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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
DSSG2017/florence	dev/notebooks/FirenzeCard_NetworkAnalysis_MM-2.ipynb	1	1485472	null	mit
otavio-r-filho/AIND-Deep_Learning_Notebooks	embeddings/Skip-Grams-Solution.ipynb	1	814578	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like machine translation.\n", "\n", "## Readings\n", "\n", "Here are the resources I used to build this notebook. I suggest reading these either beforehand or while you're working on this material.\n", "\n", "* A really good [conceptual overview](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) of word2vec from Chris McCormick \n", "* [First word2vec paper](https://arxiv.org/pdf/1301.3781.pdf) from Mikolov et al.\n", "* [NIPS paper](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) with improvements for word2vec also from Mikolov et al.\n", "* An [implementation of word2vec](http://www.thushv.com/natural_language_processing/word2vec-part-1-nlp-with-deep-learning-with-tensorflow-skip-gram/) from Thushan Ganegedara\n", "* TensorFlow [word2vec tutorial](https://www.tensorflow.org/tutorials/word2vec)\n", "\n", "## Word embeddings\n", "\n", "When you're dealing with words in text, you end up with tens of thousands of classes to predict, one for each word. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. The matrix multiplication going into the first hidden layer will have almost all of the resulting values be zero. This a huge waste of computation. \n", "\n", "![one-hot encodings](assets/one_hot_encoding.png)\n", "\n", "To solve this problem and greatly increase the efficiency of our networks, we use what are called embeddings. Embeddings are just a fully connected layer like you've seen before. We call this layer the embedding layer and the weights are embedding weights. We skip the multiplication into the embedding layer by instead directly grabbing the hidden layer values from the weight matrix. We can do this because the multiplication of a one-hot encoded vector with a matrix returns the row of the matrix corresponding the index of the \"on\" input unit.\n", "\n", "![lookup](assets/lookup_matrix.png)\n", "\n", "Instead of doing the matrix multiplication, we use the weight matrix as a lookup table. We encode the words as integers, for example \"heart\" is encoded as 958, \"mind\" as 18094. Then to get hidden layer values for \"heart\", you just take the 958th row of the embedding matrix. This process is called an embedding lookup and the number of hidden units is the embedding dimension.\n", "\n", "<img src='assets/tokenize_lookup.png' width=500>\n", " \n", "There is nothing magical going on here. The embedding lookup table is just a weight matrix. The embedding layer is just a hidden layer. The lookup is just a shortcut for the matrix multiplication. The lookup table is trained just like any weight matrix as well.\n", "\n", "Embeddings aren't only used for words of course. You can use them for any model where you have a massive number of classes. A particular type of model called Word2Vec uses the embedding layer to find vector representations of words that contain semantic meaning.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Word2Vec\n", "\n", "The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. These vectors also contain semantic information about the words. Words that show up in similar contexts, such as \"black\", \"white\", and \"red\" will have vectors near each other. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram.\n", "\n", "<img src=\"assets/word2vec_architectures.png\" width=\"500\">\n", "\n", "In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. Here, we pass in a word and try to predict the words surrounding it in the text. In this way, we can train the network to learn representations for words that show up in similar contexts.\n", "\n", "First up, importing packages." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "import utils" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Load the [text8 dataset](http://mattmahoney.net/dc/textdata.html), a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the `data` folder. Then you can extract it and delete the archive file to save storage space." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import zipfile\n", "\n", "dataset_folder_path = 'data'\n", "dataset_filename = 'text8.zip'\n", "dataset_name = 'Text8 Dataset'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(dataset_filename):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc=dataset_name) as pbar:\n", " urlretrieve(\n", " 'http://mattmahoney.net/dc/text8.zip',\n", " dataset_filename,\n", " pbar.hook)\n", "\n", "if not isdir(dataset_folder_path):\n", " with zipfile.ZipFile(dataset_filename) as zip_ref:\n", " zip_ref.extractall(dataset_folder_path)\n", " \n", "with open('data/text8') as f:\n", " text = f.read()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Preprocessing\n", "\n", "Here I'm fixing up the text to make training easier. This comes from the `utils` module I wrote. The `preprocess` function coverts any punctuation into tokens, so a period is changed to ` <PERIOD> `. In this data set, there aren't any periods, but it will help in other NLP problems. I'm also removing all words that show up five or fewer times in the dataset. This will greatly reduce issues due to noise in the data and improve the quality of the vector representations. If you want to write your own functions for this stuff, go for it." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst']\n" ] } ], "source": [ "words = utils.preprocess(text)\n", "print(words[:30])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total words: 16680599\n", "Unique words: 63641\n" ] } ], "source": [ "print(\"Total words: {}\".format(len(words)))\n", "print(\"Unique words: {}\".format(len(set(words))))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "And here I'm creating dictionaries to covert words to integers and backwards, integers to words. The integers are assigned in descending frequency order, so the most frequent word (\"the\") is given the integer 0 and the next most frequent is 1 and so on. The words are converted to integers and stored in the list `int_words`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)\n", "int_words = [vocab_to_int[word] for word in words]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Subsampling\n", "\n", "Words that show up often such as \"the\", \"of\", and \"for\" don't provide much context to the nearby words. If we discard some of them, we can remove some of the noise from our data and in return get faster training and better representations. This process is called subsampling by Mikolov. For each word $w_i$ in the training set, we'll discard it with probability given by \n", "\n", "$$ P(w_i) = 1 - \\sqrt{\\frac{t}{f(w_i)}} $$\n", "\n", "where $t$ is a threshold parameter and $f(w_i)$ is the frequency of word $w_i$ in the total dataset.\n", "\n", "I'm going to leave this up to you as an exercise. Check out my solution to see how I did it.\n", "\n", "> Exercise: Implement subsampling for the words in `int_words`. That is, go through `int_words` and discard each word given the probablility $P(w_i)$ shown above. Note that $P(w_i)$ is that probability that a word is discarded. Assign the subsampled data to `train_words`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from collections import Counter\n", "import random\n", "\n", "threshold = 1e-5\n", "word_counts = Counter(int_words)\n", "total_count = len(int_words)\n", "freqs = {word: count/total_count for word, count in word_counts.items()}\n", "p_drop = {word: 1 - np.sqrt(threshold/freqs[word]) for word in word_counts}\n", "train_words = [word for word in int_words if random.random() < (1 - p_drop[word])]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Making batches" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now that our data is in good shape, we need to get it into the proper form to pass it into our network. With the skip-gram architecture, for each word in the text, we want to grab all the words in a window around that word, with size $C$. \n", "\n", "From [Mikolov et al.](https://arxiv.org/pdf/1301.3781.pdf): \n", "\n", "\"Since the more distant words are usually less related to the current word than those close to it, we give less weight to the distant words by sampling less from those words in our training examples... If we choose $C = 5$, for each training word we will select randomly a number $R$ in range $< 1; C >$, and then use $R$ words from history and $R$ words from the future of the current word as correct labels.\"\n", "\n", "> Exercise: Implement a function `get_target` that receives a list of words, an index, and a window size, then returns a list of words in the window around the index. Make sure to use the algorithm described above, where you chose a random number of words to from the window." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_target(words, idx, window_size=5):\n", " ''' Get a list of words in a window around an index. '''\n", " \n", " R = np.random.randint(1, window_size+1)\n", " start = idx - R if (idx - R) > 0 else 0\n", " stop = idx + R\n", " target_words = set(words[start:idx] + words[idx+1:stop+1])\n", " \n", " return list(target_words)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here's a function that returns batches for our network. The idea is that it grabs `batch_size` words from a words list. Then for each of those words, it gets the target words in the window. I haven't found a way to pass in a random number of target words and get it to work with the architecture, so I make one row per input-target pair. This is a generator function by the way, helps save memory." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batches(words, batch_size, window_size=5):\n", " ''' Create a generator of word batches as a tuple (inputs, targets) '''\n", " \n", " n_batches = len(words)//batch_size\n", " \n", " # only full batches\n", " words = words[:n_batchesbatch_size]\n", " \n", " for idx in range(0, len(words), batch_size):\n", " x, y = [], []\n", " batch = words[idx:idx+batch_size]\n", " for ii in range(len(batch)):\n", " batch_x = batch[ii]\n", " batch_y = get_target(batch, ii, window_size)\n", " y.extend(batch_y)\n", " x.extend([batch_x]len(batch_y))\n", " yield x, y\n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Building the graph\n", "\n", "From [Chris McCormick's blog](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/), we can see the general structure of our network.\n", "![embedding_network](./assets/skip_gram_net_arch.png)\n", "\n", "The input words are passed in as one-hot encoded vectors. This will go into a hidden layer of linear units, then into a softmax layer. We'll use the softmax layer to make a prediction like normal.\n", "\n", "The idea here is to train the hidden layer weight matrix to find efficient representations for our words. We can discard the softmax layer becuase we don't really care about making predictions with this network. We just want the embedding matrix so we can use it in other networks we build from the dataset.\n", "\n", "I'm going to have you build the graph in stages now. First off, creating the `inputs` and `labels` placeholders like normal.\n", "\n", "> Exercise: Assign `inputs` and `labels` using `tf.placeholder`. We're going to be passing in integers, so set the data types to `tf.int32`. The batches we're passing in will have varying sizes, so set the batch sizes to [`None`]. To make things work later, you'll need to set the second dimension of `labels` to `None` or `1`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " inputs = tf.placeholder(tf.int32, [None], name='inputs')\n", " labels = tf.placeholder(tf.int32, [None, None], name='labels')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Embedding\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "The embedding matrix has a size of the number of words by the number of units in the hidden layer. So, if you have 10,000 words and 300 hidden units, the matrix will have size $10,000 \\times 300$. Remember that we're using tokenized data for our inputs, usually as integers, where the number of tokens is the number of words in our vocabulary.\n", "\n", "\n", "> Exercise: Tensorflow provides a convenient function [`tf.nn.embedding_lookup`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup) that does this lookup for us. You pass in the embedding matrix and a tensor of integers, then it returns rows in the matrix corresponding to those integers. Below, set the number of embedding features you'll use (200 is a good start), create the embedding matrix variable, and use `tf.nn.embedding_lookup` to get the embedding tensors. For the embedding matrix, I suggest you initialize it with a uniform random numbers between -1 and 1 using [tf.random_uniform](https://www.tensorflow.org/api_docs/python/tf/random_uniform)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "n_vocab = len(int_to_vocab)\n", "n_embedding = 200 # Number of embedding features \n", "with train_graph.as_default():\n", " embedding = tf.Variable(tf.random_uniform((n_vocab, n_embedding), -1, 1))\n", " embed = tf.nn.embedding_lookup(embedding, inputs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Negative sampling\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "For every example we give the network, we train it using the output from the softmax layer. That means for each input, we're making very small changes to millions of weights even though we only have one true example. This makes training the network very inefficient. We can approximate the loss from the softmax layer by only updating a small subset of all the weights at once. We'll update the weights for the correct label, but only a small number of incorrect labels. This is called [\"negative sampling\"](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). Tensorflow has a convenient function to do this, [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss).\n", "\n", "> Exercise: Below, create weights and biases for the softmax layer. Then, use [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss) to calculate the loss. Be sure to read the documentation to figure out how it works." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of negative labels to sample\n", "n_sampled = 100\n", "with train_graph.as_default():\n", " softmax_w = tf.Variable(tf.truncated_normal((n_vocab, n_embedding), stddev=0.1))\n", " softmax_b = tf.Variable(tf.zeros(n_vocab))\n", " \n", " # Calculate the loss using negative sampling\n", " loss = tf.nn.sampled_softmax_loss(softmax_w, softmax_b, \n", " labels, embed,\n", " n_sampled, n_vocab)\n", " \n", " cost = tf.reduce_mean(loss)\n", " optimizer = tf.train.AdamOptimizer().minimize(cost)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Validation\n", "\n", "This code is from Thushan Ganegedara's implementation. Here we're going to choose a few common words and few uncommon words. Then, we'll print out the closest words to them. It's a nice way to check that our embedding table is grouping together words with similar semantic meanings." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " ## From Thushan Ganegedara's implementation\n", " valid_size = 16 # Random set of words to evaluate similarity on.\n", " valid_window = 100\n", " # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent \n", " valid_examples = np.array(random.sample(range(valid_window), valid_size//2))\n", " valid_examples = np.append(valid_examples, \n", " random.sample(range(1000,1000+valid_window), valid_size//2))\n", "\n", " valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n", " \n", " # We use the cosine distance:\n", " norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))\n", " normalized_embedding = embedding / norm\n", " valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)\n", " similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# If the checkpoints directory doesn't exist:\n", "!mkdir checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "epochs = 10\n", "batch_size = 1000\n", "window_size = 10\n", "\n", "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " iteration = 1\n", " loss = 0\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for e in range(1, epochs+1):\n", " batches = get_batches(train_words, batch_size, window_size)\n", " start = time.time()\n", " for x, y in batches:\n", " \n", " feed = {inputs: x,\n", " labels: np.array(y)[:, None]}\n", " train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)\n", " \n", " loss += train_loss\n", " \n", " if iteration % 100 == 0: \n", " end = time.time()\n", " print(\"Epoch {}/{}\".format(e, epochs),\n", " \"Iteration: {}\".format(iteration),\n", " \"Avg. Training loss: {:.4f}\".format(loss/100),\n", " \"{:.4f} sec/batch\".format((end-start)/100))\n", " loss = 0\n", " start = time.time()\n", " \n", " if iteration % 1000 == 0:\n", " # note that this is expensive (~20% slowdown if computed every 500 steps)\n", " sim = similarity.eval()\n", " for i in range(valid_size):\n", " valid_word = int_to_vocab[valid_examples[i]]\n", " top_k = 8 # number of nearest neighbors\n", " nearest = (-sim[i, :]).argsort()[1:top_k+1]\n", " log = 'Nearest to %s:' % valid_word\n", " for k in range(top_k):\n", " close_word = int_to_vocab[nearest[k]]\n", " log = '%s %s,' % (log, close_word)\n", " print(log)\n", " \n", " iteration += 1\n", " save_path = saver.save(sess, \"checkpoints/text8.ckpt\")\n", " embed_mat = sess.run(normalized_embedding)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Restore the trained network if you need to:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " embed_mat = sess.run(embedding)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Visualizing the word vectors\n", "\n", "Below we'll use T-SNE to visualize how our high-dimensional word vectors cluster together. T-SNE is used to project these vectors into two dimensions while preserving local stucture. Check out [this post from Christopher Olah](http://colah.github.io/posts/2014-10-Visualizing-MNIST/) to learn more about T-SNE and other ways to visualize high-dimensional data." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "viz_words = 500\n", "tsne = TSNE()\n", "embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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D6wVA0uklrmxtbVtkuSpfX98GAVDdM0wmk7Zv397ofeHh4Q0CIEkKDg5WcHCw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\ncWFhYZibmxMTE0NMTAz29vYEBQURFhbG9OnTm72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Ir+/XsWPHmD9/PmvWrGHevHkG17h4\n8SKrVq3S9S6bNGkSM2bMYM+ePUyZMgUHBwc6depEp06ddEGg1u6xlZSUxIIFC+jbt6/e8ZCQEGJj\nY9m5c6dBEGjXrl3U1dUxatSoVt3L7XiobCXoI4QQQgghHgiPzrs8QgghhNDT2+PO3uC+03kPouaU\nxLvdPA+VLU5OTixdupSoqCgOHjzITz/9RF1dHfb29ri5uTFmzBjc3d2bve748ePp2bMnUVFRZGZm\nEh8fj5WVFW3atGHkyJEMGzasRfs0MTFh3rx5/PTTT8TGxpKYmEhVVRXV1dVcuXIFU1NT/vOf/xAV\nFYWLi4veJ/Bv7Qm0dOlSfv75ZwA2bNig61104cIFlEolHh4eGBsbc+XKFTIyMkhISMDS0pKLFy/i\n4uKCh4eHrlfUiBEjOHfuHAUFBRw9ehR7e3u0Wi3W1tY89dRT+Pv76/Z/9uxZNm/eTGFhIXv27MHV\n1ZWXX36ZZ599lgMHDjQro+F2Dh06xM6dO/Hx8aFHjx6YmJhw7tw5du3aRUJCAkuWLNF781uIh9HN\nGSf7tn+LprKaqVOnNlh2sj7wuXPnTmpqanjttdcMfgb8/PwIDAwkISGBq1ev6n6u673yyiu6ABDc\nyPocNmwY33zzDTk5OfTr1+8e3KW+wMBAgwAQgJeXF15eXsTHx3PlyhUcHBwAqKurY/fu3VhaWrb4\ndbYpzemvJoQQQgghxP0kQSAhhBDiEeWhssXHzbFFmTC+7o6P1CeXmyqJZ25jT5/fLmh0nqWlJS+/\n/LJBH5vbaeqNwJ49e9KzZ89mrdWc7AuFQsHw4cMZPnw4ANHR0axatQovLy/69++PUqmkpKSE3Nxc\nkpKSdPu7tcTfgAEDgBs9N7y9vfHx8SE/P5+IiAjat2/P6tWrcXBwYNmyZezevZu8vDyuX79Op06d\nUCqVHD9+HCMjI7RaLVOnTjUo+VTfy2PUqFF6paEATE1NiYuL41//+hcqlQpofkZDY4YPH85zzz2H\nqamp3vGjR4+yYMECNm7c2Gi5QiEeZEfPFLJ+b7bea/yJvYlUFBWx4zS4nSm8be+ZrKws4EZJxuzs\nbIPzpaWl1NXVkZeXZ9AHrqFSavWZheXl5Xd8Py1R33uuISEhIbrXqfrX7aSkJAoLCwkJCcHCwuJX\n2aMQQgghhBAPCgkCCSGEEI+w3wR5MWd9PFpt02MVCgx6AT3sHseSeNHR0ZiYmLBixQqDMnVlZWW3\nnTdgwACsra2J2hFDtZUzdOzHiSPrcXBy5pVXXsHBwYG4uDhiY2MZNGgQoaGhzJw5k5qaGj755BMi\nIiJYvHgxxsbGrXIfO3bsoLa2lokTJzaa0dCY22X5+Pv74+7uzpEjR+56n0LcD9FHz7F0e5rBa3vN\ntSoATl2pZc76eGaP8WVkb8NykPWvBZs3b270OlVVVQbHbu71Va/+576urq5Z+79b9Rk+DQkKCmLt\n2rXExMTw0ksvoVAoiI6OBvjVSsEJIYQQQgjxIHl43+EQQgghRJP8PZ2YNdqnwTcLb6ZQwOwxvrf9\n1PjD6nEtiWdsbNxgMEapVN52ztEzhSzdeozUs0Vctssjg5NkHThKZXERP5y4jnO3QiIjIzE2Nmbm\nzJlYW1vj5OTEpUuXqKioYOLEiaxcuZKCgoJWuYcTJ04ANFjyqbm0Wi0//fQTcXFxnDlzhvLycr03\nqU1M5FdhuPE4bd++nR07dlBQUICtrS0DBw5k0qRJBmMrKiqIiYkhOTmZvLw8SktLsbKyonv37rz0\n0kt0795dN7a8vJwpU6bg6OjImjVrUCgUBuv95S9/ITExkX/+85+6DJP4+HgiIyM5f/48Go0GpVKJ\ni4sLQ4cOJSQk5N49EA+Jo2cKb/uabmJmQTVwvVKDsak5S7alorKzNHhtrw/kbNy4ESsrq19h14bq\nnw+1tbUNnq+oqGgw4HTz3IaYmZkRHBzM1q1bOXLkCO7u7iQnJ9OtWzc8PT1btMfGnosBAQG6nlkA\nY8eO1X3t7e3NokWLdN/n5OTw3XffkZGRQUVFBQ4ODvTr148JEybg6Oiod82lS5fqMiMTExPZtWsX\n+fn5dO3alZdeeokFCxYY9D+rd/36daZMmQLAunXrDLIghRBCCCHE40n+8hVCCCEecaP83XC2tyJi\nXzapZw1Lw/m6OxI21OuRCwDB41MS7+aeIJYdenAl8wTTp08nKCgIb29vevToYZAVdLP6rIKyglK9\n47XXqwHIKarhD+v2U5aSSZeOzmzduhWAS5cucfHiRb766ivs7OwwMTFpMHPgTtSXlbqbnj1r165l\n69atODo60qdPH9q0aYOZmRlwozydWq1ulb0+7P71r38RFRWFo6Mjo0aNwtjYmPj4eE6ePElNTY1e\nsOzChQvMnj0bBwcHXnvtNWxsbFCr1SQkJJCcnMz8+fN1gTsbGxuCgoKIjY3l2LFj9O7dW++6hYWF\nJCcn06VLF10AqL6coYODg0E5w9jYWAkCAev3Zt82qG/l5EpFUT5l+TlY2Dmh1ULEvmyD1/du3bqR\nk5NDRkbGPe3ho1AobpsdZGNjAzRc2vHixYuNBoGaEhISQmRkJNHR0Xh6elJXV9fiLKCmnovDhg0j\nNDRU91oSGhqqm+vs7Kz7OjExkfDwcAAGDRqESqUiJyeHHTt2cPjwYT755BO98fXWrFlDZmYmAQEB\nBAQEYGRkhL+/P+3bt2f//v289tprBo/PwYMH0Wg0PP/88xIAEkIIIYQQOhIEEkIIIR4D/p5O+Hs6\n6QULrMxN6O3h9NAFPFrqUS6J11BPEHCltMNQSgvSOfvN9ygtt6JQKPD29mbq1KkG/TwayyowNjUH\noKaqHIWxCacvlWJmYsSGDRsASEtLo7q6mqioKIyNjSkvL7/tp/pbqv4N4qKiIlxdXVs8v7S0lMjI\nSNzd3fn73/9u0Nx+7969rbLPh93x48eJioqiffv2/OMf/8DW9sbrwaRJk5g7dy7FxcW6Pk0Arq6u\nBAUFYWZmxltvvaU7XlhYyLvvvssXX3yhl70VEhJCbGwsO3fuNAgC7dq1y+DN+TstZ/i4yFVrGg1q\nt+0aQGF2MgXpe1G6dMbCri2pZ4vJVWvwUNlSWFiIk5MTY8aMISYmhi+++AIXFxc6dOigt05NTQ0n\nTpygV69ed7VfpVJ52/5drq6uWFlZER8fT2lpqe7f+9q1a3z++ed3dV0XFxf8/PxITEwkKysLa2tr\ngoKCWrRGU89Fa2trwsLCSEtLQ61WExYWZrBGVVUVS5Ysoba2lkWLFuk9nt9//z3r1q1j5cqVLFy4\n0GDuqVOnWLZsmUGA6JlnnuHf//43P/74I2PGjDHYM8DIkSNbdK9CCCGEEOLRJkEgIYQQ4jHiobJ9\n5IM+t3pUS+LdricIQJtOftDJj9rrVTzdzRyKz7B7924WLFjA6tWr9d7QbCyrwNKxHZXFFym/dBZ7\n915otVBhbMfeqG+5ePEib7zxBiqVii+++AKAiIgIXYDobnXr1o3s7GySk5PvKAhUUFCAVqvF39/f\nIABUWFjYamXrHkY3B4N/3LqRyuoaXn75ZV0ACG6U1JoyZQpz587Vm2ttba3LprqZk5MTgwcPJioq\nisuXL9O2bVsAvLy88PLyIj4+nitXruh6udTV1bF7924sLS0ZNmyY3lp3Us7wcZGS23BApZ6FTVh+\nqwAAIABJREFUXVs69nuG8wnbydrxOXau3TG3dST870lYXb+ClZUV4eHhuLq6MmPGDJYvX85bb71F\nnz596NChA7W1tajVajIzM1EqlXz22Wd3tV8/Pz/27t3LX/7yFzp37oyJiQm9evXC29sbExMTnn32\nWb755htmzJjBwIEDqa2tJSUlBUdHR4MyaS0VEhJCSkoKJSUljB07tsHnbVPu9rl4+PBhNBoNQUFB\nBgG1559/np07d5KSkqL3M1PvxRdfbDBD6KmnnuLrr78mOjpaLwiUl5dHeno6vr6+BkE9IYQQQgjx\neJMgkBBCCCEeeY9aSbzGsnduZmxqwfYzsOg3oWi1Wnbv3k1GRgaDBg0CoLK6Ri+roL7Phvb/F27T\n2Z+inKM3sgpcu2Jpr+LChfOk5lwg8psv0Wq1PP300/fkHkNCQti5cyfffPMNffr0oWNH/eb29RkN\nt1OfvZKZmUldXR1GRkbAjU/mr1y5stUylh4mDWWO1fd92pRRRZvOhXo/Az179tQ9bjcrKSnh3Llz\nTJ06lZKSEmpqavTOFxUV6b2hHRISwrJly9i9ezcvv/wyAElJSRQWFhISEoKFhYVu7BNPPMHatWtb\nVM7wcVJZXdPkGCevvljaq7h0/BDll3IpvZBFVkU7hgf66v28Dh8+HE9PT3744QdSU1M5evQoFhYW\nODo6MnjwYIYOHXrX+3399dcBOHbsGElJSWi1WkJDQ/H29gYgLCwMc3NzYmJiiImJwd7enqCgIMLC\nwpg+ffpdXTswMBClUklZWVmzSsHdminbs3cgp06duqvn4qlTp4AbwbBbGRsb4+3tzZ49ezh9+rRB\nEKhr164Nrmlra8uQIUPYs2cPx48fp0ePHsAvWUDPPPNMs/cnhBBCCCEeDxIEEkIIIcRj4VEqiddY\n9o6m4Aw2zh43BXRu9ASxLSkBwNzcXDe27Oo1bv6svYn5jQbx1ytu9AayadsR516DuZRxgKxtqzGx\ntKWy+BLjx42hc8f2+Pr68sILL+jmV1VVUVFR0Sr32LFjR958801WrVrFjBkzGDBgAC4uLpSVlZGd\nna3LaLgdBwcHgoKC2Lt3LzNmzMDf35+KigpSUlIwMzOjU6dOnD59ulX2+jC4XeZYfd+n7KLrzFkf\nz+wxvozs3RGtVsvOnTvJysqiurqaKVOmMHDgQLp27UpiYiLGxsZ07tyZ9u3bo1AoSEtLIz4+nvPn\nzzN9+nTat29P9+7deemllwgKCmLt2rXExMTwzDPP8Morr5Cbm4uLi4vBm/Pjxo3jhx9+YN++feTl\n5WFlZdVoOcPHjZV58/58s27bkU5tfwmcvjmyJ+P6exqM8/DwYNasWc1ac9GiRbc9FxwcTHBwsMFx\nOzs73n///dvOUygUjB8/nvHjxxucW7t2bbOv0xC1Wo1Go6Fnz564ubnddlzDZTUBlNj1fBqKs4iM\njGTr1sZLazak/vWwPgPuVvXZTvU90G52uzlwI7C6Z88eoqOj6dGjB9evXycuLg47OzsGDBjQ5L6E\nEEIIIcTjRYJAQgghhHisPOwl8ZrqCXJm77cYmZhh5dQBcxt7tFo4sfMsnW2q8e3VXe8T6bV1+hEB\nc9s2mFkpuXI2HYyMMLO2w9jEjA59R1J6LpOrJZdAAVcKL6Oxt8He3p7169ej0Wi4dOkS0dHRVFVV\ntdq9jhw5End3d7Zs2UJaWhqHDx9GqVTi4eHRrAykGTNm0K5dO/bt28f27duxs7Ojf//+/Pa3v200\ngPSoaV7fpwqMTc1Ysi0VlZ0libs3ExkZSWVlJe7u7gQFBREfH89nn32GQqGgf//+fPDBBwCcOHGC\nqKgolEol9vb2PPnkk5ibm5OQkEBycjLz588nODiYrVu3cvLkSfr06cOBAwfo1asXnp76gYnCwkKu\nXLnCs88+y0cffcTx48c5dOjQbcsZPm56e9xZtuKdznuYbdmyBa1Wa9A352aNldUEKLXpRJltJ978\nny50MNW0+LlobW0N3Miea0hxcbHeuJvVB/Ib0q1bNzp16sT+/ft57bXXSE5ORqPRMH78eExM5E98\nIYQQQgihT35DFEIIIYR4iDTVE6R972A0F09xtbiAsvwcjIxNMLO2I+DJsfx55lS9NwiNjfTfZFQY\nGeE57GXyj8ZRcu44dder0Wq1eI2YgnOPXz5d/lTH61w9l0pGRgYJCQnY2NjQtm1bZsyYwfDhwxvs\n4dPYJ/hnzZp122yE7t27M2fOnEbvWaVSERUVZXDc3NycSZMmMWnSJINzDWU1+Pj4NLjOw66xzDEr\nx/Y3+j6pz2Ju64BWCys27ubygSgsLCzw9vbGxcWFadOmMWnSJHr16oWxsTE2Nja6NVxdXfnyyy+Z\nM2cOpqamPPfcc/j4+FBYWMi7777LF198wfz584mMjCQ6OhpLS0u0Wi2mpqYG+9m1axd1dXWMGjUK\na2trAgICCAgIaLCc4ePIQ2WLj5tjo4HgW/m6Oz7Uge+WuHz5Mj///DP5+fnExsbi6enJkCFDGhzb\n3LKaWi2sjsth0W8Cefttw+difcnEm8tO1uvUqRMAaWlpjBgxQu9cbW0tGRkZAHTu3LnF9zp69GhW\nrFjBnj17OHToEAqFgpEjR7Z4HSGEEEII8eiTIJAQQgghxEOkqZ4gbbsG0LZrgMFxv8FdsbS01H2/\naNEi3lBreOPzvXrjrNt0wOupyY1e46WQYDxU41qwa3G/NJU55ti5N4U5RyhI34eda1dMzK04tO9n\nHMqv0kFloRc0NDMzw9/fn/3791NdXa07bmVlRUREBOfPn9db28nJicGDBxMVFYWpqSl+fn4kJiZi\na2uLvb09ly9f5sqVK7qyV3V1dWzcuBELCwuGDRumt1ZJA+UMH1e/CfJizvr4JoMXAAoFhA19fEro\nFRQUsG7dOszNzenduzfTp0+/bUZNY8FR0C+tWV9W09/TyeC5qFQqgRsBKGdnZ701Bg4ciK2tLT//\n/DOjR4+mW7duunORkZFcunSJ3r17G/QDao5hw4bx73//m02bNlFcXIy/vz/t2rVr8TpCCCGEEOLR\nJ0EgIYQQQoiHSHN7gjRnnmQVPPqayhyzadsRVfdA1FnxHN/+GQ5uPVFnHeZMUT4VvQfh1sZKb/yU\nKVPYu3cvhw8fZvXq1RgbG3P8+HFdRsPx48eZOXOmQXmroqIiQkJCSElJoaSkhJCQEHJycti9ezcv\nv/wyAElJSSQlJeHm5saSJUtwdnZGq9WSkZFBdnY2Xbp00Stn2FrmzJlDenr6Q5MF5u/pxKzRPk1m\nsSgUMHuML/6ej08puOZm8zUVHAXD0poXkuHKwfVcyjur91z08/Nj//79hIeHExAQgJmZGSqViuHD\nh2NhYcHMmTP529/+xh//+EeGDBlC27ZtycnJ4ejRozg4OPDWW2/d0b2am5vz5JNP6u731v5aQggh\nhBBC1JMgkBBCCCHEQ6S1e4JIVsGjranMMYAOfUdibuvI5ZOJFGYncbXkMibmVjgOmMDR3f+hV8df\nMilGjx6Nt7c3Fy9eJC4uDjMzM2xtbamrq6OsrAxra2uGDRtGly5dUCgUpKWlkZ6ezvXr1wkMDESp\nVFJWVsZbb73F/PnziYmJ4aWXXkKhUBAdHY2rqyuDBw/m1KlTJCUl6d5Qf+WVVwgJCZF+J/9vlL8b\nzvZWROzLJvWsYTDD192RsKFej1UAqCWaCo5Cw6U1C8w7MfWW5+LTTz+NWq1m7969bNq0idraWry9\nvRk+fDgAgYGBfPLJJ3z77bccOXKEyspK7O3teeaZZ5g4cSKOjo53fB8jRowgKioKR0dHAgMD73gd\nIYQQQgjxaJO/ooQQQgghHiKtnb0jWQWPtuZkjikUCtp260/bbv0ByNqxhsrii2jrauk1biYKxY3+\nKf6eTtTW1mJtbc2AAQNYu3YtAG+99RZ2dnb85z//oWPHjnprr1q1ivT0dADUajUajYaePXvSpUsX\ngoOD2bp1K0eOHMHd3Z3k5GSGDh3K4sWLW/lReDT5ezrh7+lErlpDSm4hldU1WJmb0NvDSbL1mtCc\n4GhDpTXDnujKi7cEwo2MjJg8eTKTJ9++jKaXlxfz5s1r1t4a65F2q9OnTwM3gkHGxsbNmiOEEEII\nIR4/EgQSQgghhHjItHb2jmQVPLruJHPMyrE9lcUXKVefxdzWQa8fSmZmJnV1dXrjL168iJubm0EA\nqL6UW70tW7ag1WoZM2YMACEhIURGRhIdHY2npyd1dXWtXtIqPj6eyMhIzp8/j0ajQalU4uLiwtCh\nQwkICGDatGm6sWPHjtV97e3tzaJFi1p1L/eKh8pWgj4t1JplNe+X2tpafvjhB4yNjaUUnBBCCCGE\naNSD81usEEIIIYRolnuRvSNZBY+mO8kcc+zcm8KcIxSk78POtSsm5lakni3m5IUi1q1bZzBepVKR\nn59PcXGxrrSVVqslIiKCnJwcioqKWL9+PZmZmXh6ejJkyBAAXFxc8PPzIzExkaysLKytrQkKCmqd\nGweio6NZtWoVDg4O9O/fH6VSSUlJCbm5ucTGxjJs2DBCQ0OJi4tDrVYTGhqqm+vs7Nxq+xAPntYu\nq/lryszMJD09nbS0NHJzcxkzZgxOTvd/X0IIIYQQ4sElQSAhhBBCiIfQvcrekayCR09LMscAbNp2\nRNU9EHVWPMe3f4aDW09QGPG75K/x7tTeoIfJuHHjWLVqFTNmzGDw4MEYGxtz/Phxzp07R9euXfnu\nu+9ITk5m4MCBTJ8+HYXilx5DISEhpKSkUFJSwtixYzEzM2u1+46OjsbExIQVK1ZgZ2end66+f1FY\nWBhpaWmo1WrCwsJa7driwdbaZTV/TSkpKWzYsAFbW1tGjhzJ1KlT7/eWhBBCCCHEA06CQEIIIYQQ\nN1Gr1UybNo3g4GBdX4alS5cSFxfH2rVrUalUd7x2XFwcS5cuZdasWQQHB9/1XiV7RzRHczPHbtah\n70jMbR25fDKRwuwkjM2tCHwyiIV/eY8ZM2bojR01ahSmpqZs3bqVuLg4zMzM6NWrFzNnzuTgwYOc\nPXuW8PBwfHx8DK4TGBiIUqmkrKzsnpS0MjY2brBXilKpbPVriYdLa5fV/LWEhYVJwFIIIYQQQrSI\nBIGEEEIIIR5ykr0jmtJU5titFAoFbbv1p223/rpjz47sibW1NWvXrjUYHxwc3GBg08PDo9E3rNVq\nNRqNhp49e+Lm5tbMu2nYrcHQnr0DOXXqFNOnTycoKAhvb2969OhhkBUkHk/3oqymEEIIIYQQDyIJ\nAgkhhBBCNGHy5MmMHz/eoAyWEA+T+syxg1kFfPhdcovn34t+KFu2bEGr1TJmzJg7XuPomULW781u\noLSXErueT0NxFpGRkWzduhWFQoG3tzdTp07Fy+vByOwQ98+9KqsphBBCCCHEg0SCQEIIIYRolntZ\nJu1B5+joKAEg8cgY1L3dfe2HcvnyZX7++Wfy8/OJjY3F09OTIUOG3NFa0UfPNZrJUWrTiTLbTrz5\nP13oYKrh0KFD7N69mwULFrB69WrJChJSVlMIIYQQQjzyJAgkhBBCiPtmzpw5pKenExUVdb+30qiG\ngl03B8XCwsL48ssvSUlJoaqqCnd3d8LCwujXr1+z1i8vL+ejjz4iMzOTSZMm8dJLLwFQUFDA999/\nT2pqKkVFRZiZmdGmTRt69OjB5MmTsbWVNyjFnbmf/VAKCgpYt24d5ubm9O7dm+nTp6NQKFq8ztEz\nhc3qc6TVwuq4HBb9JpC33w5Aq9Wye/duMjIyGDRoEEZGRgDU1dXpvhaPHymrKYQQQgghHlUSBBJC\nCCHEHZMyaTeCQe+88w7t2rXjySefRKPRsG/fPhYuXMhHH32Er69vo/MvX77MggULuHjxIrNnz2b4\n8OEAFBcX884771BZWUlAQACDBg3i2rVrXLp0iR9//JExY8ZIEEg06sKFC7z55pv4+PgQHh6ud66+\nH8rrb06nqrQI73EzMbW68Xwqy89BnRVPZVE+dTXV9OnuwVGXS3RVTcDa2lpvndTUVPbu3UtmZiaF\nhYXU1tbSrl07hgwZwosvvoiZmZne+IiICDZs2EB4eDjFxcVERkby9ttvo1QqG+w11Jj1e7MbDQBp\nCs5g4+yBQqFAq4WIfdn4ezpRUlICgLm5OQBKpRK48bPo7Ozcoj0IIYQQQgghxINOgkBCCCGEuGNS\nJg3S0tIICwsjNDRUd2zYsGEsWLCAzZs3NxoEOnPmDH/+85+pqqpiwYIF9O7dW3fuwIEDaDQaXnvt\nNZ599lm9eVVVVZKxIJrk6uqKr68vqamp5OXl0aFDB73z7hYVeFhfh469dQGgi6k/czH1J0zMreju\n7UeQXye0FUVs2bKFpKQkFi9ejJWVlW6NTZs2ceHCBbp3705AQADXr18nMzOTiIgI0tLS+Oijjxp8\nrm7ZsoWUlBT69++Pr68vFRUVLbq3XLWmyXJ2Z/Z+i5GJGVZOHTC3sedCMlw5uJ5LeWfp0qULfn5+\nAPj5+bF//37Cw8MJCAjAzMwMlUqlC8gKIYQQQgghxMNMgkBCCCGEuGO36wkUHx9PZGQk58+fR6PR\noFQqcXFxYejQoYSEhOhKqdUbO3as7mtvb28WLVr0q97H3VCpVEyYMEHvWJ8+fWjbti0nT5687byU\nlBTCw8OxtLTkb3/7G56eng2OuzWTAsDCwuLuNi0eGyEhIaSmphITE8P//M//6J2LiYnBzsqMhfPe\nwN6lMz/s3sdX0UkM7teb8IV/oadnO93YuLg4li5dSkREBK+++qru+Jtvvomzs7NBObevv/6ajRs3\ncuDAAYYOHWqwr9TUVBYvXkynTp3u6L5ScgubHNO+dzCai6e4WlxAWX4ORsYmFJh3YuorrxASEoKJ\nyY0/hZ5++mnUajV79+5l06ZN1NbW4u3tLUEgIYQQQgghxCNBgkBCCCGEaFXR0dGsWrUKBwcH+vfv\nj1KppKSkhNzcXGJjYwkJCcHa2prQ0FDi4uJQq9V6WTS/djmmW5uBu1o3o0nKTTw9PRvMdHByciIr\nK6vBOQcOHODo0aO0b9+eDz/8kLZt2xqMCQwM5KuvvuKzzz7j6NGj+Pv707NnTzp27HhH/VPE42nA\ngAE4OjoSGxvLpEmTMDU1BaCiooJ9+/bRvn17/Pz8UCgUVOQepYOjNcvDP8DNrZ3eOsHBwURGRvLT\nTz/pBYHatdMfV++5555j48aNHDlypMEg0KhRo+44AARQWV3T5Ji2XQNo2zVA71jYE1158Zb+RkZG\nRkyePJnJkyff8X6EEEIIIYQQ4kElQSAhhBBCtKro6GhMTExYsWIFdnZ2eufKysoAsLa2JiwsjLS0\nNNRqNWFhYb/6Po+eKWT93myDklLV5SWcP3+FbkXlzVrHxsamwePGxsZob9OwJCsri5qaGrp164aT\nk1ODY1QqFf/85z+JiIjgyJEjHDx4ELgRXHrhhRf0sqeEuNmtgU3/wKHE7dzKwYMHGTZsGAB79uzh\n2rVrjBw5UhdUzMrKwsTEhP379ze47vXr1yktLUWj0ej6UVVVVREZGcnhw4fJy8vj6tWres/7oqKi\nBtfq2rXrXd2jlfmd/Rlzp/OEEEIIIYQQ4mElfwUJIYQQotUZGxtjbGxscLy+Afv9Fn30HEu3p922\nqXzZ1WtsSz7HiJTzjOzdsdWvP3nyZJKSkoiNjUWr1TJz5swGs3s6duzIH/7wB2prazlz5gwpKSls\n27aNNWvWYGFhwYgRI1p9b+LhdbvA5rVKa87nlbDum826IFBMTAwmJiY89dRTunEajYba2lo2bNjQ\n6HWuXr2Kra0tNTU1zJs3j5MnT+Lu7s7QoUOxs7PT/exv2LCB69evN7iGvb393dwqvT0aDp7eq3lC\nCCGEEEII8bCSIJAQQgghGnSnZdKeeOIJ1q5dy/Tp0wkKCsLb25sePXoYZAXdL0fPFDYaANLRwpJt\nqajsLFt9D6ampvzxj3/kH//4B3FxcVy/fp133nmnwcAZ3AiqdenShS5dutCjRw/++Mc/cujQIQkC\nCZ3GAptmVkoUjp3Y9uMhvo5JoK+bLWfPntUFbepZWVmh1WqbDALVi4+P5+TJkwQHBzNr1iy9c8XF\nxY2uc7clDT1Utvi4ORoEvBrj6+6Ih8r2rq4rhBBCCCGEEA8bCQIJIYQQQs/dlkkbN24cSqWSHTt2\nEBkZydatW1EoFHh7ezN16lS8vLwanX+vrd+b3XQA6P9ptRCxL5sO92AfJiYmvP/++5iamvLjjz9S\nU1PD+++/r2tWn5OTQ/v27bG2ttabV1JSAoC5ufk92JV4GDUnsOnUNYCS88f52+qvGeWjAm705blZ\n9+7dSUxM5Ny5c7i5uTV53YsXLwIwaNAgg3Pp6ektuIM785sgL+asj2/Wz7NCAWFD7+9rjxD15syZ\nQ3p6OlFRUfd7K0IIIYQQ4jFg2MVYCCGEEI+t6KPnmLM+/rafrq8vkxaTcr7RdZ588kkWL17Mhg0b\nWLBgASNGjCA9PZ0FCxZQWlp6L7beLLlqTYsyBwBSzxZTrKm6J/sxMjJi9uzZPP300xw8eJDw8HBd\n+awff/yRyZMnM3/+fFatWsW6dev4+OOP+ec//4mpqSnPPffcPdmTePg0J7Bp284TC2Ubik4fIzI6\njg4dOuDr66s3pv45tWLFCoqLDX9OqqqqOHHihO57lepGMCktLU1vXEFBAV9++eUd3EnL+Hs6MWu0\nD00lFSkUMHuML/6eUgpOiHpxcXGMHTuWuLi4+70VIYQQQghxj0kmkBBCCCGAe1MmzdramoCAAAIC\nAtBqtezevZuMjAxd5oCR0Y3Po9TV1em+vpdScgvvaF5ecUUr7+QXCoWC3/3ud5iZmbFt2zYWLlzI\nBx98QFBQENevX+f48ePk5ORw7do12rRpw9ChQ3n++edxd3e/Z3sSD4/mBjYVCgVOXgFcSI7hSjX0\nGTDMYIyfnx9Tpkzhq6++4vXXXycgIABnZ2eqqqpQq9Wkp6fTs2dPPvzwQwD69+9P+/bt+eGHH8jN\nzaVz585cvnyZhIQE+vXrx+XLl1v9fm81yt8NZ3srIvZlk3rW8HHwdXckbKiXBICEEEIIIYQQjy0J\nAgkhhBACaL0yaampqfj4+Bj0/GiojJlSqQTg8uXLODs739G+W6KyuqbJMeY29vT57QK9Y8EvTCZs\n6EK9YyqVqtFSPosWLTI4FhwcTHBwsMFxhULBG2+8wRtvvKE71q1bN7p169bkfsWDR61WM23atAZ7\n5bSWsWPH4u3tTeDzrzdrvOZSLucTd3Kt4gqWDu2wdfducNz48ePp2bMnUVFRZGZmEh8fj5WVFW3a\ntGHkyJEMG/ZL8MjCwoLw8HC+/PJL0tLSyMzMxNnZmYkTJzJu3Dj27dvXKvfaFH9PJ/w9nQz6mPX2\ncJIeQEIIIYQQQojHngSBhBBCCHHHZdIsMSyTFh4ejoWFBd26dcPZ2RmtVktGRgbZ2dl06dIFPz8/\n3Vg/Pz/2799PeHg4AQEBmJmZoVKpGD58+F3fU0OszP+PvTuPq6paHz/+OcyDzHhQQQUUTQURUXHW\nQs2xsknB0kytq32/ZaXfe7VfeV91rzbY1cyh681b3VLqhpo4oYgalApOjKaAgKIiR5ThcJD5/P4g\nThwPsyPyvP8p995r7bXPS/Ds9az1PC376tPSdkLcbU0JbNaoqihFqwX7zr3QmljUe13v3r3p3bt3\nk/p0dnZm0aJFdZ6rK0gaHBxMcHBw0wbcTO5KGwn6iPsuJiaGsLAwsrKyUKvV2Nra0qlTJ0aMGMHE\niRP1rq2srGTr1q0cOHCAa9euYW9vz6hRo3jhhRd09eFqi4+PZ9u2baSkpFBSUoJSqWTo0KE8++yz\nBvXjauoObd++ndDQUA4fPkxOTg6jRo0iJydHV7dr9erVrF69Wtdu06ZNulSPQgghhBDi4SAzGkII\nIYS4o2nSZs2axalTpzh//jwnTpzQBXZeeuklJk6cqDexNW7cOFQqFVFRUWzdupXKykq8vb3vWhCo\nn3vLUkK1tJ0Qd1tzApSl6hsYmVrQvudACWwKcReEh4ezbt06HBwcGDRoELa2tuTn55OZmcmBAwcM\ngkArV64kOTkZf39/rKysOHHiBFu3biU/P99gF2F4eDjr16/H3Nyc4cOHY29vT2JiIqGhocTExPDJ\nJ58YBIKgemFGamoq/v7+DB48GDs7O3x8fLC2tiYmJoaAgAA8PT1119fVhxBCCCGEaN3k7U8IIYQQ\ndzRN2oQJE5gwYUKT7mtkZMTMmTOZOXNm0wd7G9yVNvh0cWzWrqe+XR1ld4F4YDUWoLyZl0PB5VTy\nLiZTXqLBwcUda2c3CWwKcReEh4djYmLC559/jp2dnd65wsJCg+uzs7NZt24dNjbV/8a8+OKLvP76\n6xw8eJBZs2bh4OAAVKeY/Oc//4mFhQX/+Mc/cHNz0/WxYcMG9uzZw1dffcX//M//GNzj2rVrrFu3\nTpd+tbaYmBiGDBlSZ5pSIYQQQgjx8JAgkBBCCCHaVJq0GSO9WLI5pkn1jxQKCB7hdfcHJR5aKpWK\nr7/+mri4OEpKSujatSvBwcEMHDjQ4NqoqCjCw8NJT0+nrKwMFxcXRo8ezdNPP42pqWmd/d8a2Cy/\nWcSV+IMUXkqhsqIMbVUVpUV5mFhYY2Zpg7PXAAlsCnEXGRsbY2xsbHC8riDMSy+9pAsAQXWdrVGj\nRvH999+Tlpam+z1x+PBhKioqmDp1ql4ACKoDR4cOHeLQoUO8+uqrBr8rXnjhhTrvLYQQQggh2g6j\n+z0AIYQQQtx/bSlNmp+HMwsn+aBQNHydQgFvTu6Ln0fre0bxYFCpVLz11luoVCoee+wxRowYwYUL\nF/jggw9ISEjQu/azzz7jk08+ITs7m6FDhzJp0iRsbGz47rvvWLZsGZWVlfXeZ8ZILxQKqCgpJmX/\nv7medhpzWyfa9wzA0d0HC1snHLv0xtKxI8amZhLYFOIOylSp+Sk2gy3RqVi69iKvUMPh91Y4AAAg\nAElEQVSCBQv48ssvOXbsGAUFBfW29fIy/Fls3749AEVFRbpj58+fB6Bv374G17dr145u3bpRVlbG\npUuXmnQPIYQQQgjRtrS+5btCCCGEuOPaWpq08X5dcLG3Ykt0KgkXDJ+5b1dHgkd4SQBI3JbExESC\ng4MJCgrSHRs1ahTLli1j27ZtugndyMhIDhw4wJAhQ1i0aBFmZma667ds2UJISAi7d+/miSeeqPM+\nNYHNN99dTqk6D2Wvwbj5P64779xzACn7vkKhgIn9u8jfayHugNMZuWyOSr3l3003ClxHUHA1iQvf\nh2JruQOFQoG3tzezZ882CMjUVX+nZhdRVVWV7phGU11/z9HRsc6x1KSNq7murnNCCCGEEKLtkiCQ\nEEIIIYC2lybNz8MZPw9nMlVq4jJzKS6twMrchH7uzq02uCUeLEqlkmnTpukd69+/P+3btyclJUV3\nLCwsDGNjY9544w29ABDA9OnT2bVrF4cPH643CAQwxqcTrpWXqbKzoYPPKL1z1k6uePcPoOLqb/Tt\n6nQHnkyIti389EVW706s899LJ09f8PSlsryEcT3N4UYGERERLFu2jA0bNhjUCmqKmmBRXl4eXbp0\nMTifl5cHgJWVlcE5RWPbXoUQQgghxENPgkBCCCGEAP7YTVDfxFaNhy1NmrvSRoI+4rbcGkh0s67+\nAfLw8MDIyDD7srOzM2fPngWgtLSUjIwMbG1t2bFjR539m5qakpWV1eAYLl26hKUJTH1sIH/633EG\ngc3ziRasXn3+Np9UCHE6I7fRfycBjE0t2J0BK2YEodVqiYiIIDk5maFDhzb7np6enhw5coTExER8\nfX31zmk0GtLT0zEzM6Nz585N7rPmd1PtHUdCCCGEEOLhJEEgIYQQQuhImjQhmq7udFBQWpRPVlYe\nPfvV3c7Y2Bjt7zPIRUVFaLVaCgoKCAkJafFYiouLAbC3t68zsHnd3r7FfQsh/rA5KrXeAJD6agbt\nXNx1u2+0WtgSnYpNfj4A5ubmLbrno48+yvfff8+uXbsIDAykY8eOunPfffcdxcXFjBs3DlNT0yb3\naWNT/TtCpVK1aExCCCGEEKL1kCCQEEIIIfRImjQhGtdQOiiAwptl7Dp5kbFxWTzer/7V+TVpnjw9\nPfnss89aPJ6aNFD5v08236q+43eaSqVizpw5BAYGsnDhwntyTyHulUyVusHaeRlR/8XIxAwrZ1fM\n29mj1cK5vRfo1q6Uvn0eMdjF01RKpZJ58+axYcMG3njjDYYPH46dnR1JSUmcPXsWNzc3XnrppWb1\n+cgjj2Bubk5YWBhqtVpXO2jy5Ml11ioSQgghhBCtlwSBhBBCCFGntpombefOnezdu5ecnBzKysqY\nO3cuTz75ZLP6SExMZOnSpQQFBREcHKw7vmTJEpKSkti5c+edHra4h5qaDgotrNqVgNLOst7dcxYW\nFnTp0oWLFy+iVqt1q/Oby83NDXNzc9LT09FoNAaTuImJiS3q90E3Z84cADZt2nSfRyLagrjM3AbP\nd+wXiDr7PDdvXKXwShpGxiaYWdsx4LEp/PWN2ZiYtPz1e+LEiXTs2JFt27Zx5MgRSktLad++PU8/\n/TTPP/98swM37dq1Y8mSJYSEhBAZGUlJSQlQvetIgkBCCCGEEA8XCQIJIYQQQvwuKiqKjRs34unp\nyRNPPIGpqSmPPPLI/R6WeMA0lA7qVjXpoBpKofjUU0+xZs0aPvvsM958802DCdiioiJycnLo1q1b\nvX2YmJgwevRo9u3bR0hICHPnztWdS01N5fDhw00bsBCiXsWlFQ2eb99jAO17DDA47jusB5aWlro/\nr1ixot4+AgMDCQwMrPOcn58ffn5+TRprQ/eo4e/vj7+/f5P6E0IIIYQQrZcEgYQQQgghfnf8+HEA\nli1bhqOj430ejXgQNZYOqi4JF26QqVLXu7Nu7NixpKWlsWfPHubNm4efnx9KpRK1Wk1OTg5JSUmM\nGTOG1157rcH7zJw5k/j4eHbs2EFqaiq9e/cmLy+P6OhoBgwYQExMTLPGLZquvt1/4uFiZd6y1+eW\nthNCCCGEEOJOkG+jQgghhBC/u3GjenJfAkCiPo2lg2qoXUPpFefPn8+AAQPYu3cv8fHxaDQa2rVr\np0v39OijjzZ6D1tbWz7++GP+85//EBsbS1paGq6urixYsAClUnnfgkCXL1/mwIEDxMXFoVKpKC4u\nxsHBgf79+zN9+nScnfV3SWm1Wg4ePEh4eDhXrlzh5s2b2NnZ0blzZ8aOHcuIESN0QZcaU6ZM0f3/\n3apHJPWORD/3+nf03Y12QgghhBBC3AkSBBJCCCFEm7dlyxZCQkJ0f649obxp06YGJ36lzk/b0lg6\nKADzdvb0f2FZve3qS9M0cOBABg4c2KRx1Pf3zcHBgTfeeKNZbe62o0ePsnfvXnx8fOjVqxcmJiZc\nvHiR/fv3Exsby6pVq3ByctJd/+233/Ljjz/i4uLC8OHDsba25saNG6SmpvLLL78wYsQIXFxcCAoK\nIiwsDIAnnnhC197T0/OeP6NoG9yVNvh0cWzWbsC+XR3bZH09IYQQQgjx4JAgkBBCCCHaPB8fHwAi\nIyNRqVQEBQXd5xGJB5Wkg2q+Rx99lCeffBJTU1O946dPn2bZsmX88MMPLFiwQHc8PDwcJycn1q1b\nh7m5uV6bwsJCAJRKJcHBwURGRgJICjZxz8wY6cWSzTFNqgumUEDwCK+7PyghhBBCCCEa0HbfRoUQ\nQgghfufj44OPjw+JiYmoVCq9CWWVSnUfRyYeNJIOSl+mSk1cZi7FpRVYmZvgZm04M157l09tfn5+\ndO3alVOnThmcMzY2xsjIyOC4ra3t7Q+6BWrvFoyMjNQFnwAWLlyIUqnU/Tk9PZ1vv/2W3377jfLy\ncnr06MHMmTPp1avXPR+3uPP8PJxZOMmH1bsTGwwEKRTw5uS++Hk8nD/7QgghhBCi9ZAgkBBCCCHa\nrFsnsAs0Zfd7SOIBJ+mgqp3OyGVzVKrB51BalE9WVh49rxfpjmm1Wg4fPkxkZCQZGRkUFRVRVVWl\nO29iov9KMnr0aHbu3MmCBQsYPnw43t7ePPLII1hbW9/dh2qAj48PGo2GsLAwPDw8GDx4sO6ch4cH\nGo0GgLS0NLZu3cojjzzCuHHjuHbtGr/++iv/7//9P9asWYOrq+v9egRxB43364KLvRVbolNJuGD4\nu6BvV0eCR3hJAEgIIYQQQjwQJAgkhBBCiDanvgns1LiLKArzOJ2RK5N3ol5tPR1U+OmLDe6CKLxZ\nxq6TFxkbl8Xj/TqzadMmduzYgaOjI/3798fJyQkzMzPgjxSMtc2dOxcXFxcOHDhAaGgooaGhGBsb\nM2DAAObMmUPHjh3v9iMa8PHxwcXFhbCwMDw9PQ3SzyUmJgJw/PhxFi5cSGBgoO5ceHg469atIyws\njPnz59/TcYu7x8/DGT8PZ4PFBP3cnR+6oK8QQgghhGjdJAgkhBBCiDalKRPYSzbH8Obkvjzer/O9\nHZxoFdpyOqjTGbmNPjcAWli1KwFLRTlhYWF07dqVTz75BEtLS73LoqKiDJoaGRnx5JNP8uSTT1JQ\nUEBycjLR0dH88ssvXLx4kXXr1hnUF3pQ9OrVSy8ABDBmzBi++OILUlJS7tOoxN3krrSRoI8QQggh\nhHigSRBICCGEEG1GUyewtb9PYCvtLHFrpwCgsrKyzmtr0kCJtqWtpoPaHJXapB1QUP1z9E14LFqt\nFj8/P4MAUG5uLlevXm2wDzs7O4YOHcrQoUMpLCwkISGBCxcu0L17d6A6YFRRUdGiZ2mK2rs8yjT5\nFJc2fC8vL8NdXyYmJtjb21NUVFRHCyGEEEK0dpGRkaxevdpgN7AQQjwoJAgkhBBCiDajuRPYW6JT\nef+5fkD1hPWtiouLuXz58p0comhF2lo6qEyVulm1kAAyChQoSis4c+YMVVVVGBkZAVBSUsLatWsN\ngqvl5eWkpaXRq1cvveMVFRW6IIq5ubnuuI2NDZmZmZSVlelSzN0JdaWMLC3KJ/nCdapiMxlVT8rI\n+uoWGRsb69VBEkIIIcTdM2XKFLy9vVmxYsX9HooQQjwQJAgkhBBCiDahJRPYCRdukKOuwM3NjTNn\nzpCVlUXnztUp4qqqqvjyyy8pKyu7G8O9pxITE1m6dClBQUEGtU5E49pKOqi4TMNAaGNMLdvRoWc/\nUlKSeP311/Hz80Oj0RAXF4eZmRmenp6kp6frri8rK+P//u//6NixI927d0epVFJWVkZcXBxZWVkE\nBATofgYBfH19SU1NZdmyZfTp0wdTU1M8PDwYNGhQi5+zsZSR2XnFkjJSCCGEEEII0WpIEEgIIYQQ\nbUJLJrBr2j399NOsWbOGxYsXM3z4cMzMzEhISKCiogIPDw8yMjLu8GjvPJVKxZw5cwgMDGThwoX3\neziiFWosFVp9HnvqBYyunCI6Oprdu3djZ2fHoEGDeOGFF1i+fLnetebm5rz00kskJiby22+/cezY\nMSwtLenYsSMLFixg7NixetdPmzYNjUZDbGysbrdRYGBgi4NADaWMVCiqU0NqtVV6KSMftpR/Qggh\nhBBCiIeLBIGEEEII0Sa0dAK7uLSCp36feN6+fTuRkZG0a9eOwYMHM3PmTINJ7NaoR48ebNiwAVtb\n2/s9FPEAszJv/NXBvJ09/V9YpnfMzsaKp158kRdffNHg+lvTtJiYmPDMM8/wzDPPNGlMFhYWLFiw\ngAULFjTp+sY0lDLS2MwShUJBeXEB8EfKSAkCCSGEENVSUlLYvn07Z86cobCwEBsbG7p27crjjz/O\n8OHDddedO3eObdu2cebMGYqKirC3t2fAgAEEBQXh6Oio1+eSJUtISkrip59+YuvWrRw4cIBr165h\nb2/PqFGjeOGFFzAxqf6OUlObByApKYkpU6bo+qnZ8V57YdRzzz3Hd999R2JiIoWFhfz973/Hx8eH\ntLQ0Dh48SGJiIrm5uZSWluLs7ExAQADTpk2jXbt29+DTFEKIO0eCQEIIIYRoE5oyge019qV6240d\nO9ZgFwIYTmID+Pj4sHPnziZd+yAwNzfHzc3tfg9DPOD6ubcs2NHSdvdaYykjjU3NsHJypUh1kcxf\ntmFu68TVRAVP9rbBzrzeZkIIIUSbsG/fPtavX4+RkREBAQF06tSJ/Px80tLS2L17ty4IFBERwdq1\nazE1NSUgIABnZ2euXLnCvn37iI2NZeXKlbRv396g/5UrV5KcnIy/vz9WVlacOHGCrVu3kp+fr9vl\n7uHhQVBQECEhISiVSgIDA3XtfXx89PrLzs7m7bffxtXVldGjR1NaWoqVlZXuWY4ePYqPjw/9+vVD\nq9WSlpbGTz/9xMmTJ/n000+xtLS8Wx+lEELccRIEEkIIIUSbcCcmsGNiYggLCyMrKwu1Wo2trS2d\nOnVixIgRTJw4UXedWq1m27ZtHDt2DJVKhYmJCd27d+fZZ5/Fz8+vzvtERUURHh5Oeno6ZWVluLi4\nMHr0aJ5++mlMTU1bNPYaW7ZsISQkBKheIRkZGak7t3DhQpRKZZ01gWpWXm7fvp3Q0FAiIyO5fv06\nSqWSqVOn8vjjjwOwd+9edu/eTXZ2NjY2NowdO5bg4GBd+qzamrPy8+rVq4SGhpKQkMD169cxMzPD\nycmJXr16MXPmTGxsHv46PA8Sd6UNPl0cm1Vbq29Xx1ZTL6kpKSPdh03l0ol9FGafp/JCElqtloMx\nPkwd6XsPRiiEEEI8mLKystiwYQNWVlZ89NFHdOnSRe98bm71v7GXL19m/fr1uLi4sGLFCpycnHTX\nxMfH8+6777Jx40beeecdg3tkZ2ezbt063fe/F198kddff52DBw8ya9YsHBwc8PT0xNPTUxcEaqjW\n5ZkzZ3juueeYOXOmwbnnnnuO+fPnY2RkpHc8IiKCNWvWsHv3bp599tmmf0BCCHGfSRBICCGEEG3C\n7U5gh4eHs27dOhwcHBg0aBC2trbk5+eTmZnJgQMHdEEglUrFkiVLUKlU9OnTB39/f0pKSjh+/DjL\nli3jtdde0wVPanz22WccOHAAZ2dnhg4dirW1NefOneO7774jPj6eDz74AGNj4xY/u4+PDxqNhrCw\nMDw8PBg8eLDunIeHBxqNpsH2n3zyCefOnWPAgAEYGxvz66+/snbtWkxMTMjIyODgwYMMHDgQX19f\nYmJi+P777zE3Nzd4OW7Oys8bN27w1ltvUVxczIABAxg6dChlZWXk5ORw6NAhJk+eLEGg+2DGSC+W\nbI6pN2VabQoFBI/wuvuDukOakjLS3MaRbo8G6R3r3rcHPj5ede7+q7Fp06bbHp8QQgjxoNqzZw+V\nlZVMnz7dIAAE4Oxcvahq7969VFRUMG/ePL0AEICvry8BAQHExsZy8+ZNg502L730kt53PwsLC0aN\nGsX3339PWloaAwcObNaY7e3tCQoKqvOcUqms8/iYMWP48ssvOX36tASBhBCtigSBhBBCCNFm3M4E\ndnh4OCYmJnz++efY2dnpXVtYWKj7/1WrVnHt2jUWL17MyJEjdcc1Gg1Llixh48aNBAQEYG9vD1Tv\nzDlw4ABDhgxh0aJFmJmZ6drU7ODZvXs3TzzxREsfGx8fH1xcXAgLC8PT09NgVWRiYmKD7a9du8a6\ndeuwtrYGYOrUqcyfP59//etfWFtb8/nnn+te5IODg5k3bx7bt29n6tSpuuBVc1d+/vrrr6jVaubN\nm2fw7CUlJQYrM8W94efhzMJJPqzendjgz5FCAW9O7tuq6uU0JWXknWwnhBBCtGaZKjVxmbkUl1aw\n++dYiksr8Pf3b7DN2bNngep6PampqQbnCwoKqKqq4vLly3Tv3l3vnJeX4cKSmsVDRUVFzR6/h4dH\nvbvtKyoqCA8PJyoqiqysLDQaDdpaX3yuX7/e7PsJIcT9JG8sQgghhGgzmjuB7WBtzk+xGRSXVnD+\nagEVFdo6d+TY2toCkJGRQVJSEsOGDdMLAAFYW1szY8YM/va3v3HkyBHdzqGwsDCMjY1544039AJA\nANOnT2fXrl0cPny4RUGg2i/nZZr8Ju10qMusWbN0ASCADh060Lt3bxISEpgzZ45eQMfa2ppBgwbp\npY6Dlq/8vPUzgeqVn+L+Ge/XBRd7K7ZEp5JwwXBnXd+ujgSP8GpVASB4+GseCSGEEHfC6YxcNkel\n6u2uT065TKn6Bp/sTeOlMRb1fgeoWTi1bdu2Bu9RUlJicKz2d9EaNd/Lq6qqmjz+Gg4ODvWe+/jj\njzl69CgdOnQgICAABwcHXcAoLCyM8vLyZt9PCCHuJwkCCSGEEKJNacoE9qDuSiLiL/GPnQm64yoj\nVy6lJBPw+HM8M2UcE0cPoVevXnq7gmpWN2o0GrZs2WLQd0FBAVCdNx2gtLSUjIwMbG1t2bFjR53j\nNTU11V3fVHW9nJcW5ZN84TpVsZmMysht1gT9rSsxAV39nrrO1QR5ageBmrvyMyAggP/85z988cUX\nnD59Gj8/P3r37k3nzp3rrDUk7i0/D2f8PJz1Ao1W5ib0c3duNTWAbvWw1zwSQgghblf46Yt1LqYy\nMbOgFIg7d4ElORrenNyXx/t1NmhfE8j54YcfsLKyugcjrl993ydTU1M5evQo/fr1469//aveAjCt\nVsvWrVvv1RCFEOKOkSCQEEIIIdqchiawz17Oq/PlVtlrCMbmVuSmnOCLr79n/97dKO2s8Pb2Zvbs\n2Xh5eaFWqwGIi4sjLi6u3vvfvHkTqE5dodVqKSgoICQk5I48W30v5zWy84pZsjmm3pfzujS08rKh\ncxUVf+w8au7KT6VSyT/+8Q+2bNnCqVOnOHLkCFCdU/7pp59mypQpTRq7uLvclTYPVRDkYa55JIQQ\nQtyO0xm59X7HtHJ2Q3P9CoVX0rCwc2bVrgSUdpYGi4569uxJWloaycnJza7h0xwKhaJFu4MAsrOz\nARg0aJBBBoCUlBTKyspue3xCCHGvSRBICCGEEG3WrRPYDb3cAjh5+uLk6UtFWQnFuVn0crlJ0qmj\nLFu2jA0bNuhWNL7yyitNClLUBFA8PT357LPPbvt5Ght/Da2Wel/O75aWrPzs3Lkzf/7zn6msrCQj\nI4O4uDh27drFxo0bsbCwYOzYsXdzyKINephrHgkhhBC3Y3NUar3/NrbvMYDc1JNcTYrCtlM3LOza\nsyU6VffvZG5uLs7OzkyePJl9+/bx5Zdf0qlTJ1xdXfX6qaio4Ny5c/Tp0+e2xmpra0tubm6L2rq4\nuADVu9drf58vKChgw4YNtzUuIYS4XyQIJIQQQgjxu4ZebmszMbPAtpMXVV0dGeNoTUREBMnJyfTs\n2ROA5OTkJgWBLCws6NKlCxcvXkStVmNjc3s7Khoaf03KC6226vf/ovdyfrfdzspPY2NjunfvTvfu\n3enVqxd/+ctfOHr0qASBxF3xsNY8Em3Hli1bCAkJYfny5fj4+DSpzZIlS0hKSmLnzp1Nvs+UKVPw\n9vZmxYoVt3VvIcSDL1OlbjBdqoVdezoPnEBW7G7O7vkndm6PcCXOEZsrR7hxNQsrKyuWL1+Om5sb\nr7/+OmvWrOG1116jf//+uLq6UllZiUql4syZM9ja2vLFF1/c1nh9fX2Jiori/fffp1u3bpiYmNCn\nTx+8vb0bbfv222+Tm5vLkSNHWLx4Mb179yY/P5+TJ0/i6uqqS4kshBCtiQSBhBBCCCFo/OVWfTWD\ndi7uevnDEy7coLIoBwBzc3O8vLzo06cPR44cISIios4gRWZmJg4ODrpaQk899RRr1qzhs88+4803\n3zRIr1ZUVEROTg7dunW7rfEbm1miUCgoLy7QG3+mSt1gv3dKc1d+pqWl0bFjR4PPIz8/H6j+vIW4\nWx7GmkdCCCFES8VlNr6rxtnLH0t7JTm/HaUoJ5OCS2c5VNKJkQO8GTdunO66Rx99FA8PD3766ScS\nEhI4ffo0FhYWODo6MmzYMEaMGHHb433llVcAiI+P58SJE2i1WoKCgpoUBFIoFAwdOhQvLy9OnDjB\nzp07cXJyYty4cUybNo0FCxbc9viEEOJekyCQEEIIIQSNv9xmRP0XIxMzrJxdMW9nj1YLGtUFbhir\nGT6gL76+vgAsWrSId955hzVr1rBz50569uyJtbU1ubm5ZGZmcuHCBVauXKkLAo0dO5a0tDT27NnD\nvHnz8PPzQ6lUolarycnJISkpiTFjxvDaa6/d1viNTc2wcnKlSHWRzF+2YW7rhEKhYP8RW4Z0s2/G\nJ9UyzV35eejQIcLDw+nduzcdOnSgXbt2XL16ldjYWExNTXnyySfv+piFeNhqHomHk0qlYs6cOQQG\nBrJw4UImT57MyJEjad++/f0emhDiIVFcWtH4RYB1+854tv+j5uSs0T3qrJ/n7u7OwoULm9Rn7d2G\ntwoMDCQwMNDguJ2dHYsXL66zjVKpbHTXo7m5OfPnz6/z3KZNm5o8DiGEeFBIEEgIIYQQgsZfbjv2\nC0SdfZ6bN65SeCUNI2MTzKztGDZuKssXzcHEpPprlbOzM6tXr2bnzp0cOXKEw4cPU1VVhb29PV26\ndGHy5Ml07dpVr+/58+czYMAA9u7dS3x8PBqNhnbt2tG+fXuefvppHn300dseP4D7sKlcOrGPwuzz\nVF5IQqvVkjm0N0O69W+07Z3QnJWfI0eOpLy8nN9++420tDTKyspwcnJixIgRTJ061eAzFEIIUc3W\n1hZbW9v7PQwhxEPEyrxl04fNbXfu3Dm2bdvGmTNnKCoqwt7engEDBhAUFKSXhi0tLY2DBw+SmJhI\nbm4upaWlODs7ExAQwLRp02jXrp1evxUVFezdu5cDBw6Qk5NDeXk59vb2eHh4MHnyZPr160dkZCSr\nV68GDOsBBQUFERwc3KLPQAghHgQSBBJCCCGEoPGX1PY9BtC+xwCD46Mf742lpaXeMUtLS55//nme\nf/75Jt9/4MCBza6VU1tTXrLNbRzp9miQ3rFBQ3vj4+NR54rIhlZeLly4sN4VnMHBwfW+KDd15WfP\nnj11NZaEEOJhV3s3z7Rp0/j6669JTEykvLycRx55hLlz59K1a1cKCgr49ttviY2NpaioCHd3d4Ma\ndA3V5YmKimLbtm1kZWVhaWlJ//79eemll+odV0VFBaGhoURGRpKbm4ujoyOjR49m+vTpzX7GS5cu\nERoaSnx8PPn5+VhbW+Pr60twcLBBilAhxIOln3vL6uA1p11ERARr167F1NSUgIAAnJ2duXLlCvv2\n7SM2NpaVK1fqdjju27ePo0eP4uPjQ79+/dBqtaSlpfHTTz9x8uRJPv30U73v56tWrSIqKoquXbvy\n2GOPYW5uzvXr1zlz5gynTp2iX79+eHh4EBQUREhICEqlUm9nj9Q4E0K0dhIEEkIIIYTg3rzc3k2t\nffxCCCEgJyeHt99+m86dOxMYGIhKpeLo0aMsWbKElStXsmzZMqysrBgxYgRqtZro6GhWrlxJeXl5\no33v2LGDL7/8Emtrax577DGsra05deoUixcvxsrKyuB6rVbLhx9+SExMDB07dmTy5MlUVFRw4MAB\nLly40KznOnnyJMuXL6eyspJBgwbRsWNHcnNzOXr0KCdOnGD58uWN1r4TQtw/7kobfLo4Nlh/8lZ9\nuzo2OaXq5cuXWb9+PS4uLqxYsQInJyfdufj4eN599102btzIO++8A8Bzzz3H/PnzMTIy0usnIiKC\nNWvWsHv3bp599lkANBoN0dHRdO/enU8//dSgjVpdXR/T09MTT09PXRBIdv4IIR4mEgQSQgghhODu\nv9zeba19/EIIIapTEL344ot6O0m///57Nm/ezNtvv83w4cNZsGABCoUCAD8/P1asWEFOTo5BXwUF\nBWzYsIETJ06QnZ1NYmIijo6ObNy4kYCAAABmzZrFhx9+SFhYGOnp6URGRtK+fXtCQkI4evQoKSkp\nuLu7s3r1al2QJjg4mLfeeguonjxdsWIF8fHxVFRUUFJSQkFBAceOHWPp0qUsXHUfdXMAACAASURB\nVLiQgIAAPvnkE8zNzfnoo4/o3PmPeiEXLlxg0aJFrFmzhs8+++yufa5CiNs3Y6QXSzbHoNU2fq1C\nQZ21gGrLVKmJy8yluLSCX8O3UqgpYenSeXoBIABfX18CAgKIjY3l5s2bWFpaolQq6+xzzJgxfPnl\nl5w+fVoXBFIoFGi1WkxNTXW/O2uzsZHvwkKIh58EgYQQQgghfnenX27vtdY+fiGEaOuUSqVu4rJG\nYGAgmzdvpry8nJdffllvEnPUqFF8/PHHFBcX67UpLS3l448/RqvV0rdvXywsLMjMzMTU1JQPP/yQ\npUuXMnDgQBQKBbNnz9alBI2NjSUmJgZ/f39sbGywsbHB1NSUZcuWsX79emxtbbGxsWH69Ol8+OGH\nHDx4kG7dujFw4EDc3d3Zu3cvJ06cID4+XjeWgwcPotFo+NOf/qQXAALo2rUrjz/+ODt27CArK8vg\nvBDiweHn4czCST6s3p3Y4HdNhQLenNwXP4+6d5ufzshlc1Sq3sKlc4dj0eRe591//sTIX08ZLFIq\nKCigqqqKy5cv0717dyoqKggPDycqKoqsrCw0Gg3aWoO6fv267v+trKwYNGgQsbGxvP766wwbNoze\nvXvTs2dPzM3NW/hpCCFE6yJBICGEEEKI392pl9v7pbWPXwgh2oraK+CtzE1ws67+pe3p6WmQqqim\nGLqrq6tBDTojIyPs7OwoKyvT7z8zk44dO/Laa6/x/PPPs2LFCrp168acOXP46quvWLVqFf/+97+x\nsLCgQ4cO2NnZAXDs2DHef/99Xa2enj178tRTT7F9+3YiIiJ45plngOr6GJmZmWi1WubPn8/EiRMB\nMDEx4fLlyyQmJmJrawvA2bNnAcjIyGDLli0Gn8Xly5cBJAgkRCsw3q8LLvZWbIlOJeGC4e7zvl0d\nCR7hVe93zPDTF+v8nlpRWh3IPhkdwalfwNPFlva2lgbtS0pKAPj44485evQoHTp0ICAgAAcHB0xN\nTQEICwszSJH55z//mdDQUH7++Wc2b94MgJmZGcOGDePll1/G3t6+eR+EEEK0MhIEEkIIIYSo5XZf\nbu+31j5+IYR4mNW1Ah6gtCifrKw8evYzjOAbGxsD1Fm3B6oDQbVXwKvVagoKCujduzdPP/00UF0T\nA6B///6kp6dz6NAhjhw5wmOPPQb8kQ5p5MiR+Pr66trY2NgwadIktm/fTkpKiu4eFRUVFBYW0rFj\nRyZMmKA3Hjs7Ozp16sSVK1d044HqQu4NuXnzZoPnhRAPBj8PZ/w8nA2C2f3cnRtMM3w6I7fehUrG\nZhYA+D7/Z4zNLFAo4P0ZAXV+X01NTeXo0aP069ePv/71r7rfkVBdy2zr1q0GbczMzAgODiY4OJjc\n3FySkpKIjIzk0KFD5OTk8NFHH7XgkxBCiNZDgkBCCCGEELdo6cvtg6K1j18IIR5G9a2Ar1F4s4xd\nJy8yNi6Lx/vVvyPm1t/tmpIKvfMqlQqA7t27Y2JS/cpvbW0NQH5+Pn379uXQoUOkp6frgkA1gZru\n3bvr+rG2tkatVutWyBcVFenOJSQkAODk5FRnjQ1PT09dEKgmePX555/j7u5e73MJIVoXd6VNs75X\nbo5Krff3n7WzK8XXr1B07SJ2rj3QamFLdGqdQaDs7GwABg0apBcAAkhJSTHYGXkrZ2dnRo8ezahR\no3j11Vc5c+YMarVaFwxXKBRUVVU1+bmEEKI1kCCQEEIIIUQ9mvty+6Bp7eMXQoiHRUMr4PVoYdWu\nBJR2lgaTnzn5xSz65qjBLqKENBUaTSmXrlcHaUpLSwF06dgAunXrxpEjR0hMTOSRRx4B/gjqXL16\nlYKCAgDatWun1yYuLo5z584B6E2KJicnA2BhYVHnY9QutP7II49w5MgRkpOTJQgkRBuVqVIb/O6q\nrX2PQVxPO8Xlk/sxt3HEwtaZhAs3yFSpcVfaUFFRwblz5+jTpw8uLi4AJCUlMWXKFF0fBQUFbNiw\nwaDvgoIC8vLyDH7/lJSUUFJSgrGxsS5gDtW/O3Nzc2/ziYUQ4sEiQSAhhBBCCCHqoFKpmDNnDoGB\ngSxcuPCe33/OnDkAbNq06Z7fWwhxZzW0Av5Wda2AVxXcJLngCl6d6p5ELauo0u0iqil0XrO7B2D0\n6NGEhISwa9cuzMzMgOqdPlqtlq+++kovnVyNMWPGEBcXx7fffqsXAFKr1URHRwN/1Oe4Ve17jxkz\nhh9++IGQkBC8vLzo0aPHLc+rJSkpCR8fnwY/FyFE6xWX2XBQxcLOmS4BT3AxJozfdn2BbcdumNs6\n8fGqeDpZV3HmzBlsbW354osv8PLyolevXhw5coTFixfTu3dv8vPzOXnyJK6urro6ajWuX7/OG2+8\ngbu7O+7u7jg7O1NcXMzx48fJy8tjypQpevXWfH19iYqK4v3336dbt26YmJjQp08fvL2978pnI4QQ\n94IEgYQQQgghhBBCiLuksRXwdam9Av50Ri4ZqkLaKR0abvT7LiJ/o+rJzLS0NCorKzE2NkapVDJr\n1iw2bdrE8uXLqaioIDU1lTfeeAONRoOLiwvp6el63Y0cOZLo6GhiYmJISkpCq9WyceNGfv31V3r1\n6sXRo0e5fv06Wq3WICVc7b5sbGxYsmQJf//731m0aBG+vr506dIFhULBtWvXOHv2LGq1mm3btjXr\nMxJCtB7FpRWNXuPo2RdLBxdUvx1DnZOB+up54oqcUPTsyrBhwxgxYgRQXQft3Xff5bvvvuPEiRPs\n3LkTJycnxo0bx7Rp01iwYIFevy4uLsyYMYPExEQSEhIoLCzExsYGV1dXXnrpJV2/NV555RUA4uPj\nOXHiBFqtlqCgIAkCCSFaNQkCCSGEEEIIIYQQd0ljK+AbaueutGn2LqL4yxrs7Oy4ceMGYWFhTJ06\nFYCnnnqKoqIi3nvvPcrLy7l48SIDBw5k9uzZvPbaawZ9KRQK/vKXvxAaGkp8fDxpaWnExMQwZswY\npk+fTmhoKEVFRezdu5eJEyfq2hUUFFBYWKiXjs7X15e1a9eybds2Tp06RXJyMiYmJjg6OuLr68vQ\noUNb9BkJIVoHK/OmTT9aOrjQdeiTuj/Pf7w3Tw3yMLjOxsaG+fPn19nHrTuora2tmT59OtOnT2/S\nGOzs7Fi8eHGTrhVCiNZCgkBCCCGEEEIIIcRd0pQV8Obt7On/wjKDdjW7iG49V1vPCfNI/ukz3Z/L\nOw1g87YZfP7x+/z73//m1KlTeHl5kZubyy+//IKvry9/+ctfCAgI0LWZO3eurkZQbSYmJkyfPp3N\nmzfj7e3NihUrdOcOHjzI4sWL2bBhAydOnMDDw4OrV6/i5OSEv78/MTExejuElEolf/rTnxr9LIQQ\nD59+7s6NX3QH2wkhhNAnQSAhhBBCCCEacenSJb7++muSk5MpLy/H09OToKAg/Pz89K4rLy9nx44d\nHD58mOzsbIyNjfHw8GDKlCkMHz7coF+tVsvu3bvZs2cPV69excbGhiFDhvDiiy8aXBseHs66desI\nDg4mKCjI4HxeXh6zZ8/Gzc2NtWvX3rmHF0LclqaugK+rXUt3EV0uNmbVqlX88MMPnDhxgqSkJCwt\nLenfvz/Tpk3Dy8urRf3W1rlzZ1auXMl//vMfEhISSEhIwN3dnaVLl3Lp0iViYmKwsrK67fsIIVo/\nd6UNPl0cm5Uas29XR9yVNndxVEII0XZIEEgIIYQQQogG5OTksGjRItzd3Rk/fjx5eXlER0ezbNky\nFi9erMslX1FRwXvvvUdSUhJubm5MmjSJ0tJSfv31Vz766CPS09OZOXOmXt//+te/2LlzJ46Ojowf\nPx5jY2NiYmJISUmhoqICE5M/vq6PHj2ar776iv379zNt2jSMjIz0+oqIiKCyspLx48ff/Q9FCNFk\nt7MC/si5q41eV98uIicnJ4PaGPUJDAwkMDCw3vM7d+6s87ibmxtLly41OP7zzz8D1YEiIYQAmDHS\niyWbY5qU3lKhgOARtx+sFkIIUc2o8UuEEEIIIYRou5KSkhg3bhwffvghs2bNYuHChXz44YcYGRmx\nbt06iouLAdi+fTtJSUn4+/uzdu1aXn75ZebPn8+6detQKpX8+OOP/Pbbb7p+f/vtN3bu3EnHjh1Z\nu3Ytr7zyCnPmzGHt2rUYGRlx44b+alkLCwseffRRcnNzOXnypN45rVbL/v37MTc359FHH737H4oQ\noslqVsA3R80K+NvZRXS3abVa8vLyDI7Hx8cTHR1N586dcXV1vevjEEK0Dn4eziyc5EOtLJF1Uijg\nzcl98fOQVHBCCHGnSBBICCGEEEIIIFOl5qfYDLZEp/JTbAYXr1XXx7C2tjZIv+bl5cXo0aPRaDQc\nPXoUqN6Jo1AomDt3LsbGxrpr7ezsdMWI9+/frzt+4MABAJ5//nlsbP5Id2JmZsasWbPqHGNN8fW9\ne/fqHT99+jQ5OTmMGDECa2vrFj2/EOLumTHSq9GJzxq1V8A/yHU0ysvLmT17Nu+++y4bN27kyy+/\n5L333uPdd9/F2Ni43qLtQoi2a7xfF1bMCKBv17oD4327OrJiRgCP97v3uwinTJnCkiVL7vl9hRDi\nXpB0cEIIIYRotbZs2UJISAjLly/Hx8enRX1MmTLFoNi1aFtOZ+SyOSrVIE99aVE+WVl5jB7WHUtL\nS4N2Pj4+REZGkp6eztChQ8nOzsbJyQk3NzeDa/v27QtAenq67tj58+cB8Pb2Nri+d+/eBuneALp0\n6YK3tzcnT54kNzcXZ+fqid59+/YBMGHChKY+thDiHqpZAb96d2KDqZBuXQH/INfRMDExYcKECcTH\nx5OSkkJpaSm2trYMGzaM5557Dk9Pz7s+BiFE6+Pn4YyfhzOZKjVxmbkUl1ZgZW5CP3dnqQEkhBB3\niQSBhBBCCPHAioyMZPXq1SxcuLDBWgVCtFT46YsNTsoW3izjl/MF7IvLMliVam9vD4BGo0Gj0QDg\n6Fj3ylYHBwcAioqKdMdq0sjV9FObsbExtra2dfY1ceJEkpKS2LdvHzNmzCAvL4+YmBg8PT3p0aNH\nA08rhLifxvt1wcXeii3RqSRcMAzq9O3qSPAIL4MUSA9qHQ0jIyNeffXVe3Kvh9mSJUtISkqqt+5S\nXeT7kXgYuCttJOgjhBD3iASBhBBCCNFqTZ48mZEjR9K+ffv7PRTRCp3OyG10VT5A+U0Nq3YloLSz\n1Juczc/PB6rTxdWkYKurPkbt47VTtVlZWen66dChg971lZWVFBYW6nb61DZkyBDs7e2JiIggKCiI\niIgIKisrGT9+fCNPLIS431qyAr6lu4hE65WYmMjSpUsJCgoiODj4fg9HiDuipKSEoKAgvLy8+Pjj\nj3XHy8rKmD59OuXl5bz11lt6tQ337NnDhg0beP311xk7diwAarWabdu2cezYMVQqFSYmJnTv3p1n\nn30WPz8/vXtWVFSwd+9eDhw4QE5ODuXl5djb2+Ph4cHkyZPp16+fLqgK1XUgp0yZomt/68/guXPn\n2LZtG2fOnKGoqAh7e3sGDBhAUFCQwUKgmgDv9u3bCQ0N5fDhw+Tk5DBq1CgWLlyoF8xt3749ISEh\npKWloVAo6NOnDy+//DKdO9/7tHhCiIeTBIGEEEII0WrZ2trWu1tCiMZsjkpt0sr6mzeyqSgrZUt0\nqt7kamJiIgCenp5YWlrSsWNHrl69ypUrV+jUqZNeHwkJCQB069ZNd6xbt26cP3+epKQkgyDQmTNn\nqKqqqnM8JiYmjBs3jv/+97/Exsayf/9+LCwsGD16dFMeWwjxAGjuCviW7iISD7633nqL0tLSZrUZ\nPHgwGzZs0O0yFaI1sLCwwMvLi5SUFG7evKlLtXvmzBnKy8sBiI+P1wsCxcfHA+Dr6wuASqViyZIl\nqFQq+vTpg7+/PyUlJRw/fpxly5bx2muv8fjjj+var1q1iqioKLp27cpjjz2Gubk5169f58yZM5w6\ndYp+/frh4eFBUFAQISEhKJVKvd11tdNNR0REsHbtWkxNTQkICMDZ2ZkrV66wb98+YmNjWblyZZ0L\n05YvX05qair+/v4MHjwYOzs7vfOxsbHExMTg7+/PhAkTyMrK4sSJE6SmprJ+/Xp51xFC3BESBBJC\nCCHEHRUZGUlsbCznz58nLy8PY2Nj3N3dmTBhgt5LHTS8Qi4nJ4ekpCQAVq9erVuhB7Bp0yaUSmWD\nNYEuXbrE1q1bSUhI4MaNG1hbW+Pq6sqoUaOYOHFio89RWVnJvn37OHjwIBcvXqSyshI3NzfGjh3L\npEmTUDS1wrd4IGWq1E2usVFRVsLVxJ9JMB1HpkqNu9KG1NRUDh8+jLW1NUOGDAFgzJgxfPvtt/z7\n3/9m6dKlupo+hYWFfP/99wC6Vaw11+/fv5///ve/BAQEYGNTPSFcVlbGN9980+CYxo8fT2hoKF98\n8QXXr19n/PjxddYtEkI8PKSOxsOpJbuZa+9AFaI18fX15bfffiMpKYmBAwcC1YEeIyMjvL29dUEf\nAK1WS2JiIh06dECpVALVQZ1r166xePFiRo4cqbtWo9GwZMkSNm7cSEBAAPb29mg0GqKjo+nevTuf\nfvqpQa1FtVoNVC/m8fT01AWB6tp9d/nyZdavX4+LiwsrVqzAyclJdy4+Pp53332XjRs38s477xi0\nvXbtGuvWras3mHPs2DHef/99XaAL4JtvviE0NJSIiAieeeaZRj9XIYRojASBhBBCCHFHrV+/Xle8\n3sHBAbVazYkTJ/jHP/7B5cuXeeGFFwza1LVCzsfHB2tra2JiYggICNArMN3YxMfx48f58MMPKS8v\nx9/fn5EjR6LRaMjIyGDr1q2NBoEqKir44IMPOHXqlC5wZGZmRkJCAv/85z9JSUnhrbfeatkHJB4I\ncZm5Tb7WxqUr19NOo8m9wj8qfsPTwYTo6Giqqqp47bXXdGndnn76aU6ePElMTAz/+7//y4ABAygt\nLeWXX36hoKCAZ555ht69e+v67dWrF1OmTGHnzp38z//8D8OGDcPY2JiYmBjatWtXb30hqJ40HDhw\nIDExMQCSCk6INkTqaNw+lUrFnDlzCAwM5Nlnn+Xrr78mOTmZ8vJyPD09CQoKMkgrVV5ezo4dOzh8\n+DDZ2dkYGxvj4eHBlClTGD58uME9YmJiCAsLIysrC7Vaja2tLZ06dWLEiBF630NurQm0evVqIiMj\nAQgJCSEkJER3bc2il4ZqAqWlpfHjjz+SnJyMRqPBwcGBgQMHMm3aNIN/V2rutWnTJk6dOsWuXbu4\ncuUKVlZWDB48mNmzZ0uwSdyWW4PWzm7dgerASe0gUPfu3Rk6dChffPEFly9fxtXVlfT0dNRqNUOH\nDgUgIyODpKQkhg0bphcAgup3gxkzZvC3v/2NI0eOMHHiRBQKBVqtFlNT0zoXb9UsvmmKvXv3UlFR\nwbx58/QCQFAd2AoICCA2NlZvh1ONF154ocHdPCNHjtQLAMEfi31SUlKaPEYhhGiIBIGEEEIIcUet\nXbuWjh076h2rqKhg2bJlhIaGMmHCBIOXp4ZWyMXExDBkyJAmFz4uLCxk5cqVVFVVsXz5cry9vfXO\n5+Y2Pvn/3//+l1OnTjF58mTmzZunWzlYVVXF2rVriYiIYNiwYQQEBDRpTOLBU1xa0eRrzawd6Dxo\nEldOR3L8l0NctregW7duTJ8+nf79++uuMzEx4YMPPuCnn37i559/ZteuXRgZGeHh4cErr7xiMGEB\nMG/ePDp16sTu3bvZu3cvtra2DB48mJkzZ/L66683OK6xY8cSExODl5eXXpo5IYQQTZOTk8OiRYtw\nd3dn/Pjx5OXlER0dzbJly1i8eDEjRowAqr/HvPfeeyQlJeHm5sakSZMoLS3l119/5aOPPiI9PZ2Z\nM2fq+g0PD2fdunU4ODgwaNAgbG1tyc/PJzMzkwMHDjS4GGXw4MFA9c5qb29vvZ3OLi4uDT7P8ePH\nWb58OQBDhw5FqVSSlpbGnj17OHbsGB9//HGdfXz11VecOnWKQYMG4efnR0JCAvv27SM7O5u///3v\nTf9Ahfjd6YxcNkelGuy6rqqs5EJ2EQeijzF37lw0Gg3nz5/nmWeeoW/fvkB1UMjV1VWXSrfm+Nmz\nZ4HqXT9btmwxuGdBQQEAWVlZQHXtxUGDBhEbG8vrr7/OsGHD6N27Nz179sTc3LxZz1Nz76SkJFJT\nU+u8d1VVFZcvX6Z79+5657y8vBrs+9brAV1NyKKiomaNUwgh6iNBICGEEELclrrS0tzKxMSESZMm\nkZCQQHx8PI899pje+cZWyDVHZGQkxcXFTJkyxSAABH+8VNVHq9Wya9cuHBwcmDt3rl7qCCMjI+bM\nmcOBAwc4fPiwBIFaMSvzxr8Gm7ezp/8Ly3R/9hw9nfmP9+apQR71tjEzM+P555/n+eefb9I4FAoF\nkydPZvLkyQbnNm3a1GDb8+fPAzBhwoQm3UsIIYS+pKQkpk6dyssvv6w7NmnSJBYvXsy6devw9/fH\nysqK7du3k5SUhL+/P++++y7GxsYABAcH89Zbb/Hjjz8ycOBAevXqBVQHgUxMTPj8888N6n8UFhY2\nOKbBgwdjbW1NZGQkPj4+daamqktJSQmrVq2isrKSFStW0KdPH9250NBQvvnmG9auXcsHH3xg0Pbs\n2bOsXbtWl5qusrKSd955h4SEBFJSUujRo0eTxiAEQPjpi6zenVhn3UUjY2Mq27lwMCaRbdHJuJoV\nUVVVha+vL507d8bR0ZH4+HgmTpxIfHw8CoVCt0umJn1bXFwccXFx9d7/5s2buv//85//TGhoKD//\n/DObN28Gqr+rDRs2jJdffhl7e/smPVPNz+22bdsavK6kpMTgWGO1u9q1a2dwrOZ3TH31IYUQorkk\nCCSEEEKIFqlvhV+ZpgDj7NPYl19DW6qmrKxM7/z169cN+mpshVxznDt3DgB/f/8Wtb98+TJqtZpO\nnTrxww8/1HmNmZmZbpWhaJ3qClbezXZ32s2bN9m7dy82NjZ17jASQgjROGtra4KCgvSOeXl5MXr0\naCIjIzl69CiBgYFERESgUCiYO3eubnIWwM7OjunTp7NmzRr279+vCwJB9SRu7Wtr3K0i78eOHUOt\nVjNy5Ei9ABDA1KlT2bt3L3FxcVy7ds2gDlFQUJDeMWNjY8aMGUNycrIEgUSznM7IrTcAVKNdBw8K\ns9P58JtdjPM0xczMTPez07dvX06ePEl5eTnJycl06dJFF0itSb/7yiuvMGXKlCaNx8zMjODgYIKD\ng8nNzSUpKYnIyEgOHTpETk4OH330UZP6qUmL+MMPP+jG0VRSR1QI8SCQIJAQQgghmq2+FX6l6jzO\nhX9JZdlN2im7MGXUQAb2dMPIyAiVSkVkZCTl5eUG/TW2Qq45NBoNgEHKuaaqWWV45coVvTz8t6q9\nylC0Pu5KG3y6OBoEMRvSt6vjfa/Dcfz4cc6fP09sbCz5+fm8/PLLzU5pIoQQbVHtnctlmnyKSyvo\n27ebQf0OQFd3Jz09naFDh5KdnY2TkxNubm4G19akqkpPT9cdGz16NJs2bWLBggWMHDkSb29vevXq\nZbAr6E6q2R16a20RqA7qeHt7c/DgQdLT0w2CQJKOStwpm6NSGwwAAdh0qN5Rrc7OYHfmNSYGPIKZ\nmRlQ/ff38OHD7Nmzh5KSEr2/zz179gQgOTm5yUGg2pydnRk9ejSjRo3i1Vdf5cyZM6jVal1tIIVC\nUe/Om549e5KWlkZycrKulpEQQrQmEgQSQgghRLM0tMJPdfYoFaXFdB3yJE7d+nFOAS8NC8DPw5mo\nqChdoeNb3ckVcjUr9a5fv467u3uz29es7hsyZAhLly69Y+MSD54ZI71Ysjmm0ckKAIUCgkfcuR1r\nLfXrr78SGRmJvb09zz33HE899dT9HpIQQjzQ6tq5XFqUT/KF61Q4FHA6Ixc/D/1dnjUpojQajW5x\niaOjY5391yxkqR0seeqpp7C1tWXPnj2EhYWxY8cOFAoF3t7ezJ49+47ugK5RM876FtbUjL+uoI6k\noxJ3QqZK3aTFNVYOHTExs6Dg0jlySzR0nPaE7lxNUPXHH3/U+zNU79Lr06cPR44cISIigrFjxxqO\nITMTBwcH7OzsKCgoIC8vz+B9oKSkhJL/z96dx1Vd5Y8ff132fRdEZBVEZBNFUXBBKbegMpdQK502\nf/O1SSutUStbTKuxdGzKcnKanERr0ExNLUQRExVQdjcUUFyv7AiCIPf3B3HH6wVBE0V9Px+PeZSf\nz/mcz/nc6eOF8z7n/a6pQVdXFz29/02LWlhYtFg7NDIykl9++YWvv/6aLl264OTkpHG+vr6eo0eP\nau3CE0KIjkKCQEIIIYS4KTda4VdbWQqAlUtjSgeVCmJ25xLkbkdWVtZN36upHs/NTEB4e3uzZ88e\nDhw4cEsp4bp27YqpqSlHjx6lvr5e45dDcX8Jcrdj5iP+raYtUSjglcgArUnCu2HmzJnMnDnzbg9D\nCCHuCTeqTQJQeP4ic1bv55XIAEb0clYfLysrAxoXljQtLiktLW22j6bjTe2aDBs2jGHDhlFVVcXh\nw4fZu3cvcXFxzJ8/n+XLl9/2XUFN928a+/VKSkqaHacQt0t6QfMBlOspdHQws3el7HRjCmeF1f92\n2Nnb2+Po6Mi5c+fQ0dHRqu85a9Ys5s2bx7Jly9i0aRPe3t6YmppSVFREQUEBJ0+eZPHixVhaWlJc\nXMyMGTNwc3PDzc0NOzs7qqurSUlJobS0lKioKI2dgIGBgSQmJvLee+/RrVs39PT08PX1xc/Pj65d\nu/Lyyy+zbNkypk+fTu/evXFycuLq1asolUoOHTqEhYUFX3755W34JIUQ4vaTWQ0hhBBCtFlrK/wM\nTBsnNC5dKMCya2PKhsyTJWzevptff/31pu/XlJ5BqVS2+ZqIiAjWrl3L1q1bCQ0N1frlsaioSJ3i\npDm6urpERUWxdu1aVqxYwfPPP69OUdGkpKSEqqoqnJ2dW+hF3CtGBrngY2br4gAAIABJREFUYGVC\nzO5cMk9q/7cd4GrDpEFeHSIAJIQQou3aUpvkcsk56q/UsmRzJvaWxuq/65sWrnh4eGBsbIyjoyPn\nz5/n7NmzdOnSRaOPzMxMALp169bsPUxNTQkODiY4OBiVSkVcXBw5OTmEhoa2OK5bWQTj4eGhHvv1\nOySuXr1KTk7ODccpxB9VXVvf5rZmnd0pO30UXQMjLO010ywGBgZy7tw5PD09tYKWdnZ2LF26lE2b\nNpGUlERCQgINDQ1YWVnh4uJCZGQkrq6uADg4ODB58mSysrLIzMykoqICc3NznJycmDp1KoMGDdLo\n+8UXXwQgIyOD1NRUVCoVEydOVP8uMXToUNzd3dmwYQOZmZmkpaVhZGSEjY0NYWFhWv0JIURHIkEg\nIYQQQrRZayv8OnXvS0leOvm7Y7Fy8UHf2JzLZUrei1MyLnI4u3fvvqn79ejRA0NDQzZu3EhlZaU6\nxUlkZGSLK1ktLCyYNWsWH374IXPnziU4OBg3Nzeqq6spKCjg4sWLrFy58ob3ffLJJ8nPz2fr1q0k\nJycTEBCAra0t5eXlnD17lkOHDvHMM89IEOg+EeRuR5C7nUa9CBNDPXq52d31GkBCCCHaRqlU8txz\nzxEREcHMmTPbVJuk/koN57N24dR7uHrncm5uLgkJCZiamjJgwAAAHnroIf7zn//wr3/9i7lz56qD\nNBUVFaxduxZAI/CSmZnJ3Llz8ff3Z9GiRerjTbt0WqvlZmFhAcDFixfb/PwDBgzA3NycXbt28cgj\nj6jrpwBs3LiRCxcu0KtXL616QELcLiaGbZ9itO8Rgn2PEADMjDUXW02fPp3p06e3eK2xsTETJkxg\nwoQJN7yHqakp0dHRREdHt2lMlpaWzJ49+4Zt3Nzc2rwj+9p3vzkRERFERES0eH7Tpk1tuo8QQrSF\nBIGEEEII0WatrfAztnbA86EpnMvYScWZXFSqBoytHHjkqWmMCutx00EgMzMz5syZw5o1a4iPj6em\npgZoXIl3o3Qmffv2ZcmSJcTGxpKRkUFaWhqmpqY4Ozszfvz4Vu+rp6fHvHnzSEhIYPv27aSkpFBT\nU4OFhQUODg489dRThIeH39SziI7Pzd5cgj5CCHEfaGttEnMHV4qPp1FVdJYznZwxPrmLnPQUGhoa\nmD59urpO4BNPPMGBAwfYv38/f/nLXwgODqa2tpbffvuN8vJyxo4dS8+ePdX9Lly4kIyMDC5dusS/\n/vUvVCoVOTk55Obm4unpqVHsvjlOTk7Y2tqSmJiIrq4u9vb2KBQKhg4dir29fbPXGBkZMWPGDD78\n8EP++te/MnDgQDp16sTx48dJS0vD2tr6hhPrQvxRvdxubdf0rV4nhBCi7SQIJIQQQog2a8sKP7NO\nzng99IzGscDePfH3d9da0dbaCjmAPn36tFjbZ9KkSUyaNKnZcy4uLrz66qut9t/SKrumyZahQ4e2\n2ocQQgghOo621iYxMLXGud8jnE2Lpzg3lbhSEwb1DSA6OprevXur2+np6fH++++zYcMGdu3axebN\nm9HR0cHd3Z0XX3yRwYMHa/Q7ZcoU0tPTKS0t5eeff8bAwAB7e3umTp3K6NGjW603qKOjw7x58/j3\nv//Nnj17uHz5MiqVip49e7YYBAIICQnh448/5ocffuDgwYNUV1djZWXFqFGjiI6OxsbGpk2fixC3\nws3eHH8XmzYFYJsEuNrIAhwhhLgDFKrW9kc/oBQKxYHevXv3PnDgwN0eihBCCNFhFCgrmfZV4k1f\n99W0wfILnhBCCCHazbXp4Oz7PMK3CcdabFt7qYycDX/H1qMXrqGPqY9PCe/OpEFet2U8UVFR+Pn5\ntWnBixD3i7T8Iuas3t9qKkYAhQIWTQ6RuotCiPtenz59OHjw4EGVStX86tY7QHYCCSGEEKLNZIWf\nEEIIITq66rKL5CWs5dLFUzRcrcfEujOdA4Zg4dhN3UalaqDyQj6521dRW1FMfW0VNXvsOREWzPjx\n4+nRo0ezfZ8+fZp169aRmZlJSUkJpqamODk5MWTIEEaPHt3q2NavX8+///1vevTowVtvvYW5ufyM\nJO4fQe52zHzEn6U/Z90wEKRQwCuRARIAEkKIO0Tnbg9ACCGEEPeWyYO9UCja1lah4LatqBVCCCGE\naM2FCxf4aeUn1F+pwdazD9YuvlSXnufEjtWUFmSr2zXUXaHibC6gwMLJi049BhAa0pfMzEz++te/\n0lxWkJSUFGbMmEF8fDwuLi48/vjjhIaG0tDQwLp16244LpVKxYoVK/jmm28YMGAACxYskACQuC+N\nDHJh0eQQAlybTz8Y4GrDoskhjOjlfIdHJoQQDy7ZCSSEEEKImyIr/IQQQgjRUWVnZzNmzBi8dX3U\nO5ftvIM59ss3FCb/jEUXTwB09A1w9AzHffB44PeJ6WcGUFRUxGuvvcbXX3+tUZOwoqKCxYsX09DQ\nwMKFC/Hz89O4b1FRy3WIrly5wieffEJSUhKRkZG8+OKLKNq6okaIe1CQux1B7nYUKCtJLyiiurYe\nE0M9ernZSYYAIYS4CyQIJIQQQtwhmzZtYuvWrVy4cIErV67w/PPP89hjj7V+YQc0MsgFBysTYnbn\nknlSOzVcgKsNkwZ5SQBICCGEEHeUqakpEydO5Mj5KnVtElNbJ2zc/CnOS6es8Ai23XrR5+l31ddc\nu3PZzs6OsLAwNm3axMWLF+nUqRMA8fHxVFdXq2v9XM/OrvmfeSorK3n//fc5cuQIU6dOZezYse3w\n1EJ0TG725hL0EUKIDkCCQEJ0APfTxLAQonmJiYmsWLECDw8PHn30UfT19VvMNX+vkBV+QgghhLhb\nrv/5o6tp4/bkbt26YWxsTJC7scbOZTMHV4rz0rlcel7dx6WLhVw8sh834yreO/A19fX1GvcoLi5W\nB4GOHj0KoLE7qDVlZWW8/vrrnD9/ntdee40hQ4b80ccWQgghhLhpEgQS4i67HyeGhRDaUlJSAJg/\nfz42Ns3nx75XyQo/IYQQQtwpaflFrE7MVad6a1J7qYzCwlK6+emrj127c/m3s2YAXL1SC0DZqcMU\np/6EW2crBvcNwdHRESMjIxQKBVlZWWRnZ1NXV6fuq6qqCgBbW9s2j7W0tJTq6mrs7Ozo2bPnLT+z\nEEIIIcQfIUEgIe6y+3liWAjxPyUljRMV8p4LIYQQQtyabWmnbliTsOLyFTYlHWZUeqG66HzTzuXV\nJkUszTYn0N+FyBE9WfflBqo97Fm6dCnOzpoF6j///HOys7M1jpmamgKNu4Pc3NzaNF53d3eGDx/O\n0qVL+etf/8oHH3xA586db+6hhRBCCCH+IAkCCXGXycSwEPe3mJgY1qxZo/5zVFSU+t83bdoEQEZG\nBuvXr+fYsWPU1NRgb29PaGgo48aNU084NJkzZw7Z2dn8+OOPxMbGkpCQwIULFxgyZAgzZ84kPj6e\npUuXMnPmTGxtbVmzZg15eXkYGBjQt29fXnjhBUxNTcnLy+O7777j0KFDXL16lYCAAKZNm4a9vb3G\n/c6fP09sbCyZmZkUFxdjYGCAra0tPj4+PPPMM5ibyw4gIYQQQrS/tPyiGwaAmlSXnGPxjynYWxpr\n1Ca8ePoEna1MmDhiABH93Fn1YTEuLi5aASCVSkVOTo5Wv97e3uzZs4cDBw7cVEq4oUOHYmBgwOLF\ni9WBICcnpzZfL4QQQgjxR0kQSIi75F6fGBZCtI2/vz/QWExYqVQyceJEjfPbtm3jiy++wNDQkIED\nB2JlZUVWVhaxsbHs37+fv/3tb1rvO8DChQvJzc2lT58+9O/fH0tLS43z+/fvJyUlhb59+zJq1CgO\nHz6sHsOUKVOYN28evr6+DB8+nIKCApKTkzl//jz/+Mc/UCgUQGOQ+tVXX6W6uprg4GBCQ0O5cuUK\nFy5cYOfOnURGRkoQSAghhBB3xOrE3FYDQAD1V2o4l7mLmN2O6iBQbm4uCQkJmJqaMmDAAADs7e05\ne/YsJSUl6gV5KpWKmJgYCgsLtfqNiIhg7dq1bN26ldDQUPz8/DTOFxUVYWdnp3UdQFhYGHp6enz0\n0UfMmTOHBQsW4OLicjOPL4QQQghxyyQIJMRdci9PDAsh2s7f3x9/f3+ysrJQKpVMmjRJfU6pVPLV\nV19hZGTEp59+SteuXdXnli9fzpYtW/jmm2946aWXtPq9ePEin3/+ORYWFs3ed//+/XzwwQfqCQqV\nSsXbb79Neno677zzDi+99BLh4eHq9suWLSMuLo7k5GRCQkIA2LNnD5WVlbzwwgs8+uijGv3X1NSg\no6Nzy5+LEEIIIURbFSgrtWoAtcTcwZXi42nE/vMsncsj0L1aw+7du2loaGD69OmYmJgA8Pjjj/P5\n55/z8ssvExYWhq6uLocPH+bUqVP069eP5ORkjX4tLCyYNWsWH374IXPnziU4OBg3Nzeqq6spKCjg\n4sWLrFy5ssVxhYSE8Oabb/LBBx+oA0Hu7u63/qEIIYQQQrSRBIGEuEvu5YlhIcTtkZCQQH19PWPG\njNF4zwGefvppdu7cyc6dO5k2bRr6+voa55966qkW33OAIUOGaKxQVSgUDB06lPT0dFxdXTXec4Bh\nw4YRFxdHXl6e1rtuYGCg1b+RkVFbH1MIIYQQ4g9JLyhqc1sDU2uc+z3C2bR4Nmz8GXsLA7p160Z0\ndDS9e/dWtxs5ciT6+vr89NNPxMfHY2BggK+vLzNmzCApKUkrCATQt29flixZQmxsLBkZGaSlpWFq\naoqzszPjx49vdWy9e/fmnXfe4b333mPu3Lm89957eHl5tfnZhBBCCCFuhQSBhLiDCpSVpBcUUV1b\nj4mhHr3cmk8XcK9MDAshWnb9+15edUWrzYkTJwAICAjQOmdmZka3bt3Izs7m9OnTWitFW5sw8PT0\n1DrWlOqkuXO2trZAYyqTJiEhIaxatYovv/yStLQ0goKC6NmzJ87OzrIzUAghhBB3THVtfattDM2s\n6P3UfPWfPcKjmRLenUmDWv6ZKSIigoiICK3jbm5uGov0ruXi4sKrr77a6niaUnxfz9/fn//+97+t\nXi+EuDc899xzADfcCSiEEHebBIGEuAPS8otYnZjbbAqDsqwzGF7WnBzu6BPDQoiWtfS+56afQlFR\nSlp+kTo/fVVVFfC/d/B61tbWGu2aO9eS5tJF6urqAqjToDR37urVq+pj9vb2fPrpp8TExHDw4EGS\nkpIAsLOz44knntCoZSaEEEII0V5MDG9t6uJWrxNCiCZN9ZdbCuwKIcS9QH4iEqKdbUs7xdKfs1os\nYnqx4jKXlKX8kl7IiF7OQMefGBZCNK+1973i8hXmrN7PK5EBjOjlrH4fS0tLmy0OXFpaCjT/bt6p\nnTjOzs688cYbXL16lfz8fNLT09m8eTMrVqzAyMiIhx9++I6MQwghhBAPrpYyKLTXdUIIIYQQ9xOp\n6CxEO0rLL7rhhHATlQqWbM4kLb9xt821E8PN6QgTw0IITbfyvnt4eACQlZWl1a6qqoq8vDwMDAxw\ndnZujyHfFF1dXTw9PRk3bhyzZ88GYO/evXd5VEIIIYR4ELjZm+Pv0vwCuZYEuNrgZm/eTiMSQggh\nhLh3yE4gIdrR6sTcVieEm6hUELM7lyB3Ozw8PEhKSiIrK4vAwECNdh1tYlgI0ehW3vfZI4eydu1a\nNm/eTEREBI6Ojuo23333HdXV1QwfPlyr9tedcvz4cRwdHbV2EJaVlQFgaGh4N4YlhBBCiAfQ5MFe\nzFm9v00/bykU3LAWkBDij4uKisLPz49Fixa1S//79+9n48aNFBYWUllZiYWFBV26dGHQoEGMHj1a\n3e7s2bOsXbuWjIwMKioqsLCwIDAwkOjoaLp06aLR59KlS4mPj2flypXY29trnMvKymLu3LlMnDiR\nSZMmoVQq1fV+mp63SXPPXVNTQ0xMDLt376asrIxOnToxfPhwxo4dK4t1hRB3nQSBhGgnBcrKZmsA\n3UjmyRIKlJUMHdqxJ4aFEJpu9X2vxo8XXniB5cuXM2PGDAYOHIilpSXZ2dkcOXKErl27MnXq1PYZ\ndBvs3LmTbdu20bNnTzp37oyZmRnnz58nOTkZfX19Hnvssbs2NiGEuN2OHj3K+vXrOXToEJcuXcLK\nyorg4GAmTpyoTtH7//7f/+PChQt8++23WFhYaPURGxvLt99+y7Rp04iMjFQfLyoqIjY2ltTUVIqL\nizE2NsbHx4fo6Giteo4xMTGsWbOGhQsXUlJSwsaNGzl16hQWFha8++67/PnPf8bf35+FCxc2+xwv\nvfQSp0+f5l//+leLqYWFuBcFudsx8xH/VndeKxTwSmSAugajEOLes23bNj7//HOsra3p168fFhYW\nlJWVUVBQwPbt29VBoNzcXN58800uX75Mv379cHFx4fTp0yQkJLB//34WLFjQat3klpiamjJx4kTi\n4+NRKpVMnDhRfc7BwUGjbX19PW+//TYlJSUEBwejo6PDvn37+Pbbb6mrq9O4Vggh7gYJAgnRTtIL\nim75usf7uXfoiWEhhKY/9L6PHo2joyPr168nKSmJ2tpaOnXqxBNPPMGECROareN1pwwePJi6ujoO\nHz7M8ePHuXLlCra2tgwaNIgxY8bg6up618YmhBC3U1xcHP/4xz/Q19cnJCQEOzs7zp49yy+//EJy\ncjKLFy+mU6dOREREsGrVKnbt2qWxIrjJjh070NPTY8iQIepjJ06c4K233uLSpUv07t2b0NBQKioq\n2LdvH6+//jrz5s0jODhYq68ff/yR9PR0+vXrR0BAAFVVVXTt2pWAgAAyMzM5c+YMTk5OGtccPnyY\nkydPEhoaKgEgcV8aGeSCg5UJMbtzyTypvQAnwNWGSYO8JAAkxD1u27Zt6Onp8dlnn2FpaalxrqKi\nAgCVSsWnn35KdXU1r732GuHh4eo2u3fv5uOPP+aTTz5h+fLlt7QTx9TUlEmTJpGVlYVSqWTSpEkt\nti0pKcHd3Z0FCxZgYGAAwKRJk5g2bRo//fQT48ePR09PpmCFEHeP/A0kRDuprq3/Q9eN7sATw0II\nTW15370entridUFBQQQFBbXpXq2lW4iIiCAiIqLZc/7+/mzatKnZc/b29lrnvL298fb2btO4hBDi\nXnXmzBm++OILHBwcWLRoEba2tupzGRkZvPXWW6xYsYJ58+YxdOhQ/vOf/7Bjxw6tIFBubi6FhYWE\nhoZibt5Yh+Tq1at89NFH1NTUsHDhQvz8/NTtS0pKeOWVV1i2bBkrV67U2uGdmZnJ4sWL1fXjmowe\nPZrMzEx++eUXnn32WY1zv/zyCwCjRo364x+MEB1UkLsdQe52FCgrSS8oorq2HhNDPXq52UkNIHFf\nqampYeLEiXh5efHxxx+rj1+5coXo6Gjq6up49dVXGTp0qPrcli1bWL58OS+//DIPP/wwAJWVlaxf\nv559+/ahVCrR09NT1/q8/neQ+vp6tm7dyvbt27lw4QJ1dXVYWVnh7u5OZGQkvXr1Ij4+nqVLlwKQ\nnZ2t8X3YlErtdtDV1UVXV1freNNO3CNHjnD69Gl69OihEQACGDRoEJs3b+bQoUPk5ORofP+2l2nT\npqkDQACWlpaEhISwY8cOzpw5IwvohBB3lQSBhGgnJoZte72unxi+9rqOOjEshNDU1vf9dl0nhBDi\nj7l28njPtnVUVNUwd+4LGgEggMDAQEJCQkhOTuby5cvY2dkRGBhIeno6p06dwsXFRd02Pj4egGHD\nhqmPpaamcu7cOcaMGaM1AWVjY8PYsWP55z//SUZGhtZuoJEjR2oFgAD69++PjY0N27dv5+mnn1YH\nj6qqqti9ezeOjo5aNSWFuB+52ZtL0Efc14yMjPDy8uLYsWNcvnwZY2NjAA4dOkRdXR3QuFjh2iBQ\nRkYGgPp7QKlUMmfOHJRKJb6+vvTp04eamhpSUlKYP38+06dPZ8SIEerrlyxZQmJiIq6urgwbNgxD\nQ0OKi4s5dOgQBw8epFevXri7uzNx4kTWrFmDvb29xjyDv7//LT/vtd/Nxk4+lB46yv/93/8xePBg\n/Pz88PHx0dgVdPz4cQACAgKa7S8gIIBDhw6Rl5fX7kEgU1NTjTT+TezsGnclXrp0qV3vL4QQrZHZ\nJyHaSS+3W0tBcKvXCSHuHnnfhRDi3pCWX8TqxFyNOm5HE5KpKirmra82MHjPQa1J5fLychoaGjhz\n5gyenp489NBDpKenEx8fz5/+9CegceV0YmIilpaWGsGcI0eOAHDx4kViYmK0xnP27FkACgsLtYJA\n3bt3b/YZdHV1GT58OGvXriUpKUmdem7Hjh1cuXKFESNGSAFqIYS4TwQGBnL48GGys7Pp27cv0Bjo\n0dHRwc/PTx30gcb0aFlZWXTu3Bl7e3ugMahz8eJFZs+ezeDBg9Vtq6qqmDNnDitWrCAkJAQrKyv1\nYgJPT08++eQTdHR0NMZSWVkJgIeHBx4eHuog0B/d+dPcdzN0pdxpEOXnszm5NhYL459QKBT4+fnx\npz/9CS8vL6qrqwFaTH/adLyqquoPja8tWsrU0rSTqaGhod3HIIQQNyJBICHaiZu9Of4uNjdVLD7A\n1UZWswlxD5L3XQghOr5taaeaLShfX9s4iXRgdxwHfwMPBws6WRhrXV9TUwPAgAEDMDExISEhgSlT\npqCjo0NycjKVlZU89thjGqlrmuoW/PbbbzccW1Pf17Kysmqx/ciRI/nhhx/Ytm2bOgj0yy+/oKen\nx0MPPXTDewkhhLh3BAYGsnbtWjIyMjSCQJ6enoSGhvLll1+qa8Tl5eVRWVlJaGgoAPn5+WRnZxMW\nFqYRAILGoMXkyZNZsGABSUlJjB49GoVCgUqlQl9fv9nFBE2pTm+nlr6bAWw9AsEjkKt1NQz3NoSS\nfOLi4pg/fz7Lly/HxMQEgNLS0mb7Lilp/N2sqR2gfq6rV69qtb8TwSIhhLhbJAgkRDuaPNiLOav3\nN/sDzfUUCpg0yKv9ByWEaBfyvgshRMeVll/U4iSTroERAIET3kDXwAiFAt6bHNJiYXkDAwMGDhzI\nr7/+SlpaGn369GHHjh2AZio4+N/K4DfffJOQkJCbGvONdvPY2toSEhLC3r17OX36NJWVlZw8eZJB\ngwZpFdAWQghx77i+1pVfVycMDAzUO36qqqo4ceIEY8eOVadBy8jIwMnJiczMTOB/6dGadqNWVVU1\nuxu1vLwcaNyNCo3Bkn79+pGcnMzLL79MWFgYPXv2xNvbG0NDw9v+rDf6br6Wrr4RP+fDoskTUalU\nxMXFkZOTQ7du3QDIyspq9rqm403tAMzMzIDGHbrXp2/Lzc1ttp+mHVENDQ1au6OEEOJeIUEgIdpR\nkLsdMx/xb/UHG4UCXokMaHGyQQjR8cn7LoQQHdfqxNwW/242tXOiuvgsly6ewtKpOyoVxOzOveHf\n0w899BC//vorO3bswNPTkwMHDuDm5qZVw8fb2xuAnJycmw4CtWb06NHs3buXbdu2qWsNjBw58rbe\nQ4hboVQqee6554iIiGDcuHH8+9//Jicnh7q6Ojw8PJg4caJG3dOmIvMzZ87EysqK2NhY8vLyqK6u\n1qhNmpGRwfr16zl27Bg1NTXY29sTGhrKuHHjmk3FVFlZyYYNG9i3bx/nz59HT08Pe3t7goODefLJ\nJzEyMtJou379evbt24dSqURPTw9PT0/GjRunVaO1vr6erVu3sn37di5cuEBdXR1WVla4u7sTGRlJ\nr1691G1zcnJYt24deXl5lJeXY2ZmhoODA3369GHixIm382MX97jmU6I1qqizoOhwLuXl5Rw5coSG\nhgYCAwNxdnbGxsaGjIwMRo8eTUZGBgqFQl0PqCl9W3p6Ounp6S3e+/Lly+p/f+ONN4iNjWXXrl2s\nXr0aaFz8EBYWxrPPPnvDXao360bfzZXn8zFzcFMviGj6bjYvKwPA0NAQHx8fnJycOHToEHv27CEs\nLEx9/Z49e8jJycHJyQlfX1/18aZUq7/88otGLaGCggI2btzY7FgsLCyAxsCRg4PDrT+wEELcRR0i\nCKRQKGyBMcAjgD/gBFwBsoBvgG9UKpVWAk2FQhEKvAn0B4yBXOBfwGcqlUp7b6cQd8HIIBccrEyI\n2Z1L5kntH+gCXG2YNMhLJoSFuA/I+y6EEB1PgbLyhuk6O3XvR/Hxg5w58CuG5jYYWdiRebKEAmUl\nbvbm1NfXc/ToUY1JJB8fH7p06cK+fftwdnamvr6+2TRsISEhODo68vPPPxMQEKBV9wcaV2q7u7vf\n9CrrwMBAnJyciI+P58qVKzg5ObVYHFuIu+HChQvMmjULNzc3Ro4cSWlpKbt372b+/PnMnj2bQYMG\nabTfs2cPBw4coE+fPowaNQqlUqk+t23bNr744gsMDQ0ZOHAgVlZWZGVlERsby/79+/nb3/6mEQi6\ncOECc+fORalU4unpyejRo1GpVJw5c4YNGzYwatQodRBIqVQyZ84clEolvr6+9OnTh5qaGlJSUpg/\nfz7Tp09nxIgR6r6XLFlCYmIirq6uDBs2DENDQ4qLizl06BAHDx5UB4EOHDjAu+++i4mJCSEhIdja\n2lJZWcnp06f5+eefJQgk1G6UEg2g2qQzJ47l8M/YOCyulmBgYICPjw/QuOvnwIED1NXVkZOTg4uL\ni3pHaFMatBdffJGoqKg2jcXAwIBJkyYxadIkioqKyM7OJj4+np07d3LhwgU++uijP/7AtP7dnJ/4\nAzp6BpjYOWFoZoVKBUe3nqSbWS0Bvj0IDAxEoVDwyiuv8NZbb/HRRx/Rv39/unbtypkzZ9i7dy/G\nxsa88sorGjtrQ0JC6NKlC4mJiRQXF9O9e3cuXrzI/v37CQkJaTZ9a2BgIL/99hsLFy4kODgYAwMD\n7O3tGTp06G35LIQQ4k7oEEEgYDywHDgH7AROAQ7AE8DXwCiFQjFepfrfV6JCoXgMWAfUAN8DJUAU\nsAQI+71PITqEIHc7gtzttLZ293Kzk5ogQtxn5H0XQoiOJb2g6Ia/RzKmAAAgAElEQVTnjSztcAl5\nlFP7N3J485dYOHbD0MKWj5dk0MW0gUOHDmFhYcGXX36pcd2wYcP47rvv+P7779HV1SU8PFyrbz09\nPebOncvbb7/Nu+++i4+PjzrgU1RURG5uLufPn2fVqlU3HQRSKBSMGjWKr7/+GpBdQKLjyc7OZsyY\nMTz77LPqY4888gizZ8/m888/p0+fPhq1OlJTU5k/fz59+vTR6EepVPLVV19hZGTEp59+SteuXdXn\nli9fzpYtW/jmm2946aWX1McXL16MUqnkmWeeYfx4zamBiooKjV1AS5Ys4eLFi8yePVujbkpVVRVz\n5sxhxYoVhISEYGVlRVVVFbt378bT05NPPvlEKzVU084LgF9//RWVSsWiRYtwd3fXGoMQ0LaUaOad\n3TmrgpU/bsff+go9evTAwMAAaAxQJCQksGXLFmpqatS7gEBzN2pbg0DXsrOzIzw8nCFDhjBt2jQO\nHTpEZWWlujaQQqGgoUFrvXabtPbd7NgrgspzJ7hccp6Ks8fR0dXDwNSS4GFRvDPjT+jpNU5nent7\ns2TJEr7//nvS09NJTk7GwsKCIUOGEB0djZOTk0a/BgYGfPDBB6xcuZL09HRyc3NxdXVl1qxZmJub\nNxsEGj58OEqlksTERNatW8fVq1fx8/OTIJAQ4p7SUYJAx4BHgZ+v3fGjUCjmAsnAWBoDQut+P24B\n/BO4CoSrVKrU34+/BewAxikUimiVSrX2jj6FEK1wszeXSWAhHhDyvgshRMdQXVvfahsbjwCMrR1Q\nHt5H5YV8Ks+fIP2SLQpvV8LCwrR2LEBjEGj16tXU19fTt2/fFmvxuLm58dlnn7FhwwaSk5PZvn07\nOjo6WFtb4+HhwaRJk9SpZm5WREQEK1euRF9fn4iIiFvqQ4j2YmpqqrXbxcvLi/DwcOLj49m7d6/G\nf7chISFaASCAhIQE6uvrGTNmjEYACODpp59m586d7Ny5k2nTpqGvr8/x48c5cuQIHh4ejBs3Tqu/\na9+3/Px8srOzCQsL0wgANY1/8uTJLFiwgKSkJEaPHo1CoUClUqGvr99s3a6myfFrNU3WtzQG8WC7\nUUq0JibWjugZGFFeeJQDZ+oYF/W/oH/TDtD//ve/Gn+GxvfN19eXpKQk4uLiePjhh7X6LigowNra\nGktLS8rLyyktLcXNzU2jTU1NDTU1Nejq6qqDL9D433FR0Y2DOS1p7bu5U/dgOnXX3j0bGNYdY2Nj\njWNOTk68+uqrbb63nZ0db7zxRrPnrk1B2URHR4dnnnmGZ555ptlrVq5c2eK9mnZVCSHE3dYhgkAq\nlWpHC8fPKxSKL4EPgHB+DwIB44BOwKqmANDv7WsUCsWbQDzwZ0CCQEIIIYQQQjzATAzb9iuPsbUD\nrqGPqf/85xE9ebyfe4vtO3Xq1GL9gOtZWloyZcoUpkyZ0mrbm5kwys/PR6VSERYW1uzks+g4oqKi\n8PPzY9GiRXd7KLfd9bufu5o2zmh369ZNa7IWwN/fn/j4ePLy8jSCQE21Oq534sQJgGbTHZqZmdGt\nWzeys7M5ffo07u7uHD16FIDevXs3G6i51pEjR4DGXT8xMTFa58vLywEoLCwEGtNr9evXj+TkZF5+\n+WXCwsLo2bMn3t7eWrv5hgwZQlJSEq+99hqDBg0iICAAHx8f7OwkLbBo1FpKtCYKHR3M7F0pO32U\nOsC2q6f6nL29PY6Ojpw7dw4dHR38/Pw0rp01axbz5s1j2bJlbNq0CW9vb0xNTSkqKqKgoICTJ0+y\nePFiLC0tKS4uZsaMGbi5ueHm5oadnR3V1dWkpKRQWlpKVFSUxjsdGBhIYmIi7733Ht26dUNPTw9f\nX1+tMTSnrd/Nt+s6IYR40N0Lf3vW/f7Pa5cJDPv9n9uaaZ8IVAOhCoXCUKVS1bbn4IQQQgghhBAd\nVy+3W5twvdXr7qR16xrXyD3yyCN3eSTiQdRSIfvaS2UUFpbSzU+/2euaCstXVVVpHLe2tm62fVM7\nGxubZs83XdfUrrX212pK35aenk56enqL7S5fvqz+9zfeeIPY2Fh27drF6tWrgcbdPmFhYTz77LPq\n5wsNDeXtt99mw4YNbN++nW3bGqcvPD09mTJlirp2kHhwtZYS7Vpmnd0pO30UXQMjynU0d54GBgZy\n7tw5PD09NWpjQeOul6VLl7Jp0yaSkpJISEigoaEBKysrXFxciIyMxNXVFQAHBwcmT55MVlYWmZmZ\nVFRUYG5ujpOTE1OnTtXaFfviiy8CkJGRQWpqKiqViokTJ7YpCHQ/fzcLIURH1KGDQAqFQg9o2m95\nbcDH+/d/Hrv+GpVKVa9QKPIBX8ADONzKPQ60cKrHzY1WCCGEEEII0dG42Zvj72LTptXWTQJcbTps\nSs+CggJSUlI4fvw4Bw4coG/fvuq6D6LjWr58+U3XferIWitkX3H5CpuSDjMqvZARvZw1zpWVlQFo\nTVa3tGunqV1paSkuLi5a50tLSwHU9YWa2peUtP7ON13z4osvtrlmioGBgXrHXlFREdnZ2cTHx7Nz\n504uXLjARx99pG7bt29f+vbtS01NDceOHSM5OZmtW7fy7rvvsmzZMpydnW9wJ3G/a0u60ib2PUKw\n7xECQE2dZh2e6dOnM3369BavNTY2ZsKECUyYMOGG9zA1NSU6Opro6Og2jcnS0pLZs2e3qe317rfv\nZiGE6Oh0Wm9yV30I+AFbVCrVL9ccb1r2UN7CdU3HrdprYEIIIYQQQoh7w+TBXrSSFUpNoYBJg7za\nd0B/wIkTJ1i1ahXp6ekMHDiQmTNn3u0hiTbo2rUrnTp1utvDuC3aUsgeoLrkHIt/TCEtX3O3Q1ZW\nFgAeHh5tul9Tu6brrlVVVUVeXh4GBgbqgEpTUPTgwYOoWhlkU9ucnJw2jeV6dnZ2hIeH89577+Ho\n6MihQ4fUu4uuZWRkREBAAM8//zzjx4+nvr6e1NTUZnoUD5IHPSXa/fTdLIQQHV2H/eZQKBQvA68B\nR4Cn2+s+KpVKu/Ik6h1CvdvrvkIIIYQQQog7I8jdjpmP+Lc6ca1QwCuRAQS5d9x0MxERERp1VMTN\n2b9/Pxs3bqSwsJDKykosLCzo0qULgwYNYvTo0ep2lZWVrF+/nn379qFUKtHT08PT05Nx48YRFBSk\n0Wd8fDxLly5l5syZWFlZERsbS15eHtXV1eoi4y3VBLp69Sq//PILO3bs4NSpU1y9epWuXbvy8MMP\n88gjj2jtjmnr+NtTWwrZA9RfqeFc5i5idjuq36nc3FwSEhIwNTVlwIABbbrf0KFDWbt2LZs3byYi\nIgJHR0f1ue+++47q6mqGDx+Ovn5j+jlPT098fHw4fPgwsbGxjB8/XqO/yspKDA0NMTAwwMvLC19f\nX5KSkoiLi+Phhx/Wun9BQQHW1tZYWlpSXl5OaWkpbm5uGm1qamqoqalBV1cXPb3GaZbs7Gx8fHzQ\n1dXVaNu0E+p+2hkmbs2DnhLtfvpuFkKIjq5DBoEUCsVLwN+BQ0CESqW6fn9o004fS5rXdLysHYYn\nhBBCCCGEuMeMDHLBwcqEmN25ZJ7UTj8T4GrDpEFeMsl0H9u2bRuff/451tbW9OvXDwsLC8rKyigo\nKGD79u3qIIpSqWTOnDkolUp8fX3p06cPNTU1pKSkMH/+fKZPn86IESO0+t+zZw8HDhygT58+jBo1\nCqVSecPx1NfX8/7773Pw4EGcnJwYMmQIBgYGZGZm8tVXX3Hs2DFeffXVmx5/e2prIXsAcwdXio+n\nEfvPs3Quj0D3ag27d++moaGB6dOnq1Oxtcbe3p4XXniB5cuXM2PGDAYOHIilpSXZ2dkcOXKErl27\nMnXqVI1rXnvtNebMmcOqVatISkrC398flUrF2bNnSUtL48svv8Te3h6AWbNmMW/ePJYtW8amTZvw\n9vbG1NSUoqIiCgoKOHnyJIsXL8bS0pLi4mJmzJiBm5sbbm5u2NnZUV1dTUpKCqWlpURFRWFsbAzA\nihUrKC4uxsfHBwcHB/T09Dh+/DiZmZnY29szePDgtn/w4r4kKdHku1kIIe6UDhcEUigUM4ElQDaN\nAaDmfnI+CgQD3QGNmj6/1xFyB+qBvPYdrRBCCCGEEOJeEeRuR5C7HQXKStILiqiurcfEUI9ebnb3\n1aSaaN62bdvQ09Pjs88+w9JScz1hRUWF+t+XLFnCxYsXmT17tsZEfVVVFXPmzGHFihWEhIRgZaWZ\nfTw1NZX58+fTp0+zySa0/PDDDxw8eJDIyEheeOEFdHQas7U3NDTwj3/8g7i4OMLCwggJCbmp8ben\nmylkb2BqjXO/RzibFs+GjT9jb2FAt27diI6Opnfvm0u6MXr0aBwdHVm/fj1JSUnU1tbSqVMnnnji\nCSZMmKBVX8jBwYG///3vrFu3jn379rF582YMDAywt7dnzJgxGp+fnZ0dS5cuZdOmTSQlJZGQkEBD\nQwNWVla4uLgQGRmJq6urut/JkyeTlZVFZmYmFRUVmJub4+TkxNSpUxk0aJC63wkTJrB3715yc3PJ\nyMhAoVDQqVMnJkyYwKOPPoqZmdlNfQbi/jR5sBdzVu9v0+66+zUlmnw3CyFE++tQQSCFQvEGjXWA\n0oGHVSpVSz9h7gAmAyOBNdedGwyYAIkqlaq2vcYqhBBCCCGEuDe52ZvLxNIDSldXVys9F4CFhQUA\n+fn5ZGdnExYWprVTw9TUlMmTJ7NgwQKSkpK0dt6EhIS0OQCkUqnYvHkz1tbWPP/88+oAEICOjg7P\nPfcc27dvJyEhQR0Easv429vNFLIHMLLshEd4NFPCu7c4ed3WFIdBQUFaqfhuxNzcnKlTp2rtEmqO\nsbExEyZMYMKECTdsZ2pqSnR0NNHR0a32OXDgQAYOHNjW4YoHlKRE+x/5bhZCiPbTYYJACoXiLeA9\nGnf2DG8mBdy1YoGPgGiFQvGZSqVK/b0PI2DB722Wt+d4hRBCCCFEx3JtXY4HoWaKUqnkueeeIyIi\ngpkzZ97t4QjRIV27stzYyYfSQ0f5v//7PwYPHoyfnx8+Pj4au0KOHDkCNO76iYmJ0eqvvLwxM3lh\nYaHWue7du7d5XGfOnKGyspIuXbrw/fffN9vGwMBA4z7h4eGsXLnyhuNvbw96IXsh2oOkRBNCCNHe\nOsRPYgqFYgqNAaCrwG7g5esLYAIFKpXq3wAqlapCoVC8QGMwKEGhUKwFSoBHAe/fjzf/k7QQQggh\nhBBCiPtaWn4RqxNzr6u10ZVyp0GUn8/m5NpYLIx/QqFQ4Ofnx5/+9Ce8vLyorKwEID09nfT09Bb7\nv3z5stYxa2vrNo+v6T5nz55lzZrrk1s0f5/HH38cCwsLtmzZwsaNG/npJ+3xt7cHvZC9EO1FUqIJ\nIYRoTx0iCERjDR8AXaClZYy7gH83/UGlUm1QKBRDgHnAWMAIOA68CixTqdqSUVUIIYQQQjxoZAeN\nEPe3bWmnWkytZOsRCB6BXK2rYbi3IZTkExcXx/z581m+fDkmJiYAvPjii0RFRbXpfl9//TXJyck0\ns5CxRU33GTBgAHPnzm3zdcOGDWPYsGFUVVVx+PBh9u7dqzH+9t4VJIXshWhfkhJNCCFEe9BpvUn7\nU6lU76hUKkUr/wtv5ro9KpVqtEqlslapVMYqlcpfpVItUalUV+/CYwghhBBCCCGEuIvS8otara0B\noKtvxM/5CgZGTuShhx6isrKSnJwcvL29AcjJyWnXcXbt2hVTU1OOHj1Kff3N1dmBxto0wcHB/OUv\nf9EY/50webAXN4p3GZpZ0fup+biGPnbfFrIX/6NUKomKimLp0qV3eyhCCCGEaEFH2QkkhBBCCCEe\nMNfuyBk/fjzfffcdWVlZVFRU8MEHH+Dv709lZSXr169n3759KJVK9PT08PT0ZNy4cTdVILyoqIjY\n2FhSU1MpKipCV1cXpVJJbm6uVgqlkpISfv31Vw4ePMi5c+e4dOkSFhYW+Pn5ER0djbOzs1b/+/fv\nZ+PGjRQWFlJZWYmFhQVdunRh0KBBWsXjb/aZLl++zOrVq/ntt9+oqKjA3t6ekSNH0r9//zY/vxAP\nitWJuS0GgCrP52Pm4KbesaNSQczuXMzLygAwNDTEy8sLX19fkpKSiIuL4+GHH9bqp6CgAGtr6z+0\n60ZXV5eoqCjWrl3LihUreP755zEwMNBoU1JSQlVVlfrvnMzMTPz9/bV2HJVdM/47QQrZCyGEEELc\nWyQIJIQQQggh7qpz587x2muv4eTkRHh4OLW1tZiYmKBUKpkzZw5KpRJfX1/69OlDTU0NKSkpzJ8/\nn+nTpzNixIhW+z9x4gRvvfUWly5donfv3oSGhlJRUcG+fft4/fXXmTdvHsHBwer22dnZ/Pe//yUg\nIIDQ0FCMjY05e/YsSUlJJCcn8/HHH+Pu7q5uv23bNj7//HOsra3p168fFhYWlJWVUVBQwPbt2zWC\nQDf7THV1dcybN4/c3Fzc3d0JDw+nqqqKtWvXkp2dfZv+HxDi/lCgrLxhmrL8xB/Q0TPAxM4JQzMr\nVCo4uvUk3cxqCfDtQWBgIACzZs1i3rx5LFu2jE2bNuHt7Y2pqSlFRUUUFBRw8uRJFi9e/IdTrz35\n5JPk5+ezdetWkpOTCQgIwNbWlvLycs6ePcuhQ4d45pln1EGghQsXYmRkhLe3Nw4ODqhUKnJycsjN\nzcXT01M9/jtBCtkLIYQQQtw7JAgkhBBCCCH+sKNHjzJr1iz69+/PvHnzmm3z5z//mfPnz7Nq1SrM\nzRvz3ZeXl7NhwwZsbGyora2loqKCAQMG0LlzZxYsWMDFixeZPXs2gwcP5rnnngNg8eLFREdHM23a\nNHx9fZk8eTKTJk2itraWs2fPsmzZMr766itUKhUWFhZkZ2djaWnJp59+ip+fn3oHUv/+/Tl27BjL\nli1j5cqV6OvrU1JSQmpqKmZmZqSnp2NiYoKvry8TJkxg4sSJvP7663z77be88847xMfHs3TpUgwM\nDKiqqsLT05Ndu3ahUCjw9fXllVde0ZokXrJkicYzNamqqmLOnDmsWLGCkJAQrKysAPjxxx/Jzc0l\nNDSUv/71r+odAOPGjZN6RkJcJ72g6IbnHXtFUHnuBJdLzlNx9jg6unoYmFoSPCyKd2b8CT29xl+P\n7ezsWLp0KZs2bWL9+vXExcVRXV0NgLW1Nd7e3hw+fFidOq7J1atX+eGHH9i+fTsXL17EysqKIUOG\n8NRTTzU7npycHK5cuUJVVRXHjh3jt99+w9LSEm9vb7p27cpTTz1FeHg4f/vb30hMTOTJJ5/k5MmT\nnDhxgtTUVPLz8yktLaV3794sXLhQPf7Lly8zceJEevTowYcffvhHP9YWSSF7ERMTw5o1awCIj48n\nPj5efW7mzJlEREQAcPDgQTZu3MixY8e4fPkydnZ2DBgwgCeffBJTU1ONPpu+6z/77DNiYmLYu3cv\nxcXFTJgwgUmTJqnvuXDhQkpLS1m/fj2FhYWYmZkxaNAgpkyZgr6+PpmZmaxZs4YTJ06go6NDv379\neOGFF9Q/fwghhBAPEgkCCSGEEEKIP8zb2xsnJydSU1OprKzUmmQ5duwYp0+fJjQ0VH1u/fr1HD16\nFBMTE8aOHYuNjQ0FBQX8+OOP7Nixg5KSEgYPHqwRLKmvr2fhwoWYmppibm6Ol5eXekX8t99+y+nT\np+nWrRvDhw9HV1eXgwcPcubMGfr374+fn5/GmExNTRk7diz//Oc/ycjIwNnZmddff52SkhICAgLo\n3r07RUVF/Pbbb6SkpDB37lwCAgJIS0vTqOFx/vx58vPzCQ0NZdSoURQWFpKamkpubi5ffPGFul1+\nfj7Z2dmEhYVpPFPTWCZPnsyCBQtISkpS7x7avn07CoWCqVOnaqSAcnBwICoqSj35drtdO8nm7++v\nPh4VFYWfnx+LFi26pbZCtKfq2hvX1unUPZhO3YO1jgeGdcfY2FjjmLGxMRYWFlRVVdG3b1+tXX6J\niYk8/vjjALi5udGvXz/S0tLIycmhT58+mJiYkJqayrp16ygrK2PTpk0a/W/bto0vvvgCQ0NDHnvs\nMaysrMjKyuLo0aNYWVkxf/589eR4YGAgiYmJ2NraagSUpk6dSnFxMdCYXq5JdnY2V69evWM7g6SQ\n/f3l2lStrS028Pf3p6qqio0bN+Lu7q6RprRpx+yaNWuIiYnB3Nycvn37Ymlpqf6uT01NZfHixZiY\nmGj0W19fz7x586isrCQoKAgTExMcHBw02mzevJnU1FT69++Pv78/aWlp/PTTT1y6dImQkBA+/vhj\n+vbty8iRIzl8+DA7d+6koqKCd9555/Z8UEIIIcQ9RIJAQgghhBDitoiIiGDVqlXs2rWLyMhIjXPx\n8fFU19aj29mHmN25KE/lsm7tD5iZmTF+/HhmzZql0Xbu3LnU1NRQVVVFTEwM0JjWraKiAhcXFx56\n6CHi4uLw8fEhIiKCgoICTp06hbW1NdOmTVOvPtbR0aGgoABbW1t1P+Xl5Zw5c4YDBw6ogzmFhYVs\n3LiRkpISnn76adzd3dm6dSvHjx+noqKCnJwcHn/8cXr16oWOjg4VFRXq8dbV1eHl5cWpU6dwc3Nj\nxIgR2Nvbs2XLFuLi4hg7diwAR44cAdB4pmuVl5erxwKNq/nPnTuHnZ0djo6OWu39/f3bLQgkxL3I\nxPDWfr1t6bpt27ahp6fHZ599prWr79q/A5qcO3eOzz//XB3ofvrpp3n55ZfZsWMHU6ZMwdraGmic\nZP/qq68wMjLi008/pWvXruo+li9fzpYtW/jmm2946aWXAAgICAAgIyODUaNGAXDmzBmKi4vp1asX\n6enpHD58WB30ycjI0LhOiPbi7++Pg4MDGzduxMPDg0mTJmmcz8zMJCYmhh49evDOO+9o7Ppp2k0b\nExPD888/r3FdSUkJzs7OLFq0CCMjo2bvnZ6eztKlS9XpEuvq6pgxYwY7duwgOTmZ999/X734Q6VS\n8fbbb3PgwAHy8vLw8PC4nR+DEEII0eFJEEgIIYQQQtwWQ4cO5T//+Q87duzQCAKl5J5n2aofKa+u\nQ/eUDorTx8jb9T0lhaWYGdlQp2em0U9ERAQ2NjZkZWWRnp5Oeno6AHl5edTW1mJtbU1cXBzQGCi5\nlo6OjsafmyZqU1JSSElJAaC2tpYzZ85QW1urLqh+8eJF0tLS6NSpE/r6+rz33nuYmZnRq1cvwsPD\niY+P5/DhwxgaGlJXV6exE+jJJ58kKCiILVu2sHHjRn766SeuXLlCXl4eSUlJ6iBQZWUlgMYzNafp\nmaqqqgDUE8fXa+n47RAZGcngwYPp1KnTLfexfPnyO1aoXgiAXm63Vn/m2uuuTW124nw59fUqjV02\nTSwsLLSOTZ06VWMXpJGREUOGDGHt2rUcP36cvn37ApCQkEB9fT1jxozRCABBY+Bo586d7Ny5k2nT\npqGvr0/nzp2xt7cnMzMTlUqFQqFQB3qeeuopMjMzycjI0AgCNdUOEqI9XPueXKkqa3EXXtMOuL/8\n5S9aad8iIiLYuHEjCQkJWkEgaEwL11IACBp3mzYFgAD09fUZPHgwq1evJjg4WGP3r0KhIDw8nPT0\ndPLz8yUIJIQQ4oEjQSAhhBBCCHHLrq8F4erZg9zcwxQWFuLs7My2tFPMX/4DRSVl2Pv0R6HTOJla\nVXQaHV1dSktLWLN5B/qmlgS42qr7VSgU1NXV8fTTTzNhwgSgcUKorKyM2NhYjdRoAC4uLjg6OpKX\nl8eKFSsoKyujZ8+e6gmkN998k5CQEKD5VDfJycls2rQJHx8fvv/+e6ytrVm6dCk2Njbq/v/+979T\nV1en9Rl4enoybNgwhg0bRlVVFYcPH2bPnj0sWrSIbdu28fbbb2NpaalOd/Piiy8SFRXV6mfbNGFW\nWlra7PmWjt8OFhYWzU5y34zrJ7eFaG9u9ub4u9iQdaqkzdcEuNrgZm9OWn4RqxNzNa5V6jhx+lgO\nISPGMzZqOKPDB+Dj46O1K6iJl5eX1rGmQOqlS5fUx06cONF472Z26piZmdGtWzeys7M5ffq0OqVW\nYGAgcXFx6gnsjIwMbGxs8Pb2xtPTUx0UKi8v5+TJkwQFBalrBAnRVq3V+LHxCGR1Yi579iWjPLKf\n6uKz1NVc4tKFk5wpr6ffw2MI9XVVX3PkyBEyMzN55plnmDJlCklJSeTm5qpTtqlUKg4dOsSoUaP4\n+OOPKS0tZd++fZSVlfHOO+80W+Nn586dFBYW4uHhwWOPPaYReLWxsaG6uprMzEyee+45SkpKMDEx\nwc7ODktLSxoaGtQpFIUQ/5+9Ow+oqswfP/5m33cEFQTBDZTFBcVdk9TKTDNzodLKGrNmJjNrxvo2\n1mj2a5kys5z6jjM2GWoZFWhBCpG4hArIKqKyqsgqcNnhcn9/8L03rveyueT2ef2l55znnOdevefc\n+3ye5/MRQtxJ5FuhEEIIIYToMX0DpgAVlY5UFF7i3zu/44H5i9m4N43ys22rXhy9f6tP0dJYT6tS\nSUN1Oa0tzfz78y/wcXPAztIU+G01TGpqqiYIBGBnZ6cTAIK2FUBPPvkkZ8+epbKykm3btgFtq2nK\nyso4ceKEJgikj3rVjbm5ObW1tQQGBmoCQNC26kapVFJcXEzfvn212lpb/7aSycrKiqCgIIKCgvjf\n//1fGhoayMjIYPz48ZpZ+RkZGd0KAllYWNCnTx8uXrxIUVGRTkq4tLQ0rb+3D24tXLiQbdu2kZaW\nRnNzMz4+Pjz11FN4enpSVVXFF198wdGjR6mpqaF///48/vjjWgPSHdX56YmOagLV1taye/dujhw5\nQklJCaampgwePJh58+YxfPhwndf4yiuvsHjxYsaOHcsXX3zByZMnaW5uZvDgwSxZsgRfX98r6p+4\nPT0yeRBrvkxAper6WAMDCJ00iKjkAjbuTdNp4+I7DiMzS8qyj/PPbTv56ce9uNhZ4ufnxxNPPKET\n9Ll8pQP8VquntbVVs019v2l/j2lPvcpPfRz8FgRKSUnBy2gp8s8AACAASURBVMuLtLQ0goKCNPu+\n+eYbamtrNauFfq96QOL20lmNn4I6M979MoELKb9QlBqHsZkldm6DUGFAXXkR53KzWfTks3y08QPm\njBsMtK2AValUnD17lnXr1tHS0oKtra2mZp+zc9sqPKVSqanxY2VlhYODAzY2Nnpr/AQEBFBaWsqJ\nEyf4xz/+oVXjp7S0lMzMTJqampg9ezaurq7U1dVRVFTEoUOHMDMz01rJK4QQQtwpDLs+RAghhBBC\niN9EJRew5ssEvbPt7fv5UKs05LOw79i0J5Wm+lqqL5zF0qE3lg69NccZmZhhZGaOndtg3Efdw4hH\n1hK89HUiIyOJjIzk559/5oknniA1NVWT+g3QCgDl5eVp6uhAW9DEw8ODl19+mc8++4w///nPBAQE\nUFVVxccff8zx48f1vp6srCxMTEwAaGpqwszMjDNnztDQ0KA5pqysjIKCAr2DRzk5Oaj0jDirVw2p\nU6INGjSIYcOGcfjwYa3X1N7lr+nuu+9GpVKxbds2rWsUFxfrFJpvv+/FF1+ksrKSkJAQRo4cSUpK\nCmvWrOHChQusXr2a06dPM2nSJCZOnEhubi6vv/46paWles93LdXW1vLSSy+xe/duLC0tmTNnDuPH\njycrK4u//e1vREVF6W135swZXnrpJZqampgxYwZjxowhIyOD//mf/+H8+fPXvd/i1jHCy5mVs/zR\nEyvWYmAAL9zfFvjUFwBSc/IOZMg9y/B/+GUMht6P78hxpKens3btWq3Pak90d5WfevUg/LZq6MSJ\nE+Tk5KBQKDSBnoCAAFpbW0lLS5N6QOKq+Pv7M2fOHABNjZ/Q0FB8x81gV0oV1UW5FKXGYdWrH0Pn\n/AnP8XPpEzAFK2c3HPr7UV9ZyqtvbyY5twxo+z9sbGzM8OHDCQ0NJTs7m/T0dFJSUjhy5AiRkZGs\nXr0aMzMzTY0ff39/hgwZwgcffEC/fv2IjY3lo48+Yt26dbzyyitMnToVX19ffHx8NDV+1JKTk2lt\nbWXhwoW8/PLLLF26lBUrVvD3v/+dDRs26KSMFUIIIe4UshJICCGEEEJ0W3JuWacDpobGJjh4DKXs\nTBJZmWk0VJWhalVqrQICsHJ251J+BvDbiVLzK8grUdDfpS21y+rVq3n11VfZtGkTkZGRZGZmYmJi\nwnvvvUdeXh75+fm89957elMz9enThz59+jBlyhSys7PJzMzkjTfewNfXF2dnZwoKCoiJiSEjI4OL\nFy/ywQcfAJCZmcmsWbMIDw/nueeeY+zYsbS0tPDFF19QWVnJlClTdArCh4WFcejQIYYMGYKrqysq\nlYqMjAwqKyvx8PDQmpF/+WsaMmQIVlZWlJWV6X1NDz74IL/++iuHDx/m+eefZ+TIkdTW1hIfH4+f\nnx8JCQk6rz09PV0rjR7Azp07+fLLL3nxxReZOHEizz77rCagNmLECN5//32+//57vXUZrqVt27ZR\nWFjIPffco9WH+fPn88ILL/Dpp58ycuRIXFxctNodO3aMlStXEhISotkWFRXFxx9/TEREBCtWrLiu\n/Ra3lntGeOBqb0lY/GlS83WD1QGejoROGsQIL2dWf36kW6uGjE3Nse07iFZPR+52tGLfvn2aVX49\n5e3tzeHDh0lLS9NZsVNbW0tOTg6mpqZa9U4cHBzo168fGRkZJCYmAmjaDh06FBMTE1JSUkhNTdWk\nlBPiWvnywGlUKig9dRQAj+D7MTZtS7eqvo9bOvbB0MiIitw0wuJPM8LLGR8fHw4ePEh9ff01r/ET\nFBRERESE3ho/+lIhWlpa6l1JLIQQQtwJZBqEEEIIIYToNvVAUGccB7Sl9KrISaUiNwUDQyMcvLRT\nirn4tqVmq79UjLL5txU3J/LaZg83NDRQXl7Oxo0beeyxxzA0NOTixYsUFBRw8uRJevXqxXPPPYen\nZ1vtgeLiYioqdAd7a2pqMDMzY8yYMcyfP5/a2loOHDhAaWkpZWVleHt7s2rVKry8vBg+fDglJSXY\n2NiwbNkyzMzMiIqKIioqiurqakaPHq2TrgxgxowZDBo0iLNnz7J3717279+PUqmkX79+TJkyRWsw\nytnZWes1xcXFERkZqfc1Qdsg2Pr165kzZw5VVVVERESQlpbGwoULmTl3ERcr60g8W8p3R3MpKG2r\nOeLi4sL8+fO1+qgOnjQ3N/Pkk09qDYRNmTIFIyMjrdnU10NLSws///wz5ubmLFmyRKsPffv2Zfbs\n2bS0tBAbG6vT1tfXVysABG2rpIyMjMjOzr6u/Ra3phFezry7ZByfLp/MiplDWTp1MCtmDuXT5ZN5\nd8k4Rng5k1ei6LR+kOJirs4qv9T8CvLOFwO/rfLrqbvuugtjY2P27NlDUVGR1r7t27dTV1fH1KlT\nNSsU1QIDA2lsbCQiIoK+fftqUmmZmpri4+NDfHw8RUVF+Pv7y2C36La8EgXfHc0lLP601rOk/X71\n50Rdz6+yIJOi1DiKUuMoPXWMRkU5l/LSaFUqaW6oJSn7HHklCs2qooKCAr115hoaGrhw4QKgv6aW\nOmXiwIEDdfbZ29sDaNX4Uf/f//LLL3n//feJjY3V+YwJIYQQdyJZCSSEEEIIIbqlqwFTNete/TCz\ncaSyMJNWpRI798GYmGvXyrDp7Y376HsoOhFLddFZcg98jam1PRGVx0n5sW01y9ChQ3njjTdYsGAB\nCxYsQKFQALB161ada+bm5vLFF18wePBg0tLSOH/+PFVVVSQkJNDS0sLSpUuZO3cuS5cu1aqds3Ll\nSs05nnvuOV5++WU+//xzhg8fztixYykrK+PgwYMMGjSIv/71rwQHB/PSSy9pXXvMmDE6AQpom9V8\n+SAutKWtU7+m7rC0tOSpp57SrNLR1GP69hSOM1ZRAmyJzqSxppLCwkt4DPHXSXmjHkhzc3PDwsJC\na5+hoSH29vaUlZV1qz9X6ty5czQ2NuLr66tVyFstICCAXbt2cfbsWZ19+gYHjY2Nsbe3p6amRmef\nEGr9XWw0qwsvpw46dyT3wFcYGpti6eyGmbU9KhXUluRTYaRgYlDAFdfdcXFx4emnn2bLli08//zz\nTJw4ETs7O9LT08nKysLd3Z3HH39cp11gYCB79uyhqqpKZwVSYGCgpk6Y1AMS3dFRbT/1s2RIedu9\ntf3npKWxHlWrkqLUX7TatDTUUVWdjYlFEYYmplxM/YWfDvfmD3MnMXDgQM6cOcPy5csJCgrC1dWV\nhoYGSkpKSE9PR6lUAtrpD9XUNbX01dtSP+fap2l1d3fH19cXT09PDh06xM8//wy0Pfs6qwsohBBC\n3O4kCCSEEEIIIbqlqwHT9py8A7mQ8vP//Vl39QxA72ETse7lQempo9SUFqA8f4qz9S5YD/Zk5syZ\nTJkypdvXGzhwIPPnzyc9PZ3ExERqamqws7Nj4MCBzJ49m1GjRnV5jt69e/PBBx+wa9cujh8/Tnp6\nOhYWFowcOZKFCxfqDUT83joqYK9WXd9ETGYZ0ScKmTn8t7Q66oE0fYNs6v3qgbjrpa6uDvgtIHU5\n9fba2lqdffoGAKGt362trdeoh+JOU9fYeYH4PsNDUBSdpb7iItUXzmBoZIyplR0TZjzIhtXL9Kac\n6q777ruPPn36EB4ezuHDh2lsbKRXr17MmzePBQsW6P0/r17loFKpdGr+BAYGsn37dkDqAYmudedZ\nsiexgOknCrU+J0YmZoCKgIdf1jq+UVHBuePR1JadQ9lUT8mpo+TljQYm4eXlhYODA6NHjyYzM5OE\nhAQsLS1xcnJi5syZVFdXa4I114K1tTWPPfYYkydP5syZMyQlJREZGalTW08IIYS4k0gQSAghhBBC\ndEtXA6bt9fafTG//yV0eZ+3igbWLh+bvny6f3OGsfX0rgNScnZ1ZsmRJt/rm4uJCZGSk3n1OTk48\n++yz3TpPSEiI3hVAah1d40p1VY9JQwUf7EnFxc6CEV7O17QPV0MdgFIXvb+cOp1fR4EqIa41S7PO\nfw73GhxEr8FBOtunzhyqtaLurbfe6vAcnd0nRowYwYgRI7rZ27ZgaEREhN59Pj4+1/yeI25PXT1L\n1KkEVa2tfLAnlftH/faMtnJ2p+p8NvWVJVjY/1a7zczGkQF3LdY6z5jxQzV/tre35y9/+Yve64WF\nhWn9vbNnPUBoaCihoaGalW/tXf558/X1xdfXl759+/L+++9z//33Exoa2un5hRBCiNuR1AQSQggh\nhBDd0tWA6dUK8HTsMAAkulePSU2lgrD409e3Qz3k7u6OmZkZubm5elf7qAf09NV+EOJ6GN7/yoKk\nV9pOiJtBV88SI1MLDAwMaK6rQqWCU+erNPvU9fwKEvbQXKfQaatsbqK27BxwYz4nJ0+epKmpSWd7\nZWUlcOV1vIQQQohbnawEEkIIIYQQ3XI9B3QMDCB00o1Pt3az6m49pvZS8yvIK1HcNIE1Y2Njpk6d\nSnR0NNu3b2f58uWafUVFRURGRmJsbMxdd911A3t5/emrSbVx40ZiYmLYunUrLi4uWsdHRkby448/\nUlxcTFNTE0899ZSm2Lq4Ov1dbPD3cOzRZ0uC1eJW1p1niZGJKZZObtSUFJB3MJwiWyf6OFhSb+2B\nTW9v+o4IoehELBkRH2HXdxCm1va0tjTTVFtJTUk+Vr08mPfEn27I5+Sbb74hNTWVYcOG4erqioWF\nBfn5+SQmJmJtbc3MmTN/9z4JIYQQNwMJAgkhhBBCiG65kgFTd0crzl+q7XTWsYEBvHB/wE2Vuuxm\n05N6TJe3u5kGrJcuXUpGRgZ79uzh9OnT+Pv7U11dzcGDB6mvr+eZZ57B1dX1RnfzpnHgwAE+++wz\nvL29eeCBBzAxMcHHx+dGd+u28sjkQaz5MqFbq+wkWC1udd19lvSf8CDnjkdTXXQWZX46KjsLzIdO\nx8LBVW89P0MTM0wtbHEaOBJHL78b9jmZNWsW1tbWZGdnk5mZiVKpxNnZmVmzZjF37lydILsQQghx\np5AgkBBCCCGE6LaeDpj+8T4/oC01WWq+bvAowNOR0EmDJADUhZ7UY7oW7a4XGxsb3nvvPb7++msO\nHz7Md999h5mZGYMHD2bevHk9qo9yO1myZAnz58/H0dFRa/uxY8cAWLt2rc4+cW2M8HJm5Sz/Lutt\nSbBa3A66+0y4vMbP0qmDcbQ203xOLq/np6bvc9LdGj/6dFZTy9/fX6cOVk/rbAkhhBB3CgNVdxOL\n32EMDAwSR44cOTIxMfFGd0UIIYQQ4qYSlVzQ7QHTmcP7abbllSg4kVdGXWMLlmbGDO/vfFOtUrmZ\nfXc0ly3RmT1ut2LmUOaO8boOPRJXSl86uI68+uqrpKam6gx0imsvObdMgtXitne1zxL5nAghhBA9\nN2rUKJKSkpJUKtWoG9UHWQkkhBBCCCF65J4RHrjaW/Z4IKi/i40Efa6QFLC/vV1eEygsLIwdO3Zo\n9s+ePVvz5/YBoXPnzrF7925SUlKorKzEysqKwMBAQkNDcXNz+11fw61uhJczI7ycJVgtbmtX+yyR\nz4kQQghxa5IgkBBCCCGE6DEZCPp9SQH7O4u/vz8AMTExlJSUsHjxYp1jEhMT2bBhA0qlkjFjxtCn\nTx/Kyso4cuQIx48fZ8OGDQwYMOD37vot73oHq5ctWwZ0nSLrWurJ6jN90tLSeOWVV1i8eHGHabvE\nreFaPUtkUocQQghxa5EgkBBCCCGEuGIyEPT7kQL2dw5/f3/8/f1JS0ujpKREZ+C9pqaGd999FzMz\nM95++2369fst7WJ+fj6rV69m06ZNfPjhh7931287VxtAEeJmI88SIYQQ4s4jQSAhhBBCCCFuAVLA\n/tZ0+Wo5d6urr8kaGxtLbW0tzzzzjFYACMDT05OZM2fy/fffU1hYqLNf3HkcHR3ZsmULlpaWV9R+\n8ODBbNmyBVtb22vcM3EjyLNECCGEuPNIEEgIIYQQQvyuYmJi2LhxIytXriQkJESz/UakSbrVXGk9\nJvH7S84t48sDp3XSLjXWVFJYeIkh5TVXfO6srCwAcnNzCQsL09l//vx5AAkCCQCMjY1xd3e/4vZm\nZmZX1V7cfORZIu5E8j1TCHEnkyCQEEIIIYQQtxCpx3Tzi0ou6HSWfXV9E3sSC5h+opCZw3sepFEo\nFABER0d3elx9fX2Pzy1+ExYWxo4dO4C24HVMTIxmX/sgdlJSEhEREWRnZ1NfX4+zszPjxo1j4cKF\nWFlZdft6Bw4cICoqipycHJqamnB1dWXq1KnMmzcPExMTAMrLy3niiSfw8vLqMN3f66+/TmJiIps3\nb8bT07PDlHaVlZWEh4dz9OhRysrKMDY2xt7eHh8fHxYtWkTv3r2BzmsCXbhwgZ07d5KSkkJ1dTW2\ntrYEBgayaNEi+vbtq/f93LBhA9XV1XzzzTfk5+djamrKiBEjWLZsGU5OTt1+v8TVkWeJuN2sWbOG\n9PR0IiMjb3RXhBDipiNBICGEEEII8bsaO3YsW7ZswcHB4UZ35ZYm9ZhuTsm5ZV2mWQJABR/sScXF\nzqLH11Cn9froo4/o379/zzspusXf35/a2loiIiLw8vJi7Nixmn1eXl4A7Nixg7CwMGxsbBg9ejR2\ndnbk5eXx7bffcvz4cd57771upWH78MMP2b9/P87OzowfPx4rKytOnTrF9u3bSUlJYd26dRgZGeHk\n5MTw4cNJTk4mLy9P59+/oqKC5ORkBg4ciKenZ4fXa2xs5OWXX6aoqIjhw4czZswYVCoVJSUl/Prr\nr0yYMEETBOrI6dOn+Z//+R/q6+sZM2YMHh4enDt3jri4OBISEli/fj2DBunWk/nhhx9ISEggODgY\nPz8/srOziY+PJzc3l02bNmkCXuL3Ic8SIYQQ4vYnQSAhhBBCCPG7srKy6tHseCFuJV8eON2tgusA\nKhWExZ/GrYfX8PHx4fDhw2RkZEgQ6Dry9/fH1dWViIgIvL29dVbBpKamEhYWho+PD6+//rrWfU2d\n9jIsLIynnnqq0+vExMSwf/9+xo0bx+rVqzE1NdXsU6+e2bt3Lw888AAAd999N8nJycTGxvLkk09q\nnSsuLo7W1lamTZvW6TVTUlIoKipizpw5Ov1raWmhubm50/YqlYr333+furo6XnzxRaZOnarZFx8f\nzzvvvMM//vEPtmzZgoGBgVbbxMRE3n//fa3/u++++y4HDhwgISGBiRMndnptIYQQQgjRMxIEEkII\nIYS4iWRnZ/Ptt9+SmZlJdXU1NjY2mkLv7QfGDh48yJ49e8jNzaWlpYU+ffowZcoU5s6dqzOLevbs\n2fj5+fHWW2/pXG/jxo3ExMSwdetWXFxcALRSB4WGhrJt2zZOnDhBQ0MDnp6ehIaGMnr0aL39j4+P\n16QzamxsxMHBAR8fH+bOnauZEd5RTSAhbnV5JQqdGkBdSc2vwIKGHrW5++672bVrFzt27GDQoEEM\nHjxYa79KpSI9PR1/f/8enVegkxrL3arjiJ465dCf/vQnncB2SEgIERERxMXFdRkEioiIwMjIiOef\nf14rAASwaNEi9uzZQ1xcnCYINHbsWKysrIiLi+Pxxx/H0NBQc3xMTAzGxsZMmTKlW6/38utBWw0h\nY+POhwqysrI4d+4cPj4+WgEggEmTJrFnzx4yMzPJyMjAz89Pa//s2bN1gpczZ87kwIEDZGdnSxBI\niFvQlXx3bG5u5vvvvycuLo6ioiKMjIzw8vJi9uzZOveB9ud/+OGH2b59O2lpaVRXV/P888+zceNG\nzbGzZ8/W/Fnf99+GhgbCwsKIj4+nsrKSXr16MWPGDB566CGdoLUQQtwuJAgkhBBCiJvenRI0iI6O\n5pNPPsHQ0JDg4GD69u1LZWUlZ86cYe/evZofxP/973/5+uuvsbW1ZcqUKZibm5OYmMh///tfkpKS\nWLduXZcDeN1RUlLCqlWr6N27N9OmTUOhUBAfH8+6detYv349AQEBmmNVKhUffvghMTEx2NraMm7c\nOOzs7CgvLyc1NRU3Nze9aYGEuJ2cyCu7onbnK2p7dLyNjQ1r1qzhzTffZPXq1QQGBuLh4YGBgQGl\npaVkZWWhUCgIDw+/ov7ciZJzy/jywGmdIF5jTSWFhZcYUl6j0yYrKwtjY2MOHjyo95zNzc1UVVWh\nUCiwsdGfbquxsZHc3FxsbW35/vvv9R5jYmJCYWGh5u+mpqZMnDiR6OhokpKSCAoKAuDMmTMUFBQw\nbtw4bG1tO329fn5+ODk5sXv3bs6ePUtQUBC+vr54e3trBZU6cubMGQCt50B7AQEBZGZmkpOToxME\n0vcs6NWrFwA1NbrvsxDi1tHd744tLS387W9/Iz09HXd3d2bNmkVjYyOHDh3i7bffJicnhyVLluic\nv6ioiBdffBE3NzemTp1KY2Mj/fv3Z/HixcTExFBSUsLixYs1x7u6umq1V1+3oqKCoKAgDA0N+fXX\nX/n8889pbm7WaiuEELcTCQIJIYQQQtwECgsL2bJlC5aWlrz99tt4eHho7S8raxtczsrK4uuvv8bZ\n2Zn3339fU1dn6dKlvPnmmxw7dozw8HAWLFhw1X1KS0sjNDRU6wfxlClTWLt2LeHh4VqDf9HR0cTE\nxDBo0CDWrVunNSu+tbWVysrKq+6PEO1FRkby448/UlxcTFNTE0899RRz5sy5oX2qa2y5onZNLa09\nbhMYGMjmzZsJDw8nKSmJjIwMjI2NcXR0JDAwkPHjx19RX+5EUckFndZxqq5vYk9iAdNPFDJzeD/N\ndoVCgVKpZMeOHZ2ev76+vsMgUE1NDSqViqqqqi7P015ISIjmvqsOAsXGxmr2dcXS0pL33nuPsLAw\nEhISSEpKAsDW1pb77ruPhQsXdjqZoK6uDgBHR0e9+9Xba2t1A5z60oEaGRkBbc8L0T3tV0asXLny\nRndHCKD73x2//fZb0tPTGTVqFK+99prmHhAaGsqqVav4+uuvGT16NL6+vlrnz8zM5OGHH9YJEA0Y\nMIC0tDRKSkp0Une2V1FRgZeXF+vXr9eshAwNDWX58uV8//33PPzww9dkIpUQQtxs5M4mhBBCCHET\n+OGHH1AqlSxatEgnAATg7OwMwL59+wBYuHChJgAEbQNoy5Yt4/jx4/z000/XJAjk4uLCwoULtbaN\nHDmSXr16kZ2drbV9z549APzxj3/UGeAzNDTscKBQiCtx4MABPvvsM7y9vXnggQcwMTHBx8fnRncL\nS7Ouf16ZWdsz8tG1Wtseeuwp5o7x0jlWXwrH9lxcXHjmmWd61kmhJTm3rNMAkIYKPtiTioudBSO8\n2u7HlpaWqFSqHgVvLqe+X3p7e/Phhx92u52vry99+/bl6NGj1NbWYmZmxi+//IKtrS2jRo3q1jmc\nnZ3585//jEqlorCwkJSUFPbu3cvOnTtRqVQ8+uijHba1tLQE4NKlS3r3V1RUaB0nhLgzdPe74759\n+zAwMOCpp57SBIAA7OzsWLRoEZs2beKnn37SCQLZ29tf9Wqd5cuXa6XCtLOzIzg4mNjYWM6fP4+n\np+dVnV8IIW5GXa/zFkIIIYQQV2Xjxo3Mnj2bkpISre15JQq+O5pLWPxp9v5ylLrGlk4H79asWcMH\nH3wAtK0CuJybmxvOzs4UFxfrnX3dU15eXnrTAjk7O2ul7GloaCA/Px97e3u8vb2v+rpCdOXYsWMA\nrF27lqVLlxIaGsqQIUNucK9geH/n37WduHpfHjjdaQBIXR9CpWpFpYKw+NOafT4+PtTU1FBQUHDF\n1zc3N8fDw4OCggIUCkWP2oaEhNDU1ER8fDzHjx+nurqaKVOm9HgWu4GBAR4eHsyePZv169cD8Ouv\nv3baZsCAAUDbrH991NvVxwkhbi/tv8N+dzSXgtK274Xd+e5YX19PUVERjo6OuLu76xyrXi2Uk5Oj\ns8/Ly0un9mVPWFlZ0adPH739A0lJKYS4fclKICGEEEL87tqnMJk/fz7btm0jIyOD5uZmvL29Wbx4\nMSNGjOjyPKmpqRw4cIDMzEzKyspQKpX07t2biRMn8tBDD+kteN3a2kp0dDQ///wz+fn5tLS04OTk\nhJ+fH/Pnz6dv376aY5VKJdHR0cTGxlJQUIBSqcTd3Z3p06cza9asKy4eq6/2REb2eRoVFbz74xke\nv9tcM9P8ckqlEkBrFVB7jo6OlJaWUltbqzflTk9YW1vr3W5kZISq3aipOuDk5OR0VdcTorvUqwxu\nthVm/V1s8Pdw1Kkr05kAT0f6u+hPFSaur7wSRZf/VkamFhgYGNBcVwVAan4FeSUK+rvYMGfOHI4d\nO8ZHH33EmjVrdP4/qgPkXQUo586dy6ZNm/jwww954YUXdO7dNTU1FBcX6wRUpk2bxvbt24mNjcXe\n3h6Au+++u1uvvaCgAFtbW007NfXKHjMzs07b+/r64ubmRmZmJocOHWLChAmafYcOHSIjIwM3NzeG\nDRvWrf4IIW4NXdZPG66/XfvvjurvjR09w9XfcfUFZDr6/ttdHX03lpSUQojbnQSBhBBCCHHDFBcX\ns3r1avr3788999zDpUuXiI+PZ+3atbz00ktMmjSp0/bffPMN586dw8fHh6CgIJqbm8nMzCQsLIy0\ntDTWr1+vNRuxpaWFN954gxMnTuDs7MyUKVOwtLSkuLiYX3/9lWHDhmmCQC0tLaxbt46kpCTc3NyY\nMmUKpqampKam8umnn5Kdnc2qVau69TqXLFnC/PnzcXR07LD2hLGpOY3AiVP5rCmu5YX7A7RqT6ip\nf6ReunRJ70xG9eB4+x+5BgYGmuDR5a7FjEf1tcrLy6/6XEJ0JiwsTCv11uzZszV/joyMBCAlJYXw\n8HCys7NpaGjAxcWF8ePHM3/+fJ3Bn2XLlgGwdevWDq+1YcMG/P39ta7p5+fHyy+/zBdffEFiYiKX\nLl3i+eefJyQkhEcmD2LNlwldpxcDDAwgdNKgHr0H4to5kVfW5TFGJqZYOrlRU1JA3sFwzGyd2Py/\nOfzxkdkEBgaydOlS/vvf//KHP/yBoKAgXF1daWhovGoFFQAAIABJREFUoKSkhPT0dIYOHcobb7zR\n6TWmT5/OmTNn+OGHH3j66acZMWIELi4uKBQKiouLSU9P5+677+a5557Taufs7ExAQAApKSkYGRnR\nv3//bq/GTE5O5j//+Q8+Pj707dsXe3t7ysrKSEhIwMDAgHnz5nXa3sDAgBdeeIHXXnuNt99+m7Fj\nx+Lu7s758+c5cuQIFhYWvPDCC1c8WUL0zLlz57o9oSY+Pp6oqChycnJobGzEwcEBHx8f5s6dy6BB\ncj8SHbvS+mmXUz+LO0onqd6uL2Aj9xQhhLgyEgQSQgghxA2Tnp7Ogw8+yJNPPqnZNmvWLF566SU+\n/vhjRo0a1Wk9gRUrVuDq6qrzg3D79u3s2rWLQ4cOaQWSwsLCOHHiBGPGjOGvf/2rVjqJ5uZmTaFr\ngK+++oqkpCTuv/9+nn76aU0wqbW1lc2bN7Nv3z4mTJhAcHBwl6/T0dERR0fHTmtPWDq7U1t+geoL\nZzC3c9apPaE57v/ej/T0dJ0gUFFREWVlZbi6umr9cLa2tqasTHews7W1ldzc3C773xVzc3M8PT3J\nz88nJydHUsKJ60YdjImJiaGkpESnLkBUVBSffPIJZmZmTJw4EXt7e9LS0ti9ezcJCQm8++67na6Q\nO3fuHCtWrMDf3x8/Pz+9x6SlpZGRkUFhYSF2dnaMHz+ewsJCdu3axdatW6mvr8fAwJxzKhdc/SZj\nbGqu1V5xMZdL+enUlhbS386A9cn/7nQFY/tgVEVFBREREZpVHOrgVUJCAhERERQWFqJQKLC1taVv\n375MmjSJ++67r8fv852irrGlW8f1n/Ag545HU110FmV+OrEXrLh37FD69+/P/PnzGTp0KJGRkWRm\nZpKQkIClpSVOTk7MnDmTKVOmdOsaK1asICgoiB9//JGUlBRqa2uxtramV69ezJs3j7vuuktvu5CQ\nEFJSUlAqlUybNq3br33kyJGUlpaSkZFBQkICdXV1ODo6Mnz4cObOnatTh0OfIUOG8MEHH7Br1y5O\nnDjB0aNHsbW1ZcqUKSxatAg3N7du90dcue5OqFGpVHz44YfExMRga2vLuHHjsLOzo7y8nNTUVNzc\n3CQIJDp0NfXTLmdhYUGfPn24ePEiFy5c0FqFD20r/aHn6STbf1fXl5JOCCHuZBIEEkIIIcQNY2Vl\npTOIO2jQIKZOnUpMTAxHjhwhJCREa393BjuTk5M5evQox44dIy8vj7i4OMrLy0lLS8PJyYnly5fr\n5BM3MTFBoVDwn//8h5SUFPbv34+JiQkTJ06kqKhIM5hlaGjIsmXL2L9/P/v376ewsJBDhw5x7tw5\noG1m9ogRI1iwYIEmzc7GjRuJiYmh34xntH48l589QdX5bOorLtKgKKfmYi6n922jtaWJ3v6TCYs/\nrfkBrQ7iqHOW79y5kzFjxmBnZwe0/eDdunUrKpWKGTNmaL22wYMHk5iYSHJystas4F27dunUKbpS\ns2fPZvPmzWzevJl169ZpDbSrVCouXbp006XuErcef39//P39SUtLo6SkhNDQUM2+kpISPv30U8zN\nzXn//fe16gxs2bKFH374gf/85z/88Y9/7PD87u7uBAQEkJqaqjMoBXDy5Enq6+txdHRk5MiRPP/8\n83z11VekpKRgY2PD6NGjsbOzIy8vj9pDCZQfLsR5/CMYtQsEFWcexrJVwbxpowkY5NHlCka1b7/9\nVhPEDggI0KTTiYqK4uOPP8bBwYExY8Zga2tLZWUleXl57N+/X4JAnbA0697PYTMbRwbc9duzasXM\noYSM8dL8fejQoQwdOrRb59K36kxt9OjRjB49ulvnUbvrrrs6DBCpubi4aFbKqfXr14+nnnqqW9fw\n9/fXaa/m5ubW7VWxoaGhWp/Zrvoouqe7E2qio6OJiYlh0KBBOs/p1tZWKisrb0T3xS2iq/pp7anr\np3UUBIK21JVffPEF//73v3nllVc0z73q6mp27twJtK2S7AlbW1sASktLcXV17VFbIYS43UkQSAgh\nhBDXXV6JghN5ZdQ1tmBpZoy7VduvyAEDBmBhYaFzvL+/PzExMeTk5GgFgY4ePcrhw4c1g50WFhYc\nPHiQn376ifDwcP75z3+iUqk4efIk0JYeysPDgwkTJlBZWUlycjLl5eX885//5LXXXtNaQZSYmMiG\nDRtQKpUMGTIEJycnzM3N2b17N99++y0LFy7U+kFpaGjIrl278PT0xM3NjenTp2NsbMzFixfZt28f\n48aN06q1UNfYQua5Ssysf9tWeOwHzO16Ye3iib3nMKocXCnNOkrWD59RfPIIFwaPxubCYSouFmpW\nANnY2PDQQw/xzTff8NxzzzFhwgTMzc1JTEwkPz+foUOH6qTxefDBB0lKSmL9+vVMmjQJa2trsrKy\nuHjxomZA/WrNmDGDjIwMfv75Z5YvX05wcDB2dnZUVFSQkpLC9OnTOxz8E+JaiIuLo6WlhQcffFCn\n0PRjjz3Gzz//rPn/2VlR6fvuu4/U1FTNTOT2oqOjAejduzfLli0jIyODsLAwfHx8eP3117UGVWNi\nYti4cSMTHIrwmThLc/9ze/DvBA0b0O0VjGqpqam89957OivtoqKiMDY25qOPPtIEhdWqq6s7fJ0C\nhvfveIDyerQT4nro7oSaPXv2APDHP/5RZ0WkoaGhTNQQHepO/bTLta+fps+8efNITEwkISGBP/3p\nTwQFBdHY2MjBgwepqqrioYce6nZwXS0wMJCDBw+yYcMGgoKCMDU1xcXFpctAuRBC3AkkCCSEEEKI\n66ar4rED/PQPxKqDJ+qZ7mrHjx/XDHZaWVnxl7/8hbKyMsaNG4enpye9e/fGyMiInTt3kpCQQGVl\nJZGRkVhbW3Py5El++eUXqqurOXbsGHFxcZofhTU1Nbz77ruYmZnx9ttvU1NTQ2ZmJgB1dXWcPHmS\nTZs2aaWHOnv2LDU1NTzzzDOsWLFCa0C3oaFBpwZPdX0Tlyeh8p31DGY2vw26uI0IQTF6Fll7/0ll\nwUmUjfX83ODB5CA/ZsyYoRnAefzxx/H29mbPnj3ExsaiVCrp3bs3jz32GHPnzsXYWPsrXmBgIK++\n+io7d+7kwIEDmJubM3z4cF5++WXCwsL0/hv0lIGBAatWrWLkyJFER0dz8OBBmpubcXBwYNiwYd1K\nmydERy4PJFfVNukcc/bsWQACAgJ09llbWzNgwADS09M5d+4cXl5eWvvbn9/M2BVTSxvS09O1ClDX\n1tYSHx+Pubk5gwcPxs7OTrNy4U9/+pPOoGpISAgRERGkJ/3KX1d1vPpIbc6cOezatYukpCS9QaB7\n7rmnw1SLRkZGmnph7alnRQv9+rvY4O/h2KPBzQBPxw4HNYW4nq5mQs2ECRPIz8/H3t5eUraKHutO\n/bSO2nV0vzQ2NmbdunV89913/PLLL+zZswdDQ0O8vLz4wx/+wOTJk3t8vRkzZlBSUsKBAwf45ptv\nUCqV+Pn5SRBICCGQIJAQQgghrpPuFI+NPHySe/UUj1WnJNFXu0M92JmQkEB2djYhISGsXLlS65ij\nR4+SkJCAr68v1tbWmnMZGhri7e1NRUUF+/bt0/wojI2Npba2lmeeeYZ+/fqRn58PwLhx43jllVf4\n17/+xffff88nn3xCv379qKqq4rHHHsPBwYEnn3xSZ0a/ubl2DRAAZavuG9E+AKRm4+rJoLuXkHPg\nKzzHz+XpJx/WFI5XB4EAJk+e3KMfyMHBwXoDMStXrtR5/7pKy/PWW291uG/q1KlMnTq1076EhITo\npPmDztMkiTuTvkCyojiPE2Fh2NjYkpxbpkk3ow4adzSbXR3QaR9cLlc0cPZiNcs/PaB1bJGyL8Xn\nE3FtaNZsi42NpampiV69emmukZWVhbGxMQcPHtR7zebmZqqqqlAoFNjYtA2ENTQ0EBERwa+//sr5\n8+epr69H1e5GWV5ervdcgwcP1rt96tSpbN26lWeffZbJkyfj5+eHr6+vzqogod8jkwex5suEbqU5\nMjBAcz8W4vdyLSbUqO97Tk5O17ez4rbUnfppZtb2jHx0bYft9H13NDU1ZcGCBSxYsKDL83cnZaSh\noSFLlixhyZIlevd39j2zs3SVQghxO5AgkBBCCCGuue4Wj62rKOK9b4/pFI9Vpyfz9vYmr0TBoawi\nzlfUMmTYAEryTvHss89iZWXFpUuX9M76V9fP6dWrl2abu7s7VlZWKBQKWlpayMnJ0ezLysoCIDc3\nl7CwMFpbWykrK+PHH3+kX79+nD9/HoDCwkL69etHdnY2KpWKYcOG6Q346GNkaKCzram2iuKMQygu\n5tBUV01rS7PW/uY6RbdrVghxOykpKWH2/FCqrL3xHDdH7zH1zS2s+TKBF+4PYObwfpqg8aVLl/Dw\n8NA5/tKlSwCa1IpRyQUczylDpVRyefUfp0GjyD0Uzpn8cxzKakubGB0djbGxsaYuF4BCoUCpVLJj\nx45OX099fT02Nja0tLTw6quvkp2djaenJ5MmTcLOzk6zimfHjh00NzfrPUf79JLtzZ07F1tbW374\n4QciIiL4/vvvMTAwwM/PjyeeeEIKvXdhhJczK2f5d/nMMjCAF+4P6LTGhbh2SkpKWLZsmd6JHneS\nazWhRn1/7CjILERnrvS7qHyHFUKIm4fckYUQQghxzXW3eGxLUwNFqb8QFt9HM7B2+vRp4uLiaMKY\niLOGZB0+QPnZfArLajBs6gVuLlRdTKclM5GiwnxWrVrFfffdpxnsvHjxoqaWh5mZmeZahoaGzJo1\ni6+++oqioiKt1EkKhQKVSsWePXs0tUIaGxvJycnhnXfewcPDA0NDQ+rr64G2WbXNzc06K4A6Y2th\nSvsEcY2KS5yK+hfKpnqsXTyw7TsQQxMzDAwMaKqpojznBKrWFqk9Ie4ICQkJREREUFhYiEKhoLZJ\nRVpGBo5e2j9XWhrrKMn6labaKprrqzmx622e3deH9S8tx9vbm8OHD5OWlsbp06f5/PPPefrpp3ng\ngQeora0lJycHU1NT+vXrR1zyaZaEPkZDZSnm9i60KpUYGhmhalVSdiaJipxUGqvKaWmqZ+3f/kZ+\n1gny8/OZNGkS8fHxQNsg9bFjx3B1dSUqKopt27Zx4sQJGhoa8PT0JDQ0lNGjR+u8zo5WMFZUVHQa\nTOrsfjNt2jSmTZtGbW0tJ0+e5MiRI+zbt4+1a9eyZcsWWRXUhXtGeOBqb0lY/GlS83VTwwV4OhI6\naZAEgK6x2bNn4+fn1+nq0jvZtZxQY25ujqenJ/n5+eTk5EhKONEjUj9NCCFufRIEEkIIIcQ11ZPi\nsTaunpSfSWb3/16gd1UIRsoG4uPjuXipFtWgu8kqrtNp4+QdCN6BNNdVY5WwjaqS8+zevZvY2Fju\nu+8+0tLScHJyori4mMbGRq22ixcv5uTJkxw7dozq6mq2bNmCpaUliYmJpKSk8M477/DII48A0NLS\nwv/7f/+PhIQEnJycCAgI4Ny5c2zatInExEROnDihtdKoK5ZmxvRzt+ds28RcSrKO0NJYh+e4OTgN\nGK51bEVeOuU5J/BwtpbaE4KNGzcSExPD1q1bcXFxudHdueaioqL4+OOPcXBwYMyYMdja2vJZxCFU\nrSrqys5rjmusqeTM/s+pqyjCwMAQIzMLHDyHUnX+NK+9tpY3//pHjI2N2bNnD6+99hoGBgbExsby\nwAMPsH37durq6pgxYwYmJia8v/VrVK2t2PUbQkNVGRU5J3D0Hk7OLzupvnAGACNzS1qVzTQoyvnf\nf27B1d6CN998UxMEgrZaQwqFguXLlzNgwACmTZuGQqEgPj6edevWsX79eq3VikVFRQCMHz9e531I\nT0+/6vfSysqKoKAggoKCUKlU7Nu3j4yMDL3XE9pGeDkzwstZp+7K8P7Och8WN8S1mFBjZWXFuHHj\ngLag2+bNm9m8eTPr1q3TSrmrUqm4dOlSh+k0xZ1N6qcJIcStT4JAQgghhLimelI81tTKgX5jZnEh\nOYbvIvbiYmuKtXNfDF0nY9NnYKdtTSxtIXgp0w1Oc2j/XvLz8zlx4gSPPPIIR48eJTMzk9LSUq02\nxsbGLFiwgP379wNtNT5UKhXNzc04OjrS2tqqdeyrr75KXFwc+/fv59ixYzQ0NGBra4u9vT3u7u40\nNTXR0NDQ7ZRwD4315t3oHFSqtpVAAPYevjrH1RTnYWAA44f07tZ5hbiVRUVFYWxszEcffYSdnR0f\nbtnKqbRkrF08aFUqSdr+BgC1pYWYWtnjPvoecqp2YmxuifOg0TQqLlGUfpDnV77A2OAxFBcX88Yb\nb9DU1ERcXBx/+MMfOH78OBUVFbz66qvs/O5Hft79L5rrqjAwMsbQyJjCo3s5nxyD4uJZUKlQNjdh\nZGKGSqWipbEBpYEBlVUKzWpANWdnZ9LT00lKSqKlpYXCwkIGDhzI/Pnz2bFjB1999RVmZmYMGTIE\nQBPES0tLY8yYMZrzXLx4kW3btl3R+5eamoq/v7/OSiF1Kqj2KyJF1/q72MjApbjhrsWEmtbWVp57\n7jlNCswZM2aQkZHBzz//zPLlywkODsbOzo6KigpSUlKYPn261EQRHZL6aUIIcWuTIJAQQgghrqnu\nFI9tz9yuF95TF7F06mBCJw1i9edHuHjZwIfTgOE4DRiO4mIuKpVKM9hpYmmHsecM7jc34NixY7z6\n6quMGjWK48ePM2bMGGxsbKipqcHa2hqApqYmtm/fjqurKy+88ALTpk0D2tLBPf3000RHRxMcHKwp\nwG5gYMBdd93F1KlTSU9Px9/fX9On9957j19++YV///vfrFixQmsAtqGhAaVSqTXLFsDf04mVs6zY\nuDcNU6u29Ew1xXnYuQ/RHFN94QzlZ5PxdrWVgUgBwJIlS5g/f/5tPUPbyMjotxSNdn1x8QmmJCsB\na2c37PoNoam2kkZFOfaeQ7Ht2xYgVjY1kv3Tv7FydsfVbyJNBYkUFRVhbW1N3759KSsr4+LFi5w6\ndYrRo0dTUFDATz/9RPjen1Apm7Hr54ujdwCO/f05nxzDheR9KJsaMLd3oU/gVJrrFJRmH6O5thID\nYxNM7V2Ji4vT9Lm0tJTCwkJMTEwwMTHhwoULuLm5ERcXR2RkJHV1daSkpGBkZMQbb7QFssaMGUOf\nPn347rvvyMvLY8CAAZSWlnL06FFGjx6tE7jujg0bNmBubs6QIUNwdXVFpVKRkZHB6dOnGThwIIGB\ngVf97yNuX6dOnSI8PJzMzExqamqwt7cnKCiIxYsXa91zzpw5Q2xsLGlpaZSVldHY2IizszPBwcEs\nXLhQ85xVi4mJYePGjaxcuRJ7e3t2795NTk4OdXV1rFy5ko0bNwJtK+Bmz56tabd48WKdQERJSUm3\n0i3eTq52Qs2AAQNYtGgRI0eO1BxnYGDAqlWrGDlyJNHR0Rw8eJDm5mYcHBwYNmwYwcHB1+OliNuE\n1E8TQohbmwSBhBBCCHFNXU3x2K5mvuYe+ApDY1Msnd0ws7ZHpYJTP+YzwLqRgGE+OoOd/fr147nn\nnmPChAkYGRmRkJBAUVERo0eP5q677tIcZ2Njw5o1a3jzzTdZvXo1gYGBeHh4YGBgQGlpKVlZWSgU\nCsLDwzVtnnnmGfLz8/nxxx9JS0tj5MiRGBsbU1xcTFJSEq+99ppW0EhNXXtii0Uj3+ecIDd+N/Ye\nvphY2FBfWYJBZSGL58wkJyPpit5HcftxdHS87QJA7VNuWbj5cinzFM8++yyTJ0+mssUOB68ASrIS\nsHDoTZ+AqZRmH8fMxgkjYxNKs49hZNY2s93KyQ1rFw9aGuqw7+tB8Cg/cnNzGTJkCO+88w5LlizB\nzMyMSZMmsWvXLlJTUxkcMJrzlU30nzAP+35tAdjefhMoSonB3N4Fz3FzMDazAMCu3xDyDobTVFNB\nY2MjhYWFREZGAvDnP/+ZpqYm5s6dy6pVq4iMjCQzMxOVSkVZWRllZWW4u7vz6KOPal63ubk5GzZs\nYNu2baSlpZGZmYmrqyuLFi1i7ty5Wqnmumvp0qUkJSVx9uxZjh8/jqmpKS4uLjz++OPcd999GBvL\nTz6h3759+9i8eTMmJiYEBwfj7OzMhQsXiI6O5ujRo7z33nuatKfR0dEcOXIEf39/hg8fjkql4syZ\nM3z33XckJibyj3/8AwsLC51rHDp0iMTEREaNGsW9995LSUkJXl5eLF68mB07duDi4kJISIjm+Muf\nmyUlJaxatYrevXt3mW7xdtKdCTVm1vaMfHSt5u/tJ9R0ZurUqUydOvVquyjuQLdr/bT2Qev29yMh\nhLidyC8CIYQQQvRYSUkJy5Yt01vc/GqKx3Y187XP8BAURWepr7hI9YUzGBoZY2plR9C02bz+/BM6\ng51/+ctf2LlzJ3FxcVRUVODk5ERoaCjz58/XSZ0UGBjI5s2bCQ8PJykpiYyMDIyNjXF0dCQwMFCn\npoa1tTXvvvsuERERxMfHExUVhaGhIb169WL69Ol4eHh0+DpGeDnz2YvzWBDswT+3/ofzBXkYNBcx\nYdhgnnj0z1hZWfHKKxIEulFiYmI4evQoZ8+e5dKlSxgZGdG/f3/uvfdereCh2unTp/nvf/9LVlYW\nBgYGDB48mEcffZSkpCR27NjBhg0btAY2f/31Vw4dOkR2djbl5eUAuLu7ExISwv3336/zf1NfTaD2\nn8HQ0NBbZpZ8cm4ZXx44fVmw150qt0lUXUwnf+du6hqbyS26RN2li9i4egGgbGpLw1ZdlIOyqZ5G\nRTnGphYoivNQFOcBbWm8PD09KSgoIDs7G1NTUyZOnEh0dDT5+fkATJ8+nS/D92JibqVZUQRQdjoR\nlUqFqaUtpaeOara3NNbRXK/AwMiYuppqFAoFALm5uWRlZeHg4ICfnx9Dhw5l6NChmnYJCQk88sgj\nWFlZMWiQ9oCss7Mzq1ev1vv+qANM7YWGhnaaounee+/l3nvv7XC/EPqcP3+eTz75BFdXV9566y2c\nnJw0+1JSUnjttdf47LPPePXVVwF4+OGHWbFiBYaGhlrn2bdvH5s2bWLv3r3Mnz9f5zrHjx9n7dq1\njBo1Smu7t7e3JgjU2f/vtLQ0QkNDWbx4sWbblClTWLt2LeHh4bdtEOhqJtQIcT1J/TQhhLg1yTcE\nIYQQQlxTV1M89vCpi50e12twEL0GB+lsD5wwWO8MZBMTEx577DEee+yxbvXDxcWFZ555pnudpm1W\n/4IFC1iwYEGnx61cuVInWAZw98Qg7p6o+3pA/2DwW2+91e2+iSv3ySef4OHhgZ+fHw4ODigUCo4f\nP87777/P+fPntVZ2pKen87e//Y3W1lbGjRtHnz59yMvL45VXXulwcHLbtm0YGhoyZMgQnJycqK2t\nJTU1lc8++4zTp0+zatWqbvf1VpolH5Vc0GEaGSfvQPAORNnUgK9xNZXxcSgu5lJ2JpH+kx7CyKSt\nro170D1YOLhyet/nuPgE4x50j+Ycny6fTH8XG9LS0qipqQEgJCSE6Oho0tPTAVAqlZiomnHo74eh\nOv0c0FDZFoA2tbLDddgEzfbijEMobZ2w7T2AIc5GvPbaawBkZWVpzpeYmEhYWJjW66mqqgLQqSEk\nxI1y+YBt1sG9tLS08PTTT2sFgKBtUkRwcDBHjx6lvr4eCwsLTQD6cnfffTf/+te/SE5O1hsECg4O\n1gkA9YSLiwsLFy7U2jZy5Eh69epFdnb2FZ/3Znc1E2qE+D3cTvXTxo4dy5YtW3BwcLjRXRFCiOtG\ngkBCCCGEuOa6Kh7bPoVJ++KxMvNV3Aw2b95Mnz59tLa1tLSwdu1adu/ezb333ouTkxMqlYpNmzbR\n3NzM66+/rjXQ+eOPP/LJJ5/oPf/atWt1zq9Sqdi4cSOxsbHMmjWLIUOG6G17uVtllnxyblmXdQQA\njEzNKcYct1EzKEqNo7WlmdqSAiyd3QCoLSnAwsFVc6yaOpAMbfWFWltbAfD19aVv374kJyfj5ORE\nZmYmlmbGjJs4hcKG366rQoWBoRFV57Npqq9B2ViHsrmB5voaTMytMFPVYWnmQENDWyP1iqCqqiqS\nkpKorKzU+3qUSmXP36wbZNmyZQBs3br1BvdEXEv6V9/BqahYjGovsffnI5w+fVqnXVVVFa2trZw/\nf56BAwfS0tJCVFQUBw4coLCwkNraWlTtPtDqVY2XU9fYu1JeXl46q4+gbUWdOhh7O7qaCTVCiJ6x\nsrLSqeMphBC3GxkxEUIIIW4BV5r66cCBA0RFRZGTk0NTUxOurq5MnTqVefPmYWJiojnus88+IzIy\nkjlz5vDUU09pnUOd6mX48OH8/e9/Z8eOHezYsQNoS5sVExOjOVadS/tKi8fKzFdxM7g8QANgbGzM\nrFmzSE1NJSUlhWnTpnHy5EmKiooICAjQmel+zz338P3333P+/Plund/AwIAHHniA2NhYkpOTux0E\nulVmyX954HSH9wLFxVysXfvrpMFTtbZgYGiMobHJ/9X+8aSy8CTGltoF6NWB5Ly8PL2zeENCQjh2\n7BglJSU0NTUxdOhQnpw/VStQbWxmgamVPeY2Dlg49aW+vAgjMwts+wzAY8x9vLvsLjztDKmtrQXA\n0rKtJpH6/qtvpd+aNWs0K5DuFLNnz8bPz09WLd4kOlt919JYR219E5/8ezverrb0stVdTQtoAp/v\nvPMOR44coXfv3gQHB+Pg4KD5HhEREUFzc7Pe9lc7s97a2lrvdiMjI60g1O2oqwk17bWfUCPE76En\nqXPVz8Nvv/2W3bt3ExMTQ3l5OS4uLjz44IPMnDkTaJtAs3fvXoqKirCxsWH69OmEhobqfD8AOHXq\nFOHh4WRmZlJTU4O9vT1BQUEsXrxYp47i5dePi4ujuLiYKVOmsHLlyk5rApWVlREeHs7x48cpLy/H\n1NSUPn36MGbMGBYtWqQ5LjU1lQMHDpCZmUlZWRlKpZLevXszceJEHnroIUxNTa/VWy+EEFdEgkBC\nCCHELaQnqZ8+/PBD9u/fj7Oz8/9n784Dqq4DvnAbAAAgAElEQVTSx4+/L7vsIEsIKGCgsrgjKrnv\n+5YWlJOTlWM1aqb+JsussVzSGZcyG5fGLbQxzVBzSdwwUxSQVRTFFZBFRBaVzfv7g++9eb2XVVTU\n5/VP9NnO+SD3fu49z3meQ+fOnTEzM+Ps2bNs3LiRmJgY5syZg/7/lUN68803SUxMJDQ0lFatWqkD\nSleuXOE///kPNjY2fPjhhygUCvz8/CgsLCQ0NBR3d3c6duyobtPd3V39c20Wj5WZr+JJeLBMkqs5\nRBzeQ0xMDFlZWRQXF2scr5rxfuHCBQCNtWBUFAoFzZs31xkEys/PVw8oXL9+XT3I+uD1q+NpmCV/\nKTO/0tf0xSP/Q8/ACFM7Z4zNrVEqIS81mZK7hZjZuWD+f+sCuQWO4HzYBq7HHuZObiY3zkejLCul\no5sZa/61m8uXL7No0SKt6/fo0YMFCxaQmpqKubm5zkC1mZ0zhdmpmFjbcyfnOkZmVpg7umFkZoln\ncSI//ieSxMRE/vKXv+Dq6qoO0qkygoSob6rKvlNl0rUc/f8wMDbhn68FVLiYe3JyMn/88QetW7fm\ns88+U392gPIsxq1bt1bYD12Dt6J6ajuhRojHoSalc1UWLlzI2bNnad++Pfr6+vz+++988803GBgY\ncPHiRQ4cOIC/vz+tWrXixIkTbN68GWNjY61Sk7/99hvffPMNhoaGBAQEYGdnR1paGnv37iUiIoJF\nixZhb2+v1f7cuXNJTk6mXbt2dOzYESsrq0rvMTk5mdmzZ5Ofn4+vry+dO3emqKiIK1euEBISohEE\n2rp1K9euXaN58+a0b9+ekpISEhMTCQkJIS4uji+++ELn5zUhhHhcJAgkhBBCPEWqW/opLCyM/fv3\n06lTJ6ZNm6Yx+ywkJIRNmzaxa9cuhg4dCpRnOfy///f/mDx5MkuWLGHZsmWYm5uzYMECiouLmTVr\nFtbW1gD4+fnh6OhIaGgoHh4elS7oXJvFY+ti5qvMQhfVoatMUlH+Tc7uWY2pfhndOralX79+mJqa\noqenR2ZmJmFhYeoZ77dv3wZQvzYepGsGfGFhIR988AEZGRl4eXnRs2dPzM3N0dfXVwdXK5pRr8vT\nMEv+9KXsSvc7te5FfvoF7uRcJy/tPHr6Bhg0sKCBlT1Wzl7qtXuMzKxoNuBtrpzYxdWIXdzLS8fh\ntgn3bjli37gxgwcPpkmTJlrXt7e3x9XVlWvXrqGvr0/37t0BzUD13VsduHE+CuW9ezRq3ZP86xfR\ny72MvdKI2xl2mNnb061bN/W5np6eNGvWjNjYWM6ePavzvm7dulWjf0sh6lJl2XdQHvi8fSONgqwr\nWDl7ERKeXGEQIT09HYAOHTpoBIAAzp07pxUory6FQqEu3Sh0q82EGiEeh+qWzr1fVlYWy5cvV5de\nGzFiBBMnTmTVqlWYmZnx9ddfq88JDg7m7bff5ueff2bEiBHq957U1FS+/fZbHB0dmTdvnkYbMTEx\nzJo1i5UrV/Lxxx9r9VnVvqWlZZX3V1payvz588nPz2fatGl069ZNY392tuZnm4kTJ+Lo6KgV+N64\ncSM//vgjv//+O126dKmyXSGEeFQkCCSEEELUQw8GTVzMykdyqlv6KTQ0FH19fSZPnqxVfuDVV19l\n586dHDp0SB0EgvISVe+//z4LFy5k0aJFvPDCC1y5coUxY8bQqlWrh7qfmiweKzNfxeNQUZmkzKQ/\nKC26jU2nYaQ5t6ZJh5b0a+0KlJdXvL/8oaokWEXrwdy8eVNr2759+8jIyCAoKEgrgJqUlERoaOjD\n3Fa9dLuotNL99l7tsfdqr7X97J413L6RyqWj2zC2bIhCoWDUoJ5YNX+ZUGUa48eNrTQIfb8xY8ZQ\nVlbG3LlzNYJ2fwaqfdngYUjo5v+idz2KXh398fHyoKysjMzMTHW5GTu7P99vZs+eTXFxMdeuXWPS\npEk0a9YMMzMzsrOzuXTpElevXmXp0qXV/C09Hkqlkl27dvHrr79y/fp1LCws6NSpE2PHjtU6trCw\nkL179xIZGUlqaiq3bt3C1NSU5s2bM3r0aJo3b64+VlVKByA+Pp4hQ4ao993/t16T8kGi9qrKvgOw\n9yoPfKZG7sPYwpbYy+XnqZ7VpaWlnD17Fh8fHxwdy9fhevDf9tatW6xYsaLW/bS0tNQaSBXaajOh\nRoi6puvv70G6Sufe74033tBYe+eFF17A29ub2NhYxo8frxHQMTMzo0OHDhql46C8ZFxpaSlvv/22\nVpCpVatWBAQEEBERwZ07d2jQQLPM5euvv16tABBAREQEmZmZBAQEaAWAAI3PA6p70WXYsGH8+OOP\nREVFSRBICPFESRBICCGEqEcqWsC5qCCXq1dv0tjLt8rST0VFRVy8eBFLS0t++eUXne0YGhpy9epV\nre1du3YlJiaGffv2ER8fj7e3N6+99lod3FnNyMxX8ShVViapKL88cGPduAVKJSzeGYuDVQPauNsR\nFxencayHhwcAiYmJWtdRKpU6y7GlpaUB0LlzZ619z+r6MabGtfvK4RY4gmun9pKXfoGyy/HlmU3d\n/OjZtRX7a3nNCttysGDWu8GMHdiZ7du3Exsby85ziZiYmGBra0tgYKDW4I2dnR1Llixhx44dHDt2\njEOHDnHv3j2sra1pXElm0pO0atUqduzYga2tLf3790dfX58TJ05w7tw5SktLMTD48/d67do1NmzY\ngI+PD/7+/pibm5OZmUlERASRkZHMmjVLvRaWu7s7QUFBbNq0CQcHB401Ffz8/NQ/16Z8kKi5qrLv\nAEys7GgcMJQrJ0I5s/M7LJ2asqgojpaNbdWBT0tLS7777js8PT1p0aIFx44dY/r06Xh7e5Obm0tk\nZCTOzs5a629UV6tWrThy5Aj//Oc/adq0KQYGBvj4+ODr61ur6z3rajKhRoi6UtF3k+LCW+inR2Nd\nkoWyKL/C0rn3e/HFF7W2qd4/dO1TBXnuDwKpPlvFx8eTnJysdc6tW7e4d+8eqampWtf09Kz+ulmq\ndh5c87Eid+/eJTQ0lOPHj5OamsqdO3c0MrJrUupXCCEeBQkCCSGEEPVEZQs4A+TdKSbszA32nr6q\nzkxQub/0U0FBAUqlklu3brFp06Ya9yMwMJB9+/YB5Yt8P6n61TLzVTwqlZVJMjIrrw9fkHEJK5dm\nKJUQEp6M8uYV9etCxdvbGycnJ2JjY4mMjNQYKNizZ4/O9YBUM+rj4uJwc3NTb09JSWHLli0PeWf1\nk67ZwtVhbGFL0x5BGttebOmFn58nO3bsqPC8NWvWaG0LDg6uVtaQm5sbU6ZMqXYfGzRowJgxYxgz\nZky1z3lSzpw5w44dO3BycuJf//oXFhbl76Njx45l5syZ5OTkqAfZAFxcXFi3bp3WrOns7Gw+/PBD\nVq9erf6b9/DwwMPDQx0Equh3XZvyQaLmqsq+U7H1aEkDG0cyzxwnP+MiJ45kcMPZTivwqaenx6xZ\ns9i4cSOnTp1ix44dNGzYkL59+/LKK6/w7rvv1qqf77zzDlBewunUqVMolUqCgoIkCCREPVHRdxNV\n6dyy4juYOzRmSDd//Ju56Cyde7/7s4BUVGXeKttXWvrne1peXh4A27Ztq7TvD663CLrL9FaksLAQ\noFrPpNLSUj7++GPOnTtHkyZN6NKlC1ZWVur+b9q0ScrDCiGeOAkCCSGEEPVAVQs4qz2QmaCL6kuU\nh4dHjUsR5eXlsWzZMoyNjYHyWeN+fn5VLpz6KMnMV1GXqiqTZO/lT07KaS6G/4R14xYYNrDg/IFM\noo1y6durO+Hh4epjFQoFf//735k9ezZz5syhc+fOODk5cfHiRU6fPk27du2IjIzUqA/fs2dPtm3b\nxqpVq4iLi6NRo0akpaVx8uRJOnXqpHH9Z4WbgwV+jW2rLE9VHbXNKnpe3R9EP/jLj9wuKmXMmDHq\nABCAkZERb7zxBjNnztQ4V9eAHJRnQAUGBrJjxw6ysrJ0Lr5dkQcDQFB1+SBRczV5nTSwcaRJ52EA\nTOznzfAO7jqPs7CwYOLEiTr36Qq89urVSyMjTBcrKyumT5+uc5+Dg0OlwV5Z+0+IR6uy7yaq0rlN\nOg2jYdPWnFXAuMAA2rjbaZXOrWuqZ9OPP/6oLstbXQ+u11OddqqTwaPKqO3Vq5fWRJKcnJxaTcoT\nQoi6Jt+ihBBCiHqgqgWc76fKTKgoCGRiYkLjxo25cuUK+fn5GoN9lV9XyeLFi7lx4wZ///vfAfj6\n669ZvHgxs2fP1vjipMoOkgWdxdOmqjJJDWwcebH3G6THHCQvNRml8h4NrB3p8/pbDOjgqRWk8fPz\nY968eWzcuJGTJ08C0KxZM+bOncuhQ4cANAYpbG1tWbBgAWvXriUxMZGoqChcXFyYOHEirVu3fiaD\nQACvdfXkox9OVPt9riK1zSp63ugq35P0ezS3c26wNeEuDZtmazxDvL29dWZ9njlzhtDQUJKSksjN\nzdWYjQ3lg2M1CQJlZWXx008/ERMTQ1ZWVrXKB4maq+3rRF5fQgiVyr6b3F86FzS/mzxYOreuNWvW\njPPnz5OQkIC/v/8ja0e17l1kZCQDBgyo9Nj09HTg+Sr1K4R4+kgQSAghhHjCqrOA84NiL+doLOD8\noOHDh7Ns2TKWLl3KBx98oDWju6CggIyMDJo2baretn37dk6dOkWXLl3o27cvAKdPnyY8PJxt27Yx\natQo9bHm5uYoFAqysrJq1G8hnrTqlEkyt3fFs/dfNLa5elVchqxZs2bMmTNHa/v333+Pnp4ejRo1\n0ryWqyuzZs3S2bau60+ZMkVrZunTNku+jbsdUwb5VS/jsQItm9hKVmA1VFS+p6ykCIDkGyV89MMJ\nPhjcUl1aVF9fX6vs2x9//MG8efMwMjKidevWODk5YWJigkKhIC4ujvj4+BqVt7l+/TpTp06loKAA\nHx8f2rZti6mpaZXlg0TN1Sb7Tl5fQgiVqr6bPFg6F8q/m+zcH65VOreuDR48mL1797J69WoaNWqE\ns7Ozxv7S0lLOnj2Lj4/PQ7XToUMHHBwcOHHiBEeOHKFr164a+7Ozs7GzKw+cq0qpxsXF0aFDB/Ux\n169fZ+3atQ/VDyGEqCsSBBJCCCGesOos4FzReRUN2PTp04fz58/z66+/8vbbb9OmTRscHBzIz88n\nIyOD+Ph4evfuzXvvvQdAcnIy69evx9HRUb0N4P333yc5OZkNGzbg6+tLs2blX/RMTEzw8vIiISGB\nRYsW4ezsjJ6eHgEBARrrnAhR39S2nFhF5xUVFVFaWqoVaA0LC+PMmTO0a9cOExOTWrX5rOnfpjGO\n1qaEhCcTe7lmgW+FAoK7VH9B5+dVZeV79A3Ly3yW3i1E39BIo7RoWVkZeXl56gEtgI0bN2JoaMji\nxYtxddVch2758uU1nt28fft28vPzmTJlilaZsEddPuh5VJPsO3l9CSHuV9V3E12lc+/kZvLP3zJ5\neXDfR5rV7OLiwqRJk1i2bBnvvfcebdu2xdnZmbKyMjIzM0lMTMTS0pLvvvvuodoxMDDgH//4B59+\n+ikLFy5k9+7dNG/enOLiYq5evUpMTAy//PILUB4wcnJyYvv27Vy6dImmTZuSlZVFREQE/v7+MmlO\nCFEvSBBICCGeEjt27GD37t1kZGRQXFzMW2+9xbBhwx57P4YMGYKvr2+9m2X+NKvuAs41PW/ixIm0\nb9+e3bt3ExMTQ2FhIebm5tjb2zNy5Eh69OgBlC98umDBAgBmzJihMZhtamrKjBkzmDFjBl999RXL\nli1T7//www9ZtWoVUVFRHDlyBKVSiZ2dnQSBRL1W12WSsrKymDx5sjpT4t69e1y4cIHExETMzMwY\nP378w3T3mdPG3Y427nbqtWpiLt3g2NmMSs9RKOCDwS0rLIEp/lRZ+R5TWydu56RTkHkZYwsbjfI9\niYmJWuU909PTady4sVYASKlUkpCQoLMNhUJRYZnQysrlPOryQc+j6mbfPYuvr8zMTMaPH69zfQ4h\nRNWq+o5RUencQa9PYEBg80de2rZHjx64u7uzfft2YmNjiY6OxsTEBFtbWwIDA+nSpUudtOPp6cmy\nZcv46aefOHXqFElJSTRo0AAnJydee+019XEmJibMnTuXtWvXEhcXR2JiIo6Ojrz66qsMHz78mS31\nK4R4ukgQSAghngJHjhxh5cqVeHh4MHToUAwNDdV1iuuaasBS1yK/4tGoTmaCsbk1bV+fXeF5FQXl\n/P39q6yXbWZmxurVqyvc7+npyc8//6y13cnJiU8//bTSawtR39R1mSRra2u6detGfHw8sbGxlJaW\nYm1tTe/evRkzZgxOTk511fWnkq4JDKtXr1ZPJhjewZ3oi9kVZge1bGJLcBfPZ2qA+lGpqnyPbdPW\nZJ+P4np8OFYuXhgYmxJ7OYdz126wbt06reMdHBxIS0sjJycHW1tboDwAFBISwtWrV3W2YWlpSXa2\n7hnkFZXLiYqKeuTlg55XVWXfyetLCKFLdb6b6Cqd26qtN35+7lrlaiubPKir5K1KcHAwwcHBOve5\nublVO8hb1eTFXr16aWWoqtjb2zNx4sQq27Czs2PatGk691VWvlcIIR4XCQIJIcRTQLXY+OzZs9UD\nMeLZ8TgWcJbgnhB/qssySebm5kyaNKkOe/fsqO4Ehgezg24XlWJqbEBrNztZo6QGqirfY27vikPz\nADKTTnBm13fYNPYGhR7vR27E18NJ6/PF8OHDWb58OZMmTSIwMBB9fX3OnDnDlStX6NChAxEREVpt\ntGrViiNHjvDPf/6Tpk2bYmBggI+PD76+vgwaNIj9+/czf/58AgMDsbW15fLly0RFRfHSSy89sZnS\nT0PWyMNkYT+Pry9bW1tWrFiBqanpk+6KEE+lx/HdRAghxOMlQSAhhHgK5OSUz96UANCzqS4yEz76\n6CPi4+NlppkQ1fA8l0l6nCqawLBixQqMjY21jndzsHhmB6Ufh+qUFnVu1w9jC1uyzp0kO/kU+sam\nBPTsypx/TtMKZvbv3x9DQ0N++eUXwsLCMDIywsfHh8mTJ3Ps2DGdQaB33nkHgJiYGE6dOoVSqSQo\nKAhfX1/c3NyYO3cuGzdu5OTJk5SVleHu7s7MmTMxMzOTcjmP2PP0+jIwMMDFxeVJd0OIp1ZdZ00L\nIYR48iQIJIQQ9VhISAibNm1S//+QIUPUP69Zs6bSmau6ggJxcXHMnDmToKAg2rdvz6ZNm0hKSqKg\noIApU6awZMkSnW3paiMvL4/169cTERFBfn4+Tk5OjBw5kt69e+u8l6ioKEJDQzl37hx37tzBzs6O\nTp068corr2gtqK7KWvn6668JCQnhjz/+4MaNG4wZM6bCkgBPO1nAWYjHS8okPXoVTWCQwdlHozrl\nexQKBfbNOmDf7M9ybEP7eWNmZqYzU7SiEjlubm46n8dWVlZMnz69wvZbtGjBl19+qXOfTGIQdUVX\ndteSJUsICwtj9erVnDx5kl9//ZXr169jY2NDv379GD16NAqFgqNHj7Jt2zauXLmCiYkJL730Em++\n+SZGRkYabRw/fpzff/+dc+fOcePGDaD8va1Xr14MHjwYhUKh1a/U1FTWr19PTEwMpaWluLu7M2bM\nGPLy8liyZAlTpkzRer1lZ2er1yO5ceMGDRo0oEWLFrz66qt4espnQfHoyHcTIYR4tkgQSAgh6jE/\nPz8AwsLCyMzMJCgoqE6um5SUxJYtW/D29qZPnz7k5eXRqFEjgoKCCA0NBWDo0KHq4z08PDTOLyws\nZMaMGRgYGBAYGEhJSQlHjx5l6dKlKBQKrS+wmzZtIiQkBAsLC/z9/bGysuLSpUv8/PPPnDp1ikWL\nFmmV7CgtLeXjjz8mPz+fNm3aYGpqiqOjY53cf30kmQlCPH7PY5mkx6GyCQw7duzQKm21fPly9uzZ\nwyeffEJAQIDW9c6ePcu0adPo3LkzH330kXp7UVERoaGhhIeHk5aWhkKhoEmTJgwdOpSuXbs+wjus\nn6R8jxBV+/7779XrUrVp04YTJ06wYcMGSktLsbCwYO3atXTs2BEfHx9Onz7Nrl27uHfvHu+++67G\nddauXYuenh7NmjWjYcOGFBYWEhsby8qVK0lOTmbq1Kkax1+7do3p06dTUFCAv78/bm5uXL9+nblz\n59KuXTudfb1w4QKzZs2ioKCAtm3b0rlzZ/Ly8jh+/DgzZszg448/pn379o/sdyWeb/LdRAghni0S\nBBJCiHrMz88PPz8/4uLiyMzM1Jh1m5mZWevrRkdH895779G/f3+N7S1atCAsLAyg0oybixcv0qdP\nH95//3309PQAGDZsGO+//z5bt27VCALFxsYSEhJC8+bN+eyzzzSyfsLCwliyZAkhISG89dZbGm3k\n5OTg6urKvHnzMDExqfW9PiphYWFERERw4cIFbt68ib6+Pm5ubgwYMIAePXpoHKvKytq+fTtbt25l\n//79ZGVlqReUf/311zEwMNDKTMi/nkJG4h/cvpHKvdJiXnB0ZNSg3rzk2V19bdVsV5X7B1t1rR9w\n9+5dQkJCCA8PJzc3F3t7e/r27cuoUaN0zlo9e/Ys27ZtIzExkYKCAqytrWnfvj1BQUFas/tV9/nz\nzz/z008/cejQITIyMujWrVu9XWdBCHi+yiQ9DjWdwNCrVy/27NnDgQMHdAaBDhw4AKCRaVpYWMjM\nmTNJSUmhadOm9OnTh3v37hEdHc3ChQu5fPkyY8eOrcO7qv+kfE/dysnJ4ccff+TUqVPk5ORgamqK\nj48PY8aM4cUXX9R5Tnh4OHv27CElJYWioiJsbGxo3rw5w4cPV2dtFBYWsnfvXiIjI0lNTeXWrVuY\nmprSvHlzRo8erXPdLFF3zp8/z9dff03Dhg2B8s+7b7/9Ntu2bcPY2JglS5bg6uoKQElJCZMnT+a3\n337jtddew8rKSn2d2bNn4+TkpHFtpVLJkiVLOHDgAIMGDaJZs2bqfStWrKCgoICJEycycOBA9fbI\nyEg+++wzrX6WlZWxYMEC7t69y9y5c/H19VXvy8nJ4YMPPmDZsmWsWbMGQ0PDOvndCPEgyZoWQohn\nhwSBhBDiOeTh4aEVAKoJY2Nj3nrrLXUACMDV1RVvb2/i4+O5e/euOnCjKu/y97//XavsW69evQgN\nDeXQoUNaQSAoLwtXHwNAAN9++y2NGzfG19cXGxsb8vPzOXXqFP/+979JTU3l9ddf1zpn0aJFJCQk\n0K5dO0xNTTl16hRbt24lNzdXHSRRZSas27yN5b/9gp2+Ib69u+Ht3oj0y+c5eXgv01MSWbhwIWZm\nZpiZmREUFKRzsPXBzKnS0lI+/fRTcnJyaN++PXp6ehw/fpx169ZRUlKiNVD722+/8c0332BoaEhA\nQAB2dnakpaWxd+9eIiIiWLRoEfb29lr3OXfuXJKTk2nXrh0dO3bUGDQRQjz7KpvAoEvz5s1xdnZW\nlxe1sPgzKFFSUsKRI0ewsrKibdu26u2rVq0iJSWFcePGMWrUKPX24uJivvzyS7Zs2UJgYKBWJuuz\nTsr31I2MjAxmzJhBTk4OLVu2pGvXrmRnZ3P06FFOnjzJzJkz8ff3Vx+vVCpZunQpYWFhWFpa0qlT\nJ6ysrLhx4waxsbE4Ozurg0DXrl1jw4YN+Pj44O/vj7m5OZmZmURERBAZGcmsWbMqzAwRD+/VV19V\nB4AAzMzMCAgIYP/+/YwYMUIdAAIwNDSkS5cuhISEcPXqVY3PMw8GgKC83OLQoUM5cOAA0dHR6iBQ\ndnY2sbGxODk5MWDAAI1z2rVrR+vWrTl9+rTG9lOnTpGens6IESM0AkBQXmJz1KhRrFq1ipiYGMkG\nEo+UZE0LIcSzQYJAQghRDz34IftWYXGdXt/Ly+uhzm/UqJFW+TYAO7vyWWAFBQXq4E1SUhIGBgYc\nPXpU57VKSkq4deuW1sCfkZERbm5uD9XPR+mbb77RGgAoLS1l9uzZ/PTTTwwYMEBjkAEgPT2d5cuX\nq+9z7NixTJo0iQMHDvDGG29gY2MDlGf3bP9xA24v2PLvf/9bY/2MFStW8Ouvv/Lf//6X999/HzMz\nM4KDg6s12JqTk4O7uztffPGFurZ9cHAwEyZM4JdffmH06NEYGJR/NEhNTeXbb7/F0dGRefPmadxL\nTEwMs2bNYuXKlXz88cda7WRlZbF8+XIsLS1r8isVQjzlHubZ1bNnTzZs2MCRI0cYNGiQentERAQF\nBQUMGzYMfX19APLz8zl48CCenp4aASAof3aMGzeOqKgoDh8+/NwFgaR8T91Yvnw5OTk5jB07ljFj\nxqi3Dxw4kH/84x8sXryY77//Xv1ZZ+/evYSFheHp6cmcOXM0Jr3cu3eP3Nxc9f+7uLiwbt06rWdk\ndnY2H374IatXr5YgUA09+N7jYlbxH7+uLC5VZrOufarPP9nZ2Rrb8/Pz2bZtG6dOneL69evcvXtX\nY79qnSCAlJQUoDzgrSvr2tvbWysIlJSUBJR/pgoJCdE6Jy0tDYCrV69KEEg8FpI1LYQQTzcJAgkh\nRD0SfTGbH44ka5VyST59BUXeTaIvZtfJgI21tfVDnf9gRo+KaoDu3r176m35+fmUlZVprA+hy507\ndzSCQFZWVjq/KNcXumaAGhgYMGjQIGJjY4mJiaFnz54a+8eNG6dxjyYmJnTr1o3Nmzdz/vx59azi\nQ4cOUVpayogRI7QWUB87diwHDx7k4MGDTJgwocYlQCZMmKCxuLGVlRUBAQEcOHCA1NRUmjRpAsDu\n3bspLS3l7bff1gpmtWrVioCAACIiIrhz5w4NGjTQ2P/6669LAEiI50hdPLt69uzJxo0bCQsL0wgC\nqUqU3l8K7ty5c+rnjK7B0bKyMqB8cPR5JOV7KldVwCA7O5vo6Gjs7e0ZOXKkxr4WLVrQrVs3Dh48\nyLFjx9TP+Z07dwKoJ2fcT09PT6N8akWfoezs7AgMDGTHjh1kZWXpzLQVmip67ykqyOXq1Zs0u1Gg\ndY6u37/q86uuCU6qfar3FSgv6ffBB6vrvU0AACAASURBVB+QkZGBl5cXPXv2xNzcHH19fQoLCwkN\nDaWkpETjeKj487eu7Xl5eQAVTqJSeTD4JIQQQgihiwSBhBCintgTfaXSmbt5d4r56IcTfDC4Jf1a\nu6oDJPd/Kb2f6gunLo8zuGJqaopSqawyCPSg+hYAenDQyNUcIg7vISYmhqysLIqLNWe83z8DVEVV\nCuZ+qkGegoI/ByouXLgAQMuWLbWONzc3p2nTpsTHx3Pt2jXc3d2rfQ9mZmY6g1f3Z3CpqGagxsfH\nk5ycrHXOrVu3uHfvHqmpqVozZ3XdpxDi2VTTZ1dF7OzsaNWqFadPn+bq1au4urpy69YtoqKi8PDw\n0MgMzc/PByA5OVnn+5PK8zw4KuV7tFU3YKDK2vDx8VFnx96vZcuWHDx4kJSUFHr27Mndu3e5fPky\n1tbW1c48O3PmDKGhoSQlJZGbm0tpaanG/hs3bkgQqArVee/ZGXmFPqevVvreUxv79u0jIyODoKAg\nrQzspKQkQkNDNbapgkv3Z4TdT9d2VbDqk08+0blWmhBCCCFETUgQSAgh6oHoi9lVlm4BUCph8c5Y\nHKwa0PwFc0C7PAXA7du3SU1NrVVf9PT0tAYjHkbz5s05efIkV65coXHjxnV23cdF16BRUf5Nzu5Z\njal+Gd06tqVfv36Ympqip6dHZmYmYWFhGjNAVSqbfXp/9pQqgHf/zOH7qcrGVRbo06UmGVyqGajb\ntm2r9Jq6BllV/RNCPNtq8+yqLPukV69enD59Wl0i89ChQ5SVlWllVarey4YNG6ZzPTnxJynfU64m\nAQOj/3u2VvQsU21XTZxQPYsfzJqtyB9//MG8efMwMjKidevWODk5YWJigkKhIC4ujvj4eJ2fIcSf\nqvvew33vPXVJVYqtc+fOWvvi4+O1tqmCg0lJSSiVSq2JTomJiVrnqNYTSkhIkCCQEEIIIR6aBIGE\nEKIe+OFIcrUWcYbywbSQ8GQW/qUTLi4uJCYmqmdNQ/lA/urVq7UyU6rLwsKCS5cuUVxcrFE2rLaG\nDRvGyZMn+frrr/noo4+0AhuqGbSqL7v1SUWDRplJf1BadBubTsNIc25Nkw5/znA/cuSIunxRbakG\nOG/evKkzcHbz5k1Ad9mSuqLqw48//ljjdupbFpcQ4tGozbOrsiBQ586dWbFiBQcPHuQvf/kLYWFh\n6Ovr0717d43jvLy8UCgUOgdOhXhQTQMGr/mWl1mtKGtD9QxWPSdV/9WVAazLxo0bMTQ0ZPHixerP\nbirLly/XGUQQmmrz3uNch+07OjoCEBcXp5GlmJKSwpYtW7SOt7e3x8/Pj7i4OHbv3s3AgQPV+yIj\nI7XWAwIICAjAycmJXbt20bJlS53r/iQlJeHu7o6xsXEd3JUQQgghnmUSBBJCiCfsUma+VmmSqsRe\nzuFSZj4jR45k2bJlTJ8+nZdeegkjIyNiY2MpLS3F3d2dixcv1rg/rVq1Ijk5mdmzZ+Pj44OhoSHu\n7u506NChxtdSXe+NN95g/fr1vPPOO7Rv3x5HR0fu3r1LZmYm8fHxeHt78/nnn9fq+o9KZYNGRfnl\nA0DWjVtozXCPi4t76LY9PDw4duwYcXFxtGrVSmNfYWEhKSkpGBkZaQwe6enpAeVBQNXPD6NZs2ac\nP3+ehIQE9VpFQgih8jDProoYGRnx0ksvsW/fPrZv387FixcJCAjAyspK4zgrKyu6d+/OwYMH2bx5\nM2PGjNF630tPT0dPT089WCueXzUNGBxPLS+zm5CQQFlZmTpbViU2NhaApk2bAuXr+zVp0oTLly+T\nkpJSZUm49PR0GjdurBUAUiqVJCQkVK+jz7Havvc0oO7KQ/bs2ZNt27axatUq4uLiaNSoEWlpaZw8\neZJOnToRHh6udc7EiROZPn06K1as4NSpU7i7u3P9+nWOHTtGQEAAJ06c0JhEY2BgwMyZM/n000/5\n/PPPadGihTrgk52dTXJyMtevX2f9+vUSBBJCCCFElR5+lEgIIcRDOX1Ju5xbdc/r06cPkyZNwtbW\nlrCwMMLDw2nRogULFy6ssPRXVV555RUGDBhAeno6W7ZsYePGjRw7dqxW11J5+eWXmT9/Pv7+/uo6\n+EePHuXGjRv069eP119//aGu/yhUNmhkZFY+IFmQcQn4c5ZpVFQU+/bte+i2e/TogYGBATt37iQ9\nPV1j38aNG7l9+zbdu3fH0NBQvd3S0hKArKysh24fYPDgwRgYGLB69WqdpQVLS0tlsEqI59jDPLsq\n06tXLwDWr18PoFUKTuVvf/sbzZo144cffmDixIksXbqUdevWsXjxYqZOnco777zD2bNna9VH8eyo\nTcDgfM493DxbkJmZqbW2y9mzZzl8+DDm5uZ06tRJvX3IkCEAfPPNN1qlWpVKJTk5f/bBwcGBtLQ0\njW1KpZKQkBCuXr1ao74+j2r73pOaU7MSupWxtbVlwYIF+Pv7k5iYyM6dO8nMzGTixImMGzdO5zmu\nrq4sWrSITp06kZiYyC+//EJGRgYzZ87Ex8cH0M7wdnNz4+uvv+bll1+msLCQ/fv3s3v3bs6fP4+H\nhwdTp05Vf/4TQgghhKiMZAIJIUQllixZQlhYGGvWrMHBweGRtHG7qOr1dzz7jKvwvD59+tCnTx+t\n/fPmzdPa5ufnx44dOypty8TEhHfffZd3331X5/7Kzp8yZQpTpkzRuc/b2xtvb+9K21ZZs2ZNtY57\nVKoaNLL38icn5TQXw3/CunELDBtYcP5AJtFGufTt1V3nDNCacHBw4O2332bFihVMnjyZl156CSsr\nK+Lj40lKSsLFxUVrkKFVq1YcPXqUuXPn0r59e4yMjHBwcKBHjx616oOLiwuTJk1i2bJlvPfee7Rt\n2xZnZ2fKysrIzMwkMTERS0tLvvvuu4e6VyHE06k6z67anOft7Y2TkxPp6elYWFhUmIVqamrK/Pnz\n2bNnD4cPH+bYsWMUFxdjbW1No0aNeOutt2jTpk2t+iieHbUNGLTt8zK3sr/h+++/JyoqCk9PT7Kz\nszl69Ch6enpMmTKFBg3+XGemb9++JCQkcPDgQSZMmKDOYMvJySEmJoY+ffoQHBwMwPDhw1m+fDmT\nJk0iMDAQfX19zpw5w5UrV+jQoQMRERF1cu/Pquq89xibW9P29dka23qN/AvBXeboPD44OFj97/Og\nXr16qYPT93N1dWXWrFk6z6nos7KLiwszZ87U2n748GH1NR9kZWXFG2+8wRtvvKHzmkIIIYQQ1SFB\nICGEeMJMjWv3Vlzb80TVqho0amDjyIu93yA95iB5qckolfdoYO1In9ffYkAHz4cOAgEMHDgQJycn\ntm3bxrFjxygqKsLe3p6RI0cyZswYrUyvvn37kpmZyZEjR9i6dStlZWX4+vrWOggE5RlJ7u7ubN++\nndjYWKKjozExMcHW1pbAwEC6dOnysLcphHhKVecZpGsCg6mxQZWTEVauXFmtPhgYGDB48GAGDx5c\nrePF86e2wUpjcxsWL17Mjz/+yKlTp4iPj6dBgwa0bduWV155BU9PT43jFQoFU6dOpW3btuzdu5ej\nR49SUlKCjY0NPj4+BAQEqI/t378/hoaG/PLLL4SFhWFkZISPjw+TJ0/m2LFjEgSqwtP6uVmpVJKb\nm4uNjY3G9piYGMLDw3F1dcXZuS5XLhJCCCGE+JNCWd0Cyc8ZhUIR2bZt27aRkZFPuitCiCfocWQC\nXcrMZ8J/jtT4vP9M6Iqbg8Uj6JEICU9m3aFzNT7vje5eBHfxrPpAIYR4ysmzSzwNtkdcZMXexBqf\nN7GfN8M7uD+CHomH9bS+9xQXFzNmzBj8/PxwdXVFT0+PK1eucPr0aQwMDPj888/x8/N7Yv0TQggh\nxKPTrl07oqKiopRKZbsn1QeZRi6EEE+Ym4MFfo1ta1SzvmUTWxlEe4Se1lmmQgjxuMizSzwNWrvZ\nPdbzxKP3tL73GBgYMGDAAGJiYjh37hxFRUVYWloSGBjI6NGj8fDweKL9E0IIIcSzTUarhBD11rVr\n15g4cSJ+fn7MnTtX5zHvv/8+165d4/vvv8fW1halUsmePXv47bffuHr1KkqlksaNG9O7d28GDBiA\nQqHQOH/IkCH4+voyY8YMNmzYQGRkJDdv3mTy5Mk663+rXLx4kc8++4w7d+4wc+ZMWrdu/VD3+lpX\nTz764QTVSc5UKHgi2SaZmZmMHz+eXr16Vbjuz7NCBo2EEKJqT8OzSzzfntaAgajc0/jeo6enx4QJ\nE550N4QQQgjxnNJ70h0QQoiKuLi40LJlS+Li4khNTdXaf+bMGS5fvkxAQAC2trYA/Otf/+Lbb7/l\n5s2b9O3bl/79+3Pr1i1WrFjBv/71L53tFBQUMG3aNM6ePUvnzp0ZPHgw1tbWFfYrJiaGf/zjHwDM\nnz//oQNAAG3c7ZgyyI8HYlRaFAr4YHBL2rg/vcGG8ePHM378+CfdjUqpBo1qoq4GjZYsWcKQIUPI\nzMx86GsJIcSj9Dw9u8TT67WunlX+jarUl4CBqJy89wghhBBC1IxkAgkh6rWBAwcSGxvL3r17efPN\nNzX27d27F4ABAwYAcOTIEQ4fPoyHhwcLFizAxMQEgNdff52PPvqIw4cP4+/vT7du3TSuc+nSJXr0\n6MHkyZPR19evtD8HDx5k2bJlODk58dlnn9XpOkH92zTG0dqUkPBkYi9rz1ht2cSW4C6eT+yLrK2t\nLStWrMDU1PSJtP+4vdbVk6n/2Uv8z0tp6NGaJp2HVXisDBoJIZ5X9f3ZJYQqYLBkV1ylmSMSMHi6\nyHuPEEIIIUT1SRBICFGvdezYEVtbW/bv38/YsWMxNDQEoLCwkPDwcJycnGjVqhUAv/32GwDjxo1T\nB4AATExMGDduHJ988gn79u3TCgIZGBgwfvz4KgNAP/30E+vXr6dFixbMmjULc3PzurxVoHygoo27\nHZcy8zl9KZvbRaWYGhvQ2s3uiZcmMTAwwMXF5Yn24XFq427HhD4tmLS98uNk0EgI8byrz88uIUAC\nBs8qee8RQgghhKgeCQIJIeqdB7/ItQnoQtjuXzh27Jg6gHPgwAGKi4vp16+fep2fCxcuoFAo8PPz\n07qmr68venp6XLhwQWufo6MjVlZWlfZp1apVHD9+nM6dO/Phhx9iZGRUB3daMTcHi3r35VXXmkBL\nliwhLCyMNWvWEBUVxc6dO0lLS8PU1JSOHTvy17/+FTMzMwDi4uKYOXOm+npDhgxR//zgOkMxMTFs\n27aNc+fOcffuXRwcHOjcuTMvv/yy+nqPQ08/F5o722Bgqzv7SQaNhBDiT/Xx2SWEigQMnl3y3iOE\nEEIIUTkJAgkh6o3oi9n8cCRZa/He4ttmXE3NZd3mbeog0N69ezEwMKB3797q4woLC7GwsMDAQPut\nTV9fH0tLS27duqW1z8bGpsq+JSQkANChQ4dHHgB6Gv33v/8lKiqKDh060KZNG3UJv/T0dL788kug\nPNgWFBREaGgoAEOHDlWf7+Hhof55z549fPvttxQXF2NoaMjNmzeJj49n165dfP3118ybN48+ffqo\njz969Cg7d+7k4sWLlJaW4uTkRLdu3Rg+fLg6c0xFtRbR8uXLCQkJITw8nNzcXOzt7enbty+jRo1S\nBxVDQkLYtGkTVqZGkHeB2+FnybtTTNk9JcOD3+Qvo4eRn3GJmZP+SlBQEO3bt2fTpk0kJSVRUFDA\nmjVr1OUCz58/z5YtW0hISKCwsBAbGxv8/f155ZVX1OtZCSGEEOLRk4CBEEIIIYR43kgQSAhRL+yJ\nvlJhrXYjU0sUth7sPPgHG/dG0K6xBZcvX6ZLly4aGTxmZmbk5+dTWlqqFQgqKysjLy+v1uvZfPzx\nxyxdupSlS5dSWlpKv379anWdZ1VSUhLffPMN9vb2QPnv++OPPyY2NpZz587h5eWFg4MDwcHBhIWF\nARAcHKx1nczMTP7zn/+Qm5uLiYkJBgYGDB48mEaNGrFz505OnjzJwoUL1UGg9evXs2XLFiwtLenW\nrRsmJiZERkayfv16oqKimDNnjtbfQmlpKZ9++ik5OTm0b98ePT09jh8/zrp16ygpKSEoKAgAPz8/\nCgsLCQ0Nxd3dnY4dO6qv0bFjR9wcLIjL+PP+t2zZgre3N3369CEvL0/d7smTJ5k7dy4AnTt3xsHB\ngfPnz/Prr79y/PhxvvrqKxwdHevwX0MIIYQQQgghhBBCiHISBBJCPHHRF7OrXKzXzqs9uVfPMH/F\nRvr7lWdX9O/fX+MYDw8PYmJiSEhIUK8TpJKQkMC9e/do2rRprfpob2/P/Pnz+eSTT1i+fDmlpaUM\nGjSoVtd6FgUFBakDQFCeedW7d28SEhLUQaDqOHToEPn5+RQWFuLs7MyCBQto3LgxACNHjuTNN9/k\n9u3blJSUcOHCBbZs2YKdnR3//ve/1Rldb7zxBl9++SUnT55k27ZtjBkzRqONnJwc3N3d+eKLL9RZ\nXcHBwUyYMIFffvmF0aNHY2BggJ+fH46OjoSGhuLh4aEzaKUSHR3Ne++9p/U3effuXRYvXkxZWRnz\n5s3Dx8dHve+nn35i3bp1fPPNN8yZM6davx8hhKivxo8fT2ZmZoX77y/7WVRURGhoKOHh4aSlpaFQ\nKGjSpAlDhw6la9euGuepSolKxqUQQgghhBBC1I4EgYQQT9wPR5IrDQABWLzgjollQ26kxBCaqkfP\nds1o2bKlxjF9+vQhJiaGdevWMW/ePIyNjYHywaa1a9eqj6ktW1tb5s2bxyeffMJ3331HcXExI0aM\nqPX16rsHa+a7mFX8j/Tiiy9qbbOzK18np6CgoNptbQ07wflL13C0b8irr76qDgABmJub07RpU+Lj\n47l27Rq//fYbAK+88opGST99fX3Gjx/PqVOn2Ldvn1YQCGDChAkaZf2srKwICAjgwIEDpKam0qRJ\nkyr7fD8PDw+tABDA8ePHyc/Pp2vXrhoBIIARI0awe/duTp8+TVZWlkYQTQghnjZDhw6lsLBQa3tE\nRAQXLlxQP5MLCwuZOXMmKSkpNG3alD59+nDv3j2io6NZuHAhly9fZuzYsVrXkYxLIYQQQgghhKgd\nCQIJIZ6oS5n5WmsA6aJQKLDzbM+1yL3cLIK2HbtpHdOtWzeOHz/O0aNHeffdd+nUqRNQPhCfkZFB\nly5d6N69+0P118rKirlz5zJ79my+//57SkpKdAYZnmYVrc1UVJDL1as3aXZDO6hjbm6utU1fXx+A\ne/fuVbut5KRUMm7kkq804dcUaHwxmzbudurjVcGewsJCLly4AKCV9QXg7OyMnZ0dGRkZFBYWYmZm\npt5nZmaGk5OT1jk1CVo9qKJMp8r6qK+vj6+vLwcOHCAlJUWCQEKIp9qwYcO0tp0+fZr//e9/ODk5\n8dprrwGwatUqUlJSGDduHKNGjVIfW1xczJdffsmWLVsIDAzUWCsOJONSCCGEEEIIIWpL70l3QAjx\nfDt9Kbvax9p6tEKhUKBnYIhFE1+dx8yYMYOJEydiaWnJ7t272b17N+bm5vztb39j+vTpddJnCwsL\nvvjiC1q0aMGGDRvYuHFjnVy3PtgTfYWPfjhRYWAu704xOyOvsPf01UfSlr6RMUrlPZRlpVy4WcZH\nP5zQaOvmzZsAmJqacvv2bQCNLKD7qcr/PDgz/f6A0P2qE7SqiLW1tc7tqrar6mNtAk9CPCs++ugj\nhgwZ8ljbDAkJYciQIcTFxT3Wdp8llzLz2R5xkZDwZLZHXORSZr7G/suXLzNv3jxMTU357LPPsLS0\nJD8/n4MHD+Lp6akRAAIwMjJi3LhxKJVKDh8+rNVeVRmXXbp00Zlx6eDgoM64FEIIIYQQQojnkWQC\nCSGeqNtFpdU+9k5uBkqlEhvXFigNTHQeo1AoGDhwIAMHDqzWNXfs2FHp/ilTpqjXMLifqakpX331\nVbXaeFpUZ20mAJSweGcsDlYNatWOnp4eGTcLdLZlavMCCoUepUV3KLmdj76hsbotL4cGpKSkYGRk\nhKurK6ampkB5YEhXZk9OTnlwqaKgT11SKBQ6t6vazs3N1bn/cfZRiCdlyZIlhIWFaazfIp5eFWWL\nAvg1tuW1rp40sdLj888/p6SkhNmzZ9OoUSMAzp07pw60h4SEaJ1fVlYGwNWr2hMNJONSCCGEEEII\nIWpHgkBCiCfK1Lj6b0MZCb8DYN/Mv0bnieqpztpMKkolhIQn41yLdiwsLDgUnYybTwl6BoYa+2zc\nW2JoYkpxYS43LkTj3LaPuq0X78Ry+/Zt+vbti6GhIR4eHly4cIH4+HitIFB6ejrZ2dk4Ojo+VIBF\nT688YbY22UGAupxRXFyc1npUZWVlJCQkANC0adNa91GIp93UqVMpKip60t0Q1bAn+kqlkwXiruQw\nY2045ud3UpafzbRp0/D29lbvz88vzxZKTk4mOTm5wnbu3r2rtU0yLoUQQgghhBCidmQUVQjxRLV2\ns6t0/52bGdxKTeZ2Thp5aeexcvbCzM6lyvNEzVR3bab7xV7OoQHaA3VVcXb3InfXUS4c/AEzhybo\n6enTwMYRK5dmGJtb07jTMJL3reX8gR+4cyuLBtaOnN17maamt/Fq6qYuG9WnTx9+++03Nm/eTIcO\nHbCysgLKAzZr1qxBqVTSt2/fGvfvfubm5igUilqXEerUqRMWFhYcPnyYQYMG0axZM/W+0NBQMjIy\naN26tcxOF881+ft/OlQnW1R57x4Xw7eSl3aOD99/h65du2rsVwXlhw0bxltvvVWj9iXjUgghhBBC\nCCFqR4JAQognys3BAr/GthUGIG7npJN2Ogx9IxNsmvjg6j+Qlk1scXOweMw9fbbVZG2m+6XmFFZ9\n0ANcW3fHziuWvGvnKMi6ivLePRp6tMbKpTxA4tymN2XFd7lyPJTUU3sxMDHFyMya0uYe6OnpsXLl\nSubOnUuLFi0YNWoUW7du5b333iMwMBATExMiIyO5fPky3t7ejBw5slb3pWJiYoKXlxcJCQksWrQI\nZ2dn9PT0CAgIwM3NrVrnT548mfnz5/OPf/yDl156CXt7e86fP090dDQ2Nja89957D9VHISqTmZnJ\n+PHj6dWrFy+//DJr164lISGBkpISPDw8CAoKok2bNurjCwsL2bt3L5GRkaSmpnLr1i1MTU1p3rw5\no0ePpnnz5lptDBkyBF9fX2bMmMGGDRuIjIzk5s2bTJ48mSVLlqiPGz9+vPpnBwcH1qxZA5SvCRQf\nH6+zPGd0dDQ7duzg3LlzFBYWYm1tTdOmTRk8eDCtW7cGICwsjCVLljBlyhR69epVYf/mzZtX5e/r\n+PHj/P7775w7d44bN24A4OLiQq9evRg8eLBWIEJV6m7VqlWcPHmSffv2kZaWhpeXV7Xae5yq+j1V\npTrZotci93Ir9RwNX2xDtqWP1n4vLy8UCgWJiYk1br8iknEphBBCCCGEEJWTIJAQ4ol7rasnH/1w\nQufgUsOmrWnYtLX6/xUKCO7i+Rh793yoztpMxubWtH19tsa2XiP/QnCXOTqP9/Pz0zmoW4o+jTsM\ngg6DKmyrccBgGnq0IuPMHxRmXqGs5C5KpQI7OzuN7J5x48bh4eHBzp07OXDgAGVlZbzwwguMHTuW\n4cOHY2Dw8I+5Dz/8kFWrVhEVFcWRI0dQKpXY2dlVKwgEEBAQwFdffcX//vc/oqKiuH37NtbW1gwY\nMIBXX31VXapIiEcpIyODadOm4ebmRv/+/bl58ybh4eHMnj2b6dOn06VLFwCuXbvGhg0b8PHxwd/f\nH3NzczIzM4mIiCAyMpJZs2bRrl07resXFBQwbdo0TExM6Ny5MwqFAmtra4KCgjh+/DgXL15k6NCh\n6myM6mRl/PDDD2zevBkTExM6deqEnZ0dOTk5nDlzhkOHDqmDQHVp7dq16Onp0axZMxo2bEhhYSGx\nsbGsXLmS5ORkpk6dqvO8lStXkpiYSPv27Wnfvr26lGRduz+op2u9ukelOtmimWeOk3U2AksnD1z9\nBxF7OYdLmfkakzasrKzo3r07Bw8eZPPmzYwZM0brd5Weno6enh6Ojo7V6ptkXAohhBBCCCFE5SQI\nJIR44tq42zFlkF+VZWYUCvhgcEvauEspuLpW2zWWanNedc8xs3fFw95V/f8T+3kzvIO71nFdu3bV\nKjlUEVXmgS7BwcEEBwdrbXdycuLTTz/VeU5Fga4HeXp68vHHH1erj1OmTHmsg7uPS0hICJs2bWLu\n3Ln4+fk96e6os1Iq+5uoS7UZvH/YzI37xcfHM2LECN588031tkGDBjF9+nSWL19Ou3btMDU1xcXF\nhXXr1mFpaalxfnZ2Nh9++CGrV6/WGQS6dOkSPXr0YPLkyejr66u3t2vXjszMTC5evMiwYcNwcHCo\nVn+jo6PZvHkzjo6OLFiwgIYNG2r151GYPXu21hpjSqWSJUuWcODAAa0gg8qFCxdYunRptQMXT0LH\njh3561//yq5du1i5ciWlpaU4OTnRrVs3hg8fjqHhn2u0qV4fy5cvJyQkhB+2/Up8SiqGplY0fLEN\njt6BGllRJXcKSI3aR1lJEfmZVzj134+4V1rMoG02+LfypnPnzrRs2ZKOHTvyt7/9jbS0NH744QcO\nHjyIt7c31tbW5OTkcPXqVZKTk5k+fXq1f5eScSmEEEIIIYQQlZMgkBCiXujfpjGO1qaEhCcTe1l7\ntnHLJrYEd/GUANAjUts1lmpz3uNsS4jnzaXMfE5fyuZ2USmmxga4mJVH1s3MzAgKCtI41tPTk+7d\nuxMWFsYff/xBr169KszQsbOzIzAwkB07dpCVlaWVVWFgYMD48eM1AkAPQxVcHT9+vFYASNWfR+HB\nABCUr0UzdOhQDhw4QHR0tM4g0KhRo+p1AAhg69atbNmyBUtLS7p166Yun7l+/XqioqKYM2eORvZk\naWkpn376KTk5Obh6+pCucCT3WhJp0WEoy8pwatlNfey9slKKC29x52YGKBQYmpij0Dfg5o1s9uzZ\nQ1hYGBMmTKBjx46Ympoyf/58E8ICxAAAIABJREFU9uzZw+HDhzl27BjFxcVYW1vTqFEj3nrrLY0S\nhdUhGZdCCFH/3D8BJjg4mLVr13L69Gnu3r1LkyZNCA4Oxt/fX+u8I0eOsGfPHlJSUiguLsbR0ZHu\n3bszcuRIjQkL8GyXcRVCCCHqkgSBhBD1Rht3O9q422kNYrZ2s5M1gB6xqtZm0qW2azM9zraEqC9s\nbW1ZsWIFpqamj+T60Rez+eFIstbrqqggl6tXb9I98EUaNGigdZ6fnx9hYWGkpKSos43OnDlDaGgo\nSUlJ5ObmUlqqWS7yxo0bWkEgR0dHrKys6ux+zp49i0Kh0Jl19Cjl5+ezbds2Tp06xfXr17l7967G\nftUA04O8vLweed9U2XRQniX266+/EhMTg52dHbNmzeKf//wnxsbGuLu7qwfXGjduzFtvvUVBQQE5\nOTnk5OTw9ddfM3z4cNauXcvly5e5cOECMTEx+Pj4UFxczK5duwgLC6OkpARra2t69+5NAyMDXvDt\nwgstu3Em9Buyko5j7uhG1tkTFGZdpbjwFkX5OTSwdcJ35AeYNWwElGdwuhvnMWvWLI2BOAMDAwYP\nHszgwYOrvO9HkXEphHj2PKlymaJqmZmZTJ06lRdeeIGePXuSn59PeHg4c+bM4YsvvqBly5bqY5cu\nXcr+/fuxs7Ojc+fOmJmZcfbsWTZu3EhMTAxz5szRmHBS38u4CiGEEPWFBIGEEPWOm4OFDPg/AZWt\nzfSgh12b6XG2JUR9YGBggIuLyyO59p7oK5WW08y7U8zRC7fYe/oq/Vq7auyztrYGoLCwEIA//viD\nefPmYWRkROvWrXFycsLExASFQkFcXBzx8fGUlJRotWFjY1On91RYWIi5uTlGRkZ1et2q2vzggw/I\nyMjAy8uLnj17Ym5ujr6+PoWFhYSGhuq8d6j7+9fFz89P3Q93d3datGhBdnY2rq6ubNq0CT09PUxM\nTOjSpYt6cG3gwIEAZGVlUVRURKNGjbCwKH++xsTEYGxsjK+vL8ePH+err77C3d2dS5cuoaenh6Gh\nIdbW1uTl5XEt/RhJWYfw6vtXrFyakR53hLN7VmFgbIqVSzMKM69w52YGCj09Ug5vplm/8RiZWf3f\nJA53AgICiIiI4M6dOzqDkUIIIZ5dcXFxBAcHa2Qkd+vWjdmzZ7Nt2zZ1ECgsLIz9+/fTqVMnpk2b\npvEZQDURYteuXQwdOlS9/Vku4yqEEELUJQkCCSGEAB7v2kyyDlT9d/+M2tGjR7Nx40bi4uLIy8vj\nyy+/xM/Pj7S0NDZv3kxMTAx5eXlYWlrSqlUrXn31VRo1alTttq5du8ZPP/1ETEwMubm5mJmZ0apV\nK4KDg3F2dq71PSiVSnbt2sWvv/7K9evXsbCwoFOnTowdO1br2MLCQvbu3Utk5P9n784Doi73xY+/\ncdj3HRFkc2UTQRTXRKCslDJPGaCZ5bF70ltueM7F6njuqfSYZupx6VaWmrn8NMotVxTBJVD2RRQE\nXAAZEJRNkWV+f3BmZJwBxjXR5/WXftdnRuE783yez+eTRFFRETdu3MDQ0JC+ffvyxhtv0LdvX8Wx\n165d45133sHV1ZUVK1aovfc//vEPkpKSWLVqFc7Ozu2uUC4pKWHDhg2kpqbS2NiIq6srEyZM0Og1\nphSUd/hzBNBws5av9qRja2ag9PN0/fp1AEUZuE2bNqGjo8NXX31F9+7KAaPVq1eTmZmp0bgelJGR\nEdXV1dy+fbvDQJA8w6SpqUllnzy4pYmDBw9SWlpKeHi4Sn+wnJwcdu3a1eEYHiVvb2/s7OzYtWsX\nbm5u/OlPf+K3336jubmZiIgISkpKOHr0KH/6058YOXIk8/7nI77+YRNGppbU3m6mrroGFxcXAGpq\narhw4QLe3t5cvXoVLS0t6urq0NHRwdnZmR49egAo3vv3wsKYv+jfXE7Yg46hKbeuS7Fw9sT9lRno\nGppybv86DK26YebQC2lOAmf3rMV/RAgnD9/gJHDjxg2am5spKiqiZ8+ej/y9UicqKorMzEyNsooE\nQRCEh8fW1pY333xTaZufnx82NjacP39esW3Xrl1IJBJmzpyp8uwPCwtjz549xMbGKgWBnuYyroIg\nCILwMIkgkCAIgqDwOHsziT5QnUNJSQlz587FwcGBwMBA6uvrMTQ0JDc3l48//pibN28yaNAgnJyc\nuHLlCrGxsSQkJPDZZ5/Rq1fHGVxJSUksXLiQpqYmBg0ahL29PeXl5Zw6dYozZ86wcOFCxYT0vfr2\n22/ZvXs3lpaWvPjii0gkEhISEjh//jyNjY1K/U+uXLnCjz/+iKenJwMHDsTY2BipVEpiYiJJSUl8\n8sknitJkVlZW9O/fn5SUFAoLCxUT63IVFRWkpKTQs2dPnJ2d2x1jcXExkZGRVFdXM2DAANzc3Cgp\nKeHzzz/XqBTaT3G5GmXU3awoofF2PZvjc5V+pjIyMgBwc3MDWv69nZycVAJAMpmMrKysjm+khrzE\nirogTVv69OnD6dOnSUpKYsiQIe0ea2xsDLRku9wtNzdX43sWFxcDMHToUJV9jyv4dT/kk2tHjx7l\n6NGjRB+M54qOGznljVQU5NJ94Mtcv51PzY0acq9Wk1tyA4P0dGQyGT4+Ply9ehUdHR3MzMyoqqri\n+eefJycnBwAvLy+OHDnCwIED8e7tzMn0XCR6RsiQYesxFF1DUwAa6+sAuFGUS9PtW1RezKTSWo8t\nV84ojfXu8nqCIAjC06OtvoSurq5qy61ZW1srnjf19fUUFBRgamrKzp071V5fR0eHy5cvK217ksu4\nCoIgCMKTRASBBEEQBCWPszeT6AP15MvOzuaNN95g8uTJim0ymYzp06dTV1fH3LlzCQwMVOyLj4/n\niy++4Msvv2Tt2rXtZkjU1NSwZMkS9PT0WLx4sVLg4eLFi0RGRrJy5co2s23ac/bsWXbv3o29vT1f\nfvmlogTWW2+9xfz586moqMDW1lZxvKOjIxs2bMDU1FTpOuXl5cydO5fvvvtOKSgTEhJCSkoKR44c\n4d1331U6JzY2lubmZoKCgjoc59q1a6murmbatGlKK1vlgbT2FEqrNe6t1Xj7FlczjpGu8wKF0mpc\nbE3Izc0lNjYWIyMjRaDF1taW4uJiKioqsLS0BFr+vTdv3qwy8aIp+XtfVlamdsWuOqGhoZw+fZp1\n69bRu3dvrKyslPZfu3ZNsa1nz55oaWlx7NgxXn/9dfT09ICWiaEffvhB43HKVwRnZGQoBfby8/PZ\nvn27xtd5mFr/brx1u4mq6+UUVdSScfEal8pqgDuTaz4+Pkhv3GT11v24jQyjSxdtmhtuY9LVleqS\nfGpKC6m8Xsm6mLMMOdsy6ebj48OBAweQyWSKwKiPj49iUs7auiVgePPmTUYO8afkainnCosAqKso\npiQ9FoBbN8qpr6nEeXAoteWXsetygw3fff2HZf2oM2fOHOrr6//oYQiC8BhIpVLWr19Pamoqt27d\nUvRJGzhwoOIYeXmxhQsX4u3trXK+uuzd5cuXExMTw3fffcfp06cVmcYWFhaMHj2aN954Ay0tLY4f\nP050dDSXLl1CX1+f4cOH8+6776pkt/z++++cOHGC8+fPK4IVjo6OBAcHM3bsWJXPUPL7r1u3juTk\nZPbs2UNxcTGGhoYMHjyYd955R5HZ+zh11JewT3/150kkEmT/WclSU1ODTCbjxo0biv53HXnSy7gK\ngiAIwpNEBIEEQRAEtR5nbybRB+rJZW5urlTDHVpKY125coW+ffsqBYAARowYwZ49e8jOziYrKwsv\nL682r33kyBFqa2v5y1/+opJ54uzszOjRo9m5cyeXL19W2a9O6wnzozu3UVffyIQJExRBCGgpb/X2\n228zf/58pXPbmjSxtrZm2LBh7N69m7KyMmxsbAAYPHgwRkZGxMbGMmXKFKUVrjExMWhrazNy5Mh2\nx1teXk5qaip2dnaMHTtWaV9AQABeXl7tZqCkFpa3e/3WTOycuZaXQm15Mcsaz+JmoU18fDzNzc3M\nmDEDQ0NDAMaNG8fq1av58MMPGTZsGBKJhLNnz3Lp0iUGDRpEYmKixveU8/HxITo6mlWrVjF06FAM\nDAwwMjJSec2t+fr68uabb7Jt2zbef/99Bg8ejI2NDZWVlWRnZ9O3b1/FxJylpSWBgYEcPXqUDz/8\nkIEDB1JXV8eZM2fw9PQkPz9fo3EGBQURHR3Nt99+S0ZGBt26daO4uJjTp08zZMgQ4uPj7/m13692\nJ9TKa6jLKeXCxlNKk2uXq6H4pg6NlReRNTdz+2YVMmSYdHXFxKEn5XnJNNRWIZPBr4eO09dWl169\nelFXV0djYyOGhoZIJBKliTF58+3m5mYsLS2xNTPgurE2V250oSI/jRtXWsr41FeXU19znevZR3Fz\n7IqZocETl/Uj/9kVBOHpJpVKmTNnDl27diUoKIjq6mpFn7TPPvtM0X/mQXz//fdkZGQwaNAgfH19\nSUhI4Mcff6SxsRETExPWr1/P4MGD8fT0JDU1lb1799Lc3Mz06dOVrrN+/Xq6dOlCnz59sLKyora2\nlvT0dL755htyc3OZM2eO2vv/8MMPJCcnK+6fnp7OgQMHFJnEj5MmfQn3JF3ieTV9CVuTfw5zc3PT\nePHPk17GVRAEQRCeJCIIJAiCIAhCuyU8dHR0lI7Ny8sDaHMipV+/fmRnZ5Ofn99uEEiebVBQUMDm\nzZtV9hcVtWQcdBQEUjdhnnMihbqKa/ycdQurHuVKJdA8PDzUliU5e/Ysu3btIicnh+vXr9PY2Ki0\n/9q1a4qJZF1dXYYPH86BAwdITk7G398faHlvLl26xJAhQ1Syiu4mD060NR5vb+92g0B19Y1t7rub\nrpEF3QeNoTglhtPHj1Jkrk+PHj0ICwvDz89PcdyLL76Ijo4OO3fuJCYmBl1dXTw9PZk5cyYnT568\nryCQn58fU6dO5cCBA+zcuZPGxkZsbW3bDQIBTJo0ib59+7J7925Onz7NrVu3MDc3p2fPnipZVh98\n8AHm5ubExcWxd+9ebGxsCA0NZfz48Rw/flyjcVpaWrJ48WLWr19PdnY2ycnJODo68v7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VcD1q9fz7asLBoaGnBzcyM8PBxfX1+l4xsaGti5cyexsbGUlJQgkUhwdXUlNDSU4cOHK47b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\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PPlERXhQKBaWlpYI30/3gt99+IzY2Fh8fH/r06SOUTcnLy1OJXoWmTKcPP/wQmUxG7969\n8fPzo7a2lqysLLZv396mCHQn5ObmsnTpUmQyGf369cPFxUXoW3t+T28lMjKSc+fOCYJ8dnY20dHR\npKamsn79epWSP9euXePdd9+lsrKSAQMG4OTkRH5+PitXrryja4uIPGweJeHkXvluimLj3dGed4Cd\nnR2zZs0iICAAaAqUUtJ8sTomJoaVK1fS0NCAj48P9vb2FBUVERERQXR0NCtXrtSYKf3TTz9x8eJF\n+vfvT//+/dHS0lLZn5aWxu7du+nZsydjxozh+vXrnDlzhvfff5+1a9eqjDUSExPZuXMnnp6e+Pn5\nYWhoSG5uLuHh4URGRvLVV1/h7OwMgIeHB1VVVQQEBODs7MzAgQOFdpTHtERqairvv/8+N27cwMfH\nh86dO3Pt2jVOnjxJ4cFj6HhORWqlXrO3KCWa8mvJmHVypVO/MVQV5VJZmImuoTF+4/5BeXk5NTU1\nODo6EhQUREREBB4eHnh7e6NQKIRAG0dHR978v4+4XFBFda2cqEPHqalrwMaqqbRZ87G8q6uriuhR\nW1uLubk5o0aNIiIigoiICJU+amlpMWrUKKqrq9X637t3b3744Qe17Y6OjowZMwZtbW3Wrl2r8vwO\nHDjA1q1b+euvvwRfncWLFzN19nzCrxYRl1tDQXEpOnoGJCYmoq+vz6xZs7hx4wavvfYa6enpZGZm\nUlZWxoQJEzAwMKChoQEtLS369++v1pcOHTqoiEBw0zdRKpViZmamdk56ejoA7u7uGkuDe3p6cuLE\nCdLT09VEoFuDWpTP0M3NTRDtbG1tSUlJEcrjbd++Xe0cZfm97OxstX0iIiIiDxNRBBIREXnieJQm\nzn8HNBmww01j+5HOroKPhhKl6XxVVRVrNvxCZpGM2vpG9HW16GJtgh5NUfbKwbOpqSnDhw8nICCA\nBQsW8Oyzz+Lm5qYWjd9eqqurVUSohIQEYZ9UKuX555/n008/JScnR+1cX1/fdi8aRkVFAfDhhx+q\nLP5u2LABfX39O+q7iDp36keQU63NqlWrBOPYxMREDA0N6du3L88++yzdu9+fkpDt6a+2rh5GVg5U\nFmZx9fQe9E2tWPdzOm88P6nNc+8FnTp14s0332Tt2rUsWrSIvn374uDgQENDA4WFhVy8eBFTU1ON\nCwgPi7aeq4GZNZ19J5N1LoBLB3/A1L4r+qZWfLUqjo7SRpV76t69O7169SI8PJx3330XNzc3ysrK\niImJwcHBQaOYM2bMGJKSkjhx4gSvvvoqvr6+mJmZCeVVRo8erRZ9fC+5fPky69atw8bGBkAomxIf\nH09KSgo9evQAmgS8L774AplMxtKlSxk2bJhKO0VFd/b31BYbNmxAJpOxYMEClYXHc+fO3ZH3z9mz\nZ/n444/x8vIStm3evJldu3YRHBysEp27YcMGIZrZ399f2B4TE8NHH310ZzckIvII8CgIJ38n383H\nmfa8A2xtbYUgKkDjO6myspKvv/4afX19vvzyS5Vsj8zMTJYuXSr42tzKlStXWLNmjRBIcStRUVFq\nWZeBgYF8//33BAQEsHDhQmG7l5cX27ZtUwvGycjIYNmyZWzevFn4/fbw8MDOzo6AgABcXFza/a5V\nKBR89913VFdX88477zB8+HBhX1hYGEtWfETOmb30mrRIzWOqIjcN13GvoGMgRcdAikQiIeP0bkqv\nJnL14nl+Dk0DmrJZevTowcKFC9VEsQ1bdvHNd6s583+r6ODeVOo7KS0PWWEF5XIdUvPKaZ6fqqWl\nhYnJzb+byspKFAoF5eXlQiZUe2nup9USrflIGhkZqc0Bi6/kUlZZQ01+OZ+u+QUHKynG+tokJiZS\nW1uLsbExRkZGdOzYkbFjx5KRkYFCocDMzEyjh5emuVLz62tC6dHY0v0pt1dWVqrtU3oKtXSOsm2Z\nTAY0CYipqakaz4FH0zdTRETkyUYUgURERJ5YHoWJ8+NOSwbsSipu1BGaVk7QLcb2MpmM8uo6th84\nQcUNVSFH0SBHUluOTmMdWVlZgtdPY2MjDg4OVFZW8vvvvwNQVlZGSUmJxoF8a1RWVmJkQ4oIowAA\nIABJREFUZERVVRXbt28nKyuLnJwczpw5AzRF+wNUVFSoCVjKhdT2UFLSNCm6dcFYrOd+b2mPH4Gi\noekYSbMIyupaOVZWVrz++uvtus7IkSNbLRelKfNq8eLFaiWxlP01sXNS80hojtPgaVyLDqIi7woN\nmYkcz5UyfqCbSmbb/eTpp5/G2dmZffv2ER8fz/nz5zEwMMDS0pLBgwczZMiQB9KP9tKe74GliyeG\nFnYUXjqLrCADWf4VLlRaIXHtonJPWlpafPDBB2zbto3o6GgOHDiAlZUVY8aM4dlnn9X4nZFIJCxZ\nsoS+ffsSFBTE6dOnqa+vx8LCAnd3d3x9fW/7nlrKsNTErFmzhMU/uFk2JSkpSUUEioyMpLCwEF9f\nXzUBCFCryX8vKCoq4sKFC9jZ2TFx4kSVfb6+vvTu3ZvExMTbanPo0KEqAhDAuHHj2LVrFykpKSrX\njo+Px97envHjx6sc369fP6EspIiIyJ3zd/HdfJxp7zugLY4fP05VVRWvvfaaWmnNLl26MHbsWPbv\n3092drba/n/84x8tCkDQVI7r1nHUqFGj+OGHH1R+twGNWR7QlNnj6enJ+fPnkcvlGrM92svly5e5\ndu0aPXv2VBGAAIYMGUI/b092Hz1DZWEmJnZOKvttXH0wtLAj53wIpVcTMLFzolFez43SfAK2rMPe\n1pp+/foxePBgjQJH4Pks9mUaUNWghSIvXRCBtHWbSrqVV93gl5BLOPe+OYdqbGxEJpNhZWUF3BRp\nXFxcNIpy95PTl/LYn5Kl9jcvkTQJXc6T3kZH34D+Rrloa2sLZfiqqqo4cuQIxcXFXLlyBS0trRY/\na00ZTG2hfCZlZWUa95eWlqoc15z2nqP875QpU3jllVduu48iIiIiDwtRBBIRERERuSNaM2BXQSFR\nM7a/lFfF5ZxSHPqNo1vPmwujtbJSkgM30qCji6FtZ4YP68cA105oaWlRWFhISEgIs2bNYsyYMSQm\nJrJx40ZSUlL4448/mDJlCoAw0VKm6d9KVVUVcnnTYvGFCxe4cOECFRUV5OTkEBERQVZWlnCsXC5X\nE4HaEznXvDY5qNZcP3DgAJMmTVLzBGrusVFSUkJAQABZWVmYmpoKfhjnzp0jICCA7OxsZDIZpqam\ndOzYkSFDhuDv7y/Uitd03cfBg6it+1Mik8nYs2cPZ8+epbCwkIKKWnLrpNi5D8bUXr08CcD15Ciq\niq5Rf0NGSXocuobGBBX2Y0CHl1Uyfurr69m/fz8nT54kLy8PbW1tnJ2dmTRpEk899ZRKm81r88+e\nPZtNmzZx4cIFampq6NKlC7Nnz9bop6OtkHMtJoiyzIvIa6vRk5ph3b0fZp1USwXqm1jS9embpRQX\njnVjpE9TWQ5NglNrn68mMUrJ7NmzW4yadXJyavG8R432+lIYWtjRxW+K8O+FY92Y6qNeLsbExEQl\nKrk5zT1qbi2/OHz4cLXFpNvl47W/8XtoKq/+GCpsywzfz/XUaEz0dXD1Vhe+laVZmqMUdJoL5cpM\nzAdZBk1ZnsXNzU0tEhqaorhvVwRq7/0qr92zZ0+NC3Fubm6iCCQicpeIvpv3h9sJBGjvb2JbKN8R\nGRkZGktdKbPkNYlAbQlNmjKsdXR0MDc319jHqKgojhw5QlpaGhUVFUKZLSUVFRV3VWY1La0pW8fT\n01Pj/iED+xMcFsWN0gI1EcjIqiMApvbO3CjNpyLvCnVVFVBXjdRAj5deeonJkycjkUiQy+UEBgYS\nGhpKdnY2uddLuXStRPhbqa+uENo1tLSn7FoKDbU3UChQmUMlJyerPAMDAwM6d+5MVlYWMplMJUvo\nflJeXce20FSMb3kmANp6TT5ASv/FXSdisa6uw8+vyV+pqqqKzZs3o6uri5GREZaWltTW1moU9JT+\nSLeDsqxhUlISDQ0Nap5B8fHxABrLGSYkJKiVw21sbMoUb952jx49kEgkwnYRERGRxwVRBBIRERER\nuSNaM2C/leYG7Oczijh2tR6FAqoKs6CZCFR4OQJ5bTVdBk3Bqqs3yRKYN9iXPs7WhIaGCuUrrK2t\nGT58OHK5nLCwMDIzM4XJj7GxMdA0ybi1hER1dTU5OTnChOCf//wnkyZNatEsVllGo7mgpGkR8VaU\nNaVDQkIoLCxU80Rqjb1793LhwgV8fHzw9PQUSg8oy2VYWFjg4+ODqakpZWVlXL16lWPHjuHv749U\nKmXWrFkar9taZOajQHvuD5qEl+XLl1NYWIi7uzv9+vUj53oZP/51hCvHf8fRZwLW3W8ubleX5JMW\nsoXSzIugUGDVtQ+GFh2or66g6noWUVFRwqKEXC7nP//5D4mJiXTq1IkJEyZQW1vLmTNn+PLLL0lP\nT2fOnDlqfS8sLGTJkiV06NCBESNGIJPJCAsL45NPPuHTTz9VWVyor68nePv3FF6KxMiiAxbOHjTU\n1ZCfEEplQWarz+hOfReeFO6VL8W9oDUD6rZoK8NSVlPPwZgsRt+SYan87WuO8reu+W+Y8jdFGUn8\nIFBes61SK7fD7d5vS9duabuIiMjtIfpu3jvaKrXsWqwumLT3N7EtlKWugoKCWj3uxo0batva+i3X\nlH0BTf28tY8BAQH8/PPPGBsb4+3tjY2NDfr6+kgkEs6ePUtGRoYQ1HWnKDNNWhKSLC0tcbCSUl6v\nXtZLKXaYdHDBpEOTOFBXVUZD1BamTRrP9OnThWO/+uorIiIi6NChA76+vgQlFdPBoqnN65fPoWi8\nKexYOnuSF3+KWlkJDfI6YQ7l4WjOli1b1PoxdepUoTzf22+/rfaMKysrKSgo0Ch63Ck5xVW0NKvQ\nk5qjkNcJ/ou6UnNyrlWRkJCAk5MTlpaWfPHFFxgaGrJ8+XLMzc1pbGzk2LFjjBs3TminqKiI2tra\n2y6fbW1tLWT4BgQEMG3aNGFfcnIyp06dwtjYmEGDBqmdGx8fT1RUlEoA18GDB8nLy8PT01PIxDcz\nM2P48OGcOHGCHTt2MHPmTLUAl7y8PLS0tNqcf6WkpLB3714uXrxIRUUFJiYmQsZd8+Cz06dPc/Dg\nQeF7b29vz7Bhw5g6dapawKIyIPD7779n27ZtnDlzhoqKChwcHJg9ezYDBw6koaGB3bt3c+zYMYqK\nirCysmLKlClq2drNx7N9+/Zl27ZtpKam0tjYSK9evXjxxRfVxN2SkhKOHj1KbGwseXl5VFZWYmpq\nSu/evXnuuefUxOPbDapTzhlnz56tcY5dWlrKSy+9RKdOnVi3bl2rz19E5ElDFIFERERERG6b9hjb\n34rSgP330FSMLB0wtu1CWfYlitPOY9WtD9CUCQRg3rkXN0oL0DE0ZntYKi6Wupw8eVKtzbq6OsFQ\nVBk9ZmhoSKdOnTh9+jR2dnZCxFxjYyMbN26krq5OmCgnJSWpZMvcitJY/Pr167d1rx4eHnh4eJCQ\nkEBhYeFtLQLHx8fzzTffqBj0QtOAV0dHh//+979qZRMqKpoiCKVSKbNnz76j6z5s2nN/AKtWreL6\n9eu8++67DB06VNheYtWXfb+u5lpMEGadXNE1bPqM8xNDKclIQN/YAlf/BVg6NQl0nl0s+fIFX5XS\nD3v37iUxMZF+/frxwQcfCIsns2fPZsmSJezcuZMBAwbQq1cvlf4lJCSoTUSGDRvGhx9+yJ49e1RE\noL1791KQk0lPz34YekwQREU798EkH/m5xecj+ie0zcPypViyZAm1tbV31YaS1jIsO3qPwMK5N6nH\ntkKz6ODbRblAVFxcfMf9VC523BqVDZojzttbnuV+oPQNaOnaLW0XERG5fUTfzbunPaWWNQUC3CuU\nv5n//e9/cXJyuq1z2xMo1R4aGhrYvn07FhYWrF69Wk2kUWYr3S3Ke23pHVRSUoKZkR5PD+rBuSra\nzHJ7bYwb25L0VLanpqYSERGBt7c3H330EdnF1Rz5MRT7zk2eRAUXw2meq2Ji54SZQ3fyE8PIPnuA\nOlkJObFa5IZsxM7KDEtLS5XnPHr0aNLS0jh8+DALFiygT58+2NraIpPJKCgoIDExkVGjRrFo0aI7\nfk7NuV5+g4obdS2KQNq6elh270dZ9kUuHfwBqXUnSq5fZ9l7y1m7di0ymUwo8T1o0CCKi4spLCxk\n/fr1xMXFYWNjw+HDh7l69Spz5sy5o2ybRYsWsWzZMn799VdiY2Pp3r07RUVFnD59Gi0tLRYvXqwW\nKAjg4+PDZ599xqBBg7C3tyc9PZ2YmBiNmeGvvfYaubm5/P7775w4cQI3NzfMzc0pKSkhOzub1NRU\n3n333VZFoKCgINavX4+Wlha+vr507NiRsrIy0tLSOHTokCACbdmyhZ07d2JqasqwYcMwMDAgJiaG\nLVu2EBsbyyeffKKWRSWXy3n//feprKzE19cXuVzOqVOnWLlyJZ988gmHDx8mOTmZfv36oaury+nT\np/nxxx8xMzPTWO45JSWFnTt34u3tzYQJE8jLyyM8PJykpCQ+/vhj3N3dhWMTExPZuXMnnp6e+Pn5\nYWhoSG5uLuHh4URGRvLVV1/h7Kyegd/eoLrhw4fz22+/cfToUZ599lk1AS44OJiGhgYVUVFERKQJ\nUQQSEREREblt2mNsr4njCTnCAq3T4GmkhWwl82wA15MjMbJ2oLokj+qSPBJ3f0eDvA7XcfOJz5Sy\nMyCQ9evXY2hoyKFDh6ivr6e6upr9+/dTX1+Pj4+PymB++vTpREREcOnSJdauXYuzszPx8fHI5XKc\nnZ3JyMjA3d2d8PBwgoOD6dChg1pfr169Srdu3Th9+jQrV64UBrAXLlxo1Rfmbhk3bpyaAKREW1tb\nrawB3BSrHnfaur+MjAwSExMZPHiwigAEMG+0J+dihnPl5A7Ksi9h06MpYqy2ohjTjt3o6f8qRpZN\nn7PSj0BLS0tlYSE4OBiJRMIrr7yi0g8zMzOee+451q5dy9GjR9VEIFtbW5599lmVbX379sXGxkat\nxv2xY8eQSCR8sHQR3wVnCgsK+sYW2Lj6kBd/Su3+Rf+E9vMwfCmaezDcLa1lWOoamdDY2CAs/iij\ngx1u8xo9ezaVHYyJiVHzyGkvSlGnqEj9XaAsr9Mc5W/axYsXaWxsVJuwJyQk3FE/2oPy2pcvX0ah\nUKgtUorlXERE7j2i7+ad0f5Sy3ceCABNQn5LWTQ9e/YUFndvVwS6V1RUVFBVVYWXl5eaAFRTU8OV\nK1fUzlG+V24n60mZHdPSO0i5fcbogUyVdmB7WCp58erHKbPcHKSNbLtlX15eHtAkMGhra6vMoaqL\nc2iU16u1Z9NzIOXZyUi0dShKjUZb3wiTMcP55D9LmDdvHvb29irHL1y4kP79+3PkyBHi4uKoqqrC\n2NgYGxsbpk+fztNPP93eR9ImmUWyNo8xsXfBzn2w4L+oa2hMdW0VmZmZSKVS3NzcmDt3Lt7e3oSF\nhQnZ/pGRkWhra6OlpYWbmxtOTk539I7u0KEDq1at4s8//yQ6OprExEQMDQ3p27cvzz77rMayhAB+\nfn6MGzeOP//8k6ioKHR0dPDz82POnDk4OKiOtoyMjPjiiy8IDAzk1KlThIeHU1dXh7m5OR07duSV\nV16hT58+LfYxOzubDRs2YGRkxJdffknnzp1V9ivHV5cvX2bnzp1YW1vz3XffCdl2c+fO5bPPPiMq\nKoo9e/Ywc+ZMlfNLSkro2rUrn3/+uZAp9PTTT/Pee+/xxRdfYG9vz/fffy+M56ZOncrChQvZtWuX\nRhEoJiaGV199VSVT6Ny5c3z66aesWbOGH3/8URhfeXl5sW3bNjWhLSMjg2XLlrF582Y++ugjtWu0\nN6jOwOD/s3fmAVVVa///HGaZQQYBRUGZDyKiOOU8j9ms5VVvWt2y4VZ0b2Zl7630Vt6bdi27+trP\nzMQKLWdFMQMnQIbDJMgskwwyHY7M8PuD9xw5nsOolsP6/AV7r73XWpvD2Xvt53m+XyMmT57M4cOH\niYmJUasSam1tJTQ0FENDw9v6uRcI7hdEEEggEAgEPaY7BuzayC65UdFhYGKBx+znKE2LovLKJSpy\nEmmqr6WlsZ6aklwsnb0pz06kKOE3tjQUMWXKFKKjo8nLy+OXX37BzMwMKysrBg8ezNy5c9X6mT59\nOs8//zyffvopwcHBODo64unpyaxZs4iLiwMgKCiINWvW8MUXX2BmZkZOTg6//fYbhYWF5OTkkJub\ny6effsoTTzxBeHg4Fy9eJD8/n4sXL3Y4v5szX6sUDT2+Rh3pqU+aNInt27fz0ksvMWHCBKRSKV5e\nXh2aqd4LtL9efZy8qEhJ63R+ysxPhUKhVad+RN9asiRQV9W2cGpubKC2sgT9PqZqASBtfgS1tbUU\nFRXRt29f+vfvr3Fu5cJD6S/SHhcXF60+JzY2NmrZqso+bGxsmDFKSouBudrLnjZtdfUgkPBP6Bk9\n9aXwcjDlkUcewc3NjU8//VS1v6GhgUWLFtHY2Mgbb7yhtpA8cuQIW7Zs4dVXX2X69OkankAbN25U\nSVcGBwer+YOtW7dOJRepJCEhgeDgYGTJqcRmlWFq54zT8OkYWagHl5SeQO0nFp2cxf5f/ou+ng6l\npaUaEhqBgYEacw8MDMTOzo7IyEjCw8M1AqplZWUqH4mOUH5PnTx5ksmTJ6uCpmVlZWrzVdJenuXQ\noUMsWLBAtS8yMrLbfkBhYWHs3r0bmUzGBx98wH//+18GDRrE7NmztS7209PT2blzJxkZGcTExLB4\n8WI++OADYmNjCQ4OZsmSJRp+QPn5+YSEhCCTyaisrMTExAQ/Pz+efvppjZdAAoFAcDvpjdRyb76V\nlM+9DQ0NGBioV65MmzaNH374geDgYNzc3DSeS1tbW0lKStK4l91OLC0tMTQ0JCMjg7q6OoyM2qTX\nmpqa2Lp1q1qFuBJTU1MkEkmPqve9vLxwcnIiJSWFs2fPMm7cONW+s2fPkpycjJOTEz4+PkgkEvxd\nbOivSGHX1VgWjBiIp7e3WpVbSUmJRh/KSpCkpCTmz5+vWkM11inIizqidVwSwMDUUiWPDTBhkjtV\nVVXU1dVpyGkBjBw5UqsPpTa0+Uq2pzMfyRGT5pKM9iBK38HDVOMF1PwXl01y15p409VYbq7A8fX1\npbi4uNNjoE3y9qWXXuqy3c305Drq6ekxb948DQm17nDkyBGam5tZtGiRRgAIbvh5nThxAoCnnnpK\nTW5RV1eXFStWcPHiRUJDQzWCQADPPfecmlScj48P9vb2FBcXs3z5cjXpwH79+uHl5dVhso6Dg4PG\nenvUqFFIpVKSkpJITk5GKpUCdLg2dXFxYejQocTFxWn1gOpJUt2cOXM4fPgwR48eVft7xcXFUVxc\nzLRp0zqUnxQIHmREEEggEAgEPaY7BuyGppYMX7K20za6+ob0k46nn/RGxlFNaR5Fsl+pLS+iPCue\nPpb2LPjTSzw6zlPDYyMsLIyNGzdqlZ946aWXcHR05Pjx41y9epXS0lKOHz+uZui+ceNGDh48yLlz\n5/D09KSsrIympiacnZ2ZN28eLi4ueHl5sXTpUlVfK1eu1OirI+329PgrSKoriMsu6/ZL/I78KRYu\nXIi5uTlHjhzhwIED7N+/H4lEglQq5c9//nOHWW13I9qvV3+qnMZTdTWJ3D0hmPfRnJ9Spz4+Pr5D\nI3dPJyv0zNo+n83/p+Gu36dtcd6ZH4HSN6QjXXjlwkub1JU2HX5oW6C1tnubo+xDea6b/RP0jdTP\nI/wTekdPfSnc3Ny4fPkytbW1qqzFlJQUGhvbsnNlMplagEEmkwFtmY7aGD16NND2/SSVStVelN0s\nCxIVFUVkZCQBAQG4Dh3N5ZoEqgrSuX6tEK95L6FnZNzlfOsam2ltaWLt2rUMGjRITUIjLi5O7TMI\nbS8t3n77bd5//30+++wzjh49iqenJw0NDeTl5SGTydi/f3+nfXp4eKgW/m+88QZ+fn5UVlYSFRWF\nv78/Z86c0TjmxRdfJCgoiG3bthEXF4eLiwtFRUWcP3+ewMBAoqKiupzrV199BbS9wBw7diwDBgzg\n4sWL/Pvf/6agoIAlS5ao2iYlJfH+++/T0tLCwoULOXXqFHFxcTz66KN4eHiQmZnJf//7X6ZMmUJk\nZCQSiYSYmBjWrVtHc3MzgYGBODg4UFZWxvnz57l48SLr1q27rb4KAoFAoKS3Ust90PSr6Qo/Pz/S\n09NZu3YtPj4+6Ovr4+LiQmBgIGZmZqxevZqPP/6YoKAg/Pz8cHZ2VgVYUlNTkcvl7Nu3r8f9dheJ\nRML8+fMJCQlh1apVjB49mqamJhISEpDL5QwdOpSEBPWSHCMjI9zd3UlOTmbDhg04OTmpZLY6qmiS\nSCS8/vrrvPfee3zyySeMHj2a/v37U1BQwPnz5+nTpw+vv/662jrD1qIP/SyNmRswEF9fTUmrm3Fz\nc8PLy4tz587x1ltvUWdkS+7FdKoLMzA074u+sWbFXFODpt+SvqSZbdvaZIO1+dn8XnRnDXg7j7uf\naJ/8dvi3KK7XNxEQENDpMcqqN23PnE5OTtjY2FBcXIxCoVALepiYmGhUjEHbOqcjj6i+ffvS3NxM\nRUWFhm+kMhB6M76+viQlJZGZmakKAgFER0dz9OhRMjIyqK6u1pAPrq6u1lhzdTepDsDZ2RmpVEpM\nTIxa8pLSy6y3le4Cwf2O+CYWCAQCQY/prZG6i70ZURmdZ+iZ2g7AbdpStW1+w73x9XXRyBabOnVq\np9JsCxcuZOHChR3u79OnD08++aTW7Kmb6aiv7mi3r/4+ktfnDe2WdntneupTpkxhypQpKBQKLl26\nxPnz5zlx4gRr165ly5Yt90RVUGfXq6+rH7j60dxYxwwPQyjPVpufUrv9+eef79TLCdoWWlFpBfwz\n3BRzcx3++8KETqVplAunjnThldtvJatMWx/t/RP2nzyD/Lwp/p72vNfFeAWd0xNfCj8/Py5dukRS\nUpIqm1Amk6Gjo4NUKlUFfaAtAzoxMZF+/fqpDIJvZvTo0ZiYmBAWFoavr2+n3lwXLlzgH//4B35+\nfuyOSCfdUEpBXBjFyWe4lhmHvc+4Do9V0tLaSnV1NdOmTeMvf/mLavvEiRN5/fXXVVI07XFzc+OL\nL74gJCSEixcvkpqaSp8+fXBwcOCZZ57psk+Ad999l2+++YbIyEgOHjyIo6Mjy5cvZ/jw4VqDQI6O\njvzrX/9ix44dyGQylUn0mjVrqK6u7lYQaPPmzaSkpLBx40ZmzJjB1KlTaWpqC4CFhISoFv2tra18\n8cUXNDY28sEHHxAQEEB+fj47d+7k+PHjXLhwAWNjY5577jmMjIyIjIwE4LPPPsPQ0JBPPvlELdM6\nNzeXoKAglfm2QCAQ3G56K7VcUK7o8TFPPfUUCoWCqKgoVeb/1KlTVdWjfn5+bN68mX379hEbG0ty\ncjJ6enpYW1vj5+fH2LFjezXWnrBkyRIsLCwIDQ3l2LFjGBsb4+/vz5IlS7RWgwO8+eabbNu2jdjY\nWMLDw2ltbcXGxqZTWTsPDw+VdFh8fDxRUVEq75VFixbdcgWojo4O7733Hrt27eLixYsUXE2jprSe\nvkP86SedwKVDX2kcU3klFfnVbIpTzlJXfY2m2hp+TLlOXU0VAQEBahVLvze9XQP29rj7AW3Jb8mX\nC6iXl/PZ0QyWTzPqMOHr+vXrAGpVQO2xtramtLRUaxBIG8rKbW37lfu0+T12lKSoHJdynAAHDhxg\n27ZtmJqaMmzYMGxtbTE0NEQikXDhwgWys7O1ylF2N6lOyZw5c0hKSuL48eM888wzVFRUEBkZiaur\na4fKGgLBg44IAgkEAoGgx/TWgH2KtD8/nNWU0+qKu3Xh0F3t9tZ22u23o6rDxMSEESNGMGLECFpb\nWzlx4gTJycmqRXl7XXRtGVV/FN29Xrr6RhzOhvXPLFabn4eHBwDJycldBoHa/Ag8Cff3Jjc3l5aa\nUugkqKJ8AX716lUKCwtxdHRU26/MOL2VKoD2fRQVFall6A2yM8OWCpysTfAd2FcEgG4T3fGl8PPz\nY8+ePchkMrUg0JAhQxg7dixff/01BQUFODk5kZWVhVwuv20vwCZMmKDK7lRmydq4Dac4+QyKawUa\n7XV09fBZ+BqGpjcW4w79nRnqPojnn39ere3w4cMZPHgwTk5OWgNRtra2GjIr2mhfPdkeExMTXnnl\nFV555RWNfR3Juzg4OLB69Wqt+24OsueUyJn/wrtcr2/il6jstgCegwMODg5qbfX09Jg7dy4JCQnI\nZDIOHjxISkoKf//73xk6dKgqy7Z///688847rF69mhdffJGCggKkUqkqazQ3NxeFQsFf/vIXDamd\ngQMHMnPmTPbv309eXp5WKR6BQCC4Fbojtaytyn7qo0t5evyHWtv7+vpq/T42MjLipZde6lQuy87O\nTi2xoDM6kw/rbBxKtN1ndHV1O0zm6qg/BwcH3n///R6PwcnJiTfeeKPD8bXn6aef7jC5w87OTmsf\nZmZmavfboG/Pq9ZQPgtf02jv6DcRXX0DaiuuUpp6HgsTIxzdhzLxiUdZsGBBpwljd5rergEf1Ofa\njpLf9AyMqAfi03JZXazoMFlQmQBXUVGhtbKnvLzt73Cnpc8qKyu1blcmtinH2dzczO7du7GysmLj\nxo0a1T43V/TcCmPGjMHS0pITJ06wePFiTpw4QXNzM7NmzbptfQgE9xsiCCQQCASCXtEbA/Y7vXAo\nKSlhxYoVTJ06tdPF6K0wf/58pFIp69ev75V2e2+DQAkJCfj6+mos/JQP5YaGhqpt5ubmAJSWlmrI\nT/2RdHa95FezMbUfpGF6b9Zufm5ubvj4+HDu3DlOnDjB9OnTNc6Tk5ODlZWVqipq/vz5bN68mc2b\nN/Phhx+qLZJaW1upqKhQLVCmTZvGd999xzfffMM777yjCqBVV1ezZ88eAK199gRlHzt27ODtt99W\nzbe4uLhLXXTB7eHm6iBpfycMDAxUFT8KhYLMzEwee+wxlReUTCbDyclJFQxUbr+FU4KjAAAgAElE\nQVRVhgwZovpZGew2MG77/21u6J7Ej3kfgx5JaNztdCSvCTDYUoJlZTLlBZmUlpbS0KDuu3bt2jXg\nhnyKt7c30Pa/XllZiZWVFRKJBE9PTwoKCkhLSyMiIoIBAwaofCSys7O1ZpkXFLQF5UQQSCAQ3AmE\nzNaDQ1drKLN+rpj1cwXa1lDrnxl1V0kD92YNeDfTlbJEb+ks+c3Ypj+Ka4VUF2ZgZGHTYbKgq6sr\nmZmZJCUlaQSBioqKKCsrw97e/o4HgVJSUmhtbdVYhyYmJgI3kuSqq6tRKBT4+flpBIDq6upUz2e3\nAz09PWbMmMGPP/5IVFQUoaGhGBkZMWnSpNvWh0BwvyGeGAQCgUDQK3pqwK58qL1fFg691W7PKZH3\nqr9169ZhZGSEh4cH9vb2tLa2kpycTHp6OkOGDFHTivbz8+PMmTOsW7eOESNGYGBggJ2dnVbj9N+L\nrq5XdviP6OgZYGzjhKGpJa2tkHY0l8Gm9Qz18VTNLygoiDVr1vDFF19w8OBBPDw8MDExoaysjJyc\nHHJzc9mwYYMqCDRjxgySk5P59ddfeeGFFxg1ahQWFhaUl5cjk8mYPn26KqPz0UcfJSYmhsjISF55\n5RVGjBhBfX09Z86coaqqiscee0z1Urm3PPLII1y4cIFz587x2muvMXz4cBQKBREREUilUpUsleD2\n01lwobrRnLJL6VRVVZGamkpLSwt+fn4MGDAAa2trZDIZc+bMQSaTIZFIOvQD6intpS80guStLV0e\n7z3AkrwUvR5LaNytdCYXWS+v4Oef/pfmhlomjx3BzJkzMTY2RkdHh5KSEsLCwlQ+TkpZEqV8SWNj\nI3/+85/x9fVlwIABJCQkkJaWxubNm7G2tubFF1/kxx9/BG7oyXdEba2mV4NAIBDcKkJm68Ght2uo\nu4V7ffy/F50lv9m6j6AsPYarSeGYOw7GyMJWLVlQ6XMzffp0Tpw4wZ49ewgMDFStb1paWti+fTut\nra3MmDHjjs+lsLCQw4cPM2/ePNW2yMhIVXDKx8cHaHvuMjQ0JCMjg7q6OoyMjABoampi69atVFdX\n39ZxzZo1i5CQEL7++muuXbvGrFmzVP6eAoFAExEEEggEAkGv6akBO9z7C4ctW7ZgaGjI2ezeabf3\nVvN92bJlxMbGkpmZycWLF1WBneXLlzNnzhz09G7c0mfMmEFJSQnh4eHs3buX5uZmpFLpHxoE6mre\nDsOmIi/KpLb8KtWFGejo6mFgYsGIKfP54LU/q+ZnY2PDxo0bOXjwIOfOneP06dO0tLRgaWmJs7Mz\n8+bNY+DAgarzSiQS3njjDYYPH87x48c5c+YMjY2NWFlZ4ePjw6hRo1Rt9fT0+PDDD/nll1/47bff\nOHToEDo6Ori4uPD8888zYcKEW74O+vr6fPTRR+zevZuIiAgOHDiAnZ0dTz31FGPGjBFBoDtEV95d\n1437kXk5mW0hJzBvLsfAwAAvLy+greonJiaGxsZGkpOTcXZ2vmP+W8ogeXeQSOCxUa5s7Dxmcc/Q\nlVxkSep5muqvM3DMw1S5DmPk9BuZ0eHh4YSFhanaKmVJlJWSenp6zJ49G5lMxuXLl0lJSeH69ev4\n+/vzyiuv4OrqyqFDhwD4z3/+06l/hEAgENwJhMzWg0Vv1lB3E/f6+O80XSW/GVnYMmDkbPKiDpN6\n5L9Y9PekMN4as8JzlF/Nw9jYmHXr1uHl5cVjjz3G3r17WbVqFePGjcPIyIiYmBhyc3Px9vbm0Ucf\n7XQs7ZUyuktYWBgbN25kwYIFAAQEBLB9+3bef/99zMzMmDNnDufOncPAwIDXXntNVSEkkUiYP38+\nISEhrFq1itGjR9PU1ERCQgJyuZzU1NTbmpxka2vLyJEjVeunrqTgNm7cSFhYGNu3b+/Q21MguJ8R\nQSCBQCAQ3BI9MWBXci8vHPr37w/A9VTt2sjtcZu+XGPb9fomrbJfnWmMA8yePVtlet4VOjo6LF26\nlKVLl3ar/e9BV1r3tu4jsHUfobHdb5y7RkZXnz59ePLJJ3nyySe73f+kSZO6JQ9gYGDQ7XN3pP2u\nZP369Vq3Gxsbs3LlSlauXKmxT0jC3X6640Vl1s+FwlbY/vNJfK0a8PT0xMDAAGirrDt9+jRHjhyh\nrq6uW1VA7X25eoIySL50V+ftlEFyX0fDzhveQ3Qlr1kvb9Odt3T20pDXVMqRKHF1bZPRSUlJAdr+\nHi+88ALQJg2n9AR69tlnVW09PT05d+4cycnJIggkEAj+EO6XanlB9+jNGupu4l4f/52kO0l/Nm4B\n9LG0o/jSeWqKc6jKT+XXOkf6WxsTHR1NWFgYU6dOZfny5apklVOnTtHc3Ey/fv3405/+xMKFC9US\nAe8U7u7uLFq0iEWLFnHlyhUuXrzI0KFDWbp0KW5u6t9DS5YswcLCgtDQUI4dO4axsTH+/v4sWbKE\nyZMnU19ff1vHNn36dCIjI3Fzc7sl71aB4EFABIEEAoFAcFvojgF7e+70wiE/P58dO3aQnJxMY2Mj\nrq6uLF68GH9/f4224eHhHDt2jKysLBoaGrC3t2fSpEk8+uij6Ovrq7VVegKNeuSGCXtRwmmKEn7D\nbfoymuquU5JyltqqUnR09TDr54pTwAyVz0d77fb09HR27txJamoqEokEd3d3lixZQmxsLMHBwaxb\ntw5fX99bvhZ3A0LrXvBH0R3vLmMrB/QMjKjKSyOmoJHH59/IJFT6//z0009qv3dGe1+unjLL3xlP\nJyvqO5Cz8B5gyXNz26pgSkpKenz+u5HuyGsamLRVX9UU52DR30Mlr1men05oaKhaW29vbxwcHEhI\nSCAmJoaAgADVvmPHjqn8fdozbdo0fvjhB4KDg3Fzc8Pd3V1tf2trK0lJSffNd7JAILj7uNer5QW9\no6drqLuNe338d4Kukt+UmNgOwNX2hs/gsknu2DdcYePGjWrtJkyY0G1Fgu3bt3e4r6MENYC//vWv\nnXrqenp6cvToUQANf6L26OrqsnDhQhYuXKixz9XVFalUqlaF09ukOiVKn6HuJksKBA8y4s2KQCAQ\nCP5Q7sTCobi4mKCgIAYNGsSsWbOoqKggIiKCtWvX8tZbbzF+/HhV202bNnH48GEuXbrEyJEjmTt3\nLmlpaezatQuZTMaHH36Irq6uRh/aNNjLLl+kKj8Ni/4emNoPRFFWSEVuMrWVxXjOeYHKvFT2bzvE\n95+VUVpaSlVVFQMHDmTMmDE4ODiQk5PDO++8c9tM539PVq9eTVJSktpDfGJiIu+88w6LFy9m7LT5\nvTpvV1r37SUOOlu4CB5MuuvdJdHRwdRuIJX5aTQCffsPUe2zs7PDwcGBoqIidHR0kEqlXZ7PycmJ\nvn37Eh4ejq6uLnZ2dkgkEiZPntwt+QkLYwOkUideeGGCKkgeVm5PTnNfPnhyJHZ299eLv+5kzNq6\nj6Q8K57siBAsnb3Q72PG22tCuV6czUMPPURERISqrUQi4ZVXXmHt2rV8+OGHjB07FgcHB7Kzs4mP\njycgIICYmBg1g2MzMzNWr17Nxx9/TFBQEH5+fjg7OyORSCgtLSU1NRW5XM6+ffvuyDUQCAQCuLer\n5QUCQRu3lPzWcJsHcxvpLPjzR1BbW8vRo0cxMzO7LbLdAsH9jggCCQQCgeC+IykpiUceeYRnn31W\ntW3u3Lm89dZbfPnllwQEBGBsbExYWBgnT55kxIgR6OvrM2HCBFasWAHA7t27CQ4O5vDhwyo95PZo\n026vLszAY9ZK+ljZq7Zln9lLRU4SV5MiqM2MRH+kF7Nnz2bXrl2Ympry3nvvqWWpHz16lK+++upO\nXJY/FKF1L/gj6IkHl2k/Fyrz09A1MKJKR93zx8/Pj6KiIoYMGYKJiUmX59LR0WHNmjXs2LGDs2fP\nUltbS2trK97e3j3SIG8fJC+J6UtJxv356N6djNk+VvYMmbaMItmvVBek09raQk0fL9595x1MTEzU\ngkAAvr6+rF+/nl27dhEdHQ2Ah4cH69at4/Tp08AN7yAlfn5+bN68mX379hEbG0tycjJ6enpYW1vj\n5+fH2LFjb8+EBXcFYWFhREVFkZmZSUVFBbq6ugwaNIjZs2dreOgpEx1+/vlnQkJCOH36NMXFxUyc\nOFEtAaEnlcUXLlzg7NmzXL58mWvXrgFtkrNTp05l3rx5akFKwYOFkNkSCO4d0tLS2LdvHykpKdTU\n1GBpaYmLh5TG607oG9/4f62XV1Cccgb51Rwaa+Xo6Oqh38cME9sBOA6bgp6hMaG7NpOXfRlo869p\nXxGk9LEpLy8nNDSU2NhYioqKqKmpwdzcHKlUyqJFixgwYIDGGJX0RClDG8p1cvtqo6amJo4ePcrJ\nkycpLi6msbGx7Rq4uDBv3jyGDRumcZ7q6mp27txJVFQUcrkcBwcHHn30UaZNm6a139jYWA4cOMDl\ny5dVz9T29vYYGBhQWVnJs88+i6HhDYnk+Ph4goODyczMRF9fHx8fH5YvX96tOQoE9zP350pSIBAI\nBA8ENy+O+5u0aWeYmJiwePFitbZubm5MmjSJsLAwzp8/z9SpUzlw4AC6uro899xzvPrqq2rtFy1a\nxKFDhzh9+rTWIBBomrjbegSqBYAAbIYMpyIniYrsBFytjXn99ddpbW3l559/ZujQoWoBIGgztNy/\nf79WuaJ7HaF1L/i96a4cB4Cd5yjsPEcBUNeo7uWzatUqVq1apfW4jmQq3Nzc+Pjjj7Xumzp1aqcG\nvdpkMbTJdNyqhMbdQnczZk1tB+A27YbX2cqZ3owOdAG0XzMPDw8+/PBDje3ffPMNOjo6ODo6auyz\ns7PjL3/5S3eHLriH+eqrr3B2dkYqlWJlZYVcLufixYv8+9//pqCggCVLlmgcs27dOtLT0wkICGD0\n6NFYWNwIGG/atImTJ09iY2PD2LFjMTEx6bSyeMeOHejo6ODh4UHfvn1RKBQkJCSwdetW0tPTeeON\nN36X6yC4exEyWwLB3c2JEyfYvHkz+vr6jBo1ChsbGwoLC4k89xvF5U3YPbQEAxMLGq/LSTv2vzQ3\n1mPhOKTN37C5ifqaCsqzE7D1CGS4e39mjZ3D+fNWREZGMmrUKJVvIaBKQkpKSuKnn35i6NChjB07\nlj59+lBYWMi5c+eIiori008/xcXFRWOsPVHK6Amff/454eHhDBw4kClTpmBoaMi1a9dISUkhNjZW\nIwikUCj429/+hp6eHuPGjaOxsZEzZ86wadMmJBKJxvNxcHAwu3fvxszMjJEjR2JhYcEPP/zAL7/8\ngqWlJUFBQWrSc2fPnuWTTz5BX1+f8ePHY2VlRUpKCkFBQVqvi0DwICGCQAKBQCC454jLLuP78HSN\nqpL6mkry8iqYNG4IfbT4afj6+hIWFkZWVhYPPfQQ2dnZmJubqzwiYmJi2L17t6q9vr4+eXl5HY5D\nqd3+N9lpAIz7ar5QNDCxQCKBgZa6WBjr0bdvXyIj2wJH3t7eGu0lEgmenp73ZRBIaN0Lfm+EF9W9\nQVeyj705rr6+nqamJo3KrbCwMC5dukRAQABGRka96ldwf7B582YNaZumpibWrl1LSEgIs2fPpm/f\nvmr7S0tL+fLLL1W+X0qUlcVjxowhKCgIAwMD1b6OKovXrl2r0X9raysbN27k1KlTzJ07Fw8Pj9s1\nXYFAIBDcRgoKCvjqq6+wt7dn/fr1avcLmUzGq2/+nfyLx3Cd+BQVV1Joqr9O/xGzVAlHSpobG9DR\nkajJPEZGRjJmzBitCUN+fn7s2rVLY62bnZ3N3/72N7799ls++OADjeO6q5TRHldX106TjRQKBRER\nEQwZMoR//etf6OjoqO2Xy+Uax2RnZzN9+nRefvllVfuHH36Yl19+mb1796rNOSEhgd27d+Pp6ckH\nH3ygeqZ79tlnCQsLY+PGjUgkElXlbF1dHV9++SU6Ojr885//xM3tRkLh//7v/7J///4O5yIQPAiI\nFa5AIBAI7imOxV3pNIhQXdvAmcwqjsfnMXOYejm8paUl0PbAWlNTQ2trK1VVVfz8888UFBRQX19P\nUVER+fn5yOVyWlpaMDY2Ji4uTmuZfHh4OOHHjkHmaa4XZ5F77hfqvMdi5z0WHd22W6x57RWay7Kp\n1FVg7erKihUrKCwsJD8/n0WLFqnOlZGRwU8//URycjKXLl2itLSUH374AScnJ6ytrdX63bhxI2Fh\nYWzbto3o6GhCQ0MpLCzE3d1dLfP/5tJ5GxsbxowZw/Dhwzl+/DgpKSlUV1djZmbGwIEDmTlzJg89\n9BDQM5mcnqDUut8RGk/Y8SNU5afSoKhCoqOLsbUDD02bzevPzNMIANXW1vL9999z5swZqqursbOz\nY9asWYwePbrXYxHc/9yJ4ILg9nMn5CJLS0t57bXXGDZsGA4ODrS0tJCZmUlKSgomJiYqSRPBg4s2\nbwM9PT3mzp1LQkICMpmMKVOmqO1fsmSJRgAIUFUWv/baa2oBIOi4slhb/xKJhAULFnDq1Cni4uJE\nEEggEAjuUo4ePUpTUxPPPfecRsKAn58fMyaP59CJ32hpqldtV64P26NnYNCj5Lf2FajtcXFxYejQ\nocTFxdHU1ISennpf3VXK6AkSiYTW1lb09fW1SpiamWk+pxkaGrJy5Uq1gNGAAQPw9vYmKSmJuro6\nVZKOMgD1yiuvaCT1KFU9Tp8+zcqVK4E2mVW5XM6UKVPUAkAAixcv5uTJkygUih7NUSC4nxBBIIFA\nIBDcM8Rll3VZRQLQWKvg80MJ2Fn0UXugrqysBNoegpUPkq6urqxZs4YVK1YglUrJzs5mxIgReHl5\ndVom3172ZZjUC0lzA+5eA8kpuIixYRVLXwoiYLA9JTm2/OXsXkxNTQFYsGABaWlpnDp1irq6OgCi\no6NZt24dAGPHjqWlpQW5XE5ERAR5eXl8+umn2Nury8wBbN26lZSUFEaMGMGIESPUHqa1lc7n5OSw\ndetWiouLkUqljBs3DkdHRyorK8nIyODw4cOqIFBvZHK6i5NJCzVRwdjXFOLuNZC+/QYgaWni2pU0\nKqP3URLoAC4zb/w9GxtZs2YN6enpuLi4MGnSJBQKBXv27CEpKYlLly6RlZWlIZUlEAgvqnuH2y0X\naWlpycSJE0lKSiIhIYGmpiYsLS2ZNm0aTz755F1nbiy489wsITvAFKJ+O4ZMJqO0tJSGBnU3bqVP\nT3tufqkEbVVnysrijrKMtVUWy+Vy9u3bx8WLF7l69arqmaCz/gUCgUDwx9H+PnLo10iu1zeRlJRE\nenq6RtuqqipszAxZOak/x20N2Sc7RV70UaqLMjF3GIyJ7QACh3rwzAT3HqsfREdHc/ToUTIyMqiu\nrqa5uVltf3V1tUYS4eDBg7tUyuhpEMjY2JjAwECioqJ49dVXGTduHN7e3nh4eKh59LTH0dFRo+II\nwMam7RrU1NSogkCpqano6elx5swZredqbGykqqoKuVyOmZkZmZmZAEilUo22JiYmuLi4kJSU1KM5\nCgT3EyIIJBAIBIJ7hu/D07v1grC2vIimhnp2R6SrPVQnJiYCbYEfIyMjnJ2duXLlCjU1NUD3yuQB\ncnJySEpKUsm+hISEUFFRwftr/k5iYiLBwcHol6YwaMwQjBmEk5MTenp6NDU18fDDD1NaWkpaWhpZ\nWVnU1dXx+eef09zczPr16/H29ubFF1/Ew8ODSZMmcfr0aTZv3qzV1yIzM5NNmzZpBIg6Kp3Py8sj\nLCyMhoYGHnroIf7+97+rHVdWVqb6uTcyOd3l888/p7S0lLXvrmbChAmq7QqFgtWrV7N161ZGjRql\nqtz6+eefSU9PZ+zYsbz99tuqTLPHH39cBH4EXSK8qO4NbrdcpKmpqYbX271ASUkJK1asYOrUqarv\nN2X1p9IUujckJibyzjvvsHjxYp5++unbOeS7nvYSsrnn9nMtKx63acvIjvgJY91mJo4ezsyZMzE2\nNkZHR4eSkhLCwsJobGzUOJeVlZXGtvaVxcHBwd0ak0Kh4PXXX6e4uBh3d3emTJmCqakpurq6KBQK\nDhw4oLV/gUAgEPz+aJMiT07Lo15ezkebtuPU1wQLYwOtxw62Nearl+eyZJwrX2/fweWURJqz89G/\nasC18n7kWz+Kv8v8bo/lwIEDbNu2DVNTU4YNG4atrS2GhoZIJBIuXLhAdnY2TU2anpjKdVVH23tb\nIfP3v/+dkJAQfvvtN77//nsADAwMGDduHM8++6xGvzdX9ChReua1tNzw5ZTL5TQ3N3d5b62trcXM\nzEw1h47mqu0eLhA8SIggkEAgEAjuCXJK5N3O5m9qqONq4m8k6M8gp0TOIDsz0tPTOX36NCYmJowZ\nMwaAhQsX8sUXX7B161aam5sxNzdXK5OvqalBR0dHrUweID09nf79+/dI9qU93t7eODg4kJCQwLff\nfotcLmfChAn4+Phw9OhRlR/Q1KlTSUlJIT4+ntLSUmxtbdXO89hjj2mtEOqodP7IkSNYW1tjbW2t\nCoi1R5mBBb2TyekO2dnZJCUlMW7cOLUAELQtCp555hk++ugjzp07x5w5cwA4efIkEomE5cuXq0kN\n2NvbM3/+fKKjo3s8DsGDg/CiundQykXujkgnIVfz+37oQGs1zXyBoCs6kpAty7hIU/11rMY8TKHT\nMAYGDlVJyIaHhxMWFqb1fNrkbtpXFm/atKlb4woNDaW4uFhrUC41NZUDBw506zwCwYOAtuB4Vyj9\nQv7617/2uLpBIGhPR/cRXYO2ahWX+a+jZ2jEy/OGakiRt2esvxdjN39Cc3Mz2dnZxMfHc+jQIbZu\n3YqRkRHTp0/vcizNzc3s3r0bKysrNm7cqFHtk5qa2uGxSkWMjrZ3FJzpCgMDA55++mmefvppysrK\nSEpKIiwsjF9//ZXi4mI++eSTXp0X2iqNWltbu51goZxDR3OtqKjo9VgEgvsBEQQSCAQCwT1BfE5Z\n143+DzP7gVzLiENRVsi/my7haqVHREQELS0trFq1ipKaZuKTsrluNAgn70DOXviVrPQ0PD09+fHH\nH5HL5RQXF5OUlMS0adPUyuSbm5upqqrC09NTJfty9uxZCgoKOHz4MImJiVplX9ojkUh45ZVXWLt2\nLV9//TX19fV4eXnxj3/8g/j4eAICAoiJiUFPTw+pVMqpU6fIysrSCAK5u7trPX/70vnSqlpyy+TU\nN7Zw/uQB6uUVBPj7UVFRoSqd10ZpaSkhISE9ksnpDsrFiUKhYPfu3Rr7q6qqADhx4gQymYy0tDSO\nHz+OkZERn3/+uZonUUlJCf/973+Ry+UYGhoyf/6NLDqpVKrmjyR4sBHBhXsHfxcb/F1sNKS7hg2y\neWBl+pYuXcrjjz+u8bKnJ7i7u7Nlyxatfjb3K9okZB2HTcHeZxx50UcBsHT2orUVNQlZbUkSndG+\nsriz+2p7CgsLgTYJ2JsRUjUCgUBwd9CZFLmJjRPXrxVSU3oFCyd3rVLk2tDV1WXIkCEMGTIELy8v\n3n77bc6fP68KAinlvdtXxCiprq5GoVDg5+en8UxQV1enkkPTRmZmJrW1tRqScO2VMm4VGxsbJk2a\nxMSJE3nhhRdISUnp9n1RG56enkRHR3PlyhWcnZ27bD948GCg7T56c1BNoVCQnZ3dq3EIBPcLIggk\nEAgEgnuC6/WaZe0dYWBixYDAuRTGhRF95lcKLI0YPHgww8bPZH+WHomnw280Nh3O9YGt1KZlUHC1\nlF9++QVTU1NsbW159NFHmTx5MsXFxUDbw2Nzc7OG7EtBQQEFBQUcPXq02y/YfH19Wb9+Pa+++iqJ\niYnExMQwZswY1q1bx+nTp4G27CflA75Ssq49HZW0y+VyyuW1vPfpV1TX3gje1BTn0NzUwHUdEwbY\nmKlK52/m6tWrvPHGG9TU1ODj48Pw4cO7JZPTHeRyOQDx8fHEx8d32O7UqVNMmTKFIUOGEB8fj7W1\nNSUlJWqeRCYmJjz++ON89tlnAGpVXNoqpAQPNiK4cG8xyM5M/F3+D2UF561gaGhI//79b9OI7g20\nScjqG5uhjxmGZlbIr7bdFy36e9DaCrsj0mmtuEJoaGiP+1JWFm/atInXX39dI6O6pqaG4uJi1Qsq\n5T0qMTGRQYMGqdplZWXx008/9bh/gUAgENx+OpMit3UP5FpGLAUxoRiaWWNkbqMmRd7U1ERaWho+\nPj5kZGTg4OCgcW9QVqy0989Rrs1KSko0+rS0tMTQ0JCMjAzq6upU3jlNTU1s3bqV6urqDueiUCgI\nDg5Wkz3XppTRE6qqqqioqFC7j0FbQKqurg5dXV309Hr/2vnhhx8mOjqa//znP6xevVpr4Cs3NxcP\nDw8ARo8ejampKb/99hvz5s1T8/ELDg7uteSdQHC/IIJAAoFAILgnMDbs+pZlaGrJ8CVrVb+7TlrE\nizO9WRjo0q6Uv07jOLN+g9E170ejjQt/WbtBo5Q/LS0NaCsx379/P0888US3ZV/s7Ow4ePCgytOh\nPR4eHjz55JMYGBjw6quvqjKWvvnmG3R0dHB0dKS8vFzV981ok6UBqKpvJb20jqFPrFafx9FtKK4V\n4jz9BfpY2hBXWM9MLfYSv/zyC3K5XKuERmcyOd1BaQT6/PPPq1Xu3ExRUREODg7U1tYSGRmJjY0N\n27Zt0/AkmjRpkurv8KD5XAh6hwguCO41bvYESktLIygoiNGjR7NmzRqtx7z44otcvXqVnTt3YmZm\n1qEn0OrVq0lKSuKXX35h7969nDx5ktLSUiwtLZk4cSJLlizR+gLn9OnT/Pzzz+Tn59OnTx+GDx/O\n8uXL+eyzz0hKSlLJkranvaTTE088wa5du0hMTKS6upqPP/4YX19f5HI5+/bt48KFC5SUlKCnp8eQ\nIUN4/PHH8ff31zinsqr07NmzVFdXY2dnx6xZs3AaIuXbda/R13UYA8c+rGqv9AQaPPlpyrPiyY4I\nwdLZC/0+ZiTtS+anyiwszE0pKSmhvr4efX19Fi5cqNHvihUrAPjyyy/ZvRxa/oUAACAASURBVHs3\nERER5OXlkZCQwLFjx1iwYAH29vYalcWrVq0CYMqUKezbt49t27aRmJiIo6MjhYWFREdHM2bMGCIi\nIrr4VAgEAoHgTtKVFLmRhQ3OoxZwJfIAlw59jbnDYPLN+2JVEk1LXTUpKSmYm5vz9ddf8+uvv3Ls\n2DG8vb3p168fpqamXL16laioKPT19Xn44Rv3KU9PTwwNDTlw4AByuVyV9Ddv3jxMTEyYP38+ISEh\nrFq1itGjR9PU1ERCQgJyuZyhQ4eSkJCgdbxSqZTQ0FAuX76Ml5cXFRUVakoZyjVaT7h27RqvvfYa\ngwYNYtCgQdjY2HD9+nWio6OpqKhg/vz5GpVHPcHPz49ly5axc+dOnn/+eUaMGIG9vT11dXWUlJSQ\nlJSEt7c3//M//wO0Vea+/PLLfPLJJ7z99tuMHz8eKysrUlJSyM3NRSqVimpbwQONCAIJBAKB4J5g\n2KDeyTQNG2TTaSl/e66XF7Hh52iNUv72ZfK9kX3RRn19PU1NTarS+8TERKZPn05YWBiXLl0iICAA\nfX19kpOTgRvl7V0Rl11GXoMZTfVF1FaW0MfyRpTH2KY/imuFVBdmYGRh06FsQVFREaBdpqanMjk3\no8zUSk5O7jQIpPQk6tOnDw4ODly9epXS0lINT6JbHY9AIBDca3h4eODk5MTFixe13ocuX75Mfn4+\nY8eO7fY9asOGDSQnJxMQEICxsTEXL15k7969VFZWanhw7N27lx07dmBqasqUKVMwMTEhLi6Ot956\nq1ueAkVFRbz55ps4OTkxadIk6uvrMTY2pqSkhNWrV1NSUoKPjw8BAQHU1dURHR3N2rVrWbVqFTNn\nzlSdp6GhgTVr1pCZmYmrqyuTJk1CoVDw448/IrE40+kYjCxsGTJtGUWyX6kuSOd6xVUar8vxcHdj\nztTxHDp0iNbWVnbu3ElsbKxWWZ6mpibef/99ysvLGTFiBIGBgRw6dIiMjAwOHDiAra2tRmWxEmtr\naz755BN27NhBSkoKsbGx9O/fnxdffJFhw4aJIJDgD6Ouro7Fixfj5ubGp59+qtre0NDAokWLaGxs\n5I033lD7PB85coQtW7aoJTQVFhayZ88eZDIZ1dXVmJub4+fnx6JFi3B0dFTrc/fu3QQHB7Nu3TrK\ny8s5cOAAV65cwdzcnO3bt3c63qKiIr799lvi4+NpamrCxcWFJ5988jZeEcGDSnekyK1dh9LHyp6S\nSxeQF2cjv5rJ0etZDHVzZty4cYwfPx6ACRMm0NjYyKVLl8jIyKChoYG+ffsyfvx4HnnkEQYOHKg6\np6mpKatXryY4OJiwsDDq6toSGCdPnoyJiQlLlizBwsKC0NBQjh07hrGxMf7+/ixZskSr1LYSe3t7\nXnrpJb799luOHj1KY2MjgwcPZtGiRQwfPrxX18je3p5nnnmGxMREEhISqK6uxszMDCcnJ5YvX66a\n/63w+OOP4+3tzcGDB0lJSSEyMhJjY2P69u3LzJkzmThxolr7cePG8Y9//EOVoKGvr49UKmXDhg2E\nhISIIJDggUYEgQQCgUBwTzDIzgxfZ+tOM7JuZuhAawbZmbH526QuA0AATQ11FCX8xu4IB1VgRFuZ\nfE9lX7RRWlrKa6+9ho+PDyUlJezcuZOUlBSKi4sxMTFhxYoVHDhwgOLiYoYNG6bhB9QR34enY+c5\niqr8y1yJPITr+CfQN257CWjrPoKy9BiKEk6jZ2SMtctQNdmCsrIybGxssLNrCxwlJiYSGBioOnds\nbGyvZHLa4+bmho+PD+fOnePEiRNMnz5dQ57LRkeBg1UfQkNDkclkyGQycnJymDJlCoMHD0YikXDt\n2jWKi4u1ZpsLBALB/c7UqVPZuXOnSvKkPcpqzZ6YoRcVFfHll1+qgkZ/+tOfePXVVzl16hTLli1T\nZSJfvXqV7777DnNzczZt2oSNTdv9Y9myZWzYsIHw8PAO+1CSkpLCE088wdKlS9W2r169mtLSUt56\n6y0mTJig2q5QKFi9ejVbt25l1KhRWFpaArBv3z4yMzOZMGECQUFBqurYp556iocXP0tXmNoOwG3a\nUhSleaQd/wZTu4G89P56npszgo8//pjm5mY+/vhjoqOj+dOf/qRhbl1eXo6LiwsfffQRBgYGQFtF\n6gsvvADArl27OpXBGTBgAO+9957WfeLeJvijMDIyws3NjcuXL6v5h6SkpKikgGUymVoQSCaTAW1Z\n+9D27Pzuu+9SW1tLYGAgzs7O5Ofnc/r0aSIjI/noo4/UZJqU/Pzzz8THxxMYGMjQoUO7lG4qLCwk\nKCgIuVxOQEAArq6uFBUV8fHHHxMQEHBbrofgwaW7UuR9rOzVKk6XTXLn6fHqn28PDw9VIlx3CAgI\n6PAzrKury8KFC7VWqf71r3/VSNxQqlIoeffdd7vsf+rUqVqfIW4OypqYmLBo0SIWLVrU5Tmh83ub\ntrEr8fb2xtvbu1t9AAwbNoxhw4b1qA+B4EFABIEEAoFAcM/wzAQ3Vn8f2a2AjkQCT49367KUvz1m\n9gO5lhFHyLZC+lVNRbe5TmuZ/PTp08nIyODIkSM899xz+Pv7Y2dn16HsizaUUjtJSUmYmJiQl5fH\niRMn8Pf3Z+zYsWzbto24uDisrKw6PU97lHM16+eKo/9UiuJPkXzgP1g4umFgaklLUyN6BkZcy5Ih\nL87GyX86hfHWmBWeo/xqHsbGxqxbt465c+dy8uRJ/vnPfzJu3Disra3Jzc0lNjaWhx566JYzlIOC\nglizZg0f/nMDf/90K7WGNugaGNGoqKa2shjFtQIsTI0ZYGPG2JH+LF++XBUQq66uprq6msOHD7N3\n716kUinR0dG3NB6BQCC415g8eTLfffcdp06dUgsCNTU1ERERgYWFRY9egi5fvlytasjIyIiJEyey\nZ88eMjIyGDlyJAC//fYbzc3NzJ8/XxUAgjZ50mXLlnHmzBmtVTPtsbS0VPNwA8jOziYpKYlx48ap\nBYCg7SXTM888w0cffcS5c+eYM2cO0OYdp+y3vTyqjY0NYyfP4PK3O7s192uZbf50/aTjsbXpq9qu\nq6vLihUruHjxIqGhoVqrC1544QVVAAjAwsKCUaNGcerUKQoKCtSyuwWCewU/Pz8uXbpEUlKS6n9f\nJpOho6ODVCpVBX0AWltbSUxMpF+/ftjZ2dHa2sq///1vrl+/zptvvsmkSZNUbSMiIvj000/517/+\nxZYtWzRkjRMSEtiwYUO3Deq3bNmCXC7nueeeY8GCBartykCTQHArdEeK/HYeJxAIBHca8e0kEAgE\ngnsGfxcb/jrXt0tpN4kEXp83FH8XG36Jyu72+Q1MrBgQOJfCuDB+OXAYO3ODDsvkX3zxRUaMGMHR\no0eRyWQoFIoOZV+0YWpqyquvvqr6PT09nR9//JGUlBR+/fVXLC0tmT17NosWLeq2IXh72YJ+Pg9h\nautMaVoUNaVXaC5IQ0ffEIM+5gwInENjnYKa4hyq8lP5tc6RCSOkzJgxA4BBgwaxbt06du3aRXR0\nNM3Nzbi4uPDOO+9gYmJyy0EgGxsb5ix/k8iN31Bx5RL1RYm0traib2SCkYUtSCRUXyviuv8cJj21\nlJnDBrBs2TJ2795NSEgI6enp5OXl8fLLLzNmzBh27NhxS+P5PTh48CBHjx6luLiYhoYGVq5cqab/\n3R2U3h3ts+g68vn4vWnv9SEy7ASC7nNzJWR/k25kOdD2Pern50d8fDx5eXkMGNDmZRcVFYVcLufh\nhx9GV1e32+PQlpWvrECtqalRbcvKygLQmpFrZ2eHjY2Nysy6o7m5uLigr6+vdmxqaipww+PnZqqq\nqgDIy8sD4Pr16xQVFalVr7Zn2riR7OhmEOh6eZsEqlk/Fw3pWScnJ2xsbCguLkahUKhV/pqYmKik\nS9ujDI61v24Cwd2MRkV2/yFAW+CnfRBoyJAhjB07lq+//pqCggKcnJzIyspCLperJIRTU1PJz8/H\n09NTLQAEMH58m9RiSkoKycnJSKVStf2zZs3qdgCorKyM+Ph47O3tNaohR40aJbw/BLfMrUiRCwQC\nwd2ICAIJBAKB4J5ilr8z9pbG7I5IJyFXs8Jn6EBrnh7vppI4604pv6GpJcOXrFX97jppkdZS/psZ\nOXKkanHcFcry88TERFasWKHx0t7Nza1Dg++OznUzN8/V1M4ZUzvnLs+nba5eXl58/PHHWttrK+Vf\nv369xjZfX1+tbeOyy9gSlo69dDz2Uk2t6IxT3yPRKcZigJeab9HKlStVRt2LFy/mkUceAdqkfxIS\nEmhpaUFHR6fL+f7ehIeHs3XrVlxdXVmwYAH6+vp4enr+0cMSCAR/IHHZZXwfnq5RqVpfU0leXgUe\n17oOIEybNo34+HjCwsJYvnw50DspOECrl48yiNS+skcpz6SUZLsZKysr0nPyCfr2fIdzc/cz0DhO\nLpcDEB8fT3x8fIfjrK2tBdqCQMr+tOEz2AnzPpr9aKO5sR4Af/cBDLLT9FCytramtLRUaxBIG9qu\nm0BwN9LR91BLczO5RTWcjLjAypUrUSgUZGZm8thjjzF06FCgLSjk5OSkMqFXbs/IyFD7/WaGDh1K\nSkoKWVlZGkEgd3f3bo+9fUBa27Ofr6+vCAIJbolbkSIXCASCuxERBBIIBALBPYe/iw3+LjYamYvD\nBtloPHj/EaX8f1RVxL0iW/B9eHqnlVwGJhYA1BTnYNHfQ+Vb1JEnkbm5OdDms2Rvb39HxnwrKOXq\n1q5d2+2qrnsNa2trtmzZopJMFAgEHXMs7kqnFa3VtQ0cirnC9Pg8Zg4b0OF5xowZg7GxMb/++itL\nly5FLpcTExODi4sLLi4ud2Tsyv/xyspKnJ01kwwSM/JILaigTwcvzaprGzgce4UZN81Ned7nn3+e\n+fPnd3scFRUVWvdXVlbi1NeEEonW3Wro6hsikcBsX+3Z2+XlbXPpKOgjENyLdPY9pKOrS7OpPaci\nE9kXkYyTQQ0tLS34+fkxYMAArK2tkclkzJkzB5lMhkQiUfkBKQO0HT3vKLdr8/vpKLisje4EpAWC\nW6U3UuQCgUBwtyKCQAKBQCC4ZxlkZ9ZlttWDVMp/L8y1Ox5Ntu4jKc+KJzsiBEtnLwpizWhMPEBW\nWrJWTyI/Pz/OnDnDunXrGDFiBAYGBtjZ2XUpyfd7oXyBeL8GgAD09PTo37//Hz0MgeCuJy67rEtJ\nUwBaUVVCdoSBgQEPPfQQoaGhKlm45ubmHlcB9QRXV1fOnz9PSkqKRqZ/WPQl4tJyezQ3ZdWu0jA7\nOTm520Ggfv36UVxcTElJiYYkXEpKChbGBnh5OJArodMxGVv3w8FAgaS6CFCvTCgqKqKsrAx7e3sR\nBBLcN3Tne8i0nwvVRVn889tDzHDVx8DAAC8vL6CtmicmJobGxkaSk5NxdnbGwqItgaerAK3ymUhb\n0sjNHkGdofx/rKys1Lq/o/4Fgp7QGylygUAguFsRQSCBQHDf8HtUX8yfPx+pVKpV+kpwd/IglfLf\nC3Nt71vUEX2s7BkybRlFsl+pLkintbWFXFOfDj2JZsyYQUlJCeHh4ezdu5fm5makUukfHgTavXs3\nwcHBqt/bv9hUyuTJZDL27dvH5cuXqaurw87OjrFjx/L444/f8gvHwsJC9uzZg0wmo7q6GnNzc/z8\n/Fi0aBGOjo6qdseOHePLL7/k5ZdfZubMmartJ0+eZNOmTRgYGLBnzx41D48333yT7Oxs9uzZg4GB\nQYffvxs3biQsLIzt27cTGxvLoUOHKCwsxNjYmNGjR/PnP/9Z6zxjY2PZs2cPWVlZ6Ovr4+Pjw/Ll\nywkJCVGdT5sPyIPImTNnOHToENnZ2TQ1NeHg4MDEiRNZuHCh6m8WFBREZmYmwcHBGBkZqY5V+kxN\nnz5dzaMsLy+Pl156icmTJ/PGG28ANz7P69ato7q6mr1795Kbm4uBgQH+/v6sWLGCvn373tJcIiMj\nOXDgAHl5ecjlcszNzXF0dGT8+PHMmTNH1U4ul7Nv3z4uXLhASUkJenp6DBkyhMcffxx/f3+1cyoU\nCo4fP05MTAwFBQVUVVVhbGyMp6cnTzzxxO8uzdhVJWR7Wlthd0Q6Tp20mTZtGqGhoZw6dYq8vDx0\ndXU1fDhuJxMnTmTPnj0cPHiQadOmqfxvWltb+ceGL2ntpgSacm7KF2Zubm74+Phw7tw5Tpw4wfTp\n0zWOycnJwcrKSvWyecqUKezevZtvv/2WoKAg1QvksrIy9u/fD4BXfyv+8vCoTiVknx+9jB1frGfP\nnj0EBgaqzt/S0sL27dtpbW1VeeYJBPcD3fkeMuvXVk0oL8rmcE4pc0Z5YmDQJrHo5+fH6dOnOXLk\nCHV1daoqIIDBgwcDbZ6F2lBuV7brLUrvoJSUFK1ywB31LxD0lJ5KkQsEAsHdiggCCQQCgeC+5/cs\n5W//4j8sLEzlzwBtXj7tX1xnZWXx3XffcenSJRobG3F3d2fp0qWqTMv2NDc3c/z4cU6dOsWVK1do\nbm6mf//+TJ8+nblz56peft3tsgXd8WgCMLUdgNu0parfn5jkzujRbWO92WdIR0eHpUuXsnTpUu4m\nfH19gbbPQUlJCYsXL1bbf+zYMb766isMDQ156KGHsLS0JDExkZCQECIjI/nss896HQhKT0/n3Xff\npba2lsDAQJydncnPz+f06dNERkby0UcfqYzglS9vZDKZWhBIJpMB0NDQQGpqqmo+CoWCjIwMfHx8\nVC+EuuL//b//R2xsLIGBgfj7+5OQkMDx48cpKirS8J4KDw9nw4YN6OvrM378eKysrEhNTSUoKOiO\nSVzdq+zcuZOffvoJc3NzJk6ciJGRETExMezcuZPY2Fg+/PBD9PT08PPzIy0tjeTkZAICAgCor68n\nNTUVuPG3VqL8vf2LPSVHjhwhMjJSZbx9+fJlIiIiyM7O5osvvlALFvYEZTDSysqKwMBAzM3Nqays\nJCcnh5MnT6qCQCUlJaxevZqSkhJ8fHwICAigrq6O6Oho1q5dy6pVq9Q+x/n5+Xz33Xf4+PgwcuRI\nTE1NKSkpISoqipiYGN577z3VNbnTdKcS8mYScsvpQ12H+728vHBwcODs2bM0NTWpBTHuBA4ODjzz\nzDPs3LmTV155hfHjx7N161Za0KFc1wZjq37UVhZ361wJueXklMhViQhBQUGsWbOGL774goMHD+Lh\n4YGJiQllZWXk5OSQm5vLhg0bVPN77LHHuHDhAuHh4eTn5zN8+HAUCgVnzpzBx8eHCxcuIJFI1CRk\nPyy/QFy1KcsnuzNpuKeqb3nB/2fvzuOiLNfHj3+GYd9BZBFEFklEcNxJcc0108odKZdzXPqZHjPL\nc77a1+M53067qZVKy7Gy1PS45EEzTVHUhEBRBgZEUBYVUUAWYZB9fn/QPDLOoLhgoPf79eqVPPsz\nPMzAfd3XdU1g586dzJ8/n5CQEOlnKTs7m4CAAMaPH988L6ggPGJNfR+ydHDD2NScksvnKKhQ4zbl\neWmdNgtw+/btOl9D/XuSu7s7KSkpnDhxgpCQEGndiRMnSE5Oxt3dnS5dujzQfTg5OdGtWzcSEhLY\nu3cvzz9/6/piY2NFPyDhobqXUuSCIAgtlQgCCYIgCI+9R5nKHxQUhFqtJiIiAm9vb55++mlpnbe3\nt1TD/Pz58+zcuRN/f39GjBhBfn4+J06c4H//93/59NNPcXe/Nfe7pqaGt99+m9OnT+Pu7s6gQYMw\nNTUlMTGRL774grS0NGm2fksvW9Ba+hY9DEFBQQQFBZGUlEReXh5hYWHSury8PL744gvMzc1ZtWqV\nTim18PBw9u3bxzfffMOCBQvu+bwajYZVq1ZRXl7OG2+8oZMVcPz4cT788EM+/vhjwsPDkclkuLm5\n0bZtWxITE9FoNFJAMTExka5du5KUlIRSqZSCQCqVirq6ukabPhuSmprK2rVradu2LVAf1HzrrbdI\nTEwkLS1NagZ98+ZN1q9fj1wuZ+XKlTpBn40bN7Jjx457fj0eV6mpqWzfvh0nJydWrVol9T+YMWMG\n77zzDidPnmTXrl1MnjwZhULBf/7zH5RKpRTwSE5OpqamRhpEy83Nxc3NDbhzECg+Pp5Vq1bh5eUl\nLfvoo484duwYsbGx9O/f/77uZ//+/RgbG/PZZ5/pBTFu3Lgh/Xv16tXk5+ezZMkSBg4cKC1Xq9Us\nXbqUL7/8kuDgYKlPhIeHBxs3bpR6h2kVFBTwxhtv8O9///uRBYGakglpSE6hfu+MhoYOHcqmTZuk\nfze3SZMm4eTkxO7duzl06BD5+fm0cevAU8P/zPnDm5CbmDX5WAlZBdIAmpOTE2vWrGHPnj1ER0cT\nFRVFXV0d9vb2eHp6MmbMGDp06CDta2pqyrvvvsvmzZs5ceIEu3fvxsXFhUmTJklBoIYlp7ycbQjq\n0Ia881aM7tEB5wYDdzNnzsTHx4e9e/dy+PBhamtrcXV1Zdq0abz44osYG7e+zyBBMKSp70MyIyOs\nnTtQfPlc/df2t35PcXZ2xs3NjdzcXIyMjAgMvFVGUSaT8frrr7N8+XI++OADnn76aTw8PMjJySEm\nJgYLCwtef/31eyr91ph58+bx5ptv8tVXX3HmzBm8vb3Jzc0lJiaGPn36EBcX98DnEISGmlKKXBAE\noaUSv80KgiAIT4RHlcofFBSEi4sLERER+Pj46Az8w63yFCdPnmTRokU6A3bamfARERHMmzdPWv6f\n//yH06dPM2bMGObMmSOVvKirq2Pt2rUcPHiQkJAQgoODH+m93o/W0LfoUYiKiqKmpoZx48bp9dKZ\nNm0aR44c4ciRI7zyyiv3nFmRmprK5cuX8ff31ysLNWDAAPbu3UtKSgrJycnSwE3Xrl2JjIwkOzsb\nLy8vLl26RGFhIVOmTOHmzZsolUpefvll4M4BgsZMnTpVCgAByOVyhg0bRnJysk4Q6LfffkOtVjNs\n2DC9rJ8pU6bw888/G2wm/SQ6ePAgUP+6NGyALZfLmTVrFqdOneKXX35h8uTJ+PvXl/FpmPGjVCqR\ny+W89NJLJCQkoFQqcXNzQ6PRkJSURLt27aRSXw2NHTtWJwAEMHLkSI4dO0ZaWto9BYEazqi9cLWE\nmhoNcrlcbzttACczMxOVSkVISIhOAAjq+0O89NJL/Otf/yI6OlrKHGosm87JyYmQkBD27NlDfn6+\nzvPZXJqSCWlmbU+Pl1foLBs6fjphA95udJ8pU6YwZcqUOx43KChIL4sSuGN526FDhzYaVBoyZIhU\ncnPs2LHI7NpRbmxCZVkRFvYuBvcxdG+3vyYWFhZMnjyZyZMn3/F+tKysrJg7dy5z587VWX7gwAEA\n2rdvr7N80aJFjZYMHjhwoN5z1ZgNGzY0ui4sLEzvs18QWoqmZmRDfV+g4svnkJuaY+es+7uKQqEg\nNzeXjh076r3PdurUidWrV7Nt2zYSEhKIi4uTMlZDQ0N1Jjo9iHbt2vHxxx/z7bffolQqSUpKwsvL\ni7feeosbN26IIJAgCIIgNCCCQIIgPPYqKyuJiIjg+PHjXLlyBZlMRocOHXj++ecN/rFfU1Mj9Z0o\nKCjA0dGRwYMHExoa+gdcvfAwtaRU/s6dO+sNrg0bNozPP/+ctLQ0aZlGo2Hv3r04ODgwe/ZsnZrn\nRkZGzJo1i0OHDhEVFSUFgaBl3WtDraFv0YO6/TUvUVfpbXPhwgUAg9k01tbW+Pr6olKpuHz58j2X\nQDt//nyjx9YuT0lJISMjQwoCKRQKIiMjUSqVeHl56QR68vLy2L17Nzdv3sTCwgKlUom5ubkUuGmK\njh076i3TBhjKysqkZRkZGQAEBATobW9ubo6Pj88TXee/4bP1y4nTlFfWGAzGubu74+TkxLVr11Cr\n1VhZWeHv709SUhKlpaXY2NiQmJiIn58f/v7+2Nvbo1QqGTVqFOfPn0etVjNgwACD16AtI9iQNoDS\n8Ht5J2cyC9h8LF3nfSDPyJ3LackEj5zEhLEjGD24L507d9bJCtKWr1Or1WzZskXvuCUlJUB9T6OG\nzp49S0REBKmpqRQXF1NTozsIev369UcSBHpcMiFLSkqwsrLSyY4xlkHO6YPU1VRj377pfZYe9N4K\nCwtxdHTUWZafn8/WrVuRy+X06dPngY4vCI+be/mZc/YPxtm//ndLawvd8q/z589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xIhUV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rHbp3QkNDiYyM5OeffyYkJAQTExMi4q/xyf4EsqL2qK17O9aGfxZOnL6IvedDOPo8LIwVJJ+g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+fj5eWlsbF3ZmYmycnJjBw5UkUAgoYgy9y5c/nwww8FixRoCNDExcUREhLCggULhO3p6elk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uyiZvRwd0dHQUQaCKigquX7/OtGnTFDMToqOjcXBwICYmBlA/S0HH1I6D4TeorJZRU3GL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uwoUIg6Ojo6jB8/nn379hEZGamSYRYUFATApEmT1LZvb5ZrSUkJx44do0ePHioCkGLfS5cuJTY2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\nge4D4/s5YWtm2K7JuJezBUHDu4vvkYeMtuPKUE8bK4duQKvw01YE6tatG35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1a6xYsULK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U1khG+h+mYehuadGRIwnJ62huzYseOOQdT2Amlubm5qLUGU95m9vb1KKxNoreBpkuvTUNOa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\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UFxfj6+vLyJEjKSoq4vz588TFxbFq1Sq5h1jfvn3R1tbm6tWrCkGgq1evKvy/7Bo0NDSQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5f8c12e358>" ] }, "metadata": { "image/png": { "height": 793, "width": 832 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(14, 14))\n", "for idx in range(viz_words):\n", " plt.scatter(*embed_tsne[idx, :], color='steelblue')\n", " plt.annotate(int_to_vocab[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]), alpha=0.7)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
yw-fang/readingnotes	machine-learning/Hitchhiker-guide-2016/ch05.ipynb	1	10382	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading Great Code\n", "\n", "## Commom Features of great code\n", "\n", "每个定义的函数包含的代码大多不超过20行；含有很多空行；对于交互型代码（例如Requests和Flask等），都有大量的doctoring或者comment，综合下来五分之一的代码内容是可以作为文档使用的。不过像HowDoI这种直接的不是用语交互的代码，并没有必要去包含大量的comment。\n", "\n", "下面，我们就跟着学习下如何读不同风格的代码。\n", "\n", "## HowDoI\n", "\n", "HowDoI，code代码总数只有300行左右，可以作为阅读代码的首选。\n", "\n", "### Reading a single-file script\n", "\n", "scirpt通常有一个清晰的starting point和一个清晰的ending point，以及其他定义清晰的操作。这使得sciprt会比一般的提供API的库\n", "更容易follow。\n", "关于howdoi，可以Google下，然后从github下载到。\n", "\n", "安装： pip install --editable .　\n", "\n", "unit test： python test_howdoi.py\n", "\n", "#### Read howdoi's documentation\n", "\n", "HowDoI的文档是README.rst文件，从中可以看出 HowDoI是一个很小的命令行应用，它可以允许使用者从互联网上获取关于编程问题的答案。\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/ywfang/miniconda3/envs/plotenv/bin/howdoi\n" ] } ], "source": [ "!which howdoi" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: howdoi [-h] [-p POS] [-a] [-l] [-c] [-n NUM_ANSWERS] [-C] [-v]\n", " [QUERY [QUERY ...]]\n", "\n", "instant coding answers via the command line\n", "\n", "positional arguments:\n", " QUERY the question to answer\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " -p POS, --pos POS select answer in specified position (default: 1)\n", " -a, --all display the full text of the answer\n", " -l, --link display only the answer link\n", " -c, --color enable colorized output\n", " -n NUM_ANSWERS, --num-answers NUM_ANSWERS\n", " number of answers to return\n", " -C, --clear-cache clear the cache\n", " -v, --version displays the current version of howdoi\n" ] } ], "source": [ "!howdoi --help #注意我这里是在jupyter notebook里面直接使用的，所以需要加感叹号。如果是在terminal上，不需要加叹号。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过帮助文档，我们可以了解到HowDoI大概的工作模式以及它的一些功能，例如可以colorize the output，get multiple answers，\n", "keep answers in a cache that can be clared等。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use HowDoI\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "★ Answer from https://stackoverflow.com/questions/6076270/python-lambda-function-in-list-comprehensions ★\n", "[(lambda x: xx)(x) for x in range(10)]\n", "\n", "================================================================================\n", "\n", "★ Answer from https://stackoverflow.com/questions/28268439/python-list-comprehension-with-lambdas ★\n", "In [177]: bases = [lambda x, i=i: xi for i in range(3)]\n", "\n", "In [178]: print([b(5) for b in bases])\n", "[1, 5, 25]\n", "\n", "================================================================================\n", "\n", "★ Answer from https://stackoverflow.com/questions/452610/how-do-i-create-a-list-of-python-lambdas-in-a-list-comprehension-for-loop ★\n", "def square(x): return lambda : xx\n", "listOfLambdas = [square(i) for i in [1,2,3,4,5]]\n", "for f in listOfLambdas: print f()\n" ] } ], "source": [ "!howdoi --num-answers 3 python lambda function list comprehension" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "★ Answer from https://stackoverflow.com/questions/568962/how-do-i-create-an-empty-array-matrix-in-numpy ★\n", ">>> import numpy\n", ">>> a = numpy.zeros(shape=(5,2))\n", ">>> a\n", "array([[ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.]])\n", ">>> a[0] = [1,2]\n", ">>> a[1] = [2,3]\n", ">>> a\n", "array([[ 1., 2.],\n", " [ 2., 3.],\n", " [ 0., 0.],\n", " [ 0., 0.],\n", " [ 0., 0.]])\n", "\n", "================================================================================\n", "\n", "★ Answer from https://stackoverflow.com/questions/4535374/initialize-a-numpy-array ★\n", "numpy.zeros\n", "\n", "================================================================================\n", "\n", "★ Answer from https://stackoverflow.com/questions/21088133/how-to-construct-a-ndarray-from-a-numpy-array-python ★\n", ">>> x = np.array([1, 2, 3])\n", ">>> type(x)\n", "<type 'numpy.ndarray'>\n" ] } ], "source": [ "!howdoi --num-answer 3 python numpy array create" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read HowDoI's code\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在howdoi的目录中，除了__pycache__之外其实只有两个文件，即__init__.py 和 howdoi.py。\n", "前者只有一行，包含了版本信息；而后者则是我们即将精读的代码。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__init__.py \u001b[34m__pycache__\u001b[m\u001b[m \u001b[31mhowdoi.py\u001b[m\u001b[m\n" ] } ], "source": [ "!ls /Users/ywfang/FANG/git/howdoi_ywfang/howdoi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过浏览howdoi.py，我们发现这里面定义了很多新的函数，而且每个函数都会在之后的函数中被引用，这是的我们可以方便follow。\n", "其中的main function，即 command_line_runner()接近于 howdoi.py的底部" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XDG_CACHE_DIR = os.environ.get('XDG_CACHE_HOME',\n", " os.path.join(os.path.expanduser('~'), '.cache'))\n", "CACHE_DIR = os.path.join(XDG_CACHE_DIR, 'howdoi')\n", "CACHE_FILE = os.path.join(CACHE_DIR, 'cache{0}'.format(\n", " sys.version_info[0] if sys.version_info[0] == 3 else ''))\n", "howdoi_session = requests.session()\n", "\n", "\n", "def get_proxies():\n", " proxies = getproxies()\n", " filtered_proxies = {}\n", " for key, value in proxies.items():\n", " if key.startswith('http'):\n", " if not value.startswith('http'):\n", " filtered_proxies[key] = 'http://%s' % value\n", " else:\n", " filtered_proxies[key] = value\n", " return filtered_proxies\n", "\n", "\n", "def _get_result(url):\n", " try:\n", " return howdoi_session.get(url, headers={'User-Agent': random.choice(USER_AGENTS)}, proxies=get_proxies(),\n", " verify=VERIFY_SSL_CERTIFICATE).text\n", " except requests.exceptions.SSLError as e:\n", " print('[ERROR] Encountered an SSL Error. Try using HTTP instead of '\n", " 'HTTPS by setting the environment variable \"HOWDOI_DISABLE_SSL\".\\n')\n", " raise e\n", "\n", "\n", "def _add_links_to_text(element):\n", " hyperlinks = element.find('a')\n", "\n", " for hyperlink in hyperlinks:\n", " pquery_object = pq(hyperlink)\n", " href = hyperlink.attrib['href']\n", " copy = pquery_object.text()\n", " if (copy == href):\n", " replacement = copy\n", " else:\n", " replacement = \"[{0}]({1})\".format(copy, href)\n", " pquery_object.replace_with(replacement)\n", "\n", "def get_text(element):\n", " ''' return inner text in pyquery element '''\n", " _add_links_to_text(element)\n", " return element.text(squash_space=False)\n", "\n", "\n", "def _extract_links_from_bing(html):\n", " html.remove_namespaces()\n" ] } ], "source": [ "!sed -n '70,120p' /Users/ywfang/FANG/git/howdoi_ywfang/howdoi/howdoi.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
BrownDwarf/Starfish	notebooks/Stellar Interpolation.ipynb	2	12679	{ "metadata": { "name": "", "signature": "sha256:a87de098e371d1c15f08703571d621107a21c4b788ae6e3d6daa6360bea44df2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#A suite of tests to explore interpolation amonst the stellar parameters" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.interpolate import InterpolatedUnivariateSpline as IUS\n", "from StellarSpectra.grid_tools import HDF5Interface\n", "myInterface = HDF5Interface(\"../libraries/PHOENIX_submaster.hdf5\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def load_pixel(index, temp, logg, Z):\n", " '''\n", " Return the flux value at a specific pixel in a spectrum, defined by params\n", " '''\n", " params = {\"temp\":temp, \"logg\":logg, \"Z\":Z, \"alpha\":0.0}\n", " flux = myInterface.load_flux(params)\n", " return flux[index]\n", "\n", "def get_wl(index):\n", " return myInterface.wl[index]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Determine truncation indices for order of interest\n", "wl = myInterface.wl\n", "inds = np.argwhere((wl > 5120) & (wl < 5220))[np.array([0, -1])]\n", "inds[0], inds[1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "(array([345476]), array([355478]))" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "flux = myInterface.load_flux({\"temp\":6000, \"logg\":4.0, \"Z\":0.0, \"alpha\":0.0})\n", "print(len(flux))\n", "#plt.plot(flux[inds[0]:inds[1]])\n", "#plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "983561\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "temps = myInterface.points[\"temp\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#Pixel indices for lines\n", "cont = 5018\n", "mg_bot = 5268\n", "mg_side = 5242\n", "fe_bot = 5053\n", "fe_side = 5050" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "flux_cont = [load_pixel(cont, temp, 4.0, 0.0) for temp in temps]\n", "flux_mg_bot = [load_pixel(mg_bot, temp, 4.0, 0.0) for temp in temps]\n", "flux_mg_side = [load_pixel(mg_side, temp, 4.0, 0.0) for temp in temps]\n", "flux_fe_bot = [load_pixel(fe_bot, temp, 4.0, 0.0) for temp in temps]\n", "flux_fe_side = [load_pixel(fe_side, temp, 4.0, 0.0) for temp in temps]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(6,6))\n", "ax = fig.add_subplot(111)\n", "ax.plot(temps, flux_cont, label=\"continuum\")\n", "ax.plot(temps, flux_cont, \"ko\")\n", "ax.plot(temps, flux_mg_bot, label=\"Mg bottom\")\n", "ax.plot(temps, flux_mg_bot, \"ko\")\n", "ax.plot(temps, flux_mg_side, label=\"Mg side\")\n", "ax.plot(temps, flux_mg_side, \"ko\")\n", "ax.plot(temps, flux_fe_bot, label=\"Fe bottom\")\n", "ax.plot(temps, flux_fe_bot, \"ko\")\n", "ax.plot(temps, flux_fe_side, label=\"Fe side\")\n", "ax.plot(temps, flux_fe_side, \"ko\")\n", "\n", "#try fitting a spline to fe_side\n", "myspline = IUS(temps, flux_fe_side)\n", "fine_temps = np.linspace(5000, 7000, num=300)\n", "fine_fe_side = myspline(fine_temps)\n", "ax.plot(fine_temps, fine_fe_side, \"k\", lw=0.5)\n", "\n", "\n", "ax.set_xlabel(r\"Temperature ($K$)\")\n", "ax.set_ylabel(r\"$\\propto f_\\lambda$\")\n", "ax.legend(loc=\"upper left\")\n", "fig.savefig(\"../plots/interpolation_temp.png\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# log g tests" ] }, { "cell_type": "code", "collapsed": false, "input": [ "loggs = np.arange(3.5, 5.6, 0.5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "flux_cont = [load_pixel(cont, 6000, logg, 0.0) for logg in loggs]\n", "flux_mg_bot = [load_pixel(mg_bot, 6000, logg, 0.0) for logg in loggs]\n", "flux_mg_side = [load_pixel(mg_side, 6000, logg, 0.0) for logg in loggs]\n", "flux_fe_bot = [load_pixel(fe_bot, 6000, logg, 0.0) for logg in loggs]\n", "flux_fe_side = [load_pixel(fe_side, 6000, logg, 0.0) for logg in loggs]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(6,6))\n", "ax = fig.add_subplot(111)\n", "ax.plot(loggs, flux_cont, label=\"continuum\")\n", "ax.plot(loggs, flux_cont, \"ko\")\n", "ax.plot(loggs, flux_mg_bot, label=\"Mg bottom\")\n", "ax.plot(loggs, flux_mg_bot, \"ko\")\n", "ax.plot(loggs, flux_mg_side, label=\"Mg side\")\n", "ax.plot(loggs, flux_mg_side, \"ko\")\n", "ax.plot(loggs, flux_fe_bot, label=\"Fe bottom\")\n", "ax.plot(loggs, flux_fe_bot, \"ko\")\n", "ax.plot(loggs, flux_fe_side, label=\"Fe side\")\n", "ax.plot(loggs, flux_fe_side, \"ko\")\n", "\n", "ax.set_xlabel(r\"Temperature ($K$)\")\n", "ax.set_ylabel(r\"$\\propto f_\\lambda$\")\n", "ax.legend(loc=\"lower right\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Metallicity tests" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Zs = myInterface.points[\"Z\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "flux_cont = [load_pixel(cont, 6000, 4.0, Z) for Z in Zs]\n", "flux_mg_bot = [load_pixel(mg_bot, 6000, 4.0, Z) for Z in Zs]\n", "flux_mg_side = [load_pixel(mg_side, 6000, 4.0, Z) for Z in Zs]\n", "flux_fe_bot = [load_pixel(fe_bot, 6000, 4.0, Z) for Z in Zs]\n", "flux_fe_side = [load_pixel(fe_side, 6000, 4.0, Z) for Z in Zs]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(6,6))\n", "ax = fig.add_subplot(111)\n", "ax.plot(Zs, flux_cont, label=\"continuum\")\n", "ax.plot(Zs, flux_cont, \"ko\")\n", "ax.plot(Zs, flux_mg_bot, label=\"Mg bottom\")\n", "ax.plot(Zs, flux_mg_bot, \"ko\")\n", "ax.plot(Zs, flux_mg_side, label=\"Mg side\")\n", "ax.plot(Zs, flux_mg_side, \"ko\")\n", "ax.plot(Zs, flux_fe_bot, label=\"Fe bottom\")\n", "ax.plot(Zs, flux_fe_bot, \"ko\")\n", "ax.plot(Zs, flux_fe_side, label=\"Fe side\")\n", "ax.plot(Zs, flux_fe_side, \"ko\")\n", "\n", "ax.set_xlabel(r\"Metallicity\")\n", "ax.set_ylabel(r\"$\\propto f_\\lambda$\")\n", "ax.legend(loc=\"lower right\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "flux_fe_bot" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "[6378120.0, 5492418.5, 3951947.0]" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spline interpolation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.interpolate import RectBivariateSpline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.interpolate import InterpolatedUnivariateSpline as IUS" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "temps = myInterface.points[\"temp\"]\n", "print(temps)\n", "loggs = myInterface.points[\"logg\"]\n", "print(loggs)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100 6200 6300 6400\n", " 6500 6600 6700 6800 6900 7000]\n", "[ 3.5 4. 4.5 5. 5.5]\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "#This is the part that actually takes a while, loading all of the spectra. \n", "#If we could do it on a computer with a lot of memory, it might not\n", "#be so bad\n", "values = np.empty((temps.size, loggs.size))\n", "for i,temp in enumerate(temps):\n", " for j, logg in enumerate(loggs):\n", " values[i,j] = load_pixel(0, temp, logg, Z=0.0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "def interpolate_pixel(index, mytemp, mylogg):\n", " \n", " #Create a 2D array of shape (temps.size, loggs.size) with the value of the flux at the pixel\n", " #values = np.empty((temps.size, loggs.size))\n", " \n", " #for i,temp in enumerate(temps):\n", " # for j, logg in enumerate(loggs):\n", " # values[i,j] = load_pixel(index, temp, logg, Z=0.0)\n", " \n", " #create a bivariate spline over this grid\n", " spl = RectBivariateSpline(temps, loggs, values)\n", " \n", " return spl(mytemp, mylogg)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "timeit value = interpolate_pixel(0, 6010, 3.8)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1000 loops, best of 3: 787 \u00b5s per loop\n" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "print(value)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 6129205.16129755]]\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.imshow(values,origin=\"upper\",interpolation=\"none\")\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
JorgeGH97/test-git-Jorge	Clase4_OsciladorArmonico.ipynb	1	2717267	null	mit
suvarchal/JyIDV	examples/CreateFunctionFormulas.ipynb	1	3562	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Write a Jython function for IDV and export as IDV Formula in GUI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### \n", "### Define a function to calculate Moist Static Energy from Temperature, Specific Humidity and Geopotential Height." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def moistStaticEnergy(T,Q,GZ):\n", " \"\"\" Calculates Moist Static Energy with Temperature, Specific Humidity and Geopotential Height. \"\"\"\n", " from ucar.visad.quantities import SpecificHeatCapacityOfDryAirAtConstantPressure,LatentHeatOfEvaporation\n", " cp=SpecificHeatCapacityOfDryAirAtConstantPressure.newReal()\n", " L=LatentHeatOfEvaporation.newReal()\n", " return cpT+LQ+Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Above function was created for use in this session, it will not be available for IDV in next session so let us save it to the IDV Jython library." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "saveJython(moistStaticEnergy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a IDV formula, once created it will be in the list of formulas in IDV. The arguments to saveFormula are (formulaid,description,functionastring,formula categories). formula categories can be a list of categories or just a single category specified by a string. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "saveFormula(\"Moist Static Energy\",\"Moist Static Energy from T, Q, GZ\",\"moistStaticEnergy(T,Q,GZ)\",[\"Grids\",\"Grids-MapesCollection\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check if the formula was created in IDV GUI . At anytime to show a IDV window from notebook use function `showIdv()`. Currently some displays cannot be made when using GUI from notebook, will be implemented in future. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3D [dev] 1.6.0-pre9-daily-experimental daily\n", "\n", "libEGL warning: failed to create a pipe screen for i965\n" ] } ], "source": [ "showIdv()" ] } ], "metadata": { "kernelspec": { "display_name": "IDV", "language": "python", "name": "jyidv_kernel" }, "language_info": { "codemirror_mode": { "name": "text/x-python", "version": 2 }, "file_extension": ".py", "help_links": [ { "text": "Jython", "url": "www.jython.org" }, { "text": "Jython Kernel Help", "url": "https://github.com/suvarchal/IJython" } ], "mimetype": "text/x-python", "name": "jython", "pygments_lexer": "python" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
rezpe/JupyterViz	LineChart.ipynb	1	4561	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "exp = pd.read_csv(\"data/tweetdata.csv\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>day</th>\n", " <th>value1</th>\n", " <th>value2</th>\n", " <th>value3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>11</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>8</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>14</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>10</td>\n", " <td>29</td>\n", " <td>16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " day value1 value2 value3\n", "0 1 1 2 5\n", "1 2 6 11 3\n", "2 3 3 8 1\n", "3 4 5 14 6\n", "4 5 10 29 16" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.head()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exp = pd.concat([exp,pd.DataFrame([[16,2,3,4]],columns=exp.columns)])" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import IFrame" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"900\"\n", " height=\"900\"\n", " src=\"tmp/tmpbox.htm\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x7f37f38caad0>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(\"Itemplates/Ilinechart.html\") as input:\n", " template = input.read()\n", "template=template.replace(\"{data}\",exp.to_json(orient=\"records\"))\n", "template=template.replace(\"{width}\",str(800))\n", "template=template.replace(\"{height}\",str(800))\n", "with open(\"tmp/tmpbox.htm\",\"w\") as output:\n", " output.write(template)\n", "IFrame(\"tmp/tmpbox.htm\",900,900)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
pinga-lab/magnetic-ellipsoid	code/warrego.ipynb	1	179166	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Impact of neglecting the self-demagnetization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import the required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib.ticker import MaxNLocator\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import BoundaryNorm\n", "from matplotlib.cm import get_cmap\n", "from fatiando import utils, gridder\n", "import mesher, triaxial_ellipsoid\n", "from plot_functions import savefig" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set some plot parameters\n", "from matplotlib import rcParams\n", "rcParams['figure.dpi'] = 300.\n", "rcParams['font.size'] = 6\n", "rcParams['xtick.labelsize'] = 'medium'\n", "rcParams['ytick.labelsize'] = 'medium'\n", "rcParams['axes.labelsize'] = 'large'\n", "rcParams['legend.fontsize'] = 'medium'\n", "rcParams['savefig.dpi'] = 300." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Warrego magnetite body" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# semi-axes (in m)\n", "a = 490.7\n", "b = 69.7\n", "c = 30.\n", "\n", "# orientation angles (in degrees)\n", "strike = -34.\n", "dip = 66.1\n", "rake = 45." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# isotropic susceptibility (in SI)\n", "chi_true = 1.690" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "warrego = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n", " props={'principal susceptibilities': [chi_true, chi_true, chi_true],\n", " 'susceptibility angles': [strike, dip, rake]})" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# demagnetizing factors\n", "n11, n22, n33 = triaxial_ellipsoid.demag_factors(warrego)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local-geomagnetic field (nT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Main coordinate system" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "F_xyz = np.array([32610., 0., 39450.])\n", "F, inc, dec = utils.vec2ang(F_xyz)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "51183.1476172 50.4223208627 0.0\n" ] } ], "source": [ "print F, inc, dec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Local coordinate system" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "F_local = np.dot(warrego.transf_matrix.T, F_xyz)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 49844.03074679 -11610.88695533 688.8417986 ]\n" ] } ], "source": [ "print F_local" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation points (m)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "area = [-2000., 2000., -2000., 2000.]\n", "shape = (100, 100)\n", "xp, yp, zp = gridder.regular(area, shape, z = 0.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Total-field anomaly (nT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\chi = \\chi_{true}$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epsilon = 8.403 percent\n" ] } ], "source": [ "# true magnetization in the main system\n", "mag_true = triaxial_ellipsoid.magnetization(warrego, F, inc, dec, demag=True)\n", "\n", "# approximated magnetization\n", "mag_true_approx = triaxial_ellipsoid.magnetization(warrego, F, inc, dec, demag=False)\n", "\n", "# relative error\n", "mag_true_norm = np.linalg.norm(mag_true, ord = 2)\n", "mag_true_approx_norm = np.linalg.norm(mag_true_approx, ord = 2)\n", "delta_mag_true_norm = np.linalg.norm(mag_true - mag_true_approx, ord = 2)\n", "epsilon_true = delta_mag_true_norm/mag_true_norm\n", "\n", "print 'epsilon = %.3f percent' % (epsilon_true100)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 44.36562777 -3.34636713 48.66805892] 65.9400262282\n" ] } ], "source": [ "print mag_true, mag_true_norm" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.38558608e+01 -2.88105245e-15 5.30546982e+01] 68.834130496\n" ] } ], "source": [ "print mag_true_approx, mag_true_approx_norm" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_true = triaxial_ellipsoid.tf(xp, yp, zp, [warrego], F, inc, dec)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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2b97cV9mZmZn5d5I0dtMWgJrba4+bVK5+zxdmZzv9P7c0ffymKs/GpudLO+zX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\n/E7Z3ws4Zn5rJ0lSdzuOuwKSpOHKzFsi4h+A/wkcHxG/UzY538n/ori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"text/plain": [ "<matplotlib.figure.Figure at 0xade0be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.15, 7./3))\n", "plt.axis('scaled')\n", "\n", "ranges = np.max(np.abs([np.min(tf_true), np.max(tf_true)]))\n", "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n", "cmap = plt.get_cmap('RdBu_r')\n", "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n", "\n", "plt.contourf(0.001yp.reshape(shape), 0.001xp.reshape(shape),\n", " tf_true.reshape(shape), levels=levels,\n", " cmap = cmap, norm=norm)\n", "plt.ylabel('x (km)')\n", "plt.xlabel('y (km)')\n", "plt.xlim(0.001np.min(yp), 0.001np.max(yp))\n", "plt.ylim(0.001np.min(xp), 0.001np.max(xp))\n", "cbar = plt.colorbar()\n", "\n", "plt.tight_layout()\n", "savefig('f05.pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max = 482.486\n", "min = -70.649\n", "peak-to-peak amplitude = 553.135\n" ] } ], "source": [ "print 'max = %.3f' % (tf_true.max())\n", "print 'min = %.3f' % (tf_true.min())\n", "print 'peak-to-peak amplitude = %.3f' % (tf_true.max() - tf_true.min())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_true_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego], F, inc, dec,\n", " demag = False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "residuals_true = tf_true_approx - tf_true" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "residuals min = -3.388\n", "residuals mean = 0.243\n", "residuals max = 40.446\n" ] } ], "source": [ "print 'residuals min = %.3f' % (residuals_true.min())\n", "print 'residuals mean = %.3f' % (residuals_true.mean())\n", "print 'residuals max = %.3f' % (residuals_true.max())" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "misfit_true = np.sum(residuals_trueresiduals_true)/residuals_true.size" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "misfit = 10.371\n" ] } ], "source": [ "print 'misfit = %.3f' % misfit_true" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 43.834 absolute\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n", "(residuals_true.max() - residuals_true.min())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 7.925 percent\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f percent' % \\\n", "(100(residuals_true.max() - residuals_true.min())/(tf_true.max() - tf_true.min()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\chi = 0.1$" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# isotropic susceptibility commonly used as an upper limit\n", "# to neglect the self-demagnetization\n", "chi_usual = 0.1" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "warrego_usual = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n", " props={'principal susceptibilities': [chi_usual, chi_usual, chi_usual],\n", " 'susceptibility angles': [strike, dip, rake]})" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epsilon = 0.675 percent\n" ] } ], "source": [ "# true magnetization in the main system\n", "mag_usual = triaxial_ellipsoid.magnetization(warrego_usual, F, inc, dec,\n", " demag=True)\n", "\n", "# approximated magnetization\n", "mag_usual_approx = triaxial_ellipsoid.magnetization(warrego_usual, F, inc, dec,\n", " demag=False)\n", "\n", "# relative error\n", "mag_usual_norm = np.linalg.norm(mag_usual, ord = 2)\n", "mag_usual_approx_norm = np.linalg.norm(mag_usual_approx, ord = 2)\n", "delta_mag_usual_norm = np.linalg.norm(mag_usual - mag_usual_approx, ord = 2)\n", "epsilon_usual = delta_mag_usual_norm/mag_usual_norm\n", "\n", "print 'epsilon = %.3f percent' % (epsilon_usual100)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.59924155 -0.01822971 3.11927956] 4.06033175251\n" ] } ], "source": [ "print mag_usual, mag_usual_norm" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.59502135e+00 -2.17834865e-16 3.13933125e+00] 4.07302547314\n" ] } ], "source": [ "print mag_usual_approx, mag_usual_approx_norm" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_usual = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_usual], F, inc, dec)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_usual_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_usual], F, inc, dec,\n", " demag = False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "residuals_usual = tf_usual_approx - tf_usual" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "residuals min = -0.021\n", "residuals mean = 0.001\n", "residuals max = 0.192\n" ] } ], "source": [ "print 'residuals min = %.3f' % (residuals_usual.min())\n", "print 'residuals mean = %.3f' % (residuals_usual.mean())\n", "print 'residuals max = %.3f' % (residuals_usual.max())" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "misfit_usual = np.sum(residuals_usualresiduals_usual)/residuals_usual.size" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "misfit = 0.000\n" ] } ], "source": [ "print 'misfit = %.3f' % misfit_usual" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 0.213 absolute\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n", "(residuals_usual.max() - residuals_usual.min())" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 0.616 percent\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f percent' % \\\n", "(100(residuals_usual.max() - residuals_usual.min())/(tf_usual.max() - tf_usual.min()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $\\chi = \\chi_{max}$" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chi_max = 0.116 SI\n" ] } ], "source": [ "# proposed upper maximum isotropic susceptibility\n", "# to guarantee a relative error lower than or equal to epsilon\n", "#epsilon_max = 0.07 # this epsilon generates a misfit similar \n", " # to that obtained by using the usual chi = 0.1\n", "epsilon_max = 0.08\n", "chi_max = epsilon_max/n33\n", "\n", "print 'chi_max = %.3f SI' % chi_max" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "warrego_max = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n", " props={'principal susceptibilities': [chi_max, chi_max, chi_max],\n", " 'susceptibility angles': [strike, dip, rake]})" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epsilon = 0.781 percent\n" ] } ], "source": [ "# true magnetization in the main system\n", "mag_max = triaxial_ellipsoid.magnetization(warrego_max, F, inc, dec,\n", " demag=True)\n", "\n", "# approximated magnetization\n", "mag_max_approx = triaxial_ellipsoid.magnetization(warrego_max, F, inc, dec,\n", " demag=False)\n", "\n", "# relative error\n", "mag_max_norm = np.linalg.norm(mag_max, ord = 2)\n", "mag_max_approx_norm = np.linalg.norm(mag_max_approx, ord = 2)\n", "delta_mag_max_norm = np.linalg.norm(mag_max - mag_max_approx, ord = 2)\n", "\n", "epsilon_calculated = delta_mag_max_norm/mag_max_norm\n", "\n", "print 'epsilon = %.3f percent' % (epsilon_calculated100)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 3.01645036 -0.02440124 3.61542732] 4.70859670485\n" ] } ], "source": [ "print mag_max, mag_max_norm" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 3.01081106e+00 -2.17834865e-16 3.64233353e+00] 4.72562978042\n" ] } ], "source": [ "print mag_max_approx, mag_max_approx_norm" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_max = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_max], F, inc, dec)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf_max_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_max], F, inc, dec,\n", " demag = False)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "residuals_max = tf_max_approx - tf_max" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "residuals min = -0.028\n", "residuals mean = 0.002\n", "residuals max = 0.257\n" ] } ], "source": [ "print 'residuals min = %.3f' % (residuals_max.min())\n", "print 'residuals mean = %.3f' % (residuals_max.mean())\n", "print 'residuals max = %.3f' % (residuals_max.max())" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "misfit_max = np.sum(residuals_maxresiduals_max)/residuals_max.size" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "misfit = 0.000\n" ] } ], "source": [ "print 'misfit = %.3f' % misfit_max" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 0.285 absolute\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n", "(residuals_max.max() - residuals_max.min())" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "peak-to-peak field amplitude = 0.712 percent\n" ] } ], "source": [ "print 'peak-to-peak field amplitude = %.3f percent' % \\\n", "(100(residuals_max.max() - residuals_max.min())/(tf_max.max() - tf_max.min()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparison" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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bkoqIHSJidkTMiojZbPpn1TewrXj1DWt3dkRsLF7zR+j6AuB+ajeNujIiDi7a\nbRkRrwPOLY67ODN/25lPJ0mSJEntZbgtrxuAZcADxXJesf1tdduWAf82SvsccWPmCuAEYDmwP3B9\nRKwAVgEfB7YAFgJvbsunkCRJknpQMeD0ioj4YkTcGBGrI2JtRNwdEd+IiL9soI85EfHhiLglItZE\nxEMRcU1EvGoyPkOv8YZS5ZWMElBHOG6s9cc2yFwcEQcA7wCOB3an9sifJcClmXlJk7VKkiRJU839\nwLTifQKPAGuBucALgBdExFXASZm5ZnjjiHgqtUGlHYv2q6hdHngocGhEnAScmJnrOv1BeoUjtyWV\nmXtm5rQGXq8c1u6czNys2HfnGP0vy8y3Zua+mdmXmbMy83CDrSRJktSQacB1wOuAxxW/qbcD9gI+\nWxxzLPCp4Q0jYibwbWrB9ibgkMycCWwLvAFYBxwNfLTTH6KXGG4lSZIkqXnPzcxnZeanMvOOgY2Z\nuTQzX82mUPuyiJg3rO2ZwBygHzguMxcXbddl5ieAs4rjXhMRe3f0U/QQw60kSZIkNSkzfzzOIQOj\ntwk8bdi+04rlZZm5dIS2F1GbpjwNOLXlIqcYw60kSZIktd/aYhnU5a6I2IfaPW8ArhqpYWauBhYV\nq0d1qsBeY7iVJEmSpPZ7TrFM4Nd12w+s275kjPYD+/Zrb1m9y3ArSZIkSW0UEdsD7ypWF2XmbXW7\nd617f88Y3dxbLLeLiOntrK9X+SggSZIkSZXR/2hrT8ZZs259mysZWURsBnwB2AVYQ+3ux/Vm1L3v\nH6Or+n0zxjlWGG4lSZIkVch+F36p2yWM52PA8dSmHZ+RmWNNPVYbOS1ZkiRJktogIi4AzqAWbN+c\nmZ8b4bCVde/Hmm5cv2/lqEdpkCO3kiRJkrpq+73nscN2fQ0de9/F7xr/oBEsX7GaA8/815baNiIi\nPgS8hVqwPTMzRztZ/XW284CbRzlut2K5IjOdktwAw60kSZKkyujbasuW2q3ZqrVrdRsREecDb6UW\nbN+emR8Z4/CBacpB7c7Jo4Xbgbsq39iWIqcApyVLkiRJUouKqcj1wfbDYx2fmbcCdxarx4zSZx9w\nWLF6dZtK7XmGW0mSJElqQRFs66cijxls63y+WJ4SEQtG2H8G0AesB0p/B62yMNxKkiRJUpPqrrEF\neMs4U5GHuwC4n9pNo66MiIOLPreMiNcB5xbHXZyZv21Xzb3Oa24lSZIkqQkRMR84s1jdCLwrIsa6\n09X59aNVcZ/pAAAgAElEQVS6mbkiIk4AFgL7A9dHxCpgazZltIXAm9tefA8z3EqSJElScwZmwCa1\nG0PtNM7xj7kVdGYujogDgHdQey7u7tQe+bMEuDQzL2lfuVOD4VaSJEmSmpCZd9CGSzwzcxm1m1G9\ndaJ9yWtuJUmSJEk9wHArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60k\nSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3Ar\nSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPc\nSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqb/NuFyBJkiRpattywd5stf12\nnT3HH1d0tH91nyO3kiRJkqTKM9xKkiRJkirPcCtJkiRJqjzDrSRJkiSp8gy3kiRJkqTKM9xKkiRJ\nkirPcCtJkiRJqjzDrSRJkiSp8gy3kiRJkqTKM9xKkiRJkirPcCtJkiRJqjzDrSRJkiSp8gy3kiRJ\nkqTKM9yWWETMiIizI+LXEbEqIv4UEf8TEW+JiC1a7PPsiNjYwGuvdn8eSZIkSeqUzbtdgEYWEQuA\nHwELik2rgS2ApxWvUyPiyMz8Y4unWAcsH2P/+hb7lSRJkqRJ58htCUXE5sC3qAXbe4HnZeYMoA84\nBVgJPAX44gROc21m7jrG686Jfg5JkiRJmiyG23J6OXAgkMCLMvMHAFlzOfDa4rjjIuKILtUoSZIk\nSaVhuC2nlxfLH2bmdcN3ZuZlwO3F6mmTVpUkSZIklZThtmQiYjrw58XqVWMc+p1i+fzOViRJkiRJ\n5We4LZ/9gKA2JXnJGMcN7NslIrZv4TwHRsSSiOgv7sR8S0RcHBEHtdCXJEmSJHWV4bZ8dq17f88Y\nx907SptGzQL2YdNdmPcGTgd+ERHnttCfJEmSJHWNjwIqnxl17/vHOK5+34xRj3qsW4G3Ad8Ebs/M\nDcXdmZ8LnAc8FXh3RDycmRc20e+4+vv7Wb16dUtt+/r62lmKJElSZbT6+6m/f6yfkpqIiNgGeA61\n384HF8vdi93nZOY5DfQxB3g7cAIwH1hDbXbmpZn52Q6U3fMMt1NMZv7HCNvWA9+NiGuAa4BDgLMj\n4jOZuaJd5z74wP1bbvvgitb+UJckSaq6BXN37nYJeqxnAFeOsi/HaxwRTwUWAjsWx6+i9tjPQ4FD\nI+Ik4MTMXNeecqcGpyWXz8q699PHOK5+38pRj2pCZq4F/rFY7QOObEe/kiRJUo9J4GHge8CHgJcA\n9zfSMCJmAt+mFmxvAg7JzJnAtsAbgHXA0cBH2192b3Pktnzqr7Odx+g3ldqt7v29oxzTip8XywD2\nbGO/LF5yI7NmzW5nl5IkST1v6X3LWmq3fPmDE5o5pzEtysxZ9Rsi4oMNtj0TmEPtMsPjMnMpQDFK\n+4mI2I7a5YKviYiPZuZtbay7pxluy+dman8TFMCBbHrkz3AHFsv7M/OPk1HYRE2fPt1rZyVJkprU\n6u+nNWu85rZTMnPjBJqfViwvGwi2w1xEbTbltsCpwNkTONeU4rTkksnMfuAnxeoxIx0TEUFtqgLA\n1W0u4Zl1729vc9+SJEnSlBUR+7DpxlNXjXRMZq4GFhWrR01GXb3CcFtOlxbL50bE00fYfzK1KcMJ\nfL5dJ42IrYD3F6urgO+3q29JkiRJg7Mvk9EvP6Ru336dLae3GG7L6VLg19SmJl8REUcARMRmEXEy\n8OniuKsy84f1DSPi7IjYWLzmD9t3eEQsjIhTImKXuu1bRMSR1P6G6OnU/mN7bzvvlCxJkiSJXeve\n3zPqUZvuqbNdRIx1k1nV8ZrbEiqePXsi8ENgD+B7EbGG2l9GbFUctpjaHPxRuxlhWwDPL14UffYD\nM9n078IG4AOZecEEP4YkSZLUdqvXPNJSu/5H1ra5kpbMqHs/1kXR9ftmjHOsCobbksrMpRHxJGp3\nU3shtWnI66iN6H4ZuKh4Pu1jmo7R7a+K/p4JPBGYDWwHrKZ2fe0i4OLM/E27PockSZLUTjse/Ypu\nl6CSMtyWWGauonZ3tLObaHMOcM4o+x4CLmxHbZIkSZKatrLu/XRq97kZSf1U5JWjHKNhDLeSJEmS\numra3McxbdYODR37pyU/Gf+gETz40MM87tl/0VLbNqq/znYetceAjmS3YrmieJqKGmC4lSRJklQZ\nfdO3aaldf4vX6rbZwF2Qg9qdk0cLtwN3Vb6x4xX1EO+WLEmSJEmTIDNvBe4sVo8Z6ZiI6AMOK1av\nnoy6eoXhVpIkSZImz+eL5SkRsWCE/WcAfcB64EuTVlUPMNxKkiRJUpMiYoeImB0RsyJiNpuyVd/A\ntuLVN6zpBcD91G4adWVEHFz0t2VEvA44tzju4sz87WR8ll5huJUkSZKk5t0ALAMeKJbziu1vq9u2\nDPi3+kaZuQI4AVgO7A9cHxErqN05+ePAFsBC4M2d/wi9xRtKSZLGNWPDaE8q6KyV07btynklSWpA\nFq9Gjhu6IXNxRBwAvAM4Htid2iN/lgCXZuYl7Sx0qjDcStIU163g2oixajP4SpK6KTP3nGD7ZcBb\ni5fawHArSVNAmQNsq0b7TIZeSZKmJsOtJPWQXgyxzRr+HRh2JUmaGgy3klRBhtjGGXYlSZoaDLeS\nVAGG2fap/y4NupIk9Q7DrSSVjEF28hh0JUnqHYZbSeoyw2w5DPxzMORKklRNhltJmmSG2XJzNFeS\npGoy3EpShxlmq8vRXEmSqsNwK0ltZpjtPYZcSZLKz3ArSRNkmJ06DLmSJJWX4VaSWmCgndpmbFhl\nwJUkqWQMt5LUAMOshnMUV5KkcilduI2IAA4BngE8CZgP7ABsA6wBHgKWAr8CrsvM67tUqqQeZ6BV\nIwy5kiSVQ2nCbUQcCfwNcAK1MBsNNMuIWA58C/hiZv6wgyVKmgIMtGqVU5UlSequrobbiNgCeCXw\nJmCfVroAZgOvAP42Im4BPgpckpnr21aopJ5lmFU7OYorSVL3dC3cRsTLgHOBBXWbN1Cbbvxz4Drg\nZuBhYDmwApgJ7Fi89qM2dfkZwBOBacC+wL8D74yIf8rML03Kh5FUKQZadZqjuJIkTb6uhNuIuBZ4\nVt2mnwJfAr6SmQ+N0XR58YJa+P1c0d8s4MXAy4p+9wC+EBGvy8xD21q8pEoy0GqyGXAlSZpcm3Xp\nvM8C1gOXAHtn5qGZ+clxgu2oMnN50f7Pgb2LftczNEBLmmJmbFg1+JK6wX//JEmaPN0Kt5cAT8jM\n0zPzd+3sODN/l5mnU7uG9/+1s29J5WegVRn576MkSZ3XlWnJRfjs9DluBzp+HkndZ3BQFThNWZKk\nzirNo4AkqRkGWlWRAVeSpM4x3EqqDAOteoEBV5KkzjDcSio1A616kQFXkqT2K3W4jYjNgMcD2wNb\nN9ImM6/paFGSOs5Aq6nAgCtJUnuVMtxGxJHAW4DnAlsNbB6jSRb7E5jW2eokdYqhVlONAVeSpPYp\nXbiNiPOBtzbbbNhSUkUYaDXVGXAlCTZsP5cNO8zu7Dk2NDQRVBVWqnAbESczNNjeBvwEWAasbaCL\n7ERdktrLQCsNZcCVJGniShVugTcUy/XAKzPzi90sRlL7GGilsRlwJUmamLKF24OK5acNtlJvMNSq\nXaY9fHdL7TbsMK/NlUiSpDIqW7jdrFh6x2Opwgy0mohWQ2yz/ZUx9Dp6K0lS68oWbu8ADgC27HId\nkppkoFWr2h1mWzlvmYKuAVeSpNZsNv4hk+q/iuWhXa1CUsNmbFhlsFVLpj18d9eC7XADtZSlHv+b\nkiSpeWUbuf034LXA30TERZm5pNsFSXosf3irVWUJj2Mp64iuJEkaW6lGbjPzPuCvgI3A9yLipC6X\nJKkwMEJrsFUryjQq2oxu1u1/a5IkNadsI7dk5jUR8RTgm8DlEXE/8AtgObXQO177V3a4RGlK8Qe2\nJqKKgXYkA59jskdyvf5WkqTGlS7cRsSOwNnAE4pNuwDHN9g8AcOtNEEGWk1Ur4Ta4boRcg24kiQ1\nplThNiJmAD8AntRqF20sR5pSDLRqh14NtcNNe/hur8eVJKlkShVugTeyKdjeS+0GU9cCy4C13SpK\n6mWGWrXDVAm19SZzFNfRW0mSxle2cPvXxXIpcEhmPtjNYqReZaBVO03FYFvPUVxJksqhbOF2r2L5\ncYOt1H6GWrXTVA+19SZjFNfRW0mSxlaqRwEBq4vlHd0sQuolPsJHnWCwHZnfiyRJ3VO2cHtTsdyl\nq1VIPcBAq04xwI2tk9+P/01LkjS6soXbS4vli7tahVRhhlp1yrSH7zbYNsjvSZKkyVe2a24/B7wU\nOCIi3pmZH+hyPVIlGGbVaWUPaxvuvrWp46fNe8L4B01Qp2405bW3kiSNrFThNjM3RsQLqIXc8yLi\n2dQeB3RdZi7vanFSCRlqNRnKFmybDbKN9tGJwOudlCVJmjylCrcRsRFIIIpNxxSvjIhR2w00BzIz\np3WuQqn7DLSaTGUJtu0ItM2co51BtxMB19FbSZIeq1ThtjBSih032TZ5nFQ5hlpNtm4H28kItI2c\nux1B1xFcSZI6r2zh9hqGjtw2K9tYS9dFxAzgrcCLgD2BDcAtwGXARZm5bgJ9zwHeDpwAzAfWAEuA\nSzPzsxMsXW1ioFW3dDPYdjPUjmTD3beWMuA6eitJ5dDJ3+xqTqnCbWY+p9s1lEVELAB+BCwoNq0G\ntgCeVrxOjYgjM/OPLfT9VGAhsCO1vxBYBfQBhwKHRsRJwIn+h9g9hlp1k8H2sdo1kusIriT1lk7+\nZlfzyvYoIAERsTnwLWr/kdwLPC8zZ1ALoKcAK4GnAF9soe+ZwLepBdubgEMycyawLfAGYB1wNPDR\niX8SNcvH+KjbuhVsN9x9a2mD7XATrbOd37F/XkhS93TyN7ta01PhtpgS0AteDhxIbVT1RZn5A6jd\nLSszLwdeWxx3XEQc0WTfZwJzgH7guMxcXPS9LjM/AZxVHPeaiNh7gp9DDRgItP5I1VRUpVBbb6J1\nd/t6ZklSW3TyN7taUKpwGxFvnEDbGdSm2vaClxfLH2bmdcN3ZuZlwO3F6mlN9j1w/GWZuXSE/RdR\nm6Y8DTi1yb7VBAOtymayA1cVQ+1wvfAZJEkt6+RvdrWgVOEWuDAiXtpsoyLYXg08o/0lTa6ImA78\nebF61RiHfqdYPr+JvvcBdh+r78xcDSwqVo9qtG81zlCrMjLYtq7Vz9Ku79w/TyRp8nXyN7taV7Zw\nG8D/i4ijG26wacS28sG2sB/FM3up3b14NAP7domI7Rvs+8Bi2Wjf+zXYr8bh1GOVmcF24lqdpuz0\nZEmqrE7+ZleLSnW3ZGrD9nsCX4uI5400vF+vLtg+s9h0eYfrmwy71r2/Z4zj7h3WppE7sDXb93YR\nMT0z+xvoWyMwzKrsejXYrr395qaO32rPfdty3nY9NkiSVHqd/M2uFpUt3B4FXAvsDHw7Ig7LzBF/\noRTB9jsMDba9cI1o/U2xxgqV9fsavZFWq323Jdz29/ezevXqltr29fW1o4RJY6iVHquTwbbZMDte\n+4mE3WYDbjseD+Qzb6Xe1urvp/7+3hyfKMn30cnf7GpRqcJtZv4uIo6l9qyoWcDVEfFnmTlkaCEi\ntqUWbJ9VbPoqcGpmbpzMetWcgw/cv+W2D65o7Q+xyWSgVdVM5qhtJ4LtRANto323EnS7EXAl9a4F\nc3fudgmlsuO8vbpdgkqqbNfckpk3AH8JPArMoxZwdxzYXwTbhWwKtl8DXpqZGya71g5ZWfd++hjH\n1e9bOepRk9f3lOW1tNLY2h1s195+c0eDbbvO14vXFkuSBvm7uoRKNXI7IDN/GBGnUptqvC/w38Wz\noTbjscH2JT0UbGHonP15jH6B+m517+8d5Zjx+h7t19pA3yvaeb3t4iU3MmvW7HZ113UGWlXZZI3a\ntjPgTWagHev8zYzkNjOC6+itpNEsvW9ZS+2WL39wQjPnJtOqaX1s3eDlFSX5Pjr5m10tKmW4BcjM\nKyLi9cAngacD3wC2ZVOwvYLeC7ZQC5xJ7e5rB7Lp9uHDDdz5+P7MbPTC9IH/6Ab6Hu2X4kDfNzbY\nb0OmT59euWtnhzPQqhcYbCdm7e03dyzgToTX3Uq9q9XfT2vW9OY1tyX5Pjr5m10tKt205HqZ+Sng\n7GL1+WwKtl8HTunBYEsxUvqTYvWYkY6JiAAGHpd0dRN93wrcOU7ffcBhzfbd65x6LDWnXcF2sqcg\nN6pTdfloIEmqhk7+ZlfrSh1uATLzvcAn6jZ9gx4NtnUuLZbPjYinj7D/ZGqPTErg8032PXD8KRGx\nYIT9ZwB9wHrgS0323XMMteo1VQpPZQy1wzVao9ffSlJP6uRvdrWgK9OSI+Isav+QG7Uc+BO1em8E\n/rH2FyGPVYThqrsUeCPwROCKiHh5Zv4gIjYDXgR8ujjuqsz8YX3DiDgb+OdidY/MvJOhLgBOB3YB\nroyI0zJzcURsCbwKOLc47uLM/G27P1hVGGil1rUjyFUh2A5odJpyo9OTJ3LtrVOTJWlStfybXZ3R\nrWtuz5pA23ePsS+ByofbzNwQEScCPwT2AL4XEWuojbRvVRy2mLGf6zviXx5k5oqIOIHajbn2B66P\niFXA1mz692Eh8OaJfo4qMtSql03GqG3Zgu3DNy1tus0O+400qWVsjd5sarKuv5UkdV6bfrOrjUo/\nLblJIw/nVlBmLgWeRC2s/xrYAKwFrgfeCjwzM/80UtMG+l4MHAB8BLgVmEbt1uSLgNMz89jMXNeO\nz1EFA1OPDbZS97Uj2D5809LB12S3b1cwr9L0cUmayibwm10dEJnNzA5u00kjntOhrjMzf9yhvtWE\niNgJGHKf9pt/fwezZ+/UpYoeyzCrqaQKo7YTDYathtlGNDOa28gU5UZGb1udmuy0ZEkDHnzwAfbd\na4/hm3fOzAe6UM6gbv1OLOv3ofbpyrTkzPxRN84rgaFW6oRuBttOhtqRzjFe0G3kGtxGpif73FtJ\nkprTa9OSpVE59VhTVS9PcZ2MYNvKObt5Qyz/nJMkTVWGW/U8Q63UWd0ate1GsG3m3ON9rka+t17+\niwlJktqtW3dLljrKMCtVQyvBtpuhtt5AHWNNU270MUGSJGniujJyGxEv7KXzqDwcpZWG6rWRv3YF\n2+U33fOYV6dqGivAt+PRSZIkqaZbI7dXRMT/AWdn5jfb3XkRas+i9kDlae3uX+VjoJW6YyLhrNlR\n24kE20bC60jHzNpvt4b6f/impS09H7cR3lhKkqTGdOua237gycDXI2JJRLwjIib0f+6ImB8R74qI\nG4ErqD1vqr8NtarEHKmVRtdro7atmOiobDPtxwrfjt5KktR53Qq3+wKXAwHsD/wLcEdE/CgizoqI\nYyNi1lgdRMTsiDg+Is6JiB8DtwPvL/pO4DJgn45+CnWNoVbqvrKP2k4k1I7UVyNBt9WA227++ShJ\nmoq69Zzbu4FTIuJ84BzgOGpB+9nFCyAj4iFg4LUS2A7YsXjtQC0cD+ka+BZwTmbe0OnPocnnDzZp\n6mk22LYz1I7W/1jTlVuZojzec2+dmixJ0vi6erfkzPwFcEJE7Av8HfASYKdidwCzitd4/gB8Gfhk\nZt7WiVrVXYZaqXd0cgSz08G2/jyNXo9bz7snS5LUOaV4zm1m3pyZbwJ2BQ4H3gd8F7hvlCb3AAuB\n9wKHAbtl5lsMtr1lYOqxwVZqXqevt52s60SbGbWdrGDbyPlamUbttbeSJE1MqZ5zm5kbgEXFC4CI\n2BLYHtgKWAv8MTMf7U6FmgyGWUnNmuxgO/y8I43ijjY92dFbSZI6o1ThdiRFkF3W7TrUeYZaqfc1\nMyW50dHPVoLtshsfbPjYnfef3VANzQTcVnjdrSRJYyvFtGRNbU49ljRZlt34YFPBtpk2zYTs0UK+\nU5MlSWpd6Udu1bsMtJJG0+5R22YD7Vh9NDKSW6+do7eSJGl0jtxq0jlSK3VWr9xMajyTGWwb7W+0\nmkYK65P53FtJkqYCw60mzbYbVhtqpSmsG2Gu3cG2vt/R+u7Wza0kSZrqDLeSpFJpZEpyIwGyU8G2\nkXOMVF+jU63LMjIuSVLVGG4lSZqAiYToZkezOz3lXJKkKvOGUpKkntNs4LztD6tH3bf3nL6Wahjt\n8UCSJKkzHLmVJFVKO69pve0Pq8cMto0e02iYbnRqsiRJap7hVpLUUxoNmuMF1pGOH6vNSOdtJIiP\nNDXZ624lSWqe4VaSNOU0G2wbbTsZN7GSJEkjM9xKUg8p6w2HGr1x0kSn7TYSLicSbFvpY/jorVOT\nJUnqjFKF24jYe4LtX9+uWiRJj1X16bLtCLbj9eXorSRJ3VG2uyUvjog3ZuYlzTSKiNnAJcDxwCc6\nUpkkiWnznlD5gNtOt/1hdct3U6639vab2WrPfdtQkSRV0wOrN7Bhm/UdPcdDqzd0tH91X6lGboE+\n4NMRcXlEzGykQUQcBfwKOKGjlUmSWjbVg1s77/Bc1qnnkiR1W9nC7UoggJOA/4uIQ0c7MCK2jIgL\ngauAXYrNizpfoiSpito5JXm8fsebmtzIdbeOkEuS1JyyhduDgOuK9/OBH0bEeyNiSJ0RsW9x3Juo\nheH1wHuA505irZKkNtthvwXdLkGSJFVUqcJtZt4OHAa8H9gITKMWWhdFxB4AEfF3wC+AJxfNfg8c\nmpnnZWZOds2SpN6yZMXaIa9GdGpUWJIkNa5U4RYgM9dn5j9RG4W9q9j8LOCGiPgetRtGbVNs/wJw\nUGb+z+RXKkmaChoNuMMNn5rczutuJUnSY5Uu3A7IzEXURme/WmyaCRxRvP8T8NLMfHlmrupGfZJU\nRht2mNftEiRJkrqitOG2sIratOPh/hdYOMm1SJKmqEZGb52aLElSd5U23EbE44GfAu8oNq0HVhTv\nnwf8KiKOGKmtJEmSJGlqKWW4jYhXADcATys23Q48G3gimx73sxtwdUR8MCI2n/wqJWlqmjbvCS21\nm+rPupUkSZ1VqnAbETMj4ivAZ4G+YvN/ULtp1M8z8y5qN5r6J2ADtfrfBvw8Ilr7tSVJKpXxHgc0\na7/dRt238/6zR92395y+Ufc1otUbS0mSpMlRqnAL/Ao4uXi/EjgtM1+WmSsHDsjMjZn5fuBQNl2P\nezDwi4h49aRWK0mSJEkqhbKF292L5XXURmu/ONqBmXkd8BRg4Jg+4N87W54kqdcduN1WQ97Xv8rA\nO2JLkjSysoXbBM4DDs3M28c9OHNlZp4GnMqmm01Jkkqq0etuuz01uZUwO9Fpz5IkaWLKFm6PyMz3\nZOaGZhpl5peBg6jdXVmSprTJGNlr9aZSZTBZIXR4yB4rkI+kyt+xJEndUKpwm5k/nkDbO4DD21eN\nJKnMWh297QRHbSVJ6r5ShduJysyN3a5BkjS2dk1NngjDqCRJvaenwq0kafKUfdrseKO37Qq4BmVJ\nksrBcCtJKq2J3FgKOh9wR2s/3vW2wz9Xo6PZjVo5bdu29idJUhUYbiWpB5X9cTHtDHPtCLithNxG\ng20rRhsVL/s/V0mSuslwK0lq2USmJrfz2tuJBlxoLuQ2E4bHG7WVJEntsXm3C5AkaTw77LeAh29a\nOqE+dt5/NstufHDc4yYyVbmVUdt2T0mWJGmqcuRWknrU/2fvzuPtquq7j39+CWHIJQmQYBCSICoi\nQxVRBhUqghURhVqxxWJRH619EDswaJ0eBal9HEBtaW2l1Uccaa1aCxTjWIkDOAQHZm0RBMRggiS5\nN4SE/J4/zr7JyeXce8989j7383699uucffbaa6+zWdzc7117r92vS1j7MXrbjGaeI9vLRwQ1qrvV\nZ9tKkmamiNglIk6MiLdGxOci4o6I2FIsb2+hnsURcXFE3BoRGyJiTURcExGv6mX7y8KRW0lSJTQz\nervwwH1YffPdU5ZpdgS3Fc2G5mYuSS77LNSSpJ44Erhqkm3ZTAUR8VRgObBHsc96YAQ4Gjg6Ik4F\nTs7MTZ03t5wcuZUkdaxfo7fduP8WamG0G6O4U9XTTDta+e5OJiVJQy2B+4GvAO8BXgrc2+zOEbEA\nuJJasL0ZODwzFwC7Aq8DNgEnAB/obrPLxZFbSRpiD+++hNn33zXoZkxrp/2eyMbbb2mqbLMjuEBT\no7jjWhnNnS4YNwq2TiQlSZrCisxcWP9BRLy7hf3PAxYDY8DzM/MOgGKU9oMRMR/4a+A1EfGBzPxp\nl9pdKo7cSpK6otPLabs9ggut3fM6PgpbH1zrP2u0vdljNmpvo+/bjUuSfcatJFVPZm7psIozitfL\nx4PtBJdQu0x5NnB6h8cqLcOtJKlr+nm/aC8C7rh2L1t2AilJUr9FxAHA0mL16kZlMnMUWFGsPrcf\n7RoEw60kDbkq3avZ6uzJrQTcXgbPqepvdtR2KlX6byhJ6rtDitcEbpii3Pi2A3vbnMEx3EqSuqqf\nlydDa/eydjvkTldfK8HWWZIlSW3au+79VJNN3FO8zo+IuT1sz8A4oZQkzQD9nlhq9pIn8PBdt7W9\nfysTTEFzk0zVqw+k0006Nd3+U7VJktR9Y2Ojbe23YWysyy0pjXl176f6kvXb5k1TtpIMtyUVEfOA\nc4EXA/sBDwO3ApcDl7T7fKqIOB94WxNFH5+Z/9POMSSpG9oJuEBLIRe6f5/sVKG2nVHbVi5JdjIp\nSTPB4Y/be/pCPRYRrwA+0kEVJ2bm8i41RwXDbQlFxL7AfwHjvyGNAnOApxXL6RFxfGb+poPDbAJW\nT7F9cwd1Syqhqo3eQusBF9oPud3Q7WArSSqtnPDa7v7dsK7u/VxqsyI3Un8p8rpJylSa4bZkImIH\n4ApqwfYe4IzM/FpEBPAS4J+ApwCfAF7QwaG+lZnHddpeSZrKoAIu9DfkTncJcqv3EY9zIilJM8Ud\nD3F6cBkAACAASURBVGzgN7M3NFX2M9//77aO8cD9q3n17xzR1r4NfJra7+ztWtuthrD9fbZLgMn+\n0Ry/VGltZg7dJclguC2jl1Ob8SyBF2fmdQCZmcC/RsQs4FPA8yPiuMz82uCaKqlq+j16C90LuEDp\nQm4z99VOFWwdtZWk1u08d6St/TY+2Fx4bkZmPgSs6VqFnRmfBTmo5YjJ/rEcn1X5pp63aECcLbl8\nXl68fn082NbLzMuB24vVMyZul6Qy6laIa3cEFGpBdHzpVLP1dBJsHbWVJDUjM28D7ixWn9eoTESM\nAMcUq1/qR7sGwZHbEimm5H5msdrwAcyFLwJnAr/T80ZJGjqDGL2F7ozgQvuXKdebKpjWj/B2EoT7\nPWLrZFKSNKN9DHgrcFpEXJiZEy9XOgsYoTavzif73bh+ceS2XA6kdjlBsw9g3isidmvzWIdExA0R\nMRYR6yPi1oi4NCIObbM+SRUyqFHBbo7gdjKKO5VOR3i70TZHbSVp5omI3SNiUUQsjIhFbMtqI+Of\nFUuj67IvAu6lNmnUVRFxWFHnjhFxJnBhUe7SzPxZr7/LoBhuy6XVBzBP3KcVC4ED2DYT8/7Aq4Ef\nRMSFU+0oSZ3o5qhlL0NuO5ppi/fZSpImcT2wCriveB3/S+fr6z5bBfzdxB0zcy21yWZXAwcB34+I\ntdRmTv57ar/vLwfO7u1XGCwvSy6Xdh/A3IrbqP0P8gXg9sx8uJih+dnAXwNPBd4SEfdn5vtarHtK\nY2NjjI6299DtkZH2Jg6QNLlBXZ4M3btEeVwnE0518/jTaSbYOmoraaJ2f38aGxvKCXGHWdLcI4Ia\nlsnMlRFxMPCXwEnAUmqP/LkBuCwzO3kubyUYbjtUtQc4Z+anGny2GfhyRFwDXAMcDpwfEf9c/BWo\nK57w5MPb3nfj/b/qVjMk1RmmgAv9Dbmtjhj3csTW+22l4bbvox816CaoDzJzvy7UsQo4t1hmHMNt\n57r5AOeJD2CeTE8ewJyZGyPizcCXqd1wfjzw+W7VL0kT9SLgwvbBs5tBt91LoJsNto7aSpLUPsNt\n57r5AOeJD2CebFKpfere3zNJmXZdW7wG0PFfj+rd9qPvsWjhwm5WKakLBjl6C9uCXy9CLkweSJsJ\nvZ3ez9vKaK3BVtJk7vjlqrb2W7361xx2yEFdbo1UXobbDnX5Ac63UBvJHX8A8xcnKTf+AOZ7M/M3\nXTp2z82dO9d7Z6WSGnTAhd6N4k6m1xNROXGUpG5p9/enDRu851Yzi7Mll0hmjgHfLFYnewBzACcU\nq714APNRde9v70H9kkqqDCOHs5c8YShCYavfoQznXpKkqjPcls9lxeuzI+KIBttfQu1y4aT2sOau\niYidgHcWq+uBr3azfknlV5aQVdWA20447/ScO5mUJEk1htvyuQz4CbVLkz8bEccBRMSsiHgJ8E9F\nuasz8+sTd46I8yNiS7Esm7DtWRGxPCJOi4i96j6fExHHAyuAI6gF53d0c6ZkSdVRpoBbpZHcdtpZ\nlnMtSdIw8J7bkimeO3sy8HXgMcBXImIDtT9E7FQUWwmcPl1VDT4L4HeKhaLeMWAB2/rCw8C7MvOi\nDr6GpIorwz249Xo96VS7qhK8JUmaCQy3JZSZd0TEk4DzgBdRuwx5E7UR3U8DlxTPpm24+xRV/7io\n8yjgt4BFwHxglNr9tSuASzPzxm58D0nqtrKE3G6EWkdtJUnqLsNtSWXmeuD8YmllvwuACybZtgZ4\nX6dtkzQzjIevMo3gjqsPl/0Kut0cpe1WsPV+W0mStjHcSpKmVLZLlCeaGDq7GXZ7cdmxI7aSJPWG\n4VaSNK2yB9x6rQTSh++6ra/3zRpsJUnqHWdLliQ1ZRiDWZWDrZckS5K0PcOtJKlpwxhw+8HzJklS\n7xluJUktMag17+Hdl3i+JEnqE8OtJKllhrbpeX4kSeovw60kqW0GuMZ6fV6831aSpEcy3EqSOuIo\n7vY8F5IkDYbhVpLUFTM91BnyJUkaLJ9zK0nqmvFwV5Vn4nZDvwOtlyRLktSY4VaS1HUzIeQ6SitJ\nUrl4WbIkqWeG9VLdYfxOkiRVnSO3kqSeG5aR3EGHWi9JliRpcoZbSVLf1IfDqgTdQQdaSZLUHMOt\nJGkgyhx0DbSSJFWP4VaSNHATw2S/w24VwqyXJEuSNDXDrSSpdBqFzW4F3ioEWUmaaf57zSi7btm5\np8dY/5vRntavwTPcSpIqwVAqSZKm4qOAJEkqOS9JliRpeoZbSZIkSVLlGW4lSZIkSZVnuJUkqcS8\nJFmSpOYYbiVJkiRJlWe4lSRJkiRVnuFWkqSS8pJkSZKaZ7iVJEmSJFWe4VaSpBJy1FaSpNYYbiVJ\nkiRJlWe4lSRJkiRVnuFWkqSS8ZJkSZJaZ7iVJEmSJFWe4VaSJEmSVHmGW0mSSsRLkiVJao/hVpIk\nSZJUeYZbSZJKwlFbSZLaZ7iVJEmSJFWe4VaSJEmSVHmGW0mSSsBLkiVJ6ozhVpIkSZJUeYZbSZIG\nzFFbSZrZImJhRLwyIj4RETdFxGhEbIyIuyLi8xHxu03WszgiLo6IWyNiQ0SsiYhrIuJVvf4OZbDD\noBsgSZIkSTPcvcDs4n0CDwIbgUcDpwCnRMTVwKmZuaFRBRHxVGA5sEdRx3pgBDgaODoiTgVOzsxN\nvfwig+TIrSRJkiQN1mzgOuBM4HGZOZKZ84HHAh8uypwIfKjRzhGxALiSWrC9GTg8MxcAuwKvAzYB\nJwAf6OWXGDTDrSRJA+QlyZIk4NmZ+fTM/FBm/nz8w8y8IzP/mG2h9mURsaTB/ucBi4Ex4PmZubLY\nf1NmfhB4e1HuNRGxf8++xYAZbiVJkiRpgDLzG9MUGR+9TeBpDbafUbxenpl3NNh+CbXLlGcDp7fV\nyAow3EqSNCCO2kqSmrSxeA0mZLiIOABYWqxe3WjnzBwFVhSrz+1FA8vAcCtJkiRJ5XZs8ZrATyZs\nO6Ru2w1T1DG+7cDuNatcDLeSJA2Ao7aSpGZExG7Am4rVFZn50wlF9q57f/cUVd1TvM6PiLndal+Z\n+CggSZIkSZWxccNYW/s99GDDJ+iUWkTMAj4O7AVsoDbz8UTz6t5PdXLqt82bpmwlGW4lSeozR20l\nqX1vPuFJg24CEfEK4CMdVHFiZi5votzfACdRu+T4rMyc6rLjGc9wK0mSJGmgbrp7HTuvq1Q0yQmv\n7e4/qYi4CDirKHt2Zn50kqLr6t7PpTYrciP1lyKvm6RMpVWqB0mSJEma2f7ww99sa7+N6+7ns3/x\nwm4149PAFR3sv3aqjRHxHuAcasH2vMz82ymK199nuwS4ZZJy+4wfOzOH7pJkMNxKktRXXpIsSZ2Z\ns/Mube338EMPdq0NmfkQsKZrFdaJiPcC51ILtm/IzPdPs8v4pcpBbebkycLt+KzKN3XcyJJytmRJ\nkiRJKoHiUuT6YHvxdPtk5m3AncXq8yapdwQ4plj9UheaWkqGW0mS+sRRW0nSZIpgW38p8rTBts7H\nitfTImLfBtvPAkaAzcAnO2poiRluJUmSJGmA6u6xBTiniUuRJ7oIuJfapFFXRcRhRb07RsSZwIVF\nuUsz82fdaHMZGW5LJiJ2iYgTI+KtEfG5iLgjIrYUy9u7eJzFEXFxRNwaERsiYk1EXBMRr+rWMSRJ\n2zhqK0lqJCKWAecVq1uAN0XEvVMs506sIzPXAi8AVgMHAd+PiLXUZk7+e2AOsBw4ux/faVCcUKp8\njgSummRbu1ONbycinkqtc+9R1Lme2mUKRwNHR8SpwMmZuakbx5MkSZI0qfEBx6Q2KdSe05QfafRh\nZq6MiIOBv6T2bNyl1B75cwNwWWZ28lzeSjDclk8C9wM/AFYC1wPvB/bqRuURsQC4klqwvRn4o+J/\nhDnAHxfHOgH4ALVr8yVJHXLUVpI0mcz8OV26ojYzV1GbkOoRo7szgeG2fFZk5sL6DyLi3V2s/zxg\nMTAGPD8z7wAoRmk/GBHzgb8GXhMRH8jMn3bx2JIkSZLUE95zWzKZuaXHhzijeL18PNhOcAm1y5Rn\nA6f3uC2SJEmS1BWG2xkkIg6gdu09wNWNymTmKLCiWH1uP9olScPMS5IlSeoPw+3MckjxmtRuLJ/M\n+LYDe9scSZIkSeoOw+3Msnfd+7unKHdP8To/Iub2sD2SNNQctZUkqX+cUGpmmVf3fmyKcvXb5k1T\ntmljY2OMjo62te/ISMMZzyVJkoZeu78/jY115Vc4qTIMtx2KiFcAnTwz6sTMXN6l5pTaE558eNv7\nbrz/V11siST1nqO2krpl30c/atBNkCrBcNu5nPDa7v79sK7u/VxqsyI3Un8p8rpJykiSJElSaRhu\nO/dp4IoO9l/brYY0of4+2yXALZOU26d4XZuZXbue5bYffY9FCxdOX1CSKs5RW0nddMcvV7W13+rV\nv+awQw7qcmuk8jLcdigzHwLWDLodTRqfBTmozZw8Wbgdn1X5pm4efO7cud47K0mS1KJ2f3/asMF7\nbjWzOFvyDJKZtwF3FqvPa1QmIkaAY4rVL/WjXZI0TBy1lSRpMAy3M8/HitfTImLfBtvPAkaAzcAn\n+9YqSZIkSeqA4baEImL3iFgUEQsjYhHb/juNjH9WLI+4RiUizo+ILcWyrEH1FwH3Ups06qqIOKzY\nb8eIOBO4sCh3aWb+rPvfTpKGl6O2kiQNjuG2nK4HVgH3Fa9Lis9fX/fZKuDvpqij4SzMmbkWeAGw\nGjgI+H5ErKU2c/LfA3OA5cDZHX8LSZIkSeoTw205ZQtLo32nrjxzJXAw8H7gNmA2tUf+rABenZkn\nZuamzr+GJM0cjtpKkjRYzpZcQpm5Xwf7XgBc0ES5VcC5xSJJkiRJlebIrSRJHXLUVpKkwTPcSpIk\nSZIqz3ArSVIHHLWVJKkcDLeSJEmSpMoz3EqS1CZHbSVJKg/DrSRJkiSp8gy3kiS1wVFbSZLKxXAr\nSZIkSaq8HQbdAEmSqsZRW0nqrtvvXsucB3o77rZp/dqe1q/Bc+RWkiRJklR5hltJklrgqK0kSeVk\nuJUkSZIkVZ7hVpKkJjlqK0lSeRluJUmSJEmVZ7iVJKkJjtpKklRuhltJkiRJUuUZbiVJmoajtpIk\nlZ/hVpIkSZJUeYZbSZKm4KitJEnVYLiVJGkSBltJkqrDcCtJkiRJqjzDrSRJDThqK0lStRhuJUmS\nJEmVZ7iVJGkCR20lSaoew60kSZIkqfIMt5Ik1XHUVpKkajLcSpIkSZIqz3ArSVLBUVtJkqrLcCtJ\nkiRJqjzDrSRJOGorSVLVGW4lSTOewVaSpOoz3EqSJEnSAEXEYRHx9oj4j4i4JSJWR8SmiFgTEddF\nxAUR8agm6lkcERdHxK0RsaHY/5qIeFU/vseg7TDoBkiSNEiO2kqSSuB/Aa8t3ifwIDAKLAAOL5a/\niIjfz8zljSqIiKcCy4E9ijrWAyPA0cDREXEqcHJmburlFxkkR24lSZIkabCuA84DjgJ2z8yRzNwN\nmA+8HLgPmAf8a6MR3IhYAFxJLdjeDByemQuAXYHXAZuAE4AP9OG7DIzhVpI0YzlqK0kqg8z8eGa+\nLzO/m5lr6z4fzcyPAy8rPpoHnNygivOAxcAY8PzMXFnsvykzPwi8vSj3mojYv2dfZMAMt5IkSZJU\nbtfVvW/0l9kzitfLM/OOBtsvoXaZ8mzg9C63rTQMt5KkGclRW0lShRxTvCbw3foNEXEAsLRYvbrR\nzpk5CqwoVp/biwaWgeFWkiRJkkomInaKiMdExOuAj1MLth/OzG9PKHpI8ZrADVNUOb7twO62tDyc\nLVmSNOM4aitJKquIeBDYccLH1wKXZOanG+yyd937u6eo+p7idX5EzM3MsQ6aWUqGW0nSjGKwlaRq\ne/ihDW3u92CXW9IzvwR2onZv7QgQwMHAb0fEfxSXGNebV/d+qsBav23eNGUryXArSZIkqTKufdtJ\ng24CEfEK4CMdVHHiZM+rzcz96o6zJ/BHwFuAPwGOioijMnNjB8ceWoZbSdKM4aitJJXT6nvXscPc\nSk0HlBNe291/6kKZ9wHvi4gVwHeAJwN/Bry3rti6uvdzqc2K3MjcSfYZGoZbSZIkSZWx/5mXt7Xf\n5rG13H7Za7rVjE8DV3Sw/9rpi2yTmd+LiG8Cvw2cwPbhtv4+2yXALZNUs8/4sYfxflsw3EqSZghH\nbSVpOMyas3Ob+3XvSt7MfAhY07UKmzM+IdTeEz4fnwU5qM2cPFm4HZ9V+aYut6s0KjX2L0mSJEkz\n1GOL11/Vf5iZtwF3FqvPa7RjRIyw7Vm5X+pJ60rAcCtJGnqO2kqSyioips1kEXE8cESx2iicfqx4\nPS0i9m2w/SxqMy9vBj7ZTjurwHArSRpqBltJUskti4gfRsRrImK/iIjxDRGxNCLeCHyh+OgXwCUN\n6rgIuJfapFFXRcRhxf47RsSZwIVFuUsz82c9+yYD5j23kiRJkjRYTwL+sXi/KSLWAruw/QzHNwIv\nysxHzIacmWsj4gXAcuAg4PsRsR7YmW2Zbzlwdo/aXwqO3EqShpajtpKkCrgbeAnw98D3gFXA+D9g\ntwOfA14GHDrVqGtmrgQOBt4P3AbMpvbInxXAqzPzxMzc1KsvUQaO3EqSJEnSgBSB87PF0mldq4Bz\ni2XGceRWkjSUHLWVJGlmMdxKkoaOwVaSpJnHcCtJkiRJqjzDrSRpqDhqK0nSzGS4lSRJkiRVnuG2\nZCJil4g4MSLeGhGfi4g7ImJLsby9C/WfX1ffVMtju/F9JKmfHLWVJGnm8lFA5XMkcNUk27KLx9kE\nrJ5i++YuHkuSes5gK0nSzGa4LZ8E7gd+AKwErqf2IOa9unycb2XmcV2uU5IkSZIGwnBbPisyc2H9\nBxHx7kE1RpKqwFFbSZLkPbclk5lbBt0GSZIkSaoaw60kqdIctZUkSWC4nckOiYgbImIsItZHxK0R\ncWlEHDrohklSswy2kiRpnOF25loIHACMAnOA/YFXAz+IiAsH2TBJkiRJapUTSs08twGvB74A3J6Z\nD0fEDsCzgb8Gngq8JSLuz8z3dfPAY2NjjI6OtrXvyMhIN5siaQg4aitppmj396exsbEut0QqN8Nt\nhyLiFcBHOqjixMxc3qXmTCszP9Xgs83AlyPiGuAa4HDg/Ij458xc261jP+HJh7e978b7f9WtZkiS\nJFXKvo9+1KCbIFWClyV3Lute211KITM3Am8uVkeA4wfYHEmalKO2kiRpIkduO/dp4IoO9u/ayGiX\nXFu8BrBfNyu+7UffY9HChdMXlKQpGGwlzTR3/HJVW/utXv1rDjvkoC63Riovw22HMvMhYM2g21EF\nc+fO9d5ZSZKkFrX7+9OGDd5zq5nFy5I10VF1728fWCskqQFHbSVJ0mQMt9oqInYC3lmsrge+OsDm\nSJIkSVLTDLclFBG7R8SiiFgYEYvY9t9pZPyzYnnENSoRcX5EbCmWZRO2PSsilkfEaRGxV93ncyLi\neGAFcAS1Sa7e0c2ZkiWpU47aSpKkqXjPbTldDyxr8Pnri2XcZcArJ6mj0SzMAfxOsRARG4AxYAHb\n+sLDwLsy86LWmy1JvWGwlSRJ0zHcllOzjwhqVGaq/X4MnEftvtrfAhYB84FRavfXrgAuzcwbW2qt\nJEmSJA2Y4baEMrPtR/Bk5gXABZNsWwO8r926JWkQHLWVJEnNMNxKkkrLYCtJM8Nv7v45s3bq7c/8\nLRvX97R+DZ4TSkmSJEmSKs9wK0kqJUdtJUlSKwy3kiRJkqTKM9xKkkrHUVtJktQqw60kqVQMtpIk\nqR2GW0mSJElS5RluJUml4aitJElql+FWklQKBltJktQJw60kSZIkqfIMt5KkgXPUVpIkdcpwK0ka\nKIOtJEnqBsOtJEmSJKnyDLeSpIFx1FaSJHWL4VaSJEmSVHmGW0nSQDhqK0mSuslwK0nqO4OtJEnq\nNsOtJEmSJKnyDLeSpL5y1FaSJPWC4VaS1DcGW0mS1CuGW0mSJElS5RluJUl94aitJEnqJcOtJKnn\nDLaSJKnXDLeSJEmSVEIR8caI2DK+NFF+cURcHBG3RsSGiFgTEddExKv60d5B22HQDZAkDTdHbSVJ\nal1EHAC8ve6jnKb8U4HlwB5F2fXACHA0cHREnAqcnJmbetPiwXPkVpLUMwZbSZJaFxGzgI8AOwHf\naaL8AuBKasH2ZuDwzFwA7Aq8DtgEnAB8oFdtLgPDrSRJkiSVy58CTwc+AXypifLnAYuBMeD5mbkS\nIDM3ZeYH2TYC/JqI2L8H7S0Fw60kqScctZUkqXURsR/wTuDXwNlANLHbGcXr5Zl5R4Ptl1C7THk2\ncHo32llGhltJUtcZbCVJats/AXOBczJz9XSFi3tzlxarVzcqk5mjwIpi9bndaGQZGW4lSZIkqQQi\n4o+B44AvZ+YnmtztkOI1gRumKDe+7cA2m1d6hltJUlc5aitJUusiYh/gvdTum/2TFnbdu+793VOU\nu6d4nR8Rc1tsXiX4KCBJUtcYbCVJvZabN7a530NdbknXfQiYD7whM3/ewn7z6t6PTVGuftu8acpW\nkuFWkiRJUmX8+so3D7oJRMQrqD2qp10nZubyuvpeBjwfuB54X2etm7kMt5KkrnDUVpLUrgd+cQux\nw86DbkYrcsJru/sTEYupPX92M/DHmbmlxbrW1b2fS21W5EbqL0VeN0mZSjPcSpI6ZrCVJPXLDr/1\nsrb2y4c38vBNn+lWMz4NXNHB/mvr3r8L2AP4IHBrREz8R3XH8TcRMULt0UAbM3NT8XH9fbZLgFsm\nOeY+48fOzKG7JBkMt5IkSZIqJGbPaW/HfLhrbcjMh4A1Xapuv+L1tcUymWDbiOvfUHsGLmybBTmo\nzZw8Wbgdn1X5pvaaWX7OlixJ6oijtpIkdSSnWSYrW/sg8zbgzmL1eY0OUIz4HlOsfqlbDS8bw60k\nqW0GW0mSOpOZz87M2ZMtwAXbim79/JwJ1XyseD0tIvZtcJizgBFq9/V+sjffZPAMt5IkSZJUXtFE\nmYuAe6lNGnVVRBwGEBE7RsSZwIVFuUsz82e9aebgec+tJKktjtpKklQOmbk2Il4ALAcOAr4fEeuB\nndmW+Zaz7T7doeTIrSSpZQZbSZL6pqnHDWXmSuBg4P3AbcBsahNQrQBenZkn1s2wPJQcuZUkSZKk\nksrMC9h23+10ZVcB5xbLjOPIrSSpJY7aSpKkMjLcSpKaZrCVJEllZbiVJEmSJFWe4VaS1BRHbSVJ\nUpkZbiVJ0zLYSpKksjPcSpIkSZIqz3ArSZqSo7aSJKkKDLdSSYyOjrLT7ovZaffFjI6ODro5leA5\na00752umB9vR0VEWzR9h0fwR+1gTPF+t85y1xvMlaSqGW0mSJElS5RluJUkNzfRRW0mSVC2GW0nS\nIxhsJUlS1RhuJUnbMdhKkqQqMtxKkiRJkirPcCtJ2spRW0mSVFWG25KJiIUR8cqI+ERE3BQRoxGx\nMSLuiojPR8Tvduk4iyPi4oi4NSI2RMSaiLgmIl7VjfolVY/BVpIkVdkOg26AHuFeYHbxPoEHgY3A\no4FTgFMi4mrg1Mzc0M4BIuKpwHJgj+IY64ER4Gjg6Ig4FTg5Mzd18kUkSZIkqV8cuS2f2cB1wJnA\n4zJzJDPnA48FPlyUORH4UDuVR8QC4EpqwfZm4PDMXADsCrwO2AScAHygky8hqVoctZUkSVVnuC2f\nZ2fm0zPzQ5n58/EPM/OOzPxjtoXal0XEkjbqPw9YDIwBz8/MlUX9mzLzg8Dbi3KviYj92/4WkirD\nYCtJkoaB4bZkMvMb0xQZH71N4GltHOKM4vXyzLyjwfZLqF2mPBs4vY36JUmSJKnvDLfVs7F4DVr8\n7xcRBwBLi9WrG5XJzFFgRbH63HYaKKk6HLWVJEnDwnBbPccWrwn8pMV9D6nb94Ypyo1vO7DF+iVV\niMFWkiQNE2dLrpCI2A14U7G6IjN/2mIVe9e9v3uKcvcUr/MjYm5mjrV4HKiNLG9n9eo1bVQzc4yN\nbTvNv169mrENbU2GPaN4zloz8Xxteci/b06n/pytXv1rNmxo58fhzOH5ap3nrDWer9asWb260ceP\n+B2tFDY/SPbhGBpukdnzbqQuiIhZwBeAk4ANwJGZOdXoa6M63gz8FbWR2zmZuWWScuMTVyWwd2b+\nqo32PpHabMySJEkqjwMz85ZBNiAi9gRWDbINdR6VmfcNuhHqDv9s36GIeEVEbOlgOaHJQ/0NtWCb\nwFmtBltJkiRJGmZelty5nPDa7v6TioiLgLOKsmdn5kfbPNa6uvdzqc2K3MjcSfaRJEmSpFIy3Hbu\n08AVHey/dqqNEfEe4Bxqwfa8zPzbDo5Vf5/tEmCyS1L2GW9bm/fbSpIkSVJfGW47lJkPAT2ZKSki\n3gucSy3YviEz399hleOXMge1mZMnC7fjsyrf1MGxfsojZ1teQ/sj3JIkSWpNAHtM+KzVCUl7YTXw\nqEE3otBw1i1Vk+G2pIpLkcdHbN+QmRd3Wmdm3hYRdwLLgOcB/9bguCPAMcXqlzo41sNMHp4lSZLU\nH2WZuGmrYlJTJ3FS1zmhVAlNCLbndSPY1vlY8XpaROzbYPtZwAiwGfhkF48rSZIkST1juC2Zunts\nAc5p9VLkiDi/bibmZQ2KXATcS23SqKsi4rBivx0j4kzgwqLcpZn5s/a+hSRJkiT1l8+5LZEijP68\nWN0C/HqaXd47cVQ3Is4H3kZt1He/zLyzwXEOA5YDC4uP1gM7s+0y9eXAyZm5qfVvIUmSJEn95z23\n5TI+kp7UJgDYc5ryIw0+m/avFZm5MiIOBv6S2rNzl1J75M8NwGWZ+ZGmWyxJkiRJJeDIrSRJkiSp\n8rznVpIkSZJUeYZbSZIkSVLlGW4lSZIkSZVnuJUkSZIkVZ7hVpIkSZJUeYZbSZIkSVLlGW4lSZIk\nSZVnuJUkSZIkVZ7hVpIkSZJUeYZbdSwiFkbEKyPiExFxU0SMRsTGiLgrIj4fEb/bpeMsjoiLCQ4S\nNAAAIABJREFUI+LWiNgQEWsi4pqIeFU36u+niNglIk6MiLdGxOci4o6I2FIsb+9C/efX1TfV8thu\nfJ9e6/X5qjvO0PQxgIiYV/SFn0TE+oh4ICK+GxHnRMScDuqtZP/q1fko6h6qvgO9OV9V7TtT6cfP\np2HrX708Z8PYx6A/v2sNWz/TzLTDoBugoXAvMLt4n8CDwEbg0cApwCkRcTVwamZuaOcAEfFUYDmw\nR3GM9cAIcDRwdEScCpycmZs6+SJ9dCRw1STbsovH2QSsnmL75i4eq5d6fr6GrY9FxL7AfwH7Fh+N\nAnOApxXL6RFxfGb+poPDVKZ/9fJ8DFvfgb70n8r0nSb09OfTMPYv+vNv4DD1Mejx71pD2s80Azly\nq26YDVwHnAk8LjNHMnM+8Fjgw0WZE4EPtVN5RCwArqT2A/dm4PDMXADsCryO2j9gJwAf6ORL9FkC\n9wNfAd4DvJTaP1zd9q3M3HuK5c4eHLMXenq+hq2PRcQOwBXUgsk9wHMycx61X1ROA9YBTwE+0eGh\nKtG/enk+hq3vQN/6TyX6TpN69vNpGPtXoR//Bg5TH4Me/q41xP1MM1Fmurh0tADPmmb7PwBbimVJ\nG/VfWOy7Hti3wfY3Fts3AfsP+nw0+Z1mNfjs58X3eFsX6j+/qOtrg/6uFTlfQ9XHgFcV7X0YOLLB\n9tPq/p88ro36K9W/enk+hq3v9OF8VarvNPmdevbzaRj7Vx/O2dD1seJ7PWua7W3/rjWs/cxlZi6O\n3KpjmfmNaYqM/0UxqV3O1qozitfLM/OOBtsvofYDeTZwehv1911mbhl0G6qkD+dr2PrYy4vXr2fm\ndRM3ZublwO3F6hkTtw+hXp6PYes7YP9pSY9/Pg1j//LfwDb0+HetoexnmpkMt+qHjcVr0GKfi4gD\ngKXF6tWNymTmKLCiWH1uOw3UzDVsfSwi5gLPLFYbfp/CF4vX3+ltiwarl+dj2PoO2H/KZBj7l3qq\nrd+17GcaNoZb9cOxxWsCP2lx30Pq9r1hinLj2w5ssf5hd0hE3BARY8Vsp7dGxKURceigG1Yiw9bH\nDqT2y02z32eviNitzWNVoX/18nwMW9+B/vWfKvSdQRvG/tVPM62PHVu8tvq7lv1MQ8Vwq54qful5\nU7G6IjN/2mIVe9e9v3uKcvcUr/OLkQfVLAQOYNtMp/sDrwZ+EBEXDrJhJTJsfazV7zNxn1ZUoX/1\n8nwMW9+B/vWfKvSdQRvG/tVPM6aPdfi7lv1MQ8Vwq56JiFnAx4G9gA3UZtxr1by692NTlKvfNm/S\nUjPHbcDrqf3DvnNm7kltptMTgB9QG5l5S0ScM7gmlsaw9bF+fJ8q9a9eno9h6zvQ++9Upb4zaMPY\nv/phRvWxLvyuZT/TUDHczkAR8YomH3A+2XJCk4f6G+Akape6nJWZU13uUmp9PGddkZmfysyLM/Nn\nmflw8dnmzPwytWfWfa8oen5EzO/28at2vgataudr0P1L1WXfUa/NwD42NL9rSd1guJ2Zsu613WVK\nEXERcFZR9uzM/GibbV1X936qy2Dqt62btFT7en7O+iUzNwJvLlZHgON7cZi617KfrzL0sW6er4F+\nnz71r1b08nyUoe9028C+Uwn7zqANY/8aqGHrY136Xct+pqGyw6AboIH4NHBFB/uvnWpjRLwHOIfa\nD9vzMvNvOzhW/f0fS4BbJim3z3jbMnOqy2ra1dNzNgDXFq8B7NeD+qt0vsrQx7p5viZ+n8n+ir9P\n3ft7JinTrl73r1b08nyUoe9026D7T5n6zqANY/8qg6HoY138Xct+pqFiuJ2BMvMhYE0v6o6I9wLn\nUvth+4bMfH+HVY7/YhXUZvSb7Ifu+Gx/N3V4vIZ6ec6GUcXO18D7WJfP1y3U/v8b/z5fnKTc+Pe5\nNzN/06Vjl1Evz8fA+04P2H/KYxj7l7qgy79r2c80VLwsWV1TXB5T/8P24k7rzMzbgDuL1edNctwR\n4Jhi9UudHnOGOKru/e0Da0UJDFsfK/6i/s1idbLvE9QmV4HefJ/S9K9eno9h6ztQiv5Tmr4zaMPY\nv0qi0n2s279r2c80bAy36orih2395TEdB9s6HyteT4uIfRtsP4vavTObgU928bhDKSJ2At5ZrK4H\nvjrA5pTFsPWxy4rXZ0fEEQ22v4Ta5XjJtu/eFSXtX708H8PWd2BA/aekfWfQhrF/DUzV+1gPf9ey\nn2l4ZKaLS0cL8B5gS7H8eRv7n1+3/7IG2+dTu6drC7XLZw4rPt8ROBPYWGz7u0Gfixa/9+7AImrP\n4ltE7S+nW4B31322CBhp5ZwBzwKWA6cBe9V9Pofa5BnfLfZ7mNo/jgM/F4M8X8PYx4DZwI+KNv8C\nOK74fBa1YPJAse3KSfYfqv7VyfmYaX2nl+erin2nhXPW1s+nmdi/ennOhryPtf271kzuZy4zbxl4\nA1yqvQDL6n5gbgbunWY5t0Ed4z90H270Q7cocxhwX92x1gIP1a1fDcwZ9Plo8dz9vK79Uy3/r5Vz\nBhw7Yf/R4tzVn69NwIWDPgdlOF/D2seAfYH/mdAPNtStfx9YMMm+Q9e/2j0fM7Hv9Op8VbXvNHm+\n2vr5NFP7V6/O2bD2MTr8XWsm9zOXmbc4oZQ6NX5p+/gEJHtOU36kwWfTPvYlM1dGxMHAX1J7nttS\nalPR3wBclpkfabrF5dHsI28alZlqvx8D51G7r+i3qP3lez61f+RvB1YAl2bmjS21dvB6db5qBYas\nj2XmHRHxJGp94UXULiPdBPyE2uzMl2Tm5sl2n6LqSvavDs7HjOs70LPzVcm+06R2fz7NyP5V6MU5\nG9Y+1unvWjO5n2mGicxmfq5IkiRJklReTiglSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIq\nz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKk\nyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIk\nqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIk\nSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIk\nSZIqz3BbQhGxMCJeGRGfiIibImI0IjZGxF0R8fmI+N0uHGNxRFwcEbdGxIaIWBMR10TEq7rxHSRJ\nkiSpnyIzB90GTRARm4DZxWoCDwIPAyNAFJ9fDZyamRvaqP+pwHJgj6L+9cDOwJyiyHLg5Mzc1O53\nkCRJkqR+cuS2nGYD1wFnAo/LzJHMnA88FvhwUeZE4EOtVhwRC4ArqQXbm4HDM3MBsCvwOmATcALw\ngU6/hCRJkiT1iyO3JRQRz8rMb0yx/R+APylWl2XmXS3UfSHwFmAMODgz75iw/Y3AX1MbKT4oM3/a\navslSZIkqd8cuS2hqYJtYXz0NoGntVj9GcXr5RODbeESapcpzwZOb7FuSZIkSRoIw201bSxegxb+\nG0bEAcDSYvXqRmUycxRYUaw+t90GSpIkSVI/GW6r6djiNYGftLDfIXX73TBFufFtB7bWLEmSJEka\njB0G3QC1JiJ2A95UrK5o8Z7Yveve3z1FuXuK1/kRMTczx1ppI0BEzAb2n/DxGmrBWpIkSb0X1CYR\nrffTzHx4EI0ZFxGzgIWDbEOd1Zm5ZdCNUHcYbiuk+EHwcWAvYAO12Y1bMa/u/VSBtX7bvGnKTmZ/\narMxS5IkqTwOBG4ZcBsWAqsG3IZxjwLuG3Qj1B1ellwtfwOcRG3086zMnOrSYkmSJEmaMQy3FRER\nFwFnUQu2Z2fmR9uoZl3d+7lTlKvftm7SUpIkSZJUEobbCoiI9wDnUAu252Xm37ZZVf19tkumKLdP\n8bq2nfttJUmSJKnfvOe25CLivcC51ILtGzLz/R1UN34Zc1CbOXmy+y3GZ1W+qYNjrZn4wbe/9wP2\nWFiWuQPKZ2xsjMMOOQiAlTfcxNy5Uw2uCzxnrfJ8tc5z1hrPV+s8Z63xfLVmzerVPOPwpz7i40G0\nZTq/z97s3ONxtwfZwr9unTdVw8hwW2LFpcjjI7ZvyMyLO6kvM2+LiDuBZcDzgH9rcMwR4Jhi9Uud\nHG7iB3ssXMiiRXt2UOVwGx0d3fp+4cJFjIyMDLA11eA5a43nq3Wes9Z4vlrnOWuN56srSvnkip2Z\nxS7MHnQzVHFellxSE4LteZ0G2zofK15Pi4h9G2w/CxgBNgOf7NIxJUmSJKmnDLclVHePLcA5rVyK\nHBHnR8SWYlnWoMhFwL3UJo26KiIOK/bbMSLOBC4syl2amT9r/1tIkiRJUv94WXLJFIH0vGJ1C/Cm\niHjTFLu8d5JR3YaXnGTm2oh4AbAcOAj4fkSsB3ZmW39YDpzdTvslSZIkaRAMt+UzPpqe1CZ+mu4m\n1Yk3m0x7H0VmroyIg4G/pPbc3KXUHvlzA3BZZn6kpRZLkiRJ0oAZbksmM39OB5eLZ+YFwAVNlFtF\nbRbmc9s9liRJkiSVhffcSpIkSZIqz3ArSZIkSao8w60kSZIkqfIMt5IkSZKkyjPcSpIkSZIqz3Ar\nSZIkSao8w60kSZIkqfIMt5IkSRo6f/GnZ7Hngl3Zc8GuvOoVZ0xabrzMngt25ZSTTuxZe37/Rads\nPc7/efMbe3YcaSaLzBx0GzSEImJPYFX9Z7f8z89ZtGjPAbVIkiTNFNevXMkJxz2LzGTOnDl887rv\n89jHPa5h2T0X7Lr1/TOPOYZ/v/LqnrTpxz/6Ic951jFb23TNd77L4/ffvyfHGvfrX9/HEx/7mIkf\nPyoz7+vpgafR6PfEM1jCLszu6XE38DAf466JHw/8fKh7HLmVJEnSUHnrG9/A+ADO77/0DycNtv30\npCcfygtOPgWATZs28ba3vGnALZKGj+FWkiRJQ+PLy7/Id6+7FoBZs2bxp39+9oBbtM2f/sU5W99/\nefkX+cH3vjfA1kjDx3ArSZKkofGuv/6rre+f9/yTeNzjHz/A1mzvKYcdxjOPOWbr+rveeeEAW6Nu\niYh5EXF+RPwkItZHxAMR8d2IOCci5rRZ54KIOCUi3hERV0bELyNiS7G8vMk6doqIsyLi6xFxX0Rs\nioh1RTs/EBGPbadtZWa4lSRJ0lD4xte/xo9/+MOt66981asH2JrGXvG/trXpv77+NW74yY8H2Bp1\nKiL2BX4MvA04GEhgDvA04CLg2ojYrY2qXwR8Hngr8Hxgcd22aSdNioi9gR8AlwDPAvYARoEdi3b+\nGXBjRJzaRttKy3ArSZKkofChf/jg1vfL9t2XY487foCtaeykF57MHnss3Lp+aV2bVS0RsQNwBbAv\ncA/wnMycB4wApwHrgKcAn2ij+gR+Cfwn8FfA77W4/z8CBxX1vB1YlJm7ATsDxwI3AjsBlxVBeCgY\nbiVJklR5v7jzTr7ypeVb1099yR8MsDWTmzNnDr/7ey/euv65f/sMax94YIAtUgdeDhxCLUC+ODO/\nBpA1/wr8SVHu+RFxXIt1fzwz98nMF2Tm2zLz35vdMSJGgJOK1csy88LMvL+ubdcApxTbdwFe0GLb\nSstwK0mSpMr77Gf+ZesMyRHBSSefPOAWTe6kF75w6/uNGzfyH19oOreoXMbvff16Zl43cWNmXg7c\nXqxO/rDlBjJzSwft2hmI4v33J6n/f4D7i9WRDo5VKjsMugGSJElSpz77mX/d+v7Re+/Nk558aFfq\nvfmmG/nh9StZ9atfscOcOey99z487fAjWLpsWdt1PvOY32b+ggVbR2w/+5l/4WVnNDVHkEoiIuYC\nzyxWp3o48heBM4Hf6XmjCpm5OiJuB/YDDm9UJiIeB+xObdS5YQCuIsOtJEmSKu0Xd97JLTffvHX9\n6GN+u+M6r/yPL/B//+pCbrv1lkdsiwiOOPIozr/wnTztiCNarnv27NkcedTT+fLyLwJw7be/zbp1\n65g3b17H7VbfHEhtdDSBG6YoN75tr4jYLTN/0/OW1fwZ8Dng5UXQ/bvMXBMRs4Gjgb8vyn0mM1f0\nqU0952XJkiRJqrSvffUr260/4+ijO6rvjeedyyv/6PSGwRYgM7nu2u9w0gnP4eL3vLutY9S3cfPm\nzVzzX19vqx4NTP0kTHdPUe6eSfbpqcy8CjiG2oRXbwF+HREPAA8CX6d26fIbgJf2q039YLiVJElS\npV377W9tt37oUw5rq55MuOjd7+LD//ShrZ/NHRnhwIMO4ree/GR222337cpv2bKFd73zQv72/Re3\nfKwnH/qU7da//a1vttXmmWgTW9peuqh+mH1sinL12/o9ND8CzKV2tW4Cu7It/80FFlILuUPDy5Il\nSZJUaT+qe7btDjvswBMOeGJb9dz+P//Nd6/9DgCPWryY8y98J6e86PfYcccdgVqY/cbXv8Zb3/TG\n7UZ13/mOC3jGM49p6RLlAw86aPvvcP31bbV5JvoIvxh0E0ovIv4I+H/UwuynqT1z91Zqz7s9Dvi/\nwF8Cz4mIYzNzdFBt7SZHbiVJklRZGzdu5Gc/vW3r+pKly5gzZ05bdf3ynnvYvHkzy/bdl69e801e\n8genbQ22ALNmzeLZxz+Hr3xjBUce9fStn2/ZsoVz/vx1LR1r0aI9txsJvvHGqW7bVAmtq3s/d4py\n9dvWTVqqiyJiIXAJtax3WWaenpnXZ+ZYZt6VmR8DngNsBJ5KLeQOBUduJUmSVFl3/eIXWx8BBLD3\nPvt0VN+sWbP48GWfYK+9Hj1pmV122YWPfvJTHHnYoVtnPL75ppv4r699lWOPO77pY+316L34zW9q\nT2MZXb+e39x/P7vtvvs0ew2nJ4zsyK6zmosm787Ht3WM0S2becfYz9vat4H6+2yXMPmkUvUd8p5J\nynTbc4D51C5FvqhRgcy8OSKuAn4PeDHwtj61raccuZUkSVJl3X33XdutL168uKP6XnjKizj0KU+Z\nttyiRXvy2tf92XafffLjH2vpWIsX77X1fWY+4ruosZ1iVlvLjtHV6HMLtfAYwCFTlBvfdm8fZ0re\nr+79f09R7mfF62N615T+MtxKkiSpstatXbvd+siuu3ZU3x+8tPnJY//gpX9IRGxdb3VSqIltXTvh\nu6i8MnMMGP8P/rxGZaLWOU4oVr/Uj3YV6jvSY6YoN/6XoL5cLt0PhltJkiRV1tjY9hPV7rJz+5O/\nzpo1i2ccfUzT5ZcsXcqyZftuXb9v1Sruvqv50ddddtllu/Wx0aGY02cmuax4fXZENJpN7CXURlET\naG1YvzPXFq8BnNmoQETsBbyoWP1OPxrVD4ZbSZIkDY26229btmTpUkZGRlra54l1sx5nJnfeeUfT\n+2YnjVUZXAb8hFqI/GxEHAcQEbMi4iXAPxXlrs7M7R5kHBHnR8SWYlnWqPKIWFQsCyNiUd2meeOf\nFct2fyXJzJXANcXq6yLi4oh4dFHnzhHxvGL7fGAL8L5OTkKZGG4lSZJUWXPnbj9R7YMbH2y7rt13\n36ONfbafAOqBYoKpZjz44PZtndtisNZgZebDwMnAz6lNHPWViBgFRoF/ofZc25XA6VNVM8W2VcVy\nX/E67pK6z1YBb2iw72nAjdSC99nA3RGxrmjbfwKPBzYDf5GZK6b6nlViuJUkSVJlzV+wYLv19eva\nv31wl7lTPdGlsblztw+ko+vXN73vxLLz589v+fgarMy8A3gS8A5qo7gPU3vEzveBc4GjMrPRXzya\nHbbPJpeJ7bqX2mN+Xgt8jVoI3hEYA24G/hE4LDP/rsl2VIKPApIkSVJl7bPPku3Wf/WrX7Vd14YJ\n9+82Y2xs+/tkW5nQ6t57f7n1fUSwZMnSlo+vwcvM9cD5xdLsPhcAF0xTpqOByMx8iFqI/cdO6qkS\nR24lSZJUWfssWbLdjMW/vPvuKUpPbc2a1W3ss2a79QUTRpKncu8v7936ftd581iw224tH1/SNoZb\nSZIkVdZOO+3E/k84YOv6XXf9goceeqituu6+6y7WtXhZ88033bj1fUSw776PaWq/++5bxQMPbHvs\n6UEHT/WoVEnNMNxKkiSp0p586KFb32/evJlbbr6prXq2bNnCd771rabL/+LOO/nFnXduXX/U4sXs\nvc8+Te178403brd+6FOe0vRxJTVmuJUkSVKlHfWMZ263/qMf/rDtuv7l8k81X/bT25d9+oR2TOWH\nP7x+u/VnPPPopveV1JjhVpIkSZV23PHP2W79O9/6Ztt1XfHvn+f6lSunLXfffav44N/97Xafnf5H\nZzR9nG9/c1sb58yZwzHPOrbpfSU1ZriVJElSpS1ZupQnHnjg1vVvrrim7boyk1e/4o+2m8l4og0b\nNvCK0/+QdWvXbv3siQceyLHHHd/UMTZv3sx3r7t26/oRRx3FvHnz2m6zpBrDrSRJkirvxS/5/a3v\nf3nPPfzw+uunKN3Yo/femx122IE777iD4455Jp/5l8vZuHHj1u1btmzha1/5Ms951jHbhdNZs2bx\nvr9p/nGh31pxzXbB+MUv+YOW2yrpkQy3kiRJqrxTf/+07R4JdNUVX2i5jsc+7nG8/o1vBuC+Vat4\n7WtezRP2W8ZvP/0Inn3MM3jCY5bxBy9+Ebfdest2+73xLf+Hw488sunjXHXFFVvf77zzzpzyuy9q\nua2SHslwK0mSpMpbsnQpz3nuCVvXP/uZfyUzW67nnNe/gdf87zO3ro+NjnLzTTdxw49/vN2jewBm\nz57NG970Fs4+7/VN1//QQw/xhc9/buv6i158KvNbeDaupMkZbiVJkjQU/vdrz9r6/hd33sl/fe2r\nTe0XEduN+r7z3e/lo5/4FE844ImTlj/yqKdz5Re/zOvf+KaW2vifV17BmjWrt9bzmjNf29L+kia3\nw6AbIEmSJHXDbx/7bJ586FP4UfGYnY/88z/x7AkzKU903wPrG35+0gtP5qQXnszNN93ID69fyX2r\nVjFnzo4s3msvDj/iSJYuW9ZWGz/6kX/e1t5nHcshv/WktuqR9EiGW0mSJA2NN77lrbz0JS8GYPnV\n/8nPfvpTHr///m3Xd+BBB3PgQQd3pW3Xr1zJt1asAGqjtm9669u6Uq+kGi9LliRJ0tD4/+zde7hk\nVX3n//e3mwbpQ9NAtwqCNBhU0JbBC6gjJgOoXORHNEqCkoCJjv6c+DyTeMlFzdhgNCZjiBkyZgYd\nE4wiMfEWVIQQMWAyYgATINwCtk3k8oPuFvpy2pbu/v7+2Lvo4nCqTl12Ve19zvv1PPXUZa+19qpD\nd1Of81171StedTIvfslLgeJrfS784z+a8Ix2u/BjFzz2+BWvOpkXHnvsBGcjzT+GW0mSJM0rv/uR\nP2DRouJj7uc/dwl333XXhGcEN/3LP3PZV74MwJ577sn5H/q9Cc9Imn8Mt5IkSZpXjnn+83njL50D\nwM6dO/nw754/4RnBh85b89jGVW9+69uGWiotaXYxyBbp0lwi4snAg+2v3f79H7By5ZMnNCNJkqSF\nZf36hzjyGYfNfPkpmfnQBKbzmNk+J/7u1DPYZ9FotwPasmsH79/6/ZkvT/znoepYuZUkSZIkNZ7h\nVpIkSZLUeIZbSZIkSVLjGW4lSZIkSY1nuJUkSZIkNd5otySTJEmSpDkcvu9eLF882mjyyM7FsHWk\np9CEWbmVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7itqYjYOyJOjYj3R8QXI2JdROwq\nbx8Ycuw1bWN1uz2jqvcjSZIkSaPkbsn19WLgax2OZUXneBTY0OX4jorOI0mSJEkjZbitrwR+BNwA\n3Ah8D/gj4MAKz/EPmXliheNJkiRJ0kQYbuvr2sxc0f5CRPz+pCYjSZIkSXXmNbc1lZm7Jj0HSZIk\nSWoKw60kSZIkqfEMtwvb6oi4JSKmI2JLRNwRERdFxDGTnpgkSZIk9cNwu7CtAJ4NbAWWAM8E3gLc\nEBEfnOTEJEmSJKkfbii1MN0JvAf4CrA2M3dGxB7ACcCHgRcC74uIH2XmBVWddHp6mq1btw7Ud2pq\nqqppSJIkNcqgn5+mp6crnolUb4bbBSgzL5nltR3A30bENcA1wLHAmoj4ZGZuquK8L1j9nIH7rt80\n2D/qkiRJTbfqoKdMegpSI7gsWY+TmduB95ZPp4CTJjgdSZIkSeqJlVvN5jvlfQCHVzXojbfcyooV\nK6saTpIkaUFYd/+DA/XbsGH9UCvnpKYx3Gpsli5d6rWzkiRJfRr089O2bV5zq4XFZcmazUvaHq+d\n2CwkSZIkqUeGWz1OROwFfKh8ugX4uwlOR5IkSZJ6YritsYjYPyJWRsSKiFjJ7v9eU63XytvUjH5r\nImJXeTt0xrGfiYgrIuKsiDiw7fUlEXEScC1wHJDA+VXtlCxJkiRJo+Q1t/X2PeDQWV5/T3lruRj4\n5Vna5SyvBfDK8kZEbAOmgeXs/vOwE/hIZn50sGlLkiRJ0ngZbustmT2gztau2/N2NwHvpriu9nnA\nSmBfYCvF9bXXAhdl5r/2PVtJkiRJmhDDbY1l5kBfw5OZ5wHndTi2EbhgmHlJkiRJUt14za0kSZIk\nqfEMt5IkSZKkxjPcSpIkSZIaz3ArSZIkSWo8w60kSZIkqfEMt5IkSZKkxvOrgCRJkiRN1AHP3J/9\n99xzpOdY/JOfwP0jPYUmzMqtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnx\nDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElq\nPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEnS\ngCJiWUSsiYibI2JLRDwSEd+NiHdGxJIBx1weET8bEedHxFcj4v6I2FXezu2h/xER8a5BopERAAAg\nAElEQVSIuCwi1kXE9ojYGhF3RsQnI+IFg8yr7vaY9AQkSZIkqYkiYhXwLWBV+dJWYAnwovJ2dkSc\nlJkP9zn0a4FPdTiWc8zpZcC1M9pvBvYCjihvb4qID2XmB/qcV61ZuZUkSZKkPkXEHsBlFMH2PuAV\nmbkMmALOogiUzwc+M8DwCdwPfB34XeDn+ui7BNgJfAl4PbAyM/cDlgLHAd+myIG/ExG/MsDcasvK\nrSRJkiT171xgNUUQfV1mXgeQmQl8PiIWAZcAp0XEiZn5zT7G/ovMvLj9hYjote+/AUdm5t3tL5bz\nuj4iTgL+CTga+G06V4gbx8qtJEmSJPWvde3r1a1g2y4zLwXWlk/P6WfgzNw16KQy896ZwXbG8UfZ\nXU1+RkQsH/RcdWO4lSRJkqQ+RMRS4GXl08u7NP1Gef/K0c6ob9vbHi+e2CwqZriVJEmSpP4cBQTF\nkuRburRrHTswIvYb+ax695/K+/szc+MkJ1Ilw60kSZIk9edpbY/v7dLuvg59JiYiXgq8pnz6yUnO\npWpuKCVJkiSpMbbt3DlQvx8P2K+DZW2Pp7u0az+2rGOrMYmIJwOfo6g63wn8wWRnVC3DrSRJkqTG\nOOHqa+dupCeIiH2AvwEOBTYBZ2Zmt2DeOC5LliRJkqT+bG57vLRLu/Zjmzu2GrGImAK+Bry4nMdp\nmXnzpOYzKlZuJUmSJE3U/ocfwAF779VT239e9bMDneNH27ZzwiXfmLthb9qvsz2EzptKHdz2+L4O\nbUaqLdi+HNgCvDoz/3EScxk1w60kSZKkxli6ZLAI8+MdlV5zezvFTskBrGb3V/7MtLq8fyAzH65y\nAr1oC7Y/DWylCLbfHvc8xsVlyZIkSZLUh/Ja1VZIPGW2NhERwMnl0yvHMa8Z558Cvk4RbLdQLEWe\n1xcsG24lSZIkqX8Xl/cnRMRxsxw/EzicosL76bHNiscF29ZS5HkfbMFwK0mSJEmDuBi4mWJp8hci\n4kSAiFgUEWcCnyjbXZ6ZV7d3jIg1EbGrvB062+ARsbK8rYiIlW2HlrVeK297z+i3FPgqRbDdDJw6\nn5cit/OaW0mSJEnqU2bujIgzgKuBw4CrImIbRQGxtTvWjcDZ3YbpcuzBDq9fWN5azitvLa8HfqZ8\nvIQieHc7/89l5v/tMo/GMNxKkiRJ0gAyc11EHA28G3gtxTLkRykqup8DLszMHbN17fUUA7SJttf3\nAp48R/8lPc6l9gy3kiRJkjSgzNwCrClvvfaZWW2drc1Al5Bm5sXsvh54QfGaW0mSJElS4xluJUmS\nJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mS\nJElS4xluJUmSJEmNZ7iVJEmSJDWe4baGImLviDg1It4fEV+MiHURsau8faCiczw1Iv4wIu6IiG0R\nsTEiromIN1cxviRJkiSN0x6TnoBm9WLgax2O5bCDR8QLgSuAA8rxtgBTwPHA8RHxeuCMzHx02HNJ\nkiRJ0jhYua2nBH4EXAX8AfAG4IEqBo6I5cBXKYLtbcCxmbkc2Ad4B/AocDLwsSrOJ0mSJEnjYOW2\nnq7NzBXtL0TE71c09ruBpwLTwGmZuQ6grNJ+PCL2BT4MvDUiPpaZ/1bReSVJkiRpZKzc1lBm7hrh\n8OeU95e2gu0MF1IsU14MnD3CeUiSJElSZQy3C0hEPBt4evn08tnaZOZW4Nry6avGMS9JkiRJGpbh\ndmFZXd4ncEuXdq1jR412OpIkSZJUDcPtwvK0tsf3dml3X3m/b0QsHeF8JEmSJKkSbii1sCxrezzd\npV37sWVztO3Z9PQ0W7duHajv1NRUFVOQJElqnEE/P01PV/IRTmoMw63G5gWrnzNw3/WbBvtHXZIk\nqelWHfSUSU9BagSXJS8sm9sed1tu3H5sc8dWkiRJklQTVm4XlvbrbA8Bbu/Q7uDyflNmVrae5cZb\nbmXFipVVDSdJkrQgrLv/wYH6bdiwfqiVc+O07089jf32Ge1WLzu2uEx7vjPcLiytXZCDYufkTuG2\ntavyrVWefOnSpV47K0mS1KdBPz9t22aY08LisuQFJDPvBO4pn54yW5uImAJeXj69chzzkiRJkqRh\nGW4Xnk+X92dFxKpZjv8qMAXsAD47tllJkiRJ0hAMtzUVEftHxMqIWBERK9n932qq9Vp5m5rRb01E\n7Cpvh84y9EeBByg2jfpaRLyg7LdnRLwd+GDZ7qLMvGs0706SJEmSqmW4ra/vAQ8CD5X3h5Svv6ft\ntQeBP+nQP2d9MXMTcDqwAXgOcH1EbAK2AP8TWAJcAfx6Je9CkiRJksbAcFtf2cdtZr/uA2feCDwX\n+CPgTmAxxVf+XAu8JTNPzcxHq3kbkiRJkjR67pZcU5l5+ID9zgPO66Hdg8C7ypskSZIkNZqVW0mS\nJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaS\nJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iV\nJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS4+0x\n6QlIkiRJWtimDj2EfZbvM9JzbHtky0jH1+RZuZUkSZIkNZ7hVpIkSZLUeIZbSZIkSVLjGW4lSZIk\nSY1nuJUkSZIkNZ7hVpIkSZLUeIZbSZIkSVLjGW4lSZIkSY1nuJUkSZIkNZ7hVpIkSZLUeIZbSZIk\nSVLjGW4lSZIkSY1nuJUkSZIkNZ7hVpIkSZLUeIZbSZIkSVLjGW4lSZIkSY1nuJUkSZIkNZ7hVpIk\nSZLUeIZbSZIkSVLjGW4lSZIkaUARsSwi1kTEzRGxJSIeiYjvRsQ7I2LJkGM/NSL+MCLuiIhtEbEx\nIq6JiDf3McYR5Ri3lHPbGhHfj4gvR8Tbh5lf3ewx6QlIkiRJUhNFxCrgW8Cq8qWtwBLgReXt7Ig4\nKTMfHmDsFwJXAAcACWwBpoDjgeMj4vXAGZn5aJcxfg34CLBnOcY0sKOc72HATwN/2u/c6srKrSRJ\nkiT1KSL2AC6jCIr3Aa/IzGUUAfQsYDPwfOAzA4y9HPgqRbC9DTg2M5cD+wDvAB4FTgY+1mWMdwIX\nAAF8GHhGZi4rxzkAOAX4ZL9zqzPDrSRJkiT171xgNUVF9HWZ+U2ALHweeFvZ7rSIOLHPsd8NPJWi\n0npaZt5Yjv1oZn4c+EDZ7q0R8cyZnSPieRQV2wTOysz3Z+a61vHMfCQzr8zM3+hzXrVmuJUkSZKk\n/p1b3l+dmdfNPJiZlwJry6fn9Dl2q/2l7aG0zYUUy5QXA2fPcvy9FJegfjkzv9TnuRvLcCtJkiRJ\nfYiIpcDLyqeXd2n6jfL+lX2M/Wzg6d3GzsytwLXl01fN6D8FvK58+he9nnc+MNxKkiRJUn+OoriW\nNYFburRrHTswIvbrcezV5X2vYx814/XjKKq2CdwYEcdHxFci4qGI+HFErI2IT0XEc3ucT2MYbiVJ\nkiSpP09re3xvl3b3dehT5dj7lpXklme1Pf4F4Brg/6HYMXk7xQZYb6IIvm/qcU6N4FcBSZIkSWqM\nrT/ePlC/6e0/qXIay9qH7nbaDn1GMXbr+f7lfQC/B3wPeFtmXg8QEccBFwFHAxdFxL9m5j/1OLda\nM9xKkiRJaownv+G3Jz2FumtfnbsNeHVmPtB6ITO/GxGnA3cAewPvA14z3imOhuFWkiRJ0kTtcdBh\n7LH/8klPox+b2x4v7djq8cc2d2zVfewtfY7d/viS9mDbkpk/jIhLgDcDJ0ZEZGb2OL/aMtxKkiRJ\naowf/f1fDtRv/cObeObP/ueqptF+LewhdN746eC2x/d1aDPX2LfPMfamzGxfovzDtse3dTlP69gU\nsAJY3+P8astwK0mSJKkxpvZ+0kD9pge8VreD2yl2Iw6K3Y2/0aFda+fjBzLz4R7HbgXl1tidwm1r\n7FtnvH5Tj+eJtseNr9qCuyVLkiRJUl/KSum3y6enzNYmIgI4uXx6ZR9j3wncM8fYU8DLZxs7M+8G\nvl8+fU6XU7WObcrMDb3Or84MtzUWEcsiYk1E3BwRWyLikYj4bkS8MyKWDDjmmojY1cPtGVW/H0mS\nJGkeubi8P6HcgXimM4HDKaqin+5z7Fb7syJi1SzHf5ViOfEO4LOzHP/z8v6NEXHQzIMR8XTgDeXT\nr/c5t9oy3NZU+Yf4JuC/Ac+l+EuxBHgR8FHgO318EfRsHgUe6HLbMcTYkiRJ0nx3MXAzxfLeL0TE\niQARsSgizgQ+Uba7PDOvbu84o+B06Cxjf5TiM/lS4GsR8YKy354R8Xbgg2W7izLzrln6XwCsa+t/\nbNu5jwO+CjyJ4uuDzh/gvdeS19zWUETsAVxG8QXL9wHnZOY3y6UNrb8ozwc+A5w+4Gn+ITNPrGK+\nkiRJ0kKTmTsj4gzgauAw4KqI2EZRQNyrbHYjcHa3YTqMvan8up4rKJYPXx8RWygCaSvDXQH8eof+\n0xFxCnAVcAxwXURsLQ9PlfebgTdk5h1zvdemsHJbT+dSXCCewOsy85sAWfg88Lay3Wmt3xBJkiRJ\nGq/MXAccTVH9vBnYCWwHrgfeBbwkMx+ZrWsPY99IsYLzj4A7gcUUgfRa4C2ZeWpmPtql/x1l//OB\nfwF2UVSZbwf+GFidmfNmSTJYua2rc8v7qzPzupkHM/PSiPgQxRr+c4BvjnNykiRJkgqZuQVYU956\n7XMecF4P7R6kCMnvGnBum/qdW5NZua2ZiFgKvKx8enmXpq3txl852hlJkiRJUv0ZbuvnKIrlAknn\nL4Om7diBA24stToibomI6XIn5jsi4qKIOGaAsSRJkiRpogy39fO0tsf3dml3X4c+vVoBPBvYSrEL\n8zOBtwA3RMQHu3WUJEmSpLrxmtv6Wdb2eLpLu/Zjyzq2eqI7gfcAXwHWlru87QGcAHwYeCHwvoj4\nUWZe0Me4c5qenmbr1q1zN5zF1NTU3I0kSZLmoUE/P01Pd/soKc0/htsFJjMvmeW1HcDfRsQ1wDXA\nscCaiPhkeRF6JV6w+jkD912/abB/1CVJkppu1UFPmfQUpEZwWXL9bG57vLRLu/Zjmzu26kNmbgfe\nWz6dAk6qYlxJkiRJGjUrt/XTfp3tIXTeVOrgtsf3dWgziO+U90HxVUOVufGWW1mxYmWVQ0qSJM17\n6+5/cKB+GzasH2rlnNQ0htv6uZ1ip+QAVrP7K39mWl3eP5CZD49jYsNaunSp185KkiT1adDPT9u2\nec2tFhaXJddMZk4D3y6fnjJbm4gI4OTy6ZUVT+ElbY/XVjy2JEmSJI2E4baeLi7vT4iI42Y5fibF\nkuEEPl3VSSNiL+BD5dMtwN9VNbYkSZIkjZLhtp4uBm6mWJr8hYg4ESAiFkXEmcAnynaXZ+bV7R0j\nYk1E7Cpvh8449jMRcUVEnBURB7a9viQiTgKuBY6jCM3nV7lTsiRJkiSNktfc1lD53bNnAFcDhwFX\nRcQ2il9G7FU2uxE4u9sws7wWwCvLG+WY08Bydv9Z2Al8JDM/OuTbkCRJkqSxMdzWVGaui4ijgXcD\nr6VYhvwoRUX3c8CF5ffTPqFrl2FvKsd7CfA8YCWwL7CV4vraa4GLMvNfq3ofkiRJkjQOhtsay8wt\nwJry1muf84DzOhzbCFxQxdwkSZIkqU4Mt5KkoS3bNfvXTWxetHTMM5EkSQuV4VaSFrhOwXTSYxuM\nJUlSPwy3kjSPjTK4jlq3uRt8JUnSTIZbSWqwJofXYbgMWpIkzWS4laSaW6gBdhAzf1aGXUmSFg7D\nrSTViEG2WoZdSZIWDsOtJE2AIXYy2n/uBl1JkuYXw60kjZhBtp5a/10MuZI0eYueciiLVhww2nMs\n2TjS8TV5hltJqphhtlms5kqSND8YbiVpCAbZ+cVqriRJzWW4laQeGWQXDqu5kiQ1j+FWkjowzAqs\n5kqS1BSGW0kqGWbVjSFXkqR6M9xKWrAMsxrEsl3TBlxJkmrIcCtpwTDMqipWcSVJqh/DraR5yzCr\nUTPkSpJUH4ZbSfOGYVaT4lJlSZImz3ArqbEMs6oTq7iSJE2W4VZSYxhm1QRWcSVJmgzDraRaM9Cq\niQy4kiSNX+3CbUQEcCzwYuBo4FBgf2BvYBuwEVgH3ARcl5nXT2iqkkbAMKv5woArSdJ41SbcRsRJ\nwC8Bp1OE2eihW0bEBuAy4DOZefUIpyhpBAyzms+8DleSpPFZNMmTR8SSiHhbRNwG/C1wDnAAvQVb\nynYrgV8GroqIWyPirRFRm9Au6YmW7Zp+7CYtBP5ZlyRp9CYWAiPiF4EPAqvaXt5Jsdz4O8B1wO3A\nj4ANwCZgOUX4PQA4imLp8ouB5wGLgSOB/wX8VkT8TmZ+dixvRlJXfrCXXKYsSdKoTSTcRsQ/AC9t\ne+kfgc8Cf5mZG7t03VDeoAi/f16OtwL4eeAXy3EPA/4iIt6emcdXOnlJPTHQSk9kwJUkaXQmtSz5\npcAO4FPAMzPz+Mz80zmCbUeZuaHs/zLgmeW4O3h8gJY0Qu1LjQ22Umf+/ZAkaTQmFW4/BTwrM9+S\nmXdXOXBm3p2ZbwGeDfxZlWNLejzDrDQY/85IklS9iSxLLsPnqM+xFhj5eaSFxg/lUjVcoixJUrXc\nVVhSV4ZZaXQMuJIkVcdwK+kJDLTS+BhwJUmqhuFWEmCgVb0s3nR/JePs3PegSsaRJEn1V+twGxGL\ngCOA/YAn9dInM68Z6aSkecIwqzqoKsT2M34dA6/VW0mShlfLcBsRJwHvBE4A9mq93KVLlscTWDza\n2UnNZaBVHYw60PZ7/rqEXQOuJEnDqV24jYj/Dryr324z7iWVDLSatEmH2bm05leHkGvAlSRpcLUK\ntxFxJo8Ptv8GfBt4ENjewxA5inlJTWOgVR3UPdTO1D7fOgRdSZLUn1qFW+Ad5f0O4Fcy8zOTnIzU\nFIZZ1UXTAm0nk6zmWr2VJGkwdQu3x5T3nzDYSt0ZaFUn8yXUzjSpkGvAlSSpf3ULt4vKe3c8lmZh\noFXdzNdQO1OdrsuVpPlo57KnsnPflaM9x0+WjHR8TV7dwu0PgOcCe054HlJtGGhVRwsl1M40zpBr\n9VaSpP4smrvJWP1NeX/8RGchTdiyXdOP3aS6WajBtt24fgb+GyBJUu/qFm7/BNgI/FJErJ70ZKRx\nMtCq7hZvut9g28afhyRJ9VKrcJuZ9wM/B+wCroqI1094StJIGWjVBIa47kb9s/HfB0mSelO3a27J\nzGsi4vnAV4DPR8QDwA3ABorQO1f/XxnxFKWh+EFVTWKo7c3iTfe72ZQkSRNWu3AbEQcAa4BnlS8d\nCLy6x+4JGG5VK4ZZNZXBtj+j3GzKzaUkSZpbrcJtRCwDvgkcPegQFU5HGpiBVk1mqB2OVVxJkiaj\nVuEW+K/sDrb3UWww9Q/Ag8D2SU1K6oWBVvNBnYPtrgfWVjbWogMPr2ys2Ywi4Fq9lSSpu7qF218o\n79cBx2bm+klORpqLgVbzSd2CbZVhdq6xRxF2reBKkjRedQu3zyjv/6fBVnVloNV8VJdgO8pA2+t5\nqwy6BlxJksanbuF2K7A38IMJz0N6HAOt5rM6BNtJhdrZtOZSVcitMuC6NFmSpM7qFm5vA15OsUOy\nNFEGWi0Ekw62dQq1M1UZcq3gSpI0eosmPYEZLi7vf36is9CCtWzX9GM3aT5bvOn+iQbbXQ+srXWw\nbVe3ufrvkyRJs6tbuP1ziq8COj4ifmvCc9ECYqCVxqNuQbEfw8590lVySZLmu1qF28zcBfws8AXg\nwxHx9Yg4LSJWTHhqmoes0mqhmlTIamqoncmAK0lSPdXqmtuI2AUkEOVLp5S3jIiO/VrdgczMxaOb\noZrOIKuFbhLhar6E2nbDXI9bxfW3biwlSdIT1apyW5otxUYPt059tcBZoZUKBtvqzff3J0lSk9Sq\ncgtcw+Mrt/3KCueiBjPISo+3EILtjnvv7rntHgf/VGXn3fXA2r4ruO6eLElS9WoVbjPzP016DnUS\nEcuAdwGvAw4HdgJ3AJcCF2bmo0OM/VTgN4DTgUOBbcAtwMWZ+X+GnPpEGGil2Y072I4j1PYTZHvp\nP2zYnUTAdWmyJNVDnT+zR8RPlf1fBRwEbAZuAC7KzC8OOq+6qlW41W4RsQr4FrCqfGkrsAR4UXk7\nOyJOysyHBxj7hcAVwAEU1e4twBRwPMVO1a8HzhjmL+K4GGil7uZbsB021PYy7qBBd5CAK0lqtjp/\nZo+I04C/AvYu+28C9qMIuq+KiD/LzDf3O686q+M1twMrf2vSeBGxB3AZxV+S+4BXZOYyij/MZ1H8\nxuX5wGcGGHs58FWKvyS3Acdm5nJgH+AdwKPAycDHhn8no+E1tFI9jSrY7rj37sdu4zDM+fr9Gbh7\nsiQ1V50/s0fE4cDnKYLtt4FnZ+b+FOH2/LLZL0fEe/qdW53VKtxGxH8dou8yit9szAfnAqspfsPy\nusz8JhRbQWfm54G3le1Oi4gT+xz73cBTgWngtMy8sRz70cz8OPCBst1bI+KZQ76Pyhhopf41PTiN\nM9BWOQcDriQtGHX+zH4+sBS4Hzg9M+8q+2/NzDXARWW790XEfn3OrbZqFW6BCyLijf12KoPtlcCL\nq5/SRJxb3l+dmdfNPJiZlwKtT0/n9Dl2q/2lmbluluMXUix5WAyc3efYlTLQSoNr8nLkOoTamfqd\n07g20/LfR0maqFp+Zo+IKYrrfwH+NDM3zdL/98r7fYHX9Dm32qpbuA3gzyLi5J477K7YzotgGxFL\ngZeVTy/v0vQb5f0r+xj72cDTu42dmVuBa8unr+p17CoZaKVmqTrY1tmoAq7VW0lqlpp/Zj8eeBJF\nRblT/3UUy51n699YdQu3aykuwP7riJgzrLYF25eUL31+hHMbl6MoQn5S7ITWSevYgX0sJVhd3vc6\n9lE9jjs0q7RSdcYZlKoKtnWs1nbSz1z9HlxJmrfq/Jl99SxtuvV/To/zqr26hdtXAQ9SXIT91Yg4\nslPDMth+g8cH24kuo63I09oe39ul3X0d+lQ59r7lb6VGwkArVa+pwbaJqg64Vm8lqVHq/Jm91f9H\nmbm9h/69zqv2avVVQJl5d0ScSrGd9grgyoj4j5n5w/Z2EbEPRbB9afnSXwFnZ+aucc53RNp3fO6W\n+tqP9bpL9KBjV5I+p6enWbT5oceeb+2j79TUVBVTkFSRugbbLWvvGbjvPocf2nefHffe3dNXB43y\na4L8vltp/tu6tZ9PTbtNT8/PAkJNfh51/sy+bJbj3frPi2+cgZqFW4DM/F5EvIZiffghFAH3+Mzc\nCI8F2yvYHWz/GnhjZu6cyITVsxesHnzFw/aHH5q7kbTAjavyV7dgO0yg7TROP0G314Dbi8Wb7mfn\nvgdVMpak+WPVQU+Z9BRq5YCDD5v0FFRTtQu3AJl5dUScTbHU+Ejg6+X22Yt4YrB9wzwLtpvbHnf7\nVXz7sc0dW3Ufe0uFY0tSz4YNtlUF2rnG7zXk9hJwR1m9laSm27Job57UrFUodf7MvnmW4936z5vP\n+7UMtwCZ+YWI+C/AnwLHAV+i+NLiVrD9AvMv2MLj19UfQueLwA9ue3xfhzZzjX37HGNvyszK1m/c\nedMNrFyxoqrhJLVpUtV2mGA76lDb6Xy9hNyqAq7VW0kzrbv/wYH6bdiwfqiVc3VVk59HnT+zt/rv\nHxF7dbnuttW/13nVXm3DLUBm/u+IeCqwhsdvn/1F4Kx5GGyh+MObFLuvrWb39uEztXZBeyAzH+5x\n7NZfutbYnf6itMa+tcdxe7J06VKvnZVGwGA7er2G3CqXKEtSy6Cfn7Ztm5/X3Nbk51Hnz+ztQft5\nwPVz9P/XHudVe3XbLfkJMvN84ONtL32J+RtsKX/r8u3y6SmztYmIAFrfBXxlH2PfCbQ+HXYaewp4\neb9jS9Jcmhps221Ze8+cc5nrffr1QJLUfDX/zP5t4McU4bhT/1UUl3/2Nbe6m0jlNiI+QPGbjl5t\nAB6hmO+twHuLPytPVIbhpruY4g/rCRFxXGZ+d8bxM4HDKX6Gn+5z7E8D7wfOiogPll/g3O5XKb6K\naQfw2b5nLmlemlQgq0uonWnL2nu6VnGHreC6NFmSGqGWn9kzczoi/hr4ReDtEfE/MnPTjP6/Wd5v\nAr7c59xqKzL7yZgVnTRiVF/Zk5m5eERjj01ELAZupFhGcC9wbmZ+MyIWAa8DPkmxZffXM/P0GX3X\nAP+tfHpYZt4z4/i+FEsbDqT4RcE5mXljROwJvBn4GLAE+HhmvmOI9/Bkiu8sfswP77qNJ69cOeiQ\nkmbRlCXJg1Rtqwi2D9/57z233e9ZT+97/LmWKXcLuHNde9tvuPXrgCTNtH79Qxz5jMNmvvyUzJzo\n11DM9jnx9u//gJUrnzzS81b986jzZ/aIOAy4mSIAXwu8OTPvKiu+76K47BPgNzLzo4O8/zqq9TW3\nA5i9nNswmbkzIs4ArgYOA66KiG0Uy8j3KpvdCJzdbZgOY2+KiNMpdp1+DnB9RGwBnsTuPw9XAL8+\n7PuQND80Ldj2E2i79esl7M5VwZUkzV91/syemT+IiJ8H/oqiunxnRGyi2KB3UXneP5tPwRYmF25P\nHNG44y9Dj0hmrouIo4F3A6+lWNLwKMVvYD4HXJiZO2br2sPYN0bEcymWI7waeDrFFuC3ABdn5qeq\neReSRmlcVdthjDPYDhpq5xpvrpDbLeB2W548187JLk2WpPqr82f2zLy8nNtvAq8ADqK43PN7wP/O\nzC/19i6bYyLLkjX/uSxZGr1xhNtxV20HCbZVh9rZ9FLF7VbBHXR5cj/h1mXJkmZyWfLj1fXnoerU\nfrdkSdITzdeqbb/GEWxb55nrXHXd/EqSpIXCcCtJmtW4d0juJxz2EjZHYa7zdnyfokwAACAASURB\nVHoPgwb9JvwSQ5KkujDcSpIqN8rlyFWF2o13bWDjXRsG6ltlwPV7byVJqsakvuf2teO4gHlc55Gk\ncVrI1bxBgu1cAXa24wccsaKnuQzy9UGSJGk0JlW5/UJEfC8ifnYUg0fEayPin4G/HsX4kjTfDVNN\nHFXVtt9gO0xlttV3rv6d5lRl9XYh/zJDkqR+TCrcTgP/AfhiRNwSEb8ZEYcMM2BEHBoRvx0RtwJf\nAI4uzyNJqqlRBNthQm2n8brpN+BKkqTRmFS4PRL4PBAUX0r8e8APIuJbEfGBiDg1IrquCYuIlRHx\n6og4LyL+HlgLfKgcO4FLgWeP9F1I0phZxeuuylA7c9xuY/cTcMexi7QkSQvRRK65zcwfAmdFxH8H\nzgNOowjaP13eADIiNgKt22ZgX+CA8rY/RTh+3NDAZcB5mfm9Ub8PSZqPxrUkueqq7aiC7WznmO2a\n3GGvwd31wNqu33krSZK6m+huyZl5Q2aeTlG9/R9A+xcoB7ACeCbwYuAVwHHAERThtj3Y/n/Ax4Aj\nM/M1BltJmh/qFGx7Od9s8x1X9XbZLq/EkSQtbBOp3M6UmbcDvxYR7wL+I/BKikC7Gjholi73ArcA\n1wF/C/zfzNw1pulKkmqk32C7/vbe2q88svuOyRvv2tDTrspVWLzpfnbuO9v/DiVJUkstwm1LZu4E\nri1vAETEnsB+wF7AduDhzPzJZGYoSZNT9+ttq16S3EvVttdg22ugna1Pt5A7W8CdbXnylrX3sM/h\nh855TpcmS5I0uIkuS+5FZv4kMx/MzH8v7w22kjQiw1xvO26jDLYz+3cbY7Z59BLM3VhKkqRq1T7c\nSpIWnn6/07aTYYNtr2P1ErT9aiBJkkbLcCtJGqsqQl4vYbLKYNs+Zq/jVhXQJUlSbwy3kqR5ZxTB\ndq7xB9mx2aXJkiRVx3ArSRpalSFtrornXCGyn2B71yPbO97m0kvAnfleeqlaN+m6Z0mS6sRwK0kN\nUPedkuui12DbS4Dtpc2oK8T92Lxo6aSnIEnSRBluJUmN0a1q20vQ7LUqO0yffqu3vVa9/QWHJEnd\nGW4lSWMzyR2D+w21vfavU/VWkqSFzHArSZr3hg227ePMNtbMgDtX9VaSJFXPcCtJAtzIaFhWcCVJ\nmqw9Jj0BSZJaBq1wdguWVVVtZ455xPK9urbZeNcGDjhixazHtqy9h30OP7TyeUlSU23cthO27Rj9\nOTSvWbmVJDXCIN8jO6jbNs8diPsNzd2C+8xNpayiS5LUv1qF24h45pD9/0tVc5EkNV+vAfS2zdsf\nd2t/rZ/xXZosSdLk1CrcAjdGxK/02ykiVkbE3wAXjmBOkqR5bK4A20sVt5NxVpslSVro6hZup4BP\nRMTnI2J5Lx0i4lXATcDpI52ZJM1ziw48fNJTGLteg+swAVeSJI1H3cLtZiCA1wP/EhHHd2oYEXtG\nxAXA5cCB5cvXjn6KkqT5oN/A2qn9QlmavH7bjsdukiTVUd3C7THAdeXjQ4GrI+L8iHjcPCPiyLLd\nr1GE4R3A+4ETxjhXSdICU2UFd8vaeyoba9RmBlqDriSpjmoVbjNzLfBy4EPALmAxRWi9NiIOA4iI\n/xe4AfgPZbfvA8dn5oczM8c9Z0lS7+b6+pv9nvX0Mc1kcLMF3G4bV7VfdzvoVx1N0lwB1oArSaqL\nWoVbgMzckZm/Q1GFbX0KeCnwvYi4Cvg4sHf5+l8Ax2Tmd8c/U0lSU436GtpxL03evGjpSMbtNbga\ncCVJdVC7cNuSmddSVGf/qnxpOXBi+fgR4I2ZeW5mbpnE/CRJu+1x8E+N/BwHHLGisrGOWrZXZWPN\nVwZWSVLT1DbclrZQLDue6Z+AK8Y8F0mS1IFhWJI0abUNtxFxBPCPwG+WL+0ANpWPXwHcFBEnztZX\nkqT5Zue+B43tXAZVSVIT1TLcRsQvA98DXlS+tBb4aeB57P66n4OBKyPi9yNij/HPUpI0Ck3YVGo+\nM9hKkpqqVuE2IpZHxF8C/weYKl++hGLTqO9k5r9TbDT1O8BOivm/B/hORDxrEnOWpPlk0YGHj/wc\nc+2Y3E2n625XHjn760cs73xtrdfdVs9gLEmapFqFW+Am4Mzy8WbgnMz8xczc3GqQmbsy80PA8ey+\nHvcFwA0R8Z/HOltJ0mPGsamURstwKklqsrqF29ZatOsoqrWf6dQwM68Dng+02kwB/2u005OkyRjn\n9ZaavHFU0CVJmm/qFm4T+DBwfGaunbNx5ubMPAc4m92bTUmSam6upcndrrutemnyKJYnt8+lfb7t\n72uY5dmSJOmJ6hZuT8zM92fmzn46ZebngGModleWJA1hmKrhpJcmDxJwofeQ26ndXOOP0uZFSyd2\nbkmS6qRW4TYz/36Ivj8Afqa62UiSRmkU1dtuegmgswXXVqAd5QZUk/6lgCRJ88G8+gqdzNw16TlI\nkiZr5ZErWH/7hoH79xtiZ4bmTtXjunMzKUlS09WqcitJqoe6LE2eRPW2SoPMcTZuKCZJ0twMt5LU\nEPMx4IxqU6Vu1dMqA24/Y/W6mZQ7JUuSNBjDrSSpcv1Ub0d17e1cAXfYkDtb/067JHfj9baSJFXD\ncCtJmlWdKoijCLgweMidK9jO1G3+w6jbTskr955XW3lIkhrGcCtJDdKkpclVVm9hdAEXdofc2UJr\n+7FObWaeo9t8+l2K3aT/5pIkTZK/YpUkdbTowMPZ9cDaSU+jJwccsYKNd82+S3IrfPayi3K/ldxh\ndkee+QuAOlXLJUlqGiu3kqSRGWf1Fua+zrXqr+mZbbyZc+h1IylJkjQcw60kqatxVhPHFXCHDbmd\nxugWbOc7r7eVJE2a4VaSGqZp12D2uxvwOAIu7A6o/QTdbu3nCrYz31cvS5K7/beuejOpYcKpwVaS\nVAf+30iSNKdhr73d4+CfYse9d/fcfp/DD2XL2nu6ttnvWU/n4Tv/vePxVtjsdB1uu2EruQu5YitJ\nUl0YbiWpgXbuexCLN90/6Wn0pd+A24u5Ai70F3L71et32Q5StW0Kq7aSqnDv5u1sXbJ9pOd4ePNo\nx9fkuSxZktSTcQewXjdf2u9ZT++pUnrAESt6DqPDjNXvcuROmrb8XJKkSTPcSpJ6NmzAHcX1ty29\nLgVuBdN+g24v/ca1HLnq621b+qnCrtx7D6u2kqRa8f9KktRQTVyaDINdfwvMeQ0u7A6Xcy1Vbqmq\nktsp1PZSte13I6lRW7n3HqzftqPrcUmS6sjKbU1FxLKIWBMRN0fEloh4JCK+GxHvjIglQ4y7JiJ2\n9XB7RpXvR9L8UcXy5H4ruNB/FXccVdRu52nyd9q2qrIzg6zBVpJUZ/5fqoYiYhXwLWBV+dJWYAnw\novJ2dkSclJkPD3GaR4Fuu6t0/rW9pNqYVPV22N2TYbANpnrZRbldv5XcfvQbanut2taNgVaS1BT+\nH6tmImIP4DKKYHsfcE5mfjMiAjgT+ATwfOAzwOlDnOofMvPEYecrScMYNOBCb8uUW9qD6DBBd65q\ncBXBdq4lyaO63laSpKYz3NbPucBqIIHXZeZ1AJmZwOcjYhFwCXBaRJyYmd+c3FQl1UGTq7cw+FcE\nDRJyYe6A2gq//S5r7ifYSpKk6nnNbf2cW95f3Qq27TLzUqD1afKcsc1KkmZR1bLaYQJg1de29nu9\n7j6HH9p3sB20aitJkjoz3NZIRCwFXlY+vbxL02+U968c7YwkNcUkQ1GVAXfQkNstYI7KXOfsN9j2\nwiXJkiR1Zritl6OAoFiSfEuXdq1jB0bEfgOea3VE3BIR0+VuzHdExEURccyA40masPkQcGH4Ku6o\ng24v4w8SbK3aSpI0HMNtvTyt7fG9Xdrd16FPP1YAz2b3TszPBN4C3BARHxxwTEkLWF0CbkuVQbef\nsbzGVpKkyXBDqXpZ1vZ4uku79mPLOraa3Z3Ae4CvAGszc2e5Q/MJwIeBFwLvi4gfZeYFfY7d1fT0\nNFu3bh2o79TUVJVTkeatSW0u1VLVJlOwOyQOstnUTONYsjxXqLVqK2lQg35+mp7u9nFSmn8Mt0OK\niDcBnxpiiFMz84qKpjOnzLxkltd2AH8bEdcA1wDHAmsi4pOZuamqcz/r6BcO3Hf7ww9VNQ1JI1Zl\nwIVqQ+6oDBNse+X1ttLCteqgp0x6ClIjuCx5eNl2P+itZXPb426fYtqPbe7Yqk+ZuR14b/l0Cjip\nqrEljU8dqoBVLlFuGWbDqVHpZU5z/Szq8N9LkqT5wMrt8D4HXDZE//bKaPt1tofQeVOpg9se39eh\nzaC+U94HUOmn0ztvuoGVK1ZUOaSkDia9PBmqr+C21KGS22vINthKqsK6+x8cqN+GDet5wernVDwb\nqb4Mt0PKzJ8AGysa7naKSm4Aq9n9lT8zrS7vH8jMhys698gtXbrUa2elMapLwAVGGnJhPEG336px\nldVrlyRLC9ugn5+2bfOaWy0shtsayczpiPg28HLgFOCjM9tERAAnl0+vHME0XtL2uPpPo5IWpFFV\ncVtmBs+qwu4gy6B7DbVWbSVJqpbhtn4upgi3J0TEcZn53RnHz6RYLpzAp6s8cUTsBXyofLoF+Lsq\nx5c0fnWo3raMsoo7Uy+htBWAq7yO12ArSdLkuKFU/VwM3EyxNPkLEXEiQEQsiogzgU+U7S7PzKtn\ndo6INRGxq7wdOuPYz0TEFRFxVkQc2Pb6kog4CbgWOI4iOJ9f5U7JkianbkFqFJtNDaLqDaoMtpKk\nYUXEsvLz/M0RsSUiHomI70bEOyNiSQXjPzUi/jAi7oiIbRGxMSKuiYg39zHGEeUYt5Tz2xoR34+I\nL0fE24ed4zCs3NZM+b2zZwBXA4cBV0XENopfROxVNrsROHuuoWZ5LYBXljfKcaeB5ez+s7AT+Ehm\nPmFJtKTmqlMFF8ZbxR21UYd1r7eVpIUhIlYB3wJWlS9tBZYALypvZ0fESYPuuRMRLwSuAA6gyApb\nKL4h5Xjg+Ih4PXBGZj7aZYxfAz4C7FmOMQ3sKOd8GPDTwJ8OMr8qWLmtocxcBxwNnE9Rxd0JbAeu\nB94FvCQzH+nUvcvQNwHvBv4auIPiD+O+FH9x/hm4EDgmM3+ngrchqWbqWDFcdODhtank9muQudfx\nv4EkafIiYg+Kb2BZRfFtKK/IzGUU4fMsiq//fD7wmQHHXw58lSLY3gYcm5nLgX2AdwCPUuzr87Eu\nY7wTuICiYPZh4BmZuawc5wCKPYM+Ocj8qhKZ3bKQNJiIeDLwuH3rf3jXbTx55coJzUhSS50quDM1\npZI7SCAfJNhatZU0jPXrH+LIZxw28+WnZOZDE5jOY2b7nPhX37mV/Q4Y7efEhzeu58yXPOGrkSb+\n8wAolwV/gqJQ9R8z87oZx88CLimfviIzv9nn+B8E3kdR3HpuWUxrP/5bFIF1J/CczPy3GcefB9wA\nLAZen5lf6uf842LlVpIWmDpXD+tcyW3NbVzBVpK0oJxb3l89M9gCZOal7P4mk3MGGL/V59KZwbZ0\nIcUy5cXMfvnjeykuY/xyXYMtGG4laUGqe9gaJkiOai6DqvvPWpI0WRGxFHhZ+fTyLk2/Ud6/ss/x\nnw08vdv4mbmVYnNZgFfN6D8FvK58+hf9nHvcDLeStEA1JXRNIuhWdc5hfsYuSZakBeMoiutYE7il\nS7vWsQMjYr8+xl9d3vc6/lEzXj+OomqbwI0RcXxEfCUiHoqIH0fE2oj4VEQ8t485jYS7JUvSAla3\nXZTnMlvYHPY63VGF5qb88kCSNHFPa3t8b5d2983o0+uuyf2Ov29ELM3M6fL5s9ra/ALw++XjzRSb\n3q4C3kSxm/PbMvPPe5xX5Qy3krTANS3gzlSHpcszGWwlaXS2TW8dqN+Pt03P3WgylrU97jbJ9mPL\nOraqbvzW8/3L+wB+D/ge8LbMvB4gIo4DLqL4tpeLIuJfM/Of+phfZQy3kqTGB9w6qSLYuiRZkjo7\n45jJ/1IzIt4EfGqIIU7NzCsqms6otV/Kug14dWY+0HohM78bEadTfNXo3hS7Mr9mvFMsGG4lScDu\nUGbIHZwVW0kazL8/vI2Ho7aV1dnkjPtB+0OxvLel2283249t7tjqiWaOv6XP8dsfX9IebFsy84cR\ncQnwZuDEiIicwHfOGm4lSY9jFXcwBltJGo8/+datA/Xb/PBGfvs1x1c1jc8Blw3Rf1Pb4/brYA+h\n86ZPB7c9vq9Dm9nMHP/2Ocbf1Ha9LcAP2x7f1uU8rWNTwApgfR9zrIThVpL0BAbc3lUdal2SLEnd\n7bX3YP9O/uTH2yqbQ2b+BNhY0XC3U1Ryg2Jn4290aNfa9fiBzOx1MynYHZZb43cKt63xZ/724KYe\nzxNtj8detQW/CkiS1MHOfQ+yGjkHfz6SpGGVVdJvl09Pma1NRARwcvn0yj7HvxO4Z47xp4CXzzZ+\nZt4NfL98+pwup2od25SZG/qZY1UMt5KkrgxwTzSq4G/VVpIWrIvL+xPK3YdnOhM4nKIi+ukBxm/1\nOSsiVs1y/FcplhPvAD47y/E/L+/fGBFP+B9gRDwdeEP59OsDzK8ShltJ0pys4u7mz0GSNAIXAzdT\nLO39QkScCBARiyLiTOATZbvLM/PqmZ0jYk1E7Cpvh84y/keBByg2jfpaRLyg7LdnRLwd+GDZ7qLM\nvGuW/hcA69r6H9t27uOArwJPovj6oPP7fO+V8ZpbSVLPFvKOyoZaSdKoZObOiDgDuBo4DLgqIrZR\nFCP3KpvdCJw911Adxt9Ufl3PFRTLh6+PiC0UgbSVCa8Afr1D/+mIOAW4CjgGuC4iWl84PFXebwbe\nkJl3zDHHkbFyK0nq20Kq5I7rvbokWZIWtsxcBxxNUfm8GdgJbAeuB94FvCQzH+nUvYfxbwSeC/wR\ncCewmCKQXgu8JTNPzcxHu/S/o+x/PvAvwC6KSvPtwB8DqzNzYkuSwcqtJGkI87mSu1DCuySpPjJz\nC7CmvPXT7zzgvB7aPUgRlN81wPTIzE0MML9xMdxKkoY2n0LuJEKtVVtJkoZnuJUkVaY9GDYp6Fql\nlSSp+Qy3kqSRqHvQNdBKkjS/GG4lSSNXl6Bbx0DrkmRJkqphuJUkjdVsAXMUgbeOQVaSJI2O4VaS\nNHELNYhatZUkqTp+z60kSZIkqfEMt5IkSZKkxjPcSpI0AS5JliSpWoZbSZIkSVLjGW4lSRozq7aS\nJFXPcCtJkiRJajzDrSRJY2TVVpKk0TDcSpIkSZIaz3ArSZIkSWo8w60kSWPikmRJkkbHcCtJkiRJ\najzDrSRJY2DVVpKk0TLcSpIkSZIaz3ArSdKIWbWVJGn0DLeSJEmSpMbbY9ITkCRJkrSw3f3QVpb+\nZK+RnmP6ka0jHV+TZ+VWkqQRckmyJEnjYbiVJEmSJDWe4VaSpBGxaitJ0vgYbiVJkiRJjWe4lSRp\nBKzaSpI0XoZbSZIkSVLjGW4lSaqYVVtJksbPcCtJkiRJajzDrSRJFbJqK0nSZBhuJUmSJEmNZ7iV\nJKkiVm0lSZocw60kSZIkqfEMt5IkVcCqrSRJk2W4lSRJkiQ1nuFWkqQhWbWVJGnyDLeSJEmSpMYz\n3EqSNASrtpIk1YPhVpIkSZLUeIbbmomIvSPi1Ih4f0R8MSLWRcSu8vaBCs/z1Ij4w4i4IyK2RcTG\niLgmIt5c1Tkkab6zaitJUn3sMekJ6AleDHytw7Gs4gQR8ULgCuCAcswtwBRwPHB8RLweOCMzH63i\nfJIkSZI0alZu6yeBHwFXAX8AvAF4oKrBI2I58FWKYHsbcGxmLgf2Ad4BPAqcDHysqnNK0nxk1VaS\npHqxcls/12bmivYXIuL3Kxz/3cBTgWngtMxcB1BWaT8eEfsCHwbeGhEfy8x/q/DckiRJkjQSVm5r\nJjN3jfgU55T3l7aC7QwXUixTXgycPeK5SFIjWbWVJKl+DLcLSEQ8G3h6+fTy2dpk5lbg2vLpq8Yx\nL0mSJEkaluF2YVld3idwS5d2rWNHjXY6ktQ8Vm0lSaonw+3C8rS2x/d2aXdfeb9vRPgpTpIkSVLt\nuaHUwrKs7fF0l3btx5bN0bZn09PTbN26daC+U1NTVUxBkoZi1VbSJAz6+Wl6upKPcFJjGG6HFBFv\nAj41xBCnZuYVFU2n1p519AsH7rv94YcqnIkkSVJzrDroKZOegtQIhtvh5Yz7QfuPw+a2x0spdkWe\nTXtpYnOHNpK0oFi1lSSp3gy3w/sccNkQ/TdVNZEetF9newhwe4d2B5f3mzKzsvUsd950AytXrJi7\noSRJkh6z7v4HB+q3YcN6XrD6ORXPZjRuu38Te21ZPNJzbN88zo/dmgTD7ZAy8yfAxknPo0etXZCD\nYufkTuG2tavyrVWefOnSpV47K6mRrNpKmqRBPz9t2+Y1t1pY3C15AcnMO4F7yqenzNYmIqaAl5dP\nrxzHvCRJkiRpWIbbhefT5f1ZEbHq/2fv3uPkqMrE/3+eXAQyJFyScA1EEEQgIKCCq7hcFRB/XsFF\nUfC2u1/F3VVQd9VdCeD63XVBVNRVdNkNKt7drwvIRQQlugpiQImAgGKQm4FwSTITQpJ5fn90TdIz\nTM9M93RPd/V83q9Xvaqr6pxTT5fHST+cqlPDHD8N6AHWA1+dsKgkqUM5aitJUjmY3HagiNgmIuZE\nxOyImMOm/516BvYVy9PuUYmIhRHRXyy7DtP8ucBDVCaNujwiDirqPSMi3gmcU5S7MDPvbv63kyRJ\nkqTmM7ntTDcDy4GHi/W8Yv/7q/YtBz4zQhvDzsKcmSuBVwArgH2AmyJiJZWZkz8LTAeuAt477m8h\nSSXnqK0kSeVhctuZso5luLojN565BNgXOB+4E5hK5ZU/i4F3ZOZxmblu/F9DkiRJkiaGsyV3oMzc\nbRx1zwLOGkO55cAZxSJJGsJRW0mSysWRW0mSJElS6ZncSpI0hKO2kiSVj8mtJEmSJKn0TG4lSari\nqK0kSeVkcitJkiRJKj2TW0mSCo7aSpJUXia3kiRhYitJUtmZ3EqSJEmSSs/kVpI06TlqK0lS+Znc\nSpIkSZJKz+RWkjSpOWorSVJ3MLmVJEmSpA4QETMjYmFE3BoRqyPiiYi4MSJOj4jp42h3q4h4VUSc\nHRGXRcSDEdFfLKeOof4eEXFGRFwaEcsiYm1E9EbEnRHxpYg4qNHYmmlauwOQJKldHLWVJHWKiJgP\n/AiYX+zqBaYDzy+WkyPiqMx8vIHmXwNcVONYjhLXi4HFQ8qvAjYD9iiWt0TEP2fmmQ3E1jSO3EqS\nJElSG0XENOBSKontA8DRmTkT6AFOopJMHgh8pcFTJPAg8H3go8Br66g7HdgA/DdwAjAnM7cGZgAH\nAz+hklf+U0S8rcH4msKRW0nSpOSorSSpg5wKLKCShL4uM28AyMwEvhkRU4BLgJdHxJGZeW2d7X85\nMxdV74iIsda9C3hOZv6uemcR200RcRTwC2B/4IPUHiFuOUduJUmSJKm9Bp57vW4gsa2WmV8H7ik2\nT6m38czsbzSwzLx/aGI75Pg6No0o7x4RWzV6rvEyuZUkTTqO2kqSOkVEzABeXGxeMULRK4v1S1sb\nUUPWVn2e2q4gTG4lSZOKia0kqcPsDQSVW5KXjlBu4NgOEbF1y6Oqz+HF+sHMfLRdQZjcSpIkSVL7\n7FT1+f4Ryj1Qo05bRcSfAa8uNr/UzlicUEqSNGk4aitJ5bd+7ZqG6m146skmR9I0M6s+941QrvrY\nzJqlJlBEzAW+RmXk+U7g4+2Mx+RWkiRJUmn84D1HtzsEIuItjG9W4OMy86omhdMWEbEl8D/ArsBK\n4MTMHCk5bzmTW0nSpOCorSSpiXLIutH6UHmH7YCR/rGqPraqZqkJEBE9wOXAIUUsL8/MW9sZE5jc\nSpIkSWqzFQ/3Mr1v+pjKHvih7zV0jnW9j7P0U6eOXnBsvgZcOo76K6s+Vz9nO4/ak0rtXPX5gRpl\nWq4qsX0JsBo4PjP/t13xVDO5lSR1PUdtJal7TH3G5g3V61/XWL3hZOZTQLNmBb6DykhuAAvY9Mqf\noRYU64cy8/EmnbsuVYntnwO9VBLbn7QjluE4W7IkSZIktUnxnOpAgnjscGUiIoBjis2rJyKuYWLo\nAb5PJbFdTeVW5MXtiKUWk1tJUldz1FaSVAKLivUREXHwMMdPBHajMsJ78YRFVahKbAduRe64xBZM\nbiVJXczEVpJUEouAW6ncmvydiDgSICKmRMSJwBeLcldk5nVDK0fEwojoL5ZdhztBRMwpltkRMafq\n0MyBfcWyxZB6M4DLqCS2q6jM9NwxtyJX85lbSZIkSWqjzNwQEa8ErgOeCVwTEWuoDEZuVhRbApw8\nWlMjHFteY/8FxTLgrGIZcAJwWPF5OpXke6TzvzYzfzZKnC1hcitJ6kqO2kqSyiQzl0XE/sD7gNdQ\nuQ15HZUR3a8BF2Tm+lrVx3qaBspE1f7NgLmj1B/btNctYHIrSZIkSR0gM1cDC4ulnnpDR1uHK9PQ\nI6mZuYhNzwR3NJ+5lSR1HUdtJUmafExuJUmSJEmlZ3IrSeoqjtpKkjQ5mdxKkiRJkkrP5FaS1DUc\ntZUkafIyuZUkdQUTW0mSJjeTW0mSJElS6ZncSpJKz1FbSZJkcitJkiRJKj2TW0lSqTlqK0mSwORW\nklRiJraSJGmAya0kSZIkqfRMbiVJpeSorSRJqmZyK0mSJEkqPZNbSVLpOGorSZKGMrmVJEmSJJWe\nya0kqVQctZUkScMxuZUklYaJrSRJqsXkVpIkSZJUeia3kqRScNRWkiSNxORWkiRJklR609odgCRJ\no3HUVpK62+N/Ws3ULaa29Bwb1qxuaftqP0duJUkdzcRWkiSNhcmtJEmSJKn0TG4lSR3LUVtJkjRW\nJreSJEmSpNIzue0wEbFFRBwXEf8YEd+NiGUR0V8sZzah/YVV7Y207N6M7yNJjXLUVpIk1cPZkjvP\nIcDlNY5lE8+zDlgxwvH1TTyXJNXFxFaSJNXL5LbzJPAY8EtgCXAzcD6woLmefwAAIABJREFUQ5PP\n89PMPLLJbUqSJElSW5jcdp7FmTm7ekdE/Gu7gpGkieaorSRJaoTP3HaYzOxvdwySJEmSVDYmt5Kk\njuGorSRJapTJ7eS1ICKWRkRfRKyOiN9GxIURcUC7A5M0OZnYSpKk8TC5nbxmA3sBvcB0YE/gHcAv\nI+KcdgYmSZIkSfVyQqnJ507g/cD3gHsyc0NETAOOAD4GPA/4cEQ8lpmfaOaJ+/r66O3tbahuT09P\nM0OR1GEctZWk2hr9/dTX19fkSKTOZnI7ThHxFuCicTRxXGZe1aRwRpWZlwyzbz3wg4i4HrgeeAGw\nMCK+lJkrm3XuZ+//vIbrrn384WaFIUmSVCrzd9yu3SFIpeBtyeOXVetGl46QmWuBDxWbPcBRbQxH\n0iThqK0kSWoGR27H72vApeOo37SR0Sb5ebEOYLdmNnznr3/JnNmzRy8oadIwsZWk0S17cHlD9Vas\neISDFuzT5GikzmVyO06Z+RTwaLvjKIMZM2b47KwkSVKdGv39tGaNz9xqcvG2ZA31wqrP97QtCkld\nz1FbSZLUTCa32igiNgP+udhcDfywjeFIkiRJ0piZ3HagiNgmIuZExOyImMOm/516BvYVy9PuUYmI\nhRHRXyy7Djl2WERcFREnRcQOVfunR8RRwGLgYCqTXJ3dzJmSJamao7aSJKnZfOa2M90M7DrM/vcX\ny4BFwFtrtDHcLMwBvLRYiIg1QB+wFZv6wgbgXzLz3PrDlqTRmdhKkqRWMLntTGN9RdBwZUaq92vg\nfVSeq90PmAPMAnqpPF+7GLgwM39TV7SSJEmS1GYmtx0oMxt+BU9mngWcVePYo8AnGm1bksbLUVtJ\nktQqPnMrSZIkSSo9k1tJ0oRw1FaSJLWSya0kqeVMbCVJUquZ3EqSJEmSSs/kVpLUUo7aSpKkiWBy\nK0lqGRNbSZI0UXwVkCRJkqS2Wvng75my2ZYtPUf/2tUtbV/t58itJKklHLWVJEkTyeRWkiRJklR6\nJreSpKZz1FaSJE00k1tJUlOZ2EqSpHYwuZUkSZIklZ7JrSSpaRy1lSRJ7WJyK0lqChNbSZLUTia3\nkiRJkqTSM7mVJI2bo7aSJKndTG4lSZIkSaVncitJGhdHbSVJUicwuZUkNczEVpIkdQqTW0mSJElS\n6ZncSpIa4qitJEnqJCa3kqS6mdhKkqROY3IrSZIkSSo9k1tJUl0ctZUkSZ3I5FaSNGYmtpIkqVOZ\n3EqSJElSB4iImRGxMCJujYjVEfFERNwYEadHxPRxtLtVRLwqIs6OiMsi4sGI6C+WU8fYxmYRcVpE\nXBcRD0fEuohYVcT6yYjYvdH4mmVauwOQJJWDo7aSJLVORMwHfgTML3b1AtOB5xfLyRFxVGY+3kDz\nrwEuqnEsxxDbTsDVwD5VdVYBWwD7FstfR8SbM/PbDcTXFI7cSpIkSVIbRcQ04FIqie0DwNGZORPo\nAU6ikkgeCHylwVMk8CDwfeCjwGvrrP95KoltAmcCczJza2Bz4HDgN8BmwKIiEW4LR24lSaNy1FaS\npJY6FVhAJXl8XWbeAJCZCXwzIqYAlwAvj4gjM/PaOtv/cmYuqt4REWOqGBE9wPHF5qLMPGfgWBHf\n9RHxKuBuKiO5rwAurDO+pnDkVpI0IhNbSZJabuC51+sGEttqmfl14J5i85R6G8/M/nHEtjkwkAnf\nVKP93wOPFZs94zjXuJjcSpIkSVKbRMQM4MXF5hUjFL2yWL+0tRENlpkr2JRYv2C4MhHxLGAbKiPP\nwybAE8HkVpJUk6O2kiS13N5URkYTWDpCuYFjO0TE1i2ParC/BdYBp0bERyJiW4CImBoRhwHfK8p9\nKzMXT3BsG5ncSpKGZWIrSdKEqJ6A6f4Ryj1Qo07LZeblwEuoTHr1YeCRiHgCeBK4jsqtyx8A3jCR\ncQ3lhFKSJEmSSiPXP9VYvQ2N1ZsAM6s+941QrvrYzJqlWqcHmEElh0xgy6pjM4DZVJLcNRMfWoXJ\nrSTpaRy1lSR1qkevPLPdIRARb6H2e2PH4rjMvKpJ4bRcRLwZ+E8qd/5+DTgX+C2wLXAk8H+BvweO\njojDM7O3HXF6W7IkaRATW0mSRpVV60aXAauqPo/0j3D1sVU1SzVZRMwGLqCSOy7KzJMz8+bM7MvM\n+zLzYuBoYC3wPCpJbls4citJkiSprVY9+Hti2uZjKjttvzc1dI7csJYNt32robrD+BqV508btbLq\nc/VztvOoPanUzlWfH6hRphWOBmZRScjPHa5AZt4eEZcDrwVeB3xk4sLbxORWkrSRo7aSpE4XU6c3\nVjE3NC2GzHwKeLRJzd1BJXEMYAGbXvkz1IJi/VBmPt6kc4/FblWffzdCubuL9TNbF8rIvC1ZkiRJ\nktokM/uAnxSbxw5XJiICOKbYvHoi4qpSPcr8zBHKbV+sJ+yW6aFMbiVJgKO2kiS10aJifUREHDzM\n8ROpjKAmcPGERVXx82IdwDuHKxAROwCvKTZ/NhFBDcfkVpJkYitJUnstAm6lkkB+JyKOBIiIKRFx\nIvDFotwVmXnd0MoRsTAi+otl1+FOEBFzimV2RMypOjRzYF+xbFFdLzOXANcXm++OiPMiYseizc0j\n4tji+CygH/hEoxdhvExuJUmSJKmNMnMD8ErgD1QmjromInqBXuAbVN5ruwQ4ebSmRji2vFgeLtYD\nLqjatxz4wDB1TwJ+QyX5fi9wf0SsKuL7PrAHsB54T2YuHiXGljG5laRJzlFbSZLaLzOXAfsDZ1MZ\nxd1A5fU6NwFnAC/MzCdqVR/raca4DI3tISqv+XkXcC2VJPgZQB9wO/B54KDM/MwY42gJZ0uWpEnM\nxFaSpM6RmauBhcVST72zgLNGKTOugc1ihujPF0tHcuRWkiRJklR6JreSNEk5aitJkrqJya0kTUIm\ntpIkqduY3EqSJEmSSs/kVuoQvb29bLb1XDbbei69vb3tDqcUvGb18XrVr7e3lzmzepgzq8drNgZe\nr/p5zerj9ZI0EpNbSZIkSVLpmdxKkiRJkkrP5FaSJgknkZIkSd3M5FaSJgETW0mS1O1MbiVJkiRJ\npWdyK0ldzlFbSZI0GZjcdpiImB0Rb42Ir0TEbRHRGxFrI+K+iPjviHh1k86zfUScFxG/jYg1EfFo\nRFwfEW9vRvuSOoOJrSRJmiymtTsAPc1DwNTicwJPAmuBHYFXAa+KiCuAEzJzTSMniIjnAVcB2xbn\nWA30AIcCh0bECcArM3PdeL6IJEmSJE0UR247z1TgBuCdwLMysyczZwG7A/9RlDkO+EIjjUfEVsBl\nVBLb24EXZOZWwJbAu4F1wDHAJ8fzJSS1n6O2kiRpMjG57TxHZOafZeYXMvMPAzszc1lm/iWbkto3\nRcS8Btp/H7A90Ae8PDOXFO2vy8zPAWcW5f4qIvZs+FtIkiRJ0gQyue0wmfnjUYoMjN4m8PwGTnFK\nsf56Zi4b5vgFVG5Tngqc3ED7kjqAo7aSJGmyMbktn7XFOqjzf7+I2AvYpdi8YrgymdkLLC42X9ZI\ngJLay8RWkiRNRia35XN4sU7g1jrrLqiqu3SEcgPH9q6zfUltZmIrSZImK2dLLpGI2Br4YLG5ODPv\nqrOJnao+3z9CuQeK9ayImJGZfXWeByojy4OsWPFoA81MHn19my7zIytW0LemocmwJxWv2dOtnrJF\nzWPV12vFikdYs6aR/2tPLl6z+ni96uc1q4/Xqz6Prlgx3O6n/UbrCOufJCfgHOpukdnybqQmiIgp\nwPeA44E1wCGZOdLo63BtfAj4KJWR2+mZ2V+j3MDEVQnslJl/aiDe51CZjVmSJEmdY+/MvKOdAUTE\nXGB5O2Oosl1mPtzuINQc3pY8ThHxlojoH8dyzBhP9SkqiW0Cp9Wb2EqSJElSN/O25PHLIetG69cU\nEecCpxVl35uZ/9XguVZVfZ5BZVbk4VQ/tLeqRhlJkiRJ6hgmt+P3NeDScdRfOdLBiPg4cDqVxPZ9\nmfnpcZyr+jnbeUCtW1J2HoitwedtJUmSJGlCmdyOU2Y+BbRkpqSI+DfgDCqJ7Qcy8/xxNjlwK3NQ\nmTm5VnI7MKvybeM41108fbblR2l8hFuSJEn1CWDbIfvqnZC0FVYA27U7iMKws26pnExuO1RxK/LA\niO0HMvO88baZmXdGxL3ArsCxwLeHOW8P8JJi8+pxnGsDtZNnSZIkTYxOmbhpo2JSUydxUtM5oVQH\nGpLYvq8ZiW2Vi4v1SRExf5jjpwE9wHrgq008ryRJkiS1jMlth6l6xhbg9HpvRY6IhVUzMe86TJFz\ngYeoTBp1eUQcVNR7RkS8EzinKHdhZt7d2LeQJEmSpInle247SJGM/qHY7AceGaXKvw0d1Y2IhcBH\nqIz67paZ9w5znoOAq4DZxa7VwOZsuk39KuCVmbmu/m8hSZIkSRPPZ247y8BIelKZAGDuKOV7htk3\n6n+tyMwlEbEv8PdU3p27C5VX/iwFFmXmRWOOWJIkSZI6gCO3kiRJkqTS85lbSZIkSVLpmdxKkiRJ\nkkrP5FaSJEmSVHomt5IkSZKk0jO5lSRJkiSVnsmtJEmSJKn0TG4lSZIkSaVncitJkiRJKj2TW0mS\nJElS6ZncSpIkSZJKz+RW4xYRsyPirRHxlYi4LSJ6I2JtRNwXEf8dEa9u0nm2j4jzIuK3EbEmIh6N\niOsj4u3NaH8iRcQWEXFcRPxjRHw3IpZFRH+xnNmE9hdWtTfSsnszvk+rtfp6VZ2na/oYQETMLPrC\nrRGxOiKeiIgbI+L0iJg+jnZL2b9adT2Ktruq70BrrldZ+85IJuLvU7f1r1Zes27sYzAxv7W6rZ9p\ncprW7gDUFR4CphafE3gSWAvsCLwKeFVEXAGckJlrGjlBRDwPuArYtjjHaqAHOBQ4NCJOAF6ZmevG\n80Um0CHA5TWOZRPPsw5YMcLx9U08Vyu1/Hp1Wx+LiPnAj4D5xa5eYDrw/GI5OSKOyszHx3Ga0vSv\nVl6Pbus7MCH9pzR9Zwxa+vepG/sXE/NvYDf1MWjxb60u7WeahBy5VTNMBW4A3gk8KzN7MnMWsDvw\nH0WZ44AvNNJ4RGwFXEblD+7twAsycytgS+DdVP4BOwb45Hi+xARL4DHgGuDjwBuo/MPVbD/NzJ1G\nWO5twTlboaXXq9v6WERMAy6lkpg8ABydmTOp/FA5CVgFHAh8ZZynKkX/auX16La+AxPWf0rRd8ao\nZX+furF/FSbi38Bu6mPQwt9aXdzPNBllpovLuBbgsFGO/zvQXyzzGmj/nKLuamD+MMf/oTi+Dtiz\n3ddjjN9pyjD7/lB8j480of2FRVvXtvu7luR6dVUfA95exLsBOGSY4ydV/X/yyAbaL1X/auX16La+\nMwHXq1R9Z4zfqWV/n7qxf03ANeu6PlZ8r8NGOd7wb61u7Wcuk3Nx5Fbjlpk/HqXIwH9RTCq3s9Xr\nlGL99cxcNszxC6j8QZ4KnNxA+xMuM/vbHUOZTMD16rY+dmqxvi4zbxh6MDO/DtxTbJ4y9HgXauX1\n6La+A/afurT471M39i//DWxAi39rdWU/0+RkcquJsLZYB3X2uYjYC9il2LxiuDKZ2QssLjZf1kiA\nmry6rY9FxAzgxcXmsN+ncGWxfmlrI2qvVl6Pbus7YP/pJN3Yv9RSDf3Wsp+p25jcaiIcXqwTuLXO\nuguq6i4dodzAsb3rbL/bLYiIpRHRV8x2+tuIuDAiDmh3YB2k2/rY3lR+3Iz1++wQEVs3eK4y9K9W\nXo9u6zswcf2nDH2n3bqxf02kydbHDi/W9f7Wsp+pq5jcqqWKHz0fLDYXZ+ZddTaxU9Xn+0co90Cx\nnlWMPKhiNrAXm2Y63RN4B/DLiDinnYF1kG7rY/V+n6F16lGG/tXK69FtfQcmrv+Uoe+0Wzf2r4k0\nafrYOH9r2c/UVUxu1TIRMQX4MrADsIbKjHv1mln1uW+EctXHZtYsNXncCbyfyj/sm2fmXCoznR4D\n/JLKyMyHI+L09oXYMbqtj03E9ylT/2rl9ei2vgOt/05l6jvt1o39ayJMqj7WhN9a9jN1FZPbSSgi\n3jLGF5zXWo4Z46k+BRxP5VaX0zJzpNtdOtoEXrOmyMxLMvO8zLw7MzcU+9Zn5g+ovLPuF0XRhREx\nq9nnL9v1areyXa929y+Vl31HrTYJ+1jX/NaSmsHkdnLKqnWjy4gi4lzgtKLsezPzvxqMdVXV55Fu\ng6k+tqpmqca1/JpNlMxcC3yo2OwBjmrFaarWnX69OqGPNfN6tfX7TFD/qkcrr0cn9J1ma9t36sC+\n027d2L/aqtv6WJN+a9nP1FWmtTsAtcXXgEvHUX/lSAcj4uPA6VT+2L4vMz89jnNVP/8xD7ijRrmd\nB2LLzJFuq2lUS69ZG/y8WAewWwvaL9P16oQ+1szrNfT71Pqv+DtXfX6gRplGtbp/1aOV16MT+k6z\ntbv/dFLfabdu7F+doCv6WBN/a9nP1FVMbiehzHwKeLQVbUfEvwFnUPlj+4HMPH+cTQ78sAoqM/rV\n+qM7MNvfbeM837Baec26UcmuV9v7WJOv1x1U/v838H2urFFu4Ps8lJmPN+ncnaiV16PtfacF7D+d\noxv7l5qgyb+17GfqKt6WrKYpbo+p/mN73njbzMw7gXuLzWNrnLcHeEmxefV4zzlJvLDq8z1ti6ID\ndFsfK/6L+k+KzVrfJ6hMrgKt+T4d079aeT26re9AR/Sfjuk77daN/atDlLqPNfu3lv1M3cbkVk1R\n/LGtvj1m3IltlYuL9UkRMX+Y46dReXZmPfDVJp63K0XEZsA/F5urgR+2MZxO0W19bFGxPiIiDh7m\n+IlUbsdLNn33pujQ/tXK69FtfQfa1H86tO+0Wzf2r7Ypex9r4W8t+5m6R2a6uIxrAT4O9BfL3zVQ\nf2FV/V2HOT6LyjNd/VRunzmo2P8M4J3A2uLYZ9p9Ler83tsAc6i8i28Olf9y2g/8a9W+OUBPPdcM\nOAy4CjgJ2KFq/3Qqk2fcWNTbQOUfx7Zfi3Zer27sY8BU4FdFzH8Ejiz2T6GSmDxRHLusRv2u6l/j\nuR6Tre+08nqVse/Ucc0a+vs0GftXK69Zl/exhn9rTeZ+5jL5lrYH4FLuBdi16g/meuChUZYzhmlj\n4I/uhuH+6BZlDgIerjrXSuCpqu0rgOntvh51Xrs/VMU/0vKf9Vwz4PAh9XuLa1d9vdYB57T7GnTC\n9erWPgbMB34/pB+sqdq+CdiqRt2u61+NXo/J2Hdadb3K2nfGeL0a+vs0WftXq65Zt/YxxvlbazL3\nM5fJtzihlMZr4Nb2gQlI5o5SvmeYfaO+9iUzl0TEvsDfU3mf2y5UpqJfCizKzIvGHHHnGOsrb4Yr\nM1K9XwPvo/Jc0X5U/sv3LCr/yN8DLAYuzMzf1BVt+7XqelUKdFkfy8xlEbE/lb7wGiq3ka4DbqUy\nO/MFmbm+VvURmi5l/xrH9Zh0fQdadr1K2XfGqNG/T5OyfxVacc26tY+N97fWZO5nmmQicyx/VyRJ\nkiRJ6lxOKCVJkiRJKj2TW0mSJElS6ZncSpIkSZJKz+RWkiRJklR6JreSJEmSpNIzuZUkSZIklZ7J\nrSRJkiSp9ExuJUmSJEmlZ3IrSZIkSSo9k1tJkiRJUumZ3EqSJEmSSs/kVpIkSZJUeia3kiRJkqTS\nM7mVJEmSJJWeya0kSZIkqfRMbiVJkiRJpWdyK0mSJEkqPZNbSZIkSVLpmdxKkiRJkkrP5FaSJEmS\nVHomt5IkSZKk0jO5lSRJkiSVnsmtJEmSJKn0TG4lSZIkSaVncitJkiRJKj2TW0mSJElS6ZncSpIk\nSZJKz+RWkiRJklR6JreSJEmSpNIzuZUkSZIklZ7JrSRJkiSp9ExuJUmSJEmlZ3LbgSJidkS8NSK+\nEhG3RURvRKyNiPsi4r8j4tVNOMf2EXFeRPw2ItZExKMRcX1EvL0Z30GSJEmSJlJkZrtj0BARsQ6Y\nWmwm8CSwAegBoth/BXBCZq5poP3nAVcB2xbtrwY2B6YXRa4CXpmZ6xr9DpIkSZI0kRy57UxTgRuA\ndwLPysyezJwF7A78R1HmOOAL9TYcEVsBl1FJbG8HXpCZWwFbAu8G1gHHAJ8c75eQJEmSpIniyG0H\niojDMvPHIxz/d+Cvi81dM/O+Oto+B/gw0Afsm5nLhhz/B+BjVEaK98nMu+qNX5IkSZImmiO3HWik\nxLYwMHqbwPPrbP6UYv31oYlt4QIqtylPBU6us21JkiRJaguT23JaW6yDOv43jIi9gF2KzSuGK5OZ\nvcDiYvNljQYoSZIkSRPJ5LacDi/WCdxaR70FVfWWjlBu4Nje9YUlSZIkSe0xrd0BqD4RsTXwwWJz\ncZ3PxO5U9fn+Eco9UKxnRcSMzOyrJ0aAiJgK7Dlk96NUEmtJkiS1XlCZRLTaXZm5oR3BDIiIKcDs\ndsZQZUVm9rc7CDWHyW2JFH8IvgzsAKyhMrtxPWZWfR4pYa0+NnOUsrXsSWU2ZkmSJHWOvYE72hzD\nbGB5m2MYsB3wcLuDUHN4W3K5fAo4nsro52mZOdKtxZIkSZI0aZjclkREnAucRiWxfW9m/lcDzayq\n+jxjhHLVx1bVLCVJkiRJHcLktgQi4uPA6VQS2/dl5qcbbKr6Odt5I5TbuVivbOR5W0mSJEmaaD5z\n2+Ei4t+AM6gkth/IzPPH0dzAbcxBZebkWs9bDMyqfNs4zvXo0B3/+4tfsu3sTpk7oPP09fVx0IJ9\nAFiy9DZmzBhpcF3gNauX16t+XrP6eL3q5zWrj9erPo+uWMGLXvC8p+1uRyyjeT07sXmLx92epJ9v\nbpw3Vd3I5LaDFbciD4zYfiAzzxtPe5l5Z0TcC+wKHAt8e5hz9gAvKTavHs/phu7YdvZs5syZO44m\nu1tvb+/Gz7Nnz6Gnp6eN0ZSD16w+Xq/6ec3q4/Wqn9esPl6vpujIN1dszhS2YGq7w1DJeVtyhxqS\n2L5vvIltlYuL9UkRMX+Y46cBPcB64KtNOqckSZIktZTJbQeqesYW4PR6bkWOiIUR0V8suw5T5Fzg\nISqTRl0eEQcV9Z4REe8EzinKXZiZdzf+LSRJkiRp4nhbcocpEtL3FZv9wAcj4oMjVPm3GqO6w95y\nkpkrI+IVwFXAPsBNEbEa2JxN/eEq4L2NxC9JkiRJ7WBy23kGRtOTysRPoz2kOvRhk1Gfo8jMJRGx\nL/D3VN6buwuVV/4sBRZl5kV1RSxJkiRJbWZy22Ey8w+M43bxzDwLOGsM5ZZTmYX5jEbPJUmSJEmd\nwmduJUmSJEmlZ3IrSZIkSSo9k1tJkiRJUumZ3EqSJEmSSs/kVpIkSZJUeia3kiRJkqTSM7mVJElS\n13nP35zG3K22ZO5WW/L2t5wy4ef/xQ03bDz/M3fegYceenDCY5AmG5NbSZIkdZWblyzhki9fDMD0\n6dP58D+dOeExvOCQQzj25ccD0Lt6Neec+ZEJj0GabCIz2x2DulBEzAWWV++74/d/YM6cuW2KSJIk\nTRbHv+xobrzh5wCcfMqpfPKCz7Yljttv+w2HveiFZCYRwVXX/pgDDzpows7/yCMP85zdnzl093aZ\n+fCEBTGM4X4nnsI8tmBqS8+7hg1czH1Dd7f9eqh5HLmVJElS1/jBVVduTGynTJnC3/zde9sWy977\n7MtRL30ZAJnJv3z07LbFIk0GJreSJEnqGv/ysY9u/Hzsy4/nWXvs0cZo4G/esym5vvaH12xMvCU1\nn8mtJEmSusKPr7uWX99yy8btt779HW2MpuJFLz6UZ+/1nI3bn/nUJ9sYjdTdTG4lSZLUFb7w75/b\n+HnX+fM5/Mij2hjNJiefsmm25quu+D73LlvWxmik7mVyK0mSpNL74733cs3VV23cPuHEv2hjNIOd\n8Pq/YMqUys/u/v5+Fv3nf7Q5Iqk7mdxKkiSp9L7zrW8w8BaQiOD4V76yzRFtst122/OCgw/ZuP2d\nb32zjdGo2SJiZkQsjIhbI2J1RDwRETdGxOkRMb3BNneOiHdFxLci4u6IWFMs90TEJRFxRIPtXhER\n/cVyXSNtdLJp7Q5AkiRJGq/qhHHHnXZi/+ceMK72nnj8cX5x4w386U8PsWLFCrK/n1lbbc3uu+/O\nvvvtV/frDY89/nhu+PnPALj/vvv42f/+lD970YvHFaPaLyLmAz8C5he7eoHpwPOL5eSIOCozH6+j\nzV2A6nvXE+gDAti1ONdJEXER8FeZ2T/Gdt8CHDOk3a5icitJkqRS++O993LH7bdv3D70JX/eUDv9\n/f3893e+zRc//+/cvOSX9PcPnzNEBPs997mccOJf8MY3vZmttt561LYPfclhg7avvvIKk9uSi4hp\nwKVUks0HgFMy89qICOBE4IvAgcBXgFfU0fTAC3+vAS4GrsnMh4pz7g18DHgV8LbivB8ZQ6w7AJ8A\nHgMeAvauI57S8LZkSZIkldq1P7xm0PaLDj207jbuuvO3HP7iF/J/3vE2fnnTL2omtlB5Z+2vb7mF\nj3z4g4MmsRrJfvvvz5YzZ26K+Zof1B2jOs6pwAIqI6Cvy8xrAbLim8BfF+VeHhFH1tHuo8BBmfmy\nzPzKQGJbtH17Zr4GuLLY9Z6I2GwMbX4O2Bp4P7C8jlhKxeRWkiRJpfbz//3poO0DDjyorvqLr/8x\nxx59JLffdtug/RHB3LlzWbDffhx40PN45m67M3Xq1KeVGYupU6eyYL/9N27fftttrHziibriVMc5\ntVhfl5k3DD2YmV8H7ik2Txl6vJbMXJmZt4xS7KJi3cMoo7AR8Xrg1cCPMvMiKrc3dyWTW0mSJJXa\nr6rebTtt2rRB75Udzb3LlvHWN508KNHcfPPNOe1v/46f3ngTt919D9f95Gdcfd2P+cUtv+Z3f3yA\nb/+//+GUt75t0EjsWOyzz74bP2cmt9y8pK766hwRMQMYuK/8ihGKDoywvrTJIayt+lwzp4uI2cAF\nwJPAXzU5ho7jM7eSJEkqrbVr13L3XXdu3J63y65Mnz72CWr/+u3/M8exAAAgAElEQVRv5YknNs31\ns+NOO/HN7/4/nrP3PsOW7+np4bAjjuSwI47kn848i/vu++OYz7XHnnsO2l669Fb+/PCGJr1V++1N\nZQQ0gaUjlBs4tkNEbF3PxFKjOLxYPwXcOUK5TwNzgQ9n5t1NOnfHMrmVJElSad33xz9ufAUQwE47\n7zzmutf98Bpu+sWNG7c333zzERPbobbeZhu23mabMZ9vhx13HLR93733jrmuOs5OVZ/vH6HcA0Pq\njDu5jYjdgP9TbH4jM1fXKPf/AW8AbgU+Pt7zloHJrSRJkkrr/vvvG7S9/fbbj7nuFz732UHb7/67\n9445sW3E9jvsMGj7/vtHyolUyzrG9OabptWrofqe9L4RylUfq+8+9mFExBbAt4AtgIeBf6hRbivg\n88AG4C8zc8N4z10GJreSJEkqrVUrVw7a7tlyyzHVW7duHT/9yeKN29OnT+dtf/mXTY1tqJ6enkHb\nK1c6oVQjLmLst4J3k+LVQ5cAB1G5Hfnk6pmUhzgP2BG4IDNvrFGm65jcSpIkqbT6+gYPmm2x+eZj\nqnfLkiU8+eSTG7f32/+5zJ27XVNjG2qLLWYM2u7rHWnAb3J5zpbPYOaUMaYmK0cvMgFWVX2eUbPU\n4GOrapYaRURMBb5K5f2264A3ZuY1NcoeTeUduH8EPtToOcvI5FaSJEldo+rx2xHdc8/vB23X+/qg\nRuRYg9OIzp25R0P1VvevZ2HvH5oVRvU95fOoPalU9UPgD9QoM6Iisf0KcCKwHnhTZn53hCpfLNYf\nqFSPgdsZksokWAPvs5oWET3Fvr7MbOp92+1gcitJkqTSmjFj8KDZk2ufrFFysMcfe2zQ9py5c5sW\nUy1PPrlm0PaMnpEG/FTLZtHY20yfarBeDXewKVlcwKZX/gy1oFg/1MhMyVUjtq9nU2L7rVGqzS/W\nXxul3KFsGk1+DfC9euPrNL7nVpIkSaU1a6utBm2vXjW2Oz9Xrx48wWzPlj01SjZPb2/voO1Zs7aq\nUVKdLjP7gJ8Um8cOVyYiAjim2Ly63nMUie0lDE5svznWEEdYapUrPZNbSZIkldbOO88btP2nP/1p\nTPW2HDLxVO/q3holm+ehBx8ctD1v3rwaJVUSi4r1ERFx8DDHTwR2o5I4XlxPw1UjtidSecb25LEm\ntpk5JTOn1lqAHxdFf1S1/3/qia9TmdxKkiSptHaeN4/KAFnFg2N8vc7Q99M+vHx5U+MaztDkdpdd\n59coqZJYROUdsgF8JyKOBIiIKRFxIpuefb0iM6+rrhgRCyOiv1h2HXJs4BnbgRHbN47hVuR6xOhF\nysnkVpIkSaW12Wabseez99q4fd99f+Spp54atd4ee+w5aPuWm5c0Pbah7rrzzkHb+yxYUKOkyqB4\nd+wrgT9QmTjqmojoBXqBb1B5r+0S4OSRmhlm34uBv6g6/tmIeKjG8mBEvL5JX6n0TG4lSZJUas89\n4ICNn9evX88dt982ap39Dzhg0GRUt/76VyxfPrZbmht1+22/2fg5IjhwAmZoVmtl5jJgf+BsKqO4\nG4C1wE3AGcALM3O4FxqP9IxrVJWZBswdYdkOGNv7rwafuyuesR3K5FaSJEml9sIXvXjQ9q9uuWXU\nOtOmTeMlhx2+cXv9+vVc9MUv1q4wThs2bGDp0ls3bu+z777MnDWrZefTxMnM1Zm5MDOfm5mzMnPr\nzDw4M8/PzPU16pxV9WzsvUOO/Xi052aHLHU9z5uZRxT1jhzP9+5EJreSJEkqtSOPOnrQ9s9++pMa\nJQf7q3e+a9D2Zz51/qDR1Wb61S230Fs1Q/MRQ2KWNH4mt5IkSSq1ebvswnP23nvj9k8WXz+men9+\n2OGDRn3Xrl3L61/76jEnuI89+ihLb/31mMr+9CeDY3rpMcO+PUbSOJjcSpIkqfRed+KmOXUefOAB\nbrn55jHV+/wX/4Ntttl24/ZDDz7Iy444jIX/9GHuuuvOp5Xv7e3lR9f+kPf+7bs5YMHefP+yy8Z0\nnisvv3zj553nzeNFLz50TPUkjd20dgcgSZIkjdcJrz+Jj51zNpmVeXIuv/R7HHDggaPW23nePP7r\nq5dwyhtO4oknHgfgySef5LOf/hSf/fSnmDt3Ltttvz3Tpz+Dxx59lHvvXbbxHMCg1xDVsnz5n/jF\njTds3H7tCSfW+/UkjYEjt5IkSSq9ebvswtEvO2bj9ne+9c1BSehIXvTiQ/n+D67h2Xs952nHHn74\nYX6zdCm33LyEZcv+UHdiC/Dtb35jY72pU6fylre9Y0z1JNXH5FaSJEld4f+867SNn/9477386Nof\njrnus/d6Dot/fiOf+uzn2O+5z2XKlNo/k6dNm8YhL/wz/vW883nX3/ztqG1/9eJNk9m+7Njj2HX+\n/DHHJWnsvC1ZkiRJXeHPDz+C5x5wIL+6pfK87UVf+mJdsxJPmTKFN77pFN74plN4+OHl/OLGG3nk\n4Yd57NEVTJ02ja233obdn/Us9ttv/zG/xud/f/oT7vztHUBlpPfdf/ee+r+YpDExuZUkSVLX+IcP\n/yNvOPF1AFx1xfe5+6672GPPPetuZ+7c7Xj58a8YdzwXfPL8jZ+POPIoDj7kheNuU9LwvC1ZkiRJ\nXePolx3DIS/8MwAykws+df4oNVrn9tt+wzVXXwVURoX/4R8/0rZYpMnA5FaSJEld5aP/8vGNz8x+\n82uX8Lu7725LHB875+yNn094/V9w4EEHtSUOabIwuZUkSVJXOeDAA3njm08BYMOGDXzso2ePUqP5\nbrrxRq78/uVEBFvOnMlHzj5nwmOQJhufuZUkSVLXOf/Tn+H8T3+mbed//sEH8/ATq9t2fmkycuRW\nkiRJklR6JreSJEmSpNIzuZUkSZIklZ7JrSRJkiSp9ExuJUmSJEmlZ3IrSZIkSSo9k1tJkiRJUumZ\n3EqSJEmSSs/kVpIkSZJUeia3kiRJkqTSM7ntUBGxRUQcFxH/GBHfjYhlEdFfLGeOs+2FVW2NtOze\nrO8jSZIkSa00rd0BqKZDgMtrHMsmnWMdsGKE4+ubdB5JkiRJaimT286VwGPAL4ElwM3A+cAOTTzH\nTzPzyCa2J0mSJNVt97kz2Hra9Jae4/H162B1S0+hNjO57VyLM3N29Y6I+Nd2BSNJkiRJncxnbjtU\nZva3OwZJkiRJKguTW0mSJElS6ZncTm4LImJpRPRFxOqI+G1EXBgRB7Q7MEmSJEmqh8nt5DYb2Avo\nBaYDewLvAH4ZEee0MzBJkiRJqocTSk1OdwLvB74H3JOZGyJiGnAE8DHgecCHI+KxzPxEs07a19dH\nb29vQ3V7enqaFYYkSVKpNPr7qa+vr8mRSJ3N5HYSysxLhtm3HvhBRFwPXA+8AFgYEV/KzJXNOO9B\nC/ZpuO4jKxv7oy5JklR283fcrt0hSKXgbckaJDPXAh8qNnuAo9oYjiRJkiSNiSO3Gs7Pi3UAuzWr\n0SVLb2P27DnNak6SJGlSWPbg8obqrVjxyLjunJPKxuRWE2bGjBk+OytJklSnRn8/rVnjM7eaXLwt\nWcN5YdXne9oWhSRJkiSNkcmtBomIzYB/LjZXAz9sYziSJEmSNCYmtx0sIraJiDkRMTsi5rDpf6+e\ngX3F0jOk3sKI6C+WXYccOywiroqIkyJih6r90yPiKGAxcDCQwNnNmilZkiRJklrJZ247283ArsPs\nf3+xDFgEvHWYcjnMvgBeWixExBqgD9iKTf1hA/AvmXluY2FLkiRJ0sQyue1syfAJ6nDlRtqu9mvg\nfVSeq90PmAPMAnqpPF+7GLgwM39Td7SSJEmS1CYmtx0sMxt6DU9mngWcVePYo8AnxhOXJEmSJHUa\nn7mVJEmSJJWeya0kSZIkqfRMbiVJkiRJpWdyK0mSJEkqPZNbSZIkSVLpmdxKkiRJkkrP5FaSJEmS\nGhQRMyNiYUTcGhGrI+KJiLgxIk6PiOkNtrlVRLwqIs6OiMsi4sGI6C+WU8fYxmYRcVpEXBcRD0fE\nuohYVcT5yYjYvZHYOpnvuZUkSZKkBkTEfOBHwPxiVy8wHXh+sZwcEUdl5uN1Nv0a4KIax3IMce0E\nXA3sU1VnFbAFsG+x/HVEvDkzv11nbB3LkVtJkiRJqlNETAMupZLYPgAcnZkzgR7gJCrJ5IHAVxpo\nPoEHge8DHwVeW2f9z1NJbBM4E5iTmVsDmwOHA78BNgMWFYlwV3DkVpIkSZLqdyqwgEoC+brMvAEg\nMxP4ZkRMAS4BXh4RR2bmtXW0/eXMXFS9IyLGVDEieoDji81FmXnOwLEitusj4lXA3VRGcl8BXFhH\nbB3LkVtJkiRJqt/As6/XDSS21TLz68A9xeYp9TScmf3jiGtzYCATvqlG+78HHis2e8Zxro5icitJ\nkiRJdYiIGcCLi80rRih6ZbF+aWsj2iQzV7ApqX7BcGUi4lnANlRGnYdNgMvI5FaSJEmS6rM3ldHR\nBJaOUG7g2A4RsXXLo9rkb4F1wKkR8ZGI2BYgIqZGxGHA94py38rMxRMYV0uZ3EqSJElSfaonYbp/\nhHIP1KjTUpl5OfASKhNefRh4JCKeAJ4ErqNy6/IHgDdMVEwTwQmlJEmSJJXGk/0bGqw3nsdYn2Zm\n1ee+EcpVH5tZs1Rr9AAzqOR8CWxZdWwGMJtKkrtmguNqGZNbSZIkSaXx6t/9ut0hdLyIeDPwn1Tu\n1P0acC7wW2Bb4Ejg/wJ/DxwdEYdnZm+7Ym0mk1tJkiRJbTV3r9lss9kzxlb4rtbGMkarqj7PGKFc\n9bFVNUs1UUTMBi6gktguysy3Vh3uAy6OiF8AS4DnUUlyPzIRsbWaya0kSZKk0vjp8Uc2VO+xtU/x\nimt+0qwwqp+znUftSaV2rvr8QI0yzXY0MIvKrcjnDlcgM2+PiMuB1wKvw+RWkiRJkibWFtOmNlTv\nyQ2N1avhDirJYwAL2PTKn6EWFOuHMvPxZgYwgt2qPv9uhHJ3F+tnti6UieVsyZIkSZJUh8zsAwaG\ngY8drkxEBHBMsXn1RMRVWFn1+ZkjlNu+WE/I7dITweRWkiRJkuq3qFgfEREHD3P8RCqjqAlcPGFR\nwc+LdQDvHK5AROwAvKbY/NlEBDURTG4lSZIkqX6LgFupJJHfiYgjASJiSkScCHyxKHdFZl5XXTEi\nFkZEf7HsOlzjETGnWGZHxJyqQzMH9hXLFtX1MnMJcH2x+e6IOC8idiza3Dwiji2OzwL6gU+M5yJ0\nEpNbSZIkSapTZm4AXgn8gcrEUddERC/QC3yDynttlwAnj9TMCMeWF8vDxXrABVX7lgMfGKbuScBv\nqCTe7wXuj4hVRWzfB/YA1gPvyczFI33PMjG5lSRJkqQGZOYyYH/gbCqjuBuAtcBNwBnACzPzieGq\njvUUY1yGxvUQldf8vAu4lkoS/AwqrwK6Hfg8cFBmfmaMcZSCsyVLkiRJUoMyczWwsFjGWucs4KxR\nyoxrIDIzn6KSxH5+PO2UiSO3kiRJkqTSM7mVJEmSJJWeya0kSZIkqfRMbiVJkiRJpWdyK0mSJEkq\nPZNbSZIkSVLpmdxKkiRJkkrP5FaSJEmSVHomt5IkSZKk0jO5lSRJkiSVnsmtJEmSJKn0TG4lSZIk\nSaVncitJkiRJKj2TW0mSJElS6ZncSpIkSZJKz+RWkiRJklR6JreSJEmSpNIzuZUkSZIklZ7JrSRJ\nkiSp9ExuJUmSJEmlZ3IrSZIkSSo9k1tJkiRJUumZ3EqSJEmSSs/kVpIkSZJUetPaHYAkSZKkyW2b\nZ+/I7Bmbt/YkfU/Cla09hdrLkVtJkiRJUumZ3EqSJEmSSs/kVpIkSZJUeia3kiRJkqTSM7mVJEmS\nJJWeyW0HiogtIuK4iPjHiPhuRCyLiP5iObNJ59g+Is6LiN9GxJqIeDQiro+ItzejfUmSJEmaSL4K\nqDMdAlxe41iOt/GIeB5wFbBt0d5qoAc4FDg0Ik4AXpmZ68Z7LkmSJEmaCI7cdqYEHgOuAT4OvAF4\nqBkNR8RWwGVUEtvbgRdk5lbAlsC7gXXAMcAnm3E+SZIkSZoIjtx2psWZObt6R0T8a5Pafh+wPdAH\nvDwzlwEUo7Sfi4hZwMeAv4qIT2bmXU06ryRJkiS1jCO3HSgz+1vY/CnF+usDie0QF1C5TXkqcHIL\n45AkSZKkpjG5nUQiYi9gl2LziuHKZGYvsLjYfNlExCVJkiRJ42VyO7ksKNYJLB2h3MCxvVsbjiRJ\nkiQ1h8nt5LJT1ef7Ryj3QLGeFREzWhiPJEmSJDWFE0pNLjOrPveNUK762MxRyo5ZX18fvb29DdXt\n6elpRgiSJEml0+jvp76+pvyEk0rD5FYT5qAF+zRc95GVjf1RlyRJKrv5O27X7hCkUvC25MllVdXn\nkW43rj62qmYpSZIkSeoQjtxOLtXP2c4D7qhRbudivTIzm3Y/y5KltzF79pxmNSdJkjQpLHtweUP1\nVqx4ZFx3zkllY3I7uQzMghxUZk6uldwOzKp8WzNPPmPGDJ+dlSRJqlOjv5/WrPGZW00u3pY8iWTm\nncC9xeaxw5WJiB7gJcXm1RMRlyRJkiSNl8nt5HNxsT4pIuYPc/w0oAdYD3x1wqKSJEmSpHEwue1Q\nEbFNRMyJiNkRMYdN/1v1DOwrlp4h9RZGRH+x7DpM0+cCD1GZNOryiDioqPeMiHgncE5R7sLMvLs1\n306SJEmSmsvktnPdDCwHHi7W84r976/atxz4TI36OezOzJXAK4AVwD7ATRGxElgNfBaYDlwFvLcp\n30KSJEmSJoDJbefKOpah9UZuOHMJsC9wPnAnMJXKK38WA+/IzOMyc11zvoYkSZLUvSJiZnH35K0R\nsToinoiIGyPi9IiY3mCbW0XEqyLi7Ii4LCIerLo789Qx1N8jIs6IiEsjYllErI2I3oi4MyK+NHD3\nZrdxtuQOlZm7NVjvLOCsMZRbDpxRLJIkSZLqVMxh8yNgYC6bXip3Qj6/WE6OiKMy8/E6m34N/P/s\n3XvcZXVd9//XhwEG5mI4zaioCEiioiN5Au0WMw4qoFkeKIwCK391e9fjl4nVXVYOmHa4jfzddFs/\nNGo8EkVmqAhxOyZUooh3gnLwME5xChkOc7jGcZj53H+stZ3Nxd772oe1915rX6/n47Ee+7DW97u+\n+5oZ2O/r813fxSVd9vUsZkXEiyiKVu3HbwGWA08ptzdExDsz8+0DjqvWrNxKkiRJ0oAiYm/gCopg\nexdwamaupFic9SyKQPkc4ENDdJ/A3cCngN8DXjNA232AXcDHgNcBqzPzYIo1d04ArqPIgb8TET83\nxNhqy8qtJEmSJA3uXGANRRB9bWZeD5CZCVwWEXsBHwHOiIiTM/MzA/T9wcxc1/5GRPTb9uvA0zPz\nm+1vluO6ISJOAb4IHAf8Jt0rxI1j5VaSJEmSBte69nV9K9i2y8xLgQ3ly3MG6Tgzdw87qMy8c2Gw\nXbB/J3uqyUdHxEHDnqtuDLeSJEmSNICIWAG8qHx5ZY9DP10+vnS8IxrYjrbny6Y2iooZbiVJkiRp\nMMcCQTEl+eYex7X2HRYRB499VP37kfLx7sy8f5oDqZLhVpIkSZIG84S253f2OO6uLm2mJiJ+CPjx\n8uX7pzmWqrmglCRJkqTGmP/ezqHabd/5cJXDWNn2fL7Hce37VnY9akIi4jHARymqzrcDfzTdEVXL\ncCtJkiSpMY698MPTHkIjRcQBwD8ARwCbgTMzs1cwbxzDrSRJkqSpOviYwznkwLlpD2MQW9qer+hx\nXPu+LV2PGrOImAM+CbygHMcZmXnTtMYzLoZbSZIkSY1x98W/OVS7TZu3seat/7OqYbRfZ3s43ReV\nemLb87u6HDNWbcH2xcBW4BWZ+S/TGMu4GW4lSZIkNcbc8n2Hard9+XDX6nZxK8VKyQGsYc8tfxZa\nUz7ek5kPVjmAfrQF2x8GtlEE2+smPY5JcbVkSZIkSRpAea1qKySe1umYiAjg5eXLqycxrgXnnwM+\nRRFst1JMRb520uOYJMOtJEmSJA1uXfl4UkSc0GH/mcCTKSq8H5jYqHhEsG1NRZ75YAuGW0mSJEka\nxjrgJoqpyZdHxMkAEbFXRJwJvK887srMXN/eMCLWRsTucjuiU+cRsbrcVkXE6rZdK1vvldv+C9qt\nAD5BEWy3AKfP8lTkdl5zK0mSJEkDysxdEfEqYD1wFHBNRGynKCAuLw+7ETi7Vzc99t3b5f2Lyq3l\n/HJreR3wkvL5PhTBu9f5X5OZ/9pjHI1huJUkSZKkIWTmxog4Dngr8GqKacg7KSq6HwUuysyHOzXt\n9xRDHBNt7y8HHrNI+336HEvtGW4lSZIkaUiZuRVYW279tllYbe10zFCXkGbmOvZcD7ykeM2tJEmS\nJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mS\nJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqS\nJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeSJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnxDLeS\nJEmSpMYz3EqSJEmSGs9wK0mSJElqPMOtJEmSJKnx9p72ACRJkiQtbfseeQzLDz5wvOd4cPNY+9f0\nWbmVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS\n4xluJUmSJEmNZ7iVJEmSJDWe4VaSJEmS1HiGW0mSJElS4xluJUmSJEmNZ7iVJEmSJDWe4bbGImJl\nRKyNiJsiYmtEPBQRX4iIt0TEPkP2uTYidvexHV3155EkSZKkcdl72gNQZxFxJPBZ4MjyrW3APsDz\ny+3siDglMx8c8hQ7gU099j88ZL+SJEmSNHFWbmsoIvYGrqAItncBp2bmSmAOOAvYAjwH+NAIp/nn\nzHxCj+3fR/0ckiRJkjQphtt6OhdYAyTw2sz8DEAWLgN+sTzujIg4eUpjlCRJkqTaMNzW07nl4/rM\nvH7hzsy8FNhQvjxnYqOSJEmSpJoy3NZMRKwAXlS+vLLHoZ8uH1863hFJkiRJUv0ZbuvnWCAopiTf\n3OO41r7DIuLgIc6zJiJujoj5ciXm2yLi4oh49hB9SZIkSdJUGW7r5wltz+/scdxdXdr0axXwNPas\nwnwM8EbgSxHxjiH6kyRJkqSp8VZA9bOy7fl8j+Pa963setSj3Q78GvBxYENm7ipXZz4JeBfwPOBt\nEfFAZl44QL+Lmp+fZ9u2bUO1nZubq3IokiRJjTHs96f5+V5fJVWViFgJnAe8FngysAu4DbgUuCgz\nd47Q9+OAXwdeCRwBbKeYwbkuM/+izz6eArwJeDnwJIoM+J/AV4CrMvPPhh1f3Rhul5jM/EiH9x4G\n/jEiPgd8DjgeWBsR78/MzVWd+7lrnjF02/s2D/cfdUmSpKY78vGPnfYQ1EVEHAl8luIWnrBnVuTz\ny+3siDglMx8cou/nAVcBh1JcsriV4tagJwInRsTrgFf1Cs8R8WbgD4B9yz7mgYfL8R4F/DAwM+HW\nacn1s6Xt+Yoex7Xv29L1qAFk5g7gt8qXc8ApVfQrSZIkzZpy9uMVFEHxLuDUzFxJ8T36LIrv6M8B\nPjRE3wcBn6AItrcAx2fmQcABwC8DOykqse/p0cdbgAsp1vN5F3B0Zq4s+zkUOA14/6BjqzMrt/XT\nfp3t4XRfVOqJbc/v6nLMMD5fPgbFtIrK3Hjz11i1anWVXUqSJM28jXffO1S7TZvuG2nmnBZ1LrCG\noiL62tYtPDMzgcsiYi/gI8AZEXFyZn5mgL7fCjyOotJ6RmZuLPveCbw3Ig6kCKy/EBHvycyvtzeO\niGdRVGwTOCszP9a+PzMfAq4ut5lhuK2fWyn+EgbFP5ZPdzluTfl4zzDTHKZhxYoVXjsrSZI0oGG/\nP23f7jW3Y3Zu+bi+FWzbZealEfFOioLROcAg4fac8vHSVrBd4CKKGZcHAGcDaxfs/y2KrPexhcF2\nljktuWYycx64rnx5WqdjIiIopiFA9b9teWHb8w0V9y1JkiQ1XkSsAF5Uvryyx6GtQtVLB+j7aRQL\nP3XtOzO3AdeWL1+2oP0cxeJWAB/s97yzwHBbT+vKx5Mi4oQO+8+k+A1QAh+o6qQRsRx4Z/lyK/C/\nq+pbkiRJmiHHUsy0TLpfRkjbvsMi4uA++27N0Oy372MXvH8CRdU2gRsj4sSI+HhEfCcivhsRGyLi\nkoh4Zp/jaQzDbT2tA26i+AdzeUScDBARe0XEmcD7yuOuzMz17Q0jYm1E7C63Ixbse0lEXBURZ0XE\nYW3v7xMRp1D89ucEin8IF1S5UrIkSZI0Q57Q9vzOrkc9cm2cJ3Q9arS+DywryS1PbXv+kxR3Q/lR\nihWTd1AsgPUGiuD7hj7H1Ahec1tD5b1nXwWsp1ii+5qI2E7xy4jl5WE3Usyv79pNh/eCYkrESwHK\nPueBg9jzd2EX8AeZ+e4RP4YkSZJUuW3bvztUu/nv7qhyGCvbu+512i5txtF36/Uh5WMAvw98GfjF\nzLwBoJwZejFwHHBxRHw1M7/Y59hqzXBbU5m5MSKOo1gp7dUU05B3UlR0P0pxQ+iHOzXt0e1Xyv5e\nCDwLWA0cSHE/rg0UlduLM/OrVX0OSZIkqUqHvvxnpz2EumufnbsdeEVm3tN6IzO/EBGvBG4D9gfe\nBvz4ZIc4HobbGsvMrRQrn60doM35wPld9t1Pca8rSZIkScPb0vZ8RdejHrlvS9ejeve9dcC+259/\npD3YtmTmHRHxEeDngZMjIspbGDWa4VaS9Cgrd3X7/+j4bFl2wMTPKUmqh2WP/wGWrTpk8QOBh26+\nbvGDOrjv/gf4gR/+0aHadtB+LezhdF/46Yltz+/qcsxifd+6SN+byzuutNzR9vyWHudp7ZsDVgH3\n9Tm+2jLcStISMo3Q2q/Fxmb4lSQBzK3Yf6h280Neq9vFrZDnWwQAACAASURBVBSXAwbF6saf7nJc\na+XjezLzwT77bgXlVt/dwm2r768teP8rfZ4n2p43vmoLhltJmhl1Dq5V6Pb5DL2SpEnLzPmIuA54\nMXAa8KjFWCMigJeXL68eoO/bI+LfgSPKvv+2Q99z5bkf1XdmfjMivgUcDTyjx6la+zZn5qZ+x1dn\n3gpIkhpk5a6tXbelyp+FJGlK1pWPJ5UrEC90JsWisAl8YMC+W8efFRFHdtj/SxTTiR8GPtxh/1+V\njz8VEY9fuDMingS8vnz5qQHHVluGW0mqIQPsaPy5SZImYB3FnUwCuDwiTgaIiL0i4kzgfeVxV2bm\n+vaGEbE2InaX2xEd+n43cA/FolGfjIjnlu32jYg3Ae8oj7s4M7/Rof2FwMa29se3nfsE4BPAfhS3\nD7pgiM9eS05LlqQpM3yNX/vP2GnMkqQqZOauiHgVsB44CrgmIrZTFBCXl4fdCJzdq5sufW8ub9dz\nFcX04RsiYitFIG1luKuAX+3Sfj4iTgOuAZ4NXB8R28rdc+XjFuD1mXnbYp+1KazcStIEWY2dPn/2\nkqSqZOZG4DiK6udNwC5gB3ADcB7wwsx8qFPTPvq+EXgm8CfA7cAyikB6LfDGzDw9M3f2aH9b2f4C\n4N+A3RRV5luB/w9Yk5kzMyUZIGbgdkaqoYh4DHBv+3u3fuvbrF79mCmNSJo8w1OzWNGVNGvuu+87\nPP3ooxa+/djM/M4UhvN9nb4n3v3Fa3hMn7cCGtZ3Nj3A448/deHbU/95qDpOS5akChhkm6/1Z2jI\nlSSpmQy3kjQEw+zs8vpcSZKayXArSYswyC5dVnMlSWoOw60kLWCY1UKGXEmS6s9wK2nJM8yqX4Zc\nSZLqy3ArackxzGpUK3dtNeBKklQzhltJM88wq3GwiitJUr0YbiXNHMOsJsmQK0lSPRhuJTWeYVZ1\n4FRlSZKmy3ArqXEMs6orA64kSdNjuJXUCAZaNYXTlCVJmg7DraRaMsyq6aziSpI0WbULtxERwPHA\nC4DjgCOAQ4D9ge3A/cBG4CvA9Zl5w5SGKqlChlnNIgOuJEmTU5twGxGnAD8DvJIizEYfzTIiNgFX\nAB/KzPVjHKKkihlotRQYcCVJmoyphtuI2Af4OeDNwNOG6QJYDfws8IaIuA14D3BJZj5c2UAlVcIw\nq6XKgCtJ0vhNLdxGxE8D7wCObHt7F8V0488D1wO3Ag8Am4DNwEHAoeV2LMXU5RcAzwKWAU8H/hz4\n7xHxO5n54Yl8GEldGWilggFXkqTxmkq4jYh/Bn6o7a1/AT4M/HVm3t+j6aZygyL8/lXZ3yrgJ4Cf\nLvs9CvhgRLwpM0+sdPCSejLMSt0ZcCVJGp+9pnTeHwIeBi4BjsnMEzPzzxYJtl1l5qay/YuAY8p+\nH+aRAVrSmKzctfX7m6Te/LciSdJ4TCvcXgI8NTPfmJnfrLLjzPxmZr6R4hrev6yyb0mF9jDrl3Rp\nOP7bkSSpWlOZllyGz3GfYwMw9vNIS4VfxCVJklRntbkVkKT6MdBK4+U1uJIkVcdwK+n7DLPS5Blw\nJUmqhuFWWuIMtNL0GXAlSRpdrcNtROwFPAU4GNivnzaZ+bmxDkqaAQZaNdmyB+541Hu7Djl8CiOp\nlgFXkqTR1DLcRsQpwFuAk4Dlrbd7NMlyfwLLxjs6qZkMtGqCTsG1qnazEIAlSVJ3tQu3EfE/gPMG\nbbbgUVryDLNqgmHDbFXnqlvgtXoraanadfDj2XXI6vGeY1dfE0HVYLUKtxFxJo8Mtl8HrgPuBXb0\n0UWOY1xSUxhoVXeTDLP9aB9PXYKuAVeSpOHUKtwCv1w+Pgz8XGZ+aJqDkZrAQKsmqFuo7aQ1xjqE\nXAOuJEmDq1u4fXb5+D6DrdSdgVZN0IRA20kdq7mSJGlxdQu3e5WPrngsLWCgVVM0NdR2suyBO6YW\ncK3eSpI0mLqF228DzwT2nfI4pFow0KpJZinUtqvTdGVJktTdXosfMlH/UD6eONVRSFO0ctfW729S\nEyx74I6ZDbbtpvE5/e+AJEn9q1u4/VPgfuBnImLNtAcjTUJ7mPWLrJpkqYTahZbq55Ykqe5qFW4z\n827gNcBu4JqIeN2UhySNhWFWTWe4m9zPwP9OSJLUn7pdc0tmfi4ingN8HLgsIu4BvgRsogi9i7X/\nuTEPURqKX1A1Cwy1jzTNBackSdIj1S7cRsShwFrgqeVbhwGv6LN5AoZb1YaBVrPEYNvZJBaccuVk\nSZIWV6twGxErgc8Axw3bRYXDkYZioNWsMdT2xyquJEnTVatwC/wKe4LtXRQLTP0zcC+wY1qDkhZj\noNWsMtgOZpwB1+qtJEm91S3c/mT5uBE4PjPvm+ZgpF4MtJp1BtvhWMGVJGk66hZujy4f/5fBVnVk\noNVSUedgu+uO20fuY9nhT138oFH6H1PAtXorSVJ3dQu324D9gW9PeRzS9xlotdTULdhWEWYX63Mc\nYdcKriRJk1W3cHsL8GKKFZKlqTHQaimqU6gdR6Dt93xVBt1xBFyrt5IkdbbXtAewwLry8SemOgot\nSSt3bf3+Ji01dQm2u+64feLBdtxjqMvPVpKkWVe3cPtXFLcCOjEi/vuUx6IlwEAr1UMdQu1CVY7J\ngCtJ0vjVKtxm5m7gx4DLgXdFxKci4oyIWDXloWnGGGilPaYZvOoYaheqaoxV/pz975ckSY9Wq2tu\nI2I3kECUb51WbhkRXdu1mgOZmcvGN0I1mV8GpUebdrBtktZ4x73SsiRJGk6twm2pU4pdNNkOeJyW\nCAOt1N20gm3TQu1Cu+64feiA6wrKkiSNT93C7ed4ZOV2UFnhWKYuIlYC5wGvBZ4M7AJuAy4FLsrM\nnSP0/Tjg14FXAkcA24GbgXWZ+RcjDn2qDLTS4qYRbJseatuNUsWtKuC6arIk1UOdv7NHxA+U7V8G\nPB7YAnwJuDgz/27YcdVVrcJtZv7ItMdQFxFxJPBZ4MjyrW3APsDzy+3siDglMx8cou/nAVcBh1L8\nQmArMAecSLGY1+uAV43yD3HSDLRS/wy21Rm2imsFV5JmQ52/s0fEGcDfAPuX7TcDB1ME3ZdFxF9m\n5s8POq46q9WCUipExN7AFRT/SO4CTs3MlRR/mc+i+I3Lc4APDdH3QcAnKP6R3AIcn5kHAQcAvwzs\nBF4OvGf0TzJernQsDc5gW71Z/3ySpM7q/J09Ip4MXEYRbK8DnpaZh1CE2wvKw342In5t0LHVWa0q\nt6OKiJWZuWXa46jAucAait+wvDYzr4ditSzgsojYC/gIcEZEnJyZnxmg77cCjwPmgTMyc2PZ907g\nvRFxIPAu4Bci4j2Z+fXKPlUFDLJSs0wq+O3YcOvQbZc/+ekjn3+YCq7VW0lqvDp/Z78AWAHcDbwy\nMzeX7bcBayPiMOAXgLdFxPuGqSzXUa0qtxHxKyO0XUlRtp8F55aP61v/SNpl5qXAhvLlOQP23Tr+\n0tY/kgUuopjysAw4e8C+x8IKrVSNSVdtxxlsd2y49RFbVX2NYpjPO+qfif9dlKSpquV39oiYo7j+\nF+DPWsF2gd8vHw8EfnzAsdVWrcItcGFE/NSgjcpgezXwguqHNFkRsQJ4Ufnyyh6Hfrp8fOkAfT8N\neFKvvsvf5lxbvnxZv31XzUArVWtWgm0VIbSf/oc9h1OUJWlpqPl39hOB/Sgqyt3ab6SY7typfWPV\nLdwG8JcR8fK+G+yp2DY+2JaOpbxnL8VKaN209h0WEQf32fea8rHfvo/ts9/KGGil6s1CsB13qK3y\nnIN+/mnea1iSNLQ6f2df0+GYXu2f0ee4aq9u4XYDxepifxsRi4bVtmD7wvKty8Y4tkl5QtvzO3sc\nd1eXNlX2fWD5W6mxskorzY6qg+00Qm0VY7CCK0kzr87f2VvtH8jMHX2073dctVe3BaVeBvwz8Fjg\nExHx4szs+I2iDLaf5pHBthbXiI5oZdvz+R7Hte9b2fWoavrudWzf5ufn2bZtW9FpW5Dd1kfbubm5\nKoYgLTmTrAqOI9jWSWs8/S5ANcgiUy4uJamX1venQc3PV/IVrnZq8vOo83f2lR3292rf77hqr1bh\nNjO/GRGnU9wrahVwdUT8l8x8xLeziDiAItj+UPnW3wBnZ+buSY5Xg3numuFnPOx44D8rHImkqlUZ\nbOsWahfaseHWsQTcYa3ctZUtyw4Y6zkkTdeRj3/stIdQK4cefvS0h6Caqtu0ZDLzyxQrdn0POJwi\n4B7a2l8G26vYE2z/FvipzNw16bGOSfutjHpNCW7f1+/tj8bZt6SaaeK1nHUPti2DjLPf4N/EPy9J\nWsLq/J19S4f9vdrPzPf9WlVuWzJzfUScTTHV+OnApyLiZIowvjDYvn6Ggi08cl794XS/CPyJbc/v\n6nLMYn13+3bW6ntzZlY2f+P2f/siq1etqqo7ST00cTpyU4Jty6DTlCVpWBvvvneodps23TfSzLlJ\n2rpsjv36nIVSk59Hnb+zt9ofEhHLe1x322rf77hqr5bhFiAzL4+I/wb8GXAC8DHgAPYE28uZvWAL\nxV/epFh9bQ17lg9fqLUK2j0D3HS59Y+u1Xe3fyitvr/WZ799WbFihdfOSjOmjsH2gVs63Q5wcYcc\ne+RQ7fqZptzv9GSvvZXUybDfn7Zvn81rbmvy86jzd/b2oP0s4IZF2n+1z3HVXu2mJbfLzP8fWFu+\nfCl7gu3fAWfNYLCl/K3LdeXL0zodExEBtG6XdPUAfd8O/Psifc8BLx60b0n1Mamqbd2C7QO3bBw6\n2La3H6affj6DKyhL0uyo+Xf264DvUoTjbu2PpJghO9DY6q7W4RYgMy8A3tv21seY0WDbZl35eFJE\nnNBh/5nAkyl+W/SBAftuHX9W+Zd6oV8C5oCHgQ8P2LckDWTUYDtsGB2k735VFdK99laSGqOW39nL\n4P235cs3RcSBHdr/Rvm4Gfj7AcdWW1OZlhwRb6f4Q+7XJuAhivF+Dfit4hchj1aG4aZbB/wKxTSC\nyyPi3Mz8TETsBbwWeF953JWZub69YUSsBX63fHlUZv47j/Ru4I3AYcAnI+KczLwxIvYFfh54R3nc\nxZn5jao/mKTxalLVdpQwOI4wu9i5+pm2vNgU5XGtnuyKyZI0FXX+zv67wKuBxwNXRMTPZ+Y3yorv\necB/LY/7vcx8aKhPX0PTuub27SO0fVuPfQk0Ptxm5q6IeBWwHjgKuCYitlNU2peXh91I7/v6dvzl\nQWZujohXUizM9QzghojYCuzHnr8PVwG/OurnkKRumhJsF563zgFXkjRZdf7OnpnfjoifoLhl6ouB\n2yNiM8UaRnuV5/3LzHx3nx+3EWo/LXlAncu5DZSZG4HjKML6TcAuYAfFBeHnAS/s8luWRSvimXkj\n8EzgT4DbgWUUS4BfC7wxM0/PzJ1VfA5Jk9Okqu2wphVs288/iTE4NVmSmqHO39kz88pybO8DNgD7\nUsyIvRp4XWa+sc+P2RiROcjs4IpOGvEjY+o6M/OfxtS3BhARjwEesU77HV//Ko9ZvXpKI5JmX1PC\n7TBV22mH2k4Wq+IutoLyYtXbQVdNdlqypIXuu+87PP3ooxa+/djM/M4UhvN9nb4n3vqtb7N69WPG\net66/jxUnalMS87Mz07jvJKk0cxCsN10y52Pem/VsU/scGRvi01T7ucWQZIkqTq1vc+tJKl/TmPt\nrFOQ7ee4fsPuKAF3sWtvveetJEmDMdxKkvrSpKptv6G2n/aLBd1+F5qSJEnjNWsLSkmSZsSgwXbT\nLXd+f6tSP/31GmuvUD/NxbkkSZo1Uwm3EfHqWTqPJE1TE6YkD1q1HSbYjlM/oXnYgNtLE/5sJUmq\ni2lVbi+PiC9HxI+No/OIeHVE/B/gb8fRvyQtNaNUGJsebBeeq9f56riqsyRJS8W0wu088IPA30XE\nzRHxGxEx0qoZEXFERPxmRHwNuJzink7zFYxVklRTkwy2/Z63W8DtFvKrmpq8ctfWSvqRJKmpphVu\nnw5cBgTwDOD3gW9HxGcj4u0RcXpErOrVQUSsjohXRMT5EfFPFDcmfmfZdwKXAk8b66eQpCmbtWmr\ng1Q+Rw22937tvu9vw5hWsJYkSZ1N6z63dwBnRcT/AM4HzqAI2j9cbgAZEfcDrW0LcCBwaLkdQhGO\nH9E1cAVwfmZ+edyfQ5KWgklOSe7XMMGyV4jttu+xz1i96Dg6rabcbQXlbrcGWuy2QJIkaXFTXS05\nM7+Uma+kqN7+T+A7bbsDWAUcA7wAOBU4AXgKRbhtD7b/CbwHeHpm/rjBVpKap9+q7aDBdpTqbD/t\nuo2nqutvZ606L0nSuNTiPreZeSvw5og4D/gvwEspAu0a4PEdmtwJ3AxcD/wj8K+ZuXtCw5UkNcCw\ngbZbP72quN0quJ10q95KkqTR1CLctmTmLuDacgMgIvYFDgaWAzuABzPze9MZoSSpX4NMSa66altV\nsO3UZ7eQ2yngdpue3IlTkyVJGs1UpyX3IzO/l5n3ZuZ/lI8GW0li6U1XnWawHaV/bw8kSdJk1D7c\nSpKmp6rb1EzKuIPtYufpN4CPa6EtSZKWMsOtJGkmTCrYLnY+bxEkSdJ01OqaW0nS0tPPtN2qA+PX\n/3NbX8cd87i5nvvv/dp9i94uCAa79raTZQ/cwa5DDh+6vSRJS4GVW0lS5SY97XaQqm2/wbZ17GLH\ndzp3P2G808+oadPAJUmqE8OtJDXQUlpMqqqqbT9Bddi2/YRrF5aSJGm8DLeSpEbrJ1gOG2pH6cdr\nbyVJmizDrSRpplUVbBfrb5gFraqcvr1l2QGV9SVJUhMZbiVJGlC/gXlh9dapyZIkjY/hVpI0NaOG\nvcWqpVVXbRfre9K3I5IkSXsYbiVJWuDmzTumdm5XTJYkaTiGW0nSTBqkanvz5h2P2NrfG/QcC6u3\nLiwlSdJk1CrcRsQxI7b/b1WNRZI0+/oJsMME3F4WTsWe9D2BJUmaVXtPewAL3BgRv5KZlwzSKCJW\nA5cArwDeO5aRSZKWrJs372DNgcunPQxJmlnf2baLXfs/PNZz3L9t11j71/TVqnILzAHvi4jLIuKg\nfhpExMuArwCvHOvIJEkzZdDrantVeRdWb11YSpKkyatbuN0CBPA64N8i4sRuB0bEvhFxIXAlcFj5\n9rXjH6IkaSkbZrEpr7uVJGn86hZunw1cXz4/AlgfERdExCPGGRFPL497M0UYfhj4beCkCY5VkjSi\nQ449ctpDkCRJM6JW4TYzNwAvBt4J7AaWUYTWayPiKICI+K/Al4AfLJt9CzgxM9+VmTnpMUuSmmcc\nt/oZ5z11JUnS4moVbgEy8+HM/B2KKux/lG//EPDliLiGYsGo/cv3Pwg8OzO/MPmRSpKWqn7C8SSv\nu92y7ICJnUuSpLqqXbhtycxrKaqzf1O+dRBwcvn8IeCnMvPczNw6jfFJkrpb/uSnT3sIkiRpialt\nuC1tpZh2vNAXgasmPBZJ0ozwtj6SJM2e2obbiHgK8C/Ab5RvPQxsLp+fCnwlIk7u1FaSZt2uQw6f\nyHmWHf7UsZ9jsUWlVh37xK77HvuM1VUPR5IkNVQtw21E/CzwZeD55VsbgB8GnsWe2/08Ebg6Iv4w\nIvae/CglSXV2zOPmpj0ESZI0QbUKtxFxUET8NfAXQOtbyUcoFo36fGb+B8VCU78D7KIY/68Bn4+I\n8ZcXJEl987pbSZI0SbUKt8BXgDPL51uAczLzpzNzS+uAzNydme8ETmTP9bjPBb4UEf/PREcrSWos\nr7uVJGm21C3cPql8vJ6iWvuhbgdm5vXAc4DWMXPAn493eJK09DT5utvFpiYPG3D7aTfs9cCT+HlL\nkjSL6hZuE3gXcGJmblj04MwtmXkOcDZ7FpuSJNXEpKYmj7KwVFUV3GGv8e3nZzSpBcQkSWqyuoXb\nkzPztzNz1yCNMvOjwLMpVleWpCVh1gLPKNXbXvoJnWsOXL5oyG0dM0wYHnbs/diy7ICx9S1JUpPU\nKtxm5j+N0PbbwEuqG40kqUlGmZ7c0im49hNoF/bvLYokSZq8WoXbUWXm7mmPQZJm0SjXgVY5NXmU\nCuggAXeUKu1iFqtQS5Kk4cxUuJUkNduowW8aFVPvpytJUj0YbiWpwSZ53W1TqreLTU+uMox26mvh\n+XuNd+HPxZWSJUkanuFWklQr/VRvRwm4UE21dZg+hqlMz9rCYZIkjYvhVpI0EYNUbycVcIcNud3a\nDVK1rYIrJUuStIfhVpLUt0lOm51EwIU9IbdX0G0/Zthgu/DzTOoewJIkLRWGW0lquCZNW51GoBtk\nkamFIbbf6m4VC1l1+sVBk/5sJUmji4iVEbE2Im6KiK0R8VBEfCEi3hIR+1TQ/+Mi4o8j4raI2B4R\n90fE5yLi5wfo4yllHzeX49sWEd+KiL+PiDeNOsZRGG4lSQMZtXo76enJMN5VlDv1Pe7pyJKk2RMR\nRwJfAX4XeCaQwD7A84F3A5+PiINH6P95wFeBXwWeAnwPmANOBN4XEVcuFqAj4s3AzWUfx1LkyYeB\nI4FXAe8cdnxVMNxKkmqtyoBbZcjt1l+nsTglWZLUS0TsDVxBERLvAk7NzJUU4fMsYAvwHOBDQ/Z/\nEPAJ4FDgFuD4zDwIOAD4ZWAn8HLgPT36eAtwIRDAu4CjM3Nl2c+hwGnA+4cZX1UMt5I0AyY9fXWS\n1VvoP+AOEnJHCbrd2g4bbIeZkuxiUpI0U84F1lBUa1+bmZ8ByMJlwC+Wx50REScP0f9bgccB88AZ\nmXlj2f/OzHwv8PbyuF+IiGMWNo6IZwF/UI7vrMz87czc2NqfmQ9l5tWZ+etDjK0yhltJ0lDqGHBh\nsCnB7UG3W2BdeMwgoXiYW/9Ikpakc8vH9Zl5/cKdmXkpsKF8ec4Q/bfaXNoeSttcBGwFlgFnd9j/\nW8DewN9n5seGOP9EGG4laUY0cfGhcQbcYa57HTbIdjpfp7H2W7WVJC0dEbECeFH58soeh366fHzp\ngP0/DXhSr/4zcxtwbfnyZQvazwGvLV9+cJBzT5rhVpI0tCqC2bgCLox/YadhQ/RimviLCknS0I6l\nuI41KRZr6qa177ABF5ZaUz722/+xC94/gaJqm8CNEXFiRHw8Ir4TEd+NiA0RcUlEPHOAMY2F4VaS\nZsg0QtE0Ko+DBtxxhNBe/Y27auv1tpI0U57Q9vzOHsfd1aVN1f0fWFaTW9r/Z/WTwOeAHwX2BXZQ\nLIL1Borg+4YBxlW5vad5cnUXESuB8yimADwZ2AXcBlwKXJSZO4fsdy3F8uKLeUpmfmuYc0jSoFrB\nb8eGW/tu0wqQD9zS6dKhztoD6aZbev3/ffH2i42rnasjS1J15ue3DdVu+/x8xSOpzMq2570G2b5v\nZdejquu/9fqQ8jGA3we+DPxiZt4AEBEnABcDxwEXR8RXM/OLA4yvMobbGirvcfVZit+CAGxjzz2u\nng+cHRGnZOaDI5xmJ7Cpx/6HR+hb0hTtOuRwlj1wx0TPuezwp7LrjttH7mf5k58+UMCFIkwOEnBb\nxjGdeJBg261q65RkSert+B8YpGg5HmWF8pIRujg9M6+qaDjj1j7bdzvwisy8p/VGZn4hIl5JUYjb\nH3gb8OOTHWLBack1M+57XLX558x8Qo/t30fsX9ISU9X05GGqnIcce+RUVybudv5Bg60kqTGy7XHY\nrWVL2/P26cALte/b0vWoRxu1//bnH2kPti2ZeQfwkfLlyRERA4yvMlZu62fhPa6uh+IeV8BlEbEX\nxV+cMyLi5NY9sCSp3TSqtzDdCi4MN1V5FL0C9TAhvZ+qrdfbSppFGx/azoPLtvd17N/c8M2hzvHQ\nA5t440tPGKptBx+lKEgNa3Pb8/brZA6n+6JP7VOO7upyTCcL++/2P9hW/5szs32KcvsXilt6nKe1\nbw5YBdw3wBgrYbitn0XvcRUR76S4DvccwHArqVamHXDhkaFzHEF3sSpxr2Br1VaSRrPfirmh2u34\nbn/huR+Z+T3g/oq6u5WisBUURa5PdzmuterxPQNentgKy63+u/3PtdX/1xa8/5U+z9Nerc2uR42R\n05JrZNz3uJK0tEzz2s0qpyiPuhhTa8rwKNOW2/sYV7D1WltJWprKKul15cvTOh1TTvN9efny6gH7\nvx1oXXLYrf854MWd+s/MbwKthWaf0eNUrX2bM7PX2j5jY+W2Xoa6x9WQC0utiYibgaOB3RTTFf4J\neG9m/p8h+pOkR6iqggujVXHbjfu63HFXbJ2SLEkzax1FuDwpIk7IzC8s2H8mxczNBD4wRP8fAH4b\nOCsi3pGZC6c1/RLFdOKHgQ93aP9XwAXAT0XE2zPz7vadEfEk4PXly08NMb5KWLmtl3Hf46rdKuBp\n7FmJ+RjgjcCXIuIdQ/YpqWamXQ2scgpuFVXccRl1bNP+c5IkTd064CaKQtflEXEyQETsFRFnAu8r\nj7syM9cvbBwRayNid7kd0aH/dwP3UCwa9cmIeG7Zbt+IeBPQ+v5/cWZ+o0P7C4GNbe2Pbzv3CcAn\ngP0obh90wYCfvTJWbutl3Pe4Argd+DXg48CGzNxVrtB8EvAu4HnA2yLigcy8cMC+e5qfn2fbtuHu\nSzY3N9y1FZKmt7hUS5UVXBjunrjj1E+o9TpbSaMY9vvTfH3v66oFyu/krwLWA0cB10TEdopi5PLy\nsBuBsxfrqkv/m8vb9VxFMX34hojYShFIW5nwKuBXu7Sfj4jTgGuAZwPXR0TrL2bri/oW4PWZedsi\nYxwbw+2ImnaPq8z8SIf3Hgb+MSI+B3wOOB5YGxHvz8zNC48f1lN/8PjFD+pixwP/WdUwpCVp1gIu\nTD/k9lupXSzYWrWVtJgjH//YaQ9BE5CZGyPiOOCtwKsppiHvpKjofhS4qPze3rF5H/3fGBHPBH4D\neAXwJIpAejOwLjN7ZprMvK1s/xbgxygub1xGsUDVVcCFmfkfi37QMTLcji4XPA7bHsZ/j6veA8nc\nERG/BfwjxW9gTgE+VlX/kpa2cQRceGTIHHfQHWTqC21G8AAAIABJREFUcT/V2kGCrdfbStLsy8yt\nwNpyG6Td+cD5fRx3L3BeuQ2sLHytZcDxTYrhdnRNusdVPz5fPgbFb4sqc/u/fZHVq1ZV2aWkAUy7\negt7At84Qi48OnyOGnaHvY7WaciSqrTx7nuHardp0308d02vxW2l2WK4HVHD7nE1VStWrPDaWWnK\n6hBwYXxV3IUWC6c7Ntxa+SJV/QZbpyNL6tew35+2b/eaWy0trpZcI+O+x1WfXtj2fMMY+pc0ZXUJ\nVcsOf+rUK5xNCbZOSZYkaXGG2/pZVz6eVC6rvdCo97jqKiKWA+8sX24F/neV/Uuqj7oEXJiNKbyD\nBPU6/ewlSZolhtv6Gds9riLiJRFxVUScFRGHtb2/T0ScAlwLnEARnC+ocqVkSeqlDlXcYQw6boOt\nJEnj4zW3NTPme1wF8NJyo+x3HjiIPX8XdgF/kJnvHuFjSGqAulx/227cC05VaVJh3CnJkiT1x3Bb\nQ2O8x9VXyj5fCDwLWA0cCGyjuL72WuDizPxqFZ9DUv3VMeBCfUPuKIHWqq0kSeNluK2pcdzjKjPv\nBy4cdWySZktdAy48MkxOM+iOWqU12EqSNH6GW0lSrQNuy6SDblXTjkcJtk5JliSpf4ZbSRLQjIDb\n0il4jhp4x3ENrRVbSZImx3ArSfq+VhhrSshtV7fVlg22kiRNlrcCkiQ9isFsNFX8/JySLEnSYAy3\nkqSODLjD8ecmSdJ0GG4lSV0Z1Abjz0uSpOnxmltJUk9Nvg53UqoOtU5JliRpcFZuJUl9sSrZmT8X\nSZLqwcqtJKlvVnH3GFeotWorSdJwrNxKkga21KuVS/3zS5JUR1ZuJUlDWYpVXEOtJEn1ZbiVJI1k\nKYTcSYVapyRLkjQ8w60kqRKzGHKt1EqS1ByGW0lSpdoDYVODrqFWkqTmMdxKksamSdXcaQdapyRL\nkjQaw60kaewWBse6hN1pB1pJklQdw60kaeKmFXbrGmat2kqSNDrDrSRp6jqFzlECb11DrCSps2/e\nv40Ddu831nNsfXDbWPvX9BluJUm1ZECVJEmD2GvaA5AkaSlzSrIkSdUw3EqSJEmSGs9wK0nSlFi1\nlSSpOoZbSZIkSVLjGW4lSZoCq7aSJFXLcCtJkiRJajzDrSRJkiSp8Qy3kiRNmFOSJUmqnuFWkiRJ\nktR4hltJkibIqq0kSeNhuJUkSZIkNZ7hVpKkCbFqK0nS+BhuJUmSJEmNZ7iVJEmSJDWe4VaSpAlw\nSrIkSeNluJUkSZIkNZ7hVpKkMbNqK0nS+BluJUmSJEmNZ7iVJGmMrNpKkjQZhltJkiRJUuMZbiVJ\nGhOrtpKkQUTEyohYGxE3RcTWiHgoIr4QEW+JiH1G6PegiPixiLggIj4REXdHxO5yO7eP9k+JiPMi\n4oqI2BgROyJiW0TcHhHvj4jnDju2Ku097QFIkiRJ0lIXEUcCnwWOLN/aBuwDPL/czo6IUzLzwSG6\nfzVwSZd9uci4XgRcu+D4LcBy4Cnl9oaIeGdmvn2IsVXGyq0kSWNg1VaS1K+I2Bu4giLY3gWcmpkr\ngTngLIow+RzgQ0OeIoG7gU8Bvwe8ZoC2+wC7gI8BrwNWZ+bBwArgBOA6ilz5OxHxc0OOrxJWbiVJ\nkiRpus4F1lCE0Ndm5vUAmZnAZRGxF/AR4IyIODkzPzNg/x/MzHXtb0REv22/Djw9M7/Z/mY5thsi\n4hTgi8BxwG/SvUI8dlZuJUmqmFVbSdKAWte9rm8F23aZeSmwoXx5zqCdZ+buYQeWmXcuDLYL9u9k\nT0X56Ig4aNhzjcpwK0mSJElTEhErgBeVL6/sceiny8eXjndEQ9nR9nzZtAZhuJUkSZKk6TkWCIop\nyTf3OK6177CIOHjsoxrMj5SPd2fm/dMahOFWkqQKOSVZkjSgJ7Q9v7PHcXd1aTNVEfFDwI+XL98/\nzbG4oJQkSZKkxtixfX6odt/77vaKR1KZlW3Pe3249n0rux41QRHxGOCjFJXn24E/muZ4DLeSJFXE\nqq0kjd9vvfy4aQ+BiHgDo60KfHpmXlXRcKYiIg4A/gE4AtgMnJmZw/3moSKGW0mSJEkaTC54HLY9\nFPewbVnRo037vi1dj5qAiJgDPgm8oBzLGZl50zTHBIZbSZIqYdVWkob3tTu3sN+W/qLJT/3FdUOd\nY8eWB7j8zT86VNsOPgpcMUL7zW3P26+zPZzui0o9se35XV2OGbu2YPtiYCvwisz8l2mNp53hVpIk\nSVJj7LPf/kO12/W971Y2hsz8HlDVqsC3UlRyA1jDnlv+LLSmfLwnMx+s6NwDaQu2Pwxsowi2w/22\nYQxcLVmSpBFZtZUkDau8TrUVEE/rdExEBPDy8uXVkxhXhzHMAZ+iCLZbKaYiXzuNsXRjuJUkSZKk\n6VpXPp4UESd02H8m8GSKCu8HJjaqUluwbU1Frl2wBcOtJEkjsWorSarAOuAmiqnJl0fEyQARsVdE\nnAm8rzzuysxcv7BxRKyNiN3ldkSnE0TE6nJbFRGr23atbL1XbvsvaLcC+ARFsN1CsdJzbaYit/Oa\nW0mSJEmaoszcFRGvAtYDRwHXRMR2imLk8vKwG4GzF+uqx757u7x/Ubm1nF9uLa8DXlI+34cifPc6\n/2sy818XGedYWLmtmYjYPyJOj4jfjoi/i4iNbb+FeXuF53lcRPxxRNwWEdsj4v6I+FxE/HxV55Ck\nWWfVVpJUlczcCBwHXEBRxd0F7ABuAM4DXpiZD3Vr3u9p+tzaRVvb5cBjemyPpQjAU2Hltn5eQLEC\nWSfD3kfrESLiecBVwKFln1uBOeBE4MSIeB3wqszcWcX5JEmSJC0uM7cCa8ttkHYLq62djhmqsJmZ\n69hzTXCtWbmtnwQeAK4B/gh4PXBPVZ1HxEEUc+YPBW4Bjs/Mg4ADgF8GdlKsxPaeqs4pSbPIqq0k\nSfVi5bZ+rs3MVe1vRMQfVtj/W4HHAfMUq5xtBCirtO+NiAOBdwG/EBHvycyvV3huSZIkSRoLK7c1\nk5m7x3yKc8rHS1vBdoGLKKYpL2PxC9YlaUmyaitJUv0YbpeQiHga8KTy5ZWdjsnMbUDrnlUvm8S4\nJEmSJGlUhtulZU35mMDNPY5r7Tt2vMORpOaxaitJUj0ZbpeWJ7Q9v7PHcXeVjweWN22WJEmSpFpz\nQamlZWXb8/kex7XvW7nIsX2bn59n27ZtQ7Wdm5urYgiSNBKrtpKmYdjvT/PzlXyFkxrDcDuiiHgD\ncMkIXZyemVdVNJxae+oPHj902x0P/GeFI5EkSWqOIx//2GkPQWoEw+3ocsHjsO0nYUvb8xUUqyJ3\n0j4VeUuXYyRpSbFqK0lSvRluR/dR4IoR2m+uaiB9aL/O9nDg1i7HPbF83JyZlc1nuf3fvsjqVasW\nP1CSJEnft/Hue4dqt2nTfTx3zTMqHo1UX4bbEWXm94D7pz2OPrVWQQ6KlZO7hdvWqspfq/LkK1as\n8NpZSY1k1VbSNA37/Wn7dq+51dLiaslLSGbeDvx7+fK0TsdExBzw4vLl1ZMYlyRJkiSNynC79Hyg\nfDwrIo7ssP+XgDngYeDDExuVJNWUVVtJkprBcFtDEXFIRKyOiFURsZo9f05zrffK7VFzVCJibUTs\nLrcjOnT/buAeikWjPhkRzy3b7RsRbwLeUR53cWZ+o/pPJ0mSJEnVM9zW05eBe4HvlI+Hl+//Wtt7\n9wJ/2qOPjqswZ+Zm4JXAJuAZwA0RsZli5eT/BewDXAX86sifQpIazqqtJEnNYbitpxxg69S2d+eZ\nNwLPBP4EuB1YRnHLn2uBN2bm6Zm5c/SPIUmSJEmT4WrJNZSZTx6h7fnA+X0cdy9wXrlJkhawaitJ\nUrNYuZUkaQGDrSRJzWO4lSRJkiQ1nuFWkqQ2Vm0lSWomw60kSZIkqfEMt5IklazaSpLUXIZbSZIk\nSVLjGW4lScKqrSRJTWe4lSRJkiQ13t7THoAkSdNm1VaSpmvDnZvZ56Hx1t12bt081v41fVZuJUmS\nJEmNZ7iVJC1pVm0lSZoNhltJkiRJUuMZbiVJS5ZVW0mSZofhVpK0JBlsJUmaLYZbSZIkSVLjGW4l\nSUuOVVtJkmaP4VaSJEmS1HiGW0nSkmLVVpKk2WS4lSRJkiQ1nuFWkrRkWLWVJGl2GW4lSZIkSY1n\nuJUkLQlWbSVJmm2GW0mSJElS4xluJUkzz6qtJEmzz3ArSZppBltJkpYGw60kSZIkqfEMt5KkmWXV\nVpKkpcNwK0mSJElqPMOtJGkmWbWVJGlpMdxKkmaOwVaSpKXHcCtJkiRJNRARKyNibUTcFBFbI+Kh\niPhCRLwlIvYZod+DIuLHIuKCiPhERNwdEbvL7dw++1geEb8UEesj4jsRsTMitpRjfU9EHD3s+Kqy\n97QHIElSlazaSpKaKCKOBD4LHFm+tQ3YB3h+uZ0dEadk5oNDdP9q4JIu+7KPsT0BuBp4RlubLcD+\nwDPL7Rcj4mcy82+HGF8lrNxKkiRJ0hRFxN7AFRTB9i7g1MxcCcwBZ1EEyecAHxryFAncDXwK+D3g\nNQO2/3OKYJvA24HVmXkwsB/wI8BXgeXAujIIT4WVW0nSzLBqK0lqqHOBNRTh8bWZeT1AZiZwWUTs\nBXwEOCMiTs7MzwzY/wczc137GxHRV8OImANeUb5cl5nvaO0rx/e5iPgx4BsUldxXAhcPOL5KWLmV\nJEmSpOlqXfe6vhVs22XmpcCG8uU5g3aembtHGNt+QCsJ39Cl/28BD5Qv50Y410gMt5KkmWDVVpLU\nRBGxAnhR+fLKHod+unx86XhH9EiZuYk9wfr4TsdExA8Ah1BUnjsG4Ekw3EqSGs9gK0lqsGMpKqMJ\n3NzjuNa+wyLi4LGP6pH+X2AncG5E/G5EHAoQEcsi4iXAx8vj/iYzr53w2L7PcCtJkiRJ09O+ANOd\nPY67q0ubscvMTwIvplj06m3AfRHxEPBdYD3F1OVfB14/yXEt5IJSkqRGs2orSUvLru9tH7Lddyse\nSWVWtj2f73Fc+76VXY8anzlgBUWGTKD9f8ArgFUUIXe4P6AKGG4lSZIkNcbnf/cVix80ZhHxBrrf\nN7Yfp2fmVRUNZ+wi4meAv6SY+ftR4N3AbcChwMnA7wO/AZwaET+SmdumMU6nJUuSGsuqrSRpSrLt\ncditZUvb8xU9ztm+b0vXoyoWEauAiyiy47rMPDszv5yZ85l5R2Z+ADgV2AE8jyLkToWVW0mSJElT\ntemeLey9or+62zFvunSoczw8v5kN635hqLYdfJTi+tNhbW573n6d7eF0X1TqiW3P7+pyzDicChxI\nEcjf3emAzLwlIj4JvAZ4LfC7kxveHoZbSVIjWbWVpKVpr332G7LdjsrGkJnfA+6vqLtbKYJjAGvY\nc8ufhdaUj/dk5oMVnbsfT257/s0ex32jfDxqfEPpzWnJkqTGMdhKkmZFZs4D15UvT+t0TEQE8PLy\n5dWTGFeb9irzUT2Oe1z5OLEp0wsZbiVJkiRputaVjydFxAkd9p9JUUFN4AMTG1Xh8+VjAG/qdEBE\nHAa8unz5r5MYVCeGW0lSo1i1lSTNoHXATRQB8vKIOBkgIvaKiDOB95XHXZmZ6xc2joi1EbG73I7o\ndIKIWF1uqyJidduula33ym3/9naZeSPwufLlL0fEH0fE48s+94uI08r9BwK7gQuH/SGMynArSZIk\nSVOUmbuAVwHfplg46pqI2AZsA/6a4r62NwJnL9ZVj333ltt3yseWi9reuxf49Q5tzwK+ShG+fxW4\nMyK2lOP7FPAU4GHgzZl57SJjHBvDrSSpMazaSpJmVWZuBI4DLqCo4u6iuL3ODcB5wAsz86Fuzfs9\nTZ/bwrHdQ3Gbn/8GfIYiBO8LzAO3AH8OPDcz/7TPcYyFqyVLkhrBYCtJmnWZuRVYW26DtDsfOH+R\nY0YqbJYrRP95udWSlVtJkiRJUuMZbiVJtWfVVpIkLcZwK0mSJElqPMOtJKnWrNpKkqR+GG4lSZIk\nSY1nuJUk1ZZVW0mS1C/Dbc1ExP4RcXpE/HZE/F1EbIyI3eX29gr6X9vWX6/t6Co+jyQNy2ArSZIG\n4X1u6+cFwCe77Ov35sz92Als6rH/4QrPJUmSJEljZbitnwQeAL4E3Ah8GfgT4LCKz/PPmXlyxX1K\nUiWs2kqSpEEZbuvn2sxc1f5GRPzhtAYjSZIkSU3gNbc1k5m7pz0GSZomq7aSJGkYhltJUm0YbCVJ\n0rAMt0vXmoi4OSLmI2JrRNwWERdHxLOnPTBJkiRJGpThdulaBTwN2AbsAxwDvBH4UkS8Y5oDk7Q0\nWbWVJEmjcEGpped24NeAjwMbMnNXROwNnAS8C3ge8LaIeCAzL6zyxPPz82zbtm2otnNzc1UORZIk\nqTGG/f40Pz9f8UikejPcjigi3gBcMkIXp2f+3/buPOqSqjzU+PPSjShtN1OjTIJovIqCN+Ic8cqg\n4pCgLMWgKM5GoismiCQalzYS1/WqJEaMRlQERXBI9HrRQDsEFE2ERG8UIjhEaBUuIt0i9GDTw3v/\nqH3sw8c3nbFO1Xl+a9WqadeuXZuX8523d52qXD2k5iwoMy+cZdtW4MsR8XXg68CjgVUR8eHMvH1Y\n5/5v//3RfR+7+Ve/GFYzJE0gR20laW4H7XufupsgNYK3JQ8uu+b9ThMhMzcDbyqry4BjamyOpClh\nYitJkobBkdvBXQRcPMDxQxsZHZJvlXkABw+z4h9+999YuddeCxeUJEnSb635f7f0ddzatbdy+KEP\nHXJrpMllcjugzLwTWFd3O5pg11139bezku7CUVtJWli/3582bfI3t5ou3pasmR7XtXx9ba2Q1Hom\ntpIkaZhMbvVbEbEL8Payuh74ao3NkSRJkqRFM7mdQBGxR0SsjIi9ImIlO/47LetsK9Pd7lGJiFUR\nsb1MB87Y96SIWB0RJ0bEPl3bd46IY4ArgMdQPeTqbcN8UrIkdXPUVpIkDZu/uZ1M/xc4cJbtbyhT\nx/nAS+eoY7anMAfwlDIREZuAjcBu7IiFbcA7MvPdvTdbkiRJkuphcjuZFvuKoNnKzHfc94DTqH5X\nexiwElgBbKD6fe0VwDmZ+Z89tVaSeuCorSRJGgWT2wmUmX2/giczzwDOmGPfOuCv+61bkgZlYitJ\nkkbF5FaSJElSrW678QZ22mW0/wC6ffP6kdav+vlAKUnSWDhqK0mSRsnkVpIkSZLUeCa3kqSRc9RW\nkiSNmsmtJGmkTGwlSdI4mNxKkiRJkhrP5FaSNDKO2kqSpHExuZUkjYSJrSRJGieTW0mSJElS45nc\nSpKGzlFbSZI0bia3kiRJkqTGM7mVJA2Vo7aSJKkOJreSpKExsZUkSXUxuZUkSZIkNZ7JrSRpKBy1\nlSRJdTK5lSQNzMRWkiTVzeRWkiRJktR4JreSpIE4aitJkiaBya0kSZIkqfFMbiVJfXPUVpIkTQqT\nW0lSX0xsJUnSJDG5lSRJkiQ1nsmtJKlnjtpKkjR8EbE8IlZFxNURsT4ifh0RV0XEqRGx8wD17h8R\nfxwRn4mIH0fEpjJdHxEXRsRRfdZ7SURsL9Nl/bZvWJbW3QBJUrOY2EqSNHwRcRBwOXBQ2bQB2Bl4\nVJlOiohjMvO2Huu9H7Cma1MCG4EADiznOzEizgVelZnbF1nvS4BjZ9RbK0duJUmSJKlGEbEUuJgq\n0bwJeHJmLgeWAScCdwCPAC7oo/olZf4V4GRg/8xcnpn3Bg4FPl/2vwxYtcj27gP8NfAr4No+2jQS\nJreSpEVz1FaSpJF4MVWimcBzMvOfAbLyaeCPSrlnRMTRPda9Djg8M5+amRdk5s2dHZl5bWYeD1xa\nNv1pROyyiDrfD+wOvAG4pcf2jIzJrSRpUUxsJUkamReX+WWZeeXMnZn5SeD6snpyLxVn5u2Z+R8L\nFDu3zJcBh8xXMCKeBzwbuDwzz6W6vXkimNxKkiRJUk0iYlfgCWX1knmKdkZXnzKCZmzuWp4zR4yI\nvYCzgd8ArxpBOwZicitJWpCjtpIkjcwhVKOfCVwzT7nOvn0iYvcht+HIMr8T+OE85d4L7A2cmZk/\nHnIbBmZyK0mSJEn12a9r+cZ5yt00xzEDiYiDgVeX1U9l5vo5yv0B8HzgauCdwzr/MPkqIEnSvBy1\nlSRNkty6eeFCsx5355BbMjTLu5Y3zlOue9/yOUv1ICLuBXwGuBfwS+Av5ii3G/D3wDbglZm5bRjn\nHzaTW0nSnExsJUmT5tYvvKnuJnTe8XruQuXm8fTMXD2k5vSlvH7oQuBwqtuRT+p+kvIMZwH7Amdn\n5lVjamLPTG4lSZIkqTc5Y97v8VC9w7Zj13mO6d53x5ylFiEilgCfAJ4FbAFekJlfmaPsk6negfsz\noP5/WZiHya0kaVaO2kqSxuXXP7uOWHrPRZVdetgL+zpHbtvMtu9/pq9jZ3ERcPEAx9/etdz9O9sD\nmPuhUvt3Ld80R5kFlcT2AuAEYCvwwsz87DyHfKjMT68Oj84XhKR6ENaSsr40IpaVbRszc3u/beyX\nya0k6W5MbCVJkyqW7NzfgUP8mWhm3gmsG1J117EjUTyUHa/8menQMr85M2/r50RdI7bPY0diu1DG\nf1CZX7RAuSPYMaJ8PPD5fto4CJ+WLEmSJEk1ycyNwDfK6tNmKxMRARxbVr/Uz3lKYnshd01sP73Y\nZs4zzVVu7ExuJUl34ai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"text/plain": [ "<matplotlib.figure.Figure at 0x16c38d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.15, 7))\n", "plt.axis('scaled')\n", "\n", "ranges = np.max(np.abs([np.min(residuals_true), np.max(residuals_true)]))\n", "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n", "cmap = plt.get_cmap('RdBu_r')\n", "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n", "plt.subplot(3,1,1)\n", "plt.contourf(0.001yp.reshape(shape), 0.001xp.reshape(shape),\n", " residuals_true.reshape(shape), levels=levels,\n", " cmap = cmap, norm=norm)\n", "plt.ylabel('x (km)')\n", "plt.xlim(0.001np.min(yp), 0.001np.max(yp))\n", "plt.ylim(0.001np.min(xp), 0.001np.max(xp))\n", "cbar = plt.colorbar()\n", "plt.annotate(s='(a)', xy=(0.88,0.92),\n", " xycoords = 'axes fraction', color='k',\n", " fontsize = 10)\n", "\n", "ranges = np.max(np.abs([np.min(residuals_usual), np.max(residuals_usual)]))\n", "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n", "cmap = plt.get_cmap('RdBu_r')\n", "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n", "\n", "plt.subplot(3,1,2)\n", "plt.contourf(0.001yp.reshape(shape), 0.001xp.reshape(shape),\n", " residuals_usual.reshape(shape), levels=levels,\n", " cmap = cmap, norm=norm)\n", "plt.ylabel('x (km)')\n", "plt.xlim(0.001np.min(yp), 0.001np.max(yp))\n", "plt.ylim(0.001np.min(xp), 0.001np.max(xp))\n", "plt.colorbar()\n", "plt.annotate(s='(b)', xy=(0.88,0.92),\n", " xycoords = 'axes fraction', color='k',\n", " fontsize = 10)\n", "\n", "ranges = np.max(np.abs([np.min(residuals_max), np.max(residuals_max)]))\n", "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n", "cmap = plt.get_cmap('RdBu_r')\n", "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n", "\n", "plt.subplot(3,1,3)\n", "plt.contourf(0.001yp.reshape(shape), 0.001xp.reshape(shape),\n", " residuals_max.reshape(shape), levels=levels,\n", " cmap = cmap, norm=norm)\n", "plt.ylabel('x (km)')\n", "plt.xlabel('y (km)')\n", "plt.xlim(0.001np.min(yp), 0.001np.max(yp))\n", "plt.ylim(0.001np.min(xp), 0.001*np.max(xp))\n", "plt.colorbar()\n", "plt.annotate(s='(c)', xy=(0.88,0.92), \n", " xycoords = 'axes fraction', color='k',\n", " fontsize = 10)\n", "\n", "plt.tight_layout()\n", "savefig('f06.pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
wcmckee/brobeur-web	imgs/skins.ipynb	1	20039	{ "metadata": { "name": "skins" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from PIL import Image\n", "from PIL import ImageEnhance\n", "from PIL import ImageChops\n", "from PIL import ImageFilter\n", "import random\n", "import os\n", "import sys\n", "import glob" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 362 }, { "cell_type": "code", "collapsed": false, "input": [ "devInput = ('/home/will/Desktop/skins/')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 363 }, { "cell_type": "code", "collapsed": false, "input": [ "os.chdir(devInput)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 364 }, { "cell_type": "code", "collapsed": false, "input": [ "daLizt = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 365 }, { "cell_type": "code", "collapsed": false, "input": [ "for daLoopz in range(0,3):\n", " daLizt.append(random.choice(os.listdir(devInput)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 366 }, { "cell_type": "code", "collapsed": false, "input": [ "daLizt.append(random.choice(os.listdir(devInput)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 367 }, { "cell_type": "code", "collapsed": false, "input": [ "print daLizt" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['cas25437.jpg', 'cas33252.jpg', 'cas47628.jpg', 'cas24242.jpg']" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 368 }, { "cell_type": "code", "collapsed": false, "input": [ "daLimp = len(daLizt)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 369 }, { "cell_type": "code", "collapsed": false, "input": [ "liztnum = random.randint(0, daLimp)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 370 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg = Image.open(daLizt[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 371 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 371 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg2 = Image.open(daLizt[1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 372 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg5 = Image.open(daLizt[2])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 373 }, { "cell_type": "code", "collapsed": false, "input": [ "imgz2 = Image.open(daLizt[3])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 374 }, { "cell_type": "code", "collapsed": false, "input": [ "width = 1920\n", "height = 1080" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 375 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg.resize((width, height), Image.NEAREST)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 376, "text": [ "<PIL.Image.Image image mode=RGB size=1920x1080 at 0x5EE3EA8>" ] } ], "prompt_number": 376 }, { "cell_type": "code", "collapsed": false, "input": [ "near1 = iimg.resize((width, height), Image.BILINEAR)\n", "near2 = iimg.resize((width, height), Image.BICUBIC)\n", "near3 = iimg.resize((width, height), Image.ANTIALIAS)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 377 }, { "cell_type": "code", "collapsed": false, "input": [ "ext = '.jpg'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 378 }, { "cell_type": "code", "collapsed": false, "input": [ "near1.save('Near' + ext)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 379 }, { "cell_type": "code", "collapsed": false, "input": [ "near3.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 380 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg3 = ImageChops.difference(iimg, iimg2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 381 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg3.save('04.jpg')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 382 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg8 = ImageChops.difference(iimg2, imgz2).show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 383 }, { "cell_type": "code", "collapsed": false, "input": [ "iimg5 = ImageChops.darker(iimg5, iimg)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 384 }, { "cell_type": "code", "collapsed": false, "input": [ "img6 = ImageChops.blend(iimg5, iimg, .5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 385 }, { "cell_type": "code", "collapsed": false, "input": [ "iimgz7 = ImageChops.darker(iimg5, img6)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 386 }, { "cell_type": "code", "collapsed": false, "input": [ "iimgz8 = iimgz7.filter(ImageFilter.DETAIL)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 387 }, { "cell_type": "code", "collapsed": false, "input": [ "iimgz2 = ImageChops.blend(img6, img6, .2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 388 }, { "cell_type": "code", "collapsed": false, "input": [ "imgz10 = iimgz2.filter(ImageFilter.CONTOUR)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 389 }, { "cell_type": "code", "collapsed": false, "input": [ "InImgz10 = ImageChops.invert(imgz10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 390 }, { "cell_type": "code", "collapsed": false, "input": [ "fixImg = ImageEnhance.Color(InImgz10)\n", "DieImage = ImageEnhance.Contrast(InImgz10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 391 }, { "cell_type": "code", "collapsed": false, "input": [ "newFix = fixImg.enhance(50)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 392 }, { "cell_type": "code", "collapsed": false, "input": [ "dieFix = DieImage.enhance(10)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 393 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 393 }, { "cell_type": "code", "collapsed": false, "input": [ "randNumz = random.randint(1,10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 394 }, { "cell_type": "code", "collapsed": false, "input": [ "os.chdir('/home/will/Desktop/output')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 395 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "imgz10.save(daLizt[1])\n", "iimg8.save(daLizt[2])\n", "iimg5.save(daLizt[3])" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'NoneType' object has no attribute 'save'", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-396-b577c03bf458>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mimgz10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaLizt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0miimg8\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaLizt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0miimg5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdaLizt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'save'" ] } ], "prompt_number": 396 }, { "cell_type": "code", "collapsed": false, "input": [ "img7 = ImageChops.invert(img6)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 397 }, { "cell_type": "code", "collapsed": false, "input": [ "enchane = ImageEnhance.Brightness(iimg)\n", "blendz = enchane.enhance(2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 398 }, { "cell_type": "code", "collapsed": false, "input": [ "bluzImage = ImageChops.blend(iimgz2, iimg5, .3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 399 }, { "cell_type": "code", "collapsed": false, "input": [ "cassie = random.choice(os.listdir('/home/will/Desktop/output'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 400 }, { "cell_type": "code", "collapsed": false, "input": [ "mixCas = Image.open(cassie)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 401 }, { "cell_type": "code", "collapsed": false, "input": [ "mixCok = ImageChops.blend(iimg, mixCas, .5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 402 }, { "cell_type": "code", "collapsed": false, "input": [ "os.chdir('/home/will/Desktop/cook/')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 403 }, { "cell_type": "code", "collapsed": false, "input": [ "cassie = random.choice(os.listdir('/home/will/Desktop/cook/'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 404 }, { "cell_type": "code", "collapsed": false, "input": [ "cassie2 = random.choice(os.listdir('/home/will/Desktop/cook/'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 405 }, { "cell_type": "code", "collapsed": false, "input": [ "cassie3 = random.choice(os.listdir('/home/will/Desktop/cook/'))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 406 }, { "cell_type": "code", "collapsed": false, "input": [ "arcImage = Image.open(cassie)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 407 }, { "cell_type": "code", "collapsed": false, "input": [ "arcImage2 = Image.open(cassie2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 408 }, { "cell_type": "code", "collapsed": false, "input": [ "arcImage3 = Image.open(cassie3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 409 }, { "cell_type": "code", "collapsed": false, "input": [ "os.chdir('/home/will/Desktop/arcOut/')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 410 }, { "cell_type": "code", "collapsed": false, "input": [ "arcSwap = ImageChops.blend(arcImage, arcImage2, .5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 411 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "arcSwap.save(cassie)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 412 }, { "cell_type": "code", "collapsed": false, "input": [ "arcBlend = ImageChops.darker(arcSwap, arcImage2)\n", "arcBlend.save(cassie2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 413 }, { "cell_type": "code", "collapsed": false, "input": [ "arcCompo = ImageChops.blend(arcImage2, arcImage3, .8)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 414 }, { "cell_type": "code", "collapsed": false, "input": [ "ls" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "04.jpg arctic1498.jpg arctic3089.jpg arctic4767.jpg arctic6415.jpg\r\n", "arctic0040.jpg arctic1568.jpg arctic3190.jpg arctic4893.jpg arctic6421.jpg\r\n", "arctic0068.jpg arctic1626.jpg arctic3191.jpg arctic4901.jpg arctic6598.jpg\r\n", "arctic0165.jpg arctic1640.jpg arctic3197.jpg arctic4903.jpg arctic6620.jpg\r\n", "arctic0180.jpg arctic1658.jpg arctic3336.jpg arctic4923.jpg arctic6635.jpg\r\n", "arctic0218.jpg arctic1674.jpg arctic3401.jpg arctic4986.jpg arctic6686.jpg\r\n", "arctic0220.jpg arctic1730.jpg arctic3440.jpg arctic4995.jpg arctic6694.jpg\r\n", "arctic0227.jpg arctic1741.jpg arctic3662.jpg arctic5043.jpg arctic6699.jpg\r\n", "arctic0293.jpg arctic1788.jpg arctic3825.jpg arctic5156.jpg arctic6727.jpg\r\n", "arctic0301.jpg arctic1875.jpg arctic3829.jpg arctic5218.jpg arctic6737.jpg\r\n", "arctic0318.jpg arctic1894.jpg arctic3843.jpg arctic5239.jpg arctic6787.jpg\r\n", "arctic0379.jpg arctic1903.jpg arctic3845.jpg arctic5244.jpg arctic6922.jpg\r\n", "arctic0440.jpg arctic2023.jpg arctic3869.jpg arctic5368.jpg arctic7149.jpg\r\n", "arctic0459.jpg arctic2067.jpg arctic3893.jpg arctic5389.jpg arctic7152.jpg\r\n", "arctic0546.jpg arctic2265.jpg arctic3895.jpg arctic5471.jpg arctic7173.jpg\r\n", "arctic0653.jpg arctic2330.jpg arctic3940.jpg arctic5554.jpg cook0230.jpg\r\n", "arctic0671.jpg arctic2352.jpg arctic3951.jpg arctic5555.jpg cook0294.jpg\r\n", "arctic0677.jpg arctic2440.jpg arctic4037.jpg arctic5602.jpg cook0337.jpg\r\n", "arctic0683.jpg arctic2488.jpg arctic4106.jpg arctic5630.jpg cook0345.jpg\r\n", "arctic0724.jpg arctic2501.jpg arctic4118.jpg arctic5738.jpg cook0390.jpg\r\n", "arctic0842.jpg arctic2513.jpg arctic4164.jpg arctic5794.jpg cook0427.jpg\r\n", "arctic0902.jpg arctic2601.jpg arctic4207.jpg arctic5803.jpg cook0562.jpg\r\n", "arctic0942.jpg arctic2666.jpg arctic4293.jpg arctic5807.jpg 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"arctic1497.jpg arctic3030.jpg arctic4764.jpg arctic6273.jpg\r\n" ] } ], "prompt_number": 415 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "arcCompo.save(cassie3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 416 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 416 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 416 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 416 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
karst87/ml	dev/001_titanic/a_journey_through_titanic.ipynb	1	10873	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# https://www.kaggle.com/omarelgabry/a-journey-through-titanic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Imports\n", "\n", "# pandas\n", "import pandas as pd\n", "from pandas import Series, DataFrame\n", "\n", "# numpy, matplotlib, seaborn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_style('whitegrid')\n", "%matplotlib inline\n", "\n", "# machine learning\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC, LinearSVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get titanic & test csv files as a DataFrame\n", "titanic_df = pd.read_csv('input/train.csv')\n", "test_df = pd.read_csv('input/test.csv')\n", "\n", "# preview the data\n", "titanic_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n", "--------------------------\n", "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", "PassengerId 418 non-null int64\n", "Pclass 418 non-null int64\n", "Name 418 non-null object\n", "Sex 418 non-null object\n", "Age 332 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 36.0+ KB\n" ] } ], "source": [ "titanic_df.info()\n", "print(\"--------------------------\")\n", "test_df.info()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# drop unnecessary columns, these columns won't be useful \n", "# in analysis and prediction\n", "titanic_df = titanic_df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)\n", "test_df = test_df.drop(['Name', 'Ticket'], axis=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-6-54e16572b8a2>, line 22)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-6-54e16572b8a2>\"\u001b[0;36m, line \u001b[0;32m22\u001b[0m\n\u001b[0;31m ['Embarked', as_index=False]).mean()\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "# Embarked\n", "\n", "# only in titanic_df, fill the two missing values \n", "# with the most occurred value, Which is 'S'\n", "titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].fillna(\"S\")\n", "\n", "# plot\n", "sns.factorplot('Embarked', 'Survived', data=titanic_df, size=4, aspect=3)\n", "fig, (axis1, axis2, axis3) = plt.subplots(1, 3, figsize=(15,5))\n", "\n", "sns.factorplot('Embarked', data=titanic_df, kind='count', \n", " order=['S', 'C', 'Q'], ax=axis1)\n", "sns.factorplot('Survived', hue='Embarked', data=titanic_df, kind='count', \n", " order=[1,0], ax=axis2)\n", "sns.countplot(x='Embarked', data=titanic_df, ax=axis1)\n", "sns.countplot(x='Survived', hur='Embarked', data=titanic_df, order=[1, 0],\n", " ax=axis2)\n", "\n", "# group by embarked, and get the mean for suvived passengers \n", "# for each value in Embarked\n", "embark_perc = titanic_df[[\"Embarked\", \"Survived\"]].groupby(\n", " ['Embarked', as_index=False]).mean()\n", "sns.barplot(x='Embarked', y='Survived', data=embark_perc, \n", " order=['S', 'C', 'Q'], ax=axis3)\n", "\n", "# Either to consider Embarked column in predictions,\n", "# and remove \"S\" dummy variable,\n", "# and leave \"C\" & \"Q\", since they seem to have a good rate for Survival.\n", "# OR, don't create dummy variables for Embarked column, just drop it,\n", "# because logically, Embarked doesn't seem to be useful in prediction.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
python-control/python-control	examples/bode-and-nyquist-plots.ipynb	2	1229504	null	bsd-3-clause
ledeprogram/algorithms	class1/homework/najmabadi_shannon_1_3.ipynb	1	2907	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The following code will print the prime numbers between 1 and 100. Modify the code so it prints every other prime number from 1 to 100" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "5\n", "7\n", "11\n", "13\n", "17\n", "19\n", "23\n", "29\n", "31\n", "37\n", "41\n", "43\n", "47\n", "53\n", "59\n", "61\n", "67\n", "71\n", "73\n", "79\n", "83\n", "89\n", "97\n" ] } ], "source": [ "for num in range(1,101): # for-loop through the numbers\n", " prime = True # boolean flag to check the number for being prime\n", " for i in range(2,num): # for-loop to check for \"primeness\" by checking for divisors other than 1\n", " if (num%i==0): # logical test for the number having a divisor other than 1 and itself\n", " prime = False # if there's a divisor, the boolean value gets flipped to False\n", " if prime: # if prime is still True after going through all numbers from 1 - 100, then it gets printed\n", " print(num)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "7\n", "13\n", "19\n", "29\n", "37\n", "43\n", "53\n", "61\n", "71\n", "79\n", "89\n" ] } ], "source": [ "counter = 1\n", "\n", "for num in range(1,101):\n", " prime = True\n", " \n", " for i in range(2,num):\n", " if (num%i==0):\n", " prime = False\n", " \n", " if prime:\n", " if counter%2==1:\n", " print(num)\n", " counter = counter + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extra Credit: Can you write a procedure that runs faster than the one above?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
muatik/dm	KNN_movie_genre.ipynb	1	12814	{ "cells": [ { "cell_type": "code", "execution_count": 357, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pylab as plt\n", "import operator\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 572, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv(\"data/datingTestSet.txt\", delimiter=\"\\t\", header=None)\n", "X = np.array(df.iloc[:, :3])\n", "Y = np.array(df.iloc[:, 3:])" ] }, { "cell_type": "code", "execution_count": 573, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "class KNN(object):\n", " def __init__(self):\n", " super(KNN, self).__init()\n", "\n", " @classmethod\n", " def normalize(cls, features):\n", " fmax = features.max(axis=0)\n", " fmin = features.min(axis=0)\n", " features = (features - fmin) / (fmax - fmin)\n", " return features, fmin, fmax\n", " \n", " @classmethod\n", " def classify(cls, features, labels, x, k=5):\n", " datesize = features.shape\n", " distances = np.tile(x, (datesize[0], 1)) - features\n", " distances = np.power(distances, 2)\n", "\n", " minDistanceIDs = distances.sum(axis=1).argsort()[:k]\n", " neighbors = {}\n", "\n", " for i in minDistanceIDs:\n", " label = np.asscalar(labels[i])\n", " neighbors[label] = neighbors.get(label, 0) + 1\n", " #print neighbors\n", " sortedClassCount = sorted(\n", " neighbors.iteritems(), key=operator.itemgetter(1), reverse=True)\n", " return sortedClassCount[0][0]" ] }, { "cell_type": "code", "execution_count": 564, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'didntLike'" ] }, "execution_count": 564, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q = np.array([7.00960000e+04,1.09659260e+01, 1.21232800e+00])\n", "KNN.classify(X, Y, q, k=6)" ] }, { "cell_type": "code", "execution_count": 620, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error rate: 0.115\n" ] } ], "source": [ "def test(features, labels):\n", " tp = 0\n", " tp2 = 0\n", " datasize = features.shape\n", " features, fmin, fmax = KNN.normalize(features)\n", " testNumber = 200\n", " \n", " for i in range(testNumber):\n", " predicted = KNN.classify(\n", " training_x[:-testNumber], \n", " training_y[:-testNumber], \n", " training_x[testNumber+i], \n", " k=3)\n", " \n", " y = training_y[testNumber+i]\n", " \n", " if predicted != y:\n", " tp += 1 \n", "\n", " return float(tp) / testNumber\n", "\n", "print \"error rate: \", test(X, Y)" ] }, { "cell_type": "code", "execution_count": 623, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "def readDigits(path, sampleSize=10):\n", " # (number of digit * sample size, feature size=widthheight)\n", " X = np.ndarray((10 sampleSize, 32 * 32)) \n", " Y = np.ndarray((10 * sampleSize, 1))\n", " for i in range(10):\n", " for s in range(sampleSize):\n", " with open(\"{}{}_{}.txt\".format(path, i, s)) as f:\n", " for c, line in enumerate(f):\n", " colPos = c * 32\n", " X[i+s, colPos:colPos + 32] = list(line.strip())\n", " Y[isampleSize: (i+1) sampleSize] = i\n", "\n", " return X, Y\n", "\n", "X, Y = readDigits(\"data/trainingDigits/\", 2)" ] }, { "cell_type": "code", "execution_count": 675, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error rate: 0.8\n" ] } ], "source": [ "def testDigits():\n", " X, Y = readDigits(\"data/trainingDigits/\", 150)\n", " test_X, test_Y = readDigits(\"data/testDigits/\", 3)\n", " tp = 0\n", " datasize = X.shape\n", " \n", " for test_x, test_y in zip(test_X, test_Y):\n", " predicted = KNN.classify(X, Y, test_x, k=5)\n", " #print test_y[0], predicted\n", " if predicted != test_y:\n", " tp += 1\n", "\n", " return float(tp) / len(test_X)\n", "\n", "print \"error rate: \", testDigits()" ] }, { "cell_type": "code", "execution_count": 686, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error rate: [[ 0.]\n", " [ 0.]\n", " [ 1.]\n", " [ 1.]\n", " [ 2.]\n", " [ 2.]\n", " [ 3.]\n", " [ 3.]\n", " [ 4.]\n", " [ 4.]\n", " [ 5.]\n", " [ 5.]\n", " [ 6.]\n", " [ 6.]\n", " [ 7.]\n", " [ 7.]\n", " [ 8.]\n", " [ 8.]\n", " [ 9.]\n", " [ 9.]]\n", "(array([[ 12.40967365, 12.52996409],\n", " [ 14.10673598, 14.28285686],\n", " [ 14.10673598, 14.56021978],\n", " [ 12.08304597, 12.16552506],\n", " [ 10.77032961, 10.90871211],\n", " [ 11.83215957, 12.68857754],\n", " [ 13.49073756, 14.86606875],\n", " [ 13.03840481, 13.15294644],\n", " [ 11.61895004, 12.64911064],\n", " [ 9.64365076, 10.04987562],\n", " [ 11.04536102, 11.18033989],\n", " [ 9.89949494, 11.18033989],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ]]), array([[ 0, 6],\n", " [ 1, 45],\n", " [ 2, 43],\n", " [ 27, 3],\n", " [ 31, 11],\n", " [ 5, 28],\n", " [ 6, 8],\n", " [ 22, 21],\n", " [ 8, 6],\n", " [ 13, 15],\n", " [ 16, 11],\n", " [ 16, 34],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97],\n", " [198, 97]]))\n", "None\n" ] } ], "source": [ "def testDigits():\n", " X, Y = readDigits(\"data/trainingDigits/\", 40)\n", " test_X, test_Y = readDigits(\"data/testDigits/\", 2)\n", " from sklearn.neighbors import NearestNeighbors\n", " nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X, Y)\n", " print test_Y\n", " print nbrs.kneighbors(test_X)\n", "print \"error rate: \", testDigits()" ] }, { "cell_type": "code", "execution_count": 704, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.99308301 0.11026755 -0.03899004 -0.01034343]\n", " [-0.05833671 -0.74906615 -0.52732983 -0.39676186]\n", " [-0.08961563 -0.52119915 0.84024944 -0.11958829]\n", " [-0.04849752 -0.39381773 -0.1199329 0.91003939]]\n", "[ 53.32030733 22.47097741]\n", "[[-0.99784741 -0.06557862]\n", " [ 0.06557862 -0.99784741]]\n" ] } ], "source": [ "a = np.array([\n", " [53, 1],\n", " [2, 17],\n", " [4, 12],\n", " [2, 9]\n", " ])\n", "\n", "U, S, VT = np.linalg.svd(a,full_matrices=True)\n", "print U\n", "print S\n", "print VT" ] }, { "cell_type": "code", "execution_count": 733, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.48552086 0.30425039 -0.7530737 -0.32339017]\n", " [-0.75301257 0.32710408 0.49684413 0.28128438]\n", " [-0.37295365 -0.68251946 0.21788421 -0.58957547]\n", " [-0.2411372 -0.57844775 -0.37222626 0.68461571]]\n", "[ 34.2353521 6.84438172 4.66426921 0.58283658]\n", "[[-0.60176255 -0.73821769 -0.13428175 -0.27365103]\n", " [ 0.77798126 -0.61137578 0.09583771 -0.10853543]\n", " [-0.09936173 -0.28428032 0.07008324 0.95099963]\n", " [-0.15084505 -0.02095235 0.9838048 -0.09452452]]\n" ] } ], "source": [ "a = np.array([\n", " [12, 12, 2, 1],\n", " [17, 17, 4, 9],\n", " [4, 12, 1, 5],\n", " [2, 9, 1, 1]\n", " ])\n", "\n", "U, S, VT = np.linalg.svd(a,full_matrices=True)\n", "print U\n", "print S\n", "print VT" ] }, { "cell_type": "code", "execution_count": 737, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 12., 12., 2., 1.],\n", " [ 17., 17., 4., 9.],\n", " [ 4., 12., 1., 5.],\n", " [ 2., 9., 1., 1.]])" ] }, "execution_count": 737, "metadata": {}, "output_type": "execute_result" } ], "source": [ "k = 4\n", "U[:,:k] * np.mat(np.diag(S)[:k, :k]) * VT[:k,:]" ] }, { "cell_type": "code", "execution_count": 738, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.48552086, 0.30425039, -0.7530737 , -0.32339017],\n", " [-0.75301257, 0.32710408, 0.49684413, 0.28128438],\n", " [-0.37295365, -0.68251946, 0.21788421, -0.58957547],\n", " [-0.2411372 , -0.57844775, -0.37222626, 0.68461571]])" ] }, "execution_count": 738, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U[:,:k]" ] }, { "cell_type": "code", "execution_count": 739, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.60176255, -0.73821769, -0.13428175, -0.27365103],\n", " [ 0.77798126, -0.61137578, 0.09583771, -0.10853543],\n", " [-0.09936173, -0.28428032, 0.07008324, 0.95099963],\n", " [-0.15084505, -0.02095235, 0.9838048 , -0.09452452]])" ] }, "execution_count": 739, "metadata": {}, "output_type": "execute_result" } ], "source": [ "VT[:k,:]" ] }, { "cell_type": "code", "execution_count": 729, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 32.63308966, 0. ],\n", " [ 0. , 6.78833256]])" ] }, "execution_count": 729, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.diag(S)[:k, :k]" ] }, { "cell_type": "code", "execution_count": 746, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -5.82625026],\n", " [-12.80121364],\n", " [ -1.49181458],\n", " [ -0.48227441]])" ] }, "execution_count": 746, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U[:,:1] * a[:, :1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
nkmk/python-snippets	notebook/scipy_shortest_path_timeit.ipynb	1	7321	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy.sparse.csgraph import shortest_path, dijkstra, floyd_warshall, bellman_ford, johnson\n", "from scipy.sparse import csr_matrix" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "n = 100\n", "c = n * 2\n", "np.random.seed(1)\n", "d = np.random.randint(0, n, c)\n", "i = np.random.randint(0, n, c)\n", "j = np.random.randint(0, n, c)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "csr = csr_matrix((d, (i, j)), shape=(n, n))\n", "a = csr.toarray()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 100)\n" ] } ], "source": [ "print(a.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "198\n" ] } ], "source": [ "print((a > 0).sum())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3 ms ± 73.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(a)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.52 ms ± 21.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(csr)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.25 ms ± 65.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(a, method='D')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.51 ms ± 25.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(csr, method='D')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.9 ms ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "dijkstra(a)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.4 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "dijkstra(csr)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.11 ms ± 33.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(a, method='FW')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "555 µs ± 71.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "shortest_path(csr, method='FW')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "737 µs ± 13.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "floyd_warshall(a)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "433 µs ± 22.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "floyd_warshall(csr)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "590 µs ± 28.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "dijkstra(a, indices=0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "111 µs ± 6.29 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%%timeit\n", "dijkstra(csr, indices=0)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "a_n = a.copy()\n", "a_n[0, 1] = -10\n", "csr_n = csr_matrix(a_n)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.5 ms ± 17.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "johnson(csr_n)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.12 ms ± 141 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "bellman_ford(csr_n)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "402 µs ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "floyd_warshall(csr_n)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "201 µs ± 6.84 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "johnson(csr_n, indices=0)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
KshitijT/fundamentals_of_interferometry	6_Deconvolution/6_5_source_finding.ipynb	2	19509	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "**\n", "\n", " [Outline](../0_Introduction/0_introduction.ipynb)\n", "* [Glossary](../0_Introduction/1_glossary.ipynb)\n", "* [6. Deconvolution in Imaging](6_0_introduction.ipynb) \n", " * Previous: [6.3 Residuals and Image Quality](6_3_residuals_and_iqa.ipynb) \n", " * Next: [6.x Further Reading and References](6_x_further_reading_and_references.ipynb)\n", "\n", "**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import matplotlib\n", "from scipy import optimize\n", "import astropy.io.fits\n", "\n", "matplotlib.rcParams.update({'font.size': 18})\n", "matplotlib.rcParams.update({'figure.figsize': [12,8]} )" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 6.5 Source Finding\n", "\n", "In radio astronomy, source finding* is the process through which the attributes of radio sources -- such as flux density and mophorlogy -- are measured from data. In this section we will only cover source finding in the image plane.\n", "\n", "Source finding techniques usually involve four steps, i) charecterizing the noise (or background estimation), ii) thresholding the data based on knowledge of the noise, iii) finding regions in the thresholded image with \"similar\" neighbouring pixels (this is that same as blob detection in image processing), and iv) parameterizing these 'blobs' through a function (usually a 2D Gaussian). The source attributes are then estimated from the parameterization of the blobs.\n", "\n", "### 6.5.1 Noise Charecterization\n", "\n", "As mentioned before, the radio data we process with source finders is noisy. To charecterize this noise we need to make a few assumptions about its nature, namely we assume that the niose results from some stochastic process and that it can be described by a normal distribution\n", "\n", "$$ G(x \\, \| \\, \\mu,\\sigma^2) = \\frac{1}{\\sigma \\sqrt{2\\pi}}\\text{exp}\\left( \\frac{-(x-\\mu)^2}{2\\sigma^2}\\right) $$ \n", "where, $\\mu$ is the mean (or expected value) of the variable $x$, and $\\sigma^2$ is the variance of the distribution; $\\sigma$ is the standard deviation. Hence, the noise can be parameterized through the mean and the standard deviation. Let us illustrate this with an example. Bellow is a noise image from a MeerKAT simulation, along with a histogram of of the pixels (in log space)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "noise_image = \"../data/fits/noise_image.fits\"\n", "with astropy.io.fits.open(noise_image) as hdu:\n", " data = hdu[0].data[0,0,...]\n", "\n", "fig, (image, hist) = plt.subplots(1, 2, figsize=(18,6))\n", "histogram, bins = np.histogram(data.flatten(), bins=401)\n", "\n", "dmin = data.min()\n", "dmax = data.max()\n", "x = np.linspace(dmin, dmax, 401)\n", "\n", "im = image.imshow(data)\n", "\n", "mean = data.mean()\n", "sigma = data.std()\n", "peak = histogram.max()\n", "\n", "gauss = lambda x, amp, mean, sigma: ampnp.exp( -(x-mean)2/(2sigma*2))\n", "\n", "fitdata = gauss(x, peak, mean, sigma)\n", "\n", "plt.plot(x, fitdata)\n", "plt.plot(x, histogram, \"o\")\n", "plt.yscale('log')\n", "plt.ylim(1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now, in reality the noise has to measured in the presence of astrophysical emission. Furthermore, radio images are also contaminated by various instrumental effects which can manifest as spurious emission in the image domain. All these factors make it difficult to charercterize the noise in a synthesized image. Since the noise generally dominates the images, the mean and standard deviation of the entire image are still fairly good approximations of the noise. Let us now insert a few sources (image and flux distribution shown below) in the noise image from earlier and then try to estimate noise." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "noise_image = \"../data/fits/star_model_image.fits\"\n", "with astropy.io.fits.open(noise_image) as hdu:\n", " data = hdu[0].data[0,0,...]\n", "\n", "fig, (image, hist) = plt.subplots(1, 2, figsize=(18,6))\n", "histogram, bins = np.histogram(data.flatten(), bins=101)\n", "\n", "\n", "dmin = data.min()\n", "dmax = data.max()\n", "x = np.linspace(dmin, dmax, 101)\n", "\n", "im = image.imshow(data)\n", "\n", "mean = data.mean()\n", "sigma_std = data.std()\n", "\n", "peak = histogram.max()\n", "\n", "gauss = lambda x, amp, mean, sigma: ampnp.exp( -(x-mean)*2/(2sigma*2))\n", "\n", "fitdata_std = gauss(x, peak, mean, sigma_std)\n", "\n", "plt.plot(x, fitdata_std, label=\"STD DEV\")\n", "\n", "plt.plot(x, histogram, \"o\", label=\"Data\")\n", "plt.legend(loc=1)\n", "\n", "plt.yscale('log')\n", "plt.ylim(1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The pixel statistics of the image are no longer Gaussian as apparent from the long trail of the flux distribution. Constructing a Gaussian model from the mean and standard deviation results in a poor fit (blue line in the figure on the right). A better method to estimate the variance is to measure the dispersion of the data points about the mean (or median), this is the mean/median absolute deviation* (MAD) technique. We will refer to the to median absolute deviation as the MAD Median, and the mean absolute deviation as the MAD Mean. A synthesis imaging specific method to estimate the variance of the noise is to only consider the negative pixels. This works under the assumption that all the astrophysical emission (at least in Stokes I) has a positive flux density. The Figure below shows noise estimates from methods mentioned above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "mean = data.mean()\n", "sigma_std = data.std()\n", "sigma_neg = data[data<0].std() * 2\n", "mad_mean = lambda a: np.mean( abs(a - np.mean(a) ))\n", "sigma_mad_median = np.median( abs(data - np.median(data) ))\n", "\n", "mad_mean = lambda a: np.mean( abs(a - np.mean(a) ))\n", "sigma_mad_mean = mad_mean(data)\n", "\n", "peak = histogram.max()\n", "\n", "gauss = lambda x, amp, mean, sigma: ampnp.exp( -(x-mean)2/(2sigma*2))\n", "\n", "fitdata_std = gauss(x, peak, mean, sigma_std)\n", "fitdata_mad_median = gauss(x, peak, mean, sigma_mad_median)\n", "fitdata_mad_mean = gauss(x, peak, mean, sigma_mad_mean)\n", "fitdata_neg = gauss(x, peak, mean, sigma_neg)\n", "\n", "plt.plot(x, fitdata_std, label=\"STD DEV\")\n", "plt.plot(x, fitdata_mad_median, label=\"MAD Median\")\n", "plt.plot(x, fitdata_mad_mean, label=\"MAD Mean\")\n", "plt.plot(x, fitdata_neg, label=\"Negative STD DEV\")\n", "plt.plot(x, histogram, \"o\", label=\"Data\")\n", "plt.legend(loc=1)\n", "\n", "plt.yscale('log')\n", "plt.ylim(1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The MAD and negtive value standard deviation methods produce a better solution to the noise distribution in the presence of sources." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### 6.5.2 Blob Detection and Charercterization\n", "\n", "Once the noise has been estimated, the next step is to find and charecterize sources in the image. Generically in image processing this is known as blob detection. In a simple case during synthesis imaging we define a blob as a group contiguous pixels whose spatial intensity profile can be modelled by a 2D Gaussian function. Of course, more advanced functions could be used. Generally, we would like to group together near by pixels, such as spatially 'close' sky model components from deconvolution, into a single complex source. Our interferometric array has finite spatial resolution, so we can further constrain our blobs not to be significantly smaller than the image resolution. We define two further constraints of a blob, the peak* and boundary thresholds. The peak threshold, defined as\n", "\n", "$$ \n", " \\sigma_\\text{peak} = n * \\sigma,\n", "$$\n", "\n", "is the minimum intensity the maximum pixel in a blob must have relative to the image noise. That is, all blobs with peak pixel lower than $\\sigma_\\text{peak}$ will be excluded from being considered sources. And the boundary threshold\n", "\n", "$$\n", " \\sigma_\\text{boundary} = m * \\sigma,\n", "$$\n", "\n", "defines the boundary of a blob, $m$ and $n$ are natural numbers with $m$ < $n$. " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### 6.5.2.1 A simple source finder\n", "\n", "We are now in a position to write a simple source finder. To do so we implement the following steps: \n", "\n", "1. Estimate the image noise and set peak and boundary threshold values.\n", "2. Blank out all pixel values below the boundary value.\n", "3. Find Peaks in image.\n", "4. For each peak, fit a 2D Gaussian and subtract the Gaussian fit from the image.\n", "5. Repeat until the image has no pixels above the detection threshold." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def gauss2D(x, y, amp, mean_x, mean_y, sigma_x, sigma_y):\n", " \"\"\" Generate a 2D Gaussian image\"\"\"\n", " gx = -(x - mean_x)*2/(2sigma_x2)\n", " gy = -(y - mean_y)2/(2sigma_y2)\n", " \n", " return amp np.exp( gx + gy)\n", "\n", "def err(p, xx, yy, data):\n", " \"\"\"2D Gaussian error function\"\"\"\n", " return gauss2D(xx.flatten(), yy.flatten(), p) - data.flatten()\n", "\n", "def fit_gaussian(data, psf_pix):\n", " \"\"\"Fit a gaussian to a 2D data set\"\"\"\n", " \n", " width = data.shape[0]\n", " mean_x, mean_y = width/2, width/2\n", " amp = data.max()\n", " sigma_x, sigma_y = psf_pix, psf_pix\n", " params0 = amp, mean_x, mean_y, sigma_x,sigma_y\n", " \n", " npix_x, npix_y = data.shape\n", " x = np.linspace(0, npix_x, npix_x)\n", " y = np.linspace(0, npix_y, npix_y)\n", " xx, yy = np.meshgrid(x, y)\n", " \n", " \n", " params, pcov, infoDict, errmsg, sucess = optimize.leastsq(err, \n", " params0, args=(xx.flatten(), yy.flatten(),\n", " data.flatten()), full_output=1)\n", " \n", " \n", " perr = abs(np.diagonal(pcov))0.5\n", " model = gauss2D(xx, yy, params)\n", " \n", " return params, perr, model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def source_finder(data, peak, boundary, width, psf_pix):\n", " \"\"\"A simple source finding tool\"\"\"\n", " \n", " # first we make an estimate of the noise. Lets use the MAD mean\n", " sigma_noise = mad_mean(data)\n", "\n", " # Use noise estimate to set peak and boundary thresholds\n", " peak_sigma = sigma_noisepeak\n", " boundary_sigma = sigma_noiseboundary\n", " \n", " # Pad the image to avoid hitting the edge of the image\n", " pad = width2\n", " residual = np.pad(data, pad_width=((pad, pad), (pad, pad)), mode=\"constant\")\n", " model = np.zeros(residual.shape)\n", " \n", " # Create slice to remove the padding later on\n", " imslice = [slice(pad, -pad), slice(pad,-pad)]\n", " \n", " catalog = [] \n", " \n", " # We will need to convert the fitted sigma values to a width\n", " FWHM = 2np.sqrt(2np.log(2))\n", " \n", " while True:\n", " \n", " # Check if the brightest pixel is at least as bright as the sigma_peak\n", " # Otherwise stop.\n", " max_pix = residual.max()\n", " if max_pix<peak_sigma:\n", " break\n", " \n", " xpix, ypix = np.where(residual==max_pix)\n", " xpix = xpix[0] # Get first element\n", " ypix = ypix[0] # Get first element\n", " \n", " # Make slice that selects box of size width centred around bright brightest pixel\n", " subim_slice = [ slice(xpix-width/2, xpix+width/2),\n", " slice(ypix-width/2, ypix+width/2) ]\n", " \n", " # apply slice to get subimage\n", " subimage = residual[subim_slice]\n", " \n", " \n", " # blank out pixels below the boundary threshold\n", " mask = subimage > boundary_sigma\n", " \n", " # Fit gaussian to submimage\n", " params, perr, _model = fit_gaussian(subimagemask, psf_pix)\n", " \n", " amp, mean_x, mean_y, sigma_x,sigma_y = params\n", " amp_err, mean_x_err, mean_y_err, sigma_x_err, sigma_y_err = perr\n", " \n", " # Remember to reposition the source in original image\n", " pos_x = xpix + (width/2 - mean_x) - pad\n", " pos_y = ypix + (width/2 - mean_y) - pad\n", " \n", " # Convert sigma values to FWHM lengths\n", " size_x = FWHMsigma_x\n", " size_y = FWHMsigma_y\n", " \n", " # Add modelled source to model image\n", " model[subim_slice] = _model\n", " \n", " # create new source\n", " source = (\n", " amp,\n", " pos_x,\n", " pos_y,\n", " size_x,\n", " size_y\n", " )\n", " \n", " # add source to catalogue\n", " catalog.append(source)\n", " \n", " # update residual image\n", " residual[subim_slice] -= _model \n", " \n", " return catalog, model[imslice], residual[imslice], sigma_noise\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Using this source finder we can produce a sky model which contains all 17 sources in our test image from earlier in the section." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "test_image = \"../data/fits/star_model_image.fits\"\n", "with astropy.io.fits.open(test_image) as hdu:\n", " data = hdu[0].data[0,0,...]\n", " \n", "catalog, model, residual, sigma_noise = source_finder(data, 5, 2, 50, 10)\n", "\n", "print \"Peak_Flux Pix_x Pix_y Size_x Size_y\"\n", "for source in catalog:\n", " print \" %.4f %.1f %.1f %.2f %.2f\"%source\n", "\n", "fig, (img, mod, res) = plt.subplots(1, 3, figsize=(24,12))\n", "vmin, vmax = sigma_noise, data.max()\n", "\n", "im = img.imshow(data, vmin=vmin, vmax=vmax)\n", "img.set_title(\"Data\")\n", "\n", "mod.imshow(model, vmin=vmin, vmax=vmax)\n", "mod.set_title(\"Model\")\n", "\n", "res.imshow(residual, vmin=vmin, vmax=vmax)\n", "res.set_title(\"Residual\")\n", "\n", "cbar_ax = fig.add_axes([0.92, 0.25, 0.02, 0.5])\n", "fig.colorbar(im, cax=cbar_ax, format=\"%.2g\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The flux and position on each source varies from the true sky model due to the image noise and distribution. The source finding algorithm we above is heuristic example. It has two major flaws : i) it is capable to handling a situation where two or more sources are close enough to each other that would fall within the same sub-image from which the source parameters are estimated, and ii) the noise in radio images is often non-uniform and 'local' noise estimates are required in order to set thresholds. More advanced source finders are used to work on specific source types such as extended objects and line spectra." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "source": [ "**\n", "\n", "Next: [6.x Further Reading and References](6_x_further_reading_and_references.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "<div class=warn><b>Future Additions:</b></div>\n", "\n", " describe MAD and negative standard deviation methods\n", "* figure titles and labels\n", "* discussion on source finders commonly in use\n", "* example: change the background noise or threshold values\n", "* example: kat-7 standard image after deconvolution\n", "* example: complex extended source\n", "* example: location-dependent noise variations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
wrightaprilm/squamates	ExploratoryNotebooks/.ipynb_checkpoints/Untitled0-checkpoint.ipynb	3	181	{ "metadata": { "name": "", "signature": "sha256:9b63681516d3a5d69665d3bcf755a07dc8bfdd7e10b2d0d04907766efbb8a826" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }	mit
napsternxg/ipython-notebooks	System_Identification_DMD_Control_Example.ipynb	1	64558	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "System Identification: DMD Control Example.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyN8VgB+xIcA3ktE0A3cb+5j", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/napsternxg/ipython-notebooks/blob/master/System_Identification_DMD_Control_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "source": [ "# System Identification: DMD Control Example\n", "\n", "Source: https://www.youtube.com/watch?v=tuPRiA5kk3w" ], "metadata": { "id": "8g0iH9OWipBT" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "srdQ15lXilyk" }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "source": [ "A = np.array([[0.9, 0], [0, 1.1]])\n", "B = np.array([[0], [1.]])\n", "K = np.array([[0, 0.3]])\n", "A, B, K" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cx_2uHAtitEi", "outputId": "c4259ea0-311b-4f38-ecbd-58680d4ad15c" }, "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(array([[0.9, 0. ],\n", " [0. , 1.1]]), array([[0.],\n", " [1.]]), array([[0. , 0.3]]))" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "code", "source": [ "CL = A - B@K\n", "CL" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oLnYmQZqi0_c", "outputId": "ac254c88-9568-405d-db25-a1c1ea7358be" }, "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0.9, 0. ],\n", " [0. , 0.8]])" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [ "x = np.array([[10], [10]])\n", "x.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sAPdeb_nlfuE", "outputId": "812da790-caf6-4fd2-97dd-33e8f381460c" }, "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(2, 1)" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "A@x-B@K@x" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8Wbr94b1lzwE", "outputId": "00dde266-49c6-4d99-c0bd-44c292b72902" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[9.],\n", " [8.]])" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "X = []\n", "U = []\n", "x = np.array([[10], [10]])\n", "for i in range(10):\n", " X.append(x)\n", " u = -K@x + np.random.randn()\n", " U.append(u)\n", " x = A@x+B@u\n", "X.append(x)\n", "\n", "X = np.hstack(X)\n", "U = np.hstack(U)\n", "X.shape, U.shape, x.shape, u.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MoUhT0fnjC8p", "outputId": "bb1b5e38-c0ce-45cc-be7b-78d1d1e7a1cc" }, "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((2, 11), (1, 10), (2, 1), (1, 1))" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "plt.imshow(X.T)\n", "plt.xlabel(\"$X$\")\n", "plt.colorbar()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 302 }, "id": "syePMPMPkuR_", "outputId": "a6424029-5650-401a-8797-7bac47201cf5" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f1b22228b90>" ] }, "metadata": {}, "execution_count": 7 }, { "output_type": "display_data", "data": { "image/png": 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fBP4ucJqkUyWtptpl9e5l9mni6Y3AtueAjwP3UZ1TcLvtx0e1V08W/A5wuqSt9QTBqSO7KgunNxGcHBxS4MJJgQsnBS6cFLhwUuDCSYELZ+IElvSnkr644P3fSvrn5fSpz0xcR4ekQ4CngF8D3gP8DXCe7biTpwti4gQGkPQPwKFUkwMutP2DZXapt0yqwL9C1V99me0ckFiCiXsG1/wlsINc/jqUiRNY0qeoTuS8nOqE7GQJJioCJL0f+CjwW7Z3SzpC0lm2Ny+3b31lYiJY0slUx53/vu195wJ8Hrh2+bzqPxOZZCXNmZgITkYjBS6cFLhwUuDCSYELJwUunBS4cP4XXaf24JcCRY0AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "plt.plot(X[0], label=\"$X[0]$\")\n", "plt.plot(X[1], label=\"$X[1]$\")\n", "plt.plot(U[0], label=\"$U$\")\n", "plt.legend()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "aRsJyW7jkzVC", "outputId": "f8c10018-9588-4bec-c233-530824e13bcc" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1b1b8525d0>" ] }, "metadata": {}, "execution_count": 8 }, { "output_type": "display_data", "data": { "image/png": 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Tp07lnXfeufBca03jxo0pKrLN+kUS5A2Iv5crjw9uxYbnBvHh2Gi83Z3564ID9HpjJf9YlFC1LemUgs53w582GIvw//AwxE2AYiusrS5EHbVv3z46d+584XlSUhKBgYF4etpmMIB0rTRALk4OxEaHEBsdws7UHGZsTOarTcYa6Te3a8qEvs3p1eIa66P7RsBDi2DjB8Y0/xPbjBUWm/evvQ8iGrS3tr3FoexDFj1nW7+2PNfzuWsed3mQ792795LntU2CvIHrFu5Lt3BfTt3ajm+2JPPd1lSWJZymXbNGTOgTye1dgnF3ucpNTQdH6Pc0tLgJ5v8Rvh4JfR6HQS/L5hWi3srJycFkMhEUFHThNQlyUScE+bjx7LC2PD6oFQt2pzNjYzJT5+3l74sSGBkdzNge4XQK9bnym0O6GTNCl70Mmz4ypviP+twYviiElVSl5WwN+/bto1OnTpe8tnfvXsaNG2eTekD6yMVl3JwduadHOL9M6cf3k3sxpENT4nakcfvHG7jto/V8szmZvOIrjEl38TRWT7x3NuSfhOkDYOt/5EaoqHcOHz58yWbLZrOZjRs30r+/7boVJcjFFSmluKGFP+/dHc22l27m73d0BOD/Fhyg5+sreOr73Ww5nvXbFRjb3AJ/3mz0lf8yFWaOgYJTNvgEQlhHREQE27dvp6SkBIB//vOf9O/fH39/f5vVJF0r4pp83J0Z3yuC8b0i2J+ex+ztqSzYlcH8Xek0D/Dk7pgwRncPoYl35X4iXk3gvjnGLkTLXjbWaxn5L2h7m20/iBAWMHToUJYuXUrbtm1xdXWlV69efPbZZzatSWZ2iutSXFbBz/tO8v32E2xLzsbJQTGobRPG9gyjf6vAX9d3yTxs3Ag9uQe6PQDD3gBXL9sWL+yWzOyUmZ3CgtxdHBndPZTR3UM5llnInPgTzNuRxrKE0wQ1cuOumFDujgkjLLAN/GEFrH4dNn4IyRuMG6Gh3W39EYSoN6SPXNRYy0AvXrilHZueH8xn93ejbTNvPl59lH7TVnP/51v56cBZSm/6qzHuvLwMvhgCa6dBRRVnlAohfpe0yIXFuDg5MLxjM4Z3bEZGbjFz49OYE3+Cx2ftwtfDmTu7hnLfmKVEbXvFaKEfXQGjpst6LaJatNa/P1mtHqhul7f0kQurMps1G4+dZfa2EyxLOIWpQtM1vDFTg/fS6+DrKA3cOg263CvbyolrSkpKwtvbG39//3ob5lprsrKyKCgooHnz5pf87Gp95BLkotZkFZbyw650Zm8/wdEzhUS5ZDPd+7+0KNqDbn8HasT74OFn6zJFHWYymUhLS7sw9K++cnNzIzQ0FGdn50telyAXdYbWmp2puXy/PZXFe9J5wLyAp53nUurqjzn233i3v9nWJQpRJ0mQizqpoMTET3tOEr95JY9mv0VLh5Os9L0b56Gv0Kd1cPW3qROiHpMgF3XewdTTFC58jh5nf+CwOZR/Ov+Z5l0GMjI6mK5hjettn6gQVSVBLuxG2cElVCycgmvxGb4y38K0sjEE+jUmtksIsdHBtGp6HVvVCVEPSJAL+1KSDytegfgvKfQI4yPPx/k8LRSzhnbNGhEbHcztXYIJaexu60qFqDUS5MI+JW+AhY9D9nGKO41nvv9k4hIK2JWaC0DPSD9GRgdza6dm+Hm62LhYIaxLglzYr7JzsOYN2PwxeDWFEe+TGjCAn/Zm8OOudBLPFOLkoOjXKoDY6BCGtG+Kp6vMdRP1jwS5sH/pO2HBY3DmAHQcDbdMQ3v4c+hUAQt2Z7BwdzoZeSW4Oztyc/umxHYJpn/rQFycZOSLqB+sGuRKqcbA50BHQAMTtdabr3a8BLm4buVlxj6ha6eBqzfcMg06jQGlMJs1O1JzWLA7ncV7T5JzzoSPuzO3dmpGbHQwPSP9cHCQkS/Cflk7yL8G1mutP1dKuQAeWuvcqx0vQS5q7MxBo3WeHg+thsGI98An9MKPTRVmNiSeZcHudJYlnOZcWQXNfNy4vUswI7sE0yG4kQxnFHbHakGulPIBdgMtdBVPJkEuLMJcYWwnt+rvoBxh6GvQ7SFwuLQr5VxZOSsOnmHh7nTWHM6k3KxpGehJbHQII7sEExngaZv6hagmawZ5NDAdSAC6ADuAKVrrosuOmwxMBggPD++ekpJSo+sKcUF2Evw0BZLWQsSNMPIj8G95xUNzisr4Zf8pFuxOZ2tSNgBdwhoT2yWYEV2a/brLkRB1kDWDPAbYAvTVWm9VSn0I5Gut/+9q75EWubA4rWHXN7D0ZagohZtegl5/Bserj17JyC1m0d4MFuzO4EBGPg4Kerf0Z3iHIIZ2CKJpIwl1UbdYM8iDgC1a68jK5/2A57XWV92gUYJcWE3+SVj8FBz+GYK7QezH0LTDNd929EwhC/dksGhPBsfPGr9MRoc1ZliHIIZ1aEqLQNmeTtietW92rgcmaa0PK6VeBTy11s9e7XgJcmFVWsOB+fDzVCjJhX5PG19OrlV6+9EzBSw9cJqlB06xNy0PgFZNvCpDPYiOIXKjVNiGtYM8GmP4oQtwHJigtc652vES5KJWFGXB0hdg7/cQ2M5onYf+5u/A78rILWbZgVMsPXCarUlZmDWENHZnSPumDOsQRI9IX1mhUdQamRAkGq4jS2HRk5CfYfSbD3oJXKo/UiW7qIwVB0+z7MAp1iWepazcjK+HMze3M0L9xlYBuDk7WuEDCGGQIBcNW0k+rHgV4r8w9gi9/SNoMeC6T1dUWs7aI5ksPXCKVYfOUFBSjoeLIwPbBDKsQxA3tW1CIzfna59IiGqQIBcCLlmEi24PwJC/g3vjGp2yrNzM5uNZLD1wiuUJp8ksKMXZUdG7ZQDDOjRlSPumMqxRWIQEuRDnmYqNRbg2/ctYhOu296DtrRY5tdms2XUi58LN0pSscygFXcMaM7yjcbM0wl8mIInrI0EuxOWusAgXngEWO73WmsOnC1hWGeoHMvIBaBvkzdDKYY3tm8kIGFF1EuRCXEl5GWz8ENZNAxevSxbhsrQT2edYlmCEenxyNmYNob7uDG1vhHpMpB+OsqiX+B0S5EL8njOHYOFjkLYdOt1ldLe4NbLa5c4WlrLy4GmWHjjNhsSzlFWY8XF3pl+rAAa2acKA1oEEeldt3LtoOCTIhbgWcwVseA9WvwGNw2DMlxDS3eqXLSwtZ83hM6w+lMnaI5mcLSwFoGNIIwa2bsLANoFEhzWW8epCglyIKkvdAvMmQcFJGPwK9H7sNysqWovZrEk4mc/aI5msOXyGnam5VJg1jdyc6NcqkAFtAhnYOpAmsg5MgyRBLkR1FOfAwifg4EJoORju/Ay8mtR6GXnnTGw4epY1h8+w9kgmZwqM1nr7Zo0Y2CaQgW2a0C1cWusNhQS5ENWlNcR/CUtfBNdGMOo/0HKQDcu5uLWeyY6UHCrMGm83J6NvvXUTBrQJlFUb6zEJciGu1+kEiJsAmYeg7xQY9H/gaPtZm/klJjYmnmXNYaNv/VR+CWAMbxzYxuhb7x7hi7O01usNCXIhaqLsnLEA146vjBugo78Av+a2ruoCrTWHThVc6FuPT86h3KzxdnWib1QAA9sY/evNfNxtXaqoAQlyISzhwA+wcAqgYcT7xpjzOqigxMTGo1msPXKGNYczOZn3a2vduGHahO4Rvrg4SWvdnkiQC2EpOSnGqJa0bdD1fmMS0XWsplhbtNYcOV14IdS3J2djqtB4uTrRp6U/A9oE0qdlAJH+HjLLtI6TIBfCkipMxnot69+DgFYwZgYEdbR1VVVSWFrOpqNnWXMkk7WHM0nPLQagmY8bvVv607uFP32iAghpLN0wdY0EuRDWcHwtzJ9sDFcc9jr0mGSV6f3WorUm6WwRm49nselYFluOZZFVVAZAuJ8HfVr6Xwh3GbtuexLkQlhL0Vn48RFIXAZtbjN2IvLws3VV1+V8N8ymY2fZfCyLLcezyC8pByCqiZfRWm/pT68W/vh6uti42oZHglwIa9IatnwKy18xJg6N+i9E9rV1VTVWYdYkZOSz+fhZNh3LYntSNkVlFQC0a9bIaLG38KdnCz/ZSKMWSJALURsydkHcRMhJhgHPQf9nwaH+bP9mqjCzNy2PzcfOsvl4FvHJOZSWm3FQ0CnEh94tA+jT0p+YSF88XJxsXW69I0EuRG0pLYDFz8De2RDRF0ZNB59QW1dlFSWmCnal5rL5eBabj51l94lcTBUaZ0dFdFhjerfwp3fLALqGN5b9TC1AglyI2rZnNix+2pgFGvsJtL3N1hVZ3bmycuKTc9h0LIvNx7PYl5aLWYOrkwPdI3wv3DztHNpYZpxeBwlyIWwh65gxvf/kHug52dgj1LnhjP7ILzGxPSmbTceMUTEHTxq7JHm4ONIj0o+ezf3oHuFLdJi02KtCglwIWykvhZWvweaPoWlHY53zwDa2rsomsovK2Ho868Jwx6NnCgFwclB0DPEhJsKXmEhfukf4ycYaVyBBLoStHVkGP/7J2Pz5lreg63i7GnNuDTlFZexMzSE+JYcdyTnsTsulrNwMQIS/B90jfImJ8CMm0peoQC8cGvhWeFYPcqWUIxAPpGutR/zesRLkosHKPwk/TIakddBhFNz+Abj52LqqOqOs3Mz+jDzik7OJT85hR0rOhQlKPu7OdAtvTEyk0R3TJbQx7i4NqzumNoL8KSAGaCRBLsTvMFfAxg9g1evGaJYxX0Lob/5u1m8V5ZC+w/j8PiFXPUxrTXLWOeKTs9mRYrTcL+6O6XC+OybCl+6RvjTxrt/3H6wa5EqpUOBr4HXgKQlyIaogdWvllnIZMOhl6DOl1raUs5msY7DrW9gzy9hKD8CvJTTvB837Q2S/a+7EdHl3zJ60XEoru2PC/TwuhHpMhB+tmtSv7hhrB3kc8AbgDTxzpSBXSk0GJgOEh4d3T0lJqfF1hbB7xbnw0xOQsAAah0PH0cZX0471p/+8rMj4fLu+hZSNoBwgagh0ugsKT0PyekjeCGUFxvGB7X4N9oi+11zu4Hx3zI7kHOJTjJb72UKjO6aRmxPdzrfYI/yIDrPv7hirBblSagRwq9b6z0qpgVwlyC8mLXIhLqI1JPwIu2bC8dVgLoeA1kagdxgFga1tXWH1aQ1p8bDrf7D/ByOk/Voay/52uRcaNbv0+IpyY4hm0loj2FM2Q3kxoCCokxHqzftDeG9wa3SNS2tSss4ZLfYUo6898eLumOBGdKvsY+8c6kOkv6fdtNqtGeRvAOOBcsANaATM11rff7X3SJALcRVFWcaGz/vnQfIGQBtBdj7UfSNsXeHvKzxjTITa9S2cPQzOHtDhTiPAw3tX/beM8jKjDz15vXFj+MQ2qCgF5QjB0b92w4T3qtJa8LnnKrtjko0umb1puZSYjO4YbzenC6HeObQxXcJ8CGrkVifXZq+V4YfSIhfCgvJPGl0S++cZm1gAhPYwQr39Hb9t1dpKRTkcXW6E95Elxm8UoT2N8O5w5zVb0FViKjbC/Hywp+8wruPgbNwoPh/soT2qNOGqvMJM4plC9qblsictjz0ncjl8qoBys5GHgd6udKkM9s6hPnQJbVwnVnuUIBfCnuWkGNvM7Z8Hp/YCCiJvhI6joF0sePrXfk1nE3+9cVl4GjwDoctYY3y8tSc8lRZC6hZIXgdJ6+HkbtBmcHKDsJ6Vwd4fQrpVeaPsElMFCSfz2Xsil71peexJy+X42SLOR2S4n8eFUO8c6kPHEB88XWt3YTCZECREfXE20Qj0/fPg7BGju6HlTUZLve1t1h2XXlpo9Ofv/AZObDGu3WoodBtvfK9iaFpccS6kbjZa60nr4fQ+43VnT4jo/WuLvVmXaq1GWVBiYl96nhHslQF/fkclB2Ws0d45tPGF1nvbZt64OlnvZqoEuRD1jdZwev+voZ6bCo4uRqB2HAWth1tmL1GtjW6N8zcuTUXgH2W0vLuMBe+gml/D0oqyIGWDEepJ64z+egBXH2Od+KibjW6f69gA5GxhqdElcyKPvWlGuJ+ftOTi6EC7Zt6/dsmENaZloBeOFrqZKkEuRH2mtdFvvH8e7J8PhaeMG41tbjFa6lE3g1M11y4pOG10m+z6FrISjdZtxzuNAA+7wb6GRxacMm4eJzZP9mQAABUYSURBVK0zvnKSjH/02twCXe6DqMHX/duE1pr03OIL3TF7TuSyPz2fwlJjZyVPF0c6hPhcaLX3jQrA7zr72yXIhWgozBVGN8P+eXDgRyjONlqi7UYYLfXmA64eWhUmY8u6Xd/CkaWgKyCsV+WNyzvA1bt2P4u1nNxr/CO1dw6cO2v073e6G6LvNUYJ1ZDZrDl+tvBCq31PWh4JJ/MpKzczY0IPbmrz+5OerkaCXIiGqMJkjM3ePx8O/gSl+eDhD+1jjZZ6eB9jNmnmEdj1jTF0sOgMeDYxQi36fvscx15VFSZIXA57voPDS8BsMoK8y33GhCWvQItdqqzczJHTBTQP8Lzum6QS5EI0dKYSOLbSaKkf/gVM58C7mfGVsdO4cdl6uNH6bjXEdjcubeVctvHfZvdMY8s+BydjBmr0vcZ/l+p2TVmBBLkQ4ldlRcaY733zID/d6HLpPBa8m9q6srrhzCGjlb7ne+N+g7svdBxjhHpwN5vdH5AgF0KI6qooh+NrjFA/tBjKSyCgDUTfB53vqfVJWRLkQghRE8W5xhj63bMqx9A7QIubjFBvexs4u1u9BAlyIYSwlKxjxqiXPbMh7wS4NjJG9USPs+rQTAlyIYSwNLPZmHi0+ztIWGhMlvJrYazw2GWssTSxBUmQCyGENZUWGitX7v7OWNwLjGUBou+DdiPB1avGl5AgF0KI2pKTAnu/N0I9J8mYFdt+pNFSj+x33TtBXS3I6/m+UkIIYQO+ETBgKjyxCyYuhU6jjVEv/xsJBxdY/HK1uwajEEI0JEoZm1+E94Jbphlh3mqYxS8jQS6EELXB2R06jbHKqaVrRQgh7JwEuRBC2DkJciGEsHMS5EIIYeckyIUQws5JkAshhJ2TIBdCCDsnQS6EEHZOglwIIexcjYNcKRWmlFqtlEpQSh1QSk2xRGFCCCGqxhJT9MuBp7XWO5VS3sAOpdRyrXWCBc4thBDiGmrcItdan9Ra76x8XAAcBEJqel4hhBBVY9E+cqVUJNAV2HqFn01WSsUrpeIzMzMteVkhhGjQLBbkSikvYB7wF611/uU/11pP11rHaK1jAgMDLXVZIYRo8CwS5EopZ4wQn6m1nm+JcwohhKgaS4xaUcAXwEGt9Xs1L0kIIUR1WKJF3hcYDwxSSu2u/LrVAucVQghRBTUefqi13gAoC9QihBDiOsjMTiGEsHMS5EIIYeckyIUQws5JkAshhJ2TIBdCCDsnQS6EEHZOglwIIeycBLkQQtg5CXIhhLBzEuRCCGHnJMiFEMLOSZALIYSdkyAXQgg7J0EuhBB2ToJcCCHsnAS5EELYOQlyIYSwcxLkQghh5yTIhRDCzkmQC9GALTy2kL9u/Ctnzp2xdSmiBmq8+bIQwv4Ulxfz+pbXWXBsAQArU1fy4g0vcmvzW1FK9lK3N9IiF6KBSc5LZtzP41h4bCEPd36YBbELiPSJ5Pn1z/P02qfJLsm2dYmimiTIhWhAliYvZezisWSey+TTmz/lsa6P0aJxC/43/H882f1J1pxYw50L7mRl6kpblyqqwSJBrpQarpQ6rJQ6qpR63hLnFEJYjqnCxBtb3+CZtc8Q1TiKubfP5caQGy/83NHBkYkdJ/L9iO9p6tGUv6z+Cy+sf4G80jwbVi2qqsZBrpRyBD4BbgHaA/cqpdrX9LxCCMvIKMzgwSUP8t2h7xjffjwzhs0gyDPoise28m3FzNtm8ucuf2ZJ0hJGLRjFhvQNtVyxqC5LtMh7Ake11se11mXAbCDWAucVQtTQurR13L3obpLyknhv4HtM7TEVZ0fn332Ps4Mzj0Q/wre3fUsj10Y8suIRXt30KkWmolqqWlSXJYI8BDhx0fO0ytcuoZSarJSKV0rFZ2ZmWuCyQoirKTeX8+HOD3l05aMEeQTx/YjvGRIxpFrn6ODfge9HfM/EjhP54egPjF44mu2ntlupYlETtXazU2s9XWsdo7WOCQwMrK3LCtHgnC0+y+Tlk/l83+eMbjWab2/9lvBG4dd1LhdHF57s/iRfD/8aJwcnJi6dyJvb3qS4vNjCVYuasESQpwNhFz0PrXxNCFHLtp/azl0/3cW+zH38o+8/eLXPq7g5udX4vNFNopl7+1zGtRvHzIMzueunu9h9ZrcFKhaWYIkg3w60Uko1V0q5AGOBhRY4rxCiiszazOf7PmfSskl4OXsx87aZxEZZ9laVu5M7z/d8ni+GfoGpwsSDSx7k/R3vU1ZRZtHriOqrcZBrrcuBx4ClwEFgjtb6QE3PK4SomrzSPB5f9Tgf7vyQoRFDmT1iNq19W1vtej2b9WR+7HzujLqTL/d/yT2L7iEhK8Fq1xPXprTWtX7RmJgYHR8fX+vXFaK+2Ze5j2fWPsOZ4jNM7TGVsW3G1uoU+/Vp63l106tkl2QzufNkJnWehLPD74+KEddPKbVDax1z+esys1MIO6S15ruD3/HAkgcA+N/w/3Fv23trfZ2UfqH9mB87n+HNh/Ppnk8Zt3gciTmJtVqDkCAXwu4UmYp4dt2zvLHtDfoE92HO7XPoFNjJZvX4uPrwRr83+GDgB5w+d5p7Ft3DF/u+oMJcYbOaGhoJciHsyJGcI4xdNJblKcuZ0m0K/xr0L3xcfWxdFgCDIwbzQ+wPDAwbyAc7P+DBJQ+SnJds67IaBAlyIezEgqMLGLd4HIWmQj4f+jmTOk3CQdWtv8J+bn68O+Bd3ur3Fkl5Sdz1013MPDgTszbburR6rW79KRBC/EZJeQmvbHqFlze+TKfATsy9fS49gnrYuqyrUkpxa4tb+SH2B3o268mb295k0rJJpBfK9BJrkSAXog5LyU/h/p/vZ37ifP7Y6Y9MHzKdAPcAW5dVJU08mvDxoI95rc9rJGQlMGrBKOKOxGGLkXL1nQS5EHXU8pTl3LPoHk6dO8Ungz/hiW5P4ORgX5t6KaW4s9WdzB85n04Bnfjb5r/xyMpHOF102tal1SsS5ELUMaYKE29te4un1jxFC58WzBkxh/6h/W1dVo0EewUzfeh0XrzhRXae3smdC+/kp2M/yaxQC5EJQULUIaeKTvH02qfZm7mX+9rexzMxz1xz2Vl7k5qfyssbX2bXmV0AeLt44+/mj7+7P/5u/gS4B1x4fPlrLo4uNq7etq42IUiCXIg6YkP6Bl5Y/wIms4m/9fkbwyKH2bokq6kwV7A8ZTkp+SlklWSRVZzF2eKzZJdkk1WcRYGp4Irv83b2xt/dHz83v98NfH93f1wdXWv5U1nf1YLcvjrcbMRUYeJA1gHa+rW1yEpyQlwsrzSPL/Z/wVf7vyLKN4r3BrxHpE+krcuyKkcHR4Y3H37Vn5dWlJJVbAT8+aDPKjHC/vzjIzlHyMq4euh7OXv9JujPh3ywZzB9Q/pa6+PVOgny35Gan8q8xHn8ePRHskuyaebZjKe6P8WwyGG1PhVa1D8ZhRl8k/AN8xLnUVxezB1Rd/DiDS/i7uRu69JsztXRlWCvYIK9gq95bGlFKdnF2Ze07C8P/8ScRLaUbKGgzAj95j7NWRhSfxZplSC/jKnCxMoTK4k7EsfWk1txVI4MCB3AgLABzDo0i2fXPcusQ7OY2nMqHfw72LpcYYcOZx9mxoEZLElagsIYc/1ghwetumJhfebq6Eozr2Y082p2zWPLKsrILsnmXPm5Wqis9kgfeaXkvGTmJ85nwbEFZJdkE+wZzOjWo7kj6g6aeDQBjH69H4/+yEe7PiKnJIfYqFimdJtiN+N6he1ordl6aisz9s9gU8YmPJw8GNN6DOPbj7/qRshCXE5udl5BWUUZK1JWMC9xHttObcNJOTEwbCBjWo+hd3Dvq05/Ligr4L97/8s3B7/BxcGFP3b+I+Pbj6+XN1dEzZSby1mespwZ+2dwMPsgAe4BjGs3jrvb3E0jl0a2Lk/YGQnyiyTlJRF3JI6FxxaSW5pLiFcIY1qPIbZlLIEeVd9PNDU/lXfi32H1idWEeIXwTMwzDA4fLP3ngnOmc/xw9Ae+SfiG9MJ0IhtFMqHjBEa0GNHgh9CJ69fgg7y0opTlKcuZd2Qe8afjcVJO3BR+E2Naj6FXs141Wnxoy8ktvLXtLY7mHqVHUA+e6/EcbfzaWLB6YS+yirOYdWgWsw/PJq80j65NuvJQh4cYGDawzi1wJexPgw3yY7nHiDsSx0/HfyKvNI8w7zBGtxpNbFSsRfu2y83lxB2J45Pdn5Bfls+oVqN4LPox/N39LXYNUXel5qfy9YGvWXBsAaUVpdwUdhMTOk6ga5Outi5N1CMNKshLyktYnrKcuCNx7DyzEycHJ24Ov5kxrcfQI6iHVVtGeaV5fLbnM2Yfmo2bkxt/6vIn7mt7X72bnScM+zL3MePADFakrMDJwYmRLUfyQIcHaOHTwtaliXqoQQT50ZyjxCUafd8FZQVENIpgTKsxjIwaiZ+bn8Wv93uO5x3nne3vsD59PRGNIngm5hkGhA6Q/vN6QGvN+vT1zNg/g/jT8Xg7e3NP23u4r+191brHIkR11dsgLy4vZlnyMuKOxLE7czfODs7cHHEzd7W+i5imMTYPzg3pG5i2fRpJeUn0btabqT2mEuUbZdOaxPUxVZj4OelnvjrwFUdzj9LUoynj249nTOsxeDp72ro80QDUuyA/knOEuCNxLDq2iAJTAZGNIhnTegwjW47E183XQpVahslsYs7hOXyy+xPOmc5xV+u7eDT6URq7NbZ1aaIKCssKiTsSxzcHv+HMuTNENY5iYseJDG8+XHaMF7WqXgT5OdM5liYvJS4xjr2Ze3FxcGFI5BDGtBpD96bdbd76vpackhw+2f0Jc4/MxcvZiz9H/5m729wtYVBHnTl3hm8Pfsvcw3MpNBXSM6gnEzpOoG9w3zr/Z03UT/UiyF9Y/wKLji+ihU8LxrQew+0tbrfLVm1iTiLTtk9jy8kttPBpwbM9nuXGkBttXVadYdZmtNY4KAebBObx3ON8deArfjr+E2ZtZkjEECZ0mECHAFmSQdiWVYJcKfU2cDtQBhwDJmitc6/1vusN8sPZhykyFdG1SVe7bxFprVmbtpa3t79NakEq/UL68WyPZ2nu09zWpVmd1pqskizSC9NJL0gnoyjjkscZhRmYzCYAnJQTjg6OOCpHHB0cLzx3UA6X/MzJwek3x1zp+cXHXfK88nFqQSrr0tbh5uhGbFQsD7Z/kLBGYTb+LyaEwVpBPhRYpbUuV0q9BaC1fu5a77P1zM66xFRh4rtD3/HZns8oKS9hbNux/KnLn/Bx9bF1addNa01OaQ7pBemkF6WTUZhxyeOMwgxKK0oveY+fmx/BnsZqdyFeIXg4e1ChK6gwV1Cuy6kwV1ChKyg3l194/fxzszb/5mcXv+f3znH5ce5O7oxuPZqxbcfW+kgnIa7F6l0rSqk7gTFa63HXOlaC/LeyirP4ePfHzDsyDx9XHx6LfozRrUfXyT0atdbkl+WTVpj2a0gXVrasK1vVxeXFl7zHx9WHYM9gQr1DLwT2xY89nD1s9GmEsB+1EeQ/Ad9rrb+9ys8nA5MBwsPDu6ekpFjkuvXNoexDvLXtLeJPxxPVOIrnej5Hr2a9rutcWmvM2nyhxXr549+8Zq78jhmz2UypuZRTRacu7f4oNFrVRaaiS67l5exFiFcIIV4hF1rVFz/2cvGyxH8eIRq06w5ypdQK4ErrbL6ktV5QecxLQAwwSlfhXwZpkf8+rTUrUlfwbvy7pBemE+YdhkJdNYgvDuTLn1uKh5MHId4hhHheIai9Q2QlPyFqwXVv9aa1vvkaJ34IGAEMrkqIi2tTSjEkYgj9Q/sz6+As9p3dh6NyxMHBwfiufv1+/utKr5+/KXjJa1d4fOG7gyMKdeFazg7OBHkEEeIVgo+rj93fYBaivqpRB6xSajgwFRigta5fW27UAa6OrjzU8SFblyGEqONqunrUx4A3sFwptVsp9ZkFahJCCFENNWqRa61l0RAhhLAxWeleCCHsnAS5EELYOQlyIYSwcxLkQghh5yTIhRDCzkmQCyGEnbPJeuRKqUzgehdbCQDOWrAceyCfuWGQz9ww1OQzR2itf7MxrE2CvCaUUvFXWmugPpPP3DDIZ24YrPGZpWtFCCHsnAS5EELYOXsM8um2LsAG5DM3DPKZGwaLf2a76yMXQghxKXtskQshhLiIBLkQQtg5uwpypdRwpdRhpdRRpdTztq7H2pRSYUqp1UqpBKXUAaXUFFvXVBuUUo5KqV1KqUW2rqU2KKUaK6XilFKHlFIHlVK9bV2TtSmlnqz8M71fKTVLKeVm65osTSn1pVLqjFJq/0Wv+SmlliulEiu/+1riWnYT5EopR+AT4BagPXCvUqq9bauyunLgaa11e6AX8GgD+MwAU4CDti6iFn0ILNFatwW6UM8/u1IqBHgCiNFadwQcgbG2rcoqvgKGX/ba88BKrXUrYGXl8xqzmyAHegJHtdbHtdZlwGwg1sY1WZXW+qTWemfl4wKMv+Ahtq3KupRSocBtwOe2rqU2KKV8gP7AFwBa6zKtda5tq6oVToC7UsoJ8AAybFyPxWmt1wHZl70cC3xd+fhr4A5LXMuegjwEOHHR8zTqeahdTCkVCXQFttq2Eqv7AGMfWLOtC6klzYFMYEZld9LnSilPWxdlTVrrdOAdIBU4CeRprZfZtqpa01RrfbLy8SmgqSVOak9B3mAppbyAecBftNb5tq7HWpRSI4AzWusdtq6lFjkB3YB/a627AkVY6NftuqqyXzgW4x+xYMBTKXW/bauqfdoY+22R8d/2FOTpQNhFz0MrX6vXlFLOGCE+U2s939b1WFlfYKRSKhmj62yQUupb25ZkdWlAmtb6/G9acRjBXp/dDCRprTO11iZgPtDHxjXVltNKqWYAld/PWOKk9hTk24FWSqnmSikXjJsjC21ck1UppRRG3+lBrfV7tq7H2rTWL2itQ7XWkRj/f1dpret1S01rfQo4oZRqU/nSYCDBhiXVhlSgl1LKo/LP+GDq+Q3eiywEHqx8/CCwwBIndbLESWqD1rpcKfUYsBTjLveXWusDNi7L2voC44F9Sqndla+9qLX+2YY1Cct7HJhZ2UA5DkywcT1WpbXeqpSKA3ZijMzaRT2cqq+UmgUMBAKUUmnAK8CbwByl1B8wlvK+2yLXkin6Qghh3+ypa0UIIcQVSJALIYSdkyAXQgg7J0EuhBB2ToJcCCHsnAS5EELYOQlyIYSwc/8PLt/1dR3Y/XEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "X1 = X[:, :-1]\n", "X2 = X[:, 1:]" ], "metadata": { "id": "wGMPjhQonhFW" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "# Naive DMD\n", "Anaive = [email protected](X1)\n", "Anaive" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PZU5YhhZlDuw", "outputId": "15a15b76-69a2-44cf-92c5-b98952e64d65" }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[9.00000000e-01, 1.99665317e-16],\n", " [1.78001441e-01, 7.11400863e-01]])" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "# Correct DMD\n", "Upsilon = U\n", "Acorrect = (X2 - B@Upsilon)@np.linalg.pinv(X1)\n", "Acorrect" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a__58S8sm4J7", "outputId": "add1b3e5-2482-4065-f915-a2c99008a51f" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[ 9.00000000e-01, 1.99665317e-16],\n", " [-1.25647527e-15, 1.10000000e+00]])" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "X2_naive = Anaive@X1\n", "X2_correct = Acorrect@X1 + B@Upsilon\n", "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", "for i in range(X.shape[0]):\n", " ax[i].plot(X2[i], linestyle=\"-\", label=\"true\")\n", " dist = np.linalg.norm(X2_naive[i] - X2[i])\n", " ax[i].plot(X2_naive[i], linestyle=\"--\", label=f\"naive ($\\Delta={dist:.3f}$)\")\n", " dist = np.linalg.norm(X2_correct[i] - X2[i])\n", " ax[i].plot(X2_correct[i], linestyle=\"-.\", label=f\"correct ($\\Delta={dist:.3f}$)\")\n", " ax[i].legend()\n", " ax[i].set_ylabel(f\"$X[{i}]$\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 320 }, "id": "fsWsSwoMs3Ke", "outputId": "571c8df2-ab72-4d56-828d-0e54425324c5" }, "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "wn_ShHz9n1yl" }, "execution_count": 12, "outputs": [] } ] }	apache-2.0
zzsza/Datascience_School	10. 기초 확률론3 - 확률 분포 모형/10. 카테고리 분포.ipynb	1	173189	{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "fc43483dea504fb59854a3592af59b4d" }, "source": [ "# 카테고리 분포" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "115d34ded35d4cf6bbde05075823fd18" }, "source": [ "카테고리 분포(Categorical distribution)는 베르누이 분포의 확장판이다. 베르누이 분포는 0 이나 1(또는 -1 이나 1)이 나오는 확률 변수의 분포였다. 카케고리 분포는 1부터 K까지의 $K$개의 정수 값 중 하나가 나오는 확률 변수의 분포이다. 따라서 주사위를 던져 나오는 눈금의 수를 확률 변수라고 한다면 이 확률 변수의 분포는 $K=6$인 카테고리 분포이다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "6d6632b90b444b97894467fb490a6984" }, "source": [ "카테고리 분포의 모수 $\\theta$ 는 베르누이 분포와 달리 다음과 같은 제약 조건을 가지는 벡터값이 된다.\n", "\n", "$$ \\theta = ( \\theta_1, \\cdots , \\theta_K ) $$\n", "\n", "$$ \\sum_{k=1}^K \\theta_k = 1 $$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f0c185669d2b47daa9e3b448408ff058" }, "source": [ "카테고리 분포는 원래 단일 변수 확률 분포이지만 일반적으로는 0 또는 1 값만 가지는 $K$개 베르누이 분포 벡터를 가지는 다변수 확률 분포로 가정하여 사용한다. 다만 이 경우 다변수 확률 변수의 각 원소 중 하나만 1이 될 수 있다는 제약 조건을 가진다. \n", "\n", "이를 수식으로 나타내면 다음과 같다. 카테고리 분포는 $\\text{Cat}(x;\\theta)$로 표기한다.\n", "\n", "$$ \\text{Cat}(x;\\theta) = \\prod_{k=1}^K \\theta_k^{x_k} $$\n", "\n", "이 식에서 $x = k $일때 $x_{j=k} = 1$ 이고 $x_{j\\neq k} = 0 $ 이다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "8ba15aaafdca447ca50f95467d9349fd" }, "source": [ "카테고리 분포의 기댓값과 분산은 다음과 같다.\n", "\n", "* 기댓값\n", "$$\\text{E}[x_k] = \\theta_k$$\n", "\n", "* 분산\n", "$$\\text{Var}[x_k] = \\theta_k(1-\\theta_k)$$" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "17f958ab83ef4d3984683ad07f551bf5" }, "source": [ "SciPy는 카테고리 분포를 위한 별도의 클래스나 명령어를 제공하지 않는다. 다만 NumPy의 random 서브패키지의 `multinomial` 명령에서 `n` 인수를 1로 설정하고 `pvals`에 모수 벡터 $\\theta$를 설정하면 1부터 `len(pvals)`까지의 카테고리 분포를 따르는 데이터 샘플을 생성할 수 있다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "school_cell_uuid": 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RichardTMR/homework	week3/mxnet-part/cifar10/.ipynb_checkpoints/step_by_step_debug-checkpoint.ipynb	1	173651	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import mxnet as mx \n", "import matplotlib.pyplot as plt \n", "import numpy as np\n", "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (2, 3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<mxnet.io.MXDataIter object at 0x7f951f4e01d0>\n", "DataBatch: data shapes: [(128L, 3L, 28L, 28L)] label shapes: [(128L,)]\n", "DataBatch: data shapes: [(128L, 3L, 28L, 28L)] label shapes: [(128L,)]\n", "DataBatch: data shapes: [(128L, 3L, 28L, 28L)] label shapes: [(128L,)]\n", "DataBatch: data shapes: [(128L, 3L, 28L, 28L)] label shapes: [(128L,)]\n", "DataBatch: data 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f94e0b12710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Step 1 data\n", "# input data debug\n", "data_iter = mx.io.ImageRecordIter(\n", " path_imgrec = 'data/cifar10_train.rec',\n", " data_shape = (3,28,28),\n", " label_width = 1,\n", " batch_size = 128\n", ")\n", "print (data_iter)\n", "i = 0\n", "for each in data_iter:\n", " i+=1\n", " if i>5:\n", " break\n", " print each\n", "batch_numpy = each.data[0].asnumpy()\n", "label_numpy = each.label[0].asnumpy()\n", "print (type(batch_numpy))\n", "print (type(label_numpy))\n", "\n", "#show img\n", "randidx = np.random.randint(0,128)\n", "img = batch_numpy[randidx]\n", "img = np.squeeze(img).sum(axis=0)\n", "plt.imshow(img, cmap='gray')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['data', 'bn_data_gamma', 'bn_data_beta', 'conv0_weight', 'stage1_unit1_bn1_gamma', 'stage1_unit1_bn1_beta', 'stage1_unit1_conv1_weight', 'stage1_unit1_bn2_gamma', 'stage1_unit1_bn2_beta', 'stage1_unit1_conv2_weight', 'stage1_unit1_sc_weight', 'stage1_unit2_bn1_gamma', 'stage1_unit2_bn1_beta', 'stage1_unit2_conv1_weight', 'stage1_unit2_bn2_gamma', 'stage1_unit2_bn2_beta', 'stage1_unit2_conv2_weight', 'stage1_unit3_bn1_gamma', 'stage1_unit3_bn1_beta', 'stage1_unit3_conv1_weight', 'stage1_unit3_bn2_gamma', 'stage1_unit3_bn2_beta', 'stage1_unit3_conv2_weight', 'stage2_unit1_bn1_gamma', 'stage2_unit1_bn1_beta', 'stage2_unit1_conv1_weight', 'stage2_unit1_bn2_gamma', 'stage2_unit1_bn2_beta', 'stage2_unit1_conv2_weight', 'stage2_unit1_sc_weight', 'stage2_unit2_bn1_gamma', 'stage2_unit2_bn1_beta', 'stage2_unit2_conv1_weight', 'stage2_unit2_bn2_gamma', 'stage2_unit2_bn2_beta', 'stage2_unit2_conv2_weight', 'stage2_unit3_bn1_gamma', 'stage2_unit3_bn1_beta', 'stage2_unit3_conv1_weight', 'stage2_unit3_bn2_gamma', 'stage2_unit3_bn2_beta', 'stage2_unit3_conv2_weight', 'stage3_unit1_bn1_gamma', 'stage3_unit1_bn1_beta', 'stage3_unit1_conv1_weight', 'stage3_unit1_bn2_gamma', 'stage3_unit1_bn2_beta', 'stage3_unit1_conv2_weight', 'stage3_unit1_sc_weight', 'stage3_unit2_bn1_gamma', 'stage3_unit2_bn1_beta', 'stage3_unit2_conv1_weight', 'stage3_unit2_bn2_gamma', 'stage3_unit2_bn2_beta', 'stage3_unit2_conv2_weight', 'stage3_unit3_bn1_gamma', 'stage3_unit3_bn1_beta', 'stage3_unit3_conv1_weight', 'stage3_unit3_bn2_gamma', 'stage3_unit3_bn2_beta', 'stage3_unit3_conv2_weight', 'bn1_gamma', 'bn1_beta', 'fc1_weight', 'fc1_bias', 'softmax_label']\n", "['softmax_output']\n" ] }, { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n", " -->\n", "<!-- Title: resnet8 Pages: 1 -->\n", "<svg width=\"2256pt\" 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font-size=\"14.00\">stage2_unit2_bn1_beta</text>\n", "</g>\n", "<!-- stage2_unit2_bn1_moving_mean -->\n", "<g id=\"node72\" class=\"node\"><title>stage2_unit2_bn1_moving_mean</title>\n", "<ellipse fill=\"#8dd3c7\" stroke=\"black\" cx=\"1411\" cy=\"-2849\" rx=\"47\" ry=\"29\"/>\n", "<text text-anchor=\"middle\" x=\"1411\" y=\"-2845.3\" font-family=\"Times,serif\" font-size=\"14.00\">stage2_unit2_bn1_moving_mean</text>\n", "</g>\n", "<!-- stage2_unit2_bn1_moving_var -->\n", "<g id=\"node73\" class=\"node\"><title>stage2_unit2_bn1_moving_var</title>\n", "<ellipse fill=\"#8dd3c7\" stroke=\"black\" cx=\"1523\" cy=\"-2849\" rx=\"47\" ry=\"29\"/>\n", "<text text-anchor=\"middle\" x=\"1523\" y=\"-2845.3\" font-family=\"Times,serif\" font-size=\"14.00\">stage2_unit2_bn1_moving_var</text>\n", "</g>\n", "<!-- stage2_unit2_bn1 -->\n", "<g id=\"node74\" class=\"node\"><title>stage2_unit2_bn1</title>\n", "<polygon fill=\"#bebada\" stroke=\"black\" points=\"1290,-2972 1196,-2972 1196,-2914 1290,-2914 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"<polygon fill=\"black\" stroke=\"black\" points=\"1959.89,-6673.9 1950.85,-6667.69 1957.29,-6669.63 1954.69,-6665.36 1954.69,-6665.36 1954.69,-6665.36 1957.29,-6669.63 1958.54,-6663.02 1959.89,-6673.9 1959.89,-6673.9\"/>\n", "</g>\n", "<!-- softmax->softmax_label -->\n", "<g id=\"edge164\" class=\"edge\"><title>softmax->softmax_label</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M1999.38,-6665.24C2005.26,-6655.58 2011.48,-6645.35 2016.95,-6636.36\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"1994.11,-6673.9 1995.46,-6663.02 1996.71,-6669.63 1999.31,-6665.36 1999.31,-6665.36 1999.31,-6665.36 1996.71,-6669.63 2003.15,-6667.69 1994.11,-6673.9 1994.11,-6673.9\"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.dot.Digraph at 0x7f94e0b54190>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# debug model\n", "from importlib import import_module\n", "net = import_module('symbols.'+'resnet')\n", "sym = net.get_symbol(10,20,\"3,28,28\")\n", "model_prefix = 'cifar10_resnet'\n", "#check_point = mx.callback.do_checkpoint(model_prefix)\n", "arg_name = sym.list_arguments()\n", "out_name = sym.list_outputs()\n", "print (arg_name)\n", "print (out_name)\n", "mx.viz.plot_network(sym,hide_weights=True,save_format='pdf',title='resnet8')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function ImageRecordIter in module mxnet.io:\n", "\n", "ImageRecordIter(args, kwargs)\n", " Iterating on image RecordIO files\n", " \n", " Read images batches from RecordIO files with a rich of data augmentation\n", " options.\n", " \n", " One can use ``tools/im2rec.py`` to pack individual image files into RecordIO\n", " files.\n", " \n", " \n", " \n", " Defined in src/io/iter_image_recordio_2.cc:L568\n", " \n", " Parameters\n", " ----------\n", " path_imglist : string, optional, default=''\n", " Path to the image list file\n", " path_imgrec : string, optional, default=''\n", " Filename of the image RecordIO file or a directory path.\n", " aug_seq : string, optional, default='aug_default'\n", " The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.\n", " label_width : int, optional, default='1'\n", " The number of labels per image.\n", " data_shape : Shape(tuple), required\n", " The shape of one output image.\n", " preprocess_threads : int, optional, default='4'\n", " The number of threads.\n", " verbose : boolean, optional, default=True\n", " If or not output verbose information.\n", " num_parts : int, optional, default='1'\n", " Virtual partition data into n* parts\n", " part_index : int, optional, default='0'\n", " The i-th virtual partition will read\n", " shuffle_chunk_size : long (non-negative), optional, default=0\n", " The data shuffle buffer size in MB. Only valid if shuffle is true\n", " shuffle_chunk_seed : int, optional, default='0'\n", " The random seed for shuffling\n", " shuffle : boolean, optional, default=False\n", " If or not randomly shuffle data.\n", " seed : int, optional, default='0'\n", " The random seed.\n", " batch_size : int (non-negative), required\n", " Batch size.\n", " round_batch : boolean, optional, default=True\n", " If or not use round robin to handle overflow batch.\n", " prefetch_buffer : long (non-negative), optional, default=4\n", " Maximal Number of batches to prefetch\n", " dtype : {None, 'float16', 'float32', 'float64', 'int32', 'uint8'},optional, default='None'\n", " Output data type. None means no change\n", " resize : int, optional, default='-1'\n", " Down scale the shorter edge to a new size before applying other augmentations.\n", " rand_crop : boolean, optional, default=False\n", " If or not randomly crop the image\n", " max_rotate_angle : int, optional, default='0'\n", " Rotate by a random degree in ``[-v, v]``\n", " max_aspect_ratio : float, optional, default=0\n", " Change the aspect (namely width/height) to a random value in ``[1 - max_aspect_ratio, 1 + max_aspect_ratio]``\n", " max_shear_ratio : float, optional, default=0\n", " Apply a shear transformation (namely ``(x,y)->(x+my,y)``) with ``m`` randomly chose from ``[-max_shear_ratio, max_shear_ratio]``\n", " max_crop_size : int, optional, default='-1'\n", " Crop both width and height into a random size in ``[min_crop_size, max_crop_size]``\n", " min_crop_size : int, optional, default='-1'\n", " Crop both width and height into a random size in ``[min_crop_size, max_crop_size]``\n", " max_random_scale : float, optional, default=1\n", " Resize into ``[widths, heights]`` with ``s`` randsomly chosen from ``[min_random_scale, max_random_scale]``\n", " min_random_scale : float, optional, default=1\n", " Resize into ``[widths, heights]`` with ``s`` randsomly chosen from ``[min_random_scale, max_random_scale]``\n", " max_img_size : float, optional, default=1e+10\n", " Set the maximal width and height after all resize and rotate argumentation are applied\n", " min_img_size : float, optional, default=0\n", " Set the minimal width and height after all resize and rotate argumentation are applied\n", " random_h : int, optional, default='0'\n", " Add a random value in ``[-random_h, random_h]`` to the H channel in HSL color space.\n", " random_s : int, optional, default='0'\n", " Add a random value in ``[-random_s, random_s]`` to the S channel in HSL color space.\n", " random_l : int, optional, default='0'\n", " Add a random value in ``[-random_l, random_l]`` to the L channel in HSL color space.\n", " rotate : int, optional, default='-1'\n", " Rotate by an angle. If set, it overrites the ``max_rotate_angle`` option.\n", " fill_value : int, optional, default='255'\n", " Set the padding pixes value into ``fill_value``.\n", " inter_method : int, optional, default='1'\n", " The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.\n", " pad : int, optional, default='0'\n", " Change size from ``[width, height]`` into ``[pad + width + pad, pad + height + pad]`` by padding pixes\n", " mirror : boolean, optional, default=False\n", " If or not mirror the image.\n", " rand_mirror : boolean, optional, default=False\n", " If or not randomly the image.\n", " mean_img : string, optional, default=''\n", " Filename of the The mean image.\n", " mean_r : float, optional, default=0\n", " The mean value to be subtracted on the R channel\n", " mean_g : float, optional, default=0\n", " The mean value to be subtracted on the G channel\n", " mean_b : float, optional, default=0\n", " The mean value to be subtracted on the B channel\n", " mean_a : float, optional, default=0\n", " The mean value to be subtracted on the alpha channel\n", " scale : float, optional, default=1\n", " Multiply the image with a scale value.\n", " max_random_contrast : float, optional, default=0\n", " Change the contrast with a value randomly chosen from ``[-max_random_contrast, max_random_contrast]``\n", " max_random_illumination : float, optional, default=0\n", " Change the illumination with a value randomly chosen from ``[-max_random_illumination, max_random_illumination]``\n", " \n", " Returns\n", " -------\n", " MXDataIter\n", " The result iterator.\n", "\n" ] } ], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
sspickle/vpython-jupyter	Demos/Graphs.ipynb	1	3098	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glow.2.1.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glowcomm\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require([\"nbextensions/glowcomm\"], function(){console.log(\"glowcomm loaded\");})" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id=\"glowscript\" class=\"glowscript\"></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from vpython import \n", "\n", "oscillation = graph(xtitle='time', ytitle='Drag mouse to see value')\n", "funct1 = gcurve(color=color.blue, width=4)\n", "funct2 = gvbars(delta=0.4, color=color.red)\n", "funct3 = gdots(color=color.orange, size=3)\n", "\n", "for t in range(-30, 74, 1):\n", " rate(50)\n", " funct1.plot( pos=(t, 5.0+5.0cos(-0.2t)exp(0.015t)) )\n", " funct2.plot( pos=(t, 2.0+5.0cos(-0.1t)exp(0.015t)) )\n", " funct3.plot( pos=(t, 5.0cos(-0.03t)exp(0.015*t)) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "VPython", "language": "python", "name": "vpython" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
marioberges/F16-12-752	projects/akhil_anand_vasu_yoolhee_zeal/Section 4.2.1 Appliance Power Consumption Clustering.ipynb	1	1289331	null	gpl-3.0
fccoelho/Curso_Blockchain	lectures/intro_cripto.ipynb	1	17858	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introdução à criptografia e às funções Hash\n", "\n", "As criptomoedas, como o Bitcoin, utilizam-se de tecnologias criptográficas como criptografia de chave publica,e funções de Hash. Neste notebook vamos nos familiarizar com estes conceitos que nos serão úteis em nosso estudo da bitcoin e outras criptomoedas.\n", "\n", "## Funções de Hash Criptográfico\n", "\n", "As funções de Hash criptográfico são o componentes mais fundamental da maioria das blockchains pois é a \"cola\" que garante a coesão, correção, imutabilidade e outras características fundamentais das blockchains.\n", "\n", "Uma função de Hash é uma função que apresenta algumas características básicas:\n", "\n", "1. é fácil de calcular para qualquer tipo de dado (baixo custo computacional)\n", "1. É impossível ou extremamente difícil de inverter, isto é, de encontrar o input correspondente a um hash.\n", "1. É extremamente improvável que dois inputs diferentes gerem o mesmo valor de hash.\n", "\n", "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/2b/Cryptographic_Hash_Function.svg/740px-Cryptographic_Hash_Function.svg.png\" width=\"30%\"/>\n", "\n", "A biblioteca padrão do Python nos oferece uma biblioteca com implementações das principais funções de hash, a [Hashlib](https://docs.python.org/3/library/hashlib.html).\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:04:45.350396Z", "start_time": "2021-08-20T13:04:45.344891Z" } }, "outputs": [ { "data": { "text/plain": [ "{'blake2b',\n", " 'blake2s',\n", " 'md4',\n", " 'md5',\n", " 'md5-sha1',\n", " 'ripemd160',\n", " 'sha1',\n", " 'sha224',\n", " 'sha256',\n", " 'sha384',\n", " 'sha3_224',\n", " 'sha3_256',\n", " 'sha3_384',\n", " 'sha3_512',\n", " 'sha512',\n", " 'sha512_224',\n", " 'sha512_256',\n", " 'shake_128',\n", " 'shake_256',\n", " 'sm3',\n", " 'whirlpool'}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import hashlib\n", "hashlib.algorithms_available" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criptografia com curvas elípticas\n", "\n", "A Bitcoin se utiliza de curvas elípticas para suas necessidades criptográficas. Mais precisamente, utiliza o algoritmo de assinatura digital por curvas elipticas (ECDSA). A ECDSA envolve três componentes principais: uma chave pública, uma chave privada e assinatura.\n", "\n", "A Bitcoin usa uma curva elíptica específica chamada [secp256k1](https://bitcoin.stackexchange.com/questions/21907/what-does-the-curve-used-in-bitcoin-secp256k1-look-like). A função em si parece inofensiva: $$y^2=x^3+7$$ onde $4a^3 +27b^2 \\neq 0$ (para excluir [curvas singulares](https://en.wikipedia.org/wiki/Singularity_(mathematics)).\n", "$$\\begin{array}{rcl}\n", " \\left\\{(x, y) \\in \\mathbb{R}^2 \\right. & \\left. \| \\right. & \\left. y^2 = x^3 + ax + b, \\right. \\\\\n", " & & \\left. 4a^3 + 27b^2 \\ne 0\\right\\}\\ \\cup\\ \\left\\{0\\right\\}\n", "\\end{array}$$\n", "\n", "<img src=\"http://andrea.corbellini.name/images/curves.png\" width=\"30%\" align=\"right\"/>\n", "\n", "Porém, em aplicações criptográficas, esta função não é definida sobre os números reais, mas sobre um campo de números primos: mais precisamente ${\\cal Z}$ modulo $2^{256} - 2^{32} - 977$. \n", "\n", "\\begin{array}{rcl}\n", " \\left\\{(x, y) \\in (\\mathbb{F}_p)^2 \\right. & \\left. \| \\right. & \\left. y^2 \\equiv x^3 + ax + b \\pmod{p}, \\right. \\\\\n", " & & \\left. 4a^3 + 27b^2 \\not\\equiv 0 \\pmod{p}\\right\\}\\ \\cup\\ \\left\\{0\\right\\}\n", "\\end{array}\n", "\n", "\n", "\n", "Para um maior aprofundamento sobre a utilização de curvas elítpicas em criptografia leia [este material](http://andrea.corbellini.name/2015/05/17/elliptic-curve-cryptography-a-gentle-introduction/).\n", "\n", "## Encriptando textos\n", "\n", "A forma mais simples de criptografia é a criptografia simétrica, na qual se utilizando de uma chave gerada aleatóriamente, converte um texto puro em um texto encriptado. então de posse da mesma chave é possível inverter a operação, recuperando o texto original. Quando falamos em texto aqui estamos falando apenas de uma aplicação possível de criptografia. Na verdade o que será aplicado aqui para textos, pode ser aplicado para qualquer sequencia de bytes, ou seja para qualquer objeto digital." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:11:00.435756Z", "start_time": "2021-08-20T13:10:59.505569Z" } }, "outputs": [], "source": [ "from Crypto.Cipher import DES3\n", "from Crypto import Random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Neste exemplo vamos usar o algoritmo conhecido como \"triplo DES\" para encriptar e desencriptar um texto. Para este exemplo a chave deve ter um comprimento múltiplo de 8 bytes." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:17:35.666014Z", "start_time": "2021-08-20T13:17:35.662300Z" } }, "outputs": [], "source": [ "chave = b\"chave secreta um\"\n", "sal = Random.get_random_bytes(8)\n", "des3 = DES3.new(chave, DES3.MODE_CFB, sal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note que adicionamos sal à ao nosso encriptador. o \"sal\" é uma sequência aleatória de bytes feitar para dificultar ataques." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:18:24.436267Z", "start_time": "2021-08-20T13:18:24.431461Z" } }, "outputs": [ { "data": { "text/plain": [ "b'\\xbd\\'W\\xd7\\x1b&\\x95\\xdd\\xa2\\x1b 5t\\xb7\\xfd\\x93/Fa\\xa4\\xc3\\x9c\"\\xcf\\x87\\xdc\\x03\\x00\\xe5 \\x18\\xd6)\\x0f\\xb7\\xd6\\r\\x07\\x80PK\\x895V\\x084?\\xd1.cS\\'\\xec\\x02}\\xa7j\\xbf\\x1f\\n\\x06EA\\'s\\xf0C\\xd0T\\x95\\xa2]\\x10\\x19}\\xc9\\xa5\\xdf\\xd5\\x8b\\xa0\\xd7\\xdd\\tlRL\\xe1\\x0eb\\x06\\xc4\\xd1\\x1a\\xbckT9\\x12l'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "texto = b\"Este e um texto super secreto que precisa ser protegido a qualquer custo de olhares nao autorizados.\"\n", "enc = des3.encrypt(texto)\n", "enc" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:17:13.034283Z", "start_time": "2021-08-20T13:17:13.023641Z" } }, "outputs": [ { "data": { "text/plain": [ "b'Este e um texto super secreto que precisa ser protegido a qualquer custo de olhares nao autorizados.'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "des3 = DES3.new(chave, DES3.MODE_CFB, sal)\n", "des3.decrypt(enc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Um dos problemas com esta metodologia de encriptação, é que se você deseja enviar este arquivo encriptado a um amigo, terá que encontrar uma forma segura de lhe transmitir a chave, caso contrário um inimigo mal intencionado poderá desencriptar sua mensagem de posse da chave. Para resolver este problema introduzimos um novo métodos de encriptação:\n", "\n", "## Criptografia de chave pública\n", "\n", "Nesta metodologia temos duas chaves: uma pública e outra privada." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:19:03.870590Z", "start_time": "2021-08-20T13:19:03.692911Z" } }, "outputs": [], "source": [ "from Crypto.PublicKey import RSA\n", "from Crypto.Random import get_random_bytes\n", "from Crypto.Cipher import AES, PKCS1_OAEP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos criar uma chave privada, e também encriptá-la, no caso de termos que mantê-la em algum lugar onde possa ser observada por um terceiro." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:20:20.593103Z", "start_time": "2021-08-20T13:20:19.574827Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'-----BEGIN RSA PRIVATE KEY-----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\\nXcuBs1aLt23XJKf9bI+6v+ypYY9MCZ5diJ7vR6I/7+aFLugmyEZrHA==\\n-----END RSA PRIVATE KEY-----'\n" ] } ], "source": [ "senha = \"minha senha super secreta.\"\n", "key = RSA.generate(2048) # Chave privada\n", "print(key.exportKey())\n", "chave_privada_encryptada = key.exportKey(passphrase=senha, pkcs=8, protection=\"scryptAndAES128-CBC\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:21:04.000119Z", "start_time": "2021-08-20T13:21:03.994922Z" } }, "outputs": [ { "data": { "text/plain": [ "b'-----BEGIN PUBLIC KEY-----\\nMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxBCxxYLcfPm9AQyvE+58\\nd3ZYtKVZ/o1tbzKOIHFvwDXO6RDS8lej3Tt7T4zgMKNNwfLW64IMgWpobgwBpq6m\\nQIFUWoKSE5SDrlPWvbrwe1Kt6ESDCsb64DhnXuaqz0rVyAqdRbBHQ4SVcYXOMvub\\nTC5Ff5MgRPNBWiFegc5VRLjk1HEECUiNG0cwrXsMHhyunA1Fa/c5EgQ2kTa8CKoa\\n5vf0ptcSmHa+WaH665BjSdeM9VG7iMmDu2fASnIoHQ9Kr6KCd545n2d4vuGN9XDq\\np+Togb2YOUo/PEoFMSOXkuj0HtWBMK18+8xc7H+HIubUYg81T2wZbhXc1gOmzZD3\\n7wIDAQAB\\n-----END PUBLIC KEY-----'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "publica = key.publickey()\n", "publica.exportKey()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De posse da senha podemos recuperar as duas chaves." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:22:14.539413Z", "start_time": "2021-08-20T13:22:14.326878Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "key2 = RSA.import_key(chave_privada_encryptada, passphrase=senha)\n", "print(key2==key)\n", "key.publickey().exportKey() == key2.publickey().exportKey()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Agora podemos encriptar algum documento qualquer. Para máxima segurança, vamos usar o protocolo PKCS#1 [OAEP](http://en.wikipedia.org/wiki/Optimal_asymmetric_encryption_padding) com a algoritmo RSA para encriptar assimetricamente uma chave de sessão [AES](https://en.wikipedia.org/wiki/Advanced_Encryption_Standard). Esta chave de sessão pode ser usada para encriptar os dados. Vamos usar o modo [EAX](http://en.wikipedia.org/wiki/EAX_mode) para permitir a detecção de modificações não autorizadas.\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:24:52.477979Z", "start_time": "2021-08-20T13:24:52.469798Z" } }, "outputs": [ { "data": { "text/plain": [ "b'uB3b\\xc6\\xe4\\x94D\\xf2\\x1d\\xd3\\x82\\x9f\\xcf\\xb6-\\xdaKi\\\\a\\xd5\\xfb\\xae\\xc8\\x8f\\xd3\\\\\\xe4\\xd1'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = \"Minha senha do banco é 123456\".encode('utf8')\n", "chave_de_sessão = get_random_bytes(16)\n", "\n", "# Encripta a chave de sessão com a a chave RSA pública.\n", "cifra_rsa = PKCS1_OAEP.new(publica)\n", "chave_de_sessão_enc = cifra_rsa.encrypt(chave_de_sessão)\n", "\n", "# Encrypta os dados.\n", "cifra_aes = AES.new(chave_de_sessão, AES.MODE_EAX)\n", "texto_cifrado, tag = cifra_aes.encrypt_and_digest(data)\n", "texto_cifrado" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O destinatário da mensagem pode então desencriptar a mensagem usando a chave privada para desencriptar a chave da sessão, e com esta a mensagem." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-08-20T13:26:19.269797Z", "start_time": "2021-08-20T13:26:19.260373Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minha senha do banco é 123456\n" ] } ], "source": [ "# Desencripta a chave de sessão com a chave privada RSA.\n", "cifra_rsa = PKCS1_OAEP.new(key)\n", "chave_de_sessão = cifra_rsa.decrypt(chave_de_sessão_enc)\n", "\n", "# Desencripta os dados com a chave de sessão AES\n", "cifra_aes = AES.new(chave_de_sessão, AES.MODE_EAX, cifra_aes.nonce)\n", "data2 = cifra_aes.decrypt_and_verify(texto_cifrado, tag)\n", "print(data.decode(\"utf-8\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nbTranslate": { "displayLangs": [ "*" ], "hotkey": "alt-t", "langInMainMenu": true, "sourceLang": "en", "targetLang": "fr", "useGoogleTranslate": true }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	lgpl-3.0
sophie63/FlyLFM	Notebooks/.ipynb_checkpoints/960Large_FlyLFMpaper-checkpoint.ipynb	1	3643072	null	bsd-2-clause
mne-tools/mne-tools.github.io	0.21/_downloads/a1ab4842a5aa341564b4fa0a6bf60065/plot_dipole_orientations.ipynb	1	12951	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# The role of dipole orientations in distributed source localization\n\nWhen performing source localization in a distributed manner\n(MNE/dSPM/sLORETA/eLORETA),\nthe source space is defined as a grid of dipoles that spans a large portion of\nthe cortex. These dipoles have both a position and an orientation. In this\ntutorial, we will look at the various options available to restrict the\norientation of the dipoles and the impact on the resulting source estimate.\n\nSee `inverse_orientation_constraints` for related information.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data\nLoad everything we need to perform source localization on the sample dataset.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mne\nimport numpy as np\nfrom mne.datasets import sample\nfrom mne.minimum_norm import make_inverse_operator, apply_inverse\n\ndata_path = sample.data_path()\nevokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')\nleft_auditory = evokeds[0].apply_baseline()\nfwd = mne.read_forward_solution(\n data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif')\nmne.convert_forward_solution(fwd, surf_ori=True, copy=False)\nnoise_cov = mne.read_cov(data_path + '/MEG/sample/sample_audvis-cov.fif')\nsubject = 'sample'\nsubjects_dir = data_path + '/subjects'\ntrans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The source space\nLet's start by examining the source space as constructed by the\n:func:`mne.setup_source_space` function. Dipoles are placed along fixed\nintervals on the cortex, determined by the ``spacing`` parameter. The source\nspace does not define the orientation for these dipoles.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lh = fwd['src'][0] # Visualize the left hemisphere\nverts = lh['rr'] # The vertices of the source space\ntris = lh['tris'] # Groups of three vertices that form triangles\ndip_pos = lh['rr'][lh['vertno']] # The position of the dipoles\ndip_ori = lh['nn'][lh['vertno']]\ndip_len = len(dip_pos)\ndip_times = [0]\nwhite = (1.0, 1.0, 1.0) # RGB values for a white color\n\nactual_amp = np.ones(dip_len) # misc amp to create Dipole instance\nactual_gof = np.ones(dip_len) # misc GOF to create Dipole instance\ndipoles = mne.Dipole(dip_times, dip_pos, actual_amp, dip_ori, actual_gof)\ntrans = mne.read_trans(trans_fname)\n\nfig = mne.viz.create_3d_figure(size=(600, 400), bgcolor=white)\ncoord_frame = 'mri'\n\n# Plot the cortex\nfig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,\n trans=trans, surfaces='white',\n coord_frame=coord_frame, fig=fig)\n\n# Mark the position of the dipoles with small red dots\nfig = mne.viz.plot_dipole_locations(dipoles=dipoles, trans=trans,\n mode='sphere', subject=subject,\n subjects_dir=subjects_dir,\n coord_frame=coord_frame,\n scale=7e-4, fig=fig)\n\nmne.viz.set_3d_view(figure=fig, azimuth=180, distance=0.25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Fixed dipole orientations\nWhile the source space defines the position of the dipoles, the inverse\noperator defines the possible orientations of them. One of the options is to\nassign a fixed orientation. Since the neural currents from which MEG and EEG\nsignals originate flows mostly perpendicular to the cortex [1]_, restricting\nthe orientation of the dipoles accordingly places a useful restriction on the\nsource estimate.\n\nBy specifying ``fixed=True`` when calling\n:func:`mne.minimum_norm.make_inverse_operator`, the dipole orientations are\nfixed to be orthogonal to the surface of the cortex, pointing outwards. Let's\nvisualize this:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.create_3d_figure(size=(600, 400))\n\n# Plot the cortex\nfig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,\n trans=trans,\n surfaces='white', coord_frame='head', fig=fig)\n\n# Show the dipoles as arrows pointing along the surface normal\nfig = mne.viz.plot_dipole_locations(dipoles=dipoles, trans=trans,\n mode='arrow', subject=subject,\n subjects_dir=subjects_dir,\n coord_frame='head',\n scale=7e-4, fig=fig)\n\nmne.viz.set_3d_view(figure=fig, azimuth=180, distance=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Restricting the dipole orientations in this manner leads to the following\nsource estimate for the sample data:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Compute the source estimate for the 'left - auditory' condition in the sample\n# dataset.\ninv = make_inverse_operator(left_auditory.info, fwd, noise_cov, fixed=True)\nstc = apply_inverse(left_auditory, inv, pick_ori=None)\n\n# Visualize it at the moment of peak activity.\n_, time_max = stc.get_peak(hemi='lh')\nbrain_fixed = stc.plot(surface='white', subjects_dir=subjects_dir,\n initial_time=time_max, time_unit='s', size=(600, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The direction of the estimated current is now restricted to two directions:\ninward and outward. In the plot, blue areas indicate current flowing inwards\nand red areas indicate current flowing outwards. Given the curvature of the\ncortex, groups of dipoles tend to point in the same direction: the direction\nof the electromagnetic field picked up by the sensors.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Loose dipole orientations\nForcing the source dipoles to be strictly orthogonal to the cortex makes the\nsource estimate sensitive to the spacing of the dipoles along the cortex,\nsince the curvature of the cortex changes within each ~10 square mm patch.\nFurthermore, misalignment of the MEG/EEG and MRI coordinate frames is more\ncritical when the source dipole orientations are strictly constrained [2]_.\nTo lift the restriction on the orientation of the dipoles, the inverse\noperator has the ability to place not one, but three dipoles at each\nlocation defined by the source space. These three dipoles are placed\northogonally to form a Cartesian coordinate system. Let's visualize this:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.create_3d_figure(size=(600, 400))\n\n# Plot the cortex\nfig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,\n trans=trans,\n surfaces='white', coord_frame='head', fig=fig)\n\n# Show the three dipoles defined at each location in the source space\nfig = mne.viz.plot_alignment(subject=subject, subjects_dir=subjects_dir,\n trans=trans, fwd=fwd,\n surfaces='white', coord_frame='head', fig=fig)\n\nmne.viz.set_3d_view(figure=fig, azimuth=180, distance=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When computing the source estimate, the activity at each of the three dipoles\nis collapsed into the XYZ components of a single vector, which leads to the\nfollowing source estimate for the sample data:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make an inverse operator with loose dipole orientations\ninv = make_inverse_operator(left_auditory.info, fwd, noise_cov, fixed=False,\n loose=1.0)\n\n# Compute the source estimate, indicate that we want a vector solution\nstc = apply_inverse(left_auditory, inv, pick_ori='vector')\n\n# Visualize it at the moment of peak activity.\n_, time_max = stc.magnitude().get_peak(hemi='lh')\nbrain_mag = stc.plot(subjects_dir=subjects_dir, initial_time=time_max,\n time_unit='s', size=(600, 400), overlay_alpha=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Limiting orientations, but not fixing them\nOften, the best results will be obtained by allowing the dipoles to have\nsomewhat free orientation, but not stray too far from a orientation that is\nperpendicular to the cortex. The ``loose`` parameter of the\n:func:`mne.minimum_norm.make_inverse_operator` allows you to specify a value\nbetween 0 (fixed) and 1 (unrestricted or \"free\") to indicate the amount the\norientation is allowed to deviate from the surface normal.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set loose to 0.2, the default value\ninv = make_inverse_operator(left_auditory.info, fwd, noise_cov, fixed=False,\n loose=0.2)\nstc = apply_inverse(left_auditory, inv, pick_ori='vector')\n\n# Visualize it at the moment of peak activity.\n_, time_max = stc.magnitude().get_peak(hemi='lh')\nbrain_loose = stc.plot(subjects_dir=subjects_dir, initial_time=time_max,\n time_unit='s', size=(600, 400), overlay_alpha=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discarding dipole orientation information\nOften, further analysis of the data does not need information about the\norientation of the dipoles, but rather their magnitudes. The ``pick_ori``\nparameter of the :func:`mne.minimum_norm.apply_inverse` function allows you\nto specify whether to return the full vector solution (``'vector'``) or\nrather the magnitude of the vectors (``None``, the default) or only the\nactivity in the direction perpendicular to the cortex (``'normal'``).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Only retain vector magnitudes\nstc = apply_inverse(left_auditory, inv, pick_ori=None)\n\n# Visualize it at the moment of peak activity.\n_, time_max = stc.get_peak(hemi='lh')\nbrain = stc.plot(surface='white', subjects_dir=subjects_dir,\n initial_time=time_max, time_unit='s', size=(600, 400))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n.. [1] H\u00e4m\u00e4l\u00e4inen, M. S., Hari, R., Ilmoniemi, R. J., Knuutila, J., &\n Lounasmaa, O. V. \"Magnetoencephalography - theory, instrumentation, and\n applications to noninvasive studies of the working human brain\", Reviews\n of Modern Physics, 1993. https://doi.org/10.1103/RevModPhys.65.413\n\n.. [2] Lin, F. H., Belliveau, J. W., Dale, A. M., & H\u00e4m\u00e4l\u00e4inen, M. S. (2006).\n Distributed current estimates using cortical orientation constraints.\n Human Brain Mapping, 27(1), 1\u201313. http://doi.org/10.1002/hbm.20155\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
kissf-lu/jupyter_app	ipython/py36_erzhou_input/packages_data_clean.ipynb	1	135472	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from collections.abc import Iterator, Iterable, Generator, Hashable\n", "from collections import defaultdict, namedtuple\n", "import re\n", "import os\n", "import datetime\n", "import bson\n", "\n", "from sqlalchemy.types import Integer, Text, DateTime, Float, VARCHAR\n", "from sqlalchemy import Table, MetaData, Column\n", "import itertools as it\n", "import pickle\n", "from tools import deco_logging\n", "import numpy as np\n", "# add tools funcs\n", "from tools.func_abc import \n", "\n", "MHS_SHEET_NAME = ['Parcel pcs', 'Small bag pcs', 'Irregular pcs', 'NC pcs', 'Mail pcs', 'sorting by pcs']\n", "SHEET_DF = {}\n", "DEFAULT_DATA_PATH = ('data\\\\')\n", "EXCEL_DIR = 'data//2025OD_matrix_Pre-Sort.xlsx'\n", "SHEEF_FOOT_SKIP_NUM = 57\n", "\n", "log_ipy = deco_logging(user='jupyter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. DATA" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "excel_dir = 'data//2025OD_matrix_Pre-Sort.xlsx'\n", "pcs_sheet_name = 'sum_pcs'\n", "pcs_left_parse_cols = 'A:Q'\n", "pcs_right_parse_cols = 'D,R:ZD'\n", "other_left_parse_cols = 'B:F'\n", "other_right_parse_cols = 'C,G:YS'\n", "sorted_left_parse_cols = 'B:H'\n", "sorted_right_parse_cols = 'C,I:YU'\n", "cross_d_left_parse_cols = 'B:G'\n", "cross_d_right_parse_cols = 'C,H:AR'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_df_excel_sheet(excel_dir: str=None, sheet_lst: list=None, skip_foot: int=0):\n", " df_dic = dict()\n", " if excel_dir is None:\n", " raise ValueError('Should Set Excel Path!')\n", " if sheet_lst is None and not isinstance(sheet_lst, list):\n", " raise ValueError('Should Set Excel Sheet List, Value is List Type!')\n", " for st in sheet_lst:\n", " yield pd.read_excel(io=excel_dir, sheetname=st, skip_footer =skip_foot)\n", " \n", " return df_dic" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "base_col =['type', 'apt', 'model', 'Flight ID', 'Flight type', 'by pcs of ULD', 'MHS ULD', 'landing_time']\n", "round_col = ['by pcs of ULD', 'pcs_sum']\n", "dest_col = ['dest_city', 'dest_opt', 'dest_model']\n", "new_col = ['apt', 'model', 'Flight ID', 'by pcs of ULD', 'MHS ULD', 'landing_time', 'dest_city', 'dest_opt', 'dest_model', 'pcs_sum']\n", "\n", "#air_df_parcel = sorted_df.loc[sorted_df.model.isin(['D', 'I', 'INF']),:]\n", "\n", "# melt_df = df_melt_data_format(\n", "# df=air_df_parcel, base_col= base_col, melt_name='dest', value_name='pcs_sum', round_col=round_col)\n", "\n", "# melt_df['dest_city'], melt_df['dest_opt'], melt_df['dest_model'] = melt_df.dest.str.split('_').str\n", "# melt_df = melt_df.drop(labels=['dest'], axis=1)\n", "# melt_df = melt_df.loc[:,new_col]\n", "# melt_df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Parcel Data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ==============================================parcel data===========================\n", "df_parcel_left, df_parcel_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='Parcel pcs', left_parse_col=other_left_parse_cols, \n", " right_parse_col=other_right_parse_cols, skip_footer=57)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Small Bag Data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ================================================= small bag data===================\n", "df_small_left, df_small_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='Small bag pcs', left_parse_col=other_left_parse_cols, \n", " right_parse_col=other_right_parse_cols, skip_footer=57, fill_nan=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4 Irregular Parcel Data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ================================================= Irregular Parcel Data ===========\n", "df_irregular_left, df_irregular_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='Irregular pcs', left_parse_col=other_left_parse_cols, \n", " right_parse_col=other_right_parse_cols, skip_footer=57, fill_nan=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.5 NC Parcel Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ================================================= NC Parcel Data =================\n", "df_nc_left, df_nc_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='NC pcs', left_parse_col=other_left_parse_cols, \n", " right_parse_col=other_right_parse_cols, skip_footer=57, fill_nan=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.6 Mail Parcel Data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ================================================= Mail Parcel Data ==============\n", "df_ims_left, df_ims_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='Mail pcs', left_parse_col=other_left_parse_cols, \n", " right_parse_col=other_right_parse_cols, skip_footer=57, fill_nan=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.8 Sorted Parcel Data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ================================================= Sorted Parcel Data ===========\n", "df_sorted_left, df_sorted_right = read_excel_split(\n", " excel_dir=excel_dir, sheet_name='sorting by pcs', left_parse_col=sorted_left_parse_cols, \n", " right_parse_col=sorted_right_parse_cols, skip_footer=57, fill_nan=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Air Side Data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "air_model_list = ['I', 'D', 'INF']\n", "select_list = ['MHS', 'type', 'apt', 'model', 'Flight ID', 'Crossdock ULD', 'Dom-ULD', 'Inter-ULD', 'Loaded ULD', \n", " 'sum pcs', 'landing_time']\n", "sort_parcel_selctc_left = ['apt', 'model', 'Flight ID', 'landing_time']\n", "sort_parcel_sum_selctc_left = ['apt', 'model', 'Flight ID', 'by pcs of ULD', 'MHS ULD', 'landing_time']\n", "cross_parcel_sum_selctc_left = ['apt', 'model', 'Flight ID', 'by pcs of ULD', 'landing_time']\n", "left_round_list = [[['Crossdock_ULD', 'Dom_ULD', 'Inter_ULD','Loaded_ULD'], 0], [['sum_pcs'], 2]]\n", "sort_parcel_sum_left_round_list = [[['by_pcs_of_ULD'], 2]]\n", "index_slip_name = ['dest_city', 'dest_apt', 'dest_model']\n", "right_round_list = [[None,2]]\n", "in_tabel_merge_on = ['index_num']\n", "merg_data_on = [\n", " 'apt', 'model', 'Flight_ID', 'landing_time','dest_city','dest_apt', 'dest_model'\n", "]\n", "\n", "a_pcs = TableT(air_model_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 PCS Data\n" ] }, { "cell_type": "code", "execution_count": 651, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "# # ====================================get left table =======================================\n", "# a_df_pcs_left = a_pcs.get_left_table(df=df_parcel_left, select_list=select_list, left_round_list=left_round_list)\n", "# a_df_pcs_left.tail(7)\n", "# # ====================================get right table ======================================\n", "# a_df_pcs_right = a_pcs.get_right_table(df=df_pcs_right, drop_col='model', \n", "# round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='pcs_sum')\n", "# a_df_pcs_right.tail(2)\n", "# # ====================================merge data ===========================================\n", "# a_df_pcs_data = a_pcs.get_merge_table(\n", "# df_left=a_df_pcs_left, df_right=a_df_pcs_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "# log_ipy(f'PCS Size: {a_df_pcs_data.index.size}')\n", "# a_df_pcs_data.tail(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Parcel" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:20,723><jupyter>: Parcel Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>parcel_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>23.75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model parcel_sum\n", "0 WUX D CSS224 00:10:36 10 PEK D 0.00\n", "1 WUX D CSS224 00:10:36 20 CAN D 23.75" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_parcel_left = a_pcs.get_left_table(df=df_parcel_left, select_list=sort_parcel_selctc_left)\n", "a_df_parcel_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_parcel_right = a_pcs.get_right_table(df=df_parcel_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='parcel_sum')\n", "a_df_parcel_right.query(\"dest_model=='INF.1'\").tail(2)\n", "# ====================================merge data ===========================================\n", "a_df_parcel_data = a_pcs.get_merge_table(\n", " df_left=a_df_parcel_left, df_right=a_df_parcel_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "\n", "log_ipy(f'Parcel Size: {a_df_parcel_data.index.size}')\n", "a_df_parcel_data.query(\"dest_model=='INF.1'\").tail(2)\n", "\n", "a_df_parcel_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Small Bag" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:26,102><tools>: Small Bag Left Size: 104\n", "<2017-11-09 18:18:26,672><tools>: Small Bag Right Size: 68952\n", "<2017-11-09 18:18:26,708><tools>: Small Bag Data Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>small_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model small_sum\n", "0 WUX D CSS224 00:10:36 10 PEK D 0.0\n", "1 WUX D CSS224 00:10:36 20 CAN D 0.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_sb_left = a_pcs.get_left_table(df=df_small_left, select_list=sort_parcel_selctc_left)\n", "log(f'Small Bag Left Size: {a_df_sb_left.index.size}')\n", "# a_df_sb_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_sb_right = a_pcs.get_right_table(df=df_small_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='small_sum')\n", "log(f'Small Bag Right Size: {a_df_sb_right.index.size}')\n", "# a_df_parcel_right.head(2)\n", "# ====================================merge data ===========================================\n", "a_df_sb_data = a_pcs.get_merge_table(\n", " df_left=a_df_sb_left, df_right=a_df_sb_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "log(f'Small Bag Data Size: {a_df_sb_data.index.size}')\n", "a_df_sb_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Irregular Parcel" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:30,943><tools>: Irregular Parcel Left Size: 104\n", "<2017-11-09 18:18:31,541><tools>: Irregular Parcel Right Size: 68952\n", "<2017-11-09 18:18:31,587><tools>: Irregular Parcel Data Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>irregular_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model \\\n", "0 WUX D CSS224 00:10:36 10 PEK D \n", "1 WUX D CSS224 00:10:36 20 CAN D \n", "\n", " irregular_sum \n", "0 0.0 \n", "1 0.0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_irregular_left = a_pcs.get_left_table(df=df_irregular_left, select_list=sort_parcel_selctc_left)\n", "log(f'Irregular Parcel Left Size: {a_df_irregular_left.index.size}')\n", "# a_df_sb_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_irregula_right = a_pcs.get_right_table(df=df_irregular_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='irregular_sum')\n", "log(f'Irregular Parcel Right Size: {a_df_irregula_right.index.size}')\n", "# a_df_parcel_right.head(2)\n", "# ====================================merge data ===========================================\n", "a_df_irregular_data = a_pcs.get_merge_table(\n", " df_left=a_df_irregular_left, df_right=a_df_irregula_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "log(f'Irregular Parcel Data Size: {a_df_irregular_data.index.size}')\n", "a_df_irregular_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 NC Parcel" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:37,147><tools>: NC Parcel Left Size: 104\n", "<2017-11-09 18:18:37,617><tools>: NC Parcel Right Size: 68952\n", "<2017-11-09 18:18:37,650><tools>: NC Parcel Data Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>nc_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model nc_sum\n", "0 WUX D CSS224 00:10:36 10 PEK D 0.0\n", "1 WUX D CSS224 00:10:36 20 CAN D 0.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_nc_left = a_pcs.get_left_table(df=df_nc_left, select_list=sort_parcel_selctc_left)\n", "log(f'NC Parcel Left Size: {a_df_nc_left.index.size}')\n", "# a_df_sb_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_nc_right = a_pcs.get_right_table(df=df_nc_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='nc_sum')\n", "log(f'NC Parcel Right Size: {a_df_nc_right.index.size}')\n", "# a_df_parcel_right.head(2)\n", "# ====================================merge data ===========================================\n", "a_df_nc_data = a_pcs.get_merge_table(\n", " df_left=a_df_nc_left, df_right=a_df_nc_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "log(f'NC Parcel Data Size: {a_df_nc_data.index.size}')\n", "a_df_nc_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6 ISB (国际-不需要小件拆包直接终分拣的小件包)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:45,013><tools>: ISB Left Size: 104\n", "<2017-11-09 18:18:45,476><tools>: ISB Right Size: 68952\n", "<2017-11-09 18:18:45,514><tools>: ISB Data Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model isb_sum\n", "0 WUX D CSS224 00:10:36 10 PEK D 0.0\n", "1 WUX D CSS224 00:10:36 20 CAN D 0.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_isb_left = a_pcs.get_left_table(df=df_ims_left, select_list=sort_parcel_selctc_left)\n", "log(f'ISB Left Size: {a_df_isb_left.index.size}')\n", "# a_df_sb_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_isb_right = a_pcs.get_right_table(df=df_ims_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='isb_sum')\n", "log(f'ISB Right Size: {a_df_isb_right.index.size}')\n", "# a_df_parcel_right.head(2)\n", "# ====================================merge data ===========================================\n", "a_df_isb_data = a_pcs.get_merge_table(\n", " df_left=a_df_isb_left, df_right=a_df_isb_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "log(f'ISB Data Size: {a_df_isb_data.index.size}')\n", "a_df_isb_data.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.8 Sorted Parcel Data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:50,366><tools>: Sorted Left Size: 104\n", "<2017-11-09 18:18:50,997><tools>: Sorted Right Size: 68952\n", "<2017-11-09 18:18:51,062><tools>: Sorted Data Size: 68952\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>sorted_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>68950</th>\n", " <td>CGN</td>\n", " <td>INF</td>\n", " <td>INF142</td>\n", " <td>213.7</td>\n", " <td>8</td>\n", " <td>23:25:00</td>\n", " <td>728</td>\n", " <td>728</td>\n", " <td>R</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>68951</th>\n", " <td>CGN</td>\n", " <td>INF</td>\n", " <td>INF142</td>\n", " <td>213.7</td>\n", " <td>8</td>\n", " <td>23:25:00</td>\n", " <td>792</td>\n", " <td>792</td>\n", " <td>R</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "68950 CGN INF INF142 213.7 8 23:25:00 \n", "68951 CGN INF INF142 213.7 8 23:25:00 \n", "\n", " dest_city dest_apt dest_model sorted_sum \n", "68950 728 728 R 0.0 \n", "68951 792 792 R 0.0 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ====================================get left table =======================================\n", "a_df_sorted_left = a_pcs.get_left_table(df=df_sorted_left, \n", " select_list=sort_parcel_sum_selctc_left, \n", " left_round_list=sort_parcel_sum_left_round_list)\n", "log(f'Sorted Left Size: {a_df_sorted_left.index.size}')\n", "# a_df_sorted_left.tail(7)\n", "# ====================================get right table ======================================\n", "a_df_sorted_right = a_pcs.get_right_table(df=df_sorted_right, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name='sorted_sum')\n", "log(f'Sorted Right Size: {a_df_sorted_right.index.size}')\n", "# a_df_sorted_right.head(2)\n", "# ====================================merge data ===========================================\n", "a_df_sorte_data = a_pcs.get_merge_table(\n", " df_left=a_df_sorted_left, df_right=a_df_sorted_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", "a_df_sorte_data = a_df_sorte_data.rename_axis(\n", " {\"by_pcs_of_ULD\": \"sorted_by_pcs_of_ULD\", \"MHS_ULD\": \"sorted_MHS_ULD\"}, axis=\"columns\")\n", "log(f'Sorted Data Size: {a_df_sorte_data.index.size}')\n", "a_df_sorte_data.tail(2)\n", "# a_df_sorte_data.loc[a_df_sorte_data.Flight_ID.isin(['CSS68']), ['sorted_sum']].agg(['sum'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Merge Data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:18:57,855><tools>: Air Data Size: 68952\n", "<2017-11-09 18:18:57,856><tools>: No Zero Air Data Size: 18349\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>sorted_sum</th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>42080</th>\n", " <td>HFE</td>\n", " <td>D</td>\n", " <td>CSS34</td>\n", " <td>84.68</td>\n", " <td>13</td>\n", " <td>02:00:21</td>\n", " <td>LAX</td>\n", " <td>LAX</td>\n", " <td>I</td>\n", " <td>81.11</td>\n", " <td>73.35</td>\n", " <td>1.47</td>\n", " <td>2.04</td>\n", " <td>0.05</td>\n", " <td>4.19</td>\n", " </tr>\n", " <tr>\n", " <th>20557</th>\n", " <td>PVG</td>\n", " <td>D</td>\n", " <td>CSS81</td>\n", " <td>131.92</td>\n", " <td>32</td>\n", " <td>01:13:32</td>\n", " <td>23</td>\n", " <td>CKG</td>\n", " <td>D</td>\n", " <td>29.49</td>\n", " <td>29.49</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>47059</th>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>CSS68</td>\n", " <td>116.30</td>\n", " <td>26</td>\n", " <td>02:10:36</td>\n", " <td>27</td>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>99.53</td>\n", " <td>76.30</td>\n", " <td>12.98</td>\n", " <td>10.07</td>\n", " <td>0.17</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>20018</th>\n", " <td>SZX</td>\n", " <td>D</td>\n", " <td>CSS102</td>\n", " <td>139.29</td>\n", " <td>32</td>\n", " <td>01:12:04</td>\n", " <td>562</td>\n", " <td>HFE</td>\n", " <td>D</td>\n", " <td>0.56</td>\n", " <td>0.56</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>36773</th>\n", " <td>SWA</td>\n", " <td>D</td>\n", " <td>CSS94</td>\n", " <td>72.87</td>\n", " <td>14</td>\n", " <td>01:48:39</td>\n", " <td>DXB</td>\n", " <td>DXB</td>\n", " <td>I</td>\n", " <td>44.49</td>\n", " <td>40.23</td>\n", " <td>0.80</td>\n", " <td>1.12</td>\n", " <td>0.03</td>\n", " <td>2.30</td>\n", " </tr>\n", " <tr>\n", " <th>32630</th>\n", " <td>CKG</td>\n", " <td>D</td>\n", " <td>CSS15</td>\n", " <td>68.01</td>\n", " <td>24</td>\n", " <td>01:39:52</td>\n", " <td>591</td>\n", " <td>FOC</td>\n", " <td>D</td>\n", " <td>15.29</td>\n", " <td>15.29</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>12894</th>\n", " <td>PVG</td>\n", " <td>D</td>\n", " <td>CSS77</td>\n", " <td>131.92</td>\n", " <td>32</td>\n", " <td>00:53:02</td>\n", " <td>990</td>\n", " <td>URC</td>\n", " <td>D</td>\n", " <td>0.04</td>\n", " <td>0.04</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "42080 HFE D CSS34 84.68 13 02:00:21 \n", "20557 PVG D CSS81 131.92 32 01:13:32 \n", "47059 PEK D CSS68 116.30 26 02:10:36 \n", "20018 SZX D CSS102 139.29 32 01:12:04 \n", "36773 SWA D CSS94 72.87 14 01:48:39 \n", "32630 CKG D CSS15 68.01 24 01:39:52 \n", "12894 PVG D CSS77 131.92 32 00:53:02 \n", "\n", " dest_city dest_apt dest_model sorted_sum parcel_sum small_sum \\\n", "42080 LAX LAX I 81.11 73.35 1.47 \n", "20557 23 CKG D 29.49 29.49 0.00 \n", "47059 27 27 R 99.53 76.30 12.98 \n", "20018 562 HFE D 0.56 0.56 0.00 \n", "36773 DXB DXB I 44.49 40.23 0.80 \n", "32630 591 FOC D 15.29 15.29 0.00 \n", "12894 990 URC D 0.04 0.04 0.00 \n", "\n", " irregular_sum nc_sum isb_sum \n", "42080 2.04 0.05 4.19 \n", "20557 0.00 0.00 0.00 \n", "47059 10.07 0.17 0.00 \n", "20018 0.00 0.00 0.00 \n", "36773 1.12 0.03 2.30 \n", "32630 0.00 0.00 0.00 \n", "12894 0.00 0.00 0.00 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# parcel data #--------------------------------------------------------------------------------\n", "# df_right_sample = a_df_parcel_data.head(test_num)\n", "# merge data #---------------------------------------------------------------------------------\n", "a_df_data = merg_data(a_df_sorte_data, \n", " [a_df_parcel_data, a_df_sb_data, a_df_irregular_data, a_df_nc_data, a_df_isb_data\n", " ], \n", " 'left', merg_data_on)\n", "# excel out #----------------------------------------------------------------------------------\n", "a_df_data_no_zero = a_df_data.query('sorted_sum>0')\n", "# df_to_excel(file_name='a_pcs', sheet='a_pcs', df=a_df_data_no_zero)\n", "log(f'Air Data Size: {a_df_data.index.size}')\n", "log(f'No Zero Air Data Size: {a_df_data_no_zero.index.size}')\n", "# log(a_df_data.loc[a_df_data.Flight_ID.isin(['CSS68']),['sorted_sum']].agg(['sum']))\n", "a_df_data_no_zero.sample(7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Land Side Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "land_model_list = ['R']\n", "land_sort_parcel_left = ['apt', 'model', 'Flight ID', 'landing_time']\n", "index_slip_name = ['dest_city', 'dest_apt', 'dest_model']\n", "right_round_list = [[None,2]]\n", "in_tabel_merge_on = ['index_num']\n", "merg_data_on = [\n", " 'apt', 'model', 'Flight_ID', 'landing_time','dest_city','dest_apt', 'dest_model'\n", "]\n", "l_pcs = TableT(land_model_list)\n", "\n", "# =================== sorted data ============================\n", "def merg_all_data(cls_func,df_l, df_r, value_name, log_name):\n", " # left\n", " df_left = cls_func.get_left_table(df=df_l, select_list=land_sort_parcel_left)\n", "# log(f'{log_name} Left Size: {df_left.index.size}')\n", " # right\n", " df_right = cls_func.get_right_table(df=df_r, drop_col='model', \n", " round_list=right_round_list, index_slip_name=index_slip_name, column_index_data_name=value_name)\n", "# log(f'{log_name} Right Size: {df_right.index.size}')\n", " # merge\n", " df_merge = cls_func.get_merge_table(\n", " df_left=df_left, df_right=df_right, merge_on=in_tabel_merge_on , drop_cloum=in_tabel_merge_on)\n", " log(f'{log_name} Merge Data Size: {df_merge.index.size}')\n", " return df_merge\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Data LS" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:19:13,512><tools>: sorted Merge Data Size: 66963\n", "<2017-11-09 18:19:13,911><tools>: Parcel Merge Data Size: 66963\n", "<2017-11-09 18:19:14,303><tools>: small Merge Data Size: 66963\n", "<2017-11-09 18:19:14,707><tools>: irregular Merge Data Size: 66963\n", "<2017-11-09 18:19:15,111><tools>: NC Merge Data Size: 66963\n", "<2017-11-09 18:19:15,497><tools>: IMS Merge Data Size: 66963\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>66961</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE39</td>\n", " <td>23:50:00</td>\n", " <td>728</td>\n", " <td>728</td>\n", " <td>R</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>66962</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE39</td>\n", " <td>23:50:00</td>\n", " <td>792</td>\n", " <td>792</td>\n", " <td>R</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model \\\n", "66961 27 R CSSLocalE39 23:50:00 728 728 R \n", "66962 27 R CSSLocalE39 23:50:00 792 792 R \n", "\n", " isb_sum \n", "66961 0.0 \n", "66962 0.0 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ========================================sorted data=================================\n", "l_df_sorte_data = merg_all_data(l_pcs, df_sorted_left, df_sorted_right, 'sorted_sum', 'sorted')\n", "l_df_sorte_data.tail(10),\n", "# =======================================parcel bag data===============================\n", "l_df_parcel_data = merg_all_data(l_pcs, df_parcel_left, df_parcel_right, 'parcel_sum', 'Parcel')\n", "l_df_parcel_data.tail(10)\n", "# =======================================small bag data================================\n", "l_df_small_data = merg_all_data(l_pcs, df_small_left, df_small_right, 'small_sum', 'small')\n", "l_df_small_data.tail(10)\n", "# =======================================Irregular Parcel Data ==========================\n", "l_df_irregular_data = merg_all_data(l_pcs, df_irregular_left, df_irregular_right, 'irregular_sum', 'irregular')\n", "# l_df_irregular_data.tail(10)\n", "# ================================================= NC Parcel Data ==========================\n", "l_df_nc_data = merg_all_data(l_pcs, df_nc_left, df_nc_right, 'nc_sum', 'NC')\n", "# l_df_nc_data.tail(10)\n", "# ================================================= Mail Parcel Data ==========================\n", "l_df_ims_data = merg_all_data(l_pcs, df_ims_left, df_ims_right, 'isb_sum', 'IMS')\n", "l_df_ims_data.tail(2)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:19:28,521><tools>: Land Data Size: 66963\n", "<2017-11-09 18:19:28,522><tools>: No Zero Land Data Size: 31498\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>sorted_sum</th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>18206</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>852</td>\n", " <td>HKG</td>\n", " <td>I</td>\n", " <td>2.24</td>\n", " <td>2.03</td>\n", " <td>0.04</td>\n", " <td>0.06</td>\n", " <td>0.0</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>18207</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>886</td>\n", " <td>TPE</td>\n", " <td>I</td>\n", " <td>1.02</td>\n", " <td>0.92</td>\n", " <td>0.02</td>\n", " <td>0.03</td>\n", " <td>0.0</td>\n", " <td>0.05</td>\n", " </tr>\n", " <tr>\n", " <th>18208</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>BKK</td>\n", " <td>BKK</td>\n", " <td>I</td>\n", " <td>0.73</td>\n", " <td>0.66</td>\n", " <td>0.01</td>\n", " <td>0.02</td>\n", " <td>0.0</td>\n", " <td>0.04</td>\n", " </tr>\n", " <tr>\n", " <th>18209</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>DXB</td>\n", " <td>DXB</td>\n", " <td>I</td>\n", " <td>0.37</td>\n", " <td>0.34</td>\n", " <td>0.01</td>\n", " <td>0.01</td>\n", " <td>0.0</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>18210</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>ICN</td>\n", " <td>ICN</td>\n", " <td>I</td>\n", " <td>1.23</td>\n", " <td>1.11</td>\n", " <td>0.02</td>\n", " <td>0.03</td>\n", " <td>0.0</td>\n", " <td>0.06</td>\n", " </tr>\n", " <tr>\n", " <th>18211</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>KUL</td>\n", " <td>KUL</td>\n", " <td>I</td>\n", " <td>0.40</td>\n", " <td>0.36</td>\n", " <td>0.01</td>\n", " <td>0.01</td>\n", " <td>0.0</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>18212</th>\n", " <td>27</td>\n", " <td>R</td>\n", " <td>CSSLocalE24</td>\n", " <td>00:20:00</td>\n", " <td>LAX</td>\n", " <td>LAX</td>\n", " <td>I</td>\n", " <td>1.59</td>\n", " <td>1.44</td>\n", " <td>0.03</td>\n", " <td>0.04</td>\n", " <td>0.0</td>\n", " <td>0.08</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model \\\n", "18206 27 R CSSLocalE24 00:20:00 852 HKG I \n", "18207 27 R CSSLocalE24 00:20:00 886 TPE I \n", "18208 27 R CSSLocalE24 00:20:00 BKK BKK I \n", "18209 27 R CSSLocalE24 00:20:00 DXB DXB I \n", "18210 27 R CSSLocalE24 00:20:00 ICN ICN I \n", "18211 27 R CSSLocalE24 00:20:00 KUL KUL I \n", "18212 27 R CSSLocalE24 00:20:00 LAX LAX I \n", "\n", " sorted_sum parcel_sum small_sum irregular_sum nc_sum isb_sum \n", "18206 2.24 2.03 0.04 0.06 0.0 0.12 \n", "18207 1.02 0.92 0.02 0.03 0.0 0.05 \n", "18208 0.73 0.66 0.01 0.02 0.0 0.04 \n", "18209 0.37 0.34 0.01 0.01 0.0 0.02 \n", "18210 1.23 1.11 0.02 0.03 0.0 0.06 \n", "18211 0.40 0.36 0.01 0.01 0.0 0.02 \n", "18212 1.59 1.44 0.03 0.04 0.0 0.08 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ================================================= merge all data =========================\n", "l_df_data = merg_data(l_df_sorte_data, \n", " [l_df_parcel_data, \n", " l_df_small_data, l_df_irregular_data, l_df_nc_data, l_df_ims_data\n", " ], \n", " 'left', merg_data_on)\n", "# excel out #----------------------------------------------------------------------------------\n", "l_df_data_no_zero = l_df_data.query('sorted_sum>0')\n", "# df_to_excel(file_name='l_pcs', sheet='l_pcs', df=l_df_data_no_zero)\n", "log(f'Land Data Size: {l_df_data.index.size}')\n", "log(f'No Zero Land Data Size: {l_df_data_no_zero.index.size}')\n", "# log(a_df_data.loc[a_df_data.Flight_ID.isin(['CSS68']),['sorted_sum']].agg(['sum']))\n", "l_df_data_no_zero.query(\"isb_sum>0\").head(7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 Data Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Air" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "parcel_data = ['parcel_sum', 'small_sum', 'irregular_sum', 'nc_sum', 'isb_sum']\n", "head_data = [\n", " 'apt', 'model', 'Flight_ID', 'landing_time','dest_city','dest_apt', 'dest_model',\n", " 'parcel_sum', 'small_sum', 'irregular_sum', 'nc_sum', 'isb_sum'\n", "]\n", "all_data = ['apt', 'model', 'Flight_ID', 'sorted_by_pcs_of_ULD', 'sorted_MHS_ULD',\n", " 'landing_time', 'dest_city', 'dest_apt', 'dest_model', \n", " 'sorted_sum', 'parcel_sum', 'small_sum', 'irregular_sum', 'nc_sum', 'isb_sum']\n", "\n", "# 精度控制，值小于1的精度取法\n", "def right_back(dec, right_p):\n", " if dec>right_p:\n", " dec = 1\n", " else:\n", " dec = 0\n", " return dec\n", "\n", "# 精度控制，值大于1的精度取法\n", "def left_back(dec, left_p):\n", " int_dec = int(dec)\n", " point_dec = round((dec - int_dec), 2)\n", " \n", " if point_dec>=left_p:\n", " re_dec = int_dec+1\n", " else:\n", " re_dec = int_dec\n", " return re_dec\n", "\n", "\n", "def creat_od_table():\n", " db_eng = MetaData(bind=MySQLConfig.engine)\n", " machine_table_sche = \\\n", " Table(\n", " \"i_od_parcel\",\n", " db_eng,\n", " Column(\"small_id\", VARCHAR(length=10, )),\n", " Column(\"parcel_id\", VARCHAR(length=20, )),\n", " Column(\"src_type\", VARCHAR(length=5, )),\n", " Column(\"plate_num\", VARCHAR(length=20, )),\n", " Column(\"uld_num\", VARCHAR(length=20, )),\n", " Column(\"parcel_type\", VARCHAR(length=10, )),\n", " Column(\"arrive_time\", DateTime()),\n", " Column(\"dest_code\", VARCHAR(length=10, )),\n", " Column(\"dest_apt\", VARCHAR(length=10, )),\n", " Column(\"dest_type\", VARCHAR(length=5, )),\n", " )\n", " machine_table_sche.create(checkfirst=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1.1 原始数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AirSide" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "file_name = \"a_pcs\"\n", "file_dir = os.path.join(DEFAULT_DATA_PATH, file_name+'.xlsx')\n", "a_pcs_data2 = pd.read_excel(io=file_dir, sheetname='a_pcs')\n", "a_pcs_data3 = pd.read_excel(io=file_dir, sheetname='a_pcs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1.2 精度控制前，未拆小件包数据" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " <th>sorted_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>313017.03</td>\n", " <td>6460.95</td>\n", " <td>8378.24</td>\n", " <td>196.84</td>\n", " <td>2456.06</td>\n", " <td>330509.12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " parcel_sum small_sum irregular_sum nc_sum isb_sum sorted_sum\n", "0 313017.03 6460.95 8378.24 196.84 2456.06 330509.12" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f0 = a_pcs_data2.loc[:, parcel_data]\n", "f0['sorted_sum'] = f0.loc[:,parcel_data].sum(axis=1)\n", "f0_t = f0.sum().to_frame().T\n", "f0_t\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " <th>sorted_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>105650.1</td>\n", " <td>17193.88</td>\n", " <td>13412.71</td>\n", " <td>212.51</td>\n", " <td>24.64</td>\n", " <td>136493.84</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " parcel_sum small_sum irregular_sum nc_sum isb_sum sorted_sum\n", "0 105650.1 17193.88 13412.71 212.51 24.64 136493.84" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_f0 = l_df_data_no_zero.loc[:, parcel_data]\n", "l_f0['sorted_sum'] = l_f0.loc[:,parcel_data].sum(axis=1)\n", "l_f0_t = l_f0.sum().to_frame().T\n", "l_f0_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1.3 精度控制前，拆小件包数据" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f1 = a_pcs_data2.loc[:, parcel_data]\n", "# small sum * 20\n", "f1.loc[:, ['small_sum', 'isb_sum']] = f1.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "f1['sorted_sum'] = f1.loc[:,parcel_data].sum(axis=1)\n", "f1_t = f1.sum().to_frame().T" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " <th>sorted_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>befor</th>\n", " <td>313017.03</td>\n", " <td>129219.0</td>\n", " <td>8378.24</td>\n", " <td>196.84</td>\n", " <td>49121.2</td>\n", " <td>499932.31</td>\n", " </tr>\n", " <tr>\n", " <th>after_round</th>\n", " <td>313059.00</td>\n", " <td>129920.0</td>\n", " <td>8338.00</td>\n", " <td>205.00</td>\n", " <td>49136.0</td>\n", " <td>500658.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " parcel_sum small_sum irregular_sum nc_sum isb_sum sorted_sum\n", "befor 313017.03 129219.0 8378.24 196.84 49121.2 499932.31\n", "after_round 313059.00 129920.0 8338.00 205.00 49136.0 500658.00" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# =============================== 不同列的精度取值\n", "parcel_round = {'parcel_sum': {'L':0.97, 'R': 0.01}, 'small_sum': {'L': 0.6, 'R': 0.01}, \n", " 'irregular_sum': {'L': 0.98, 'R': 0.2}, 'nc_sum': {'L': 0.98, 'R': 0.2}, \n", " 'isb_sum': {'L': 0.5, 'R': 0.01}}\n", "# =============================== 获取需要进行精度控制数据并拆小件包\n", "f3 = a_pcs_data2.loc[:, parcel_data]\n", "f3.loc[:, ['small_sum', 'isb_sum']] = f3.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "# ============================== 进行精度计算 \n", "for col in parcel_data:\n", " f3.loc[:, col] = f3.loc[:, col].apply(\n", " lambda x : left_back(x, parcel_round[col]['L']) if x >=1 else right_back(x, parcel_round[col]['R']))\n", "\n", "f3['sorted_sum'] = f3.loc[:,parcel_data].sum(axis=1)\n", "# f3.tail(10)\n", "# ========================= data analysis==================\n", "f3_t = f3.sum().to_frame().T;f3_t\n", "# ============================== 合并取精度前后的数据进行对比 =======================\n", "ft_parcel = pd.concat([f1_t, f3_t])\n", "ft_parcel = ft_parcel.reset_index(drop=True).rename({0:'befor', 1:'after_round'})\n", "ft_parcel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " <th>sorted_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>befor</th>\n", " <td>105650.1</td>\n", " <td>343877.6</td>\n", " <td>13412.71</td>\n", " <td>212.51</td>\n", " <td>492.8</td>\n", " <td>463645.72</td>\n", " </tr>\n", " <tr>\n", " <th>after_round</th>\n", " <td>105878.0</td>\n", " <td>344777.0</td>\n", " <td>13414.00</td>\n", " <td>216.00</td>\n", " <td>483.0</td>\n", " <td>464768.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " parcel_sum small_sum irregular_sum nc_sum isb_sum sorted_sum\n", "befor 105650.1 343877.6 13412.71 212.51 492.8 463645.72\n", "after_round 105878.0 344777.0 13414.00 216.00 483.0 464768.00" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_f1 = l_df_data_no_zero.loc[:, parcel_data]\n", "# small sum * 20\n", "l_f1.loc[:, ['small_sum', 'isb_sum']] = l_f1.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "l_f1['sorted_sum'] = l_f1.loc[:,parcel_data].sum(axis=1)\n", "l_f1_t = l_f1.sum().to_frame().T\n", "\n", "# =============================== 不同列的精度取值\n", "l_parcel_round = {'parcel_sum': {'L':0.99, 'R': 0.05}, 'small_sum': {'L': 0.55, 'R': 0.3}, \n", " 'irregular_sum': {'L': 0.99, 'R': 0.27}, 'nc_sum': {'L': 0.6, 'R': 0.08}, \n", " 'isb_sum': {'L': 0.5, 'R': 0.4}}\n", "# =============================== 获取需要进行精度控制数据并拆小件包\n", "l_f3 = l_df_data_no_zero.loc[:, parcel_data]\n", "l_f3.loc[:, ['small_sum', 'isb_sum']] = l_f3.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "# ============================== 进行精度计算 \n", "for col in parcel_data:\n", " l_f3.loc[:, col] = l_f3.loc[:, col].apply(\n", " lambda x : left_back(x, l_parcel_round[col]['L']) if x >=1 else right_back(x, l_parcel_round[col]['R']))\n", "\n", "l_f3['sorted_sum'] = l_f3.loc[:,parcel_data].sum(axis=1)\n", "l_f3.tail(10)\n", "# ========================= data analysis==================\n", "l_f3_t = l_f3.sum().to_frame().T;l_f3_t\n", "# ============================== 合并取精度前后的数据进行对比 =======================\n", "l_ft_parcel = pd.concat([l_f1_t, l_f3_t])\n", "l_ft_parcel = l_ft_parcel.reset_index(drop=True).rename({0:'befor', 1:'after_round'})\n", "l_ft_parcel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1.4 应用配置精度到整体数据" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>sorted_sum</th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>23</td>\n", " <td>23</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>WUX</td>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>22</td>\n", " <td>TSN</td>\n", " <td>D</td>\n", " <td>58</td>\n", " <td>58</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "0 WUX D CSS224 94.98 28 00:10:36 \n", "1 WUX D CSS224 94.98 28 00:10:36 \n", "\n", " dest_city dest_apt dest_model sorted_sum parcel_sum small_sum \\\n", "0 20 CAN D 23 23 0 \n", "1 22 TSN D 58 58 0 \n", "\n", " irregular_sum nc_sum isb_sum \n", "0 0 0 0 \n", "1 0 0 0 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opt_df = a_pcs_data3.copy()\n", "# small sum * 20\n", "opt_df.loc[:, ['small_sum', 'isb_sum']] = opt_df.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "# # ===========================round for each columns==========\n", "for col in parcel_data:\n", " opt_df.loc[:, col] = opt_df.loc[:, col].apply(\n", " lambda x : left_back(x, parcel_round[col]['L']) if x >=1 else right_back(x, parcel_round[col]['R']))\n", "\n", "opt_df['sorted_sum'] = opt_df.loc[:,parcel_data].sum(axis=1)\n", "opt_df.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>apt</th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>sorted_sum</th>\n", " <th>parcel_sum</th>\n", " <th>small_sum</th>\n", " <th>irregular_sum</th>\n", " <th>nc_sum</th>\n", " <th>isb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7112R</td>\n", " <td>R</td>\n", " <td>CSSIPE05</td>\n", " <td>00:00:00</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " <td>987</td>\n", " <td>217</td>\n", " <td>741</td>\n", " <td>28</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>7112R</td>\n", " <td>R</td>\n", " <td>CSSIPE05</td>\n", " <td>00:00:00</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>374</td>\n", " <td>82</td>\n", " <td>281</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " apt model Flight_ID landing_time dest_city dest_apt dest_model \\\n", "0 7112R R CSSIPE05 00:00:00 10 PEK D \n", "1 7112R R CSSIPE05 00:00:00 20 CAN D \n", "\n", " sorted_sum parcel_sum small_sum irregular_sum nc_sum isb_sum \n", "0 987 217 741 28 1 0 \n", "1 374 82 281 10 1 0 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_opt_df = l_df_data_no_zero.copy()\n", "# small sum 20\n", "l_opt_df.loc[:, ['small_sum', 'isb_sum']] = l_opt_df.loc[:, ['small_sum', 'isb_sum']].applymap(lambda x : x20)\n", "# # ===========================round for each columns==========\n", "for col in parcel_data:\n", " l_opt_df.loc[:, col] = l_opt_df.loc[:, col].apply(\n", " lambda x : left_back(x, l_parcel_round[col]['L']) if x >=1 else right_back(x, l_parcel_round[col]['R']))\n", "\n", "l_opt_df['sorted_sum'] = l_opt_df.loc[:,parcel_data].sum(axis=1)\n", "l_opt_df.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 生成包裹详情表" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>parcel_type</th>\n", " <th>num</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "0 D CSS224 94.98 28 00:10:36 \n", "1 D CSS224 94.98 28 00:10:36 \n", "\n", " dest_city dest_apt dest_model parcel_type num \n", "0 20 CAN D parcel 23 \n", "1 20 CAN D parcel 23 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base_col = ['model', 'Flight_ID', 'sorted_by_pcs_of_ULD', 'sorted_MHS_ULD', 'landing_time', 'dest_city', 'dest_apt', 'dest_model']\n", "melt_col = [\n", " #'sorted_sum', \n", " 'parcel_sum', 'small_sum', 'irregular_sum', 'nc_sum', 'isb_sum'\n", " ]\n", "opt_melt_df = df_melt_data_format(df=opt_df, base_col=base_col, melt_col=melt_col, melt_name='parcel_type', value_name='num')\n", "opt_melt_df.parcel_type = opt_melt_df.parcel_type.str.replace('_sum', '')\n", "opt_melt_df = opt_melt_df.query('num>0')\n", "# T data\n", "tt = opt_melt_df.T\n", "# 按照件量扩展整体数据\n", "def map_col(series, key):\n", " yield from [series]series[key]\n", "# 生成重复性的列，根据parcel数量\n", "temp = []\n", "for iter_col in tt.items():\n", " for col in map_col(iter_col[1], 'num'):\n", " temp.append(col)\n", " \n", "opt_pca_data_a = pd.DataFrame(data=temp)\n", "opt_pca_data_a = opt_pca_data_a.reset_index(drop=True)\n", "opt_pca_data_a.head(2)\n", "# 添加objectid作为parcel id\n", "# opt_pca_data_a['id_parcel'] = pd.Series(data=[str(bson.objectid.ObjectId()) for _ in range(opt_pca_data_a.index.size)])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "500658" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opt_pca_data_a.index.size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "base_col = ['model', 'Flight_ID', 'landing_time', 'dest_city', 'dest_apt', 'dest_model']\n", "melt_col = [\n", " #'sorted_sum', \n", " 'parcel_sum', 'small_sum', 'irregular_sum', 'nc_sum', 'isb_sum'\n", " ]\n", "l_opt_melt_df = df_melt_data_format(df=l_opt_df, base_col=base_col, melt_col=melt_col, melt_name='parcel_type', value_name='num')\n", "l_opt_melt_df.parcel_type = l_opt_melt_df.parcel_type.str.replace('_sum', '')\n", "l_opt_melt_df = l_opt_melt_df.query('num>0')\n", "# T data\n", "l_tt = l_opt_melt_df.T\n", "# 按照件量扩展整体数据\n", "def map_col(series, key):\n", " yield from [series]*series[key]\n", "# 生成重复性的列，根据parcel数量\n", "l_temp = []\n", "for iter_col in l_tt.items():\n", " for col in map_col(iter_col[1], 'num'):\n", " l_temp.append(col)\n", " \n", "l_opt_pca_data = pd.DataFrame(data=l_temp)\n", "l_opt_pca_data = l_opt_pca_data.reset_index(drop=True)\n", "# 添加objectid作为parcel id\n", "# opt_pca_data_a['id_parcel'] = pd.Series(data=[str(bson.objectid.ObjectId()) for _ in range(opt_pca_data_a.index.size)])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>parcel_type</th>\n", " <th>num</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>297798</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>763</td>\n", " <td>734W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>92</td>\n", " </tr>\n", " <tr>\n", " <th>295302</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>552</td>\n", " <td>552W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>43</td>\n", " </tr>\n", " <tr>\n", " <th>294997</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>535</td>\n", " <td>745W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>292695</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>315</td>\n", " <td>553W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>83</td>\n", " </tr>\n", " <tr>\n", " <th>294973</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>535</td>\n", " <td>745W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>298772</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>826</td>\n", " <td>745W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>293997</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>467</td>\n", " <td>451WA</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>295429</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>555</td>\n", " <td>025WA</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>299455</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>931</td>\n", " <td>710W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>91</td>\n", " </tr>\n", " <tr>\n", " <th>297390</th>\n", " <td>R</td>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>745</td>\n", " <td>734W</td>\n", " <td>R</td>\n", " <td>small</td>\n", " <td>45</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model Flight_ID landing_time dest_city dest_apt dest_model \\\n", "297798 R CSSLocalE18 00:20:00 763 734W R \n", "295302 R CSSLocalE18 00:20:00 552 552W R \n", "294997 R CSSLocalE18 00:20:00 535 745W R \n", "292695 R CSSLocalE18 00:20:00 315 553W R \n", "294973 R CSSLocalE18 00:20:00 535 745W R \n", "298772 R CSSLocalE18 00:20:00 826 745W R \n", "293997 R CSSLocalE18 00:20:00 467 451WA R \n", "295429 R CSSLocalE18 00:20:00 555 025WA R \n", "299455 R CSSLocalE18 00:20:00 931 710W R \n", "297390 R CSSLocalE18 00:20:00 745 734W R \n", "\n", " parcel_type num \n", "297798 small 92 \n", "295302 small 43 \n", "294997 small 85 \n", "292695 small 83 \n", "294973 small 85 \n", "298772 small 12 \n", "293997 small 11 \n", "295429 small 41 \n", "299455 small 91 \n", "297390 small 45 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_opt_pca_data.query(\"Flight_ID=='CSSLocalE18' & parcel_type=='small'\").sample(10) #.num.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2.1 随机分配包裹随机选择ULD" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>parcel_type</th>\n", " <th>num</th>\n", " <th>id_uld</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>94.98</td>\n", " <td>28</td>\n", " <td>00:10:36</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " <td>parcel</td>\n", " <td>23</td>\n", " <td>18</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "0 D CSS224 94.98 28 00:10:36 \n", "1 D CSS224 94.98 28 00:10:36 \n", "2 D CSS224 94.98 28 00:10:36 \n", "3 D CSS224 94.98 28 00:10:36 \n", "4 D CSS224 94.98 28 00:10:36 \n", "5 D CSS224 94.98 28 00:10:36 \n", "6 D CSS224 94.98 28 00:10:36 \n", "\n", " dest_city dest_apt dest_model parcel_type num id_uld \n", "0 20 CAN D parcel 23 28 \n", "1 20 CAN D parcel 23 26 \n", "2 20 CAN D parcel 23 17 \n", "3 20 CAN D parcel 23 25 \n", "4 20 CAN D parcel 23 12 \n", "5 20 CAN D parcel 23 13 \n", "6 20 CAN D parcel 23 18 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# INF174 uld数量为零，实际配置为2\n", "opt_pca_data_a.loc[opt_pca_data_a.Flight_ID=='INF174', ['sorted_MHS_ULD']] = 2\n", "opt_pca_data_a['id_uld'] = opt_pca_data_a.sorted_MHS_ULD.map(lambda x: np.random.randint(1, int(x)+1))\n", "# opt_pca_data_a.query(\"Flight_ID=='CSS1' & parcel_type=='small'\").groupby(['id_uld']).Flight_ID.count()\n", "opt_pca_data_a.head(7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 ARC 包裹仿真基础数据" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arc_df = pd.read_pickle(path='data\\\\arc.xz', compression='xz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3.1 ARC 不同航班不同ULD到达槽口时间" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FltIn</th>\n", " <th>ULDIn</th>\n", " <th>AMIT</th>\n", " <th>PAR_NUM</th>\n", " <th>ULD_CON</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CSS1</td>\n", " <td>ULD40230158</td>\n", " <td>2025-02-08 00:38:10</td>\n", " <td>172</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CSS1</td>\n", " <td>ULD40230159</td>\n", " <td>2025-02-08 00:37:56</td>\n", " <td>170</td>\n", " <td>14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " FltIn ULDIn AMIT PAR_NUM ULD_CON\n", "0 CSS1 ULD40230158 2025-02-08 00:38:10 172 14\n", "1 CSS1 ULD40230159 2025-02-08 00:37:56 170 14" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arc_col_select = ['ID', 'Origin', 'Destination', 'TypeIn', 'TypeOut', 'FltIn', 'FltOut',\n", " 'ULDIn', 'ULDOut', 'AMIT']\n", "# ================================================取出空侧数据，统计每个航班ULD数量\n", "selec_arc_df = arc_df.query(\"TypeIn == 'A'\").loc[:, arc_col_select]\n", "flyid_uld_df = selec_arc_df.groupby(by=['TypeIn', 'FltIn']).ULDIn.apply(set).apply(len).to_frame()\n", "flyid_uld_df = flyid_uld_df.reset_index()\n", "flyid_uld_df = flyid_uld_df.rename_axis({'ULDIn': 'ULD_CON'}, axis=1)\n", "flyid_uld_df\n", "# ================================================ 统计每个航班每个ULD的parcel 的件数\n", "uld_amit_par_df = selec_arc_df.groupby(by=['TypeIn', 'FltIn', 'ULDIn', 'AMIT']).ID.apply(set).apply(len).to_frame()\n", "uld_amit_par_df = uld_amit_par_df.reset_index()\n", "uld_amit_par_df = uld_amit_par_df.rename_axis({'ID': 'PAR_NUM'}, axis=1)\n", "# =============================================== 合并表格\n", "uld_amit_par_df = pd.merge(left=uld_amit_par_df, right=flyid_uld_df, on=['TypeIn', 'FltIn'], how='left')\n", "# 规整数据\n", "uld_amit_par_df = uld_amit_par_df.loc[:,['FltIn', 'ULDIn', 'AMIT', 'PAR_NUM', 'ULD_CON']]\n", "uld_amit_par_df.head(2)\n", "# df_to_excel(df=uld_amit_par_df, file_name='uld_con', sheet='uld_con')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land - 到达分拣场地时间" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>ID</th>\n", " </tr>\n", " <tr>\n", " <th>FltIn</th>\n", " <th>ALDT</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>CSSLongEInf11</th>\n", " <th>2025-02-08 00:05:10</th>\n", " <td>2565</td>\n", " </tr>\n", " <tr>\n", " <th>CSSLongEInf12</th>\n", " <th>2025-02-07 23:35:10</th>\n", " <td>170</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID\n", "FltIn ALDT \n", "CSSLongEInf11 2025-02-08 00:05:10 2565\n", "CSSLongEInf12 2025-02-07 23:35:10 170" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arc_col_select = ['ID', 'FltIn', 'ALDT']\n", "# ================================================取出空侧数据，统计每个航班ULD数量\n", "l_selec_arc_df = arc_df.query(\"TypeIn == 'L'\").loc[:, arc_col_select]\n", "l_arc_landtime_df = l_selec_arc_df.groupby(by=[ 'FltIn', 'ALDT']).ID.apply(set).apply(len).to_frame()\n", "l_arc_landtime_df.tail(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3.2 生成uld 小编号" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Flight_ID</th>\n", " <th>ULD_ID</th>\n", " <th>arrive_time</th>\n", " <th>PAR_NUM</th>\n", " <th>ULD_CON</th>\n", " <th>id_uld</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CSS1</td>\n", " <td>ULD40230158</td>\n", " <td>2025-02-08 00:38:10</td>\n", " <td>172</td>\n", " <td>14</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CSS1</td>\n", " <td>ULD40230159</td>\n", " <td>2025-02-08 00:37:56</td>\n", " <td>170</td>\n", " <td>14</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Flight_ID ULD_ID arrive_time PAR_NUM ULD_CON id_uld\n", "0 CSS1 ULD40230158 2025-02-08 00:38:10 172 14 1\n", "1 CSS1 ULD40230159 2025-02-08 00:37:56 170 14 2" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uld_id_df = flyid_uld_df.T\n", "def map_col(uld_id_df):\n", " for col in uld_id_df.iteritems():\n", " for i in range(1, col[1]['ULD_CON']+1):\n", " col[1]['id_uld'] = i\n", " yield col[1].copy()\n", " \n", "col_df = pd.DataFrame(data=list(map_col(uld_id_df))).reset_index(drop=True)\n", "uld_amit_par_df['id_uld'] = col_df.id_uld\n", "uld_match_table = uld_amit_par_df.rename_axis(mapper={'FltIn': 'Flight_ID', 'ULDIn': 'ULD_ID', 'AMIT':'arrive_time'}, axis=1)\n", "uld_match_table.head(2)\n", "# df_to_excel(df=uld_amit_par_df, file_name='uld_amit_par_df', sheet='uld_amit_par_df')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>Flight_ID</th>\n", " <th>sorted_by_pcs_of_ULD</th>\n", " <th>sorted_MHS_ULD</th>\n", " <th>landing_time</th>\n", " <th>dest_city</th>\n", " <th>dest_apt</th>\n", " <th>dest_model</th>\n", " <th>parcel_type</th>\n", " <th>num</th>\n", " <th>id_uld</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>470469</th>\n", " <td>D</td>\n", " <td>CSS100</td>\n", " <td>139.29</td>\n", " <td>32</td>\n", " <td>00:58:54</td>\n", " <td>BKK</td>\n", " <td>BKK</td>\n", " <td>I</td>\n", " <td>isb</td>\n", " <td>48</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>470470</th>\n", " <td>D</td>\n", " <td>CSS100</td>\n", " <td>139.29</td>\n", " <td>32</td>\n", " <td>00:58:54</td>\n", " <td>BKK</td>\n", " <td>BKK</td>\n", " <td>I</td>\n", " <td>isb</td>\n", " <td>48</td>\n", " <td>16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model Flight_ID sorted_by_pcs_of_ULD sorted_MHS_ULD landing_time \\\n", "470469 D CSS100 139.29 32 00:58:54 \n", "470470 D CSS100 139.29 32 00:58:54 \n", "\n", " dest_city dest_apt dest_model parcel_type num id_uld \n", "470469 BKK BKK I isb 48 24 \n", "470470 BKK BKK I isb 48 16 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "opt_pca_data_a.loc[opt_pca_data_a.Flight_ID.isin(['CSS100']) & opt_pca_data_a.parcel_type.isin(['isb']),:].head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3.3 匹配uld编码和时间" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>src_type</th>\n", " <th>plate_num</th>\n", " <th>uld_num</th>\n", " <th>arrive_time</th>\n", " <th>parcel_type</th>\n", " <th>dest_code</th>\n", " <th>dest_apt</th>\n", " <th>dest_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>ULD40245213</td>\n", " <td>2025-02-08 00:32:42</td>\n", " <td>parcel</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>D</td>\n", " <td>CSS224</td>\n", " <td>ULD40245211</td>\n", " <td>2025-02-08 00:34:16</td>\n", " <td>parcel</td>\n", " <td>20</td>\n", " <td>CAN</td>\n", " <td>D</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " src_type plate_num uld_num arrive_time parcel_type dest_code \\\n", "0 D CSS224 ULD40245213 2025-02-08 00:32:42 parcel 20 \n", "1 D CSS224 ULD40245211 2025-02-08 00:34:16 parcel 20 \n", "\n", " dest_apt dest_type \n", "0 CAN D \n", "1 CAN D " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merg_col = ['Flight_ID', 'id_uld']\n", "a_par_data_df = opt_pca_data_a.merge(right=uld_match_table, on=merg_col, how='left')\n", "a_par_output_data_df = a_par_data_df.loc[:, [\n", " 'model', 'Flight_ID', 'ULD_ID', 'arrive_time', 'parcel_type', 'dest_city', 'dest_apt', 'dest_model']\n", " ]\n", "# s1 = pd.Series(data={'model.1': 'src_type', 'Flight_ID': 'plate_num.4', 'ULD_ID': 'uld_num.2'})\n", "# s1.str.replace('\\.[0-9]$', '')\n", "a_par_output_data_df = a_par_output_data_df.rename_axis(mapper={\n", " 'model': 'src_type', 'Flight_ID': 'plate_num', 'ULD_ID': 'uld_num', 'dest_city': 'dest_code', 'dest_model': 'dest_type'}, axis=1)\n", "\n", "# a_par_output_data_df.loc[a_par_output_data_df.ULD_ID.isnull(),:]\n", "# df_to_pickle(df=a_par_output_data_df, file_name='parcel_data_erzhou_airside.pkl')\n", "# df_to_excel(df=a_par_output_data_df, file_name='parcel_data_erzhou_airside', sheet='parcel_data_erzhou_airside')\n", "a_par_output_data_df.dest_type = a_par_output_data_df.dest_type.str.replace('\\.[0-9]$', '')\n", "a_par_output_data_df.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### small ID生成" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>plate_num</th>\n", " <th>uld_num</th>\n", " <th>arrive_time</th>\n", " <th>parcel_type</th>\n", " <th>con</th>\n", " <th>sb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>14</th>\n", " <td>CSS100</td>\n", " <td>ULD40657472</td>\n", " <td>2025-02-08 01:13:22</td>\n", " <td>isb</td>\n", " <td>29</td>\n", " <td>1.45</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " plate_num uld_num arrive_time parcel_type con sb_sum\n", "14 CSS100 ULD40657472 2025-02-08 01:13:22 isb 29 1.45" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a_small_data_con = a_par_output_data_df.query(\"parcel_type == 'small' \| parcel_type == 'isb'\"\n", " ).groupby(['plate_num','uld_num', 'arrive_time', 'parcel_type']\n", " ).parcel_type.count().to_frame().copy()\n", "a_small_data_con = a_small_data_con.rename_axis(mapper={'parcel_type': 'con'}, axis=1)\n", "a_small_data_con = a_small_data_con.reset_index()\n", "a_small_data_con['sb_sum'] = a_small_data_con.con.map(lambda x : round(x/20, 2))\n", "a_small_data_con.query(\"plate_num=='CSS100' & uld_num == 'ULD40657472' & parcel_type == 'isb'\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### land" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Flight_ID</th>\n", " <th>landing_time</th>\n", " <th>parcel_type</th>\n", " <th>par_num</th>\n", " <th>small_num</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>106</th>\n", " <td>CSSLocalE18</td>\n", " <td>00:20:00</td>\n", " <td>small</td>\n", " <td>7778</td>\n", " <td>388.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Flight_ID landing_time parcel_type par_num small_num\n", "106 CSSLocalE18 00:20:00 small 7778 388.9" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_small_data_con = l_opt_pca_data.query(\"parcel_type == 'small' \| parcel_type == 'isb'\"\n", " ).groupby(['Flight_ID', 'landing_time', 'parcel_type']\n", " ).model.count().to_frame().copy() #.tail(5)\n", "l_small_data_con = l_small_data_con.reset_index()\n", "l_small_data_con = l_small_data_con.rename_axis(mapper={'model': 'par_num'}, axis=1)\n", "l_small_data_con['small_num'] = l_small_data_con.par_num.map(lambda x : round(x/20, 2))\n", "l_small_data_con.query(\"Flight_ID=='CSSLocalE18'\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Air 小件包号统计" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>uld_num</th>\n", " <th>parcel_type</th>\n", " <th>sb_sum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ULD40230158</td>\n", " <td>small</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ULD40230159</td>\n", " <td>small</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " uld_num parcel_type sb_sum\n", "0 ULD40230158 small 4\n", "1 ULD40230159 small 4" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def _round_one(df, col_list, round_map):\n", " for col in col_list:\n", " df.loc[:, col] = df.loc[:, col].apply(\n", " lambda x : left_back(x, round_map[col]['L']) if x >=1 else right_back(x, round_map[col]['R']))\n", " return df\n", "\n", "a_small_data_con_round = _round_one(a_small_data_con, ['sb_sum'], {'sb_sum': {'L': 0.00001, 'R': 0.000001}})\n", "small_bag_map = a_small_data_con_round.loc[:, ['uld_num', 'parcel_type', 'sb_sum']]\n", "small_bag_map.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### land 小件包号统计" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Flight_ID</th>\n", " <th>parcel_type</th>\n", " <th>small_num</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>106</th>\n", " <td>CSSLocalE18</td>\n", " <td>small</td>\n", " <td>389</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Flight_ID parcel_type small_num\n", "106 CSSLocalE18 small 389" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l_small_data_con_round = _round_one(l_small_data_con, ['small_num'], {'small_num': {'L': 0.00001, 'R': 0.000001}})\n", "l_small_bag_map = l_small_data_con_round.loc[:, ['Flight_ID', 'parcel_type', 'small_num']]\n", "l_small_bag_map.query(\"Flight_ID=='CSSLocalE18'\").head(10)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<2017-11-09 18:43:37,925><tools>: data to excel success!\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>small_id</th>\n", " <th>parcel_id</th>\n", " <th>src_type</th>\n", " <th>plate_num</th>\n", " <th>uld_num</th>\n", " <th>arrive_time</th>\n", " <th>parcel_type</th>\n", " <th>dest_code</th>\n", " <th>dest_apt</th>\n", " <th>dest_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>438401</th>\n", " <td>10438402</td>\n", " <td>ULD40685541s3</td>\n", " <td>INF</td>\n", " <td>INF1182</td>\n", " <td>ULD40685541</td>\n", " <td>2025-02-08 01:24:26</td>\n", " <td>small</td>\n", " <td>512</td>\n", " <td>WUX</td>\n", " <td>D</td>\n", " </tr>\n", " <tr>\n", " <th>299878</th>\n", " <td>10299879</td>\n", " <td>10299879</td>\n", " <td>INF</td>\n", " <td>INF1182</td>\n", " <td>ULD40685537</td>\n", " <td>2025-02-08 01:25:10</td>\n", " <td>parcel</td>\n", " <td>7110</td>\n", " <td>BKK</td>\n", " <td>INF</td>\n", " </tr>\n", " <tr>\n", " <th>63855</th>\n", " <td>10063856</td>\n", " <td>10063856</td>\n", " <td>D</td>\n", " <td>CSS99</td>\n", " <td>ULD40598481</td>\n", " <td>2025-02-08 01:04:52</td>\n", " <td>parcel</td>\n", " <td>7110</td>\n", " <td>ICN</td>\n", " <td>INF</td>\n", " </tr>\n", " <tr>\n", " <th>217714</th>\n", " <td>10217715</td>\n", " <td>10217715</td>\n", " <td>I</td>\n", " <td>CSS43</td>\n", " <td>ULD40358823</td>\n", " <td>2025-02-08 00:47:22</td>\n", " <td>parcel</td>\n", " <td>10</td>\n", " <td>PEK</td>\n", " <td>D</td>\n", " </tr>\n", " <tr>\n", " <th>72353</th>\n", " <td>10072354</td>\n", " <td>10072354</td>\n", " <td>D</td>\n", " <td>CSS7</td>\n", " <td>ULD40643977</td>\n", " <td>2025-02-08 01:14:06</td>\n", " <td>parcel</td>\n", " <td>DXB</td>\n", " <td>DXB</td>\n", " <td>I</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " small_id parcel_id src_type plate_num uld_num \\\n", "438401 10438402 ULD40685541s3 INF INF1182 ULD40685541 \n", "299878 10299879 10299879 INF INF1182 ULD40685537 \n", "63855 10063856 10063856 D CSS99 ULD40598481 \n", "217714 10217715 10217715 I CSS43 ULD40358823 \n", "72353 10072354 10072354 D CSS7 ULD40643977 \n", "\n", " arrive_time parcel_type dest_code dest_apt dest_type \n", "438401 2025-02-08 01:24:26 small 512 WUX D \n", "299878 2025-02-08 01:25:10 parcel 7110 BKK INF \n", "63855 2025-02-08 01:04:52 parcel 7110 ICN INF \n", "217714 2025-02-08 00:47:22 parcel 10 PEK D \n", "72353 2025-02-08 01:14:06 parcel DXB DXB I " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SMALL_ID = 1000_0001\n", "a_par_small_bag_df = a_par_output_data_df.merge(small_bag_map, how='left', on=['uld_num', 'parcel_type'])\n", "a_par_small_bag_df.sb_sum = a_par_small_bag_df.sb_sum.fillna(value=0)\n", "a_par_small_bag_df['sb_id'] = a_par_small_bag_df.sb_sum.map(lambda x : np.random.randint(1, int(x)+1) if x >0 else x)\n", "a_par_small_bag_df.sb_id = a_par_small_bag_df.sb_id.apply(int)\n", "\n", "a_par_small_bag_df['parcel_id'] = a_par_small_bag_df.sb_id.mask(a_par_small_bag_df.sb_id > 0, \n", " a_par_small_bag_df.uld_num.apply(str)+a_par_small_bag_df.parcel_type.str.get(0)+a_par_small_bag_df.sb_id.apply(str))\n", "\n", "\n", "a_par_small_bag_data = a_par_small_bag_df.loc[:,[\n", " 'parcel_id', 'src_type', 'plate_num', 'uld_num', 'arrive_time', 'parcel_type', 'dest_code', 'dest_apt', 'dest_type']]\n", "\n", "a_par_small_bag_data['small_id'] = pd.Series(data=np.arange(SMALL_ID, SMALL_ID+a_par_small_bag_data.index.size))\n", "a_par_small_bag_data['parcel_id'] = a_par_small_bag_data.parcel_id.mask(a_par_small_bag_data.parcel_id == 0, \n", " a_par_small_bag_data.small_id)\n", "a_par_small_bag_data\n", "\n", "a_par_small_bag_data = a_par_small_bag_data.loc[:, [\n", " 'small_id', 'parcel_id', 'src_type', 'plate_num', 'uld_num', 'arrive_time', 'parcel_type', 'dest_code', 'dest_apt', 'dest_type']]\n", "df_to_excel(df=a_par_small_bag_data, file_name='i_od_parcel_air')\n", "a_par_small_bag_data.sample(5)\n", "\n", "\n", "# a_par_small_bag_data.to_sql('i_od_parcel', MySQLConfig.engine, index=False, index_label='small_id', if_exists='append')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Land 小件包号分配" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>small_id</th>\n", " <th>parcel_id</th>\n", " <th>src_type</th>\n", " <th>plate_num</th>\n", " <th>uld_num</th>\n", " <th>arrive_time</th>\n", " <th>parcel_type</th>\n", " <th>dest_code</th>\n", " <th>dest_apt</th>\n", " <th>dest_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>419335</th>\n", " <td>20419336</td>\n", " <td>CSSLocalAir12s19</td>\n", " <td>R</td>\n", " <td>CSSLocalAir12</td>\n", " <td>NaN</td>\n", " <td>2025-02-07 23:30:00</td>\n", " <td>small</td>\n", " <td>736</td>\n", " <td>736W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>446054</th>\n", " <td>20446055</td>\n", " <td>CSSIPE08s4</td>\n", " <td>R</td>\n", " <td>CSSIPE08</td>\n", " <td>NaN</td>\n", " <td>2025-02-07 23:45:00</td>\n", " <td>small</td>\n", " <td>7112</td>\n", " <td>7112</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>428044</th>\n", " <td>20428045</td>\n", " <td>CSSLocalAir16s111</td>\n", " <td>R</td>\n", " <td>CSSLocalAir16</td>\n", " <td>NaN</td>\n", " <td>2025-02-07 23:30:00</td>\n", " <td>small</td>\n", " <td>316</td>\n", " <td>531W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>15571</th>\n", " <td>20015572</td>\n", " <td>20015572</td>\n", " <td>R</td>\n", " <td>CSSLocalE32</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:00:00</td>\n", " <td>parcel</td>\n", " <td>577</td>\n", " <td>WNZ</td>\n", " <td>D</td>\n", " </tr>\n", " <tr>\n", " <th>115104</th>\n", " <td>20115105</td>\n", " <td>CSSIPE06s42</td>\n", " <td>R</td>\n", " <td>CSSIPE06</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:00:00</td>\n", " <td>small</td>\n", " <td>355</td>\n", " <td>TYN</td>\n", " <td>D</td>\n", " </tr>\n", " <tr>\n", " <th>333475</th>\n", " <td>20333476</td>\n", " <td>CSSIPAir09s141</td>\n", " <td>R</td>\n", " <td>CSSIPAir09</td>\n", " <td>NaN</td>\n", " <td>2025-02-07 22:30:00</td>\n", " <td>small</td>\n", " <td>571</td>\n", " <td>551W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>224089</th>\n", " <td>20224090</td>\n", " <td>CSSLocalE31s269</td>\n", " <td>R</td>\n", " <td>CSSLocalE31</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:10:00</td>\n", " <td>small</td>\n", " <td>471</td>\n", " <td>710W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>234933</th>\n", " <td>20234934</td>\n", " <td>CSSLocalE37s87</td>\n", " <td>R</td>\n", " <td>CSSLocalE37</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:10:00</td>\n", " <td>small</td>\n", " <td>735</td>\n", " <td>020W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>48797</th>\n", " <td>20048798</td>\n", " <td>20048798</td>\n", " <td>R</td>\n", " <td>CSSLongEInf07</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:15:00</td>\n", " <td>parcel</td>\n", " <td>412</td>\n", " <td>552W</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>229322</th>\n", " <td>20229323</td>\n", " <td>CSSLocalE31s284</td>\n", " <td>R</td>\n", " <td>CSSLocalE31</td>\n", " <td>NaN</td>\n", " <td>2025-02-08 00:10:00</td>\n", " <td>small</td>\n", " <td>912</td>\n", " <td>710W</td>\n", " <td>R</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " small_id parcel_id src_type plate_num uld_num \\\n", "419335 20419336 CSSLocalAir12s19 R CSSLocalAir12 NaN \n", "446054 20446055 CSSIPE08s4 R CSSIPE08 NaN \n", "428044 20428045 CSSLocalAir16s111 R CSSLocalAir16 NaN \n", "15571 20015572 20015572 R CSSLocalE32 NaN \n", "115104 20115105 CSSIPE06s42 R CSSIPE06 NaN \n", "333475 20333476 CSSIPAir09s141 R CSSIPAir09 NaN \n", "224089 20224090 CSSLocalE31s269 R CSSLocalE31 NaN \n", "234933 20234934 CSSLocalE37s87 R CSSLocalE37 NaN \n", "48797 20048798 20048798 R CSSLongEInf07 NaN \n", "229322 20229323 CSSLocalE31s284 R CSSLocalE31 NaN \n", "\n", " arrive_time parcel_type dest_code dest_apt dest_type \n", "419335 2025-02-07 23:30:00 small 736 736W R \n", "446054 2025-02-07 23:45:00 small 7112 7112 R \n", "428044 2025-02-07 23:30:00 small 316 531W R \n", "15571 2025-02-08 00:00:00 parcel 577 WNZ D \n", "115104 2025-02-08 00:00:00 small 355 TYN D \n", "333475 2025-02-07 22:30:00 small 571 551W R \n", "224089 2025-02-08 00:10:00 small 471 710W R \n", "234933 2025-02-08 00:10:00 small 735 020W R \n", "48797 2025-02-08 00:15:00 parcel 412 552W R \n", "229322 2025-02-08 00:10:00 small 912 710W R " ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATE_END = '2025-02-08 '\n", "DATE_BEGIN = '2025-02-07 '\n", "L_SMALL_ID = 2000_0001\n", "\n", "\n", "def counter_rand(map_num, cho_data, cho_counter, end_cho):\n", " \"\"\"\n", "\n", " :param map_num:\n", " :param cho_data:\n", " :param cho_counter:\n", " :param end_cho:\n", " :return:\n", " \"\"\"\n", " choise_data = cho_data\n", " for _ in map_num:\n", "\n", " try:\n", " cho_num = np.random.choice(choise_data)\n", " cho_counter[cho_num] += 1\n", " yield cho_num\n", " if cho_counter[cho_num] >= end_cho:\n", " choise_data.remove(cho_num)\n", " except ValueError:\n", " raise ValueError('计数器的截止数太小！')\n", " \n", "\n", "\n", "l_par_small_bag_df = l_opt_pca_data.merge(l_small_bag_map, how='left', on=['Flight_ID', 'parcel_type'])\n", "l_par_small_bag_df.small_num = l_par_small_bag_df.small_num.fillna(value=0)\n", "\n", "l_par_small_bag_df['sb_id'] = l_par_small_bag_df.small_num.map(lambda x : np.random.randint(1, int(x)+1) if x >0 else x)\n", "l_par_small_bag_df.sb_id = l_par_small_bag_df.sb_id.apply(int)\n", "\n", "l_par_small_bag_df['small_id'] = pd.Series(data=np.arange(L_SMALL_ID, L_SMALL_ID+l_par_small_bag_df.index.size))\n", "\n", "l_par_small_bag_df['parcel_id'] = l_par_small_bag_df.sb_id.mask(\n", " l_par_small_bag_df.sb_id > 0, \n", " l_par_small_bag_df.Flight_ID.apply(str)+l_par_small_bag_df.parcel_type.str.get(0)+l_par_small_bag_df.sb_id.apply(str))\n", "\n", "l_par_small_bag_df['parcel_id'] = l_par_small_bag_df.parcel_id.mask(l_par_small_bag_df.parcel_id == 0, l_par_small_bag_df.small_id)\n", "\n", "l_par_small_bag_df = l_par_small_bag_df.rename_axis(mapper={\n", " 'model': 'src_type', 'Flight_ID': 'plate_num', 'landing_time': 'arrive_time', 'dest_city': 'dest_code', 'dest_model': 'dest_type'}, axis=1)\n", "l_par_small_bag_data = l_par_small_bag_df.loc[:, [\n", " 'small_id', 'parcel_id', 'src_type', 'plate_num', 'uld_num', 'arrive_time', 'parcel_type', 'dest_code', 'dest_apt', 'dest_type']]\n", "l_par_small_bag_data.arrive_time = l_par_small_bag_data.arrive_time.map(\n", " lambda x: DATE_END+x\n", " if pd.datetime.strptime(x, '%H:%M:%S').hour == 0 \n", " else DATE_BEGIN+x)\n", "l_par_small_bag_data.dest_type = l_par_small_bag_data.dest_type.str.replace('\\.[0-9]$', '')\n", "l_par_small_bag_data.sample(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2rc1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
rocketproplab/Guides	Guides/experimentalTechniquesAndSensors/ADC.ipynb	1	770	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analog to Digital Conversion (ADC)\n", "\n", "Also see [Digital to Analog Conversion](DAC.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
alope107/nbgrader	docs/source/user_guide/submitted/Bitdiddle/Problem Set 1/Problem 1.ipynb	6	8024	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel$\\rightarrow$Restart) and then run all cells (in the menubar, select Cell$\\rightarrow$Run All).\n", "\n", "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\", as well as your name and collaborators below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "solution": false } }, "outputs": [], "source": [ "NAME = \"Ben Bitdiddle\"\n", "COLLABORATORS = \"Alyssa P. Hacker\"" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "checksum": "535c21960d4663d5edac398cb445d087", "grade": false, "grade_id": "jupyter", "locked": true, "solution": false } }, "source": [ "For this problem set, we'll be using the Jupyter notebook:\n", "\n", "![](jupyter.png)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "---\n", "## Part A (2 points)\n", "\n", "Write a function that returns a list of numbers, such that $x_i=i^2$, for $1\\leq i \\leq n$. Make sure it handles the case where $n<1$ by raising a `ValueError`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "8f1eab8d02a9520920aa06f8a86a2492", "grade_id": "squares", "solution": true } }, "outputs": [], "source": [ "def squares(n):\n", " \"\"\"Compute the squares of numbers from 1 to n, such that the \n", " ith element of the returned list equals i^2.\n", " \n", " \"\"\"\n", " if n < 1:\n", " raise ValueError\n", " s = []\n", " for i in range(n):\n", " s.append(i**2)\n", " return s" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "Your function should print `[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]` for $n=10$. Check that it does:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "solution": false } }, "outputs": [], "source": [ "squares(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "8e029652317e6c6a37a72710dc8d2429", "grade": true, "grade_id": "correct_squares", "points": 1 } }, "outputs": [], "source": [ "# \"\"\"Check that squares returns the correct output for several inputs\"\"\"\n", "# from nose.tools import assert_equal\n", "# assert_equal(squares(1), [1])\n", "# assert_equal(squares(2), [1, 4])\n", "# assert_equal(squares(10), [1, 4, 9, 16, 25, 36, 49, 64, 81, 100])\n", "# assert_equal(squares(11), [1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "c6ff383fa27ce1c2eb97789816c93069", "grade": true, "grade_id": "squares_invalid_input", "points": 1 } }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "---\n", "\n", "## Part B (1 point)\n", "\n", "Using your `squares` function, write a function that computes the sum of the squares of the numbers from 1 to $n$. Your function should call the `squares` function -- it should NOT reimplement its functionality." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "166e1abc6621f93f8084ec312c49f757", "grade_id": "sum_of_squares", "solution": true } }, "outputs": [], "source": [ "def sum_of_squares(n):\n", " \"\"\"Compute the sum of the squares of numbers from 1 to n.\"\"\"\n", " total = 0\n", " s = squares(n)\n", " for i in range(len(s)):\n", " total += s[i]\n", " return total" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "The sum of squares from 1 to 10 should be 385. Verify that this is the answer you get:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "solution": false } }, "outputs": [], "source": [ "sum_of_squares(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "8cd2b086064694cb2352074a421b8964", "grade": true, "grade_id": "correct_sum_of_squares", "points": 0.5 } }, "outputs": [], "source": [ "\"\"\"Check that sum_of_squares returns the correct answer for various inputs.\"\"\"\n", "assert_equal(sum_of_squares(1), 1)\n", "assert_equal(sum_of_squares(2), 5)\n", "assert_equal(sum_of_squares(10), 385)\n", "assert_equal(sum_of_squares(11), 506)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "b1e1ec376ddc63d803b109befacde06a", "grade": true, "grade_id": "sum_of_squares_uses_squares", "points": 0.5 } }, "outputs": [], "source": [ "\"\"\"Check that sum_of_squares relies on squares.\"\"\"\n", "orig_squares = squares\n", "del squares\n", "try:\n", " assert_raises(NameError, sum_of_squares, 1)\n", "except AssertionError:\n", " raise AssertionError(\"sum_of_squares does not use squares\")\n", "finally:\n", " squares = orig_squares" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "---\n", "## Part C (1 point)\n", "\n", "Using LaTeX math notation, write out the equation that is implemented by your `sum_of_squares` function." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "f3cc38a3e522c0be10852ebbe2a638b7", "grade": true, "grade_id": "sum_of_squares_equation", "points": 1, "solution": true } }, "source": [ "$\\sum_{i=0}^n i^2$" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "solution": false } }, "source": [ "---\n", "## Part D (2 points)\n", "\n", "Find a usecase for your `sum_of_squares` function and implement that usecase in the cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "5a96910dbc324f5565edf92f5c98af1b", "grade": true, "grade_id": "sum_of_squares_application", "points": 2, "solution": true } }, "outputs": [], "source": [ "# YOUR CODE HERE\n", "raise NotImplementedError()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
CartoDB/cartoframes	docs/guides/06-Data-Services.ipynb	1	252263	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Services\n", "\n", "You can connect to [CARTO Data Services API](https://carto.com/developers/data-services-api/) directly from CARTOframes. This API consists of a set of location-based functions that can be applied to your data to perform geospatial analyses without leaving the context of your notebook. For instance, you can geocode a pandas DataFrame with addresses on the fly, and then perform a trade area analysis by computing isodistances or isochrones programmatically.\n", "\n", "Using Data Services requires to be authenticated. For more information about how to authenticate, please read the [Authentication guide](/developers/cartoframes/guides/Authentication/). For further learning you can also check out the [Data Services examples](/developers/cartoframes/examples/#example-data-services)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", "\n", "set_default_credentials('creds.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Depending on your CARTO account plan, some of these data services are subject to different [quota limitations](https://carto.com/developers/data-services-api/support/quota-information/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geocoding\n", "\n", "To get started, let's read in and explore the Starbucks location data we have. With the Starbucks store data in a DataFrame, we can see that there are two columns that can be used in the geocoding service: `name` and `address`. There's also a third column that reflects the annual revenue of the store." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>address</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Franklin Ave & Eastern Pkwy</td>\n", " <td>341 Eastern Pkwy,Brooklyn, NY 11238</td>\n", " <td>1321040.772</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>607 Brighton Beach Ave</td>\n", " <td>607 Brighton Beach Avenue,Brooklyn, NY 11235</td>\n", " <td>1268080.418</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>65th St & 18th Ave</td>\n", " <td>6423 18th Avenue,Brooklyn, NY 11204</td>\n", " <td>1248133.699</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Bay Ridge Pkwy & 3rd Ave</td>\n", " <td>7419 3rd Avenue,Brooklyn, NY 11209</td>\n", " <td>1185702.676</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Caesar's Bay Shopping Center</td>\n", " <td>8973 Bay Parkway,Brooklyn, NY 11214</td>\n", " <td>1148427.411</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name address \\\n", "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", "3 Bay Ridge Pkwy & 3rd Ave 7419 3rd Avenue,Brooklyn, NY 11209 \n", "4 Caesar's Bay Shopping Center 8973 Bay Parkway,Brooklyn, NY 11214 \n", "\n", " revenue \n", "0 1321040.772 \n", "1 1268080.418 \n", "2 1248133.699 \n", "3 1185702.676 \n", "4 1148427.411 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('http://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Quota consumption\n", "\n", "Each time you run Data Services, quota is consumed. For this reason, we provide the ability to check in advance the amount of credits an operation will consume by using the `dry_run` parameter when running the service function.\n", "\n", "It is also possible to check your available quota by running the `available_quota` function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from cartoframes.data.services import Geocoding\n", "\n", "geo_service = Geocoding()\n", "\n", "city_ny = {'value': 'New York'}\n", "country_usa = {'value': 'USA'}\n", "\n", "_, geo_dry_metadata = geo_service.geocode(df, street='address', city=city_ny, country=country_usa, dry_run=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'total_rows': 10,\n", " 'required_quota': 10,\n", " 'previously_geocoded': 0,\n", " 'previously_failed': 0,\n", " 'records_with_geometry': 0}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_dry_metadata" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4999940" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_service.available_quota()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success! Data geocoded correctly\n" ] } ], "source": [ "geo_gdf, geo_metadata = geo_service.geocode(df, street='address', city=city_ny, country=country_usa)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare `geo_dry_metadata` and `geo_metadata` to see the differences between the information returned with and without the `dry_run` option. As we can see, this information reflects that all the locations have been geocoded successfully and that it has consumed 10 credits of quota." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'total_rows': 10,\n", " 'required_quota': 10,\n", " 'previously_geocoded': 0,\n", " 'previously_failed': 0,\n", " 'records_with_geometry': 0,\n", " 'final_records_with_geometry': 10,\n", " 'geocoded_increment': 10,\n", " 'successfully_geocoded': 10,\n", " 'failed_geocodings': 0}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_metadata" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4999930" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_service.available_quota()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the input data file ever changes, cached results will only be applied to unmodified\n", "records, and new geocoding will be performed only on _new or changed records_. In order to use cached results, we have to save the results to a CARTO table using the `table_name` and `cached=True` parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting data is a `GeoDataFrame` that contains three new columns:\n", "\n", "* `geometry`: The resulting geometry\n", "* `gc_status_rel`: The percentage of accuracy of each location\n", "* `carto_geocode_hash`: Geocode information" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>the_geom</th>\n", " <th>name</th>\n", " <th>address</th>\n", " <th>revenue</th>\n", " <th>gc_status_rel</th>\n", " <th>carto_geocode_hash</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>POINT (-73.95746 40.67102)</td>\n", " <td>Franklin Ave & Eastern Pkwy</td>\n", " <td>341 Eastern Pkwy,Brooklyn, NY 11238</td>\n", " <td>1321040.772</td>\n", " <td>0.97</td>\n", " <td>c834a8e289e5bce280775a9bf1f833f1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>POINT (-73.96122 40.57796)</td>\n", " <td>607 Brighton Beach Ave</td>\n", " <td>607 Brighton Beach Avenue,Brooklyn, NY 11235</td>\n", " <td>1268080.418</td>\n", " <td>0.99</td>\n", " <td>7d39a3fff93efd9034da88aa9ad2da79</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>POINT (-73.98978 40.61944)</td>\n", " <td>65th St & 18th Ave</td>\n", " <td>6423 18th Avenue,Brooklyn, NY 11204</td>\n", " <td>1248133.699</td>\n", " <td>0.98</td>\n", " <td>1a2312049ddea753ba42bf77f5ccf718</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>POINT (-74.02750 40.63202)</td>\n", " <td>Bay Ridge Pkwy & 3rd Ave</td>\n", " <td>7419 3rd Avenue,Brooklyn, NY 11209</td>\n", " <td>1185702.676</td>\n", " <td>0.98</td>\n", " <td>827ab4dcc2d49d5fd830749597976d4a</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>POINT (-74.00098 40.59321)</td>\n", " <td>Caesar's Bay Shopping Center</td>\n", " <td>8973 Bay Parkway,Brooklyn, NY 11214</td>\n", " <td>1148427.411</td>\n", " <td>0.98</td>\n", " <td>119a38c7b51195cd4153fc81605a8495</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " the_geom name \\\n", "0 POINT (-73.95746 40.67102) Franklin Ave & Eastern Pkwy \n", "1 POINT (-73.96122 40.57796) 607 Brighton Beach Ave \n", "2 POINT (-73.98978 40.61944) 65th St & 18th Ave \n", "3 POINT (-74.02750 40.63202) Bay Ridge Pkwy & 3rd Ave \n", "4 POINT (-74.00098 40.59321) Caesar's Bay Shopping Center \n", "\n", " address revenue gc_status_rel \\\n", "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 0.97 \n", "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 0.99 \n", "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 0.98 \n", "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 0.98 \n", "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 0.98 \n", "\n", " carto_geocode_hash \n", "0 c834a8e289e5bce280775a9bf1f833f1 \n", "1 7d39a3fff93efd9034da88aa9ad2da79 \n", "2 1a2312049ddea753ba42bf77f5ccf718 \n", "3 827ab4dcc2d49d5fd830749597976d4a \n", "4 119a38c7b51195cd4153fc81605a8495 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, to prevent geocoding records that have been previously geocoded, and thus spend quota unnecessarily, you should always preserve the ``the_geom`` and ``carto_geocode_hash`` columns generated by the geocoding process.\n", "\n", "This will happen automatically in these cases:\n", "\n", "1. Your input is a table from CARTO processed in place (without a ``table_name`` parameter)\n", "2. If you save your results to a CARTO table using the ``table_name`` parameter, and only use the resulting table for any further geocoding.\n", "\n", "If you try to geocode this DataFrame now that it contains both ``the_geom`` and the ``carto_geocode_hash``, you will see that the required quota is 0 because it has already been geocoded." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "_, geo_metadata = geo_service.geocode(geo_gdf, street='address', city=city_ny, country=country_usa, dry_run=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_metadata.get('required_quota')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Precision\n", "\n", "The `address` column is more complete than the `name` column, and therefore, the resulting coordinates calculated by the service will be more accurate. If we check this, the accuracy values using the `name` column are lower than the ones we get by using the `address` column for geocoding." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success! Data geocoded correctly\n" ] } ], "source": [ "geo_name_gdf, geo_name_metadata = geo_service.geocode(df, street='name', city=city_ny, country=country_usa)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.93, 0.96, 0.85, 0.83, 0.74, 0.87])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_name_gdf.gc_status_rel.unique()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.97, 0.99, 0.98])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geo_gdf.gc_status_rel.unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Visualize the results\n", "\n", "Finally, we can visualize the precision of the geocoded results using a CARTOframes [visualization layer](/developers/cartoframes/examples/#example-color-bins-layer)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe\n", " frameborder=\"0\"\n", " style=\"\n", " border: 1px solid #cfcfcf;\n", " width: 100%;\n", " height: 632px;\n", " \"\n", " srcDoc=\"\n", " <!DOCTYPE html>\n", "<html lang="en">\n", "<head>\n", " <title>None</title>\n", " <meta name="description" content="None">\n", " <meta name="viewport" content="width=device-width, initial-scale=1.0">\n", " <meta charset="UTF-8">\n", " <!-- Include CARTO VL JS -->\n", " <script src="https://libs.cartocdn.com/carto-vl/v1.4/carto-vl.min.js"></script>\n", " <!-- Include Mapbox GL JS -->\n", " <script src="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.js"></script>\n", " <!-- Include Mapbox GL CSS -->\n", " <link href="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.css" rel="stylesheet" />\n", "\n", " <!-- Include Airship -->\n", " <script nomodule="" src="https://libs.cartocdn.com/airship-components/v2.3/airship.js"></script>\n", " <script type="module" src="https://libs.cartocdn.com/airship-components/v2.3/airship/airship.esm.js"></script>\n", " <script src="https://libs.cartocdn.com/airship-bridge/v2.3/asbridge.min.js"></script>\n", " <link href="https://libs.cartocdn.com/airship-style/v2.3/airship.min.css" rel="stylesheet">\n", " <link href="https://libs.cartocdn.com/airship-icons/v2.3/icons.css" rel="stylesheet">\n", "\n", " <link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet" type="text/css">\n", "\n", " <!-- External libraries -->\n", "\n", " <!-- pako -->\n", " <script src="https://libs.cartocdn.com/cartoframes/dependencies/pako_inflate.min.js"></script>\n", " \n", " <!-- html2canvas -->\n", " \n", "\n", " \n", " <style>\n", " body {\n", " margin: 0;\n", " padding: 0;\n", " }\n", "\n", " aside.as-sidebar {\n", " min-width: 300px;\n", " }\n", "\n", " .map-image {\n", " display: none;\n", " max-width: 100%;\n", " height: auto;\n", " }\n", "\n", " as-layer-selector-slot 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background-color: white;\n", " visibility: hidden;\n", " padding: 1em;\n", " font-family: "Courier New", Courier, monospace;\n", " margin: 0 auto;\n", " font-size: 14px;\n", " overflow: auto;\n", " z-index: 1000;\n", " color: black;\n", " }\n", "\n", " .error-section {\n", " padding: 1em;\n", " border-radius: 5px;\n", " background-color: #fee;\n", " }\n", "\n", " #error-container #error-highlight {\n", " font-weight: bold;\n", " color: inherit;\n", " }\n", "\n", " #error-container #error-type {\n", " color: #008000;\n", " }\n", "\n", " #error-container #error-name {\n", " color: #ba2121;\n", " }\n", "\n", " #error-container #error-content {\n", " margin-top: 0.4em;\n", " }\n", "\n", " .error-details {\n", " margin-top: 1em;\n", " }\n", "\n", " #error-stacktrace {\n", " list-style: none;\n", " }\n", "</style>\n", " <style>\n", " .popup-content {\n", " display: flex;\n", " flex-direction: column;\n", " padding: 8px;\n", " }\n", "\n", " .popup-name {\n", " font-size: 12px;\n", " 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as-panel--top">\n", " \n", "\n", "<div class="as-panel__element" id="legends">\n", " <as-layer-selector id="layer-selector">\n", " \n", " \n", " \n", " \n", " <div slot="as-checkbox-layer-0-slot">\n", " \n", " \n", " <as-legend\n", " heading="Geocoding Precision"\n", " description="">\n", " <as-legend-color-bins id="layer0_map0_legend0" slot="legends"></as-legend-color-bins>\n", " \n", " </as-legend>\n", " \n", " \n", " </div>\n", " \n", " \n", " </as-layer-selector>\n", "</div>\n", " </div> <!-- as-panel -->\n", " </div> <!-- as-map-panels -->\n", " \n", " </div> <!-- as-map-area -->\n", " </main> <!-- as-main -->\n", " </as-responsive-content>\n", "\n", " \n", "\n", " <div id="error-container" class="error">\n", " <section class="error-section">\n", " <span class="errors" id="error-name"></span>:\n", " <section id="error-content">\n", " <span class="errors" id="error-type"></span>\n", " <span class="errors" id="error-message"></span>\n", " </section>\n", " </section>\n", "\n", " 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" " : specifier.fill + "";\n", " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", " this.zero = !!specifier.zero;\n", " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", " this.comma = !!specifier.comma;\n", " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", " this.trim = !!specifier.trim;\n", " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", " }\n", "\n", " FormatSpecifier.prototype.toString = function() {\n", " return this.fill\n", " + this.align\n", " + this.sign\n", " + this.symbol\n", " + (this.zero ? "0" : "")\n", " + (this.width === undefined ? "" : Math.max(1, this.width \| 0))\n", " + (this.comma ? "," : "")\n", " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision \| 0))\n", " + (this.trim ? "~" : "")\n", " + this.type;\n", " };\n", "\n", " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", " function formatTrim(s) {\n", " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", " switch (s[i]) {\n", " case ".": i0 = i1 = i; break;\n", " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", " }\n", " }\n", " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", " }\n", "\n", " var prefixExponent;\n", "\n", " function formatPrefixAuto(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1],\n", " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", " n = coefficient.length;\n", " return i === n ? coefficient\n", " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", " }\n", "\n", " function formatRounded(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1];\n", " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", " }\n", "\n", " var formatTypes = {\n", " "%": function(x, p) { return (x * 100).toFixed(p); },\n", " "b": function(x) { return Math.round(x).toString(2); },\n", " "c": function(x) { return x + ""; },\n", " "d": function(x) { return Math.round(x).toString(10); },\n", " "e": function(x, p) { return x.toExponential(p); },\n", " "f": function(x, p) { return x.toFixed(p); },\n", " "g": function(x, p) { return x.toPrecision(p); },\n", " "o": function(x) { return Math.round(x).toString(8); },\n", " "p": function(x, p) { return formatRounded(x * 100, p); },\n", " "r": formatRounded,\n", " "s": formatPrefixAuto,\n", " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", " "x": function(x) { return Math.round(x).toString(16); }\n", " };\n", "\n", " function identity(x) {\n", " return x;\n", " }\n", "\n", " var map = Array.prototype.map,\n", " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", "\n", " function formatLocale(locale) {\n", " var group = locale.grouping === undefined \|\| locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", "\n", " function newFormat(specifier) {\n", " specifier = formatSpecifier(specifier);\n", "\n", " var fill = specifier.fill,\n", " align = specifier.align,\n", " sign = specifier.sign,\n", " symbol = specifier.symbol,\n", " zero = specifier.zero,\n", " width = specifier.width,\n", " comma = specifier.comma,\n", " precision = specifier.precision,\n", " trim = specifier.trim,\n", " type = specifier.type;\n", "\n", " // The "n" type is an alias for ",g".\n", " if (type === "n") comma = true, type = "g";\n", "\n", " // The "" type, and any invalid type, is an alias for ".12~g".\n", " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", "\n", " // If zero fill is specified, padding goes after sign and before digits.\n", " if (zero \|\| (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", "\n", " // Compute the prefix and suffix.\n", " // For SI-prefix, the suffix is lazily computed.\n", " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", "\n", " // What format function should we use?\n", " // Is this an integer type?\n", " // Can this type generate exponential notation?\n", " var formatType = formatTypes[type],\n", " maybeSuffix = /[defgprs%]/.test(type);\n", "\n", " // Set the default precision if not specified,\n", " // or clamp the specified precision to the supported range.\n", " // For significant precision, it must be in [1, 21].\n", " // For fixed precision, it must be in [0, 20].\n", " precision = precision === undefined ? 6\n", " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", " : Math.max(0, Math.min(20, precision));\n", "\n", " function format(value) {\n", " var valuePrefix = prefix,\n", " valueSuffix = suffix,\n", " i, n, c;\n", "\n", " if (type === "c") {\n", " valueSuffix = formatType(value) + valueSuffix;\n", " value = "";\n", " } else {\n", " value = +value;\n", "\n", " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", " var valueNegative = value < 0 \|\| 1 / value < 0;\n", "\n", " // Perform the initial formatting.\n", " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", "\n", " // Trim insignificant zeros.\n", " if (trim) value = formatTrim(value);\n", "\n", " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", "\n", " // Compute the prefix and suffix.\n", " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" \|\| sign === "(" ? "" : sign) + valuePrefix;\n", " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", "\n", " // Break the formatted value into the integer “value” part that can be\n", " // grouped, and fractional or exponential “suffix” part that is not.\n", " if (maybeSuffix) {\n", " i = -1, n = value.length;\n", " while (++i < n) {\n", " if (c = value.charCodeAt(i), 48 > c \|\| c > 57) {\n", " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", " value = value.slice(0, i);\n", " break;\n", " }\n", " }\n", " }\n", " }\n", "\n", " // If the fill character is not "0", grouping is applied before padding.\n", " if (comma && !zero) value = group(value, Infinity);\n", "\n", " // Compute the padding.\n", " var length = valuePrefix.length + value.length + valueSuffix.length,\n", " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", "\n", " // If the fill character is "0", grouping is applied after padding.\n", " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", "\n", " // Reconstruct the final output based on the desired alignment.\n", " switch (align) {\n", " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", " default: value = padding + valuePrefix + value + valueSuffix; break;\n", " }\n", "\n", " return numerals(value);\n", " }\n", "\n", " format.toString = function() {\n", " return specifier + "";\n", " };\n", "\n", " return format;\n", " }\n", "\n", " function formatPrefix(specifier, value) {\n", " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", " k = Math.pow(10, -e),\n", " prefix = prefixes[8 + e / 3];\n", " return function(value) {\n", " return f(k * value) + prefix;\n", " };\n", " }\n", "\n", " return {\n", " format: newFormat,\n", " formatPrefix: formatPrefix\n", " };\n", " }\n", "\n", " var locale;\n", " var format;\n", " var formatPrefix;\n", "\n", " defaultLocale({\n", " decimal: ".",\n", " thousands: ",",\n", " grouping: [3],\n", " currency: ["$", ""],\n", " minus: "-"\n", " });\n", "\n", " function defaultLocale(definition) {\n", " locale = formatLocale(definition);\n", " format = locale.format;\n", " formatPrefix = locale.formatPrefix;\n", " return locale;\n", " }\n", "\n", " function formatter(value, specifier) {\n", " const formatFunc = specifier ? format(specifier) : formatValue;\n", "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", " return `> ${formatFunc(first)}`;\n", " }\n", " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", " if (typeof value === 'number') {\n", " return formatNumber(value);\n", " }\n", " return value;\n", " }\n", "\n", " function formatNumber(value) {\n", " if (!Number.isInteger(value)) {\n", " return value.toLocaleString(undefined, {\n", " minimumFractionDigits: 2,\n", " maximumFractionDigits: 3\n", " });\n", " }\n", " return value.toLocaleString();\n", " }\n", "\n", " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", " return {\n", " 'version': 8,\n", " 'sources': {},\n", " 'layers': [{\n", " 'id': 'background',\n", " 'type': 'background',\n", " 'paint': {\n", " 'background-color': basecolor\n", " }\n", " }]\n", " };\n", " }\n", "\n", " function getImageElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `map-image-${mapIndex}` : 'map-image';\n", " return document.getElementById(id);\n", " }\n", "\n", " function getContainerElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `main-container-${mapIndex}` : 'main-container';\n", " return document.getElementById(id);\n", " }\n", "\n", " function saveImage(mapIndex) {\n", " const img = getImageElement(mapIndex);\n", " const container = getContainerElement(mapIndex);\n", "\n", " html2canvas(container)\n", " .then((canvas) => setMapImage(canvas, img, container));\n", " }\n", "\n", " function setMapImage(canvas, img, container) {\n", " const src = canvas.toDataURL();\n", " img.setAttribute('src', src);\n", " img.style.display = 'block';\n", " container.style.display = 'none';\n", " }\n", "\n", " function resetPopupClick(interactivity) {\n", " interactivity.off('featureClick');\n", " }\n", "\n", " function resetPopupHover(interactivity) {\n", " interactivity.off('featureHover');\n", " }\n", "\n", " function setPopupsClick(map, clickPopup, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureClick', (event) => {\n", " updatePopup(map, clickPopup, event, attrs);\n", " hoverPopup.remove();\n", " });\n", " }\n", "\n", " function setPopupsHover(map, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureHover', (event) => {\n", " updatePopup(map, hoverPopup, event, attrs);\n", " });\n", " }\n", "\n", " function updatePopup(map, popup, event, attrs) {\n", " if (event.features.length > 0) {\n", " let popupHTML = '';\n", " const layerIDs = [];\n", "\n", " for (const feature of event.features) {\n", " if (layerIDs.includes(feature.layerId)) {\n", " continue;\n", " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " <span class="popup-name">${item.title}</span>\n", " <span class="popup-value">${value}</span>\n", " ` + popupHTML;\n", " }\n", " }\n", " }\n", "\n", " if (popupHTML) {\n", " popup\n", " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", " .setHTML(`<div class="popup-content">${popupHTML}</div>`);\n", "\n", " if (!popup.isOpen()) {\n", " popup.addTo(map);\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " }\n", "\n", " function setInteractivity(map, interactiveLayers, interactiveMapLayers) {\n", " const interactivity = new carto.Interactivity(interactiveMapLayers);\n", "\n", " const clickPopup = new mapboxgl.Popup({\n", " closeButton: true,\n", " closeOnClick: false\n", " });\n", "\n", " const hoverPopup = new mapboxgl.Popup({\n", " closeButton: false,\n", " closeOnClick: false\n", " });\n", "\n", " const { clickAttrs, hoverAttrs } = _setInteractivityAttrs(interactiveLayers);\n", "\n", " resetPopupClick(map);\n", " resetPopupHover(map);\n", "\n", " if (clickAttrs.length > 0) {\n", " setPopupsClick(map, clickPopup, hoverPopup, interactivity, clickAttrs);\n", " }\n", "\n", " if (hoverAttrs.length > 0) {\n", " setPopupsHover(map, hoverPopup, interactivity, hoverAttrs);\n", " }\n", " }\n", "\n", " function _setInteractivityAttrs(interactiveLayers) {\n", " let clickAttrs = [];\n", " let hoverAttrs = [];\n", "\n", " interactiveLayers.forEach((interactiveLayer) => {\n", " interactiveLayer.interactivity.forEach((interactivityDef) => {\n", " if (interactivityDef.event === 'click') {\n", " clickAttrs = clickAttrs.concat(interactivityDef.attrs);\n", " } else if (interactivityDef.event === 'hover') {\n", " hoverAttrs = hoverAttrs.concat(interactivityDef.attrs);\n", " }\n", " });\n", " });\n", "\n", " return { clickAttrs, hoverAttrs };\n", " }\n", "\n", " function renderWidget(widget, value) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", " function renderBridge(bridge, widget, mapLayer) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}`);\n", "\n", " switch (widget.type) {\n", " case 'histogram':\n", " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", " break;\n", " case 'animation':\n", " widget.options.propertyName = widget.prop;\n", " bridge.animationControls(widget.element, widget.value, widget.options);\n", " break;\n", " case 'time-series':\n", " widget.options.propertyName = widget.prop;\n", " bridge.timeSeries(widget.element, widget.value, widget.options);\n", " break;\n", " }\n", " }\n", "\n", " function bridgeLayerWidgets(map, mapLayer, mapSource, widgets) {\n", " const bridge = new AsBridge.VL.Bridge({\n", " carto: carto,\n", " layer: mapLayer,\n", " source: mapSource,\n", " map: map\n", " });\n", "\n", " widgets\n", " .filter((widget) => widget.has_bridge)\n", " .forEach((widget) => renderBridge(bridge, widget, mapLayer));\n", "\n", " bridge.build();\n", " }\n", "\n", " function _getWidgetType(layer, property, value) {\n", " return layer.metadata && layer.metadata.properties[value] ?\n", " layer.metadata.properties[value].type\n", " : _getWidgetPropertyType(layer, property);\n", " }\n", "\n", " function _getWidgetPropertyType(layer, property) {\n", " return layer.metadata && layer.metadata.properties[property] ?\n", " layer.metadata.properties[property].type\n", " : null;\n", " }\n", "\n", " function createLegends(layer, legends, layerIndex, mapIndex=0) {\n", " if (legends.length) {\n", " legends.forEach((legend, legendIndex) => _createLegend(layer, legend, layerIndex, legendIndex, mapIndex));\n", " } else {\n", " _createLegend(layer, legends, layerIndex, 0, mapIndex);\n", " }\n", " }\n", "\n", " function _createLegend(layer, legend, layerIndex, legendIndex, mapIndex=0) {\n", " const element = document.querySelector(`#layer${layerIndex}_map${mapIndex}_legend${legendIndex}`);\n", "\n", " if (legend.prop) {\n", " const othersLabel = 'Others'; // TODO: i18n\n", " const prop = legend.prop;\n", " const dynamic = legend.dynamic;\n", " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", " const formatFunc = (value) => formatter(value, legend.format);\n", " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", " }\n", "\n", " AsBridge.VL.Legends.rampLegend(element, layer, prop, options);\n", " }\n", " }\n", "\n", " function SourceFactory() {\n", " const sourceTypes = { GeoJSON, Query, MVT };\n", "\n", " this.createSource = (layer) => {\n", " return sourceTypes[layer.type](layer);\n", " };\n", " }\n", "\n", " function GeoJSON(layer) {\n", " const options = JSON.parse(JSON.stringify(layer.options));\n", " const data = _decodeJSONData(layer.data, layer.encode_data);\n", "\n", " return new carto.source.GeoJSON(data, options);\n", " }\n", "\n", " function Query(layer) {\n", " const auth = {\n", " username: layer.credentials.username,\n", " apiKey: layer.credentials.api_key \|\| 'default_public'\n", " };\n", "\n", " const config = {\n", " serverURL: layer.credentials.base_url \|\| `https://${layer.credentials.username}.carto.com/`\n", " };\n", "\n", " return new carto.source.SQL(layer.data, auth, config);\n", " }\n", "\n", " function MVT(layer) {\n", " return new carto.source.MVT(layer.data.file, JSON.parse(layer.data.metadata));\n", " }\n", "\n", " function _decodeJSONData(data, encodeData) {\n", " try {\n", " if (encodeData) {\n", " const decodedJSON = pako.inflate(atob(data), { to: 'string' });\n", " return JSON.parse(decodedJSON);\n", " } else {\n", " return JSON.parse(data);\n", " }\n", " } catch(error) {\n", " throw new Error(`\n", " Error: "${error}". CARTOframes is not able to parse your local data because it is too large.\n", " Please, disable the data compresion with encode_data=False in your Layer class.\n", " `);\n", " }\n", " }\n", "\n", " const factory = new SourceFactory();\n", "\n", " function initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex) {\n", " const mapSource = factory.createSource(layer);\n", " const mapViz = new carto.Viz(layer.viz);\n", " const mapLayer = new carto.Layer(`layer${layerIndex}`, mapSource, mapViz);\n", " const mapLayerIndex = numLayers - layerIndex - 1;\n", "\n", " try {\n", " mapLayer._updateLayer.catch(displayError);\n", " } catch (e) {\n", " throw e;\n", " }\n", "\n", " mapLayer.addTo(map);\n", "\n", " setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends);\n", " setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource);\n", "\n", " return mapLayer;\n", " }\n", "\n", " function getInteractiveLayers(layers, mapLayers) {\n", " const interactiveLayers = [];\n", " const interactiveMapLayers = [];\n", "\n", " layers.forEach((layer, index) => {\n", " if (layer.interactivity) {\n", " interactiveLayers.push(layer);\n", " interactiveMapLayers.push(mapLayers[index]);\n", " }\n", " });\n", "\n", " return { interactiveLayers, interactiveMapLayers };\n", " }\n", "\n", " function setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends) {\n", " if (hasLegends && layer.legends) {\n", " createLegends(mapLayer, layer.legends, mapLayerIndex, mapIndex);\n", " }\n", " }\n", "\n", " function setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource) {\n", " if (layer.widgets.length) {\n", " initLayerWidgets(layer.widgets, mapLayerIndex);\n", " updateLayerWidgets(layer.widgets, mapLayer);\n", " bridgeLayerWidgets(map, mapLayer, mapSource, layer.widgets);\n", " }\n", " }\n", "\n", " function initLayerWidgets(widgets, mapLayerIndex) {\n", " widgets.forEach((widget, widgetIndex) => {\n", " const id = `layer${mapLayerIndex}_widget${widgetIndex}`;\n", " widget.id = id;\n", " });\n", " }\n", "\n", " function updateLayerWidgets(widgets, mapLayer) {\n", " mapLayer.on('updated', () => renderLayerWidgets(widgets, mapLayer));\n", " }\n", "\n", " function renderLayerWidgets(widgets, mapLayer) {\n", " const variables = mapLayer.viz.variables;\n", "\n", " widgets\n", " .filter((widget) => !widget.has_bridge)\n", " .forEach((widget) => {\n", " const name = widget.variable_name;\n", " const value = getWidgetValue(name, variables);\n", " renderWidget(widget, value);\n", " });\n", " }\n", "\n", " function getWidgetValue(name, variables) {\n", " return name && variables[name] ? variables[name].value : null;\n", " }\n", "\n", " function setReady(settings) {\n", " try {\n", " return settings.maps ? initMaps(settings.maps) : initMap(settings);\n", " } catch (e) {\n", " displayError(e);\n", " }\n", " }\n", "\n", " function initMaps(maps) {\n", " return maps.map((mapSettings, mapIndex) => {\n", " return initMap(mapSettings, mapIndex);\n", " });\n", " }\n", "\n", " function initMap(settings, mapIndex) {\n", " const basecolor = getBasecolorSettings(settings.basecolor);\n", " const basemapStyle = BASEMAPS[settings.basemap] \|\| settings.basemap \|\| basecolor;\n", " const container = mapIndex !== undefined ? `map-${mapIndex}` : 'map';\n", " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", " map.flyTo(settings.camera);\n", " }\n", "\n", " return initLayers(map, settings, mapIndex);\n", " }\n", "\n", " function initLayers(map, settings, mapIndex) {\n", " const numLayers = settings.layers.length;\n", " const hasLegends = settings.has_legends;\n", " const isStatic = settings.is_static;\n", " const layers = settings.layers;\n", " const mapLayers = getMapLayers(\n", " layers,\n", " numLayers,\n", " hasLegends,\n", " map,\n", " mapIndex\n", " );\n", "\n", " if (settings.layer_selector) {\n", " addLayersSelector(layers.reverse(), mapLayers.reverse(), mapIndex);\n", " }\n", "\n", " setInteractiveLayers(map, layers, mapLayers);\n", "\n", " return waitForMapLayersLoad(isStatic, mapIndex, mapLayers);\n", " }\n", "\n", " function waitForMapLayersLoad(isStatic, mapIndex, mapLayers) {\n", " return new Promise((resolve) => {\n", " carto.on('loaded', mapLayers, onMapLayersLoaded.bind(\n", " this, isStatic, mapIndex, mapLayers, resolve)\n", " );\n", " });\n", " }\n", "\n", " function onMapLayersLoaded(isStatic, mapIndex, mapLayers, resolve) {\n", " if (isStatic) {\n", " saveImage(mapIndex);\n", " }\n", "\n", " resolve(mapLayers);\n", " }\n", "\n", " function getMapLayers(layers, numLayers, hasLegends, map, mapIndex) {\n", " return layers.map((layer, layerIndex) => {\n", " return initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex);\n", " });\n", " }\n", "\n", " function setInteractiveLayers(map, layers, mapLayers) {\n", " const { interactiveLayers, interactiveMapLayers } = getInteractiveLayers(layers, mapLayers);\n", "\n", " if (interactiveLayers && interactiveLayers.length > 0) {\n", " setInteractivity(map, interactiveLayers, interactiveMapLayers);\n", " }\n", " }\n", "\n", " function addLayersSelector(layers, mapLayers, mapIndex) {\n", " const layerSelectorId = mapIndex !== undefined ? `#layer-selector-${mapIndex}` : '#layer-selector';\n", " const layerSelector$ = document.querySelector(layerSelectorId);\n", " const layersInfo = mapLayers.map((layer, index) => {\n", " return {\n", " title: layers[index].title \|\| `Layer ${index}`,\n", " id: layer.id,\n", " checked: true\n", " };\n", " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", "\n", " layerSelector.build();\n", " }\n", "\n", " function createMap(container, basemapStyle, bounds, accessToken) {\n", " const map = createMapboxGLMap(container, basemapStyle, accessToken);\n", "\n", " map.addControl(attributionControl);\n", " map.fitBounds(bounds, FIT_BOUNDS_SETTINGS);\n", "\n", " return map;\n", " }\n", "\n", " function createMapboxGLMap(container, style, accessToken) {\n", " if (accessToken) {\n", " mapboxgl.accessToken = accessToken;\n", " }\n", "\n", " return new mapboxgl.Map({\n", " container,\n", " style,\n", " zoom: 9,\n", " dragRotate: false,\n", " attributionControl: false\n", " });\n", " }\n", "\n", " function init(settings) {\n", " setReady(settings);\n", " }\n", "\n", " return init;\n", "\n", "}());\n", "</script>\n", "<script>\n", " document\n", " .querySelector('as-responsive-content')\n", " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "va0276a", "title": "Precision"}, "event": "hover"}, {"attrs": {"format": null, "name": "vc66218", "title": "Address"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "Geocoding Precision", "type": "color-bins", "variable": null}], "map_index": 0, "options": {"dateColumns": []}, "source": "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", "title": "Geocoding Precision", "type": "GeoJSON", "viz": "@va0276a: prop(\\u0027gc_status_rel\\u0027)\\n@vc66218: prop(\\u0027address\\u0027)\\ncolor: opacity(ramp(globalEqIntervals(prop(\\u0027gc_status_rel\\u0027), 3), purpor),1)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", " init({\n", " basecolor,\n", " basemap,\n", " bounds,\n", " camera,\n", " has_legends,\n", " is_static,\n", " layer_selector,\n", " layers,\n", " mapboxtoken,\n", " show_info\n", " });\n", "});\n", "</script>\n", "</html>\n", "\">\n", "\n", "</iframe>" ], "text/plain": [ "<cartoframes.viz.layer.Layer at 0x7f80e07ecc70>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes.viz import Layer, color_bins_style, popup_element\n", "\n", "Layer(\n", " geo_gdf,\n", " color_bins_style('gc_status_rel', method='equal', bins=geo_gdf.gc_status_rel.unique().size),\n", " popup_hover=[popup_element('address', 'Address'), popup_element('gc_status_rel', 'Precision')],\n", " title='Geocoding Precision'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Isolines\n", "\n", "There are two Isoline functions: isochrones and isodistances. In this guide we will use the isochrones function to calculate walking areas _by time_ for each Starbucks store and the isodistances function to calculate the walking area _by distance_.\n", "\n", "By definition, isolines are concentric polygons that display equally calculated levels over a given surface area, and they are calculated as the intersection areas from the origin point, measured by:\n", "\n", "* Time in the case of isochrones\n", "* Distance in the case of isodistances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Isochrones\n", "\n", "For isochrones, let's calculate the time ranges of 5, 15 and 30 minutes. These ranges are input in `seconds`, so they will be 300, 900, and 1800 respectively." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from cartoframes.data.services import Isolines\n", "\n", "iso_service = Isolines()\n", "\n", "_, isochrones_dry_metadata = iso_service.isochrones(geo_gdf, [300, 900, 1800], mode='walk', dry_run=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember to always check the quota using `dry_run` parameter and `available_quota` method before running the service!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "available 115540, required 30\n" ] } ], "source": [ "print('available {0}, required {1}'.format(\n", " iso_service.available_quota(),\n", " isochrones_dry_metadata.get('required_quota'))\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success! Isolines created correctly\n" ] } ], "source": [ "isochrones_gdf, isochrones_metadata = iso_service.isochrones(geo_gdf, [300, 900, 1800], mode='walk')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>data_range</th>\n", " <th>the_geom</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>300</td>\n", " <td>MULTIPOLYGON (((-73.96279 40.67224, -73.96254 ...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>900</td>\n", " <td>MULTIPOLYGON (((-73.96897 40.67430, -73.96872 ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>1800</td>\n", " <td>MULTIPOLYGON (((-73.97653 40.67842, -73.97627 ...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>300</td>\n", " <td>MULTIPOLYGON (((-73.96537 40.57869, -73.96494 ...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>900</td>\n", " <td>MULTIPOLYGON (((-73.97223 40.57800, -73.97181 ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id data_range the_geom\n", "0 0 300 MULTIPOLYGON (((-73.96279 40.67224, -73.96254 ...\n", "1 0 900 MULTIPOLYGON (((-73.96897 40.67430, -73.96872 ...\n", "2 0 1800 MULTIPOLYGON (((-73.97653 40.67842, -73.97627 ...\n", "3 1 300 MULTIPOLYGON (((-73.96537 40.57869, -73.96494 ...\n", "4 1 900 MULTIPOLYGON (((-73.97223 40.57800, -73.97181 ..." ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isochrones_gdf.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe\n", " frameborder=\"0\"\n", " style=\"\n", " border: 1px solid #cfcfcf;\n", " width: 100%;\n", " height: 632px;\n", " \"\n", " srcDoc=\"\n", " <!DOCTYPE html>\n", "<html lang="en">\n", "<head>\n", " <title>None</title>\n", " <meta name="description" content="None">\n", " <meta name="viewport" content="width=device-width, initial-scale=1.0">\n", " <meta charset="UTF-8">\n", " <!-- Include CARTO VL JS -->\n", " <script src="https://libs.cartocdn.com/carto-vl/v1.4/carto-vl.min.js"></script>\n", " <!-- Include Mapbox GL JS -->\n", " <script src="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.js"></script>\n", " <!-- Include Mapbox GL CSS -->\n", " <link href="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.css" rel="stylesheet" />\n", "\n", " <!-- Include Airship -->\n", " <script nomodule="" src="https://libs.cartocdn.com/airship-components/v2.3/airship.js"></script>\n", " <script type="module" src="https://libs.cartocdn.com/airship-components/v2.3/airship/airship.esm.js"></script>\n", " <script src="https://libs.cartocdn.com/airship-bridge/v2.3/asbridge.min.js"></script>\n", " <link href="https://libs.cartocdn.com/airship-style/v2.3/airship.min.css" rel="stylesheet">\n", " <link href="https://libs.cartocdn.com/airship-icons/v2.3/icons.css" rel="stylesheet">\n", "\n", " <link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet" type="text/css">\n", "\n", " <!-- External libraries -->\n", "\n", " <!-- pako -->\n", " <script src="https://libs.cartocdn.com/cartoframes/dependencies/pako_inflate.min.js"></script>\n", " \n", " <!-- html2canvas -->\n", " \n", "\n", " \n", " <style>\n", " body {\n", " margin: 0;\n", " padding: 0;\n", " }\n", "\n", " aside.as-sidebar {\n", " min-width: 300px;\n", " }\n", "\n", " .map-image {\n", " display: none;\n", " max-width: 100%;\n", " height: auto;\n", " }\n", "\n", " as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n", " font-size: 14px;\n", " line-height: 14px;\n", " }\n", "</style>\n", " <style>\n", " .map {\n", " position: absolute;\n", " height: 100%;\n", " width: 100%;\n", " }\n", "\n", " .map-info {\n", " position: absolute;\n", " bottom: 0;\n", " padding: 0 5px;\n", " background-color: rgba(255, 255, 255, 0.5);\n", " margin: 0;\n", " color: rgba(0, 0, 0, 0.75);\n", " font-size: 12px;\n", " width: auto;\n", " height: 18px;\n", " font-family: 'Open Sans';\n", " }\n", "\n", " .map-footer {\n", " background: #F2F6F9;\n", " font-family: Roboto;\n", " font-size: 12px;\n", " line-height: 24px;\n", " color: #162945;\n", " text-align: center;\n", " z-index: 2;\n", " }\n", "\n", " .map-footer a {\n", " text-decoration: none;\n", " }\n", "\n", " .map-footer a:hover {\n", " text-decoration: underline;\n", " }\n", "</style>\n", " <style>\n", " #error-container {\n", " position: absolute;\n", " width: 100%;\n", " height: 100%;\n", " background-color: white;\n", " visibility: hidden;\n", " padding: 1em;\n", " font-family: "Courier New", Courier, monospace;\n", " margin: 0 auto;\n", " font-size: 14px;\n", " overflow: auto;\n", " z-index: 1000;\n", " color: black;\n", " }\n", "\n", " .error-section {\n", " padding: 1em;\n", " border-radius: 5px;\n", " background-color: #fee;\n", " }\n", "\n", " #error-container #error-highlight {\n", " font-weight: bold;\n", " color: inherit;\n", " }\n", "\n", " #error-container #error-type {\n", " color: #008000;\n", " }\n", "\n", " #error-container #error-name {\n", " color: #ba2121;\n", " }\n", "\n", " #error-container #error-content {\n", " margin-top: 0.4em;\n", " }\n", "\n", " .error-details {\n", " margin-top: 1em;\n", " }\n", "\n", " #error-stacktrace {\n", " list-style: none;\n", " }\n", "</style>\n", " <style>\n", " .popup-content {\n", " display: flex;\n", " flex-direction: column;\n", " padding: 8px;\n", " }\n", "\n", " .popup-name {\n", " font-size: 12px;\n", " font-weight: 400;\n", " line-height: 20px;\n", " margin-bottom: 4px;\n", " }\n", "\n", " .popup-value {\n", " font-size: 16px;\n", " font-weight: 600;\n", " line-height: 20px;\n", " }\n", "\n", " .popup-value:not(:last-of-type) {\n", " margin-bottom: 16px;\n", " }\n", "</style>\n", " <style>\n", " as-widget-header .as-widget-header__header {\n", " margin-bottom: 8px;\n", " overflow-wrap: break-word;\n", " }\n", "\n", " as-widget-header .as-widget-header__subheader {\n", " margin-bottom: 12px;\n", " }\n", "\n", " as-category-widget {\n", " max-height: 250px;\n", " }\n", "</style>\n", "</head>\n", "\n", "<body class="as-app-body as-app">\n", " <img id="map-image" class="map-image" alt='Static map image' />\n", " <as-responsive-content id="main-container">\n", " \n", " <main class="as-main">\n", " <div class="as-map-area">\n", " <div id="map" class="map"></div>\n", " \n", " \n", " <div class="as-map-panels" data-name="Legends">\n", " <div class="as-panel as-panel--vertical as-panel--left as-panel--top">\n", " \n", "\n", "<div class="as-panel__element" id="legends">\n", " <as-layer-selector id="layer-selector">\n", " \n", " \n", " \n", " \n", " <div slot="as-checkbox-layer-0-slot">\n", " \n", " \n", " <as-legend\n", " heading="Isochrones"\n", " description="">\n", " <as-legend-color-category-polygon id="layer0_map0_legend0" slot="legends"></as-legend-color-category-polygon>\n", " \n", " </as-legend>\n", " \n", " \n", " </div>\n", " \n", " \n", " </as-layer-selector>\n", "</div>\n", " </div> <!-- as-panel -->\n", " </div> <!-- as-map-panels -->\n", " \n", " </div> <!-- as-map-area -->\n", " </main> <!-- as-main -->\n", " </as-responsive-content>\n", "\n", " \n", "\n", " <div id="error-container" class="error">\n", " <section class="error-section">\n", " <span class="errors" id="error-name"></span>:\n", " <section id="error-content">\n", " <span class="errors" id="error-type"></span>\n", " <span class="errors" id="error-message"></span>\n", " </section>\n", " </section>\n", "\n", " <details class="error-details">\n", " <summary>StackTrace</summary>\n", " <ul id="error-stacktrace"></ul>\n", " </details>\n", "</div>\n", "</body>\n", "\n", "<script>\n", " var init = (function () {\n", " 'use strict';\n", "\n", " const BASEMAPS = {\n", " DarkMatter: carto.basemaps.darkmatter,\n", " Voyager: carto.basemaps.voyager,\n", " Positron: carto.basemaps.positron\n", " };\n", "\n", " const attributionControl = new mapboxgl.AttributionControl({\n", " compact: false\n", " });\n", "\n", " const FIT_BOUNDS_SETTINGS = { animate: false, padding: 50, maxZoom: 16 };\n", "\n", " /** From https://github.com/errwischt/stacktrace-parser/blob/master/src/stack-trace-parser.js /\n", "\n", " /\n", " This parses the different stack traces and puts them into one format\n", " * This borrows heavily from TraceKit (https://github.com/csnover/TraceKit)\n", " /\n", "\n", " const UNKNOWN_FUNCTION = '<unknown>';\n", " const chromeRe = /^\\sat (.?) ?\$((?:file\|https?\|blob\|chrome-extension\|native\|eval\|webpack\|<anonymous>\|\\/).?)(?::(\\d+))?(?::(\\d+))?\$?\\s$/i;\n", " const chromeEvalRe = /\$(\\S)(?::(\\d+))(?::(\\d+))\$/;\n", " const winjsRe = /^\\sat (?:((?:\\[object object\\])?.+) )?\$?((?:file\|ms-appx\|https?\|webpack\|blob):.?):(\\d+)(?::(\\d+))?\$?\\s$/i;\n", " const geckoRe = /^\\s(.?)(?:\$(.?)\$)?(?:^\|@)((?:file\|https?\|blob\|chrome\|webpack\|resource\|\\[native).?\|[^@]bundle)(?::(\\d+))?(?::(\\d+))?\\s$/i;\n", " const geckoEvalRe = /(\\S+) line (\\d+)(?: > eval line \\d+) > eval/i;\n", "\n", " function parse(stackString) {\n", " const lines = stackString.split('\\n');\n", "\n", " return lines.reduce((stack, line) => {\n", " const parseResult =\n", " parseChrome(line) \|\|\n", " parseWinjs(line) \|\|\n", " parseGecko(line);\n", "\n", " if (parseResult) {\n", " stack.push(parseResult);\n", " }\n", "\n", " return stack;\n", " }, []);\n", " }\n", "\n", " function parseChrome(line) {\n", " const parts = chromeRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " const isNative = parts[2] && parts[2].indexOf('native') === 0; // start of line\n", " const isEval = parts[2] && parts[2].indexOf('eval') === 0; // start of line\n", "\n", " const submatch = chromeEvalRe.exec(parts[2]);\n", " if (isEval && submatch != null) {\n", " // throw out eval line/column and use top-most line/column number\n", " parts[2] = submatch[1]; // url\n", " parts[3] = submatch[2]; // line\n", " parts[4] = submatch[3]; // column\n", " }\n", "\n", " return {\n", " file: !isNative ? parts[2] : null,\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: isNative ? [parts[2]] : [],\n", " lineNumber: parts[3] ? +parts[3] : null,\n", " column: parts[4] ? +parts[4] : null,\n", " };\n", " }\n", "\n", " function parseWinjs(line) {\n", " const parts = winjsRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " return {\n", " file: parts[2],\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: [],\n", " lineNumber: +parts[3],\n", " column: parts[4] ? +parts[4] : null,\n", " };\n", " }\n", "\n", " function parseGecko(line) {\n", " const parts = geckoRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " const isEval = parts[3] && parts[3].indexOf(' > eval') > -1;\n", "\n", " const submatch = geckoEvalRe.exec(parts[3]);\n", " if (isEval && submatch != null) {\n", " // throw out eval line/column and use top-most line number\n", " parts[3] = submatch[1];\n", " parts[4] = submatch[2];\n", " parts[5] = null; // no column when eval\n", " }\n", "\n", " return {\n", " file: parts[3],\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: parts[2] ? parts[2].split(',') : [],\n", " lineNumber: parts[4] ? +parts[4] : null,\n", " column: parts[5] ? +parts[5] : null,\n", " };\n", " }\n", "\n", " function displayError(e) {\n", " const error$ = document.getElementById('error-container');\n", " const errors$ = error$.getElementsByClassName('errors');\n", " const stacktrace$ = document.getElementById('error-stacktrace');\n", "\n", " errors$[0].innerHTML = e.name;\n", " errors$[1].innerHTML = e.type;\n", " errors$[2].innerHTML = e.message.replace(e.type, '');\n", "\n", " error$.style.visibility = 'visible';\n", "\n", " const stack = parse(e.stack);\n", " const list = stack.map(item => {\n", " return `<li>\n", " at <span class="stacktrace-method">${item.methodName}:</span>\n", " (${item.file}:${item.lineNumber}:${item.column})\n", " </li>`;\n", " });\n", "\n", " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", " // Computes the decimal coefficient and exponent of the specified number x with\n", " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", " // For example, formatDecimal(1.23) returns ["123", 0].\n", " function formatDecimal(x, p) {\n", " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", " var i, coefficient = x.slice(0, i);\n", "\n", " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", " return [\n", " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", " +x.slice(i + 1)\n", " ];\n", " }\n", "\n", " function exponent(x) {\n", " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", " }\n", "\n", " function formatGroup(grouping, thousands) {\n", " return function(value, width) {\n", " var i = value.length,\n", " t = [],\n", " j = 0,\n", " g = grouping[0],\n", " length = 0;\n", "\n", " while (i > 0 && g > 0) {\n", " if (length + g + 1 > width) g = Math.max(1, width - length);\n", " t.push(value.substring(i -= g, i + g));\n", " if ((length += g + 1) > width) break;\n", " g = grouping[j = (j + 1) % grouping.length];\n", " }\n", "\n", " return t.reverse().join(thousands);\n", " };\n", " }\n", "\n", " function formatNumerals(numerals) {\n", " return function(value) {\n", " return value.replace(/[0-9]/g, function(i) {\n", " return numerals[+i];\n", " });\n", " };\n", " }\n", "\n", " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", "\n", " function formatSpecifier(specifier) {\n", " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", " var match;\n", " return new FormatSpecifier({\n", " fill: match[1],\n", " align: match[2],\n", " sign: match[3],\n", " symbol: match[4],\n", " zero: match[5],\n", " width: match[6],\n", " comma: match[7],\n", " precision: match[8] && match[8].slice(1),\n", " trim: match[9],\n", " type: match[10]\n", " });\n", " }\n", "\n", " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", "\n", " function FormatSpecifier(specifier) {\n", " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", " this.zero = !!specifier.zero;\n", " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", " this.comma = !!specifier.comma;\n", " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", " this.trim = !!specifier.trim;\n", " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", " }\n", "\n", " FormatSpecifier.prototype.toString = function() {\n", " return this.fill\n", " + this.align\n", " + this.sign\n", " + this.symbol\n", " + (this.zero ? "0" : "")\n", " + (this.width === undefined ? "" : Math.max(1, this.width \| 0))\n", " + (this.comma ? "," : "")\n", " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision \| 0))\n", " + (this.trim ? "~" : "")\n", " + this.type;\n", " };\n", "\n", " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", " function formatTrim(s) {\n", " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", " switch (s[i]) {\n", " case ".": i0 = i1 = i; break;\n", " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", " }\n", " }\n", " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", " }\n", "\n", " var prefixExponent;\n", "\n", " function formatPrefixAuto(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1],\n", " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", " n = coefficient.length;\n", " return i === n ? coefficient\n", " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", " }\n", "\n", " function formatRounded(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1];\n", " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", " }\n", "\n", " var formatTypes = {\n", " "%": function(x, p) { return (x * 100).toFixed(p); },\n", " "b": function(x) { return Math.round(x).toString(2); },\n", " "c": function(x) { return x + ""; },\n", " "d": function(x) { return Math.round(x).toString(10); },\n", " "e": function(x, p) { return x.toExponential(p); },\n", " "f": function(x, p) { return x.toFixed(p); },\n", " "g": function(x, p) { return x.toPrecision(p); },\n", " "o": function(x) { return Math.round(x).toString(8); },\n", " "p": function(x, p) { return formatRounded(x * 100, p); },\n", " "r": formatRounded,\n", " "s": formatPrefixAuto,\n", " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", " "x": function(x) { return Math.round(x).toString(16); }\n", " };\n", "\n", " function identity(x) {\n", " return x;\n", " }\n", "\n", " var map = Array.prototype.map,\n", " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", "\n", " function formatLocale(locale) {\n", " var group = locale.grouping === undefined \|\| locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", "\n", " function newFormat(specifier) {\n", " specifier = formatSpecifier(specifier);\n", "\n", " var fill = specifier.fill,\n", " align = specifier.align,\n", " sign = specifier.sign,\n", " symbol = specifier.symbol,\n", " zero = specifier.zero,\n", " width = specifier.width,\n", " comma = specifier.comma,\n", " precision = specifier.precision,\n", " trim = specifier.trim,\n", " type = specifier.type;\n", "\n", " // The "n" type is an alias for ",g".\n", " if (type === "n") comma = true, type = "g";\n", "\n", " // The "" type, and any invalid type, is an alias for ".12~g".\n", " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", "\n", " // If zero fill is specified, padding goes after sign and before digits.\n", " if (zero \|\| (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", "\n", " // Compute the prefix and suffix.\n", " // For SI-prefix, the suffix is lazily computed.\n", " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", "\n", " // What format function should we use?\n", " // Is this an integer type?\n", " // Can this type generate exponential notation?\n", " var formatType = formatTypes[type],\n", " maybeSuffix = /[defgprs%]/.test(type);\n", "\n", " // Set the default precision if not specified,\n", " // or clamp the specified precision to the supported range.\n", " // For significant precision, it must be in [1, 21].\n", " // For fixed precision, it must be in [0, 20].\n", " precision = precision === undefined ? 6\n", " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", " : Math.max(0, Math.min(20, precision));\n", "\n", " function format(value) {\n", " var valuePrefix = prefix,\n", " valueSuffix = suffix,\n", " i, n, c;\n", "\n", " if (type === "c") {\n", " valueSuffix = formatType(value) + valueSuffix;\n", " value = "";\n", " } else {\n", " value = +value;\n", "\n", " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", " var valueNegative = value < 0 \|\| 1 / value < 0;\n", "\n", " // Perform the initial formatting.\n", " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", "\n", " // Trim insignificant zeros.\n", " if (trim) value = formatTrim(value);\n", "\n", " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", "\n", " // Compute the prefix and suffix.\n", " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" \|\| sign === "(" ? "" : sign) + valuePrefix;\n", " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", "\n", " // Break the formatted value into the integer “value” part that can be\n", " // grouped, and fractional or exponential “suffix” part that is not.\n", " if (maybeSuffix) {\n", " i = -1, n = value.length;\n", " while (++i < n) {\n", " if (c = value.charCodeAt(i), 48 > c \|\| c > 57) {\n", " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", " value = value.slice(0, i);\n", " break;\n", " }\n", " }\n", " }\n", " }\n", "\n", " // If the fill character is not "0", grouping is applied before padding.\n", " if (comma && !zero) value = group(value, Infinity);\n", "\n", " // Compute the padding.\n", " var length = valuePrefix.length + value.length + valueSuffix.length,\n", " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", "\n", " // If the fill character is "0", grouping is applied after padding.\n", " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", "\n", " // Reconstruct the final output based on the desired alignment.\n", " switch (align) {\n", " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", " default: value = padding + valuePrefix + value + valueSuffix; break;\n", " }\n", "\n", " return numerals(value);\n", " }\n", "\n", " format.toString = function() {\n", " return specifier + "";\n", " };\n", "\n", " return format;\n", " }\n", "\n", " function formatPrefix(specifier, value) {\n", " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", " k = Math.pow(10, -e),\n", " prefix = prefixes[8 + e / 3];\n", " return function(value) {\n", " return f(k * value) + prefix;\n", " };\n", " }\n", "\n", " return {\n", " format: newFormat,\n", " formatPrefix: formatPrefix\n", " };\n", " }\n", "\n", " var locale;\n", " var format;\n", " var formatPrefix;\n", "\n", " defaultLocale({\n", " decimal: ".",\n", " thousands: ",",\n", " grouping: [3],\n", " currency: ["$", ""],\n", " minus: "-"\n", " });\n", "\n", " function defaultLocale(definition) {\n", " locale = formatLocale(definition);\n", " format = locale.format;\n", " formatPrefix = locale.formatPrefix;\n", " return locale;\n", " }\n", "\n", " function formatter(value, specifier) {\n", " const formatFunc = specifier ? format(specifier) : formatValue;\n", "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", " return `> ${formatFunc(first)}`;\n", " }\n", " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", " if (typeof value === 'number') {\n", " return formatNumber(value);\n", " }\n", " return value;\n", " }\n", "\n", " function formatNumber(value) {\n", " if (!Number.isInteger(value)) {\n", " return value.toLocaleString(undefined, {\n", " minimumFractionDigits: 2,\n", " maximumFractionDigits: 3\n", " });\n", " }\n", " return value.toLocaleString();\n", " }\n", "\n", " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", " return {\n", " 'version': 8,\n", " 'sources': {},\n", " 'layers': [{\n", " 'id': 'background',\n", " 'type': 'background',\n", " 'paint': {\n", " 'background-color': basecolor\n", " }\n", " }]\n", " };\n", " }\n", "\n", " function getImageElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `map-image-${mapIndex}` : 'map-image';\n", " return document.getElementById(id);\n", " }\n", "\n", " function getContainerElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `main-container-${mapIndex}` : 'main-container';\n", " return document.getElementById(id);\n", " }\n", "\n", " function saveImage(mapIndex) {\n", " const img = getImageElement(mapIndex);\n", " const container = getContainerElement(mapIndex);\n", "\n", " html2canvas(container)\n", " .then((canvas) => setMapImage(canvas, img, container));\n", " }\n", "\n", " function setMapImage(canvas, img, container) {\n", " const src = canvas.toDataURL();\n", " img.setAttribute('src', src);\n", " img.style.display = 'block';\n", " container.style.display = 'none';\n", " }\n", "\n", " function resetPopupClick(interactivity) {\n", " interactivity.off('featureClick');\n", " }\n", "\n", " function resetPopupHover(interactivity) {\n", " interactivity.off('featureHover');\n", " }\n", "\n", " function setPopupsClick(map, clickPopup, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureClick', (event) => {\n", " updatePopup(map, clickPopup, event, attrs);\n", " hoverPopup.remove();\n", " });\n", " }\n", "\n", " function setPopupsHover(map, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureHover', (event) => {\n", " updatePopup(map, hoverPopup, event, attrs);\n", " });\n", " }\n", "\n", " function updatePopup(map, popup, event, attrs) {\n", " if (event.features.length > 0) {\n", " let popupHTML = '';\n", " const layerIDs = [];\n", "\n", " for (const feature of event.features) {\n", " if (layerIDs.includes(feature.layerId)) {\n", " continue;\n", " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " <span class="popup-name">${item.title}</span>\n", " <span class="popup-value">${value}</span>\n", " ` + popupHTML;\n", " }\n", " }\n", " }\n", "\n", " if (popupHTML) {\n", " popup\n", " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", " .setHTML(`<div class="popup-content">${popupHTML}</div>`);\n", "\n", " if (!popup.isOpen()) {\n", " popup.addTo(map);\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " }\n", "\n", " function setInteractivity(map, interactiveLayers, interactiveMapLayers) {\n", " const interactivity = new carto.Interactivity(interactiveMapLayers);\n", "\n", " const clickPopup = new mapboxgl.Popup({\n", " closeButton: true,\n", " closeOnClick: false\n", " });\n", "\n", " const hoverPopup = new mapboxgl.Popup({\n", " closeButton: false,\n", " closeOnClick: false\n", " });\n", "\n", " const { clickAttrs, hoverAttrs } = _setInteractivityAttrs(interactiveLayers);\n", "\n", " resetPopupClick(map);\n", " resetPopupHover(map);\n", "\n", " if (clickAttrs.length > 0) {\n", " setPopupsClick(map, clickPopup, hoverPopup, interactivity, clickAttrs);\n", " }\n", "\n", " if (hoverAttrs.length > 0) {\n", " setPopupsHover(map, hoverPopup, interactivity, hoverAttrs);\n", " }\n", " }\n", "\n", " function _setInteractivityAttrs(interactiveLayers) {\n", " let clickAttrs = [];\n", " let hoverAttrs = [];\n", "\n", " interactiveLayers.forEach((interactiveLayer) => {\n", " interactiveLayer.interactivity.forEach((interactivityDef) => {\n", " if (interactivityDef.event === 'click') {\n", " clickAttrs = clickAttrs.concat(interactivityDef.attrs);\n", " } else if (interactivityDef.event === 'hover') {\n", " hoverAttrs = hoverAttrs.concat(interactivityDef.attrs);\n", " }\n", " });\n", " });\n", "\n", " return { clickAttrs, hoverAttrs };\n", " }\n", "\n", " function renderWidget(widget, value) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", " function renderBridge(bridge, widget, mapLayer) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}`);\n", "\n", " switch (widget.type) {\n", " case 'histogram':\n", " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", " break;\n", " case 'animation':\n", " widget.options.propertyName = widget.prop;\n", " bridge.animationControls(widget.element, widget.value, widget.options);\n", " break;\n", " case 'time-series':\n", " widget.options.propertyName = widget.prop;\n", " bridge.timeSeries(widget.element, widget.value, widget.options);\n", " break;\n", " }\n", " }\n", "\n", " function bridgeLayerWidgets(map, mapLayer, mapSource, widgets) {\n", " const bridge = new AsBridge.VL.Bridge({\n", " carto: carto,\n", " layer: mapLayer,\n", " source: mapSource,\n", " map: map\n", " });\n", "\n", " widgets\n", " .filter((widget) => widget.has_bridge)\n", " .forEach((widget) => renderBridge(bridge, widget, mapLayer));\n", "\n", " bridge.build();\n", " }\n", "\n", " function _getWidgetType(layer, property, value) {\n", " return layer.metadata && layer.metadata.properties[value] ?\n", " layer.metadata.properties[value].type\n", " : _getWidgetPropertyType(layer, property);\n", " }\n", "\n", " function _getWidgetPropertyType(layer, property) {\n", " return layer.metadata && layer.metadata.properties[property] ?\n", " layer.metadata.properties[property].type\n", " : null;\n", " }\n", "\n", " function createLegends(layer, legends, layerIndex, mapIndex=0) {\n", " if (legends.length) {\n", " legends.forEach((legend, legendIndex) => _createLegend(layer, legend, layerIndex, legendIndex, mapIndex));\n", " } else {\n", " _createLegend(layer, legends, layerIndex, 0, mapIndex);\n", " }\n", " }\n", "\n", " function _createLegend(layer, legend, layerIndex, legendIndex, mapIndex=0) {\n", " const element = document.querySelector(`#layer${layerIndex}_map${mapIndex}_legend${legendIndex}`);\n", "\n", " if (legend.prop) {\n", " const othersLabel = 'Others'; // TODO: i18n\n", " const prop = legend.prop;\n", " const dynamic = legend.dynamic;\n", " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", " const formatFunc = (value) => formatter(value, legend.format);\n", " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", " }\n", "\n", " AsBridge.VL.Legends.rampLegend(element, layer, prop, options);\n", " }\n", " }\n", "\n", " function SourceFactory() {\n", " const sourceTypes = { GeoJSON, Query, MVT };\n", "\n", " this.createSource = (layer) => {\n", " return sourceTypes[layer.type](layer);\n", " };\n", " }\n", "\n", " function GeoJSON(layer) {\n", " const options = JSON.parse(JSON.stringify(layer.options));\n", " const data = _decodeJSONData(layer.data, layer.encode_data);\n", "\n", " return new carto.source.GeoJSON(data, options);\n", " }\n", "\n", " function Query(layer) {\n", " const auth = {\n", " username: layer.credentials.username,\n", " apiKey: layer.credentials.api_key \|\| 'default_public'\n", " };\n", "\n", " const config = {\n", " serverURL: layer.credentials.base_url \|\| `https://${layer.credentials.username}.carto.com/`\n", " };\n", "\n", " return new carto.source.SQL(layer.data, auth, config);\n", " }\n", "\n", " function MVT(layer) {\n", " return new carto.source.MVT(layer.data.file, JSON.parse(layer.data.metadata));\n", " }\n", "\n", " function _decodeJSONData(data, encodeData) {\n", " try {\n", " if (encodeData) {\n", " const decodedJSON = pako.inflate(atob(data), { to: 'string' });\n", " return JSON.parse(decodedJSON);\n", " } else {\n", " return JSON.parse(data);\n", " }\n", " } catch(error) {\n", " throw new Error(`\n", " Error: "${error}". CARTOframes is not able to parse your local data because it is too large.\n", " Please, disable the data compresion with encode_data=False in your Layer class.\n", " `);\n", " }\n", " }\n", "\n", " const factory = new SourceFactory();\n", "\n", " function initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex) {\n", " const mapSource = factory.createSource(layer);\n", " const mapViz = new carto.Viz(layer.viz);\n", " const mapLayer = new carto.Layer(`layer${layerIndex}`, mapSource, mapViz);\n", " const mapLayerIndex = numLayers - layerIndex - 1;\n", "\n", " try {\n", " mapLayer._updateLayer.catch(displayError);\n", " } catch (e) {\n", " throw e;\n", " }\n", "\n", " mapLayer.addTo(map);\n", "\n", " setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends);\n", " setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource);\n", "\n", " return mapLayer;\n", " }\n", "\n", " function getInteractiveLayers(layers, mapLayers) {\n", " const interactiveLayers = [];\n", " const interactiveMapLayers = [];\n", "\n", " layers.forEach((layer, index) => {\n", " if (layer.interactivity) {\n", " interactiveLayers.push(layer);\n", " interactiveMapLayers.push(mapLayers[index]);\n", " }\n", " });\n", "\n", " return { interactiveLayers, interactiveMapLayers };\n", " }\n", "\n", " function setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends) {\n", " if (hasLegends && layer.legends) {\n", " createLegends(mapLayer, layer.legends, mapLayerIndex, mapIndex);\n", " }\n", " }\n", "\n", " function setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource) {\n", " if (layer.widgets.length) {\n", " initLayerWidgets(layer.widgets, mapLayerIndex);\n", " updateLayerWidgets(layer.widgets, mapLayer);\n", " bridgeLayerWidgets(map, mapLayer, mapSource, layer.widgets);\n", " }\n", " }\n", "\n", " function initLayerWidgets(widgets, mapLayerIndex) {\n", " widgets.forEach((widget, widgetIndex) => {\n", " const id = `layer${mapLayerIndex}_widget${widgetIndex}`;\n", " widget.id = id;\n", " });\n", " }\n", "\n", " function updateLayerWidgets(widgets, mapLayer) {\n", " mapLayer.on('updated', () => renderLayerWidgets(widgets, mapLayer));\n", " }\n", "\n", " function renderLayerWidgets(widgets, mapLayer) {\n", " const variables = mapLayer.viz.variables;\n", "\n", " widgets\n", " .filter((widget) => !widget.has_bridge)\n", " .forEach((widget) => {\n", " const name = widget.variable_name;\n", " const value = getWidgetValue(name, variables);\n", " renderWidget(widget, value);\n", " });\n", " }\n", "\n", " function getWidgetValue(name, variables) {\n", " return name && variables[name] ? variables[name].value : null;\n", " }\n", "\n", " function setReady(settings) {\n", " try {\n", " return settings.maps ? initMaps(settings.maps) : initMap(settings);\n", " } catch (e) {\n", " displayError(e);\n", " }\n", " }\n", "\n", " function initMaps(maps) {\n", " return maps.map((mapSettings, mapIndex) => {\n", " return initMap(mapSettings, mapIndex);\n", " });\n", " }\n", "\n", " function initMap(settings, mapIndex) {\n", " const basecolor = getBasecolorSettings(settings.basecolor);\n", " const basemapStyle = BASEMAPS[settings.basemap] \|\| settings.basemap \|\| basecolor;\n", " const container = mapIndex !== undefined ? `map-${mapIndex}` : 'map';\n", " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", " map.flyTo(settings.camera);\n", " }\n", "\n", " return initLayers(map, settings, mapIndex);\n", " }\n", "\n", " function initLayers(map, settings, mapIndex) {\n", " const numLayers = settings.layers.length;\n", " const hasLegends = settings.has_legends;\n", " const isStatic = settings.is_static;\n", " const layers = settings.layers;\n", " const mapLayers = getMapLayers(\n", " layers,\n", " numLayers,\n", " hasLegends,\n", " map,\n", " mapIndex\n", " );\n", "\n", " if (settings.layer_selector) {\n", " addLayersSelector(layers.reverse(), mapLayers.reverse(), mapIndex);\n", " }\n", "\n", " setInteractiveLayers(map, layers, mapLayers);\n", "\n", " return waitForMapLayersLoad(isStatic, mapIndex, mapLayers);\n", " }\n", "\n", " function waitForMapLayersLoad(isStatic, mapIndex, mapLayers) {\n", " return new Promise((resolve) => {\n", " carto.on('loaded', mapLayers, onMapLayersLoaded.bind(\n", " this, isStatic, mapIndex, mapLayers, resolve)\n", " );\n", " });\n", " }\n", "\n", " function onMapLayersLoaded(isStatic, mapIndex, mapLayers, resolve) {\n", " if (isStatic) {\n", " saveImage(mapIndex);\n", " }\n", "\n", " resolve(mapLayers);\n", " }\n", "\n", " function getMapLayers(layers, numLayers, hasLegends, map, mapIndex) {\n", " return layers.map((layer, layerIndex) => {\n", " return initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex);\n", " });\n", " }\n", "\n", " function setInteractiveLayers(map, layers, mapLayers) {\n", " const { interactiveLayers, interactiveMapLayers } = getInteractiveLayers(layers, mapLayers);\n", "\n", " if (interactiveLayers && interactiveLayers.length > 0) {\n", " setInteractivity(map, interactiveLayers, interactiveMapLayers);\n", " }\n", " }\n", "\n", " function addLayersSelector(layers, mapLayers, mapIndex) {\n", " const layerSelectorId = mapIndex !== undefined ? `#layer-selector-${mapIndex}` : '#layer-selector';\n", " const layerSelector$ = document.querySelector(layerSelectorId);\n", " const layersInfo = mapLayers.map((layer, index) => {\n", " return {\n", " title: layers[index].title \|\| `Layer ${index}`,\n", " id: layer.id,\n", " checked: true\n", " };\n", " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", "\n", " layerSelector.build();\n", " }\n", "\n", " function createMap(container, basemapStyle, bounds, accessToken) {\n", " const map = createMapboxGLMap(container, basemapStyle, accessToken);\n", "\n", " map.addControl(attributionControl);\n", " map.fitBounds(bounds, FIT_BOUNDS_SETTINGS);\n", "\n", " return map;\n", " }\n", "\n", " function createMapboxGLMap(container, style, accessToken) {\n", " if (accessToken) {\n", " mapboxgl.accessToken = accessToken;\n", " }\n", "\n", " return new mapboxgl.Map({\n", " container,\n", " style,\n", " zoom: 9,\n", " dragRotate: false,\n", " attributionControl: false\n", " });\n", " }\n", "\n", " function init(settings) {\n", " setReady(settings);\n", " }\n", "\n", " return init;\n", "\n", "}());\n", "</script>\n", "<script>\n", " document\n", " .querySelector('as-responsive-content')\n", " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", " const bounds = [[-74.0437317, 40.5725098], [-73.8569641, 40.705719]];\n", " const camera = null;\n", " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", " const layers = [{"credentials": null, "data": 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IHZoidoeVE7sjANkdmiJ6h5UjvYNyonew/cvoHWwWc/TOjsg4egcA5b2wIS13nWMKDtVwmJZss9bnF63uHgLEEdAUMUWsnFgWPi7RJgSSePcdmUqtjR1VnnKqI3jcJOFjjeAXaQtLh4Eh+fzziykxPhpKwvZhzymMYw/odoEHqH2+VwKlPMG2VtiLl3SvO8xvlv14naQ+AeY3V64HyN32XYNbdYCle2wKEkSuHOapHGDTas6UTcS5ym0WyQN3FsmZslkkV7nNIiU9rxUERVcuDqwGhiAgS49oAFgIT73BJnHdo/Jsv3eADN/cI1MSyR6SnrrEqRnQem4Q4CgOMC7uTBnrdZUbQRJat2d+M6rU9tm5TgxM/uNsXCfu58prevabeEMy9p6N+VCzAq2d8+LwrB+gn/338NoeOJ/1Uz7Hc5jKxtiHwEpO/hrGH6D1U7XEeAQuFwaP8AH2eI5Bgw9hIKdyTgqzKX2KsPIsI6FGfBvSO/cLSyFdHZbeuaey9M4BPZ3945/0QHzImfSVPwKgrVO5JWsfYByhD0jW3C8sWXN1WLLmgRvmnKkVVXwjKJdbxOXMB0L0J1XK56wFBPUPIJ22+5l/B7SUEOj5OQEidZUKwOqncvmkw55KXPM4M5l15RAwOuNMGQFylRtlcoDNxTlTc2QwBZUbX3OAru5DW0mnePIMzfQAGEf+hhNRn6BSbry/Mjeuusx6cru5LUOt8hyHhrd81uE/QLnEXfpBuW9R5cscqt8kTNzPV/SE5AoBiT0P0POwOiS1aw8Vrr2nbb+QSHmB1a3yItR0PsmBUKORDSg3a5B/p/1CRtVDdTyQehqRqT7TqYMrb+OcRHWPK0PotIjEAiuvkluOoHFLHTfNt3RJgaIOwic/H0C+XzRhgIDlOM4UTXsIQ3g+bNFdue+XgMyH3xoyn0875V0OsM6GhQ+wDol0wKi7bpcrfcol3Tw5lHSjGXxVHpR6TKTkEFjneAabqhITT1ZCrwH9mcc3dnQcxtLM/QwZYbD296XfrNkS8A8gSeIrB/4Ao595Jkunw+HKqkbpS33F0u3ZwXCVvHU8s97c/6ueJjpDaS3wFbo8+AAznU1JCrR11ko8UPZxqlO78AWksQ6wSgcAWleo0ZzR93NAnse1rb53CzpP3dIbWtDdhLBaXEfnwuXmW3ReDYZGOsldy0IpYZT1k5G1nCeMDRvfDpA0M83IVD6fT8JTtmEvZOxZ1eRnhXJqpiJE8KlA+mAGP5j60fOosw9oJukixxfRV0WgqyiQAdLTc9DX8s7rABR+VPzrkV2pTRpgRwCNMhm6La1gXErfzBfA2VndHHtmCiXVRkDatryHvrC9owVDrgLK2ROCIXZT8N6wEPoBxpHhYHIE388DunXQrxhzz4E1Zu5rzMygdzrA+jMsBHyAOo6rJ/aHwcHWFJyvd8CN+cwwJWfux6XboucHmPsEAdtR48KGzYt9nEg6E5seuE6dTYHbgYVVB1BbSa88a9/0OfQMygy+uHThMz3L5FYe79Fj1LQJykc6q5Iu9pl/drFvrDPL7GKfARz7pNOfEECxb02b4KbYt3bpJYp9ANA41m1nz2FsRzxfcYmDYv7KoNik6jPyySW0Us/6Kq8JSY5/pmjYuzQZ4YcuUls1SUxTDuiAOP5+ViKIvYtfyzUi6S21mSK/KmnsXdQg1lt1U//zBYdu5DSgtbNEQd5ez1QdUxQfsDOs3SwySXzJx9QuawTEgkIWdGhp5rP7mkaAWBdvFgwZ6VMybAzIz5mIoTv7Ww1GqweuR2gqngAZkxCT/ACz7RLwQmmxDZUb9dTpjBIlRvIQw+qAxKj1lTa4PHXZDyBRPEpzPHCpiDNlaY6r3Fy3A26G0Ebb3aoQL/f8/UeOz6Kf8tsI0H3DO7AkA2C0EjyshM4j+CQZ1gRA3NEB6Gtk6fePG+EPi0Dp5xCAM1WUm1vl0n+eX+SxrLsRgIwDLBE1w7pTWzMC6s7ZmJPq/j6qUsxVc6l5zmBYSoBrEeXGH9AAz1cIkz0ClFMajIYoDYCaKf/HcmJsYIjcGtRMfhDLMWtWLZUcJM3SkH23IGlGgJx2Fjd2HomZX5pHxIvTbPm+6zwUAdIwMwdpYpIUeAdpJZS72S8zxHQtCSFsLSBf8ExMpfAXtn3M1UFUCp+KaA4CxJjQlC0xfp5KXM4MJh2hbWGHJo8KB9jA44lNsTvn8PuQP4DwjfGeIgUf5crnSjsyZEkDLJR+0ox6FD946hTyD/cL4PpcB3B9fChIo1z5zdRgkzXnBqoltGLgbvmh5BH2CHHOYPPRkC/ANmAm+Q6wGSCe75YwXA5ge5A/wD6SHjzZDlzeAzeLh43UnPfzhD4EaA/ckO5M2eyoA0qpt4672MxEg9cfYCrEAV04SI1MGS1yj2vTLdwkCND3kEjf75bLu1/7Q/TrWUHljiDBpp89kdRzCMC+JiOynE251DuBDvIoBsrJnkI1XXI0xhDg/M1oLRx+YIbMHg9YOPtI4O3sbpvEjAugg5Zod5ct0KNDue0AcYYosxOiUlM0q0kAzKiiJcr4oGpK+KycglIrKoXwDmLvdIhzsfKlG/0k8jznnbYMi2SLO9JrH90Sab3VYO22nrOXW0jRhH1461FV63pcPNg958rvMjMaooXpvgdoS9qmOikXQmYL1l13cj1HZ+XP2nt93R22RUDoUl+RqVpWWu+Fd91zse2wrW2bFaD0FOxcdadwbXesO2yLLeWAs/DuTuHehXcpLyW9tz6qKg0weWEUK/x74cbno1oF7mvbS9DCO55KpvV1bPNvQHrO3MFXCvsgD4D6pQOgr/Uc39OthaVG7+EA4VgHmPgaNmJ0n/1+t+EQUjxneY8YPTbaTEQP92w44O4rBVNuw8gnw7MOnfsZNPZUtFtFXkMz/Pcpoq7yBz0GcCCDJRqv93CRH68IKF2JLJnTcVX3PHYI8EC+m2D9QL5bWv1Yyr0dUzZoGOCD62Bq7TxMSGAXCYk70CqwHbgq2pZKcOLqBax7hptN2YcVhpDtcDz4trX2SbGpV3nAHK57j7tj3h3y101Qx0VLpga+7W5zf3meL/dId5sH9ira0tFV+KqEgPX0D9DH9EPjU16P6IHzCS8vwi6sfaULE4/QzkY82jUFGTjvmoIsEfadcSbqAOljrUX7rFY/94vALjLe6+E2YNkOJb8zy3Zg2dY9t8nLdtyRK/bA9cXelOTde0SV2ykCBySdJotMaRIU7bGEox68cRB2+3H5GcluF6eNfd72ifsJaZ8obPD3wHFIzlJJj+SCr9o2/nvgbPxnQ2ffP9eL2zQZmDq03nbgoAC1EZbblsEPIHnC083kT3Dr6D1ZIFGv1BXtKRW2sUKgTJCuRVPio2aJKpfUru4QkASnR6YogGPl0p1KAHTtWT04IaFzmQPOfd/dtAJIJTUCLnNxlogKYN1EBfAX8idpvrf4MhdYbfVbjlzAtherVmbvIUDcHU2RHo5tSHZ6OAQgF7CTIY67L3GDtQdMHH9h8eoDyM97EBEJQFEhO2TiZG/sRKSL0wQgSUBTJJQDj0viS9AirNZkCgBOrA8B0qux3d4ClN160EdYr8bGgEpmJ1O4sUHuhGxsi7YD7OClMyUkox4RQVLRQTJH74HUjAFjkqztA2yV1YBUDuj8glS1JO+Zpw55I6ijt0ef3f/C8lGWIrLzYirdtMoMkkgP3CQSTFEgA60vomymq8jFE+aRLU6qC4CZ9UvwgL9SwMVe7gCj20wVTbaLa4YR5oE7Jv9t5FK68DxUcULbid845Uu3gb+n7h2HzGmk/M4ejauhN6f1FfT/vPCis9m9v5dwkkShBOX7KLCnIg2+3otKEBC5vMn7wCKUvMMhnHqAxmrwTJRp8JfKBO7Rnllv+XDL/BCkxuJ3yoxOX4gv35EreAGXRjlTxqNc5dcPCdAa+AihNw/BEie2ZyTfsORzt0DCRtxhznCGvayHayyxiu70ciypauWITC2hqiMG8oTjjWBKb2obwakavncHTtXw7TdwrMYD91wNXYuzJefK0VU9IHdCNxcBPXbll047Q0bAuWbrOfxycFiX7ydCQEIAzJHYuZ01U4LDyHYgWLxHh+O9dqCHPxIC0mjDutTYz6qI62p+YPCgHF86vaa37Xx7vV3ka7TgOLf0a1i+h0Rs19p3lFZ54ExGCEAyL/eAk9ASaCebXVPNVoiQ9vUccKOws3TD9lCxPQzbt0c54FIDvRsgwXSOKfHsvisA1sulP6b3SWtXegQnnBVTzJKHzjsEGo4Jk7SgRoVxzV8BPAH/wA7kSciZLTrCRzXDAW8ut2OFzpAdRIQ+xge/t4xkAOzgN7trOPjNYwhUSnjQEVDqBlO5PpUvSQehDlNCcW7Z1HCcW0aA3LIdR3du2WRy+J40UMNh74sAOk20hJeSgULPqrocGD1sTT1UAXKAfT+6lQw++CrCV2egDSApR8qRGhlf1IeBjS4yu6PIFd9ZDr4JEKZF+N4zjGp0vRkGKQZswPO9Z+A7+N4z4Ct8Yy9EBA9cSRdnylwj34EIZz/dm9vZT/4Y4Jb5syJAYfgeFrVgi+weYy0HiqXcewerJxBrHWDjyJmykUf5AI5VDkYwut0vjKXxYg+4FjrzCpzSlUsHhnVWaeQTOXvKBrxCKsfz+bXCMLpMHn0mO9QIlIjPR64hD16DE5XgvPi8pmSYsPkNzp26D2tnej1wJ4jXShU6jx0odl8cAGPTPKUM/Pvb8mU63c1CAwPyH7CpF0xBGsGV6zWKOZq2dr0EgBss+SS1MRbut3rZzBzv4acCrNFKzxBnWWDW+q4NCZ9NvQVyUUp0Gy6IH94lgAyC/uZdHri8a0rejOuBl8rPVCQRDoT6pl7nBeuB10F74Lr0qWcz5jsGSPlK6S3IJ15XOEUgyDd1IxLMS96M7gU8GZ2z9KiSSb2q3fHmSx64yZl7UguHrpUsgLp2tRli9+0IqBumN/V41wq6AdI4YYZgykRGt8ocl/ek6wswVibxdEYcizsnkBBpt249CnR6PHB5jjz6SsGdAUOvn0o7EPDhIXPvDNAr6HYJy4Gyg97P7n0HisN68qmBor5peFA2AtdpEXHG8jJmCQ2VjJr9ULX5IfesNAEC72auzv2ApjlAzIgJNTytA/SiyBxMjMCFZDwzAleY8cwIKBAxNwchHubmIOYCMYnUqh03R4CmAtCUERoW72HSjndcE2nHX9CMBtZBN1DbFSKOtuOt2MSpQfiGOTL+grgwiOsws/3+ldwGMIHFe+74PnC4545vEId77vjOcdOK0vs4awlUdzxwVXecKZPdcZV/915z67vOFFyBx5WbHo8D7n0h/m68cyXJ0OsOoO47NPmSPfCRDjCvyvcrgx+eM6Gq6F3jGlOvMCzvtToB6gBTFgT4RmbQCHK/MI0gV4dlgx642aAzZdmgq9wmNxiA2ZA5JJaOYH6XLz20dSYBFqqd2+ykB+70JF8HjQkhXrgI05N8QyNMNvKdjpgP4iWQkPXNAu4QhA/h9mjOizxwUyyzxGkR3EtJ6Y9YaSMqbxlVVcpM7XVpNjNe+BJMquHb8bYS6M68EQWGGEvlgDti7ZklvjO3KJexRNtlP5Zos6oKJNq8oQYSbQfcRJtFVTjTfuVwnECuL1yl0eA5nq1HXU/BnL655udS1AcYts9h6cXsB6jparjpwcj6zKN1lVY0S03/7AHEqFmSDPWhcl3PjJmpokcoH0CIZzFAr4Z5AIk5Zirr7vT3/ccCnD5F1x8vvYe2vO8yFh8lPONpD71P+BrShPUCcMnx0lD/RJ+u96SbqanbCN63H+sujhP0/S/Ss/PW1zHSuZHWPVXXswnB++kN5Q9NoIuiGRBmMSJT9Jn0kvcninIX0VuM6wHyAFPw5gxAW0neHDauHu+Y0edQ0asafL+0e/S5ddvYjPqHbiZqQY9q21ghdkE9xQmWxC2ec7h6f022AZP3E789cEeSMzV1Bjd6qKl3Ij7AtAyVAPFRUN7O2TBq1/foZteyv9S1bBXqiUaxPFfP0SheOrMWdFqNzGOHgPkWHvfgW2jEaAQ+PYHGmEbgYwqvdcc+pXe/56ALau7agj5Ld5gvvZe6HqClZL+QrH3mEMil5Pet50s55TMmR1JuE4xJvA5dZSrmzFH59SzOUJeEYkVVmwNxD9v0VskQEJpQogapEjnH+xp4ApYeUQ9Mde0tBnQ9nxb5VATIp4Ip9qlVD85GPrXmFvtUAGi8oika4VA5+wRoRPYi0hD7nJtnQGydc/Nsyvw2uTaIMq78hiVnyLy2pH8N3sLcttS0dw56ehNXCo6q6FGrEDA/rNubVuSI9Y7WFjnilWxubOml5o+TdIDuCu2RKXPE3kPrHoAongjvzFFM1Hy5ntAnJD4EqhAkc0hbwm3Igl7+k5x3SV/pvHX8P+df2XlLD107GgQIdN0KakC9jpIGmk63PIvvPDR1xaKWgDkhQH5BJ2jycRjLVFV1TnFdL62HNw3IeUS+BwB2Y0ra2w5c5RL+/qxCStaX7GmrjKhTLj3JqrhhgIuld/bAjOQj+fy9Hj++gGSDY759uk7mpMDX65pjz4Eh6Jv8RAiUNlIN+v8o+yikcmyXHH+UILSrc68joE0OsDyDLVmeoe512Q/aPjPDHhgttGRUQP0xvIS5Fv1lCgEpHzsgD0OD5Q78mlZyZbWRkap+SA4orDrqDhnLcwnm0Bh/b+RAsu2B68/ZEkQMDf8AWIzR/6wjivkM6KGU6L1ViOMATRfJgljJbas9L40QkDh/gGo3pCwdhY9KI39wPROzg86pJw8evUfuzggwdbmWHMfL/fg64oTSmdvzSOxu9EEOQA6KgJ7R1K1C3BNY0hWiwDfq3fMzBGTslShBVbvP0j87Zv39o97ArhwBcv5oisKF3py4cxDd8KlwGkE3jp/AQ/MOuhn6WKIcsetS0zsngnbi4tuslFDil8OMEr8PUYB/C4W7o8gDl2s7gmXs3BEs4HBEl5DEEcdBEjfXQK6W+zElj1F6ROJM48gBJsj0AdrZ8gjySpQqgcQR+bTWhh3JxnzPAeb8nSXgdlw1pOX0sAgIy8N8XSdpg5ZCmkhfA2giTyKsdCboXYK/VEItmtZQ57qCBB/YI5brnaG3l6Mhnd18SAN1Z+AMnKoAWXGAsRu2JB/mUC6uGrgYDVTdaXv4Hg55XagbPaDG8oOdZuA83uyUqfGX6uVVSb6PPBArdZjiCMsDoXqh3Ub60UFpZ++h0/YwUTcHiK1YtNFEZFkN5Ptqjqb25gCThmFTIOrGcj8oPklqRqhkwyo9ZirvOkHXByRuQGAfFLhJHx7L7c4Up4hDqjsofMMSM9YecMsK6+V54Cposr6+6WE6eZvaaiTUbzqZ7u9NMthVgAI6rJNjAjr82iCHQ/JE/y45HP7adZ41Nu4e0q73Mk03YvxQ4nH8pVorTT740cGi4YoKMzRcUZKGBh8IYv4FxR0UeG1pn1MirGOGcj8OuJqULDsLmpQs3oPqRCTeA5/c6bWCaCrJ+oDKkgO65lT/B4lXuqYAXRtJBKFQLQOSax+fRxcbgFSVk4sF4XPWUDeRSQ9cWX42hRK9JFULsl58cwMB2CRoilWLESDVYlCPZw1ik750wsEmfSmEYQMAWvfs1E3Jn3u7aV/yoLVyp69myv9OYM3k+p0yGoqsofNGwG6pcqYkJ0rwULmW6q+vYu1lD9xGl9ylmm8FiWW+gAWDmd1rxd3NAaClRpe8oJYa3ReDWmp2RRYPGw/cgcZ30piSLNypxaSDb7dBKTUHXJFlZ8o0k13dJnvr3s+E0VyLmDAaXDPGMmdwRynr/cIdpax/xlET3DTfVQOOnbXiJJkaAUkRd1RPgCdaAwCHxlY1/Qhi4zsKcQis/zEh0LTUiH7+NeXdrrg068ej6jSpjqLqNN1pihrSdEcpakizTrXSzB6op5KmNwOm70ym8OYIqhxYEguxogC5Ay5Fc6YsyDoJcqOB7pYNuITCrrl0V0o44Hqkv3CNDomWG1/2l+LYlRIMmGd1t+WY3L27fsZiqb8tJ53tg+6Km/+8l+KUc3MF34lzZU/5Kie46sLdigNSrHzHDfyC77IBkVYmTwggc8M6yJGgPqwDTFGW+CRq0PK1EqBaS5xVSEjdJQTs6goiaFkVuCOKjRq7xNYJwEvF0BQRNJTlJYKGAGUdoDXOzE2i3jwvyHq9BnDcAlMc6aByjowEBC/HTA+ag5keApRboynKieHiEU6ioeswADexwGWo3KEdm7R+67ghiAvTHXwIEAUE1WG7xZ2vMMkyxoBl5nkahHgeliM5A0NpzwFTJrfmtJFb25OmtVPwoEn3rwTqyHzfIjgi0JKi/MCVX69JFzqCJj8LeoHSMkuAgYt3gAUFFj+DMMJyaSDBzAJrENw8cMMhm4IAypVDyOXHBUrvgUvpuUmACFAbAnPnNgeu4QCbAuDPCnyG+gEQIOpOMMPB2RJoCvMYQhZOow5ZOI1TYJfsC4yOsvOActKEblVyjvkXeT5lrsTzMdf96/P89pVaLEs3tpdADGDqp4zO8NtefRZMgPMAfAoKj5GRogGcyHeAHbx3puDsFz8VAvgaWRdzvHiFO15GR60QsAOczhQdtcK66agVnNTns1Z4hJ+lCOAMv5MiMP0AJ0UAGgX33KyTO2CNAju85wA7G+JM2dkQV7l9cve4dgTEK91cuT2njWO6NU7PAVQpuNnPKVj+SqeUP6o45laibuC7Pw+9r5VBElexAzEU0BehPo5CRHSMEYSI+OCjCRGpmhkcujyNpCvqOzoHygDoeehWghH0GH/c83sHROG4pwOuSoUz9f1TnesssWzdShAco3Ll99iVOwUKZyhlOEANc7Rzfp/Pe9oxTfJbU+9AahFg59DcCVE7ucYqM0IMzuQkHx21w3F8dBTK+egoGOKjo1A1aa7gw/KZUrur3p0ptZNu7sAnnh1FR4e/4HOd9jHcuU47ZsduCwE+vgmm+PgmPBWJQkGXYgA6oVNpEbJVIlGt76q3WDD0gPlSlLZCQS+WbzFVHqfSAoJerPNhZ+PYFHgWko4xzRWSBbmlrKhlB+xAPoJkGd23NllG123QBdJZZDiSxxEVARaCAxkn6uVwiI/HhUk/upEkv9gtiCHej3MI+VrRLiGYN+CRaBeIm7Bol3DV5YKOE/n6ngqF+Scn/mWuzgF2qP57uhWszsxRCgUtKOyYEpvXrfgPVaG4g8iJR5hwl66JQBy56hGu9KgETr39EQR4LyWfkkLNFolH6Aaz9nYpM/XeQF74OiG9ziPvt9dy5cfLeTvXLeo5JtPVNsEFeSKhFfMVgvkNLBo4fQ3z7U5JwyKtB27wchIbcJ6eVS7gdDwzj5n0UFXQ/5RXHIB6rIbkUgOuIm51loi6yYcfO4r/4rTqBSD+jzbPLb0U/7GcvBYaIj8nufyZFGMnq+JfO/DKKpvQZ0DqESAHD6/NwXyscmbROJgjQNEFTVGkxcoxbspLPdk2hTTxGEfxABXMpk4A1ECPTOhPPj/4/9oJf3XtBGNiDjA1EmfKtFxc5cbddKvkDhTCPQBaCGQKtRCoctRCoMeF1MkDJnlAGhAoeUAiECh5wGIPIHlAAEoekCmTmOS6ge454IrpsZwEiOmxPgOI6TkZBiNRDjAhTmfKlDtd5SCewK8HAL8GmKLvh7p81BXwF+JuUVjWRF9Zfs/u93Hye6bcwJ4ExEz1lE+NynWbXH1fS+WkYVD6j1RwQfmPVXBNyI+F3Yx6OWG3oxyOUjmunNTb7OoNp9eHIns3yLA6hFdWMz20/1tlNSGpK9Lfe7FjpubzK2d3hFyv9FrY/QDpSFPAOrsCRYbT8mvEH6Cfe1hgVfkDSPMWvwytQJ7nqFDtAwJv+zRqCNQjIMKmmh7g8Gv8n/J6dujDrgAFJFV8NC5h4foD5DOWYOHaAbZw7UzZyvUHkMx+upv7Pg/Vj1oml39WZPxSN792kXSlBu0EVwYqoLQhLL+siQ2JnUda0NUsBOw0LD4qfjo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"title": null, "type": "GeoJSON", "viz": "color: opacity(hex(\\"#826DBA\\"), 0.5)\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", " init({\n", " basecolor,\n", " basemap,\n", " bounds,\n", " camera,\n", " has_legends,\n", " is_static,\n", " layer_selector,\n", " layers,\n", " mapboxtoken,\n", " show_info\n", " });\n", "});\n", "</script>\n", "</html>\n", "\">\n", "\n", "</iframe>" ], "text/plain": [ "<cartoframes.viz.layer.Layer at 0x7f80e0798d00>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes.viz import Layer, basic_style, basic_legend\n", "\n", "Layer(isochrones_gdf, basic_style(opacity=0.5), basic_legend('Isochrones'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Isodistances\n", "\n", "For isodistances, let's calculate the distance ranges of 100, 500 and 1000 meters. These ranges are input in `meters`, so they will be 100, 500, and 1000 respectively." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "_, isodistances_dry_metadata = iso_service.isodistances(geo_gdf, [100, 500, 1000], mode='walk', dry_run=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "available 115510, required 30\n" ] } ], "source": [ "print('available {0}, required {1}'.format(\n", " iso_service.available_quota(),\n", " isodistances_dry_metadata.get('required_quota'))\n", ")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success! Isolines created correctly\n" ] } ], "source": [ "isodistances_gdf, isodistances_metadata = iso_service.isodistances(geo_gdf, [100, 500, 1000], mode='walk')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>source_id</th>\n", " <th>data_range</th>\n", " <th>the_geom</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>100</td>\n", " <td>MULTIPOLYGON (((-73.95850 40.67139, -73.95721 ...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>500</td>\n", " <td>MULTIPOLYGON (((-73.96262 40.67276, -73.96219 ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>1000</td>\n", " <td>MULTIPOLYGON (((-73.96880 40.67345, -73.96820 ...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>100</td>\n", " <td>MULTIPOLYGON (((-73.96125 40.57800, -73.96065 ...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>500</td>\n", " <td>MULTIPOLYGON (((-73.96605 40.57800, -73.96563 ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " source_id data_range the_geom\n", "0 0 100 MULTIPOLYGON (((-73.95850 40.67139, -73.95721 ...\n", "1 0 500 MULTIPOLYGON (((-73.96262 40.67276, -73.96219 ...\n", "2 0 1000 MULTIPOLYGON (((-73.96880 40.67345, -73.96820 ...\n", "3 1 100 MULTIPOLYGON (((-73.96125 40.57800, -73.96065 ...\n", "4 1 500 MULTIPOLYGON (((-73.96605 40.57800, -73.96563 ..." ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isodistances_gdf.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe\n", " frameborder=\"0\"\n", " style=\"\n", " border: 1px solid #cfcfcf;\n", " width: 100%;\n", " height: 632px;\n", " \"\n", " srcDoc=\"\n", " <!DOCTYPE html>\n", "<html lang="en">\n", "<head>\n", " <title>None</title>\n", " <meta name="description" content="None">\n", " <meta name="viewport" content="width=device-width, initial-scale=1.0">\n", " <meta charset="UTF-8">\n", " <!-- Include CARTO VL JS -->\n", " <script src="https://libs.cartocdn.com/carto-vl/v1.4/carto-vl.min.js"></script>\n", " <!-- Include Mapbox GL JS -->\n", " <script src="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.js"></script>\n", " <!-- Include Mapbox GL CSS -->\n", " <link href="https://api.tiles.mapbox.com/mapbox-gl-js/v1.0.0/mapbox-gl.css" rel="stylesheet" />\n", "\n", " <!-- Include Airship -->\n", " <script nomodule="" 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100%;\n", " height: auto;\n", " }\n", "\n", " as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n", " font-size: 14px;\n", " line-height: 14px;\n", " }\n", "</style>\n", " <style>\n", " .map {\n", " position: absolute;\n", " height: 100%;\n", " width: 100%;\n", " }\n", "\n", " .map-info {\n", " position: absolute;\n", " bottom: 0;\n", " padding: 0 5px;\n", " background-color: rgba(255, 255, 255, 0.5);\n", " margin: 0;\n", " color: rgba(0, 0, 0, 0.75);\n", " font-size: 12px;\n", " width: auto;\n", " height: 18px;\n", " font-family: 'Open Sans';\n", " }\n", "\n", " .map-footer {\n", " background: #F2F6F9;\n", " font-family: Roboto;\n", " font-size: 12px;\n", " line-height: 24px;\n", " color: #162945;\n", " text-align: center;\n", " z-index: 2;\n", " }\n", "\n", " .map-footer a {\n", " text-decoration: none;\n", " }\n", "\n", " .map-footer a:hover {\n", " text-decoration: underline;\n", " }\n", "</style>\n", " <style>\n", " #error-container {\n", " position: absolute;\n", " width: 100%;\n", " height: 100%;\n", " background-color: white;\n", " visibility: hidden;\n", " padding: 1em;\n", " font-family: "Courier New", Courier, monospace;\n", " margin: 0 auto;\n", " font-size: 14px;\n", " overflow: auto;\n", " z-index: 1000;\n", " color: black;\n", " }\n", "\n", " .error-section {\n", " padding: 1em;\n", " border-radius: 5px;\n", " background-color: #fee;\n", " }\n", "\n", " #error-container #error-highlight {\n", " font-weight: bold;\n", " color: inherit;\n", " }\n", "\n", " #error-container #error-type {\n", " color: #008000;\n", " }\n", "\n", " #error-container #error-name {\n", " color: #ba2121;\n", " }\n", "\n", " #error-container #error-content {\n", " margin-top: 0.4em;\n", " }\n", "\n", " .error-details {\n", " margin-top: 1em;\n", " }\n", "\n", " #error-stacktrace {\n", " list-style: none;\n", " }\n", "</style>\n", " <style>\n", " .popup-content {\n", " display: flex;\n", " flex-direction: column;\n", " padding: 8px;\n", " }\n", "\n", " .popup-name {\n", " font-size: 12px;\n", " font-weight: 400;\n", " line-height: 20px;\n", " margin-bottom: 4px;\n", " }\n", "\n", " .popup-value {\n", " font-size: 16px;\n", " font-weight: 600;\n", " line-height: 20px;\n", " }\n", "\n", " .popup-value:not(:last-of-type) {\n", " margin-bottom: 16px;\n", " }\n", "</style>\n", " <style>\n", " as-widget-header .as-widget-header__header {\n", " margin-bottom: 8px;\n", " overflow-wrap: break-word;\n", " }\n", "\n", " as-widget-header .as-widget-header__subheader {\n", " margin-bottom: 12px;\n", " }\n", "\n", " as-category-widget {\n", " max-height: 250px;\n", " }\n", "</style>\n", "</head>\n", "\n", "<body class="as-app-body as-app">\n", " <img id="map-image" class="map-image" alt='Static map image' />\n", " <as-responsive-content id="main-container">\n", " \n", " <main class="as-main">\n", " <div class="as-map-area">\n", " <div id="map" class="map"></div>\n", " \n", " \n", " <div class="as-map-panels" data-name="Legends">\n", " <div class="as-panel as-panel--vertical as-panel--left as-panel--top">\n", " \n", "\n", "<div class="as-panel__element" id="legends">\n", " <as-layer-selector id="layer-selector">\n", " \n", " \n", " \n", " \n", " <div slot="as-checkbox-layer-0-slot">\n", " \n", " \n", " <as-legend\n", " heading="Isodistances"\n", " description="">\n", " <as-legend-color-category-polygon id="layer0_map0_legend0" slot="legends"></as-legend-color-category-polygon>\n", " \n", " </as-legend>\n", " \n", " \n", " </div>\n", " \n", " \n", " </as-layer-selector>\n", "</div>\n", " </div> <!-- as-panel -->\n", " </div> <!-- as-map-panels -->\n", " \n", " </div> <!-- as-map-area -->\n", " </main> <!-- as-main -->\n", " </as-responsive-content>\n", "\n", " \n", "\n", " <div id="error-container" class="error">\n", " <section class="error-section">\n", " <span class="errors" id="error-name"></span>:\n", " <section id="error-content">\n", " <span class="errors" id="error-type"></span>\n", " <span class="errors" id="error-message"></span>\n", " </section>\n", " </section>\n", "\n", " <details class="error-details">\n", " <summary>StackTrace</summary>\n", " <ul id="error-stacktrace"></ul>\n", " </details>\n", "</div>\n", "</body>\n", "\n", "<script>\n", " var init = (function () {\n", " 'use strict';\n", "\n", " const BASEMAPS = {\n", " DarkMatter: carto.basemaps.darkmatter,\n", " Voyager: carto.basemaps.voyager,\n", " Positron: carto.basemaps.positron\n", " };\n", "\n", " const attributionControl = new mapboxgl.AttributionControl({\n", " compact: false\n", " });\n", "\n", " const FIT_BOUNDS_SETTINGS = { animate: false, padding: 50, maxZoom: 16 };\n", "\n", " /** From https://github.com/errwischt/stacktrace-parser/blob/master/src/stack-trace-parser.js /\n", "\n", " /\n", " This parses the different stack traces and puts them into one format\n", " * This borrows heavily from TraceKit (https://github.com/csnover/TraceKit)\n", " /\n", "\n", " const UNKNOWN_FUNCTION = '<unknown>';\n", " const chromeRe = /^\\sat (.?) ?\$((?:file\|https?\|blob\|chrome-extension\|native\|eval\|webpack\|<anonymous>\|\\/).?)(?::(\\d+))?(?::(\\d+))?\$?\\s$/i;\n", " const chromeEvalRe = /\$(\\S)(?::(\\d+))(?::(\\d+))\$/;\n", " const winjsRe = /^\\sat (?:((?:\\[object object\\])?.+) )?\$?((?:file\|ms-appx\|https?\|webpack\|blob):.?):(\\d+)(?::(\\d+))?\$?\\s$/i;\n", " const geckoRe = /^\\s(.?)(?:\$(.?)\$)?(?:^\|@)((?:file\|https?\|blob\|chrome\|webpack\|resource\|\\[native).?\|[^@]bundle)(?::(\\d+))?(?::(\\d+))?\\s$/i;\n", " const geckoEvalRe = /(\\S+) line (\\d+)(?: > eval line \\d+) > eval/i;\n", "\n", " function parse(stackString) {\n", " const lines = stackString.split('\\n');\n", "\n", " return lines.reduce((stack, line) => {\n", " const parseResult =\n", " parseChrome(line) \|\|\n", " parseWinjs(line) \|\|\n", " parseGecko(line);\n", "\n", " if (parseResult) {\n", " stack.push(parseResult);\n", " }\n", "\n", " return stack;\n", " }, []);\n", " }\n", "\n", " function parseChrome(line) {\n", " const parts = chromeRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " const isNative = parts[2] && parts[2].indexOf('native') === 0; // start of line\n", " const isEval = parts[2] && parts[2].indexOf('eval') === 0; // start of line\n", "\n", " const submatch = chromeEvalRe.exec(parts[2]);\n", " if (isEval && submatch != null) {\n", " // throw out eval line/column and use top-most line/column number\n", " parts[2] = submatch[1]; // url\n", " parts[3] = submatch[2]; // line\n", " parts[4] = submatch[3]; // column\n", " }\n", "\n", " return {\n", " file: !isNative ? parts[2] : null,\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: isNative ? [parts[2]] : [],\n", " lineNumber: parts[3] ? +parts[3] : null,\n", " column: parts[4] ? +parts[4] : null,\n", " };\n", " }\n", "\n", " function parseWinjs(line) {\n", " const parts = winjsRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " return {\n", " file: parts[2],\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: [],\n", " lineNumber: +parts[3],\n", " column: parts[4] ? +parts[4] : null,\n", " };\n", " }\n", "\n", " function parseGecko(line) {\n", " const parts = geckoRe.exec(line);\n", "\n", " if (!parts) {\n", " return null;\n", " }\n", "\n", " const isEval = parts[3] && parts[3].indexOf(' > eval') > -1;\n", "\n", " const submatch = geckoEvalRe.exec(parts[3]);\n", " if (isEval && submatch != null) {\n", " // throw out eval line/column and use top-most line number\n", " parts[3] = submatch[1];\n", " parts[4] = submatch[2];\n", " parts[5] = null; // no column when eval\n", " }\n", "\n", " return {\n", " file: parts[3],\n", " methodName: parts[1] \|\| UNKNOWN_FUNCTION,\n", " arguments: parts[2] ? parts[2].split(',') : [],\n", " lineNumber: parts[4] ? +parts[4] : null,\n", " column: parts[5] ? +parts[5] : null,\n", " };\n", " }\n", "\n", " function displayError(e) {\n", " const error$ = document.getElementById('error-container');\n", " const errors$ = error$.getElementsByClassName('errors');\n", " const stacktrace$ = document.getElementById('error-stacktrace');\n", "\n", " errors$[0].innerHTML = e.name;\n", " errors$[1].innerHTML = e.type;\n", " errors$[2].innerHTML = e.message.replace(e.type, '');\n", "\n", " error$.style.visibility = 'visible';\n", "\n", " const stack = parse(e.stack);\n", " const list = stack.map(item => {\n", " return `<li>\n", " at <span class="stacktrace-method">${item.methodName}:</span>\n", " (${item.file}:${item.lineNumber}:${item.column})\n", " </li>`;\n", " });\n", "\n", " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", " // Computes the decimal coefficient and exponent of the specified number x with\n", " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", " // For example, formatDecimal(1.23) returns ["123", 0].\n", " function formatDecimal(x, p) {\n", " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", " var i, coefficient = x.slice(0, i);\n", "\n", " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", " return [\n", " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", " +x.slice(i + 1)\n", " ];\n", " }\n", "\n", " function exponent(x) {\n", " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", " }\n", "\n", " function formatGroup(grouping, thousands) {\n", " return function(value, width) {\n", " var i = value.length,\n", " t = [],\n", " j = 0,\n", " g = grouping[0],\n", " length = 0;\n", "\n", " while (i > 0 && g > 0) {\n", " if (length + g + 1 > width) g = Math.max(1, width - length);\n", " t.push(value.substring(i -= g, i + g));\n", " if ((length += g + 1) > width) break;\n", " g = grouping[j = (j + 1) % grouping.length];\n", " }\n", "\n", " return t.reverse().join(thousands);\n", " };\n", " }\n", "\n", " function formatNumerals(numerals) {\n", " return function(value) {\n", " return value.replace(/[0-9]/g, function(i) {\n", " return numerals[+i];\n", " });\n", " };\n", " }\n", "\n", " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", "\n", " function formatSpecifier(specifier) {\n", " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", " var match;\n", " return new FormatSpecifier({\n", " fill: match[1],\n", " align: match[2],\n", " sign: match[3],\n", " symbol: match[4],\n", " zero: match[5],\n", " width: match[6],\n", " comma: match[7],\n", " precision: match[8] && match[8].slice(1),\n", " trim: match[9],\n", " type: match[10]\n", " });\n", " }\n", "\n", " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", "\n", " function FormatSpecifier(specifier) {\n", " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", " this.zero = !!specifier.zero;\n", " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", " this.comma = !!specifier.comma;\n", " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", " this.trim = !!specifier.trim;\n", " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", " }\n", "\n", " FormatSpecifier.prototype.toString = function() {\n", " return this.fill\n", " + this.align\n", " + this.sign\n", " + this.symbol\n", " + (this.zero ? "0" : "")\n", " + (this.width === undefined ? "" : Math.max(1, this.width \| 0))\n", " + (this.comma ? "," : "")\n", " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision \| 0))\n", " + (this.trim ? "~" : "")\n", " + this.type;\n", " };\n", "\n", " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", " function formatTrim(s) {\n", " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", " switch (s[i]) {\n", " case ".": i0 = i1 = i; break;\n", " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", " }\n", " }\n", " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", " }\n", "\n", " var prefixExponent;\n", "\n", " function formatPrefixAuto(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1],\n", " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", " n = coefficient.length;\n", " return i === n ? coefficient\n", " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", " }\n", "\n", " function formatRounded(x, p) {\n", " var d = formatDecimal(x, p);\n", " if (!d) return x + "";\n", " var coefficient = d[0],\n", " exponent = d[1];\n", " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", " }\n", "\n", " var formatTypes = {\n", " "%": function(x, p) { return (x * 100).toFixed(p); },\n", " "b": function(x) { return Math.round(x).toString(2); },\n", " "c": function(x) { return x + ""; },\n", " "d": function(x) { return Math.round(x).toString(10); },\n", " "e": function(x, p) { return x.toExponential(p); },\n", " "f": function(x, p) { return x.toFixed(p); },\n", " "g": function(x, p) { return x.toPrecision(p); },\n", " "o": function(x) { return Math.round(x).toString(8); },\n", " "p": function(x, p) { return formatRounded(x * 100, p); },\n", " "r": formatRounded,\n", " "s": formatPrefixAuto,\n", " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", " "x": function(x) { return Math.round(x).toString(16); }\n", " };\n", "\n", " function identity(x) {\n", " return x;\n", " }\n", "\n", " var map = Array.prototype.map,\n", " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", "\n", " function formatLocale(locale) {\n", " var group = locale.grouping === undefined \|\| locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", "\n", " function newFormat(specifier) {\n", " specifier = formatSpecifier(specifier);\n", "\n", " var fill = specifier.fill,\n", " align = specifier.align,\n", " sign = specifier.sign,\n", " symbol = specifier.symbol,\n", " zero = specifier.zero,\n", " width = specifier.width,\n", " comma = specifier.comma,\n", " precision = specifier.precision,\n", " trim = specifier.trim,\n", " type = specifier.type;\n", "\n", " // The "n" type is an alias for ",g".\n", " if (type === "n") comma = true, type = "g";\n", "\n", " // The "" type, and any invalid type, is an alias for ".12~g".\n", " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", "\n", " // If zero fill is specified, padding goes after sign and before digits.\n", " if (zero \|\| (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", "\n", " // Compute the prefix and suffix.\n", " // For SI-prefix, the suffix is lazily computed.\n", " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", "\n", " // What format function should we use?\n", " // Is this an integer type?\n", " // Can this type generate exponential notation?\n", " var formatType = formatTypes[type],\n", " maybeSuffix = /[defgprs%]/.test(type);\n", "\n", " // Set the default precision if not specified,\n", " // or clamp the specified precision to the supported range.\n", " // For significant precision, it must be in [1, 21].\n", " // For fixed precision, it must be in [0, 20].\n", " precision = precision === undefined ? 6\n", " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", " : Math.max(0, Math.min(20, precision));\n", "\n", " function format(value) {\n", " var valuePrefix = prefix,\n", " valueSuffix = suffix,\n", " i, n, c;\n", "\n", " if (type === "c") {\n", " valueSuffix = formatType(value) + valueSuffix;\n", " value = "";\n", " } else {\n", " value = +value;\n", "\n", " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", " var valueNegative = value < 0 \|\| 1 / value < 0;\n", "\n", " // Perform the initial formatting.\n", " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", "\n", " // Trim insignificant zeros.\n", " if (trim) value = formatTrim(value);\n", "\n", " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", "\n", " // Compute the prefix and suffix.\n", " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" \|\| sign === "(" ? "" : sign) + valuePrefix;\n", " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", "\n", " // Break the formatted value into the integer “value” part that can be\n", " // grouped, and fractional or exponential “suffix” part that is not.\n", " if (maybeSuffix) {\n", " i = -1, n = value.length;\n", " while (++i < n) {\n", " if (c = value.charCodeAt(i), 48 > c \|\| c > 57) {\n", " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", " value = value.slice(0, i);\n", " break;\n", " }\n", " }\n", " }\n", " }\n", "\n", " // If the fill character is not "0", grouping is applied before padding.\n", " if (comma && !zero) value = group(value, Infinity);\n", "\n", " // Compute the padding.\n", " var length = valuePrefix.length + value.length + valueSuffix.length,\n", " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", "\n", " // If the fill character is "0", grouping is applied after padding.\n", " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", "\n", " // Reconstruct the final output based on the desired alignment.\n", " switch (align) {\n", " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", " default: value = padding + valuePrefix + value + valueSuffix; break;\n", " }\n", "\n", " return numerals(value);\n", " }\n", "\n", " format.toString = function() {\n", " return specifier + "";\n", " };\n", "\n", " return format;\n", " }\n", "\n", " function formatPrefix(specifier, value) {\n", " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", " k = Math.pow(10, -e),\n", " prefix = prefixes[8 + e / 3];\n", " return function(value) {\n", " return f(k * value) + prefix;\n", " };\n", " }\n", "\n", " return {\n", " format: newFormat,\n", " formatPrefix: formatPrefix\n", " };\n", " }\n", "\n", " var locale;\n", " var format;\n", " var formatPrefix;\n", "\n", " defaultLocale({\n", " decimal: ".",\n", " thousands: ",",\n", " grouping: [3],\n", " currency: ["$", ""],\n", " minus: "-"\n", " });\n", "\n", " function defaultLocale(definition) {\n", " locale = formatLocale(definition);\n", " format = locale.format;\n", " formatPrefix = locale.formatPrefix;\n", " return locale;\n", " }\n", "\n", " function formatter(value, specifier) {\n", " const formatFunc = specifier ? format(specifier) : formatValue;\n", "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", " return `> ${formatFunc(first)}`;\n", " }\n", " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", " if (typeof value === 'number') {\n", " return formatNumber(value);\n", " }\n", " return value;\n", " }\n", "\n", " function formatNumber(value) {\n", " if (!Number.isInteger(value)) {\n", " return value.toLocaleString(undefined, {\n", " minimumFractionDigits: 2,\n", " maximumFractionDigits: 3\n", " });\n", " }\n", " return value.toLocaleString();\n", " }\n", "\n", " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", " return {\n", " 'version': 8,\n", " 'sources': {},\n", " 'layers': [{\n", " 'id': 'background',\n", " 'type': 'background',\n", " 'paint': {\n", " 'background-color': basecolor\n", " }\n", " }]\n", " };\n", " }\n", "\n", " function getImageElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `map-image-${mapIndex}` : 'map-image';\n", " return document.getElementById(id);\n", " }\n", "\n", " function getContainerElement(mapIndex) {\n", " const id = mapIndex !== undefined ? `main-container-${mapIndex}` : 'main-container';\n", " return document.getElementById(id);\n", " }\n", "\n", " function saveImage(mapIndex) {\n", " const img = getImageElement(mapIndex);\n", " const container = getContainerElement(mapIndex);\n", "\n", " html2canvas(container)\n", " .then((canvas) => setMapImage(canvas, img, container));\n", " }\n", "\n", " function setMapImage(canvas, img, container) {\n", " const src = canvas.toDataURL();\n", " img.setAttribute('src', src);\n", " img.style.display = 'block';\n", " container.style.display = 'none';\n", " }\n", "\n", " function resetPopupClick(interactivity) {\n", " interactivity.off('featureClick');\n", " }\n", "\n", " function resetPopupHover(interactivity) {\n", " interactivity.off('featureHover');\n", " }\n", "\n", " function setPopupsClick(map, clickPopup, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureClick', (event) => {\n", " updatePopup(map, clickPopup, event, attrs);\n", " hoverPopup.remove();\n", " });\n", " }\n", "\n", " function setPopupsHover(map, hoverPopup, interactivity, attrs) {\n", " interactivity.on('featureHover', (event) => {\n", " updatePopup(map, hoverPopup, event, attrs);\n", " });\n", " }\n", "\n", " function updatePopup(map, popup, event, attrs) {\n", " if (event.features.length > 0) {\n", " let popupHTML = '';\n", " const layerIDs = [];\n", "\n", " for (const feature of event.features) {\n", " if (layerIDs.includes(feature.layerId)) {\n", " continue;\n", " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " <span class="popup-name">${item.title}</span>\n", " <span class="popup-value">${value}</span>\n", " ` + popupHTML;\n", " }\n", " }\n", " }\n", "\n", " if (popupHTML) {\n", " popup\n", " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", " .setHTML(`<div class="popup-content">${popupHTML}</div>`);\n", "\n", " if (!popup.isOpen()) {\n", " popup.addTo(map);\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", " }\n", " }\n", "\n", " function setInteractivity(map, interactiveLayers, interactiveMapLayers) {\n", " const interactivity = new carto.Interactivity(interactiveMapLayers);\n", "\n", " const clickPopup = new mapboxgl.Popup({\n", " closeButton: true,\n", " closeOnClick: false\n", " });\n", "\n", " const hoverPopup = new mapboxgl.Popup({\n", " closeButton: false,\n", " closeOnClick: false\n", " });\n", "\n", " const { clickAttrs, hoverAttrs } = _setInteractivityAttrs(interactiveLayers);\n", "\n", " resetPopupClick(map);\n", " resetPopupHover(map);\n", "\n", " if (clickAttrs.length > 0) {\n", " setPopupsClick(map, clickPopup, hoverPopup, interactivity, clickAttrs);\n", " }\n", "\n", " if (hoverAttrs.length > 0) {\n", " setPopupsHover(map, hoverPopup, interactivity, hoverAttrs);\n", " }\n", " }\n", "\n", " function _setInteractivityAttrs(interactiveLayers) {\n", " let clickAttrs = [];\n", " let hoverAttrs = [];\n", "\n", " interactiveLayers.forEach((interactiveLayer) => {\n", " interactiveLayer.interactivity.forEach((interactivityDef) => {\n", " if (interactivityDef.event === 'click') {\n", " clickAttrs = clickAttrs.concat(interactivityDef.attrs);\n", " } else if (interactivityDef.event === 'hover') {\n", " hoverAttrs = hoverAttrs.concat(interactivityDef.attrs);\n", " }\n", " });\n", " });\n", "\n", " return { clickAttrs, hoverAttrs };\n", " }\n", "\n", " function renderWidget(widget, value) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", " function renderBridge(bridge, widget, mapLayer) {\n", " widget.element = widget.element \|\| document.querySelector(`#${widget.id}`);\n", "\n", " switch (widget.type) {\n", " case 'histogram':\n", " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", " break;\n", " case 'animation':\n", " widget.options.propertyName = widget.prop;\n", " bridge.animationControls(widget.element, widget.value, widget.options);\n", " break;\n", " case 'time-series':\n", " widget.options.propertyName = widget.prop;\n", " bridge.timeSeries(widget.element, widget.value, widget.options);\n", " break;\n", " }\n", " }\n", "\n", " function bridgeLayerWidgets(map, mapLayer, mapSource, widgets) {\n", " const bridge = new AsBridge.VL.Bridge({\n", " carto: carto,\n", " layer: mapLayer,\n", " source: mapSource,\n", " map: map\n", " });\n", "\n", " widgets\n", " .filter((widget) => widget.has_bridge)\n", " .forEach((widget) => renderBridge(bridge, widget, mapLayer));\n", "\n", " bridge.build();\n", " }\n", "\n", " function _getWidgetType(layer, property, value) {\n", " return layer.metadata && layer.metadata.properties[value] ?\n", " layer.metadata.properties[value].type\n", " : _getWidgetPropertyType(layer, property);\n", " }\n", "\n", " function _getWidgetPropertyType(layer, property) {\n", " return layer.metadata && layer.metadata.properties[property] ?\n", " layer.metadata.properties[property].type\n", " : null;\n", " }\n", "\n", " function createLegends(layer, legends, layerIndex, mapIndex=0) {\n", " if (legends.length) {\n", " legends.forEach((legend, legendIndex) => _createLegend(layer, legend, layerIndex, legendIndex, mapIndex));\n", " } else {\n", " _createLegend(layer, legends, layerIndex, 0, mapIndex);\n", " }\n", " }\n", "\n", " function _createLegend(layer, legend, layerIndex, legendIndex, mapIndex=0) {\n", " const element = document.querySelector(`#layer${layerIndex}_map${mapIndex}_legend${legendIndex}`);\n", "\n", " if (legend.prop) {\n", " const othersLabel = 'Others'; // TODO: i18n\n", " const prop = legend.prop;\n", " const dynamic = legend.dynamic;\n", " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", " const formatFunc = (value) => formatter(value, legend.format);\n", " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", " }\n", "\n", " AsBridge.VL.Legends.rampLegend(element, layer, prop, options);\n", " }\n", " }\n", "\n", " function SourceFactory() {\n", " const sourceTypes = { GeoJSON, Query, MVT };\n", "\n", " this.createSource = (layer) => {\n", " return sourceTypes[layer.type](layer);\n", " };\n", " }\n", "\n", " function GeoJSON(layer) {\n", " const options = JSON.parse(JSON.stringify(layer.options));\n", " const data = _decodeJSONData(layer.data, layer.encode_data);\n", "\n", " return new carto.source.GeoJSON(data, options);\n", " }\n", "\n", " function Query(layer) {\n", " const auth = {\n", " username: layer.credentials.username,\n", " apiKey: layer.credentials.api_key \|\| 'default_public'\n", " };\n", "\n", " const config = {\n", " serverURL: layer.credentials.base_url \|\| `https://${layer.credentials.username}.carto.com/`\n", " };\n", "\n", " return new carto.source.SQL(layer.data, auth, config);\n", " }\n", "\n", " function MVT(layer) {\n", " return new carto.source.MVT(layer.data.file, JSON.parse(layer.data.metadata));\n", " }\n", "\n", " function _decodeJSONData(data, encodeData) {\n", " try {\n", " if (encodeData) {\n", " const decodedJSON = pako.inflate(atob(data), { to: 'string' });\n", " return JSON.parse(decodedJSON);\n", " } else {\n", " return JSON.parse(data);\n", " }\n", " } catch(error) {\n", " throw new Error(`\n", " Error: "${error}". CARTOframes is not able to parse your local data because it is too large.\n", " Please, disable the data compresion with encode_data=False in your Layer class.\n", " `);\n", " }\n", " }\n", "\n", " const factory = new SourceFactory();\n", "\n", " function initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex) {\n", " const mapSource = factory.createSource(layer);\n", " const mapViz = new carto.Viz(layer.viz);\n", " const mapLayer = new carto.Layer(`layer${layerIndex}`, mapSource, mapViz);\n", " const mapLayerIndex = numLayers - layerIndex - 1;\n", "\n", " try {\n", " mapLayer._updateLayer.catch(displayError);\n", " } catch (e) {\n", " throw e;\n", " }\n", "\n", " mapLayer.addTo(map);\n", "\n", " setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends);\n", " setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource);\n", "\n", " return mapLayer;\n", " }\n", "\n", " function getInteractiveLayers(layers, mapLayers) {\n", " const interactiveLayers = [];\n", " const interactiveMapLayers = [];\n", "\n", " layers.forEach((layer, index) => {\n", " if (layer.interactivity) {\n", " interactiveLayers.push(layer);\n", " interactiveMapLayers.push(mapLayers[index]);\n", " }\n", " });\n", "\n", " return { interactiveLayers, interactiveMapLayers };\n", " }\n", "\n", " function setLayerLegend(layer, mapLayerIndex, mapLayer, mapIndex, hasLegends) {\n", " if (hasLegends && layer.legends) {\n", " createLegends(mapLayer, layer.legends, mapLayerIndex, mapIndex);\n", " }\n", " }\n", "\n", " function setLayerWidgets(map, layer, mapLayer, mapLayerIndex, mapSource) {\n", " if (layer.widgets.length) {\n", " initLayerWidgets(layer.widgets, mapLayerIndex);\n", " updateLayerWidgets(layer.widgets, mapLayer);\n", " bridgeLayerWidgets(map, mapLayer, mapSource, layer.widgets);\n", " }\n", " }\n", "\n", " function initLayerWidgets(widgets, mapLayerIndex) {\n", " widgets.forEach((widget, widgetIndex) => {\n", " const id = `layer${mapLayerIndex}_widget${widgetIndex}`;\n", " widget.id = id;\n", " });\n", " }\n", "\n", " function updateLayerWidgets(widgets, mapLayer) {\n", " mapLayer.on('updated', () => renderLayerWidgets(widgets, mapLayer));\n", " }\n", "\n", " function renderLayerWidgets(widgets, mapLayer) {\n", " const variables = mapLayer.viz.variables;\n", "\n", " widgets\n", " .filter((widget) => !widget.has_bridge)\n", " .forEach((widget) => {\n", " const name = widget.variable_name;\n", " const value = getWidgetValue(name, variables);\n", " renderWidget(widget, value);\n", " });\n", " }\n", "\n", " function getWidgetValue(name, variables) {\n", " return name && variables[name] ? variables[name].value : null;\n", " }\n", "\n", " function setReady(settings) {\n", " try {\n", " return settings.maps ? initMaps(settings.maps) : initMap(settings);\n", " } catch (e) {\n", " displayError(e);\n", " }\n", " }\n", "\n", " function initMaps(maps) {\n", " return maps.map((mapSettings, mapIndex) => {\n", " return initMap(mapSettings, mapIndex);\n", " });\n", " }\n", "\n", " function initMap(settings, mapIndex) {\n", " const basecolor = getBasecolorSettings(settings.basecolor);\n", " const basemapStyle = BASEMAPS[settings.basemap] \|\| settings.basemap \|\| basecolor;\n", " const container = mapIndex !== undefined ? `map-${mapIndex}` : 'map';\n", " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", " map.flyTo(settings.camera);\n", " }\n", "\n", " return initLayers(map, settings, mapIndex);\n", " }\n", "\n", " function initLayers(map, settings, mapIndex) {\n", " const numLayers = settings.layers.length;\n", " const hasLegends = settings.has_legends;\n", " const isStatic = settings.is_static;\n", " const layers = settings.layers;\n", " const mapLayers = getMapLayers(\n", " layers,\n", " numLayers,\n", " hasLegends,\n", " map,\n", " mapIndex\n", " );\n", "\n", " if (settings.layer_selector) {\n", " addLayersSelector(layers.reverse(), mapLayers.reverse(), mapIndex);\n", " }\n", "\n", " setInteractiveLayers(map, layers, mapLayers);\n", "\n", " return waitForMapLayersLoad(isStatic, mapIndex, mapLayers);\n", " }\n", "\n", " function waitForMapLayersLoad(isStatic, mapIndex, mapLayers) {\n", " return new Promise((resolve) => {\n", " carto.on('loaded', mapLayers, onMapLayersLoaded.bind(\n", " this, isStatic, mapIndex, mapLayers, resolve)\n", " );\n", " });\n", " }\n", "\n", " function onMapLayersLoaded(isStatic, mapIndex, mapLayers, resolve) {\n", " if (isStatic) {\n", " saveImage(mapIndex);\n", " }\n", "\n", " resolve(mapLayers);\n", " }\n", "\n", " function getMapLayers(layers, numLayers, hasLegends, map, mapIndex) {\n", " return layers.map((layer, layerIndex) => {\n", " return initMapLayer(layer, layerIndex, numLayers, hasLegends, map, mapIndex);\n", " });\n", " }\n", "\n", " function setInteractiveLayers(map, layers, mapLayers) {\n", " const { interactiveLayers, interactiveMapLayers } = getInteractiveLayers(layers, mapLayers);\n", "\n", " if (interactiveLayers && interactiveLayers.length > 0) {\n", " setInteractivity(map, interactiveLayers, interactiveMapLayers);\n", " }\n", " }\n", "\n", " function addLayersSelector(layers, mapLayers, mapIndex) {\n", " const layerSelectorId = mapIndex !== undefined ? `#layer-selector-${mapIndex}` : '#layer-selector';\n", " const layerSelector$ = document.querySelector(layerSelectorId);\n", " const layersInfo = mapLayers.map((layer, index) => {\n", " return {\n", " title: layers[index].title \|\| `Layer ${index}`,\n", " id: layer.id,\n", " checked: true\n", " };\n", " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", "\n", " layerSelector.build();\n", " }\n", "\n", " function createMap(container, basemapStyle, bounds, accessToken) {\n", " const map = createMapboxGLMap(container, basemapStyle, accessToken);\n", "\n", " map.addControl(attributionControl);\n", " map.fitBounds(bounds, FIT_BOUNDS_SETTINGS);\n", "\n", " return map;\n", " }\n", "\n", " function createMapboxGLMap(container, style, accessToken) {\n", " if (accessToken) {\n", " mapboxgl.accessToken = accessToken;\n", " }\n", "\n", " return new mapboxgl.Map({\n", " container,\n", " style,\n", " zoom: 9,\n", " dragRotate: false,\n", " attributionControl: false\n", " });\n", " }\n", "\n", " function init(settings) {\n", " setReady(settings);\n", " }\n", "\n", " return init;\n", "\n", "}());\n", "</script>\n", "<script>\n", " document\n", " .querySelector('as-responsive-content')\n", " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", " const bounds = [[-74.0416718, 40.5738831], [-73.8624573, 40.6993675]];\n", " const camera = null;\n", " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", " const layers = [{"credentials": null, "data": 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", "title": null, "type": "GeoJSON", "viz": "color: opacity(hex(\\"#826DBA\\"), 0.5)\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", " init({\n", " basecolor,\n", " basemap,\n", " bounds,\n", " camera,\n", " has_legends,\n", " is_static,\n", " layer_selector,\n", " layers,\n", " mapboxtoken,\n", " show_info\n", " });\n", "});\n", "</script>\n", "</html>\n", "\">\n", "\n", "</iframe>" ], "text/plain": [ "<cartoframes.viz.layer.Layer at 0x7f80e04324f0>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes.viz import Layer, basic_style, basic_legend\n", "\n", "Layer(isodistances_gdf, basic_style(opacity=0.5), basic_legend('Isodistances'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-3-clause
luisibanez/tensorflow_tutorial	tf-tensors-manipulation-01.ipynb	1	10050	{ "metadata": { "name": "", "signature": "sha256:e80f563ee7a7dfd8a151d744d7030ddd4fcc77343c9e366ea0fdb34106482932" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Manipulating Tensors" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import tensorflow as tf" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vector Addition" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " fibonacci = tf.constant([1, 1, 2, 3, 5, 8], dtype=tf.int32)\n", " ones = tf.ones([6], dtype=tf.int32)\n", " beyond_fibonacci = tf.add(fibonacci, ones)\n", " \n", " with tf.Session() as sess:\n", " print beyond_fibonacci.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2 2 3 4 6 9]\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tensor Shapes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " scalar = tf.zeros([])\n", " vector = tf.zeros([3])\n", " matrix = tf.zeros([2,3])\n", " \n", " with tf.Session() as sess:\n", " print 'scalar has shape', scalar.get_shape(), 'and value: \\n', scalar.eval()\n", " print 'vector has shape', vector.get_shape(), 'and value: \\n', vector.eval()\n", " print 'matrix has shape', matrix.get_shape(), 'and value: \\n', matrix.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "scalar has shape () and value: \n", "0.0\n", "vector has shape (3,) and value: \n", "[ 0. 0. 0.]\n", "matrix has shape (2, 3) and value: \n", "[[ 0. 0. 0.]\n", " [ 0. 0. 0.]]\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Broadcasting" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " fibonacci = tf.constant([1, 1, 2, 3, 5, 8], dtype=tf.int32)\n", " ones = tf.ones(1, dtype=tf.int32)\n", " beyond_fibonacci = tf.add(fibonacci, ones)\n", " \n", " with tf.Session() as sess:\n", " print beyond_fibonacci.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2 2 3 4 6 9]\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrix Multiplication" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " x = tf.constant([[5, 2, 4, 3], [5, 1, 6, -2], [-1, 3, -1, -2]], dtype=tf.int32)\n", " y = tf.constant([[2, 2], [3, 5], [4, 5], [1, 6]], dtype=tf.int32)\n", " xy = tf.matmul(x, y)\n", " \n", " with tf.Session() as sess:\n", " print xy.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[35 58]\n", " [35 33]\n", " [ 1 -4]]\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tensor Reshaping" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " a = tf.constant([5, 2, 4, 3], dtype=tf.int32)\n", " b = tf.reshape(a, [2, 2])\n", " c = tf.reshape(b, [4, 1])\n", " \n", " with tf.Session() as sess:\n", " print 'a', a.eval()\n", " print 'b', b.eval()\n", " print 'c', c.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a [5 2 4 3]\n", "b [[5 2]\n", " [4 3]]\n", "c [[5]\n", " [2]\n", " [4]\n", " [3]]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "with tf.Graph().as_default():\n", " a = tf.constant([5, 3, 2, 7, 1, 4])\n", " b = tf.constant([4, 6, 3])\n", " \n", " c = tf.reshape(a, [2, 3])\n", " d = tf.reshape(b, [3, 1])\n", " \n", " cd = tf.matmul(c, d)\n", " \n", " with tf.Session() as sess:\n", " print 'a shape', a.get_shape(), 'value \\n', a.eval(), '\\n'\n", " print 'b shape', b.get_shape(), 'value \\n', b.eval(), '\\n'\n", " print 'c shape', c.get_shape(), 'value \\n', c.eval(), '\\n'\n", " print 'd shape', d.get_shape(), 'value \\n', d.eval(), '\\n'\n", " print 'cd shape', cd.get_shape(), 'value \\n', cd.eval(), '\\n'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a shape (6,) value \n", "[5 3 2 7 1 4] \n", "\n", "b shape (3,) value \n", "[4 6 3] \n", "\n", "c shape (2, 3) value \n", "[[5 3 2]\n", " [7 1 4]] \n", "\n", "d shape (3, 1) value \n", "[[4]\n", " [6]\n", " [3]] \n", "\n", "cd shape (2, 1) value \n", "[[44]\n", " [46]] \n", "\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Variables, Initialization and Assignment" ] }, { "cell_type": "code", "collapsed": false, "input": [ "g = tf.Graph()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "with g.as_default():\n", " v = tf.Variable([3])\n", " w = tf.Variable(tf.random_normal([1], mean=1.0, stddev=0.35))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "with g.as_default():\n", " with tf.Session() as sess:\n", " try:\n", " v.eval()\n", " except tf.errors.FailedPreconditionError as e:\n", " print \"Caught expected error\", e" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Caught expected error Attempting to use uninitialized value Variable\n", "\t [[Node: _send_Variable_0 = _Send[T=DT_INT32, client_terminated=true, recv_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=-7334369538008366203, tensor_name=\"Variable:0\", _device=\"/job:localhost/replica:0/task:0/cpu:0\"](Variable)]]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "with g.as_default():\n", " with tf.Session() as sess:\n", " initialization = tf.initialize_all_variables()\n", " sess.run(initialization)\n", " print v.eval()\n", " print w.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3]\n", "[ 1.46645296]\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "with g.as_default():\n", " with tf.Session() as sess:\n", " sess.run(tf.initialize_all_variables())\n", " print w.eval()\n", " print w.eval()\n", " print w.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1.80774593]\n", "[ 1.80774593]\n", "[ 1.80774593]\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "with g.as_default():\n", " with tf.Session() as sess:\n", " sess.run(tf.initialize_all_variables())\n", " \n", " print v.eval()\n", " \n", " assignment = tf.assign(v,[7])\n", " print v.eval()\n", " \n", " sess.run(assignment)\n", " print v.eval()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3]\n", "[3]\n", "[7]\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	apache-2.0
JonathanReeve/JonathanReeve.github.io	content/presentations/dataviz-2016/dataviz-workshop-executed.ipynb	2	294101	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Introductory Examples for the NLTK Book \n", "Loading text1, ..., text9 and sent1, ..., sent9\n", "Type the name of the text or sentence to view it.\n", "Type: 'texts()' or 'sents()' to list the materials.\n", "text1: Moby Dick by Herman Melville 1851\n", "text2: Sense and Sensibility by Jane Austen 1811\n", "text3: The Book of Genesis\n", "text4: Inaugural Address Corpus\n", "text5: Chat Corpus\n", "text6: Monty Python and the Holy Grail\n", "text7: Wall Street Journal\n", "text8: Personals Corpus\n", "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n" ] } ], "source": [ "# Get the Natural Language Processing Toolkit\n", "import nltk\n", "\n", "# Get the data science package Pandas\n", "import pandas as pd\n", "\n", "# Get the library matplotlib for making pretty charts\n", "import matplotlib as plt\n", "\n", "# Make plots appear here in this notebook\n", "%matplotlib inline\n", "\n", "# This just makes the plot size bigger, so that we can see it easier. \n", "plt.rcParams['figure.figsize'] = (12,4)\n", "\n", "# Get all the example books from the NLTK textbook\n", "from nltk.book import " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sperm Whale; Moby Dick; White Whale; old man; Captain Ahab; sperm\n", "whale; Right Whale; Captain Peleg; New Bedford; Cape Horn; cried Ahab;\n", "years ago; lower jaw; never mind; Father Mapple; cried Stubb; chief\n", "mate; white whale; ivory leg; one hand\n" ] } ], "source": [ "# Let's explore these texts a little. \n", "# There are lots of things we can do with these texts. \n", "# To see a list, type text1. and press <Tab>\n", "text1.collocations()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# But what if we get tired of doing that for each text, and want to do it with all of them? \n", "# Put the texts into a list.\n", "alltexts = [text1, text2, text3, text4, text5, text6, text7, text8, text9]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<Text: Moby Dick by Herman Melville 1851>,\n", " <Text: Sense and Sensibility by Jane Austen 1811>,\n", " <Text: The Book of Genesis>,\n", " <Text: Inaugural Address Corpus>,\n", " <Text: Chat Corpus>,\n", " <Text: Monty Python and the Holy Grail>,\n", " <Text: Wall Street Journal>,\n", " <Text: Personals Corpus>,\n", " <Text: The Man Who Was Thursday by G . K . Chesterton 1908>]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's look at it to make sure it's all there. \n", "alltexts" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sperm Whale; Moby Dick; White Whale; old man; Captain Ahab; sperm\n", "whale; Right Whale; Captain Peleg; New Bedford; Cape Horn; cried Ahab;\n", "years ago; lower jaw; never mind; Father Mapple; cried Stubb; chief\n", "mate; white whale; ivory leg; one hand\n", "---\n", "Colonel Brandon; Sir John; Lady Middleton; Miss Dashwood; every thing;\n", "thousand pounds; dare say; Miss Steeles; said Elinor; Miss Steele;\n", "every body; John Dashwood; great deal; Harley Street; Berkeley Street;\n", "Miss Dashwoods; young man; Combe Magna; every day; next morning\n", "---\n", "said unto; pray thee; thou shalt; thou hast; thy seed; years old;\n", "spake unto; thou art; LORD God; every living; God hath; begat sons;\n", "seven years; shalt thou; little ones; living creature; creeping thing;\n", "savoury meat; thirty years; every beast\n", "---\n", "United States; fellow citizens; four years; years ago; Federal\n", "Government; General Government; American people; Vice President; Old\n", "World; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;\n", "God bless; every citizen; Indian tribes; public debt; one another;\n", "foreign nations; political parties\n", "---\n", "wanna chat; PART JOIN; MODE #14-19teens; JOIN PART; PART PART;\n", "cute.-ass MP3; MP3 player; JOIN JOIN; times .. .; ACTION watches; guys\n", "wanna; song lasts; last night; ACTION sits; -...)...- S.M.R.; Lime\n", "Player; Player 12%; dont know; lez gurls; long time\n", "---\n", "BLACK KNIGHT; clop clop; HEAD KNIGHT; mumble mumble; Holy Grail;\n", "squeak squeak; FRENCH GUARD; saw saw; Sir Robin; Run away; CARTOON\n", "CHARACTER; King Arthur; Iesu domine; Pie Iesu; DEAD PERSON; Round\n", "Table; clap clap; OLD MAN; dramatic chord; dona eis\n", "---\n", "million U; New York; billion U; Wall Street; program trading; Mrs.\n", "Yeargin; vice president; Stock Exchange; Big Board; Georgia Gulf;\n", "chief executive; Dow Jones; S&P 500; says T-1; York Stock; last\n", "year; Sea Containers; South Korea; American Express; San Francisco\n", "---\n", "would like; medium build; social drinker; quiet nights; non smoker;\n", "long term; age open; Would like; easy going; financially secure; fun\n", "times; similar interests; Age open; weekends away; poss rship; well\n", "presented; never married; single mum; permanent relationship; slim\n", "build\n", "---\n", "said Syme; asked Syme; Saffron Park; Comrade Gregory; Leicester\n", "Square; Colonel Ducroix; red hair; old gentleman; could see; Inspector\n", "Ratcliffe; Anarchist Council; blue card; Scotland Yard; dark room;\n", "blue eyes; common sense; straw hat; hundred yards; said Gregory; run\n", "away\n", "---\n" ] } ], "source": [ "for text in alltexts: \n", " text.collocations()\n", " print('---')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Displaying 13 of 13 matches:\n", "want ? HEAD KNIGHT : We want ... a shrubbery ! [ dramatic chord ] ARTHUR : A wh\n", "ase ! No more ! We will find you a shrubbery . HEAD KNIGHT : You must return he\n", "IGHT : You must return here with a shrubbery or else you will never pass throug\n", "d fair , and we will return with a shrubbery . HEAD KNIGHT : One that looks nic\n", " in this town where we could buy a shrubbery ? [ dramatic chord ] OLD CRONE : W\n", " do not tell us where we can buy a shrubbery , my friend and I will say ... we \n", "s of Ni , we have brought you your shrubbery . May we go now ? HEAD KNIGHT : It\n", "o now ? HEAD KNIGHT : It is a good shrubbery . I like the laurels particularly \n", "irstly , you must find ... another shrubbery ! [ dramatic chord ] ARTHUR : Not \n", "matic chord ] ARTHUR : Not another shrubbery ! RANDOM : Ni ! HEAD KNIGHT : Then\n", "T : Then , when you have found the shrubbery , you must place it here beside th\n", "you must place it here beside this shrubbery , only slightly higher so you get \n", "T : Then , when you have found the shrubbery , you must cut down the mightiest \n" ] } ], "source": [ "text6.concordance('shrubbery')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3beac4fba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "text1.dispersion_plot(['Ahab', 'Ishmael', 'whale'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1fHN/5OVyzHaVkftgNvXm7fL5Md22i91vMPNzYVi+3eVcmks7y21msn09T862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\nX26e1/E41ZjfXR9D+vKbNF8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3beacd8390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "text2.dispersion_plot(['Elinor', 'Marianne', 'Edward', 'Willoughby'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3bea734400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "text6.dispersion_plot(['Ni', 'shrubbery'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "260819" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's count the words in a text\n", "len(text1)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Put the texts and their wordcounts into a lookup table\n", "lengths = {text.name: len(text) for text in alltexts}" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Chat Corpus': 45010,\n", " 'Inaugural Address Corpus': 145735,\n", " 'Moby Dick by Herman Melville 1851': 260819,\n", " 'Monty Python and the Holy Grail': 16967,\n", " 'Personals Corpus': 4867,\n", " 'Sense and Sensibility by Jane Austen 1811': 141576,\n", " 'The Book of Genesis': 44764,\n", " 'The Man Who Was Thursday by G . K . Chesterton 1908': 69213,\n", " 'Wall Street Journal': 100676}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lengths" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Chat Corpus 45010\n", "Inaugural Address Corpus 145735\n", "Moby Dick by Herman Melville 1851 260819\n", "Monty Python and the Holy Grail 16967\n", "Personals Corpus 4867\n", "Sense and Sensibility by Jane Austen 1811 141576\n", "The Book of Genesis 44764\n", "The Man Who Was Thursday by G . K . Chesterton 1908 69213\n", "Wall Street Journal 100676\n", "dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(lengths)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f3beaa5a7b8>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JdURERERETUmqIyIiIiJqSlIdEREREVFTkuqIiIiIiJqSVEdERERE1JSkOiIiIiKipiTV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3beac44400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.Series(lengths).plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['the', 'the', 'the', 'the', 'the', \"that's\", 'all', 'folks']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# That in itself is not very interesting. \n", "# So let's see if we can not only count the words, but count the vocabulary\n", "# of a text.\n", "# To do that, we can use `set()`, which will count every word once. \n", "porky_sentence = \"the the the the the that's all folks\"\n", "porky_words = porky_sentence.split()\n", "porky_words" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can count the words in the sentence easily: \n", "len(porky_words)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'all', 'folks', \"that's\", 'the'}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# To count the words, but ignore repeated words, we can use the function set(). \n", "set(porky_words)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# So if we count this set, we can determine the vocabulary of a text. \n", "len(set(porky_words))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "19317" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see if we can find the vocabulary of Moby Dick.\n", "len(set(text1))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "13.502044830977896" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pretty big, but then again, Moby Dick is kind of a long novel. \n", "# We can adjust for the words by adjusting for the total words: \n", "len(text1) / len(set(text1))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This would get tedious if we did this for every text, \n", "# so let's write a function!\n", "def vocab(text): \n", " return len(text) / len(set(text))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab(porky_words)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let's go through each text, and get its vocabulary, and put it in a table. \n", "vocabularies = {text.name: vocab(text) for text in alltexts}" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Chat Corpus 7.420046\n", "Inaugural Address Corpus 14.941050\n", "Moby Dick by Herman Melville 1851 13.502045\n", "Monty Python and the Holy Grail 7.833333\n", "Personals Corpus 4.392599\n", "Sense and Sensibility by Jane Austen 1811 20.719450\n", "The Book of Genesis 16.050197\n", "The Man Who Was Thursday by G . K . Chesterton 1908 10.167915\n", "Wall Street Journal 8.113798\n", "dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's put that table into Pandas so we can see it better: \n", "pd.Series(vocabularies)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f3bead190f0>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AITJSO8CQGKkdYEiM1A4wREZqBxgSI7UDDJGR2gGGxEjtAENipHaAWVmTEd4HAr+0fZnt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\nF/MsI7wxTt9Oa0soI755ncRsbdz8e3n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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3beacb9eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now let's plot that. \n", "pd.Series(vocabularies).plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# OK, now let's make a famous wordcloud\n", "from wordcloud import WordCloud" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3.5/site-packages/PIL/ImageDraw.py:99: UserWarning: setfont() is deprecated. Please set the attribute directly instead.\n", " \"Please set the attribute directly instead.\")\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f3beac200b8>" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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H/njTkKBlGkc8Xq26Ijo9jOK1ITn9EmDR2gcAEUOj/uX7ZI8qmZXufIOsD9Mq/SmeYhSv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He5HmpYXh1Zzs5o8lTeSeXHsyW6Iem+QwFhyCx5RTSi96RJlVwlGmNCy7Q/AJ01plTJgfQE9421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\nFbbsinBMf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xngzInxzpPrb3Lz2eepSVv+F/Gs71tm+b7irezrOcV8ZPMHAMvC2zir6nGm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3beaa75f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rawtext = ' '.join(text1.tokens) # Stitch it back together. \n", "wc = WordCloud(width=800, height=600, background_color='white')\n", "im = wc.generate(rawtext).to_image()\n", "plt.pyplot.imshow(im)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Now let's take a look at the inaugural address corpus in detail. \n", "from nltk.corpus import inaugural" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KLfSMibtXKbVYKVUBnAD8LNrjJbOcu9pM+zDNLHcv7g5E3+CXL9ciX7S2+LBB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n2AC6l8MX3idjGlg6uUOgYtoahSeUop1UJsQYE+uX9Az3D84OC1Bu4TkRsd6z3zfXIvBCZK1Qjr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\nDAwMDAyiwDjLBgYGBgYGBgYGBlHw/5l/u4r7Og1cAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3beabca710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Now let's set up a conditional word frequency distribution for it, \n", "# pairing off a list of words with the list of inaugural addresses. \n", "cfd = nltk.ConditionalFreqDist(\n", " (target, fileid[:4])\n", " for fileid in inaugural.fileids()\n", " for w in inaugural.words(fileid)\n", " for target in ['america', 'citizen']\n", " if w.lower().startswith(target))\n", "cfd.plot()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['adventure',\n", " 'belles_lettres',\n", " 'editorial',\n", " 'fiction',\n", " 'government',\n", " 'hobbies',\n", " 'humor',\n", " 'learned',\n", " 'lore',\n", " 'mystery',\n", " 'news',\n", " 'religion',\n", " 'reviews',\n", " 'romance',\n", " 'science_fiction']" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's play around with the Brown corpus. \n", "# It's a categorized text corpus. Let's see all the categories: \n", "nltk.corpus.brown.categories()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Now let's create another conditional frequency distribution, \n", "# this time based on these genres. \n", "genres = ['adventure', 'romance', 'science_fiction']\n", "words = ['can', 'could', 'may', 'might', 'must', 'will']\n", "cfdist = nltk.ConditionalFreqDist(\n", " (genre, word)\n", " for genre in genres\n", " for word in nltk.corpus.brown.words(categories=genre)\n", " if word in words)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ConditionalFreqDist(nltk.probability.FreqDist,\n", " {'adventure': FreqDist({'can': 46,\n", " 'could': 151,\n", " 'may': 5,\n", " 'might': 58,\n", " 'must': 27,\n", " 'will': 50}),\n", " 'romance': FreqDist({'can': 74,\n", " 'could': 193,\n", " 'may': 11,\n", " 'might': 51,\n", " 'must': 45,\n", " 'will': 43}),\n", " 'science_fiction': FreqDist({'can': 16,\n", " 'could': 49,\n", " 'may': 4,\n", " 'might': 12,\n", " 'must': 8,\n", " 'will': 16})})" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cfdist" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f3be80d8668>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3be80cca90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.DataFrame(cfdist).T.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
joshzarrabi/e-mission-server	emission/analysis/notebooks/Filter Evaluation Ignore Zeros Outliers.ipynb	2	15506904	null	bsd-3-clause
mari-linhares/tensorflow-workshop	code_samples/RNN/colorbot/colorbot_solutions.ipynb	1	10379	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Colorbot Solutions \n", "\n", "Here are the solutions to the exercises available at the colorbot notebook.\n", "\n", "In order to compare the models we encourage you to use Tensorboard and also use play_colorbot.py --model_dir=path_to_your_model to play with the models and check how it does with general words other than color words.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EXERCISE EXPERIMENT\n", "\n", "When using experiments you should make sure you repeat the datasets the number of epochs desired since the experiment will \"run the for loop for you\". Also, you can add a parameter to run a number of steps instead, it will run until the dataset ends or the number of steps.\n", "\n", "You can add this cell to your colorbot notebook and run it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# small important detail, to train properly with the experiment you need to\n", "# repeat the dataset the number of epochs desired\n", "train_input_fn = get_input_fn(TRAIN_INPUT, BATCH_SIZE, num_epochs=40)\n", "\n", "# create experiment\n", "def generate_experiment_fn(run_config, hparams):\n", " estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)\n", " return tf.contrib.learn.Experiment(\n", " estimator,\n", " train_input_fn=train_input_fn,\n", " eval_input_fn=test_input_fn\n", " )\n", "\n", "learn_runner.run(generate_experiment_fn, run_config=tf.contrib.learn.RunConfig(model_dir='model_dir'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EXERCISE DATASET\n", "\n", "0. Run the colorbot experiment and notice the choosen model_dir\n", "1. Below is the input function definition,we don't need some of the auxiliar functions anymore\n", "2. Add this cell and then add the solution to the EXERCISE EXPERIMENT\n", "3. choose a different model_dir and run the cells\n", "4. Copy the model_dir of the two models to the same path\n", "5. tensorboard --logdir=path" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_input_fn(csv_file, batch_size, num_epochs=1, shuffle=True):\n", " def _parse(line):\n", " # each line: name, red, green, blue\n", " # split line\n", " items = tf.string_split([line],',').values\n", "\n", " # get color (r, g, b)\n", " color = tf.string_to_number(items[1:], out_type=tf.float32) / 255.0\n", "\n", " # split color_name into a sequence of characters\n", " color_name = tf.string_split([items[0]], '')\n", " length = color_name.indices[-1, 1] + 1 # length = index of last char + 1\n", " color_name = color_name.values\n", " return color, color_name, length\n", "\n", " def input_fn():\n", " # https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/data\n", " dataset = (\n", " tf.contrib.data.TextLineDataset(csv_file) # reading from the HD\n", " .skip(1) # skip header\n", " .map(_parse) # parse text to variables\n", " .padded_batch(batch_size, padded_shapes=([None], [None], []),\n", " padding_values=(0.0, chr(0), tf.cast(0, tf.int64)))\n", " \n", " .repeat(num_epochs) # repeat dataset the number of epochs\n", " )\n", " \n", " # for our \"manual\" test we don't want to shuffle the data\n", " if shuffle:\n", " dataset = dataset.shuffle(buffer_size=100000)\n", "\n", " # create iterator\n", " color, color_name, length = dataset.make_one_shot_iterator().get_next()\n", "\n", " features = {\n", " COLOR_NAME_KEY: color_name,\n", " SEQUENCE_LENGTH_KEY: length,\n", " }\n", "\n", " return features, color\n", " return input_fn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result you will see something like:\n", "\n", "![](imgs/dataset_exercise_sol.png)\n", "\n", "We called the original model \"sorted_batch\" and the model using the simplified input function as \"simple_batch\"\n", "\n", "Notice that both models have basically the same loss in the last step, but the \"sorted_batch\" model runs way faster , notice the `global_step/sec` metric, it measures how many steps the model executes per second. Since the \"sorted_batch\" has a larger `global_step/sec` it means it trains faster. \n", "\n", "If you don't belive me you can change Tensorboard to compare the models in a \"relative\" way, this will compare the models over time. See result below.\n", "\n", "![](imgs/dataset_exercise_relative_sol.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EXERCISE HYPERPARAMETERS\n", "\n", "This one is more personal, what you see will depends on what you change in the model.\n", "Below is a very simple example we just changed the model to use a GRUCell, just in case..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_model_fn(rnn_cell_sizes,\n", " label_dimension,\n", " dnn_layer_sizes=[],\n", " optimizer='SGD',\n", " learning_rate=0.01):\n", " \n", " def model_fn(features, labels, mode):\n", " \n", " color_name = features[COLOR_NAME_KEY]\n", " sequence_length = tf.cast(features[SEQUENCE_LENGTH_KEY], dtype=tf.int32) # int64 -> int32\n", " \n", " # ----------- Preparing input --------------------\n", " # Creating a tf constant to hold the map char -> index\n", " # this is need to create the sparse tensor and after the one hot encode\n", " mapping = tf.constant(CHARACTERS, name=\"mapping\")\n", " table = tf.contrib.lookup.index_table_from_tensor(mapping, dtype=tf.string)\n", " int_color_name = table.lookup(color_name)\n", " \n", " # representing colornames with one hot representation\n", " color_name_onehot = tf.one_hot(int_color_name, depth=len(CHARACTERS) + 1)\n", " \n", " # ---------- RNN -------------------\n", " # Each RNN layer will consist of a GRU cell\n", " rnn_layers = [tf.nn.rnn_cell.GRUCell(size) for size in rnn_cell_sizes]\n", " \n", " # Construct the layers\n", " multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)\n", " \n", " # Runs the RNN model dynamically\n", " # more about it at: \n", " # https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn\n", " outputs, final_state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,\n", " inputs=color_name_onehot,\n", " sequence_length=sequence_length,\n", " dtype=tf.float32)\n", "\n", " # Slice to keep only the last cell of the RNN\n", " last_activations = rnn_common.select_last_activations(outputs,\n", " sequence_length)\n", "\n", " # ------------ Dense layers -------------------\n", " # Construct dense layers on top of the last cell of the RNN\n", " for units in dnn_layer_sizes:\n", " last_activations = tf.layers.dense(\n", " last_activations, units, activation=tf.nn.relu)\n", " \n", " # Final dense layer for prediction\n", " predictions = tf.layers.dense(last_activations, label_dimension)\n", "\n", " # ----------- Loss and Optimizer ----------------\n", " loss = None\n", " train_op = None\n", "\n", " if mode != tf.estimator.ModeKeys.PREDICT: \n", " loss = tf.losses.mean_squared_error(labels, predictions)\n", " \n", " if mode == tf.estimator.ModeKeys.TRAIN: \n", " train_op = tf.contrib.layers.optimize_loss(\n", " loss,\n", " tf.contrib.framework.get_global_step(),\n", " optimizer=optimizer,\n", " learning_rate=learning_rate)\n", " \n", " return model_fn_lib.EstimatorSpec(mode,\n", " predictions=predictions,\n", " loss=loss,\n", " train_op=train_op)\n", " return model_fn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is the tensorboard comparison of a model using a GRUCell called \"gru\" and a model using LSTMCell called \"simple_batch\".\n", "\n", "![](imgs/hyperparameters_exercise_sol.png)\n" ] } ], "metadata": { "colab": { "default_view": {}, "last_runtime": { "build_target": "//experimental/users/jamieas/transform_colab:notebook", "kind": "private" }, "name": "Copy of CustomEstimator.ipynb", "provenance": [ { "file_id": "0BwN-JPfIIHwgdFkwUTVIWTQwU00", "timestamp": 1496845355496 } ], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
machinelearningdeveloper/lc101-kc	November 10, 2016/Covered in class.ipynb	2	3048574	null	unlicense
mholtrop/Phys605	Python/Plotting/Plots_with_Bokeh.ipynb	1	98237	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting with Bokeh\n", "This example is a demo of what you can do with a different plotting package, Bokeh. \n", "Bokeh works very nicely in a Jupyter Notebook, since it outputs a bit of HTML and Javascript. The result is a plot that can be setup to be more interactive than Matplotlib, but some of the features are different." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import bokeh as b" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.1.1'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"1001\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" \|\| force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /*\n", " Render data to the DOM node\n", " /\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /\n", " Handle when an output is cleared or removed\n", " /\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /\n", " Handle when a new output is added\n", " /\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") \|\| (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " / Handle when an output is cleared or removed /\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " / Handle when a new output is added /\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /\n", " Register the mime type and append_mime function with output_area\n", " /\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " / Is output safe? /\n", " safe: true,\n", " / Index of renderer in `output_area.display_order` /\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1001\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", "\n", " for (var i = 0; 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If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null \|\| js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined \|\| force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", " <div class=\"bk-root\" id=\"408809dd-cd2c-40a1-99a9-eab5701d31e5\" data-root-id=\"1002\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", " \n", " var docs_json = {\"1403fc13-5f75-44e5-b789-8912d688a95b\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1011\"}],\"center\":[{\"id\":\"1014\"},{\"id\":\"1018\"}],\"left\":[{\"id\":\"1015\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"1036\"}],\"title\":{\"id\":\"1039\"},\"toolbar\":{\"id\":\"1026\"},\"x_range\":{\"id\":\"1003\"},\"x_scale\":{\"id\":\"1007\"},\"y_range\":{\"id\":\"1005\"},\"y_scale\":{\"id\":\"1009\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1019\",\"type\":\"PanTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1043\"},\"ticker\":{\"id\":\"1012\"}},\"id\":\"1011\",\"type\":\"LinearAxis\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1025\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1039\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1007\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1005\",\"type\":\"DataRange1d\"},{\"attributes\":{\"axis\":{\"id\":\"1011\"},\"ticker\":null},\"id\":\"1014\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"1041\"},\"ticker\":{\"id\":\"1016\"}},\"id\":\"1015\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1016\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis\":{\"id\":\"1015\"},\"dimension\":1,\"ticker\":null},\"id\":\"1018\",\"type\":\"Grid\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1019\"},{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"}]},\"id\":\"1026\",\"type\":\"Toolbar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"navy\"},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"value\":\"navy\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1034\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1020\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1003\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"HelpTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1025\"}},\"id\":\"1021\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1041\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"data\":{\"x\":[1,2,3,4,5],\"y\":[6,7,2,4,5]},\"selected\":{\"id\":\"1045\"},\"selection_policy\":{\"id\":\"1046\"}},\"id\":\"1033\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"source\":{\"id\":\"1033\"}},\"id\":\"1037\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"navy\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"navy\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1035\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"1033\"},\"glyph\":{\"id\":\"1034\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1035\"},\"selection_glyph\":null,\"view\":{\"id\":\"1037\"}},\"id\":\"1036\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1045\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1046\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.1\"}};\n", " var render_items = [{\"docid\":\"1403fc13-5f75-44e5-b789-8912d688a95b\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"408809dd-cd2c-40a1-99a9-eab5701d31e5\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "1002" } }, "output_type": "display_data" } ], "source": [ "from bokeh.plotting import figure, output_file, show\n", "\n", "# output to static HTML file\n", "# output_file(\"line.html\")\n", "b.io.output_notebook()\n", "p = figure(plot_width=400, plot_height=400)\n", "# add a circle renderer with a size, color, and alpha\n", "p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color=\"navy\", alpha=0.5)\n", "\n", "# show the results\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"1101\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" \|\| force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /\n", " Render data to the DOM node\n", " /\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /\n", " Handle when an output is cleared or removed\n", " /\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /\n", " Handle when a new output is added\n", " /\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") \|\| (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " / Handle when an output is cleared or removed /\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " / Handle when a new output is added /\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /\n", " Register the mime type and append_mime function with output_area\n", " /\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " / Is output safe? /\n", " safe: true,\n", " / Index of renderer in `output_area.display_order` /\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1101\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " if (url in hashes) {\n", " element.crossOrigin = \"anonymous\";\n", " element.integrity = \"sha384-\" + hashes[url];\n", " }\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined \|\| force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"1101\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" \|\| force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1101\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null \|\| js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined \|\| force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1101\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ipywidgets import interact\n", "import numpy as np\n", "\n", "from bokeh.io import push_notebook, show, output_notebook\n", "from bokeh.plotting import figure\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0, 2np.pi, 2000)\n", "y = np.sin(x)\n", "p = figure(title=\"simple line example\", plot_height=300, plot_width=900)\n", "r = p.line(x, y, color=\"#2222aa\", line_width=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def update(f, w=1, A=1, phi=0):\n", " if f == \"sin\": func = np.sin\n", " elif f == \"cos\": func = np.cos\n", " elif f == \"tan\": func = np.tan\n", " r.data_source.data['y'] = A * func(w * x + phi)\n", " push_notebook()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", " <div class=\"bk-root\" id=\"9e3fa587-7021-4dbf-92ae-12a84eaf54b6\" data-root-id=\"1102\"></div>\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[2000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[2000]}},\"selected\":{\"id\":\"1155\"},\"selection_policy\":{\"id\":\"1156\"}},\"id\":\"1135\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1126\",\"type\":\"HelpTool\"},{\"attributes\":{\"activ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ZhukovGreen/UMLND	finding_donors/finding_donors.ipynb	1	214872	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Supervised Learning\n", "## Project: Finding Donors for CharityML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n", "\n", "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. \n", "\n", ">Note: Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. \n", "\n", "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Exploring the Data\n", "Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>workclass</th>\n", " <th>education_level</th>\n", " <th>education-num</th>\n", " <th>marital-status</th>\n", " <th>occupation</th>\n", " <th>relationship</th>\n", " <th>race</th>\n", " <th>sex</th>\n", " <th>capital-gain</th>\n", " <th>capital-loss</th>\n", " <th>hours-per-week</th>\n", " <th>native-country</th>\n", " <th>income</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>39</td>\n", " <td>State-gov</td>\n", " <td>Bachelors</td>\n", " <td>13.0</td>\n", " <td>Never-married</td>\n", " <td>Adm-clerical</td>\n", " <td>Not-in-family</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>2174.0</td>\n", " <td>0.0</td>\n", " <td>40.0</td>\n", " <td>United-States</td>\n", " <td><=50K</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>Self-emp-not-inc</td>\n", " <td>Bachelors</td>\n", " <td>13.0</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Exec-managerial</td>\n", " <td>Husband</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>13.0</td>\n", " <td>United-States</td>\n", " <td><=50K</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>38</td>\n", " <td>Private</td>\n", " <td>HS-grad</td>\n", " <td>9.0</td>\n", " <td>Divorced</td>\n", " <td>Handlers-cleaners</td>\n", " <td>Not-in-family</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>40.0</td>\n", " <td>United-States</td>\n", " <td><=50K</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>53</td>\n", " <td>Private</td>\n", " <td>11th</td>\n", " <td>7.0</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Handlers-cleaners</td>\n", " <td>Husband</td>\n", " <td>Black</td>\n", " <td>Male</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>40.0</td>\n", " <td>United-States</td>\n", " <td><=50K</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>28</td>\n", " <td>Private</td>\n", " <td>Bachelors</td>\n", " <td>13.0</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Prof-specialty</td>\n", " <td>Wife</td>\n", " <td>Black</td>\n", " <td>Female</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>40.0</td>\n", " <td>Cuba</td>\n", " <td><=50K</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age workclass education_level education-num marital-status \\\n", "0 39 State-gov Bachelors 13.0 Never-married \n", "1 50 Self-emp-not-inc Bachelors 13.0 Married-civ-spouse \n", "2 38 Private HS-grad 9.0 Divorced \n", "3 53 Private 11th 7.0 Married-civ-spouse \n", "4 28 Private Bachelors 13.0 Married-civ-spouse \n", "\n", " occupation relationship race sex capital-gain \\\n", "0 Adm-clerical Not-in-family White Male 2174.0 \n", "1 Exec-managerial Husband White Male 0.0 \n", "2 Handlers-cleaners Not-in-family White Male 0.0 \n", "3 Handlers-cleaners Husband Black Male 0.0 \n", "4 Prof-specialty Wife Black Female 0.0 \n", "\n", " capital-loss hours-per-week native-country income \n", "0 0.0 40.0 United-States <=50K \n", "1 0.0 13.0 United-States <=50K \n", "2 0.0 40.0 United-States <=50K \n", "3 0.0 40.0 United-States <=50K \n", "4 0.0 40.0 Cuba <=50K " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import libraries necessary for this project\n", "import numpy as np\n", "import pandas as pd\n", "from time import time\n", "from IPython.display import display # Allows the use of display() for DataFrames\n", "import matplotlib.pyplot as plt\n", "\n", "# Import supplementary visualization code visuals.py\n", "import visuals as vs\n", "\n", "# Pretty display for notebooks\n", "%matplotlib inline\n", "\n", "# Load the Census dataset\n", "data = pd.read_csv(\"census.csv\")\n", "\n", "# Success - Display the first record\n", "display(data.head(n=5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Data Exploration\n", "A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \\$50,000. In the code cell below, you will need to compute the following:\n", "- The total number of records, `'n_records'`\n", "- The number of individuals making more than \\$50,000 annually, `'n_greater_50k'`.\n", "- The number of individuals making at most \\$50,000 annually, `'n_at_most_50k'`.\n", "- The percentage of individuals making more than \\$50,000 annually, `'greater_percent'`.\n", "\n", " HINT: You may need to look at the table above to understand how the `'income'` entries are formatted. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of records: 45222\n", "Individuals making more than $50,000: 11208\n", "Individuals making at most $50,000: 34014\n", "Percentage of individuals making more than $50,000: 24.78%\n" ] } ], "source": [ "# TODO: Total number of records\n", "n_records = len(data.index)\n", "\n", "# TODO: Number of records where individual's income is more than $50,000\n", "n_greater_50k = len(data[data.income == '>50K'].index)\n", "\n", "# TODO: Number of records where individual's income is at most $50,000\n", "n_at_most_50k = n_records - n_greater_50k\n", "\n", "# TODO: Percentage of individuals whose income is more than $50,000\n", "greater_percent = n_greater_50k / n_records * 100\n", "\n", "# Print the results\n", "print(\"Total number of records: {}\".format(n_records))\n", "print(\"Individuals making more than $50,000: {}\".format(n_greater_50k))\n", "print(\"Individuals making at most $50,000: {}\".format(n_at_most_50k))\n", "print(\"Percentage of individuals making more than $50,000: {:.2f}%\".format(\n", " greater_percent))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Featureset Exploration \n", "\n", "* age: continuous. \n", "* workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. \n", "* education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. \n", "* education-num: continuous. \n", "* marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. \n", "* occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. \n", "* relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. \n", "* race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. \n", "* sex: Female, Male. \n", "* capital-gain: continuous. \n", "* capital-loss: continuous. \n", "* hours-per-week: continuous. \n", "* native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Preparing the Data\n", "Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as preprocessing. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transforming Skewed Continuous Features\n", "A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number. Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`. \n", "\n", "Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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AmwK3llIm/HFN3UdfLaV8Em67b+IvWd4XGSbet4Mf+pt13g/3t5yqXNtNUrHN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\n1lQp5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7efbef175f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Split the data into features and target label\n", "income_raw = data['income']\n", "features_raw = data.drop('income', axis = 1)\n", "\n", "# Visualize skewed continuous features of original data\n", "vs.distribution(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a <a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.\n", "\n", "Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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caFtNd/5N3RdLJnYH1RwxB3NEP8pp5swzal/7HShdsJYAv46I59di4wXA1pRu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\n3/0awxJ47syHI0maI8wNkjS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BkJnHAsfOwqJWqZl7xLguo2e+rAe4LqNqPq3LlMwNQ+d2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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7efc28720f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Log-transform the skewed features\n", "skewed = ['capital-gain', 'capital-loss']\n", "features_log_transformed = pd.DataFrame(data = features_raw)\n", "features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))\n", "\n", "# Visualize the new log distributions\n", "vs.distribution(features_log_transformed, transformed = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalizing Numerical Features\n", "In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.\n", "\n", "Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>workclass</th>\n", " <th>education_level</th>\n", " <th>education-num</th>\n", " <th>marital-status</th>\n", " <th>occupation</th>\n", " <th>relationship</th>\n", " <th>race</th>\n", " <th>sex</th>\n", " <th>capital-gain</th>\n", " <th>capital-loss</th>\n", " <th>hours-per-week</th>\n", " <th>native-country</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.301370</td>\n", " <td>State-gov</td>\n", " <td>Bachelors</td>\n", " <td>0.800000</td>\n", " <td>Never-married</td>\n", " <td>Adm-clerical</td>\n", " <td>Not-in-family</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>0.667492</td>\n", " <td>0.0</td>\n", " <td>0.397959</td>\n", " <td>United-States</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.452055</td>\n", " <td>Self-emp-not-inc</td>\n", " <td>Bachelors</td>\n", " <td>0.800000</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Exec-managerial</td>\n", " <td>Husband</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.122449</td>\n", " <td>United-States</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.287671</td>\n", " <td>Private</td>\n", " <td>HS-grad</td>\n", " <td>0.533333</td>\n", " <td>Divorced</td>\n", " <td>Handlers-cleaners</td>\n", " <td>Not-in-family</td>\n", " <td>White</td>\n", " <td>Male</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.397959</td>\n", " <td>United-States</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.493151</td>\n", " <td>Private</td>\n", " <td>11th</td>\n", " <td>0.400000</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Handlers-cleaners</td>\n", " <td>Husband</td>\n", " <td>Black</td>\n", " <td>Male</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.397959</td>\n", " <td>United-States</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.150685</td>\n", " <td>Private</td>\n", " <td>Bachelors</td>\n", " <td>0.800000</td>\n", " <td>Married-civ-spouse</td>\n", " <td>Prof-specialty</td>\n", " <td>Wife</td>\n", " <td>Black</td>\n", " <td>Female</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " <td>0.397959</td>\n", " <td>Cuba</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age workclass education_level education-num \\\n", "0 0.301370 State-gov Bachelors 0.800000 \n", "1 0.452055 Self-emp-not-inc Bachelors 0.800000 \n", "2 0.287671 Private HS-grad 0.533333 \n", "3 0.493151 Private 11th 0.400000 \n", "4 0.150685 Private Bachelors 0.800000 \n", "\n", " marital-status occupation relationship race sex \\\n", "0 Never-married Adm-clerical Not-in-family White Male \n", "1 Married-civ-spouse Exec-managerial Husband White Male \n", "2 Divorced Handlers-cleaners Not-in-family White Male \n", "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", "4 Married-civ-spouse Prof-specialty Wife Black Female \n", "\n", " capital-gain capital-loss hours-per-week native-country \n", "0 0.667492 0.0 0.397959 United-States \n", "1 0.000000 0.0 0.122449 United-States \n", "2 0.000000 0.0 0.397959 United-States \n", "3 0.000000 0.0 0.397959 United-States \n", "4 0.000000 0.0 0.397959 Cuba " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import sklearn.preprocessing.StandardScaler\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "# Initialize a scaler, then apply it to the features\n", "scaler = MinMaxScaler() # default=(0, 1)\n", "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n", "\n", "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n", "features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])\n", "\n", "# Show an example of a record with scaling applied\n", "display(features_log_minmax_transform.head(n = 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Data Preprocessing\n", "\n", "From the table in Exploring the Data above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called categorical variables) be converted. One popular way to convert categorical variables is by using the one-hot encoding scheme. One-hot encoding creates a _\"dummy\"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.\n", "\n", "\| \| someFeature \| \| someFeature_A \| someFeature_B \| someFeature_C \|\n", "\| :-: \| :-: \| \| :-: \| :-: \| :-: \|\n", "\| 0 \| B \| \| 0 \| 1 \| 0 \|\n", "\| 1 \| C \| ----> one-hot encode ----> \| 0 \| 0 \| 1 \|\n", "\| 2 \| A \| \| 1 \| 0 \| 0 \|\n", "\n", "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n", " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.\n", " - Convert the target label `'income_raw'` to numerical entries.\n", " - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "103 total features after one-hot encoding.\n", "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n" ] } ], "source": [ "# TODO: One-hot encode the 'features_log_minmax_transform' data using pandas.get_dummies()\n", "features_final = pd.get_dummies(features_log_minmax_transform)\n", "\n", "# TODO: Encode the 'income_raw' data to numerical values\n", "income = income_raw.map({'<=50K': 0, '>50K': 1})\n", "\n", "# Print the number of features after one-hot encoding\n", "encoded = list(features_final.columns)\n", "print(\"{} total features after one-hot encoding.\".format(len(encoded)))\n", "\n", "# Uncomment the following line to see the encoded feature names\n", "print(encoded)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shuffle and Split Data\n", "Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.\n", "\n", "Run the code cell below to perform this split." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set has 36177 samples.\n", "Testing set has 9045 samples.\n" ] } ], "source": [ "# Import train_test_split\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Split the 'features' and 'income' data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(features_final, \n", " income, \n", " test_size = 0.2, \n", " random_state = 0)\n", "\n", "# Show the results of the split\n", "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n", "print(\"Testing set has {} samples.\".format(X_test.shape[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Evaluating Model Performance\n", "In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a naive predictor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Metrics and the Naive Predictor\n", "CharityML, equipped with their research, knows individuals that make more than \\$50,000 are most likely to donate to their charity. Because of this, CharityML is particularly interested in predicting who makes more than \\$50,000 accurately. It would seem that using accuracy as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that does not make more than \\$50,000 as someone who does would be detrimental to CharityML, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \\$50,000 is more important than the model's ability to recall those individuals. We can use F-beta score as a metric that considers both precision and recall:\n", "\n", "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n", "\n", "In particular, when $\\beta = 0.5$, more emphasis is placed on precision. This is called the F$_{0.5}$ score (or F-score for simplicity).\n", "\n", "Looking at the distribution of classes (those who make at most \\$50,000, and those who make more), it's clear most individuals do not make more than \\$50,000. This can greatly affect accuracy, since we could simply say \"this person does not make more than \\$50,000\" and generally be right, without ever looking at the data! Making such a statement would be called naive, since we have not considered any information to substantiate the claim. It is always important to consider the naive prediction for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \\$50,000, CharityML would identify no one as donors. \n", "\n", "\n", "#### Note: Recap of accuracy, precision, recall\n", "\n", " Accuracy measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).\n", "\n", " Precision tells us what proportion of messages we classified as spam, actually were spam.\n", "It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of\n", "\n", "`[True Positives/(True Positives + False Positives)]`\n", "\n", " Recall(sensitivity) tells us what proportion of messages that actually were spam were classified by us as spam.\n", "It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of\n", "\n", "`[True Positives/(True Positives + False Negatives)]`\n", "\n", "For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1 - Naive Predictor Performace\n", "* If we chose a model that always predicted an individual made more than $50,000, what would that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later.\n", "\n", " HINT: \n", "\n", "* When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. \n", "* Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n" ] } ], "source": [ "'''\n", "TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data \n", "encoded to numerical values done in the data preprocessing step.\n", "FP = income.count() - TP # Specific to the naive case\n", "\n", "TN = 0 # No predicted negatives in the naive case\n", "FN = 0 # No predicted negatives in the naive case\n", "'''\n", "# TODO: Calculate accuracy, precision and recall\n", "TP = np.sum(income) * 1.0\n", "FP = income.count() - TP\n", "\n", "accuracy = precision = TP / (TP + FP)\n", "recall = 1\n", "\n", "# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n", "# HINT: The formula above can be written as (1 + beta*2) (precision * recall) / ((beta*2 precision) + recall)\n", "beta = 0.5\n", "fscore = (1 + beta ** 2) * precision * recall / \\\n", " (beta ** 2 * precision + recall)\n", "\n", "# Print the results\n", "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(\n", " accuracy, fscore))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Supervised Learning Models\n", "The following are some of the supervised learning models that are currently available in [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) that you may choose from:\n", "- Gaussian Naive Bayes (GaussianNB)\n", "- Decision Trees\n", "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n", "- K-Nearest Neighbors (KNeighbors)\n", "- Stochastic Gradient Descent Classifier (SGDC)\n", "- Support Vector Machines (SVM)\n", "- Logistic Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2 - Model Application\n", "List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen\n", "\n", "- Describe one real-world application in industry where the model can be applied. \n", "- What are the strengths of the model; when does it perform well?\n", "- What are the weaknesses of the model; when does it perform poorly?\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", "\n", " HINT: \n", "\n", "Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: \n", "\n", "In order to choose three algorithm for the problem, I use the scikit-learn [algorithm cheet-sheet](http://scikit-learn.org/stable/tutorial/machine_learning_map/). \n", "\n", "Inputs: \n", "- we have a supervised learning classification task with a dataset around 45K datapoints and 103 hyperparameters. \n", "- our data is labeled\n", "- it is not a text classification\n", "- some labels are skewed (imbalanced data)\n", "\n", "Then we'll try in sequence (order matter):\n", "1. SVC (linear)\n", "2. KNN\n", "3. Ensemble Classifiers\n", "\n", "#### Support Vector Machines (SVM)\n", "- Describe one real-world application in industry where the model can be applied.\n", "\n", "> Image classification, handwritten character recognition, text classification etc.\n", "\n", "- What are the strengths of the model; when does it perform well?\n", "\n", "> Strengths:\n", "> * you're able to apply a kernel trick (non-linear separation). The kernel trick allows to reduce a computational expense. This is a reason of algorithm efficiency in a very high-dimensional spaces\n", "> * training is relatively easy, becasue of a known solution to a quadratic programming (find global optimum)\n", "\n", "> Performs well in:\n", "> * domain with a clear margin separation\n", "> * highly dimensional space\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", "\n", "> Weaknesses: \n", "> * not easy to tune (i.e. select a right kernel and its parameters)\n", "\n", "> Performs poorly:\n", "> * On big datasets the training time is cubic to the size of a dataset.\n", "\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", "\n", "> SVM is a general purpose robust algorithm where you can apply a kernel trick when non-linear separation take place. It is highly tunable and very powerful.\n", "\n", "#### K-Nearest Neighbors (KNeighbors)\n", "- Describe one real-world application in industry where the model can be applied.\n", "\n", "> Applications where search of similarities take place. For example: [Concept Search](https://en.wikipedia.org/wiki/Concept_search) or [Recommender Systems](https://en.wikipedia.org/wiki/Recommender_system). KNN also used as a feature extraction algorithm (reduce dimensionality).\n", "\n", "- What are the strengths of the model; when does it perform well?\n", "\n", "> Strengths:\n", "> * It is so called lazy learning algorithm, what means it is more robust to data noice and when data is not linear separable \n", "> * Very simple and almost no training required\n", "\n", "> Performs well in:\n", "> * small training sets with small number of dimensions\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", "\n", "> Weaknesses:\n", "> * The algorithm generelly slow, because of need to scan all the dataset to make prediction\n", "\n", "> Performs poorly:\n", "> * In high dimensions feature space\n", "> * In big datasets\n", "\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", "\n", "> if the data not linearly separable (SVC with linear kernel will be high biased), then KNN is a good candidate to try next. It is simple, quick to train and, though, capable to give good results for non-linear separable data\n", "\n", "#### Gradient Boosting\n", "\n", "- Describe one real-world application in industry where the model can be applied.\n", "\n", "> Applications where even samallest improvement in accuracy is tremendous are the case. It is healthcare aerospace etc.\n", "\n", "- What are the strengths of the model; when does it perform well?\n", "\n", "> Strengths:\n", "> * Generally more accurate then a single algorithms, because of ability to combine different algorithms (or different parameteres of the same algorithm) together in a different ways \n", "> * Bagging algorithms (i.e. Random Forest) is possible to run in parallel (easy to scale)\n", "\n", "> Performs well in:\n", "> * big datasets\n", "> * usually tollerates data noice and outliers\n", "\n", "- What are the weaknesses of the model; when does it perform poorly?\n", "\n", "> Weaknesses:\n", "> * can be sensitive to noisy data and outliers\n", "> * can be prone to overfitting when dataset is too small\n", "> * can be computationaly expensive\n", "> * can be hard to tune\n", "> * can be hard to parallelize\n", "\n", "> Performs poorly:\n", "> * on small dataset\n", "\n", "- What makes this model a good candidate for the problem, given what you know about the data?\n", "\n", "> It is powerfull algorithm which could handle data which are not linear separable. Moreover it is doing well in highly skewed hyperparameters, what is our case.\n", "\n", "### Main References:\n", "1. http://www.dataschool.io/comparing-supervised-learning-algorithms/\n", "1. https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms\n", "1. http://svms.org/\n", "1. http://scikit-learn.org/stable/tutorial/machine_learning_map/\n", "1. http://www.nickgillian.com/wiki/pmwiki.php?n=GRT.AdaBoost\n", "1. https://www.analyticsvidhya.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation - Creating a Training and Predicting Pipeline\n", "To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.\n", "In the code block below, you will need to implement the following:\n", " - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).\n", " - Fit the learner to the sampled training data and record the training time.\n", " - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.\n", " - Record the total prediction time.\n", " - Calculate the accuracy score for both the training subset and testing set.\n", " - Calculate the F-score for both the training subset and testing set.\n", " - Make sure that you set the `beta` parameter!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import fbeta_score, accuracy_score\n", "\n", "\n", "# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n", "\n", "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):\n", " \"\"\"\n", " inputs:\n", " - learner: the learning algorithm to be trained and predicted on\n", " - sample_size: the size of samples (number) to be drawn from training set\n", " - X_train: features training set\n", " - y_train: income training set\n", " - X_test: features testing set\n", " - y_test: income testing set\n", " \"\"\"\n", "\n", " results = {}\n", "\n", " # TODO: Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n", " start = time() # Get start time\n", " learner = learner.fit(X_train[:sample_size], y_train[:sample_size])\n", " end = time() # Get end time\n", "\n", " # TODO: Calculate the training time\n", " results['train_time'] = end - start\n", "\n", " # TODO: Get the predictions on the test set(X_test),\n", " # then get predictions on the first 300 training samples(X_train) using .predict()\n", " start = time() # Get start time\n", " predictions_test = learner.predict(X_test)\n", " predictions_train = learner.predict(X_train[:300])\n", " end = time() # Get end time\n", "\n", " # TODO: Calculate the total prediction time\n", " results['pred_time'] = end - start\n", "\n", " # TODO: Compute accuracy on the first 300 training samples which is y_train[:300]\n", " results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n", "\n", " # TODO: Compute accuracy on test set using accuracy_score()\n", " results['acc_test'] = accuracy_score(y_test, predictions_test)\n", "\n", " # TODO: Compute F-score on the the first 300 training samples using fbeta_score()\n", " results['f_train'] = fbeta_score(y_train[:300],\n", " predictions_train,\n", " beta=0.5)\n", "\n", " # TODO: Compute F-score on the test set which is y_test\n", " results['f_test'] = fbeta_score(y_test,\n", " predictions_test,\n", " beta=0.5)\n", "\n", " # Success\n", " print(\"{} trained on {} samples.\".format(learner.__class__.__name__,\n", " sample_size))\n", "\n", " # Return the results\n", " return results\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Initial Model Evaluation\n", "In the code cell, you will need to implement the following:\n", "- Import the three supervised learning models you've discussed in the previous section.\n", "- Initialize the three models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.\n", " - Use a `'random_state'` for each model you use, if provided.\n", " - Note: Use the default settings for each model — you will tune one specific model in a later section.\n", "- Calculate the number of records equal to 1%, 10%, and 100% of the training data.\n", " - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.\n", "\n", "Note: Depending on which algorithms you chose, the following implementation may take some time to run!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SVC trained on 361 samples.\n", "SVC trained on 3617 samples.\n", "SVC trained on 36177 samples.\n", "KNeighborsClassifier trained on 361 samples.\n", "KNeighborsClassifier trained on 3617 samples.\n", "KNeighborsClassifier trained on 36177 samples.\n", "GradientBoostingClassifier trained on 361 samples.\n", "GradientBoostingClassifier trained on 3617 samples.\n", "GradientBoostingClassifier trained on 36177 samples.\n" ] }, { "data": { "image/png": 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TJlzt06dP8pEjR+zGjRtXi4igjd+2bduqBwcHJ+7YseNCXl4eXb582WLgwIG1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\nT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"text/plain": [ "<matplotlib.figure.Figure at 0x7efbe64d6048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Import the three supervised learning models from sklearn\n", "from sklearn.svm import SVC\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "# TODO: Initialize the three models\n", "random_state = np.random.seed(0)\n", "clf_A = SVC(kernel='linear', random_state=random_state)\n", "clf_B = KNeighborsClassifier()\n", "clf_C = GradientBoostingClassifier(random_state=random_state)\n", "\n", "# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data\n", "# HINT: samples_100 is the entire training set i.e. len(y_train)\n", "# HINT: samples_10 is 10% of samples_100\n", "# HINT: samples_1 is 1% of samples_100\n", "samples_100 = len(y_train)\n", "samples_10 = int(0.1 * samples_100)\n", "samples_1 = int(0.01 * samples_100)\n", "\n", "# Collect results on the learners\n", "results = {}\n", "for clf in [clf_A, clf_B, clf_C]:\n", " clf_name = clf.__class__.__name__\n", " results[clf_name] = {}\n", " for i, samples in enumerate([samples_1, samples_10, samples_100]):\n", " results[clf_name][i] = \\\n", " train_predict(clf, samples, X_train, y_train, X_test, y_test)\n", "\n", "# Run metrics visualization for the three supervised learning models chosen\n", "vs.evaluate(results, accuracy, fscore)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion matrix (optional task)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7efbe6510a58>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7efbe61f9cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "import seaborn as sns\n", "\n", "# Compute confusion matrix for a model\n", "model = clf_C\n", "cm = confusion_matrix(y_test.values, model.predict(X_test))\n", "\n", "# view with a heatmap\n", "sns.heatmap(cm, annot=True, cmap='Blues', xticklabels=['no', 'yes'], yticklabels=['no', 'yes'])\n", "plt.ylabel('True label')\n", "plt.xlabel('Predicted label')\n", "plt.title('Confusion matrix for:\\n{}'.format(model.__class__.__name__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Improving Results\n", "In this final section, you will choose from the three supervised learning models the best model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3 - Choosing the Best Model\n", "\n", "* Based on the evaluation you performed earlier, in one to two paragraphs, explain to CharityML which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \\$50,000. \n", "\n", " HINT: \n", "Look at the graph at the bottom left from the cell above(the visualization created by `vs.evaluate(results, accuracy, fscore)`) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:\n", "* metrics - F score on the testing when 100% of the training data is used, \n", "* prediction/training time\n", "* the algorithm's suitability for the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: \n", "\n", "* Algorithm learning\n", "\n", "You start from the very simple algorithm, like, for example, if you're over 35 years old, then you earn more than 50k. Let's call it model 1. Then you apply the algorithm to the dataset and compare real results with the results from your algorithm. You have a misclassified some part of the dataset, because the model 1 was, perhaps, too simple. Let's imagine, you see that the model 1 performs especially poorly when the capital gain is bellow 60000. OK, lets add a model 2 to our intial model 1, which will compensate this model imperfection. Then, again, we apply our comulative Model 1+2 to the dataset and focus on misclassified subset and compensate it by addditional model. We repeat this process until we're satisfied with the result.\n", "\n", "* Algorithm predicting\n", "\n", "For example, we decided to send mails to people in a new district within the same city. We gathered, somehow, the data about district's inhabitants. Here we already have a good model from the step `Algorithm learning`. Then we just plug the those new parameters into the model we have the good predictions in a seconds. \n", "\n", "\n", "> #### More technical explanation (for history) if someone wants dive deeper:)\n", "\n", "> Gradient Boosting Classifier gives the best F0.5 score on the testing set. It gradually outperforms the rest classifiers. F-score on Training and Testing subsets are almost identical when 100% training set size being used. This indicates that our model is neither overfitted or undefitted at this training set size.\n", "\n", "> CharityML is going to spend money per each person we'll predict as earning more than 50k. This means even slight improvement in the model performance will save company's money. Let's imanging it is important for me:) Because of that training time don't really matter within reasonable frames. To train GBC takes longer than KNN, but much faster than SVC. However all of those training time may be accepted " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4 - Describing the Model in Layman's Terms\n", "\n", "* In one to two paragraphs, explain to CharityML, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.\n", "\n", " HINT: \n", "\n", "When explaining your model, if using external resources please include all citations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: \n", "Gradient Boosting algorithm is a subclass of boosting algorithms, when hypotesis (weak learner) shortcomings are compensated by an additional model. This additional model being tuned to minimize the training error. In this way we'll derive a strong learner what is our final hypotesis.\n", "\n", "Gradient Bossting algorithm trained in the following steps:\n", "* Pick an initial learning model which could be very simple, i.e. average value over the labels\n", "* Calculates the error rate over the entire dataset\n", "* Fits the additional model (usually decision tree) to minimize this error\n", "* Updates initial model with the additional model\n", "* Repeat previous steps until converged (the model do not show the imporvement)\n", "\n", "Gradient boosting predicts:\n", "By plugging new inputs (features) into the result algorithm from training stage.\n", "\n", "References:\n", "1. http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf\n", "1. https://en.wikipedia.org/wiki/Gradient_boosting\n", "1. https://www.quora.com/What-is-an-intuitive-explanation-of-gradient-boosted-trees\n", "1. http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Model Tuning\n", "Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:\n", "- Import [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n", "- Initialize the classifier you've chosen and store it in `clf`.\n", " - Set a `random_state` if one is available to the same state you set before.\n", "- Create a dictionary of parameters you wish to tune for the chosen model.\n", " - Example: `parameters = {'parameter' : [list of values]}`.\n", " - Note: Avoid tuning the `max_features` parameter of your learner if that parameter is available!\n", "- Use `make_scorer` to create an `fbeta_score` scoring object (with $\\beta = 0.5$).\n", "- Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.\n", "- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`.\n", "\n", "Note: Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Student Note: I found usefull to split a grid search into 3 levels. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unoptimized model\n", "------\n", "Accuracy score on testing data: 0.8630\n", "F-score on testing data: 0.7395\n", "\n", "Optimized Model\n", "------\n", "Final accuracy score on the testing data: 0.8722\n", "Final F-score on the testing data: 0.7532\n" ] } ], "source": [ "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import make_scorer\n", "\n", "# TODO: Initialize the classifier\n", "clf = GradientBoostingClassifier()\n", "clf_tuned = GradientBoostingClassifier(learning_rate=0.1,\n", " n_estimators=300,\n", " max_depth=5,\n", " min_samples_split=100,\n", " min_samples_leaf=1)\n", "# TODO: Create the parameters list you wish to tune, using a dictionary if needed.\n", "# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}\n", "parameters_level1 = dict(\n", " n_estimators=(50, 100, 150, 200, 300),\n", " learning_rate=(0.05, 0.1, 0.3, 0.5)\n", ")\n", "\"\"\"\n", "best params {'learning_rate': 0.1, 'n_estimators': 300}\n", "f0.5 0.7622 \n", "\"\"\"\n", "parameters_level2 = dict(\n", " max_depth=(2, 5, 10),\n", " min_samples_split=(2, 20, 50, 100)\n", ")\n", "\"\"\"\n", "best params max_depth=5, min_samples_split=100\n", "f0.5 0.7806 \n", "\"\"\"\n", "\n", "parameters_level3 = dict(\n", " min_samples_leaf=(1, 5, 10, 20, 50)\n", ")\n", "\"\"\"\n", "best params min_samples_leaf=1\n", "f0.5 0.7806 \n", "\"\"\"\n", "parameters = dict(\n", " loss=('deviance', 'exponential')\n", ")\n", "# TODO: Make an fbeta_score scoring object using make_scorer()\n", "scorer = make_scorer(fbeta_score, beta=0.5)\n", "\n", "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n", "grid_obj = GridSearchCV(estimator=clf_tuned, param_grid=parameters,\n", " scoring=scorer,\n", " n_jobs=-1)\n", "\n", "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n", "grid_fit = grid_obj.fit(X_train, y_train)\n", "# Get the estimator\n", "best_clf = grid_fit.best_estimator_\n", "# Make predictions using the unoptimized and model\n", "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n", "best_predictions = best_clf.predict(X_test)\n", "\n", "# Report the before-and-afterscores\n", "print(\"Unoptimized model\\n------\")\n", "print(\"Accuracy score on testing data: {:.4f}\".format(\n", " accuracy_score(y_test, predictions)))\n", "print(\"F-score on testing data: {:.4f}\".format(\n", " fbeta_score(y_test, predictions, beta=0.5)))\n", "print(\"\\nOptimized Model\\n------\")\n", "print(\"Final accuracy score on the testing data: {:.4f}\".format(\n", " accuracy_score(y_test, best_predictions)))\n", "print(\"Final F-score on the testing data: {:.4f}\".format(\n", " fbeta_score(y_test, best_predictions, beta=0.5)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 5 - Final Model Evaluation\n", "\n", "* What is your optimized model's accuracy and F-score on the testing data? \n", "* Are these scores better or worse than the unoptimized model? \n", "* How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in Question 1?_ \n", "\n", "Note: Fill in the table below with your results, and then provide discussion in the Answer box." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Results:\n", "\n", "\| Metric \| Benchmark Predictor \| Unoptimized Model \| Optimized Model \|\n", "\| :------------: \| :-----------------: \| :---------------: \| :-------------: \| \n", "\| Accuracy Score \| 0.25 \| 0.86 \| 0.87 \|\n", "\| F-score \| 0.29 \| 0.74 \| 0.75 \|\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: \n", "Both unoptimized and optimized model shows the great improve comparing to the naive predictor. Optimized model f score is higher at 1% compraing to the unoptimized model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Feature Importance\n", "\n", "An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \\$50,000.\n", "\n", "Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier. In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 6 - Feature Relevance Observation\n", "When Exploring the Data, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['age', 'workclass', 'education_level', 'education-num',\n", " 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n", " 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country'], dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_raw.columns.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "1. education_num: allow a person to be more efficient at work, by knowing more and use this knowledge to produce better results\n", "1. occupation: occupation defines how are you contributing to the company assets and barier of your intelligence in order to sufficiently cope with your duties.\n", "1. workclass: defines how many additional cells in the chain between final costumer and you. Less cells - bigger monetary flow to you\n", "1. age: linked with ability to think outside the box and expertise\n", "1. marital_status: affects your ability to dedicate time to a work" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation - Extracting Feature Importance\n", "Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.\n", "\n", "In the code cell below, you will need to implement the following:\n", " - Import a supervised learning model from sklearn if it is different from the three used earlier.\n", " - Train the supervised model on the entire training set.\n", " - Extract the feature importances using `'.feature_importances_'`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZZZcVDTvyyCM59thjGTNmDKeeemrR8D59+jBo0CAg1KLdcMMNtG7dmoKCAt56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\nMgjoE4/lWyj+mCmrzP5XdFwVc/xuVo5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"text/plain": [ "<matplotlib.figure.Figure at 0x7efbe643dd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: Import a supervised learning model that has 'feature_importances_'\n", "\n", "# TODO: Train the supervised model on the training set using .fit(X_train, y_train)\n", "model = best_clf\n", "\n", "# TODO: Extract the feature importances using .feature_importances_ \n", "importances = best_clf.feature_importances_\n", "\n", "# Plot\n", "vs.feature_plot(importances, X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 7 - Extracting Feature Importance\n", "\n", "Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \\$50,000. \n", "* How do these five features compare to the five features you discussed in Question 6?\n", "* If you were close to the same answer, how does this visualization confirm your thoughts? \n", "* If you were not close, why do you think these features are more relevant?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", " education_num, occupation, workclass, age, marital_status\n", " I surprised that occupation outside of the top 5. Also surprised that the age has such importance. Thus, I am not close to the reality (if data set is still relevant in our times). One observation is that first four features still has very similar level of importance, thus saying that age is more important then hours-per-week wouldn't be that confident.\n", " \n", "I overlooked capital loss and capital gain. OK, probably they are important, I just had problems with understanding its definitions, even though I was googling them,\n", "\n", "Hours-per-week - OK. It is important. Hard to say what is better when making more than 50k:)\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Selection\n", "How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of all features present in the data. This hints that we can attempt to reduce the feature space and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set with only the top five important features. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final Model trained on full data\n", "------\n", "Accuracy on testing data: 0.8722\n", "F-score on testing data: 0.7532\n", "\n", "Final Model trained on reduced data\n", "------\n", "Accuracy on testing data: 0.8419\n", "F-score on testing data: 0.6969\n" ] } ], "source": [ "# Import functionality for cloning a model\n", "from sklearn.base import clone\n", "\n", "# Reduce the feature space\n", "X_train_reduced = X_train[\n", " X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n", "X_test_reduced = X_test[\n", " X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n", "# Train on the \"best\" model found from grid search earlier\n", "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n", "\n", "# Make new predictions\n", "reduced_predictions = clf.predict(X_test_reduced)\n", "\n", "# Report scores from the final model using both versions of data\n", "print(\"Final Model trained on full data\\n------\")\n", "print(\"Accuracy on testing data: {:.4f}\".format(\n", " accuracy_score(y_test, best_predictions)))\n", "print(\"F-score on testing data: {:.4f}\".format(\n", " fbeta_score(y_test, best_predictions, beta=0.5)))\n", "print(\"\\nFinal Model trained on reduced data\\n------\")\n", "print(\"Accuracy on testing data: {:.4f}\".format(\n", " accuracy_score(y_test, reduced_predictions)))\n", "print(\"F-score on testing data: {:.4f}\".format(\n", " fbeta_score(y_test, reduced_predictions, beta=0.5)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 8 - Effects of Feature Selection\n", "\n", "* How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?\n", "* If training time was a factor, would you consider using the reduced data as your training set?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:\n", "The f score and accuracy of the final model, at reduced features, degraded gradually (9%). If I would need to reduce a training time, then, I think, you need to start exploring the ways wihtout compromising the algorithm score. And I learned here that there are a lot of them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
simpeg/simpegdc	notebooks/DCinverse.ipynb	1	76965	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['linalg']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "source": [ "from SimPEG import \n", "import simpegDCIP as DC\n", "%pylab inline\n", "from pymatsolver import MumpsSolver" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cs = 12.5\n", "nc = 500/cs+1\n", "hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n", "hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n", "hz = [(cs,7, -1.3),(cs,int(nc/2+1))]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ---- 3-D TensorMesh ---- \n", " x0: -541.97\n", " y0: -416.97\n", " z0: -548.22\n", " nCx: 55\n", " nCy: 35\n", " nCz: 28\n", " hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 4112.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", " hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 2112.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n", " hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 2112.50\n" ] } ], "source": [ "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n", "print mesh" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sighalf = 1e-2\n", "sigma = np.ones(mesh.nC)sighalf\n", "p0 = np.r_[-50., 50., -50.]\n", "p1 = np.r_[ 50.,-50., -150.]\n", "blk_ind = Utils.ModelBuilder.getIndecesBlock(p0, p1, mesh.gridCC)\n", "sigma[blk_ind] = 1e-3" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nElecs = 21\n", "x_temp = np.linspace(-250, 250, nElecs)\n", "aSpacing = x_temp[1]-x_temp[0]\n", "y_temp = 0.\n", "xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63\n" ] } ], "source": [ "srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n", "survey = DC.SurveyDC(srcList)\n", "print len(survey.srcList)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10db0fa50>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ev0t6C3BKRLy+qF2tnOF0cu+dwqzx+f3yisodr3/bjjcz3sQ2DRovW94rr0x+aSV66bWb\nslcf3Y93mUM2U+mXMzvlTO9T94iIeF36/Gngo/0a0coBx8zsqeSoudlrzNlf2S3lZmBeerry/cDx\nwJKunFXAUmClpIXAIxGxVdJDfepulPSaiPgy8JvA3f3a6QHHzKzJhtBLR8R2SUuB68jWsV0aEesl\nnZrKV0TEakmLJW0EngBO6lc3/fQpwD9Kejrw0/R9KnfFzMymzJB66Yi4BrimK7ai6/vSsnVT/GZ2\nX3xQyAOOmVmT+V5qZmZWiRb10i3aFTOzFmpRL92iXTEzayEfUjMzs0q0qJf2nQbMzKbA0O400PdS\nyoJ6/2s42x+2Fo2du3Ry753CrPH5/fKKyh2vf9uONzPexDYNGi9b3iuvTH5pPqRmZmaVaFEv3aJd\nMTNroRb10i3aFTOzFmpRL+0HsJmZWSVaNHaambVQixYNeFm0mdkUGNqy6FWTqHesl0VXppN77xRm\njc/vl1dU7nj923a8mfEmtmnQeNnyXnll8ktrUS/dol0xM2uhFh1S84BjZtZkLeqlW7QrZmYt1KJe\nukW7YmbWQi06pOZVamZmU2Boq9RunES9hV6lVplO7r1TmDU+v19eUbnj9W/b8WbGm9imQeNly3vl\nlckvrUW9dIt2xcyshVrUS7doV8zMWqhF53BqvZeapI9J2irp9lxsP0nXS7pb0hckzciVLZO0QdJd\nkt5QT6vNzEaPpEWp79wg6YyCnOWp/DZJCyaqK2mlpFvS6x5Jt/RrQ9037/w4sKgrdiZwfUQcCnwx\nfUfSfOB4YH6qc7GkuttvZja19p7Eq4ukacBFZH3nfGCJpMO6chYDh0TEPOAU4JKJ6kbECRGxICIW\nAJ9Jr0K1r1KTNBe4OiJenL7fBbwmIrZKmgmsjYhfkbQM2BkR56e8a4FOdK3h8Co1M2uCoa1S2zCJ\nevPGb1/SK4H3R8Si9P3M1MbzcjkfAdZExL+l73cBRwEHl6gr4F7g6Ij4flG7mngO54CI2Jo+bwUO\nSJ8PAvKDy2ZgVq8f6OTeO70SeuT3yysqd7z+bTvezHgT2zRovGx5r7wy+aUN5xzOLOC+3PfNwJEl\ncmaR9b0T1X0VsLXfYAPNHHCeFBExwYzFsxkza7fh9NJl+8rJzsqWAJdPlNTEAWerpJkR8YCkA4EH\nU3wLMCeXNzvFdrMm9/kesvmgmdlUuSe9r+mbNUkleum1/5G9+ujuP+eQzVT65cxOOdP71ZW0N/Am\n4GUTtbOJA84q4O3A+en9qlz8ckkXkE3z5gHrev3A0en9y3iwMbOpN9bP5PueoSnRSx/16uw15uwL\ndku5GZiXzpnfT7YAa0lXzipgKbBS0kLgkXQu/aEJ6r4OWB8R9w9hV6aOpCuA1wD7S7oPeB9wHnCl\npJOBTcDvAUTEnZKuBO4EtgOnRd0rHszMplgM4RxORGyXtBS4juys0KURsV7Sqal8RUSslrRY0kbg\nCeCkfnVzP388cEWZdtS+Sm3YvErNzJpgWKvUtv148HrTn+V7qVWmk3vvFGaNz++XV1TueP3bdryZ\n8Sa2adB42fJeeWXyy9rRol66RbtiZtY+26dN5vr2nUNvxzB4wDEza7Ade0+mm/750NsxDL41jJmZ\nVcIzHDOzBtsxrT23i/YqNTOzKTCsVWo/jH0GrvdcPe5ValXp5N47hVnj8/vlFZU7Xv+2HW9mvIlt\nGjRetrxXXpn8sra36IE4rRxwzMzaYkeLuun27ImZWQvt8AzHzMyq0KYBx4sGzMymwLAWDWyI2QPX\nm6fNXjRQlU7uvVOYNT6/X15RueP1b9vxZsab2KZB42XLe+WVyS/LiwbMzKwSXjRgZmaVaNM5HA84\nZmYN1qYBx/dSMzOzSniVmpnZFBjWKrV18WsD1ztC3/Uqtap0cu+dwqzx+f3yisodr3/bjjcz3sQ2\nDRovW94rr0x+WV40YGZmlWjTORwPOGZmDeYBx8zMKuEBx8zMKtGmOw14lZqZ2RQY1iq1a+Kogesd\no7W7bV/SIuBCYBrw0Yg4v8f2lgPHAD8B3hERt0xUV9LpwGnADuDzEXFGUbtaOcPp5N47hVnj8/vl\nFZU7Xs02Ok9Gp2oL9cab1ZrR+G+iinjZ8l55ZfLLGsYhNUnTgIuA1wFbgG9KWhUR63M5i4FDImKe\npCOBS4CF/epKOho4FnhJRGyT9Nx+7WjlgGNm1hZDOodzBLAxIjYBSFoJHAesz+UcC1wGEBE3SZoh\naSZwcJ+6fwicGxHbUr0f9muE7zRgZtZg25k28KuHWcB9ue+bU6xMzkF96s4DXi3pRklrJf16v33x\nDMfMrMGGdOFn2XPbg5532ht4dkQslPQK4Erghf2SzcxshN2x9kfcsfahfilbgDm573PIZir9cman\nnOl96m4GPgsQEd+UtFPScyKiZ2O8Ss3MbAoMa5XaFfHGgest0VXjti9pb+B7wGuB+4F1wJIeiwaW\nRsRiSQuBC9PMpbCupFOBgyLi/ZIOBW6IiOcXtauVM5xO7r1TmDU+v19eUbnj1WzDq9RGL97ENg0a\nL1veK69MflnDWDQQEdslLQWuI1vafGluwCAiVkTEakmLJW0EngBO6lc3/fTHgI9Juh34OfC2fu1o\n5YBjZtYWw7rwMyKuAa7piq3o+r60bN0U3wacWLYNHnDMzBrMd4s2M7NK+F5qZmZWiTYNOF6lZmY2\nBYa1Su2iOHngekt1qZ/4WZVO7r1TmDU+v19eUbnj1WzDq9RGL97ENg0aL1veK69Mflltult0Kwcc\nM7O28KIBMzOrRJvO4YzczTslLZJ0l6QNkgqfu2Bm1gY7mDbwq6lGatFAei7D98g9l4Hdb88wOjtk\nZq01rEUD58b/HrjeMl04mosGJL0b+GREPFxBeyZS5pkOXjRQcXyqt+FFA6MXb2KbBo2XLe+VVya/\nrDYtGihzSO0Asie8XZkOZ9U5apZ5psOUed/Onbxv507n17iNnTvfx86d73vK5Dft77iJ/01UsQ91\n2sHeA7+aasIBJyLOAg4lu0nbO4ANkj4o6UVT3LaezSmTtCa9AO6ZuraYmQG7+pl83zMsbTqHU2oo\njIidkh4AtgI7gGcDn5Z0Q0S8Zyob2KXMMx04Or1/mezZqMPy13sNtsbiqZZfxTb22uuvn1L5Tfs7\nbuJ/E1Xsw0TG+pl83zMsTR5ABlXmHM4fk91y+iHgo8CfR8Q2SXsBG4AqB5ybgXmS5pI9l+F4YEmF\n2zczq1SbBpwJV6lJOhv4WETc26NsfkTcOVWNK2jPMcCF7Houw7ld5V6lZma1G9YqtT+Lcwau93f6\nq9FcpRYR7+9TVulgk7bZ87kMeZ3ce6cwa3x+v7yicser2YZXqY1evIltGjRetrxXXpn8spq8CGBQ\n7dkTM7MWatMhNQ84ZmYN5gHHzMwq0aYLPz3gmJk1WJvO4YzUvdTK8Co1M2uCYa1SOzkuGrjepVo6\nmqvURlEn994pzBqf3y+vqNzxarbhVWqjF29imwaNly3vlVcmv6w2ncMZuccTmJk9lQzr1jZlHu0i\naXkqv03SgonqSupI2izplvRa1G9fWjnDMTNri2HMcNKjXS4i92gXSau6Hu2yGDgkIuZJOhK4BFg4\nQd0ALoiIC8q0wwOOmVmDDWmVWplHuxwLXAYQETdJmiFpJtmt4vrVLX2uyIfUzMwabEiPJyjzaJei\nnIMmqHt6OgR3qaQZ/fbFq9TMzKbAsFapvTGuGLjeVVoybvuS3gwsioh3pe9vBY6MiNNzOVcD50XE\n19P3G4AzgLlFdSU9D/hh+olzgAMj4uSidrXykFon994pzBqf3y+vqNzxarbhVWqjF29imwaNly3v\nlVcmv6wy53B+tPYOHlp7R7+UMo926c6ZnXKmF9WNiAfHgpI+ClzdrxGtHHDMzNqizDmcGUe9hBlH\nveTJ73ef/enulDKPdlkFLAVWSloIPBIRWyU9VFRX0oER8YNU/03A7f3a6QHHzKzBhnGngYjYLmkp\ncB27Hu2yXtKpqXxFRKyWtFjSRuAJ4KR+ddNPny/pcLLVavcAp/ZrhwccM7MGG9aFn70e7RIRK7q+\nLy1bN8XfNkgbPOCYmTVYm+404FVqZmZTYFir1I6Kvs+b7GmtjvG91KrSyb13CrPG5/fLKyp3vJpt\neJXa6MWb2KZB42XLe+WVyS/LjycwM7NKtOnxBO3ZEzOzFmrTORwPOGZmDdamAceLBszMpsCwFg0c\nHt8YuN6teqUXDVSlk3vvFGaNz++XV1TueP3bdryZ8Sa2adB42fJeeWXyy/KiATMzq4QXDZiZWSXa\ndA7HA46ZWYN5wDEzs0q06RyOV6mZmU2BYa1Smx0bBq63WfO8Sq0qndx7pzBrfH6/vKJyx+vftuPN\njDexTYPGy5b3yiuTX1abDqntVXcDzMzsqaGVMxwzs7Zo0wzHA46ZWYPt2OkBx8zMKrB9e3sGHK9S\nMzObAsNapbbPEz8cuN7jz3yuV6lVpZN77xRmjc/vl1dU7nj923a8mfEmtmnQeNnyXnll8sva0aIZ\nTisHHDOztvCAY2Zmldi+rT0DTi3X4Uh6i6Q7JO2Q9LKusmWSNki6S9IbcvGXS7o9lX24+labmVVv\n5469B371ImlR6lc3SDqjIGd5Kr9N0oKydSX9maSdkvbrty91Xfh5O/Am4Cv5oKT5wPHAfGARcLGk\nsRNflwAnR8Q8YJ6kRRW218ysHtunDf7qImkacBFZvzofWCLpsK6cxcAhqY89hazPnbCupDnA64F7\nJ9qVWlepSVoD/FlEfDt9XwbsjIjz0/dryc6/3Qt8KSIOS/ETgKMi4g96/KZXqZlZ7Ya1So3v7xy8\n4ov2Grd9Sa8E3h8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"text/plain": [ "<matplotlib.figure.Figure at 0x10d9ac710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n", "indz = 22\n", "dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n", "ax.set_xlim(-300, 300)\n", "ax.set_ylim(-300, 300)\n", "cb = plt.colorbar(dat[0])\n", "ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n", "\n", "txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n", "ax.plot(txLocs, txLocs0, 'w.', ms = 3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "expmap = Maps.ExpMap(mesh)\n", "m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n", "imap = Maps.IdentityMap(mesh)\n", "problem = DC.ProblemDC_CC(mesh, mapping= imap )\n", "problem.Solver = MumpsSolver\n", "problem.pair(survey)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1038621d0>,\n", " <matplotlib.lines.Line2D at 0x103862450>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e82c610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datasyn = survey.dpred(sighalfnp.ones(mesh.nC))\n", "survey.makeSyntheticData(sigma,std=0.01,force=True)\n", "plot(np.log(np.c_[survey.dobs,datasyn]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "problem.mapping = expmap m2to3" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dmis = DataMisfit.l2_DataMisfit(survey)\n", "reg = Regularization.Tikhonov(mesh,mapping=m2to3)\n", "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n", "opt.remember('xc')\n", "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n", "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n", "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n", "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SimPEG.InvProblem will set Regularization.mref to m0.\n", "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " *Done using same solver as the problem\n", "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", "============================ Inexact Gauss Newton ============================\n", " # beta phi_d phi_m f \|proj(x-g)-x\| LS Comment \n", "-----------------------------------------------------------------------------\n", " 0 7.28e+00 1.86e+03 1.05e+04 7.84e+04 2.20e+03 0 \n", " 1 7.28e+00 6.57e+02 1.06e+04 7.80e+04 1.15e+03 1 \n", " 2 1.46e+00 5.58e+02 1.06e+04 1.61e+04 1.03e+03 3 Skip BFGS \n", " 3 1.46e+00 1.36e+02 1.08e+04 1.59e+04 1.69e+02 0 Skip BFGS \n", " 4 2.91e-01 8.24e+01 1.07e+04 3.20e+03 2.05e+02 0 \n", " 5 2.91e-01 4.61e+01 1.07e+04 3.16e+03 1.35e+02 0 Skip BFGS \n", " 6 5.83e-02 4.40e+01 1.07e+04 6.65e+02 1.16e+02 0 \n", " 7 5.83e-02 3.99e+01 1.07e+04 6.61e+02 1.35e+02 0 \n", "------------------------- STOP! -------------------------\n", "1 : \|fc-fOld\| = 4.7528e+00 <= tolF(1+\|f0\|) = 7.8397e+03\n", "0 : \|xc-x_last\| = 4.6314e-01 <= tolX(1+\|x0\|) = 1.8172e-13\n", "0 : \|proj(x-g)-x\| = 1.3494e+02 <= tolG = 1.0000e-01\n", "0 : \|proj(x-g)-x\| = 1.3494e+02 <= 1e3eps = 1.0000e-02\n", "1 : maxIter = 7 <= iter = 7\n", "------------------------- DONE! -------------------------\n" ] } ], "source": [ "m0 = np.log(np.ones(problem.mapping.nP)sighalf)\n", "mopt = inv.run(m0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AY8DLwM/N4ue+64HjJb0GHBvWC16G4ziO0146yZvIzEbn2b4OOCWxvhBYmKPe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\njuM4+GDgOI7j4IOB4ziOA/w/91GdssuxpB0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103879a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colorbar(mesh.plotSlice(np.log10(expmap m2to3 * mopt), normal='Y', ind=indz, grid=True)[0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dpred = survey.dpred(mopt)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f42a790>,\n", " <matplotlib.lines.Line2D at 0x10f42aa10>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10fa4d110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(np.c_[dpred,survey.dobs])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
IS-ENES-Data/submission_forms	test/forms/test/test_testsuite_1234.ipynb	7	70879	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generic DKRZ national archive form \n", "\n", "\n", "This form is intended to provide a generic template for interactive forms e.g. for testing \n", "\n", "... to be finalized ..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fVGv94rzznrO/b01ZYgshBqNH/PSnv/h0UOlURBlA8gWuPphF4CZX4O7m/Bb/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BG4CFwEbhqYcs1mkh8WgYvAzZnA9RO0CFwELgKXiXqWl/YmfnlvmMFplOTTdzqqfiiICMhmuxTL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\nNm1L3AI3nbQr6Qhcf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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dkrz_forms import form_widgets\n", "form_widgets.show_status('form-submission')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "MY_LAST_NAME = \"....\" # e.gl MY_LAST_NAME = \"schulz\" \n", "#-------------------------------------------------\n", "from dkrz_forms import form_handler, form_widgets\n", "form_info = form_widgets.check_pwd(MY_LAST_NAME)\n", "sf = form_handler.init_form(form_info)\n", "form = sf.sub.entity_out.form_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Edit form information" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "form.myattribute = \"myinformation\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save your form\n", "\n", "your form will be stored (the form name consists of your last name plut your keyword)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "form_handler.save_form(sf,\"..my comment..\") # edit my comment info " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# officially submit your form\n", "the form will be submitted to the DKRZ team to process\n", "you also receive a confirmation email with a reference to your online form for future modifications " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "form_handler.email_form_info(sf)\n", "form_handler.form_submission(sf)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
tensorflow/docs-l10n	site/pt-br/tutorials/quickstart/advanced.ipynb	1	11458	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "rX8mhOLljYeM" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "BZSlp3DAjdYf" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "3wF5wszaj97Y" }, "source": [ "# TensorFlow 2 início rápido para especialistas" ] }, { "cell_type": "markdown", "metadata": { "id": "DUNzJc4jTj6G" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/quickstart/advanced\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">Ver em TensorFlow.org</a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/pt-br/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Executar no Google Colab</a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/pt-br/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">Ver código fontes no GitHub</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/pt-br/tutorials/quickstart/advanced.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">Baixar notebook</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "hiH7AC-NTniF" }, "source": [ "Este é um arquivo de notebook [Google Colaboratory] (https://colab.research.google.com/notebooks/welcome.ipynb). Os programas Python são executados diretamente no navegador - uma ótima maneira de aprender e usar o TensorFlow. Para seguir este tutorial, execute o bloco de anotações no Google Colab clicando no botão na parte superior desta página.\n", "\n", "1. No Colab, conecte-se a uma instância do Python: No canto superior direito da barra de menus, selecione * CONNECT.\n", "2. Execute todas as células de código do notebook: Selecione Runtime * > * Run all ." ] }, { "cell_type": "markdown", "metadata": { "id": "eOsVdx6GGHmU" }, "source": [ "Faça o download e instale o pacote TensorFlow 2:\n", "\n", "Note: Upgrade `pip` to install the TensorFlow 2 package. See the [install guide](https://www.tensorflow.org/install) for details." ] }, { "cell_type": "markdown", "metadata": { "id": "QS7DDTiZGRTo" }, "source": [ "Importe o TensorFlow dentro de seu programa:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0trJmd6DjqBZ" }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function, unicode_literals\n", "\n", "import tensorflow as tf\n", "\n", "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n", "from tensorflow.keras import Model" ] }, { "cell_type": "markdown", "metadata": { "id": "7NAbSZiaoJ4z" }, "source": [ "Carregue e prepare o [conjunto de dados MNIST] (http://yann.lecun.com/exdb/mnist/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JqFRS6K07jJs" }, "outputs": [], "source": [ "mnist = tf.keras.datasets.mnist\n", "\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "x_train, x_test = x_train / 255.0, x_test / 255.0\n", "\n", "# Adicione uma dimensão de canais\n", "x_train = x_train[..., tf.newaxis]\n", "x_test = x_test[..., tf.newaxis]" ] }, { "cell_type": "markdown", "metadata": { "id": "k1Evqx0S22r_" }, "source": [ "Use `tf.data` para agrupar e embaralhar o conjunto de dados:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8Iu_quO024c2" }, "outputs": [], "source": [ "train_ds = tf.data.Dataset.from_tensor_slices(\n", " (x_train, y_train)).shuffle(10000).batch(32)\n", "\n", "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)" ] }, { "cell_type": "markdown", "metadata": { "id": "BPZ68wASog_I" }, "source": [ "Crie o modelo `tf.keras` usando a Keras [API de subclasse de modelo] (https://www.tensorflow.org/guide/keras#model_subclassing):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "h3IKyzTCDNGo" }, "outputs": [], "source": [ "class MyModel(Model):\n", " def __init__(self):\n", " super(MyModel, self).__init__()\n", " self.conv1 = Conv2D(32, 3, activation='relu')\n", " self.flatten = Flatten()\n", " self.d1 = Dense(128, activation='relu')\n", " self.d2 = Dense(10, activation='softmax')\n", "\n", " def call(self, x):\n", " x = self.conv1(x)\n", " x = self.flatten(x)\n", " x = self.d1(x)\n", " return self.d2(x)\n", "\n", "# Crie uma instância do modelo\n", "model = MyModel()" ] }, { "cell_type": "markdown", "metadata": { "id": "uGih-c2LgbJu" }, "source": [ "Escolha uma função otimizadora e de perda para treinamento: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "u48C9WQ774n4" }, "outputs": [], "source": [ "loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n", "\n", "optimizer = tf.keras.optimizers.Adam()" ] }, { "cell_type": "markdown", "metadata": { "id": "JB6A1vcigsIe" }, "source": [ "Selecione métricas para medir a perda e a precisão do modelo. Essas métricas acumulam os valores ao longo das épocas e, em seguida, imprimem o resultado geral." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "N0MqHFb4F_qn" }, "outputs": [], "source": [ "train_loss = tf.keras.metrics.Mean(name='train_loss')\n", "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n", "\n", "test_loss = tf.keras.metrics.Mean(name='test_loss')\n", "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "ix4mEL65on-w" }, "source": [ "Use `tf.GradientTape` para treinar o modelo:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OZACiVqA8KQV" }, "outputs": [], "source": [ "@tf.function\n", "def train_step(images, labels):\n", " with tf.GradientTape() as tape:\n", " # training=True é necessário apenas se houver camadas com diferentes\n", " # comportamentos durante o treinamento versus inferência (por exemplo, Dropout).\n", " predictions = model(images, training=True)\n", " loss = loss_object(labels, predictions)\n", " gradients = tape.gradient(loss, model.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", "\n", " train_loss(loss)\n", " train_accuracy(labels, predictions)" ] }, { "cell_type": "markdown", "metadata": { "id": "Z8YT7UmFgpjV" }, "source": [ "Teste o modelo:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xIKdEzHAJGt7" }, "outputs": [], "source": [ "@tf.function\n", "def test_step(images, labels):\n", " # training=True é necessário apenas se houver camadas com diferentes\n", " # comportamentos durante o treinamento versus inferência (por exemplo, Dropout).\n", " predictions = model(images, training=False)\n", " t_loss = loss_object(labels, predictions)\n", "\n", " test_loss(t_loss)\n", " test_accuracy(labels, predictions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i-2pkctU_Ci7" }, "outputs": [], "source": [ "EPOCHS = 5\n", "\n", "for epoch in range(EPOCHS):\n", " # Reiniciar as métricas no início da próxima época\n", " train_loss.reset_states()\n", " train_accuracy.reset_states()\n", " test_loss.reset_states()\n", " test_accuracy.reset_states()\n", "\n", " for images, labels in train_ds:\n", " train_step(images, labels)\n", "\n", " for test_images, test_labels in test_ds:\n", " test_step(test_images, test_labels)\n", "\n", " template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'\n", " print(template.format(epoch+1,\n", " train_loss.result(),\n", " train_accuracy.result()100,\n", " test_loss.result(),\n", " test_accuracy.result()*100))" ] }, { "cell_type": "markdown", "metadata": { "id": "T4JfEh7kvx6m" }, "source": [ "O classificador de imagem agora é treinado para ~98% de acurácia neste conjunto de dados. Para saber mais, leia os [tutoriais do TensorFlow] (https://www.tensorflow.org/tutorials)." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "advanced.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
chusine/dlnd	autoencoder/Simple_Autoencoder_Solution.ipynb	2	40311	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Simple Autoencoder\n", "\n", "We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder and decoder will be built with neural networks, then trained on example data.\n", "\n", "![Autoencoder](assets/autoencoder_1.png)\n", "\n", "In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11abae4a8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11aa66da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = mnist.train.images[2]\n", "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building basically the simplest autoencoder with a single ReLU hidden layer. This layer will be used as the compressed representation. Then, the encoder is the input layer and the hidden layer. The decoder is the hidden layer and the output layer. Since the images are normalized between 0 and 1, we need to use a sigmoid activation on the output layer to get values matching the input.\n", "\n", "![Autoencoder architecture](assets/simple_autoencoder.png)\n", "\n", "\n", "> Exercise: Build the graph for the autoencoder in the cell below. The input images will be flattened into 784 length vectors. The targets are the same as the inputs. And there should be one hidden layer with a ReLU activation and an output layer with a sigmoid activation. The loss should be calculated with the cross-entropy loss, there is a convenient TensorFlow function for this `tf.nn.sigmoid_cross_entropy_with_logits` ([documentation](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits)). You should note that `tf.nn.sigmoid_cross_entropy_with_logits` takes the logits, but to get the reconstructed images you'll need to pass the logits through the sigmoid function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Size of the encoding layer (the hidden layer)\n", "encoding_dim = 32\n", "\n", "image_size = mnist.train.images.shape[1]\n", "\n", "inputs_ = tf.placeholder(tf.float32, (None, image_size), name='inputs')\n", "targets_ = tf.placeholder(tf.float32, (None, image_size), name='targets')\n", "\n", "# Output of hidden layer\n", "encoded = tf.layers.dense(inputs_, encoding_dim, activation=tf.nn.relu)\n", "\n", "# Output layer logits\n", "logits = tf.layers.dense(encoded, image_size, activation=None)\n", "# Sigmoid output from\n", "decoded = tf.nn.sigmoid(logits, name='output')\n", "\n", "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(0.001).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create the session\n", "sess = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here I'll write a bit of code to train the network. I'm not too interested in validation here, so I'll just monitor the training loss and the test loss afterwards. \n", "\n", "Calling `mnist.train.next_batch(batch_size)` will return a tuple of `(images, labels)`. We're not concerned with the labels here, we just need the images. Otherwise this is pretty straightfoward training with TensorFlow. We initialize the variables with `sess.run(tf.global_variables_initializer())`. Then, run the optimizer and get the loss with `batch_cost, _ = sess.run([cost, opt], feed_dict=feed)`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "epochs = 20\n", "batch_size = 200\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " feed = {inputs_: batch[0], targets_: batch[0]}\n", " batch_cost, _ = sess.run([cost, opt], feed_dict=feed)\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking out the results\n", "\n", "Below I've plotted some of the test images along with their reconstructions. For the most part these look pretty good except for some blurriness in some parts." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x125d34908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs})\n", "\n", "for images, row in zip([in_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "fig.tight_layout(pad=0.1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Up Next\n", "\n", "We're dealing with images here, so we can (usually) get better performance using convolution layers. So, next we'll build a better autoencoder with convolutional layers.\n", "\n", "In practice, autoencoders aren't actually better at compression compared to typical methods like JPEGs and MP3s. But, they are being used for noise reduction, which you'll also build." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
dsavransky/MAE4060	Notebooks/Spinning Symmetric Rigid Body.ipynb	1	45444	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dynamics of a Spinning Symmetric Rigid Body" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from miscpy.utils.sympyhelpers import \n", "init_printing()\n", "th,psi,thd,psidd,thdd,psidd,Omega,I1,I2,t,M1,C = \\\n", "symbols('theta,psi,thetadot,psidot,thetaddot,psiddot,Omega,I_1,I_2,t,M_1,C')\n", "diffmap = {th:thd,psi:psid,thd:thdd,psid:psidd}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![spinning symmetric rigid body](img/ssrb.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spinning Symmetric Rigid Body setup: The body's orientation in inertial frame $\\mathcal I$ is described by a 3-1-2 $(\\psi,\\theta,\\phi)$ rotation: the body is rotated by angle $\\psi$ about $\\mathbf e_3$ (creating intermediate frame $\\mathcal A$), by an angle $\\theta$ about $\\mathbf a_1$ (creating intermediate frame $\\mathcal B$), and finally spinning about $\\mathbf b_2 \\equiv \\mathbf c_2$ at a rate $\\Omega \\equiv \\dot\\phi$, creating body-fixed frame $\\mathcal C$. Note that while the body fixed frame is $\\mathcal C$, all computations here are performed in $\\mathcal B$ frame components." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "${}^\\mathcal{B}C^\\mathcal{A}$:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left[\\begin{matrix}1 & 0 & 0\\\\0 & \\cos{\\left (\\theta \\right )} & \\sin{\\left (\\theta \\right )}\\\\0 & - \\sin{\\left (\\theta \\right )} & \\cos{\\left (\\theta \\right )}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡1 0 0 ⎤\n", "⎢ ⎥\n", "⎢0 cos(θ) sin(θ)⎥\n", "⎢ ⎥\n", "⎣0 -sin(θ) cos(θ)⎦" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bCa = rotMat(1,th);bCa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\left[{}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{B}\\right]_\\mathcal{B}$:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left[\\begin{matrix}\\dot{\\theta}\\\\\\dot{\\psi} \\sin{\\left (\\theta \\right )}\\\\\\dot{\\psi} \\cos{\\left (\\theta \\right )}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ θ̇ ⎤\n", "⎢ ⎥\n", "⎢ψ̇⋅sin(θ)⎥\n", "⎢ ⎥\n", "⎣ψ̇⋅cos(θ)⎦" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWb_B = bCaMatrix([0,0,psid])+ Matrix([thd,0,0]); iWb_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "${}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{C} = {}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{B} + {}^\\mathcal{B}\\boldsymbol{\\omega}^\\mathcal{C}$. \n", "\n", "$\\left[{}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{C}\\right]_\\mathcal{B}$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left[\\begin{matrix}\\dot{\\theta}\\\\\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\\\\\dot{\\psi} \\cos{\\left (\\theta \\right )}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ θ̇ ⎤\n", "⎢ ⎥\n", "⎢Ω + ψ̇⋅sin(θ)⎥\n", "⎢ ⎥\n", "⎣ ψ̇⋅cos(θ) ⎦" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iWc_B = iWb_B +Matrix([0,Omega,0]); iWc_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\left[ \\mathbb I_G \\right]_\\mathcal B$:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} & 0 & 0\\\\0 & I_{2} & 0\\\\0 & 0 & I_{1}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡I₁ 0 0 ⎤\n", "⎢ ⎥\n", "⎢0 I₂ 0 ⎥\n", "⎢ ⎥\n", "⎣0 0 I₁⎦" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IG_B = diag(I1,I2,I1);IG_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\left[{}^\\mathcal{I} \\mathbf h_G\\right]_\\mathcal{B}$:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\theta}\\\\I_{2} \\left(\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\right)\\\\I_{1} \\dot{\\psi} \\cos{\\left (\\theta \\right )}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ I₁⋅θ̇ ⎤\n", "⎢ ⎥\n", "⎢I₂⋅(Ω + ψ̇⋅sin(θ))⎥\n", "⎢ ⎥\n", "⎣ I₁⋅ψ̇⋅cos(θ) ⎦" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hG_B = IG_BiWc_B; hG_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\vphantom{\\frac{\\mathrm{d}}{\\mathrm{d}t}}^\\mathcal{I}\\frac{\\mathrm{d}}{\\mathrm{d}t} {}^\\mathcal{I} \\mathbf h_G = \\vphantom{\\frac{\\mathrm{d}}{\\mathrm{d}t}}^\\mathcal{B}\\frac{\\mathrm{d}}{\\mathrm{d}t} {}^\\mathcal{I} \\mathbf h_G + {}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{B} \\times \\mathbf h_G$.\n", "\n", "$\\left[\\vphantom{\\frac{\\mathrm{d}}{\\mathrm{d}t}}^\\mathcal{I}\\frac{\\mathrm{d}}{\\mathrm{d}t} {}^\\mathcal{I} \\mathbf h_G\\right]_\\mathcal{B}$:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\psi}^{2} \\sin{\\left (\\theta \\right )} \\cos{\\left (\\theta \\right )} + I_{1} \\ddot{\\theta} - I_{2} \\dot{\\psi} \\left(\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\right) \\cos{\\left (\\theta \\right )}\\\\I_{2} \\left(\\ddot{\\psi} \\sin{\\left (\\theta \\right )} + \\dot{\\psi} \\dot{\\theta} \\cos{\\left (\\theta \\right )}\\right)\\\\I_{1} \\ddot{\\psi} \\cos{\\left (\\theta \\right )} - 2 I_{1} \\dot{\\psi} \\dot{\\theta} \\sin{\\left (\\theta \\right )} + I_{2} \\dot{\\theta} \\left(\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 ⎤\n", "⎢I₁⋅ψ̇ ⋅sin(θ)⋅cos(θ) + I₁⋅θ̈ - I₂⋅ψ̇⋅(Ω + ψ̇⋅sin(θ))⋅cos(θ)⎥\n", "⎢ ⎥\n", "⎢ I₂⋅(ψ̈⋅sin(θ) + ψ̇⋅θ̇⋅cos(θ)) ⎥\n", "⎢ ⎥\n", "⎣ I₁⋅ψ̈⋅cos(θ) - 2⋅I₁⋅ψ̇⋅θ̇⋅sin(θ) + I₂⋅θ̇⋅(Ω + ψ̇⋅sin(θ)) ⎦" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dhG_B = difftotalmat(hG_B,t,diffmap) + skew(iWb_B)hG_B; dhG_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the $\\mathbf b_2$ component of ${}^\\mathcal{I}\\boldsymbol{\\omega}^\\mathcal{B} \\times \\mathbf h_G$ is zero:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}I_{1} \\dot{\\psi}^{2} \\sin{\\left (\\theta \\right )} \\cos{\\left (\\theta \\right )} - I_{2} \\dot{\\psi} \\left(\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\right) \\cos{\\left (\\theta \\right )}\\\\0\\\\- I_{1} \\dot{\\psi} \\dot{\\theta} \\sin{\\left (\\theta \\right )} + I_{2} \\dot{\\theta} \\left(\\Omega + \\dot{\\psi} \\sin{\\left (\\theta \\right )}\\right)\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 ⎤\n", "⎢I₁⋅ψ̇ ⋅sin(θ)⋅cos(θ) - I₂⋅ψ̇⋅(Ω + ψ̇⋅sin(θ))⋅cos(θ)⎥\n", "⎢ ⎥\n", "⎢ 0 ⎥\n", "⎢ ⎥\n", "⎣ -I₁⋅ψ̇⋅θ̇⋅sin(θ) + I₂⋅θ̇⋅(Ω + ψ̇⋅sin(θ)) ⎦" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "skew(iWb_B)hG_B" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define $C \\triangleq \\Omega + \\dot\\psi\\sin\\theta$ and substitute into $\\left[\\vphantom{\\frac{\\mathrm{d}}{\\mathrm{d}t}}^\\mathcal{I}\\frac{\\mathrm{d}}{\\mathrm{d}t} {}^\\mathcal{I} \\mathbf h_G\\right]_\\mathcal{B}$:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left[\\begin{matrix}- C I_{2} \\dot{\\psi} \\cos{\\left (\\theta \\right )} + I_{1} \\dot{\\psi}^{2} \\sin{\\left (\\theta \\right )} \\cos{\\left (\\theta \\right )} + I_{1} \\ddot{\\theta}\\\\I_{2} \\left(\\ddot{\\psi} \\sin{\\left (\\theta \\right )} + \\dot{\\psi} \\dot{\\theta} \\cos{\\left (\\theta \\right )}\\right)\\\\C I_{2} \\dot{\\theta} + I_{1} \\ddot{\\psi} \\cos{\\left (\\theta \\right )} - 2 I_{1} \\dot{\\psi} \\dot{\\theta} \\sin{\\left (\\theta \\right )}\\end{matrix}\\right]$$" ], "text/plain": [ "⎡ 2 ⎤\n", "⎢-C⋅I₂⋅ψ̇⋅cos(θ) + I₁⋅ψ̇ ⋅sin(θ)⋅cos(θ) + I₁⋅θ̈⎥\n", "⎢ ⎥\n", "⎢ I₂⋅(ψ̈⋅sin(θ) + ψ̇⋅θ̇⋅cos(θ)) ⎥\n", "⎢ ⎥\n", "⎣ C⋅I₂⋅θ̇ + I₁⋅ψ̈⋅cos(θ) - 2⋅I₁⋅ψ̇⋅θ̇⋅sin(θ) ⎦" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dhG_B_simp = dhG_B.subs(Omega+psidsin(th),C); dhG_B_simp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume an external torque generating moment about $G$ of $\\mathbf M_G = -M_1\\mathbf b_1$:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$$\\left \\{ \\ddot{\\psi} : \\frac{\\dot{\\theta} \\left(- \\frac{C I_{2}}{\\cos{\\left (\\theta \\right )}} + 2 I_{1} \\dot{\\psi} \\tan{\\left (\\theta \\right )}\\right)}{I_{1}}, \\quad \\ddot{\\theta} : \\frac{C I_{2} \\dot{\\psi} \\cos{\\left (\\theta \\right )} - \\frac{I_{1} \\dot{\\psi}^{2} \\sin{\\left (2 \\theta \\right )}}{2} - M_{1}}{I_{1}}\\right \\}$$" ], "text/plain": [ "⎧ 2 \n", "⎪ ⎛ C⋅I₂ ⎞ I₁⋅ψ̇ ⋅sin(2⋅θ) \n", "⎪ θ̇⋅⎜- ────── + 2⋅I₁⋅ψ̇⋅tan(θ)⎟ C⋅I₂⋅ψ̇⋅cos(θ) - ─────────────── - M₁\n", "⎨ ⎝ cos(θ) ⎠ 2 \n", "⎪ψ̈: ──────────────────────────────, θ̈: ─────────────────────────────────────\n", "⎪ I₁ I₁ \n", "⎩ \n", "\n", "⎫\n", "⎪\n", "⎪\n", "⎬\n", "⎪\n", "⎪\n", "⎭" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solve([dhG_B_simp[0] + M1,dhG_B_simp[2]],[thdd,psidd])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
dataewan/deep-learning	gan_mnist/Intro_to_GANs_Solution.ipynb	1	207985	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n", "\n", "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n", "* [CycleGAN](https://github.com/junyanz/CycleGAN)\n", "* [A whole list](https://github.com/wiseodd/generative-models)\n", "\n", "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n", "\n", "![GAN diagram](assets/gan_diagram.png)\n", "\n", "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can foold the discriminator.\n", "\n", "The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pickle as pkl\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Inputs\n", "\n", "First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def model_inputs(real_dim, z_dim):\n", " inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') \n", " inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " \n", " return inputs_real, inputs_z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generator network\n", "\n", "![GAN Network](assets/gan_network.png)\n", "\n", "Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n", "\n", "#### Variable Scope\n", "Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n", "\n", "We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n", "\n", "To use `tf.variable_scope`, you use a `with` statement:\n", "```python\n", "with tf.variable_scope('scope_name', reuse=False):\n", " # code here\n", "```\n", "\n", "Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n", "\n", "#### Leaky ReLU\n", "TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can use take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alphax`, and the output for positive `x` is `x`:\n", "$$\n", "f(x) = max(\\alpha x, x)\n", "$$\n", "\n", "#### Tanh Output\n", "The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('generator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(z, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " # Logits and tanh output\n", " logits = tf.layers.dense(h1, out_dim, activation=None)\n", " out = tf.tanh(logits)\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discriminator\n", "\n", "The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(x, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " logits = tf.layers.dense(h1, 1, activation=None)\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameters" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Size of input image to discriminator\n", "input_size = 784\n", "# Size of latent vector to generator\n", "z_size = 100\n", "# Sizes of hidden layers in generator and discriminator\n", "g_hidden_size = 128\n", "d_hidden_size = 128\n", "# Leak factor for leaky ReLU\n", "alpha = 0.01\n", "# Smoothing \n", "smooth = 0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build network\n", "\n", "Now we're building the network from the functions defined above.\n", "\n", "First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n", "\n", "Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n", "\n", "Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'n_units' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-6426fea9cab4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Build the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mg_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_z\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_units\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_units\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;31m# g_model is the generator output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'n_units' is not defined" ] } ], "source": [ "tf.reset_default_graph()\n", "# Create our input placeholders\n", "input_real, input_z = model_inputs(input_size, z_size)\n", "\n", "# Build the model\n", "g_model = generator(input_z, input_size, n_units=g_hidden_size, alpha=alpha)\n", "# g_model is the generator output\n", "\n", "d_model_real, d_logits_real = discriminator(input_real, n_units=d_hidden_size, alpha=alpha)\n", "d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, n_units=d_hidden_size, alpha=alpha)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discriminator and Generator Losses\n", "\n", "Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will by sigmoid cross-entropys, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n", "\n", "```python\n", "tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", "```\n", "\n", "For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example, using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n", "\n", "The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n", "\n", "Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Calculate losses\n", "d_loss_real = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, \n", " labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n", "d_loss_fake = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, \n", " labels=tf.zeros_like(d_logits_real)))\n", "d_loss = d_loss_real + d_loss_fake\n", "\n", "g_loss = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n", " labels=tf.ones_like(d_logits_fake)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizers\n", "\n", "We want to update the generator and discriminator variables separately. So we need to get the variables for each part build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n", "\n", "For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables to start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n", "\n", "We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n", "\n", "Then, in the optimizer we pass the variable lists to `var_list` in the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Optimizers\n", "learning_rate = 0.002\n", "\n", "# Get the trainable_variables, split into G and D parts\n", "t_vars = tf.trainable_variables()\n", "g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", "\n", "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n", "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "batch_size = 100\n", "epochs = 100\n", "samples = []\n", "losses = []\n", "# Only save generator variables\n", "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " \n", " # Get images, reshape and rescale to pass to D\n", " batch_images = batch[0].reshape((batch_size, 784))\n", " batch_images = batch_images*2 - 1\n", " \n", " # Sample random noise for G\n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n", " \n", " # Run optimizers\n", " _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n", " _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n", " \n", " # At the end of each epoch, get the losses and print them out\n", " train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n", " train_loss_g = g_loss.eval({input_z: batch_z})\n", " \n", " print(\"Epoch {}/{}...\".format(e+1, epochs),\n", " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", " \"Generator Loss: {:.4f}\".format(train_loss_g)) \n", " # Save losses to view after training\n", " losses.append((train_loss_d, train_loss_g))\n", " \n", " # Sample from generator as we're training for viewing afterwards\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", " samples.append(gen_samples)\n", " saver.save(sess, './checkpoints/generator.ckpt')\n", "\n", "# Save training generator samples\n", "with open('train_samples.pkl', 'wb') as f:\n", " pkl.dump(samples, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training loss\n", "\n", "Here we'll check out the training losses for the generator and discriminator." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f4e8947fbe0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PTB4BBt/J7vUFRmWRu22eeyHJPY1yl8kdINI0K/doigpVkQJ59Bm2efI6OCd1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\nyus7eODSbD74z+X8vyVTeG77ce5+dS99JrNN8spLDic3OZyE8ABuWZb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e7fda4be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "losses = np.array(losses)\n", "plt.plot(losses.T[0], label='Discriminator')\n", "plt.plot(losses.T[1], label='Generator')\n", "plt.title(\"Training Losses\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generator samples from training\n", "\n", "Here we can view samples of images from the generator. First we'll look at images taken while training." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def view_samples(epoch, samples):\n", " fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n", " for ax, img in zip(axes.flatten(), samples[epoch]):\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)\n", " im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " \n", " return fig, axes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load samples from generator taken while training\n", "with open('train_samples.pkl', 'rb') as f:\n", " samples = pkl.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4jl9++aXpWrVqmebYsYDR9/mqqo3G7CRmXvoYOXKk6cro5ZfWRuPzxTEt1LOT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\nleIk8bikmnCEEEKIiiJLTQghRBA04Qg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zE3mPHHTQQaZHjx5tulmzZqZp+/gWkiqWbKi0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\nCCGECML/A9aD2hgAgC7ZAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d1d1550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = view_samples(-1, samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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33BBIdlGPi9wu5EK7OOyww2q4sH/D7nrP0/yKs7jkYp111uGPf/zjkHBRb420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maM1nK+rzkqIP3TKpwzzzwTSApFBWy2khXkdlvw/J933nkAzD777EDK7HrppZdGuP9V6p2WZ9yZ\noWnGXR4jahTjIBNMULvOoPVh9RROFXEsZpppJiBlwzqfPDY9IFU6z/VQsXjtvPnmd0t22Y3B2J4s\ns8wyQIrleS14zeS1aGanlbV4YiicIAiCoBR6EsPRAr7xxhsBWHTRRYFkeWgRa6FoAfv/mWeeGUjr\nmOTL4T755JOt7lrX6nCKfO76l7VcRvB7QN8+U/4122SeeeYBknLafffdgfrZJ3n8op1OA2aVmTVW\nL0NLK915YWaVytVuDMcffzwA559/PpDGTp+9atK/m2yyCZDiV61ab73w1VtLYpeFvEreY1HFGdc0\nW22DDTYY7nZVwmZuNWvtlzkW22yzDZDOe6MYL3UM9IyY3dgpujkWedxTxfLjH/8YSPfO+eefH0jn\nM/ci+HrFFVcE4JJLLnHfgXQ/8Z7bKhHDCYIgCCpFT2I41pJojfsU96nu+ie5VadK+PnPvxMhDzzw\nAABXX301kPqRVXF9myILwmp5fbUeo1a6sRjHymO3itqeasY9tHx/97vfASkOVo9OjJXb8Jg8D/qH\n867OogJyDLRE9957byApHKuoVUQqGDP0zORy/SWz1/Lu1Y0y5phjNl3B3ylc28exNKtIVP8eo2N9\n7bXXAulYHXvHvFVl0yp23G4FlU2jtUzeL6w9s/p+zz33bOn3y6AoO9B74r/+9S+gb32NsWBje3mX\nFtWhce5f/epXNZ9z1Vwzf838bFbp9OvXr6m5FAonCIIgKIWeKJy8N5IrP/rUzdfi0EpbZZVVADj2\n2GMBOPvss4H09Leav8rk2SBaByuvvDKQViPUajeL5OCDDwaSRbT//vsDqc+Y27ObQ+77L1qDppsU\nVYgX4Xk//PDDATjxxBOB1OHWDD7nybzzzgukLEdX8HzxxReBpA7MbmsUx+jf//53x3qhNYsdBYxH\n2F/O1S4lt3z11Rvbs07LzhT16HQPuFa2k18jjW5DVWAsd+eddwaSMl5++eWBvn3nOn3MzVCUFeg+\nGbPJcY7eddddQIpT2u3bYzdjL8f4tzWO3jes3/MaqnefaPY+EgonCIIgKIVSFY7WuRar/cJc4dF1\nSHKM+Vh57nb+9Kc/AdXsoZaj0tBq04Kxs4A+erPKjGNsu+22QDpme6upcLRktYgPOeQQIK0kaqaY\nmV1lKJtcRTWa4+/3jEtpcWq9aeUfeuihQFJExrOst9CaX3vttWu+36gFm3ev7gVWiBvPtFJchZPv\no93HrUA3lmMNW6P0StENS6OKOF8/yViNsV07C9ihIj+2KsV686w072l5vzjx/bXWWgtIcc6//e1v\nQJoPRbh2kDVqV111FZBq1fQWdLo7QyicIAiCoBRKVTibbbYZAJtuuimQLFL7jVltP+eccwLpKWun\nZK14USEdeOCBQMoxryJF+fH6TvUvG8vRd6sadIxmmWUWINVp2E9On6vZKdZlGO+yxsUOy52mX79+\nfaw0VVyu6vJ4kvi+Vr0xOmsF7CxgnznHwA4DxnbsTKAiajbzxuwm1WQvsIYk7zDgGDrWznl9+Sut\ntBKQ9t151G6dRS8wFmMMxs7Yqvqll14agJtuuqnme46Z9xFrWJwPdpP2/aJ+dGUqH68Zr1O9AkWr\nlOZZad5HVDp57MbP2Xfy17/+NZC8AC+88AKQYjv1Ovu3SiicIAiCoBTa6jRQzzefr8miorGPU+5D\nzS0MLRkt3Bz9i1osG2+8MdCWNde1TgM5WqzGH26++WYgZVRZHb3mmmsCcPnllwMpi01LSEvHinPH\n3LEz3qF1Z15+PZqtou7Xr9/3PfCKVvQcZtvD3Y7WnPNKxWON0WKLLQYkNWcdjrE/+0TtsssuQKqq\nd3+G6aLQ6HExdOjQSnZIzmub7FRhXY7v+7lOUeZYOB+MbxqbswbFa8C/HrNW/jPPPFOzPddN8trw\n2snvP6oN71dFtVjdGAuzxryeTzvtNCB1eXafndP77bcfkLLdLr300ppjsCbN7S688MJA6tbi/cHt\nmaVmDLjRLtLRaSAIgiCoFF3ppZYrF1fia3ZNlkax3sJOyY1muZjBM0w1ftcVjtlmSy65JACvvfYa\nkGqS9KV6DKoG/+/qmR6rFenGO1QF+u5dG8YqfHus1aOT6+FIvraLFqwKVrVnho5WmDEa59H9998P\npJigY+IxqxZd8dNMnKK5nlu6KqbXXnutsgpHXM/EeWK9lmNSL1upWcoYi7yThHPaLFWvBT+XrxFk\n/EJvgFmwqj+Vj98ff/zxgRQTNlasajCuYZeGYdZ16vhYuE92lzcuaV/BI444AkhxKuOVnn9rEY1f\n6SExNmzGntvV++T9RKXcbAZfKJwgCIKgUnRE4ZhNlq+46dNaa0tLolP1De67T2uzWNqg6wonzy7Z\nfvvtgVRTVFQHofVu5bn+ZTN1XOHPnkn5CoDGzVQXbqeITlhvWteefzPsrBVxLMYZZxwgzSOV5znn\nnAOkTCz32doiu1LbVyyPKZrF5hohI1O36GaxJsl5obVvRujpp5/ekd/pxFjUq+z3PKkwjD/k96qi\nLEdjPr42I8vOFVr92223HZBiO2ZmNWrdd3NeqNJU8WbiGvvVq1M0p1XpKiIzfa+//nogjal1N96b\nW83MC4UTBEEQVIpS1sOx66+1BVphWjpatlrjWrB2Ms0tGX2oZi8ZC7Czcht0XeFoebjPdoM1s8bz\nUa/i28yccccdF0iqYPDgwUDyQ1troBJyraB61n4z1lv//v2HjjLKKN+rJ9HKVnXZ1dvzreJRjbm+\njThGVkXbKfuKK64A0hiYlZavm6Sfu6iDRT0GDhzIN998U4pV3yx5zZP95PTFOwaeg3Z/d8opp+S9\n997j66+/7rhVb7eNO+64A0gZUkcddRSQ7hc5eXaZ15JxiZ122glIakGlbD2XscMLLrgASPcnz5X3\nH2M4w/n9rivf3LNRD1W+qt/6PWPDZqN5DTp//J1W629C4QRBEASVoicrfuaYY27Glt2Cfdr+8Y9/\nBFL+vD5Xa1Yef/xxIFk49RiBj7bjCkdfqRaHfmSrqI855hgg1ZCo7tzHXIlowfhXH68KJ18HxTEx\nvmVn7nq0Y7257yoQrWytqfzY7GytgrFvmOsb2RfMrglapP7fOJhWf16PkSunnLya2/0bZv2eysZw\nXDfJeJXnPe/y4PxrlzLGwlVLXQnYuEW92K91OmajOS+8BrTu7RtmbM/4pivN2q3DMbQ2Jb9fVHFe\nOJf965jsscceQLoG9YDYXfyvf/1rW78bCicIgiCoFG0pHH2u+mDb7b6a+7nNqNDC1VevRVyvk2kL\nfvOOKxyta/3SHos1SVqgrrhnhbH7bo2QefnGqYoyArVkfW2mj1koWjh5vCWnHetNVadCMGvIfdaq\nOuuss4DkK3fe2FPPehuz2lSBrotkF3Grp0VlY5acdRvGilRceUcKY4jD9ptrtw4n98F7jdjDytoi\nrWpf33fffTWfL7qmVPn5uji+r/XeqPqvR5lWvVa4HSPqKRzP56OPPgqkzD0zQP2+157X3NNPPw2k\nGKPV9c6Xov6DVVI4ehP0AnjsZvCqgO3EPkwtUUd+PxROEARBUCkqEcPJ0WeqhWOGhVaiiqXVzBsV\nkrGfYeiYwtGXbqbNY489BqS6iPnnnx9IFqkWiVa2GVZm2GiN6Z/Oq+Ofe+45IGV+/eEPfwDSKpl+\nvqgvVE6zWWqjjz7695Zgnj3kWKh0jCOpxsxis8OA1phWm2sCnXHGGTXvi7+jarOLg2rQ33f/jAk4\nxvl23P8BAwbw7bffdsWSVfn4m8b21l9/fSApFGNzrn+kYnasPJairsKdXtOnF1a914QdA4zxeP07\nt+22sdFGG41wO9aFmanlfHKtIetzvKaKqILC8fyayZd3VLfzhJmeU001FZCujVA4QRAEwQ+SSikc\nn9a/+c1vALj11luB5JM33qG/uqiWpCh2U+S7H4a2FU5uJWuRat3b/fXII48EYN111wVSHzF9q1rl\nuVrwtRaw2BXW7RkL0HJulmast4EDBw4da6yx+vjaja0Yj/C85B0BVLLus35oxy5XcyrdvBbFeFXe\nMTnH/fP7zpO8m3Qnu0WrVM289PwZ75phhhmAFH+wR559v/IVPnPrPsftmtk5gv0FOt8z67/b7sr9\nYtCgQUA6v6r/ovPttafqN+tVhdvsyrTSibHI53CzeP5UY64lZW81s171oMw000wAnHrqqQDssMMO\nQPvrJYXCCYIgCCpFpRROs1gpXM+Ka4Kudxqw/kbLw55IWr7Gl/I4hedJtaCvX2VjRpY+3HZpx3rT\n6jY7TOWhn9lYSr7yp+uzO0ZmbjkWWnH2jZt33nmB5I92THNrMe9AIG63XlyrTKu+aM0ex0gFNM88\n8wBpbB1zlU2rfeMa2L+eK5xOYy8/u3I0GhsucyyK4o7W15kx7DXnfHF+5J20fW2mbyicIAiC4AdF\nVxSO2Uf1aj06Rd6BOffJ21+qgSr7riuc3GdufUxeVyOqAsfSugqt+W7RbAxnzDHH7LPSZ05RvMD3\ntbqmm246ICkaYzzGbnJrrGjl2GGzzSCNpTUpec1Lvr1+/foxZMiQrliydstwzRZxX80ysgLctVqM\nX6hgtGi7zaijjsr//d//MWTIkB+cwmmVXqo973nWqBmnsju06+eo9s877zwg3Wfsb9kpQuEEQRAE\nlaKrMZx2O+S2m8HRAl1XOCML3bTe2u1I0SztzsMfYtyiVWIsEjEWiVA4QRAEQaUY2M2Nt7sGR4nK\nJiiBXNnUUx7NZiEWKadW52H//v07tn5NEAShcIIgCIKS6KrCyVluueUAuOGGG4Di6ulg5MTzaJ+w\nXJlYyW3GnUrE2hHrYew0YW+svNNAUeynKPutXqzIz1mPYbZdN+djo1XtZce7ev27wfCx76N1eiMr\noXCCIAiCUmg2S+0j4M3u7U7PGTR06NAJGvngD3wsGh4HiLEYlhiLRIxFIsbiO5p64ARBEARBq4RL\nLQiCICiFeOAEQRAEpRAPnCAIgqAU4oETBEEQlEI8cIIgCIJSiAdOEARBUArxwAmCIAhKIR44QRAE\nQSnEAycIgiAohaaad1Z9EaEONBz8uInWNpUei3aJxaUSMRaJMsbCJqqfffZZK18vjU6ORaPNXLvF\n3HPPDcBjjz3W0OdHH310AP7zn/8AjY9FSyt+tjs4ja7k2YOOtZVd8bPdVSubJW6yiVbGoqy5O+qo\nowKpA3e3aXYsBgwYUNq6VgcccAAA++yzD9D8farZa6yVeTFw4Hc2vmPi/PAG7m/XO5/Or+Hs03Df\nt4P7l19+2eguN0Ws+BkEQRBUipYUTj3KtsZbZThWaMsKp1PH7HbctyLrsFkLOv98ve+Hwkm0MxYj\n27oy9eZxO2Ohi0yXWZGn4/rrrwdg+eWXb/SnhkujnpTcPbTaaqsBcNlll9V8znM52mij8fXXXzNk\nyJCOK99WvUf5dlVS33zzzXA/P9FEEwHwySefjPBzMuWUUwLwxhtvAEmBqbBD4QRBEASVoiMKp97T\ndCSi9BhO7ovVKsstTN8fbbTRgGTJ+DnH3vdzH3GzhMJJxFgkWhmLiSeeGEgrqX711Vc1nxtvvPGA\nZG1Lt+MOOUXqzhVp//Wvf9W83415ocLx+s33pareo1A4QRAEQaVoKi26CK3rsp++s802GwALLrgg\nAKeeemopvzsiGh0DFYvWnUrHv25nxhlnBNKa5r4/9thjA8n6+9nPfgYkH2ueUporqZElpgDFfu98\nrPz/lltuCcCGG24IwM9/3pBorRTGFb7++mugbzaTx27coR4q40Y/30k++OADIB2Df3/0ox8BfRWP\nlJXdJvk16H7mymb88cdvOWU737bXv9dxfn7y+0mz99YnnngCgGWXXRaADz/8sJXd7hihcIIgCIJS\n6GiWWh5/aNaKrpfB4dP+oYceAmDaaacFkqXk7+60004ATDrppADsueeeNdsdgQrpWAwnj2vl1pyv\ntTynmGIKABZYYAEAJptsMgAWXnhhAMYaaywAzjvvPCD5txdZZJGa9++77z4gKUvJ+UQAACAASURB\nVJ1JJpkEgHfffRfoG+MpGvMy4xaLLrooAPfffz+QMnQuvfRSIFmY22+/PQCrr746AL/5zW+ANGaO\nZV7L4LnQP95s7Uonx6Leb6tcPWavqTHHHBOAlVZaCYAddtgBSPPm+OOPB9Kxq4xOO+00AGaaaSYA\nHnjgAfcTaD7u2smx8Dx6ns844wwANttss+Hum9ftFltsAcCJJ55Y8/9HH30UgJlnnhmAww47DID9\n99+/0V1uik6OxcYbbwzAmWeeOcLt1ItrbbDBBgCcc8457iOQxjKPAXeKiOEEQRAElaIrdTjdQuvw\nwAMPBGDnnXeu+f8ee+wBpKe/8Y+tttoKSNkybuftt98GOlOHM5z/A8lCFfPftUS1PLX21lprrZrv\nmSGjdWcmT56vf/LJJwNw+eWXA/DSSy/V7IfKqlH12YvMrJ/+9KcAHHTQQQAsscQSAIwxxhgAvPba\nawBst912QLLipp9+eiCpxjxepdU3/vjjA3DssccC3a0or0decT7HHHMA8Oqrr9b8/5FHHgFSjM55\n4Pn3WFU08o9//AOAY445BoDTTz8dSLG9Vr0Q3RiLCy64AEgq/ZprrgHSHHZfvZ5zHIt6inXfffcF\n4OGHHwbgjjvuAGraszSyu9/Tja4Lzarv3EPhdZ6r/X//+98AjDvuuEDr8bEi71AonCAIgqBStJWl\npmWR14A0i/7p3C+Zb884hsomr8Y3W01f8AknnADAn/70JwB22WUXoHXrrhnyzKncMph66qkBWHXV\nVYEUx3j55ZeB1EzPWMw777wDwIQTTgiksbJqW+Xj7xm/eu+994C+VqAKqkq1U45BHrcwy8xsJ3GM\nll56aQCuu+46IB2rYzF48GAA1llnHaC3NQx59plz+JlnngGSsjnllFMAuPjiiwHYeuutgb6K2fky\nwQTf9Zz12FWLfn6qqaYC4OOPPwbgrbfeAhrvPNEN3AfjUCrQPL5kTCbHzzWqBvQm2GtNj0dRllyn\n+fbbb+sqmGZ74uXX+0cffQSkY3M+6QlpN/Ov7U4qbX07CIIgCBqkLYXTbittLRT9i0XWlXEMM7By\nH73W4q9+9SsAVlllFSCpCjN6/J777dO/kxhvcJ987b74V5/8c889B6T8+F/84hcA/Pa3vwWSFWiV\ntr5Z/y/GhnILWCtRtfDpp5/WvF+lXl/PPvssAHfffTcA22yzDVBs9Xn+3n///Zr3PTa/N//889e8\nX0TeU6uTFNXNeAz+f/LJJweSgtVSvf3224GUceV8cS57/vbee28gHbv1F+eff37N72qplnn+c+t+\nqaWWqvn/nHPOCaRMuiJlI3oH6nHvvfcCSe17LakKVH2Ncsghh/TJkGuUbnX1/vvf/w6kOZyr/Py+\n0CtC4QRBEASl0JMsNS0du8HecsstQHr6533A9F+bXZQrHJ/uWiytdlCmySy1fv36Ff6WykaLdtCg\nQTX7qALy2MzUeeGFF4CUTaRf2/iFFrEKyfx9rTdrUoxfqaDcXt6DrSjGVGaWmr89++yzA/Dmm2/W\n7HPuh5Zf//rXAFx00UU1n/OYzPiabrrp2tm9jo5FPufy2qC1114bSPU2Zh1q9RvLE70Dbseuvma3\n6RUw3uVfVWGz9ThV6CvntfX6668DqVpfvI9ceOGFAGyyySZAytByfpn1aIZgs3RyLOya8vTTT7e0\nL/PNNx8ADz744HD//8UXXwDJW9RpIkstCIIgqBSdD2I0gL5XrTZVQJ7BteuuuwLJQinqB2Z2WhEj\nqKZv7QAa+L4qzdoQFcj6668PwJVXXgkki8QYjArHWI2+VyvQc9/srbfeCiR/uFlsZq+5HbefZ3pJ\nLzO3PD95tplxDJWLx7LccssBSdmokESf/CyzzNLN3W6JXFGq2lR3HpNKxOwza8b8fN5TT+vdKnyt\nfzPAXnnlFSApolzZdDOWoyLpVDbYyiuvDKRMPFW9nQaKUAWogBZffPERfj7PNO1mnKtVZSN2Xcmz\nDsUaxV4TCicIgiAohZ4onGuvvRZIT+EVVlgBgKuvvhqAhRZaCEiZWEXrd1uJrE+2iF5kYGk5apHO\nO++8QFI2WiT2RrKeQnWnstEaM7vMWiQVyayzzgok69ExMT5mlpuxHK22KtXfqAZvvvlmIKk8FbC1\nKM4HjznH82ydVrcygtqhqFL7+eefB1JvPHvomV2W+/hVsB6jHbKtZZphhhmAFMNRIdl1w8wts9+6\neY3kysbzaKeIRnnssccAmGuuuYA0b5588skRfs94qd4Bu4j7PRWz14hUbc2Z4eG9Ua+A+5xnpXlN\ndZrxxhuvqc7ZoXCCIAiCUuiJwsn7Pql4zI/XmjdzJ8cnal6L0irdWMcntxitJLcLtJk1Zo2YcaXi\n0UK58847gWSd6Q83A8sYkZatcQ8r1R3Lou4KZaxh1GgXcDOnrD0xi+iII46o2U4RKt285qRKFK3g\naA3ZU089BaQ6G2M5yyyzDJC6SO++++5Aiodqtbt9415+367R119/PZCuwV6sINmssvG8q2xUde6z\nvfRUKH7euFV+H7GDuvGtZvdj4oknbrp2B2pVR54t6vlaY401gJS1Wg+34/l17ttJ3e2rZDuFx5Kv\n0lqPUDhBEARBKfRE4eT1FFogVsMX5Yr7PTNyOoXWRSd9/nm2iJaGNUNmrVldrZVmnYUqTsvXTrn6\nna3C1tIyz/7GG2+s+bwWsRazsZt8vZ5u0mh84PPPP6/5vPtcT9nk2Y1V6JrQKFaGX3XVVUCKvdhx\nwKxG62rspGynY+ux8nnm/HJtGWN6KujFFlsMSF6CZrsUdwKt5Hr9vfKVN425OCZ+f5pppgHSNeN8\nEDtYHHXUUS3tr/vhNdwsIzpOr0OVTb1VbvP3VUa5UrVreKM91MxyzVcGza9Bt/fQQw993429EULh\nBEEQBKXQlsLpVO5+XgtQhPGJRi2jRinTqjPmYp2EnQS0dB0LFc9NN90EJB+sMSDralQyjoXKyNiP\nFm1OlTJw3BezmYwr/O53v2vo+54/la9xq5EBj9k5rRVvzM64lBl7rmJp1w3Pt2tEqRK9Vg499FAg\nKRzjpGa55R3Xy6TR31R9OUZm6uXXrXFNa9K8L1nrZheGKmVoFpHfU50PXiv52OnxMDbn/cT5432n\naKVQsdt00f5Yx+V9xdqmRgmFEwRBEJRCWwqnU75y16/x6WxFec5f//pXID2ttQarjFZZ3g9MX711\nMsZuVD5atGYZOdZaLGZyWb+hVW/VtX5m/6oa8t5sVUQrzjEx20grPY9XvPjiiwD8+c9/BtIql9ag\nVJFczdtRYL/99gNSht5ZZ50FpBVhtTDtzbfaaqsBcMABBwDp2tEHP88889T8tR7s3HPPrdkPv+f2\nu0GzHpF8tVpfO9e9Xzh2qjY7k1iTZgd2vQH21rP2rSz69evXZ+42StGaY27PLgsqG/E+0eh59fPe\nN7xvqTK997ZKKJwgCIKgFFpSOJ3uu6Rlau54UT8gsyHuueceIPWdqgL5mOSWTF5trUWp0sgVi5k4\nxiVUQm43X7nRbCUzd7T+zF7TB6wqHBkyuew4se222wLJynd9I+NcWrCu/eL8qLLCycffeeA+ewx2\njb7iiisAePzxx4HUB0wrXovUDC3jFsY5rFGxHkzFq+Wa18Z1g2bnnGov75Wn1b3XXnsBKS6hCvCY\nVDB6GRwjM7eapd2apQEDBvSpu/G1f+2N5/nwmIrq6HztsRrL8ZjN+M1X/BV/z/tEnoHn73eq514o\nnCAIgqAUerIejvgU1kLxKWz1qt1g9Wcbn7BKvws+2KbWwxne+7k1Jlo0HpNWupaMlqeds82HN+bj\nGNlHzGwkrf4zzzyzZntvvPEGkHy3vnY/zEYpstaqsO5JPYvS+aNFu++++wKpX91DDz00wu83SjfH\nIlca++yzD5BibZdddhmQ4g6XXnopkOIRft9+hGY1usrpSSedBKSaFOeH80BFvc466wApnlpEJ8ZC\nK9nzV5Q1tsQSSwBw2223AX1XKc0z7OxUYScBuzYYAzTO1Whn5nrZsGVcIx6jGXZex/l927orj128\nfxjbcQzrrW7rvdh5Uq/Td6yHEwRBEFSKnnQaEDvc+jT1aW6OtyrAPkF5XKLM9diLyPch99GKPdC0\nQPSxu2bLu+++CyTL1Iwbs0L0xar2tDhuuOGGms+rXPycFrCfz/tQVZl6+6jl6TFZf+F6KXZebqXv\nVdk4XzwmYzgqX630X/7yl0Cyvp0/+uKN6amQN9poIyDNLztvi/OynrJph1wp1KsJcc6q4p0HqjVj\nuXYmya81P28XD39PldeowilSNgMHDmyplmdEKwQX3cvy1U1zHCvHIqdojP29Ii9CUew5//4oo4zS\np3PMiAiFEwRBEJRCR2I4KhSfdD79tL600sQqeavntd70Q+t7N0tJzOwq8jt2gLZjOLl/2X1Wodgj\nSwtC37l1OX7O7DQ7aefZRcZyjGO5volYq6CV6Do6jfZW6oZ/2n0/+OCDAVhxxRWBtNqlczGfT/Vi\nOVY75x2Tt9tuu5rPtVpN38pYNNoNw2Pz884bY3OOgWPlteP5NKvokksuAZKqs1+Y9Th6DTz/xoxc\nl+mJJ55o6Pg6MS+MrXje66FaN47hekd5TzQ9IXaocCztL2gdV7MekXy13CWWWIJHHnmEzz//vKmx\n6N+/f8N9G70GnD9F61jp2bA+z3khvm+n7VYz9MRsWGNGEjGcIAiCoFI0FcMZY4wxmGGGGVhllVWA\nlCdvxoPWmBaEVrlxC+MLZp34tLd+whiN2xe3lyubRvPiy1zzI18H3TGx55VWl9XRZuKocFREWq7W\nzZidok/XnlgqmLzDtmNtRbo+fL9vHKyb5OPuX6vnra+xxsT54v8dg6K4k2Pp2LimkPEM+42pAp1X\nf/vb34a7vU5ST9nkdVrOF7MYraewrsbtOW8cG2MydlvQCnf7Wudmfvp9sT7HGEA35kU+D+aYY46m\nvn/88ccDaV2koriE144qIK9pa5X891pdW2bIkCHfX4/1MmxzZWuX8BzrtIo67Ju52cyqnFB8zzQT\nsFVC4QRBEASl0JEYTlGGRd7XR7+kcQs7DPg9s4l22203AM4+++ya7VuHY4ZXFzrcthzDycdACyGv\ns8izi7Rg9Uvn67SfeuqpQPJD67PVn63P1l5KqkCr742H6XN1f1STRee/mzUGqjPHxH3Sv2z8ygy8\nu+66C0gZfOuttx4ACy+8MJAUkmOhla+/2e/ZhaHZDKNujkU+b1T9KhNVoNlCvq9v/uijjwaSIrL/\nnNfa5ptvDsCtt94KJEvYDgVvv/020Hj2YjfGouj+4fzwmOwvuO666wJJjTlmzmlrlDy2bsV8mx2L\n/v37d1xVe517jXgtecx2jTe+qULxPqLyyWOOfs91k7wPbbrppsPdj4jhBEEQBJWiI3U4RVayVvgL\nL7wAJH9znn3m/12xT7+yVpp/zW7ptLLpRD1P/t3cktGC9LdUHB6TWUIzzTQTkDJ4VEJ2V7DWRJ/9\n/fffX/O+Vp1xsrwnUy9rljyPxpFUICoSrXcxa3Hw4MFAOgaVkJatx+T/jW+YmaVlXEXyuadVryWq\ncnVeGPOzan777bcHkq/fTtnGHcwAffbZZ4Fk+VqT0sWMz4bxPNpdwfPnWj7GPbbeemsgzek8Hunc\n17NiPVajPRe7vQpuJ9WN88ZaxTx2bDwr/007aduZRPJ7qspGcmXjGH/99ddN3VNC4QRBEASl0NUY\nTp7pYA2KSkfrXR+tlcF5jnqnOwqMIGut7TqcnLyCPK+7MGtop512AlLWiYpH9WcWm77XvA5DS9bf\nsSdXvXz/orHoZtxCpaMlaSZevtZGURcH1Z0KydoTlY8ZeaqFdld0LSNukZ8H541WvJlWxl6OO+64\nmu1cfvnlQKq/coycL8Y1zNxrlU6OxeGHHw4kte++ORa5gnFsPEZr1KaaaiogeU78XNG6WvVw5VA9\nLkU0Mxajjz760EGDBn2fnVa0rk2z9zjnet5Dze24npbHlGchqhqNlzvvjLN6TeZrEnmfsXtCxHCC\nIAiCStGVbtH50zrPgND/Z18o6yLKii/k6+xoUX377bctK5wipZB3Hsg7EGjt+9p6CF9rcZhppc/d\nWhPXT7EWJV/vxt9rdj2LXnSL1go3/mBsR+Vrxl2ztFuH1c5Y5F0TRvC9/DeBvt3HnRcqY5XP9ddf\nD8AUU0wBwMMPPwy0r+5y2hkLM6rsiuHcds6aSWVHbM+X8SYzN1UeHuOkk05a87t5bK9btDMWt9xy\nC5Duge3Gpc3cM17V7vaK1vdqN6s1FE4QBEFQCj1dD6fTmOXSRnfgjsdwilD15dZ3npmXZ5kVWRr5\nWiGNWiZFVGE9nKoQY5EocyzqrdkynN8Dile3zLEe8IILLqh5v1FFPDLPi3rH2GzniVA4QRAEQaXo\nqMLZYostADj55JPb26vG9wdo31c7zHY6pnAatZKKfPeNHlunM/hGGWUUvvnmG4YMGTLSWm+dHpNO\nWrJVWMOpHao8FsZ6zOw0S7Eefs7Mz0Y9JJ0ciz322AOAQw45pOb9RruOd4pue0RC4QRBEASl0JLC\n6dZTN8/oqVf5a474v//9707tQscUTtkKpcpWfVk4Bs6bZlYiHBFl9lKrOs2OxYABA0qzzrvF8LJu\nv/3225bmxVZbbQXAiSeeWLNts1Ctq7n66quB1Fmi1XVs6s2volo1ryE9Nfn/fQYATY1FKJwgCIKg\nFJpVOB8Bb3Zvd3rOoKFDh05Q/2M/+LFoeBwgxmJYYiwSMRaJGIvvaOqBEwRBEAStEi61IAiCoBTi\ngRMEQRCUQjxwgiAIglKIB04QBEFQCvHACYIgCEohHjhBEARBKcQDJwiCICiFeOAEQRAEpRAPnCAI\ngqAUBjbz4WYbE7ocrouIjQR83ERrmx90i4aRsXlnt4ixSFRpLHrd+LRKY9FrGh2Lph44zeL66i+/\n/HI3f6ZP9+p8Hfgm1rL/ofY6CipKszfNRtdZGrabL6Rro6jTe74uk1S59VWjK3sG1aGjD5z84vFB\nU7TImORLyc4111wAPPHEE8P9fL4sQX7x5BfjOOOMA8Bnn33W1PGMTLRr7TV6I+slzS45XEXGG288\nAD755BOg9QdNvpRHTtGSAPWWCsj3Z4wxxgDgq6++amg/m6HVOZdf/yPD3IXvrtFOP8CHt3QCFJ/n\nevOm3vbrvV+PiOEEQRAEpdDWEtNl+VAb/R2f7rkV6CJDX3zxRb3tdWwBthF8r+i3ge5ba42O5cjo\nn+7CgnxAb8ai3WtrxhlnBNJyy/XILeOi3x8Z50W3qNJYFCkbF1Lz/W7dq2MBtiAIgqBStKVwqk69\n2NFw6LrCaWC7QPG+5j7YPEEiZzgWakO/V6b1Vi+Q3a5VZgzvn//8Z83rfNneMsZiZI9DVcmq7zVl\njMXIEp8KhRMEQRBUiqaz1LqRadFpepGf36oloo/1m2++qdlOHo+aaKKJgBSHmmWWWQB48cUXgZQa\n6ue//PLLhvarU2M0YMCAuhlQReel6Hud2rc8OzFXNp38vX79+jHKKKN8fz7y8VfZjDvuuCPcl05R\nz7fvvPshk8+7XtXvtHLvLFvZjDnmmEC6z4hj5rxpNMstJxROEARBUAodieHUy+2ulxveKFqLdjBw\ne9YKaDXeeuutAEw77bQ1v9+ANdd2DKfZvPU8BrPBBhsAcM011wBwwgknAHDZZZcBMNVUUwEpE2uC\nCb5rjKCiOfPMM4FU57H66qsDcMMNNwB9LRcpM4ZTLyPK1/5t1cq75557AFh22WUBmG666QB45513\ngKQu6m2/nbH4yU9+AsC//vWvRjdRQ5FC/fGPfwykLh7+32vjwAMPBODdd98F0ny6++67gVTjphLb\nd999AXj22Wdr3pdRRhmFb775hiFDhow0MRzHzuzFscYaC4BPP/0USGpz0kknBdJYNcoPMZ7ltTn7\n7LMDaZ44liPwRkQMJwiCIKgOlcxS07L1qfrHP/4RgIMPPhiAtdZaC4BVV10VgNtvvx1I1t7FF18M\nwOeff17zV0bQEqO0LLU8g87spfHHHx+ApZZaCoAlllgCgKWXXhpIlq1q7b333gPg5JNPBuCtt94C\n4Prrrwf6+lwbPd+9tN6smyraZ+eFf601cexUvJ5nrbKnnnoKgJ122gmAO++8c7jbz+nFWNxxxx0A\nzDTTTADsuOOOAKy00koATDnllACce+65AFx44YVAaid11FFHAbDwwgsDyZpXAWnla/2r/n72s58B\nybLN6eRYFCnZPJPPOez/J5tssprtzDDDDEBS+6+99hqQju2nP/0pANNPPz0AZ511FpDuC84T1X89\nT0j//v0ZMmRIpRSOKs3zftNNNwFw9tlnA/VVvOegyFtVz0sUCicIgiCoFF1t3im5YvFpq2Wp1W5c\nwqesfm+fqssss0zN/yeeeGIAFllkEQAeeOABIFlxV199NQDPPfdczX70otlfHqvxtSrNHluPP/44\nALvuuisAk0wyCZD2Xassj2MZp9ASzn38eZykG/n9ncr+qXd+dtllFwB22203IM0HLWPx2J555hkg\nWbDGbtZcc00ALrroIqBzmVvtZHLOPPPMAFx77bU176tkxHlgTM99V/Eaw/NzWvkem2Ox//77A/Dh\nhx/W/C0Dxyi/D3jeFl988Zp9VdkceuihQIqPeR/QCnd7xjXzc/HRRx8BSeUZ31RZ1zv/VaqJ2Xnn\nnQH461//CsD8888PJC/Q2muvDaT7QxGOkcduvMtz4NifeOKJbe1vKJwgCIKgFLoSw8ktxdxXK7nP\n9t577615rf86z2ayrkKL6O233wZgmmmmAZJl43YmnHBCIFkyPrWHU+3dsRiOCiK34owvqFz+/ve/\n17yebbbZANhss82A5FPX8nzssceAZAmbTaLPde655waSWszVZKPKph3/dN7TzPjSFlts0egma3Bs\nrDmyVsBjyJWVHQW08h1Dlcwpp5wCwAcffACkc1CkrMr01buvr776KpCupXy5gfvvvx9IsT27OefX\n2I033ljzuffffx9IdV3+/fjjjxvav3bGoigb0fOr8jA+tdFGGwGw5JJLAkmtzzPPPEC6lvL7i/ed\nPAbofNx6662BdN8wBqjyHRninB7r5ptvDiTl4TXhfHEsVb6OSdG5EO8TgwYNAlL26/LLLw8Md4wj\nhhMEQRBUh650Gsh9oFdddRUAgwcPBpLP3afn+uuvD6SnsfU0csUVVwApVuNT/PzzzweSJaSFqsWy\n8cYbA8niXWihhWq+302KfkNL4o033gBSLEYfu9lC+q21LF566SUAHn74YQD22GMPIPnwn3zySaBv\nd9ii/SpSoZ2ovs67NbeqbLSy3nzzu3XxnDfuo7E5M7PyXmmOldb79ttvX7Nd/7ZaNd0NjCtYN7Pc\ncsvV/H+HHXYA4JxzzgH6Xmv5+bPuxmtHVeeaU2Uwgh51QFI2xlDM0DO+ZKzGuX7bbbcBqWbIrhsq\na5WLng7ng/PFDNAtt9wSaL4+sJGuGkXkY9Fsbz2Vy8orrwykeTLffPMBKWbj/UOVn+9voz0Wp556\naiBlR7a7LlIonCAIgqAUmlY4zVjAPo233XZbIMUnXKvDHPGbb7655nsqFavuzdyyEtjtHXDAAUCy\n1sWq6eeffx5IKkIrolOdD4al0Q4DWgjug//XPz3rrLMC8MorrwDJ566VrpWX9wczDtHoqqb1LONe\notVXZE05P+aYYw4g7buxHWN0nvfFFlsMSGPt9rWAy+itVe83xh57bAC22WYbIClbrXyz1G655Rag\n/vomXhP77bdfze+rAlQ6qsZuUm9cVTbWyahojzzySCBdKyoh7xsqWDOz9IwYs1XxbrjhhkBS3qrI\norhFvftCO/eNfCxyZZOvCCuTTz45kOJWZrE++uijQLoW9GB4fvXy2LFEb4D1ekU4RsaIVEx5j8dY\n8TMIgiCoJE0rnGb8lz4FfZpaJ6PCMVfcKuljjjkGgOOPPx5IFo3b8Smvz92/eYdkK479vPESqbeq\nYTPU20aRr9R90EdqdtmCCy4IJD+1ikZL1Kwi4yLHHXcckKqnq6RUWsUMLfGYXn/9dSD1yMuP1fqM\nn//8u0TDRRddFEg1Lfk86RbDWs55lmIRWuNeI+6rmZjGQfNrooh11lkH6Gu1e+1JvfhVN9fvcd+0\n2s0S0+NhnEnVrsdD9aYV7l+r7b3erVFx+47lIYccAnTWw9Eu1hLp0cjvK2bWGbtdYIEFgJS56Rh4\nTHZbUCmrdP1bhL+rSsznR9v9MNv6dhAEQRA0SNMKZ9gnXKPWvdln+v+0WOyAfOmll9a8b7xCS2aV\nVVap2a4KRt+vPn2rYy+44IKa7xfRCTXQ6jYcO632o48+Gkg9kNZdd10gxa/M1NN3axcG8+uNybTr\nY+0ErSpHYzDWZWi16ac2A0efvOf7jDPOAJIyUulYl2XWYhGdHqNWtmeWoj3MrJ8xrmHna7t//+Uv\nfwGS2vOasK+Ylq1oORvDaVTllbEyqde7c1dV7zxyH7yPqAJXW201IMUvzDrL6/X8vrE84xsqLK+d\n3Hqv1wW/k+TKRox76wVyH4355R4R/68Cuu6664DU0aRePZ7vu31jSmb4tUsonCAIgqAU2uqlVmTJ\n5cpDpWEGVr56nE9ffe/2Bcr9mj7FtYSefvppIFlE7o/V2ma7FNGLlf/y33QM9t57byBV8m633XZA\nOnYVkJapsZy8irqos3KRz78bGXutjqcxN+fNYYcdBsA+++wDJIvz1FNPBZKPXl+/dVkqI7sxvPzy\ny0C1fPaiqtNHbxaa+671bdcN+wleeeWVADz44INAik947eWYsZnXSBWRX8N2SO4kzhOVqH0Azbj0\n2FVlWttmIZqVtskmmwAp3qQice57P3B7/p5jr2LKyZVNGfcLPRfWHunByDsI5J1ENt10UyCpPuu1\nDj/8cCAde1EWnGPsmBorzDtQ6GFxu80SCicIgiAohY50i857GWkVaZHqC7/irAAAIABJREFUk/fp\nnPcZ83tW2dv3yRoEs1fy72kFWmWvhVOUY66fXAu4F/ENf9PqZ616/cr2+7LCXJ++NQfm1Vuj4tjm\n1lij2VHdsPpbtQS32morIPnixZiNyteYjn/tZGt/MX9fRVzPMu9G5+xGUbHqazcO5TVkVpK1Z2Y1\nmsmplZ53IxfnhbHBeivPFnVUb2dsin7TGJxzX+ve9/NeiyuuuGLN/92ex2itiP0Gvf94nzCW45jr\nPSjCmJFZb2XUaRm79Xx4fu2lmB+zWHOWj6XXhJ6UvPYsj/la66hiHlbhQuvKRkLhBEEQBKXQlW7R\n+Tru9jbSJ2+9jU9V/YVa64888giQnq5mXOhPNH5hNb5+SVd8bLYz8jCfK23FTy0Os45UZcaxPFb9\n2eeddx6Qjl1VZ42BvbJ87ZgPs781r6uwyqX7pHWWd+u16v6kk04CklWnr/+0006r+Xwej+pUllE3\nxkJfuZlU+t6di8ZctHgPOuggIPnqrSUx1pL3hXMsnD95BqDWfqPXvz0UOzkW7rP76nXsSqz2DzQL\nzbntPuddE4x/meFnzNj1k1zx1ZomV8d0vuSr4w7neGp+vxurn1qP9//bO/N4z+r5jz9nmvsra6kp\nkWRrISWlaNcmWklUiiRrKEmRptKmHVFatGgTRTRJWaNISilLiBKmxFQYohhzf3/Ucz73+773zHc7\n33O/w/v1z33c73K+57zP55zzer1XVZrXezs1pgq0p5pKRRtomxjzrbpGvJca62nXQSW7RScSiURi\nqDCQiZ8qGyH7tsOtikWGYc63T3HrJi699FKg1KDEp72sz/iGFeVbbbUV0LnfuUnfvYxUNieOP/54\noPjoZRw33HADUPzWwvqdM888EyjHYHwqIrK3qnnyTSAqTzNoYuzPLDSrqU877TSgZKmJqmMYpi7Q\nImZ/WRuiWrOjtczxiCOOAAq7Vw26nVhvpTqwi7BZj/6OTHf99dfvar8HEb9wmx6Dqst4larfeKad\nSeyWMGvWLKDUYXl/ER7jgQceCJR1YhW+MV3vV7F3X9WE4kHAbats7M7s1OJ2cD15X1AJazPj4jG+\n7TUSu8e3U8C92iIVTiKRSCQaQV8xHBlDuy6rZlaZaWHmhAzETC0zaXbYYQegZK3tueeeLduTcZi1\nZFZTDT2yBh7DMTtNn71qzH3XRsYzZCzGdrSRn5fZWGviDPt2rGwYYjh2A5aB2v3bDrUeizbRP+2+\ny/JlZ8ZF9F/XhV5sEX3cKlfZtVC1W0umklGRaBPjHHbR8HszZ84EShcGs9r0AtilQwUVuwVb59HE\nJNgxr7utCf/3WGX5qnzfd56N95urr74aKP3otI3bcZ2ZCWiHE+MexrmEdTvt1lEdttC7o8rqNLu0\nCnYmsNOEGXoqZev5Yhai15KKRy9D1RTciIzhJBKJRGKoUEungSplE/2Cxlh8eupP3n333YHik/Up\nLMuXdcnCzOyxbqNTZTOZ9RayOGMxxqXslmD3ZzNorMfYcsstgVJR7twcbWWGjr79qmMbpi7S2sJO\n17IqY3JmFblOjAHG7gwqZDNuzGqabEw0FTcqGzM5Tz/9dKBkpan2nHoZVYAZeipdz7f1EdrGLhux\n3sL15LRM+xhOZreNWEPkefdaiDZQoRhn8Ji1hfcHPSVeM2boWWOiB0WFYz1XzPAcJGL80f/d16i+\n2sF15vnWS9Cuo0jsCjOoe2QqnEQikUg0glqy1GLmTXzdTqexStb/r7zySqDM6TYLJdYY+HlrVzrt\nCyUmQ9mIOL9dW62wwgpA6aqgT1fVZu2AvbSsepbdyYj0AXd6jJPRRy7+tgpWOLnROi1jf7J+VaBq\nT1sZ3+p02qmwp1acv9MvJrJp7B9oRtQpp5wClNieM11i/EBm6rpwgqM2sav4NddcA5RrRl++3zN2\no3pY0D4PCnGNVsUbq/5XsWizGPcwA9ROyqp/vQv2ozMGLOqO/S0I0VsjPIZ2dTdVUB3aRdx7pP0G\n23UWiZOA60YqnEQikUg0gp4UTozNyNat/PWp2k6BRJ+r1fWx+7OKSFZmVsrCCFm5x6avdu211waK\nzfTd2s/Jil/7SWkrbRLrakS33aObQKy/cUa9+24Mz860kXU5I8Y4hn7qblG3soFHjmHRRRcdN0dG\nu8u2Pc92y7A6XtZtdpoqPzJTs9gikzUrSbau7956Dm1tvEKbt5txXycmqFJveb8dC/f+E/t8ye49\nZjP+rNOzJ5/qUNtZ56VN2nWZrwOD6t7uujMO6rVillqnGJQHJBVOIpFIJBpBTwqnys9nnYQZNlWI\nWSc+lY1XyFhl9fq7zWZbmOExeezGt2S22lYbnX/++UDJajIrTVamYrIGQeYUGVJVNf4g5uG0w1VX\nXQWU+ikzr2IH7LjOrCh3n2On237hOdhtt9163sbo6GiLuol2V4nI0q2O32677QA455xzgGIjM+/M\nrDK7cY011gAKq7dnlnVbZiu5ToS1SsbP7LBcxWgHOfWyWxYd1btr37XrfUP7W7OkstGmeg1URtaw\nNanyqtDvdajqU/3buaDb8zeomF4qnEQikUg0goF0i17A94HCTGWwsWtsnINht9jrrruun5/vBAPv\nNBA745qVZDaRleAyk0MPPRQo1c9OQVUNWMskO2t3Pjtlld1WUU9Ue9IO9v1yYqMV5frSzeAzQ0+1\nZxzEv71m1pjl1C67rc6uC8Y5o5L1vG666aZAidH5+iabbAKU2hKvDc+nymbGjBnA+Pqadgy3yU4D\nvcJj9djtKKDq99qyR+Muu+wCFDVnx2wz+8zcM37msXeqmCfTFp1Cb8HKK68MwLXXXtvyfuwy3iuy\n00AikUgkhgp91eFEtmyutzUBEX4uMtJYZWtmjgykAWXTGPTRqmz8K5O1nsZ4ljUHMl4VkEwlzquo\nqokSg/LN9rJdM62MI7gOzNDTBtZT+H5dExi7rdupAzFzU4apgrGK3mth1113BUqMR3Yv7A4uSzdu\n0W1cZDKzFjtFvH8Yi3E2kNeIHbK1ld6E8847D4D99tsPgA022KBl+03aoKk6ONWfKi/GiGIPtUEj\nFU4ikUgkGkEtvdR8Wlcpm04run36xhqGQaPJqnt/S5ZunzDjEWb4HXzwwUCJMzgjSBanKoj7PIwz\nYKrgPBIz8PSxy9b1OxuvkoE6L6XJzLpBQ/YtzA5z0qu2kp2bheb7J510EtC9sonx1GFEzE6zH5wd\nR6ybWWuttYCiaOzq4TqxJsVu03HirNdUE2h3nmKtY6/QixBrF+3haHdpMeg6vVQ4iUQikWgEPWWp\nVc32qJprIXM1riDjsBLYGpJlllmm5Xv6HY1jVM3PqBEDz1KLDMJj0Rb6VJ2KKtvXJn5exiubu+22\n23rZnfkY5Lz2XqEtZLD2lzJW2BT6sUWskxKdMsl4Xqq21xTqmA3UDhtttBEAN910E1DUnrOjjFve\neuutABx11FEAHHTQQUCpbbMuUAWjAvJacvu93k+auEaMQ5lxZ51WFczstNOE68s4l50m9JjUhcxS\nSyQSicRQodY6nHasraqqXZ+sbF70WuXchx9y4AonQqWimhuWuTXDoHCEkxqvv/56oHRYlukOGr3Y\nwrUbu0N3u6br8qnHmTO9dhWvc1302uUi9mxcwO+3/K065mFWOGIYeiAuCKlwEolEIjFUaLTTwEKA\nxhWOLM/zMCwMZpgUjjCbzVqlplCHLXpVGJONWM0/jOtiUGinKoah68LC5hFJhZNIJBKJRtBtHc59\nwOCHRUweVujis7XYYkhrSbqxAzS0LppWNo+iFlssLIomQmXzKIZyXQwKbc7ZpNpiWJTNo+jYFl25\n1BKJRCKR6BXpUkskEolEI8gHTiKRSCQaQT5wEolEItEI8oGTSCQSiUaQD5xEIpFINIJ84CQSiUSi\nEeQDJ5FIJBKNIB84iUQikWgE+cBJJBKJRCPoqrXNwt6MrwPcNzo6unQnH/xvt8X/UpPGdkhbFHRj\ni5GRkdHFFlus59HNdTWobDcGIY6w7nTsdq6Lgk5t0W0vtaHAADulLrR9nxL/neh09sswYu7cuTz4\n4IMsscQSAPzlL38BShdmjy0+kHzf67tqhpD3AScKx157vu+U1DhzygfL9OnTgdI3zgdTVbfoxRZb\nbP4U4kR3SJdaIpFIJBrBQqlworJ5zGMeA5SpimLYZkYk6kGnkxwXRkT3T7/Kxu2pJprsuD0yMsKy\nyy7LrFmzWvbFY3vooYeA8Yomnle/p8Jxiuqiiy7a8r5KSsXk+9owziRaeulHvOd//etfAXj6058O\nwJw5c1r+Rjz88MON31M85nZuvmFHKpxEIpFINIKFcuKnkx9VND71F1tsMaAwpx7Q+MTPYcXCEBCN\nPvaq4HC/SrcfW7T77bpm1S+11FIA/OlPf2rZrn+9NrxWYjykU3Rri7GqItqgagqqNvu///u/ltf9\n379uTyXieY/rwP/j9lUN2kIbPvDAAxPu19j9njdv3lBeI9rmX//6VxM/Nx858TORSCQSQ4WuYzhT\npkypzX9Zxf583Xnqj3vc4wDYd999gRKz8Xv+3WuvvWr5/cRwQAYcffrR51/1PT8/mee3am3Htdst\n3M4666wDwHXXXTfh5yI7jynAg7wGpkyZwrRp0+az7Sc96UlAyRaL+xaVid8zi2z27Nktr5ud5ueX\nXXZZYNyU0nHrwGN2O66X++67DygqoUopTyZUZe7LqquuCsBvfvMbAI4//ngAfvGLXwBw5ZVXArDz\nzjsDcMQRRwDl2Jq+NlLhJBKJRKIRdK1wenkiylzMGnn/+98PwOqrrw7ApZdeCsAb3/hGAJ7ylKcA\n8KxnPQsoTMTYTWSH119/PQBf+tKXANhuu+2A6kKvfo4l0RxiHMJ6CiHzNQvK+IQK+MEHHwQKY7V2\not26GCTqUjjGHby2In74wx+2/L/uuusC1eovsv86MDo6yr///e/5v2kdjlhvvfUA+N73vgeU8+c+\neL/46le/CsApp5wCwHnnnddyDNbp+PlYt+P/MSZkVtpdd90FlDiXWWvC/X/a054GwN13392xDeqC\nXh7X7vOf/3wA9thjj5b/tanr4phjjgHGewu+/e1vA/CjH/0IKOdm0PfEVDiJRCKRaAQDzVLT3/i1\nr30NKE/dl7zkJQDceeedANx6660ArLbaakDJj9dH+4c//AEoDEWmYTaaT20Zi/n4t99+eze7C5OQ\npbbhhhsC8IMf/AAocauXvvSlADzzmc8E4OabbwYKS9d2+qtl83UxlMnMwJGNeb6f8YxnAEX5rrXW\nWgB885vfBOCXv/wlAM973vOAsl5UyDfeeCPQe/biMGUjaZt77rkHKOtFGMfcZpttWv4uueSSANx/\n//0Tbreqli2iW1uMjfnG+inXvHEoIVs35vPyl78cgJ///OdAOe8vetEjl6os3fuN51/F4/e/+MUv\ntmzH39l2222Bsk7uvfdeoKyXVVZZpeV7HtMg14Xnw/PteV5mmWUA2GyzzYByvqOK917r592OCkkV\nt/HGGwPws5/9DCg2j5l67ZBZaolEIpEYKgxE4fg0teLXKmeZiPnuT3ziE1s+97a3vQ0oGRcyE5nG\nO9/5TgCWW2459wcorH6TTTYBCuuXBXaBgSkc/chrrrkmUPZRRiLL+tCHPgSUY5ahRP+zbM74hVko\nZqfok+21NmkYFM7KK68MlErxHXfcEYBNN90UgJNPPhkosUD90jLc5ZdfHigZXL12JujFFoPK/nK7\nV1xxBQCveMUrWt7/1a9+BcBKK60EFFvqNYgZXHG77fa3H4UTYQxOtv3Upz4VKOdPVfamN70JKIrk\nxBNPBIpaM7Z78cUXAyWb9fOf/zwAM2bMAMazez0iXkM/+clPWrZr1lrM6PJaGsQ14vVqzM1rYMUV\nVwRghRVWAOCnP/0pUO4bn/zkJ4Gi6txHj0FPin/N6NMLtPXWWwMljtbtuk2Fk0gkEomhwkAUjv5H\nn7Zrr702ALvuuitQMi6MT2y11VbA+B5Jwupps04+85nPAOWpLwM57bTTADjnnHOAnp7WtSkcfa7G\nH/SRGr+66KKLgBKnUgGJqlqT6AcXH/vYxwDYf//9gd7z7Cezilp299GPfhQoWYueR5mvf6+55hoA\nfvvbR5p8W3GuIvrud78LlPiWMSDrQDrtKzYMMRyVsEq33ec8766/qGy67XAwZ84cNt54Y26++eaO\nbTF16tTRkZGR+b/hmnSt6+mIal1Phdljb37zmwHYfffdgaKMjKlsueWWAGy++eZAucZuueWWlu8b\ni1H5qGSuuuoqoCjh6EUwO3JsB4R7772Xhx9+uO91Ec+Dvyn2228/AC655BKgZJ39+Mc/btkn17pe\nJBWO68F7qh6WD3/4wy3H7v3De2y3mZypcBKJRCIxVBhIt2ifkvvssw8A73vf+4DCWKybUbmYGWNW\n0Ze//GUADj74YKBkl+jjteJY5vGNb3yjZTsyGZ/St912W41H1xn0nVpDdMghhwCFichkrr76aqCw\nM7PPjFNZo+AxyNZURjIcYz2i19jBZHRe1hYq3T333LPlfRXN6aefDhQWJ2tTPbrufF3FJH7/+99P\n+PsyZs/ZMEFvQZWy8Xyp7i644AKg1LZ5jVV9r1MYb+0Go6OjzJ07d9zcGxXrZZddBhSlYmzOGiLX\ntnEMlZHn2/Pl/7vssgsARx99NFCuGbMYzzjjDKDEvcxKc50YD1EV+Nftez+ZNWtWbddJ3I62sZ5G\n9W5GnvcP74Xa9I477gDKOqjq4uCxquaMZ/m+51nb1n0/SIWTSCQSiUYwEIXjU9qnr3UzsvITTjgB\ngLPOOgsoMZlzzz0XKH5FmY5MQ6ZrVpLZJ1tssQVQfLL65vVbm5Fhfr2oc8ZEzPKRDRkv+MAHPgAU\nH+pzn/tcAHbbbTegxLdkGKo4/zcOoa2sbbLmyKyWyayi7xbaTLZ24YUXAoVt6d92PXz2s58Fynn0\nWN2OWWlR6Wi7qiw1181rX/taoGQ7DRLtxh6LdvNwjGMZ9xDD1CcwdmlW8ahkVZ4x5hs7j5iZZR2W\nHg4Vi0rG9WSfMa+t9773vUDpdGLW64EHHgiUmLBxEhWPcTB/vwkvgDEa1V9U7Spfj801LmKM1302\nzu29z/hX7DLttRdnEfWLVDiJRCKRaAS1ZqlFX7hPza9//etA8cWapWa8Qt+rykXWfvbZZwPl6Syj\nkRX6lLdiWV+ryuawww4DSl1PBxh4p4FXvvKVQFEusvaqjroRsjd9tjIfYz0bbbQR0D+zbSIzS0Z5\n+eWXA2V9qDj1VxvbqaqCl4WpGlW4sn6VsllQsadXO0xGllq789dvz7OqLDW9EHonJtivrm3heVGB\nWDfjNaDCVMlYH2MPNWM37tMGG2wAlPuGHZI9loMOOggodVlrrLEGUNSimV9mahnHcB16LcaZMu7v\nP//5Tx5++GHmzZs3sHVhDZL3UOtkXBdf+MIXgBLr9V7qPnuPFcbLXvjCFwJFQVm/M3PmTKDcU7VJ\npx3XM0stkUgkEkOFWmM4MhXZl0rk+9//PlAYjJkXZ555JlCq4/0rw5HZxApfVYKxGd+3L5DKxqy4\nYYJMIs7ckFnEepwI63viXAy/F6cYDhNiDZEqTTYmO3PfrQ2oml4Yj93YjkxYZW3MIHYBrlIJkxn3\naKdcVPP9br8qDlGlbPqBHgZ/WwXrPvz6178GShW9GZ1+3o4hqn/38dOf/jQA73rXu4Di4ZC1q3yt\nTVPh6D34+Mc/DpQuHWZ4GSeJ/cfGKuNBrZHY0857np4MO+K7r9rC9/2+8D5jVpo1SnoXvBcbJ4/1\nf3Faar/xq1Q4iUQikWgEA8lS8+loLMZeSPod9cEaoxFmGalczj//fKAwJBmMnVL1R8p8fDob55Ap\nqXiGCbG3mTarii/I9szgkXn4PesuhjlLLWYFysq+9a1vAcUnbwxQ5qlv3WOOKsCOFXa41dduLM/f\nMT7h77frjDwZiJXmETLUbqHNImNuArJn90G7G0ewM7pxBtfDJz7xCaAoGGM6xx57bMvnVcTOy/E+\nYYd1Mzi17VFHHQWU7LMDDjgAgK985StAsZHZb07N9Nq78cYbB6Zw/G3vac95znOAYjPXdMyGVb2p\ncPQaqEh22mmnlv9V/3qVrF0ym1Zb1aVsRCqcRCKRSDSCWhROVT3LDTfcABTGaTdomYdPaZ/G+trN\nOlHBrL/++kDxvfr5yNLMRddna++lduinZqHfege/LwuM0Afr9mXpEc7FMOvNc9FDx+yBQSbqenEO\nihNahWwuVj+rZPQ7m7Xouojzk1S8vu7/VR2T68KCOiRXQZtUrYN+1ZhsPvro65zwORGmTZs2jiW/\n5jWvAUocwTVuVwQ9FK5p7xeu7e233x4o68KOAnYcsWO29w1jy37e+h7rA7WFsUJVg1lu7vdNN900\n/5jqipFWTYA1nuXv2P27ndLwGDxmv+eaV0GpcFRE1iaJupWNSIWTSCQSiUbQk8KJT2XZtDGbON8i\nZlTZ68xYjqzOSX4qF5/KMgsVT+wr5UwHWcGLX/xioHQyaKdC+vHHdvtdGUOcMV4Ve4mVv7ELrBla\nsn47FMTeasMEj0Um6jGYeWMsT5+8mX1m2uijl9GatejrsUefdTlj5pjUf1Bj0Mv2vYZiR2TnI6nm\nOoU21WZOQ3W9WRszaIy1hefdWK61JipUe5vZO1HlYtaYsViPQSXrfcSuC7J1Y8dmdrmO3vCGNwCl\n64f3l5gxGqv39azEbMd+oH20jYi1g0ceeSRQVL/7onfA7Zip+bvf/Q4ottMmxobiPTZCNVhVD9gr\nUuEkEolEohH0pHCqGJwZVioQ4wf63v2esRqz0azTMStFhnP33XcDxdcqQ7FSOfo7hZXpdhWuqhye\nDMQ6CBlLO2hTbSlz0cdrloqfsz9Vu4mfnfb0qgMeu/usElXZyOrtWeX6OeKII4ASh/C8us8qX+F5\nNgMn2riqP1mdvfXawWvAfVGRmLlpV/BOlU3srOz0yqqsN7OSXvWqV3W9793gP//5T2VXAxWo9XPW\nz3jMZifKylUmTgI2tmM2q2pOT4m9GV37Zvj5ufe85z3A+Pio7N/9s35QNdBLjK4dtI3Kxe7Qqr6q\n+4bqXQXkNXPttdcC5VpRDarOVC5VMTzfjz3W+kUqnEQikUg0glp7qemTtRrezqRmixh3OO6444DS\n40p/4a233gqUeIRPVVm6vbVkgdZtOOtcVi97fPaznw2UPHy/v4BjHngvNWG8S/ZnDUkVZMDWLhgH\nM+PGjBptV4VOGUsT/cO+853vAPCCF7wAKOdNdiYrMzvJ+JSsTZvYG8tJjnZ9duKj85diDcOY/Z/w\nddGELVz7XjPvfve7Adh3332B0g/MOolXv/rVQKlZcR0Zx3J9ROiFcPZUtz76fmyhnd1XPRUqVa8J\nrwXjWe6zr9sF2liL9wfnaKlMtKGdS5yHY9zDbtHeJ1wvsVOyXgVjjo/aYaDrwvi3a1zlosLxmK1h\n8q+1QioZrwn33Z5rwpq32FVBZez/7bLVspdaIpFIJIYKtSicWJWqwpFRyCw/9alPAaWy1ywQWXr0\nU8b+Yk4QdYZM7HxqZpbfM9vJzI0OjrUxhdNt7ETfrhlY+pll784Eqqt6vglWL8vWp67qsv7COi5j\nKsZ6oiIx5qdKeOtb3wqUHl077rgj0HtNUhO28JhU6V4bsm+P3b/GLY1vHXroocD4GUHCqbsnnnhi\nL7s3H73YwrUe17zn3/NUFU9QkXheDz/8cKDMcnFG1A477ACUrtQf+chHAFhqqaWAMlHU2hSVsl3E\n7UsW15f3E2NB06ZNY+7cubV2i65Sf17n7osxPxWO90ZrkzwGP6c6jHFtrxUVj7OovMdaK2UczUzg\nKqTCSSQSicRQoWuF00mGhk9dMyz0ucps7OJsjYGs3Tx6n+pmpfh0NpvF3/ep7qx7MzZix1R/vwMM\nXOH02pnADK5Yu7L33nsDpXNuXZXBTbB6feOuC8+z2UXveMc7gJK95OdUPJ5nGbCxQW1jzy2Vdayr\n6BRN2ELWbRzLa0fGq7IxzuH60bugDS655JKW14UKud0E0XboxhYjIyOj06dPn+/hcJ/8qxqPWWza\nQMVrJp37bnzKWK42cRaUf10Hr3vd64DC1o2TWcdl3MO6HNeb8Jp1vc2bN29g83BiRqXxbffBDD5j\nL87D0VZ2bzA+rq3MelxttdWAMv3UGrZtttkGKHN3vCd3WnOUCieRSCQSQ4Wu63AWxMxlKtZZ6HP1\nKWr2kMw2Vr+a7242ik912Zl+ROdY2PXVz5n1JHPyc1WYjLnv7X4rsj0Zjz5VbRH94VXKZphm20fE\naurzzjsPKFXxdu+VEcc+YDJYY3lLL710y+sXXXQRUGxUVQ8SMRk223bbbYGiyiLTlcWL2KEi9uLS\nl6+vv1dl00/Ptblz586Pn0K5L1hX53WuZ8Jjjj337CTgfKS11loLKB4S2b3ZjdrEDD5jRHaZdzqu\nGZvGOVQ2sR5LxWWW7aMZal1aY2LEteba1MtjLCde9ypaFY9xc9d+VINm4NlhW1VnDFgvgHFvPSnt\n6vi6RSqcRCKRSDSCWufhRFYu45RBWAFsfr255jKI2PvKGgGVkrEaeyTZ8fTAAw8EygRJK4aNe+iP\njBhG1q8No99YP3Zk57FiOB7TMB5jFWRl9tASriNZlszTdWGsRzamsomTYjvFZNjM7DEZqp0AOkXs\nOu41cNBBB7Vst9sux3V2k3ZbKtuoWF3bZpsa+zETT7ZvlqqdSYxjqKbsXKISsg7npJNOAoqtrWny\nvqFaMNYXY011d06G6uvVNWtmpbEZawrtK2c3jYsvvhgosR27tJjRZyafikdlY/cFO1OYGar6q+pe\n3itS4SQSiUSiEQxk4qc+VCfl+XQ2312mY2cCK35lJmZG2B/IDCz9j6eeeipQMitkHjJffbtWXUfG\nvDAgdoe2R5bqUebRrlK8bh9sE4jzcPTR+7p1OsYGtZVsz/qLOD07jqx8AAAd2UlEQVR1k002AeDq\nq68e2L73i2OOOablr+y8XQcJmbH1OGbsTTamTZs2vzO1LNrrP06t9fqVpfs517g91Izp7LHHHkDJ\nVjPLzOzVK664AoCDDz4YKPU5ZmgZ51JReY0ZC4oem7FqpCkVrDJVFZpxZ2cRVZxq33uecU0nAdub\nzf+1sdvxeIyjj+2qUCdS4SQSiUSiEdTaS20B3wNKnY2V5Wad6V+MMyHiBD59qsZ8VEIeg35HWYF/\nI9NdQLZS33U4dU0AlbmYraKvVbZmXEq2VxWn6hVN1J50Cs+rmVr+NXZjxbrszzhFXR1ue7GFU2ed\nXtsvrAi3Y7aZV6qHJrp9Q2+2iFX0/q+iibEcM7JUKrLwzTffHChV8sZ4rDkxc8vYjLbyr1X0n/vc\n54DSn05V4P0ldhyQ9dvbz31t8hqJfefMarQ+6+1vfztQ6rmsOdIb5P3Bri72JzReHj0q3SLrcBKJ\nRCIxVKhF4fTK6juti+j186KLDJ3GeqlVIbJA/cqrr746UHpoOQskdguua9b6MCmciKiEZcRmF0Xf\ne7/sf5ht0TTqUDiqdrMSY4zEz6ksNtxww5bPmY0mm7dbuB1FrL8zw0s1oAJeb731ALjggguA8UrY\nusAYHx2bsde0wokwe9V9V8UZu1EFek3oDfJaiMfcbwZeKpxEIpFIDBUaieEsRJg0heP0U9lbO5bl\n58f6levEwsTqu1W+3SryhckWvcKOyvfff/8CP9etLaZOnTquC0JUnrHmzF5oV111FVA6Tli3ZyzY\nuIYZXGbmGaew1mSdddYBSizXqnvjYML6vrPOOgsYv67GKrB58+YN1bqInpG4z4NGKpxEIpFIDBVS\n4bSiY4Wz5JJLjm622WbzJ+oNCrI6mYsKSHZoRbmZNXVhmNjbZCNtUdCtLRZZZJH5tWCuWf8abzTG\n6twi6/esl3FGkB0FjF/a7dmsRSfCzpgxA4ATTjgBKGw/zooy+7EqM8v9dj9VgX/729946KGHBtIt\neoLvAcPfMSQVTiKRSCSGCqlwWjHpWWrDgmT1Bf9NtjDjy2r6btFPllqsl7MOx5hOjMGpKFQu9hGz\nji/CzhMzZ85sed34hnU2UdHE31dxxdiT+yv+m9ZFv0iFk0gkEomhQrcKZzaw4KZOCzdWGB0dXbqT\nD/6X26JjO0DaYizSFgVpi4K0xSPo6oGTSCQSiUSvSJdaIpFIJBpBPnASiUQi0QjygZNIJBKJRpAP\nnEQikUg0gnzgJBKJRKIR5AMnkUgkEo0gHziJRCKRaAT5wEkkE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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e7c13e0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rows, cols = 10, 6\n", "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n", "\n", "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n", " for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n", " ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling from the generator\n", "\n", "We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x6bj3EO8/fg5FqQkhhCgXaMIRQggRhLS1J0iFZJZx3IfWVaK6ZuPHjzcdtSxw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\nip1Ay3JcaKMR2jS0YJYvX27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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e80057c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", "_ = view_samples(0, [gen_samples])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
kit-cel/wt	mloc/ch4_Autoencoders/BinaryAutoencoder_AWGN.ipynb	2	342738	{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "# Learn Modulation and Bit-Wise Demodulation in the AWGN Channel with Deep Neural Networks by Autoencoders and End-to-end Training\n", "\n", "This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>\n", "\n", "This code illustrates\n", "* End-to-end-learning of modulation scheme and demodulator in an AWGN channel with binary autoencoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We are using the following device for learning: cuda\n" ] } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from ipywidgets import interactive\n", "import ipywidgets as widgets\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "print(\"We are using the following device for learning:\",device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper function to compute the Bit Error Rate (BER)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "# helper function to compute the bit error rate\n", "def BER(predictions, labels):\n", " return np.mean(1-np.isclose((predictions > 0.5).astype(float), labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we consider the simple AWGN channel. We modulate using a constellation with $M = 2^m$ different symbol. To symbol $i$, we assign the binary representation of $i$ as bit pattern." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "# number of bits assigned to symbol\n", "m = 5\n", "\n", "# number of symbols\n", "M = 2*m\n", "\n", "\n", "EbN0 = 10\n", "\n", "# noise standard deviation\n", "sigma_n = np.sqrt((1/2/np.log2(M)) 10(-EbN0/10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we define the parameters of the neural network and training, generate the validation set and a helping set to show the decision regions" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "# Bit representation of symbols\n", "binaries = torch.from_numpy(np.reshape(np.unpackbits(np.uint8(np.arange(0,2m))), (-1,8))).float().to(device)\n", "binaries = binaries[:,(8-m):]\n", "\n", "# validation set. Training examples are generated on the fly\n", "N_valid = 100000\n", "\n", "# number of neurons in hidden layers at receiver\n", "hidden_neurons_RX_1 = 50\n", "hidden_neurons_RX_2 = 128\n", "hidden_neurons_RX = [hidden_neurons_RX_1, hidden_neurons_RX_2]\n", "\n", "# Generate Validation Data\n", "y_valid = np.random.randint(M,size=N_valid)\n", "y_valid_onehot = np.eye(M)[y_valid]\n", "y_valid_binary = binaries[y_valid,:].detach().cpu().numpy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the architecture of the autoencoder, i.e. the neural network\n", "\n", "This is the main neural network/Autoencoder with transmitter, channel and receiver. Transmitter and receiver each with ELU activation function. Note that the final layer does not use a `softmax` function, as this function is already included in the `CrossEntropyLoss`." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [], "source": [ "class Autoencoder(nn.Module):\n", " def __init__(self, hidden_neurons_RX):\n", " super(Autoencoder, self).__init__()\n", " \n", " # Define Transmitter Layer: Linear function, M input neurons (symbols), 2 output neurons (real and imaginary part) \n", " self.fcT = nn.Linear(M, 2) \n", " \n", " # Define Receiver Layer: Linear function, 2 input neurons (real and imaginary part), m output neurons (bits)\n", " self.fcR1 = nn.Linear(2,hidden_neurons_RX[0]) \n", " self.fcR2 = nn.Linear(hidden_neurons_RX[0], hidden_neurons_RX[1]) \n", " self.fcR3 = nn.Linear(hidden_neurons_RX[1], m) \n", "\n", " # Non-linearity (used in transmitter and receiver)\n", " self.activation_function = nn.ELU() \n", " self.sigmoid = nn.Sigmoid()\n", "\n", " def forward(self, x):\n", " # compute output\n", " encoded = self.network_transmitter(x)\n", " \n", " # compute normalization factor and normalize channel output\n", " norm_factor = torch.sqrt(torch.mean(torch.mul(encoded,encoded)) * 2 ) \n", " modulated = encoded / norm_factor \n", " received = self.channel_model(modulated)\n", " \n", " bitprob = self.network_receiver(received)\n", " return bitprob\n", " \n", " def network_transmitter(self,batch_labels):\n", " return self.fcT(batch_labels)\n", " \n", " def network_receiver(self,inp):\n", " out = self.activation_function(self.fcR1(inp))\n", " out = self.activation_function(self.fcR2(out))\n", " logits = self.sigmoid(self.fcR3(out)) \n", " return logits\n", " \n", " def channel_model(self,modulated):\n", " # just add noise, nothing else\n", " received = torch.add(modulated, sigma_ntorch.randn(len(modulated),2).to(device))\n", " return received" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the NN and evaluate it at the end of each epoch\n", "\n", "Here the idea is to vary the batch size during training. In the first iterations, we start with a small batch size to rapidly get to a working solution. The closer we come towards the end of the training we increase the batch size. If keeping the batch size small, it may happen that there are no misclassifications in a small batch and there is no incentive of the training to improve. A larger batch size will most likely contain errors in the batch and hence there will be incentive to keep on training and improving. \n", "\n", "Here, the data is generated on the fly inside the graph, by using PyTorch random number generation. As PyTorch does not natively support complex numbers (at least in early versions), we decided to replace the complex number operations in the channel by a simple rotation matrix and treating real and imaginary parts separately.\n", "\n", "We use the ELU activation function inside the neural network and employ the Adam optimization algorithm.\n", "\n", "Now, carry out the training as such. First initialize the variables and then loop through the training. Here, the epochs are not defined in the classical way, as we do not have a training set per se. We generate new data on the fly and never reuse data. We change the batch size in each epoch.<br>\n", "\n", "To get the constellation symbols and the received data, we apply the model after each epoch." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start Training\n", "Validation BER after epoch 0: 0.486800 (loss 0.69531417)\n", "Validation BER after epoch 1: 0.392910 (loss 0.67964846)\n", "Validation BER after epoch 2: 0.364116 (loss 0.65048879)\n", "Validation BER after epoch 3: 0.328666 (loss 0.60374552)\n", "Validation BER after epoch 4: 0.282752 (loss 0.53719771)\n", "Validation BER after epoch 5: 0.237608 (loss 0.45808160)\n", "Validation BER after epoch 6: 0.174876 (loss 0.35196051)\n", "Validation BER after epoch 7: 0.153402 (loss 0.28338176)\n", "Validation BER after epoch 8: 0.143322 (loss 0.26819330)\n", "Validation BER after epoch 9: 0.125086 (loss 0.23904413)\n", "Validation BER after epoch 10: 0.097586 (loss 0.20571463)\n", "Validation BER after epoch 11: 0.064588 (loss 0.16371432)\n", "Validation BER after epoch 12: 0.043776 (loss 0.13498044)\n", "Validation BER after epoch 13: 0.036798 (loss 0.11493500)\n", "Validation BER after epoch 14: 0.030254 (loss 0.08791406)\n", "Validation BER after epoch 15: 0.028794 (loss 0.08501137)\n", "Validation BER after epoch 16: 0.026342 (loss 0.07543315)\n", "Validation BER after epoch 17: 0.021488 (loss 0.06120609)\n", "Validation BER after epoch 18: 0.019112 (loss 0.05278707)\n", "Validation BER after epoch 19: 0.017960 (loss 0.04775571)\n", "Validation BER after epoch 20: 0.017102 (loss 0.04406153)\n", "Validation BER after epoch 21: 0.014990 (loss 0.04206623)\n", "Validation BER after epoch 22: 0.012894 (loss 0.03469373)\n", "Validation BER after epoch 23: 0.011780 (loss 0.03697656)\n", "Validation BER after epoch 24: 0.012040 (loss 0.02982548)\n", "Validation BER after epoch 25: 0.011642 (loss 0.03828009)\n", "Validation BER after epoch 26: 0.011462 (loss 0.03526101)\n", "Validation BER after epoch 27: 0.011524 (loss 0.03011780)\n", "Validation BER after epoch 28: 0.011350 (loss 0.03639561)\n", "Validation BER after epoch 29: 0.011178 (loss 0.03242466)\n", "Validation BER after epoch 30: 0.011124 (loss 0.03145263)\n", "Validation BER after epoch 31: 0.010960 (loss 0.02628735)\n", "Validation BER after epoch 32: 0.010854 (loss 0.03295664)\n", "Validation BER after epoch 33: 0.010946 (loss 0.03382872)\n", "Validation BER after epoch 34: 0.010842 (loss 0.02913460)\n", "Validation BER after epoch 35: 0.010866 (loss 0.03123650)\n", "Validation BER after epoch 36: 0.011174 (loss 0.02300410)\n", "Validation BER after epoch 37: 0.010824 (loss 0.02761109)\n", "Validation BER after epoch 38: 0.010726 (loss 0.03135696)\n", "Validation BER after epoch 39: 0.011172 (loss 0.02975094)\n", "Validation BER after epoch 40: 0.010526 (loss 0.02757191)\n", "Validation BER after epoch 41: 0.010658 (loss 0.03120482)\n", "Validation BER after epoch 42: 0.010592 (loss 0.02780099)\n", "Validation BER after epoch 43: 0.010590 (loss 0.02798905)\n", "Validation BER after epoch 44: 0.010190 (loss 0.03072020)\n", "Validation BER after epoch 45: 0.010422 (loss 0.02632508)\n", "Validation BER after epoch 46: 0.010030 (loss 0.02934284)\n", "Validation BER after epoch 47: 0.009986 (loss 0.02910344)\n", "Validation BER after epoch 48: 0.010008 (loss 0.02518122)\n", "Validation BER after epoch 49: 0.010066 (loss 0.03444438)\n", "Validation BER after epoch 50: 0.010038 (loss 0.02793099)\n", "Validation BER after epoch 51: 0.010050 (loss 0.02839644)\n", "Validation BER after epoch 52: 0.010124 (loss 0.03038305)\n", "Validation BER after epoch 53: 0.009982 (loss 0.02843680)\n", "Validation BER after epoch 54: 0.009916 (loss 0.02544533)\n", "Validation BER after epoch 55: 0.009752 (loss 0.03138152)\n", "Validation BER after epoch 56: 0.010082 (loss 0.02426642)\n", "Validation BER after epoch 57: 0.009902 (loss 0.02353315)\n", "Validation BER after epoch 58: 0.009736 (loss 0.02426081)\n", "Validation BER after epoch 59: 0.009834 (loss 0.02453304)\n", "Validation BER after epoch 60: 0.009648 (loss 0.02345995)\n", "Validation BER after epoch 61: 0.009832 (loss 0.02355838)\n", "Validation BER after epoch 62: 0.010076 (loss 0.03111228)\n", "Validation BER after epoch 63: 0.009794 (loss 0.02385832)\n", "Validation BER after epoch 64: 0.009586 (loss 0.02210572)\n", "Validation BER after epoch 65: 0.009536 (loss 0.02397677)\n", "Validation BER after epoch 66: 0.009882 (loss 0.02656456)\n", "Validation BER after epoch 67: 0.009438 (loss 0.02444416)\n", "Validation BER after epoch 68: 0.009722 (loss 0.02807107)\n", "Validation BER after epoch 69: 0.009250 (loss 0.02775311)\n", "Validation BER after epoch 70: 0.009420 (loss 0.02827078)\n", "Validation BER after epoch 71: 0.009482 (loss 0.02571028)\n", "Validation BER after epoch 72: 0.009652 (loss 0.02352219)\n", "Validation BER after epoch 73: 0.009592 (loss 0.02726550)\n", "Validation BER after epoch 74: 0.009488 (loss 0.02152265)\n", "Validation BER after epoch 75: 0.009666 (loss 0.02723907)\n", "Validation BER after epoch 76: 0.009554 (loss 0.02430050)\n", "Validation BER after epoch 77: 0.009280 (loss 0.02561529)\n", "Validation BER after epoch 78: 0.009134 (loss 0.02365524)\n", "Validation BER after epoch 79: 0.009392 (loss 0.02584594)\n", "Validation BER after epoch 80: 0.009474 (loss 0.02723803)\n", "Validation BER after epoch 81: 0.009186 (loss 0.02638854)\n", "Validation BER after epoch 82: 0.009662 (loss 0.02239287)\n", "Validation BER after epoch 83: 0.009444 (loss 0.02435002)\n", "Validation BER after epoch 84: 0.009210 (loss 0.02435908)\n", "Validation BER after epoch 85: 0.009144 (loss 0.02599248)\n", "Validation BER after epoch 86: 0.009350 (loss 0.02668979)\n", "Validation BER after epoch 87: 0.009828 (loss 0.02430218)\n", "Validation BER after epoch 88: 0.009258 (loss 0.02573795)\n", "Validation BER after epoch 89: 0.009494 (loss 0.02833278)\n", "Validation BER after epoch 90: 0.009390 (loss 0.02485977)\n", "Validation BER after epoch 91: 0.009268 (loss 0.02537241)\n", "Validation BER after epoch 92: 0.009250 (loss 0.02646681)\n", "Validation BER after epoch 93: 0.009558 (loss 0.02744564)\n", "Validation BER after epoch 94: 0.009092 (loss 0.02616633)\n", "Validation BER after epoch 95: 0.009212 (loss 0.02482910)\n", "Validation BER after epoch 96: 0.009274 (loss 0.02742478)\n", "Validation BER after epoch 97: 0.009150 (loss 0.02691468)\n", "Validation BER after epoch 98: 0.009332 (loss 0.02564413)\n", "Validation BER after epoch 99: 0.009278 (loss 0.02890083)\n", "Validation BER after epoch 100: 0.009098 (loss 0.02173120)\n", "Validation BER after epoch 101: 0.009386 (loss 0.02610632)\n", "Validation BER after epoch 102: 0.009216 (loss 0.02720360)\n", "Validation BER after epoch 103: 0.009366 (loss 0.02677977)\n", "Validation BER after epoch 104: 0.009282 (loss 0.02599082)\n", "Validation BER after epoch 105: 0.009506 (loss 0.02534137)\n", "Validation BER after epoch 106: 0.009138 (loss 0.02559844)\n", "Validation BER after epoch 107: 0.009190 (loss 0.02396739)\n", "Validation BER after epoch 108: 0.009300 (loss 0.02314747)\n", "Validation BER after epoch 109: 0.008934 (loss 0.02874488)\n", "Validation BER after epoch 110: 0.009090 (loss 0.02234589)\n", "Validation BER after epoch 111: 0.009244 (loss 0.02540740)\n", "Validation BER after epoch 112: 0.009130 (loss 0.02515250)\n", "Validation BER after epoch 113: 0.009132 (loss 0.02377273)\n", "Validation BER after epoch 114: 0.009386 (loss 0.02484569)\n", "Validation BER after epoch 115: 0.009126 (loss 0.02673006)\n", "Validation BER after epoch 116: 0.009104 (loss 0.02195922)\n", "Validation BER after epoch 117: 0.009318 (loss 0.02471648)\n", "Validation BER after epoch 118: 0.009232 (loss 0.02430898)\n", "Validation BER after epoch 119: 0.008914 (loss 0.02294928)\n", "Validation BER after epoch 120: 0.009046 (loss 0.02510118)\n", "Validation BER after epoch 121: 0.009124 (loss 0.02479158)\n", "Validation BER after epoch 122: 0.009292 (loss 0.02691045)\n", "Validation BER after epoch 123: 0.009036 (loss 0.02562723)\n", "Validation BER after epoch 124: 0.009088 (loss 0.02371221)\n", "Validation BER after epoch 125: 0.009278 (loss 0.02647229)\n", "Validation BER after epoch 126: 0.009180 (loss 0.02434589)\n", "Validation BER after epoch 127: 0.009228 (loss 0.02541258)\n", "Validation BER after epoch 128: 0.009294 (loss 0.02496370)\n", "Validation BER after epoch 129: 0.009284 (loss 0.02437430)\n", "Validation BER after epoch 130: 0.009014 (loss 0.02602797)\n", "Validation BER after epoch 131: 0.008886 (loss 0.02822077)\n", "Validation BER after epoch 132: 0.009052 (loss 0.02485039)\n", "Validation BER after epoch 133: 0.009398 (loss 0.02268189)\n", "Validation BER after epoch 134: 0.009208 (loss 0.02091005)\n", "Validation BER after epoch 135: 0.009102 (loss 0.02404522)\n", "Validation BER after epoch 136: 0.009202 (loss 0.02563511)\n", "Validation BER after epoch 137: 0.008786 (loss 0.02643481)\n", "Validation BER after epoch 138: 0.009004 (loss 0.02382357)\n", "Validation BER after epoch 139: 0.008820 (loss 0.02316237)\n", "Validation BER after epoch 140: 0.009186 (loss 0.02687752)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Validation BER after epoch 141: 0.008924 (loss 0.02529114)\n", "Validation BER after epoch 142: 0.009074 (loss 0.02620668)\n", "Validation BER after epoch 143: 0.008892 (loss 0.02605776)\n", "Validation BER after epoch 144: 0.009096 (loss 0.02067445)\n", "Validation BER after epoch 145: 0.009134 (loss 0.02398598)\n", "Validation BER after epoch 146: 0.009100 (loss 0.02372427)\n", "Validation BER after epoch 147: 0.009170 (loss 0.02717586)\n", "Validation BER after epoch 148: 0.009072 (loss 0.02417607)\n", "Validation BER after epoch 149: 0.008990 (loss 0.02444244)\n", "Training finished\n" ] } ], "source": [ "model = Autoencoder(hidden_neurons_RX)\n", "model.to(device)\n", "\n", " \n", "loss_fn = nn.BCELoss()\n", "\n", "# Adam Optimizer\n", "optimizer = optim.Adam(model.parameters()) \n", "\n", "\n", "# Training parameters\n", "num_epochs = 150\n", "batches_per_epoch = np.linspace(1, 1000, num=num_epochs).astype(int)\n", "\n", "# Vary batch size during training\n", "batch_size_per_epoch = np.linspace(200,5000,num=num_epochs)\n", "learning_rate_per_epoch = np.linspace(0.001, 0.00001, num=num_epochs)\n", "\n", "validation_BERs = np.zeros(num_epochs)\n", "validation_received = []\n", "constellations = []\n", "\n", "print('Start Training')\n", "for epoch in range(num_epochs):\n", " \n", " batch_labels = torch.empty(int(batch_size_per_epoch[epoch]), device=device)\n", " batch_labels_binary = torch.zeros(int(batch_size_per_epoch[epoch]), m, device=device)\n", " \n", " \n", " for step in range(batches_per_epoch[epoch]):\n", " # Generate training data: In most cases, you have a dataset and do not generate a training dataset during training loop\n", " # sample new mini-batch directory on the GPU (if available) \n", " batch_labels.random_(M)\n", " batch_labels_onehot = torch.zeros(int(batch_size_per_epoch[epoch]), M, device=device)\n", " batch_labels_onehot[range(batch_labels_onehot.shape[0]), batch_labels.long()]=1\n", "\n", " batch_labels_binary[range(batch_labels_onehot.shape[0]), :] = binaries[batch_labels.long(),:]\n", "\n", " \n", " # Propagate (training) data through the net\n", " NN_output = model(batch_labels_onehot)\n", "\n", " # compute loss\n", " loss = loss_fn(NN_output, batch_labels_binary)\n", "\n", " # compute gradients\n", " loss.backward()\n", " \n", " # Adapt weights\n", " optimizer.step()\n", " \n", " # reset gradients\n", " optimizer.zero_grad()\n", " \n", " optimizer.param_groups[0]['lr'] = learning_rate_per_epoch[epoch]\n", " \n", " # compute validation BER\n", " out_valid = model(torch.Tensor(y_valid_onehot).to(device))\n", " validation_BERs[epoch] = BER(out_valid.detach().cpu().numpy(), y_valid_binary)\n", " print('Validation BER after epoch %d: %f (loss %1.8f)' % (epoch, validation_BERs[epoch], loss.detach().cpu().numpy())) \n", " \n", " # calculate and store constellation\n", " encoded = model.network_transmitter(torch.eye(M).to(device))\n", " norm_factor = torch.sqrt(torch.mean(torch.mul(encoded,encoded)) 2 ) \n", " modulated = encoded / norm_factor \n", " constellations.append(modulated.detach().cpu().numpy())\n", " \n", " \n", "print('Training finished')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plt decision region and scatter plot of the validation set. Note that the validation set is only used for computing SERs and plotting, there is no feedback towards the training!" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option);\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"1000\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cmap = matplotlib.cm.tab20\n", "base = plt.cm.get_cmap(cmap)\n", "color_list = base.colors\n", "new_color_list = [[t/2 + 0.5 for t in color_list[k]] for k in range(len(color_list))]\n", "\n", "# find minimum SER from validation set\n", "min_BER_iter = np.argmin(validation_BERs)\n", "\n", "plt.figure(figsize=(10,8))\n", "font = {'size' : 14}\n", "plt.rc('font', font)\n", "plt.rc('text', usetex=True)\n", "\n", "bin_labels = [np.binary_repr(j).zfill(m) for j in range(2m)]\n", "\n", " \n", "plt.scatter(constellations[min_BER_iter][:,0], constellations[min_BER_iter][:,1], c=range(M), cmap='tab20',s=50)\n", "for i, txt in enumerate(bin_labels):\n", " plt.annotate(txt, xy=(constellations[min_BER_iter][i,0], constellations[min_BER_iter][i,1]), xycoords='data', \\\n", " xytext=(0, 3), textcoords='offset points', \\\n", " ha='center', va='bottom')\n", " \n", "plt.axis('scaled')\n", "plt.xlabel(r'$\\Re\\{r\\}$',fontsize=16)\n", "plt.ylabel(r'$\\Im\\{r\\}$',fontsize=16)\n", "plt.xlim((-1.7, +1.7))\n", "plt.ylim((-1.7, +1.7))\n", "plt.grid(which='both')\n", "plt.title('Constellation with Bit Mapping',fontsize=18)\n", "plt.savefig('learning_AWGN_BitAE_EbN0%1.1f_M%d.pdf' % (EbN0,M),bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate animation and save as a gif. (Evaluate results III)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option);\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"800\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "%matplotlib notebook\n", "# Generate animation\n", "from matplotlib import animation, rc\n", "from matplotlib.animation import PillowWriter # Disable if you don't want to save any GIFs.\n", "\n", "font = {'size' : 18}\n", "plt.rc('font', **font)\n", "\n", "fig = plt.figure(figsize=(8,6))\n", "ax1 = fig.add_subplot(1,1,1)\n", "\n", "ax1.axis('scaled')\n", "\n", "written = False\n", "def animate(i):\n", " ax1.clear()\n", " ax1.scatter(constellations[i][:,0], constellations[i][:,1], c=range(M), cmap='tab20',s=50)\n", "\n", " for j, txt in enumerate(bin_labels):\n", " ax1.annotate(txt, xy=(constellations[i][j,0], constellations[i][j,1]), xycoords='data', \\\n", " xytext=(0, 3), textcoords='offset points', \\\n", " ha='center', va='bottom', fontsize=12)\n", " \n", " \n", " ax1.set_xlim(( -1.7, +1.7))\n", " ax1.set_ylim(( -1.7, +1.7))\n", " ax1.set_title('Constellation', fontsize=18)\n", " \n", " ax1.set_xlabel(r'$\\Re\\{r\\}$',fontsize=16)\n", " ax1.set_ylabel(r'$\\Im\\{r\\}$',fontsize=16)\n", "\n", " \n", "anim = animation.FuncAnimation(fig, animate, frames=min_BER_iter+1, interval=200, blit=False)\n", "fig.show()\n", "anim.save('learning_AWGN_BitAE_EbN0%1.1f_M%d.gif' % (EbN0,M), writer=PillowWriter(fps=5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.12" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/structured/labs/6_serving_babyweight.ipynb	1	8737	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LAB 6: Serving baby weight predictions\n", "\n", "Learning Objectives\n", "\n", "1. Deploy a web application that consumes your model service on Cloud AI Platform.\n", "\n", "## Introduction \n", "Verify that you have previously Trained your Keras model and Deployed it predicting with Keras model on Cloud AI Platform. If not, go back to [5a_train_keras_ai_platform_babyweight.ipynb](../solutions/5a_train_keras_ai_platform_babyweight.ipynb) and [5b_deploy_keras_ai_platform_babyweight.ipynb](../solutions/5b_deploy_keras_ai_platform_babyweight.ipynb) create them.\n", "\n", "In the previous notebook, we deployed our model to CAIP. In this notebook, we'll make a [Flask app](https://palletsprojects.com/p/flask/) to show how our models can interact with a web application which could be deployed to [App Engine](https://cloud.google.com/appengine) with the [Flexible Environment](https://cloud.google.com/appengine/docs/flexible)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Review Flask App code in `application` folder\n", "Let's start with what our users will see. In the `application` folder, we have prebuilt the components for web application. In the templates folder, the <a href=\"application/templates/index.html\">index.html</a> file is the visual GUI our users will make predictions with.\n", "\n", "It works by using an HTML [form](https://www.w3schools.com/html/html_forms.asp) to make a [POST request](https://www.w3schools.com/tags/ref_httpmethods.asp) to our server, passing along the values captured by the [input tags](https://www.w3schools.com/html/html_form_input_types.asp).\n", "\n", "The form will render a little strangely in the notebook since the notebook environment does not run javascript, nor do we have our web server up and running. Let's get to that!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Set environment variables" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Your current GCP Project Name is: asl-ml-immersion\n" ] } ], "source": [ "%%bash\n", "# Check your project name\n", "export PROJECT=$(gcloud config list project --format \"value(core.project)\")\n", "echo \"Your current GCP Project Name is: \"$PROJECT" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"BUCKET\"] = \"your-bucket-id-here\" # Recommended: use your project name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Complete application code in `application/main.py`\n", "We can set up our server with python using [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/). Below, we've already built out most of the application for you.\n", "\n", "The `@app.route()` decorator defines a function to handle web reqests. Let's say our website is `www.example.com`. With how our `@app.route(\"/\")` function is defined, our sever will render our <a href=\"application/templates/index.html\">index.html</a> file when users go to `www.example.com/` (which is the default route for a website).\n", "\n", "So, when a user pings our server with `www.example.com/predict`, they would use `@app.route(\"/predict\", methods=[\"POST\"])` to make a prediction. The data that gets sent over the internet isn't a dictionary, but a string like below:\n", "\n", "`name1=value1&name2=value2` where `name` corresponds to the `name` on the input tag of our html form, and the value is what the user entered. Thankfully, Flask makes it easy to transform this string into a dictionary with `request.form.to_dict()`, but we still need to transform the data into a format our model expects. We've done this with the `gender2str` and the `plurality2str` utility functions.\n", "\n", "Ok! Let's set up a webserver to take in the form inputs, process them into features, and send these features to our model on Cloud AI Platform to generate predictions to serve to back to users.\n", "\n", "Fill in the TODO comments in <a href=\"application/main.py\">application/main.py</a>. Give it a go first and review the solutions folder if you get stuck.\n", "\n", "Note: AppEngine test configurations have already been set for you in the file <a href=\"application/app.yaml\">application/app.yaml</a>. Review [app.yaml](https://cloud.google.com/appengine/docs/standard/python/config/appref) documentation for additional configuration options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Deploy application\n", "So how do we know that it works? We'll have to deploy our website and find out! Notebooks aren't made for website deployment, so we'll move our operation to the [Google Cloud Shell](https://console.cloud.google.com/home/dashboard?cloudshell=true).\n", "\n", "By default, the shell doesn't have Flask installed, so copy over the following command to install it.\n", "\n", "`python3 -m pip install --user Flask==0.12.1`\n", "\n", "Next, we'll need to copy our web app to the Cloud Shell. We can use [Google Cloud Storage](https://cloud.google.com/storage) as an inbetween." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "gsutil -m rm -r gs://$BUCKET/baby_app\n", "gsutil -m cp -r application/ gs://$BUCKET/baby_app" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the below cell, and copy the output into the [Google Cloud Shell](https://console.cloud.google.com/home/dashboard?cloudshell=true)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rm -r baby_app/\n", "mkdir baby_app/\n", "gsutil cp -r gs://asl-ml-immersion/baby_app ./\n", "python3 baby_app/main.py\n" ] } ], "source": [ "%%bash\n", "echo rm -r baby_app/\n", "echo mkdir baby_app/\n", "echo gsutil cp -r gs://$BUCKET/baby_app ./\n", "echo python3 baby_app/main.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Use your website to generate predictions\n", "Time to play with the website! The cloud shell should now say something like `* Running on http://127.0.0.1:8080/ (Press CTRL+C to quit)`. Click on the `http` link to go to your shiny new website. Fill out the form and give it a minute or two to process its first prediction. After the first one, the rest of the predictions will be lightning fast.\n", "\n", "Did you get a prediction? If not, the Google Cloud Shell will spit out a stack trace of the error to help narrow it down. If yes, congratulations! Great job on bringing all of your work together for the users." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab Summary\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this lab, you deployed a simple Flask web form application on App Engine that takes inputs, transforms them into features, and sends them to a model service on Cloud AI Platform to generate and return predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
iandeniset/GeophysicsNotebooks	LoadingOpendTectSeismic.ipynb	1	3102374	null	apache-2.0
manipopopo/tensorflow	tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb	5	13975	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "custom_layers.ipynb", "version": "0.3.2", "views": {}, "default_view": {}, "provenance": [], "private_outputs": true, "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "metadata": { "id": "tDnwEv8FtJm7", "colab_type": "text" }, "cell_type": "markdown", "source": [ "##### Copyright 2018 The TensorFlow Authors." ] }, { "metadata": { "id": "JlknJBWQtKkI", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "cellView": "form" }, "cell_type": "code", "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "60RdWsg1tETW", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# Custom layers" ] }, { "metadata": { "id": "BcJg7Enms86w", "colab_type": "text" }, "cell_type": "markdown", "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\"><td>\n", "<a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", "</td><td>\n", "<a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a></td></table>" ] }, { "metadata": { "id": "UEu3q4jmpKVT", "colab_type": "text" }, "cell_type": "markdown", "source": [ "We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution.\n" ] }, { "metadata": { "id": "pwX7Fii1rwsJ", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "import tensorflow as tf\n", "tfe = tf.contrib.eager\n", "\n", "tf.enable_eager_execution()" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "zSFfVVjkrrsI", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Layers: common sets of useful operations\n", "\n", "Most of the time when writing code for machine learning models you want to operate at a higher level of abstraction than individual operations and manipulation of individual variables.\n", "\n", "Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers.\n", "\n", "TensorFlow includes the full [Keras](https://keras.io) API in the tf.keras package, and the Keras layers are very useful when building your own models.\n" ] }, { "metadata": { "id": "8PyXlPl-4TzQ", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "# In the tf.keras.layers package, layers are objects. To construct a layer,\n", "# simply construct the object. Most layers take as a first argument the number\n", "# of output dimensions / channels.\n", "layer = tf.keras.layers.Dense(100)\n", "# The number of input dimensions is often unnecessary, as it can be inferred\n", "# the first time the layer is used, but it can be provided if you want to \n", "# specify it manually, which is useful in some complex models.\n", "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "Fn69xxPO5Psr", "colab_type": "text" }, "cell_type": "markdown", "source": [ "The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),\n", "Conv2D, LSTM, BatchNormalization, Dropout, and many others." ] }, { "metadata": { "id": "E3XKNknP5Mhb", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "# To use a layer, simply call it.\n", "layer(tf.zeros([10, 5]))" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "Wt_Nsv-L5t2s", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "# Layers have many useful methods. For example, you can inspect all variables\n", "# in a layer by calling layer.variables. In this case a fully-connected layer\n", "# will have variables for weights and biases.\n", "layer.variables" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "6ilvKjz8_4MQ", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "# The variables are also accessible through nice accessors\n", "layer.kernel, layer.bias" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "O0kDbE54-5VS", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Implementing custom layers\n", "The best way to implement your own layer is extending the tf.keras.Layer class and implementing:\n", " * `__init__` , where you can do all input-independent initialization\n", " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", " * `call`, where you do the forward computation\n", "\n", "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." ] }, { "metadata": { "id": "5Byl3n1k5kIy", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "class MyDenseLayer(tf.keras.layers.Layer):\n", " def __init__(self, num_outputs):\n", " super(MyDenseLayer, self).__init__()\n", " self.num_outputs = num_outputs\n", " \n", " def build(self, input_shape):\n", " self.kernel = self.add_variable(\"kernel\", \n", " shape=[input_shape[-1].value, \n", " self.num_outputs])\n", " \n", " def call(self, input):\n", " return tf.matmul(input, self.kernel)\n", " \n", "layer = MyDenseLayer(10)\n", "print(layer(tf.zeros([10, 5])))\n", "print(layer.variables)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "tk8E2vY0-z4Z", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`.\n", "\n", "Overall code is easier to read and maintain if it uses standard layers whenever possible, as other readers will be familiar with the behavior of standard layers. If you want to use a layer which is not present in tf.keras.layers or tf.contrib.layers, consider filing a [github issue](http://github.com/tensorflow/tensorflow/issues/new) or, even better, sending us a pull request!" ] }, { "metadata": { "id": "Qhg4KlbKrs3G", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Models: composing layers\n", "\n", "Many interesting layer-like things in machine learning models are implemented by composing existing layers. For example, each residual block in a resnet is a composition of convolutions, batch normalizations, and a shortcut.\n", "\n", "The main class used when creating a layer-like thing which contains other layers is tf.keras.Model. Implementing one is done by inheriting from tf.keras.Model." ] }, { "metadata": { "id": "N30DTXiRASlb", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ "class ResnetIdentityBlock(tf.keras.Model):\n", " def __init__(self, kernel_size, filters):\n", " super(ResnetIdentityBlock, self).__init__(name='')\n", " filters1, filters2, filters3 = filters\n", "\n", " self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))\n", " self.bn2a = tf.keras.layers.BatchNormalization()\n", "\n", " self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')\n", " self.bn2b = tf.keras.layers.BatchNormalization()\n", "\n", " self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))\n", " self.bn2c = tf.keras.layers.BatchNormalization()\n", "\n", " def call(self, input_tensor, training=False):\n", " x = self.conv2a(input_tensor)\n", " x = self.bn2a(x, training=training)\n", " x = tf.nn.relu(x)\n", "\n", " x = self.conv2b(x)\n", " x = self.bn2b(x, training=training)\n", " x = tf.nn.relu(x)\n", "\n", " x = self.conv2c(x)\n", " x = self.bn2c(x, training=training)\n", "\n", " x += input_tensor\n", " return tf.nn.relu(x)\n", "\n", " \n", "block = ResnetIdentityBlock(1, [1, 2, 3])\n", "print(block(tf.zeros([1, 2, 3, 3])))\n", "print([x.name for x in block.variables])" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "wYfucVw65PMj", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential" ] }, { "metadata": { "id": "L9frk7Ur4uvJ", "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } } }, "cell_type": "code", "source": [ " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n", " tf.keras.layers.BatchNormalization(),\n", " tf.keras.layers.Conv2D(2, 1, \n", " padding='same'),\n", " tf.keras.layers.BatchNormalization(),\n", " tf.keras.layers.Conv2D(3, (1, 1)),\n", " tf.keras.layers.BatchNormalization()])\n", "my_seq(tf.zeros([1, 2, 3, 3]))" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "c5YwYcnuK-wc", "colab_type": "text" }, "cell_type": "markdown", "source": [ "# Next steps\n", "\n", "Now you can go back to the previous notebook and adapt the linear regression example to use layers and models to be better structured." ] } ] }	apache-2.0
data-cube/agdc-v2-examples	notebooks/07_hovmoller_space_time_visualisation.ipynb	1	415568	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualising variation in space and time (Hovmoller plot)\n", "\n", "This notebook describes how to generate a space-time (Hovmoller plot) visualisation of NDVI, the example shown here is for the Mitchell River in Queensland. The river channel migrates, and a Hovmoller plot generated from a transect that crosses the river shows the channel migration and associated vegetation changes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-06-09T11:09:06.502514", "start_time": "2016-06-09T11:09:04.805211" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab notebook\n", "from __future__ import print_function\n", "import datacube\n", "import xarray as xr\n", "from datacube.storage import masking\n", "from datacube.storage.masking import mask_to_dict\n", "from matplotlib import pyplot as plt\n", "from IPython.display import display\n", "import ipywidgets as widgets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-06-09T11:09:07.599525", "start_time": "2016-06-09T11:09:07.553759" }, "collapsed": true }, "outputs": [], "source": [ "dc = datacube.Datacube(app='linear extraction for Hovmoller plot')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#### DEFINE SPATIOTEMPORAL RANGE AND BANDS OF INTEREST\n", "#Use this to manually define an upper left/lower right coords\n", "\n", "\n", "#Define temporal range\n", "start_of_epoch = '1998-01-01'\n", "end_of_epoch = '2016-12-31'\n", "\n", "#Define wavelengths/bands of interest, remove this kwarg to retrieve all bands\n", "bands_of_interest = [#'blue',\n", " 'green',\n", " 'red', \n", " 'nir',\n", " 'swir1', \n", " #'swir2'\n", " ]\n", "\n", "#Define sensors of interest\n", "sensors = ['ls8', 'ls7', 'ls5'] \n", "\n", "query = {'time': (start_of_epoch, end_of_epoch)}\n", "lat_max = -15.94\n", "lat_min = -15.98\n", "lon_max = 142.49522\n", "lon_min = 142.4485\n", "\n", "query['x'] = (lon_min, lon_max)\n", "query['y'] = (lat_max, lat_min)\n", "query['crs'] = 'EPSG:4326'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'time': ('1998-01-01', '2016-12-31'), 'x': (142.4485, 142.49522), 'y': (-15.94, -15.98), 'crs': 'EPSG:4326'}\n" ] } ], "source": [ "print(query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# retrieve the NBAR and PQ for the spatiotemporal range of interest\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Define which pixel quality artefacts you want removed from the results\n", "mask_components = {'cloud_acca':'no_cloud',\n", "'cloud_shadow_acca' :'no_cloud_shadow',\n", "'cloud_shadow_fmask' : 'no_cloud_shadow',\n", "'cloud_fmask' :'no_cloud',\n", "'blue_saturated' : False,\n", "'green_saturated' : False,\n", "'red_saturated' : False,\n", "'nir_saturated' : False,\n", "'swir1_saturated' : False,\n", "'swir2_saturated' : False,\n", "'contiguous':True}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-06-09T11:09:49.335761", "start_time": "2016-06-09T11:09:32.888099" }, "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "#Retrieve the NBAR and PQ data for sensor n\n", "sensor_clean = {}\n", "for sensor in sensors:\n", " #Load the NBAR and corresponding PQ\n", " sensor_nbar = dc.load(product= sensor+'_nbar_albers', group_by='solar_day', measurements = bands_of_interest, query)\n", " sensor_pq = dc.load(product= sensor+'_pq_albers', group_by='solar_day', query)\n", " #grab the projection info before masking/sorting\n", " crs = sensor_nbar.crs\n", " crswkt = sensor_nbar.crs.wkt\n", " affine = sensor_nbar.affine\n", " #This line is to make sure there's PQ to go with the NBAR\n", " sensor_nbar = sensor_nbar.sel(time = sensor_pq.time)\n", " #Apply the PQ masks to the NBAR\n", " cloud_free = masking.make_mask(sensor_pq, *mask_components)\n", " good_data = cloud_free.pixelquality.loc[start_of_epoch:end_of_epoch]\n", " sensor_nbar = sensor_nbar.where(good_data)\n", " sensor_clean[sensor] = sensor_nbar" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Conctanate measurements from the different sensors together\n", "nbar_clean = xr.concat(sensor_clean.values(), dim='time')\n", "time_sorted = nbar_clean.time.argsort()\n", "nbar_clean = nbar_clean.isel(time=time_sorted)\n", "nbar_clean.attrs['crs'] = crs\n", "nbar_clean.attrs['affine'] = affine\n", "#calculate the normalised difference vegetation index (NDVI)\n", "all_ndvi_sorted = ((nbar_clean.nir - nbar_clean.red)/(nbar_clean.nir + nbar_clean.red))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of time slices at this location is 578\n" ] } ], "source": [ "print('The number of time slices at this location is '+ str(nbar_clean.red.shape[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting an image, select a location for extracting the hovmoller plot\n", "The interactive widget allows you to select a location (x, y coordinates), the plot will then show all of the time series that fall into the same x coordinate." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/v10/public/modules/agdc-py3-env/20170427/envs/agdc/lib/python3.6/site-packages/xarray/core/variable.py:1143: RuntimeWarning: invalid value encountered in less\n", " if not reflexive\n" ] } ], "source": [ "#select time slice of interest - this is trial and error until you get a decent image\n", "time_slice_i = 481\n", "rgb = nbar_clean.isel(time =time_slice_i).to_array(dim='color').sel(color=['swir1', 'nir', 'green']).transpose('y', 'x', 'color')\n", "#rgb = nbar_clean.isel(time =time_slice).to_array(dim='color').sel(color=['swir1', 'nir', 'green']).transpose('y', 'x', 'color')\n", "fake_saturation = 4500\n", "clipped_visible = rgb.where(rgb<fake_saturation).fillna(fake_saturation)\n", "max_val = clipped_visible.max(['y', 'x'])\n", "scaled = (clipped_visible / max_val)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/ Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"1200\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8851b6852a434ecbade3b76159b561b3" } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Click on this image to chose the location for time series extraction\n", "w = widgets.HTML(\"Event information appears here when you click on the figure\")\n", "def callback(event):\n", " global x, y\n", " x, y = int(event.xdata + 0.5), int(event.ydata + 0.5)\n", " w.value = 'X: {}, Y: {}'.format(x,y)\n", "\n", "fig = plt.figure(figsize =(12,6))\n", "plt.imshow(scaled, interpolation = 'nearest',\n", " extent=[scaled.coords['x'].min(), scaled.coords['x'].max(), \n", " scaled.coords['y'].min(), scaled.coords['y'].max()])\n", "\n", "fig.canvas.mpl_connect('button_press_event', callback)\n", "date_ = nbar_clean.time[time_slice_i]\n", "plt.title(date_.astype('datetime64[D]'))\n", "plt.show()\n", "display(w)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#this converts the map x coordinate into image x coordinates\n", "image_coords = ~affine * (x, y)\n", "imagex = int(image_coords[0])\n", "imagey = int(image_coords[1])\n", "\n", "\n", "#This sets up the NDVI colour ramp and corresponding thresholds\n", "ndvi_cmap = mpl.colors.ListedColormap(['blue', '#ffcc66','#ffffcc' , '#ccff66' , '#2eb82e', '#009933' , '#006600'])\n", "ndvi_bounds = [-1, 0, 0.1, 0.25, 0.35, 0.5, 0.8, 1]\n", "ndvi_norm = mpl.colors.BoundaryNorm(ndvi_bounds, ndvi_cmap.N)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"1169\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f23fd0faf60>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#This cell shows the x transect that you've chosen in the context of an NDVI image with a suitable colour ramp\n", "fig = plt.figure(figsize=(11.69,4))\n", "plt.plot([0, all_ndvi_sorted.shape[2]], [imagey,imagey], 'r')\n", "plt.imshow(all_ndvi_sorted.isel(time = time_slice_i), cmap = ndvi_cmap, norm = ndvi_norm)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var 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width=\"1169\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0x7f23fd09e860>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Hovmoller plot for the x transect\n", "fig = plt.figure(figsize=(11.69,7))\n", "all_ndvi_sorted.isel(#x=[xdim],\n", " y=[imagey]\n", " ).plot(norm= ndvi_norm, cmap = ndvi_cmap, yincrease = False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "toc": { "toc_cell": false, "toc_number_sections": false, "toc_threshold": 6, "toc_window_display": true }, "widgets": { "state": { "cfb57ac3502744dd9549a01a567e525e": { "views": [ { "cell_index": 12 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
ioam/holoviews	examples/reference/streams/bokeh/Selection1D_points.ipynb	1	2042	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### Title: Point Selection1D stream example\n", "\n", "Description: A linked streams example demonstrating how to use Selection1D to get currently selected points and dynamically compute statistics of selection.\n", "\n", "Dependencies: Bokeh\n", "\n", "Backends: [Bokeh](./Selection1D_points.ipynb), [Plotly](../plotly/Selection1D_points.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "from holoviews import opts\n", "from holoviews import streams\n", "hv.extension('bokeh')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opts.defaults(opts.Points(tools=['box_select', 'lasso_select']))\n", "\n", "# Declare some points\n", "points = hv.Points(np.random.randn(1000,2 ))\n", "\n", "# Declare points as source of selection stream\n", "selection = streams.Selection1D(source=points)\n", "\n", "# Write function that uses the selection indices to slice points and compute stats\n", "def selected_info(index):\n", " selected = points.iloc[index]\n", " if index:\n", " label = 'Mean x, y: %.3f, %.3f' % tuple(selected.array().mean(axis=0))\n", " else:\n", " label = 'No selection'\n", " return selected.relabel(label).opts(color='red')\n", "\n", "# Combine points and DynamicMap\n", "points + hv.DynamicMap(selected_info, streams=[selection])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<center><img src=\"https://assets.holoviews.org/gifs/examples/streams/bokeh/point_selection1d.gif\" width=600></center>" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-3-clause
bosmanoglu/Digital_Image_Processing_Book	tr/Sekil_3_24.ipynb	1	3546	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Yapıcı ve Yıkıcı Girişim\n", "### Sekil 3.27\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'matplotlib' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-59152b5ffac2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'font.size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# ornekleme araligi ornekleyecegimiz frekansin en az iki kati olmali.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'matplotlib' is not defined" ] } ], "source": [ "# Tanimlar\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "t=np.linspace(0,1,100) # ornekleme araligi ornekleyecegimiz frekansin en az iki kati olmali.\n", "\n", "## Yapici Girisim icin ayni fazli 2 isaret\n", "ton1=np.sin(2np.pi10t)\n", "ton2=0.5np.sin(2np.pi10t)\n", "\n", "f,ax=plt.subplots(2, 2, sharey=True, sharex=True)\n", "ax[0,0].plot(t[0:100],ton1[0:100], 'k'); \n", "ax[0,0].plot(t[0:100],ton2[0:100], 'r--'); ax[0,0].set_title(u'Eş Fazlı İki İşaret (Zaman)')\n", "\n", "ax[0,1].plot(t[0:100],ton1+ton2[0:100], 'k'); ax[0,1].set_title(u'Toplam İşaret - Yapıcı Girişim (Zaman)')\n", "\n", "## Yıkıcı Girişim için pi kadar ötelenmiş 2. işaret\n", "ton2=0.5np.sin(2np.pi10*t+np.pi)\n", "ax[1,0].plot(t[0:100],ton1[0:100], 'k');\n", "ax[1,0].plot(t[0:100],ton2[0:100], 'r--'); ax[1,0].set_title(u'Faz Farklı İki İşaret (Zaman)')\n", "ax[1,1].plot(t[0:100],ton1+ton2[0:100], 'k');ax[1,1].set_title(u'Toplam İşaret - Yıkıcı Girişim (Zaman)')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
slundberg/shap	notebooks/tabular_examples/tree_based_models/tree_shap_paper/Performance comparison copy.ipynb	1	392037	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Performance comparison copy" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xgboost\n", "import numpy as np\n", "import shap\n", "import time\n", "from tqdm import tqdm\n", "import matplotlib.pylab as pl" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from iml.common import convert_to_instance, convert_to_model, match_instance_to_data, match_model_to_data, convert_to_instance_with_index\n", "from iml.explanations import AdditiveExplanation\n", "from iml.links import convert_to_link, IdentityLink\n", "from iml.datatypes import convert_to_data, DenseData\n", "import logging\n", "from iml.explanations import AdditiveExplanation\n", "\n", "log = logging.getLogger('shap')\n", "from shap import KernelExplainer\n", "class IMEExplainer(KernelExplainer):\n", " \"\"\" This is an implementation of the IME explanation method (aka. Shapley sampling values)\n", " \n", " This is implemented here for comparision and evaluation purposes, the KernelExplainer is\n", " typically more efficient and so is the preferred model agnostic estimation method in this package.\n", " IME was proposed in \"An Efficient Explanation of Individual Classifications using Game Theory\",\n", " Erik Štrumbelj, Igor Kononenko, JMLR 2010\n", " \"\"\"\n", " \n", " def __init__(self, model, data, kwargs):\n", " # silence warning about large datasets\n", " level = log.level\n", " log.setLevel(logging.ERROR)\n", " super(IMEExplainer, self).__init__(model, data, kwargs)\n", " log.setLevel(level)\n", " \n", " def explain(self, incoming_instance, *kwargs):\n", " # convert incoming input to a standardized iml object\n", " instance = convert_to_instance(incoming_instance)\n", " match_instance_to_data(instance, self.data)\n", " \n", " # pick a reasonable number of samples if the user didn't specify how many they wanted\n", " self.nsamples = kwargs.get(\"nsamples\", 0)\n", " if self.nsamples == 0:\n", " self.nsamples = 1000 self.P\n", " \n", " # divide up the samples among the features\n", " self.nsamples_each = np.ones(self.P, dtype=np.int64) * 2 * (self.nsamples // (self.P * 2))\n", " for i in range((self.nsamples % (self.P * 2)) // 2):\n", " self.nsamples_each[i] += 2\n", " \n", " model_out = self.model.f(instance.x)\n", " \n", " # explain every feature\n", " phi = np.zeros(self.P)\n", " self.X_masked = np.zeros((self.nsamples_each.max(), X.shape[1]))\n", " for i in range(self.P):\n", " phi[i] = self.ime(i, self.model.f, instance.x, self.data.data, nsamples=self.nsamples_each[i])\n", " phi = np.array(phi)\n", " \n", " return AdditiveExplanation(self.link.f(1), self.link.f(1), phi, np.zeros(len(phi)), instance, self.link,\n", " self.model, self.data)\n", " \n", " \n", " def ime(self, j, f, x, X, nsamples=10):\n", " assert nsamples % 2 == 0, \"nsamples must be divisible by 2!\"\n", " X_masked = self.X_masked[:nsamples,:]\n", " inds = np.arange(X.shape[1])\n", "\n", " for i in range(0, nsamples//2):\n", " np.random.shuffle(inds)\n", " pos = np.where(inds == j)[0][0]\n", " rind = np.random.randint(X.shape[0])\n", " X_masked[i,:] = x\n", " X_masked[i,inds[pos+1:]] = X[rind,inds[pos+1:]]\n", " X_masked[-(i+1),:] = x\n", " X_masked[-(i+1),inds[pos:]] = X[rind,inds[pos:]]\n", " \n", " s = time.time()\n", " evals = f(X_masked)\n", " #print(\"n\",time.time() - s)\n", " \n", " evals_on = evals[:nsamples//2]\n", " evals_off = evals[nsamples//2:][::-1]\n", " \n", " return np.mean(evals[:nsamples//2] - evals[nsamples//2:])" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 9/9 [00:26<00:00, 3.55s/it]\n" ] } ], "source": [ "tree_shap_times = []\n", "sample_times = []\n", "Ms = [20,30,40,50,60,70,80,90,100]\n", "for M in tqdm(Ms):\n", " \n", " X = np.random.randn(N, M)\n", " y = np.random.randn(N)\n", " model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n", " \n", " #print()\n", " e = shap.TreeExplainer(model)\n", " s = time.time()\n", " e.shap_values(X)\n", " tree_shap_times.append((time.time() - s)/1000)\n", " #print((time.time() - s)/1000)\n", " \n", " tmp = np.vstack([X for i in range(1 * M)])\n", " s = time.time()\n", " model.predict(xgboost.DMatrix(tmp))\n", " sample_times.append(time.time() - s)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5.633111 , 5.85872889, 5.49962997, 5.51635981, 5.31605005,\n", " 5.07364988, 4.98589039, 4.95893955, 4.9760294 , 4.86562967,\n", " 4.64447975])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(tree_shap_times)10000" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "image/png": 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kLbuqRfDcn+Dxn9aditStD+zyXdjmDOjavXz1SVIb1OzQEBHvLtZ0VkSc0MCl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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a13c17ef0>" ] }, "metadata": { "image/png": { "height": 263, "width": 390 } }, "output_type": "display_data" } ], "source": [ "pl.plot(Ms, np.array(tree_shap_times[:-1])10000 / (60))\n", "pl.plot(Ms, np.array(sample_times[:-1])10000 / (60))\n", "pl.ylabel(\"minutes of runtime\")\n", "pl.xlabel(\"# of features\")\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.271293222904205" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(np.array(tree_shap_times[:-1])10000)" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "947.6979966976681" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4995.5940246/5.27129322" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "947.6979966976681" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4995.5940246/5.27129322" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1016.5784222579987" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_times[-5]/tree_shap_times[-5]" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4995.594024658203" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(np.array(sample_times[:-1])10000)" ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "data": { "image/png": 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QUBD16tWjRYsWPHjwgPXr19OiRQtmz55Nnz5WFoawwY4dO5g0aRKNGjWiU6dOZM2aldOn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\nG6h9jmWU5F9V/OZg887BHGz+8x6VORjmX/P9fJuUjcgcLKpgN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"text/plain": [ "<matplotlib.figure.Figure at 0x1a16837c88>" ] }, "metadata": { "image/png": { "height": 209, "width": 624 } }, "output_type": "display_data" } ], "source": [ "f = pl.figure(figsize=(10,3))\n", "pl.subplot(1, 2, 1)\n", "pl.plot(\n", " Ms[:-1], np.array(sample_times[:-1])10000 / (60),\n", " label=\"Model agnostic sampling\",\n", " color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], np.array(tree_shap_times[:-1])10000 / (60),\n", " label=\"Tree SHAP\", color=\"#1E88E5\", linewidth=3\n", ")\n", "pl.ylabel(\"minutes of runtime\\nexplaining 10k predictions\")\n", "pl.xlabel(\"# of features in the model\")\n", "pl.legend()\n", "#pl.savefig(\"runtime.pdf\")\n", "#pl.show()\n", "\n", "pl.subplot(1, 2, 2)\n", "pl.plot(\n", " Ms[:-1], (ime_std[:-1] / ime_m[:-1])100, \"--\",\n", " label=\"IME\", color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])100,\n", " label=\"Kernel SHAP\",\n", " color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], np.zeros(len(Ms)-1),\n", " label=\"Tree SHAP\",\n", " color=\"#1E88E5\", linewidth=3\n", ")\n", "pl.ylabel(\"Std. deviation as % of magnitude\")\n", "pl.xlabel(\"# of features in the model\")\n", "pl.legend(loc=\"upper left\")\n", "pl.savefig(\"perf.pdf\")\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "image/png": 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a8c8s/hTg4F5Xa39wX/7H/OOFF/KWpsQHgi/zUTJfCtbaHcaY/wFjUTIvIhIT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\n4eofuVV4r4PLSovh4Ttdr/ruPns9JHpNSuattQ/W+P6hQKIREZFA+W1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vIptTrcc9a4A+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\nV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"text/plain": [ "<matplotlib.figure.Figure at 0x1a21d65080>" ] }, "metadata": { "image/png": { "height": 263, "width": 377 } }, "output_type": "display_data" } ], "source": [ "pl.plot(\n", " Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])100,\n", " label=\"Kernel SHAP\",\n", " color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], (ime_std[:-1] / ime_m[:-1])100, \"--\",\n", " label=\"IME\", color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.ylabel(\"Std. deviation as % of magnitude\")\n", "pl.xlabel(\"# of features\")\n", "pl.legend(loc=\"upper left\")\n", "#pl.savefig(\"std_dev.pdf\")\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.017660769984570657" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(kernel_shap_std[:-1] / kernel_shap_m)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.019387321525750678" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(kernel_shap_std[:-1] / kernel_shap_m[:-1])" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.041465549361301236" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(ime_std[:-1] / ime_m[:-1])" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "image/png": 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a8c8s/hTg4F5Xa39wX/7H/OOFF/KWpsQHgi/zUTJfCtbaHcaY/wFjUTIvIhIT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\n4eofuVV4r4PLSovh4Ttdr/ruPns9JHpNSuattQ/W+P6hQKIREZFA+W1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vIptTrcc9a4A+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\nV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"text/plain": [ "<matplotlib.figure.Figure at 0x1a21d65080>" ] }, "metadata": { "image/png": { "height": 263, "width": 377 } }, "output_type": "display_data" } ], "source": [ "pl.plot(\n", " Ms[:-1], (ime_std[:-1] / ime_m[:-1])100,\n", " label=\"IME\", color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], (kernel_shap_std[:-1] / kernel_shap_m[:-1])100,\n", " label=\"Kernel SHAP\",\n", " color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.plot(\n", " Ms[:-1], (ime_std[:-1] / ime_m[:-1])100, \"--\",\n", " label=\"IME\", color=\"#7C52FF\", linewidth=3\n", ")\n", "pl.ylabel(\"Std. deviation as % of magnitude\")\n", "pl.xlabel(\"# of features\")\n", "pl.legend(loc=\"upper left\")\n", "#pl.savefig(\"std_dev.pdf\")\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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VGtVVpFVmHXq0sNWSCktKmbs2CkvEljn1RqgXSH4OboYFL7qNJ5K2L4OJP/fH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\nSpoz9+UAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a13d30b38>" ] }, "metadata": { "image/png": { "height": 263, "width": 393 } }, "output_type": "display_data" } ], "source": [ "pl.plot(Ms, kernel_shap_std / kernel_shap_m)\n", "pl.plot(Ms, ime_std / ime_m)\n", "pl.ylabel(\"minutes of runtime\")\n", "pl.xlabel(\"# of features\")\n", "pl.plot()\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 320.80395406, 552.44233763, 684.03119462, 841.14715592,\n", " 1016.57842226, 1226.48211761, 1450.68094181, 1690.75300588,\n", " 1728.4646152 ])" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(sample_times) / np.array(tree_shap_times)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "image/png": 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oYkhJUumoXQLP3QZPXgeLZufq1S3hwJ/CAT+FFm2yyydJTciDwCRJleW94ckp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\nPAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a16822320>" ] }, "metadata": { "image/png": { "height": 263, "width": 384 } }, "output_type": "display_data" } ], "source": [ "pl.plot(Ms, np.array(tree_shap_times)10000 / (6060))\n", "pl.plot(Ms, np.array(sample_times)10000 / (6060))\n", "pl.ylabel(\"hours of runtime\")\n", "pl.xlabel(\"# of features\")\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1a212c1d68>" ] }, "metadata": { "image/png": { "height": 250, "width": 381 } }, "output_type": "display_data" } ], "source": [ "pl.semilogy(Ms, tree_shap_times)\n", "pl.semilogy(Ms, sample_times)\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3761.9642857142862" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2.1067/0.00056" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0020101070404052734" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = time.time()\n", "model.predict(xgboost.DMatrix(X[:100,:]))\n", "time.time() - s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "M 20\n", "0.0007393969481046989 0.08655239076881058 0.008542767467621969\n", "KernelExplainer 1.5829526567459107\n", "0.0034187560235558454 0.08727476235122503 0.03917233265898269\n", "IMEExplainer 0.40989099979400634\n", "\n", "M 30\n", "0.0012877561556381184 0.10746270935466279 0.0119832839072398\n", "KernelExplainer 2.7619515800476075\n", "0.003955039524908129 0.10750344677696065 0.03678988575234914\n", "IMEExplainer 0.6094264173507691\n", "\n", "M 40\n" ] } ], "source": [ "N = 1000\n", "X_full = np.random.randn(N, 100)\n", "y = np.random.randn(N)\n", "\n", "tree_shap_times = []\n", "kernel_shap_times = []\n", "ime_times = []\n", "tree_shap_std = []\n", "kernel_shap_std = []\n", "kernel_shap_m = []\n", "ime_std = []\n", "ime_m = []\n", "for M in [20,30,40,50,60,70,80,90,100]:#,30,40,50]:\n", " print(\"\\nM\", M)\n", " X = X_full[:,:M]\n", " \n", " model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n", "\n", " def f(x):\n", " return model.predict(xgboost.DMatrix(x))\n", " \n", " e = shap.TreeExplainer(model)\n", " start = time.time()\n", " e.shap_values(X)\n", " iter_time = (time.time() - start)/X.shape[0]\n", " tree_shap_times.append(iter_time)\n", " tree_shap_std.append(0)\n", " \n", " e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n", " nsamples = 1000 * M\n", " start = time.time()\n", " out = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)])\n", " std_dev = out.std(0)[:-1].mean()\n", " mval = np.abs(out.mean(0))[:-1].mean()\n", " kernel_shap_m.append(mval)\n", " iter_time = (time.time() - start)/50\n", " kernel_shap_times.append(iter_time)\n", " kernel_shap_std.append(std_dev)\n", " print(std_dev, mval, std_dev / mval)\n", " print(\"KernelExplainer\", iter_time)\n", " \n", " e = IMEExplainer(f, X.mean(0).reshape(1,M))\n", " nsamples = 1000 * M\n", " start = time.time()\n", " out = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)])\n", " std_dev = out.std(0)[:-1].mean()\n", " mval = np.abs(out.mean(0))[:-1].mean()\n", " ime_m.append(mval)\n", " iter_time = (time.time() - start)/50\n", " ime_times.append(iter_time)\n", " ime_std.append(std_dev)\n", " print(std_dev, mval, std_dev / mval)\n", " print(\"IMEExplainer\", iter_time)\n", "\n", "ime_std = np.array(ime_std)\n", "ime_m = np.array(ime_m)\n", "kernel_shap_std = np.array(kernel_shap_std)\n", "kernel_shap_m = np.array(kernel_shap_m)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.00341876, 0.00395504, 0.00228658, 0.00303863, 0.00220854,\n", " 0.0014588 , 0.00276436, 0.00156815, 0.00030343])" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ime_std" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.08727476, 0.10750345, 0.08316281, 0.07246471, 0.04027761,\n", " 0.04097318, 0.05724012, 0.03294186, 0.02315394])" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ime_m" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7.39396948e-04, 1.28775616e-03, 8.63459855e-04, 1.29795187e-03,\n", " 1.82790054e-03, 4.39236264e-04, 1.71100994e-03, 6.29363519e-04,\n", " 8.80791006e-05])" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_shap_std" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.08655239, 0.10746271, 0.08271485, 0.07177752, 0.04009051,\n", " 0.04008376, 0.05701191, 0.03229693, 0.02288745])" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_shap_m" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 10/10 [00:59<00:00, 6.11s/it]\n", " 0%\| \| 0/10 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TreeExplainer 0.5667633771896362\n", "KernelExplainer 1.6998610496520996\n", "IMEExplainer 36.06944036483765\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 10/10 [00:43<00:00, 4.03s/it]\n", " 0%\| \| 0/10 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TreeExplainer 0.5962720394134522\n", "KernelExplainer 2.410202980041504\n", "IMEExplainer 24.168269634246826\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 10/10 [04:23<00:00, 26.14s/it]\n", " 0%\| \| 0/10 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TreeExplainer 0.5785245656967163\n", "KernelExplainer 3.6281538009643555\n", "IMEExplainer 170.16926431655884\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 10/10 [06:38<00:00, 41.04s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TreeExplainer 0.5870000839233398\n", "KernelExplainer 5.659902095794678\n", "IMEExplainer 254.79452514648438\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from tqdm import tqdm\n", "\n", "tree_shap_times = []\n", "kernel_shap_times = []\n", "ime_times = []\n", "nreps = 10\n", "\n", "N = 1000\n", "X_full = np.random.randn(N, 20)\n", "y = np.random.randn(N)\n", "\n", "for M in range(4,8):\n", " ts = []\n", " tree_shap_time = 0\n", " kernel_shap_time = 0\n", " ime_time = 0\n", " for k in tqdm(range(nreps)):\n", "# print()\n", " #+ ((X > 0).sum(1) % 2)\n", " X = X_full[:,:M]\n", "\n", " model = xgboost.train({\"eta\": 1}, xgboost.DMatrix(X, y), 1000)\n", "\n", " def f(x):\n", " return model.predict(xgboost.DMatrix(x))\n", "\n", "\n", " start = time.time()\n", " shap_values = shap.TreeExplainer(model).shap_values(X)\n", " tree_shap_time += time.time() - start\n", "# print(\"Tree SHAP:\", tree_shap_time, \"seconds\")\n", "\n", " shap_stddev = shap_values.std(0)[:-1].mean()\n", "\n", "# print(\"mean std dev of SHAP values over samples:\", shap_stddev)\n", "\n", " e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n", " nsamples = 200\n", "# print(shap_stddev/20)\n", " for j in range(2000):\n", " #print(nsamples)\n", " start = time.time()\n", " std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n", " iter_time = (time.time() - start)/50\n", " #print(std_dev)\n", " if std_dev < shap_stddev/20:\n", "# print(\"KernelExplainer\", nsamples)\n", "# print(\"KernelExplainer\", std_dev)\n", "# print(\"KernelExplainer\", iter_time, \"seconds\")\n", " kernel_shap_time += iter_time * 1000\n", " break\n", " nsamples += int(nsamples * 0.5)\n", "\n", " e = IMEExplainer(f, X.mean(0).reshape(1,M))\n", " nsamples = 200\n", " for j in range(2000):\n", " # print()\n", " # print(nsamples)\n", " start = time.time()\n", " std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n", " # print(\"time\", (time.time() - start)/50)\n", " # print(std_dev)\n", " iter_time = (time.time() - start)/50\n", " if std_dev < shap_stddev/20:\n", "# print(\"IMEExplainer\", nsamples)\n", "# print(\"IMEExplainer\", std_dev)\n", "# print(\"IMEExplainer\", iter_time, \"seconds\")\n", " ime_time += iter_time * 1000\n", " break\n", " nsamples += int(nsamples * 0.5)\n", "\n", " tree_shap_times.append(tree_shap_time / nreps)\n", " kernel_shap_times.append(kernel_shap_time / nreps)\n", " ime_times.append(ime_time / nreps)\n", " print(\"TreeExplainer\", tree_shap_times[-1])\n", " print(\"KernelExplainer\", kernel_shap_times[-1])\n", " print(\"IMEExplainer\", ime_times[-1])\n" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.035476692" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(xgboost.DMatrix(X)).mean()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.35859036, 0.02820558, -0.0797407 , ..., 0.257011 ,\n", " 0.14601958, -0.03547668],\n", " [-0.13089252, 0.05794828, -0.25855556, ..., 0.13599055,\n", " 0.05735664, -0.03547668],\n", " [-0.06776153, 0.10805923, -0.15713027, ..., -0.03505304,\n", " -0.126518 , -0.03547668],\n", " ...,\n", " [ 0.2136814 , 0.26080823, -0.28865245, ..., 0.37536186,\n", " 0.0192902 , -0.03547668],\n", " [-0.26988652, -0.4866938 , 0.08402091, ..., -0.9819344 ,\n", " 0.10559861, -0.03547668],\n", " [ 0.2289274 , 0.34278524, 0.04976799, ..., 0.17089835,\n", " -0.2589223 , -0.03547668]], dtype=float32)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shap.TreeExplainer(model).shap_values(X)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.03536954622922005" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n", "np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=100) for i in range(50)]).std(0)[:-1].mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.01028014048933983\n", "200\n", "time 0.017876300811767578\n", "0.030893178278808088\n", "240\n", "time 0.01951019763946533\n", "0.031826491957925064\n", "288\n", "time 0.020209121704101562\n", "0.02404333546749491\n", "345\n", "time 0.026800222396850586\n", "0.02191601539734573\n", "414\n", "time 0.03098787784576416\n", "0.019460386139078127\n", "496\n", "time 0.035751018524169925\n", "0.016387066172673846\n", "595\n", "time 0.04151914119720459\n", "0.014285933327475487\n", "714\n", "time 0.04223478317260742\n", "0.007730075891320169\n", "714\n" ] } ], "source": [ "e = shap.KernelExplainer(f, X.mean(0).reshape(1,M))\n", "nsamples = 200\n", "print(shap_stddev/20)\n", "for j in range(2000):\n", " print(nsamples)\n", " start = time.time()\n", " std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n", " iter_time = time.time() - start)/50\n", " print(std_dev)\n", " if std_dev < shap_stddev/20:\n", " print(nsamples)\n", " break\n", " nsamples += int(nsamples * 0.2)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.01028014048933983\n", "200\n", "time 0.00873462200164795\n", "0.11283049739292224\n", "240\n", "time 0.008001022338867188\n", "0.11367197853719405\n", "288\n", "time 0.011501140594482422\n", "0.1015653587243526\n", "345\n", "time 0.009341177940368652\n", "0.09609185923926047\n", "414\n", "time 0.011126718521118163\n", "0.09085558541161791\n", "496\n", "time 0.013986682891845703\n", "0.07316596561338537\n", "595\n", "time 0.015461082458496095\n", "0.07250843647631118\n", "714\n", "time 0.01717233657836914\n", "0.06397322007857624\n", "856\n", "time 0.022870540618896484\n", "0.05976986733191383\n", "1027\n", "time 0.023901219367980956\n", "0.05245597953754562\n", "1232\n", "time 0.027827000617980956\n", "0.04745224231486926\n", "1478\n", "time 0.03509308338165283\n", "0.044717441080350424\n", "1773\n", "time 0.03991847991943359\n", "0.04005476391599552\n", "2127\n", "time 0.049641480445861814\n", "0.03812949961499427\n", "2552\n", "time 0.055928120613098146\n", "0.035810585105934746\n", "3062\n", "time 0.06629895687103271\n", "0.03023017573129038\n", "3674\n", "time 0.08427309989929199\n", "0.028151972927120038\n", "4408\n", "time 0.09465731620788574\n", "0.0269187187096272\n", "5289\n", "time 0.11998224258422852\n", "0.022239860334659148\n", "6346\n", "time 0.14299814224243165\n", "0.021938981389563468\n", "7615\n", "time 0.16221713542938232\n", "0.018939869158391444\n", "9138\n", "time 0.19562265872955323\n", "0.017731913242184698\n", "10965\n", "time 0.2298459815979004\n", "0.016393279654396718\n", "13158\n", "time 0.274345440864563\n", "0.014646114662805663\n", "15789\n", "time 0.3319106578826904\n", "0.013088638305933157\n", "18946\n", "time 0.39471452236175536\n", "0.012587389782775047\n", "22735\n", "time 0.47068305969238283\n", "0.010845919708344176\n", "27282\n", "time 0.5693935012817383\n", "0.009881009462255178\n", "27282\n" ] } ], "source": [ "e = IMEExplainer(f, X.mean(0).reshape(1,M))\n", "nsamples = 200\n", "print(shap_stddev/20)\n", "for j in range(2000):\n", " print()\n", " print(nsamples)\n", " start = time.time()\n", " std_dev = np.vstack([e.shap_values(X[:1,:], silent=True, nsamples=nsamples) for i in range(50)]).std(0)[:-1].mean()\n", " print(\"time\", (time.time() - start)/50)\n", " print(std_dev)\n", " if std_dev < shap_stddev/20:\n", " print(nsamples)\n", " break\n", " nsamples += int(nsamples * 0.2)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "569.39" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0.56939 * 1000" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.10860389655048514" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.std([IMEExplainer(f, X.mean(0).reshape(1,M)).shap_values(X[:1,:], silent=True, nsamples=1000)[0,0] for i in range(10)])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "[-0.023676379073087367, -0.016465743042426484, -0.01704981192550009, -0.018463699458183006, -0.015413553059016158, -0.018366587057752404, -0.018345955714953288, 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-0.01541739020484878]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[shap.KernelExplainer(f, X.mean(0).reshape(1,M)).shap_values(X[:1,:], silent=True, nsamples=1000)[0,0] for i in range(100)]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 7%\|▋ \| 74/1000 [00:04<00:50, 18.27it/s]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-ad2b02590797>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mshap_values2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKernelExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m 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\u001b[0mnum_paired_subset_sizes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddsample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 261\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/slund1/projects/shap/shap/explainers/kernel.py\u001b[0m in \u001b[0;36maddsample\u001b[0;34m(self, x, m, w)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msynth_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaskMatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsamplesAdded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m 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2.97it/s]" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-d6ca7fbbc83a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mIMEExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeep_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_instance_with_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_value\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0mexplanations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m 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time.time()\n", "IMEExplainer(f, X.mean(0).reshape(1,M)).shap_values(X)\n", "print(time.time() - start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
wgong/open_source_learning	learn_stem/fun_with_mypets.ipynb	1	81639	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "<br><br>\n", "<a href=http://wwwgong.pythonanywhere.com/cuspea/default/list_talks target=new>\n", "<font size=+3 color=blue>CUSPEA Talks</font>\n", "</a>\n", "<br><br>\n", "<img src=../images/open-source-learning.jpg><br> \n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML(\"\"\"\n", "<br><br>\n", "<a href=http://wwwgong.pythonanywhere.com/cuspea/default/list_talks target=new>\n", "<font size=+3 color=blue>CUSPEA Talks</font>\n", "</a>\n", "<br><br>\n", "<img src=../images/open-source-learning.jpg><br> \n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "6ee77bce-39d1-46a1-802d-c7aa0f07f653" } }, "source": [ "# Fun with MyPETS" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "5676905a-4d3a-478a-bd10-06df67ffce84" } }, "source": [ "## Table of Contents\n", "\n", "* [Motivation](#hid_why)\n", "* [Introduction](#hid_intro)\n", "* [Problem Statement](#hid_problem)\n", "* [Import packages](#hid_pkg)\n", "\n", " \n", "* [History of Open Source Movement](#hid_open_src)\n", "* [How to learn STEM (or MyPETS)](#hid_stem)\n", "\n", "\n", "* [References](#hid_ref)\n", "* [Contributors](#hid_author)\n", "* [Appendix](#hid_apend)\n", "\n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation <a class=\"anchor\" id=\"hid_why\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Current Choice\n", "\n", "<img src=http://www.cctechlimited.com/pics/office1.jpg>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* A New Option\n", "\n", "> The __Jupyter Notebook__ is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.\n", "\n", "Useful for many tasks\n", "\n", "* Programming\n", "* Blogging\n", "* Learning\n", "* Research\n", "* Documenting work\n", "* Collaborating\n", "* Communicating\n", "* Publishing results\n", "\n", "or even\n", "\n", "* Doing homework as a student\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=../images/office-suite.jpg>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"<img src=../images/office-suite.jpg>\")" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "031da43c-0284-4433-bd3d-c6c596c92b27" } }, "source": [ "## Introduction <a class=\"anchor\" id=\"hid_intro\"></a>" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "81e52f61-9b24-49b2-9953-191e6fe26656" } }, "source": [ "## Problem Statement <a class=\"anchor\" id=\"hid_problem\"></a>" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "30861e9b-f7e6-41b2-be2d-d1636961816b" } }, "source": [ "## Import packages <a class=\"anchor\" id=\"hid_pkg\"></a>" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "nbpresent": { "id": "40d4fcce-0acd-452d-b56a-0caf808e1464" } }, "outputs": [], "source": [ "# math function\n", "import math\n", "\n", "# create np array\n", "import numpy as np\n", "\n", "# pandas for data analysis\n", "import pandas as pd\n", "\n", "# plotting\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# symbolic math\n", "import sympy as sy\n", "\n", "# html5\n", "from IPython.display import HTML, SVG, YouTubeVideo\n", "\n", "# widgets\n", "from collections import OrderedDict\n", "from IPython.display import display, clear_output\n", "from ipywidgets import Dropdown\n", "\n", "# csv file\n", "import csv" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "nbpresent": { "id": "1c05a501-0b6a-4bc1-ae0b-214839767968" } }, "source": [ "## History of Open Source Movement <a class=\"anchor\" id=\"hid_open_src\"></a>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "nbpresent": { "id": "82ac8516-d950-4887-9da5-98a17d3a449c" } }, "outputs": [ { "data": { "text/html": [ "<table>\n", " <tr><td>1983</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/f/f7/Richard_Matthew_Stallman.jpeg/353px-Richard_Matthew_Stallman.jpeg></td> \n", " <td><table>\n", " <tr><td>Richard Stallman</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/GNU_Project>GNU Project : gcc, Emacs, gdb</a></td></tr>\n", " <tr><td>Launch of the free software movement and founder of Free Software Foundation </td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1984</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/X.Org_Logo.svg/375px-X.Org_Logo.svg.png></td> \n", " <td><table>\n", " <tr><td>X.Org</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/X_Window_System>X Window Systerm</a></td></tr>\n", " <tr><td>basic framework for a GUI environment</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1985</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/en/thumb/2/22/Heckert_GNU_white.svg/270px-Heckert_GNU_white.svg.png></td> \n", " <td><table>\n", " <tr><td>Richard Stallman</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/GNU_Manifesto>GNU Manifesto: GNU's Not Unix</a></td></tr>\n", " <tr><td>disagreement between Stallman and Symbolics, Inc. over MIT's access to updates Symbolics had made to its Lisp machine, which was based on MIT code</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1987</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/b/b3/Larry_Wall_YAPC_2007.jpg/330px-Larry_Wall_YAPC_2007.jpg></td> \n", " <td><table>\n", " <tr><td>Larry Wall</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Perl>Perl programming lang</a></td></tr>\n", " <tr><td>Common Gateway Interface (CGI )</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1989</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/Guido_van_Rossum_OSCON_2006_cropped.png/375px-Guido_van_Rossum_OSCON_2006_cropped.png></td> \n", " <td><table>\n", " <tr><td>Guido van Rossum</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Python_(programming_language)>Python programming lang</a></td></tr>\n", " <tr><td>creator of popular Python scripting lang</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1990</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Tim_Berners-Lee-Knight-crop.jpg/330px-Tim_Berners-Lee-Knight-crop.jpg></td> \n", " <td><table>\n", " <tr><td>Tim Berners-Lee</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol>HyperText Transfer Protocol (HTTP)</a></td></tr>\n", " <tr><td> creator of the World-Wide-Web at CERN</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1991</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Linus_Torvalds.jpeg/330px-Linus_Torvalds.jpeg></td> \n", " <td><table>\n", " <tr><td>Linus Torvalds</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Linux>Linux</a></td></tr>\n", " <tr><td>creator of freely modifiable Linux kernel source code</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1993</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/e/e3/Ian_Murdock_interview_at_Holiday_Club_hotel_2008_%282%29.jpg/375px-Ian_Murdock_interview_at_Holiday_Club_hotel_2008_%282%29.jpg></td> \n", " <td><table>\n", " <tr><td>Ian Murdock</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Debian>Debian GNU/Linux</a></td></tr>\n", " <tr><td>Debian Social Contract</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1995</td> \n", " <td><img src=http://www-ksl.stanford.edu/people/robm/phbbt.jpg></td> \n", " <td><table>\n", " <tr><td>Rob McCool</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Apache_HTTP_Server>Apache HTTP Server</a></td></tr>\n", " <tr><td>a web server that powers the digital world</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1995</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Monty-Widenius-David-Axmark-MySQL-2003-05-09.jpg/270px-Monty-Widenius-David-Axmark-MySQL-2003-05-09.jpg></td> \n", " <td><table>\n", " <tr><td>David Axmark and Michael 'Monty' Widenius</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/MySQL>MySQL</a></td></tr>\n", " <tr><td>popular RDMS acquired by Oracle</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1996</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/2/2b/Jonathan_Schwartz.jpg/375px-Jonathan_Schwartz.jpg></td> \n", " <td><table>\n", " <tr><td>Jonathan Ian Schwartz</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Java_Platform,_Standard_Edition>Java / JDK</a></td></tr>\n", " <tr><td>general computing platform</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1997</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Eric_S_Raymond_portrait.jpg/330px-Eric_S_Raymond_portrait.jpg></td> \n", " <td><table>\n", " <tr><td>Eric S. Raymond</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/The_Cathedral_and_the_Bazaar>The Cathedral and the Bazaar</a></td></tr>\n", " <tr><td>open-source software advocate and author</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1998</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Marc_Andreessen.jpg/300px-Marc_Andreessen.jpg></td> \n", " <td><table>\n", " <tr><td>Marc Lowell Andreessen</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Netscape_Communicator>Mosaic</a></td></tr>\n", " <tr><td>Netscape opened source codes of web browser</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1999</td> \n", " <td><img src=https://www.wired.com/wp-content/uploads/blogs/wiredenterprise/wp-content/uploads//2012/02/apache_feather.png></td> \n", " <td><table>\n", " <tr><td>Brian_Behlendorf</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Apache_Software_Foundation>Apache Software Foundation</a></td></tr>\n", " <tr><td>Open source software incubator</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>1999</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/2/2f/Richard_Baraniuk_at_the_SPARC_2014_OA_Meeting_-_DSC00777_%28cropped%29.JPG/330px-Richard_Baraniuk_at_the_SPARC_2014_OA_Meeting_-_DSC00777_%28cropped%29.JPG></td> \n", " <td><table>\n", " <tr><td>Richard Baraniuk</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/OpenStax_CNX>OpenStax CNX</a></td></tr>\n", " <tr><td>formerly called Connexions, is a global repository of educational content provided by volunteers</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2000</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/en/c/c0/StarOffice_9.1.0_Start_Center.png></td> \n", " <td><table>\n", " <tr><td>Sun Microsystems</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/StarOffice>StarOffice</a></td></tr>\n", " <tr><td>office suite to counter evil empire Microsoft</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2001</td> \n", " <td><img src=https://avatars3.githubusercontent.com/u/57394?v=3&s=400></td> \n", " <td><table>\n", " <tr><td>Fernando Perez</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Ipython>IPython Notebook</a></td></tr>\n", " <tr><td>active documents that contain live code, equations, visualizations and explanatory text</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2001</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Wikipedia-logo-v2.svg/225px-Wikipedia-logo-v2.svg.png></td> \n", " <td><table>\n", " <tr><td>Wikimedia Foundation</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Wikipedia>Wikipedia</a></td></tr>\n", " <tr><td>the free encyclopedia</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2003</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Arduino_Uno_-_R3.jpg/330px-Arduino_Uno_-_R3.jpg></td> \n", " <td><table>\n", " <tr><td>Massimo Banzi</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Arduino>Arduino</a></td></tr>\n", " <tr><td>open-source electronics platform based on easy-to-use hardware and software</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2005</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Linus_Torvalds.jpeg/330px-Linus_Torvalds.jpeg></td> \n", " <td><table>\n", " <tr><td>Linus Torvalds</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Git>Git</a></td></tr>\n", " <tr><td>version control system</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2005</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/3/35/YouTube_TaiwanVersionLaunch_SteveChen-2.jpg/330px-YouTube_TaiwanVersionLaunch_SteveChen-2.jpg></td> \n", " <td><table>\n", " <tr><td>Steve Chen</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/YouTube>YouTube</a></td></tr>\n", " <tr><td>Video Hosting Service</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2006</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/d/d2/Salman_Khan_TED_2011.jpg/330px-Salman_Khan_TED_2011.jpg></td> \n", " <td><table>\n", " <tr><td>Salman Khan</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Khan_Academy>Khan Academy</a></td></tr>\n", " <tr><td>learn anything - \n", "For free. For everyone. Forever</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2007</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/d/db/Android_robot_2014.svg/113px-Android_robot_2014.svg.png></td> \n", " <td><table>\n", " <tr><td>Google</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Android_(operating_system)>Android</a></td></tr>\n", " <tr><td>mobile operating system</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2008</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/c/c5/Bitcoin_logo.svg/378px-Bitcoin_logo.svg.png></td> \n", " <td><table>\n", " <tr><td>Satoshi Nakamoto</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/Blockchain>Blockchain / bitcoin</a></td></tr>\n", " <tr><td>an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way.</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2008</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/Tom_Preston-Werner.jpg/330px-Tom_Preston-Werner.jpg></td> \n", " <td><table>\n", " <tr><td>Tom Preston-Werner</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/GitHub>GitHub</a></td></tr>\n", " <tr><td>Open source project repository</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2015</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/a/a4/TensorFlowLogo.png/330px-TensorFlowLogo.png></td> \n", " <td><table>\n", " <tr><td>Google</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/TensorFlow>Tensorflow</a></td></tr>\n", " <tr><td>An open-source software library for Machine Intelligence</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \n", " <tr><td>2017</td> \n", " <td><img src=https://upload.wikimedia.org/wikipedia/commons/thumb/4/49/Elon_Musk_2015.jpg/330px-Elon_Musk_2015.jpg></td> \n", " <td><table>\n", " <tr><td>Elon Musk</td></tr>\n", " <tr><td><a target=new href=https://en.wikipedia.org/wiki/OpenAI>Open AI</a></td></tr>\n", " <tr><td>promote and develop friendly AI in such a way as to benefit, rather than harm, humanity as a whole</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " </table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('../dataset/open_src_move_v2_1.csv') as csvfile:\n", " reader = csv.DictReader(csvfile)\n", " table_str = '<table>'\n", " table_row = \"\"\"\n", " <tr><td>{year}</td> \n", " <td><img src={picture}></td> \n", " <td><table>\n", " <tr><td>{person}</td></tr>\n", " <tr><td><a target=new href={subject_url}>{subject}</a></td></tr>\n", " <tr><td>{history}</td></tr>\n", " </table>\n", " </td> \n", " </tr>\n", " \"\"\"\n", " for row in reader:\n", " table_str = table_str + table_row.format(year=row['Year'], \\\n", " subject=row['Subject'],\\\n", " subject_url=row['SubjectURL'],\\\n", " person=row['Person'],\\\n", " picture=row['Picture'],\\\n", " history=row['History'])\n", " table_str = table_str + '</table>'\n", " \n", "HTML(table_str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to learn STEM <a class=\"anchor\" id=\"hid_stem\"></a> " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "Wen calls it -<br><br><br> <font color=red size=+4>M</font><font color=purple>y</font><font color=blue size=+3>P</font><font color=blue size=+4>E</font><font color=green size=+4>T</font><font color=magenta size=+3>S</font><br>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"Wen calls it -<br><br><br> <font color=red size=+4>M</font><font color=purple>y</font><font color=blue size=+3>P</font><font color=blue size=+4>E</font><font color=green size=+4>T</font><font color=magenta size=+3>S</font><br>\")" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "27bcc25f-414c-429f-970f-d72996f62336" } }, "source": [ "### Math <a class=\"anchor\" id=\"hid_math\"></a>" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "340d4e41-b5c4-4b84-add3-cdb9d2d720a2" } }, "source": [ "* [Awesome Math](https://github.com/rossant/awesome-math)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "08712a89-cfa5-4e72-a288-110c91c90268" } }, "source": [ "$$ e^{i \\pi} + 1 = 0 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "see more [MathJax](https://www.mathjax.org/) equations [here](https://jupyter-notebook.readthedocs.io/en/latest/examples/Notebook/Typesetting%20Equations.html#Maxwell's-Equations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Science <a class=\"anchor\" id=\"hid_science\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Physics <a class=\"anchor\" id=\"hid_physics\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Computational Physics, 3rd Ed - Problem Solving with Python by Rubin Landau](http://physics.oregonstate.edu/~landaur/Books/CPbook/index.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Engineering <a class=\"anchor\" id=\"hid_engineer\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [How To Be A Programmer](https://github.com/braydie/HowToBeAProgrammer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Technology <a class=\"anchor\" id=\"hid_tech\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/) @MIT\n", "* [Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) @Stanford" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "nbpresent": { "id": "58dc82ce-5499-45ba-a45d-5983a5c22edb" } }, "source": [ "## References <a class=\"anchor\" id=\"hid_ref\"></a>" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "ac06c191-b6a8-48f6-86f6-c78c76462861" } }, "source": [ "### Websites" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "bcf75c29-93f1-453d-81c1-fbd5d2c95c2c" } }, "source": [ "* [DataCamp - Jupyter Notebook Tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook#gs.ClmI4Jc)\n", "\n", "\n", "* http://docs.python.org\n", "\n", "It goes without saying that Pythonâ€™s own online documentation is an excellent resource if you need to delve into the finer details of the language and modules. Just make sure youâ€™re looking at the documentation for Python 3 and not earlier versions.\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "66662698-f8b7-4482-b9dc-d220a918e51d" } }, "source": [ "### Books" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "06ce80e4-29de-4994-9808-c8feffa25d8d" } }, "source": [ "### Other Resources\n", "\n", "* Idea\n", " - [Google Search](http://www.google.com)\n", "* Text\n", " - [Wikipedia](https://www.wikipedia.org/)\n", "* Image\n", " - [Google Images](https://www.google.com/imghp)\n", "* Video\n", " - [YouTube](https://www.youtube.com/)\n" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "095385ad-d26d-4168-9bd6-09029a9fe701" } }, "source": [ "## Contributors <a class=\"anchor\" id=\"hid_author\"></a>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "nbpresent": { "id": "3c647eed-ff6d-4b34-ae0b-08ee99798711" } }, "source": [ "* [email protected] (first created on 2017-03-09)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "nbpresent": { "id": "08d52625-4758-4290-b292-0f166e9ae95d" } }, "source": [ "## Appendix <a class=\"anchor\" id=\"hid_apend\"></a> " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "nbpresent": { "id": "9ff36507-0d51-4b5e-a714-c9dfb0dcd272" } }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "nbpresent": { "slides": { "036ced81-275e-46c6-b01d-ac7f0fa5a92f": { "id": "036ced81-275e-46c6-b01d-ac7f0fa5a92f", "prev": 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Hammit/wais-pop	src/fluxtream-ipy/Display-Fluxtream-time-series-chart.ipynb	2	16992	{ "metadata": { "name": "", "signature": "sha256:264729427d9cf398452a8d819977aaa3c680ccf523862600b9b8df5ad4e78b24" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# iframe_with_source wraps HTML+javascript into an iframe, to avoid DOM and javascript conflicts\n", "\n", "from IPython.display import HTML\n", "import json\n", "\n", "def iframe_with_source(source, height='200px'):\n", " name = 'iframe-%d' % get_ipython().execution_count\n", " source = json.dumps(source).replace('</script', '</scr\"+\"ipt')\n", " template = \"\"\"\n", "<iframe id=\"%s\" style=\"width:100%%; height:%s\"></iframe>\n", "<script>\n", "document.getElementById('%s').srcdoc = %s;\n", "</script>\n", "\"\"\"\n", " # Fill in the %s slots with id, width, height, and the HTML source\n", " return HTML(template % (name, height, name, source))" ], "language": 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url:'http://fluxtream-api-proxy.cmucreatelab.org/api/bodytrack/tiles/1968/ACHD_Liberty.H2S_PPM/'+level+'.'+offset+'.json', \n", " success:function(data){successCallback(JSON.stringify(data))},\n", " failure:failureCallback,\n", " headers: {Authorization: 'Basic ' + btoa('achd:achdmirror')}\n", " });\n", " }\n", " var plot = new DataSeriesPlot(datasource, dateAxis, series[0].axis, {});\n", " plot.setStyle(\n", " {\n", " \"styles\" : [\n", " {\n", " \"type\" : \"line\",\n", " \"lineWidth\" : 1,\n", " \"show\" : true,\n", " \"color\" : \"#0000ff\"\n", " },\n", " {\n", " \"type\" : \"lollipop\",\n", " \"lineWidth\" : 1,\n", " \"radius\" : 0,\n", " \"fill\" : false,\n", " \"show\" : true,\n", " \"color\" : \"#0000ff\"\n", " }\n", " ],\n", " highlight : {\n", " \"lineWidth\" : 1,\n", " \"styles\" : [\n", " {\n", " \"type\" : \"lollipop\",\n", " \"color\" : \"#ff0000\",\n", " \"radius\" : 1,\n", " \"lineWidth\" : 1,\n", " \"fill\" : false\n", " },\n", " {\n", " \"show\" : true,\n", " \"type\" : \"value\",\n", " \"fillColor\" : \"#ff0000\",\n", " \"marginWidth\" : 10,\n", " \"font\" : \"7pt Helvetica,Arial,Verdana,sans-serif\",\n", " \"verticalOffset\" : 7,\n", " \"numberFormat\" : \"###,##0.0##\"\n", " }\n", " ]\n", " }\n", " }\n", " );\n", " series[0].pc = new PlotContainer(\"series0\", false, [plot]);\n", "\n", "\n", " series[1] = {}\n", " series[1].axis = new NumberAxis('series1axis', \"vertical\", {min: -0.01, max: 0.05});\n", "\n", " var datasource = function(level, offset, successCallback, failureCallback) {\n", " $.ajax({\n", " url:'http://fluxtream-api-proxy.cmucreatelab.org/api/bodytrack/tiles/1968/ACHD_Avalon.H2S_PPM/'+level+'.'+offset+'.json', \n", " success:function(data){successCallback(JSON.stringify(data))},\n", " failure:failureCallback,\n", " headers: {Authorization: 'Basic ' + btoa('achd:achdmirror')}\n", " });\n", " }\n", " var plot = new DataSeriesPlot(datasource, dateAxis, series[1].axis, {});\n", " plot.setStyle(\n", " {\n", " \"styles\" : [\n", " {\n", " \"type\" : \"line\",\n", " \"lineWidth\" : 1,\n", " \"show\" : true,\n", " \"color\" : \"#0000ff\"\n", " },\n", " {\n", " \"type\" : \"lollipop\",\n", " \"lineWidth\" : 1,\n", " \"radius\" : 0,\n", " \"fill\" : false,\n", " \"show\" : true,\n", " \"color\" : \"#0000ff\"\n", " }\n", " ],\n", " highlight : {\n", " \"lineWidth\" : 1,\n", " \"styles\" : [\n", " {\n", " \"type\" : \"lollipop\",\n", " \"color\" : \"#ff0000\",\n", " \"radius\" : 1,\n", " \"lineWidth\" : 1,\n", " \"fill\" : false\n", " },\n", " {\n", " \"show\" : true,\n", " \"type\" : \"value\",\n", " \"fillColor\" : \"#ff0000\",\n", " \"marginWidth\" : 10,\n", " \"font\" : \"7pt Helvetica,Arial,Verdana,sans-serif\",\n", " \"verticalOffset\" : 7,\n", " \"numberFormat\" : \"###,##0.0##\"\n", " }\n", " ]\n", " }\n", " }\n", " );\n", " series[1].pc = new PlotContainer(\"series1\", false, [plot]);\n", "\n", "\n", "\n", "\n", " $(window).resize(setSizes);\n", " setSizes();\n", "};\n", "\n", "function setSizes() {\n", " dateAxis.setSize($('#dateAxis').width(), $(\"#dateAxis\").height(), SequenceNumber.getNext());\n", " for (var i = 0; i < 2; i++) {\n", " series[i].axis.setSize($('#series'+i+'axis').width(), $('#series'+i+'axis').height(), SequenceNumber.getNext());\n", " series[i].pc.setSize($('#series'+i).width(), $('#series'+i).height(), SequenceNumber.getNext());\n", " }\n", "}\n", "\n", "function displayValue(val) {\n", " $(\"#valueLabel\").html(val ? val['dateString'] + \" \" + val['valueString'] : \"\");\n", "}\n", "\n", "</script>\n", "</head>\n", "<body>\n", "<div style=\"width:100%; height:39px\">\n", "<div id=\"dateAxis\" style=\"position: fixed; left:100px; right:30px; height:37px; border:1px solid black\"></div>\n", "</div>\n", "<div style=\"width:100%; height:75px; margin-top:-1px\">\n", "<div id=\"series0title\" style=\"position: fixed; left:0px; width:99px; height:73px; border:1px solid black\"></div>\n", "<div id=\"series0\" style=\"position: fixed; left:100px; right:30px; height:73px; border:1px solid black\"></div>\n", "<div id=\"series0axis\" style=\"position: fixed; right:0px; width:29px; height:73px; border:1px solid black\"></div>\n", "</div>\n", "<div style=\"width:100%; height:75px; margin-top:-1px\">\n", "<div id=\"series1title\" style=\"position: fixed; left:0px; width:99px; height:73px; border:1px solid black\"></div>\n", "<div id=\"series1\" style=\"position: fixed; left:100px; right:30px; height:73px; border:1px solid black\"></div>\n", "<div id=\"series1axis\" style=\"position: fixed; right:0px; width:29px; height:73px; border:1px solid black\"></div>\n", "</div>\n", "<canvas id=\"narrow\" style=\"width:100%; height:73px\"></canvas>\n", "</body>\n", "</html>\n", "\"\"\"\n", "\n", "iframe_with_source(src)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "<iframe id=\"iframe-10\" style=\"width:100%; height:200px\"></iframe>\n", "<script>\n", "document.getElementById('iframe-10').srcdoc = \"\\n<html>\\n<head>\\n<script 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]\\n }\\n }\\n );\\n series[0].pc = new PlotContainer(\\\"series0\\\", false, [plot]);\\n\\n\\n series[1] = {}\\n series[1].axis = new NumberAxis('series1axis', \\\"vertical\\\", {min: -0.01, max: 0.05});\\n\\n var datasource = function(level, offset, successCallback, failureCallback) {\\n $.ajax({\\n url:'http://fluxtream-api-proxy.cmucreatelab.org/api/bodytrack/tiles/1968/ACHD_Avalon.H2S_PPM/'+level+'.'+offset+'.json', \\n success:function(data){successCallback(JSON.stringify(data))},\\n failure:failureCallback,\\n headers: {Authorization: 'Basic ' + btoa('achd:achdmirror')}\\n });\\n }\\n var plot = new DataSeriesPlot(datasource, dateAxis, series[1].axis, {});\\n plot.setStyle(\\n {\\n \\\"styles\\\" : [\\n {\\n \\\"type\\\" : \\\"line\\\",\\n \\\"lineWidth\\\" : 1,\\n \\\"show\\\" : true,\\n \\\"color\\\" : \\\"#0000ff\\\"\\n },\\n {\\n \\\"type\\\" : \\\"lollipop\\\",\\n \\\"lineWidth\\\" : 1,\\n \\\"radius\\\" : 0,\\n \\\"fill\\\" : false,\\n \\\"show\\\" : true,\\n \\\"color\\\" 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id=\\\"series1title\\\" style=\\\"position: fixed; left:0px; width:99px; height:73px; border:1px solid black\\\"></div>\\n<div id=\\\"series1\\\" style=\\\"position: fixed; left:100px; right:30px; height:73px; border:1px solid black\\\"></div>\\n<div id=\\\"series1axis\\\" style=\\\"position: fixed; right:0px; width:29px; height:73px; border:1px solid black\\\"></div>\\n</div>\\n<canvas id=\\\"narrow\\\" style=\\\"width:100%; height:73px\\\"></canvas>\\n</body>\\n</html>\\n\";\n", "</script>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "<IPython.core.display.HTML at 0x1064272d0>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
miaecle/deepchem	examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb	1	183679	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" }, "colab": { "name": "05_Putting_Multitask_Learning_to_Work.ipynb", "provenance": [] }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "ElXOa7R7g37i", "colab_type": "text" }, "source": [ "# Tutorial Part 5: Putting Multitask Learning to Work\n", "\n", "This notebook walks through the creation of multitask models on MUV [1]. The goal is to demonstrate that multitask methods outperform singletask methods on MUV.\n", "\n", "## Colab\n", "\n", "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb)\n", "\n", "\n", "## Setup\n", "\n", "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment." ] }, { "cell_type": "code", "metadata": { "id": "Fc_4bSWJg37l", "colab_type": "code", "outputId": "d6d577c7-aa9e-4db1-8bb2-6269f2817012", "colab": { "base_uri": "https://localhost:8080/", "height": 462 } }, "source": [ "%tensorflow_version 1.x\n", "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n", "import deepchem_installer\n", "%time deepchem_installer.install(version='2.3.0')" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "TensorFlow 1.x selected.\n", " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 3477 100 3477 0 0 14733 0 --:--:-- --:--:-- --:--:-- 14733\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n", "python version: 3.6.9\n", "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n", "done\n", "installing miniconda to /root/miniconda\n", "done\n", "installing deepchem\n", "done\n", "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", " warnings.warn(msg, category=FutureWarning)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "WARNING:tensorflow:\n", "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", "For more information, please see:\n", " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", " * https://github.com/tensorflow/addons\n", " * https://github.com/tensorflow/io (for I/O related ops)\n", "If you depend on functionality not listed there, please file an issue.\n", "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "deepchem-2.3.0 installation finished!\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "CPU times: user 3.1 s, sys: 736 ms, total: 3.84 s\n", "Wall time: 2min 19s\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "9Ow2nQtZg37p", "colab_type": "text" }, "source": [ "The MUV dataset is a challenging benchmark in molecular design that consists of 17 different \"targets\" where there are only a few \"active\" compounds per target. The goal of working with this dataset is to make a machine learnign model which achieves high accuracy on held-out compounds at predicting activity. To get started, let's download the MUV dataset for us to play with." ] }, { "cell_type": "code", "metadata": { "id": "FGi-ZEfSg37q", "colab_type": "code", "outputId": "1ac2c36b-66b0-4c57-bf4b-114a7425b85e", "colab": { "base_uri": "https://localhost:8080/", "height": 85 } }, "source": [ "import os\n", "import deepchem as dc\n", "\n", "current_dir = os.path.dirname(os.path.realpath(\"__file__\"))\n", "dataset_file = \"medium_muv.csv.gz\"\n", "full_dataset_file = \"muv.csv.gz\"\n", "\n", "# We use a small version of MUV to make online rendering of notebooks easy. Replace with full_dataset_file\n", "# In order to run the full version of this notebook\n", "dc.utils.download_url(\"https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/%s\" % dataset_file,\n", " current_dir)\n", "\n", "dataset = dc.utils.save.load_from_disk(dataset_file)\n", "print(\"Columns of dataset: %s\" % str(dataset.columns.values))\n", "print(\"Number of examples in dataset: %s\" % str(dataset.shape[0]))" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Columns of dataset: ['MUV-466' 'MUV-548' 'MUV-600' 'MUV-644' 'MUV-652' 'MUV-689' 'MUV-692'\n", " 'MUV-712' 'MUV-713' 'MUV-733' 'MUV-737' 'MUV-810' 'MUV-832' 'MUV-846'\n", " 'MUV-852' 'MUV-858' 'MUV-859' 'mol_id' 'smiles']\n", "Number of examples in dataset: 10000\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "c9t912ODg37u", "colab_type": "text" }, "source": [ "Now, let's visualize some compounds from our dataset" ] }, { "cell_type": "code", "metadata": { "id": "KobfUjlWg37v", "colab_type": "code", "outputId": "01025d0f-3fb1-485e-bb93-82f2b3e062f9", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "from rdkit import Chem\n", "from rdkit.Chem import Draw\n", "from itertools import islice\n", "from IPython.display import Image, display, HTML\n", "\n", "def display_images(filenames):\n", " \"\"\"Helper to pretty-print images.\"\"\"\n", " for filename in filenames:\n", " display(Image(filename))\n", "\n", "def mols_to_pngs(mols, basename=\"test\"):\n", " \"\"\"Helper to write RDKit mols to png files.\"\"\"\n", " filenames = []\n", " for i, mol in enumerate(mols):\n", " filename = \"MUV_%s%d.png\" % (basename, i)\n", " Draw.MolToFile(mol, filename)\n", " filenames.append(filename)\n", " return filenames\n", "\n", "num_to_display = 12\n", "molecules = []\n", "for _, data in islice(dataset.iterrows(), num_to_display):\n", " molecules.append(Chem.MolFromSmiles(data[\"smiles\"]))\n", "display_images(mols_to_pngs(molecules))" ], "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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AHDp0SKSGZWZm0ge5dLzHpk2bTO/kEz6EhYWFAODh4aHdHde3b18ASE1NFfx0vE2aNAkAvvzyS/OfWqEg4eEEgLRuTUSoudOI7OxsAOjUqZO5T2wkuhK9dicfF7RrcOfOnWI06dSpU3QYk2ZYmCCdfMLPonj11Ve9vb3Ly8vz8vI0Gy1w+gKrOb4qFYwbB/v3g5sb/PQTPD0Sxhx69erl5uZ26dKly5cvm/vcxuD3NyPe8NHffvttyJAh1dXV48ePX7lyJd3o4eHxxhtv/PXXX8eOHeN9ZFHmEw4ZMgSe/vGJURTARAMGDLC3tz979mxpaanZTkoITJ4Mu3aBszMcPgy+vmY78xNSqXTw4MFgYZ+J9Q0ZMsTa2vrEiROPHj3i/iqRQnj58uWwsLCKiooRI0Zs3LhRe8SS6Z/mooSw/mdY586dO3TocP/+/TNnzohxRsPUavXu3bt1NtrZ2YWEhKjVanNWnZk1CzZsAHt7SE8HhjV3LPDCpD4XF5eePXvK5XJaKY8jMUJ48+bNgQMHlpWVDRo0KCUlhfbKaNAfZlpaGuE9P06Yi+WnyeVyOgbyxo0bmo2sRpZqSsrWLzoaHx9vZWXl6OhonmElc+cSAGJjQ0wo6icMsZeeFUpiYiI83cnXqFWrVoExK142qqSkhD5lDQwM1DsGQNPJ9+uvv/I7hVglD0eMGAEAa9eu1WzRO7LUDGbNmgUAdnZ2Ok+oCwsLXV1dtdecAQBXV9fIyMilS5eeOnVKoVAI2IwVKwgAkUqJhXROibr0rFD0dvIZRoeLjB07VpAG3L17lz7R6d69u4FZS3T+Ou9OPrFCuHnzZgAYOnSoZgvvTmdTzJs3DwBkMhmdTa9x9erVVq1aAUBYWFhRUVGj04VM/MbYvJlIJEQiIZs2mfZ+hCPq0rMCousXcB9ql5aWRi8RTT91VVXVG2+8Qb85DFdDpfVmunfvzu9EYoVQb9dt/ZGlovrXv/4FAFKpVOfz/vbt2zRvISEhOiswX79+PTk5OSYmxsfHR2c57ujoC9Onk337iLHP1b//nkilRCIha9aY/p4EI+rSswKKjY0FgC+++ILj/llZWQAQFBRk4nlrampose327dvfuXPH8M61tbX29vZWVlZ//vknj3OJWIG7Z8+emhtWqv7IUvF89913dF2KDRs2aG/XXGD06NHD8LRo7elCTZo08fJ6DEDof15eJDqaJCWRRmcLqdUkMpIAkG++Mf09CUy8pWcF9NNPPwGAv78/x/3Pnz9v+l2PXC6ndTrbtGnDsWQ7LV+0ideljoghXLBgAQDExMRotugdWSqGH374gXZhLVu2THt7ZWUlLQTu5+dXf9EYA6qrqzMy1AkJJCSE2NsTTRoBSNu2ZOxYsnYtKSwkdAxcfj6RSMilS4QQEhdHdu4k4jw6NtX8+av79ftywQKuq3AyobeTzwBaAc3T05P3GZVKJa3T6e7u/t///pfjq5KSkgDg7bff5nFGEUNIP5N0Zj3WH1kquCNHjjRp0gQAvvrqK+3tNTU1tBB4hw4d+F02UAoFOXWKLFlCIiOJq+tTgXR1JR9+SPLzSbt2hA4Oj4sj339v4hsSy/HjBICIsPKswPr06SORSPz8/L7++uuGlqDUqKioAAAnJyd+51Kr1R988AEAODs7G9XbWVJSIpFIHBwcdG5wuBB3QRi6Nv25c+c0W+qPLBVWTk6Og4MDAEydOlV7u+YCo23bthw/Uzm6do1s3UpiYki7dgSAjBpF8vPJ6NGkZ09y755Fh1Cp/PtDxJImmek6duyYTCazsnryQNva2jogICA2NjY1NbX+5YxKpaLzG/g9bTJleTa6JhSPYavihnDy5MkAMG/ePM0WHp3O3J05c0ZvjRbNBYaHhwf3CwwebtwgRUUkP5+MGkW2bCELFlh0CAkh775LAIjQK88KRvMLlUgkNjY2MTExfn5+2oGUSqXdunWLjY39/vvvNVc3Tk5OAMCjQ3v27NkAYGNjc/jwYR6t/b//+z8A+PTTT419obghpGN53njjDe2N7dq1c3R0FHyOb0M1WtRqNS0E3qxZM96PU41CQyiXk27dyLRpFh3CHTsIABF65VlhaP9CaSffjz/+SAh5+PBhRkZGQkJCaGgondCgQct5NG/eHIxfA++rr76iT7PS09P5NZguEcPjdlTcED5+/NjR0VEikdy+fVuzsaioSPBVxa9cudKyZUsAGDZsmHafu1qtpg9SmzZtanotEI5oCAkhCxaQ9u0tOoQPHhBrayKTEUsrm6jzC124cCEATJw4UWc3hUKRl5dH6+vQ7GmPuwgPD09MTMzOzm503MW///1vALCyskpJSeHdZrVaTRdvLygoMOqFoq/UGxkZCQA6zwmEdevWLU2NFp3bYjp3zs7OzpxzWDUhvHeP2NtbdAgJIcHBBMCyGln/F6q3k0+HUqksLCxMSkry8PCApzk6OoaGhiYkJGRkZNTvOElOTqa3kaYX4KOrsHz99ddGvUr0EK5fv55+nol0/PLy8oZqtHz55Zf0AoNVLZlp00j79kTkshWmWryYABBBV541SUNFd2gsuaz3TBe0WL9+PR0I5evrqzPuQrtfZ+/evfRp1uLFi01v/N69ewGgd+/eRr1K9BDeuXNHIpHY29vX1tYKfvDKysqGarTQgbxSqVSk+Z1chIYSAGLCBY45/P47ASDu7sT4ymbCM/AL/fjjj3U6+RpCC+knJydrtpSUlOzcufPTTz/t2rWrTr8OrdrK5bBcVFdX29raWllZlZWVcX+V6CEkhNABeAeFnjtQU1MTFBQEAB07diwtLdX+p61bt9ILjPXr1wt7UqMsX04AyJgxDJvASYcOBICIWZaFEwO/UEIInXGm08mnF41rQ6MjHz16pN2vM3369Pj4eAFa/z/0SdjWrVu5v8QcIZwxYwYAdOrUad26dYWFhULVViovL/f39/fy8tLu9SGE7Nmzh15gLF26VJAT8XbtGgEgzZsTyx6eSaZOJQBE0D9Fo9XW1vbv35/2LhYX6xnEo7eTT6/PP/8cABYuXMjlpIJfoNE+nqioKO4vET2E9+7d69ixI33aQzk5OWnukk3sJq2oqNB58v7TTz/R4TILFiwwreHC8PEhACQri3U7DMrMJACka1dmDVAoFHRqbOvWra9du9bQbsOGDaM3e4aPRmchsir6fvPmTYlE4uTkJJfLOb5E3BBWVVXRMrve3t6JiYljxoyh0x81HBwcQkJCEhISMjMzTa+anJubS4fLTJkyRZD2m27mTAJALGkRAD3kcuLkRKytiTE3MoJRqVSjR48GADc3t4sXLxrYc8OGDVw6+datW6f3eYbZdO3aFQAyMjI47i9iCGtra+nKHt7e3tpV/URaXSg/P5/OV/zHP/7BsJqojqwsAkAsaREA/bKz2fTiauoeODk5nT171vDOmk4+w6XN6OJqI0eOFLSlRpgzZ45R3wRihVChUNCe4tatWxuYxaszXUj7S9Ko1YUuXLhAi+2PHTvWolY+0YzPvHyZdVMs0meffQYA9vb2Wdwu2Wknn84UbR10CUrzzJjT6+TJk/QPmOP+ooRQqVTSopHu7u6GLzC01dTU8Ftd6OrVq3R0RWRkpAVOUR0zhgAQXivPmo/O9CvzPLungy1tbGy495zTUgmTJ082sA/NQI8ePYRoIx8qlcrNzQ04V+MXPoRqtXrixIkA4OzszOXRql51dXV5eXkrVqyIioqi70ejadOm2v06t27dotPkBwwYwGMWiRmkpBAAwmvlWfMx//QrWrpTKpUaVRKaFrNt27atgTuOixcvAtPqxqdOnbK1tY2MjOT4Byl8COkDCXt7e6GmbKvV6sLCwrVr144dO9bT01M7kPb29vQqtHfv3uZZDYuHykoikxGZjFhyZTOd6VfTp5M+fcicOeTgQVJVJfzptmzZQusebNy40agXakqbnT9/vqF97t+/Hx0d3bx5cyYV33/77Tc67nzcuHEc74wEDuEXX3xBLzDEWwxAu1/HysqqTZs27u7uFl66r29fAmApddb00pl+NXDgk5nKUinx9SUxMWTrVqLvAZ7Rdu/eTYvc8VvZj3bkGH4MSNeylkqlW7Zs4dlKXi5fvkzXZnv77be53xkJGcIVK1bQd/69uYYDFxcX29jYSKVSw8WwmFuyhDg4VH/22bnGdzU7+rBaZ/pVcjLJyCAJCSQ0lNjaPlU9oGVLEhX1d30dHp3QaWlpMpkMABYtWsSvwbSeWs+ePQ3vRp8WSiQS0xfx5OjmzZt0FvvAgQONegAuWAg3b95MLzA2b94s1DG5oKvwbN++3ZwnNdalS7dtbW3d3d15LD0rqqoqEhBA4uIMTb+qrSUnTpAFC0hYGGnaVDeQEyZ8snLlyvz8fC5v7ejRo3QGYP1CzNxxL222evVqOlI0Li6O9+k4Kisro4WzeNwZCRPC7du307Ga2tV+zYPe348ePdrM5zVWhw4dACA3N5d1Q56oriaBgQSAdOxIsrM5Tb9SKklhIUlKItHRpG1b4uHxp+b+XHu6kN6xYKdPn6YDpz755BMTW06ffnH5uN+2bRsdwzhr1izxnh7fvXv31VdfBQB/f38ed0YChPDHH3+k7zMxMdH0oxmLVtdydnYWtmC24FitAtAQuZy89RYBIG3aECPnoD9x6VLlxo0bx48f7+3trd1h1qRJk6CgoPj4+AMHDlRVVRGtafLR0dGmP8hdu3YtALzzzjtcdt63bx99BP3RRx+J8QxZUyO4S5cu/G6LTA2hZs3EuXPnmngo3uiH0DOx9Kz5VwHQS6kkUVEEgHh4EKFq7pSUlOzatUvvdKEuXbrQui9hYWELFizQLkXL+1wSicTR0ZHjrdeBAwfs7OwAYMyYMcJ+WNfW1tK1N729vRutEdwQk0KoWTORR3EbAT3fS88KTq0mH3xAAEizZkSkmjs604Vob2FoaCj9BouMjDT9FP7+/gBw5MgRjvtnZWXRD4KIiAihnicrFAq6CmCbNm1M+bXyD+Fvv/1Gq3pwfx4iErr0bPv27Rm2gQszrwLQkGnTCABxcCA5OeKeKC8vb/jw4TNmzMjKyrpz505dXR0d/GniurbU3LlzQWvFXC7Onj1LB37079/f9PLT2sPCOI6MaQjPEF6+fLlFixbGPg8RiVKppD/cS3TYlaUy5yoADZk9mwAQOztihot3vdXH6Kwaw4M/uaALXb788stGverixYt0IaDu3bsbVYJdh1qtpuVknJ2dtcvq8sMnhMXFxfyeh4jnvffeg3pF7y2N2VYBaMjChQSAyGSEb1E/46jVavoXf+HCBc3G+fPnNzr4kwuVSkUfi3MZ36/tjz/+oOsNduvWjcdkHWr69OnAt0ZwfUaHsLS0lPfzEPHs2rWLXmawbkgjzLAKQENWryYAxMrKrAtj0JLy2tXHzp071+jgT45oOVkeffLFxcUdO3YEgM6dO9+6dcvYl8fHx4MJNYLrMy6EmuchhtdMNL9nZelZsVcBaMiOHYeaN1dLJMTIoZqmql99TDP4Mz8/38SD79mzB/iuglZaWvraa6/RC1qj6lAvX75c8GFhRoTQ9Ochonpel541HR2r2anTqFWrzP0oVW/1MTr40/T6I5qDZ2Zm8hiKVFFR0atXLwB46aWXOJbr1dQI3rFjh/HtbRDXENbW1nJfM5GJZcuWwbOw9Cx9rr1+/Xrz3E5riu7Mnz/fDKerr371MTr4U5D5flOmTOnSpQsAODg40GWVGxqvo1d1dfXAgQMBwMXFpdHFgLdt20aHha1bt87khj+FUwhVKlVYWBiIsKSRgJ6VpWcTExPpB7Dh1YUEoSm6ExsbK8bxuVi9ejU8XX3MxHVtdcyePZsOCdSwsbHp3bt3XFxcenr6g8aKdjx+/Hj48OG0n9NAL8u+ffvosLBvRFjtles34Zo1a1q0aGHi8xCx0R4jQTqsRLVq1aqGVhdKTU0V5E+TaBXdef/99xkW3SkuLq5ffYzWVhNwrH9paWlaWhqtk0JnaWh4eXnFxMRs3fWIAlwAAAYOSURBVLq1oSVi6urqxo0bR3s79fa1ZGRk0CEHCQkJQjVYm3H3hGK0QEB0PrEZhswLotHVhWh9HX75KSoqoksyvPPOO8ynbtDqY5mZmZotdOjMiBEjxDid9ngdOlpN5wdL66Ro/2CVSiV97mdjY6PT43Ly5Ek6LMyogQFGMUfxX7M5fvw4APha/tqz9RhYXahFixbcVxeirl69Sh/QRUZGWsK4dtqnr71s661btyQSSXNnZ4XIN8badVLoCHINDw8PzQ9WLper1Wr6IS6VSjVf0efPn6e/jvHjx4t3NfFchVCpVNIftOCLH5qTZnWh6Ojotm3bav/dNDpdiBBy+/ZtWnQnJCTEQoru6K0+dvHdd1VuboTz4E/TqVQqzQ+WjjbR0PTr0O9DiUSyfPnyS5cu0WFhI0aMELWj4bkKISGElpFdabFrzxrv2rVrXFYXojuXl5f7+vrSvkedNaoYUqlU7u7uwW3b3i8qerJ17lwCQES7xmvU1atXt2zZMmHCBPrgXvv+nP4Pnf04aNAgsfuxn7cQbt++HQAGWubasyYzMF3I399/0qRJtGuqa9euInW38lb96acEgGgvP3bmDAEgRg7+FElZWZmmX4eu0wQArq6ugYGBZhgWJiGEwHPkwYMHLVq0sLKyunfvnvYCGM+f6urq06dP5+Tk5Obm5uTkPH78GAA6d+6sUql+/vlnOq7SguzeDVFREBwMWVl/b1GroXVrKC2FCxegSxemjXvKo0ePcnNzf/zxx1mzZrm5uZnjr0jslJsfHVSwe/du1g0xn7/++isrK2vhwoUFBQWWOZSCPHpEmjQhUinRHms1YQIBIHwrPj03rBpN6TOHFiDZv38/64aYj62tbXBw8Jw5c/z8/Ggxcovj6AjBwaBSwZEjTzaGhwMAvEi/Kb2ewxBGREQAwIEDB9RqNeu2IC1DhwI8HblBg8DWFk6fhnv3WDXKEjyHIfTx8Wnfvv3du3fppFJkKSIiAAAOHoS6ur+3ODhAv36gUsGhQwzbxdxzGEJ4Ia9InwFeXuDrC1VVcPLkk431vx5fPM9nCOnQxPT0dNYNQU+rfxMYGQkAcPgwKBRsmmQBns8Q9u3bt1mzZgUFBbQqKbIU9UPo6Ql+fvDwIeTksGoUc89nCGUyWVhYWGBg4IMHD1i3BWnp3RtcXaGoCK5cebLxhe8jfT5DCAApKSk5OTmvv/4664YgLVIpDB4M8HTkwsPBxQX+N07lBfS8jZhBlm7XLnj3XQgJgaNH/96iVgMh8L8Rmy8ga9YNQC+YwYNh0CAYNuzJFqvn9nKMI/wmROxs2gRLlgAhMGYMJCSwbg0zGELEyPXrEBwM+fng6Ai9esG330L//qzbxMaLfiWAmDl2DMLCwM0NbG1h1KinxpS+YDCEiJEHD8DV9e//d3V9kYePYggRI+7ucPfu3/9/9y5Y5uQPs8AQIkZCQuDIESgvB7kcUlNhyBDWDWIGH1EgRjw9YeFCCA4GQmD8eOjVi3WDmMHeUYQYw8tRhBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWLs/wFFO9mssTqu9wAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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gAFQq/PADANjY4Lff4O6OefPwzjvIz8df/2q26aSn4+ZNuLnB09NsY9Yz+obw9OnT27Zte3C7ra1tixYt2rRp4+bmpvuDt7e3bpuOh/rxxx//9a9/KRSKlStXSsXMTSQpCQ0b4rnnNGdnT3kIZTIsWgQACxYgNhZTpmg2/uc/aNoU06fj449x+TIiI80zHekLoaAOgvWDviEMDg7esmVLZmZmVlbWn3/+ee3aNemHsrKyjIwMqdBNDU5OTtLR0t3dvWXLltIP0hYHB4fNmzdPnDhRJpMtXrxYWnNpOkOGIDcX16+jeXPgqQ+h5OJFTJmCqirk5GDOHM0FqGnT0Lw5PvwQ33yDu3excCHkJr8owF8In0jfELZo0WLIkCEPbs/Ly8vKysrMzNTmU/ohMzOzoKCgoKDg1KlTD36qYcOGlZWVKpUqMjJy3LhxBv0OnqS8HHl5sLaG9oqPxYXQFHf22rXDunV491385z+4cQPLlkFaIDd2LJycMHo0fvgB2dn47Tfca3BpIpWVaNCAQ/g4JrxZX1paev369YyMjOzsbN0f0tPTCwsLmzRpIpfLs7OzFdI5IgDgzJkzTk5Obm5uRpxGRgbatkWrVrh8WbNl6FDExiImBsOGGXEcA8TGYutW/PQTkpMxaxa2bzfanuPjMWwYiooQFIRNm+DoqNm+fz+GDkVhIV59FTEx0CncYApVVZDJoPPnzO4n5HLQ9evX27ZtC2DRokXajb/88ouVlVVoaKhxx9q/nwDq2bN6i7QAOCXFuOMYoEYNK+NKTSVXVwIoIIDy8qq3nzxJ7u5lbdu+9corJlpetHMnP6OrFzH3CZs3bz5nzhwAX375ZX5+vrSxT58+VlZWK1asSE5ONuJYmZnA/SefFnc6+uCdPSPq0gVHjsDbG8nJ6N1b858DgK8vDhwY26zZhvj4Xr16XblyxcjjAgA/o6sXYTfrhw8fHhQUlJ+fL3XSANC6devJkycT0aeffkrGO0mWIufhoXn54FdE8bp2RXw8ACQnw9fX+Ptv0wYJCXjpJZw9i27dcPKkdvuCTZv8/PwuXLjQvXv3Y8eOGWW0khJs3IhlywBg6FCsWQOLeTzZUgk8Ch87dkyhUNjY2JyXCjcR3blzR/pCuHr1amONMmkSARQVpXl56RIB1KqVsXZvmIQEOnyYSktr1rAyhYIC6t2bAHJyooMHtZuN1dQ2P5/WraOQEHJw0JTd2r6dvv6a5s6llSv5dPRxBD8xIxWiG6rzR/Tzzz8DaNmyZXFxsVGGGDu2xNaWtK1a9+0jgHr1Msq+Dda3LwGkLbuSl0eXLplwuLIyeustsrb+qmfP7du3azeXl5dLZXmVSuX69etrtcvsbPrvf6l/f7K21qzYlMupe3eaM4e2bKGvv6bCQurXj954gwYNIoN7Lj2dBIcwJydHuqf/+++/S1tUKlXXrl0B/Pvf/zbKEP7+/gCSkjS1IjduPNm376HJky8aZecGqaoiBweSyUjbmWTBAgJo4kSTDrpt+nQA1tbWK1as0G5Wq9WTJ0+G3k1tr1yhqCgKCiIrK032FAoKDKSoqOraBTt3akojTJ1KbdsSQE2aUGKiSX5b9Zr4Z0e//vprAD4+PtqGrAcPHpTJZA4ODllZRqhcKPWs0u5K6ts6depUw/dsqLQ0Aki37Ir0hOdPP5l0WN0KnN98843uLz2xqe2JEydmzZrl59fVxUWlrVQwdCgtX063bj1u0LIyGjFCU5JSp7E8I7KEEJaXl3t7ewP48ccftRtHjBgB4L333jNw5xUVFXK53MrKSlv1eeLEiQAWLFhg4J6N4L//JYDGjKne0ro1AXTihBkG/+6777RNbXU7mSxbtszKygrAxIkTpe1qtToxMfHvf/+71MxI0r//yXfeofXra1E+q6qKPvqIALKyIp3G8swCQkhE69evB9C0aVNtsceMjAxbW1u5XJ6cnGzIni9fvgzA09NTu2XYsGEANtaqjKKJvPfefXVXcnIIIEdHMleV+I0bN0rNa0NCQnTLjW3evNnW1nbMmDEHDhwIDw/X7T3k4uISEhISGxtbh2ZGRKRWU0QEASST0f2N5Z9pFhFCIurTp0+Ns8Rp06YB6N69uyE9Nw4cOACgR48e2i3SF86kpCSDpmsUUh047UMDmzcTQP36mXMKe/bskfqcBAcHa6+EJSQkDBkyxMXFRZs9T0/PTz75ZN++fUZpI7FwIcnlBFB4OPfyIbKcEKalpcnlchsbG23F/6KiIul2xdq1a+u8299++w3A22+/rd0i9bUyyrdNgxQUkFxOtrZUXq7ZMn06ATRjhpknkpKSom1qm5eXd/To0bFjx0rZM27vIV2rVmmupoaGktCSnxbBUkJIRB988AGAN6V1qEREtGTJEgAeHh51vl0hPZfz+eefSy/Ly8vlcrm1tbX5GwPVtGuXprWb1iuvEECxseafS3p6uvQUoY+Pz5gxYwAMGDDgzJkzJh10925ydCSABg8mI92Nqq8sKIQ3btyQTo3+97//SVtUKpXUJ2D27Nl126e0bH/+/PnSS2nJlZeXl1EmbBCpzca9vx2oqooaNiSATNbG/fGkprYeHh4dO3YEkJCQYIZBk5OpaVPq2PFOnz6Dbj7D7bQsKIRENHv2bAAvvvii9kgVHx8PwMHBoW7NcVQqVVZW1q17l8+lr4iBuscfUQYOJKC62MTx4wQY/+nt2sjPzz958qSVlZW1tbWxnpR4orNn1Z069QHg6+v757NaHd2yQlhWViadF+neL37zzTcBfPDBB4bv/8GviEKoH6y7sngxAfTuu0LnRfv27QPQpUsXcw76qM7yzw7x1dZ0KZVK6XnuL7/8srCwUNo4d+5cpVK5fPnyP/74w8D9Z2ZmAmgpegFFenq6t0o177XXquuuWMb688TERADdzFuLws3NLT4+vmfPnlevXu3Ro8eRI0fMObolsKwQAhg1alTv3r1zc3OlJ2kAtG3bdtq0af/85z/bt29ft30WFRXt3bt3zpw5P/30k/FmWndHjhy5VFBwWKe86vhr15b27FnQo4fAWQFISkoC8LLZ/y5wcnLavXv38OHD8/Pz+/fvv2vXLqMPsWsXWrZEZSVu37aYxdxaog/FD/HHH3/UuF1RWxUVFadOnYqOjg4LC/Px8ZHrVFJxdnZ2dHTcs2ePcedcK3/7298AaB8Zu337tlwuVyqVdbsDbkQtWrQAYLq+0I9Xh87yDyotpcuX6eBB2rCBFiyg6dMpNJT+/W/auZO6dKFffrHEFcaWGEIieu+99wCMGDFCz/dXVlampaX99NNPYWFhnTt31jYclyiVyoCAgAkTJixdunTUqFHSH/OaNWtM+lt4DD8/PwD79++XXv7vf//D/U8UCCGt63VxcTH6XUH96dNZvrS0NCMj4+DBg+vXr1+wYMGcOQmhofTaa9SxI7m4aJ4mr/FPz560cyf961/0+uuUn29xIdS30JOZRUZGbtq0aePGjbt37w4KCnroe7Kzs48ePXro0KGDBw+mpaWVlJRof0mhUPj4+HS5p2vXrrb3yhm9//77Xl5ec+fOfeedd7Kzs6WlA+ZUWloqXYTUdmmWvomZ/ySwBu0XQpm43gAymWzmzJkNGzacMmXK1KlTjx075u/vf/369evXr0sFiq5fv64txSDp02fK/v09tS9tbODqCg+P+/7dpg1KSiy3AYmFhrBZs2ZTp0795z//OXXq1NTUVKkYlJQ6SWJi4s2bN3U/4ubmJkWuZ8+e3bt3t7e3f+ieZTLZnDlz3N3dJ0+e/Nlnn+Xk5ESaqwKnJCUlpbKy0s/PTztDUd/EarCQaQD47LPPXF1d33vvvQMHDqx6oDyGjY1Ns2bNWrZsKf27Xbue771XHblmzR6+T+lrZlgYhg+vrndlISw0hACmTJmydOnSY8eOjRgxQqVSpaSk5OTk6L7B3d3d39/f39+/a9eu/v7+Tk5O+u/8k08+cXZ2Hjdu3DfffJObm7tkyRJp6YAZ1LgCSUTS//1mvib5oKNH/4BlhBDAmDFjZsyYce3atdGjR/v6+rq7uzdv3tzd3b1Zs2aGdCtp2BBNmmi6RJm+5KreRJ8PP86aNWtatWqlnWrDhg0DAwPDw8Ojo6MzMjIM3//WrVsbNGgAYNiwYaWlpYbvUB9SpePoe+XVLl68CMDNzc08oz9KeTk5Olb4+KQWFOi9NsmUbt68KZPJ7O3ttatMjUKl0iwv3rLFiHs1lEWHUK1Wl5WVTZ8+ffXq1enp6aYYIjExUVou0LdvX+1CKpNyd3cHoC2rs2LFCtz/xKwQSUkEkI+P2FlU27p1q/SHYvQ9f/stAfTqq0bfcd1ZziH5IWQymXT7fvTo0dKTNEb38ssv79u3z93dfd++fX/9a+T9J7zGJ5Und3Z21i6QtZBvYpbWMcJ0p+jjxqFRI+zdCyMVlzMCiw6hefj6+h46dCg4eNzmzTMDA/GwthpGo70Qqr0CaSGXRi3jiZ1qNf6zZGVlxcfH37171/A9Ozri/fcBYOFCw3dmJKIPxZbi1i3q3p0AataM/vjDVKOkpqaOGzcuNDR0zJgxv//+e2Zmpo2NjUKhuHPnjqmG1E+bNgTQ8eNiZ6GhVqsbN24MnWWf33//PYCxUt/FOjl7lq5c0fx8+TIpFKRU0vXrBs/VGDiE1e7epQEDCCAHB7q3msporl27tnjx4uDgYO2DBDY2Nm3bth00aNDAgQONPFgtSYU17O3JqBdB6k5qIqS74iwkJAT3N02ole+/J5mM/vrX6i1vvkkAPaKclblxCO9TXk7vvKPpaWvAgv5qZ8+enT17dteuXbXnn0qlctCgQfPmzZOWDri5uZmtRfGjxMYSQK+8InYW1aTas7qLXaSv0H/U9RTlwgWSy8nOjrSLFqUOJa6uZK6L4o/DIaxJraYpUzSFNP/73zru4ejRY//4xz86dOigPe13cHAYOXLk6tWrCwsLpbcZq/S14WbMIICmTxc4hft89NFH0FmKfevWLZlM1qBBA0NuV7zxBgH01VfVW/z9CaBlywycrBFwCB8uMpJkMgJo2jR9P6JSUUICTZtG7dpRYOCPUvacnZ1DQkLWrVt392G1AcvLy6VnWZVK5QZtkXCz69ePANq8WdT4NXXq1AnA4cOHpZfbt28H0Lt3b0P2GRdHALVoUV3T59dfCSBfX/HFpjiEj7R8uaa89IQJpFOYs6byctqxgz76SNOATPrntdf+nDBhwu7du5/4l3dVVdX48eOhd+lro1OpNIU1LOQqRVFRkUKhsLa2Likpkbb83//9H4C///3vBu75hRcIoFWrNC/Ly8nNjWQy2r+/yMA9G4hD+DgxMWRrSzIZxcXV/KWSEoqNpZAQatSoOnutWlF4OCUkPC60D/XE0temc+IEAdS6tZmHfaQ9e/YACAgI0G6RTtoNLxX7008EkG7ZgIUL0729hw4ePNjAPRuIQ/gE+/fTvHn39bvs25eGDiU7u+rsvfQSzZplaOFsbenrSZMmqWobYgNkZtKsWRQZabYBn0CqMxQeHi69VKvV0lPBhlegKSmhpk0JoIMHNSegeXl5dnZ2MplMbFkNDqFedJeEvvqqpvdQly4UEUFG/OOLiYmRlly9++67Fc9qOc4hQ4YAWHXvrPHMmTO4v4a6ISIjbwYGLho9OkS7RVpGPGnSJKPsv244hHqpsSQ0OprqVPztyeLj46U2Vf369SsqMu13FctsZy1VZ750r0XcsmXLAIwcOdIoO8/JyVEqlQqFQrsA4NSpU1L3oYKCAqMMUQf82Jq+dJeEhobCzc0ko/Tt2zchIaFFixZ79uzp169fXl6eSYa5x9LaWV++fDknJ6dp06Zt2rSRthj32VpXV9dRo0apVKpFixZJWzp27BgUFHT37t2lS5caZYg64BDWQlgYfvkFpl533qlTp4MHD7Zr1y4lJaV3797Xrl0z7v5Pn8bMmejQARcuWFw76wfLvRm9ANynn34KYMmSJUVFRbpbvvvuu6qqKmONUiscwlpo2BB+fqHk2uAAAAu/SURBVOYYqHXr1gkJCZ07dz537ly3bt2OHz9u4A6rqrB7NyZMgLs7fH0xaxbOnUNy8n2H97t3UVBghMkbosZxr7i4+PTp09bW1n7G++/u5+fXu3fvoqKiX3/9VdoycODADh06XL16dfPmzcYapXZEnQezJ7pz507//v0BODk51a0ufWkpxcZSWBg1a1Z9LdfTk8LCKDaWtm2rbmc9ZAgNGEA+PnTtmtF/H7UgxW/37t3Sy7179wLw9/c37iibNm0C4O3trb0K/cMPP0BcaXYOoV527tS0jbh40azXMMrKyrSP1Oh/o+z27dsbN1556y2yt6/Ono8PzZhBqanVb9NtZz1wIHXqpImoqM7yZWVlSqVSLpdrn+yTas9ONHb/cJVKJS1Pjb3Xfqe4uFha2y2kZx6HUC+iQkhEVVVVH3/8MQCFQvHTYztp37x5Mzo6Ojg4WKlUdus2XJu9iAg6ffrJAxUUUK9eBJCzMx06ZLT560+qve3r66vdInV0XbFihdHH+vbbb729vTfrPKon9cN8V0QnAg6hXnbupFat6PXXqVcvMVfzH/NIzZUrV6Kionr37i3VpJPiOmDAGwsX1vrcUmxn+W+//RbAhx9+qN0iFSO+ePGi0ccqLy+v8UTEtm3brKysxo8fb/SxnohDqBeBR0Kt77//XrfL/KVLl6KiogIDA3UXSQUFBUVFRV034DFQgZ3lR48eDUB7tJdanZunGPGuXbscHBwAzDB7k1biEOrJEkJIRGvXrlUqlQCk3roSBweHUaNG6S6SMpCozvJSZb2TJ09KL8vLyw8cOLB+/XpTjxsdHS2ttA4NDRXyoBKHUC8WEkIi2rt3b+fOndu2bfv4RVKGM3NneamorKOjo5mbKEdFRUmnEuHh4aLq/3MI6x/pXNS4BTkfSttZ/rPP8k03XFlZ2Y4dOwYOHAigVatWZiu3U1VVJXXmUSgUP/zwg3kGfSgOIXuc3bupQ4dyV9cXBg8ebNz2vSUlJbGxsSEhIdKzsgCaN28OwN3dfeXKlaY+KJWVlY0cOVL6Ir3WKIVMDMAhZE+QlJTSpEkTAD179szPzzdwb7du3Vq+fPnQoUPt7Oy0X2tfeumlWbNmrV27tse9Do3+/v5HjhwxyvwflJ+f36tXL+kpCLGFRSQcQvZk6enp0t1tHx+fzMzMOuwhNzdXuodpY2MjxUwul3fp0iUiIkJ3LZ9arV63bp2Hh4f0hpCQkBs3bhjv90FEdOVKptRt1svL68yZM8bded1wCJle6tZZ/vLly9J9FG2fVoVCERgYGBUVpa0p+qC7d+9GRERI14EbN24cGRlZrq0MY5iTJ6ldu2Jf326+vr51+9vEFDiETF/5+fk9e/YE4OzsrK3C9FCnTp2KjIwMDAzUnnDa2toGBwcvXrw4JydHz+EuXLgQHBwsffz555/fYfDTA3Fxmmo6b755w1i3c4yCQ8hqoaysTOoqZW9vXyMVKpUqNTU1IiLi+eef12bP3t4+ODg4Ojq6zguUt27dqu3bER6+o87NuNavJ1tbAuittyyi1qguDiGrnRqd5auqqhISEsLDw6VuUxIXF5eQkJDY2FijnEZWVFRERUX5+fVWKKpsbCg8nGqb6Kio6nueZizfoy8OIau1tWvXjh07VnqW1VGn7W2rVq0mT56ckJBgikJVWVnqkBBNMVgPD1qzRq9HCLSlnM389E+tcAhZ7ZSXl0sLjiIjI4cNGyaTyTw8PMaNG5eQkGCGJ05SUjR9ewAKCKDHLzwqK6O339Y0NVi92tRTqzsOIasdqd5Ex44diUitVl+5ckX6lrhmzRrzTECtpuhozTJluZxCQuih13ry86l3bwKocWPav988U6sjLm/Bake36ItMJvPy8pK2GLECxePJZAgNRXo6IiJgbY0VK9C+PRYsgG6BmMJC9OqFAwfg4YGDB9G7t3mmVkccQlY7NcrAXL16NTs728XFxdvb25zTcHDAzJlIS8Nrr6GgAJ9+ij590LIlKitx+zbGjkW/fujYEYcOoWNHc86rLjiErHZqlD/TvpSZugrdw3TogN9/R1wcOnTAgAH3VXCcPx+HD8PDw/yTqjUOIauF3Nzcy5cvOzo6+vj4SFuMWxe0boKCcPw4/P3vq+CoUKBhQ4GTqgUOIasFqQxMQECAtpSGJYQQgNT+WLeCYz1iJXoCrD6pEbnKysq0tDSZTObv7y90XtXCwjB8OHRuXtYDfCRktVAjhGlpaaWlpR06dJAaJ1kCsxVoNiI+EjJ9qdXq1NRU6ITQ6DXqDTFgAAYMwKJFuHABU6eKnk1t8JGQ6evc6dNFRUWtW7eWGifBYr4Q6kpLw5YtOHVK9Dxqg4+ETF8+SUmqZs2yhg7VbrGoI6GkRQsAuH5d9Dxqg0PI9JaYKM/J8WjdWvMyL++UtfXFoKCOlnQ7XGpZl50teh61waejTG+JiQCgPe4lJtqdP/+CSqW9XWEJpBDWryMhh5Dpp6gIZ89CqcSLL2q2JCUBOpm0DNLpKB8J2dMoKQlqNfz8oFRqtkgHRku6KgM+ErKnWY3jnlqN1FTA4kLYvDkUCuTkQFDX3brgEDL9SCHURu7MGRQWolUrNG8ucFIPsrJCkyZQq5GbK3oqeuMQMv0kJwP3XZW576UlqXdfCzmETA/p6cjNhasrvLw0W2ocGC1JvftayCFkepAid69GffUWSz0S2tggN7dC9ET0xSFkeqhx3LtzB2fOwMYGnTsLnNSjeHjMraiQZWZ+JXoi+uInZpge+vdHaSn69dO8TE6GSoWuXatvV1iSpk0dAFyvP+ejHEKmh8GDMXhw9UtbWwwebLFLhqRO99n158oMh5DpR63G+PG4ehUVFfj+e8TGip7QI7m5uaFeHQn5OyHTz7ZtUKuxaxciIy18uV69OxJyCJl+0tLQqxcABATg3DnRs3mc5s2by+Xy3NxclUolei564RAy/chkmjJmACxp2cSDrKysmjRpolKpcuvJUzMcQqafrl0RHw8Aycnw9RU9myeoX2ekHEKmn4ED0aABBgzAjBmIjBQ9myeoX9dm+Ooo049MhkWLRE9CX3wkZEww6Uh48eJF0RPRi2LmzJmi58CYkd29ezcrK2vDhg3p6enPPfecq6ur6Bk9Dh8J2VNoyJAhvXv3VqvVK1as6NSpU3BwcLx0VckiyUh73Zmxp0tGRsaCBQuWLl1aXFwM4KWXXpo8efI777xjLXWusBgcQvaUKywsXL58+dy5c7OysgC4ubmFhYV98sknllO6n0PIngkVFRVr1qyZO3fuqVOnADg6Or7//vuff/65p6en6KlxCNkz5uDBg99888327duJSC6XDxo06MsvvxRbyZ9DyJ5Fx48fnzdv3po1ayorKwEEBgZOmzYtODhYSL9hDiF7dt24cePHH39cuHBhQUEBgHbt2k2YMCEsLMzOzs6c0+AQsmfdnTt3li1bNn/+/GvXrgFwdXUdP378pEmTXFxczDMBDiFjAKBWq7dv3z579myp35tSqRw1atQ//vGP9u3bm3poDiFj99m7d++8efN27twpXbl58803Z8yY0dmUJa34iRnG7vPqq69u3779woUL4eHhSqVy48aNZ8+eNemIfCRk7JFycnKWLl06depUkz5kwyFkTDA+HWVMMA4hY4JxCBkTjEPImGAcQsYE4xAyJhiHkDHBOISMCcYhZEwwDiFjgnEIGROMQ8iYYBxCxgTjEDImGIeQMcE4hIwJxiFkTDAOIWOCcQgZE4xDyJhgHELGBOMQMiYYh5AxwTiEjAnGIWRMMA4hY4JxCBkTjEPImGAcQsYE4xAyJhiHkDHBOISMCcYhZEwwDiFjgnEIGROMQ8iYYBxCxgTjEDImGIeQMcE4hIwJxiFkTDAOIWOCcQgZE4xDyJhgHELGBOMQMiYYh5AxwTiEjAnGIWRMMA4hY4JxCBkTjEPImGAcQsYE4xAyJhiHkDHBOISMCcYhZEwwDiFjgnEIGROMQ8iYYBxCxgTjEDI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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "kDUrLw8Mg37y", "colab_type": "text" }, "source": [ "There are 17 datasets total in MUV as we mentioned previously. We're going to train a multitask model that attempts to build a joint model to predict activity across all 17 datasets simultaneously. There's some evidence [2] that multitask training creates more robust models. \n", "\n", "As fair warning, from my experience, this effect can be quite fragile. Nonetheless, it's a tool worth trying given how easy DeepChem makes it to build these models. To get started towards building our actual model, let's first featurize our data." ] }, { "cell_type": "code", "metadata": { "id": "eqEQiNDpg37z", "colab_type": "code", "outputId": "e1b919ac-1bb3-4224-ff91-65d2e3d16f3b", "colab": { "base_uri": "https://localhost:8080/", "height": 357 } }, "source": [ "MUV_tasks = ['MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644',\n", " 'MUV-548', 'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712',\n", " 'MUV-737', 'MUV-858', 'MUV-713', 'MUV-733', 'MUV-652',\n", " 'MUV-466', 'MUV-832']\n", "\n", "featurizer = dc.feat.CircularFingerprint(size=1024)\n", "loader = dc.data.CSVLoader(\n", " tasks=MUV_tasks, smiles_field=\"smiles\",\n", " featurizer=featurizer)\n", "dataset = loader.featurize(dataset_file)" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "Loading raw samples now.\n", "shard_size: 8192\n", "About to start loading CSV from medium_muv.csv.gz\n", "Loading shard 1 of size 8192.\n", "Featurizing sample 0\n", "Featurizing sample 1000\n", "Featurizing sample 2000\n", "Featurizing sample 3000\n", "Featurizing sample 4000\n", "Featurizing sample 5000\n", "Featurizing sample 6000\n", "Featurizing sample 7000\n", "Featurizing sample 8000\n", "TIMING: featurizing shard 0 took 38.166 s\n", "Loading shard 2 of size 8192.\n", "Featurizing sample 0\n", "Featurizing sample 1000\n", "TIMING: featurizing shard 1 took 8.325 s\n", "TIMING: dataset construction took 46.915 s\n", "Loading dataset from disk.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "QQfINH2Ag371", "colab_type": "text" }, "source": [ "We'll now want to split our dataset into training, validation, and test sets. We're going to do a simple random split using `dc.splits.RandomSplitter`. It's worth noting that this will provide overestimates of real generalizability! For better real world estimates of prospective performance, you'll want to use a harder splitter." ] }, { "cell_type": "code", "metadata": { "id": "-f03zjeIg372", "colab_type": "code", "outputId": "5472a51a-42e9-43bc-e73e-d947ae3c6a33", "colab": { "base_uri": "https://localhost:8080/", "height": 136 } }, "source": [ "splitter = dc.splits.RandomSplitter(dataset_file)\n", "train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(\n", " dataset)\n", "#NOTE THE RENAMING:\n", "valid_dataset, test_dataset = test_dataset, valid_dataset" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "Computing train/valid/test indices\n", "TIMING: dataset construction took 0.529 s\n", "Loading dataset from disk.\n", "TIMING: dataset construction took 0.254 s\n", "Loading dataset from disk.\n", "TIMING: dataset construction took 0.272 s\n", "Loading dataset from disk.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "6nRCpb08g375", "colab_type": "text" }, "source": [ "Let's now get started building some models! We'll do some simple hyperparameter searching to build a robust model." ] }, { "cell_type": "code", "metadata": { "id": "BvfbTbsEg376", "colab_type": "code", "outputId": "9f96de90-ad90-4492-cced-0f5e74dcacb6", "colab": { "base_uri": "https://localhost:8080/", "height": 853 } }, "source": [ "import numpy as np\n", "import numpy.random\n", "\n", "params_dict = {\"activation\": [\"relu\"],\n", " \"momentum\": [.9],\n", " \"batch_size\": [50],\n", " \"init\": [\"glorot_uniform\"],\n", " \"data_shape\": [train_dataset.get_data_shape()],\n", " \"learning_rate\": [1e-3],\n", " \"decay\": [1e-6],\n", " \"nb_epoch\": [1],\n", " \"nesterov\": [False],\n", " \"dropouts\": [(.5,)],\n", " \"nb_layers\": [1],\n", " \"batchnorm\": [False],\n", " \"layer_sizes\": [(1000,)],\n", " \"weight_init_stddevs\": [(.1,)],\n", " \"bias_init_consts\": [(1.,)],\n", " \"penalty\": [0.], \n", " } \n", "\n", "\n", "n_features = train_dataset.get_data_shape()[0]\n", "def model_builder(model_params, model_dir):\n", " model = dc.models.MultitaskClassifier(\n", " len(MUV_tasks), n_features, *model_params)\n", " return model\n", "\n", "metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean)\n", "optimizer = dc.hyper.HyperparamOpt(model_builder)\n", "best_dnn, best_hyperparams, all_results = optimizer.hyperparam_search(\n", " params_dict, train_dataset, valid_dataset, [], metric)" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "Fitting model 1/1\n", "hyperparameters: {'activation': 'relu', 'momentum': 0.9, 'batch_size': 50, 'init': 'glorot_uniform', 'data_shape': (1024,), 'learning_rate': 0.001, 'decay': 1e-06, 'nb_epoch': 1, 'nesterov': False, 'dropouts': (0.5,), 'nb_layers': 1, 'batchnorm': False, 'layer_sizes': (1000,), 'weight_init_stddevs': (0.1,), 'bias_init_consts': (1.0,), 'penalty': 0.0}\n", "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "If using Keras pass _constraint arguments to layers.\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n", "\n", "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n", "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n", "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "computed_metrics: [nan, nan, nan, 0.3168604651162791, 0.525, nan, 0.7647058823529411, 0.26775147928994086, 0.18300653594771243, nan, nan, nan, 0.5405405405405406, nan, 0.24614197530864193, nan, nan]\n", "Model 1/1, Metric mean-roc_auc_score, Validation set 0: 0.406287\n", "\tbest_validation_score so far: 0.406287\n", "computed_metrics: [1.0, nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n", "Best hyperparameters: ('relu', 0.9, 50, 'glorot_uniform', (1024,), 0.001, 1e-06, 1, False, (0.5,), 1, False, (1000,), (0.1,), (1.0,), 0.0)\n", "train_score: 1.000000\n", "validation_score: 0.406287\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n", " warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "QhZAgZ9gg379", "colab_type": "text" }, "source": [ "# Congratulations! Time to join the Community!\n", "\n", "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n", "\n", "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n", "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n", "\n", "## Join the DeepChem Gitter\n", "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!\n", "\n", "# Bibliography\n", "\n", "[1] https://pubs.acs.org/doi/10.1021/ci8002649\n", "\n", "[2] https://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00146" ] } ] }	mit
fabriziocosta/EDeN_examples	prot/interactive_display_ligand_protein.ipynb	1	744924	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive visualization" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>.container { width:95% !important; }</style><style>.output_png {display: table-cell;text-align: center;vertical-align: middle;}</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "import logging\n", "from eden.util import configure_logging\n", "configure_logging(logging.getLogger(), verbosity=1)\n", "from IPython.core.display import HTML\n", "HTML('<style>.container { width:95% !important; }</style><style>.output_png {display: table-cell;text-align: center;vertical-align: middle;}</style>')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pdb_fname = '4uug.pdb'\n", "ligand_marker = 'PXG'\n", "\n", "from eden_prot.io.pdb import load \n", "structure = load(pdb_fname)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8N7NK+q1Mn1k1RUwxrDiHzFQR+V5C9fzX2UigvTohw0AxRciZUObc5/t+oXNf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\nwLKQpkACwHe22C8fIU5sxClgWTbiGIhjIAxf/eP7r/45Hu/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14Aw4mLiIy+fegH/73/wD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MDFAzzP4fijDsN9Tz6KGb4wut1xTB7chQHfAfiw9RBMO/gz+LBPRU87B59bf87efPNNPPmn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\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6669x33334Z577gEAbNmyBddccw0KCgpQXFyM+fPno3bt2hlebfycPXsWJSUlWLp0KZYv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\nn7W7vWVE5HMYpoi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"text/plain": [ "<matplotlib.figure.Figure at 0x1007e7d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from eden_prot.display import interactive_plot3d\n", "int_w = interactive_plot3d(structure, ligand_marker)\n", "\n", "from IPython.display import display\n", "display(int_w)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IkiRJkiRJkiS9r4jYR6e0+G7ZWTQY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Pl9mCrPKUlav1HA0OIW860816L4e7oqX2PXy2M0Ln3I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\nPNtP94rKBQfORsR14CFgAngqIk5m5pV6q5Ok2j0REdcy81Tdhag3RMQOY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WfRFabH3tjYhtFF1RpiosSdIy+HBsjCfZ8ECdeAa4n3JSY+3mcxs/0PzD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ODkB5IkSdvqEWAFON04h8bHsW55tmkK3ZGTSEmSJEnaLr3WASRJkiRJkiRJ2ohuhnbtsKp6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\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OeiRJkqTV+Nvx0MAozX8YxMCd1pumvXueYgRgkObVYZrXNqumgvz6b+WJ/7gR+4qIpQDj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pBdkiRtnzGgBlzNTKcuV69ND7ukS5LUR56KiBcyc36brtcYpbHhBg91Yrn9/7N3p89ynul9379X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fVy6ywASY4B5zyPLUmSJEmS9DOWFiVJkiRJkiSNnSSPA3P9zfeq6nzDOJsuSYBfA75rUVHS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\nOSR9ping+SSe59tm+sWAp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VWSRJkiRJkiStz6ZFSZIkSepBETEJPAckRdPScsWRPhQRRygGKgNY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\nZFuSr6/YdRv4dHpif+KlPxOl+3PSosbJz6rqVusQktRCP1nx/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"text/plain": [ "<matplotlib.figure.Figure at 0x114879690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from eden_prot.display import interactive_draw_ligand_protein\n", "int_w2 = interactive_draw_ligand_protein(structure, ligand_marker)\n", "\n", "from IPython.display import display\n", "display(int_w2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": { "d99c0f78b2d5486c8ea7e0ee7ecff735": { "views": [ { "cell_index": 3 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
atulsingh0/MachineLearning	ML_UoW/Course01_Regression/PhillyCrime2.ipynb	1	53306	{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#importing the graphlab\n", "import graphlab as gl" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>Finished parsing file C:\\learn\\ML\\Regression\\week1\\Philadelphia_Crime_Rate_noNA.csv</pre>" ], "text/plain": [ "Finished parsing file C:\\learn\\ML\\Regression\\week1\\Philadelphia_Crime_Rate_noNA.csv" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Parsing completed. Parsed 99 lines in 0.036002 secs.</pre>" ], "text/plain": [ "Parsing completed. Parsed 99 lines in 0.036002 secs." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Finished parsing file C:\\learn\\ML\\Regression\\week1\\Philadelphia_Crime_Rate_noNA.csv</pre>" ], "text/plain": [ "Finished parsing file C:\\learn\\ML\\Regression\\week1\\Philadelphia_Crime_Rate_noNA.csv" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Parsing completed. Parsed 99 lines in 0.032002 secs.</pre>" ], "text/plain": [ "Parsing completed. Parsed 99 lines in 0.032002 secs." ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------\n", "Inferred types from first 100 line(s) of file as \n", "column_type_hints=[long,float,float,float,float,str,str]\n", "If parsing fails due to incorrect types, you can correct\n", "the inferred type list above and pass it to read_csv in\n", "the column_type_hints argument\n", "------------------------------------------------------\n" ] }, { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">HousePrice</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">HsPrc ($10,000)</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">CrimeRate</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">MilesPhila</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PopChg</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">County</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">140463</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.0463</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Abington</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Montgome</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">113033</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.3033</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ambler</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Montgome</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">124186</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.4186</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19.5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Aston</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Delaware</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">110490</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.049</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">49.4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bensalem</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bucks</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">79124</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.9124</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">54.1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.9</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bristol B.</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bucks</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">92634</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.2634</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48.6</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.6</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bristol T.</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bucks</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">89246</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.9246</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.8</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">15.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.6</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brookhaven</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Delaware</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">195145</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">19.5145</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.8</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.5</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bryn Athyn</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Montgome</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">297342</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.7342</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.6</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bryn Mawr</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Montgome</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">264298</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.4298</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.4</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.0</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Buckingham</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bucks</td>\n", " </tr>\n", "</table>\n", "[99 rows x 7 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n", "</div>" ], "text/plain": [ "Columns:\n", "\tHousePrice\tint\n", "\tHsPrc ($10,000)\tfloat\n", "\tCrimeRate\tfloat\n", "\tMilesPhila\tfloat\n", "\tPopChg\tfloat\n", "\tName\tstr\n", "\tCounty\tstr\n", "\n", "Rows: 99\n", "\n", "Data:\n", "+------------+-----------------+-----------+------------+--------+------------+\n", "\| HousePrice \| HsPrc ($10,000) \| CrimeRate \| MilesPhila \| PopChg \| Name \|\n", "+------------+-----------------+-----------+------------+--------+------------+\n", "\| 140463 \| 14.0463 \| 29.7 \| 10.0 \| -1.0 \| Abington \|\n", "\| 113033 \| 11.3033 \| 24.1 \| 18.0 \| 4.0 \| Ambler \|\n", "\| 124186 \| 12.4186 \| 19.5 \| 25.0 \| 8.0 \| Aston \|\n", "\| 110490 \| 11.049 \| 49.4 \| 25.0 \| 2.7 \| Bensalem \|\n", "\| 79124 \| 7.9124 \| 54.1 \| 19.0 \| 3.9 \| Bristol B. \|\n", "\| 92634 \| 9.2634 \| 48.6 \| 20.0 \| 0.6 \| Bristol T. \|\n", "\| 89246 \| 8.9246 \| 30.8 \| 15.0 \| -2.6 \| Brookhaven \|\n", "\| 195145 \| 19.5145 \| 10.8 \| 20.0 \| -3.5 \| Bryn Athyn \|\n", "\| 297342 \| 29.7342 \| 20.2 \| 14.0 \| 0.6 \| Bryn Mawr \|\n", "\| 264298 \| 26.4298 \| 20.4 \| 26.0 \| 6.0 \| Buckingham \|\n", "+------------+-----------------+-----------+------------+--------+------------+\n", "+----------+\n", "\| County \|\n", "+----------+\n", "\| Montgome \|\n", "\| Montgome \|\n", "\| Delaware \|\n", "\| Bucks \|\n", "\| Bucks \|\n", "\| Bucks \|\n", "\| Delaware \|\n", "\| Montgome \|\n", "\| Montgome \|\n", "\| Bucks \|\n", "+----------+\n", "[99 rows x 7 columns]\n", "Note: Only the head of the SFrame is printed.\n", "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns." ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#importing the input data into graphlab SFrame dataset\n", "crimeData = gl.SFrame.read_csv(\"Philadelphia_Crime_Rate_noNA.csv\")\n", "crimeData" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = document.getElementsByTagName('script');\n", " var thisScriptTag = scripts[scripts.length-1];\n", " var parentDiv = thisScriptTag.parentNode;\n", " e = document.createElement('div');\n", " parentDiv.appendChild(e);\n", " } else {\n", " e = element[0];\n", " }\n", "\n", " if (typeof requirejs !== 'undefined') {\n", " // disable load timeout; ipython_app.js is large and can take a while to load.\n", " requirejs.config({waitSeconds: 0});\n", " }\n", "\n", " require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n", " var app = new IPythonApp();\n", " app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"crimeData\"], \"descriptives\": {\"rows\": 99, \"columns\": 7}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"HousePrice\", \"x\": \"CrimeRate\", \"columns\": [\"HousePrice\", \"HsPrc ($10,000)\", \"CrimeRate\", \"MilesPhila\", \"PopChg\", \"Name\", \"County\"], \"view\": \"Scatter Plot\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"HousePrice\"}, {\"dtype\": \"float\", \"name\": \"HsPrc ($10,000)\"}, {\"dtype\": \"float\", \"name\": \"CrimeRate\"}, {\"dtype\": \"float\", \"name\": \"MilesPhila\"}, {\"dtype\": \"float\", \"name\": \"PopChg\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"County\"}], \"column_identifiers\": [\"Name\", \"PopChg\", \"County\", \"HousePrice\", \"MilesPhila\", \"HsPrc ($10,000)\", \"CrimeRate\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": [[29.7, 140463], [24.1, 113033], [19.5, 124186], [49.4, 110490], [54.1, 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119566], [24.1, 100231], [21.2, 95831], [9.8, 229711], [29.9, 74308], [7.2, 259506], [19.4, 159573], [41.1, 147176], [11.2, 205732], [21.2, 215783], [42.8, 116710], [9.4, 359112], [61.7, 189959], [19.4, 133198], [6.6, 242821], [15.9, 142811], [18.8, 200498], [13.2, 199065], [34.5, 93648], [22.1, 163001], [22.1, 436348], [71.9, 124478], [31.9, 168276], [44.6, 114157], [28.6, 130088], [24.0, 152624], [13.8, 174232], [29.9, 196515], [9.9, 232714], [22.6, 245920], [13.0, 130953]], \"columns\": [{\"dtype\": \"int\", \"name\": \"HousePrice\"}, {\"dtype\": \"float\", \"name\": \"HsPrc ($10,000)\"}, {\"dtype\": \"float\", \"name\": \"CrimeRate\"}, {\"dtype\": \"float\", \"name\": \"MilesPhila\"}, {\"dtype\": \"float\", \"name\": \"PopChg\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"County\"}]}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# setting plot canvas to this IPython notebook\n", "gl.canvas.set_target('ipynb')\n", "\n", "# plotting scatter plot \n", "crimeData.show(view=\"Scatter Plot\", x=\"CrimeRate\", y=\"HousePrice\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Calculating Linear Regression model\n", "\n", "crimeData_model = gl.linear_regression.create(crimeData, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">176626.046881</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11245.5882194</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">CrimeRate</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-576.804949058</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">226.90225951</td>\n", " </tr>\n", "</table>\n", "[2 rows x 4 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\tindex\tstr\n", "\tvalue\tfloat\n", "\tstderr\tfloat\n", "\n", "Rows: 2\n", "\n", "Data:\n", "+-------------+-------+----------------+---------------+\n", "\| name \| index \| value \| stderr \|\n", "+-------------+-------+----------------+---------------+\n", "\| (intercept) \| None \| 176626.046881 \| 11245.5882194 \|\n", "\| CrimeRate \| None \| -576.804949058 \| 226.90225951 \|\n", "+-------------+-------+----------------+---------------+\n", "[2 rows x 4 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crimeData_model.coefficients" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#importing matplotlib library for plotting\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x224efac8>,\n", " <matplotlib.lines.Line2D at 0x224efb70>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x289768d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(crimeData['CrimeRate'],crimeData['HousePrice'],'.',\n", " crimeData['CrimeRate'],crimeData_model.predict(crimeData),'-')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Tryting the same linear model withput high influencial point\n", "\n", "crimeData_woHI = crimeData[crimeData['HousePrice'] != 96200]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "crimeData_woHI_model = gl.linear_regression.create(crimeData_woHI, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">225204.604303</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16404.0247514</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">CrimeRate</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2287.69717443</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">491.537478123</td>\n", " </tr>\n", "</table>\n", "[2 rows x 4 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tname\tstr\n", "\tindex\tstr\n", "\tvalue\tfloat\n", "\tstderr\tfloat\n", "\n", "Rows: 2\n", "\n", "Data:\n", "+-------------+-------+----------------+---------------+\n", "\| name \| index \| value \| stderr \|\n", "+-------------+-------+----------------+---------------+\n", "\| (intercept) \| None \| 225204.604303 \| 16404.0247514 \|\n", "\| CrimeRate \| None \| -2287.69717443 \| 491.537478123 \|\n", "+-------------+-------+----------------+---------------+\n", "[2 rows x 4 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# getting linear model coefficients\n", "crimeData_woHI_model.get('coefficients')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x29417160>,\n", " <matplotlib.lines.Line2D at 0x2937dac8>]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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E0z6A9wG/SD2fA5zb6LhS8ewFPJJ6/ntgXLK8O/D7Rsc4RMz/ARyZ11iB7YH7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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x293c42e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(crimeData_woHI['CrimeRate'],crimeData_woHI['HousePrice'],'.',\n", " crimeData_woHI['CrimeRate'],crimeData_woHI_model.predict(crimeData_woHI),'-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
28ideas/quant-econ	examples/numpy_scipy_examples.ipynb	1	56109	{ "metadata": { "name": "", "signature": "sha256:952cee497edf16bfbed678655b06d5e6d3792aeebb855147f8e586a5a3221f51" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "NumPy and SciPy examples" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Basic NumPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy defines a basic data type called an array (actually a numpy.ndarray)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "a = np.zeros(3) # Create an array of zeros\n", "print a" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0. 0.]\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "type(a)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "numpy.ndarray" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that array data must be homogeneous\n", "\n", "The most important data types are:\n", "\n", "* float64: 64 bit floating point number\n", "* float32: 32 bit floating point number\n", "* int64: 64 bit integer\n", "* int32: 32 bit integer\n", "* bool: 8 bit True or False\n", "\n", "There are also dtypes to represent complex numbers, unsigned integers, etc\n", "\n", "On most machines, the default dtype for arrays is ``float64`` \n", "\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = np.zeros(3)\n", "type(a[0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "numpy.float64" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we create an array such as \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.zeros(10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "``z`` is a \"flat\" array with no dimension--- neither row nor column vecto" ] }, { "cell_type": "code", "collapsed": false, "input": [ " z.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "(10,)" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the shape tuple has only one element, which is the length of the array (tuples with one element end with a comma)\n", "\n", "To give it dimension, we can change the ``shape`` attribute \n", "\n", "For example, let's make it a column vector" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z.shape = (10, 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "array([[ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.],\n", " [ 0.]])" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.zeros(4)\n", "z.shape = (2, 2)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "array([[ 0., 0.],\n", " [ 0., 0.]])" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Creating arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating empty arrays --- initializing memory:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.empty(3)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "array([ 6.92366233e-310, 6.92366233e-310, 0.00000000e+000])" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are just garbage numbers --- whatever was in those memory slots\n", "\n", "Here's how to make a regular gird sequence" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(2, 4, 5) # From 2 to 4, with 5 elements\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "array([ 2. , 2.5, 3. , 3.5, 4. ])" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.identity(2)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "array([[ 1., 0.],\n", " [ 0., 1.]])" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arrays can be made from lists or tuples" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.array([10, 20]) \n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "array([10, 20])" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.array((10, 20), dtype=float) \n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "array([ 10., 20.])" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.array([[1, 2], [3, 4]]) # 2D array from a list of lists\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "array([[1, 2],\n", " [3, 4]])" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Array indexing" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(1, 2, 5)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "array([ 1. , 1.25, 1.5 , 1.75, 2. ])" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "z[0] # First element" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "1.0" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "z[-1] # Special syntax for last element" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "2.0" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "z[0:2] # Meaning: Two elements, starting from element 0" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "array([ 1. , 1.25])" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.array([[1, 2], [3, 4]])\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "array([[1, 2],\n", " [3, 4]])" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "z[0, 0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "1" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "z[0,:] # First row" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "array([1, 2])" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "z[:,0] # First column" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "array([1, 3])" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(2, 4, 5)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "array([ 2. , 2.5, 3. , 3.5, 4. ])" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "d = np.array([0, 1, 1, 0, 0], dtype=bool)\n", "d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "array([False, True, True, False, False], dtype=bool)" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "z[d]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "array([ 2.5, 3. ])" ] } ], "prompt_number": 28 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Array methods" ] }, { "cell_type": "code", "collapsed": false, "input": [ "A = np.array((4, 3, 2, 1))\n", "A" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "array([4, 3, 2, 1])" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "A.sort()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "A" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "array([1, 2, 3, 4])" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "A.mean(), A.sum(), A.max(), A.cumsum(), A.var()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "(2.5, 10, 4, array([ 1, 3, 6, 10]), 1.25)" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "A.shape = (2, 2)\n", "A" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "array([[1, 2],\n", " [3, 4]])" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "A.T # Transpose, equivalent to A.transpose()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "array([[1, 3],\n", " [2, 4]])" ] } ], "prompt_number": 37 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Operations on arrays" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = np.array([1, 2, 3, 4])\n", "b = np.array([5, 6, 7, 8])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "a + b" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "array([ 6, 8, 10, 12])" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "a - b" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "array([-4, -4, -4, -4])" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "a + 10" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "array([11, 12, 13, 14])" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "a.shape = 2, 2\n", "b.shape = 2, 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "a" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "array([[1, 2],\n", " [3, 4]])" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "b" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "array([[5, 6],\n", " [7, 8]])" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "a * b # Pointwise multiplication!!" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "array([[ 5, 12],\n", " [21, 32]])" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "np.dot(a, b) # Matrix multiplication" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "array([[19, 22],\n", " [43, 50]])" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in this is slated to change to ``a @ b`` in future versions of NumPy" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Comparisons" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.array([2, 3])\n", "y = np.array([2, 3])\n", "z == y" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "array([ True, True], dtype=bool)" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "y[0] = 3\n", "z == y" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "array([False, True], dtype=bool)" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(0, 10, 5)\n", "z" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "array([ 0. , 2.5, 5. , 7.5, 10. ])" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "z > 3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ "array([False, False, True, True, True], dtype=bool)" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "z[z > 3] # Conditional extraction" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "array([ 5. , 7.5, 10. ])" ] } ], "prompt_number": 52 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "SciPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's just cover some simple examples --- references for further reading are below" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Statistics and distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display figures in browser:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use ``scipy.stats`` to generate some data from the Beta distribution" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.stats import beta\n", "q = beta(5, 5) # Beta(a, b), with a = b = 5\n", "obs = q.rvs(2000) # 2000 observations" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's histogram it and compare it to the original density" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pylab import hist, plot, show\n", "hist(obs, bins=40, normed=True)\n", "grid = np.linspace(0.01, 0.99, 100)\n", "plot(grid, q.pdf(grid), 'k-', linewidth=2)\n", "show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IVbFXYCYBfwkzQmY+JqkH55B9HDNL1Uh73cPAdYkEkJn2A/9N9Zcg2hdFxA+C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\nRERERERERERERETc9P8BGX+oIdaRwooAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x2a40090>" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other methods" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(q)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "scipy.stats._distn_infrastructure.rv_frozen" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "dir(q) # Let's see all its methods" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "['__class__',\n", " '__delattr__',\n", " '__dict__',\n", " '__doc__',\n", " '__format__',\n", " '__getattribute__',\n", " '__hash__',\n", " '__init__',\n", " '__module__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", " '__weakref__',\n", " 'args',\n", " 'cdf',\n", " 'dist',\n", " 'entropy',\n", " 'interval',\n", " 'isf',\n", " 'kwds',\n", " 'logcdf',\n", " 'logpdf',\n", " 'logpmf',\n", " 'logsf',\n", " 'mean',\n", " 'median',\n", " 'moment',\n", " 'pdf',\n", " 'pmf',\n", " 'ppf',\n", " 'rvs',\n", " 'sf',\n", " 'stats',\n", " 'std',\n", " 'var']" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "q.cdf(0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "0.50000000000000011" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "q.pdf(0.5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "2.4609375000000009" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "q.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "0.5" ] } ], "prompt_number": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic linear regression:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.stats import linregress\n", "n = 200\n", "alpha, beta, sigma = 1, 2, 0.1\n", "x = np.random.randn(n) # n standard normals\n", "y = alpha + beta * x + sigma * np.random.randn(n)\n", "gradient, intercept, r_value, p_value, std_err = linregress(x, y)\n", "print \"gradient =\", gradient\n", "print \"intercept =\", intercept" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gradient = 2.01866616159\n", "intercept = 1.00087870427\n" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Roots and fixed points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's choose an arbitrary function to work with" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def f(x):\n", " return np.sin(4 * (x - 0.25)) + x + x*20 - 1\n", "x = np.linspace(0, 1, 100)\n", "plot(x, f(x))\n", "plot(x, 0 x)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "[<matplotlib.lines.Line2D at 0x21e9750>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ePdVHXyRTKPilxpYuhS5d4Fe/gj/9CWow57OI+EDnZ1Ij+flw1VUwbJjdoCUimUPBL0mb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\niIiIiIiIiIiIiIiIiIiISLD9P0XSCg3SmJILAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x21e9250>" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.optimize import bisect # Bisection algorithm --- slow but robust\n", "bisect(f, 0, 1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "0.4082935042797544" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.optimize import newton # Newton's method --- fast but less robust\n", "newton(f, 0.2) # Start the search at initial condition x = 0.2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "0.40829350427935679" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "newton(f, 0.7) # Start the search at x = 0.7 instead " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "0.70017000000002816" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see that the algorithm gets it wrong --- ``newton`` is fast but not robust\n", "\n", "Let's try a hybrid method" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.optimize import brentq\n", "brentq(f, 0, 1) # Hybrid method" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ "0.40829350427936706" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "timeit bisect(f, 0, 1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 160 \u00b5s per loop\n" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "timeit newton(f, 0.2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 38.9 \u00b5s per loop\n" ] } ], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "timeit brentq(f, 0, 1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 42.5 \u00b5s per loop\n" ] } ], "prompt_number": 68 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the hybrid method is robust but still quite fast..." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Numerical optimization and integration" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.optimize import fminbound\n", "fminbound(lambda x: x2, -1, 2) # Search in [-1, 2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "0.0" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.integrate import quad\n", "integral, error = quad(lambda x: x2, 0, 1)\n", "integral" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ "0.33333333333333337" ] } ], "prompt_number": 70 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "More information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* linear algebra: http://docs.scipy.org/doc/scipy/reference/linalg.html\n", "* numerical integration: http://docs.scipy.org/doc/scipy/reference/integrate.html\n", "* interpolation: http://docs.scipy.org/doc/scipy/reference/interpolate.html\n", "* optimization: http://docs.scipy.org/doc/scipy/reference/optimize.html\n", "* distributions and random number generation: http://docs.scipy.org/doc/scipy/reference/stats.html\n", "* signal processing: http://docs.scipy.org/doc/scipy/reference/signal.html\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
openp2pdesign/FabLab-Mapping--Python	List of FabLabs.ipynb	1	271878	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting a list of FabLabs, with details, from fablabs.io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Requisites:\n", "\n", "1. pip install requests\n", "2. pip install pycountry\n", "3. pip install incf.countryutils" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import requests\n", "from collections import OrderedDict\n", "import pycountry\n", "from incf.countryutils import transformations\n", "import json" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load FabLab list\n", "#url = \"https://api.fablabs.io/v0/labs.json\"\n", "#fablab_list = requests.get(url).json()\n", "json_data=open('labs.json')\n", "\n", "fablab_list = json.load(json_data)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print a beautified version of the FabLab list for debug\n", "# print json.dumps(fablab_list, sort_keys=True, indent=4)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 436 FabLabs.\n" ] } ], "source": [ "labs = {}\n", "print \"There are\",len(fablab_list[\"labs\"]),\"FabLabs.\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Name: G.Wiz -- The Science Museum Falhaber Fab Lab\n", "E-mail: None\n", "Links:\n", "http://www.gwiz.org\n", "Address:\n", "City: Sarasota\n", "County: Florida\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Stoughton High School\n", "E-mail: \n", "Links:\n", "https://sites.google.com/a/stoughton.k12.wi.us/fablab-stoughton/\n", "Address:\n", "600 Lincoln Ave\n", "\n", "City: Stoughton\n", "County: Wisconsin\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Marymount School Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://marymountnyc.org/97th-street-campus-the-fab-lab-and-more\n", "Address:\n", "116 East 97th street\n", "Room 403\n", "City: New York\n", "County: New York\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: DEUSTO FabLab\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Avda. Universidades, 24\n", "\n", "City: Bilbao\n", "County: Vizcaya\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Happylab Salzburg\n", "E-mail: [email protected]\n", "Links:\n", "http://www.happylab.at\n", "Address:\n", "Jakob-Haringer-Straße 8\n", "Techno-Z Salzburg, Techno 5\n", "City: Salzburg\n", "County: \n", "Country: Austria\n", "Continent: Europe\n", "\n", "Name: Blue Valley School District's Center for Advanced Professional Studies\n", "E-mail: None\n", "Links:\n", "http://www.bvcaps.org\n", "Address:\n", "City: Overland Park\n", "County: Kansas\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab Venezia\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabvenezia.org\n", "Address:\n", "Venezia\n", "via della libertà 12 - Edificio Porta dell'innovazione - PST Vega\n", "City: Marghera\n", "County: Venezia\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Mt. Elliott Makerspace\n", "E-mail: None\n", "Links:\n", "http://www.mtelliottmakerspace.com\n", "Address:\n", "City: Detroit\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Lawrence Technological University -- makeLab\n", "E-mail: None\n", "Links:\n", "http://www.ltu.edu/architecture_and_design\n", "Address:\n", "City: Southfield\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab ABQ\n", "E-mail: None\n", "Links:\n", "http://fablababq.com\n", "Address:\n", "City: Albuquerque\n", "County: New Mexico\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Mobile Fab Lab, Fab Labs Carolinas\n", "E-mail: None\n", "Links:\n", "http://www.fablabcarolinas.org\n", "Address:\n", "City: Durham\n", "County: North Carolina\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Leon\n", "E-mail: \n", "Links:\n", "http://fablableon.blogspot.com\n", "http://www.linkedin.com/company/fablab-le-n\n", "http://vimeo.com/user7819089\n", "https://www.facebook.com/FabLabLeon\n", "https://twitter.com/FabLabLeon\n", "http://www.fablableon.org\n", "Address:\n", "Polígono Industrial de Onzonilla Fase 2, Parcela M-24\n", "\n", "City: Ribaseca (León)\n", "County: Castile and León\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: MindGear Labs, LLC\n", "E-mail: \n", "Links:\n", "http://mindgearlabs.com\n", "Address:\n", "\n", "\n", "City: Huntsville\n", "County: Alabama\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: MC2STEM High School\n", "E-mail: \n", "Links:\n", "http://mc2stemhs.wordpress.com/\n", "http://mc2stemhs.com\n", "Address:\n", "\n", "\n", "City: East Cleveland\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Century Community and Technical College\n", "E-mail: None\n", "Links:\n", "http://www.century.edu/currentstudents/fablab/default.aspx\n", "Address:\n", "City: White Bear Lake\n", "County: Minnesota\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fox Valley Technical College\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: Appleton\n", "County: Wisconsin\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: University of Wisconsin-Stout\n", "E-mail: None\n", "Links:\n", "http://www.uwstout.edu/discoverycenter\n", "Address:\n", "City: Menomonie\n", "County: Wisconsin\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Universidade de São Paulo\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: São Paulo\n", "County: São Paulo\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: FabLab Nerve Centre\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/nervecentre.org\n", "https://twitter.com/nerve_centre\n", "http://www.youtube.com/user/thenervecentre\n", "http://www.linkedin.com/company/nerve-centre\n", "https://soundcloud.com/nervecentre\n", "http://www.nervecentre.org/projects/fab-lab#.UMJ0R7YWWbI\n", "Address:\n", "7-8 Magazine Street\n", "\n", "City: Derry\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: µLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.synergia3.com/\n", "https://www.facebook.com/fablabLaPlata\n", "Address:\n", "Calle 8 n° 977 1/2\n", "e/ 51 y 53\n", "City: La Plata\n", "County: Buenos Aires\n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: FabLab Breda\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabbreda.nl\n", "Address:\n", "Belcrumweg 19\n", "4815 HA\n", "City: Breda\n", "County: North Brabant\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FAB LAB BUENOS AIRES\n", "E-mail: [email protected] \n", "Links:\n", "http://www.reprapargentina.com/blog/\n", "https://twitter.com/FabLabBsAs\n", "https://www.facebook.com/FabLabBuenosAires\n", "Address:\n", "\n", "\n", "City: Buenos Aires\n", "County: Autonomous City of Buenos Aires\n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: Howard University Middle School of Mathematics and Science\n", "E-mail: None\n", "Links:\n", "http://www.howard.edu/ms2\n", "Address:\n", "City: Washington\n", "County: District of Columbia\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Lake Michigan College (Benton Harbor)\n", "E-mail: None\n", "Links:\n", "http://www.lakemichigancollege.edu\n", "Address:\n", "City: Benton Harbor\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Valencia\n", "E-mail: [email protected]\n", "Links:\n", "http://vimeo.com/user10405535\n", "https://twitter.com/fablabvalencia\n", "http://fablab.upv.es\n", "Address:\n", "cno. de vera s/n Edif. 8G bajo\n", "\n", "City: Valencia\n", "County: Valencian Community\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Hradec Kralove\n", "E-mail: \n", "Links:\n", "http://fleq.cz\n", "Address:\n", "\n", "\n", "City: Hradec Kralove\n", "County: Hradec Králové Region\n", "Country: Czech Republic\n", "Continent: Europe\n", "\n", "Name: EHove Career Center Fab Lab\n", "E-mail: \n", "Links:\n", "http://www.ehove.net/fablab\n", "Address:\n", "316 Mason Road West\n", "\n", "City: Milan\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Incite Focus FabLab\n", "E-mail: \n", "Links:\n", "http://www.incite-focus.org/Fab_Lab.html\n", "Address:\n", "\n", "\n", "City: Detroit\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Mott Community College Fab Lab\n", "E-mail: None\n", "Links:\n", "http://www.mcc.edu\n", "Address:\n", "City: Flint\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FFL Fab Lab\n", "E-mail: \n", "Links:\n", "http://www.fayettevillefreelibrary.org/about-us/services/fablab\n", "Address:\n", "\n", "\n", "City: Fayetteville\n", "County: New York\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: The Community College of Baltimore County\n", "E-mail: \n", "Links:\n", "http://www.fablabbaltimore.org\n", "Address:\n", "800 S. Rolling Road\n", "HTEC145\n", "City: Baltimore\n", "County: Maryland\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Neuchâtel\n", "E-mail: \n", "Links:\n", "http://www.pinterest.com/fablabneuch/\n", "https://twitter.com/fablabneuch\n", "https://www.facebook.com/fablabneuchatel\n", "http://fablab-neuch.ch\n", "Address:\n", "Place de la Gare 4\n", "\n", "City: Neuchatel\n", "County: Canton of Neuchâtel\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: Stanford Learning FabLab\n", "E-mail: None\n", "Links:\n", "http://www.blikstein.com/paulo/contact.html\n", "Address:\n", "City: Palo Alto\n", "County: California\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Lorain County Community College\n", "E-mail: None\n", "Links:\n", "http://www.lorainccc.edu/Academic+Divisions/Engineering+Technologies/Fab+Lab\n", "Address:\n", "City: Elyria\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: DenokInn Basque fab Lab\n", "E-mail: \n", "Links:\n", "http://www.denokinn.eu\n", "Address:\n", "\n", "\n", "City: Santurtzi ( Antes Bermeo)\n", "County: Basque Country\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Syskrack Lab\n", "E-mail: [email protected], [email protected]\n", "Links:\n", "http://www.syskrack.org\n", "Address:\n", "Via Meridionale, 23\n", "\n", "City: Grassano\n", "County: Basilicata\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Lake Michigan College\n", "E-mail: None\n", "Links:\n", "http://www.lakemichigancollege.edu/BX\n", "Address:\n", "City: Niles\n", "County: Michigan\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Metropolitan Community College Tech Center-FabLab\n", "E-mail: None\n", "Links:\n", "http://www.mcckc.edu/fablab\n", "Address:\n", "City: Kansas City\n", "County: Missouri\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Sustainable South Bronx\n", "E-mail: None\n", "Links:\n", "http://www.ssbx.org/index.php?link=35\n", "Address:\n", "City: Bronx\n", "County: New York\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Sinclair Community College Fab Lab\n", "E-mail: None\n", "Links:\n", "http://www.sinclair.edu\n", "Address:\n", "City: Dayton\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Solvik Gard\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab.no\n", "http://www.fablab.no/\n", "Address:\n", "\n", "\n", "City: Ørnes\n", "County: Lyngseidet\n", "Country: Norway\n", "Continent: Europe\n", "\n", "Name: Fab Lab Liverpool\n", "E-mail: \n", "Links:\n", "Address:\n", "John Lennon Art and Design Building\n", "Duckinfield St\n", "City: Liverpool\n", "County: Meseyside\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Cape Craft and Design Institute\n", "E-mail: \n", "Links:\n", "http://www.youtube.com/user/capecraftanddesign?feature=watch\n", "http://www.pinterest.com/capecraftdesign/\n", "https://www.facebook.com/pages/Cape-Craft-and-Design-Institute/152396544910856\n", "http://www.ccdi.org.za\n", "Address:\n", "\n", "\n", "City: Cape Town\n", "County: Western Cape\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: North West University\n", "E-mail: \n", "Links:\n", "http://www.nwu.ac.za/fe/fablab\n", "http://fablabpotchefstroom.yolasite.com\n", "Address:\n", "\n", "\n", "City: Potchefstroom\n", "County: North West\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: Central University of Technology\n", "E-mail: \n", "Links:\n", "Address:\n", "\n", "\n", "City: Bloemfontein\n", "County: Free State\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: FabLab Luzern\n", "E-mail: \n", "Links:\n", "http://luzern.fablab.ch\n", "Address:\n", "Technikumstrasse 21\n", "Trakt I\n", "City: Horw\n", "County: Lucerne\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: Fox Valley Technical College, site #2\n", "E-mail: None\n", "Links:\n", "http://www.fvtc.edu/public/content.aspx?ID=1873&PID=1\n", "Address:\n", "City: Oshkosh\n", "County: Wisconsin\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Liepaja\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: Liepāja\n", "County: Liepājas pilsēta\n", "Country: Latvia\n", "Continent: Europe\n", "\n", "Name: Fab Lab Tulsa\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabtulsa.com\n", "Address:\n", "710 S Lewis Ave\n", "\n", "City: Tulsa\n", "County: Oklahoma\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: STEM East\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: Kinston\n", "County: North Carolina\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Linden-McKinley STEM High School\n", "E-mail: None\n", "Links:\n", "http://www.columbus.k12.oh.us\n", "Address:\n", "City: Columbus\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Addis\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabaddis.wordpress.com\n", "http://www.fablabaddis.org\n", "Address:\n", "Addis Ababa University\n", "Piaza\n", "City: Addis Ababa\n", "County: Oromia\n", "Country: Ethiopia\n", "Continent: Africa\n", "\n", "Name: PiNG\n", "E-mail: None\n", "Links:\n", "http://fablab.pingbase.net\n", "Address:\n", "City: Nantes\n", "County: Pays de la Loire\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: The Technology Innovation and Entrepreneurship Project\n", "E-mail: None\n", "Links:\n", "http://www.thetieproject.org\n", "Address:\n", "City: Boston\n", "County: Massachusetts\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Afghanistan\n", "E-mail: None\n", "Links:\n", "http://www.fablab.af\n", "Address:\n", "Jalalabad, Afghanistan\n", "City: Jalalabad\n", "County: Nangarhar\n", "Country: Afghanistan\n", "Continent: Asia\n", "\n", "Name: Fab Lab Tenerife\n", "E-mail: \n", "Links:\n", "http://fablabtenerife.com\n", "https://www.facebook.com/FabLabTenerife\n", "Address:\n", "\n", "\n", "City: Santa Cruz de Tenerife\n", "County: Canary Islands\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fablab Kamakura\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabkamakura\n", "http://www.facebook.com/pages/FabLabKamakura/359621684057046\n", "http://www.fablabkamakura.com\n", "Address:\n", "Yui no Kura #1, Ougigayatsu\n", "Kamakura-city\n", "City: Kamakura\n", "County: Kanagawa Prefecture\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: Mediterranean Fab Lab\n", "E-mail: \n", "Links:\n", "http://www.pinterest.com/medfablabcava/\n", "https://www.facebook.com/medfablab.cava\n", "Address:\n", "Via Alcide de Gasperi, 23\n", "\n", "City: Cava de' Tirreni\n", "County: Campania\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab Goes\n", "E-mail: \n", "Links:\n", "http://fablabgoes.nl/\n", "Address:\n", "\n", "\n", "City: Goes\n", "County: Zeeland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: SIAT, SFU\n", "E-mail: \n", "Links:\n", "http://www.interactionart.org\n", "Address:\n", "\n", "\n", "City: Vancouver\n", "County: British Columbia\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: Fab Lab Windhoek\n", "E-mail: \n", "Links:\n", "https://www.facebook.com/fablab.namibia\n", "Address:\n", "\n", "\n", "City: Windhoek\n", "County: Khomas\n", "Country: Namibia\n", "Continent: Africa\n", "\n", "Name: FamiLAB\n", "E-mail: None\n", "Links:\n", "http://familab.org/blog/about-our-lab\n", "Address:\n", "City: Orlando\n", "County: Florida\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Happylab Vienna\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/happylab.at\n", "http://www.happylab.at/\n", "Address:\n", "Haussteinstraße 4, 1020\n", "\n", "City: Vienna\n", "County: \n", "Country: Austria\n", "Continent: Europe\n", "\n", "Name: Artilect FabLab Toulouse\n", "E-mail: [email protected]\n", "Links:\n", "http://www.artilect.fr\n", "http://vimeo.com/user4871340\n", "http://www.youtube.com/user/fabLabArtilect\n", "http://twitter.com/FabLab_Toulouse\n", "http://www.facebook.com/pages/Artilect-FabLab-Toulouse\n", "Address:\n", "27bis Allées Maurice Sarraut\n", "\n", "City: Toulouse\n", "County: Midi-Pyrénées\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Akranes, Innovation Center Iceland\n", "E-mail: None\n", "Links:\n", "http://fablabakranes.is\n", "Address:\n", "City: Akranes\n", "County: West\n", "Country: Iceland\n", "Continent: Europe\n", "\n", "Name: Netaji Subhas Institute of Technology\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: New Delhi\n", "County: Delhi\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: FabLab Enschede\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabenschede\n", "https://www.facebook.com/fablabenschede\n", "http://www.fablabenschede.nl\n", "Address:\n", "M.H. Tromplaan 28\n", "\n", "City: Enschede\n", "County: Overijssel\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FabLab Bergen op Zoom\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/FabLabBergenopZoom\n", "http://www.fablabbergenopzoom.nl\n", "Address:\n", "Nobellaan 25\n", "\n", "City: Bergen op Zoom\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fab Lab Vestmannaeyjar Iceland\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.fablab.is\n", "http://www.fablab.is\n", "http://www.nmi.is/impra/fab-lab\n", "Address:\n", "Faxastígur 36\n", "\n", "City: Vestmannaeyjabær\n", "County: \n", "Country: Iceland\n", "Continent: Europe\n", "\n", "Name: College of Engineering, Pune\n", "E-mail: None\n", "Links:\n", "http://www.coep.org.in\n", "Address:\n", "City: Pune\n", "County: Maharashtra\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: échoFab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.youtube.com/user/Communautique\n", "https://twitter.com/qcechofab\n", "https://www.facebook.com/echoFab\n", "http://www.echofab.org\n", "Address:\n", "355 Peel St\n", "Suite 111\n", "City: Montreal\n", "County: Quebec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: The Edge, State Library of Queensland\n", "E-mail: None\n", "Links:\n", "http://edgqld.org.au\n", "Address:\n", "City: Brisbane\n", "County: Queensland\n", "Country: Australia\n", "Continent: Oceania\n", "\n", "Name: Timelab\n", "E-mail: \n", "Links:\n", "http://www.timelab.org\n", "Address:\n", "\n", "\n", "City: Ghent\n", "County: Flanders\n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: FabLab Lille\n", "E-mail: \n", "Links:\n", "http://www.flickr.com/photos/fablablille/\n", "https://twitter.com/FabLab_Lille\n", "http://www.fablablille.fr\n", "Address:\n", "2 Allée Lakanal\n", "\n", "City: Villeneuve-d'Ascq\n", "County: Nord-Pas-de-Calais\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: fablab iMAL\n", "E-mail: \n", "Links:\n", "http://www.imal.org/fablab\n", "Address:\n", "30-34 Quai des Charbonnages Koolmijnenkaai\n", "\n", "City: Brussels\n", "County: \n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: Dingfabrik Koeln e.V.\n", "E-mail: \n", "Links:\n", "https://www.facebook.com/dingfabrik\n", "https://twitter.com/dingfabrik\n", "http://www.dingfabrik.de\n", "Address:\n", "\n", "\n", "City: Cologne\n", "County: North Rhine-Westphalia\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Instituto Tecnologico de Costa Rica\n", "E-mail: \n", "Links:\n", "Address:\n", "\n", "\n", "City: \n", "County: Cartago\n", "Country: Costa Rica\n", "Continent: North America\n", "\n", "Name: FabLab INSA Strasbourg\n", "E-mail: None\n", "Links:\n", "http://www.ideaslab.fr\n", "Address:\n", "City: Strasbourg\n", "County: Alsace\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: National Innovation Foundation\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: Ahmedabad\n", "County: Gujarat\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: Vigyan Ashram\n", "E-mail: None\n", "Links:\n", "http://www.vigyanashram.com\n", "Address:\n", "City: Pabal\n", "County: Maharashtra\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: FabLab Saudarkrokur, Innovation Center Iceland\n", "E-mail: \n", "Links:\n", "http://www.fablab.is/w/index.php/Main_Page/%C3%8Dslenska\n", "Address:\n", "\n", "\n", "City: Sauðárkrókur\n", "County: Northwest\n", "Country: Iceland\n", "Continent: Europe\n", "\n", "Name: Indian Institute of Technology\n", "E-mail: None\n", "Links:\n", "Address:\n", "City: Kanpur\n", "County: Uttar Pradesh\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: Funlab Zürich\n", "E-mail: [email protected]\n", "Links:\n", "http://funlab.ch\n", "Address:\n", "Am Holbrig 10\n", "\n", "City: Zurich\n", "County: Canton of Zurich\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: Fab Lab Unal Medellín\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabunalmed.blogspot.com\n", "http://www.facebook.com/FabLabUnalMedellin\n", "Address:\n", "Universidad Nacional de Colombia sede Medellin\n", "calle 59A No 63 - 20\n", "City: Medellin\n", "County: Antioquia\n", "Country: Colombia\n", "Continent: South America\n", "\n", "Name: University of Nairobi\n", "E-mail: None\n", "Links:\n", "http://fablab.uonbi.or.ke\n", "Address:\n", "City: Nairobi\n", "County: Nairobi\n", "Country: Kenya\n", "Continent: Africa\n", "\n", "Name: DèmosLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.demoslab.org\n", "Address:\n", "Frelighsburg (moving)\n", "\n", "City: Frelighsburg\n", "County: Quebec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: FabLab Kitakagaya\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabkitakagaya.org\n", "Address:\n", "5-4-12 Kitakagaya\n", "Suminoe-ku\n", "City: Osaka-shi\n", "County: \n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: labfab de Rennes\n", "E-mail: \n", "Links:\n", "http://labfab.fr\n", "Address:\n", "\n", "\n", "City: Rennes\n", "County: Brittany\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Groningen\n", "E-mail: \n", "Links:\n", "http://www.fablabgroningen.nl\n", "Address:\n", "\n", "\n", "City: Groningen\n", "County: Groningen\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fabulous St. Pauli\n", "E-mail: \n", "Links:\n", "http://www.fablab-hamburg.org\n", "Address:\n", "Sternstraße 2\n", "(im Centro Sociale Nord-Ost Ecke)\n", "City: Hamburg\n", "County: Hamburg\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab Truck\n", "E-mail: None\n", "Links:\n", "http://fablabtruck.nl\n", "Address:\n", "City: Weesp\n", "County: North Holland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FabLab Brainport\n", "E-mail: \n", "Links:\n", "http://www.flickr.com/photos/97007574@N04/with/10958203735/\n", "http://www.fablabbrainport.nl/\n", "http://www.brainportdevelopment.nl/project/fablab-brainport\n", "Address:\n", "\n", "\n", "City: Eindhoven\n", "County: North Brabant\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Protospace/FabLab Utrecht\n", "E-mail: \n", "Links:\n", "http://www.protospace.nl\n", "Address:\n", "\n", "\n", "City: Utrecht\n", "County: Utrecht\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Takoradi Technical Institute\n", "E-mail: \n", "Links:\n", "http://www.takoraditech.org/?q=node/34\n", "http://ttifab.wikispaces.com/How+to+Use+the+TTI+Fab+Lab+Wiki\n", "Address:\n", "\n", "\n", "City: Takoradi\n", "County: Western\n", "Country: Ghana\n", "Continent: Africa\n", "\n", "Name: Fab Lab Ellesmere Port\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabEllesmerePort\n", "https://twitter.com/FabLabEllesmere\n", "http://www.fab-lab-ellesmereport.org/\n", "Address:\n", "53 Whitby Road\n", "\n", "City: Ellesmere Port\n", "County: Cheshire\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab SUPSI Lugano\n", "E-mail: [email protected]\n", "Links:\n", "http://twitter.com/fablablugano\n", "http://www.fablab.supsi.ch\n", "Address:\n", "Campus Trevano Canobbio\n", "Laboratory of visual culture\n", "City: Canobbio\n", "County: Tessin\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: MiniFabLab Utrecht\n", "E-mail: [email protected]\n", "Links:\n", "http://www.minifablab.nl\n", "Address:\n", "Nobeldwarsstraat 33\n", "\n", "City: Utrecht\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Gateway Technical College - Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.usfln.org\n", "https://www.gtc.edu/wedd/industrial-design-fab-lab\n", "Address:\n", "2320 Renaissance Blvd.\n", "\n", "City: Sturtevant\n", "County: WI\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Underes Ätzisloo\n", "E-mail: [email protected]\n", "Links:\n", "http://www.randelab.ch/\n", "Address:\n", "Switzerland\n", "Kirchgasse\n", "City: Merishausen\n", "County: \n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: Fablab Taipei\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/fablabtaipei/\n", "http://facebook.com/FablabTPE\n", "http://fablabtaipei.org\n", "Address:\n", "1F 9 ln 15 Sec 3 Chongqing S Rd\n", "\n", "City: Taipei City\n", "County: Taiwan\n", "Country: Taiwan, Province of China\n", "Continent: Asia\n", "\n", "Name: Fab Lab Brasil\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabbrasil.org\n", "Address:\n", "Praça Gen. Craveiro Lopes, 19 Sobreloja 1\n", "\n", "City: São Paulo\n", "County: São Paulo\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Fab Lab Reggio Emilia\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabreggioemilia.org\n", "Address:\n", "Reggio Emilia\n", "City: Reggio Emilia\n", "County: Emilia-Romagna\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: icecairo\n", "E-mail: [email protected]\n", "Links:\n", "http://www.icecairo.com/fab-lab.php\n", "http://www.icecairo.com\n", "Address:\n", "32 Sabri Abo Alam Street\n", "1st floor, apartment 8, Downtown\n", "City: Cairo\n", "County: \n", "Country: Egypt\n", "Continent: Africa\n", "\n", "Name: Les Fabriques du Ponant / TyFab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.lesfabriquesduponant.net\n", "http://twitter.com/fabduponant\n", "http://www.tyfab.fr\n", "http://wiki.lesfabriquesduponant.net\n", "Address:\n", "40 Rue Jules Lesven\n", "Bâtiment X - Lycée Vauban\n", "City: Brest\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: DAD-workshop\n", "E-mail: \n", "Links:\n", "http://dad-workshop.com\n", "https://www.facebook.com/dadworkshop?fref=ts\n", "Address:\n", "\n", "\n", "City: Lubartów County\n", "County: Lublin Voivodeship\n", "Country: Poland\n", "Continent: Europe\n", "\n", "Name: CabFabLab\n", "E-mail: \n", "Links:\n", "http://cabfablab.nl\n", "http://vimeo.com/cabfablab\n", "Address:\n", "\n", "\n", "City: The Hague\n", "County: South Holland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fablab EDP\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabedp\n", "https://twitter.com/Fablabedp\n", "http://www.fablabedp.edp.pt\n", "Address:\n", "Rua Cidade de Goa,4\n", "\n", "City: Sacavém\n", "County: Lisbon\n", "Country: Portugal\n", "Continent: Europe\n", "\n", "Name: FabLab Milano - Frankenstein Garage\n", "E-mail: \n", "Links:\n", "https://twitter.com/FablabMilano\n", "https://www.facebook.com/fablabmilano.page\n", "http://www.frankensteingarage.it\n", "http://www.fablabmilano.it\n", "Address:\n", "\n", "\n", "City: Milan\n", "County: Lombardy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Bright Youth Council\n", "E-mail: \n", "Links:\n", "http://blogs.fabfolk.com/sosh\n", "Address:\n", "\n", "\n", "City: Soshanguve\n", "County: Gauteng\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: Limpopo Fablab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab.co.za\n", "Address:\n", "1025 Science Education Centre, University of Limpopo(Turfloop campus)\n", "Private Bag X1106, Sovenga, 0727\n", "City: Polokwane, Mankweng \n", "County: Limpopo\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: Fab Lab Manchester\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabmcr\n", "https://www.facebook.com/FabLabMcr\n", "http://www.fablabmanchester.org/\n", "Address:\n", "Chips\n", "2 Lampwick Lane\n", "City: Manchester\n", "County: Greater Manchester\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab Shibuya\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabshibuya\n", "http://fablabshibuya.org\n", "Address:\n", "\n", "Co-lab Shibuya Atelier 1-3\n", "City: Shibuya\n", "County: Tokyo\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: Urban FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.urbanfablab.it\n", "Address:\n", "Via Coroglio, 104 e 57 \n", "\n", "City: Napoli \n", "County: ITALY\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Kimberly FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab.co.za/index.php?option=com_content&view=article&id=5&Itemid=29\n", "Address:\n", "1317 Solomon Mekgwe Street\n", "Old Kitsong Training Centre\n", "City: Kimberley\n", "County: Northern Cape\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: FabLab Region Rothenburg o.d.T. e.V.\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-rothenburg.de\n", "https://de-de.facebook.com/FabLabRothenburg\n", "Address:\n", "Deutschherrngasse 1\n", "über dem Jugendzentrum\n", "City: Rothenburg ob der Tauber\n", "County: Bavaria\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fablab We Do\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/wedofablab\n", "http://www.wedofablab.com\n", "Address:\n", "Via Alfieri Vittorio 7\n", "\n", "City: \n", "County: Borgomanero (NO)\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab CHAMPAGNOLE\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabChampagnole\n", "http://www.netvibes.com/fablabchampagnole \n", "Address:\n", "Lycée Paul Emile Victor de CHAMPAGNOLE 625 Rue de Gottmadingen\n", "8 rue Marandet Le Pasquier 39300\n", "City: Champagnole\n", "County: Jura/Franche-Comté/FRANCE\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Stadslab Rotterdam\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/254279061267294/\n", "http://www.stadslabrotterdam.nl\n", "Address:\n", "Wijnhaven 99, 3011 WN\n", "\n", "City: Rotterdam\n", "County: South Holland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: MAKE aberdeen\n", "E-mail: [email protected]\n", "Links:\n", "http://www.make-aberdeen.com\n", "Address:\n", "17 Belmont Street\n", "\n", "City: Aberdeen\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fab Lab Airedale\n", "E-mail: \n", "Links:\n", "http://www.linkedin.com/company/2570096?trk=tyah\n", "https://twitter.com/FabLabAiredale\n", "https://www.facebook.com/FabLabAiredale\n", "http://www.fablabairedale.org\n", "Address:\n", "\n", "\n", "City: Keighley\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab Firenze\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabfirenze.org\n", "Address:\n", "Via Panciatichi, 14\n", "\n", "City: Firenze\n", "County: Toscana\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Starship Factory\n", "E-mail: [email protected]\n", "Links:\n", "https://secure.flickr.com/groups/2341518@N21/\n", "https://www.facebook.com/starshipfactory\n", "https://twitter.com/StarshipFactory\n", "https://plus.google.com/+Starship-factoryOrg/\n", "http://wiki.starship-factory.ch/\n", "http://www.starship-factory.ch/\n", "Address:\n", "Sankt Alban-Rheinweg 62\n", "\n", "City: Basel\n", "County: \n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: FabLab Aachen - RWTH Aachen\n", "E-mail: [email protected]\n", "Links:\n", "https://plus.google.com/photos/112431217600462712880/albums/5885215609266891137\n", "http://hci.rwth-aachen.de/fablab\n", "Address:\n", "Ahornstr. 55\n", "Room 2214 (2nd floor)\n", "City: Aachen\n", "County: North Rhine-Westphalia\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: RuralLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.rurallab.org\n", "Address:\n", "Rue de l'École\n", "\n", "City: Néons-sur-Creuse\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: AV-Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.av-exciters.com/AV-Lab\n", "Address:\n", "37 rue des frères\n", "\n", "City: Strasbourg\n", "County: Alsace\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: La Machinerie\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/Machinerie\n", "https://www.facebook.com/LaMachinerieAmiens\n", "http://lamachinerie.org\n", "Address:\n", "70 rue des Jacobins\n", "\n", "City: Amiens\n", "County: Somme\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Arabia\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/pages/Fab-Lab-Arabia/162135563892237\n", "http://fablabarabia.com\n", "Address:\n", "Dallah Tower\n", "Palestine St.\n", "City: Jeddah\n", "County: Makkah Province\n", "Country: Saudi Arabia\n", "Continent: Asia\n", "\n", "Name: TechLab LR\n", "E-mail: [email protected]\n", "Links:\n", "http://techlablr.fr\n", "Address:\n", "49 rue Super Nova\n", "\n", "City: Vailhauquès\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Santiago\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsantiago.org\n", "Address:\n", "Condell 1097 L2\n", "Providencia\n", "City: Santiago\n", "County: Santiago Metropolitan Region\n", "Country: Chile\n", "Continent: South America\n", "\n", "Name: FabLab Arnhem\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabArnhem\n", "https://www.facebook.com/FabLabArnhem\n", "http://www.fablabarnhem.nl\n", "Address:\n", "Ruitenberglaan 26\n", "\n", "City: Arnhem\n", "County: Gelderland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Wanger family FabLab MadaTech\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/192976580730340/\n", "http://www.pinterest.com/fablabmadatech\n", "Address:\n", "Balfour 12\n", "MadaTech Museum, Education Building\n", "City: Haifa\n", "County: \n", "Country: Israel\n", "Continent: Asia\n", "\n", "Name: FabLab Genk\n", "E-mail: [email protected]\n", "Links:\n", "http://www.twitter.com/fablabgenk\n", "http://www.facebook.com/FablabGenk\n", "http://www.fablabgenk.be\n", "Address:\n", "Houtparklaan 1\n", "\n", "City: Genk\n", "County: Flanders\n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: Fablab Torino\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabtorino\n", "https://www.facebook.com/fablabtorino?fref=ts\n", "http://www.fablabtorino.org\n", "Address:\n", "Via Egeo 16\n", "\n", "City: Torino\n", "County: Piedmont\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab Jerusalem\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Jerusalem\n", "\n", "City: Jerusalem\n", "County: \n", "Country: Israel\n", "Continent: Asia\n", "\n", "Name: la refabrique\n", "E-mail: [email protected]\n", "Links:\n", "http://www.la-refabrique.fr\n", "Address:\n", "14 rue Jean Giono\n", "les olivarelles 2 - Villa 8\n", "City: Cannes\n", "County: Provence-Alpes-Cote d'Azur\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab DC\n", "E-mail: \n", "Links:\n", "https://twitter.com/FabLabDC\n", "https://www.facebook.com/pages/FAB-LAB-DC/320444572757\n", "http://www.awesomefoundation.org/en/projects/2258-fab-lab-dc\n", "http://fablabdc.blogspot.com\n", "http://www.fablabdc.org\n", "Address:\n", "\n", "\n", "City: Washington\n", "County: District of Columbia\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: BUDA::lab\n", "E-mail: \n", "Links:\n", "http://www.budalab.be\n", "Address:\n", "Dam 2a\n", "\n", "City: Kortrijk\n", "County: \n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: FabLab Bern\n", "E-mail: \n", "Links:\n", "https://foursquare.com/v/fablab-bern/519b5eda498e1dd2ad2d3c49\n", "https://twitter.com/FabLab_Bern\n", "https://www.facebook.com/fablab.bern\n", "http://www.fablab-bern.ch\n", "Address:\n", "Eigerstrasse 12\n", "\n", "City: Berne\n", "County: Canton of Bern\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: FabLab Contea\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabcontea.blogspot.com\n", "Address:\n", "Località Contea, 108\n", "\n", "City: Contea\n", "County: Firenze\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: LimouziLab\n", "E-mail: [email protected]\n", "Links:\n", "http://twitter.com/limouzilab\n", "http://www.fb.me/limouzilab\n", "http://lab.limouzi.org\n", "Address:\n", "2 bis impasse daguerre\n", "\n", "City: Limoges\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Mexico\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabmex\n", "http://www.fablab.mx\n", "Address:\n", "Universidad Anahuac México Norte\n", "Av. Universidad Anáhuac # 46, Lomas Anáhuac, Huixquilucan, Estado de México\n", "City: Mexico City\n", "County: Edomex\n", "Country: Mexico\n", "Continent: North America\n", "\n", "Name: Fab Ed Carolina\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "301D Advanced Technology Center\n", "Charlottetowne Ave.\n", "City: Charlotte\n", "County: NC\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Tecnoparque Bogota\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Calle 54 # 10 39\n", "\n", "City: Bogotá\n", "County: Bogota\n", "Country: Colombia\n", "Continent: South America\n", "\n", "Name: IZOLAB\n", "E-mail: [email protected]\n", "Links:\n", "http://facebook.com/fablab.ua\n", "http://izolab.org\n", "Address:\n", "Donetsk\n", "Svitlogo shlyahu str. 3\n", "City: Donetsk\n", "County: \n", "Country: Ukraine\n", "Continent: Europe\n", "\n", "Name: Le Petit FabLab de Paris\n", "E-mail: [email protected]\n", "Links:\n", "http://lepetitfablabdeparis.fr\n", "Address:\n", "156 Rue Oberkampf\n", "2ème atelier sur la droite \n", "City: Paris\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: machbar potsdam\n", "E-mail: [email protected]\n", "Links:\n", "http://www.wissenschaftsladen-potsdam.de\n", "http://www.machbar-potsdam.de\n", "Address:\n", "Potsdam\n", "Friedrich-Engels-Strasse 22\n", "City: Potsdam\n", "County: Brandenburg\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab Madrid Medialab-Prado\n", "E-mail: [email protected]\n", "Links:\n", "http://medialab-prado.es/\n", "Address:\n", "Calle Alameda, 15\n", "PLAZA DE LAS LETRAS\n", "City: Madrid\n", "County: Madrid\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fab Lab Athens\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabathens\n", "https://www.facebook.com/pages/Fab-Lab-Athens/344269965659213?ref=hl\n", "http://www.fablabnetwork.gr\n", "http://www.fablabathens.gr\n", "Address:\n", "\n", "\n", "City: Athens\n", "County: Attica\n", "Country: Greece\n", "Continent: Europe\n", "\n", "Name: Fab Lab San Diego\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsd.org\n", "Address:\n", "4685 Convoy St\n", "#200\n", "City: San Diego\n", "County: California\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Isafjordur\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.fablab.is/wiki/Fab_Lab_Portal\n", "Address:\n", "Torfnes\n", "\n", "City: Ísafjörður\n", "County: \n", "Country: Iceland\n", "Continent: Europe\n", "\n", "Name: Green Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://vimeo.com/valldauralabs\n", "http://www.youtube.com/valldauralabs\n", "https://twitter.com/valldauralabs\n", "https://www.facebook.com/valldauralabs\n", "http://www.flickr.com/photos/valldauralabs\n", "http://www.valldaura.net/\n", "Address:\n", "Cerdanyola\n", "Valldaura\n", "City: Barcelona\n", "County: Catalonia\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fablab Amsterdam\n", "E-mail: [email protected]\n", "Links:\n", "http://fabacademy.org\n", "http://facebook.com/fablab.amsterdam\n", "http://twitter.com/waag\n", "http://fablab.waag.org\n", "Address:\n", "Nieuwmarkt 4\n", "\n", "City: Amsterdam\n", "County: North Holland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: 8 FabLab Drôme\n", "E-mail: [email protected]\n", "Links:\n", "http://www.8fablab.fr\n", "Address:\n", "8 rue courre-commère\n", "\n", "City: Crest\n", "County: Drôme\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Universidad de Chile\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab.uchile.cl\n", "Address:\n", "Av. Beauchef / Av. Rondizzoni\n", "\n", "City: Santiago\n", "County: Santiago Metropolitan Region\n", "Country: Chile\n", "Continent: South America\n", "\n", "Name: Fablab TI\n", "E-mail: [email protected]\n", "Links:\n", "http://instagram.com/opfindnu\n", "https://www.facebook.com/fablabti\n", "Address:\n", "København\n", "Gregersensvej 1\n", "City: København\n", "County: Danmark\n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: Defaral Sa Labo\n", "E-mail: [email protected]\n", "Links:\n", "http://www.ker-thiossane.org/spip.php?article137\n", "http://wiki.fablab.is/wiki/DefaralSaLabo_Dakar\n", "Address:\n", "S.I.C.A.P. Liberte 2\n", "\n", "City: Dakar\n", "County: \n", "Country: Senegal\n", "Continent: Africa\n", "\n", "Name: Latvijas Universitātes FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.flickr.com/photos/103422353@N02/\n", "https://www.facebook.com/biznesa.inkubators?ref=hl\n", "http://www.biznesainkubators.lu.lv/fablab/kas-ir-latvijas-universitates-fablab/\n", "Address:\n", "Aspazijas bulvāris 5\n", "\n", "City: Riga\n", "County: \n", "Country: Latvia\n", "Continent: Europe\n", "\n", "Name: Fablab Lannion - KerNEL\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-lannion.org\n", "Address:\n", "14 Rue de Beauchamp\n", "\n", "City: Lannion\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Zürich\n", "E-mail: [email protected]\n", "Links:\n", "http://www.flickr.com/photos/fablabzurich/ \n", "https://www.facebook.com/fablabzurich\n", "http://zurich.fablab.ch\n", "Address:\n", "Zimmerlistrasse 6\n", "\n", "City: Zurich\n", "County: Canton of Zurich\n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: Fablab013 XL\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab013.nl\n", "Address:\n", "Galjoenstraat 37\n", "\n", "City: Tilburg\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FabLab Treviso\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Via Principale, 39\n", "\n", "City: Casier - Treviso\n", "County: TV\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Perú\n", "E-mail: \n", "Links:\n", "http://www.fab.pe\n", "Address:\n", "Calle Manuel Fuentes\n", "\n", "City: San Isidro\n", "County: Lima\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: FabLab Net-IKi\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-net-iki.org \n", "Address:\n", "3 Rue de l'Église\n", "\n", "City: Biarne\n", "County: Franche-Comté\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab MDP\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabmdp.org\n", "https://www.facebook.com/FabLabMardelPlata\n", "Address:\n", "Belgrano 3568\n", "\n", "City: Mar del Plata\n", "County: Buenos Aires\n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: Fablab Tainan\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/fablabtainan\n", "Address:\n", "No.21, Nanmen Rd., West Central Dist.,\n", "\n", "City: Tainan City\n", "County: Taiwan\n", "Country: Taiwan, Province of China\n", "Continent: Asia\n", "\n", "Name: 3dlab-fabcafe\n", "E-mail: [email protected]\n", "Links:\n", "http://www.3dlab-fabcafe.com\n", "Address:\n", "Costa Rica 5198\n", "\n", "City: Buenos Aires\n", "County: \n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: Fablab Fribourg Freiburg\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FablabFR\n", "https://www.facebook.com/FabLabFribourg\n", "http://www.fablab-fribourg.ch\n", "Address:\n", "Passage du Cardinal 1\n", "\n", "City: Fribourg\n", "County: \n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: FabLab Robert-Houdin\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-robert-houdin.org/\n", "Address:\n", "39D Allée des Pins\n", "\n", "City: Blois\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Tecsup i+De\n", "E-mail: [email protected]\n", "Links:\n", "http://www.tecsup.edu.pe/i+de/index.php\n", "Address:\n", "Cascanueces 2221\n", "Santa Anita\n", "City: Lima\n", "County: Perú\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: FunLab Tours\n", "E-mail: [email protected]\n", "Links:\n", "http://funlab.fr\n", "Address:\n", "30, Rue André Theuriet\n", "\n", "City: Tours\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Makerspace 56\n", "E-mail: \n", "Links:\n", "http://www.makerspace56.org\n", "Address:\n", "Place Albert Einstein\n", "\n", "City: Vannes\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab BCN\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.fikket.com/\n", "http://vimeo.com/fablabbcn/videos\n", "https://github.com/fablabbcn\n", "http://twitter.com/fablabbcn\n", "https://picasaweb.google.com/fablabbcnphotos\n", "https://www.facebook.com/FabLab.BCN\n", "http://iaac.net\n", "http://fablabbcn.org\n", "Address:\n", "Carrer de Pujades, 102\n", "\n", "City: Barcelona\n", "County: Catalonia\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Garagem Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/garagemfablab\n", "http://www.garagemfablab.com\n", "Address:\n", "Praça General Craveiro Lopes, 19 - sobreloja 01\n", "\n", "City: São Paulo\n", "County: SP\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: FabLab VdA\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabvda\n", "Address:\n", "Via Garibaldi 7\n", "\n", "City: Aosta\n", "County: AO\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Provence\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-provence.com/\n", "Address:\n", "Aix-en-Provence\n", "\n", "City: Aix-en-Provence\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Budapest\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabbudapest.com\n", "Address:\n", "Eötvös St 29-27.\n", "\n", "City: Budapest\n", "County: \n", "Country: Hungary\n", "Continent: Europe\n", "\n", "Name: Photonic FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.institutoptique.fr/\n", "http://bit.ly/ProtoListes\n", "Address:\n", "Bâtiment 503\n", "Rue du Belvédère\n", "City: Orsay\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabCafe Taipei\n", "E-mail: [email protected]\n", "Links:\n", "http://taipei.fabcafe.com/\n", "Address:\n", "No. 1, Bade Road Sec. 1, Zhong Zhen District, \n", "\n", "City: Taipei\n", "County: \n", "Country: Taiwan, Province of China\n", "Continent: Asia\n", "\n", "Name: WeCreate Workspace\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "North Tipperary Enterprise Park\n", "\n", "City: Cloughjordan\n", "County: Co Tipperary\n", "Country: Ireland\n", "Continent: Europe\n", "\n", "Name: technistub\n", "E-mail: \n", "Links:\n", "http://www.technistub.fr\n", "Address:\n", "130 Rue de la Mer Rouge\n", "\n", "City: Mulhouse\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Kannai\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabKannai\n", "http://fablab-kannai.org/\n", "Address:\n", "中区相生町３丁目６１泰生ビル２F\n", "\n", "City: 横浜市\n", "County: 神奈川県\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: Fablab Varese\n", "E-mail: \n", "Links:\n", "https://plus.google.com/+FablabvareseIt\n", "https://www.facebook.com/FablabVarese\n", "http://www.fablabvarese.it\n", "Address:\n", "Via Varese\n", "\n", "City: Varese\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab MET\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabmet\n", "http://www.fab.pe\n", "Address:\n", "Metropolitan Museum of Lima\n", "\n", "City: Lima\n", "County: \n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: Fab Lab Polytech\n", "E-mail: [email protected]\n", "Links:\n", "http://instagram.com/fablabpolytech\n", "http://twitter.com/fablabpolytech\n", "http://fb.com/fablabpolytech\n", "http://vk.com/fablabpolytech\n", "http://fablab1.org\n", "http://fablab.spbstu.ru\n", "Address:\n", "Politekhnicheskaya st., b.29-12\n", "\n", "City: St. Petersburg\n", "County: St. Petersburg\n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: Fabriques Alternatives\n", "E-mail: [email protected]\n", "Links:\n", "https://plus.google.com/114201372084258307863\n", "https://www.facebook.com/pages/Fabriques-Alternatives/712873455394908\n", "http://www.fabriques-alternatives.org\n", "Address:\n", "Mont-de-Marsan\n", "\n", "City: Mont-de-Marsan\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab UNI\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabUni\n", "http://www.fablabuni.edu.pe/\n", "Address:\n", "Av. Túpac Amarú 220 Rímac \n", "\n", "City: Lima\n", "County: Lima\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: Aalto Fab Lab\n", "E-mail: \n", "Links:\n", "http://mediafactory.aalto.fi/fablab \n", "http://www.flickr.com/photos/70184309@N07/10960603285\n", "http://www.flickr.com/people/aaltomediafactory\n", "https://twitter.com/AaltoMedia\n", "http://vimeo.com/aaltomediafactory\n", "https://www.facebook.com/aaltomediafactory\n", "Address:\n", "Hämeentie 135\n", "\n", "City: Helsinki\n", "County: Suomi\n", "Country: Finland\n", "Continent: Europe\n", "\n", "Name: Fab Lab Siegen\n", "E-mail: [email protected]\n", "Links:\n", "http://twitter.com/fablabsiegen\n", "http://facebook.com/fablabsiegen\n", "http://fablab-siegen.de\n", "Address:\n", "Siegen\n", "\n", "City: Siegen\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab.re\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.re\n", "Address:\n", "La Rivière Saint-Louis\n", "\n", "City: La Riviere\n", "County: \n", "Country: Réunion\n", "Continent: Africa\n", "\n", "Name: Fab Lab Moscow\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablab77\n", "http://www.fablab77.ru\n", "Address:\n", "Крымский вал, 3\n", "\n", "City: Moscow\n", "County: Moscow\n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: Laboratoire d'Aix-périmentation et de bidouille\n", "E-mail: [email protected]\n", "Links:\n", "http://www.labaixbidouille.com\n", "Address:\n", "Département Informatique de l'IUT d'Aix-en-Provence\n", "413 Avenue Gaston Berger\n", "City: Aix-en-Provence\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLabGenova\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablab.genova?fref=ts\n", "http://fablabgenova.it\n", "Address:\n", "c/o LSOA Buridda, via vertani 1, Genova\n", "\n", "City: Genoa\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: TalentLab Padova\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/talentlabcivitasvitae\n", "http://www.talent-lab.it\n", "Address:\n", "Via Monselice, 15\n", "\n", "City: Padova\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FryskLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.frysklab.nl\n", "Address:\n", "Zuiderkruisweg 4\n", "\n", "City: Leeuwarden\n", "County: Friesland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FabLab Napoli\n", "E-mail: [email protected]\n", "Links:\n", "http://bitsforfun.com\n", "http://twitter.com/fablabnapoli\n", "http://facebook.com/fablabnapoli\n", "http://fablabnapoli.it\n", "Address:\n", "Corso Campano, 134\n", "\n", "City: Giugliano In Campania\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Central\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fabfoundation.org\n", "http://fab.cba.mit.edu\n", "Address:\n", "20 Ames St, E15-401\n", "\n", "City: Cambridge\n", "County: MA\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab at Patrick Henry Community College\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/pages/Fab-Lab/199543143543441\n", "Address:\n", "54 W Church St\n", "\n", "City: Martinsville\n", "County: Virginia, Martinsville\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: fablab013\n", "E-mail: [email protected]\n", "Links:\n", "http://facebook.nl/fablab013\n", "http://fablab013.nl\n", "Address:\n", "Stadhuisplein 354\n", "\n", "City: Tilburg\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FabLab Southern Federal\n", "E-mail: [email protected]\n", "Links:\n", "https://github.com/orgs/FabLab61\n", "https://vk.com/fablabrnd\n", "http://fablab61.ru/\n", "Address:\n", "Ростов-на-Дону\n", "ул. Зорге, 5 а.044\n", "City: Ростов-на-Дону\n", "County: Ростовская область\n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: FABLabs For America Inc.\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsforamerica.org\n", "Address:\n", "70 Esmond St\n", "\n", "City: Boston\n", "County: MA\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Social FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.socialfablab.it\n", "Address:\n", "Via Pò, 2\n", "Via Misericordia, 3\n", "City: Orvieto\n", "County: TR\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Mini FabLab MakerHousehold\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Amelinkhorst 4\n", "\n", "City: Enschede\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: CARBON Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/pages/--Carbon-Ideas-Fab-Lab/120457534687417\n", "http://www.carbonstudio.ir/wordpress/\n", "http://digitalfabrication.ir/\n", "Address:\n", "#112, Bisotoun St, Yousefabad\n", "\n", "City: Tehran\n", "County: \n", "Country: Iran, Islamic Republic of\n", "Continent: Asia\n", "\n", "Name: Open Edge\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.fablab.is/wiki/OpenEdge\n", "http://openedge.cc\n", "Address:\n", "6 Avenue Foch\n", "\n", "City: Folschviller\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: District3\n", "E-mail: \n", "Links:\n", "Address:\n", "\n", "\n", "City: Montreal\n", "County: Quebec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: Makespace Madrid\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.makespacemadrid.org\n", "http://www.meetup.com/Makespace-Madrid/\n", "https://www.facebook.com/pages/Makespace-Madrid/477334925648477\n", "http://www.flickr.com/photos/makespacemadrid\n", "https://twitter.com/@MakeSpaceMadrid\n", "http://makespacemadrid.org\n", "Address:\n", "Pedro Unanue 16\n", "\n", "City: Madrid\n", "County: Madrid\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fablab Web-5\n", "E-mail: mailto:[email protected]\n", "Links:\n", "http://fablab.web-5.org\n", "Address:\n", "3, place du 14 juillet\n", " IUT de Béziers\n", "City: Béziers\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Nouvelle Fabrique\n", "E-mail: [email protected]\n", "Links:\n", "http://www.nouvellefabrique.fr\n", "Address:\n", "104 Rue d'Aubervilliers\n", "\n", "City: Paris\n", "County: France\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: ACoLab\n", "E-mail: [email protected]\n", "Links:\n", "http://acolab.fr\n", "Address:\n", "32 Rue du Pont Naturel\n", "\n", "City: Clermont-Ferrand\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Belfast, Ashton Centre\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabni.com \n", "Address:\n", "Ashton Community Trust \n", "5 Churchill Street \n", "City: Belfast\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab Gezhi\n", "E-mail: \n", "Links:\n", "Address:\n", "Gezhi High School\n", "66 Guangxi North Road\n", "City: Shanghai\n", "County: China\n", "Country: China\n", "Continent: Asia\n", "\n", "Name: The Glass Fablab\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/TheGlassFablab\n", "https://twitter.com/CerfavFablab\n", "http://www.cerfav.fr/fablab/\n", "Address:\n", "Rue de la Liberté\n", "\n", "City: Vannes-le-Châtel\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: WOELAB\n", "E-mail: [email protected]\n", "Links:\n", "http://www.lafricainedarchitecture.com/hubciteacute.html\n", "http://www.lafricainedarchitecture.com/\n", "https://twitter.com/woelab\n", "https://www.facebook.com/pages/W%C9%94%C9%9BLab-Lom%C3%A9/183098345158986?ref=ts&fref=ts\n", "http://www.woelabo.com/\n", "Address:\n", "Lomé\n", "WOELAB-Lomé Villa 227('Djidjolé en allant vers le marché), 110 Aflao-Gakli, Lome, Togo\n", "City: Lomé\n", "County: \n", "Country: Togo\n", "Continent: Africa\n", "\n", "Name: TIS FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://tis.bz.it/fablab\n", "Address:\n", "TIS innovation park\n", "Siemensstraße 19\n", "City: Bozen\n", "County: BZ\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Spitfire Fab Lab (Eastleigh, UK)\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsuk.co.uk/spitfirefablab/\n", "Address:\n", "157 Leigh Rd\n", "\n", "City: Eastleigh\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fab Lab Terni\n", "E-mail: [email protected]\n", "Links:\n", "http://www.greentales.it\n", "http://www.fablabterni.org\n", "Address:\n", "via luigi casale\n", "\n", "City: Terni\n", "County: Italia\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: El Reactor\n", "E-mail: [email protected]\n", "Links:\n", "http://www.reprapargentina.com\n", "http://www.elreactor.com\n", "Address:\n", "Buenos Aires\n", "Pacheco de Melo 2888\n", "City: Buenos Aires\n", "County: Buenos Aires\n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: esiaulab\n", "E-mail: [email protected]\n", "Links:\n", "http://atelier.rfi.fr/profiles/status/show?id=1189413%3AStatus%3A409652\n", "Address:\n", "[email protected]\n", "[email protected]\n", "City: Bamako\n", "County: Mali\n", "Country: Mali\n", "Continent: Africa\n", "\n", "Name: Fablab Reykjavik\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.is/reykjavik\n", "Address:\n", "Eddufell 2\n", "\n", "City: Reykjavik\n", "County: \n", "Country: Iceland\n", "Continent: Europe\n", "\n", "Name: Fab Lab Lima\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fab.pe\n", "http://www.slideshare.net/fablablima\n", "https://www.facebook.com/FabLabLima\n", "https://twitter.com/fablablima\n", "http://www.fablablima.org\n", "Address:\n", "\n", "\n", "City: Lima\n", "County: Lima Region\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: Nybi.cc\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.nybi.cc\n", "http://nybi.cc\n", "Address:\n", "9 Rue d'Alsace\n", "\n", "City: Jarville-la-Malgrange\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Reynoldsburg Battelle Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "https://sites.google.com/site/fablabreyn/home\n", "Address:\n", "8579 Summit Rd\n", "\n", "City: Reynoldsburg\n", "County: Ohio\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: L'ETABLI\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Pole Associatif Résano-Lapègue\n", "rue de Moscou\n", "City: Soustons\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: BEC Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.becfablab.org\n", "Address:\n", "Unit 4, Derwent Mills Commercial Park\n", "Wakefield Rd\n", "City: Cockermouth\n", "County: Cumbria\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fab Lab Sassari\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsassari.org\n", "http://pinterest.com/fablab.sassari\n", "http://instagram.com/fablabsassari\n", "https://issuu.com/fablabsassari\n", "https://twitter.com/FabLabSassari\n", "http://facebook.com/fablab.sassari\n", "Address:\n", "\n", "\n", "City: Sassari\n", "County: Italy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab Zeeland\n", "E-mail: [email protected]\n", "Links:\n", "https://www.linkedin.com/groups/FabLab-Zeeland-6701557/about\n", "http://www.facebook.com/fablabzeeland\n", "http://www.twitter.com/fablabzeeland\n", "Address:\n", "Kousteensedijk 7\n", "\n", "City: Middelburg\n", "County: Zeeland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: MAKILAB\n", "E-mail: [email protected]\n", "Links:\n", "http://makilab.org\n", "Address:\n", "Louvain-la-Neuve\n", "chemin du cyclotron, 6\n", "City: Ottignies-Louvain-la-Neuve\n", "County: \n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: Fablab Brussels\n", "E-mail: [email protected]\n", "Links:\n", "https://maps.google.be/maps?q=Fablab+Brussels&hl=nl&sll=50.8387,4.363405&sspn=0.307434,0.718231&hq=Fablab+Brussels&t=m&z=11&iwloc=A\n", "http://www.fablab-brussels.be\n", "Address:\n", "Nijverheidskaai 170\n", "\n", "City: Anderlecht\n", "County: Brussels\n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: Fab Lab Alicante\n", "E-mail: \n", "Links:\n", "https://www.facebook.com/fablabalicante\n", "https://plus.google.com/u/0/109169507820175971471/posts\n", "https://www.youtube.com/channel/UCcLITo4_XRluWORsuCbdW2w\n", "https://twitter.com/FabLabALC\n", "http://blogs.ua.es/lips\n", "Address:\n", "Ctra. San Vicente del Raspeig, s/n\n", "\n", "City: San Vicente del Raspeig\n", "County: Alicante\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fab Lab London\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablablondon.org\n", "Address:\n", "1 Frederick's Place\n", "\n", "City: London\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fair Use Building and Research (Fubar) Labs\n", "E-mail: [email protected]\n", "Links:\n", "http://fubarlabs.org\n", "Address:\n", "403 Cleveland Ave\n", "\n", "City: Highland Park\n", "County: NJ\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Baltimore\n", "E-mail: [email protected]\n", "Links:\n", "http://www.meetup.com/fab-lab-baltimore\n", "https://twitter.com/FabLabBaltimore\n", "https://www.facebook.com/FabLabBaltimore\n", "http://www.fablabbaltimore.org/\n", "Address:\n", "Building H Room 145 800 S Rolling Rd\n", "\n", "City: Catonsville\n", "County: MD\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Champaign-Urbana Community Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.flickr.com/groups/1847246@N22/pool\n", "http://cucfablab.org\n", "Address:\n", "1301 S Goodwin Ave\n", "Art Annex 2\n", "City: Urbana\n", "County: Illinois\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLabFultonMO\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fulton58.org/vnews/display.v/TP/52176a9ff08e3?cssfile=/teacherpages/Plain_Label_Blue/default.css\n", "Address:\n", "2 Hornet Dr\n", "\n", "City: Fulton\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Orange mécanique\n", "E-mail: [email protected]\n", "Links:\n", "http://www.cegepth.qc.ca\n", "Address:\n", "671, boul. Frontenac Ouest\n", "\n", "City: Thetford mines\n", "County: Québec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: Le Garage\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Saint-Denis\n", "\n", "City: Saint-Denis\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Frosinone - Officine Giardino\n", "E-mail: [email protected]\n", "Links:\n", "http://officinegiardino.org/\n", "Address:\n", "Via Giordano Bruno, 83\n", "\n", "City: Frosinone\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fablab Shanghai\n", "E-mail: [email protected]\n", "Links:\n", "http://weibo.com/fablab\n", "Address:\n", "No.281, Fuxin Road\n", "\n", "City: Shanghai\n", "County: China\n", "Country: China\n", "Continent: Asia\n", "\n", "Name: NCC Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.northampton.edu/personal-enrichment/the-fab-lab.htm\n", "Address:\n", "511 East Third Street\n", "3rd Floor \n", "City: Bethlehem\n", "County: PA\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Magdeburg\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLab.Magdeburg\n", "http://www.inkubator.ovgu.de/FabLab\n", "Address:\n", "Universitätsplatz 2\n", "Otto-von-Guericke-University\n", "City: Magdeburg\n", "County: Saxony-Anhalt\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Creaticity FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://creaticityfablab.com/\n", "https://www.facebook.com/creaticityfablab\n", "Address:\n", "via XXX giugno 28\n", "\n", "City: Tolentino\n", "County: MC\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Rinoteca FabLab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/rinoteca/\n", "http://www.rinoteca.com\n", "Address:\n", "Via Anders Wladislaw\n", "\n", "City: Ancona\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: 7elektron\n", "E-mail: [email protected]\n", "Links:\n", "http://www.7elektron.com.br\n", "Address:\n", "Rua Joaquim Zenir Leite, 406 Paraiso\n", "\n", "City: Belo Horizonte\n", "County: Minas Gerais\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Verona Fablab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.youtube.com/channel/UCrNHf7SWyCJ4XLaeTlZPbvQ\n", "http://twitter.com/fablabverona\n", "http://www.facebook.com/Fablabverona\n", "http://www.veronafablab.it/\n", "Address:\n", "Viale del Lavoro, 2\n", "\n", "City: Grezzana\n", "County: Verona\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fablab Catania\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablab_catania\n", "https://www.facebook.com/pages/Fablab-Catania/189873531201389\n", "http://www.fablabcatania.eu\n", "Address:\n", "Via Cifali \n", "\n", "City: Catania\n", "County: Italy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab München\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabMuc\n", "https://twitter.com/fablabmuc\n", "http://www.fablab-muenchen.de\n", "Address:\n", "Gollierstraße 70 D\n", "\n", "City: Munich\n", "County: Bavaria\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fab Lab Salerno\n", "E-mail: \n", "Links:\n", "Address:\n", "Piazza S. Agostino\n", "\n", "City: Salerno\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Industrial Studio \n", "E-mail: [email protected]\n", "Links:\n", "http://www.industrialstudio.cl\n", "Address:\n", "Talca\n", "\n", "City: Talca\n", "County: Talca\n", "Country: Chile\n", "Continent: South America\n", "\n", "Name: Bamako Fablab\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "ACI 2000\n", "\n", "City: Bamako\n", "County: HAMDALLAYE\n", "Country: Mali\n", "Continent: Africa\n", "\n", "Name: fablab@strathclyde\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "75 Montrose St\n", "\n", "City: Glasgow\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fab Lab Belém\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/161326217395967/\n", "Address:\n", "Travessa Benjamin Constant, 1337\n", "\n", "City: Belém\n", "County: Pará\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Fab Lab Bahrain\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-bahrain.blogspot.com\n", "http://bahrainfablab.wordpress.com\n", "Address:\n", "\n", "\n", "City: \n", "County: \n", "Country: Bahrain\n", "Continent: Asia\n", "\n", "Name: RElab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/relabliege\n", "http://relab.be\n", "Address:\n", "Rue Lambert Lombard, 1\n", "(place Saint-Etienne)\n", "City: Liege\n", "County: \n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: FabLab-Bayreuth\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-bayreuth.de\n", "Address:\n", "Ritter-von-Eitzenberger-Straße 19\n", "\n", "City: Bayreuth\n", "County: Bavaria\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab Essex\n", "E-mail: [email protected]\n", "Links:\n", "http://www.mic2c.com/fablabessex\n", "Address:\n", "Oakdene Business Centre\n", "Cranes Close\n", "City: Basildon\n", "County: Essex\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: ECODESIGN FAB LAB\n", "E-mail: [email protected]\n", "Links:\n", "http://www.apedec.org \n", "http://webtv.montreuil.fr/festival-m.u.s.i.c-et-fablab-video-415-12.html\n", "http://www.wedemain.fr/A-Montreuil-un-fab-lab-circulaire-dans-une-usine-verticale_a421.html\n", "Address:\n", "Montreuil\n", "2 à 20 avenue Allende, MOZINOR\n", "City: Montreuil \n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: fablab Q8\n", "E-mail: [email protected]\n", "Links:\n", "http://instagram.com/fablabq8\n", "http://www.sacgc.org\n", "Address:\n", "الكويت‎\n", "\n", "City: kuwait city\n", "County: kuwait\n", "Country: Kuwait\n", "Continent: Asia\n", "\n", "Name: Fab Lab Paramaribo\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabParamaribo\n", "http://fablab.vicepresident.gov.sr/\n", "Address:\n", "Dr. Sophie Redmondstraat 116-118\n", "\n", "City: Paramaribo\n", "County: Paramaribo District\n", "Country: Suriname\n", "Continent: South America\n", "\n", "Name: FabLab Santiago\n", "E-mail: \n", "Links:\n", "http://www.designlab.uai.cl/fablab\n", "Address:\n", "Av. Diagonal Las Torres 2640\n", "Penalolen\n", "City: Santiago\n", "County: Santiago Metropolitan Region\n", "Country: Chile\n", "Continent: South America\n", "\n", "Name: Metropolitan Community College Fab Lab, Omaha Nebraska, United States\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/mccnebfablab\n", "Address:\n", "5300 N 30th St\n", "\n", "City: Omaha\n", "County: Nebraska\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Maine FabLab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.mainefablab.org\n", "Address:\n", "265 Main St\n", "\n", "City: Biddeford\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab Roma Makers\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.romamakers.org\n", "Address:\n", "Via Giovanni Battista Magnaghi, 59\n", "\n", "City: Roma\n", "County: Rm\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Egypt\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablab.egypt\n", "http://www.fablab-egypt.com\n", "https://twitter.com/fablabegypt\n", "Address:\n", "10 Abdulrahman El-Rafei (infront of Shooting club gate #5) St., from Makkah St., Dokki\n", "\n", "City: Dokki\n", "County: Giza\n", "Country: Egypt\n", "Continent: Africa\n", "\n", "Name: fabLAB Asturias\n", "E-mail: [email protected]\n", "Links:\n", "http://www.laboralcentrodearte.org/es/files/2014/blog-fablab\n", "http://www.laboralcentrodearte.org/es/plataformacero/fablab\n", "Address:\n", "LABoral Centro de Arte y Creación Industrial\n", "Los Prados 121\n", "City: Gijón\n", "County: Principality of Asturias\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: FAB Newport\n", "E-mail: [email protected]\n", "Links:\n", "http://fabnewport.wordpress.com/\n", "Address:\n", "Newport\n", "\n", "City: Newport\n", "County: RI\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab Nordvest\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/fablabnordvest\n", "http://www.instagram.com/fablabnordvest\n", "http://www.fablabnordvest.dk\n", "Address:\n", "Glentevej 70B\n", "\n", "City: København\n", "County: \n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: FabLab SENDAI\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabsendai-flat.com\n", "Address:\n", "青葉区一番町2-2-8 IKIビル4F\n", "\n", "City: 仙台市\n", "County: 宮城県\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: McKinley South End Academy Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.bostonpublicschools.org/school/mckinley-south-end-academy\n", "Address:\n", "90 Warren Avenue\n", "\n", "City: Boston\n", "County: MA\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Limerick\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.saul.ie\n", "Address:\n", "Limerick\n", "7 Rutland Street\n", "City: Limerick\n", "County: Limerick\n", "Country: Ireland\n", "Continent: Europe\n", "\n", "Name: WeMake - Milan's Makerspace\n", "E-mail: [email protected]\n", "Links:\n", "http://wemake.cc\n", "Address:\n", "Via Stefanardo da Vimercate, 27\n", "\n", "City: Milan\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fablab Istanbul\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabist\n", "https://www.facebook.com/Fablabist\n", "http://fablabist.com/\n", "Address:\n", "Kadir Has Caddesi Cibali / İSTANBUL \n", "\n", "City: Istanbul\n", "County: Fatih\n", "Country: Turkey\n", "Continent: Asia\n", "\n", "Name: Fablab Rostov\n", "E-mail: [email protected]\n", "Links:\n", "http://vk.com/fablabrostov\n", "http://fablabrostov.ru/\n", "Address:\n", "просп. Михаила Нагибина, 3А\n", "\n", "City: Ростов-на-Дону\n", "County: \n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: Ekurhuleni fablabs (Fablab Tembisa and Fablab Thokoza)\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "45 Thami Mnyele Drive \n", "\n", "City: Tembisa\n", "County: Guateng\n", "Country: South Africa\n", "Continent: Africa\n", "\n", "Name: FabLab Roma - InnovationGym\n", "E-mail: [email protected]\n", "Links:\n", "http://www.innovationgym.org\n", "http://www.palestrainnovazione.org\n", "Address:\n", "Via del Quadraro\n", "\n", "City: Roma\n", "County: Rome/Lazio\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: MADE Makerspace Barcelona\n", "E-mail: [email protected]\n", "Links:\n", "http://www.made-bcn.org\n", "Address:\n", "C/CONSELL DE CENT 159, PRINCIPAL B \n", "ANTIGUA FABRICA LEHMANN\n", "City: Barcelona\n", "County: Catalunya\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: FabLab Paderborn e.V.\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-paderborn.de\n", "Address:\n", "Westernmauer 12\n", "\n", "City: Paderborn\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FABLAB Sardegna Ricerche \n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/pages/FAB-LAB-Sardegna-Ricerche/405778596221361\n", "http://sardegnaricerche.it/fablab\n", "Address:\n", "Sardegna Ricerche-Parco Tecnologico della Sardegna-Località Piscina Manna\n", "\n", "City: Pula\n", "County: Italy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Puebla\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabPuebla\n", "https://www.facebook.com/FabLabPuebla\n", "http://www.fablabpuebla.org\n", "Address:\n", "Blvd. del Niño Poblano 2901\n", "\n", "City: Puebla\n", "County: Puebla\n", "Country: Mexico\n", "Continent: North America\n", "\n", "Name: Fab Lab La Casemate\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FablabGrenoble\n", "http://fablab.ccsti-grenoble.org\n", "Address:\n", "CCSTI Grenoble La Casemate\n", " 2 place Saint Laurent\n", "City: Grenoble\n", "County: Rhone-Alpes\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Monterrey\n", "E-mail: [email protected]\n", "Links:\n", "https://www.udem.edu\n", "http://www.fablabmty.org/\n", "https://www.facebook.com/FabLabMonterreyCRGS\n", "Address:\n", "Av. Ignacio Morones Prieto 4500 Pte.\n", "\n", "City: San Pedro Garza García\n", "County: Nuevo Leon\n", "Country: Mexico\n", "Continent: North America\n", "\n", "Name: FabOutaouais\n", "E-mail: [email protected]\n", "Links:\n", "http://www.agoralab.ca/portfolio-tag/fablab/\n", "https://www.facebook.com/Fablab.Outaouais\n", "Address:\n", "820 Boul. de la Gappe\n", "\n", "City: Gatineau\n", "County: Québec \n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: The S.T.E.A.M. Room Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.thesteamroom.org\n", "Address:\n", "Iowa City\n", "Iowa City Marketplace\n", "City: Iowa City\n", "County: IA\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: CMIT \"Druzhba\"\n", "E-mail: [email protected]\n", "Links:\n", "http://vk.com/cmit_ru\n", "http://cmit.ru\n", "Address:\n", "Tomsk\n", "Krasnoarmeyskaya 147, office 103\n", "City: Tomsk\n", "County: \n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: FabLab caen\n", "E-mail: [email protected]\n", "Links:\n", "http://www.relais-sciences.org/fablab/\n", "Address:\n", "Rue Léopold Sédar-Senghor\n", "\n", "City: Colombelles\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Mahtomedi\n", "E-mail: \n", "Links:\n", "https://www.facebook.com/FABLabMahtomedi\n", "http://www.mahtomedi.k12.mn.us\n", "Address:\n", "8000 75th St. N\n", "\n", "City: Mahtomedi \n", "County: Minnesota\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: fablab Kelle FabriK\n", "E-mail: [email protected]\n", "Links:\n", "http://kellefabrik.blogspot.fr/\n", "Address:\n", "Dijon\n", "IUT Dijon\n", "City: Dijon\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Technoport FabLab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablablux\n", "https://twitter.com/fablablux\n", "http://www.technoport.lu\n", "http://fablablux.org\n", "Address:\n", "9, Avenue des Hauts-Fourneaux\n", "\n", "City: Belval\n", "County: Esch-sur-Alzette\n", "Country: Luxembourg\n", "Continent: Europe\n", "\n", "Name: FABLAB Hamamatsu\n", "E-mail: [email protected]\n", "Links:\n", "http://www.take-space.com\n", "Address:\n", "3645, Nishikamoe-cho, Nishi-ku\n", "\n", "City: Hamamatsu-city\n", "County: Shizuoka-ken, Japan\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FABLAB Chihuahua\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabchihuahua.com\n", "Address:\n", "Av. Heroico colegio militar #4709 Colonia Nombre de Dios C.P.31300\n", "PIT3\n", "City: Chihuahua\n", "County: Chihuahua\n", "Country: Mexico\n", "Continent: North America\n", "\n", "Name: L1A Makerspace\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/L1A.Makerspace\n", "http://www.l1a.de\n", "Address:\n", "Lauteschlägerstraße 1A\n", "\n", "City: Darmstadt\n", "County: Hessen\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fablab Roma SPQwoRk\n", "E-mail: [email protected]\n", "Links:\n", "https://it.foursquare.com/v/fablab-spqwork\n", "http://spqwork.com/fablab\n", "https://twitter.com/FablabSPQwoRk\n", "https://www.facebook.com/FablabSPQwoRk\n", "http://www.flickr.com/photos/spqwork\n", "Address:\n", "Via Ignazio Pettinengo 9\n", "\n", "City: Roma\n", "County: Roma\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Social Fabrication Center Keio University, SUPER FABLAB\n", "E-mail: \n", "Links:\n", "http://sfc.sfc.keio.ac.jp\n", "Address:\n", "馬車道駅\n", "\n", "City: YOKOHAMA\n", "County: Kanagawa\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FabLab DAZAIFU\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabdazaifu.com/\n", "Address:\n", "2-19-30 Tofurominami\n", "EK JAPAN Co., Ltd\n", "City: Dazaifu-shi\n", "County: Fukuoka-ken\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FabLab Bielsko-Biala\n", "E-mail: [email protected]\n", "Links:\n", "http://www.arrsa.pl\n", "http://www.fablab24.pl/\n", "Address:\n", "1 Dywizji Pancernej 45\n", "\n", "City: Bielsko-Biala\n", "County: woj. Śląskie\n", "Country: Poland\n", "Continent: Europe\n", "\n", "Name: LH3D fablab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.lh-fab-lab.e-monsite.com\n", "Address:\n", "1 Rue Dumé d'Aplemont\n", "\n", "City: Havre (Le)\n", "County: Haute Normandie\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab SFedU\n", "E-mail: [email protected]\n", "Links:\n", "http://vk.com/fablabrnd\n", "http://fablab61.ru/\n", "Address:\n", "Milchakova 5/2\n", "\n", "City: Ростов-на-Дону\n", "County: \n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: HONFablab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.vimeo.com/fablabyogyakarta\n", "http://www.facebook.com/fablab.yogyakarta\n", "https://www.twitter.com/honfablab\n", "http://www.honfablab.org\n", "Address:\n", "Jalan Taman Siswa\n", "Yogyakarta\n", "City: Yogyakarta City\n", "County: Special District of Yogyakarta\n", "Country: Indonesia\n", "Continent: Asia\n", "\n", "Name: Fellesverkstedet\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fellesverkstedet.no\n", "Address:\n", "Urtegata 11\n", "\n", "City: Oslo\n", "County: \n", "Country: Norway\n", "Continent: Europe\n", "\n", "Name: FabLab Innovation\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabInno\n", "https://www.facebook.com/FabLabInno\n", "http://www.fablabinnovation.dk\n", "Address:\n", "Sverigesgade 5\n", "3th floor\n", "City: Odense\n", "County: \n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: OPO LAB\n", "E-mail: [email protected]\n", "Links:\n", "http://www.opolab.com\n", "Address:\n", "Rua D. João IV, 643\n", "\n", "City: Porto\n", "County: Porto District\n", "Country: Portugal\n", "Continent: Europe\n", "\n", "Name: FabLab Pau\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-pau.org\n", "Address:\n", "18 Rue Latapie\n", "\n", "City: Pau\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab@Marguerite\n", "E-mail: \n", "Links:\n", "http://wp.csmb.qc.ca/fablab\n", "Address:\n", "8700, boul. Champlain\n", "\n", "City: LaSalle\n", "County: Québec/Canada\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: Smart Materials\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.ifts.net/\n", "Address:\n", "Boulevard Jean Delautre\n", "\n", "City: Charleville-Mézières\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Brasília Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/brasiliafablab\n", "http://www.brasiliafablab.com.br\n", "Address:\n", "CLN 305\n", "Bl B, Sala 06, Subsolo\n", "City: Brasilia\n", "County: DF\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Fab Lab Plymouth \n", "E-mail: [email protected]\n", "Links:\n", "http://www.plymouthart.ac.uk\n", "Address:\n", "PLYMOUTH COLLEGE OF ART\n", "Tavistock Place\n", "City: Plymouth\n", "County: Devon\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: ffablab Pontio\n", "E-mail: [email protected]\n", "Links:\n", "http://www.bangor.ac.uk\n", "http://www.innovationquarter.co.uk\n", "http://www.pontio.co.uk\n", "Address:\n", "Pontio\n", "Ffordd Deiniol Road\n", "City: Bangor\n", "County: Gwynedd\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab Quito\n", "E-mail: \n", "Links:\n", "Address:\n", "Quito, Urb Marisol Calle 9 N71-126 y Calle 13\n", "\n", "City: Quito\n", "County: Pichincha\n", "Country: Ecuador\n", "Continent: South America\n", "\n", "Name: Hackspace Catania\n", "E-mail: [email protected]\n", "Links:\n", "http://www.hackspacecatania.it\n", "Address:\n", "Via Grotte Bianche, 112\n", "\n", "City: Catania\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: ON/OFF Fablab Parma\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabparma.org\n", "Address:\n", "Strada Naviglio Alto, 4/1\n", "\n", "City: Parma\n", "County: Emilia Romagna\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: icealex\n", "E-mail: [email protected]\n", "Links:\n", "http://icealex.com\n", "Address:\n", "Ibrahim Sherif\n", "\n", "City: Alexandria\n", "County: \n", "Country: Egypt\n", "Continent: Africa\n", "\n", "Name: Copenhagen Fablab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.kulturvalby.dk/fablab\n", "http://kulturogfritid.kk.dk/kultur-valby/copenhagen-fablab\n", "Address:\n", "Valby Kulturhus\n", "Copenhagen Fablab\n", "City: Valgårdsvej 4-8\n", "County: Valby\n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: ICER-Lab\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Hutteweg 32\n", "\n", "City: Ulft\n", "County: Netherlands\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: fablabq8\n", "E-mail: [email protected]\n", "Links:\n", "http://www.instgram/fablabq8.com\n", "http://www.sacgc.org\n", "Address:\n", "in youth center\n", "\n", "City: hawalli\n", "County: \n", "Country: Kuwait\n", "Continent: Asia\n", "\n", "Name: FabLab-Lübeck\n", "E-mail: [email protected]\n", "Links:\n", "http://www.gruendercube.de\n", "http://www.unitransferklinik.de\n", "http://www.tzl.de\n", "http://www.tzl.de/fablab/\n", "Address:\n", "Seelandstraße 5\n", "TZL Building 4\n", "City: Lübeck\n", "County: Schleswig-Holstein\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fab Lab Adelaide\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabAdelaide\n", "http://fablabadelaide.org.au\n", "Address:\n", "39 Light Square\n", "\n", "City: Adelaide\n", "County: SA\n", "Country: Australia\n", "Continent: Oceania\n", "\n", "Name: FabLab Settimo\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablansettimo.org/\n", "Address:\n", "Via Ariosto, 36bis\n", "\n", "City: Settimo Torinese\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Machines Room\n", "E-mail: [email protected]\n", "Links:\n", "http://makerlibrarynetwork.org/makerspace/machines-room-limewharf/\n", "http://limewharf.org/machines-room/\n", "Address:\n", "LimeWharf Annex\n", "Vyner St\n", "City: London\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: CITC FabLab\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "3600 San Jeronimo Ct\n", "\n", "City: Anchorage\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Madrid-CEU\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabmadridceu\n", "https://www.facebook.com/profile.php?id=100006287478794\n", "https://www.flickr.com/photos/126001619@N08/sets/\n", "http://fablabmadridceu.wordpress.com/\n", "Address:\n", "Escuela Politécnica Superior, Universidad CEU San Pablo \n", "Campus de Monteprincipe\n", "City: Boadilla del Monte\n", "County: Madrid\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Olabi\n", "E-mail: [email protected]\n", "Links:\n", "http://www.templo.co\n", "http://olabi.co/\n", "Address:\n", "R. Barão de Lucena, 85 A\n", "\n", "City: Rio de Janeiro\n", "County: \n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Bio-Fab\n", "E-mail: [email protected]\n", "Links:\n", "http://www.crbm.cnrs.fr/index.php/fr/news-du-s-e-m/417-bio-fab\n", "Address:\n", " CRBM-IGMM-CPBS - CNRS 1919 Route de Mende\n", "\n", "City: Montpellier\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab CEPT\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "CEPT Universty -- Kasturbhai Lalbhai Campus\n", "University Road, Navrangpura\n", "City: Ahmedabad\n", "County: Gujarat\n", "Country: India\n", "Continent: Asia\n", "\n", "Name: FabLab IL (Fab Lab Israel)\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabilholon\n", "http://fablabil.org\n", "Address:\n", "4 Haamoraim st\n", "\n", "City: Holon\n", "County: Tel-Aviv\n", "Country: Israel\n", "Continent: Asia\n", "\n", "Name: Parthlab\n", "E-mail: [email protected]\n", "Links:\n", "http://parthlab.cc-parthenay.fr/\n", "Address:\n", "5 Rue Jean Macé\n", "\n", "City: Parthenay\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: La Fabulerie\n", "E-mail: [email protected]\n", "Links:\n", "http://www.lafabulerie.com\n", "Address:\n", "4 Rue de la Bibliothèque\n", "\n", "City: Marseille\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fablab Regensburg\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabregensburg\n", "http://www.fablab-regensburg.de\n", "Address:\n", "Grunewaldstraße 5\n", "\n", "City: Regensburg\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fablab Pesaro\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabPesaro\n", "https://plus.google.com/110591340885713597484/posts\n", "https://www.facebook.com/FablabPesaro\n", "Address:\n", "Via Della Produzione 61\n", "\n", "City: Montelabbate\n", "County: Pesaro\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FAB LAB Château Thierry\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablab.chateau.thierry\n", "Address:\n", "Château-Thierry\n", "\n", "City: Château-Thierry\n", "County: avenue de l'Europe\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: NOVOTECNA\n", "E-mail: None\n", "Links:\n", "http://www.novotecna.pt/fablab\n", "Address:\n", "City: Coimbra\n", "County: Coimbra District\n", "Country: Portugal\n", "Continent: Europe\n", "\n", "Name: The Wellington Makerspace\n", "E-mail: [email protected]\n", "Links:\n", "http://facebook.com/thewellingtonmakerspace\n", "http://www.meetup.com/wellingtonmakerspace/\n", "http://www.wellingtonmakerspace.com\n", "Address:\n", "6 Vivian St\n", "[LV1]\n", "City: Wellington\n", "County: new zealand\n", "Country: New Zealand\n", "Continent: Oceania\n", "\n", "Name: Fab Lab ICC\n", "E-mail: [email protected] or [email protected]\n", "Links:\n", "https://www.fablabs.io/fablabicc\n", "https://www.facebook.com/pages/Fab-Lab-ICC/269608433227931\n", "Address:\n", "Independence Community College\n", "1057 W College Ave.\n", "City: Independence\n", "County: KS\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Sibillini\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Comunanza\n", "Via A. Merloni, 11\n", "City: Comunanza\n", "County: AP\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Bogotá\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Bogota\n", "Cll 63 No. 3 09\n", "City: Bogota\n", "County: Cundinamarca\n", "Country: Colombia\n", "Continent: South America\n", "\n", "Name: Museum of Science and Industry Chicago Wanger Family Fab Lab\n", "E-mail: \n", "Links:\n", "http://www.msichicago.org/whats-here/fab-lab/\n", "Address:\n", "5700 S. Lake Shore Drive\n", "\n", "City: Chicago\n", "County: Illinois\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab@Champlain\n", "E-mail: \n", "Links:\n", "http://www.champlainonline.com/champlainweb/\n", "Address:\n", "900 Riverside Drive\n", "\n", "City: St-Lambert\n", "County: Québec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: Fablab Aldeias do Xisto\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabAldeiasDoXisto\n", "http://www.llcb.pt\n", "Address:\n", "Rua dos Três Lagares\n", "\n", "City: Fundão\n", "County: \n", "Country: Portugal\n", "Continent: Europe\n", "\n", "Name: FabLab North Greenwich\n", "E-mail: \n", "Links:\n", "Address:\n", "Mitre Passage\n", "\n", "City: London\n", "County: London\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: AS220 Labs\n", "E-mail: \n", "Links:\n", "http://www.youtube.com/user/tv220/videos?view=0\n", "http://www.flickr.com/photos/as220fablab/\n", "http://www.as220.org/labs/blog/fab-lab\n", "Address:\n", "131 Washington St\n", "\n", "City: Providence\n", "County: Rhode Island\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Intilaq Tohoku Innovators Hub\n", "E-mail: [email protected]\n", "Links:\n", "http://www.impactjapan.org\n", "Address:\n", "卸町１丁目２−９\n", "\n", "City: Sendai\n", "County: Miyagi-Ku\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FAU FabLab\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FAUFabLab\n", "http://fablab.fau.de\n", "Address:\n", "Erwin-Rommel-Straße 60\n", "\n", "City: Erlangen\n", "County: Bavaria\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: MakerBar\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/MakerbarTaipei\n", "http://www.makerbartaipei.com\n", "Address:\n", "5F., No.9, Sec. 1, Jinshan S. Rd., Zhongzheng Dist\n", "\n", "City: Taipei\n", "County: T'ai-pei city\n", "Country: Taiwan, Province of China\n", "Continent: Asia\n", "\n", "Name: Fab Lab Maastricht\n", "E-mail: [email protected]\n", "Links:\n", "https://www.linkedin.com/groups/FabLab-ZuidLimburg-3663893\n", "https://www.pinterest.com/fablabzl/\n", "https://www.facebook.com/fablab.maastricht\n", "https://twitter.com/FabLab_Mtricht\n", "http://www.flickr.com/photos/99070534@N07/\n", "http://www.fablabmaastricht.nl\n", "Address:\n", "Herbenusstraat 89, 6211RB Maastricht\n", "\n", "City: Maastricht\n", "County: Limburg\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fabcafe Bangkok\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Ari 2\n", "\n", "City: Bangkok\n", "County: \n", "Country: Thailand\n", "Continent: Asia\n", "\n", "Name: FabLab Bremen\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablab-bremen.org\n", "Address:\n", "Bremen\n", "\n", "City: Bremen\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabCafe Tokyo\n", "E-mail: [email protected]\n", "Links:\n", "http://fabcafe.com/\n", "Address:\n", "1-22-7 Dogenzaka \n", "\n", "City: Shibuya \n", "County: Tokyo\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FabLab Ventura\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabnet.it/\n", "Address:\n", "Via Privata Giovanni Ventura, 20\n", "\n", "City: Milano\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Smart Fab Lab \n", "E-mail: [email protected]\n", "Links:\n", "http://www.smartfablab.org/\n", "Address:\n", "Sofia Center\n", "1 Hristo Smirnenski Street\n", "City: Sofia\n", "County: \n", "Country: Bulgaria\n", "Continent: Europe\n", "\n", "Name: FabLab Spinderihallerne \n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "Danmark\n", "Spinderigade 11E\n", "City: Vejle\n", "County: Denmark\n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: FabLab Puerto Rico\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabpr?ref=bookmarks\n", "https://www.facebook.com/#!/groups/FABLABPR/\n", "http://centrointernacionaldediseno.com/\n", "Address:\n", "Carretera 189, KM 3.3\n", "\n", "City: Gurabo\n", "County: PR\n", "Country: Puerto Rico\n", "Continent: North America\n", "\n", "Name: Fab Lab Sevilla / Escuela Tecnica Superior de Arquitectura Universidad de Sevilla\n", "E-mail: [email protected]\n", "Links:\n", "http://www.pinterest.com/fablab/\n", "https://www.facebook.com/sevillafablab\n", "http://fablabsevilla.us.es\n", "https://twitter.com/fablabsevilla\n", "http://htca.us.es/blogs/fablab\n", "http://htca.us.es/blogs/talleresfablab\n", "Address:\n", "\n", "\n", "City: \n", "County: \n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: FabLab Groene Hart\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabgroenehart.nl\n", "Address:\n", "Achttienkavels 8\n", "\n", "City: Nieuwkoop\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fab Lab Sitges\n", "E-mail: [email protected]\n", "Links:\n", "https://github.com/Fablabsitges\n", "http://www.pinterest.com/fablabsitges\n", "http://vimeo.com/fablabsitges\n", "http://twitter.com/fablabsitges\n", "http://www.facebook.com/fablabsitges\n", "http://fablabsitges.org\n", "Address:\n", "Passeig de la Ribera 46\n", "\n", "City: Sitges\n", "County: Catalonia\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: Fab Lab Antofagasta\n", "E-mail: [email protected] \n", "Links:\n", "http://www.fablabafta.cl/\n", "Address:\n", "Avenida Universidad de Antofagasta S/N\n", "Campus COLOSO\n", "City: Antofagasta\n", "County: Antofagasta\n", "Country: Chile\n", "Continent: South America\n", "\n", "Name: MakeRN\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/MakerSpaceRiminiLab?ref=bookmarks\n", "http://Twitter: @makernlab\n", "http://www.makern.it\n", "Address:\n", "Via Umberto Cagni, 14\n", "\n", "City: Rimini\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Sulbiate\n", "E-mail: \n", "Links:\n", "http://www.makeinprogress.org/\n", "Address:\n", "Via Madre Laura 1\n", "\n", "City: Sulbiate\n", "County: Italy / Monza Brianza / Lombardia\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab SV (El Salvador)\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabSanSalvador\n", "http://fablab.org.sv/\n", "Address:\n", "Calle La Reforma 249, Colonia San Benito, San Salvador, El Salvador.\n", "\n", "City: San Salvador\n", "County: San Salvador\n", "Country: El Salvador\n", "Continent: North America\n", "\n", "Name: KAIST Idea Factory\n", "E-mail: [email protected]\n", "Links:\n", "http://risti.kaist.ac.kr\n", "Address:\n", "291 Daehak-ro, Yuseong-gu\n", "\n", "City: Daejeon\n", "County: \n", "Country: Korea, Republic of\n", "Continent: Asia\n", "\n", "Name: Fablab Dynamic\n", "E-mail: [email protected]\n", "Links:\n", "http://vimeo.com/user24433735\n", "http://www.fablabtaiwan.org.tw/\n", "http://www.youtube.com/watch?v=WklbEAPbtoQ\n", "https://www.facebook.com/pages/Fablab-Dynamic/532700190113349\n", "Address:\n", "180 Fúhuá Road, Shilin District\n", "\n", "City: Taipei City\n", "County: Taiwan\n", "Country: Taiwan, Province of China\n", "Continent: Asia\n", "\n", "Name: Fab Lab FBI\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabfbi.oddist.org\n", "Address:\n", "\n", "\n", "City: Taicang\n", "County: Jiangsu\n", "Country: China\n", "Continent: Asia\n", "\n", "Name: ouagalab\n", "E-mail: [email protected] [email protected]\n", "Links:\n", "http://www.ouagalab.info\n", "http://www.ouagalab.info\n", "Address:\n", "ouagalab.info\n", "https://www.facebook.com/pages/Ouagalab/159514010814943\n", "City: Ouagadougou\n", "County: Kadiogo\n", "Country: Burkina Faso\n", "Continent: Africa\n", "\n", "Name: Fab Lab Dhahran\n", "E-mail: \n", "Links:\n", "http://instagram.com/fablabdhahran\n", "http://fablabdhahran.org\n", "https://www.facebook.com/KingAbdulazizCenterForWorldCulture\n", "https://twitter.com/fablabdhahran\n", "Address:\n", "\n", "\n", "City: Khobar\n", "County: Eastern Province\n", "Country: Saudi Arabia\n", "Continent: Asia\n", "\n", "Name: Fab Lab NCCU-Durham, North Carolina\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "North Carolina Central University\n", "\n", "City: Durham \n", "County: NC\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Karlsruhe\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-karlsruhe.de\n", "Address:\n", "Alter Schlachthof 13a\n", "\n", "City: Karlsruhe\n", "County: Baden-Württemberg\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab WA\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabwa.org\n", "Address:\n", "15 Grosvenor St\n", "\n", "City: Beaconsfield\n", "County: Western Australia\n", "Country: Australia\n", "Continent: Oceania\n", "\n", "Name: FabLab@SP\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/635485616525389/\n", "Address:\n", "500 Dover Rd\n", "T11C\n", "City: Singapore\n", "County: \n", "Country: Singapore\n", "Continent: Asia\n", "\n", "Name: MUSE Fablab\n", "E-mail: [email protected]\n", "Links:\n", "https://github.com/musefablab\n", "https://vimeo.com/user22102097\n", "http://www.flickr.com/photos/musefablab/\n", "https://www.facebook.com/Muse.Fablab.Trento\n", "https://twitter.com/MUSE_Fablab\n", "http://fablab.muse.it\n", "Address:\n", "Corso del Lavoro e della Scienza 3\n", "\n", "City: Trento\n", "County: Trentino-Alto Adige/Südtirol\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab Lisboa\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablablisboa.pt\n", "Address:\n", "Rua Maria da Fonte\n", "Mercado do Forno do Tijolo\n", "City: Lisbon\n", "County: \n", "Country: Portugal\n", "Continent: Europe\n", "\n", "Name: FabLab Alessandria\n", "E-mail: [email protected] \n", "Links:\n", "Address:\n", "Via Verona, 95\n", "\n", "City: Alessandria\n", "County: Italy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: cssm\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/CssmTecnos\n", "Address:\n", "Via Campo Sportivo\n", "\n", "City: Uggiano La Chiesa\n", "County: \n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: OpenLab\n", "E-mail: [email protected]\n", "Links:\n", "http://jugendzentrum-schwabach.de/forder-verein/openlab/\n", "Address:\n", "Königstraße 20A\n", "\n", "City: Schwabach\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FaberLab Varese\n", "E-mail: [email protected]\n", "Links:\n", "https://www.youtube.com/user/FaberLabVarese\n", "https://twitter.com/FaberLabVarese\n", "https://www.facebook.com/FaberLabVarese\n", "http://www.faberlab.org\n", "Address:\n", "Viale Europa 4/a\n", "\n", "City: Tradate\n", "County: Varese\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Makernow\n", "E-mail: [email protected]\n", "Links:\n", "https://plus.google.com/113776356809222537580/about\n", "http://www.facebook.com/MakernowFabLab\n", "http://www.makernow.org\n", "Address:\n", "Penryn\n", "Falmouth University, Penryn Campus \n", "City: Penryn\n", "County: Cornwall\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Atelier Pobot\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "190 Rue Frédéric Mistral\n", "Centre International de Valbonne - Agora\n", "City: Valbonne\n", "County: Alpes-Maritimes\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Berlin\n", "E-mail: [email protected]\n", "Links:\n", "http://www.meetup.com/FabLabBerlin/members/117574612/\n", "http://www.3dhubs.com/berlin/hubs/fab-lab\n", "https://twitter.com/FabLabBLN/\n", "https://www.facebook.com/FabLabBerlin\n", "http://www.fablab-berlin.org\n", "Address:\n", "Saarbrücker Straße 24\n", "Haus C\n", "City: Berlin\n", "County: \n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fab Lab El Paso\n", "E-mail: [email protected]\n", "Links:\n", "http://www.twitter.com/FabLabEP\n", "http://www.flickr.com/FabLabElPaso\n", "http://facebook.com/FabLabEP\n", "http://fablabelpaso.org\n", "Address:\n", "806 Montana Ave\n", "\n", "City: El Paso\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: ViNN:Lab (TH Wildau)\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/ViNNLab\n", "http://www.offene-werkstaetten.org/werkstatt/vinnlab\n", "http://www.th-wildau.de/creativelab\n", "Address:\n", "Hochschulring 1\n", "Building 16A \| Room 2094\n", "City: Wildau\n", "County: Brandenburg\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab Dresden\n", "E-mail: [email protected]\n", "Links:\n", "http://www.werkstadtladen.de\n", "http://www.fablabdd.de\n", "Address:\n", "Wernerstraße 11\n", "\n", "City: Dresden\n", "County: Sachsen\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: Fab Lab Recife\n", "E-mail: \n", "Links:\n", "http://www.facebook.com/fablabrecife\n", "Address:\n", "Rua Dona Ada Vieira, 87\n", "\n", "City: Recife\n", "County: Pernambuco\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: Fablab Danmark\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabdanmark.dk\n", "Address:\n", "Maglemølle\n", "\n", "City: Næstved\n", "County: Sjælland\n", "Country: Denmark\n", "Continent: Europe\n", "\n", "Name: (Fab)Lab Digiscope\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabdigiscope.wordpress.com\n", "Address:\n", "660 Rue Noetzlin\n", "\n", "City: Gif-sur-Yvette\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab ESAN\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/fablabesan\n", "http://fablab.esan.edu.pe/\n", "Address:\n", "Lima\n", "Alonso de Molina 1652\n", "City: Lima\n", "County: Lima\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: Fablab-Leuven\n", "E-mail: [email protected]\n", "Links:\n", "http://www.flickr.com/photos/fablableuven/\n", "https://www.facebook.com/groups/321746647866201/\n", "http://www.fablab-leuven.be\n", "Address:\n", "Celestijnenlaan 300\n", "Katholieke Universiteit Leuven\n", "City: Leuven\n", "County: Flanders\n", "Country: Belgium\n", "Continent: Europe\n", "\n", "Name: Chantier Libre\n", "E-mail: [email protected]\n", "Links:\n", "http://www.chantierlibre.org\n", "Address:\n", "Place de la Gare\n", "L'Hôpital sur Rhins\n", "City: Saint-Cyr-de-Favières\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Amersfoort\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabamersfoort.nl\n", "http://dewar.nl/?en/home\n", "Address:\n", "Kleine Koppel 40\n", "\n", "City: Amersfoort\n", "County: Utrecht\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Fabrique d'Objets Libres\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-lyon.fr\n", "Address:\n", "Allée Gaillard Romanet\n", "MJC Louis Aragon\n", "City: Bron\n", "County: Rhône\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLab Biella\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabBiella\n", "https://www.facebook.com/fablabbiella\n", "http://www.fablabbiella.it/\n", "Address:\n", "Via Corradino Sella, 10\n", "\n", "City: Biella\n", "County: Italia/Biella\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Oita\n", "E-mail: [email protected] \n", "Links:\n", "http://www.faboita.org\n", "Address:\n", "51-6 Higashi Kasuga\n", "\n", "City: Oita\n", "County: Oita\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: Fab Lab Region Nuernberg e.V.\n", "E-mail: [email protected]\n", "Links:\n", "http://wiki.fablab-nuernberg.de\n", "http://www.fb.com/FabLabNuernberg\n", "http://www.fablab-nuernberg.de\n", "Address:\n", "Muggenhofer Straße 141\n", "\n", "City: Nuremberg\n", "County: Bavaria\n", "Country: Germany\n", "Continent: Europe\n", "\n", "Name: FabLab Cali\n", "E-mail: [email protected] [email protected]\n", "Links:\n", "http://www.Twitter.com/FabLabCali\n", "http://www.facebook.com/FablabCali\n", "http://FabLabCali.org\n", "Address:\n", " Calle 25 # 115 - 85 Km. 2 Vía Cali - Jamundí\n", "Universidad Autónoma de Occidente\n", "City: Cali\n", "County: Valle del Cauca\n", "Country: Colombia\n", "Continent: South America\n", "\n", "Name: Insper FAB LAB\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "R. Quatá\n", "\n", "City: São Paulo\n", "County: São Paulo\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: FabLab Graz\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab.tugraz.at\n", "Address:\n", "Inffeldgasse 11/I\n", "\n", "City: Graz\n", "County: Styria\n", "Country: Austria\n", "Continent: Europe\n", "\n", "Name: Antibes NavLab\n", "E-mail: [email protected]\n", "Links:\n", "http://imaginationforpeople.org/fr/project/le-navlab-un-fablab-nautique-a-antibes/\n", "http://www.kisskissbankbank.com/navlab\n", "http://www.viadeo.com/v/company/navlab\n", "http://www.linkedin.com/company/navlab?trk=company_name\n", "https://twitter.com/NavLabAntibes\n", "https://www.facebook.com/navlab?ref=hl\n", "https://www.facebook.com/pages/Navlab-English/455206721245993?ref=hl\n", "http://navlab.avitys.com\n", "Address:\n", "Antibes\n", "3 Boulevard Wilson\n", "City: Antibes\n", "County: Provence-Alpes-Côte d'Azur\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Cascina\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/fablabcascina/\n", "http://www.fablabcascina.org\n", "Address:\n", "Via Mario Giuntini 25 interno 28\n", "\n", "City: Cascina\n", "County: PI\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: FabLab la Côte\n", "E-mail: [email protected]\n", "Links:\n", "https://www.twitter.com/fablablacote\n", "https://www.facebook.com/fablablacote\n", "http://www.fablab-lacote.ch\n", "Address:\n", "Route de Champ-Colin 11, Nyon\n", "\n", "City: Nyon\n", "County: \n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: ZB45 Makerspace\n", "E-mail: [email protected]\n", "Links:\n", "http://www.zb45.nl\n", "Address:\n", "Zeeburgerpad 45\n", "\n", "City: Amsterdam\n", "County: \n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: FacLab\n", "E-mail: \n", "Links:\n", "http://www.youtube.com/user/Faclabucp/\n", "https://twitter.com/FacLabUcp\n", "https://www.facebook.com/faclab\n", "http://www.faclab.org\n", "Address:\n", "Avenue Marcel Paul\n", "Allée des Pierres Mayettes\n", "City: Gennevilliers\n", "County: Île-de-France\n", "Country: France\n", "Continent: Europe\n", "\n", "Name: FabLabDevon (Exeter)\n", "E-mail: [email protected]\n", "Links:\n", "http://www.facebook.com/fablabdevon\n", "http://www.twitter.com/fablabdevon\n", "http://www.fablabdevon.com\n", "Address:\n", "Exeter Library\n", "Castle Street\n", "City: Exeter\n", "County: Devon\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: \"Experimentarium1502 MPEI\" (Fab Lab)\n", "E-mail: [email protected]\n", "Links:\n", "http://www.lyceum1502.ru/pages/overtime/fablab/\n", "http://www.planetseed.com/ru/forums/fablabs/moscow-russia-fablabschool-experimentarium-1502\n", "Address:\n", "Molostovukh street, 10 A\n", "\n", "City: Moscow\n", "County: Moscow\n", "Country: Russian Federation\n", "Continent: Europe\n", "\n", "Name: Fab Lab Wgtn, New Zealand\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabwgtn.co.nz\n", "https://www.facebook.com/FabLabWGTN\n", "http://creative.massey.ac.nz/enterprise/fab-lab-wgtn/\n", "Address:\n", "Buckle St Entrance\n", "Massey University\n", "City: Wellington\n", "County: North Island\n", "Country: New Zealand\n", "Continent: Oceania\n", "\n", "Name: Open Dot\n", "E-mail: [email protected]\n", "Links:\n", "http://www.opendotlab.it\n", "https://www.facebook.com/pages/Opendot/699330510128661?fref=ts\n", "Address:\n", "Via Tertulliano, 70\n", "\n", "City: Milano\n", "County: (MI)\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: fablab noord-brabant\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabnoordbrabant.nl\n", "Address:\n", "Zuid-Willemsvaart 215, 's-Hertogenbosch, the Netherlands\n", "\n", "City: 's-Hertogenbosch\n", "County: Noord-Brabant\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: South End Technology Center\n", "E-mail: \n", "Links:\n", "http://www.tech-center-enlightentcity.tv/home.html\n", "Address:\n", "359 Columbus Ave\n", "\n", "City: Boston\n", "County: Massachusetts\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Floripa\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabfloripa.wordpress.com\n", "https://www.facebook.com/fablabfloripa\n", "Address:\n", "Rua Lacerda Coutinho, 100\n", "\n", "City: Florianópolis\n", "County: Santa Catarina \n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: FabLab IRSC\n", "E-mail: [email protected]\n", "Links:\n", "http://www.irsc.edu\n", "Address:\n", "3209 Virginia Ave\n", "\n", "City: Fort Pierce\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab BH\n", "E-mail: [email protected]\n", "Links:\n", "http://@FabLab_Bahrain\n", "Address:\n", "Manama\n", "\n", "City: Manama\n", "County: \n", "Country: Bahrain\n", "Continent: Asia\n", "\n", "Name: Castlemont High School, Sustainable Urban Design Academy (SUDA) Fablab Oakland CA\n", "E-mail: [email protected]\n", "Links:\n", "http://www.sud-academy.org\n", "Address:\n", "8601 MacArthur Blvd\n", "\n", "City: Oakland\n", "County: \n", "Country: United States\n", "Continent: North America\n", "\n", "Name: JeugdFabLab \n", "E-mail: [email protected]\n", "Links:\n", "http://www.fabfunclub.nl\n", "http://www.jeugdfablab.nl\n", "Address:\n", "Langstraat 48\n", "48\n", "City: Dronten\n", "County: FlevoLand\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: Kaasfabriek \| FabLab regio Alkmaar\n", "E-mail: [email protected]\n", "Links:\n", "http://www.twitter.com/kaasfab\n", "http://www.facebook.com/kaasfabriek\n", "http://www.kaasfabriek.nl\n", "Address:\n", "Pettemerstraat 15\n", "\n", "City: Alkmaar\n", "County: Noord-Holland\n", "Country: Netherlands\n", "Continent: Europe\n", "\n", "Name: District 3\n", "E-mail: \n", "Links:\n", "http://d3center.ca/opportunities/makerspace/\n", "Address:\n", "1515 Rue Ste-Catherine O\n", "\n", "City: Montreal\n", "County: Quebec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: MAKLAB\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/themaklab\n", "http://www.youtube.com/channel/UCH2kOIIZQto_2TkSl4QDu5A\n", "https://twitter.com/theMAKLab\n", "http://www.maklab.co.uk\n", "Address:\n", "30 St Georges Road\n", "Charing Cross Mansions\n", "City: Glasgow\n", "County: Lanarkshire\n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: FabLab Imperia\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/FabLabImperia/\n", "https://www.facebook.com/fablabimperia\n", "Address:\n", "Imperia\n", "\n", "City: Imperia\n", "County: IM\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab xChc\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabxchc.org.nz\n", "http://www.fabriko.org.nz\n", "Address:\n", "The Exchange - XCHC\n", "376 Wilsons Rd North\n", "City: Christchurch\n", "County: New Zealand\n", "Country: New Zealand\n", "Continent: Oceania\n", "\n", "Name: La Fabrique\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/SherbrookeFabrique\n", "http://www.sherbrookefabrique.org\n", "Address:\n", "1801 Rue Denault\n", "\n", "City: Sherbrooke\n", "County: Québec\n", "Country: Canada\n", "Continent: North America\n", "\n", "Name: ICTP SciFabLab\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/triesteminimakerfaire\n", "https://www.facebook.com/scifablab\n", "http://scifablab.ictp.it\n", "Address:\n", "Via Beirut 7\n", "\n", "City: Trieste \n", "County: Trieste\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Fab Lab Cardiff\n", "E-mail: [email protected]\n", "Links:\n", "http://fablabcardiff.com\n", "Address:\n", "Western Avenue\n", "Llandaff Campus\n", "City: Cardiff\n", "County: \n", "Country: United Kingdom\n", "Continent: Europe\n", "\n", "Name: Fab Lab Seoul\n", "E-mail: [email protected]\n", "Links:\n", "http://fablab-seoul.org/\n", "http://www.tideinstitute.org\n", "Address:\n", "Seun-Sangka Room 550 \n", "Chungkyechun-Ro 159, Jongno-Gu \n", "City: Seoul\n", "County: \n", "Country: Korea, Republic of\n", "Continent: Asia\n", "\n", "Name: Tecnoparque Medellin\n", "E-mail: [email protected]\n", "Links:\n", "http://tecnoparque.sena.edu.co/sedes/medellin/Paginas/default.aspx\n", "Address:\n", "Carrera 46 #56 11\n", "\n", "City: Medellín\n", "County: Antioquia\n", "Country: Colombia\n", "Continent: South America\n", "\n", "Name: Fablab-Shanghai\n", "E-mail: [email protected]\n", "Links:\n", "http://Fablab.tongji.edu.cn\n", "http://www.fablabshanghai.tongji.edu.cn\n", "Address:\n", "No.281 Fuxin Road\n", "College of Design and Innovation, Tongji University\n", "City: Shanghai\n", "County: China\n", "Country: China\n", "Continent: Asia\n", "\n", "Name: Cherokee Trail High School\n", "E-mail: [email protected]\n", "Links:\n", "http://cherokeetrail.cherrycreekschools.org/Departments/preeng/Pages/default.aspx\n", "Address:\n", "25901 E Arapahoe Rd\n", "\n", "City: Aurora\n", "County: Colorado\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: FabLab Bohol\n", "E-mail: \n", "Links:\n", "http://bohol.fablabphilippines.com\n", "Address:\n", "Main Campus of Bohol Island State University\n", "CPG North Ave., 6300\n", "City: Tagbilaran City\n", "County: Bohol\n", "Country: Philippines\n", "Continent: Asia\n", "\n", "Name: Fab Lab Olbia\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/FabLabOlbia\n", "https://twitter.com/FabLabOlbia\n", "http://fablabolbia.wordpress.com\n", "Address:\n", "Via Petta 124\n", "\n", "City: Olbia\n", "County: Olbia-Tempio\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: Maker Space Vienna\n", "E-mail: [email protected]\n", "Links:\n", "http://www.makeraustria.at\n", "Address:\n", "Schönbrunner Straße 125\n", "\n", "City: Wien\n", "County: Austria\n", "Country: Austria\n", "Continent: Europe\n", "\n", "Name: FabLab Winti\n", "E-mail: [email protected]\n", "Links:\n", "https://m.facebook.com/FabLab.winti\n", "http://www.fablabwinti.ch\n", "Address:\n", "Lagerplatz 13\n", "\n", "City: Winterthur\n", "County: \n", "Country: Switzerland\n", "Continent: Europe\n", "\n", "Name: University of Texas at Arlington Fab Lab\n", "E-mail: [email protected]\n", "Links:\n", "Address:\n", "702 Planetarium Pl\n", "University of Texas at Arlington Libraries\n", "City: Arlington\n", "County: TX\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fab Lab Richmond, California\n", "E-mail: [email protected]\n", "Links:\n", "http://www.wccusd.net/fablab\n", "Address:\n", "4300 Cutting Blvd\n", "\n", "City: Richmond\n", "County: California\n", "Country: United States\n", "Continent: North America\n", "\n", "Name: Fablab Bratislava\n", "E-mail: [email protected], [email protected]\n", "Links:\n", "http://www.facebook.com/fablabslovakia\n", "http://www.fablab.sk\n", "Address:\n", "Ilkovičova 2\n", "FIIT STU\n", "City: Bratislava\n", "County: \n", "Country: Slovakia\n", "Continent: Europe\n", "\n", "Name: LABSud Montpellier\n", "E-mail: [email protected]\n", "Links:\n", "http://forum.labsud.org\n", "http://www.labsud.org\n", "Address:\n", "Hotel d'Entreprise de l'Agglomeration de Montpelllier\n", "120 Allée John Napier\n", "City: Montpellier\n", "County: \n", "Country: France\n", "Continent: Europe\n", "\n", "Name: Fab Lab Atikux\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabatikux.edu.pe\n", "Address:\n", "Distrito de miraflores\n", "Distrito de miraflores\n", "City: Miraflores\n", "County: Lima\n", "Country: Peru\n", "Continent: South America\n", "\n", "Name: Fab LAB Trojmiasto\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/groups/178918095579486/\n", "http://fablabt.org\n", "Address:\n", "Niepodległości 291\n", "\n", "City: Gdynia\n", "County: \n", "Country: Poland\n", "Continent: Europe\n", "\n", "Name: SENAI FABLAB\n", "E-mail: [email protected]\n", "Links:\n", "http://www.senaifablab.com.br\n", "Address:\n", "Praça Natividade Saldanha, 19\n", "\n", "City: Rio de Janeiro\n", "County: Rio de Janeiro\n", "Country: Brazil\n", "Continent: South America\n", "\n", "Name: 新Fab - XinFab \n", "E-mail: [email protected]\n", "Links:\n", "http://www.xinfab.com\n", "Address:\n", "Yu Yuan East Road, 29, Building 3\n", "\n", "City: Shanghai \n", "County: Shanghai\n", "Country: China\n", "Continent: Asia\n", "\n", "Name: FPGA-CAFE/FabLab Tsukuba\n", "E-mail: [email protected]\n", "Links:\n", "http://fpgacafe.com\n", "Address:\n", "Ritchmond 2 street\n", "2-9-2 Amakubo\n", "City: つくば市\n", "County: Ibaraki Prefecture\n", "Country: Japan\n", "Continent: Asia\n", "\n", "Name: FabLabUPM\n", "E-mail: [email protected]\n", "Links:\n", "http://www.fablabupm.com\n", "Address:\n", "Campus de Montegancedo\n", "Edificio C.A.I.T.\n", "City: Pozuelo\n", "County: Madrid\n", "Country: Spain\n", "Continent: Europe\n", "\n", "Name: FAB LAB ARGENTINA + SCA\n", "E-mail: [email protected]\n", "Links:\n", "https://twitter.com/fablabargentina\n", "http://issuu.com/chungsong/docs/cv_fablab_argentina\n", "https://www.facebook.com/FablabArgentina/\n", "http://www.fablabargentina.com.ar\n", "Address:\n", "Montevideo 938\n", "\n", "City: Buenos Aires\n", "County: Buenos Aires\n", "Country: Argentina\n", "Continent: South America\n", "\n", "Name: FabLab Palermo\n", "E-mail: [email protected]\n", "Links:\n", "https://plus.google.com/u/0/b/100116212512482122910/100116212512482122910\n", "http://twitter.com/fablabpalermo\n", "http://www.facebook.com/fablabpalermo\n", "http://www.fablabpalermo.org\n", "Address:\n", "Via mariano stabile, 52\n", "\n", "City: Palermo\n", "County: Italy\n", "Country: Italy\n", "Continent: Europe\n", "\n", "Name: MakeInBo\n", "E-mail: [email protected]\n", "Links:\n", "https://www.facebook.com/makeinbo\n", "http://www.makeinbo.it\n", "Address:\n", "Piazza dei colori, 25/b\n", "\n", "City: Bologna\n", "County: BO\n", "Country: Italy\n", "Continent: Europe\n" ] } ], "source": [ "# Print an analysis of the data\n", "for i in fablab_list[\"labs\"]:\n", " print \"\"\n", " print \"Name:\",i[\"name\"]\n", " print \"E-mail:\",i[\"email\"]\n", " print \"Links:\"\n", " for l in i[\"links\"]:\n", " print l[\"url\"]\n", " print \"Address:\"\n", " if i[\"address_1\"] != None:\n", " print i[\"address_1\"]\n", " if i[\"address_2\"] != None:\n", " print i[\"address_2\"]\n", " print \"City:\",i[\"city\"]\n", " if i[\"county\"] != None:\n", " print \"County:\",i[\"county\"]\n", " country = pycountry.countries.get(alpha2=i[\"country_code\"].upper())\n", " print \"Country:\",country.name\n", " print \"Continent:\",transformations.cca_to_ctn(i[\"country_code\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load data for reordering by continent - country later\n", "groupedlabs = {}\n", "\n", "for i in fablab_list[\"labs\"]:\n", " labs[i[\"name\"]] = {}\n", " labs[i[\"name\"]][\"name\"] = i[\"name\"]\n", " labs[i[\"name\"]][\"email\"] = i[\"email\"]\n", " labs[i[\"name\"]][\"links\"] = {}\n", " for f,l in enumerate(i[\"links\"]):\n", " labs[i[\"name\"]][\"links\"][f] = l[\"url\"]\n", " labs[i[\"name\"]][\"address_1\"] = i[\"address_1\"]\n", " labs[i[\"name\"]][\"address_2\"] = i[\"address_2\"]\n", " labs[i[\"name\"]][\"city\"] = i[\"city\"]\n", " labs[i[\"name\"]][\"county\"] = i[\"county\"]\n", " country = pycountry.countries.get(alpha2=i[\"country_code\"].upper())\n", " labs[i[\"name\"]][\"country\"] = country.name\n", " continent = transformations.cca_to_ctn(i[\"country_code\"])\n", " labs[i[\"name\"]][\"continent\"] = continent\n", " \n", " # Save by continent and country\n", " if continent not in groupedlabs:\n", " groupedlabs[continent] = {}\n", " if country.name not in groupedlabs[continent]:\n", " groupedlabs[continent][country.name] = {}\n", " groupedlabs[continent][country.name][i[\"name\"]] = labs[i[\"name\"]]\n", " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Africa\n", "- Burkina Faso\n", "- Egypt\n", "- Ethiopia\n", "- Ghana\n", "- Kenya\n", "- Mali\n", "- Namibia\n", "- Réunion\n", "- Senegal\n", "- South Africa\n", "- Togo\n", "\n", "Asia\n", "- Afghanistan\n", "- Bahrain\n", "- China\n", "- India\n", "- Indonesia\n", "- Iran, Islamic Republic of\n", "- Israel\n", "- Japan\n", "- Korea, Republic of\n", "- Kuwait\n", "- Philippines\n", "- Saudi Arabia\n", "- Singapore\n", "- Taiwan, Province of China\n", "- Thailand\n", "- Turkey\n", "\n", "Europe\n", "- Austria\n", "- Belgium\n", "- Bulgaria\n", "- Czech Republic\n", "- Denmark\n", "- Finland\n", "- France\n", "- Germany\n", "- Greece\n", "- Hungary\n", "- Iceland\n", "- Ireland\n", "- Italy\n", "- Latvia\n", "- Luxembourg\n", "- Netherlands\n", "- Norway\n", "- Poland\n", "- Portugal\n", "- Russian Federation\n", "- Slovakia\n", "- Spain\n", "- Switzerland\n", "- Ukraine\n", "- United Kingdom\n", "\n", "North America\n", "- Canada\n", "- Costa Rica\n", "- El Salvador\n", "- Mexico\n", "- Puerto Rico\n", "- United States\n", "\n", "Oceania\n", "- Australia\n", "- New Zealand\n", "\n", "South America\n", "- Argentina\n", "- Brazil\n", "- Chile\n", "- Colombia\n", "- Ecuador\n", "- Peru\n", "- Suriname\n" ] } ], "source": [ "# Order alphabetically\n", "\n", "# Get list from continents and countries in the data\n", "continents = []\n", "countries = []\n", "for m in groupedlabs:\n", " continents.append(m)\n", " for j in groupedlabs[m]:\n", " countries.append(j) \n", "continents = sorted(continents)\n", "countries = sorted(countries)\n", "\n", "# Order continents and countries alphabetically\n", "sortedcontinents = OrderedDict(sorted(groupedlabs.items(), key=lambda t: t[0]))\n", "for k in sortedcontinents:\n", " sortedcontinents[k] = OrderedDict(sorted(sortedcontinents[k].items(), key=lambda t: t[0]))\n", "\n", "# Print output for debug\n", "for k in sortedcontinents:\n", " print \"\"\n", " print k\n", " for h in sortedcontinents[k]:\n", " print \"-\",h" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "***********************************************************\n", "\n", "Africa\n", "\n", "----------------------------------------------\n", "Burkina Faso\n", "\n", "ouagalab\n", "ouagalab.info\n", "https://www.facebook.com/pages/Ouagalab/159514010814943\n", "Ouagadougou\n", "Kadiogo\n", "http://www.ouagalab.info\n", "http://www.ouagalab.info\n", "[email protected] [email protected]\n", "\n", "----------------------------------------------\n", "Egypt\n", "\n", "Fab Lab Egypt\n", "10 Abdulrahman El-Rafei (infront of Shooting club gate #5) St., from Makkah St., Dokki\n", "Dokki\n", "Giza\n", "https://www.facebook.com/fablab.egypt\n", "http://www.fablab-egypt.com\n", "https://twitter.com/fablabegypt\n", "[email protected]\n", "\n", "icealex\n", "Ibrahim Sherif\n", "Alexandria\n", "http://icealex.com\n", "[email protected]\n", "\n", "icecairo\n", "32 Sabri Abo Alam Street\n", "1st floor, apartment 8, Downtown\n", "Cairo\n", "http://www.icecairo.com/fab-lab.php\n", "http://www.icecairo.com\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Ethiopia\n", "\n", "FabLab Addis\n", "Addis Ababa University\n", "Piaza\n", "Addis Ababa\n", "Oromia\n", "http://fablabaddis.wordpress.com\n", "http://www.fablabaddis.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Ghana\n", "\n", "Takoradi Technical Institute\n", "Takoradi\n", "Western\n", "http://www.takoraditech.org/?q=node/34\n", "http://ttifab.wikispaces.com/How+to+Use+the+TTI+Fab+Lab+Wiki\n", "\n", "\n", "----------------------------------------------\n", "Kenya\n", "\n", "University of Nairobi\n", "Nairobi\n", "Nairobi\n", "http://fablab.uonbi.or.ke\n", "None\n", "\n", "----------------------------------------------\n", "Mali\n", "\n", "Bamako Fablab\n", "ACI 2000\n", "Bamako\n", "HAMDALLAYE\n", "[email protected]\n", "\n", "esiaulab\n", "[email protected]\n", "[email protected]\n", "Bamako\n", "Mali\n", "http://atelier.rfi.fr/profiles/status/show?id=1189413%3AStatus%3A409652\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Namibia\n", "\n", "Fab Lab Windhoek\n", "Windhoek\n", "Khomas\n", "https://www.facebook.com/fablab.namibia\n", "\n", "\n", "----------------------------------------------\n", "Réunion\n", "\n", "FabLab.re\n", "La Rivière Saint-Louis\n", "La Riviere\n", "http://fablab.re\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Senegal\n", "\n", "Defaral Sa Labo\n", "S.I.C.A.P. Liberte 2\n", "Dakar\n", "http://www.ker-thiossane.org/spip.php?article137\n", "http://wiki.fablab.is/wiki/DefaralSaLabo_Dakar\n", "[email protected]\n", "\n", "----------------------------------------------\n", "South Africa\n", "\n", "Kimberly FabLab\n", "1317 Solomon Mekgwe Street\n", "Old Kitsong Training Centre\n", "Kimberley\n", "Northern Cape\n", "http://www.fablab.co.za/index.php?option=com_content&view=article&id=5&Itemid=29\n", "[email protected]\n", "\n", "Bright Youth Council\n", "Soshanguve\n", "Gauteng\n", "http://blogs.fabfolk.com/sosh\n", "\n", "\n", "Central University of Technology\n", "Bloemfontein\n", "Free State\n", "\n", "\n", "North West University\n", "Potchefstroom\n", "North West\n", "http://www.nwu.ac.za/fe/fablab\n", "http://fablabpotchefstroom.yolasite.com\n", "\n", "\n", "Limpopo Fablab\n", "1025 Science Education Centre, University of Limpopo(Turfloop campus)\n", "Private Bag X1106, Sovenga, 0727\n", "Polokwane, Mankweng \n", "Limpopo\n", "http://www.fablab.co.za\n", "[email protected]\n", "\n", "Ekurhuleni fablabs (Fablab Tembisa and Fablab Thokoza)\n", "45 Thami Mnyele Drive \n", "Tembisa\n", "Guateng\n", "[email protected]\n", "\n", "Cape Craft and Design Institute\n", "Cape Town\n", "Western Cape\n", "http://www.youtube.com/user/capecraftanddesign?feature=watch\n", "http://www.pinterest.com/capecraftdesign/\n", "https://www.facebook.com/pages/Cape-Craft-and-Design-Institute/152396544910856\n", "http://www.ccdi.org.za\n", "\n", "\n", "----------------------------------------------\n", "Togo\n", "\n", "WOELAB\n", "Lomé\n", "WOELAB-Lomé Villa 227('Djidjolé en allant vers le marché), 110 Aflao-Gakli, Lome, Togo\n", "Lomé\n", "http://www.lafricainedarchitecture.com/hubciteacute.html\n", "http://www.lafricainedarchitecture.com/\n", "https://twitter.com/woelab\n", "https://www.facebook.com/pages/W%C9%94%C9%9BLab-Lom%C3%A9/183098345158986?ref=ts&fref=ts\n", "http://www.woelabo.com/\n", "[email protected]\n", "*********************************************************\n", "\n", "Asia\n", "\n", "----------------------------------------------\n", "Afghanistan\n", "\n", "Fab Lab Afghanistan\n", "Jalalabad, Afghanistan\n", "Jalalabad\n", "Nangarhar\n", "http://www.fablab.af\n", "None\n", "\n", "----------------------------------------------\n", "Bahrain\n", "\n", "Fab Lab Bahrain\n", "\n", "http://fablab-bahrain.blogspot.com\n", "http://bahrainfablab.wordpress.com\n", "[email protected]\n", "\n", "FabLab BH\n", "Manama\n", "Manama\n", "http://@FabLab_Bahrain\n", "[email protected]\n", "\n", "----------------------------------------------\n", "China\n", "\n", "新Fab - XinFab \n", "Yu Yuan East Road, 29, Building 3\n", "Shanghai \n", "Shanghai\n", "http://www.xinfab.com\n", "[email protected]\n", "\n", "Fab Lab FBI\n", "Taicang\n", "Jiangsu\n", "http://fablabfbi.oddist.org\n", "[email protected]\n", "\n", "Fablab-Shanghai\n", "No.281 Fuxin Road\n", "College of Design and Innovation, Tongji University\n", "Shanghai\n", "China\n", "http://Fablab.tongji.edu.cn\n", "http://www.fablabshanghai.tongji.edu.cn\n", "[email protected]\n", "\n", "Fablab Shanghai\n", "No.281, Fuxin Road\n", "Shanghai\n", "China\n", "http://weibo.com/fablab\n", "[email protected]\n", "\n", "FabLab Gezhi\n", "Gezhi High School\n", "66 Guangxi North Road\n", "Shanghai\n", "China\n", "\n", "\n", "----------------------------------------------\n", "India\n", "\n", "FabLab CEPT\n", "CEPT Universty -- Kasturbhai Lalbhai Campus\n", "University Road, Navrangpura\n", "Ahmedabad\n", "Gujarat\n", "[email protected]\n", "\n", "College of Engineering, Pune\n", "Pune\n", "Maharashtra\n", "http://www.coep.org.in\n", "None\n", "\n", "Netaji Subhas Institute of Technology\n", "New Delhi\n", "Delhi\n", "None\n", "\n", "Vigyan Ashram\n", "Pabal\n", "Maharashtra\n", "http://www.vigyanashram.com\n", "None\n", "\n", "National Innovation Foundation\n", "Ahmedabad\n", "Gujarat\n", "None\n", "\n", "Indian Institute of Technology\n", "Kanpur\n", "Uttar Pradesh\n", "None\n", "\n", "----------------------------------------------\n", "Indonesia\n", "\n", "HONFablab\n", "Jalan Taman Siswa\n", "Yogyakarta\n", "Yogyakarta City\n", "Special District of Yogyakarta\n", "http://www.vimeo.com/fablabyogyakarta\n", "http://www.facebook.com/fablab.yogyakarta\n", "https://www.twitter.com/honfablab\n", "http://www.honfablab.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Iran, Islamic Republic of\n", "\n", "CARBON Fab Lab\n", "#112, Bisotoun St, Yousefabad\n", "Tehran\n", "https://www.facebook.com/pages/--Carbon-Ideas-Fab-Lab/120457534687417\n", "http://www.carbonstudio.ir/wordpress/\n", "http://digitalfabrication.ir/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Israel\n", "\n", "FabLab IL (Fab Lab Israel)\n", "4 Haamoraim st\n", "Holon\n", "Tel-Aviv\n", "https://www.facebook.com/fablabilholon\n", "http://fablabil.org\n", "[email protected]\n", "\n", "Wanger family FabLab MadaTech\n", "Balfour 12\n", "MadaTech Museum, Education Building\n", "Haifa\n", "https://www.facebook.com/groups/192976580730340/\n", "http://www.pinterest.com/fablabmadatech\n", "[email protected]\n", "\n", "FabLab Jerusalem\n", "Jerusalem\n", "Jerusalem\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Japan\n", "\n", "FabLab Kannai\n", "中区相生町３丁目６１泰生ビル２F\n", "横浜市\n", "神奈川県\n", "https://www.facebook.com/FabLabKannai\n", "http://fablab-kannai.org/\n", "[email protected]\n", "\n", "Social Fabrication Center Keio University, SUPER FABLAB\n", "馬車道駅\n", "YOKOHAMA\n", "Kanagawa\n", "http://sfc.sfc.keio.ac.jp\n", "\n", "\n", " FABLAB Hamamatsu\n", "3645, Nishikamoe-cho, Nishi-ku\n", "Hamamatsu-city\n", "Shizuoka-ken, Japan\n", "http://www.take-space.com\n", "[email protected]\n", "\n", "FabCafe Tokyo\n", "1-22-7 Dogenzaka \n", "Shibuya \n", "Tokyo\n", "http://fabcafe.com/\n", "[email protected]\n", "\n", "FabLab DAZAIFU\n", "2-19-30 Tofurominami\n", "EK JAPAN Co., Ltd\n", "Dazaifu-shi\n", "Fukuoka-ken\n", "http://fablabdazaifu.com/\n", "[email protected]\n", "\n", "Intilaq Tohoku Innovators Hub\n", "卸町１丁目２−９\n", "Sendai\n", "Miyagi-Ku\n", "http://www.impactjapan.org\n", "[email protected]\n", "\n", "FabLab Shibuya\n", "Co-lab Shibuya Atelier 1-3\n", "Shibuya\n", "Tokyo\n", "https://www.facebook.com/fablabshibuya\n", "http://fablabshibuya.org\n", "[email protected]\n", "\n", "FPGA-CAFE/FabLab Tsukuba\n", "Ritchmond 2 street\n", "2-9-2 Amakubo\n", "つくば市\n", "Ibaraki Prefecture\n", "http://fpgacafe.com\n", "[email protected]\n", "\n", "Fablab Kamakura\n", "Yui no Kura #1, Ougigayatsu\n", "Kamakura-city\n", "Kamakura\n", "Kanagawa Prefecture\n", "https://twitter.com/fablabkamakura\n", "http://www.facebook.com/pages/FabLabKamakura/359621684057046\n", "http://www.fablabkamakura.com\n", "[email protected]\n", "\n", "FabLab SENDAI\n", "青葉区一番町2-2-8 IKIビル4F\n", "仙台市\n", "宮城県\n", "http://www.fablabsendai-flat.com\n", "[email protected]\n", "\n", "Fab Lab Oita\n", "51-6 Higashi Kasuga\n", "Oita\n", "Oita\n", "http://www.faboita.org\n", "[email protected] \n", "\n", "FabLab Kitakagaya\n", "5-4-12 Kitakagaya\n", "Suminoe-ku\n", "Osaka-shi\n", "http://fablabkitakagaya.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Korea, Republic of\n", "\n", "Fab Lab Seoul\n", "Seun-Sangka Room 550 \n", "Chungkyechun-Ro 159, Jongno-Gu \n", "Seoul\n", "http://fablab-seoul.org/\n", "http://www.tideinstitute.org\n", "[email protected]\n", "\n", "KAIST Idea Factory\n", "291 Daehak-ro, Yuseong-gu\n", "Daejeon\n", "http://risti.kaist.ac.kr\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Kuwait\n", "\n", "fablab Q8\n", "الكويت‎\n", "kuwait city\n", "kuwait\n", "http://instagram.com/fablabq8\n", "http://www.sacgc.org\n", "[email protected]\n", "\n", "fablabq8\n", "in youth center\n", "hawalli\n", "http://www.instgram/fablabq8.com\n", "http://www.sacgc.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Philippines\n", "\n", "FabLab Bohol\n", "Main Campus of Bohol Island State University\n", "CPG North Ave., 6300\n", "Tagbilaran City\n", "Bohol\n", "http://bohol.fablabphilippines.com\n", "\n", "\n", "----------------------------------------------\n", "Saudi Arabia\n", "\n", "Fab Lab Arabia\n", "Dallah Tower\n", "Palestine St.\n", "Jeddah\n", "Makkah Province\n", "http://www.facebook.com/pages/Fab-Lab-Arabia/162135563892237\n", "http://fablabarabia.com\n", "[email protected]\n", "\n", "Fab Lab Dhahran\n", "Khobar\n", "Eastern Province\n", "http://instagram.com/fablabdhahran\n", "http://fablabdhahran.org\n", "https://www.facebook.com/KingAbdulazizCenterForWorldCulture\n", "https://twitter.com/fablabdhahran\n", "\n", "\n", "----------------------------------------------\n", "Singapore\n", "\n", "FabLab@SP\n", "500 Dover Rd\n", "T11C\n", "Singapore\n", "https://www.facebook.com/groups/635485616525389/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Taiwan, Province of China\n", "\n", "FabCafe Taipei\n", "No. 1, Bade Road Sec. 1, Zhong Zhen District, \n", "Taipei\n", "http://taipei.fabcafe.com/\n", "[email protected]\n", "\n", "Fablab Dynamic\n", "180 Fúhuá Road, Shilin District\n", "Taipei City\n", "Taiwan\n", "http://vimeo.com/user24433735\n", "http://www.fablabtaiwan.org.tw/\n", "http://www.youtube.com/watch?v=WklbEAPbtoQ\n", "https://www.facebook.com/pages/Fablab-Dynamic/532700190113349\n", "[email protected]\n", "\n", "Fablab Tainan\n", "No.21, Nanmen Rd., West Central Dist.,\n", "Tainan City\n", "Taiwan\n", "https://www.facebook.com/groups/fablabtainan\n", "[email protected]\n", "\n", "Fablab Taipei\n", "1F 9 ln 15 Sec 3 Chongqing S Rd\n", "Taipei City\n", "Taiwan\n", "https://www.facebook.com/groups/fablabtaipei/\n", "http://facebook.com/FablabTPE\n", "http://fablabtaipei.org\n", "[email protected]\n", "\n", "MakerBar\n", "5F., No.9, Sec. 1, Jinshan S. Rd., Zhongzheng Dist\n", "Taipei\n", "T'ai-pei city\n", "https://www.facebook.com/MakerbarTaipei\n", "http://www.makerbartaipei.com\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Thailand\n", "\n", "Fabcafe Bangkok\n", "Ari 2\n", "Bangkok\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Turkey\n", "\n", "Fablab Istanbul\n", "Kadir Has Caddesi Cibali / İSTANBUL \n", "Istanbul\n", "Fatih\n", "https://twitter.com/fablabist\n", "https://www.facebook.com/Fablabist\n", "http://fablabist.com/\n", "[email protected]\n", "*********************************************************\n", "\n", "Europe\n", "\n", "----------------------------------------------\n", "Austria\n", "\n", "Maker Space Vienna\n", "Schönbrunner Straße 125\n", "Wien\n", "Austria\n", "http://www.makeraustria.at\n", "[email protected]\n", "\n", "Happylab Salzburg\n", "Jakob-Haringer-Straße 8\n", "Techno-Z Salzburg, Techno 5\n", "Salzburg\n", "http://www.happylab.at\n", "[email protected]\n", "\n", "FabLab Graz\n", "Inffeldgasse 11/I\n", "Graz\n", "Styria\n", "http://fablab.tugraz.at\n", "[email protected]\n", "\n", "Happylab Vienna\n", "Haussteinstraße 4, 1020\n", "Vienna\n", "https://www.facebook.com/happylab.at\n", "http://www.happylab.at/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Belgium\n", "\n", "BUDA::lab\n", "Dam 2a\n", "Kortrijk\n", "http://www.budalab.be\n", "\n", "\n", "fablab iMAL\n", "30-34 Quai des Charbonnages Koolmijnenkaai\n", "Brussels\n", "http://www.imal.org/fablab\n", "\n", "\n", "Fablab-Leuven\n", "Celestijnenlaan 300\n", "Katholieke Universiteit Leuven\n", "Leuven\n", "Flanders\n", "http://www.flickr.com/photos/fablableuven/\n", "https://www.facebook.com/groups/321746647866201/\n", "http://www.fablab-leuven.be\n", "[email protected]\n", "\n", "MAKILAB\n", "Louvain-la-Neuve\n", "chemin du cyclotron, 6\n", "Ottignies-Louvain-la-Neuve\n", "http://makilab.org\n", "[email protected]\n", "\n", "FabLab Genk\n", "Houtparklaan 1\n", "Genk\n", "Flanders\n", "http://www.twitter.com/fablabgenk\n", "http://www.facebook.com/FablabGenk\n", "http://www.fablabgenk.be\n", "[email protected]\n", "\n", "Fablab Brussels\n", "Nijverheidskaai 170\n", "Anderlecht\n", "Brussels\n", "https://maps.google.be/maps?q=Fablab+Brussels&hl=nl&sll=50.8387,4.363405&sspn=0.307434,0.718231&hq=Fablab+Brussels&t=m&z=11&iwloc=A\n", "http://www.fablab-brussels.be\n", "[email protected]\n", "\n", "RElab\n", "Rue Lambert Lombard, 1\n", "(place Saint-Etienne)\n", "Liege\n", "https://www.facebook.com/relabliege\n", "http://relab.be\n", "[email protected]\n", "\n", "Timelab\n", "Ghent\n", "Flanders\n", "http://www.timelab.org\n", "\n", "\n", "----------------------------------------------\n", "Bulgaria\n", "\n", "Smart Fab Lab \n", "Sofia Center\n", "1 Hristo Smirnenski Street\n", "Sofia\n", "http://www.smartfablab.org/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Czech Republic\n", "\n", "Hradec Kralove\n", "Hradec Kralove\n", "Hradec Králové Region\n", "http://fleq.cz\n", "\n", "\n", "----------------------------------------------\n", "Denmark\n", "\n", "Copenhagen Fablab\n", "Valby Kulturhus\n", "Copenhagen Fablab\n", "Valgårdsvej 4-8\n", "Valby\n", "http://www.kulturvalby.dk/fablab\n", "http://kulturogfritid.kk.dk/kultur-valby/copenhagen-fablab\n", "[email protected]\n", "\n", "FabLab Spinderihallerne \n", "Danmark\n", "Spinderigade 11E\n", "Vejle\n", "Denmark\n", "[email protected]\n", "\n", "FabLab Innovation\n", "Sverigesgade 5\n", "3th floor\n", "Odense\n", "https://twitter.com/FabLabInno\n", "https://www.facebook.com/FabLabInno\n", "http://www.fablabinnovation.dk\n", "[email protected]\n", "\n", "Fablab Danmark\n", "Maglemølle\n", "Næstved\n", "Sjælland\n", "http://fablabdanmark.dk\n", "[email protected]\n", "\n", "Fablab Nordvest\n", "Glentevej 70B\n", "København\n", "http://www.facebook.com/fablabnordvest\n", "http://www.instagram.com/fablabnordvest\n", "http://www.fablabnordvest.dk\n", "[email protected]\n", "\n", "Fablab TI\n", "København\n", "Gregersensvej 1\n", "København\n", "Danmark\n", "http://instagram.com/opfindnu\n", "https://www.facebook.com/fablabti\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Finland\n", "\n", "Aalto Fab Lab\n", "Hämeentie 135\n", "Helsinki\n", "Suomi\n", "http://mediafactory.aalto.fi/fablab \n", "http://www.flickr.com/photos/70184309@N07/10960603285\n", "http://www.flickr.com/people/aaltomediafactory\n", "https://twitter.com/AaltoMedia\n", "http://vimeo.com/aaltomediafactory\n", "https://www.facebook.com/aaltomediafactory\n", "\n", "\n", "----------------------------------------------\n", "France\n", "\n", "Le Garage\n", "Saint-Denis\n", "Saint-Denis\n", "[email protected]\n", "\n", "ACoLab\n", "32 Rue du Pont Naturel\n", "Clermont-Ferrand\n", "http://acolab.fr\n", "[email protected]\n", "\n", "Fabrique d'Objets Libres\n", "Allée Gaillard Romanet\n", "MJC Louis Aragon\n", "Bron\n", "Rhône\n", "http://fablab-lyon.fr\n", "[email protected]\n", "\n", "Fab Lab La Casemate\n", "CCSTI Grenoble La Casemate\n", " 2 place Saint Laurent\n", "Grenoble\n", "Rhone-Alpes\n", "https://www.facebook.com/FablabGrenoble\n", "http://fablab.ccsti-grenoble.org\n", "[email protected]\n", "\n", "FabLab Net-IKi\n", "3 Rue de l'Église\n", "Biarne\n", "Franche-Comté\n", "http://www.fablab-net-iki.org \n", "[email protected]\n", "\n", "RuralLab\n", "Rue de l'École\n", "Néons-sur-Creuse\n", "http://www.rurallab.org\n", "[email protected]\n", "\n", "FabLab caen\n", "Rue Léopold Sédar-Senghor\n", "Colombelles\n", "http://www.relais-sciences.org/fablab/\n", "[email protected]\n", "\n", "Atelier Pobot\n", "190 Rue Frédéric Mistral\n", "Centre International de Valbonne - Agora\n", "Valbonne\n", "Alpes-Maritimes\n", "[email protected]\n", "\n", "Open Edge\n", "6 Avenue Foch\n", "Folschviller\n", "http://wiki.fablab.is/wiki/OpenEdge\n", "http://openedge.cc\n", "[email protected]\n", "\n", "Chantier Libre\n", "Place de la Gare\n", "L'Hôpital sur Rhins\n", "Saint-Cyr-de-Favières\n", "http://www.chantierlibre.org\n", "[email protected]\n", "\n", "Photonic FabLab\n", "Bâtiment 503\n", "Rue du Belvédère\n", "Orsay\n", "http://www.institutoptique.fr/\n", "http://bit.ly/ProtoListes\n", "[email protected]\n", "\n", "FabLab CHAMPAGNOLE\n", "Lycée Paul Emile Victor de CHAMPAGNOLE 625 Rue de Gottmadingen\n", "8 rue Marandet Le Pasquier 39300\n", "Champagnole\n", "Jura/Franche-Comté/FRANCE\n", "https://www.facebook.com/FabLabChampagnole\n", "http://www.netvibes.com/fablabchampagnole \n", "[email protected]\n", "\n", "LimouziLab\n", "2 bis impasse daguerre\n", "Limoges\n", "http://twitter.com/limouzilab\n", "http://www.fb.me/limouzilab\n", "http://lab.limouzi.org\n", "[email protected]\n", "\n", "The Glass Fablab\n", "Rue de la Liberté\n", "Vannes-le-Châtel\n", "https://twitter.com/TheGlassFablab\n", "https://twitter.com/CerfavFablab\n", "http://www.cerfav.fr/fablab/\n", "[email protected]\n", "\n", "FabLab INSA Strasbourg\n", "Strasbourg\n", "Alsace\n", "http://www.ideaslab.fr\n", "None\n", "\n", "TechLab LR\n", "49 rue Super Nova\n", "Vailhauquès\n", "http://techlablr.fr\n", "[email protected]\n", "\n", "Fablab Lannion - KerNEL\n", "14 Rue de Beauchamp\n", "Lannion\n", "http://fablab-lannion.org\n", "[email protected]\n", "\n", "La Machinerie\n", "70 rue des Jacobins\n", "Amiens\n", "Somme\n", "https://twitter.com/Machinerie\n", "https://www.facebook.com/LaMachinerieAmiens\n", "http://lamachinerie.org\n", "[email protected]\n", "\n", "Antibes NavLab\n", "Antibes\n", "3 Boulevard Wilson\n", "Antibes\n", "Provence-Alpes-Côte d'Azur\n", "http://imaginationforpeople.org/fr/project/le-navlab-un-fablab-nautique-a-antibes/\n", "http://www.kisskissbankbank.com/navlab\n", "http://www.viadeo.com/v/company/navlab\n", "http://www.linkedin.com/company/navlab?trk=company_name\n", "https://twitter.com/NavLabAntibes\n", "https://www.facebook.com/navlab?ref=hl\n", "https://www.facebook.com/pages/Navlab-English/455206721245993?ref=hl\n", "http://navlab.avitys.com\n", "[email protected]\n", "\n", "L'ETABLI\n", "Pole Associatif Résano-Lapègue\n", "rue de Moscou\n", "Soustons\n", "[email protected]\n", "\n", "technistub\n", "130 Rue de la Mer Rouge\n", "Mulhouse\n", "http://www.technistub.fr\n", "\n", "\n", "Makerspace 56\n", "Place Albert Einstein\n", "Vannes\n", "http://www.makerspace56.org\n", "\n", "\n", "AV-Lab\n", "37 rue des frères\n", "Strasbourg\n", "Alsace\n", "http://www.av-exciters.com/AV-Lab\n", "[email protected]\n", "\n", "Fab Lab Provence\n", "Aix-en-Provence\n", "Aix-en-Provence\n", "http://fablab-provence.com/\n", "[email protected]\n", "\n", "Fabriques Alternatives\n", "Mont-de-Marsan\n", "Mont-de-Marsan\n", "https://plus.google.com/114201372084258307863\n", "https://www.facebook.com/pages/Fabriques-Alternatives/712873455394908\n", "http://www.fabriques-alternatives.org\n", "[email protected]\n", "\n", "LH3D fablab\n", "1 Rue Dumé d'Aplemont\n", "Havre (Le)\n", "Haute Normandie\n", "http://www.lh-fab-lab.e-monsite.com\n", "[email protected]\n", "\n", "FabLab Robert-Houdin\n", "39D Allée des Pins\n", "Blois\n", "http://fablab-robert-houdin.org/\n", "[email protected]\n", "\n", "8 FabLab Drôme\n", "8 rue courre-commère\n", "Crest\n", "Drôme\n", "http://www.8fablab.fr\n", "[email protected]\n", "\n", "Smart Materials\n", "Boulevard Jean Delautre\n", "Charleville-Mézières\n", "http://fablab.ifts.net/\n", " [email protected]\n", "\n", "Laboratoire d'Aix-périmentation et de bidouille\n", "Département Informatique de l'IUT d'Aix-en-Provence\n", "413 Avenue Gaston Berger\n", "Aix-en-Provence\n", "http://www.labaixbidouille.com\n", "[email protected]\n", "\n", "FabLab Pau\n", "18 Rue Latapie\n", "Pau\n", "http://www.fablab-pau.org\n", "[email protected]\n", "\n", "Nybi.cc\n", "9 Rue d'Alsace\n", "Jarville-la-Malgrange\n", "http://wiki.nybi.cc\n", "http://nybi.cc\n", "[email protected]\n", "\n", "PiNG\n", "Nantes\n", "Pays de la Loire\n", "http://fablab.pingbase.net\n", "None\n", "\n", "FabLab Lille\n", "2 Allée Lakanal\n", "Villeneuve-d'Ascq\n", "Nord-Pas-de-Calais\n", "http://www.flickr.com/photos/fablablille/\n", "https://twitter.com/FabLab_Lille\n", "http://www.fablablille.fr\n", "\n", "\n", "labfab de Rennes\n", "Rennes\n", "Brittany\n", "http://labfab.fr\n", "\n", "\n", "Fablab Web-5\n", "3, place du 14 juillet\n", " IUT de Béziers\n", "Béziers\n", "http://fablab.web-5.org\n", "mailto:[email protected]\n", "\n", "Bio-Fab\n", " CRBM-IGMM-CPBS - CNRS 1919 Route de Mende\n", "Montpellier\n", "http://www.crbm.cnrs.fr/index.php/fr/news-du-s-e-m/417-bio-fab\n", "[email protected]\n", "\n", "Les Fabriques du Ponant / TyFab\n", "40 Rue Jules Lesven\n", "Bâtiment X - Lycée Vauban\n", "Brest\n", "http://www.lesfabriquesduponant.net\n", "http://twitter.com/fabduponant\n", "http://www.tyfab.fr\n", "http://wiki.lesfabriquesduponant.net\n", "[email protected]\n", "\n", "LABSud Montpellier\n", "Hotel d'Entreprise de l'Agglomeration de Montpelllier\n", "120 Allée John Napier\n", "Montpellier\n", "http://forum.labsud.org\n", "http://www.labsud.org\n", "[email protected]\n", "\n", "FunLab Tours\n", "30, Rue André Theuriet\n", "Tours\n", "http://funlab.fr\n", "[email protected]\n", "\n", "FAB LAB Château Thierry\n", "Château-Thierry\n", "Château-Thierry\n", "avenue de l'Europe\n", "https://www.facebook.com/fablab.chateau.thierry\n", "[email protected]\n", "\n", "fablab Kelle FabriK\n", "Dijon\n", "IUT Dijon\n", "Dijon\n", "http://kellefabrik.blogspot.fr/\n", "[email protected]\n", "\n", "Le Petit FabLab de Paris\n", "156 Rue Oberkampf\n", "2ème atelier sur la droite \n", "Paris\n", "http://lepetitfablabdeparis.fr\n", "[email protected]\n", "\n", "Parthlab\n", "5 Rue Jean Macé\n", "Parthenay\n", "http://parthlab.cc-parthenay.fr/\n", "[email protected]\n", "\n", "FacLab\n", "Avenue Marcel Paul\n", "Allée des Pierres Mayettes\n", "Gennevilliers\n", "Île-de-France\n", "http://www.youtube.com/user/Faclabucp/\n", "https://twitter.com/FacLabUcp\n", "https://www.facebook.com/faclab\n", "http://www.faclab.org\n", "\n", "\n", "la refabrique\n", "14 rue Jean Giono\n", "les olivarelles 2 - Villa 8\n", "Cannes\n", "Provence-Alpes-Cote d'Azur\n", "http://www.la-refabrique.fr\n", "[email protected]\n", "\n", "(Fab)Lab Digiscope\n", "660 Rue Noetzlin\n", "Gif-sur-Yvette\n", "http://fablabdigiscope.wordpress.com\n", "[email protected]\n", "\n", "La Fabulerie\n", "4 Rue de la Bibliothèque\n", "Marseille\n", "http://www.lafabulerie.com\n", "[email protected]\n", "\n", "ECODESIGN FAB LAB\n", "Montreuil\n", "2 à 20 avenue Allende, MOZINOR\n", "Montreuil \n", "http://www.apedec.org \n", "http://webtv.montreuil.fr/festival-m.u.s.i.c-et-fablab-video-415-12.html\n", "http://www.wedemain.fr/A-Montreuil-un-fab-lab-circulaire-dans-une-usine-verticale_a421.html\n", "[email protected]\n", "\n", "Nouvelle Fabrique\n", "104 Rue d'Aubervilliers\n", "Paris\n", "France\n", "http://www.nouvellefabrique.fr\n", "[email protected]\n", "\n", "Artilect FabLab Toulouse\n", "27bis Allées Maurice Sarraut\n", "Toulouse\n", "Midi-Pyrénées\n", "http://www.artilect.fr\n", "http://vimeo.com/user4871340\n", "http://www.youtube.com/user/fabLabArtilect\n", "http://twitter.com/FabLab_Toulouse\n", "http://www.facebook.com/pages/Artilect-FabLab-Toulouse\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Germany\n", "\n", "FabLab-Lübeck\n", "Seelandstraße 5\n", "TZL Building 4\n", "Lübeck\n", "Schleswig-Holstein\n", "http://www.gruendercube.de\n", "http://www.unitransferklinik.de\n", "http://www.tzl.de\n", "http://www.tzl.de/fablab/\n", "[email protected]\n", "\n", "Dingfabrik Koeln e.V.\n", "Cologne\n", "North Rhine-Westphalia\n", "https://www.facebook.com/dingfabrik\n", "https://twitter.com/dingfabrik\n", "http://www.dingfabrik.de\n", "\n", "\n", "FabLab Dresden\n", "Wernerstraße 11\n", "Dresden\n", "Sachsen\n", "http://www.werkstadtladen.de\n", "http://www.fablabdd.de\n", "[email protected]\n", "\n", "Fab Lab Berlin\n", "Saarbrücker Straße 24\n", "Haus C\n", "Berlin\n", "http://www.meetup.com/FabLabBerlin/members/117574612/\n", "http://www.3dhubs.com/berlin/hubs/fab-lab\n", "https://twitter.com/FabLabBLN/\n", "https://www.facebook.com/FabLabBerlin\n", "http://www.fablab-berlin.org\n", "[email protected]\n", "\n", "Fablab Regensburg\n", "Grunewaldstraße 5\n", "Regensburg\n", "https://www.facebook.com/fablabregensburg\n", "http://www.fablab-regensburg.de\n", "[email protected]\n", "\n", "FabLab Bremen\n", "Bremen\n", "Bremen\n", "http://www.fablab-bremen.org\n", "[email protected]\n", "\n", "FabLab Karlsruhe\n", "Alter Schlachthof 13a\n", "Karlsruhe\n", "Baden-Württemberg\n", "http://fablab-karlsruhe.de\n", "[email protected]\n", "\n", "Fab Lab Region Nuernberg e.V.\n", "Muggenhofer Straße 141\n", "Nuremberg\n", "Bavaria\n", "http://wiki.fablab-nuernberg.de\n", "http://www.fb.com/FabLabNuernberg\n", "http://www.fablab-nuernberg.de\n", "[email protected]\n", "\n", "FabLab Magdeburg\n", "Universitätsplatz 2\n", "Otto-von-Guericke-University\n", "Magdeburg\n", "Saxony-Anhalt\n", "https://www.facebook.com/FabLab.Magdeburg\n", "http://www.inkubator.ovgu.de/FabLab\n", "[email protected]\n", "\n", "OpenLab\n", "Königstraße 20A\n", "Schwabach\n", "http://jugendzentrum-schwabach.de/forder-verein/openlab/\n", "[email protected]\n", "\n", "Fabulous St. Pauli\n", "Sternstraße 2\n", "(im Centro Sociale Nord-Ost Ecke)\n", "Hamburg\n", "Hamburg\n", "http://www.fablab-hamburg.org\n", "\n", "\n", "FabLab Paderborn e.V.\n", "Westernmauer 12\n", "Paderborn\n", "http://www.fablab-paderborn.de\n", "[email protected]\n", "\n", "FabLab Region Rothenburg o.d.T. e.V.\n", "Deutschherrngasse 1\n", "über dem Jugendzentrum\n", "Rothenburg ob der Tauber\n", "Bavaria\n", "http://www.fablab-rothenburg.de\n", "https://de-de.facebook.com/FabLabRothenburg\n", "[email protected]\n", "\n", "ViNN:Lab (TH Wildau)\n", "Hochschulring 1\n", "Building 16A \| Room 2094\n", "Wildau\n", "Brandenburg\n", "http://www.facebook.com/ViNNLab\n", "http://www.offene-werkstaetten.org/werkstatt/vinnlab\n", "http://www.th-wildau.de/creativelab\n", "[email protected]\n", "\n", "FAU FabLab\n", "Erwin-Rommel-Straße 60\n", "Erlangen\n", "Bavaria\n", "https://twitter.com/FAUFabLab\n", "http://fablab.fau.de\n", "[email protected]\n", "\n", "FabLab München\n", "Gollierstraße 70 D\n", "Munich\n", "Bavaria\n", "https://www.facebook.com/FabLabMuc\n", "https://twitter.com/fablabmuc\n", "http://www.fablab-muenchen.de\n", "[email protected]\n", "\n", "Fab Lab Siegen\n", "Siegen\n", "Siegen\n", "http://twitter.com/fablabsiegen\n", "http://facebook.com/fablabsiegen\n", "http://fablab-siegen.de\n", "[email protected]\n", "\n", "FabLab-Bayreuth\n", "Ritter-von-Eitzenberger-Straße 19\n", "Bayreuth\n", "Bavaria\n", "http://www.fablab-bayreuth.de\n", "[email protected]\n", "\n", "machbar potsdam\n", "Potsdam\n", "Friedrich-Engels-Strasse 22\n", "Potsdam\n", "Brandenburg\n", "http://www.wissenschaftsladen-potsdam.de\n", "http://www.machbar-potsdam.de\n", "[email protected]\n", "\n", "FabLab Aachen - RWTH Aachen\n", "Ahornstr. 55\n", "Room 2214 (2nd floor)\n", "Aachen\n", "North Rhine-Westphalia\n", "https://plus.google.com/photos/112431217600462712880/albums/5885215609266891137\n", "http://hci.rwth-aachen.de/fablab\n", "[email protected]\n", "\n", "L1A Makerspace\n", "Lauteschlägerstraße 1A\n", "Darmstadt\n", "Hessen\n", "https://www.facebook.com/L1A.Makerspace\n", "http://www.l1a.de\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Greece\n", "\n", "Fab Lab Athens\n", "Athens\n", "Attica\n", "https://twitter.com/fablabathens\n", "https://www.facebook.com/pages/Fab-Lab-Athens/344269965659213?ref=hl\n", "http://www.fablabnetwork.gr\n", "http://www.fablabathens.gr\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Hungary\n", "\n", "FabLab Budapest\n", "Eötvös St 29-27.\n", "Budapest\n", "http://www.fablabbudapest.com\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Iceland\n", "\n", "FabLab Saudarkrokur, Innovation Center Iceland\n", "Sauðárkrókur\n", "Northwest\n", "http://www.fablab.is/w/index.php/Main_Page/%C3%8Dslenska\n", "\n", "\n", "Fab Lab Vestmannaeyjar Iceland\n", "Faxastígur 36\n", "Vestmannaeyjabær\n", "http://wiki.fablab.is\n", "http://www.fablab.is\n", "http://www.nmi.is/impra/fab-lab\n", "[email protected]\n", "\n", "FabLab Isafjordur\n", "Torfnes\n", "Ísafjörður\n", "http://wiki.fablab.is/wiki/Fab_Lab_Portal\n", "[email protected]\n", "\n", "Fablab Reykjavik\n", "Eddufell 2\n", "Reykjavik\n", "http://fablab.is/reykjavik\n", "[email protected]\n", "\n", "Fab Lab Akranes, Innovation Center Iceland\n", "Akranes\n", "West\n", "http://fablabakranes.is\n", "None\n", "\n", "----------------------------------------------\n", "Ireland\n", "\n", "Fab Lab Limerick\n", "Limerick\n", "7 Rutland Street\n", "Limerick\n", "Limerick\n", "http://fablab.saul.ie\n", "[email protected]\n", "\n", "WeCreate Workspace\n", "North Tipperary Enterprise Park\n", "Cloughjordan\n", "Co Tipperary\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Italy\n", "\n", "Fablab Roma Makers\n", "Via Giovanni Battista Magnaghi, 59\n", "Roma\n", "Rm\n", "http://fablab.romamakers.org\n", "[email protected]\n", "\n", "Fablab Varese\n", "Via Varese\n", "Varese\n", "https://plus.google.com/+FablabvareseIt\n", "https://www.facebook.com/FablabVarese\n", "http://www.fablabvarese.it\n", "\n", "\n", "FabLab Palermo\n", "Via mariano stabile, 52\n", "Palermo\n", "Italy\n", "https://plus.google.com/u/0/b/100116212512482122910/100116212512482122910\n", "http://twitter.com/fablabpalermo\n", "http://www.facebook.com/fablabpalermo\n", "http://www.fablabpalermo.org\n", "[email protected]\n", "\n", "Fab Lab Reggio Emilia\n", "Reggio Emilia\n", "Reggio Emilia\n", "Emilia-Romagna\n", "http://www.fablabreggioemilia.org\n", "[email protected]\n", "\n", "FabLab Napoli\n", "Corso Campano, 134\n", "Giugliano In Campania\n", "http://bitsforfun.com\n", "http://twitter.com/fablabnapoli\n", "http://facebook.com/fablabnapoli\n", "http://fablabnapoli.it\n", "[email protected]\n", "\n", "Open Dot\n", "Via Tertulliano, 70\n", "Milano\n", "(MI)\n", "http://www.opendotlab.it\n", "https://www.facebook.com/pages/Opendot/699330510128661?fref=ts\n", "[email protected]\n", "\n", "Creaticity FabLab\n", "via XXX giugno 28\n", "Tolentino\n", "MC\n", "http://creaticityfablab.com/\n", "https://www.facebook.com/creaticityfablab\n", "[email protected]\n", "\n", "Fablab We Do\n", "Via Alfieri Vittorio 7\n", "\n", "Borgomanero (NO)\n", "http://www.facebook.com/wedofablab\n", "http://www.wedofablab.com\n", "[email protected]\n", "\n", "Fablab Catania\n", "Via Cifali \n", "Catania\n", "Italy\n", "https://twitter.com/fablab_catania\n", "https://www.facebook.com/pages/Fablab-Catania/189873531201389\n", "http://www.fablabcatania.eu\n", "[email protected]\n", "\n", "Hackspace Catania\n", "Via Grotte Bianche, 112\n", "Catania\n", "http://www.hackspacecatania.it\n", "[email protected]\n", "\n", "ON/OFF Fablab Parma\n", "Strada Naviglio Alto, 4/1\n", "Parma\n", "Emilia Romagna\n", "http://www.fablabparma.org\n", "[email protected]\n", "\n", "MUSE Fablab\n", "Corso del Lavoro e della Scienza 3\n", "Trento\n", "Trentino-Alto Adige/Südtirol\n", "https://github.com/musefablab\n", "https://vimeo.com/user22102097\n", "http://www.flickr.com/photos/musefablab/\n", "https://www.facebook.com/Muse.Fablab.Trento\n", "https://twitter.com/MUSE_Fablab\n", "http://fablab.muse.it\n", "[email protected]\n", "\n", "WeMake - Milan's Makerspace\n", "Via Stefanardo da Vimercate, 27\n", "Milan\n", "http://wemake.cc\n", "[email protected]\n", "\n", "TIS FabLab\n", "TIS innovation park\n", "Siemensstraße 19\n", "Bozen\n", "BZ\n", "http://tis.bz.it/fablab\n", "[email protected]\n", "\n", "MakeRN\n", "Via Umberto Cagni, 14\n", "Rimini\n", "https://www.facebook.com/MakerSpaceRiminiLab?ref=bookmarks\n", "http://Twitter: @makernlab\n", "http://www.makern.it\n", "[email protected]\n", "\n", "FabLab Contea\n", "Località Contea, 108\n", "Contea\n", "Firenze\n", "http://fablabcontea.blogspot.com\n", "[email protected]\n", "\n", "FabLab Roma - InnovationGym\n", "Via del Quadraro\n", "Roma\n", "Rome/Lazio\n", "http://www.innovationgym.org\n", "http://www.palestrainnovazione.org\n", "[email protected]\n", "\n", "Social FabLab\n", "Via Pò, 2\n", "Via Misericordia, 3\n", "Orvieto\n", "TR\n", "http://www.socialfablab.it\n", "[email protected]\n", "\n", "Fablab Pesaro\n", "Via Della Produzione 61\n", "Montelabbate\n", "Pesaro\n", "https://twitter.com/FabLabPesaro\n", "https://plus.google.com/110591340885713597484/posts\n", "https://www.facebook.com/FablabPesaro\n", "[email protected]\n", "\n", "FabLab Alessandria\n", "Via Verona, 95\n", "Alessandria\n", "Italy\n", "[email protected] \n", "\n", "TalentLab Padova\n", "Via Monselice, 15\n", "Padova\n", "https://www.facebook.com/talentlabcivitasvitae\n", "http://www.talent-lab.it\n", "[email protected]\n", "\n", "FabLab VdA\n", "Via Garibaldi 7\n", "Aosta\n", "AO\n", "https://www.facebook.com/fablabvda\n", "[email protected]\n", "\n", "Verona Fablab\n", "Viale del Lavoro, 2\n", "Grezzana\n", "Verona\n", "http://www.youtube.com/channel/UCrNHf7SWyCJ4XLaeTlZPbvQ\n", "http://twitter.com/fablabverona\n", "http://www.facebook.com/Fablabverona\n", "http://www.veronafablab.it/\n", "[email protected]\n", "\n", "Fablab Torino\n", "Via Egeo 16\n", "Torino\n", "Piedmont\n", "https://twitter.com/fablabtorino\n", "https://www.facebook.com/fablabtorino?fref=ts\n", "http://www.fablabtorino.org\n", "[email protected]\n", "\n", "FabLab Milano - Frankenstein Garage\n", "Milan\n", "Lombardy\n", "https://twitter.com/FablabMilano\n", "https://www.facebook.com/fablabmilano.page\n", "http://www.frankensteingarage.it\n", "http://www.fablabmilano.it\n", "\n", "\n", "Syskrack Lab\n", "Via Meridionale, 23\n", "Grassano\n", "Basilicata\n", "http://www.syskrack.org\n", "[email protected], [email protected]\n", "\n", "Fab Lab Terni\n", "via luigi casale\n", "Terni\n", "Italia\n", "http://www.greentales.it\n", "http://www.fablabterni.org\n", "[email protected]\n", "\n", "cssm\n", "Via Campo Sportivo\n", "Uggiano La Chiesa\n", "https://www.facebook.com/CssmTecnos\n", "[email protected]\n", "\n", "MakeInBo\n", "Piazza dei colori, 25/b\n", "Bologna\n", "BO\n", "https://www.facebook.com/makeinbo\n", "http://www.makeinbo.it\n", "[email protected]\n", "\n", "FabLab Sibillini\n", "Comunanza\n", "Via A. Merloni, 11\n", "Comunanza\n", "AP\n", "[email protected]\n", "\n", "Fablab Roma SPQwoRk\n", "Via Ignazio Pettinengo 9\n", "Roma\n", "Roma\n", "https://it.foursquare.com/v/fablab-spqwork\n", "http://spqwork.com/fablab\n", "https://twitter.com/FablabSPQwoRk\n", "https://www.facebook.com/FablabSPQwoRk\n", "http://www.flickr.com/photos/spqwork\n", "[email protected]\n", "\n", "FabLab Ventura\n", "Via Privata Giovanni Ventura, 20\n", "Milano\n", "http://www.fablabnet.it/\n", "[email protected]\n", "\n", "FabLabGenova\n", "c/o LSOA Buridda, via vertani 1, Genova\n", "Genoa\n", "https://www.facebook.com/fablab.genova?fref=ts\n", "http://fablabgenova.it\n", "[email protected]\n", "\n", "FaberLab Varese\n", "Viale Europa 4/a\n", "Tradate\n", "Varese\n", "https://www.youtube.com/user/FaberLabVarese\n", "https://twitter.com/FaberLabVarese\n", "https://www.facebook.com/FaberLabVarese\n", "http://www.faberlab.org\n", "[email protected]\n", "\n", "Fab Lab Cascina\n", "Via Mario Giuntini 25 interno 28\n", "Cascina\n", "PI\n", "https://www.facebook.com/groups/fablabcascina/\n", "http://www.fablabcascina.org\n", "[email protected]\n", "\n", "FabLab Biella\n", "Via Corradino Sella, 10\n", "Biella\n", "Italia/Biella\n", "https://twitter.com/FabLabBiella\n", "https://www.facebook.com/fablabbiella\n", "http://www.fablabbiella.it/\n", "[email protected]\n", "\n", "Fablab Venezia\n", "Venezia\n", "via della libertà 12 - Edificio Porta dell'innovazione - PST Vega\n", "Marghera\n", "Venezia\n", "http://www.fablabvenezia.org\n", "[email protected]\n", "\n", "Fab Lab Sulbiate\n", "Via Madre Laura 1\n", "Sulbiate\n", "Italy / Monza Brianza / Lombardia\n", "http://www.makeinprogress.org/\n", "\n", "\n", "Urban FabLab\n", "Via Coroglio, 104 e 57 \n", "Napoli \n", "ITALY\n", "http://www.urbanfablab.it\n", "[email protected]\n", "\n", "Rinoteca FabLab\n", "Via Anders Wladislaw\n", "Ancona\n", "https://www.facebook.com/rinoteca/\n", "http://www.rinoteca.com\n", "[email protected]\n", "\n", "FabLab Firenze\n", "Via Panciatichi, 14\n", "Firenze\n", "Toscana\n", "http://www.fablabfirenze.org\n", "[email protected]\n", "\n", "FabLab Settimo\n", "Via Ariosto, 36bis\n", "Settimo Torinese\n", "http://www.fablansettimo.org/\n", "[email protected]\n", "\n", "Fab Lab Sassari\n", "Sassari\n", "Italy\n", "http://www.fablabsassari.org\n", "http://pinterest.com/fablab.sassari\n", "http://instagram.com/fablabsassari\n", "https://issuu.com/fablabsassari\n", "https://twitter.com/FabLabSassari\n", "http://facebook.com/fablab.sassari\n", "[email protected]\n", "\n", "Fab Lab Frosinone - Officine Giardino\n", "Via Giordano Bruno, 83\n", "Frosinone\n", "http://officinegiardino.org/\n", " [email protected]\n", "\n", "FabLab Treviso\n", "Via Principale, 39\n", "Casier - Treviso\n", "TV\n", "[email protected]\n", "\n", "Fab Lab Salerno\n", "Piazza S. Agostino\n", "Salerno\n", "\n", "\n", "FABLAB Sardegna Ricerche \n", "Sardegna Ricerche-Parco Tecnologico della Sardegna-Località Piscina Manna\n", "Pula\n", "Italy\n", "https://www.facebook.com/pages/FAB-LAB-Sardegna-Ricerche/405778596221361\n", "http://sardegnaricerche.it/fablab\n", "[email protected]\n", "\n", "Fab Lab Olbia\n", "Via Petta 124\n", "Olbia\n", "Olbia-Tempio\n", "https://www.facebook.com/FabLabOlbia\n", "https://twitter.com/FabLabOlbia\n", "http://fablabolbia.wordpress.com\n", "[email protected]\n", "\n", "FabLab Imperia\n", "Imperia\n", "Imperia\n", "IM\n", "https://twitter.com/FabLabImperia/\n", "https://www.facebook.com/fablabimperia\n", "[email protected]\n", "\n", "Mediterranean Fab Lab\n", "Via Alcide de Gasperi, 23\n", "Cava de' Tirreni\n", "Campania\n", "http://www.pinterest.com/medfablabcava/\n", "https://www.facebook.com/medfablab.cava\n", "\n", "\n", "ICTP SciFabLab\n", "Via Beirut 7\n", "Trieste \n", "Trieste\n", "https://www.facebook.com/triesteminimakerfaire\n", "https://www.facebook.com/scifablab\n", "http://scifablab.ictp.it\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Latvia\n", "\n", "Latvijas Universitātes FabLab\n", "Aspazijas bulvāris 5\n", "Riga\n", "http://www.flickr.com/photos/103422353@N02/\n", "https://www.facebook.com/biznesa.inkubators?ref=hl\n", "http://www.biznesainkubators.lu.lv/fablab/kas-ir-latvijas-universitates-fablab/\n", "[email protected]\n", "\n", "Fab Lab Liepaja\n", "Liepāja\n", "Liepājas pilsēta\n", "None\n", "\n", "----------------------------------------------\n", "Luxembourg\n", "\n", "Technoport FabLab\n", "9, Avenue des Hauts-Fourneaux\n", "Belval\n", "Esch-sur-Alzette\n", "https://www.facebook.com/fablablux\n", "https://twitter.com/fablablux\n", "http://www.technoport.lu\n", "http://fablablux.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Netherlands\n", "\n", "FabLab Breda\n", "Belcrumweg 19\n", "4815 HA\n", "Breda\n", "North Brabant\n", "http://www.fablabbreda.nl\n", "[email protected]\n", "\n", "Kaasfabriek \| FabLab regio Alkmaar\n", "Pettemerstraat 15\n", "Alkmaar\n", "Noord-Holland\n", "http://www.twitter.com/kaasfab\n", "http://www.facebook.com/kaasfabriek\n", "http://www.kaasfabriek.nl\n", "[email protected]\n", "\n", "FabLab Arnhem\n", "Ruitenberglaan 26\n", "Arnhem\n", "Gelderland\n", "https://twitter.com/FabLabArnhem\n", "https://www.facebook.com/FabLabArnhem\n", "http://www.fablabarnhem.nl\n", "[email protected]\n", "\n", "FabLab Zeeland\n", "Kousteensedijk 7\n", "Middelburg\n", "Zeeland\n", "https://www.linkedin.com/groups/FabLab-Zeeland-6701557/about\n", "http://www.facebook.com/fablabzeeland\n", "http://www.twitter.com/fablabzeeland\n", "[email protected]\n", "\n", "FabLab Groene Hart\n", "Achttienkavels 8\n", "Nieuwkoop\n", "http://www.fablabgroenehart.nl\n", "[email protected]\n", "\n", "FabLab Enschede\n", "M.H. Tromplaan 28\n", "Enschede\n", "Overijssel\n", "https://twitter.com/fablabenschede\n", "https://www.facebook.com/fablabenschede\n", "http://www.fablabenschede.nl\n", "[email protected]\n", "\n", "FabLab Groningen\n", "Groningen\n", "Groningen\n", "http://www.fablabgroningen.nl\n", "\n", "\n", "FabLab Truck\n", "Weesp\n", "North Holland\n", "http://fablabtruck.nl\n", "None\n", "\n", "Mini FabLab MakerHousehold\n", "Amelinkhorst 4\n", "Enschede\n", "[email protected]\n", "\n", "FabLab Amersfoort\n", "Kleine Koppel 40\n", "Amersfoort\n", "Utrecht\n", "http://www.fablabamersfoort.nl\n", "http://dewar.nl/?en/home\n", "[email protected]\n", "\n", "Stadslab Rotterdam\n", "Wijnhaven 99, 3011 WN\n", "Rotterdam\n", "South Holland\n", "https://www.facebook.com/groups/254279061267294/\n", "http://www.stadslabrotterdam.nl\n", "[email protected]\n", "\n", "JeugdFabLab \n", "Langstraat 48\n", "48\n", "Dronten\n", "FlevoLand\n", "http://www.fabfunclub.nl\n", "http://www.jeugdfablab.nl\n", "[email protected]\n", "\n", "fablab013\n", "Stadhuisplein 354\n", "Tilburg\n", "http://facebook.nl/fablab013\n", "http://fablab013.nl\n", "[email protected]\n", "\n", "FabLab Brainport\n", "Eindhoven\n", "North Brabant\n", "http://www.flickr.com/photos/97007574@N04/with/10958203735/\n", "http://www.fablabbrainport.nl/\n", "http://www.brainportdevelopment.nl/project/fablab-brainport\n", "\n", "\n", "Fablab Amsterdam\n", "Nieuwmarkt 4\n", "Amsterdam\n", "North Holland\n", "http://fabacademy.org\n", "http://facebook.com/fablab.amsterdam\n", "http://twitter.com/waag\n", "http://fablab.waag.org\n", "[email protected]\n", "\n", "ZB45 Makerspace\n", "Zeeburgerpad 45\n", "Amsterdam\n", "http://www.zb45.nl\n", "[email protected]\n", "\n", "CabFabLab\n", "The Hague\n", "South Holland\n", "http://cabfablab.nl\n", "http://vimeo.com/cabfablab\n", "\n", "\n", "FabLab Bergen op Zoom\n", "Nobellaan 25\n", "Bergen op Zoom\n", "http://www.facebook.com/FabLabBergenopZoom\n", "http://www.fablabbergenopzoom.nl\n", "[email protected]\n", "\n", "fablab noord-brabant\n", "Zuid-Willemsvaart 215, 's-Hertogenbosch, the Netherlands\n", "'s-Hertogenbosch\n", "Noord-Brabant\n", "http://www.fablabnoordbrabant.nl\n", "[email protected]\n", "\n", "Fab Lab Maastricht\n", "Herbenusstraat 89, 6211RB Maastricht\n", "Maastricht\n", "Limburg\n", "https://www.linkedin.com/groups/FabLab-ZuidLimburg-3663893\n", "https://www.pinterest.com/fablabzl/\n", "https://www.facebook.com/fablab.maastricht\n", "https://twitter.com/FabLab_Mtricht\n", "http://www.flickr.com/photos/99070534@N07/\n", "http://www.fablabmaastricht.nl\n", "[email protected]\n", "\n", "FabLab Goes\n", "Goes\n", "Zeeland\n", "http://fablabgoes.nl/\n", "\n", "\n", "FryskLab\n", "Zuiderkruisweg 4\n", "Leeuwarden\n", "Friesland\n", "http://www.frysklab.nl\n", "[email protected]\n", "\n", "ICER-Lab\n", "Hutteweg 32\n", "Ulft\n", "Netherlands\n", "[email protected]\n", "\n", "MiniFabLab Utrecht\n", "Nobeldwarsstraat 33\n", "Utrecht\n", "http://www.minifablab.nl\n", "[email protected]\n", "\n", "Fablab013 XL\n", "Galjoenstraat 37\n", "Tilburg\n", "http://fablab013.nl\n", "[email protected]\n", "\n", "Protospace/FabLab Utrecht\n", "Utrecht\n", "Utrecht\n", "http://www.protospace.nl\n", "\n", "\n", "----------------------------------------------\n", "Norway\n", "\n", "Fellesverkstedet\n", "Urtegata 11\n", "Oslo\n", "http://www.fellesverkstedet.no\n", "[email protected]\n", "\n", "Solvik Gard\n", "Ørnes\n", "Lyngseidet\n", "http://www.fablab.no\n", "http://www.fablab.no/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Poland\n", "\n", "Fab LAB Trojmiasto\n", "Niepodległości 291\n", "Gdynia\n", "https://www.facebook.com/groups/178918095579486/\n", "http://fablabt.org\n", "[email protected]\n", "\n", "DAD-workshop\n", "Lubartów County\n", "Lublin Voivodeship\n", "http://dad-workshop.com\n", "https://www.facebook.com/dadworkshop?fref=ts\n", "\n", "\n", "FabLab Bielsko-Biala\n", "1 Dywizji Pancernej 45\n", "Bielsko-Biala\n", "woj. Śląskie\n", "http://www.arrsa.pl\n", "http://www.fablab24.pl/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Portugal\n", "\n", "FabLab Lisboa\n", "Rua Maria da Fonte\n", "Mercado do Forno do Tijolo\n", "Lisbon\n", "http://www.fablablisboa.pt\n", "[email protected]\n", "\n", "OPO LAB\n", "Rua D. João IV, 643\n", "Porto\n", "Porto District\n", "http://www.opolab.com\n", "[email protected]\n", "\n", "Fablab EDP\n", "Rua Cidade de Goa,4\n", "Sacavém\n", "Lisbon\n", "https://www.facebook.com/fablabedp\n", "https://twitter.com/Fablabedp\n", "http://www.fablabedp.edp.pt\n", "[email protected]\n", "\n", "Fablab Aldeias do Xisto\n", "Rua dos Três Lagares\n", "Fundão\n", "https://www.facebook.com/FabLabAldeiasDoXisto\n", "http://www.llcb.pt\n", "[email protected]\n", "\n", "NOVOTECNA\n", "Coimbra\n", "Coimbra District\n", "http://www.novotecna.pt/fablab\n", "None\n", "\n", "----------------------------------------------\n", "Russian Federation\n", "\n", "Fab Lab Polytech\n", "Politekhnicheskaya st., b.29-12\n", "St. Petersburg\n", "St. Petersburg\n", "http://instagram.com/fablabpolytech\n", "http://twitter.com/fablabpolytech\n", "http://fb.com/fablabpolytech\n", "http://vk.com/fablabpolytech\n", "http://fablab1.org\n", "http://fablab.spbstu.ru\n", "[email protected]\n", "\n", "Fablab Rostov\n", "просп. Михаила Нагибина, 3А\n", "Ростов-на-Дону\n", "http://vk.com/fablabrostov\n", "http://fablabrostov.ru/\n", "[email protected]\n", "\n", "FabLab Southern Federal\n", "Ростов-на-Дону\n", "ул. Зорге, 5 а.044\n", "Ростов-на-Дону\n", "Ростовская область\n", "https://github.com/orgs/FabLab61\n", "https://vk.com/fablabrnd\n", "http://fablab61.ru/\n", "[email protected]\n", "\n", "CMIT \"Druzhba\"\n", "Tomsk\n", "Krasnoarmeyskaya 147, office 103\n", "Tomsk\n", "http://vk.com/cmit_ru\n", "http://cmit.ru\n", "[email protected]\n", "\n", "\"Experimentarium1502 MPEI\" (Fab Lab)\n", "Molostovukh street, 10 A\n", "Moscow\n", "Moscow\n", "http://www.lyceum1502.ru/pages/overtime/fablab/\n", "http://www.planetseed.com/ru/forums/fablabs/moscow-russia-fablabschool-experimentarium-1502\n", "[email protected]\n", "\n", "Fab Lab Moscow\n", "Крымский вал, 3\n", "Moscow\n", "Moscow\n", "https://www.facebook.com/fablab77\n", "http://www.fablab77.ru\n", "[email protected]\n", "\n", "FabLab SFedU\n", "Milchakova 5/2\n", "Ростов-на-Дону\n", "http://vk.com/fablabrnd\n", "http://fablab61.ru/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Slovakia\n", "\n", "Fablab Bratislava\n", "Ilkovičova 2\n", "FIIT STU\n", "Bratislava\n", "http://www.facebook.com/fablabslovakia\n", "http://www.fablab.sk\n", "[email protected], [email protected]\n", "\n", "----------------------------------------------\n", "Spain\n", "\n", "Fab Lab Madrid-CEU\n", "Escuela Politécnica Superior, Universidad CEU San Pablo \n", "Campus de Monteprincipe\n", "Boadilla del Monte\n", "Madrid\n", "https://twitter.com/fablabmadridceu\n", "https://www.facebook.com/profile.php?id=100006287478794\n", "https://www.flickr.com/photos/126001619@N08/sets/\n", "http://fablabmadridceu.wordpress.com/\n", "[email protected]\n", "\n", "FabLab Madrid Medialab-Prado\n", "Calle Alameda, 15\n", "PLAZA DE LAS LETRAS\n", "Madrid\n", "Madrid\n", "http://medialab-prado.es/\n", "[email protected]\n", "\n", "Fab Lab Tenerife\n", "Santa Cruz de Tenerife\n", "Canary Islands\n", "http://fablabtenerife.com\n", "https://www.facebook.com/FabLabTenerife\n", "\n", "\n", "Fab Lab BCN\n", "Carrer de Pujades, 102\n", "Barcelona\n", "Catalonia\n", "http://fablab.fikket.com/\n", "http://vimeo.com/fablabbcn/videos\n", "https://github.com/fablabbcn\n", "http://twitter.com/fablabbcn\n", "https://picasaweb.google.com/fablabbcnphotos\n", "https://www.facebook.com/FabLab.BCN\n", "http://iaac.net\n", "http://fablabbcn.org\n", "[email protected]\n", "\n", "fabLAB Asturias\n", "LABoral Centro de Arte y Creación Industrial\n", "Los Prados 121\n", "Gijón\n", "Principality of Asturias\n", "http://www.laboralcentrodearte.org/es/files/2014/blog-fablab\n", "http://www.laboralcentrodearte.org/es/plataformacero/fablab\n", "[email protected]\n", "\n", "FabLabUPM\n", "Campus de Montegancedo\n", "Edificio C.A.I.T.\n", "Pozuelo\n", "Madrid\n", "http://www.fablabupm.com\n", "[email protected]\n", "\n", "Fab Lab Sevilla / Escuela Tecnica Superior de Arquitectura Universidad de Sevilla\n", "\n", "http://www.pinterest.com/fablab/\n", "https://www.facebook.com/sevillafablab\n", "http://fablabsevilla.us.es\n", "https://twitter.com/fablabsevilla\n", "http://htca.us.es/blogs/fablab\n", "http://htca.us.es/blogs/talleresfablab\n", "[email protected]\n", "\n", "MADE Makerspace Barcelona\n", "C/CONSELL DE CENT 159, PRINCIPAL B \n", "ANTIGUA FABRICA LEHMANN\n", "Barcelona\n", "Catalunya\n", "http://www.made-bcn.org\n", "[email protected]\n", "\n", "Green Fab Lab\n", "Cerdanyola\n", "Valldaura\n", "Barcelona\n", "Catalonia\n", "http://vimeo.com/valldauralabs\n", "http://www.youtube.com/valldauralabs\n", "https://twitter.com/valldauralabs\n", "https://www.facebook.com/valldauralabs\n", "http://www.flickr.com/photos/valldauralabs\n", "http://www.valldaura.net/\n", "[email protected]\n", "\n", "Fab Lab Sitges\n", "Passeig de la Ribera 46\n", "Sitges\n", "Catalonia\n", "https://github.com/Fablabsitges\n", "http://www.pinterest.com/fablabsitges\n", "http://vimeo.com/fablabsitges\n", "http://twitter.com/fablabsitges\n", "http://www.facebook.com/fablabsitges\n", "http://fablabsitges.org\n", "[email protected]\n", "\n", "DenokInn Basque fab Lab\n", "Santurtzi ( Antes Bermeo)\n", "Basque Country\n", "http://www.denokinn.eu\n", "\n", "\n", "DEUSTO FabLab\n", "Avda. Universidades, 24\n", "Bilbao\n", "Vizcaya\n", "[email protected]\n", "\n", "Fab Lab Leon\n", "Polígono Industrial de Onzonilla Fase 2, Parcela M-24\n", "Ribaseca (León)\n", "Castile and León\n", "http://fablableon.blogspot.com\n", "http://www.linkedin.com/company/fablab-le-n\n", "http://vimeo.com/user7819089\n", "https://www.facebook.com/FabLabLeon\n", "https://twitter.com/FabLabLeon\n", "http://www.fablableon.org\n", "\n", "\n", "Makespace Madrid\n", "Pedro Unanue 16\n", "Madrid\n", "Madrid\n", "http://wiki.makespacemadrid.org\n", "http://www.meetup.com/Makespace-Madrid/\n", "https://www.facebook.com/pages/Makespace-Madrid/477334925648477\n", "http://www.flickr.com/photos/makespacemadrid\n", "https://twitter.com/@MakeSpaceMadrid\n", "http://makespacemadrid.org\n", "[email protected]\n", "\n", "Fab Lab Alicante\n", "Ctra. San Vicente del Raspeig, s/n\n", "San Vicente del Raspeig\n", "Alicante\n", "https://www.facebook.com/fablabalicante\n", "https://plus.google.com/u/0/109169507820175971471/posts\n", "https://www.youtube.com/channel/UCcLITo4_XRluWORsuCbdW2w\n", "https://twitter.com/FabLabALC\n", "http://blogs.ua.es/lips\n", "\n", "\n", "FabLab Valencia\n", "cno. de vera s/n Edif. 8G bajo\n", "Valencia\n", "Valencian Community\n", "http://vimeo.com/user10405535\n", "https://twitter.com/fablabvalencia\n", "http://fablab.upv.es\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Switzerland\n", "\n", "Funlab Zürich\n", "Am Holbrig 10\n", "Zurich\n", "Canton of Zurich\n", "http://funlab.ch\n", "[email protected]\n", "\n", "FabLab SUPSI Lugano\n", "Campus Trevano Canobbio\n", "Laboratory of visual culture\n", "Canobbio\n", "Tessin\n", "http://twitter.com/fablablugano\n", "http://www.fablab.supsi.ch\n", "[email protected]\n", "\n", "FabLab Zürich\n", "Zimmerlistrasse 6\n", "Zurich\n", "Canton of Zurich\n", "http://www.flickr.com/photos/fablabzurich/ \n", "https://www.facebook.com/fablabzurich\n", "http://zurich.fablab.ch\n", "[email protected]\n", "\n", "FabLab la Côte\n", "Route de Champ-Colin 11, Nyon\n", "Nyon\n", "https://www.twitter.com/fablablacote\n", "https://www.facebook.com/fablablacote\n", "http://www.fablab-lacote.ch\n", "[email protected]\n", "\n", "Fablab Fribourg Freiburg\n", "Passage du Cardinal 1\n", "Fribourg\n", "https://twitter.com/FablabFR\n", "https://www.facebook.com/FabLabFribourg\n", "http://www.fablab-fribourg.ch\n", "[email protected]\n", "\n", "FabLab Winti\n", "Lagerplatz 13\n", "Winterthur\n", "https://m.facebook.com/FabLab.winti\n", "http://www.fablabwinti.ch\n", "[email protected]\n", "\n", "Starship Factory\n", "Sankt Alban-Rheinweg 62\n", "Basel\n", "https://secure.flickr.com/groups/2341518@N21/\n", "https://www.facebook.com/starshipfactory\n", "https://twitter.com/StarshipFactory\n", "https://plus.google.com/+Starship-factoryOrg/\n", "http://wiki.starship-factory.ch/\n", "http://www.starship-factory.ch/\n", "[email protected]\n", "\n", "FabLab Underes Ätzisloo\n", "Switzerland\n", "Kirchgasse\n", "Merishausen\n", "http://www.randelab.ch/\n", "[email protected]\n", "\n", "FabLab Neuchâtel\n", "Place de la Gare 4\n", "Neuchatel\n", "Canton of Neuchâtel\n", "http://www.pinterest.com/fablabneuch/\n", "https://twitter.com/fablabneuch\n", "https://www.facebook.com/fablabneuchatel\n", "http://fablab-neuch.ch\n", "\n", "\n", "FabLab Bern\n", "Eigerstrasse 12\n", "Berne\n", "Canton of Bern\n", "https://foursquare.com/v/fablab-bern/519b5eda498e1dd2ad2d3c49\n", "https://twitter.com/FabLab_Bern\n", "https://www.facebook.com/fablab.bern\n", "http://www.fablab-bern.ch\n", "\n", "\n", "FabLab Luzern\n", "Technikumstrasse 21\n", "Trakt I\n", "Horw\n", "Lucerne\n", "http://luzern.fablab.ch\n", "\n", "\n", "----------------------------------------------\n", "Ukraine\n", "\n", "IZOLAB\n", "Donetsk\n", "Svitlogo shlyahu str. 3\n", "Donetsk\n", "http://facebook.com/fablab.ua\n", "http://izolab.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "United Kingdom\n", "\n", "Fab Lab Liverpool\n", "John Lennon Art and Design Building\n", "Duckinfield St\n", "Liverpool\n", "Meseyside\n", "\n", "\n", "Fab Lab London\n", "1 Frederick's Place\n", "London\n", "http://www.fablablondon.org\n", "[email protected]\n", "\n", "fablab@strathclyde\n", "75 Montrose St\n", "Glasgow\n", "[email protected]\n", "\n", "BEC Fab Lab\n", "Unit 4, Derwent Mills Commercial Park\n", "Wakefield Rd\n", "Cockermouth\n", "Cumbria\n", "http://www.becfablab.org\n", "[email protected]\n", "\n", "Fab Lab Ellesmere Port\n", "53 Whitby Road\n", "Ellesmere Port\n", "Cheshire\n", "https://www.facebook.com/FabLabEllesmerePort\n", "https://twitter.com/FabLabEllesmere\n", "http://www.fab-lab-ellesmereport.org/\n", "[email protected]\n", "\n", "FabLab Belfast, Ashton Centre\n", "Ashton Community Trust \n", "5 Churchill Street \n", "Belfast\n", "http://www.fablabni.com \n", "[email protected]\n", "\n", "ffablab Pontio\n", "Pontio\n", "Ffordd Deiniol Road\n", "Bangor\n", "Gwynedd\n", "http://www.bangor.ac.uk\n", "http://www.innovationquarter.co.uk\n", "http://www.pontio.co.uk\n", "[email protected]\n", "\n", "Fab Lab Cardiff\n", "Western Avenue\n", "Llandaff Campus\n", "Cardiff\n", "http://fablabcardiff.com\n", "[email protected]\n", "\n", "Fab Lab Airedale\n", "Keighley\n", "http://www.linkedin.com/company/2570096?trk=tyah\n", "https://twitter.com/FabLabAiredale\n", "https://www.facebook.com/FabLabAiredale\n", "http://www.fablabairedale.org\n", "\n", "\n", "Machines Room\n", "LimeWharf Annex\n", "Vyner St\n", "London\n", "http://makerlibrarynetwork.org/makerspace/machines-room-limewharf/\n", "http://limewharf.org/machines-room/\n", "[email protected]\n", "\n", "Makernow\n", "Penryn\n", "Falmouth University, Penryn Campus \n", "Penryn\n", "Cornwall\n", "https://plus.google.com/113776356809222537580/about\n", "http://www.facebook.com/MakernowFabLab\n", "http://www.makernow.org\n", "[email protected]\n", "\n", "Fab Lab Manchester\n", "Chips\n", "2 Lampwick Lane\n", "Manchester\n", "Greater Manchester\n", "https://twitter.com/fablabmcr\n", "https://www.facebook.com/FabLabMcr\n", "http://www.fablabmanchester.org/\n", "[email protected]\n", "\n", "FabLab North Greenwich\n", "Mitre Passage\n", "London\n", "London\n", "\n", "\n", "Fab Lab Plymouth \n", "PLYMOUTH COLLEGE OF ART\n", "Tavistock Place\n", "Plymouth\n", "Devon\n", "http://www.plymouthart.ac.uk\n", "[email protected]\n", "\n", "MAKLAB\n", "30 St Georges Road\n", "Charing Cross Mansions\n", "Glasgow\n", "Lanarkshire\n", "https://www.facebook.com/themaklab\n", "http://www.youtube.com/channel/UCH2kOIIZQto_2TkSl4QDu5A\n", "https://twitter.com/theMAKLab\n", "http://www.maklab.co.uk\n", "[email protected]\n", "\n", "FabLab Essex\n", "Oakdene Business Centre\n", "Cranes Close\n", "Basildon\n", "Essex\n", "http://www.mic2c.com/fablabessex\n", "[email protected]\n", "\n", "Spitfire Fab Lab (Eastleigh, UK)\n", "157 Leigh Rd\n", "Eastleigh\n", "http://www.fablabsuk.co.uk/spitfirefablab/\n", "[email protected]\n", "\n", "FabLabDevon (Exeter)\n", "Exeter Library\n", "Castle Street\n", "Exeter\n", "Devon\n", "http://www.facebook.com/fablabdevon\n", "http://www.twitter.com/fablabdevon\n", "http://www.fablabdevon.com\n", "[email protected]\n", "\n", "FabLab Nerve Centre\n", "7-8 Magazine Street\n", "Derry\n", "https://www.facebook.com/nervecentre.org\n", "https://twitter.com/nerve_centre\n", "http://www.youtube.com/user/thenervecentre\n", "http://www.linkedin.com/company/nerve-centre\n", "https://soundcloud.com/nervecentre\n", "http://www.nervecentre.org/projects/fab-lab#.UMJ0R7YWWbI\n", "[email protected]\n", "\n", "MAKE aberdeen\n", "17 Belmont Street\n", "Aberdeen\n", "http://www.make-aberdeen.com\n", "[email protected]\n", "*********************************************************\n", "\n", "North America\n", "\n", "----------------------------------------------\n", "Canada\n", "\n", "DèmosLab\n", "Frelighsburg (moving)\n", "Frelighsburg\n", "Quebec\n", "http://www.demoslab.org\n", "[email protected]\n", "\n", "échoFab\n", "355 Peel St\n", "Suite 111\n", "Montreal\n", "Quebec\n", "http://www.youtube.com/user/Communautique\n", "https://twitter.com/qcechofab\n", "https://www.facebook.com/echoFab\n", "http://www.echofab.org\n", "[email protected]\n", "\n", "District 3\n", "1515 Rue Ste-Catherine O\n", "Montreal\n", "Quebec\n", "http://d3center.ca/opportunities/makerspace/\n", "\n", "\n", "Fablab@Champlain\n", "900 Riverside Drive\n", "St-Lambert\n", "Québec\n", "http://www.champlainonline.com/champlainweb/\n", "\n", "\n", "SIAT, SFU\n", "Vancouver\n", "British Columbia\n", "http://www.interactionart.org\n", "\n", "\n", "FabOutaouais\n", "820 Boul. de la Gappe\n", "Gatineau\n", "Québec \n", "http://www.agoralab.ca/portfolio-tag/fablab/\n", "https://www.facebook.com/Fablab.Outaouais\n", "[email protected]\n", "\n", "District3\n", "Montreal\n", "Quebec\n", "\n", "\n", "La Fabrique\n", "1801 Rue Denault\n", "Sherbrooke\n", "Québec\n", "https://www.facebook.com/SherbrookeFabrique\n", "http://www.sherbrookefabrique.org\n", "[email protected]\n", "\n", "Orange mécanique\n", "671, boul. Frontenac Ouest\n", "Thetford mines\n", "Québec\n", "http://www.cegepth.qc.ca\n", "[email protected]\n", "\n", "FabLab@Marguerite\n", "8700, boul. Champlain\n", "LaSalle\n", "Québec/Canada\n", "http://wp.csmb.qc.ca/fablab\n", "\n", "\n", "----------------------------------------------\n", "Costa Rica\n", "\n", "Instituto Tecnologico de Costa Rica\n", "\n", "Cartago\n", "\n", "\n", "----------------------------------------------\n", "El Salvador\n", "\n", "Fab Lab SV (El Salvador)\n", "Calle La Reforma 249, Colonia San Benito, San Salvador, El Salvador.\n", "San Salvador\n", "San Salvador\n", "https://www.facebook.com/FabLabSanSalvador\n", "http://fablab.org.sv/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Mexico\n", "\n", "Fab Lab Puebla\n", "Blvd. del Niño Poblano 2901\n", "Puebla\n", "Puebla\n", "https://twitter.com/FabLabPuebla\n", "https://www.facebook.com/FabLabPuebla\n", "http://www.fablabpuebla.org\n", "[email protected]\n", "\n", "FABLAB Chihuahua\n", "Av. Heroico colegio militar #4709 Colonia Nombre de Dios C.P.31300\n", "PIT3\n", "Chihuahua\n", "Chihuahua\n", "http://www.fablabchihuahua.com\n", "[email protected]\n", "\n", "Fab Lab Mexico\n", "Universidad Anahuac México Norte\n", "Av. Universidad Anáhuac # 46, Lomas Anáhuac, Huixquilucan, Estado de México\n", "Mexico City\n", "Edomex\n", "https://www.facebook.com/fablabmex\n", "http://www.fablab.mx\n", "[email protected]\n", "\n", "Fab Lab Monterrey\n", "Av. Ignacio Morones Prieto 4500 Pte.\n", "San Pedro Garza García\n", "Nuevo Leon\n", "https://www.udem.edu\n", "http://www.fablabmty.org/\n", "https://www.facebook.com/FabLabMonterreyCRGS\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Puerto Rico\n", "\n", "FabLab Puerto Rico\n", "Carretera 189, KM 3.3\n", "Gurabo\n", "PR\n", "https://www.facebook.com/fablabpr?ref=bookmarks\n", "https://www.facebook.com/#!/groups/FABLABPR/\n", "http://centrointernacionaldediseno.com/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "United States\n", "\n", "Castlemont High School, Sustainable Urban Design Academy (SUDA) Fablab Oakland CA\n", "8601 MacArthur Blvd\n", "Oakland\n", "http://www.sud-academy.org\n", "[email protected]\n", "\n", "STEM East\n", "Kinston\n", "North Carolina\n", "None\n", "\n", "Sinclair Community College Fab Lab\n", "Dayton\n", "Ohio\n", "http://www.sinclair.edu\n", "None\n", "\n", "Fab Lab at Patrick Henry Community College\n", "54 W Church St\n", "Martinsville\n", "Virginia, Martinsville\n", "https://www.facebook.com/pages/Fab-Lab/199543143543441\n", "[email protected]\n", "\n", "Fab Lab Mahtomedi\n", "8000 75th St. N\n", "Mahtomedi \n", "Minnesota\n", "https://www.facebook.com/FABLabMahtomedi\n", "http://www.mahtomedi.k12.mn.us\n", "\n", "\n", "FAB Newport\n", "Newport\n", "Newport\n", "RI\n", "http://fabnewport.wordpress.com/\n", "[email protected]\n", "\n", "South End Technology Center\n", "359 Columbus Ave\n", "Boston\n", "Massachusetts\n", "http://www.tech-center-enlightentcity.tv/home.html\n", "\n", "\n", "FabLabFultonMO\n", "2 Hornet Dr\n", "Fulton\n", "http://www.fulton58.org/vnews/display.v/TP/52176a9ff08e3?cssfile=/teacherpages/Plain_Label_Blue/default.css\n", "[email protected]\n", "\n", "Incite Focus FabLab\n", "Detroit\n", "Michigan\n", "http://www.incite-focus.org/Fab_Lab.html\n", "\n", "\n", "The S.T.E.A.M. Room Fab Lab\n", "Iowa City\n", "Iowa City Marketplace\n", "Iowa City\n", "IA\n", "http://www.thesteamroom.org\n", "[email protected]\n", "\n", "AS220 Labs\n", "131 Washington St\n", "Providence\n", "Rhode Island\n", "http://www.youtube.com/user/tv220/videos?view=0\n", "http://www.flickr.com/photos/as220fablab/\n", "http://www.as220.org/labs/blog/fab-lab\n", "\n", "\n", "Fab Lab Richmond, California\n", "4300 Cutting Blvd\n", "Richmond\n", "California\n", "http://www.wccusd.net/fablab\n", "[email protected]\n", "\n", "University of Wisconsin-Stout\n", "Menomonie\n", "Wisconsin\n", "http://www.uwstout.edu/discoverycenter\n", "None\n", "\n", "The Community College of Baltimore County\n", "800 S. Rolling Road\n", "HTEC145\n", "Baltimore\n", "Maryland\n", "http://www.fablabbaltimore.org\n", "\n", "\n", "Fab Ed Carolina\n", "301D Advanced Technology Center\n", "Charlottetowne Ave.\n", "Charlotte\n", "NC\n", "[email protected]\n", "\n", "Fox Valley Technical College, site #2\n", "Oshkosh\n", "Wisconsin\n", "http://www.fvtc.edu/public/content.aspx?ID=1873&PID=1\n", "None\n", "\n", "Fab Lab Baltimore\n", "Building H Room 145 800 S Rolling Rd\n", "Catonsville\n", "MD\n", "http://www.meetup.com/fab-lab-baltimore\n", "https://twitter.com/FabLabBaltimore\n", "https://www.facebook.com/FabLabBaltimore\n", "http://www.fablabbaltimore.org/\n", "[email protected]\n", "\n", "The Technology Innovation and Entrepreneurship Project\n", "Boston\n", "Massachusetts\n", "http://www.thetieproject.org\n", "None\n", "\n", "MC2STEM High School\n", "East Cleveland\n", "Ohio\n", "http://mc2stemhs.wordpress.com/\n", "http://mc2stemhs.com\n", "\n", "\n", "Fab Lab NCCU-Durham, North Carolina\n", "North Carolina Central University\n", "Durham \n", "NC\n", "[email protected]\n", "\n", "MindGear Labs, LLC\n", "Huntsville\n", "Alabama\n", "http://mindgearlabs.com\n", "\n", "\n", "FamiLAB\n", "Orlando\n", "Florida\n", "http://familab.org/blog/about-our-lab\n", "None\n", "\n", "Linden-McKinley STEM High School\n", "Columbus\n", "Ohio\n", "http://www.columbus.k12.oh.us\n", "None\n", "\n", "Fab Lab Central\n", "20 Ames St, E15-401\n", "Cambridge\n", "MA\n", "http://www.fabfoundation.org\n", "http://fab.cba.mit.edu\n", "[email protected]\n", "\n", "Fab Lab El Paso\n", "806 Montana Ave\n", "El Paso\n", "http://www.twitter.com/FabLabEP\n", "http://www.flickr.com/FabLabElPaso\n", "http://facebook.com/FabLabEP\n", "http://fablabelpaso.org\n", "[email protected]\n", "\n", "Century Community and Technical College\n", "White Bear Lake\n", "Minnesota\n", "http://www.century.edu/currentstudents/fablab/default.aspx\n", "None\n", "\n", "Lorain County Community College\n", "Elyria\n", "Ohio\n", "http://www.lorainccc.edu/Academic+Divisions/Engineering+Technologies/Fab+Lab\n", "None\n", "\n", "Fablab ABQ\n", "Albuquerque\n", "New Mexico\n", "http://fablababq.com\n", "None\n", "\n", "Stoughton High School\n", "600 Lincoln Ave\n", "Stoughton\n", "Wisconsin\n", "https://sites.google.com/a/stoughton.k12.wi.us/fablab-stoughton/\n", "\n", "\n", "Blue Valley School District's Center for Advanced Professional Studies\n", "Overland Park\n", "Kansas\n", "http://www.bvcaps.org\n", "None\n", "\n", "Stanford Learning FabLab\n", "Palo Alto\n", "California\n", "http://www.blikstein.com/paulo/contact.html\n", "None\n", "\n", "McKinley South End Academy Fab Lab\n", "90 Warren Avenue\n", "Boston\n", "MA\n", "http://www.bostonpublicschools.org/school/mckinley-south-end-academy\n", "[email protected]\n", "\n", "Museum of Science and Industry Chicago Wanger Family Fab Lab\n", "5700 S. Lake Shore Drive\n", "Chicago\n", "Illinois\n", "http://www.msichicago.org/whats-here/fab-lab/\n", "\n", "\n", "Sustainable South Bronx\n", "Bronx\n", "New York\n", "http://www.ssbx.org/index.php?link=35\n", "None\n", "\n", "Lake Michigan College (Benton Harbor)\n", "Benton Harbor\n", "Michigan\n", "http://www.lakemichigancollege.edu\n", "None\n", "\n", "FABLabs For America Inc.\n", "70 Esmond St\n", "Boston\n", "MA\n", "http://www.fablabsforamerica.org\n", "[email protected]\n", "\n", "Lake Michigan College\n", "Niles\n", "Michigan\n", "http://www.lakemichigancollege.edu/BX\n", "None\n", "\n", "Gateway Technical College - Fab Lab\n", "2320 Renaissance Blvd.\n", "Sturtevant\n", "WI\n", "http://www.usfln.org\n", "https://www.gtc.edu/wedd/industrial-design-fab-lab\n", "[email protected]\n", "\n", "EHove Career Center Fab Lab\n", "316 Mason Road West\n", "Milan\n", "Ohio\n", "http://www.ehove.net/fablab\n", "\n", "\n", "FFL Fab Lab\n", "Fayetteville\n", "New York\n", "http://www.fayettevillefreelibrary.org/about-us/services/fablab\n", "\n", "\n", "FabLab IRSC\n", "3209 Virginia Ave\n", "Fort Pierce\n", "http://www.irsc.edu\n", "[email protected]\n", "\n", "Lawrence Technological University -- makeLab\n", "Southfield\n", "Michigan\n", "http://www.ltu.edu/architecture_and_design\n", "None\n", "\n", "Metropolitan Community College Tech Center-FabLab\n", "Kansas City\n", "Missouri\n", "http://www.mcckc.edu/fablab\n", "None\n", "\n", "Reynoldsburg Battelle Fab Lab\n", "8579 Summit Rd\n", "Reynoldsburg\n", "Ohio\n", "https://sites.google.com/site/fablabreyn/home\n", "[email protected]\n", "\n", "Cherokee Trail High School\n", "25901 E Arapahoe Rd\n", "Aurora\n", "Colorado\n", "http://cherokeetrail.cherrycreekschools.org/Departments/preeng/Pages/default.aspx\n", "[email protected]\n", "\n", "Fab Lab San Diego\n", "4685 Convoy St\n", "#200\n", "San Diego\n", "California\n", "http://www.fablabsd.org\n", "[email protected]\n", "\n", "Fab Lab DC\n", "Washington\n", "District of Columbia\n", "https://twitter.com/FabLabDC\n", "https://www.facebook.com/pages/FAB-LAB-DC/320444572757\n", "http://www.awesomefoundation.org/en/projects/2258-fab-lab-dc\n", "http://fablabdc.blogspot.com\n", "http://www.fablabdc.org\n", "\n", "\n", "CITC FabLab\n", "3600 San Jeronimo Ct\n", "Anchorage\n", "[email protected]\n", "\n", "University of Texas at Arlington Fab Lab\n", "702 Planetarium Pl\n", "University of Texas at Arlington Libraries\n", "Arlington\n", "TX\n", "[email protected]\n", "\n", "Fab Lab Tulsa\n", "710 S Lewis Ave\n", "Tulsa\n", "Oklahoma\n", "http://www.fablabtulsa.com\n", "[email protected]\n", "\n", "Mott Community College Fab Lab\n", "Flint\n", "Michigan\n", "http://www.mcc.edu\n", "None\n", "\n", "Fair Use Building and Research (Fubar) Labs\n", "403 Cleveland Ave\n", "Highland Park\n", "NJ\n", "http://fubarlabs.org\n", "[email protected]\n", "\n", "Fox Valley Technical College\n", "Appleton\n", "Wisconsin\n", "None\n", "\n", "G.Wiz -- The Science Museum Falhaber Fab Lab\n", "Sarasota\n", "Florida\n", "http://www.gwiz.org\n", "None\n", "\n", "Marymount School Fab Lab\n", "116 East 97th street\n", "Room 403\n", "New York\n", "New York\n", "http://marymountnyc.org/97th-street-campus-the-fab-lab-and-more\n", "[email protected]\n", "\n", "Metropolitan Community College Fab Lab, Omaha Nebraska, United States\n", "5300 N 30th St\n", "Omaha\n", "Nebraska\n", "http://www.facebook.com/mccnebfablab\n", "[email protected]\n", "\n", "Fab Lab ICC\n", "Independence Community College\n", "1057 W College Ave.\n", "Independence\n", "KS\n", "https://www.fablabs.io/fablabicc\n", "https://www.facebook.com/pages/Fab-Lab-ICC/269608433227931\n", "[email protected] or [email protected]\n", "\n", "Howard University Middle School of Mathematics and Science\n", "Washington\n", "District of Columbia\n", "http://www.howard.edu/ms2\n", "None\n", "\n", "NCC Fab Lab\n", "511 East Third Street\n", "3rd Floor \n", "Bethlehem\n", "PA\n", "http://www.northampton.edu/personal-enrichment/the-fab-lab.htm\n", "[email protected]\n", "\n", "Mt. Elliott Makerspace\n", "Detroit\n", "Michigan\n", "http://www.mtelliottmakerspace.com\n", "None\n", "\n", "Maine FabLab\n", "265 Main St\n", "Biddeford\n", "http://www.mainefablab.org\n", "[email protected]\n", "\n", "Champaign-Urbana Community Fab Lab\n", "1301 S Goodwin Ave\n", "Art Annex 2\n", "Urbana\n", "Illinois\n", "http://www.flickr.com/groups/1847246@N22/pool\n", "http://cucfablab.org\n", "[email protected]\n", "\n", "Mobile Fab Lab, Fab Labs Carolinas\n", "Durham\n", "North Carolina\n", "http://www.fablabcarolinas.org\n", "None\n", "*********************************************************\n", "\n", "Oceania\n", "\n", "----------------------------------------------\n", "Australia\n", "\n", "Fab Lab Adelaide\n", "39 Light Square\n", "Adelaide\n", "SA\n", "https://www.facebook.com/FabLabAdelaide\n", "http://fablabadelaide.org.au\n", "[email protected]\n", "\n", "The Edge, State Library of Queensland\n", "Brisbane\n", "Queensland\n", "http://edgqld.org.au\n", "None\n", "\n", "FabLab WA\n", "15 Grosvenor St\n", "Beaconsfield\n", "Western Australia\n", "http://www.fablabwa.org\n", "[email protected]\n", "\n", "----------------------------------------------\n", "New Zealand\n", "\n", "The Wellington Makerspace\n", "6 Vivian St\n", "[LV1]\n", "Wellington\n", "new zealand\n", "http://facebook.com/thewellingtonmakerspace\n", "http://www.meetup.com/wellingtonmakerspace/\n", "http://www.wellingtonmakerspace.com\n", "[email protected]\n", "\n", "Fab Lab xChc\n", "The Exchange - XCHC\n", "376 Wilsons Rd North\n", "Christchurch\n", "New Zealand\n", "http://www.fablabxchc.org.nz\n", "http://www.fabriko.org.nz\n", "[email protected]\n", "\n", "Fab Lab Wgtn, New Zealand\n", "Buckle St Entrance\n", "Massey University\n", "Wellington\n", "North Island\n", "http://www.fablabwgtn.co.nz\n", "https://www.facebook.com/FabLabWGTN\n", "http://creative.massey.ac.nz/enterprise/fab-lab-wgtn/\n", "[email protected]\n", "*********************************************************\n", "\n", "South America\n", "\n", "----------------------------------------------\n", "Argentina\n", "\n", "El Reactor\n", "Buenos Aires\n", "Pacheco de Melo 2888\n", "Buenos Aires\n", "Buenos Aires\n", "http://www.reprapargentina.com\n", "http://www.elreactor.com\n", "[email protected]\n", "\n", "FAB LAB BUENOS AIRES\n", "Buenos Aires\n", "Autonomous City of Buenos Aires\n", "http://www.reprapargentina.com/blog/\n", "https://twitter.com/FabLabBsAs\n", "https://www.facebook.com/FabLabBuenosAires\n", "[email protected] \n", "\n", "µLab\n", "Calle 8 n° 977 1/2\n", "e/ 51 y 53\n", "La Plata\n", "Buenos Aires\n", "http://www.synergia3.com/\n", "https://www.facebook.com/fablabLaPlata\n", "[email protected]\n", "\n", "3dlab-fabcafe\n", "Costa Rica 5198\n", "Buenos Aires\n", "http://www.3dlab-fabcafe.com\n", "[email protected]\n", "\n", "Fab Lab MDP\n", "Belgrano 3568\n", "Mar del Plata\n", "Buenos Aires\n", "http://www.fablabmdp.org\n", "https://www.facebook.com/FabLabMardelPlata\n", "[email protected]\n", "\n", "FAB LAB ARGENTINA + SCA\n", "Montevideo 938\n", "Buenos Aires\n", "Buenos Aires\n", "https://twitter.com/fablabargentina\n", "http://issuu.com/chungsong/docs/cv_fablab_argentina\n", "https://www.facebook.com/FablabArgentina/\n", "http://www.fablabargentina.com.ar\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Brazil\n", "\n", "7elektron\n", "Rua Joaquim Zenir Leite, 406 Paraiso\n", "Belo Horizonte\n", "Minas Gerais\n", "http://www.7elektron.com.br\n", "[email protected]\n", "\n", "Garagem Fab Lab\n", "Praça General Craveiro Lopes, 19 - sobreloja 01\n", "São Paulo\n", "SP\n", "http://www.facebook.com/garagemfablab\n", "http://www.garagemfablab.com\n", "[email protected]\n", "\n", "SENAI FABLAB\n", "Praça Natividade Saldanha, 19\n", "Rio de Janeiro\n", "Rio de Janeiro\n", "http://www.senaifablab.com.br\n", "[email protected]\n", "\n", "Fab Lab Belém\n", "Travessa Benjamin Constant, 1337\n", "Belém\n", "Pará\n", "https://www.facebook.com/groups/161326217395967/\n", "[email protected]\n", "\n", "Fab Lab Floripa\n", "Rua Lacerda Coutinho, 100\n", "Florianópolis\n", "Santa Catarina \n", "http://fablabfloripa.wordpress.com\n", "https://www.facebook.com/fablabfloripa\n", "[email protected]\n", "\n", "Fab Lab Brasil\n", "Praça Gen. Craveiro Lopes, 19 Sobreloja 1\n", "São Paulo\n", "São Paulo\n", "http://www.fablabbrasil.org\n", "[email protected]\n", "\n", "Fab Lab Recife\n", "Rua Dona Ada Vieira, 87\n", "Recife\n", "Pernambuco\n", "http://www.facebook.com/fablabrecife\n", "\n", "\n", "Insper FAB LAB\n", "R. Quatá\n", "São Paulo\n", "São Paulo\n", "[email protected]\n", "\n", "Fab Lab Universidade de São Paulo\n", "São Paulo\n", "São Paulo\n", "None\n", "\n", "Brasília Fab Lab\n", "CLN 305\n", "Bl B, Sala 06, Subsolo\n", "Brasilia\n", "DF\n", "https://www.facebook.com/brasiliafablab\n", "http://www.brasiliafablab.com.br\n", "[email protected]\n", "\n", "Olabi\n", "R. Barão de Lucena, 85 A\n", "Rio de Janeiro\n", "http://www.templo.co\n", "http://olabi.co/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Chile\n", "\n", "Fab Lab Universidad de Chile\n", "Av. Beauchef / Av. Rondizzoni\n", "Santiago\n", "Santiago Metropolitan Region\n", "http://www.fablab.uchile.cl\n", "[email protected]\n", "\n", "Fab Lab Antofagasta\n", "Avenida Universidad de Antofagasta S/N\n", "Campus COLOSO\n", "Antofagasta\n", "Antofagasta\n", "http://www.fablabafta.cl/\n", "[email protected] \n", "\n", "Fab Lab Santiago\n", "Condell 1097 L2\n", "Providencia\n", "Santiago\n", "Santiago Metropolitan Region\n", "http://www.fablabsantiago.org\n", "[email protected]\n", "\n", "Industrial Studio \n", "Talca\n", "Talca\n", "Talca\n", "http://www.industrialstudio.cl\n", "[email protected]\n", "\n", "FabLab Santiago\n", "Av. Diagonal Las Torres 2640\n", "Penalolen\n", "Santiago\n", "Santiago Metropolitan Region\n", "http://www.designlab.uai.cl/fablab\n", "\n", "\n", "----------------------------------------------\n", "Colombia\n", "\n", "Tecnoparque Medellin\n", "Carrera 46 #56 11\n", "Medellín\n", "Antioquia\n", "http://tecnoparque.sena.edu.co/sedes/medellin/Paginas/default.aspx\n", "[email protected]\n", "\n", "Tecnoparque Bogota\n", "Calle 54 # 10 39\n", "Bogotá\n", "Bogota\n", "[email protected]\n", "\n", "FabLab Cali\n", " Calle 25 # 115 - 85 Km. 2 Vía Cali - Jamundí\n", "Universidad Autónoma de Occidente\n", "Cali\n", "Valle del Cauca\n", "http://www.Twitter.com/FabLabCali\n", "http://www.facebook.com/FablabCali\n", "http://FabLabCali.org\n", "[email protected] [email protected]\n", "\n", "Fab Lab Bogotá\n", "Bogota\n", "Cll 63 No. 3 09\n", "Bogota\n", "Cundinamarca\n", "[email protected]\n", "\n", "Fab Lab Unal Medellín\n", "Universidad Nacional de Colombia sede Medellin\n", "calle 59A No 63 - 20\n", "Medellin\n", "Antioquia\n", "http://fablabunalmed.blogspot.com\n", "http://www.facebook.com/FabLabUnalMedellin\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Ecuador\n", "\n", "FabLab Quito\n", "Quito, Urb Marisol Calle 9 N71-126 y Calle 13\n", "Quito\n", "Pichincha\n", "\n", "\n", "----------------------------------------------\n", "Peru\n", "\n", "Fab Lab Perú\n", "Calle Manuel Fuentes\n", "San Isidro\n", "Lima\n", "http://www.fab.pe\n", "\n", "\n", "Fab Lab UNI\n", "Av. Túpac Amarú 220 Rímac \n", "Lima\n", "Lima\n", "https://www.facebook.com/FabLabUni\n", "http://www.fablabuni.edu.pe/\n", "[email protected]\n", "\n", "Fab Lab Atikux\n", "Distrito de miraflores\n", "Distrito de miraflores\n", "Miraflores\n", "Lima\n", "http://www.fablabatikux.edu.pe\n", "[email protected]\n", "\n", "Fab Lab MET\n", "Metropolitan Museum of Lima\n", "Lima\n", "https://www.facebook.com/fablabmet\n", "http://www.fab.pe\n", "[email protected]\n", "\n", "Fab Lab Tecsup i+De\n", "Cascanueces 2221\n", "Santa Anita\n", "Lima\n", "Perú\n", "http://www.tecsup.edu.pe/i+de/index.php\n", "[email protected]\n", "\n", "Fab Lab Lima\n", "Lima\n", "Lima Region\n", "http://www.fab.pe\n", "http://www.slideshare.net/fablablima\n", "https://www.facebook.com/FabLabLima\n", "https://twitter.com/fablablima\n", "http://www.fablablima.org\n", "[email protected]\n", "\n", "FabLab ESAN\n", "Lima\n", "Alonso de Molina 1652\n", "Lima\n", "Lima\n", "https://www.facebook.com/fablabesan\n", "http://fablab.esan.edu.pe/\n", "[email protected]\n", "\n", "----------------------------------------------\n", "Suriname\n", "\n", "Fab Lab Paramaribo\n", "Dr. Sophie Redmondstraat 116-118\n", "Paramaribo\n", "Paramaribo District\n", "https://www.facebook.com/FabLabParamaribo\n", "http://fablab.vicepresident.gov.sr/\n", "[email protected]\n" ] } ], "source": [ "# Get finally the sorted list of labs\n", "\n", "for k in sortedcontinents:\n", " print \"***********************************************************\"\n", " print \"\"\n", " print k\n", " for h in sortedcontinents[k]:\n", " print \"\"\n", " print \"----------------------------------------------\"\n", " print h\n", " for u in sortedcontinents[k][h]:\n", " print \"\"\n", " print sortedcontinents[k][h][u][\"name\"]\n", " if sortedcontinents[k][h][u][\"address_1\"] != None and sortedcontinents[k][h][u][\"address_1\"] != \"\":\n", " print sortedcontinents[k][h][u][\"address_1\"]\n", " if sortedcontinents[k][h][u][\"address_2\"] != None and sortedcontinents[k][h][u][\"address_2\"] != \"\":\n", " print sortedcontinents[k][h][u][\"address_2\"]\n", " print sortedcontinents[k][h][u][\"city\"]\n", " if sortedcontinents[k][h][u][\"county\"] != None and sortedcontinents[k][h][u][\"county\"] != \"\":\n", " print sortedcontinents[k][h][u][\"county\"]\n", " for l in sortedcontinents[k][h][u][\"links\"]:\n", " print sortedcontinents[k][h][u][\"links\"][l]\n", " print sortedcontinents[k][h][u][\"email\"]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ireapps/pycar	project2/json_to_csv_notebook.ipynb	1	1953	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convert JSON to CSV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import your modules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use a local version of this file from now on to save on\n", "bandwidth." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Open the file 'bills.json' in the project2/data/ directory\n", "\n", " # Convert it to a dict\n", " \n", " # Each bill is stored in an array in `data` with the key `objects`\n", " # Create a variable for easy access to the data we care about\n", " \n", " # Create a csv file to output in the same directory\n", " \n", " # Create a csv writer. This will help us format the file\n", " # correctly.\n", " \n", " # Write out the header row\n", " \n", " \n", " \n", " \n", " \n", " \n", " # Iterate through each dict in the array `objects`\n", " \n", " \n", " \n", " \n", " \n", " \n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
phobson/statsmodels	examples/notebooks/tsa_filters.ipynb	1	8880	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Filters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.macrodata.load_pandas().data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n", "print(index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta.index = index\n", "del dta['year']\n", "del dta['quarter']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm.datasets.macrodata.NOTE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(dta.head(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "dta.realgdp.plot(ax=ax);\n", "legend = ax.legend(loc = 'upper left');\n", "legend.prop.set_size(20);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hodrick-Prescott Filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Hodrick-Prescott filter separates a time-series $y_t$ into a trend $\\tau_t$ and a cyclical component $\\zeta_t$ \n", "\n", "$$y_t = \\tau_t + \\zeta_t$$\n", "\n", "The components are determined by minimizing the following quadratic loss function\n", "\n", "$$\\min_{\\\\{ \\tau_{t}\\\\} }\\sum_{t}^{T}\\zeta_{t}^{2}+\\lambda\\sum_{t=1}^{T}\\left[\\left(\\tau_{t}-\\tau_{t-1}\\right)-\\left(\\tau_{t-1}-\\tau_{t-2}\\right)\\right]^{2}$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gdp_cycle, gdp_trend = sm.tsa.filters.hpfilter(dta.realgdp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gdp_decomp = dta[['realgdp']]\n", "gdp_decomp[\"cycle\"] = gdp_cycle\n", "gdp_decomp[\"trend\"] = gdp_trend" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "gdp_decomp[[\"realgdp\", \"trend\"]][\"2000-03-31\":].plot(ax=ax, fontsize=16);\n", "legend = ax.get_legend()\n", "legend.prop.set_size(20);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baxter-King approximate band-pass filter: Inflation and Unemployment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Explore the hypothesis that inflation and unemployment are counter-cyclical." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Baxter-King filter is intended to explictly deal with the periodicty of the business cycle. By applying their band-pass filter to a series, they produce a new series that does not contain fluctuations at higher or lower than those of the business cycle. Specifically, the BK filter takes the form of a symmetric moving average \n", "\n", "$$y_{t}^{}=\\sum_{k=-K}^{k=K}a_ky_{t-k}$$\n", "\n", "where $a_{-k}=a_k$ and $\\sum_{k=-k}^{K}a_k=0$ to eliminate any trend in the series and render it stationary if the series is I(1) or I(2).\n", "\n", "For completeness, the filter weights are determined as follows\n", "\n", "$$a_{j} = B_{j}+\\theta\\text{ for }j=0,\\pm1,\\pm2,\\dots,\\pm K$$\n", "\n", "$$B_{0} = \\frac{\\left(\\omega_{2}-\\omega_{1}\\right)}{\\pi}$$\n", "$$B_{j} = \\frac{1}{\\pi j}\\left(\\sin\\left(\\omega_{2}j\\right)-\\sin\\left(\\omega_{1}j\\right)\\right)\\text{ for }j=0,\\pm1,\\pm2,\\dots,\\pm K$$\n", "\n", "where $\\theta$ is a normalizing constant such that the weights sum to zero.\n", "\n", "$$\\theta=\\frac{-\\sum_{j=-K^{K}b_{j}}}{2K+1}$$\n", "\n", "$$\\omega_{1}=\\frac{2\\pi}{P_{H}}$$\n", "\n", "$$\\omega_{2}=\\frac{2\\pi}{P_{L}}$$\n", "\n", "$P_L$ and $P_H$ are the periodicity of the low and high cut-off frequencies. Following Burns and Mitchell's work on US business cycles which suggests cycles last from 1.5 to 8 years, we use $P_L=6$ and $P_H=32$ by default." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bk_cycles = sm.tsa.filters.bkfilter(dta[[\"infl\",\"unemp\"]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We lose K observations on both ends. It is suggested to use K=12 for quarterly data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(12,10))\n", "ax = fig.add_subplot(111)\n", "bk_cycles.plot(ax=ax, style=['r--', 'b-']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Christiano-Fitzgerald approximate band-pass filter: Inflation and Unemployment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Christiano-Fitzgerald filter is a generalization of BK and can thus also be seen as weighted moving average. However, the CF filter is asymmetric about $t$ as well as using the entire series. The implementation of their filter involves the\n", "calculations of the weights in\n", "\n", "$$y_{t}^{}=B_{0}y_{t}+B_{1}y_{t+1}+\\dots+B_{T-1-t}y_{T-1}+\\tilde B_{T-t}y_{T}+B_{1}y_{t-1}+\\dots+B_{t-2}y_{2}+\\tilde B_{t-1}y_{1}$$\n", "\n", "for $t=3,4,...,T-2$, where\n", "\n", "$$B_{j} = \\frac{\\sin(jb)-\\sin(ja)}{\\pi j},j\\geq1$$\n", "\n", "$$B_{0} = \\frac{b-a}{\\pi},a=\\frac{2\\pi}{P_{u}},b=\\frac{2\\pi}{P_{L}}$$\n", "\n", "$\\tilde B_{T-t}$ and $\\tilde B_{t-1}$ are linear functions of the $B_{j}$'s, and the values for $t=1,2,T-1,$ and $T$ are also calculated in much the same way. $P_{U}$ and $P_{L}$ are as described above with the same interpretation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CF filter is appropriate for series that may follow a random walk." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm.tsa.stattools.adfuller(dta['unemp'])[:3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm.tsa.stattools.adfuller(dta['infl'])[:3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[[\"infl\",\"unemp\"]])\n", "print(cf_cycles.head(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(14,10))\n", "ax = fig.add_subplot(111)\n", "cf_cycles.plot(ax=ax, style=['r--','b-']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filtering assumes a priori* that business cycles exist. Due to this assumption, many macroeconomic models seek to create models that match the shape of impulse response functions rather than replicating properties of filtered series. See VAR notebook." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
gastonstat/stat259	tutorials/genotypes.ipynb	2	10945	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_This notebook will allow you to practice some basic skills for using python: working with different data types, using various data structures, reading and writing text files, using conditionals, control flow structures, creating functions, and of course working with ipython notebooks._\n", "\n", "### Motivation:\n", "\n", "A biologist is interested in the genetic basis of height. She measures the heights of many subjects and sends off their DNA samples to a core for genotyping arrays. These arrays determine the DNA bases at the variable sites of the genome (known as single nucleotide polymorphisms, or SNPs). Since humans are diploid, i.e. have two of each chromosome, each data point will be two DNA bases corresponding to the two chromosomes in each individual. At each SNP, there will be only three possible genotypes, e.g. AA, AG, GG for an A/G SNP. In order to test the correlation between a SNP genotype and height, she wants to perform a regression with an additive genetic model. However, she cannot do this with the data in the current form. She needs to convert the genotypes, e.g. AA, AG, and GG, to the numbers 0, 1, and 2, respectively (in the example the number corresponds the number of G bases the person has at that SNP). Since she has too much data to do this manually, e.g. in Excel, she comes to you for ideas of how to efficiently transform the data.\n", "\n", "### Part 1:\n", "Create a new list which has the converted genotype for each subject ('AA' -> 0, 'AG' -> 1, 'GG' -> 2)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "genos = ['AA', 'GG', 'AG', 'AG', 'GG']\n", "genos_new = []\n", "# Use your knowledge of if/else statements and loop structures below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check your work" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genos_new == [0, 2, 1, 1, 2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 2:\n", "\n", "Sometimes there are errors and the genotype cannot be determined. Adapt your code from above to deal with this problem (in this example missing data is assigned NA for \"Not Available\")." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "genos_w_missing = ['AA', 'NA', 'GG', 'AG', 'AG', 'GG', 'NA']\n", "genos_w_missing_new = []\n", "# The missing data should not be converted to a number, but remain 'NA' in the new list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check your work" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "genos_w_missing_new == [0, 'NA', 2, 1, 1, 2, 'NA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Main Practice\n", "\n", "__Setup:__ Open a terminal and run the following commands:\n", "```bash\n", "# create a new directory\n", "mkdir python-intro\n", "cd python-intro\n", "\n", "# download data file, and ipython notebook\n", "curl -O https://raw.githubusercontent.com/gastonstat/stat259/gh-pages/tutorials/genos.txt\n", "curl -O https://raw.githubusercontent.com/gastonstat/stat259/gh-pages/tutorials/genotypes.ipynb\n", "```\n", "\n", "__Data File:__ The raw data for this practice is in the file `genos.txt` which contains one column of genotypes (one genotype per row). Each genotype consists of two characters: e.g. `'AA'` or `'GG'`. In addition, there are some rows that contain missing values denoted as `'NA'`.\n", "\n", "I. Read in the data and store the contents in a list called __genos__.\n", "\n", "II. Find out how what are the different (i.e. unique) values are in __genos__.\n", "\n", "III. Calculate the number of occurrences of each genotype, and store the results in a dictionary called __geno_counts__. Use the following 3 approaches:\n", "1. Use a for loop to count the genotypes (store the result in a dictionary)\n", "2. Get the same counts but this time using the `count()` method\n", "3. Another alternative is to use `Counter` from __Collections__\n", "\n", "IV. Once you've counted the genotypes, make a function __get_proportions()__ that takes `geno_counts` and returns a dictionary with relative frequencies (i.e. proportions) of genotypes. Also, test your function with the provided assertion.\n", "\n", "V. Convert the string values in __genos__ into integers ('NA' remains as 'NA') and put them in a new list called numeric_genos:\n", "- `'AA'` = 0\n", "- `'AG'` = 1\n", "- `'GG'` = 2\n", "- `'NA'` = `'NA'`\n", "\n", "VI. Write the data in numeric_genos to a text file called `genos_int.txt`\n", "\n", "VII. Finally, convert your notebook to html (and open it) by running these commands from the shell:\n", "\n", "```shell\n", "ipython nbconvert genotypes.ipynb\n", "open genotypes.html\n", "```\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# things to be imported\n", "from __future__ import division # if you use python 2.?\n", "from collections import Counter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## I. Reading a text file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some refs about Reading Files:\n", "\n", "- File Operations: [https://github.com/dlab-berkeley/python-fundamentals/blob/master/cheat-sheets/12-Files.ipynb](https://github.com/dlab-berkeley/python-fundamentals/blob/master/cheat-sheets/12-Files.ipynb)\n", "- Reading Text Files: [http://www.jarrodmillman.com/rcsds/lectures/reading_text_files.html](http://www.jarrodmillman.com/rcsds/lectures/reading_text_files.html)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# open 'genos.txt' and store values in \"genos\"\n", "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## II. Unique Genotypes" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Find the unique values in genos\n", "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## III. Counting Genotypes\n", "\n", "### a) Using a for loop" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# For loop to count occurrences of AA, AG, GG, NA\n", "# (store results in dictionary \"geno_counts\")\n", "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### b) Using count method" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### c) Using Counter from collections" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## IV. Function to Calculate Proportions" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write a function \"get_proportions()\"\n", "# Parameters: geno_counts (dictionary)\n", "# Returns: dictionary of proportions\n", "# YOUR CODE" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# apply your function:\n", "# get_proportions(geno_counts)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# test for function get_proportions()\n", "def test_get_proportions():\n", " # We make a fake dictionary\n", " input_val = {'AA': 2, 'AB': 4, 'BB': 14}\n", " expected_result = {'AA': 0.1, 'AB': 0.2, 'BB': 0.7}\n", " # run function\n", " res = get_proportions(input_val)\n", " assert res == expected_result" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# run the test and see what happens:\n", "# test_get_proportions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## V. Converting to numeric genotypes" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# convert genotypes: AA = 0, AG = 1, GG = 2, NA = NA\n", "# (create a list called \"numeric_genos\")\n", "# YOUR CODE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## VI. Write Numeric Genotypes to a text file" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# write values in \"numeric_genos\" to a file \"genos_int.txt\"\n", "# YOUR CODE" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
phockett/ePSproc	notebooks/ePS_ePSproc_degenerate_states_demo_030821.ipynb	1	2959594	null	gpl-3.0
jphall663/GWU_data_mining	05_neural_networks/src/py_part_5_neural_networks.ipynb	1	161585	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# License \n", "*\n", "Copyright (C) 2017 J. Patrick Hall, [email protected]\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# imports\n", "import h2o \n", "import numpy as np\n", "import pandas as pd\n", "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n", "from h2o.grid.grid_search import H2OGridSearch" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# display matplotlib graphics in notebook\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: java version \"1.8.0_112\"; Java(TM) SE Runtime Environment (build 1.8.0_112-b16); Java HotSpot(TM) 64-Bit Server VM (build 25.112-b16, mixed mode)\n", " Starting server from /Users/phall/anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpu7y9qi1u\n", " JVM stdout: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpu7y9qi1u/h2o_phall_started_from_python.out\n", " JVM stderr: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpu7y9qi1u/h2o_phall_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", "Connecting to H2O server at http://127.0.0.1:54321... successful.\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n", "<td>02 secs</td></tr>\n", "<tr><td>H2O cluster version:</td>\n", "<td>3.10.3.4</td></tr>\n", "<tr><td>H2O cluster version age:</td>\n", "<td>1 month and 2 days </td></tr>\n", "<tr><td>H2O cluster name:</td>\n", "<td>H2O_from_python_phall_sxxj60</td></tr>\n", "<tr><td>H2O cluster total nodes:</td>\n", "<td>1</td></tr>\n", "<tr><td>H2O cluster free memory:</td>\n", "<td>3.556 Gb</td></tr>\n", "<tr><td>H2O cluster total cores:</td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster allowed cores:</td>\n", "<td>8</td></tr>\n", "<tr><td>H2O cluster status:</td>\n", "<td>accepting new members, healthy</td></tr>\n", "<tr><td>H2O connection url:</td>\n", "<td>http://127.0.0.1:54321</td></tr>\n", "<tr><td>H2O connection proxy:</td>\n", "<td>None</td></tr>\n", "<tr><td>Python version:</td>\n", "<td>3.5.2 final</td></tr></table></div>" ], "text/plain": [ "-------------------------- ------------------------------\n", "H2O cluster uptime: 02 secs\n", "H2O cluster version: 3.10.3.4\n", "H2O cluster version age: 1 month and 2 days\n", "H2O cluster name: H2O_from_python_phall_sxxj60\n", "H2O cluster total nodes: 1\n", "H2O cluster free memory: 3.556 Gb\n", "H2O cluster total cores: 8\n", "H2O cluster allowed cores: 8\n", "H2O cluster status: accepting new members, healthy\n", "H2O connection url: http://127.0.0.1:54321\n", "H2O connection proxy:\n", "Python version: 3.5.2 final\n", "-------------------------- ------------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# start and connect to h2o server\n", "h2o.init()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load clean data\n", "path = '/Users/phall/workspace/GWU_data_mining/03_regression/data/loan_clean.csv'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define input variable measurement levels \n", "# strings automatically parsed as enums (nominal)\n", "# numbers automatically parsed as numeric\n", "col_types = {'bad_loan': 'enum'}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: \|█████████████████████████████████████████████████████████\| 100%\n" ] } ], "source": [ "frame = h2o.import_file(path=path, col_types=col_types) # multi-threaded import" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows:163987\n", "Cols:18\n", "\n", "\n" ] }, { "data": { "text/html": [ "<table>\n", "<thead>\n", "<tr><th> </th><th>id </th><th>bad_loan </th><th>GRP_REP_home_ownership </th><th>GRP_addr_state </th><th>GRP_home_ownership </th><th>GRP_purpose </th><th>GRP_verification_status </th><th>_WARN_ </th><th>STD_IMP_REP_annual_inc </th><th>STD_IMP_REP_delinq_2yrs </th><th>STD_IMP_REP_dti </th><th>STD_IMP_REP_emp_length </th><th>STD_IMP_REP_int_rate </th><th>STD_IMP_REP_loan_amnt </th><th>STD_IMP_REP_longest_credit_lengt </th><th>STD_IMP_REP_revol_util </th><th>STD_IMP_REP_term_length </th><th>STD_IMP_REP_total_acc </th></tr>\n", "</thead>\n", "<tbody>\n", "<tr><td>type </td><td>int </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td><td>real </td></tr>\n", "<tr><td>mins </td><td>10001.0 </td><td> </td><td>1.0 </td><td>1.0 </td><td>1.0 </td><td>1.0 </td><td>1.0 </td><td>NaN </td><td>-1.767455639 </td><td>-0.39219617 </td><td>-2.119639396 </td><td>-1.6213902740000001 </td><td>-1.907046215 </td><td>-1.587129405 </td><td>-2.22445124 </td><td>-2.164541326 </td><td>-0.516495577 </td><td>-2.058861889 </td></tr>\n", "<tr><td>mean </td><td>91994.0 </td><td> </td><td>2.5740028172964924 </td><td>11.409337325519703</td><td>2.5740028172964924 </td><td>3.2449401476946345</td><td>2.340356247751345 </td><td>0.0 </td><td>2.38744452882879e-11 </td><td>2.2959296297769782e-12 </td><td>6.807013811211564e-11</td><td>-3.566867876239133e-11 </td><td>-8.948753565861857e-12</td><td>8.311927579716105e-11 </td><td>5.0612534090153816e-11 </td><td>-1.4734128080190765e-11 </td><td>-1.5009542966560638e-10 </td><td>8.060924856225354e-13 </td></tr>\n", "<tr><td>maxs </td><td>173987.0 </td><td> </td><td>5.0 </td><td>37.0 </td><td>5.0 </td><td>14.0 </td><td>3.0 </td><td>NaN </td><td>4.6180619798 </td><td>4.1566950661 </td><td>3.0371487270000004 </td><td>1.2288169612 </td><td>2.8376799992 </td><td>2.7671323946 </td><td>3.1431598296 </td><td>3.0363495275 </td><td>1.9718787627 </td><td>3.0684672884 </td></tr>\n", "<tr><td>sigma </td><td>47339.11363414683</td><td> </td><td>0.6675260435449262 </td><td>9.971926133461404 </td><td>0.6675260435449262 </td><td>2.2672892075259754</td><td>0.5040864341768772 </td><td>-0.0 </td><td>0.9999999999982868 </td><td>0.9999999999212518 </td><td>1.0000000000037712 </td><td>1.0000000000339833 </td><td>1.0000000000199503 </td><td>0.999999999985285 </td><td>0.9999999999850594 </td><td>1.000000000017688 </td><td>1.0000000000642086 </td><td>1.0000000000331841 </td></tr>\n", "<tr><td>zeros </td><td>0 </td><td> </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td></tr>\n", "<tr><td>missing</td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>163987 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td></tr>\n", "<tr><td>0 </td><td>10001.0 </td><td>0 </td><td>3.0 </td><td>14.0 </td><td>3.0 </td><td>3.0 </td><td>2.0 </td><td>nan </td><td>-1.1992995020000001 </td><td>-0.39219617 </td><td>1.5712460425 </td><td>1.2288169612 </td><td>-0.7047730510000001 </td><td>-1.019182214 </td><td>1.6839024850000002 </td><td>1.1858716502 </td><td>-0.516495577 </td><td>-1.359278248 </td></tr>\n", "<tr><td>1 </td><td>10002.0 </td><td>1 </td><td>3.0 </td><td>10.0 </td><td>3.0 </td><td>8.0 </td><td>2.0 </td><td>nan </td><td>-1.04507688 </td><td>-0.39219617 </td><td>-1.9861534850000002 </td><td>-1.6213902740000001 </td><td>0.3572732234 </td><td>-1.3347084310000001 </td><td>-0.42059567400000003 </td><td>-1.7882703350000002 </td><td>1.9718787627 </td><td>-1.7965180230000002 </td></tr>\n", "<tr><td>2 </td><td>10003.0 </td><td>0 </td><td>3.0 </td><td>7.0 </td><td>3.0 </td><td>7.0 </td><td>3.0 </td><td>nan </td><td>-1.501267394 </td><td>-0.39219617 </td><td>-0.9556422520000001 </td><td>1.2288169612 </td><td>0.5158905241 </td><td>-1.34732948 </td><td>-0.7212382690000001 </td><td>1.7782983174 </td><td>-0.516495577 </td><td>-1.271830292 </td></tr>\n", "<tr><td>3 </td><td>10004.0 </td><td>0 </td><td>3.0 </td><td>2.0 </td><td>3.0 </td><td>4.0 </td><td>2.0 </td><td>nan </td><td>-0.303921333 </td><td>-0.39219617 </td><td>0.5500788236 </td><td>1.2288169612 </td><td>-0.051913437 </td><td>-0.388129779 </td><td>0.0303682169 </td><td>0.0325652593 </td><td>-0.516495577 </td><td>1.089264497 </td></tr>\n", "<tr><td>4 </td><td>10005.0 </td><td>0 </td><td>3.0 </td><td>14.0 </td><td>3.0 </td><td>10.0 </td><td>2.0 </td><td>nan </td><td>-0.890854259 </td><td>-0.39219617 </td><td>-0.624597193 </td><td>-0.7663281030000001 </td><td>-1.3369434530000002 </td><td>-1.019182214 </td><td>-0.8220262690000001 </td><td>-1.0317254690000002 </td><td>-0.516495577 </td><td>-1.0969343820000002 </td></tr>\n", "<tr><td>5 </td><td>10006.0 </td><td>0 </td><td>3.0 </td><td>2.0 </td><td>3.0 </td><td>8.0 </td><td>2.0 </td><td>nan </td><td>-0.5824090160000001 </td><td>-0.39219617 </td><td>-1.4054897720000001 </td><td>0.9437962377 </td><td>1.1319693155000001 </td><td>-1.271603188 </td><td>-1.623166051 </td><td>1.3379811999 </td><td>-0.516495577 </td><td>-1.7965180230000002 </td></tr>\n", "<tr><td>6 </td><td>10007.0 </td><td>1 </td><td>4.0 </td><td>2.0 </td><td>4.0 </td><td>7.0 </td><td>2.0 </td><td>nan </td><td>-0.788039178 </td><td>-0.39219617 </td><td>-1.37879259 </td><td>-0.48130738 </td><td>1.7388529011 </td><td>-0.9434559220000001 </td><td>-1.17220216 </td><td>-0.8596015050000001 </td><td>1.9718787627 </td><td>-1.0094864270000001 </td></tr>\n", "<tr><td>7 </td><td>10008.0 </td><td>1 </td><td>3.0 </td><td>4.0 </td><td>3.0 </td><td>4.0 </td><td>2.0 </td><td>nan </td><td>-1.430633434 </td><td>-0.39219617 </td><td>0.2937858745 </td><td>-1.6213902740000001 </td><td>-0.235817553 </td><td>-0.971853281 </td><td>-1.17220216 </td><td>-0.703489072 </td><td>1.9718787627 </td><td>-1.883965979 </td></tr>\n", "<tr><td>8 </td><td>10009.0 </td><td>0 </td><td>4.0 </td><td>14.0 </td><td>4.0 </td><td>2.0 </td><td>3.0 </td><td>nan </td><td>0.0344814697 </td><td>-0.39219617 </td><td>0.032153489 </td><td>-0.196286656 </td><td>0.2147475328 </td><td>-0.8298664840000001 </td><td>-0.270274377 </td><td>-1.339947451 </td><td>1.9718787627 </td><td>-0.135006875 </td></tr>\n", "<tr><td>9 </td><td>10010.0 </td><td>0 </td><td>4.0 </td><td>2.0 </td><td>4.0 </td><td>2.0 </td><td>2.0 </td><td>nan </td><td>0.1115927805 </td><td>-0.39219617 </td><td>-0.680661276 </td><td>1.2288169612 </td><td>-0.235817553 </td><td>-0.13570880500000002 </td><td>1.0826172966 </td><td>0.5213930910000001 </td><td>-0.516495577 </td><td>0.8269206315000001 </td></tr>\n", "</tbody>\n", "</table>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frame.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# split into 40% training, 30% validation, and 30% test\n", "train, valid, test = frame.split_frame([0.4, 0.3])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bad_loan\n", "['GRP_REP_home_ownership', 'GRP_addr_state', 'GRP_home_ownership', 'GRP_purpose', 'GRP_verification_status', 'STD_IMP_REP_annual_inc', 'STD_IMP_REP_delinq_2yrs', 'STD_IMP_REP_dti', 'STD_IMP_REP_emp_length', 'STD_IMP_REP_int_rate', 'STD_IMP_REP_loan_amnt', 'STD_IMP_REP_longest_credit_lengt', 'STD_IMP_REP_revol_util', 'STD_IMP_REP_term_length', 'STD_IMP_REP_total_acc']\n" ] } ], "source": [ "# assign target and inputs\n", "y = 'bad_loan'\n", "X = [name for name in frame.columns if name not in ['id', '_WARN_', y]]\n", "print(y)\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# set target to factor - for binary classification\n", "train[y] = train[y].asfactor()\n", "valid[y] = valid[y].asfactor()\n", "test[y] = test[y].asfactor()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning Model Build progress: \|██████████████████████████████████████\| 100%\n", "Model Details\n", "=============\n", "H2ODeepLearningEstimator : Deep Learning\n", "Model Key: nn_model\n", "\n", "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on train data. \n", "\n", "MSE: 0.14411464647275385\n", "RMSE: 0.3796243491568393\n", "LogLoss: 0.4545508130295154\n", "Mean Per-Class Error: 0.3641766804848612\n", "AUC: 0.6879894198982001\n", "Gini: 0.3759788397964001\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.20919837042173123: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>5628.0</td>\n", "<td>2396.0</td>\n", "<td>0.2986</td>\n", "<td> (2396.0/8024.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>818.0</td>\n", "<td>1082.0</td>\n", "<td>0.4305</td>\n", "<td> (818.0/1900.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>6446.0</td>\n", "<td>3478.0</td>\n", "<td>0.3239</td>\n", "<td> (3214.0/9924.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- ---- ---- ------- ---------------\n", "0 5628 2396 0.2986 (2396.0/8024.0)\n", "1 818 1082 0.4305 (818.0/1900.0)\n", "Total 6446 3478 0.3239 (3214.0/9924.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.2091984</td>\n", "<td>0.4023801</td>\n", "<td>210.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.1311033</td>\n", "<td>0.5748118</td>\n", "<td>311.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.2699799</td>\n", "<td>0.3646270</td>\n", "<td>145.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.4676336</td>\n", "<td>0.8089480</td>\n", "<td>7.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>0.4876967</td>\n", "<td>0.7142857</td>\n", "<td>2.0</td></tr>\n", "<tr><td>max recall</td>\n", "<td>0.0641674</td>\n", "<td>1.0</td>\n", "<td>396.0</td></tr>\n", "<tr><td>max specificity</td>\n", "<td>0.4960472</td>\n", "<td>0.9998754</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_mcc</td>\n", "<td>0.2237859</td>\n", "<td>0.2257185</td>\n", "<td>192.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.1905967</td>\n", "<td>0.6314806</td>\n", "<td>234.0</td></tr>\n", "<tr><td>max mean_per_class_accuracy</td>\n", "<td>0.1748815</td>\n", "<td>0.6358233</td>\n", "<td>254.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.209198 0.40238 210\n", "max f2 0.131103 0.574812 311\n", "max f0point5 0.26998 0.364627 145\n", "max accuracy 0.467634 0.808948 7\n", "max precision 0.487697 0.714286 2\n", "max recall 0.0641674 1 396\n", "max specificity 0.496047 0.999875 0\n", "max absolute_mcc 0.223786 0.225718 192\n", "max min_per_class_accuracy 0.190597 0.631481 234\n", "max mean_per_class_accuracy 0.174882 0.635823 254" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 19.15 %\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>group</b></td>\n", "<td><b>cumulative_data_fraction</b></td>\n", "<td><b>lower_threshold</b></td>\n", "<td><b>lift</b></td>\n", "<td><b>cumulative_lift</b></td>\n", "<td><b>response_rate</b></td>\n", "<td><b>cumulative_response_rate</b></td>\n", "<td><b>capture_rate</b></td>\n", "<td><b>cumulative_capture_rate</b></td>\n", "<td><b>gain</b></td>\n", "<td><b>cumulative_gain</b></td></tr>\n", "<tr><td></td>\n", "<td>1</td>\n", "<td>0.0100766</td>\n", "<td>0.4433253</td>\n", "<td>2.6115789</td>\n", "<td>2.6115789</td>\n", "<td>0.5</td>\n", "<td>0.5</td>\n", "<td>0.0263158</td>\n", "<td>0.0263158</td>\n", "<td>161.1578947</td>\n", "<td>161.1578947</td></tr>\n", "<tr><td></td>\n", "<td>2</td>\n", "<td>0.0200524</td>\n", "<td>0.4216453</td>\n", "<td>2.3741627</td>\n", "<td>2.4934673</td>\n", "<td>0.4545455</td>\n", "<td>0.4773869</td>\n", "<td>0.0236842</td>\n", "<td>0.05</td>\n", "<td>137.4162679</td>\n", "<td>149.3467337</td></tr>\n", "<tr><td></td>\n", "<td>3</td>\n", "<td>0.0300282</td>\n", "<td>0.4054876</td>\n", "<td>2.6907177</td>\n", "<td>2.5589968</td>\n", "<td>0.5151515</td>\n", "<td>0.4899329</td>\n", "<td>0.0268421</td>\n", "<td>0.0768421</td>\n", "<td>169.0717703</td>\n", "<td>155.8996821</td></tr>\n", "<tr><td></td>\n", "<td>4</td>\n", "<td>0.0400040</td>\n", "<td>0.3912729</td>\n", "<td>1.8993301</td>\n", "<td>2.3944956</td>\n", "<td>0.3636364</td>\n", "<td>0.4584383</td>\n", "<td>0.0189474</td>\n", "<td>0.0957895</td>\n", "<td>89.9330144</td>\n", "<td>139.4495559</td></tr>\n", "<tr><td></td>\n", "<td>5</td>\n", "<td>0.0500806</td>\n", "<td>0.3758540</td>\n", "<td>2.1937263</td>\n", "<td>2.3540993</td>\n", "<td>0.42</td>\n", "<td>0.4507042</td>\n", "<td>0.0221053</td>\n", "<td>0.1178947</td>\n", "<td>119.3726316</td>\n", "<td>135.4099333</td></tr>\n", "<tr><td></td>\n", "<td>6</td>\n", "<td>0.1000605</td>\n", "<td>0.3265151</td>\n", "<td>1.8428480</td>\n", "<td>2.0987311</td>\n", "<td>0.3528226</td>\n", "<td>0.4018127</td>\n", "<td>0.0921053</td>\n", "<td>0.21</td>\n", "<td>84.2848048</td>\n", "<td>109.8731118</td></tr>\n", "<tr><td></td>\n", "<td>7</td>\n", "<td>0.1500403</td>\n", "<td>0.2928796</td>\n", "<td>1.6322368</td>\n", "<td>1.9433375</td>\n", "<td>0.3125</td>\n", "<td>0.3720618</td>\n", "<td>0.0815789</td>\n", "<td>0.2915789</td>\n", "<td>63.2236842</td>\n", "<td>94.3337457</td></tr>\n", "<tr><td></td>\n", "<td>8</td>\n", "<td>0.2000202</td>\n", "<td>0.2654448</td>\n", "<td>1.6532980</td>\n", "<td>1.8708641</td>\n", "<td>0.3165323</td>\n", "<td>0.3581864</td>\n", "<td>0.0826316</td>\n", "<td>0.3742105</td>\n", "<td>65.3297963</td>\n", "<td>87.0864112</td></tr>\n", "<tr><td></td>\n", "<td>9</td>\n", "<td>0.2999798</td>\n", "<td>0.2251197</td>\n", "<td>1.3637076</td>\n", "<td>1.7018687</td>\n", "<td>0.2610887</td>\n", "<td>0.3258314</td>\n", "<td>0.1363158</td>\n", "<td>0.5105263</td>\n", "<td>36.3707555</td>\n", "<td>70.1868713</td></tr>\n", "<tr><td></td>\n", "<td>10</td>\n", "<td>0.4000403</td>\n", "<td>0.1952777</td>\n", "<td>1.0467356</td>\n", "<td>1.5380029</td>\n", "<td>0.2004028</td>\n", "<td>0.2944584</td>\n", "<td>0.1047368</td>\n", "<td>0.6152632</td>\n", "<td>4.6735570</td>\n", "<td>53.8002917</td></tr>\n", "<tr><td></td>\n", "<td>11</td>\n", "<td>0.5</td>\n", "<td>0.1709584</td>\n", "<td>1.0372602</td>\n", "<td>1.4378947</td>\n", "<td>0.1985887</td>\n", "<td>0.2752922</td>\n", "<td>0.1036842</td>\n", "<td>0.7189474</td>\n", "<td>3.7260187</td>\n", "<td>43.7894737</td></tr>\n", "<tr><td></td>\n", "<td>12</td>\n", "<td>0.5999597</td>\n", "<td>0.1488687</td>\n", "<td>0.8266490</td>\n", "<td>1.3360547</td>\n", "<td>0.1582661</td>\n", "<td>0.2557944</td>\n", "<td>0.0826316</td>\n", "<td>0.8015789</td>\n", "<td>-17.3351019</td>\n", "<td>33.6054665</td></tr>\n", "<tr><td></td>\n", "<td>13</td>\n", "<td>0.7000202</td>\n", "<td>0.1297643</td>\n", "<td>0.7679567</td>\n", "<td>1.2548511</td>\n", "<td>0.1470292</td>\n", "<td>0.2402476</td>\n", "<td>0.0768421</td>\n", "<td>0.8784211</td>\n", "<td>-23.2043250</td>\n", "<td>25.4851091</td></tr>\n", "<tr><td></td>\n", "<td>14</td>\n", "<td>0.7999798</td>\n", "<td>0.1111519</td>\n", "<td>0.4633447</td>\n", "<td>1.1559502</td>\n", "<td>0.0887097</td>\n", "<td>0.2213125</td>\n", "<td>0.0463158</td>\n", "<td>0.9247368</td>\n", "<td>-53.6655348</td>\n", "<td>15.5950173</td></tr>\n", "<tr><td></td>\n", "<td>15</td>\n", "<td>0.8999395</td>\n", "<td>0.0922571</td>\n", "<td>0.4528141</td>\n", "<td>1.0778502</td>\n", "<td>0.0866935</td>\n", "<td>0.2063599</td>\n", "<td>0.0452632</td>\n", "<td>0.97</td>\n", "<td>-54.7185908</td>\n", "<td>7.7850185</td></tr>\n", "<tr><td></td>\n", "<td>16</td>\n", "<td>1.0</td>\n", "<td>0.0596773</td>\n", "<td>0.2998187</td>\n", "<td>1.0</td>\n", "<td>0.0574018</td>\n", "<td>0.1914551</td>\n", "<td>0.03</td>\n", "<td>1.0</td>\n", "<td>-70.0181269</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate cumulative_response_rate capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- -------- ----------------- --------------- -------------------------- -------------- ------------------------- -------- -----------------\n", " 1 0.0100766 0.443325 2.61158 2.61158 0.5 0.5 0.0263158 0.0263158 161.158 161.158\n", " 2 0.0200524 0.421645 2.37416 2.49347 0.454545 0.477387 0.0236842 0.05 137.416 149.347\n", " 3 0.0300282 0.405488 2.69072 2.559 0.515152 0.489933 0.0268421 0.0768421 169.072 155.9\n", " 4 0.040004 0.391273 1.89933 2.3945 0.363636 0.458438 0.0189474 0.0957895 89.933 139.45\n", " 5 0.0500806 0.375854 2.19373 2.3541 0.42 0.450704 0.0221053 0.117895 119.373 135.41\n", " 6 0.10006 0.326515 1.84285 2.09873 0.352823 0.401813 0.0921053 0.21 84.2848 109.873\n", " 7 0.15004 0.29288 1.63224 1.94334 0.3125 0.372062 0.0815789 0.291579 63.2237 94.3337\n", " 8 0.20002 0.265445 1.6533 1.87086 0.316532 0.358186 0.0826316 0.374211 65.3298 87.0864\n", " 9 0.29998 0.22512 1.36371 1.70187 0.261089 0.325831 0.136316 0.510526 36.3708 70.1869\n", " 10 0.40004 0.195278 1.04674 1.538 0.200403 0.294458 0.104737 0.615263 4.67356 53.8003\n", " 11 0.5 0.170958 1.03726 1.43789 0.198589 0.275292 0.103684 0.718947 3.72602 43.7895\n", " 12 0.59996 0.148869 0.826649 1.33605 0.158266 0.255794 0.0826316 0.801579 -17.3351 33.6055\n", " 13 0.70002 0.129764 0.767957 1.25485 0.147029 0.240248 0.0768421 0.878421 -23.2043 25.4851\n", " 14 0.79998 0.111152 0.463345 1.15595 0.0887097 0.221313 0.0463158 0.924737 -53.6655 15.595\n", " 15 0.89994 0.0922571 0.452814 1.07785 0.0866935 0.20636 0.0452632 0.97 -54.7186 7.78502\n", " 16 1 0.0596773 0.299819 1 0.0574018 0.191455 0.03 1 -70.0181 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on validation data. \n", "\n", "MSE: 0.14716412316013278\n", "RMSE: 0.38361976377675433\n", "LogLoss: 0.46347712915408495\n", "Mean Per-Class Error: 0.3757937526837185\n", "AUC: 0.6690848124829848\n", "Gini: 0.3381696249659696\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.1974456585232734: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>26099.0</td>\n", "<td>13560.0</td>\n", "<td>0.3419</td>\n", "<td> (13560.0/39659.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>3905.0</td>\n", "<td>5627.0</td>\n", "<td>0.4097</td>\n", "<td> (3905.0/9532.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>30004.0</td>\n", "<td>19187.0</td>\n", "<td>0.355</td>\n", "<td> (17465.0/49191.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- ----- ----- ------- -----------------\n", "0 26099 13560 0.3419 (13560.0/39659.0)\n", "1 3905 5627 0.4097 (3905.0/9532.0)\n", "Total 30004 19187 0.355 (17465.0/49191.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.1974457</td>\n", "<td>0.3918660</td>\n", "<td>230.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.1116928</td>\n", "<td>0.5627238</td>\n", "<td>338.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.2842587</td>\n", "<td>0.3550024</td>\n", "<td>137.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.4518233</td>\n", "<td>0.8065093</td>\n", "<td>19.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>0.4727366</td>\n", "<td>0.5208333</td>\n", "<td>10.0</td></tr>\n", "<tr><td>max recall</td>\n", "<td>0.0603167</td>\n", "<td>1.0</td>\n", "<td>399.0</td></tr>\n", "<tr><td>max specificity</td>\n", "<td>0.5091984</td>\n", "<td>0.9999244</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_mcc</td>\n", "<td>0.2029996</td>\n", "<td>0.2017317</td>\n", "<td>223.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.1889088</td>\n", "<td>0.6219052</td>\n", "<td>240.0</td></tr>\n", "<tr><td>max mean_per_class_accuracy</td>\n", "<td>0.1974457</td>\n", "<td>0.6242062</td>\n", "<td>230.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.197446 0.391866 230\n", "max f2 0.111693 0.562724 338\n", "max f0point5 0.284259 0.355002 137\n", "max accuracy 0.451823 0.806509 19\n", "max precision 0.472737 0.520833 10\n", "max recall 0.0603167 1 399\n", "max specificity 0.509198 0.999924 0\n", "max absolute_mcc 0.203 0.201732 223\n", "max min_per_class_accuracy 0.188909 0.621905 240\n", "max mean_per_class_accuracy 0.197446 0.624206 230" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 19.38 %\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>group</b></td>\n", "<td><b>cumulative_data_fraction</b></td>\n", "<td><b>lower_threshold</b></td>\n", "<td><b>lift</b></td>\n", "<td><b>cumulative_lift</b></td>\n", "<td><b>response_rate</b></td>\n", "<td><b>cumulative_response_rate</b></td>\n", "<td><b>capture_rate</b></td>\n", "<td><b>cumulative_capture_rate</b></td>\n", "<td><b>gain</b></td>\n", "<td><b>cumulative_gain</b></td></tr>\n", "<tr><td></td>\n", "<td>1</td>\n", "<td>0.0100018</td>\n", "<td>0.4449025</td>\n", "<td>2.6012866</td>\n", "<td>2.6012866</td>\n", "<td>0.5040650</td>\n", "<td>0.5040650</td>\n", "<td>0.0260176</td>\n", "<td>0.0260176</td>\n", "<td>160.1286552</td>\n", "<td>160.1286552</td></tr>\n", "<tr><td></td>\n", "<td>2</td>\n", "<td>0.0200037</td>\n", "<td>0.4214264</td>\n", "<td>2.1607461</td>\n", "<td>2.3810163</td>\n", "<td>0.4186992</td>\n", "<td>0.4613821</td>\n", "<td>0.0216114</td>\n", "<td>0.0476290</td>\n", "<td>116.0746088</td>\n", "<td>138.1016320</td></tr>\n", "<tr><td></td>\n", "<td>3</td>\n", "<td>0.0300055</td>\n", "<td>0.4027877</td>\n", "<td>2.0768336</td>\n", "<td>2.2796221</td>\n", "<td>0.4024390</td>\n", "<td>0.4417344</td>\n", "<td>0.0207721</td>\n", "<td>0.0684012</td>\n", "<td>107.6833618</td>\n", "<td>127.9622086</td></tr>\n", "<tr><td></td>\n", "<td>4</td>\n", "<td>0.0400073</td>\n", "<td>0.3886123</td>\n", "<td>2.0138993</td>\n", "<td>2.2131914</td>\n", "<td>0.3902439</td>\n", "<td>0.4288618</td>\n", "<td>0.0201427</td>\n", "<td>0.0885439</td>\n", "<td>101.3899266</td>\n", "<td>121.3191381</td></tr>\n", "<tr><td></td>\n", "<td>5</td>\n", "<td>0.0500091</td>\n", "<td>0.3756632</td>\n", "<td>1.9929211</td>\n", "<td>2.1691373</td>\n", "<td>0.3861789</td>\n", "<td>0.4203252</td>\n", "<td>0.0199329</td>\n", "<td>0.1084767</td>\n", "<td>99.2921149</td>\n", "<td>116.9137335</td></tr>\n", "<tr><td></td>\n", "<td>6</td>\n", "<td>0.1000183</td>\n", "<td>0.3258714</td>\n", "<td>1.8355853</td>\n", "<td>2.0023613</td>\n", "<td>0.3556911</td>\n", "<td>0.3880081</td>\n", "<td>0.0917961</td>\n", "<td>0.2002728</td>\n", "<td>83.5585269</td>\n", "<td>100.2361302</td></tr>\n", "<tr><td></td>\n", "<td>7</td>\n", "<td>0.1500071</td>\n", "<td>0.2907210</td>\n", "<td>1.7880625</td>\n", "<td>1.9309477</td>\n", "<td>0.3464823</td>\n", "<td>0.3741699</td>\n", "<td>0.0893831</td>\n", "<td>0.2896559</td>\n", "<td>78.8062453</td>\n", "<td>93.0947713</td></tr>\n", "<tr><td></td>\n", "<td>8</td>\n", "<td>0.2000163</td>\n", "<td>0.2637421</td>\n", "<td>1.4076317</td>\n", "<td>1.8001054</td>\n", "<td>0.2727642</td>\n", "<td>0.3488159</td>\n", "<td>0.0703945</td>\n", "<td>0.3600504</td>\n", "<td>40.7631675</td>\n", "<td>80.0105407</td></tr>\n", "<tr><td></td>\n", "<td>9</td>\n", "<td>0.3000142</td>\n", "<td>0.2235013</td>\n", "<td>1.2526482</td>\n", "<td>1.6176320</td>\n", "<td>0.2427323</td>\n", "<td>0.3134571</td>\n", "<td>0.1252623</td>\n", "<td>0.4853126</td>\n", "<td>25.2648209</td>\n", "<td>61.7632039</td></tr>\n", "<tr><td></td>\n", "<td>10</td>\n", "<td>0.4000122</td>\n", "<td>0.1944564</td>\n", "<td>1.1340978</td>\n", "<td>1.4967546</td>\n", "<td>0.2197601</td>\n", "<td>0.2900340</td>\n", "<td>0.1134075</td>\n", "<td>0.5987201</td>\n", "<td>13.4097751</td>\n", "<td>49.6754611</td></tr>\n", "<tr><td></td>\n", "<td>11</td>\n", "<td>0.5000102</td>\n", "<td>0.1700730</td>\n", "<td>0.9588949</td>\n", "<td>1.3891870</td>\n", "<td>0.1858101</td>\n", "<td>0.2691901</td>\n", "<td>0.0958875</td>\n", "<td>0.6946076</td>\n", "<td>-4.1105140</td>\n", "<td>38.9187034</td></tr>\n", "<tr><td></td>\n", "<td>12</td>\n", "<td>0.6000081</td>\n", "<td>0.1488376</td>\n", "<td>0.8162147</td>\n", "<td>1.2936949</td>\n", "<td>0.1581622</td>\n", "<td>0.2506861</td>\n", "<td>0.0816198</td>\n", "<td>0.7762274</td>\n", "<td>-18.3785338</td>\n", "<td>29.3694874</td></tr>\n", "<tr><td></td>\n", "<td>13</td>\n", "<td>0.7000061</td>\n", "<td>0.1293534</td>\n", "<td>0.7438254</td>\n", "<td>1.2151444</td>\n", "<td>0.1441350</td>\n", "<td>0.2354649</td>\n", "<td>0.0743810</td>\n", "<td>0.8506085</td>\n", "<td>-25.6174556</td>\n", "<td>21.5144380</td></tr>\n", "<tr><td></td>\n", "<td>14</td>\n", "<td>0.8000041</td>\n", "<td>0.1108183</td>\n", "<td>0.6410118</td>\n", "<td>1.1433796</td>\n", "<td>0.1242122</td>\n", "<td>0.2215587</td>\n", "<td>0.0640999</td>\n", "<td>0.9147084</td>\n", "<td>-35.8988228</td>\n", "<td>14.3379628</td></tr>\n", "<tr><td></td>\n", "<td>15</td>\n", "<td>0.9000020</td>\n", "<td>0.0927616</td>\n", "<td>0.4689562</td>\n", "<td>1.0684454</td>\n", "<td>0.0908721</td>\n", "<td>0.2070383</td>\n", "<td>0.0468947</td>\n", "<td>0.9616030</td>\n", "<td>-53.1043761</td>\n", "<td>6.8445388</td></tr>\n", "<tr><td></td>\n", "<td>16</td>\n", "<td>1.0</td>\n", "<td>0.0590042</td>\n", "<td>0.3839776</td>\n", "<td>1.0</td>\n", "<td>0.0744054</td>\n", "<td>0.1937753</td>\n", "<td>0.0383970</td>\n", "<td>1.0</td>\n", "<td>-61.6022408</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate cumulative_response_rate capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- -------- ----------------- --------------- -------------------------- -------------- ------------------------- -------- -----------------\n", " 1 0.0100018 0.444902 2.60129 2.60129 0.504065 0.504065 0.0260176 0.0260176 160.129 160.129\n", " 2 0.0200037 0.421426 2.16075 2.38102 0.418699 0.461382 0.0216114 0.047629 116.075 138.102\n", " 3 0.0300055 0.402788 2.07683 2.27962 0.402439 0.441734 0.0207721 0.0684012 107.683 127.962\n", " 4 0.0400073 0.388612 2.0139 2.21319 0.390244 0.428862 0.0201427 0.0885439 101.39 121.319\n", " 5 0.0500091 0.375663 1.99292 2.16914 0.386179 0.420325 0.0199329 0.108477 99.2921 116.914\n", " 6 0.100018 0.325871 1.83559 2.00236 0.355691 0.388008 0.0917961 0.200273 83.5585 100.236\n", " 7 0.150007 0.290721 1.78806 1.93095 0.346482 0.37417 0.0893831 0.289656 78.8062 93.0948\n", " 8 0.200016 0.263742 1.40763 1.80011 0.272764 0.348816 0.0703945 0.36005 40.7632 80.0105\n", " 9 0.300014 0.223501 1.25265 1.61763 0.242732 0.313457 0.125262 0.485313 25.2648 61.7632\n", " 10 0.400012 0.194456 1.1341 1.49675 0.21976 0.290034 0.113407 0.59872 13.4098 49.6755\n", " 11 0.50001 0.170073 0.958895 1.38919 0.18581 0.26919 0.0958875 0.694608 -4.11051 38.9187\n", " 12 0.600008 0.148838 0.816215 1.29369 0.158162 0.250686 0.0816198 0.776227 -18.3785 29.3695\n", " 13 0.700006 0.129353 0.743825 1.21514 0.144135 0.235465 0.074381 0.850608 -25.6175 21.5144\n", " 14 0.800004 0.110818 0.641012 1.14338 0.124212 0.221559 0.0640999 0.914708 -35.8988 14.338\n", " 15 0.900002 0.0927616 0.468956 1.06845 0.0908721 0.207038 0.0468947 0.961603 -53.1044 6.84454\n", " 16 1 0.0590042 0.383978 1 0.0744054 0.193775 0.038397 1 -61.6022 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>timestamp</b></td>\n", "<td><b>duration</b></td>\n", "<td><b>training_speed</b></td>\n", "<td><b>epochs</b></td>\n", "<td><b>iterations</b></td>\n", "<td><b>samples</b></td>\n", "<td><b>training_rmse</b></td>\n", "<td><b>training_logloss</b></td>\n", "<td><b>training_auc</b></td>\n", "<td><b>training_lift</b></td>\n", "<td><b>training_classification_error</b></td>\n", "<td><b>validation_rmse</b></td>\n", "<td><b>validation_logloss</b></td>\n", "<td><b>validation_auc</b></td>\n", "<td><b>validation_lift</b></td>\n", "<td><b>validation_classification_error</b></td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:57:57</td>\n", "<td> 0.000 sec</td>\n", "<td>None</td>\n", "<td>0.0</td>\n", "<td>0</td>\n", "<td>0.0</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:57:59</td>\n", "<td> 3.062 sec</td>\n", "<td>76298 obs/sec</td>\n", "<td>1.5226220</td>\n", "<td>1</td>\n", "<td>99951.0</td>\n", "<td>0.3802783</td>\n", "<td>0.4553597</td>\n", "<td>0.6859534</td>\n", "<td>2.6115789</td>\n", "<td>0.3452237</td>\n", "<td>0.3844228</td>\n", "<td>0.4645550</td>\n", "<td>0.6688402</td>\n", "<td>2.5068850</td>\n", "<td>0.3755972</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:01</td>\n", "<td> 4.515 sec</td>\n", "<td>81583 obs/sec</td>\n", "<td>3.0449089</td>\n", "<td>2</td>\n", "<td>199880.0</td>\n", "<td>0.3797884</td>\n", "<td>0.4551946</td>\n", "<td>0.6893369</td>\n", "<td>2.4548842</td>\n", "<td>0.3432084</td>\n", "<td>0.3837157</td>\n", "<td>0.4637102</td>\n", "<td>0.6707354</td>\n", "<td>2.4544397</td>\n", "<td>0.3816552</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:02</td>\n", "<td> 5.929 sec</td>\n", "<td>82905 obs/sec</td>\n", "<td>4.5618031</td>\n", "<td>3</td>\n", "<td>299455.0</td>\n", "<td>0.3798108</td>\n", "<td>0.4546644</td>\n", "<td>0.6872953</td>\n", "<td>2.5593474</td>\n", "<td>0.3660822</td>\n", "<td>0.3841954</td>\n", "<td>0.4646253</td>\n", "<td>0.6666972</td>\n", "<td>2.6432428</td>\n", "<td>0.3563863</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:03</td>\n", "<td> 7.582 sec</td>\n", "<td>85776 obs/sec</td>\n", "<td>6.0878527</td>\n", "<td>4</td>\n", "<td>399631.0</td>\n", "<td>0.3833328</td>\n", "<td>0.4624014</td>\n", "<td>0.6882186</td>\n", "<td>2.5593474</td>\n", "<td>0.3750504</td>\n", "<td>0.3871330</td>\n", "<td>0.4718812</td>\n", "<td>0.6701115</td>\n", "<td>2.4649288</td>\n", "<td>0.3870627</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:06</td>\n", "<td>10.107 sec</td>\n", "<td>71733 obs/sec</td>\n", "<td>7.6121809</td>\n", "<td>5</td>\n", "<td>499694.0</td>\n", "<td>0.3798377</td>\n", "<td>0.4545102</td>\n", "<td>0.6881884</td>\n", "<td>2.5071158</td>\n", "<td>0.3404877</td>\n", "<td>0.3840824</td>\n", "<td>0.4642912</td>\n", "<td>0.6691557</td>\n", "<td>2.5383522</td>\n", "<td>0.3875912</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:08</td>\n", "<td>11.420 sec</td>\n", "<td>74286 obs/sec</td>\n", "<td>9.1359149</td>\n", "<td>6</td>\n", "<td>599718.0</td>\n", "<td>0.3803389</td>\n", "<td>0.4567269</td>\n", "<td>0.6898939</td>\n", "<td>2.5593474</td>\n", "<td>0.3692060</td>\n", "<td>0.3845155</td>\n", "<td>0.4656183</td>\n", "<td>0.6692889</td>\n", "<td>2.5173741</td>\n", "<td>0.3575857</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:09</td>\n", "<td>12.670 sec</td>\n", "<td>76750 obs/sec</td>\n", "<td>10.6607001</td>\n", "<td>7</td>\n", "<td>699811.0</td>\n", "<td>0.3800611</td>\n", "<td>0.4556208</td>\n", "<td>0.6847244</td>\n", "<td>2.3504211</td>\n", "<td>0.3445183</td>\n", "<td>0.3839916</td>\n", "<td>0.4640350</td>\n", "<td>0.6681922</td>\n", "<td>2.3915054</td>\n", "<td>0.3925108</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:10</td>\n", "<td>13.871 sec</td>\n", "<td>78923 obs/sec</td>\n", "<td>12.1840534</td>\n", "<td>8</td>\n", "<td>799810.0</td>\n", "<td>0.3800529</td>\n", "<td>0.4558997</td>\n", "<td>0.6850540</td>\n", "<td>2.8205053</td>\n", "<td>0.3120717</td>\n", "<td>0.3841183</td>\n", "<td>0.4648343</td>\n", "<td>0.6658078</td>\n", "<td>2.6117756</td>\n", "<td>0.3621801</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:11</td>\n", "<td>15.033 sec</td>\n", "<td>81064 obs/sec</td>\n", "<td>13.7086710</td>\n", "<td>9</td>\n", "<td>899892.0</td>\n", "<td>0.3796760</td>\n", "<td>0.4542979</td>\n", "<td>0.6890318</td>\n", "<td>2.6638105</td>\n", "<td>0.3758565</td>\n", "<td>0.3839098</td>\n", "<td>0.4643072</td>\n", "<td>0.6704357</td>\n", "<td>2.6642209</td>\n", "<td>0.3624850</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:13</td>\n", "<td>16.193 sec</td>\n", "<td>82809 obs/sec</td>\n", "<td>15.2324660</td>\n", "<td>10</td>\n", "<td>999920.0</td>\n", "<td>0.3808994</td>\n", "<td>0.4566182</td>\n", "<td>0.6866072</td>\n", "<td>2.5593474</td>\n", "<td>0.3147924</td>\n", "<td>0.3855863</td>\n", "<td>0.4676274</td>\n", "<td>0.6673428</td>\n", "<td>2.3495491</td>\n", "<td>0.4046269</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:14</td>\n", "<td>17.374 sec</td>\n", "<td>84153 obs/sec</td>\n", "<td>16.7539912</td>\n", "<td>11</td>\n", "<td>1099799.0</td>\n", "<td>0.3803267</td>\n", "<td>0.4556301</td>\n", "<td>0.6875346</td>\n", "<td>2.4026526</td>\n", "<td>0.3493551</td>\n", "<td>0.3845816</td>\n", "<td>0.4650365</td>\n", "<td>0.6677390</td>\n", "<td>2.3915054</td>\n", "<td>0.4101157</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:15</td>\n", "<td>18.601 sec</td>\n", "<td>85075 obs/sec</td>\n", "<td>18.2777710</td>\n", "<td>12</td>\n", "<td>1199826.0</td>\n", "<td>0.3796243</td>\n", "<td>0.4545508</td>\n", "<td>0.6879894</td>\n", "<td>2.6115789</td>\n", "<td>0.3238613</td>\n", "<td>0.3836198</td>\n", "<td>0.4634771</td>\n", "<td>0.6690848</td>\n", "<td>2.6012866</td>\n", "<td>0.3550446</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:16</td>\n", "<td>19.737 sec</td>\n", "<td>86261 obs/sec</td>\n", "<td>19.7992200</td>\n", "<td>13</td>\n", "<td>1299700.0</td>\n", "<td>0.3808349</td>\n", "<td>0.4575195</td>\n", "<td>0.6848141</td>\n", "<td>2.5071158</td>\n", "<td>0.3716243</td>\n", "<td>0.3850006</td>\n", "<td>0.4663161</td>\n", "<td>0.6665559</td>\n", "<td>2.5383522</td>\n", "<td>0.3568742</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:17</td>\n", "<td>20.930 sec</td>\n", "<td>87106 obs/sec</td>\n", "<td>21.3266864</td>\n", "<td>14</td>\n", "<td>1399969.0</td>\n", "<td>0.3843492</td>\n", "<td>0.4652862</td>\n", "<td>0.6818501</td>\n", "<td>2.7160421</td>\n", "<td>0.3478436</td>\n", "<td>0.3881900</td>\n", "<td>0.4733023</td>\n", "<td>0.6640504</td>\n", "<td>2.5593303</td>\n", "<td>0.3793987</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:58:17</td>\n", "<td>21.125 sec</td>\n", "<td>87078 obs/sec</td>\n", "<td>21.3266864</td>\n", "<td>14</td>\n", "<td>1399969.0</td>\n", "<td>0.3796243</td>\n", "<td>0.4545508</td>\n", "<td>0.6879894</td>\n", "<td>2.6115789</td>\n", "<td>0.3238613</td>\n", "<td>0.3836198</td>\n", "<td>0.4634771</td>\n", "<td>0.6690848</td>\n", "<td>2.6012866</td>\n", "<td>0.3550446</td></tr></table></div>" ], "text/plain": [ " timestamp duration training_speed epochs iterations samples training_rmse training_logloss training_auc training_lift training_classification_error validation_rmse validation_logloss validation_auc validation_lift validation_classification_error\n", "-- ------------------- ---------- ---------------- -------- ------------ ----------- --------------- ------------------ -------------- --------------- ------------------------------- ----------------- -------------------- ---------------- ----------------- ---------------------------------\n", " 2017-03-05 14:57:57 0.000 sec 0 0 0 nan nan nan nan nan nan nan nan nan nan\n", " 2017-03-05 14:57:59 3.062 sec 76298 obs/sec 1.52262 1 99951 0.380278 0.45536 0.685953 2.61158 0.345224 0.384423 0.464555 0.66884 2.50689 0.375597\n", " 2017-03-05 14:58:01 4.515 sec 81583 obs/sec 3.04491 2 199880 0.379788 0.455195 0.689337 2.45488 0.343208 0.383716 0.46371 0.670735 2.45444 0.381655\n", " 2017-03-05 14:58:02 5.929 sec 82905 obs/sec 4.5618 3 299455 0.379811 0.454664 0.687295 2.55935 0.366082 0.384195 0.464625 0.666697 2.64324 0.356386\n", " 2017-03-05 14:58:03 7.582 sec 85776 obs/sec 6.08785 4 399631 0.383333 0.462401 0.688219 2.55935 0.37505 0.387133 0.471881 0.670112 2.46493 0.387063\n", " 2017-03-05 14:58:06 10.107 sec 71733 obs/sec 7.61218 5 499694 0.379838 0.45451 0.688188 2.50712 0.340488 0.384082 0.464291 0.669156 2.53835 0.387591\n", " 2017-03-05 14:58:08 11.420 sec 74286 obs/sec 9.13591 6 599718 0.380339 0.456727 0.689894 2.55935 0.369206 0.384516 0.465618 0.669289 2.51737 0.357586\n", " 2017-03-05 14:58:09 12.670 sec 76750 obs/sec 10.6607 7 699811 0.380061 0.455621 0.684724 2.35042 0.344518 0.383992 0.464035 0.668192 2.39151 0.392511\n", " 2017-03-05 14:58:10 13.871 sec 78923 obs/sec 12.1841 8 799810 0.380053 0.4559 0.685054 2.82051 0.312072 0.384118 0.464834 0.665808 2.61178 0.36218\n", " 2017-03-05 14:58:11 15.033 sec 81064 obs/sec 13.7087 9 899892 0.379676 0.454298 0.689032 2.66381 0.375857 0.38391 0.464307 0.670436 2.66422 0.362485\n", " 2017-03-05 14:58:13 16.193 sec 82809 obs/sec 15.2325 10 999920 0.380899 0.456618 0.686607 2.55935 0.314792 0.385586 0.467627 0.667343 2.34955 0.404627\n", " 2017-03-05 14:58:14 17.374 sec 84153 obs/sec 16.754 11 1.0998e+06 0.380327 0.45563 0.687535 2.40265 0.349355 0.384582 0.465036 0.667739 2.39151 0.410116\n", " 2017-03-05 14:58:15 18.601 sec 85075 obs/sec 18.2778 12 1.19983e+06 0.379624 0.454551 0.687989 2.61158 0.323861 0.38362 0.463477 0.669085 2.60129 0.355045\n", " 2017-03-05 14:58:16 19.737 sec 86261 obs/sec 19.7992 13 1.2997e+06 0.380835 0.45752 0.684814 2.50712 0.371624 0.385001 0.466316 0.666556 2.53835 0.356874\n", " 2017-03-05 14:58:17 20.930 sec 87106 obs/sec 21.3267 14 1.39997e+06 0.384349 0.465286 0.68185 2.71604 0.347844 0.38819 0.473302 0.66405 2.55933 0.379399\n", " 2017-03-05 14:58:17 21.125 sec 87078 obs/sec 21.3267 14 1.39997e+06 0.379624 0.454551 0.687989 2.61158 0.323861 0.38362 0.463477 0.669085 2.60129 0.355045" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# neural network\n", "\n", "# initialize nn model\n", "nn_model = H2ODeepLearningEstimator(\n", " epochs=50, # read over the data 50 times, but in mini-batches\n", " hidden=[100], # 100 hidden units in 1 hidden layer\n", " input_dropout_ratio=0.2, # randomly drop 20% of inputs for each iteration, helps w/ generalization\n", " hidden_dropout_ratios=[0.05], # randomly set 5% of hidden weights to 0 each iteration, helps w/ generalization\n", " activation='TanhWithDropout', # bounded activation function that allows for dropout, tanh\n", " l1=0.001, # L1 penalty can help generalization \n", " l2=0.01, # L2 penalty can increase stability in presence of highly correlated inputs\n", " adaptive_rate=True, # adjust magnitude of weight updates automatically (+stability, +accuracy)\n", " stopping_rounds=5, # stop after validation error does not decrease for 5 iterations\n", " score_each_iteration=True, # score validation error on every iteration\n", " model_id='nn_model') # for easy lookup in flow\n", "\n", "# train nn model\n", "nn_model.train(\n", " x=X,\n", " y=y,\n", " training_frame=train,\n", " validation_frame=valid)\n", "\n", "# print model information\n", "nn_model\n", "\n", "# view detailed results at http://localhost:54321/flow/index.html" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6879894198982001\n", "0.6690848124829848\n", "0.6768012585175186\n" ] } ], "source": [ "# measure nn AUC\n", "print(nn_model.auc(train=True))\n", "print(nn_model.auc(valid=True))\n", "print(nn_model.model_performance(test_data=test).auc())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning Grid Build progress: \|███████████████████████████████████████\| 100%\n" ] } ], "source": [ "# NN with random hyperparameter search\n", "# train many different NN models with random hyperparameters\n", "# and select best model based on validation error\n", "\n", "# define random grid search parameters\n", "hyper_parameters = {'hidden':[[170, 320], [80, 190], [320, 160, 80], [100], [50, 50, 50, 50]],\n", " 'l1':[s/1e4 for s in range(0, 1000, 100)],\n", " 'l2':[s/1e5 for s in range(0, 1000, 100)],\n", " 'input_dropout_ratio':[s/1e2 for s in range(0, 20, 2)]}\n", "\n", "# define search strategy\n", "search_criteria = {'strategy':'RandomDiscrete',\n", " 'max_models':20,\n", " 'max_runtime_secs':600}\n", "\n", "# initialize grid search\n", "gsearch = H2OGridSearch(H2ODeepLearningEstimator,\n", " hyper_params=hyper_parameters,\n", " search_criteria=search_criteria)\n", "\n", "# execute training w/ grid search\n", "gsearch.train(x=X,\n", " y=y,\n", " training_frame=train,\n", " validation_frame=valid)\n", "\n", "# view detailed results at http://localhost:54321/flow/index.html" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " hidden input_dropout_ratio l1 l2 \\\n", "0 [100] 0.18 0.02 0.008 \n", "1 [100] 0.1 0.02 0.008 \n", "2 [100] 0.1 0.06 0.006 \n", "3 [320, 160, 80] 0.0 0.01 0.001 \n", "4 [170, 320] 0.06 0.01 0.007 \n", "5 [170, 320] 0.12 0.03 0.004 \n", "6 [80, 190] 0.18 0.02 0.008 \n", "7 [80, 190] 0.1 0.03 0.009 \n", "8 [50, 50, 50, 50] 0.08 0.05 0.004 \n", "9 [80, 190] 0.18 0.06 0.005 \n", "10 [80, 190] 0.0 0.06 0.008 \n", "11 [100] 0.02 0.04 0.006 \n", "12 [100] 0.02 0.06 0.004 \n", "13 [80, 190] 0.18 0.06 0.007 \n", "14 [170, 320] 0.06 0.07 0.007 \n", "15 [80, 190] 0.14 0.03 0.004 \n", "16 [50, 50, 50, 50] 0.12 0.01 0.007 \n", "17 [320, 160, 80] 0.02 0.01 0.0 \n", "18 [50, 50, 50, 50] 0.1 0.06 0.0 \n", "19 [320, 160, 80] 0.16 0.01 0.003 \n", "\n", " model_ids \\\n", "0 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_9 \n", "1 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "2 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_4 \n", "3 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "4 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_6 \n", "5 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_3 \n", "6 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "7 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "8 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "9 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_7 \n", "10 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "11 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "12 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "13 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_1 \n", "14 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_8 \n", "15 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "16 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_0 \n", "17 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_5 \n", "18 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_2 \n", "19 Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_mode... \n", "\n", " logloss \n", "0 0.4665108466360082 \n", "1 0.4672577727589049 \n", "2 0.49180760688559993 \n", "3 0.49340422513101256 \n", "4 0.4935917111436136 \n", "5 0.4940674960651805 \n", "6 0.4943429023102421 \n", "7 0.4952134113980504 \n", "8 0.497018064417741 \n", "9 0.49821296269581355 \n", "10 0.49823191879660195 \n", "11 0.498394425377021 \n", "12 0.49845713499201155 \n", "13 0.4985832770417643 \n", "14 0.49956120207440785 \n", "15 0.5065674270865724 \n", "16 0.5123765682901726 \n", "17 0.5143173692053821 \n", "18 0.5622461586498995 \n", "19 0.6299455225981917 \n", "Model Details\n", "=============\n", "H2ODeepLearningEstimator : Deep Learning\n", "Model Key: Grid_DeepLearning_py_7_sid_bc2c_model_python_1488743871002_32_model_9\n", "\n", "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on train data. \n", "\n", "MSE: 0.14725977717296032\n", "RMSE: 0.38374441647138047\n", "LogLoss: 0.46372711571707503\n", "Mean Per-Class Error: 0.3731369293059327\n", "AUC: 0.6717227633960028\n", "Gini: 0.3434455267920056\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.19698055628842004: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>5491.0</td>\n", "<td>2663.0</td>\n", "<td>0.3266</td>\n", "<td> (2663.0/8154.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>818.0</td>\n", "<td>1130.0</td>\n", "<td>0.4199</td>\n", "<td> (818.0/1948.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>6309.0</td>\n", "<td>3793.0</td>\n", "<td>0.3446</td>\n", "<td> (3481.0/10102.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- ---- ---- ------- ----------------\n", "0 5491 2663 0.3266 (2663.0/8154.0)\n", "1 818 1130 0.4199 (818.0/1948.0)\n", "Total 6309 3793 0.3446 (3481.0/10102.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.1969806</td>\n", "<td>0.3936596</td>\n", "<td>201.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.1204921</td>\n", "<td>0.5644273</td>\n", "<td>328.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.2445548</td>\n", "<td>0.3516508</td>\n", "<td>131.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.3455796</td>\n", "<td>0.8080578</td>\n", "<td>33.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>0.3977974</td>\n", "<td>0.5526316</td>\n", "<td>4.0</td></tr>\n", "<tr><td>max recall</td>\n", "<td>0.0669954</td>\n", "<td>1.0</td>\n", "<td>392.0</td></tr>\n", "<tr><td>max specificity</td>\n", "<td>0.4062546</td>\n", "<td>0.9985283</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_mcc</td>\n", "<td>0.2107221</td>\n", "<td>0.2120594</td>\n", "<td>178.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.1889417</td>\n", "<td>0.6232033</td>\n", "<td>214.0</td></tr>\n", "<tr><td>max mean_per_class_accuracy</td>\n", "<td>0.1914947</td>\n", "<td>0.6268631</td>\n", "<td>209.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.196981 0.39366 201\n", "max f2 0.120492 0.564427 328\n", "max f0point5 0.244555 0.351651 131\n", "max accuracy 0.34558 0.808058 33\n", "max precision 0.397797 0.552632 4\n", "max recall 0.0669954 1 392\n", "max specificity 0.406255 0.998528 0\n", "max absolute_mcc 0.210722 0.212059 178\n", "max min_per_class_accuracy 0.188942 0.623203 214\n", "max mean_per_class_accuracy 0.191495 0.626863 209" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 19.28 %\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>group</b></td>\n", "<td><b>cumulative_data_fraction</b></td>\n", "<td><b>lower_threshold</b></td>\n", "<td><b>lift</b></td>\n", "<td><b>cumulative_lift</b></td>\n", "<td><b>response_rate</b></td>\n", "<td><b>cumulative_response_rate</b></td>\n", "<td><b>capture_rate</b></td>\n", "<td><b>cumulative_capture_rate</b></td>\n", "<td><b>gain</b></td>\n", "<td><b>cumulative_gain</b></td></tr>\n", "<tr><td></td>\n", "<td>1</td>\n", "<td>0.0100970</td>\n", "<td>0.3686537</td>\n", "<td>2.5929158</td>\n", "<td>2.5929158</td>\n", "<td>0.5</td>\n", "<td>0.5</td>\n", "<td>0.0261807</td>\n", "<td>0.0261807</td>\n", "<td>159.2915811</td>\n", "<td>159.2915811</td></tr>\n", "<tr><td></td>\n", "<td>2</td>\n", "<td>0.0200950</td>\n", "<td>0.3510992</td>\n", "<td>2.5672434</td>\n", "<td>2.5801428</td>\n", "<td>0.4950495</td>\n", "<td>0.4975369</td>\n", "<td>0.0256674</td>\n", "<td>0.0518480</td>\n", "<td>156.7243377</td>\n", "<td>158.0142827</td></tr>\n", "<tr><td></td>\n", "<td>3</td>\n", "<td>0.0300931</td>\n", "<td>0.3344523</td>\n", "<td>2.4645536</td>\n", "<td>2.5417398</td>\n", "<td>0.4752475</td>\n", "<td>0.4901316</td>\n", "<td>0.0246407</td>\n", "<td>0.0764887</td>\n", "<td>146.4553642</td>\n", "<td>154.1739841</td></tr>\n", "<tr><td></td>\n", "<td>4</td>\n", "<td>0.0400911</td>\n", "<td>0.3237729</td>\n", "<td>1.9511050</td>\n", "<td>2.3944457</td>\n", "<td>0.3762376</td>\n", "<td>0.4617284</td>\n", "<td>0.0195072</td>\n", "<td>0.0959959</td>\n", "<td>95.1104967</td>\n", "<td>139.4445712</td></tr>\n", "<tr><td></td>\n", "<td>5</td>\n", "<td>0.0500891</td>\n", "<td>0.3116707</td>\n", "<td>2.3618639</td>\n", "<td>2.3879422</td>\n", "<td>0.4554455</td>\n", "<td>0.4604743</td>\n", "<td>0.0236140</td>\n", "<td>0.1196099</td>\n", "<td>136.1863907</td>\n", "<td>138.7942229</td></tr>\n", "<tr><td></td>\n", "<td>6</td>\n", "<td>0.1000792</td>\n", "<td>0.2730895</td>\n", "<td>1.7765324</td>\n", "<td>2.0825397</td>\n", "<td>0.3425743</td>\n", "<td>0.4015826</td>\n", "<td>0.0888090</td>\n", "<td>0.2084189</td>\n", "<td>77.6532417</td>\n", "<td>108.2539702</td></tr>\n", "<tr><td></td>\n", "<td>7</td>\n", "<td>0.1500693</td>\n", "<td>0.2507378</td>\n", "<td>1.4890012</td>\n", "<td>1.8848240</td>\n", "<td>0.2871287</td>\n", "<td>0.3634565</td>\n", "<td>0.0744353</td>\n", "<td>0.2828542</td>\n", "<td>48.9001159</td>\n", "<td>88.4824026</td></tr>\n", "<tr><td></td>\n", "<td>8</td>\n", "<td>0.2000594</td>\n", "<td>0.2354586</td>\n", "<td>1.5403460</td>\n", "<td>1.7987471</td>\n", "<td>0.2970297</td>\n", "<td>0.3468580</td>\n", "<td>0.0770021</td>\n", "<td>0.3598563</td>\n", "<td>54.0346026</td>\n", "<td>79.8747139</td></tr>\n", "<tr><td></td>\n", "<td>9</td>\n", "<td>0.3000396</td>\n", "<td>0.2106872</td>\n", "<td>1.3811769</td>\n", "<td>1.6596030</td>\n", "<td>0.2663366</td>\n", "<td>0.3200264</td>\n", "<td>0.1380903</td>\n", "<td>0.4979466</td>\n", "<td>38.1176937</td>\n", "<td>65.9602994</td></tr>\n", "<tr><td></td>\n", "<td>10</td>\n", "<td>0.4000198</td>\n", "<td>0.1926937</td>\n", "<td>1.0268974</td>\n", "<td>1.5014657</td>\n", "<td>0.1980198</td>\n", "<td>0.2895323</td>\n", "<td>0.1026694</td>\n", "<td>0.6006160</td>\n", "<td>2.6897351</td>\n", "<td>50.1465726</td></tr>\n", "<tr><td></td>\n", "<td>11</td>\n", "<td>0.5</td>\n", "<td>0.1764488</td>\n", "<td>0.8985352</td>\n", "<td>1.3809035</td>\n", "<td>0.1732673</td>\n", "<td>0.2662839</td>\n", "<td>0.0898357</td>\n", "<td>0.6904517</td>\n", "<td>-10.1464818</td>\n", "<td>38.0903491</td></tr>\n", "<tr><td></td>\n", "<td>12</td>\n", "<td>0.5999802</td>\n", "<td>0.1618927</td>\n", "<td>0.9036697</td>\n", "<td>1.3013776</td>\n", "<td>0.1742574</td>\n", "<td>0.2509487</td>\n", "<td>0.0903491</td>\n", "<td>0.7808008</td>\n", "<td>-9.6330331</td>\n", "<td>30.1377644</td></tr>\n", "<tr><td></td>\n", "<td>13</td>\n", "<td>0.6999604</td>\n", "<td>0.1466439</td>\n", "<td>0.6880212</td>\n", "<td>1.2137677</td>\n", "<td>0.1326733</td>\n", "<td>0.2340546</td>\n", "<td>0.0687885</td>\n", "<td>0.8495893</td>\n", "<td>-31.1978775</td>\n", "<td>21.3767690</td></tr>\n", "<tr><td></td>\n", "<td>14</td>\n", "<td>0.7999406</td>\n", "<td>0.1294717</td>\n", "<td>0.6315419</td>\n", "<td>1.1409985</td>\n", "<td>0.1217822</td>\n", "<td>0.2200223</td>\n", "<td>0.0631417</td>\n", "<td>0.9127310</td>\n", "<td>-36.8458129</td>\n", "<td>14.0998469</td></tr>\n", "<tr><td></td>\n", "<td>15</td>\n", "<td>0.8999208</td>\n", "<td>0.1092314</td>\n", "<td>0.5391211</td>\n", "<td>1.0741306</td>\n", "<td>0.1039604</td>\n", "<td>0.2071279</td>\n", "<td>0.0539014</td>\n", "<td>0.9666324</td>\n", "<td>-46.0878891</td>\n", "<td>7.4130563</td></tr>\n", "<tr><td></td>\n", "<td>16</td>\n", "<td>1.0</td>\n", "<td>0.0507288</td>\n", "<td>0.3334115</td>\n", "<td>1.0</td>\n", "<td>0.0642928</td>\n", "<td>0.1928331</td>\n", "<td>0.0333676</td>\n", "<td>1.0</td>\n", "<td>-66.6588471</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate cumulative_response_rate capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- -------- ----------------- --------------- -------------------------- -------------- ------------------------- -------- -----------------\n", " 1 0.010097 0.368654 2.59292 2.59292 0.5 0.5 0.0261807 0.0261807 159.292 159.292\n", " 2 0.020095 0.351099 2.56724 2.58014 0.49505 0.497537 0.0256674 0.051848 156.724 158.014\n", " 3 0.0300931 0.334452 2.46455 2.54174 0.475248 0.490132 0.0246407 0.0764887 146.455 154.174\n", " 4 0.0400911 0.323773 1.9511 2.39445 0.376238 0.461728 0.0195072 0.0959959 95.1105 139.445\n", " 5 0.0500891 0.311671 2.36186 2.38794 0.455446 0.460474 0.023614 0.11961 136.186 138.794\n", " 6 0.100079 0.273089 1.77653 2.08254 0.342574 0.401583 0.088809 0.208419 77.6532 108.254\n", " 7 0.150069 0.250738 1.489 1.88482 0.287129 0.363456 0.0744353 0.282854 48.9001 88.4824\n", " 8 0.200059 0.235459 1.54035 1.79875 0.29703 0.346858 0.0770021 0.359856 54.0346 79.8747\n", " 9 0.30004 0.210687 1.38118 1.6596 0.266337 0.320026 0.13809 0.497947 38.1177 65.9603\n", " 10 0.40002 0.192694 1.0269 1.50147 0.19802 0.289532 0.102669 0.600616 2.68974 50.1466\n", " 11 0.5 0.176449 0.898535 1.3809 0.173267 0.266284 0.0898357 0.690452 -10.1465 38.0903\n", " 12 0.59998 0.161893 0.90367 1.30138 0.174257 0.250949 0.0903491 0.780801 -9.63303 30.1378\n", " 13 0.69996 0.146644 0.688021 1.21377 0.132673 0.234055 0.0687885 0.849589 -31.1979 21.3768\n", " 14 0.799941 0.129472 0.631542 1.141 0.121782 0.220022 0.0631417 0.912731 -36.8458 14.0998\n", " 15 0.899921 0.109231 0.539121 1.07413 0.10396 0.207128 0.0539014 0.966632 -46.0879 7.41306\n", " 16 1 0.0507288 0.333412 1 0.0642928 0.192833 0.0333676 1 -66.6588 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomial: deeplearning\n", " Reported on validation data. **\n", "\n", "MSE: 0.14836589527056177\n", "RMSE: 0.3851829374083979\n", "LogLoss: 0.4665108466360082\n", "Mean Per-Class Error: 0.37686646501331533\n", "AUC: 0.669025639866052\n", "Gini: 0.338051279732104\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.18331602718201165: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>0</b></td>\n", "<td><b>1</b></td>\n", "<td><b>Error</b></td>\n", "<td><b>Rate</b></td></tr>\n", "<tr><td>0</td>\n", "<td>23892.0</td>\n", "<td>15767.0</td>\n", "<td>0.3976</td>\n", "<td> (15767.0/39659.0)</td></tr>\n", "<tr><td>1</td>\n", "<td>3395.0</td>\n", "<td>6137.0</td>\n", "<td>0.3562</td>\n", "<td> (3395.0/9532.0)</td></tr>\n", "<tr><td>Total</td>\n", "<td>27287.0</td>\n", "<td>21904.0</td>\n", "<td>0.3895</td>\n", "<td> (19162.0/49191.0)</td></tr></table></div>" ], "text/plain": [ " 0 1 Error Rate\n", "----- ----- ----- ------- -----------------\n", "0 23892 15767 0.3976 (15767.0/39659.0)\n", "1 3395 6137 0.3562 (3395.0/9532.0)\n", "Total 27287 21904 0.3895 (19162.0/49191.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n", "<td><b>threshold</b></td>\n", "<td><b>value</b></td>\n", "<td><b>idx</b></td></tr>\n", "<tr><td>max f1</td>\n", "<td>0.1833160</td>\n", "<td>0.3904441</td>\n", "<td>222.0</td></tr>\n", "<tr><td>max f2</td>\n", "<td>0.1332325</td>\n", "<td>0.5648486</td>\n", "<td>306.0</td></tr>\n", "<tr><td>max f0point5</td>\n", "<td>0.2265355</td>\n", "<td>0.3486420</td>\n", "<td>157.0</td></tr>\n", "<tr><td>max accuracy</td>\n", "<td>0.4063481</td>\n", "<td>0.8061231</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max precision</td>\n", "<td>0.4063481</td>\n", "<td>0.4786325</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max recall</td>\n", "<td>0.0592609</td>\n", "<td>1.0</td>\n", "<td>396.0</td></tr>\n", "<tr><td>max specificity</td>\n", "<td>0.4063481</td>\n", "<td>0.9984619</td>\n", "<td>0.0</td></tr>\n", "<tr><td>max absolute_mcc</td>\n", "<td>0.2117499</td>\n", "<td>0.1999202</td>\n", "<td>178.0</td></tr>\n", "<tr><td>max min_per_class_accuracy</td>\n", "<td>0.1860778</td>\n", "<td>0.6189516</td>\n", "<td>218.0</td></tr>\n", "<tr><td>max mean_per_class_accuracy</td>\n", "<td>0.1833160</td>\n", "<td>0.6231335</td>\n", "<td>222.0</td></tr></table></div>" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.183316 0.390444 222\n", "max f2 0.133233 0.564849 306\n", "max f0point5 0.226536 0.348642 157\n", "max accuracy 0.406348 0.806123 0\n", "max precision 0.406348 0.478632 0\n", "max recall 0.0592609 1 396\n", "max specificity 0.406348 0.998462 0\n", "max absolute_mcc 0.21175 0.19992 178\n", "max min_per_class_accuracy 0.186078 0.618952 218\n", "max mean_per_class_accuracy 0.183316 0.623134 222" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 19.38 %\n", "\n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>group</b></td>\n", "<td><b>cumulative_data_fraction</b></td>\n", "<td><b>lower_threshold</b></td>\n", "<td><b>lift</b></td>\n", "<td><b>cumulative_lift</b></td>\n", "<td><b>response_rate</b></td>\n", "<td><b>cumulative_response_rate</b></td>\n", "<td><b>capture_rate</b></td>\n", "<td><b>cumulative_capture_rate</b></td>\n", "<td><b>gain</b></td>\n", "<td><b>cumulative_gain</b></td></tr>\n", "<tr><td></td>\n", "<td>1</td>\n", "<td>0.0100018</td>\n", "<td>0.3718031</td>\n", "<td>2.3180820</td>\n", "<td>2.3180820</td>\n", "<td>0.4491870</td>\n", "<td>0.4491870</td>\n", "<td>0.0231851</td>\n", "<td>0.0231851</td>\n", "<td>131.8081968</td>\n", "<td>131.8081968</td></tr>\n", "<tr><td></td>\n", "<td>2</td>\n", "<td>0.0200037</td>\n", "<td>0.3490529</td>\n", "<td>2.2236804</td>\n", "<td>2.2708812</td>\n", "<td>0.4308943</td>\n", "<td>0.4400407</td>\n", "<td>0.0222409</td>\n", "<td>0.0454259</td>\n", "<td>122.3680440</td>\n", "<td>127.0881204</td></tr>\n", "<tr><td></td>\n", "<td>3</td>\n", "<td>0.0300055</td>\n", "<td>0.3328878</td>\n", "<td>2.0034102</td>\n", "<td>2.1817242</td>\n", "<td>0.3882114</td>\n", "<td>0.4227642</td>\n", "<td>0.0200378</td>\n", "<td>0.0654637</td>\n", "<td>100.3410207</td>\n", "<td>118.1724205</td></tr>\n", "<tr><td></td>\n", "<td>4</td>\n", "<td>0.0400073</td>\n", "<td>0.3190451</td>\n", "<td>2.1607461</td>\n", "<td>2.1764797</td>\n", "<td>0.4186992</td>\n", "<td>0.4217480</td>\n", "<td>0.0216114</td>\n", "<td>0.0870751</td>\n", "<td>116.0746088</td>\n", "<td>117.6479676</td></tr>\n", "<tr><td></td>\n", "<td>5</td>\n", "<td>0.0500091</td>\n", "<td>0.3084206</td>\n", "<td>2.0873227</td>\n", "<td>2.1586483</td>\n", "<td>0.4044715</td>\n", "<td>0.4182927</td>\n", "<td>0.0208770</td>\n", "<td>0.1079522</td>\n", "<td>108.7322677</td>\n", "<td>115.8648276</td></tr>\n", "<tr><td></td>\n", "<td>6</td>\n", "<td>0.1000183</td>\n", "<td>0.2710756</td>\n", "<td>1.8397809</td>\n", "<td>1.9992146</td>\n", "<td>0.3565041</td>\n", "<td>0.3873984</td>\n", "<td>0.0920059</td>\n", "<td>0.1999580</td>\n", "<td>83.9780892</td>\n", "<td>99.9214584</td></tr>\n", "<tr><td></td>\n", "<td>7</td>\n", "<td>0.1500071</td>\n", "<td>0.2489377</td>\n", "<td>1.6600439</td>\n", "<td>1.8861883</td>\n", "<td>0.3216755</td>\n", "<td>0.3654967</td>\n", "<td>0.0829836</td>\n", "<td>0.2829417</td>\n", "<td>66.0043897</td>\n", "<td>88.6188331</td></tr>\n", "<tr><td></td>\n", "<td>8</td>\n", "<td>0.2000163</td>\n", "<td>0.2330295</td>\n", "<td>1.4600770</td>\n", "<td>1.7796497</td>\n", "<td>0.2829268</td>\n", "<td>0.3448521</td>\n", "<td>0.0730172</td>\n", "<td>0.3559589</td>\n", "<td>46.0076968</td>\n", "<td>77.9649663</td></tr>\n", "<tr><td></td>\n", "<td>9</td>\n", "<td>0.3000142</td>\n", "<td>0.2087375</td>\n", "<td>1.2956621</td>\n", "<td>1.6183314</td>\n", "<td>0.2510673</td>\n", "<td>0.3135926</td>\n", "<td>0.1295636</td>\n", "<td>0.4855225</td>\n", "<td>29.5662093</td>\n", "<td>61.8331405</td></tr>\n", "<tr><td></td>\n", "<td>10</td>\n", "<td>0.4000122</td>\n", "<td>0.1903785</td>\n", "<td>1.0868874</td>\n", "<td>1.4854772</td>\n", "<td>0.2106119</td>\n", "<td>0.2878488</td>\n", "<td>0.1086865</td>\n", "<td>0.5942090</td>\n", "<td>8.6887391</td>\n", "<td>48.5477153</td></tr>\n", "<tr><td></td>\n", "<td>11</td>\n", "<td>0.5000102</td>\n", "<td>0.1748160</td>\n", "<td>0.9935158</td>\n", "<td>1.3870889</td>\n", "<td>0.1925188</td>\n", "<td>0.2687835</td>\n", "<td>0.0993496</td>\n", "<td>0.6935585</td>\n", "<td>-0.6484209</td>\n", "<td>38.7088881</td></tr>\n", "<tr><td></td>\n", "<td>12</td>\n", "<td>0.6000081</td>\n", "<td>0.1604152</td>\n", "<td>0.8487374</td>\n", "<td>1.2973667</td>\n", "<td>0.1644643</td>\n", "<td>0.2513976</td>\n", "<td>0.0848720</td>\n", "<td>0.7784305</td>\n", "<td>-15.1262645</td>\n", "<td>29.7366667</td></tr>\n", "<tr><td></td>\n", "<td>13</td>\n", "<td>0.7000061</td>\n", "<td>0.1453477</td>\n", "<td>0.7522184</td>\n", "<td>1.2194906</td>\n", "<td>0.1457613</td>\n", "<td>0.2363071</td>\n", "<td>0.0752203</td>\n", "<td>0.8536509</td>\n", "<td>-24.7781603</td>\n", "<td>21.9490604</td></tr>\n", "<tr><td></td>\n", "<td>14</td>\n", "<td>0.8000041</td>\n", "<td>0.1282796</td>\n", "<td>0.6305206</td>\n", "<td>1.1458712</td>\n", "<td>0.1221793</td>\n", "<td>0.2220415</td>\n", "<td>0.0630508</td>\n", "<td>0.9167016</td>\n", "<td>-36.9479419</td>\n", "<td>14.5871222</td></tr>\n", "<tr><td></td>\n", "<td>15</td>\n", "<td>0.9000020</td>\n", "<td>0.1090739</td>\n", "<td>0.4825948</td>\n", "<td>1.0721755</td>\n", "<td>0.0935149</td>\n", "<td>0.2077611</td>\n", "<td>0.0482585</td>\n", "<td>0.9649601</td>\n", "<td>-51.7405212</td>\n", "<td>7.2175505</td></tr>\n", "<tr><td></td>\n", "<td>16</td>\n", "<td>1.0</td>\n", "<td>0.0489660</td>\n", "<td>0.3504058</td>\n", "<td>1.0</td>\n", "<td>0.0679000</td>\n", "<td>0.1937753</td>\n", "<td>0.0350399</td>\n", "<td>1.0</td>\n", "<td>-64.9594219</td>\n", "<td>0.0</td></tr></table></div>" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate cumulative_response_rate capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- -------- ----------------- --------------- -------------------------- -------------- ------------------------- --------- -----------------\n", " 1 0.0100018 0.371803 2.31808 2.31808 0.449187 0.449187 0.0231851 0.0231851 131.808 131.808\n", " 2 0.0200037 0.349053 2.22368 2.27088 0.430894 0.440041 0.0222409 0.0454259 122.368 127.088\n", " 3 0.0300055 0.332888 2.00341 2.18172 0.388211 0.422764 0.0200378 0.0654637 100.341 118.172\n", " 4 0.0400073 0.319045 2.16075 2.17648 0.418699 0.421748 0.0216114 0.0870751 116.075 117.648\n", " 5 0.0500091 0.308421 2.08732 2.15865 0.404472 0.418293 0.020877 0.107952 108.732 115.865\n", " 6 0.100018 0.271076 1.83978 1.99921 0.356504 0.387398 0.0920059 0.199958 83.9781 99.9215\n", " 7 0.150007 0.248938 1.66004 1.88619 0.321675 0.365497 0.0829836 0.282942 66.0044 88.6188\n", " 8 0.200016 0.23303 1.46008 1.77965 0.282927 0.344852 0.0730172 0.355959 46.0077 77.965\n", " 9 0.300014 0.208737 1.29566 1.61833 0.251067 0.313593 0.129564 0.485522 29.5662 61.8331\n", " 10 0.400012 0.190378 1.08689 1.48548 0.210612 0.287849 0.108687 0.594209 8.68874 48.5477\n", " 11 0.50001 0.174816 0.993516 1.38709 0.192519 0.268784 0.0993496 0.693559 -0.648421 38.7089\n", " 12 0.600008 0.160415 0.848737 1.29737 0.164464 0.251398 0.084872 0.778431 -15.1263 29.7367\n", " 13 0.700006 0.145348 0.752218 1.21949 0.145761 0.236307 0.0752203 0.853651 -24.7782 21.9491\n", " 14 0.800004 0.12828 0.630521 1.14587 0.122179 0.222042 0.0630508 0.916702 -36.9479 14.5871\n", " 15 0.900002 0.109074 0.482595 1.07218 0.0935149 0.207761 0.0482585 0.96496 -51.7405 7.21755\n", " 16 1 0.048966 0.350406 1 0.0679 0.193775 0.0350399 1 -64.9594 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n", "<td><b>timestamp</b></td>\n", "<td><b>duration</b></td>\n", "<td><b>training_speed</b></td>\n", "<td><b>epochs</b></td>\n", "<td><b>iterations</b></td>\n", "<td><b>samples</b></td>\n", "<td><b>training_rmse</b></td>\n", "<td><b>training_logloss</b></td>\n", "<td><b>training_auc</b></td>\n", "<td><b>training_lift</b></td>\n", "<td><b>training_classification_error</b></td>\n", "<td><b>validation_rmse</b></td>\n", "<td><b>validation_logloss</b></td>\n", "<td><b>validation_auc</b></td>\n", "<td><b>validation_lift</b></td>\n", "<td><b>validation_classification_error</b></td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:59:33</td>\n", "<td> 0.000 sec</td>\n", "<td>None</td>\n", "<td>0.0</td>\n", "<td>0</td>\n", "<td>0.0</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td>\n", "<td>nan</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:59:33</td>\n", "<td> 1 min 14.205 sec</td>\n", "<td>160107 obs/sec</td>\n", "<td>1.0</td>\n", "<td>1</td>\n", "<td>65644.0</td>\n", "<td>0.3839785</td>\n", "<td>0.4652994</td>\n", "<td>0.6700643</td>\n", "<td>2.2878669</td>\n", "<td>0.3757672</td>\n", "<td>0.3850745</td>\n", "<td>0.4674797</td>\n", "<td>0.6674973</td>\n", "<td>2.3075929</td>\n", "<td>0.3717753</td></tr>\n", "<tr><td></td>\n", "<td>2017-03-05 14:59:36</td>\n", "<td> 1 min 17.521 sec</td>\n", "<td>183568 obs/sec</td>\n", "<td>10.0</td>\n", "<td>10</td>\n", "<td>656440.0</td>\n", "<td>0.3837444</td>\n", "<td>0.4637271</td>\n", "<td>0.6717228</td>\n", "<td>2.5929158</td>\n", "<td>0.3445852</td>\n", "<td>0.3851829</td>\n", "<td>0.4665108</td>\n", "<td>0.6690256</td>\n", "<td>2.3180820</td>\n", "<td>0.3895428</td></tr></table></div>" ], "text/plain": [ " timestamp duration training_speed epochs iterations samples training_rmse training_logloss training_auc training_lift training_classification_error validation_rmse validation_logloss validation_auc validation_lift validation_classification_error\n", "-- ------------------- ---------------- ---------------- -------- ------------ --------- --------------- ------------------ -------------- --------------- ------------------------------- ----------------- -------------------- ---------------- ----------------- ---------------------------------\n", " 2017-03-05 14:59:33 0.000 sec 0 0 0 nan nan nan nan nan nan nan nan nan nan\n", " 2017-03-05 14:59:33 1 min 14.205 sec 160107 obs/sec 1 1 65644 0.383978 0.465299 0.670064 2.28787 0.375767 0.385075 0.46748 0.667497 2.30759 0.371775\n", " 2017-03-05 14:59:36 1 min 17.521 sec 183568 obs/sec 10 10 656440 0.383744 0.463727 0.671723 2.59292 0.344585 0.385183 0.466511 0.669026 2.31808 0.389543" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show grid search results\n", "gsearch.show()\n", "\n", "# select best model\n", "nn_model2 = gsearch.get_grid()[0]\n", "\n", "# print model information\n", "nn_model2" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6717227633960028\n", "0.669025639866052\n", "0.675823306490083\n" ] } ], "source": [ "# measure nn AUC\n", "print(nn_model2.auc(train=True))\n", "print(nn_model2.auc(valid=True))\n", "print(nn_model2.model_performance(test_data=test).auc())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PartialDependencePlot progress: \|█████████████████████████████████████████\| 100%\n" ] }, { "data": { "image/png": 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1Hsw5H2bd+Q48EObNG8sll8Bb3zq0+7rKRB1XmZAkSWo/Z5wB++4LZ50Fe+yR\nbvPSzZIkScrCjTfCl74EEye+2gwPlQ2xJEmS2tJf/gLbbAP/+79w4onF92NDXJJZs2ZVXYKawJzz\nYdZ5MOd8mHXnefHFtKLECiukdYeXWaZ4zjbEJZk2bVrVJagJzDkfZp0Hc86HWXeWGGH8eHjoIbjs\nMlh55XR70ZydVFdnOJPqenp6GD169MgUppZhzvkw6zyYcz7MurMceyx8/eswcyZsueWrt9fn7KS6\nCvgiy4M558Os82DO+TDrznHZZakZnjJl0WYYiudsQyxJkqS2cO+9sNNOsPXWqSkuiw2xJEmSWt5T\nT8Hmm8Pb3w7nngtLldjF2hCXZNKkSVWXoCYw53yYdR7MOR9m3d7+/W8YNw6eey5dlvl1r+t/u6I5\nLz2M2lRnzJgxVZegJjDnfJh1Hsw5H2bd3g49NF2A49prYc01B96uaM6uMlHHSzdLkiS1lh/8AHbf\nHU47Dfbbr/H7ucqEJEmS2t6tt8K++8Kee6Y/R4oNsSRJklrO3/+ellX78Ifh1FMhhJF7LBviksyZ\nM6fqEtQE5pwPs86DOefDrNvLSy+lyzIvuyxcckn6sxFFc7YhLsnkyZOrLkFNYM75MOs8mHM+zLp9\nxAh77AH33ZcuwvHGNzZ+36I5u8pESU499dSqS1ATmHM+zDoP5pwPs24fxx8PF1wAF14IH/zg0O5b\nNGfPEJfE5VzyYM75MOs8mHM+zLo9XHUVHHYYHH54Wnd4qIrmbEMsSZKkyj3wAGy/PWyyCRxzTHMf\n24ZYkiRJlXr2WejqgjXWgB//uNzLMjfChrgkU6dOrboENYE558Os82DO+TDr1rVgQToz/MQTaRLd\n619ffF9Fc26ZhjiEsH8I4eEQwkshhNtCCB8aZNstQwjXhBCeCCE8F0K4JYQwts8264QQLq7tc2EI\n4YCRrL+np2ckd68WYc75MOs8mHM+zLp1ffWrcM018JOfwLveNbx9Fc25JS7dHEIYB5wH7AXcARwM\nbAu8O8Y4r5/tTwIeAa4HngUmAF8GPhxj/G1tm/Vr+7gLOAmYGmM8eQl1eOlmSZKkJjn/fNh5Zzjx\nRDj44HL3PZRLN7fKsmsHA9+PMf4QIISwD7AJqdGd1nfjGGPfH9kRIYTNgc2A39a2+Q3wm9r+/JxE\nkiSphdx5Z1pveNdd4aCDqq2l8iETIYRlgPWAX/beFtNp6+uADRrcRwBWAJ4eiRolSZJUnsceS1ei\n+8AH4IwWld5iAAAgAElEQVQzRvayzI2ovCEGVgFGAY/3uf1xYLUG9zEJWB64qMS6hmTevMVGdqgD\nmXM+zDoP5pwPs24dL78MW22Vrkg3cya89rXl7btozq3QEA9LCGEH4OvAtv2NN26WCRMmVPXQaiJz\nzodZ58Gc82HWrSFG2HdfuPtu6O6G1Vcvd/9Fc26FhngesAB4U5/b3wT8Y7A7hhC+CJxJaoavL6ug\njTfemK6urkW+NthgA7q7uxfZ7pprrqGrqwuAKVOm/P/b999/f6ZPn77ItrNnz6arq2uxdy5HHXXU\nYkuEzJ07l66uLubMmbPI7aeccgqTJk1a5Laenh66urqYNWvWIrfPmDGD8ePHL3Zs48aNG/Q46nkc\nix9Hfc7tfBz1PI7+j6M363Y/jl4eR//H0Ztzux9HL49j4OPozbrdj6NXux7HySfDuefCWWfBhz9c\n3nGceeaZdHV18dxzz9HV1cVaa63FNttss9g+BtIqq0zcBtweYzyw9u8AzAVOjjEeP8B9tgfOBsbF\nGK9cwv4fBk5ylQlJkqRqXHcdfOELaQLdCSeM/OO14yoTJwLnhhDu4tVl10YD5wKEEI4DVo8x7lr7\n9w617x0A3BlC6D27/FKM8fnaNssA6wABWBZYI4TwfuCFGOOfmnRckiRJ2XvoIdhuO/jsZ6EVr5HS\nCkMmiDFeRFpH+GjgbuC/gc/HGJ+sbbIa8Na6u+xJmoh3GvBo3dd36rZZvbavu2r3/zIwGzhrxA5E\nkiRJi3j+edh8c1hlFZgxA0aNqrqixbVEQwwQYzw9xvifMcblYowb1NYR7v3e+Bjjp+v+/akY46h+\nvibUbfPXGONS/Wzz6b6PXYa+Y1rUmcw5H2adB3POh1lXY+FC2Gkn+Nvf0mWZ3/CGkX28ojm3TEPc\n7mbPHnRoijqEOefDrPNgzvkw62oceSRceWU6M7z22iP/eEVzbolJda3CSXWSJEnluOgiGDcOjjsO\nDjus+Y8/lEl1niGWJElSqe65B8aPh+23h698pepqlsyGWJIkSaV5/HHo6oL3vAfOPrv6yzI3woZY\nkiRJpXjlFdh66/RndzeMHl11RY2xIS5Jf1dUUecx53yYdR7MOR9mPfJihP32gzvvhEsvhbe+dcn3\nKVvRnFvlwhxtb+LEiVWXoCYw53yYdR7MOR9mPfJOOQWmT0+XZt5gg2pqKJqzq0zUcZUJSZKkobv2\n2lcvy/ztb1ddTeIqE5IkSWqKBx9My6t97nOteVnmRtgQS5IkqZDnnksrSqy6Klx4ISzdpoNxbYhL\n0t3dXXUJagJzzodZ58Gc82HW5VuwAHbYAR57DC6/HFZaqeqKiudsQ1ySGTNmVF2CmsCc82HWeTDn\nfJh1+Q4/HK6+Gn7yE1hrraqrSYrm7KS6Ok6qkyRJWrIf/Qh22SVNoDvkkKqr6Z+T6iRJkjQibr8d\n9twTdtsNDj646mrKYUMsSZKkhjzyCGy5Jay7LpxxRntclrkRNsSSJElaopdeSs3wqFEwcya85jVV\nV1QeG+KSjB8/vuoS1ATmnA+zzoM558OshydG2GMPuPdeuOwyWG21qivqX9Gc23S1uNYzduzYqktQ\nE5hzPsw6D+acD7MenmnT4IIL0lrDrbzuQNGcXWWijqtMSJIkLerKK9PFNw4/HI49tupqGucqE5Ik\nSRq2P/whXXyjqwuOPrrqakaODbEkSZIW89RTqRF+29vSusNLdXDX2MGH1lyzZs2qugQ1gTnnw6zz\nYM75MOuhmT8fttsOnnsuXZZ5hRWqrqgxRXO2IS7JtGnTqi5BTWDO+TDrPJhzPsx6aA49FG66CS6+\nGNZcs+pqGlc0ZyfV1RnOpLqenh5Gjx49MoWpZZhzPsw6D+acD7Nu3FlnwV57wfe+B/vsU3U1Q1Of\ns5PqKuCLLA/mnA+zzoM558OsG3PTTbDffrDvvu3XDEPxnG2IJUmSxF//CltvDR//OHz3u1VX01w2\nxJIkSZl74YW0osQKK8BPfwrLLFN1Rc1lQ1ySSZMmVV2CmsCc82HWeTDnfJj1wBYuhF13hT//Oa0o\nscoqVVdUXNGcvXRzScaMGVN1CWoCc86HWefBnPNh1gM7+miYORO6u+G97626muEpmrOrTNTx0s2S\nJCknF18M226bLsl8xBFVV1MuV5mQJEnSoO65Jw2VGDcODj+86mqqZUMsSZKUmccfT5Po3vMe+MEP\nIISqK6qWDXFJ5syZU3UJagJzzodZ58Gc82HWr3rllbS82iuvpHHDnbREc9GcbYhLMnny5KpLUBOY\ncz7MOg/mnA+zTmJMF96480649FJ461urrqhcRXN2lYmSnHrqqVWXoCYw53yYdR7MOR9mnZxyCkyf\nDueeCxtsUHU15Suas2eIS+JyLnkw53yYdR7MOR9mDddeCwcfDIcckibTdaKiOdsQS5IkdbgHH0yr\nSXzuczB1atXVtB4bYkmSpA723HNpRYlVV4ULL4SlHTC7GBvikkz17VYWzDkfZp0Hc85HrlkvWAA7\n7ACPPZYuy7zSSlVXNLKK5ux7hJL09PRUXYKawJzzYdZ5MOd85Jr14YfD1VfDVVfBWmtVXc3IK5qz\nl26u46WbJUlSpzj/fNh5Z/j2t9NEutx46WZJkqSM3X477LEH7LZbWllCg7MhliRJ6iCPPAJbbgnr\nrgtnnOFlmRthQ1ySefPmVV2CmsCc82HWeTDnfOSS9UsvwRZbwKhRMHMmvOY1VVfUXEVztiEuyYQJ\nE6ouQU1gzvkw6zyYcz5yyDpGmDAB/vAH6O6G1VaruqLmK5qzq0yUZMqUKVWXoCYw53yYdR7MOR85\nZH3ccWmd4YsugjSXLD9Fc3aViTquMiFJktrRpZfCVlvBUUdBBr1/Q1xlQpIkKRO//W1aXm2bbeDI\nI6uupj3ZEEuSJLWpJ55Il2V+17vg3HNhKTu7QvyxlWT69OlVl6AmMOd8mHUezDkfnZj1v/4FW2+d\n/rzsMlh++aorql7RnG2ISzJ79qBDU9QhzDkfZp0Hc85Hp2UdI+y7L9xxRxo/PGZM1RW1hqI5O6mu\njpPqJElSOzjppHQ55vPOg112qbqa1uSkOkmSpA7185/Dl78MkyfbDJfFhliSJKlN3H8/fPGLsNFG\n8M1vVl1N57AhliRJagNPP51WlHjLW+CCC9LlmVUOG+KSdHV1VV2CmsCc82HWeTDnfLR71vPnw3bb\nwTPPwBVXwOtfX3VFralozl66uSQTJ06sugQ1gTnnw6zzYM75aPesDz4YbrwRrr0W3v72qqtpXUVz\ndpWJOq4yIUmSWs0ZZ6Ql1s44A/beu+pq2oerTEiSJHWA66+HL30JJk60GR5JNsSSJEkt6E9/gm22\ngQ03TOsOa+TYEJeku7u76hLUBOacD7POgznno92yfv552GwzWHlluOgiWNpZXw0pmrMNcUlmzJhR\ndQlqAnPOh1nnwZzz0U5ZL1gA228Pjz4Kl18Ob3hD1RW1j6I5O6mujpPqJElS1SZNghNPhKuugs9/\nvupq2tdQJtV5Al6SJKlFnHcenHBCGjNsM9w8DpmQJElqAbfcAnvtBbvvDgceWHU1ebEhliRJqtjc\nubDllvCRj8Dpp0MIVVeUFxv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Xc9VV6ezwIYfAXntVXU1jzDoPRXN2lYk6rjIhSdLgfv97\n+NjH4FOfShfgGDWq6oqk/rnKhCRJKt0//pFWlHjHO+DHP7YZVuewIZYkSUv00kuwxRYwfz5ccQW8\n7nVVVySVx4a4JLNmzaq6BDWBOefDrPNgzo1ZuBB22w1+97vUDL/lLVVXNHRmnYeiOdsQl2TatGlV\nl6AmMOd8mHUezLkxU6bARRfB+edDGpLZfsw6D0VzdlJdneFMquvp6WH06NEjU5hahjnnw6zzYM5L\ndv75sPPOcNxxcNhhVVdTnFnnoT5nJ9VVwBdZHsw5H2adB3Me3KxZsPvuMH48fOUrVVczPGadh6I5\n2xBLkqTF/PnPsOWWsMEGcMYZEELVFUkjx4ZYkiQt4tlnYZNN4A1vgEsugWWXrboiaWTZEJdk0qRJ\nVZegJjDnfJh1Hsx5cfPnw7bbwuOPw5VXwsorV11ROcw6D0VzXrrkOrI1ZsyYqktQE5hzPsw6D+a8\nqBjhS1+CG26Aa66Bd7+76orKY9Z5KJqzq0zU8dLNkqScfec7cPDBMH06TJhQdTXS8LjKhCRJGpIr\nr4RDDoHJk22GlR8bYkmSMvfb38IXvwibb57WG5ZyY0Nckjlz5lRdgprAnPNh1nkwZ3jsMdh0U1hr\nrXQRjqU6tDMw6zwUzblDn/bNN3ny5KpLUBOYcz7MOg+559zTA11dsHAhXH45LL981RWNnNyzzkXR\nnF1loiSnnnpq1SWoCcw5H2adh5xzXrgQdtkF7rsPbr4Z1lij6opGVs5Z56RozjbEJXE5lzyYcz7M\nOg855/y1r8HMmXDppZDDwko5Z52TojnbEEuSlJlzz02T544/Pk2kk3LnGGJJkjJy442w116wxx5w\n6KFVVyO1BhvikkydOrXqEtQE5pwPs85Dbjk/9BBstRX87//C6adDCFVX1Dy5ZZ2rojnbEJekp6en\n6hLUBOacD7POQ045P/MMbLIJrLoqXHwxLLNM1RU1V05Z56xozl66uY6XbpYkdaL58+ELX4B77oHb\nb4d3vrPqiqSRN5RLNzupTpKkDhYj7LdfWlrtuutshqX+2BBLktTBvv1tOPvstLLEJz5RdTVSa3IM\ncUnmzZtXdQlqAnPOh1nnodNz7u6GyZPhq1+FXXetuppqdXrWSormbENckgkTJlRdgprAnPNh1nno\n5Jxnz4Ydd4Stt4Zjj626mup1ctZ6VdGcbYhLMmXKlKpLUBOYcz7MOg+dmvMjj8Bmm8F//Recdx4s\n5f/2HZu1FlU0Z1eZqOMqE5Kkdvfii2md4Xnz0ooSb35z1RVJ1XCVCUmSMrRwIey0Ezz4IMyaZTMs\nNcqGWJKkDvHVr8Lll8Nll8H73191NVL7cFRRSaZPn151CWoCc86HWeehk3KePh2mTUvLrG26adXV\ntJ5OyloDK5qzDXFJZs8edGiKOoQ558Os89ApOV9/PeyzD+y9Nxx4YNXVtKZOyVqDK5qzk+rqOKlO\nktRu/vhH+OhHYf314Wc/g2WWqboiqTUMZVKdZ4glSWpT8+bBJpvAaqvBRRfZDEtFOalOkqQ29K9/\nwZZbwnPPpeXVVlqp6oqk9mVDLElSm4kR9tgD7rwzjR9ec82qK5Lam0MmStLV1VV1CWoCc86HWeeh\nXXM+5hg4//x0FboNNqi6mvbQrllraIrmbENckokTJ1ZdgprAnPNh1nlox5wvuACOOgqOPRbGjau6\nmvbRjllr6Irm7CoTdVxlQpLUymbNgs98BrbfHs45B0KouiKpdbnKhCRJHeZPf4IttkhDJM4802ZY\nKpMNsSRJLe6ZZ9LyaiuvDDNnwrLLVl2R1FlsiEvS3d1ddQlqAnPOh1nnoR1yfuUV2GqrtObwz34G\n//EfVVfUntohaw1f0ZxtiEsyY8aMqktQE5hzPsw6D62ec4zpcsy33AKXXgrvfGfVFbWvVs9a5Sia\ns5Pq6jipTpLUSo47Dg4/HH70I9hpp6qrkdqLk+okSWpzF12UmuGjjrIZlkaaDbEkSS3mtttgl11g\nhx1SQyxpZNkQS5LUQh5+GLq6YP31Yfp0l1eTmsGGuCTjx4+vugQ1gTnnw6zz0Go5P/tsWl7t9a+H\n7m547WurrqhztFrWGhlFc1665DqyNXbs2KpLUBOYcz7MOg+tlPP8+bDttvCPf8Ctt8Iqq1RdUWdp\npaw1corm7CoTdVxlQpJUhd7l1c45B669FjbcsOqKpPY3lFUmPEMsSVLFTjgBzjorNcQ2w1LzOYZY\nkqQKzZwJX/lKWmJtt92qrkbKkw1xSWbNmlV1CWoCc86HWeeh6pzvvDOtMbzddnDMMZWW0vGqzlrN\nUTRnG+KSTJs2reoS1ATmnA+zzkOVOc+dm5ZXe//701CJpfwfeUT5ms5D0ZydVFdnOJPqenp6GD16\n9MgUppZhzvkw6zxUlfPzz8PHPgYvvAC33w5vfGPTS8iOr+k81OfspLoK+CLLgznnw6zzUEXO//43\njEN1oyYAACAASURBVBsHf/sb3HKLzXCz+JrOQ9GcbYglSWqSGOHAA+G66+DnP4d11qm6IklgQyxJ\nUtN897tw+ulw5pnw2c9WXY2kXg7hL8mkSZOqLkFNYM75MOs8NDPnyy+HQw6BSZNgzz2b9rCq8TWd\nh6I52xCXZMyYMVWXoCYw53yYdR6alfPs2bD99rDllvCtbzXlIdWHr+k8FM3ZVSbqeOlmSVLZ/v53\n+MhHYI014IYbwLldUnMMZZUJzxBLkjRCXngBNtsMll46DZmwGZZak5PqJEkaAQsWpGESf/oT/PrX\nsNpqVVckaSCeIS7JnDlzqi5BTWDO+TDrPIxkzocempZWu+gieN/7Ruxh1CBf03komrMNcUkmT55c\ndQlqAnPOh1nnYaRyPu20tMTaKafAF74wIg+hIfI1nYeiOTuprs5wJtXNnTvXGawZMOd8mHUeRiLn\nq65K44YPPBBOPLHUXWsYfE3noT5nJ9VVwBdZHsw5H2adh7Jz/t3v0mWZN90Ujj++1F1rmHxN56Fo\nzjbEkiSV4LHHUiP8rnfBj38Mo0ZVXZGkRtkQS5I0TC++mIZJLFwIV1wBr3td1RVJGgob4pJMnTq1\n6hLUBOacD7POQxk5L1gAO+0Ec+bAlVemC3Co9fiazkPRnF2HuCQ9PT1Vl6AmMOd8mHUeysj5sMPS\nRTcuuww+8IESitKI8DWdh6I5u8pEHS/dLEkaijPPhL33TkusHXBA1dVIqucqE5IkjbBf/AL22w8m\nTrQZltqdDbEkSUP0u9/Bttumi26cdFLV1UgaLhviksybN6/qEtQE5pwPs85DkZwffRQ22QTe+U64\n8EJY2tk4bcHXdB6K5mxDXJIJEyZUXYKawJzzYdZ5GGrOL7yQlleL0eXV2o2v6TwUzdn3tSWZMmVK\n1SWoCcw5H2adh6HkvGAB7LAD/PGPMGuWy6u1G1/TeSiasw1xSVyVIg/mnA+zzsNQcj7kELjqqrTW\n8PvfP4JFaUT4ms5D0ZxtiCVJWoKTT05f3/temkgnqbM4hliSpEFcfjkcdBB8+cuwzz5VVyNpJNgQ\nl2T69OlVl6AmMOd8mHUelpTzXXfB9tvDlluCV/5tb76m81A0ZxviksyePegFUNQhzDkfZp2HwXKe\nOxc23RTe+1740Y9gKf/HbGu+pvNQNGcv3VzHSzdLkgCefx4+/nH45z/httvgTW+quiJJQzWUSzc7\nqU6SpDrz56er0M2dC7fcYjMs5aDwB0AhhP8NIZwfQrg1hLBG7badQwgfL688SZKaJ0bYf3/41a9g\n5kxYZ52qK5LUDIUa4hDC1sAvgJeADwKvqX1rReDwckqTJKm5jj8ezjorfX3601VXI6lZip4h/hqw\nT4xxT2B+3e2/BrIcfNvV1VV1CWoCc86HWeehPueLL4avfAW+9jXYbbfqatLI8DWdh6I5F22I1wJu\n6uf254CVCu6zrU2cOLHqEtQE5pwPs85Db8633QY775yWWDv66IqL0ojwNZ2HojkXWmUihPBnYK8Y\n43UhhH8C748x/jmEsAtwWIyxLUdducqEJOXnz3+Gj34U1loLrrsOXvOaJd9HUusbyioTRc8QnwV8\nN4TwESACq4cQdgROAL5XcJ+SJDXVM8/AxhvDSitBd7fNsJSrosuufYvUTP8SGE0aPvEv4IQY4ykl\n1SZJ0oh55RXYaiuYNw9uvRVWXrnqiiRVpdAZ4pj8H/AfwHuBjwKrxhi/XmZx7aS7u7vqEtQE5pwP\ns+5sMcKee8KsWd10d8O73lV1RRppvqbzUDTnYV2IMsb4SozxPmAO8NkQwtrD2V87mzFjRtUlqAnM\nOR9m3dmOOQZ++ENYf/0ZfNzV87PgazoPRXMuOqnuIuCmGOOpIYTlgHuANYEAfDHGeEmhairmpDpJ\n6nznn59WlDj2WDjiiKqrkTRSmjGp7hPAzbW/b1nbz0rAAaQ1iiVJajk33QS7757WGT7cy0hJqina\nEK8IPF37+xeAS2KMPcDPAEdiSZJazgMPwBZbwMc/Dt//PoRQdUWSWkXRhvhvwAYhhOVJDfE1tdvf\nALxcRmGSJJXlySfT8mqrrQaXXALLLlt1RZJaSdGG+DvAj4G/A48CN9Ru/wTw++GX1X7Gjx9fdQlq\nAnPOh1l3jpdfTmeGX3gBrroqrTncy5zzYdZ5KJpzoXWIY4ynhxDuAN4KXBtjXFj71p/JdAzx2LFj\nqy5BTWDO+TDrzrBwYRovPHs23Hgj/Od/Lvp9c86HWeehaM6FVpnoVK4yIUmd5fDD4VvfgosvThfh\nkJSPoawyUegMcQhhFLAb8BngjfQZehFj/HSR/UqSVJbp0+G44+CEE2yGJQ2u6KWbv0tqiH8G3At4\nmlmS1DKuuw722Qf23RcOOaTqaiS1uqKT6r4IbBd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Jv/wl3HYbvPGNra/BrGMw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iipZZPa5JRaI6U2Et5GZGyyGshujje8ZVORF2ZyykxTpwul4DQoRgIDWUAYkOQoihdAoPEIT3+8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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118b67320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# partial dependence plots are a powerful machine learning interpretation tool\n", "# to calculate partial dependence across the domain a variable\n", "# hold column of interest at constant value\n", "# find the mean prediction of the model with this column constant\n", "# repeat for multiple values of the variable of interest\n", "# h2o has a built-in function for partial dependence as well\n", "par_dep_dti1 = nn_model2.partial_plot(data=train, cols=['STD_IMP_REP_dti'], server=True, plot=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H2O session _sid_bc2c closed.\n" ] } ], "source": [ "# shutdown h2o\n", "h2o.cluster().shutdown(prompt=False)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
aasensio/elecciones2015	Elecciones 2016 New.ipynb	2	1729722	null	mit
ryanpdwyer/hdf5plotter	Untitled0.ipynb	1	181	{ "metadata": { "name": "", "signature": "sha256:1747a2dc602117fb265f9fc1b96a5ba40cee78a20c54e8dad9ada528c8a18a80" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }	mit
c-bata/akashipy	notebooks/python_grammer.ipynb	1	13061	{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:ea1a214345d7dc07018c23ee9d099be0cd13f8ac2d184c0602b70f63fb849c33" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Python3 \u57fa\u790e\u6587\u6cd5\u30c1\u30a7\u30c3\u30af\n", "\n", "akashi.py \u7b2c\u4e00\u56de(2015/06/14) \u8cc7\u6599\u3002\u30cf\u30de\u308b\u3068\u3053\u308d\u3082\u5c11\u306a\u3044\u306e\u3067Python3\u306b\u7d5e\u3063\u3066\u8aac\u660e" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Hello World\n", "\n", "python\u306b\u306f\u5bfe\u8a71\u74b0\u5883\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u308b\u306e\u3067\u3001\u308f\u3056\u308f\u3056\u30d5\u30a1\u30a4\u30eb\u3092\u4f5c\u3089\u306a\u304f\u3066\u3082\u7c21\u5358\u306b\u52d5\u304b\u305b\u308b\u3002" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('Hello World')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hello World\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## \u5909\u6570" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### \u578b\n", "\u52d5\u7684\u578b\u4ed8\u3051\u3002C\u8a00\u8a9e\u306e\u3088\u3046\u306bint\u7b49\u306f\u6307\u5b9a\u3057\u306a\u304f\u3066\u3044\u3044\u3002" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = 'Hello World' # str\n", "b = 1 # int\n", "c = 1.0 # float\n", "d = [1, 2, 3] # list\n", "e = (1, 2, 3) # tuple\n", "f = {'a': 1, 'b': 2,} # dict\n", "g = True # bool" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\u64cd\u4f5c\u306b\u95a2\u3057\u3066\u306f\u5fc5\u8981\u306b\u5fdc\u3058\u3066Google\u3067\u691c\u7d22\u3057\u305f\u65b9\u304c\u65e9\u3044\u3067\u3059\u3002" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### \u5909\u6570\u306e\u8868\u793a" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = 'c-bata'\n", "print('Hello %s !' % a)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hello c-bata !\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## \u5236\u5fa1\u69cb\u6587" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### \u6761\u4ef6\u5206\u5c90\n", "\n", "Switch\u6587\u306f\u7121\u3044" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = -5\n", "if a == 0:\n", " print('a\u306f0\u3067\u3059')\n", "elif a >= 0:\n", " print('a\u306f\u6b63\u3067\u3059')\n", "else:\n", " print('a\u306f\u8ca0\u3067\u3059')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a\u306f\u8ca0\u3067\u3059\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### for\u30eb\u30fc\u30d7" ] }, { "cell_type": "code", "collapsed": false, "input": [ "items = ['c-bata', 'c_bata_', 'kare-meshi']\n", "for item in items:\n", " print(item)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "c-bata\n", "c_bata_\n", "kare-meshi\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(3):\n", " print(i)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1\n", "2\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### while\u30eb\u30fc\u30d7" ] }, { "cell_type": "code", "collapsed": false, "input": [ "i = 0\n", "while i < 3:\n", " i += 1\n", " print(i)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "2\n", "3\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## \u95a2\u6570" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add(x, y):\n", " return x + y\n", "\n", "add(2, 3)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "5" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## import" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "math.sqrt(2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "1.4142135623730951" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "from math import pow\n", "pow(2, 10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "1024.0" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "from math import pi as ensyuritsu\n", "ensyuritsu" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "3.141592653589793" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\u81ea\u5206\u3067\u4f5c\u3063\u305f\u30e2\u30b8\u30e5\u30fc\u30eb\u3082import\u3067\u304d\u307e\u3059\u3002\u540d\u524d\u306e\u885d\u7a81\u306b\u306f\u6ce8\u610f\u3002" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## \u30af\u30e9\u30b9\n", "\n", "\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u6307\u5411\u3068\u304b\u7fd2\u3063\u3066\u306a\u3044\u4eba\u306f\u307e\u3060\u5206\u304b\u3089\u306a\u304f\u3066\u3082\u5927\u4e08\u592b\u3067\u3059\u3002\u4e00\u5fdc\u7d39\u4ecb\u3002" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " def __init__(self, x, y):\n", " self.x, self.y = x, y\n", " self.name = None\n", "\n", " def get_euclidean_distance(self):\n", " return math.sqrt(self.x2 + self.y2)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(3, 4)\n", "point.get_euclidean_distance()" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "5.0" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "$\\sqrt{(3^2 + 4^2)} = 5$ " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## \u305d\u306e\u4ed6" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### \u6a19\u6e96\u5165\u529b" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(input())" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "hello\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "hello\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### \u30b3\u30e1\u30f3\u30c8" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# 1\u884c\u30b3\u30e1\u30f3\u30c8" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\u8907\u6570\u884c\u30b3\u30e1\u30f3\u30c8\n", "\u30c0\u30d6\u30eb\u30af\u30aa\u30fc\u30c6\u30fc\u30b7\u30e7\u30f33\u3064\u3067\u56f2\u3063\u3066\u304f\u3060\u3055\u3044\u3002\n", "\u3053\u308c\u306f\u30d6\u30ed\u30c3\u30af\u6587\u5b57\u5217\u3067\u3059\u304c\u3001\u8907\u6570\u884c\u30b3\u30e1\u30f3\u30c8\u306e\u3088\u3046\u306b\u6271\u308f\u308c\u307e\u3059\u3002\n", "\"\"\"" ], "language": "python", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "'\u8907\u6570\u884c\u30b3\u30e1\u30f3\u30c8\\n\u30c0\u30d6\u30eb\u30af\u30aa\u30fc\u30c6\u30fc\u30b7\u30e7\u30f33\u3064\u3067\u56f2\u3063\u3066\u304f\u3060\u3055\u3044\u3002\\n\u3053\u308c\u306f\u30d6\u30ed\u30c3\u30af\u6587\u5b57\u5217\u3067\u3059\u304c\u3001\u8907\u6570\u884c\u30b3\u30e1\u30f3\u30c8\u306e\u3088\u3046\u306b\u6271\u308f\u308c\u307e\u3059\u3002\\n'" ] } ], "prompt_number": 22 } ], "metadata": {} } ] }	mit
fabiencampillo/systemes_dynamiques_agronomie	Partie3_Identification_moindres_carres.ipynb	1	354643	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Partie 3: Identification des paramètres d'un modèle\n", "\n", "- [Problématique générale](#PbGeneral)\n", "- [Problème des moindres carrés](#moindresCarres)\n", "- [Modèles de régression linéaire](#regressionLin)\n", "- [Cas non linéaire: algorithmes de minimisation](#casNLin)\n", " - [Minimum global ou local : importance de la condition initiale](#minLocalGlobal)\n", " - [Méthode du gradient](#gradient)\n", " - [Méthode de Newton-Raphton](#NewtonRaphton)\n", "- [Quelques remarques pour finir](#remarquesFinir)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>\n", "code_show=true; \n", "function code_toggle() {\n", " if (code_show){\n", " $('div.input').hide();\n", " } else {\n", " $('div.input').show();\n", " }\n", " code_show = !code_show\n", "} \n", "$( document ).ready(code_toggle);\n", "</script>\n", "Pour afficher le code python, cliquer sur le bouton: \n", "<button onclick=\"javascript:code_toggle()\">Afficher code python</button>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# -- coding: utf-8 --\n", "from IPython.display import HTML\n", "\n", "HTML('''<script>\n", "code_show=true; \n", "function code_toggle() {\n", " if (code_show){\n", " $('div.input').hide();\n", " } else {\n", " $('div.input').show();\n", " }\n", " code_show = !code_show\n", "} \n", "$( document ).ready(code_toggle);\n", "</script>\n", "Pour afficher le code python, cliquer sur le bouton: \n", "<button onclick=\"javascript:code_toggle()\">Afficher code python</button>\n", "''')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from ipywidgets import interact, fixed\n", "#from IPython.html.widgets import interact, fixed\n", "#import scipy.integrate as scint\n", "#from matplotlib import patches as pat\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <a name=\"PbGeneral\">Problématique générale</a>\n", "Une fois que l'on s'est fixé un modèle, se pose la question de la valeur des paramètres de ce modèle. Les valeurs des paramètres du modèle peuvent être tirées de la litérature ou bien être identifiées à partir de données expérimentales. Dans ce chapitre, la question que nous allons nous poser se formule de la manière suivante:\n", "\n", "> Question: étant donné certaines données expérimentales, et étant donné un modèle de paramètres $\\theta$, quelles sont les valeurs $\\hat{\\theta}$ des paramètres $\\theta$ qui permettent d'obtenir des sorties simulées les plus proches des mesures?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Cette question est formulée mathématiquement comme un problème de minimisation d'une fonction objectif $J(\\theta)$, de la forme:\n", "$$\n", "\\hat{\\theta}=\\text{argmin}_{\\theta \\in \\Omega}J(\\theta)\n", "$$\n", "où $\\Omega$ est l'ensemble des valeurs des paramètres $\\theta$ admissibles.\n", "\n", "La notion de proximité qui apparaît dans la question est évidemment à clarifier: on souhaite que les sorties simulées soient proches des valeurs mesurées, mais en quel sens? selon quelle distance?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "En mathématique, la notion de distance est primordiale, car elle permet justement de quantifier l'éloignement entre deux objets de même nature. Dans ce chapitre, nous allons considérer un distance \"intuitive\", qui est la distance euclidienne, définie comme la racine de la somme des différences au carré, c'est à dire:\n", "$$\n", "d(x,y) = \\sqrt{\\sum_{i=1}^N (x_i-y_i)^2}\n", "$$\n", "où $x=(x_1,...,x_N)^T$ et $y=(y_1,...,y_N)^T$ sont deux vecteurs de $\\mathbb{R}^N$ qui dans notre cas vont représenter le vecteur des données simulées (obtenues avec le modèle et donc dépendantes des paramètres $\\theta$) et le vecteur des données mesurées expérimentalement.\n", "\n", "Plus exactement, nous utiliseront la distance euclidienne pondérée définie de la manière suivante:\n", "$$\n", "d(x,y) = \\sqrt{\\sum_{i=1}^N \\beta_i(x_i-y_i)^2}\n", "$$\n", "où les poids $\\beta_i$ sont positifs.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Pour répondre à la question précédente, nous allons donc chercher à minimiser cette distance. Minimiser la racine d'une quantité ou cette quantité revenant au même, par simplicité, nous chercherons à minimiser la somme des distances au carré, ce que l'on appelle le problème des moindres carrés. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## <a name=\"moindresCarres\">Problème des moindres carrés</a>\n", "\n", "Dans le problème de moindres carrés, la fonction objectif $J(\\theta)$ considérée est donnée par: \n", "$$\n", "J(\\theta)=\\sum_{i=1}^N\\beta_i (y_i(\\theta)-y_i^m)^2\n", "$$\n", "où:\n", "- $\\theta$ est le vecteur de paramètres du modèle\n", "- $N$ est le nombre d'instants $t_i$ d'observation\n", "- $y_i^m$ est le vecteur des observations (mesures) à l'instant $t_i$\n", "- $y_i(\\theta)$ est le vecteur des estimations données par le modèle à l'instant $t_i$ en fonction de $\\theta$\n", "- $\\beta_i$ est un coefficient de pondération pouvant être différent selon l'instant d'observation\n", "\n", "La solution du problème de moindre carrés ainsi que la méthode à utiliser pour la trouver dépend de la forme du modèle utilisé." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## <a name=\"regressionLin\">Modèles de regression linéaire</a>\n", "On se place dans un premier temps dans le cas où le modèle considéré s'écrit linéairement par rapport à $\\theta$, c'est à dire sous la forme:\n", "$$\n", "y_i(\\theta)=\\phi_i^T \\theta\n", "$$\n", "où $\\phi_i$ est un vecteur de même taille que $\\theta$ appelé régresseur.\n", "\n", "Ce type de modèle est appelé modèle de regression linéaire." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Le problème de moindres carrés évoqué précédemment s'écrit alors dans ce cas:\n", "$$\n", "\\min_{\\theta \\in \\Omega} J(\\theta)=\\min_{\\theta \\in \\Omega}\\sum_{i=1}^N\\beta_i (\\phi_i^T \\theta-y_i^m)^2\n", "$$\n", "\n", "C'est un problème que l'on sait résoudre analytiquement. Sa solution, que l'on sait unique, est appelée estimateur des moindres carrés et est donnée par:\n", "$$\n", "\\hat{\\theta}=\\left[ \\sum_{i=1}^N \\beta_i \\phi_i \\phi_i^T \\right]^{-1} \\sum_{i=1}^N \\beta_i \\phi_i y_i\n", "$$\n", "Cet estimateur existe si la matrice $\\sum_{i=1}^N \\beta_i \\phi_i \\phi_i^T$ est inversible. \n", "\n", "Rappel: pour rechercher le minimum ou maximum d'une fonction, on se ramène souvent au problème de recherche des points qui annulent la dérivée de cette fonction.\n", "\n", "$$ x_{min} = argmin_{x\\in \\mathbb{R}} f(x) \\Longrightarrow f^\\prime(x_{min})=0$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Les modèles de la biologie sont souvent non linéaires. Néanmoins, il est quand même parfois possible de transformer le modèle de sorte à faire apparaître les paramètres de manière linéaire ce qui facilite grandement la résolution du problème de moindres carrés. L'exemple ci-dessous va dans ce sens.\n", "\n", "> <u>Exemple</u>: modèle d'une fonction de croissance de type Monod\n", "> $$ \\mu(S)=k\\frac{S}{a+S} $$\n", "> Supposons qu'aux instants $t_i$ $i=1:N$ on mesure le taux de croissance et le substrat. Notons $\\mu_i$ et $S_i$ ces mesures. On a alors, pour tout $i=1:N$:\n", "> $$ \\mu_i=k\\frac{S_i}{a+S_i} $$\n", "> On souhaiterait trouver les paramètres $a$ et $k$ de ce modèle à partir des mesures $S_i$ et $\\mu_i$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "> Le modèle de Monod n'est pas linéaire en les paramètres. Cependant, en multipliant l'équation par $(a+S)$ on a:\n", "> $$ \\mu_i=k\\frac{S_i}{a+S_i} \\Longleftrightarrow (a+S_i)\\mu_i=k S_i \\Longleftrightarrow S_i\\mu_i=k S_i -a\\mu_i \\Longleftrightarrow S_i\\mu_i=[S_i\\, -\\mu_i] \\left[\\begin{array}{c}k \\\\ a \\end{array}\\right] $$\n", "> On retombe donc bien sur un modèle de regression linéaire de la forme:\n", "> $$ y_i(\\theta)=y_i^m $$\n", "> avec:\n", "> $y_i^m= S_i\\mu_i$, $y_i(\\theta)=\\phi_i^T \\theta$, $\\theta=\\left[\\begin{array}{c}k \\\\a \\end{array}\\right]$ et $\\phi_i =\\left[\\begin{array}{c}S_i \\\\ -\\mu_i \\end{array}\\right]$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Application numérique\n", "\n", "Test de la qualité de l'estimation de la fonction de Monod en fonction:\n", "- du nombre de mesures $N$\n", "- de l'écart type $\\sigma_1$ du bruit de mesure sur $\\mu_i$\n", "- de l'écart type $\\sigma_2$ du bruit de mesure sur $S_i$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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cCZs2Fe40IgLOPReuugratoU2bax3c6gypQRiPSuLnNgQ2ucRKhkrUricYGUp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\ndSaFLCCXAyMljZDUDbgKmJ99gqRxwE9JisddBczSpDVrkse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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa716718e80>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.identifMonod>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Identification des paramètres a et k d'une fonction de Monod via l'estimateur des moindres carrés linéaires\n", "# -----------------------------------------------------------------------------------------------------------\n", "def identifMonod(N,sigma1,sigma2):\n", " # N : nombre d'observation\n", " # sigma1 : ecart type bruit de mesure sur mu\n", " # sigma2 : ecart type bruit de mesure sur S\n", " \n", " # Generation de donnees d'observation\n", " # -----------------------------------\n", " # parametres du modele de Monod\n", " # mu(S)=kS/(S+a)\n", " coeffk = 1.34 # coefficient k\n", " coeffa = 1.57 # coefficient a\n", " \n", " # calcul des mesures bruitées de S et de mu\n", " S = np.linspace(0,15,num=N) # mesures du substrat\n", " mu = coeffkS/(coeffa+S) # sorties du modèle correspondantes\n", " mub = mu+sigma1(np.random.rand(N)-0.5) # bruitage des sorties\n", " Sb = S + sigma2(np.random.rand(N)-0.5) # bruitage des entrées\n", " \n", " # calcul du modèle exact\n", " Sabs = np.linspace(0,15,num=200) # mesures du substrat\n", " muexact = coeffkSabs/(coeffa+Sabs) # sorties du modèle correspondantes\n", " \n", " # trace des observations\n", " plt.figure(1)\n", " plt.plot(Sabs,muexact,'r',label='modèle exact')\n", " plt.plot(Sb,mub,'ro',label='mesures bruitées')\n", " plt.xlabel('Substrat')\n", " plt.ylabel('Taux de croissance')\n", "\n", " # Identification des parametres k et a du modele de Monod\n", " # -------------------------------------------------------\n", " beta = np.ones(N) # coefficients de pondération\n", " \n", " # calcul de l'estimateur de moindres carrés\n", " matM = np.zeros((2,2))\n", " vecb = np.zeros((2,1))\n", " for i in np.arange(0,N,1):\n", " phii = np.array([[S[i]], [-mub[i]]]) # regresseur\n", " matM = matM+beta[i](phiinp.transpose(phii)) # matrice à inverser\n", " vecb = vecb + beta[i]phiiS[i]mub[i] \n", " theta_hat = np.linalg.solve(matM, vecb) #estimateur des moindres carrés\n", " \n", " # calcul du modèle identifié\n", " muhat = theta_hat[0]Sabs/(theta_hat[1]+Sabs) # calcul du mu correspondant\n", " \n", " # tracé du modèle identifié et des valeurs des paramètres k et a identifiés\n", " plt.plot(Sabs,muhat,'b',label='modèle identifié')\n", " plt.text(16.5,0.8,'coefficient k exact ='+str(coeffk)+'; estimé ='+str(theta_hat[0][0]))\n", " plt.text(16.5,0.7,'coefficient a exact ='+str(coeffa)+'; estimé ='+str(theta_hat[1][0]))\n", " plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", " \n", " plt.show()\n", " \n", "interact(identifMonod, N=(2,200,1), sigma1=(0,0.5,0.1), sigma2=(0,2,0.1))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## <a name=\"casNLin\">Cas non linéaire: algorithmes de minimisation</a>\n", "\n", "La plupart du temps, et notamment dans le cas où $y_i$ est une fonction non linéaire des paramètres, il est impossible de calculer analytiquement la solution du problème:\n", "$$\n", "\\hat{\\theta}=\\text{argmin}_{\\theta \\in \\Omega}J(\\theta)\n", "$$\n", "On doit donc passer par des algorithmes d'optimisation, qui permettent de trouver le minimum (ou maximum) d'une fonction objectif sur un domaine $\\Omega$ donné.\n", "\n", "Ces algorithmes sont généralement itératifs et démarrent donc d'une valeur initiale que l'utilisateur doit donner. Cette valeur (ou condition) initiale est une première estimation \"grossière\" des paramètres. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### <a name=\"minLocalGlobal\">Minimum global ou local : importance de la condition initiale</a>\n", "\n", "Avant de présenter les algorithmes d'optimisation, une remarque s'impose. Une des difficultés rencontrées par les algorithmes d'optimisation vient du fait que les fonctions non linéaires peuvent posséder des minima locaux.\n", "\n", "Il existe en effet deux types de minimum.\n", "\n", "Pour tout $a\\in\\Omega$, $f(a)$ est un\n", "- minimum global de $f$* si $f(a)$ est la plus petite valeur atteinte par $f$ sur tout le domaine $\\Omega$\n", "- minimum local de $f$ si il existe un voisinage $V$ de $a$ tel que $f(a)$ est la plus petite valeur atteinte par $f$ sur $V$\n", "\n", "La condition initiale donnée à l'algorithme peut donc être très importante car les <u>algorithmes convergent généralement vers un minimum local, souvent le plus proche de la condition initiale</u>.\n", " \n", "Exemple: la fonction $f\\mapsto -3e^{-x^2}-e^{-(x-3)^2}$ admet deux minimums locaux sur $[-5,10]$ dont un est global" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa7166ee710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Exemple d'une fonction ayant deux minimum locaux dont un est global\n", "x = np.arange(-5,10,0.1)\n", "f = -3np.exp(-x2)-np.exp(-(x-3)2)\n", "\n", "# tracé\n", "plt.figure(1)\n", "plt.plot(x,f,'b',label='$f(S)=-3 e^{-x^2}-e^{-(x-3)^2}$')\n", "plt.text(-2,-3.3,'minimum global')\n", "plt.text(1.7,-1.3,'minimum local')\n", "plt.xlabel('Substrat $S$')\n", "plt.ylabel('fonction $f$')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.ylim(-3.5,0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "### <a name=\"gradient\">Méthode du gradient</a>\n", "\n", "L'algorithme du gradient* est un algorithme d'optimisation (c'est à dire de minimisation ou maximisation) de fonction. Il est aussi appelé algorithme de plus forte pente ou de plus profonde descente.\n", "\n", "Dans ce paragraphe on s'intéresse donc au problème de la minimisation d'une fonction $f:x\\in \\Omega \\subset \\mathbb{R}^n \\mapsto f(x) \\in \\mathbb{R}$ sur un domaine $\\Omega$:\n", "$$ \\min_{x\\in\\Omega} f(x)$$\n", "\n", "On notera $\\nabla f(x)$ le gradient de $f$ en $x$, c'est à dire le vecteur colonne des dérivées partielles de $f$ par rapport aux $x_i$ ($i=1:n$):\n", "$$\n", "\\nabla f(x) =\\left[ \\begin{array}{c} \\partial_{x_1}f(x) \\\\ \\vdots \\\\ \\partial_{x_n}f(x) \\end{array} \\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "L'algorithme de gradient repose sur la notion de direction de descente. Une direction de descente est définie localement autour d'un point $x\\in\\Omega$: c'est une direction, donc un vecteur $d\\in \\Omega\\backslash \\{0\\}$ selon laquelle, au voisinage de $x$, la fonction $f$ décroit. Cela assure que si on suit cette direction, on va bien se rapprocher d'un minimum. Mathématiquement, cela s'écrit de la manière suivante. \n", "\n", "$d\\in \\Omega\\backslash \\{0\\}$ est une direction de descente en $x$ pour $f$ si il existe un intervalle $\\left[0,\\alpha_0 \\right]$ tel que:\n", "$$ f(x+\\alpha d)\\leqslant f(x),\\,\\,\\forall \\alpha\\in \\left[0,\\alpha_0 \\right] $$\n", "On parlera de direction de descente stricte si l'inégalité est stricte ($<$ au lieu de $\\leqslant$).\n", "\n", "On peut montrer que, si $\\nabla f(x) \\neq 0$, alors $d=-\\nabla f(x)$ est une direction de descente stricte en $x$ pour $f$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "L'algorithme du gradient est donc donné par:\n", "\n", "> <u>Algorithme du gradient</u>:\n", "> - on choisit un nombre d'itérations maximal $N$, un seuil de précision $\\epsilon$ et une valeur initiale $x_0$ pour $x$\n", "> - initialisation: $k=0$ et calcul de $\\nabla f(x_0)$\n", "> - Tant que $k+1\\leqslant N$ et $\\left\\\|\\nabla f(x_k)\\right\\\|>\\epsilon$ alors\n", ">> \\begin{eqnarray} x_{k+1} & = & x_k - \\alpha_k \\nabla f(x_{k})\\\\\n", "k & = & k+1 \\end{eqnarray}\n", ">> où $\\alpha_k$ peut être choisi selon différentes méthodes. \n", "> - $\\hat{x}\\simeq x_{k}$ et $\\min_{x\\in\\Omega} f(x)\\simeq f(x_k)$\n", "\n", "Parmi les différents choix possible de $\\alpha_k$ on notera les deux suivantes:\n", "- Si $\\alpha_k$ est une constante indépendante de $k$, on parlera d'algorithme de gradient à pas fixe.\n", "- Le coefficient $\\alpha_k$ peut être également choisi de sorte à minimiser $f(x_k-\\alpha_k \\nabla f(x_{k}))$. On parlera alors d'algorithme de gradient à pas optimal." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "> <u>Exemple</u>:\n", "> On veut minimiser la fonction:\n", "> $$ f(k,a)=\\frac{1}{N}\\sum_{i=1}^N \\left( k\\frac{S_i}{a+S_i}-\\mu_i \\right)^2 $$\n", "> Le gradient de $f$ est donné par:\n", "> $$ \\nabla f(k,a)= \\left[ \\begin{array}{c} \\partial_{k}f(k,a) \\\\ \\partial_{a}f(k,a) \\end{array} \\right] = \\left[ \\begin{array}{c} \\frac{2}{N}\\sum_{i=1}^N \\frac{S_i}{a+S_i}\\left( k\\frac{S_i}{a+S_i}-\\mu_i \\right) \\\\ -\\frac{2}{N}\\sum_{i=1}^N k\\frac{S_i}{(a+S_i)^2}\\left( k\\frac{S_i}{a+S_i}-\\mu_i \\right) \\end{array} \\right]$$\n", "\n", "Application numérique: utilisation des algorithmes de gradient à pas constant et à pas optimal" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Critères d'arret pour les algorithmes\n", "1. Nombre d'itération maximal = 35\n", "2. Seuil de précision = 0.001\n" ] }, { "data": { "image/png": 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G95ZUFNdgiNChUikpyiv3qK6/nKRT71R2b8qi7xUZ/LzhCAOu7sZPPxxk8OgebFm2l8tG\ndmXjt3vI6J3Gns1HiEmIQHJLVJbWMnJCP7Z8t4fr7h7C0rkbiU6IoGOv1vy89iC3Trua797biNsl\ncffzY5n7xDekZTTj8ut78/FzS+k+KJ1LR3Rl9rSvaNe9FTc9MpI3pn6C2+XmyY8mk7n7BN/MWsPw\nW/ozdNylrPn0R7Yt3cNtT19Lj8EdOX3kLG8/8CkZl7Zl6swJABzYmsV7j35B72FduO/NW/0K67Zv\ndzP30S/pc1U3nlhwDwqlIujx/3nVPh4f8RoqjYp3t/yL/qP+O5H7VGqVx5pS35vtxdG9pwFIv6R1\nUPovaw8C0G9UHYHdumQ3giAwZFxff93c7HOMvMOjvv647FdqK01ce88VuN0Syz/cTIeeqX/2lEII\nIYQQ/lb8ZQRWluXTsix38346ybL8qjf9I1mWP/Key7IsPyDLchtZlrvIstwgVmyT7VNHImXqiGxd\n/0BAuuQvG2ATqKfQehaCNaKigp/4+f2p9csEqpUBlgF/f/WU0AZrjOrZDRrYA2gsvS4/iKg2Ma4g\nK0FT5DWwmd9hH/Dli4Ln2qemeiwCnralANLrswX4fLJ+64DfNuAhsz4VVimKiF6y7pZkXC4P+XS6\n3DidEk6XhMPhxmF343B41Fa7w4Xd7vSkO9y4XG4kt4QoCKhVCnQaj+qqVStRKkUEwWMlsNlcmM12\n7HYXSqWCsDANep0apULAYXdjNNqw213odWrCDVoUouAhrTUW1GoFkZF6BARqazxEMzY2HJ1OhbHW\nitloIz4+Ao1GSUWZEYDYeANWi4PKMiPJzWOQJZmisxWkNI9BEAUKcstpkRaHy+mitLCKVm0TKS+u\nRaEUiYkLJ+dIAe06N6Oq3IQoCkRGh3H88Fk69mhJ1v5cuvRpTdHZCpqlJWA12VEqFZ6FUdVm9GEa\nKoqq0YdrMVabUShE74ItK537tOFU5jlG3zmQ9Yt2MeLWfqz4+EeSWsaiN2g4nXWOu565lk9eWU6b\nzi2ISYxg75Ys7nh6DCvnb6O63Mgj79zGnMe+RhAFHnn3dmZP+xKHzcETH97N8o82c2T3Ce6feQv6\nCB1vTP2E5m0TuX/mLeT8lsu8Z5bQ+4rOjJ92NbWVJl66dS7hUXqe/eIBVGolecfO8eod79MyPYWn\nP7vPT1J/WXeQN+/9mC4DOvDPL+5Hpa6z+MuyzPfvb+DlW+fSunMLZm97jtROzRt56v8aNEVcfcja\nnYNGr6ZN15ZB6T+v3k+zNom07JDib2frkt10H9yR+GYxAKz59Ef0Bi1DbuiLLMusnL+F1I7N6HpZ\ne35Zf4jCM6XccP+VDfoMIYT/NcyePTu2sLDwvxHeM4T/D3BRys+B1oG6a68qG5DmWcsjNCC2PoU2\nMC04P0BZDfC2SnJ9stsI0W3K/9pI3n/kf62HRv9/DFRjG8lvYB8IuikBpwH2Ad91oH1ADlRVA9Ik\nqa4dvxdWwvPKP0ChVXh9rqLgGZTkJbU+K4AkyXVjFYK9sb6PUhF8FEXB3yd4VVe3h+zabE6sVicO\nuwtkz2IsvU6NXqdCqRSRZRm73YXZZMdud6JWKTGEa9BqVLjdEiaTHYvJhl6nJiJCiygImEx2TLVW\nDAYtBoMWh8NNRZkRtVpJVLQeh8NFWUkt4QYtERE6agO8r06nm5KCapKaRaNQiBTklpPSIgZRFCg4\nU0artglYzHaqyow0T4vjXG45MXEeK0LeiRLadWpG7vFiWrZNwG5zYTbaiE2M4OhvuaR3b8m+n47R\n5/KOHNp1kkuHZZC97wz9ruxM1r4zDBnTg/0/HuPKm/qybfkBrrypD2u+3En3Ae35ee1BImPDiYgM\no+BUKeP/cRVL5myk34huZP5ykpoKE5OevZb5zy+jY+80klrGsuU7j3Ug65dTZO4+wb2v3MTeTUfY\nt8VjI7AYbXz5+koGXduboeP6Muv+TzFWmT2+V7fEa3d9RFR8BE/Mn4wsw+uT5lFRVM2zXz5ATGIk\n1eW1PH/TbNQaFS8u+QdhEToADu04xqsT36dN15a8sPjhoGgCkiQx/+lvmPfk1/Qf1ZOZa54iOjE4\nWsFfDafDhdIbgaIxZO7OoeMlbVCq6v7vNtdYOPTTMfqN6umvl7X7BEW5ZVwxvh8AtZUmflqxl6E3\n9UNv0HL45xxOZxZw7b1XAPDdextIahXHZQEK7v+vaNasWZeioqI/hfy88cYb8XPnzo0FmDNnTmxu\nbq7qr+jn90Kv1/+tX+C0adNSnnvuuUSARx55JGXFihWGf6edXbt26ZYsWdLkP74ePXpYH3jggf/e\nL8t/A+Xl5YrXX3/dH80jNzdXNWLEiNbnq/N7EXifQ7gwLkoCi19JDVZePa+zg1VWT3qw7xXqiKwv\nTaIxb2ygylvXd/A4GlNiz+9/rRtIU4prwNjrK7EN2iCIqAaqqoFoYCs4n31Abto+EHhdN/dg+0D9\nNAi2DgjgtQ14+vL5XAOJqgCIooDKZxsQBf/iLd9/5rLP+yrV1fe04X06RBGlQoFaqUCrUaLVeFRX\nlUqBUuH57lxuCbvdhcXiwGZ1IggCeq2aML0GlUqB2y1htTgxGm3IyBgMWvR6NbIMRqMds9FGWLiH\ntMqyTE21BYfDRUxsOFqNktoaKxazg/j4CNQqn/oq+9XXilIjySnRCAIUna0gMTkKlVpB/pkyUlrG\ngiBwLq+ctPZJ1FZZsFscJKVEcepoIantk7BZHdRWmklqHk3W/jN06Z3K2RMltGqXhLnWhigK6MM0\nFOaWk5ASxdH9Z2jRNpGDP+eQlp7M/h+P0rx1AscO5BIVF47FaMNuc5DRO40Th/O5ceoVfL9gKwNH\n92D78n0olAoGX9eL9V/v4vqpV7Duy53YLHbueelG5j7xDakdmzFwdE8+e3UFfa7sQsdLWrPg+e/o\nNbQTw27uxxtTPyYmMZKHZt3Kig+3sHdzJve8chNpnZrz1n2fUnauimcW3ktETDhfvLKcA9uyeGDW\nbXS8pA0Ou5OXb51LZXE1Lyz+B4kt4wA4eSiPF8fPJjktgVe+n4beoPM/906Hi5l3z+P7uRu4dupw\nnl304F8WKut8sJpsaMO1jeaZayycPpxPp37tg9J3rz2I2+UOsjlsXLQTXbiGy0b3AjyLuZx2F1d7\nF2+tnLeZiJhwho7rS9aekxzbd5rr7xvWwEoRQtNwOp088cQTZQ8++GAFwFdffRV39uxZ1YXqXWxw\nOp3/Vr1333238LrrrjP+O3X37dunX7NmTZMEdtCgQZbx48dX/bd/IPwRVFRUKD755JME33Vqaqpz\n/fr1p//OMf1fxf/sQ3I+BCqwntfTYlAeeENXNbaAq1482MA4sJ70QItBPTX2Av7X870lrJ/VUAEW\n6imzvkKN2AcaazCocaFBGV9sWV/6H7EPyHKwfcB37bcXBOQLBFgM3AFjFUBBHcGVvGGqfP37VFUf\ngfaT0QC11xduQvAdfW17FeI664YnXwos75ur7FVvBQGNWkQURJBl3E63x5rgcOPCM3C1UkG4XgOS\njN3mwGF34bC5UClFDOEaJLeMxWzHWGtFpVAQGaHH6fAqt1YjkVF6tDo1NVVmykqcxMaG47QrMNZY\n0epUJKVEUVpUTVFBFSnNo6korvHbBkqLajh7qpTUtgmcPVVKcX4lqe0TyTvu8b5GxoRxIrOADl1b\ncPRAHh26tqC6wkT+qVJS2ydxcFcOPQe0Y/+OHC67qjM/rzvC4FHd2b76AEOv68nW5fvpOTCdDd/s\n5tpJg1n56XZufmAYS+Zu4tq7B7Pyk+10H9CeX7dkotao6NQ7jY/+tZQpL1zP56+vIiUtnjadmrF0\n7kbufGYMPyzcTnW5kec+n8rsaV+h0al56M1bePnOj1BpVEybfQfznv2OwjNlzFwxjaIzZSx8+Xv6\nj+rBqElD+P6DTfyy7iBTZ4yn4yVt2LlyH0veXsPIOwYxYuIgZFlmzsOfkbX7BM98dh8densEj4IT\nxfxz7CzCo8J4dfljRMbWCUM2s52Xb3uPfZuOMOmlm7jp0aubVEAbg9stUZZfgc1iR6VRkZwW/28v\n9jJWmTFEhzWal7n7BLIs03VAh6D0HSv2Et88xu+LtZnt/LR8L4PGXoIuXIskSaxZuJ2Ol7ShdecW\nFOWWsXvtIW5+dCQanZqlczdiiA7jygmX4XK6/q1xnxeTJrUgM1P/p7bZubOFTz/NP1+RYcOGtSkq\nKlLb7XZx6tSpJY8//niDnR6nT5+e/N1338XGxsY6U1JSHD169LC89NJLJbt27dLdd999raxWq9iq\nVSv7119/nRsfH+/u06dPh86dO1t+/fXX8BtuuKHSaDQqwsPD3WlpaY7MzEz9xIkTW2u1Wmnfvn1H\nAd54442EDRs2RLpcLmHJkiWne/ToYZs2bVpKbm6uOi8vT1NUVKSeMWNG/u7du8O3bt0akZiY6Ny8\nefNJjUYj79ixQz9t2rQWFotFjI6Odi1atCi3VatWQWzy2LFj6vHjx7e2WCziiBEjqn3pP/zwg2HW\nrFmJ27ZtOwkwceLElr179zY//PDDQav0tm/frp8yZUqqKIoMHjy4duvWrZEnTpzImjNnTuyKFSui\nLRaL6Ha7hc2bN58YMWJE25qaGoXL5RKee+65wttuu60a4Mknn0xasmRJXOA9BLjhhhtSR40aVXPX\nXXdVNTWXPn36dOjVq5dp586dEUajUfHRRx/lDhkyxDxjxowUm80mpqenhz/22GNFU6ZMqfKN+fjx\n4+oJEyakWa1WEWD27Nlnhw8fbg6c1/Hjx9UjRoxo16VLF0tmZqa+ffv21u+++y7XYDBIjz/+ePL6\n9euj7Ha72Lt3b9OiRYvyRFHklVdeSVi4cGG8QqGQ27dvb/vhhx+CyKbFYhEmTpzY6vDhw3qFQsEb\nb7yRP3r0aOOcOXNiV65cGWU0GpUlJSWqG2+8sWLWrFlFjz32WPP8/HxNenp6xuDBg2unTZtWOmrU\nqHa++7tq1aooi8Ui5uXlaR944IFih8MhLlmyJFatVksbN248kZiY6J41a1bcwoUL451Op5Cammpf\nunTpGYPBIBHCH8JFqcA2FqPe54WtT1obW+zV0AsbEMFADia1jb76b/Q8mBA39L/WEdML2OFoSGL9\nqcFp9QlvvbymFNb6Y5Dr9fN77ANivWtoIvIA3rIy/ogDyD7fbOACLtm/eMvtU2LlOi+twqvAqgI/\nSs8CLbXvE7CYS+tbzOVNUylFlArP5gWSJOPyRiewWh3YbE5kGbQazyIurUaFADi8ZNRmdaBWqzAY\ntGg0SpxON7W1Nux2JxEROsLCNDidbqqrzEiSTExsGGq1gtpqCzarnbj4CFQqRZD31WZ1Ul5aS2JK\nFEqFSOHZSuISI9Dp1eSfKScxJQq1RkneqRJS2yZiszioKq2lRet48k+VkpjisRucO1NGWnoyxw+d\nJb17SyrLjOjDNWg0KorOVpLUIobDv5wivUdLdm86Qs+BHdix5hCXDuvEjyv20+eKDLYt30enS1qz\na8NhElvEUFlcg93moNegdA79fILxD1/J12+vI71XGlWltRTlljP5ubHMf24Zbbq0oGX7ZLZ8u4eb\nHrqS3348yvH9Z7j/9fFsWLSL4wdyefit2zi6/zQbFu1k3MNX0bZrK2ZMnk90QiSPzr6D7D0n+fSF\nZfQf1YNr772Cs8cLmXX/J6Rf0ob73rwVgG/fWcvmb3Zx+zPXMeh6TzS+iuJq/nn9LABeW/G43xMK\nYKo289SYNziwJZNH5t7FzdOu+V3ktfB0Cd+8uYppw1/h2vgp3NHpMe695BkmdZ3Ojc3v55Xb55Kf\nc95AKY2iqqSGqPiIRvMO7ziKSq0k/ZI2/jRzjYUDWzIZcG1vP2nesXIfNrOdK28dAMDB7Uc5d7KE\nUXcPAWDV/K2ICpFRk4aQf6KIX9YfYvSkIUhumUl/Xhitvx2LFi3KzcrKOnrw4MHsefPmJRYXFwfJ\ny9u3b9evXr06Ojs7O2vz5s0nDh8+7P/lcOedd6a99tprBTk5OdmdOnWyPvnkkym+PIfDIWRmZh59\n8cUXS3xpd911V1Xnzp0tX3zxxeljx45lh4eHywBxcXGu7Ozso5MmTSp7/fXX/a988/LyNLt27cpZ\ntmzZyalTp6YNHTq0NicnJ1ur1UrffvttpN1uFx5++OGWK1euPJWVlXX0jjvuKH/88ceb1Z/j/fff\n33Ly5MllOTk52cnJyX9YKp0+uAXEAAAgAElEQVQ8eXLaBx98kHfs2LFshUIR9D9OVlaWfuXKlaf2\n7t17XK/XS2vWrDmZnZ19dPv27TnPPPNMc0mS2LFjh3758uUxR44cyd60adOJQ4cONfj1daG5uFwu\n4ciRI0dnzpyZ/9JLL6VotVr56aefLhw9enTVsWPHsgPJK0BKSoprx44dOdnZ2UeXLFly+tFHH21Z\nv0+A3Nxc7YMPPlh6+vTpLIPBIL355pvxANOnTy/NzMw8euLEiSyr1SouXrw4EmDOnDlJmZmZ2Tk5\nOdmfffZZXv32Zs6cmSAIAjk5Odlff/316XvuuSfVYrEIAIcPHw5btWrVyaysrKxVq1bF/PTTT/pZ\ns2YVtGjRwn7s2LHsefPmFdRvLycnR7dmzZpTe/fuPTpjxoxmer1eOnr0aHbv3r3N8+bNiwW49dZb\nqzIzM48eP348u0OHDtY5c+bE/Z7vNYRgXJQKrEetFKlvDZD9eZ5jYHpguCyoI5hywDWB6uzviURQ\nr5/6loIg/2sQcbyQ/7URNCXhBpDTRu0DgUoldefnsw802mfArwKflzXoWq6bp09RlfCQVv8mAoJP\nBZa9vlk5eGxeUusfo1fZlWTZvxKsKfW1oUrra8PTuIjHUqBSem0I4A25JSG5PCG3XE63x96gENFq\nVChFAZdDwm53YrU6QAaNxhM+y+XwRDCoqbGgUSmJidZjszqwWhzYLQ4iInXIOpnaagsOWy0xMeE4\n7U6/+pqYEkV5cQ3FBVUkp8RQVV7LubwKmrWKparcxNlTpbRqm0DR2QrOniqldYdkTh8tRKtTk5AS\nRU5mARk9WpG9P5fI6DCi4w0c+y2XjF6pZO87Q9+hGezZmk3foRn8uiWb8Aitd3MGGVEhIrslZElG\nH67FVGulfbcWLF/wIxOnX80Xb6zhxqlDWfrRFjr0SCXvWCFmo5Wx91zOzPs/46oJ/dm19hC1VSae\nXnA3M+/9hNT0FPqN7M5jV7/BwDE9SU6N4837P+HyG/qQ0bcN9w16iXbdWnH7k2N4+6HPKMkr540f\npiPLMjPunk9Cixgem3sXllorL054D41Owz8/vw+1RsXuNQdY+MJSBt/QlwlPemJ2m2ss/HPsLGrK\njbyx5kmat0vyP6rVpbU8fe2bnD12jme+eICB113C+SDLMvs2Hmbp7HUc3J4NQNvuqYy+5wpapqcQ\nFqHHarRybN9pflz6C7/8cICpb97GqMlDz9tuICqKqkjr3KLRvEM7jpHep02QtWH32oM4Ha6gsW9c\ntJOU1gl06tcOgB8++ZHIOAMDr+uNudbC+q92MGhsb+JSonn7H5+j1qoYM2Uoaz7bTmlB5e8e6+/G\nBZTSvwozZ85MXLNmTRRAcXGxKisrS5uUlORX6rZv3x4+cuTIar1eL+v1enn48OHV4Hn1azQaFddc\nc40JYMqUKRXjxo3zexdvueWW332TJkyYUAXQp08fy6pVq6J96cOGDavRaDRynz59rG63W7jxxhtr\nATp16mQ9c+aM+vDhw5oTJ07ohg4d2h48/uz4+PgGBPXAgQPh69atOwVw7733Vrz88su/2xdaXl6u\nMJvN4rBhw8wAd9xxR+WmTZv8QYQHDhxYm5iY6Pb2LzzyyCPNf/nll3BRFCktLVUXFBQot23bFn71\n1VdX+1TBK6+8srp+Pxeay7hx46oA+vfvb54+ffoFfTsOh0O4++67W2VnZ+tEUSQvL0/TWLmkpCTH\nlVdeaQa4/fbbK+bMmZMAlKxbt87w9ttvJ9lsNrG6ulqZkZFhBWo6dOhgHTt2bNqYMWOqb7311gbz\n2LVrV/hDDz1UCtCjRw9bSkqK48iRI1qAAQMG1CYlJbkBrrnmmqoff/wx/Oabb27QRiD69+9vjI6O\nlqKjo6Xw8HD3uHHjqgG6dOliOXz4sB5g//79uueee66Z0WhUmM1mxeDBg5sOEh1Ck7goCWwAd/KT\nWD+BIkBlrbeAy/PWuuECrkASLNUjtRfewOD858GhshrLI3jg9cZWl34B0nuetgLtA74oAY31F9hW\noH3Ar7B6iatcLz9wM4OgTQuoZynwblIQSGjFOmMDsi8agZ981o0FwbdATPC3JyD4LQw+9dffvnc3\nL59dQZJBdkm4ZRCkum9DKYpo1EpEQUCWZJxOF26XhNXpRpBApRQJ03v+9tqsDuw2J3arE41aSWSk\nZ3GW1ezAYXMSFqYmOiYMU42VmmoLWq2KuHgDNZUWKsqMRERoiY03UFFmxOWoISEpkopSI4X5FaQ0\nj6ZWtHAur4LmrWJBlsk7WUrrdomcPVVGYV45aR2SOHOsiFbtEjFE6jiZWUB695Yc+y2PLn3SyN6f\ni7HKRHxyFFn7z9CxR0v2bsumz9CO7NmcyZAxPfhxpWeh1sbFexg5oR/rv/mF4Tf1Ye1Xu7hkaAZb\nlu4luVUcNRUmjNUWJj7Rl/emf8O4B4bz7XubiIwNp9eQjrw25WNu/sdVbPpmN1VlRv75yT3MmfYV\nYZF6Jr94A/8cN5vo+AimzriZ16d8jMPu5ImP7ubHZb+y7bs93P7UGDL6tOH5m+dQU27k7Q1PoY/Q\n8fJt71N0poyZq6cT3yyG05n5zJw8n/Y905j2wSQEQcBhc/LCLXMoyCnipe8epX3PNP/zV1FUxZPX\nzKS0oIKXlj5Kryu6NPGQe56VA1sy+fS5bzl5KI+4lGjueP5Ght86IEjN9eGqOwZz+z/H8vZ9HzP3\nkc+JaxbNpSMvvLZGkiRK8yvoO7J7g7zq8lpOHTrLbc9cF5Tusw907ONRZc+dKuHIzuPc+a/rEQSB\n0vwKfll3kBsfHoFao2L1x9uwmuxcf99wyouq2PrtL4ycOBB9uJbvP9xEj8Ed2fD9BYf6P48ffvjB\nsH37dsO+ffuOGQwGqU+fPh18r5z/U/yRV7harVYGUCqVssvl8v9J1Wg0MoBCoUCpVMo+9VwURVwu\nlyDLstC2bVvrwYMHj12oj8bi7apUKlmS6oZpt9t/vyfGC71e729g3rx5MRUVFcojR44c1Wg0crNm\nzbr83vt5obkE3CPcbvcFx/nqq68mJiQkOJctW3ZGkiR0Ol2vxsrVf5MiCAIWi0V47LHHWu3Zsye7\nbdu2zmnTpqXYbDYRYNu2bSfWrVtnWLlyZeRbb72VfPz48SyV6vdZmhvr60JQq9X+700URf998D0D\nAPfcc0/a0qVLT/br1886Z86c2O3bt/9bi+L+r+OisxDIAR/wb+wEfsIaqIgGENzANrxENri9wAez\nYfn6/YN/t9Og/poac5N59YhuA3tA/Uaayg9MO5/C2tiAmlBdG7UPUC+Mljdf9Nb1pflCa0HTmxn4\nvK5uKTjSgD/mqyigUgQs3sITtN0Xesu/uYHLE07L6XTj9l5Lbsnbnyd0lmc3LhU6jQq1SoHCu4jL\n6ZKw2Ty7cNntLpQKBWF6NWE6NQqFgNPpxmy2Y7E40HhtBGqVAofdSU2NBWSZ6Cg9ao0Ss8kTBzbc\noCXcoMVmdXiIa6QOg0GLscaKyWglMTkKWYaigipi4sIJC9dQlF+JIUqHIVJHQW45sQkGdHoVZ3JK\naNU2AbvVQWVpLc3T4sk7UUKzVM9GB2WFVTRvHU/mr2fo0qc1+afKaNkuEYvRhigIaHUaSs9VEJsY\nyYkjBSS1iOXo3lwSW8RwMuscUXHh1FSYQJZp3jqec6dLuWbiADZ9u4cxdw1i6fubSEmLR2fQcioz\nn7ufvY6PX/ieFu2S6NAjlU2Ld3Pj/cPZvzWLU0fyeeitCXz/4WbyTxQzbe6dbP12D79tP8q9L9+E\nKAq8/8TXdLmsPeMfu4alczawd3Mm9752M+26p7Lk7bXs+uEAd780ji6XdaC6vJYXxs8mPFLP8988\nhEanRpIk3rx3AUd2HmfaB3fTc2gn//NaWlDBY1e9RnlhFa9+/9h5yWvBiSKeHTuLZ659E2O1mWkf\nTuazrFlMeGJMo+TVh5ikKJ5d9BBturZk1r0LqK248FqWyuJqHDYnyWkJDfJ+25qFLMv0vqJuQx1j\nlZn9m48w8LpL/P9hbvhyB6IoMPzWywBY+9l2ZBmuvmswbpeblfO30rlfO9p1b8WKeVuQ3BLX3zec\njV//TFVpLeMfGXnBcV4MqK6uVkRGRroNBoP022+/aRt7tT148GDThg0bIi0Wi1BTUyNu3rw5CiA2\nNtYdERHhXr9+fTjAJ598EtuvXz/ThfoMDw9319TU/Cmr4Lp27WqrrKxUbt68OQw8BHTfvn0NVvf1\n7NnTtGDBghiABQsWxPrS27RpYz958qTOarUK5eXlip07dzbwpcTFxbnDwsKkrVu3hgF8+eWXTT7Q\nNTU1iri4OKdGo5FXr15tKCwsVAMMHTrUtHbt2iiTySRUVVWJgQruH51LICIiItwmk6lR3lFTU6NI\nTk52KhQKPvjgg1i3291YMYqKitS+PhctWhTTv39/k8ViEQGSkpJcNTU14urVq6MB3G43p06dUo8e\nPdr4/vvvnzOZTIr63+Vll11m+uqrr2LAoyoXFRWpu3btagPYuXNnRElJicJkMglr166NGjx4sCky\nMtJtNpv/I+5ksVjEli1bOu12u7B48eKm/+CEcF5cdATWAwH/FrL1FmVB4wu45CYWcMkBt0BqyjZA\n/Rix9c8JHktjaq1MwLVvUE3MrRGyGkRVz9dGY3k+Yli/rfrnTbXru2E+4hqgoPoXcXmLikI9FTaA\nzArUpUtSXR1fGC2fHxYZbwgt78e785YkSUGqtF+Z9XsoBH+eJHsW4fhCZ3l243JiszpwuiQEBLRq\nJXqddyeuwPBZZjtWqxOVUoEhXItOp0KWZSwWOyajDaVSJMK7eYHV4qS6yoxapSAqWo8AVFeZcbvc\nxMYZUCg83ldB8Hhf7TYX5SU1xCdGotWqKC6oItzgIa7FBVWEhWuIiNKTf7qMhKRoNBolZ0+U0Do9\nmZpKMy6ni/ikSI4fyqdDtxZUlNSi06vRh2s4c6yI1h1T+O3nHHoObE/Wvlx6DWrPmaNFdL20DedO\nl9K1XxvyT5XQZ2gGJw6dZejY3uzZlMnVt1/Gmi9+5rKru7Fx8W4SvCG9inLLueXRkSx+dz39RnTj\n2P4zlBVWMfWVcXzw9BJadkjm0qu6sviddQwd15cwg44V87YwZvLlRMVF8OlL33PpiG5ccfOlzJg8\nH6VKwRMf3s3RPSf57JXlDBrbm2smDeG3H7P54pXvGXx9H65/4Ep/xIHq0lqe+/ohYpOjkWWZ+U8v\nZsfyvUx59WaG3tzP/4gW55UxfcQMasqNzFg1nS4D0mkMToeLr2asYGqff5K95wT3vj6Bj3+byVUT\nBwXFjT0ftHoNj82bQm2FiTWfbrtg+YITHktls7ZJDfL2bc4kIiactj1S/Wk7V+3D5XRz+bhLAXC7\n3Gz6+mcuubIrscnROGxO1n2xg74jupHUMo6ff/iN0vwKxt43DFONhbWf/cTAMb2IS4nm2znr6XhJ\n6wYLxC5W3HDDDTUul0to3bp1p+nTpzfr1q2buX6ZwYMHW0aMGFGTkZHRaejQoe06dOhgjYyMdAMs\nXLjwzJNPPtm8ffv2GYcPH9a9/vrrhRfqc+LEieUPPfRQq/T09AyTyfSHFc9AaLVaefHixaeeeuqp\n5h06dMjo1KlTxvbt2xvsYfzBBx+cnT9/fkL79u0zzp0755cL27Zt6xw9enRVenp6p2uvvbZ1p06d\nLI31M2/evNypU6e2Sk9PzzCbzaLBYGiUDU6ePLny0KFDYe3bt8/4/PPPY9PS0mwAAwYMsIwdO7ay\nc+fOnYYNG9aua9euDe7z751LIEaOHGnMycnRpaenZyxYsCA6MO+RRx4p/eabb2I7dOiQcezYMa1O\np2tUEU9NTbW99957Ca1bt+5UXV2tfPzxx8vi4uLct956a1nHjh07XX755e19z4XL5RImTJiQ1r59\n+4zOnTtnTJ48uTQuLi7oXjzxxBOlkiQJ7du3z7j55pvbzJs3L1en08kAXbt2NY8ZM6ZNp06dOo0e\nPbpq0KBBlqSkJHevXr1M7dq163Tvvff+WyG/nnrqqcI+ffp07N27d3q7du1s/04bIYBwoQDb/2to\n3SVMfuH7zrhkJW5ZwCkrcSPikhW4ZAE3ClySiAsFbknEKSuQEHDJIi5JREKBSxJwIyLJIk5JxC2L\nSAi4JRGX7NnJyy2BS/aUccsCbklA8pfzeHAlb7qM4D9HFjzkSfK88JYkD6FGFvwk16NGigHxUwX/\nEVnwK5m+c2QBwZfv80/40gNfswfU8aV7PKI+KdSrjgbI2H5F1pcuBZBadx2pFbzXfnsBIAfk+xTY\nQH+HiE9lDfSken9++Mcke72ZDceiwPPKRgjI822AQL02weN39Yytro4g+PoA2S0jeePK1vXjqeNb\nECYALqcLh90NXo+uUiGi1apAlrGaHX51N0yvRqVUYDbacDkllKKAIVLniURgtCEAMdHhOOxOTLVW\nNBolUTHhVJTW4HJIJCZHYjHaMNVaiU+OxGl3UV1hIrl5NGajjdpKC607JFFwuhSFQiSlZSynjxXR\nOj2ZkvxKZEmieVo8OYfz6XFZW37beYKMnqnk5hQRFROO5JawmqwkNo8h/1QJ7Tq3IOdQHh17pHIy\ns4AWbROpLKlBoRRJbBbN0f25jLlzIEvmbuL+V25k3nNLGTK2NxXFNeQczGPaO7fzyt3zuXbyEOxW\nJxu//pmZK6bx/vSvqa0089aax5k++i20ejXvrHuK6WPepKbCxIc/Pc93s9ez7P2NPP/VA2T0bcMD\ng15CrVXx3rZ/Yam18uCgF4iMj2D2lmfRhmmY/fBnrP/8J55eOJXBN3h2nFo6Zx0fP/stY++/kntm\njPerkz7yajHZeG3F43To1XhIxlOHz/Lm5HmcycpnyLhLmTrz1v8oHuz0Ea9RWVLDJ7/NPG+51fO3\nMHfaF3x1/J0gdVeSJG5p+wg9hmTw1KdT/elPjppJeWE1H+9/DUEQ2L32N1685T2e//oh+l3Tg82L\nd/HWfZ8yY8VjdB+UziPDZ2CsNrNgz8t8994GPntlOe9v+xcnDuXx7iNf8PLih+g5JAOlSrlfluXe\n//aEgUOHDuV269atwar//zXU1NSIkZGRktFoFPv169fho48+yhswYECjZO//R/jmD/DMM88kFRUV\nqRYuXPi3eJb/TBw/flztW/H/V/c1Z86c2H379oV98cUXZ//qvv5dHDp0KK5bt26pf/c4/i5cdAqs\nTzn1nwem+1TZxhZw1VdfA72u+NTYOtVUkpv2wp7X8+ofX724rwFtBflf60/Of2zEPnA+BNZpTLmt\nr+I21nbgeb1duGjMPoBvYZZPgfbk+dOkAPJK3WYGHoXUG7fVt8hKoG5jAq8SKwcqsQFWAVf9j9uz\nCMtjI/DuzOXw7Mhls7mw2904nW5kSfYu0KpTXpVKEQFwOt1YLA7MZjuyhGe3rTANKpWIyyVhMtqw\nWhzodGoMEVoUCgGzyUFttRWtzrN1rCTJVFeakdwSsXHhKESRinIjAl711e6itLia2HhPtIGSwmp0\nejVRMWGUFdWgUiuIjgunKL+SiEg94RFazhwvplXbRBw2J5VlRpqnxXM6u5DUdp7IBMYaCwnNosn8\n9TRd+7Yh+0AuXfu2oTC3nNQOydRWWYiON+C0u9CHa3E53UTEhGE22kjrmELx2QoGjerBgZ+Oc93k\nISz/+Ef6j+zKtu/3Ehahp22XFhzccZw7nhzNwtdWkNgilu4D01n/1U6uv384+zZnknu0kH+8cxuL\n3vyBypIapn8wia9nrSH3aCHT5tzByUN5LHt/I6MmDaHviK68dd+n1JQbeWbhVFQaJa9MfB+nw8W/\nvnoAXbiW1fO3sP7znxj/+Cg/ed2+bA8fP/stA8dewpTXbg4ir0+MfB2Lycbrq59olLxKksR3767l\n4UHPU11Wy4vfPcrTn93/H29mcNmY3hTkFFF0pvS85XKzC9BH6IhLCRKcOPFbLjXlRi65sqs/rbyw\nisM7jjPkxr7+Oa7//CeiEyPpc5Wn3KoFW2nZIZnug9LJ2nOS4wfOMPa+YbgcLlbM20zvKzqR2jGF\nb2evp123VmT0bcvkS5//j+Z6seG2225rlZ6entG1a9eOo0ePrvq/RF4Bvv3228j09PSMdu3addq1\na1f4q6+++sdDZ4QQwv84LsJFXD5Pqy9sVmNEtpEFXBBkF4DgOK+BdYP7ahwNwlA1Va6JOnXXjRPd\n8zbmK98ECW7U5+rLa4rcNkFk/Yu4fFYB77U/HmxAO4FpvvYVvmEGWAp8eaLgWcQl4VFI3T4lNlAd\nFoIXbwlei4EoBo87UKXFF7UAkCUZ2S17ybCE2w1O3xzwkGaNRoUoCkhuGafdidPhxml3IeCJOmAw\nqHE53disDswm7+YAOjUKnYjJZMVUa0OlEIiK1mO3O7GY7NgtTqJiwnDanBhrrag1ShKTI6ksM1Jy\nrpr4xAhUSpGy4hpi4w3ExBsoK/Is7IqJM1BwppxWbeKRXBJnjhfRpmMKJ7LOoQ9Te3bZOphHx56t\nyNp7hk69UqkqraWipIbE5tEc2XOKjj1bsXdbFn2uyODXzVkMHt2DH1fsZ9iNfdiybC9X3NCbTUv2\n0H9EVzYu+YU2nZtz/LdcFAqRdl1a8Pmag0x9ZRxfvbWGzn3bUFZYxblTpTz32b18+MwSmrdNpM/w\nzjx13TsMv6U/ToeLzYt3M+GxazDXWln+0WZG3z2Etl1bcd/AF0jt2IwpL49j2Xsb2LvpCPe/MYG2\nXVsy97EvOb7/DM9++QAt2iVzcHs2Hz31DZde3Z2Jz44FIHNXDm/d+zGd+rVj+rwp/rBSpfkVPHnN\nTMxGK6+vfoJ23VMbPPLVZbW8OWU++zYd5rIxvfjHe5OIjPtz1kt0HeixKRzdc7JRf6sPZ7LySevU\nvMECkH2bjiAIAr2H13l1ty/bgyzLfvtAWUElezce5qZp16BQKji27zQ5B3K5/40JCILA9x9swhAd\nxvDx/dn0zS6qy4zc9PAIti/fR+GZUp79bCqrP/6R4rz/edH0T8Xq1avP/N1j+DsxZcqUqvphqv5/\nQIcOHRz/DfUVwBtbt+KCBUP423DRKbBQj8f5yGq9bWQ9x4YLuHyRCALbahgXNlhxDVJ0620t6y/z\nO/yvdYNrgnz+Ef9rAGE7r8rayKYGTbPyOtIY+Kugzp4QULTedrKBaeAhrlAX+1WWfV5Ywe+H9W0Z\n649iINT5YZUBaizgX7zlckm4nB6l1emsW7zlcLq9yqvbQ0C96iwyKETRsxuX1qO6atQKVArPM+Dy\nLeIyO3DaXahUSsLCNOh1KkQR7DYXxlorLqcbQ7iWsHANsixjMtoxmayEh2kxRGhxuSWqKs0gQ0xs\nOIIoUOmN+xoTZ8Bhd1FaVEN0nAF9mIay4hpUKo/iWlFmRJYk4hIjKC2q9qfnnSwluUU0oiiSf6aM\nNunJFOZWEBNvQK1RkX/S443N2udZwHUut5zmafFYzQ5EUUCtVVFRVE1kbDhnjxcRmxRJXk4RUXHh\nVHitAxHReipLarlsRFcObD/GDVOv4Lv3N9F9YAeyfj2FzeLg2ilD+f6jLQwf34/927IpO1fFgzNv\n4b1pi4hJjGT8I1cxZ9qXtO3aklGThjDrwc9o2T6ZSc9dz9sPLsRca+WpBVM4dSSfhS8vZ+B1vRk9\n+XK2LNnNDx9v44aHRjBgTC+KzpTy6sQPaNE+mScW3OOZd04RL94yh8RWcbzwzcOotR47YNm5Sp64\negbGKjMzVk5vlLxm7znBgwOe49BPR3nw3Tv419cP/2nkFaBlegoKpYLco+eaLCNJEmcy82nduWFI\ny91rfyP9ktZBmy9sWbyL9j3T/GHB1n/xE7IMIyZ6dtpa/uFmwiJ0DBvfn3OnSti95iDX3DUYpVrB\nd3M3kN67NRl92/LN22tIzWhG94Ed+P7DzfQZ3vSCthBCCCGEixEXJYH1L+DyXtVxrcD4sAH5AQu4\n/OUDNjjwlKFBmUB7gI8gB1oEAsvJdRfB6mxjKmsjJDUQfyh8lr9SIGmmabU1qE4T51ITRLaRa5/g\niRy8qEsK9MIKdeqsL9pAIKFVikKdGivXbWbg29hADohQgOBRX4MsB96PKAooRAK2mvVYEJwuCYd3\nEZfV6sTh8Jh5NWoFYTq130rgliTsVidmkw273YVGoyY8XItKpcDpdGOstWKzOgn3bR0rQW2NFbvV\nSXR0GDq9GovZTnWlmcgoPeHhGky1Voy1FhKTI1GpFJSeq0anVxEVraeyzIgsycQnRVJVbsLtcpOQ\nHEXJuWr0ejWR0XpOHysmtZ3HQlBVbqRZahwnjhTQNiOF2ioLsiz7d+Vq36U5B3bk0HtwB7L2nuaS\nwR05mVlAr0EdOH200LNw63A+g0b14LefjjNiQj82Lv6FYeP6sPrzHbTt0oIzWQW4nG4GjunJjlUH\nGP/wVSx+dx2RseH0G9GVNZ/9xLVTLmfflkzyTxTzyDu3seD5pVhMNh5//y4+ePobaiqMTP9wEuu/\n2MnezZlMefkmYpMjmTFpHoktYnnk3YnkHT3HnEc+p8tl7Zn0wg1YTTZevGUOsgzPf/0QeoOO6rJa\n/nXjOyiUCl5e+iiGGM/6kKqSGp4a9QY15UZeW/l4UBgtz/cus/bTbUy/6jWUKiXvbPkXo6dc8Yd2\n4fo9UKqUxDePofRs0+pm4alSLEYbbbu3CkovK6jg5ME8+l1TF4brdGY+p4/kc8X4/oBn8daGL3fQ\nc2gnklrFUV5Yxc5V+7nytgHoDVqWvb8RpVrBtfdcwY/L91JytoKb/zGCXT/8Rv6JYiZMu4bVn2zH\nWGVmwvRr/tS5hxBCCCH83bgoCWwdkWxqM4NgguovH2At8H2kemT0/F7YgLIBhPQP+199nQW2U5+0\nBk62wbExVZcgVbYx/F77QGAeBPhgAwipb+w+H2xgjFgC0iAgjJZct2VscBgt2RuZQK4r4yWlKoUv\nnJaIQhRRIHgXpXlCasezNNUAACAASURBVEkBi8A8Y/fWUyrQqJRo1Ep0Gs9R5Q2fBT7l1YXFuxOX\nIECYTk2YXo1apUCSZKwWByajFVEQiDBo0enVuN0SxhobNosdQ4RHkXU4XFRVmhEEiPaSrMoyI4Ig\nEBNnwOlwU1pUQ2R0GGEGLRWlnqgEcYkRVFeasVsdJDWLpqrchMvpJj45knN5FURFhxEWruH0/2Pv\nvMOjqtY1/tvTZ9J7IUBoSUgIXXpv0gUp0qWKICrS1GNHsVBUUKQqiiDtoCBVpIMU6SWQEAIkQEJ6\nm0zJlH3/mJJJMgHPucd7jvfwPc88s2ettddee88OvPPu9/vepAzqxlYjL6sYQQJ+gZ4kXUojtmlN\nZ+UBbaEeQRDw8FJx92Ym4TUDuXwymXrxEZz+9SpxT9Ti+J5L1G8WyYlfLlMzOozrZ2/h5eeBRAKF\nOcV0Htic33ZfZNCUbmz8bC81o8NAgJQrd5n07iBWvv13wmsF0bJ7PFuX7qfn6HbkZhZycs8lxr4x\nkJuX0zj+83lGv9YfqVTCN+9tpVWvRvQd35FPp31LflYhr38zGQSB90ctReOl5rVvnkeQCCya8jVp\nien87dsphNcJwaAz8u6wJeRnFvLeppedj+iLcrW81m8+2fdzmfv3GcQ0L3OvAlsC3hfTv2Pxi2to\n1DGWL469Rz2XDP9/dfgGeVOYU3UpraRzKQBENSsPsk/uugBA675NnW0HNvyGVCal0xCb7vf3fZfJ\nSc+n97iOAOxacwSrxUr/SV0oyC5i/8aTdB3aGp9AT7Ys+YXI+uE80b0BP3y6i4i6ITTuWJ8fv9pP\nix7xRP+J1+BxPI7H8Tj+HfGXBLBQAaw69LBuWFkHaC2/j1CJJa3oylVRC1vx2O623a3R9RiV+yoc\nww1Yfah8oKqDuRlfFdvrOtaJU11ZVsrLBXD5LHH57JABOABtuTahYnkt0cnEOvpcZQPgcMmySwYs\nImaL1a5hFcvtL4o2kwJHm6N0lslkT+QymjHoTRhLbQYFAgJKuQy1WoHGXj4LwFRqQVdSik5nBLDJ\nCDS2WrBGo11GYLbYrWMVmC1Wigr0mEvN+Pt7oFLL0WmNFOaX4OvrUca+FuoIDvVBrpCSnVGIUiHF\nL8CDgtwSjHoTIeG+FBXoKCnWE1bdn7wsm5wgKNSHtJQsQuzlrO6mZFEnNpx7KdmERPgDAtnp+VSr\nFcTl0yk0blOXpItpNHiiNhmpOUTGhFGQazM10GmNBIT6UlJkoEbdELLv59O8U30Sz6fSZ0w79m08\nzZMj2rBt5UFqRodRnK8lJ6OA4dN7smnxXtr1bULS+ds8SM1h6kfD+HLOBoIi/BjwXGeW/20j8a3r\n0bp3I756dQNxrerSd3wnPpqwEi9/D6YvfpafVx7k5O6LjH93EPUa1+SzaWvIuJPN39Y8T0CoLxsX\n7uT49rNMeH8oTbvEYbVaWTh5NUlnbzFn9XNEN7clZtnctxZyPyWT9zZNJ75t+dJQRXlaXu+/gF2r\nDzLklT68/+NMvPwqlQn9l4ZKo8SoN1bZf+3UTTReKmrWL19t58SuC1SPCqN6vTDAps8+tOU0T/SI\nd0oKdq4+RECYL616NcaoL2X3msO07NmIsMggdnx9mFKDiUEvdOf03sukJqYz5MWenNp7mTvX7jN8\nRh92rD6EtlDHyFl9WfXO3/+8i/A4Hse/KBYvXhyQnp7+F8zNeRz/jvjTAawgCFJBEC4IgrDTTd9Y\nQRCyBUG4aH9NfNR8ZWDTDesKdiArVAKZDoDr2matME/FpK1yWlhctkWoSvP6z+tfy86v8tgq9qnI\nuFZxLFedavl+99tVyQdcE7oc8oGKLK2jxBWU1X51SApcAa20nBYWp1zAYimTCzjYWpnEZmYgk0qQ\n298VMilymQS5TIpCJkUhl6KUS1EqbGyrUm5rU8gkSKU2xy6rXUNrNJrQ60rRG0xYRVuilqdGgUol\nRyLYzQu0RvT6UhQKOd5eKhRKGaZSC0UFekylFnx8NKg1CoxGG/sqk0rw9fMAEfLsjJx/kBcmk4Ws\nBwXOqgJ5OVosJhtALS7Soy3SEV7D32ZyUKgnvIY/OQ+KkMsk+AV6cisxg8h6oRj1pRTkaAmvGUDi\nhVTqN6lBdkYh3r4aFAoZ6bdzCI8M5NLJGzRoUZvT+6/SqnscJ3+5Svs+jTi+6yKd+jfhwNYztO/b\nmF82nKRByzqc3HsZv2AvpBIbIO4/sRO7vjtOn7Ht2bHmCEqVnC5DWvLz6sP0G9+R84eucT8lk5c/\nHcWy1zchWkVe/nwMn077DoDZX43nm3e3cu9mJrO+Gk/2/TxWv72FVr0aM3BKd3766leObz/LuHcG\nEd82mtN7L/H9vG10HtqKp6c9CcC3723l+PazTPxgKG372cx4DDojbw/5jJTLaby1fhpNOpcZGACk\n38rklS5zuX4qmdmrJjPxg2eQSv/83+dSmQSLuWoDp4STN4h5ok65tRTnl3D5WCKt+5bJBy4cTCDv\nQQHdhtuMCtJvZXHuwFV6je2IVCbl4JZTFOZqGTi1G4YSIz+vOkirXo2IqBfKhs92ExYZSMeBzdmw\naBfVagfTvGscPy0/QOtejci8m8OPyw/8eRfhPySqVasWn5GR8S8BP/Pnzw/68ssvA8BWTunOnTvO\nWqz/yuP80dBoNI+2fPsTY8aMGeFvv/12CMD06dPDt23b9k+JyU+cOKHetGlTleU/mjRpon/hhRf+\nqdqq/4mRlJSkWL58+f/KqGDu3LnBxcXFf1my8c+M/4uL8jJw/SH9m0RRbGx/rf7j0zosZCvrW23v\nZf2ufVY7wHUNsULJLKtrZYIKUoEq2dxykoDy+1aWG+AedFYMt2MEN2PKgKqjrSJgrWQA4Nh+hHyg\nIpCVCEAFqQC4YVux/wBwPNoX7GW0cGVhKQdWHSysRLAnb4m2cluuyVtl5bRspbEcL9fSWUaj2ZnQ\n5Ujkkklt4FajkqNS2dy4BAEsZisGvYkSbSmmUjMKhQxPTyUqlRxRBL2ulOIiPQLg7a1GrZJjKjVT\nmK9DtFjx8/dAqZShLTZQXKjH18/GvpYUGyguKCEoxBulUk7OgyJkUgkBgZ4UF+rQaQ32eq9G8nO0\nREQGUlygp6TYQHh1fzLu5uHprcbTR03KtfvUaxBBbmYhCoUMbz8NyVfuEd24BtcvpBLXvBaZ9/II\nq+5PqcGEKIoo1QryMorw9vcg/U4O3v4eZGfko1QrECQC+hIDMU0juX3tPk9N6MTutcfoOaotO74+\nQkCYD2E1Akg4ncK4Nwfw9XtbCYrwo9WTDflp+QF6j+3AvZsPuHgskefeH8qJXRe4eiqZqZ8M59aV\nu+xac4SnX+hOdNNafDR+Bb5B3sz4cizXz6Tw9dtbaNO3CYNf6sm95Ad8MnEFtRtW5+UlYxEEgb1r\nj7L5s930mdDZCWhNpWbeH/kFCSeTmbN6Mi17lrdkTTybwoAJI9kfu52my2sS3t2Pu4V3ydRmkqfP\no9hYjMFswGJ1U8v9t9/ggw/g5MnKfX8gzCYLUrl7k6bi/BLuXLtPgzblmeKTuy9gtVhp27fMKfOX\ndcd42XqWtoNagSAQWjeMF/Qn6TmmA6Iosm3Zfmo3qE7DttH8sv44xfklDHmxJxeOXufGhTsMebEn\nZ/ZfJeXKXYbN6M32VYcoKdIzfEZvvp+/kxrRYf/U+f03hslkYs6cOdnTpk3LBVi3bl1gWlraH/Me\n/QuFyWT6p/b7/PPP0wcMGPBoCzo3cfbsWc2uXbuqBLAdOnTQDRs2LP//+gfCnxXJycnKTZs2/a8A\n7IoVK0Kqci/7b48/9SYRBCEC6APMA2b8K+d2cB7lpQJVlNVy0bU697frZ8uNcQW7brSwrn3/rP61\n3D4V2VU3+5WfpMJ7xXb7diUdrLs23PTb11YO1LpIA0TKgKrE5bPDTlYQHeCUcrVfRUC02KeyXShn\nApcT+Nu1sM51iiC4aEIkAggSm0GBpGy1SJxjbfM6f0zYtbGiVcQi2iwFXeeUSQQUcplNsiCCyWi2\nlcqyOACvBA+1jZE16G02s0a9GaVCho+vBqPe5uplNJjw8FSiVisozC8hL6cYT08l/oGe5OVobWWy\nAr1QyGUU5JXg6akkJNyXzPQCzGYL1WoGcP9ODjkWkeq1gmymBRKB0Ag/7t6yVR64p892SghSEu7T\noHktrp2/g7ZQT2CoDwlnbhPbPJLzx5Jo1S2Ok/uu0Kl/Ew5vO0+PoS3Yt/l3+o5uy861xxkwoSPb\nVh+mz5h27PruGM061efotrP4BHrh6+/JncR0pi8ayYq3ttCkYwzpt7O5n5LFu99P5atXNxJc3Z9e\no9sxs88nPNGtAfUa12T6kx/Rrn9TGrePYUr796jbqAbPvjGAT19Yw4PUHObvmI3VauXDZ5cRHOHP\njKUT0GsNzB3xBXK5lHd+eAmVRsnFI9f4YvpamnaJY+qCkQiCgMVitZfBusIrS8fTaXDLcrfuqT0X\nmDX7bc4MPgACvJ/4Lu8nvlvFje64lwUEQUCKBIXBjMwK8p/eQvaTBLkooBAFzBKBUolIoElBgEWB\nTJAgR2p7CY6XjHyfAmQSGdNe24xckCGXyJBL5cglcrR5ejyb3uNGbhpfLPgeuVSBXKbg4sEkwpvo\nuXTxC64nqLCWgufJr1EGZXPcCnXzIExrpa8pEeGDNzg/dBqpienMWDoOq8XK1qW/EtuyLnGt6jLn\nqYUEhPrS9ZlWzOw9n7DIQJp3iWNCi7dp168pd5LSuZv8gDe+nsTKY+889Lr8VaJbt251MjIyFEaj\nUfL8889nzpo1q1IW3ezZs8O2bNkSEBAQYAoPDy9t0qSJbu7cuZknTpxQT5kypaZer5fUrFnT+MMP\nP9wJCgqytGjRIrpBgwa633//3XPQoEF5xcXFUk9PT0utWrVKr169qhkzZkxtlUplPXv27HWA+fPn\nB//yyy8+ZrNZ2LRp060mTZoYZsyYEX7nzh1FamqqMiMjQ/HRRx/dPXnypOfBgwe9Q0JCTPv377+p\nVCrFY8eOaWbMmFFdp9NJ/Pz8zOvXr79Ts2bNcmgyMTFRMWzYsNo6nU7Ss2fPAkf7zp07vRYtWhRy\n6NChmwBjxoyp0bx58xJ7ySdnHDlyRDNp0qRIiURCx44diw4ePOiTnJycsGTJkoBt27b56XQ6icVi\nEfbv35/cs2fPuoWFhVKz2Sy8/fbb6aNGjSoAePXVV0M3bdoU6HoNAQYNGhTZt2/fwnHjxuVXdS4t\nWrSIbtasmfb48ePexcXF0uXLl9/p1KlTyUcffRRuMBgkMTExnjNnzsxwLfWVlJSkGDFiRC29Xi8B\nWLx4cVr37t3LOYAlJSUpevbsWS8+Pl539epVTVRUlH7Lli13vLy8rLNmzQrbu3evr9FolDRv3ly7\nfv36VIlEwgcffBC8Zs2aIKlUKkZFRRl27tx5y3VOs9nM1KlTIw4dOuQjCIL47LPP5rzxxhtZ27dv\n93rttdeqWywWGjVqpFu7dm2qWq0Wq1WrFj906NDcit//rl27PGfOnFkDbInEJ06cSHzjjTeq3bp1\nSxUTExM7fPjwnGHDhhW4O8edO3d6zZ07N9zf39+UlJSkjo+P123btu32hx9+GJyVlSXv2LFjlJ+f\nn/n06dM3/vG/mP+/8Wf/yvkcmAM87HHDIEEQOgA3gFdEUXykW4iNCZW4kQkATlZWUqG9zCpWLDdP\nRdmAeztZHHNXOGaVa3QZ8Gizs39C//oQUGpLrhKcwK6Smreqk3CMtbOsri5crkyqE1faPzt0sg7w\n65rk5VqNwKGPBQHRYWLgAkAFwQZOBQEkomM+W7KW6JKsZXWUMXACVzswdRwD2zxSicRWsUAiICDY\nXLgsVkSz1VblwGgG0VZUTS6V4KFRIAgCRoMJs8mC2WRBAqhUcjQaKUa9iVKD7aVWyfHz06ArKaWk\n2IBUIsHPz8NWxaDYgNEgISjEm+ICHblZxfj4qAkK9iInswiz2WJjWe/lkZVRYAeu2eRmFlK9dhB3\nU7IIruZHYIg3KdfSiW4YQfKVexTnlxBa3Z+Ec7ayWZdPpdC4dV2unE6hVF+Kt5+GW9fvU61WEBeP\nJ1M3PoJT+xOIblyDE79cpnZcNS4cTSQo3I/cjAKsFiv1GlZn4+cJPPfeIL6Zt512/Zrw264LWK0i\nfcd2YN74lfQc2ZaLR69z/1YW8/7+Ml/OXo9cKWfqx8N5d9RSvP09mTZ/BB9PWk2p0cSrKydxaMtp\nDm/9nTFvDKB+izq8NfgzCnOL+ezXN9B4q/hg9FLu3XzAR9tnEVw9gHvJD/hg9FIi6oXyxndTkcqk\niKLIl6+s5cjW00z84Bl6Ptux3H3+y3dH+PzFNVie0iEIAiIiAgJxwXHU8K5BiakEnUmHzqRDb9Zj\nMBsoNhZTXFps005jxaR0nbGCFECETJWZ+GLQWCSYBCsmrJgQne/GahYsEjCTjUkQMQlgEsAiAAFA\nF9hKOriW0bc/DF5z/1JZ28iyzcV74KXT9r/FlSv5KTsK3yAvOg1qwZGfzpB1N5cpHw0j4fRNLv92\ng+feH8KFw9e5eTmNGUueZduKgxh0pQx7pRfvj1tB3YbVadvnX/8Eevz28dWvZl3V/CvnbBDcQPfN\nU9889P+A9evX3wkJCbFotVqhSZMmsaNGjcoPDQ110utHjhzR7Nixw+/atWsJRqNRaNy4cawDfI0d\nO7bWZ599ltanTx/t9OnTw1999dXwb76xHa+0tFS4evXqdbA9MgcYN25c/rJly4IXLlx4t0OHDs5v\nMTAw0Hzt2rXrH3/8cdDHH38csmnTplSA1NRU5YkTJ26cP39e1aVLl5jvvvsuZfny5fe6d+9eZ/Pm\nzT5Dhw4tfOmll2rs2rXrZnh4uHnVqlV+s2bNqrZly5Y7ruc4derUGhMnTsyeNm1a7kcffRT0j17H\niRMn1lq2bNmdbt26lUydOrWaa19CQoLm8uXLCSEhIRaTycSuXbtu+vv7WzMyMmQtW7aMGTFiRMFv\nv/2m+emnn/yvXLlyzWQy4XoNHWE0GoWHnYvZbBauXLlyfdOmTT5z584N79mz543XX389vSp3q/Dw\ncPOxY8duaDQa8cqVK8rhw4fXdnwfrnHnzh3VihUr7vTo0aNkyJAhkQsWLAiaO3du5uzZs7MWLlyY\nATBgwIBaGzdu9BkxYkThkiVLQlNTU6+o1WoxJyen0uOSRYsWBaWlpSmuXbuWIJfLyczMlOp0OmHy\n5Mm19u3bl9SwYUPjwIEDIxcsWBD09ttvZ1X1/S9atCh0yZIlqT169CgpLCyUaDQa67x58+67/uAo\nLi6WVHWO169fV1+8ePFWZGSkqVmzZjG//vqr55tvvpm1bNmykCNHjtwICwsz/6P3wf/3+NMArCAI\nfYEsURTPCYLQqYphO4ANoigaBUGYDHwHdHEz13PAcwA14zzKgVUrNuvXhyVwVawFWxGYVqwD6xhT\n1l82/mGaV3duXZXY1Ycg4H+4fJbLcR5WMkt00/aH5AOu2w6G1eX/eEGwMauuLKmDbXXOKZTNawO5\nYjmjAokg2JO+7OC0HEi1/2QQKbPBdR5bcH6r9iuAwxZZtIJFFLFarWXnYJ9LKgjI5FKkEgkCAhaT\n2SlBMJXaFq6QS/H0VCFarDatrL4UfQmoVXJ8fDQY9LY2g74UT08VarWcogIdeTlaJ/uan2tjX/0D\nPFEq5RTmlaBRywmt5kdmej5ZGQVUq+FPRloeD+7mUaN2MGk3M8nPLraB2FvZVK8ViLevhpsJ94mK\nr07ixVTqxoVTpFFyJ+kBteuHc+nkTZq1j+bs4Wu06dGAE79coW3PhpzYc5n4VnW4lXCfiE71SbqQ\nStteDdmx5hjPvNiDTUt+YfCUrmxbeZAnusZxYs8lVGo5DVvX46vXNzHxnYF8//EO/EJ8aNOnCe8M\n/5J+4zuSfPEOiedu8+ryCfy8+hCpiel8sOkl9m84ycUj13n5s9GIVitfzfmBxh1ieOaV3mxcuJPz\nBxN46fMx1G1Uk40Ld3Jix3me+3AYjTrUpzhPyztDP0cqlfDe5ul4+Ngw0dr3f2T3N4cYOqMPQ6b3\nLnf7bvlsF6vf3ESzrg3o8dZYem85SamlFIVUwcq+K2ldvXXFOx6A9w++zzvH3kF03FeOG8oK3QKb\nExRej4TsBC5nXrZrvaU8M+BNXm//utv5ng5/nq7D2/DCgjHl2kWLhWkd3kDjKeXN7ydjMuoxleo5\nvv0kGz/dxpT5wwit6YvJZOCb9zbyTMrPmKVglkBUbvl5zuy/yui/PYVcIWPz4r3UiA6jZc+GvD38\nC3wCveg1pj1z+i8iLDKQpp1jWfb6ZjoObM61Mylk3s3l+XlDeX3wYrfr/yvGJ598ErJr1y5fgAcP\nHsgTEhJUoaGhTqbuyJEjnr169SrQaDSiRqMRu3fvXgCQm5srLS4ulvbp00cLMGnSpNwhQ4Y4rduG\nDx+e90fXMGLEiHyAFi1a6H7++WenxVq3bt0KlUql2KJFC73FYhEGDx5cBBAXF6e/ffu24vLly8rk\n5GR1ly5dosBWJzgoKKjSs/zz58977tmzJwVg8uTJue+///4f1oXm5ORIS0pKJN26dSsBePbZZ/N+\n/fVXX0d/+/bti0JCQiz24wvTp0+POHXqlKdEIiErK0tx79492aFDhzx79+5d4OXlZQXo0aNHQcXj\nPOpchgwZkg/Qpk2bktmzZysete7S0lJhwoQJNa9du6aWSCSkpqYq3Y0LDQ0t7dGjRwnA6NGjc5cs\nWRIMZO7Zs8fr008/DTUYDJKCggJZbGysHiiMjo7WDxw4sFb//v0LRo4cWek8Dh486P38889ny+U2\nlUhISIjl5MmT6oiICGPDhg2NAGPHjs1dunRpMJAF7r//Vq1aaWfNmlV96NChecOHD8+vU6dOJXH8\nw84xPj6+pE6dOiaAuLg4XUpKyiOv2X97/JkMbFugvyAIvQEV4C0IwjpRFEc5Boii6PrYYzUw391E\noiiuBFYCRDbwrCQjdY6rIoHL8bjencVsefa1/BhrRalAucQtXNpdtl3b3UoLKiy8Imh12+7S7w7M\nuh1btu36WN7dvlXKB8pdnLLPEhzsdRmrKjrAbQW2VbSUB9ASQXBeYVG0uW+Vc96iLHnLefWtOB24\n7BfUfoHLxjiYXQkCgtTO5Io2lle0gmixYrXYksWsFotzYTKJBKVCjkxie1xdajTbXgYzUomARmPT\njBpLbIDVoCtFo1Hg66tBV2JEW6x3JnCVurCvAUHeaIv05GVr8fJSERziTfaDQsxmK2HV/cm8n096\nWi7VagSSnppLRloOkfVCuHPjAVKZhPCaAdxNyaZubDh3U0q5eyuL2vXDuXn1Po1a1eHSqRSCwnzw\n9FZz6/p9akaFcu5oIo1a1+XUviu07tGA33Zfol2fxhzefo6O/Zvw66bTNO9cn4Nbf6d6vRDSbqQj\nitCgZR3WfPgzk959mnULdhHdNBJtoZ47iem88fVzLH/dJh3oNLglc/ovoF3/pvgGeTvdtnyDvPn2\ngy9p07cJnYe05JUeH6HSKJi9YiKXjyWy7qPtdB3Wml5jO3L21yt89/6PdBrckoEv9MBsMvPBmKVk\n3c3l451zCK0ZCMD2Zb/yw/yf6flsB8a/N6TslhRFvn3v72xcsIMOg1owZ/XzyBUyDow5wOE7h+kU\n2akSeDWVmjm54xz7fzjOweunYZAA0sp/DAFF9fl20mrOZZ+j69quTkDcKbKTmz8cMJQYKSnUERDq\nV6mvKF9HyuUHjH5jID7BZSYGl387iKdfQ7oOt2l+ky/eIfncPtqUSJBYK/1/hyhIUKoV9B3fiTP7\nr3Ln2n1mLh1HyuW7nD2QwLi3BnLpWBLJl1J5ZfEYti79lVJDKYNf7MFbw7+kQet63EvJ5NJv//on\nj49iSv+M2Llzp9eRI0e8zp49m+jl5WVt0aJFtONx7P82HGDtj4RKpRIBZDKZaDabnf9wK5VKEUAq\nlSKTyUSHa5xEIsFsNguiKAp169bVX7x4MfFRx5BIJJVuUrlcLlpd7hOj0fgQzZn70Gg0zglWrFjh\nn5ubK7ty5cp1pVIpVqtWLf6PXs9HnYvLNcJisTxynfPmzQsJDg42bd269bbVakWtVjdzN65iPWdB\nENDpdMLMmTNrnj59+lrdunVNM2bMCDcYDBKAQ4cOJe/Zs8dr+/btPgsXLgxLSkpKcIDVfzbcff8f\nfvjhgwEDBhRu377dp3379jG7du1K/kfO0XHvgO3+cb2vHof7+NOEwaIovi6KYoQoipHAMOCgK3gF\nEATBNbOgPw9P9nLsZQOqdnbV6gCPuOI39wlcroxs2TqrMDlw9lcAvm60sJVBatX33R81NvhD8gHX\nvnKfy1jZf1g+4Nh2ZaccoLTCcl2lAg42y7XNqZd1tNuZVkcJLAdDW2ZmIDh1sRZnGS3Hy5a8ZXEt\np+WsXGArnWV2uHKVWjAaTBiMZkqNNikAIshlUtQqOWq1HIVCagOtZqvNIrbESGmFJC6r1UpJiZGS\nYoMtecpHjUwmQVdipKhQh1Ilx9tHjcViJT9Hi4CIf6AnFouVnMxCVCo5vv4eFBfpKSrQERrhh9Vi\n5cG9PEKr+SGTSrl3O4eIyECsVpH01BxqRYdSmFeCyWAmONyXmwn3qRUThq7YgF6rJzjcl6t2562U\nq/eJaVyDvMxCAoK9bVa4pWbUGiW5mUV4+mrIychH7aFCrzXaTBPC/ci+n0/nAc35/dcEBk2xOW/F\nt67HjYup6Ir1DJnajS1f/EKXwS24ejKZ9NvZvLhwJEvnrMfTV8P4t57m05e+pVqdEEbN6ccnk1bh\nE+jF9M/HsPqtLdxOuMesZRNAFPlk4kqqR4UybdFoHtzJ5uMJK4iMi2D6F+MA+Gr2ei4dTeTlJWOJ\na1UPgMN/P82yOetp07cpLy0e6/wPy2q1smz2OjYu2EHv8Z15bc1U5Arbb/DW1VvzevvXy4FXXbGe\nzYt2MjpqOvNGTLLTcAAAIABJREFUfcmty3cZ2XsYdb3qlmlNnH8bAre/yeKtpz+lecgTHBhzgPc7\nv8+BMQeqZHOz7tqkl8E1Air1nT94FVEUadatgbOtMLeY84eu0WHgE85z2vvdURQqOeZxEyrNIQK7\nlTF0faY1PgFebFm8l8BwPzoPbsEPi3bi6aOhz7iOrJu/g7DIQBq3j2HXt0fp+kxrzh5IID+riGde\nepLNX+yjWedYt+fwV4uCggKpj4+PxcvLy3rhwgXVpUuXKtVJ69ixo/aXX37x0el0QmFhoWT//v2+\nAAEBARZvb2/L3r17PQG+/vrrgNatW2sfdUxPT09LYWGh+0y9fzAaNmxoyMvLk+3fv98DbAD07Nmz\nqorjmjZtql21apU/wKpVq5w3WJ06dYw3b95U6/V6IScnR3r8+HHvivsGBgZaPDw8rAcPHvQA+P77\n76tMIiosLJQGBgaalEqluGPHDq/09HQFQJcuXbS7d+/21Wq1Qn5+vsSVwf1Hz8U1vL29LVUlJBUW\nFkrDwsJMUqmUr776KsBicZN0CWRkZCgcx1y/fr1/mzZttDqdTgIQGhpqLiwslOzYscMPbLkPKSkp\nin79+hUvXbr0vlarlVb8Lrt27Vq0YsWKQEdSW2ZmprRRo0aG+/fvK65evaoEWLt2bUD79u0fmriW\nkJCgbNGihX7evHkPGjZsWHL16lWVj4+PRavVOo/3R8/RNTw8PCyFhYWPk7jcxP/5RREEYa4gCP3t\nH18SBCFBEIRLwEvA2D8yh2sCF06w6j6Bq6LuFR4CWh+SxFVe/ypUwo2uIZYtrrz+1e1OD5MaPEQ+\nAFSyiXUDbN3qb90wrW4ht9WV3aRcrVfnZwcra9fNVl37FRy/NqRCVWYGDnArOl29JBIBmdT2cpTQ\nksskKGQSZ+kshaN8lrysnJZcZhsrsSNqi8VKaandjUtn07hKpRI0Gpt5gVwuwWoVMehL0RYbsZot\neHoo8fBQIkFAV2KkuFCPUmlL4pJIJWiL9Oi0RpvhgIeSEq2RwnwdAYFeqDUKCnJLMBnNBIf6UGo0\nkZ1RSEi4L1KplPupuQSH+6JQyLh7M4vqtYKwmG3gNrJeCNkZBbZj+XuQfOUu0Y2qk5GWh3+wDzK5\nlAdpuVSrFcjF35Jo3KYe548l0aJLLNfO3KZF11iSLqbSukc8187cptOApvx+IIGeI9qwb+NJOj/d\nnF3fHaV2XDVSkzIw6E10GtiMIz+dZci0HmxcvBcvXw86PNWcn1cdot/4jiScSiblyl1eXjSa7z/Z\nQV5mIXOWTWDth9u5m/yAWV+N5+rJZHZ+c5hB03rQtHMsH09cib7EwBtrpyJIBN4f9SUg8ta6aag8\nlGxfvp/d3xxm6Cu96TbCVj7qwqEEFkxaQVzrery2ZgpSmdT5/X3+wjdsX/YrT7/Yk5eWjK2yTJbZ\nZGbHiv2MazCLr9/cRK0G1Xn/p5l8l/gpjV+uQ7K+Mhu5vP9y5r7zNy4eSmDFnPVuAXHFSL+dBeA0\nWXCNs/uv4OXvQb0mtZxtR388g8VsofNQ25yGEiOHtpyiw8AnUKxeiWHsBCyOf1ukUhJb9ORLVWsG\nTulGwqmbXDlxg8HTepCWlMHJPZcYMLkrV367wc3LaQyf0YctX+7DarHy1KTObPlyHy26x3P11E2K\n80sY+7enqjyPv1IMGjSo0Gw2C7Vr146bPXt2tUaNGpVUHNOxY0ddz549C2NjY+O6dOlSLzo6Wu/j\n42MBWLNmze1XX301IioqKvby5cvqjz/+OP1RxxwzZkzOiy++WDMmJiZWq9X+r1gxlUolbty4MeW1\n116LiI6Ojo2Li4s9cuSIZ8VxX331VdrKlSuDo6KiYu/fv++kC+vWrWvq169ffkxMTNxTTz1VOy4u\nTldxX4AVK1bcef7552vGxMTElpSUSLy8vNwipYkTJ+ZdunTJIyoqKva7774LqFWrlgGgXbt2uoED\nB+Y1aNAgrlu3bvUaNmxY6Tr/0XNxjV69ehXfuHFDHRMTE7tq1apyjy6mT5+etWHDhoDo6OjYxMRE\nlVqtdsuIR0ZGGr744ovg2rVrxxUUFMhmzZqVHRgYaBk5cmR2/fr14zp37hzluC/MZrMwYsSIWlFR\nUbENGjSInThxYlZgYGC5a/HKK69kR0RElMbExMRFR0fHfv311/4ajUZcvnz5nSFDhtSJioqKlUgk\nzJo1K/th5zZ//vzgevXqxUVFRcXK5XJx8ODBhS1atNBLpVIxOjo69r333gv+o+foGs8++2xOz549\no1q2bBn1qLH/bSGIbhHOf27UbOAlvvr3pliQYBElmEQpVqSYrBLMSLFYHW0CZlGC2Sqx9wtYkGIV\nBcxWCWZRglW0zWG2J32ZrQIWJFhFAYtVwCLat+39Fqtgq14gClhEnNtWa/ltEYld72lnfK2Ao80K\ntiQm2zuiDaTawKAdsFoF+2N1x2f7WIfFqwOAikJZspWD0XTOa9su14fLtn1eZ5tL0pZTb1rhM1Se\nz6GLdbRLXI7hkB0I2NodYLXcHK79CPb5RHtimO3xv+BqpFBF8la5Nvv+gp3RlUps0gIJYLFasZht\nyVxYbPMJgMxeS1YASu1JXGCrV6tWKZAIoCsxYjFbkUjA00OFgM1KFquIp4cChVJOYX4JVovVBnIR\nyM/VopBLCQjyIierCLPJQliEHwU5xei1pVSvFUh2RgGlBhOR9UJITc7Ew1OFb4AHd29mUS+uGmk3\nM1EopQSG+nI7MYMmbepy4Xgy8S1qceNSGmE1/CkusCUyaTyUFOXrCAzzJScjn6BQX4oKtMgVMry8\nNdy/lUXrnvHs++EkE98ZyKp3f2T49J7s33wKtYeKTgOasfaTHcxeOo4fFuzEYrEyY8kYXh34KV2G\ntKRF93g+nLCSUXP6UbdhDd4d8SWDXujBU891YWqH9wirFcyne19j/Sc/s3HhTmYtn0DXYW1YOHk1\nBzedZO6W6TzRoyHnDlzlrcGf0eLJhrz9w4tIJBJSLqcy68kPCa4eyKJ9f8PT10auWcwWFj2/mgMb\nfmPEa08x5s2nq7SFvfpbEl++8h23r9wlvl00Ez4YRv2WdZ39DZc25ErOFec9gwBz2s7hk26fALB8\n9jp++vIXvjzx/iMdvLYu2cPK1zewJW0p3gFleapWq5URdafTqGN9Xl8zxdk+o/s8Sor0rDj9AQD7\n1h/n0ylfs2DPa8S3jWb9/J/5ft42Vp/7kMBwP8bEv0psy7q8+8M03h62hMSzt1l76WMWvriG84eu\n8e35D3n96c/QaQ28v/FFprR/nx4j2+Llq2HzF/uYt/FF3hu3nNa9GtOyRzydBz5xThTF5g89qUfE\npUuX7jRq1Khq79z/kCgsLJT4+PhYi4uLJa1bt45evnx5art27dyCvf+P4Th/gL/97W+hGRkZ8jVr\n1vyfSz7+1ZGUlKTo27dvveTk5IR/91r+E+LSpUuBjRo1ivx3r+PfFX9RWtrx6N6WwOVwEgUXmaRT\nLlChWoHokBiUjS+nT3WMeWjZrLJx5ZjOcqxnBfa24n6u7OrDZAcu+7lLxKr8WXDb9w/JB1yXV4Vc\nwV09WAHHtcMpD7A7tzrZVVfG1r2ZgbWc01aZzKDMXtbGyNpNDWRlBgdSqR2sSmzfr8O4oNRoZ14N\nJkylFiSCgEopR6NRoFLKkEgEzCYLuhIjuhIjUqkETy8VarUCqyhSojVQojWiVivw9lYjCALFRXp0\nJaX4lWNfS/AL8ETjoaQwT4dBX0pImA8Ws4XM9HwCg71Rq+VkpOXi6+9ps329lU1wmC9KpYy0lCwi\no0IpLtChKzYSGuFP8tV71IkNp7hAj9lkwT/Ym+vnU4ltFsmV0yk0bhvFnaQHxDSuSU56ATXqhVKY\nqyW8ViCFOVpqx1YjMy2Pll3juH7uts1564eTPDmyDdtWHqJmdBhFeVpy0gsYMaMnGz7fQ9s+jUm+\neIf7t7KYtmAEX8xaj3+wN0Nf7MmSmeuIahJJjxFt+ezFb6ndIILRr/Xjk+dWY7VYeX31JC4dTWTj\nwp30GNWObsPbsmPVQQ5sPMHI1/rzRI+G3Et+wIdjv6JGTDhzVj2HRCIhMy2HNwcuQuOt4YOfZpYD\nrwsmreTAht949p3BPPvWILfgVVes58vp3zGz2wdo83W8teElFux7oxx4fXX/q2Xg1R6RvpFO8Aow\n+s2nUWoU7P76YKVjVIy0xHR8Ar3KgVeAlEup5GcV8kSPhs62B6k5XDt9k85DWznbdn9zmIh6oTRo\nE4XZZGbX14do1rUBEXVD+fWHExTlaRn84pPcSrjH7/uu8NTkrmTey+W3HRfoP7EzV07YWPERM/uw\n+fNfkEgl9BzVlm2rDtJxQDOO7jiPxWylZbcGfDJlzSPP5/9TjBo1qmZMTExsw4YN6/fr1y//vwm8\nAmzevNknJiYmtl69enEnTpzwnDdvXsa/e02P43H8q+MvCWArSgVsbVUlcDlAa8WyWkK58TbC0AWo\nUllGUM7EwJ3+FahYzaDcuisB1Yoglqof51fsf9h+Fce4bj5CPuDcdpEPuIJzx7zlrGNxIycQbG3W\nCiBU6gJYH2ZmYHuXILOXwIIyqYHTYtZssVvN2l6iRbSX4xKQ22UFaqUMlUqOUi5FKrV9RyaTBYPO\nhK7EiMlkQS6X4uGhRG2v+2o0mNAWGzCVmvHUKPHwVCKKItpiAyVaA54eKry8VZjNFvJztYCIX4Dt\nyVluVhFSqQS/AE/0ulJys4oIDvNBoZTz4F4e3r4aPL3VZKTl4e2jxstbzd2ULEIi/JFKJdy7lUXt\n+mHkZhYik0vwDfAk6VIa9ZvW5G5KFuE1AzCbLBQXlBAQ6sO1c7eoFx/B7weu0axTjK0KQa+GHN95\nkfb9GnPgx99p06sh+zaeIvaJ2pzadxn/EG8kEgk5GQX0G9+R3WuP03dcB37++ghKtYKuQ1uxfeUh\n+ozrwPlDCaTdyGD64mdZ+fZmTEYTs5eN54sZ36MvMfLqykls+eIXEk4lM23hKBQqBQueW0lkXAQv\nLBzFtdM3WfHaBlo82YgRr/anOL+Ed4ctRiaT8u7Gl9F4qSnK0/LGgIWUGk3M+2kmQdVskj2L2cLH\n45dxaPNJxs8dyog5DuVR+Ug4cYMpLd5g58oDDHjhSVZf/Jh2A54oB3RP3j3J/N9cckTt7Ovr7cpX\nF/Dw0dC6T1NO7DiH1U1SlWukXr9HzdjKyeG//3IZQRBo3jXe2XZ4yykAOg22AdhbV++SeCaF3uM6\nIQgCx7adJe9BIf0nd8VisfLTsl+JblaLuFZ12fz5HtSeSvpP6syGRbtRahQ89VwX1s3fQbU6IUQ1\njuTA5lP0GduBvet/w2yy0PWZ1uzbcILeY9rx48qDBIVXTjT7/xw7duy4nZiYeO327dsJH3300YN/\n93r+r2PSpEn5iYmJ15KTkxMOHz58Mzw8/P9FCabo6OjSx+zr43DEXxfAimXAxxXMuiZwVWJlxTKt\nLOXaXNlWqJjEVRHiVSQ+3S6Q8sDvYTv90+Wz3ABV1zGVAG0V/e6YZFedazkdrAvjWm68UEEra9/P\nmcAl2kGr/XtzAFpb8pZNEyuKdhbWBaRaLK5srFgmZbAvyjUXxyriTPIylVow2plXg96EyWxBQECl\nkKFRK1Cr5MhlNt2r0WCmRGug1Gi22cp6qVAqZZhNVrTFBgy6Ujw8VXh7qRARKSrUoS8x4efngUaj\nQGdnX339PfDwVFJcoEenNRAS5oMAPLhfgI+fBg8vFZn381GrFfj4aci4m4eXjxovHzWpyZlERAZi\nMVvJvJdHZFQI925lExzui1QmJSM1l8ioUK7+fpv4lrW5ezOTWtGhaAv0qD1USKQSSor0eHipyL6f\nj6e3moLsYhRKOTKpBJ3WQP2mkdxOuE+/8Z3Y8/1xeo5qy/ZVhwgK9yMo3Jdrv6cw/s2BfP3OVoKr\n+9Omd2N+XLaf3s924MGdbM4eSGDCO4O4cOgaZ/ZfZeLcIRTlatmwYCfdhrWm49NP8NH45RgNJt74\ndgq6Yj3zxiwlOMKf2SsnIYrwyYQVPLiTzZvrphFaM5BSQynvPrOYB6k5vLPhZSLtgNBitjB/wgqO\nbv2difOG8czMvpVucYvFyvcf/Mis7rZH8ov2v8mUhaNQeVTOI3lt/2uV2hqFNOK5Zs9Vam/aLZ6C\nrCLu3aiatLJardxOuEetuMoA9tTuC8Q8URvfYFt+jSiK7N/wGw3aRDmrLOz++hBypYzuI9oiiiI/\nffUr1eqG8ET3eE7sPE/6rSwGv/gk6beyOPrTGfqO70RBdhFHfjpD/wmduXryJrcT7jFyVl/WL9iJ\nQqWgw4Bm/LL+N3qNbsfu746i1CipERVO8qU0nn2tX5Xn8jgex+N4HH/F+EsC2DIJwcMTuJw1YCvY\nvzq1qS4zljMwcB7Dtb+MXf0j9V/Lb1dOtnJGRYDshll1C2bdglY38gF3IPdR8gHHELE8UC0HZF2S\ntQQ721rxkb/tMX5ZbVcHUC1L3sKevOUYI5YxtRIBubQsgUtmT+CSSiRIBQlSu7ZVKpUgk0iQS6Uo\nZFIUChlKpQyVUoZSIUUmkyCV2MCx2Wy1SQlKSjEYbBmnarUCjUaBQiHFahXR60rRFhsQreDtpULj\nocRiFW0JWzojXp5qPL1c2Vds7KsAednFCAgEBHrZkrYeFOIf7I1aoyArvQC12laVIDvDZgkbEORF\nRlou3n4eeHqruZ30gFrRYeiKjRTllxBWI4Abl+9SLz6CglwtEomAt6+GlKv3qNsggvPHkmjeKYbL\nJ5Jp2S2OxPOptOgaR9LFVNr1bsTlE8l0G9yCozsu0HNEG3Z+e5QnusZxZNsZ/IK98fRWcTf5ASNn\n9Wb9wt007xLL3RsZ3L+VxdSPh/HF7PUEV/enz9iOrHr37zTtVJ/4NlGsfufvPNE9nk5PN+eT51YR\nGhnE1Pkj+f7DbSScTOalz8cQXjuYj8YtR1ug46310/Dy8+Cbd7Zwdv8Vpi4cRXzbaKxWKwueW0XC\nyRvMWjGJhu1jABswXTR5FYf/forxc4dWqgELkPeggNd6fcS6eT/ReVgblv0+j7g27nMcTt49ydG0\no+XvfwGW9VnmdnxU01oA3LyY6rYfID0lE0OJkToNa5Zrz7qXy43zt2ndp6mzLensLe4lP3Amqum1\nBg5uPkmHgS3w8vfk+pkUbpy/zVPPd0MQBLYs2Ut47WDa9G3K5sV7kSlkPD21Bxs+3Y1CLWfA5K58\n/8nPVK8XSkS9UI79fJ6Bz3dl28pDyOQymnaM5eTeyzz9fBc2f7mP2g0i8PL7l/oNPI7H8Tgex789\n/qIAtjJYdUoCXMBs+bHldag42qgIZF0AKW5qwTr2rcBaii7b7kGsY432NneAsiJT6kY24FY+gIss\n4GHyAddxbsa6BbLWyp+d1rHgrDzguEoOMGt1JIjZta5l1QjKZAOuYFUqsb0LOJjUshJatm1LhTJa\ntpejvJbJZMFUaqa01IzRYMZgsNVztVpEJIJgkxOo5GhUcuRymxmt2WwzKtCVGBGtoFEr8PBQIpNJ\nKDWaKS7SU2ow4eWpwtNTidVqY1+NehO+dvZVX2JnX3098PBUoS0yUFyoIzjUB7lMSlZ6ARoPJT5+\nGnIyixAEgYBgW01YiUTAP8iL+3dy8A/yQqVWcDsxnbqx4eRlFSOT2SQENy6lEdu0JreupVO7fhjF\nBTqkMgG1h4r7t7IIrRHA1dMp1KofzpmDCUQ1qsGpfVdszlvHEgmO8Cfzrq3kcq361bh9LZ0hU7vx\n04qDdBrYnMM/nkEiEeg5sh3bVhyk99gOnD1wlYzb2bzy+Ri+nLMeuVzGiwtHseD5r1F7qpi+eAyL\np39PflYRr61+jmunb7Jx0S6eHN2eLkNb8+3crVw+lsiLn42hdnwN9m84wdYle+k3qQu9x3UC4Ju3\nt3D0x9+ZOO8Zp0Ws1Wrls6lfc2DjCca+O9gt83rleBIvtH6TpLO3mLV6MnO+fh6Nl7rSOEdM/nly\npT+GOa1nV1lhoFrdEAAy7FUG3MWNc7cBKiV6ndxxHoA2/cpKWO7/4TeUagXtBzwBwKEtp9AVG+g9\nvhMA2776FQ8fNd2Ht+XKiRvcOH+HQS/0IC+jgAObTvLkqHaUFOk5vPV3+o7tyMWjiaQlZTD61f6s\n+2QHnr4aGneM4ci2swx4rjNblu7DP8QHqUxG1r08ug9tyTujl1d5Lo/jcTyOx/FXjL8cgHWCUWct\n2AqsK1Cmb61sN2t1A1orOXM9REbgYHDLbzs+l5/j4eEOxP4T8gFnW/kxbuUDboCyO1Au2B/5O8c5\nluPyGRfQXq4erEvtVxvAtUsAXBhbd8lbjpqujjU42FqZm6QthcxeSsu1jJb9XS6X2hO7bH73oihi\ntlgxGs02Ry2dCatFRKGQotEoUKvlSKUSTPYkrpISIzKpFG8vFSq1HLPFSnGRHoPehJe3Gg9PJSaT\nhQIX9lUiEcjLKQZRJCDIC5PJQlZGoVM2kJtVBlzzs7WYTWZCwn3JeVCIVCbBP8iLtORMwqr7I5FK\nuHc7m9r1w23a2Or+CIJA5t18ImoHc/nUTRq3qUfShTTiW9Yh/XYOteOqkZdVRLVaQWgLdFSrHUR+\ndjGxzSK5m5xJ5wHNOHsggf4TOrJ99SFaPdmQw9vOovFSE9W4JheOJjLmtf6s+WAbQRF+tOzegJ9X\nH+Kp57qQeO4218/c4oVPhrNrzRFuXb3HK0ue5fTeS/y24zzj3n6agFAfFkxeRWRsNaYuGMmJnefZ\n8vkeeo/vRLcRbUk6d4vFL60hvl00kz8eDsCOVQfY8vlu+k7swuCXetnvH5t17K/rjjH6jYEMn11Z\n87pz1QFe7fURak81i4++S/eR7ar4Q7GF28QtwZdPerj1TAFAoVLg6auhIKuoyjHXTiej9lRV0sCe\n2HmeGtHhVI+ylbguNZo4vPV3Wvdpgoe3GlEU2bHqILXjqxPbsi5Zd3M5/vM5eo7piNpTxebP9uAT\n6EW34W3Y8sVeRBEGv/gkPyzaiVwl5+kp3Vi/YAe14iLwC/HmzP6rDJnWg81LfsHLz4Pq9cK4fvY2\ng6Z2Y+uy/TTvEsvhn84SEOrz0Ov0OB7H43gcf7X4ywFYVyBZljj16AQuV52rrb0qAwOh3JhKDGqF\ndVSKKkGpm37KA8h/Sj7gdp+y7UoM6x+RDzjGVwCyzoQuF5AKleUEjjlc53Ekb4mPSN6SSASkdjMD\nq2jTwzpYVrPZitlkZ1vNVkpLLWUvo5lSk80O1mK2IooiMomAQm5L4lKp5CjkUiR2xy2j0YyupBSD\n3oRMKsHTQ4lGo0AiETDoTRQX6bGYrHh5qfDwUGKxWCkq0GEymvH1LWNfC/K1eHnbZAUl9kf/QSFe\nKJUysjOLUMil+AV4UpCrpVRvIqSaL4V5JRj1pYRE+NnkBHIZvgGe3LqeTmS9EIz6Ugqyi6gWGUjS\nxTSiG9UgN7MQDy8lKrWce7ezqFYriIvHk2jQsg6nf71Kq+4NOLHnEu37NObI9vN0sDtvtejegP2b\nT1E7thrJl9KQyKTUjY8g6fwdRs7szfqFu4hvU4+c9Dzup2Ty/AfP8OWrG6hWO5hOA1uwbv4OOjzV\nHP8QH7Z+uY/eYzsQFhnEir9tokmn+jz1fFc+mbQKg87I376dQm56PgufX029JpE8/8kI8jILmTvi\nC/xCfHhj7VRkchln9l3iq5nf07JXY6YuHOX8obHy9Q3sWn2QoTP6MPL1AeVueYvZwpfTv+WLl76l\nadc4vjj+HrUaVOdh4TZxi8qJW+5C7alGX2Kosj/hZDIxT9QpV4u2KFfL5eOJtO5XJh84tfsi2oIS\np3zg2umb3L56l36TuiIIAtuW/wrAgOe7cevKXc4euMqAyV0pKdKzZ+0xuj7TCpPRbGNfx3Xi3OHr\n3L+VxcjZfVn74c/4BXsTGRfBuUPXGDKtBxs+20ONqDAepOag1xpo2KYeSRdS/2s1sC1atIg+evTo\nQ/UT33//ve+5c+ecwunp06eHb9u2zeth+/wnxJIlSwLu3Lnzv7OUehyP4y8cf0EA6wCl9m1ct10S\nuMTK/Y5arbjuW0EPW5GNte1XERC76F/F8gDX/bbLIly3XcZVPsmyd7fygUpjBbdjxErjKs/rXIUr\n6wplQNWxiztjAztj6wCnFasRCILgrEZQDtA6krewMW+O5C0nE+tqS+vQu0rK62EdxgYyqYBUagO/\njrVZLaId5Jox6E0Y9KVYzFakUgGNWu7UvQIYjbYkLoPBhFIhw8tLhUIhxWSyUFyop7TUhLePGg8P\nJaVGCwV5WhBt7KtUKiU/V4vVIhIY7IXVKpKdUYiHlwpvXw35uVpMJjPBYb4UF+rRFuqoViOAwrwS\nDLpSQiP8eHAvDw9vFV4+alKu3adugwjysouRyqT42CUE9ZvWJOliKvWbRZJ9P5/gan6YSy1YrVZU\nagV5mYV4+Xnw4G4unj5qivNLECQCvr4a8jKLaN2rERePJTFoSlf+vnQ/zTrHcuHIdUylZvqN68hP\nyw7Qc3Q7zhy4Qva9PF76dDSLZ6zFy8+DsW8OYMGUbwivE8zYNwfy8cSVqDyUzF4+kY0Ld9qkAp+O\nJrh6AO+PXopUKuGNtS8A8MHopWgLdLy74SV8A71JuZzKvDFfUTu+Bq+7GBWs+/AnfvxiLwOm9GD8\n3KHlKgiUFOp4a+Aidqw4wODpvXlv60w8fB6t6XSbuKX35rmucx65r1QmwWp2X4WgpFDH7at3aVBB\nc3ty13msFivt+peVWv1l7VECq/nRpHMcADtXHUTjrabzkFboivXsXXuM9gOaExThz+bFtmoD/SZ1\nYevSfVhMFoa90psNn+5CrpQxYFIX1i/YQb1GNVGo5Fw9dZPhr/Ri3fwdBEf4o1DKuX8ri/4TO7L7\n++N0f6YVO745Su24CB7czXvkOf8ZsXz5cv/w8PB4iUTSLDw8PH758uVVukL9u2Lbtm2+ly9fdmpQ\nPv/88/T6E1/HAAAgAElEQVQBAwY81HXpPyHWrVsXmJaW9hjAPo7/2vhrAlinRKAMfJYHshXAbMW+\nCu5cZbID12NQHuxWqlTgOs51/z+gfy07qNvH+o+UD1S1n5t5/iH5AOWBbLldxcrWsRVrvzrAbVnV\nARHspgKOagRlGlnRWZXAMbcTpLq8S+y0b1kJrTLbWEf5LEelAgGQSiTIZVKn5lWllNtkBVIBq1XE\nVGpBrzehK7EBWqVShqeHzTpWFEX0OhPaIj0g4OWtQqNRYDbZ2NfSUgu+fhob+6qzsa+eniq8vNXo\ntEbyc7QEBHqi9lCQl12MKIoEBnujLdRTXKAjrLo/umIj+TlFRNQKtDGxulJCq/tz/1Y2gaG2cltp\nyQ+o2yCCtJuZVKsRgMVsJT+riJDq/lw5lUJ8yzpcOFrmvPVEl1huXEqjVbcGJF1IpUP/ppw/ksiT\nw1uzf8tpug9rxY41R4lpFsn1M7cAaNm9Aad+uczwV3qzbv5O/EN9ad41jt3fHeXpqd05s/8Kd66n\nM2PJs6z9cLvNeWv5BH5YsJPbCfeYuXQcd5PS2TB/B91GtKXrsDYsnbmOOwn3eHX1ZEJrBrJsznqu\nnUpm5rIJ1I6vQW5GPm8P/gwPHw1z//4Kak8b6bV1yR7WfbiNHqPbM3n+iHLgNSsthxld/4e9946P\not6//5+zLZvee6WEhECooUvvAqIUkSICimAXxcJVuYr9YkUFVARBEOldSui9QyihQ0jvyaZsyZb5\n/THb0pBb/Nzr95fzeKw78573vHdm3ZCTs+d1Xu9zfn8qMxY8ydSPx9bbgcsZtQq3rJ/hBR3f+8Nz\nAalhhaLu17l07DqiKNKya1y18QPrTxISE2j3xRZkFnF272UGjO+OXC6jOE/DoY2n6D+uG2p3F3b+\ncghtmY4Rzw0g924BBzec4sFJPTGbzGxbcoCeIzpgNlvYt/YEQyf34mTyRfLSi3j8zYdY+uFmQqID\n8PB150ZKOo++OJCVX20nsUssZ/dfReWixMvfg4LsEjr2b8mvX+64r/v+T2LhwoV+M2bMiM7JyVGJ\nokhOTo5qxowZ0f8OiS0rK5P16tWraVxcXEJsbGwLWzenTZs2eTZv3jyhWbNmCaNHj47R6XS1lAE3\nN7e2tu0lS5b4jhw5MiY5Odl99+7dPm+//XZEfHx8wuXLl11GjhwZs2TJknuuGx4enjhjxoywhISE\n5s2aNUs4d+5cregLk8nEtGnTIlq2bNm8WbNmCXPnzg0AWLZsmU+XLl2aWSwW7t69q4yJiWmZnp6u\nuHbtmqp9+/ZxCQkJzRMSEponJyfb2+S+9dZbIc2aNUuIi4tLePbZZ8OXLFnie+nSJbeJEyc2tnUI\nO3TokFuHDh3iWrRo0fyBBx6IvXv3bgO5bcD/0/iLEtjqqmt9BVyObZtXtrr66hh3jFUr2sJGXGv7\nX6njmGO8xsXWQl3qrJP/9Y9QU2n9Q5W37u06ia7zFda49mqqaI19ZzuBwxog1EgjoFq8llwm2NvK\n2iO0rEVbdkXWYqkV7QUO8ixdp2g/32SyYKwyOSK0rF21BKRCLjdXFWq15Hs1my3odUYqK/QYjWZc\n1So8PFxQKK1FXBqpeYCXtytu7iqMBhOlxZVW9dUduVxOaXEFxiozAUGeyASBglwNLi5S2kB5qZaK\nch0hEb7otVUU55UREROA3poPG9k4kJLCCkxVJgJDpS5bUU2CMFaZKM7XEN4ogNQzabRIiiE3o4jA\nUB9Ei0h5SSV+QV5cv5BBdLMQzh68SlybKI7uTCGubTTHtqcQ0zyMS8du4BPoRZWuisoyLV0GtubM\nvlQefXEgKz7bRmybaLRlWtKv5TDtg1Es/NtvRMaGkNQngXXfJTP48e5UlGrZv+4k418fhqawnI0L\ndzN8Wl+atY3hk6d+IKxJMM/NHc+OZQdJXnGYsa8NI6l/Ir8v2c/2JQd49JUH6TGiI/pKA7NHf0mF\nRsucNS/jHyrlku5ceoAfZq2k+4iOvPzdk8hkjn+Sbl9M56We71GQWcwHm2YyaFIv7hfPbnu2xocX\nXr/kTZdHXryv83WVetRuLnUeu3joKgqlnPiOTexjmsJyzu27TM+RHe0EfNeKI4iiyIAJkk93+5L9\nmIxmhk3ti9lkZsOCZFp0iSWufWPWfL0DuULOI8/0Z/38ZAzaKsa+OpRfP9+GUq1k+NQ+/Pr5NhI6\nNaGyTMftSxmMm/kgy+duJSY+jILsUjRFFfR6JImj21MYMqkHWxYfoFP/RJJXnyS21b3tFn8G5syZ\nE67X66v9jtHr9bI5c+aE/6trrl+/3iskJMR47dq11Bs3blweMWJEmVarFaZNm9Zo1apVt65fv55q\nMpmYO3du4P2s179//8p+/fqVfvDBB5lXr15NbdGihcF27I/WDQgIMKWmpl6ZMmVKwSeffBJcc+2v\nvvoqwNvb23zp0qUrKSkpV5YuXRp49epV1cSJE0uDgoKMn3zySeCkSZOiZ82alR0VFWUKCwszHTp0\n6HpqauqVVatW3Z4xY0YUwOrVq71+//13nzNnzly9du1a6t///vfcyZMnl7Rs2VK7bNmy21evXk1V\nKpW8+OKLUZs2bbp1+fLlK0888UThzJkz/+X3uQEN+CvgL0pgHQpq3WTVWsBVU1G1nVsrI7a2jaB2\n+sC/6H91Hq/veF1zqpHael6jxrx7Ud8/TB+oobrWecz2ljgRyjrtBPYxsUYagUOFtRdv2Tp04bAW\n2CK05NaHzSogd7ISSIVgUoSWQi5DqZQitNROEVpKhQyZVQ42GS0Y9EZ0uioMeiMymSClDripUCrl\nmM0WtJVVVFbokcvkeHqpcXVVYjSaKSvVYqqy4OPrhqurUlJfiytxd3fB09tVIqcF5VLRloea0uJK\njAYTgSHeElnNLScsyh+T0URuZjGRjYPQ64wU5mqIahJEYa4GhUKGb4AHNy5mEpsYKaUQyOV4+blz\n44Lkg7104hZtusWSdjWHuLbRFOaU2gu3AkN9qSzTE94okKJcDa27xHLrUiaDxndl/8YzDJ3Uk3UL\ndhPXNoaM69lUaLSMmN6XdfN3039sV07tvkxRTinP/2McX7+ynJBofx6Z3o9vZ64goWMTBo7vyhfP\nLyEmIZzJs0fw2fRFVJRW8refnyH7dj7zZy6nbe8Exs8aTuqJm8yfuZz2fVvyxDsjsVgsfPrkQm5f\nSOdvS5+1R08d3nSKr55fTPt+iby+aFo1ZTXl4BVe7fcBMpnAF3vepl2flvf4dFfHG7vf4Hze+Wpj\nMSXwaYc3QfbH/+SZzRYqS7V4+rrXeTzl4BXiOzatRnCPbD6NxWyh50hHmkLyisO06dmckJhAjFUm\nti3eT1K/RCn6auNp8tOLGPXCIIrzNOz69Qj9xnbFxU3Flh/30X14exAkVfehJ3tzaPMZinJLmfDa\nMJZ/uoWYhHC0FXqy7xQw8rn+bPxhDz0fbs+OFUcJDPOlJE+DyWgmKNKPotxSJr81/L7fv/8UcnNz\nVf/M+P2gXbt2ukOHDnk988wz4Tt27PDw9/c3p6SkqCMiIgytWrUyAEyaNKno8OHD/7aH9Y/WHTdu\nXAlAx44dtRkZGbX+2tm9e7fX6tWr/ePj4xPatm3bvKSkRJGamqoGWLRoUfpXX30VqlKpxGnTphUD\nVFVVCePGjYuxqr1Nbt26pQZITk72mjBhQqGnp6cFIDg42FzztS5cuOBy48YN1z59+jSLj49PmDt3\nbmh2dnaDAtuA/6fxlyWwDvX0HgVcgMV2/A+8r9jGatDAWi1m6/G/VvPCVvPFOq9TT0FXHTaAauP3\nUFxr2QfuZUn4Z+wDNdiwICB5Yp2Ia822sfYxC3ZPga14S7IU4Cjesh6zEVKZrXDL1sTALGI2WTCb\nHEVcZqtdwPGwYDKaraqrVMhl0BulCK0qMyaziEwAlUKyE7iqJTsBgLHKhNYWoQW4u7ng4e6CXCZ1\n4irX6LCYLXh5u+LqpqLKqr4KSOqrQiFHU1JJlc5EQJAnCoVc6sKlEPAL8KCyQo+muILQSD8sZgu5\nGUWERfkDkHW3kKjGgegrDRRbldic9CK8/dxx9XDhztVsmrWK5O71XKKaBErEu1KPf7A3V8+mEdsq\nklP7UknqFc/xXRfpNrg1R7an0OuhduzfeIZeD7dn18qjtOvZnP0bThMaE4CmqJzKMi19H+3EvnWn\nGPXcAFZ9tQOfAE/a90lg54rDjHphIAc2niYvvYhX5k3iu9dXYDZbePW7yXz14jK05Xre/PFpNn+/\nh9O7LzH947EERfjzwePf4e3vyRuLplFaUMYHj39HYIQ/by6ejlwuY/HsNRzdepapH4+l06A2AJzf\nn8onkxYQ37Eps399EZWL43ft0c2neeuhuQSE+/HlvtnEtLh/9bBW4Zb1szfrmAwmT76vNcqLJPuH\nT6BXrWNlxRXcPJ9Gm57Nq40fWHeSiNgQGidGAXBuXyq5aQUMnNhDuqctZyjOLWXY030RRZF13+4k\nvGkwnQa3ZuOCZMxGM6NfHMjGhXvQVuh57NUhLP90CypXFQ8+0YPV83bQrldzctIKyb5TwNhXBrPy\ni+0kdo3lwtHrmM0izdrEcONCOoMndGPvulP0G9OZnb8dp/PARD59bul9v4f/KYSEhFT9M+P3g1at\nWhnOnj2bmpiYqHvnnXfCZ86cGXq/5zpbU+qyGPyzUKvVIoBCoRBNJlOt9URRFD7//PP0q1evpl69\nejU1Kyvr4ogRI8oA7ty5o5LJZBQWFirMZomPfvjhh8FBQUHGK1eupF68eDHVaDTe9+9nURSFpk2b\n6myvdf369dQjR47c+HfvsQEN+F/GX5TAOp6drQSOAq7a3bJEBKlZQT2ktSavrJ0Fa5vvIHwiTsQU\nx7PovF3LFlAXiRVqPNd3005K659tH7BUn2/bt3tenbNfwV7A5fDBWglpDZJrK94SnMiqpUbUlkxA\nSiOQOdRX5+dq0VkqhfSwx2hJc2Qya4SWyUKVNUJLr7dFaClwc1Ph5qpELhcwGc1UVhjQVhpQKqUi\nLrVaQVWVpL6aTRa8fWzqaxWlxZW4uqnw8nbFoDdSlF+Op6fkhS2zdeEK88FkNJObUUJwuA8KhZzM\ntEIpKksmI/N2AdGxIVSU6SjXaAmL8iftWi4RjQIxGy0UZpcQ3iiQy6fukNixCek38ohuFkKlRoeL\nWoVSqaCksBwvP3cyb+fhG+hJ5u18PLzdqNRosZgthDUKJPtOAYPGd2P/+lMMn9qbNd/sJCouFESR\ntNQsps4ZxY/vrCY6PoyEDo0lD+wz/bh+Lo2UQ9eY/tEYTiVf5PSeS0x9/1H0Wj1LP9hA94eTGDy5\nJ18+v5j8jCL+tvQZ3L3d+GDCt1SWaZn9q9S8YNfyQ6z56neGTe3LI88OAOD62du8O+YrwpuGMGft\nK6jdHeJV8vJDvD92Hk1aRfF58lsERQXU8wPhQNbNXH77xyb+NvQTJsx8stZnvXW+jKcbjYLgWt/y\n1omCrBIAAiNqWzXP7r2ExSLSvp+jTWxRTgkpB6/Qc2QnO0na/vMBvP096PaQlAe7aeFuQhsF0WFA\nIhcOX+PGuTRGPDeQyjIdWxfvp/vwJLz8Pdm4cDddh7RBEODgptM8/HQf9q07iaaogsdeeZAVc7fS\nolNT7l7NQVNUweAJD7B71QmGTOzOhh/30rRVJGcPXsXLzx2N9Q8uuUJOZbnuvu79P4nZs2dnqdXq\napVwarXaMnv27Kx/dc20tDSlp6en5dlnny1+5ZVXcs+fP+/WunVrfVZWlurSpUsuAMuWLfPv3r17\nrSIsf39/49mzZ9Vms5lNmzbZe+t6eHiYy8rKav0uvN9160P//v01CxYsCDQYDAJIKmlZWZnMaDQy\nZcqUmKVLl96OjY3Vv/fee8EAGo1GHhoaapTL5cyfP9/fRmwHDhxYtnz58oDy8nIZQF5entx23RqN\nRg7QqlUrfXFxsWL37t3uAAaDQTh9+nTtlnQNaMD/Q/jLEVi7eur8zL2sBHWT2TqzYOtoKfsf87/W\nuIh/Oz6rxrz/mH2gpgJrm2ezBYgOD6v9PqxzHD5YEdEa3WCbKxOoHaFVQ4WVy0CGTYm12D2t0sOM\nyWypHp9lkBoXSM9SIwOT0YLZIjUvUCnkuLpIyquLSo5cbo3Q0hsdEVoKOe7uLri5qhAE7BFaiODl\n5Yqrq4oqvQlNiUQGfPzcUSjllJVq0WuN+Ad6olLJKS4sx2Kx4B/khV4v2QOCw3xRKuVk3y0iINgL\nV1clGbcLCI30RSaTkXUnn8bxoZQWVmAymQkI8eb6hQyatZIsBC5qOR7erty6nEmTFhGcO3SN9j3j\nuXTyFkm9mnPrYibte8RzJzWbTv1acv38XXo/3J5Tey4z+PEH2LHiCL0eTmL7skOENwlCV66nMLuU\nR58fwNpvd9F7VEdO77lEcX4Zz3w8hm9m/kp0fBi9HunIz+9voMuDbYhtE81Pf19Lp4GtpFaxk7/H\nP8yHl76exMb5yRzZfIYp740ioVMs37+5kisnb/Hq/Cdp1CKSi4ev8vULS2jbuwXPzB2PIAhk3sjh\n7Uc+x8vPg482v1bta/qN3+3ks6k/0LpXAp9ufxMv//q/BTabLRxaf4KZ/d5nSstXWTJ7NcV5GoKj\nA6t9dgURFmyxcCiozT1+QqojN60AoE7yfHrXBTz93IlLcvhf9605jiiK9H5UaoxQnKfh2LZz9BvX\nDZWLkmtnbpN64ibDp/VFJpOx9mtH1uvmH/aiLdczZsaDbFy4m8oyHeNfH8byf2zBzUPNgLHdWPvt\nTroMbs2Vk3coyS9j5HNSxmv3Ye3YveYE7t6uuLiqKMwupfPAVlw6fpO+oztxbMcFej2SxJHfUxj5\ndJ/7vv//FKZPn1785Zdf3g0NDa0SBIHQ0NCqL7/88u706dP/5UiEM2fOuLZp06Z5fHx8wocffhg2\ne/bsHDc3N3HhwoVpo0ePbtKsWbMEmUzGzJkzC2qe+95772UNHz68abt27eKDg4ONtvHx48cXz5s3\nL6R58+YJly9ftv81db/r1ocZM2YUxsfH6xMTE5vHxsa2mDp1arTRaBRmzZoV2rlz5/KBAwdWLFiw\nIOOXX34JOHv2rPrll1/OX7lypX9cXFzC1atX1a6urhaAUaNGlQ0ePLjUdt/vv/9+CMDEiRMLX3jh\nhej4+PgEk8nEb7/9duvNN9+MiIuLS2jRokXCgQMHPP7V97kBDfgrQBD/OHH/fwqRLb3EF1d3xoIc\no0WGCTlmiwyjKMeMDJNFwCzKMCOXtpFjtgiYRDkma4yWSZRJc6xzLQjSHIsMiyjDIgqYrUVhZosg\nxW+BY1sEi23bYm2OYBFAFCTF0SIAMkesFIL1a3Ur6bVYCbCN/FkEiWSKgv1rekTbmJMiaj1u27ef\nY7MOOB8D+/rVjolO5wKCpcY852fbm26xzsMae+W0LyDNF83OdgcRmU15tVRfU1JfBQTRQXKrXZP9\ndaU1BARkorQvIDiuW5LG7deHbT2LoxkComi9P4nQKuQy5HIZMgHM1sxYURTBIpFoFxcFcpmMKl0V\nRqMZRBEXlQK1WkWVvgq9Vvrm09NLjSAIlJVowSLi4+uGKIqUlWhRKuX4BXpQnFeG0WAmJNyH8hIt\n2go9oVF+lJVoqdToiGkWTNadAmQyGWHR/ty5kk1U02BKCssxaA00igvj2vk0WndpyoVjN2naIpzc\n9CLcPNQSgS6pIDQqgMxbeTROCCftShZRsSHkZxTh7u2Km7VLV4+H2rH154NM/2A0C99azUNP9iL1\nxC0Kc0p4es5oPp2+iLGvDiE3rYCDm04zd8trfPPKckryNczb8xZvj/6a8pJK5h/6O9+9upxj287x\n+c43ES0wc9DHdBrUmndWPE/yisN88exiRr44iKkfjCE3rYAXeryLl78nX+97Bw8fd4pzS3m5zxz0\nlQa+3PMO4U1D7D/Xqz7byuJ3VtHtoSTeXPZsNUuBM0RR5PDGUyybs5b0K1kERwUwdFo/ej3alaBI\nf45lHKP30t5UmauQCTK+SYnk0V2VjKvqx6zlL9BrdN3dt5xhu5b1eT/g7uXo8GWxWBjb+EXa9Exg\n1lJHkdizXd9BrpDzzcF3pfO/2MaSd9fy45mPiIwN5dOnvufE9vP8cuUL8jOKeKbrbCa+/QiPPNOf\nia3eIKFTU17//kmeaD2LxG7NGP/6MJ7v8wHjZw6lymBk7be7mLtlJu+On0/LLrH4hXiT/Nsxnv/H\nOL56ZTnjX32QtQv20K5nPLcvZ+Hq4YIgl1NeUol3gCcl+WW079WcV7+ccEYUxaSa9/vPICUlJa11\n69aF/84aDWhAA/4zSElJCWjdunXMf/s6/lv4yymwNtStuoKVPlX3qzodt9yHH9au4Dq9XrUWs6LD\nVuBQZoXq2zYCRw0bQR1qay2val03ey/7gPM6/6R9oNZYzflOrWMBR/KAUN06YLtDm1fW4pRQ4Fy8\n5VBhRae0AqpFZ8llgl2JFS0i5ppqrNGCqcpczRtrMVtjtOQyqXmBi+R7VVmLuUDEaDSj11VVi9By\nt0ZoWSwiusoqKsp0yOQCXl6uqNVKDFb1FST1ValSUK7Roa0wSJFZrko0xZUYq0wEBnthNpnJyyrF\nN8ATV3cXcjNLcPdS4+XrRk56Md6+7rh7qkm7nkdk4yBMRhMF2SVENQ0i/WYeYdH+WMwihbmSheDi\niZu07hrLjQsZJCQ1Ii+zmIgmQVRqdPj4e1ClN+Ll646u0kB4o0AKc0pJ6p3AlTN3GDalJ78vO8TA\nsV3Zsng/wZH+eHi7cfNCOlNmj+DHv68hpnkY0XGh7Ft3krGvDOHo1nPcuZzJjG8msebrHaRfzWbm\n/Ckc3XKWw5tO88TbjxDWKJiPJs0nMMKPV+Y/yc2Uu3wzYxltejZnyrujqCzT8c6oL7BYLMxZ8zIe\nPu5Ulul46+HP0BSW8/76V+3kVRRFfvlgPYvfWUWvR7vw1orn6yWvty+mM7Pf+3ww9msQRWYte54l\nV77k0VeHERQp+Yu7RHZh3xP7+LDPhxzq+TPPrE/D++2ZtOgax9fP/URpQf3dtWzIuJ6NX4h3NfIK\ncPN8GqUFZXQY2No+lpaaya0L6fQZ0xWQlOHfl+wn8YE4ImNDKcop4eD6UwyY0B13L1fWfbMDtbsL\nw57szbYlBygvqWTsq0PY+P1eKjRaxr02lGUfb8LDx41eIzuwedFeeo/qyLHfU9BV6Bk4oRs7Vxxh\n0IQHWL9wD2GNAsnNKMJsthAaHUBeRhFtusdzJzWLDn1acOtSJm0eiCN5zck/vO8GNKABDfgr4U8n\nsIIgyAVBOCcIwtY6jrkIgrBKEISbgiCcEAQh5j5WdCKojgKuur6Fd3hi60gkuAdptY85EVXbfp2F\nWX9wvTW364zPEoXaY86ozz5ADYtATdyPfcD5eA37QDWyayOugrTtnP3q8MZKC9pbyeIUoeVkG5BZ\nI7QEHIkEtugss9liJ7jOBWY2K4JcAMHatMB+qdZmCCZb8wK9Eb3WaFVZQalU4OqqxNVViVJhjdDS\nG6ksN2AymnBzVeLu4YJSIcOgN1Gm0WKxiHbvq97qfXVxUeDt44bJaKYovxyVSoGPnzu6SimJICDY\nGxe1gvysUjw8XPD2daMguxSVixIffw+y0wrx8XPH3cOFO9dyaNQslAqNjspyAyERflw7l05cmyiK\ncjW4ubugdnMh40YOUbHBnDt0jTbdYjm15zJdBrbi5J7L9BjWlqPbU+gzsgN71p6k1yNJ7FhxlMQu\nTTm6/TzeAZ64eriQdSufx2YMYvW8nTwwrB3nD15BU1TO1DmPsmDWb8S2jqZl56as+3YXg5/oASJs\nWbSPR57tj3+IN9/PWkm7Pi0Y8cIA5k77kdL8Mt5a+iwWs4X3J3yLT6AXby6ZDoLAJ1MWkHUzj3eW\nv0B40xCMVSbmjP2au1eymP3ri8S1b2z/f7bk72tY/uEGBkzsweuLp9sbGzjDoKti0Vsrea7zW2Rc\ny+alb59k4ZlP6fVolzozYbtEdmFW91l0WXsCVCoUT07hxe+eRFumY9eyA/f4YZFwNzWLqPjaCUQn\nd6QgCAJJ/R3+1z2/HUUml9FrlJQ+cGb3RfLuFjL0yd4AbPlxLxazhYem9SM/o4h9a04w8PHuqFxV\nrPt2J216NieyWQgb5ifTeXBrzCYzJ5MvMuq5AWz8fi8mo5kHn+jB5p/203dMF3auOILa3YWQmADS\nr+cwaMID7F17ioFju/D7L4fp0Lcle9efpnlSI47ssMaqJV8kqVfzWvfTgAY0oAF/ZfxfKLAvAVfq\nOfYkUCKKYlPgS+DT+120Omm1qZ5OjQvE6vOqNT9wWgOsX/vX8LfWpdI6H6/mf3VWWu9DAa11IzW3\nnYhmfQVbdamodR0Taozdl0fXNl+ofk/2JIJ6WsliK97Ciew7kVab3cBi7bhlK/ASRGltuworl5RY\nhVxmb2YgFcc43neJOAvIrDFaKqXUvMDFGqOlUspRKGQISKpYlcGETiv5XgFcXVW4ubmgcorQ0lbo\nUSikCC0XtdLufUUU8fF1R+WioKJMR2W5Hj8/D9zcVJSVatFqDQSGeCPIBPKzS/D0dMXT25WCXA1y\nmQy/IE8pKksubWelFeIf5IXaVUXa9RxiW0ZQlKdB5aLAy1eKzYpvG82NixnEt4mmKLcMLz8PBEGg\nvFSLl6876ddzCAzz5caFdALDfUlLzcLTxw2Drooqg5G4NjHcuZzFiOl92fzTfvqN6cyOX47g5uFC\np4GJ7Fl9nEdfGszWxfvQVuh57h/j+OKFpYQ2CuTRlwbyxfNLaNQigvGvDeXjKd/j5unKa98/xfpv\ndnJq1wWmfvQYjVtF8cmTCynJ1fD2L8/hE+DFkr+v4eSOFJ75x3ja9ErAYrHw+bQfpUYE302xFz+J\nosiS2atZNXcLDz7ZmxkLnqyTjF49dYtnO/2NNZ9vZcDjPVh04TMefKrPHzcz0Grhl19g1CgICCAm\nIbO0T3oAACAASURBVILmnZpyYO3xe55mNlu4eyWzzuSDY1vPEt+xiT2dwGy2sPe3oyT1S8Q32BuA\nbT/twzfIi67D2qPXGtj20z66DG1LWOMg1n+3E4CRzw9kxy+HKMkvY+xMh/o64Y2HWPrRJrz9PejY\nP5Edyw8xeGIPdvxyBEGA9n0SOLHrIiOm9WXtt8k079CYk7sv4enrToVGi8lkwcvfg4rSSoIi/Kgo\n1eJtVembtIy49/t1/7BYLJY/+qu9AQ1owJ8M689h3e0C/3+CP5XACoIQAQwBFtUzZTiw1Lq9Fugr\nOGed1AGHvbF2AZfjW2sbUa3eqADn406WAKqtWTdRtd5R9WupR40VndXUOgmt4/gf/iaozz5QY84f\n2gfEGq9XHxl23nfKcEXEkURQw05gV1udirfAoZji1ErWUbwltX61WwtEHJ22TA411abGWqxr28mv\nre2sSYrSkgq7TBj0kvpqrDIjWkQUChlqtaS8qlRyZIKUOqDTVaGtMIAo4u7mgrs1QktvjdDCIuLt\n44qrWoleZ6S0pBKlUo63jxtms4XiwnLkchm+/h5U6U0U5mqsyqqawtwy5DIZAUFelBSWYzZaCArz\nodBOaL1Iv5lHSKQfgkwg804BjeJDSb9hsxBYKMrTEBYdwIVjN2jdNZZLJ27Rvmc8ty5l0rpbLBk3\n82jVtSkZN/Lo2CeBGynp9B3dkWM7LjBkYne2LNlP54GJ7F9/Em8/D8JiArl25g6T3nqYxXPWE5MQ\nTki0P8e2pzDpbw+zbfF+CrOKmTl/MvNfX4m2XMcbP0xl8bvruHsli9e+f4qsW3n8PGc93R/pwLCp\nffjlww2c3XuZZz+bQFz7xuxeeYQ1X/3O0Kf6MOzpvgD8/O5a9q0+xuR3R9N/Qnfpc2Mjr59t5cGn\n+vDCvEnVGhiARA5XfrqJGb3exaCt4qOtbzJj4VS8/O6zLmXVKtBoYNo0+1CHgW24eS6NitLKek/L\nupmLQVtFk9ZR1cZz7xZwM+Uu3Ya1t4+d3XuJwuwSBljvKy+9kJM7LzBwYg+UKgV7Vh6lvKSSEc8N\npKy4gu1LD9J7dCd8g71Z8/UOWnSOpXHLSLv6WqnRcu7AFca8PJg13+xEoZTTbUhb9q45wUNP9WL9\ngt0EhPlKn8fCcjoPSOTS8ZsMHNuFAxvP0GdkB/ZtOE23IW04tOUcnQckcnJPKh36tGDVd7vv7337\nY1wqKCjwbiCxDWjAfw8Wi0UoKCjwBi79t6/lvwnFn7z+V8DrQH3lxOFABoAoiiZBEDSAP1CtSEAQ\nhKeBpwHCW3hhI6gO1VXmRFbrjsSqu4FBHWqrlTA6r1Gf/xVqqLHVSKzzOtY5tYilUOO5HtRJWoX6\nj9VzXl2WAedtJ+eAXRV1JuF1Zb9WU1pxshNYSatQYxywElrRruYKTusJCHZCbC9Sw6mAywKCzHE/\njnkScbYlIFjMNm8sGKukgiyZIKBUyFC4KBBFMBpMGK3FXIIAahclbkoZBr0Rg86EQWfEVa3Ax9cd\nrdZAZbkeuUyGj687VXqp5axSKScw2IuyUq2VxHrgG+BBSUE5Hp5qgsN9ycsqwcvsSkikH7npRQSF\n+eAb6MmdK9nEtgzn1pUcNIXlhDcK4Oq5NBI7NeHi8Zs0bxdNcZ6G/OwSgiP8uHTyFs1aR3Ei+RKt\nu8VyeOt52vWMZ/+G0yR2ieXQlnNExoZw50omMrmcqGahHN9xgWc+fJTFc9bTcUAiF49dR1NUzox5\nT/DJ1B9J6NSEwAg/fnxnDY+9+iA3U9I5lXyRZz8dR+bNXH5fsp9RLw6iSesonn/gXUKiA3j5m8kc\n336e3z7bysCJ3Rn0RA+unrrFV88voVX3eJ6ZOx6ALT/sZtXnWxnyVB/GzBwqfbZEkWVz1jnI69dP\n1CKvpfkaPn7iO87vu0zP0Z158ZspePjU3VSgXixcCM2bQ/fu9qFmSZJ14c7FdBK71/2V+o2zdwCI\nbduo2viRTacB6DbcUQOVvPwwnn7udHpQSjjYtngfggAPTu6FxWJhw/xdxLaNoUWXWFZ8uhmDtopR\nLw1m98qjFGaX8PK8J9i4YDcVGi3jXx/Gglm/ERDqQ0LHJvzwzhrGvDyY9Qt24+7tSniTYNYt2MNT\n745k2adb6D68PduXHyE6LpQLR6/jE+hJdlohrh4uFOaU4u7lStadAvxDvLl2/i6xrSLZkfHPvYV1\nwWQyPZWbm7soNze3JX/hGooGNOAvDgtwyWQyPfXfvpD/Jv40AisIwlAgXxTFM4Ig9Pp31hJF8Qfg\nB4CIlt61vqmvbSWAmp247HNFoTZppQ7iW4uo1q+42he5r3upcUG1nusiutQ7VlN5vZd9oNq8usir\njWzaxp0KsUAipYIz0XVuLWsjs05faNiIqGidK9kFJKIpw2YBsK4rYFdY7QTZyQdiO0+wOMaqjTvd\nhwxQyGTIFZL9wGIRMZvMmK2ZsFXWe1Qp5KjdXRAtIgZdFXpdFXqtiIuLEi9vV4x6ozSmM+Lu7oKr\nr5LyEh0lhRW4u6vwC/SkpKiCglwNfgEeuLgoKC2swMNDTXCYD/lZJVjMZsKi/MlOK0REJCxa2g6L\n9sfs687N1GyaJUZw7Vw6kU3UePt72C0EV06nkdS7Oaf3XaFD7+ac2X8Fj5YRWMyiRPpEEZVKjkFv\nJDQmgIvHbjDmxYGs+noH4199kDXf7qLL4NYc2nwWhVLBA0Pb8vkLPzNu5hA2LNyN2Wxh6rujeHfc\ntzRtHUWP4Um83P8jOvRrSceBiTzf/T2atWvE4289zPvjvkVTVM6Xu99GU1DGZ9N+pGmbaJ777HGK\nc0t5b+w8/EN9ePuX51EoFRzffo75r/5C5wfb8tznj9vzUVd8vJFfP9nEoMm96iSvqcdv8MG4rykv\nrmDGgqkMnNSTP/hSpjbOn4eTJ+GrrxxfFwDhTaTCsZw7+fUS2NTjN3DzVBPVvLoH9siWMzROjCKs\nsZQlW15SydGtZxk8qScqFyUGXRU7fj5I5yFtCYr05/j282TeyOWNRU+jrzSwaeFuOg1uQ0TTYP7+\n2DfEtW9Es7YxfDTlB7oNbUdJfhmpJ2/xwtzxLP/HFjx83Gie1JjVX+/kibeG89vXO2iSGMnNC+kA\nhET6c2jzWUY/15813yUzdHIPti49TP9HO5G85iQ9hrXl4NbzdOibwKm9V+j9SBJs++fexrrQvn37\nfOChf3+lBjSgAQ349/Bn/gXdDXhIEIQ04DegjyAIy2vMyQIiAQRBUADeQNEfLVxXAVddbWMlf2vt\nTlyOObUtBvWlFDheux7/q/0/Qk1WXf1Fa6YRUGNeXcSzxjyhrv0/ILr3tB7geP+EuuaLToqsba6I\n1fOKXfm0zXG2GNQq3hIkhVWkjkYGomg/Xy6T2sgq5DKUCumhUji2lQoZKqUMpVIaV1ibFwiAxSwl\nDhgMJnQ6IwaDEVEEF2sDA7VaiQyBKoOJygoDBl0VLmolHp5qlCo5Bp2RslItIOLt64aLi4LKCj3l\npTq8vF3x9FKjrTCgKa6UkgjcXSguKMdiFgkI9qKiXEdpUQVhUf5SK9k8DZGNAykv0VJRpiPUSmj9\nAj1xdVORdjWH2MQIMqwpBCajGU1huV11bdGhMWf2X6FT/5acPXCVroNbce7gVXo+3J7jOy/Sf0wn\ndq8+Qe+RHdj+yyGaJzUm5cg1lCoFcW1juHTsBo+/OYzF76+nUYsIfAI8ObsvlSdnj2Dl59vQVRp4\n+esn+PzZxbh6qHnxq4l8Nv0nLBYLb/40jY3zkzm9+yLTPxlHRGwI7z/+HTKZjLd/eR4QmTPuG7Tl\nOt797SW8Azy5fvY2H038jqZtYpj187P2wqzVn2/ll/fX0//x7rz07eRa5HXbj3t4rf/7qNQqvjr4\nHoMm9/rnySvA99+DWg0TJ1YbtvlU75VEcPnYdeI7Nq3msS3KKSX12I1q6uu+1ccwGowMeFxSeA+s\nP0lZcQXDp/UDYN28HQRF+tP9kQ78vmQ/5SWVPPbqEPasOk5eeiHjXx/GhgW70ZbrGPvaUJZ+tJHQ\nmABCGwdyes9lxrw0mF8/20ZQhB8gkJ9RzIBxXdm/4TSDH3+ArT8fJKlPAnvXnaRpq0hO7kklulkI\nZw5cpVFCOKcPXKV5+xjO7L9K626xrP9h3z//PjagAQ1owP8w/jQCK4riLFEUI0RRjAEeA/aKojih\nxrTNwBPW7VHWOX+oZdapujoXaYl1c0iLky+22lr1dOj6Y/9rfdaBGnPqIbRCHWO1brQu/+sfnVff\nsbpU15rbtmtzmm/nEM4eWKu31RGFJTjSCWqQVtvX/JYaCqsM7NFZshrxWdUaGRgtGI1Su1ijybpt\n26+SmhuYzRYEpBgtF5UUoaW2to6VCQJmk7WBgbaKKr0JpVJqYKB2VSKKoNNaI7QEKULL5n3VlGiR\ny2V4+7ojCAKlxZWYjGb8Az0RBIHCvDKUKjm+/h5UaHRUaLSERvhhrDKRn1NKeHQAxioTeVklRDUJ\noqy4Er3WQEikH2nXcwmLDsBsspCfVUxk02CunEmjRYdG5NwtIiTSD5PRjL5Sj6ePG2lXsgmNDiD1\n1C0iY4O5ePQ6YY0CuZGSgZevO6YqE9pyPW17xHPp2E0efXEgq77cTtue8Vw/l0ZZcQVPzBrO4vc3\n0KZHPHK5nJO7LjJl9gj2rz3BrYsZzJj3BDuWHeTS0es89/njFOeVsvT99fQY0YHBk3vy7YxlpF3O\n5I1F0wiO8mfeS0u5euoWr//4NI1aRpKXXsjfR32JT4BXtS5bm+bv4qe3V9FrdGdmLHiqGnk1GU18\nN2Mp815YTJveLZh3eA5NWkXf48N9D5SXw/LlMGYM+PpWO2S7Fn2loe5TSypJu5RJy25x1cYPbzqF\nKIp0f7gDAKIosmPpAZq2iSa2TQyiKLLlhz1ExYfRqns8187c5uKRazz8TH8sFpH13+2idfd4mrWN\n4bcvttG0dRTN2sWw8fvddB/enuxbedy6mMH414ax7KNNBIT64BPoxY2UdEa9MIC13+0iqW8LDmw4\njW+QF+WlWim2LcyXolwNjVtGkp9ZTEzzcIrzy/AP9paK+arMeHi7kXY1h2Y1PL0NaEADGvBXx/+5\nh0kQhDmCINi+gvoJ8BcE4SbwCvDm/axhU0Rr8sJapFakljXANtdC/aS1uiJrW486bQX38r863bXT\nuO1CbTKlUH2sPmW22n7dpPZe9gFnsmzbFmvMrfYazkTciZDa952yX6UxRytYoZ7iLUEQHK1kbYVb\n1ugsybPqnA8rOFrP2vJh5Y6HwrZtjdMSkQp/TFVmDAajFKOlM2I2mZHLZbiplbi6qVCq5ICIQW+k\nssKA0WDC1VWJh4cahVKGQSdFaImiFKHlolairTRQVlKJh4caLy9X9NoqigvL8fJxxcNLTVlxJbrK\nKoJCfagymCjIKSE0UmpDmn23kIiYAMwmM7mZxUTHBlNSUI7FbCEg1JubFzOJbRmOpqgCRBGfAE+u\nn08nvl0MKcdu0u6BOG5dzqJlpybk3C0kNjGC/MwSmrWOIietkHa9mnPzQjoDHuvCoc1nGTq5Bxt/\n2EOrrrGkHLyKKEKPh5PYs/o4o18cxLr5ychkAhNeH8aPs1fTunscUfFhrPt2Fw9O6omHjxsr/7GF\nvo91oUO/RD6ZspDgqABemjeZ7T8fYPfKo4x74yGS+ieycX4yySsOM2HWw3R7KIlKjZZ3RnyOQW9k\nzrpX7IrnzmUHmf/qL3Qd1l6KynJSNytKK3nn4blsXrCLkS8/yJwNr91/oVZdWLkSKiqqFW/VRH1/\nIl86cg1RFGnVPb7a+IG1x4lJiCDaaiu4cS6NWxfSGfRETwCunLzJjXNpPPR0XwRBYN03O3DzcmXg\nxB7sWXmUopxSxrwyhL1rjpNzp4Dxrw9j7Te7MGirGDdzKMs+2UR0fBgqVxVXz9zhsVeH8Otn22jc\nIoL0G7noKg206R5P6qnbDBrfjX3rTtFnVEeSV5+g6+DW7NtwmqQ+CRz+/TxJfRI4vf8K7bpLBX+N\nEsIoK6kkrFHgv/6eNqABDWjA/yD+TwisKIr7RVEcat2eLYriZuu2XhTF0aIoNhVFsaMoirfvaz1n\nxdVewFWPugpIVgLb/JrXVj15QBqraRuoR1mttu/0onVt176J2ts1iWcdiuufYR+wK63O5wnV5zkX\ndNn3sdkJRAdpherNDQTnJgY2uwCIZmktmYBdfXVuZmCzJthUW7OV4Do/RLN0sTKZDKVcitGyNTBQ\nKqXWsRZRKuLS6arQaauwmKTuWu7uLri4KLBYRLTaKirKdchlcry8bQ0MjGhKtchkAj6+7ijkcjQl\nlRgMVfgHeaJUKSguKAeLiH+gp0Rq8zUEh/kgk8nIvltEcJgPSpWCjFsFRDQKwGw0k5dZTEyzEPKz\nS1GrVXj5utnjsjJu5hHeKACjwURFmRb/YG+unkujaWIkp/Zcpm33OI7uuECXgYkc2HiGLoNbs2fN\nSdp2j+PAxlNENAkm504BZpOZ9n1acHb/FcbNfJBlH28iJiEcT283Lh69zlPvjWbJnPUIgoxpHz3G\nF88tIaxJEONeG8o/pv5IcHQgz/xjPJ9NX4SmoJy/LX2G7Ft5LHz9V9r3bcn4Nx/i/P5UfvjbSroO\nbcf4WcMxGU18+Pi3ZN7IZfavLxKTIMU2HVp/kq+eWUT7fonM+uW5ajmveXcLmNH7PVIOXOGVhVN5\n+pPxfxyP9Uf4/ntITITOnWsdMhml3vJKl7qt/ykHUlGplcR1cLSJzc8o5PKxG/Qc7Vhv+5L9uLiq\n6GNtHbthfjIePm70G9uN7Nv5HN54miFTeqF2U7Hqi23EtW9Eq+5x/Dp3K01aRRHXvjFbFu2j58iO\nXD17h8ybeTz+xjCWfbSRqGah6CsM5N4tZPjTvdm+7BD9H+vM5kX7aZQQTsqR63j5uVOcp0GhkGEw\nmBAEAW2FAbWbiryMYgJCfbhy/i6Nm4dx4ehNEpIasX/z2X/vfW1AAxrQgP8x/CWrSOtWXcGmnlpw\n+F5rElo7ma0jrcBSw99qsxfY59Tjf3UM1OFvrXaxjuO1CqvqvMk/wT7gRELrJbdQ3S4gOvYdnlcH\nkbV12LJbAwSnAi6bD9Yi/Zkgs/pg5TJpWxSxq6+Oh6V6MZd0JXbSLLPeiIjVR2uyYDSaqLJGaOn1\nRjtZcVHKcXNVonZVolDIsJgt6HWS+mo2mXFzkxoYKBQyu/fVWX3VVRooLa7E1VWJt48bVQYTRfnl\nuLqp8PJxo6JcT1mpluBwHwRBIDejGP9gL1zdXci6U0hAkBdqNyXpN/KJbBIkKbS5GiKbBJF5K5/g\ncF/kchk5aQVENwvh8onbJHZqQubNfGLiQ6go1eLmrkKQydBX6lG7qdAUlaN2VWI0GLGYzARF+JGX\nUUzvkUmc2HWRR6b1Y83XO2nRqSl3r2VTWljO468PY9knm+jQryUVJZVcPn6TZz55jFWfb6Mkv4zX\nFj7JwjdXUpRTyps/TWPnsoOc3JnC1A/HEBIVwPuPf4tPkBevL3qa/IwiPpz4HRFNQ3jtx6cRBIHv\nXvmFM3su8eK8SbTplQDAqZ0pfDJpPvEdmzL7txerddi6lZLGy73epTinhI+2vsHASb3u8YG+T5w+\nDWfPSuprHd5ZbZkOADdPdZ2nnz+QSkLn2GrXuX/NCQB7owJdhZ79a4/T/ZEOuHu7UZBZxJHNZxg4\nsQdqdxfWf7sTuULOw8/0Z9/aE+TeLeSxmUPZv+4kOXcKmPDGMFZ9uR1jlYkxLw1ixdytxLdvhKa4\ngsybeYyZMZhVX++gfe8Eju24gEqtwtvfi/ysYjoPbEXqqdv0fDiJ03tT6TG8PWf2X6GLdbx112Zk\n3Mwjsmkw2nIdRpMZLz93bl3OIqlXfJ333IAGNKABf1X85Qisc8KAg2Q6HXeeW9MbW9daouAgls5j\nNS0A9SiwohMxdY5HqLN9rH1bcDw7H/8z7ANOlgHnedWE1hrXV60bVw0iKzoTWSRVFUs9xVvWF7AV\nbwlYu3KZRSxmxxybtcBuDbA3MZBJ44LNdmDbFlBYGxgolQ7lVaWSo1LK7Sqe2WSRCrlsDQxEcFUr\ncXNzQamSY7I2MKis0KOQy/G0to+tsqqvgoDUwECloEyjQ1uhxy/AA7WtfazBSGCIN2aTSH5WqVSU\n5e5CbkYxnl5SMwNb0wJXdxV3b+QSExeCtlxPRamW0Gh/blzMoEmLcDQllYgWC76Bnly/kEGz1lGc\nPXCNpN7NuXDsJp36teDKmTS6DGhF6snb9BwukZgB47qS/Nsx+o7uxNbFB2mSGEn6lWwM+ir6jO7I\n7t+OMfqFgaybn4zSRcmIZ/vzy8eb6Dq0LTKZwIENp3j8zYdIu5xpbxWLKLL472vpOqwdQ57qzWfT\nF1GcU8rby57DxVXFnLHzsFgsvLvqJdw8XVn/zQ5+X7yPMa8OtX+tfvnYdd4fO4+YFhF8sHEmancH\naUw5kMrMfu8jk8n4fO/fadOrBf8RLFwIbm4woabVXoKmqBwAL//aqX7FuaXcuZhB2z7Vr+XA2uPE\nJTW2pw/sW30MbbmeB6f0BmDLor0gigyb2pfSwjJ2LT9E7zFd8A32ZvUX24hpEUFSv5as+McWmiRG\n0qRVFL//fID+Y7tyZv8VCrNLGD9zKMs/3UJCxybcvphJpUbHAw+14/jOCzw0pSebf9pPxwGJ7F59\ngkYJ4Zw9cIXQmAAunbxNaHQAl07eIjoulHOHrxPfPoZzh6/TslMTMm7kERThh8ViIS+z5D/zHjeg\nAQ1owP8I/nIE1uFRdcqCdVZVrb5WSw3y51BfqWYZsHFOSy3SWsP/yj38r9S9jfN4faqsfUz4c+wD\ndaE+BbbmthORtacUVLMOSKSyWoSW9RxHcwOn4i2kY3KZNWXA7oW1NSWQ1FSzxdHEwGxtVmBvXGC2\nNTuQHkar59WgM1FlzXUVRRGFQu7UwECBTCZgMlrQaavQVhrAIuLuppIaGMhl6HVGyq3eVy9vN9S2\n9rFFlahUcnx83DCbLBQXVKBUKvD1d0enraIor4yAIE/UaiX5WSW4e7jg5etGflYJalcVPv7uZN4u\nsHfeuns9lyYJYZQUlIMo4hfkxbXzd2mR1Ij063lENglGV2nAZDJJhVvXsglvHEjKkes0TYzkRPIF\nYltHcSL5ItFxoVw5eQtvf08sZgua4gp6PZzE0e3nefSlgfz62Vai48NwdXch9eQtnn5/NIveWYO7\ntyvjXh3Kd6/9SkLHJnQZ0oYFb/xKmx7NGfREDz6avAD/UB9mfDuFNV9u58SOFJ7+6DGatW/EV88v\n4fbFDN74aTrhTUM4tu0sP/7tNx54uAOT3h0FwO2L6bzzyOcEhPvx4abXcPd2s3+sDm04yVvDPiUg\nwp+vDrxrtxr829BoJP/r2LHg7V3nlKKsYgD8QnxqHTudfAGA9v1a2cfSUjO5mXKXXlb7gCiKbF20\nl0YtI0no1BR9pYHfFx+gy9B2hEQHsHnhHqr0Rka9OIjDm86Qfi2Hx14Zwr41JyT19c2HWPn57wA8\nPL0fq776XfIwX0ynJL+Mh6dJXdP6PtqZLUsOEBTpR0FOKSaTmeBIfwqyS0jo0JjMm3m06BxL9p0C\nmiRGUJSrwTfQE5PRTEWpDr9gL66fT6dJy3BuXMwgOi6UrLTCWvfcgAY0oAF/ZfwFCawEZ+W1tqVA\nYlmO1IG6kwcsdfhfa7eVraHw1iTA9yKA1bbrUGPr2nee/2fbB5xQzdLgfKye7FfBdpK1eMtmJ7AV\nbzm/b5Jy6pRGYJaKtywi9s5d9ugsaytZpS1CSy5YY7PkKBWSwiq1jZWjUins+0qlDIVcItMWs4ix\nyoReZ0SvrcJoNKGQy3BzVeHmpkKhkGE0mqX2sZUGlNb2sTb1tUyjRbRYI7TUSirK9JSX6fDxc8fN\n3YVyjZaKMj2Bwd7I5TLys0tx93TBy8eNwrwye7etgpxSFEo5voGeZNzKJyjcB4VSTsbNPJokhJFz\ntxDfQA9ULkoybuYSEx/KheM3adM1ltuXskhIiiE/o4TIpsGUl1QSFO5DhUZHZNNginI1tO7WjJsX\nMxg0oSv71p1k2OSerP0umdg20eSlF1Ocp2H8zKGs+GwrXYe0JeumVO3+wheP8/3ffgNEZnw7mblP\nL8JFrWLm908y7+WlFGQWM2vJM9y+lMGyD9bTc2Qnhj3dlw3f7WTf6mM8MXskHQe25taFdD6ZvICm\nbaJ57cenkclk5N4t4K2H5uLq7sLHW9/AJ8hBJnf+vJ+Pxs8jtl0jPt8zm8AI/3t8kCUU55ay97cj\nfPvyz7w7+gtmDfmYL6f/yI4l+6kyGB0TV6yQ2sfeo3grP10iccFRtQuaTu5IwS/Eh6Ztou1ju389\ngkwuo/doyet67fRtbl1IZ8iTvREEgeRfj1BRWsmI5weiLdex+YfddB3WjojYEH6du4WI2BC6DG3L\nys+k5IGouDB2rTjCoIndObjpNGXFlYx6biBr5u2ky+DWHNl6HrlCRqMW4dy+lMngCQ+wb90p+o/p\nxM5fj9J5YCv2rjtFmwfiOLz1HK26xnJs50Xa9Yzn/JEbJHZpSubtfILC/ax/bJUTEuXPjYuZ9Hm4\nfa17bkADGtCAvzL+kgTWobTKqm/XUEwd853OEeuwElCDtNY1VlOhrfECzu1jxRqEseZ8oZqNoN6b\nrGP/P2sfqOs+qq0lVldc7Tmv1n2bzH3P4i0kkmpPI7A4yKrUSlayBSBavbAWEZPJ9rBGaJksUscs\noxljlcnaNtYsNSWoMmE0mDEZzVgsUoyWSqWQ1Fe1EqVS+ohXGczotAa0lVUIAri7u+Dm7oJMBnqt\n1D5WtFikIi5XaxFXkRaFXOq8hShSUlQBiPgFeGI0minI0eDt44anl5qivDIEAfyDvSgpKMdUZSQo\nzJfCHA1KpRy/QE/uXs8jIiYQs9FCXlYxUbHB3LqURZOEcMpLdICIt58Hty5l0LhFuFRN3iOOJt7+\nHgAAIABJREFUk8mX6Dq4NUd/T6Hn8Hbs33Cang+3Z+evR0nq04K9a08S1igQTVE5FaWVDBzfleSV\nRxj5nBTB5OruwqDx3Vjz9Q4GjO9G9s1cLh69zjOfjmXH0oPcTLnLy99M4sT2FA5vPM2k2SMIivLn\nk8kLCGsSzEvzniDl4FV+fGsV3R5K4rGZQynOLWX2qC/w8HbjvTUzULu5UFpQxqwhn1JlMPLhltcJ\njg6wf7Q2fLuDL6b/SNu+iXy87c17Jg1YLBaObjnN34Z+wvjGz/PppPnsWXGI3LQCKst0HP/9LF8+\n8yNTWrzK+f2XpQ/WwoXQrh0kJdW7bvbtfOQKOYHWhAgbTEYTZ3ZfpOOg1vbcWbPZwr5VR0nqn2hP\nU9j6017U7i70GdMVi8XCxvm7iGvfmIROTdn+8wEqSrWMmTGEEztSSLucyWMzh0rqa1oBE954iOWf\nbkahlDN4Ync2LNxNj+FJHN+Zgl5XRa+RHTm48TTDnuzF2vnJxLdvxKk9l/H296C0sEKKiFPIMBqM\nqFxVmM1SUw61mwvZdwoJjvQj9fQdYltFcvVsGk1bRVJSWIHZbMEvyIvd68/U+740oAENaMBfEX9J\nAgt1c8PqRFVWS3l1zBVqK61gJ7jVcW//a11taO3n3TM+q8ZYfTdkfb6nfaD2DdZ/rC5yW8ea1RRX\nqvtbbRNkVuJ5z+Itp4QC20MUrbYCa7GWvWDL6nW1+WGVcgGFQmZ/qOwNDKyKq0KGUi5DLhcQEBBF\nEZPRQpVBitDS66owm0VUSmsDA1clcrmAscqMttKArtKAUqmwqq8KqgwmyjQ6LBapiEvtqqSyQk9Z\naSWePm542BoYlFQQEOiJ2k1FYV4ZMpkMvyBPNCVa9JUGQiJ8KSvRoqvUERLpR35WCSq1Eh9/D26l\nZtO4eSiVGh0GrYGgcN//j73zjo+i2t//++wm29N7DyWh9w6ho/SqiAoqKvau6LX33hUbVqqKSO9N\nuiC9BkhIAklI73WzbX5/zOxmk2zU6733+73+vvnwymtnzpxzZnZ2gSfPPOd5SD6STqc+rbl0Po/W\nnSKpKK1Bp/fG29uLsqJK/IJ8yLyQQ3CEP5kX8vALMlFeVIkQgtCoAPIzixl94yB2rTjMlLtHsuzD\nTcQkhKM1aEg5fok7X7mO+c8sIzgygKtnDmLha6tJmtSLwDA/VszbzITbhxPRKoT5T/1A71FdmHrf\n1bx52xfUVJl5dsn9VJXX8LqyaGvu/DlYzFZeuv4jKkureGn5IwRFBFBTWctzU96l6EoJr6x8rIE0\n4Ps3V/PF3MUMmtKHl1Y81kAP614Ohwwa53R9nJemf0B2Si7XPzGZTw68ys95X/HF4Tf4eO/L/Hj5\nM15f/yR6k5bnp71H9pJVcPp0s4u3nJWVkkNEq1C8vBu6EJz5NYWailr6je3uaju5O5minFJG3ZgE\nQHlRJbuW/8bI6wdi9NVzaMsprqTlM+Xeq7BabKz6dCtdB7cnsVcrvn9rLRHxISRN6sX376wnsWc8\nobFB7FpxmMl3jmDjor1YzFbG3JTEhgW7GT1zEGu/3ElAiC+SBKUFFfQZ2Znkw+mMnN6PA5tPMWxq\nb/ZvOEHShB4c2nGWvqM6c/7YJTr1bU1eVjFB4f447A5KCioIiwmS42O7RFOYU4ba24vQyKayiZZq\nqZZqqb9z/e0ArAtouradj/Ub9cENrLqkBB76NQNa/6z+1dn/n3oDjbfdAKo7O/un5QPNHWuGAW5O\nPtDEKsyNgXXGytbvS/Ljf+rlA54Wbwknu+qQpQOuOFrRNMRAUSS49LA2q8PFwsphBnYlxEAOMLBZ\n7djsDiSHhFot0Gi80Gpl9lWrUWy0HBJ1Zhu11XXU1VpRq1RyLKxBgxDu7KuEr58evUEjL+IqkRdx\nBQQaUakE5e4BBgiK8irQ6bzxDzRSUSr7wIZFBVBdYaa0qJKo+GAqy2qprqglMi6IvKwSjD46fPz0\npCVnk9AlmvzsUnz89OgMWrLS8mjVIZIT+1LoOaQdyYcz6DmkPelnr9BtUAKZKXn0GtaBtLPZJE3s\nyfE95xl3cxKbl+5n5HX9WP/tLmLbRVBZUkVRTik3PDaeZR9sYsjk3qQcy+BKWgEPfnAT8x5ZjG+Q\nidnPTeW9e74htl0ENz01hddv+Ryjn4G58+ew9M21nN53gQc+uIXIViG8MnMeVouV5394EL1Jxwf3\nfiOHF3x9F227x2Oz2njlho+5ePIyzyx9gI79E5TvkMTCl35m4YvLGXnDIJ5Z8gDeGs8WVmd/vcD9\nA57lzVs+RaPX8NTi+/ku+X1ueXE6CT1aNbDXEkLQa1QX3tr8DD4BRi49+BySySTrX3+nLidnE9cx\nqkn7bxuO4631pseIzq627Uv3YfQzMGB8DwA2L9qDtc7KpMZJW1N6s+PHXynKKWXGo+M5tOUkqScu\nc/3cCez48QAFWcXc/NQUFr+xFr1JR9LkXmxatIexNw1m46I9eGu9SewRT/LhdKbcNYJ13+5i8MSe\nbP5+P606RXF01zlCogJIPZ1FUIQfF89kExots61x7SI4sS+Vjn1ak3wkg8RusRTnlaP2VmHyN5Bx\nLpdWHaIozCkjJDqgyftuqZZqqZb6O9ffDsA6q0ECl7Jdv5CrIXarH+NZStAsKyvRlHFtrH9tTi7Q\nYLuedW2gNW3+zXnY/wvygcbHaHR+9/k89G+w0EtpcLe2cpcXSA5ci7cA1+ItF8h1ygbcZAVO2yzZ\nlcCBpHxwLsZWAbZOXaxarXIFGDjlBwL5PDab/Ei1TgkwsFrsCCHQab0xGDRotV4IIbBYbNRU12Gu\ntaDReGHy0aHV1rOvNqsdP38DeoOG2moLZSXVGIxafP2UAIPCSnwVNrasuAprnZXQCH9qq+soya8g\nKi5ITuHKKSWmTSiVZXJ8bGRcEFfSCwkO95W9YS/m07ZTtCwh6BRFRYnsOWvy1XM5JZeYtmGc2Hee\njr1bcWDTCboPbsfuNUfplpTIntVHSOgWy/E95/ALMqFWCYpyypgwewhblu5n8p0jWPnZNox+eoZM\n6c3ar3Yy9e5RHN1xhsvnc3h03my+eX45lSXV/OPru/jmxeVkp+bxxFd3knr8Ej++u54xtwxh1A0D\n+eTRxaQczeDxr+4iJjGCZe+ul3WwL1xL0uQ+OBwOPrjnG47tOMPDn91O/3E9lO+ExDfP/sj3b6xi\nzOxhPPb13Q08YJ1VWVrN+3d9yaMjXqaiuIp/LLiXz357jWHTB3js716B4f48/Po19Cm7QO6AUeDT\n1F3AWXW1FnIu5hHfKaZBuyRJ7F9zhB7DO6I3ycxwVVk1e1cfZtj0/mh08uP6Dd/8QtfB7YnvGN0g\naUsIwU/vbyShRzw9hndkyZtrCYsNZvCU3vzw3gY69m2DwVfPgY0nuPaBq1nxyVa8NF70GdWZfWuP\nMe3uUfz08RbiO0SSdjYbISA4KoDCK6V0G9SOS+dz6D28I+lnsuk6sB3ZaQW07hRFaWElepMOlZcg\nP7uEiLggko9eol33OHIuFeHjb0Rr0JB5MZ+YhDDOHrn0u/eypVqqpVrq71Z/SwDrZF6d2+6vruPu\noPZ3PGGdLGzDsb8nDaD+sbnreDN9m7Ceov61SZuHN6m8/qvygQZAtNGcUuM+0CC0wLWvpG+52FYF\ntDr33RdvOceplMVboLCzdiXEQDmh2gMLqxb198bhkFO6nE4E7mEGTmmCWiVLCrRap5WWF95eKhBg\ns8mer7XVdVgsdry8FPZVr0Egs69VFbUgSfgo7KvVYqO8RF7EFRBoRKNRU1leS22NhaAQH7w1akoK\nKhESBIf6UFNVR2lxJRExsl1RblYxkXHBOOwO8rOKiU0IU+QEcnxsxrkcYtuGYamzUlZUSURcMGcP\np9OlfxvSzmTTrkccRTllhEYFUFdrRaP1kuUZKpW8sCzQRGVZDR37tCbj7BUmzB7C1u8PMP7WIaz4\nfBuRrULRGbVcPJXJnJeuZf7Ty4hOCKfHsA6s+nw7k+4YQUFWMQc2HOfWF68lKyWXrYv3MuOx8US3\nDeOdO7+iVecY7nl7Jhu/28WWRXu44fGJDJzQk1/XH+W7F5czbHp/bnh8IgALXvyZ7Uv3cfPz1zD6\n5iHyR6eA1+XvrWfCnaN46LPbPQYUHNp8gjt7PMG2JXu5bu5Evj75NiOuH9QgZvaPqlfuSbTY2aBq\n+7v9Mk5n4nBItOkW16A99VgG+ZlFJCkxsQA7lx/EYrYydvZQQPazzb9cxMQ7RgKwct4WjH56xtwy\nhL2rj5CbUcD1cydwdMcZUo9f4vrHxrN16X6Kckq56enJLHh1Ff4hPnQZmMju1UeYevcofvp4MwGh\nvmh0GvIuFXH1jYPYs+Yoo2cmsXHRXgaM7sr25b/RqV8b9m04TvterTi49TSd+7Xl0I5kegxux/lj\nl2jXPZ7ivHK8vL0w+ui4lJJLfPtIrmQU4hdoQmfQcOVSIX2HdfjT97SlWqqlWurvUH9PANvsAi5V\nE10quOM2N4a2EZhtTkrQxIGguWCDZmQFzdpnNQaTnoDpv0k+0PgammhdG9PVbjIAWT+h7Cr7klso\nAdBk8ZYTgzqUxVvOczr1rUIobgQOGgQXuKJkHTSQJzg9YN2/rJIk4bBJ2JTFXXVKiIGlzobDIeGl\nVqHTeWPQe8vxsZKEpU5hX81u7KtGjaXORmV5LTarDV8/A3qjBnOthbLiKjQaL/wCDFitdooLKtDr\ntfgFGKisqFUCDAKQHJCXVUJopD/e3l5kpxcSGReEJEnkXS4mPjGc0oJKHA4HweF+ss9r11iKcssw\nmHTojVoyU3JlL8+95+k5pB1Hd52j/9VdOLH3AkmTenJs9zlGXNuXPWuOcvX1A9i0eB/9ru7Cjp9+\nIzwuGLvdQX5mMdc/MpafPtrM0Cm9ObH7HMV5Zdz79g18/MhiohPCuXrWIOY/9SM9h3ei35iufPzQ\nAjr2a8v1c8fz+uzPsVpsPLPwXjLOZvHZY4vpNbIzNz07jUtns3n79vkk9mrNo5/PQQjB+q92sOyd\ndYybM4Ibn5zs+lyc4HXiXaO478NbmgBSi9nCJw99x3NT3sE3yMTH+17m9levb1Yb22xJEqov55MT\nHM+2s1Uyg99MXTiSBkBCz9YN2veuOozaS82AifWr9Lcs3E3rLrG07R4PwKrPthIcFcjAiT3Ju1TI\n3tWHGTt7GDqjlh/fXUdsu0j6j+vO4tfXEBobRNLk3vz4/ga6DW6Pwy5xat8FZjwyjiVvr8MvyER0\n2zCSD6Ux/f7R/PzJVnqN6MjOVYcJCvejOK8MSQKdSUdNpZnAUF+qK8wY/QzYbPJCRKOvjksXcolq\nE8rp39Jo3zOerIv5BEc6wzSKiW4dSs7lYgw+egwmHYd2nfvn7m1LtVRLtdR/ef09ASyesV09q+rG\nvCKa4LPGUoKG5dS/1pdDasySNhrjCXS6tj2wsb/3xtylAp7a/qp8wJ11bQxeG1+DJ3q2EbPqmkdq\nCFqdYFZIUv2iLmW8w01WICnncbKwanWjKFnlR/7jZHqdx+UQA423Gp1W1r1qNGo5PlYl616tVjvm\nGiu1NRbsNgcaJT5Wp5dTlpzsqySBj6/CvlrtVJTW4LA6CAg0yhZalWaqKswEBhnRGzSUl1RTV2sl\nNMIPu9VOQU4pQWG+aHXe5F4uJjDEB4NRQ9bFAiLjgpGQyL1cTKt24RRkl2AwaWUdrMK2pp3Npm3n\nGMqL5bQvrU5DQXYJIVEBXDiaQUzbME7tu0BcuwhOH0ghLCaI7LR8vLzVhEQGkJNewMTbh7FxwW4m\n3DaUNV/9gm+gSTa+/+EAMx4ey9bF+ygtqOCRebP58P4FaPUaHvr4Ft6e8xVCJfjHN3ex5I21nDuU\nxsPzZuMTYOS1mz4lMNyfJ7+9h6rSap6f/j56k44XfnwQrV7DwY3H+fThhfQb1537P7gZIeRFdN89\n/5OLeb3vw9lNwGtWSg4PDXmBdfO3M+2hsczb/woJPVr93t+K5mvfPjh3jrzRUygvrKA0v7zZrmcP\npBAcFUhIdL0DgSRJ7F11iG5DO7hcEdJOZZJ6/BKjbx6CEIJLZ7M5sSuZSXeOxMvbixXzNruStg5u\nPMGl5CvMeGw8R3ecJeVYBjfMncimhXsoK6zkpqcm890rKwmNCSI8PpiT+y5wnZK+Fdc+kpxLhdRW\nmek8IIHUE5cZNWMA+zecYOS1fdm16ghJE3qwb/0JBozpytFd52QN9Jls2naJpbSwEi9vNQYfLZcv\n5BLXPoKM87mExwbhsDsozC0nslWwbOXmpSaqVXBzt6alWqqlWupvWX9TAOt89E+93lVqgLHc+oKT\nSXXQvJSgqf5VNGBV62UJzuPOwc3JBZpue7TP8nzBzR770/KB5kBqY0lCo/ECXN6vrnaH2zGkeu9W\nZ6MTtOImJwDFbQAkuzyPU9eqciZrucsEbG5Rsm7BBU6XAueP3S5hs9mxWRxYLDbq6mzUmeUQA5vV\njhDIwFYJMfDyVikLuazUVNdhMVvRurGvVouNyoparBYbPj56DEYtdXVWyoqrUKsF/oFGJEn21FSr\nVQQEmzDXWijOLyc43A+tTs6f9/WTk7dyM4vxCzDKWtbUfKJbhWCz2im4UkJcYjiZqflExASBJFGU\nU0pU6xBOH0yl68C2nD9+ma4D2pKdVkBi1xgKc0pp1UlehJPYPY7siwUMHNeNMwdSmXjrUDYu3Muo\nGf1Z/+0uwuOCMfjoSTudxW3PT+Or55bTpksMMQlh7FpxiBufmMDBDce5ePIyj3xyK+u++oULR9N5\neN6tXDqbzc8fbWL87cNJmtKHN279nNKCCp5bej8GXx2v3fQJJXnlvLDsIYIjA7lwJJ3Xb/qENt3j\neXrRfS6t6tLXVrLsnbWMu30E9314i8uSyln71xzmwUHPUZxTysur5nLXW7PQ6DSevo1/rubPB19f\nbFPlAIWCrGKP3SRJ4sz+C3Qe2K7BNaWdvExuegGDp/V1tW36bifeWm9GzJC9X9d8sQ2Nzpuxs4cp\nSVv7GD5jAEER/vzwzjoiWoUy9Jq+LHljDWGxwQyc0IOfPtpEn1GdKc4t4+LJTGY9OZGFr68hIj4Y\nSYLcjEImzRnOpkX7uOrGgaz7djeJ3eM4tP0MIZEBZJzLwTfQSE56AX7BJjLO5RAWE8jZQ+m07hTF\nyf0pdOrTmssX8oiIDcZmtVNSUEFkXDCXUvIIjQ5CAgpyyoiIDaKspJrqqrq/fp9bqqVaqqX+C+tv\nCGAbJnG5L+ZyAtXmpALOch3Hg5SAxkBWPpd7p6ZRsh76up9bAveFXJ7aGrOrrnf7V+UDDa6/vt0j\noG1EYzdZzOUGSJ2Mq2u/SbQsLoDrbHeScJKEyzpLcuplFTDr5RYl61yoJQcZKHZZSpiBxluN1lsO\nMZDDDGRLLdlKS46PtVhsmGvkEAPJATqtzL5qtF5IUkP21eSjw2DUYrM5qCyrxVpnwz/AgN6opaba\nQnlpNb5+Bkw+OqrK5QjY0Ag/vLzUSoCBDl9/AwU5ZWi03gQEm8jNLMbH34CPn4GM83nEJ8rpWhUl\nVUTGB5NyMpOEbrEU51eg02swmHRkpeYR0zaM43vO06V/Ww5uOUW/q7uwb/0JBk/syc6Vhxk4rjvb\nfjxAx75tOLTtDH5BJnRGLTkZBVz34Bh+/mQLw67py6Etp6gqq2HOS9fy2RM/0L53azr0bs3PH29m\n7OyhaPUafv5oE+NuG0b73q159+6vadMtlrveuIHFr67k+M6z3P/+TST0aMUX//iek3vO8dC8W2nf\nuw15lwt5/pr38A/x5eUVj7oe+y97Zy2LX13JVTcN4YF5tzZgXh0OBwte+ImXZ3xIdLtIPjnwGv3G\n9vDwhf4nqqgIli+Hm2/GGCkHE1SVVXvsmpOeT3FOKZ0Ht2/QvuunA6i91AyaJHvHmqvr2PHjrwyZ\n2gffIB8qiqvY8eOvjJgxAN8gE+vm76Cu1sK1D47h6HZZ73rdo+M4uuMMKccyuH7uBNbM30FVWQ2z\nnprMwtdXE98hCodDkpnah8ex7MNN9BjagQObTqI3atGbdJTkl9NlUCIZyVfoN7or549dos+oTlw8\nnUWX/gnkXCoiqnUY1ZW12O0SPgEGMs7nEN8+gtRTWcS1j6C6spaqylrCogPJTi8kINQHb60XuZnF\nhEYFUNsCYFuqpVrq/7P62wFYCRS3ATf85gGEytvuSVz1oLbhfM1LCdxtt+r1r001r05A7T72D99E\n457NSQWU7d9jXv+KfKDxdTSY0snGuoFS4WHf5TrgBOuSk4EVrnZ3BhaUhV2KG4FayFIDlxbWJmG3\nOrA5bbRsDpltVey0rFY7FosNS50dS51VCTOQNbCSEmLgstHSeqFWq7ArC7lqquuwWe1otd4YTVoX\n+1pVaabObMFk0mL00WKz2SkvqUZyOAgIMuLlpaasuAq7zUFQqA+SQ6IwpwwfPz0mPz3FeeUIlSAo\nzJdi5RF2cLgfuZnF+PrrMfnqSD+XQ5uOUUp8LK742I69W5F2JpvEbrGU5FfgH+wDEtTVmDGYdBTl\nlOLjb6AopwSdQYPDZsdcYyGxWywZZ7OZctcINny3i9Gzklj/3S58A010HZTI3jVHmfmPiSyftwWr\n1ca9b9/I+/d9S2TrUGY8Oo537vyS2PaR3PbStbw++3PsNjtPf3cPR3ec5od31jH65iGMuWUomxfu\nZt387Vz70FiumplEZWk1z015F5vFxqur57oiWdd8toVvn1vG8BkDeeSLOxqA15rKWl6a/gE/vLWG\nMbOH8d6O5wmN+eMErj+shQvBYoG77kKjlWUhVvdkLrc6uSsZgG5DO7raHA4HO386SK+ruuAXLLsX\n7Pr5IDUVtYyfMwKADd/upK7WwtR7R1NbZWbtlzvoP647MYkRLH1rDaExQYy8fiCLXltNRHwIfUd3\nZdXn20ia3IvUE5fJSS9g1pMTWfLWOhJ7xHP5/BWqymoYOL4nR39JZsJtQ9mwcA+DJ/Vk6w8H6NS3\nNfs3HKdt1xgObT1DYvdYDu04S5cBbTm25zxdByRy+UIuEfEhWOqslBdXERYbyMUz2cQlRlBZLstd\nQqMDyc+Wo4x9A40UXJHT3FqqpVqqpf5/qr8dgJXLybY6F3Mhv7oCDGgAPqEhWenw4ErgnLfJOE9h\nCL8nF/g9htVTf/5o37NEQTTp5+G1cZvbuAbX07iP82Aj71fJISGkhlZZkpu8QHYrcAsmoGGAAeAm\nDVD0sNTLDupZWFnj6u3lzr42DDHQKEEGarUsSZBDDOzU1cnsq6XOigB0Om+MBg0ajRrJIWGusVBd\naW7AvtrtElXlZupqLPj66TGatJhrrZQWVaHXe+MbYKC22kJpYSUBwSYMJi3FBZWogKBQX8qLq6ir\ntRAWHUBpYSV2m52QCH+uZBQRGOKD3uDN5ZRc2naOJiejkKAwX7y81OReLiKmbRgn96fQIymR0wdS\n6T28AyknMuk9ohNpZ7LpP7or545kMOKavhzccopxNyWxYcEeBo7vzvYfDxAU4Y9foJH0M9nMfnYq\n3764gva9W6PXazn2y1nmvDyd5R9upDS/gie+vpNPH1tCdUUtT313N8ve26DoXm9FpVbxzp1f0bZ7\nHPe/fxNnD6Yy76EF9BrZmdtemYHNauPVGz8mJy2f55c9TGx72U91y8LdfPboIgZO6s3cr+9q4DZQ\nkFnEo8Nf4tDmE9z34S08/PkcF9j8l0qS4MsvYeBA6NwZh2I+LFTCY/eTu5IJjPAnJjHC1XZm/wWK\nrpQwYsZAV9uGb3YS1yGKjv0TsFpsrJ2/nZ4jOhHfKZqNC3ZTWVrNjEcncHxXMucOpXHdI+M4vPUU\naacyufEfk1jxyRbqaizMeHgsS99eR+cBCWRfzKcop5Spd41k3Te7GHX9ANZ9s5PIViFkpuahUqnQ\nGrRUV9QSFhtMWVEVoVGBVFXUojPqkIDSwkoCw/xIOZlJ605RnD92mTadoiktqsJmcxAU5kfG+Vyi\n24RirrVQXlLl+i5aLXaCwny5ePbKv37fW6qlWqql/ovqPwZghRA6IcQhIcRJIcRZIcRLHvrMFkIU\nCiFOKD9z/szcnvBaw596WUG9L6xn9rUJ04obaG1gr+VBu9okPrY5PazwICP4A/lAY5BK8/0avSnP\n280cbyIXcL23+kusB7a/s3hLqgezKvd2u8LCyje2gb+ry8tVkgMMnDZZMvOqBBW42FclwKDOjtUi\nBxrYFK2sSgiX7lWn80bjrUYlhGKjZaGmxoLN5kCr9cJo0qJxY1/NtRZMRi1GHx0Ou0RFaTVWix3/\nICNavTcVZTWYqywEh/ri7a2mKK8cL281AcEm2R6rykx4dCBV5bVUlFQTGRcsAwurndBIfzJT8wiP\nCQIhyLtcRHy7CFJPZ5PQNYbSggp0em90etnqKCI+mFP7U0joFsvBrafo3L8t+9Yfo1PfNuzfcJy4\ndhGknshEq9cQHBVAZkou0x8cw4rPtjH82r7sX3sUi9nKzCcm8O3LK+g9sjM6vTd7Vx/hpqenkHwg\nlcNbTzHn1RkUZpew/ENZRtBvbHdemTkPIeC5JQ9QUVLFqzd+TEhMEE8tvA+VSvDR/d9xYlcyD392\nO92GyJZMe1b8xof3fEWvUV14avH9DRKuLp64xENDXqAgq5hXVz/OpLuvbqKJ/cu1axekpMDddwPy\no38AnUHbpKvD4eD4rrP0GNapwfl3/PArOqOWARNkKUPKsXRSjqYz/vbhCCHYs/I3SvLKmPbAGCx1\nVlbO20zXwe3p0LcNP7y9juDIAEbNHMTiN9YQ1SaMLoMSWff1TkbdMJDD289QWlDB9IfG8NNHm+k/\nphv71h9D7a0mslUoWSl5jJzRnwObTnLVDQP4ZflvDJnUk12rj9J/dBcObD5Fn1GdOHXgIl0HJpCd\nVkB4bBAWi5XykmpCowNIOZFJm85RFOeVIwGBoX5kXSwgJEoOLMi/IluxWS02iguraNf6niHMAAAg\nAElEQVQt9t9z71uqpVqqpf5L6j/JwNYBIyRJ6gZ0B8YIIfp76LdMkqTuys/Xf2biprIAt0VdeMZ0\nzsf8Dg/OBC7QilMmUN/eeNGVO7B1nvNP22e5bzdhZT3IB5oDqW7j/hn5wB9Gx0qN9h2Nzu1uzyCo\ndxyg6eItyekbq0gFVKJ+jDO4wD1GVp5D8YN1aWHrWVgnE+vlJbOzTs2rJNWHGJjNVsw1VmxWOyqV\nCr1eDjHw1qhxOCTMNVaZfXVIGE0y+yo5JKoqzJirzJh8tJh8dVgsNsqKqvBSq/APNGK12ijKL8dg\n0uIbYKCitAZztYXQSH9qa+oozi8nKj4Yc62FkoIKYlqHUFpQgcPuICTCn/TkK8QnhlNTaaamWn7E\nm6x4v6aeyqJjn1YUZJUQFR9CdaUZ3wAjNqsdg0krOx5EB1KSV07vEZ04dziNyXeMYN3XOxkxvR+b\nF+/FN9BE+x6tOLT1NLOfncLiN9ag1WuY9Y+JfP7E93QemEivEZ345vmf6D+uO/3Hdefdu76mdZcY\n7n7zRj6bu5i0U5k88fVdBIb78fINH1NTZebFZQ/jE2Dkp3fXs3XRHm58agpXzRoMwJFtp3jzlk/p\n0D+R55c93IBZPbrtFI+NfBm1l5r3d75Ar6u6NvMF/ov1xRcQEADXyou3KkqqAPBRnATcK+3EZcoL\nK+g5qourzWK2sG/VIQZN6u3S8K6dvwOdUcuoG5OQJIkVH28mtl0kvUd14ZdlByjOLeO6R8Zxcu95\nTu+/wPSHx/HbxpNcOpvNrKcn8+P7G0GCiXNGsPzjzQwc350jO85grrUweFJP9q8/zuQ7RrDqix10\nTUpkz9pjhMUEkn72CkY/AwVXSjH46MjPLMYv2ET62RwiW4Vw+mAa7XrGkXwkg3bd4ijOK0elUuEb\naCTjfC7x7SIoKajAZncQFO4nJ7756jH56mWNtq8ek4+OC6ey/r2fQUu1VEu11P9y/ccArCRXlbLr\nrfw0B8X++flpjO+coFRGSo0DDDySkdKf1L/iBLbNaV6hKUj9A/mApzfUZN8zqP2r8gFnedLDSm7A\n1J2BrQe28oldIQcKcPW0eMvZ7oqXlWS5AG5pXfXBBfK2kOTxDqcjgZOFdUXJys4DNqtd8Y2Vx3mr\n1Wg1su5Vo1HLIQaAzWqntsZCbY0Fh81Rv5BLo8Zms1NdZcZcU4fBoMHkK4OYyvJazLVW/AIMGIxa\nqivNVJTVEhBkwmDUUFZUhdViJyTcj9qaOkoKKoiIDcLhcMjem61CsFisFOaUEds2lKK8MtReKgKC\nfUg9lUVit1gKskvx9Teg1XmTraQkHd97ge5JiRz55azLMmnwhB4c2nZGtlRaeZiR1/Vj48K9dB/c\njl83HsMvyERguB/pZ7K5+clJLHx9DV0GJVJdUUvKsUvc985Mvnx2GQjBgx/ezNt3fIlfsA8PfTyb\nt+d8iaXOytML7mXXzwfZvHAPM+ZOoO/obnz66GIuHEnnia/vIr5jNHtXHuLb539i2PT+3PzcNEC2\npHp5xofEdYzilVVzG/i37vh+H89NfZfINmF8tOcl4jtGN/6m/2tVUACrVsEtt4BeD0BJXhkAgWH+\nTbof3HAMIQS9r+7mavt13TGqymoYOXMQABXFlez++SAjbxiE0c/AsZ1nSTuVyTUPjsFhd7DsvQ0k\n9Iin18jOLHljNYHh/lw9K4nFb6wmvmMUrTvHsHXpfsbdOpRtP+ynrtbK2FuGsnHhHsbMSmL1/F8I\njgygpsJMdXkN7Xu14vL5HAaM7U7yoTQGjutG8uF0eg/rQHryFdr3bEVhTil+wT5IkkRxXgVhMYGc\nO36ZhC4x5GUWY/DRYfTRcfliPjFtwqgsq6G6ykxIpD8lBRXY7Q4CQ30pL6nGbLbSukNEk3vTUi3V\nUi31d67/qAZWCKEWQpwACoBtkiT95qHbNUKIU0KIn4UQMR6OI4S4UwhxRAhxxImtHIoswPnqCaTW\n4zcnqHUDrU3P8tf0r3/EtLq2G8kHmgOaymtjkPpPyQekRmyrp3k8sbWN53QLNGjAyDazeEsI4Urp\ncthxMbAqt4VdDYCqUwtrl5oEF7jLDNSNtuVzyCDXapUXdJnNVixmOw6HhLe3up599VZhdzhk9rVK\nYV+NWgxG+XFzdWUdNZVm9AYNPn567MoiLofDQUCwCZUQlBQoFlohPnJkbGEl4VEBCAE5l4sJiwrA\ny0tNdnoB0a1CsNbZKMwpJTYhjDzFkcBgks3nE7rGcPF0FgldZS9Po48Ob40XxbllBEf4k3oqi6jW\noZz97SJRbUI5fzidoAg/SnLLcDgctOocTfqZbKY/MJpVn29n+DV92b3qCA6Hg2n3XMWP721k+LX9\nyE3PJ/ngRe5/bxYr5m0mOzWPx7+8g7Xzd3Dm1xQe+OBmLHUW5j28kO5DO3LLs9NY/9UONi/czY3/\nmMSgSb1JOZrOO3Pm06FfWx778g7FFzWL56e+Q3BkAK+vexKjn8H1dVn96Wbevu1zOg9qx7vbniMo\nMsDDl/BfrO++A6sV7rzT1VSQWYS31hu/kKZRsgc3HqNDv7b4h/i62rYu3kNoTBA9hncCYMvivVjM\nVlfS1oqPNhEY5seI6weyZ+UhV9LWqX0XOL3vAtc9Mo59a4+SnZrHrCcns/iNtWj13gyf3o8N3+1h\nzE1JbFiwG61OQ1z7CFJPXmbi7cPZtHgfI67rz8ZFe+kyIIE9a4/RulM0h3ecpVXHKA7vTKZdz3gO\n70ym26AEzh3JoF3PeIpyy/DWemPy1XNJ8XzNzSxGZ5Q9hbPSCwmPCcRuc1CUW05IpD+WOhslRVUE\nhPig9lKRfiHv3/9ZtFRLtVRL/S/WfxTASpJklySpOxAN9BVCdG7UZR0QL0lSV2AbsLCZeb6UJKm3\nJEm965Wa7oyoU8uqahRg4AGUuub8ff2r9Af619+Lj/1d+yxl2/V4vzFo/XfIB37nPFKj464rb3Qt\nDeZzMqvNLN5CWbzlss8SoFbVs7UuqYCihXVKBbwasbAqVf29auD9qrw6b6zKGWSgkeNjtTpvvL3V\neKnlEANLna2efXVI6LTeGBqwr3XU1tSh02nw8dWhUgmqK81UV9bhozxyNddYKC2swuSjw9dPT1V5\nLVXlNYRG+iOQk7cCQ3wxGDXkXCoiKMwXnUFL1sUCYhNCqTNbZTlBm1AyU/JcfrAlBeVEtgrhzG8X\n6TYokfNHL9FtQAJZqXm06xlPQVYxbbvGkJ9VQud+bbicksvQyb05tusck+YMY93XOxk0sSc7fjqI\nb6CJNl1iOLH7HLc9P43vXl5BQKgvo2cNYsmbaxl6TV+8NV5sWbSX6Y+MBeDHd9dz9awk+o/rwauz\nPsHkb+Sp7+7h/OE0Pn98KX3HdGPWM9MozC7hhWs/wD/ElxeWPaxEnhby9MS30Og1vL7uHwSE+ckf\nlySx+JUVfP7YYgZN6cOrax5vAGybK4vZwqm959i2eA9rv9jGgXVHXWyqx3I45MVbQ4dCh/po1Jy0\nfCJahTQJTSjMLubi8Uv0H9/Tra2EY9vPMGpmEiqVCrvdwbovt9MlqR2tOseQcSaLozvOMOnuq/Dy\nVrPs/Q3EtpeTtpa8LrOvV81MYskba0joHkdwdCD71h7lmvtHs+KTrXhp1PQY1pGDm08y7b6rWPbh\nZhJ7xHFq3wX0yi9NNVV1hMeHUJJfToyS0hYc4Y+5ug6HA3RGLVlphUTGh3D2UDrtesSRnVaAf7AJ\nL0394r/CnDLUXt74B/uQm1Ui/6Lko6Mwtxyjrw6Tr47SoiqEShDdEmTwtykhxFQhRML/9nW0VEv9\nt9f/iAuBJEllwE5gTKP2YkmSnAaFXwO9Go/1PF89GfgnyEgaa2Yb9/Okf5WruShZd51rU/1r8xfe\nXLsnOcK/UT7Q+Ao9gVW3BVvua7fcjzW3eMtdGgDyvsPuBnLdrLOc7KnDXh9c4AossDtBbv2n5Lrb\nkjLOocgLrHYsdTbqzFbqzFZsFjtIcoiBXq+EGHjJAMVcY6VGYV8NBpl9FUIGElUVtWi1Xvj6GxAC\nKspqsNRZCQg2odV6U1ZchbXORnCYL3a7RMGVMsWJQEd+donsAxtgIOdysdxu1HLpfC7x7cKpVkBv\nRGwQF05m0q57LMW55eiNGnQGLdmK9+uJvW7er1d1Zu/aYwya0J1ffj5E/9Fd2fbjARJ7xHNy3wX0\nJi0RcSGknc5i5uMTWPLWOnoO60hOej6ZF3K5/92ZfPLYEgLCfLnhsfF89OACEnu2YuIdI3nr9vnE\nJEZwz9szef+er8nNKOTphfdit9t5deY8wuKCeeLru7GaLbx43QeYq828vPJRAsL8KCus4JlJb1FX\nU8cb658kvFWo8vlLfPnkUpa8tpLRtwzlmSUP/G44gc1qY8/PB3l28ttMC72DuSNf4Z3bv+CTB7/j\nhWveY2brB5j34HdUl9c0Hbx9O6Snw113NWjOupBDdGJkk+4HNxwDoP+EegC744d9SJLEVTcNdvXJ\nv1zE5HuuBmD5hxvRGbWMv304BzYc51LyFa5/bAKn9l7g9P4LzHh0PL8sO0B+ZhE3PzuVBS+vxC/I\nROcBsn3ZNfddzbIPNxESFYCl1kpJfjmDlV9ARs8axI7lvzFsWm9++fk3Boztxt51x+kzshOHd5yl\n17COpJ7KJKFLDCUFFXhp1Bh9dFy6kEdcOzkEIyjcD5VaRc7lIqJahVJaVInFYiU43I+SggocDomA\nEB95gWGNBf9gEzabg+yMomY/k//mEsrKOyHEi859T20expmEEJ8LIdKEEMeEEEeFEHf8i9cyWwjx\nibJ9txDi5r84T7wQ4sbf6XIMeF8I8V/tEiSEeLrR/q//pnld97lRu1Du3ezfa2up/zv1n3QhCBFC\n+CvbeuAq4HyjPu7CrEnAnwzsFm4MaiOnAee2OxOqlDtm+2ettP5Y8+q27W6f9XvgspmL+7fLB9wZ\n1WZAdD3YbNTfecy572Hxlmyf5QZwaWidJblZZ6FIBZwyAad1llql2Gc55QJCcShAoALUQjnuJUfG\nahX2VaNR4+WlQiUEdrvDxb6aa61IUlP2taa6jtrqOrQab3x89Xh5qampqqOyvAa9QYOvnx6LxU5p\nQRUajZqAIKOcvFVYSWCwD3qDhsKcMrQ6L/yDTBTmlOHl7UVAiA85GUUEBJvQm7RcOp9D646RlBZU\nIlSCgFCfeu/X09kkdo+lOL8c/yATkkOirtYie7/mluHjb6A0rwKN1huVWkVNRS2dB7TlwtEMrrn3\nalbP387QaX3YveIwKpWK0TcNYvUXO5g4ZziHt57iysV8Hv30Nj59fCl2m4Mnvr6DD+//TrbPWnA3\nWxbtYd+aI9z28nTa92nDq7M+oabKzAs/PoTRT8/bt88n/eRlnlx4H/GdYqitMvPc1HcoyCzipRVz\nie8kK30cDgcfP/AtKz/axOR7r+bhz+e4Urkal6XOyupPtzC7w6O8euPHpJ/OZPwdI3lp5WN8e+Y9\nfrj0KR/tfYmxtw9nw1c7eGbS21gttoaTzJ8PwcEwbVr9vGYLVy7mEdchqsk5f117hKi24S7LL4fD\nweYFu+mS1I7I1rIv6urPtxEaE8TACT3Jzyxi5/KDjLt1GD4BRr5/ey2RrUMZMq0Pi19bRXBUACOu\nG8AP766j88BEQHBiz3munzueJW+tJSDUl+DIAC6eypQ/py93MHhSLzYv2kd0QhjnjmTgE2Cg8EoZ\nOoOG0oIKDD46ci4VEhzpz/njl2nVMZJTBy7SsU8rMlPzFVcBiYIrpUS2DiE7vRD/YB+0eg1XLhcR\nHheExWyjuKCC4AhZOlBaXKWwtV6UlVTj5aUmIjawyf35m9RMIcTjgE4I8QQws5m2xvU1UAokSJLU\nE5k8aXIThBBejdv+TEmS9IUkSYv+ylggHmgWwEqSdBl4E2j9F+f/n6oGAFaSpIHNdfw31RdAEhAr\nhPhGCBHVTFtL/R+p/+RveBHATiHEKeAwsgZ2vRDiZSHEJKXPg4rF1kngQWD2n5nYKc1szMDWb9cn\nczmkhqC2KYBzkxI0YFtFE4a1yfjmLsC575IVuMkIPKVv/aflA42n83Dd9XIB+aUeyEr1IFVhWxsv\n3sIhNQGzTayzXLICxT1A0b7abQ4XE2uzOVxWWvUSAofC1tZba1ldEbJymIHd5kAIgUbrhV7vjU7r\nhdpLCTFQ2FeHXWZfjSYtKrWgttpCZXkNXl4qfP0NqL1UVFXUUl1Vh7+yiKuyvJbqilqCw/xkC63c\nMrRaGbiWFla6XAZKCioQkkRwuB/Z6QUEh/mi1WnISsmTvV8vFREUKqd35VwqJCYhjJP7LtB9sOz9\n2mtER1JOZtJreEfSTmfRf3RXkg+nMXJ6P35df5yxNyex/tud9LmqM3vWHMHkbyC+XSSnf03h1uem\n8M3zPxPVJoxug9uz4dtdTLvvalKOZnB63wXueWcmv647xtEdZ7jrjRswV9fx5dM/0n98D659cCxf\nPLGE5IOpPPr5HOI7RrP4lZXsX3OEO964gX5ju2Oz2nh91jwuHsvg6SUP0HlQO0C2PHv/zi/Z+PUv\nzJg7kXveu7nJI3yQGdrdyw8yp+tcPntkISHRgby8ai6LUz/mnvduZsCEXkQnRhAUGUCHfgk8OO82\nnlx4H8kHUlj88s/1E+XkwJo1cOutoK23y7qcfAWH3UHrrnENzlteVMmJXckkTe3rss86ufscuekF\njL1tOAAZZ7I4teccE+8chdpLzcp5mxFCMPX+0Rzacoq0k5lcP3cCJ/ecJ/m3i1z/2AQ2LdxNSV45\nNz8zlQWvrCQ8Lpig8ADOHEhlxiNj+f7d9ST2iCf5cBoAEfEh5GQU0n9MN84dSSdpYi9OH0il3+hu\nnD92iW6DEpXFfBFUltdgt0n4+BvISM4hvn0k6ck5RMaHYLXYKC2sJCIumLysEnQGLb7+BvKySvBV\nkuOK8sox+Ogw+eopK5Z13L7+BqxWO7lZpU0+m79DSZK0BMgGHgcyJUla4qnNfYwQog3QF3hWkuRf\nzSVJKpQk6S3l+DAhxF4hxFogWWlbrbC0Z4UQd7rNdasQIkUIcQgY5Nb+ohBirvN8QojNyvi9Qoj2\nSvsCIcTHQohfhRDpQohrleFvAoMV68hHGl27SQixA5gHrBJCTPZ0X4QQVUKID5Tr3SGECFHa7xBC\nHBayfeUKIYRBaZ8uhDijtO/xMJ8QQryj9DkthJjhdq/2CCE2CCEuCCG+EEKohBBvAnrlPSx1XpPb\nmN1CiDXK+35TCDFTyLaap5XPByHERCHEb0KI40KI7UKIP0rbuBe4AbgNeEqSpCvNtLXU/5H6T7oQ\nnJIkqYckSV0lSeosSdLLSvvzkiStVbafkiSpkyRJ3SRJGi5J0vnfn9U5NzT2d63XrjZPcDqBraM5\n/Sue9K/QQNeKO7h1jm8sK3Af3HS74YKpf7N8oDE4dXttAnYl9ytX+omGcwhoYKflBLJOMC/LAhQJ\nhjN1i3oGVgj5XrkWa0lKyIEL1CoMrFuErLdileUeJavxlmNkNRolTlYjM7H1EbJ2LGaZfa2rs4Ek\nuRZyaTRq7HaZfa2uMuPtpcbHV49Wo8ZcY6GirAaNxgu/AAOS5KCsqBokB4HBJnlhTH45RpMsFSgr\nrsJqsREa6U9FaTXVlWYiYgIpKajEbpcDDDJT8wmPCUQCcjOLiG8fIS/c6hJDWWElWq0XeqOOKxfz\nZaukX1NJ7B7Hb07v13VH6dCnNQc2niAmIZyM5GzUXmri20eRdjqLGx4dzw/vbaDv1V1IOXGZopxS\n7n5jBp88upj4TtEMGN+DRa+tYui0vsQkhLPwlVUMntqHpCm9ef2WzwiOCmDuF3ew/fv9rP/qF6Y/\nPI6h1/Rj57IDfP/mGkbfMoSpD4xBkiQ+uu9bDm0+wf0f3cqACbLCxwlety3Zy03PXcOtr8zw6PF6\n5WIeT41/k9dmfozBpOf1DU/ywa4X6T++Z4PAg8Y17LoBjJo1mBUfbaK8qEJu/PZbsNsbLN4CSD2W\nAUDb7vEN2vetOoTD7mDotf1cbRu/2YkpwMjgqX0AWPnJZrR6DWNuGUJ5USWbFu5mxIwBhEQF8sPb\nawmNDWL4df1Z9OoqmaWd1IufPtxEn6u7UnilhLTTWcx6cjILX1tFbGIElaXVFOWWcdUNA9iz+gjj\nZw9l3Te76XNVZ3Ys/422XWI4tO00cR0iObbrHG26xHDkl2Q69W3D8b0X6DoggczUPEJjgrDb7BTn\nlxMRF0zGuRwi4mUNtbOtpKBSts4K86WksNIlHagsq6Gmqg6/ICMgqCirQe2lIjzm78nAKo/ao4F3\nkFm2Gz21NRrWCTjpBK/NVE/gIUmSEpX92yRJ6gX0RiZWgpQnhC8hA9ckoKPnqfgSeEAZPxf4zO1Y\nhDJ2AjJwBXgS2KtYR37QaC4zMFVhjYcD7wlPf7nACByRJKkTsBt4QWlfKUlSH8W+8hxwu9L+PDBa\naZ/UZDaYhmx32Q0YBbzj9oS0L/CA8v7bANMkSXoSqFXegycGvBtwN9ABuAlIlCSpLzIz/oDSZx/Q\nX5KkHsCPwBMe5nGvT4AfgG+B14QQkc20tdT/kfqv1tg0V/VBBe7bosl2vUNB8y4Ff1b/iqtPI83r\n7wBW0Ux704vwMMZ9/5+RD9RfWfPnbgRoG/Rx/ydfuWkN5nJfvIUsD5DcGFgnCSeD1np3AadkwAVs\nJXms3WWbpbCxNrcgA2eMrNWuxMjKulfnqytCViVHyOr1zghZgd3moLZajpB12B0Y9BoMJi1eahXm\nGiuV5TUIIVyWVjVVdVSU1mAy6fHx01NTbaGsqAr/IBNGH50rYSs43I/qilrKi6uIiAuittpMaWEl\nUa1k71fJIREc4U9Gcg6t2kdQW1VHdXkNYdGBnD2cRpcBbbl4OpsOveIpvFJKRFwwNVVmDD467DYH\nBqOWOrONiNgginLK6De6C6d/TWXq3aNY9cV2kib1ZPeKQ2h03iRN7MW2pfuZ/vBYNi3YTXV5DQ99\neDPv3fMNwZEB3PbSdN68fT5Bkf488MHNvHvXV5TklfHMovvIu1TIxw9+R7chHbj1pemkHsvg/bu/\novOgdjzw8a0IIVj8ygq2LtrNzKenMl5ZoW+3O3hvzhdsX7qXW164llnPTGsCXh0OB2s+28LdvZ/i\n/KGL3PvBLXx66HV6/xN+sFPuG421zsrhzSdl4PrVVzByJLRt26Bf8sEU/EJ8iWgd2qB9988HiU6I\ncDGzZYUV/Lr2CKNuTEKj01CSV8bOZQe4alYSvkE+rP5sK3U1FqY/PI6j289w/kg61z82gSPbz3Dh\naDo3/mMSa+fvoKqsmplPTGLha6tp2y2W6ooarqQVcO2Do/n5060kTerJ1qW/EhzhT1lhJTabnYBQ\nP8oKK4ltH0lRbhlRrUOpKKnCYNIhSRJlJVUEhftz4cRl2nSOJvVUFnHtIqmurKWmspbQmECyLuYT\nHOGPUKvIyy4lLCZQXihYWElQuB82q53Soip8A41oDRrKS2sACZOfAbvdQW7235OBBX6QJOkdwCxJ\n0tvIYMVTW7MlhHhGYQpz3JoPSZKU4bb/oPIk8CAQAyQA/YBdCntrAZZ5mNsEDASWC9lxZz4yaHXW\nakmSHJIkJQN/Js9XAK8rTy63A1HNjHO4Xc8SZJAM0FlhgU8jSys6Ke37gQVC1gF70vkkId9XuyRJ\n+ciguI9y7JAkSemSJNmR73WSh/GN67AkSbnKGpc0YKvSfhpZQgHyLyFblGt93O1am6t7kUFvpiRJ\nd0iSlNNMW0v9H6m/H4B1Y1mdr+7++s1jOzfQ+0f61wYOAwpobU460Pik7kC4gYyg4XYTPeq/Sz7Q\nDCD2BGgbs8VCcuvoPOZ86062VriNlWSGtYEXrL2+ryfrLIein5VtZYWLhXUFFyjMq3twgbeXCm9v\ntduPCrWXCpVKIDmUCFmFfbXU2RBCoNO5s68Oamos1FSaUavV+Pjq0Om8qTNbqSirQQB+AQa8vNVU\nlNZQV2shKMQHb42aksJKBBAY6kN1hZnykioiYgKxWe0UXCklqpWcS1+cX0ZMG8X7VS0ICKn3fi3M\nKcPgo0Nv1JGVmktcuwiO7zlPz6HtObrrHANGd+PEnvMMntiTQ9vPMHJ6P3b+fJgR1/Vjw4LddB2U\nyKFtpzH66olLjCT5UBqzn53Cty/9TOvOMYRFB/Lr+uPMfn4aG7/bTf7lQh7/8g6+eeEniq6U8tS3\n97Bl0R4ObT7JnW/cQESrUF6Z+TG+QT48veg+ygorXY4Dz33/AN4aLzZ/t4ulr6/iqpuGcNNz1wAK\neL1jPjt+2M/sl67jxqemNvl6luSV8eykt/n04YV0Gdyer068zZT7Rv8u4+qp2vaIx+RvIPlgCmzZ\nApmZTRZvgRwJ22lAYgMQXZJXxum95xhybX9X+9ZFe7BZ7Yy7XZYPrP/6F2xWO1PvG01NZS1rv9zO\noIm9iG0fyZI31xAaE8TIGway5PXVRLYOpdeITqz6bCtDr+nL2d9SKcgq5sYnJrL07fV0G9yOk/su\n4HBIJHaPJ/XkZcbMSmLXysOMmtGfHcsPMmh8d3avPkLfq7twYNMpeo3oyOkDqXQZmMCV9EKCIvxw\nOCSK88sJjwsm9XQWcYkRVJTVUFNlJiQqgNzMYow+Boy+evKvlOIbYERv1FKcX4HOoMXHXw7ZqKuz\n4uNvAASVFbWgEoRF/wcszf4HSpLkf6UkSXrRue+prdGwZKCbUBZBSZL0muKG4+vWp9q5IYQYhsw6\nDlAYyuOAjj9XKqDMLYynuyRJHdyO17lte2JSG9dMIATopVxz/p+8Fuc9WADcL0lSF2T2WAcgSdLd\nwLPI4PyoECLoT8zZeO7m9j2V+/t2uO07AKfueB7wiXKtd/EH71P56C9JkrTg99pa6v9O/eH/KkKI\nBCF7tCYrepZ0IUT6/8TFearGrKvr0b1rAVejEANP+lbXVjP6V5zguKmUoEG8bLW6QssAACAASURB\nVCNQ2qx9lieGtbFUwFPbX5EPNNfHw/ndz+GugRXOBqnhvjNhy6V3RWFgG8gJlHbJs3WW2o2Flfsg\na2CdrKub1tUZXGCzOeT4WIsNq8WOzWLHYXcgEHh7qdHq5CADrUaNSi2wWe2y9lVhX/V6DUaTFi9v\nFXW1VirLa3E4HPj46dEbNEp+fLUiIzBitdgpKahAZ9DgF2CkqryWytJqwqICkOwSeVklhEUHoFar\nuJJeKHu/mq0U5srer/mZJZh89Rh99aQnXyGhWwwZyVdo0ymK8qJqtFovdHoNeZnFhEQFcOFoBjEJ\nYZzaf4GYxHDOHUojKMKf0vwy7DY77XrEk3riMjMeGstPH21iwNhuHN99jqqyGmY/O4Wvnl1G18Ht\nCY4MZNvSfcx4bALZF/PYs/IwNz87FYfdzrcvLCdpcm/GzxnBW7d9QXFOKc8tfQCDr55Xrv+I6ooa\nXlrxKP6hfhzeepKP7v+GXqO68PBntyOEwOFw8NG9X7Pj+33c8sK13PCPptK84zvPclevJzm15xwP\nzLuV19Y+QUj0P/P/ZH2pVCriOkZz+dwVefFWWBhMbnjOgqxicjMK6JLUvkH7nhUHcTgkhk6Xg//s\ndgfrv9pB18HtiesQRV2thfVf7aDf2O5EJ0Sw4ZudVJXVMGPuBI79cpbzh9OY8dh4ftt4grRTmcx8\ncjLL3t+IzWLn2gfG8MN7G+g5ohPJv6VRWVrN6FlJ7PjpIONnD2Xl59vo0Kc1B7ecJijCnyvpBeiM\nWkqLKtEZNBTllOEXbCLj7BWi2oRy+kAa7XrGk3Iik9YdoygvrsZaZyMowo+McznEtA2jtrqOitJq\nwmKCXE4DgaE+8lMBuwP/YB+qK81UVpjxCTTirfGmsqwGhyRh9NUjVIL8nL8tA/tPlyRJF4EjwKtC\nCDWAEEJH8wDSDyiV/h975x1eVZW2/d86vaZXQkJCb5FeRQRELKACitiwK/Yy1rHrqGPBMirqiIIV\nFBGUJoJ0pPfeCQkhpNfTy/r+2PucnDRgZtT3ne/Nc13n2nuvvdba67TkOfe+n/uW0qnyV0OOkRuA\n81U6gR4Y18i1qoBjQohx6nWEEKJb/X71ohpoKFpcu5YiKaVPCDEUaNVEPw0Q4tReh4JCos5boK43\nfGtfCNFGSrlBSvkcUIySyEbGamC8ULTbE4HBwEb1XF8hRJb6g2B8xLV86nX+3YgGQpzVm/6DeZrj\n/2icDSwyDfgI8KNwcr5EuWXxPxrh/KwRakCDPlAnua1PD6g9Xxd9DZ1sLLltQCX4T56Euv+70Aca\nJMRNzFevb6REVgjWDiOuEYlsneKtUIFXaI760ln1E9pArRqBDEi1H2GjghAKG2kha9CrKKw+ZCMr\n0GiVKwb8QaWgy+XH7fLi8wXQqOirOYy+SlxOL45qNxqhwWY3YbEY8Hn9VFe68PsDRMdaMZn1OKrc\n1FQ6iY2zYrEZFctYp4dEFR0rzC8nPjkKo1nPyeOlJCRHYzIbyD1cSHqbJHweP6UFlaS3TSbvSBGp\nGfEgJcX55aS3TWLXusN0O7cdB7cfp0u/tpw8Vkzb7HSKC8rJ6pRGcX45nXu3JvdAAeeP7sWW5XsZ\nfecF/DhlGQMu7c7K2RsxWYz0GtaF3+Zu5fonL+fbtxcgNBpueW4s7z/8BR16tea8Mb35+PHp9Bja\nmRE3DOLVmz4kuVUCf/nwNma8/hObl+zk7kkT6NC7Ne/dN5V9Gw/z6JSJtM7O4MjO47xy3XtkdU3n\nmRkPotPrkFLy/gPT+OULhU5QH3mVUjL97z/y5MWvEhVnY/L6V7hs4oWN8mL/lUhIi4PcPJg/H269\nFQx15bl2rNwLQLfzO9VpXzr9N9p0axV2Adu8eCeFx0sYdedwAJZ8s4bKkmqufOASPC4vP7y3iO5D\nOtO+ZxZfvfojiS3jGDZ+AF++MoeMji1o3yuLn79YxSU3D2bF7E04Kl2MnngBP/1zKReM78+8qSuI\nTYoiGJRUFFeTPbADR3blce5lPdi17hADL+3Ong1H6DW0C0f3nKB9t1aUFFRgjbKg0WkozCsjNTOB\n/duO0za7JaWFlcigJC45mtzDhSSlxSElFBdUkNQyFo9L0SiOS44mEAhSUVqDLdqMxWakusKJz+vD\nFm1Go9XiqHbjV4sN/4/F7UA8cFgIsRlFZ7wpjuUiQCeE2IfCU10PIKUsAF4A1qHcgm9KJed64DaV\ngrAHaLTwKiJ2AgG1oOrheue+AXqrt9VvpJ5yT0Q4UBLL3cAw4CW1/VmUxPu3emPfVAuodgNrgR31\n5pujrmsHsAx4XEoZcr/YhMI13QccU/uCwv3dGSri+jfiBRTqxRbgv1PnrTn+R0M0vPtSr4MQW6SU\nvYQQu1SoP9z2p6ywXiR3jpNXf30xASnwSS1BqcEX1OCXGoJSQ0Cq+0GBXwoCUkMgqGyV80LZBgWB\noAaJIBCsRW2DQeV8MAhBqVETWKWdEKobRNkPggJNClXcX/2HHQQRVNHhICpvVNkPSVwJdY5wm1Tb\nIvqF2onYF5GoaLBen0i5q8h5oTYJjZinyfmlSrYI1EuKI441KOhreI6g0iZCCX/EOaF+xrQotAFU\nSkGY/xFhQRv6hSBCCXJEWy01QaEnaIVQVA0kBAOKGkEwEFSLziQ6rUBv0KLVaPB6fPjcAZASvU6D\n2WwgEAzirHFDUGIyGzCbDFRXOAj4AlhsRkxmg0IhCEoSUqKoqXTiqlESWrfLR3WZg9SMOKrKa3BW\nucnqmErOvpPYY61Yo8ycPFpE595Z7N10lMyOqZSdqkQIiE20k3+0iC5927Dzt4P0G5HNhkU7GHRZ\nT9Yu2E6fC7qwZ8MhUlolIjSQf6SIUTcPZsZbC7nvzev5/G+zSW+fSp8Ls/ny5dk88vFtLPl6DYe2\n5fDOr0/z2m3/pLywkslrXuDd+6axfcVe3l3+HGUFFTx31dsMv+5cHvnnHfw4eTEfP/Y1Nzw9hgnP\njKX4RCkPnf88AO+t/hvxLWIVndcnvmH2ez8z/tHLGhRsOatdvHHLR6ydu5mh1wzkoQ9vx2w727uv\np4937ppC+oxPuKp6Cxw5AllZdc6/edvHbFy0je/yPgorIOTuz+eO7o8z8Y0bGPuAYt7w7JhJHN6e\nw1cH30VoNNze4wlsMVbeW/k8cz/+lQ8f+5o3f/4rHpeXZ696h/vfvRG9Uc/bd3/Gs9/cx8ofNrLh\nlx28Mf8JHr30dYaO64fL4WHj4l3c/tJVTH5iBjf+9Qq+eXMeg1XN15btUig8UUZUrJXKshqi4+0U\nnigjvV0Kh3bk0rV/W3auO0yXfm3Yu+kYiWmx+P0BKkpqyGiXQs6BAqLibehNOkpPVRGXHIPX46Om\n2k1MvA2fz4+j2oPFbkKr11Jd4UJoBVa7CZ8viNvtQ2gFJquRYDCIx+Pnl52vbJFS9v5d3pzm+B8N\nIUSNlNL2J1xnCPColHLUH32t5miOfzXOBoH1qLcODgkh7hNCjAH+8C/OmeIsAEnqF3UpeZBoUk6r\nUQS2fjFX/bGNHdfhvda2h/mofzZ9IDKRjewWSR+IfB4hp4jI+Rop3grTD8J0ghCSKyMsZAkTZ0N0\ngaA/GC7+UsaLWvtYbV0kVqfThFUKtBpFcSAYlPh9iuarx+XD7fLi9wXQaBqir26nD0e1GyTY7Eas\nViN+f5DqKhcel5coFblyO7xUlNZgtZmwx1hw1nioLK0hISkKo0lP8UlFuzM6zkpxQSV6vZbYRDsF\nx0uIirVijTJxbN9JsjqnUVlaQ8DnJyE1hv1bj9GlT2ty9p0kq3MLqkprMFuN6HRaqkpriIq1UpBT\nTHSCncLjJZitRiQSR5WL7IHt2L/5GFfdN4JZ7y/m3Mt6sm7hNnxeP+MevJjpr//EeaN7U1ZQzs7V\n+7n7jetY8Nlycvac4NGPb2fpt+vYtHgnE1+7DluMhddv/5isrunc9+5NbFu+h0+enM7Ay3px/VOj\ncVa7eHbMJJxVLv724+NhG9gvX5zF7Pd+ZvS9FzVIXguOFfHQ4BdYv2ArE9+8gSe/uPd3S14BNAQZ\n6tgHI0Y0SF6llGxdtosew7rWke9a8vVqNFoNQ8crkpQnjxay6ZedXHzLUHR6Hevmb+HkkULGPXQp\nfl+Ame8upMuA9mQP6sDXf/9JUR4Y15+v//4j7XpkkpAWx6o5m7jyvov48eNfERpB34vOYfVPWxhz\n93BmvreI1l1bsnfjYYxmAzq9nppKFy1aJ1NaUEHLtorTVmxStCL75vZhVS1h09ulsGfjUdr3aEVR\nfrliFxtj4fjBU6S3S1YUBaoV/mtZURVBqSgNKEoYAWIS7MoPqQoXthgzJouRmio3Ho8fi82E0aTH\n5fDgcfux2c2/2/vSHM3RHM3xvyHOJoF9ELCg6LT2Am7gf5CvUpuIRgJztfzXSBOD2tv9keMhkj97\nWv5rvaSwcftYEXFc5yIN9xsupPE+jSSxTfWr379uktx0/7qIZyN96hVzNVa8FZbUChVuhSgDQnnt\nlIItGUZSI+kCIetYoU4qZYQurE/i9wcU7Vefog0bkuDSaFTeq0HhvRqMOvR6LQjC3FeXw0PAH8Rk\n0oVNDHzeADVVbtwuL1arAZvdRDAgqSp34vcEiImzYjTqqCp3KEVcyVFoNIKSggrMKhe2vEiRK0pM\njaassAopgySkxlCQU0JMnA2z1UjOgZO0U61g7TEWTBYjxw+eUgTqfztIz/M7sm/zMXqe34mje07Q\n/bwO5OzNp+/wrhzYmsOwcX1Zt3A7o245n3mfLafvhdmsmbsFi91E5z6t2bJsDzc9PZov/jabqHgb\nI28dwpcv/8ig0b2xxVqZN2UZY+4dgcVu4vMXZ3HemD6MmDCIl69/HxmUPDv9fsoLK3l1wmTSO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SIiuBipIa3C4v8SkxlIdsaBMUB7iaajdRcTa0Wi1VFU40Wg1Wu4lgUOF0oxGYbQa06ufc6/Wj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sjrxCRE8+qip4hPDlXIzDl0igd7PcNfS7Zy/8e38/jU+4J+rtXZZ59BnTowbBilBUrcJy45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\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vDeB/6DjGArjS+FH1pYg0OM4+GNKB64EFxnYW+vsdmeJ4LXC+iMQZ73Vf4Lj3899kJ93j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\nTshm3YIIeI1M47qC53moQiqVMaBqCDI4w+hKlmHNgjFM5ik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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa7164d3da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Identification des paramètres a et k d'une fonction de Monod à l'aide de l'algorithme du gradient\n", "# -------------------------------------------------------------------------------------------------\n", "# les algorithmes de gradient à pas optimal et à pas constant sont testés\n", "\n", "import scipy.optimize as scop # pour utiliser des algorithmes d'optimisation déjà codés sous python\n", "\n", "# ------------------------------------------------------------------------------\n", "# PARAMETRES POUVANT ETRE MODIFIES\n", "# ------------------------------------------------------------------------------\n", "# * Paramètres des données\n", "sigma1=0.2 # ecart type bruit de mesure sur mu\n", "sigma2=0.9 # ecart type bruit de mesure sur S\n", "N =100 # nombre d'observation\n", "\n", "# * Paramètres des algorithmes de gradient\n", "x0 = np.array([0.01,0.01]) # valeur initiale pour l'algorithme à pas optimal\n", "x0c = np.array([0.01,0.01]) # valeur initiale pour l'algorithme à pas constant\n", "\n", "x0 = np.array([1.9,4.5]) # valeur initiale pour l'algorithme à pas optimal\n", "x0c = np.array([1.9,4.5]) # valeur initiale pour l'algorithme à pas constant\n", "\n", "alpha_constant = 2 # valeur de alpha pour l'algorithme de gradient à pas constant\n", "\n", "Nbiter = 35 # nombre d'itérations\n", "\n", "precision = 0.001 # seuil de précision pour le critère d'arrêt des algorithmes\n", "\n", "# * Parametres du modèle de Monod\n", "coeffk = 1.34 # coefficient k\n", "coeffa = 1.57 # coefficient a\n", "\n", "# ------------------------------------------------------------------------------\n", "\n", "print('Critères d\\'arret pour les algorithmes')\n", "print('1. Nombre d\\'itération maximal = '+str(Nbiter))\n", "print('2. Seuil de précision = '+str(precision))\n", "\n", "# * Generation de donnees d'observation\n", "S = np.linspace(0,15,num=N) # mesures du substrat\n", "mu = coeffkS/(coeffa+S) # sorties du modèle: calcul du mu\n", "mub = mu+sigma1(np.random.rand(N)-0.5) # bruitage des sorties mesurées\n", "Sb = S + sigma2(np.random.rand(N)-0.5) # bruitage des entrées mesurées\n", " \n", "# ** fonction qui calcule la valeur de la fonction f à minimiser, ainsi que son gradient\n", "def fonction_f(paramk,parama):\n", " global mub, Sb, N\n", " # f : fonction f\n", " # partialf_k : dérivée partielle de f par rapport à k\n", " # partialf_a : dérivée partielle de f par rapport à a\n", " # gradf : gradient de f\n", " \n", " # initialisation\n", " if len(paramk.shape)>0: # si les entrées sont vectorielles\n", " f = np.zeros((np.size(paramk,0),np.size(paramk,1)))\n", " partialf_k = np.zeros((np.size(paramk,0),np.size(paramk,1)))\n", " partialf_a = np.zeros((np.size(paramk,0),np.size(paramk,1)))\n", " else: # si les entrées sont scalaires\n", " f = 0\n", " partialf_k = 0\n", " partialf_a = 0\n", " \n", " # Boucle sur les données\n", " for i in np.arange(0,len(Sb)-1,1):\n", " mu = paramkSb[i]/(parama+Sb[i])\n", " # calcul de f qui est définie comme la somme sur les données (i=1:N) de\n", " # (kS_i/(a+S_i)-mu_i)^2 divisée par N\n", " f = f + (mu-mub[i])*2\n", " # calcul de la dérivée partielle de f par rapport à k qui est définie comme\n", " # la somme sur les données (i=1:N) de 2S_i/(a+S_i)(kS_i/(a+S_i)-mu_i) divisée par N\n", " partialf_k = partialf_k + 2(mu-mub[i])Sb[i]/(parama+Sb[i])\n", " # calcul de la dérivée partielle de f par rapport à a qui est définie comme\n", " # la somme sur les données (i=1:N) de -2kS_i/(a+S_i)^2(kS_i/(a+S_i)-mu_i) divisée par N\n", " partialf_a = partialf_a - 2(mu-mub[i])paramkSb[i]/((parama+Sb[i])2)\n", " # division par N\n", " f = f/N\n", " partialf_k = partialf_k/N\n", " partialf_a = partialf_a/N\n", " # stockage du gradient\n", " gradf = np.array([partialf_k, partialf_a])\n", " return [f, gradf]\n", "\n", "# ** Calcul de la fonction f pour le tracé\n", "# on fait un maillage de l'espace des valeurs des paramètres k et a\n", "# On se limite ici aux valeurs de k comprises entre 0.5 et 2\n", "# et les valeurs de a comprises entre 0.5 et 5: ce choix est arbitraire\n", "x , y = np.meshgrid(np.linspace(0.5,2,201),np.linspace(0.5,5,200))\n", "z = fonction_f(x,y)\n", "z = z[0]\n", "\n", "# tracé des courbes de niveau de f, c'est à dire des courbes sur lesquelles f est constante\n", "plt.figure(1)\n", "graphe = plt.contour(x,y,z,400)\n", "plt.xlabel('coefficient k')\n", "plt.ylabel('coefficient a')\n", "\n", "# fonction à minimiser dans l'algorithme de gradient à pas optimal pour obtenir la valeur de alpha\n", "def fonction_falpha(alpha,x,gradfx):\n", " # dans l'algorithme de gradient à pas optimal, on cherche la valeur de alpha qui\n", " # minimise f(x_k-alpha gradf(x_k)) pour un x_k donné à chaque itération\n", " x1 = x - alphagradfx\n", " f = fonction_f(x1[0],x1[1])\n", " f = f[0]\n", " return f\n", "\n", "# Algorithme du gradient\n", "\n", "# 1. Gradient à pas optimal\n", "# initialisation des vecteurs où seront stockées les valeurs intermédiaires obtenues avec les algorithmes itératifs\n", "xval = np.zeros((Nbiter+1,2))\n", "\n", "# stockage de la valeur initiale: le choix de cette valeur est effectué en début de code\n", "xval[0]=x0\n", "# le choix du nombre d'itérations ainsi que le seuil de précision du critère d'arrêt sont\n", "# également effectués en début de code\n", "\n", "# Tracé de la valeur initiale dans l'espace des paramètres\n", "plt.plot(x0[0],x0[1],'ro')\n", "\n", "# initialisation de la valeur du gradient de f\n", "temp = fonction_f(np.asarray(x0[0]),np.asarray(x0[1]))\n", "fx0 = temp[0] # valeur de f en x_k\n", "gradfx0 = temp[1] # gradient de f en x_k\n", " \n", "# Boucle itérative de l'algorithme de gradient à pas optimal\n", "noiter = 1 # numéro de l'itération courante\n", "while (noiter < Nbiter+1) and (np.sqrt(gradfx0[0]2+gradfx0[1]*2)>precision):\n", " \n", " # calcul de la valeur de alpha optimale en utilisant ici une méthode de minimisation codée sous\n", " # python appelée algorithme de Nelder-Mead\n", " alphaopt = scop.minimize(fonction_falpha,0,args=(x0,gradfx0),method='Nelder-Mead')\n", " if alphaopt.success == True:\n", " alpha_k = alphaopt.x\n", " else:\n", " alpha_k = alpha_constant \n", " \n", " # stockage et mise à jour des valeurs de paramètres\n", " xval[noiter] = x0 - alpha_kgradfx0 # algorithme de gradient optimal\n", " \n", " # Tracé de la direction de descente dans l'espace des paramètres\n", " plt.plot(np.array([x0[0],xval[noiter][0]]),np.array([x0[1],xval[noiter][1]]),'r')\n", " \n", " # mise à jour pour l'itération d'après\n", " x0 = xval[noiter]\n", " noiter = noiter + 1\n", " \n", " # calcul de f et du gradient de f en x_k\n", " # pour le gradient à pas optimal\n", " temp = fonction_f(np.asarray(x0[0]),np.asarray(x0[1]))\n", " fx0 = temp[0] # valeur de f en x_k\n", " gradfx0 = temp[1] # gradient de f en x_k\n", " \n", " # Tracé des points dans l'espace des paramètres\n", " plt.plot(x0[0],x0[1],'r.')\n", "\n", "# Affichage des légendes et des résultats\n", "plt.plot(np.array([x0[0],xval[noiter-2][0]]),np.array([x0[1],xval[noiter-2][1]]),'r',label='algorithme du gradient à pas optimal')\n", "plt.text(2.2,3.3,' Gradient à pas optimal ')\n", "plt.text(2.2,3.0,'nombre d\\'itération ='+str(noiter-1))\n", "plt.text(2.2,2.7,'norme du gradient ='+str(np.sqrt(gradfx0[0]2+gradfx0[1]2)))\n", "plt.text(2.2,2.4,'coefficient k exact ='+str(coeffk)+'; estimé ='+str(x0[0]))\n", "plt.text(2.2,2.1,'coefficient a exact ='+str(coeffa)+'; estimé ='+str(x0[1]))\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "\n", "# 2. Gradient à pas constant\n", "# initialisation des vecteurs où seront stockées les valeurs intermédiaires obtenues avec les algorithmes itératifs\n", "xvalc = np.zeros((Nbiter+1,2))\n", "\n", "# stockage de la valeur initiale: le choix de cette valeur est effectué en début de code\n", "xvalc[0]=x0c\n", "# le choix du nombre d'itérations ainsi que le seuil de précision du critère d'arrêt sont\n", "# également effectués en début de code\n", "\n", "# Tracé de la valeur initiale dans l'espace des paramètres\n", "plt.plot(x0c[0],x0c[1],'ro')\n", "\n", "# initialisation de la valeur du gradient de f\n", "temp = fonction_f(np.asarray(x0c[0]),np.asarray(x0c[1]))\n", "fx0c = temp[0] # valeur de f en x_k\n", "gradfx0c = temp[1] # gradient de f en x_k\n", "\n", "# Boucle itérative de l'algorithme de gradient à pas constant\n", "noiter = 1 # numéro de l'itération courante\n", "while (noiter < Nbiter+1) and (np.sqrt(gradfx0c[0]2+gradfx0c[1]2)>precision):\n", " \n", " # stockage et mise à jour des valeurs de paramètres\n", " xvalc[noiter] = x0c - alpha_constantgradfx0c # algorithme de gradient à pas constant\n", " \n", " # Tracé de la direction de descente dans l'espace des paramètres\n", " plt.plot(np.array([x0c[0],xvalc[noiter][0]]),np.array([x0c[1],xvalc[noiter][1]]),'g')\n", " \n", " # mise à jour pour l'itération d'après\n", " x0c = xvalc[noiter]\n", " noiter = noiter + 1\n", " \n", " # calcul de f et du gradient de f en x_k\n", " # pour le gradient à pas constant\n", " temp = fonction_f(np.asarray(x0c[0]),np.asarray(x0c[1]))\n", " fx0c = temp[0] # valeur de f en x_k\n", " gradfx0c = temp[1] # gradient de f en x_k \n", " \n", " # Tracé des points dans l'espace des paramètres\n", " plt.plot(x0c[0],x0c[1],'g.')\n", " \n", "# Affichage des légendes et des résultats\n", "plt.plot(np.array([x0c[0],xvalc[noiter-2][0]]),np.array([x0c[1],xvalc[noiter-2][1]]),'g',label='algorithme du gradient à pas constant')\n", "plt.plot(coeffk,coeffa,'ko',label='solution exacte')\n", "plt.text(2.2,1.7,'Gradient à pas constant')\n", "plt.text(2.2,1.4,'nombre d\\'itération ='+str(noiter-1))\n", "plt.text(2.2,1.1,'norme du gradient ='+str(np.sqrt(gradfx0c[0]2+gradfx0c[1]2)))\n", "plt.text(2.2,0.8,'coefficient k exact ='+str(coeffk)+'; estimé ='+str(x0c[0]))\n", "plt.text(2.2,0.5,'coefficient a exact ='+str(coeffa)+'; estimé ='+str(x0c[1]))\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### <a name=\"NewtonRaphton\">Méthode de Newton-Raphton</a>\n", "\n", "La méthode de Newton-Raphton est un algorithme qui est destiné à trouver une approximation numérique d'un zéro (ou racine) d'une fonction $f$, c'est à dire la valeur de $x$ telle que $f(x)=0$.\n", "\n", "Cette méthode peut être également utilisée en optimisation car le problème de minimisation d'une fonction $g$ revient au problème de recherche des zéros de la dérigée $g^\\prime$ de la fonction.\n", "\n", "> <u>Algorithme de Newton-Raphton</u>:\n", "> - on choisit un nombre d'itérations maximal $N$, un seuil de précision $\\epsilon$ et une valeur initiale $x_0$ pour $x$\n", "> - initialisation: $k=0$ et calcul de $f(x_0)$\n", "> - Tant que $k+1\\leqslant N$ et $f(x_k)>\\epsilon$ alors\n", ">> \\begin{eqnarray} x_{k+1} & = & x_k - \\frac{f(x_k)}{f^\\prime(x_{k})} \\\\\n", "k & = & k+1 \\end{eqnarray}\n", "> - $\\hat{x}\\simeq x_{k}$ \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "D'où vient cette formule?\n", "\n", "Si on considère la formule de Taylor à l'ordre $1$ de f au voisinage de $x_k$:\n", "$$ f(x) = f(x_k)+f^\\prime(x_k)(x-x_k)+R_1(x) $$\n", "où $R_1(x)$ est une fonction de $x$ négligeable par rapport aux autres termes du développement au voisinage de $x_k$.\n", "\n", "Cela veut dire que si $x$ est proche de $x_k$ alors on peut négliger $R_1(x)$ et on a alors:\n", "$$f(x) \\simeq f(x_k)+f^\\prime(x_k)(x-x_k)$$\n", "Rappelons que la droite d'équation $y=f(x_k)+f^\\prime(x_k)(x-x_k)$ est la tangente au graphe de $f$ en $x_k$. Faire cette approximation revient donc à considérer qu'autour de $x_k$ la courbe de $f$ est à peu près égale à sa tangente.\n", "\n", "Au lieu de chercher le zéro de $f$ on va alors chercher le zéro de son approximation, c'est à dire de la tangente qui va nous donner une nouvelle valeur $x_{k+1}$ supposée plus proche du zéro de $f$ que $x_k$. Le point $x_{k+1}$ est donc défini par:\n", "$$ 0 = f(x_k)+f^\\prime(x_k)(x_{k+1}-x_k) \\Longleftrightarrow x_{k+1} = x_k - \\frac{f(x_k)}{f^\\prime(x_{k})}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Exemple\n", "> On cherche à trouver la valeur du point d'équilibre du modèle de croissance d'une population de micro-organisme $B$ sur un substrat $S$ dans un réacteur batch donné par:\n", "> $$\n", "\\left\\{\n", "\\begin{array}{crl}\n", "\\frac{dB}{dt}= & \\mu(S)B &-\\frac{Q}{V}B\\\\\n", "\\frac{dS}{dt}= & -k\\mu(S)B&+\\frac{Q}{V}(S_0-S) \\\\\n", "\\end{array}\n", "\\right.\n", "$$\n", "> Trouver l'équilibre pour lequel $B\\neq 0$ revient à résoudre l'équation $\\mu(S)-\\frac{Q}{V}=0$, c'est à dire trouver les zéros de la fonction $S\\mapsto \\mu(S)-\\frac{Q}{V}$. Nous allons résoudre cette équation grâce à l'algorithme de Newphton-Raphton." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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i5s01llhERIpHBbOI+IaTJ7Fr1xG/IprEH6IJ2BhF7djN+Ns0ABKpQzRhRDGe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\nvo8E5udxzGqgpjGmXsbzAcBmD2Rzu/H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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa7164d3cc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.plot_iter>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Illustration du principe de l'algorithme de Newton pour trouver les zéros d'une fonction\n", "# ----------------------------------------------------------------------------------------\n", "def fonction_f(S):\n", " # parametres du modele de Monod\n", " coeffk = 1.34 # coefficient k\n", " coeffa = 1.57 # coefficient a\n", " QsurV = 1 # valeur de Q/V\n", " \n", " # calcul de mu(S)\n", " mu = coeffkS/(coeffa+S)\n", " # calcul de la dérivée de mu\n", " muprime = coeffkcoeffa/((coeffa+S)2)\n", " # calcul de f=mu(S)-QsurV\n", " f = mu-QsurV\n", " # calcul de la dérivée de f\n", " fprime = muprime\n", " return [f, fprime]\n", "\n", "# Calcul de la fonction f=mu(S)-Q/V pour le tracé\n", "N = 200 # nombre de points pour le tracé\n", "S = np.linspace(0,7,num=N) # mesures du substrat\n", "f = fonction_f(S) # fonction f\n", "\n", "def plot_iter(i):\n", " global xval\n", " # ième itération de l'algorithme de Newton\n", " x0 = xval[i] # valeur initiale du zéro\n", " temp = fonction_f(x0) # calcul de f=mu(S)-Q/V et sa dérivée\n", " fx0 = temp[0] # fonction f\n", " fprimex0 = temp[1] # dérivée de f\n", " x1 = x0 - fx0/fprimex0 # mise à jour de la valeur du zéro à l'itération i\n", " \n", " # tracé\n", " plt.figure(1)\n", " plt.plot(S,f[0],'b')\n", " plt.plot(x0,fx0,'ko')\n", " plt.text(x00.93,-0.98,'$x_{'+str(i)+'}$',fontsize=14)\n", " plt.plot(x1,0,'ko')\n", " plt.text(x11.02,-0.98,'$x_{'+str(i+1)+'}$',fontsize=14)\n", " plt.plot(np.array([0, 7]), fprimex0(np.array([0, 7])-x0)+fx0,'r',\n", " label='tangente à $f$ en $x_{'+str(i)+'}$ d\\'équation $y=f^\\prime(x_{'+str(i)+'})(x-x_{'+str(i)+'})+f(x_{'+str(i)+'})$')\n", " plt.text(3.1,0.3,'tangente à $f$ en $x_{'+str(i)+'}$' ,fontsize=12)\n", " plt.text(3.1,0.2,'$y=f^\\prime(x_{'+str(i)+'})(x-x_{'+str(i)+'})+f(x_{'+str(i)+'})$',fontsize=12)\n", " plt.plot(np.array([0, 7]), np.array([0, 0]),'k--')\n", " plt.plot(np.array([x0, x0]), np.array([-1, fx0]),'k--')\n", " plt.plot(np.array([x1, x1]), np.array([-1, 0]),'k--')\n", " plt.xlabel('Substrat $S$')\n", " plt.ylabel('$f(S)=\\mu(S)-Q/V$')\n", " plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", " plt.ylim(-1,0.4)\n", " plt.show()\n", "\n", "# Algorithme complet de Newton\n", "Nbiter = 8 # nombre d'itérations\n", "x0 = 0.5 # valeur initiale du zéro\n", "xval = np.zeros((Nbiter+1,1)) # initialisation\n", "xval[0]=x0 # stockage de la valeur initiale\n", "for i in np.arange(1,Nbiter+1,1): # boucle d'itération\n", " temp = fonction_f(x0) # calcul de f=mu(S)-Q/V et de sa dérivée\n", " fx0 = temp[0] # fonction f\n", " fprimex0 = temp[1] # dérivée de f\n", " xval[i] = x0 - fx0/fprimex0 # mise à jour de la valeur du zéro à l'itération i\n", " x0 = xval[i] #stockage de la nouvelle estimation du zéro à l'itération i\n", "\n", "# tracé intéractif pour voir l'évolution de l'estimation du zéro au cours des itérations\n", "interact(plot_iter, i=(0,Nbiter,1))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## <a name=\"remarquesFinir\">Quelques remarques pour finir</a>\n", "\n", "### Analyse de sensibilité: quels paramètres doit on identifier?\n", "\n", "Avant d'identifier les paramètres d'un modèle, il peut être intéressant, surtout si le nombre de paramètres est important, de faire une analyse de sensibilité. Cette analyse permet de quantifier l'impact de la variation des paramètres sur les sorties du modèle. \n", "\n", "Les résultats de l'analyse de sensibilité pourront être utilisés pour réduire le nombre de paramètres à identifier: seuls les paramètres ayant un impact non négligeable sur la sortie seront identifiés.\n", "\n", "### Fonctions pré-codées\n", "\n", "Il existe évidemment et heureusement des fonctions déjà codées en python/scilab/R/etc qui reprennent les algorithmes de minimisation.\n" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
jskksj/cv2stuff	cv2stuff/notebooks/CameraCalibration.ipynb	1	242173	{ "cells": [ { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pathlib import Path\n", "import numpy as np\n", "# cfg should save and preset the proper criteria.\n", "from cfg import (winSize, zeroZone, criteria, pattern_size)\n", "import cv2\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class CornersNotFound(Exception):\n", " pass" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def findCorners(image, pattern_size, winSize, zeroZone, criteria):\n", " gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n", " found, corners = cv2.findChessboardCorners(gray, pattern_size)\n", " \n", " if found:\n", " return(cv2.cornerSubPix(gray, corners, winSize, zeroZone, criteria))\n", " else:\n", " raise CornersNotFound(\"CornersNotFound\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/jsk/GitHub/cv2stuff/data/images/left01.jpg'" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_path = str(Path('/home/jsk/GitHub/cv2stuff/data/images/'))\n", "left_image = image_path + '/left01.jpg'\n", "left_image" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f9215d8aef0>" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ORPfAq6zoWQCamPLxus4NwxU8ScoaVktaN0oe4Zwxtk3kbmOhXENNCnIt+TzG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gZcG3lqgdAkuX5HRl5XVlbg9/uFn2Gkj99NpVIIh8MIhUJwu91yMid7fVZVVcn3a2tr4fF4MDc3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\nRRZZ9BhkgadFFllk0WPQ/wHbbFhHWlEmyQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f921028fdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = cv2.imread(left_image)\n", "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", "plt.imshow(image_rgb)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "try:\n", " corners = findCorners(image_rgb, pattern_size, winSize, zeroZone, criteria)\n", "except CornersNotFound:\n", " corners = []" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f9215d70d68>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Q7vSiYKkQKroEOjWU6v6nUimTMGKiiAqAW/+ohJhVp5Kenp7G1NQUZmZmzDMOHTpkkkic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\nK9iyZQs2b96MH/7wh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0v/WZvNhubmZjFK2mnhXmLITeeHPWz5vDqijEQi8Hg88lo6nZYK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\nOuLLBHe+hpFdDSFpKES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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9215da3278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cv2.drawChessboardCorners(image_rgb, pattern_size, corners, True)\n", "plt.imshow(image_rgb)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(480, 640)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "height, width = image_rgb.shape[:2]\n", "height, width" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "images_path = Path('/home/jsk/GitHub/cv2stuff/data/images')\n", "images_paths = set(images_path.glob('left[0-9].jpg'))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corners_reshape = (-1, 2)\n", "corners_new = corners.reshape(-1, 2)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(54, 1, 2)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corners.shape" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(54, 2)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corners_new.shape" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "244.40532" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corners[0,0,0]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('float32')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corners.dtype" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def intializePatternPoints(pattern_size):\n", " pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)\n", " pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)\n", " return(pattern_points)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def findAllCorners(pattern_size, image_paths, winSize, zeroZone, criteria):\n", " object_points, image_points = [], []\n", " \n", " pattern_points = intializePatternPoints(pattern_size)\n", " for image_path in image_paths:\n", " try:\n", " image_bgr = cv2.imread(image_path)\n", " image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n", " corners = findCorners(image_rgb, pattern_size, winSize, zeroZone, criteria)\n", " except CornersNotFound:\n", " pass\n", " \n", " object_points.append(corners)\n", " image_points.append(pattern_points)\n", " return(object_points, image_points)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def initializeImagePaths(glob_pattern):\n", " image_paths = set()\n", " for path in glob_pattern:\n", " image_paths.add(str(path))\n", " return(image_paths)\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'/home/jsk/GitHub/cv2stuff/data/images/left01.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left02.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left03.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left04.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left05.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left06.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left07.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left08.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left09.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left11.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left12.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left13.jpg',\n", " '/home/jsk/GitHub/cv2stuff/data/images/left14.jpg'}" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_path = Path('/home/jsk/GitHub/cv2stuff/data/images')\n", "image_glob = image_path.glob('left[0-9].jpg')\n", "image_paths = initializeImagePaths(image_glob)\n", "image_paths" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "image_points, object_points = findAllCorners(pattern_size, image_paths, winSize, zeroZone, criteria)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[ 0., 0., 0.],\n", " [ 1., 0., 0.],\n", " [ 2., 0., 0.],\n", " [ 3., 0., 0.],\n", " [ 4., 0., 0.],\n", " [ 5., 0., 0.],\n", " [ 6., 0., 0.],\n", " [ 7., 0., 0.],\n", " [ 8., 0., 0.],\n", " [ 0., 1., 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}, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(640, 480)" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_bgr = cv2.imread(undistort_image)\n", "heigth, width = image_bgr.shape[:2]\n", "width, height" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rms, camera_matrix, distortion_coeficients, rotation_vectors, translation_vectors = \\\n", " cv2.calibrateCamera(object_points, image_points, (width, height), None, None) " ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 628.99803077, 0. , 321.61760204],\n", " [ 0. , 640.7759953 , 199.33760571],\n", " [ 0. , 0. , 1. ]])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "camera_matrix" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.14329251214954217" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rms" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.39739852, -0.03035544, 0.0228357 , 0.01347597, 0.41850775]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distortion_coeficients" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[-0.35358963],\n", " [ 0.51719743],\n", " [ 1.30454888]])]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rotation_vectors" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([[ 2.8379715 ],\n", " [ -3.68537301],\n", " [ 14.95393764]])]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "translation_vectors" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	isc
poldrack/fmri-analysis-vm	analysis/efficiency/DesignEfficiency.ipynb	1	197511	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook covers the concepts underlying design efficiency.\n", "\n", "In order to examine the factors that affect efficiency, we need to be able to generate experimental designs that vary in their timing and correlation between regressors. Let's first create a function that can generate such designs for us." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f29de2a8d10>]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GydQpCMjuAIwO5zIzXyp5BLBdIyAXuSGgbGUc6N7H8X5LVgFFhYBMEYBKB+Dq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\nllaHSjsALS0trQ7V/wd6pha4M2vDIQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f29ddb56fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "import numpy\n", "%matplotlib inline\n", "import sys\n", "sys.path.insert(0,'../utils')\n", "from mkdesign import create_design_singlecondition\n", "import matplotlib.pyplot as plt\n", "#from spm_hrf import spm_hrf\n", "from nipy.modalities.fmri.hemodynamic_models import spm_hrf,compute_regressor\n", "\n", "tr=1.0\n", "\n", "# the \"blockiness\" argument controls how block-y the design is\n", "# from 1( pure block) to 0 (pure random)\n", "d,design=create_design_singlecondition(blockiness=0.95)\n", "regressor,_=compute_regressor(design,\n", " 'spm',numpy.arange(0,len(d)))\n", "plt.axis([0,400,-0.2,1.2])\n", "plt.plot(d)\n", "plt.plot(regressor,color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our design, let's generate some synthetic data. We will generate AR1 noise to add to the data; this is not a perfect model of the autocorrelation in fMRI, but it's at least a start towards realistic noise.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 400)\n" ] } ], "source": [ "from statsmodels.tsa.arima_process import arma_generate_sample\n", "\n", "ar1_noise=arma_generate_sample([1,0.3],[1,0.],len(regressor))\n", "beta=4\n", "y=regressor.T*beta + ar1_noise\n", "print y.shape" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f29dda48dd0>]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ldOWuk7KJ3l4abXBoXpDn7qfcgejJfX5ebfoAkI3R00Okb3rup58OnHaaipbx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\nP+Vu2jKVFvUk0XMXghJO3X9/eDIzPs9Vq4A3aBs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gHLk7ODg4tCAcuTs4ODi0IP4/6s3L\nQVg7qEYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f29ddb6b4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(y.T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's fit the general linear model to these data. We will ignore serial autocorrelation for now." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.5, 1.5, 400.0, -50.0)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29dde82050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X=numpy.vstack((regressor.T,numpy.ones(y.shape))).T\n", "plt.imshow(X,interpolation='nearest',cmap='gray')\n", "plt.axis('auto')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400, 2)\n" ] }, { "data": { "image/png": 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lQXorC4lEqit3ZcuofPqK3FUoZDot5A4w3Cttso++triI6QRyOekXS5ZA4i/S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\noUPhzDPVY52WKSry/mgZXbHTPpcgGFT7FQyqEzGeq6+sTL1j1OYnGIT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29dd9e4e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beta_hat=numpy.linalg.inv(X.T.dot(X)).dot(X.T).dot(y.T)\n", "y_est=X.dot(beta_hat)\n", "plt.plot(y.T,color='blue')\n", "plt.plot(y_est,color='red',linewidth=2)\n", "print X.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make a function to repeatedly generate data and fit the model." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def efficiency_older(X,c=None):\n", " if not c==None:\n", " c=numpy.ones((X.shape[1]))\n", " else:\n", " c=numpy.array(c)\n", " return 1./c.dot(numpy.linalg.inv(X.T.dot(X))).dot(c)\n", "\n", "def efficiency(X,c=None):\n", " \"\"\" remove the intercept\"\"\"\n", " if not c==None:\n", " c=numpy.ones((X.shape[1]))\n", " else:\n", " c=numpy.array(c)\n", " return 1./numpy.trace((numpy.linalg.inv(X[:,:-1].T.dot(X[:,:-1]))))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's write a simulation that creates datasets with varying levels of blockiness, runs the previous function 1000 times for each level, and plots mean efficiency. Note that we don't actually need to run it 1000 times for blockiness=1, since that design is exactly the same each time." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nruns=100\n", "blockiness_vals=numpy.arange(0,1.1,0.1)\n", "meaneff_blockiness=numpy.zeros(len(blockiness_vals))\n", "\n", "for b in range(len(blockiness_vals)):\n", " eff=numpy.zeros(nruns)\n", " for i in range(nruns):\n", " d_sim,design_sim=create_design_singlecondition(blockiness=blockiness_vals[b])\n", " regressor_sim,_=compute_regressor(design_sim,'spm',numpy.arange(0,len(d_sim)))\n", " X=numpy.vstack((regressor_sim.T,numpy.ones(y.shape))).T\n", " eff[i]=efficiency(X,c=[1,0])\n", " meaneff_blockiness[b]=numpy.mean(eff)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f29dde205d0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29dde50ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(blockiness_vals,meaneff_blockiness)\n", "plt.xlabel('blockiness')\n", "plt.ylabel('efficiency')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(400, 2)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's do a similar simulation looking at the effects of varying block length between 10 seconds and 120 seconds (in steps of 10). since blockiness is 1.0 here, we only need one run per block length." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "blocklenvals=numpy.arange(10,120,1)\n", "meaneff_blocklen=numpy.zeros(len(blocklenvals))\n", "sims=[]\n", "for b in range(len(blocklenvals)):\n", " d_sim,design_sim=create_design_singlecondition(blocklength=blocklenvals[b],blockiness=1.)\n", " regressor_sim,_=compute_regressor(design_sim,'spm',numpy.arange(0,len(d_sim)))\n", " X=numpy.vstack((regressor_sim.T,numpy.ones(y.shape))).T\n", " sims.append(numpy.mean(regressor_sim))\n", " meaneff_blocklen[b]=efficiency(X,c=[1,0])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f29dda58f90>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/hauvdt9MVeGWW+ArX0m7ZaYjSdZUpPGNfQRwgog8IyJNIvIp7/gA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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f29dde868d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(blocklenvals,meaneff_blocklen)\n", "plt.xlabel('block length')\n", "plt.ylabel('efficiency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at the effects of correlation between regressors. We first need to create a function to generate a design with two conditions where we can control the correlation between them." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from mkdesign import create_design_twocondition\n", "\n", "d,des1,des2=create_design_twocondition(correlation=1.0)\n", "regressor1,_=compute_regressor(des1,'spm',numpy.arange(0,d.shape[0]))\n", "regressor2,_=compute_regressor(des2,'spm',numpy.arange(0,d.shape[0]))\n", "\n", "X=numpy.vstack((regressor1.T,regressor2.T,numpy.ones(y.shape))).T" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nruns=100\n", "corr_vals_intended=numpy.arange(-1,1.1,0.1)\n", "\n", "corr_vals=numpy.zeros(len(corr_vals_intended))\n", "\n", "meaneff_corr=numpy.zeros(len(corr_vals))\n", "sumx=numpy.zeros(len(corr_vals))\n", "\n", "for b in range(len(corr_vals_intended)):\n", " eff=numpy.zeros(nruns)\n", " corrs=numpy.zeros(nruns)\n", " for i in range(nruns):\n", " d_sim,des1_sim,des2_sim=create_design_twocondition(correlation=corr_vals_intended[b])\n", " regressor1_sim,_=compute_regressor(des1_sim,'spm',numpy.arange(0,d_sim.shape[0]))\n", " regressor2_sim,_=compute_regressor(des2_sim,'spm',numpy.arange(0,d_sim.shape[0]))\n", " X=numpy.vstack((regressor1_sim.T,regressor2_sim.T,numpy.ones(y.shape))).T\n", " # use contrast of first regressor\n", " eff[i]=efficiency(X,c=[1,0,0])\n", " corrs[i]=numpy.corrcoef(X.T)[0,1]\n", " corr_vals[b]=numpy.mean(corrs)\n", " sumx[b]=numpy.sum(X[:,0])\n", " meaneff_corr[b]=numpy.mean(eff)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f29dd92a490>" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Z3zCzqWa2exw5q5PMax+t183M1prZkdnMtzFJ/u6URn+rb5pZeZYj1iiJ358W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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f29dd897690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(corr_vals,meaneff_corr)\n", "plt.xlabel('mean correlation between regressors')\n", "plt.ylabel('efficiency')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at efficiency of estimation of the shape of the HRF, rather than detection of the activation effect. This requires that we use a finite impulse response (FIR) model." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f29dd5b7f50>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29dd6b12d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d,design=create_design_singlecondition(blockiness=0.0)\n", "regressor,_=compute_regressor(design,'fir',numpy.arange(0,len(d)),fir_delays=numpy.arange(0,16))\n", "plt.imshow(regressor[:50,:],interpolation='nearest',cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's simulate the FIR model, and estimate the variance of the fits." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "nruns=100\n", "blockiness_vals=numpy.arange(0,1.1,0.1)\n", "meaneff_fit_blockiness=numpy.zeros(len(blockiness_vals))\n", "meancorr=[]\n", "for b in range(len(blockiness_vals)):\n", " eff=numpy.zeros(nruns)\n", " cc=numpy.zeros(nruns)\n", " for i in range(nruns):\n", " d_sim,design_sim=create_design_singlecondition(blockiness=blockiness_vals[b])\n", " regressor_sim,_=compute_regressor(design_sim,'fir',\n", " numpy.arange(0,len(d_sim)),fir_delays=numpy.arange(0,16))\n", " X=numpy.vstack((regressor_sim.T,numpy.ones(regressor_sim.shape[0]))).T\n", " eff[i]=efficiency(X)\n", " cc[i]=numpy.corrcoef(X.T)[0,1]\n", " \n", " meaneff_fit_blockiness[b]=numpy.mean(eff)\n", " meancorr.append(numpy.mean(cc))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f29ddee3fd0>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29ddcb6790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(blockiness_vals,meaneff_fit_blockiness)\n", "plt.xlabel('blockiness')\n", "plt.ylabel('efficiency')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f29dd87a910>]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29dd73ce90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(blockiness_vals,meancorr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "__Exercise:__ write a function to generate random designs, and then do this a large number of times, each time estimating the efficiency. Then plot the histogram of efficiencies. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "git": { "suppress_outputs": true }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
maxmouchet/tb	F4B101/TP1.ipynb	1	551818	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# F4B101A / TP1 / MCMC Methods\n", "\n", "D'après le notebook IPython par P. Tandeo et T. Guilment." ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Distributions\n", "using Gadfly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Monte-Carlo approximation of $\\pi$" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", " fill=\"#000000\"\n", " stroke-width=\"0.3\"\n", " font-size=\"3.88\"\n", ">\n", "<g class=\"plotroot xscalable yscalable\" id=\"img-642069c7-1\">\n", " <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" 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compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// drag actions, i.e. zooming and panning\n", "var pan_action = {\n", " start: function(root, x, y, event) {\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " root.data(\"tx0\", root.data(\"tx\"));\n", " root.data(\"ty0\", root.data(\"ty\"));\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", " },\n", " end: function(root, event) {\n", "\n", " },\n", " cancel: function(root) {\n", " set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n", " }\n", "};\n", "\n", "var zoom_box;\n", "var zoom_action = {\n", " start: function(root, x, y, event) {\n", " var bounds = root.plotbounds();\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", " var ratio = width / height;\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " x = xscalable ? x / px_per_mm : bounds.x0;\n", " y = yscalable ? y / px_per_mm : bounds.y0;\n", " var w = xscalable ? 0 : width;\n", " var h = yscalable ? 0 : height;\n", " zoom_box = root.rect(x, y, w, h).attr({\n", " \"fill\": \"#000\",\n", " \"opacity\": 0.25\n", " });\n", " zoom_box.data(\"ratio\", ratio);\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " var bounds = root.plotbounds();\n", " if (yscalable) {\n", " y /= px_per_mm;\n", " y = Math.max(bounds.y0, y);\n", " y = Math.min(bounds.y1, y);\n", " } else {\n", " y = bounds.y1;\n", " }\n", " if (xscalable) {\n", " x /= px_per_mm;\n", " x = Math.max(bounds.x0, x);\n", " x = Math.min(bounds.x1, x);\n", " } else {\n", " x = bounds.x1;\n", " }\n", "\n", " dx = x - zoom_box.attr(\"x\");\n", " dy = y - zoom_box.attr(\"y\");\n", " if (xscalable && yscalable) {\n", " var ratio = zoom_box.data(\"ratio\");\n", " var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n", " var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n", " dx = width * dx / Math.abs(dx);\n", " dy = height * dy / Math.abs(dy);\n", " }\n", " var xoffset = 0,\n", " yoffset = 0;\n", " if (dx < 0) {\n", " xoffset = dx;\n", " dx = -1 * dx;\n", " }\n", " if (dy < 0) {\n", " yoffset = dy;\n", " dy = -1 * dy;\n", " }\n", " if (isNaN(dy)) {\n", " dy = 0.0;\n", " }\n", " if (isNaN(dx)) {\n", " dx = 0.0;\n", " }\n", " zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n", " zoom_box.attr(\"width\", dx);\n", " zoom_box.attr(\"height\", dy);\n", " },\n", " end: function(root, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var zoom_bounds = zoom_box.getBBox();\n", " if (zoom_bounds.width * zoom_bounds.height <= 0) {\n", " return;\n", " }\n", " var plot_bounds = root.plotbounds();\n", " var zoom_factor = 1.0;\n", " if (yscalable) {\n", " zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n", " } else {\n", " zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n", " }\n", " var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n", " ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n", " zoom_box.remove();\n", " },\n", " cancel: function(root) {\n", " zoom_box.remove();\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " var scalable = root.hasClass(\"xscalable\") \|\| root.hasClass(\"yscalable\");\n", " var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n", " var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n", " var drag_action = zoomable ? zoom_action :\n", " panable ? pan_action :\n", " undefined;\n", " root.data(\"drag_action\", drag_action);\n", " if (drag_action) {\n", " var cancel_drag_action = function(event) {\n", " if (event.which == 27) { // esc key\n", " drag_action.cancel(root);\n", " root.data(\"drag_action\", undefined);\n", " }\n", " };\n", " window.addEventListener(\"keyup\", cancel_drag_action);\n", " root.data(\"cancel_drag_action\", cancel_drag_action);\n", " drag_action.start(root, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.update(root, dx, dy, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", " window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n", " root.data(\"cancel_drag_action\", undefined);\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.end(root, event);\n", " }\n", " root.data(\"drag_action\", undefined);\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n", " event.preventDefault();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), -0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), 0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "// Map slider position x to scale y using the function y = aexp(bx)+c.\n", "// The constants a, b, and c are solved using the constraint that the function\n", "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n", "var scale_from_slider_position = function(position, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return a * Math.exp(b * position) + c;\n", "}\n", "\n", "// inverse of scale_from_slider_position\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return 1 / b * Math.log((scale - c) / a);\n", "}\n", "\n", "var increase_zoom_by_position = function(root, delta_position, animate) {\n", " var scale = root.data(\"scale\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var position = slider_position_from_scale(scale, min_scale, max_scale);\n", " position += delta_position;\n", " scale = scale_from_slider_position(position, min_scale, max_scale);\n", " set_zoom(root, scale, animate);\n", "}\n", "\n", "var set_zoom = function(root, scale, animate) {\n", " var min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"scale\");\n", " var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n", " if (animate) {\n", " Snap.animate(\n", " old_scale,\n", " new_scale,\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", " } else {\n", " update_plot_scale(root, new_scale);\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", " var root = this.plotroot();\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#img-3b10254d\");\n", "fig.select(\"#img-3b10254d-5\")\n", " .init_gadfly();\n", "fig.select(\"#img-3b10254d-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-3b10254d-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-3b10254d-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-3b10254d-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-3b10254d-16\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3b10254d-16\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3b10254d-16\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#img-3b10254d-18\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-3b10254d-18\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-3b10254d-18\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#img-3b10254d-19\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-3b10254d-19\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-3b10254d-19\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3b10254d-19\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3b10254d-19\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ";\n", "fig.select(\"#img-3b10254d-20\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3b10254d-20\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3b10254d-20\")\n", " 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In[100]:2.\n" ] }, { "data": { "text/plain": [ "3.084" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count the number of darts inside the circle to estimate the area\n", "insideCircle(x, y) = ((x^2 + y^2) < R^2) ? 1 : 0\n", "N = reduce(+, mapslices(p -> insideCircle(p[1], p[2]), points, 2))\n", "\n", "# Estimate Pi\n", "pi_hat = 4(N/T)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Monte-Carlo integration\n", "\n", "We would like to approximate the integral $I$ defined by:\n", "\n", "$$\n", "I=\\int_a^b \\frac{1}{\\sqrt{2\\pi}} \\exp^{-x^2/2} dx.\n", "$$\n", "\n", "With $a = -1.96$ and $b = 1.96$ (95% confidence interval).\n", "\n", "We rewrite\n", "$$\n", "I = \\int_a^b g(x)\\,f(x)\\,dx \\\\\n", "h(x) = \\frac{1}{\\sqrt{2\\pi}} \\exp^{-x^2/2} \\\\\n", "g(x) = h(x)\\,(b-a) \\\\\n", "f(x) = \\frac{\\mathbb{1}_{[a,b]}}{b-a}\n", "$$\n", "\n", "(Théorème de transfert)\n", "\n", "$$\n", "I = \\mathbb{E}_f[g(X)] = \\int_a^b g(x)\\,f(x)\\,dx = \\int_a^b h(x)\\,(b-a)\\,f(x)\\,dx \\\\\n", "= \\frac{(b-a)}{(b-a)} \\int_a^b h(x)\\,dx = \\int_a^b h(x)\\,dx\n", "$$\n", "\n", "$\\{x_i\\}_{1\\lt i\\lt N} \\sim \\mathcal{U}_{[a,b]}$, for large N:\n", "\n", "$$\n", "\\hat{I} = \\frac{1}{N}\\sum_{i=1}^{N}g(x_i)\n", "$$" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Method definition h(Any) in module Main at In[92]:7 overwritten at In[101]:7.\n", "WARNING: Method definition g(Any) in module Main at In[92]:8 overwritten at In[101]:8.\n" ] }, { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " 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= this;\n", " while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n", " element = element.parent();\n", " }\n", " return element;\n", " };\n", "\n", " Element.prototype.svgroot = function () {\n", " var element = this;\n", " while (element.node.nodeName != \"svg\" && element.parent() != null) {\n", " element = element.parent();\n", " }\n", " return element;\n", " };\n", "\n", " Element.prototype.plotbounds = function () {\n", " var root = this.plotroot()\n", " var bbox = root.select(\".guide.background\").node.getBBox();\n", " return {\n", " x0: bbox.x,\n", " x1: bbox.x + bbox.width,\n", " y0: bbox.y,\n", " y1: bbox.y + bbox.height\n", " };\n", " };\n", "\n", " Element.prototype.plotcenter = function () {\n", " var root = this.plotroot()\n", " var bbox = root.select(\".guide.background\").node.getBBox();\n", " return {\n", " x: bbox.x + bbox.width / 2,\n", " y: bbox.y + bbox.height / 2\n", " };\n", " };\n", "\n", " // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n", " // does everything right.\n", " // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n", " var events = [\"mouseenter\", \"mouseleave\"];\n", "\n", " for (i in events) {\n", " (function (event_name) {\n", " var event_name = events[i];\n", " Element.prototype[event_name] = function (fn, scope) {\n", " if (Snap.is(fn, \"function\")) {\n", " var fn2 = function (event) {\n", " if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n", " return;\n", " }\n", "\n", " var reltg = event.relatedTarget ? event.relatedTarget :\n", " event.type == \"mouseout\" ? event.toElement : event.fromElement;\n", " while (reltg && reltg != this.node) reltg = reltg.parentNode;\n", "\n", " if (reltg != this.node) {\n", " return fn.apply(this, event);\n", " }\n", " };\n", "\n", " if (event_name == \"mouseenter\") {\n", " this.mouseover(fn2, scope);\n", " } else {\n", " this.mouseout(fn2, scope);\n", " }\n", " }\n", " return this;\n", " };\n", " })(events[i]);\n", " }\n", "\n", "\n", " Element.prototype.mousewheel = function (fn, scope) {\n", " if (Snap.is(fn, \"function\")) {\n", " var el = this;\n", " var fn2 = function (event) {\n", " fn.apply(el, [event]);\n", " };\n", " }\n", "\n", " this.node.addEventListener(\n", " /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n", " fn2);\n", "\n", " return this;\n", " };\n", "\n", "\n", " // Snap's attr function can be too slow for things like panning/zooming.\n", " // This is a function to directly update element attributes without going\n", " // through eve.\n", " Element.prototype.attribute = function(key, val) {\n", " if (val === undefined) {\n", " return this.node.getAttribute(key);\n", " } else {\n", " this.node.setAttribute(key, val);\n", " return this;\n", " }\n", " };\n", "\n", " Element.prototype.init_gadfly = function() {\n", " this.mouseenter(Gadfly.plot_mouseover)\n", " .mouseleave(Gadfly.plot_mouseout)\n", " .dblclick(Gadfly.plot_dblclick)\n", " .mousewheel(Gadfly.guide_background_scroll)\n", " .drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend);\n", " this.mouseenter(function (event) {\n", " init_pan_zoom(this.plotroot());\n", " });\n", " return this;\n", " };\n", "});\n", "\n", "\n", "// When the plot is moused over, emphasize the grid lines.\n", "Gadfly.plot_mouseover = function(event) {\n", " var root = this.plotroot();\n", "\n", " var keyboard_zoom = function(event) {\n", " if (event.which == 187) { // plus\n", " increase_zoom_by_position(root, 0.1, true);\n", " } else if (event.which == 189) { // minus\n", " increase_zoom_by_position(root, -0.1, true);\n", " }\n", " };\n", " root.data(\"keyboard_zoom\", keyboard_zoom);\n", " window.addEventListener(\"keyup\", keyboard_zoom);\n", "\n", " var xgridlines = root.select(\".xgridlines\"),\n", " ygridlines = root.select(\".ygridlines\");\n", "\n", " xgridlines.data(\"unfocused_strokedash\",\n", " xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,\|$)/g, \"$1mm$2\"));\n", " ygridlines.data(\"unfocused_strokedash\",\n", " ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,\|$)/g, \"$1mm$2\"));\n", "\n", " // emphasize grid lines\n", " var destcolor = root.data(\"focused_xgrid_color\");\n", " xgridlines.attribute(\"stroke-dasharray\", \"none\")\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " destcolor = root.data(\"focused_ygrid_color\");\n", " ygridlines.attribute(\"stroke-dasharray\", \"none\")\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " // reveal zoom slider\n", " root.select(\".zoomslider\")\n", " .animate({opacity: 1.0}, 250);\n", "};\n", "\n", "// Reset pan and zoom on double click\n", "Gadfly.plot_dblclick = function(event) {\n", " set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n", "};\n", "\n", "// Unemphasize grid lines on mouse out.\n", "Gadfly.plot_mouseout = function(event) {\n", " var root = this.plotroot();\n", "\n", " window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n", " root.data(\"keyboard_zoom\", undefined);\n", "\n", " var xgridlines = root.select(\".xgridlines\"),\n", " ygridlines = root.select(\".ygridlines\");\n", "\n", " var destcolor = root.data(\"unfocused_xgrid_color\");\n", "\n", " xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " destcolor = root.data(\"unfocused_ygrid_color\");\n", " ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n", " .selectAll(\"path\")\n", " .animate({stroke: destcolor}, 250);\n", "\n", " // hide zoom slider\n", " root.select(\".zoomslider\")\n", " .animate({opacity: 0.0}, 250);\n", "};\n", "\n", "\n", "var set_geometry_transform = function(root, tx, ty, scale) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", "\n", " var old_scale = root.data(\"scale\");\n", "\n", " var xscale = xscalable ? scale : 1.0,\n", " yscale = yscalable ? scale : 1.0;\n", "\n", " tx = xscalable ? tx : 0.0;\n", " ty = yscalable ? ty : 0.0;\n", "\n", " var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n", "\n", " root.selectAll(\".geometry, image\")\n", " .forEach(function (element, i) {\n", " element.transform(t);\n", " });\n", "\n", " bounds = root.plotbounds();\n", "\n", " if (yscalable) {\n", " var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n", " root.selectAll(\".xfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(xfixed_t);\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .transform(xfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1, 1/scale, cx, cy).add(st);\n", " element.transform(unscale_t);\n", "\n", " var y = cy * scale + ty;\n", " element.attr(\"visibility\",\n", " bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " if (xscalable) {\n", " var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n", " var xtrans = new Snap.Matrix().translate(tx, 0);\n", " root.selectAll(\".yfixed\")\n", " .forEach(function (element, i) {\n", " element.transform(yfixed_t);\n", " });\n", "\n", " root.select(\".xlabels\")\n", " .transform(yfixed_t)\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var cx = element.asPX(\"x\"),\n", " cy = element.asPX(\"y\");\n", " var st = element.data(\"static_transform\");\n", " unscale_t = new Snap.Matrix();\n", " unscale_t.scale(1/scale, 1, cx, cy).add(st);\n", "\n", " element.transform(unscale_t);\n", "\n", " var x = cx * scale + tx;\n", " element.attr(\"visibility\",\n", " bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n", " }\n", " });\n", " }\n", "\n", " // we must unscale anything that is scale invariance: widths, raiduses, etc.\n", " var size_attribs = [\"font-size\"];\n", " var unscaled_selection = \".geometry, .geometry \";\n", " if (xscalable) {\n", " size_attribs.push(\"rx\");\n", " unscaled_selection += \", .xgridlines\";\n", " }\n", " if (yscalable) {\n", " size_attribs.push(\"ry\");\n", " unscaled_selection += \", .ygridlines\";\n", " }\n", "\n", " root.selectAll(unscaled_selection)\n", " .forEach(function (element, i) {\n", " // circle need special help\n", " if (element.node.nodeName == \"circle\") {\n", " var cx = element.attribute(\"cx\"),\n", " cy = element.attribute(\"cy\");\n", " unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n", " cx, cy);\n", " element.transform(unscale_t);\n", " return;\n", " }\n", "\n", " for (i in size_attribs) {\n", " var key = size_attribs[i];\n", " var val = parseFloat(element.attribute(key));\n", " if (val !== undefined && val != 0 && !isNaN(val)) {\n", " element.attribute(key, val old_scale / scale);\n", " }\n", " }\n", " });\n", "};\n", "\n", "\n", "// Find the most appropriate tick scale and update label visibility.\n", "var update_tickscale = function(root, scale, axis) {\n", " if (!root.hasClass(axis + \"scalable\")) return;\n", "\n", " var tickscales = root.data(axis + \"tickscales\");\n", " var best_tickscale = 1.0;\n", " var best_tickscale_dist = Infinity;\n", " for (tickscale in tickscales) {\n", " var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n", " if (dist < best_tickscale_dist) {\n", " best_tickscale_dist = dist;\n", " best_tickscale = tickscale;\n", " }\n", " }\n", "\n", " if (best_tickscale != root.data(axis + \"tickscale\")) {\n", " root.data(axis + \"tickscale\", best_tickscale);\n", " var mark_inscale_gridlines = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " var mark_inscale_labels = function (element, i) {\n", " var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n", " element.attribute(\"gadfly:inscale\", inscale);\n", " element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n", " };\n", "\n", " root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n", " root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n", " }\n", "};\n", "\n", "\n", "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n", " var old_scale = root.data(\"scale\");\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " // compute the viewport derived from tx, ty, and scale\n", " var x_min = -width * scale - (scale * width - width),\n", " x_max = width * scale,\n", " y_min = -height * scale - (scale * height - height),\n", " y_max = height * scale;\n", "\n", " var x0 = bounds.x0 - scale * bounds.x0,\n", " y0 = bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " var xgridlines = root.select(\".xgridlines\");\n", " var ygridlines = root.select(\".ygridlines\");\n", " var xlabels = root.select(\".xlabels\");\n", " var ylabels = root.select(\".ylabels\");\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " if (xlabels) {\n", " xlabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", " }\n", "\n", " if (ylabels) {\n", " ylabels.selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", " }\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n", " if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n", " if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " if (xgridlines) {\n", " xgridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " if (ygridlines) {\n", " ygridlines\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", " }\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// drag actions, i.e. zooming and panning\n", "var pan_action = {\n", " start: function(root, x, y, event) {\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " root.data(\"tx0\", root.data(\"tx\"));\n", " root.data(\"ty0\", root.data(\"ty\"));\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", " },\n", " end: function(root, event) {\n", "\n", " },\n", " cancel: function(root) {\n", " set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n", " }\n", "};\n", "\n", "var zoom_box;\n", "var zoom_action = {\n", " start: function(root, x, y, event) {\n", " var bounds = root.plotbounds();\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", " var ratio = width / height;\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " x = xscalable ? x / px_per_mm : bounds.x0;\n", " y = yscalable ? y / px_per_mm : bounds.y0;\n", " var w = xscalable ? 0 : width;\n", " var h = yscalable ? 0 : height;\n", " zoom_box = root.rect(x, y, w, h).attr({\n", " \"fill\": \"#000\",\n", " \"opacity\": 0.25\n", " });\n", " zoom_box.data(\"ratio\", ratio);\n", " },\n", " update: function(root, dx, dy, x, y, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " var bounds = root.plotbounds();\n", " if (yscalable) {\n", " y /= px_per_mm;\n", " y = Math.max(bounds.y0, y);\n", " y = Math.min(bounds.y1, y);\n", " } else {\n", " y = bounds.y1;\n", " }\n", " if (xscalable) {\n", " x /= px_per_mm;\n", " x = Math.max(bounds.x0, x);\n", " x = Math.min(bounds.x1, x);\n", " } else {\n", " x = bounds.x1;\n", " }\n", "\n", " dx = x - zoom_box.attr(\"x\");\n", " dy = y - zoom_box.attr(\"y\");\n", " if (xscalable && yscalable) {\n", " var ratio = zoom_box.data(\"ratio\");\n", " var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n", " var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n", " dx = width * dx / Math.abs(dx);\n", " dy = height * dy / Math.abs(dy);\n", " }\n", " var xoffset = 0,\n", " yoffset = 0;\n", " if (dx < 0) {\n", " xoffset = dx;\n", " dx = -1 * dx;\n", " }\n", " if (dy < 0) {\n", " yoffset = dy;\n", " dy = -1 * dy;\n", " }\n", " if (isNaN(dy)) {\n", " dy = 0.0;\n", " }\n", " if (isNaN(dx)) {\n", " dx = 0.0;\n", " }\n", " zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n", " zoom_box.attr(\"width\", dx);\n", " zoom_box.attr(\"height\", dy);\n", " },\n", " end: function(root, event) {\n", " var xscalable = root.hasClass(\"xscalable\"),\n", " yscalable = root.hasClass(\"yscalable\");\n", " var zoom_bounds = zoom_box.getBBox();\n", " if (zoom_bounds.width * zoom_bounds.height <= 0) {\n", " return;\n", " }\n", " var plot_bounds = root.plotbounds();\n", " var zoom_factor = 1.0;\n", " if (yscalable) {\n", " zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n", " } else {\n", " zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n", " }\n", " var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n", " ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n", " zoom_box.remove();\n", " },\n", " cancel: function(root) {\n", " zoom_box.remove();\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " var scalable = root.hasClass(\"xscalable\") \|\| root.hasClass(\"yscalable\");\n", " var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n", " var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n", " var drag_action = zoomable ? zoom_action :\n", " panable ? pan_action :\n", " undefined;\n", " root.data(\"drag_action\", drag_action);\n", " if (drag_action) {\n", " var cancel_drag_action = function(event) {\n", " if (event.which == 27) { // esc key\n", " drag_action.cancel(root);\n", " root.data(\"drag_action\", undefined);\n", " }\n", " };\n", " window.addEventListener(\"keyup\", cancel_drag_action);\n", " root.data(\"cancel_drag_action\", cancel_drag_action);\n", " drag_action.start(root, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.update(root, dx, dy, x, y, event);\n", " }\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", " window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n", " root.data(\"cancel_drag_action\", undefined);\n", " var drag_action = root.data(\"drag_action\");\n", " if (drag_action) {\n", " drag_action.end(root, event);\n", " }\n", " root.data(\"drag_action\", undefined);\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n", " event.preventDefault();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), -0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " increase_zoom_by_position(this.plotroot(), 0.1, true);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "// Map slider position x to scale y using the function y = aexp(bx)+c.\n", "// The constants a, b, and c are solved using the constraint that the function\n", "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n", "var scale_from_slider_position = function(position, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return a * Math.exp(b * position) + c;\n", "}\n", "\n", "// inverse of scale_from_slider_position\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n", " b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n", " c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n", " return 1 / b * Math.log((scale - c) / a);\n", "}\n", "\n", "var increase_zoom_by_position = function(root, delta_position, animate) {\n", " var scale = root.data(\"scale\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var position = slider_position_from_scale(scale, min_scale, max_scale);\n", " position += delta_position;\n", " scale = scale_from_slider_position(position, min_scale, max_scale);\n", " set_zoom(root, scale, animate);\n", "}\n", "\n", "var set_zoom = function(root, scale, animate) {\n", " var min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"scale\");\n", " var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n", " if (animate) {\n", " Snap.animate(\n", " old_scale,\n", " new_scale,\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", " } else {\n", " update_plot_scale(root, new_scale);\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", " var root = this.plotroot();\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", " event.stopPropagation();\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#img-3263f0db\");\n", "fig.select(\"#img-3263f0db-5\")\n", " .init_gadfly();\n", "fig.select(\"#img-3263f0db-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-3263f0db-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-3263f0db-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#img-3263f0db-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#img-3263f0db-12\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3263f0db-12\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3263f0db-12\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#img-3263f0db-14\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-3263f0db-14\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-3263f0db-14\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#img-3263f0db-15\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#img-3263f0db-15\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#img-3263f0db-15\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3263f0db-15\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3263f0db-15\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ";\n", "fig.select(\"#img-3263f0db-16\")\n", " .data(\"mouseover_color\", \"#CD5C5C\")\n", ";\n", "fig.select(\"#img-3263f0db-16\")\n", " .data(\"mouseout_color\", \"#6A6A6A\")\n", ";\n", "fig.select(\"#img-3263f0db-16\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T = 100;\n", "a = -1.96;\n", "b = 1.96;\n", "x1 = linspace(a, b, T);\n", "x2 = linspace(-5, 5, T);\n", "\n", "h(x) = pdf(Normal(0, 1), x);\n", "g(x) = h(x)(b-a);\n", "\n", "# Plot the integral to compute (note: only between a and b)\n", "plot(x=x2, y=h(x2), Geom.line)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9575386316986803" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = rand(Uniform(a, b), T);\n", "G = g(X);\n", "\n", "# Estimate I by E_f[g(X)]\n", "I_hat = (1/T) sum(G)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimate of I for T = 100: 0.9517082598351836\n", "Estimate of I for T = 1000: 0.9723372002996593\n", "Estimate of I for T = 10000: 0.9440884457988756\n", "Estimate of I for T = 100000: 0.9489644213631062\n" ] } ], "source": [ "for T = [100,1000,10000,100000]\n", " X = rand(Uniform(a, b), T);\n", " G = g(X);\n", " I_hat = (1/T) * sum(G)\n", " println(\"Estimate of I for T = \", T, \": \", I_hat)\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.5.0", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
mathisonian/consumer-complaints	analysis/Untitled1.ipynb	1	3390	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from altair import Chart, load_dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "csv_path = \"../data/consumer-complaints.csv\"\n", "csv_path_out = \"../data/consumer-complaints-formatted.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "complaints = pd.read_csv(csv_path, parse_dates=[0], infer_datetime_format=True, low_memory=False)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "complaints[complaints['ZIP code'].notnull()]['ZIP code'].value_counts().to_csv('vals')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Company has responded to the consumer and the CFPB and chooses not to provide a public response 80793\n", "Company chooses not to provide a public response 52476\n", "Company believes it acted appropriately as authorized by contract or law 30745\n", "Company believes the complaint is the result of a misunderstanding 2734\n", "Company disputes the facts presented in the complaint 2557\n", "Company believes complaint caused principally by actions of third party outside the control or direction of the company 2361\n", "Company believes complaint is the result of an isolated error 2106\n", "Company can't verify or dispute the facts in the complaint 1457\n", "Company believes complaint represents an opportunity for improvement to better serve consumers 955\n", "Company believes complaint relates to a discontinued policy or procedure 39\n", "Name: Company public response, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "complaints['Company public response'].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
zebulasampedro/singularity-jupyter-demo	managing-custom-jupyter-envs-with-singularity.ipynb	1	35307	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Managing custom Jupyter environments with Singularity\n", "---\n", "In this tutorial we will cover the basic workflow for managing custom software environments for Jupyter Notebooks using Singularity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Singularity is a developing platform, so version matters a lot. The version we will be using is the latest development HEAD of 2.3." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3-HEAD.gadf5259\n" ] } ], "source": [ "singularity --version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pull the base image from ~~SingularityHub~~ DockerHub\n", "We maintain a base image on SingularityHub for running Jupyter* (https://singularity-hub.org/collections/440/). This image contains the minimum dependencies and configuration needed to run containerized Notebooks (standalone or JupyterHub-spawned), and is intended to serve as a base for user-built software environments.\n", "\n", "At this time though bootstrapping from SingularityHub is still an upcoming feature (https://github.com/singularityware/singularity/issues/833) so we will instead be using the `jupyter/base-notebook` docker container as a base image. The base-notebook is provided by the Jupyter Docker Stacks project (https://github.com/jupyter/docker-stacks), which provides pre-built stacks ready to be run standalone or behind JupyterHub." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A basic pull\n", "Start by pulling the Jupyter base image from SingularityHub. We specifically want commit _ae885c0a6226_, so we'll specify that by adding the `:ae885c0a6226` tag to the end of the repo path:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Singularity image subsystem\n", "\u001b[0mOpening image file: jupyter-base.img\n", "\u001b[0mCreating 892MiB image\n", "\u001b[0mBinding image to loop\n", "\u001b[0mCreating file system within image\n", "\u001b[0mImage is done: jupyter-base.img\n", "\u001b[0mDocker image path: index.docker.io/jupyter/base-notebook:ae885c0a6226\n", "Cache folder set to /home/vagrant/.singularity/docker\n", "Importing: base Singularity environment\n", "Importing: /home/vagrant/.singularity/docker/sha256:e0a742c2abfd5e2a6f8ed15b1c78e873cf9559b96a04204daf6de5df01e3124c.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:486cb8339a27635fa93dc47aa0c689326a0a7cce388966d16daf8d265436cf7f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:dc6f0d824617ad8a5d1163a5b2084814665dd83156317ad06ccf14deb517a053.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:4f7a5649a30e3f318ce5d7e4dbcbbeb6c0938c4cbae4d4a641fe910562ff4978.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:672363445ad2c734e29221a6b47f4e614b5adc8a3cdca3364f62db2ed2bdff0c.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:b337aaee648d9f87e96fae8b24ae2dd887a2ded309b38dbee691fcdb040878cc.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:7f77b4eaa7ff7da43552cca1a34f9d16a1bbe90ba779d2c4355a4c4d1ab6dd0f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:94e7bffb8046310f285abb93104a0cddd073c5a44ec7cfab05a3db6128fb3006.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:fa94652128009709e53b85cf57087d739108eaa98fd6e599343a32267deda620.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:79c35484f704c0792d062e5749c5df82fa85820bf072531daccefb532d88f294.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f1756f88332d61a74c658fae7b6a5b8509e59b24e7f4ae7412bb183e23c17bc8.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:e73fef5319c18fa96bf5654b990f993622087a8caa56c2a5673db488a549ab1f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:ea6e64c66b3a6af070c29a97433acaf94f875d99b4d43509f874686afa66a7d8.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f3e50561218a351fb914efee8fb3bcb05eb619f70615520375e864b40a0227c4.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f2de9a241e20a4ca75822db878a3eecf6afee5c4a96f099b76fe24306a51c1ca.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:6684df1dccb5994df682ca325871418c86770373ca27638919dff8dbb42b063a.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:1033de5a165ffa098da08b2da7325b577e5585d55bee04439033d0e72103bd64.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:ff59b2aa8f66f44d88ee7f218edd92bf0ced0d585b880ea6e9fc267100718f1b.tar.gz\n", "Importing: /home/vagrant/.singularity/metadata/sha256:583daeeee9e135a869cbe39637eecb8a3b3790096af37bfdf5a2d98b51016110.tar.gz\n", "Done. Container is at: jupyter-base.img\n" ] } ], "source": [ "singularity pull --name \"jupyter-base.img\" docker://jupyter/base-notebook:ae885c0a6226" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There it is! Your container is good to go." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: jupyter [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]\n", " [--paths] [--json]\n", " [subcommand]\n", "\n", "Jupyter: Interactive Computing\n", "\n", "positional arguments:\n", " subcommand the subcommand to launch\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " --version show the jupyter command's version and exit\n", " --config-dir show Jupyter config dir\n", " --data-dir show Jupyter data dir\n", " --runtime-dir show Jupyter runtime dir\n", " --paths show all Jupyter paths. Add --json for machine-readable\n", " format.\n", " --json output paths as machine-readable json\n", "\n", "Available subcommands: bundlerextension kernelspec lab labextension labhub\n", "migrate nbconvert nbextension notebook run serverextension troubleshoot trust\n" ] } ], "source": [ "singularity exec -e jupyter-base.img jupyter -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customizing the base image\n", "The base image is meant to capture the _minimum_ config and dependencies to run Jupyter Notebooks. Here we detail how to customize the base image to better suit your needs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resize the image\n", "This image uses the default size set by Singularity _(image size + 200M of padding)_ which is great for quick builds and pulls, but it is likely you'll need more space to accommodate your custom software stack." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "669M -rwxr-xr-x. 1 vagrant vagrant 893M Sep 27 15:41 jupyter-base.img\n" ] } ], "source": [ "ls -lsah \| grep jupyter-base.img" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When pulling from a Docker registry, you can use the `--size` flag to specify the built image size. Notice that Singularity isn't grabbing Docker layers from the registry, because the specified commit _(ae885c0a6226)_ has already been pulled. Singularity Docker cache is located in `$HOME/.singularity/docker`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Singularity image subsystem\n", "\u001b[0mOpening image file: jupyter-ext.img\n", "\u001b[0mCreating 3000MiB image\n", "\u001b[0mBinding image to loop\n", "\u001b[0mCreating file system within image\n", "\u001b[0mImage is done: jupyter-ext.img\n", "\u001b[0mDocker image path: index.docker.io/jupyter/base-notebook:latest\n", "Cache folder set to /home/vagrant/.singularity/docker\n", "Importing: base Singularity environment\n", "Importing: /home/vagrant/.singularity/docker/sha256:e0a742c2abfd5e2a6f8ed15b1c78e873cf9559b96a04204daf6de5df01e3124c.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:486cb8339a27635fa93dc47aa0c689326a0a7cce388966d16daf8d265436cf7f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:dc6f0d824617ad8a5d1163a5b2084814665dd83156317ad06ccf14deb517a053.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:4f7a5649a30e3f318ce5d7e4dbcbbeb6c0938c4cbae4d4a641fe910562ff4978.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:672363445ad2c734e29221a6b47f4e614b5adc8a3cdca3364f62db2ed2bdff0c.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:b337aaee648d9f87e96fae8b24ae2dd887a2ded309b38dbee691fcdb040878cc.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:7f77b4eaa7ff7da43552cca1a34f9d16a1bbe90ba779d2c4355a4c4d1ab6dd0f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:94e7bffb8046310f285abb93104a0cddd073c5a44ec7cfab05a3db6128fb3006.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:fa94652128009709e53b85cf57087d739108eaa98fd6e599343a32267deda620.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:79c35484f704c0792d062e5749c5df82fa85820bf072531daccefb532d88f294.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f1756f88332d61a74c658fae7b6a5b8509e59b24e7f4ae7412bb183e23c17bc8.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:e73fef5319c18fa96bf5654b990f993622087a8caa56c2a5673db488a549ab1f.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:ea6e64c66b3a6af070c29a97433acaf94f875d99b4d43509f874686afa66a7d8.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f3e50561218a351fb914efee8fb3bcb05eb619f70615520375e864b40a0227c4.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:f2de9a241e20a4ca75822db878a3eecf6afee5c4a96f099b76fe24306a51c1ca.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:6684df1dccb5994df682ca325871418c86770373ca27638919dff8dbb42b063a.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:1033de5a165ffa098da08b2da7325b577e5585d55bee04439033d0e72103bd64.tar.gz\n", "Importing: /home/vagrant/.singularity/docker/sha256:ff59b2aa8f66f44d88ee7f218edd92bf0ced0d585b880ea6e9fc267100718f1b.tar.gz\n", "Importing: /home/vagrant/.singularity/metadata/sha256:583daeeee9e135a869cbe39637eecb8a3b3790096af37bfdf5a2d98b51016110.tar.gz\n", "Done. Container is at: jupyter-ext.img\n" ] } ], "source": [ "singularity pull --size 3000 --name \"jupyter-ext.img\" docker://jupyter/base-notebook:ae885c0a6226" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: jupyter [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]\n", " [--paths] [--json]\n", " [subcommand]\n", "\n", "Jupyter: Interactive Computing\n", "\n", "positional arguments:\n", " subcommand the subcommand to launch\n", "\n", "optional arguments:\n", " -h, --help show this help message and exit\n", " --version show the jupyter command's version and exit\n", " --config-dir show Jupyter config dir\n", " --data-dir show Jupyter data dir\n", " --runtime-dir show Jupyter runtime dir\n", " --paths show all Jupyter paths. Add --json for machine-readable\n", " format.\n", " --json output paths as machine-readable json\n", "\n", "Available subcommands: bundlerextension kernelspec lab labextension labhub\n", "migrate nbconvert nbextension notebook run serverextension troubleshoot trust\n" ] } ], "source": [ "singularity exec -e jupyter-ext.img jupyter -h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Installing software _(the quick way)_\n", "By default Singularity containers mounted as read-only volumes, which means you won't be able to add content or install software _(even as a privileged user)_, save for default or system-mounted paths. In order to add content you must run your Singularity command with the `--writable` flag.\n", "\n", "For an interactive shell into your container, use the `shell` subcommand. The command below also passes the `-e` flag, which tells Singularity to strip the host environment before entering the container." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Singularity: Invoking an interactive shell within container...\n", "\n" ] } ], "source": [ "sudo singularity shell -e --writable jupyter-ext.img" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can use the `exec` subcommand to execute commands in your container without leaving your host environment." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching package metadata ...........\n", "Solving package specifications: .\n", "\n", "Package plan for installation in environment /opt/conda:\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " cycler: 0.10.0-py36_0 conda-forge\n", " dbus: 1.10.22-0 conda-forge\n", " expat: 2.2.1-0 conda-forge\n", " fontconfig: 2.12.1-4 conda-forge\n", " freetype: 2.7-1 conda-forge\n", " gettext: 0.19.7-1 conda-forge\n", " glib: 2.51.4-0 conda-forge\n", " gst-plugins-base: 1.8.0-0 conda-forge\n", " gstreamer: 1.8.0-2 conda-forge\n", " icu: 58.1-1 conda-forge\n", " jpeg: 9b-1 conda-forge\n", " libiconv: 1.14-4 conda-forge\n", " libpng: 1.6.28-0 conda-forge\n", " libxcb: 1.12-1 conda-forge\n", " libxml2: 2.9.5-0 conda-forge\n", " matplotlib: 2.0.2-py36_2 conda-forge\n", " mkl: 2017.0.3-0 defaults \n", " numpy: 1.13.1-py36_0 defaults \n", " pcre: 8.39-0 conda-forge\n", " pyqt: 5.6.0-py36_4 conda-forge\n", " pytz: 2017.2-py36_0 conda-forge\n", " qt: 5.6.2-3 conda-forge\n", " sip: 4.18-py36_1 conda-forge\n", " xorg-libxau: 1.0.8-3 conda-forge\n", " xorg-libxdmcp: 1.1.2-3 conda-forge\n", "\n", "expat-2.2.1-0. 100% \|################################\| Time: 0:00:00 1.48 MB/s\n", "gettext-0.19.7 100% \|################################\| Time: 0:00:00 9.27 MB/s\n", "icu-58.1-1.tar 100% \|################################\| Time: 0:00:01 21.29 MB/s\n", "jpeg-9b-1.tar. 100% \|################################\| Time: 0:00:00 25.47 MB/s\n", "libiconv-1.14- 100% \|################################\| Time: 0:00:00 16.77 MB/s\n", "mkl-2017.0.3-0 100% \|################################\| Time: 0:00:03 35.19 MB/s\n", "pcre-8.39-0.ta 100% \|################################\| Time: 0:00:00 1.21 MB/s\n", "xorg-libxau-1. 100% \|################################\| Time: 0:00:00 22.14 MB/s\n", "xorg-libxdmcp- 100% \|################################\| Time: 0:00:00 20.33 MB/s\n", "dbus-1.10.22-0 100% \|################################\| Time: 0:00:00 3.40 MB/s\n", "glib-2.51.4-0. 100% \|################################\| Time: 0:00:00 13.82 MB/s\n", "libpng-1.6.28- 100% \|################################\| Time: 0:00:00 68.13 MB/s\n", "libxcb-1.12-1. 100% \|################################\| Time: 0:00:00 11.46 MB/s\n", "libxml2-2.9.5- 100% \|################################\| Time: 0:00:00 12.21 MB/s\n", "freetype-2.7-1 100% \|################################\| Time: 0:00:00 14.38 MB/s\n", "gstreamer-1.8. 100% \|################################\| Time: 0:00:00 21.42 MB/s\n", "fontconfig-2.1 100% \|################################\| Time: 0:00:00 15.21 MB/s\n", "gst-plugins-ba 100% \|################################\| Time: 0:00:00 24.25 MB/s\n", "numpy-1.13.1-p 100% \|################################\| Time: 0:00:00 36.10 MB/s\n", "pytz-2017.2-py 100% \|################################\| Time: 0:00:00 48.89 MB/s\n", "sip-4.18-py36_ 100% \|################################\| Time: 0:00:00 7.02 MB/s\n", "cycler-0.10.0- 100% \|################################\| Time: 0:00:00 22.60 MB/s\n", "qt-5.6.2-3.tar 100% \|################################\| Time: 0:00:02 20.00 MB/s\n", "pyqt-5.6.0-py3 100% \|################################\| Time: 0:00:00 10.44 MB/s\n", "matplotlib-2.0 100% \|################################\| Time: 0:00:00 21.28 MB/s\n", "Fetching package metadata ...........\n", "Solving package specifications: .\n", "\n", "Package plan for installation in environment /opt/conda:\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " libgfortran: 3.0.0-1 defaults \n", " pandas: 0.20.3-py36_1 conda-forge\n", " patsy: 0.4.1-py36_0 conda-forge\n", " scipy: 0.19.1-np113py36_0 defaults \n", " seaborn: 0.8.1-py36_0 conda-forge\n", " statsmodels: 0.8.0-py36_0 conda-forge\n", "\n", "libgfortran-3. 100% \|################################\| Time: 0:00:00 17.90 MB/s\n", "scipy-0.19.1-n 100% \|################################\| Time: 0:00:01 35.76 MB/s\n", "pandas-0.20.3- 100% \|################################\| Time: 0:00:01 16.39 MB/s\n", "patsy-0.4.1-py 100% \|################################\| Time: 0:00:00 58.21 MB/s\n", "statsmodels-0. 100% \|################################\| Time: 0:00:00 11.16 MB/s\n", "seaborn-0.8.1- 100% \|################################\| Time: 0:00:00 55.75 MB/s\n" ] } ], "source": [ "singularity exec -e --writable jupyter-ext.img /opt/conda/bin/conda install -y matplotlib\n", "singularity exec -e --writable jupyter-ext.img /opt/conda/bin/conda install -y seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now seaborn is installed in your image." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seaborn 0.8.1 py36_0 conda-forge\n" ] } ], "source": [ "singularity exec -e jupyter-ext.img conda list \| grep seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Installing Software _(the reproducible way)_\n", "Shelling into your container and making ad-hoc changes is excellent for debugging and initial development, but it is considered bad practice as the steps needed to construct your software environment are not captured and cannot be reproduced.\n", "\n", "To make durable, reproducible changes you need to build a spec file from which you can bootstrap your container. Bootstrapping must be done by a privileged user" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BootStrap: docker\n", "From: jupyter/base-notebook\n", "\n", "%environment\n", " export PATH=/opt/conda/bin:$PATH\n", "\n", "%post\n", " export PATH=/opt/conda/bin:$PATH\n", " echo \"Installing seaborn...\"\n", " conda install matplotlib\n", " conda install seaborn\n" ] } ], "source": [ "cat jupyter-bootstrapped.def" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Singularity image subsystem\n", "\u001b[0mOpening image file: jupyter-bootstrapped.img\n", "\u001b[0mCreating 2500MiB image\n", "\u001b[0mBinding image to loop\n", "\u001b[0mCreating file system within image\n", "\u001b[0mImage is done: jupyter-bootstrapped.img\n", "\u001b[0mSanitizing environment\n", "\u001b[0mBuilding from bootstrap definition recipe\n", "\u001b[0mAdding base Singularity environment to container\n", "Docker image path: index.docker.io/jupyter/base-notebook:latest\n", "Cache folder set to /root/.singularity/docker\n", "Exploding layer: sha256:e0a742c2abfd5e2a6f8ed15b1c78e873cf9559b96a04204daf6de5df01e3124c.tar.gz\n", "Exploding layer: sha256:486cb8339a27635fa93dc47aa0c689326a0a7cce388966d16daf8d265436cf7f.tar.gz\n", "Exploding layer: sha256:dc6f0d824617ad8a5d1163a5b2084814665dd83156317ad06ccf14deb517a053.tar.gz\n", "Exploding layer: sha256:4f7a5649a30e3f318ce5d7e4dbcbbeb6c0938c4cbae4d4a641fe910562ff4978.tar.gz\n", "Exploding layer: sha256:672363445ad2c734e29221a6b47f4e614b5adc8a3cdca3364f62db2ed2bdff0c.tar.gz\n", "Exploding layer: sha256:b337aaee648d9f87e96fae8b24ae2dd887a2ded309b38dbee691fcdb040878cc.tar.gz\n", "Exploding layer: sha256:7f77b4eaa7ff7da43552cca1a34f9d16a1bbe90ba779d2c4355a4c4d1ab6dd0f.tar.gz\n", "Exploding layer: sha256:94e7bffb8046310f285abb93104a0cddd073c5a44ec7cfab05a3db6128fb3006.tar.gz\n", "Exploding layer: sha256:fa94652128009709e53b85cf57087d739108eaa98fd6e599343a32267deda620.tar.gz\n", "Exploding layer: sha256:79c35484f704c0792d062e5749c5df82fa85820bf072531daccefb532d88f294.tar.gz\n", "Exploding layer: sha256:f1756f88332d61a74c658fae7b6a5b8509e59b24e7f4ae7412bb183e23c17bc8.tar.gz\n", "Exploding layer: sha256:e73fef5319c18fa96bf5654b990f993622087a8caa56c2a5673db488a549ab1f.tar.gz\n", "Exploding layer: sha256:ea6e64c66b3a6af070c29a97433acaf94f875d99b4d43509f874686afa66a7d8.tar.gz\n", "Exploding layer: sha256:f3e50561218a351fb914efee8fb3bcb05eb619f70615520375e864b40a0227c4.tar.gz\n", "Exploding layer: sha256:f2de9a241e20a4ca75822db878a3eecf6afee5c4a96f099b76fe24306a51c1ca.tar.gz\n", "Exploding layer: sha256:6684df1dccb5994df682ca325871418c86770373ca27638919dff8dbb42b063a.tar.gz\n", "Exploding layer: sha256:1033de5a165ffa098da08b2da7325b577e5585d55bee04439033d0e72103bd64.tar.gz\n", "Exploding layer: sha256:ff59b2aa8f66f44d88ee7f218edd92bf0ced0d585b880ea6e9fc267100718f1b.tar.gz\n", "Exploding layer: sha256:0ec5668b017f4b07564d4b53a6f4bd19c61dc4892342ed092c076c99ad065c2e.tar.gz\n", "Running post scriptlet\n", "+ export PATH=/opt/conda/bin:/bin:/sbin:/usr/bin:/usr/sbin:/usr/local/bin:/usr/local/sbin\n", "+ echo Installing seaborn...\n", "Installing seaborn...\n", "+ conda install matplotlib\n", "Fetching package metadata ...........\n", "Solving package specifications: .\n", "\n", "Package plan for installation in environment /opt/conda:\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " cycler: 0.10.0-py36_0 conda-forge\n", " dbus: 1.10.22-0 conda-forge\n", " expat: 2.2.1-0 conda-forge\n", " fontconfig: 2.12.1-4 conda-forge\n", " freetype: 2.7-1 conda-forge\n", " gettext: 0.19.7-1 conda-forge\n", " glib: 2.51.4-0 conda-forge\n", " gst-plugins-base: 1.8.0-0 conda-forge\n", " gstreamer: 1.8.0-2 conda-forge\n", " icu: 58.1-1 conda-forge\n", " jpeg: 9b-1 conda-forge\n", " libiconv: 1.14-4 conda-forge\n", " libpng: 1.6.28-0 conda-forge\n", " libxcb: 1.12-1 conda-forge\n", " libxml2: 2.9.5-0 conda-forge\n", " matplotlib: 2.0.2-py36_2 conda-forge\n", " mkl: 2017.0.3-0 defaults \n", " numpy: 1.13.1-py36_0 defaults \n", " pcre: 8.39-0 conda-forge\n", " pyqt: 5.6.0-py36_4 conda-forge\n", " pytz: 2017.2-py36_0 conda-forge\n", " qt: 5.6.2-3 conda-forge\n", " sip: 4.18-py36_1 conda-forge\n", " xorg-libxau: 1.0.8-3 conda-forge\n", " xorg-libxdmcp: 1.1.2-3 conda-forge\n", "\n", "Proceed ([y]/n)? \n", "expat-2.2.1-0. 100% \|################################\| Time: 0:00:00 1.50 MB/s\n", "gettext-0.19.7 100% \|################################\| Time: 0:00:00 7.85 MB/s\n", "icu-58.1-1.tar 100% \|################################\| Time: 0:00:01 23.17 MB/s\n", "jpeg-9b-1.tar. 100% \|################################\| Time: 0:00:00 20.14 MB/s\n", "libiconv-1.14- 100% \|################################\| Time: 0:00:00 12.02 MB/s\n", "mkl-2017.0.3-0 100% \|################################\| Time: 0:00:03 35.69 MB/s\n", "pcre-8.39-0.ta 100% \|################################\| Time: 0:00:00 1.31 MB/s\n", "xorg-libxau-1. 100% \|################################\| Time: 0:00:00 18.72 MB/s\n", "xorg-libxdmcp- 100% \|################################\| Time: 0:00:00 23.09 MB/s\n", "dbus-1.10.22-0 100% \|################################\| Time: 0:00:00 2.58 MB/s\n", "glib-2.51.4-0. 100% \|################################\| Time: 0:00:00 9.27 MB/s\n", "libpng-1.6.28- 100% \|################################\| Time: 0:00:00 55.61 MB/s\n", "libxcb-1.12-1. 100% \|################################\| Time: 0:00:00 11.54 MB/s\n", "libxml2-2.9.5- 100% \|################################\| Time: 0:00:00 17.10 MB/s\n", "freetype-2.7-1 100% \|################################\| Time: 0:00:00 23.24 MB/s\n", "gstreamer-1.8. 100% \|################################\| Time: 0:00:00 9.55 MB/s\n", "fontconfig-2.1 100% \|################################\| Time: 0:00:00 5.83 MB/s\n", "gst-plugins-ba 100% \|################################\| Time: 0:00:00 8.40 MB/s\n", "numpy-1.13.1-p 100% \|################################\| Time: 0:00:00 41.53 MB/s\n", "pytz-2017.2-py 100% \|################################\| Time: 0:00:00 4.68 MB/s\n", "sip-4.18-py36_ 100% \|################################\| Time: 0:00:00 6.29 MB/s\n", "cycler-0.10.0- 100% \|################################\| Time: 0:00:00 23.92 MB/s\n", "qt-5.6.2-3.tar 100% \|################################\| Time: 0:00:02 21.20 MB/s\n", "pyqt-5.6.0-py3 100% \|################################\| Time: 0:00:00 19.36 MB/s\n", "matplotlib-2.0 100% \|################################\| Time: 0:00:00 20.04 MB/s\n", "+ conda install seaborn\n", "Fetching package metadata ...........\n", "Solving package specifications: .\n", "\n", "Package plan for installation in environment /opt/conda:\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " libgfortran: 3.0.0-1 defaults \n", " pandas: 0.20.3-py36_1 conda-forge\n", " patsy: 0.4.1-py36_0 conda-forge\n", " scipy: 0.19.1-np113py36_0 defaults \n", " seaborn: 0.8.1-py36_0 conda-forge\n", " statsmodels: 0.8.0-py36_0 conda-forge\n", "\n", "Proceed ([y]/n)? \n", "libgfortran-3. 100% \|################################\| Time: 0:00:00 4.70 MB/s\n", "scipy-0.19.1-n 100% \|################################\| Time: 0:00:01 29.71 MB/s\n", "pandas-0.20.3- 100% \|################################\| Time: 0:00:01 12.90 MB/s\n", "patsy-0.4.1-py 100% \|################################\| Time: 0:00:00 10.76 MB/s\n", "statsmodels-0. 100% \|################################\| Time: 0:00:00 14.35 MB/s\n", "seaborn-0.8.1- 100% \|################################\| Time: 0:00:00 55.54 MB/s\n", "Adding environment to container\n", "Finalizing Singularity container\n" ] } ], "source": [ "singularity create --force --size 2500 jupyter-bootstrapped.img\n", "sudo /usr/local/bin/singularity bootstrap jupyter-bootstrapped.img jupyter-bootstrapped.def" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seaborn 0.8.1 py36_0 conda-forge\n" ] } ], "source": [ "singularity exec -e jupyter-bootstrapped.img conda list \| grep seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using your environment in a notebook\n", "This next section will cover basic strategies for using your very new, very custom software environment in a Jupyter Notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom kernels\n", "IPython notebooks interface with the system via an abstraction called _Kernels_. A wide variety of languages are supported via Kernels, and they can be customized by editing the kernelspec JSON file that defines them. Here is the default Python 3 kernelspec for reference:\n", "```json\n", "\"argv\": [\n", " \"python\",\n", " \"-m\",\n", " \"ipykernel_launcher\",\n", " \"-f\",\n", " \"{connection_file}\"\n", " ],\n", " \"display_name\": \"Python 3\",\n", " \"language\": \"python\"\n", "}\n", "```\n", "\n", "The `argv` key in this JSON object is the list that Jupyter uses to construct the kernel command when a notebook is started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember the `singularity exec` subcommand? We can leverage that here to start a kernel in our container from a notebook server running in our host environment. All we need to do is prepend the components of the exec command to the `argv` list:\n", "```json\n", "\"argv\": [\n", " \"singularity\",\n", " \"exec\",\n", " \"-e\",\n", " \"jupyter-bootstrapped.img\",\n", " \"python\",\n", " \"-m\",\n", " \"ipykernel_launcher\",\n", " \"-f\",\n", " \"{connection_file}\"\n", " ],\n", " \"display_name\": \"Python 3\",\n", " \"language\": \"python\"\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generating a new kernel\n", "We'll start by generating a new kernelspec in a temporary location:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[InstallIPythonKernelSpecApp] WARNING \| Installing to /tmp/share/jupyter/kernels, which is not in ['/home/vagrant/.local/share/jupyter/kernels', '/home/vagrant/venv-jupyter/share/jupyter/kernels', '/usr/local/share/jupyter/kernels', '/usr/share/jupyter/kernels', '/home/vagrant/.ipython/kernels']. The kernelspec may not be found.\n", "Installed kernelspec python3 in /tmp/share/jupyter/kernels/python3\n" ] } ], "source": [ "ipython kernel install --prefix /tmp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Editing the kernel\n", "Now edit your kernelspec. An example can be found in this repo at <a href=\"/user/vagrant/edit/singularity-jupyter-demo/singularity-kernel.json\">singularity-kernel.json</a>. Make sure to rename the kernelspec directory to avoid conflicts with existing kernels." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "mv /tmp/share/jupyter/kernels/python3 /tmp/share/jupyter/kernels/seaborn\n", "# Then edit /tmp/share/jupyter/kernels/seaborn/kernel.json (in our case we'll just copy the example)\n", "cp singularity-kernel.json /tmp/share/jupyter/kernels/seaborn/kernel.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Install the kernel\n", "Finish by installing your new kernel to a location where your notebook will look when it starts. The `--user` flag specifies that you wish to install the kernel only for your user, and prevents the install from attempting to use `sys.prefix`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[InstallKernelSpec] Removing existing kernelspec in /home/vagrant/.local/share/jupyter/kernels/seaborn\n", "[InstallKernelSpec] Installed kernelspec seaborn in /home/vagrant/.local/share/jupyter/kernels/seaborn\n" ] } ], "source": [ "jupyter kernelspec install --user /tmp/share/jupyter/kernels/seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IPython Parallel\n", "A similar approach can be used to execute ipengines on Summit, providing a mechanism for multi-node ipyparallel workflows that run in Singularity containers. This approach is detailed in the _ipyparallel-using-singularity_ notebook in this directory." ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mcs07/ChemSpiPy	examples/Getting Started.ipynb	1	9583	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "# ChemSpiPy: Getting Started\n", "\n", "Before we start:\n", "\n", "- Make sure you have [installed ChemSpiPy](http://chemspipy.readthedocs.io/en/latest/guide/install.html#install).\n", "- [Obtain a security token](http://chemspipy.readthedocs.io/en/latest/guide/intro.html#securitytoken) from the ChemSpider web site.\n", "\n", "## First Steps\n", "\n", "Start by importing ChemSpider:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from chemspipy import ChemSpider" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "Then connect to ChemSpider by creating a ChemSpider instance using your security token:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# Tip: Store your security token as an environment variable to reduce the chance of accidentally sharing it\n", "import os\n", "mytoken = os.environ['CHEMSPIDER_SECURITY_TOKEN']\n", "\n", "cs = ChemSpider(security_token=mytoken)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "All your interaction with the ChemSpider database should now happen through this ChemSpider object, `cs`.\n", "\n", "## Retrieve a Compound\n", "\n", "Retrieving information about a specific Compound in the ChemSpider database is simple.\n", "\n", "Let’s get the Compound with [ChemSpider ID 2157](http://www.chemspider.com/Chemical-Structure.2157.html):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJYAAACWCAYAAAA8AXHiAAAAAXNSR0IArs4c6QAAAARnQU1BAACx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\nrHSzHCxpCygt38FSaljpZv0FqtR95byLG1IAAAAASUVORK5CYII=\n", "text/plain": [ "Compound(2157)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comp = cs.get_compound(2157)\n", "comp" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "Now we have a [Compound](http://chemspipy.readthedocs.org/en/latest/api.html#chemspipy.Compound) object called `comp`. We can get various identifiers and calculated properties from this object:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C_{9}H_{8}O_{4}\n", "180.1574\n", "CC(=O)Oc1ccccc1C(=O)O\n", "Aspirin\n" ] } ], "source": [ "print(comp.molecular_formula)\n", "print(comp.molecular_weight)\n", "print(comp.smiles)\n", "print(comp.common_name)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "## Search for a name\n", "\n", "What if you don’t know the ChemSpider ID of the Compound you want? Instead use the `search` method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compound(5589)\n", "Compound(58238)\n", "Compound(71358)\n", "Compound(96749)\n", "Compound(2006622)\n", "Compound(5341883)\n", "Compound(5360239)\n", "Compound(9129332)\n", "Compound(9281077)\n", "Compound(9312824)\n", "Compound(9484839)\n", "Compound(9655623)\n" ] } ], "source": [ "for result in cs.search('glucose'):\n", " print(result)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "The search method accepts any identifer that ChemSpider can interpret, including names, registry numbers, SMILES and InChI." ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
sassoftware/sas_kernel	notebook/SAS Magic.ipynb	1	62717	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/json": { "cell": { "!": "OSMagics", "HTML": "Other", "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", "javascript": "DisplayMagics", "latex": "DisplayMagics", "perl": "Other", "prun": "ExecutionMagics", "pypy": "Other", "python": "Other", "python2": "Other", "python3": "Other", "ruby": "Other", "script": "ScriptMagics", "sh": "Other", "svg": "DisplayMagics", "sx": "OSMagics", "system": "OSMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "writefile": "OSMagics" }, "line": { "alias": "OSMagics", "alias_magic": "BasicMagics", "autocall": "AutoMagics", "automagic": "AutoMagics", "autosave": "KernelMagics", "bookmark": "OSMagics", "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", "colors": "BasicMagics", "config": "ConfigMagics", "connect_info": "KernelMagics", "cp": "Other", "debug": "ExecutionMagics", "dhist": "OSMagics", "dirs": "OSMagics", "doctest_mode": "BasicMagics", "ed": "Other", "edit": "KernelMagics", "env": "OSMagics", "gui": "BasicMagics", "hist": "Other", "history": "HistoryMagics", "install_default_config": "DeprecatedMagics", "install_ext": "ExtensionMagics", "install_profiles": "DeprecatedMagics", "killbgscripts": "ScriptMagics", "ldir": "Other", "less": "KernelMagics", "lf": "Other", "lk": "Other", "ll": "Other", "load": "CodeMagics", "load_ext": "ExtensionMagics", "loadpy": "CodeMagics", "logoff": "LoggingMagics", "logon": "LoggingMagics", "logstart": "LoggingMagics", "logstate": "LoggingMagics", "logstop": "LoggingMagics", "ls": "Other", "lsmagic": "BasicMagics", "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", "man": "KernelMagics", "matplotlib": "PylabMagics", "mkdir": "Other", "more": "KernelMagics", "mv": "Other", "notebook": "BasicMagics", "page": "BasicMagics", "pastebin": "CodeMagics", "pdb": "ExecutionMagics", "pdef": "NamespaceMagics", "pdoc": "NamespaceMagics", "pfile": "NamespaceMagics", "pinfo": "NamespaceMagics", "pinfo2": "NamespaceMagics", "popd": "OSMagics", "pprint": "BasicMagics", "precision": "BasicMagics", "profile": "BasicMagics", "prun": "ExecutionMagics", "psearch": "NamespaceMagics", "psource": "NamespaceMagics", "pushd": "OSMagics", "pwd": "OSMagics", "pycat": "OSMagics", "pylab": "PylabMagics", "qtconsole": "KernelMagics", "quickref": "BasicMagics", "recall": "HistoryMagics", "rehashx": "OSMagics", "reload_ext": "ExtensionMagics", "rep": "Other", "rerun": "HistoryMagics", "reset": "NamespaceMagics", "reset_selective": "NamespaceMagics", "rm": "Other", "rmdir": "Other", "run": "ExecutionMagics", "save": "CodeMagics", "sc": "OSMagics", "set_env": "OSMagics", "store": "StoreMagics", "sx": "OSMagics", "system": "OSMagics", "tb": "ExecutionMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "unalias": "OSMagics", "unload_ext": "ExtensionMagics", "who": "NamespaceMagics", "who_ls": "NamespaceMagics", "whos": "NamespaceMagics", "xdel": "NamespaceMagics", "xmode": "BasicMagics" } }, "text/plain": [ "Available line magics:\n", "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%lsmagic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using SAS Config named: default\n" ] } ], "source": [ "%load_ext saspy.sas_magic" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/json": { "cell": { "!": "OSMagics", "HTML": "Other", "IML": "SASMagic", "OPTMODEL": "SASMagic", "SAS": "SASMagic", "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", "javascript": "DisplayMagics", "latex": "DisplayMagics", "perl": "Other", "prun": "ExecutionMagics", "pypy": "Other", "python": "Other", "python2": "Other", "python3": "Other", "ruby": "Other", "script": "ScriptMagics", "sh": "Other", "svg": "DisplayMagics", "sx": "OSMagics", "system": "OSMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "writefile": "OSMagics" }, "line": { "alias": "OSMagics", "alias_magic": "BasicMagics", "autocall": "AutoMagics", "automagic": "AutoMagics", "autosave": "KernelMagics", "bookmark": "OSMagics", "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", "colors": "BasicMagics", "config": "ConfigMagics", "connect_info": "KernelMagics", "cp": "Other", "debug": "ExecutionMagics", "dhist": "OSMagics", "dirs": "OSMagics", "doctest_mode": "BasicMagics", "ed": "Other", "edit": "KernelMagics", "env": "OSMagics", "gui": "BasicMagics", "hist": "Other", "history": "HistoryMagics", "install_default_config": "DeprecatedMagics", "install_ext": "ExtensionMagics", "install_profiles": "DeprecatedMagics", "killbgscripts": "ScriptMagics", "ldir": "Other", "less": "KernelMagics", "lf": "Other", "lk": "Other", "ll": "Other", "load": "CodeMagics", "load_ext": "ExtensionMagics", "loadpy": "CodeMagics", "logoff": "LoggingMagics", "logon": "LoggingMagics", "logstart": "LoggingMagics", "logstate": "LoggingMagics", "logstop": "LoggingMagics", "ls": "Other", "lsmagic": "BasicMagics", "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", "man": "KernelMagics", "matplotlib": "PylabMagics", "mkdir": "Other", "more": "KernelMagics", "mv": "Other", "notebook": "BasicMagics", "page": "BasicMagics", "pastebin": "CodeMagics", "pdb": "ExecutionMagics", "pdef": "NamespaceMagics", "pdoc": "NamespaceMagics", "pfile": "NamespaceMagics", "pinfo": "NamespaceMagics", "pinfo2": "NamespaceMagics", "popd": "OSMagics", "pprint": "BasicMagics", "precision": "BasicMagics", "profile": "BasicMagics", "prun": "ExecutionMagics", "psearch": "NamespaceMagics", "psource": "NamespaceMagics", "pushd": "OSMagics", "pwd": "OSMagics", "pycat": "OSMagics", "pylab": "PylabMagics", "qtconsole": "KernelMagics", "quickref": "BasicMagics", "recall": "HistoryMagics", "rehashx": "OSMagics", "reload_ext": "ExtensionMagics", "rep": "Other", "rerun": "HistoryMagics", "reset": "NamespaceMagics", "reset_selective": "NamespaceMagics", "rm": "Other", "rmdir": "Other", "run": "ExecutionMagics", "save": "CodeMagics", "sc": "OSMagics", "set_env": "OSMagics", "store": "StoreMagics", "sx": "OSMagics", "system": "OSMagics", "tb": "ExecutionMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "unalias": "OSMagics", "unload_ext": "ExtensionMagics", "who": "NamespaceMagics", "who_ls": "NamespaceMagics", "whos": "NamespaceMagics", "xdel": "NamespaceMagics", "xmode": "BasicMagics" } }, "text/plain": [ "Available line magics:\n", "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%IML %%OPTMODEL %%SAS %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%lsmagic" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<html xmlns=\"http://www.w3.org/1999/xhtml\">\n", "<head>\n", "<meta charset=\"utf-8\"/>\n", "<meta content=\"SAS 9.4\" name=\"generator\"/>\n", "<title>SAS Output</title>\n", "<style>\n", "/<![CDATA[/\n", ".body.c section > table, .body.c section > pre, .body.c div > table,\n", ".body.c div > pre, .body.c article > table, .body.c article > pre,\n", ".body.j section > table, .body.j section > pre, .body.j div > table,\n", ".body.j div > pre, .body.j article > table, .body.j article > pre,\n", ".body.c p.note, .body.c p.warning, .body.c p.error, .body.c p.fatal,\n", ".body.j p.note, .body.j p.warning, .body.j p.error, .body.j p.fatal,\n", ".body.c > table.layoutcontainer, .body.j > table.layoutcontainer { margin-left: auto; margin-right: auto }\n", ".layoutregion.l table, .layoutregion.l pre, .layoutregion.l p.note,\n", ".layoutregion.l p.warning, .layoutregion.l p.error, .layoutregion.l p.fatal { margin-left: 0 }\n", ".layoutregion.c table, .layoutregion.c pre, .layoutregion.c p.note,\n", ".layoutregion.c p.warning, .layoutregion.c p.error, .layoutregion.c p.fatal { margin-left: auto; margin-right: auto }\n", ".layoutregion.r table, .layoutregion.r pre, .layoutregion.r p.note,\n", ".layoutregion.r table, .layoutregion.r pre, .layoutregion.r p.note,\n", ".layoutregion.r p.warning, .layoutregion.r p.error, .layoutregion.r p.fatal { margin-right: 0 }\n", "article, aside, details, figcaption, figure, footer, header, hgroup, nav, section { display: block }\n", "html{ font-size: 100% }\n", ".body { margin: 1em; font-size: 13px; line-height: 1.231 }\n", "sup { position: relative; vertical-align: baseline; bottom: 0.25em; font-size: 0.8em }\n", "sub { position: relative; vertical-align: baseline; top: 0.25em; font-size: 0.8em }\n", "ul, ol { margin: 1em 0; padding: 0 0 0 40px }\n", "dd { margin: 0 0 0 40px }\n", "nav ul, nav ol { list-style: none; list-style-image: none; margin: 0; padding: 0 }\n", "img { border: 0; vertical-align: middle }\n", "svg:not(:root) { overflow: hidden }\n", "figure { margin: 0 }\n", "table { border-collapse: collapse; border-spacing: 0 }\n", ".layoutcontainer { border-collapse: separate; border-spacing: 0 }\n", "p { margin-top: 0; text-align: left }\n", "span { text-align: left }\n", "table { margin-bottom: 1em }\n", "td, th { text-align: left; padding: 3px 6px; vertical-align: top }\n", "td[class$=\"fixed\"], th[class$=\"fixed\"] { white-space: pre }\n", "section, article { padding-top: 1px; padding-bottom: 8px }\n", "hr.pagebreak { height: 0px; border: 0; border-bottom: 1px solid #c0c0c0; margin: 1em 0 }\n", ".stacked-value { text-align: left; display: block }\n", ".stacked-cell > .stacked-value, td.data > td.data, th.data > td.data, th.data > th.data, td.data > th.data, th.header > th.header { border: 0 }\n", ".stacked-cell > div.data { border-width: 0 }\n", ".systitleandfootercontainer { white-space: nowrap; margin-bottom: 1em }\n", ".systitleandfootercontainer > p { margin: 0 }\n", ".systitleandfootercontainer > p > span { display: inline-block; width: 100%; white-space: normal }\n", ".batch { display: table }\n", ".toc { display: none }\n", ".proc_note_group, .proc_title_group { margin-bottom: 1em }\n", "p.proctitle { margin: 0 }\n", "p.note, p.warning, p.error, p.fatal { display: table }\n", ".notebanner, .warnbanner, .errorbanner, .fatalbanner,\n", ".notecontent, .warncontent, .errorcontent, .fatalcontent { display: table-cell; padding: 0.5em }\n", ".notebanner, .warnbanner, .errorbanner, .fatalbanner { padding-right: 0 }\n", ".body > div > ol li { text-align: left }\n", ".c { text-align: center }\n", ".r { text-align: right }\n", ".l { text-align: left }\n", ".j { text-align: justify }\n", ".d { text-align: right }\n", ".b { vertical-align: bottom }\n", ".m { vertical-align: middle }\n", ".t { vertical-align: top }\n", ".aftercaption {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", " padding-top: 4pt;\n", "}\n", ".batch > colgroup {\n", " border-left: 1px solid #c1c1c1;\n", " border-right: 1px solid #c1c1c1;\n", "}\n", ".batch > tbody, .batch > thead, .batch > tfoot {\n", " border-top: 1px solid #c1c1c1;\n", " border-bottom: 1px solid #c1c1c1;\n", "}\n", ".batch { border: hidden; }\n", ".batch {\n", " background-color: #fafbfe;\n", " border: 1px solid #c1c1c1;\n", " border-collapse: separate;\n", " border-spacing: 1px;\n", " color: #000000;\n", " font-family: 'SAS Monospace', 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " padding: 7px;\n", " }\n", ".beforecaption {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".body {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " margin-left: 8px;\n", " margin-right: 8px;\n", "}\n", ".bodydate {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " text-align: right;\n", " vertical-align: top;\n", " width: 100%;\n", "}\n", ".bycontentfolder {\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: none;\n", " margin-left: 6pt;\n", "}\n", ".byline {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".bylinecontainer > col, .bylinecontainer > colgroup > col, .bylinecontainer > colgroup, .bylinecontainer > tr, .bylinecontainer > * > tr, .bylinecontainer > thead, .bylinecontainer > tbody, .bylinecontainer > tfoot { border: none; }\n", ".bylinecontainer {\n", " background-color: #fafbfe;\n", " border: none;\n", " border-spacing: 1px;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " width: 100%;\n", "}\n", ".caption {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".cell, .container {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".contentfolder, .contentitem {\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: none;\n", " margin-left: 6pt;\n", "}\n", ".contentproclabel, .contentprocname {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".contents {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: decimal;\n", " margin-left: 8px;\n", " margin-right: 8px;\n", "}\n", ".contentsdate {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " width: 100%;\n", "}\n", ".contenttitle {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: bold;\n", "}\n", ".continued {\n", " background-color: #fafbfe;\n", " border-spacing: 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", " width: 100%;\n", "}\n", ".data, .dataemphasis {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".dataemphasisfixed {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", "}\n", ".dataempty {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".datafixed {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".datastrong {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".datastrongfixed {\n", " background-color: #ffffff;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".date {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " width: 100%;\n", "}\n", ".document {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".errorbanner {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".errorcontent {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".errorcontentfixed {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".extendedpage {\n", " background-color: #fafbfe;\n", " border-style: solid;\n", " border-width: 1pt;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", " text-align: center;\n", "}\n", ".fatalbanner {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".fatalcontent {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".fatalcontentfixed {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".folderaction {\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: none;\n", " margin-left: 6pt;\n", "}\n", ".footer {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".footeremphasis {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", "}\n", ".footeremphasisfixed {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", "}\n", ".footerempty {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".footerfixed {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".footerstrong {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".footerstrongfixed {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".frame {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".graph > colgroup {\n", " border-left: 1px solid #c1c1c1;\n", " border-right: 1px solid #c1c1c1;\n", "}\n", ".graph > tbody, .graph > thead, .graph > tfoot {\n", " border-top: 1px solid #c1c1c1;\n", " border-bottom: 1px solid #c1c1c1;\n", "}\n", ".graph { border: hidden; }\n", ".graph {\n", " background-color: #fafbfe;\n", " border: 1px solid #c1c1c1;\n", " border-collapse: separate;\n", " border-spacing: 1px;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " }\n", ".header {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".headeremphasis {\n", " background-color: #d8dbd3;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", "}\n", ".headeremphasisfixed {\n", " background-color: #d8dbd3;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: normal;\n", "}\n", ".headerempty {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".headerfixed {\n", " background-color: #edf2f9;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".headersandfooters {\n", " background-color: #edf2f9;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".headerstrong {\n", " background-color: #d8dbd3;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".headerstrongfixed {\n", " background-color: #d8dbd3;\n", " border-color: #b0b7bb;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #000000;\n", " font-family: 'Courier New', Courier, monospace;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".index {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".indexaction, .indexitem {\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: none;\n", " margin-left: 6pt;\n", "}\n", ".indexprocname {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".indextitle {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: italic;\n", " font-weight: bold;\n", "}\n", ".layoutcontainer, .layoutregion {\n", " border-width: 0;\n", " border-spacing: 30px;\n", "}\n", ".linecontent {\n", " background-color: #fafbfe;\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 0 1px 1px 0;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".list {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: disc;\n", "}\n", ".list10 {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: square;\n", "}\n", ".list2 {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: circle;\n", "}\n", ".list3, .list4, .list5, .list6, .list7, .list8, .list9 {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: square;\n", "}\n", ".listitem {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: disc;\n", "}\n", ".listitem10 {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " list-style-type: square;\n", "}\n", ".listitem2 {\n", " background-color: #fafbfe;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " 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.systemfooter3, .systemfooter4, .systemfooter5, .systemfooter6, .systemfooter7, .systemfooter8, .systemfooter9 {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".systemtitle, .systemtitle10, .systemtitle2, .systemtitle3, .systemtitle4, .systemtitle5, .systemtitle6, .systemtitle7, .systemtitle8, .systemtitle9 {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: small;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".systitleandfootercontainer > col, .systitleandfootercontainer > colgroup > col, .systitleandfootercontainer > colgroup, .systitleandfootercontainer > tr, .systitleandfootercontainer > * > tr, .systitleandfootercontainer > thead, .systitleandfootercontainer > tbody, .systitleandfootercontainer > tfoot { border: none; }\n", ".systitleandfootercontainer {\n", " background-color: #fafbfe;\n", " border: none;\n", " border-spacing: 1px;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " width: 100%;\n", "}\n", ".table > col, .table > colgroup > col {\n", " border-left: 1px solid #c1c1c1;\n", " border-right: 0 solid #c1c1c1;\n", "}\n", ".table > tr, .table > * > tr {\n", " border-top: 1px solid #c1c1c1;\n", " border-bottom: 0 solid #c1c1c1;\n", "}\n", ".table { border: hidden; }\n", ".table {\n", " border-color: #c1c1c1;\n", " border-style: solid;\n", " border-width: 1px 0 0 1px;\n", " border-collapse: collapse;\n", " border-spacing: 0;\n", " }\n", ".titleandnotecontainer > col, .titleandnotecontainer > colgroup > col, .titleandnotecontainer > colgroup, .titleandnotecontainer > tr, .titleandnotecontainer > * > tr, .titleandnotecontainer > thead, .titleandnotecontainer > tbody, .titleandnotecontainer > tfoot { border: none; }\n", ".titleandnotecontainer {\n", " background-color: #fafbfe;\n", " border: none;\n", " border-spacing: 1px;\n", " color: #000000;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", " width: 100%;\n", "}\n", ".titlesandfooters {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".usertext {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".warnbanner {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: bold;\n", "}\n", ".warncontent {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: Arial, 'Albany AMT', Helvetica, Helv;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", ".warncontentfixed {\n", " background-color: #fafbfe;\n", " color: #112277;\n", " font-family: 'Courier New', Courier;\n", " font-size: normal;\n", " font-style: normal;\n", " font-weight: normal;\n", "}\n", "/]]>/\n", "</style>\n", "</head>\n", "<body class=\"l body\">\n", "<h1 class=\"body toc\">SAS Output</h1>\n", "<section data-name=\"Print\" data-sec-type=\"proc\">\n", "<div id=\"IDX\" class=\"systitleandfootercontainer\" style=\"border-spacing: 1px\">\n", "<p><span class=\"c systemtitle\">The SAS System</span> </p>\n", "</div>\n", "<h1 class=\"contentprocname toc\">The PRINT Procedure</h1>\n", "<article>\n", "<h1 class=\"contentitem toc\">Data Set SASHELP.CLASS</h1>\n", "<table class=\"table\" style=\"border-spacing: 0\">\n", "<colgroup><col/></colgroup><colgroup><col/><col/><col/><col/><col/></colgroup>\n", "<thead>\n", "<tr>\n", "<th class=\"r header\" scope=\"col\">Obs</th>\n", "<th class=\"header\" scope=\"col\">Name</th>\n", "<th class=\"header\" scope=\"col\">Sex</th>\n", "<th class=\"r header\" scope=\"col\">Age</th>\n", "<th class=\"r header\" scope=\"col\">Height</th>\n", "<th class=\"r header\" scope=\"col\">Weight</th>\n", "</tr>\n", "</thead>\n", "<tbody>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">1</th>\n", "<td class=\"data\">Alfred</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">14</td>\n", "<td class=\"r data\">69.0</td>\n", "<td class=\"r data\">112.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">2</th>\n", "<td class=\"data\">Alice</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">13</td>\n", "<td class=\"r data\">56.5</td>\n", "<td class=\"r data\">84.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">3</th>\n", "<td class=\"data\">Barbara</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">13</td>\n", "<td class=\"r data\">65.3</td>\n", "<td class=\"r data\">98.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">4</th>\n", "<td class=\"data\">Carol</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">14</td>\n", "<td class=\"r data\">62.8</td>\n", "<td class=\"r data\">102.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">5</th>\n", "<td class=\"data\">Henry</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">14</td>\n", "<td class=\"r data\">63.5</td>\n", "<td class=\"r data\">102.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">6</th>\n", "<td class=\"data\">James</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">12</td>\n", "<td class=\"r data\">57.3</td>\n", "<td class=\"r data\">83.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">7</th>\n", "<td class=\"data\">Jane</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">12</td>\n", "<td class=\"r data\">59.8</td>\n", "<td class=\"r data\">84.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">8</th>\n", "<td class=\"data\">Janet</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">15</td>\n", "<td class=\"r data\">62.5</td>\n", "<td class=\"r data\">112.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">9</th>\n", "<td class=\"data\">Jeffrey</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">13</td>\n", "<td class=\"r data\">62.5</td>\n", "<td class=\"r data\">84.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">10</th>\n", "<td class=\"data\">John</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">12</td>\n", "<td class=\"r data\">59.0</td>\n", "<td class=\"r data\">99.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">11</th>\n", "<td class=\"data\">Joyce</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">11</td>\n", "<td class=\"r data\">51.3</td>\n", "<td class=\"r data\">50.5</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">12</th>\n", "<td class=\"data\">Judy</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">14</td>\n", "<td class=\"r data\">64.3</td>\n", "<td class=\"r data\">90.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">13</th>\n", "<td class=\"data\">Louise</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">12</td>\n", "<td class=\"r data\">56.3</td>\n", "<td class=\"r data\">77.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">14</th>\n", "<td class=\"data\">Mary</td>\n", "<td class=\"data\">F</td>\n", "<td class=\"r data\">15</td>\n", "<td class=\"r data\">66.5</td>\n", "<td class=\"r data\">112.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">15</th>\n", "<td class=\"data\">Philip</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">16</td>\n", "<td class=\"r data\">72.0</td>\n", "<td class=\"r data\">150.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">16</th>\n", "<td class=\"data\">Robert</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">12</td>\n", "<td class=\"r data\">64.8</td>\n", "<td class=\"r data\">128.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">17</th>\n", "<td class=\"data\">Ronald</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">15</td>\n", "<td class=\"r data\">67.0</td>\n", "<td class=\"r data\">133.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">18</th>\n", "<td class=\"data\">Thomas</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">11</td>\n", "<td class=\"r data\">57.5</td>\n", "<td class=\"r data\">85.0</td>\n", "</tr>\n", "<tr>\n", "<th class=\"r rowheader\" scope=\"row\">19</th>\n", "<td class=\"data\">William</td>\n", "<td class=\"data\">M</td>\n", "<td class=\"r data\">15</td>\n", "<td class=\"r data\">66.5</td>\n", "<td class=\"r data\">112.0</td>\n", "</tr>\n", "</tbody>\n", "</table>\n", "</article>\n", "</section>\n", "</body>\n", "</html>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%SAS\n", "proc print data=sashelp.class;\n", "run;" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
ES-DOC/esdoc-jupyterhub	notebooks/nasa-giss/cmip6/models/giss-e2-1h/seaice.ipynb	1	99815	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: NASA-GISS \n", "Source ID: GISS-E2-1H \n", "Topic: Seaice \n", "Sub-Topics: Dynamics, Thermodynamics, Radiative Processes. \n", "Properties: 80 (63 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:20" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --> Model](#1.-Key-Properties--->-Model) \n", "[2. Key Properties --> Variables](#2.-Key-Properties--->-Variables) \n", "[3. Key Properties --> Seawater Properties](#3.-Key-Properties--->-Seawater-Properties) \n", "[4. Key Properties --> Resolution](#4.-Key-Properties--->-Resolution) \n", "[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n", "[6. Key Properties --> Key Parameter Values](#6.-Key-Properties--->-Key-Parameter-Values) \n", "[7. Key Properties --> Assumptions](#7.-Key-Properties--->-Assumptions) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid --> Discretisation --> Horizontal](#9.-Grid--->-Discretisation--->-Horizontal) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Seaice Categories](#11.-Grid--->-Seaice-Categories) \n", "[12. Grid --> Snow On Seaice](#12.-Grid--->-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --> Energy](#14.-Thermodynamics--->-Energy) \n", "[15. Thermodynamics --> Mass](#15.-Thermodynamics--->-Mass) \n", "[16. Thermodynamics --> Salt](#16.-Thermodynamics--->-Salt) \n", "[17. Thermodynamics --> Salt --> Mass Transport](#17.-Thermodynamics--->-Salt--->-Mass-Transport) \n", "[18. Thermodynamics --> Salt --> Thermodynamics](#18.-Thermodynamics--->-Salt--->-Thermodynamics) \n", "[19. Thermodynamics --> Ice Thickness Distribution](#19.-Thermodynamics--->-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --> Ice Floe Size Distribution](#20.-Thermodynamics--->-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --> Melt Ponds](#21.-Thermodynamics--->-Melt-Ponds) \n", "[22. Thermodynamics --> Snow Processes](#22.-Thermodynamics--->-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --> Model \n", "Name of seaice model used." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Variables \n", "List of prognostic variable in the sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the sea ice component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Seawater Properties \n", "Properties of seawater relevant to sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant seawater freezing point, specify this value." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Resolution \n", "Resolution of the sea ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Tuning Applied \n", "Tuning applied to sea ice model component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which simulations had tuning applied, e.g. all, not historical, only pi-control? " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any observed metrics used in tuning model/parameters" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Which variables were changed during the tuning process?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Key Parameter Values \n", "Values of key parameters" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### What values were specificed for the following parameters if used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "Is Required:* FALSE    Type: STRING    Cardinality: 0.N\n", "### If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Assumptions \n", "Assumptions made in the sea ice model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### General overview description of any key* assumptions made in this model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.N\n", "### Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### List any key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the sea ice component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.1\n", "### Provide a general description of conservation methodology." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in sea ice by the numerical schemes." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does conservation involved flux correction?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any variables which are conserved by more* than the numerical scheme alone." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --> Discretisation --> Horizontal \n", "Sea ice discretisation in the horizontal" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Grid on which sea ice is horizontal discretised?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the type of sea ice grid?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the advection scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model thermodynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model dynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional horizontal discretisation details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Sea ice vertical properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using multi-layers specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional vertical grid details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Seaice Categories \n", "What method is used to represent sea ice categories ?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Set to true if the sea ice model has multiple sea ice categories." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using sea ice categories specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### If using sea ice categories specify each of the category limits." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the sea ice thickness distribution scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --> Snow On Seaice \n", "Snow on sea ice details" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow on ice represented in this model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels of snow on ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how the snow fraction on sea ice is determined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional details related to snow on ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "Sea Ice Dynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of horizontal advection of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice transport in thickness space (i.e. in thickness categories)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Which method of sea ice strength formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which processes can redistribute sea ice (including thickness)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Rheology, what is the ice deformation formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --> Energy \n", "Processes related to energy in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the energy formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of thermal conductivity is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of heat diffusion?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method by which basal ocean heat flux is handled?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which the heat content of precipitation is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --> Mass \n", "Processes related to mass in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which new sea ice is formed in open water." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs the vertical growth and melt of sea ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice lateral melting?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs sea ice surface sublimation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of frazil ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --> Salt \n", "Processes related to salt in sea ice thermodynamics." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does sea ice salinity impact the thermal properties of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --> Salt --> Mass Transport \n", "Mass transport of salt" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the mass transport of salt calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --> Salt --> Thermodynamics \n", "Salt thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the thermodynamic calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --> Ice Thickness Distribution \n", "Ice thickness distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice thickness distribution represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --> Ice Floe Size Distribution \n", "Ice floe-size distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice floe-size represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Please provide further details on any parameterisation of floe-size." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --> Melt Ponds \n", "Characteristics of melt ponds." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are melt ponds included in the sea ice model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What method of melt pond formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What do melt ponds have an impact on?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --> Snow Processes \n", "Thermodynamic processes in snow on sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has a snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has snow ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow ice formation scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What is the impact of ridging on snow cover?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the heat diffusion through snow methodology in sea ice thermodynamics?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "Sea Ice Radiative Processes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method used to handle surface albedo." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method by which solar radiation through sea ice is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
martinggww/lucasenlights	MachineLearning/DataScience-Python3/PolynomialRegression.ipynb	1	36957	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Polynomial Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if your data doesn't look linear at all? Let's look at some more realistic-looking page speed / purchase data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x723aeb8>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cF9/zrlTYz/VcrtVvueXWYbt4ZjLNcZFZGDOorQ37GsnMpZCXMRttYK/4APDu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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x42b5048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from pylab import *\n", "import numpy as np\n", "\n", "np.random.seed(2)\n", "pageSpeeds = np.random.normal(3.0, 1.0, 1000)\n", "purchaseAmount = np.random.normal(50.0, 10.0, 1000) / pageSpeeds\n", "\n", "scatter(pageSpeeds, purchaseAmount)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "numpy has a handy polyfit function we can use, to let us construct an nth-degree polynomial model of our data that minimizes squared error. Let's try it with a 4th degree polynomial:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.array(pageSpeeds)\n", "y = np.array(purchaseAmount)\n", "\n", "p4 = np.poly1d(np.polyfit(x, y, 4))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll visualize our original scatter plot, together with a plot of our predicted values using the polynomial for page speed times ranging from 0-7 seconds:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7281ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "xp = np.linspace(0, 7, 100)\n", "plt.scatter(x, y)\n", "plt.plot(xp, p4(xp), c='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks pretty good! Let's measure the r-squared error:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.82937663963\n" ] } ], "source": [ "from sklearn.metrics import r2_score\n", "\n", "r2 = r2_score(y, p4(x))\n", "\n", "print(r2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Activity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try different polynomial orders. Can you get a better fit with higher orders? Do you start to see overfitting, even though the r-squared score looks good for this particular data set?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
probml/pyprobml	notebooks/book1/10/logreg_poly_demo.ipynb	1	79270	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "logreg_poly_demo.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "## Fit logistic regression models to 2d data using polynomial features\n" ], "metadata": { "id": "bVC9NBe2Obud" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "CsJeGYeAQByu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "70a7cfaf-9d5f-4ea6-96a6-4fafbe2aaf01" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K \|████████████████████████████████\| 272 kB 14.4 MB/s \n", "\u001b[K \|████████████████████████████████\| 72 kB 502 kB/s \n", "\u001b[?25h Building wheel for probml-utils (PEP 517) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for TexSoup (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for umap (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "\"\"\"\n", "Created on Tue Oct 27 10:08:08 2020\n", "\n", "@author: kpmurphy\n", "\"\"\"\n", "\n", "try:\n", " import probml_utils as pml\n", " from probml_utils import savefig, latexify\n", "except ModuleNotFoundError:\n", " %pip install -qq git+https://github.com/probml/probml-utils.git\n", " import probml_utils as pml\n", " from probml_utils import savefig, latexify\n", "try:\n", " from sklearn.datasets import make_classification\n", "except ModuleNotFoundError:\n", " %pip install -qq scikit-learn\n", " from sklearn.datasets import make_classification\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LogisticRegression\n", "import matplotlib.pyplot as plt\n", "import jax\n", "import jax.numpy as jnp\n", "import seaborn as sns\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "source": [ "latexify(width_scale_factor=2, fig_height=1.85)" ], "metadata": { "id": "TI6aUAf3D-ef" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "def make_data(ntrain, ntest):\n", " n = ntrain + ntest\n", " X, y = make_classification(\n", " n_samples=n, n_features=2, n_redundant=0, n_classes=2, n_clusters_per_class=2, class_sep=0.1, random_state=1\n", " )\n", " Xtrain = X[:ntrain, :]\n", " ytrain = y[:ntrain]\n", " Xtest = X[ntrain:, :]\n", " ytest = y[ntrain:]\n", " xmin = jnp.min(X[:, 0])\n", " xmax = jnp.max(X[:, 0])\n", " ymin = jnp.min(X[:, 1])\n", " ymax = jnp.max(X[:, 1])\n", " xx, yy = jnp.meshgrid(jnp.linspace(xmin, xmax, n), jnp.linspace(ymin, ymax, 200))\n", " return Xtrain, ytrain, Xtest, ytest, xx, yy" ], "metadata": { "id": "QdHrPdtkQH8x" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "def plot_data(ax, X, y, is_train=True):\n", " X0 = X[:, 0]\n", " X1 = X[:, 1]\n", " colors = [\"blue\", \"red\"]\n", " if is_train:\n", " markers = [\"*\", \"x\"]\n", " else:\n", " markers = [\"s\", \"o\"]\n", " for i in range(0, 2):\n", " ax.plot(\n", " X[y == i, 0], X[y == i, 1], color=colors[i], marker=markers[i], linestyle=\"None\", label=\"Class {}\".format(i)\n", " )\n", " ax.set_ylim(-2.75, 2.75)\n", " plt.legend(loc=\"upper right\")\n", " plt.xlabel(\"$x_{0}$\")\n", " plt.ylabel(\"$x_{1}$\")\n", " sns.despine()" ], "metadata": { "id": "JdyjGMa7CpAQ" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "def plot_predictions(ax, xx, yy, transformer, model):\n", " grid = jnp.c_[xx.ravel(), yy.ravel()]\n", " grid2 = transformer.transform(grid)[:, 1:]\n", " Z = model.predict(grid2).reshape(xx.shape)\n", " plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.55)" ], "metadata": { "id": "vcaPOF_j7CLA" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "ntrain = 50\n", "ntest = 1000\n", "Xtrain, ytrain, Xtest, ytest, xx, yy = make_data(ntrain, ntest)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1gnRGNplTyY4", "outputId": "661b7c7b-5587-436f-8750-c4e68039b24d" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" ] } ] }, { "cell_type": "code", "source": [ "def train_plot():\n", " C_list = jnp.logspace(0, 5, 7)\n", " degree = 4\n", " plot_list_C = [1, 316, 100000]\n", " err_train_list = []\n", " err_test_list = []\n", " weights_list = []\n", " for i, C in enumerate(C_list):\n", " transformer = PolynomialFeatures(degree)\n", " Xtrain_poly_feature = transformer.fit_transform(Xtrain)[:, 1:]\n", " model = LogisticRegression(C=int(C))\n", " model = model.fit(Xtrain_poly_feature, ytrain)\n", " weight = model.coef_[0]\n", " weights_list.append(weight)\n", " ytrain_pred = model.predict(Xtrain_poly_feature)\n", " nerrors_train = jnp.sum(ytrain_pred != ytrain)\n", " err_train_list.append(nerrors_train / ntrain)\n", " Xtest_poly_feature = transformer.fit_transform(Xtest)[:, 1:]\n", " ytest_pred = model.predict(Xtest_poly_feature)\n", " nerrors_test = jnp.sum(ytest_pred != ytest)\n", " err_test_list.append(nerrors_test / ntest)\n", "\n", " if int(C) in plot_list_C:\n", "\n", " fig, ax = plt.subplots()\n", " name = \"Inv Reg (C) = {:d}, Degree = {}\".format(int(C), degree)\n", " plot_predictions(ax, xx, yy, transformer, model)\n", " plot_data(ax, Xtrain, ytrain, is_train=True)\n", " ax.set_title(name)\n", " savefig(\"log_reg_poly_InvReg_{:d}_deg_{:d}\".format(int(C), degree))\n", " plt.draw()\n", "\n", " plt.figure()\n", " plt.plot(C_list, err_train_list, \"x-\", label=\"Train\")\n", " plt.plot(C_list, err_test_list, \"o-\", label=\"Test\")\n", " plt.legend()\n", " plt.xscale(\"log\")\n", " plt.ylim(0, 0.5)\n", " plt.xlabel(\"Inverse regularization (C)\")\n", " plt.title(\"Train and Test Error v/s C\")\n", " plt.ylabel(\"Error Rate\")\n", "\n", " sns.despine()\n", " savefig(\"Train & Test Error vs C,degree_{}\".format(degree))\n", " plt.show()" ], "metadata": { "id": "ipwoRvZFypnD" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "train_plot()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "pEgoc8eY6oyl", "outputId": "ff38eff2-7e10-4f1b-bb06-3b134e2fb77b" }, "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 432x288 with 1 Axes>" ], "image/png": 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\n" 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\n" 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\n" 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\n" 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CivicKnowledge/metatab-packages	homelessness/notebooks/Explore.ipynb	1	665351	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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e05a08XrPnOXt9SaIJO2S9r98+XJmzZrFggUL2Lt3L+l0mp07d3Lvvffyxz/+sTBOXrE//elPXHLJJcyYMYNXXnmFLVu2FNbdeuutADQ0NLB79+6SytQbJMBQxmpra1m4cCFXXHEFI0aMIB6P8/7779PU1ERzczOxWAzbLu0PRAghhBB9yzAMLMuSAIMQQvSASr9FOn3msRXS6QBhf9eHMVy5ciUvvfQSq1evZuPGjVx88cWkUik2btzI4sWLefDBB/nc5z7X7jnJZJJ77rmHJ554gk2bNvH5z3+eZDJZWO/z+YBs43J/qtNJgKGMhUIhJkyYwLRp05g8eTJDhw4lEomwc+dOtmzZwgcffEAkEunrYgohhBCiBPkxGJRSaK0lwCCEEN2wZPoEoi0Tz7hNtGUiS6ZP6PK+W1tbqampIRgM8t577/HWW29x9OhRXNflk5/8JN/73vd455132j0nH0yoq6sjGo0WxmXo72QWiTKmlMKyLIYOHcrcuXOJRqO8/fbbHDlyhJUrV6K15qabbmLIkCF9XVQhhBBCdJFhGHg8HgzDkACDEEJ00ycuHsdTTbOJx/Z1OtBjPFaNis/mExeP6/K+r7vuOh588EEuuugipkyZwoIFC9i/fz+LFy8udFn/4Q9/2O451dXVfP7zn2f69OkMHz6cefPmlfbCzjMJMJSxfN/M6upqZs6cSVtbGzt37mT79u28++671NbWsmjRosIFiUxdKYQQQgwc+S4SxQGG4kCDfK8LIcS5G1EV4Ec3L+S+pyER3EBFzU683gTpdIBoy0RUfDY/unlhSVNU+nw+nnvuuVOWf+UrXzll2SOPPFK4/73vfY/vfe97p2yzcuXKwv26urp+NQaDBBgGgYqKCqZMmcKJEyd4/fXX2bVrFydOnODIkSMkEglc18UwpLeMEEIIMZAUZzAAaK0LYzFIcEEIIbquYVwt/3nXVTy1fhLPbN7F8aRN2G9x6/QJfOLicSUFFwYbCTAMAl6vl7q6OkaMGEFtbS1+v58TJ04QjUZJJpPYto1lWYW5tIUQQgjR/3XMYHBdF9u2cV1XvtOFEKJEI6oC3LN4KvcsntrXRRmQpNl6EMlPXxkMBjFNE8dxSKfTpNNpma5SCCGEGGBM08Tj8RSCCY7jkMlkcBynj0smhBBisJIMhkHAdV0ymQzJZJJ0Oo3jOO3mzhZCCCHEwJPvIpH/Ps9/30uAQQghRF+RDIZBwLZt2traOHz4MM3NzRw/fhyAQCBAMBjE7/dLKqUQQggxwHSWwZBvSBBCCCH6gmQwlLF8f8yWlha2bt3Khg0bOHDgAK7rMnz4cMaOHUtlZSUej6eviyqEEEKILjIMA6/XWwgwuK4rAQYhhOim5tYET6/bz4sbm4mkbMI+i6tnjeDmhlEyyOM5kAyGMua6LqlUir179/LMM8/whz/8gX379lFVVcWcOXOYN28eQ4YM6etiCiGEEKIElmXh9XqxrGx7kW3bhQBDfqpKIYQQ527dnuN88aE1bHvjANfGPPy1E+LamIdtbxzgiw+tYd2e4yXtd/fu3UyfPv2U5YsXL6axsbHL+3vkkUf40pe+VFJZepsEGMpQfg7sSCTC+++/z5o1a3j77bfZvn07lmUxYcIE5syZw8yZM6muru7r4gohhBCiBKZp4vP52nWRSKVSZDKZPi6ZEEIMPM2tCf758U1cmfCywAxQbZgYSlFtmCwwA1yZ8PLPj2+iuTXR10Xt1yTAUKZc16W5uZlnnnmGp556il27duHz+Zg0aRLz5s2joaGByZMnEw6H+7qoQgghhCiBaZqFDAalFJlMhng8LgEGIYQowdPr9jMxoRhhdj6KwAjTYkJC8Yd39pe0f9u2ueOOO7joootYunQp8Xi83fovfvGLzJ07l2nTpvGd73ynsHzt2rV89KMfZdasWcyfP59IJNLuec888wyXXnopR48eLalcPU0CDGUoFouxa9cu1q9fz+rVq9m8eTPJZJL6+nrmzJnDJZdcwqRJkxg6dCh+v7+viyuEEEKIEuQDDPkMBtu2SSQSEmAQQogSvLixmanKe8ZtLlJeXtzYXNL+33//fe655x7effddKisr+eUvf9lu/fe//30aGxtpamri1VdfpampiXQ6ze23387PfvYzNm7cyEsvvUQgcHIciKeeeoof/ehHPPvss9TV1ZVUrp4mgzyWoSNHjvDSSy/x6quvsm3bNtLpNEOHDmXatGlcccUVzJ8/n9ra2r4uphBCCCG6Id9FIj8GQ3EGg4zBIIQQXRNJ2VQq3xm3CSuDtqRd0v7HjBnDwoULAfj0pz/N8uXL263/7W9/y69+9Sts26a5uZmtW7eilGLEiBHMmzcPgMrKysL2r7zyCo2Njbzwwgvtlve1c85gUEqZSqn1SqkVucdfUkp9oJTSSqlTwiVKqXlKKVsptbRo2V1Kqe25211FyxuUUpty+1uulFK55bVKqRdz27+olKrJLVe57T5QSjUppeZ0500oF4lEgv3797N161beeust1q9fT0tLC5WVlcyYMYNLL72U6dOnM2bMGEKhUF8XVwghhBDd0DHAIBkMQghRurDPok27Z9wmol0q/aW10eequJ0+3rVrFz/5yU94+eWXaWpqYsmSJSSTyTPu74ILLiASibBt27aSytNbutJF4ivAu0WP3wCuAvZ03FApZQI/Bl4oWlYLfAe4BJgPfCcfMAD+F/B5YFLudl1u+X3Ay1rrScDLuccA1xdte3fu+YPeiRMnWLNmDa+88gqbN2/myJEjmKbJxIkTue6667j22msZMWJEXxdTCCGEED2geJBHpZQEGIQQohuunjWC93T6jNu8q9NcPau0+tSHH37I6tWrAfjNb37DZZddVljX1tZGKBSiqqqKQ4cO8dxzzwEwZcoUmpubWbt2LQCRSATbzmZQjBs3jieffJI777yTLVu2lFSm3nBOAQal1GhgCfDv+WVa6/Va692necq9wJPA4aJl1wIvaq2Pa61bgBeB65RSI4BKrfVbOpvP95/ALbnn3Az8R+7+f3RY/p866y2gOrefQSmdTnPs2DF27tzJmjVraGxsZP/+/ZimyZQpU7j00ktlUEchhBCiDBV3hUilUrS1tZFMJqWLhBBCdNHNDaPYGdA0O513gWh2bHYFNB+fM6qk/U+ZMoVf/OIXXHTRRbS0tPDFL36xsG7WrFlcfPHFTJ06lb/8y78sdKXwer089thj3HvvvcyaNYurr766XWbD1KlTefTRR7ntttvYsWNHSeXqaeea3/EA8HXgrLVTpdQo4BPAlcC8olWjgL1Fj/fllo3K3e+4HKBea50fReMgUH+WfZU24sYAF41G2bZtG2vXrqWxsZFt27aRSCS44IILuOmmm1i8eDFjxow5JS1HCCGEEANXflpK27bRWhOPxzly5AixWKyviyaEEAPOiKoA/3jbDP758U1MSGS4SHkJK4OIdnlXp9kV0PzjbTMYURU4+846GD9+PO+9994py1euXFm4/8gjj3T63Hnz5vHWW2+1W7Zs2TKWLVsGwMUXX8zWrVu7XKbectYAg1LqRuCw1nqdUmrxOezzAeAbWmu3Jyu0WmutlOpSOF4pdTfZLhSMHTu2x8rSX9i2TTKZ5MCBA7zzzjusWbOGDz74gGQyybhx45g/fz4LFixg+vTpeDweCTAIIYQQZUYpVfh+d10Xx3FwXVcyGIQQvW7Xrl388Y9/xOfrfGDE/Lmp+Gf+ZhgGhmGgtS5082ppaeHQoUNccMEFne7DNM3C8yzL6vR+fhvTNE+5H4/HC/vLz77T8TizRlXy8880sGJDM883HSSStAn7La6eNZKvzRnJyOpgz76JZehcMhgWAh9XSt0A+IFKpdSvtdafPs32c4H/l/sl1QE3KKVsYD+wuGi70cDK3PLRHZbnJxc9pJQaobVuznWByHe52A+MOc1zCrTWvwJ+BTB37tyy+6ZNpVIcOnSIrVu38tprr7FmzRqOHTvGqFGjuOGGG1i8eDETJkzA6/VKcEEIIYQoM16vl1AoVPie9/v91NTUEAgE5HtfCNHr/vCHP/DQ8v9iwfSPtVuuKap2aQ2qaJkGlMZ13aItNVrD8bbDtEVb2DmqpfB0pRRaazQ699PJ/tQuLm42oEr2cf6nq7PbuNpFawdXa77+7XvZ9+EBABzHRqMxDONkmTqU+4rhmiuG1xXKBmla9u3i+D5Q5IMeAKrwM/uv48/222TPzZrxE8YXBugtN2d9VVrrvwf+HiCXwfB3ZwguoLWekL+vlHoEWKG1/n1ukMcfFA3seA3w91rr40qpNqXUAuBt4E7g57lt/gDcBfwo9/PpouVfUkr9P7KDRrYWdaUoe7Ztk0qlOHDgABs3bmT16tVs2rSJo0ePUl9fz+zZs7n00kuZPXs2VVVVp0TohBBCCDHw5Vv08hfg+Ra8RCLB0aNHMQyjkNFg23ahK0V++3Q6TSaTIZ1Ot8t4yAcnDMNgxowZeL1nnhdeCDE4hcNhLpn2Z1xzydKzb9zHQgEPdVU9MGSfzodEiv7XHZZ03EYXBy80R1sP4jjO4A0wnI5S6stkx2UYDjQppZ7VWn/udNvnAgn/DKzNLbpfa308d/8e4BEgADyXu0E2sPBbpdRnyc5W8anc8meBG4APgDjwmVJfx0CUz1zYtGkT//3f/82aNWtobm6mrq6OK6+8kkWLFjF58mSqqqrweDx9XVwhhBA9JNtqowsp8Kd77DgOtm2TyWSwbbvwOF+hzK/LZDK0trbium5hFoL8LZPJ4DhOdrt0hlQ6TSqZJp1MkcpVSLXWaFfjdkjHN5TC1TrbSlQ045dS5Jtzci+ocB1WeH1KKZRx6nqlwMgFzF3HwXU1rutgGGa71vrihvv8NkWHO3nf1WjXLZTdNAwMM3tgx862eLlOtvDKaJ/m2xnXcQstbB231Vpj206uC4ON67iF7gyO4+K4Dq5zMgiQDwhkMplCK1+2PE4hYOC4LslEgkQiQSqVJuSrYuvG7ezdcRB/wE/AH8QwjGwqsjIxjeytmGlaWKYH0zBRuTdda022fU1z8Ng+/uXnP+Lyyy8/7esWQgxygy1ZKt9lo/j/Lr4HplmegYW8Lr06rfVKst0a0FovB5afZftlHR4/DDzcyXaNwPROlh8DPtbJcg387TkXvMxEIhG2bt3KW2+9xbp169izZw+VlZVMmjSJ+fPn09DQwIgRI/D7/X1dVCG6zXVdUqlU4aK9uD9dcd/jvuS67sk0uw7i8TjRaLRQCcy3IBbLTzeU70fo9XrbRbUty8Lj8eD1egsVhoEgX9EtrvQqpXAch0QiUahIZStQFF63YRgkk8nCLf+aU6kUyWQS13VJJpOcOHHilAqxbduFwGr+mMXHcHL3Xa2xC5U7F9uxSWds0naGTMYmbdtk8pVw28Z2nOzPTHa5bdvYTr782UpstpKfS73U2ZTK7LLsNvnfe3EwoNDK0SFIkE/5dHPvX34boFARVIaRTb/M1dqzj3MXO6aJYVoo00RZJqpw3wLLyv40sz93rHyFMZ5qZk9dgEGuf6syMVS20mliYhgWlmkVKqSWme2Dmk8BNdWpn38TsFT7z6vOteTknnxyH518djqu12QDAtnXqjCUASh0hznL861EJ1NYjXYXf8XHU0Xlc20nFyAg9x4YJy+cdfufnQUR8r+Lzl4PgOExcu+pQimjcIzs5ztbDtOwMA0TQxmYZvY+Kv9awTBMjFzAQClFJBYl0tpGLBYnFotRVVVJdXUNwWCQYDBAd6/8f/vqLwrnJyGE6Egp1T5KLM6RHjDXcqUo7/DJAPbTn/8bf3j+ecaNH8+Y+npG1A3ByrXcNDc3s+LFl9i+fRukUoTDYWbMmMGCBQuYNm0ao0ePJhDo+uimr61axR9f+dNpK0qunWHk0KEEg0H8fj8+nw+fz9duwJa84gGnigdzAU62vhRd+He2zHVdWlpaSDnuqfvvcBwAV2sOHTqIrYxsy5mbqzw4Dk5+31pnW/NcF7fDOshd5Hc8UWrdrkJY3D+r44U/HVriOixqv23hsVtYXsqgXOlUCjdjU1lTjeO4KMPAtjM4jpur9GQrMYW+a/rUcpdy3HhbG47r4PFlA1nZi/8Ory336t3c+2uaVruKc2eEp/TqAAAgAElEQVQpue2erzWZTBoME5UbCAid/d1p7UIuJTj7+To58I9hGKj84EGuW0hX01qjOgwAZFkmhmFimgaWaRVaKs+FdjWHDx8mkYgTDIaoCFcQDARBQTKZ4kRLC6lMBisUylYKlcpW9vKtiLlKr7KsQmVTOzZuOp2tSOXeE23buJkMrm0DGsvKBhs8Xm+23JaFZXlyP81cxcQo/N0ZKlv5NHMVUsMwMJWBMk7+bSraD76UrShx8r3NtUin0imS6TSpZIpkKtuSnUolSaXSuZbudK4C7+LY9snXbahcxVijTAvT60OZJhhG4T13MxlwXVzHwfT6MHw+DK83e+2iXZTHi/J6s8/xeHFD4ex7Zxpow0RbFtowMXLTS2mlQBnZn2b+vKZy5cmVy8y13BoGygyiAiaqwkJZJoZZVDHPVc5V8TIj9zwjW9FVZvY99ygDjPw5q6jiXzzYlVLZ5xZXds2iAIFhZk90Kr9/suXthQuS5gMH+WhoBtcuuK3H9y16mUsuMyL/HUZRX+Wut6p1pM5tNnMhxCCllEJ3bQz+AeFQJMkL7x3izW1HiaRtwl6Lj06u45qp9dSHe6ABV585I26gkwBDPxWNRtlh+ojMmM87x49h7z0CucFK0skULfVjiG7ejDedZvTo0cyePZv58+czbtw4qqqqSjrm75/7I49v20nt/I/mlrQ/YWQSJ+DAcZR9GJVJo+wMKp0GchcxbqFpp/BTG7nWHNfNXixrnb3gz12Y53JesxUAI7dcKXSu1UYrA1c7WOHKkwU5Q2VY+6qwKiowfYHcxbrRrhLQrlJRWG6evODP/7F3+KM/5STQ2Umhw0i57bc780mk8JyTkZOzHy/HqzV2NEoiXwnKvTbLMMEsaj0sbknMv9bcFWgpJ7kayFW4Tp5G8i2qQG4Qn8IKlFLZCrLrFNKg272u01wNK8uDcZo+ajrbRJwNNrga7TrZirmrc8vcUypyGg1OdrvszUE7DtpxSWsX7TjnfFWulGJoTS1WMIQTj2PHYySTCQAMr5f6ikrMUKhHv0S04+DaNjqTwbXzFXIb7TjYtkMmd59cCziaXHDFzb1X+RbkXGuwezLARYcW9UJ+ui5KXfd4MDweDI83d9+Lx+PB5/GgvF4MyypUjpUls9cMBIZlFjI8xEBzMnAMJ0/tqjCgWPfJZ0MIcTrl+B2/8UArv3x5G5c7Jn9v+RniMzjmuqzacoxvv3eYez42mVkju17X+nDvHv7izqW88ae16KIMhm9/+9tcfvnlXHXVVT39UvqMBBj6qT9f+kkeffFlai6eW2i9yqdXR1tbOfK1L2ElE1RWVzNmzBjmzp3LnDlzqKmpOcueT892XeoWXcnQhVf01MsQop2eHnBUKQWmiaLvBzK1KiqwKip6/TgqN9USp5kSSoguM0xsJ9PXpRAlUMrAtMxChmA+46inLvqVUhJgEEIMGociSX758jbuxc+FvpPj2A0zTT5pBpllZ/j5y9u4/5YZ3cpkKM7gvf/++7tV5v5Ict/6qUmTJjGmuorItncLy7TWOLZD65YmPG0teL1ehg4dyvjx45kwYQIjR44sqWtEnu3YhQG0hBBCDA7KNHG1VCIHIpUbn0EVZcv15Ng0ClVSFzohhBiIXnjvEJc7JhdanQ+Sf6HlYZFj8sJ7h0rav+M4fPXvvsTHb76J66+/nkQiwbJly3jiiScAuO+++/jIRz7CzJkz+bu/+zsAli1bxhe+8AXmzp3L5MmTWbFiRWkv7jySAEM/9hc33Uj07TcKj7XW2I5NfEMjIQXBYJDx48dzwQUXUNEDLaeZjNMu3V0IIcQgYJo4rgzkN2D1YoZyfjpLIYQYDN7cdpRF1pkzRC+3fLy57WhJ+9+5awefXXY3T//+D1RXV/Pkk08W1h07doynnnqKLVu20NTUxLe+9a3Cut27d7NmzRqeeeYZvvCFL5BMJks6/vkiAYZ+7IbrryO97m3cTHacg0Jf6hMtWLkB7DweDx6P57QDM3ZFxs5kB10TQggxeBhmYSBWMcD0cvdn6SIhhDiTcgtARtI2Q85Sp6o1DCLp0oLy48aOZ8b0mYCmoaGB3bt3F9ZVVVXh9/v57Gc/y+9+9zuCwWBh3ac+9SkMw2DSpElMnDiR9957r6Tjny8SYOjHhg8fzuwpkznRtB7Ij1VnYOQGPEwmkxw8eJADBw6QSCS6fTzbcSTAIIQQg4wyTRxXAgwDUq9f20sGgxDi9MptkMew1+LYWYKqx12XsLe0jG+v15sbTFthWVa7aYAty2LNmjUsXbqUFStWcN111xXWdXyf+/v7LgGGfu7Pb7qR5FuvA7nBnEwLz8RJZIzsqN+HDx9m165d7Nq1q9uBBjtjS4BBCCEGGWXKII8DWi/W/xUyi4QQ4vRUfkayMvHRyXWsslNn3OY1O8VHJ9eVfAxN55OVRaNRWltbueGGG/jpT3/Kxo0bC+sef/xxXNdlx44d7Ny5kylTppR8/PNBAgz93FVXXYX97mbsWBRlKDxeD3XzFhBxXUzT5Pjx47z//vusXr2axsZGjh07VvKxMo4EGIQQYtAxLRnksYz0bMOWZDAIIU5PKZWdErtMXDO1ntdMhw/szoPuH9gZVpkO10ytL/kYhanaO4hEItx4443MnDmTyy67jH/9138trBs7dizz58/n+uuv58EHH8TvL30Gi/NBRvTr5yorK1l86QLeanybYZd/DNMwCQwZSuWSW0mveBKVTHDw4EHWr1+Pz+ejpqaGUChEKBTKpuF0gZ2xs/PXCyGEGDSUaeBKFwnRCaWkHUoIcXr9PVW/q+rDfu752GR+/vI2FqUyXG75qDUMjrsur9kpVpkO93xscklTVI4dM443/rQ2932rCrNEFFuzZk2nz73qqqt48MEHu3zMviLfHAPAp266Efvt1wuPPR4PYz/551hzLgGfn3g8ztatW1m9ejUbNmxg586dRKPRLh8nY0sGgxBCDDqGjMFQLnr8Wl9LFwkhxJmVW5bTrJFV3H/LDPS0IfxQJflCqo0fqiR62hDuv2UGs0ZWdf8g5RWXOYVkMAwAl112GeY/3U/y6CH8Q+sxTYNAMMTYe/4/dvzL9wlsWk8iFmX79u2sWbMGj8eTnRvbMAgGg+ecyeA4DsqSj4QQQgwqlikZDKJTypAuEkKI0yu3DIa8+rCfv5o3jr+aN66vi8IjjzzS10XoMslgGAC8Xi9Lb7iOltdfzS1RGIbCHwwx/stfR31kBp5QBceOHeP111/n2WefpbGxkT179hCLxc75OLZtQw9MdymEEGLgKM/LQ9ETFDJNpRBC9LRy/96V5uoBYunNN/Pol76CvnkpyjDITm9iEqqqZuxX72PHj+9Hv7uJ/W0Rjq5t5P19+xn1h/9mxPDhDKmtJRQIEPD78VoWHo8HrTWO66JdFxeN67rs3bWTYTIGgxBCDCpaBvITZyCfDSHE6ZRrBkNvyp5Ry/t9kwDDADF16lTGVIU5+OwfGHHjLUBu2koLAuEqxvyPb3JsSxOJSBQnk+aoUrSaJrv9fnweC8swUFqjkw46lgClsoM3KZW7WQT+8q/xDx/Rx69UCCGEEP2BwpAAgxBC9KjyP6dKgGGAUErxr9/9Jz752c9hL74Kq6ICFBjKwOPxUFFVhZrVQCQSIZGIY9s2yrQwKyrwhUIEAgG8Xi+GYaAMhaEMlFLtI4/lHUwTQgjRCWUYuIPggkeUQrJbhBBnVk7TVOYdiiR5ftt+Vu38kGjKocJnsmjiWK6dPKqkGSTa0b0wIG8/Ix3uB5AZM2Zw/eLFHHvztXbLDcPA5/cTDldQVV1FOFyJx+PBtm2i0SgtLS20tLTQ2nqCaCxKKpnEtm1c15ULByGEGOS0UiDfBWWk565csx8N+WwIITpXjl0kNh5o5ZvPraK19bcsm/Zr/vGj/4dl035Na+tv+eZzq9h4oLVb++/YRWLx4sU0NjZ2eT8rV67kxhtv7HTd5z73ObZu3QrAD37wgxJK2T2SwTDA3Hn7p3jxH/8JffX1hT9qZSgsw8JQCshmJbjaBRUnk86QSCRwXYdMJo3X68Xj8eLxeDAtE0MZGEb+efmjZO8YhoFpGhiGiWFku1Rkt1HFmwkhhBjAFFKJFKcjgzwKIU6v3AIMhyJJlr++htsnP8XYquOF5UMCMa6esJEptXtZ/jr84PpF3chk0L2ewfDv//7vhfs/+MEP+OY3v9m7B+xAMhgGmDlz5jDUVEQ/2HbKOmUYeL1eKkIhamtqqKmuxufzorUmlU4Ti8VpbW3j+PFjHD5ymEMHD3HwYDPNzc0cPHiQgwcPcehQ9nb48GGOHjtKW1uERCJBOp3BcRxcuQgVQojyYhhoqUSKTijpIiGEOKvyOUc8v20/s+sa2wUXio2tOs6sunU8v21/l/f94d49LLh8Dv/ja19hyY1LuOaaa0gkEgA8/vjjzJ8/n8mTJ7Nq1SoAlixZQlNTEwAXX3wx999/PwDf/va3eeihhwCIRqMsXbqUqVOncscddxTO1/msiPvuu49EIsHs2bO54447APj1r3/N/PnzmT17Nn/zN3+D4/T8NNUSYBhglFJ8ZulSIq++1Ok60zLxer0EgyEqKsJUVIQJhYL4vD5M00SjsW2HdCpFIpEgFosTjcaIRqNEohEikSjRaIRIpOhW9DiaexyNRolFY8RjcZKJJJl0Bsd20K7OnmfK51wjhBDlrcxaoETPkgCDEGKwWLXzQ+YM/+CM2zQM387rOz8saf87d+3gM3d9lmefeY7q6mqefPJJAGzbZs2aNTzwwAN897vfBWDRokWsWrWK1tZWLMvijTfeyJZx1Souv/xyANavX88DDzzA1q1b2blzZ2GbvB/96EcEAgE2bNjAo48+yrvvvstjjz3GG2+8wYYNGzBNk0cffbSk13Im0kViALr54zfxowf/N3YsihWqOGW9UgaWBYFgAMsySafDpDNpMpkMdiZDxrZxbAfHsXEctzAWg3Y1LidbsVzXxbEd4ol4u0Eh84NEGoaRDWh4vPgDfvw+P16vF9OUqS6FEGLAUKosB+ka1HooZqSUdJEQQpxe9vxQPkHqaMqhxh8/4zbVvjiRVGmt/uPGjmf6tBm0JY7R0NDA7t27Abj11lsB2i1btGgRy5cvZ8KECSxZsoQXX3yReDzOrl27mDJlCs3NzcyfP5/Ro0cDMHv2bHbv3s1ll1122uO//PLLrFu3jnnz5gGQSCQYNmxYSa/lTCTAMADV1NRwzaLLePXNVdRfff2pG6hskMGjDCzLwudzSKcz2QCDncG2bewOAYZCkEFrtHZxXY2rXXQuyGBrG62LBoVU2SCDZZl4PF4ymQwZfwa/34fX68Pr9WBIoEEIIfo/w8Bxez5FUgx8Mk2lEOJMym0MhgqfSUsyyJBA7LTbnEgFCftKq+N4vV6yYzAoTNMsdJHw+XwAmKaJbdsAzJs3j8bGRiZOnMjVV1/N0aNHeeihh2hoaCjsL/+8js89Ha01d911Fz/84Q9LKv+5ki4SA9Rf3baUzOuvnPWLX6EwDBOvz0swGKCiIkxVVTW1tTXU1dUxbNhQ6uvrGT5iOCNyt/r64dTV1VFbU0NlZSWhUAi/34/H48U0LZQyQGscNxu4SCQStLW1cfz4cY4ePcqJEydIpzPn6Z0QQgjRLWV2gTi4aHqzX6KrnbKrQAghek65nR8WTRzLOwcvPOM26w5O4rKJY0s+htacU9KH1+tlzJgxPP7441x66aUsWrSIn/zkJ4XuEefK4/GQyWTrZR/72Md44oknOHz4MADHjx9nz549XX0JZyUZDANUQ0MDdUoT3bGN8IVTOt8oP9mDUpiYcLaMAp2NbGVnnLDJ2Lmsh4ydy3xwsgM9uk42w8F10drFcdxcVoRNJpMGFKFQCK/Xm52hosxOPkIIUU4KMw+JstCTX7mtiSOMGjWq53YohChD5ZPldO3kUXzzublMqd3b6UCPH7bWsvFoAz+Y153zokadY7eSRYsW8fLLLxMIBFi0aBH79u1j0aJFXTra3XffzcyZM5kzZw6PPvoo3/ve97jmmmtwXRePx8MvfvELxo0bV8oLOS01WFLf5s6dq0uZY7Q/+7+P/Af/8vZ6Rn72iz2zw9xH4dRuErnHhS4UufEaXIeMbZNKpojFoqRSKRzHJRAIMHToUCrCFVimhWFKoowQQvRX7/3X/2X0ynf46u3f7+uiiC5qa2ultbWVWCxGLBanpqaGIUNq8fv9udTZ7kUbHn7+B/zkF//M9OnTe6bAQoiy8thjj/Hsr99kyaWf7uuinNXEP/Mwcdyks2638UAry19fw6y6dTQM3061L86JVJB1Byex8WgDX75sPrNGVpVcjnQmSTzTysQLJpa8j/Ph3Xff5aKLLmq3TCm1Tms992zPlQyGAezmj9/Ej3/1EHY8hhUMdX+HRRkPChPjdAkPGjQaN5e54PV4MAyFaVqkUik0mkQigVIq17XCg2maKEMyGYQQor9RhsItoxYo0YO0zCIhhDg9pRTldoqYNbKKH1y/iOe3TeQ/tnxIJOUQ9plcNnEsP5g3ivqwv9vHKLf3rCMJMAxgtbW1XLXwUl574zWGdzbYYy/Kju1g5IIHBl6vj0AgSTQaIZk8+bOiooJgKEjAH8Ay5OMmhBD9jTKMdjMIiYGlN69TlZIAgxDizMqxJ3R92M+dDRdwZ8MFvbB3VfbnVcldH+DuvO027DdXnt8PqsrelKEwTAPL8uD3+wmFQlRUhPH5/Ni2TSwWIxqNEo1EicdjpFIpXMftzfGohBBCdJHh8cosEgNc732llmHNQQjRo8q9stzTslkf5f2eSYBhgJs7dy61dobYrh19XRQsyyQUChGqCGFZHhzHIRaL0drayvGWFtra2gqjmAohhOgflGniyCCPA9cp16kSFBBCnB9KKbS0GnaJGgQZDJKzPsAZhsFnblvKT1e+RMXEM0+r0msKYzcYeLwGAX+AYCiIm5vGMplK4roO2tUYyiiMWmqaZnZ6G7kWGhh09j+dG4ODc5xmp3fKkUvJU+rkSLzyORKiJMqycKSLhOjEYGhpE0KUTmuNkvbqrlGUfVBGAgxl4JabP87//Pf/g5O4EzMQ7Ovi4PF4qKmuxmN5aG09QTKZJJ3J4LpRMpkMyWSSqqoqAoEAhmGU3Ry65UqjT84o4mo0uuQ6fbdPqzrXRccwMJR8hoToDkMyGIQQQoiCQ5EkK7Yd5oWdR4mmbCp8FtdMrOPGycO6PcjjYMhgkJBTGRgyZAhXLriEo6tf7+uiAGCaJoFAkHC4gopwmEAwiGVaOI5DIhEnEokQiUaIxWKk02kcO5vdIPoZTTag4Gpc10W7bm6a0u7vukfCAbnyublyydgeQpTG8PiwXbuviyF6VM8EXbV2JYArhDit7PmhvC6+Nh5o5YvPbebJtjbS06sJLKwnPb2aJ9va+OJzm9l4oLVb+y/ODDtx4gS//OUvC+tWrlzJjTfe2K399wcSYCgTd33qNjKrXunrYgDZPxzDMPD5/dTW1FI3ZAihihAejweAVCpJy/EWjh0/RjQaJZ1O47rSetbfaJ2tvDuOjWPbOI6Tu9jMBpEsy8Lsxs3qzs1jYRgGrnZzAQa37KPBQvQWw2NJBoPolNZIgEEIcVrZMRjK5/vjUCTJd1/fjj2litCESqyAhVIKK2ARmlCJPaWK776+nUORZDeOogrzVHYMMHSXbfePxgIJMJSJ+fPnU2OniO78oK+LUphhwjItAoEAoYoKwhVhQqEK/H4/hmGQSqWIRWNEohGi0SiJRIJMPtAgLdF9K5+5oE9mLGR/HSo75oEyMPLdE/rwpgxVuPB1XRdXuyfHhpDPjxDnzLA8OFpmkRCn0mgMQy4VhRCdy54fyicIuWLbYZJD/fiqfJ2u91X5SA7188y2wyXt/5f/++cs+tglfPzmj/PAAw9w3333sWPHDmbPns3XvvY1AKLRKEuXLmXq1KnccccdhQa0devWccUVV9DQ0MC1115Lc3MzAIsXL+arX/0qc+fO5Wc/+1lJ5eppMgZDmTAMg7s+eSvLV73Sd4M9nobH8lBZWYnX6yUe9xGLxYjHY2QyGSKRCOl0mnQ6RTCYnYHC6/H2dZEHNa11LltB52IKCtMwc/Mcq8LPvmYohTItHNfBcRwMDYYy+kPRhBhQlCVjMAxsuveCqlpLBoMQ4ox0GX1/vLDzKL7p1Wfcxjc8yAubj/LXDWO7tO8NTev5zWP/xQsr/sTB4x+y7DPL+PWvf83mzZvZsGEDkO0isX79erZs2cLIkSNZuHAhb7zxBpdccgn33nsvTz/9NEOHDuWxxx7jH/7hH3j44YcBSKfTNDY2lvaie4EEGMrIrbfcwr88/AjO7X+F6Q/0dXEKFT3DNPCa3lzLs8q1PEMikSSTyZBwErm+9NkrJDdwcpaJ4v2ULHfhpbXOzvWuKQwuKbNYFNE61y1CF6KlClX4vdHfLjJV9nNkaAOXbPaC656hv3AvFF/l/m93zH72Noke0qUKnO7k3rnsQ5fwnA66+vnTFAJ10lWtr6bG6br895f0DBNC9KVym6YymrIJ+M0zbmP6TCKprndFeHvNmyy57iZCoQqCySCf+MQnWLVq1SnbzZ8/n9GjRwMwe/Zsdu/eTXV1NZs3b+bqq68GwHEcRowYUXjO7bff3uXy9CYJMJSRuro6rrxkPqvfXEX9n13T18U5hWma+P0BTNPC5/USi8VobW0jlU6RTCZxHJt0OkUoVEFVVRV+v7/HWk40GsexSaXSaK3xeDxYliWzWBTRWmM72QCMUtlpR092Q+i/75EyFJZl5caLcHKzW3Q+dWXPvors3rLdRUz5HA1KOj97a7tlhf91hy07bNd+m9M8LkFXPokacJXCcZx+03ez+0oJFOiiZ/X/v2XTNE4G4XuRYZg4jnSfEUJ0TilVVt1SK3wW6aSDFTh9FdlJOYR93axCn+Ga0ec72T3DNE1s20ZrzbRp01i9enWnzwmFQt0rTw+TjnVl5s7blmK/8ae+LkanlKGwPBY+v49gKERFRZhwuIJAIIBSinQ6QzweJxqNEovFckEHp+Q+9drV2BmbZDJJLBqjLRIhEokU9p9IJshkMti2jW3bOLbTfkaCcqPJZinkZl5wnQ43VxcGnckHFwpTQPbj6+32WRZFRS2KMfRmiERrCoNNdvq+OkXr5Nb7t1Le99M956z70rnZVdzsZyA3havr5qd0zd/covu6MLbJyYhCZ4GK4s94125dZZhmWbVAlUYNmOBCVr6c+Yyz3vn9KWWUUeBJCNHTyq1x5ZqJdaQOxs+4TepgnGsm1nV53wvmL+TZ51cQj8dJxBP8/ve/Z+HChUQikbM+d8qUKRw5cqQQYMhkMmzZsqXLZThfJIOhzCxYsIDKRJzY7p2Exk/s6+J0ylDZFudgMIjlsfD7A7S2niCRSOA4LolkAk5kR0Ktrq4uuZUmOy1mgng8TiweI51K47oOhmHg8XoJ+P0Eg/nZLTSGkZsZwTTBOHm5WT5Odn8oBFKKqNwAjvlZQAbad4YyDKziSHpvl7+o601+cNJTW6lpF+gQva9dRf0cYmOdVcvONOmW6uRe8SLVcUFn+2g3jsnJwFinFdzz8MGxcuPeWJZcEgwU+cwyrelwPs8HrHom4OC1vCST3RktXQghBo4bJw/jmecOk6r1dTrQY6o1hf9IkiVzuz7e3ayZs/mLT32aq5dcge1k+MIXvkBDQwMLFy5k+vTpXH/99SxZsqTT53q9Xp544gm+/OUv09raim3bfPWrX2XatGldLsf5IFcTZcYwDO5aeiv/9urL/TbAgFIYSmF4jGw3BWXguA5KKRKJBLbtkEjEUUrh8/kwjNx25lkSbnLXU67rYtu5zIVYjFg8RjwWx7bt7HgQHg+KfGW6UCjyLUGu62Kg0Opki1Z/b8UHshVcnU/P7jwLI996CrR/PbncYMM4GWTo9683r1CBV+c3kp6rybouKK1yXTNOX2EV59k5foRPt83pn3tqAOB0gYHTtYi3CzCoDsHMjo/PXqBuM0wTrWW2gIGoXUZMYRk9ltDgMf1Eo9Ge2ZkQouxkr7vKJwOuPuznO5dN4ruvbyc21I9veBDTZ+KkHFIH4/iPJPnOZZOoD/tL2v89f3Mv9/zNvRxu2ccFk7L1tN/85jfttlm8eHHh/r/9278V7s+ePZvXXnvtlH2uXLmypLL0JgkwlKFbb7mFB/7jVpzbP90/Bns8A6UUXq+X6upqvB4PGkjE4ziOSyqVzKUNaUIVFXjNc5tdwrZtIpFIdraKRJx0Ko3jOJimidfnJRgIEq4ME/AHcuMwZK/cda7CmB2LIDvY2cn0+/4/VoPWOjtVYz41u0OQQeUGRcxmKJid1IfU6Ss34jQUhgFKmbTrM3++sihEnziXLIWzPblb++hhSrpIDGi6KG1HFwWae4LX9BOLxXpsf0KI8lNu3x+zRlbxv66fzjPbDvPC5qNEUjZhn8VNE+tYMvfCkoML7SjVo+fq/kYCDGVo2LBhXNbQQOPbbzLsio/1dXFOL3dNbZpmoRtEOpMBrYnHE2QyNvFEPNulwePBNIzsYHpG5xfj+cyFeCJONBYlHouTTqfQGnw+L16fD7/PTyAYIBQM4fV627XUF1LdXX0y3Z2TFXflnLkScHIax17OiS8eBK6o5UqT6/fdLqpwaiGV6qezQgw0Rdkvhd9935VGiJKV2yjgg0k+aNyu804PnogMwySTyfTcDoUQZaW/N76Vqj7s568bxnZ5KspzpejZYHB/IwGGMrXsU7ex+n/+FPpzgKEDj8dDVVUVZm7U6kQiSSqZwlAGXm92mkufz49pdD4mg23bRKNRotHi4ILG5/NTVV1FMBjE6/FgeTxYpnXKSTGfrZC9TtOFViFXa1wn18f1dOdRnc8MMArdDHqbq3V21gQ3PzBjdkAuIzdAY+dduVX79GwhhDCNcspwHVSUys4mke/eUl2obTgAACAASURBVAg49NgpXrrOCCHORr5AukwyGMRA9NGPfpSK+P3E9uwiNG5CXxfnzPKZDIaJ3+dHuy7JVBLX1aRSKVKpFLFYLDurgWGe7K7Q4QJK5yrcjuOgtca0LDyWh1AoSLgiO1uFaVqnzYAodA/It0bnUk0NXByli4va/rhFK7JZEKCU226f3brgKxpMsLjbw6mzXahckCMX4JAYghDiHCgkg2GgUoWxcwpLiv7vPq112bZQCiG6L5sBN0Do/nNOU/TvAEN3yyZh6TJlGAZ3ffJWWl99ua+L0iVKKTxeL9VVVVRVVeLxenKZCdkpJtPpFK7rdPrBN4zs7BQ+n49AwE+4Ikxd3RBqamrx54MLXTypKKWysxOYVnaGiU5ulmVhmRamma3U57tqnJz6svPydkW++4ZjO4V9u65bGAAze8sHX+TPWghx7pRh9OsLHXEmqugG+ey7nvptyuCfQogz6Q+V9XOVimgisdZ+833XX8rRkdaaY8eO4feXPtaEZDCUsaW33srP/vNWnE99GrMbH5LzJneOskwTfyCA4zjEYnEy6QyZjE0ykSSRSOaCCKd2lTAMA68vOxCkx2NhmhahUBCP14uhShhzoGh2gjMOfpibgUFrjcJt18VCo3E14LooXfpJuN3sD/niFWcrFJVXCCG6ZABdIIpz1EPXrYZh4DhOz+xMCCH60MEmGziML3ykz6+ZY4k2EqkYXu+5DWB/vvn9fkaPHl3y8yXAUMaGDRvGwjkXs37Nmwy9/M/6ujjnTCkDy1R4/n/27jtOrrpc/Pjn+z3nTNuSLcmmLakkEJAihCZRuEBo0iQJTVTa9SLlqj9UbNcu1ytXRbxeBSnKVURFRJqAEEwA6RBaCC11Uzfby7Rzzvf3x5nZbMomO1tmZmefN68lszNzznnO7uzMOc/5fp/HCREOh0mnU6RSKTzPpaurC611ZvTAjgmGYIpFyAkFRRmVwrbsILmQh3cSpRQ6MzXBoHuGYhnjZw7QMpmIAYSSnWYRjJJQ290nhBCDUuJzQUefoftdKnRQ/FgIIXZBjaDPDy8F6190Cx0GAL9+9Kdcd8N/MGfOnEKHMiwkwVDiPrVoIc/99OcwghIMZIpUOY5NLBbD8z08LzhJTyTiOI5NWVkZhLY9H0BphaUtLHZdBHI44912s9doB9O7JoOHMWqA+QXV8zNR2QKOQggxRLS0qRy5dsifB20qCxaNEGKY+L7fM102+2/2drb22J4ey37F43G6urpIp9M9I2R93++pY+a6bs+/vb9SqVTmK00iniCRSPLCC88T9scW+scjiowkGErc0UcfTfTb3yW+voHo5IEPdSkE23aorKxAKUUykSSdTpNKpUkmUz11DYr9Cn62s4TSqmcAQ87r6P3/It9fIcQIpLWclY5gapeZa/l9CpGrRx95lNt+9Zue73tfme/rNoDvZ0erbjuJz57Y9/Wv73uZLmXedgmBbR3JdE9NLy/zmM62Gs+Mlg3+DUbPql7/9lyQIhgVnG1PrtCZ0b0OkVAUS9sosh1osuuw0CpoC68JantpZaHQWMpGaxtbB9OQHdthnzHz2Gv8zOH9xYgRRxIMJc62bS4460xuf3Ix0fM+WehwcqK1JhQKEw6nse2g+0P2zTaVShMOp7FsO3iDLUbZkRVK9V2/QQghCkxrLaejI9SuP1kGNh6lub2Ru5f+As+k8Y2H7xuaWrdwtjOCRkAKMQhvLl+O7qjmkH2O3v6Bndqa7/iw6nWCrne4nU0KqG0n+7t5vm98si3SdfaEX1tSbLUElfJFQ0kwjAKLPnYWN51/If6C89BOcRYT2Z1sdwjHtkmn3e2mSkSjMXnTFUKIwVAaI/PsR7Rth6lBgeGBZBhS6QRNXRv481/+hOM4PS2hJ0yYMHSBClHEjG+orhzLlAl7FzoUUfJUSde3kQTDKLDXXnvxwX1m8+7LL1J7xIcKHU7/ZY6YtLaIRCKk02l8vxvP8zLdJBxCoTC2Iy9jIYQYKKnBMJINrHDwrlRXjEMZzfTp00v6ypoQfRkpxQrFyKdVaXfokUu/o8RFixaQfGpxocMYENsOijqWlZdh2XYmwdBNPN5d0n+cQgiRD9oJlfSVlFLXu07PYIRDERwrTFNT06DXJcRIJVNaRT5obZX0OYwkGEaJ4447Dt2wlkTj5kKHkjOtNeFwmHAojKV1pkhOUEynP9nmjY88wKsXLWLT3+7H7ezMQ8RCCDFyaNvCGEkwjFy9JkgYGEyBx5rKcaxfv37wIQkxAimlZDSXyA/jl/QU79LdM7GdUCjEuad/lOYljxc6lJwppbAsjWUF1XKz32tL92sYp/YNY0M28+PNbPzqZ9lwy8/peO9tGQonhBCAcmyML++HpWIwSYbK6FhJMAghxDDzR0AnvMGQBMMoct6CBbjPPoUZYUNylNrWZifbjitbbXdPI9m2Ln2C6NJH+fsDD/Cj667jmYce4JojD8X+v5tZ/+0vs3nxo3iJeH52RAghipC2HAxGpkmUkIGmiyojNTSsaxjSWIQYKeTCkxBDQxIMo8jMmTPZZ/JEWl97pdCh5E7t2CWo15BQ38dPpXZapOm5f6IfuJu7fnUzkyZNAqCqqoqLL/oUS+6/j9u+/mWO2riK9ddexYY7bqF73Zph3gkhhCg+SiuUtkilEoUORQzAzhfBBn6SVFU+ljWr1w0qHiFGKmOM1GAQeaFUaXeRkATDKHPRwgXEn3qi0GHkSGXe8Hd+009u2cy7V13M6msup+XlF3rub1n2Et6f7uDOX/4v06ZN22k5rTVHHnkkv/zJj1n6l3u4Yr+ZxG/8AY1PLxnG/RBCiOKkLIuUt3OiVhQ5lfnfjh+PA8wxVFeMY91amSIhRiffL+158aJ4lHoiS/6KRpmTTjoJ897bpFpbCh3KwGXmlyY3rKPx5htZcMJx3HPTL3D+/Fs23nUHra8vI3HHzfz2f37G7Nmz97i6uro6rrj8ci49ZxHJzZuGPXwhhCg2yrJIu8lChyFypHb4FwyDGeVdXTmW9Q2SYBCjky+1aESelHL9BZAEw6gTi8U468T5NC0dYaMYslMkMn+PnuvReuvP+ddj5/HD736XAw88kL/98Q8ckWin4xc/5jc3/JgPfOADOW0iGomg0umhj10IIYqc0hrXcwsdhsiZ2jaKYQhUldfS1LSVtHwWilGq1K8sC5EPkmAYhc5fcDbpf/4DM4Lm/vSeImGMIfneCiLdnRx7zDFYlgXAmDFjuPnGn/LkQw9yyCGH5LyNcDgsCQYhxKikLJtUWkYwjEQ7nw4N/CqsZdnEIhU0NjYOJiQhRiQp8ijyxYDUYBClZf/992dqVSXtb71R6FD6LehLbHq+Szz1BJMqynd6ntaasWPHDmgb8XgcPxQeeJBCCDFCKa3xfRnBMDL1miBhtrWpHOi5UnXFODZs2DAkkQkxkiilMsebQgy/Up4mYRc6AJF/SikuXrSQ7/39Ccbsf2Chw+kfY4LMcuZ9P/32m4w/bO6QbqK5rZ3W5a9iPVyB19EBloUViaIjYSJ1ExjzgYOGdHtCCFEslG2TSkuRx1IxmFOkMdFaGhoamDt3aD9jhSh2vu+jlFx7FWKw5K9olDr1lFPwlr9KuqO90KHskTEGz/PxfD/ILKfTaAO1tbU4jjNk2zny8MNYuP++nBfy+fze9Vy11zg+HlPMb9tC8y3/M2TbEUKIYqMdh5QUeRyZdtWmchAZhopILQ1S6FGMQr7vo0v4qrIQ+SIjGEapyspKTv2XY/n7U0uYcMrphQ5nt4IEg4fnehjfYDraiMai1NfXE41Gh2w7H543jw/Pm7fT/U1NTTyw9Kkh285AGGNIbN5Icstmut5+izFzjyC21xRaXniW+PoGdDSKFY1hx2JY0Vgw8iIaDb6PxLCiUVSmVoUQQuxI2TbJdKLQYYhcZWo8DuU5UU3FOFa/v2boVijECBHUJpNjJTH8gvft0k1mSYJhFLtw4UIe+srXMSefVtQvcmN8XM/F9VyMMZi2NirLy6mrqyMcHv6aCZ7ngS7sz+ed73wVZ+0qxlRVMbNuLCv/8Shb0mk8z2XRSScytmIMrZ2ttDWupb2zi/aOTjo6O+ns6qK7q4t4vBvthLAziQgdjUEkAtEYKhLDi4Txw1F0JIIViaAjUWJ7TaVsyrSC7rcQIj90KExKEgwjVO/Pp8G1qQSoGVPHi2ufH9xKhBiJivhYWJQWQ2kXFZUEwyh28MEHMyHk0PnOCir2mVPocPrkG4ObdnFdF2N8/JYmasvLqKqqIhQKDf/2i2BOXsR4/Pf3vsPxxx9PJBJBKUU6naahoYHp06fvMUFkjCEej9PZ2UlXV1fPV/b7zs5OOjs7aenopKO7g87WzTz74D00OCGsw+dRe9Q8QjW1edpbIUS+6VCIeKK70GGIHKld3BqsmspxNDwnUyTE6BMcS5XuSZ8oHr7v9XTBK0WSYBjFgmKPC/jh0seLOsFgfBOMYHBdfGPwGzcxobaWmpqa/CUYrAInGMormDBhArFYrOe+UCjEjBkz+rW8UopYLLbd8nvi+z7Lli3j7vvu5/7vfBlTPxXnyHnUzD0CKzJ0U1OEEIWnwxESKUkwjDxqp9zC9l2XclceHUN3Vzfd3d05fWYIMdLJFAmRT8U8enywpMjjKHfG6afjvvYybldnoUPpkzEGz+1Vg2HjBqbstRe1tbV5SzAUetic8X20zu+fq9aaQw45hOu+9U1efPzv/OTST3LI+2/S8MUr2XDzz2h97ZXMh7EQYqTT0agkGEao7SZIZHILg7kGq5SiunIcDQ0Ng4xMiJGnlE/6RPEwmJJ+rckIhlGuqqqK+fOOZsk/n2TC/FMKHc4u9RR59DyMMfhbtzB16kl5HcGQ7uhg469vxoRC+KEwOhzGisUIVdcS3WsKkXHjhzUGVeA3onA4zPz585k/fz4tLS08/Mgj/O6v9/Hub24idNiHqDzqw5RNnV6w+IQQg6PCYVKd0kViZBr6z4aqslo2bdrE7Nmzh3zdQhQr48v0CJEfpZtaCEiCQfCJRQt5/LvXYU44uSizab0TDH46jelsZ8aMGXlLMNTX1/Pz736bjo4O4vE43d3ddMbjNLd38O6zr/PqvVuY8p3rhzUG45u8j2DoS3V1Neefdx7nn3ceq1ev5q8PPMhdN/+UBtuReg1CjFAqEiGRihc6DDFA2390D3IIA1ARqWH9eqnDIEYXAwWvuSVGB9/kf2RyPkmCQTB37lyq3RRdq96nfMbehQ5nJ8YYfN/D9328lq2Ew2EqKyspLy/Htof/Jay15sQTT9zlY+vXr2f+py4e9hgwxZNg6G3atGl89qorufqKz7Bs2TL+fP8D3PedL2Pqp+AcfjQ1hx8l9RqEGAFUOELabSt0GGIgdqrBMHiV0bGsXSNTJMTo4vt+yV9ZFsWh1BMMpbtnot+01ly8cCHtSx4rdCh9MPi+HyQYmrYSdRwikQjhcLjgf5zhcBg/mRr27RhT3LUOsvUavv/Nb/DS4se44bKLOHT1CtZ/8Uo2/PKntL76MsbzCh2mEKIPKhwhnR7+9zIxxBSoHdpUDkWGoaZyHGtWrR38ioQYQYqha5gYHRSqpNtUyl+RAOCsM88g/coLeIniGyJrDPh+JsnQvJVy28JxHGzbLviUjnA4jJePg3LfjJh2NqFQiBNOOIFbbvwpzzz0AF+ffwzjH3+AtV/4DBvu/DXdDXLQKkSxUU6IpCc1GEYuNaS1iGsqx7F27bqhW6EQI4AxpV14TxSPUn+dSYJBADB27FiOPfwwmp55qtCh7MRkRjAYY/DXN1AWiRTNH2Y4HMZP5SHBMEI/9Kqrqznv3HO5/87f8ehv7+DTM+vZ+INvFTosIcQOrFCIpC8jGEaaHT8VTM//B3dlrLpyHJs2bizpK2xCCFEonufiOE6hwxg2kmAQPT65aCHpp54odBjbmG1fxpig2OPmDVRWVmJZFkqpgp90O46D8b3hb9do/ILv62BNnTqVq6+4Att38RKJQocjhOhFh8OkjUxjGol2/GgYfHoBIqEoGpuWlpZBrkmIkUMpVfRTUkVp8IyXlzpyhVK6eyZydtRRR1He3UnXmlVF1nLQ9FxFMU2NVE2rL3jthSylFKFQmIY/34WyNPgmGG0A4PuAQRlQJhiBQebLOA5O3QQikyYTnVSPU1G5+w0VaZHHXCmlqK6uJt3RhhWJFDocIUSGsizMkJQHFPml2G4cw1BkFzKqK8exfv16ampqhmaFQhQ5y7LwZdSOyAdT2tMkJMEgemit+dSCj/GLpYsp+8SlhQ4HyHaQMBgMJpGAZJKqqqqiqkfw3S99gaatW9Fao5Ta6d8dbyuliMfjvL1mDW899BwrV60irRShCZNQtePwasfhjK0jUldHeFwdoZqxYNkkk6UxP7qmuobWtjYYN77fy2z+/W9w31uBZTvB1YVsr2rj9yRtgpEuvb83pOJxrGkzqL/yGlQJJGiEGE6SYBi5djpOHYJfZVVsLOvXr+eAAw4Y/MqEEEL0Ykp6CpokGMR2zj7rLH56x0K8cy7ECocLGovB4Bsf3/hgwG9pJBwO5a09ZX8tWrhwUMsbY2hpaWH16tVs2LCBdQ0NvLt2HatefY516xtY39qGwbBp0yYOPPDAIYq6cMbV1rDi7w8Ree3lfi/T/uiD/OQbX2f27Nl7TN70vs91Xa744pdofuEZao84ehj3SoiRTUlzthFr54tgQ3PQWhGpoWGdtKoUQoihprWFP9zTqwuoeM7SRFGYMGECH/rgwSx7/p+M+/C/FDQWYwy+5+F7XnC7tYVoqPgSDIOllKKmpoaamhoOOeSQnR5PpVJs3LiRiRMnFiC6oXf1xRdx9Mv9Ty4AVP3H1zjrrLMGVBDne1/5Mp/64rWUTZtJZPyEnJcXQoiRZKiuiVWXj2PVyjVDtDYhhBBZSmm8Em7fXjpnaWLIXHTOIq648X+h0AkG3+C6Hq7rYYyP6eigIhyisrKypBIMexIKhZg6dWqhwxgyc+fOZe7cuXnb3pw5c6iNhEls2SQJBiF2o5SHa5Yslf3fDnUYhkB1xThWrH1raFYmxAgg74Eib0Zod7j+kknJYifz5s0j1tpMd0Nhe2AH7Sk9fD8YwWCSCcpCIWKx2KhKMIjBuf6nN9Iyc1/GfOCgQociRPEq4QOdkmayJ0WZQsgm29Z58ENvayrHyRQJMaqYbJFuIYZbib/QJMEgdmJZFhd+7Cxalz5W2EAM+H6myKMBfI9QKEQ4HC6Jjgpi+L3//vv85u67oboWt6O90OEIUbx0iR/tlKhtCYXgc9L3fTzPHZK5vWPKa2hq2orrukMQqRDFzxiDUnJ8KfKgxAfLyF+R2KVFZ3+M9HNP46dShQ1EbftSoTBeCRdEEUOvqqqKL152KemH/kKycUuhwxGiaEl6YWQymUrkPa2ceyUcBsuybCpiVWzZIu+dQggxpJQq6SKPkmAQuzRp0iSOOPADNL3wTMFiUAq00kFHABQ4IRJpl0QiUdKFUcTQqa2txVOa0NyjKJ85q9DhCCHE0Mq05+2dT8iOZhgKVeVjaWiQaRJiNCnxS8tC5IEkGESfLlq0iNRTTxQwgqDdoFYapUCFQiTcNMlksqSzfmJo/eq2W/EqxhQ6DCGEGHoqqEaeLaGhFD1teofCmOhY1q9fPyTrEqLYKaWk0KPICy0jGMRo9ZGPfIRw42biGwpz9UKpTIJBa0ChQmGSaU8SDKLfWlpaIBxh7EcK2xFFCCGGg1Yay7IySQVQWmPZ9pDVKaqM1rBurYxgEKODUkrGL4i8UEqXdDJLSvGLPtm2zYVnf4zbly4met4n8x9A5kpM9mqMCkdIeS6pVEoSDKJffnjDT9EfOpbYXqXT5lMIIbKyifjsiAWFwvVSNLVuJu0ncGwHpTSWpdHKRmsVLKOySYm+Rzu4bhpLO7z7zvvDvh++7/Pqq69miuxt2yetNclkknA4TDqdxnXdTCHLoLuU53m4rovnedt9VVdX8+EPf3jY4xalR+rRiPwo7REMkmAQu3XO2R/jpvM/jr/gPLQTyuu2FcGBUM/BTyRK2vMkwSD65fXXX+eeJUup/+6PCh2KECNC6V5LGV0Wv/wXnnnrYcpjsZ4uTNlCkL2LQRoAA1pbKKWoGzee8rIKlFJ4vkfaTeIbn9P2/uiwx7xixQou+9TlTBw7NRNnpjMGhmdfWcIx/1KP7WgsDdpSaAVKg9YKrcHSYFmgNSSTLsuWJVi27J1hj1uUFhnBIPLF0lZJd+iRBIPYrfr6eubO2Ze3XnyOsUfl+WqAAqVV8KVARSK4riQYRP/c89f7SLW2su5LV6FDocxXGOWEUKEQhMIox4FQCOOE8G0H4zgQjTHxlNPznlAToqCUxhh5Xx2Jghz8tuuunu/xL8eX89WvHbrHWgy+75NI+Nx88zrOO/e7HH300RhjsCyLMWPG5K0ldCqVYnxNPZ884Ys7PfbCsqe49stTse3+xZJIuHzxC6uGOkQhhBgyWmlJMIjR7eJzFvHZm2+DPCcYFAqtgi9QEIniyRQJ0U/f+NpX+cqXvkgikSCRSJBMJnf6N3u79/c3/+73bIlEiEyYhHYclO2gLCtz20bZNtoObmsneMz4hlRTI4lNG0ls3gRbNqIat6CnzaRu4fmF/lEIsWdy5W4E2z6JYOsQrtu/36bWmlhMM6YyOBysra0d8uj6w/M8tLL6fFw+8oUQpUTLCAYx2h1zzDGEv3cd8U0biE6YlN+NZ6dIKFDhCJ7rSZtK0S9KKUKhEKFQiMrKyn4vZ2ybJc8+R3L126TSadJpl2QqRTqVIu2me+5LpVJ4rksqnUIrzdhx49h32lT2nT6NWR8+gnfeeYc/bGoZvh0UYggprTCSYhh5dhqgYNBK4/u5/S6VoqCfq67rovXuEgw+UpdcDDelFEPW41WI3fB8l3A4XOgwho0kGMQeOY7DBWedyW+WLCZ67oV53bYKenAF9Ri0Atumo6NDEgxi2Fx84YVcfOHgX+ff+cEPYMLEIYhIiOGntIUvB9YjjgJ2nAWhtUU6xwSD1hR0ZGAwgmHXCYTcW26WdvE0McyGqMWrELuTSMUpLy8vdBjDRtLBol/OOftjpJ5dip9O5W+jmZkRCtVT+VqVV9DS0kIymSSdTstBhChaGxq3EqqqLnQYQvSL0hpPajCMQMEHZfacyAAahZdjgqHQXNftM8EQJAz6v67gIvTI2n9RHOR1I/IllU4Qi8UKHcawkQSD6JcpU6bwwdmzaXn5hbxvWymFtiy0trD32Y8NGzfR3d1NMpmUBIMoWo5tY7xtr08zgNeq76aHMiQh+qTyVMxPDD+lLfwcB/ll20MW1q63r5TCdfv//lnw3RBCiD0o/Pvt8JIpEqLfLlq0gGt+/Ts44ui8bldrjeM4OI5N+NAj2bDsBd544w3Gjh3LzJkzqampyWs8QvRHyLZpWfI47rNLSW1oINXWRqRuPM7Eybh1EwhNnEx0Uj3RyfVY0e2z2G5nJ21vvsba//0J4049A6WtbfNCe11hic7Ym5rDjsznbolSJUUeRyjDdg1GTTDqL9cEg+8rbLs4DwlzrSmhtUyREEIUN6VK+32qOD9NRFE67rjjcK77AYnNm4iMn5C37WqtCYfCpEIpUlOm0x0r595778XzPCoqKiTBIIrS+R87i0P2e5vp06czffp0amtrWb9+PatWrWLlqlW8+d77vPXsEtasWYOJRglNmEwincJxQnir3mPf2bM4dcFZTJ82rWed2Yy3UorNmzfz28ceBEkwiCEjKYaRxphg9EF2aLcxJkgw5HjcqrUpgormu379KaVIpyXBIIQoHQpV0lNyJMEg+i0UCnH+Gafz26WPM3HRx/O2XW1pwpEw6XSKRCKOc9RHWP7Ifez77rt0dnbmLQ4hcjF37lzmzp273X3ZZMNxve7zfZ/NmzezatUq1qxZQ21tLcceeyyhUGi361+9ejV3LF4yDJGLUanEh2uWtkzBoux3WuOb3H6f2qKgCQal+u5iokv8Sp8QYvRRSpV0wXqZdClycu7CBaSeWZrXueGWtohGokQiUSzLwp5zIF2eR2trK+m0zFEXI5vWmokTJ/KhD32I888/nxNPPHGPyQWAyspK3C5JsImhU8pXU0qVUtuKIPe+L9fzcUsXPsHQdw0Gjevm9tqU17IQopgppUs6cSoJBpGTadOmcdDMGbS+8lLetqm1wnZsHMfBsm2csWNR4yfR0NBAR0cH3d3dRTC0U4j8qqysJN3ZOaDikULsSGurzyvIorgF59LZKRI+xjc5n2BrbQp/Na2PmIOESf/3RwbjCCGKnVK6pBOhMkVC5OxTCxdw7Z1/zOPcb4VWCsuycGwby7YJzzuOtX+/j6amJlpaWqipqSnaAlVCDAfbtolEI3iJOHasrNDhiBFO2Rae7+G6aVAKrTR6CDpLZA+glFI9tQKM8YN/yd4OnusbD2MMvvH7PNn0TVDU0BhDMp3A9baNYut9sKbItDbe4WxzxwM6ldnX3uvtiYFtJ+p+5sS9d5y912UwaKVRSgfbzV6NV/2rFq6U3m6fs8mekB3eLvFjaztIBmWem0jE6e7uoq29g87OdqyETVP7JtrcJK+/1oy2NEqBVuB6hnjcxbI0OhOX0kESf82adiZO3MSGDRt6flda657YPc+jrq5u0J+zLS0tvP3226RSKZLJJK7rYozhjTfe6HNEosqxyKPKFCz1fX9IXsNCCDHUpMijEDs44YQT0D/4IYnGzUTGjR/+DWaOzSzLIhyOkEqlcQ84mNb7/sjy5cuZPHkyoVCIaDQ6/LEIUUQqKyvZ9PD9OFU16HAYKxRGh0LocBgdCufcerDnhKn3SVhQRS64mT3RMez0+LbHtl/WGNPr+TusN7vNXW13h8cqZu2LXV7er/3wXRe3vQ0rFkNZFtrZ85ST0c6yHToT7fzwj1fj+wYv04YgOFE2maH4Vs/JcxsQYQAAIABJREFUs1Kq51Kx73uZl4GPMcEBU5BA2Fn2pF8rhdZ6u5NYrTWWZQVb3SE50PuEN3t/LBYjFAptK36qg/UGMfV9Rb73en3j47lesF0V7K9lWZlpBxqtg5NVKxObMQbbsXsSCVlaKzzPx8ucMGfbPva+3XtfejPG4Pv+DusLrm51d8e32+dUKrXdc7u7u+nu7qa5uZmmpq3YtoPve3ie4j+/1bjd6AYAxw4D2ydxjDFsbm7m2Sf/h7/+6WGM8TMFyDK/S6ClrYlrrv0cl1126S5/pv114QWfYuvGNibXTcVSNloFr6nX3n2Bmtiujye01qTTuR2IyyAGMRCl3jpQFA9d4lMkJMEgchYOhzn39I9y19LFTFxwft62a1kW0WgU13NJpcrRM2fz2GOPUVtby8SJExk3blzeYhGiGHz5is+w/J136Iy30t0SpyueoDsRJx5PEE8kBvbhlT3ZUzvP697x357b2ft3taza4blKoXtd3e29ru3W3WtbmzdsYPnWZmpmzsJ4HngueB4mncYtr2Cvz14LBCdKjY8+SPzh+7Bdl+7uLkLTZjLp37+EM6aKzpXv0fbYw4yZfwrxDQ24He1UH3p4fhKlRS4cjXDSSSdy1y0399zX+/Xj+z6+7+N53rar+pkT3d6Jgh2TANkryNnnygH80HrnnXdYsWIFd999N3/4wx8YXzueaDTK/hPnce78f+v3z/uGu77GjAn7ccaxuy7g/OJbS3n/3ZWDjjfshDn32Cupr5u+3f0OUdZseHeXywykBoMQQhSzbAK6VEmCQQzIeQsWcMel/4o56xyUZeVlm9rSRCIR0uk0nVYn9oGH8u79f+S9996jo6Nju6G4QowGZ515JmcVOog8aG5u5uWXX8a27e2+XNflvE9f3vO89jdfo/KfT/Cn225hxowZpFIpbrr1Nn75tc9hjxuP39zEcXMP4bXf/IIjZs9m09q1rHjtZaZ86ZsF3Lvi4ZntE1K9h5cPdqi5DFUfHrZtE4lEcBwHCC4AlJWVYUxu9RS01j2jVnaldsx4lq18ZVCxQvbzuY+D6j4+u+UTXQhRaoJ2wjKCQYjtzJw5k/32qmfdspeoOfTwvGxTa00o5BAKhYJuEtP3pj0ep6GhgXg8vtMwVCFEaaipqeGEE07Y6f5UKoWVmRO+6fe/oeOxh/jfH/wns2bNAiAajfK5q67k8ssuZeXKlYwbN267kU6pVIqPnHY6mx9/GH/zJmhppuz4k6iYPQelNen2NjreXYFdVk7lvvvnZ2cLxRgsSQKMOCpTnyj72ReJRCgvL8+5YKelLPzdJBhqKsex7sV1g4o1IJ/RQgghNRiE6MOl557Dtb+/G/KUYFAolLawbRvHsXFqaqFiDOvXr2fjxo1s3bqVyspKIpFIXuIRQhSWbds9J0WqrZWfff97nHLKKTs9LxKJsN9+++10fygU4ub/vp4bbr6FD8yexaQjDubWP9zJytWrMUAsEuHgAw9g+dvv0HHZ1VTsM2e4d6lgJEE7Mvm+Tzqd7jlQdV0X13XxrNw6K2mld5uUqCyrpqOjqyeZv3r1ambOnEk4HB5U/NsZwuHCpT78WAgx0pX2e5QkGMSAzZ8/n6/+1/Ukt24hPLYub9sNRjKEgq8DD2HjK8/y7rvvMmnSJPbZZx9JMAgxSiilgkKSvo/nuQMahn/ggQdy2//c2PP9eeeeSzqdxhiD4zgopbj1ttv4yQvPlnSCAXYuPiiKn+u6xONxUqkUAJ2dnQBUVU8hmIrQv6SR0nq3LW+VUtRU1rJx40aWLFnCD7//Y77/w29z9tlnD3YXgEwB2T4SXFpbORd5BJkuKYQoXkqpwrcGHkaSYBAD1lPsccliJi44b/g32KubRCQSJZ12iR4xj63PLmX58uWMHz+eiRMnUltbO/yxCCEKLjs8PLm1kdYXn2PKNf8+JOvNzmfPOvKII3B/dxcb769CuWnqTjur5DpTyMnYyNS7sCYE7SSzrR9zWo/WmdacfXNUlAvOvZDu7m7SSY8bfnwjD973CN1dXSSTyUyrT3o6eSgVJPw838NkOpO8/urrHF5/+s7bR/U5gkErhZdjkUelgp+F1P4QQhQjpXRJJ/UlwSAG5bwFC7jjkn/FnLUob8UeLcsiFovheR7x+r3onDKDfz7zDHV1dRx66KF5iUEIURyM57HuZ9dzyOy92XvvvYdlG/vttx9XLjqbtrY2Nje28cgXr6R80YWMPfqYYdlewZTusU7Jchynp10nbCv66OOTS70Ds0ObzF1KO+hEjMs/+lXKouVs3LqWtJsmXB7Btp1dtOLMFl7WPe1Nl730RVx35+kbWluZmHemtYXn51hTwgquDu6YLBRCiGKgpQaDEH2bOXMm+02pp+HVl6k+5LC8bLP3FAnLsokcdzIbbvs5a9eupaOjg3Q6neljLlcuhCh1CkV403p+du9fhm16lFKKqy/f1q3inXfe4cIrrmTtmtU4bc1YM2Yxbv6pqJH+niODGEYcx3EoKyvrSTA4jkM4HM65i0SQYNj967euejIdG1sYWxW0dZ0xOfcpQ47t7LLWg1Yas5skgufldiCudWn3mBdCjGylPoJhhB8NiWJwyTmLiD/5eN62lx0SatsWlmXhzJyNV1Xdk2BIJpNyYCHEKBGKRuh0vbzOZZw9ezb3/Pp2Lqyr5Ptnnsq+7y+n4frvkGzamrcYhkXpHuuULNu2icViPfVCsiMYcqb6nqKQNbZqPB3x1gFG2mtTfWSy+ioyqZRiNw0udr2MLu35zUKIka60RzBIgkEM2vz581Gr3ie5tTE/G1TBwYNlWcFIhnCY6AkfZeXGTaxatYo1a9bQ3d2dn1iEEAUVCkdQlkVjY57efzLq6+v5+pe/zBlnnMGdt/yKa06Zz4b//A+8+Ah971EKbw9z8EXxsW2bcDiMnWnX6rouyWQSL8czcqXUHltbjq2eQFd3x4BjzdrV6MJgekVfNRisnK/0aSVFS8UAyetG5EGpt6mUBIMYtEgkwqLTTqXlySfyul2tLSKRCJFIhOjBh9KBYsmSJbzyyiu0tg7+KosQovhZlkX4Q8fwy9tuL1gMWmsuu+QSzjj6KDb+6U68RLxgsQyUkiHlI5LWGsdxsDI1kDo7O2ltbSXlJsllSEp/ijyOr5lEV3zwCYa+DOV5nRQtFQOhtdrj34EQQ0FJm0oh9uyChQu587J/w5yxIK/FHqPRCK6bJpGIEPrw8Tz9zD+YPn06+++/PxMnTpRaDEKUOMuyiE6uZ8MrzxY6FL52zTW0fvNbPPn1axh31Rcomzaj0CH1n1K7nQMvCmf58uU9nSGyX1mdnZ10dXWxdetWjDF0dnaSTqdJdcFbK1/b6UTb9YIWrKqn00NwkNvYvJGuUAdvvvdyzwmWMUHnB6WCg2GtLdJummdeXQwE3SEwQXtJvcN2eneQwJhMpwtNZ3c7b6x8kXgqaKfpGx/f+LzfsJwNW9fwzOt/D7pOGLAtm7SboqmtkQceSLHi7S34HriuwXUN6XRwO5UypNM+vge+CR5buya+y2KSQuyOZeU+WkaIgVBKl/Q0LkkwiCGx9957s0/9JDbksdhj73aVltWBc/jRbHniEV588UWOO+444vE40WhUEgxClDDL0my6/y986tyFhQ6Fmpoabv3ZjTz2+ON89lvfoeuscxk379i8JV0Ho9QPdkaqp556in+96DNMr5/d617VkyAwvo8BWpqbmVK7L8b4dKc6aW5t5I5HfrSt3kEmkWBpHVw5C5YOBjkoaGzeDMDWjg2ZdQdLaq0xJqiPYIyP66b5/WM3UjOmDktboHRm6MH2J2XZ71QmVmMMBh/Pd0mWbaYlpCDTZtayNB84agbTO+uonQWOE0EpSHQnCEdjVLwUo+G9OtJNk1FKY1s2lmVjWw62ZROyw5Q5YbTSaMvCDtussv8in/0id7uZqiPEULK1QyKRKHQYw0YSDGLIXLJoIV+7+695SzAEBa2COgzhSJh05RjSZ5zDC/f9gRdffJHKykpmzZpFbW1tXuIRQuTfpy84n3dXruLsM88odCg9Tjj+eO6pr+cbP/ghrz5wD6EjP8K4U0/HisYKHVrftMaXK3dFZ8uWLRy6z0c44+iL+nhGMKKhsXErm7dsxnNdtrZt4r4XfsV/XPRLQk4Ix7HZU4uQh5/+E2+sfp4vfPz63T7vht9/jVlTPsBHjz5/QPtz28PX8c1vf50DDjig38u8+NxLHL7XGUydMKvfy7zZ8IwkzMSA7KkWiRBDodQTDJLeFUPmxBNPRK16N8+V1IOq2WWxMmJlMWKHHYm79xx+/X+/5bHHHmPLli15jEUIkW8Xf/KTXPetbzJjRnFNR9hnn334w+23cv/Nv+AEE2fjz/670CHtkQwNLj57HrIdjBBwHJtoJIrtOBjA9w3tbe0kEol+1TZQ9K8GR01FHY0tG/od/460tnKeupB2XWzLyXlbUodB5Er1o5uKEEMh7SWHrbV2MZAEgxgy0WiUhR89leali/OzwWAUJ5ZtEYtFiUVjOKEQ0QXns7Y7zpIlS1i1ahWNjY0kk8n8xCSEEL3MmjWLCxYuQCWLvPCjVN0vSkopUHv6vShCoTAVFRWEQ2EwBkNQjyGRiJN20/h76CqhtdWvK7e1leNpbh94xxZrIAmGVHpACQZ5PYtcaa1lBIPIC9dPU1ZWVugwho0kGMSQOn/BAlJP/wOTx2rklg5qMURjUUJOCLu8kuj5F/Ps8rd46aWXeP/99+nq6spbPEII0Vt7ezsmFMnr+2KuSr2i9UhlWVa/qtqHw2HGjBlDNBoJTpAMdHd3090dJ5FIkE677G5ueX+v9ldXjqUzMfAuTQOp9ZGt4ZDrdqQrisiVZcnrRoihIAkGMaRmz57NPpMm0PraK3nbptIKy7YIh8KUlZURjUYJz9oXddQx3PHHP7Fs2TKam5vxPE8OoIUQeTdv3jwOqa5g7ec/zcaf/4im558pvvcirYsvJoHuZ/tQ27YIh0OEQmEsS6OUynRYitPR0UEymcz8fnf3O97z739s1QS6ujv7vwM7UOQ+skBrjW9yS0roEu8xL4ZH7w4rQoiBkwSDGHKXnLOI+NLH875dx3GorKygsrIS23YIHXcK67XDnXfdxZYtWyTBIIQoiFAoxG9vvokn//Jn/nPBGYx9/AEavv1lulavLHRo25Eij8XHsiwM/TlRVpnCxzahUDjTA0KRSCRobWklHu/OtLjsY+l+nlTVVtXRnRj4iECtrJxHMDiOs8cpHjvy5URRDJi8bkR+lPI5iSQYxJA78cQTYeU7pJqb8rpdrTXhcIRoNEIkEsaJhImcfzEvrVzNY489xttvv01HR0deYxJCiKy6ujpOO+00/vanP/KlBWfS/tC9hQ5pGyluVpRs2+7XFImAIhwOB+2ZLY3jOBhjSCYTxONxurq6SKVT7GqkgqXtfp3EV1fWYoyhtWNgxZy1zj3BEA6HSLvpAW1PiNzJ+6DIj1JupdvvPVNKWUqpV5RSD2S+v0op9Z5SyiilxvZ63seVUq8ppV5XSv1TKXVQr8dOVkq9nVnuy73un66Uei5z/x+UUqHM/eHM9+9lHp/Wa5mvZO5/Wyl10uB+DGIoxWIxFpx8Ms1PPpHX7Sql0FrjOCGi0RiRSITQuDqcE0/njj/+iSVLltDYOPDiVEIIMRS01pxyyimYt99kw+9up+m5p+lc9X5BYxrIPHcx/Gy7fyf+WZFImPLyMixtEY1GsG0bY6A7Hqe5uZl4d/cu80haK0w/EhlaW9SOqWPlhrdz2Y1ty6vcizw6oRCel9syICMYRO7kNSPE0MgldfJZ4K1e3z8NnACs2eF5q4BjjDEHAN8FboYgQQH8HDgF2A84Xym1X2aZ/wJ+YozZG2gBLs3cfynQkrn/J5nnkVnuPGB/4GTgfzPrF0XigkULST39RH6LmqmgHoNt28RiMWKxWDBc9IOHsb6tnSeffJIVK1awYcMG4vEir+guhChpEyZM4LE/381lM/di7vtv0v3z69n44L20vr4Mr0C9saV6evHRWvfrxD/Lsmwcx0FbmoqKSiKRCFpr3HSarq4uurq7icfjpNNpel+p1crq9xSZ2srxbGhcneOeZLejc04w+L6X84mfbYVKuse8EEIUs34lGJRS9cBHgVuy9xljXjHGrN7xucaYfxpjWjLfPgvUZ24fDrxnjFlpjEkBdwFnquBT4zjg7szzfgOclbl9ZuZ7Mo8fn3n+mcBdxpikMWYV8F5m/aJI7LPPPuw9vo6215flfdu2bVNWVkasrAzHCUE4jH3kR3jq+ed5/vnnef3112lvb897XEII0duECRP43FVXctOPf8Q9t93KsV3N1P/jb6z7/tfpbliHL8PCR72gi0RuUwqMMWilqa6uIhYry4yCMCSTKbq7umhtayWRSGw3kiGXREbtmIlsbl6fU0zbtpP7CAbjG5TKbSixpS0p8iiEEAVi9/N5NwBfAipyXP+lwN8ytycD63o91gAcAdQCrcYYt9f9k3dcxhjjKqXaMs+fTJC8YBfLiCJxyaKFfOOvf6PqoEPyut1soatIOEKsLIbv+5iPHM/W55/m0b//nXQ6TXt7OzNnzqSuro4xY8YQiURwnNz7bAshxFCYMWMGP/vhf2GM4X9+eRN33XwDqxu3UDb/NOrOWoQq4bmaom+WZQ1o6opSQT2GWCxGKpWkuztOPB4nkUhiDDi2QywWRWsdtHQ0/T+Jr60cz2srCzulRwghRrpSnpa4xwSDUuo0YIsx5iWl1LH9XbFS6l8IEgzzBh7e4CilPg18GmDKlCmFCmPUOvnkk/nGj39CqqWZUHVN3rfvOA5jKivRmb7bsU/9G6/c8jM2/v73vPrqqxx00EHMmzePOXPmMH78eEkwCCEKTinF1Z+5nKs/cznNzc18+nOfZ/k3vkDow8cxbv6pw5dokLZ+RUlrzTtrlvP4C0FB0KAThI+PD8ZgMPjG65neYqsQDZtX0dbRwk9+/1VAgQE37ZJMJkEpFIZIJEo0GgtGSHge67asorm9kV/8+bv4vodnPIzxMPgYPDBgCDoxbW7eQFt7G9f/9ppMTPR0ulBots1mUPRMw8jcuX7LKkJ3JXn5xVcwvsHzfXzfD6ZsGEi7aXzP4PkexgvW+fprrzPnI8fk/LMr5YN3MTyqqsZw+2t3sPi1P6NV0IlFKYXnuew39TAWHXt5oUMUJSJ4XeU2Om0k6c8IhqOBM5RSpwIRoFIp9VtjzIV9LaCUOpBgOsUpxphsK4H1wF69nlafua8JqFJK2ZlRDNn7ey/ToJSygTGZ5/e1ru0YY24mUwNi7ty58kmTZ7FYjI+ddBL3PvkEE89YkL8NZw5uLEsTiUTwfYPrptGz9sW66lo2/+4WGl94iVWrV9PW1sb69euZNGkSkydPpr6+nqqqKizLKunqrkKI4ldTU8Mff307b775Jt/64fW89fLzWIccTvXBc4mMn1Do8EQezJ49m89+4YqgDgEKbWm01ti2jVIKy7KwLKunRkE8Huf9921efe+fzJp0AGnXxfNdEokEiWQ8GNFnwLYtbMdGaUW0PEZLezPt8QZmz30Dy1ZYOij8qDRopdBaBXWOlGLLljQPPdDNwTOz149UZvvbCkX6JkiA9GQbMif7B0w7irJkGc0rgpiV0sH6VfB92LKC/dQ6aLWpFM1N9xPPsZ6C1loSZiJnl152KZdedmkmkWcyfy+G559/nv/6xs8KHZ4oIQpd0knQPSYYjDFfAb4CkBnB8IU9JBemAPcAnzDGvNProReAWUqp6QTJgPOAC4wxRin1BLCQoC7Dp4C/Zpa5L/P9M5nHF2eefx9wp1Lqx8AkYBbwfL/3WuTNxxct5E9XXI057WN5H+KrlMayVNCyS2tCoTBaa6zPfZXkitdZ8/Lz3HbvfZQ/+BBzpuzFMcccw+mnn86cOXN6lhFCiELSWnPAAQdw12238vTTT/PoP5bw4H99k6Yj5jHhzIVYkSgAxvPwXRcrHB7wtqSCevGprKzkM5/J7arp8uXLefmp5Zx2zAWAwRjo6Ginvb2dzq4uujq7CIVChMIhamtqqa2t5ZlXH2fpO+u59LID9rj+7u40D/9tCccfcWZePidj0fKc61AoZESOyF32PTD7r2UF9eNjsVhO3VyE6I9RnWDoi1Lq3wnqMkwAXlNKPWSMuQz4BkGdhP/N/IG6xpi5mRoKVwGPABZwmzHmzczqrgXuUkp9D3gFuDVz/63A/yml3gOaCZISGGPeVEr9EVgOuMCVxuT46SPyYt9992Vm3Vga33iVqgM/mN+Nq231GLRWmTbvPo7j4B56BOkDDyWdiJN47RWef+BuNt9zDzU1NRhjmD17NjU1+Z/WIYQQu+I4DsceeyzHHnss11x9FV/7zndZctONTLr8c2x99EG6H7kfZQzR+adSd8bCnoSuMf1r12c8D8sa8CGBKDLbOoIEn32hUJjy8go8zyOZSOB5Hol4nFQqlTlxUvS3WUUs5hCNajY3NzBx7PBPP1VArsfhSqb8iCEUDofx/FxbpQrRt1JvDZ3T0YQx5h/APzK3bwRu3MVzLgMu62P5h4CHdnH/SnbRBcIYkwAW9bGu7wPf73fwomAuXrSQbz3wSP4TDBlKKbS2iESi2LZDRUWaVCpNPB6ns9NGH3o4zux9abjjZn55660kEgmqqqokwSCEKEq1tbXceP0PueKaL/CPqy9m7sEHc8O996C15uovXctrP/gWXuMm3I4OVDhMzb9eTfXBh+52nSadJhIK5WkPxHDb8cA1FHKwLItkMonjdOO6aVzXxfM8XNdDK42fw7Hu+Looaza9m58Eg9I5Xz3W0kVCDKFYLEYyLW1PxVCSBIMQg3LqKafwzR/fQKq1hVBVdf4DyIxksLSFZVs4noPjuJk6C4quLou4UnDRZ1h34w9Y/MQTHHbYYYwfP56ysjJCctAthCgyoVCIW362U46f3/zyFzz55JPMnj2b+vp6nn/+eS759vepOuiQ3Y5k8D0X25FDglJgdtERQmsLrS1CoRCRSJh43JBOu7hummQyGdRMyOF8fOKkMA2bV8IHjh/i6HemtYXn5dayVSmdcztMIfoSjUZJSYJBDKFgZJYkGIQYsKDY44n8deni/BZ77IPWCtuxiekY4XCYUCiMb4JK1uEzzmHZ3f/He++9x8SJE5kyZYokGIQQI0YkEmH+/Pk93x955JGMszVdq1dSPn1m3wsa0zPfWJQux7GJRKK4rksiAem0Szzeje+ZXtMq9mzS5BBv/HPtMEa6vdUb3gWCpImPh8kMtzCZz+6e25kD9i2Nm1i8eDGrVq3qqRORLdwXjGrUO33Zts28efOk/pLYSSgUoq2rhb89cxfRcHnP/cb38Y3PvINOJhyKFDBCMRJJgkGIQbpg4QLuvvpzBSn2uBOlMlWxNbZj4xufsmQZxjf4++xHZzTGo48+Snl5ObFYjGg0Kl0lhBAjUldXF+0d7VSWle/2ecbzsOQ9riRorTF9TA+wLJtIJEwiEQfA8zxSqTS5lrGaMD7KM4mNg461P/aZciBvrn6e5ete6BmZoZQi+I+g9abK3taZRlKaJ+9/lWedN7dLm2QbZ2bbfULQ8cLgs2rjW/z5vj8wbdq0vOyXGDlqamr4vzt/zcsvv0xHeyeQGRlrW/zqplvYZ+qB1NfNKHCUYiQJpn6V7jQuSTCIvJgzZw61Gp45/wwsx2HqxZcz4fiTCh0WAI7tUFlRgQLS6TT2cSfz+D13UlNTw7Rp06irqyMSiUiCQQgx4rz11lt0h6NU2Xv4uC/xglOjida6z9EIlhVMkwhaWwZX/YNaDH5OXUQmTCyjK7VhqELerfPmX5GX7fzygW+U9AG/GJwPfvCDfPCDO9cS+8fjTxYgGjHSmf5W1R2h5IxJ5IVSirNPPikYgltVQ9trrxQ6pB6WpQmHI0SjUcKRMNEPHk5qwmQee+IJli5dyosvvkhjYyPpdFoOPoQQI8pBBx3E+cd8mObvfJkN1/0HGx+4l/jG9Ts/0fcliVoilFJ9JhiyRY+3dRkJruZrrfFzqPI4fnwZ8UTHkMRbLLS2pG6DyJlWqmdqjhD9Vep1YuRoQuTN5ZdfTv24seCmia9eVehweigVzL0MhcLEojGisRgVF32GzeEY/3PTzdx9992sWrUqKIQlCQYhxAgSCoX49te+yktPPM6t117DwpBP/CfXseHWX+C7LqnmJrYsWUzbow8yffLkQocrhplSwC4GKgTDdfu/nvF15aTdFIlE95DFVmi6xA/4xfDQWpf81Wgx9Cxt43m5TU0bSWSKhMibsrIyzjzxRO5cvZ6ND92Hv4srZru6b9hlukw4jkOsLJaZmzkWddWXaP397fz+r/dRXV2N67rMmTOHsWPHBovlMJxUCCEKyXEcjjjiCI444giu/X+f58prvsDiqy+hMhJh3hGHM//8hZx44omFDlMMld1cUd1VjkHtKuuwG9pSVFWHWbvlfWZPOSD3+IqQfKaLgQhG/0iCQeRKlfR7jiQYRF5duGghd1xwIcbzeOPaf6fyoEOoX3gBfqKb1b+9nY0P3UfFlKkc8ss78h6bbduUl5VjaZ2Zi6zgk/9GfPHf+Nltt9PW1sYll1xCTU2NDCUWQoxYkUiEm2/8KWvXrmXq1KnyflZigikSuQkKQ+a2zITxYdZueq9kEgxSg0QMhO45ZhSi/7RSJT1iShIMIq/mzJnDB2btzfLDPoSuqmbLk4tp+PNd+K5LbPa+7PvdH/HuD79Nw/33UH/62XmNTWmFrW0iJoLreYDB933M8afQ1dzE3x5fzMknn8zUqVMpKyuT9pVCiBHLsiymT59e6DBGFdd1ufPOO0mlUjtd8fR9H89zM20XPXzf9HwfnLv4PW0WPc/DmOD5rucFrfJ8D9/3MMbQ3NxMY3NDTrFpbeHlUIMBYMKkMBsbcmtV+fr7z/PPtx7Ctp0g7syiU17vAAAgAElEQVR+YQz+LjIcxhhc38X4wVBi0+t+CH4eJvtvz3q2tdzUvbpOaKWDEYt9jNaIp7qIRKTVoBBi+GllyRQJIYaKUopLzlnEdx56jEnnXohZ9HHaV68kNmECTqwMgK3Hn0zD727fKcGw+vabaF/xJrXz/oXNjz6ItjTG87GiUVJNTRz805uxy3ffiq0/bNumory8Zz6m7/uY0xaw4b+/zYoVK5g5cyaTJk2SBIMQQoh+27JlC9df/3VOPbV6Wy2EzBmz0sEVLaWDk19LB0lvpYL7gyeReSy4PxxRRDPLKAU6s2xHRwvNbY05xWZpa3ezKnZpwgSH55fn1qpy49a1fPBDc7j44ovRWve0gM6OolFq27BhYwxKKUKhEFrr7R7r/dzsY72f03sd2S/f35ak2eXPwLKoqqrK7YcgRr3gb1ZGMIjclfLIF0kwiLw79ZRT+NZPbiDd1oozpooxM2Zu9/jkE05i2b1/YOPD9zPx5NMBePtH17Hhob+iqmqINzUR2Wc/olOmoi0bL5mg7cF7Wfnrm5h91TX4qRR6ECf/SmtsrQmHQ4TDEVLpNG6sDPugQ3n2uec47LDDqK2tpaKiYlA/ByGEEKNLXV0FCxZOG9Zt1NZYPPxQW07LaG3lVOQRYNy4KJ2pTTkt4+NxwAEHsP/+++e2MSGKlFIyRUIMRGm3hpYEg8i78vJyzpw/nweWPsHE0z+20+NVM/Zm8qf+jbd/8l80/PG3KBTdrS3oMVVUHHAQh3zrBzst41SOYfPvbuP9cJi1d/6Gmf/270w57xODilProF94yAmR1Ems6Xvz/ktP0djYyIwZMwa1biGEEKOLUirnk/gBb2u3RRt3fMxgaSunNpUAkyZV0JXIsSOUkWKKorQoVdonimL4lPLrRhIMoiA+vmgh937+C5iPntnTj7u3Wed+nJoDD6Zl+etEasdRd+jhPVModqXukMPofH0ZXZs2oSvHkNiS21WVXdFaEQo5OCEHrRUqWkZrZxdNTU2kUqlBr18IIcToYds2nlec1ea1yr3I4+TJFXTHOwrT/UmIIiEJMzEQuXbuGWnkE0EUxP7778+UMRW0v/VGn8+pnbM/ey84j/pjjydUUYGyNMra9Uu2fHI9B3712xz4H9+jbL8D2PTIA/iDTAIopbFtB8d2giFwvksqmaS5uZl0Oj2odQshhBhdLCv3aQgDZfY4J3z7g1uldM6zyMeMCWPZ0Ny+pf8LqdK+aidGH601vindYn1i+JTye6EkGERBKKW4eNFCupY8PuTr3u/zX8H3fXx3cEkApRW2bWPbVjAEzvdJpVK0tLTICAYhhBA5yddQaqUVOVdshJyXUUpRNy7C6o3v5LKUXPEVpcX47DztSIg9kwSDEMPgo6eeir/iddLtuRWj2pN4UyN4Pk8vOJmnP3Yir1x1KYnGzTmvRyuFZVlYloXSChOKEE8maW1tlREMQgghcpJOp+noiBc6jF0yxh/Qif+EiWHWN64e+oCEGCmURrpIiFyVeu0OSTCIgqmoqOD0E06g6al/DOl6q/eezcRPXMKML36Dvb9+Hd1dXbxy5aWkWptzXJPqaZ+llcaeNJlEOs3WrVuJx+M9LSyFEEKIPfE8j5qawbdS3pMBDRAY4KiCiRNDbGpe1/8FSviAWoxOMiJHDESpdx+RBIMoqI8vXED6qSeG9I9MOw77fOJS9jpuPuMPncvhP78VPxJhxY+uG1BCQCmFZWmsaBnWjNmsXLmSrq4uksmkJBiEEEL0SzQaJZUq0rnaA/wMrhsfoa27/0WVfeNJQUhRUrRSkjcTAyIJBiGGyQEHHEB9eWy3xR4Hy4mVMev/fYXml1/kuQvPZtlnP03La6/seUEVfGXbVfor3yW9fh3vNqxn5cqVrFmzhu7u7mGLWwghROkoKysjkXCHfTtKqX4UedxpoQFta/z4GPFUY7+fbzBYljWgbQlRjAb09yYEkmAQYtgopbj03HOGpdhjb3UHH0rtKWeQ6mgnFY3xxte/wD9Onser/+8z/Ypx3dc+T+uvf0HkyA+TNIZnnnmGV199lfb29mGNWwghRGlwHAfQpNPDO/JN8f/Zu+84uerz0P+f7/eUOTOzZbZqd1VXSKjQiwQIW8ZgIGBMqJJMccNJbppvmvPK78Y3v9/NtX19U26uU66TOMWOnRtjB9sxNtgUI7oAI0BCAoP6rqRt2jr1zCm/P6ZoVQCVXc3s7PN+Mcxqd3bOd8rOOd/nPN/nOXMrETo746SyJ1NHKZSUclFTlFaEJ9vjVcx6KqztGgxmpQcgxE0f/jB//Jd/RX5sFKsxMW3bOe83fofw134bgNTB/ez+/ncYfvCBd+zhPfTsUwxsfJSRV35G3nVZ/Cd/TdILGB0fZdMLL9Dd3c1FF100beMVQghRW5qaWvkv/2UnixbarDzH4vzzm2lpcaZ0G0rDSRedO8UD3Tlz6shk0++4Hz0eCTBUr3Q6je8fXsZTKkRXukz+fkkYhgRBcMzPS5eSybcxTROtC2vQS0WzDx06xMGDB4/4Pd/38X2fIAhobm7m4osvnp4HfhoUtT1RFOJUSIBBVFxdXR23XncdP3jycTpvvn1at6V0YacV75yL8vIobRC4Lto59gBv+//4I3SimeiKc1nxiV9BNTWT6R/AXrWGfa++xO7du0mlUtM6XiGEELXj+9//CRMTE7z66qs89dSjfPELT9CYCGhpLhyOhYDvhYQhBGGhA14Ygh8UJzAh+AEQhsVrCIJCgrZR3L8NDo6RSuf46kN/XPiV4tlVpTR5L4/neSQnUoyNjqFNhWFq/CBPKp3lc3+4s7DtAHw/IAwPTyYnxw/CsHDJZj3ynuKL//qrRJ0YKI6YkAZhQBD45d/x/Dx3xD403U+zOAVbt27lrvX3YJvRw4EFQhTHBgtKE2oFcJxgwtEBicLNFAoFqhA4mBxsAJiYGKe9aS5NjS3l+JjWhTbhqXSSAyM7ee31E1jeeoYlmht54MG/5XvPGWil0No44nkztFFYbqs0Suny81Do3DLpe8XvH/t18TaAH/r4vlf8uwoIQp8g8PEDv/jvgLB4HQQ+edfnD+7+S2LO9BeXFScnwC+/92tR7T4yMaPcs+5OHvj1zxDedCvqDBSAyo0c4tDjPyHIZQlcFxyHoeefpu/RhyAIGXttMypWx6Vf/nuc5lYgZCKZxDRNrAWLSDlRdu/ezdDQEOPj40Sj0WL6qxBCCHF89fX11NfX09XVxY033ojv+2zfvp2hoSGAcueicovkYrvkUnaA1rr8vVItg9L3SmeSgyBgbGyMpqamY7IKRkZGGBwc5Fvf+hb3338/CxYsYMmyZVx//fVcddVVNDU1AYVJX+ngtxwsCEqBClXeruM45TPRnueVf17uwFS8lH5HKYVt29P5FItT5LouC+cs42PXfrbSQznGWHKYb2z8n5UexnF98Uuf548///+V/0Y8zzsi86P0d1EKqpSutdblrI7S9eSvS3/Lk39W+rs0TbP8uXD0v0vfMwyDq9deh5vPSoChCoVhIAEGIabbihUrWDKnjf4tr9B04SXTvj2npZUrvvldXvnD3+X5DR+BICA0DNpuug0didDQ2MTyX/kNrFi8GEkvHDBZloVl20Quex97Nz/PgQMH6O/vp7OzUwIMQgghTophGJx33nlnbHuDg4PU1dXR2tpanuzHYjHa2tpYuXKl7MdmMdM0CcIq7XJC9S6t0VpXddBMKSm3V41cP0ckEqn0MKaNBBhE1filDev5w3//jzMSYACwGxpZ/b//nqGtr+LnciSWnI3T0nLsDYv7NMMwcByHvJfHufRy+p96jE2bNmFZFhdddBHz5s0jFovJAZoQQgghZhTLssrLWapNKMVBRY2xDYfBgRPvwDPTSIBBVI3rr7+ez/3pn5MbGiDS2n5GtqkMTduFJ1Y0yDRM4vEYfuCTa++ElefzwH/8B+Pj4xiGgWmadHV1SYBBCCFE1ZKJmjge0zTxgulvo3pKpIjiKZG/9eoVjzSQzmQqPYxpI3kzomo4jsOGm29ieOP0tqw8VYU0uAhRxyHiRGi4ZQPjsXqeeuZZnnzySZ588kk2b95cLv54vCJHQgghhBDVxjRNfL86AwxhyAl3KRFiRlCKwK/d9qby1yqqyl133on73EYCL1/poRyjUIPBJBJxiMfixFrbaPy132XQifFvDzzAN77xDR588EFeeuklRkZGKj1cIYQQQogTYlkWfhUvkRCnptRFRlQXrXS5cG4tkiUSoqp0d3dzwVlnsfPll2i5bE2lh3MkVSiWY1kWsXgMpUDPX4D12T9i/MEHeHHT04yMjpJKpUgmk8ydO5dIJILjOESjUerr60kkEuU6DaUK4EIIIYQQlWRZFr5ffSd3ALRSNT0Zmy6GYRBIJq2oAAkwiKpz34Z1/M4//QtUW4ChyDRN4rE4tmUTcRxSdgR9x92kzr+Et+//Or3f/S6bNm2ivb2dpqYm2tra6OjoYPHixZxzzjnMnTuX+vp6CTAIIYQQoiqYplm1GQyqxs/2TpdCu0x53qqRH/g1XbNNAgyi6lx99dVE/sf/JHOgl2jXvEoP5xhKK0xtFnuVF/uPE2KuPA/ns3/ExMM/4PVnn8DavYf6iE0ikaClpYXu7m56enpYsGABc+bMobOzk7lz51JXV+hPLMV4hBBCCFEJ1bxEQgIMQswsEmAQVceyLO69/Tb+ceNjRO/6RKWH8460VpiWRVxrInaEdDTNmGmib1mPu/Yastu3MLFnF+MTY+xLZdn87PN87/EnmNfUyOpLLmHNmjXccMMN5QCDEEIIMZ2UUuVLiRQjFlCoM1Wtk3itlLxPT0Ehg6HSoxDHk82ncKKdlR7GtJEAg6hK62+/jb+9cz3BHXehbbvSwzk+pdBKoZXGNEyUUgRhgG1b5OMxgq65BEFIEAT4vo/v++QO9ND7xCMceOxxMpkMra2t5PN5urq6JNAghBBCiIowDKOq0+klwHDyqv01nc0836W5ubnSw5g2EmAQVamrq4s1F17Aq5ueoW3t1ZUezntSSmHbNo2NjQR19YTh4cBCPp8nl8uSzWbR8xZgbvgEuZc38ePv349hGExMTPChD31IAgxCCCGmnSzHE8djmmbVZjCgFJ6X57XXXiu/f0uZOHV1dSxatKiy46tSYRjK33uVUkrj+9W5JGkqSIBBVK1PbVjPf/rzL0O1BxiKn93a0NiGTambUiHI4JPPe1iWhWXZWJZFJptFr1pDOpfl0UcepL6+npaWFnzfZ86cOcTj8co9FiGEEELMOkqpqq3B4FhR2usX8v/85z8GCq0Xw7DQvrJ/pIfNr72E1rrCo6w+kvVRvRS1vexHAgyial155ZXU//cvkNqzi/iixZUezklTSqG1gW1rTMvEcRzq6upIppIMB8P4V6wluX0rj/70pziOQyaT4f3vf78EGIQQQghxRhXOdFfnhMc0LT52/e8e92df+rdfJ5vNEovFzvCoqp/WuqYnsaJ6SbhPVC2tNZ9adwdjGx+t9FBOjjp8UVqhDY1pmti2jeM4xGNx6uvricfrqLvjbvpdj41PPskLL7zA1q1b2bdvH+l0utKPQgghhBCzxExNpbcsm2w2W+lhVCVpUykqRQIMoqrdfuuteJtfxEunKj2UKaGUIhKJ0NTURCKRINrRSfy+X+ft0XF+8KMfsXHjRl599VXGx8crPVQhhBBCzBJqhnZqMA2LXC5X6WFUpZkaNJoNwirNFpoqEmAQVa2lpYUPXXkFh559qtJDOX3FrAbD0EQiEeLxGHX19dSfvZLG3/kcfQ3N/Nt3v8emTZt4++23GRgYkJ2mEEIIIc6QGTghlUKG78j3fbQ2Kj0M8Q5q+X0rAQZR9T6+bh35px+fkZH141NorYlEHJqammhMNOK0thH79G9yaO4i/uXb3+Gll15i586dslRCCCHEtAnDsHwRs5tSakaeVQ3CUAo8voNCV5DancTObLUdGJMij6LqXXrppbQZiuRbb1K/bEWlh3P6ip8nhmHgRByCICDv5gstl9Z/nOGvfpnvfv/7NDQ00NXVRVNTU2XHK4QQQoiaNlMnO+PJUb75zW8SsSPYEZtsNkcumyOXy9HW3ko0GiUIAjzPI5/PE/gBfvG4y3Xz5F2XfN7Dzbn4gc+v/vqvsHjxzCssfjyGYRCE1dkZZLYLazwwJgEGUfWUUvzShvV8aeOjtRFgKFKqUADSiTg0NSUwTQPfDwhuv5uX/+p/suKFF1i9enWlhymEEEIIUZWuvvA2tjzRg2VECMMQ07AwDYvR5BA7rUOEhCil0RhoZRSOvZRGGwamNtHawDQiaN3Aazue4s1r3qyZAEMYhmhVu5PYmSwkwDBqd/mKBBjEjHDzRz7CF//mK+THx7AaGis9nKmhCn1wTdNEqSj5vIdlJfHmdKBWnMtLL79MX18fCxYsIB6PY1lWpUcshBCiBszUs9Vi+pSWyYQzrKbB+y+4ccruq2fo5/h+7Zzx931fAgxVSxWXsNQmedeJGaGhoYGPfOgahp7eWOmhTDmlFIZhYFmFVpamaeJcdR27BobYvXs3Bw4ckBZMQgghTptS6pjJo9RfEFAMLDC7g08KXXt/D7P49axmNflem0QCDGLGuHfdnYVij7UW8VOgtMI0LaLRKI7jYM9bQNA1n4cefpg333yTZDJZ6VEKIYQQokYFQTCrgwsFM7NV57sJwxo7Zq4ZMytT6GRJgEHMGOeccw7dzQnGXn+t0kOZFqZpUlcXJxaPYxgm1lXX8vTmV3j55ZcZHR2t9PCEEELUgFo+qBWna5a/NxQ1l7auZvtrKipCAgxixlBK8ekN60k/+XilhzItDENjRyJE7AimaWCetYx0NM7mzZsZHR0lm83W1NpAIYQQQohqoVXtpa3PxNajs0WtvdcmkwCDmFFuvOEG1M43yR0aqvRQppxSGtMwMU0TwzDRhoF9zQ1s3bmL4eFhUqmUBBiEEEIIMU1qd8JzIrQya6rmlWmaNZeRUSuUkiKPQlSNaDTKnTd9mOGNj1V6KFOvXIvBxHEcIhGbyLkXMex6PPPMM7z99tukUqlKj1IIIcQMVVoeUboOw7B8EbNb4T0wu9PpG6LN7O89UOlhTBnDMAikBkOVqr16H5NJgEHMOPesW4f73EYCz6v0UKaFaZrEYjGi0Rim42CtvZrv/eghfvazn0ktBiGEEKdFukiIdzLb63Mk6lvYt7e30sOYMlprgkAyX6uTFHkUoqosXryY87oXMfrKzyo9lGlhGAZO1MFxnEL7ykvXsPfQMK+//jrJZJIgCOSAUAgxK/i+Ty6Xw/M8+dybApZlEYvFsG0bpRS5XI6xsTEymYw8v2LWvwdMwyKXzVV6GFNGKSU1GERFSIBBzEj3rV9H5skaXCZBIeIciUSIRCIYhoGKRtErz+fVV18lnU7P+gMAIcTskc/nyWQyuK4rn31TIBKJkEgkiEajaK1JpVL09fVJK2SB1prZXoMhnU3S3NxU6WFMmVo+Qz7zSQ0GIarONddcg923n0xf7ayVK1FKYWgD0zQwDAOtNOaylRwcGyeVSpHL5Wr6Q0kIIUo8zysHGMTpM02TSCSCZVkopchmswwPDzMwMMCBAwcYHx+XQM4spZQimOWvfTqborm1dgIMonrVevtQCTCIGcm2be6+5RcZrcVij0VKqWKQQaOicVzPI5VKkU6npZuEEGJWKAUY8vm8THyngFKqELjWGq11eYlEb28vb775JgMDA/I8z1JhGKJn+RnvTC5JU3Oi0sMQs0Qtf9ZKgEHMWB+98w7cTU8T1NqZLVW4KKXQ2kBrA6UgCELS6TSZTEYCDEKIWSEMQ3zfl6ytKaKUQilFe3s75513Hu3t7Xiex86dO3n88cfZtGkT27Ztk0CDmLUMw6j0EKaMYWj5O65SIQGmaVZ6GNOmdh+ZqHlz587lsvPOZetLz9N25QcqPZyppxTaKJxlQhd2EhJgEEIIcboWL17M9ddfz9NPP83AwABvvvkmfX199PT0MDQ0xGWXXUZLS0tNTbbEu1NKMdvnopZhMz42UelhTBmlNKG0qRQVIAEGMaPdt2E9v/blv4EaDDAoFEYxjVUZJn7gyxIJIYQQp23+/PmsWbMGz/PI5XL09PSwb98+IpFIuWuHUop58+bR1dVVLAAoapl0HID6eIK9u3sqPYwpo7XU1ahmtZxdIgEGMaO9733vI/75L5Dau5v4wu5KD2dKlZdIGBpdX4/r+YyPjzMxMYHneZUenhBCiBmqo6ODxsZGLMuipaWFxx57jAMHDtDT08Pw8DDJZJL+/n6uueYaOjo6JMAwC5Q6DoRhOGu7D2zZ8yy/ctvHKj2MKaO1ZDCIypAAg5jRDMPgk3fczt9sfIz4x3+p0sOZUoViXBpDa4zmNlzPY3h4WAIMQgghTkssFiMWi7F06VKi0SipVIpkMsnevXvp6enh5z//OZlMhrq6OhKJhGQyzCKzOcAwlh5izZorKj2MKaO1Jgxq9yz5TBaG1PTnqQQYxIx3+6238uV/uR1//T0YTrTSw5kyk4s8atNEN7ewf/9+CTAIIYSYEq2trdTV1REEAW1tbTz88MMcOHCAwcFBJiYmiEQipNNprr76aslkmAVmexeJ1sZOdu3axZw5cyo9lCmhtSaQDIaq5AcukUik0sOYNhJgEDNee3s7H7xsNc899xRzrr6+0sOZMqUMBm1olAKjax4DAwNSg0EIMavM1rOpZ4LjODiOw+LFi4nFYoyOjjIyMlLOZNixYwf5fJ729naWLl1Kc3MzjY2N8prUqNKEVM/SJnOdicU8/dQzXHFFbWQxaEPDLK+rUa3S+QmampoqPYxpMzs/QUTN+cT6deSferymCqYoRblXOShUoplkMkk2m5WWbUKIWUUmtNOrqamJs846i7Vr13LXXXdxwQUXoLVmYGCA119/nS1btvDaa69x8ODBmtrPiiNprQmC2XsCY9GcZWx59fVKD2PKFJZHyGdnNcrlM9TX11d6GNNGMhhETVi1ahWtBCR3vEX90mWVHs4UUuWrMJPGNE050BZCCDGlIpEIkUiE7u5uotEo4+PjpFIpdu/ezd69e3njjTdoaGjAdV0aGxtpaGggHo/L/qjGmKY5qwMMSim0qp1zr0orKfJYpYIgwDRrdxpeu49MzCpaa+5bt44/3fhozQQYwhDCMCicLQohnBgnGo1iWZYc1AkhhJhyTU1NxONxgiBg3rx5/OAHP2D//v28/fbb9Pf3o5Rizpw5dHd3E4vFZF9UY0yz0BJ7tgrDEG0YlR7GlNHakIyjKqW1JpvNVnoY06Z2wnRi1rvlF2/G37KZfHKi0kOZMkEYFncOIeHYKHV1dViWJYW2hBA1LZ/Pk06nyWaz5PN5qTtzhkQiEerr61m0aBGrVq3isssu44orriCRSLB//35eeeUVHnnkEbZs2cLQ0BCpVEomMDXENK1ZHWDw/DxmDQUYDK2kyGOVao53sG/fvkoPY9pIBoOoGY2Njdx49VU8+tQTdNx4c6WHc9pCQsKgeAGC5Dj19fXYti0BBiFETXNdl4mJCZLJJJlMhng8LhPZM6i+vh7HcbjiiiuIxWI89NBD7N+/n23btrF//37y+Txz5sxh3rx5xGKxSg9XTBHTNPH92dulqmdwB+e8f0WlhzFllGQwVC2tzJoOnMssRdSUj69fT/6ZnxLWQhHEsJCuV14ikSkUhIlEIhJgEELUJNd1SSaT5HI5wjDEtm0aGhqIRqOSjn8GWZZFLBZj0aJFrF69mhUrVtDZ2Ynv++zZs4fXX3+dF154gZ6eHik6XENm+xKJvJejo7M2WlQCGFoTSheJqhSEXk3v02SWImrKeeedx4KGOsbfmPlVgEPCcoAh9PLg5YnH45LBIISoWdlslpGREXK5HLZtk0gk6OzspLGxUT73KiCRSHDWWWexdOlSli1bRmtrKwC7du3iscceY8eOHXKGtIYUijzO3gyGEGrq/ay0KhT0ElXHD72azv6SvbWoKUop7lu/juTGRys9lNMXQhAGhGGAPzGBYVo4jiMBBiFEzfA8j0wmQyqVYmJiotyGV2tNJBIhGo0Si8Wwbbumz/ZUq1JNhu7ublavXs28efNQSpHL5RgbG6vpImWzkWVZeLM4wGBok3w+X+lhTBmF1GCoVmEY1vQ+TWYpouZ8+MYb4a03cEeGKz2U01SovxCEIWE2g2EY5VZiEmAQQtSC0pKIkZERhoaGyGaz2LaN4zhEIpGabuM1kyxcuJC1a9eyePFitNY4jkMikcBxnEoPTUwhy5rdbSo1Rk2ti9eGrqmMjFoiAQYhZph4PM5tv3A9w0/9tNJDOS3h5C+KO4kgCPB9X3YYQogZpfTZlc1mmZiYYHx8nPHx8SMyFizLKmct2LaNYRhorWv6IGymaGxsZOHChSQSCZRSBEGA53lSf6HGzPYaDErpmspgMC2TIJy9r2d1C2v6ZGHtPjIxq927fh3uM08QzuBItAKUKiz70JEovu+Ry+VwXVcO6oQQM0ppQppKpRgaGmJwcJDBwUHS6TSmaVJfX09LSwtNTU3lWjMSWKgetm2XiwxDoVaGLJGoPZZl1dQZ/JOllcb3aufxK6QEQ7UKoab3cZJ7KGrS2WefzfK5nex/bTNNF6+q9HBOidIa07IwTRPd0ICvNG+99Rbd3d0sX768nMVQyx9QQojqlsvlypPMdzsbEwRBOYvBsqzy51dp2Zdt21iWJZ9nVco0TaLRaPm1m5iY4MCBA/T29rJ7924Mo5Ba7vt++bUOgoCOjg7a29vf8X5fe+01BgYGyhl6x7uEYciqVatYsGDBGXzEs5NtW3h+7ZzBP1me7xJxIpUexpQxDJNQajBUJd/PMzo6WulhTBsJMIiadd/6dfzBtx6YsQEGQxs4EQc34mKYJsaKc3nhpZdYsGABl19+eaWHJ4QQpNNphoaGgMIktBQgOHoZl1IKw4yFvK0AACAASURBVDBwHIfm5maUUoXsLK3LF1G9TNPEtm0AokaEdjOBmgh55Fs/5OVHN2EaBloptCpdazK5LH5c8cNHHzrufe7bt49Prr+X9y1djUaBUhgUlsQYSqOVAhS7+veyf91+PvNbnzmDj3h2mu0nuwP8cpaOENMpmUpiGEalhzFtJMAgatZ1113H5/70z8kO9OO0z7y+xlorLMvCsi1Mw8T54PX0b3mFrVu38vLLL6O1Zv78+dTX11d6qEKIKVKamHueh+u65Va1JYZhYFnWMZPyUz3zX7r/yWefSynS75YlVfq9XC6H1hrDMI4IMEz+faB8m6ML1UrGwsxQqpGhtcYxI/zvmz6HVoo5HR10dHQc93X0fI/b/ulX8TzvuMU6fd9nXmsXf3jdr7/rtr/z4g9xa2hdfDXLu3nMeqvSw6goqXElzoTWpjk1HViXAIOoWZFIhLtu/gjf3PgonevuqfRwTppSGsNQWKZVKH7WNR/3g9fz/JOP0NDQQDqd5oYbbpAAgxA1KJvNMj4+jud5RxzwOo5TrlEwFUsKSqnpruuSzWaPW+dl8jZKX5eCEdFolKamJmzbfteOD6XfKxVuFDNLKdvEMIxy2zttGO/6/jMNk+Z4E/39/cydO/fYn5sm3gms9ze0UVPr4qtZNpfDMmdvgKHWKvsrBeGsz0sRlSABBlHTPrruTv7p7nsJbl2HtuxKD+fkFAs8WrZFXV0dEBJedxPJTJpHnn0OwzCYM2cOvu/T0dFBPB6v9IiFEKeoNGHP5/Pli1LquJN213XxfZ9cLgecXhbA5AyGyd0cjrfEYTLDMAiCAMdxiMVimKaJ8R4TTjFzTQ4whEBwgmd5O+pb2b9//zsGGE6kY4GhDTzJYDgjMukMljm7lwjU0meY1powkABDVQpr6712NAkwiJq2YMECLl2+nDdeeoHWNe+v9HBOiW3ZJBIJDMPA83yCm+8k2zaHH/34+xiGQS6XY+3atRJgEGIG830f13VJJpMkk0kcx6GhoaFQ5HXSGf9SUcVMJnNMdgO883KGdzqQKdVCsCwL27aJRCLHzYw4+t+l7Wqtj1kaIWpTuW4GCj/wCALjPdPJO+Kt9Pb2snr16mN+Zpom3gkEGEzDIO9Jt4ozIZfLYVsSYKgVSmukskaVUrW9HEcCDKLm3ffR9fzm//kqzNAAg9Ya27aIxWK4rls4wLvyKtzmFn7w7a8D0NzcTBAEtLa2Eo1GKzxiIcTJyufzJJNJPM8rLIkq1io4OsAAhY4MpTPKxztAeadgwPEOnEuTRtu2y8suJgcYaulgW5y6ye8D13f506f/gd9ccy9zKNQ3CgKfwA+Omcq0RhJse30ba9asAQqZL6VlMplMhryXJ3iPZRIaRSadYWJi4rg/L3Wa8H3/iOvJ7/vJ72fTNLGsk1sGkMvlyOVyWMXOTtFotCb3ta7rYhqze2pQS5M+rVRNPZ6aEoZS5FGImWzt2rVEP/9F0r09xObNr/RwTlFhAtDU1FRIK/V9gmUrCT/2n/jB1/+WlpYWwjDk4osvrsmDHiFqneu6TExMEI1GaWlpKdc0OHqCb1lWIU39qOKPp0trXQ5aSFBBvButDGJWlN968Au01jdjR2z8ICAMAvwwQCtNzI7S6NQxkhqjabCdD179QeBwW9JIJEI+nyeXd/F8D4DJb+fJb0HXy/PjHz7E0z99klwuRxAGBEFIGAblWiFaFd+/SqN14bqwAD0s3ndYXoseGPCN+//1pPaVIyMjbNjwUQgUhIrGhkYiEQfHcUg0JQpdNEodUQwDw9CsPHcFn/393z2dp7ps06ZNbNmyhTAM8fIenje5KGuA73n4vl/+vu/75dT44B0+KwYHB8nlMsyfv7DYsQMODR/C0LN3auDmXfr7+ys9jCmjtDrh5UzizKr12hiz91NEzBqmafLxO27j7554hNi991V6OCeveKBlGBqtbXw/huvmCmdlFp0Ft2zg/h9+h0QiQTweJwxDEomEtFoSooImd4PI5/Pl7g+T2zhOrrngui6GYWDbNo7jvGPBxNIZYCEqQSmFZZjcd+md+KFPiiyNDY2Fugy+j1YaP/QZSY0yOD7ClgNv0jcyUl7CV1qKUwqgBYGPUoU07mPjWoVv7OjfSzRv8ZXb/huOFUFRCCAY2ih8fZJp4B/9+m8RBEG57eaJcByHr37173nooYd57IdP8UvX/xGGaeDmc6SzSYIwIAzCQvAjDBhLHuLRHz82ZQGGf/i7f2bHlv2cPf/8YhCwEMwoPX6tDJQy0NouBFmULtRxQmGUn9jJT3DI8K4DbNq2kU/e9HvliM5tV/4yMaduSsY8E9U5jVgzrV7Xu4g6DqO+W+lhiOMICWu64LEEGMSssO722/nrW2/Hv/NuDMep9HBOUSHNMxKJkGhqwjBN/EM+/rkXknr9Fb717w/Q3NyMYRgsX75cAgxCVIFSZkIkEqGurq4cOPB9n3w+TyqVYmJiAtM0qaurIxaL1fRBh6gNISHtDa20tbXR3t5e+M6kYnKlr4aTo/zGg/+NJUuWAByRJeN5HiiFab57wMyxbeY3d9LW0DIlY1da0d7eTmNj4wn/TmNjI52dncydO5dnn9zERHqU+R1nAdDSeGwb7OGxAV7e98iUjBfAtm2uuvgjrOy+eMru84Kll/NWzxaGxwe59rLbpux+ZzLLssm7tVNQtCHRwMHsUKWHIY4jDCXAIMSMN2fOHN5/6SX8bNMztF/1oUoP59SUMxkMnIhD4Ae4ORelFfzienr+8kv8+wMPMD4+zr59+1i0aBGdnZ0kEglisdhJrzkVQpy8UuZCLpc7ohCj53mk0+lyBkMQBOVLqeZCLBbDtm1ZoiCqmlKKIPRR6vBSBqU06jhxgtaGZnKZHFrrYwoRl2olFDIY3plpWHjlTIfTF4SF9qonGoQPwxDbtgnDkEwmQ2NjA7l87t1/aYrXvhtaE/jBe9/wJGhtsObc69n81tMSYChSqCNa9M509Q11uP7+Sg9DHEcYBjWdjVi7oRMhjvKpDevJP/14pYdx2pRSaEPjOBGamhI0NjQQSSSI3fcb/KxnP1/5+6/yla98hfvvv5/XXnuNwcFBXFdS5IQ4U4IgIJVK0dfXRzKZLE9kxsbGOHToEIcOHWJsbIx0Oo1hGLS0tJBIJIhGo++4NEKIauIHAWF4ZN2E41FK0Zlop7e395ifaa0LSwve405sw8J7j0KQJ0Od4uS/1G3FMM33/H2tFP5UjtnQhOHUT3zntMxjeHxwyu93pjrV90a18vJeYbmMqDpRq549u/dUehjTRo5kxKxx+eWXk3CzJHftoG7xkkoP59SV1lWaJo4TJQgCPM/DWLAI87f+kPEf/jubtrzCnt5exsfH6enpYcGCBSxcuJCFCxfS0NBQuBs5SyrElHNdl2w2i+u65baPjuOQz+fLxdfgcHvHUsG7Wj6TIWqPF/iEwXsHBwA669vo7e1l2bJlR3y/1N3h3dqoQqFNpR94pz3m8nY5uX3f5LopSimME5jsK3X8Di+nSvHewZxT0dLYVliqIoB3b+k7E2UyWUyjdmpK1JL2xDz27ump9DCmjQQYxKyhteZTd97JXzzx6MwOMBRppVGmIhqLobWBHUkxYRgYd38a97p+Dj3/FN98+Mc89NjjXLBiOddccw233HIL9fX1NbUDFaKaZLNZhoeHsW2btra2cuvHUvHGyUrr0Wt5HaaoPSHg+R77Rg7y0sA24nvrypkIflBY9jCSHsc2TcIg5Gc/f5UVb1zCNddcc8x9aa15eOsTKBQhIUGxUGJhO4XuB5t2buaVnjf4/fu/UOwGUSySWtxm4euQIPQJghA/DAgJ6Ui00dXUgRd65IPCxQ3yJHOp00qDN7TxnsEDpfSUptqHHNlZY6qkMsmaKmoojiTHetVLKTXly56qiQQYxKxy6y2/yJ/9wz/iJZOYdTO8UrIqpmyaJjpaOBOkUNgRm3wsSr5zLrkP3UDq+afZ+MRPGBoeJhKJcOmll7J8+XKam5sr/QiEmPFKExzXdct1F8IwxDRNHMcp1z5RSsnyB1ETXN/jDx79MkoZtDfNxXGixTaRGqUKa/uDYnZDNBJj1PV5fcu2497Xvfd9nH1Dwyh9uPijUbxWxW4JrZEFhHu2Ma4bUKjidgoTJ0XxdihMpdGq8POhsYMciExw1e0fLtc4Kf1N3hP75ZMq8Hi0E8lMKHTIrP5U+662+YxNDFd6GFUj56ZpaOys9DCmzPjYOI49Uwub17Yg8HHs2q2NJkc7YlZpbm7m2vddycZnN9Jx/U2VHs6UUEpjGIpoVGNHbLy8Rz7vkkqnGdcK9cHrMc+7iK3/+Nf82f/6C26/9RY+/elPS4BBiClQCjCk02kOHTqE1ppoNIrjOLLsQdQkLwxZs+wGzlmwirld8+ns7HzXM6Vv7HmF/f4rx/3ZZ377P7/n9jZv3kzPW4f46LW/esJjfPWt58k19XDXXXed8O+cqCAM3vPM8FTHFry8h6Gn/pDdzbuYRu1Ock5WQFiDgWDJYqhGQRDU4HvtMMnLFLPOJzasx3vmiRlxduGEqELbLcM0ymnY0ViMungdjQ2NxONx7LZ2Yvf9OgcyWV599VV6enoYHh6W4o9CnKIgCPB9n1wux9jYGLlcDsuyiEajR3SDKF2EqAWFGgQm7Y1zMfSJTUwbYk08/tPHTm+7J3n7MAzQenoCfL4fnMCypqlfyz8dxyx7DrxNQ11iyu93pgpDv6YmfX4wfX8H4vR4gYdVwxkMEmAQs85FF11EZ8Ri4ufbKz2UaaG1xjIt4vEYLS3NJBIJLMvGaGrBufbDvLV3H3v27KG3t5dsNlvp4QoxI4VhiO/7JJNJhoaG8DyPlpYWWlpaCkE9aTcpapDWhWUIIWF5ScN7aahroq//IOPj46e0zUJbzFPr+jAdAt9HH68n5zRuv/ScT7WBkQM0N7RN+f3OVEoZtVX0skbOo9WiIPCxrNoJZh1NAgxi1lFKcd/6dSQ3nt4ZlapVymgwTGw7QjQapa4uTjQaw1l9JYcyWV588UW2b9/OxMREpUcrxIwShiFBEJDL5RgfHyefz5czhxzHwbZtTNPEMAwJMIiaUwoq+EH+hH+nPtaIY9h8/r9//pTbQ57sTCkIA/R0BRiCE1kiMbUzO1Uo6jCl9wng+x5K2hjWLM+TNpXVyg98bLt2C6zKu07MSh+56SaCN7aQHxut9FCmnW3bNDYmaGiox65vwDj/Yh776U/ZvHnzKZ9REmK2KtVcyGQyDA8P4/s+LS0tNDY2Ss0FUfO01qBDcvlscRJ9YpPe+lgDD3/7QX75E59mx44dADz80MNs3LjxBDoynEKgIARtTM8hbj7v4fnee3aJmNLuMNMULJk/ZzGHxvqn5b5nIoWa0u4flea6eUyjds+Sz2S+79V0BkPtPjIh3kV9fT23XHstDz75Uzpvvq3Sw5kexeMRwzBwHId8Po9lJXHWXkP/5hfZtm0b27ZtIxqN0t7eTiwWq+x4j/JOB51yVlhUkuu6ZDKZIzIXotFoTa3bFeKdGIYBKiSbTwPhCZ9Ub0nM4RMX34hjRvnlDZ/kF267iaeefIr9O/cRrY8XblQM3pXaU5YuqUwax2o5qXGG01ADoWTHzrd5+WefLfxDlbpaFCanoMqxgPqG+JRtM5fLTcta+sGRPmKR6tr3V1KtBRiiUYdkPlfpYYjjCGq8BoMcEYlZ6551d/K93/wtwptuQdVwH3qtNZZllS925zzMy97HC6+8wLLnnsM0TS6//PKqCzCUTA40SHBBVFo2m+XQoUM4jkNLSwuRSGRqz1QKUcW01himJp2bOKmM/Xishb7RQT6z5j4+cPblfH3Tv3Ngby+fvfZXuGLJxUBpgl74nC+ldSsFD7/2BH/99HdPapyFIo/T83d51uIl3HD+J5nbvhA/CAhDv9iWE4LQIwhgLDXM91/8ypRsb3x8nC2vbuWyG6b2ZEhP324e2vR/ufWqT0zp/c5oNXaM0ZhoYCg/VulhTCvPyzMwcgDPz2OaFoY2sQwLwzAxDQulFI4dq7r9dEBQbmNdiyTAIGatlStXclZ7KwNbXyVxwcWVHs70UYUDNtu2iMXjBEFI8JE7mDg0yLf/4wfk83kymQwrVqxg0aJFNDQ0nNHK95Or8WcyGXK5HPl8nmw2SzqdJhaL0dbWVi6cV+k09Mljm5iYIBqN0traSiQSqei4xPQoBbhc1yWXy5HNZlFKYVkWjuNI5oKYVQzDIBKJkMyMUlgecWJRhni0nvFcEoDGWD2fufqT3HbhL9BclyBmR999m9o46fIDQRhO24QiDArBC62NYlbBsZMEP8xPyfZd1+V3f/uzrJi3mvrY1HZ7ePyl77Ns4fl84OIPT+n9znQ102EMsGyLwPcrPYwpk3Uz9A/30jfUw8HRPQyM9XBovJ958+YSjUbJ5Vzy+Tyu6+K6Lvm8i+/7pDMZFArLsrFMC1Ob2JbDxd1XsWrlBysSfAhqrGPJ0Wr3kQlxAj69fh2f++6DtR1gKLIsi4b6BhSFg5bYvb/MyH/cz9e+8+9s376da6+9lltuuYV4PH5GC9SVggsjIyP09fUxMjJSrszf19dHR0cHq1evZt68eRiGUfEAQzabZXh4mP3797N3717a29u56KKLJMBQwybXXNBaE4/HiUajVXdGRIjpZhiFdsip3DhheOKlF8eTw7S2nHvE9+Y1d57Q755KkcfDvzf1/CB4z8KIpjbJ5U4vNd3zPH7vd36f4b0ud171ydO6r+N5q2cLH73uV6f8fme0acx8qYTp6j5yJqSzSQ4M7uXgoX0MjPfQN7qXVHaM7u5ulq9cxu0Xf4gVK1Zw1llnvefxVxiGRwQeXNdlYGCAL33hT/nGo5u56fKP0dI45ww9soIgDCTAIEStuv766/mvf/a/yA0NEmmt7VZNWhvYEZuYHyOfz2MYBvqOe8guWMSzP/gO6UyGWCzG+eefz8KFC2lpaSEajU57Clcmk2FgYIAdO3awdetW+vr6jshksG2bMAwxTfOIA8ajzzKcqYBILpfj0KFDpFIpLMuSFPka5Xkevu/jeR6e5+G6LqZpYts2sViMSCRyRjN9hKgGpeyd1HixA1FY+t+7/x2MjfdzdsfNp7jNU/h8DcNp7SLxXpX5HTtGOp06re184b9/kT3bBtlw9W9Mef2FLW+9RDo7wYpFF07p/c50IVNcnLPCtNZVn5ERhiEjE0McGNzDweF9DCX3MzDaS97Psmz5MlZeuILrzruZFStW0N3dfUonmQpZvPYRXRs6Ojr45r99nX/95r/yN1/+EpctvYErzv3QGXv9g9CTAIMQtSoajbLuphv59pOP03n7hkoPZ1oppTAMg2g0hmGYWJZdWDe66kqMOV28cv/X2flnf851a9/PnXfeybnnnktHR8e0BxiSySS7d+/m+eef55FHHqG3t5cgCGhpaaG7uxvTNKmvr6eurq4qdvy5XI7h4WGUUixZsoS2tjai0XdP8RUzj+d5pNNpstks2WwWx3FoamrCsixM0yy36xNitjEMg0wuSSmrIAxBqXcPMqTT4yzpWHRK29NKlQs+wokFk4MwQOnpbFM5vfuiXbt28eMfPs6vfeTz09IF4OHnv8UHLv4wplm7bfJOVbVPyE+GZVv4gVfpYZRl3UxhecOhvYwkBzmUOkjf8D7idVFWrFzBhdesYOU517N06VK6urqm/ZhPa829H7uXD1z1Af7rf/l/+ecf/4ybr/gkbU0nll11OoJAMhiEqGl3r1vHNz95H8Ev3oGu4T92ilWuDcMg4kQIggDP9zBNg+zipZi//Tkyz23ku08+ysChYT75sXu5/PLL6erqwrKsafugtyyLxsZGWlpaaG5upq+vj/7+fgzDwPcLa9RKkzo4XLMBKJ9Bns6dUOlgI5vNkkql6O/vp7+/n8bGRhKJBIlEoqZ3ErNVKcAQBEG53oLjOOXlQxJcELNRKaMnk0vxZu/LHJhoJDHQWA5glyvwK0UQ+ARh4d95L8+/vfADFrTNRStdTN0u9IIPggA/9AnCED/wmfDSDGfHGMmMkcnnGJ0YpW94H7/2Jx+hs2U+l57z/vJ4Dqd/Hw52QMj2Xa9w6zk3TstzsHffbn7g/Qv18SYUqvh5oNFKF68VQRgwOjbG1772tUJhzOLnhmEY5X+XrkuXyf/+0Y9+RPeclURsZ1oeQ+/ALuriDTz6QqF4ZqFzR4Cbz+H5eYKw8O8wCAjCoJBi7rt4fh7f9wnDgOA4qfea0mdj4brUyjQMw+Pe3lAGh1+7wm0MZRxz5j0MS50dDhcCDcMAPwzKt7ctB9/P4weHjw+0MgjDwrHOsdspbKPQBaQw1p37t6PnvI9YLFa4XRAUb6OOGFMp4JXL5XBz7uHtGbp8PDI5CF26n0LtDn3E/U0OnoVhiGVZbNiw4Yiz7acqEomwr/9tnn714XKHlskUh/dlQRgQ+D5+4BWeb6XLhVcBXD9DJj9x+DUp39fhWxWex9LXxecJ8HyXobEDpHLjLFmyhHPOW8EFiy7g7LPvZPny5TQ3N5/2Yz0dCxYs4J//5R/59re/zf/+sz/hrPYLiViRYnKWOnw96flTTP5+6fFyxHN2+LalPK/iz5Sid2B3TR871u4jE+IELV68mPO7F7H7lZ/RvOrySg9n2imlMJQmGi0UqIvYEUZGR8gS4qz9EMEFl/D0P/wl4T/9E11dXTQ1NZV3itOhsbGR5cuXA4WDV8dxeOaZZ8qpuEcvjQiCANct7NBLB2PTPeELgoBkMklvby979uzhwIED5bXIjuNURWaFmFqe55HJZIhGozQ1NZULjEpgQcxmnueRzWbxwhwPv/wNVl92GVecX+hCZJkGhnk44800C8vHtKG5rfMOOufPQ1ul4LZf2BeZJpZllLOCDMNgeSJBa2srra2txGIxXNdl7969vP3224yOjrJgwYLyZ/7kz/7JX39EvZ9LLrlkWp6DkJCcnyXIHCp+IyTwfUIKAZIwDAjCkO45K3jqe68Xfh4GxQnZ4a/DICCkNIEvXlOczHs5zl905bSMH8Dz8xjKZMuOF8rtNVFgGTamYRa+pxSGNgoTcBSmYWIZDo5hFNtzqiMmU6X2ohAW/wuL9wOgigEYVXzKwvJzUPo5FCeoQUAQ+sUJ7lEZK+HhbZUCO6Xbu66LaZiYZqTQ8hQIAh9Dm0SM2BHb0UqXg1+lAIhCcX735ST3WTzyby+Vx6OULgRbjsqgUWi0MrAM+8jWqoTFSNekKXhpklp6F4QBoSreX1h4rkvP5bPbHmLt2rUsWrTo9F5k4Morr6TvYP+kx6KOfA0mBTYM08DQBpZtYVlm+WeloGHEjtDZ1XnEfZVflqPua/LzpLXGtm0WLVrEggULqvZ4SWvNhg0bWLt2LT/96U+PeEwlRz/Go69P9DYAl4aLWLVq1TQ8kuogAQYhgE+tX8fvff3/wiwIMJR2soZhonUhuu/7PoY2yGQz5Juaid7zyzz3d/+L73//+2SzWS666CJaW1unZSJfWhfX1dXFypUrOXDgAK+88gqu6zIxMUEymSSfzxfTUlX5+1pr6urqjglATLVS14i+vj7eeustUqkUc+bMoauri1gsVvGik+Lk+L5fPANX2NkfXTi01C0il8uVa2xEIpGaPtMgxImKx+N0dHTgeR59fX0sX3E26zesY+7cuXR0dEzbZ/HZZ5/NtddeOy33fbJam9v4xI2/Q128odJDOSX5vItSik98+LdxnOpsTz2b7Rx47XAm0GlavHgxn/3935uS+5oturq6uOeeeyo9jBmvOsNIQpxh11xzDfbBXjJ9Byo9lDNKKYUdidDUlKCpKUEsGsMyLYyOTvQHf4F//ta3ePDBBzl48OARk7LpEI/HmT9/Pl1dXcV2QzkGBgYYHBwkm82Wt++6LsPDw4yPjxei7tM8wS/VXOjp6eGNN94gk8lwwQUXcO655xKPx6d122LqlTIT0uk0mUwG13WPOJjLZDIMDQ2RzWaJxWJEo1HJWhCiqLW1leXLl9PWViiKnE6nGRoaIpVK1dTa9Xejjjj7PfOMjA9hWxEJLlQpraq/MKMQ70VOyQhB4Sz63bfewtc3PkZ0w8cqPZwzozhnMrSBtjXRIMT3A5SCYCLAvuIDjD/zBM8++yxLliwhmUyyYMECmpqacBxnys/oRiIRmpqaaGlpoampiUOHDjEyMsLevXt54403UErR0NBQXgM8XVX8S50DoBCASaVSDA4O0tfXx8GDB9Fak8lkSKVSuK6L4zjE4/Hy8yGT0eqSz+fJ5/PlAFUpg6EkDMPy610KYBmGQSQSIRaLYdu2vKZCFNXX1zN37lz27NmDUop0Os3g4CCdndNfFK0aFJZ3uAyN9dFQ18h7dc+oRq/vfFmCC1WstPxDiJlMAgxCFG2443a+uuEugts3oK3ZVVlZUWjh09hYKNaVzeXwPA/7otXs+ulDfOc736G3t5frrruOFStW0NraOuUBBtM0icfjNDU10dbWxv79+xkeHmbHjh0899xzjI+PHxHgmK72kL7vk8lkytkR4+PjDAwMMDAwwMjICIZh8NZbb5FMJolGo7S2tjJ37lxZn1+lXNdlfHwc13Xxfb+87KH0WmWz2XIWQxiGOI5DIpHAtu1ycVN5XYUoiMVi5doIMPsyGHzfJ5d1mdM094S6Z1SbfQd38h/PfI37PvL7lR6KeAez4e9I1D4JMAhRNG/ePFafu5LXX3yetis/UOnhnDnFY6NSIcdoNEpDfT1aabyly0k+8WN27txZnkAfPHiQpUuXMnfuXNra2nAcZ0oyCUpnjScHD/L5PKlUiuHhYfbs2cPQ0BDd3d3l5QnTsTyiVOl7bGyMQ4cO8fbbb/Pqq6/S39+PaZo0NTWRSCSor68vr88vVaCWiWj1KGWiuK5bzlAoBQ1s2z6i0jccrvJdeg9KK0ohjhWLxWhrayMej6OUKi8pSqfTQruyJgAAIABJREFUlR7aGeE4DkprTMPC9320VsUuBFDtgYa+oV7+8cE/4dLl7+f8pZdVejjiHcg+R9QCCTAIMcmn1q/nN/76b2E2BRiOUliq0IxhmuSXryTV0sb4gR7efPNNent72bp1K5dddhmrVq3ikksuOeJs8OkoVRouZUZMrkSstWZoaIht27aRyWQ477zzqKurm5YAg2EYRKNRDhw4wNatW9m0aRPPP/880WiU8847j6VLl7Jy5Ura29vLVc+lAGD1yefz5RoLSilisVh5KcvkzJdSK7KSyS3lhBBHisfjtLe3U1dXBxRqlgwPD8+aAEOpsDAoPM8rFokttWWs9Oje2YHBffz5v/4+5y1dzd2/8JlKD0e8C8lgELVAjoqFmGTt2rXEPv9FUvv2EF+wqNLDObNKmQyGJhKJEM1HicbjNN5zH6N/9SdMTEyQz+fZvXs3AMlkktHRUZYuXcr8+fNpbGzEcZxTnvSXsiBM0yyfaVZKlYvylYo+jo6OAhw3EHE6giAor80vBTVM0ySfzzM0NEQkEmFkZATXdYlEItTV1R3R7klUVilj4eiLUopoNFrONpGuH0KculIXifr6epRSjIyMsGvXLoaHh2v+c7DUOrmQtVAo9giFfYfWCqjOTIZsLs3ff++LXHj25dx7429VejjiBEiAW8x00kVCiEkMw+Djd9zG2JOPVXooFaMoTK4tyyIWjVG/bCXONTfiRyLlIotbtmzhRz/6Ed/4xjd48MEHeeuttxgbGyOfz5/29ksZBJOXSYyPjzMxMUEul5uy9k1H832fXC5XTqmPRqPMmzePjo4OotEoqVSKPXv20NvbSzqdPqKrxnQUmxQnJ5/Pk06nGR0dZXBwkPHxcTzPwzRN6urqiEajVdt/W4iZoq6ujs7OznKAYWhoiO3btzMwMFDzAYYSQxvFYIOJUuD7Hr4fEIZBVT4H33zorzFMLZkLM4QstxS1QDIYhDjKuttv569uuQ3/zrsxnGilh3PmFfdrpmkSi8UIgoCmm25lNJ9j8NmNJEyjXD189+7dKKUIgoDe3l6WLFnC3LlzaW1txXGcwt2d5I6ysbGRZcuW0d/fz8DAAOl0mt7eXhzHKa+TT6fTpFKp8hr5Uv2I0r9PZMJ/9IGg53mkUinS6TTpdJqensKykLfffptkMglQTp0vZTtIKn3lhWFIGIblJRG+7x9RayESiWCapmQuCDEFSoHfOXPm0N3dzejo6KxaIgHFfVoxg0GpEKUL+5IgCFBKo3VAYclE5fcN+w7uYsuuTXzxV78mAdYZpBreO0KcDgkwCHGU9vZ2PrDqUl58/hnaP3htpYdTMaUAAxTO0HD7XYx3zuPQA//KoqYEdfE4IyMjvPHGG+zatYslS5awZs0aLr30Ui6++OJTrs3Q2trKqlWrGB0dZfv27ezbt49du3bR2NhIe3s7AOPj44yMjGBZFpZllesglL4+0clk4YCwcCDoui4TExPs37+fnp4eNm/ezDPPPMPevXsZGxujs7OT1tZWEolEOchQjWerZptS68lSgCESiZBIJModIEoXIcTpMwwDrTXz5s3jwgsvZPv27QwPD5cLqc4GWutyJl3h80Xh+0Gx6COALl5XfpL47GuPsHjucupiDZUeihBiFpEAgxDH8ckN63nmi39CeNWHZm0kuVAPwSDiRKjz6wtna9Z8AHtOJz3/8Fcs0pr29nby+TwTExP09fXx4osv4rouhmFw9tln097eTjR6clkg9fX1LFq0iMWLF9PZ2cnQ0BATExMAWJbF7t27ee6559i9ezeRSIT29nYWLFhAS0sLpmm+5+tVyj7IZrOk02lM06ShoYFkMsnevXvZsWMHu3btYvv27ezcubPcmrKlpYVzzz2XJUuWUFdXJx0GqoTruqTTafL5PJFIhFgsVu4CIYSYWqVgXXd3N1dffTXZbJY333yTgYEBtm7dSldXF3PmzKm6z8ZSplMQBMcssysFiienpr/b+JVSBEcshyjVZFDlgGcYarQOi1kOlXkuDo328/zrj/C7d3+pItsXp8bzPPr7+1m4cGGlhyLEKZMjMCGO47LLLqPJy5HatYO6s5ZWejgVpLAtC6O+vlh9X6GXLse/95fY88//h+uXLaOlpYWhoSH6+/vZsmULw8PD5XoGq1evPukAg+M4tLW1MW/ePObPn8/BgwfJ5XJkMhn6+vpwXZf+/n4aGhqIRCKcf/75XHfddcTjceLx+HuerfZ9H9d1GR0dpa+vj3g8TiQSYWxsjJ///Ods27aNvXv30tvbSy6XK7fPnDt3LpdffjkXXHABzc3N2LZddQfRs1E2m2V4eLjY/aRJCjkKcQYsXbqUjo4Odu/ezU9+8hP27NnD448/zhVXXEFbW1vV/Q2WAgul4q+lgAMcDj6UAgyTl9gdL+AQhiE5N4OhNCGl+yhuIyxmMhQDC7ZlY9uRM/xoIQh8vvbDv2BF90Us7Dz7jG9fnLqI7ZSzR4WYqSTAIMRxaK351Lp1/MXGx2ZvgEGVrjSmqXGcCEFQRwj4K84jvHkdzz36INeuuYIFCxYQj8fJZrMkk0leeeUVLMsqH2jW19dj2/YJbba0xMFxnHLbSqUUvu/j+z5DQ0NkMpnymerm5mbS6fQ7npU6OghQOngcGxvjzTffxLZtstksu3btYvPmzbz11lsMDQ1x6NAhXNeltbWVpUuXcvHFF7N48WJaW1vL65BF5XieVy7IaRgGtm0f0eZUCDF96uvrqaur45JLLuHDH/4wvu/z7LPPEo1G6ejooLm5maampjMWhC193ruuSy6XO+7PJ2cxvNPP3quGTxiGxOqi/MUDn8WyrOPdgCA4nAlhGTZ3vP/XOGveijMakP7J89+lf6SHL2745zO2TTE1/MCjvr6+0sMQ4rTIkZgQ7+DWX7yZP/37r+Il78Us9vyezUzTIl5XOCvjex7hlVcx8vYbPPviS/zSJz7OvHnzsCyLXbt28cYbbxAEAfPnzycajdLd3X3CAYaS0tmmyd0agPKSDNd1icViZLPZ8oHhidRE0Fpj2/8/e+cdH0d55//3bJ2t2l31bkmWbMkNV4wRYAhgeiCAKWkQSLncped+uUvvCckll567Sy4XkgsJJCEXese2jI0rttzkKsvqXbvaXmZ+f0gzSLbc1fW8XyzWzu7MPDu7M/M8n+f7/Xwt9Pf3s2fPHhKJBD09PdTX17N161YaGxuJx+MkEgmSySQFBQWsWbOGVatWkZ2dLcSFSYLmmaGqKi6X66JKpAoEgvNHkiRWrFhBdnY2f/nLX3jiiSdwu91kZ2dTVVWl+9WMJ6FQSC9lPHTfQ8sgD02lO7nU8MlRDRpDRYg/PfHYiN4uQ4VwTeioq6vjkW/+G5f23MiqhdePy/HY+NbLPP/mH/n4Pd/AZDq/+65g4tE8ngSCqYwQGASC0+Dz+bj+imrWbVpPzvU3T3RzJg49kkHCZDRhtVpxOgciGZJr30vHD7/F+g0bWLF8OSUlJUiSREdHB+3t7WzZsgWDwYDNZsNkMmGxWM55EGg2m3G5XNjt9mHrqKo6zFBMm62KRqN6ykNaWtppBY1oNEogEKCjo4Pe3l66urro6uqipaWF5uZm/H4/qqrqpTo9Hg+FhYWkp6cPhMYOpk1oD5EmMTFoaThms1mPdhHlQgWC8UE7z7KysnC73bz88sv09vayf/9+bDYbyWSS9PR0nE4nDodjzM7LVCpFMpkkkUjoovBI14KTBYahn+Fkcfrk56dLpTiT0ByPx7FYLCxfvpyf/eeP+fIXvkbvpk5uXHkvRuPYdb3Xb3+ev67/Lz58xxeZXTBvzPYjGDsMkkEYSAumPEJgEAjOwAP33sPLX/gy6nU3iYHLINrAH0kimUigPPwxNvzHD+np6+NDDz2E0+mkpaWFxsZGNm7cSDwep7CwELfbjcfjOWeBwWq1kpGRgc/no6Gh4ZTXU6kUkUiEeDyOwWAgFArR0dGBx+OhoqLitAJDf38/x44do6WlhWQySVdXF7t376a3t5dgMKjf2E0mk+7rIMsyqqoSCARIJpPYbDZkWUaSJDFrPkFoES5aJZFzMfgUCASji5aWpEUQHTlyhNbWVmRZpri4mMLCQhwOx5jtP5FIEIvFCAaDBINB0tLS8Pl8I5rwnmsJ49Nx8qDvTNvRPB+i0SgFBQX88Mff50tf+CpPvP5L7lr9YcxjEFmwcedL/HX9r/jHO7/KnFmLRn37gvFDRDAIpjpCYBAIzsDixYvJtZrpP7gf99wZPhsw2Jca6LiZsck2Ek4XFM+Cj/0L+3/xfWpqaigvL2fu3LmYzWb279/P4cOH2bx5MwCXXHKJPvA/WyfP4/FQWVlJR0cHx48fJxgM6uZc8PZskhYpMWvWLMxmM1arlVQqddrtms1mHA6HHhkRi8Xo7u4mEAgMe58WKdHZ2cnu3bvp7+/H6/XqURWZmZnk5+frZkxicDv+aL8FUYpSIJgYtEiu4uJiLr/8co4dO0ZDQwO7du3C4/Fw2WWX4Xa7h0UZjQapVApFUYjFYoTDYVRVRZZlrFarXiJ5oiOahvoASZLEdx75Jj/64U/47QuPcO/V/4TL4R21fR1rrOPPr/8nH77jC0JcmAaI/oRgqiMEBoHgDEiSxENr7+bbr74iBIYhSJKExWLBneZGMkgkMzNJ3nEvTz32G25RFFavXk1mZiatra20tbXx0ksvkUgkKCwsxOv1ntNgMCMjg2XLluH3+9m1axednZ2Ew+FTxIPjx4/z17/+lYULF7Jy5UqKi4vPeHN2Op2UlpbS2dmJ1Wo9JddWI5lMEg6HOXToEKFQiKKiIkpKSvB4PMiyTGVlJWlpaciyLAa3AoFgRjN//nzuu+8+nnnmGU6cOMHevXtpamoCBipOaJV3RgvN5DUcDhMKhXC73bjd7sFqR5PneqxFeBiNRkwmE//8uc/w37/+Df/97Hd5z7WfJsOTfdH7SCYT/Pa5H7J66S3MK106Cq0WCASCi0MIDALBWbjt1lv51s9+QcLfhznNM9HNmXiGRDJYLBbsNhtxpxMWLaVn5zZe27SZ2bNnYzabmTNnDg0NDbS1tXHo0CHq6+tJS0vTywmeCZvNRnZ2NhUVFaxYsQJVVamrq8Pv9w97XzAY5Pjx4xgMBqxWK2azGafTqadlqKpKJBIBBqIXYrEYgUCAzs5OOjo68Pv9wzwdtM+meU3k5uYya9Ys/eF2uzGbzfh8Psxms5hpmCCG5kOLfFWBYGLJzc1l2bJltLa20tzcrKfJbd++nYyMDJYuXcqSJUswm80jpi+cK9r5Ho1G6e/vR5Ik7HY7NptNj1yYLAyNotCiN1KpFB946EEKiwr51c+/w13VH2VW3sWVkdy6dz3xVIR3Xvn+UWq5QCAQXBxCYBAIzoLL5eK2667luZp15Nxy+0Q3Z9IgSRJGyYDVKpPmHjAmSt7/IIE/Pcr//PFP3HrdtSxatAiPx8OLL75IY2MjdXV1eL1ePZT1TBiNRux2OyUlJVx33XUYDAba2tpOERg0L4aGhgb6+/sJBoO6P0JZWRmpVIq2tjYkScLlctHX18fx48fZu3cvR48epaOjg0QioW/PYDBgNBpJS0ujoKCAxYsXc8UVVzB79mxycnKw2WxIkoQsD9Sqnkwd2pmGEBYEgsmB1+vF4XCwYsUK4vE4NTU1tLa2smPHDhobG0kkElRWVuqD7QtFKzMZDofp6ekhPT2d9PT0Se+Fo0UxaOL1DTesQZat/OSHP+PGpe9lfunyC9726zufonrhDZMqckNwcYh7m2CqIwQGgeAceN89a/m/j30S9abbkMRNfABp4H9GoxFZlkkmk0QdTqT7H6T7p9+jtraWxYsXk0wmsdvthMNhjh8/TmFhIcXFxWfdvNZZ8vl8zJ07l2AwiN/vp7a2lhMnThAIBIjH4/qMVjAYJBaLceDAAb2GtMfjwWAw0NraSjgcxmQy0d7eTl1dHbW1tbS3tw9Lu5AkCZ/PR35+PmVlZcyZM4f58+ezcOFCcnNz9egFwcSRSCSIx+Mkk0msVisWi0V0rAWCCcZisWCxWCgrK9Ovx62trfT19XHkyBG2b99OcXExlZWVzJ0794KF2Wg0ql+zHQ4HNpttVL0dxgItikFVVb2SUiKR4PLLL6e4uJgvfO7L9AY6qV50w+DnOPfPcuj4Xjr6mrlh5T1j9wEE44ooUymYDpyzwCBJkhHYDjSrqnqLJEn/BHwSKAMyVVXtGnyfBPwYuAkIAw+oqrpz8LX3A18c3OQ3VVV9dHD5UuC3gA14DviEqqqqJEk+4HFgFnAcWKuqau+Z9iEQjAWVlZWUZvjo2rsbz8LFE92cSYUW7irLMk6nEwDrDbdx4LHfYLVa8Xg8pKWl0d/fT2NjI01NTXppyXPBbreTn5+P0WgkPT2doqIinnvuOY4cOaKXKIMB1+V4PM6JEycIBoMYjUYqKyuRZZnm5mZaW1vp7++noaGBAwcO0NzcTE9Pj24cqVWEyM3Npbq6muXLl3PJJZeQm5uLw+E4rxKbgrEjFovR19eHwWDA6XQKDwyBYBKRm5uL1+ult7eX7u5uamtr6ezs5K233iISiXD33XczZ86cC97+0GpBubm5U07w1e6XbrdbT+v45ne/xne/9X38m7u5aeX9GAznfp955o0/sKJqtV56UzD1kUSZSsE04Hx6ZZ8ADgx5/gZwLXBy/bgbgfLBx4eAXwIMigVfAS4FVgBfkSRJs9D9JfDBIevdMLj8X4BXVVUtB14dfH7afQgEY4UkSXxg7d2E17860U2ZfEggGSRMZtNAh0m2YimtIGI2s3v3bgKBAC6XC6vVSnd3N11dXcTj8XPevMk0sN2srCwqKyuZPXs2Pp9vxIGlqqqEQiFaW1s5cuQIe/bsYffu3dTW1rJr1y62bdtGbW0t9fX1dHd3E4vF9Bu5xWLB7XZTUFDAggULmD9/PiUlJWRlZeFwOPTcYcHEkkql9MgVi8UifDAEgkmEzWbD6/VSWVnJNddcQ3l5uX7t37NnD21tbSiKct7eKdFoVPfLsVqtyLKMzWabUgProZ4MZrMZm82GzWajqKiIH/30ByQcPTy7+bFz3l5jWz31rXXcec0Hx7DVgvFG8+oQCKYy59RbliSpALgZ+LW2TFXVt1RVPT7C298J/E4d4E3AI0lSLrAGeFlV1R5VVXuBl4EbBl9zq6r6pjpwt/kdcPuQbT06+PejJy0faR8CwZhx0403wtE64j3dE92USYnRYNS9FUxmM6bLruLVDTU0NTXhcDhwOBwEAgF6e3uHeR6cK1arVc+31SIKRhrwK4pCIpGgtbWVLVu2sGHDBrZu3cqOHTvYuXMnhw8fpq+vTx+kDt2+z+ejoKCAiooKCgoKkGX5oo6JYPTRcrBhwLRTRJUIBJOP0tJS1qxZQ1VVFU6nk3g8TltbG/39/Rc0OxsOh+nq6kJVVTIzM3E4HGPQ6vFF8wXKycnB6XTy6c9+guM9tTS0HT7rui2dJ/ifp7/P/LKlyBZxn5pOiAgGwXTgXKfjfgT8P+BckoLygcYhz5sGl51pedMIywGyVVVtHfy7DdDq+ZxuWwLBmGG327nzhhvoqXl9opsyKTEYJIwmI2azGYvFjH3ZSg43DziJ22w27Ha77qMQjUb11IRzxWg0YrVaKSgo0FMYcnJyThvJoIXnahELbW1t9PT0EAqFRty3LMtkZmaSlZWFz+fD4XCIiIVJjDYTeDGO9AKBYGxwOp1kZ2eTk5NDbm4usizrUQidnZ0Eg8Fz2k48HicYDOpRb9rMv5YaoV0HphInRzLIsowsy/h8Ph768AO8vOMJFOX0M9gbd77EI7//NLPyy/ngOz8/ji0XjAcSTLnftEBwMmftPUuSdAvQoarqjnFoz2kZjG44L0lPkqQPSZK0XZKk7Z2dnWPUMsFM4t1r7ya+8XVUYcAzAhIGyYDJZMJqlbH4MlBnlVJ38KAezhoOhwkEAkQiERKJxHkbGUmSRElJCXfeeSe33HILZWVluFyuEWex/X4/dXV11NXV0d7eTjAYPKUc5VC0NIyMjAxkWcZoNIqb/CRkaOdcfD8CweQmLS2NoqIi0tLSUBRFr+LT19d3TutHIhF6e3tJpVLY7faBCDmTadqJvw6Hg4yMDK6//noM9gR7j24f8X1b9qzjz+v+k4dv+3+876ZPTbvjIBAVJATTg3O5Ml0O3CZJ0nHgT8A1kiT97xne3wwUDnleMLjsTMsLRlgO0K6lPgz+23GWfQxDVdX/UlV1maqqyzIzM8/0GQWCc6KiooI5eTn07RaeoqcwaH5tMpqQZRmLxYxl1VW0B0OkUilUVSWZTNLf309bWxtdXV3nlSqhDSgdDgcFBQUUFhaSkZGBy+UaMQ83kUjoZStjsdhpcxo1E8nMzEy9FObWrVvZtWsXHR0dRCIRkQ85ydDyt0VHTCCY3GRkZFBZWYnWB+vs7GT//v10dHSc8fxVFIVkMkkikSAWi2EwGLDb7XrFiOkmMGqRGSaTiYc//CCv1f6VRDLByfNq+45tZ3nVahbMXjExDRUIBIJz4KwCg6qq/6qqaoGqqrOAe4HXVFV9zxlWeQp4nzTASsA/mObwInC9JEneQXPH64EXB18LSJK0crA6xPuAvw/Z1vsH/37/SctH2odAMOZ8YO3dRDa8MtHNmLQYjUZsNhmrVUauqCJmMNLT00M8HkdRFILBIA0NDTQ3NxONRi9oH1otdbfbjcPhuKg8fI/HQ2VlJfn5+USjUfbu3cuTTz7JSy+9xPHjx+nv7xcCwyRCiAsCwdQhOztbL/MrSRJtbW3s3r2b1tYzd9lSqRSJRIJUKoWiKJhMJt17ZzpiMAxE/xmNRpYsWUJFVQlv7n2Fky9zLrsHf1D4QE1rBkuaCgRTmQuOrZIk6eOSJDUxED1QK0mSZgD5HHAMOAL8CvgogKqqPcA3gG2Dj68PLmPwPb8eXOco8Pzg8u8C10mSdJiBihXfPdM+BILxYM2aNUj1R4l1ibSbkTAYDJgtFiwWMyZZxnppNYeOHiUUCmG324nH4+zZs4d9+/bR19d33l4MQ3NXnU4ndrv9opzE09PTWbRoEatWrWLVqlUsWLCA/Px8PB6Pnus7nWbKpiqaqKAoij7oEAgEk5uMjAzmzp2rR4j19vZy+PBhuru7z3jdj8ViBAIBVFXF5XLpaWvT0XNlaESG1WrF6XTy0Y99hM11zxOK9A97bzKVxCCJtIjpjPBgEEwHzqtXrqrqOmDd4N8/AX4ywntU4B9Ps/5vgN+MsHw7MH+E5d3AO85nHwLBWCPLMvfcejOPr3+V3DvvnejmTDoMBgNmyYzZPFBCUF6+isbNG7BZLKSlpZFKpdixYwc2m41FixaRnZ2NJEnnHYWgzWjZ7XbdL+FCVP/09HQWL17MvHnzcDgchEIh2tvb8Xg8FBYW4nQ6RaWCSYSWaqOl3QgEgsmL1+vFbreTkZGBwWDA7/fT0NBwVg+GSCRCT08PXq8Xn883Y67BNpsNi8VCUVERq666lBe3PM67Vj+sDzj31m/ljtUPTGwjBQKB4CxMnQLCAsEk4v677+b3Dz6E8s67MEyhOtzjhSRJmEwmZKuMUlhMMDObjo52cnNzicVidHR0cPToUfbt24fdbqeoqAin03le+/B6vcyfP59UKoXL5aK9vZ1QKKQ/wuHwMP8EWZb1iAdZlrHb7dhsNpYuXcrs2bMpKirC4XAQi8Xwer1YLBacTqfuVi6YWOLxONFolEQioX93wuBMIJjcWK1WLBaLfs4mk0l6enoIh8Mjvj8WixGLxUgmk1gsFv0xE2Z0NaHdYDBgNpv50Icf5itf+jq/f+mHLC2/mrbOJvrDfSwovfS80vYGor+UU1PLJGlwuRYNJgEqqgrqyZ7qqooyuK5BkpAGoygkaaCsooSkb9MgDRgyDfXJGPgbfblAIJjeiJGRQHABlJaWsmBWMcd3bce3bOVEN2dSYjQZ9Q6lvOZWOn/zc3IZMLOKxWI0NzezdetWvTzX+QoM2dnZVFdXU1xcTGVlJY2NjbS2ttLS0kJzczNtbW20t7frHTGHw0FxcTE5OTlkZGSQnZ1NdnY2c+fOpaioSK9GYTQasVgsek6sYHIQjUbp7u7GZDLh9Xr1kGmBQDC50UL/HQ4H4XB4WNnJkwmHw/T09CDLMl6vF5vNNs6tnRzIskxOTg4/+Pfv8dRTT1Gz7nXe3Pom1196F9/9w8eQjCqSwYAEKIMCAjCYOjZcHFBUdXDQD4Yh10xVUTAY3q6WpKrqYOnfkUSAtwUCFVWvpKUob4sW2nLdJ2dgo0iSAZcjjbKc+Vw+/way0wsQnB4VUUlCMPURvWeB4AJ5cO3dfO6xJ0AIDMMZ7JcYjUZkWSaVSpJ2yVKUG2/n4CvPki5bkSSJQCDAnj178Hq9VFRU4HQ6kWX5nAf1siyTmZmpRxoUFhbS3d1NZ2cnnZ2dtLS06KJDe3s7WVlZLFy4kNLSUr1qhM/nIzs7m7S0NH2/JpNJCAuTiEQioT8MBgMWi0V4YwgEUwTtHNWE20gkoqc4DSWRSOhVI1RVxWQy6ef5TET73JIkceedd3LXXXfx5S9+jYxEEVulOM88+3ckSUJRFD1SwGAw6B4VQyMHgIuO9tJEg/Ot3qF91729vax7bR2//8O/cePS91JVskRcv0+DQZKEsbRgyiN60QLBBXLdddfx+Ue+T7SjHTkre6KbM+nQBAZVVUmlFKSbbqfbaKLx6T/jVBSi0SgHDhzA6/WyYsUKMjIyyMzMPOfBvdaR8ng82O12CgoK9A5qPB6no6OD+vp6amtr2bJlCxkZGSxfvpyqqiqKi4txu92YTCbMZvO0dSafDsTjcQKBAMAwEUqkRwgEUwctIux0XjnxeJxwOEwqlcJqteqpFTP1PNcqSmgCOsDSZZfw5nOHMBpNFBYWnmULkwdVVXG73ay9dy1Lly/ls5/4F3zuLHIybFPJAAAgAElEQVQzps5nGE8kySAiGARTHiEwCAQXiMVi4f7bbuV/179C7t3vnujmTDokJCTDQDlJh8MBEqhrbsYyq5T+J35HuOkEaiRMY2MjO3bsQJZlbDYbVqt1YP2zzG4MnbUZOsul3Zg9Hg8ejwe3243X68XlcjF//nwKCwvJzMxEluWx+/CCUSOVShGLxbBYLMMGHWL2SyCYOmjX6pPP22QySTKZJBaLEY/HdfNeq9U6Y8/zoVECQ9PAioqLeDW+FaPBqN8npwIDkwwDprzl5eV86B8/wB9+9Ss+ePMXB+/dM+87PhtCYBBMdWamNCwQjBL33n0X8U3rUZKJiW7KpMVkMmG323G73HjSPHiqFpD2yX/FsGwlitlCZ2cnNTU1bN++Hb/fP2o3VofDQUFBAStWrOCee+7h1ltvZf78+WRlZYmIhSmE1jmFAVFPmwUVCARTi6G5/hrxeJxQKEQsFiOVSmE2m3G5XOIaPQJtbW1YTQ6MxqnXdZdlGZfLhcFg4Morr2TuJSW8sPVxxDj6VCQkUYZZMOWZelcpgWASUVxczOKKCnp3bJ3opkw+BoykkQwSRpMRq3XASdzhcOBwpeG97wEMOXn09PRw9OhR6urqOHLkCO3t7ac1ADun3Q7O/mg5vD6fj4KCAnJzc3G73frMmGByo0UuJJNJDAaD7q4+U2c1BYKpjHZdPvnc1c5tzVvFarXq6QHiPB9OS1MrDov75PoOkx6tOobZbEaWZRwOB5/+7CdpDOxn79HtE928SYfK2SM4BYLJjuhlCwQXyQfuuZvohlcnuhmTHsOgJ4PD4cDldmF3peF45z1EjSba2to4evQoe/bs4dixY6ctYSaYOSSTScLhMMlkErPZLCIXBIIpzkjnryYEu1wuPB6PSF07A8tWLGXj/meQbZYpOcMtSRJOp5P09HQcDgef+uzHeGHHH/AHeya6aZMMVdzrBFMeITAIBBfJ1Vdfja2znXDTiYluyqRGkiQMxoEqAA67fSCaYeFiTMUlpFIp2tra2LZtG7W1tfT29pJIJKZkJ0owOiSTSSKRCKlUClmWsVqtotMlEExREokE4XBYrxKhpUlo5o/aQ0QunJ4bbriBZ174PzZveWNKRuFpkYVWqxVZllmwYAF33387f9v436RSyYlu3qRCnAOCqc7Uu0IJBJMMs9nMg2vvou/VFye6KVOCgeoSNmw2G7LNhuuO+5DcaXR2drJlyxZ27NhBR0cHsVhMCAwzGE1gUBRl4Lciy1OyUy0QCCAWi+H3+4lGo8M8GE5n/ig4FUmSKC0tnfLHSpIk7HY7Pp+P++6/F9mrsLH2hYlu1qRCmDwKpjqityYQjAL33HUXyR1vkgwFJ7opkx4tH3OguoQdd9V8LFULicRidHZ2cuzYMWprazl69CiRSGSimysYZzTvBW2mU5vhHOqmLhAIpgaasDDUyHHoAHmoN8NUHzgLzh3Nj8FisfDP//IZth97hcb2o2JgjTB5FEwPhMAgEIwCGRkZrKm+nK6adRPdlCmD2WzG6XLhcDhw3nEPiiuNWCxGS0sLmzdvZteuXQQCgYlupmCc0SIXEonEMHNHgUAw9QiHw3R1dREMBkkmk7poKMSEmY1m+mixWCgoKOAzn/sEf934X0Tjwn9JkgxCaBFMeUSvTSAYJR56z7uJr38ZVSjPZ2awuoTRaMBqsSDLMs78Ilw334Fkd9Dd3c2ePXvYt28fra2t9Pf3k0yK/MyZQjKZJBqN6qkRmveCmOEUnA9ann8ymSQej5NMJlEUZVj+v2DsaWhoYP369Rw7dgxFUSgsLKS6uppZs2ZNdNMEE4R2LTcYDFitVux2O1dddRWrVi/j7xsfJZVKoaoKA/UUxuIxuZEkRASDYMpjmugGCATThQULFlDq89C1ZxeeRUsmujmTHkkyYDRKWK1WHA4HyrU3Etm8gd5jh4lGo+Tk5NDQ0EBGRgbZ2dmYTOJyNRNIpVJEo1EsFgsul0uUFRVcEJqQkEgkiMfj+mypmD0fXw4cOMCTTz7JoUOHUFWVqqoqbrnlFhYsWCC+B4FeltTv9/OBhx7gC//6ZR5//RdcVnU9Tpsbg0FCkgxII8yHar8fSZIGBQlQURn4b0BIkJAGymUPrm8wDM5wICFJg68P3ygSEioqEm+L2gaDcfD5QN9FkgwYJMMY3ZskIYIKpjyixy4QjBKSJPH+d93BV//2tBAYzgVp4JiZTWbsdjupVAr3fQ/Q+5NHCPn7aG9v5+jRo+Tk5ODxeLDZbBPdYsE4IKIVBBdDNBolEonQ0NDA0aNHCYfDxGIxPSRbExhkWcZms+F2u3G73fh8PrxeL2azWVQyGAXa29tpa2tjz5497N+/n3A4jMvloqSkhGXLlpGfny+O8QxH82OSpIGJBq/Xy/d/8F3++NifWFfzJ8LhMKo6MJt/8j1BExBPHohrA37tX0VJaXsbfP+gEKGCFs2gbUPblLY/bbmiKCiqgjokAiqlKANRBqoKSBgMEgZp4BpjMg48DAYjBkkTNAfFEDSBQtv+222QJANGg5GmjnrC4fB5pYhqEQ9axSXt+AoEp0NRFBKJBMlkcjBqaHTLowqBQSAYRW666SY+/91HSPj7MKd5Jro5UwKTyTgoMCRxzplHZOWVRDe8jN/v59ChQ+Tl5VFaWorHI47nTMBgGChlKknSMB8GgeBcCIVCdHZ28uqrr/Lkk0/S19dHPB4fNkCRJIn09HQyMzMpLS1l9uzZVFVVMW/ePJxOp/i9jQL19fVs2rSJ2tpa2tracDqdZGRkUFhYSEVFhRCMBTpaVQntur/2nru58aYbCIfDeklLu92OLMsjrnumZcqgEKAtGypKjEaUgLY9RVFIpVLE43E9JUsbuA1930iiyNC2aevk5+fT3d19zu1IJBLAgB+YdhwFgjOhKArhcFgvH5xMJkc1wk8IDALBKOJyuVh72208s/41cm9710Q3Z0ogSQaMJgmL1YrdYSd97Xtob2umteEYBw4coKioiMsuu2yimykYJxRFIZlM6pUjxGBPcC4Eg0ECgQD79+9n9+7d1NTUsG/fPlKpFFarlUQiMWy2pq2tDY/HQ0dHB01NTRw/fpy6ujqcTic2mw2Hw4HD4cDr9eLz+XA6nTidzmEiheBU+vr66Ovro7a2lpqaGurr60kmk5SWlrJs2TIh4ghOQZIkTCYTBoMBm82mVxrR0pq0ihPazPzZtjUUzXdFYzp6sKiqitFoRFVVYrEYfX19yLKMLMsiGlAwLBJHE780UUwTFcxmMyaT6RQh/mIQAoNAMMq89561/O1jn0S95XYkkTt+TkhIWMwW3C4XqCrRD36c3p//G7v37GH27NmiXOUMQitTKUmS3rkUHSTB2ejt7aW+vp7XXnuNp59+mra2NkKhED6fj+zsbCKRCH6/n0gkQjQa1VMnenp6OHz4MFarFZvNht1ux263k5ubS15eHlVVVVRVVVFcXIzNZhNeMGeho6ODuro6tm3bxhtvvEEsFsNsNrNkyRIeeOAB8vLyhKeKYEQkScLhcCDLMj6fb1hqhMFw4X4H001QOJmhhrY9PT309fWRnp6up4QJMU8AA32rYDBIKBTSzy3tfqelBsLoiefiTikQjDJVVVWUZabTKcwez43Ba5nRaMRqlbFYY5idThz3PUjgh9+gs7OTlpYWcnNzcblcWCyWiW2vYExIJpMkEglSqZQ+cyVKVApORhssxGIxotEofX199Pb2cuDAAfbs2cPWrVs5duwYXq+XJUuWUFpaSmlpKYlEglAoRCwW08OYE4kEvb29dHd3EwgE6OnpoaurCxjwEGhsbKS9vZ3jx4+Tk5NDVlYWbrcbp9NJYWEhRUVF+szrTBfBtFDbvXv38vrrr7N37156enooLy9n7ty5rFixgvLy8hHD3AUCeDuSQYh454d2TTQajXoESDKZJBAI6JEfFyPQCKYW2u9BS9lJJBJ6xIKiKJjNZgC9iotmtDra5504iwWCMeDhe9bypb89IwSG80AL0zKbzBgNRowZWUhmC11dXXoHv6SkRAgM05R4PE4wGERRFD3fVnSIBCOhqirhcJju7m4OHjzI/v372bFjB9u2baO3t5d4PE5ZWRnXXXcdCxcuZN68eRiNRt3ISlEUIpEIoVCIAwcOsHfvXo4cOcKxY8fo6enB7/fT1tZGZ2en7iWgddTz8/MpLCzk2muvJSMjA4fDIX6nQH9/P21tbWzbto2nnnoKv98PwOLFi7n33nupqKgQx0ogGENMJhNpaWnYbDb8fj/d3d2kpaXp6Sbi3Js5aBEtfr+fUCiEqqqYTCacTicej0efvNEeYyGQC4FBIBgD1qxZw5d+8ENi3V1Y0zMmujlTA62qhNmELMsDudNLVnCidju7du0iLS2NrKwsXC7XRLdUMAZo5SlNJtMwwy/BzCQWixGLxWhvb6e1tZVwOEwkEtF9FPr7+/H7/Zw4cYKGhgaOHTumRzotXryY6upqLrvsMkpKSvRIg6FEo1Gi0ShOp5P09HRmz55Nc3Mzfr+f/v5++vv7CQQC9Pb20tPTQyAQIBwO4/f7aW9vBwZ8H7Kzs0lPT9eFh9EOM50qHD58mJqaGnbs2EFLSwv5+fksWrSI5cuXs3DhQr1TO9bHRVEUPv3xT5GIxFAUddhyzXDvXDk5F3lo2wdm242nvP9064xkLniyN8BAO9VT3n86tBlJFBVFVWhqbeaGW27iM//8mfP6nIKpzdDfmCYkxONxUqkUiqIQCoV0T6OpivZZtMHwdPWW0M75i/lsQ41HtYpJMBDhMp4RLUJgEAjGALvdzt0338Rf1r9K7rvumejmTClMJhMOhwNFUYhdvYbuHVuoqanB4/GwZMkScnJyJrqJgjFA817QqkgI74WZTSQSoaenhx07dlBTU0NLSwtdXV2EQiGi0ahu2qgJEfF4HFVVmTdvHrfffjvz5s3TZ81H6lhbLBZMJhNlZWXk5+ef4v7e0tJCY2Mj+/fvZ9++fZw4cYKmpiZd2Ojo6GDjxo2UlZVRUVHBO97xDnJzc6d0J/5i2LNnD7///e/p6OhAURQqKyu56aabWLJkCdnZ2XpY7lgTi8XY8Mo6vn7jpwDQLiESEgbDuQscugjACIN8dWBAr5xsHsjZqxPowgPSiM+HttkgnXkAIEkGDJpHgWTgzeRO/vDo74XAMMMxGo24XC5kWSYQCBAIBKb0gFxVVf3arBk/T9f0SU0cGOn7Oh8vES1iweVy4fF49O2N53ETAoNAMEbcf9ddPPbwh1HfeRfSDO10XghGowmbzUYikcCSlYN83c0ce+HvHDt2jKNHj+J0OvH5fOfkKC2Y/CQSCb3zoOUDTtfOg+D0RKNRIpEIra2tNDc3097eTkdHB/v27aO2tpbu7m78fr8+k2WxWLBarbhcLnJycvD5fKSnp7Nq1SqWLVtGbm6uPms+EtoMjiZoamgdPJ/PR1ZWFunp6RQUFOjtaWpqorGxUU+fCIfD9Pb2oigK/f395ObmkpmZSUZGBpmZmecd0XC6TuTp1j9dybvxQnPp7+vr48SJE7jdbubPn8/ixYtZsWIF+fn5upv9eGG1WFleumjc9jdZKMks5IX6jRPdDMEEMfQc0yIZbDbbsDKdUxGtSobm06R5CpjNZr36wXThdALDhRiVahELWjToeP8Gps+3IhBMMsrLy6kqKqDxrW34lq2c6OZMGYwmIzaDjXg8jtFkxDx/McGnnmTHjh0sW7YMl8uFzWYTAsMUJBAI8P7338Ndd93Pffe9Ww/l9Pv9GAwGXC6XLjAIZhahUIj29nbeeOMNXnvtNRobG2lra9NdrzX/BFmWcblcuqCQl5dHYWEhFRUVVFZW6maMFovlgkUqSZLweDw4HA7y8vJYsmSJbpa1bds2Nm3axPbt2+np6aGzsxO/309raytvvPEGixYtYunSpSxevFifOZrOYpmqqrqpXDKZpKioiJUrV7JixQrmzp0rTB3HFWlYVIVgZmMwGHA4HNhstoluykWjqqpuyhsMBjGZTFitVr2s8HRCExhGg/FISzsdQmAQCMaQh++9h8899gQIgeGckSQJyThQopCGoyS//wglaaV0djTy5ptv4nK5KCoqIi0tTX+/YGrQ3d3NkSN7eOyx73DiRAMf/OBHsFgsxGIxLBaLSI2YQWgpCc3NzRw/fpzW1lba2trYvXs3O3fuxO/3Ew6HycjIoKysDK/Xi8fjwWazYbPZcLvduN1uMjMzyczMpLCwUC8leTH+Hdp62uyY3W4H3p5BSiaTmM1m3XehqamJlpYWQqEQPT09uj9Ea2srR48e1aMgtPZqA3LN50HzlNA8IbTnWidTCwc2mUx6moHW2daqrmjGqHa7nYKCAgoKCpBleUzPJS3//8iRIxw+fJj29nby8/NZuHAhV155JRUVFbhcrmktrkw2JGn6l2QUnDtaVY7pgtFoxOl0DvNhSCQSAPpMPYg+4WRh+vzyBIJJyLXXXovxke8T7WhHzsqe6OZMGM1P/42u55/GXjqbsk//yzl1Oo1GI8Gf/YSbl9xHWfYCXtj+B9a9vp7MzEyqq6tHVeUVjA+JRILi4nQ+8ck8nnjiL9x//3P87Ge/BdDrME+nDpHg9MRiMfx+P2+++SbPPPMMjY2NdHR00N/fTzAY1CMVFi1aRHV1NRUVFZSVlemRCdpvRRt4a+k1Yz2gLSoqIiMjg3nz5rF69WreeOMNNmzYQH19PSdOnNDNJrdv347L5aKyspL58+dTUlJCSUmJbmba2NjI8ePHCQaDekWM7u5u/bl2fdNSQRwOh57KoSgKwWBQFygSiQTZ2dnk5uZyzTXXcN111+Hz+cbU90BRFJLJJDt37uSJJ54gmUxSWVnJsmXLqK6uxul0iuvzOCMhgRAYBNMUzVvCZrPpgmx/fz+KoujXO3HNmTyInpxAMIZYrVbuv+1W/nf9K+Te/e6Jbs6E0fbLn1PsKSG4aRe7192IvGwJnpWrSFu0BDk7d8RBQXDPW7gkmcsXXktvXx9Xzr+N468NlJTbtWsXZrOZ/Pz8YfnTgslNMpnEYIC0NAsPP1zEM0838eEPv5f/+Z/HsVgsExrOJxgftBn6gwcPsnPnTjZt2sSuXbsIBALE43EyMzOZN28eWVlZZGVlMW/ePBYuXEhhYSF5eXkTJkBpv0stUsBut5ORkYGqqjgcDhobG2lubqalpYXm5mb6+/vp6OggFovR19fHsWPHyM3NJZVKkUgk9OoYWkdZM2PT/Ei0/WleEZqAAgODe83cMpVKkUql6OnpobW1FUmSCAaD5Ofnk5OTQ0FBAfn5+bqL/IWeX5oA0tXVRWdnJ5FIhFgsxvr169m9ezcrVqxg1apVLFiwgPT0dJHmNAFIkoSiKhPdDIFgTNAiMgwGw7BIHS0azu/364KzJj4LJg5x9AWCMea+tXfzm3e/F+WOtRhM4+OkPdmwpKVx1eKbWbnwao411fFG7cs0PPoXDvf+hHgihsnpYNY3vo134WJ9nd43NzI7fx42m51oNIaS8lFdeQubDzzDhg0b9Jx9ITBMHQY6BgN/q4qK3S6Rn1+Aw+FAlmURTj0DSCQSBINBtm/fzqOPPkpjYyM9PT3Y7Xa8Xi/Lly/nyiuvpKysjKKiItxuN3a7fdJ5c1itVsxmM0uWLGHOnDl6ysPmzZvZsGEDBw8e5MiRIzQ3N9PZ2YnZbMZisegmXpq5qWaSONS80m636yHA8XhcFxOGli7UjFC1SI5IJEIwGKS3t5ctW7Ywe/ZsysvLWb16te4ncTHHr7W1ldraWnbs2MHOnTsJBAJ6RQ2/309+fj533HEHHo9nws/jmZomYJAMM/azC2YOkiTp11+bbcCvq6+vj76+vmECsBAYJhZx9AWCMaaoqIglc+ZwaPsW0ldWT3RzJgTrypXs3PcGly26hrLCSsoKK/XXEskEz9U8zkvf/jqu3/wvJruDcGMDwdfXsWjVh7BYLDidTlRVYf6sFexv3Mrzz79AZmYm8+fPx+v16uH1gsmNwWDQ67zHEymefdbPV77yEaxW60XlzQumDu3t7Rw8eJDa2loOHz6M0WikuLiY0tJSysvLWbRoEZdccolu1jhZz2vNGyEtLY20tDR9Fi2VSmG1WikvL+fEiRN69QnNJFIbAGozbZmZmeTk5GC325FlWX8MFRi0dZPJpL5/TbDQIhw6Ojpob2+nra2N1tZW3RAtkUjQ3d1Nbm6unkahldPUcplHQhMzTpw4QUNDA7t372bXrl0cOnSIgwcPkkwmURQFl8tFSUkJRUVF5ObmTninXpIkmMGXESEwCKY72rVxaNlFm82mp5XF43FgoPS15u0kGH+EwCAQjAMP3buWT/znb2CGCgxF976X2vfdy84Dm1hSuWrYa2aTmZuq19La08Ceu+/A4vUS7+pm9cKbWLHgSkDC5XKhKAqhcJhrFt7FExt/Sk1NDatXryY3Nxe32z1pByIzkTfe2MjLLz/HggVLmTt3LuXl5VgsFhoaGujvj4EK69e3U1a2lKqqKmHuOINoaGjgtddeY+/evYTDYWbPns3SpUu5/PLLqa6uxufz4XA49DJrUwUtBaGqqopZs2YRCoUIBoPDTCtDoZAesaC9vmzZMlatWkV2djYZGRmniKXaQF8TGjQ03wmNAwcOsGfPHjZt2kQgENBn9E6cOMGrr75KVVUVCxYsoLq6Gq/Xi81mO+PxVVUVRVHYu3cvTz/9NHV1dRw8eJBIJEI0GsXpdOLxeHRvieLiYnH+TjDC5FEwE9GEXrvdTigUIhqNEo/HCYfDeDweITBMEEJgEAjGgauuugr5W98h0tKELa9gopszZhz771/S99KLOObPZ86XvqkvDx47jMVgxpeWOeJ6ZrOFD7/r85xoPUJPoJvi3LIh71WRJBNW2YrDbic7vZBbVzzEC7t/x/r165EkiUsuuYT09PRx+ISCc+GZZ57kwIG/0t7+PL/9bYq2thgrV15FTc0G8vKShCNxXnihn29/66PYbDY9r1Iw/fH7/dTX19PZ2UkymSQvL4/q6moWL15MSUnJlC0/q/1+XS4XLpdLL9toMplwuVyEQiEikYguGESjUSKRCHPmzNEjsdLS0oY5pA9FqwGvcXKOsclkwul04nK5yM/P59ChQxw6dIj+/n66urr0yhXBYJDW1lY8Hg9ut1tfX2uXNkDVhITNmzezbds2urq6CAaDlJaWMnv2bL2qR25uLnl5eZSWlp4xIkIw9ogUCcFMQrvWaKWAByIkFb26hKIoRCIRPapMi5IUfY3xQQgMAsE4YDabee+d7+LX617Bdv8DE92cMaN//TosfVF6Xn+N5KeCmJxOFEWh+d9/wJ3VDzErr/y060qSRHFeOcWnvEdCksBiNuN0ulBVKMwso8g3l8f+8EeMRiOzZs0SAsMkIhzu5/o1BSxblgFALJbib09uY8WlMg88MJvnnm1iwfxVLF26dFyc/wWTh0gkQnd3N6FQCFVVKSwspLq6mry8vDGtejDeaKG7s2bNIicnR/dY0NDKPGplN7UIiLNt73Tk5ubi9XqZM2cOa9as4dlnn+Xvf/87DQ0NhMNh2tra6O3t5ciRI7z88svk5OSQm5uLJEl6tIL2UFWVnp4e/dHb24vFYsHlcnHFFVewdu1avF4vbrdbjz7SfCMEE4eEJAQGwYxFS5WwWCwkEglisZjuE6MJqhOdwjWTEEdaIBgn1r7rDn5x11pSd96HcYrO0p0JRVFQEwluufw9HGs7wK67bsP5jncQ3bePTMlN9eLrLmr7RqMJm81GIpkgHA5zeeXN/O+673HgwAF6e3uJRqPCi2GScHIn12o1cu99RcDATOxrrwX53vce1t2eBWOPqqocPXqUjTUbObB7Hw63gy9+9cvjJu5ovwmtqkI0GsVoNGK320lPT8fpdI5LO8YLLQpBMxwbre2dDq3KhMvlQlVVLr30UpLJJK2trbovQ2trK729vdTX1w+rOqFFLmjigqqqBINBgsGgXi2jvLyciooKVq9ezbx583Rj1skoKszkQfZM/uyCmc1QTwYtmkqryJNKpQgGg5jNZv0h+h5jizi6AsE4kZeXx6pLFrF76yYyr7h6opsz6nRtXIccTHD5JddyrXwb9U2H+N5jn2XF3NXcd+dHMF1kBQ2j0Ygsy8TjccxmM06bmwXFq9i/b0BgCIVC08aLob+/H4PBMGUrZAyYLY38Wnd3DLBRUlIyKQcn04lkMsmrr77Kxtdr2LppC4a4yrK8+SzKns2/PfErPveFfx33lIRYLEYgECAWi+kVEMTvYPTQhIgFCxZQXFxMJBIhEonw5ptvsnHjRvbu3atXfujv7weGD0pPHqBmZGRQVlbG9ddfzw033IDP5yMtLW3SXmdn8m9J++ya2Z1AMFMxGo16pYl4PE4gEMDv92OxWLBarTidTiEwjDHi6AoE48gH7r2Hj/z7T2EaCgwdf/gdy2Zfhk0emK0rKajgR598HIt5dGa5tI6z2WxGtsmkUkkqC5fx5Jat1NXV4fP5KC8vn/Jh1vv37+e2+9+N1WajvLwC2WLm4x94kFWrVp195XOkpaWFF198gfz8AmbNmkVRURGyLI/a9oFTBAZVHej4Hq8PMmdOpZ4PKTrCY4OqqnztS1/l2OYDrC69lNvf8SnyvTn68f73Db8Z02OvhdtrhoPd3d10d3dTW1tLIBDAbDbj9XpxuVwid38U0Y7jUC+IVCpFMpnEarVSUlLCvHnziMViuqfD0Fxm7W9tli8vL4/8/HyWLl1KaWmpLiyI72vyIQQGgeDt80Cr9CNJ0rAqE6lUilAoRDweR5Zl3QRSnDOjixAYBIJxpLq6Gtc3vkWooR5HcclEN2d06QuwcMGlwxZZLbZR343JZMRus5NKJvG5sjAbZDZs2IDX6yUvL2+YcdlUJCcnB0m24f3c12kLBuisWcd7/uGjzC6v4JKquSyaM4e5c+dSWrXAsEAAACAASURBVFqK3++ns7OTvLw8EomEbsym/av9rT2CkQhbt27ixPH9FBZEsNldtLcl6OiI4vOlM2tWGWVllZSUVFBcXIzT6dRnmYc+Tl6mPX873P7UMF1VVVFSKdat83PzzWv0EnuC0aWlpYWXXnqJfbv3Ur/tIP92x+exWU4Vj0xGE8lkcswctlOpFIlEgtbWVg4ePMjevXvZu3cvBw8eJBAIkJGRQV5eHl6vV/wOxhAtZLiiooKCggJCoZDutB6NRoeVfNMM0LQ685oxmjbjN1mjFgRvIwZJAsFwjEYjTqcTWZb1615/fz+qquLz+USViTFCCAwCwThiMBh44K538fN1r+B4/wfHdd9KMok0qOaODRIm49hfUgwG45Cyhgay0gpoamrSa75PdXw+Hx6HA4PFgnvuPNxz55F638MEm07wYkM9z23ZhfTkU0RbmrC43CDbSPl7Mco2DFYrksUKg/+qFguqxYpqtqCYLUhWK23rXuZ9azO4774qfZ+KotLZGaWtrYHW1jo2bFDp6FCJxxVSKUilVJIplVRKRUmppFKKvkzR/1YwGAyYjEaSyQSXLM4/6ZOpdHRG6Oiwcscd78JqtYrO8Cjz/HPP872vfYcr8pcw11vAR955+4jiAoDZaBrV80Uro9jc3ExDQwN+v59gMEhLSwuNjY3U19dz7NgxAoEAJpOJ2bNnc+WVV1JZWSk6eGOIJhw4HA4cDgc+nw9FUfTv62SB4eQIhqHRDYLJjzB6FAiGI0mSPgEy9NzQjCD9fr9+vdOiHgQXjxAYBIJx5s477uBHj76L1D3vwSiP/gz/SIQa6mn5jx9jn7eQvHc/ODadRUUZl5lIg0EanDUfyN22W5wEwr3EYrFp0bE6cuQI/SkFT3qGvsxoteIsK8dZdvoqHOdK37rnMZk6hi0zGCSys21kZ9tYtOjCtjtgEgeplEIspuB0mk553ek0EY+HCYVCpKWliUHLKBGLxfj+dx5hy4sb+db1n6Ysq/iM71cUhUQqOarpRFpHbevWrTz77LM0NTXR1dVFJBIhFosRjUaJxWJYLBacTicLFy7kzjvvpKCgQAgM44gWoSDL8in+G0PPR5G+NDWRJAlFUcQgSSA4CS0yy2w2Y7PZiMfj9PX14ff7cblcOJ1OrFarOHdGCSEwCATjTFZWFldfuoLNm2rIvub6Md1XoG4fsa5O+v7yB66aO4cTR+to/sKnMeQX4L7xnThLZ4/avpR4DLvsGrXtnQ5VRXc7BxUksFgsyLI8LUKtH3/yScyXXYE0Rjc5QyqJ0Tj6A4cBB+eBcESLZeS2W60GMjPNdHR0UFBQMOptmA4cOHCAT/3Dx+nq7MJisVJRUc7cBVVULZzHlVdeicfjGfZ+RVH4+Ef+CWu7wo/v/CoO69lFy2AshNlivqjqBlq1gba2NpqammhqaqK5uZnt27fz1ltv0dfXRywWw2634/F48Hq9+sPj8VBdXc2sWbNwOp3T4rydKkx34WA6f7ZzZToI7QLBaDNSKpjmzaCqKtFolGQySTwe14UIwYUjBAaBYAJ48N572PCN76Befd2YdYja//Q7Yq88z1VXr2be/ffyTx/5CMlkkoaGBna+9RZf+8kjxN/7ML6ll559Y2cheOwIxJPkZoz9oFFRFJLJJMlkElVV6Y/2kVOQPuEVJHbv3s1f/u//KCos5KEHHrigQVM0GuXxp5/F+/lvjEELB0nLoKGhZey2fxY6OmPk55+cPiHQ2LlzJ1WOEj59+7eIJeIc7Wjg6OEGXtvyND/69g/43Nc+zxVXXEFzczMdHR1sWLeeSIOfr77zc+f8m7NbbEgKNDY2UlhYeEHt1Ewcjxw5wiuvvEJtbS11dXX09PTQ39+PzWbD7XZTXFxMWVkZc+fOpbKykpycHDIyMvB4PDgcDiEuCASjyMlh4AKB4FS0tIm0tDTsdjuhUIhIJIKqqhiNRjwejxAYLhIhMAgEE8Dy5ctJV5IEjx7CNXsOkdZmemrWoVosuCvn4Z5TdfaNnAZVUehc9wq9Lz3Lz7/9TW695Rb9NYvFQnl5OeXl5VTOncuDn/gULUcOY/ClY7BYSZu3AGtG5nnvs/HPj7Go7DLM5rEPdVZVVXdGV1WVnv42lhWsntAbwqFDh7j/Y58gNu8SpG1vsXXHTm69YQ233Hwzu3btYvbs2edkPvn0M8+QKi1Hzswes7bm3fsA67/4AW67rYeyMt+Y7WckgsEEqmI6ZRZe8Dab17/B8rxKzEYzZqOZRUVVLCoauB4caDnCf3z7l3yl54vkpGWS6fSR68jk89d/9LwG6iajiatmLeelF1/ioYcfOq/2aRUhWlpaaG5uZteuXWzbto0TJ07Q0tJCeno6ZWVl5OfnU1BQQG5uLnl5eRQVFVFcXIzH48HtdgthQSAYI4TAIBCcnqG+MgaDAYPBQCqVAgZKOyuKQiQSIZlMIssyZrNZVDq6AITAIBBMAAaDgQ+svZsfrHuFeHcXkT89yofX3oUkSfz21z+j46Y7yLjiagznUKdXVVV6tm4mtmUjqi8TpeEI5bKFJ/7yBBUVFaddb+HChTz9h9/zxz//hb5AgGCgg5e/+RgsXIpt8XLSFizCYDrzgF1JxIl1dRF67XVW3fF1vT0wdqGqiqKQTCVJpZJE4xEi8SAlJSV4PJ4Jq2tcV1eHobSc0of+gYS/jzc31VDz45/zha98la5Egtz0dP76P7+hpOTMlUM27diJeeHSMW1rWtV8mm0uenujY7qfkVi/rpOlSy8VN+rTEI1G2bppC//8gfeN+Hpl3mx+fNdXRmVfZenFHDx4+LzX6+joYM+ePWzatImamhra29vx+/2kUimMRiNz5szhmmuuYf78+cydOxeHw4HZbNarEhjH1GhWMNOZ6YNrgyQiGASC88FgMGC327FarcTjcWKxGP39/SQSCXw+Hy7XQOqv8GY4P4TAIBBMELe/8zZ++ItfkN3Tznd//lMWLlwIwC033cQnvvBF6p7/OwVf+CbmtFNne5OhID3bt5LYUkPMZMbX3szXPvoPhMNh8m+7ntWrV5/TDGFeXh6f+cTH9ed+v58n/vwXnl//IvtffIa8f/osJqdzxHVVRWHfB9+NIRbFbjXz+Pofc0X3beyqX4+/389n7/0Bbof3Ao/O6VFVlVQyRSql0NPfjs1mIysrC6/XO2ERDE+9+BJqRhYA5jQPuTfeSl9ePsf//TvM+/Gv6Hz892zfvv2MAoOiKLy58y2c/3D1mLY17u8j0d9HRcWCMd3PyYRCCV59NcyvfvWpcd3vVKKlpQWL0Yxstp79zRfJoa565l+98rSv9/f309fXR09PD93d3XoIaUNDA0eOHGHfvn0cPHgQi8VCZmYmBQUFFBcXs2jRIpYsWUJxcTH5+fkizFQwrmi17mcqkiSqSAgE58OAf5RRj1KQJIlUKkUsFiORSBAMBvUqE1ppbsHZEUdJIJggvF4vuzZvOiX0qrS0lKf/+Bg//eUv+cUjX8WSk4eS5sV91TuINDcRq32L1L5drFq2lPd85GH2HjjAtVdfTVXVhadVaKSlpfHBhx/ioQ88yHd/8AMe/d7XyPt/X8bsHMG8UVWxmc3s374VWZZ5+ulneOpvz/Crr/2CT33sMzxZ82uKM+eQ6c0jFOknHOnHaDRhMVuxmKwYjEYMkgGDYeDfRDKO1SxjMVtx2NxkenJHFElUVSWVSv1/9s47Pooy/+PvmdlesptCEhIIhNCLQUBRqmLB3k/PXk5/Z2+n3qmnd6eeeqeeZ8feTs9TsXuKXU5BbAiCICUUKSEkm02278zO/P7YzEikJaQCz/vlSnZ3duaZ2dmZ5/k83+/nS0bPUBfZgMvrpEePHuTl5XXJhX/58uV89Pnn9L793mavBytHscdj/0a22fCO3487Hn6E0aNH069fvy2u56WXXyacE6RX6Y7lxLe4vffdyV6jA+TkdK5z/3ffhRg+fAw9erQ+BWd3oaGhgYC7441SQ7EwX66dz5ljLt76MqEQS5cuZcGCBXz//fesW7eOjRs30tDQQCQSIR6Pk0wmKS0tZcSIEUyYMIFJkyZRUFCA3+/H6XSKjpig0zEMA3bj8bWEiOIQCHYURVFwuVw4HA7S6TQNDQ3U19fjcrlwuVx4vV5xX2sh4igJBF3ItkKuLj7/fMaPHUskEuHr7+bx0iP3MLx/P6YeMIEj777dyumfMmVKu7dLlmWuveoqbIrC008/QulFv9tsGV3TsCmyVersyCOP4Mgjs34PDz5yP1VVVSxc8ANVy34gv28efYuLSCVTxKNx4vFGUqpKJqOjqSq6YZAmTXhjGLfbTc3iGmKNKfYddAgjB47Dbvt5MJwth5hBz+hsCK/G63MTCATw+XxdcuH/98sv45p4AM6mCIZNMVNcAsNGUHvkCRx+6mmc/+uTuOyyy5otV1NTw1/vf4D8K2/o8Nk3I1xD6RBXh26j2faaHJoXLkgxbtwBOJ3Odt/H2bNnc/HFF/Lyy69sNw2lO2Oz2dA7YXDw+Kz/cPRpxzdLoTIHJevWrWPlypUsXryYRYsWsXz5cqqqqqivr6ehoaGpSoiD/v37U1ZWxoABAxg0aBAjRoxgwIABuFyuXb5SgaB7szufe2aZSoFA0Do2vW+ZqXxmlQlJklBVlVgsZlWZMMsr787Xm20hBAaBoJsiSRKjRo0CYPLkyc1SGTpr+5dddBEvTD2U2MoqvH2bz7zrahqn07XFi+ugQYMYNGgQhx566A5vf/78+dx/z0Pc9fJLVvlLCQnFsOMkB68jSFX1QoaO7I/P58Pj8XS6cVxVVRUvvf0OBdfetN1lC8ZPpiGYx9/vvJkxY8Ywfvx46717pj2MPm4/PL06NnoBwL3HWJYvbn3u/Y5iGAY1NXF+WJTmhhsOwO12t/v3NGbMGACOPXYqfr+bsj59mThhKieccCIFBQXtuq2OJJVK4XF2vPjjtru2OMtrGAZLlizh7bffZv78+fz4449EIhFSqZTVyQoGgxQVFTF58mSmTp1KSUkJeXl5eL1eS1wQCARdgyRJLFiwAK/X2yySwfz7l9ENQ4cOtSYJBALBzyiKgt/vx+12k0gkSCQSNDY2YhgG+fn5lsAg2DJCYBAIBFvF6XTy599dwTUPT8P9x782M3000mkcHdgx2WOPPXjk8YeIx+OEw2Eg61Pw+uuv8/jjj/PVD6tJJZMEAoFOz4v79ttveeu/7/DcjPdwTz6wxVUfAsNGoJ57MdfefAuHHXAAZb1KOeH441m2ejWe8Qd2cKuzFIybyILpD7FiRZjy8o6r5mAY2f9VVTXy8LR1nHH6JRQWFuJwONp9EGq32znjjNN59NE7uPFPFaxbG+abbx/h6KMf5Nxzr+CMM87q9gZN6XSa5595jgm9O9bk0zAMjh95CJc//1eOP/EEUqkUqVSKUCjExo0bmTNnDnPmzGHNmjWEQiF69uxJeXk5gUAAr9dLbm4ueXl5VFZWMnz4cAKBAE6nU1SFEAi6AaNLh3PfH+9EYvNrrOWe3/R8TWg9v7/1jxxyyCGd2EKBYOfALGUpy7IVFSRJEpqmkUwmMQwDh8OB3W63PBwEPyMEBoFAsE2OPOII3pjxHl+++SrFx55ovW4YBrLc8bOVHo8Hj8djPa+oqCA/P5/Vq1eDkr3Ad+aFPZFIcOI5vyGR14N+1/wJd8/SVn0+f9xEFj77KE/N/R7l+0Xc+/gTRKJR8o8/fbufja/5ifAn7yOnU6Bp6G43utePPSeAze/H5s/BnhPEnpODzedH2sJx8fcfCDYn4fokdGQ2gWFQV5fgvvvWc+EFN3LwwQdbN+KOmOVetmw55eU5BAIOAgEHQ4YGmTo1yTNP383Mme/zwANPNDuPuhOffPIJV118BQPy+nLhEVd20FayuemGYdDDl0+0McqCBQtIpVKEw2EWLlzI3LlzrXKTAG63m9GjR3PsscfSu3dva9bGZrPh9XqtfFQRtSAQdA+unXphi5e944NHrPJ8AoFgy0iShNPpxG6343a7SafThMNhGhsb8fv9+Hw+IbJvASEwCASCbSJJErfeeANTT/w1G3sUUjB+ctapWs90yaxw1oNB3yzPtLMGOU6nk18deyzvffgR0eVLWy0wSJLE8GnPAqBGGll22XkokoR79UpSNRvIGTpii/ti6Dq1j9zDbw+aQnl5OXa7nWg0Sl0oRHVdiJrlq6mpC1FXX09dKEQ0GsXhz8GWk4PsDyD5/egeHxmvD11Ls3RphJyAh4qKnHY5LlY7mzwXQqEU99+/mmOOPouDDz4Yt9ttzQZ0BIsXz+XIo/Kavdajh4srruzHU0/+wE03Xc/tt9/dIdtuC0uWLOHKCy/j2ikXMGnQ2HZbr6qqpNNpMppGJpPBwMAwfv5++gd689BDD1FSUkIkEmHlypUsW7YMVVVxuVxUVFQwZMgQ9t13X0aOHElhYSF+v7/bR4IIdm+EwWHLkUD4NQgE22HTKhPmBInH47EqtiQSCTRNw263WxENAiEwCASCFlBYWMh/HpnGZdddz6pvv6TwjPPIxGJ4Pd4uaY9pxtMVnUlZlrn1xhv4ZNYs5F5lbVrXxhlvcebxxzGgfwUfzZrNipUr2TjhQAoP2ty7Ir5mNYWyxOWXX94iMUXTNBoaGgiFQoRCIcLhMOFwmLpQiNkHHc28eT/x7ruLeOLJ9hvURqMqn3xSzfJlKsuWJzn6qDM588xzyMnJ6dBIkzVr1mSNlxyb+xfIssSpp5Vx01/e5eOPj2L//Tu2DGhrUVWVHJe/HcWFbKRCKpWisbGRRCJBKpXKDiQMw7JeGFc0kts+fhjD+XNJrmQySY8ePSgtLWXKlCkce+yxlJSUEAgEsNvtYoZmF+aBe+5j9sxZSJKMrmfQNA1d17MVezI6mYyGnvl5MGpgNEWzZDvVDocDm92GoihZAbhpZlxCAllClmW8Xg+apmVfb7qGW2H7W/hX2qTCkrmsLMtI8s9/A8iKjCRllw3V1VGWW9I5B20nR5ZkIcgIBK1AkiTsdjuBQACPx0M0GiUej1vpFOa9UiAEBoFA0EIGDhzIG/9+ngcfeYRpN/2BukSSq3594vY/2M7Y7Xb8fj8ej4dkMkk6nSYej5NIJHA4HJ0ywxqJRKhtaKS875ZLTraERPU6+PwTLvzPvykpKeHMU09l/vz5nHzdDbAFgSFdu5HBvXu1OFLDZrORn59Pfn7+Zu9deMEF3HDDNdjt0R1uP2CF3CcSKu9/UM1HH8XZd5+DOOaYyQwcOJDi4mLcbneHfi/r16/nssvO48ADYc9Rm+8rgNOpcOZZRdx88zWMGvUBgUCgQ9rSWnRd529/vZ1jhraf/4aqaqTTaWKxKLFYlFQqjaqqgDlIA5AYkN8HmyGjSVBUVEROTg5+v59evXrRp08f9t57b/r3zxqoiqoQuz6v/Gc6Zw4/ht55PVFkG7IsoUgKiiwjyzKyJKPIzX/DakZFy2ikNQ1N11AzGrqhZ814NxGjDMMgY+gk0knsbls2igbjFyaEWdHCXB6amxIaxs+z7bqhW9VWdONn4cwwDAbZejDsoKM66jDtUoiftEDQcjYVO+Wm66IpmGYyGQzDIB6PW1GApt/U7nrvFAKDQCBoMXa7ncsuuogjDz2U6a+9xnnnnNPpbXA6neTm5pKTk0M4HLZmamOxGIqidIrAsHr1avQ23jTq3/8vF556MiUlP8+29evXj/TGGsuxf1O0RJyA39+mbZrE43E+/PBt/nJTGyIwmsrNz5q9gZdfCjF02L7cfttv6NevHz6fr9PMj26++VoGDKzm4KnbrsAxcGCAEXs0cM89d3Djjbd0WHtaQ1VVFd9/M49rzjyr3daZTqebfg9R4vEEup6dSVZkBaXJL8EsZedzesgpK2DUqFGUl5fTp08fevXqRa9evSxTx921c7S7oakq4/qPxu/2dXVTBJ2IiGAQCHaMbFSWF6fTSTqdJplMEo1G0TSN/Px8KyV0d72HCoFBIBC0mn79+nH1lR1lRrdtXC4XPXr0IDc3l3Xr1hGPx6murqa2ttaaLe8I4vE469evp7y8nD/fdjuOSdufdTYMg9rPPyX90QwSa1ajOBy4evdBLy0jPft/HH/dVc2W9/l8BH0+ktXrNvN2UNeuYXDfPu2yL1999RV9+tgJBHb8WOmGwdy5G3nrTYObbprG4MGDgawAZBoidYYBoCRBnz6eFm3n6KN78sc/Tuf88y+lsLCwQ9vVEr6YNRu33YXX6W7zugxDR9cNUqkU8ViMZDKFnsngcDrxeNzYFBtyU/5oNr1Ip2dBMSP2yZZMLS4upqioiLy8PPLy8oTXwm5GKp3GbhOhvbsTErLwYBAIdhDTm2HTvkcmkyGdTltCv91ut/pCnVnprDuwe+2tQCDY6XG73RQWFpKfn4/dbicej7N27Vo2bNhAUVERXm/7+kIkk0lCoRC/u/Zavp03n2OOOJx5CxdSdMIZ2/xcYv1aap99nAGKwR//+AcqKytJJpMsXryY7xcsIH//8RQVbV7ect/Ro3jz5ReouODS5mVBHQ7mfDePi9thn+bO/Zp+/do28NczGd5+K8TZZ9/A3nvv3SwcsKMqRWyKpmnceOPv+f77eQwb3rLqED6fneHDXXz22Wccd9xxHdq+7fH111/z9CNPcvtRv8fjaLvAoOsGmpZNj4g3mU4hSXi9Hnr0KLTEnqYMCQwDpg6bRNxv54ADDmjWERJeC7sXhmGgqioORQgMuxNd5WMkEOxKmL4MNpvNimYw/a/cbjdutxuPxyMEBoFAIOjumPlvJh3VSQqHwxx3+hmsi8QI127EWzmaN2IaysAhIG15EGZkMmx461WMT97j+vN/y8m/PsmaDXY6nYwdO5axY7du6HfOKSdTe/+DfHP1Rdj2noCtsBBHbj7xHxfRv3JIu+zX3O9ms99+2063yFYboCm/2QCa8vebcqHfeHMtmUwxU6ZMsSIWoPOqecycOZNFi2Zw2eVF9OrVclGppKfEihXLOrBl22fFihVceeHlTCndm4HFbasValZUSSaTJOJxYrEYmqZhUxScLhceTzaE02ZTyPrGZ9nYWMf8DT+yz7j9tujTIdh9SKfT2QgXISztVuyegdsCQfuyqem4mRLhdrut91RVJRaLkU6ncblcnd5X6iqEwCAQCHYqdF23QtAMw0BRFDweDy6Xq107yOl0mnMvvYzaoSMp+9WptMStwNB11j74D0Y5ZO56+cUtRihsj5EjR/L8Y4+wYsUKZrz3Pmtqalj93RLCdjju6KNbvyO/QFVVFi9ayDnnbNugUtcN3n9/HZ9/lqChMU3Png6OODyX8nIfTz21msbG3tx334NWrmFnoqoqDzxwBwccmEPv3q3LGbfZZVQ11UEt2z7RaJTbbvorh/WdyNkT226Squs6qqoSjUapr68nnU6j6zoOj8dyus7+Lpp3Zp6a8zKjj5zABRdf2OY2CHZuUqkUTlvHpJYJui+mF4tAIGg/bDYbOTk5uN1uEokEiUTCqjSRl5e321SZEAKDQCDYqdA0jXg8TjKZzA6kHA5ycnLw+Xztljeu6zpX//GP/GD3UHr8yS3+3Mb/fcJgPc2TDz7e5ptIeXk55//2/9q0ji2xdOlS8vNtuN1bufw3ubl/+kk1X30Z5Oqr/0ZxcTHz58/n6acfYMOGJUyefAi33HItRUVF2O32TndKfuaZJ3G61rDXXq2f/Q/VaVRWtq286I7y5Zdfcv8/7sNTBycdcmSb1qVnMmQyGRLJJPF4nFg0SiqVQpIkXC4XHk82LNNMXdkUwzCojtZy1N6n7HZhm4LNSSaTwn9hN0RCpEgIBO3FpvdZMyXRFPBkWSaTyTTrt3aGCXZXInoWAoFgp0LTNGKxGIlEgkwmg8PhIBgM4vf7222wdNe99zJj2Sp6Xf1HpFZc/DNzPuPyi3/brRXq+fPnU16+9eNkYGDoBl9+meS8826gsrISn89HeXk5Bx10EMlkEr/fn6173wkmjr8knU7z7LMPc/kVxTu07dpaid69t11xoiP44YcfuObCKzl7z+OZMnY8jjYO6LRMhlQqSSQSIRwOo6oqmUwGl8uF1+vF4/Hicrmw2Tb3w3j12xloAZl99tmnTW0Q7Bqk0+k2n4+CnREhMAgEHYUsy1a5SrfbbXkzNDQ0EAgE8Pl81nK7IkJgEAgE3ZpfdoAymQypVMoKBVcUBZfLhdPpbJcL9Wuvv8H9L7xEv9vvQba3LmxYq6uhoqKizW3oSObOnU15v23sl0FT6kn2qam0m9+Dx+OxyoF2RQmm9957j549VUpKWmbs+EsaGnT8fj8ff/wxkydP3uo5YxgGCxcu5OMPP2LV8pVUjt6T0888vdXbSyaTLFq0iGn3P8iRA/bnkD3226F2/xI1nSYSiRJvyu00fwcejwev14vb7camKEi/8Ap5atZL/G/jXB5++rEOq7gi2LlIp9MiRWI3REKUqRQIOgrT8Nr0DDO9GSRJIpPJEI/Hsdls2O12q5+1K7FryiYCgWCXxfRgUFXVMtVpz5KILrcLRU0TX7Wi1Z+1+XIIhUJtbkNHMm/eV/Tvn7PV9w0MdMOgd5lEVdUyHA4Hsixjs9lwuVyWIt8VofWGYfDccw+z3/5bb//2KCmR+evNf+GKcy/hjJNOY+bMmSxcuJDGxkZrGV3XufWmW7jm3CuIzVqP7yd44dnnW52vrOs6111zLX+66Fr6RPM5bsyhO9zuX5JqKoMVTyQwdB2n00kwGCQYDJKTk4PL5UKSm/8evqqax6y6+Tw3/QVKS0u3smbB7oamadhkMd+0uyFSJASCzkGWZSvaNj8/H1mWiUajNDY2EolESKfTXd3EdkfcUQQCQbcmkUjQ0NCAoigEAgHsdnszz4VIJEJVVRVFRUX069evzSrwIQcfzMsFBZx2yWU4r70ZV3HPln+4uISqqipGjBjRpjZ0FLquU129gYKC7VWjMLDZDDQtbDmoUgAAIABJREFUY4k2phrflSxcuJCNG5dTWdm/2evJpAaAy7X9W9q48T5+d/nHvHvJdBatX8pTt0wjnGhE88DTLzxLY2Mj1191LbZQhgd+9Rc8Dje1kXrmvDqPYw85ij//7Wb23HPPFrX3tVdfo+rrH7nn+BvbpRQlQCajoWkaqVQKtcno1Ga3Z1MjPB6cLtdWxbY19esZO34fAoFAu7RF0LGk02luvO4GXE4XeT3yCeYF8fl8+Hw+/H6/9TCf72hEiqqq2OSu/W0LOh9RplIg6Hg27UOZkQwulwv4uQpUPB5HVVVrAqezfa06AiEwCASCbk00GmXFihVWGoTL5aKoqIj8/HwcDgd1dXV89913BINBioqK8Pu3XX6xJYwaNYpfHXoIr3zzJT0Pb3nlBqNnKYuWLqXttR46BlmWGTx4CFVVEQYPDm51uWQyw+xZKe64Y2K36oA+9/yTTJ7sQd5kZn7DhjhnXTifWNLAjsZ5p5cwbFguQ4YEsdk2D9J7+aUV7Nd/MqV5RZTmFXHgsAkYhsGtMx7kqCmH4XV4OLD/OE47+ljrBl/gz+WpM+7ki+XfcuX5l/Hv11+kuLh4m22NxWLc9dc7+McR17abuACgqhqJRJxUKkVG15Fl2fpduNxbFxcA+hf25dmF/223tgg6lkgkwmfvf8q5e59I/Yo6VqtriGkJ4lqSaDpOLB0nlk4QS8WJJKIoNgWv14ff7yM3N5dgbpBAfi65+Xnk5udaES7BYNDK/wVYtWoVihAYdjtEioRA0PkoioLP58PlcpFKpUgkEkQiETKZDPn5+btMGUshMAgEgm5JMpm0QsgURbE8Fvx+P+Xl5VRVVeFyuQiHwyxYsIDS0lL22msvdF1vF/V36pT9mX7XPdAKgcFV1JNli79r03Y7mpEjx7Jq5WtbFRgMA2a8u57KyskMGzasy6MWTNLpNO+/9yZ/vyMbvfDpJ+t57KnlrK1TyJlyGCMvupKlzz7Oo2+8A29uQE4sZ9QQJ9dePYhg0AnA/Hk1fPd1jPcu/X2zdUuSxPWHXLTdNhT6C0glktTW1m5TYFi2bBnXX3Mt+/XZi7492sdQUtM0VFW1KkaYbtQulxOfz4fb7ULZgufCpvTK68nKj1ZQX19Pbm5uu7RL0HHouo7T4eTQPfbf7rKGYZDWVGLpOJFkjEgiSkMiQuOGCA0rw6xKrWGBGqMxFaUxHSOWiiNvcq7s2XNoR+6KoBsiUiQEgs7HjAbdtI9qGAbpdJpUKkU4HMbpdOJwOCy/q50RITAIBIJuSTweZ+3atWQyGXJzc8nJycHhcBAIBBg4cCDLly/H7XazYcMGvv/+ezKGwdv/+4yzT/wVvznrrDZ7BIwZMwZj/VrS4XocwZYNxtLhegqC3Tv8vKysgjlztu4lsHFjgo8/TvLQQ79tV2+LltDQ0GBFqhiGYZl4GobB+x98QFVNnNv//gNlpQ5eeG0jWk4BFX+4ipJ9JyEpMoPPvRDOvTC7rhVVLP7Ps5x87sfccm0/+vf384+7fuD00WcQ9OyYh8O6cDU9+5QyfPjwrS7zv//9jxuuuI5DB0zkjH2O36HtbAlVVYlFo0SiUaLRKJqmYRgGDoeTQE4OTpdrm+ICQK43QP+8MubNm8d+++3Xbm0TdAy6rqNs5zs1kSQJp92B0+4gz7v16CSBwEISEQwCQVchSZLVxzKjGUKhULMqE06nUwgMAoFA0J5omma57Hq9Xstzwe12U1hYSK9evSgtLSUUChFKJFgg2Sk57Hhun/46Mz78iJRhUFFWxj9vv22Hqks4HA5OOOxQXv3gXYpPOHm7yxuGgfb1bA684Lwd2d1OY/jw4Tz5ZIpMxkBRJLL9SwPDMGhoSPPwtJ845ZT/o0+fPp1So1nTNO68+588+8orpJFwYTB+3DjmL1xIQ0MjgwYOJFRfT3Vao/Sy6/n+i8/4YvZ8Cs+9nIpjT0RStty+QHk/RvzhT6z8755cdcs/8aWjJGMyekV24LYj+7WkegUZtm30+MLTz3PmyGM4rHJKq9e/KYZhNJvVSCQSxONxkskkmqZZvwWPx4PD6WyqGLHtda6qW0tVwxoqKyvb1DZB57Cj56lA0BIkpFYb1woEgvbBjLQ1zcolScLj8VivmaXYNU3rMmPttrBztVYgEOw2GIZBJpPBZrPh8Xhwu91WuZ+8vDxKS0upqKigtraW6LLl6KkUmeJS/GdfyNJVVbgCAZZOf54nn3mGXx13HPX19Tz9r39RtXYdl//feYwcORLDMFixYgVz5sxh1tzv2G+fsUyeNMnq1B9+8EE8de7/UXDYUdg83q23Vdepnv5vBjht7L//9sOZu5IRI0bg9ZawalWUfv38gEFdbZIZM6qZNSvOkUeewumnn4XH4+mUwc3rr7/OU5/NpvDG23EWFBL58Qe+DIVw73sAPYK5rF27BsXlond5BZIs03Ny6wbufQ87ivAHMwiuDHHykRfw/Hv3sjEW4pbjrmp1W0uCRdg2LNnq+19//TVV3y/l9yed1ep1/xLz/I/FYoTD4aypo5pG17PCg8vlJCcngNfrzYoLskQ2q3rrfLNiPvtPPUCkR+wkZGeXd+48XEH3RW5hdIxAIOh4FEUhGAzi9XqJRCIkEgnS6TR2ux2/3y8EBoFAIGgPzAGWqe6aYWKmE29paSkTJ05E13UaGxvZULWU0MyPUA6YSmDPMbhdbly9y7jlz7/n9ocexgCUMftiL+zNKRdezMQxo/lmwUJikoQyaDhK33I+mv4G2t33WOZXitOJ7najhuu3KDDE1/xE+KvZZL6eTWVpTx68/75uH84mSRIFBQXE42upDyV49dVqvvtOZerU45g27Th69+6Nx+PBbrd3qMBQU1PD/Pnz+eM995F/6e9x9SgCIGfwsGbLtTQ9ZWusffNV7D+u4JLT76YwrydnHvY7Hnv9Zm5p5XpenfsuT331CgcfOXWL7xuGwYN3389pex6N075jbv7Q5CqdyZBsilqIxaIk4nEyug5IuFxOnE4nHo8Hr9eL0+lAkmVaMhDN8wZZuH7BDrdN0LlkMhnkndzoS9CNaUpDEwgEXccvq0zIsoymaUiSRCaTsapMaJpmVZnYGRACg0Ag6JaYOfhbyxEtLS1l6tTsYO+HH34gFFpI5MVnUHx+5H2zlQH8wXzK/3Y/itvTLJS+sWIAX6xfR2DqceQWFv3sMTBly4PHX6JrGtXPPYFr4TxOP/wwpv7tViorK3ca11+fL4c1a5bw1JPrmDDhBJ544kwKCwubCQsdJS6EQiGmv/IK9z39LJo/h5xzL8bbt1+HbEuLx9jw0AOcf9i1FOZly43aFDu60bpO9aq6tfx7/ttMf/c1SktLt7jM3Llzqalax5RfX9KmNmcyGdLpNJFIhPr6elRVJZPJoCiKNZMRDAYtAyhZlrabGmHyY00Vow4b06b2CToPwzBEdQdBhyFLsvBgEAi6GbIsN00eOEkkEpbhOUBeXp4QGAQCgaAtbFozeEuYSu6gQYOYNGkSdrud77//nsiTD5KpWkrm2JPImIpvRsOGram8oYR/0DD8g4Zl1729wZlh/mOAAZIEG/7zLGPUOA++9QYej6dd97ujMMWadDpNLJbgvffWcs45v+Pkk0/F4XBY5oodFYGxfPlyotEo/7j/Af5XG6bogivxDxzcIduytvnwfQwoGsoeA/eyXgv4c0mpaR795N+ct9/2vTUA7vn0SS66+tKtiguGYTDtngc5ccRhbRBmsueXKS7EYlHSqRSyLON2u5tKUTrxeLy4XK7NXKhbQp/cUj779HPOPvvsnUYM250xK+IIBB2BJAQGgaDbsWmViU0jjFRVJZFIZKsLNVWZ2FYfuasRAoNAIOiWmBfZbV1AZVmmT58+HHHEEbhcLmpqali9ejWxGW8S/+YL/CedSd6YfcjNzcXtdiFJdsDYpJQltCS03DAMS2BIherQZn/Kve/N2GnEBWgyodQ0otEogUABkyYdyTnnnIvL5cJms3WooeNXX33FqZdchrO4BAYPp/zMC1Fc7g7ZlomWjBP/4EOOOOamZq/nBXpw3OT/476PprVYYBha1J9nHnkSu93OEUceYdWpNnnnv++wYelaDjjxtzvcXsPI/i+ZTFqeC7qu43K78fv9eD0e3B4PNpuCLG/f0HFLHDR8Is/+6zXWrVu3VbFE0H3QdV2kSAg6DpEiIRB0WyRJwul0YrfbcTqdJJNJGhoaaGhoIC8vz5oM6q5puUJgEAgE3RLT3FGWZSKRCAAej8cyujFFh5ycHCoqKojFYqRSKebPn8/y5cupra2l8dF7Sc/5HP203+Dr2RO73YHDYcfucKBISpNhnp4Nmd/qRI6xSaUFiFWvp7Exwu9v/BPXXJad1bbZbN3W7d2sQmDm8um6zqWXXoHT6cTtdneYCt7Q0MARv/oVw4aPIBKqw3XYMRQfdnS7bmNbrHhsGn0L+jOgz7DN3utV1Ben3dlil/5zx/+afdeO4l8Pvsj0f7/EU/9+xjoPo9Eod97yN/4y5VJsyo7cUrPnVSqVIplMEotGSaVSgIGj6Tvyeb243C4cDvt2S1FuCwmJhJoiJ2fHynQKOpeswNA9ryuCnR9ZFhEMAkF3xawyYT4gG8VgejREIhEcDodVYaK7CQ3iziUQCLoldrudnJwcFEUhFApZ+ei/xOVyUVBQwN577815553HaaedxujRo+nZsydONU1yzmes/uMVrH7jFWo31hCJRjF0A0kCw9DRNI10Kk0ylSS1xUeKdDpFOp1GVdM4ysrpedu9fICTw089jXfffbdbzwKl02nC4TD19fVEIhEkSSI/P5+8vDycTmeHCSP3TZvGhrwiZheW8VVNiODosR2ynS2hp9NE353BEfuessX3+5YMIJFKEk3FW7zOYaUDufWoq1Fr4kx/ebr1+rfffkt5Ti8GFu+Yj4RhYJWkqqurIxKNNHku2JqMHD14vB4cdkebRaCGRASbI+vj0BZ0XaeqqopFixaJAUoHIlIkBB2JLMpUCgQ7BbIs43Q6yc3NJT8/n0wmQzgctqpNaJrW1U3cDBHBIBAIuiUOh4NAIABAfX09siyTk5OzWTi/qqrE43EMwyA/P59hw4aRTCYpLS1l2bJlVFVVUVVVReSFp0h88T+0s85HGTS42ey9oijIxjYG2tLP/xgGKMEgruNOIjF6by7/xz18/Pnn/OHKK8nPz+82pYRUVbVmxSVJwm63Y7fbLa+Fjmznq6+9xrMffkKv627B5vPBgYd02La2xIqnH6V3sIwh/Sq3+L4sy7idXj5dPJsj9zyoxeuVJIkrJ5/DdXfexZ6j9mTgwIHEYjH8jq2XMN0aZn1rNZ0mlU4TjUatuteKIuNyufD7fbjdHhTF1lQpom0EPTkoukR1dTXFxcWt/nw8HueO2/7Oe2/PIOjyk0olOeq047j0isvb3DbBlhECjqCjkCQJPSMEBoGgO/PLKhOSJOF2/5ximkqlrBRYh8OxWQpnV9E9esICgUDwCxwOB8FgEFVVqampIRaLkUgkLENCk2QySU1NDZBNlygpKaG4uJhRo0axdOlSZs6cSWNjI2vWrEH9YT4bbr+RxImnU3zgIU2OvE4UW8tCyySaMimM7ACRsr7Yf/8X3nz+Cb4+8yym3XUnw4ZtHpLfFaTTaRoaGjAMA6fTaT06Op1j3rx5XHfX3RRe86esuNDJ6JpG5K23OOnAK7a6jCwrnHTgRVz3yh0cXnlAq4+HrhgEg8GmdcktsfHYDE3TSCQSRKNRIpEIqppG036uFuHxuPH7c3A47E3mpG0nHG8ko2e22gGZNWsWY8aM2cyl2jAMFi1axF9vvJkyNZ8nTrqdoCeHTxbN5ttVK9qlbdtj9erVrF69mrKyMnr37r1bzOy7XC5SWrqrmyHYRZElmVmffU60McKJJ59Enz59urpJAoFgOyiKQk5ODm63m0gkQjKZjbQ1o36FwCAQCATbwCyV6PV6KSgosNz1IXuBTaVSRCIRok056y6XC7vdjtfrtUQIt9uNJEk4HA7mzZvHokWLiMViRJ5/AimVxHb0Cc1c+YHtDhZ/fttAN2xIPh/F515MaNZMjj3nXG6+8nIOO+ywThnMbwlVVUmn0ySTSXRdtwyCTLOgjmxPPB7nvN9dhf+s83H37BoTwVXPPE6Ru5DKgdtOydhr+ESeffcf1MXC9PDntXj9y2pWsc+EfSksLASyQoGyjWxDwzCsVBxV1chkNDIZnXQ6RSqVIpHIpuJISM3OW6/X1/R9tS2vUs2oLFizhD+8fBuy28aFV1xCfn6+9X4ymWT+/Pl8+cWXPPHQo5x06q/5/R+vxTAM3vnvO9x9+13EojGCTj9HDzmAo/c82BrcO+0O5n47lysuuBRVVUkmU8SiMXQ9g8/vp7ikmJ69SykuKW4mWmSPidHsuYnb7cbn82Gz2WhsbGTd2nXMfP9jVv64gr75vVjXsAG738nEAybjdLlQbAqKoqDYFOv3pigKvXr1Yv/992/TsetqPB4PiXSyq5sh2EXZf8g4InOjPP/Ys/StKBcCg0CwEyBJEjabzYpkkCQJTdMwDIN4PI5mVk9ruud2lRgvBAaBQNCt8fl8OJ1OQqEQa9euRVVVfD4fyWSSVatWoaqqVTPYLOcnSRJer5c+ffqQn5/PyJEj+fjjj3n22WdZvHgxqUiExunPo0syPQ87aocMcmRZwWGXm4wTM+SN349ISS+uuftulq5YySUXnI/X6+0SgaGxsbEp1F5pKm/osm5IHcmsWbOIF5VSUjmqQ7ezNdLhEOHp0zn/sD9sd19VNTszrGqb+3psC5fdSTKesJ4rikLG2HqYsaHraJkMiUSSWCxGMpkknU6haZlmxptOpxOPx4Pf78Pvz85CKG08d5ZWr+DW9x8gr3chh550JBddcjFer5eZM2cy95tvmfvFNyz5cQn98ssY0WMAV+93Hg+/9gIur4clixazbuEqbph8IaW5RXidns2O6ciyYZyrHY8iK9idNuxBO16HB1mWiCRj1DaGqP1iI3OTS1B1bbOKCFKTXGc0/W1gkMykiasJND2Dz+Eh6PBzVK+J7LX3xdgUG4ZhsKR6Bd8vWkRGT6HqGVKGTkbX0THI6BoNiQj3r/maOd991abj19V4PB5SqohgEHQMPYOFDCkZwKsL3xfigkCwkyHLMh6PB4fDYaXDRqNRdF2noKBgs0jEzkYIDAKBoFujKNkZyuzgy4+u69TX15PJZHC5XHi9Xnw+Hz6fz/JUgGwVCpvNZoWNjR49mnA4TFFREfPmzSMUChGd/hw1Dge2Q45odSQDUlNOHNkQecUAX3kFzmtv5onH7mPhjz9y/513EAgErJrG7YnRVGLM9FowzboymQyGYWC323E4HDidzg4tQbkpiUQCyd01pTvT4RA/XHAue1dMZMSAMdtd/rsfvyDX56ckt6jF20iqKd5d/CnDDx1jHXe73c5HCz+npyOPnxqryXMFmFgxhl65JWAY2YGvrlsdAFVNo6rZ2QZJAptNQVGcTYaOXqvD0Nbv65nPXua+D54kEAxSn4oQCoX4zSlnk4jE6N+jL8PyKzip98EM2ftC3I6fU476FPRizldzGePpz0G/Ohu7svVwS7fDxeTB+7Spna1FkiQG9ezHoJ5bN9VMqilmPv11J7aqY3C5XCTVZIurnQgErWH2sm+47eNp9OnXd6cquSwQCH4u5b5p39LsE5oRrE6n0+pPdPY9RAgMAoFgp8DtdlNUVEQ4HKampgan00lhYaEVTr21KATTFGfQoEEUFxdTVlaGqqp8//33JDdsoOHfT6LLCsVTDtqhSAZJklAkBdkuZ9tR0IOiS67m40vP5ZtvvmHChAnWIL890XWdTCZDPB63KmyYTsNut9uK5uiMyAWTsWPHYvz9DhoWzicwbI9O2aauaax+4Rnq//Mf9iofz+mHXbTdzxiGwYw5L3LsHge2altvz/sQd/9cLrj4IlKpFA0NDUQiERSvnecX/xeHx0m0McrTX0znpOGHMXXgxGz4v2GgNwlCZjqALMvYFAWnlRLhwev1YbMpbfZc+KluHf9491HuP+UmJgwdSzydoCGeTS8qDvTY5vlQUdiHisKdezZTkeVdwh1flmUcdgcpLd1MBBII2oPCnAKkDFz75+sZPnx4VzdHIBDsAKaJtxmxmkwmqa+vp6Ghgdzc3GxfowvSdYXAIBAIujXmTH0mk0FVVSRJwu/3W7nabrfbEhG2hFlD2OPx4Ha7GTFiBKFQiEAgwJdffsnGjRtpfP5xFLcbefxkvF5Pk3N/yyMZsv9IZGIx6r+ZQ+Td15m4915WlQHImla2JZJBVVVUVbXC6s08dk3TLJXa9JswK2R09k2lsLCQx+74O1f95SbW5uThO+xocoaO6BCBQ400UjPjv6x+6F7KCwfymwMvZ9SQ8S367PKfFlHXsI5LDjqrxdszDJ2V9WvpPawvM2fOZMOGDWzcuJHFixfj8rkJh8M01mYNl+LpOI99+SK1jSGOGjoFt8OdFROcDmyKDaVJyDINHZ0OBw6nE7utfapFNCQayfMGmTA060PhcbjxONzb+dSugyIpWRPWXQC3x01STQmBQdDuFOUUNF2XnF3dFIFAsIOYfVyzygRgpedqmkY0GrU8uEwhojMQAoNAIOjWmAJDMpmkoaEBm81GcXExbre71QNoSZIoLy8nPz+fgoICotEo8+fPJ7l+PXWP3YdqGBTuPQ6brfUXYS0aZc1tNzCuoi8n/e5yRo0aRTgcbhbe3JZIBtNbIZVKWYMnRVFwuVwEg0Hsdnuzm4z5d2ezzz778PEbr/Puu+/yj0cfY+1rDryHHk1wzzHt1p7osiXUPXAnR+83mdcKirjuzH+2+DyoWvMj0169iTP3PaZV546uG1TklvHRF1/xw6JFLF++nHXr1tHY2Eg8HrdSU0xPBV3XeeH7t3ln6UymDNiXs8aeQCAQxOVyNaXiyEg0dQzM76udqkWkNHWbqQ27Oub3uiukFrjdWaPHXG+gq5si2MWQZZl0Os3SpUsZOXJkVzdHIBC0ETOaIRgM4vF4aGhooLGx0ep3eL1eITAIBILdGzNqIZ1Ok0gkUFXVukiaeeqtwRzculwunE4nw4cPZ+rUqXg8HmbOnEl1dTUNj92H3ePBOWYsup71eJBbeDHe+Mn7HD5qD+6+/XYgW1FB13XS6TSNjY3EYjErusDr9WKztezyu2lVCNO40awGIcuytT/m+rpD+T673c6RRx7J4YcfzkcffcRdDz/Cyvf/S8mlV6O00aMhvuYnQg/exWO33sL48eN5+6XXW7zPVWt+5IGXbuCc8cdw4QFn/OJdw/wv+0zX0Q0dVdVQ02lUTSNo9zHvm+9wBNzU1tYSi8XIzc1lwIAB5OXlEQwGsdlsVhqE2a5Vy1dyzXt/J5aI45Id1nclS1lhQW56SJKMLMkoiowiKdjk7MOu2CnNLWZAUTlHjDyQHPe2y3+m1BT2Fp5fuyqyJJPJZHZ6gcHjcZNQRSUJQftz83/vRXIqVFZWEo/HCYfDlJSUdHWzBALBDmD2NzadZDKrTEC2LxmLxVBV1Ypo6Eh27x6IQCDotphmNbFYjHA4jMPhID8/v7kR4w4iSRJ9+/aloKAAt9vN8uXLqa+vJx6up/7R+7AX9kQv7YXNZsexlW2lw/XUPvcE9r79MTBw/e8DLnnsEet9s2xmKBRi/fr1pNNpFEUhNzfXSl9oCZtWhTAFBY/H8/MgtQvMe1qKLMsceOCBTJkyhT/ccCPvvD6dnr8+fYfXl6qrZeM9t3PX769m3LhxvPjiixQGS1osMDw/415O2evQLYgLWOKC6ZmgZTJomkYsFiMaibChoZY3571P9cYNpGpUSywaOnQokyZNYujQoQwcOBCXq3kou1k2saGhgbffeos50z/l1DFHYxgGGT1DRtfJGNlKJBldJ6Nn0HQNVdNQMypaJkNSTbEmXM09Mx7ntjfvJ8fjJ+jNYVzFaBw2B0U5BYzoNZhBJRV4HG5SWhq7snvf3rMmml0vtrUVj8dLUk11dTMEuyAum5P8HgWUlpbyp+tvZPas2Xw25/OubpZAIGgHFEXB7/fjcrmIx+MkEglSqRSKolhRrx3J7t0DEQgE3YZM04BO0zTLa8AMOXe73dZMfVsviptGMrhcLkaMGMERRxxBIBDgq6++IhKqJfzeW3hOOTvbBr1poPKLscr6F55hgh0WfDqDgoICnnzheQoLC633zYG/x+MhGAxaynEikaC6urqZSLKp8mx6K5gPM5LDHNCa/gotFVnM2XRN00in05bzsPnojEGYLMuc8qsTeOemv+7wOtRohOq7b+UPZ53OEYcfzhtvvMH9dz7GMePPbdHnG6P1VNf9xP9NvJPGhgaruoMZs2BYoQsGBtnzsTEe4fs1i/hoyRfMWvUtUTVOflEBvXv3pri4mKKiIgYPHszw4cMpKyujtLR0q5E1paWlBINB3nnxLfYdtGPpItcfcykb6mvQMZiz9Fvmr1lMox7hh7VLefbzVwjFwjjtDpJqil7B4lavf1ejOwoMmUyGTz/9lC9nzcHpcuL2evB4PdZv23yYQmJDtIFEOrH9FQsEreSqg37LZdP/wuWXXc7XM+dgd3dtWTuBQNB+SJJkmXybpseqqqLrOvF4HFVVm/kytHfqhBAYBAJBtyCTyZBMJonFYsRiMQzDwOFw4PF4yMvLa9WgujUMGDCAM844g9zcXFauXEn9kiVkPv+E9NG/QsvkZfO4m/LlTWpnfox7yUL+/Ny/rLZtTfgw0zmi0Sj19fU0NjZSW1uLqqrNZllNQULXdTRNs4wc3W43gUCAvLw8a+DR2oGTYRhWqoZZacLpdG7RHHPt2rUsX76cUChEKpWyxB7z37y8PAYMGMCIESNa9X2EQiHCy5Yg/SMrMqgZncKzz0dxuZEUeZupE5lkkvX3/I1zDzmIs888E13X+fzzz4knopSs/1KpAAAgAElEQVQWNK94sHrDcnwuP3mBn8UeXc/w7aLPyfcHSSVTbAzV8eGPn/PFqu/YGAsRSUXxOjzYZRs2xUZGz9CQjNCYjGJIBvWJRtxuNzm5ASorK9lvv/0YMmQIFRUV5OTkNDPV3BYlJSV4g35W1v5EeY+yFh87E6fdQVlhLwD6FvbmJI5u9r6qqVSHN7Kmdh2+LioX2l0w6F4RDHV1dbz2yqu8+K//UCDnMKFsNJoeI6qGqM2kSelpElqKVCZNUkuR1FIk1BT19bX4R/q7uvmCXRCv0825Y09k2v+e4/zxp/Bh7Vdd3SSBQNDOmJGvdrvdSreNRqNomobP58Pr9XZIpTMhMAgEgk4lk8k0i1YwZ9jNmXpZlq28MbvdvpnHQHthDj7MQXtZWRl9+vQhFApRF4/S+OXn+HLzwAC7mia2dBHJmg1QtxHPou955Zmn6d2793a3YyrDZkSCzWbDbrejadpm7ZFlORs631R+MhKJkEqliEQi1rEzaxpvut5Noz3M7ZjrNA0HzWNuftYUNLxeL4Zh8Orrr/P09FfZGIvhLCsHfw6G3YGhKNmHrIAkoSxYgv7Mc/QwMky78w4GDx7couM9adIk3nzmaRKJ7Gzsn2+9jeW/vwS3z0cylcQ9+WCKT948dUHXNNZN+ydHDRvMVZddBkBtbS1vv/4umqrz+Lu3Yrc5GF0xBVVN8c63z5BKaOQG8tHSOvm5PVhdvZRYPEqZt5C3v/uIx+e8SG28nmj6Z3NGu91u3WRlWcZut+MIusnNzWV4QSVFRUVWxMKIESPo3bs3RUVFrfYC2Xfivny7fMEOCQzbw26z07ughN4FIo/asEJSug5d1/n666956fkX+eLTzxnfZzQ3TryQ/kV9u7ppAgEA+1SMYp+KUbw970OC+bld3RyBQNDOmFGrmwrupj8YQDKZJJ1OY7PZLCGiPUzChcAgEAg6FV3XSaVSJBIJy30fsC5upqBgXhA72mPAvJAGg0EqKirYuHEj0cWLibzxMj+tX4cno+JZv4YDxo9jYJ8+FA/qw4E3/oGioqJWbcfhcBAMBvH7/RQVFW13AFRbW8tPP/1ENBolEokQi8Woq6vDZrNhs9msKATzeGqaZpWwNMPh4Gfhxul04vV6kSSpWSqKzWbjzIsuxjNuMjlnX0hZ334turHUzZnF8ef9lodv+ysTJkzY7vI2m62ZU/mMN9+w/m5sbGTq8ScQHjaC4B57Wq8bus76J6cxLuDl1j//yWpXYWEhjzzxEJqmkZ+fT0NDA3+6/iYaGhp57MlHWL16Nfn5+Xw39zti8Rjz5inMnz+f5dUr+PuGR2hMRa1jaEaMBAIBCgsLrWgEu92Ow+FgwIABVFZW0q9fP/r06dOqiIUtsc/Ecbww+0mO57BWf1bQOroqgiEcDvPG62/w8nMvYkvAoQMmccGpd+J17t5RJYLuSzyVoKiXSKsSCHZVzJQJpak0rekxlUgkrPTZvLy8djMMFwKDQCBoFzbN9TdLKf7STR9oNgB2uVzWMuZFz+FwdFg6xJYw21ZcXMyECRNIpVJUV1dTu3EDxof/ZcQ++3DDP+5k/Pjxm+1LazCFku0NSs0ID7P8pNvtbrYOyB7rTX0UNq1cYEYymG01IxzMdBNzG9FolIaGBtatW4etqCfFZ1+QXX8Ldy9/7Dgieflccv0NzJ7xzmbmhq0hJyeHK847l5vf/6SZwFD90vMMSTTywCMPb3bcxo0b1+z5m++8ZvlcyLLMunXrcHvc/LDoBxYuXEhtbS3xdJyioiKG9R1Bjx49yMvLQ5az1Qa8Xi/BYNASDsxHaWkp5eXlFBcXU1BQgN1u58svv+SpaY9b36fSVC5VsTV9JzYFWVaw2W0osoxis2XfsynEEwm+X/cjWkbDtpsbMXYUW7rudMY2586dy8v/fon/ffgpY3tVcsWo0xlSMqBbpWoIBL9E13UaEhFqNzR0dVMEAkEHsWlUghmpuekEnxnV0NDQYPUrt5X+uz1E70YgELQbhmGQSqUIh8OkUqktdvTNi5bT6cTtdjcbOJqeAF3RIS8tLeWggw4iHo8za9YsqqurSeg6wWCQnJycTnOlz2Qy1NbW8sMPP1BWVkZBQQEej8cSXFRVJZlMWhEL5ky8OSDeUhsNw2gWCWIYBqFQCMMwcLlcqBtrSCdiOD2+Vu2jf8AgIj1LmTNnDpMnT27TfmuZDMm6OgxdR5JlNn7wLj0Wz+Pxfz3bIvHCFKW+/fZbPvroI+bOncuCBQuora0lEolYqTfDhg3j0EMPZciQIZSVlVkpDmbqiHn+mQ8zdcJut1vn6sKFC1HWpDlo6MSsWaSRIZPR0TW9yTwyk60MYehoTUJbRk+RMXQ8eobTRx6NLHXPyh+7DDpc9JvzsTWJP6bwlo3o0a2/dV0H3bC+J73p+9N1I/s9Ngl+ZiRQ8+c6eqap+kdGo8iXzyEDJvHkr/+GfzulRAWC7sLyjauYvnAG9z32YFc3RSAQdBJmn8jlcjUTFxoaGqx+ZSAQEAKDQCDoGsxOuWkeo6oqiqI0m3nfdNCqKIo1aOvMSIXtYbanoKCA3Nxc3G43yWSScDjMypUryc/Pp6CgAKfT2aHtkGWZv//9FuZ8+V/UtI3evfswZEglffsOYsiQoYwaNQqfz2cNdBRFsY7jpnl22xMK/H4/mqYRiUQo7dmT2IoqnMNGsGkIgxaNkli/lmT1OtL1IYxMBnsgSM6gobhLs2aDDBzKl9/ObbPAcMLxx/PajPdY8MBdOEftjTzjDZ791zMEAoEtLm+KV/X19dTU1LB+/XrWr1/PwoULmTt3LitWrOCnn37C4/FQUVFBr169KCsro7Kykr322otevXrRo0ePHbp5GoZBSbCYsRV7bn9hQacjSRJ3H38DkUQUTdfI6BlkSUGSQJEVJCQUWQZJQpZk5E3+lSQ5+7e8yXuygiLJKLLy83NZRpF+fi5J4La33oBVIOhqSoLFFPrz+eabb6xIPYFAsGtjejOYEbGSJFn9dnOCJZFIWKm05qOlCIFBIBC0CXNmLx6PEwqFUBSF/Pz8ZtUOftnpNr0VultnXJIkXC4XeXl5BAIBVFWlvr6eRYsWkZuba7ntdiSJRILPPnufu/4xDJtN5qfVUX5a8xnLln3E+x8YhOs9XH/9bUycONGKqtiRqA+Px4PT6SSTyVCal8vM118i9dMqpI3VSNXrSFevR9bS9O3Tl4Hlfelb0hObLPNTTQ0f3/c36nw5uA8+Ak+fcuZ89n6b99vhcPCvRx7m/mkP89ATD/HMQw9SWlq63c/V1NTw9ddfM2fOHL744guqq6uJRCJWOaaSkhL22msvxo4dy7777kuPHj3wer3NIhJai2EYLc0kEXQRg3tWdHUTBIKdAq/TTX19mFdffoVLL720q5sjEAi6AJvNRk5ODl6vF1VVSaVSRKNR0um0VT7Z6/W2fH0d2FaBQLALY1YrSKfTxGIx0um0ZTxohpR3VbrDjrCpF4M5eP/iiy8IhULMmzeP/Px8KioqCAaD212Xqqr8+c/XscceoznooIPJy8trcTt++uknevb04HZnL8/9KnLoV5Fjvb94cZg//ekCrrvubqZOPaSVe5lF13U++OADXnzxSb799msaG+vxu3I5ZtQI9pg4lvLycvr06UOPHj22+P3pus5nn33GLf+8lzUFxSxYsoSamhoKCwu3sLWW43A4uPLSSzj3rDPJycnZ4jKpVIpkMsnq1atZsmQJP/74o/VYtmwZiqLQo0cPSktL6d27N/3792fgwIEMHjyY8vJyy4eiLei6vtOc1wKBQLA9UlqKk44+qqubIRAIOplfpjCb6bSSJFlRspIkkU6nW1WdSQgMAoFghzBTI5LJJPX19dhsNvLz83G73ZuVxNmZKCsr4/DDDwfgxx9/pLa2lrlz51JSUsJ+++3XonUYhsGbb01n7dp3+ec//0xl5d4ceujx7L///lsdOJv4fD7iicxW3x88OMgll9q45ZbfUVzck8rKyhbvG8CSJUu46eZriTT+yNSpOZx8Sj8efWQpkUhf/nDllfh8288dl2WZSZMm8dqYMRx96mksdHn5+z33cudfb2lVW7bGto5RMpmkrq6Ozz77jBdeeIFVq1YRjUat9Jzy8nL22GMPxo0bx+TJkyksLKSmpoaPP/qEhx98lL/c8qdWVwD5JYZhIO+k57dAIBD8koNGTKK4p6giIRDs7pjVJmRZxuFwNKs2EY/HW7we4TIlEAhahVmVIJVK0djYSDKZxOVyWekDdru9S80a24rH46G0tJTS0lJyc7N1wWtra1m3bh0bNmwgHA7/P3v3HRbF1T1w/DvbOx0RBCsoqGDXWKMmRk1ijEnemGhieq+m+ia/N71XU43pRVOM0WgsMcUWu8YGKiAWpPeywPb5/YG7gYgCiiB6P8/j8wg7O3NnYYe9Z849B5fLdcJ9aDQaevfqw4ABZl55tQtx3VNZsOApxowZwH333cqKFSuoqqqq87kRERGUljhxOI4fZIiKMjHthkDuv/8m0tPTG3RelZWVvPbaS9x002UkJGQw47+d6Nc/BL1eRWSUHmh8EUuDwcBTD03H4HLy07oNfPTJJ416fmPk5OSwceNGfv31V3766SdWrlzJvn37yMnJwWq10q5dOy655BIuv/xyxowZw4ABAwgJCeGdt9/jzpvvI3FNFpn7Svnyi69OeSwejwdJLJIQBOEsYVTpSd2b3NLDEAShhXk/u3vrpWk0GnQ6HQaDoVEZoCLAIAhCo3hrLlRVVVFYWIjNZsNisRAQEHDS1WbPJGq1GrPZjL+/P4GBgWi1WioqKsjPzycjI4PCwkKcTme9+7nppntZvLgEtVrBoEGh3HV3e155tTPtO+ziiy8fZdSovsyY8RAbNmzwtQqC6iKYERHh5OTUHYDwio8PZPx4Fbfddi0pKSnH3c7j8fDHH39w2WWjSd3/PU8/04ERI8JQKLwTZIkBAwKxWgtPKiA0dOhQbrxkHK4KK8/MfJefFy1q9D4a4vDhwyxbtoxvvvmG2bNn8/vvv1NcXAxUF+js06cPd9xxB7fccguXXXYZSqWSaVNuInefjXsue5GxA//DuPMms/DHxWRkZJzSWDxusURCEISzx9acJC4cd1FLD0MQhDOMt9uEn58fQUFBDX6eWCIhCEKDuN1uXC6Xr/iL0+lEp9Oh1WrR6XQnXTDvTONt5RgYGEi3bt0oLi5m3759FBQUsGvXLvz8/PD396/VJaMuw4cP59NP49iyJZNBg6prE+j1KoYNC2PYMCgrc7Bx42peeWUFJSUaxo+fxLhxE+jRowcdO0aTnb2bqKgTL1c4f2QYOl0+N900gcGDL2LEiDEEBARQXl5OZmYG6en72bhxDWp1KVOnBhEbd2yNBEmCqioPFouf79wb64lHHuH8wYP56LPPcdST3dFYVVVVWK1W0tLS2L59O3v27CErK4vAwEDi4uIICwsjODiYQYMGERsbS3BwMD/8MI8PZn7M2D5TiOvYx7evYP8w4iIG8cP385j+0IMnPSYZWWQwCIJw1hjcvjdvvPgaAxbOO2v+lguCcOr+3W2iocRVRBCEBnG73dhsNiorK6mqqkKtVuPv749OpztjWk02paCgIBISEigqKiI9PZ38/Hy2bt3qm9jWF8mVJInrr7+DTz55mEGDjn3cYtEwZkwEY8ZATk4lGzfM5/77vyIlpYCQkEh0J45f+Aw6L4T4hAA2rF/PTwvWYKsCrU4iMMBDcIiaW26xEBkVdNw77pIkUVnpwc8v4JTuyg8ZMuS0tDizWq1kZmaSlpbGnj17yMzM9NVauPTSS+nZsyedO3f2/Twenv4oyTsOccOFjxNoCTlmfz06DWDpkk95cPoDJ32+SoUSj+w5pfMSBEE4U9w8+Gp+/uhWCgoKCAsTtRgEQTg1IsAgCMIJuVwuHA6HL2sB8GUueGsunI38/f2Ji4sjPT2ddevWUVRUxOHDh8nOzsZut/uq6Z5okjpkyBCefNJOZaULg+H4l9uwMAMTLzcw/mI3zz9fxO5dqXTp0rfBYzUYVIy+IJzRFzT8/HxkyM+zsnXnbpYsW8akiRNPYid17FaWycnJITg4+Li/I5WVlWRlZXHw4EGys7O57rrrjnk9c3Nz2bx5M0lJSRQXF2OxWIiMjGTAgAH07duX6OhowsLCSE5O5qH7H6WtIYYbx81Apaz79W4bFIW1rIojR44QFRV1UuemN+gpdDtO6rmCIAhnogndR3PD5Ov46oc5p9wRSBCEs0tjb8iIAIMgCCfkdDopKyvzBRe0Wi0mkwmNRnPSKfWtgZ+fH127diU5ORmj0Uh2djY5OTnk5+f72vXUd8HV6/UMGDCU9et2cMGF4fUeU6NRcu+9Cdx5xwY0muZJwZeR2bGrmBxFKLO+mdPgAIPT6eTQoUPIskznzp2PyWJZu3Yt1997HyqlCqPJRGREBL3jYolqG8bWpD1s27mLkvIydMGhWB1OtIV5TJ069ZjXNCsrizVr1rB7924qKyuJjY1l5MiRDBkyhO7du6PRaJg9+2O++ew7Luozhe6dThyYkSQJf1MQBQUFJx9g0OuxeUSAQRCEs8ctQydTtKKUz2Z/yuNPzmjp4QiC0IqJAIPQ6nnvJLtcLmw22zEV/pVKJRqNplZ/1+bkzQBwu914PB7fmL3rmlQqFWq1usWXGXjbTrpcLux2u2+sbrcbWZZRqVSoVCq0Wq3v9TybeX8mgYGBhIWFkZubi9Vqpbi4mOzsbEJDQ/Hz80Oj0ZxwP/fd9xg33jiR2LgKIiKM9R43JMTIiPODWfDTIWb8t1dTnc5xyTLs2FFOu1sfJ3Pht8yZMweLxULnzp3p3LkzWq326HYyW7ZsYeXatWxJTCJp714UAUEgSRjsNu678QYmXjYBvV7Pb7/9xmMvvUKb6U9g6dYdZ3kZBbnZ/Hz4IO59B9F2isM0ZiIBwdV3yTLefJHHpk2t9d70vq/Lyso4fPgwWVlZOBwObDYb2Vk5rPxjFQvmLWLvnn10atOTm8c+iZ8psEHnXGWvwN/f/6RfM51Oh11kMAiCcJZpZ25DyuHDLT0MQRBaubN7hiCcM2RZxuFwUFJSckz7P61Wi9lsxmAwtEg6v8vlwmq1YrPZfEEGb7EUtVqNwWDAZDK1eIChZneI0tJSHI7qCZRGo0Gv1/uWRXgDNWc7bwDIbDbTrl07srKyqKyspLS0lEOHDhEaGoper683wNClSxceffRF3nn3Mf7v/zqj09X/c+7Rw59vvslsqlOpR/VEXh0UjD02nhnvfYh/z154sjOw52TRPjKK3t1jOXw4nd2FxSj7DMQwbAxtb7gblbG6CGXF4YO88ssCnnl7JrLbjbZjZ/zumo65S1cANP4BaPwDsHSNO+bouYvm000pM/Xaa+ocXVVVFXl5eThtHhI6DKNjUAzGijZIbj2dLXGMvPQ6DLoTF8P8N4fLXm+RzhPR6/XYRIBBEISzTO/2Pfhq/gI2btzIoLqKBwmCIDSACDAIrZY3M8DlcuF2u3G73ahUKgwGQ600a4VCgcvloqKiAoVC4XusrvR2SZJQKBSoVCqUSiVKpfKE29fFmwXg7bjgdDrxeDyo1WpfgKPm+n2n04nVavX1mm3qzADvsWqOq2Y2Rc3tZFnG7Xb7MhQkSUKtVvvqLXhfl3OB9+cdFBREfHw8xcXF5ObmUlhYyO7duwkICKBt27aYTPVPbi+9dAIbNqxhwYI/ueaayHq3T0how9tvpeDxeJolmGO2qHHk5xJ+670olAqko8f0OB1UZhxhxcE05Ngg2o2+yPdYTcb2HTHePb36d83jQWrg70jB6j8wblrDR19/dczvfU5ODgcPHiQxMZHi4mK0Kj0X9PoPoSGhhIWFodFoT/p8FZLymN//xtDr9dhc9pN+viAIwpkoOScNSaMgJOTYArmCIAgNJQIMQqvlzQyorKzE5XKhVqvx8/NDr9fXCgY4HA7fdh6PxzexkCTpmKCBJEm17th772I3htvtxm63U1lZidVqRZIkTCYTer2+1lII75IOm81GSUkJWq3Wt2TidHC5XFRWVvqO6Xa7fY/JsuxbPqLT6TCbzb47896gTF2v17kgODiYfv36UVBQwPbt2ykoKODvv/8mNDSUvn37NviD2GOP/R+XX76aPn1K6drV74TbBgToUColCgsdhITomuI0TsjPoiIzLxdJoagVQFCoNZg6dsbUsXOD9iNJEjTg/SJ7POQu+AHz9o3Mnf1RnQXFDh06xC+//MKmTZuotNoYGHMRWo0WhUIJp9gisspe0aDA0PHodDrsLpHBIAjC2eWShNF8v+MXNq7fQOfODbvuC4Ig/JsIMAithtvt9t2B9/6TZRmNRoNarfZlAKjV6mMmw3q9HoVC4Vue4HW8LAaPx4PNZsNut590BoMsy76MBG8qvVqt9t2RliTJNxZv9oDVasVut/u2VSqVx72D7Q2WeDM5agZOvPusa3ulUoler6/1uLcmhEKhqJWt0JjzPluZTCaioqIIDw/HaDRSWlpKRkYGOTk5vkBNzcyY4/Hz8+Opp17nmWfv4Omnjeh0x7/8VgelVGQcsTZLgCEgQMnBwvzTfhwAd1Ul2Z+8T3dcfDR3DoGB1XUTvO+BrKws0tLSWL9+PZs2bSIpcQ9BprYMiBmN0WhCp9OdclaHVq2juLgYP78TB3qOR61W4/K4699QEAShFVEpVUzpM5HVv61iynVTW3o4giC0UiLAILQa3voA3n8KhQKTyYTRaPQVIKxrouedqBuNxmMm3TV5JzhOpxObzUZVVRVOp7PWnf6G8C6xqJkJUDMLwEupVPoCIlqtloqKCkpLS5EkCbPZjNFoRKvVnjDA4HA4qKqqwmq14nQ6a+3fez7wT2aGTqdDp9Oh0WiOm5nREoUwz2R6vZ42bdoQGhqKyWTC7XaTn59PQUEBNpsNj8fT4OyOESNGMHzYJL6du4Qbb2p/wm21WgXbtxfSu09wU53KcQX6q3AWFJ3249jyc8l5+2UmDx3M/z3+WK2aKN7lOfv372fhwoWsX7+e5L37aevXkTG9r8VoMGOxWDAYDKf8+9mlbQLLli7jzrvuPKnnq9Vq3CLAIAjCWWhn5h78ewS19DAEQWjFRIBBOGP9+w69d7IvSZIvM8A7WT5R4cGGLnPwTsi9E0WFQlE9kTjJAINGo6mVCVDXuGpmR3jP1ZtlUVVV5cug+Pfk1TtW72vk7exQ1zl591+zA8SZ0LWitVAoFGg0GgICAmjfvj0FBQXk5ORQWFhIWloa/v7+tGnTpsFFAx999AmuvHIty5YeoXefIPR6Fev+ymXP3hIKC+xUVbkpLXVSWFhFSkrpaT67aoFBaqS9Gaf1GGV7Eyn++F2evO0Wrp/6z50xm81GRUUFhw4dIjk5mS1btrBs6TIK88oYETeRXp2HoNNVF0LVG/RoNOpTzqrp3r4/K5YtOOkAg0qlwulx1b+hIAhCK9OjbVdSnc2T0SYIwtlJBBiEM5Y3m6CyspKysjI8Hg86nc5XH8G73KAp77Z7J+Le7IITZTzUtx/vkoOGbOtdtqDVan1ZCd4lGt4UfC9vcMEbyNDr9bVqJtQ3pnO1lsKpslgsdOnShby8PIqKiigqKiIpKQl/f38sFkuDAwwGg4Gnn36dsWNHIEkeNFoFUZEGomNM9IzXYzKpMRo0uD0efvg++zSfVbVu3fyQ56Wdln3Lskz+b8uRViziq9deYeDAgbUer6ysJCcnhzVr1vD1119zYP8hgo0R/GfIvQSYQzEazQQGBtRY/lT/cpT6RIV15ueNRSQmJtKjR49GP1+tVuNyiwCDIDQXu9PB+v1byS7LR5Y9XNZrDCZd/W1/hcZLzj9AYKf6ixELgiAcjwgwCI1WM7Og5hKCmmv/a945P9nJgPc4brfbt1TAewfem7XQlBpba6Gpj+vtWgH/1ERQKpXHpODXzLT4d1aC0PRqdpPo1asXhYWFpKamUlxcTFJSEmFhYcTGxjZqnwMGDGDYsPPoFpvLuHHtMRiO/dmtXnWYZvlVlKFDez9Ush3rwf1YOkcD0qnWUQTA43KS8/VnROSk89mcr4mIiPBdGwoKCsjIyCAtLY3k5GQWLVrEwdQjDOhyEb07DcNgMGIwGDGZTOj0etQqNQqFRFMMTKFQMiR2PP974mk++/IT/P39G/V8tVqNUwQYBKHZ/Jq4mi8SF9KzRw+CgoO5Ye4jTOg6ipGxg4kMDG/p4Z01Fmxfzt8Fe/no6odaeiiCILRiIsAgNJo3s8C79t/hcByz9t9bcPDfNRGOV4CwLt4Jtk6nw2KxoNVqfVkBZ/MdeO9yj7oyKGoWhayZJSFqJpx+ISEh9O/fn+zsbFauXElBQQGJiYl06NCBysrKRu8vLi6BkNANdQYXAMIjzFitp79TgQwoFBJxcSZS//gVU6cuKJrg/eUoKiRn1kxGRIbx1tdfYTAYaj2emZnJ6tWrWb16NevWbsCsDuLK8+4h2C8MpVKJwWAkODj4aHcV73u+6d73fbsNJ39TFo8+PIPZn3zYqOeKDAZBaHoOl5NfdvxOZGA4DreTxOxk/s7eg06tJbM4h9c+eIPBgwcDcODAAb756mv+b+lbPHnh3SRlpbAlYzcPjbqFAOPJFW8V4O+MJEZcMJKYmJiWHoogCK2YCDA0I5fLhd1u9/3zpr4rlcpaa+K9d+29k0xvkcDmnkQeL1Oh5tp/bybBvyf8NQMMXnXVETgR7zl7axmcK3foj1czoq6uD97thdNPq9USHBxMcHAwJpOJ3NxcCgoKKCoqwul01sraaYiyshIio45fB8Nud6FUnv73vLfWSK8EM4krN+By3gZIR69NNSb2DTgtWZaxF+RR/NdqnKtW8OCN07j1pptQKBQ4nU4cDgdHjhxh//797Nixg2XLlpG69yC9OoykX/RI9DoDarUatVqNyWQ8YQ2TUyVJEmP6XwpzLeMAACAASURBVMW7C2eQkpLSqA/UootE8zuYn87qlE1c1e8SkrP3syjpDxKzU7hnyHX07dATs/7k244KZ4ad6Xv4Yf+vGPUGVGo1XaKjeeX5t7Db7ciyTHx8vG/bTp068b+nn2JB/AKeeuMdevTqyQFbFruO7GVEt0EteBat2w0Dr+S/K96A559p6aEIgtCKiQBDM3K5XJSVlVFcXExpaSl2u91XqNBoNPrW0Lvdbmw2G2q1muDgYN8EoLn9O1PBbrcD/0xIanZJqOu5cOqT38bUMjiXiKBC8/MGAb2dSxQKBVarFavVitvtrhX0qU9lZSU7dmxh4uXHX+eal1eJyXTiuhpNQalUoFBoiI8PwTN3D1U2GwpJQqlSoaG628iJTsvjcFC4eT2u3dtxpOzFqFQycfQobv12DlFRUb7tHA4HpaWlbNu2jXnz5rFl0xYULj0TB9xBkLkNBr0RP38/DHo9Go0WlUqJQnF6C5EqlSp6RJ3HL4uXMP2hhgcYVCoVTrfzNI5M+Lfnf32PHGcRP+5eTpeYaK64+Somms388PV3fDDnG9664v8IMQehUZ0bgeizSXL2AZ5d8S5WRwVPPPd/XHrZhAY/9/JJl3P5pMsBWLt2La88+hxDovuhUoqPt41V5bAxd8NCwtq2bemhCILQyokrcBNwu904nU7sdjs2mw2Hw+G7gw//TJK9bQUBzGYzJpPJl73g7YoA1Xf/vf/3Ru5rdknw3uFWKpW1lgvUlUXgdrt9k5/GFiz0to2rmakAtbskiLX/zUcEFVpWzWKcQUFBmM1mCgoKqKyspKSkBKvVisFgqPeOuyzLPPPM/+gZr8LP7/gBhKpKF1rt6Q+sea9PnToGoNd4sKUmY47rgSRJvvd/Xb96CoWC4s0bqZg/h0Fx3Zg0YRx9+z5JeHh4rZohxcXF5OXlcfDgQdLS0nxLIkJN7Rl73lQsRn90Oh0Gg6G6NatGg0p16p0iGio6sifr1ixk+kMPNup5UhMu1zgXuT1uNqXtoHtEDH4Gc73bXxI7il9zNvDZt1/i7+/v+/24aOxFXD52AjfPeYxgcyBvXf4kIRbRYq+1SM4+wPO/v8cDTz3MyFEjMRpPvnDjsGHDeAo7ZTYrgcbG1VUR4MctS9iQt4vX/vt6Sw9FEIRWTgQYmoDb7fZNMgoLCykvL/elTHvT/CVJQqPRYDZX93L39/dHr9fXWdnfGxiw2WyUlpZitVqP3kWUkGUZpVKJVqv1pQ97H4Pak1CXy+ULeHgnCvWpGYTwLt+oK1NBZBYI5yqdTkdwcDD+/v4UFRVRVVVFQUEBpaWl9RYfTU9P59WZM/nkiy945rFOJzyO1eqkvNyB0+lBrT797zOlSsXoUcEs/+xNQt6di9vtwul04ZE9IMvUjE/Kskzx4vm0SdnNp2+/QXx8fK3WqTWX+OTl5bF161bWrVvHkl+WYC2xMTBmLL06D0Grqb62+Af4o9V4ay0omqe45VFmgz8lJY1rB+otvCo0Xl5ZAb8n/cWmrJ2UKquQNrm5tNsoIgLC6N8xgd/3/MVfh7bSIzSG5IIDKBRKbh18NWF+IZgrzQQEBByzz6dfeY78vHy2bdvG9xsXc8/oG5r9vISTs2TPn1x167Vccuklp7wvb5aUXq1rgpGdW1JzD7L8yDrmzvuWrl27tvRwBEFo5c6pAIPb7cbhcPgyDVyupinS5fF4cDqduFwuDAYDSqWSiooKPB4PBoMBjUbjq/hvNBoxGo0YDAZ0urr/CHo/pHvvIHqfX/MDrUKhQJZlXC7XMV0cau7HW8PA+3VjeO/YikwFQfiH0WikXbt2HD58mPT0dNLT0/nw008pn/URLrfnmMKb1RPv6mtEucOJZugoIu99jDc/epshQ8LQaOoOHvTq3YZvv8ti06Z8hg5tc9rPS5IgPj6AX9cXH72+KFGpZDweBVAzwCBTtHYVgYnbePvVVwgKCiIvL8/XQlahUODxeMjKyiI1NZWkpCTWrVvH+r82YVYFM2HE1QQHtEWn1aLV6TAaDOi0ulqB0uakVChxOhu33EEEGBpnZ/oelu9bw9aM3Tg9Li65YgJ33v4ggwcPZsOGDXz7zVw+Xv4dJqUBS3gg190yjSOH0hnRaTwLvp/P9V89xOXdL0Shq3vJTEJCAgDhEeHcOPd6/s7aQ1ZxDm9e8SRxEaJY3ZnK6XaSW1bAyMimaYmYmppKhH8Yeo0IMDTG70l/8fZfn/PwE4+K4IIgCE3inAswWK1WSktLKS4urrfye0Mn5DXv8oeEhKBUKikqKsLj8RAUFORL+as58ajvzr+3JaNarT5mHN4lGd6ghsfjqdVdwMvbwtC7dvxksg1EpoIg1GYymWjfvj0HDhxAlmUOVjko79IT/25x+IWGodJpwP2vbCFJQqNUEhAYhKRQIHtkdvz1B6+9sYcnZvSo8ziyrKBrTDd+WVxKv35B6HSn/3KdnV2JHFi9/rb2NeOf64q9sADnLz/yzDNPUVJSQllZGUbjPx0fJEnC6XSyd+9e5s2bx5rVaykpKOe8ruOJi+yHwWjCYrZgNBrR6/W+5V8tNWE3G/woKSnG7XbXWVy1LrIso5DENfF4HC4n+7L3k5Sdypas3eTZirj9wbu4r98MAgMDsVgsvm2HDx9OSEgIL5ZbmfSfKxgxYgSBgYG+xydNmsTrr7zGgd92U6wuO+Fx4+LiuPeRB3jrzbeQXR6eW/wOY3uOYNqQq07buQqNV1RRwk/blvHr/nXE909gwIABp7zPyspKnnniKYa079MEIzy3HCrM4PKrr+Dqaya39FAEQThLnDMBBrfbTUFBgS9zwfvhtiHqCzR4lyx41xBDdcV5t9vtu6vXGDWXO9Q1sfd+GPfWQvAuffj3OL31HdRqtWhlKAhNxGKx0KlTJ1JSUtDr9biCQ9ANHIrGYkbrH4BaU3+mj6SQiL73Uf64ZxpX7CuhW7fa64UX/JTF2rU2pk//Hzt2bOPLL39h4sRw/Pw06I5zF7cp6HRKJKfd1zHin0n/P5P/oiULuXHSRHr27ElBQQGSJPmysfLz832ZCytXrmT50hX4a9syedgtBPmFVl8PDQaMBiNanRaNRo3UwhN1hUKJQWeisLCQ0NDQBj3H4/GI62kdHC4n76z6nN05KdgUTrpEd2H6qzOIiYnBbD5+nYXY2Fi+/vabOh9TKpVccNGF3PL5HCZOnlTvGLpEdwG3B6VJQ7GtjITI7id9PkLTc7icfL1uPgVtHHzz87e1CsGeCrfbzYHUNF6Ydl+T7O9cYLVVsGTXn/x+ZCNfvvF1Sw9HEISzyDkTYHC5XGRnZ6NSqdDr9QQHB59SMaGaat7lVyqV2O32Y5Y0NCVvF4e6shtqfv3v2g6CIJw6s9lMp06daNeuHUajkaLMI9htNpx6faOWIZki2hHyn2n878UvmPtZH1QqBW63zI/zjrBnj5mff15OUFAQF1xwEQ8/nMH77x2goKCQ0FANnTopiemqJybGj8BAbZOdm9GoRuE4fmaXy2rFtW0jtz79M35+fvj7VwdGFAoF+fn5pKens3LlSr768isKc0sZ1HUcPaIGolSpMBgMBAQEotPpUKtbNmvh3/yMQeTk5DQ4wFCdwXBmjP1M8t2WRRSYq3jntQ+Jjo5usp9v3759uemuW+ge37PebXv37s30Rx+moqKCnN9S6BUV1yRjEE5dhb2K+398Gr92wbz4v5ebLLgAsGPHDtxONzpRf6HBVidvZM6eX/hq7tdN+rMQBEE4ZwIMUJ3arNFofJkGjc0saIzTObEXQQNBaDkajQZ/f38sFgsGgwFKy3Dk5+K2WBpd56TLVVPYtnEt907fyfNPxfLeu0do06YX3377gW/ybrFYmD27+u6ux+MhMTGR3bt3sXHjKn74fgtOZxXh4XoGDNQxcmQbFIr6rw0bN+aRmmpDlmViovX07ReMWq3AYFAhO0qO+7yCv1YxdvgwgoODfd1tsrOz2b9/P/v372f79u0sWbQMjcfC5GE3Emhpg1arQavV+a653uACZ1AXBj9jMFlZWcTHxzdoe4/HU3PVyFmt0FrMj38vpcxu5a7h12HUGoDq16DCXonN5SDEHIgsy2zLSuSh154gJqbp6x7c++D9DdpOq9UyZeoU3nlrJvvy05p8HMLJ8Xg8PLH4NYZeMpLHnpzR5PvfvH4jSqWC77cu5rpB9We6CHBh9+F8t2uJqK8lCEKTO2cCDAqFgtDQ0Fo1CQRBEBrLe/3wFXB1ubCnH8TdoRNyY2edSgVBbcNxHy7jkUcSufeeJ7n11tuPm36vUCiIj48nPj6eKVOmIssy5eXlJCcn8867r7Bm9V4mTgygd5+62/Q5HG5+nJdJaqof06Y9iNPpZPXq5cyfv5VrpwQRGmrEUbb/uEsAXOtXMe2FZ2t978CBAyxcuJANGzaQsieNQTHj6B45EJ1Oj8FgwGQyHe1Co66RtXDmBBcATFp/MjMyG7z9ubRE4r01X+EXH8bKpZtYlbyRfp0SKLOVk5J7EI1GjVqtQSupqXRU4R8cQHh4eEsPGYCLJ1zCF7M+xe50oFUfvx2s0DxScw+R6yji0ScePy37f+ixR+h/3kAeuP1ehnTqR6dQcUe+PmqlCpfH3WTZvIIgCF7nVIDB26P+dAcXvN0XvN0gXC6XqIEgCGcJ7/tbrVZXFyl0OnAm7sAzdGR1wVWPXD2JbsAcujxlL+H2Sr79eSkHDhxg6NChjR6LxWKhf//+fPXlPDZs2MDjj99JYaGN8nIXR45IOBwykVESQYFKfvutmH79Lubbb5/3FdqbMmUKW7Zs4cknH8DhzEPpcVDw1ypCh4+qdSxbfi66ygri4+PxeDxkZGSQkpLCunXrWLt2LWkph7mg52RiIhPQaXXo9Tr0egN6vR6tVoNCceYGdQNMoaQfzmjw9rIsnxNZZEeKsvg7O4nFX72GyWxm/MXjyc7OJigoiISEBN9yvczMTIxGY50tJFtKSUkJskzjg35Ck/J4PDyzdCaJealce9PU0/q++eyjTxgYnkDHkKbpSnG2252xj1J7OUFBdQekBUEQTtY5E2CQJAm1Wt0sHwq9xRfhn9aYGo1GBBgE4Szireei0+koTdqF22nH4/bg8XhQKhu2BMD61ypuv+IKwsPDT/nOryRJDB48mLff/pw33ngBWVZwyy13YjQa2bVrJ0eOpHHzzT2YMuXYD/n9+/dn2bK1XHHFJLSaHA68+hT29asJnDwNY4dOAJQl7uL8QQN97XNTUlL44YcfWL9+PZmHchjTewod28Sh0+oICAhAb9Cj1WiPdqM4syfj/qYgDqZvavD2Ho/nnOgi4fF4cDid+Pn58cSTTwDVNQ7+rV27ds09tHrFx8czYOhAlu9excQ+F7X0cM5JDpeTRTtWsDljF4tXLCEiIuK0HSsjI4PdiYmoFErunvskap2GcEMo0SEduKjn+Ri1p29JbGtl1hmJMrXl2Wee5eZbbqZ9+/YtPSRBEM4S51SAobkm+AqFAp1Oh8PhwOl0Hp1wKH1BB0EQWj+VSoXRaMRoNFJaZqUyeS+2oGCUSgVarQ6F8sST6pLdO9Ds3c2kl5894XaN1adPH+bMmV/rewMHDqz3eQqFgo8+ms3nn3+OSqWkQ4eOPD3zZexXTCFwyAhcKXsZfOEIUlNTSUpKYt26dfz++x+UF1Vycf8b6BLRA51Oj9Fo9C0fUalUreJOv8XoT97hvAZvf7YFGPZmpbJ2/xa6t41hSHQ/ZFmmwFpMiCUIk0rPnj176NWrV0sPs1G8GUYau1hf3lI+XPM1pSEu5vz47WkNLkB1kGvDlo0AvPbyq2TmZLF67Qb+3LeePu17YKyR1VBSWYa/wXK8XZ0zOoZE8fTFD/D9lsXce+tdLFqxpKWHJAjCWaLBM15JkpTAViBTluVLJEnqCHwHBAHbgOtkWXZIkhQFfAn4A0rgcVmWlx7dxwzgZsAN3CfL8q9Hvz8WmHl0+09kWX756PePdwwt8BXQFygErpZl+VA942/oqZ4y7/pshUJBSUkJdrsdnU6HVtt01d4FQWhZarXaV7hQkZtL1Zb1VPTqe3T5hAaF8tgJqNtWRcmuHdi2bUabto/P3n4TPz+/Fhh93UJDQ3nsscd8X4eEhHDPhx/jGXAe9sQdxD90Lzt27GDWrFns3pFEiCmK8UNvI8AcgtFoIjAwEK1Wi0qlRJLOnC4R9dFpDFit1gZv7/F4aCWnVq9D+Ud4+rd3uOaW6/hq4SLeXvM5MjKVThshWn8GjBzc4OKXZ6JyR0VLD+GcZHPaWXVwM8u+WNFs1ziNprrWxvhLL+b2a2/mqp5j6dO+B+0C2+LxeNidsY/H57+MR/Ywe9orpOUeomvbzrT1CyWjOId2AWHnXKZpmF8IDtlFVkEOxcXFZ9QyJ0EQWq/G3FK/H9gLeMO+rwBvybL8nSRJs6gOHHwIPAn8IMvyh5IkxQFLgQ5H/z8Z6A6EA79LkuQtNf0+cCGQAWyRJGmRLMt7TnCMm4FiWZa7SJI0+eh2V5/ka3BaeGsueOswNLa6vCAIZ7Y2bdowdOhQXC4XRUVFlO3ajq3Cik6rwyN7jtm+aMtGyud8Rv/usUy4aBRjx77sq4NwpurSpQuOnCwKN2+ga2Q7Nm/ezPz589m9fR8j4iYRG9UPnU6PXq/HZDKi0+mOZi3AmVbI8UTKK0sICQlu8PayLKOUztyaEg1hc9r5fc9a5uxczGPPPsG48eO49bZbyczMxOFwEBAQwKpVqxg9enSrnXTl5uXi7xGZgy1BrVShkpSUlpY2W4ChvLycD955n+/nfofscrPDkcafm7agqQRZhhx7IWgkcEj897c36BrXjVmLv8VeZcdgNqLxqOgQGMG4riMY2Kl3qwmQnqotB3firnKwfPlyrrnmmpYejiAIZ4EG/eWVJKkdcDHwAjBdqr7qjgKuPbrJl8DTVE/+Zf4JQvgBWUf/fxnwnSzLduCgJEn7gQFHH9svy/KBo8f6DrhMkqS9JzjGZUf/D/Aj8J4kSZIsZvGCIDSTyMhIxo8fj8vlYseOHZQWFFKxdw/GAYOQPbUvRda0VFzff8HCj2cRGxvbQiNuHI/Hw5wffsCjN2Bb8wc9ojvy3nvvcTDlCJf0vYl2IZ3Q6bSYzWb8/f2PdolQtsoP5RZTANnZ2Q0u3lidwdD6zhNg9b6N7Mvez4Kk35AUEvN++YnOnTsD1YHxyMh/UsknTWrd7f48Tg+yJFNeZcWsN7X0cOqVW5qPn8GCTt06sh0r7JV8uWE+ReXFpJdnc/fQ60iIigNAqVASG9yZjRs3EhXVPB0dsrOz+X7udyDDf1/4H1f95yqKi4tZunQp5WXlfDz7YwJCApkwYQJ333s3KpWKqqoqysvLCQ4O5tChQyQnJ/PZB5/w3qqvGNAhAa1KQ4RfGBf1HI4sg0qhbLUBt+P59LpXeH7Ju7z+0quMGjWKNm3atPSQBEFo5Roa2n8beBQwH/06CCiRZdl19OsMwLvA7mlghSRJ9wJG4IKj348ANtbYZ83nHPnX9wfWc4wI73NkWXZJklR6dPuCBp7PadVaP3gKgtBwer2ekJAQQkND8ff3R52ZSdWWdbj69MftdiN7PEiSAiQoW76Ip++9p9UEF+x2O488+STLUw9iuuo6it56kb0uGwdS0rm0/81EhnRBr9cfrUFRXW+hod15CkpyOZydQlRYF0IC2p7mM6mbLMukZexhTeLPlFUV43Q5sLsqKS8vr5VV4vF4uOn6G9m9azcqpZLggGAcdhuFxcV09D/zChueyOGCDN7+4zP2lRwkpksM9z8+HaVS6QsunI3e+/gDHrp/Oh+s+prHxt3Z0sOp064je7HoTDg9Lh5e9BL+OgvPjnuAQKM/wGkJjOSXFaJUKn3HOBmbD+zgtVUfM/qSC9GUwZjofrz67Sfo12lQSyqu6DmWTE8BY8eObcKRn1hMTAzrt21EpVL5al4FBAQwZcoUAC6beBlhYWG1PqPp9dUZWACdOnWiU6dOjBkzhplvzcTib0Gn07Fh9Tq++Hw+1kor50cP4rGxd55Vn/PMehPppdn06BV/Ri3ZEwSh9ao3wCBJ0iVAnizL2yRJOr8B+7wG+EKW5TckSToP+FqSpB6nOM6TIknSbcBtQLNF0AVBODcolUp0Oh0mkwl/f3+MBgNFO7dhL8zHHRSI2+1BqZSQJAlPxmEG9O/f0kNukKysLC64+BIKVRq6vfouh76fg2wtZ+3K9YzrPY3IkC4YDHpMJjMWi+Vo5kLD7ugdzkll/roPGDpiMN+vW4xRFUD3yEH06Nwfg6557jBbq8r4ae1sZG0lDz55D71790aj0Rxtp1n7zrEkSUR3i0GhUbJ9zRaev/x+NCoNWpUavUbXLONtKr/s+gNDtyAW/m/mWf/3UJZlnvrv/ygvK2PQ0PP45P3ZtNkQwg3nXdmg5+/LTmP9/m1c1W98nRP8oooSSirK6BR66q/jU0vfxiY70Cu1TLvtJqI6RPHwUy/i8XhAguig9tw04Cpiwjo1et8ej4eDBUcINPqjUihxyx4yi3N4eOELyB54atx9DOzUu0HvX4/Hw5aDO1l7cCsh+gCWpq7mnc8/qFX888abbiQzM5PXX3yV13+fzczP3m/2ZWA63fHfl23bNiygqVQqmf7wdN/XU6ZMYdeuXRw6dIhP3p/NC8vf45bBkwnzCznl8Z4pDHo9Dzz4wAlfP0EQhIZqSAbDEGCCJEnjAR3Vyx9mAv6SJKmOZhi0AzKPbn8zMBZAluUNkiTpgOCjj9dsTlzzOXV9v/AEx/DuK0OSJBXVSzEK/z1wWZZnA7MB+vXr12zLJ8RKDUE4+0lSdfAgKCiIHj16UF5ezt+7dpP3+nM4h4zA1KEjyrJS1MUFOPLyCAwMbOkhN0hVVRX9eiVwOK+ApAduoyo3F02Fgwt6TaZLux5Hgwumo50iqpdF1KeoLJ9Ne35nX9YW3njnFYYMGYLL5WL9+vXMn7eA9xbNwKg1YzYEYNYHoFOacLircLhtqJRqNAo9eo0JizEAg86EJEk4nHbySjKxOa20DehIn65D650oZeUfZt7aD7j6usu55967691ekiSeePIJZFlmzLALcHpchJtbX/pwhb2SPw9t4qePfiY0NLSlh3Pa7du3j18WLOK+YdNYs2At/hoT+nqWHew6speFu1dwpCQHh8aNOciP/b9+wIsTH/Vt43Q7eXfll/yVvg2Xy8Vrl86ga9vGT/wBth3cRaWjiu5tozn/hvGMHj2asLAwAAadNwhJktDpdLz00kv8sGUJT158b6P2b3PaeXThy5RgxVpuxWazYTQakRQKnnvtJZJ2J/Ll2kV8sXk+H05+/rjvBVmW2XpwF59v+RGHzsNlV0+iqrKKR6bOOKaziMFgIDo6mgcfe4i1g9YwdOjQk3ptzjSSJJGQkEBCQgLDhw/nh+++56a3H2HhHR+jUVV3KVmfug2NSkW/jgktPNrGkWWZHel7KCwrPuNrAgmC0HrUG2CQZXkGMAPgaAbDw7IsT5EkaR5wJdVdHqYBPx99SjowGvhCkqRYqoMS+cAiYK4kSW9SXeQxGthMdSWw6KMdIzKpLgR5rSzLsiRJK49zjEVHv95w9PE/Rf0FQRBaQkhICP3796esrIyDBw/iKC5At/YPInJjGD96FH0vHUPYo9Mxmc78NeAA4eHhvPnaayxatIjnXnyRypJyEqIvIDaqLwaDAbPZhNlsQa1Wo1DUnyZ8IHMfCzd8xNVTr+Slax/1TaJUKhXDhw9n+PDhOJ1OsrKyyM3NJSenupq5n58fRqMRm81GeXk5hYVFZGVkk1dyCJfLhd6op1ffzrQJa8PihUv4csUG+seMJjwoigBLSK0UZlmW2ZGynlVJ83nmhf9jzEVjGvWaSJLETXfczFPvvs3z46fTLrBllnY0VkllGW+t/JTNh3YycMigsz648Pwzz5F+JJ3i/CKu7XUp4+JHMi5+5Amf43A52Zj2Ny+tnMWUaVOYMvQ2+vTpw4EDB5g6aTKHCzJoH9wOu9PBrJVfc0iZx7wlP3HPzXeydNefrNu/FbPOwJjuI/AzmHG4nPyWtAaDRo9WpSXEEkh0m45UOqpYn7qNlPwDpBYfpshTTnRMNNsyExlgHeF7XwAEBQX5/n/nnXdy+8Zb+C1pLRd2H1bnORwuyKCoopQ/ktdxUexwekZ2Y2f6Htz+Cpb//BsVFRXk5+fTvn17oLrWxsWXXowsy1w4ZBQZxTlEBYXXue/vtyxmZf5W7n7qfi644IIGLQ3o1q0b3bp1q3e71iggIIDrpl3P5x9+SkllKd9tW4xepWPJ3pX4BQVwfmYyNwy56oxfQrEiaQ25pfkk5aRSpKlg4rVXNDjDQxAEoT6nUl75MeA7SZKeB7YDnx79/kPAx5IkPUh1wccbjk7+kyRJ+gHYA7iAu2VZdgNIknQP8CvVbSo/k2U5qZ5jfEr10ov9QBHVQYkziizLvkwG751OQRDOPv7+/sTGxpKenk5gYCC5ubnIsoeuHdozcuRIunXr5lvjeybzeDx4PB4OHTrE1q1bWbNmDRVlZXhkJ3szttIr+jzahLZBrzegVqsAmbyibCzGAHRawzH7sztsbE9Zx4aUJcz88A0GDhx43GOr1Wrat2/vmwA11qRJk1jw0wJ+XfY7a9bNp6y0nNCAtmjUehwuG6XWIsLbhfLl3E+JiYmpf4d1uGbqtVTZbXy9cAEzxtx1UvtoTn/sWceryz5EFaTj9gfu4oYbb2jpIZ12ap2GzX+sp1dkd7p06IDH4znmzrzH42H9/q0EmQIwaA3cPve/SGoFb30wkxEjRvi2i4uL4+IrJvDk4jdoYwiioLKYPGcxj/33ccLDw+k7qD+bN2zikgsmsD85lZvnPMprE2fw3KJ3yPEUMXLESCqtFezdvI/re0xgU/pOXGEqhl06ggk9p9K9e3d0Ol29BUPDw8N55pXnue/Gu+gVgH3TTgAAIABJREFU1Z0Qc+1MKJfbxW1fz0BhUCGpFJjVBrqFd8asM3Ig7QBVVVWYTKY6A5ySJDH15ut548uPufW8yWQUZdO3Q09CLNUBjr9SNrM4bRVf/TiH8PC6AxDnIoPBwBXXXMX1XzzEyDGjKaoqIzQyjNmff8IVF09kUKc+xIZ3aelh1qm4opR3Vn3BQVsWYy4dh2Z3LrNeelsEFwRBaFLSuXLjv1+/fvLWrVub5Vje1pQ2m42SkhJkWSYgIACj0dgsxxcEofm4XC6cTidLly5l5syZJCUlYbPZGDVqFHfccQe9evUiODj4mPX9ZxqXy4XDUd2qbPbs2SQmJpKfn4/RaMRisaD0aOnfbSRqtZr88gxyizNoExpKSXEZVw69i6iw6g/UJdZC/tq1lH2ZWxk0ZAB33HUbcXFxzXouZWVlHDp0iKqqKgwGA0FBQbRt2/aUA73Jycncf/1dfHrNy6iUZ3b7w4/Xfsf87UsBkJQKfv7tl1odIs5Gb7/5Fku+WkAFNoLbhDLQrzujug7G7nLgdDv5dPMPeGxujjjz8FS5mNp3ImnGPN78YGadSwQcDgdffPEFyxYv5fKrJnH++ecft37Fqy++wreff4OkVfLwjEe5dkp1A6y0tDQeve9hLP4W3v/4QwyGY4NxDTu3t8lcnczDF9x2zGOfrP6OFHUWM556grdefoOi/TncO/wGpi98nvU7NqHRaI67X4/Hw/sz32XBvAWUVpYRrg6mQ2g7OgVE8VPir3zw9Wx69ux5UmM+m7ndbhITE4mPj6+us3M0mDV71kekLd/F9NG3tPQQ6zRj4SsYogP537NPn/UZTYIgND1JkrbJstyv3u1EgKHpee8E2mw2ysrKfAGGk/1gIQjCmW/jxo3MmzePDRs2kJSUROfOnRk3bhxDhw5lwIABtdKezyTevwFpaWls376dlStXsnz5csrLyzGZTMTExNC7d29iYmJwOByYTCZiY2Pp1q0bRqORNWvW8Pj0/zFh4E1Yq8r5fef3XHv9VVw9+T9nZbuzEf2H8sHlzxJsDmjpoRzX0l1/8uXfC+gSF8PIi0bTNrwtI0eOPOsz6Ww2G2vXriUoKAh/f3+uvHQSwUHBlJaU4Kiyc9v0u9j01waefuFZnnzkvxTmF3D39Hu5ZMKlTXL8We9/SM+EeIYMHdIk+6spOTmZay67imv7XMZF3YcTagkGqt+/49+/kVlff8yAAQOQZZk3X3mdNX+uISM7gz//WtWgzgCyLFNaWsqKFStQKBQcSEmjU5dOXPmfq5r8XM5mRUVFTBg9nq+mvIlRe2ZlrmUUZXP/oudYuvJXUW9BEISTIgIM/9LcAQaXy4XdbsdqtQLg5+cnAgyCcBY7fPgwO3fuZMWKFSxcuBCn00nHjh0ZP348119/PR06dGjpIdbJu5xr2bJlzJo1i127dpGXl0doaCixsbGMHDmSiy++mMjISIxGo6/9W03Lly1n9qzP0Ot1PPzYg/Tu3bsFzqR5TJt8HQO0Xbmy38UtPZQ6/ZWyhU92/8j7n82iU6eTK0B4tsjKyqJt27b8/tvv7E9J5c57zvylLccjyzKbN29m2eKlrFn+J3EhXQgyBHDj4Ku4ce6jzFn8/THLGCorK8XnjhYw7eqpXBIylOFdj78srCV8svY7tH2CefjxR1p6KIIgtFINDTCc2TmerYzb7cZms+F0OnG5XLjdbiRJQq1WN7hHvCAIrZOfnx9du3blwIEDBAUFkZmZSUZGBocOHSI7Oxt/f39MJlOdE/SWlJuby8GDB9myZQspKSkUFRWhVCrp2LEj559/PgMHDiQyMvKEd0HHjhvL2HHN1+++Jd181618/twHXMmZFWCosFcxf9sSftm/mnc+ef+cDy4Avgn3hWMu5MIxF7bwaE6NJEkMHDiQgQMHsus/V7B//342rtvAs0tmUlpRVmcgQQQXWsadD97D4/c9gt3lOG5hzuZW6ahiWepqfnpvUUsPRRCEc0DDmpcLDeJyubBarRQVFVFUVITVakWhUKDVahvcJ14QhNbJbDbToUMHOnToQEhICEqlkuLiYrKyssjIyKCwsBCn09nSwzxGRkYGv/32Gxs2bCA/Px+Px+NbGnHRRRfRr1+/VtMBozl0796ddGsOB/PTW3ooQHWRv5ScAzy84AUKI1x8t2geCQmtq1We0Djx8fFMmjSJ5196gY7DYomOjREp72eQQYMG8fbsd3lz5ac4XLWv+eVVVl+drr9StpBfXnRSxygoL+aHzb+QXZLXoO0LrcV4kEXdBUEQmsWZdSutlZNlGZfLhSzL6HQ6tFotOp3uaDs3EWAQhLOZUqlEqVQSERHBoEGDcLvd7Nixg6ysLNavX49arcbPz++M6SjhXR5XXFzMvn37OHz4MHa7ncjISAYOHMjQoUOJiIgQxWn/JSgoiP9cP5nlf6zhzhFTW3QsVlsFn/z1Heuyd3DbPbcz5bqpZ32dBeEfGo2G/3vmqZYehlCHhIQEwtuFc7gwg+g2HQGYs2kh3/y9kM7B7XG4HGRVFjAxZjS3DGt4IzSPx8Obf3zC1szd9BsxiPsWPYtBoWNwZG8u6zWGML+QY54jyzIzV3/BtNtubLLzEwRBOBERYDgNVCqVr4e7t0Wl+NAnCOeG8PBwRo4cicPhIC0tjczMTP7880/0ej0JCQkEBwe39BBr8XZcyMnJwel0Eh0dzeTJk+nZs2eDisOdi84bMpgXfljeYse3Oe08teQtUgsOERYZzsLliwgMDKz/iYIgNAtJkojuFsORwiyi23TE6XayZN9Kvls4j6KiIjQaDcXFxfz4xtcN3qfd6eD91V9yWJHPR99+RkxMDG63m507d/Lnb39w3/xniQlozzW9JxAXHg3Az9tX8PPePwjpGMYtt916uk5XEAShFhFgaELeYmlQfTfzTFtrLQjC6WexWOjcuTMdO3YkNDTUN3lPT0/nyJEjWCwWLBYLarW6RcdZVFREdnY2qampFBQUoFAoCAsLIyoqiqioKIKCglCr1SI4Wofg4GByyguYu3EhV/W/GLWy+X+WOzP28sxrzzNhwoRmP7YgCCdWUFDA6pWruXBsHxwuJx+s+oqeA3sRExPj22bu3LmUlpVSUF58wq40VQ4b32xawNaMRKIHxvH581/6MsuUSiV9+vShT58+3PvAfSxdspQ335+NVOXBT2vGZvLw+mcz6datm8ikFQSh2YgZsCAIQhMyGAxotVoiIyMJCwsjNzfXN5k/ePAgQUFBvqVTLSkvL49NmzaRmJhIaWkpOp2O9u3b065dOywWC1qtVgQXjiM8PJz3Pv+Q5596lqzfc7l9+BTM+uapU/H34UR2pu/BYDAQFRXVLMcUBKFxTCYTw84fzssrZxOgNxPcJZzXn3+j1jYXXnghf2/exiu/z+K1y2ccsw+3x83n6+fx2/6/GDZmJA/e/RjDhg07bqBAq9Vy+aTLuWziZezfv5+srCzi4uJE3QVBEJqdCDAIgiA0oZq1GAYPHozb7Wbz5s1kZWWxbt06NBoNISEhLV7bIC8vj61bt7J3714qKiqIjo7mvPPOo0ePHphMJnG3qx49e/Zk5gfvMuv9D7lh7iPc1P9KLo4ffdzt88oKeGbJTML9Q5lx0d0n9fr+nvQXXyb9zKVXXsY3r3xLx44dT+UUBEE4TXQ6HTM/eIecnBzy8/OJjY09Jqs1JCSEu+67mxsmTeW3pLUcKcoiOrQDGpWGxKxkUgoOoe8UwLe//Ejbtm0bfGyFQkFMTEytbAlBEITmJAIMgiAIp0FERASjR4/Gbrezb98+srKy+PPPPzGbzQwYMKDF7yrl5+ezdetWkpOTsdlsREREMGLECBISEkR7uwYKCwvj6eee4aZbb+b2abewP/8wFq2ZdgFhdA+PITygjW9btVJNkbWEA8VHyC7NIyIgrEHH2JS2Hafbydb03Wwt2MPrH7wlukQIQisRFhZGWNjx3+vh4eGEd2rHgiMr6RIbzfvrv0Wr0XDVdZPpFjCQSy65BI1G04wjFgRBOHUiwCAIgnAamEwmoqKiiIiIwGw2k5+fT35+Punp6Rw4cACj0UhgYGCzf3h0OBxUVVVRUlJCaWkpTqcTg8FAYGAgbdu2JSAgQNSPaaSoqCi+//lH3nnzbfak7WfexuUMCOvJ0xc/4NumtLIMlVaNhII/966nY3A7QszB5JTm0Sk0isjAcN+2Ho+HI8XZfLrhe4448+jQoQM9xyTwwJX/R5s2beoagiAIrZBOp+O7n37wfe1wOABEUEEQhFZNfIoUBEE4Dbx1FoKDg31LDqqqqsjJyWHfvn34+flhMBia/YOkzWajuLiYsrIynE6nr31mQEAAFosFvV4vai80ksfjIS8vj/Codvw07ydGdRzE6G5D+GTtd2zLSUIjqcl3FnPjPTcz6sLRvPj086SWZZO8JZnYuDhmLfqWF8Y/RKfQKCrsVXy9fj4/7/uDW+66jTduuB6TqXnqOwiC0LJEYEEQhLOBCDCcBqItpSAICoUChUJBmzZt6Nu3L06nk3379lFYWEhiYiIhISF06NABi8XSrOMqLCxk586dpKamUlVVRVBQEP/P3p3Hx1XX+x9/nTP7lkz2NEmzdF/TQluWspT9IpsK/kRE0KuCgMhFFhG9CHrxCqJyBVkusohXFq9eFRUBgUJLW0r3fUvTpFmafTLJ7Ns5vz/Sc5ik+5q2fJ6PRx5pJ7OcM5k5me/7fL6f75QpUxgzZgwej0d6LxyEd999l+/edQ96MoOe0ljYtIJ3Ny9E9dh44be/IRgMUltbi9/vB+BXzzw56PYvvPACv37pFR68/C5+v/Rv1Nnb+fMbr1FVVTUcuyOEEEIIcdAkYBBCiCOotLSUs846i0QiQUtLC4FAgPXr11NZWcnpp59+1Leno6ODxYsXs2HDBmKxGCNHjmTWrFlMnDgRl8t11LfnRHDBBRfw+lv/IBgM0tzczIwZM7jwvAu4/DNXUFtbu8/bX3PNNSyev4jv/Pkn9CcifO/RByRcEEIIIcRxSQKGw0zXdTRNQ9f14d4UIcQxIC8vj4kTJ9LY2Ijf76ejo4PW1lZaW1vp7e2lqKgIh8OBxWI5KtsTjUbNpTPT6TR5eXlMmDCByspKHA7HUdmGE42iKGYztwkTJgDwhz/9kcLCwv26vdPp5IlfP8XLv3uJbVu3SRNHIYQQQhy3JGA4zHRdN7+EEMLotbBp0yb8fj9tbW10dHTQ1tZGd3c3ZWVlWK3WoxYwJJNJ+vr6iEQiaJpGbm4uY8aMoby8XOb/HkajRo06oOtbrVau/8qXj9DWCCGEEEIcHTLZVgghjiCLxYLT6aS0tJTa2lpqamqwWq00NDTwxhtvsGDBAjo7O0kkEmiadlS2KTsIzWQyJBIJUqmUBKNCCCGEEOKQSAXDESJNHoUQ2YqLizn11FOJRCK0tbVRX19PT08P/f395lKWFovliDdZVBTFfBxFUYjH4wQCAfr7+3G5XLJEpRBCCCGEOGjySfIwUhTF/HCeTqdJJpNYLJajVvoshDh25efnM3XqVPr7++ns7KSuro5AIMDq1av561//SktLCxMnTqSkpIScnBxsNtsR2Q6Px0N5eTktLS10dnbS399PfX09RUVF5OXlSR8GIYQQQghx0CRgOIxUVcVms6HrOqlUing8jsvlkoBBCEFeXh5er5dkMkkgEEDTNPr6+ti0aROdnZ00NDRwxRUDqw44nc4jFjDk5OQwatQompubqaurIxgMsnnzZsrKyhgzZgw+n++IPK4QQgghhDjxScBwGKmqisvlQlVVkskkmqZhs9mO2EBBCHH8sFqtWK1WKisrmT17NjDQcLGxsZHe3l7WrFmDzWZj48aNlJSUUFlZyahRoygqKsLv9x+240h+fj6TJ0+mqamJjz76yFw2s7q6mng8flgeQwghhBBCfDJJwHAYWSwW3G43iqIQjUbJZDJ4PJ7h3iwhxDGktLQUv9+P3W4nEomg6zrr1q1j69attLa24nQ6cTqdzJo1i0svvZTp06fjcrkOa8AwdepUNm/ejNPppLW1lbVr1zJhwgQJGIQQQgghxCGRgOEwUhQFRVHMJm1Gh/ZMJjPociHEJ5dR1TRq1CjmzJlDYWEh1dXVbNmyhbq6Onp6elAUhbVr12Kz2di2bRsjR46ksrKSyspKCgsLyc3NPejAwW63k5ubi9frxWq1kk6niUajJBIJWUVCCCGEEEIcEgkYjhBjFQlN00in09KZXQgxSFlZGQUFBUyaNInt27czd+5cgsEgsViMVCpFQ0MDO3bsICcnh7y8PGbOnMkFF1zA1KlTD6miwVhBwmazmStJCCGEEEIIcTjIqPcIsFqteDweksmk2YvB6XRit9uxWCwoimKeKTSqHoQQnyx2ux273Y6iKDgcDlKpFHa7ndbWVkKhEG1tbWzfvp1wOExraysWi4V0Ok1HRwczZsygurqa4uJinE7nfj2epmlommZWU2Ufd+QYJIQQQgghDgcJGI4Aq9VKTk4O8Xic3t5eotEomqYB4HA4sFgsg0qR5cO9EJ9cbrcbh8NBXl4eM2bMIBAI0NXVxZIlS/jnP/9JXV0dHR0dbNq0icbGRpqbm4lGo6RSKXJycvY7YDCmbA0NNY3/y3FICCGEEEIcKgkYjgBFUbBYLNhsNtxuN/F4nEQiQSKRwGq1oigKmqZhtVrx+Xz7PUAQQpx4VFU1pyx4vV48Hg+5ubkoioLL5WLbtm20trZSX1/P5s2baWhoYMGCBeZxZcyYMZSXl+NyufYZFCiKQiwWIxKJ0NPTQyqVAgamTRjVVUIIIYQQQhwsCRiOIJvNZjZj6+zsJBQKoes6uq6jaRputxubzSYBgxDCZEynysnJYeLEiXR1ddHS0sI777xDd3c3bW1tBAIBAoEA4XCYaDRqVjLsK1xQVZVIJEJTUxPt7e0kEgkURcFqtWKxWI7iXgohhBBCiBORBAxHkFHJYLfb8fl8WCwWUqkUsViMYDBINBo151V7vV4JGoQQZkWD1WrF7XZjt9txu92Ew2FisRjLly9nw4YNtLa2snTpUvr6+mhoaCA3N9c83tjtdiorK5k4cSI+n8/suaAoCh0dHSxatIh169YRjUZxu90UFxeTn58vzWiFEEIIIcQhkU+TR4HNZiM/Px+fz0cikaCnp4eOjg5CoRDpdJpkMklFRYUEDEKIXRg9Gk455RRz5YnOzk5aWloIBoOsX7+eN954A7vdjs1mM6dYnH/++RQXF+PxeAYtodvW1sZ7773H6tWrCYfDFBUVUVFRQVFR0UGvTCGEEEIIIQRIwHBUGGckjQ/4ubm5lJeXEw6HUVUVXdcJBAIkk0lycnJwuVzDvclCiGOEcfzw+/1YrVZOPfVUIpEIGzdupLGxkUAgQCgUor+/H03TzIoHY8pEZWUlfr+fyspKxowZQyQSoaOjg97eXjKZDCUlJZx22mlMnjwZt9s93LsrhBBCCCGOYxIwHEVGIze/34/P5yMej5sDg66uLoLBINXV1RIwCCF24XA4sNlszJgxg8rKSlauXMn777/Ppk2baGpqMlesMfoyLFq0iFWrVjFixAiqq6s577zzKCgoIJFIEAqFiEaj6LpOeXk5c+bMYerUqXg8nuHeTSGEEEIIcRyTgOEoyi5TtlqtZlVDJpOhr6+PZDJpLmcphBDZjEqGnJwc7HY7mUwGq9XKxIkT6e7upr+/n3A4TGNjI5s2baK3t5eenh6SySSRSARFUQiHw2zdupVAIGAea5xOJ4WFhWYPByGEEEIIIQ6WBAzDyGq14vF4SKfTWK1W0un0cG+SEOIYZxw3xowZw8iRI0mn02QyGWKxGOFwmLlz5/LSSy+RTCbJZDJmtYLR3DGZTBIOhwfdn8vl2ucqFEIIIYQQQuyLBAzHiH2tXy+EEPDxscLhcOBwOMzLU6kUiUSCWbNmkUgkqK+vp729nYaGBrZu3UokEhkULBh6e3vZuHEjDoeDsrIyaTYrhBBCCCEOmgQMw0jXdTKZDJlMBpCQQQhx8KxWKxaLhdraWsaMGUNrayv19fXMnTuX7u5us+fCUJ2dnSxduhSn00leXp4EDEIIIYQQ4qBJwDCMkskkvb29RCIRPB7PLmckhRBifxkBpcvlwuVyYbFYzLDA5/Oxdu1aNmzYQHd3N5FIxAw2u7q6WL58OcXFxUyfPp28vLzh3A0hhBBCCHEck4BhGCWTSTo7O0kkEpSXl5Ofny/r0AshDgufz4fb7aa0tJTZs2czd+5cXn75ZdauXUsikTADhs7OTiKRCKNHjyYWiw3zVgshhBBCiOOZBAzDSNM0UqmU2eTRbrejqupwb5YQ4gRgsViwWCzYbDa8Xi9Tp07lU5/6FC6Xi7lz55JIJICB3g3G0payio0QQgghhDgUEjAMM2NOtLF8pRBCHG6KolBTU0NBQQGZTIbly5fT09MDDByDjJUodtej4Xik6zpzzjgbp8Wx27423b3dYFF4/sUXmDZt2jBsoRBCCCHEiUkChmGm6/qgkEEIIQ4n47jidDqx2WwUFRVRUFBAZ2cnsVjMnCoRi8Xo6emhr68Pj8eD1Xr8/nlQFIVoKMKPLvsWhd78XX7+8pLXeGPD+zRsa5CAQQghhBDiMDp+P0GeAE6Us4VCiGOfqqqoqorX66WwsJDc3FySyaRZuRCNRmlvb6e0tBSHw3FcBwwANVXVWFQLRTkFgy5v6GrijU3zuOL/fZZPf+bTw7R1QgghhBAnpuP7E+Rxzuj6LstTCiGONOMYU1FRwfnnn4/NZmPBggUEAgEA2tvbWbRoEVarldzc3MO2XKURpBrVWsaXpmmDej4Y22d8V1UVi8Wyy+X7a9rM6axv3MK40lGDLo8kYoysruSGW2486H0SQgghhBC7J5P+h5mEC0KIo6mmpoYrrriCM888k5ycHPPylpYW3n33XZYvX04kEjnsj2uECul0mmQySSwWIxKJEIlEiEajRKNR4vE4sViMWCxGMpk86CqvUCjERws/oqqwYpef5bp9dLd0cP2nv8inP3UFzc3Nh7prQgghhBBiJ6lgGGYSMAghjiaXy0VJSQmFhYV4vV7sdjupVIpIJEJrayvLli2joKCAGTNmMGXKFHw+H7BrBYFRgbBt2zZWr15NX1+f2c9BUZRB/WWGVi1omkYmkxl0/aHHQmNlHbfbjcfjoaqqinHjxuH1elFVdY/HzXg8zm033crM3AmcXDVll5+PzC/jzzc9w3sbFvHTD3593E8FEUIIIYQ4lsgnKyGE+ASx2WxYrVZ8Ph8ejweHw0EmkyGRSJBKpVi2bBltbW309PRQUVGB1+vd7WDeWH1izZo1PP300zQ0NJBMJoGB6Q3ZgUJ20LC777u7f1VVsVqt5OfnU1JSwoUXXkhRUZHZrHJ3t0mlUtz1b3eQ3+/ixgu+uMfn4HeL/sT9/3yK2265mXQ6fYDPoBBCCCGE2BMJGI4hspqEEOJIMyoFRo8ezVVXXUV5eTkrVqygq6uLeDxOX18f6XSahQsXYrVaqa2tZcKECRQVFeHz+Uin00SjURoaGtiwYQPz58+nrq6Ojo4OMpmMWXlgLLtrTImwWq1mOGC1WkmlUiQSCXRdR1EULBaLWU2gaRrJZJJ0Om1uk8vlQtd1Tj75ZGpra/H7/dhsNvNxNE3jvnv/nUxjhDs+desel/3928q3eWDBbxl52z28Fu7jjeu+zO+eeJzJkycfnV+AEEIIIcQJTAIGIYT4BJo4cSJVVVWUlZXR29tLPB43B/2pVIpFixaxatUqzj33XK6++mpqa2txOp3E43E6OztZtGgRv//976mrqyMYDJJKpYCBygOHw2EGCfF4HE3T8Hg8FBQU4Ha7cTgcRCIRgsGgGUo4HA6cTqdZGRGNRolEIiQSCUKhEOFwmA0bNnDZZZdRWFiI0+k0Awld13noxz9hx4oGHrzsTqyW3f9pm7/5I+5461dUffd+yk4/E4CmVJoHHnqYP/zPb4/OEy+EEEIIcQKTgGEYqaqKzWZD13VSqRTxeBy73Y7FYhnuTRNCnOCcTidOp5Pa2lquvPJKNm7cyPbt22loaKChocFsvLh27Vq8Xi/Nzc1MnjyZvr4+Nm3axPLly6mvr0fTNCorK6moqKC6upqcnBxzmUtVVc1jm8PhwOfzYbfbsdlsJBIJIpEIuq6b0yHsdju6rpPJZEgmk8TjcZqamti8eTPd3d10d3ezbNkyPB4Ps2bNYtq0aRQWFvKb515g1T+X8NCn78Fhs+92f1c3beCGP/+EstvuNsOFwPIluFct4acvPHc0n3ohhBBCiBOWBAzDyDjTZwQMsVjMXJpNCCGOhnHjxlFRUUF9fT1r167l/fffp6uri1gsBsC2bdtoa2tj8+bNNDc3097ezuLFi83rVFZWMmnSJObMmcNFF11EUVHRLg0bjWkQ2V9GjwbYdYlKwOzdsHjxYl5//XVWrFjBpk2bWL16NQ0NDbS1teH1evn7X//G279/nUc+8z08Dtdu93FLewPXvPx9ir56E5XnXwRA34a1JF96jleffpKampoj8twKIYQQQnzSSMAwjKxWq9lALZFIoCgKDocDh8Mx3JsmhPiEMI451dXVWCwWVFXF4/GwatUq1q1bRygUIplM0tDQgKIo9Pf3097ejqqqlJaWMnXqVM477zxmzJhBZWXlHledOFBGT5qJEyeSSCTw+/34fD6ampro6Ohg1apVdHZ0EtjWwS8/dz9eu9sMMrLt6O3g//32LnL+3zXUfPpKAMLbthJ69nH+59FfMGnSpEPaTiGEEEII8TEJGIaR0ckdoKenh2QyOWhdeiGEOFr8fj9er5eSkhKmTJnC3/72N1paWgiFQgB0dnYSDAbRNI1UKkVpaSmjRo1i1qxZXHzxxYwcORK7fffTEw5FVVUVpaWllJSU4PP5WLRoET09PWzYsIGGTfX84F++hcfiJJVK7bJ8ZUdfN5eH/JyEAAAgAElEQVT9+pvY/uUSxnzxywDEWlvoefynPPPgj5gxY8YBbcuqVasYP348LtfuKyWEEEIIIT7pJGAYRsYUiVgsRjweN6dI9Pf379ftjRJjI6gwOrTLFAshxIGyWq3mspB2u51TTjmFQCDAsmXLPq5kSKRw2JwoNoWioiLOOOMMTjvtNEpKSg77oNsICux2O3a7nZqaGtLpNJqmEQ6H2bZtG50dnTy+4Lfke/zMGFWLoijmyhKRRIwvPXMbyf5+RtZOQ1EH7i8wfy7Tq0Zy1llnHdD2fPjhh3zj1lt57umnmTVr1mHdVyGEEEKIE4UEDMNIURSzqWMsFqO7u9ts9Lg/MpkMqVQKl8tFRUUFhYWF0sNBCHFI7HY7eXl5nHzyyRQXF+P3+2lubiYUCqEoKiPyaqgpnUQo1cnrf/4nH7z3IVNqJ3PSjGlMmTKZ2tpavF7vYd+u0tJS8vPz0XWdvr4+83tLXzt3/eXHXD/rKm4871oURUFVFK7971uZXDKWO87/Ovc9+B+kv30HpRd+itLPfp7VP/8x//nII3z/O9/Z61QOXdeZO3cu//HQw2zr6CCtc0SqNIQQQgghThQSMAwj44Ot0+lkxIgReL3eA1pFQtM00uk0uq4Ti8Xo7e01O7ELIcTBUFUVgNzcXCwWC+PGjWPixInouk5nZyfd/Tu46ORrqKkYS3FxMdFkP61djXz498385Xdv0dnfws3fupHrrvuSuZLE4WCz2bDZbIwaNYpzzjkHv99PUVERK1esJNkb5Q8r/0EgGuTOi27g1j88QKErj4e+8D2cdgcep5u7Hn2ITCxG+RVXUnb7PfzPI/+BRf05373rzt2GDOl0mn+75x7+On8B3rMvYMwVV9L16E/MpTGFEEIIIcSu5JPSMcDtdlNdXY2maQfcGE3TNPr7+2lubiaRSJCTk3NEzh4KIT5ZHA4HNpuNqqoqpk6dSjQaJRQK0R8K8M6q3/OV0nvQdQ2/t5D8nGIm18wknU7TF+7mN089SVdnF3fdfedhCxgMFRUVFBUVMXLkSBKJBJuXruf+i24CFB6e999c9MvrGFNczSNf+nccNhsAZ086jV/Z7udb//1D2p1OSi+6hPK77uPFR/8Tq+2X3H377bs8zrx583ivsYXxjz2Laix9mUlj23mfQgghhBBiVxIwHANUVT2oqgNd19F13fzAa1QzCCHEoVJVFVVVqaioYM6cOei6Tm9vL83NzbT21LOm/kNcrvPweb14PB4URcViUcnLKeb6C+/ilb88Rjgc5nvfvxen03nYKxkSiQRv/+VNHrz0Tkqd+cTicWZXncz/rXuTscU1aKk0iURyoC+NqnLK2JP4xVXf4/b/+jGqy0XxWecy4tv38uxD91NaVMR1115LOp1m9erV2O12lq9ahaV6NIrVRsfcf+Iqr6C3abtUMAghhBBC7IV8UjrODV1vXgghDiejYiCVSrFlyxbC4TBtbW28s+IPlPirqCyrwel0YrdbUBQrmqbhcnj44vm387/vPck9fffy8E9/gtvtPqzbVVVVRfGIEjb1bGPcpBrmNyzlb5vfI6YleW3t23jsLr55/pcHejLYbSgozJ4wizOrTuK9Z56k+KxzsXl9lPzbPTz40APU19Xxt3nzae/uQQF8o8dSfNPtpPqChF96jggKp06exIgRIw7rfgghhBBCnEiUT8oZ75kzZ+rLli0b7s04IoLBIFu3biWdTjNmzBgKCwuHe5OEECeY9evXs3DhQurq6mhpaWHZ0mXoUQfXnncH+XmFeDxuXC43qqqalVSZTIo/ffAseZV2Hn7kJzgcDqxW62GraOjp6eHr132V/IiLNd1bmHHWKWYA0rx1O5+feDGfO/lTWCzWgRV70jEufexr2K/8LLlTp+GprMY9sopwQz3tzz1JyZdvxDd2vHn/mXiMtldeJPzBXN7+w/8yfvz4vWyNEEIIIcSJS1GU5bquz9zn9SRgOP719vaaAcPYsWMlYBBCHHaJRIJ4PE57ezvNzc288cYbvPDci4wrmcG5tVeRl5dHUVERDocDi0U1K6vS6TSvLfgNaXcvP/rxA+bKFIerl0Fvby8P/+dDfP6aqxk5ciT19fWsWbOGuXPn8v5bc/nX6Vdy3ujTyMnNIT+/gGUNa7jvb48SSURQcryMe/532P35u73vWFsrnffdyaP/+WMuvvjiw95PQgghhBDieLG/AYNMkRBCCEEymSQej6Oq6s6QwDJoCpbD4cDhcKCqKi6Xi61btzJ6bA0bNy6lOLeCkx1n09sbwOv14fV6sVotgILFYuXTZ36FNz56le/ceS+P/OIhLBYLTqfTfJxDkZeXx0OPPGz+X9d1rFYriUSC3t5efrv0L7isDs4acwqqGmT6yIm8csMv0dF5fsEf+NttNzHx+ZdR99BbobiigksuueSQtlEIIYQQ4pNCTscIIYQwB+ShUIhUKrXHhrEej4cRI0ZQVVU1MCWrJI/5G16jqWMr3d099Pf3kclkzOsrioLVauWS075ImXMS37rldurq6giHw2iadtj3Iz8/nylTpnDmmWdy5ZVXMv30GTz50cssaVhFbyBAX18/flcOZXklfP+KWznJVsTmu761+22R/jZCCCGEEAdEAgYhhBBYrVY8Hg+qqhIOh4lEIrsddKuqis1mo6amhosvvpizzz4bu1vlH8teJBzrJxaL09/fTzQaRdOMoEFBVVXOn/lZJpecxV2330NjYyP9/f1EIpFBgcQLz/+GX/7XYwcdPhg9HsrKyjjppJO46KKLmDhjCk8sfYmNHVuJxWIEg0H6QyEy6Qw/u/p7lLf3Uf/QD3e5Lz2dkSa6QgghhBAHQAIGIYQQOBwO/H4/drudUChEOBweNPAfatSoUXz2s5/lnHPOobKyklAiwD9XvkwsHiMY7N15+49DAkVRUFWFM2ov4rQxl/Fv37yDpUuXEgqFzDDhlVde4fmnXuL1P7zHAz/44V4ff18KCgqora3ljDPO4KqrrmLcSZP42cLn2dK+jUAgQDAYJBaL4bQ6efJL/4FryUoaf/PMoPvQ0ymcDudBb8OBisVirFmz5qg9nhBCCCHE4SYBwwnAmCetKAqappHJZPZY3iyEELujKIrZd0HXdTRNM792dzyx2+3k5ORQW1vL1VdfzUX/ciGd4SY+2vxPEokE4XCYYDC4sxLi40oGUJgx/iwunPpFfnDvj1i1ahXBYJA//vGPPPbI01x7/re5/qI7WPHBZu664zskk0nWrFnDjh07Dmh/jEqGiooKZs6cyaWXXsrIiTX8fNHzdIV6iMfj9PX1EYmEKfTm8/gXHiD6yqu0vfV38z60ZJJ0MsHixYtZunQpGzduPIRneN/+9Kc/ccV11/ObF397wPsrhBBCCHEskCaPJwhVHejanslkzLN+h9o8TQjxyZPd2FHXdXRd3+s0gYkTJ1JVVUVxcTE7duxg+Yb3KMqpYEzZZJLJFOlMGrvdhqpasu4fJo+egcPu5L7v/pCLL7+QN157m+sv/A75OUUAfPH82/jj/Gc4d855BAP95Obm8pvfPce4ceMOaH8KCwvx+/1YLBasVisvv/QSD3/wa+4/71aSySQwEJasa96EpmWIrFkN/3LZwOX5BXTk5vONx54ETSO6aT3rl3yE3W4/4Od1X9auXctLf/4L9nP/hUf+9Bo/+fWzfObcc3joRz+UaRpCCCGEOG7IMpUngGAwSH19PalUipqaGgoKClBVVZZUE+IYZISA2RUDwDEziIzFYub0CEVRcDqdeDwerHtYZcEIIVauXMk777zDG2+8wdoVm/jc7Fso9Jfh9Xjx+3Nxud04HU5UVUXTNFR1oFLi/eV/59V3nuSS2ddy6ewvoCiqGZhqWob3VvyVmhETCcf6eW/9//LUrx+ntrb2gPerra2NxsZG5s6dywvPPs8I8rj7zK/j8Xh4adVfeb1pKSO+8z2Kz5gzcIMhvw5d16m/4Rq2rlg+6Ni6+KOP+Mfb7wy6bllJMTfdcMN+bVcmkyEQCHDRlVeRmHwS5f/6DRSLhWRvgMY7b+b1V15i8uTJB7y/QgghhBCHkyxT+Qmi6zqZTGbnh3ZVKheEOIZpmkYikRi0HOSxxOFwYLPZ6Ovro6urC7fbjdPp3GPAAAPhyLhx4ygsLMRqtbJx40Ze++g5Pn/mbVhUdeeUCx27zcYf5z3HP5b/mdsu/z7FeSNY2fgeTz3zBI8/+iQfrivi1EnnoaoDUylU1cL5Mz/78bbZnHzjq9/k5489zOzZsw9ov4qKivD7/QSDQbadt4133nybp5a8AorCh6FGiv7jYfwTJqKjowxJFzKJBKm+IOi6GS4Y4fzf//k2Lze04Bs/EXTIJOLEfvXEfgUM8+fP55u3f5twJoPr1DMp/9rNZtBkz8vHdeGlXPv1G/hw7ru4XK4D2l8hhBBCiOEgAcMJwjiLCPs+E2rMr45EInR2dppf8Xgcp9OJxWIxezkYwcXuvozHU1UVq9WKzWbDZrPh8XjIzc3F6/XidrtxuVy4XC5z4GIMqI6VM7ZC7E4mkyGdTpvLLB5qRZDxHk0kEvT395vvF+P9kEwmicViWCwW830IR/99YlRWOBwOvF4viqIQDodJp9ODtiv7+gAul4uioiLGjh3LnDlz+GD+At5a+Ts+c9qNgLLzGGFlxaZFZHJz+dU7j1Ljy+exp37BmWeeybRp07j5xltJpuOcPe2SnVVYlkH7P76qFof9Jr7+lZt48aXnmDVr1n7vl9VqxWq1Mnr0aC666CI0TePPf/wLmYIibN+5n6TDSU9PAK/Xi8fjNqd0oED7s0/AhtWMrhxp3t8XvvKvLFm1Ck2Hom/eQf6MUwHQ0inW/+Elxpw8A13Xseo6b/zp/xg1ahRNTU1s2rSJzs5Onnv197T29aOfPoeqz34euz9vl232TZqCvvxDHA7Hfu+nEEIIIcRwkoDhE8ioeAgGg2zYsIGVK1eyatUqAoEA+fn5OBwOksmk+ZVKpUin06RSKfPf6XQaTdNQFAWbzWaWUXu9XoqLi6mpqaG8vJyioiKKioooKCjA7/fjdrt3dpOX6Rvi2JbJZMwB/+GYcmQEe/F4nGAwiMvlwufzYbPZAEgkEvT29mK32wcFD8PF4XCQl5dHNBqlr6+PVCq11+0ygpHq6mrmzJlDJpPh7bfe5YP1f+PsyVcQjaqoqkJYSTLqW3djd7nof/ZxbDYbVquVsWPH8j8v/4YbvvoN3lwc4sJZ/w+7XWUgX/g4ZOiP9KJaFDwez0HtV3V1NSNGjKChoYGEz0POt+8h43QSjcZIJBJkMmmcTsfHAQNgSSV56hc/54wzzjAva9jRRuXDv8JRWDTo/lWrjcnPvgLoKIpK24PfJxgM8uqrr3LPQw+Te/IpBNavoeiKz1F14SUoe3ldxTs7uHD2aXK8FEIIIcRxQwKG41gymaS/v5/+/n7zbKMxWNmdVCpFJBKhra2NdevWsWXLFhobG2lsbGT79u1EIhE8Hg82m80MEYyzuEZFQ3YFg8GoYLDb7TgcDlpaWmhubiYvL4+cnBx8Pp9ZzWBUMRgDNmOwkslkzAFNYWEhJSUl5Ofn4/V6j0hDNSH2xKg0SKVSxONxrFbrIZ1BNoKFRCJBJBIhmUya74fsHgxWqxW3242u60QikT1WDBxpxvZYLBbsdjvpdBqbzYamacRiMTRNw2637zLoNZpDFhcXc/LJJ9Pe3s6KFStY07iQQt8IEukYK9qXEnapjK09CZfPh/uOf+dfb7+Dt/73VaqrqykrK+O/n32KW2++jdc//B2fOu2L2KwDAURbdxNvfvR7mgNbeP7FZ5g0adJB7Z/dbsdut3PZZZexYfNm/vLs49i/fDO6z0c6rRGLxQn29OC0WnH7fKh2O+jaLpUk0UgE7x6mLahZ00li0Shfuulm0skkSdVC6S13ULqf26rFoljUY2sKjRBCCCHE3kjAcByLx+N0dHQQjUbJyckhNzd3rwOhZDJJIBBg9erVvPTSSyxdupR4PE4ymTRDhOxBw+4agBrVB0M7zRtLYxo/N8ID49/Zt7NYLGZHdyMQSSaT5ObmMm7cOKZMmcKMGTMYN26cGVwIcTTpuk46nSYWi2G32w9p2VcjYIhGo/T09OBwOCgsLMTlcg0KD+x2O7m5uUSjUUKh0D4rBo40Y2qI3W7H6XSSTqd3Ljmp7XXKiLFqQ319Pfn5+XR2dvKPNa+C34/7ymsoPf0MNFVF0zKEFn/AqSdNp6KiwjymlJWV8cJvn+OWb9zKH957mivP/jqJSIRX5z1G5egRPPajX3Dqqace8v6NHTuWxx59lJ4vf4W5v/4lrpvvRHG6SCTiND77OM7W7Xg8HjKZDFZFwefzEY1GuepL1xFOJgmGQoxw7rsvQvuOVpy5uUz49wcotu45AN4de1Exr7/wJCdPncIXrr76YHf1qOro6ODu+x8gmc6gqB8voawoCplYjJu/+q/4/X5sNhvjx4+X6gwhhBDiBCMBw3FI0zTz7GoikUDTNFwuF16vd7eN2IzBUXd3N/PmzWP+/Pls2rSJUCiEz+ejuLgYv99Pbm4uPp/PHPhYrVYzDMgOCoZOcTAqGoxpFOFwmJ6eHkKhEPF43NxOY7qFsc3GdhlVEv39/aRSKfr6+mhtbaW8vJySkhJGjhzJmDFjKCkpkYoGccSl02kSiQTpdBqHw4Hdbj+kPgjJZJJwOEwymcTj8Zj9SIYGB0YlkKIopNNprFbrIQUbh2poZUUsFiMWi5FMJkkkEgP9BXZubzaj18H48eP59Kc/TWFhIQsWLCCma5BfQEqDYLCXdNpHdMsGNkXDvPXPf3L5ZQNLQ1osFvx+Pz//r0f4zl3f5fl/PERKi3PLt7/Gdddfd9gCF6OS4b57v0vihz9i2R9/h+O6G4hvWkdky0bOmF7LV77yFSZMmEBlZSU5OTm8+eabbFOsFN94C6OsNpQ9bEu8p5tw246Bx6kZg6W3B0/1qAPeRk/NaOrjSTbX1x/Svh5NO3bsYMn2ZnKvvh5d0yDrNdz79hvc8ujjKKpCqG4Lf3z+WWbO3GczaiGEEEIcRyRgOA5lMhkSiQTxeJx0Oo2qqrhcLrO/wZ50dnby1ltv8e677xIKhXA6nYwYMYJRo0YxevRoqqurqayspKCgAKfTicPhMAdYRtiwu/s3SsqTySSRSIQdO3awceNGmpubCQQCBINB+vr66O/vJxwOE4lEzOAhHo+TSqXQNI1QKEQ0GqWpqYklS5bg8Xjw+/1MmzaNK664gpkzZ2Kz2SRgEEeUcaYewO12Y7fbD2lQm0gkCAQC2O12CgsL9zrtwQjxjKqHY4Hx3s9kMkSjUTMkNILGPe3L+PHjKSgowOVysWnTJpqamog/+kOsP/g5yWQhmqZTetd9hJZ8yJ0//Tnb6uu54YYbzICztLSUp595kv988CEKCvO44MILyGQyh72io7a2lt+98Dxf+Mq/svIvvyfe2kSmfQfbtnl5/fXXSSQSlJeXA7Bw6TIcM07FVVax1/vc9NjP6F/+EY5cP1oySd7Z5x7UtrlKy6j60lexWFIHdfvhoGkadl8OuVOm7fIz/7STzX8rDz8g1QtCCCHECUgChuNQJpMhEokQjUbNpe72VrJsVBdEIhF6e3vp6+sDoKSkhLPPPpuTTjqJ4uJiCgsLKSgoMHs5GM3XsqsYYNeu9kbAkE6nzbO0Xq+XUaNGmdsZjUYHVTOkUqlBDSSNwUtvby/d3d20t7cTCoXo6upi7dq1WK1Wtm7dSllZGTU1NUyYMIHi4uJB2yXE4WD0GzEGz3sK1vbFGIgHg0ESiQS5ubmDwoqh95ldMeByudB1fdBqE8b78Wi/3o2qJbvdjtfrJZlMEo1GzakS2SXw2Vwul9lLxagC8TrTJJ98EP3uh4lGowPVEafMxlpUwkO/fIhx48bxqU99ChioZPB6vXz/vnvp6+tDURQzGD0cvSmM7TXC1B99715uuesutmU08OXQ09PD6tWr8fv9FBcXM3bsWFas34Dn8s/v8771VBJFAVd5ObU//dUBb1vP+rWoNjt548Yf8G2Hm6Zpe21cadBTKQmLhRBCiBOQBAzHISNgiMViWK3Wfa5Rb3SuN8qbNU3DZrMxYsQILrjgAs455xxzoL67IGF/BlfGahLG9hQVFQ1aztI4I5u9nGb25el0mq6uLurq6li3bh3Lly9ny5YtdHR0sHXrVpqbm8nNzWXEiBGcdtppuN1u/H4/DodDAgZx2BkrpBzKiifJZJKuri5uuesu6uob+O63vklVZSVb6+vp7Q9x+qyZnHPOObu8v2w2G16vl0gkQjAYNAfaQ3s2HG1Gg9a+vj76+vrQNG2XRpXZjHDS6XSavSQsqs7Ekn7WP/MIys3fJZFIkJPIIb9mDN6TZvGt797LtN//LzMnT+b2W79phgmKohCNRgkGg3g8nkOuKslm/J5ra2v57dNP8+lrvkiH1Upvb68ZDum6zsyZMwn396Hr+64sqf7KNwhu2cT2J352wNvTtnghdT+6F9CZ/POnD2KPhpemaej78TdDT6clYBBCCCFOQBIwHIesVis5OTkAhEKhnUurZfZ4faOxojFQMpox2mw2PB4PPp/vkLYne3BhDMj2FngMZQQRxtx0v99PRUUF27dvp7m5mYaGBurq6ggGg6RSKfOs8vLly83QYdSoURQVFZmDGSEOlsViwe12m1ORdF3H5XLtd9BgvJ67u7u55Y472arYKPzOA/zXgrmo8z9EG1EObg//84tfcsk77/CzBx8c9B6yWCxms1ZVVc3eJsb7yhisH0pfiINhHEecTie5ubnmaheZTMYMGoZeP7sRrK7reDwq11yTy3PP19P46A/Qb7ybmN1GX18Qz+evx3HVF9ke6GbFa38gnclw33fvMVezyGQyJJNJ83GNKVwHGwAZQeeOHTvYtm0bLS0t7NixgwvPmM0f/vEGCbeHtGphe34Jz73+Bq/MW4AWizDK7hy4rQ6aliEdDmPzeFCzjjt548Zj83ho3Y8mkNnS0SgNP3+Q275SQjia5sXv3UYmnWH9Becf1D4Oh/2tYCCdkmO1EEIIcQKSgOE4ZLfbKSgowGKx0NfXRywW22fAYAxMjsWz/cbAJScnB4/HQ2VlJaeccgqBQIDGxkYWL15MPB5n/fr15hSPzZs34/f7KS0t5aSTTuKyyy4zp4rIh1ZxKIwKAmM1B6PZ4/4yKnJ++9JLbHT6qLrlduwOFzljd5a778wFtAs/xT9+eC9XL13KKaecYt5eVVXsdjs2mw23221WMmQ3VTR6NQwHp9OJ3W4nHA4TDAbNJWb3VFGQXb1UXm7D7bZw800FvPZaF0seuZvQtf9GauIU0r4crHl5pFqbcQU6Of/ss4CPnw9j6koqlSIUCpHJZA75mKbrOo2Njbz55pssX76cDRs2MH78eE6bPo33P1qC76u3YB07EUVRcHu95Ppzsbo96NpAYBJpb6P5/rvxFBbhOPM8cmqn466sRlEUOpYuxjOifL+3ZftbrxNYsRS/Pcnn/l8NAOeeE+Ivf97O+LE1B72P+5JMJvn73/9OOp02g6DsKrO9fd+dZcuWoe3l75FBy6QPKIgWQgghxPFB/rofh4wBud1uN8+07q1c2AgYsudvGx/U0+m02ShyuMIHY6BkVCYYZbPGNhvzvzdu3GhWNezYsYP29nai0ai5L8uXLzdXmdjd9I6hH553N3c8+zpGpUf2du5urnl2Q77s6wy9PPv+h56xNn432dNGjLn4Fotl0MohiqKYlRqqqpJKpYjFYsDAvHfj8nQ6TTQaRdd1cw6/3W7f6xQY47Vls9nIz8+nrKzMXP50OMvzj6bs5yeTyZhLsO6vTCZDe3s7L//175R95wGsNsdAqDAkD1BtdpTak1m9evWggCH795JdtQAMmnY0XIzXiKIoe92WoVOhbDYLo0c7cbvduN1uvvY1PxMWdPLqyz8jctoV2D7zBXa8+xa2f/wfv3vqSWbPnm0+Hnzcm8JYZSN7GdEDCRqMba6rq2PlypWsWLGC5cuX09XVhcViobu/n26XlwlPvEjcZiMei5m9Y0L9IXRtYL+SO1rofvyn/ODWbzJ18iTenT+ft577FU2RCLZJtbTNf5/SU0/fr23KpJJs/+VDnDvTwaW3jTQvr6ryMXFSHjbr/gdcB6qpqYnv/OwXuGbNNi/TjWOYccGQY4Q+5LJBrB6cc07e/c+y6OmMhMFCCCHECUgChuOYxWLB4/GYg9E9MQYE2QGD0ZAxmUySTqePyQ96TqeT0tJSCgoKqK2tpaGhgYULF7Jo0SIikQgtLS0kk0mCwSAbN24c1JQyOwgwvhuDM2OAYQzesqeOGD/fXUXI0OsbjGU2jesYX8aZbOOxjG0wGNtonKm2WCxms8x0Oo3L5aKwsBCHw0EqlTLPZKuqSl5eHm63G5vNRjQapaurC0VRKCgowOPxYLPZiEQidHV1oWma2XjTmL+e/fhDvxwOB16vl3HjxnH66aebQc8nJWCAj1dzOJgBfSaTYd68eSgTJuMqr0BVVJSh6YLxOB4Pgf7QXu/PCN329tocDrsL24bKDus8HpURI+x4PB4KCwuJx+OccQaMKHPw5FN/pddmQ0ml8UcilJWV7XJfxmvQWLHG6EWzr+Pf7rYpk8mwZs0annnmGXP6VUFBAUVFRdR19TD1v/8Li8tNLB6nT1XNY6Wx4k10ywbSL7/Ao/d9j8svuwxVVZk9ezb3fXdgwP7BBx9wx+t/QVuykHXrVjPhZ09gdXv2uE1dK5aTn+fghz/c98D8cLNYLDhzchlx3deO6uPqaalgEEIIIU5E8tf9ODZ0YLgn2WfSvV4vOTk55ooNy5Ytw+fzMXHiRIqLi4/Wpu8XY6BuzPuuqqoimUySm5vLqFGjaGpqoqenh46ODtra2giFQiSTyT1WJhhBgHFG2ii9NgbOxuDeOGud3egPMM/sDx1oG35hVxwAACAASURBVGGNruuDghxN08ymmsZtjMfOvl+r1WrOJTceN5PJYLfb6evrw2q1mv0AotEoiqIQDAbNbU8mk4RCIRRFoa+vb5fLNU0z56tnd9/fXQhjVLs4nU5aWlpoamqitLQUv9+Px+PB6XSa+1xcXMyECRPIz88fltUNjhTjuchkMsTj8UEl/tk/3xOLxcKGrdvQS8vRNR1N1bAoKruUMADe0WN5+/9+x93fvn2vz1921ZLNZjMrZPanKme4plIYgVhPT8/O96VOeYULi8WKxTJQjaD5NUaPVvn611I89sT/od/2EKFAN7fddTcPfO9eJk2ahMfjGbRvxu8lGo2SSCTw+XxmD5fsCp2hjOBu8+bNLF++nHnz5rF161b6+/uxWCyMGTOGOXPm8P6SJXS8/hqlX7gelxPSqTSpZIp4Ik4ymaTvo4Xor/2eGz7/OSZPmkQ0Gh3Ug6KyspJrr72WSy+9lOu/cRPr02DZRy+GnkXzGFd5YH+ONU1j5cqVZrhpPEdD7e49nn29zs5O9GEIrWSKhBBCCHFikr/ux7HdDTD2dL1UamAddZ/Ph9/vJ5VK0dPTwwcffICiKOTn5x9zAcNQfr+f6dOnM2HCBKLRKK2traxfv56VK1eyePFiWlpaBg3gDdnLaGaHLUZgYHzIzT4znB0uGF97ChgA8/k1pnkYoUAqlTJL3bN/R9nbp2kaiUTC3AZje5PJJJFIxBxMGvsCEI/HzfvJ/v0bAUT2fQPm/ezPWWfjOuvWrWP+/Pl4PB78fr/5paoq0WiUk046Ca/Xi8/nG9YpNkdKJpMhFouZ4dTQ19We2Gw2eoK9qEUVaJqGioKuqLutKPeNn0RLRue9997j/PP33MjP6L+QvWLM0Iob43VmfB3pYGFfVR2hUIimpiY6OzuJx+O43Sp5eXYs6sBzMbAqhR1d16mqjHDqzCjz/vw/+L5+O/Oe/Dn/fv/9/PaFF8yAwWBM/QkEAvT29uL3+82pVHsLuoxgYtmyZTzxxBPU19cTjUZxuVzk5OQwdepUPv/5z3PjjTfypZtupuvdNyi+8FLc7gyaloF+ndA7bxD9+//hzqRpbW2lqakJl8u1y7KZra2tfOmmm+meOI1Rn/viXpseaqkUvfPe5t9+NeEAnn1oaGjgmpu/iXP0WPMyBdD1j6MsHX3nZR9/N69n/gwsEyYf0GMD6JrOtj+9QqqnmwnfuO3Ab5/J7DNg2LRpE3/5+98HtlkZPLVL3SU4GTK9KDtA3Xl/2cc/Vfl4hZgpU6aYU3KEEEIIcWgkYDgMjFL4VCplltAeyIf77IGMy+Xa44eudDpNIpEgnU6jKAqJRMJssmZ8UN7TcnFut5uCggLOPfdc7HY77733Hm1tbTQ0NFBaWkpHRwc1NTX7XPJyOOyuR4MxPcBms5GXl0dVVRWBQGDQFIRsxmDM+P0YFQzZc7eNgaQxYBt6xnroNBODESQYFQxG1UX242VPkdhTb4fs/gvZZ8z3FCQNDVGyLzP+P7QHRPbPsv+d/T0ej9PX10d3d7fZ5yIWi9HT02MO9lKplBmKTJ8+ncmTJw/q13AkGL+X3t5eduzYYTY4NapOUqmUWcpuTP3RdR2v10thYSHl5eVUVVXh9XoHraqyJw6Hg/z8fHPVAl3Xcbvd+3x/KIrCDV+6lndv+zb6eRdjPt3Gd2XwdT2XfIYnXvztHgMGo4InkUjQ0dFBV1cXLS0t9Pb2Eg6HzdcYfDy1Y09B0tDXx+5+Zgy4S0pKqKiowO/34/V6zdDLeL5DoRD9/f0kk0kzfIOBJrROp5Ouri5WrlxJXV0d0WiUsWPt5vYZD29RVRwOB74cHxf/S4al/7mOeEM91vZO1rV28MrLL/Ol664jNzfXbO5o7LPRy8Go6DF+/263e9CSnsZxecOGDSxZsoT333+f7du3E41GsVqtTJo0idmzZzNnzhxKS0vJzc3lxSd+xWeu/zKBvEJ8007G6XDQ9MeXic1/B5fdTjo00HT2T3/6k3kbu92O1WolGo3y1EuvoF52FSMuunRg5KsP/r1nS4bDKFqa8vI9T6HYtq2RRYsWUVFRQVlZmRlgOopLKb3z3/d4uyMl2d/Hhp89SGb1IrTCCjiogGHfU/PmzZvHk0tWkjtlOpBBN95EQ1+/um5etstrO/tyfdfbxzvaOH3FSgkYhBBCiMPk2BpJHqc0TSMejxOLxYhGo+ZZ6/0NGRKJBD09PWiaRn5+Pm63e7ePYQQKRqO/dDptLhPX1dVllsYbc8dhYNDg8/koLy+npKSESy+9lIKCAurq6mhoaKClpYVt27bR0dFBKBQyB9HHMqNvQX5+Pjk5OYwdO5Y5c+aY+5w92B76YXPo/3f3O9rbmdk9/U73NfA/EEZglb0/u5t/n/0a212QkH397MH00MBi6FdPTw+NjY2sW7eOJUuW0NjYSCAQMEMHQ1dXFxs3bmTWrFlceeWVzJo1C6fTeUQDhnQ6zY4dO/jwww9paGggEAgQjUbNag9j0Ju9fGtZWRlTpkzhjDPOMKd57E+1hcvloqSkxFwtIZVKmYPIfbFarTiLS3Y+p8bZ4t2/dvzTTmbts0+Q3sOcdKP3QDgcpqWlheXLl/PBBx9QX19Pe3u7OY1jqKGv1X29Lo3XU2FhISNHjmTmzJmcddZZjB492uwBEQ6HicfjxONxIpEI4XAYp9OJpmnmYNHr9WK1Wuno6GDp0qVs3ryZdDrB2HH+rIBh4HlBAYfdQW5uLgAnT+9hyZP/xZUTLuCCiWfyi6d/Q1l5OZdffjmRSIQdO3aYgUZeXh7FxcUEAgE2b97MwoUL+cxnPkNpaemgpqSpVIpwOMzSpUt56qmnaGxsJBqJkufKAYvC9OnT+epXv0p1dTUu18BUhrKyMp79xc+55pZbURy30Pi/L5He3sD4Xz5L+0svEFkwl40bN9LY2GgGnw6HA03Xidgd5N9wG0WzTh9owIsFRd3z34JQ83Zcjj33N5kyJY+O9mX86lfL6ehMEuxNUlRURG5uAe2N24n88VXcpWXYfDkoqjJQLaEO9P1QLANTcxRVNb8wQqid/x54LyhgUVEVFZSB2ymKgmq1oVgtqKrVvLx77Wq2Pnw/tSNifPvxaVx32xbS0Sg6Ono6q8eMalRuaQNLeqZTZOIxbL4cLG4P2n70/okmEvgmTGbEJVfs9XqHIrh6BeqyD47Y/QshhBCfNMf2SPIYZQz40uk08Xh80BlEl8tlVhLsb8AQDofNOfvG2cOhj2dMczAa/gHmHH+jfDq7uWH2AMLlcuHxeHC5XKiqSnl5OVOmTDHPhHZ0dPDBBx9gsVg4/fTTqaysBIZv7va+DK1oOFID2uGSXcEwtIohuwJiTwPGodUQxmVDp1rsrnoBID8/n7y8PIqKiqiqqqKrq4tQKEQsFjOn1rS2ttLZ2UkgEKCuro53332XTCZj9mo4lBJ940x0R0cHjY2NdHd3EwqFzPdaW1sbdXV1dHd3Ew6HzYZ/iUTCHPwa183uuxGNRtmxYweFhYU4nU5GjBjB6NGjdw7Wcs3BjllCvbNHh9vtxliJY38aXSqKQk5ODunOdqL1W8gZP2n34cLO4AHVipqTy8KFCykqKjKDylAoZFZpJJNJent76erqoqGhgS1bttDT02MeO4wz+burejH+b7x2situdtenJJlMmqFNd3c3o0ePprq6msrKSkaOHGk2Ec1+/+Xk5JjhiBHgGA1YI5EILpdOTY0Lj8eL0+XEYhmYJqEAFutAhUY8Hufss3NZsbCfmeVTKHEXcPOMa3joBz+mqqqKUaNG4XK5cLvdA1UPPp9Z8fXc08+yYcVamrZt5/7/eMCstkgmk6xfv54lS5Ywb948WltbSSWSjMqv4L4Lv8Xf6ubSH+ijrKzMrM4xeqpUVVVx3623cMOdd+Osqmb6ky+SURUsX72FrWtXUVKgkEom6e/vp6+vDxQVpaQU/1dvRa8aTTAYJBqN7tzXjytLBkJcy8CAX1HA7iCV0cmkMwMBATvf2zt/NWVlbq67vsr8PaXTGt3dcVpbe0AJ0/zh87T3KiRS8HGglX1SX8co7Mr+Oca/jbCHnd/1nXUCOjursAZWGVIAFLBZVW78YjFXXz0NTdNw6nEWf/7irG1Wdj6mTkFBEbk5uQOhsN1OJNiLddYZlN/wTTKazr0P/BCH3Y7DbsNpd+CwWXE6HGZ12rp161ErP54CciToB1hxKIQQQoi9k4DhIBgfxOPxOL29vSQSCZzOgeXXjC79B/KBxWq1EggESKVS5OTkUFBQsMfHHVomv6cz59my52YD5OXlMX36dPPDf2dnJ2+//TbxeNwcSIjhYwxChjqYaogDZUwpKCoqYty4cZx11lnmAD0WixGJRNi4cSMLFixgxYoVZi+M3t5eLBYLM2bMoKys7IACtqHS6TShUIjNmzfz5ptvsm7dOlpbWwmFQmb4YIQH2UGMVbHgtrmwqCq5Vg85Hh/hRIRYLMHGjRupr6/n/fffx+l04nQ6mTlzJpdffjnTpk0zpwYNnbpisVjMkntgv/tMjB07lusuu4Tfr1tF/uSpe3q2P56WY7OxceNGmpub6e7uNsO/hoYGuru76e/vJx6Pm4PfdCpNrsNHubuYntj/Z++846Mo1zZ8zWxvyaaSDiFACFUpIoqgIKhg99iwo8cKFux6REVR7BUVxN6VIxZU7EpRFBGk95KQkF6215nvj80Mm5CQAMGjn3P9ftFkd/q+s8xzv89zP/VkZWVhMBiadEpRrk3834oQo5R8xAtBStmLz+fD6/VSXl7OsmXLyMvLo7CwkGOPPZa8vDySkpKIRCLqdTGZYhkIisAQb8bo9/vx+/3o9VDQ1UGCw4HJbGqyX52oQzSKGA1G8vJsHHuih0e/ns29I68l25HG+KJx3H7Drcx6fTZJSUmq6KN8b9448Qa6yulMvfI5zpx1DfPnz2fChAkEg0FcLhdLly5l5syZ7Nixg6A/QPfULkwZNZFu2fkc1mcgd33xOO+/+x5XXHWleh08Hg/btm3jiRdnk3X62eRdcGms9avfT/k7LzGkdxF9uhXw+++/U1xcjN/vJ2q2oEtJx/X5R0SOOwl9Vo4qFgiCYlqrw2QyNno26BF1ImVffUZhbiwjTdQ1emgIutYqKtDrRTIyrGRkWBk4MK1d4/FAuOP2xYiiifunDSTSmKGg18fuA1EUmf9R09ICrzfC++/vpHRnGq+99gFJSUnqex9++CH3fLcIQRRJn3gTX7rdyJEQUjiC7A8ghcMQiSBEIwiRCII5gYRerd0/HYQst/u+1tDQ0NDQ0GgbTWBoB0pgpzwwK1kDiju/2WzGYDA0mpYZ9rnEIL5uWpkVPJgkJibSr18/fD4fu3btwu/343K52LJlCytWrMBut5Obm6umLXcU8XXyymx4Wy0AZVnGaDRit9vVIPD/+8Pg3oSiP4v4MRzvXxEMBtWZfafTicViYfPmzerM+qJFi4hGo/Ts2ROn07lP+/R6vdTV1bFlyxZWrlzJqlWrWLVqFbt27aKhoSEWgIkiBp0eh82GIIGISE5SBl1TcumalEuWsxO5SZmk2JIQBZGoFGXlznV8uOorVu5aT70/VsOv1+tZvXo1Op2O1atXk5KSgtlsbrcBZnOvg3gRT/l967btuLxBTNU1e2SdyMjIkowsS0SjErXbt/Hzzz+rwa3b7aa2tlbN0ggEAuq5J5sTObJoIP/qdzxd0ztz9dy7OXr0aDIyMppkKjT/UV5X7sHmx6Tcn9XV1ZSVlVFSUkJJSQmlpaVqdkpqaiq9evUiNzcXo9FIJBJRS7Piu7EAaoDv9Xrp28+MwahDbPS+aHKdBRAQMJvNJCclceppBurqdvLQwheZMnISgzL7UBto4MpLLmfmqy+SnZ2NwWBgx44dTLrsakZlHsa5h53C7IXvMmT4EYwbN46dO3eyefNmVq5cyeLFiyktLSUSCtMjNZ9pY28kNz0Lu92O3eZg6omTmfzaNDplZDDq2FGEQiFWrVrFDffci3jK2eQNHxXLyGioo/zJ6Zzat4jzzzqLnTt30q1bN2pra6mvr6eiooKKigqW/PIrweXLCBuMSAMGYTxiOPrOBQhCFEGINma/RRFFEf/6Nfi+/ohzbkmlpqYmlsEgiDEDw8br0piDEsuCEWJmh6KynBgztlQySsTG0ohW1Yn9oHv3RJYti7VSVYSFvbH892qW/Sbw/fcf72HQ6ff7kYyxjLOkAYM77iAPAFmWVcNIDQ0NDQ0NjQNHExj2gXA4TENDAz6fD7HRnEwJstrr0P9XICEhgX79+iHLMuvWraOsrIxdu3axc+dOli5dis1mw2azHRSBQTEQrK2txev1NikvgT3rxCVJwm63k52dTXJy8v/LbgV/B5TxbjQasVgs5OXl0alTJ/WzaGhooKSkhG+++QZZlunUqdM+Cwwej4ft27ezYMEC5s6dy5YtWwgEAujRYTNayHNk0yM9n+6pXchxZtI5NYcMZxoWs6WJ+NTkHpRlUlNSGdJtAGtKNjBn+Rcs2vYbDQE3GzduZMeOHWp5kXIuTYSAZhlCinigBNTxvyv+Jcr/Q6EQ9eWVeDNyMPUfhCAK6EQxlr4uNTXzdNXX8+GHH+6ReaCYjTpMNmxGKyO7DeWsAePontNVFTbzMnM49dRT6dWrV9xp7120a+l95fVNmzbx66+/snDhQmpqatRMJyWIVVqUOhwO1ZOi+feeLMuqZ4wgBOnXL7GJKNMSZrMZoym2vfMviPB0dSlPLn6F20ZcyYisQVSsr+aBe+9n5ssvsmLFCm6+ZjIX9z+NMb2HU1pXzrfFS/jgsw/xer1s27aNb7/9lrlz58Z8GwJBuqd24Z4x15GTlkVKSmpjS0uBZJuTe0+4nlunTUev1+H2ernn6WdJuORqkg4diICAv3Qnlc88xA3nnMlll1yCJEkUFhYyfPhw9TNTMnyefuIpfvzsW87peyJryzfx08yZeAQJb04WFBZh7NkHMSMbnU4ksLOYnNQgNpuXykrfHgag8QgIsTEUN950Op1aAqe0MFWEiY5AisqUlPiw2fRNzGpb49dfq5g/38Vxx520h7gAsS43ksHYIcfWYchaiYSGhoaGhkZHogkMNHXcD4fDakp4vMme8sCv1+vVtnxGo7Hdpm/tOYbWHvw7GuXBNCsri+HDhyPLMt999x3V1dWsXr0ah8NBfn4+aWlp+9VVQul2odR5N68DNxqNJCUlYbPZ1IdzheYCgzLL6vf7qaioUGtz21MPH7/vv4v4cyA07yRxMLav+H1YrVZycnIYOnQogUCA4uJiqqqq2LJlCwaDgWAwSJcuXUhMTFS7ESQkJKiz3fHU1tZSXFzMunXr+OOPP1i+fDnFxcUgyWQ4Ujm51yjG9j6GTolp6HSxNPP4wD42HvSx1wUh5tofd7xGYyyg6Z/fm55Z3ahoqGL+2h/5bN0PeMN+GgIufI3ZEfFiQ7zZZpP2ds3aQDZpjReXwRCNRiEYxP3mbPxrV2E74zxEvU5tDQggeT2E16wAWUIniTiMViwGM2ajCavRQqLFQWdnFn06FTIgrw+JVgcmkwmLxaLeA6FoWPWRAAiFQgQCAbX7RHszopTxE4lEgFjAn5yczKpVq1izZg0VFRUsX74cQRAIh8P07NmT/Pz8PbrX+Hw+GhoaqKmpaTwOiW7ddvukSJKMLElIsrSHgCOKIiaziSSnk0nXww1Xb2Lumi85q+9YLhpwGs8te4dxJ4wjWOfjluGXMTC/HwBzV8znuFPGUlxczOrVq1m+fDnLly9n165d6ASRok7dmDZ2MtmpWaqHxO7uMRI5zgxuPfpy7rh1CqXRAPlTH8XRtQABAdfGtTTMfIpHb7mJk048EQCXy4UoiiQmJrJy5UqcTicZGRkEAgGW//Y7k46+mOEFgzk5MppQOMT6sk0sL1nH7yvWsv3bH/CGAwgGA6ZIhHpEnnrEjckSwWKXsFpFHAk6EhJ0JCbqyMnR43A0mjUKMaFWFHa3I1XKZpr78OwuyTA1+mLs+/fC4p92snhxDaIocPeUFdx3/4AWlwuHJR6avgW/P5F77pnN4MEtZycEQyFow9jxT0f+6/oNaWhoaGho/B3RBIY44muGlXaQCopLuhIYxwcSHcGfJS7Ek5aWxsiRIzGbzWzYsIEdO3awevVqTCYTQ4YMoaCgYL+6Sig19H6/v4lQYzAYVBNMpXd9W+csyzIul0utwRcEAZPJpAYJba2rdFFQHrw7kvZ0lGjP8q2t197jbW7SeLDEFCXgttls6PV6UlNTGTp0KB6Ph8WLF1NWVkZxcTGVlZWsWrWK7OxsCgoKOPTQQxk2bJgaADUPeKuqqliyZAmLFy/m559/pqysDDki0TujO1OOn0S3zHxsdjsGvV4VD2JecrsDe2H3i3tcG8VHwWqxICOTnJxEfmZnzh18Csu2rmTNro3MWTUfvxBrcaiIIOFwmEAgEAvo4lqZKvuMN3uNzzpoXpIgeTxEf/oRwe7A0LM3AgLRuhqiq5YT3byeRJORbrk5uKrrOaNwDGcPPBGDQY9eb8Cg16PT62PZGUravBBLjxeAsBShxlNHWtruWnwlc0Ap2drXkqvU1FScTqdqBvvZZ5+xc+dOysvLqa+vp76+nl27djFmzBi1XCF+vHk8HkpLS6mqqsLn85GcrIsFyGqHQIlINCbiKhkaAo3mhwY9RqMRtyzx0H2b6ZLYmfkbF3FE3gC6W7ty7dALeWP5R4wfcyo9MrsCUO9z8eOOpdx//XQWL16sjqOqqiqioQj9s4uYevz15HTKJiEhoVFciO+2EjOEXLJtBfYEO6InijkpCUEQqfn1J8LvvcYrD0/n8MMPB2Dr1q2M/9c5pKakkl9YwG8/LMFgMJCY4sTv95NlSmXcgFGxbJVG08TMjExGHDJMLUOp9zRQWr2LqvoaPEEv3pAfd8CDK+ChvtpFQ6mLLQE3OxsqiRpd3HFPIsgyW7YG+fknHxmZerp0NpKbZ8RkEvF4PHElFLH736A3YDAaYq1jjUYQ9730rnv3JP7973zefHMHAwcm73XZrVvrGD/+XI488shWlwlFIogHuQRwX1G+IzQ0NDQ0NDQ6hn+MwCBJEnV1da2+H5/FoG98oI8P+JTX483gOiqIU9JcZVmmvr4eAIfDgdls7pDtt4ZerycxMZG8vDwGDx6Mz+djy5YtlJaWsmDBAgRBaNJVQiE+gFKCBCXrQ3lfcbaPJz7ro6WZ7OYo199ms5GSkqI65ev1+iZt6Pa2fvzs88EIultKDd+X5dtab19FiX1Zd19Rsk1MJpN6j+j1evLz8xk9ejQJCQmsW7eOuro6amtrCQaDuN1u6urq1Hr1bt26kZubS0ZGBl6vV/X9WLx4MWvXrqWiogKryUKv7AKmjp1Mp+Q0tRRgX2bi5d0W+jEhMOayp1wZVRw8qtcQhvYYSEQvU2ypYeDgQWoGgxIMKmOotf20JC4o10sRudxuN6s2bsKzbQOiKOK0Wumc2YkeIybgdDpxOBwIgsCbs18nfWsqpx56XKMYE2sdGKvNV8bw7mNZtn0Vhb2LmpQzKfuMb2naHpRzVD7XlJQU9Ho9hx12GA0NDSxbtoy1a9dSVVWFIAjY7XYMBgOHHHIIRUVF2O32JvtXxJesbKPqTSM0BsBrVrt46YViaiti4qNOL2IwCBiMsc+otibI8YWjuGjQ6Tz9w2u8vOy/3DPmWvQ6HZcPOYcEuwOfz4csScz5dR5Z+Tl8//33/PHHH2zatImqqipsRgsGg55UezK5nbKxWK1NMheU8RwMh3j829nUJQR4d+77fDxvHk8+/RCmQUOx/PQ978yeRY8ePQBYu3Ytky69mmuHXIgsy6zYvoaZ50wjxZ7EroZKDDoD6Y6UFoVnWZZUgS1VryfB6qAgY7cXhiQpn5kiXMuU1VUycc5dTL+/nnBQRyQkkpfQFXd1Ip8vLKPW34DBIGC1y9iTQqRnCBQUGOnZ04RkkolKUVVcVcTdWCvK9o2JjAw7o0Z14c03Szj1tC6tLmcwiBx/fBaff/4xV155DcnJLYsRwVAIKRxCioQR9X+RTAatREJDQ0NDQ6ND+ccIDNFolOrq6r0uo9frsdlsmM1mNYhSiDdE6+iHEZ1Oh91uJxKJUFtb2+i6rj/oAoMS8GdkZDB06NDYzFp9PTt37txrV4n4VnehUIhgMKiaNgJq27qWTBmbp5W3B6WlYEv18HtjfzMCNFpHuQ/iP9Pc3FxOOukkUlJSiEajrF27lpqaGqqqqqiqqmLTpk0sWLCAoqIijjrqKIYOHYrVamXXrl188803LFmyhJUrV1JVVYUoCxzSuScPnXob6UmpmMzmWEu/ffnsZKU9p7xHQA40llmYMBiMjXXiMsf2GcZ3/hVMnjx5jzKd9lyT1l5XWsn6/f7YrHKzEp/mBpFjxozh0nMvZkSvw8lOymyynZhGIhB/KSpdNeR379r+a7MPKB0iBg0aREpKCg6HQ81k8Hq9BAIBysrKaGhoIDs7G5vNtofxpSzLOOw6jEYTekNMmP36qzJeemYXlx9xHsNPGIoogi/gxxcK4Av6kWSZ3jndSbIl4vP5uW7kxZz/6o18vPobTu41CpndGUmBUID3fp+HNTOR5av+oKKiAp/Ph0U0cVy3ozj/sFO5ce4DvLb0Q244/t9NzPxkSaLO08ADX80gtU8Oz01/Ervdzr8nTODjjz5l8ztv8vl3X5KTk6Ou89kn8zgxfwSjesVm6Y/tPUx9Lzc5a6/XUxBEdLqY+GowxL7f9xg7yt9CrNVjeno6r1/2FLO+fYv+eb04osdAMhLTCYdjnVS8fj8l1aVsr9nJrvpKtpTs4IcV2/ivr5bMLg2c+i87WVkSwWCwsQOHCUEv7LNHQ3tuvzPPyiYcLuHOO2/iqadeUEuT4unWtYDEF19kx5fzkE0m7EeMIP2cC/fpWDocWd6v8hENDQ0NDQ2N/RGOawAAIABJREFUlvnHCAyAOkvYGkpPd8VX4c9Km9Tr9SQkJCCKsVTXaDRKTU0NDQ0NammG3W5vUnfdESjXwuFwUFhYSG1tLStXrlT3vW7dOr755hv8fj+9evUiJSVFLR2JN6NTUtCVh+X4jhoHYsrYvLZd46+J1WolKyuLAQMGoNPpGDx4MG63m+rqasrLyyktLaWkpITt27djMBiorKxk5cqV1NfXs3LlSrZt24bL5cLpdJKuT+LBk28hLSkFo8mEXqdDEPbts49lpUtALHNBEJq76gvqTLqSt7+zoZysgixVELNYLPt9nynim5Jto/ytzOobDIZWvRHsdjtjTjyehRuWcu6QU2Lns5fyKWV7e1yDDii3Uu5dp9NJfn4+gwcPprq6mhUrVrB+/Xq148cPP/xAIBAgKSkJURRxu93U1NSwcuVKgsEgYCAcDuHxeFi4oJp3X/IwdexNDGr0TwCZBJsd4j0BozK1tXWEwyEioSi3H3MFd3/9FOm2ZIYVDMbtdiEgsHDLUlwhL3WlHjweD36/nxRbEuMPOYnxh52Cw+HgqXPv5qo376RremdOOuRYQAYZyl3V3P35kxx2whFMvvkmzGYz4XCY22+9E8FtoiirPzOfn8XU++9Vv4s2rF7PqZ2GH8BVVQQioV1BO0D3rK48csFd6rWKRqON3iPGWItQRwJFeT2IRCLqd/PO2l18uuobXnjsew49so7TTk/C7XYjy3LMxNcaK/Nrj84giLt1j70uJwicc24us2b9xsSJlzFlygNNxBmAM884nTPPOB1Jkli8eDFXTJsO/G8FBlmWETuy7YaGhoaGhsY/nH+MwCCKIsnJyW0Gq/8LM0CDIVYnazabMRqN1NfXU1lZidfrVfvNZ2VlkZKSgs1m63Dhw2azUVBQgMvlIi8vj61bt1JdXc3mzZuZN28eDQ0NJCUl4XQ68Xq9BINBVVhQxIT4jI+/U0cNjQNHEZP69+9Pz5491XKZHTt2sGzZMhYvXkxDQwMVFRXU1taybNkyDAZDLD09GFQ9OnJzczki/RCy0jPVEpj9GkKyjCTJiAKw13EoI8uwYscaPt3yAzP+8wL19fXtNhFtjUgkgt/vb2JCqdfr1UyfSCSyVwHg9LPO4Krz/s0JfY/BaU0AWW4UTGJp8/FRYVSKojeYWttUh2C1WjGbzQwYMACr1YrNZqO0tJTKykp8Ph/V1dX8/PPP6nkqJVOBQAC/348s6wgGggSDId591c2Vgy4hx5JORUU50NhRQylnQRGIYiUuMjFxJcfRiWuPuIinf3qdBLODovQCkGXeWfYpO6vKkGUZu9FKtr0T1w2/mFF9hzUet4mKkhpSM9KY/ev79MnqQU5SJlsqd3D/tzM4/+qLufDii9DpdLhcLiZefR3BKj0XjJ5MVIryxtePMf3Bh7nt9lsIBAKsW7OWO8+/7KBe770joBN1iEYdyvgFKK+r5Ou1P9Lg82Axmkm2J3LOkFMY2e0Ipn/3PDPLd3HZFUl4vd7dLVnF9oXV+3IP6nQC//53Zz7/bA3nnnsSH3/8bYvlEqIo0qNHD+RAoP0bP1hIMoImYGtoaGhoaHQY/yiBoaO9EzoKQYgZnBmNRux2u5pKHQgE1CyBYDBIVVUVXq8Xq9WK3W7HZOqYwELZX6dOnTjiiCMIhUIsXryY8vJyiouL2bRpE9u2bSMtLQ2j0ai2H1NmaJUfJSD7q11fjYOLMn6VDKDmM+5Wq5X09HQ2bNjAtm3bqKmpwe/3A7GWqTk5OfTu3ZuUlBTKftva6M8hdsg42usWZNhYvo2HfpzFIy88QU5ODtXV1Qcs4CntWBVT0/hygWAwiMFg2KvAUFhYyLAxI/hh3c+cOvC4xoC75UAvGAlhMDoO6HjbQslkSE5OpkePHmqb3rVr17JlyxZqampwu91IUsxjoLk3hSCCJEsgg98n0zOlq1pOBahCQoyYxNDS5emXUciZfY7jiUWvcFz3o9hQvZWShl10T+3CIbm9GJDdmyPyB+B0JKp+Cx8s+5z5Oxfz8nuv8+uvv3L3k08yvv/JvPz7HK659VpOP+MMdDodu3bt4orLribd2J0TR5wZ+/cCA+cfewOvfPIQmZmdGDlqJHajFavRclCvd5sIAj+u/ok+uYWkJqZQ723gkpcnY+7kJjlNwO8Cb4nIrpIAXZw5nN73eP678gvmfFDB6Wck4PX6qKmpwW6zY7VZ25UdJssyL7xQQnwykFJ9JLB7bIoiiKKM1xvC5Qrg8Xha9WNISEgg7HET8XrQ2+zIsoy/dCd6ux2jM2m/Lo1vZwmVzz2GXooqB44sKX4sMggigl6HqNODXo+g01G7YzvGscfv1/40NDQ0NDQ09uQfIzD8FYWF5iilBhaLheTkZDUIcbvdlJSUUFtbG0uJTUxUjQ47ClmWSUlJYfjw4YiiyObNm9XWgzt27GDbtm107tyZzp07q4Zy++OnoPHPQBAEkpOTSUhIoEuXLgwePJhly5bx5Zdfsn79etUPJTExkaFDh3LuueficDi45acbmnSL2P/902b6tz8cZOpXT3PbtP8wYMAAvF7vAe1TQREYlCC7rddb4tDBA1jy2jdt7muHu4yRXQce8DG3B4fDgdVqJSEhgaKiIhYtWsS8efNYt24dgUCAYDDYxOhV+f76bamf6qoIggDhqJ7nf30Hk86IXqdrLGHRoYu1P0DX2CFDFER0gkhYiuAJ+ajzN1DurqLO78If9vPf1V8yLH8gOlHH3cdNYnDhoRhNppgYIgjIyDz345tslEuZ/eYravefefPmcdOc+7n/oQc4ZuRIdDod69ev5+rLJ3Fo3iiG9hnd5JzNJivjR17HrGcfxOP1YNQdfGPCecu+5rkfXqPKXauejyiIscwFQcRiNFFWV4lRb6BnZkGsi0j3ANOmNx0Hfl+YD+du592P3sPtlyn/SSA1zcPRR4uEQkGkqITZYm5TYNDpdGRmZnPO2dPV15Tx21IrZ6XV84QJmXv498RjNpsZ3K8vf9w6iahejyxJGCJh5BGjyTn7gv26dmFXPYWpyTz/8ENN/E2U35VyJaWcRPnJzs7er/1paGhoaGho7Mk/RmCAv34gHC+CxM+iWq1WUlNTMZlM6gNRVVWV2nFCIX5WNN6Usvn70WiUUCjUpIWjkuaekJBAYWEhI0aMQK/Xs3HjRiorK1m4cCGyLDNq1Ci1lZ/mi6DREvFjWDHjMxgM9OvXD1EUOfTQQ9WWoxaLhW7dupGfn4/T6aTWW09UltAJB14GtH7XVv7YubaJod3umXJYVbKerK65jBo16oD3FY8oiur9JAgCkUhEDWqUDipt3TuFhYW8XPnCXoUIb9DP/FU/kLYqlw8+eJmammpEUUSSZKLRSGNWlKnRJFNUAy2drunfgiAgiAJjTziDM888Z6/nJYoiDocDURQ55JBD0Ol0HHbYYdTV1altfT0eD3V1dWzfvp3169fj8QiUlCTSqVMn+g5yYG0UTyPNum7IailIVH09NnYsdDIn0cVShMlkYuPGjaz4bSn5SdnkJ+Vw27xH+W/eC2RZY7PxgUiI6V8+j5Rl5IXHZqHX63G73bz39rvUb6nkpjFX8OFbHzB8+HBWrlzJ3Xfcx5hDx9On6+AWzzvRnsxZIybyxCO3869ex7bx6R8YHy75jIe+fZYrrs9h0KBDGrNeokQiMlJUJhyRcDWECQQScSYZWb++HrPZxJgxexp9WqwGzjuvO6eemsW33xazeFEFX3/lobDQTEaGTCAYwOf1xbq0GA2t/vsoy2C12hgzZkyHnqsgCLzz8kvIskxdXR3RaJQHn3iS71L2P9gXRBGDwbiH94OGhoaGhobGn8c/SmD4u2IymcjIyMDpdFJXV6fWswea1a8qD+XKA3tzLwTl9VAohNvtVtc3mUwxg730dDp37kxBQQGjR4/GbDbT0NDAhg0b+Pbbb/F4PHTt2pXc3FxEUdR6h2u0C6U2v7CwkK5du6pmh7C7PEdpW5qUnESNp470hNQD2mcoEubBL2eQ0bczRx5xZNM3G+Oow8hUg+SOJL6drSiKRCIRfD4f0WhU9StpS+wsKirClp7Ab9tXMqhz3xYTOmb/+A6mxGoCwbmMHOUgJcWCLEM4HCUajXUKEESZWMAeQZKUjhSxEgRJagzsJVi9uo5ly37eq8AQf35Wq5VevXpRWFiolkIolJWVsXnzZr766itKS0uxWCz06tWLUaNGMW7cONLS0tol9saLDIIgEAgE8Pl8PPfcc6xb9zPvr5/LrBMfoT7kZsJrNzN34izCUoR7vniKgqG9uO0/txONRmloaOCaqybiKq7m2bOnkmJPxL7UxqUXXkooKHPuMdfSJatwr8eSldqZU464jDnfPs+YPsPpk9ezzePfV75ZuZDpX8/gPw90o3//lHat07Ons81lLBYr48b2YMiQZN55ZyOzX6zh5pvTCIVCeLxeEGKfqSA2N0PdTXu1eb/fz7x589SSOUVg7NevH4mJiepr8e8p2U4Am7ZsoX7hYqiuJP2Mtsfinscp7nN7Vg0NDQ0NDY2ORRMY/gYoM4dK+0e9Xo/FYiEUCjVZLn4mMJ7mD/M+nw+Ipag6HA61Q4ViNGk2mykoKKC+vp6NGzeqRm7btm3jyy+/xO/3M2TIEPLy8tDpdFomg8ZeUYIIZfZ+bzgcCXgCPtITDmyfLyx8m4JBPXnwkYdISWlfsNZR6HQ6zGazet8qadqSJBGNRtsVAAmCwLgzTubnD39lUOe+LS7zxfrPOXtCLsce2zQNXTFZjPedaYua2iDVVe27j5U0eEEQVDPO+P34fD7VIybWktGAw+HA6XSSlJREUlJSiyVrynHvzrJoWoIViUQIhUJUV5dz0skpLFvqYs6q+Vw05EweX/gS5828FmdyEmPPO4Urr7mKQCBAbW0t9959H9UlXraV7qCsbhepjiTOHDSWz1d+z06/h4yUPNVEU+nw0BKH9x1JVX0Z1751D69f/gQ5KZntul7toaqhmns/fYyJN+e2W1xoi3ixWdSJWK1WTj+tCyUlm5k9u47rbzDj8XgQBNDp9BiNMSFQlmSiUjSW+SMIRMJRolGJurq6ve7LZDKxefNmHrhjKsf3OZqoHEWSZardtUyt2oJep0eSY9uORqMgQLfu3Xn02cfVjIMZjzzM999/zyMfzIX9EBgQRaJtlB9paGhoaGhoHFw0geFvhF6vx+FwYLPZ9iiHaAmlJrZ5JoPL5UIUYzM9eXl5OJ1ONQhSxIKcnBz8fj+bN2+mvr4ev99PaWkpn3zyCTU1NaSnp5OZmam1kNToUGRZOlD7Bb5c8yOrfVt59s7nsNvtHXNg+4AiMCj3mxLoK/XfihFiW6xZsYoeKZ1bfb/CXcHgwUe2+v6+EMsUaN99HI1GCQaDqnGlkn0SnykVf36KINqWuKRsVxEllAyQ+O3odDrKyrYwdpyTvDwDzz39Fcd0O4IrDzuHB3+YSXphDldecxWRSITS0lIuGH8RXVP7M/GMe/l+6edc9vqdfHTN82QnZzDrwgc4+dkreHv+DC4cdx06HW1eg2MGnsTS9d9x29zpvHD+A9jNtr0u316mz5tBlz4CI47uONFClmWkqASCgCiCxWzB0MnIVVfJ3HP3OubMqeaUUxIRG7OIBKzodDqiUmObVQQEUSQSDSPJEjU1NS3uQ/l/QkICkUiEnrnduX7khDaPLxwNc+0H91JSUqIKDNnZ2Tz59DNUBEJk7cc5C41CnoaGhoaGhsb/Dk1g+BsRb1q1N1oSH5SHf0mSMBgM2Gw2JEnCbDa3aBYpiiJpaWkMGTJEXXfdunW43W42bNjA/Pnz8Xq9DBw4UDXI+qt7XGj89ZFlGbGdgW5LbK7YzivL53Lvo/ej1+sJBALtTpn2+/1q5xalpKGtY42neSmSgiRJattGt9tNKBRSu8bsjU3rNjG2/2FEJYloVEIUZSRJQBBkkCESjWI26fY4zmhU2t3Bgb2b2ypHGonEshKUWeqWzk0RDgKBAF6vt0lWitFoxGQyYTQaCQaDeL1e/H4/kiQRCoVwuVx4PB7C4TDBYJBwOLyH2WU4HFZNMJXSmfhyEkUUraqqoKioMz6vm6NHebjn+4d5YvRUbht5JQ8snMmjjzzK2HFjue36W6gurSTDEkAU9Yweeip17krOnXUDn1//MjaTmbcue4KTn72ceQve4aQR5wFyrFNCC9esqm4X73z/FNffMhF3QwP3zX+G+0+6EcMBGj/+uOZnftrxC7PfPuSAtqMQ++hi13bOnI388H0tJpOO/K4W8vLMdOtm5V9npjPzhTICAZnEBC9GUzVWixmLxYxeH2uHqtPHzCUjEQmvJ8TGjRvVf3vixWjl7+TkZOrr62OfaWttT+Iw6AwkO5xNxppOp+PmGydz9d1T2fj2642ZJbFWptDYurSxhWnGEcNJ6tGstEXLYNDQ0NDQ0PifowkM/89pbvKoPNCbzWYgNivYGsnJyQwdOhSn04nH48Hn87F+/Xq2bdvGxx9/TF1dHRkZGZoDt0aHIUnSfneQcPs9TPt6BtffeSM5OTmEw2H8fn/TlojNiA9uvF4vPp+PSCSC2WxWTQtbWz7+7+YBaUvL+f1+GhoaVANIo9HYaiaDy+WitLiEnKMzkaQoUjSKLIsIQiwjKRD0IwixtoDRaDQWe8X2pJZitNnlJW6dSCSC3x+gpqZmjwyEeOFEkiSCwSB+v38PgcFms+FwOAgEArhcLlVgUEoVXC6X+pn4fD7C4XAT/4ZwOEwoFFINMU0mE1arVfXIMBgMuN1ubHYjTmcCEOX4E1KorCjnP98/zMNj7uSaAefywPMzee3FV7hjzNUcOupGTnrmCr76OYPjjzyTfx17KbWuKs6dOYm518wkLTGZ1y99lLNfuA6nI5XhA4+jpVKJNVt/48vf3+a2KTdx6qmnIEkS126bxKS37ubaURdTlNV9vwRWl9fFtM+e5rx/Z2C3711waj+xz+/jjzfzyftubj32FoLRMCtL1vLLqk28U7spNkaCOhauMGDo2QfBZMKoN2Ay6JEjYUSdDlmSoPHzMWTpuO31d1CCfDXQl6TYa5JEcFcZ/XKyECUJGRAa/7uveNweMkNQ8OPSmEDW+LqI0NhRQ2BlyTrKBWEPgUEQBCRJ82DQ0NDQ0ND4X6IJDP8Pae1BNxKJ4PV6CYVCWCwW9Ho9BkPrs2+KEVdWVhbDhg1DFEXC4TCbNm2ivr6enTt3UlFRQX19PTabrc0ZWQ2NtohlMOx7UCJJEo99N5tjTh3DyJEjqaysbNd68fdKfBmRIAiEQiFqa2vV42pOMBgkEAioJRHxZpHN/VASExNVUSFe1GvNYHLJkiWkmZOp9tTu9m0QwNDodVDlqgVkKioDqiAQDkvs3OmjrjYYOwdRQBSFxlaQIIpKqRTqLL2oi4WAv/1WS0HXmMdLc6PY+HNSMhgU7xUla0HJNjAYDE28J2RZVrM3ampq2LFjB0lJSU2yLpQSkuTkZKLRqCpAKB4xyvVS2vgGAjLRqIzZbCE1JYWLL9Hz5JPFPPDDDKYccy2XDTiTx398iWx7J9IcKbw64WHOe3EyTkcqh/c7hktPuZnH37qNK16/g5cmPExBemeeHX8XV781laSEFPp0G4Qg6GKZIgis3rKUz357lUeemM7IkSPVY75r6hTee+89Hv/0dVgQoTC5C8f1GkG/3KK2Bx5QXL2TW96bRkrXACedtHeTyX1BlmV8vhBz3i/jvhMf5JiiwxFFkTMPGwdAOBQi2Gj0e/n7U1iNgOXsi3E4EkhMTMRms2G1WXdnErV1OzbeGg1bNvL5TddwTv7hqgeIThT3KhhGpegeIvemtRv4zwnXcGzvYa2ud83rd/JLS2+IYqwsRENDQ0NDQ+N/hiYw/IOIRCK43W4kSVL7wrfHPyE1NZWjjjoKm81GcXEx5eXlVFdXU1NTQ2VlJbW1tWrwpKFxIChp/fvKe0s/xZ8kc/1NN6gmpsA+eYQogbHyM+W2//DLoiVkJKer24pHkiTV4yTeg6A5ZdXlFPTuzrPPz1CFCGUfzX0GFCLhCKWBSqYueo5INEokHEYUG2vlBZFAMIDFksSLs4LqcYiiSLdu/cjL6xYnXMjq+5IUbcwYkIhKElJ09989C6OMHj2uXYaYPp9PbbWpeCsowoJS2qCcVzAYJGhzEAyEKVn0Mx8vX4lemR2XY2nvsiQTaajjjWefYeDAgbjdburq6rBYLCQnJzcRQQVBoHfvfvzxRxkDBqRgMhkRRJEJl/qZdt86Xlz6HtcMu5BxvUdy/Tv38Nq/n6AoqxuPnXUbk99/iKSEFAq79OPqM+/m4TcmM2Xu40w97QYG5fenID2Ld797holJ95OV1hkQ+Hn1V6wo+Y633nudnj2bdo7o1KkT1157LRMnTmThwoWMP+dc3lo4l84pOfTMKKBnp64MyO9L//zeTdYLhkPM/OoNXv9lDkOOsXPTrS2beO4vsiTz5fxtJOgyGdlraOO43D029Y2fjyzLPHPanZz+5o00fPcl+jEnEolGkGUZi8UMun0rVUos6EH+zVOYM/0eTti8jMGd+2FsNAFtjags7SEw7Ni6nVP6791bJNJ43zVHEHVaiYSGhoaGhsb/GE1g+AehmKgpZRL7EngpD/s5OTlkZWXhdrsJBoPU1dVRX19PUlLSwTx0jX8IkiTtswfD7ztW89n2hbz14TuNAfhuw1Jlhr09KGn5RqMRs9lMwBvgkdNup29Oyy0JJSlKJBIrRWitW0N5QxU3fHI/06Y/gMlkUrtLKJ4FrQkM488bz/jzxgOxconq6mpMJhNpaWlqwB3fvjHWDUBQ96HsR1muI1GurdI9Ir7loPJ+fKaG9biTSR17CgkJDhITnY3XCuKD3vInpiFJkno9lNKJ5u1wPR4PHref8vIggiAiCLHuBcuXg8Fo58ftv3F450MZ3/8kyl1VTHrzLl6a8ChH9xzK5FEX8tRH07hx/CNkpecx8cypPPrmTaTZk9npLWfAMUO4bMhAnn/yWS4afSu/rPua6shW3n7/DTIzWzdf3Lx5Mw9MuZ+7jp/EqKJhLNnyO8u2r+LHLb8wa9E73DVuEu8unYfZYKJbWmd+2LAEQS9iM1tJSzd2mEluLMMEvL4Qc97fxb2j72+8xk0/f6ExhcVkMpHsTGbGSbdx3odTCOZ2hqK+jR4bsQwSg9HQtuCnvC1D9hFHsVkvcv7L1+I0Ju4eE40dKWKdKWKrBCIhJL2XtZevwGAw4vMFychIY9uaXej6X7DXXa6v2k7EV7DnoYhaiYSGhoaGhsb/Gk1g+Aeh1E9HG2cu9xWz2Ux2djY5OTkUFxcTDoepr6+nrq5O82HQ6BCSU1N485e5nD34JJyWBOxmKzqx9RnQKlcNj/4wm4deeIz09N2ZBkonApPJ1KKJaUso3guKwKDTibGab7HlAEvxRFDqwoUWAkV3wEt6Zid69uyJz+dTywuU42pNYIhHEQwU8cNkMqlGiBATDpVgXskiONi0VkYBqAaWitiDSGPWhi4u3hWab1C9DoopZnPzysrKSq6++mJy88oYNy4PAQiGorz1ViVeT18mThzGzJkzeWLRq9xtnMjkoyZw55ePcet703jmovu4YNgZFNft4tkPpnDrhY/TKSWbc4+byAufPMh1113D7XfdgSAI1NfV8/iTN3HIwL688vJsUlNTW70OP//8M3dcfytXDx7P8MIhAIzpM5wxfYYD8NXqBdz8zv3YzFYuOeosVpasY0j3Q7n71BvYULqZS1+9kZy8UkaPPvDvT1kGWZL475zNdDLnM7LXkXuVBgwGAwa9nsLsAqYMvYgpb72MMPFmAmYTHo8biHn0CLp9G0+WhAQGFQYp3Wzi9fNnkJ6Yik6vA2QkWUZGJhqVuGPOw+h7LGfChG6NJUlRgsEo110WaFPUSDBaqQ8G93xDFJGktju0aGhoaGhoaBw8NIHhH4RiuKbUdLc3EFGWMxgMJCYmkpiYiNFoJBKJ4HK5aGho2KuRnoZGe3niuad4/pkZ/OfbJ/E1mi7++/CzOW3A8XuM11AkzLSvZnDB1RczaNCgJu/Ft2Zt7wxx/DrKT2MI3fLy8b+0cisl251UVVW1uO3m7WP39Ribv9bSsgeLeHGhuZmkJEmqUaPceHFUQSI+9hOabLBJiYXdbkcURdxuN2azmdLSUiZNuoTDh4Y4/vhcJCmK1xvl+ed2kJMznCcen87KlStpaGhg/vz5PLTgRR44fjL3HXcjkz66h1vefoCHz72DO068hpLaXcz44F7OHzuRBWs+5v4H7+WKK69Qj//Syy4lJzeHAQMGIEkSbrcbu92+R6r/p598yhP3PcKdx1xF39yWs1zG9BmO/ry7yU/LIz8tt8l7hdndmHT0pbz5wcsdJDDIlFd4mPdJJa+Mv78xU6H15ZVMBrPZzLhDR7KmYhPvv/0y5hvvxKfTYzSakGl/sF7+60+4t28jGgpx1tndWbhgJ2e9cgmPnfYAQwoOQRB06BQfEIOAQa/HbNFjt8cycmy22ONIWIrw7k8fc+spV8dEqRZIdSRTmZbewjmJRKJ7mrNqaGhoaGho/HloAsM/iGg02kRg2FcMBgMJCQkkJCRgMBjw+Xy4XC7VHV5D40BJSUnhP/dMgXtif2/dupU7bryN7+f8wt3HX0eqY3cpzqyFb5PZrwsXXXJxq9vb1yC7ycz8fnhBNGfp1hUMPGxQ2wu2cUyKj8JfgebZC80FDqUUK9aFI9auML5bRItIkhrAm0wxs0m/34/L5WLZsmXcf//NnPEvK0OHZhGNRKmo8DLj2VJGjDiXm2++A6PRSO/evUlJScFisfDMU0/z4HcvMGXkRHITMvluw09Mfvd+nrngXp674D6Ofvhcpr01mbffeIUTxp7Q5FCsViunnHIKdXV1VFVV4XA4sFgs6vHJsszLs19izux3mT7uFjqn7F0cGNmrdT+BXjndqPo+zI4Xq8dGAAAgAElEQVQdbjp3dux1O20iy3w0dyt90wfSJ7dnu8e+krVzw6hLmf/yVfj+WA6HDsIatio9L1ukbNECNj18D47CXkSqyglUVSF468nrlkZujo1Jk/pTULCF62ffyEWHXszlR58XZwDauug3aqyTtz/6iCtHX0iSPbHFZewGC76vPqNs7R+g0yGIIoJOj9/tIiHob9d5a2hoaGhoaBwcNIHhH0AwGMTlclFXV4fQOGO1N+Ot1tDr9SQmJuJ0OjEYDEQiETweD263u8WWfhoaB0rXrl15+7/vMvP5mdz8xoPcfPRl9MruwTdrF7HSu5m3Hnx3r4HUXoPag0xUivL15sVcfMeVB7yttjIdlI4N8Oecc/NWlvGEQiFcLhderxdJlhERkGVJNZRsuXvh7pIPpStHOBzmv/+dwwcfzOLKqzIpKkokEomwY4eHGc+WMX78dUyYcJlaimG328nKyqJnz54MP3oEPy/6ias+uptsZyceO+UOHvpuJo9+PhNEkTJDlMTeA1i2auUeAoMSBJvNZhISEhAEgYaGBtXQ8pEHH2b517/w2Kl3kGI/MO+Zvp17cdFhZ3HH5PeY8VJ/nM72lfM0uXKSjCRLuNxBvv+ulhfPfnCfpDFREBB0OoxGAyd1HsI7vywm2vfQmBAtSYiS3GI2hLNnEaLNgWfpIh6ZfgiJicl065aAXr9bPBg7roAuXRw8+MBrLNu5kkfOuAunLQFJamyZ3DiO4sf25x9XkWpJwdJKadPO2l3oBJFJZ/+L888/XxXMo9EokUgEp9O5L5dPQ0NDQ0NDo4PRBIZ/AMFgkPLychoaGtDr9djt9j2cu9uDksGgtNxTBAaPx6MJDBoHDVEUueqaqyjqXcR9t99DZ3MGq6s28dbcd7Hb7fu0LZfLxcSJlyGKTbMBYu0Uw4RCIdWk8Pff1vLb4iWkOZIBkJoH1I3BkUzLmRJuv4cu/Xpw7LHHNnn966++4aP/fqKWBDRvBdmcTVs28shjD1FU1HL7w/ht/JniwkPTprPytxXodCJCXPcNv8+vZjaZJT0NH70Lv/2E327H53BgMJoQdSIIIoIYK6Hw7yxu4iNhNBp5aeZsnn/6Wbr3lSgqSkSKSqxaVcMrr9Ry220PM3bsuCbmmkqbzO7duzNixAgkSeLb+V/TxZlNqtHJlOOu5bZ5D1MqBOk342UcnTJ4Y/rd5GVnc+H55+9xnkorX6Wrhcvl4qlHn8C7uZZHTrsdq9HSIdfzytEXsquhgjtuXsRzLw7Y5/UlWSIajTL3wy3kObrRL7dn260l4xFiuTqiqOOEohG88dl9BDatJ2zuj5SahqyTWxzf1tQ0ss48H+tXMxkyZM9yBYVevdJ56hkHz89Yw8kvjOfl82bQJTVHHUexMSuo3SydDitXHnE+ZoO5xe19tXoBG/3FnFRwNvn5+ftwohoaGhoaGhp/BprA8P8QWZaJRqOEQiF8Ph91dXXU1dURjUZJTk4mJSWl3cZ38ej1ehwOBw6HA4PBQCgUorq6murqaoItGW5paHQgRx99NAO/+pRZz73A+AGX0b1791aXbW2GPRwOs2LFL0y+sfMe5o2SJBOJRBAb204OGpyMyxUGgsiyjK5Z275otLE9pBCbdScuCKso9zN/fgP3PDB1Dw+ITRs3Ygqk0b/70D26KcT+2v13ccVmVvtXtat95J9N6bYSJg0+n26duiA3GvgBeLweGhrq8Xi9vPbLhyyrX8fRvQoZMGAAAwYMwOl0YjQa1awLpcVm796xdo5KMPvlZ19w07FX8PBXM6iocLNhvZuPPgoybdrzDBs2TO1cER/86nQ6OnXqxKBBg9Sykl8X/ULnjQs4pd8Y7jj2SqZ89SyhigrELl3JuPY2pj10N5kZGYxuFILiMykgJlx4vV5uuf4m+jm6cc8J12GMa515oAiCwO0nT+LIad8SCkkYjbHxEuviGRvH8eJWXNMGlAWLd7j4/LMqZp117x5tKduLXq8jLzWbTMFExStPUoKOivQMrEV90ev0RN31RN0udAmJGPN7YM3tjL9yFwmGNh4jBEhOtnDbHQN4+631nP/GZYwrPBF/yI+l8fOPN+foPcDInN8/41+Hn9iiCKcTdVwx8SrGjx+/z+eooaGhoaGhcfDRBIb/h0hSbEbL5/NRWVlJdXU1DQ0NWK1WnE4nqamp+9UaTREYEhIS0Ov1BINBqqurqaysJBAIHIQz0dBoisPh4MZbb25zudZm8lNSUigsLEKWffTs2TSVOhqVVIFBr9c3zq63zt6Wf+ONHdx443/o06dPi+smO9LonNG9RYFBYWflNn7d/DVPPvt4m2nf/6sSiSRrQizDIy4A9OgsmKI6bIKFiBQhJSWFwYMHM3DgQAoLC3E6nZhMpr1+B5WVlSEFo5w2+HiWbVvJnbd+hzMll2effZ0+ffq02hYUID09neTkZGw2m5pt9faX80i2JjEkrz/XHXk+T9w3Bcuzs3DkdyV14k1cN/U+3k1Pp1+/fup2lPajVVVV3DjxBlavWs3FZ5+CKAjIcpNTPmAsJguJ1gS2b3fTo0ej74AcK30IBsNs3lxLNCpjtRjQ6QU87hA1tQFqagKsWe1l5QoPZ/c/m0M7997vA9PrDVisFsZ2H8ay8CdMuMzJls1utm5dgNlgIrW7heRkE5VVlWza/AfFP4kE68IMGN2+kgSdTuSCC3oxaHAab77+HWvL6wl9EuXzzysxGESMRh1ms57+/RP4vaGYpZtWMLj7IY0iAyj3iSRLvDL7FS6//PL9Ok8NDQ0NDQ2Ng4smMPyNUbIU/H4/Xq9XTe8WGk3VpEbjtJSUFBITEzGbzS26obcXpfWf0lpPyZT4q5jPaWi0h4svnsisWbdSVOTcI4uho2LGX3/xcucdp+73+tX1FXyw4FkeePQeBg0aRHV1dbvW+zM9JyRJAnHP2vxYVoKMLEtsqtlBVlEeycnJ2O12VQhp6zg//fgTjuo8CFEQuWrkBSx+ZRm3PDSVrl27qsu0JjDodDp0Oh2ZmZn079+fhoYGysvLmbXsPZLMDobk9uc8Ty1vTr6GHrPfwNa5K+GLruTia6/no9dfJS8vT93+6tWruf7ySZxXNA4KxnLn3Id52fkoPbMLEASxcfx0zKjJScpk6a9VdO+WgCTHTDFXr67iicc2EPGb0Ik6wlIYSZIw6U3YjHacplT6dBrOw9ecQ1pCygGpHga9HqvVxogeQ/j44/lEIlF69TYy+DAnDrsDkznWJlVpObpfuxKgqCiNaQ+kISMTiUTxeEK4GoK43SEqKrz88EMVnkgdz3w/m9e6PdPo3YF6vU885FjeX/3Ffp+nhoaGhoaGxsFFExj+xihZCrW1tZSVleHxeFQBQKfTYbVaSUtLIy0tDbPZjMFg2G9xAWIP3DqdTp093NcWexoafxZ7G5cnnDCWN998kV9/reTww1uoHT/AIV1VFcBgiN17LaH4NrSEJEksW/8jC9fN45b/XM/IkSNxuVxtBuV/5r2oHIskSegEkRYUBmRZJhwJ4wp4KHQ4cDqd2Gw2gDYFydraWt599W0eOu5mJEkiM6kT5x52Cot+WMDw4cPbLWgmJyeTmJiolol9EQrx6KKXuXvUJEbnD6XcXc13111Fr5ffwtlvAFXjzuCCq67mozffICkpiR9//JF7bvoP1x1xEUO7DUSSJMrqK7jijdt5/8oZpCemIQj7GWi3wBVHnceN70wlK7uMoYenMmfOZubOqeSqoVcyYcQ5ex+We8mEaS96gwGdXkefvCL6pvfmzdf+4OIJiUiSRCgUwm6347A7MJpMjWUcB7A/IVYKZNDrSXLqcTqtAPTuAyNHdeXJJ5bhWWsHZFVg0OlAFHTYTVZMBuMBnauGhoaGhobGwWPf8+Q1/meEw2FcLheVlZUUFxdTXFxMVVUVoVCIpKQkcnNzyc7OJisri8zMTNLT00lISMBqtaoGaPtTGqGgtLn0+/1q5oLS717LYtD4uyCKIjfccCfzPm1Akjp+xn/JkkrGjTuzRTFPFeZaWM/ja+DNrx+nNLyK19+Zzemnn95kvb0Rb5jXngyBA0XZR0vtBiU55mURioRBiJVWKTPfSvlWa8cXjUa546bbOb7rMPJSsxFFAZ1O5JzBJ/PTd4vYsGEDPp+vXd83Op0Oo9FIbm4uw4YN45hjjgGHnocXzKLe52LC4H/RX5fCmuuvJBKJkHb0sdT1HcRl117Hu++8y9Qb7+Ke0dcxtNtAIPYZXHrMOYzpOYyLXroRf9CPrLbfPPDrfWTRYTxw8m08/dAOpk79nS8/8vPq+TO5ZPjZMRFDEFr/6YAsCkEAURCxWCzcNPpy6rYn8/HHbqJRiWAwiMfjobaujpqaGqqrqqmvr8fn8xEOh/d/vDUeevPTGXNcHn/U/Mo3axcjijo1Yy4SjSAiEG78N+d/2SVGQ0NDQ0NDo2U0geFvRCQSoaGhgV27drF161a2bt1KRUUFkUiEzMxMioqK6NGjBwUFBeTn55Odnd2ueuf2omRM+Hw+NUiIRCIH9oCpobEPrFq1im+//bbN8dbWjP7gwYNJSenGHytqm7wuq//Zf+rroKCgxz6v9+nPrzFi3GDeePtVevTYvX57s4WUrII/S+yTZImWpBJJknaLjrKsllbpdLo2S6qm3HkXS75fTJYjne/X/cSCDb+waNNS1pRuIF2fxEPTpuPxeIhGo+0+zqysLIYNG8agQYPo3ac3dbKHhxa8iMvTwDVDzqNzlZ8tD94NQPqpZ7Fo2Qqeu+9JHjn5dnpmFqjbEQQBnV7H7SdPokdaPhNevZlINIIsSR2hL4AsM6LX4ZgwU705mc8mvkvvnMIOLcPYO7Ho3mIxU9ilO4//awqlK9J5bHot69bGugXV1tRQVVlJeUU5tbW1eDxeQsEQHf3137t3GtdN7sZdn9/L5spt6EQRZJlwWBEWNHFBQ0NDQ0Pjr4pWIvEXQJnVCwaDeL1egsFgizORynJGo5G0tDQEQcBgMGC1WrHZbBg60Nm8Jfx+PyUlJRQXF+P1ejEYDCQmJuJ0Og/6vjX+Gcx4+llWr1iFxWxGp9ej0+swmoykdkpnx7btrFnyB5Wuar5fsoDExMT93o8gCJxxxoV88cX9HDpgfzs0xGauZYRYq8rGV/V69rlta0VtKbWBUq67/qVWMx9aQxAERFH802d0ZVnew8MCaMw6aKzTJ/Z7ezOojjnmaDwuN+ttFTGxJCqpwklKryxsiTEPGb/fjyzLGI3GNrep1+vR6/X07NmT0047jdTUVL764kue+ul1bhp2KXeMvJLbvniMtc88irx6FX1I4Jnz7yWlsT1pUwT0eh0Pnv1/7J13fBR1+sffM9s3u+khvQBJCBBCB6WLJ4JdVE7Fs4sFy51nryfWs5dTfxbsKOrZFdupiIL03gMEQkJ6215nfn/szpCQAAkCKs7bF8JO/c7sTvl+vs/zeW7hspk3ccO79/P4OXeBLEcG439FvoSMzOodG5AEmQ+vehmLybJPE9BDhSiK6PUCvbLy+b+pD/LBsjl89Po3BPGRkOqnqK+eESOt0d+cTDAYxB8IYDGbMZnNiEJ7X44DYfToHBYuqGf2ok+5+7R/IKJDL0AoKlI5HA6MRiMGgwGDwaCl62loaGhoaPxO0ASG3wGyHHlJc7lcVFdX09LSoo70tRYZTCYT8fHxxMfHk5CQgMUSqcOuGDseajweD9u3b6esrAy3243RaCQpKemAy15qaOzJV5/NYWzSILrH5EQENV+YgCtIY3kjuWI8fz/7Qc6f9c8ujWDvjfHjx/P4EzP48stKjj02DZ3uQK+htp16nV7uctnWpRt/YMq5Z2I0dj23XBEYRFE8KOels8iS3GEEgyiIGAwRI1iEyP1JERiU9u6Nvxw/gb8cP6HDeT6fTw3Jd7vdyLKsGs52hsLCQvLy8rBarezYsYN1q9by8tL3mTbsr1w+5Czu+/B5xhYexWMX34HFZNnrdgRBJNZq48mp/+KCl/7Bo1++wI0nXA6Iv0oQkGV4d/FnjCkcjtVs/c06zIIgotPJ2GwxWK1Wrk29lGnjp7Jy23qW71jDgiVLeejrMgYc7eDkU+z4/T58Xi9SfBxGoxFZpztokkh6hon1m7dG2iRGomF+2bqCUDhEc3OzKq7vq6qIhoaGhoaGxuHlTyMwKGG7yov4oUCp6qD4FAQCgTbzlZE4aCsKtA5tNpvNao14ZZ6CEq1gtVpV08bDidvtbiMwJCQkMGzYMI466iiSkg50FFhDYzdxsfGUZPemd0YBRn3Hv29RaN+R9vl8mM3mDpdXrqE9OyCJiYnMfudzHnvsfm65eR4l/Y3k9zRSWBhLSsqB3xr1erndtb8vJCnMqm0LuKD3qaxevbpdu91uN83NzRgMBoqKisjOzm63jdb3i8MVxSDLMmIHHgyt5wsIbQSQrqZvhEIhKisrEQSBcDiM2+3m1lv/yUUXXc7IkSPbRG3sr4OpjHQXFxdz1lln8V1iIvP+Nxdf0M+6mlJOKhrHdRMuIRwMIxnCiOK+DHEFUuNTeGbqDKY8fxUmvZFrjrsoavrYOp6la6zYuY57Tv/nAa17cBGipr6RT6IoMLigH32zCzl7+Cls3LWVx394kWe3lXPVdbpIlQeHQDgsYbGYMZst6HRi1G9EiWgQumyIOemE7nzy0UKWbV/D4Lx+CMDCshWIVgNmsxlJkvB4PAQCAcxmM2az+ZA93zU0NDQ0NDQ6x59KYAgGg7/a6HBfKB4Fzc3N1NfX43K52qU4hEKhNiNvysu3Xq/HZrORmppKbGzsXis+KOv8FqM1e0Yw5ObmMnLkSEaPHv2rwtU1jmx8Ph/Lly8nNjaWbt26kZycvNdr0Bobwy2f/RuDrOfjq17qcDmrwcyWLVtISUmhrq6Oh2Y8wA/ffc/dD8zg1NNO7VLbcnJyeOqpF6isrOSrr75k/vzveO+9VVitkF9gomdPPb16xZOebtnHNdd2HF8Q9l8usvXcJkcDVVWVzLz/uQ6XVVKj3D4PPQb14pn/+0/bvUfvIZ0tAXmwkGWpw7KIMpHReDn6X+v2dZXnn3+SF198irS0uMg2ZRmL1cULLzzOkCFDVIGhK9suKioiNzcXQRBYtmwZ87YvoX9aEWeXnERTYxPIRH1r9l1xRxRF5qz9AZ2o4435H9A/pzfjeo/gQDrSoHhtiCDIhzkpYt/odCKiaMSgNxATE0M4LJGQkMBTKZnM+OxJHr1/FdP/EUMoFMTj9hAXH9fGd0MQRBSPSuSupU8kJVmZfGYGt39yL3OumY0oisRZ7dxwww3ExMTgcrnweDyEQiHi4uK0SDoNDQ0NDY3fAX8agQEiHeSuvIjKskw4HCYUCnWqUoKyvCRJxMXFYbfb2+xPmQ+0iaRQyj+azWZsNhtmszmaw/z7GIlRzoHL5aKhoYGmpiZCoRBGo5HY2FhVENHQ6Ihvv/2Wp+95jCR7Apt2bUUQBeLi4rBaY4iNjSUxKYGEpEQSU5IYfNQQHM4WcgN7FyEuH34Ot11zEyVDBlC6aTND4nvz5Gl3ce99T/Dp+x9jsZppbGrisumXk5ubi9Fo3O/IpsViYcKE4xkyZCh6vR63283mzZtZuXIR//t2CV7vTvILrPToIVBYEEtOri16bXeQJCBDIBDA4XCooqIy+t7S0oLb4yEcthIOhwCBZkcjMSYrD590c4d9r7AkEQ6HWLp9DV+1LKKxsa0xZSgUIhAIqBVd/H4/oVAo2rnrele1tUjRkZjpdDppbm7GHwhCtG27lxXVYw6GQgjsjtBS2qkIJvv6PpT9b99exvkXpDFyZKo6TwqHefjhbcyZ8wWnnHIqMTEx2Gy2/ZbgVY5DKeU7YMAAzj//fObOncumletZVrGWIdnFeNxumvR6YqxWLFZrh+2UJImb33+AldvX8fblT7N51zZu++BhZl74MEWZ+RH/iS6ee0EQSI9LYUttOUcXDO7SuocWQa1iofziRVGgW2IK959+E09/9wpP/vsHpl0rkpqKmr6i0+kRRWH3dSK02l7kr92fWkU4RJ6NOgx6PXqDntNO784vC5Zy1yePcN/pNyMjq74eig9HMBgkHA7T0NCAxWLBYrH8pmK8hoaGhoZGZ2hdkU/pG+r1+v2+0/ze+dMJDMqLSGdQjBd9Ph8+n49gMLjP5fV6PTExkU5TSkoKNputSy84v2ak71AiSbvLlDU3N+NwONQLQAlL1dDYG+tWrWVy3wlMHjIJgFA4hNPnxhvw4fS5afE6aG5x0FLloN5XwwB9T84bdfpetze0R3+eS53Byh3rOGvkMeR3y0MQBF6Z+jAry9fh8rp5bv2bbNmyBUEQ0Ov1xMXFtfE3aD3Kr1x3rVMR4uPjGTFiBGPGjMFqteLz+Vi3bh3Lly/ivfd+orJyM7m5Jrp3lyksjKWgIB6zJXI7lZHxer00NzcTDAbVtCmn00lLSwsOhwNBMhEKhREE8AW8CIKgGkPuefmHw5EOuYCA0+WkoqKizT1Cp9OpIoYkSej1ejwejyowdPV+oggAre9HrbfhdDppamrC43GzZNtqyup2IgqRDqFOp8Pr9tLY2IjD48AfCuHz+WhsbFQNGY1G437FXuX78fp8SOEw4VammTIyk89I4qUXn2fAgIFkZGRgsVg6/TBWjqekpIScnBxiY2N5rLSUJ+e/xg2jL2ZgVnG0DGK4wwo8oVCIi169AZfbxZvTniQtoRtFmflUtdRy+Ru38d+r/o/UhGRlb5076dF2ZSWkU1Zf3qX1DjeCKKJDICYm4tFwy4nTSf0xhf974gMmni4zbKiE1+ttvUar/9NOWGhdJUWI+ncYDAYsZgsWqwWTycTU83J56elfgEiEjCAIqsGjMgDQ3NxMQ0MDcXFx6PX6vUYBamhoaGho/F6QJEntZyplvZWB5z8ynRYYBEHQAUuBSlmWTxIEoTswG0gClgF/k2U5EF12CvAvIu8Cq2RZPjc6/QLgjugm75Nl+fXo9MHAa4AFmANcJ8uyLAhCIvAukAdsB6bIstwkRN5MnwJOADzAhbIsL99X+0VRxGazqSkKnUEdiQsGOyVMKKMqygiK4mz9exMMukpLSwvr169n2bJl1NfXYzKZ6NatG9nZ2cTExPzhj0/j0LJmxWou6nmK+lmv05MQE0dCzIGn1STGxDO+z8g204x6A8N6DODnTYtxhD1069aN559/mtWrF7fqbENLixtBkImNtQEgywZmzfqA2NhYwuEwBoOB2NhYRFHE7/cTDAZJSUlhwoQJTJgwAUEQcDgcLFiwgPnz5/HFF0vZubOU7BwLPXsIbNvmQBR/YcKECdjtdnUEX6m6YrFYCIgCok5EFAQCQT9mgzHSRuigbykAMin2RHaUbufScy5S54TCIa664RomTZqkpguIoqhGQB1oBIMkSXstj6l4KTS4KnluxaPRdaLrShGX/1AohNcbQjKE2bTpZ+68ay1eT4jExAzefPO9TpfOTUxI4c0365k9uzk6uq14OoDLFWTFihVkZmYSCARUMWl/x6zMt1gs6PV6+vXrx6mnnsr8+fN5fP5r3DT6UvplFOF0upBl0Ot10Ye9Hm/Iy6Vv3kKaLYXXLnuMuJhYiHaULz3mHHY113DBzH/w/pXPYTXHdDmKJDshneUV6zq9/G+HEK0gImOxWrlo7BS6p2Tz1Oev8OPXDnr0DpGVFRH0GhuD1NWHCPhkzFYYMthKSoqehER91FhVQJIiaSGiTkAKhwkFQ4SCIXx+HwaDAY/XRTAYpL6+nhZHCwsWLCArK4u8vDzsdrtq9ggRoT8QCKhRPIrYoD2nNDQ0NDR+b8hyxLcrEAioXlG/lwj2X0NXIhiuAzYAsdHP/waekGV5tiAI/wdcAjwvCEIBcCswMioGdAOIigV3A0OICA/LBEH4VJblJuB54DJgERGBYSLwJXAL8J0syw8JgnBL9PPNwCSgIPpneHT94ftqvCiKxMXFdTk/WVm+s+u1HY05Ml5ompqaWLx4MQsXLqS+vh6r1Upubi7du3dXX+o0NDoiGAyybctW8kfkHZb91TkbeXbhLF6b/Qbdu3ePRNw413HXXQVqTv0jj6zGbocrrigB4MYbNpOcnExMTIwacp2UlEQgEMDr9RIMBtulR8XGxjJy5Eh69eqF0Tgdm83G5s2bWbZsKeU757B2zWrOO+9s3nnnfXXk3mKxEBcXR0xMDCFRh14X6QxbrTb8oSAGfce3Y0GI7LtHtxw+uvyFSBnAKC//NBu9Xk9aWlq7qIxDhclkQqfTkZ7ZjZtvTiEpORLBpIxIO1oc1NfXU7a9mf88U82Me7NIS0ujsTHMc8+6sNls2O32Tqnzd999L7fffrdqgts65cTj8ahhhH6/X4246Kzqr9Pp0Ol09O7dW610MWvWLB796RXuHH8VPRKz8bjdEaFGFGnyO7h1ziMMzenPvyZfT4zJGjE3jJ5qnV7HnZOv4+rX7uTiV2/ijUuewGI2d+hTsTfS47tRt+mnTi//W1HRWMXm6m0kxMTjCXipaNhFTIyVB0+/le01O1myYyVbtu0CGWymGPA4aXDWU+2qY91aB3o90WtNIByWCQYjaTkxMSIFBSb69jXRvYeJmBgdggCVlV6cngA1NTU4HQ4WfLeAhIQEjjvuOHJzc9XflM1mw+v14vF4gMh1oAhJR8rzWENDQ0PjyEGpJBgIBLDb7UeMl1CnBAZBELKAE4H7geujEQTjgXOji7xOJGJBEQqejQoHyLJcG13meOBbWZYbo9v8FpgoCMJcIFaW5YXR6W8ApxERGE4FxrXax1wiAsOpwBty5I16oSAI8YIgpMuyXLWv4zgSFKHDiTKS6XA42Lp1K9u2bcPn85Gens6IESMYMWIEycnJ+9+Qxp+W0tJS0uO6YTEe+jQaSZJ4/PuXOPvSqQwcOBCXy8XYsWOZPfslNm1y0Lt3PIKS/y3LCAK43SH8foG0tDTc0c6k0kk1mZUzcQcAACAASURBVEzExsYiSRJutxuv19umk+JyuWhpacFojEQf9OzZk549ezJp0glUV1fT3NxMYmKiauCqdJBFUaSybjuyLCGKOrpn5NPiduAJ+rAa25dHVA3yhN258Ltndq1TfTBQQvhkIukKyFFBQ2lWNOxdltrm3cNu75nOtlmJxBg2cChyMBJBplQmMJgMnHneX6mrqmX6dVcDdOnBrHyXCQkJFBQUkJ2djd1up66ugjv+9yjn9Z/M0TkDSbDGUlq9g3/Pe5GT+4znvOGn42xx4HG50ekiqWJ6vQGj0YjRaOThv97GxTNv4JYPHuKp8/5FNKi/U23KSEjD6XV1+hi6SigcIhgORf6WQupnvagj1mIHot9pFAFBvXZlWWbVzg28v3IOGxq3MmrMaOp3LcVsMdNzYAFry8rZsOYbmuoaGJMzlPz07qyt28Kmum2kZaczJD2fnJwcNXVBkiS12opOpyMcDtPS0sL27WXMnbudjz+uIy5eJjkJ1q7xYSGOYDCIHJaprq7m559/prGxkaSkJOLi4ujWrRupqamkpaWRlpamjgJJkoTL5cJoNGIymTShQUNDQ0PjN0fpY4XD4X36Xv1R6WwEw5PATYA9+jkJaJZlWUmMrQAyo/8uBBAEYT6gA/4ly/JX0fk7W21TWScz+u89pwOkthINqgHF6Wtv22ojMAiCMA2YBhG3eI2uIcsyoVAIp9PJ9u3bKS8vByA1NZXRo0czevToNnntGhp7snbtWpoamyitKaMgtfsh3ddHK75GSjFyyWWXAqij0hMmnM7iRa9TVBTXbjS5vNxFQWFhh+Kj0WgkPj5eNWcMBoNtbvxut5umpqZIZzsa+QARjwKHw0FiYiJmsxm73a6mWzidTupqqlm+9luWrf0fifHdSE3uDoLAy3Pf5toJlxzCM3RwUCoEyNGoDjnagW5zZgUQdeBwSNx+WwVJSW6cTj8NdSFOOeF0dDq9mj4QDofo268P/3nu6Q73p9PpGD5sGMOFXkwsGadOX71zA9/NW8DPpYvJ7ZHLyaecgt1ub7d+S0sLFRUV9O3bt8Pt2+121SgyEGghv8DIju1eXln2X2av/gKT3kSzt4Wc+AxyYtMpqyonyRqvpqCYzWasFgs2ux29Xo89xs6z59/PBS/9g39/8Ty3nHhVpy0VspLScHoOjcDw8+bF3P+/5zGbTBgMevTRUEyDwUBtXV0kRUEpOxp9yfF5vDx48o3saKhkzua56OJMnD1tKv85+aR23jvhcJhwOMyOHTt4e9bbBAMBLht9DYMGDcJqtbYp07znC5RiLCrLMrt27aK8vJzVq1czb9481qxeDUE9AVnmge+fp8IREe8WLFjAkiVLVMGqb9++DBo0iLFjx5KZman6crjdbhwOBzabrU0JaA0NDQ0Njd+K1oUElPTWI+n5tF+BQRCEk4BaWZaXCYIwrpPbLCASeZAFzBMEod+vaSRA1JOhS/kNsiy/CLwIMGTIkMNTu+0IwuFwsG3bNlatWkVdXR0Gg4HMzEx69epFcnIyFkv70VYNjdaMGjWK/0t4jn/8914+n/7KIdnH2opNfL7hB1bWbOCtD99RR8YVgWzYsKP4Ys7M3dELraiscNOrcIz6ufXNvXUHUlGZWyOKIsFgEKPR2MZEUhRFQqEQMTExGI1GtSStIlBkZGZy06TLGZE/hOVla1m9cwO7LLG88vP7VHjrKE7OpziriN7p+ViMZrXs4+/luaMYvMoyiCLRMpG7R+llKXLeDQYZnU6gf/9juO6662hubuaRGc8wdcz1aodbliNRHa98/QDl5eVthGCl6oROp6OwTy9aljvbtKMkuzcl2b05oe847nz1SY4ZPx6r1aoaz4qiSEVFBVdfeiX1NfU8PfNZBg0a1OHxVFRU8OqrzzD+WAMZGUZefKGBnkUFjBgxgnA4zMqVK1m7oZTHlr2HGAxgEvX0TMphZPZAjs4bqL4o+P3+iEGhwcTDZ9zKVW/fSTdrAqcPnKTuL+IJoEev06PbI3S/W1wywXAQl8+DzWw9iN8aePxeTjrtJGY8eF+n15l2wSXc+eUTjB4/ltueuIchQ4bs9QVISTkpKCjg7n/d3eX2KQKDktZgt9vJzc2loaGBxsZGGhsb2bFjB42bPBQkF+B0OmloaFBfzsrKygiFQrS0tLB582ZKSkooKSmJCEBWK4Ig4Ha71SoiWpUJDQ0NDY3fitbRsXq9HovFckQN2nYmgmEkcIogCCcAZiIeDE8B8YIg6KNRDFlAZXT5CmCRLMtBoEwQhM1EBIdKdqc7EF1nbnR61h7TlW3VKKkPgiCkA0q6RSWQvZd1NA4Szc3NrFixgqVLl9LQ0IDVaqWwsJCioiJiY2P3vwGNPz0ZGRnM/uA9zjnhrEO2j9U7N0APKy88+jKZmZnq9HA4TDAYpLm5GbNJjOYaROYpamNzc4jCwtw222vd4VAEBqUkXmtal8lLSkpSHwxKGLYSMr9ndISy/YzEVDKT0jh5yHGcNmwir5R+zOX/vIZlS5bx7oJv2PTtM+QlZtEnqQdF3XpSnNULk9FEKBxGjHaMgqF9V7Y5FCgRDESTJNhDupVkKersLyEIqAIMgN0WR7w9MXpOdp9nmzVWDZdXCAaDeDyeSFWPxATKvOUdtqcwrQfH5AznP08+wz9vvkE16ty4cSPXTbuGs4smkVmcxo3Tr+elWa/Qo0ePNuuvXr2aa6+9iIkToXuPWBYubCEYhP79+3PNNdeQnJyMx+PhndmzefrV2SQeezkeZxOrtyxn5bqveHv155w/4FSG5/RH79BjNpuwWKwkmeO5e+I13PbZo4ghkVE9BiMgYIp2eM1mM2adLiocKSkkOmyGGG6YfS8PnnkLep2IQW/AqDOoHiIHik6nJxjo2u/lxjtuUdMPDgeCIKglnrOyshg8eLCaWqTkqM6bN49vvvmGNWvW4HK5VPGvpqaGuro61q5di9Fo5KyzziIhIYGcnBxSUlLUSkiK4AD84V26NTQ0NDT+mITDYZxOJx6Ph5SUFBISEo4owXu/AoMsy7cSMW0kGsFwgyzLUwVBeB84k0gliQuAT6KrfAycA7wqCEIykZSJbcBW4AFBEBKiy00AbpVluVEQBIcgCEcRMXk8H3gmusyn0W0/tMc+PgWuFgRhNhFzx5b9+S9odB6lI+V0Otm0aRMbN27E6XSSmprKgAEDGDhw4BF3IWgcOoLBIEaD4YDXl2UZfyiA2bD3/Pr8/HwKCwsB1JCzUChEeXk5Tz11P8dPjFHjF4xGkYA/0tHy+gLMeu1Ntm4oJeCPlJMUdSJmk7lNJERHbQoGA7jdHvoN6s+0K6apy7f2GehohFRu3yfHbDARCARUbxOuA7/fz7p165g/fz6fzvuZR3+eiSiIGE0mQEaWZCRZ4takYw/ktB4wSgSDJMlRfWHPo4kccyAQqWqxdu1C7r//Vvz+ENvKd/HOD0+jE3XoRANGvZlQKEijo1bt+MuyzIsvvIjBaKBfv36kpqYyfPhw3nr2VS4MnNXOp0IALh45hatn38mrL7/CVddMZ968edx/6z1ce/T5HJ0/GICLXJO59rLpvP7eWyQlJQHwzTdfc++913PBhYlkZljYVbWL0s0+fD4Zg8GA3W4nISEBm83Gueecg9Pl5q0v3iFt4pUk5xQSGnMm9Rt+4T/zP+DrLfO5YOBp5KfkIkmRcMe8uEyuH30xj/74Mh6vmzE9hxMKhwkGA3jcBvQGAxaLBavVGvWbEMhKTOfHDb8w7sGzMOj1SNHUAlEQoudNhyhEf1tAVmI6Zw6axMSBx+4z6sF4AAJDQUFBl5b/NSjXiWLWqd/D9FRJwSguLkan01FSUkJ1dTV+v59QKMSGDRtYtWoVLpcLh8PB8uXLMRqN9OnTh169epGenk5qaioGg0EVLQKBgFZlQkNDQ0PjsBEOh/H5fPj9/khaZdTY8UjzCexKFYk9uRmYLQjCfcAKYGZ0+tfABEEQ1gNh4EZZlhsABEG4F1gSXW6GYvgIXMXuMpVfRv9ARFh4TxCES4AdwJTo9DlESlRuIVKmcnfdNo2DgizLOJ1ONm/ezKZNmwgGgyQmJjJw4EAGDRp0xLicahx6BEHocvWW1ny/YQGPzn2JlNgkMuK6kWCOozA5j9MHTmy1k93/VG7eVVVV3Hbb1UyebGHUqHR1Ga8vjN0WuZGXlCTy0VubmJz3F5Ji4pBECWQRXVBEDO37Zi/LMmsaNvLdl98y7Ypp7ebt95hbef8JtD9HJpOJQYMGUVBQwEknnYRer/9dpCapAoMsIYrt2y1EO8JeD9jtOs4+R0diYgM7d/oJhZK5+h8XqaUEPR4PsiyTlnaR+nANhUI8/cSz9M4eyjM1z3PbXTcxceJEho8fyZsLP+TyMVPb7E8GlpStotbViCAIfPbpZ7zzwpvcffy1FKX3VJc7ru9oapz1XDNtOi+9MZPZs2fx1luPc911meTk2qivrwdgy9ZAm2NSKlVkZmZy9513IIgiH83/kJwTrkAQddjsx9FcOJjSH97ljv89RZLZTt/UAgqT8whJYdx+L/lJuTzx02t8u3k+qfZkBmcUMyizLzqdjoSEePR6PSaTiSVbVrOjqYIpQ0/mmw0/8d/pz5OZmB6pniGF8AUD+II+QuEwsiwRkiR+3PgLry/6kMe/ncmk4nGcMfQE+mQXtvve9Do9Aa//YP0MDjuKaFdYWEiPHj1UwUERE999911qamrYtWsXgUCA1atXs3HjRgYPHsyIESMYM2YMeXl56HQ6tfx0IBDQqkxoaGhoaBw2WqdGWK1WrFbrEZUaodAlgUGW5blE0hqQZXkbMKyDZWTg+uifPee9ArRLxJZleSlQ3MH0BqDd8Fx0H9O70naNzuPxeKipqWHLli3U19cjCALp6enk5eWpxnVHmtKmceiQJOmAXt6rmmtZtG0FH6/9lrsevIdhw4ZRUVHB1q1beeT+f/Pe6ogO6fV7uXT8Fep6isAwd+5cSvoLjBqdxp4ue3p95HP//kkMHlnFrAUf8MiUO8hNzkIUhEiHYz+/cUmSSIyJY9vW2oNyPexLjxAEQa2o8HtAFMWIyWMHX6sggCCKyAjoDQL5+TGkpydgMrqp2pXCyJEj1VKXe0On03PG2EvZULaSRx96ggEDBnDZldO45JwLGV8zoo1haHlDJfd/9yyPPfskOTk5nHvm2VzQ7zSK0nu02+7U4adR87+XOX7CscTGtXDLrXkkJJii7Rbw+2XcLkmtdKBMbz2qPuPuu6i9+joWzZtN9vi/RUpRxiUS7DOchq3L8IWD/FI/j+2GReh0YDIJmHIFxvbRY7GW43Ru4/H5PxEjxnPzmMspNvWisbGRX8qW89j3L3PluPP46/CTsehNXPzKjXx2zUwMRiNGfeRPrMXW5pjOH3Um5486k41VW/nP/17jgpevZ1yv4dxz5g1toj1MeiN+f9s0lD8Syj1EMaZUCIfDSJLE0KFDCQQCbNiwgc2bN7Nz504qKyspKysDoKGhgY0bN9KvXz/69euHyWTCbDZHzCx9PtWbQRMaNDQ0NDQOFYrhsSRJalWu38u73cHk10QwaByhuFwuSktL2bhxo+qSn5OTQ/fu3YmNjdXyVjW6xP5e2BvdzXy1ei4NvmYEQaDO08SW+h1gEBg1fgw3/u12xo0bhyiKZGRkMGzYME444QRCoZC67fj4eHV7isDQ3NxEYqIYccbfRxNuv6uY/zy9kb+9ci3XjLmIKUef0unjEgQRXXRktSvsKSb8Efs0ezOejHTGhch8ZRoQlmQEofPnSRR19OkxiOVb53Ldtf8gKzMbe3IS09++m8+veRmjPqL4ZydmYDPbyMvLQxRFHn7yUe6+4Q6Ks3rRq1UEg9K2XY2VeL2bmHHv0djsrVN3IiYdin+GUuViT3Q6HU899jBnn38xFYvnkDH8RGpX/Yj7y9e498Rr2NG0iyX+9/n79T0jhpfhUNTgUyQQkLn3rnKKk4spSs7ngR+e5/rRF7PLUcO7a77k1glXMqpgKG63h2ljz2F91RaufvtuXrjwgVZt7Jii9J48PfUebvnoIWr1Dv7+wb3cefzVZCdmAODwuvD7fJ0+/38UlMiGwYMHU1xczKJFi5g3bx4LFiygtraWXbt2UV1dzcqVK7FarZx33nnk5eWpqSl+vx+fz4csy3v9zjU0NDQ0NA4WSpRkR+mARwpH5lFp/Cq8Xi+VlZVUVFTg8Xiw2WxtzB21FzCNrhAOh3eXv9uDZo+DWz99hIHHDaOk70ggIhb079+flJSUvXbcFdPAjmiTntAqDaHtMm0/X31tEWOPqefeW2fSo1suR/cavN/jipT1i4gMHV0Te7tORLHj6bIs7WX6brVbkiTVNX9vf5TlWof5dyZlQ6kC0HqEWDHX8/v97SJRQqGgarAnEBnhF0Qh2sYw4ajJI0TtIKWIKaTb7cbn86nbUtqsRAoEg8FWVTtkJg45l62VGxE9IonxhWwIlvL+4i84e9jJalvS7Mls2bKFtLQ00tLSmH7Ttdz94JM8dsptpMWlKF8I1S01zN32Hc+9MHwPcSHSSrNJwGAQcLmC6sh467YpWK1WXn3hWU6bMpVlL8/HUrOTp6bcwYheQ3j2u9fRh3XEWGOQkZHCEmEpjMcd5rZbSxmcPILrj70UJMhJyuDRn2YSksI8OPGfFKR0x+V0qt/FjeMv5e8f3Mv9Hz7DFWPORafXYTKZO4wic3idfLHqewIJIp+8+xmff/Y5Nz30b07tdSybG7aztqGUK/9+9T5/A39ElO9FMVXNz89HlmVSU1Pp3bs3K1asYPny5WpI6sKFCzGZTBQUFNCjRw/S0tJISUlBp9Op3zewVw8VDQ0NDQ2NX4vyTnakPmM0gUGjHT6fj127dlFZWYnX6yUlJYVevXpp1SM0DghZlmloaWTepkWRji+Rzq4oiPx39Zccd/YJTL/24Hd89jZaLu7lZt6nTyKDRlTy6cpvOLpo/wID0bB5UWzfCdmb4HAgD5LWokEoFAJoIzgonXFFVFBy0pWOkrL+/gQGg8GA2WzGYrGoUUrBYBC3243T6WwTMQIQDIWR5RChUGSaDh06QRdtZ5BwWGoV4SBHzQoj3i5K22C3eZ+SBhIKhaLTQsiAzRJPcfehCKKAXqfn55Vf0ehqIhQOqUKRIENjY6Pa9sLCQk4851Qe+Og5Hp98u3r+31n4GcUlsaSmtk0ziDYRBMjK0lNb61PboZRO3JPExEQmnzCB156eydMXPkxRVkF0PyI6nQ6L1RLx1ohuvKa6BZ9Tx2N/vwtREPH5fZw+bBIWg5mn5r3G0/PfoDA5j262JIqSe2AzWbEazUwfPpX7vn+OHrGZDM3rT3x8HCajMVIjFGjxOFlStorXl31Ij+IC7pvxAEajkclnTCa/IJ+Lzr0AwaRj3k/z1OoJRzJZWVmkpaUxcOBAHA4Hr7/+Ohs3bqSlpYVgMMiSJUtYs2YNw4YNY9SoUYwYMYLMzEx0Ol27UrRatJ6GhoaGxsGmUz5df3A0gUGjHX6/n7q6Ompra/H5fBiNRhITE0lKStLMHTW6TFJSEmOPP4af3GsRlM64ELnBjj1rAmdPPYfGxsb9b6gVe+uoy7KMy+WiubkZr9cT6XyHIp0G5VYuSRKSLBAKhdsEN4SlMJNOSOXRu5YRDIbQtR4h3osuEJbCBAKRUpgKLpeLlpYWNcxeMe/xer24XC68Xh922UgoHEaQIpUAgqEQoVC4w/PgdDqpq6sjEAhQX1+PKIptzO1ycnKwWCxtBIxAIBDp5EY73PtTyhsbG3nvvXfJysrm6acfZ+zY0QwcOJyioiJWrVpJWVkpkhSOihWRvx0tLdx5x3p6906OuvBHSk86nS4aGhopL3fRUK1jxm3VGI1NBAMSGbmGNgIGQFNTEzde+08S4uKRZQiHQzQ21vPIW/+MVKpAxmQ1YjQaEESRivodfOatZGHVGjXqo9nrICUlhbS0NPW8jD/2WD5+64NW+xJYWrGcoZPs7b9OOeIbodfrKeptYfVqPy0tLWzfvh2z2UxycnKbvH+AJx99nE/f+ohZ054iJTap7caQIykiooAYLUNZ1DuBoBSMilI6jAYjWAUmDRrP4O7FzN24iK83zuO7soUkxScSjv4mgsEgwVCA77f8Qp9uPfGF/Lww/x0kUaZ7Ug4/bl9EXv8Cbn/0X4wdO7ZNG0tKSvjg84945eVX+Ncdd2MyGpElOSKcKH/LMlKraI2IYLU7CiYcDoMkR40l5YjwI7daV5KRZanV59biV+T3IkkR4WvY0cN5+MlHDumIjV6vR6/XI4oiZrOZ0aNHI0kS27dvVyPzdu7cyZYtW5AkidraWjZu3EhBQQEFBQXExcVhs0UEqNbRK0fqKJOGhoaGxqGndfUIURSJiYk5YtMjQBMYNDogEAi0ERgMBgNxcXHEx8cfkU6nGocWQRC4+h/X4vf7O1RsuyouKNvcE2XbLpeLxsZG3G43bo+fbWUthMMy4ZBEKCzT3OynoSHEsqW1hCU5Mi8sEwqGCQTD1Dsbmf7G7djMMYTlcCSHPhwmJIUJy3J0WpiwLOEJ+DDGm2hoaFDb4Xa7aWpqwmAwIMuyes34fD5cLhfBUJC3V3zFh+u+QZKlSKUBr5vc/O6qqWprXC4XN954Nc3N1SQmxqDTi+h1AjqdQPnOJq6efidTpkxRTSCDwSDBYBC9Xo/NZsNkMu23k/TQQzNYtOh9CgpT8Xqrscf+xHffz+WFF/xkZxvp29eMThfxshCEyPm/+JIM/vt+A4MHX05eXp5abWHRokV8+OGHVFVtI04Xw7SSacTHxbO1fgfLvaXYbLY2If4rVqzAU+PguoHnRVwQBIETU0fiDwbQiSLfbphPzpheTJ48mYSEBGRZVks6KX/0ej0JCQlIkoTD4aCpqSmasiGi10UfcwLsdJRxUXFhW8FIhpoaL+s3tOBw+PF6ZKxWkebmZrZs2UJcXBxxcXHtBIbK8kouG3H2HuICSK1SVSLpMHtGt0SOW6/Xo4tWj4iNtZOS1I3vKxdx4WkXYbNHqlrU19dTUVHBtm3b+Ll8ObXOBkRRR6mjnLHjx5LdP4Yrz/87kyZN2ut3m5eXR0tzM1t+Xsdfh50cOWeCiCgIiFEPEXQCgl5QS2GKgoCAgLDHskpKkChEIndEQYykxwiRZXXRdKHW6yAI6EQRSZK564vHmfPFHE486cQO23owUUwbR40axbBhw1i5ciWLFy9m3rx5VFdXU1FRQWVlJatWrSIuLo4TTzyRM844A71er6YChsNhRFHUIhk0NDQ0NH4VkiRFB5mO7OoRCprAoNGOcDiM1+vF6/WqL1gmk+mIrNOqcehROrpms5kXn3uB9WvWYTaZ1ZFwWY6UJgxHR57DocjofIvHyaz33u5y1IwSZt+zZz6ff57Cu7P16qimTqfDbDYTCgVYtSoHvd4Qna5HliEQCGG0NuEUYxCNSehEHTpRj1HUoxN1kU5U9N86nQ6Hu5kGaUub1CFl/0ajkdjYWPUBojgFTz1vKqdPPh2LxaKO5ut0OkwmU7tOrLI9QQhxz4yepKXZ2phWvv9euRqp0NrDQDlW5e/W4kJHHdFx4yZQVTWPK67I5fLLc9oIQaIQ6ajvuVo4HOa7/wUZO3Ysffv2bROp8e2336LT6UiyxHNU94FkZKQzXhjJ+bP+yfbt2+nTp4/aacvNzSUhPp7+OX3UbffL7q3+e0fTLuJSU8nOziYpKWmvD2TFpE+pBGCxWPD4PKoT5c6GXfjDXvIL2goCMvDFF7Vs2xaP16unujpIKOShrq6O5cuXk5CQQI8ePdqlF4iiiF7suOMpt/r/XhEi0SuRDr/Al+vmIsQbOWvKWbhcrohAFhWrGhoaWLp0KfN//DlyjQgSgUCA1LRUMjIy8Pv9mM3mve7qiulX8o8lV3Nc39G/6T38urEXMeOBRxk5amQbY9ZDgfKbN5sjnhU9evRAFEXi4+MpKChgw4YNbNy4EbfbTV1dHYsXLyYYDNK7d2969epFbm4u2dnZmrigoaGhoXHAKOmtgUAgOvAgYjAYjtjqEQqawKDRDkmS8Pv9qrGbEuZtNBq1MFGNLmM0GtXR/OqaasrXbeXmiVeqxo+CIKAXdejFSFizQadHL+q46dN/U1tby4ABA7q0P0WQOOWUU7jooos6LVC4XC4aGhr45aeVXHjy9VjMlv2us6O6lCUVDSQl7e60mkwmJEnCZDK16RAr0UCCIGAymdRran8PGJPJFBFIdBEhpLWHhCiKGI1GVWCAiG+CMnqrjPDvj4kTJ/L00/dTWekhOysSHt7GH7MLl30oFMLn80UqJ1h315Ew6g2cUnQsb776Og8+8m91+e7du7OrpZZAKEhNSx03vn8/4fBur44mdwsn5p/a+QZEMRgMbdo9Z/UP9OplV0uUtkXgpJPOIDExkW+++YbPP/+c2tpali1bRm5uLqNHj+7y/jtLJGVIYGLJOH6pWMlnH33KLXfcqv5uQqFIFYo333yTNWvWUFVVRTgcZsOGDXz00UcEg0Hy8vL2KTD07t2b1Lx0ftm6nJEFQw7ZseyPXuk9GJU+iCceeZx77p9xWPednp5Ot27d6Nu3L263mzlz5vDBBx+wZcsWmpqaWLZsGWvWrGHAgAEMHTqUcePGkZGRcVjbqKGhoaFxZCHLMoFAQK1WpNfr25VbPhLRBAaNdhgMBuLj44mPj8fhcKjqWygU0uqEa3SZ1qPnM+6/lxPGHs+A3L4YdPu+uZ7RbyKfffQpgwd3wnCxFa1D57sS3tx6HZDoTK9aINJ5b70PZRtC1ARSmdfRdCWtYX/tglYdfqH9/I620ZXccaPRyLhxE9m48Quys210sJu90N6oSDk+AHfAS4vXQQYZIMCo/KHc89OzbZY3mUwUFBawflcpq8vXs6NmJ7eeMB0Q0IkCP2xeqObE74vWERodHfcvZYspHtvRdiLHYLVax+kcugAAIABJREFUKSwsZN26dRiNRjweD9XV1TQ3N7cz/zsUpMQm8tjpt/HYdy9z9WVXcd8jDxAbG4vBYMButzN06FAuueQSFi1axLJly3C73WzevJlFixYRHx9Pnz59KCgo2Kug9tcLzuX9x9/4TQUGgAtGnMEV797O0tOXMmTI4WtL6+vNbDYzaNAgBEFg8+bNlJaWsnXrVrZu3Up5ebkayrpjxw569+6tGhzbbLYjesRJQ0NDQ+PgoLwbKd4LXq8Xk8mE2Wz+UwzYagKDRjsMBoNq6ujz+ZCkSDhuMBjs9IiohkZHJCQkYDAa8AX8GCz7FhiO6zuay96/jetv+udhrV6SnpFORV0ZBdn91GnbKjeyoXyp+ll5cFTV78Sa3PmHxKF4oBwsJ2K7PY7Gxl/fkVaqQYiiSIvPyUVv30xqXDJDcktIj02huq6mXZuHjBzO+qWbAeib1YtThh6vzqv3NBM4CPecspYtTC7ObjddlkFGxmKxUFBQQFZWFgaDAa/XSygUUkXWPaltqOc/S37mrV8+BuBvw05lTN+j99mGUCjE9NdvR6B9uooyTUZm5fL1TJl8Fm/NnkVsbKzaIS4qKiI1NZVdu3ZRXl7Otm3b0Ov1+P1+AoEA2dnZexUYjjrqKO6ouo3KpmoyE9I6d9IOAVajhSuOmsp9d9zDe59+cNhzUBWhoaSkhD59+rBp0ybWrl3LN998Q2VlJTU1NVRXV1NaWsqPP/7IiSeeiNlsJi8vj5iYmMPaVg0NDQ2NPy6KIbKSdm6z2bDZbEe8uACawKDRCUKhEG63G5fLpeY4a2gcKILYuRtrQkwcwzJL+OTjT/jb+X87xK3azQUXn8czj75ERvJtxFjsAGzauYKUXgaGDRumLqc8ILp3735A+zkYZYr29Yz6rUogGQwGYmJiMJlM5FkyeOLMOyn31vDj5oV8uuZ/VDRWMWnsBAYOHcSgo4YwaNAgUtNTWecrI1a/lw7crzyWJncLDm8LvXsP6mjjql2C2WzGarUSExODwWBQq3W0LvOpfO8eUWZLQTqxfYqp/voLBuzctF+BQRZgYF4xkizjD/rxhwLROQK2aEqOLMtYzRZqDA5sNhuSJNHY2IjZbMZutzNgwADOPvtsZr3xFhXbd1JTU8PatWsxmUz4fD769+9PcXExFsvuFJ9wOMxxx/yFMTlDSIiJ+1Xn8mAwomAw35XOZ+ZLL3Pl9KsO676V709JUVJKVAqCQGpqKmvXrmXdunX4fD5qampYtGgRgUCA7t27k5eXR35+PgUFBWqVmNraWqqqqggEAoRCITIyMo5o74bGxkYSExN/62ZoaGho/K6RJAm3243X60Wv1xMfH/+n8rLTeooaHaLUplfUN0VgsFqt+8z11dDoDPL+DPCinNTnGB5/8w2mnjf1sNyUZVlm1KhRVO2q5vXZjzD12L8TZ0tEEAT69+/PlClTDtp+DuU6ByouzJ9fiyBGUhPGjkvHbO56J8lgMKjVK3RRb41RBUMZ33cEa3Zs5Np3/sWjE29mTeUmVr07n7efeY3y+krcLS5MeiMer5dvV85leMEgYmNiD4rS/+26n8jNjcFo7OB4ohEMSm6k2WxW2+/xeFQhSJblNm1J6taN9OFjSRo+kpY1q9ivqSOgE0VKHeUIgsCq7evRmwzE2mNxOh3ccex0hvccCECdo4G/z3mA+Ph4mpubqa+vJy4uDqvVSr9+/bBYLLz70ixuHHsZT81/jfXr19PU1ERpaSmTJ0+mZ8+ebQSGuXPngi/MtcdejNW4f2+Rw8EVo8/jmlf/xcQTJh2wSHcwSEpKIiEhgezsbMaMGcMnn3yipkc0NzezdOlSVq1aRY8ePSgsLOSEE04gIyNDjYSoqqpi8eLFOJ1OfD4fw4cPV+cfabhcLo4/5jhOP2My19/0T+1dQENDQ2MvhMNhHA4HHo+H5ORk4uLi/hSRCwqawKDRDiUlQnE8VUSGw5GHrHHk43S7+GXLcuxmmyo0xJisDGhVRUChd0YB1l/0LFy4kBEjRhy2Nl551RVYLGY+mPUCF0+6NdIJ/Y0iAuDQpFbsyTHH/IVQKIQgCMya9Rq5eU6Kivbt9F9f7+LTTz9l5cqVAJx22mlqiUZZllUjTwWlk54Wl0JafDeO6xsxT2x0NbF0yyqWla1hfukSbvrvQwDEWWwEQ0H+Ej/pgI5J+c7mbfqF4v4d+zjI0QgGJQ3CZrORlZVFc3MzPp+PlpYWduzYgdlsJjExsWNjJkFgxY51PP3lTJZsXw2ZHd8r49OsXP3ADRFjU72ekpISRFHkhmuu59t1P7GpeisA4WjUgizLali+IAg0NTVhNBoxm80kxSUwacAxWExmHvzueRoaGpBlWfUSCIfDJCQkIIoid990B0+eeRcxpt+HuACQYk/knJKTuO+uGbz0+szfbFRHEQoUUWno0KGIosimTZvYsmUL27dvp7y8nNraWvX6qKysJDk5mcTERDZt2sSaNWsIhUIAZGZmHrHPys8/+5z8+BzqF+9g6hnncP9jD1JUVPRbN+sPR0e+NRoaGkcurb2p/ixoAoNGOxSBQakiAbQZxdPQ+DUcN/E4FrdsRh+IjPAJgsCPX8/lsytnote1vSUJgsDxBaP54J33D6vAAHDZtMt4beabuDwt+85F+A2ROXjCR0lJCSUlJQCsX7+UcLhqv+uccmoKVdUf4HTqWLnSRX5+fhtBUuzkeUu0JTBhwDgmDBgHgMvnYfmWVSzctJyvNszlv/99i/Kd6xk2bDRHHTWGIUOGkJyc3Olj29y0gYv6Jux1vtzKrNJut5OdnU11dTVVVVU0Nzezbds24uPjVdPFPck85UyWzfmIZezCLTQzbC+HbTYbGTJkSLsOxZS/na2KNAp/P+l6zGYzgiAQExNDU1MT9fX12O12tXRwUlISx/QZgcfr5YmfX6GyspLt27ezefNmLBYLS5cs5fknn8XtcFGY1qPT5+twcXL/v/DDhwv55ONPOH3y6b9pW5RStoMGDaK4uJh169axYsUKfvzxRxoaGnA6nTQ0NFBWVsbXX39NVlYWubm57Nq1i7KyMkwmEzExMQwdOrRDz44/OqtXr+bFx5/j0ZNvJSM+le83LOCq8y7jjAvOZtoV0454R3QNDQ2NrqIYev/ZxAXQBAaNDtgzYsFoNJKQkEBCQsJhN+TSOPL492OPtPksSRLD+g3G7fcSZ7W3W/6Y3iN4bdYN1NfXd6lT+WsRRRGLxUpAzZP/belIR9hb//3XjoiFQp0TB048MR1RFNHr9dTVVQAQCARoaWnB5/Ohi2kVJt4FHcRmtjKm+GjGFB+N8QsjS0Ifce5UKNv2NbNnf8mMGV4SE1O57rrbOO644/e5rUAoQJ2zjuJ+hftcThEYkpKSKC4upqamho0bN9LY2Mj69etJTk4mLy+vTeqB8qUkDhlG4pCIP8e2N15CLH278wcLDBs2rI2/x57t0ul0WK1WEhISEASBhoYGkCOdYqvVyoSS0ej1Oh758WU2bdrEF198gdvtZvvWMoqNedzzt+ldas/hQhRFrhlzAXc8/ARjx439TXP7lWtGKR+WnZ2NKIrY7Xby8/PZsGEDGzZsoK6ujoaGBqqqqvD7/WoIbDAYJBQKsWjRImbOnIleryccDiNJEpIkodfr0el0mM1mTCYTaWlpZGRkkJycTFJS0u/6BXTHjh3cd+cMLhw4WTUIPbbPSAbk9OGZL15n6rffM+Ph+7VoBg0NDY096CjF8s+AJjBotEMpS6kIDEpo8O/9JUjjj4koipxzwbk89+ObnD98MtB2VF5GJteWziszX2HKX6d0GEmjfJZlGZfLRVNTk9r52l+ecG1tLfX19bjdbpxOJ4WFuzuifr+P8potON3NXT6upqYmdu7cidFoRJZlfD4fHo8HSZLo06dPh2JdY2MjpaWlbaa5XC4cDiff/c9FbGxDm4fUurXNGA1bWLBggTrN7XbT0tJCVlbWAQsyfr+flSsb8PpC9OkTj9nc9lHhcgVZv74xGqUQUeibmrzqus3NzXi93vYP1H1EW8zfvITVFRsIt4qaAlhYtgJLT5Hu3e0UFEQ62JIk8/nnO/nqq4/3KzD8vHkJKSlGYmM7ly+enJxMSUkJ27Ztw2g00tDQwNq1a8nLyyMQ2C02CQI4Sze1e3Fwby9jZ7mbt97ciiCKCEIrY0iPv1Nt6AiLxYLZbMbpdOJwOPAHA4iigNViQafT8Ze+o6huqePVZR/wccXHCIKAzRxDSUIO6fGpB7zfQ03Pbrkcm3MUjz70CA88/OBv3RyV1NRUkpOT6dOnD4FAgG+++YZPPvmEtWvX0tzcjMPhwOl0qilBEHl2LliwgLVr1xIKhfD7/apYr5Qni4uLIz4+nsGDBzN8+HB69+6tprL8Hnnz9Td44cnn+NuA09SUJoUkWwJ3n3Ad362fr0YzXH7l5ZoRtIaGhgYHx8z7j4r2FNBQcTqd7Ny5kzVr1lBTU0MoFMJisWC32/9Uzqcah5/p117DDVuu564fn1Y7Y63/rmqp5rv/LOP17+eqw/Zqp26PTqwUjcBpqdjJHVddwdVXX73X/QYCAUZNOB5Tbg8kScK1rZRP355FSkoKAOP/cgyrV83FFGuiZ8+eXTqmqy65AptkJiU+CUDtiFQ11XLW5edy/vnnt1vn1Vdf5o03nqR3n90dQkmSKN1Ujbs+D3tMvFrOMEICSx0bWLdou3pOJEnC5XFiTdDxwcfvdanNCscffyqLFv3EC/+3mAsuDDNiRNsO6rwfq/npJwu5ubnqqGxBQaSUX21trVqFoW1bFTp+2J7/9u0YCoqwJKeoSwmCgNPkZ6ivbU67KAoUFcXx5ZzKfR6HLMBjX71I0bCO/Rc6QvFgSE5OxmQy4Xa7qayspKGhQc2zBzh+7FjEeT9BeaS8phQ9rvV+Nw0taTQtK2j124zWw3av7vJIxp7Xg8lkikRSJNl4+vvXuXzUOZiMRswWM6cNmMCa2k38VLaUjRs3YjfH0Dc7E1mWEITfr+ng1OGnceV7d/LLL79w9NH7rsRxqFHOs+LNYDAYMJvNFBcXI0kSgwYNorq6miVLlrBo0SL8/raikcfjIRAIEA6HCQaDbSIYDAaDmmoRDAaprq5m1apV5OXl0bNnT/Lz84mLi1PTYH5rPvn4Ex6//1HuOH46owo7jrARBIG/9B3FwNy+XPrCLYwcPZIBAwYc5pb+sfizjWRqaGj8+dAEBg2VlpYWVq9ezbJly6iqqiIYDGK32/ead6yhcbAwm83858Xn9jpflmXGnnwKofOmYcvfd6i7FI5E4Aiff0RwP2ZrkiQhGQyk3/kAUlhix03T23Qi773/nq4dSCvMRjP3HXc9mQnpu/clSbz683tt9tG2PSHOPDOd4ydmqdPCYYkFP9Zxycm3kZ2WhyDsu+MRDofZWb2NuaXvHHDbL7zwUi688FJuu+0fyPIv7dspy0yYcBJnnfVXzGYzycnJakTGihUriI2NxWw2I7YuSSqwz7abYuwU3nwX9uycNtPXP/cksY2ft1/epGvXuWuNKIo88+Kz3Hvv7fQfsP/OmjLKEBMTg8ViISkpCaPRSHNzM7t27aKxsbGNed+pJ53EqSed1G47n376KXOe/S93HH81er2+TWdi0nMX/urOhdlsJjMzk1dmvcZtN9zCVe/fRZI9geD/s3ee8XFU5x5+Zna2S7ta9W5ZkuUiWe4NA7bBYMCY3kO4lBSS3ISEe0MJhBZuEiBACEmAQIBAEloILbRQAtjGDRfhXiRZva/K9jIz98NqxpIly5Ixxkb78DOSds+cOWd2dmbe/3lLKEwwEMQbCWA2mNi+bTuSYMBd00pzsB2TwYQoCphEE5IgElVktrur2NO2l5OLj+O7J1w2IAfKkcJqsnBJ+VJefu6lr1xgGAxRFBk/fjyFhYVEo1EikQiPPPIImzZt0r1atCRefUub9j1fIpEIkUiEQCBAZ2cnLS0tbNiwgeTkZFJTU1myZAlLly6loKCAhIThC2JfFqqq8sG77/GThVcfUFzoS727CUeKs58HWJw4ceKMZgRBGLWCYlxgiIPX66W5uZktW7awYsUKNm3aRHd3N+np6cyfP58FCxaQnp4+ar8kcb56BEHg8nPO5ncrPzqowNBno0Pa1+F0Zxusr6G+R4qiMHDR/+h2r9t/PtoNVRCE/h4MX8E05syZg8UiUlKSMmS7tFSRiop1XH755QiCoOc8SE5Opqenh2AwiM/nw+/3EwqFMBqNX9kKs3ZsnU4n9z/8IDt27CAajdLY2EhNTQ2rV6+m+pNGfD4fckTGkGLBOS8Xl8uF2WwmGo0SDoURRZFvly2lpKSEu35+B3e89RA/W/L9r6yMpdOWSDQU+Ur2PRTa+a0lgdSEQrPZDMTEB0EQmDlzJvPmzcNoNA6aFFn73e/34/V6qa6upqqqikgkQmNjI6tWrcLj8TBu3DjGjRtHcXEx48aN08tdHun774YNG6iu2M3153/joG3D0QgPr3iGm351Czab7QiMLk6cOHGOfrT7xWgkLjDEwev1snPnTlavXs3y5cvZuXMngiBQVFTEKaecwsKFC49ocr04cQbj7GXL+M2TT6NcdiWicXjJRlVlGFbtlxQfJwgj73pII2KEfR2OWQmCiCyPvCfNCN6/TOWhDEw9hOPYF6/XS0LC0AJD2WQHzz+3Tf+7rq4Or9dLcnIyra2teDwe/H4/Pp+PcDj8hWLMD1eyJ6PRiCRJTJ06lUgkwtSpUxFFkdTUVGpqaqiurqY73M2YsQWMHz+e0tJSxowZM2juj9/+4Xf88q67ueHVX3PX0p+QbB+6POmXgSiIB/TsOZrQPBUEQdA/S0mSmDdvHj/96U8P6H2gCQytra00Nzfz3nvvEQqFaGxspKWlhbVr17JhwwYmTJhAWVkZZ555JmPHjtUFhiPN22+8yeKi+VhNB85d8vGO1bT2tLOydgOlx01l4cKFR26AceLEiXOUM5or8I0agcHj8bB161ays7NxuQ5crmw0odV437FjBytWrGD16tU0NzeTlJTElClTmD9/PhMmTCA5OTlePSLOV05GRgZTJ4ynatMGkmfNHdY26kGs2S9i6GnJ2zTXZ02l9nq9dHd3o8gxF2m512hSVBVFiWWV9/l8dHZ26rlNtJtPIBBEUWWi0X2u1YoS+11WYq+LooKq9rlx7TfHWIhIhFAwxN69e/uFNw1nvpriLggCublF/OtfbzB1ihOLVdJDHmRZwe/30dXVFasW0bvib7FYCIfD+qr/vv2p/cYbleUBjhqq2ntMo/KA17W8GkIszgKIVbqIRFTcbne/9loSxHA4rHsZhEIRolEDg7iHoMgyiiITjSp0dnZiMplYtuwkLJYobndAn3NHRwdbt25FkiTGjh1LJBKhq6sLRVEQRRGTyYTZbMbr9SL3uscLAv28OBQlNt6+n/mBPhetOodW1WB/jwlNxNEMUK0cVnFxMUuWLOHTTz9lzZo1VFVV8dZbbxGJRMjIyNDPh777lCSJn995O3/Keozrn76bu06/nvyU7AFj+jIRBRFZPvpXerQSzsFgENj3OVksFhISEkhMHFgJB/p7M1mtVk444QRSUlKoq6ujvr6eXbt2sWvXLlpaWlAUhcTERARBYOLEiYwfP/6IJk4MBAK89+a/+eO5dw3Z7p4PHuOUZUu47MKrWXrm0iM0ujhx4sQ5NhjNnt+jRmDo7u5m06ZNWK3WuMDQSzAYpKmpiS1btvDxxx+zYcMGotEopaWlnHbaaSxatIj8/Hzsdvuo/pLEOXr4xtlnccvLr8NBBYZ9VSVGynC3URSFUCiE3+/H7/frq68+X8zwjlViiRKVewUGRdWNd5/Ph9vtxmQy6SuhqqoSCAQQDXK/lVxFkVFRY8KBHEVQBFDRY7z3H6+iKIR7Y70rKyv7GSbD+R5rMeSCIDB2bCHJydN4/oVNXHZZHgZD78qtIuML+nG73VitVj25pCRJBIPBfVUk9GPaK4goSq+QMNhKtarvu9+rvcdGE100g12RZSIROVaysQ9aFRGLxYKqqiQmOmlr9eFySYMmnZSVXqEoGhMRYoadyq/vKaamppP161v5+98UWltb+fzzz3E4HGRlZdHR0UFVVRWCIGA0GklISMDhcNDT04PSR1jq/9nItLe36wKDtgI+2OdiMpkwmUzY7fYhjUtRFPuJD4WFhSxbtoxgMEhFRQVVVVW0traSnJzMggULDmgAC4LAd793LVnZ2dx49z3cuPC7TM2fRJe/hxtf+TUXT1/KSRPnH3AcXxSDwdAvZ8HRilaaMhgMoqqqLjAM19MgISEBu91Oamoq8+bNo66ujpqaGt544w2am5vp7u6mtbWVSCRCW1sbZ599NsXFxUdUYHjvvfeYmFJIauLQz0o2i5Wf/fwWHA7HERpZnDhx4hxbjNY8DKNGYPB6vbzzzjs0NDRQWlpKaWkpOTk5GAyGoyJb81dBV1cXa9eu5dNPP6WpqQmTyURBQQFTp06lpKSE7OxsbDbbqD0+cY4+TjnlFG657zeEO92YXMlDtBSI9HTx2xce470V7+qhEn3LWULMmG6tr6Jw32b9+NX997O24nOk3lXfvtvHDGZ1n6GPZghHCYfDbGus5ZY3HyDDlaZvIysyO+v3cPm8K3E4HP08GFRVxWQyIggiUh9jRRYgEgzz8Av/i8VkAUHAYJD6xQ30kxhUlUgkTFD18tBDv9BvbPvPff/fQeCyy77LnDlzelffY67gF1/8TX7/+88xGqXea4GAaDBgMhlxOBxYrVacTic2m02/nmo/NzRu479fvZOU1BQkg0RjWxPdQQ/feenWAcZ+2O+j5Z7b8SbFjBo1GiHi8eBrbeHfgQAfrP2834ekKjK2oMyl51zUrx9FURAMIg89+jBOp5OpU+fy4IPPYDI19/uAfb4gnrawPg5Xqgun0xkrayqAUTKSm+MgIUFh+Sdu2tvbaWhowO12oygKTz/xFG+88hoTJ05i0tRSpkybSlpaGgkJCTGjv7cKQd/nClEUSUpKGpYHg8FgQJIkXYQ6EPu/l5iYSG5uLpmZmTidTt0grqio4B//+AczZsygvLxcL9+6//ZnnX0W2TnZ3PDf/8PVU8/n7R0fM3bOeJ6peJ0OXycXzFj6pTwsxTwYjn6BoaOjg8rKSlpaWpBlmYSEBDIyMnA6nUPeK/evBmIwGDCbzWRkZGAymVi8eDEpKSmsWbOG1atX09nZydatW3E6nciyzOTJkykrKzsiQsOrz/+TpeOOP2g7RVW+shCOOHHixDka2f85UVGUeIjE1xm/38+bb75JRUUFs2bNwmq16okLR6sB7Xa7Wb16NStWrKCjowOn08mkSZOYPn06hYWFpKSkjNpjE+foxGazcdbixbzx6XKylp49ZFt/TTWGMWBYst8DcB/bSIkohFcH+rzV33B67Z138S86HWtO3oDt+7XtY3DF3Pmj2Nra+fFNPyU/P1YVIRwO4/f7ASguLtaT7hkMBv3mY7XakGUjBknSexcUAYvJwK+W3czYjHwUVSYUGSQZXu8GqqJS1VLDL997iKuvMQ9qDPZ9Tfv1vX/HKseMGzeuX9uYZ4KxN0wq1thkkhCwkJycjNVq1cOoNJd9za2/K+zhxOnjuO6660hLS8Pj8RCNRklLSxtgKPn9fux2u/63JEm4XC5+ee+9vBhQyDztrH1VGQSBjjUrkX7/GK9869F+/ciyzLef/xlms5nU1FRuueV2brjhZwPm/e677/Lh4//iugVXsrupmke3vkhKSkpvckowSBIWqwVTwETZZCtvv9VDU1MTXV1dsf1Eolx3wpVkONPYunMX/1z5HPe1/wprgo2KrdvItKVy5rSTKUjdVxFEFA2kpqYOS2AYznuDYbPZ9Puby+Wiq6tLrxDU3d1NMBikuLhYFxgGY+bMmTz6zONcev7FLD71FH51369pb2/nh9/5AY0ftvKDhVcc9ooToiCgHgPJsNrb29m6dStNTU0oioLD4SAvLw+Xy3VIwotWljIjI4MTTjiBJ598kp07d+J2u2ltbdU9kS655BImTJjwpQsMdXV17N1VxdzLfzBkO1VVCUZCWK1fTVLQOHHixDla6SsuxAWGrzkWiwVJkmhvb2fTpk2YzWaqq6spLS2loKAAp9M5am6U4XAYn89HS0sLra2t9PT0IIoimZmZzJo1i5kzZ5KamhpfmYhzVPJfl17C69d8i+2frSZ9yVIEoxEUlajPS9TnRYlESCifDoA1w0ZizuAu4QByWO6fJwD63QwsNjvSpMnYcvMO2Mf+aGUyvTY7kyZN0g32YDCIx+MBYsaz5treV8QTRYGorO5XAEPAJBnJT82hMDN/WGUqVUXBbJIYMyZx2EaPw2HSV837ohn1/UQJ9iW7238e2k3V0LuC73K5yMnJISMjg+7ubsxm84hK35pMJsSoisFiwWCQEHpzIohGIwbRgEnqnx9GFmQ9CZ8oirrYsT9msxnJYMBqsmAxmfsl7wN6cy/EPDFSkg2IYpCenh4CgUDvOQKJFjuzC6cwu3AqEMum/+t//YGVjr085tvJA39+FZtBYrwzi3m5ZYTCwQGf+eFG63vChAlcdNFFLF++nOXLl+P1eqmpqWHFihVIksS0adMoLy/Hbrf3elr0P09KSkr4ZPUKrFYroiiSnp7OU3//Czf85Kfc+dZD3Lzk+1gkM79+9xGCkRC3nvFDTNKhlzOWRMMxkYMhGAzidrvx+/2oqkpSUhKFhYW6cDRStPNBEARMJhOzZs3im9/8JitWrNDFf1mW2bt3L3V1daSlpeFwOL60c+itf73JiWNmHlRACkcj+nUsTpw4ceLEUBSFYDBIKBTCZDJhNBr1qkOjiVEjMNhsNhISEujo6GDLli1UV1ezadMmLrzwQv0EGC0CQyQS0VdH3G43gUAAm82mCwyzZs06ovGeceKMhPHjx/Ovvz7Ln5/+Cx01OwicPT7RAAAgAElEQVQEQxhEkZQkJ8kOJ6FAlOceuJue2hoSTrQP3ZmwX3hBbz4EDUWOfiF38JGq1qIoHrDKwsESVvZFUVX8/jA33bBuQH9GM9xw41Qcjv6G+UinqaoHNga1xIdGY8zzQVuxl2V5xCWbTJKEKgcO3vAwExMcYgJDosMAyHi9XgKBAKqq0tDYyN8+eA67NXaOXTLjdH6w+L9ISXDhLJ1C6W3/hyiIdFftpnFLBc9+vglfezVLlsxjxozjmDlzPtOnT2fs2LFfSshBaWkp+fn5GAwGKioqaGlpwefzsWLFCnbt2sUFF1xAXl6e/vkMNoa+HiUQu4/+9g8P8X93/oKbX7uX8aljabN5Sc/M4ObX7+H206/DYT2woDcUgiASjR59ZSr3Rws50c6DLyowaGhCw/Tp0ykuLkaWZdasWYPH49HFob179yJJ0gHzaHxRFEXh9Zde5WfHffegbUPREBbL6HhmihMnTpzhoigKgUCAQCCA3W7HZrONSptq1MzY6XRy8cUXs2XLFiorK2ltbaWmpoYPPviA1tZWxo8fT0lJCUVFRaSlpX2tk3L09PSwZcsWKioq6OzsxG63U1JSQmlpKampqfGKEXGOegoKCvjFHbcP+l5PTw/nnLWMBx98kA3ChqE76vMVV3v/7CsKTCgqYs3WzVhzcgdseqgMlgtBw2AwDlIW8tDKRKJKnJl1Ta+3wT7D54GPHqK7KzBAYBiyrxGg5aSI9IZxRKPRAe6CI8FwKEabemgJPvdHm3piogFFieJ2u/F6vSiKgjUhgROnn01Z4Sw+/fx9tjTsHLi9QcRVMgFXyQQ472I2nPE2P/iBg917VvL++x/xyCNhwmEjSUkp5OcXsXTpOUydOpXMzMwvvDJsMplISkpi9uzZfOtb32Ljxo1s3rwZn89Ha2srK1euBGD69OlMmzaNpKQk7Hb7QfcrSRK33XUH9/zfr/nXG2/wxjtv4nQ6efi3v+P6F+/mrjOuJ9uVMeLxioKIMpyysl8xihLzUNLOY5vNpufe+CLPDNq2VqtV9zA577zz2Lx5M1u3bmXHjh289tprnHrqqeTk5Oif0+F8Tlm1ahUJspnijIKDtg1GQlgso29VLk6cOHEOhlZlTEsCPBo9vUaNwJCUlMRVV13F+++/zyeffMLatWupqanh/fffp6KigvLycubNm4fVatVzD3xdBQYtHnfjxo243W4cDgelpaWUl5fjdDq/6uHFifOFEASBxMREpk6dyudbPx+6LQKCqtJ8750x1bmlud/7N133I86+8iqaUUg78SQMI1yxO5CRO+TrB7rsjMBgFgC7xcY35p074Dr2+5V/GHY/h4JmgGmlOwcTGL78eMTh9T/cUTgcMeO3o6MDj8eDLMs4Eh1k5OYzbkwp26o2EOjsOHhHQGamlewcGwsWxP52u0Pcfts62to2Ewiu48YbOxBFE3PnzmX27IXMn388EyZMGOZI96GFqMyePZvy8nLefPNNIpEIO3bsoK6ujk8//ZSKigrOPvtskpOTMRgMejjEwRAEgZtuvZkbfnaj3v66639Mdm4ON9z3a2479YeUZBYepJf9xiuKeknWo5m+SbtUVcVqtZKamnrYqi1JkqQLDImJibz44ovs3LmT7du3U1tbS3p6OosXLx52iNFIeOWFlzlt3InDahuKhofM4xEnTpw4oxHNW1NLlj1aw81HjcAgCAJpaWnMmDEDm81GTk4OW7ZsYceOHXR1dbFnzx4ikQidnZ1MmDCBMWPGkJeXR1ZWlm50H+uCg/ZQ7/f7qa2tpa6ujlAoRHZ2NqWlpZSVlZGUlPQVjzJOnC+O9l3tqeuh5qOafW/0WpS6gatCRno6T95wPV6vF5/PR05Ojt68sLCQl594nN8++hgf3Po/JF58BckHLZHZZ3f7GdKCIBCNRnn66b8gSVKsMoMgovS2W758BUVFA93EVVRe3fRvkquTB+oPA6pEKLR0tg9pxA/2TiQS5c03X6W9vanf621t7VRVNfHww52oSsxwbWjwI4kr8XYHMUpG5syby7Kzl+nbKIqCIhrAILFx6zZ++8dHsNls+AN+JpWUcPV//dcBx7Y/wWCAjleewffxWwjivtSakR4Phs52Hvrwyf5JNmWZJnfLoH1t376dJx59HKfTSX19HXZ1eKsKJpMBk0lAVQ+/OJKcbCYjw0JuroMrr8rn2mvz8Xgi7N5dzdatW/jd7+5h3bqtI/Ys074DRqMRSZKYPHky559/Phs3bmTjxo00NDTQ2trKxo0bMRqNTJkyhSlTppCTk0NmZuawDNj9xYgLL7oQV7KLu26+m2e/+cCI7pmxKhJHfw4Gk8lEYmIiZnMsb4fX66WpqYnMzMzD5DUTO2bJyckUFxdTXFxMYWEh7e3t9PT00NbWRn19Penp6Tgcjn7HOBwO8+tf3cPMWTM46aSTRiQA9PT0sGbFaq695J5htQ9FwqMyrjhOnDhxRsKxbjseKqNGYABwOBxMmzaNwsJCSktLWbduHaFQiHXr1lFTU0NNTQ1r165lzJgxLFiwgPnz52OxWL5Wq/qKouD3+2loaKChoQFBEHC5XLrAMFqVtjhfP6ZPn84VvisGGEqi0N+1OPmGZGbOnInH46G9vX1AwruSkhL++MD9bNy4ketvu52GD9/FfvpZOCdPHfrGcYD3nn3mWf708F84edY5+0rX9ZrNlZtbEISeAds4MgzY52SRkZ8/wNDcfwyBQABftURKS8qBxzYImzd30tb2CVOnNvTzorDZFcomJ/DJx81I/jQWTZpLAZCfkk1Ks4OGrmZeb3i1n8AAgNWG9aJv0pyQwPuOJAySRNAbZdNrb4xIYPD7fIxNdHPqScbYZ9c73053iDVrXMz85qJ+7X0+H0VnTiU7O3tAX7Is8+Hb73PtcZeRJ01k/NSiYYxAAAQcDgMdHeoReVhITDQyfXoq06enUvF5rCRiXt7wE432RQv3mzhxIsXFxYwZMwa73c7q1atpbW3l888/Z9u2bZxwwgn4fD5mz55NamrqIa+QL168mKcfe5KLnvpvrpxxHkunnDys7Y4VDwbtmcBqtSIIAj09PdTW1jJmzJgRh/8MhdPpxOFwUFRUxLhx45BlmY6ODjo6OqitrcVoNA7IxfCnP/2JF/76Mus/3s5dt/2SM886nQsuOn9YHjCvvvIq07MmkWhNGNb4wtFwPAdDnDhx4gzC1znMfriMKoFBS6Kk1QlXFEV/8Nq5cye1tbW0t7fT2NjIunXrcLvdbNu2jbFjx5Kfn09ubi4ZGRk4HA7g2FOlwuGwvgLi8/mQZRmz2YzJZMJkMo3KJCRxvp6oqkphYSGzZs06bG6806ZN4/1XX+Hdd9/lwcefoP7l57CcupSUOfMR+313VD0h42ArmoIgsGjGMhbNOKu3UsG+KhZyVGVt5dNEI1G9D1mWSUw0M3v2bE444QQSEhIGzfqv0dPTw4YNG9j80foRzU8QYMHCVJacNjDfxNKl8OADCsltC7jt7B/r44pGo1TUbeN198p+8xPFmBBgmTkXR0oq6ZmZSJKBnqpK2LV5RONKSUnh5MU5nHFG/r4ylUBtjZemJpHzzjuvX/vu7m46OjoGFUvLyso4+fRTCHQEOWfGEn0efRFFkYSERNragqSlWRBFobcahkR1dbTPZ3pk8gWkphhpbGz8QgID7HO9LywsZOHChUiSRDQapaamhubmZvbs2YMkSXR0dNDU1MSECRMYN26cvko9kvvdsy/+jerqan7yvR/h9ndz+dyBoToDx3lseDCkpaVRXl7O7t27MRgMuN1uduzYQUlJyWEVGDTvkPT0dMrKyujq6qKyspI9e/bw5ptvsnjxYnJzc/Xz/LPPPuOxR54AUSEvpYSlc69gQ8VyvvXG90nPSuHiyy5g6ZlLSUgYKCCoqspvfnkv7S1ttHS00uZxYzWauebESzhu3MxBxxeMhjHHczDEiRMnzpBozwzHms34RRmVFqXJZCItLY3k5GQmTZpEdXU1b731Fh9//DHBYJDGxkbWr1/P5s2bsdvtFBQUsHDhQubNm4fFYtEFhmONcDhMW1ubXlsb0Eu4jcYEJHG+vmjx0Ycbg8HAGWecwemnn86qVat48LE/8d5dPydh9lxMObmokTCqIKAi0LNzBx999BGVlZVIkkROTg5ZWVmx8R3AOJ07eRHvffYiP/3f9Zy4MIWUFDOqqlBX56aurg6fz6fHyQ91sxLFfWEXg6EezmR6g4xDU+9FBrrR97jdvPLKK7qgqX1O773zb8LeIMZeDw3t9S3bNzP3hPBhG+5Pbrieb5x9MadMOpHURNeA90VRJDc3j5de2sm4cQ72VrupreuipibUL/5eEAR21W7GH/TQ2F5D2NfOaxv+zc7mSiJCJ60fvIMoGvodn3BY5oEHNvcKSzEUFXbs6CEtbfCKJ8kpAo2NjV9oztp4AT30T5Ik/T7Q3t5OTU0NDQ0N1NbWUlVVxWmnnaZXmeh7rm3YsIGqqirOP//8A56DgiBQWFjIU889w4+++9+0vv8EPzrpSoyGA3tFqKjIcvQLzfNIkJGRgc1mY+3atRgMBjo6OtixYwdz5sw5rAKDRlpaGmVlZVRWViKKIrt27aK9vZ2srCxOPvlk3dPk/nt/i1PKIGBu5z+f/5OZExdw0oxzWDjtLCobtvGPJ9/l/nse4qRTFnL+hecyY8YM/bu5fft2cpIyuf/Mm3hj4/uMTyskHA1zy0v3IhpEFkyYy7WLLifdkaqPK+bBEBcY4sSJEyfOQEalwKAl3dCye2ZnZzNv3jxSUlKYPn06VVVVVFZW0tjYiM/no66ujlWrVtHS0kJFRYWenyE3N5fMzMxjxqNBkiQcDgdOpxOj0YjBYMBisWC1WodcEY0T51hAExUikYheg9hgMPSLE+5raO2P1+ulq6tLL9t3oPhibT+lpaXceeMNbPxoDWW+FLyfN2MQjaiqQiQaQcyYyX9eXY/BsAlFkalr28PMeVPJzE7vXfzW6lZo/YIqKEwuns36XR/jDwjMmjWJQDBAILCXBx/8JaIosmjRIiwWCwkJCTgcDt1Q7zuvmMBwCMbOYdVkVJSo3K+cZbClmc5de6l4fgVCn7kLgkBiSOD44hMxG/cddwGBPeu3EY0e2PDcvypH34SS2s++ZGVlcfal5/Ps8pf5ycnXEJt0TPLRklBedtm32Ly5AkGAd159lJMLFuI27qZT2IHf76e1tZUFJ51IRXKsTXnqWJDH4M6SyXWM46SWJgr8nVhtVoyGfbfZx42pZLYvGJDI0xlaT1QODjo/l0uhsbF+qAM9JG+//TY333wdc+bMZtq0+cyYMZOysjLGjh3LSSedRG5uLpMnT2br1q1s3rxZL+UsiiJut5uysjLKyspwOBw0NjbyvW//EL8nyO5dldx400+HFKeTk5N5/Jk/c/NPb+K2Nx7g52f8CJtpcLf6t7f8h9nz5xzyPI8UWu6FhIQErFYrkUiEtrY2PB5PvwSmh+t+mpqaysSJE1m7di1Go5FQKITb7aa9vZ329nZcLhfv/fs9Nq6vwCo5UC0qZsGBzRLzVBBFkXF5ZYzLK8Mb6KFi9ypuuu5OJJvCRZeez7Jly3jx7y9wWsmJlBeUUl5Qqu/7h0uuYvn2tby64V3OfOAqCtJyuXjOMs6eeirhaARzPMljnDhx4vRDFEU9hC4ajeL1enVv8dHEqBQY+qLlIJg7dy5Tp06lp6eHnTt38tZbb/Hpp59SVVVFa2sr7e3tbNiwQfdoOPHEE485jwaj0YjL5SI5ORmTyaRnDR+tNVrjfL3QjMlIJILf70eWZRRFGXYsuc/nw+12YzKZUFV1UIGhr/Fqs9kIh8PkZRdw2WnXolmNsiwTlaOIgtDr0h8zwELhIK8sf5JPV64i3VqEx9eNwRArX+QN9PDZjo/ZWreaM88+jUefv5eMjFipv+7ubi6++HSOO87P35/7NR988CZXXPEdxo0bh8ViGfS7ezAPhsE5mAv78HtS1ZjBLkcjvaUHe8eiyGQ60/jRoquGbYAlWhKAFk0H2G8/AwWGvpUqDlSx4qprruL8f5xDVWsd+cnZvf0o+rann76UM844E1EUeeaxv3LVnAv5eNcadrZX4/P5aGpqYvLkMk47bcmAh4auri46Ojqw2+2kpKT0O/9ee/Z5bj77hwOM8p/87Xbstt2Dzj811UJd3Z5hHavB8Hg8TJ1mYtr0avbs2cJ77z1KXX2IkpIJzJh+AsXFJcybN493332XxsZGWlpa9FDBNWvWcNZZZ+FyuQgEAlxw7kWcMeO/mFAwlRff/SMtzf/Lvb/59ZAPTlarlQd+9yD33P0rfvDibVx34lVkOdNw2hxYesWk3S3VfNK4gZefeOWQ53mk0Lz+bDYbNptNN/a18qVDiZiHQmpqKsnJyXrizXA4rIuhbW1tmEwmCsYWcP4lZ/PO22/j74lw0cLLB+0rwepgfvkSjpt8KnWtVfznn8v54+/+hL+rk39+77EB7Q0GiYVlx7Gw7Dg6PG7e+Ow9nln+Mve//SdsJisLlw0vv0acOHHijBY0gcFgMOD3+wmFQoiiGBcYRhN6gjVB0A1uSZIoKiri5JNPZsyYMTQ3N1NbW6t7NHg8nn4eDRs3biQ5OXnYZS2H++AhCAJGoxG73U5GRgYlJSVkZGTongeHgnaCm81m/aE3FAoRCASGXCGME+dwoJUujEQieh35A61+9g1xOFiynP3bBoNBBEHQxb+DZTpXFEWvVwyxEKqkpCSMRuOA/Wr70vYXDodpd7ewt3EPuelj+/WnCrH8CqIYW0EXBQNnzb+Sv7/ze/a6P+dP79wR6xMVu83OosUn8r+/+hPp6ekYDAa6uroA9JXRcSUOjpufyuuv7eGuu/6XK6/8EfPnz9f3qc3f7/dTWVmJu9vN+1s+0ZNaauMPhSKs39BBbV3MPV5zMGhv91NTI/PxxwPd8VVFpanJT3dHHW9v+rB3nyqyIrOzpZLGaCOfffYZRqORmpoaQqEQqiyjRCK62KARiUbYUrdzgJzRt1W330OHrxOAps4Wgrs8fPBhA6KwL+TA3RHE63Xqx0mjp6cHr9eLLMt6BYXBuOSqb/DQM09yxazzaHA3I0dlPB6P3t5gMOjXSYPBQGqCC7Nkoq6ujvfff59oNEpycrLeRjtXtBJVI3WX7+gIsX17l3YwALAnSKSmmtm4oXZEffVFURScjn1JIwFCIZndu93s3PlXVq6U2bvXj8uVyvjxReTk5NDQ0IDX66Wjo4O1a9eiKAoTJ05ENAgEwwGsZjsXnHgt9z53HRdfeiHz5s0bcgyiKHLzbbdw3ILj+dVtd9Pa0kKCNYFLypdyevkiHv7kGX50w4+PiSpGfZ8bBEHQhaxgMEhPTw8mk+mwlm/UckdlZ2czdepUqqqqaGhooKKigmeeeYbx48czbtw4rrnmasaOLWDVG7soLZxx0DnkZxSRn1FE5EOZgFBJsn3oY5+SmMyViy7mvxZexNbandz39mN4fb7DNs84ceLE+TqgeckbDAZ90evLCJ872hnVAsP+aKqTFqN6/PHH4/F42L17t+7RsGfPnn4eDVpyRC3kQmMog2iouNW+2O12srKymDp1KpIk4XQ6EUXxkAUGoXdFVVuBUVUVv9+vJ3yME+fLRJZlgsEgPp9P9zAYLDRn/5Vn7QH7QGirztr5HY1GEQQBm82Gy+XCZrMNOa5oNEo4HCu5ZrVaMZlMOByOg3o+eL1eMjIyuOJbl/LqPx7FIjiZUbyI8flTQBVQeuelKL0/VRVVVslKzWf6SUVccsklWCwWDAZDv1V2zWDWhAyfz9crABqxWIxcdFEe06Z389RT9/Hhh+9w3HELsdvt+rEKhUI0NDRQ37WH+1bfGQtFEPb5JySlKrzzdn0/hwUBkGWF6uooNTW7B71GKYpMp7iRhzdW9Hk1Nkabzc4dd1yJKIp4PD04nZ10e4JE62ogP183mKWERDoJ8sctz+8Lkeg7jt797qj8HNHoxmqV6BT9VK80Edpr0ucC0OVXkC2ddHZ2Dvhcenp6CIfDehjcYBx/wvGs+3QNj29/GVmWGVc6nu7ubr290WjUDUXJYGBsSi4G0UB1dcyLweFwMHfu3AEebIciMFgsVurcOfznw7R9x0IUqKutJTu7m46OQ78+R6NR9v/6mM0GyspclJXFclBEwgp7KnvYvbuFjRu6CYd7sFgSYwLDmrWs/2wjLmcyCXYHpYUziMpRXv7kMS674hLmzh1+2dYFCxYw6+1ZVFdXYzQaeez3j3LeY9dSPmMKy85advAOjjL6Co6awGC32/UwisNJVlYWM2fOJBKJ0NDQwMaNG9m6dSszZsxgzpw5ZGfnsHXrFmBgXpGhaOqo5cTs8cNuLwgCZWMmsHT6yUTLB88bEidOnDijGU1gUFVVX1AbbcQFhj5oDwTaiaEZ44WFhSxatIj8/HyampoGeDRoK6aD9TXSfWuoqkogECAYDBIOhwkGg2zcuJHc3Fzy8/MpLCwkJSVl2J4T+89PiweKRqOEQqHYiusozXQa5+AcjnNDO+80jyHNg+FAXgJ9GcqLQRMktOoFoVAIRVGQJKlf/wfaXlEUAoFAr6Fs00XDgyU+NZvNOBwOrrzySr7xjW/w6aef8tLz/+SjN1+mLP84ZoxfQJLZpYdICOq+MVqtVr3U3f4Cw/5z01bhDQYRySCBABMmJHP77Ym8/noVf//7ZhyOVKxWG4IQ8yzweLwkp5j5/R9mIxrEfob5QVE1b4JeCeAA28myjByVEUQRSYoJrKIg0tnVRUuLmb883cgnT/yW6Jzjeo+vBWNSEnnlk3jsySeGHMKtt15P2eTdzJiRwl+e3kr7Z5O4/+Jb+1WRqGqr5f71Tw8oIywIApFIBKvV2i9HxWDH9hf3/J/ubm6z2fq11+4BktGIw+FEMkgsLJrDv6s/pbOzkw0bNvDyyy8zY8YMysvLdZFHkiTsdrvuHqlda4c6nxyJDn76w2s49dRT+73e2dnJlVdeTGNj7SG73kejUQyGocNljCaRiROSmDAhiZNO8vPIH7fz/nstJFiSKM2bTSDkZ1rJfIrzSxEx8sbKv1AwKYOf3XLTkGMKhUIDPIhsNhulpbE4//t/9wBVVVUkJiYec4mG90/4aTabSUhIOOzigtbXmDFjWLRoEaqq4vF4aG5upr29nb1796IoCvU1jcgROPeEa1AUGUEYplclwiGlXolEI5it8SSPceLEidMXTXDW8oAlJCQcctnnY5m4wDAEoihisVjIyckhMzOT448/Hp/Pp3s0rF69mtraWjo6OgbE+o40g/3+7RVF0ctKNjU1UVFRQUZGBuXl5Rx33HH66uyh1FrV5mUymfB6vbrAECfOgbj2O99j3fp1PPLIH8nIyKC9vR1FUcjNzSUrK2tY56CW8+Nwug8PhsfjIRQK9TNGhyISieDz+fS68sM1EEwmU7+bxnnnncd5551HRUUFf3nqWf7877soypzMzJKFFGSVIKoiiqBgEEXsdjupqam6B8NQaHkWRNGAQZIQhZhB4HQa+eY3i7j0UoXGRj/BYOw7LEdVKitlnnrKT2dnGIfDgs12+C/12jESxZjwIQgxEcNgEDEaJebNS2DTpjY6XvwLtiu/pwsQkmQkJSVlQH+rV6/ko48+RBBE6urqmTKlt19RQBCF3jKL+zxeJIMBo2QkOTm5Xz8Gg4FoNIrdbsflch30xq7FRWrX1L6ChCAIGCUjSUlODAaRy2eew2f1m6ntbmLDhg10dHTg9/spKirSBQaTyURCQgLRaBS/36+LRH2P2XBxuVw8//xrbNu27ZCN1pin0MHvR93dYT7+pJWVK/ykp09l/vGwbcMe5k5YgkEwIhmNhINR/vPZ6wTMbdx7/7NDnruvvfoad956O/c8cB+nnHrKAdsVFhYe0ry+ajQhtK9omJSUhNU6eBLLL4pWLjsUCtHS0oIgCLjdburr62lsbCTRmsTZc75DQfokuj1dGAwiFrMVk3Ho660oGpCVkd//Q3KEpC9prnHixIlzrKItWgUCAaxWK1arNS4wxOnP/h4NWkxuQUEBixYtYuzYsbjdbvx+/2F3f9FWKRobG6mvr6empoauri527txJKBSitbWVkpIS8vPzycvLIycnR1/JO9iDqNVqJTc3l4KCApqamrBarfEkj3GGpLm5hWhI5uorvo3LkYozIQUBgY6eFvIKsgmHwkRlmcyMDLJyMsnOzSIrK5P09HQyMjJIT08nISHhkASxkaIZeLF8AyE9YeNwQotGMr4DtS0uLua6n/yQa7//HdasWcMLf3+Rd9fLlI85ninj5vbb9mDhH33bxjI60CfcIfZ/o9HAmDGJentZlnEmSTzyiMr3vrea4mI79/3muGHNacRoiRf1wYHJZMbpdDK5XGTipB42vf9P3DOPQ5o6A7OqIAiDH7vvf//bzD9eJCvLxvzjYcLEdL3PfQJsnx31Hoj9j58WRqYd24MdX83Lpe8/6BtrD4IgYjZbKMwew91nXM9Nb9xHa5ebOrGO3bt3s23bNiKRCOnp6UiShNVq1UOBJEn6QiVTrVYrM2YMHVM/FNFoFOEgzgE7dnTxm/tquOCCy/njH7/BxIkT2b59Ow/c/yDv/OdZlky5HFEW2VGziZV73uDnd9xMTU0NRUVFgyau+vTTT3no7vu58cTvcN+tv6S9rY1Lv3HZIc/haCIYDBIIBPB6vQSDQUwmEy6XC4fD8aVWY9KeQcaPH8+yZcsoKytj7969bN68mc2bNxOM+Glor6Tb38b6mg+QJAMOazI/vfRBJMnAgVyRxN444ZESkkNfulgcJ06cOMcaWpiklttrOB6xX0fiVuUI0ErXZWdnk5GRoYcVfJGHx6FobW1l/fr1rFq1ilAoxM6dO6mqqqK6uppVq1YxZswYTjzxRN2jYX9X4QNhs9nIz8+nqKhIX+mLCwxxhqKosJid2/YwYZSiL+gAACAASURBVMwULlt8nf66oihs27sBmzkBq9lGj6+Lrr0dbNi6F1+4Am+oE4+/i26vG4MkYjKb+OlN/8M555z9pY1VkiQSEhL0BKZasr/hCgyHi8TERC655BKuuOIKPvvsM579y9/4/Ws30+XuoWjeFSPubyRXmdRUC6+9fjIff9zKa6/uHfG+hjsgtfe/vhU3zWYTkuRCVVXOPTeZ3bua6Lz/Vgw/uIXMCRMP2F1eXgannGIiO7t/zoxD+UQOVcga6lqueX2VKRO4ffEPueGte2lqaqKqqorNmzdjsVhwuVx62IsW3qZVJTnYeL8sotEIBnHo/rOybNjtNq6//iYSEmLlDYuLi/ntQw9y2SXf4O31f6U0by5vfvYU+WNzWbVqFYmJieTm5g4QGHbu3MktP76JW0/+PmW545mQXcyVd/wPrpRkTjvttC9tnkeKYDBIZ2cnPT09BAIBHA4HmZmZOJ3OIxJeWFJSQlFREZ2dnbjdbp577jlqa2tpb29n3e4PMBpM+KJdnHHGGaxfvYmXPn4Ek9GEP+jFZLIgCJr3hYKKys7aDdTXG+kQvLFvsxpTDRVVxWVxcOm0ZRSk5Q0YR1iJHDSBbpw4ceKMNvoKDFpusNEYeh63KkfA/h4NXzapqamUlpZis9nIy8tj165dVFZWUldXR2trK83Nzaxbt06vW56SkqK749psNn3VVosNtdlsWCwWfD4fdrud8vJyCgsLycrKIj09/bCX14rz9UEQYEH5WZww9fR+r4uiSFnhTP3vrNT8QbdXVZVQOMA7q19i6+atX6rAIIoiRqNRr1YhiuJBV+j6GoBf9DvQN4OwZujOmjWLKVOm0NDQwCuvvMr06dOGrWgLgkBrh4c7/6+NRIcZRQGvN4I9IfZwr6gqqiogKyoqArKsEoko2M1RkmxQX+/l5X/s7Z1nbG6hsIAiQ2dngG9cXkBysglVUVH65Ik46HHobacqKqFwCENvCEds/rHPIDMzkVOX+Hn77W7ML9zN3m4VhzOJK644F1WNJY7UbsTbtm3hx9eFMRoNCAJEIgqRqEwoGIFwD9976ef9Vojbu92k5KYNOrSRCr9953qg66DQ6y1htpiZlFeC2WpDUEPU1tayfPly7HY7JSUlelJRzZNBFEUCgUAsjI7+1TL0fTLysLrhIssRRMPQn6XTaWL8BCPvvPM2F1xwIYCevPi6n/yIn910Kx9seR6DScDj8bBp0yZUVaW9vZ3y8nImT56M2WymubmZH1x9Ld+dfjETMotAVdlQs5ncgjxmz579pczvSNPU1MSWLVuora1FlmXS0tKYPHky2dnZX6oHg0YsVCiW9NlkMrFgwQKsViu7du1i/fr1tLW1Ee2Msnv3boomFJCQqZKZ6SIjYwLl5eX6w642Vr//m8iyrIdcatclURTZvm07P3vkAWZmlPLN2eeRlrgvHCkkR760cJA4ceLEOVbRwtC1XHqRSISEhATd42u02FlxgeEoJiEhgXHjxlFYWMiCBQuorq7m/fffZ8WKFQSDQerr6+ns7KSiogKz2awbUlarlYyMDJxOJ0ajEafTqYsISUlJJCYmYrPZmDJlCikpKfq/AyXdixMHIDUpE5Px0FasBEHAYraRkZx9SAnF+hIOh/n7X//Glo2bY4Zkr3igGWhK789oNEJikoMfXPfDYRlvh8sbSVOs9xchRVEkIyOD73zn2/0e8IfTX4cnSmTOWQRz81GCARqefowx//2j2PsICAYRRAOCGAshiLS30vLGPzjvzMspLW2hpGS63hfEVuONRiN3330zBkMsMaRm7Gu5Eg7mOqAZIxE5QigUxmiUsBhEBMGA2HsMLBYzixYls2aNlzmz7SxenElPj4jD0YFkNCCKQu8/uODCMmRZQVFUVEXFbJFISDDywgtVJLvO4pJLLtHLh2oMVtZw/1Kiwzm+fUsODtESQYgl93Q6nTgSXTSbBKpb26n96BNWbtnKH//6d6LhEFdcfDHf//a3BuRiMFvNXP3cjew7uLH9tXa1c5bxkmGNd6SYzVb8voO7wB9/vIOXXnqa88+/oF+YyNSpU/nzU4/z9NNP849//IO2tjZaW1upr69n/fr1nHfeeeTl5SFJEj/+3o9YVrCAmfmTiUajrNv7OX/f9i/+/NxfBuTKOFZpampi7dq17N27F1mWSU9PZ8qUKeTk5BxRN1iLxYLZbNa9GFetWkVBQQErVqxg+fLlbNu2DYPBQFZWFjk5OVx++eUsWLBgRF4HM2fO5Jxzz+HpPz/FD/56O6cVn8BFM5aSYLETUsLxEIk4ceLE2Q8tJw+A2+0mFArposNoIi4wHMX0NVSMRiNZWVnMmTOHlJQUpkyZQmVlJbt27aKhoYGenh699F8oFCIajeJ2uzEYDFgsFurr60lMTNST7JnNZmw2Wz/PBrPZjNFo1N/LyMiguLiYjIwM3SCJE+eL8kWM+F27dnHL/9xMctjGSUXzkAwGMICelUAzjBBQJJmH33uGU05fQlra4CvdfdG2/aIVMyRJwmazDShdq3lW9C2/OdzkmPnFxUTPPJfEgrGEPV6an/sLhcvOHbS9Iiv4mhqIfr6Om2++ecjv7a9+9TPs9thtQFVBVVRUQe0b8XBAtBV9LbxKVSEcjiAZFCSjhMlkIjExEVVVueACF0/+uYPFp7gYW+giOTlx2NeTjIwEkl0ZFBQUkJKS0m+7yspK9u7dq/+tqiperxev18v06dOH1X/fVdu+VQEOhMEgYbFYUESBxPMuRczLR1XBnJSEmJpK+PONbN65s1dgiXmMadmkX3v7DSKRyID9C4JARkbGsMY7Uk466WSee+4hLrhQxTCEJ0NZmYvnn9vDtm3bKC0t1Y+BzWYjOzubJUuWoCgKb/7rTZqam/D5fDQ2NrJp0yZSU1P58N0PmGgaw7nTTkOWZXY07eG+j/7EL393L9nZ2V+bKkVut5tdu3bR1taGqqqkpaVRVlZGVlbWEfFs1NDOG5PJhMlkoqioCIiJbvn5+VRUVFBRUUFHRwfhcJiVK1dis9mYPHkykydPHvZYExMT+eGPf8TFl13CIw//kW+9cDMXlZ2OLxSIh0jEiRMnzn70td2CwSDd3d2YzWb9mWC0XDfjAsMxgiAIuFwuZs6cSXl5OYFAgB07dvCvf/2LVatWUVlZqZfaC4VChEKhfqtQB05gti/ZnCRJeum9tLQ0pkyZwtKlS/VM93GBYfQiKwqfbHqTJnfNgPcEROZPPpUE23BygAiMLJvAPl5/7XUevPs+rplxIYsnHX9QQ0WRZZ7Z8OoRrz+sCQx9DVfYd9M5lP4Moojcr2bkgY/hviSKIyhh29tnbOVf63/obfteUwwGiUgkVk5XkYy91wsTiYkS0WiUccV2MjK6efedVs4734aifHFPkfr6ei488zwm507o97qqqmxr3M2jzzw+bGFJE3u00oNDrURLkmFfEkmTCSk5LRZr6XQiJLkwpqQidjbpYTKBQIBQKITRaCQtLe2IX0fHjRtHXt4EPq9oY9r0gdU7NERRYP7xVl566W+Ulv5Sf10rs1laWso9v/wNxqgDAx0EwzFRu6Kigp3bdlCeNI5rL7gsdmwEgadWv0RSdgpOp5NIJPK18Y7r7Oxkz549tLe3A5Cenk5ZWRkul+srTeSVl5dHdnY2xcXFzJ49m2effZatW7fi8Xjwer2sWLGCxsZGLr30UiZNmjRiMSQ9PZ3bf3EH37zqCn53/2+paNg26lbk4sSJE2c4aPf/YDBIV1eXXnVMy+U3GogLDMcAfcUALUN632oWBQUFdHR04PV6URRFj2kOh2MP/FrW60AgQDAYxOPx0NbWhsfjIRwO6+0VRSESiRAOhwkEAkSjUYLBINu2bWP8+PEUFhaSl5c3qFtynK83V11zBdNmbBrweigU4g8PP0JWSj7lxXOG19kheDC43W7uvOk2HrnoF4xJzR3WNnvb6/EJIaZPnz7kw7RWnlJRFN0LaKjykYqioCgKdXV1bNu2je7ubiBW7lIT94LB4KAhRwfL9dC37J2GLMvUNzRidHcSSuiAcKwyhhyNIhoMCJoQoHcnIAoxg3F4Bl0fAVKMhQGMJLXi/tcnQYiFqYjCvooOBkli5kwba9f6Ofc8hZGJTKp+zPsev0AgQEFaLveedVO/1rIsc81zN47IU2Z/D4ahvRhix0gUhJjXl8WC3+8nFArS1dmJ7PPR1tZGXV0dmZmZmEwm7Ha7LjYoiqI/aBwpLrroap5//tYhBYbmZj8N9UHWr3+VO+7YJzAIgkAkEuHGn/6MHPskLrnoTFZsepd31z9HR3cLHa3tlKYWc90JVyKJsbAfVYAzS0/mVx89SjgcprKyEpfLRWpqqp5R+1gTG6LRKNFoFJ/PR3d3N6qq4nQ69dK2X3WiZO2BNjk5GUmSOPnkkzGbzXz22WesX7+e7u5uKisrWb9+PdnZ2Xr45UjPw8LCQn77h99RWVlJQUHBlzOZOHHixDnGEQQBq9VKYmIikiTphQFGC3GB4RhEEAQ9ZCI9PZ0TTzyxX8yxJi54vV4907T2r7OzUzeM6uvr8Xq9hEKhAdt5PB5aWlrYvHmzXq3ihBNOICEhIS4wjEKmTJnClClTBryuKApPP/kMFtPwkn0JgoB6CKvXXq+XTFf6sMUFgFp3A1OnT9HLxx0ITWDw+/14vV4ikciQVScURSEajVJZWck///lPampq9GQ+PT099PT04PF4iEajg27f11V8MPGhb34GbUXdbzST1N6G1WrHLMRCGSLRKCZB7M27sI99XkmGYRgPMWNZEAVEpU+5xoNsNaAXAURBxKAZmKoCqgFBBFE0IEkGMrNM9PR4RnyDFRAGzakQO3Zf3EjdP0RiuB4voihiMpmwWK0EgwGCwRChUBjR66G1tZW9e/eSkpKCyWTC4XDowq2qqkfcGD3llFO4555b6GgPkpK6b9U5GlXYsKGD5Z94aWkxcs45V3Pnnf1zQaiqym233oG3WeWihRejqionzzmLzOQ8nn33QeSQh+vmXoEkGKD33GnsbOHP61/i3t/+BovFws6dO8nLyyMxMXFYpUOPRjTB3efz4fF4kCQJl8ulV2E6WgQTu92OzWZj8eLFzJo1i8cff5xt27bh9Xrp6upi/fr1GI1GFEWhoKDgkD8LLSQjTpw4ceL0R1VVRFHEbrcTjUb1ctVxgSHOUc3BqlloBpDFYsFms+FyucjKysLn8xEIBOjq6mLKlCl0dXXp+Rr6btfV1UVtbS11dXXU19fT2trKxo0bCYVCdHV1UV5ezsSJE3X346PlwSrOkUcURSaUTGC4hl4g5CPLPrxyqn0Z6hyLGYUy4XCEcChEVI6iKCqtHe1s7djOE088QVJSEi6XC5fLhdPpxOl0kpCQoGf513KVaMZ934fuSCRCMBjUSxI2NzfjdrvZsWMHmzZtwu12o6qqbkAGg0EikQgqIBkMumucoiiEQiHdgNVCkjTPoUgkgqLICMK+vAaaR5JilvFvXIeQkoZsEFFVhfb2diTD4IZNuK2dYFMT999/f78QgL4hUZFIhK6uTtra2jEY0KtIWCyWmOIuSCOz3wUB0SDqeWAkKZaHQQvZsFpEZHnfzVVVY+Ut9aSM/bwa9u1YVmQ6OztpbGzs517Y1dWFLMtEo9F+w5QVBVlW6OnpobOzs19FncHKV2pVd7QwsZEk3zQaJf0BIhgMEAqFiUYiNDY28sknnyAIAnl5eaSmpiIIAoFAIFZ54wg/ZFgsFs4880KWL3+Fc87No60tyMcft7J6VZCioslcddXVnHTSSQNKTgK88MILrP5oA98+8+f62AVBICdtLAvKzmHVln+SaLbr4jQGgR+//Auuvv67zJw5E4/HQ0FBAYIgUFNTg9PpJCkpSQ+9OFbuHzU1NWzZsoUdO3boWcGTk5Ox2+2DnldfFdq1y2azIUkSc+fOxePxsHbtWtatW0dzczObNm2irKzsiIePxYkTJ87XHUVRdO9xo9GIw+FAUZQjUmXoaCIuMHwN0TwctAfnvqqZ9ntfd+P936urq2PlypWsWLECv9/P3r17qaiooL6+npqaGlpbW3G5XMOKb47z9UdleCJTVcN2Nu39mB/+4tHDu39FIRKJ4vf76O7uIRgMEo1GaWtvY+3aNVTVVVNQUEBRURGFhYWMHTuWgoICMjMz9aSmmlhnNpsHzCUcDtPT08OOHTt45513qKiooLq6mq6uLsLhMIpoQLAngCRhyMhGTErGmJxC5LPVGP1e7Ha7boRqng2qquqGvCzL+Hw+VFXFbldZsiQRRRGIRlWiUZVIVCEYVNn7+cu0ffIanpmLkaMyra2tg4RHACrI7nY89fXceeed+8IUeoWTvr97PD088fhORIOKKAhMLrcyZUpGb5JKaYT6wj7hIhQM6cKK/p5BIBJR+eijTpKSVCSDhKL2hiQoAwUGbd+7dnaRnNxOXV0d5v9n77zj46jv9P+e2d531Xu1JdtyN7bBxqZjQwhgCDU9uXCphHRSLrkklwvJJeGSXCAh/QeEQCChdwwYjHGVqyTLsnpvqy3aPjO/P3ZnvJLliivs8/K+rC3TZ77l83k+z2MyaerMXq+XhJSkradfMkmSkSWJsbExRkZGkCRJO96p7lODwTCBUTDVbx7c8K+DREAD4XHCO+thzEs8GiMWCRMJhYm2NjPW0cED9z9A/bZ6KisrKS0r1XQIbrjhhtOSxfjAB25hzZr7aGtL0N0tcPXVN/HXv95CZWXlYZezWCy4HdkY9MngwwHdDZGi7ApGQmNEpWRpXSAQwGK1UplTyraNW1i+fDlut5uCggI6OzvZt28fubm52kDLYDCcNQOu/fv389xzz9HQ0EA8HsdsNpOVlaXprZxpUMcAS5cu1Uoh6uvrGRwcxO/309fX957KpmWQQQYZnArIssz4+DihUAiLxYLT6dTYi2cje+94kQkwvAuRPtg5loGPGmDIz89nwYIFmM1msrOz2blzJw0NDQQCAdrb27VJQ1tbG/PmzaOoqEirq83gvQedXuS5jQ9iMnySsoJpE75LJOKMRwJsbnyVvYOb+MX//YRZs2Yd8zYEQUBW1GxbMvMdjcUIjY8TjSWzxtGU5sjYuI/usX4aBloIRyMMDAyQSCTwer20tbWRnZ2tMRnsdvuE+mlJkrTMe3L/kxaDfr+fjo4OGhsb6e/vJxAIoJgtGHLzsV54GY6FSzEXlyIKEOvtIeEdZbRxFx++4XoKCgowmUxaZl+SJAAtuCHLMrFYjJGREd548xE+8IHSZNmDAkqa8GIsFmX3bi//evwF9kbB97MfgNGELisHndujaScoKEhjY8jxGIlEQmM5Tc5WCoJAImHklVeSAZlkhF1h+rQQIyMjWK1WLBbL1DXzqX2bsD5SZQMGY7JcQp9W5iHJiCIkErB1Sz6rVq0iK8uDJCkplkZCO87JGhCzZo1RXFyqZb7VAIPT6USXKr+YGGGREHU6HA4Hbrf7qCjsh/vuK9/+uqazof1eFLix8hqys7IQBIGxsTGa+9vYuXMnvb29eIwursg7D8OYEXNjjPG2HnQ6kf9X/xTXXnutptFxKrUYpk2bxqc//TXKy6u47LLLjlpoatWqVfz4Bz9lLDiC235Aw8FkMuNwOhBEkeeb3+D6uasYHx9HEAS+fcln+dqTd/H0U0/ziU9+gng8jtVqpbS0FIChoSEikQgul0tzMTrT+49IJILX69WCgWVlZVx44YVMnz79tOsvTAX1nrbb7YiiiMPhSD3zCc2bPYMMMsgggxMPlREejSY1s9SEy3tJLP/M6xUzOK0QBAG32828efMoLS1l2rRpFBUV4ff72bFjBz09PYyOjtLW1kZ7ezsOh4Pc3Nyztq42g3eOX/zvz1i3bh13/ehnFDgqsZmcAAz6uugb7cBud7Bsxbn88w//IDv70CJzh8PBk9vkgH9kZIRQOIQkyfT7B3lw+1Ns72tEVmSiiShRu5WoohAYHqGtqwsxTXhRpcObTCZNDT0Wi2EymXC5kmUc4XBY02ZQy4kEuxMxrxD76vfjOncFDrcHh8OB0Whk6KVnEf/5N1YuW07+rTfzjS99aUKGc2odATTmUEPjKxQUFkx5DkKhEOecY6aszMHnvjmE/YprCa1/DentdZhTdGhVkC8eCqEXBExpAQJFUSYIuqqv9M/2Nkfo6w8QiUZwOp2aSJEoiBMMLJTURZgQZEjpMIhGEaPJiKrvoCgKkiwRjcqAwIwZs/jIRz5GdXX1Ic9LOnp6eti3bx9ZWVlkZ2dPYDDo9LqDJndqdt3lcpGVlTUl7f9YcOsHbz3s9+p5fOutt3j22Wd57rnnyAvZuax6OYIADnsy0GGxWnli76sYjcbTpsXwuc/dfky/b25u5uGHHqGzu4N1W1/g6gsOnAuTycjLOx5mWk0VjzW8gFlv5PLpywFY2/wWfjHELbfegslkIhAIYLPZ8Hg89PX10dXVRSgU0tg8Z4OqtsrQUDWLKioquOSSSygtLT2l9pTHinRbakB73tVAZwYZZJBBBicOKktcTdzIsozb7daCvO8VZAIMGWiYrAbvcDgoLS1l6dKlGAwG6urqtAxdIBBg586d/P3vf6e5uZm5c+dSUVGBx+PRJgAZvDfgdDq56qqrWLFiBW+88UZahu8mlixZ8o4nUWrGLRGP4/f7icfj2meRaJREQmJnXxP/t/EBrLlOVqy6kMLCQjo7O9lqz8K26mrCXR1EujuJ7W8m1tmGNDpMXBCQ4nESKW0SdQKu1+sJh8MAEybfOk8W5vIqHKvfj3PeIgxGE3qDHlPKh16n16FICT7zyU/wpduPbSIHSSp6Ii4jSWAwHAjWqbaRBoMBm82G0ymgM/oou+wKhqUEc3LdLF+2TCsDiMfj2v6nawqkl0eluyWoQYauri42bdrA//16kA9+yEVtrSGVsQ1hMOjR6fSpbQiHCCYmyxqSThSi9rvkOZRpa40CSRvcdEbBkerXTSYTbrcbu90+QR9DFEV1ixN+L6baL2ta0OVweKcdvhpcVTPaw8PDDG3uwGw2E41GicVjBMeDiDod0ViExsZG7bjsdrsm/qTeg/F4fILadGVl5RHLGE4kIpEIzz//PA/d/wjdHf3MKT+Pq8//OC9ueJSrVtyEKCYn02/tfhFbro5P3vp5nn76aR577UVGQl7mF8/i/zY9wFU3XE1DQwOlpaXk5eWh0+mQJAmLxUJeXp7m2qIGxex2u5ZtPxMRDocZGRkhHA6j0+mw2+3k5uae0fsME+1k05lI76WBbgbvXiiKwpNPPsn4+PgErSFI9n+XX345drv9NO9lBu9FTLbAVss13yvIBBgyOCTMZjOFhYXk5uYyb948duzYwYMPPsi6devo6elhz549NDc3U19fz0033aSJ1p3qAEM0GuWOL3yR7u5unnr26VO67QwOwOVycdVVV53w9arBhFgsGWAIhcaJRKJIiQSSLPNq60b+su0xckrzWbNmDeeeey6zZ8/mzTffpHXTdnKmTSdaWkY4HGZ8fJxYLEYiEiY+0Eeir4d4ZzvxthYSA30o0SgxWSIcl1LuDCKi3YZx7kKyLryM7Lq5qdIB1cZSSP1754P2goIC5sxZzre/tZ5LL7VzwYX5mEw6SJVJ6PWqoKARUewlLy+XuNvF5Zdfzqf//d+1basBhMl2l4eC+vuNGzeSk5PDa6+9yu/v28cVV8RYtjyMXm/AYNBjMBgxGY3oU8EGURA4IAFx4O/khDtZuqDT6ZESEtFogrVrx3G7i7QAw9FCZZSoQozpmIr5IKS2b7PZTunEr7S0lKKiIrq6uljbMIzFbCYeixGLxZHlcYxGE40D3Xz2f36BzmhC1E0q7zAaky4MegOIAggi3tb9XFI3gz/f85uTvv9tbW08/Pd/8MQ/n6LAWcGC6otYs2AuoigiyzLPrn+Infs2M7/2XJo7d7FuzxO8tPZ5AoGAVrry9GNP8HTTawQTIXbs2IHb7WbFihWUlZWhKArj4+OYzWaKiorwer0MDw8TjUYJBoPk5uZOeY3PFITDYYaHhwmHw9qz6PF4NGbAmQ61PVDPbybAkMG7AeFwmO/f+V2unnVpsqSQA3o+Gzu2k5eXx/nnn3+a9zKD9xrSg7o6nQ5FUUgkEmds/3YykAkwZHBIqA+IatlXUVHB6tWr8Xg8bNiwgX379uH1eunu7mbt2rX4/X4WL17MjBkzKCgowOFwnPR9bG9v5xt3fI292xu44MpLTvr2Mjj5iEajjI+P093dzb59++jr66OjowN/wK/ZSIajYbb3N/FCyxsMxL2sXvM+Fi5cSF1dHRUVFRMmK3qDPmmjqBMxGAwaI0EpLIR5C1EUGVlRUCSJ9h98E4M7i+wrr0EwGNG5XBiyc9CJuqRmgtmE0WBMsgJOcEdhNBr59a//QFNTE7/73S/59rde4+JLbFx0UT5mc3IiKgjw2ds3IclGFCVlV5liLBiNRsxms2Y3qwb8jhQxV9kM1dXVXH755ZSWlrJt2zZeffVlurp8rLnOhcWiUv7iSStKUdS0EiZJJqTajSSDQSfqCEciNDaME43qqaoqxePxHLOacnoWNv2zQ/7+qNd84qCKZ2ZnZydp6WYTQlBEliSkRAJZkhBMZgq+9p8Y7PaUu0Zaxm2KnTbWb0HevuGk7XMsFmPt2rX87f6HaW7cz5zyZXzskm/hceZM+J0oiiysXc66+meZO30Jb+1+nlWrL6egoACj0UhtbS0rV64kGo2yc+dOOjo66O7uZv369QSDQQYGBqitrWX69OmaDoCqrq2yhAKBAJIk4XQ6cblcZ0ymJ5FIaBaVkUgEXUrfQw1gnS0Tdc2tJeUCkhF4zODdAEVRsBjNfObCDx303cCzI++pCV0GpxeqI1g0GkUQBMxmM2azGb1eTyKRIBgMaqzF9wLeG0eZwTuGKIoUFhZyxRVXUFpaiiRJmiXf0NAQa9eupauri0gkgiAIEiPZyQAAIABJREFU2Gy2kx5gePqpp/n5D3/Kh+dezYKl0/HMLT+p28vg1CASiTA8PEx9fT1PPfUUu3fvZmBggCzRwe6uvbzevon1HdvQWfTUzJ7BF6/5GBdffDGlpaWaYKNqORiPRtGJOnRGHQajEZvVltpKml1i2h8d8Ri5c+ZRccHFyc8mmDRMZCscCu904D5jxgzuvvteWlpauO++X/ONrz+P1apPrRv8EQMFN30kGShRZHbu3s3evXspLy9Hp9MRi8Xw+Xxa7fWRJmrqBKmsrIySkhJmz57NvHnzyM/P529/+ystLYOcf76V/PykbaUsp14SyArIsoIsgyKDJCe1Gex2kcoqI3a7DhTo7IyiKBbcbjd6vR6v13vUnazP52N4eBi73a5NTCEpFDjkG6Ghp3nC72VFZsTvPdbTfsLg8Xgwm02YTCZEQSChKEiKkrTPjEUJNOxCZ7UkbzxZgVQpiSknByH9WikK4b4e/GM+2tvbgWQ5UlZW1jvex1gsxm/+7x4ee+RxPJYC5lWu4Io1t6HTHfqaXL3iw3zzno/yj7W/w1Nq5kc//iEAbrcbl8uFoig4nU6sViuDg4P09fXR29tLV1cXjY2NXHfddcycORNRFDX7Lo/HozGLAoEAw8PDFBUV4XA4zqgAgxpciEQimvixak95NiHdPSqDDE42AoEAzzzzTJI1mGIiqs5mwEGleop8IACm0090PlLZN+mfaeVl0fCU25cVhe3btxMOh7VndXBwcEIAU80yGwwGTYhvqudalmVGR0dZtmxZpuQigymhKArRaFS736xWa8qRS6ex384GvaEThUyAIYMjQm1s1QxdcXExF110Ebm5uezdu5fm5mb27t3L4OAgb7/9NkajkfLycgoLCycsf6IQCoX48Q//mx2vb+HHq79CZW4Z//3Cb5hfsfKEbieDU4NQKITP56Onp4fW1lZ6e3sZGBigpaWFhoYGBgcHk+r0Bpmfvv0H8koLuOrma6itraW4uFiznFQbcvV+CwQC6JzuKWn86eli7S8FDDYb9orKiVmPo7x9pXAIKRoFTgxletq0afz0p79kbGxM01QA+NJ//Af7amYg6kRyV1zMtqe9fPRLX+Gy5efx8VtuIS8vD5/PRywWw2g0YrVaMRqNh8zkpOswJBIJ7HY71dXVXHbZZWRnZ7Njxw52725gy9ZAMgsvJEsQBEFMLa+eYAEUgVAoRDjiRyf6WLPGyfwFFqIxBW80zto9jWxs7+SeR/+F3jBF9zNFWzHc0008OE5u9TR0OlEL8EiyRF/IyzWPfW/ySpB8Xq2+/1QjeZ4FdDo9JrMZBYVEPEEiEccYjhH9yU8xGlThSQUQGBn34qosJis/f+LKhoZYNzTEVZ/+HJFgAI8osHnd6+94HxsbG7n3V3/gjht/TI57alHRybBbXeR6Ctmy71V2/LNeGyip91Vubi51dXXE43FcLhdbt25l69atjI6OArB9+3ZKS0u151VRFGKxmCa0qg7yw+Ew7e3tuN1usrKyTnugYXR0lK6uLvr7+5EkCZfLRXl5OVlZWWdVdjSdvXA2MS9UTGW3PdX7Q32uHu/hJpEZnFjs2rWLn3znR1y3YDXRWBSL0YxPEBBIstxIXRcBIVlyBwechxQFWZGRkQ/8rSip98lXQpEYj4e5bMaKKbe/tHguO5/dyjN/+ie7O/cm25x4jJkl05lXOStVSpF0qIpLceKyREJOoKCwqy2plTOnciYArX0dhIjywCN/Oy4nrAze/ZBlmWg0SiQSwWazYbFYMBgME9qfMxnp7eWJ6CMyAYYMjhm5ublcfPHFzJkzh8bGRt588018Ph8NDQ1s3LgRg8HAJZecnHKFffv28fXbv0KNqZRfXf89LMak+n9PYJDy8gyD4WxEKBSip6eHt99+mxdffJGmpiaGhoYIhUKa2J0oiuQU53PppZdy/vnns2zZMvLz8ydkNKZqDAWOLVsnQDKrfAyI+8YY+MvvkPc2YLFaKfncZ45p+SPB7Xbjdru19zZrMnOqE3VYc/OwfvwzJG78MOteeZ4Xbr+DeVWVXLh0CUuWLNEmboezkVUDDLGUzacaICwuLmbZsmXs27eP7du3MzAwkAza6JL6Bmpd4YQMlKKwb98+tm7dSl9fH48+6iM7JzlBNC5Zhusjn8LjdpOTk4vBeHR2TcG//B5xXxOl/3kXukmijRWHWKb3O1/GZrMd4ttTA71OhzllT5pISMTjCexGCw98/G4Kc/MBhXg8aSP6f+v+H0s+cjHXXXfdhHW8+eabfPa3f6TgjjuJDg8R/vkPT8i+1dTUkJXjxqA/NoeNj1z5JZ7cfN+UGbysrCzcbje5ubnMnz8fq9VKQ0MDPp+PQCBAfX09NpuNCy+8kIqKCu2e0+l0mM1mjEYjiqIwOjpKd3c3ZWVlZ0SpxPDwMA0NDfT19WkBhsrKSrKzs8+KQeNkJF1WdGdVcAQODiKkO+FMfj/5s/QBvt1uf09ZxZ1uLJ4+n9tWHt6J52Th6vmXMXuwhlvu/Tzvm30RDpONbHsWH1qx5rBsrX7fELePfJ9AbJxb511NTUEVn33kO3z29tszwYUMDgmVwRCJRDQ2H0A8Hj9qTazTjfT2MhNgyOCUQxRFjEYjbreb6upqzc99y5Yt1NfX09vbyxNPPMHo6CgLFy6kpKTkHQ9oFEXhsUcf4zc//SX/tuhGLq07f8J3vWMDmsd6Bmc2VCp0T08P7e3t9PT00NfXR2trK3v37sXn86HX66mqqqKqqor8/Hw8Hg8lJSVUV1drg3vVWvJQjeAxN46qmMDBxgSHRKi7k6H/vYt/v+5abv/zfaektm7D+vXYZi+eYBupt9kpfP/1JC57H81vvsauF19h9sZN3Pvzn5FIJPD5fIc8H+qAXWUwqMEatdykrKwMnU6nWfSpz/9ki0l1PTNnzmTBggVs3ryZl156iaefClBaloziJ+IJgsFxFAVN4FAQhaS9pU5Mal0YDZg0xoVanJLMeJ0N0zlVT8FgNOJwOpBTg45EIgGKQiwWTQXNhBQjA0i5R8gpG9UJ6zoJsFgsvP/a97Fl2+tccs6ao16uorAGhzGHN954g8WLF08INKgUZrW8YenSpfj9frZu3cr27dvp7e1l8+bNVFRUkEgkMJvN2Gw2YrEY0WhUG9ioy8uyTEdHhxYcMxqNWsnPZOr0VJoeJ+rcRaNRfD4fkUgERVEwm8243e4J9rNnMtIn2SrSs/xnOrxeL16vV9PqUK8/TF2ONvkz9b26XMae89Th5N1jSqo078B9rbY/qjaQijsfvYsbFr2Pr1716aNe+yNbnuaSqy6jvLqS3/72QTxWF7WL6rj1Qx88wceRwbsB6WOgdIZYev+QHvCUJOmMDDhIkqTZR1utVm2MfbzIBBgyOG5YrVbKysrIyclh+vTpVFRU4PV62bFjB48++ijd3d24XC7y8vK0TOrxIBgM8oPvfp/WTU38z1V3UppVNOH7keAYZqs5Uxd3liAQCLB//342bNjA2rVr2b9/P2NjY0QiEWRZxmazkZ2dTV1dHatWrWLevHlUVFRoEw91QnFUOImD6NjoCIP/exd3f/0rvO/KK0/adiZjbt0sdvf14GHxhM8FQcBgsVB02RWM185k7P7fYrFYtBr3Qw2sJ3eIanZPHbAVFBSQl5c3YVJyuImV2oE++uijNDY20tnZxJhPQpmWnEQnpESy5CW1PZ1ORKfTp2pgk1acep0uJYBI0llBZELA4UyHoiiYjEYMBn3K/cSX9MRWZP6y8TFys3LQ6XQYBB0Okw0plc0/3IBcNJnoGx6mesFCFFmmrraWJx/++3FPcm+48Xo+9q/buGjhNcfUNs8oXsQXP307Jr2BeQvn890f/SdlZWXa96r2x5IlSyguLsZsNtPU1ER/fz8jIyMsXryYeDyOw+HAYDBoTCV1suByufB4PAwODtLW1qbdkw6HA6fTiTFlC2swGLS66XQR0BM96Y/H44yPjxONJgNDqm2qxWI5awIM6vmFAwPdsyXAMDo6yv79+zV7YpMpqW+S7ooxuQ2b6jP1nnk3Bhjq6+t54IE/sHTp+dx44y2ne3dOPhSQJYmElEBO6TYcKHsRtEq7h95+ghGfl09f+pGjWq0sy6xtfIuNg7t49Mv/wul0YrFYsNlsXHDBBSfveDI465Ee6Joq4J0eXDhT7SplWdb6OoPBkAkwZHD6oFItVdGrOXPmcPPNN1NeXs6mTZtobGzk0UcfpbOzk/nz51NeXo7dbj8mkZOGhga+8cWvMt8xnbuv/w+M+oOpjb1j/RMGuBmcGVAbXJ/PR19fH93d3XR1dWn/t7W10draSigUwmq1UlVVRXV1NSUlJeTl5VFZWUltbS1FRUW4XK5jFsdJ1nYe144f9uvY6Agjm94i/trL3PmxD5/S4ALAtOk1NJrSrGCnPEgFQRAn1LarE4zJUDs+Vf04FAoBHNQBpothmc3mQ9rdqde9pqaGa665hrVrHWzfsYmEsZ34/r0IeqM2ABQEEK129HkFSFKCeDxOPJ4gHI6kOmmIhCMkIlHGfGNYLdakTaaYymCiMLkKRlEUJEVmaGgIWZYJBAIaa6a/vx9AY2fodDrNtURtz9RMuerOoTILCgsLqaqqorCwkPz8fC0QM3VpTup8CSI6nYgoiEjIxGWJJqGbqvPm4vF4UBSFF558juY9+3i8620eeuY5YtEI58yo5dylS2lqaiIWjQJgcDiZ8bsHIBXo2fedL9PU1MTMmTOnvK5HQk1NDWVVxTR1bGdW5cKjXm5orJ9iYw73feIuntr+Mh+6/hY+99XbKSoq0kSs1Mm3KvxYUlJCX18ffr+fjo4O3nrrLWbOnElFRQVGoxGbzZa69nH0+mSwKScnB6MxWcKh6jSYTCYCgQBDQ0OMjo7i9XqxWCzY7XZEUZyy9n7ywM5qtWK32/F4PGRlZaVsZy2HDLI4nU7Kyso0zQW/309XVxc1NTVnxSRdPS/qfXo2lkgoioLH49GCUipTbHJgaXKQKf29ymA41TbaJxKKovDGG2/w2GP3EwqPI0kJfGM+fL5OLrzIxi9/+SoXXHAx+ZO1XN5tEJL39bbW3fxl/aNEE3Eun72Cm5dejSgIhGJhvvvYz9jQso2f3fBt7OYj6yIN+Ia4Z90D+B0xfvWH32hliTfeeOPJPpoMznLIsqyJ3uv1ei0QrkJl9un1emKxGMPDw9r4Iz2xo9frtdfpxIliuGUCDBm8Y+j1ehwOBzNnzqSqqoqysjLa29vZsGEDPT097N2bFNex2WxJq7+jmCgqisLfHnyQP/7yPj679FZW1C495G97vP2UV1eeyEPK4ATC6/Wye/duNmzYwBtvvEFHR4dmNynLMi6Xi+zsbBYuXMgVV1xBXV0dhYWFmg3cOxGbOVIjGfOO4tuzk8T+ZgTfGNLQwGGXkWMxeu/6HjesPJ8rv/8fLF++/Lj2651AlmUGnn+axJuvoTMYYOYcXEuXIYg69DY7ers9KZ4F2qTscMeUSCSIxWIEAgFCodAEm6V0qJF5m82mTQIPd12qq6u5/vrr0el0tLW10d/aTPB3vwTxQOBHUUjafRqNmJaej3HRUnT5RQiioIk5hiNhEuEwY96xlEVmcuIuCKnrq0wMNCiKgizJDA4OMjo6Sm9vLz09PezZs4f6+nogOWlUM+0+n4/BwUHNVsput+NwOBgfH2d0dJRwOEwsFmPJkiVcfvnlnHPOOXg8nsPWcSuKkmReqKUdoggoGEU9breb+fPnM2PGDAoLC6mpqeGmWz6Mfs5qvFdeTairnWfa9vPsS6/jbW4kIoMaPlWtUQXAsHgZTzz9zHEHGABu+dCN/OVXjx5TgKGpdTM3z7kYvajj6nmX4TY6+fG3f4jJZSU7P8lmKyoq4rUX1xIPx/AUZFNaWkokEsHn89HR0cHrr7+OKIoUFxdrQljhcFjLRJrNZqxWK3l5eQedV9UauaGhgYaGBux2Ozk5OQfE4SbV4quZ72g0SiwWIy8vj8LCQqqrq6mpqSEnJ+ewmRq3262VZYmiiM+XdPUYGRk5ZNDuTMLkgMKhMmxnKtT9z83NpbKy8oj7/U6/PxMhyzKvvvoqv/vdLwiF2rn4YjsulxFRJ2A0ilRVTUenE/COdvLQQ/dzxx1fPd27fJIh8OT2l/nJM/dw8znvx2Iw88jGp/jdaw/isjoYGBvinPK5/P0zv6E0p+iIa2sd7OTbz/6c9910NZ+7/fPvOHObwXsLsiwTDAYJh8O43W7sdvuEBI1Op8PpdGIymRgYGGBsbGxCoFSn02mOE4IgnPYAw4nCu+MoMjitUDtstVY2Ly+PWbNmMTQ0xMDAAG1tbTz33HP09fUxe/Zspk+fTklJCU6nc8LyKnw+H9/71nfp29XB3dd8hwJX7mG33zPWT9nS2pNzcBkcNdSop6q63t3dTW9vL52dnXR0dNDa2sr+/fsJSBKCqKOsvAizw8EVl1xCQUEBlZWVzJw5k/z8fGw224QI8PFAEAQSoXHC/b0Y3VnExkbxbn4bY04usf0tKM170Pl9LFuymIvOX0J+fj5/Moms37ie+JYNKIMDiGYzusJiMBoRJJn42CiXzJvDj/5zsnPBqcMHP3A9NeVlzJo1C71ezyNPPMkb9/4CRVEYHBkhGo0AcPXq1RMyeYeCmrVXO8UjCfwIgkAwGDzs+tTOs6CgQNPLEAUBXSTErFmzOO+883A4HCQSCfx+Pz09Pezft5u+rW8T0enA5QGrFTGvAKmvBwWBcDjEqFfAEDAcqMGewGBQs9cwNjbGo48+iiAI+P1+fD4f/f39DA0NoSgKfr9fyxREIhHtePR6PWNjY5hMJmKxmFaPKEkSzc3NCILA3r17ef311yewOABNTLOlpYVXdr5Jx0A3kLRdHR8fJyElCMUjtLa28vjjj3PRRRdx8cUXs3TpUnQGHcbCIhzTanFMq4WLkuuM//NhwhvXT2RppC6L+7wV/OPuH3HbJz/B+Pg4fr+fQCBAIpHQtAzcbjcOh2NCeVG6dsbll1/Of/3nXQwM92K3uFKTcikt65+ykUNBQECWZfoHO5iZV83AwCCyLDPNVcp/XXQHb7ZuobFrPy9uf5ZALMjKisVcM+cqfv/2wzSNthKRkiUgfX19bNy4kfHxcTo7O5kzZw6zZ8/GZDJhNpuJxWJaYKi7u5uRkRFGRkY0wdfh4WEGBgYYGBigv78fk8mkWUZO5SagMhfSXVKcTifbt28nNzeXgoICCgsLmTlzJrNnz55QcgFJUcDi4mLcbjeiKOL1emlpaWF4ePisCDDAxOdfLRFQRV3VzP5Uz/sv/udu/vznv+J2ebR7Q5IkQuEQF1y4kt/c++uTvu82m438/HycTudZFRg5EZBlmbVr13LvvT8jHu/myve5WLCg+jB6OjKRSPQU7+WhcTIZPr997QG+deXnef85lwHwqUtvZXPLDvyhALPLainwHB2LY+P+en7x5p/42ve/yfuuet9J298M3r1Id+ICDmqn0udITqcTg8EwIeirOiqpItrvFivLTIAhgxMKURRxu93U1dUxNDREMBiku7ub/v5+GhoauPDCCwmHw9hsNhwOx0Ed5fbt2/nmHV/nvNy5fPW6b2HQTZ0lDISDJGQJj81F1/gAy6qvOBWHl8FhoDayQ0NDbN68mbfffptNmzbR3d2drLO22tCVV2FbcQmRfz3E+JyFhEZHMVpt3HbbbZjN5gmUsXeKkpIScjofQvrNz+gaHsZus3HNhSvpa97J8gXzOe+2jzBjxowJk8QZM2awceNGnE4ntbW1hMNhWltbicfjiKKI2WzmvPPOe8f79k5QV1dHXV2d9n7hwgPZ53RLtqM9hypLxGAwHNZ5Qe1A1clsPB6fcgBpNBqxWCxYLBays7Nxu90aHVCv17NgwQK+8IUvUFJSQiwWo7Ozk+bmZtra2ujo6GDPnj00NzcTCASIxWJIej2C0czI/X8gdM1NiCbjQVUhk/fC5/Xy0EMPTTgn6v0JySAmHLBEm4z0yap6HltbWzXRQbWeW800yLKM0WjUJsjBSJDtfY0a9T8UCiXtO2NhWlpa6O/vx2g0smjRIux2O/f/+Y987n9+jnL9LRpLIbkfHLJkx1JYxIjdwbp168jNzaWrq4uuri5NMLG4uJjKykqKi4u1fZtMizeZTKy8cDkb97zCuTMvJx5P4A2MsK9rJ11D+xkZ66Eibxbzqy9AEKB1oAG73oIQkukL92lnX6fAytJzWFl6Doqi4IsEcJsdCAjcfs4H+d3mh1nXthlJkujt7dX6g+eeeoY8Ty6SJDNz1kxu+8KnmVWXDE5v27aNN954g8bGRvbt26dpVEx2B1Cv12QcbnKTXr9fXFxMWVkZ119/PdOmTTuImWO32zGbzbhcLkRRZGxsjPHxca0E52yDeu6i0SjRaBSj0XjIeuCtW+u5YtGHqSmbk2INJc/ZkLePl/bcf0r2Vw2Onc2lDccKSZJ4/vnnue++X4AwwJVXupk/v+rIbbqg4+8PP8D06bVcf/0HTs3OHgHHWah4RIxHQsworj6wHUFkyfQFR7Vsr3eA+956iJ19TZhtFn71p3uYO3fuSdnPDN4bUNvVw429dDodLpcLl8s14fNYLMbAwAChUOi0u1+dSGQCDBmcMKgPlcfjYdGiRZjNZvLz89m9ezd79+5laGhI80XfuXMnBQUFOJ1OCgsLqays5KUXX+Kff3mYO1Z8nKXVh+4oZFnmB8/9it39zVw8/Tw27tvG18tPX0b5TMVUquHqoDz9vZp5VQeZRxrEqOsNhUKMjY0xMDBAT08Pw8PD+Hw+enp6aGlpoaOjg+7ubsKiDn1tHVlrbsYxe15SCX7VlZgtVgyJGP/48XdZfeklrFy58oQe/wUXXMCbKWGmo1XtLSgo4JprrpnwWXV19SF+febheIIzU0XaDweVPnoosTQ1kKBG6dV1yrKsZddVcT6AvLw89Ho9hYWFzJo1i8WLFzM4OEg4HCaRSLBhwwa2WV3EvV78/3UnYnYO6HTIfj9KOASyhBKNordYEVIMDHk8iNNuJy8vD7fbjcvl0kQCTSYTxpRLxaHOV/rzEQ6HCQaDDAwM0Nvby+joKD6fj3A4POF+Uo9bkiTi8TixWEwrA1JZEJAUDQwEAmzatIn77rsPl8uVrMEcG6Pl+adxLFyisRT8/gCxaDSpHTFFVCUwvY677r6b+TNn4vP5GBsb086x0+kkOztbG9CoQREVqvDfmN/LW3vWM6NwCXvaN/Pqtr9T4sqn3F3EovK5PLb7RcxGO7UlC2jq3MI5JXUkNT4ERFGHXq9Dp9OjE0VEnQ5FkXHKDo0BYTAY+cLyj5Bry2JDZz1SahBW5Mxjcclc5pTMYFpBOU2DbXzhk5/hoqsuQxRFjfU0ODhIMBjUnmFVBFItcVFLWqYS9kt/qW1fJBIhHA7T19dHT0+Pds5efPFFRkZGWLhwIQsWLMDlcmmU1RPhCX66kJ49M5vNJBIJEokEu3fv5oEHHtA0RbxeLyMjIxOeiZ6eXopKFiDFQEIGJEAgFo3j9Xp5/PHHtftIrUFWAxfp208vX1FFOmOxGLFYbEIZnCAIGtNEfa+WuJSWllJaWkpFRcWEUomz9bpEIhF27NhBLBbTrkkikWB0dISHHvo9ZvMo167xMGvWUQQWUrj55lJC48388pc/O2MCDCcLHruLlr52phdWHfOyu3v24nfFeOpvz2nlWBlkcKxQEy7qWEBlJxyO/Zme1JAkSdO9isfjp3LXTwkyAYYMTjg8Hg+LFy+moqKCqqoq8vLyGBsbo6GhgW3btrF9+3b0+mQtcmlpKXV1dQx292MPG/nlmu+S58o55Lr7fUP88a2HMZY6ePmxV3nm6WcQtzsyFpWHwVRCZ+l/6/V6TZTtWIS/xsfH6e7uZvv27WzYsIGmpia6urrw+/1Eo1EkkwkxtwD7NTfiXLgEh9OpiTWKoqjV2Wd/6gt88bvf44VHHj6o5vpE4UxU7D3boNZCWyyWw9aopmf+0ydm6j2oTrbV9y6Xi5ycnAl0wXSRobvvvpuO4SD2i1Yx3NxIaH8LciyKmJWNPr8Ig91G8Pe/pjIWwul0Mjo6ypAUp6CggBkzZlBRUUF5eTklJSUUFxdrYnF6vX5KUbjJ1PqhoSH6+vrYsWMHmzZtoqmpSbPsPFwGXS2xmCCYlLLJkySJLVu2sH379onnZ9SLM79IYzGEA37i0Qh9fb1Tnmu5uIzNf97F1rfeOoiNkX5sKkNFDeqoz79mjYmB+1/6OaHxAb68/KPMzq/R1lPsyOMX6//K5qZnCMWCfHLO7QCIok7T1EkGbQwY9Aak1DVOxOPEtf91fHDhNdw87yqUdAcDIZnh9Hv9FBuymWWv4t5770UUxQkshXRWTlZWFtOmTcPj8eByuSgoKKCoqEgLGqnU0/SXak+YSCQYGxtjdHSUzZs34/f78fv9jI2N0dfXxyuvvMItt9yi3Y9q1nzyPXK2iSRCMsBgMpmIRCIkEgnNOnT69OnU1NSwf/9+mpqatICzoigM9Y0yzTbKqHUESGmmCAL+8aRo6iOPPKIF0lQ7SfWcTrYPTC9RUTVOgsGgVqKhvtQgxeRg0cKFC1m0aBGXXHIJ5eXlZ3XQB+Cpp57iZz/7KtOnZ6PTob0MBoUbb3IyY8axT5wB5s3PwmCYcYL39vhgNBrZ3LGDm/96OwIC5a4i7rr2G7zSsB6bycK51Uev/TIZ5VlFNPW1cAUXH9fyVZVVZGVlHff2M8hAkiStNNHj8WgBhqOBOsYIBoMaI/TdNk7NBBgyOOFQBxROp5PKykpkWcZisbBnzx5aW1u1utqxsTGi0Si9+7u4ae6V3HjuVSiRBCPxpMKqwWjAaDAiCwpPbn+JV9s3MRL1ce2Na/j05z6DyWTiwx/5MBydA9G7HsFgkOHhYY1yPjo6elA9cvqAXR3AqzXzZrMZi8WiiS5arVYF1WveAAAgAElEQVTNfSAajRIOh7Vsrt/vT07khobo7u6mvb2dwcFBfD4fssWKLicf99U34F62ElNKxE3NHGtZ7dTY0DljFv0rLuWLd36TB//w+7Nu4P5ewdEyHSYrEKdPwtOp6Wp5gSRJmkVp+nLqhMNoNKLX67HZbejr5iDVzkpR0xUgNXkuKGT1rOlUV1cTDocJBAI4HA7y8vLIysrC4/FoTAaLxXIgyHWILGj6M6LX6zWngaKiIgYHBxkZGUkG0VK6AJPXk/6ZOpkfGBjgibWvoriziA8NkohFSSQSICWSzAu9HmFkGP8d/47F4gRFRpQkTIJA9D/uRNHrUYxGFL0OQZIQI1EIhbDEZBw5OZSVlWk6GoIgMD4+js/n0yZ8iUSCcDg8QQBRkqTkuUBGlwjwk6u+RkVOCXq9QTs/BQX5LJ+5hLaRLrwhP8unnQMoiKIuxdrQodclGVCiyiBJs0ZM/n8goBSNJmv/4/E4iXg8paOhoAAV7iIMgg69KY7JZKGsrJaysjLKysq0tiMrK4ucnBwt0OV0OrX6/KmsCtOzRpIkae1YaWkpCxcuZMuWLWzatAm/3084HKa+vp7777+fOXPmUFdXh9PpxGq14vV6tfZSvSfPpgluQUEBixYtorm5mdbWVoLBoHZOhoeHGRkZ0cROVeeJRFQhnogTjiR1XUhql6Z0RULs2rVLC5iFw2FCoRDRaJSI+nsOPPfp953KcohGoxMCQYIgkEgk3WQmB7xbW1s1Bfbt27ezYMECFi5cqD2bh9YlSLYpamDzSArp6VRndXlFUTQGkLqv7xTDw0NccqmHa6999yZHFi9ezMtvvgpAf38/H7z2Zr779C/Y0rMLJaFw740/pCL3+I5/UeU8Xm/YcHw7dha4v2Rw5kFNkKgMxVgsprHq1PHy4YIEkiRpbaTavoiiiMvl0vqWqZI36WP3E9X+nApkAgwZnDRYrVbKysrIz89nzpw5NDU18fLLL7NhwwYikQiDg4MkonGcznw8egf9AwOYjUYMhmQNt81qJWqK8cOXfoO7Np9v/+/3mTdv3lnzcJ1qBINBWltbef3113niiSdoaWnR6NKTqeDpdm7qQM5ms5GdnU1ZWRk1NTXk5eVhtVpJJBL4fD68Xi/Dw8Oa5aQaIEokEigmM5gtGOrmY1t+Ia5FS3C4PZpdjygKmqr+VMh//3Vs//l/cc999/HZ227LXOOzHJMH++kTPjVwoJblhEIhgsGgRhVW70k1465moO02G3abPbXGiaqHitvN6tWrWb58+ZQMismvY4HRaMTtdlNWVsaSJUu0jl61VUx/viYHGNQJUywWo76+nlc2b8XyjR8Q6OkkMTiAEo1APA56PYLFiuwdJfHs49x67ucw6I0kJImEFCWeiBOLR4gmIkhSAr1Oj8lgwWH1MBLsZffgayxfvpzCwkLtnPX399PV1UVnZyednZ2aS0g6M0AQkorVRdZc7rrqG5TkFGKxWLBaLcmyB52IGgmsqjg4oyokL+6E9wcP3VWnjySCwQA+n59QKGmzp8gHruf5FYt4YMfjfORzOv71z5Bmdbpy5UoteDK5tGXyNT2aEi9FUVi6dCmyLPPAAw/Q1dWlBWB27NjBnj17WL58OSMjI5SUlFBQUKC5RqiDwHei9B2LxXj++edZvXr1OxazPVoUFRWxZMkSwuEwbW1tRCIRIpEI3tExutr7EIWk00nyKibLeSQ5zhMb/oDFZE99n7zCsUSUYHiM4YZ+bf1H0rxQv49EIpqehnofpmtZpPdN6UHHzs5Ourq62LJlC3q9nn/7t3+jtLSU3Nzco9JnSCQSRCKRg7aXDkVRtEBEesmGLMvY7Xbs9mT7cyL6J0EQCY0n8PliyLKCLClIsoIsK0hS8qUoIEnJz2RZQUooyMqB71FAkmQSkkIsJiNLCjt2juJ0nBllfSrjCJIMmoLyQi7++FX8+ro/svaVtXzjuz/iproruHbBqmM+pxfUnst9ax9EliVE8dgzv2dTcDCDMwNqvx8KhfD7/cRisQnMhSP1CSrjwe/3a4Fql8t1RMe09ITg2XTfZgIMGZw0qJMJk8mEIAhUVlZywQUXUF5eTn9/P4ODg/T399PU1MS99Q8jxBXOL1/EysrF1ORXkUgkeGz3C/TjZensC2hqasLn85Gfn09+fj4Wi+WgiGL6/+kvtb5RpQQrioLJZNKU1j0ej+ZccLbRlILBICMjI+zevZt169axZcsW2js6iRjN6AqyEW125KFBBDmBXgFZEEjIMqLThWgwJLNYkQhjAT+h/n5CoRA+nw+73Y7RaESWZSKRCKFQiPHxcQKBAGNjY0g6HTjdWCunYT3/AhzzzsHodGn190ZTKuOjE5OBhcO0i4Iokv+pL/DzO2/n17/9Hd+78xvcevPNp+4kZnDCoHaAwWCQsbExhoeHicViAAdNEAVBwGg0ao4S6RNfleVgs9kIPPA3en1edKWVWCsqsZSUIaZREWVZ0tg1qgijGsR4px3yoZaPx+O89NJLRCIRrFarNilRFIWamhrmzp2rTVAkSaKmpiaZbbVZiJeUEy8oQpEVrUxAFcbsbdpNx2gjqxbfOKGUIJnoP7gkIyFPY8cLr7Js2TIKCgo0BoP6nHq9XsbGxjTWQPqkrre3l+1vbuFHq79MdV4FBoMhNVBSM7UCh31wpzpfU32S9vibTGaczqSWh8PhIBKJHmA0JBLYjFaMRplVq7Po7gowc+ZMbDYbJpPpiNaoxwL1XC5YsICPfexjbN68mS1btjA0NITX66W1tRVBELQB5J49e4jH40yfPp158+Yxbdq0o+or1IBGa2srP/nJD9i7dxeDgwPE4wl27foitbW15OXlUVJSQklJyYTSnROB4eFhhoaG2LlzJ9u2baO3tzdZFiPocFo8zK1cTknOdByWpBhrko2S3IdnNv0Fc7ZCYWHhhIk/oLlxqM+0OsCeKgg0GVOV86hQB9LqOtRM4datW9myZYv2fuPGjZq/vN1u1/od9d5W+3sV6vujYTCkBxjSxwtquzK5DUvX/1D3X92f+fPnM3/+fO29isrKSv7yF4Vt2/oOCrqmv1SmUPL7ZH+q1xvQ6/Sp4GuyvTOZLOj1BmqmC5xzzukVIp4KTqeT5156QXu/+orVzKqbxS033Mwf33iYZ+74yzGtrzK3FAToGOqhMr/syAu8S/Hqq6/S3d2tvU+/ty+99FKKio5s05nB1FADjqrNcXr7YbVasdvtE5gLR9tmi6Ko2VKq4uaHK/lKarOMEolEyMrK0hz4znRkAgwZnHSo6vslJSUUFhayYsUKZDnpU9/Y2Eh9fT1vv/02u3fv5snmtbyw702yrW4uqTqXfza8REwv0dnbRXV1Neeccw7z58/XfLHj8bhmAxcMBhkfH5/w0tTbU7TYcDisDbKdTiclJSVUVFQwbdo0LVN6NgYY2tra2LRpE8888wxN+1oQyipxfvQ2LCXliDoRZIXEQC+ydxTRZsdQVIpoNiVzVVJyMBYbGSS88S1631xLT9NeGA8mqYR6A+j1yZcogiCgL63AfO4K7AsX4ywuxW5LKq0bjQYtm5BsK49+kGx0eyi68/tEhwb51n/fxfLzzqO8vPyknLMMTj5UB5mhoSGN8jyVJZ4q+nYoN4ePf/zjnHfeedTX17Np5y52vPkSbT09GPMLEcqr0JVV4Wtvw+fzMTw8nGQ/2WxYLJaTamv33//9PV5++SHKKzx4PEYsluR939LipahwKX/4Q9LFQm1PqqqqsNqsmCwWxmPJLKmEhICAIApYrVby8vIwfuw2Ntz1A25d/ZmjypILAsyuWoyiKJx77rna51NZNqbj4Yf+ztsvvMlP19zJvLI67Xk98NienPNmSl1vSGZo/X4/Y14vIUBKJDDqDUiJOCtXFvCrXzYzPDxMdXX1YSeF7wTz589n9uzZFBYWEg6H2bNnDz6fj87OTrq7u7V7SKeLIUkSOp2O2tpaampqjur6KIpCIBDga1/7PB5PCzfdbMZuc3HXXb389re/Zdq0aUybNo05c+Ywb948bZKcft/OmDHjuOvFBwcH2b17N1u3buWtt94iEAhg0luYW7mcc2tWYTXbEQRRew5V+1adTiQ/t5All9SxbNmyCewcVTtFZagZDAZtwKwuf7xUXpUxoPbHwWCQYDDIPffco4kiSpLE5s2bqa+v19oUtVRCDRCo/f3k++Zo7qP0QN7koN7kUq/04IBer9cCZap44Kc+9SktuJgeYFi1ahWrVu055vPzboIgCFj1Zr6w6oPHtXy+O5ddnY3HHGAIRkNY3wVq/Tt27OArn/8SK0oXkeM40D4ICGxo2UokEuG22247jXt4dkNlLEQiEXw+H5FIRCsldjqdhy3NOhTUYKwaoD2a5ROJBENDQ/j9fkwmUybAkEEGKtI7ZrW2EyA7O5vp06djtVopKSlhxYoV+Hw+BgcH2bt3L8/vfwsMSVq91+tl3759mm3g22+/jc1m04SjotGoltmY/JrMZlDrMFXrsaysLLKzs3E6nalJslHTDLBarZqlpqperirSpyvRnw4EAgH6+/vZs2cP69evZ9u2bfT39yO6PRR85TtYc/MmMjLyclNThtT+prKKSfqljJydjTythth1NzHeuItYbzeKAqLJjM5qQ2+3o7c7MBYUYbA70KVqr9VzlRxU6hDE4z8ftooqbBVVRHZfxLo33uDDmQDDWYvR0VF2795Ne3s70WgUj8czoZ5+qjKKqWAwGJgzZw61tbWsWbOGRCJBKBRi//79NDc3s7N5H1stJiorK/F4PNqkQA0sHgrpbhdqbfWxQK/XcfMt5axcWTDh8507bWzadHBgQ6fTISBgNVkgx0gwENTcERRFIRwJMzo6iq12FkJZGU+tv5/rLvw4cORsdl3ZEp558nluuOGGI+63LMu8/PLL/PmeP/KLa75JSXYhJyuYMCUEQWuHBCHJZHC6XChANBpJUsBlCaMRrl3j5q9/vZfZs2djSWm5nKj2Vl2POjGcNWsWa9asYebMmezdu5e9e/fS1NREPB7HYJBZfr6dnBwdsVgv999/N48/8QArzl/F8uUrMJvNB9Xsq1oEwWCQl156DtjFpZdlIUlJll3tDB2dHV6CwW3s2r2TvXuf4oknDClNHBGz2YTZbGb7jj7+/bY7uf32Ow57POrEtqWlhebmZo250tfXR29vL3v37mV8fBxJkjDrLSyefglWsx2bza4xAJKTZZWqK2IymZk+fTq1tbXasan/m81mzfpU1fGZLJw6uSxvKmcjFWpAQLVUVZ9jtW9ZuXIloijS19dHf3+/VvqjTgBU6rKKWCxGNBrVnnN1W+p+qoyEyddNTU6oy6mMBNW1JF1fJF3LRGVLqNdBZVS98sorBINBbfwwf/58jd30Xi4FlGWZ//j6t/lAzeVcNHPZca2jPLuY5v7WY16uebSdi+ZedVzbPFMQj8f5+K0f5XPnfoj3zbv4oHbRdAiL9wyODFUXStVZkiQpVTpo1cYLxzNmSMexPv+TS8bOBmQCDBmcNthsNipSCu/nnnuu1kl3dXWxceNGtm7dSn19Pe3t7RrVt6OjY0r65WT65uH+TsdkmqNa0mGz2TSxw/z8fIqKiigvL2fatGmUl5dPoF+frgCD3++nubmZ9evX8/TTT9Pa2prMnBSXklVaht1uw2g0HTUjQx1gxWIxwllZxBMJUEAUBXQ6vTYQ1+v16PS6VE0uGsX7REIsKeeV9W/x4Q996ISuN4NTB7Vsp62tjWg0SklJCXV1dVRUVGAymY55felMB7fbTVFREStWrDjodyqjKRwOE41Gp6y3Vksz1CyjTqejvb0dWZapqqo6qo5fEKb+jQAEAkHq6+sP+s43NITw1mvkXbwKnagjGosiR5KTlFAolKoRlym89aO8dNd/cdWyD6Um1eqap8aM8vk8988HGBwcPKITS0tLC3fe/lW+u+p2SrJPP33WlCqlisVi+Hw+zDojo6N+JEli0aIcHv1HUhNh9uzZJ9VOrra2lmnTptHS0kJTUxNPPfUUbW1txONxTCaBJYstFBQaEEWBiy8W6GgP8de//onHH38Qm82O2WxNMQ90FBdXI8syLS0NjI+PkkhE+MpXc/D5xlAFKVatsgIHjkctI9PrBQwGPdnZbnJycnj47woul+eI+5+uIfHkk0/S0tJCW1vbBLvUA/2gwngkgMuWhd1hJz8vf8pyGKvVSkXKDepIpQXpdcKTLWzTv5v8mYqRkRFGRkZwu91kZWVpAQ/1OV25ciVLly5l165d7Nq1i9dff52hoSGNlahOCNTl1HrldPcUldpstVpxOByavan6nSrGmrzmpgm2nm63G7fbrYlTquWYaumFqt2g/q0GPF555RXWrVtHXl4eBQUFfPKTn2T27Nnv6eACwD2//g3yQISrr7nsuNexqHwOD7/9JJe1rUQBSrIKyHFlT/nbQf8wG1q24bG5eK1pA5+u/cqUv1u3bh1jY2PAwUGwCy64AI/nyM/iqYBOp2Ppuefym/X/j2BsnJuXXD3he3kSCyqDo4csyxorWtUpcrvd2FKsl8x5PTpkAgwZnHKkP5zpk19ZljEYDOTn57NgwQJycnKYM2eORg0aHByks7OT4eFhxsfHSSQSE+oW0yfA6ZnJyS+VRaHSnlSbGXWArwqxRCIR/H4/Q0NDdHV10dzczLZt2zQWg0oHVSc+6QrTqmhiTk6OFqBwOBzv6LypA7e+vj727NlDY2MjjY2NNDU1MTw8TG5uLhUVFbTEJKxWCwZDyrEhxSiIDg8y9PS/yLv2RozugztJQUlmjJI0TismWUpKfgkCoiAi6sSk171KfT2Jjazs91FTkWEvnM0IBoN0dnYyNDSEJEkUFRVx/vnnU1dXd1SibCqOlumgwmAwaNlutWbycOuMxWLs37+fD3/kOvLzzBgM2XzqU1/i0ksv1YIPUwUSDQYz+/b5WbEif8J6c/PMxGOt/NePPn7QMkNDAwz/6be0//X36CxWLTsiSQkwWxBEHUG9HnNOLoGQjwde+DWfeP+Xj3iO9HoD0wvn8dxzz/PRjx7eVmd8fBwQmFc264jrPRUQEJJBzFS7MiOnmr1NXaxaJaPTiaxYYeP5F55i3rx5xGKxY653PVqoVPe8vDyt9r6srIxf//oXLFgABYWpjGDKSaGyysC3vp2LoggMDkhEY0FEUaCvN8Yzz7QjilBXZ2T5chtujwO3y4hOr8NgMKZKyUREQSQSjRKNRIgnEkhSAimRZLT4/ckgS0uLH4+ngz179lBbW6v1mWrGv62tjf379zM0NMTw8DANDQ3s2bOHkZERxsfHNRviuro65s6dy+7du9lZv5um7q0EI14Gx910jyZVzOOJOKrgpoLC0Gg/kUhEYwJNFdif6tmYLMKp/vZIUHVILBbLBLcZ9dqYTKZkqZHVSk5ODgsWLNACKJNLF9K1ENIZDOkMRVUfKl1rIRgMEggEtOXU9aiuJWogQWVPpgdV0q14e3p66Onpob29nY6ODkZHR4nH4zz//PN4vV4KCwspLCykqqqKqqojW1Lu2bOHzZs3/3/2zjvOjqp+/++Z28veu303W7I1dRPSCyQhhCQQAggamop0QQUUxK8iCFhAv4pfFRHBBqggIopEIAgBAgmhpPey2U2y2Wy2l9vrzPz+uHsmd1uySTaQ8LvP67XJ3b1zZ87MnXLOc57P8yS+p24So7ysnAsWX3DUz55qeHP5m/znmX/x6GU/OCGi5bxxZ/Ojlx7hmj99E5vZwoJRs3jwyu/0u+xf17xEi8tPtpTFlFnTyc/P77OMqqpcf/vXcZw1N/EHKWFOjabR+sEqnvi+xCWXXHLc7R1KSJLEWWefRX3TQdp9nX3eF4kDKQwegmQU/QZxzxHK5tTxPDakCIYUThmIGm0RKzdu3DhUVSUajeLz+aipqWHVqlXs3LmT5uZmwuGw3kkQck2LxaJ3UMRMRfKPkIKqqkpHRwd1dXXU19fT0NBAS0sLHR0deDwe3dCwvb1db1tvtYMgF8RsiBjUiA5Rfn4+Y8aMYeLEibhcriEhGGKxGPv37+eVV17hww8/ZP/+/Xi9XgAmT57MhAkTaGntxGaz9yBvggfr2fPj+6i0mWla8z6GmXNInzsfR0lZn+9AdKx6O/V/nJDr93PW+dd+rNtMYWghPBja2tpQVZWioiJmz55NRUXFCTnwHw2Hz98jD2qi0ag+SOvq6iIn28z9Dwxnx44ufvv4d/j3SxN58Ec/p6ioSL+WFEVh9erVaJrGq6++zJ49+5g1K4eRI90IHU9+vp1vf6f/AcNrq5uY8tQ/CDY3EQ8F0DQIBPx0dXYR62hHjceRVQUtFsGRlcMHq1dxTVwZVLb2uLKZLP3Xy0ckGGpra/nW1+7koQvvwm4ePMlzUtFdMiF1DwxH5ZSzpnqVHkV6zrw8Hrj/NW679U79eziZPjni+VNRUcHixYvZtGkt1dUrCARUHA5Zz1lQVQWDIXF+FRSCuEeWlJgZOcqN3S5jtchIcneUqtnUrY5z4nQ69OeEx+NNzJiGQyjK4QGrIL737u2kre1VRoxIxLAmEwytra18+OGH/Pe//2Xfvn3U1dXpg08Bu92O2+1mzpw5XHPNNTzzzDNs3bKVbfXvs6PhQ6ZMmcKMqhlEoxGUcBiL2YzFasEgGzhn9EyKiorw+Xz9mjeKQYwgfEQHfKD0lv5+EqeApCc0iN8HQklJCcXFxUyZMqVHmULy9o9kItn79/4IkKORkkdaTigzPvroIz766CPeeust6urqdC+JZcuWsWLFCiZOnMikSZNYtGjRoAiGKy/7AhNKZ2M1O5CRae1qYm/bH1i/fiNqXEkkzsTjRCNRFi0+jwULFxx1nScTtbW1rFq1kuuuu77H37dt28ZD9/yAB8//Jun2E6slz3PnkJ2WyQu3Pc7fPlzK9gO7aOxoYlhmX/IgEA9yzfXXcu655x5xnZokk3/j106oXR8HGhsbeeL/HqMiu4Rzz5jV530VNTUgPkYIU0dBMBiNRpxOp65cSOHYkCIYUjhl0HvGI7kzaTAYKC0tRdM0RowYgd/vJxaL9ata0FMMumcqkv8XngSqqupZ6B6PB4/Hg9/v11UMon5T1NCKelaPx6OrHUREo9/vJxwOJ7k+JzrK7e3ttLa2cuDAAaqrqxk7dixVVVXk5+cPKq1CMKltbW3U1dVx8OBBGhsb2b17Nxs2bNAzy4VB2NSpUxOSzaZ2UOJosoTU/YBpWfEG7miYBx78ATNnzmTu/AU0Vu+k8sH/S/oC+nwjQ/K9His0RSGyv5axY0+NGdYUjg/JsXCC9BMmcicT/Q0e+oPRaNQdnPPy8ojHwWQyMuGMbKqqMlm2rIarr76YSy+9lnA4QFNTHXv31mAwtOFwGJk2XSIYzOKx33RQVtbCddcX43KZu6XuErIYQPVqgmSQyRw1BjUep/H1V/C+v5KYz4cCxCUJyWzGYLZgdbnwKhGeW/5brl50K43t9URjUSwmK2aTlXRnJkbjYeKhvGA0L3/0JPv27aOsrCdxCImSqju+cjs3TFzC5NLxJ3ychxrC/Gpkdin+tTKxWJxYLE5ampFx48y89NKLzJ07T0+fEEaCQ42EI79FJ5knTJjKW2+9zROPt/PlL2dRWOjC6XSiAZp2OPLzMCSysgQpTSIFQJYxdD+jhMmlUDDY7TYkCWIxB7FYnEgk3C3BT0jzv3SNmyf/tJ3f/ObX7N+/X392eL1ePB4P1dXVVFdX60Rzfn6+rmYrKyvTzQ8nTZpEXl4eTqcTkzlRkoKkMnfeHO6++268Xi/Nzc2kpaX1MR8byFehv/d6P8cH+3/y+o6E3rXLyckzyZ8Vx0eYUQ6l4kVI58UEiCipEKVfmqZRXl6O0WjUlRZ79uyhpqaGtrY2Ojo62Lt3L5FIRFehTJkyhcmTJw94DNIcaSycdhl2a4KIicdjrN35Lu07NCTJhCxZkGUDW/d8iM2+akCCQVEU6urqWLVyFV6PN1E+oyioyuHzWFUUtmzbTMXIEkKhOBUVld37rKKqij7TmyiFUYlEYsycOQtJSqTm/Pf1pby/+g1CIa0HwdDQ0MCdX/kGd8y6jsq80iH5LmRJQtU0ynOH8481L3PJo1/m5tmf56b5X+ixnMlg6pEq0h+OqLLRTi1pvKqquBxpPPzZ7/b/fvcMfAqDhxgXhMNh3bi2P7+oE8HRSs0+TUgRDCmc8jAYDNhsNoqKiigoKNDrNns7bcPAg4uBbhD9uawnm3T5fD4aGxv1etYDBw5w6NAhmpubaW9vx+v1EgwG8fv9fTqakiSxa9cuNm7cSG5uLrNnz9bdZweTViGMZurr63n33XdZs2YNW7dupbm5mUgkomfoVlVVcdVVVzF58mQOHTrEn19ayv7brkdVFWz5BcjlIzFs28g9d32T+fPnYzAYuPH66/jZs38n5vdhcqahhMOEDh3EWV55bF/OAMdUCQXRYjGQZUxpxzZL0fbhe0wcMeKoteQpnNoQnW9AlxefSh0ecQ1arVYyMjISZVaGxCPRYIRLLili3Lgu1q59CodDpqzMwpSpFkpLizEYEoPHYFChauz1tLc3c/99/+bc+TYWLszHak0krsiSoQe/IF53blzHzp/cjyErh7Qp07E6nIQCAUJeL1o4jBqLEo9EsY09g7d3vEl1w0aycjIoLSklFA7j9/lpb2/D5cikMLOCoqxKMl05hANRlr+xnJtv6ekcrqoq9939Paalj2XB2NkfzwE+Rhi6CYYcVzY2g529ewPkZMcwGGTOnZ/B73//V4qLS3A6nWRmZurE8VB3+p1OJ5Ik4fF4aG5uxuPxAEYOHozw2GPtXHedg/MX5XbL8BMDM0VNeA4kx/Eml8vJcs+o3uTXVmvi2hDPHTEwDgVDxOMx3C5YssTOE0+sZfPmrbo5YTgc1kv6FEXR3c1LSkoYP3488+bNY/78+TohIX6E83lvXzGyxagAACAASURBVASTyaT7Erjd7lNqMHUkDOSDJJJAiouLT4rruiiFEElVbrdbJxgkSWL48OEUFRUxdepUwuEwy5YtY9myZWzdulUv92xsaGb79u2sXLmS2267jUmTJg143KXu803AaDRx5vi+JEIkFsZmH/g++/rrr3PvnXfjtqQxr2ImGQ43JkNPPykJI6veXInFnkZ2tpWDDStIWHRIetWALEkYDInlt271U1O7VFQTUFlh4Cf/O5Lbb9vIFVdcQFXVlEQk+eY9fH7MhcysmHy8hx2A3Q01PLr8aUxGI75IAFmSWDJ1MUumLuaP7z7H2trN3ERPgsFqMBEOh4+43ng8jmw8dZ5RR0JeXh6esI9mTyt57pw+76taSsFwrBCl08FgkKysrBNWHQ+0jdPNrPF4kSIYUjjlkTzT8XHcMIVEymAw6GSAzWajsLCQzs7OPp4NQpIqbhpCeSC8Etrb2+nq6mLjxo1IksSaNWvIzc3FarXqZlhiVjc57SIUChEIBGhsbGTPnj0cPHiQzs5ObDYb5eXllJWVMWbMGN1hPz09ncLCQla98rJuklVTU8OmTZsov/Fqpk6dqu/jzTfdxO+e/jPNb7yKragE/8v/xNTRTue4iRhVFbW1CdvFS8icMuOYjp1vz27qH/8laZEwVpuNlvZ2Rj/xVwzHYOoX37CGG7945TFtN4VPHuK6ETPRcHhGKLlm/lQZuCS3I3FfSR4AJt4vL0+nvNw94DomTXSyYcMafvGLJ7jqqqt5/PFf8YPvv8eFF7qYOTMHk7lnJyIeDrPjofvxbt2I3Z1OTpoDS9Fwss8+F08oRGtLayKGT1VIS0sj3eWm8Z5v8MPvfptLL71UrxmHxPHet28fGzduZN2aDWxr2MLZC89k/oL5fdq58t2V7PxwC09d8/AJH7eTBUO3oiQej1OVO4K1a7ZyxhkhZINMRbkLh72JgwcPMnv2bDIzM0+aF4PFYsFkMtHa2sqGDRs4cOAAiqKQn19AdnY277wDH364j8suy2T8GYnUEllLfi4dPs8Tg7HEYF5DQ1N7dyolRFyn3F1KYbPaAAmb1YYj6iAaiTJyVJTzz1d49VUf0Whillj4/BQXF1NaWkp+fj75+fkMGzaM/Px8KisrcTqdfTxEhHGiUPUJ+a+QBw/kq3CqYqB2KoqiR1qerO0aDAYURdETa1paWnS1loDwhxo7dqyeiFNbW8umTZvYvb2GbPtwDtYd5L///S+xWIwZM2b0eFYD3fXgMoqqoGkqmnbYAqn3OEVTE0bNfr9fryNPNrL0er2cN3oOd5x7Q/fZl7xPYn0av3rj9yxcWEB5RVriPq6hn8MJA8HDnztnXk73OSPrKSQAv/v9FPbv91Fb+xrLl1eTbSjnkokLicfjvLDuVUyykflVs8lwDHyPTT4GLZ42AF5e/ybv713PtbMv57aikQxLz+uxnKb1NPZVVIUmb9tR1XO9S2t6teCobfy4EAwGWTB3PkZFHrBViqaedLXgpwViMkSkRuik8Em4B4rEnWMZy0iShNVq1VXbJwuSJGGxWPQS8BNF6uxLIYVeEBnagB5RmZeXN6DaQSBZMhkMBlm7di3PP/88a9eupampiS1btrBjxw6ysrIoLS3FbrcTDoeRJAmbzZaIqguFdGmuz+fD5/PpTvii3rW0tJQJEyYwZ84c5s2bR3FxMXB4RjbZvGjkyJGMHDmyzz6azWaef/opHn/6z/h3rOfCm27ggkXns3TpUtLT03nzrbd4fW8tTJmBGo/T+tILxNZ/SNzvw5SXj/nsBWTPPkcvwQAI1O2j49GfcsPCBdz3ve9hMpmYMGcuSsB/TAQDfi95eXlHXy6FUwrCxb63qRp8fOTg8SLRkejZVUtI2I/8uQkTsnjmmc2YTCbGjRvHY4/9kTVr1vC73/2SF/+9nunTrUyfnk5enhWTyYASV/Dvr8WZmcX/3Hg9884+mz89+zfe/d6dxKeehXH8ZJTuQUskHKbD68UQj3HWWWf1Sd4wGo2MGDGCESNGcMUVVxyxnY1NjUwrOUNXaJyKMBoNWK0WIuEw4/JG8fKujYRCYUwmMzabxrxzXaxevZwlS5aQmZnZY+A0lBCdypaWFtatW8eBAweIx+MMGzZMv+fa7XYeeOBOztzvo6IijbFj0zHIco9yGDFQ07TEoEzTEgMzQShIIhmk+++m7pIPi9WKxWpBiSvElUQJ3tq1TaxeHUbTEsdJVVXdU2jq1KnMmzePMWPGMHLkSF3VoZvx9oIwLjObzaSlpeFwOHRpe3KM8+lCMAwEsT/9pcgMBcTxFQSDeF5nZWWRlXU4yUD4P40cOZJx48bR0tJCc3MzTz75JNU793LpjC/z/s7XeHv5O2zevJk77rijR6mE6G/IsqHbSFLr/n4AxIBYOIMk2vXCP57H7XZx8Wcuwu12YzKZ9P7J47/5LTMyqtBUFU06/PnEtqRefRwtaZtqd6R1QjEjSKhkEtkgG4DDgyeTKUEOrlt7iFjIzOO3/BR/OMglv7mZiCJhs9j5wSu/xWG28PI3/khOWuaAx3vZhje558WHsVmshGMRvr34q3zhzEv7LJfnzuHDvRt5df1yLpyykEgsyo9ffwxHRSYLFx45seKI572qnjIKPKvVyugxo9m/oZo2Xwf5/SkYUh4Mg0ayckHt/p5Pxv0v2ePsWL4bWZb1CcnB+DEdL2RZ1mOBUwRDCimcBAj2ThANsVgMq9V61AtOPJSF18Po0aO55JJLqKioYOfOndTW1lJXV6dLN4XLvSRJegdAzAKLjp4sy/qsVGFhIYWFhRQXF1NSUkJZWRnZ2dlAwvBLlmXsdvugb1xjx47l0Z/9tMffvvCFhKzwow0b6Hz+BTQljlq3j1k5GdzzxGPk5OSwc+dOHv7t42xb/iq2hRdiTHMRaWwgtvxVfv39Bzj/vPP09V35mYv5y/8+gDkrG61yFM4zJqOpCqH6A8Q6O7DkDyNr5ixkk/lwI7JyqKurY8KECYPajxQ+XgiyS3iUCIM1ODyrJyA6n/1Fy55KyMjIwOOJoKpad1xfN47SXKvNSGWllQ8++EDvvE6ZMoXHHnuK2tpali59kb89u5zm5jo0TaGyrJhLLv4s0ydP5pxzzsFut/PbKVOor6/noYcfZtkvfkR8wjTiecOQKkYSePdNyuxWli1bxsSJExk/frwucT+WYzly5Ehe8f7reA7NxwaDwYjFYsVoClKZNZyurYaE8WA8jqbBjBk5vPivndTX16MoCg6HA5fL1cPYbyihKIruswOQnZ1NVVUVBQUFVFVV8fjjz3L2wgUEaMeISm6WGYdVIxqXicYl4op2+CeuoigaYX8QVVExWizk5VjQkPH442Q4Jb59eyFVVek9iIF16zr5618biUUNXHPNHdhsNj2tQNT8l5eXU1lZSW5urp6+cCQIU0a73U5WVhZ2u/2IHgsp9A+hVoxGo7rhtN1u1+MsBZJ9oETcndFo5LzzzuPlfy/D7nAwe+xF+MNd7G3ZyvLlywmFQnrqVXZ2NtnZ2YkBUCiA2WDTJxtEWkZixj7x3Y0unozdnMY7S9fw6K8eIxwOUZibeziqNKZy+bwLiXb7Eaiqiqoousmqqigo3fd4oWgxyDKqCpqk6DO9Npsdm63vxIGmqcRicWRZouGgj18/spPWeitPf+m3ZDrcXPKbW8jNGsHNn/sOsmwkFovyrV9/nmZPyxEJBrvZTnF2Aa/c+fQRv5cJxWNJd7jwBH0A/H3NUmwjM3n4lz8/ah8uQUYNcP5r6ilzbciyzF3f+RbXfP5qvCF/v8uoKQXDoKGqqt6fSfaLGmrIsqybwR/L+oWaWpSanij6mxhN9lUR5dnJk5viuXMsirDU2ZdCCr0gyzJms1kvUUiOwDwakhnKESNGUF5ezqRJk3j//fdZtWqVTi6EQqEBXa7F7JPVasXpdFJSUsL06dOZNm0aU6dOZdiwYT0Y0FAohN/v12emhoLhvOvrX+dzF1/MqvdWUzhrKhdffLG+/zNmzOCF6dNZuXIlz/77JfbU1HDOpInc8tSfqKzs6eFw913f5LJLPoPH42H5O+/w3tK/YzIaqRpRSUl5Ae+t38DaD1aR/flrsRcnYimVnHwO1Nef8D6kcHIgHjahUIjOzk59ACbSVJLZ/2S59anSOesPJpOJ0tJytm3r5IwzBu7k9ofxZ5h4++3/6gSDuP7HjRtHVVUVcJ9OyCR3DpKPR2lpKY88/DB37NvHd+79Hstf/ieqOwPN76PFZuHZZ58lGAxSXl6O1Wo95mNZWVnJ/rb6U3pm2mgwIFutmEwmCty5SKqJ+np/dx2shsVs5Oy5Tl566QWWLLmKrKwsffb9ZOyTqLEXHSpBMAwbNgxZlhkzZgxP//4PfPMXj5B9+7fp3LOboN+H0eHEZrZgsJgxWm3IZgtGiwXZbKF99Ts0r1jOhJ/8itaNazFYbeTnF9C67kO+9eAfmTryAJd9tpAJE7OQZZk3Xm/nC5//OrfccgsWi6WPfDtZqTDY4yA+43A4dIIh+b0UBgdxfgiCQRABTqfziIkYDocDu93OeeedR3Z2Ng67g2g4xvwJV+L5oJ3lb7zJO++8oysfqqqqGDt2bHdZZgizHERDw2gwdhtWKyiK2kNRUJRdQWFWBZX5k3j+rV/xh889hNnYbVbXTRhEo1EkSeou8YxhMCT6FOKc1zSNuJIg94QvgaolkqyCgaCe2iXKfIQXiccT5sUXa9i8yUPd/igXjb2Y577xbQCW/ParOJ0F3Hjpt5HlRH/CZDJjMVmRpUFMjAzi/Fy25S3cFhczRiRUIP5YiBmzZg6q/6aqKtIAKgVJPbVME6uqqshwplOeO3zAZf5/qPMfCiRPmLjdbtzuo5fsHA/EJOCxwmAw4Ha7h/z5nUwcJMfvCnI9+Xcx8Sl8tQaDFMGQQgq9IGZhRX3lYF1fe1/4Yh05OTlMnDgRt9vN6NGjaWtrIxwO4/P56OxM5Be7XAlncsGcijp2i8VCVlaWbhiVnZ2tDzBisRiBQIB4PK7n9A7VA9DlcjFhwoQBVQSSJDF37lzmzp17xPXIsqyXaEybNq3P+zcpCo8+/gRP/urHxG+6HdeYcRhsNjp9vhPfiRSGFOIaCAaDdHZ2oigKZrMZm82WcMfvnlGNx+MEAgG8Xi/xeFyPehrM7OoniTvvvI8HH/rKMRMMEydm8eCP3kRRlD7SyuTXR+tYmM1mysvLuesbX2f8mNG89t/XqfF2EQ6Hqaur46233sLr9VJYWEhOTg7Dhw+ntLQUh8Nx1JpOt9tNWrqburaDlOYUH9P+fWyQJGSpW0EmG6nMHM6mTbWMHqXqCvB583L5/gOvcu21N2EwGGhra9MHdEM9OI5EIni93h5JKJmZmT1IjYsuvJCn/vECtTW7KT5nARqacGDoZ//A53Ihm0xY3G6Kzuk26NPAVVJK1tSZ7H7x7/zPT95gWEYjX7g0m7q6AKNGjSItLW3I9k+4o9vtdn1fgsEg0Wg0NSA5CpJLI4UZZywW080x09LS9Of30WCxWDCaTNgdVkymXMKhEFfMuZ33drzCjgNr8XsDBAIBdu/eTVdXF36fD4+3C1k1kShpSBBLonSh5/mRaGcsEkfSJCKBMFHCkFRyIUosxABDlhMeCsL4U1EUuro8tLUqyAaDnpgSDoeJRhJloImZ2ES5jSRLBP0K37t3E3uqw5gMFiTNyayK6UiSxNV/uBPF6OS2y+7FZBz8JIiqKsiyIdHmQX6mNLuIivxSAIyygXVr1nLllVce9RpSupUc/ePUIGfFoFCWZTq6OrEYB54JT13Pg4dQdp2I94I43pFIhKamJpqammhsbKStrU1PmxHL9f5u9ASXpFScI22j9+v+fj/SZ5PbIa75gV4n3xNSCoYUUjgBCBWCqK880ViZjIwM3G4348aN02NwvF4vhw4doqamBoDi4mLy8/P1GaXkm5yYcepdxx6LxfB6vciyTFZW1nHNbH7SMBgM3HHbreRkZvDDpf/GUVpOrPEQFKcSJE5VBINBmpqasFqt5Ofn43Q6e5BbHo+Hjo4OvF4viqKcNgRDZ2cH5uMQ/2RmWsjM0ti0aRNTpkw57u0L5cOECRPIz88nLS2Np556ikOHDuH1emltbeX999+nvLycsWPHMmfOHFwu16DVVZd98XL+8fKrfPu8rxx3G08mEveubrWLLDMubyTv79qZ6Ox0L5ORYWHMGBNr165hwYKFtLS06H4EQ31uRSKRRJpDtx+PSBsRJSoCuWlONu3YStaMWRistoFWl8AAzxEJiYySMjLu/C7Krd/kwBvLePy1lwi2a7S0tQ3ZPsFhgkHMpCcTDP+/uJufCETHWyRMiWSotLS0HsqFo0GSJAxywkA6KyONzs4OFFVlwcQrmFa5kJrGLWysfZfmhjb27duPw+LC4/EgK4OXVgcDARQlTkd7e899EG1Iet0bCTVCFy0tQZAgFFT55S/bUJUESSHJDYk0FCTiikpFhZuWlhCZ1vH88bs/R9Ng/fbVPPDyr3l753vsaqrjJ7f+FbPp2CTeVz/+daqb96EBwzKO7s2kqD1LGa6YchE3/u07HDp0iMLCwiN+9kgmj127NnPXXa9TXFwAHB4IinIVg8FAKBShqKiMv/zl+SNu55H/+yV/fvLPpDnS9O0aDDLxuJIwDO8exGkkvDfE/kSjCcm6hJQw3IwpxJT+ozcljmRYmUIyxKTiUEQfCzWEbsC8bh07d+7E5/MRCoV6nDf9pdcJwq8/U+zey/b2guvPG6532470+9H261iRIhhSSKEX+puBTL6Yj3UQL25e4sYlHkaiDha6Zxi7DbeESdfRttO7TacbuZCMyy+7jDUbNvLyg/fiCgW58d6/fdJNSmEAmM1mPV1FURTdrdxqteJwOGhvb2fLli3s3buXcDhMVlYWY8aMYfjw4SelrnEosHXrVu6//1soSgCPJ4rbfWztHD/OyNtvLz8hgkFcv3a7nby8PGbNmoXZbGbbtm3s2LGD5uZmOjs7aWxsJB6P4/P5qK6upqysjNLSUsrLyykvL+9jBinwhau/yKI/PM3SDW+Q785B1VTUbsf1fHfukOXSnygS6i0jI7LL+c9HJhRVRVESNd2ylIis/Ptzf2Lx4gtxu90YDAb8fj8Wi2VISdZQKER7ezvBYBDoWY6QjCsuv5zWp//MlrtuwVJcilZagaViJGkjRmHO6KuG6dO+Xr8aLBbKLv4sZRd/loNPPIJriNUZY8aM4YorriAtLY3MzEzy8/N1Oezp/hw5WUjuXHu9Xrq6uggEAjp5mpGRoZM1g0FyogcSGAwydrsDSZKJK3EyMzMpKSpj7pQLOdhcS11zNau3L8NsTqgaE4qDbpPJ7qhUEYJzuF8gYTAaQZKw6qkWGko84bEgPp9IpdD0EonDddYSVqsZg9GAqqgEQyp+n8SlU76NhorN4uhWsNlp6txPLB5h3BgX08aeo29/StUscrMK+M0/7yeuxLFZk1VcCbNTYSapqSrJZpV1LQd5ad1/afK2cd2cy5k9YjqF6XkJTxZ69n/EMZUkiVg8hoSkl+65rE4cVsegZl4NBsOAJKAW8nD+IjNnn23Ttyf8eoTxptdr4LHf7DjqdvbV7ONrM77I2aNmiCOBqqkYJEOiVEw67CujdqeGAJiNRgzS4Mxtj5yIkUJ/ON57Xzwex+Px0Nrayo4dO9i9ezd1dXXU1dXR0NBAS0sL8XhcPycHgvA9Sb6+kycbB/rek8cpyal2IhXCYDD0uMcbjUbdFya5vC55IrP3/8lt2blz56COS4pgSCGFAXCyOlqiU+JwOPTEh+Pp2J0Ote2Dhdls5sc/+D7nvfMOkydPZtiwYZ90k1IYABaLhYyMDL1mLzkH3mq10t7ezubNm6mtrSUSiZCZmUlVVRUlJSUDDn4/aVRX7yY7Ox+73cJvH9vDqFHpfPZzBYO+tqZOy+TXj7zA9dffpBuvHi+sVitWq5VZs2Yxc+ZM3nnnHf7zn/+wceNGvF4vbW1ttLS0sGPHDmRZZty4cUyfPp358+dTWFg44DG22+3c+LWbWf3OKsz+/XqHwR8MENrm4Ref+94JtXuoYJBljEYTldnDiYQk/P4oSry7/MQgM2KEC9jLli1bmDx5sl5upmlajxjPE0UoFKKtrY1AIADQL7kAMG/uXObNnUswGGT79u1s3rKF1RvWseG5p4iazJjKR6CVVeAoLSd0sJ54wE+0qxOD1YrBakMJh/Du3E5wwxqiXV1YRo8lZ/Y5GF1ufGtWM/rrQ6s4GTduHGVlZXpcmt/v14/fp+FZcjKhaRpdXV3U19frJWGCqDmeYyfSdiRJxuGwd084JAaF8biCosTJcGcwYvg41u1ZgclsxGazEovFdeWSqI0WJQ9CzixJEkaDEUmScDjsiUGqphHplmobjUZMJmO37FnFYjFjMpl7rM9qtWAyScSIIctgNMrkuMWzuXtQE4U8RzlGkxGzyUwwGMRsNmM0GjAYjJQWVvLDm3+Hv5cZoSAXVFUlFo/xtX/8D84kAqLd24Ev4sNoMPHyzld5Y8/y7pn77hhhZCQkTLKVMwrHYzaakGSJd3a+jzfg59G3nwbAZrTQ2tk6eGn3EQblOTkW8vMHLnWzWCJYrUcnpyUgw+HGbU8bXJuOA9KgC0pSgBPr78fjcdrb29mxYwf//Oc/WbFiBX6/X/da650e0duYOJkgEO+JSUjxmd5GvL1JAUmSdIV0JBLRI8OFwlQoKAS54HK5cDgc+naO9CPaLl6nCIYUUjgKxINYJDcI1k+wvrFYDE3TjjmzdiD0voENdWfudO8cOhwOLrzwwk+6GSkMAHF+mUwmHA5Hj+hUkYYCiRm+/fv309zcTDweJyMjg5EjR1JUVHTKKhiWLLmMJUsu48c//gHPP/8Me/d6yMiUmDevYFCfLyx0cM45Hi6++BwmTpxCQcFw8vOLcTrTCAYDnHnmWYwZM2bQJnxwuGRixIgRLFq0iPHjx+s1nQ0NDRw4cEA/zhs2bMDtdpOXl0dZWRl5eXn9mr1ee/21XHv9tT3+tm3bNh76+v2D2s+PAwZjwqzWEXdQ7B7GmjWd5OdnIMmS7nFxzjwHzz//Z84880wikQjRaFR3vB8q9K5HPZqCzW63M23aNKZNm8ZNNyY+X19fz5YtW/ho4ya2vPZvIvv3EfB04nnwHoLBAJLdAeEQE6qqmD9zBnUNDby5/BXaNBh20aVkffFGvnzXt3j28d/2MdA9XpjN5h6zUsJj4nSH2IdoNEo0GqWrq4u2trYeqpb+VH+HPQn6J+t7fzYSieBwOHA6nbrq8Hj7BwaDAUUVA19JJwkS78G6XSvx+DqIK3FUTWFr3Qd8dvj1wOEZxcMmj4m11DXt4c11/0SSZIIRP6FoiMfX/V1XLClKHFVRkWQJJAlFTSgapG6FUHtHe8J/JBxg4wYNl0sjGIrQ3q4QjyuYzGZMRhNxJY4Sj+uz90pcIaolfEtisVj3YCRBMhiNBsyylY6OjkR7VVWPbtW0RNvnXQB5+Ye9IIyGXGz2QkAmElFQFA1NTcz2i+3FFZVnn2nkwgWX62V4pYvG0dnZSX5+Pqqq0nCwAWWnhsfjIRAIHLODv34ekFCaHAmrVrbi9YaOui5N0wZnanmCOFkRrZ9GHM89MBwOU19fz759+9i0aRNbtmxh27ZtBINBXC4XpaWlVFZWUlxcjN1u1wnw/sofBJI9GPoz8E3+v/frZAWDUCtYrVZdwSBIB+GZJfxiem+rv9fJP//4xz8GdXxSBEMK/99C1DtFIhECgQDhcLgHySCMFo1G42k/eE8hhaFCfzX/4XBYH/z5fL4+BMOIESMoKio6pVy4+8PXvvYNzjlnIW+99RYfffgc8+YN/rMXLC5g0uQgDQ01eDw7qKmJEQ5LyLLKM8/8H1/60je54YYvH3ObysrKKCkpQVES9bk7d+5k/fr1rFy5kvb2djo6OmhubiYtLY2CggKMRiNZWVmDTpPpLaVt6mzmoaWPsrtpLz/47F2cOer4yz6OBwaDEavFQjQSYWL+WLZseoPzFob1TpEkScycmcNL//6Q5uZm7Ha7TjAMJZLjuwbqfO7evZuNGzdit9s599xz+6QHDB8+nOHDh3PRRRf1u/7GxkYyMzN7pItcfumlfP7ue9EuvITc+efTZrdz+U038/Izf6GoqOiE90vMSgmIjuSngWSAhHeGMFBubW0F0EmU5O9TlC2KkoDkMsZkJBMMAOnp6WRkZJCZmXncygUBf8BLu6eZTFdOn/dkWeb19X/j+i9/CafTSd64q1j2yuvIZolM18AqqXe2vIRLinPOyJk8v/4Vzl00j6u+fEOPWU/Rv0meTTUYDDQ2NvKVm7+GEo+BLLF1bT6xWJxYRMEgmRlbkCCJ7TY7kUiYsAaQUFqoqoKqKsRiXnw+HwaDnLiWrYfNfxOTOXHi8Vg3eZcoj4jH40hyiJwcCZvNgsVsIM3VbZppMmMS5jia+E/TFR4vvhjkM5/5DDk5OWRkZPQ4Fqqq0trayqjRo3C5XAQCARwOx4AEg6qqAydVDOL62F0d55Zb7jjqch8HUgqGY8ex3gPD4TDV1dWsWrWKFStWsGnTJhRF0c3Zx48fz4UXXshZZ52lKwZOJRzLhMfxIEUwpPCph8iqFtI/Ud8koGmaXo8kfofD3gkpkiGFFA6j93VgNpt1l/tIJILf79cNHoXx2WCd1T9ppKenc9ZZZ6EoCitW/IVQKI7NNvjHZH6+vV8J7Xnnh/nZT/+PgoIiFi26YFDr6m3yKu5FwqjMbrdTWFjI+vXrWbNmDfv372f58uWoqkpWVhZ5eXk4nc6jkjqizhegzdPOZY9+hUZvK0aDgc112z92gsFoNGK1WQmFQ4zILmPFNgOhcAiL1aIPMMxmA2eeaeX555/lq1+9HbfbjSzLeDwevcTkRM83YeoYj8cJBoM9orzEoPzh//0FbftDt6ug5gAAIABJREFUhGM+7vnOfTz55z8wffr0Qa1fkiQKCvoqZCZMmECeyYi/ehdpo8aQfeYcDq58m7179w4JwTDQc6y+vp4DBw6QlZVFbm4uRUVFFBYWnnLXrYiTCwaDBAKBPqqEZAM0p9NJTk4OJSUlfUzRDisA1B41z0eCpmnY7XZsNtuQGIuWlpfw19d/QUZaDkgakWgEDYU0hxuXMx2DUeLWW2/VvZo2rt3ar9Li7288znsb/oNRNqKoCksmL+KWhV9iZdMGHnnkV0c1NxSora1l4php3Hzh9wkEAgQCfjyeBGEgyxKywYDdZsPtdhGP24nF42iqiqKqxGNxvY8l1J/xeJxwOCEhV4QbvXK4hMNgSKgVJEnm78/5MRp9zJhhZ8HCdAAikSgmkxGT0YTVZsVmtSEb5O7PQGIY3VMu3htOp5MFCxKJLZqmEQgECIVC/V4HPp8vQTj1Q1Ym2q4ckcjUVI2ysjLae5lq9kY4HEY1Hl5Xb9M+SC6BTeyngCy+/0GUx35aSMNTBeI78vv9bN++nW3btrFp0ya2bt3KoUOHsFqtjBw5klGjRuk/Y8aM0Ulko9GoeyEcS6R8LBbD4/EQjUZxu92nHFExEFIEQwqfeiiKQjgcJhAIEAwGicfjei2RwWDQzemEGUp/Jo8ppHA6Yf369fztL8/idDhBgo6WNi69cgnzF8wf8m2ZTCbS0tKIRqN9CIb09HScTudpR9BNmzaNyZMv4Kkn3+Frt5ad8Pqysqxcd30eP/zRd1mwYOGgUh/6gyRJ5OXlkZubS2lpKVOnTsVut7Njxw4OHDhAfX09TqeTcePG6YOgoxEMsiyjAbF4jKt/9w2afG28+Z3nuP2Z+8hJy6K+7RAFGbkYDB9Pd8FoNCJLNvymAGWZhfg9Gn5/EIfDgZbkeX/uubk89NDf+PKXv0p6ejqBQACPx4OqqkPi9SFiKcWARBAMYjAaj8fZvGkzt178E+xWJw/9+Tbuuede3nxz+QltV5Ikrl3yOX7+7luEGuqJrV6BqbV5SMiFI6Guro53332XnJwcCgoKmDFjRr8EyCeNcDiM1+ulpaWFlpaWHq7rouZYxJampaUxbNgwKioqBix/6D2oGyyG4n72yK9/xc6dO9m1a5cuoXa73bhcLr1fcrR4W4DG9jpuPOtyvnLeNUjImAwGtjXsJqcwb9DkAsDatWu7X2kEAn5aW9u6B8EakmTAZDRis9lxudz6csJLIRQOEQqGuomJgK5YSKhED29DDJiFwsFgMGC2WHC7y2hra+Ptt3189JGf2XMcjB9nY1iBGbPZRGZmZsI3AhNGY3cNO0ceREuShNPpxG6364RUMBgkEon0IGrEwDEQCKCoiVKQ3tBURVdOJP0Vvz/GhvUdqJrG7uoWvF7vUQkGn8/H0uo3qGtrQNMSBE00FsUT8RGIhwnHI4SURBtzbBmMzC5lVH4FJVlF2CyWBBmGPLDaggQRkSIYhh6qquLxeFi9ejXLli2jtraWhoYGZFkmNzeX6dOnc9555zF27FhKS0t7EJeKougeCZIkDZpgiMfjtLW14fP5MBqNKYIhhRROFYgyCFVV9Rm9ZHmgMD050QzcjwuxWIxIJEIkEsFkMg2ZR0QKnx7U1tay5vX3uG7WFQA4/Ar/c/s3eeT3v2HOnDlDui1xDdXW1rJu3To++OADfD4f6enpjBw5kuLiYr3u+3SB1Wrl/vsfZOHC6Tz3tzpaWhVuv71Mdw0/Howe7cbhSPglDHaWOxm9azAdDgeSJDFhwgQWL17Mpk2b2LlzJzt37uSFF15g3rx5zJ8/H7fbrSfT9AdZlonFY7y05r/UdTSw4Yev4bI5McgGfrX8T3he8XPDrMu5Y/Gxl3ccDyQpMVMqyxIuaxo5jiw2b+piwcKsHstlZVsZPVrmX//6B9dccz2SJOkDGjHgPBETXJPJhNPp1MkKn8/HoUOHkGWZzMxM3n33XVy2LOzWRFlEefEIbr37uhPad4GLLlzMr3//e0bEAtx21x2cddZZx01KHQ0Wi0WXj1dXV1NXV0daWho5OTnMmDHjEytrCofDRCIRfWAoIBQHDoeDsrIyvfRBfNfCfFFVVYLBICaTqcc+nEr3ofz8fPLz85k3yFqsgQaMbW11nDXvi1iTIiDf37ue+RcsGHRbVqxYwS/+9zcsnHgVXV1d3ZMxMV3t4XQ6cbnSeqVlJGbYhQpUmNnZ7LaEeWR37KKixPXSHFkW/a+EekGWZVxpaVx59efQNI22tjbq6urYsnkTq99rQTYojBtn5eKLJVRV61bFOQBJVzMMRGCLv8myrJdFmM1mXTmQ/BnhtyVIj76QMBpkjMae7+3e1ckLL0RYvHgxl15iYvz48X1KNXrjsquvZMe2HQQ1LUGaamAwOhhmK9AVMiKCuKWlhd3bd7F8y3McamigMD2fUVlljM6tYPSwCgrT8/v0/yQADT16NtU/PDGoqko4HKajo4NNmzaxYcMG1q5dy/79+wmFQuTk5DBx4kQmTZrElClTGDVqFNnZ2X0IBKHqEUT1YCHKuYUC+3RBimBI4VMPVVWJRqNAIg7SZjscM3Q6QkTUKYqiy4FP9dr2FD5elJaWEjUoTC4ZR0FGHpqmMamkigfuvJcbvnEzC89bSF7e0TPFBwNRSnTgwAGWLl3K5s2b8fl8FBQUMGrUKJ1gON3gcrm4776fUVdXx4cf/pG6Oj9lZcfv+t3eHiEasTN+/PghaZ+49sePH6+TpXv37mXXrl3s27cPo9HIpEmTsNvtR1SQFBYW0uJv54V1rzJ75HRcNifekJ+apv0ARJUo0sdgSCZwmBhIyKAnF4xjw/rlnDtfJUnAkBhsuBSefPJ33HDDl5EkqdsRX9E71SdyjzebzX0IhoMHD+pqtzu/cRdzRl+iLy9LQ0dO5+TksGH1ex/LM0pId0OhEHv27NH9CqZMmfKJzoBGIhE8Hg8tLS26lwIkiDWRwCRSmJIhjlkwGKS9vf24iBlN0/D5fLhcruPfgZOA/r6PmoPbkTWNqRVn9Fhu9YENPHHerQSDQRoaGvjva6+zatUqLrt8CQDz588nKytB2lVXV/Pd/7mPy+d8jTRLFp2dXYRCId0oTpYNerlJn1xVEsfcbDJhNpmw2xJJGKqq6QOzSCSC2WxOSpcQ/ZXEutJcLq677joyMzN107y3336blStXUldXx4q3m9m2NcTNtwSZPLkIm83arTZNEJED+WckQ2x/oOMICfWUoR+PIQDZYEDVEskcvd5h0aKFPPTQw0fcfjIuueQSLrnkkqMv2AvhcJhNmzaxadMm1mzYwl/eXMqufbvJsLuZUTiRWZVTmFk+hTSbg6iSMNpMEQwnDpHO0NDQwCuvvMKrr76Kx+MhFArhdDopKipi8eLFLF68mMzMTFwuV7/3bkEUHI8hcfKz7XRBimBI4f8LJLtEn+43W1VVicViACkFQwr9Yvr06Xzlrtt48l//4HuLbkeSJM4oHsNPL/oON//gbn7x44f57VO/Y+bMmSe8Lb/fT0dHB/v37+fgwYN4PB5kWaawsJDp06czatSoHiZ2pxMuuugzAASDftat/fsJEQx2u5FoNERNTc2QkAyiA5OZmcmoUaM4ePAgBw4coLa2lsbGRjZv3swzzzzD7NmzmTVrli6r7N3xcTgcBGNhDrU0cM/02wBwWuwsqJpFmjWND/ZuYHJJ1Qm391gh7tVTC8fz1vvvoKoaiqogqRJNjWGeeaYRs2UETzzxQyRJ0ksaVFWlvb0dh8Ohe4McD6LRKH6/n0gkoncMo9EoiqJgs9l49LFHuPfuB8jJKGBs2WRKc8fxh9/9CY/Hw+TJkyktLe2xvg8++IBwOMyECRPIzMwc1P5/HJBlmfb2dpYvX6Z3YoUS5OOAUCS0trbS2tqq+43AYYl7snmm2WzW4yGPRCL1Vq8MtJzX66WmpoY9e/awe/dWdu/eSm1tDeGwwrvvfoTb7e73c58ENE3rY963Yt1S5o8+C1k+PMlQ3bQPW7qTsrIy7r3ne/z7hf8wbfQ8iLh55S/vsW7XSmpvrOWCCy7A6/Vyz7fvY+7oJbht2Xi9vm5yIY4sGxKEjtOhK6b6IxgSEAqrxGtZ1roVJeakuDuD7rnQG2azGavVSk5Ojk6IDh8+nG3btrFjxw6qq6v5w+/buPvuxH0s4e1jRZYNuN3uI5ZF9f7uj6TmCvt9dG3dqJs6SgYDSDJRv59du6JkZXUmrQe2buskP8/4sfTBbDYbU6ZMYfz48USvTBjbLvniEhrsDbyjreXlj94i9HKIDIebkDfClYUKI0eOZPTo0bpnSO/UlE9Dn/hEIRQ6Qv0kEIvFaG9v59ChQ2zcuJGNGzeyYcMG/H4/LpeL8vJyJk6cyOTJk5k6dSoZGRm6R4PVaj1lY7k/LqQIhhRSOM0gaoGF6dvpODucwsnH+YvO5+nf/JFgNITdnFDtDM8q4JXbnuT9mvX87tePDwnBIFIj9u3bx6FDh/D5fJjNZp1gKC8vP+07MEuWXMkttywlEtnPFVcWYzYfu2LIbjdy9dUZ3Hrr5UyYMIcHH/z5kAxeMjIySE9Pp6Ojg7a2NlRVpampic2bN1NXV4eiKEyaNOmIdZsvvbqUX/3yV9TuOQAkOlyPXP1DXt64nNc2v82YwhEn3M5jhZg5rcgajgUn27d7yczIYtlbTaxcGefWW+/hyiuv0s8tm82G1WqltbWVtrY23eTveBGNRvF6vYRCidg5QeyKgffcuXO5+95v8dQjLzC2bDLTxsxl9XOvc+Ptd5A3vJj//c7/8Pbq1UwcO5bJkybxvbsfwEo6zV31ZOdkMn3mVL7/wweG/Nqorq7G4/Hg9Xr7RDIC/RrLLV36Aj7/NszmEMHg4bSFjwPieXbo0CG2b9+eqLM3mfSkhry8PPLz8wc9SDwaAoEAf/jDE+zatYmamt34fF0UFdkZNkyioMDA+YscFBWV8N27a045ZWBD/X5+9vRtmE2HS85aOpv56pd+3GO592rWsvCi82hsbOS1V17n1s8+SG7GYT+NYZklvPnyCla9vg5FUTi36koKMyu7lQtBwuGwrgxIlMtkdxMYx+ZRIUlSt3JA/2u/y6rqYeNUcT8rKSlh9uzZrF69mtzcXFRV5YMP3ufRR+v5+tdjlJRKGAxmZFnC7XYPCSHncDi4YN48vB+u0AfjqqISVxXkwlI8XU7eX518z9aQKGDmzKEtOzwSeisxSstKsYy0kD0mkSwSC8Voq22jraaNFU0reO+B9/DWe7l40cX8/Cc/72Nqeqqd458EBMHQG7FYjMbGRjZu3Mg///lP3n33XWKxGCaTieLiYiZMmMDll1/OvHnzMJlMaJpGV1cXfr8fWZZTBMMn3YAUUjhZSHaM/jSY3cTjcT3L1maz6SaVp2upRwonFwUFBZSNKGd3414mJc1AGw1GZo2Yyp/+9gK1tbVUVFQc1/rFdVVXV8cbb7zBunXr8Pv9lJWVMW3aNGbPnk16evppTy4AlJeX8+c/v8hPf/oj/viH97jlK2UYDD2vO03TWLWqmZ07osiyxnXXF2MyycRiKk1NQXbv8nDmWXn8+CeZPHD/6zzySAH33/+DE26b6MgXFRVx9tlnEwqFOHjwIG1tbXi9XrZs2cKLL77I5MmTGT9+vF4ilozc3Fz279vHKPnwIMQfDvKL1//AN8/7MtnurD6fOdkwmUw4uuu9zy6ZxosvvMUbb0QoGT6Nv/3tEYYPH97nM5IkYbfbycrKQpIk2tvbsdlsOByOQZ+HHR0dtLe3c+DAAdrb23WCISMjg/LycrKzs/V7bkd7B05LwvE+sX6J9IWLsJdVcPt9D0BuPm/HDcT+8S+ad+/gidv+BcDehp089+IvWbT4fM4444wTIkJ644orLqa8wopB1nCmGbuN8EDtfgQK9bc4ezVNw+HQ+M63K1i56gBP/qmNUOjkPTOFSqKzs5OOjg7dY0GSJMrLy/VZRFGLfjSlwkDoXV8v8Ne/Ps0///kwX/pSJRddnE52dn4fbxVN04hElEEZLH6cKC0ezmeGz6U8ZzhKt1S6K+DpkfSiaRrvH9zIwwuv5fZbv0HAG+pBLgBMGX02U0afDWiEw4lUDp8vQabF44o+4HI4HTidTmT5RPoZg/tc8necPKsunk/BYJBQKERdXS1P/7mVr99uIx6PYzb7ueGG6xk1arTuAXEsZTG992tsecLUV5ynwuNj5tgxetlUeno6o0ePpqKiQjcMPx6I8l2/38/u3bvZu3ev7q8lSktEG8Ux6R0rW7OnhlhuDEOHIeFJgYRjuAPncKe+H/aDdt5Z+g4rV64kEAgQiUSw2+1YrVbdn8fj8RAIBHQ/suREEEiUDAoPs2Qfs2TvsuR2DvRaQPQfYrEY0WiUQCCA3++ns7NTT0xQFKW7rOZw+oJod3IbEtdrRC8bTvYcSh4H9P7/SO+Jsoj9+/ezd+9eamoShGN5eTkVFRVMmDCBM844Q/eBEVH3HR0diZSQpNJsgVgspr8vSZJ+bI+GUCikH5eOjo4hIS76Sy0ZaDnx/rHeA1IEQwqfavSO/zmdiYZ4PE4gEEBVVWw2m37jTSGFgWC1WQnHwn3+bpANjM4pZ926dSdEMKiqyv79+3nttdeorq4mEolQUVHBZZddxhlnnHHK1TCfCAoKCvjJT/6Pr371el54YTdXXVWivxcKxfnd7+poaU7j9tu/z/PPP8Xfn6umy2OhoSFEe1uUysoqXnyxmpwcI23tUQoKhjYVQMQKtrW1sWnTJkKhEB6Phy1bthAMBonFYowYMaJfgsHn81G/7wATKsv1v33zuR9Q5Mrn8jMvGtJ2DhYmkwmH04GiKswpncay5e+QPrGMz3zmCr12vD+Ie2NnZydtbW1kZGQc00Cxo6OD3bt3U1dX14dgqKys7DFbun7tJnLTiwHwBbzsrt/CqHtvxTVyLFmLLwFNQ5JlDr77Nu7ag3oHO82RTjSscNM1X+X7P76XK6644ngPUx8UFORy9dUWiop6KVaSH31J/cTEjKZGW1srVVU2XC4ZiwWam5uHrE3JEKUmTU1N1NTU0NnZSWdnJxMmTGDSpEnYbDZsNlsfU9PjQfIgQ7yORqPMO3cYkyYPfA6FQsopGa0bioSZXH4GFbklAy5T07Sfg22HEuVSe/bxvesfH3BZTYNwOERXl0cfwCeUC0bS0tLIzs4eVHznYKAocepb9iYGKoiBp0woEiQW73+gZTAYKCsrIz8/H5/Pp6cT7di+haeerufyy9KZv0Djsd88SzicIEGsVmuPGX7R9mTDx+Rza6DzS0SbC0M+q9WK3W5n2LBhlJWV8bnPfY6ioiIsFstx98M0TSMUCtHa2srKlStZvnw5Xq8Xj8dDV1cXXq9Xb2dy8pnBYND9MRSTQlZVFnSgD6wlSUKWZJAgHovj6/TR2drJ22+/TUdHBz6fT1eKiGNVV1dHc3OzXiog0kBCoRCaplFcXEx+fj4Wi0XvewqDdFGmm0w4iHb2/j153xVFIRgM4vf7aWlp4dChQ9TW1nLgwAH8fj/hcFi/nweDQaLRKJmZmfo9QhwPQC9nS0tL63H/ENvq70cotZJfi+VVVSUSidDe3o7H40HTEgajRUVFnH/++cyaNYsxY8boE38iPUs8M8Tnk889QTCIZ0pvAmIghEIhOjo68Hg8pKenDzp94kjovc+9iavex24wPie9kRqdpHDaQNSGClZVSFXFDdVkMun55+KiD4fDes2suBGerkj2XhDlESn1Qgr94dChQzzy819Su20PEz5/fb/LfGbcAn74i0SqxPHE0TU0NLB7927Wr19Pe3s7VquVgoICKisrKS4uJiMj41OnsLHb7fzwhz/jssvOZ86cAIWFDsLhON9/YA8LFnyBnz98MwUFBWRlZfLYYw/z+c/fRHZ2NlVVVbhcLkKhELt27WLjxg2cffbcIW2bePiPGTOGK664gjfffJPXX38dv9/Pvn372L9/Pw0NDUhSIr4vubPX0NCAGlX4zMSF+t/y3bl4DB7Mxk+mBMtkMmK32QmHIxSm5+E0p9HV5aGpqUmfWYS+6Rqio2S1WklLS3hmdHV16SUUvTtJwjirurpar/Xes2cPW7duJRqN4nK5yMzMJDs7u49ZZklZMW/9+yM6fa10dLVhzcnBNXLs4fZ0L+vdsZUR7sOKi7zMQn5001O8+sEzQ2baJTwMbDYXGzfWYDJm6A71h7uMGn1mlEVEXzCApqrceFMW27eH2bRpDcFg8IRL8MSMrN/vx+/369+b0WiksrKScDhMOBymoCDhoC8G9kNx3xAd5uS6c4PBiKYeeaLBYjGgqnHC4fAp5R8T8AfITTuymuid7e+z9+AB7r3rB5TnV2E19yUUhXIhEgnj8/kJhhLx3QnlggOH04nD4cBgSKhyhgJ1TTU88/bPqRpb1T24VFFUBYfDzpTpE/tV8SQng4wZM0af8W5ra2PjhjYO1rcybVpi/4all3efWxpIEImG8IS60DLyEn/TVDRFQQuF0NQ4JjWKxTxwfzCZXBBlPGKWXChudu3aRVZWFjk5OQwfPpxhw4bh8/nw+Xx0dnbi8/n0Aa+4DkRUplinIEK3b9/O3r179Uj1cCyGYjSCbEgQlUocqXu7YtYewOAwEI8nztVYNNZ93yHh1SGBqqiEI2Ei4QirV6/WYzrF/VCQI52dnfj9fp08iEajRKNR3ZCws7NTNw/uTRqI67W3WqE/JUMyRJ9W3B8aGhro7OxECccxSQYckoVsKR1/IEiuMR1sIIdl9jUdJDejOKHOQgNNQ9VUQCLmDWIyxtHQDp8PWve9oPf51f2vIL3EZzRN7V6fCZcpl/ScfKxWG0aTkV3bq7nnnnHk5+f3IHQECSQmADMyMvpMsIgUCKvV2u/7AyEYDJKenq6XEA3Gw+doOFYFw/Hck1MEQwqnDQQjKCSVYrAtbl4i891oNBIOh/H7/Xg8HoxGI3l5ed1Sv1NrRuJYIG7GYh9T6oUU+kMwGOTWm77KTFcVj13+Q91/oTeqCkdy5ZjFXHv5F7nqui9y/Y03HNP1cfDgQd566y3WrVtHV1cX6enpVFZWUlFRwbBhw04pc7ShRElJCTk5OcRiKv/5T8P/Y++8w+Oozrb/m9neVFZdlmzZlm254iJ3Aza9hUDoJoFQAoGQnrykEEKAQAp5k5CXAF8gJCFAAoTei4Fg3I0rlm25yLJ6W21vU74/dme8atZKrsDe12WQtDNnzszOnDnnfu7nfnj5pQ6qqqbx05/eoW+zaNGJLFrUNy/Xbrczc+ZMZs6cecT6N2HCBMaMGUMkEmHFihU0Nzfj8/mor6+noaEhIX12OHoQDAUFBYhmA7WteylzlyTOYfxsfvrMr/nW33+GM8uFxWbVJxvReJQrZnyBaeUTD0ufJVmizddJd8jL3o797O6spzPSjUkwcdW0LyIIIvPKT+CF7e/Q3Nx8UANCbbKnRbFCoZAegepPWqpJWbds2cLTTz+tV+HQJoM5OTl6akTv5+Pa667BZrchCgJP/+tjIlII77YtSH4/rvFVmHMTE8HQlk1MGX1Wn2N3Bzrx+/09IuzDRXt7O4899ghbtqxk/gInzS2RA+RFGsK9xOQaSktNFBaaUVUv99//O26//a5D6lc0GsXv99Pc3Exzc7Me9SwvL6eysrLPQuRwIbW91DKWOTluduw8+L4Gg0BRkY26ujqqqqoOW58OFUaDAYvp4PLoDn8XbpuLieWz+cKJX+53m4RyIYLX5yUUDBEOacoFA06Xi4L8/GRaxOGbLymKTHX1bP72+CND3tdgMFBVVUVZWRnNzc3s3LmT+nqBPXsaaWiIYzZauGzRt4AD37s32MUTy/+I86xv9mkvtHstwTX/QYjFBlS1pqZIaBJ8jSBoaWlh586dWCwWKioqqKys5KSTTmLGjBk0NTXR2Niom+2WlJSQn5+vKxNaW1vp6OjQUwM6Ozvp7u5OHMtgRHA4MYwai3nKdMS8fASTGVVRUMNBVI8Hua0FxdeN0u1BDfhRJB+yJBONRAfkgmLRGF6/l+XLlx/0OqeStr2vS0NDQ4/fh/OsDpS2BIlndISriHPGLmJkdimjckopySpIKDFSEIiFuPGlu7l0Qd/v9fBDQBQFTCYzeXluWr372BVcycKFC/Vz0ebimsJFEATC4TB5eXl9CARNsRAOh3G73WnPkWw2Gx6PB4PBoJPdhwu9ifqhfn4wZFYoGRz30PKz4vG4zhJmZWX1+2LQ6mWrqorRaNQlYBaL5VNNLvSHz1JkOIPDh3vvuoeJlpF8deElg277pVlnMa2sirsfeYC58+cxZcqUtI8TDAZpamqio6MDSZIoLy/n9NNPp7q6GqfT+Zm+Py+8cCn3/PI+qqvn8fzzT/dbLu9YQYsojRo1ihNPPJFNmzaxc+dOmpqaWL16NUajkYKCgh5qLlVNRP0aPS36306pWsCNp3yZt7d+SMHcCi66+CJ98v7wgw9T27J3yASDrMi0+7to8rRQ19FATedu9nQ10O7vJC8/D7c7j8qJ45h2+kIKCwv59z//xfI965hZMJHTxi3grdrl1NfXs2bNGsaPH095eXkfolW77wwGAxaLRVe+QSKCpMk9NVPMnTt3UlNTw8aNG6mpqcHr9WKxWJg6dSpTpkxh5MiRjBw5kqlTp/ZRjeXk5HDTTV8H4IYbb+CCK7/Mhvvu4pRFi/j4qcfoDgQIByL4WxsJl5xEIOTDaT8w6dyw9b/U/WoDD/7xAf7f3x6huro67WupLXxqa2vZvn07W7du5d133+S00y24XEI/Ro1CQlChJqN+yXoESVGwPlG22WzYbXYuv8zE/fe/xE9/eseQjOC0Ou+azFvrp91u13PWDQYDOTk5uiLkcJMLkFiwaAvE1LbHjh3LSy8NfqzSEUZqa2uPG4JBVVUkScLUp0ziAdS3NfDUupcpKx3PyTP7S21SiUSjRCOJiHEomEhPEEXUC/oxAAAgAElEQVQBhyNRrcPpcByi58IA/UdluE1q94fVamX27NmYzWa8Xi9er5dIJMJ//vUixcUlyW0BBIzehB9Df2NzZ0c21SedxIL5cwc8Zqp8XlNOaAq02tpa3bOgpaWFeDxOJBLhk08+wefz6VJ5n89HU1MTdrtdV9QGAgE91VVTMZhtNhSHC/vJp5F9yhlYC4qTKgTt2qEbTkaiCaVCIlXBxP4fXoPD4SAnL+dA1F5Nbq8qyJIMlsRYNWn6JMLhMPF4XPcy0MgE7XysVis2m033XOhNOmjKhnA4TCwW0yuZ9b5eA6UkaN4KqSkWVquVUCjEyHgetyz56kEJx8buZiwmC0VFxbpCiyQ52kOhJaTyLf3ceELvT/orvwqCkFBq2O12lm/fzLlLzxx0PBxoTEv9+1DGvOHul27bh/L5wZAhGDI47hGPx+nu7iYWi+k1sDUpJRyYSGhGKJIk6SZRqTlmGWTwWUdDQwPvvfYOj1/1v2nvU1lUwWh3GTXbaoZEMIRCIZqbm+nq6kKWZUaNGsUZZ5zBuHHjPvPqmq997SauueZrx+V5ahOQkSNHcvLJJxONRqmrq6OpqYmVK1dSVFTUZyE7cuRIfnbnz1n+1zf0vxmNRq4/+QqsJgu2SSUsWbJE/+zjteuRNwUH7Yuqquxqq2PZ9hWsbd5Cq7eDXLebsvIyxk0cz1mXXcSkSZMoLS3tM04nzAcd/Ohr32NqwTgq8soZlVPKtm3bWLlyJUajkZKSkgG/g9RcZY2cTiWpOzs7qamp4fXXX+ell17C6/USjUb18pZz585l6dKllJeX66XzDkZSC4LAN675Krv21nHzDV9DURT++c9/8vMf3828qRew5pP3ePGDv5PlzMYomlBRkeMxXrnlr3zjmaFXk9Am7TU1NTz33HNs2bKF2todVFdno8iGlPnygfKBPSeLWsHDxKTcZDRhsVrJzs4hNzchx3W7vaxevZoFCxak3S9Nrt3S0sLevXtxOp24XC5KS0spLS09LN4K6UDL8U4tyQdQUVFBc0tfX5reKCpS2b17EKnDUYSqqkQiEWoaaplUPp5wNMyLa9/k1a3vEZclJEWmw99JRVkV3/9y/+O/qkI0EsHr8xEMJKT62vOgeS4YDIfHc6G//h/qN24ymZg9ezazZs3S7//Ozk7ef/sjSkpKekbIjVJyjOhLMMgN2SyZfAo/+N530jquZvjX2dnJc889RywWo66uTicSOjs72bVrV4/If2paTipUVUUVRASLBdFkQrBYsM1eQOEXLyG7bCR2hx2b1dZnP40o7e7upmP9atqfeBCTzUK8swtXdgn5BfmoSoJUUBVVnxdHo1Fkq4zb7WbJkiV0dXURDAbJz8/X0xglSaK+vp62tjZycnJwu92MHDmSoqKiHgSDoigEg0H8fj+dnQn1ldPpxGaz6alm/f3TPtN8xLTUI83ENScnh/b2dnwbWygsLMRkMiIIYh/zVVTwEcJsslBSUpy8xtq1VlDVAwTTAYKhN+GQQikI6f0uCAKSLFHXvo2zz/llWvdMBhmCIYPjEJoJlJb/FY/HMRqNWCyWHnljqTm3ABaLhezsbBRF0UtdfRZywD/NxpQZHF3EYjEcVjs209Dyhk+tXMB7by3jkksHVz1o6D2B13xBeuepf1ZxPJILcGDhlp+fz9SpU9mxYwdms5lgMEhjY6NOCPXGxIkTebDl/j5/FwWReKynCZtoMCRzVPsiEo+yonYdG5q2sbG5BrPTyrkXfYHfnX4DFRUVaRO+giAwe/ZsCitLueXFO/nduT/m3AmL+cvGZ1mxYgXFxcVUV1djNpsHjBZB0izS4dAn/Bs2bGDjxo3s27ePhoYGtm/fjt/vJy8vj+LiYiZMmEBVVRXTp0+nvLyc7Oxs3cRsMJxx+umcoV0jUWT69OmMG1PFV86+BYBYPMKW2vXsbdrJroYtFJqsgEBboJOJE9NTg2imiDt27GDbtm1s3ryZzZs309BQT0WFidmzc7HaLJhNJgRRRFVAlrUIspjIL1a05xdiMQWTSSQ/P5FjnFjcJHK8p0418MQTj6RFMIRCId2wze/3I4oiY8eO1evBZ2VlHTZvhXTRX/53UVERsahIIBDH6Rw4B7+szM7mTZuPVlcHhSiKGG1Wrv7r9xntLqPF147J5mTetLNx2rMwGkyoisLkMf2pYFR9LhUKhQkFg0gpygWH06GnTR2570eFQ0y50N4zWgRZSxvVouc9n1EtLabvMQUEzOb0S3xrbQuCwIIFC8jNzaWtrY329nZaW1tpa2tLKEJCId1kUpu3ORwObDabnhIRi8WobWwiMnk6rur5OMaOw5qbq5efNJlSzEWTX4WqqMiylPBSiEbxvv82otjBiFMrMJqrKJ1cisFs0JULumJAVbBYLCguBTFf5OSTT9bNErV+iaKIoih4PB5dwaAF53r7YqQqGLRqFJpng0Yi9CZW+lMwaKUetdQCq9XK1q1b2fxJByaTUVfQ9LkXBZVANIjJZEEQxBRFjIqqij0JBlI/G9jQUxBIUT8k7w6h9/4quxs+YfyESoqKitK6ZwbD52KOdKw7kEEGvSFJku6zkHA1FsnKysLhcAzo2KvVnP28153N4PON0aNH0x32EZPiWEzpK3cqiyq4/4W/s+KjFSxYmF60srd52kBGThkcG+Tn55Odnc2aNWt0SXFTU1PCRKsfgqGurg5/MNDn74VZ+WztlX9rNBmJ9TInrG3dy5vbPuC/deuYOusETrpyMbfM+xHl5eXDvicEQeBPDz3AvXffw9NrX+VLladh/vh5PvzwQ6ZOnaorDg4mWdUkvLFYjEgkwrp163jooYdoaWnB7/frJFlVVRVz587l1FNP5dRTT8Vutx/y/bx582aKsg84/ptNVmZNWsisSQt5+IV7mOpy0+brIBaP0dHRQXl5+aBtdnd3s3v3bl555RWee+45fL5EecGSEiNXXV3Au8vCrFsXwJJMDTxgyiboE3BtIakR94FAAKPRiyAIxGIKkYhELKZgt9u4+eZFaZ1rKBSio6NDJ0C03PRUj4WhoL6+nrvu+x2xpFs+gBSPc/L8eXztq18ddP+BJMWCIFA5bhwNDR6qqnL67CdJCq2tYdrbIsedgkEUjdxx0z94/aOnWDxmFpPHzBp8xyTicYlwOJycWx1QLjhdTgry84flEj+0/h/+RZWqqvh8vuTCVgEOjAOKog5oPaIqMgYx/bQfSIwj2dnZzJ8/nzlz5hAOh/UywFu3bqW5uZmOjg5ycnJwuVz6GFtUVITb7darMvj9fvzvvU9owUkULjgRk8mcJOVhIBMFlUSKRiQcSRqXK7jGOsiekE1WdhY2R/8+S9q+skvGVmDj5JNPHtDYrzcxMNCCvHdFNo040P5p0BRf6XzngiAQjUbZZlyH0XhwI/ZANITJYNLTFw4OFUVREQQ1SUj03xdBUHXSVRRTr0tif1VVqalfz3lfPnPQc0kHn5c5UoZgyOC4Q2r9WJfLpedoafV5B4pUfVYhSZKeN2ez2XRlRgYZ9IYgCGRlZeEL+ykwHdxtPBXF2QVcN/MS/vGXx4ZEMKRGLFL7kMGxhzZWulwuCgoK9AW2z+ejq6sLu92uL6IBVi1fyfSRk3q0IUkScTmOuRdxKwgCiqpQ176fN2v+y9rGLShWgQsu+xJPX/CTwxblgUT99f/58a1ccPb5fNxSw7cWXs097z3IihUr+POf/0x1dTUzZ84kOztbj8ilIhqNEg6H2bBhA6tWrWLlypW0trbq5PWUKVOYPn06EydOZMKECVRWVmKz2fQx9lDu55XLVzOyYLz+u6Ik3MlVVaGlbQ9nj5zEiNxizp5wMv/8++P8+Laf6NtqJnOax4ImLW5sbGT//v16vrfT6SQrS+S66/JZt1amq6ucfz31R0aNGkVeXt6gi8bu7m69dJ3b7cZutw9YceNg0OTYLpdLdzpPXbQOdh0VRdGrTASDQa7/znepHzUe15TJ+jb+nTXs+sfjaREMJpOpRypl6vHHj5tCw/43yMuz0rA/yP6GIC3N0NQk09YWpri4hPHjZ3PVVemRK0cDPp8Po8GMw+rgS0uuQ1UVYvEYQjLimpCIqxgMxj7zA1VNmm0GAsRiidJ5Doc9oV6wa8qFI+tRZTKaWfX+SqZNnk5J8YgehHQoFOSiSy7klm99Y0ht6qUOIwH+/tZvMZlNoCZUV8FwgFCwm6ivC0uWu89+RmP6c6jUeye1aoLBYGD8+PFkZWXh9/sJBoPYbLaEaiC52HY6ndjtdj3yH41GWVezg0abHZMpOaftnQowwLkqipLwHTAYkKXE968oykELfQgIevuHe97Ym2TQfpdlWb8emrLBZDJhsVhwuVw4HI4+bRkMhoOeR+I4EIyEMBrMKWqFwfuoqgqiOLB3QSLNQkmSEQa9XVVNEFWSFKO2aROnnf6LwQ+YBoaqSu5N6nxakCEYMjjuoBnqAGRnZw+phvlnEfF4HL/fDyQIl9TSQhlk0Bt2uwNfJEBBVvoEA8C8sTN59OlnaG9vp6CgIK19+hrJZXC8QI9QOp0UFhbi8Xjwer34fD490qYtIjds2MCLzz7PXy6/F0mSuPnx2/hg+0pUVM6cvoSLv93Tjd5oMvLM5td5vX45Fy29hN+eej0TJkw4IuSS2WymqKiI679xA7/++T1cM+1LOMw21q9fT01NDZdffjnFxcW63LY3IpEIHo+HDz74gAceeEB3bNfMf6urq7nuuusoLy8nLy/vsKb4fLK1hgvnLGZfSy0dnhbe2fg0cTmGoshEhCDv1a/hCuUCrpp/EV9/5jYuW3o5Y8aMAQ6kCm7ZsoXnnnuOtrY23ZhSM1Z1uQREUWXaCQ4+/FBm8uQzeeSR3w3JZNXn89HY2EhZWRkjRowYdhRb629xcTGlpaVDbufBRx7lN//3ABank3gkgnP+SZQvvbrHeVhLSvF89F5a7WkEQ3+lqauqpnH33U+wbJnIuHFVjB8/jQXzJzFmzBhGjx59XCohOzo6cNlzUJJyeVlWUBQ5masu6gs5i6X/hWQ0GiXgDyBJCR8Sh8Ohl14VhxjNHw5Gl07gOxf9lriUCB4p6oF3xyd71vHJ1m3DatdqtfKTn91KQUEBpaWlOumgKAo3fet7RAPd/RAMCuIhPuOaSeHYsWP1Z3agajCp3gwA/3zhRTps1vTLpqekPqACoogqHUgPGRQCh60cbr/NJ981GmRZxuPx0NzcTDQaRZIk7HY7LpeLsrKyfgmGdKEoidKT6cw7EgRBgtQdjEBTkr4VvcctVVXZ1VBDxZiRh5U4Hy7J8GlCZpWSwXEHLa8OBh6wP0/Q2OHU/MPP+zXJYGDY7Da8Id+Q98u2uxiXX0FNTU1aBEM8HtfTmDQjvVAoRCQS0WvZZ3DskJpzXl1dTTwep7Ozk+3bt/PUU0+xYMECFi5cSF5eHs/++xkWlM2kzF3C8+veYN2eTRQ43ITjEd7ftoK/n//vHm1ffPHFnHTSSYwfP/6If8/aeSxcuJBrv/E1/vGXxwiLMSwWC5FIhI8//pjHHnuMyZMnM3HiREaNGsXIkSP1Ce/mzZt5++23WbFiBcFgEKPRiNPpZNasWcyfP5/Zs2dTVlaGy+Ua1MhxqCgozOfhV37B5MmTcGY5efzff2XSpEn6YuHm67/Oyxvf4cJZZ3H51HO55+d38cAjD2E2m6mtrWXTpk0sX76cmpoaPRUiEkkYFLrd2VitEmazifa2HK644mt85zvf1cnndN8R2vv2YKU/B9pPM9nr7OzEbDZTXFw8bK+F5tZWcr58LUWnDCxDNlhtafdTqyLS32L74osv5pxzzumTY360oS2GU32n+tsGYPv27ViMdiRJ0j0BtLmAICTSRLUKKT33V5BlBVmWkKQ4qgoGowFDcoF8tMZpQRDIdrr7/ay1q4GAJTzstsvKyqisrGT06NE9/p5fVIzUXzBmiAqG3uidcjNUGA0GGER50Pt4xqQCIBQKIZgtyH4FRU4YOh4r9GdgCQnyJTc3F6PRiCRJPcp9dnV10d3dradoaSkliqLotrMHg6LKaW3Xs2ND27w3tu9fzxkXn5r+4dT0CJDPOjIEQwbHHbRyTNrPn3dokxBNmpdZuGVwMIwaPYp2f9ew9u0O+7DZBs7nTEU8HteNrTTzJ03GfTQnrhkcHJoZYmtrKxs3bmTbtm3s3r2bYDDIyJEjUVWVF198kZl5E/nT24/R5uukqmQsT9z0J2JSnAsfubFPRNftduN2979YOFKYMGECP/jhDyguKeaBBx6gvr6ecDjMxx9/zKZNm5g7dy6LFy/mxBNPpLS0FIPBgKqqbNq0iUceeYTOzs5k1N9FdnY2J510ErfccgvZ2dlHzDvkgYfuJxAI6BFODVp+8//c9iOuu/RqFlfN59xpp/DwA0/w7rvvcvbZZ7Njxw6ef/55tm3bxs6dO/VJumZgfM45X+Ccc85hxIgRzJkzp08JzXSR6vI+FGjvpba2Nnbs2MH48eOpqKgY9nMvCgIMslgSkovodKCVwOuPYBBF8ZiTCxq01JJAIEAgkPBAST1HbbGyfPlydu7ZSvO0eorcZRgMhl65+wdM6lKRiMzKSLKMJCXmEUaDMfHvOFFCqqrK1k82c8fP78Rut/GDH37/sLw/Evd0f89Eb0PIowuDaBjS3FYQwGQ0JdWrBgyjRhN5+11kRU6vnaM8jTYYDOTl5envCFmWCQQCeL1e3atCFEVsNhsVFRXY7fa0r0dclhANBo7WScmyRG3TJu48+ftH5XifJRwfo0sGGaRAK2Wj/ZxBT2TUCxkcDAGfn6xhTJ4/2L6KiEli1qz0jMPy8/OZMWMGkDBkq6+v58033yQQCDBnzhyys7OH3IcMDj/cbjcTJ06kpaWF1tZWdu3aRWNjI5s2beIf//gHkydPxtfp5dX6d3GYbdjMVpZULUAQBCwmM0aDkVgspqcfaGZeWg61hiM9LmkRrxNOOIFrr72WvXv3sm/fPmpra9m5cyf79+/nww8/pLGxkTVr1ujVIFpaWgiHE9FRLSXilFNOYfbs2QA6eXsk+l9YWEhhYWG/n0mSxIgRIzjrS+dx5yt/xGwxM2LMSBwOBxs3bmTLli3s2LGDjo4OVFVl7ty5LFy4UE+R07wj3G532lUu+kNqNZh0JvnRaDThZO/14vV6URSFsWPHkp+ff0hEjaQoMMg5iGYzoiH9aWu6NeNTjeq089OIl3TND7Xrl6pEGCh3uncpQ1VVsdls5ObmDtj2Sy++gmgQCcUCSaNOMYVcSP1/jz2JxqKEQyFi0USfzGYzdrsNszlNef5RgCAIdHR2suylVXQEG7jlm984sqmx6jEOXglopQvS3sFgEJOGrUYsJ1Tj+89fCLWEkAsSCpjBnr2jQagczB9Ne3+IokhOTo6uyDWbzYnSmx0dRKJRYrGYrsrpUWI3+bOiKojC0fMg29u0ndLyEkpKSo7aMT8ryBAMGRx3kCRJZ/IzBEMGGQwNHe0d5BX3P1EdCL6wn/9b+Tj/78m/pj0RKSwsZObMmQSDQdra2ti3bx+vv/46JpOJSZMmZQiG4wRut5vc3NyEe7nfj6qqNDU16e7nixYtwmG189Bld5Of42ZUYc9KBiaDkXg83oNgiMfjKfLso0t4Tp8+nWnTprFz5062bdvGa6+9Rn19PY2NjdTX17Ny5UosFgtnnnkmX/rSl2htbUWSJERRxGq1MmfOHG666SZMJhOhUIhoNDrs6P+hQItcX3nVlaz8aAWnX3QWV375SrZt28b69evZsmULtbW1xOOJnPmFCxdy66234nQ6dZ+Iw6G8SF1cp4NoNKr7NjQ0NFBZWcn48ePTLvk3EERBgAFKn+oQBKLh0CEdZyAoiqLPPXw+H/F4vEc5vYG7JOj7a5FazaxS+3tvRYL2f02RYjabGTVqFG63e8Bn6oknH+f5519g6+ZlvLr2b5w160qmjp1z0HNSVYhGovh8ft3c0WIx43A4MQ2hytCRhtVsIxaKM2/eqbz58VOHbTF8vKbYKoqKMMRzFEUDRpOKwWjAZDFjGD+L7o8/oXxSIlVCQEAwDHyux/I6CIKgl6PU5gUasdnd3a2XyNxYt5X3PvmI+WNnYjKZe1S0SIx1vXmZ3iTN4T/HLXWrOOMLpxz2dj8PyBAMGRwzaC9azYQrFArpFSS0WryHOmn5rOB4fElmcPxBVVWaGpsoWdR/1HQgrNq9gTmL5lFVVZX2PkVFRSxYsAC/38/OnTvp6uqiubmZ+vp6GhoasNlsZGVlHTcy3M8rJEkiHo9TUFDAggULiEQidHZ2sn//fjo6Oqirq0OIKuRbcjDLRlpbW/XFuD2ZLpO6+NTk3FardUhmgoeK3pNNTRmgqiplZWWsWbOGNWvWEA6HCYfDbNu2DUEQ2L17N7FYTL8fbTabbrqmKEqy7JuMxWLRPzsS0N53WoRbEARMJhN5eXk8+cxTtLe3s2HDBlauXKn7LkiSxIQJE5g6dSozZszQSzUfTmJHu54DLeq0fnu9Xrq7u3WjUIvFwoQJE8jPzz8snitGg2HQfPK414shzfPWUracTieqqhIKhQiHwwdxkk8c22AwkJubq5Mug6UlphIMqqricrmIxWK6t8JA+2vH07wUsrOz+y2rqWH69OlMnz4dVVX54nkXYTOnZ5QnSXEikQiSJCfvOTN2u51AuJuIFERRZMwmC6qqEgj7UBQZpy0bVVWIxiPEpGjiXJREsMdssqAqKooqp1RFUZEVifbuZqKxMBazTT+/OZMWY7ceXFE3fuQ0fv7VRxEEgWf/+xAvvPACoijqpE0sFtN/tlqtOBwO7HY7VquV9vZ2fD4foigSDod7VDJoaWpA2fAejoIRqLKMtiDt2Lme/eNtbNy4US/jqnmz5OTkpJ0mOFzEY1FC+/dhKUi+p1VQk6oG0WpDTKakCaKIKgh6iUVFUSASQYxEsC05i8BDa/B1+RKGpg77gP0+1qnGvT0rUokfh8OBIAicfPLJ+H0+br33PmKRGONKxmA1JohfgyBiEA0YBZG6jv34YjIfbXp7QIJVFETUpBGkVsI0tUylVuJSFA2IggGDKCarUoiIgpBMHzKjKAq1TZu45YSr8Hg8PQKe2jVNPX48Hsfj8RCJRBBFsU+ANB6P09XVpY9D6QZQw+GwPvZ6PJ4hmdD2p0xL9XPTxu5IJEI8Hu+hqgIOicTOzPwyOOYIh8M0NjbqzthWq1WvHZwhGDLkQgbpIxwOE4/FcdmGliKxoWkb8686bUj7lJSUUFRURGtrKx988AFdXV20trbqBENeXh52uz1DMBxjSJJEKBQiPz+f8vJyQqEQ+/fvR5IkvF4v+/btI9+Wy9a67ZRmFWAQEwZwOTk5mM2mPhOUaDRKd3d3j3JjqQRE79SJIwUtx7eiooIlS5bw+OOP6xH/aDRKTU0NO3fu1BcmOTk5ZGdnY7FYdKNAVVWJRCIEg0GysrKwWq1HdLxVVTVRLtDvx+Fw6FU8BEGgtbWVVatWsWzZMt555x2i0SiqqjJx4kQuueQSJk2ahNlsPuz9S1VCHKxtj8fD3r178fl8+Hw+pkyZwsSJEw9b6TtRFFAHm3CLArmF6Tm5x2IxAoGAThYEg0G6uroGPEetKkBWVhZZWVnDJnG050VTQJjN5rSqBQx0rP3793PeOedz/hfP54qllyFJEt3tfsbMn5hOb4jHJSKRiL6oM5vNBKNe/vzyzygsKqSzqwNUyM1x485z09CwH6vFitVqw2az4XAkKnH4/D4URcVusxGOhJHiEgaDAYfTgcvpwuvzsnffHsaMHkPljBOw2mw89te/UVY4hsqySYP2VDv/pkA3d3+4GsFgBEFANRhQDUZUUQRBQJQkhGgYMRJBjUaI+rzEA35MRiMmsxlBq4ghCrTu2095VwSXXSNvRAQgF9jx3iZ+u6IGWZGRFBlFlWnsbGHUuDE8/cIzaVzb4WPGlCnUvvQSztpPgITsX1EUPB0deLq6sCTHVe1eUmQlaUop4Pf7CSTLjcpRlbb1bZgWmTCajAMTI8nSnccbRFHUy+K6XC6uu/563ntnOSNt0xOKIllKkldywqRUlshVLQS7tkNhOwaTGVVN+GxoxIWqKsjJe11WFCLhxL1qs9ux2qwospyoRCFATJKTBtUJI0pZkohGo8TjElpSxpnnnoYkSXR1dfUwYdXeeanjhCRJeDweotGEWqi3aatmtKyl7Glm9oMhHA7j8Xj0EtNpVx+BftVpoihiMpl0ss5oNOr3lbZ9KuE63Pd5ZuaXwTFDNBrVpVFOp1OPHlksFpxOp37jZ5BBBumhu7ubrCGSC+FYhHWNW7jt1HuHtJ/GgpeUlDBv3jwANm7ciMfjYfv27bjdbgoLC/stHZjB0YMkSdx+2x1s37YDq9VGIBhg585tFBRbGTHChc+n4u+OcPOLd2I0GLEYE4tsUUjUT5dVhUXzFidL4UkoyajQV65eyo9+fCt1dXWcf+4FyRAclJWP4PW3Xj3i56VN7LQIzNy5c1EUhTVr1rB27Vo8Hg9+v79P1QHtvpVlmWAw2CPiqW17pFI/BEGgo6ODXbt20dzcTHNzsz6Za2hooL6+nj179iDLMlOmTGHatGksWrSIqqqqQ/Y46A1NidDU1ER7e7vu49CfM7xmvFxQUIDdbsfpdA67WoQG7bwlSUKSJKrGjEF+8TXk087us61Gb8lywuOgs7NzwHYjkYgetGhoaNAJFKPRqJvO9Rv1TCoNNJJpOPeAtojX/EI0dcKhEG7PPvMfqkpm07VD4Iarv4lEhNmjzxykzcR1jcXjxKU4qqokCBSTCaPRQEyJ4s7L5f0P3xl2v9LB+rUbhlQOUlEUVFGk5OobEcQ09lNVIpEo8XgMi8WK2dIzIOVbey73L72DCSMq0zr+vo4G7l39l7T7O1x8++ab+PbNN+m/a89BPB4nFov1WHhq95TmSbFp0ybWrVvHiy++yPvvv57qlOgAACAASURBVE/3mm7yZuXp1UVSy1mqySdHikvE4j2fm1TCyWw260qOo4H+xhj9mQPKisZQUVqZUj5VUyOo1O7fypr6l/neD75LVlbWgMfQrqm2oHe73WRlZenjTu+xS0v983q9RCIRcnNzsVgsdHd3Ew6HycnJ6XE87TqnjhOaAkDb3+Vy9eiTpsSx2Wx6f9JBKBTC4/EgCAI5OTlpGyyn46ujKeq0ih6p+w53HNSQWb1lcMwQiURoaWlBkiRKSkqSETPzYa1DnkEGnyc0NzdTlJU/pH1W7FrPCdUzhl0VoLi4WE+V2LFjh04wlJWVMWXKlGG1mcHw8My/n+XRvzxGKBTWJxd19XvJcxVz5Wnfwmq10+5p5pOtP+f00x0sXjKaYMDL16/fy6TymextrSEmRRAQMJusTBm5gJnjTsRut+POzacwvxBBEFm9dRk+rxdBEKirq2N82XQuW3wLoUiAB1+77aiesyb1nDNnDrNmzeLZZ5+ls7OTXbt26V4+qdAWm1pZVW0xmCppP5Lvn46ODrZu3coHH3zABx98oBsKpk58RVFk+vTpfOUrX2Hs2LGUl5cf9sm/z+dj3759NDY20traqkvRe5+7pgApLS1lxIgRRCIRQqGQLukfLrQc7Gg0SiQSYdq0aUR//VsiPh/G5GJKVVWkgJ9oextKOEysq5NwZycdHR192tP64vF48Hg8OoGjLfJHjBhBUVHRUfMN0e7LQ0EsFuNXf/gjxY5SLh0znxNPOJu9TdspLxo76L7xuEQkHEGKJ0tTGoxYLRYMBiMmzCjy0KqGDB9DIxiA9MiFNJsfSklDQRAT0e1jAE1BM1h6RnV1NWVlZWzbto1ly5YR7YzirfOSl5+nqx20tJVUH5BoNNrnudGqqWhpCkeLYEgPQo+fE0ShiigYMJlMuN3uQX2eNKImFAqRl5eX1vZaqo3b7cZut+u/5+XlDUoIaARDOBwmNze3z/E0RUO67Wmw2Ww6weB2u8nLy0trv4GgkSmRSIRAIIAkSWRnZ+NwOA6ZEE1FhmDI4JjBbDaTn59PLBbTpaOH4oadQQafdxQVFdHq6zv5Phj2dNQz4+z0Kkf0h94mcQaDAavVeljysj/P0BZempQxnWv5xz/8ifljz2P8yGnJRZTA3Y/dwpmzLmdE4WiMRiN/eOrHRKUACxdNJS/PhkDCKX/pmd8EVSUWixMI+dnduJVlm54jGPVy9uwrQRGQZBmjQYv2ppo8pkwGj3K+r7ZY1BasVVVVLF26lHfeeYc33ngDv9+PJEk9vH5UVcVkMumGiVarFa1Gu8PhwOVyHfZF6L59+9izZw+rVq1ixYoV7Nq1S/cdUlVVX5BOmzaNmTNnsnDhQsaMGUNubu5hJT20RYckScRiMYqKiigtLcXhcPTrqaH1LysrC4fDgaIousQ3HWhVGbQIbWpUTJM1i6JIXl4eJ82Zzdt/uo/s3ByUjjaiba1YTUZKS0px2O0IqFScfdaAZKggCHo1BqPRiKqqlJSUMGLEiD5pD0dKoXI4sWzZMixjx+IZN5F73/gluQYbSyacyqjig0fkVTURwPH5vEST1SMsFgsuV1YiRSgWJSvr6JjwqoMZd/aAcnivoTq070QUBBRFHnzDw4x0+5hqCqotBOWgTOCTAFQn2xIFDKohoV5IDsUG44FFeX/QnlGtL/31R/OpSE35Odz3u6qqkAa5pB6kn6noPdaku33vfVM/HwwHO95Q+5Nuu0OFRrJrai1FUbBarYfduDlDMGRwzKB5LYTDYbxeL+Fw+Iib62SQwWcZ2dnZBKNDc1l327Jpa2od9jE1KaJW6s5kMun55Rkl0vARi8Xwer262e1ABIPP5+MHP76NaCxOTJVp72pl9qQDE0lZlnjy7ft5dbUbAej2d1FUkMsdt+1GVROO5qoKpSUlyehLBGfIgcPmJD+rlCff/18eeeMu3Dl5ZOdkYzIZCUdCnDluCQnTOBM1dR/z8Kt3EI2G2d+0nQsuOEUnnoCkyV6M3/72gbTLoA4VWjR8woQJTJs2jXA4zEcffUQ4HNYJBi0tQlEUzGaznprncDjo6OjQS0I6h1HmdTDU1dXx9ttvs3LlSlatWqVHu1Il/Gazmerqaq677jpGjBhBQUHBESPptCjWqFGjqKysHPQ42rMciUSGdByt6kQwGGTZsmWsWbWWH976A4xGIyaTCYvFgtVqxWKx8MNv3sL85cupqKhgxIgRlJWVpR3lS4V2XbVSoGVlZZ+6sUhVVf706F8puOTL5M2Zj3z112h4/11effUFXnj4eU4omswFJ15NedGY/vYmEonoZUQFITHfcmW5MBqMhGJHx/hPEMQ+Pv8HQ7qVTNKHmnD0SxPiMVQwpAtNaaD7pqgCkX0RPR2iPzLSIBqw2Wzk5/dUNyqKoufeRyIRYrFYv/trqcsJkso1JA+AdKGbJiIclJT6tD3H/WGo53AkSFGN0B6KYeRQkSEYMjhmSB0o4dg73WaQwacdDocDZ3YW+zoaGJVfltY+M0ZN4Z73/x8MU9kuy3IPB2JtwXSkpeafdWiqkMEm3R0dHfx37SbcCy6l3dzOlr1rOWfRZfrnqqowuWIOE0ZNQxQTTv1Gk0mXDquqymPNv9GVCCazCTsOBEHEaBzNwsln8/aGp8kptlI5dRKzZ89mzpw5jB07FkVRqK6u5j8v/lt36r/q6gv5ylWJtjRTNYCbb9rKZZdcwb+feYoJEyZgMpl6lIc8XFEZrVRgMBgkGk044AuCoPsrbN26laeffpqpU6dSVVWlV2XQJs+CkDBS0xa9h9qv9vZ2WltbWb9+PatWrWL//v2IosiMGTOYPn06WVlZOJ1OfcJ3wgknUFpaitPpPCLPUCQSIRKJoCgKLpcLm802aKWEoSA1l1yLiu7evZs//u+f2LW9jrgS0f0kNLMxLf+7qqqKiRMT5oWHct79RQo/bWPRypUr2bB9B5O+fzsABouFUWeew6gzz6F7Vy17X32B25+7lWKbm9OnnMNJ089NelYlCMMDDvoHjDyNBuMRIaxSfTq0MStB5gUQc9I/niga+1Ye7O94skLU5yXi6STQ3k6suwtTLIoYCGDwecDvQ/Z2o/ST8nPw44vHRMGgQVMHpZJ42jxZG480aARdPB5H8h247v36qKAiCv0TB1ogwGaz6WPlwaBV6endjsVi0ZUVw021SGcNIJA+EaUoCqFQCJ/P18cPYaDtNUPFaDSKKIp0dXUljR/jg1Z9kCRJL3F7OMt0p6pED8c66WiNhRmCIYNjCi0/EjIEQwYZHCpEUaR63myW71qbNsEwIreY9vY2YrHYsKq2aJFjLdWpN3GYwfCgSf4HGxdVVcVksZE39gQcheVs/O1NxOMxvc69w+ZiWuVcplfN6+FvI8tyYkGgKvCqVnILzCYT5uTi32KxMHfyqXy07XUaGxtRFIVx48YxZswY3TBLURTKysp0gsFus1BaatMXCmryv3m5Tipy5/D1627h29//Bud/8fwekbhDhUYwBINBAoGAHpXTJoVaX7du3UowGOSyyy5j6tSp+j1vsVjIysoiHo/j9/sBDkvVhra2NjZv3sz69etZvXq1rp6YNWsW1157LWVlZT2UCtpi8EghEonQ1NREV1dXoiJBd7f+mSzLPRaI8Xic0aNHpzU516Dl9vr9fvbt28e/n3qGNSs+5uQpF5A9bhSdwi4KCgqOxKl9JqAoCrfddTf/eu45iq+/BdHYN1qcUzmOnG//kPjXbmb/u2/x2MN/osvXzsWnXK9vo6oKiqIiCLBm5zvIeyJk57hQVAWvvxvB2r+D/d0/v4vVH63EaDQm1U0KUjxhxCknS/7JkkSHp4tAMERWrjuhkoBk2pSIQRQRBJFgMMjiyqVpnbcv6GHjzhUQj1H/3RtRVQVVURBNJlRJwtvdjd/rA1VFkeIYRQNWsxUDApIc5aqlV3DCtEkUFBSQn59PXl4eN1/3dX71zkMU5uQjCgnD2pisleJTEoa1qqIvvv2hAD6lr2/L0YKqqgSDwR7PpMFg0IlYjWDQ3rEmk0lfdKpK4l9/lhMDlX9NVSYM1i8tZS8QCOhpZqnve5fLhdPp1EmG4WLQ8VYQaGpuYMOGDfr3XFJS0u+mWvUYr9fbR70x0PYawdDV1aWXldS8GQbrmyzL+P1+nWw7nEhVBH5akCEYMjhm0GSrkUhEz+/KLEr6ondd2gwyOBi++b1vceWFl7OocnZaJENMiiWlocN7eWVlZTF27Fi2b9+O2Wymvb2ddevWUVxcTHV19bDa/DxAmxhqrvevvPIKgUCASy+9VI+id3d360ZVA1XU8Xg8dDbXo37wDCCgCLCjbivjR01BFMXEZEeWkGUJQYAVm99m3fYPkn1IRh4VmX+9+f/0ibfL5URBRVUUfH4fBsFIJCLT3NjKv558mo+Wr0SVVSZOmoiAkCizJitEolG6u719x6rkr1WjZnBq0fn8+fcPse2TGm74+tfIycnpEa0f7oJeM2vcvn07H3zwAatXryYajZKXl0dRURFer5f29nZ94btmzRrcbjeTJ09mwoQJusGaNsnUIlbDJUG0a7B3717efvttampqkCSJ6dOns3DhQhYsWKB7A6QqOY40/H4/P/vRT6n9ZAdF7sJEWVKDAYNoQBQERCGhLBAFkV2Ne5haPZ1/PPX4oO0Gg0G9KkcsFuPZZ57lycefYerIBXzj/F9itdjYuHMl+/dvPSrnajQasVgsx5lx3cHR1tbGj+74BSs7PFTc/yhGx8FTdUx2B2WLT6Pp0QdZOO0MIJHilIiCRwEVUTSyce9/uejyC7jmmmv061FYWNhvm2tWrubyyrMZV1SBIIiIgoDRYOxxfxhFA+9u+ZD/W/5vfnLFnxPbDXPuFo1FePa/D9HirePMs0/jpa/9i1mzZun3SCwWw2Kx8Jt7f0Nxo5VTJy/CZbFjNSd8U/a17ed7r97LN268gbFje5pfPvDIg7S3twMH0ma0iL2moNHGSG08TnXSP9LQIuyhUEhPUTCbzbhcrh7jjsFg6EH+i6JIVlYWhYWFdHZ2ElSDCEkjxN4Eg6qqdK3v4oJFF/Q5/lCeQy1nX6u0orWd2kY4HNaNczXiIt3qFOnOb7OdbtzWEdx72x+JxsN0+Jr4YPl7/W4bjUYJh8NpEfWAfm7Z2dl61Q6j0UggEEireoOm4ozFYgOec+q9li60fdI9j+MFGYIhg2MGSZLw+/36CyRjCjcwMiRDBumiuLiYsvJyfOH0IjE1zbuYPG3KsMtJ5uTkMG7cOMrKyrBYLDQ0NODxeKisrBySGdznDZpCobW1lccefYy/PvQIQYvInx56mNKxlUmJeYSzTjyRpZddOuCExWKx8L2vX6srSF5rHIs30EksHsOY3CehVpABgf+89yijiydSUTwh0YAg8Endela4OhFEA/Ftm/nmaVdSXl6OzWbjvffeI7IxwPyqs3FZczGZzOzcuYFOXzNTCpboY7aiqqixUHKRmWyaAwqGxDkrFLnLuPasH/Pch4/ww9of8ct778RqtR7yIltbLNTU1PD444/T3NxMNBqloqKCyZMnU19fj9/v1yNaa9euJRAI4Pf7GTNmDDabDaPRqKseNIJBkiQe/MsjrN28hS9f+EUWLFgwpDJhe/bs4a233sLj8SDLMjNmzOCGG26gqKiInJycoy7db2lpoa52L3+54ldUFJUnU9T778MnjTv5+55X0mo3GAzS2trKhg0bePThv5FrKeP6M3+KO7tI30ZRZaLRoXk4DBdafvHxVupaVVX27t3LunXreOu1N6ieNyehAJNl/vbc8xgWn8mIr3wd0ZSemmz/O29Qas+jJH9kkrAM4/V26zJ70SBiszqYPn068+fPH7Q9m9VKRX7ZoOS00+JAFEQMhkO7vu3dzfikFj746N2DvoOsFgvZdhcFrp7PnqoeePZ7o7KyksrK9EpUHgsoikIgEKCjowOPx0MkEmHUqFHk5+frqUP9QSMYioqKEgRFOIRoSBK0yWc54onQtasL/2Y/I00jufbqaw+pr1qVi/6+I1mW8fl8+P1+ffzUiJqhVKcYfH6r4rLlcPnibyXSWVSJ+579Lh0dHf2Oo/F4fEgEQ6pJbH5+PmazGVmW6ezsTKt6QywWQ5IkgsHgQQmG4czjh0NMHGscXyNvBp8LaO7V2gvQbDZjs9k+ddGGowUtMgcHrl1G7ZHBwdDa0Yqn0JvWtp0BD8Wl/UsM04HZbNbzyDUJfoYMGxhafrrH42HdunU89IcHmOqspLp8KssdUQynn01w7HgUoHvTxzS2t+N2uw86Nl599dX6z3W76zGGjYmoo+4KnXASNxgM2G0OTpn9RSaNma7v89qaJ6m69Q4MJiNNd/wPX/ziF6mqqsJsNlNYWMiH767i1FkX4vf7EQSB1u79WEw2RhVUYbPZsdttxONR2jzNSJLC1i0eAoE4kYhCXp6FaEwhFpP1SKfT7mLpqd/kqWUP8MWLLuHExSdjt9uxWK0YDQaikTAzpkxh6pQpmEwmzGazXm0AYPPmzZxzzjk9onr79u1j165dbNq0CY/Hg6IoOBwOJk2axHnnnUdnZyd1dXV6qkJDQwONjbvZuXMzc+bMoaysjOzsbP1+lmUZj8fDNTfdzG4MZC05gx8/9zLW3/+Rv93/ByZPnjzg96EoCr/4+R2Ulo2gtrYWv9+P3W7XKxrk5uZit9uPijeA9hx2dnaybds27vrpHZw8ajZG0UBjZzNmg5ksm7NHRFdP16D/Sb/WZ60kWygUora2lvt//wDN+zo4feaVjB85BUHo+Y4aXTKBj3b2jXweCWjVV44lwaCqqp6us3fvXl5+7kVWf7QaKyamFo3nxfde5okXXkB051Fx6VLyfvJLrIVFgzecAkFWaAq087O/38ypk85lTNFUQuEwkiwhCCIOux2Xy0VVVVV67QliIm1qEJiNJpo79vO3V+7jq+f9IK22mzr28Y+37iPLmZ3w3zBa8Pm6yR+RkxbB3d+9qB7EtOE/zz3Prr179XtNUWQUVUUATluyhNlHWWGnLRIlSaKrq4vu7m7dINntdmM2m8nNzdWrqQ30jGhR8lAolJDiJx+zQGuAjjUdxOpiGCIGFsxZwOlXn95nrBwqBntWRVHUvVwCgQCvvfoal152KaFQiHg8jtls1oOI6R1vwE8QRUFPrTYKBlRF0Svt9EY8HkeWZcLh8EGPnep9oaqqPm4M5OOiKIpuGpx6XhqZciTm50c6de5IIEMwZHDUIcsyoVCIaDSKIAg6wXAoA+BnGZoRlub6rUnnPm2DTQZHD+MqK5Hk9HIAOwMe8icNPydaM+tzOBwZ4isNPPnkk9z58zvJd+YyoWA0Xxn/BU6dsoifPvNbssa4KDv3QiBpntnUwL73P2bZsmU9nvve/7SJjSAItLW1smbHZpo69yEpEp3dbazZ/h717TsRRAF/0IcsSxhTcrs1ElM0GDEka6Nrebnjx4/H6XTidDoJh8PEYnEi0Qhb61fz80evByFJJgkgIBKOGrnvbg9mk5VOXwunnTWfgoJC8vJELBYzopggSsxmA1eccjNf//2F/OPV1xl7/c264iLSHebFf/4Lq82KGouhRCKIJhOoKvFohHhnB2PGjGHatGn6OdTV1fHWW2+xefNmvF4vRqMRl8vF5MmTOe+884jFYrS2tmIymVi/fhUmU4AvfCGLhgYPt99+Kzfe+C2WLFmiE93d3d10dXUxf3Y1+z5Yjr18FI4Tl9C1fg1f/ea3efeF5wascPDg//2Z5/7+NDE5TlCKYJGNqFEFp9NJaWkp2dnZR9S9uz+sWrWK733ruxgUgXZLByuaNiIIAtFYlFFZpXx/8XWU54/AZDIiigdMOuVBxpFQKERnZyc3XPd1FlZ9gQu/+M2EWV8/yHHl4+nqpqWlZcC86cMFLeXlSDjeDwXd3d386q572Ll+G5dMO4el5/2UwqxEPvjrO1bTUJhD2RVXUVw9d1jtj7n4MkpPPZ3dLz7HI/9+kHkl85g/7qzkgsSA3e7A6XLp5pmDIUEQD77d/AnVnDFhEbXdzWn3tdvfydgJFdz3v79GkiTdMDAdTw5BEPolE1RVQRCFfsmHXz/wAF3jJmMtHQGqmiC8RIHOlcsJRaJHnWCAxOI0Go3S3NzM/v37cblc5OTkkJeXR2FhYVrEm+aJEAwGE6VfjQJta9owbDZw41U3ctoPT2P8+PFH7V2spZ5YrVYe+OP/8czfnmLF+8v5zq3fo6KiQlenpc7xJUmipqaGwsJCrFar7t0SjUbwh310+zsRxcQYZDKa9fNO+AapmDEliAhB6KGWSL1+mgJNq9CjtaFtq0FbkwQCgR7EZH/bwgFCQjOC7I9gOJwEqnZenzbj7AzBkMFRh6IoxGIxZFnG4XBgs9n0yXEGfWE0GnE4HHo+mZYXrEU0M9ctg97wdHXjHp2b1rYzRk3hz+/8i29/99vDOpZ2/5nNZrKzs7HZbDq7n6pk+Lzdp2+//Ta/uP0uikoK+dJFF7J48WJWr17Db+/5A12dXl678VGcVkdiEpW6Y8ov0VicVZvX43+nPWmfDYJ6YDItKMlJt0IiC0GFtu42WltbybIl0hkmV8xGEEX8QS+CIGIx2ejy9yxLKiCAqvQ4dur3KggCdoeDfBWCwQBWs5VS9xjOrr6SnCw3BfmFZLmyUVWZ7//pCn550z8A+PNLt3H33f9LRUUF11x1PR9teptPdn/cI20iy+zC0+3Fs6uWymtuwGA0YjIa+w1jqYpC/VN/Z5anVScXWlpaaGhoYM2aNaxdu5b6+noURWHq1KmcfPLJLFiwQCewBUFg2rRpnHHGuaxatQxBgEsvzWL16q3ceeeN3PELJxPGV3HmmRcxb948QqEQSy+9lMlVVdz++3vguz/BPWsOTds28/Bf/8oPv/OdPn3csGEDLz/xPH/9ym+RI3He2bacSCTC37e+wPnnn8+YMWOOyvtOlmVd3tvR0YHZbOar11/D4sWLKS4u1reJx+O8+cab/Pix3/GLM7/N2MJRSJKcWJwkPUI6Ozv1Z1kzQNMMISFRHvfKLy9l28rGAckFRVHYsOMjBJGjsugPh8N0dXXhcDiO+LF6I1EaMlFf/t677sG3rZUHLr6TbOcBQkqSJPZ6Wznhvt9hLyoe3oFUkKQ4sslMqG43WQYHc8efjoCA3e7A4XBgt9sTPgppKjnSvS1NRhPTR02mtmZ52t0VBIFwOITX66WsrKyPZ8LBoKoqQj+pPIraf4UEAJvDQfFZX8BeVt7j76LZjBr1pX3sQ0VqCoFmiGw2m6msrMRqtWKz2XQfmnSgefcEAgFisRiKomD42MCzTzzL6NGjj3qwTrv2O3bs4N2X3+LZmx7kvzvW8LPv/4T7/vx7ysvLCQaDRCIRvYT15s2buX7pNeRl5RKORfQqP7vq9vCR+hFWs11vP7WyRyLdL0qW1Y6qqnjDfqqnz0u+E3uSTIqiICsK3d1duCw2XK4DniYqkMqkybKCrMgIZpHX33pD9zzQgqF+v5/Ozk4goYzwer36PEebk0MiNctut/f7vKUadQ5FrW0wGMjKyurhf3E0kOr7MJzqIBmCIYOjDo1gUFUVi8WCzWY71l06rmE0GnE6nQmznq4uRFHE4XAc9QhYBp8eFBYV0uHvSmvbiSWV1L1UN+wqEho0abndbtfzHrWcwc8buRCPx/nRd/+HvfUN5BrLueu233Bf1u/IthZx0/m/4I6/3IjR2DPHVkzo0Xu0Y3S6sGbbGH3h6LSvobvRjfcOHzddfBsGg1GX5BoMiXzp+/75PwhiPxN1RUkobYWe0UAtGuOwO7Db7BiNiQiN05qF1ehEUA1EwhHMJjNmi7HHOWiLLICOjjbauhv6jFunzb6AguwSHnr59+x2uRhz0eUYDcZ+Fzrhxv3I773Jb194Xv9bc3Mz69atY/Xq1axdu1ZPvTvhhBO49tprKS4uPkCS2O1MmzZNz5V94YU3KSg0MqvawhlnuLFYc2hoaOblV+7lqacKueGG7yCKIjNnzOD2b9zELx79M7bb78E4cgytHU39Xv/l/13O6WMW4DDZaO8OMLdsGj9/636KRpWyePFinWAYKsLhMPf/8X7mzZ/H/PnzB31WNfKgpaWFmpoaRo8ezQ033IDdbu8zSa2qqqK4uIjb7/0DvzjtW5S7SxNVBGSZWDRGR0eHLu/u7u7G4/HoHhV5eXnk5eVx0ze+znkvX0BLZwPFeQfy91VVZce+Tby/5QVKRuXx5NOPp+XofqgIhUJ0dHQMmjd9KFAUhZaWFmpra9lVu4sdW2vYWbODhoYGTCYTnoAPowSnjFvASxvfZvqoyUwtT6QqrNi9HjE7Z/jkQhLxuMTuf/4N1q7nyjnfRlSNCKKAw+EgLy8Pk8mEP+Bl+fLlzJkzhxEjRuj7yrLMyy+/rJNRsViMXXt387t9D2M321BUFVmRkGQZFRVZ1crmqsSkOI0dLWTlleILdrNx50d0BJqQFQlFlRNVIJJjgcFgxCAY6fZ3Ude0h0svWMoFF5/Pr35zz5DOtb8xUEudGuiz/sdNgQ3r1vHWW2/1yc/Py8tj7tzhqUkGgqIoeDwempqaCAaDyLLM+PHjqaioGJb0PZVgiMfjWA1WvnLpVygtLT2s/R4KFEXh7p/dyVdnfQmXzcW500/FG/Xz7L+e4fu3/oDf3PNrZFnmR7f9GKvViizLTC2v4jfn/whQE4bDisLlD97CjBlfYP7U0/tcF1VVqdmziSffuI/3f/gEggAzbz+H2778cL99UlWVeDzO/f/+CV8YN+v/s3fe8VHU6R9/z2zfZNN7SCAhhN6LIAiIIEqxlxPsvXvqed7d7+zt1PMsP8tZEBW7nigeqIhSpChSQk2AkALpZbPZbN+dmd8fuzNsQoAAtrtfPi/yCpn9Tt2Z7zzP53mez8O10y7RiIiwBlLYgQ5J4UyHr7avYr2yW8usCQQCGjHSCTKGSgAAIABJREFU2tqqHU8oFNI0M9T9GI1GjEajplXRGYkqiiImk0nreNRV6HQ64uPjtSyRXwrq9ZNl+ZhK2LsJhm78avhP7U/9SyM6kpiYmEggENDEMdW2QN3oRjTikxJwVbm7NLZo/07yC3of932kEgwWiwVBEHC73dTV1ZGUlKQp5f+3YenSpVRXVx9kLLS1taEEJSxGMzNPnIvD1USjo5ZR/SaG03QVhVCofV9tfyiIJIejISjhqI0iingdHsq/KUcQBQS9EP4tRH7rwnODqBO1v91NbqSAKuoYicxIIRQlbBzIskxFzW627P4eRQnXMIekEE1rv0M0GnG32LHb7Vq0xuFwEAgG2F9fFj5Ovw9EGbu7npLqjZpxZbVYMJqN+IN+lm9YhKzIlO8vZfny5ZSUlCArCj3S8slMzm13rRQUviv5lHv/8HvmffwvWvML0I0ai16vC5dTRL0eHN99y2W/u5BevXodWOZwsHfvXhoaGgiFQvTt25dhw4ZpYoyqarw6j6anpzN8+HDq6+upqKjgnbdLufGmJCwWD6Iokp8fw+BBPfhsUQ3z33iRRx7+B36/n8mTJ7Nq/Y+s+eRDDD1yaXN3/nw525yIgQCBQCBinCmckDOUb5s2kJ2dfczCjitXruSLT1aw6ssNVDXdzlPPPMHUqVPx+XwYjUba2tpwOp3hFOIIqaRG1vr3709ycjJWq1VLV+54DDNnzUKWZe558lnijOGof0iWSOqRpolaRutgqKrqarQuPj6em39/PW//830uPfVOAEqrdrBq+yJMcXDPo39g8uTJ/7Hv+5aWFsrKyigtLWXXjhL27NxNRXk5Fp2JXkk96GnLZFhKL8454SRyTs/i6WXzqDQ0MeOMmZSUlLB29042fL+dZ3PuA2BTxTb8zY1svuUaDGnp6NIzMWVkkjF6LNbMIziKkedWkRVq1q+jddG/uHrsbViM1ogmipWYmBgMBgM6nciIwknc96dHmT7zZJ78xxPaZl5/bR4vPPYsM0ecgiiIvLv6U2L0ZvoOGhkWcBR06EQxTPgR7iahdpLYVLmdnYEyhmQP4NUv7mfG7OmcPubMdvXrOp1OK+0MBAIUFxez8lsBr8dL3/59j+r6H6rVooJyyKwLRVEizG17xPbuw749xdz14UIQRBQhbGeFfD6CWzayu2jzUR1bR6gp9F6vF7fbrWWexsXFkZCQgMFg0HQDjsUGFkURm81GSkoKdrtd02yIzir6pbHwk4XIDT6mjT9JW3bmsGlc8e7d/DD5B4pWb6AwuRe3Xn8z9z/6IC0tLSiy2s4x7OzLskyLx0lcTNIhzkWJvMvCTr4ghDMRDt0S8kBrR52gQyUWFEWJlMwIWpcLnajju/0bOOfmOdp8pzrXVquVlJQU4uPjAbRlwWCQ2NhYrFarFuHv2J0EDpRlqJlNOp1Oc94PhWjSS5ZlrZOIIAhIktTuvvm55lS1nEctzT5aG66bYOjGr4L/VCPj14TRaCQpKQmn00lDQ4PWhqubYOhGR6RkpGLftb9LYyubqhh5wvHXohoMBmw2myZe53a7qa2tJSMjQzN2/5vQ0NDAn/9wN7I3xMTeY0iJUjdXFIWrxl7Ao4tfRlEU8rP7k58droEOG0MC3+5cw9ryTQzI6sOc0Weyo3o3Uo8hhIJhY0mWJRSdDskn41+si0QEw2mcQCRKqBwYG+nr7vN7ULxCRINDQZJlpJCEKIbHSlKQDaWfUeX6GginRGfnhFDeuRdJAH9jiOrqaq2uNRAI0Cs/h+W73sNg0CMIIi5dHQFvC0W7luASGsjMMmB0h7U4BJ2Lst2rEBDpn5DDigVfoNPpKC8uxkE1UkyCdo0qK93k5Azn9QWvkJeXx5QpU7jtf+6h+sd1ZN9wO0ajiMow+BrrUdav4aZFn7X7HlSCobGxEVmWGTBgABdddBF9+/YlPj7+IPInPT2dtLQ0jWD49lsPr8+r4fobhHDKqyxjNJo444xsnnt2H6tWrWDMmLGEQiEe/MufOfX8CzEOuYVVH22guLi4XW37pk2beOmFlzi3cCr+Xn5CwSCeoI9FJd9yyjnTyc7OJiYm5pjef4s+/Tfj+k9neN/xbC1dzwP3PsSGHzfx9pvvkNsrh5dffYl9+/ZpBqFqoOfn59OvX78j1gXbbDbmzJ3LmWedRX19vWYwx8fHY7PZtHGqU6Sqqkdv8/zzz+e9tz/ki3UfUO+sRDD7ueOvNzNt2sHRyF8Cx2NnrF27lm1bt/HNV8uo2FuOWWckJymLXnFZ9EzIZnzv2eSOySLOYjto3Z3Vu9nhKuODRR9TV1dHRkYGqSkpvPv625z5wtUsvOEVbp12JVMGTGBPfRnljfvZt6WCFZ9/ivLFp8TMPpeUyVPRWw9T3qEoBDxual5+lok9phBjjAcEYmKsJCUlYzQatTn3jPGXkZtWiF9p/07wBwJcduJ5zBkXbmG4sXQrk3qP4fKTLzzi9cnamsHinStYvWURK9aspE+fPkdc57TTTuP2228/4rhDoX2JhOq8HXq8LCvohIPvu5ieecRcd3BJoOTzUnPnDcd8fNqRKYqmTdLQ0EBraysZGRmkpaWRkJCAzWY7rntTEATi4uJITU3VyAuVVPw1xJXtdjsv/P05Hpl2R7vnPMYUw/SCCTxwz/1cP/JCZg45hXfWLeSauVeSlpuBwX/gmCVJYm9dOQ6Pk7ys/lrJRPuvXNEyTlSCQS/q+Ovzc5AViTavi7svf56M5By1BgJJkvEFfDS7WsKEeqRsAlTNBBFBELG7HFS56pk6daqW9aQSALGxsSQnJ2uaO0rUcaikwqEQ3QEumqBVv6/ocR3Xi/6/Oh+rBMMvUWanZsr4/f5jyjTvJhi68auhW2X+6KBOJiaTifj4eE0dV5ZlrcVaN7oB0KNHD75xb+rS2LKWKkYWTDzufappf+qLz+FwsHv3blJSUjQhp/8mpKWlsXHrZl5/7XXmv/gaw3UDmJQ/hhMLRmpGxMP//ieiEG7WGApJ+PxeVhX9G0kJ8uL372BOiMFV5eXSceditVhJHDoCx/KlhKr3IQWDuEIS1ph4/nTJP+jYSvDTFW/S0tYUjjSKOgQhbCg53S1sK/shqotEJIIj6tDrw637rryqkLPOyqUzPPhABdnZ2e1Sy199/WVNjFcQBJYvX86Ch17mskFncfFH1zN7tpV+/ROwWGK46QYXr1/1d62biIrbW+8jdayeuXMP1F1/9WU1BsMU+vTpgyiKDB8+nE/ffotzLructl07SRg4WKuvtn/6ETfMnXNQi8j09HQGDRqE3d5Mbm4uI0aMoHfv3iQnJ3dqhKmRn4KCAmbNmkVKSgqffPIR81+v5dLL4jCbPDQ3NWEym5h2qpl33n6V6dNPJyYmhtjYWKaMP5Fl+/dh/d3lXPX725kzexbfrFiJ4gtQt6cKwSeRak3C6/WiKAqLdn9LWn4mp5122jG34nQ6naxft4Fbz74AgMG9R7G1bC1blpdx0eTbeePLx9myZQsFBQVayrVanqR2IDlStEtdHhMTQ35+/kHLO16/jv+HcDnNHXfdxm233M6DD93P7DNm/2qir8djfLe0tHDzVTdw/uDTOTN9IkNGXUuqLbnL21yw8VOuu+1GbDYbtbW1KIrCGWeeyYyZM/n9ZTdq12RITj+GREomSmr34lkf4rlXX+AfL77I13+6Ff2IMcRPmU5Mbq+D9uH1+ih9bwGJXoFhQyZgNJoiJadWjEZj5+nMHUyucKs/ud3fW/btJBAKYNQfIXChKCSYbcTHx3eJXDhaOJ1OFrz9DtlZmRQWFuJytQHxHQ8i8rvz70WnE1EkqdPPDoVjbSEYbiscrtV3uVxa/boq3qgK5ZrN5uN+JtQouJq5oDrcKrH4S+OZvz/N5JzR5Kcd/E6Z2n8CH61fzOlDJiOKInPHnc3Y3iPYVLGNnNysdmKKi4qWkZPdH7Oxc1vhQDkM6PU6dDo9/7rpn/iDAUwGI5e+dgcujxN9ml4bDxJxMYmYDEYEAQRRRNfuOQ6/p9bu3cjoE8doGW9woKQBaGdfe71edu/eTUVFBQ6HA6fTqWlrHNhv++NWf46mbLQjyaAeU7S4MxxoyWuz2cjMzCQrK4ucnBxSUlKOK0u8IzlytOj2SLrxq6CbXDg2qCIx8fHxeL1erQbvcD2Tu/H/Dzk5OWyv243L5ybWfHiRM4ve9JMYJdEEA6ARDLm5uQwePPi4t/9bxZVXX8l5F5zHsmXLePS+h3ky5m4GZBeGP1RAEHQoCuytLubztW+Q0zuN+x66hylTpuDz+Xjuz0+Er5sgIigKri8+5dYLz6eqqoo3P1+MLIfw+DyIooBOjKQeiyLLNnzCmIGTMRnMyJHaaFkJsq+hlJSEDM0A01JEIxoMqmGizheyLBOdiarTiZqq+aGg0+nQ6fUk2OKZ1XsGn336OVlZRrwenxbdMRjUe+FAGQe0F5vT63UYDPp2WVhxcXFcdu45/OO7b4nrNxBE8O6rwLR7J1f8428HHUtmZiYJCQnY7btoc8lUVhaTmZnORRfNPaxhVVBQQO/evYmPD7fMW7jwXzz7zF7mXhwiL8+D2WwmLs6M09mipcIqisIl55/HV7f9HuPcq/Gcfg5/evIxYp1ezhwwhcE543it5gMGJOfj8XjYVL2DtXVF/P35pxkyZMgxZ/F8/vnn9Errj9loAcLG6fRRv2Pl5s95b/kznDxtIsnJyaSmpmKxWNoZyT8XDrX9yZMns2Xb8aWY/1Q41mvw7bffMrH3GK486ciR/I7Y21BJlb+RmbNmHnQsmzdtZnSPIZ2ut3LP95x+1kzy8vL43yefpLm5mY8/+YR5Lz1FdUIKxgmTMSUl4/p+NXJZKa7ehdg//ZCLh10FgMlkxGazYbF23pGrM4FEnV7XjmAY1WswLy5bwKaybYwtHNnpcUqyRFHlDtbVbCExKYHhYzofdzxwOp1cfN31FBssmC0xKPPeoHlTEX+ZeMVBY0Wh8w4SAKKoI3RUtmbnnSqOBJVgaGtro7q6msbGRmy2MPmSmZnZpS4ZR4uO7RBVDY1fukRi8+bNfL9sNS9f+Einn/dM6cGXdy7Q/tbpdBRm5lOYeYDEVBQFj9/Dkp0ruPb8Rw5pyyqKjMvbhtVoRqcLax30Sj9AalhNFnT6A/oG0U65rCiobS6jNoishEuNlu1dyyV3Xd2O/FHt7WjNBDU7ZevWraxYsYK9e/eyb98+Wltbcblc7fYbfX7q9jpb3hVErxu9PUEQMBgM2r02cuRIRo8erWU8/5qdJ7rskQiCoAM2ANWKoswSBCEPeB9IBjYClyiKEogafy7wMTBaUZQNkWV/Bq4CJOBWRVG+iiw/DXgW0AGvKYryt8jyTvchCIIJeAsYCTQDFyqKUnHMV6Eb3fgPQHTUyGAwaEKZv1bNXTd+uxgxYgQTpk/m8y3LuOiEMw871i8FfpIXUGpqKiNHjqS1tZWKigqCwSAej0frLf3fjLi4OM455xw+//BTLf1SlmX0Oh01DftYs+0rSuq+56qbL2Pq1KkkJiZisVjYuXMnAG+s/Yg9zZWIb79GjMXCnDlz8Hg8fPDZIpxtTv7npUuBSB/1iEiV3+9hw/ZvuPKsvzCq/4EMlH9+/CjflX7Llf97trYs+vJLwQA7Xmzj9Xm7DiwUDjggsizQ2tp6yHsiEAjg9XoRRZG4OBvXT7qYH9/bxPvv1XP+hXEoikJDQwNWqxWr1YJBb0Cn16PICov/XcWmjS2aked0+jnppH0HRcHPmD2bJ199jaCrDWNcHM6vF3PnZZcesiNAQ0MDY8fGM316Gm1tCsu+eZq3336Z6667k3PPPa/TiKFqnPXq1YspU6YgiiKfffYZb8zfzcSJJqZODZMjcfES8+fP58wzz2Tw4MFkZmby4G23cs8TT1Jd10gfOYY7Tr+ZHvEZLNz6FXFmGzpRxwfbl/Bd9UbmXHUJAwYM0LQXjvSs+Xw+TcDN5XKx9MulvPrSG1wy9Q/4/X6anfX8sGMZJTUbmHb6FH7/0IskJSVpHVwOpbHQVRxpPfWd81t/76iZHMcSLf7qsyVMzzs2ob+FW77iwksv0rp3yLJMdnY2cXFxbFz3I2MyD9YeCISCLC//gQVPH+hKkpyczHXXXMNVV1zBihUr+PDfi6n/sZlLTxrPwAvO4vrbfk+aMYnMxF4RETZzWJPJ0PWSSZ1Op81XADdMuZTPi74hGGpfG75l304+XP9vWgJOGn0tZOZmc+bcs7l32t9IS0s76mtUUVHBP+e/gS3GSqLNRmxMDKIo0tzcTNG2bdTU1FCe2ZNe19+m3Y9tN119MEkS0aLw+X2d7qfN0YL90w8xpaSBKCDowgStoDegALIUwpicimAyIog65GAIl7OVvXv3hjcfaQcZDAa1kpOOz5YakVZ/x8bGYrPZtO4Qx1oSdTiox6BmJqmp7G63m9jY2CNv4CdCKBTikXse4poxF2I1HotYe9i5lxWFr4pWYI5JoFdWZ9kwERFIRWH3viIGZORFMgMPhkg0QaBG/HXInRFHkfdelaMeu+Rk0qRJ7UhglYgPBALs3buXmpoaKisrKS8vp6SkhL1792K322lrawPCZWbRpQzqNlSoXSmCwSBmsxmTyXTI1t4d7zG1FEYVkoQDpRqSJGmtTzdt2kRDQwPbt2+noKCAoUOH0qdPH+Lj43/xjjpHE/K8DSgG1D47jwNPK4ryviAI/yRMHLwEIAiCLTL+B3VlQRAGAL8DBgJZwDJBECJhHl4ApgFVwI+CICxSFGXnYfZxFdCiKEqBIAi/i4w7eqq5G934D4T6YlEjxceavtSN/26cOmM68x96iYuOMK5nfBb7yiqPe39paWmMHj2a6upqVq1aRSgU0lo8/dadkZ8DiiIzpXA0L3/2IIOGDuBvf3+EUaNGhdvGRQwKQRDYXlnCtvJinn3xWc477zxtfYfDwTvzXuPRux7gjblPdmqkXv7KHXy9/hPKq0s4d0o4+qIzGMiYfS75192CXm9AUWRCkoyoC4u1bf3r7Vw+opbzzs/r9LgferBCE7PqDIFAAJ/Ph16vw2aLAwSeP/cxbv/0Xl75Z4VGMKjbsFojkVJZJiPDyPTpySgKyAqsX9+M1do+HVan05GWlsaUceP47Pm/k3P+HISSbZz71GOHPKaGhmp65JjI7pGIKAgMGJjMN8tqePjhvzBq1Gjy8jo/V4Dc3FxycnKQJInW1lbWrjXy5ZebKS31c8kliZhNfhYvXkxubi4DBgygqakJn8+HyetjAHHcedKVWI1mvF4PP+zbwqjsQTy28mVcliC33n0748ePJzc3t8vZCz6fj5aWFurr66mrq2PLlq24vS6++PEdBEGksXU/J04cy9N/eZJhw4ZhNps18a9fAtHO1G8Z0Q7Y0cDpdLLxx438zzXXHPU+m10t/FC9hT+d/TD79++nuLiYgoIC8vPzsVgsrFy1EnI8yIpC79RceiRlotfpWbXrBwYMG0SPHj0O2qZer2fq1KlMnTpVu+aBQABDAKb0mR0RgAuncsfGHt6B6PiV6XQ6ZNrPzaIoICsK5Y37eWTRc+ys2YNB1KPX6SkY2peFCz4/7s4cmzZtYsHK70iaejrS/kaEQDWCLCFLEqWfLaL31TeTPv6k9t+dILa/5yL/dfs9CKLY6f3obdpHsmc7ickmZElBlsO6DLIMoZBEi92HP+xlggLBoIy3RaG0tBQIO3ZtbW243W5sNptGFnR0GhVFwWwOEzxZWVmkp6cfsTb/eBGdKq+K8bnd7sMKB/7UqK+vp6y8jEDOMe5TCbcZlSSJ9zcuZtyQ0zt9XsOajDIen4eiPWt45+q/Ixzi2rZfHiYZDzUWBAQBvt21lhlnzTrovacSDLIsU1payurVq/nuu+/YunWr1kUHws9oXFycRiyp87FKcqokUCAQ7sjj8XhISEggPj6+nVZK9H6jfweDQa1rRWxsrKaHIMuyRiw5HA4cDgf19fUUFRWh1+vp2bMnF198MTExMVq7+18SXSIYBEHoAcwEHgHuEMJnPQWYExnyJnA/EYIBeIiw039X1GbOBN5XFMUPlAuCUAqMiXxWqihKWWRf7wNnCoJQfJh9nBn5P4SzJJ4XBEFQfutvvP/nUFk2v9/fTnG1G0eH6AhS9ATWjW5EY8iQIZTaK2l0NpMad5h0d1FHKBQ45OddhcViITU1lYSEBPR6PT6fj+bmZpxO569SF/prQBAEZEUmGAxQba+j1t3IsFEDeeSxR0lPT9fSLdWx/fr14w/3/wlJkpg+fXrnGz3Ma+28kadTtG8n/9q0iOnjLiAuJgEBEHQiOoMx4tgr6AQJURQQdToEUUQ+hCL7gV0e+vOONZkmk4m05FReOOdRHv7qOcrca1lV9iPT+k/A4XCEnXGTCVEQGT4ildNnFKBOV1arlTanqV2LVHUuu+CM2Sy79Tb2PHof9914HUajkdWrV/PME//ghttuYsqUKQiCEI7qN9cxcJAhHN8UwOHws3ixi+eem3dYckHdnyAI5OfnM336dFJTU8nOzmbDhu954YVm8vKM1NTUsGTJEmpqamhubmbVtyvJEVK4buLviDFZ0BsMGI1Gmn0O3I0++o4ZxLnnn0thYSGZmZldcnTVd2NLSwuNjY2YTCb69OnDXXf/gRtvvoGysjICgQADBw7EbDZrbctU8cbud0B7qN0LjpbcbGtrwxRj4e5Ff+O0PhOZ3G8clkPUhEdDURQ+27yUCadMoqamBkEQtO4dambJK2+8RtHmIoq2FfPB919RX1dHTmIWe2vLeea157t8fMXFxbjbfGT0y0WnEzFomgtHdw/odLpIRlQY9a2NNDtbuOujRxAFkdMGTuLaiXMYXTCMiqb9/OXLp9i1axcnnnjiUe2nI+x2O/FjJpB52ux2y2VZZu97b5F9xjmIHZwuWa/j8aUv88b6hWG7Rwh3s3C4HOjjOi8JKuzTi0suFcjJ6RDVV8L2qCRL4elVCRdG+Hwh/nR3paZBorZldblcmthpZxkMEHYy1U5K6vP+cz2ToiiSmJhIjx49qK2tpampiZKSElauXMmkSZPIyDi+1qddRXZ2Nh9//glXnH8Jw3MHkhybeFTrhy+9wve7N1LjbOSGEWfQ2T0sCCAIIpuK15Bii6dPeq924pZhAeRwRoXT2Yrd3hwhJRQkWcLrcROI9VNfX99u72GiW2bh5q84Oe9UXnjhBc2+liRJ07Vwu91UVlZSWVlJVVUVkiRhsVhITExk8ODB9OnTh4SEBGJjY9vpUIWPXdAIBkmScLvdBAIBrV1wdLCw/TkfOvNBJSRU7Q1V+0PNXNi7dy8ul4vm5mZWrlyJ3W6nb9++9O3bl8LCQq1V7c/9zuhqBsMzwB8BVS43GXAoiqJKYFYB2QCCIIwAchRFWSwIQjTBkA18H/W3tg6wv8PyEw63j8jv/QCKooQEQWiNjG/q4vl041eAKkoYCAQ0UZJuguHooU5UsiwfcwpoN/77ERcXx4jRIymuLT0swWA2mGhqqDnu/akvvvj4eHQ63f9PggGBUCjEtn27eGDps4yZciJzLp5L3759sVqtB423Wq3MnTv3kNtzu90YdIeOfM8aOY1ZI6exaMuywx2UZuS0bN6Ar2Qny+1QUlIRIQrCw1SDrLbWe1g9F1XR+ofyIq58726NaGh1ttLicGDUW3mt6DNe3bTwwP4JtzyUiyUWfujQtuX3+zEYq1jy+XLOPGsW1994HZmZmQCccMIJ3H7D9Tz59NOY9XqcTidPPvQ4I6yFvHT/M8x/aR633HUbRqMRt8eFIeqQly1r4MwzL2XSpEmHvi4dkJ+fT15eHllZWWRkZGAwGPj880W0tIS1bpYsWcIXX3yBVW9mct4JXD36fPSiHr3BEG7VaDbiDfpJTkvh6WefJjs7+6icDLWGWyUY8vLyKCws1IzPESNGdPlcunFApf1oCYbs7GxWr1/D2rVr+fjdD3n5zffAK3PlhAuZNeyUQ75vvQEfS3av5K6L/6x1F+nXr1+7d/TIkSMZOfKAZoHX69VSr0866aROtxsNRVFwOp1888035Kb2jWRp6DEdStSxk/WjodfrWVe6Eb/kZ3NdCV7Fz6zzzuCLz5fw0TUvkJl0wFHNS83lr1Nu4q+3/Ynb77mL2WfM7rj5LqPebkcXd3D3DTkQQA74WX3e6SiR7gKWgkJOeO5Vet//CO6yvchSONNBlmXkUAj/3t2YNq8/5L46ff6EcFaVjo7XLKwJk5+fHyaLZRm73Y7T6dTKkI6WOJAkCYfDgcFg0KLbxwudTkdSUhI9evSgpKQEv9/Pzp07wx2L8vMZPXr0ce+jq8jPz+eCyy7ioX/9L/8z/WZSbUlHXikKiqKwYN1ChvU/+TDXJpxpsHbbl5w9bGokcu/F4wmTDGoWiZqF1mxqjhAF4VaQXp8Pj9cbJhgiZJL6e09jBWW1lVQtWIDVatXIBZXwVbsoRJPqBoOBmJgYevbsyezZsznttNOIj4/vUnmKeqxHY7tHZ411tp7qW1VUVPDee+/hdrupqqqiubmZFStWsHnzZoYNG8bYsWOxWq1kZWX9IoT0EQkGQRBmAQ2KomwUBGHyEcaKwD+Ay3+SoztOCIJwLXAthNMgu/HrQpKkcBswSSImJgaLxdKll2I3OoeactVdItGNQ2Hi1Mm8+sSLVNqrmXvCWZ2+0CYUjuHVdz5gx44dDBw48Jj3paYkm81mbDYbLS0ttLa2UltbS3l5OSaTiaSkpP+6dpVqNpHD4cDldrGy5HtWVW/kmjtvZMiQIcTFxeHz+TRFalEUcTqdvDH/TaaccjKDBg065LaDwSAxpiPXth7OWIjOcvLV13Le6adxx623at9Dx17ser2enJycw+5v2LBhvPHBAmJiYrSa0JKSEoqKinjq8We4atr9eH0ugoEQYiRC8/Ga5zlxwOmM6HciBqMBg97I52vewuN1c/ns21m3fRlnzjwLjx5gAAAgAElEQVSXZ57/OyeeeCIGg4HKsgr8jU4ev/8x3nvzXbJNqVw3Yw4K8N3u9Tz8+3uRY0X2V1ZiMmVrx9fcDFNPOTrxOfUaZGRkMGLECKxWK/369ePbb79l06ZN+P1+Yg1WZvabzJwhsyKCmQIWs5n4+DjW79tKelwK+Wk9aWlpOWqbw263U15ejsViIT8/n+Tk5N9kdpp6nX6u41LfZWptccef6DHq/1VVdnV+UxSF1tZWPB4Pra2t2O32I+5XNeD1+rB43OjRo5kwYQJ79uxh+tRT+evCJ7l34d+ZPXwat0y9nLS4FG09WZb5fPPX5PcvoFevXgiCQFpa2kFq7x1hsVgYNGjQYeeAjtemoaGBH9b9SGZCL1TRVJPJfESR586OoV+/fqT2z6bg1BFccuJNFBQUIIoil9svodbZ2I5gABiYXcjjM+/mLw8+SVNjI1dcdWWXjrsjquobEFI6KQcxmznp02XIUojKBfOo/moxtj7hLhvGhCSMIyLOq0aKKuiSkjGW7jzkOR8pW6sjKioqGT3oAJmn3hc6nR6z2RTennr/RS6pJElRugh6JCnULtNUkRWsZgveoI+8XnkkJiWRlJxEnwGFXPC7Cw9bktYZDAYD+fn5+Hw+bd51Op3s27eP3bt3U1xcTFpa2nGXsnQV195wHStWrWTe6vf50+k3HvX6uxrKueKkSzv5JNxmUpJCbN21gfrmfczocxcNDQ0EAgGCwSCBUAC334ukSASCQYKBAD6/P6zPoSjIsoTH1xY1fyjqPwBWVfxIq8+JWQhqelEqWREKhQgEAohiWPg4LS2NwsJC8vLySExMJCsri6FDh5KQkKBlIxwOqg8UCoWwWq1dtoUCgQB2ux2fz0dSUpLWLlOFIAhYLBYyMjI45ZRTyMrKYt++fZSXl7N9+3bq6uqorKwkFApht9vZsGEDo0aNorCwUMuk+DnQlQyG8cAZgiDMAMyENRieBRIEQdBHMgx6ANWEMxwGASsik1kGsEgQhDMin0dbLeo6HGJ58yH2QdS2qgRB0BPuXdPc8cAVRXkFeAVg1KhR3R7YrwyVFVQUJdJO6VhEYbqhvsjUFFDgqKM03fj/gbPOPoteeb149smnef/HRcw54ayDxsSYLNw24TJuueJGnp33/DF3fFDvS5PJpKWJtra2UlNTQ1lZGYmJidhstv9KgiEUCtHU1MT2kh1slWSeefE5Bg8ejNls1rQoVHFWURTZu3cvLzz7MoIoHNa5UBQFgS5GOTgg+KrIEnIoiKxIgIASEchSJIneBQXHRSSpzlxmZqb2fRoMBrKysjj55JN5+cV5JCUk4fFYcLlcKBGVellSkIIKfm8Io96MyWDGaDChKBBrjWf84OlsrfhOIzcaGxv5YflaXrv8cf788eOkBGO5acrFyIqCTicyqd9YJhSOZvHmb7h/22oQDsyBTqd0zGJn6enppKWlMXDgQPx+PzabjV3bi7EIRi4YPIPZfU+Oqq/VYbZYiIuLZ9WeHxjdawhu3bG1irPb7ezevZuBAwfSu3fv3+xz8kuU5UW33VPbv6k/Hcl0NeKophqr5MKXX35JTU0NmzdvxhYbi9ftxevx4vf68Hl9BHx+0Anc89B9iKKoOSBmsznSQSROK1Mp3l3ClHGTuHbYBXy98ztm/eMK8tJzuG7SXCYWjqHN4+L9LYu54rZrMJvNWrvQn+O6NDQ0UL63kvH5YQItTDCY0OkPdm4URaG2aR+lVdvZVLqSKTPaZ0mMGDGCN95966D1krNT+aG8iBG9Dn4X5CRlkpeQzZbNW475PJqamjD26fw9o488twFHC2kzz2LA9bcefmOCcMgqsvB30HXTX5YVRAS+uPENABRZJhgKEpKkcGkZMqIQ7uaj3ocKSqSVbph3CMkh9KI+nNaPEGkhHBGq9LpoctlxeNpo9TjZ+sV2Tn7uRV547SXGjRvX5ePU68NZFjabjeXLl6PT6XA6nQSDQY1g0Ov1PyvBoCgKS5cuZdGHn1K0uYgcWwZzTj7YvugKbGYrra6WTvYBsizh9wf4rmgJY7MG0NLcwv6WWt4u+ozylir8IT86QQcCuANePD4PAb+/3Xb0oh5REJDVeTnyaAakIKsrNhIIRr7jDuSpShKZTCbS0tIYPHgws2bNYsKECVpJxNGQrWqpg9cbzhLsqmMfCoVobGzE4XBo9lU0VNsiLS2NU045hXHjxrFv3z6KiooIBoPU1NSwf/9+Kioq+OGHH8jPz+faa68lJSVFCwr9HDgiwaAoyp+BP0dOYjLwB0VR5gqC8BFwHuEuD5cBnymK0gqkqOsKgrAiMn6DIAhe4F1BEP5BWOSxD7Ce8FfdRwh3jKgmLAQ5R1EURRCE5R33Edn0osjf6yKff9utv/Dbh2qYhEIhvF6v5ox0t1c8eqjGlKpj8VuLcnXjtwFBEBg+fDiPP/0kl194Cf0yChjR82CH9qTCE/i+sog9e/YcF8EA0LNnT2bMmIHNZmPt2rVUVlayZMkSgsEgqampWgu9//R7Vo1w+Hw+PB4PBoOBBx97mJ49e9KrVy9tblNrJ9V68EcfeYz33nkfWVJ45cVXefetDwAFozHcb1uNoIRCIeoaamlu2ceKkq+0WmEIm8xmg43U+CwUFPxKgKc/vhNBCGdHBBoEKrdtRqfTI4gCAoL2O+36a3+S8+9ojKnRY4PBQExsLCaTGYvVgt/nw+vzaca+3+8HFHw+P16PD3/QR21tLSu3LiIlM4kNGzbgcDhYMP8tZvadzMj8ITw75wGufuOPvLnmY+4547aIcR8u75hSOI6Hv9ZhMokoKJSXuWixm9uloncVLpdLE3LbvHkz3333HatWrcIf8HPR4Nmc0f8UTecBRUGOqtPdULGNC4bPYFNzCQ6HQythOxI8Hg8ejweXy6URRL+F50M1qdTzU7tbNDQ0UF9fj8fjIRQKae8gj8eD2+3GaDRiMpm050Ntp6f+jl4eHeEPBAL4/X5t/WgYjUaMRmOn7d6iU4fV5evWreOzNz9mYsEJmC0BTEYfCUYrFmMSZoMJc6IJs8HEX5c8pSn9t4s4R1rRhcVM9Xz//fcUJPXktOEnc9rwk2lsbeaT9Ut4+LPn0Ov1FKT1ov+IgUycOFHLPPm50NLSgsPhwGq0IQhhZ9NsOZDBUNu0jzVbvqLOsY+A4iYhKY6JJ5/EBbc8wNixY4+4/ZUrV1K0aiNPzL6708/f/mEhnkSFZ544tOjqkeD2BzAkHL5eP2RvJqZ/FzI7RBFJPgyhdxSegVorH/23KOrQKQdsLkGbADqHkUMTgzZLLDbLAeKzR1ImX5SsxGY7uFykK8dqsVgYNWoUTU1NbNq0ibKyMrZu3Yper8dgMJCZmak9Oz8lFEXh/r/ex46Vm7lw2Exu/92cdufV5XMg/N5ItaXQ6KjhQMaChM/nxefzh3UV2hzsqd7KWeMv5c9fPEmFo4bZA6Zw5djzyU/NxWwyIwhw4eu3Ut60DVfIrhHbiqLQ3FZLqcXIZ3tXALJGSNW0NtCjdy5Xz74ufDwdxBVlWSYQCHfZSk9PJysri4KCAkwmk9blx2Qyad1Foq9PNEmh/gQCAU2sUV2vqy3mNUKrE1c3et+qX5WamsqgQYM4//zzyc/PZ82aNezYsQNJkmhsbGTp0qXU1dWRk5NDfn4+AwcO1LLu1LkwFAppP+q8fTQ4Hs/ubuB9QRAeBjYD8w43WFGUHYIgfAjsBELATYqiSACCINwMfEW4TeXriqLsOMI+5gELhLBQpJ0wKdGN3zjUFGo1ogcHevl2o+tQjU+1vc2vbYh247eP9PR0Jk+bwu7isk4JBoCdjXu5cdiw495Xr169mDVrFsFgkK1bt1JRUUFVVRWxsbFMmjSJ1NTU/4rSKLXvuMvlwuFwYLPZmDFjBkajsZ2D4fV62xEMK1asIC9lCBdceB2BkB8h0ntdkg68vAVBQJJkftj+LUXVr/HU0ydEpeAKFBc7WLc2i1dffU8zitW5wOFw0NzcTGxsLMnJyT/r/NqZI+y0N/Hwa+F+9WpWhSzL+CM11YGAn0DADwi4PR4CQS+l5bv4dtMigkYJk9nApEmTWP31St665CkEUWRAbiGnDZjEx5u/YH15EQ+cdQdDewwIby8YJBAKYjKF76lPP23iuuvuOchJPRLq6+uZftpkZpw+k/POm8PGjRt5/vnnaWxsxOvzsrupPHy+4RPX0m9VwryqqYYXVi7AoDdwy9U3ctk1V/DHP3fupEXD6/VSXV1NQ0PDbzITTW0363A4aGlpoba2lpqaGgKBsDCsaiA3NjbS0NCAzWYjLi5Oq2FWCQOTyYTZbNaWq8a1agO0tbXR1tamra86Rur66viukC+pqalM6Duam8ZfjCnSDq7TcfHJmM3mdpFet9uNy+XSCBOLxcLSxV8yvsfwdutdOelCLhgzm1e+fYe3NnzC2w+/S2FhYWe7+cmgKAotLS14PB6MOlNENyXE+uJv2LJ7LU32/UjBAMNzBxJyN3H6BbO45/57u0x4SJLE7TfcxuOz/kiPpMyDPv9q+0pW1G/grQ/f6VRTpqtoam7GcgSCAUGg+s1XyRh7ErHZ2Ycfe8hNmHjwwe0kJcUS1sAU0Ing8QSYNi2B02e0P0ed7jClZsd0BIeGoijc9uED3P7XP3S5RKYjzGYzY8aMISYmBpfLRUlJCdu2baOuro4+ffowevRobDbbT04wfP311+xYtZmnzvkfzIajm2fbIRJ0zIhLobm1XstYCAT8tLY6cTqdhEJBNu5ZiRT08dzaN+mV0IMe8elcf9LccEcFsxmjyYQoClwwaiYl9WUYgi3oRHWeEJjSZyQ6UYdf9kftWmBXUxl/eexeLr20s/KMAxkHgUBAKwdU/Re1PaXa/jFazDGaBIgmLVWCIVrkUbXlf0ro9XpSUlJITk6mX79+jBgxArfbTVlZmXbsS5cuZd26ddp9EhsbS25ubjtyQe2UoZILR5uZd1RnpSjKCmBF5P9lHOgCcajxkzv8/QjhThQdxy0BlnSyvNN9KIriA87v8oF34zcBg8FAXFwcXq9Xiw780m1T/hvQGTPajW4cCZOnnszDi+9l1tBTiDUf/Nz1jM+iaHORpqB9rLBYLKSlpdGzZ08KCwtRFAW73c727duZP38+J510EieeeCKJiUenOP1bQTAY1NK1/X4/oii2U4qP7hIB4euRnJysiX3FxcbTM3UwBr0R42GMM0mSiLHY0Bt0WK3tIyR6vRgWKvuViJrDzTupySm8MfcJ9KIBgXCXh4Dfz7Xv3YPFasVmi8Pn9yGFJEJSkP3NpbRtfg9/zx7oh4/kyxVL+X7dD5xZcDLuVhc+l4eQLLF89zoeOfcuGloaue3dB8hKSGOvax9OrwuDEuCRR3YxY4aLpsYYxo8fj8vlwmq1HhSVOhS8Xi+2WBlJ/pKrr34HSMPhcBAKhRAEgXqfnbS0tDCpEAzh8/vwen0EAgG+2raSXik5/PvO+QiCyHVv/5lPPvmEqsr93HT7LfTt2/eQ+3U6ndxw7fUkJCTw+ztvJzU19bAO4fx5r7Nx/Qb+ct9fycrKOvKXFXV+KrHf8btThTtFUWwX8VTHqer4RqMRq9VKSkoKCQkJJCQkaOuZzWaSkpI0MkE1SNXIr6pt0FkGgyiKxMbGaus7HA6+/PJLTSQxJSWFQYMGtSMXDvd9WiwWgkgYTabDPiMWo0XLplTP12g0Ehsbi9FoJCYmBp1Ox5rv1nDOzD+Fa+tlBTniLHi8Hrbb93DT7beQl5enZTL91A6dCkVRcDgcBINBlm9dSG3TboIhD33S85hWOIaxUy9lSM8BGPQGWtytXDTvFk6eOoWJEyd2afuCIDD1tGm8uf5f3J/y+3ZR6R/LtvDmtk95/b03SUo6OiG/jufQ0mInLj7hsOMGP/k8a86ZjthJ6ceB4w3PgaaI7k9HLFjwUaRM64AwtizLvPfee+wpff+g8ZLUYU4TQAzvJJIB9tNAlmWeWPYysl4hv/fRv2/V+1UVe+zduzfDhg2jrq6O/fv309bWxrp16zCbzYwfP57hw4e3W+944PF4+OczL3DlqHOPj1wgEimXJFKtieyqq6C+vh5JkiIZU15qmytYseVfuN2NnDNoGucMO41yRxWvf/8RiYmJmv6P2rXh5ulXHJRhEp31FH3+Dc4m9nxazRlnnEEwGNS2E72OOh+Koqh9pgZK1SyRjuXe0Zow0fMKhG0HtVRcbTf5cwQA2tsKetLS0pg9ezbZ2dlUVFRQXl5OaWkpTU1NVFdXo9PpwppHlZUMGTKEvLy8dtkVqs3T1tZ2VMfRHTruxi8GtVesTqfD5XJpgo/dOHZ0Ewzd6CrGjBnD2NNOYt7aD7htysHiXGcPPpUXX5nPWWd3LgbZVag1zLm5uRQWFtLa2orD4WD79u1UVFTgcrkYOHDgfzTB4Ha7tdpwm81GYmLiIQ0FcySC2tLSwhOP/50dO3bQFOuh0VETfn5R8AV8SFIQs9GCEkn9l6QQVQ3l7G928913dahGkywp1NZ6CEsc/fI4ErFpMpn4bMsydIKIAsjIoITrY9ftXMzWspX4/D7qWqpQgJCg0CxL2C6/DzE2FmXECbR99A7/2rIUggqzBp7M95VF6HU6Zgw9GZ2oY3BuP65++270J5hIbk1kTIZCjxwLH7xfxtChE9mzZw99+/btkvCWClmWcbl8DBwYS0aGniefKMbpDGoOsCTKWOJisOiNeDxeaAWfz08wGGTl7u8ZmtUPRQFBULj9lKtpcDbxyBfPM/Ps2YclGNasWYO9upHhI0Zo7c4O5Qhs3LiR915eQJYphavnXkFuz57MOmc2s7qg6O/1erHb7QdlSajfpWpMxsbGtjO01fpek8lEYmIi6enpSJKkGdgqUlJSOFqUlZbx7FNPR/V1D2eFbNy8idY6O6P7DMPj87CzcS8bt23u8naNRiOSEq6dPpxLZdDptUwMbVnEsFaDH5WVlcQZY8hP7UkoJB1Qmff5eHbFfJIKMjjrrLO0ciir1fqTEQySJNHQ0EB6ero2J1dVVaFXJNJ1ErNHnc3EgWMpzOtz0LpF+3YwdNRwJkyY0OX9iaLI3/7+OM889Q/+sPAxHp55B6lxyeypL+ep1fN45rXn6dmz53GdU1tbG+gNiIYjXyM5GMQUf7j3hIBeH/6+OiMYrFZrp5kW6enplJT4D1ouhgPe7bYviCK68IMNHZzXY8WC7z+hJc7HstXLj4usEUVRI+WGDRuG3W4nEAhQV1fHunXraGxsJCUlhWHDhh03ubBr1y4+eu8DVixdzrjMYYzKG3Jc21MhyTJWgwW3u5X6+npkWaK+pYrvdizE7W7itD4TmNH/WnqkZRNns+HAjSfoZV9bLXJk7pIVSesaIclSpIQt3IJSVg6IcipqBwkUVu76njHjTyAQCGjv7miRWBUqEQpo/oooilpbUrPZrM1favQf2mvVqL/VrjYqwfBzaR9EQxAEUlJSNO2IH3/8kTVr1uDxeKipqaG2tpbm5mb279/Pzp07ufTSS0lLSyM+Pl6bC9Wss2AweFT+RjfB0I1fDB0nuGAwqEWI1PSjbhwZaupSMBjU6ry7W1V240gQBIFLLr+USz+dw41S8KD2h0Ny+hO/ycK8V1/j6muvOWaDpKMWgyiKVFVVYbfb8Xg8NDQ0sHfvXkwmE8nJyf8xz73askqNQhiNRiwWi+bEHu56qZ8V9MknJtZKSWVYfClseIg0O+rxupsYmz8sHDEDDAhYQ15i9Gk0NY5Hr9dpKuUJCTBp0qhf5Lw7O5fDpan/5aF7sNvt7cYJgkBW9Q/Y2gyMzx9JU5udt37cz63n/4N3vn6WGnsFyvNPob9wDoYBQwhddSOByjLeefdNlny6ClGB68dfRF1dPQaDATmk4DH6mXDBaLY9vI5plxQwZGgas2cXsHRpLX/84zVMnXoWF110KT169MBmsx2UWdIRwWCQ7OwBvPnmRs47z8SFv4vn9Xl2+vUbxeTJk9n2YxGVzfsZmNUXo9GILmKUKopMi7uV/ukFQNhJLkjtSUFqT3LTe5CQkEBjY+NB+1P7l7/7+gLMOhP5BfnExcVpabOd4dXnX+ayEedwyoDxbKsqwel18bcHHmXGrJma4nkgENAyBAAcDgdut5v4+HisVmtULXn76JoapVMUBbfbrWkuqAay+rlakxudkSDLMm1tbbhcLi26p0aLVYc8Ok1Yzf5Z8Pqb6GuDTBswEZSwXyAAA/JmkjMik6yENDxBL5e+90cuv+xyLQOjb7++WuaQwWDA5XJpRF4wGOTF516gwJpNXV1d5P6DcKu76Ouq0Opw8MYbb5CSkqKdV0cnoa2tjUGpfQAFvU6HLIrodCLvb/icWoODh+56hIyMDBRF1RcJOyJqJsfRorKykgeefIrSygrqauuQQ0H+9eYbDBo0iGAwSFNTEyf2HMGN4+ai0+lIiOm8A8Hnxd9y9b03H7VtIIoid9z1B9LS07njfx/h+nFzeGndO9zz+AMMHTq03Vj1WkmShNPppLW1lcbGRpqamrT7ISYmBrPZjMPhoK2tLSxyF2n/KESUEVXnUMuk0YlIzlYUQaDV40ZxtR2YS0QRURA1YszT1kZrfQNLlizRxO9kWdbu4bi4OGw2m/bdezwetm7dyoYNjbz4YjhDQRBAFEGSZEIhBa/XG32WKMqB50OnExHFY88cW1e6kW9r1vPuwg+Oi1yAA3OZ0Wikb9++mtjw/v37CYVCVFdXs27dOmJjYxkwYAC9e/fudDvqHBAIBKiurqa2tpampibsdjuSJLF2zVq2rtnEOf2ncffoq8iMT6OpqUldGQQBvU6HTq9Hr9eh0+kPzDPhA9X2o2YoeL1e/H4fkiQTjHSEqGmu4LvtC/F57czoP4lzh59Gsi1JIzhNJhNBOUSN084N7z4cvgYcyGgSBIGWtmYEQSQhNil8n0RaRnTMKLC7Gnj++mexWCwHacEdKUtKlmXN+VZ/Ol7L6GOK3m90WfPPXd7cUZvBarXSu3dvLath1KhRbNu2jfLyckKhEGVlZXz22WeUl5fTv39/rZtRUlLSMekCdRMM3fjVEE0w/Jxphf9tUHveBoNBrT71v6GmvRs/P1JSUgjqJCqaquiTntfuM0EQuOuU67jnradYuWwFjz71t+Nq79urVy9yc3NpaWnh66+/prGxUVNDLi0tJTExUUu7/k+A3++ntbUVtZWd2o7zaJ698ePHk5mZyWP3Pc0Ns+/Vasq/XPMvqqt+4OmL79PGSpLEjqpdvFz8MQ8++Dh6vf43If4HhxchPOOMMzpdXrJ1J/2cGUwbeBKNrc18vO0rbHExODwNPHz9PDbuWMPSea/jTIjBdPYFmPoPRLzrHuo/eJOYTZsZnFxIXV0dJpOJB5Y+Q9K4JGKTYlFCEhmZ4W4W8fEGzjuvF6ee6ufThUuYfdo7TJ1+Og8/+ghpaWmHPafS0lISEpL5/ksveXkSI0dYGD7cgk5n5s477+TVf75CWel+huQMCBMMOh2KLFNUtZNGl52Sur1sryohKIUIBINIsoTH59EM/qamJs3RVsVB95aWYvboyM/uRWFhIfHx8Ye9nxrrGygY1RNBEBiS0x+A+Rs+ZvXq1QwcOJD58+bz0dvv4/f5GThkIOMmT+Cl515gyLCh/PW+e8jKytIyjA4F1VFsbW3F6XRGOVbhHzVdViUS1L/r6uqoqanRtBaia3ijf1RxNKfTyd6yMn6XN52haYURB1MmGAwRCgVRAtDQ0IhOp2PugFnYKxwkKbEUN+5l7fYa6p2NBE0yeouRhoYG4uLiiI+Px+12Y29sJi0pjqrqagxqersQ6R+vRTHBbm9h/vz5iKKoESpqCYdKrAzuO4hrh56HLCsYTHp0KHy5bSWr7UX878sv0KNHD6xWqya+qGqzAMdEMDz3yiusVvSkXXULSW4XurdfZdCgQZpj1lBdx7j0AZHabV2nBEJlUxVNkrPLpRGd4eJLLyE9I4O7b7+LP/71bqZMmdLpOFUIr6mpicrKSoqLiykuLqampob6+npSU1NJSEigoqKC6upqzGYzbdY4jHY7oiiiyHI48ixJyLKEwWDEYDTg31cOZgv2ZjshKYRODDv2Op0OnV4Xaf8o43U68dbVsXjxYtLT0zUntqGhgZaWFnJycsjKytKuU2NjI42NjQwaNBWLOUH7niHsYJsEh9b1RxN9VNuXGgyA4ZgJhsY2Ow9+9Ryvv//WcZML0TAYDBQWFpKcnMzOnTvZuHEjjY2N1NbWsm7dOgKBAGaz+ZAEA6ARZOXl5WzcuJGdO3dSVlZGQ30DVo+ee065iQRLHATQCFNVbBcEzCaTpncS/oloEQlRpSWKQigU1LKpnE5nuOzH1UpN016+ds5jVr/JnN5/EjkZ2dhsYcLVaDSoqxNrtpKelM1D18/r9N5/+I2bmTzkDCaMOFUTS5ZlWQvEybJMZe0elu18m1NPPRWLxXJYG6Sz+bgjudDVjj+yLB+RoP85YTKZ6N27N7169eKEE06gvLycd955B5fLRVVVFeXl5VRWVrJu3TqmTp3K5MmTGTdu3DFnLXUTDN34xWE0GklMTEQQBBobGwkGgyQkHL4erxsHoBIMiqJoaVbdGQzdOBLKysq486bfM6nHKPJScjodk2pL4qULH+bzLcu45OyLuPfxBzll6inHvE9RFOnbty8XXXQRa9asYcOGDezfv5+vvvoKRVFIT0/XCLLfguMcDTUS0dbWhsPh0LIX1Ehsa2srTU1NnaZVdga1U4DD4cDjdRMMBTXS4FCI9H8I//83dH0OdyzFxcVs2rQJQRDIy8vrtP2aKIYN9/0N5STFp5KZksOp487hhEEn88j8Gwi9/DxS797EXXwFrr17+P0Jc9CJIpIsU1JfRrFrL4WjCqmvrycYCBIIuPF4DFgi0f/4OBM5OWbikxL4Ydlazis6mzMvPJtrr7/uoBZfaiS0qamJ4hsO8NEAACAASURBVOJiBDFAeroVa4yVmbNiWfiJnZiYGPoO6MfaH7/Wzv/z7d8wf+1HmPVGFOD72q1YyxdrmQ06nY7CEQPo06cP5595LiOzBhFvsaETRPSiDkERSFesXDbpCv685Clyc3OP+B07nU5iOuinZJiSeOGZ5/G6PWQrSbx0zoMkxiSwevd61i/exPj04Wwu2cUHH3xAz549GTp0KMOGDdMidqrjX1RUxMaNGzVHOTqDITqLQSVJ1GVqe0eXy0VbW9tBGQzRte+qM6pmMDQ2NeHO9OB2ezRnRZLC4+yeFvzBIInWeE7oMRivP4AkSeTGZxFvtVHVWsfLGz8g4GrF7XZrgmzBYBB/0M/mmp18tG0Jvxs6EyWkIEci0TIysiIhSwoun5s2X5uWvqw6I+qzLEkSNfur6HFiBo2NjSgCLClZwfL6DTzx3N/Jzs7WnAs1TVq9TkajsZ1D0RW0tLSwZPlKejz6LPrYWGrmvcgdF1yg2UqVlZXU7q+l75iZ4bppveH/2Dvv+Cjq9I+/Z2ZrNrvZ9AYkkAgIoROlKCKCYMHG2dt553l6enbvzq5X1LOcd54N72zYewNFQaQLCEFCh4QESE82m2yvM78/ZmfYQICg3p33u3xer32xZGd2Z77Tvs/n+Tyfp9tn/4a9W5k4+bgek59aUB6LxXRyqa2tjUAwwM9/9QvqGxp44gm1Y0YkkW1ONn6LRqN4PB46Ojr0AF5TMLjdbt1Xw+PxoAgiynFDCHa4EYxGFEHN6GoKBllOBKF1exFSUgiFQyiyTCzpPBSSFAwxBfx+HxUVFaSmpup/18pwGxsbuygYNGWD3W7H6/V26WSiKGqbylZXKybJCIKurleJNlFETBBtgE7yqISz9bCS9xSThdKsYp7/x/OcNvM0+vXrR2lp6Q9CsmsZ6mOOOYZIJMLChQtZt24dDQ0N2Gw2neBMJk4ikQi1tbXs3r2bvXv36q+6ujpdidLe4mJw9gCeXfla4lwBOVGW0NdZQD9nARkpDvIcOeSlZWMKmRJqhmSl1L4MfiwWI5boStPsbWPu9q9YWLWS/hl9eeTM35Fud+qlB2azWW0vKcsIQuI5u++s7X4cDvoJ+jZU7vqa086Y0SP1YU/H/r8B2naKoojZbCY3N5eTTjopUTK0jR07dlBdXY3H46GyslI3hhw0aBADBgygb9++R1Ta2ksw9OLfDqPRSEZGBvF4nN27d+P3+yn8ji7B/4tQW/iEkCSp25ZevehFdzhv1rnE3CGevPFuDNLBb/2CIHDGyGn4Qn5Wr1z1vQgGgIEDB+J0OrFYLOzcuZM9e/bQ2NhIWloakyZNIisr60fdDcXr9bJnzx5kWVZruxMmTVrglIxDkQyanNjtdhMMBYhFoyhmdUIajgSxSN1MMv9NUsojQfK2dOfFcN6sCxjadzyBkA8fzSxasqDL8gCioE7QRSR94mgyGfEEXZglgbevfo4P187nxXvvwKDItAXdxBQFAXh4+dMoJhlfwEewOYisyDQ0tOOwJ9RcgkAkGmf+fD+PPf4k995yJ/eeeB13/vNRPn7/I55/5UUGDBjQxWdAlmXa2trYuXMLJ59soU+hkZQUGy5XFJMpjX/+czZr1izni2ULWL5lNb6QHxNGrio/n1GFR7N41xo2Wet55qXnujUuzs7I4paTfkGWXZucKYkMfwxvyE9YiZCbm3vI46woitplIYlgeG3VB+wV2lHa4kwtGMdF5WckAiCRaWXHc9LQ43h3xVzerfiUN998k9LSUiRJYvjwffXTmpri66+/5rnnntP7rR/MZ2P/43+kSF7HJBl5v/Jztjft0lsNGiSJXa691LjrSDFZCEbVsgNJULOhoVhYbwEajkUIxdTPQ6HQAb/14ZaFzN32VfLWkzzEwWiIQPTA9ZLhC/j53UcPcfJRx/PFzmUYs1K44767yMrK6mIiur8yRCNUjuTe9s577yGNGIshNZWoz0t8wzrOefB+AFpaWlixYgUmRSLbloEkiYkuHAcSDJuadlA+ZXKPflODdh40NzezZ88eXYmgBR1aOZjf7ycQCOh12ZoiJxmSpJKIgmzAbrYlSDUDqVjUAHXZcuLLliErGpmzr4wlnsh4C4qCNR6l45eXkmq1oySW0c4eQRBQEh14xKCfioqKg+7bwc7Z7o6Lw5zK3796GbPBhEGUkEQJURAxiuoz0yAZSLc6KE7vQ4EjWycWMjIELBYzh/JosJlTmDlkCn/5/HnE3UH2eppwRzw4nU6ikSgnz5zBr3597Xc2/rNYLBx77LHk5+dTV1fHmjVraGpqIh6P43K5dNNVjWAIhUJs27aNJUuWsGrVKjZs2NClVAjAacvGkT6QeGL/BUFEFAQ21ixnac1aTAYjZsmEJIqYJCPF6X0YmlNK/4y+9E3LJzNF85RJeAvJMlWuWhZUreCbuo14Ij6sthRGF5dRVNA3oYLQTGYT11FcRpQSmX+drDjEQCgH78YTjUXZWreOu0/59b/FA+HHCK1zRXZ2NieddBJjxoyhoqKClStXEgqFaGpqorKykm3btrFu3ToGDhzItGnTmDBhQo/VGtBLMPTiP4DkurH09HRdchyPx3/UgcZ/Gsm9yLW2XT/GzG8vfpz48JOPOPOUmXy68SvOGDntsMuvbdrM9dff9p1/TzsvrVYrWVlZlJaWUl5ezqZNm6ivr2fr1q288cYbHHfccYwbN460tO5rif/d0ALOzs5OXC5XF0MmzTBMm4AdiclqclY0GgsRjARIRc24tXY2MLCbtnDAASTGq6++SjAQZGjZUEaMGPEf6cRzSAd/SwqnjLuA1o4mvt79wb51xOR6UPS6WK1f+aqNi3jxkz9zzfEX0T+7L6OLhvHut59z1tiLWbjlC96s/ILSzAL8SjtlhSaqn9uDUGYHo0BLS4C8vA5V4mwxs2SJm9LSsUyaNIncgjxEUeSDX83mlVUfcP7psygaUMw1N13HxIkT2bx5M+vXr2f58uUYjTHGlqcjGSQMBokRwx1UVbXR0PASZcMMfD4/ysVjzsRqtlDebziuVnXSbpSMBP0BPB4PBoNBNRdMGiNJMuj7qQaecRRFNT7e1babktKSw3pExONq9wrNuf31VR8yv34lKHBy4XguPOZMNcObyBhGohFCoRBtvnYi8SgpKSnk5eXp2dxNmzZRWVlJS0sLra2tVFRU0NraSiAQQJblA1pIa9u1vweDllXWfIE080dt/f29OJIz35FIhBafi6G5pfsUFfEYnWEvx/cfyy+PvRAFMBmNesD+288fIZASw2q1IooiwWBQ7QBit2O32/XrTOuakZqait1ux2w266UtmnFbcicLbT+0Z6zb7aa9vZ3W1lY2Nu2g1l1PMB5meMFw5s6dy/Lly3UlZrKRm9VqxWq14vf78fv9+ueadDweVw3phg8fzrBhw7DZbNhsNmRZ5p9vvU3a1TcD0LZkEadNmYzT6SQej9PZ2cnatWsZljsQzYtF7ZLRdRo/b8OX1Alt3HvqqXR2qsZ5zc3NdHR06GUvPp/vgLZzWkmLZsqrKRFcLhcej4dAIIAoigQCASQJLBYRs1lAQUQSzTidBjIzDWTnSKSni3y5sBPF5+SlCx8j1WJTz8tkL4xEqYqsxFXvAzmGHJdRBNXXpKGzlbs+fYxx/UZyydiz1HutAChKIoMuE5PjdAQ6uG/hk5xw4vG6aiQej+P3+wkGg6SkpGC1WvXzp7NTVbykpqaSkpLS5T6uEY3rgzv1v2lmvppSR5ZlTBgwIJFnz+LikWdQXjRCP+dEUepaipP4VzsvR+QM5qXzH9bHvcXrIhhRlUIvv/4+q1et5tbf3UZhYSHZ2dmHvCfsf20KgoDFYiEtLY28vDz69u2L2+0mGo1SV1fH+vXrCYVCuiR+79697Nq1i5qaGurr6xFFkYKCAnJzcykqKqKmajdluScyouRAFVqITorKJlNYWEgoFKKjo4Pm5mYaGxv5vGkVcu1y4tEYBiRyUzOxm20Eo2HqOpuIKjGy+uRyxgVnk5GRQVtbG8Y9McwWS+I+oO2vZuKoICa8HsSkZ0a3Y6GuddDPq/ZuonhAX/Lz8/9n587deTNopHtGRgYjRozg22+/pba2lmAwSFVVFaAafSa39D0cegmGXvzHoCkZtPqoI5UT/q9Bq1/VDOaOxCG9F73o06cPL772Mtf+9GpSjFZOGHzsAUaPychOSaelpeV7/642sS4pKaG8vFzPkm3dupW2tjai0ShDhgz50REMbrebqqoqUlNTKSgo0D0jvu81F4vFGDF6GOu3L2PauHMAhYbWGtbu3MSnOz4hzZpGpiWHgtQCjKIJV6xdX1eWZR5+6FECnREGlZTR2F5LcXERx044hmPHlXPCCSd8z73vOdxut27qJ0kShYWFiKJILB5na+23tHe2IBn2ZVfjcZm6tgY27dlGOBomFo8RiQb1/fpkyQtcNPp0fjrpXMLhME9++RLHjzmbkyeexfTjzmJX3U6e+/CPlB/j5OKLs9i2vYN58zqp9cd483UXFrNMXr4JgyGFuZ90cu+9ZyLLMg6Hg1AsjCCIXD5hFhcecyaVe7fyzH1/5aX8Fzh65FDefvttaqp3MehoIxkZRr1lWf/+Dm6+OVWVzsdlrBaJcQPHUJTTh/Z2l/7cQlE9Ojwej95FIPk5ZjBIxBIZei3jqwXgO1tqKR0y8LDjLUkSCBCX49S1N/Fa5SekpaVxat/juKB8JqIodKkNj0QiuDvdvLjmXYLRkH4ep6WlIQgCGzdu5NVXX6Wqqoqamho98N23zYYuhpPav5pPgdFo1J9BkiQRDAaRZVlv26Z5MWiBtxbMa2apkUhENWc0mPnlxIsxiBJqiUSceCwR+AqoNflGoy6ttpjMFB7Vn6ysLAwGA+3t7bhcLgoKCigoKNADw/b2dtxuN3l5eeTm5upmfyaTqUvttEaUaMe8o6ODjo4Oqqurqa6uZsuWLappYdgPQEVFBevXr+9CmiQTFpopmiYx1+BwOHA4HHqweumll5Kbm0tOTg42m43ly5fjT02jsKg/iiwTXfYlP/3rY/r6Ho+H2u3VnF14on4+aISJhq0NO3l10ye8+NYcJEmis7OT7du3U1lZqUvhNRM/TYGQDI3MVBQFo2ggxWjFIEkY4yKiAkVp+eyM7GTgICOTTrCTnm4gI8OEw6ESSqIgEpcV5rzUiFPoyz+vehSnLQ1jUgmAmPDD0EpiVIsDtV2hrKhS+KbOVh798B9MHXIcv5l+dRdpv9bIQe04IlPvbiIvN5fTTjsNu92ue4I0NzfT3t5OVlaWPs+MRCLs3buX5uZm8vLyyMrKOsCMNBAI6CaPsizjcrl0g+JgMKgTR16vl41NO3hw8WzGFpbx0zGzyLKl6+RaFwJaUfTuQPtI6X3/l2SFHW27iAbCrFq0mDHHjuXkk0/WCYaeQiOyHA6HTjDEYjE8Hg979+6loqKCtrY2GhsbWb16NRs3btSve1EUsdls9O3bl1GjRjF58mRmP/08g0vKKCg4kPweVDSMsjGFXP7Ty+js7KSmpkZ/1dbW0tjYqPtgVHvriXXGdEVPTk4O551/HmeeeSa5ubl8++23fPrkO92qcfVxVA87kigdWr2AcMjPN+7+mmmzTjyicf3/DrPZTP/+/fVjX1VVxSuvvKKTRs3NzdTU1Oj3up6il2DoxX8MqilXGuFwGJ/Pp7d8024yvURDV2h1koqi6EFbr/dCL44Ew4cP58kXn+GPd/+eirpN/Gb61QdddkK/0fzjiWcZOnQoAwYcea9uDdp1nJeXx4QJE+jo6GDjxo24XC7cbjc7duxg6dKljBgxggEDBnTbVuxIkJx5CgaD+jVzKPIyObPq9Xrp7OwkHA53yToGAgF9onuk+69N/DRjyDPOmslfH36aiSNmYLFY8QTbeezxclJtIo1NPhoaOmhqbGLjZi/h8L6MgSiKvPzKi9zx27vpax/IxVNupL6tltq125nz0g0s/3rxv+W+uXv3bq678lfkODIxSAY6gx5+/9gDTJ06FZerjfe+/CeyIpNTuM/IrL6xka+WfMmLX79PKBIiHI/y6udPYDKZeOOLp8iy2rnj7OsRBIHPKhZR3bqHjvgiivKPYkjxaGKxCMGom19dezSSJHP00VbmzfVjT43T6TLwj8dlbBkd2DPbicfzWLBgAVu3bsXr99Lqadez8sgKgzKL+f3U6/nnsreY/dQzBL0hzOYoM07JIMWagi01FavFiqzIgIBBMhAX1PZnYiLIMRiM+rkqAO3t7Xz00UeMHz+ecePG6XXVsVgMURCIxqKqeaGiqFlOQQQEqjv2MrZk0mGVMIIgYDFb8IcC/PbtBzBE4Sejp3HGiKmIkqiXmmiIRqOs3bWBSDwKQFpaGkVFRWzbto21a9eyfv16du7cqTvFm0wmTCYTY8aM4ZhjjtHNz/bPdmnGjVpQrikAvouCIRqN8sLT/yAt3YHVaEm0lkvu4oBu7CfLaqbb6UznpJNOori4uFsFA6j3AO3vmkJAq+nWtmt/BYNGhGgKhuLiYkaNGkVraystLS14PB48Hg+bN29m69atupeEJjfXfBw6OjoIh8P4/f4uUnOfz6d7FyiKwooVK9SSF7sdh8PBvMVLUKacSjQSwbtpA6VZmQwdOlQnPFtbW2luaGbQ8P768Q0EAkQiEfw+H+6Ah9989CAjJ5czZ84cfT+amppoamrSiRNNjZBMKJlEIzaTFaNkpCAtl5LMfpRk9qXAmcecineo8exEMghUdVYz/ZR0Lr2sSB8vVTWz7/3rr9ewe5uVt6/4Ow6bSshKoqgnpQUEXcK075RXkGWD6oXidXHjm/cxZsBw7j3zpsT9XL0OtTIK7RgrioI1ZMVhdzBq1ChsNlsXTxBNwZCSkqITCEOGDDmogkHzJdBKPhRF0YkF7fyORqOEw2ECAVWxtGPHDio3VPLbzx/hpAHjGZE/GJvZSigapj3YSUfQgz8SxCiphI3ZYMQkGmkPdrK5pYpd7Xvxhf3IioI36qekpITly5eze/du+vTpQ35+PgUFBfTv358+ffro5+3B7hGgqoMHDx7MCSecQDQa5dtvv2Xz5s34fD78fj+dnZ00NjYiCAL5+fnk5+czaNAgjjrqKAoKCigsLKR///7MOG0aGxatpX/BgW12i/IG8c2qJVx9zS8RBIHi4mL9HqN1DElu55x8vtlsNkaOHElubi5Go1F9Xsejieto3zHuDvF4jGZ3Pbc+cSEAoUiAqWPPYdZJP9PHoLt7qSzLBEJ+drdu4+ST7zkiqf//d2jnjSRJWK1WCgoKmDFjBv369aO+vp66ujrq6+tpamrC4/F0IU4PhV6CoRf/MWgEg9frpampCb/f/51bO/0vQCMYNAOp/xb3/V78uDBixAj++swT/Oyiy/nH0je4fMJPMBkOfNieMHgc/kiAG6/+Ne988v73vi6dTifPPv0cdXX1pKSk6NmNHTt2sGTJEiRJIj8/H5/Pd1jH/0NBmygGg0FcLpfeuzlZxrw/BEEgEonoE/K6ujqcTif9+vXDYrHoklu/339E26F9tyiKeseM1NRUJk6cyLzBn7F68yLKj55MIORj0CAnBoNIUbFqeBuPx6mu7uDtt4xdtnnUqFG89uYcTj5pBtFolJOPnUVR3lGs3fWlvp//SgiCgMvlYlTfITw48zcIgsDDC2cTjaqBbEF+Ab885Q80t9dT2fqFvl6fvn254JQbGXv0Pmf7lZULqGz6it2tlQhyjKrW3ZTmFPHMkle5afrPiEdlXp73GBZbBoFwkCnTUsnJcSIg8P57DbhbMslJ7UewYwtPnfl7lu9ay0fbFtLgaeCFnS9gMpkoHzuWDbGtTDl6AlIig96ZCLIm5o9i0dZleIRWHDYBh0MlDTIy0pFE1aleEiVEg4F4RCEeV8v51My6umwsFqXJ10ZHi4tnHnuSN9Nfw+l0Iisy8ZgaTNc11NMwtJlsW4baQ12SEEQ1i7ujtYZZRZf2qNTGarVwzZw7icfiPH/5w6Sl2NW2faJKViQjGo3y7ob5dIa8KIpCWloa/fr14+OPP+aNN97A7/fr3Q5ADUocDgdTpkzhuuuuIyUlpVsT0sPVsB8p5r37MRkZmdjM1h4t70xLY+rUqZSVlfVo+WTTxuRyhsMheT8VRaGuro66ujrefvtt6urq9KTI/mVSmkmi9nsaAoGA3mZTEASWL1/OihUr9hEy+YX0v3YYkUiEwOIFXHHurC6/v2XLFnKsGaQYrcTjMZ1g0AiIt76dy46mXWx9s4pQKKT/vbv9SR4L7VXoyOPeqdeRnurUOwJYzGbq/U28sK6aq6/uT0triEsvLcPQje8DwIcf7mTZojDvXzWH/PRcDhUodod2n5trX72HQYWlPHje7w6ztNp+0GQ2YXfYGTVq1AE19d3dDw92/h6Jn4gsywSDQdxuN/Pnzyc1NZUNGzbw1sZPmbttEULCGyIcjxKKhlE5FQFJlDBJRgySRFSJI4iCXiqknac7duxg586dmEwm7HZ1v8aOHcuUKVPIyck5QLXSHUwmE0cffTSiKLJ9+3ZWr17Npk2b2LJli060gFq+WFhYyOjRo5k5cyaTJ0/uUvoUiUSY/8G93f5GUd5RfPD1bBRF0dU5RUVFPR5HjWzUjGHb/R2ox1Q45GljNllwpDi55eKHMUgSc5e/Tn1rLR5fB9FYmEDIT2Pbbnbs3pQoEwsSjqrqqqr6TYwZO4q8vLxegqEbCIKAyWQiPz+fvLw8jj/+eGpra/VzSCub6CUYevFfA82LQZNMRqNRUlNTe4mG/aBNGFTHZfnfEkz04v8nCgsLeenNV7j1hlt495t5XDT+rG6XO3X4FJbVrmXFihUHbVPWEyiKwq03/YZYu5WStLE0N82jvLycyspKWltbWblyJXV19Tz39PO0tDbzwcfvcdRRR32n3wqFQni9Xn3ikyzx7u56CYVC+zKBfj92u53hw4djt9txOp3fy+tEm2xp16oWfHR0dHDqzBk89uDfKcgsxmoxIAjKAde0Zo61772KjIwMnnjqr9x71/18tirCKeMuPOJt+z7o6VgcLhh12jMZ5BjE376Yxwfvf8BDzz5HXfUeFOCy485FEODccafx8Nxn+aDyc+xphbjdISKRKPM/a+H6s58jLSWTO5+9jJycbC7IO5PTR5zE+xs+541v59IR9tDc0sLySCcXD5+JKIpEwmFC4RAb67bz7Oo3aA51kJFViMEg8sXnQUaPtiNJhoSUe58se31FE3ZzGjmOLACMRgMpKSkEg0FC0RCj84dy2dizycnKIS83TzUlVBQ+/XYRj+yezaa920GGoQVHIZjNIMusrFqHPdtJdna2fk9PDoaTEQqFqN5dQ4bo4IWfP4LT5lDJhYTngmqGFicWjxOPxdjdspctzVWqBFuS2LJlC6+99hrbtm3r4rMwbNgwRowYQW5uLtnZ2ZSXl2O1WvUs//7boRFtKSkp2Gy27/0MCkXC3P/Z3zAajchJ+y+JYkJyLyMrckJNAnvdjT0mCTQoiqJn8DVjZJvN1sWkcX/sH3w6nU5EUWTGjBn07dtXVzBonydL7ZMl98leFoqi4HK5aGtrY8eOHezYsUM124vLWI86Go8/gKe+nuC6NVT078OmTZsANaD94rPPmVQ4Ui/JicdjRCIKcVnG5e+gxlVHLB7DaFaJIk2xoZWCZGRkkJ6eTnZ2tl5eogWSiqKwZuVqnlr/BvdOv570tHQMkgFP2M/UYSfwQeXnfP21mz/+6diDjvHSJXt49aUGnr/kGQrS83p8bDSEoiF+9dKd5Gdk8/hF3Qe1yVCrDLSuBGr5qNbJQ8PByOTucKjzWFEUXb2QnO1NS0tjzJgxpKenM2nSJN1IMVkxt//zJ5no1lQvsVhMV71oJUtutxu/3091dTXBYJD6+noWL16sl/Ukb3Py72jKIK/XS1tbG1u2bAHQyxO0Ep68vDycTicul4uKigpCoRCbN29m6NChDBo0SF8uEPJ1OyZmk4UcZx8qKys55phjejSO+0MURSwWC2PHjuVRxc/W+p0MLjgqcU87yDqCgNloJj+zL5IkUZw/kLcWzuaOZy5HkgyEwkE6vJ/zbfUKVf2U8MQQBZF2TytTz72pRyTN/yL2n3uYTCaystTnXWpqKmVlZXR0dHDDDTf06Pt6CYZe/MehEQx+v5+WlhYCgUCvkqEbJBsRae97CYZefFfk5+czZMRQ1n+xnovonmAAMBm//8PY6/Wy+Kul3HPZPxITHYE11V9gt9tpbGgk7JcpTh3N5GGT2Zaylo8++Ihbf3Nrj75bY9jnf/UVt998MwaDAZfLhc1mIzc39wCZ9/7o6OjoQjDk5eUxcODAQwYfRwIt0NDauLndbtxutyonHVDAgm/ew2ZX2/2Jgtgle6N6mnWfEZowYQLvf/QuP73s5zz7we8Jhw/thv9DIhQK8dk3i1j87UpQFCLxKPM3L+E+291s2LOLq2svQAGO6tevy3rdZVG17hznX3A+02dMp6T4KARZYPbi17jqhItw2Bz4DCH++tTf2LJ1I/fc/QHBYCu5aWUU5ZcAArkZfdjsqmZG2WTMHWZmjZhBv7QCHl36T7Zt20ZmipNvtq9HRGDZnnVUu/awrbUad9DDuHHjmDx5MkuXfcZFFxfgcDjUeytCF2PKJUtaGV84Uf+/wWDEIBnwmkykWR3Ue5pJM9lxWh2k2xw0tDdz5Yu3s9vdhCQaeHH1XCKx9wiGA2Q70inNKsQXDjD29IkHyLS7O++ampowSyb+esE9ZKVlqqUaSUG2LMeJJgKVcDjMkqrVdIZ9yIqsEwyVlZX7AviEd8KIESO4/PLL9RrcwwXugUCAtrY2MjMzfxCD0bc+fIfm5mY98NJ+XysvSC5l0Ca9Q4cO7fH3a8GXx+Nh9+7deslETk7OEZVjaVnawsJCpk+ffsDnmkmiJtHXCIjkuYyiKGzfvp1t27bxKcjW7AAAIABJREFUySefsGvXLrVcJBrBv3IJoT21KJ5O4s0NPPDAA13OgxSjhdPHT2TNng3UeZqo7ainwdNMm79DVcsoMnFZxmYyYbPZdELBarWSkpJCSUkJpaWlDBkyhMGDBx8wx1IUhReef4EH/jGb359yI3ZTKnfMf5S4GdojLkyNadx22yYuu6yAYcMyuux7VZWbh/+8jcdn/ZnhfQf3eEw1yHKc373xIBElxtOXPtDz9RSFcDSil7YdqWdBT6GVTgSDQf08tVqtOBwORo8ezejRo7t6LrBPudaj/ZBlfD4fLS0tfP7553z55Zds3boVl8tFTU0N1dXVLF++XFU/JbxCuiMsAP282397tGspKyuLgQMH6iUKL730EuvWrmPDhg3k5OQwa9YsncSw2+1EYgd/rvTJKGXN6m+6EAxHAs2UUpIkZp53Fu/Nm88d+aWHXEcUROSk58jUY85m6jFnd7uskiCg4vE4kViYJz74Deeee26v+reHMBgMZGZmkp6eritigF6CoRc/fiQ/PDXZf1paWhclg81m670ZJGAymdR2RglppPawS4Y2OZUkSTfh+q4tj3rx/x8Nu+s5vuTQk4P2QAcZGRmHXOZgaGlpoaKigunTp2O3pxIM+7GabUwefQartn3B8ccfT2VlJQ27WyjJG06KIY2xpVN48/XHmTxlMmPHjkVRFJYtW0Z1dbVev93idrNuy1aK8/NZuHIl5OTjVaDfG29w8YUX6tnJZOXB/tdKR0cHLS0thEIhYrEYKSkppKWl4XQ6u5AS35dk0K7HlJSULhPB9PR0rrnuaq656lqicT9PPVlLbq5ITo6Z3FwrGZkmorEYoZBMdXX1Qcs7br71BlatWoXJZFJ71wcCdHZ24nQ6VbO2I5SC7i+d3v+lushLjD96DPeecgNGyahOuE0WREFk6KM/YeSLbxFtaSLrs/f1spJoNKqTLYJqIo8iK9TU7tIz4h9//AmTxpzC4IKxvPrls7y3bj73nnk9pBv5ybnnEo2exUUXXcadd9zOlsoqttVuoKRwKP1yB7Jy5zpOHX6iOs6CwHHmctJTHPxp0TPsbq/nd58/isEaIi4rxGUzeX0KOO3YM5gwYQIGg4EPP2zF7TYSj8tdW/8pEApH+Wa1m1cvuUDnfwQBFAR8oQCNnhY8IR+a6/namkp+9dr9DCs5jkvOvIvMtNzE8QNf0EtN/XbWb1vOxqYN3HHy/WRmZiJJEm63+4BjrAXdc158mVmjZlCSX4wgCshxmXgspnsNaIFtPBYjFo/zzZ6NRGL7PAK0QCM9PZ3MzEzKysoYMWIEI0aMoLi4WM/Q/7tJ69LSUkpLDx1UwL7AKRaL0d7eftjl90c0GtW7O7S0tGA0GnsckGpjoo2hZhgYiUS6eDskqwtNJpPuVZHcGrCwsFDvaDF06FDd9LK6upqtW7fS1NlOQJRQTCaEhBeAIAjE5DjPrn6TuBLHFw6QlZ1Fnz59mDKoXG8/ajAY9PJJrZuFVoKRkZFBRkYG2dnZelC3f8byyl9cSX5+Pjff9Qd8Xh/X3Hodv7zmavWe29LCr371S16Zs5H77neQkrJvXqEGvgLrdm9k6tDjj+i4KIrCnz9+mk1NO/jw+uePQJmS6Mojx5ESY/yvgkZsAfr1ZjQau5wX2v1x/3KcnkDL5GdmZlJeXk5mZiYtLS00NTVRU1PDnj17aGlpweVy6evsT9YmJ6CSS2OSt8tsNjNq1CimTp1KQUGBXnNvM5uxGVLxt3tZsGABLpdLVzT5/ep9LfFt+ntFgb7ZR/Hmq3No3FtPJJQwbw2HiYQjuvdCJBIlFo0SjoSJRWNEIhHOveR8Lrvicn0bY7EYI0eO5K8PPopRMRKNRwjHItS7m2j3dxCXZe4/8yaG9xuiGmYmFCWyrHkiHWyctYSczM69myg5agCaAez3QTQapb29nVAopKtQko/JwTyfwuEwzc3NugeKVlZ4OAQCAVwul/5c358Y3B/ftfRnf2gEDagKBq1zT0/RG3n04kcB7QaelpaGz+fD7XYTCAQwGo29BEMCmvu13+/H5XLpfb+TH3IaS200GrHZbF3kdL3oRTJqamrYWrGJWy+85KDLyLLMnvYGSkpKjvj7m5qauOSSc2htbea44zZQXFxMq7uR4oKBao1o0RhCgQ6OPfZY3t37Lu8tf4ZLJt9KVnYOp469nFtu+A3vfvgWLS0tXHjtr3HOOANFFEGSEM1mbNPOoGbPbqwX/oyMYyfQuW0Lb78ym/N+8hPdLPZQk063283OnTsBsNvtFBQU0KdPnx9cOaVNNLQJv5iovU9NTWXkyJF8NPcDli1bhsvloqOjnaoqN2vXttLYWE+7201hQRFVVVVdgr/9Jy5aPXpNTY3eni8cDuvBxZEg2fQu+aVlkQOBAKFQCJvFRlZqRiKTntiefU3qUQSQE+aYWgYQOY4sx9EmqvlZRdh25TL+2InM/+JTZj/1T84/7kZWfPsF4WiQoD/AL16+iZ/94moURcFut1NWVsaDD/2Zc8+5gHhcVolocxrt3l0IgpiQ7qtkzsC8/vzh5Bu55ZMH8Coufn1dNiajgcqNEb74tJUdG2vxdQa55PIL+dOf/s6bb77Ip/O28Kc/DUUyCAQCMdraQrzzzg5CQZiz8l1avC46Q146Ah68AR/hSIS4HMdpsaMo8NnmxTy18i3OnPRzJo899YCJptlsIdOZTaunjlFTShk8eDCZmZl4vV46Ojq6BAebNm2iqqqKNUtX0bq7kesn/5RVu9ZjlIzI8TjhaIRWVytpYiotvrZ9k0kFtjdXH+APAGp5TWlpKWeccQYXXXRRl0DpSJC8TmVlJd988w1Al9aHoN5DBAQuu/yyAyTsRwIt8RAKhQ7ofgDgbm/npedfwmK2HLjfiYny0qVLicSi9OnThwcfeeg7bYdGkgWDQXw+VT6uGS73ZPJdWFhIQUEBQ4YMIRaLEQgE8Pl8LFq0iPT0dDZs2MDGjRvV6yrppQgQkNUOHoIoMGjQICZOnMiUKVM44YQTdEXK98Vpp5+GwWjgkT8/zFVX/xJQM5kFBQW8996HPPLIA9x/38vcd/8grFZ1bjFgQBp/e6Kce+95h7rX6/nbBX/ocSDy3MLXmLf5K969djapliM3+FVQ9HvTvxJaqZzf7ycQCBxU/fJdCTqNGDrmmGMoLy/XVW9Lly5lxYoVVFZW6garWrtMjcxK/rc7ckEjviwWC6NGjeKss85CkiQ+/fRTGhq2kuqMMjFzFIOyS5i77SvmvfcJvmiA7JxszJINRUn4ciagKFqnERPVO3fxs7IzMIoGjJIBQ6oBU5oRSZQwG0wYJSNGyYDRYMQkGVm2Yw1rKytZvXq1vj/BYJCGhgZisTitna2YJBMWo5mjsoqx5pr5ZNOXrKmpZGjhwETAqxCNRYnLaulDstKsCxSQFRlFVtiy5xsmzBiDx+Pp9v5xJIhGo3R0dOheJxpRkKwa6Y6s1TyevF6vfkx6gv8UwSDL6vNVu76ONJ7ojTx68R9H8qRZkiTdaV2TvYXDYSwWi1439b/cOUEjYjQlQ3cKhmg0qmdlv8/NpRf/v7FyxUpGFwwhxXTwSX99RxNZ2VlH3NmhoaGBn//8fE6YHKNqp51FixZROrCElj31FBeoLflOG3cJs+feT4rNgs2ais/XydLNH3P6+MvIy+hHScZYbrz+Fp6Z/SQZaQ6cx03GWthHD2JlRSZ14BAU1ODBnJvPjrp6tm3bxrhx4/TAujvlQltbG3V1dbS1tZGdnU1eXh7p6ekYDAa9+0R3D//uMvrJ9bTdvTRoE0AtQNJ6thcXF2Oz2XTXbUmSunSuMBgM5OTkdAkeupvEJrucR6NRHA6HnhnvKXpadhUKhfiyciXTqn6KgoI/FEBW4pgMJuKdnez82aXIsRgNBonXXn2dSy69GIvFzPtrXmVt9ZeJlm3qpNgoWMjPK+CZp5+l0DGQdZtXsLLyU+449SqqmmpY2fkBzc0L+OlPd/H22x8D8Om8z5g4+HSO7j+CeFymtmk7W3avTmRcokSjMYKJVoidAQ8yoMSt2O0STqeBU/pY+OoLD5dP/S1tngZeefoDGjtqMJigdlcHV15aQVtrAKNkxGqyEArHsAgOjIKBMYVDyUnLoiAjj76ZhYhRhY/XqJ4Pf//6Vb5t3cUVp/+WEYOOTRyrA8cvEg2yZe9qnrjrMex2OwaDAZvNpre3A3Vy94d77kcJy4lzXuGhpc8hiRIp1hTC4RCRcISavbtRYrLe/1275UfiEf1cEQSB0aNHU15eTt++fenTpw9lZWVdTBy/j3Lho/c+ZPXHSznuqHJEQfWF0GTMoiDw2rqPOHHKiQwcePh2nAeDZpBqsVj0rFoy3njtDWpXb2fW2FMRQCfy1G1Qr9ER44v4++KXycrJxmQy4fV6j7gcU7vmNdPlIylVTM5uawoCs9mMoigMGzYMh8PBuHHj9PbA2vfKssySJUtYsWKFHryUlpYyZcoUBgwY0KVbxw+B6dOnd1sGIkkSxcUlxONqu8lkFBU7eezxcu6+czWXvfBr5vzs74edq73/zaf8Y/nrXHnChVTs3oiwR2BH4y4icXVuIwkiRoMRSRQxiInOH6KEmMhYS4LE3vZ6XO1tbN++Ha/Xq4+FNr7JnUKSu55oip2e4HDj+kOo3PaHKKpk6eDBg0lJSWH48OG0tbUdUCa7fxmEpmQClZTbsWMH1dXVNDQ0EA6H2bRpE1988QVbt25i4cI3ue03/XC5nPz9L1/z6ylXcOLA8WxrrGJZ7TpW7FlHe9TFix/+BQQFWYkTjUeIRsNE4xECIT/RaJhHPptNTFbbUMblOHFZJhgJ8cj5d3Jy2aQu+9UR9LB0xWK8tS6VfEgQExIiF487m2tOuER/Zqn7E2Nn225ichRJMjB/8xLyM4uQRLVzzuFiAkVRCEUC1Ll2cNZZd5GRkfG9iTjNOFor73Y4HF1+72DHVDMG1Z7PPVWGWiwW0tPTEQRBVyL9O5Ds+6aZ/vYqGHrxX4lkgkEURfx+vy5lcjqdXSb0/8s4XAcJn89HOBzWWe5e9KI7zHnuRX434RcH/XxbYzW/eOl2Ckv6HNH37t27lyuvvIATJkeZPDmXFct34HK1Mmz4UF5f9QnlQyarDyyrjXNPuIYn37uL0cVTyHUU4w+p567X66N80InMW/MyL780h/tuvok7/vEEfe/8I6LBpE48YjE6Nm8kuH0L8uZvkVuamDZxAv379ycYDPK7m3/Dxk0bWbRscZd7RmdnJ9XV1TQ1NeFyucjJySE3N1efJPj9fjo6Og6QLybXiHeX2e/u/8mTDC3zqUmiLRYLsizjdDpJT0+nra2NhoYG3W3eYrGQk5NDZmambsp2KIRCIfx+vz7BsVgsellGT9FdNiw5W6bdTwwGA860bH4y6ZcIgsTbXz5Dv7wSUi3prN2xmKtOvRNJlNi+dwNbNm9FkiSuu/5azr/wPH2MtPv5mjVrUBSFvz78JGMHnMy7i2dz49TLmV42mT8vfIy77x/EunVBhgw5kUgkgtFoZMzY0Sz46G9I0jmIogyCzHnHnJZQSkR1GfOOphoeXDybkaVTcftb+efs1dx8m1N3bTeZLAwuHsng4lFsrq7gyXfu5urxF1BWNJhBBaXkObNAgfvefxwkuOfsmw4I5JqbmxmSW0p7sJNwh4UrT7uLwSVlgML+rSMTo8zXm75kwqRjGTNmjH6eGAyGA4i8JSuXsXv3bgwGA62trYlSjg957bXX6OzsJBgMIiIQVw5+n9fOxbFjx3LNNdeQl5dHZmbmD1oKIQgCZ42azsxRU7v9fFVj5fcmuzWzwoMhIyOdGcMnc8boaQdd5qUV73DS6Sdz3kXn6wSDFuT3FMmS8+/zjNWOi6a0GjZsGCNHjuxWCaFlepcuXaqvqxEM/86yFkVRePjhPzF9Ripm84FBWkaGlWHDc1m3po0HP3+K26Zd3W2HIoBl21fzXvVCzr/8IkRHGntkL7FojGc//wRr+QQUWQZZRonF9PfIsvpeUVDkOEo8TqC5EaWtjQfvfhzJIKkZazXFrpuDajJ59X0cj6+T226/iSuuuOJfPWTfGVrZxODBgxk8+Mh8LbRrzev18sknnzB//nzC4TC7du2ioqKCysq1ZGR4ueuuQTjTzShKLnM/aefdTfO5cMRMBmT2pX96Hy4acTqLdq2itrOODEc6aanpmA0mzEYzFoOqMnBaHPTJyMdiNGM2mrCYLJgMBq6dczcdQc8Bz61oPEp5v+E8cd7vu2xvLKYSFIakchf9WWY0E4qqirwWr4vC7AGIooQgcFjlTiwWY3PNOiYcN+6w49hTslDzWtEIBqfTedh1QJ1b2Gw2wuGwngDoCaxWK21tbTrB0NP1/tPoJRh68aNBciZFm2wJgtoqJxwOoyhqq63/Vcl/TycRB8ug9qIXGhRFITs/l7fWz+P+wu6zik8seIHiwpE0u3bh8Xi6sPQHQ21tLb/4xQVMm6Yw+cR8QqEYjY0xLr/8CuLxOO+98yFrNi/i2LKTAOiT0587L32GSDiK1+uhs9NDPK7Khg1GA6eUX8zLLzzEM88/wXFFfVn59utIWdm0L/wEuXoDsXCUn//810y/7WaGDh2KxWKhvr6e66+6juG2EjztnXpQ2tHRQWtrK6FQiIyMDKxWK7m5udhsNurq6jAajV0m9haLRSc7e4r9Jaoa+598HWpZz3A4TGdnp55xCwaDeoYgHo/T1tbG3r17ycvLQ5blboOgZNIjEAjg9XoxJUzekn/rUOUVGrTJlZad1d5rWXXtFYvF1JZkhQM4esBIBEEka10eBdlFFOUOYlnlPFZtWcClp9yIP+Khw7BTH8uMjIwu46HVpN94/c2UFU1gybcfc+epv+TCcWdxz4ePkp0HwRBs2SLw8MNXcubMs+nbtw9PP/sU98q/5+uNCzAbbLS4a7n/KlXyHgmH8Xg8bKnbwQOLn2VIyYmMH3IywVCAN5fu5aUXGrn4UjsyUd5d8Dyzpvwcmy2Fz79+m5kjTuC6U3+WyM7uyx7n2rPY1LxD3XZ9vGRkWZ0AO1McFKblkZs3ihSTHa/Xi9VqxWKxHDDewZCfNTsW8PgND7Nt27YD6nW1f7Xgtba2lurqatra2nC5XGzatAmPx4MSlclLzSIzJR0XXsqPLefoo49m06ZNVFRU4Em04iwrK2PUqFFMnDiRnJwcvfPDd302dLeuQZL0Lg8HW+f7kt2HzyIf/jrd7Kriop/+jLS0ND2JsX9rw55sh3ZPOFLSpDvFYXJwpCgKXq+XUChESkoKKSkp+Hw+Ojs78Xq9gEruWa1W/X7173zOC4LAe+/N5aKLzqSkpJ3hw7tmUufNbcDnH8DSFa/y4B8e4PaPH+a+U27Abk3tsty3e7bw9No3eHbOP7qoWkKhEG8s/JwBt9936A1R9t1rmyq+wfXon7lixh2YjD0rp12w+r0e17/vD1mW9WD4X4UfShFhNBopLi5m2LBhVFZWYjTGaGnZzqRJaVx8SSnOdDOC2kOTCy7sxyMPfMyVE1XyzefzEQgEmDHweERJIjU1ldTUVGw2W6LUSUkqnUjcF/V7GHhCPvKdXdtNK4qCQTIQjfd87AVBwGwwsXDLcmJynK+rKwiazGxoXk8wFiKuyCgklBsJAkk9N+LI8RgKIMTjPPPYIwcdV00tGI1GSUlJ6dE9ITmJcCRzc80r5UiIwWR/De17/hvwvxmp9eJHDY1g0B6kfr+fpqYmwuHwD+Ja/b+AXnKhF4eCIAjMmHkKW95dfdBl9na2MmHk8TRtqOH223/Lk08+fcA5FY1Gqaqqoq6ujmXLFvLll/OYNSuV445X25RZLAaMRpFwOIzVauXRx//M+edcTP+CweRkFAKQarOjpKgPUL/fTzgcIRpV/VdSbdkcd/SZ/ObW2xlQWoDr/RfJyTZxxgQn468YzJLFAcaOGkVubi7z5s1jzbJVbP52I7889kJOGDSOikbVOX/kyJG4XC62b9+Ow+GguLhYbYsny7hcLvbs2UM8HsdgMOjSRYfDoZdNJAfYB8vs7/83rf3Y/sqGZILB4/Ho2VBFUfTsTTweZ85Lr2A0G5g2bZo+qdOW1Y6h9r0Gg0EPRBwOBzabTScDDmV0ebBz41DLybKMzWZDFEWisSjN7r2EIgFqG3eQmpJGaWEZjW17qarbzPbdlQjpHtauXYssy4waNQq73d7l+8xmMyNHjuRvj/+dgN/DeeWn09LRxhc752NzmHhudiu33XYX69evx9ceocpTx9KlS3nuhWd44vEnefODjxhfNAzN1yEcibCrcTd//PJpPNEoo4+apMs7z5lwDe+vfIbnnq1n4GCZLTVrmeY5F4NRYlfjVv558ZwDCCVRFMh2ZOLZ40tMorVxUBJSZAVRlBhbWMbXzTsJRyJ4fV5EUcBiMe9bIYEVG7/g2IljMRgMbN26VZczJweugiAQjUaJRCIsXryYBQsW0NnZqbeXzLCmccqQk5g5+EQCSpgHVsxm5syZzJo1izfffJOGhgZ90lxWVsYll1zCgAEDyMrK+pcoAIWkcoTuIAnij0JNV+duoqSkBLPZTCAQ0NvSHimSVQzfV5mh3R80A1uv14vL5SI7Oxuz2UxnZycNDQ16612DwYDFYvnO3hnfF4sXf0l6RozS0q6E8+KvmvjmmxTmzHkRp9PJg488xBOP/42b3/0TfzztZnLTVEPNHU27+PPi2Tzy7OMHlMzIsgw9IIo0KCgo8bjqV3FQo7/u1/suMnlNtfKvJhh+KBiNRurq9vLOO8/jdHZw6WVOBg0yY7OZiMfjbN3ahssVJBKWiUTjxPDzxvq5/OrEy9TSglAIOR4nGo3Q2dFBMBhEksQezcNz7Jnc8eEjlKwtJhqPEY5GiMQi+AJ+BqYXHWQthX1GPiRKnQQkQWIHAVrTwsjTpmKw2xGKBpCWasdks2Ewm5EsFkTJgGgyYTCZkczqC0Vhzy1Xc+aZZx50W7UuHur+ST0iGJLLI3uK/QmGI8H+5TD/DeglGHrxo8P+F6x2UXb3WS8OhPYQ1N73ohfdISUlBeUQl1NnwEumM4dIvIMFC95h3rzTGD9+PFVVVSxc+Bnr139NQ0M9TqdAdo6B0lKJu+/pQ0bGvky7LCvEYvuy70VFRdx2x808/dg/ufKUO5Ekg54BMZvNOBxp+P1+gsEg4XCI9vZ2SvLK+GbLIubNfZ8nnxhDnz5qQCAKIvPmtvLw7x8i35HN6LwhnFxYzq0XXordmoqiKJxQXM78Tz8jNTWVaDRKUVERdrsdp9OpZ+Z9Pp9+vWh13unp6XpNPHRVV2keJ5psef9aWI2E0LwUNKNabTIiy7LuK+NwOLrU3QN64FlZsQV7ihODtISCggKcTid2u13PsHSX8daMXwOBgO5g35P6YU2q3d33HmwdEKjYvpwN9YvoCLWwt6Uab8BDp68dW4aRTa6FLK9cypABo/jL/bNpctVx293Xc8455xzwfdffeB3jJhzDXx/5C+c+/2ti4RBGS5wRI8bxl788i9Fo5IZf30iaJZtjh0zht7fezsrVy3nmuae4+LILefvVN7n0pRuZVjyBPql5PL7kBTLSB+BE4cUv/sRVZ9xFXmZf7HYHP53+W95dMputu3ZwxjFn4Pf7aXU3IiEQD0RpCTUnnjkGDAYJo9FEekoa3qAPBQj4AwT8fmKJWudgUB3rbFs6wZBqxBbwB4jH4gQCQf24K4pCIOTjy3UfcNKMSTz//PN0dnZ2OfbJY6/VUtfW1uLxePTWcykpKRyV3Z+rJ128r0vAaiNlZWVYrVYyMzMZMGAAoVCItrY2ve43GAwe9rh+V0gGiQ17NzKs72BKcg4MHn4IBcPhIIjCAc+7l75+lxV71qEoareBzrDaytDn832ncUhW3vwg25z0fZpHgMPh0BWciqKwcOFCKioq2Lx5M6IoUlZWRnl5OYMHD/63z4f27NnD448/wB13FnfpIlFR0cb8+TJz5ryud+YQRZEbb7mJ7Jxsbn3iAf546i1IosR9n/+Nux+5nzFjxhzw/YqidGm9eigoKKCAIscPSi488sZNBCJezGaL7sMhiiJen4cp54844v3XatG1a7O9vb3HGe//BCorK/n73+/n19dnYbFYcblcNDVG+fB9L9u3NZFqTcNhsWOSzBgEI0elD6M0pxhJEnHY7QiCkFBC+XU/hGAwhM/n059t+8Plc3PNm3fhiwW476HfM336dAwGg778hg0beOL2hxP3A0U3jNxH2ggkX16KomA1W8goH8/Am28nFo8hx+OqMgEBg+HgJRKyAh2bvqVfXh5ut/ug3SNisRhut5tQKITH4yE1NbXb5Xqy/KE8GJJNHk0mE4FA4ID1NMLRZDLpau1gMEhnZ6dugK+Z5XYXK3X3r/Zee84fzJ/qh0YvwdCLHz16CYYjg0Yw9I5VLw6GW266mcVfLeaUAd23FHtj1Ud4gwHys/ohCgrjJth55pnbeeihKHl5Fo4eIvKTc51kZfXF4Ti4LDUQiOmO/hrOOedsFn7xJYsqPmRa+U8AAUFQM9lpaWkAhCNhQiH1JQgC50+9lode2co337gpLk7Xv6uuJkKBkMFfzrr7AAdyRVGYdvRxXPfefRw1aCD9+/dn8ODBpKamdpmQtLW16Zl+s9lMamqq3qoyGcmBvEqAhHWzp+TPkz0QTCaTPgHVxkAjAmw2m66QSIYkSaxcvooTR55N+ZDJvPvVszz0p4f5+VVXMHr0aKxWK+np6YiiqKsmtKxaKBRClmX8fj+RSEQ3ktyfCNn/97Se55qR7uEgCAIKqunXtOkn0dTQTEZ0MGUl5Xyx+l0mnDmIqVOnctl5v+DqM+4B4JOVLx/w25FIhI8//oiTTppKeXk5TzzzJFMmnUwoKhII2Zg583wEQaC9vR1ZlinIKiEvsx/BYAiv10tGRgYTJkxg/Pjx3HPPPfztqWeJhaOIkpGYpFxxAAAgAElEQVRfHn8TMTnCq4sf4+OvX+Z3lz2uG3NdOOV6lm6Yy6drX6HT306qJQ2bZKG5uQlRlPSJmNlsVtUjJguBcBAUhUAgQEtrq16HSyLQMYlGYnG1JVss0UpYHV8RUZKQ4zKrtn9OfdNunn/++SPKRhklA9G4amyYlpZGa6Adu91OVlYm7f5O8vLzOOaYYxAEQScYmpqaEASBUEgl6rRuHv8KDB85gpVLV3Dtm3fz2s/+pp5zcpy4HEdBweVp/5eT3ZFoJNGhZB9qOuq49IafU15ejiAI+rW/f4vG74q//vXPVFdvw263IQhdJdOiKBKNxpg27XSuuuraw36XNs9xOBw4HA6diPz93ffjancRI44oigwbNoxZs2YxYMCA7739R4q+fftyww2/Y/azf+H6G/pQWGhjV7WH117tZPbst+nT50C/nosvvYT0jAxu+N29hEIh7vnz7znxxBO7/X61Q0YPM7vKPpJBHfMDF4kpYRYvX6iTAslKs+9Sxy6Kot4JyOfz4ff7da+EHxu2bNnCzbdcyRVXZFNaamd3rY+5n3hYtybK+NyJvHLBFZSVDDno+naHA7PFkriX+RNlCHLC68eHKArdEgyRWJSU9FQWfLFY/zzZr0QQBMKx6H5GlfueYwDBcJBQLEIkGiESi9LmaSdmFdUOGnIcOa6SExoOdmtRFAVvxRoml/Snurr6oPegWCymd5dwu92HVWgcbPnuukgk/2Zye0tZluno6OiyHqjPY63MUSP8tXJKzR9Ka6PdHaHQXYm09l4rqzrSzlLfFb0EQy968f8EWpARj8f1djK9JEMvusNXCxZxzqBpXH7cuQd8dstbf2TZzg38/IzbcaQ6Kc2fgNu9mHvvO/IJbTyuTiwaGxvJz88H1Ifdnx78A2fP/Akl9WX0LxiILKt16KqBqdoOLB6L6xMTd2cb0Wic2U/tZtKkPHJzU6iv97OzuoOt4W946OMn+eN5vwH21eZGYxHeq/gMe3oaRUVF9OnT54BAPxqNYjAYyM3N1Y3/AoEAHo9Hzw7HYjHC4XCXDKxmOHe466uqqooP3v+IYcOHMmTIEPr27YvH46G9vZ1IJIIkSdhsNr3NHahGUF+vWM3Vp/0BqzmFC6f+mmUbPuORP/2NE06ayNW/uor29na9nMJms2G32/XgSWtTaTKZsCeyUNpkTkN3Ey1FUXC73fr/tX3T/BP2Jx72Ntawe28NwxlIZ6eHjobNrN78FbWN/8femcfZWL9v/H32debMvq/GPhhk34WsIZWoSCWlkFalKFGKiErbtyLtWikqWUKULbthZoyZMWZfz5x9ec7vjzPPY8ZQpPrq93W9XoozzzznWT+fz33d931dx2nV13+v805n8+ZXzyIIAicKjtBzeJt6+zhy5Ahz5jzA0qUh3HbbFAoLSmmX0ovBncfwyfrX2bNrLyNGjCAzM5OcrAImD7sHhUJO20a9GNz/Wp6a9yRxcbHs3r2brKws7G4b0bECCoWLl9fej9EIXpmPxKg+kme6VqclNDSMwd1uIjG6Gat3vIvTbadVaCKCVxS4lElZNYfTwWsb36NFRAqFRUX+6hC3W8rAgQyZHKqdFsosJbzz4zNEBSfRNrkHsaGNEAQfPp+XipoSfsvajMvjlIJJsW1GoVCg9CnQytVolOpaJXYvaoUKpUKJ2WUlLiWB3n16Ex4ezofvvI9b5kEuV2BxWDEajNjtdvR6vWS5GhgYiEwmw2azUVZWhtVq/d1n9Y8gio96vV5MJlO9oOqagdcQFR3F7bfcxtRv5qJUnikDlsvlyAJUBAcH/87eLx1upxu324nNZWfb8V043E4OZB/h5sg7SUpKkrazWCz1qokuhfg4cGAPY8bqiIkWEHxexCHC/wz5OHiggp07t/0hwXAubQaXy8XRo0eJ0IcwuvE1fHLgW8oc1QQHB5OcnHzBwnJ/JWQyGXfeOYmoqBief+FRRowIYM1qM/PmvUHLlucPVocMHUKgKZDiwiKuG3XdebfzeDzILqZ1wQfU2qCeDwaD4aJdkH4PYnDmdDqlNji73X5ZWYKXlZUxbdrtjBmjp02bEKqqrby0OBdrQTCL+00jPCCEMEMIHrcb+e+4MCgUCgJqtZcsVqskUCwDv5uHXCHNQ79k7iWzOIcqu7lWqNmvGXL2+yUIAk63E7vLb/Go1+jw+WQoFP41QEbRSe5Y8Qgo/cclUyhwe9wYOt7o/y6fAkFxxqJTqVQiV5z7+H2CD9/Rg1w75yni4+PPe73EigS73U5wcPAftk3VtakUKwul76wlC+q2UdU9d7vdjsfjQafT1Uti1HUCORter7eeMPLZyYwLGcPE9YvZbK53v8VraDKZMBqN9UQ2LxWXx9twBVdwBZcMQRBwuVxSafTZonVXcAUiEhISSAqPlxS+PR4Pp6uKKTWXsenYHp668w2CTWEAxIYlc6jkxz/1PSaTmmuHG7nzzjG8/vpKiouL2b1rNzt37MEreHnnmxd49JbF6DQG5HI/waBSiXa0MrxeH4ezd/Lj/lV0Se2Hz+dj1hObGXldBG++VkCftJvo13kkz787mR77NjOoXV98PoFKSzXz17+GzeRl7oJnSUtLa5BlEheGog1kTU0NVbV9pmazGa/Xi06nk/4tWuMZDAYpwBIzCeeCw+Fgz5497N58lMy9hbxS/hYen4OUJimoNQr69e9Hp06diIyMrPeubt68mfjQpgToTbWki4Z+HUeiUemw2fzWmqIdm16vJzo6GpPJxIYNG8k/dQqv4HeqCAgIIDo6mtjYWGJiYoiMjDznwkEkWsxmM1VVVQ181IOCguoJyoG/ZNNpqaJ/024YrUbiFUE4I5woZHJy3TYCAgKIjIwktWUznKfK6ZjQCo3LLGWoRCgUCho3DuOOOyP46stlfPttFo0iu9A7bRhFlmxemPI2RqORGTMewFxtp9xcQkxYAkO73MLb606iVCo4dOgQK1asICMjA4PBw6S7IzAalezf7+H661tyOt/M889/yZJV2UwZ9Rw6rRa9TofdrqOdpgvhpihW/LCAoyWZzNn4KmqFCpM2gARTNNEB4eRUFZBZksP0ruMpLCwguzyPmMBItEp/249M7hcYtLntJEU0p0OTfmQWHmT9/o8RBAG9JgCP143ZXoHdaZHOXalU1j7vtWSV08cTve6hSVhSnSvk48Vt73KsPJur2l/F9OnTOXLkCJ/JPkZfq4NxvPAEEdER2Gw2dDodRqNReibOJhguJZi22+2Ul5djNBoxmUwNKnzatGnD3kP7/vT+LxUqpRKDPpCvf/uBrdX76dy5Mze0GNNAOb6u1sWlEgxarZq4OANxcecuqbbbvZjN584WejweNm3aRP/+/c85T7vdbn7eto320S3p26gzHWJb8/rOj1j18ad4nR7mPf/snz7uS8XQocMICQll8uTbmTPneXr37t1gm4qKChQKhVSV1qNHjz/crz/DfeEtEmL22+o0s2zNk6iUqtr7q0Auk+Pxuf/ybK0Y6KlUKolkcDgcknbYfxter5cZM6bRsaOHq66Kwif4WLzoMMrqRF4aNF2ysvV6vbg9blSyczuzyWT+6iuxss0rCDgc/tZFj8eDUnR10WqRy2W8+cvHBKWE06ZrGuOi21NeXl5vfyKhqtFoyK08zfXv3oscGUuvf4omkUl+1w+53E+shoTQ9NOvznuOPvwuUoIgoFAqUMjPHRBbT2UTHRBA7969fzcZ4PF4KCsrw2azERYW9oeC1m63m/Lycux2O6GhoRckgH0h8Pl8kvaO1WrF4XBIn4lkdN3zOLsq8exWTRFiFaPb7ZaqL+tup9PpSEhIkJImVwiGi4TT6aS4uJjAwECpf+UK/j2oq9p8LmX2K0Dq2/J4PFK26t8gRHQF/xx8Ph+rv15NVWEFvQd0AWDDkW08/uUiPF5we52M7HmnRC4A5BVnEBt/4TZuIqqrXGRkVFNU6GT3zhNcc/UQGse3IDqoEU2jetK+61CWf7OQleuWMnH4DECBQgEarYagoCAsNRasVgtHcncToAtmcOeb0et1vPD+Yd55vYI7h8ymTdOOAIweOI15a1+hRXRjPD4vT3+/lLQ+HbnjrjsICQmpV80jZmHcbrdUwRAUFITBYPB/r8VCSUkJBoOBkJAQVKr62VeVSoVWq/1D73mlUklZaTmpyR3o2spv31dtqSCnIIMVaxexf9dRFCpo3KQxbdu3oVWbVJo2bcrnn35Fq8Q+UpuTPwssI6f0CONHX09CQoJkQytmOg8dOsTjDz9Bl+aDEQQvHsENMh9e9lHjqKTKUobVbubOSbczddqUescp9n6LmhPnCrhEMUrwB5per5f2Tdow89qpfoG1OgvUMsFf/VFTU0OLls1pERfNNa16MXftK6z6+DMyj5+QFjbl5RXk5VQRGhrLxLvi2LmjAEt5PrNeG0/P/r1p3LgxW7ZsQa2upmsfgZc+n0hKVFfaN+mDR2Hj4MGD7N69m5MnT2KrsaNU6ikuktOsdxSNG+vQ6RQ0aRLMAw824YmZv7LooxlMGjGTQGMQMrkMnU7Hsfx9REfEYYiI97t3VFaSWZnP1oK9yD0geL2oFWoeXPs8WqWaYks5XePb8kDfO6U+eUEQMGgNeAUPMaHJJEQ2YUCHGymrKqDcXIzdaeXnY98w6sbhRERE1MsUiQvHbz9bTVBwEFFRkQCSbsApSxEao46bxt5EUFAQJSUltT/3V1j8kPUzwybdIC0Kq6qqOHnyJOXl5fV6+y910SiWlosltn/VIvSvhEfwsjV3N7NfeZZ27dqdc5u/kmAQfD5+j8P3u4w0HB+8Xi9PPvYEP3y9jqE3DOeZ5+bWC/IEQWDXrl2s+uBTbkrw226GBYYwd9iDfHpoHdm/pDNh7HgWvryIuLi4/0pg27VrV3bvPoRMJmPPnj106NBBehfKysoYPfIGvB4vKz5eSePGjS9onz6f71yX6zwb+/+jUavp0rUz85+YSXBwsFRqLzrv/JUEQ92Sc7FVwul0StbgIlmoVCqlZI9Wq5WsSP+JhM+KFe9SYznAHXcm+8eHH3I4cVTGB2PnYjNbcLtdCIIPh91OTY1KGovqtisIgt/m01d7riqVfzsxA++u0wIWLJeh0ajpnnQV+o5R3DbhtnPOieJcGRISwvbdvyCXy5l2131YndYzDhQ+HyqlEryeBs+B4PHgsdkQ7DY8dhvumho8dhs+txuf047gcOB1OBBcLrxO///Nh/Yx7dqhF33dL0S36Gx9g78K4rssk8nQarVSW59YXRgcHFyvvedclYlnj2nieylWOYvJkrokg9frpaCgQBof1Wo1Wq1W0n36U+fyp37rXwin00lpaSlqtfoKwfAvg/jA11Vw/ye9n/8tEAcFcWL7tynOXsHfj2XLlrFo3gKW374ItVLFqfICHvlsAWP6T6NLWl8EwYtcXt827WTJXm4dZvrd/fp8PsrKnBw/Xk1WppOsLDdWq5y2ba+iffvunMj8Go0rhJuuvq/eZP/Q2AXMfGMCW/aspW+nawHQqDUEmYLA58NutyMIPgJ1Idhs/sz4/Tc9BzIZwYFnJtkOLXqy5+hP3PTmFCwKL/dNvovx48cTGxvbINPqcrmkCgVAysiKAVN2djZZWVkYDAbUajWhoaEEBwdfdEClVCopOl1ClKmNtGgIDYogKCAU7QYdY/tNJT4ymbyibHL2ZrDjxw85VZpFUXERQybdjSB48R+iEpfbzemKbAYNGiT1e9psNiwWC8XFxaxbt46E8GZ0Sx2IXCFHWauroFSeaeP4fMsbxMRGNzhOMfMmkgx176nP56O6upqqqippTBEFp06VnGZ/7hFpIeir7Yn2uv02o+Xl5TgcDmlRczA3HY8yAKEwVPpeZ6mN06ccfPF5HnFxGuwVWr6692UOn0rn40NrmTjuDg5m7OWee2JISwtl5MgaVqw4yic/badps3YsWbKEsrIyVDItPVOvI1AfzAfLP6W8vIK77mqCy+3G6XTz1hvZdGxyLcdyD/Hs8qmMHzydLfvX0jyhLRt++xy1Ss0Td89Ap9ORnp7O8ePHKSwspKCgAJNPz3MDHuRUVSFmpwWby87q9I3odDrCQkPxCl5cLjdBhkA8Xndtlk6NTq8nOjIKp8vFN9tX0rdfL6ZMmUJsbCxRUVENFr0ZB48REhpKZGSU9Nm+3CPEJsXxzkcrSEhIACApKQmPXEApU/DWto8gTE3Pnj0lwkskGCoq/LoHCoXiL6tmE0t//1sOBr+H0PBQlv62kpj4ONLSzi/g95cSDH+gdeQTaDBuuN1uZj76OFVHivhs0mvM/f4V5j39DE8+Pbvecf3000/kZeeSlBoD+B1JAgNNPHjNRHR6HV/+9gN3jp3A8y8vpGPHjn/6HC4FSqWSuU/N4atPvuTWieOY/tCDlJSUcMvomxmc0J3P93zHqk9XMfOJmRe0P4/Hg+w82ehzwecDnyAQGBBAfHz8n9JV+DMQgz+1Wo3T6ZQSOnK5XGp5E0kHUVC47nP3d6GqqooVK17hkUdjAIGSYgsrl+fx7OBnMWi0uJUOvF4PXo8bu8MBMhkatRr0Ov94LwicKM4lIjAUldxPzIhVVqK9rb9S1inZOhr0emQBMlrHNuPj7T/QMrUlLpdL+uN0Ov1/dzhxOV04HU6cdgdOp5OjR44yOLiLZG8piiMLDgenHr4Xwe1CcLux15hxFZSiUqpQK1QoFEpUciVqpQqNUo1aqUarUqNSqNDUbqNSqNiZe5qeXbv8rdf8r4a4jq+7blGr1VIl0NkEw6XCV6srlJubS3FxsSQmLGpRiZWSfwb/MwSD3W4nJycHm80m9SZqNBpCQkIky63LbcK8Aj9E2xiReXM6nfV6lq/AD/H5VSgUUmbN6XRKve2XY8bpCi4egiCwdetWSktLuf766y/4PbBYLPzn9eUEGmNZueML5t8wgwc+nUenlgPokuYX3apLLjhcdua8cxc1jny6dO5z1jH4KCy0+QmFLA9ZmQ5kMgMdOnSld+8eTJ/ejsaNG0vHNm7ceO69Zwqrf36XET3ukAQK5XIlQYGBrNmxguaN0kiMaVwb8CpqHSZkJEe2ZF/2Fqm8z6ALQKlU+QNXr4fCsjw27lnN0fJMKo0qApObkVdcIi3sRIi2dKJ1pDiJnz2WmEwmycouODgYvV7/p+YHp9PJ4SNHaNNz0Dl+6ndhUKt0NI5PJSWuBV6vwOmSHBZ/8hh6nQGZXIa89juP5x6geYtm2O12ySlCLHnUarUcO5JBy8ROqDVqBK+Ay+XG4/Eil/sDXsHnJfP0IVq3nkFZWRlwRtxRdLk4n3uESMiLhIPdbqdx48ZEpcTyYe53KJRKlHXKN/VJQbRu3ZrQ0FB0Oj0Kl/97NGoNLZI60yn1zLMUW5pIpZCD4Eth0cJPaRuVhlatoU9qN3q27MLUD2eTn1XEGy9Xcde9Hpo01TPxrgT69A3gu3XHMJur0KtCubbTHTSJb4XRaKRlkzQ+/HExR48c4MnZjVj+bhYuSyypzbuwP2MnaY278uaaZ3F7HBw+uYvuaQMpqDhBeHg4zZo1IzExkQ4dOmCxWNi1axdCRg1NklJIcMbjcDiotFSxcv9qqmuq0Wg0yGQyrE4bWzJ3IvgESaRLq9Gg1eow2yoptGTy2ORFxMbGSusPmUxGfn4+AHFxcf5+3bMqzvbmHWLoDcNJTEyU7sH9k6dSXFTMLyf2st+ayaJlSwgKCpLIoNzcXA4ePEhJSQlKpZKkpCS6d+9OcnLyn5ozxWdOEARMJpNkl3q5rZdumzCB2yZM+MPt/lKCQRCQy3/H0tXnq1fy73a7efyRx7AeK2P24GmolSqeHjqdWd8uYv7c53jsycelefvg/oN0jG2NWqEGfJLwm5/ckXNdu4HEBkby8N3TmT7rYYYMGSK59fxT+GDl+xzYtIf3b1vM89+9wf3ZU8k9mctVAc1oHdOcT1XrGDHy/PaAZ0Mmk+E1V1Hw1CMgkyOTy/zBp8zfiy9TyPHh/8wj+PD6fJjzctC0a/OPVnHUHSvF+AHOEHA+n0/SfhDL6QMDA6Ve/b/q3Xn00Wls3rzeT2jqdLhcMtq1kxMRrsUreHnrrUzSIjrTt2V3vyVroBehWsDtckuEgiiUaXfZeXPrR3yfsZXUyKbc3/s2YoOiUNSO62q1Cp9PJwkbu10uvwRGbfa7SUQSW1b8RIQrAJ1K6ycA5CqUMoVfS0auRK9UY1Kq0SiNqJUqfqz6nkc+eRYfPlweN16fl8jISPpe3ZsFS16U5ufvv/+eX1du5KF+E6XrLLpOnK86q7i6lPu/fZZevXr9Jdf6n8D5novzuUVcKsTnVa1WEx4eXs8hRZzvS0tLpTWDSqW6KO2X/xmCweFwSHZP4oBgMplQqVSX7YR5BX6Iit4ymUwa3P4pFdR/I0TGUWSPxSzWFYLh3wGv18vx48fZsnUbyUmJqFQq9h86jEqpIDQ4mH2HDrNm3wFyd+2ksKSEaff9sUI5wKFDh4gJTaRRWFv2HV3HN/t+JK+yjDtvvK3Btj6fj7W/vE9K8yQczgA2biyhdRsTGRnVnDghkJVpIyAgjA4d+3LtsO60a9eO+Pj4846harWaV197mXvuupdvf3mfa7uNx+128dyKqRRXn+Tq/iG8990ibrp6Miu/X0zLpA4M7nwzgs/LqfJMyqwlOJ0OHA47coWcCnMZ3/36CfsKDlDltmJs34ng6+6jaZfuKJUKts97gq3btjFi+HDpfOx2O2VlZSiVSoxGIzqdrt7iUNxObKMTA8U/Oy98+MGHRAekEBYU1eBndZ0n/PuXAx4OnviVxKgmtd8rqm8LZOQdJCBJRVlZmfQ7dUmPo4ePcfWICWjUGqn9w+fzW36p1SqyC4/i8FjZsWMHISEhxMfHSzoSBoNBEuQ710JGFHkUYbfb0Wq1vLXibamnVpxDz4ZOp0VeI68n+Fc3EFCqVOj1BhYvepVePfsz9Z7p9HtpPLd2upbx3a7n4Kl0lt0yj/SCTBbM+pSwBBc33RKA222nqMiGXhXMqC73E2aKwmA0EBYWSoQ8nEdufYmVaxcz6Y5fkcthynWP8vKqmQzrOp64iEb8lvEzD41dSHhQDIHGIFZuWEBycjLt27evdx7F+UXEoCc6Ooaamhqqq6uRySAuMJJvjmzixnZDUKlUfHX4B3YVHsTnVbDz2A90bzOIEE0IWq2WXZk/MnX6ZPr06dOgDP6+iZM5lZPLqDE3+EuXzwp2uzZqz0tr3+Pqq6+WSrG9NjfRwRH8lLOL6269AYPBgNFolI4vLy+PgwcP4vF4UKlUJCYm0qNHD4kou1g4HA4qKyvR6/XndFf5t6Fuj/GlVvj5fD5+h1/AJ/ika+52u5nx4KPYMyuYOfA+Sf9Gr9Yxd9hD3L7iYSKiIxk3fhxOp5PAoEDW52wnKSiWAY27oVZrMBj0iCVDPp+P9omtmGuYzpy5L1F4uoDJ9937j61jf1z/I++/upzFI58gPDCU5659hAU/vkE7fRMmdL4BtVpN48ikBrorv4fIyEg2fP2VtG6pG+iIAtaiZkxhYSElJSUYjUNITU39r7SJiASDwWCQStDFHnexAq6wsJCKigqpzeCvuj8ZGRns+OUHnpvfCIVChs3mITPDTLt2MQg+ge++O8nRAy5+nDa7tqVDi1wmw26zY8GvxyL4am0igZc3v4ct0seaV9eydctWZrzyIsOb9uXGDsNQyxSSNpJQ65xjsVil9bjH6yXYEESwMYhhza6mbXLqBZ3DVUltKLdUYtToUSvULNj4BiOmjeXaa6+tt51Op8PldV/U9fn1xD569O15JRF5AVCpVISHh0sWsx6Ph+rqaiorKykqKpLcLvR6vUR2Xwj+ZwgGm83G+vXrCQkJITg4GJPJRGhoKIWFhYSHhxMaGorJZPJbUul09Xq3JFXy2r4jsYTkbN9SlUol/d6VgO6vg3htxcFbvB9XcG6IFR8+nw+LxS8qdqVV4t+BrKwsxtw1CbvOAM1awaatyNVqfI2a+NnmE/nItFrC755O7oEJvPrJKnYdPMTzs548p0WYCJ/Px5JFL9M2uRfrf/2CrvHNeW7dm1zXayJ6bUNLph2H1+PVm1nz8WpqamqYdPetHDnioUOHYdw8thvt2rUjIiLios5No9Gw7I1XuHPCJFZve4+j2b+hUelJjGiFx1MEmkIWf/oI144MZdP69QTqQtl1/CcKZRbcURF8t+cTEiNS2HtqBwXOMgKu6kzQyPtofFUnFBoNcnmt/ZxcRvhd03jyxXmktmxJeHi45BAh2kJqNJoGVk8im3+2WvOfXRCuXfM9XZufXzG9IWRk5R8mJbpVvc/kcjlumYP+/QcTFhbW4Hg2bdpEXFgKWrXWv+DDh1rtz3KKDgUGXSCt47rzydvfsPPgFqIio9Hr9YSHhxMRGUFkVDhR0RGYgkxoNBpatGhBWFiYJERYd5HmcDgky0OR/K1LMIgWXj6fj5KSUrzF1Rw+dYwqi5nKmjJKKwr91xuoMlfgdDooKyvjo48+5toe4wkPjOPrre/yxuYPubppF9olpNIsLJmuce34ZM8a5s9ej1dpIzmsM3ddMx6DIRC9QY++9hg8Hi8y5PRuO4IjJ39jbP8pvLt2Aa0bdaN5YhpvrplL19b9SYhsAtSKarldksClGKhYrVb27txN3w63I2aQAwMD8Xo8jG07jMXblhOsC6R9fCu+OPgDQSHBnD59miP52zh04ieaN+5Cv44jKao5wY2jFzZo7fvhhx/Q2RV8eserrNqzlq3btjJsVPd69zZQZ0QQfLz5+hv8smk7Mjm0jmhKqb2SALWBGrNZCvjT09PZvn07Bw4cwO12k5KSQmpqKi1atLikFglRIExsjfi3r2vqEl0i8XWhEKsLRHE/hUKJ23P+udXn83+fy+Xi0QcfwZlZxROD70OlqJ8gOZx/HJleSdduXUlPT2f//v3U1NRQZqvkk8Zkz0oAACAASURBVKPrqPJYeDT5Huo2povHnRKZyKIRM3nmo1fIzsrmyadn/e0OEwcOHGDe408zf8gjhNe2qinlCh4bMBnRPlBxDkLxjyCTyaRWoN+Dx+MhOzub06dPExcXR3R09H8l6XSuuUOj0dQT2Q4MDJS0bUSi7q9o037nndfp3z8Ak8n//huNKsLDtXi9XjKOV/HeiiKM8jAOnT5O50Zp/vmxDpEsWkP6fD4sDgu7Cw/y6VtfEBoayrjx4xhwzQCenzufqZ8/zd1dxxKiN2HUGtCpNOj0OnblHaTSWkUrpd/6WS6TYdIFUFxVesHnEKgzEqgzSgSSWqHG7W5IJKjVary++kRVvVnQ55PaLEQcLcui36jhF3ws/4s439pGLpej1+ul6gZxrSfepwvF/xzBEBoaSmRkJHFxcURERGAwGCTf6Pj4eCIiIggJCZH6FuHMRbXb7VRUVPizCLUvJiBtq9PpCAwMRK/XS6IuV3DpEBc2ou+4KOBzBeeGuOgXW0pE7YoruPyxZcsWrO26EH9zw6qCushe/ib65BTin1vCwc0buGniXXz27jvExMScc/sDBw5QeKqcflc3J7cogxiDjvDgRLqm9WuwbVb+UX7L2ciqLz+SstdfffnDBR3/7t27OXLkMLfdNuGck5der+e5F+bSq0cfjMpQZoxfhNVew/z3p3Lv9Ghatw4hMlJHVdVevtrwH4KHjSD+5tspTj/K7peeZU/FfkzDrqfNqBsJCI887wSpj0/EPGgE9z8+k1cXvIDD4SAgIIDw8PA/rEoQA5BLgdfrJSMzg9YRZimwrPdzwYNS3nBBXFxxmn5XXV/7L7+3u8frJr8skyFDXpH6MOGMcNNPm7bRNKZ9rUe5lZNFGThdDiosxew59hPlVaW4PS7/ucnkyJBz//XPo1HpqKwpp8pcRtWpCg4dK2DdjkUEGExERUZjtlbhdDm4ZfxNjBk7Rvpeh8OB3W6XFtUajaae/eczT81j394DaLV6asw12Kw23tu5FovNTKHtWwps6fWIm2YtmnDq1CmyjmXTrscQkmOa0SjueWa+MpY7et6E0+GgrKwUh92G3elAEGR0SbmJjs2uxuW1YvNUkxiWiEx2JhiuqCzl/R8W06/DSI7k7EIt19KjzSBe+/ppokLjuK7XHQg+AZ/gFzTzur0NHDRsNptfJ8LtBB9oaltqbDYbqZFNmdZtPG/v+YwVe76k0l2Dt8Y/L5k0RuaPfpR3tn3CSx88yPU33+i3XfV6pWDL4/HwxkvLuLvjaAJ1AUzsOYav93xPpbUKs72mNrPoLxu2OxwU5J7mueGP0C4hleWbP+X79C0kBETx47r1TLj9dmQyGcePH+eLL74gLy8Pj8dDo0aNGDhwoEQw/FmiTLymwGWpvXCxEANBsUXoYt910YLvxIkTnDpVhMVyfgV5H+D1CgwaMJAAu4ZlNz/TgFz49cRvLP11JS+/vYzmzZuzatUqvvnmG44fP45MJiOpWSO25O6iXUYrbupyJmASxylB8BIeEMILwx9h0YZ3mHzH3bz6n9f+Vj2CjGMZmCvNRASI3+Hzi8t6PCiVokU2eM8x9v0VENfkotWvGAz9NyGOaWe3qYgxQUVFBZWVlcjl8ksmGAoKCti27Xuem9/QOtrp9PLeymKWLHmbwMBAPnhnJQt/+g/JYfEkB8bSIjCJUIUJn+CrbRESqLCZCQ+PIDY2VtpPVFQUL726hHVr1/Hhig+xWa1UV1VTba5GIVeQdlU7kjsk8+ynr3NPhzH4ZODxesivLJTmhouFSq7A5XI1+FytVjesYKibHABk1BdUzSjLYervWKdewfkhPqM6nY6QkBDA/85ZrVZyc3MveD//MwQDnMmsCIKA1WolPz9fKltKT08nJiZG8kuPiooiNDQUvV6PIAiUlpaSn59Peno6BQUFuCUfbH9mzmQyERkZSaNGjYiNjZV6d+EMS1S37/dKhcPF4789gfxbcLa67ZXr9u+ByWRC6XL+7jauinJOf72KFvMWo9DpiRoynFK1mhtuv4NV77xdr5JBEAQ+/vhjXl70GgPajsFkDKFPu+Hkl2Zz65BpDZ6NCnMpa359m1feXEx0dENBwN/Dhx9+yAsvPInB4KW0tJCHH35c6kcFf2l9VVUVS5cuRPCZCTA0QqfVY9AbGd33Xl5/ZQnL3gzhhQUH2LnfQdMFSwm6qjPV5moCEpNwjxiNZe2X6Lr3xiaA3GpBrzc08MAWF07yABO//rSDRS8uZvZTs+o5P8Df19cIftL51deX8vLiZfx06EuSIloSF5ZCYnQTHHY7Ho+LmPD6pYZ2hxWLzUyjuPq2eln5R2jZqkW9rKQgCBQUFJCVlcWa1asx6SL4fPN/sNjMAHTr0ZWWrZqjPKHgsXFLiAyNweFycPzkQXYcXM9Tb04iMiyOvu2vpWOLPpL2xpZ93zKm3xRSU9oDsGH3F5iCTPWClboEA4BWq61XwXA6v5B7R8wjJDC83nms+/Ujel/XhlGjRtX7XLThGjZ8CF988wYdGvcjK+8IXZLb0jg8EbPZTHlNFS9ufZsCaw0TBs4mPCSKGmcV76x7FplMTlrTLtwyYBqC4MXt8fDZ5jcJCQzHZAjl5wPfM2n4LD5c/zIV5mKmXD+n1okIZAoFPp8X8Isj5ufnSz7oCoWCxMbJ5JSfpnV8c/+CltoxFWgT3ZzH+9zNY9+/iKuWwDGq9YxuPZh2ya2Y4L2R3TkHKTmQxx23TGDqw/fTq1cvZDIZ36z5hmCvgfaJZ6pVdFodi7a+y8qja6SgzGw2k5ufR6vkFsR09xNqbRJb8tb2j/nowLfMnj8Hq9VKUVEROTk5FBQUYLVa0Wg0xMbGkpaWRnR09F+mMfX/YT4Rr63JZEKpVP6h5z0gubWIgnUnT55k5gMzcFf52LqlmKZNz10xkJBgYPGCtURooll+z7IG5EKltZqlP7/H0uXLSEhI4NSpU5w4cYL09HTKy8uRy+WkpqbSs0dP3v78U1QqNde1H3jWHC9HrgC9xsDMQffx8e41jB5+Ay8sXUi7du0uWVywtLSUoqIiWrduLX12/Y3Xk5ebw8Nfz2fO4PuJCAxDrLbyPyMAfnJu69atOJ1OmjdvfskaEWLlcF1FfNFV7L+J832/GGiLP79UzQ8R77//Lt26a9Hra0M4n5/M8QkC339XSJPG3Rg4cCBGo5EBAwZgtVo5fvw427dv5/klr9E6IIVx7UairG2R0KrU2GutNs8+r6HDhjJ02FDpM0EQcDqd6HR+PYa09m35z8tvUF5VgSpIx1eZG9hetI+OMa3pkNCGNvEt0KgurK1KJVedt4LBLZwvc+7z//HJJH7hdGURLpmfZL0Y/Lefo8sFZ1+HusmEi6lc/Z8iGLxeLxaLBYvFQlFRkfS52LMeERFB8+bNadq0KSkpKcTHxxMWFobX6+XEiRMcOHCArVu3cuzYMalETuzBio6OpkmTJnTq1AmbzUZcXJy0IBRvlkhmAFcqHK7gb8fZfdpXcPng27VryTqZw+S7JtZbdBWXluLTN2xZqIuiDd9haNqCsNZnlNLD+w+iRCZj9J0T65EMT86czZ5tR7it/+OEBfnt70ZfM+mc+3W5naza8hpTHrj7olXJt27dyoIFT3HddUH06RvDohff5/jxTJYseUVyJqipqWH27BlUVu5ixXuduXviQTbuXMOArtfRNa0fvx3/man37aZVWjtMXUII69zdP6mpNWg0WjSx8TirSyib+yjyZ5YAoNFqGxAMHq+HrMXP49q8lZGdx7F7x8/k5ub+40rrffr0oXfv3hw+fJi9e/aye+detmz6nJM5ObjdDj74filtmnQmrXFn1Gotm/Z+TWJUU3SaM4rNgiCw59hPpPVsxDtvv0P6keNkZGSRf+oUOrWR4IBwOjS+msiQeGLCEwkJDOOd9fP44KP32LhxIxu/38q+rG10UPQmIiSWdi260q5FV6otFWzf9yNfbHqXHQfX88i4Rf5rJ3gIDgyRMu1yuQKj0diAYBBbJMBPMNTNICprRR/PLo1WKPz7EjMiZ2PWU09y861jWfbK66T/tJMb0gZSXlGO2VLDc5tfxynXc1OvBwkKCkZn0PPGutn0aTeC7mkD+M/q53lu5VQmDpnJrvSfKKrM49FbXuLxN8YxquckNu75EplcoHurQbzw4QM0jk1l7IApBBhNuNx+cqCqqorTp08DSNocKU1TOLUlp3aBBYiCfTKQIeNISSY2wSFV1SWERDOgeQ9kwKubVnBDxyE8POhuduccYMkTC3gvcTn3PjCFt15+nRldJ9Y7/4TIOOY+/QK9e/eWPqupqeHqLr35dOKr0mdBxkA6durI8k9Xotfryc7O5sSJE2RmZpKfn4/oClJSUsKhQ4fYunUrCoWCoKCgBtV/QUFB9O3bV+q9/T38f5lHxAqGwMDAi/KvFysXsrOzefKhx5nS6VbiB0Yz8b1H0GqyuHV8fStGl8vLRx8V0q1nD/LST5NVnEOruGb1tvl452pCEyNo1aoVp06dIj8/nxMnTnD8+HEASaRz/PjxtExtydovv+HkhlNM7TtB0nDwB7AKZDJ/y9LNnUYQGxjJA3dNZcbcJxg8ePCfvlYWi4W7J9xFTlY2s+bP4bpR/pYvuVzOQ48+QmRUNA+/PJ/Z10ylUXg8crmiVpjW/6wMad6H377ezusLX+HxZ2cxevToP30sIuoSDHUriS9niET7X3Gs1dXVrFnzCbOfOpNI8NVWkOTm1LB1q4fPPnuuXpWEwWCgffv2xMXFkZGRwYfL3ydMH8KIWvvkYnMZoWEXVvFStwJDqVQyePBgevfuzaFDhygoKCA1NRVBENj+83a+3LSZRVvf4b6ut5AQFofb48btdeP2enB5XLg8HtxeN063E4fLyW8nD5Hq6dzgOzUaDdmleTy7fhkurxuXx4XT4/89wSfg9Lpwed14BQGv4KXGbuHqgf0vasz6/zK+/Z0QtRouFP9TBMP54PV6cTqdVFRUkJGRQXl5OceOHSMoKAiTyVTbS1pCQUEBBQUFUj8vnOnJEz2nbTYb2dnZpKSkkJSURFxcHCaTSap4KC4uluzAxN5WsSe47oJM7G8V2y9Eb93/DxmEi8X/2vn+WYiTl6gVIip//38oa72cIC5w7HY7NTU1kiuB+K6KPennIxA3bNjAQy+9jEeppGXTJlxzzTXY7Xa++fZb3vpkFYH3Pfy731+x42eMHbs2+Dyi30BKZXJuvONOVr3zNvHx8Wz96Wduu/pxTMZzB3V1z2nNjvfo1LMNt9x6y4VfDCAzM5OZM6cy6noTgwf7Fz0PPpTMf976hbvvHs/SpW/hcDiYN+9JzDW7mXZ/I7RaFU8+1ZgnZ6ygSWIrsguP4NGYeW7+Im666SZGjRvPqc3rieg3EI1GjXXNZ8h+/JBRIwP5eXsh1Zt+QDtkBDarDZ1Oi0rlV1q35OVy4slHiXDruH3si8REJJCY14jHH32Sr9Z8flEBxV8BmUxG69atad26NRNun4DP5+Pdd99l4az5JGvUbNn+AR+vXURAQDCF5YWYAsJZsXYRZdVFVJpLsNpr0GmM+OReCg7ZiQyOo2/TDoR2iEKr0VK3JNTr9bD32HbatmuDwWBg1KhRtG3blgUvLGTXsc0M63artK3JGMKQnjdxPG8/0eFnep5l1NdrqTSXMXfOZ3zyweeA+Ox78XqFWhFJ/289NON+Ro4cicPhoKqqCoPu/Nf5fGORQqGgUaNGLFz0AqNuGMmzT83l/q/movIpKHc6GT/gIYxGIwaDgRXfv0B0cCLX9R2PVxCYduNcVm38Dy98dD8er4eHxi5ApdDQJqULX/38H+QyBfMmLceoD6RLq/78sOsz5n8wjTuHPcrij2eg1egZQh+0Wr+ORUlJCV6vl+iYGPZUb8fr9SJHgc/n5vsjWziSdwyry05mWU5t24ZAVHAEE7uOxqg3sj1zD8dKsxnZcRACAt2aXEWXlPZsTN/ObTfcilwmR9dLW+/8ffgalAcrlUoUZ9n2yWVyhFqV+rooKysj2BBJWnI3VCoNyio9P399lF8PbcLmrKF/x+uQyeTIalX48fmwuqp5cf4SunTvxJibR9OlSxcp2+1yuaRsotFovOzFHW02G3a7nczMTDIzM6mqqqK6ulrSWhBR1/ddbFtRqVTo9Xr0er1UZRocHExISAiRkZFERESgVqspKipi9sNPMPmqsfRp0QWZTM7rt87n7vdnoFZnM3qMP2PqdHp59ZWTNGo0iHnzFrB7924en/oIs/tNoWVsU/FAuLHjUMa8PZVFixbhdrvJysriwIEDyGQyEhMTSU5OJiEhgbi4OG677TZGjx7N7Mdn8djqF5g1aCrBhjMtU/6iAT950qdFVxLCYpn79IscPXSEyVPuRavVXpROgcvlYvq902ilTeaJmycx64XFlBQXM+meu6V3+JZxtxBgCuCJp1/g0d6TaJ/Uyu/8UItR7QYxikE8r3xDSq5dCvLz81n40lK+/W49jZISeXLGQwQHB1+265u61XI6nU7K/peWlkouExeLTz75kLQ0FcHBGvDVasT5BMzVbpYvL2Lq1DlER0fXuyZer5eamhqKioqw2WzYPA42Zf/C9W0HIpfJ2JG9lz7X9P3T56lSqYiJiUGr1eJwOFCr1Vw36jom3jWRAwcO8OysZ3BlumrfLY2/3UyrQaNRo1KrUelUeAUvHQd3P6fFbMuWLZn5/GypClyhUEiinwaDgcDAQEm4WbTU/G+Ifv5/xZ99v67cAeoL91RUVEifKxQKdDodcrkcu91+ztId8Ad0FRUVVFRUkJWVRUhICKmpqaSlpUmtEA6HA6vVSk1NDT6fj4CAACkI0Wq1kv2TuLgTlf/FYEXMqPwV/WyX62B8PtRd8P4b2Op/Cue7Fi6Xi+rqalwul2ThczH3/FKv8eXyfP1dz4ooMmqxWCgsLMThcEjvql6vJzAw8HdF1Za8/S6Kjt0w7d5OfHw8L738Cis+/wJPcmMC734AY3JjUSfLjzqXU/B4qDmZReMZs8+57/CrB1Aqk3HjnRN5d8lL2G12DNo/LgHecXg9gt7M08+8clH37/Dhw0yZMp6bxujp3PlM6ZzRqGL6A01ZuTKbO++8BYMhEEE4xNSpKajV/v7c1m1C6D+4iMUfP0pEdBgbN/8oseOvvvA8Q28dT3VkNPmvzSfEmcOUOU3R6x0kJap4453lWFq3lSwmlUolp9d8Selbb9CzxUCu6+t3VJDJoEVyO7KLjjJ3zjwWLlpwwef2d0Amk9GnTx/ei3ibWddNR6FQYLbVcCDnCPuyD2P3OBHw8dPpo/icdvp3HMXIPrfht7rzl7P6++F9Z+la+R+YzIL9jBzfTwqikpOTKS+tpF18Q6vMvMJssguPMfn6WXWPsN42ReWnaB3fjaGdx0iBqf+98kn/3nFwPYWFfuHG3NxcTIZQNOr6wbOIuiXCdQXS4EwLIUDfvn05cuQIs2bNwmX3oFUbyK/IoHtifzbs+ZIKcxkzJ7yMQqHE43GAT8boq+8mPjyFb3as5HD2bowtTZRWFRAdkkSlpZQt+79lSLexxEUmM2HIg/xycANvff0cKqWGyLCYWltNHS6XC6vVikKhIDIykiP5Gaw7sJnVB37kaEEG4foQIvWh6FVaWkY0oaimDMEngEcgKSIetUrFh/vX8MzCZ9m57Re+/fRJxrcfSZ/mXenepAORuyMYNnYkM798ifZhzRnfeRQRgWHoNboGpIFcLvfbyNV7hqhD7tS/trGhjeiWOgSdVkdwcDDBwcHYrQ4KKk8ysMu5s8dOl4P9mb8w6+HnCI4yMGfubFq2bInb7cZqtUpVmpcLwXC+cd1ms1FRUcHOnTtZt24dOTk55ObmNnBBEcdvkUARW3xCQ0MJCwvDaDRiNBpJSUkhJSWFNm3aEBYWRmZmJo9OeYgpnW6hR9NO0v5axjdl2c3Pct+HM0GWzbXDE1i6NIeWLYbyzDPPI5fL6dy5M3Nfms+T0x9n7sDpNI1qRGZxDu/+ugqf3MeGDRukFlxx7ZeUlESvXr1ITEyU5hK9Xs+ClxbyxmtvMP39ucy+ZiopEbWtVrWtOwqFAoVcTqPwBF4c9hgPfDQPwScwbfr9UtD1R2O8IAg8/eRTaEoEJg++FblczuLrnmTWR4spLiziiadmScKF3bt3h6dnsHDeQnrmXkWoLohAXUCtgF8AJp2R7LI8IiMjL+Guw+o13zBjznwUiR0IG/oAR79aQFBQEMHBwZe0338CYtZfJvO3YlVWVkqkQ1380X2x2+18/PHbTH8gDPCTkl5B4FReDa+9XsiwobczYsSIBi4pgiBQXV1NUVER1dXVeL1eSm2V/Ji5nVsiRrE97zdeH3DvRZ9X3ers2NhYgoKCSE9PJycnB5VKRUhICG3atGHVmi9+dz+ilafVaj2ndohareaaa66R/i1WozscDokg1Gq1l80YdQV+XCEYfgfiJCSTyS7Kasdut5Obm4vT6eT06dMEBgZKnuWiA4VarZYyy+LkFhkZKQlNio4WcrlcYh/Fyf5ig0VRgPLf+vKJIlNut1siWS6XIPa/CfG6WK1WSaBMr9ejVCrR6XTS30XG92L37Xa7pVagusQXnCkzFZ9jhUJx2dkBeTwe6fjrllLW/VxUjRcriM62LBR7bx0Oh7SPqqoqCgoKyMvL4+TJk9J7qdVqCQgIICQkhLCwMCIjI4mMjCQ4OFiyPlSr1ZgCAxAEgXKzmWtvux1Vp+4EP/gkuqhoQFbHW73hM16x+xeUpiCM0ecWcwQI79ufEsFLz5596Zk2EKXy3FmrGlsVvx7ZyK70jah1Clav/bLeQvyPsH//fqZNu41bxwXQtm3DRYHP5+P666O4e9JePB4P763silqtQAZ4PALvvpPJ92sriIyK5JNVH9UrvUtMTGTCyOHMmXYrgwZFMGVKF/B5qLHU0CZNSd8eTn58dS7KOa/iczrJmfM4yuM5TBr8GK2bdEAuP9MHDDCw42heWf046enptGjR4oLP8c+gpKSEFxcuok/f3vTu3btB0JiTk0OjsATpGE36AHq27EKPFp0QBB9ymYzJtnEMePEWBne9CfAvqKotlSxf9yy5JftqA3U5ESFxBGpjSYpqSVrjLpwsSqdDhxlYrVaUSiXFxcXknMhjVLumkm0l+IPU9Ts/p2Wj9mi19Re59bgtmYwAfTChpsh6W/hfJf+WGnV9Ytwf5pwdBMqQ19HjqAvx/SopKaG4uJiamhosFgv79u3D7XaTHJlK45g0vt/9MduOfENZVRGP3LoYg86I2+PG50N6xnu1H0R8ZCPe/uY5Nuz5ijaNujBmwGQqzCW8uXoeReV53Db4YZRKFb2uGkJSTBM27f2GKmcRkZGRkkuH2NcdFRVFfslplvz4DiPSBvD4kHsJVBgoLCjAKwisOriOOFMkT/S9l59yd/HY6gXEBEcR3yqZG264gVtuuYV9+/bxysKlfHl4PfHGSLpc3Z2HHn6Iu++5m5Ur3mPKijn0T+5KtbWmwTpDoVBgsVn4Zt+Pfs95QaCgqojCokJJPNTr9darqpRRX1BdJpP/LtmqUWvpnNqXTi37sD/jF+4cfw9PzZ1Jx04dqaysJDAwULL0vhxgtVqxWq3k5eWRl5cnJXfMZjNms5nMzExOnDhBVVUVHo8Hm9WGzANqub8CVA7IkaFAjVahAo8Mr8VFmb2EksJiVFo1BoOB/Px8Dh8+zK+//grAjg3beKDbBJoGJVFeVoZCqUCh8BP4zaJTeGXMPO59fyZr1xZx882TeeKJp+vNJ2lpabgUXtYd2Mz247t5d+dnJLdoTLM2LcnIyKCiogKtVkvTpk1p2rQpHTt2pEOHDiQlJdU7f7lczr1T7iWlcQpPzJrH/d1uo2vjq+pfJJnfxcHqtuNWeGl3VXssFgsejweNRvOHWgivLHmZ3F3HeW74o9I5BBtMLBj5GM/98BoPTJnOC4sXSPtJTU3l5beXceDAAaorqyioqOJo+WmqCqqoqqykUqj5XZejP0J1dTULly4jov8k1GGx5P/yLcMH9ad169b/mrWgmIQICAhAqVTicrkoLi6WqrIuBKtXryYhwUt0tF5qdzp8uJIVyyt44IF5DBt27TnfU7vdzu7du9mwYQOZmZmoVCoatWzMd4U7+GHlz0Q3jr9ovYKzUddxQKlUYrfbOXHiBGFhYX+Lq0ndhOtfpW1xBX8trhAMvwMxuLhY2O12afK7EOh0OiIiImjSpAndunWjdevW6HQ6AgICJHJDrKD4My0SWq1WKt+WnWeRB5dP5vls1M02/Fnl5/+PEK9LZWUlOTk5eL1eQkNDpT9i9c0fXauznwexv9HhcGA2m3E6nVLWpy7BoNVqJVtXrVYrPZuXy0AvZuDsdnu9BbjD4aCmpgaHw4HH40Gr1RIYGCiRDHUFmbxer0TgiPvIycnhwIEDHDlyhOPHj2M2+4X19Ho9JpOJ8PBwoqKiaNasGa1bt6ZRo0bExcUREhKCxWIhOycHTbvumGY/j1ypQqk3INnryfx97/6e2toTES+nDE69/y6hA4b84blH9BvI6ZcW0bPVsHP+vLjiNB9sfJERNwyjy9B76Nip43kdKM6FvXv3Mn36BG6/I5hWrc6RQfKBw+Fm2avZdO8+gIrKEr7/rpjhI2JYteokn31SRqghmdsHT2LbsdU4HA7pVwVBYPnyt/n6q7eJidZhNCrR6VQIggKlyj9lDRwURPrxYk4ufY6qU4W0DGvObeOWEhLU0MYR/AFoj5bDeHrWXD5e9cHfMn5MvH0SGRmZ6AwainIryNxXwKzHn6FL144MuXYQvXr1wmg0cvTIUVqEpiCv7ef3x+qizZkcGfDz8d0EmSLQavW43W6+3f4hWw99QOduRmY+25Ft20rYuOE0N9yg4vTpk6SnH2bb6uXIFXoKCwsJDQ1FoVCwefNmv7DmZ7Ow2WuobYJApZ2NUAAAIABJREFUEHycLs4hIjSaZ969F/DRsnFb3B4nG3/7nMDMIHw+gaKqPKwOM59srpZoAx8+8PmkzPrx7ENcNehefD4fSUlJFFee4r31C+uMBf7tTpw6Rj+uaiDKJrZViOXhhYWFFBcXc/z4cVwuN3FhTWgR14EurfqyL/tnfvptNd9uf59brpmKURdUe38VEumRktCC6/tM4pONy+jRZhB6nYHgoGY8dPNC3vxyHos/eYTpY+ajUetIiG7CuMH3s/jzB6Rx8+zS2rFjxnKVtxH9U3uiUMgpLi7xW2zaqlmftZ15A+7HoNFze48bGdfnBsYtf4BJI4fi8XgwGAx07tyZTp99yKZNm/jovQ+Zc/8UwN92cO+U+xg95ibeWvYGeScK66m4g3+crTab+XDH1xLdKJfJKTWXcujQIdq0aSO1eIql0vWJSR+cp+LhbMhkMto164Zea+ThBx9jxftvIwiCNL7/XXPuxc4XFouF4uJi9uzZw/bt28nKyiIrKwu73V5PfFQmk4EP2selMqhJT0INQagVapQyOTK5DK8gIL4LXp+A0+OiwFzMoeIMsstOkZ5/GK8gYHXbiTCEML3HBOI04ZSWltaS0nXKvTVqmkQnkxiRQETzWGbOfKreXFhYWMjkO+/GXmxms/lXFAFqTFEhbP5pszTPiFombdq0YejQoaSmppKamnre6zBw0EDi4uN4YPL95JTnM6bT8DrvlQyLy8a8DcuY/OhUkpOTsVqt0hpSTDadb82XfiQdJQppjBKhV+t4esj9LN20nInj72DO/LlSG3F8fPw5y9svBXv27GHytAexOd1oGnchOqYRLpcTZ85vDJ/4+L9Gx0zURRErmtVqNaWlpZjNZmQy2QW5YHi9XlaseJVx44IBv4vGxg2F/PijwKJFy+nYseN5SUCRYPjqq6+wWCyoVCqaN2/OwIED+W3XXoaPGnHJ51eXYNDpdOTm5lJQUIBSqaznfiRuf6kQNcbEdqcrTmmXH64QDJcB3G43VVVVZGdn43K5SE9PlyoYxIBHtIlSq9VS24RYEiQOXGIwKfYgicGOXC7HYrFgt9sBGvSLX+69SmKQJ07CImHyvw673U5hYSF2u52IiAgp4Bf7SC+00kMcoM1mM8XFxRQXF1NUVERZWRmVlZXYbDZcLlc9xWaRjRe/T7S0ESsn9Hp9veya2Eag1+sxGo2YTCb0en29eynaTon6I+JxWa1WqaxPnJxFazzxHajbklBdXU1JSQmnTp0iJyeHiooK7Ha7lPFzu90SuSBWMIjHbTAYCAsLIzo6WrL6FEtXq6urpQqGoqIiSktLcTgc0iLS5XJRU1Mjle9VVVWRk5NDfHw8SUlJBAYG8s5HH1PUvDUBMXGYXR58Tjcymw2ZTI5CqUClVKJSnXGaEd9puVyGx+Gk5lQuqc8vuaDnQy5TNOjfFrF87ULSOjdl5hOPXdC+6mLXrl088MDtTJoURouWDTMTgiBgs7n4P/bOOzyqMn3/n+kzyaRNMqkkIRBaCL0KSBcUca3oytrW3tvae9dVd3V3XVdd115QdBVdXUHAFZTeSYKQkN7b9D5zzu+PyXuYhFBFf+53ua+LC0jOnDn1fd/nfu7nfv76Qg3Z2TN44IHHkCSJK65YyN9f/o60hHzOn3Eno4dMQqVSYTYncvutd/PJZ4vx+/3cc88t2OzbuPe+PEKhPlx77RrGjmlk7LgsNGpNl4eNkQsWJvP4ozuZVHwWcyacjU6jR5KkA44NYwdPo3TpBt577z0uuOCCXrc5WoTDYbZu2cZ5J95Ei62BU4r7kZWai9fnYfue9Tz74Etc33IzmdkZ+F0urhp+Lk3NTYAKrVaDVqNFb4jWpKrVGlbtWU9B7nCqG/fw2r8fwmBu5/4HBzJ8RLQMZc33HUyfYWXGzHxkucsbISJz3XU7sVgsCjk9ffp07HY7b77yPlOGnhY92K663chgCa1GS1XzLva0bKJgRBrm7JMwm80KuT4+bRSyLGMyCdPHLlIkpg1ZWt/x1NTU8MgjjyDLMtNmnojP51MWneJPzsBJlJaW8sgjj3QblwKBAD6fj7a2NkXB4PF46OjoQKPSEqdPiLalC0lMHjqXCUOms2zTxzz6+nVMHj6XM6ZdgkatRhLdQ9RqigeMRrdKR21LBQP6DiUQCJBstnDz+U/w9hfP8/ib13PDgsdITchARiYQCqBSqejs7Oz2/MiyTNGoYrYu2cTsoind7vnOlj2kxSWTk5yFTqdFrdawtX4HM+bO4vTTT8dgMHSrwZ41axazZu3fFjYtLY17HryPex68r9dnK9OawaJr/rrveoWCnPm3KxVSLhQK4fP5lDVCOBJVaDmdDoKhIAG/H5/fT3NzU/RZQY7yDkpQoOsac3TUtu7li41vcuvtNzFgwADcbrfyLP1UEOWpra2ttLa2Kp0ahEeC6N7gcDiUP3a7nbq6Ourq6rDZbAQCAeLi4rBYLGRnZ5OdnU1GRgatra3kOZI5a8KhSNko0SB3kfaBYBCX383ejlrq7c2Mzh5KalwSkiR3zRuRLmIniK8raFy25zv8hgiDhxbx1FNPKSUXgwYNYulXX1FetoeUdAuWzDQ6Ojpoa2xDrVYrHguFhYUMGDBAUTAcjlv70KFDeeej97j1upupXvo3bpl1GUadgYgU4cmlLzLnvFM544wz8Hq9yvUU87mYt3vDn//2F+6/+z7u+ewZHjzlRhJM+/wTtBott86+nNe++5BzzziHv7/5j5/M28bpdNIRUDHwvIfQmeIV/6OgvZUBAwb8JN/5U0OUgiUlJaHX66PlCm1th1QyLF++nPh4B/365+N2B3j11ToC/jxefvlZBg4c2CsBGAwG2b17N9u3b2fPnj34/X6SkpLIzMykqKiIESNGcNJJJx2zlqYqlUrxM7FYLMqY6na7FZ8Ese77sd8j4phQKHTMDDSP49jilx1Z/o8gHA4rE2dlZaXycxHIiYFDEAeJiYlYLBaSkpIwm82KeY+QqpvNZtLS0sjPz1cMcNxut+IhoVarlXpxwageTub551Y4xEraPR4PgEK8/C+i5/3xer0KQzxw4EAsFkuvfguHuq+iHKKjo4Ndu3ZRUlJCSUkJtbW1tLe343Q6FRVAJBJRJhHxDMXHxyuEVkpKipIJ1Ol0BINBJdjW6/WkpqaSlZWldGhJTk5WsimBQAC3260E/SIz197eTm1tLYFAQCE1kpKSSE5OJjk5WZm0RE2okLZu27aNTZs2Ka3bBEEhiIvY2m+NRqOUNwwaNIhRo0Yp5qwlJSWsXLmSlpaWg17LUChEKBTC5XLR0tLC3r17gWg/6cLCQmxuD5U2O+mX34LNZiMcjhCRIlFJs1qNXqfHYDRgMhqJSCb0Oh0arRatRotKpaX5yyUYcnLRJySJxs8HhL+tBSkQwJqS2evv05IzKB5W3OvvDoUNG9agVrvpX1iw/y9l8HpDPP9cJfn5s3jqqT8otaevvPIuU6dMZ/boMxlTNFn5yOD8EextLOGKy66kpW0PU6aouPyK/l1lInDppQN5/vkf+MsLFiyW6Fhn0BvI6RNP4QAXwZAXj9vTRRTFHZBgUKlUTCk+lYfvf5QTTjiB/v37H9X594by8nKS4tPomz2QvtlRE7eogZyefpnFmDXpjOrjoqZ1D6vrFuPMs9PU1Ixo/WQyGjEnmLvqp2W+37uZVGsef/70as4+O4tzz5uMTieIOInaWje33V7UjRzes8dBfn4ho0ePVsbzfv36EQyEmFh0EicMn0UwFCQUDBIMhpDkqCKppHodffKy2blzpxLcezweRXIu3pXYdndiQRe7sBMKp94ySb21P4vdz4Fg1MXz9bb3KW/cxsmjf4Pf7yM1NY0LT7mB6aNO5c1/P8eums1cc9YDpCVlde0b/vD+7YwsnMzJk88mFAoTCAbQ6w0kmpO46ux7+eSbN3j23d9x0Sm/oyBzCLIcLTfr7Ozc7xxzc3P5rOMjJTAXSI1LwRlwo1Gr0Wp1eEI+/vTt67zy9j8wm83HzC9J6nF9Fm/+gtFTxlFUVKQQQV6vt4tgUBEJRwhIEsFgCKfLhd8fwOvz0dzcHA2ilXEvej/j4uIxxZvYtncVezs38+QfHlbaaSYmJu7nlXGs4ff7cTgclJeXU1paitPpxOVyKSWlbrcbl8ulqEL9fj+BwL5WvmKNZLFYyMrKYuzYsYwdO5bhw4dTU1PD928sO4yWuzKRiKSQwx63G6PRiMWczKg+Q5UyO0mKtkaMymVi1K0qWLTxc35or2RLyTa0Wi2DBg1i0KBBzJ8/n1+ffz5lu3bx5ptvKs+VqB3v27cvkyZNYsaMGUybNu2IVarp6en8453XeeSBh7nj06d44OQb+Xjrlxj6JnPDTTcqXmKi9EiQUUKFGAvxvXq9niefeYo//fE5bv34cR499VYyk6zdtjtr9Mks3voFTU1NPxnBMHnyZJINEPa50Jm6DBHlqHbqv1HFGrumT05OJj4+ntbWVoXY7NnaXkCWZZ5//mnq69t44QXYvKmRk05awLPP/qlXvyfxrgYCAXbu3MmyZcvYvXs3fr+frKws+vfvz5AhQygqKjrm5yeSW4K0qKmpoaOjg+zsbKVlriit+TExRc8SieP45eE4wfALhlis9ax/D4fD+Hw+2tvbD6lgWLVqFQkJCcrPhHw7KysLo9FIRUWFUpcnDOqEHFKv1/9Xezf8X4TwXQgGg0qWUJZl3G43sC8r1duCXpjhiRrjSCSiBMSdnZ2UlZXx/fffU15eTktLC06nE6/XSyAQIBQKKYGGLMuKL4MgAVwuF3a7nZaWFkUNoFarlTIOoWAQpT+irlf0Ihf7EURATwWD0+kkHA4rCgaj0aioGERNqVAy2Gw26urqaGhoUFQG4thFjXNP41BRhiQ6ybhcLgwGA5FIhNbWVtxu91EvsN1uN7W1tdjDEnELLyUYCuGv+AH/+u8J7d2DKj4eTaoVXX4/4sZMJJyWht8fwGDQK+flr95L1VuvUvC7e5FkaT/pak/UvvcWQ/JHdXVX2B8j+k+iraXjqM7n2mtvpL6+hhf/+jXXXV+ATqeOZsVlCbc7wJ//VMPAgfO466770ev1yrOYmZnJ6u+/5cKFlxDaHGbmmF8p+2xsq6G09iuefmYCAwd2l1Oeemou33/fyvPPb+fBB8ai0+lISk4ClYr5p5l59cW1TBg8l7hgXNczoqbN3sSu6m1UNpTR5qzC6WvC63ehUkUwGFXcc8+tLFr06TEjTTdu3Eh2SpRwEW0cg6Eg4VAYt8dNafV6Nlf8B5u7DaQQr25azJT8MRRlDGCgtSDmGfSzrnIrnoCLPmklPHhzAZmZcdhsnSDLSLLENyvbsVjUaDQempo80RbgssxrrzWSkX4ijzzyiHJckiSx6J2PWDDpBqUzgsi+yjK4vA721O8gLZxMc3OzUoomgrtYIk6Up8WOL7F/i/FEjqjRqnVo1Jqu4FjqUllIeAJOZb6C7n3irVYrWVlZypjR1taGy+bjV+MvY3vVd7z1ze85Z8p1GAxGZFkm0ZTGzec+wdJ1i7n/5cu58dxHKSoYzXvLXsCkS+CCU67vMsJUodXqlGNUq9WcM/tSrMnZvPnFH5g15kyMht5LAFQqFRkZGbS5OqIxZczjMiitAFBRbW+kOHcgf1z5GomqOO69+S7Ou2QhF1x0wTFxzheshs/nw+v1srNmF+G+Bp599lkgKr9vbGykrq6OFE1u17oBVKoISCpUqAgEPd3IBeGjIUkSHq+bJev+gSPUyJxTZrN+/XrKy8vJzs4mJydHGYO8Xi9er1cZdyORiOJnE1tKKro2CGJAbO/3+/H7/UqrcI/Ho+zT6/XS1tZGa2urolgQsmfxf9GxJyEhgczMTHJycsjJySE9PZ309HQSEhJISEjopmBwOp20ebqPc7IkKaSNShX1qFCpoh4hdM1RGo1G2W5feaCkqGQEKR8KBbuOL0iLux2NRqP8rqmpSTn27du3s3v3bsXAUavVcuKJJzJ58mTy8/OVjhFHOx4ZDAYee+pxXnv1H1zwzM3k5PfhvX8s6ka2CkWNuF9CNSISBD2hVqu55bbfkZmdxe3PP8lDc29SDCV9QT8Pfvk8l994NRMmTMButx/VcR/OeU05YQLrmioxWTLEgSH/HwgoxZpMJGsApQy4N5L8mWf+RGNjI9u2bWPb1le59tqbDtghTJZlPB4PTU1N7N69m507d2K320lKSuKEE05g7ty5P6kCRJhaGo1Gampq2LBhAxqNhqSkJCZOnMjIkSMVb6of8x3HqkQiNml2OOposb3RaPxZFOBqtRqz2awkJP5bcJxg+IUj9qURCziRzT9cGI1GkpOT6dOnD0VFRRQXFyvB2K5du7Db7SQkJJCamkpOTg4pKSkKIdGbd8P/LyVDz/////aM+LklWYo8sMvl3O/3K4tiUV8Zm5XveX3EACpKF4Q0ubW1lerqatavX8/SpUsPyzskHA4ri8ujhclkIikpCY1Go5AQQsp5JBAKBFEiEgqFcDqdvXZ96e2eCdJE1PB2dnZSXl5+1OfVE2JBrUpMxrB1A+5N6whu3YDs2LcoCwF+3So8//on+nGTMM2eR1x6OnFx8ej1Iaqfeoi0Bb/BOu4EZElC1qhQyT2e/5j/+ld/x5Rp1x3wmHwBN5nmo1MCqdVqHn30aSZMGM3y5Q2ccnIuMuB2B/njHysZWjSfhx56XFEuxD6HGRkZnH3ur7j//ttp7qhh4ZwbcHrsbCv/htPPytqPXIDo/bn33mIuv2wtn/+rgtNOGxAlf8IRTHEaQhEnX216F1kVxCs14/Z1otFK5OXGU9DfxNgCE/n5fejTJxFLiomIJPPYY+V8+eUXzJt36iHP91DjjMPh4Nnf/5FfTbgciBIMNlsnPp+fTmcbX299n9r2cgIhrzKO7rXVUdlZT4IhHq1aQ15yFmNyimnzdLK2fhNT58rMnJmAJNtpaXGh0WqQpehCavnyFkaOMtHU1AxdWfWKvQE2rO/A6/2wm4cIQHK8ld1VJfiDXvxBD76Ql2DIRyDkx+Zuw+l20Larqdv9jb1vYiEXaxgrFsexf4LBaLAV9sucf+LvSIwT3hwq1GoV63d/zX9K/qnsR1wL8f7m5+czcuRIJYtcVlbGbtteMlPyybYUsKF8OYu+fY6ZI86lf+ZQzGYzCQmJnDrlN5iM8bz8yROMGjyZ7bvXcPsFf0St1iiZWq1OiwqQpEgXGa9hyqg5JJvTeP3Lp1HpIvup+MT5JyYmotZpcAc8pGj3PZ9qVdSVQqvR4Ap6WV+5lbeveJ6EeDPvfLWEM979gEuvvYIF5y7olaQ/nPkjlhAQY5PdYefz91ful3gASMrrQ5Q8EEacEJGikv7u36dSxr1tFavZWr6acCTEnhd+IDs7mz59+ihKgMTEROLj4+no6KC9vZ3ExESSk5MJBAKKn43L5VL2LErlBPEhiGSHw4HNZqO5uVkpwWtvb1cIrUNBEFOJiYn06dOHcePGMX78eIqKihgyZEiv5HpqaipPhh7nh6a9DM6KKpYkWSYSCaMiuq1aQ9TzRq1GE3P8B4IoNYgSJB7cbg/BYBCNOhoYinVaU1MTTU1NlJSUdPu8INqnT5/OzTffrKhPfyxUKhWXXXE5w0YMJzs7u1vwJgITse4LBALYbDacTicZGRmKB0Bva6vzFy4kLc3Kffc+yu3TrmBkbhG/X/Y3Bp84nKuvvVrpvLZ48WL0ej0Wi4WUlBSSk5OxWCxYLJajasco4A8E8XY07jsX2O8+/7chdmzV6XTK+CA8MnoLcouKihg4cCBDhw5l4sSJZGZmHlDFIUmS0pZyz5497Ny5E4PBQE5ODieccAJnnXXWT1pmLAgGg8FATU0N33zzDe3t7Wi1WgKBACkpKYpq9Ggg7n8swfBj1uOCMBAE4OFsL+awI7mOR3uMQpku1vD/LeUgxwmG/wGEw2HcbjcNDQ3KxLdjxw7UarWS4dXr9ZjNZpKTk0lPTyc7O5vc3Fzy8vIUubsI4H5uRUOseaDIHvwS5HFCBRAIBPB6vfj9fsWrIDYLKI5XLKxFW6zYxfqBcCAyRavVkpKSogRxwstADLbiOIQUsyczKwbG+Ph4xVOgsbGxm9neT41QKBQNvLsyi8IT4UgRq6QQz8eRkhQ/B2SnHf+Krw68QSiEZOvAv/IrgpvXETrpVKSZcwluXENYoyNhygxcbhcGvR6dLtajQYWqh6Ih5HTSv0/v8kdZlimtX8/FdzzS6+8PBUmSuPjihSQkOJkxvRhJknC6/Dz/fC0jR5zFbbfd1U25EIt33nmLDz78M48+Ooxnn13Cm1+EKKtbzUknJ3HZZUP3BUZiAu3aRZxJz513FvPYY9sYPtyKJSWe999v4T//8SBr1FR3rGX6TDNDhyaSn5dHmrUrI61knWWCQQdNzQ5UKpg3T8Mdd1zL7t03K5mB+Ph4EhIS6NOnD3379u3mgXMwLF++nMKsEfTPKqatra2L/Auwp347X29dhDYOZsyaSp8+fcjMzESr1SqEltvtpqamhl27dvHRnmX4fG4MJok9ewyo1W7SM7TExalBVhMISHi9IfbuDRCRYNuWEH6fCpsjhEYTxGBUoderFLND0BAKgk6jY1PFSgw6E0adCYPehMkQR0qildSkDPRmmD13hlKTLTLQseoEMV6I8aq3P+Idfusf75Kenk5KQndJdVJLMqeffjpjxozpVpYnFoppaWlkZmbS2tpKTU0NtbW1GHQmDPpoWcz0EaeTZcln+bYP2F2/mfnjLwFUBINBJgw5ibz0gby85GFOmbCQ3My+SNK+xaJKJQhaIYWPGvwV9R/JpfNv51+bXlNq5nvOLSqVipycHBptLcTr44hEol4H7V4boUiY3KQs/rDiVab0H8eA7H6o1SrumHMVVW21vP72R7z32tucufAcLr300l7nLaFMEF00hMeAKAVwOJ00NDYQ8AVotrfRYG9RiJnYAMXr9SLLElqtTlmIGo1G6mxW4jrjyc7uXiYgyTKyJDPeOJOwHGJLxTc4vJ2K10woFKK6uloJSn0+Hx6PRwlUhRFwKBRS5hrxHun1+l4VDGIfbrebQCCAWq1WWnRbLBZSU1OVjLogFMT5RQmlqPotMTFRUVhYrdYDBpx6vZ5LrrqUj9/9inuzooSrFIkQCoWjJQpH2MIZiFE6GBViOzExkSG5AxgybSR6vZ6WlhZKSkooKyvbLxgQBITNZqO+vl4pKTxWGD9+/EF/LzKusR4ALS0tip9Sb8mJk+acRJo1jduuvQWDX03uyP7c2+UZIkkSPp+PF//wAucMP5kfwl4cAReOgBu7z4nN7UCj0xIfH8f4yRN57KnHj+h8Ljj3bMoefpy9X7xMzqyLCTg6sKSmHnVw+kuCSqVSzLiDwSBOp1NRHMdCKBL8fj86nY6ioqKD+hj4/X42b97MypUrqaiowGAwMGrUKMaPH09hYeERty4/WhgMBoqLi5kxYwZr166lsrKSdevWEQ6HmTNnjjLnqNVq/H4/Ho9nP0PIA+FYdpEQBJyY+w5ne6EeP1oFw4HKA3tLqMb6j4mxuee2B9tXbDlj7Fh5oORt7GfEmvxouvcdJxj+ByAIBrfbrfQr7612SdRGZWdnM3LkSIYNG6ZI8UW2uTe2/acaqHoucMWLJnwAfi4G+0ADlyRJXWZaTjo6OnA4HEqNoxgs3W63MjgYDAaFxLFYLMTHx+8X+Pf2gsdmC2Mz9YLR7LloFd0fxDGKxZmYxMT1EyUwWq2WpqYmGhoaflaCQTyXPxbiOokM6n89wiGkjjbcnyzCt3oFsiQRf9ZCnG43YUkmYjJhNEbbz0YnNw1qjdxN0aA1mWhorWJwwf6u3tVNe2izNzJ27NgjPrRIJMJ999zPd99sxGi2YbP7iYvT8OwzVYwdexb33feQkv3r+X6+8cZrvPPO09x+Wz6paUYee1zH7bd9gtms56abptNld69IkaNdJqPSZZVaxahRVqbPyObOOzaTm5fHiOGzmDcv2i5z586tlJX6mDDBSDjioKXFEf3SHrJ28T7p9WoGDnTz3HNPk5WVh1qtxmq1kpOTw/jx45XSncOZVL/4/CsGZo3E6/XQ3t6Oy+NiVckSSmvX4w24mDl+JvPmzWPcuHFKhyBZluno6KClpYV169aRmJhIeXk5tbW1eL1e9uwJs2uXG71eQrzekQgEgxLJpmxyDdPRaw3YpDaavCu55joLRqMag0GFTg8VFUF2bA9QvkvHpKGndI2XaiJSCK1Gz8Dc4WSm5tBsq0PX6OfKK68kNTWVlJQUxTj1QOd9oPFQbL9i6bc0OSpxBtvwBbzUNkfVQPWtlUwqGk3//v2ZNm3aAeviy8rK0Gq1bN68GYPOpJiwgsyw/uPJTs3nq03v8+qyh5kz8nzy0geiVmv4rvTf5Fj7M3vimUppRKwaI0o6Rn1PpEgEjVaLTqsjIT6JvLw80tLSlO4b4jzF81I8chh7G6vJT8khHI6ALFNlqyfZmECLs43tjT/wysInYh44mQJrLo/Mv5WHPn2OR+58kK//tZSb7riFiRMndssWe71e2tvb2bVrFzt37lTIFZvNhsPhwOV0sremmlc3fsCa2q34Q4FuUnsRkIfDYWrbdvP6iscwGPSY4qKZ+JbWZlQa+Mfyh2lobFDIO7VKjUqtBllFJBIlCEy6eAhK+O1eKjvLaa6o72o7GoVarUajUisvld3vxKDRkWRMQIWKNm8nNp/zoO+LgChtS05OJiMjQzE5TE9Px2q1KuVvwufHarUqZELs83YonH7G6bz8pxdpdrSRmWQlIkmEu6ToarW6e0/Pw4BQOmg0JoxGEyX1u3ln86foUk3ccMMNAAqxsGvXLmRZRqfWYNKZcAU8SglFZ2cndXV1aLVaLBbLER2DcixHeuwqVTfiRngAtLe3Y7Va96uNj93/qFGjePW913nt7//g9rvuQKfTKYQL0cmEAAAgAElEQVR+Q0MDOrWWK6ct3O87ZVnGG/SzsXIbb6z79IjPcerUE/ninx9w2pnn0LT1G+Sgh/knzfw/4cUV9UCJ69ZVIlYCH+tv4/V6cTqdWCwWkpOTD1gaAdE14KZNm1iyZInS/nTUqFHMnz9fIRh+DhiNRoqLi4lEIoo31vr166muriY9PZ3BgwdjsVgUAtLr9R6Wmgm6d5E4EMFwuKSDWA8f7ud62/5ICI6e/kW9EQGx28YmKUXc0dvnevsO8W9RRhK7punt+2M/K0qyRaLySBO7xwmG/1Ec6GUUE19ZWZlSj56Xl0dhYSF9+/YlEAiQlpZGQkLCz1YLpNVqSUhIUFQBPp9PYdp/DgjPgVAopPgEOBwO2traqK+vp7GxUck8Cb8Csb1QMIiJPVbBILotiFo1sZDSaDSKI7harSY+Pl7JcqSkpCgZJdF9xOl0YrfbaW1tpampifb2duVYZDkaiJpMJiVDK8wYtVqtYmJYUlJCU1PTjyp5OI5jjICfSEMdqvh4/KuWo8vMhtx8QqFQVy20Pkb2qlcmALVahbZPNnXN+xMMnc42PlnzCvc/dM8RL07D4TB33HYXFdsbeeLqt3n7q+e47davSU1LYt6833L99bf0KvWVZZnnnnuGzz9/lTvv6ofFEh03CgpSePKpsdx/3zaeemo7qRYDl10+EJVKjUYtmiDKhCNhQv4wDQ1OOjv8+P16Bg+agsfjZ9euXTQ0NBAMSlRUhPnrCx0s/E0y4ZCMyy0RCERbKipBhCzTRV1gMEAo1EllZbRVqc1mU2TN27dvx2w2d5Pyi/dXEDuSJOF2u1nx9UrOnzqCuqZqOmyt/HvzO7hDNkaOGcbYsWMZNmwYxcXFpKend/M1kGWZhIQEhg4dSkJCAjabTelVL8binmVPa75bi1U1iAlFJwHw2hdPMOukRKZMyVWu91tv1fP9N/GkmPPJsuiobC7pIhfC1LXuJS+jkDW7/s09F/2FFkct/QcUkJWVtV9XlwNBkLwiuBDXReDCi89n794qVIRY/fkyfPYIBVmDyc8YjL1KxePLn0V6WGLhwv2DEbH/fQosCVTR22cwRFVfiYmJXJp5J+tKVrB063ukmNMpyBjKrtot/Hbu3XR2duJyuhSpu1ar6QomVUCXykuOIHfV0wdCPuLi4/bL6okSssrKSuweJ63VVYxIG4TXG22FWN5RQ0FKLi+vW8Tk/DEkas00NTXHtKLToVZrWFn2HQ/MuwG9zsA9V96GzmJi0owpJCUlKYaabreb1tZWqqqqaG9vV0yZQ6EQBknLTf96lFHZRWQkWEkpsDJ58mTMZjMmk4mSkhK2bt2KXq+n093JxCnjmDdvHsOGDWPo0KFK1x2dTsfNN9zEUF8OZ44+GW/QTyDop76jmcvfvJ1759zCmPxiwsEwL656G5vXwS0n/hZV15gSiUhdJSb7PDS+3v0dn5et5JlT70Kn1XL30mfxJ8qkpqYqSQ3htyOSE0JCL+Y/QZAIBYP4WU8Fw+G08usNcXFxnHHuWSxZt4yrpvZ45o4yR2H3OtmwdyvfVm+kKdzBFTdezciRI2lpaWHt2rWsXbuW0tJSZFkmOSGJwuQ8Ths8gz9//xaOYDTpsHXrVsLhMBkZGWRkZJCcnExSUpJi+i3mdVunDZfNiQqwZqUzY9ZMZs+erZh6Hy2EKikxMVHxjmhra1PuS2/Z2YKCAh594jFkWVYMYQFycnJITug986xSqYg3mEiMSyCnT5+jOtb4+Hhuu/kGFpz7a5LTs/jtY18ck7KSXzpiSzeDweB+KrKeCIVC1NTU8MMPP1BRUYHD4UCn0ymm7/369TtshcCxgEajwWq1MmDAAEaNGkV7ezt1dXW43W5WrVpFOBxmxowZjBw5ksTERGWdejg4krHA5/Oxe/duKisr6ezsVMyLhWpWJFoPRRIcrUriQEqBgykIem4nSJTe7v2hyJXeFAyHUi/0/N5Yz4vDxXGC4X8UB3ogw+EwNpsNm83Gnj17MBgMFBQUMG3atG5slpj0fw5vBkEwCFWAJEnKgumnQM9rIzIOXq+3WwvE3bt3s23bNsrLy2loaMDpdB62VEuUNYiAPykpidTUVDIyMtDr9Uq7Rp1OR3p6OoWFhUpZhFhY+Hw+WlpaqK2tpaamhrKyMjZv3kxNTQ12u10hC8QiTUwyBQUF9OvXD41GQ3t7O3v27KGkpIS2traf5Hoex4+D7PEQ2rkVW10NvhNnYpx5Cuqu+sZ9LUIlZNnQ5ZeiJn78CXy1eDFTRs3BZIzWv7bbW3h3xR+56bZrOO+8847oGEKhELfdegfVJa38esZ1aDQ6Zo89h0dfW47fL3PJJVeQkhKtuY8dByRJ4tlnn2T58re4484ChVwQGDAglYcfHsmrr+5mzfctjB+fzsiRqaASNeLg9QZZsqSWL7/soLMjjMvl5rXXXuv1PWtsDPHySx1IEoRCMorhO0ImH3NdZfGnoxujv3nzZiU7IsqOJEnCaDQqQZ3JZFIUOB6nnxc/vZ+IFEanVhPRSuTl5TF9+nQuueQSsrOzMRgM3UxMRQZaEAwjRoxQXOXFsQiSMnayn/XlXM6dM59kcyp1zVW0u2t48Y5x6PVRsrWuzs3338rcuvD39M3ubuLV2tnEE2/cwH2X/YWX//kEz394F4nJcXzx8mdkZ2cf9rMgypGEs77oYiSO+4KL9rUAbW5uJV0qpqhgtPIzo/7gLcrE/mVZxh/yKrXyRqNRWRxLksSsuNMY1Gc4a0uXs6rkU+KNidQ1V6KR9Gg0WlJSktFqNahUBtRqTZS40tDN6FWSJQJBPwldHTxiJaE+nw+bzcbmzZujHTaq2jmt73TlIarqrGOwtR9bysu4YuwCXE4nLpdTKVEwx8fzwfYvSTOlMKn/WPx+Pw9Ou55VFRv4+59fweZzEJLCSkmXXqMjNS6ZcTnDsFiSMWqjpE1qfApFGYWkJ6TxyMoXGDJ6FDfccANpaWkkJiby6quvUlZWpmTkBwwYwKmnnkp6ejpJSUnd3kdbaydDB8+IzqmaeMxGEzctepg5Q6Zy5qRTkORoG970hFRaPZ3ouxR2Go2GcChEMBSKEpt6A5IsM3/4LD7a+RVrajYzd+g0dDodg0YMYeDAgbS0tNDU1EROTg65ubkKkdCvXz/69++P2WxWypN+SkiSxNJly2iuaeA3489AS2xQfmTf3e6y8fq6D1nfsIPxkydy5k0LmT17Nlqtlo0bN7Jx40aWLVvG0qVLo2OG1kBRaiEPzL0eHVr+uvZdpQ5969atbN26lQSTGbMpHpOh672QIM2cgtVsIdNsJSPeQl9jH/Q6Ha27Onlh1R9546XXuPTay5kzZ85+pY+Hg9ggRbQPFK2XRX08EFNi1D3jKcjV9vZ20tPTo10R4g5u2Of0ukhO2b+t8cEQO/b169eP1ORk9MEQGzdspG/fvv9nSYaeygWHw6EkqWJLA3oiFApRUVHBmjVrqKiowOl0kp6eTmZmJnl5efTt21fZ76EQ+ywdSFLf27ax0Gq1WK1W9Ho9o0ePxm63EwwGaW1tZfXq1VRVVZGamkpRUREJCQlK2cHhHlvPYPtAWXy3283WrVtZsWIFe/fupb6+XjE7FQnBnvs40Lke6c8P9/c/B3ojFn5KHCcYjuOAEIF1a2srGzZsoLW1lYKCAgYMGEBRURF5eXkkJSUdsG7vWCEcDittq0Q25ueQeIn2X8LRurW1ld27d1NeXk55eTl1dXU0NzfT2dmJz+c7ojowkaH0eDxKG06RQRVGUbBPwVBaWqoYJokgx+/3097eTmdnp6KoaGpqUrKgAoIYcjqd1NbWKn+r1Wq8Xi8dHR1HbBx6HD8/ZHsnvqWfE9jwPYbxk1HNOqVL+h1tw2cyRlUxOp2W5BOm0PLW69S1VDIwvxi318G7K/7ATXdew7nnnntE3xsMBrnjtruoKW3nvJnXoVapqW4o5/kP7+ZXky/FbDZz/TU38sbbrykkA0Sfu8cff4gtWz7m7nsKiY/XKj+XIhLhSNQoNDNTy913D+all/bwzDPbuOaafPr1j0OWZEpLXXzwQQfhSCJGQwaRSJ2yf1HbLTJuPp8Ph8OBxxN9X81mM+npKQwZMoQRI0bslxkRgZ0oramsrKSsrExxq491iw/4A0SCYex2O263WzGEEh1d4rUmxvYZSm5yFqubtvKrX/2KGTNmRA0Ce7QZjs2WiH34/X5FPSDGNiGJFFi/fj3JJitJ8RZkWWJT2SoKB+nR6faNu088uocpxfP3IxdioVKp+O1pt7Js7SdsqljBhQsvYeDAgcjS/mNXKBziokt+w7Tp07r9vDcp57GCVqtV7mtECncpGNQxi0pB0JrIzenLl5uaKcgqoiBjMCt3LCbFnMGs4QswGPTIMhiMBoxdz4q4xoIcV6tVhCJBki3JhEIhGhoa+OGHH9i5cyc2mw2Xy0VlZSX19fW47HbCUhitKnp/Km117G6vYkLuCCxxXYGTHDWSDIXA5nSyePMX3DnzKux2e7QTTyjMpLxRDEsfyNtbPmFr4y7a/TZSc9JQSyouG3Y2swZNQqfVou5SXUTPO3qsFouFUaNGYbFYFAWhMH/0+/1KOYnoBhULl8tFXXUtA6ZHu53IksSLK9+m02njlYufjJYjAXFxJnS66DyblpaKShVVMEgRiYgkdXtGZVnm4knn8O+S/3D+iWcwLHcwlj65TJ06FY/Hg8vlUjwThPonJSVFUQf9HLDb7TRXNyBFJJZsXsbZY05GbzCg1R6ZOduXO1by9rYlnHXBAh6+4g8YjUYkSWLHjh3s3LmTkpISSktL97UnTklnYu5Ibpt1BZakZCIRieSEJJJIJMWYSIo+EavJQmZCGpa4ZKxmC+kJqZgN8b1mKVUqFeosNb8aMpOS1nL+dPfvefy+RzjvkoVccsklSmnT0UClUpGQkKAEIKK0Uyi3YgN50fUDIDk5GZPJhMPhINF48I4pTr8bS/6Rl4II5SjAgLx+XFR8OhsWfcObL73GJddcxoJzF/xXOev3BuHFIEkSfr+fpqambmtqoTIRfloHQjAYpLy8nDVr1tDY2IjRaGTMmDFMnz6d3NxcOjs72bt3L9XV1YpKRniHiTJe4W2Sm5tLVlYWer2+1+dKzGOHU65sMBi6GbFqNBqampro7Oxk2bJleL1epk6dyujRow+6n54QJSaxnTggqj4TbW3r6uqoqqqitLSUPXv2KOO6mLuEqvhQpVe9vY89/y26kYVCIUWxJRSPYhux7hDtYoWCWXRdE4bt4rqLNYD4uSgdE/sU90+oDIQSMRYixggEAkoMFbvvnucmyjBEIgEgISFB2W9FRcVh3Z/jBMNxHBAiCG5ra6OtrY2Kigr69etHa2trN8mMUDMcayWD2J9opSjLcjcjokN97sd+dzAYxGazKZNpfX09mzZtYtOmTZSWltLe3v6jvkN0Yjjc4D7WKDI2g3qomjURxIi+9nV1dQfd/jh+wQgGkFqa8P17CYG1qzBMm03c9DloTXGE48Oo1Cpk2UD1U48yY9hpDMgrIhwOsfjbl1hw4RksWLDgyL4uGOTG62+mtdLDudOvRqPWUlFXyl8WP8BJYxYwf9p5gIon31zBzGmz+fa7b0hKSkKSJB5++F5KSj/jllv7YjRo6ap5iAbt4ZDiQh8MBAmGgsw92YzRGOL3v99NaqoGg0FNZWUQp1MCOpTFiRhfRHec1NRUrFYrNptNIfn8fj9ms5nc3FxmzZrFRRddRHp6unJe4p0Q75/L5WLlypWKCa6QpIqOLZFgmIz4NGb2m0izu402jw2n34XWoKF/bj5TC8YxLGsQHQEH65p2kJyUzLRp0wiHw8pCsadCQZRQCWUD0M1tvWemasXXKxmQNRKQCYdDbNq9kouvtkYrQACHM0hNVZCbb77wkPfVoDdx2rSFDBswjqffvpUTB5zdFch3H7u/37WMbdu270cwHC5++OEHPt75CTqtjkDIj0Fvwqg34tY1UL57LzIyaWmpXH/Dvo4nYqFrMBiQpAjIPQ2mVKhUYDIZKavZRLujmTsveI5ISGZM4XSWbnqfD757nnGFsxnedxJxcWbFQ0ivN3QpU/Zd11AkQHpqCoFAgPr6er7++ms++OCDbsQxQFpcCm3uTrIS0wGZC0aezjvbPmNWvxOE7QIAUiRKoL259RPyErPIj8vEbrPtuygqMGr0nFF0EuvqtpGZk0V2djZBbwCj0ajUJGu1WrQaDahUlLdUsWr3ehocLYwaNYqUlBSF4BEEQygUUgiGpKSk/RaZ27ZtY2B6ATqNDpCRZJklW5dx+0lXYTbFI2pRTKa4rvIrPampaYe8x8MKhvDhli9ISbFw8rDpLKlexdSpU4/8YfmJ4HK5yEnN4qHZNyKFo8S9aNl4OJBlmZdXvcvOQBWvfvAG/fr1A/a9v1u2bGHRokVUVVVRVVWFXqsjy2zlqgnnc+qIWSQmJiqGhP+58wO8Xp9igOlyubrKtpQvw+/3HeRgABVk69K4bfyllNtq+PKNz/j3J19w8VW/5eyzzz4sY9qeEASD6BZis9mUdZTwaRLXwuPxKJ3HUlJSMBgMOBwOmttbqGytpV96nrLfzVU7cPhcaNQaVu9ez4TRM4/ouCB6nf1+PzU1NWhDKuYMPZH5o2ZT2VrLO+99wnuvvc2DTz3ChAkTjnjfvxSIrgsqlUopcRWt4i0WC4mJiTGeS/sjdq1cUVHB2rVrkSSJlJQURo8ezamnnorBYKCjo4ONGzeyatUq6urqqK+vx2634/F40Ol0JCYmKt1jJk6cqJTVxt5/8bcg4Hsm+w5ktjpo0CAyMzMVrwW/309ZWRnLly+nvLwci8XCyJEjD+gncCCIAF2lUnVTn+3Zs4c1a9awbt06duzYoZhkijWECODF58Wx91xn9FRLHOznwrzV4/EoiQ8R8McSDIFAQCGFLRaL4vkUDodpa2tTSsvMZjNGY7QtszCQTklJUYJ9tVqt3L9IJKIoRnp2kAkEAjQ3N+NyuZR2vmLfvRErIl5wuVz4fD7UajWZmZmK98dxguE4jjl8Ph+NjdF2QV6vl8bGRkaPHk1hYSFWq1Vxvz1W6oJ9vaZDijTscCdP8Vm/39+r/Am6y75iyZFYcxOIsqGNjY1KlqK6ulohHX5OCKMW4evwS+2WcBw/A8IhpPZWfJ9/RGjbJswXXYkmryBaG+t2EKypYfrFdxEMhvhm66f0H5bFdddde0RfEQgEuOG6m+isCbJg2tWoVCp2V+/gxX8+xKkTL2D2xDMAFUtWvUW7rYVBfYdx5WXX8OrrL/P0049Rtutf3HBDPipVBJfbRygYIhQOKdJ6EeCLmn6tVmLylDjsjjBrvvfg9UqARploCwsLKS4uJjk5Wck6xPqZuFwu2tvb2bRpE99++y1Op5Oamhrq6+tpbW1VJnsRrIpaVlFvPHr0aGU/sccmxpElH37CuAEjmdB35H7muDIyy35YzaKSLzn/igu5/IrLFX+CngsTSZJoamrCbreTnJysGLUejDSNRCJ8vXQFF0y/E1mWWb7+M1QGOzNnFtCV4I5mGzUaDPoDt9jrCb3OgNFg6lbCEIuWzoYfRdj279+fIuuJ9O8zhA57G+32ZgBMOiPOCi3hcJCPtr/SjWBob2+ntLSUiooKwpEwpbXrmTpqXtf9ijW9UvHF2ndJT83CG3SSmpCFRqPm1yddT1XjD/xz9SuUN21nzqhfk5GaqxBPYmGp1kSvud1ho2FbDZ4Xoz4IZWVlOBwOpcWleP7iNEbcKh9WaxpqtYYhwYFoS7Usr1+HNdVKbkImAb8fSZbxB/2s2LuW+6Zfu28Bp1ajVqkwmkzEx8Xx3u4vmHLSNKbNmI7ZbOab5Sv5dOMyqn2NyraqrmeiuqWOzFH53HDvLRQVRbvDiPFfZJp1Oh1xcXFKJm/Pnj306dNHIa02rtvAcOtAZElCkiUcXic2j4MTBo6mZ6mASqVG6kXR0hv+tW0FQzKiLSCLsgfwx+9fO8qn5dhDlmU++ehjssxWrAkWAn4/dJXcHG4IvnjTv9gtN/D6e29iNptxuVw4nU6lxGHbtm1UVVXhcrlIM6cwrf8ErjnxAjJTrBiMRgyGfdlftVqjmC7r9TrMZrOy/hBzevR9k7vKt+Ruv4+Om6Jrh8RAS1+GZw2i0tHA4ufe5sU/vsDFV/2Wiy+++Kjc7cV4qFKpCIfD+Hy+/Y5NlKiK90ij0ZCXl4c+08wjX/2ZNy56FlmWaXa0cd+Xf2DOvJORIhIpw7KZMPHISABJkrDZbLS1tbF+7XpOLBiHRhM9r37peTww7ya21JRw3/V3ctrCM7n8qiuU593r9VJWVsa27dtZuXY95595Oqedeui2xP+/IOaIYDCI2+1WfM5EpvlQnmM9xwMR8G7btk3p0iAUWTU1NUoAKRJUkUgEr9dLeXk5Ho+HvXv3smLFCqXjWeyzGFtKZjAYMJlMCtEvOrsI5YnT6VT8WGw2G6WlpbS0tCjZe0mS6OzsZPny5YTDYSZNmkRRUZHidVZXV0djY6NioK7X6xX1rei00zPJ5vf7qaurU0iUUChESkoKFouF/v37K+prYZTeM5Pfmzqh57972150VOlNwSAgkhter5dwOKyUuYrYw+OJtr2NvffC8yQYDHbr9gQopR6xCoaeKhfR+jkYDCrvrVCUHehZEvsVcZBQTgN8+eWXB30WBY4TDMdx2BAsWGdnJzU1NTQ0NCi1xKLOP3ah/GOVDOIhFwSDMH3qOdD2xnaKzzocjm6dEWIDA/FCi8Ad6NZXV61WYzQaFaOvnTt3UlpaSn19/Y86r6OFOC5BMhzHcRAMEt67B+efniQ4/2z0YycR+tPvmVg4DYMujh3l6/m+9EsWP7ZovxKeg72fPp+P6665EXejzDnTrkClUlNWtZWX/vkov5p8CbMn/gqVSs0/v3mN77cv46bzHiMvq5DPvnuT4qGDGDbczO13FKLVqvD5ArjdbrweryILlGRpnzcC0We7vCLIJ/900tYWIRhUo9VGycqkpCSysrI48cQTWbBgAfn5+ZjN5v0W0KIF3jPPPMP3q9YiSWoa6huoq6ujpaWF1NTUbuVVseNWfHy8ku05EE4//XRuu/wmZo6cjEkrOmVAs6OdV9Yuwm+B95Z8QE5OjjKO9DZevfTSS/zlub+RZc3F4erEH/KhUauxWtPJyckmr28u/QcUkJ2dTXp6OhkZGezevZuQX0aSIlTUlfHV+ve456E+x6TdWESKUFq5eZ98Va1FhQqNRkuHo4VI5MhMwUSGCqLlAjqdDr3OQHZ6LoW5Q1BrtGjV0fE8GPKzetcSYN84LgiGmppaLAkZrClbyua93/DrWdcyYtB4xTuhuqGczD5pXHHVpbz017+TauzDjJFnkpaWRnz8aPpYn+LzNW/zweq/MKrfNEb3n4pWo8NkipoIioWfw2lj7ZbVrPxmJQ6HQzHlFZLkpKQkMjMzSUpMwhZykZZmRa/ToWnfw+lnn0F+YV/++t4icjSpnFY4ncx4K+vqtxGORPjLurdZUHwy43KGEW+MR93Vz9wWdtGIjSVvfE58fFQSP2PGDEpKSpT7IJ4bMUeNHj1aUeGIuSAUCikBhV6vV1pEtrS08OszzsWoNzBw0EDGTZnIxx9+xJ2Tr0Dq+uxXO79lQHpfzHH7S9vLWyrRaw9d3/7p5qV8XbKKVy95GgCzIR69rOP3TzzFGWefyaBBg47o2TmWCAaDPPLAw5SvKeHhU24GFJHJYb8zNe31fLJnBe8v+VAhA5xOJ/X19SxdupR3331XCXLiTHGcOXg2t865gtTU1C5z0f2X2Psk59HrLssSsiQT6iI1ZVnuIoG6Ew+xJtdCJRMOhQkGAqSpE7llwsU0etv45PXP+ejtD3j4948xfsLBW1b2hEoVbTNqMpno7OzE7XYrz1qsGZ6QWYtApri4mKefe4bz5p/Dw//+E7tbK5G0cNpZZ/DwE0fXDhn2EQz19fVsWL2OuyZdgUql4s01H6HRaLhgwpmMzi/mL2c/xCsr32PO27MI6tSok8x02O0Yc/si5/fDK6lpfenlXzTBINbOQjUnstQHUy7EQiTFxL0SZuHbtm1j7969NDc309HR0e37YgNkQcBWVlYqZT5iHBQmoLEGxbEBbVJSEgMHDmTIkCGMGzeOwYMHK8rAxsZGmpqalG4lDodDIXBNJhMej4fOzk5WrFhBbW0tiYmJFBYWYrfbqa2tZe3atWzZskUh5YUBbEdHh1KyKIiw3kr3RPmJ8HqYPXs2kyZNIjMzU2kP2zOGONT4cKTb9/b5nuaJB4O4tyJpcbStMY8VfvOb3xzWdscJhuM4Ygims66ujtWrV2Oz2ejs7GTw4MFkZWUpsrkf+xKIPtvCSOxADuexk6/I7gsGV7B9wizHZrMpnRdEDZrISPj9/m7KACFrFu096+vrcTgcP+qcjuM4fgpInR34PnoX3z/fI9uYwcQJc9lRvpGVpYu44JLz0Wg02O12hd0+mBJIlmV+e/HlhG0Gzp56JQBllVt5+ZPHOGvq5Uwdcwqg4qOVf2fdzpXc/Osnyc0sIBDwsr3iW8KRDsrKHDQ2mjEY1IRCQcVMKRKJICN3LVyiiiSvR81nn7lobDQzZ86Z5OVFJbYi0IqLi8NsNtOvXz8yMjIUaWBPOJ1Onv/jn/j3pyuYP/5SzPpk3vnmWXbt2sWHH37IrFmzmDNnzlHXKY8YMYLZZ8zlH2sWc/OM3yLLMusqt/Lo0r+Q1bcPi175oJsJ14GC/xNOOIE3XnmPK+c9gCTJhEJBQpEQHp8Tl89GZ3U73+7YgTu4CrffjtPTSYetA7vNzmOvXY8v4CU9O8Ko0YeWrx8Koa4ykPeXvhgNdIhdpEkEwwES8w9tCBaLiy+8lMryatQqNZ2dnXg8XwMy3oAHZNB2BV19swdw4Sk3KwtioWppb29n7969BL0Rzpl6OdkZeZTWbsQ5zKIAACAASURBVOCNL58lfV02v5l7IznWvqwq+Yybbr+eefPmMffkubz41xf5+/uPMKpgOhOLTiIlxcJ5s6+ipukkPvjmBSqadzB7+AKytQV4PB40XQoGm7Mdj8eD0WjEarUqPkNpaWmKHNVsNtPW1sbeFTvRabWo1CoaO5r5euvXXDPkWp5/6c98/NHHPLfkbcZlDWN74y7GnTiBH374gb9vWcybWz5l7sApnDp4Onq9nr+seQNL/zReeeUVRo0axZgxY8jKylKe/d7QcwEtFvAej6dbhluSJLZs2cKUfmP53czLKWssZ8f3u4jzabn0778jJSGZYTmDqWitZtagSftl8jeWb+PrH77j05v+cdD7/N7aT3nh69d5dsG9DM2LEglqtZpnz7iHxev+xTNlT/HqO68f0bNzrOB0Orn1+psxtsk8Mf82NF3dQwxH6L3wVem3nHvRr0lPT6eqqorq6mpFybhjxw6lq1ZaWho6tY6s1IwuSXnUVPTwoEKlBq026rkhlAs9VQxSJEJEkoiPjyMUChPw+/EHAgSDAYLBaLY4O87K3dOv5s4vnqa0rPSICQbliGKUDKKES2Rjxdqq59jWt29f7n/qIcW4Nisr65iUy6pUKpqbm2msq6e2o4F/rP8QlylIbWU1pxbPJCU+iZT4JO6ccw0un5uJf76ItLlXkn/KfNS66FgvhYLU3H4dJSUlFBcX/+hjOtYQ77PH41G6KajVaoVQApSMdm8QHmF79+7Fbrd3U7uKJJssy6SlpZGXl0dubi45OTlkZGQo3y/8HxobGxVSoL29XSEtxLwm5jjxGaG4qK6uxuVyUV9fT1pamqJgEOVAHo9H8TQTHXqEwlisz+vr6/n4448pLS3F4/EoJb3CW8zv9yuJTL/fr/gP9EwcCvIkMTGRMWPGMHz4cMVXol+/flitViUbD/uUB4L0OJSnR6xSoTfFwKEQ67UQHx+/XzlbT4jSCZ/Ph9Vq/Vk7gfwYHCcYjuOIIQxK6uvraW5upqmpSZl0hIGWyP4cDIeS3gqCASApKUkZEHp+TmwnzIdE3XNcXJzSdUHUOrvdbhobGxX5VENDA42NjTQ3N+NwOBRJ0OG47B7HcfySIHujdfy2kIqlGxZR31lOYVGe0qdcLArEe3ogzxRZlrFa06hta0eWI5RWbuXvS57krBMvZ/q4eahUahZ9/Tc27/qOm857gtzMAoJBP0++czXZBZ08dM0IXnllN3fdVcrNN6dhNHTtX/ylZGi1bN4UYOXKCOeccxnXXHMdVqv1iJ3BI5EIixd/xJ//+FcGZ43j+jMeIxiI4HK5mDhoLqtLP2Nx82IMBgMnnHACycnJvZ73oaBSqbjx1ps5e94ZrK/azucly+nQuXniT08zevRoRaYeWxbRG2w2G2lJmUrwoNXq0Gp1xJvMZKpz9l2oA+CBVy5j4QX7S7xVKgiGA7y9/A8kGlNINKZhTcki3ZJDWlJGr/sKhP0kmJN47Jreg8lvt35B4dAjc3532JxcfNKdpKd070zx3Pt3MyBnOPOnno/D3ckXqxfx5Bs3oTWoaWpqwmKx4PF4aG9vp6qqmmF9JpMYl4rJGMf8qecxfeyp/Pv7D3j23TvItuaSmZfC/PnzFTXX1ddczdnnnM1bb7zN60sfZ9yA2YwaMIV+eQO5/ozHWbHln3yy7hVy0waQZx0AqKjv2EtVcyken4f4+HjS09OZOHEiJ598MoWFheTn5yvzWFVVFdcuWRGVrAKdHgf9E/tQsWwHyz77ilvuuY0HH3qQN19/k4LOoeT1y+fdd9+lsrKS6upqFu34gqXl3zHE2o+tjbugWs2mTZu48MILKSwspKa6hmeffJqUpGSkmDlOrdrXIjJaIwIQzTZ2dnZSVr5HUbaJuevb5d8wNrsYo87A6PxiRucXc9EJZ3PCo6dz5eTzaXG00dTZwuJNX/BN+ToGZvRj5pBJzB0+jf5ZfRnfdyRXv3k3L1zwKIOzC/e7x2+sXsxfvn6NBWPnU5w/pNvvMpOsZCRZMQ/KOqLn5lihoqKC266/hbGJQ7js5PMIBgP4ff6ucoXD914IhkN8U7WeN+ffiCRJVFVV8e2337Jq1SpWr14NRMcEq9XKyJEjSU1NJfyDg4QEM0fSnUKYeGo00S4nhwNZlnG7XLjcblwuFIIhGAzQ5umgxdfB0KFDj9oXSxAMRqOR1tZWPB5Pt3rw3tZuer2es84664i+52DnFwudTsewsSPYldDEr393CSeffDJXXngZNR0NpMTvC7ZkICCFSZswWSEXANQ6PYkLL+Wq227n07ffwmq1HpPj/LEQ5ykCbJfLRVpaGklJSUo7RZ/PpyS8xNzY836Gw+H9CAZBADgcDrRaLYmJiVitVkaPHs2kSZMYM2YMQ4cOVdQLoVAIp9PJ5s2b2bx5M9u3b2fXrl3dAnthrBz1x9lnAi9aLFdXV7Np06Zux3Y4z57wkqmvr+9VIdzbPg70s1giJDU1ldmzZ3PWWWeRlpamzP09Ia6/8Bs4HIJBKCeEZ9CRIBKJ4PF48Pl8iqnxwRAKhWhvb1eSRMcJhuP4Pw/Bkra3t7Nhwwbcbjd2u50RI0YwePDgQ740An6/H5vNRjAY7LZAFyUOGo2mW1/sWPYwdrITpjgej4eGhgY6Ojrwer0Kg+pwOLDb7djt9m7qBZfLpdQnCQnYcRzHfyu8ARfbq1ZjMBpoafl/7J13eFTl+rXvPXtqZiY9kx5II6EkgUCA0EFAEWyAiA0VG5ajKCp2sWLvHTl2LMgRARVBAaUjLYQAoSVAepskk0ymz/fHZG8SCJoA6jnfL+u6chEmu8/M3u+7nvWspWHTpk3U1taSlpZGeno6JpNJ7qeTyMAT8dwLc7nrzlk899ldlFYWMWXULQzuPQYQ+PTH18g9sIW7Lp9LVFgc1qYG5n52E3FJddx4UwwOeyOTJwfx+WdVvPFGFffdF41Br0GlVqFW+Xrfy8vsLPi8BkGI46mnZpGdnU1AQMCfSgXbwvjzLkBh1zN16F1EhMT4+h+VDlxuF1ndRnGgJIcS8yH27dvH4sWLycrKIj09vd152y3h5+fHA088zMyb/0X/IQNZ+NYnct9sy4zyU8Hr9fLdf5bSLbIPIOVSSykJf75/r9dDpbmUgQMz8Hg9zWSqiKhQoNGIdE0M4aFnZlJSUsKRI0c5mL+XjVuXUlVVheBRUttQzRcr3qZv6lCSYnoCfz4Vaitd4o+gVCl9k+E/QIAhmCvG3cqIfhNY+PMHXDbpCi6c6DMi27hxIwqPkj5Jw9Hr9Wg0vsQHo97IpHOmM7LfBTz18a1Mm3GpLCm2WCxotVpiY2OZ88Rj3HhzMS8+/zL3vXUFKlHDVWNmctGwaQzvPYE1O5dwqDwXr8eLKTCGhJjuxKYG07dvX0wmEwkJCSQlJREUFNTq8xgYGEhVQw2WpkaMOj3VjWaSwrvywHm3sbt4P/Offpu3tW8w8YpLufqaq3nn7bcp2HeI7GHZXHHFFZSWlrJv3z6279iN0+vFbfUp5n777TcaGhrYuX0nQXUaRqan+z5HKHzeGvhIBlEh4vF6UKp8Elm3xs2KY2uprW4go+tQiqsP4XW5+XXVbxzed4BLr3mxlf/QzmN5+Kl1TB10IR6Pl7uUN2Jz2NlWkMvWwzl8vuFbnl76BqaAEPp1zaCq0cwtXzzMS5MfJrPL8apvpaWG/+z5ianXX4XoFrjx6weY1ONcJvY9r9lAEnIr9zP1uus69Lk5G9ixYwf33HoX16RfzOjuQ3C7XXg83laJHO2dauce20tMQhwlJSV8//337N69m927d3Ps2DFEUaRnz5706NGDnj170rNnT4xGI7N/vIfAXxeQEBpHQlgcscFR7Wo16SgEAdQaDUYB2Whbqgj/kLcGQSeyZ88eYmNjZQXO6e3HJy8PCgrC5XJRV1d3ylSBsw2pAi4IAnFxcTz9/Fy6NscsApgiw6my1LRa56HFL+HXtz9+4ScTqiH9sykvK+Ha227n6w//fVr3/78CLVsPWrZH6fX6VpV6qSWlrWeM3W5n+/btLF26VDbgk0giqYIfEhKCyWQiNjaW2NhYwsPDW7VIiKKIn58fSUlJ6PV6unXrRlVVlVx0k55v0jgcjvf2WywWysrKKC8vp6KigtraWrmNxmAwyMaCRqNRTn2QlMNVVVVUVFTIXknS8Wg0GvR6PVFRUURERMjrS544LY9DWqflj0KhwGg0yqk7fzYfORVx1onTRyfB0IkzgsfjoaqqiurqasrKymRzkqioKEJDfRLetiqkLX9vamqirKyMhoYGWW4kiqLMqKrVavz9/WWJmPS61WqVe5L8/PzkfmqbzcaBAwfYtWsXBw4coKioiKoqnwy2U5nQif8LcLodOBsdHDhgobKyUpYvSooe6XvQlnmU1IN+2eWXsug/n6BWGchOH4Eoisz77lkOF+1l1pXPEx4SjdXWwDOf3UhiqoXrb4ilrs4sV5SnXBbMFwvMvPpKOU8/3R3/AANqlY6ffipn7Vovd975MhMnTjotUkGCy+WivKKciQNvJSIkBkCW8jqdDho0WsZmTmXBmlfIy8uTBxGJiYlYrVYEQZDvU+3B4sWLfcZVDitHjh3zZd0398m2B+vXr+fDDz9Cp9Hz1c/voRRVqJQ+jwKlqEJQKGj2bASaSQfBZ/gH4PF6cTrszHkoH0uDg8YmB5GRRhQK8Li9VJQ7UQgKLrnkklb3XavVypo1a7hp+q3UW2qZt/hZHK4m1EotdVYz5TXFhAdHn3S8Uo9oR6AQFHi87bvPRoXFceflT1BUUcCSrz4m/2gO9dZaogISCQ+K9nkKaDTyFREE2JS3EgUiX32+iODgEMaOHSNLuKX2ma5duxIRGc6AXiOJDkrk85WvkZ48gGnjZjLVNIN6Sz0et6+VbunvHzBx4kTGjRtHaGjoKT1+rFYrJlM4hyqO0LtrT6obzSRGdMHrhYy47rwW+yi7i/J5ZO5LrPl1DUf3FfD0Bfew6uBGtpZsYfQEX3tOeWEto3pexqb8FRws3cXatWtZu3YtaqWK2wdeTZwuUjYyk6S+vh8A34TPT6fD7fFwrLKY9YX7GJ0xBbVag81tYfm2BTRYLFz53r8I0QeR1SWdvl3S+PXgFvp1SWt1bgY/PcN7ZjO8ZzYAtY11vLniI3IaDhLRLYbbr5zCq/M+YXTxAK4aeAkKhYIdR3Yzcuw5PPuiz3ehuLiY556ay21fP8p5ycMY2q0/e8oOdDhy7kzx0/KfePbhp7h32A30jU+X2ya9XhAVCrllwWytp7y+ivK6Ssrrq6iwVlNhraGioRqXy8nNA68gKyGDPSUH6NG7F5s2beKTTz6htLSUsrIyOTUrPT2dSZMmkZKSIntNfLboC9atXcfuPfl8u201RUXHiPAPo0tgNF39o+gaGkt8aCwRAWFndN8DoXnCqUalUqPVaKhpVrXoRR2H8g+ydetW0tPTZV8FOD1fLGlCWFlZSV1dnWzm/VdPxqT2AInkOPEeGxETSdW21gTDqqJc4m976ZTbNF0wkcKaah58/Alee/65v+S424OW107qr2+LYNBoNHKqgNPplI1nW/qzgK9IJxEM0muiKGI0Ghk6dChTpkwhMjLyD5UbUm9/UlISSUknq5ZOdR5erxer1Sp7lPn8c47IxEJERAQRERFER0cTGRlJSEgIOp2OsrIyORY4Ly8Pl8tFdXV1s4GvQk7U6dWrF+np6XJ8ZkxMDCaTqdVz90Qj5fZc9xPR0sj0VMu1NZf5O0mJs9Fy9Heik2DoxFmB1+ulrq6OXbt2odfriY+PJygoSL5JtrW81LJgs9kICgoiKCiolWOu2+2WJU0ScWC322VDKynL3O12Y7fbOXz4MDt27GDfvn0UFhbKObv19fXyep3oxP812Gw2qqqqyM3NBaB3795kZmbKkuqWLsZS+9O2bdt45JE7ePDhHvz4wzGeXXArIYYuHC09zL1XvUBIoInq2gqe/2IGPTMcTLsmiqYmK26Xz9RPq9Gi0Wq46+5wXnnlEHPnFnDllfH88EMpqanD+eabJ+X+zzOBUqnkvXnvcNets+kS+TjaFgkKGq2W4OAglEol/buNYdP+H+UM8Pnz5/Hdd58TG9uVL75Y0i6/GJvNxiMPzsFpdyN61Ljq1IwbewGPzHmAUaPaF7+2e/duTCGR3Dn5GewOO5bGOuxOGw6nDafbAV5fxdrrdTe3UHiaDTG9SLXX4uJ8Jsddw96yg3yz82e0njD6pY5EoRBw1W3j559/Iat/Vqv9+vn5kZKSQkxUF264eLavr7mqiH0FOaza9h3/Xvo8D1zz2knHKw16OwKFqOjQvdbr9RIV2oVLh93Kzj1bWPDrK5TXHmPjvuVcPHwaapUKr9dHenk8LtZsX8Z1E3xtEt98+AXPz32Bf828jUmTJslqklWrVrFs0QpuHP8IBj8jaclZfPz9Kzz83nSmXzCbqOCuPs8JLwhKCA8Plw38WhpwSW0vUrxoUmoSBZVHSYtNoaaxlnBjGNKbJggCabGpvHn5E/x709cMjExnWMoAhqUMIOfoHuYt+pKN+7dz4eCbSIlJIyEuheKKAn7LXcrhsjwcjia2HMvB6/WgaG5h8jbnEqpEkUBdADqVhrjgaKKCwpvJeVvzsfomFCb/aNSiiluGX8X0EVP5NX8TP+et4711C8gvPUyQnz/XfHA3WV0yuKDPaOLDYuX3QVAI+OuMBAUGcd3V13NdswJh/Pjx3DfzHmYuepIHx96KUaunoqxC/mxER0fz2ttvsHLlSr7/z1Le+WQm9fV1vPvmO/QfNIDMzMxTSpPPFjZt3MTTDzzOcxNm0yUkGofDjt3pZM2+jeyrOkRVUy1VVjNVDWa0fjqioiKJiokmKj2aXnHdiIyMJDIyksrKSh5/4FF67FvP+gO/003Zi5qaGrkNNDw8nMzMTLkqnJKSIhvFAa3IBvAZTR45coSDBw9yIP8Aq/N28P4vX1NfW098cAxTe0+gf0LvMzhzn0+UVqdF7zDgdnsY33Mku8v3s/z7H/F6vYwfP56xY8eeEPXafkhycynSV/IH+CsnOx6Ph9raWiorK9HpdMTExMhRnxJMEeHsbTrS6jW1KGIztyYdWkIQBCKvuJaFN1/FlZMn0b//6XlUnCkkQ0XJxNzlcqFUKk+qtCsUCgwGA6Io4na75XhQSZHicrkoLi4mPz9fjo8H0Ov1DB8+nGHDhtG3b1/Cw8NbRSCfTUitG9HR0ahUKmJiYqivr5dbKgwGg0w2GAwGmbAKDw9Hp9MRGBhIQkIC6enpHDx4kE2bNsmEg9ls5uDBg6jVamJjY4mPj8doNKJWq2XPCEl5LCks2kq6+DNI+5IILSmVQjq3ttIgzub1O5vL/Tehk2DoxFmD5A6rVqvJysqia9euMtvfEi1THiRX2/DwcPz9/VuxsxLsdrucxyolSkhGOEqlUu5dO3DgAIsWLWLbtm2yOWQnOvF/HZLE0mq1cvToURoaGggPD5fVQB6PRybrnE4nW7du5YEHbmXaNQH06xtOVj8TjzyyhZ3bC3jsuo8ICTRRU1/BswtuoH82TJwUTl2dWZZja7RaDEbfoEKv13PvvQHccO1Gnn2skOtvu4mnn3n6rD4s+/fvz5jzR7Di96+5cPA18utajQa1yhevlZU8kl0FGygsLGTp0i/ok+nP/ffH8tVXR3nppee5774H2jymlhPs/fv3IwgwfuDVZHUfjkKh4HDxPh6//1m+zfqOhx998A9JE6/Xy/vvzSMjpT+mkCicTgdBxjCUSmVzG8CfVTR9pm+L17zLxVnnMVlUckm/85j6/kyGpJ3PoN6jMRqNMhFxYv+10+n0udM3q76iTHFEmeI4WnaQ1Tu/4+UFs+nTbQjZvc5Bq/WTDrrD75WoUOBtp4JBOk6Px4PL7WZX4UbiwlLolzSSzQd+4ulPb2PCkKsYnDYWhULgm9XziQzpSka3/ni9HqaOvIMDx3JZ/NlPLPn2e+57YBaZmZk8MPshjhQcZXv+Wob1OZ+I0Gjuvfp5lm/4hre+eZTM1CGM6T+Z6LA4BAVyOoMU2yZ5GXi9XnQ6nUzApfbqTsGPu3G53DTnnxyPJmhGTHAkj55/Z6vXMuJ6cLtiGhv3bmdInzGIopJGayMarYbw4FhKq4r48fdPWVe4jY1HdzSTTMerY6JCgU6pxYsXlULJuJQRXNR9lM+YzO0CvM1SeSXmuhKyEjJQq9WMSRvGmLRhfL/zF95f/TlPXXIvWw7sYGPBDuav/RKjzkBSeDzZCX0Yn3EOMSGR5JTv47L+N8j7DgoK4v2PPmDBZwu4642nuG3glViO1fDME09zz/33yr4GY8eOZezYsXww7wPWfPQj+nwXX677kEfLHiQ6Lpb+QwaQNbA/vXv3bpXTfrpoSWLtzsnF2mClqqGGIJ0/3+es4rt9vxCTHMfoiWPp1q2bLLVua6IlbSspKYmX3nqV+R/Mx3KgiW+++UauMJtMJiIiIhgzZgzTpk2T43H/CGq1muTkZJKTkxk3bpz8usViYevWrbw69yV+2LuGGUOuJCLg9HwBlEolSlHEpZfGO15m9J/KQyt95xEZGcnIkSNlc7yOQlpHitaTJnOSpw+c/cmP1+ultraW4uJiHzEaE3PSMlFRUayxVrd67c7+k3n8mcewXz6N2EsuRdTqcFrqsR4txFlfh6OmGm/ONtKSk84Kwd1enEi4SgRDfX29bFJoMpkIDg5utZwk89fpdFRUVMjja0mR4nQ6OXLkCDt27KC8vFxez2AwMHbsWG688UZ5cvxXQHrf1Wo1UVFRREZGtjtJzs/PD5PJRFJSkkwWHGtWBRYUFMixlpKi4dxzz6VLly6t5g82m43GxkbsdnurSMeWqQvtgUQwSJ5vEsEgeblJpNrZJhg6Si78r5EMnQRDJ846zGYz27ZtQ61W06tXLyIjI2VfBUmGJH3xJTZSq9WeUuIk9Yap1WrZzVaj0aBQKHC5XJSWlvLbb7+xbt06SkpKTnKV7UQnOoHs9rxv3z6WLVsmu9eDj4QQRV/f7uOP38V104PIyAgBATRaJY/N6cfs+7bw9ZrXuXz0LF788mYGDRW46OIQ7PYmmaTQarXo9Xo5X/rTTw6x+KtKLuh+PlcPnsQTP79FwY0FJCQknNVzu+e+WUy86FKWb/6a1LjexJoSUChEFApwuO38uO1jBLeNIJ2R0WPg3LH+6A1urr0umldf+ZTu3Xty4YUXtbpWb7z6Ot99u5jvV/zIl19+xfx3PmbioFvoEX9c+p0QncrN4XP4LWcZF42fxG133swVV1zRpq9FQ0MDHreXgmMHmPvpHVgd9bicLgZ0H8tFw65q97kKgkCTw0aAn5Hiugo0ai2IvnQOt8vDsqU/sHHDFupqzSR364aAgKAQsFgsFBw9iMvtajVQiotMxnQkiviInmzYtZJvVn2A0eCPQhCxOays3evP1i075PuzRFJ0TYjjX3feftLxudwulmz4CFNwFGWVxZjrqgEvJZXHKCzZz85D66DFgNvpchFsCGdM5uXsL97JxOwZRATH0bv7QI5U7GXZhs9YsXkhlwy/jk27V3H75CeanyXg8XpIjk0jpUs6+4/mMuf+5wiJMOC0ublq7EyWbfiMX3csY/oFs4kNT+D8IZfRIyGTH9Z/wfOfzkIpirhxyG1B/v7+JykYpN/tdjum8HBWVn+PzWZDp9Jgttbh9rjBDSDIfhptTeRcbhd+Gj1KpQrwolapMRgMaNS+f0tqB9C9Xxd69eoly/slssPpdGI2mzGbzRw9epTF+T+z8sA6TIZgvF7f5MHpdFJRXYbdbqVnbArldZUoRSUhhiBW5P1GdnwfUqOT6BaZwOj0IVz6zq08ceEsCquOsblwJ59sWORrTVS4CQoKkics0uTo/Annk9QtiScenEMXXTiFa/dy5dbLef61F1t9n7dt2MLEvucxOLkfU5rPe3/ZYXbm7OGjn98lv+IwXRK6MmDIQPoNyKJPnz6nVV2VjC1FUeS6G6cTbArh439/RsHyw/TLzmLOK0+Rnp7eSqH1R3A0J6rk5eVhrjXjF2DAe8xLTEwMMTExsodNRkZGm5L9jsBoNDJy5EgGDx7Mpx9/wh3vPcGFKaOY0m/C6Xk2CMiFHIfdToDen3uH3cAjK15hzZo1hIWFkZWVdVptK5LSTavVEhISIqtJwac0aKuIdKZoK27wRERHR1PeUAVAsbmM9Qe2UmkzkxoQTC9LFZtm3YLSaITGRnqkphARGkqUKYxBd9zKsGHDzrBFpePwmXAeT1MC3yTbz88PhULRKtXgRAiCgFqtRqfTtYqMrK+vJycnh59//pmjR4+i0Wjo3r07ffr0ITk5+bRJpY5CIkwcDofcatERYkNSCgQHBzN69Gh0Oh2rV69mx44dNDU1UVpaysqVK3E6nWRmZtKtWzf53FQq1UmkV8v7dnsgtZ40NTURHBwsq2Wk4zrR76ET7UMnwdCJs47a2lq2b98utzJ4vV45YlIaFHi9XvR6PeHh4a2+zG3hVLmvrubc6JKSEtatW8e6desoKyuT2cdOdKITx+FyuWSCoaioCKvVSmxsrPzd2rdvH++8O5frrw+hd0YYguL491GvV/PM3H7cP3srj8y7jLHnBjB+fCD19RY8brc8wPX3D8Cg11NvgXvu3EGwEM3Lk+YwMm0wANN6X8yDd9/PJ19/dlYHpQaDgc+++JgP5v2bTVu+Y9nmWi4fcQcbclfy6+//YVCX3jx1zvV8t3MFv/z4I1n9anwO8CY/rr8+ipdeeoxBgwYTGhrKnj17ePS+h4l0B9LDEM+QgUOIDunG9ec9TIAh+KR9K5UqRvW9hPTEbF6Z+ww11WbunHnHScsZjUZ+XbuabxZ+g1Ll63W99ebbiQiOPWnZP4JaqcbqsBFoCCAy0MSggVlYNEf44pfXOLfvFei1Rnbu30Bh0THO6XUlAgJerwe3W1CBuwAAIABJREFU0cVPzhU4nDa0muMy3ABDEH46PZeMmsYlTMPSWE9ReQFerwe7w8a8Jc+QOnK4TAp4vG7qG+v4dfWyNgmGZ194mqKiIrxeL/Pe+zfhNXFEh8X7khC8btmUUsKRkoOsyVmKURNKiDGCiOBYVColKqWK7IxzyOo1nNVblvDRspdxuZ3odb7qndfr9RlQij4Sunt8H1K6pPPaVw8TbohnYPoosnoNY9naL3j+s1n06TaIaePuomtUMrde+igej5vDRfm8v/RJmWAwGo0nDSQlEzOHw0FQUBBHqoqaCQYdtY31uN3NrRbNHglKEVAcd9KQICrEFsoO4bhZXrPXnE7vR1KPHowYMQK73e4jcbp2xWaz0dTURGFhIYcPH2br1q1YLBbsDTYaHE1YnWbW5i1haM8LyDv2O/HBMSgVItd8cDdl5gpC/YMprinjjcsfx+PxIIoKXvjpfcZ2H8rYPsMBuImrsDnsPP3d66wu3kJNTU2rSZ7X68VoNJKQkMDHX33KvHfe57dlq+ipSmTmLXfw3fKlgI/k2JWTy6ypV8vnrRSV9IjuRo/obr5l3E72lRxi5+97eP+H1zlQVYjVbmXK5VMZNGwwmZmZ7VI4SK2RkjfTyJEjycjIACAkJER2u29v1U9SSm7dupUFCxbIk5PY2FgGDx7MOeecI7dCna1Kolqt5vobb+D8CeN56dkXufmrh7hpwGVkJ/Xt4JZ8nyef2aMVVaOaLkFR3Nz/ct7d+gUNDQ1oNBp69+7d6tjbcx5S3KFWqyU0NFSuNksm33q9/k8nku29Xif6c0k/ba0fGRnJ0cpiXv/lQzaW5XDuBeMYljaO29LuIz4+HovFQm1tLVFRUf/Y5LBlSoTUEiwpcQMDA/H39283QSN5zEjjaLvdTm1tLTk5OaxatQoAf39/MjIyGDduHElJSX+ZcuFESOfX2NiIUqmUVRanQzCcc845JCYmUlFRQU5ODna7nbKyMlatWkVpaSk6nY6kpKRm5Z9GXv9MIBnGazQagoKC/mdSGv7b0UkwdOKso6mpieLiYnniUltbS9++fYmOjpYdcKXe1o7Gu7REfX09hw8fJicnh8LCQmpqamSDuU50ohNtQ5K65ufns2TJEjIyMvB4XHz11btMvz6Ybt0MOJtdoxUKRXO1xIUgOLn77niefDKP8nIbDqcDr9eDRqNB56dDr9ej02lZuLCIH76pY2zqaB6+eCaaFnFh49JGsu3H3bzz5tvceffMs3peJpOJBx+6H4D58+fzwKzpxAfH8NqUR+jdpSd1dXVMyhjH5u938v0PFUyZYqCx0Up4hJaBAxXMmHEDA7IG8ePCZdyQNYVR3QfRaLdy3ft3Y2u0tUkutESwfxh+ej9GjBx+ymUCAgK4/obrAVi9ejVKdKQldqQP2NvsleFrRzlcdZSBg7O56967yczIQqlW8sOGLyiuLuT87MtJiEyVe1Pdbg8KxZ8PtI16f7on+CZqjU31KBQKkmJ6HD8Cr4fq2go2HWp7/e7du9O9uy+68MdlK/Dz70JG8sBT7s9fF8qanUtYvesbzkmfgkqtxqA3oNVqfG75KjVD+pzLT1sW0ishi2c/uZuE6O5MGjGdiNC4VhU6hUKkvKaIiwZPB0Cr0TJ59HSy00fx6Q+vM+PZcdw08UH6pQ5HoRA5UJSLubaC+vp6edJ0ItRqtTzJT0pKQqVTU1pfiZ9ai93tZHX+RpSiknN6DAZ8vgnH0xsEFAoBhaBA2SIati0UFO9l667DlG8rxOP1sPPoXtIy0wkJCkZUingF6Dcgi9TUVEaOHMnnH37KZV3GEqA28OmO7/jk5ydxIzKx+1CKSouprK3ii+tfp9xSzaHKIwxK6YdSKVJpqeH3gl18OeONVvvXqNTEmqK4c8pMTCbTSZM9QFYGXn/zDXRP68Fzjz9Dk81GeXk5BoOBQ4cOEWkMw6g7dWqBSlSRFptKWmwqAJX11Yx+7nK0+Q4+XT+Ph8oPEds1jv6DB8gKhxP77+E4ISC9R5Iy0t/fH61W22E5c15eHhs3biQ3NxePx0NiYiJJSUlkZ2czaNAg4uPjO7S9jiAyMpIXX3uJTZs28eycp8kt289NQy7v8HYEwM9PT2iogKW+nkFd+3C0vpTv89ewYcMGjEYjPXr0IDU1tV3bk0y4LRYLGo2GwMBAedIvKWzq6+tpaGhotQ74fAD8/PzaZb7Xct36+nrq6333ncjIyFMmYOj1es678Hxiu8Zx7xVPnOTzIaUO/JOQDBClll2p5UGKNlSpVO0iP6RUBcnI3OFwsHHjRtatW8fevXtRKBQEBgYSGxtLamoqPXr0aOUN8ldDUmAoFAo5Ll5SXHQEklIjLCyMCy+8kNDQULZt28bevXux2+0cOnSI7777jrKyMvr370/Pnj3l69iJ/z50EgydOOuw2WyUlJRQX19PdXU1TqeTlJQUuZdMevCfLusoPcDq6+vZs2cPu3bt4siRI5jN5rN2Dp3oxP+vkIwc9+/fT1FREYWFBVgshdx5ZxThESqcTqccmSgIxyuFFksDTlc9N94YwrvvVfLBvDKmXROMunngWVLi5rUX9qOzh/HOlQ+SmZB20r4FQeBfI67lxk8fYPioEfTufSYGZ6fGtddey6Ivv2FC1BAGdfcZHjocDvQ6P24ZeBWP//wKQ4c2oNVqUCgEwsOVfP7v9ejKRN6e/IScrW7Q6nlr2tNc9s6trN66lJH9LjjlPnMP/U5iSpxcRf0zFBQUEBOSiOJPvRd8kOZ7giDgbq6E51bu54rp16PVagkLNbFs7WfYXFZun/QkCVEpOBxOVCqpj/r0TG5PNSduz/3bbrfjL/7x4M93PxfITBxJF1MKapUao9GARqNFajv4ccPXhAabuGXyw5RVFfH0h3cw98Nb6ZHYnyvO+xchAb5+6pr6ShqsFnqdUAGONnWlW5deNNQW8dUPr/LuoicJ0AcQqQ/GX2dg/jvz8Df6o9fr8TbLj319vL688j15eT4/CpVIZVkFN3/+gK814hA43C40oprHVSpcHhcNNisBOqOPRFcqWX73p3yz7UdsDptvnVNAp9FjtTYx57w70aq17Dq2l4r6avCCo8nBz/vWox46mMmTJwOwbtVvPtf3sK48POIWdpXls2DXUpbnr6W6wUyQLgA/QUNGbCpDew6QYxrnLnuTc1IGER/e5aT3s9peTxd/f9mToiWkSOempiaamppITk7mhTde5sD+/VRXVyMIAr9v+Z30sG5/+rloidzCvUQFR3DlwIsBn8Ihv/Qwu3bu5aOV7/JAxWG6JsYzeOQQxo0/X57oS/GkarUao9EoV9SlFsqOji92797NwoULKSoqwu12k5CQwJgxYxg4cCADB56aIDubGDhwII8/9xTP3f3E6W2gOTHCz0/ni/O22bi013kcrCrkpx+WY7PZfIRhUlKrhJLjq598zSSvAJPJJE/iJRNGq9Uqy/6ldSUSSlKwtoxVbI/Lf11dHWVlZfj7+/8hwQDwzPNzO3Z9/mKcSCB6PB6sVqvsnaDVamVvoo5ASlWQyB2LxcKmTZv48ssvqaqqQhRFQkNDSUhIkAmGvxOSV4FKpaKxsZHGxsbTikaVouZNJhMTJkygb9++zJ8/n8rKSsrKyjh8+DDl5eXk5uaiUCiIi4sDOOM5RSf+GnQSDJ34y+BwOKiuriYvL49ly5ZRWVlJ3759iYmJaTdzeyp4vV7MZjO5ubns3r2burq6s3jknejE///wuYI3cPTYdmbN6kJQMLicLtweOxaLrwKrUIh4PG7cLjd2hx2nw4nBKDJjhol336niu8V2ZswI4P13j7FjvZPp2VO5buRlqJWnlnweqSrC0mAhf9++v4xgEEWRd+e/x7SJVzAkOYvwgLBm+WMgGWIPxiQO44O3V3PPg0reeesYB3eqeGjsnVzSfxzCCT2rpsBQrsi6iJd/eo3E2O7Ehbcd4ZV7ZAP/emB6u4/RaDTSaK/vwFl58Xi8vhjI5kF8tbWW6GhfvGRIWCCrV/3KEzfOI8ho8qVPeLzNkmZ7B/ZzHIJkZHiaaE8KhSD42geG9bwQkFzrVShE3/tQU1vF2pwfuPfqFwGwNjUiCB6+uOl1vt7yPXPnz6BbfD+uOPd2auur8HjdLN4wDwRw2B1MHnEzeq0/W3J/4rmJ99ItKoF5vyxArVYzc9yN9J8zgaG6NALU/mgVGhRKXwuHKIgoBAEhVMCUdhUGrZ46az12uw2rtQmlqESj1aAQFMSERlJYcYzvtq0gQGfkvPQRCIKCe796ivd/XcBH6xaiVqiwuxw88vbV6LQGNBoD/vpggv1NhAVFMSLzAnL2/YbNaUer1pIe273VdTpUc7TV/0NCQjF6jOj1eupdLjKiutMrIoUtRTl8kbMUu8vJjiN7SI9NwWptQqvV4vA42XhwB5/fdHJiCMD6vM14u2iZNm3aSX+TJvI6nU5uQzSZTKSmptLQ0MDYUWMQPQL+Hh1JIXEMTuqHKCqbiY2TJ7MSthbuolvEcXWASlTRKyaFXjG+NAaHy8m+0oO8Om8+D8x+gMkXT2LC5AvJzMzsUJ/1n6GhoYHS0lJf+4tOR3x8PNnZ2fL36++CRqPB4TrTNk8f0SBNRv+VfRWP/vI669auw+FwkJubS2pqKikpKURERLRJKIFvwmYwGOQJZMvXpYq11PYqoaV3gtlsllsA1Gr1nypWJa+TxsZGuYXgbPs7/B3weDw0NDTQ2NgI+Fr4pFaeM6m22+129uzZw86dO8nJyaG2thaNRoPJZOKcc85h+PDh7Van/FVoGa97ppDSMAICAti8eTO7du2itraW8vJyfvjhB8rLy+nfvz/p6ekEBQX944qVjqCjPhH/i+gkGDrxl0EiGKReMbPZTEhICCEhIe12mm0LUqXCbDaze/du8vLyZNOhTnSiE+2D2+3Gz8/LxRfrMRp9bRNajQab3Vexcrvcx9vIpWegQHOclo5770vkwQfyuWHaHgZ0yeTLm2cSHx53yv05XE4+3vgNv5Zs482P3mXw4MF/6fnFxcVx1U3XMG/xlzw87l/NUkolbreHSzPOZ/V/NjLrX0X0jkjn4+vuJTGyS5v3ogablUU7viepp5I3F93NnOkL8NO2rs643S6OVR7q0Dmlp6dTVnuEBmsdWm3bA/yW8Hp9A9fqxjqmfXQf56QO4GhFMRaLhV9//ZXiIxU8Ov0dwkNimo/Jjdvtwul04XQ62tUecRL+4N4sXas/MmRrz9hJOMGrQHLrFptVF8vWLaBrdDKx4T4jwSVrP2Fcj8H0jE3hqa49ua7sMt7+5WMeeP1yRIWIQadh6o0XEBAQwIqffmb+iieorqoh1t/E0B4DEQSBRybfJe9PVIiMTMnGqDegUqn/8HkU6OcvmwHKxymKiKKClOgk7os+Tj55vV5uGnYFsxY+hVIQmX/tC4T5B3Oo/AgV9dVUWWqobKihtLaI3cd2srj8CF6PB9UpFB9KQUlZWVmzj4JIWFgoBquvGlpfXw9eL0pRZFCXTLJi0lhxYB1PrnyD2IAIbu4/leToBN5c/xn9u6STFNm23D8oNJjR545p828ajeaUE0S32w0OD7dlT2NLwU5e+PE97m94lvDAMHpFpXBe2nAGJ2e1ObnaXJjD1YMnnvKaq5Uq0mO7M33wFLYc3snYgH78PG8JL5Q8S/aIwUyaMpmwsNNLYIDjA32LxUJZWRlqtRo/Pz+6du3KgAEDTnu7pwuNRoPddWatnoIgoPfzQ6vV4vG4CWj0576hN/LUqrf59ptFLFEv4cILL2T8+PGAz7Oipcqg5e9SzOCJ25fSVVqiZStNVVUVNTU1vhY6nQ6DwfCnZIFEMCz88mvy9+zj4skT6ZnWkz59+pyUsPDfhBPbiSQDRrPZ3CqC/XTJEmn7NpuNHTt2sHTpUp8ZqdlMbGwsiYmJnHPOOUyceOrv0V+JlucvCEIr1cqZQK/XM2zYMNmstampiby8PAoLC/nxxx/Ztm0bVquVwMBAlEql/Dn9X1Ay/JmJ6f8P6CQYOvGXo6WSYenSpVRVVdG/f39iY2NP60ZkNps5cOAAW7dupaKiojM1ohOdOE1YLB5++7WBHj108gDI4/XFQrkFX1VKEHwpBJLE05fgouKtNwtRqURsTV7GdB9KXGjUKSvWR6qKeP6X94nrk8TC9xed1C/7V+GqaVezZOFiNh7cxsDETJ/BoCiyMPcHGoUavAqBWwddhZ+goaGhAX1z5nhLPLvkDbQRDbz65mDmPrOd1xbezewr323V+2+2VBMSEtwhN3w/Pz969urOoZK9hAb/OcHg8bjZkLMSncbIpFG38fve1TTavdx+090cLjxIQlR3gvxD5eUVCgHfI94X/VvXYMbpcrBiyyK0ap+rviiIlFQepaauip82LgQEesT3JTw4GkFAjvr6ddv3ePHi8fhaBxptFsy1NaxZswaNRkNISEiblTOH3UZRbQEKQcRqa6C06igN1noabPXUWapRqdTU1ZtpsjeyaMM7aNV6dFotWp0Wr9dnvJh3aDtD+5zHis3f0NBYz9GyfXww9SN5H0mRXXn5qseoqqum1lbPnDVv0r9/fwICAhg3bhxer5eLzp3ANd0ubPOzqRAEPM3eFu15EomiiFqtxuVy0tTU1GxqpkJUiM2qC2lyBqPSh7Ah5VvqrQ2EGIN8xqKBoW1u967PH8fkH4JGVOFyOX3pH83fPUFQ0CU4inff/YhP3v2QoOAgKqur6DfyllbnoVAo8NPr0ev1XB02iTGpQ/km50ceXPEyPcOTyas4yKuTHpF76jUaNbQ46wa7lZEjR7bjKrSGxWKhqymW8/qM5Lw+vvXLaivYemgXWwtzmPXVU4SFhjIwIZO0sGQSwuLoEZ1MtaWWI9VFjOk19A/VLvWN9Tz7w1tcO/RSRnTPZkRqNiXmcn7MXcPDt8/GFBvBqPFjGDBgQIe9FwoKCjh8+DCHDx+WK/FKpfJvTxmQ4FMwnAUvqebPg9FgRBAU6A165o6bxbubv2RnyV62bNmC3W5nw4YNREREEBQURHBwMImJicTHx7dJLLR/1z4FRUtX/6amJuz2UyuppIl5ZWUl29Zt4Y4h11C+uZJXPn+WkqZKJl06iRtvufmUaot/GpLpeFNTEzabDaVSiclkQqfTya0ipwuHw8HBgwfZs2cPmzdvJj8/H7PZjF6vJzs7m3PPPZeUlJSzeDYdh9PplIlXyS/idNEyCcLlcqFSqejbt6/8rNFqtVRUVNDY2MjmzZsBmDBhAqGhobLnUCf+eXQSDJ34yyERDDabjZqaGurr64mMjCQiIgKh+SHYEdTW1rJjxw62bdtGRUVFZ2pEJzpxBjhwwMaePVaysgyo1GrcHjeiQsQluJrd8X0VCZ1OR0BAAJZ6gUcf3U1kpIoP5g+luLiRpx58F5fbzZXDJvqmKy2q2z/sWsWnu77jzgdmcdHFF51yEuF0Olm+fDljxoxBq9W2uUxHodVqmfPck9w/Yxa9olMoravkqvdvBb9a7rs/krW/1fHUqtd46fxHEQTQ+elaDU5yj+xhxf41zPvM574+86507p65ic9/epWrx919/Nhd9g4bWgEYjAbs9dZ2LVtdX8nC1e9x1Zi7yOo1hP5pwwDfNd5zaAe/bP2W2W9eTc+ETG64cDZKpRpRpDnKS0lljR1RoeRY6SHf++P14sGDx+0mKrgrOQe2UGku4fe9a5g55VkUCoVM3q7PWQGC0DzpBRDQqgJ5/5XPEASBoqpDfL3oi5MqdKk9U9iyaRt1lYUcOLif6vI6ukamcrh4Dx6vh+EZEwgzOIgI6MrWwm/J6K4iJtrXQy4qBdats3L+RWMZOnQwCoWCj+b9m1tHTMWgPZnICQ0IwYWHE9MbNm7ciMYuMjS17Wq0QqHA5Xa1m+iWBrBOpxObzYZKpfKtqwJBoWgh+vBdKz+tH35tHK8Mr5fSmnI2Fezgu9s/wNVs4CnFXYooEEQv56eP4vz0UbjcLmoaa6m2mIkPi8NcXdO8vIBCFDEYDJhMJhobG1Aqldw4aCqjEwfx6Y7FKBAI1QS1MkeUjtfqaMLm9GXRh4WFERwc3O5nc3FxMZH+Ya0m9zGhUcSERjGuz0h2mQ8w940XyM/PJ3dHDp//+j3OJjvW+kY0SjUlNWUkmOIQxZOHpB63mwcXPkeQfxD3jLsZvF68gMk/lKsGXsxVAy9hV/E+Vi5ex5OPPEG/EUO4+/bbyM7O/kOyQZrEFBQUsGbNGplgkPrA/6lJilarPWMFAxxXIhiMRvz0eqqqBELtDmYPv4lX133IrwW/U1BQIKd0de3alYSEBEaPHo1WqyU8PFy+p7XHq6Hl65LyQa/Xy6Z/kndHW+scPHhQlrh/u/A/jE8Zwcju2SiVKq7MvoS9JQd49OtX6N0vk/PPP/9Pj+OvxomVZ6/XZ+xqs9moq6ujsbGR8PBwWRlypn5jUmvEypUrZYJBoVAQHh5OdnY2U6ZM6TCxdjbh9XpxOBzYbDYEQZDTLs50m5Jxq1KppHfv3iQmJuL1emloaMDlcnH48GG2bNlCWVkZycnJMglxuuroTpxddBIMnfjb4HA4qKmpIT8/n1WrVmGz2cjIyCAiIqJD2/F4PLJMtVO50IlOnBksFg9LvqsjIyMYp9OBKIoEBPpMDo/3UypQq1UcOtjE00/vYfCQIO68szcqlUhwsJY5zymYc/8HONwOrhk+BVFUUNdk4fU1H1GttfLvrz75Qxf2Xbt28cSDczi09wD7b9rHrNn3nrXzy8zMZOQFY7j8vdupqqshLbIH26s2sGVLE+edH8DLe4+xNG8ll2VdiMXSgE6nkwcpn6xfRHo/HYGBPmm4n5+Khx5JZ9Zd37MhN41BaT45+YFjuaR0T+7wsTkcDozK9iXprNm2lMSonmSktJ4oC4JAz6RMeiZlUlJxlC9WvM1D703ntslzZL8Ir9eLIAoolUpuvuTh5onliYMvL2u3r2D97h/kv4tOn2v5fdNeOr6U14vH6wGv73eFQsFzX95OcHDwSRL66ddfx/U3+Hwp3nn7HY5ua2JY73EsWPE2XreHiaOvoaKiktLSEnYVL2PECAM9ekQQGRGJqBQ5UniE2bNn06tXL77++mtKCov4rPhbPtv4ra+1QgBPixaNORffhVtOcIApV1zN9u05iI12nl76BtMGT6ZLaOueeqWoxOroeIudWqWSTSGdTqdcbZP8BnzvDUiEh++7JLT6O81/zi89jMPp4OHFL3DLiKvp07WX/J54vB48ruPxlgqFgMk/hDBjMB6Pl9YeGV4EwbdfjUaLv78gu7lfq57Mnd8+QZWlGqWokFs8fHn1SkRBpF9MGo/MmE1NYy0WWyPBwcGYTCbCI8OJiIkkPCqCkJAQwsLCCAsLIyQkBIPBwN69ewnTtS1hP1RxhOi4WHr37k1aWppsUllbW8vy5cs5uO8Az2x8n/qaOtIiUugdkUp6THfiQn2pU6vzNpJbup8fZ30C+N5v6bkvikq8Xi+9Y7vTM6obizb/wN747sx4+Q005seYeuEEJl98MQkJCScdV3l5OWVlZWzevJnffvuNo0ePIggCvXr1YtCgQfTq1eukdf4OnDUFQzMEfGomg96AQiFSb7VwrKmCjL69KSoqorGxkaamJmpqanC73bhcLvbt24fBYMBoNBIREUFkZCTJyckkJCScMjb8VFCpVPj5+cmtJyeivLyc2TPvRafUIogCDQ0NvD3lCTye42ks3cITGJWazYN3zWbTuo089uScs9bnfybwer3y9ZPuQTqdrlWCxpnA5XJx5MgRDh48yMaNG9m2bRvl5eWo1WpSU1PJyMggPj7+H1XcwPGYyqamJvyaW3POxGtCEAT5M2az2eT7q0KhICMjA5VKRWBgIFqtlvLycsxmM8uXL8dqtTJ8+HD69Olztk7tL8OZEE//K+gkGDrxt8HpdMrtDUqlEo/HQ1RUFCaTqc0vm8TenvivVDmSssI70YlOnD4EQaC01MULz5dw001KUlJ9OdAqlRq1WiUPXP7zn0I+/HA/t8xI5PzxifL6CoWCnr1CeOolgUfu+QS7y0GfhF68seEzLrjiYl6+7dZT9p5aLBbefPUNflnyEzf1n0rvoTOZsfBhJlx84VmTfJrNZirKK9Cpdcy79lnSuvRg1pdzWP7LKgID4bLLAvhw3jcMSxyIUqlsjspSsSZ3I2sLN/LeE61TIWJjA7j7nmSem/s8WpUfpTWFFJp38dnLH3f42Ox2Bypt+/pyXW4nOo3+xAJ9K0SZ4ph5+VPMfPlS3vvP09x71Qv464MpqTzCpz+9giiKKJWifJ4nQmyeeErvlzTIazmh8Ho9eDyK5vuxj2BQiApCQ0P/0MTNaPRHqXSiVmtQKpU4PU5ooYjwSeSb93HCuh6Ph0Wff829595MYkgXPB43gkJEFAWUogqlQuTppW/w2H9eQtAeP9ac3N0Ej5xGfWUJC/I2Utf0IS9OfbjVtpWikib7yZXVP4NKrUKlUtFka8JqbcLlcvlMIU8wOZNaL0TJr0HhUzoclzoIjOg1iKV3/puFm5cx66sn8dP6cWHGaKYNmoRaqW71nFOKIoKgaPa38Lb2ufD6ticgyJ4JGo2Gl1fP56fcNQzq0heD0o+GhgZEUSlP/pRKJRqVmicvmCVvyul2UtNQS3WDmaoGMxV5VRT8nsN2h4WaplpqrHXUNJhBVFBwrJC0iG4w8pqTrlNO8V4GjRh8Ug99aGgot99+O2azGbPZTFlZGTk5OeTszOXzFd8TpQ3l5kGX8/XWpVzQezR+al81XUr5OB6/6sLl8rBq9zrw8yNu8lQAmkqL+XTdGuZPv4FuUZFcO2ki5557ruzgL+1vy5YtrFu3TiZc0tLSuPTSS/92c0cJGo0Gh9PZLoPUdqFZeaQ3GNAb9Py8cQP9h2fTNTme5cuXU15ejsPhoLa2lsrKSg4ePCivqlQqycjIoHfv3owTImJAAAAgAElEQVQdO5bw8HC0Wu1JPg3HdyWcsGuhTe+BlmO6wsJC4oKieHnCgxTXlFLeUE1ccLRcvZYwfeAUEoPi+K04F7fbTUlJCTOm38T4CyZw6x23/cklODsTuROVC263L2XGbDY3t0qpCQwMJCAg4Iz3I/lRHDp0iPXr17Np0yZ27NgB+AyCe/bsyejRo4mPj//HWwJaKhgMBkOHWgXbQkuCwe12yxHCGo2GtLQ0EhIS5AhQh8PBgQMHWLFiBQcPHiQkJIT09PT/CgLqjyCpt/+bj/FM0UkwdOJvR2NjI4WFhQQGBhIXF4fH46FLly4n9WVbrVYqKyspLS2lqKiI6upqmpqaKC4uJjc3l8OHD7fKX+5EJzrRcfj6Y72UlMCKlaXUW5pISTGi02rRaDVoNFreeecg69aW8MD9PRg0OKbN7SQnB3L/44k8cu9HBOQE8+6H8xgyZMgpq12rVq3i2TnPkBXSg3cvfQqjztfve23fiTz58ON88tVnZ1yVycnJ4bbrZzAuYRif3/Q6aqWvqvLS1DmM2TmcWYsf4PEnYsnMsvHMmtd5cfyjqFRqXIKbJ75/jWtnRMrqhZYYODCGiZMsfLX0ee644z5evuaR08oddzldiH5//BiWJvIutxOFIJ5ginjiVFxAFJWMHXApB4vyeOS9G5g27m7W7/mePtndqTuSitCcQiHFFv4hY/EXQSEIeL3Hq/KtK/60yOT0/bNy5Ur25+XTu28yvxftwuF0sqUgR5pP4/F48SogIjiSo/UlvPbWO2g1WjxeMHXLJCptMLubGnGaK046Fo1STYO9fW0qrSGA4EWlVOHn19rQsqXxpeQU7na7fZO95h+fckAl/z/OFMOsC2Zw+3nTWZHzKwu3LuPj9Yvo06Unt468mh7R3Zqvke+iuFwunE6nXOUVFL4WiePmmx4s1kZu+Hg2TruDf097gRBNAI1WK01WKzabjdraWtl3QFIySBddJaoIDwgjPOCPDRQb7VamvXMn+aWH2/z7ror9XJs945Tr63Q+P5CAgACSk5OZPHkyHo+H5cuX8+R7b5N7IJcBiZm4XC6UoogXr/ydEEBW0vy0Zy1kHq9c6iKj0V16Jd6JUynfncNjS39i9jPPMnrIIO6+7TbWr1/Pzz//TH5+PoIg0LdvX/r168ewYcOIjo7+xxzplUolid2S+GHXKsZnnHNa2/B4PGw6tIPFe1ZS0VCNVqkhIzwFm8vOqv0beXX+m7hcLmJjY2loaMBisVBcXExRURFHjx7l6NGjNDU14XA4KC4uxu12U1tby5YtW+QKfWJiIgkJCXK7a0fhdrtxOBwUFBTgrzGgUqmIj4gjSUw45YQ5wM+f33/dwoRR43AKbszFVXz19VcMGDyQmJgYOdEiPz+fAQMGyEkXZ3MCLqlnpR9BEAgODpaVHWejvc/hcLB7925yc3PZvn07O3fu5NixYyiVSqKiokhISKBv375kZGQQFBSEw+GQDWf/CUgxmp5m8q+hoUFOzTjT7UoEqLRtycdDUhg1NjZSUlKC2+2mqqqKX375Ba/XS1ZW1t8e19mJ1ugkGDrxt6OpqYljx44hiiImkwmtVktAQMBJrK9EROzcuZMtW7Zw6NAhampqqK2txWKx/KFhUCc60YmOobbWwtrfFGxYbyE5WcM114QTGmbg7bf2UmO28+KLWSQkBp1y/dWrS3n7rX0MHG6k0eplyZJF9OnTB6PR2IqlN5vNPD3nKfb/vpv7hl5PWmxrc8CxPYex6ruNfLFgAVdeddUZndPevL001jWQEd1dJhcAfsr9lceWP82IkaGEmQI4f4KDF/cUsiz3FyZnjefd1fMJ6Wpn3LhTD1CuuDKV4pJDGP11p0Uu+Ko0Im6P60+W9MVTFpbtJzv13BbX0le9bmyy+CIT1ToEwTfLvWDY5djtDl5aMJt5S57hmuuu5tIpk5l5+z2YLRUE+4cDCkTxn6meiAolbs/xaDuheabYbA2BFy94jydMxMfHc9OsW9FqtTQ1NbF27Vr2VZYwss9FzQoBXzWoyW5l9+Yv+a3aZzAXMvRKRHXzgF+hwNOGa7dapaa+6XSJagGlSoVSpWomFDy43b6BsDQgll7zVWTdMhGh0+l8agSFonlO7ztXjUrNBf3GcEG/MRwoOczCLd8z45MHCTYEMeeiO+kbnwEter5dLid4vQiCbyLlM/eEwxVHueHD2aRFduOZ6+5HLapwOBwgCLICUHqGiqIoD+Q7WlDzU+tQKERuGD71pL9VWcwcrDlCv379Tr5yzTs6MY1AqhKPGzeO9PR0Ljx3PPPWLOCLzYt54bKH6BmV4lvGi8xINdmb2Fiwg4THPj95P6JIYEYmgemZVKxeycF1K9m/fz8bNmxgyZIlzR4lKvr168f06dOJjIwkKiqqYxfhLOP5117k2kuvpldMKl1COq6keGftZ6wp+p1Hn5pDUlISVquVjRs24nK5eGj6WAYNGgTA0KFDAd9kf+fOnezYsYP169fLLRNms5mKigrKysrYuXMn4Hu/jEYjo0ePZtSoUcDxFAqpItuWuuFEuFwu7HY7dXV15BTmIYoKtNo/8rHxMiCpD8tmzKfYXMahiiPUx1vYULiDm6+8Hp3ej7iucVgcjRQeKOCVt15j0KBBqFSqs9Y+ILXnNjY20tDQgN1uJzQ0lKCgoA6ZC7bl4SD9K1Xmt2/fztKlS9mzZw+HD/vIO51OR1xcHJmZmWRmZtKrVy/sdrvvew3/mPeAIAiyek26PtL3qj3Hc6o0BcnPQa1WY7Va5XNVKBT06NGDiIgI8vLy2LZtG3V1dVRXV7N69WoqKioICAggNTX1/0Qrwn8rOgmGTvxjqK2tZfv27YiiKFcLwsLCUCgUmM1m8vLy+Pnnn9m5cyclJSXU1NTITsQu158NyjvRiU50FBaLB6VSQV6ek7lzi1CrISxMzWOP9cRk8pP7IFvC5fLw7Nxd7N5dzcy7khk8OAbr/2PvvOOjKPfv/57ZXtM7qYSE3pt0EFBRFBUV28UuqFivXrvXdi3YvWK/iApYsResIEV6r6EkIaT3zfYy+/tjdoY0mnKv+vvmvF55pexk9pmZ3dnnOZ/zOccd4JVXlnDbbdfz0ENPEhcXh1arZcmSJcx+8HHGZwzl5vMfabHoVyAIArNGTee25x9j3Mknk5KS8puPZ9pF08jMzuSem+/k8ajbyYxL48Uf/sMbq97g1tvyGD6iE47GRvx+C+dfEM07/3kPu8nKj3uW8swruaoUu70iv0YjcM016Tz80NMkJ6dxxhlnHNfYBEEgJi6GpuqGQxXZdiZCcn+rn9r6Crpmy9JPSZJocNbz8dJXqHGVIYVC2EzRxFjldrNwGBpcNaR1juW51z8mKysLs9nMVTMu59N353P+6Osw6I2RBcGh59p9YAvRtvZTDhTJstwiEWrWIqE9prit5hF2GlGLL+DF63Xj9DpwepsIhkI0NISoqPDi9zsRBA1er59wOExeXp7aMtPQ0IDH40HflMypw6a28Dmobajiq9UL0RgtaEQRrcGEu6YUAJ+jjorGKvZWFrUYl1YQafI0HXX8R4M8DBGNhsg1CKuVt+aKhuaLCbfHE2lbORR32fzF1iU1h7unzOKmU6/ko1+/ZNa7DzAyfzCPnnMHgUAAl8uF3+8nzKF4T0EQ+W7bLzzwyTNcNOgsZk74GzqtlpAkodfrI8kA8iLG7TqkZFDGe7wu7Mt2rKLCWc3lI85v89inmxZzxjln/ibJtMViISoqirTEVN684HEWrPyU6+bdQ6e4VO6YcC09M7qi1clT2FV7NyLERGNMOnwlXZJC+Ar30Dk1hYULF7Jt2zZAjovt27cvw4YNIyUl5Q9TLjRHZmYmPXr3oKy+4jcRDF0Tc/hp769IkkRWVhbAEau5oiiSmpqKKIokJiYyaNAgdRFdXFxMcXExRUVFlJSUEAwGcblcbNmyBYfDwerVq1ukUGRnZ5OZmdlu0UhBOBymsbGRuro6XC4XJ3cfjradz4KWEBAE+TWaEZ9KRlwqYeDM/hMJh8NUNFSzp3I/+2oOUBMu58Hb7gWdSG5eF/J65NMlP48uXbqQkpKivj/D4TB6vR6dTqd+bz1Ov9+vzjmbG4rbbDaioqIwmUy/KQ0tEAioBEtDQwNVVVXqV1lZGTt27GD37t3U1dVFYmkTyMjIYNSoUYwePZqsrCwkSaKuro66ujqMRqNqxPy/fg0riiyFHPF6vb9JyREMBmloaMDpdKr+MIp/h+IzodxPJUnCYDAwduxYjEYj33//PRs2bKCpqYmysjL27dvH3r17SUhIICbm8IWRPxLHGlPZ3PDyrxRt2UEwdOAPQ0NDA5s2bcLv95Oamkp0dLRaRSkvL2fr1q0sXryYzZs3/9FD7UAH/s9AidtyuyE6WuSaa5PQaAL4fN7Ih/yhbUtKnNx/3yZiY0VefHEoCYlyf7PVamDWDdm89dYWZs26kn/840EWzHuXg1sLuWfUDFXufTikxSTTJzaPOS/8m4cfe/R3Hc9JJ53ExVddynuff84/Js4k2mTHatUyeEgaOp0WvUHP/HfrMZlCdO8j8uzK1zl3mhHwEgwFETUiRYUNzH93L5df0ZW0tEOTt9hYA3FxTmbOuI5B6weRlJR0zOMSBIGczlks27UJSQojiuEWi2UFIUliz8Ht6LQGUhMzEAQBr8/Nwh9eoGv/TG44/QpMJhPl5eXU1tYiSXK8Y3p6OmPGjMFms2E0ymTC1VdfxYplK7jp2bMx6JpVjZXniqQpzHj8dPnv4TBen5sZj01SBq2Mvtl/gSAefbJUXlHOwsVv8t7iOfiDfvw6D+te/lmt/Ad9Eq+9FMBsdKDReGRFgt9FY2Nju+euvem81+8mFAzgXLwQAah1VtJr8CBEUSTaWU5t2MkLW1pWuX16CXfg+E0e2xkUAiAIGo5UMFVj+yLu+or6AWhD+CiwGM1MH3s+Y3sM44FPn2HC0xfzjwnXkm1Ji1QvZS8MnVbLG8vf4/Mt3/PPyTczvvdImRCMJMFoIikTinRZIex9Ph9arVY2ONXr2xAdR8Lry97jnP6ntmmFavI4+W7fct5/4WNA/rx/+qlnmHreufTp06e9XUVOo/y8VquV/fv3k5nQifjoOK4ZdzFn9Z3IglWfct38e+melse/zruDBGscm0t2EMw/8j0lFJLwbd9CQ0oiixYtUs937969ufDCC8nJyfnDlQvN4XF7MMccfzINwMndRqATdTz+wKMMHToUu93e7nbNlQapqamkpqbSv39/NRXB5/OxYsUKVqxYEfHqKZfvCV4v27dvZ9u2bSpBlpGRQU5ODqNGyek2GRkZqt+FUkVWnk+SJJqamqiqqqKuto7smPRjJLWEiEKn1RssHCY5OoF4awyjug7l6tEXEpJCVDXWsreyiL2bivjm50X8u6YYj+QjOyebzC45ZHfOZvTo0aoCQ3kNKwSEJEl4vV5cLpfa76+0iCgpGS2HcXhlQvPvkiThdrtpamri4MGDFBcXs3PnTnbs2MGOHTvYs2ePqoJSqvipqan06tWLkSNHMmGCbC7s8/mor6+npKQEo9GINRKzbLFYjin140ShOcEQCoVwu93Y7faj+oi0Vm4EAgFqa2upqqrCYDBgNpuJiZE9oRQvD8WINBQKodPpGD16NPn5+VRWVrJ582acTidlZWXs3buXgoIC1RDyf3EefguOl2T4K6GDYOjAH4pwOExNTQ3Lly9Xq4VRUVHs27ePAwcO4Hb/lt7YDnSgAycCjY0SC+bXcMutxmYTMDnjevWqap58cgunn57M36Z3Q6drOUHU6TVcfnkW1123kokTxtElIZePZryMyWg67MRDkiSqm2p58Ze3aTR6uPbS29ps81tw4cUXcfY777O1ZBeXDp/Kp1u/Zu5/dnD9DX1wOmHfPhGt1oxO50Jj8qPRmPD7/TibnBiMRl54ficFO0Q2bljLxZemcc45cjXd7Q6wcYODoV2ncd2MG3j9zVfbmNkdDjabjSlTpvDxB59RXL6X9KScSE9+qw3DsHH3cjqn9QAgEPCz6Jc3ye3ViRtvmqVWd7p06dJisqJMgtUYRWQp/KOPPcKO7TuZ2PtSctN7oBE1hxIOEEAQVef5JpeDe165nBf//mmL3n5JOtQHL4oijy28/qiTt+SkZC6YcC1j+p/BF8vns9z/OUNvHUpNdQ3lFeUU/ruIKfkz6NG5P4mJiej1et76/vHDV0Hb+E+AFJaIssRw/+X/RqPR8NSHN/PVx++1WQg0xzPPPIN3RcURx34iocS46vV6UCqpkkTA7ycYDKDV6lpcs+bISOzEG1fO5sNVX/Dgl8/TOSaD64ZcjFGrQ6PT8MDi5ymtr+C16U/QPb0Lzf0tFGg0GlnJYLEiSWE8Hjdut0eVxdvtdqJ0UZF2myNf0z3lhWw5uJNx3Ye3eezTTd8x9rTxan/+vLfe5ttPlvDz4hXk9+jMTbfeQO/evY+4/40bNtI9rjOCII87PiqWG0+5gmlDJvPm8veZ8vzV9M/sSUldObaLph5xX86qCtyV5VRp5PtM9+7d6d69O8OGDSMnJ+dPV+X0Bfw8/NULvHrpY8RZj39sA7N7Y17zMcuXL28R7XisUMxec3NzMRgM5ObmMn78eHXBXVhYSFFREbW1tdTX19PQ0MCePXvw+/0UFBQQGxurJo4kJiaqygYlYUBpK3A3OsmxdjohTjDqa12QY14To+JJtMdxUpcB6jYNbgf7qorYd/AAr37yEklJSYwYMUKN02y9r+Yy/XA4rHqnHC4lQVlk19fXU1dXR01NDbW1tTQ0NNDY2IjT6cTtdhMIBPD7/TQ2NtLY2EhNTQ3V1dVUV1dHWqtktUZycjKZmZmMHDmSYcOGkZubqz6X0mas1+vVCrdyLZTWI5vN9rtNF48VihpZo9EQDAapra1VEzXaQygUorGxkaamJvx+P4FAQI3fVOByuXC5XC3IqWAwqEaepqamYrFYGDFiBH6/nzVr1lBUVMS6desAMJvNqornrwylBemvhA6CoQN/OGpqatS+v/T0dFJSUtizZ08HwdCBDvzBCIdh714P33xdyVlnaSLxjRq++66MV17exRVXZjFlSvuVw8LCel58YRfOBivTxs1gzY6fmfjcZdx12gxO7zuuTbW+uqmOS964CZ1Zz4ybrmP65Zf9rmzvX375hUce+hdPPzubXr16cffD9/L4HQ/z/Dn38+IFj3HGnPPp0+cgxcUhrrnmRmJiErj77n8g4Gf+/Aa0Oi2jR+tY+ks5pQe0PH/Lx9z87FRemVPEryvrOXdqJyor3ESZ0zl3zNU8Of9WRg4bS1ZWFrm5ncnvnktubi7Z2dnk5ua2WehZrVby8vK4ZPo0vn7vc6adfMNh0x1qGsvJSOiCP+Bj4Y8voo8JcsONt5Gbm3vcUlSz2cz4ieOo3FlK9+z+arJEe4tJQRBVY8LfCyXuVBRlOXFzekBAQNQKeP1u4FArgcvl4r57HiA5KQWfz8ugoQNITExk165dFFeUsW7nL4TDEl6/ByksUVVf1sLb4Vhgt9upDrRvUPhfQUTpoNPr0en1qoIgFAwiSSGMRtnoTyEiWkMURc4/6Ux6JOTywOfPcdWiu7mo92R+KFxJblImC6/7N3G2GNojFwBVyWCxWtFqtdQBHo8Xb0TNoNVqsdtscBg1RXPkJmdxdu9TmPPT21w+6lCLhMvn5qvdPzPv8QUAOBwOFr77AVdOvhu7JYYNu5Zx3ZU3k98jl+tvnEG/fv1ajPXAgQN8+/U3LJj7LjcNmQ4Rjw1FJp2Zks7dk2dxwYDJzFv1MXsq9qNf9jNpp5yO1tyKTIrstnHPbsKBIHv37kWSJPLz85kyZYrqSv9nw+wXnualF//NnZ8/yavTHj3uxcWGoq2Uuat/U2Sf0Ezx0rlzZzp3llODJEnC4XBQW1vL0qVLZS+UXbtwu904nU5qampUvwCj0YjZbKZr165069aNMWPGyORVRMJvt9uxWq001jcSF9ez3df68Y058iX/pnqRtEa8PZZ4eyxDcvuztnIbSUlJCIKA2+1WF63Nz4HNZlOJWuXzSCFyFYPU5sSuYl5ZUVHB/v37KSgoUOezJSUlsmqjrk7d/nAVbFEUMRgMpKen079/f8aPH8/JJ7c0/dRqtSQmJhIXF4fT6aShoYHS0lJqa2uJjo4mNjYWjUajJn80P7bjwZE8ElqP2WazYTAYqK2tpbGxEY1G08JjpTmCwaCaION0OgmHw2RlZZGYmIjf78fj8ajETCAQUImXYDCIw+EA5M+zzMxMhg+Xk2rq6+vZuXMn69evp7q6mgEDBjBq1Ki/dGKDQi781Y6hg2DowB8OKZIjfuDAAb744gtsNhsVFRWUl5erN5EOdKADfww8njDff++kR08HnRF4/LHdHDzo5p57uzN4cNtECb8/xPx3d/Lpp6V43PDotXOJsycwZsBkVmz6noe+fpX/rPiQJ6f+g+yEDERRQBRE5q76kGETR3Hn3XeSkZFxxDG53W4MBsNhZbXvv/8+D9z9CP06j+SeO+/n408/YPTo0Wy5cDOPfP5vpg86G1eThn/eV0B6VgLffHMRBw4c4LLLruTbb7+loGA7C95tZPs2LwUFfi4a+zAbC1YSkkI8df0XfLv6fZ6Z/QUut4Pzx/wDURS589LnCAYDVDWUUV1fzsbvS/j5s7XsKFrPm/NeY+jQoe2O9ZRTTuGNV96itqGStKTMNo+LosiAriNZ/OuHRNljiErV8cCDDxEfH/+bCBij0UhcfBwfb5lLYdVWRI14SBpMRK4aMVl0eZz4j6F9oLamhvPOOwWAhgY355xzkTpxV742bdpIyB0VeR6BVgwDoknE7WsiHJaVCGHCNNQ3kByVQWpKX/bX7eTnz9YhIHCwohidPYSuU4Nc2bbK2etpLgM7ilumER1Ngmqz2XAG/jgiWzn3khQiFJJUYkVJdRAFUTaCjEDpjTcKem4beQVz13/Mu5s/Q6/R0SnuJDUZ5GjzUK1GA0aj2rsdCAQIRSqD9fX1mMxmzCbTERd+giAQCAUxaFu6xX+6cTHDJ4xW38fz5y8gJ6EXsXY5kWJwj7H07zqSTQUruXnGP8jO68R1s2awa+cuvv7kS2pKKxmROZAHxt5At9QubZ5TMbTMSurEvWfM4vudyxjq0LL2vLPQTZhA1tXXozObEYVD5829cxshVxPeSO+2IjlPTEw86jX6I5CSksLDjz7CtCnns6F4KwOzD99W0h5G5A0m5tcFJzRZQKnmx8bG0r9/f+Li4qitraWmpkatwBcVFVFcXKxW6ktKSnC73dTW1rJ27VrVmyEuLo7Y2FiKC4swJZ1MIBBQvQyOexEVUSyIopymcqzYW16oSvF1Ol0bpZNiXqioFfx+v6oQqK2tpba2FofDQVNTk+o9oKRLKPGrtbW11NXV0djYiMPhwOv1qotFxQhRFMUWpqsmk4nBgwczYMAAcnJy1MSOI10Xg8FAdHQ0oigSGxurKhoqKyupqanBarVitVrlNqgjxAn/XihEoKJa8Hg8BAIBLBaLmhgTDAaprq6mvr4egJiYGOLi4tR2BpPJhE6nw2AwYDQaiYmJUckFhWBoaGjA7/djt9vR6/XExMSQkpJCQkIC0dHReDyyKmvHjh2sWLGCzp0706lT+wlYHfjvoINg6MAfDuWGUVJSQmlpaQsDnr+SoUkHOvD/KxoaQrz+WiWiWEVqqpEnn+xLSko0ESt3dbsNG8r516NbsUeZee75YXz15X5e+OgWHrjsHfR6PWMHT2JAjxF89csCpr5yI+PyBvHAmTdT5qhka/1ePln4GWaz+YgtFIs++YSHn32eC86azN23395mm3Xr1vH07OcY0n0ck4ZezIIfnmfOSy8z7uSxdM7L5aldT/HTll9JS8whpPPwwQcfkpGRgV6v55xzzkEURSoqKnA1etm5xUByXC69Ow+h0VUT6YkVOWf0FZw96gp2H9hMz86HHPK1Wh2p8ZmkxstEQSgUZH/5LapBYXMox2cwGBhz8ggWr32PXjlDcbjqSEnIJD0xB7slBlEUOKnnBD5ZOpc1O37iylkXk5WVdUTZ/5FgNBq5/PLL6dGjB2VlZcTHx5ORkRFRpxxakAmCgMPhYMaVNxxhb/Ix+Pw+pl0IJpOWjRuCFBbNo7AIlUQIA4VF5eBolibQ6t4umjS4fI5Iy0AYwmE0Gg05nbrRLasf3bLkSmwoFOKXjV+T2FPknnvvVlUfgiBQW1vLpx991eYcHwl2u50mv+uo2/23oFSKJUlCCoXweD243R6MRgOCYEKrBU1YUBf6brebqqoq6l2NvLf5SzaUbeeyQefSL6snX+9YwqRnptMlJZurR13E6K7tk1oAGq0WjVaLyWjEazKpPdAej0eOEBUETCajWhE+HGo8dQgakZ1le+mWmovb7+GL3T8z99F3AJkQeWfuAi4Z0/K9qtVoGdhtFP3zh7N5zypmXHYj2lCAf035Oz3H5beq2IfVL0EgEj0ootFoKa47gN1k49kL72fDvq288esHbJl2NvqJp5B77Y2IEWl7YNNaXA4HWq0Wg8FASkoKPXr0+FNXBAVB4IK/XciXr3923AQDgF6ro6Ki4oSRKArBoCz6+vbtC8jvyQMHDrB//35++eUXVqxYQXFxMU1NTWrM+KZNm1DiSGNiYujSpQt5eXmUlZdjFg34/X4Mer183dtRa7S5SkLLH5qnWBwrom1RpKamYjKZVCVYa78E5WcltaWiooKioiL27NnDvn37KC0tpaysTCUUFKJAmb82J1kVUkEhx5XXolarpbGxUU1IsFqtjBw5kosuukhVKBwJSkykksgWDAZbkD5Op5OkpCQSExNVv4Tm17T1z62Pvb2f2zvPynE2JxgaGhrwemWSWiFTAoEA5eXllJaWqqplu91+zJ9pileD4vWg+DN4PB4SExOJjY2lpqZGJRji4uIwm82kpXY+bt0AACAASURBVLVvmNp6rXG0dcfxbn+4fRzv87X32vwzo4Ng6MCfCn/FN1EHOvB/AVVVIXI667nm2iQE0YfT5cRqtaLVaHF7ArzwwhaWLKlm+PBE7ruvPwAzZvbm4MHVvLToLm46fzYgYDXZOG/C1YzoeypPvHsji2dPIsmaxr1PPAjIk4f2WgV27tzJnQ8/QoEvSNS1NzHvtRc487TT1DzsyspKnnnqOZb/vIpJ/S6je7Y8htOGXMw77zzFO2++RyggYdLFoTGI2OPNvP/xJ2qV1Wq10qlTJ7p27crgwYPZsGED2oCVswbfQF19HV+tepfuWQMwGy2Ew7L5VresfpFkDblq3BoHqwvJyOx0xP5us9nMOeeew/0b/onHdoBuA3LYtmUDP/38PpqQnsF5E+jVeQgGnYF6VzVZWVm/e1Fkt9vp3r07VquVxMREOnfu3G5meX19PXrdMVS7wmFSUy2YzVrS0tqfJM4NNLJzlfyzIDRTMAiyokFjFvHUOJFbJCSVfwi3a2wlHJc51pFgtVpp8v1xBIMCQRAQNSJ6vQFREAmDKguWq4IaNBotLp+bT7d9z+c7fyTNlsSr5z1KVmoGZrOZUwaOpbK+ikXrvuXBT55D0MCpPcdw7dhLsJus7T6vwWhUTdD8fh+hUAiv14fL5VTl1UajgcORDMnJKfSZfBJPLnuDFG0ceknLoJFD1b7n9xa+R3pMPvHR7Sc8iKKGPl2G8u3KDxndpR+907u1eFxRdoBMSijEgySFkaQQX235kZ5peeh1Ogbl9WVQXh/W79/CK78sZPv5Z2I5/0JiTxqBzuslGAwSFRVFbGzsYY0P/2w4bdJpvPDks1Q0VpMclXBc/3vFwKncceNtfPXjtydUydAagiAQHR1NTk4OGo2G3NxcGhoaVMl+aWkpBw8epKysTJXF7927V/YmqK8n5A1SU10tKxiaRV22XphrlMdEIZJ4In9OiM1arsJhASWuty1aLurqnA2EQiFKS0tVgkDxRHC73bhcLjUasXXqg5Le0NTUhNPpxO/3I0WSWgwGg+qhoLTJJSQkEBsbq7b5eL1eHA4H69atY+vWrfj9foxGIwMGDGDw4MEMGTKE+Pj4w7YXHAkKSaF893q9SJKkqkqan1eFXFEUBiAT+Yr3gWIEq5A3SpKB4uUSExPT5r3UnGQQBEE9NzU1Neo+RVEkIyOD+Ph4tQXl98JmszFmzBh0Oh3fffcdmzdvprCwELPZrHquKIqx5vGeymvS4/HIqqxmaSHtIRAIHPP27ZEysom27MPhcDjUcRwOiqpMieL8Peeq+dyhvc/G9uYWRyOWDocOgqEDfyp0kAod6MCfE4FAmLLSANu2OejVSy6oGg1GPvpoL++9d4CALY3oy//Oyg//Q3W1l4QEI1qtwJ139WXGtb+ydscSBnUfc2iyqBXRmVxc8bd0vv++nm++WUR+fj75+floNBr1XuBwOHjupTks+OZbTFOm0WnkGOrWrMTd2MBPS5eSnJzM7CeeZvHX39M/dwwzJz/Eup3LmfPxIzR5GgmG3SCGiImJIT2jExmZ6eR0zmL8hPEtWjEUCWl+fj4DBw7E4/GwauUaVu/6gYG5Y9m051euO+efKDGQ4XAYKSyhEUXCYU0bJXlxxR7e+fY5pl8z7Yjn1Ww2k5eXx+v/eRWn00lsbCw3zLoBnU7H+vXreXXO6zz14QJMNgNDh40gPT39uAmGpUuWsmHjRqSQbJBlt9s49bRT1Wvxv0Lz+7skhQl6gwTdQYKeIGjB4arH4/M2M5EU2jVzPFxBXSYumk2GjsE6zm634zwCwaBEs5bVV/LM4tdJj03lpolXHHW/zfH99mXMXfsRydGJJFniSDTGkmiPI9EWT2JUPClRiei1OgRBg8GgwWDQ4/F41QmsKIq4fRLvr/6Cd35dhFlr4PaRV5Ibl0lSbKJqmCcIAsmxSVw3cTrXnnwxP2//lQ/Xfcn4J6bRo1M+M8ddyuCcvi3GZjQaMBrkCnJTU1MkRSaAy6VRT6XBYDhsp0SNu56bL7qLe++7ly8+/4J3577NbdffD8gT47lvvsMFw28+4vn5YfXnNDormDn2ybbnP/Ka1UYUF8owwuEwPp+fL7b8yL/OvkP2tECe/PfL6sXrOb35dc8GHv1oDlsXvkXPzjmUBoNYLBbS0tL+FHGUxwKTycTkc8/i2w1LuGz4eUfcVpIkAqEgB+vLeXv1IsbmDqWyrJLi4uL/qs+EKIrExMQQExNDdna2Wijy+/1s2rSJDRs2sGbNGlUar7QW7N+/n072ZBwtkmKEiJ9C5HMiovDR6bTy4lwQETWyH5DBoEcU5XjXQ61BSotFuLVIqgWavE40Wo0alVlUVKRGcZaUlKgKgOYEQut49OYLda1Wi16vx2w2YzKZkCRJbXUYM2YM+fn5ZGdnq1X82tpaCgsLqa2tZeXKlSpJM2zYMC644ALS0tJISDg+Qqn59VA+z0B+T5SVlVFZWYnT6VTvKzqdjqioKLW1QmmdCIVCOBwOqqur1dhI5To0N6A0m81qMk3rzyStVr5eRqORQCCgGlhWVVXhdrvJysqiU6dOLYiN3wuLxcKoUaPIzMyktLSUTZs2UVRUhM/nY8yYMbhcLlWp19TUhMsl3/cVgsHr9R4TwXA82yvJD8prpXlihpKm4na72yhemkMhGBwOh0z6t1MMOFa0p1Jp73EFrbfrIBg60IEOdKADJxxNTRLvv1dPZoaBcNjFnJeL2bBDwDb+ErpffyuiKLKjtoYbb/+Sd97oj1YrEh1t5MqrMnnt5afokzsUvV6Wos5b/BinnhbPWVPymHR6kB9/3McNN1zIxIlTmT79KqKjo1m6dCkPv/Ai/l4DSHv4GUSjkYKH7yO2vpJvFy5g48aNjBl5Ml5ngPuumIPJIFfPV237iR2hMtLOmopv1QquPv1U7vz7bcf04dipUyfGjBmDw+Fg8+bNrNz5NSt2fEV6Qi5p8dn4/T61ghCWpMNOYjfvW0G/od24YNr57W8QgV6vx2az4fV68fl8BAIBdaIxcOBAes7pSVNTE16vl6amJqKioo7be+Htt+ZzcFc9uek9EAWBHze/i9FkpKqqCrvdTkJCgkrqKL27iqHbgdJC5n75lDoBkqQQUliRrMuLeF/Ayw/fH0RvOGSC1vq8rFtbz969a3hk7vWUVhXjD/r4+cplQGRhFAxSIuzhuUW3YTQYEQURp6eJnzd8QWVjCUS8IYKhEDv3b+Ts7qe3e6x1jbWs2vYjgijS5Gpi06ZNWCwWPB6P6mAPEB0dTVRUlNwK4nG2u6/z58ykoOyQAaRRa8Dpdx03wVBaX8GwSaM5++yzKS8vp6y0jJKSUtaUFLDr11001tRzXv9JJFrjSYqKJ8keT6w5Wl2ohEIh7vnoSQ40lHHvaTeQqosjJElAGCniWq+Y0inQaLSM7z2S8b1HUlJTxserv+L2hY9gNBg5u/+pXDZiKka9EVlCEsZkknvrnU4nTqeTgN+PG7mlJhgMotFo2pBR4XCYisYqUlJS0Ol0nHPuOZxz7jnq45988il+p0RCdMphz82WgrV8vfIdXr70QazGQ27zcqVUIgxoNErKCYSkkLrIeWX5fDJj0hjVfUiz4xYBHaIgMLrHSXSKSua0Fy+jovgAAImJifTs2fO4ImX/l6itrWXZsmUIgoDNZiMrK4vTzzyDWR/OYGzXYRAOs2T3Ks7ufwp2U0uS5O5PZ7OpdAcxlijyB/dkjbiHi6645LDS8P8mlCp2p06d1O9Dhw6lqamJxsZG6urqKCwspL6gAovFqsYyKhVyIukqocg9R1byBNQFmNfjQaPVIAgRVYP8pPJzR8ZwpHJVaUMFbreHl156SU1zaGhowOFw4HA41B7+QwoiWXlgt9uJjo4mLS2NlJQU1UtCr9ej0+nUVB9lEZ6Tk0NmZqbqMaD4Laxbt44lS5ZQUFCAXq8nPz+f3r1707dvX1JTU1Vy4ERAFEWio6PRarUEAgECgYD6Waicb4UAaB6lqdfrSU5OVt/7zeM7lf9ramqioKAATYT0iYmJUck7SZLweDw0NTVRXV1NTU0NgUAAvV6vtnT8HiPn1lBeczabjZ49ezJixAgKCgpwOp2sWrUKi8XC4MGDycvLw263q20xwaAcz+zxeIiNjT0q+Xg82yvns7VyQDl/Csl/JJWA2+1WDTPj4uKIj48/7nPzR6CDYOhABzrQgQ4cM6qqArzxRhUuj0B9VFcyXphNXHKKbKgWhq7XzGJTcSF337eVJx+T+4bHj8/m55+qmfv1k1w75X427F6Bw7uHiy8eHuml1nPaaZ0YOtTDokWfM3HiPBKzulNrshI341aMgsjeV17At2oVtqAOc0YMs66/mdID5ehNWnoP6MYny97k/DEz0Wp1JMemczA+jvSpFxE4dTLznn0Mr+9fPHD3XUet2KelpZGWlkZhYSF2ux13UxV9skcyvv9UNS5Lp9OjFMtbVuUlHO56lm/5hvW7f+HzbxYd1VhKiTxT+nf9fn+Lffp8PlwuFyaTiZycHHWSejzQaXWc1Gt8pG0kTKO7lq/m/0IwGIpU3gyqeZ5cB5Qnk05XEy6XG50o9+ILqmneoWqMAFjMNqzW89Fq5aq3UoFsPlFKSvwJb10dY/qciSiKpCd3Jj05m9raGiorq/js1zeJMscxotdpGEwGzCYzC5c8z7TLppCTk6NO8OVM+mEMGDCgzXFKkoTP2cT2bYsRBIF0YyyvPfgiggArt65FZ7WRlZZLWVUJgZAPi9mK3++jrqqCUEhC20pF7g34eOq8exjXaziCIPLLjlXM/v7V4zr3AKIg0KlTJ/r379/msVAoxOLFiykrLaP8YBkbSlZRtqOMfUWFNDQ0gKChR1wWe2uK+eLGuXiaXNTU1CCohI9EKBRUfQnak3ekx6dy8+lXM3PidBZvWsIH677kreUf0j+rBzecfBnd0/IwGU1otVokKYTL5SIQDBIIBjGbzeqEunUrkNPrAlE4bLtB7969SM9J4rWvHmJYt0n0zBmovnaLSvewv3QXny+by/1nXMfQzi3TDsIqcaIkj4ggQCgk4fcHWLFvHV9u/pE5Fz/S7HUpt1yI4qELubV0Jym2eOI0MVRpK4mPj/9TmjsWFxdTVFTE2/+ZR/WOUjonZ7KmcDPugAdTjIVGRwPXLLgTg8HIsLEjuPL9O4k1RZNmS+TSQWdT66znokFnsql0B33HDuKJJ544IbLz3wONRqPeT5X3q8/nw+12s3//fr766iu21K7GYjETCMjKmWAw1IJskCQJQmHCNFcPtKIOWhnGHgsO1JZQVlbK66+/Lv+bQk60as2QlRIG9f6TnJxMeno6AwYMoF+/fnTu3JmsrCwMBkObyvLhCG2/38+GDRv47LPPqKmpQa/X07NnT0455RT69OmjRrueKCjx760jf4PBoNrqoRhRKoteRZGSkJDQpsVPkiR8Ph9Op5Pi4mIqKirQ6/VYLBY5oSbipRAKyfcSxeyyvr4eg8GA1WrFaDQe0aj5t0AhWa1WKz169FAjMDdu3MjatWvx+XwkJSXRo0cP9Hq9en0UUl8hSA4XjazgeLf/vTCZTNTX16vX5WieHH8WdBAMHehABzrQgWNGMAh7C0PQczBx1/0dnyS73itZ4aJOR497HmbdzL/x3Iu7uXlWPoIgMGtWd264fgkFxWfz0dJnuHh6OlbboQmZ3N8psKPAQZlfi3DSWNJOOYM9d1yDc0sBg7qNo//wGXTJ7M6T829FZ9Aw/cpLmHXj9RiNRu74+5289/NLTBt7PXG2RMKVqwHQWW2k/f1e3n/+SVz33c8TDz14TJOa5ORk0tI6EXCKDO92BlJIwulsQqPVqNJQubIjS3o37fmVj5e8RnxcIuNOHQ7R/dV4t2PF4WSLzasdv8V/QRAPeR6Ew3D6SRdHctgDqtFYe/ttbKpj+abvuGTSIaPHcFhq1sIAoiiwv3YTt956+xGlm2azjQ26A4zoP7HF31WSJiwTFka9mShrNBaLhfjYBMaNG9cixrChoYH6+vp2DcE0Gg056Vm8ffHTsomaRqtWNc8/MJOBA85mSM9xLf4nGAxw+zPn4vS6MLQavygISGGpxYI11K4nxJEhCiKhYPvxmRqNhkmTJrX5+z/uf4DXf/6FgMvFT/u28MjEmdjNVoRQmOjoKNxujxqtV1tbh91uw26zI6+123+NGHR6zhw0kTMHTaSgdB8frf2aq/5zB7G2GKYOOI1pAydjNpuJiorC6/Xi9XrxeNzU1NRgt9ux222R0ynvv6yhCk/AS2lpabtEWs+ePfno0w9Yvnw5zz71Amt2f8fo3lOIsSYw+93b8Ae9PH3BXUzuN0H9H1n9IqktMspCLyxJBELygvONZe+xcM1n3H/6TfTN6XnEc//xxm8Z1/kkzug6jtdWL2T/rn3Exsb+Zvn5fwO7du1i5vRrcNQ3EtbA/RNncVLuANx+Dz/vXMmLS+YRDkrYEmN454MFZGVl4XA4qKioYOnPS7hpzsP4vX4yY9JIikng8ssv/8PJBYX8ar5gB1k6bzKZSEpKwmKxkB6bSlRUFKHItQ1LElIzUzvlKxQKEQoF8fn8BPx+1atBISMO3Y8Uoq09HHpfeKUAVruNlJxOahylzWYjLi5OrcJbLBZV6q8cixKxmZKSQnJyMtHR0Wg0Gnw+n6qSOlpKg+JxUF9fTyAQwGg0kpmZSZ8+ff6nr0tRFLFYLOr3xMRE9XNGURi0F4Pc3GMhNTUVu93eQtHgdDpbfJ6IoqieL9k7Q27nqq2tVc/ziYRerycnJwev18v27dvZvXs3TqeToqIitm/fTnp6OmlpaX8ZJcBfFR0EQwc60IEOdOC44HUF0RwoxlVdjRiZbCiTDo2gwWCPptOVN7Do6Uf4fulqslLgwvMzOf+CFJ5+80Y6dzYw6bRDveCSJDFv3l4WfNGIZdjJDLxrJrqoaCpXLEUq2oJeZ2XK6L9RUXeAuYsfY/ypo7nltpvJy8tT9zH76Se48467WfjTi3RJ7geFh/p6NSYzabfcxVdznsb199t5/onH2yyGm5vLSpLEO/PepXhPKdNG34gWnSotNRgMhEKhCMEgEgqF+Hb1+5Q6d3LdTVeTmpbKueeeC6BW+dtDa8nk4bZTxgP8doKhuaniceDoljgRn4TwkY9BGUO7fgpAmEOpIeEwLRYMRx7fob7V5n87/GjbN7XS6/Q4PE3E2VubcbbKeRcESmrKePTzF+TfRQ3KUiYlOom/jZja7vNqNVoC/iP39SpobGxk3759bNi8mS5XzKRx2U+kJcTx5d4l5CRmMCirj3wupTAej0eNxlMqd2I7fiDtIS+tM3enzeKmU6/km40/8t7aL3h96UKGZvfjsqFTMRuN+HxePG4PXq8vsn9LRE0g7yMrvhMX9JzEJWdNY/j4UVx7/Yw2EbOCIDBy5EiGDh3Kt99+y3NPvciWTdvonprFgmteaCORlgmsQ61HinIiEAoTCAR55MsX+HLTj5zSbRSnDxx/xGP0+r3srtjPZb3PQSuIXDt4GpvqC3jmkdnc+dA9pKWltXn9/K+xevVq7rzpdmYMnEZ6bColtWWclCtX+816E5N6j2N0/lAa3A5u/exRdcEnEz528vLyOOvsKZw27hRKfFVcd91M1fj2f4XW77lDhEBIvWep3jsRuX1CQgKCIJAWnXxEw81wWG5F8wcC+P1y1dzlcsuGj5FEgmAwqN4LdDod2hZSpPavbZW7jpiEWPK7d1VbAVJTU8nJySE7O5ukpCTi4+PVsbd3vMq9WTHtU7wNjkYwhMNh3G43DQ1yzG5MTAyZmZn06tXriP93oqEQC8e7wFf8G5RoT6UVwul0UlJSQmVlpRrBqXg8JCUlqWaygUCAyspK6urq0Ol0auLEiXof6vV6srOzMRgMLFu2DJvNRn19PcXFxezYsYO0tDQMBsNfRgnwV0UHwdCBDnSgAx04boSqK/F89j6G6dfidDkRNbKcVBAFREEk4+SJWOx2tv3zTgrELjz4n3rCDU7CkpeBgw9VDrZurePh2ftpMKeR8+A/SeilRJ8FqX3tCS67LJ1ffnbxxLu30G9Ab1587Wn69+/frqnUE7Mf45abbmPH+nWEHI4Wj2sMBtJm3c7SV1/kqlk38upzz7YwlwoGg/h8PoqLi9m9ezfpmZ0wWQ2s2PEto7qdiTxRDeN2e6iursZsNuEPefl05RvEppm57KpLaWhoIBwOs379ejVeTHHeViZcrXvZXS6XGlEWHx9PVFRUG4XF0YyZjgUtY7Fa8g3t7/cQYSA1q9q3VDDIXgs6nZ7y8nJiY2MPG6nlcrkIhUKqUZooCmrvaVgKq4t/KSxFqpIhQGjx3EdDc5KiNZdwJMNHrVZPg6upxd/cfg+7y/a2+FuXlGzO7jsRr0+OXQtJEmHCePw+3l35yWEJBp2oxXMU4zAF8xcs5KGX5mCIiiYxEMBatIcnZz+J0+nk6Udn82vRRq4adgFGkwmjx0MwGCQUCuF2u6mrrcNssaju7ccCi9HM1JMmc+7QM9hUuJ33Vn3GlW/fQZI9nlNyRzIicwDhSMW1pqYGi8WK1WIBQcCg0zN92FTO7X8aC1Z/xtmnn8WaTevaVQgpSg2Px8MlV1/D+voDLFr/LVMHTUIUxTb990Kz3uRAQF5A/vPzZ9l+sIB/nnULT377Cj9sXsrJvUcd9lgXrvqcnLh04i0xEA4jihom9RjLiB5DeOT+J9m3Zx9XXXPVCTOZOx6Ew2HeeO11Xn7uJbpEZzCm60kAdE7MbLGdIAhYjRasRgujswfzyceLmHn9dS22SUxMZOGi93n+mec474Lz/xDCRHlvK8qX1gvz5mOSJInGxkb279nHL6t/5peiNSRa44gzxRBnjCbGbCfGEoXdaMVsMGE2mDFo9ehFHRaLVVbKifJ9VFEwKG94+e/tHX/Lv4V0YYYOP4kJEyao5JzVaiUmJoboaFlB1Z7hHsjXTklXaJ6qEBUV1W7Fvz0o90ilteCPVpz8HgiCoB5Hamqq6m2jKCEMBkOL8yKKohpHHQwGcTgcx6T8OF6YzWYGDhyIw+FgxYoV7Nu3j82bNyMIcltXamoqRqPxuN8vrT/j/tto7XvxV0EHwdCBDnSgAx04fgSD+Deuw91nAKFe/dFpdVgtVtl4SyNw4IP5lCx8m/QZN5F9+hQEUcBRVEjx26/z2Xcr2bx5JUazkbUFAokXXc2gKecjNKs8lbw3jySLg0mThpLXpYHHHy/izntup1+/fm2GUlhYyD/vf4iAP8CzLzzNuFETcDfUs/e9d8iddqm6najVkTbjJta/9Sp/mzGTuS/9W63AKCaKO3bs4Ouvv6ampgaDSceeks2M7Hqmug95Yuml1lXG8oLPGDKyP927d+Onn35i48aNdO3albFjx9K9e3eMRiMajYZAIIBOp1MrbM0nk06nk/LycoxGI3Fxcaojd3tVQWWS0doU6kgIh8PsL9yH32jGZJAjNgNBWY3hC3hVSbEUlpBCIfm7JCGFQzS5GvD53RSW7ZInVISRQrKEWV3FCyJ+v5+Ghgb1GJWJUPMvj8dDSeU+9h7cTjgMtY3lBEMBHE0OGhoaqKwvwe1toriiAGPEg6Gs8sBxTahCoRCF5SVcNvf2SOvKIc+ATSU7KXbU8OuWxRF/CNmvQECg1lHDa0vnM75upCy7lkKsK9qCWWfCFfAw58d5EWNLSIlN4vS+48mMP2Sc5wv4Oemhs5j41MWc0nMUt516bYtxaUQN1TXVeDyeoy5m95aUkHz5TBJHjaN6+RIG9e6NzWYjIyODBYveY9p5F3DLBw/x/NQHMJlMeDxy3Jnb7SYUCoIgYDKZWrQyHAsEQaBfTk/65fSkztnAh8s/55NNi5m3fhF9krpyUb8zSPInAgJmsxmx2f6tRgtmg4nTJ59x2PYjURT54ccfuff5F+kz5y1cdXXc9cSD6LU6pvSfqJpZKtAKqO+dQCDAzvIClhWs4YOZL5OekIon5OWhr17kP8s/4MYJVzA0r60nx5dbfuS0/JFy1KnaVy/QNTmXZ6bcw8XP30xmdiaTJ09ucy7+23j6iadY89Vy3rn02cNGiLbGpB5jeOD9f3PtzBltqup5eXm89Mqc/8ZQ26C9e5NC0Cp9783vc4cMYmWyUJIkKisrGTR0MH0H9EOjkdMc6uvrKWtysqexEk+VG6/Lg9fnwx9pP/B4PYiIZMSmcma3k5nQcwSicOj1dvjL1vaBkB7OPvtsJk6c2M72Rz5epWLvcDhUcuFYevGbk67/vxEMyvU+FnNKJR5TEAQ1rlMURVVV+Hvff8r/m81mBgwYgF6vp7S0lO3bt7N161bq6uro0aMHffv2JSYmRiU/jud5/9cL/ebvn78KOgiGDnSgAx3owG9C2OnA9eG7aDNy8JlNNDU5CHq9lDz/GI79++j60Gzie/VRt7dnZdNl5k3sfriGDTt2Eg77iB9/MpaoaArfeQNfZQVSXSUadwP+0n3cck8eOp2G/PwYbr4lzF133cDcuR+RkZGBVqvF7/fzxutv8vab8xnUZSJbClfyr0cfIxQKk5WUS8H8t0gcNgp7xqGqoKDRkHr5DHbOn8sl11zLUw89SE1NDQUFBezatYs9e/awZ88eOQfd6SIQ9FNQtpnc5N4IAoSkIGv3LGHLgWV065VHbW0N3333HTU1NVRUVJCYmEhDQwMul0vNNtdoNEiSpGaQN0d1dTUVFRXY7Xa1guPz+dTtmpqacDgcasqE4lJ+rJMhn8/HihUrMGg3YjHZ5EWWcMgMUIzEwYmiKC+3I4tvQYCaxgrMVjPry75ukYMuhSMS9kiledDQ/sTHx6sTtfYcsXv37s2qFevYWv0jTU4HuzZtIS8xR45FDATQh/0cqN6Ne2sdCYkJJCcnM3TEwDa9/Uc6bo1Gg1ZnJDNzmHpcyubbDu6nd2I2J+X0k5MXImkYkhQish6pggAAIABJREFU7Kql1uJmb0wNiYmJ6PQmnC6JXgN684tnK2tW/crAjN4ArN2/meLaUp644G71eQ06PW9ePpuPVn/J2qItbcYVCAWZ9/pbfP/Nd1x7w0ySU5KJjY0lKSmJ3NzcFsdUVFqGoWs/wuEw3qoKslOSsVgsagReanIKX/36JUv3rmJ07hC5fSBS4ff7/Hg8HjWOTa/XcTwkg4JYazTTx5zP+UMms3LnWj7b/D1//+oJEq1xTO13Gmf2n4jVasVsNgECLp+HL3b9xNuPLTjsPqurq7nx/n8Sf/NdmNKzsOWECf/9Xm576mG2HdzNrROuQqfTtenZVyToj3/zCtMGTqZTvJxIcfGwc7hg8Jk8/8N/uPX9h+menMus8ZfTJ7sHADWOOg5UH2ToxH74mrxtzsLPu38lr2c+/fr1o6KiokWs34lCOBzmxx9/xOv1UllRSdHe/WxcswF/MIDoCzPnvIexGI5dPZGdkEGcxs7SpUsZO3bsCR3rb0E4HI6YrrrVvxmNRkwmUxulVmtVk8lkIjExUfWyAaUdRlRTG+R2B20zvxvZ5HXLli28Oed13njzfdJjUjFrjYRDEnHWaOx6K2a9GZvRgtVoxmawYDfbsRks2ExWjDrZb6bOLbcn1NTUqGNUTAKV527+elSILuU+LAgy0aZ4NBxr9T0UChEIBNTkF5PJhN1u/13Rg381KISEEtcYDAYjnkB+9fyfCCjET0ZGBvn5+ezbt4+KigqamprUpJaxY8fSu3dv1YfoWImew6lb/ltor1Xnz44OgqEDHehABzrwmyHVVOH66F10V99ITck2Gl57HlN2Dv1feANTXEsTpYOfL6Lo1ZeI6zaCUXf+g9qiXZSv/579P9yL1RjEatMwenQ8mZk24mK7MGBAilwk14r06RPL/n0l3H33Lbz++rusX7+efz38BDYxiStPvY8oayyZSV3Yun81d0x7Fqs5iscX3s62e29jyOvz0TSbAAqiSMrFV7B34VtcOetGTh87htWrV7NkyRK1zaH5BOK7jfM5kLabfp3H8NPmDzhYuw9/0Mvy5VXy/pq5jjscDjVrXPFsUCqxfr9fnVwqk+2amhoqKysJhUJER0e3akeQe3UV06ympiYMBoNa/TkWOJ1OUpLSuGPaC+i1BsKR1odQUJY0a7VadHo51k9djAryT0s3fk36ABPXXHO1eox+v1+VBoPscK24hyvnoPm5U34/7bTTmDRpEoIgsG7dOl6+91ken3wH1dXVlJeXs3j3Lzy/Yh5JafGMHDmCsWPHMnbs2Hb7gw937IIgEBcTz8mDJzebqMrbrt+9lFP6jubcgS0NFcPhMOlJaeyNqeH6m2dhtVqJioritshkbtWqVbzmEHjsjNsBePLzOXy78xdeXvIuqVGJnNVProD2y+lJUdUB9mwobjOu7PhOJFnjuWX45Rz8sZA9vo3Uex1sKN7Gq++8waBBg9RtD5aWYkyQzdaE2mpSuw7CbDarrudSQOKSk87msa/n0OlvaaSbE3G53DS5Hbh8HgxGAy6XXp0s/9Y5sGLwNii3Lxm2ZOpdjfxatJGF67/gmSVvcv3Y6cyc8DdEEb7Y/AMnjRvRxn+hORZ/9x1i7wFYs2XjUykkkTJkGJoHZ/PW80/y7StX8/LUe+nZKR9BFAFZJSNJEpuKt1NaV8H0y89rce21Wi23nXoN14/7G09+8wrXvnM3gzJ7M2PsJXy3fSm9Urti1Zvx4eFQk4zAr3vX80XhEl5+6zUkSaK6uhpBENq81n7vAqK+vp47Zt3GuLxhROtt5EanMGn4DDwBL1aj5bjIBQVndTuZua+8yYgRI/7nVe+WbVZh1axQ6aXX6/Vq/OvR8FsM9hTn/uHDh5OcnMz69espLCyktrZWVh5pNFT5/Xg9DnxuD74Gr0y6RQxRnS4noWAIm8lCg1tWH9TV1an7V/wTDAaDrNKJeEYAqkqosbERl8ultlIo2x8rWhMMRqMRm832f5JgUKIeFaLB5/O1SJb4ve8/rVZLTEwMoVCI/Px89u/fj9/vp6ioiJUrV1JdXU1aWho9e/ZUyaxjITf+KHLht3ow/VHoIBg60IEOdKADvx2hEIEdW2ic9wrBgh1EnXEuOedfgmixqAv1oNfLrofuxrG9gO5T7yClqxzXl9ZjMGk9BuOpr6Js48+ULHmHBZ87GdA9zKkTDASDElqtiBCWYw8nn5nKP+5Yx8ljx2PAzikDLyQv45AxVkZyLhnJuervd1zwBPfOm8nuN+bQ/fpbWgw7GAximzyV/U4nz7z6GlFaDR6PB5vNRmxsLOnp6eTk5GAwGAgGg6xZvY5Fq/6N1igSFkLqh77dbic2NpaUlBTS0tLo06cPgwYNIj09nZiYmEi/sKianqnRaxF4vV7q6+uJjY0lPj6+xWQ1HA6rC3qFnDCbzVit1mOO99Lr9dgsdsxGi7pPJV4yHJYQNILcNhFpjwiGgoTDEiEphMNVh9GQo/YjA2pcm9IGoUywWxMLR/t+CJFWi/Ch6+J2u1v0N5+ISZUoiGw7uJuchEw0okhuUhZmvQkB6JKcw9cbVxAMBtvIUFs/t9ViZfSZJxPbNZM3X52nEgwAep2u3ZQJvc5AKBxiQo+RLf5+9Xt3tTEac9XVUD/3VTJuuwfXurUsrq+hob6B9Ix0OnXqREnxAa6bcBsxJjtXv3kH3oCXBFs8DrcDXzDAzSOnc3K3Eej1uhPizm42m4mPT8BoMDJBP5yhWf244fMHeWfjp6wu38IpXUbw4fZvmf/Z+0fcz3uLPkE/+lT1d2WhFZ2bh+2ZORz89EOmLLiTC/NGcv/ps+T7RqRl4onvX+GSIVOItrS/cDXqjdx/1s3cPPEqHvvy31z51u14fF4uH3QuHo/sl6EQYTWeep5fMY9n33iR9PR0PB4Per0eSZIoLy9HFEUevu9BKksrQCDiqq8YukqEw/L1FQQRn9/L/f96sAVB1BzKYmX64HNItJ8Yx/qR+YNZ8vVqbp11C7Ofe+qYe/5PFHw+Hz6fT608C4JAfHy8Suid6D761lBUBgkJCQwZMoQuXbrQ2NhIbGysaiKobKf43ijRvhqNBr/fT1NTE36/n+Tk5MP62yj33eaPh8NhNW3it8QshsNhNd5RiTpUWoP+Sr31JwqKF4MYMet0Op2qSu9EejIorRIajQadTqeSRRUVFSxbtgydTkePHj3Iyck5obGZ/9fRQTB0oAMd6EAHfhfCzib8mzdgu+42zN174fF60GjlCadz9y523X8nhuh0hsx8GnNUbJv/11ns1BduJpySTtb0azh48ADPLPqZ2a+sJy9Dx4jBNkaNSiIlxYTVIrJtYwWPzngMs8kcWYBGxtHa2E8QuPXsB7lr3kwK7FHkXXIFykrWH/DjcrnQjj2FqjW/UrVrO2IoSGJiIjk5OQwdOpTRo0djs9kIh8OMHj2adevWsW3bNtatW4fb7UYURaKjo8nMzKRfv34MHDiQbt26kZeXd8zGcS6Xi+joaGJiYoiNjW2zYFCICafTicPhwGKxEBMTc8wyUpPJhKCTeOL9WQBIUigyqQ0SkiS1/1kKKpFyEaIAgUDQT2zu1BbScUWpoJAlzQmGI6F19fNQbqZiOin/HgwG8Xg8+P3+4554H2lbo9nEAU0NH1X8xMYtG7mi77lMHXQ6CALZ8en/j73zDo+qTNv475zpM5lJ7z0hEAiE0KuAoKIgRYTVRda+rmXVddWVtSL2BpbFtvaGomABURRUxEjvgSRAIIQkkF6mt3O+PyZzSEgouujqt3NfcsWcOfOe95SZvM/z3M99U3moEqfTiaktMRaEJEkdBCK1Gg2DB/dl6tSpvLHgFZZtW0W8JYa48Bil9eJYaNWaTtslSaKmtZ6kpKQO2596+GFuf/t9kMFdcYjeGaPwrq9j7bcllDdVsn3HDmKnRXHFmRczLm8kK4t+ICchk0RjDIvWLWXZ7m8ZltYvkFyQ5RM1pp8SjEYDOp0uQId32ClvPITT4yK/Zz/6FBSwuaKMidMmkZmZecJk0L4DB4icHqfcdr/kb2sh0mEIDyfnkitoHDKSjxY8ycoFVzBvwt/pGZ/N+5uXIfklrhhz0UnnajGE8ciMO6hqPMK58y7ltQ0f4ff6GZ05CIPBgNak58nPn+Vvd99GQUFATDbYN15TU0NtbS37y8ooXLWGf82ci1bVpiGAjL/NplAliMqz+vzqd6ioqDhugiH4uQk3HN8l4adCJaq4+7y/Mm/VK/z1mut55oXnTrvNXxBdaS0EtWqC7iWxsbGKmO0vTeEOPlsqlYqYmBiioqKwWCw0Nzcr8zgVJCYmdvi9fetGMIHidDpxOp0dEql6vZ6wsLCTXu8TOQK53W4cjoBuChxtATqRgN/vqWr9UxBMMGi1WhoaGpS/qcGE0MlYHacqtmgwGCgoKCApKYn6+noqKyvZv38/NTU1FBYW4nA4MJvNSrvaycb7NQUef88IJRhCCCGEEEL4zyH58ezegTshCaso4nM6KXv8AWxbtpAycjrdx0xDFDtXB1qr97PtvQfQ9urFwNufRGtpq1JedjW2qkoO//g9721ex8uLd6NXy8RaJOyuFraVrGNAr5GKmJ23zcbM5/MHgrm2v/9qtYqbz7uXJxfNxdR3AKbUdHz+gNq5w26nZeUX+CsOkJaYQN++fcnNzaVHjx6kpaWRkpKieKBrNBrFuszn81FeXk5tba2iri8IAhkZGYrX96niZHTLYMUuqH/wUxc2RqORtesLlcp80HUg6F4RHh7OZZdcxdSB1xETkdDhvas2fkLv3sldDfuz0HHuXZ+z0WgkLi4Os9n8s6pJdqeN/dUlRIfH4Ze82JwBdwi7u5VnHlnAgAEDeGb+M/g2Nyvv0Wm0JFhiqaio6BSktE9gAUjICnPl5n/eysH95Wys2EN1URVlZWW0tLby9DevEWeIIj48loTwWDw+j1KND6LR3kJYWyW0PWpqapASkvC5XOCXmDnsAgx6PSCwrmwra3duQqsOLLzT4lK44syLkGWZuro6+iR0560tH+OXAkkpvySh+g/pvIIgolKByWQKVI31Bs6tHMUP27cqzhATJkygqamJyMhjbT7bXUONFmNK6lGxMhlUqja9DwLBaVS3HMKfXED5xx8y6/0HOCuuJ9sOFjH/4nsxaE+tUu/xeDGrTfRO7M6aso38e9MiypuquOXsq5j/3asMnziayVOOira2ZyIFfw4aNZSXChdy9/gbiDCFH/f6pUYnnvDzKEkSoiCi05xe+rtapea2s67h+dVvcc1lV/PcywuIiuqcuD0d6ModQqvVKj3rQceS/0YQHExonS7qeHvqvkqlQq/XK/c3yNA41VYGj8eDx+NRvm+DFHytVtvBbcLtdmO32xVGg8PhwOVyddj/9ywAeSoQRZGwsDDUarWSuLbZbDgcDoxGIwaDocM99vv9isimKIqKTemJxg9o0mgZMmQIKpWKZcuWsX79eurq6ti5cydhYWHU1tYycOBAevToQdBq1eFw4PF4OjDwfD4fzc3NeL3eE1qs/q8jlGAIIYQQQgjhP4Zst+Fa/gm+4iJck2dw+J3XMMZm0WvW/cSld2urbsm0DywPrl3G3u/eJeHiP9Htoj8hqDpWwMKSU8iZMRNmzET2S7QcKKO+aDvC+kJe/+F5MpN7EGawAAIOpwOb1Yrb7VEWa0Cgn9ZgYXDCQDb96ykSZ8/B6/Xhdjlp/ewjXN+uQO/3kZeXx8SJE+nfvz89e/ZURCSDC8TIyEgqKyvZW1qGwWBUqiHBBANASkqK4u/+U3CiBXqwKqgILP4MJen2C1SVShUQVvT5FPHAE1Xcjjcvh8PB5s2bEUWRuro6MjIylHkCpKWl0a3b0XYVu92O2+0mPDwcj8fDmqJ1PKN7DYfNRou1lU2VRfikwMIxuOgrKSkhNTUVrVaL1+vFbrejUqkwGAyMHj2605yMRiODhgxgw6Gl7P9uP6JKJC8vD6PRRMHAPIUt4HDYMQsdn7XMyBQOHDjQyaVErVZ3aHsQOWo7etnll3XY1+fzsW3bNg4cOEB1ZTXbKw7yxb617CvbS4OjibOenMnI7IFkxaZh9zhptrXw3nvvkZCQQFJSEklJSeyrOIQqJg5H+X4ijBY07ZJVakEkJqxjEB98dlQqFRkxqejUWsobq4iOim6bp4Ag/Hzab2B8FWEmkxIUXdb/AqpX1/Ltqm+JiYmhe/fuAERERHT5vOzcuRPCI1AZjPh9fsUtor2loKgSEds+/2lTpmPO78cX//wbkX5PQNjxpB+pQJuNx+Ohqv4w5U1V+Hw+bDhYUbaG7Q2ljD3vLO68564OcwxeP7PZTFhYGA0NDdw6+zY+fH8RN340h4cm3kp6bGqXRzxQe4iSN99lU+EG5TMkiAKS/6iLis1q7fK9/ylEUeSGMZfxzvqPuWjyDOY8NpcRI0Z0ue/Prba2d4dobW3FarViNpuxWCyKRsd/C0qrV7sE7M/FsboearX6Pzq3YGub3W5XtHhMbfaxOp2O8PBwhf4f3K99giFo26jX6xXBy2Pn+f8JoihiMpkwGAy4XC6cTifNzc243W5kWVbuiUqlUgJ/l8ulaBMF2SDHe87VajVmsznw92HQIFJSUigrK2Pr1q00NjYq/w4ePEhYWBiZmZlA4PvcarUqxwn+Lfb7/bS2tiJJknLsEDojlGAIIYQQQgjh9MDvw3eoHPuLzxI++AKie41AMITj9XgRtMFKE/g9Hra9/wgtTRX0mPMocf0HnnRoQSUS0S0HS1Y2GROnUvTEgyxY/hD/nPEkGo0Gv9+PWxNgMPh8RxMMPp8XpxNG95rKpuWzqftyGaqEJBzLP0YqL2Ngz1xGjBhBfn4+eXl5mM1mamtrFa2DYBXr2WeeY+WnhfRKG8TB2lqqa2tQq9U4nU6sVivl5eVs3LiRvLw80tPTT7l/NLhg+m9aUMmy1OXi9eCRvXz75BI+eG9x234B1wW/P2DTlhSVhUFnYOXaZZw9fBIgICNjc7SSmBHB62+/qoz10ksv8/LzrxJmDqO+oQ6Hw83aqnI8Hi9ul5tKax2947uTp81AV+GjtvYASzZ/TXpSLhnJ3Vi9cRnR+jAmDBjH9srdPDb3EeKiY5V5IQQW61FxMTz93FM4HA5FS6M9Wltb+XDhIu4687oO27PCUygvO3CcRerRbaKgUpgkc++7n249csjMzCQlJYXExEQGDhzIwIGdn2e73U6/3Hwkn8Tq4nWU1R0kJTKB0sWbWONspNbeQE1LHZU11ThcbmSNGr0P3v5xCfHhMcSHx/FV0ffEmLumgatUKowGI/2T8/ihfDMFWXmByhtaRO1R15CfC0EUUQNGo4m42Dhmj7uW2z57hLU/rkUURaZMmUJmZmaXNPmvVq1C7D8YCFLCvYiiCq1Wi+qYpGKwfUcty2QkJ3L5lCncuuRRpvYYxx8GnY9GrenyWfV6ffh8Xux2Gy+sfhsnAUX6YcOGMWzYMEwmE7fccstJWTFGo5HY2Fguv+oKaupqmPX6Ldw0+jKmDz6/03GPNNWS5I2jX2rG0fnLAdaCw+PkUGM1VqvtFK/wT4cgCPxp6DTyk3KZe8vdjJ0yniv+fCVxcXGd9m1vF+tyuX6Sza0sy4o7RJC9cLqU/n8ugq0MQXHO35JIYjDB4HK50Ov1WCwWRQsiqGETFOv1+XzYbDalSh4UzA3en2CL3O9N4O/noL2LgyiKCmvGZrOh1+uVZLgsy20uNoGEzbEMj2MT40HxzmDrhcViYdSoUUiSRGFhIcXFxTQ3N7N3714++eQTqqurGTRoED169FASH8FxAEV7JGg/HULXCCUYQgghhBBCOH1wu0CnQjRFBjQONGqMRmOgyiQKOOqq2P3avTQ4Gsl/5mWie+b9pOGDasq5N97K9tv+yqtfPsFNF87B7/cplW6vV2xbiAQpvn4ONx7E5WhBWrIQARm9z0tsVBTDhg3jyiuvJC0tDb1eT0NDA1VVgepn0DqyqamJjz/6jKvH30t4WCQj+o5nU1Ehi7/7N5XOcqxWKwcPHmTDhg1oNBpF9Kx9MHO8xeFvIcEgyTKC0DkwtDlayE0czKQz/ghtugxym0WlRqVFrzMCMt+s/ZwZo69X3revcjclLas7jmWzMTx3AsP6nM2PO7+iuHwL102/l7q6Wg4fPsy7qyqZ0ftczup9BtHR0ej1er4qKuTsgdPpmVXA3gM7+GOfsfxh2CS8Xg/19qaAdR1CoPVGELA6bdz0yVwg0GfdVU+21+vFZrUx/6tXeXbl68iAWhSpbW7AGGvmltv+fkrXTJZlPv3wYyb0HMM39uUcaqqm1W9n3eYNXe4fZIzcP/3WEz4LdredJlsrC75+nSLHAVSDoylvauWzbWv4YutaZEEg9/ELUNkcaGSICYsiL7k7yZZ4esV2Iz8xl1V71uL3B6prarUKjaz5T/MLgcRBW8+0SqUCWeaO0ddw91fz+eCDD8jMzGTy5MlHK/lt5+j1eln0+XLC/3wzQFvVz4dOHxBcU+bVFu9KkoTX48W66C0en30Hw4cPZ/jIEbzx8mv88NH93Hrm1eQkZHDsCfl8XhwOJxv2bWNlSSFWnwOdTsfQoUO5/vrriYiIOKkNZZDyr9frefTBh2nYUc2s/lN4+qvX0Gl0TO5/Tqf9h2QWMKbnsE5jjX5kBlaHjdTIxE6vnW70TevFgulzeWv9EqafO5XxU87jlttv7aADE2QjBJ0QlFaVdudyLARBUKrH4eHhp+QO8WsgGMA7HA5FG+G3FOi1TzCYzeZO30NBfQGdTtdB9LF9JT/YKhHUaAgmJv4/I5hgUKvVGAwG3G630opoNpuV51mtVhMdHU14ePjPas8JDw/njDPOICYmhoaGBkpLS7FarVitVmpraykuLkar1ZKenk5YWJji2tT+Oy3INvktPXe/NYQSDCGEEEIIIZxWSB4Htm1fEjn2KjxuDy0tLfg8Tmo3fI5r09dcPmgyZS1VfPf6y0Q//szPOobWZKLXA49TdMfNPLv4Pv48YTZmc4Bx4PValCqzx+NhQ8kqfti9lMS4GNLT08nOziYxMZH4+Hhyc3OJiopSnBGMxkD7g16vR61WIwgCb77xFj0SBxAeFuhzFgSZHun5XDTmrzy/9D4kvYmi4tKAcF9NDcXFxRQUFNC/f38sFssJq32n2kMc7IMOCu6dVkjHaZEALMZIIsxHFfBlWUKSjopcSZLUOXjtorVC8kuoRDV6nSFwXdu9KaD5KKER1QgCiG2LuXZSkB0giiIJ4bGBpBWCImSoV2vRnqTfPSoqitHjxmDfVceY7kMDAn5+iVanlRcL3+W8seNJTkjC4/MiCTLJKUms2ryafRUHEBA43FRDZl02hd+tARmuHXUJGpUGj8/Lha9d1+l4H3+0hGfnP0NLcwsN9Q2MfGgaZr2JcJOFeEss2bHpdItLp0diNlmxaYTpwwjTh1GQ2Yf4pG5cdtllxMTEsHz5cspS04i7/C/YDx+m5IbLibGYcbg87Kgops5Sz3vrP8Xr9+L2eXl708dkJ2SQHJ1AakwS8eGxmPVhp8H6TQXoMJpM9EjO5vrhl/DU969it9vxeDwdrP0AVq5ciT0qjuTsnA7jHDsLSZIUIUzr+kK6G3RMnjwZt9tNr169eHTe46xYsYI7n3uKiVmj+OPQKeg0R1lCLqeLuoY6nvvhLRqdLRQUFFBQUMCAAQMUO8FTxdtvvU3llv08NPk2dGoNSZZ47v1kHmN7jSRMbzy16ySqeO3KJynI/GkJ1J8LsyGMG8ZcyuXDpvPyDwv5w6QLmX3/XQwfPhxZlpUWB41G06VWxolatERR/K+2Q3QFr9eLy+VSAvXfkvp/e9vi411Xn8+Hx+NRWgDaJw+COhfBz5JKpfp/n1xoj2AyLCACq1fEPI3GwGcv+Dz+3O8yURQxm81kZmYydepUEhISWLt2Lbt378br9VJVVcXSpUuprKwkPz+f3NxcEhISlETRqYpL/q8jlGAIIYQQQgjh9EKW8dSWYy9egyp/HK2b1yFs+5I+8Vn8/ZIHGZidT52tiWVPXUzN5g3EDxj8kw8hCiLm2Hh6P/YsRXf9nac/uZfbZzyE2Wxum4KMw2lj0bcvUe88yHmTzqGpqYnc3FxGjRpFTk4OqampqNVqpSIhyzIGgwGz2aws6BwOB4sWLubKs+9qd/TAAjAtKQudRo9+9CVYi75h6+4SSkr3kJqSzNSpU0lJSUGj0WAwGE7YJxxciJ5o0RJMMBgMhi7FHk+22GptbeWpJ+bhcrkD7SQuNx5PoGq2u3g38w/NRiWqAwr5bWNX1x0iPab7KdyNrquf7aHMWaaDu0HgUHK7RIKA0H4x3e40gxaBgeMd53xPsugTBIHszCyi5BymtKtI+30+Fm35nAkZo5g25DwQBHZVlvL1rjVoBQ2XDr4gcB6yxLOrXkffKHDnWdehaXMaUIsqRWys/bl//+1qRscNYMSIgeQmZlPVcITDTTVUN9VwqLGaAzUH+aF0AzWtddjdDsIMYYTpTTTZm5l17eXKOJVVVfgioxE1GjQGHTl5eWxY+RWPzH2IqAMqxuUMo6WllWXbVvLEd6+w0VVKWXMdFTt2o9MYcdp9hBkspMckExsWRZQuHJOoJzU6iXhLDL1TctGqT16NU6nUqFQBVpIsy5zZYxilNftZ8833WG+zIooiBoNBqZDP+/crmCZM75gpan/rFOaCH5/Xi89uw7N8Cfc9PQ+1Wq24PFitVs4++2x69uzJ80//ixs+uI/bxl1NbmI2IOByu3l/w1LK6ioQBIG8vDxmzJhBTk4OFovllIORLVu28Oa/XmX+1LsJM5jwer2MzRvBO+s+5u4lj/P0zDmd5t4VTkWN/peASWfklnFXsWH/Np6842HCU6O55sZrSUlJob6+noSEBCIiIpTEwe8RsiwrCQaLxXLK7RE/9X6c6jPTldtGVwmG4Pd70KLV6/XkfMY3AAAgAElEQVQq4pFdJRh+S20fvyaChQGPx6OIM4aHh582txSVSqVoMsTExJCXF2gnq6iowG63U1NTw5dffsmmTZs4//zz8fv9CosneJ9CSYaTI5RgCCGEEEII4bRD9jqxFX2DXF1KjMvOLaOvZmyfkRiNRiRZItYcxV+HXMiLTzxE+POvo/+ZKujG6Bj6PPoMJY/P5Y43ruGOaQ8TFR5Hdd1BPl37CkNH92fqBTfS3NxMQ0MD0dHRJCcnYzAY8Pv9nUTCgotCr9fLzp07Wbx4CcmROR2q+BCgaRqNRiItsXhFH5ETr8NaW03r9hXsK9/KZ599htVqJS8vj9zcXNLT0xVXiqBdWVAJ+7vvvuPTJcvIyMjA7/cxcPAAYmKjiY6OJjIykujoaERRpKGhAUmSMBgMSv9pkMLc2trKkSNHFMG9Y1FdXc0H7y1m8vDLUQkCRhkMcluFEg3D8sYTY0lQXDkEUWDxty+jUp2eZYIgHl1oS+0WZYLQcSEfXJiLYhvHQXmpE00i8J9w9JVTDeqErgIrAXITsrG57CRFBq5DcmQCrU4bR5rrmDxovLLrJzu+ptVlpejwXnYd2YtJYyDOHIPP72P//v2KMKUsy0jIGDQ6eif3QBRFMuPTyIxP63JeNpeDyvoqqhtrePzrl4iPj1deK684hHN3MfBHfC0tSjVt/74yUo0FyrU06YyIgkBaWhpms4GoWDVDBkexaNFBrrnmHs444wyqq6s5cuQI+/fv50BrM8999jZ/GXRxh4TLyRAMfvx+P1cMnc78wtf5w/Q/cPe9dzN27FhEUeTrr7+m0g8p+f0UgVIZGbVK1ekeSJKMz+en4eNFzDh7HPn5+Z2OF7SSe2z+E6xZs4b7n3yOM5L686ch0zjYUMmSXV8hqyHaEk1KSgo5OTmnbF0IUFFRwZ233MGNwy8lITyg7yGKIlqNhodn3MHMl2+k9HAZPRKzTzqWJEuoVf+9AH5wVgEDM/L5ZnchD95yL6b4cO6ae4+SPP299/P/XOeKYOLL6XQqIoLB7cFe/SAj4qfoTARbGRwOB06nE1mWMRqNHSj0Pp+PQ4cOUVpaSnV1NX6/H5PJ1EH0MYSjdqh2ux2DwYDBYPhFWhGC9zshIYHJkyeTnp7Ovn37KCsrY//+/bS0tLB582ZsNhsHDx5U2AxJSUloNJrfHHPmt4ZQgiGEEEIIIYRfBLLLiqGllrnnzSYpKgGb1YooCOh0WkRRxW3n/YUdNWVse+Zx8u97pEMQelK07SqKIqaoaPLnPkHJi8/wwKLbmNh3KkWVP3DbP29hwoQJGAwGvF4vTqdTUbBXqVT4/X5UKpUi/tQe554zAcmpJiu+NxOGzOx0eLVahVqtQqvRojMaMFjaxCyHzUAVnUbpti8ofeU1BhbkM3HiRHw+H5GRkZhMJqV/s6WlRen7TAvPI06VyidrXidZl89eVwlOrxW724rV0cShw+X4JR8Z6ZmkpCaTkprEtdf/hejoaBZ/tJhnn16A0WRgw6Z1gWvfbuEMgUAwNTGDAblnKIvsoBuCSlTRp9tAUuOzOpzj99uXKcr+x9zZdj+lDu0Oga0drR27vn1KWqDjb0IweBA7bD8VCIJ46uJ1ncrPAr1TcllTuh5kGRnhuOeQm9QNV4aa7mcMAKCpsYm9ldU0NDVyxYWzcPk8hEeE43A42HtgH26nm8c+f4FYSzRRYRFEGsNJiUwkOz6dHglZ9EnpSZjeSJjeSG5KDrkpOXyw+wuyso7ej6GDBvJ5fQsAfpcTc1igmucXZLZX7mZoWt/A75JfSbQ0NdUyanQ0w0fEs2Wrm4yMDLp169bB3QOgqaGRSM1P668Pitb5fD68Xi/XDZnJTR/P5dmnn2XEiBHIsswTL7xI2OSLEAQBv9+H1+tDEAIsCLGD5kfgebTuLUFbtJW7nlyu0KGDCAZ+QSbMhAkTGDBgAM/Oe4a/vH8X9fX11NkbiY6JJiYmhuTkZMXX/lTgdDq56ZobuCBrHMNzBijbVW3JrvSYZC4sOI8b3rqbi4ZMAqC87hBfFq2m1WcHINIUTnp0MgICbo8H6TitR78WREFgdI+hpEQkcNPHc4mKisJsNv9ukwtdsbZOdC5dMQuCiQC73U5ra6uyPZhgCDLgThQ8Bo/Z/jvW7/fj8XhobW2ltbWV8PBwpT3uqOCwjwMHDrBx40YlwWAwGIiMjAwlGDh6PSVJCthIOxyYzWblnpxuBMU24+PjmTBhAiNGjODHH3+ksLAQj8dDbW0t27dvZ+/evVRWVlJXV4fRaCQxMVGxEA0lGI6PUIIhhBBCCCGEXwxWt51v9q/jYsv5OJxONFotercBAQFRpeL5i+cw8rlLqfj6C9LHT/jJ48vIyFKgUpw0ZTq7v/2KTeVf89S8J+jevTv19fW43W5UKpWi3G0wGBRaqkqlorq6ms2bNzNlyhRlXI2o5fyRfyYuKrnr48oykiRjdbQQHxmrqM+7XC7M/cfh7D6Y5qLv2LbnR2rq36akpIQtW7bQu3dv+vTpg9FoJCIiApVKRXR0NO5IM5lJ3dFodAzvczaaY7QElq9ZRFH5eqYO+gs2Zyv7S4uZNfMy1KKa5IjuXDD8GnYc+UbZP1gB8vl8bN68ma1bt9Lc2hig8SMjSxJ+SUKURSRZUhbe7WF3WFm1aQmlh7a1sQmENqfRdrRfScLusvL0+3cq18XhtmEIV/HkE08pi/cfvv+Bou0lbNm7hhZbIw6XncfeviVgS+Zy0epsYvHuryi1HiQ8PByVWk2TvZkvN7xPSfVGDh7Zx1NH9rNw6xdIbUKTAZ2GNqoq4PP7aHI2n/SZaWlt4YVl7/Bm4Ucd7qfd7aC2qZ5p/7oGvyzR5LIjIuBwWHlzzSLSYlLpl55HrDmKxMG5zJzZMfG0bs2PPD3xHqJM4TTYAiKULo+bnZUl3LpwLjeMvhSLIYzDzbUcaqrmh5L1fLDuM+paGwKtAHoTEUYL0aZIdtaUEB8frwQzh6qqkNpcM1y1NWSlpgDQt6AAaWMjwUTNEWs9Tp87cJ4tDURFBQKXxgY/ycmdn+XS0lJWfP0VYybmK2JyPwXB3nxJknjknFt5bO0rzLpkFtU2O9b0bOJze+Nyu/B5vXi9ASFWrU7bwfIt2Fri+Ph9HrjhuhMGFMHrodFoSExM5Nzzz6O+qYFDa48gI5OUlER+fn6X53oiPPX4k2Rrkpjaf3zHFwQUmswVo2bw8dYvWbRhGTlxGfSMz0YURDbv34HT66LaVc+wEQHNA0uEBfG/FnwEdEUkSeJgfSUPrlzAnXPvIS2ta+bM6YDD4aCmpoaGhgZaWlpoaWnB5XLRo0cPevfufdoC6ODzEmwjOJUAT5ZlbDYbDoejg61rVFRQU0fokCwIuhcEIbaJmx7b9y/LMlarFYfD0UGrIvivvdtGkLW2d+9eCgsLqaqqQqPRkJGRwYABA0hISDgt1+f3DEmSFAaIWq0mKirqV9X+0Ol05OTkoNVqSUhIID8/nw0bNlBWVkZlZSVbtmwhMTERg8FAfHw8UVFRIZHHEyCUYAghhBBCCOEXg8vnZlnptwxP70eyJQGtVovJZApU/0WRML2Rx869kWueeYzInnlY0tJPPOAxRUFZlvFLfurXfEvL+28ya+IEJk2cQFhYGE6nE7vdTl1dHSaTibi4OAwGg6I6Xltby0svvMyi9xeDIHdIMCQkJdJsazxhgsFqb6HV0UROSg80ej0mkwmn0xnwzRZFpD7j0GcPpGHPej5c9hXffPsdf5gxnejoaDIyMoiMjFQSDXXqJtLiszAaTPz700e57sK7OyQZApRdDclxmajVanIzCkjem0VMeDypCVkcaTyEfPhoX6jD4WDHjh08M+856qtbEfxqqg9X4ff7OzAYZFFGo9Zitbfgi+yYYEAQiDLH0y2pDyAgyZKioRBgKQiATGnFDrISehMkoAiCiEarpXyDrW0fgSPlLahFNQVZIxVBTY1Gg8vpxGa3s6ZoKZurijDnxDBh3BBiY2NJHZdLSkoKRqMRt66Bmn12BuWODlQb2xb0giAGRCFFEZ/Pw2frXzvpMymqVAxKy2di/thjTlfA7XYF3E48LuZ+8S9Ml12Fq7KCOZs+wlffgFqtRQRyDuWwZeduenXvxtVXXYlGo0EQVdjddmLMkcRa2qj5JkiKjOff33dDo1JxXv+xneYjSX6qGmsC2gyNRyg9sp/tR0o6uB6UHqxAlxxgHvjq68jKCdhvVpUfon9EprLfEVsdfilwj1tamomKCgSVlZUN3HnnbXz00WfKvna7nT9OuwhPq5O/vzcXh9dFTno2vVN6EG+MJt4Yjd3jJCMmhd4pPYgOOyoOaHM5eH/9p8wadgHh4QG2RpjWyF1jrmP28idpzM+lz5//itfrw+P1BHrOPQFXi/YtST6/D4/HjXNvKWkakQsuuOCE9y4YIAYdDkpKSij8sZCmpiYAkpKSGDRoEElJScDJ++4FQeC1V15lw4pCnp52T1d7KG1DZoOZG8ZextsbPub5yx7qkGjbW3OA53a8x4tvvAzArAv/yKc7vqLwwCYEBAal59M/o88J53J6EHDPcbgdfLhpOcv2fcff/nkrU6ZO+UnMhfZV/fbvc7vd1NbWUldXR0lJCas3bmLj1m202mzoomNQhUdAmBmMYUgqFSxagvdwFXm5PRgzeBCDBwwgPz+/E0PlVBEU/wvS27tKMLS/50F2gc1mo7GxUdH1iIyMVFwIgskuSZIUMUy3243X61WSERBo0wnu337choYGxcYzPDxcSZC1ZzoE2yf27NnDunXrFH2BzMxMBgwY0KEd6n8N7ZkLNpsNm81GZGQkZrP5V7VD1ev1dOvWjfT0dPr06UNBQQFut5uKigoOHz5MS0sLcXFxWCwWhg4dSnJy8u+WDfRrIJRgCCGEEEII4RdFg72ZVzd9xN1nXofH48Ha2grIqMxq1KLI+D6jGbtxGRufe5KCx549pVaJYCXL3dJE3VuvEN9UyyNz55CYmIjT6USSJJqbm9Hr9WRmZmI0GhULMJfLxfx5T/PRB5+Qnzac6yc/wBsrH+4wflJyAi2Vjcc9vizD/soSdGHhaHT6NueDQGU1kEAJWG05HWHow87Fk9WP5h8/4M13FlJfX0/fvn3p3r07GRkZbY0AMlqtnjlXvchDb97Igg/v5/oZ96DVBCo4Wo0On9+jHF8URfrmDEaWAxZ9JeXb0WjVNDU1UVlZyXvvLuTLZV8zouckpk8aR1XtQZ549+/cPH8a08dezaiCiUqLRHs7uvYw6kx0TyngvBEz2p23HEg0EGhgkGQ/KzYuYvKYzm0k7VF55CANrUc4c9D56HQ6ZWFWW1vL4cPV7KnaRr21mtTUVEaNGkVqairh4eFotVrUajVbt2ynQQ+De52p+MKrVG1tFG1juT0uvt310QlmEUBMTDTxvYZ2EezL+H1+fH4/Vpedx1a9Qu5lVwFQtuQDHMu/pv8VD+JsacBnb2Z9SwOfzv8XF067gLi4OHJ79+TaRXcTFRZJvCWGOFM08foo4izRmLRGVhX/yPkDz+kkpiiKKlJjkkiNCQTFe2sOUK6u67BPRVU1ur4BO0RVQy1pY4cDUHWokgk9joqkevxeIPD5cDgchIcHjjXn/p7cP6ekw5ivvvwK47KHcdu119Bkb+Gyd29jwdsv0draSlVVFbt2FPHOq28RZ4rmSEsdoihgNoShVWtpsDbi90s02Vu4fcK1yphGtZ5e8dl8v2UT9r2lmHIC2hNiG/tFlsHjbnuOBfB6PHg8XmyrV3L7Hy/+ycJ2LpeL5uZm3G43giCQkpLC4MGDiYuLw263U7imkKVLP+PcCeeRn5+vBC1BEb2VK1fy4b8X8tikf2DSHT/oFUURtVrN9CETeePHj/iqaA3n9BmlvC5Jcodg96Y7buHAgQOKoN+CtxaSV7aev4y8BJPO0NUh/jO0sXgkyU9ZTTkPfP08Of1zeXXhG2RlZf1kKveFf5zJ1uLiDtsEQUBQqdBFRqGOiEJOSEKT0xPz+GlER8ccN9jyu5xU7i3l5dLd/Pu7Z3BXHKB3bi7njxnN6FGjyMrKOqVALcgusNls6HQ6zGbzCZ8Xv9+P1WrFbrcjiqJiH6xWqxU2wrH6L3q9HlEU8fl8Sjtd8NiNjY0dfg8GxpGRkQpjoSumhsfjYefOnWzZsoXS0lJ8Ph/x8fGkpaXRt29fevfuTUxMTKf3/S/B6/Xi8QS+F4JWlUEXp18bQbvQ1NRUxo8fj9ls5vvvv2ffvn0UFRUprTTR0dGYzebTJj75/w2hBEMIIYQQQgi/KGRkimvL2HBoByMyB2C1WVGp1YE/zLIKBIGXLn2IwfMvOX6rhNzxF7/fT936QlrffY3xg/rzl1sfIC8vD7vdTlVVFa2trdhsNkwmE+np6UrFbOPGjdx5x73E6tL5y4Q5mI0ROFy2DtVIgNT0ZDYXl5/wvPZXlSLEJHYQTQv2pgdFJFtaWwL2exHxqExRCM0NfLbkczZu3MiZZ57JmDFj2uz5ApV+rVbPPVcs4OE3b+L+f99AfvYQHG4r+yp34fG1p+0KiKIan8/Psh/fY0/tRl574xWKior485XX0jNlEFeOv4fo8BjUag1Zqd156LrXeWPZPBpaa1CrVfj9bUrnCFTU7MXn9+B0O9Bp9YCAwxXQzGifeAjYVB7VOpClzhoMXT8EMg0tRygu39o2noxf9tPS3EJN3REarbVtu3Vdcba2tPDZd4tZtX4Rfr8Pj18iJiIh4CwRyM/g83uob6k56VRUKjU+v6+LV4LnIXeWlJRkEEREUYUpMg4i4zAneGkufF8JDp594TkkSaKuro7Dhw9TXV1NdVU1hyoqMaRGsLu4mGmvXkdrSwvXnnEJPRO70SMpG6O2Y8BZ01JLfFJHynRlVRWW2LjAXOrrSEkJtEhUVVWTMDgO2gw2TG1jeTwezGZ1ILAHvF6J7OwMZbyKigqWvPshz184F4BlO1YxefoUCgoKlH3WZqzl0MZ9PD5lNrIsU9fSQGXjYXZX7cUreymrOcg7Py6hf0YfautrKSov4bsD62nx2pg0bTI/vvIchrseRhcVYHME7U0DgYSsJMfcVive3TsY/+yTbftJyufp2J9BBBk4TqeTlpaANoUoiiQnJzNo0CCcTicOh4M1a77n+2Xf0LCrmjl15cQmxTHtoumMHDmSiIgIHrnvIW4/40riw2No6//p4rkI2jUKIMtM73ceC755s0OC4dg5Dh48mMGDjyZ+ZsyYwfwnnuL6D+/hb2dcQb/002tfGSAWyRxpruPOz+dx3ewbueiiizpU6H8KrvzTLG59ZgEJt96FNir6qDvLzxBXVOkNRPQpIKJP4NmSPB4qi4uYt20Lj7/9HpEaNeNHn8HoYcMYOHBgpxaZ9u0LQWHcuLi4Do5B7X8G/9/v99Pa2kpTUxNxcXGKWO6JtBWC7Q3BMYLjNDU10dzc3IH9JQgC4eHhREZGdun6EHy/0+lk+/btLF++nD179uD3+0lISKBv37707duXvLxfx8r0t4qgK4jbHWjtCiaV/1v6BiqVCpPJhFarZdy4caSmplJXV0dpaSnFxcU0NjaSnZ1Nt27dFIYdnLrryP8KQgmGEEIIIYQQfnG0um28seVj8hNzMROGp81FQZZBq9WgVWt57Ny/ct3Lz5I47Ay0ls6LTFmW8Pn8OFuaqH//bcIP7mXO7NvJz88nIiJCYQ8kJgaC/iCTIYhnn36O999ezLkDZ5Gb3rfD+OIxgcXw4cNZ+PpizpIuQBS7XujsP7Kb8Nyuac8Bam2gd1cUBPZ9+jzhjTVcNP4u6pqqWbn9A75Y/iVWqxWP24PRnaYwCtRqLXde9iwvffIwTfZawozh9EjPZ13RN+zYu55+ucMJUrcFAWLDE9ld7cNisfDxko/pmTSY6WP+rMwhuO6JCo/FYopoZ/cYgCnMiBTZQLPBQ21rDSa1CbPZQnSSBa32xL3T8ikK2fl8Pipq9vHRNy8jtCU1BEHE7/Ph80vY3a1dXsOjASYMSunJhD5n0mBtwu52tglUBscScPs8vL3t05POpWvhynbnFNR3OAm93tXaQFx8XIcAThRF4uPjiY+P7xCsAyxatIhH5zyEz+dj8cbltDqt1Fkb8fi9bLr/cyXRsHJXId8cWIchzMhNf78Zo9FIY2MjYX4fsiThqa8lKSkJh8OB2+kiwmgJtOUIkB6Rglalwel0EhmlUfJyDQ1uEhMzlLk88+TTTOt5DtFhkTg9Lj7f8x1vPPJOh/l+8M5ChqUEHSoEoi2RvLv5U7Y0lDB05DASu3Wjr7qeIlMlW0q38N2O1cgEqMbZWVkMHjaMZ196muTb7m0Td1ShUomIKhWSX0JqE6Rs3b2TPt1zFOo9oNjTWSyWLjUZKisrqaio4NChQ8iyrAR4Go1G6c8XBIHrbrieNV+v5voRs0iwxPLD3o0UfbqZhS+8xb7qA/hdPtZEbSDGFEliRFyg5UYUj2qOdEDgA3fxsMl8uHU5n2z6kikDxp9SYGE0Grnrvnt4IS6GV955nwXpD5z0PacCWZYCGjSyjMPt4JGvX2D6lRczefLkn+20ADBxwgRq6+p47KG7iLzmJiy5py8IFrVaIvr2J6Jv/0AAXnWIxTu2suTfb+CafSe5Od04e9hQRgwbRp8+fVCr1bjdbiUADWroAIrdY/BfEMFEQLCf3mQy/Sz3jGCCxmQyKQFve/aCXq8/rhaE3++nurqa/fv3s2PHDkpKSmhtbcVsNtOvXz8mTJhAZmZmp/f9ryGYKHQ4HEoL42/BojOYaEhMTGTkyJF4PB527NhBbW0ta9aswefzce655xIeHv5fTYj8VhFKMIQQQgghhPCroMZWzwc7lnNF/2m4PR6cTheiqGr74wzj+4zGuHQe1YWryTj3/A7vlWUJv1+iYesm6l97njG9e3L7/HmkpaURERGhLDiDGg8ej4cjR44oqvMOh4M3X3uHv055BJOhiwrZMevO/Px80rKT2FG2gYKcYZ3ORZL8VNSU0eOCq7s818DiXoXRYKTym3fRV5fzl6lzUAs6DBoTF428hR+Kl7J86QrCwg2M7B6PJPmVYFWt1nLD9DkdjpcS0433vn6O3tn90Wj0AfHEHStYW/IFY8eNpbGxEUmWSYpNP8ECrfMCOyIiggceur+TMN78+U+z78eTiyaeCoFBpVbRr/tIrp58R4cWibq6WqqrD/PpulfZVdEAHK1Etg8GoiKjyMwbwVn5o9paJAREUdUhiHJ4nCwrX31K8z1Z8iDgctBxH8nvo6l8F+XfL8EYlYAk+ekdGdn1AF3grLPOYsETz/L4+bPpk5oLgNPt5IyHLmTis5ejFTRo1BpqbfWYRAMrl63AaDExZMgQemZnceTReznU0oLrcC3/vOUO0AiE64+6Asgy9EvuSaTeQmtrKz1yjy54Gxrc+HyBpNuuXbvYtX47f7v4EQBWFK1mwIjBpKcf1T8pLi5m98ad3PrHWcq2Dzd9TmOEi08XLsVg6Mi6eOyxx/hxU6C33OVy4fV6ufLSS1m/ZSublywkdtof2xxb1KjVbe4TvkB7CFvXM270CMrLy5X7EhYWxuHDh1n4+rucN2UiM2fNJCwsTDnXyspK1q5dS0VFBbIso9PpsFgsCvU9yCQymUzcPPvvLHj2LeZPu4dz88cwXhqFz+/njEcuZFLvcew8WMw7Py4hTG+ie3xWYJ/eozDoOgvMCYBBZ+DGMy9n3levMKHPmWh12i5cSTrDarXyyftLuHX45afwtJwKAkKzkiSxr6acR1e9yKCzh3PTzTf9x9VUQRC48vLL6ZWby5W334HmH/dhSPxpwpmnehxjShrGlDSYMAXJ66FmTykv7d7JS18/jP9INYP6FTBm0ED69etHXFwcer0etVqNJEmKO5DD4VCEGYMJAFEUiY6O7iDm+FPnBkcDzVOlwrd3jTh06BBbt25l586d7N27F4PBQEJCAv369ePss8/+WeyS3xu6cvNoj/auEWFhYb+Ya8RPRVDcMyEhgeHDh6PVamltbeXgwYOsW7eOI0eOKHoNBoMhlGA4BqEEQwghhBBCCL8KPH4v35atY3TGIHrqc3A4HIpWQRBXDZjEv79cSvr4iR3s7TwOG3UfvYehaBsP3nAtw4cPJyYmJsAQ6GKRptPpiIyMRKVSUV9fT3FxMQlRaZ2SCxC0LOy4+BQEgetvvJa7b32Q/OwhnY5R01CNLIIp5sSL7rJVH2Df+gN/m/EQsZFJuN0udHodJruDrPheFB/aSH1tI+SIeDztRcU6z2fUgHPZVLqa1z+fx1WT/sGKDR/g1Ney4OWnSUlJQaVSYTQakbEddz6yLFPXdIQD1SUEyR1NLY1dLr69Hi8Hj+yltHy7ss0v+5GldsJcsg+fz9dhn2NR21TF+l0rkQSJpT++iUajDgT5yDQ3NVNbX0tFbTF6rYntm3cy74lnFIqqXq8DQWDr5i0YreD1+ZBkP06PEz9BOn2bC4THyd7yA/z12ptRqdVoNZqAJaIo4PV5ufiSGWRmZlJ+oJzdu7coyYl+6b3pHp/Joo2f02JrweP34vV78XjcitCdPjqGyiNlbHj9LoTwCKLSomjZvIGeA/qd8P63R1RUFDf+42+89OybPJVwNzqNFr1WT1JSEufNmMSypUsZf+55jB03lrtvns0Nw2ZRtruClds+Jdwu47T58NrcSJKAbU89u6r30Csph5W7fqBndCZer5dIfTiDU/JZX72TESMjAgwWIDZGz4ovVzNs2ACyk3tw9aAZaNUa/JKfT4pX8sQrTyvzdDgcPPbAI0zvfa6iF2F12vh499e88+n7nZILEOhDz8rKoqamhvr6ekUL5J7bbmXmtdfRmpNLeH7/o4J6shTQu6itQV9fw6RJk5Tnk7b7+vy85xhmzmP3Zxs454VXGTR8MJOnT2XMmDGUlZWxcuVK9u/fD8CAAQM488wzGThwoNgRrpEAACAASURBVHLPgph6wVQ+fOd9tlXsol96bxBF1G33/rqz/0SMJRq318OWAztZv28Lb675kLsWP86kwedQEJ9LfmouqVHJ2N0OokwRgMy5fccw7+t/M/qxP/DSZY+gUZ9cTb6wsJB0fTx903p12F5We5DEiLhOrTJdI9Baooi0yjLLdqzivaJl3HD7TQpz4XRh6NCh3HndX3hgwVMkz56Lup3w6C8BUaMlPK8P4XkBZpjPZmPXrh1s+GE9LY89we1/+TPTpk1DkiTsdrvyGQ5q7ByLnysmeTrg8XgoLS1l9erVVFZWotFoyMnJoaCggJSUlJ/FqPi9IuhS5HA4cLvdyrm3t0uOiIj4Tdp1Bh1r8vLyGDBgAK2trVRWVtLY2Mi3336LLMuMHDmS3r17/7en+ptCKMEQQgghhBDCr4YGZzPf7F9HTlwmTqcTnU6H3K6N4cZxl/PqU3/gyPofSRg6HJ/PT+OuHTS+/gL9UxK5ee4chgwZolQzj7dACyYYnE4nxcXFPDt/Ad3iB9Nln/UxAUkQw4YNwxxt4EB1CdkpHYOCIw1VaEyWE55rxdrPaSpcys0zHiQtKVuZV1iYmWZ1E1ZXIwICI3ueT27qADweDyqVClEUKTm4jcqaA6TEZxAflYLFGIFarWHWuTfx1Hv/4IOVL3LYtpdX33yZyMhIwsLCsFgsGA1GwH7cOcVHpbBy42Je/vjRAOMbgVZHI06ns9O+e/ftZUPRt5Qc2NbuWtGOOh6wCPX5vby5fP5xj+nz+1Cp1AwY0ovR5xaQmJiIRqNBFEWWLVvGjwu/xCdJ9MsaRV7mACJ0EWhFLYIkINkCi8/qihVY7S3UuNqEwNRaVKKqjc4ugiDg9bnx+2Fc7iUBp4I2sVBJltm29wfeenExapUah8NBi8fPwq3fU1m7n/SYeOZOuYVHl/6LYVn9AfBLfgSg/IbL0UbHoI6KoeffZmPuW4DaGIbWbKbqw/eYmJV0wmfgWFw4/UK2btrC41+/yJ3n3oDVZUdvMjJnzhzmzJkTuKqyjM6op3t8JmN6Dm1j5wTO5eui73l1xxJi+6UytE8cu3fv5slv/03f6B5cPXA6xUf2samqiNj0SGJjjlK3C/pF8d23FRR+30KEFMnI8QF9gDWlG0jqlqosjhsbG7nh6uvIkGKZNOQsZd4fblnOWZPOUbQfjkVkZCTZ2dl4PB5qampobGzkwIEDZGRk8NxDD3Lpbf/AfPcjqNv0GGQp0Nve8s2X3DTrErp169ZhvJKSEqrKK3ngor+CBDa3ncKyzTx/33xubbwFq81GxeFDSjKhoKCAyy+/HIvFolzD9j8HDBvMvp3l9M/o3RbYyHh8XiLNEYqg3Bm9BjO8x0Bu8vuZ/NKfGTx9NPv3lLFi/escqjoEMjwx+Z/0TOxGky3A7MlLyOGaN2ZTkN4LbbK5Q3X22O+UjIwM9jQdZOH6T5kxcCJqlZrqphr+sfRRzLowbhl1Rafkw7EIDC9jdVpZX7aNL/d8jzdSxSvvvU6PHj1O+N6fi1kzZ7K/ooL3nn+K5Jtno/oVg0B1WBhRg4chh1mo27gWURRxu90KW8FgMGA0GpV/vxUEq/J79uyhsLAQh8OBVqule/fujBw5ktTU1P8J9gIcddFwu92KS0fQJjro1BEVFdWBifhbgkajISEh4IBVWVlJS0sLTqeT0tJS1qxZQ319fUDkNzdXsSs9nTgZ2+63it/enQwhhBBCCOH/NcqbqpSKhuT3d6AXq0SRGwZewPxXn8fYrTsNyz5GtWENd152GWecMZKoqKgOFPvjQa/XEx0dTXNzM7PvuBNXi5/Jf7gen8/fwSYPAraK7ZMcR7cLnHf+2Wz8oqhTgsHuaEXogkIdRM2utRxe8Q7XT72XrJSjC/9gT69Op+OswRdysHYPOo0BQRZptbby/fZl/LDzi0BftSQRFWch3BJJc3MzOUl9SY7KJiE6hfUlq3jmuXkkJSWh0WiUnvOTYfLomUwaNRO/34/f70elEnlh2T1dVo565vZEe1YSo/tNVLYFRB4lRfPN43Vy+3OX8PD1b5zwuEt/eJukPANjx46lW7duSgtHeXk5Op0Or9tJWmx3uiX1JjY2Fq1Wh1qtQhACla6v1n/EqL6TOHvo1OO2SNgcLcxecCmJMWmKWnwwME+Lz+5yXqs2LGX5D6/x+poPMGoN/OvSB/D6fPgkHzPfvYVvvl3F4cOH+eabb3joXwtwN55PWFZ3wrrlIDbUkXLGoJNe8/YQBIH7HpjDTdfdyOwlj3Ll8D8olort9+nRqwf7astJiupoX2d12Tlv0gT+cecdAKz+bjW33norh+11OJxO5q15jcO2OpL00cTGtrF7BHj7rWI2r/Wx9Lp3MBtMisbE4l0r+OsDtyrjf7hoEWneaG4++0rlujbZW/hy7/csembJcc8rOTmZwYMHY7VaKS0tZdeuXSxcuJCJEydy/vnnc8uls3j6xfkk/+M+RFVg6em32/BtWc/FD9/XabxvVq5idNZgjHojkiRhMOi5YMB4pg06j9sXPUipvYy0tP7srivD7nHS3Nys2Hq2tLQgSRJ+v185B0uEhXLrQXy+gDNAo60ZjahGLao7tOSIgsC++kPIKrjgggsUn3tZllm2bBkfvrOcuxL+yh0fPkLPlBwWzHqQPdX7uXfJk1RU7Wbfvn1kZGR0KSaYm5vLwk8X8cA99/O3xXP5+5ireaHwXa79+w2kZ2bwwF33M7K8gMuHzUCn6VowUJYkVuz6nlc2fkjP/F5Mvv4iJkyY0CWr5HTi7n/8g9Z77uX9a/9E7EWXEntMC9svBVmWqVvzLc5Fb/Gns8YycuRIYmJilHsWbIUJ3qffAnw+H+Xl5ZSUlFBeXo7L5cJisRAfH0+fPn3+52wpgy2KDocDnU6HwWDowGCAo7oav+Wki8FgoHfv3siyTHNzM42Njfh8PiorK1m3bh1hYWHk5uaSlpZ22o8d/D77PSUbQgmGEEIIIYQQflWIotCmzu3DHwxWFcq9xDm9zmB+4UJ2XXcpQ/N6ce9TT9KjRw+ioqJOeSEZVAP3+XzcP/c+ln/+BW+seIw/n383YUYz7VkMapUGr8/b5ThZWVmscm3otN0veeEEi6GqDSs4s+8kemV3FPoLBsRarQ5zWBgWUwRWZzPf7/iMnQfXYjFFMXn4ZQzuPZoftq/A0s3NlVddid1uZ/Xq1ezaWczQs/KZPeZGRo0ahdFoRKfTHV2YCUev5ckQCFoCb2rve/9LoNnaSO3WOrRaLTHRscrics2aNTgcTpBhb/V2RLWMuc6CRq1h/+Fd+AQXGo2WA9WlFB/YxoqN7x8VljxKpEBGbkta+Y87h2Phcjn4esMHROnMSD4/Fw+epLwmIyvU6+zsbDIyMtAbjXz0yafUbFlHqwz1xaU8vGUzSz/8lKS0ZJLTUkhNS2XcuHEnrMRptVqeXvAMV8y6nH9+9BgpPdMpKioiMTGRqKgoBEGge5+e7FlTzqjcIR3eW2dvJDP1qEDpqNGjuPueu1k27wPWlG2iztbQllyRiI0LJBiWf17G0iWNfHDV6yRExCrv3VqxC79JYOTIkW3Xw8V3K75lesbYDs/Buxs+4fzpU04YFCUlJTFw4ED27NmDIAgUFxdTVlamWL1dNmsW6zZvYdPihSTMuASAlsLvOGf4MKVHPgi3283H7y/h/jNv7PR5lyQ/Fw+azN/3PcC9596A3eNkU3URm9cVM+vCmZw96RyGtbVP+XwBpxBBENDpdNTaGvC3uYdMf/5aNKJaabFRxERFEb1WR0ZqOgkJHZ08Zs6cyaI33+fPr93+f+ydd3wc1fX2vzOzvWhXvVqyJEu25d6NbdwNmBJjSjAECL2FX6gpJITeAwk1QGgBEqohAQPB4GBsbHDvNrblrmpJq7JN22bm/WM1I60lGwwGDO8+n4+LpN25d+7MaO855znPgy8S5PlLH0KSJPoV9OHlKx7m1eXvcvGcX3LNH27g5JNP1p/JrmuZm5vLE888yTv/eYff3nU/peV9OOfcXyCKIm/Oe4t777yHX715C9dNuogB+eX6+7TkwpbaHTy/di6PPPc4I0aM6Hb87wqiKHL5Bb9k7rz3MOYeHmvn26D+9ZfJ2bGFG267FUmSSE9PJz09/Xsb/3CgBX+RSIRdu3axfPly9u7dSzgcpqCggLKyMgYNGtRN/PWnCm09NMtcv99PZmYmqR26NT+W9hDtPMxmM/3798ftdlNZWUllZSX79u2jtraWFStWIEkSDoeDXr16AUf2/Lq2RP1YkEwwJJFEEkkk8b1BEiWKU3t1uBvEXQAgTqNv9rXx2sp3+WjnEubM/hmlfUo57rjjyMnJweFwfKPqht1uZ/To0VRUVHDvPffzv9Vvc8r48xFFzQpPE8jr+YPbZDKhqN2D1niMe/D5iLEoNsvB+5UNBomwHKKmaRctviZyU4s4YeS5DOs3lqzM7HgiAgiHo4RCIQRBYNKkSQwZMoTm5macTif19fXYbDYsFgt2uz0ujvWV+w8BQVD1oEqzYYs7eiQmGL7OZkZRuzM/ekJ9UxXhcJgac5hGYwMCcSaH1Z/P0KJJbNz9ORv2LEVxeBlVMhJbqpuR/ftxzLixZGVl8fxzL9CwNcIxg2agKiqCKCCJBl3xXxBE2iN+7n/phq+cSzjcTigWonr/LsRYmL/88l4GFsZFF+PsCBGUTvs0o9FIOBzm+BkzmHXKKdhsNgRBYMyQUdw/87f42v3U1zXSsH0nj696GI+vheNmHBdPOBQXkl+QT05ODrm5uWRnZyOKIhaLhedffoFjj5nAsi+Wc+tVN+EJtBCORcjLzyOqxGjcWYdREclPz6UgLYeCjDz2t3sY34XxoKoqVfuqyHNk8tIXb2EQDTgkAzu217Jxo4XVq+p4+cUqHjrtfnpnJLY3/HvDfH5x+fmIokgsFuM3195IXiyV8WVxVoasyLyx8j1Wt37Jq1fdfsg1dTgc3H7f/UTa2zEajciyTCQSYfny5Tz55JOMHj2ae2+9hVnnnkdrWX8cFYNQli7k0sce6Xas+fPnU2zPpTSrqNvP2ttDFLvykQSRTfXbGVU0hFMGTuPCjLNoVfx8/NkSfvPSm0yeOY1LrrhU78svLS3lvYgfQwd7IhgO8sz5DyAIIMuKnqsSJZEMZxoNjY3dngeTycQpP5/FLb+/mblXPRVPIsUtRzAZzZw/7nTGl43izw/8nXlvv8NDj/4Vh8PRjR0kCAKnzj6V8RPG6y1RAC6Xi/sevJ9PPvmEe2+5m/6uYqxGMzW+/YzOHcTPhszg08plnHH+HPr163fI6/FdoKGhgZTBw3APGf6djRH1+2hbv5boys8hEiGj3cfrL/6DhoYGqqqqvrNxjwQURaGtrY36+nrWrFnDkiVLqK2txWq1Mnz4cGbOnNmtFeinDM2iU3NzMpvNP0ohRFmW8fl8hMNhbDYbJpOJsWPHIggC77//PmvXrmXv3r04HA5GjBiht8McSVaN1nrxY0nKQDLBkEQSSSSRxPcItzWF48rHI4pxyzpBFFEUmQ83fsqzy94gv7yQORefy/Dhwxk2bBgpKSk9Br2dyvmJ3ucHBgVWqxWr1YrZbOZXV1/Jtf93AxsqlzO4bAyiqAXbqm5rdiBisZiWgkj4vizHUDucBtR4Cb0TAiBHMBpM3d4H0Orz8K/5j7N1z3qKs/sxZdCZ5Kb2jm9IVKFT4kAQiHX4g2stDZpiOqCLOprNZjIyMrBarfH5ikKP4yZC0MURo9EIwWAQWU5MpKiq2u3cD55z+IrxBBhUMpbJw07uaHGJB1WNjQ30Tqujze9h075l9Otfzjnnnk1xcTGZmZm6VkNRURFiU5DcjF49tEjEh/AHLfFz6phnPPaLz+vZd+5n+74NGAxGahr2kmKyIooSJRm9GFjYV59/vIUlnniKdvQNGwwGIpEIfr9ft0Rsa2vDZXVSnlOScJoD8sq59p+3cUbuVPY3NVK/axebgytpDDbT6PPQ1u4jIzOTvPw8QrEwPz/nLJ5/9nlaW1p58cIHUVSF+tZGqj21fBRczOJdK2nd7MXb7sPXHiAci3DBbVclXKN9O/eSZ3MRiLRz+/RrqPbW8/KmdxGFE9iw/gtSU+CpVa/x2NIXyXFlke3IQJShyeDn5FPiVPf7776XyK42rpxyIZFYlOqWOh5e9AJpfbJ54dWXcLlch7y8ny1ZQrUMAVXEYLEgdPTJr1q1it27d6MoCoMGDeKvd9zOhb+7idBxpzCivIxhw7qLZM795+uc1m9Kj+OE2ttpaWlhRN5AFu9Zxbg+I7BarRiNRspTSyjPKeH8safx7Oev86uLr+CJ556isLAQURRJsTqROpglDosDk9GIgICsdFKPDUI8CdoeDBIIBLDb7Qm/T6644gpcKSnc+fDj/HHaVVTklyMQfxaRJEqzihiTP4iFVasJBoM6fT+xJSv+/8zMTjZJV0ydOpVRo0axYMECZFlmdkEBzz35DP976w6a21q4+uxxulvG9wm3243S2vKdHLvxk4+JrVxKrHovwwYO4vxzz8LpdFJRUYHdbmf//v3fynbzu4T22RGJRGhqamLnzp2sWbOGL774QtcXGD58OCeddNJ33spyNKAn5oIkSZjNZgwGw1F5DXtC1/Noa2vD6/WSlpaG0+lk1KhRZGVlsW3bNtasWUN1dTWqqlJdXY3X6yUlJeWIJhi01sofy9pBMsGQRBJJJJHE9wgRAafZoQfz6/Zu5pX33yNiUxl7/ASGDh1KeXk5BQUFCZvoaDSqiyB21WDQehN9Ph/Nzc2dFegDoFnnXXL5hfzlvsfISeuFOyUDAaHDMSDCjh07ur2vqqqKaFQm1OHBriEWi6KoCqFQGINB7vbBL0cjiIJEKBRO0JTctHMlL37wFwaVjOH35z+MWbRRV1dPLBaLe9p3qMSLHRV6k9mcQCG3Wq2YTCYcDgdpaWl6lb21tZW2tjYaGhuIRNRu4x4Y/8eTMQqKouJpbWLHjh3dKkweTxOxmKRTzePrrcZZC6q2DrGOfw/dmhCndyodGhgykhS3kdMSAdpctY265nuviYK1trQQjUpEIhFkOdZhb6p0aDQACMRi8XYbOSajSlrCIP6zXTVfMrx8MgNLRvDagr9x1sBJXHHc+d3nrsYVQf614j/EolHefvttzjvvPCwWC6mpqcRiMVpaWti9ezdZzu5U7Z0NeynNKmJ4Uc+K4pFYlEafh/1tjVzxj5uQqsJMLxvHhys/4eJnb+RfVz5GSVYhvTMKGNdnBHQwWYSOzeXJT16cIOYnyzLVe/eRqhbhsjgpz+yNzWqjd1sRf/jDbQnVc7/fT01NDTU1NdTX1zN9+nRdC+O/H/yXqD/MuS9eh9VgweywcM3vr+dns77alSAQCHDnI4+Seck1pDsc7Lv7T/SWVBrq6wmHw3g8HhYuXEgwGGTUqFFcceYZPPzss1z21790O9bWrVvZv6eOMeN7dueQFYVoNMaJfSfx2/8+gMVswe12YbF06qE4rQ6um3Yx763/H5f/8hLOv+xCduzYQbqpU5TVbU9hX1MN/QvKMAid4omoMH/DpyiCysKFC5k+fXpCUCgIAuf84hcUFhVxyw1/5KJhpzNjwLH6zxdtW8bipnU89tQTiKKI3+8nEAjgcDi6JSsOBafTyezZs4H4s9q3b1/emzePTes30bdv3x+EKp2enk7M13bEjxtq3I/87hs8/cB9DB8+XL+WiqLg8XjYvXs3wWBQ79M/2hCNRtm+fTvbtm1jy5YtbN68mS1btiCKImVlZQwcOJCSkpKjdv5HGprDh8Zc0D6zjnRV/7uGqqqEQiH8fj8+n49AIKAnvG02G263m6FDh1JXV8e2bdvw+/0sX74cu93OuHHjGDBgwBGbiyzLcc2qHoogRyt++nd6EkkkkUQSRw3awn7e2jifk/pN4o2NH7KmbjPOLDdlhXHbSm1TlpGRkZAoiEajBAKBhE2Kpk4djUZpbm5m3759ehKiJwiCgN1uZ8Cwvjz0+g2kp2QhCKCoMo0tNfz5z/d0eXWc1VBbW8uaTevZW7tZ959QiVs9BiMBtr30m844Xtc/UPHv38M7LftYsuVN/eftkXY8zT7Omf5rjhkyDaPRiMfj6ZIsURMYGgaDhNFgJC0tTReNCwaDVFdX06dPH3JzcwkGg7S0tNDc3ExzczObN29m5dK1fLr6vY41UlABURC7nhlKB1tBFCVa/M3cfft9WG02REHUWSBVVVW0edv4aPlc3TkiztaIr4Squ0jI/OHJX6ICgXYvoysmYzOnoKLo12lnzZfsrNnK4o3z0NgT2nWVZZlQJIjVaGf50tXs2r4Pi8WMJBkIh0PU1tZgNJrxtvn496IXEpX60XIdKoqiEgz5ue6R0zEaOkUvBUEgEo2Qn9GbPoUDSHdlIQhClx79hLsEVVVZsnc1V15/NVOnTkVVVV3To6mpidbWVnbv3k2OI6PbPbarYS+FqQfvUTcZjOSn5pCfmgOCwI0nXEamK4PLxs7h0ud/xyXP/4a/X3B/vGIlSfEEmqIgCeAPB7HarLpTAsQ3ng37G2hzpJJucyNKEilWB+EDEmIQb2Po27dvj24Di79YQlNTE7FYjLq6OoqKisjI6H5+PeGxJ58i3HcgqaV9EASRfrc/QOUtN5LlduPzemloaGDJkiWsWbOGQCDARRddxOABFUycOFG/17Rr6vV6afG19RCIa/aMMrFYlJb2NiwGE2aLBZfLpTNiuuLkIdNwmu1snLucN76YRzDUTo4jgyn9x8ePqKodv2NE/fhyLMZxFceSakvh8dv/yvv/eY/b7r6d7OzshDlNmDCBZ199gWsuv5p9LbVcOO5MttXv5Nm1c3n6n8+Sk5ODz+cjGAzS3t6OIAhYrVa9EgmH16MtiiJTpk5lZoeg4w+RYKiqqsKUlfPVLzxMGFPchEFnY2lMtGg0isfjoaqqSrehPJoCVK29LBAIsGnTJhYsWMCqVavYuHEjoijidDrp168fkyZNori4OCEJ9lPCgfeiLMv4/X68Xi8OhwOHw6GzCH9IdHWW+TrPj8bA8Hq9BAKBBEaSzWbD5XIxZMgQAoEAPp+P9evXs3LlSsLhMHl5ebqrxLdlHWgMmXgRIqnBkEQSSSSRRBLdEIlFmF/5GQt3LaNdCWO2WggEAmzfvp3a2loMBgMul4uKigqKiooQBIH29nYikQjRaFyIUVOkbmtr0z90RVHUrb8OpdUQDocpKipixYoleGObmTUrO063l9KxWtcmJAsEQSC/QGHQ4FQEIYDQERgLAsRidmTFgsEQQhLjlohxLYD4m+e9ZyAvV2TUKFU/1muvtDC45FjGDZ3esfEg4Y9WQQUt+BD58MOPqKvbj6qoNDc3U1/bQDQiM3TkAP766ENx0cSMDJxOJ1lZWZQUF+PZE2L84OORJAOiICJ0JAMURQZBQBREPln9DoGAl5MmnAMIpKZkIHZoUmgB3/vh1/CY65k96UJ9/boKTQkdx1JR49RXRB569TfUNVbhcqR3OQ+wGG3kpBVy2qQLMZpMGEQDkiTR2tZKQ0MDH697jVRbJtNHnUF6ehpGowmDwYCntZEnd93BDWfdh9oRbKuKgihKGI0d5ydKHZoOAve9fB3Thp3O0L5jEQ1inJHQMV+XIy0hwNOqiYlED5WZD1+Ay+qkpaVFDxC1xJb2/mg0yqLK5VS11VO7vw6n2U6WI51lO9cyvGAAjV4P6Y7Ug96LLYE2VEUhIyW+TtnuTF66/K9c9eIfOe+Za3nx4oeQOvQCNDHORl8zObmJAV44HKaltYVmoY1sZwYCkO3MwCqYqays/Nre7KIokpWVBdDN1eJQ2LVrF//4zzvk3PZAnGWBgDkjk/wbb6b23lsZOWgQdrud5uZmWlpaWLp0KYFAgMmTJ9Pa2ordbk+wF2xsbGRgQXm3TXksGiMaixKNRFEUlfe3LmRUr8EYjYY4i+Ug85vUbyyT+o1lcskYPlj3CR9uWMRjH/+DSCzKwF5dEy3xdilRkjAKAuPKRjKyeAjPLX2DE4+byYo1K7tVn0tKSnj5zVe48dfXc+0bt1MXaOLOR+6lT58+RCIRnE4nFouFSCSCqqo0NDRgMpkwm836v18XXdsDfiixt4KCAsJ1taiyjHAEe+klsxnXhVdy8XXXs/i9eQB4PB5aWloQRZH8/Hy91U1zCjkaEA6HWbVqFStXrmTt2rVs3LiRhoYGbDYbQ4cOZciQIYwYMYIhQ4aQm5v7Q0/3O0csFsPv9xMMBhEEAbfbrScWjgbtBS3xEQgECIVCRCKRQ7bdaAUMRVFITU0lJydHf3a14/Xq1Ythw4axceNGKisr8fv9VFVVsWvXLvbs2UNGRsZXtpd9FQRBQJIknT3xY0EywZBEEkkkkcT3Cl84oLc6BINBWltb9Q2zJEm4XC5MJhPZ2dlIkoTP5yMWi+kfrhp1tqamhlgshsFgIC8vj6KiIr1KeKhNA8DHH3/KpZeeQ2paiBkzesWD68MoNEQiUcLhEAaDAbPZgijGXRFe/EclEyZks2hxM8NH5HHiSZ2WVa+86GfCmOMPqMIJneyADjcEiG8qYtEorR4ffVMmIiBgy3GQNT6PbXs3sGj7m/h8PlJTU3G73foGLr+ggECxjaHlxxyy33XDzmWkObMYUj6WWCyGJIlIUuLr01OykIwiA8tGdFm/RJvKuBaCiLZ4TruLX550PXlZiVZdL733CKFokN75ZZjNna0vZqkBJSziMLtw2tJIdWaQ7srqCOgNertITkY+gK5F0ZNNJYDJaMJpTyHVlZZgU9kVgijSFvLREuyge3fYE2oXQTCJNEe9bN3yJW3T23RnB83eVBAEjj/+eE466SR27drFRXN+yWkDj2O/r4lQNESt4OG6/96Dt81LpjOdLGc62bZ0sqxpZKakk52SwZ6mKjKd6Qlzd9td/P3C+7n6xT9yztO/5pXLH+uYsM3MKgAAIABJREFUUpwV88WONSxbsZyfzz6Tf73xCkajkZqaGlwWJ02BFjJtaSAISKLIMb2G8unCT792guGbQFVVbr77XiwnnYbJlZrws9SSMoy/v421993KwIK4uGR9fT3Lly9n+fLlAFRUVJCVlaW7oAiCwBsvvcqs/lO7jRWNxeKJxmiUQDjIhvrt/OXUP2AwGA96j3fFwMJ+upCnosg0tDWRk3qgK4ag38+qqiIqMrta9nHJpRcfdGPvdrt58rmnefQvD1NQ2ItJkyYBJAQiiqLQ0NBAU1MTdrtdF548mDbDwdCT7sz3idzcXMp65dOweQPuwT23sHwjqOAs78++9hAtLS0YjUZqa2upq6ujuLiYgoICrFbrUcMA0NY/HA6zfPlyXn31Vaqrq2loaNDtkceMGcOsWbP0+f8U0fU+VBSFaDSqaxVkZmbidrsxGAxHTVuIoij4fD6ampp0VsKhGAaCIGA0GrHb7WRnZ+suOloiJRaL6fdm7969cblceDweqqur2blzJ7t27cJoNOqMs2/DZNBsl5MaDEkkkUQSSSRxCGgU2AOrcQ0NDaxevZrs7GyGDBmC3W7Xkwh2u51AIEB9fT2CIFBYWKh7ZzudToxGI7FYjJdeeolp06ZRXFx80PFTUlK4+urfcc89N5GRsZ9Bg9IxGKQuPf1feQYJX9XVBfnTzWvweAJ89FEdshzDdkJi1SYUUkhzZSV8TxCgoaWK91c/SUwNYjQaMHQEHj6/H5spk7JenUGiqqr0LRrEF1s+4vnnnueSSy/BZrPpCYatW7fyn4Xz+N/Kf5OT3gtJMmAQDWS4crFbnMiKAqis376MGaPOPOQZCoKA+k16PntYv3hTxcGSPp2rGaeDynrVXrMzfW3ho0Dc2UCW5S7ijgIIHc0rqsL+5lr+9fGjvLP0BRRVwWywUJRbTouvCW2UPbXbaW7bzdb5cVV6RZbRnC9lOYbZaeWZp57QK+waDVqrfAG0tbWhqip+v5/y/FLOmRDvl6+NNnHlHdcxevRoYrEYDQ0N1NXVUVdXR211LVuqaliwdyWr1q6mJK174OGw2nnignu49uVbOfNvV/LyJX/FIMa3atvqdmKQRb5cu4lZx51CQa8CQrEwLrMDf6ufIkdufF1EkbG9h/HKhx9y9f9dfbhX72tj3nvvsXp/I/kXTUNRlYQ2HABHaTlpl1/D1mceZVT//rS1tekByKpVq3jyyScZP34848aNIzU1ldraWvbvqeOYcd1dCiLhMD6fj0gkzMvr3mFIXj8G9u6PxXL4tGtRlHpILiRCAC76183sjDbR2zOUf//731RUVNCnT59uNH2j0cgNv/vNwY8lCDidTj1Joaqqfj9pCYevChxiHYKjGkX7h6pkHjtiBC/t2XVkEwxA7cP3MmX0KKLRKNXV1VgsFvr3709qaioWi+WoCVI1RCIRfD4fjY2N1NbWEggEsFqtTJo0iQkTJjBs2DCKiooS2pl+qpBlWQ/YjUYj2dnZ2Gw2/bP5aIFWvDAa422HGiMSeg7+NbaapnnUFVrbgqqq2Gw2xowZQygUYtGiRezevZvVq1djNBqxWq26beW3gaY1lWyRSCKJJJJIIolDQPN1PhBNTU20t7fTp08fvF4vBoMBWZaxWCw4HA78fj9NTU2kpaWRn5+P3W5PqEKsWLGCRx65jccff4Dbb/8zJ5xwQgIVWRAENm3axI03XkltbQ3t7e08+GcPd95poHdxKpKocwgAiMUUqqoC1NfHaZ8lxQ6ysm36ObS0RFi5soGXXqzkuOOzuPDCY3jwwdUs+Lix++aqx72BwBdbP8KV2cLPfpZCiiuF9PRUJFFi8eJavlicmKSIazOYyEktJByJiyBqlRWAvn37ctJ4ByX5/dlXvwNZjRGNRaj3VFPt2YkoiPFKTrCV8UOnHfIaCYL4zRIMhz5qwldqQkuImrBxg7idZ0lpMdf+6VIEQdADM7PZjM1mw2g06htZURT54++9pMi96N97GDWNe2hqq0cQRLLdFkQk0t1ZhMNhzrn0VH73u98ddJbNzc14PB4MBgOKouiik5pmgNfrpbm5mR07dpBhdet6DnVtjbjdbqLRKFarlaKiIoqKutstvvXWW6x5ZTE93RR2i43HL7iLG165k7F3zUJVVXpnFaIqCu3REH+bfRdZ7kz2tzXyp38/SO/MXlS11NLbmaevZb+cUhpXNNDY2HhQt4JvA4/Hwy0P/YXUK25AAQRFBYluyaWskWMxihJLn3iQVJcLSZJob29nw4YNbNiwgVAoRG5urm5pV+9poNHnIdvVdc4q4UgEn89Pg7eJFVUbePj0P5GSkoLB8N1Qr29791FWRT2UX/N73mvcz7wFi1Gef5Fow35OmDKZ+267NaG1oyd01QDRRB61+1fr6T6UNkPXYEJLMGg6BD9E4BaJRJj73w9xX/XVVrCHfexQmNzMTJqbm6murmbgwIE/iBXn14F2r7a2ttLY2Eh9fT2SJJGWlsbEiRO56KKLcDgcX3l//BSgtRF4vV68Xi+5ubm6dsvRVm2XJImUlBQ96aNpMWi/0w/nmdLepygKNpuNkSNHYrPZ2LdvH19++SXr1q3D7/czdOhQRo8ejSRJ36pN5MDPxR8DkgmGJJJIIokkjhpEo1FdyHD16tUUFxfjcrn0zZrD4aCgoACj0UgoFGLLli0MGDCAzz//nPnz53HXXQ8gSWau/r9s3nzzFh588BYmTJjGgAHDcbvd7NpVyZtv/p05c1JJTy9ClODee7Zy9dWrGTs23g/f3BLF74vi90dpb49hs0m43EYUWcXjieAPxHjkkREsXtTAu+9WY7cbuOmmAYwcFQ/wbrxxBMuXLSAjI7G6qkKHBWIiBEFgwAALvQpNpKfbycvLwGg0sr8hyOpl0W6vBwhGvbjdZd02cYIgYDbZKC8cTEXJ8B43eYqicNX9J+OwuQ6pSn1EN4gH2RhptpBmg4X2SBBZVohEohiMRkzEK8UOh5OJEycCcRHAtrY2LBaLHmx1pY7m5+WTJw2mf8kQBpWPQIt4l6z9mE9W/4cdtRvxBdsSnDl6nm58Q2c0GnE4HPpGWoNGow8GAuQ7szEYDMTkGL6Qn6ysLN3//WDYvnU7GWZ3N/eNOClDwCgZeey8O3jwv0+z6MtlXDPlQhq8TZRkFTG8ZDAABWm5CCos2rIMRZGZt3Uhje0t9Mkq4pjyEZgNJiKRyCHP85ti2YoVVDU2MSAjQ2/h6AmqquIaOhzlt7ey775bOXboUAYMGMCOHTvYsWMHa9euJRaLMXnyZCZPnsyVN17N9c/ezc0zrqJ/XnlHIjKuoC7HYvzls+eZXDqGvnklGCSpR3HHb4u3Vn3AP3csYcBf/kZKYWJySA6188mrLzH73PN44fHHDkuvAsBkiouPmkwm7Ha7rs0gSZIuJNo1Ido1sNCCoB/Krm7Tpk20O1JILTo4M+ybQInFiDbW0+7z8vobbzB1ypSvfD5/SEQiETZs2MAXX3zBzp07MRgMlJWVMWDAAEpLS486McrvCoqiJGjUZGZm/uhsOLsmAb8uRFHUHUFUVSUSieBwOMjNzaW4uJji4mJaWlpoaWlh/fr15OXlUVZW9pNtlTkYkgmGJJJIIokkjhrIskx7ezvV1dWsWrUKWZYZMGCAXjGw2+3k5eWxcOFCHn30ARSliTavTDQSwu2WmDDhU9LSDAwenMbgwWl4PCE2bPiM1asXEgwKGI0qf7qlCLe7YxOvwl13D2bxoip2727FZhcZMMBJRqaFzEwrmZlWrFZDl/kp/OOFHdx4wxqiUZUbbihj+oyShEqqySRhsxlISUncZIZCYVZsXExmWqUecAfbgzS11VLeQ4wmCuAPeFm+eWHHXNV4T4WqsnnXak44a1y39dOYAIeCKIqowJ6aStwp6WyqXE0w5O/UuFAVREFg6551tEf8LFj2jnZ0NJZBfDqdehHaYoYjoYPlEthV+yWfrpmHZJB04Um/z4ffH6DRW4tRMuu9vHJM7rSwPAgOVAMPh8N89PFHnDa+OOF97yx8mQ+WvUrv3DLyMvuwZP18bvr9H3jp+Ve69cCHIu38+525upq91prTk/K/LMv42nzkp2RikCQa/R4yMuLJIS3BcOBaadixZRszUkYiyzESIXQkoeLB5PiykXy86TOmDBiHZDB0ioF2JE4K3LlkOzMZmTuQRq+HYKydDbXbeH/Lp/ijQR66989MnB4P3t1u98EX8zAx/phj+NWcs3jllhuxnHwGmZOn0VOBLt7iouDuU06goBezZs3C6/UiCAK7d+9m06ZNbNiwAUEQKCoqYvqM6ZSVl3H772/lkuE/Z1L5GGKxKLFYjI8rl9Ia8vGL4T/DIBmQDD1QJr4lVu/ZyO8XPE3xn+7qllwAkCxWci+4jMrHHuTK667jnddf/9rH1hILmgq9ps0w7913+cfTz5OZlcVt997B0KFDEwQduwrZHm6l9UjB5/Nx0113Yxw36cgdVHskRBHr+Cm8uX4DgX17mDZ16lGZYNCe4UgkwsaNG/nwww/ZvXs3RqORsrIyJk6cqCcYfsrQ1kGzo/T7/aSmpuJ0OnWdmh8DvklyAToTDCaTiUAgQCwWw263k5ubS2lpKaWlpWzZsoXW1lY2bNiAy+XC4XCQn5//jcY7cL4/FiQTDEkkkUQSSRx10LQYNMHHp55+mD6lFWzevIFYrJ3Gpm1Mm2Zn8pQyQESSBAKBGNu2teFydQb26ekWpkw5RJVRgMwMC6fOLumoJn8FBVGFy68YgN+/lnXrvEw4NrdHVkI0qmCxJEZcojHEssrXSUuz6dqO0WgUb7gZlU4BMy0WLShw40oP4CyN9/0LQlxIUhRFZhWcqG9YukKOxb7WRqSiZCiPvPkHYrEo7eEghTl9sJgtCHRqUNS37CMQ8rG6clGXdYnbQXYsne4coI3ZHg5yx3NXYDKaCYWDXHjyjYwbMgOL1UL54CIKhlvIyszCYIy3Naxdu5Ytn6+hNVJHLAT//uJprFYrNpsNm81GezjI7prd/PLci2kPBolGo9TtryMWiZGfX6DrMMTtHGVEQeTdpS/SK6eEorxSBAGafY0cM2gaF5wcp3bHojGa/Q38cupNXdY8fk7/mH8/Xq+XrKysQ4qFWq1W0tLSCLWHeOrzd1i4ZznBYJD0ogysVqvef3vg8TU0NzVTWJyPQUrchmmvuuODR1i2dx2ZjjQa/c20+NtIsTkRRUF37xBEkZZgG6cOPZ5j8ofQ0tKCzW7DYXfgTEkhTJT11V/yyTPzePD2+xkzYSxnnXc2o0aN+tabVVEUuezCCzhuymTue+xxKpcvIf3sX+LoU57wOlmWiUajtFdupZfNwvTp06mtrUUQBFwuFxs3bmTjxo1s2bKFuXPnMnPmTGbOnMnT/3yOG6++ntV7NjCr3zSaWjy8sm4eN0y+hJyMbEzfgeVdg7eJC9+4jeyLriRn5JiDvi7c1IBxdyV/ePCBbzXe7t27uff2u/HsqOeOaddQ07qfay++mpPPmsWll1+Gy+XSAziDwUBaWtoPRrv/4513UdOrlNypxx3R48pyjFhMJmv2WTSIIuMKcpkyZcpR1buvQVVVQqEQzc3N1NTUsGfPHtrb20lJSaFv376MGTPm/wu3CEjUBNBYNT+m5MKRgCAImM1mnUFnsVgYMWIEiqLQ3t7OypUr2bVrFzabjYqKCioqKhJstr/pmD8WJBMMSSSRRBJJHHXQlJ6NRgNvv/0cZ5yRwa5d62lu9jNlahbjx5UjiGpHEBgPip1OIyNHZhz+YAJfu0dSVVVUReXSy/rT1hY8aD5CllXM5sTjZWabuOzSQsaM7UwMtLa2cs/dG9ArsWr8L1UFu8NEXl4ut91xS7fjV1VVsWPHjm7fFyXpkFV/DdecdU/HPOWDuki8teAFGlqruPKMzvEP5iKhKCqhSJDfPn4uV59+B7mZBTzz7/vwePcDYDaZGDh6NHPmzKG0tFRX2F+0aBEul4ulS5eyfPlyfGo9BUX9GT16JGPHjsXr9XLPbQ9S7pyAOc2Coii8s/cl5GiYY0vPjG9uBREEEAWRkwebefLtO/jzP2/g4evfxGg0EpUjuGydLgepziyCER8p9kTnAwCbtVNwr6so34HQqOy/++PvOfPsn+uMhby8vITEwuTpx+MNtFNSUkJRr3xKCvPpXVTE7n17EMdKCAfYqsbvL4X9QQ93PXQvBoOBRx94mHX7NnNM6Ygum3kwCAJ1bQ2UZhR2mWNc5FEQBDKd6cwYcCwzBkwgGGln4ZdfcPkvLub6P/2Wc887N+F8vu7GVRsnFArR2tpKWVkZ77zyL96dN4/7n3icmswcUk45HWefuAWkxkjxffw+N/3iHPLz88nPz8fpdJKXl4csy2zZsoXt27dTU1NDUVERxx13HMXFxbz4+sv84qxzmPvi9ciywoj8gRxTOhyn09nRRnDkNtuRWITTn78e45RpFM867aCvUyIR9j/+EH+68grGjDl4EuKQY0UiPPv3Z5n74mvMGXQSp8z5FYIK/fPKGFzQj0c+/AeXLr2YOx+4G5fLRUtLC1lZWaSmpv4gAcbWrVuZv2w5Bfc8cmTGV7V/VGIxmWg0gnfZEqwrl3DHC8/rvxuOFnRlIfn9fhoaGqiurmbfvn04nU7y8/MpLy9n5MiRP/BMvz/EYjGi0aieYPi2GgM/RoiiqDPdYrEYZrOZoUOH4na7WbduHStXrmTv3r3EYjEmTZqE1+slJSXlqG+fOZjOw+E++8kEQxJJJJFEEkcdZFkmEonQ2tpGRqbAsRMzMBoNcVeEjnYEVY07CXy/W24BQQS324bFEg/meoKsqJhM0oHv7Olona4VQnzTHQ/glXhDwmGKOgmCgKoeaWHGr8brHz/JorUfIIkGstPzcTnSsJkdvPvZyyxe9wGBoBdH4axu70tPT6dfv37s2bMHk8lEKBS3qnM6nQwdOhSfz4fD5tCdNGRZJtWZQTQapSCruBvLQJZlbjznQa59+DRUVenY/EUwGzsZIgdzs4B4AkWzJxNFkeOmzqC4oIj8XgWMn3ws55x7TvwYHWNq4m5ut5v09PSEZIEsy1TV1lM85xb2e5vZ2+xhwZ7dtO2Yi1mWueV/j8Qt3VLSyXZmkGNLJ8uaTpYrgwafh8GDB2O1WvnZz09l5burGV82St/Ii6JAKBbBHw5SmJpLwB/smFdiy4ompmoSjOSnZJOS6mLkqJF4vV69+nY4gUEkEiEUCqGqKk6nE5PJhCiKzD71VE468UTenTePvz77FFWpGaTN+SVSWgZRTwPS3l2cdOKJ+nEyMjKoqKigvLyc4uJivF4vPp+PnTt38sUXX5Cfn09eXh5jxo/l0yWLIKaypnYzH29fwlnjZx1xV4Gzn72Oxuw0Rl513SFf1/DJR0wtL+UXZ8/5RuMsXbqU+267m2JjDo+ddhuZzngrgKqqSALkuDK565Qb+HDTp1w650Km/ew4pkybSkpKSkci8PsL5FRVZeXKlfzlb09iOXE20hG0idS0NRRFoemDd0hZtZQXn/zbUd2nHg6HWbVqFZ9++inbtm3DaDRSUVHB6NGj6d279w89ve8NWmtEMBjEZDJhs9kSdEP+f4TWvqT9Xhw8eDA1NTVs27YNn8/H8uXLsdvtjBs3joqKisM+/qHYdN8FurYfftOxkwmGJJJIIokkjjpogZEkSTQ2Snz5ZQsDB6YjiV0/9L7/eWmUfFEU9ECvJ6gKGAzdkw/qgZQHQT9o/OdqvAVBVbu98pD4LtSlD+eIgZCfSUNO5uyZV+rf+8WJV3NC61kICMxf8TrZ2dndNAlSU1Pp27cv69atQ5IkWlpa4m0QdXVEIhFk+QARRKFDgeAQ3uXx4FNT4odoLILJeOgN8Jbda6hr2ofX36YnGFwuF8RUfjVoDst2reW1l19JSDB0Vfvvaf3379+P2eHGmpqNtYstYn1KOhPTfDx0391EIpG4hWVtLTU1NdRU1bB+717GTTmWwsJCBEFg9uzZnPn0S6ioSJLYkWATqPbsw2ayIiDGkynxmelJN1XVevhlwrEIDyz8O5defzmSJOH3+1FVFbPZrAtWHriOPSEcDuP3+5EkCYfDkRBYmEwmzjj9dE6dNYunnnmWx+65GWHidKLNzZx74kwsXQLUjIwMMjIy6Nu3L2VlZWzfvp36+np27tzJ559/ztChQ3XXhXA4jCAI7A96+F/Laha+tYrTKo7j5CFTsZmsmjLmIa/vofDkJ/9kfaSZYbc+j/BVrhRbNzHn0gu+0YZ7/vz53PTr33D1sedz0pBEB5f4/SSBqCKqEicMnMSg3L78ffFrnP3E0xw/8wTOu+h8xo0bp6/5N+0h/7p46+23ufnJZzCUlpEzaeoRPXZcuFOm6cN5ZG1Yxd+fePwbBV7fFzSr2tWrV/Of//wHj8eDyWSioqKCadOmUVRU9KOirx8ODvzdoK2Fz+cjOzv7iGq7/BghCPG9gLZODoeDgQMH6oLE69evZ8WKFUQiEfLy8ujbt+9B2XEHfq58H+j6mSzLcoLw87cRlk0mGJJIIokkkjhq0djYSFpaKh/+t5EBA9JAAEVREcWOSvT3uafrZKHrX2stEwqdH8qKEk8SSGL8Z12hyFow2pkgEVBB7RR1k+UYimzo0HM8zE1Gh9p890kf6qTULm0VXV6vqqgkBs/xYBr9Paoq6O9VSZxvij1Vb0PISs/TqaSRSETfuGjq+Rpt1GAwYLVasVgsmEwmwuFwj7M+FAshJsf0NQaVUKQdq6lTeE0Quid6Fm36D0PH9uPyky+kV69eGAwGHA4HJcUlNAfaGF08hLff+4jjJ86gID+fgpJCbvz9bxJX8oBrVVtbi8nVvWUn4m2iz7A+QDwoP5iVpYacnBxyeuWxsXobI0sG6d+va67HYbF1VII7E2+iKOltEqIoYTAILPhyCaVD+nLCzJl6pS0ajRKNRvVkQ9eNpuZo0JXhoF2/9vZ2nE6nrjVxIAwGA7N/dgqlvYt44ZVX+OSj9xn4+NM9nltFRQVnnnkm7777Lrt27WL37t0sWLCA7du3s2zZMlasWIGqqpSWllJeXs5ZZ53FqFGjeOLhxznvpRs4rWIGJw+ZjsNq/8aV/Wc3fED+JVdgSU//6heHQ7rN3eFi8uTJXHHdr3jx2Zdp8Hs4a9Qp8QRJAgQEQUVAINuVgT/oZ3BOX8ZKfXnqtoe5M3Ib02bOYObJJ1JWVtbNdeJIYueevRgmTiXvxO7Mo28LRVEI+72EP5rHE889Q2Fh4REf40ghEomwfv161qxZw9q1a2lpacFoNJKVlUVpaSn9+vUj/evcOz9yKIqCz+cjGAwiiiKpqakJScP/36H9fhQEgd69exMKhdi8eTOVlZX4/X727t3Ljh076NOnD1lZWdhsNlpaWggEAoiiiNFoxO12J4iEai4ywDcK8r8ONDcQj8fDl19+SUNDA5mZmWRnZ5Ofn/+N7+1kgiGJJJJIIomjFpoWQ1OTTDAYw2g0dVDfRRAOFWZ+C6g9heXqAbF3p4e2oigdAW/871AoiigKrF3nSTiC1xvG748SCETjQZ6i4vfHiMYUolEItStIYhRJCmKxqLS1xi0b/X6/bpmoBRNdx0+Yi6oSDPkJhgIJzhZqR7JAzwZoKvVyvEceQJREPXAXBAiG/ARCfjxtcR0Fl92NKBq7HCtezWpqrccX9BKLxqhp2NNlxTqTLp62/fSOufWgVpIkDAaD3seqUd67JhiMRmM8aDzopqrTTaErFCWmB9cAiipjMBy679VkMHHppZcmVFENBgMxIcZ1r9yOQZJw2hw0VtXTXN3IJ58uZMSYkfTu3TuhetWVVlpfX49g7azuhbwe9m9cSvOWL8g/Z+Ih53Mgppwwjc8/XJ2QYGjyNeMw25HlTn/0TmaNqItwyqrMW1s+4s4n7tM3i36/H7/fTzgcJhKJJNghajRfIIHhoCUlNOtLq9XabcOrzSMQCCDLMqMGD2LPtv/x1NO3sGzZp/zud7eSlZWlv768vJzCwkJ27tzJBx98QHV1NXv37sXpdOJwOPD5fKiqSklJCTNmzKBPnz6UlZVx9XX/x+IRi/l0wULm/ms+pw86ntkjTsCuBeyHwWrIsDgJHca1+KaVRbPZzGVXXM6pp83msb8+yqWv3cT5w2YzY8Cx3UUNBYFVezayes8G3vzV02S7Mjhx8FT2eWr53/ol3PCfX5NVmMs5F57LySefrCdXDnRF+TZQlDjDQGiMP/9CT0nILlBVtfuK97BW2n3krd7HpCGDGDJkyLea53cFraIbCARYu3Yt7777Ltu2baO1tZWCggIKCwspLS2lrKzsh57qd4IDE8vRaBSfz4fX69Xbwo52PYHvE10TDIWFhZjNZj777DPcbjcej4fq6moqKyspLS3FaDRiNBppbm5m//7482WxWHSxSO3ZlWUZWZZ1lsR3kWCQZRmfz0dVVRWLFy+msrKS/v37M2DAABwORzLBkEQSSSSRxE8PqhomI0Pm4kvyEcUY0WhED0YlKR7UH0nEGQQKqqKgqEpC4KjqrQsqkUiUSCSMJBmImWJIBgNSR4AuyzJFRVaeeHxzwrGNRpnHH9/O449XAugMhUhEJtZu4vMFISQxgiS16RsJk8nM1DGTEAURWVJ498P3MBgMeDwempub9f9rwmh2u42VOxawpW4ZkiglUC61/nytgq1ZCNbV1RGLxtBYFdq+MhQOEomFufO5XxGJhumVU8KNv3hQXweAuQv/zpL18zFKJkBgW9X6jrE6xuywowyG/OSUOQiHw4RCIb0aE41GaW9v15Mcidehg655QJAiCiJRNXLwa6gmMhzUbqwOuh3zYA0pJ5/6MwYaipkz8hRqWuqpaa6ntqWeen8T/3niNdbu3kTvviU89ezT8URTl/WuqaklpIj6+M07N9CXKkpOnsqwYcMOOv+eMGjwIBa88n7C9xq8HlIsjgRaqyRKmEymjsS6EBsZAAAgAElEQVRM/HXvr/+EksFljBgxQr9uZrMZQRCw2Wy6GnzCvd6hmB8KhfT7RUN6evpBrfi01iaI3wPvv/8G117bl8IiOx+8v4zTTp/G5ZfdyNln/wKDwYDRaEQQBNLT0ykqKqKpqYnm5mbC4TCax7wgCBQUFDB27Fjy8/MRBIGMjAwmT57Msccey549e3j79bc478XrObViOj8bMh2nzfm1GQ1OgwVfMPj1LsQBa/FNkJWVxZ333sWW87bwwJ33MW/u/7jsmLMZ3Kt/PMmjKMixGLfN+ytXTDyX/PScjnsLirN6cUnW2Vw44UxW7FrPmw+/zJN/fYIzzzuL448/nvT09CPmNDHnjDMoLynRvz4wedHdprbnpEZXyrd2T/l8PoJlRcycOfOIzPW7QDQaZePGjWzYsIGlS5eyfft2AoEAaWlpHHvssUyfPv2obus4EtBEWgOBAO3t7UiSREZGBjabTU8QJxGHxkLQEgFWq5UxY8YQCoVYtGgRO3fuZMWKFSiKgsFgICUlBYPBgKIoLFu2jLq6OsaMGcOQIUPIy8vD6XTi8Xjwer1YLBacTud3YoEaCoXYtWsXO3bsSGhdKy4uJiMj4xszJ5IJhiSSSCKJJI5KuFwikybbmDrVidttIBwOd3rBCyJ8B1pniqJ2WKfF7dPo0EJQFQW5Q3xRUVRiHZVcqWODYDLFKxICAqKocu99wxKCXFmRkWMygihikKR4BcNkxOv1MnfuNrZ8nMvNk67G4XSSmurGarXF6aeq2jGuyqnPXq4nE1pbW2lra8NkMtHS0qIzG4455hj6P9Mfu91Oampqj1WPrl/7fD5+fdV1nDD4QgqyihPyNe8tfpWqxkquOv0WPl/3PxZvfK8jAFL1gLw93M64gccz57grel7QjuO989kLZGamEIlECIfDul2XlmDQqugatOohdA/+TUYz/qD3oNcwFAmiqArV+3cjCCLeQAst3kb21FYiSSIebwPeQBv7m2swGUwIokQ4EiISiegBrdbCUVJSwqfeD8lISSUnNZMRpYMTxmryNnP2P35NW1sbKSkpeqUJwJXqRtm7mm3PrsXiyqCxtopf/fEGLrroooPO/WCIxWJIYuINX91aT64rS08QQNxFxGQy6nMIhNt5feMH/P2154HOa28ymQ6p1t/W1kZra6vOWNAYDunp6WRkZBw0eNfEWVVVZevWrdjtPvr0yQcBTp1dwNhjgrzyyoO8/fa/uPXWBxg6dCgGg4HMzExKS0tRFAWPx0M0EiEaiUDHehYUFDBy5Eg9oElPTyc9PZ22tjZsNhupV6Syfv16Fv3vU95+5becPvB4ThsxE7v5wBaE7jAIAmos9vVER2T5iIksVlRU8MIrLzJ//nz+cv9f6bMpnwtGn06mI50/z3+abHsG50yYDWiOJp3BnKiIjCsbwZiSYWyu2c7c1/7Lo39+mMf+/gTHHnusngD4NoyG4uJiiouLj8i5QjxYbWxspLm5OX4OokhGxjdw/fmOod3rwWCQ9evX895777Fp0yZ2796Ny+WioKCACRMmcOaZZ/4kK/hdE42xWIxwOExrayuBQICcnBzS09P1349JdELT7tE+uzTbSpvNRlVVFV9++SXr1q2jubmZvn37JmgxrFmzhmXLlhEKhZCkeJLYbDbT1NREW1sbBQUFZGVlYTKZekzsfRN0dQTau3cve/fupbi4mJKSEvr06fOtLVeTCYYkkkgiiSSOOrhcIldcmU5hYTwIisWi8d5PQcRojH/vALb/EUGiqJFMvKofd4swCJ2viUajGIwGDAYjFos5ru4uShyMUCHLMrIox3UHDB1OAELHOAisrfuShkAzzpQURFHqsF8UQBQRxLjOgVa5NRqNuk1gWloa6enpeoJBY3c4HA7S0tK+klapqdJrfw4MSERBQJIMiGJ8HQyShCJ0tkhorIivCrpUVaWuro7NmzfT1NSkV9lramr0Kk1Xcamu1+LAgrH4FVXk+cveIBqL8tjcmwEBf7uX+cvfZN5n/8JqtiMIIqqicO8/rsUfbEUUDRgNRs496wJESRM8jLebXHbFRezxVB90vHSHG0EV2LhxIykpKdjtduz2uNXlnJ//nDk//znBYJDq6mpqamoYPHhwj8f5KmzZsoU+7sQ+9erWesaXjEDpwmDo2iIBAv/d+AmjJo6hT58+hzWe2WzG7Xbr9psaNVprbbHb7T1WyltbW2loaMBgMLB8+UKmz3AnPBM5OTauu66YlSubuOaas5k69XSuvfY3DB48GFEUefvtt9m1axcGJFRUUlJdZGRkHJSmazabcblcSJKE2+1m4sSJVFdXM/fVNzn3pes4pd8UThk8A6elp8pfvMVGY8uEwyHNJbYbtPOWQ6EjGlQKgsDxxx/P5MmTefnFl7n26bsYlFrGe+v+xz8uegjDQZ4rQRAQEEESGNSrH/3ySpn9zBWkp6fT1NSkJxdsNhs2m+17V6HvCaqqEg6HCQQCpKam4na7jxjb4kgiGo2ybds2Nm/ezBdffMGWLVtoa2vD6XQyceJEpkyZwtChQ3WnmZ8qNOp8IBBIqKAfDffS0QxFUWhvbycUCmG1WunVqxcjRoygpaWFnTt30tLSwsKFC4lGozor65hjjkEQBBobG5k/fz6CEGdCNjU16Qlb7feOoihH5L7TGGKxWAy3201RURGlpaUUFhbqLXLfBskEQxJJJJFEEkcdYrKK398ZcMaiMYJqUA9udD2Bb6kgfyC03n1RjNPdEdATDvE/UgcVMhqneBuMmC3mr/zAl2WZmBhDFEUMBoO+QRNFEVESMRosXD3vbmxmG6Ik9biJa2/3cs7sORiNBqKRKOFIOD6+2awnIZpbPFxy9eXMmjWL1NTUr7TzEzoSCIaeEgzEEyva97XgdXftFh55/WYEQSAUaWfGyDO+MsHg9wfYvXMLwfp4dUYQ4hoBwfYgGyqXE46GiUVkzOZOMas7b7+bj+cvoGF/A5fdcwKyHMNoNKMqcceEX/15Vuc8u8w7FGlnTP+pXDL7NwgdSRyA2569jJPGnsuogZ36B8+9+wA2k5OzT7iSA7H6y8/YtXMPktmIx99Cjjur22sEUaSXM4c3Xn+DQYMGIYpiNxqrzWajvLyc8vLyQ67RobB901aGZZUkfK+urYGS9F7IioxmTSoIIpIoIQoClft3M3fLfJ57/cXDHk8TeRRFEUVRaGtrw+/3673DgiDo+gxd176trY29e/cCsH//DsaM7t6fLggCo0dnMmhQKm+//T6zZn3Ar399M2eccQaVlZW8/fbb2E1W7CYbqkWiuLgYt9vd4zOhzdPlcgFxRo7L5SL1qlTWj1/Pwo8/Ye4/byDV5kIQBBRdq0TRW1q21O3Bokzu2MgfwJjp+K9mx4ki61XKIxlkmUwmLrjoAo4Zfww33/RHzBYze1tr6F9QFk809rCGCIJO4vKG/Ljc8WRMc3NzQkJMu05fF0f63DQoiqInGLKyso46YUSt8hwMBtm8eTMff/wxq1atorKyEqvVSnZ2NhMnTuTcc8/VWwR+Suh6z8TdZ2K6W0ROTs5RyTY5GtE1wZCamkp+fj7Dhw8nGAzi9/v1lhuPx0OvXr3o168fo0ePRhAE3nnnHdatW0dOTg52ux2fz4ckSXqSIRaLAfFCgnb/He6zql3neFI1TDQaxe12Y7VaKS4uJj8//4isw0/r6UgiiSSSSOIngYBf5aP5Pvr3N+uBAbJMJBIl2B5ERdXFAY+kunKcNCAkJAG0wObAP0cE8WI5OamFTB98Lna7Xf+w72y5iGtN3PbUBTw08/cYJQPhSIRIJIzBYMBsjgfsAP/8/N80NjYe/jR6qNArqhxvRemAdsaetnpkOcaEISeAAEajxDuLX0JVVSRJojC7D4IgUu+pIhxpRxQlNu9eSXnhEE4/9nK9/x+gPdTO8cPOoc3byv2v/V/H+ZgxGo20Nrdx9pRr6Fs0mFc/fIpAxMsvZ15LVJY73D07tSW0wFCRY7y24CkkwcjXSTypao+KngBkp+fzydZFFBTk8/qK95haMY5cdw4ZztSE180cNJlnVr/xlWN9G9RW1XBy37H616qq/D/2zjs8jvrc/p+Zne1FvVqyLcmSZcsVF8A2LrQETHMIkEsxoRhC4IYQWggkuQm5KTckEEgBfhB6Qk8INTbVvRu5d9myetf2MuX3x+yMVrZsy2ASIHueRzxmdnbazs5+3/M97zl0BrsZnlNCpDeEqmqmmgRBoCvcy0//+RB3//zHlJeXH2HLR4bRwuLxeKisrCQejxMOh4lEIrS0tJCbm0t+fr75nTDUAK+88hynn+5Bsh6+sHU6JS67bCgzZgR55pkf8fLLT5Obq6s0cl1ZXHnCPB5c/SxdXV1Eo4OzYTSUPEYP88yZMwmFQnR3dwOHRsoJgsCTzz3HMq8Xu91+WAWDpqnE4jFkRaGzs5Phw4cfscXkWGGYn+bl5XH/gw/Q1tbGIw/+iTdf/pDrTvoG44ceuddf1VQki4TX6zUJEOOvq6vrIC+W/gqg1GdZOBwmFouZRqvHCmOfAz0jjbaD1JaezxMSiQRbt25l06ZNLF26lPXr19PZ2YnP5+OUU05hxowZTJ06FafT+aUjFwwoitLPc0GSJDPxII3BI9U01263U1lZSSKRoKGhgY6ODsLhMM3NzaxYscK8xuPGjWPXrl2EQiE2btzI/v37icfjuFwumpqaKC8vJ5FI4HA4qK6uNtOOjrVlS1EUgsGg+V03njs2m+0TJ+QMhC/nNySNNNJII40vPFpaZPbsiTNihD05u6SRiMeJhCNmoSCmzFAfD6T23x9pEGkal33K/anJ5AdRtOB1ZuJ1e8n25eByOZOFeN+5WUQLOZ4s7FYbiaSXgWSV+q2X484kelCrwdFwJLKk7zz7zjSWiGJx9BL3LEIQBPyCYApJamuDfKXgEjxuD2++8TyluSOxWW1oqkY0fqiRnoCA1WrDIhrGnZa+VAnTlFJvkzjQuoeVW97HJjk4YeQMJMmiKyFSjl9VFUTBYiZIDIoHOsw6+VlD2F9fzx13fY8Nq9fx9L632FS7kWeuvJ88b7a53pljZvLoqhfo6Oj4zDLhOzo6yJ6UaR5sp78bQRBw25wElF6dYEiadMblBPe+/RAXXn0Jp59x+ifan1EoyrJMNBolNzeXvLw8mpubaWxsxO/3mwkPmZmZZluGpmlJw7C1LFgwalD7GjbMw513ulm6rJknnliBzSowq3wq4wurqcoext4DDYTD4X7mkYe7Zw1vCa/XO+ge4veXLmW1asV2cNxjypc7GouSiMWxTJ3Od+6+h0d/cx/jx48/bmSjMZtotDXU1NTw+LNP8O677/LAL35D+ZZirjn5EooGUNGA/nlZJAsej8dU0GiaZprBphIqqSavqb30BhnR29tLVlYWWVlZA+7nSDCKqoFIX2NmN9VQ9POERCLB5s2beeONN9i4cSM7d+7E7XZTUFDAnDlzuOKKK8yWky8jUj0Xent7CYfD/TwX0hg8UhOmrFYr5eXluFwuduzYQV1dHXv27KGlpYWVK1eiaRrz5s1j1KhRjBo1ivr6erZv305DQwMAPp+P9vZ2ysvLicViZGRkYLVaycnJwel0mt/fwX5GBsHQ09OjjyEk6VPFUR4OgyYYBEGwAGuBRk3TzhEEoQx4HsgB1gFXaJoWFwThe8C1gAy0A1drmrY/uY0rgXuSm/yZpmlPJZdPAp4EnMBbwM2apmmCIGQDLwDDgX3AxZqmdQv6VfwdcDYQBr6padr6T3wV0kgjjTTS+NwhEFBZtChIRYXN/PFMyDLhSLhfIappx9eHYbAwCrpPA03TXeMb2nfzyDv3IElWvK4MPZEiWTwb7SAxOc7/vPOAKVuXZUWftRbF5Oy1yJodG5jqOZnly5fj8/n6zdY2NDRQUlLSb//BYJCt27ewd8fP8Hn6CgpREGlo20tRbql+roKFpvYD3PPINQTDftxuJz/80cQ+gid5He66aw8/+dmPGDJkCKtWrmbBV+/G4/Tx6Ku/Ys32D/jJn68zyRBN60uKSCQSyKrcT7apaX20hizLtHY2suzjhfSGunlj2bNMqDoZUdB9LwQErJKN0WUnoGnqgJ/NQCVNNB6mq6dtwL7WaDyC3WZj6tSppkP/lLEnsPHANsrzSinIyMNlc+JzeiGu8uabb3LTTTcd9+JJlmX8vX5yPdn6ZdZgf2sDdouNQCCQVC8IWET9nnlq6Ut0W0MsuP66T7zPaDRqFoMZGRlmdJphZBmJRIjFYqiqyq5du0zJrizLvPPOq3z969nY7IOfWVM1hSlTMgmHPNz/m25OHjoRQRSYP+EC7nn3Afbt20d9fb3Zu3/8ceTPTJIkBIeA86zz6M4r4Bs33MjPb7+V008/3WzP+DQQk61IhgIE9ILhjDPOYObMmTz79DNcff8dTC0Zz02z5pPnO3oxYJAVqQRCahFysNLAIAD8fj9ut/uwBcfR7u/DtVgYxIOhUvq8QVX1RJ2tW7fS2dmJ3W5n+vTpzJw50zTr+zKaOhrQNI1wOEwoFMLpdOL1ek3/jjQGD6NNzmKxmDHAHo8Hr9fL9OnTkSSJN954g7Vr19Lc3MzGjRspKyvDarVSUVHB3LlzcTgcyLJMMBgkkUiwZ88e2tvbURQFn8+HzWYjFApRU1PD8OHDj0nJIIoiLpeLUChEXV0dwWAQl8v17yMYgJuBbYChn/gVcL+mac8LgvAwcA3wJ2ADMFnTtLAgCDcA/wdckiQLfgxMRn+SrxME4R+apnUn37cAWIVOMHwVeBv4PvCepmm/FATh+8n/vxM4C6hM/p2YfP+Jn/AapJFGGmmk8TlF/f44Bw4kTLNHRZaJKDJWqxWX7ErOamqgCcc7sfKI6Ov7P9aozP7rakkfiWxvPudP+TZIKpogJ9sEbAhi0qtBEHlm0X1cesc1prLCaEkweuIBMl98kYWLniUaX4fFohtFaugEzN9frmPmlLOwWx1o6LPBqqISDcUYVzON4cUjzaNTVZV4IobHpRdPU2pmkOnNRtM09jXtYvHm549+qppmtlhIokRpfgXnTLtc72cXdBPCeDxGJBld9+x7vzF7fw+WUdtsVqbWzOaqc24lEovw1tIXaO9qwgikVDUVWUmwaNWrWESJE6pOGfh4DkpiUBSZ+tbd3P7QfzF9/Fc4b8blSJJ+r9U1bWfCxAnmwE3TNC6/aj4rdm7j5TWLaGpqwiHZKfDlgqbR0tTcl3xxHJ2+Ozs78Rmxi4KuetnfdgAJkWAwiKaqWKxWJKsVi0Uk25PJ6SefcUzKnqXLlrFtxw7mX3YZNpvNNBF1uVxkZGSY7QBerxev12vms+/Zs4e6ujqzcNy+fRsuVwcnnVxxTOcpJ2S6ugI892wLX6s8jwJPLoIgUOwrYMbQSaxYtoK5c+dSXl5+3AkGm0VCSwyg+kn56CRJQrJIKIpM5qQTCfgyuP62O1jwjUv42c/u7acE+CQwFFmyLB8yw2+327lmwbWce/55/PW5v3DRQ9dTkTmM+y7+EVnuDGMDaOqh95xhOjoYKIpitlM4nU6ys7OP/qZjQGqE6eeJYEjtSW9paWHHjh2oqorX6+Wkk07iiiuuICMj40utXIA+gikYDH4uPTK+KDAIBkmSzHYISZLIyMhg6tSp5Ofns2fPHjZv3kx7ezvhcJhhw4aRk5PDhAkTqK6upquri7a2Npqbm+no6KC+vp54XI9m9ng8/VrXioqK+j1/jvabY7FYcLlc2Gw2mpqaaG5upqLi2J7Xg8GgCAZBEEqAucD/At9LKghOBS5NrvIU8D/AnzRN+yDlrSuBy5P//gqwSNO0ruQ2FwFfFQThQ8CnadrK5PKngQvQCYbzgdkp+/gQnWA4H3ha078VKwVByBQEoUjTtOZjOfk00kgjjTQ+3/D7Vd5/L8g3r9IHuxogaH1xeAZzrycdiP8yJYNoEbHZbYPep1FA9JESye0kTSNdDi8uh168ZWdn43K5cDjsgC5nTsgJnA4XPT09RCIRrFYrTqeTvLw8CgsLyczMxOfzsWbNGs4+u4B580oRRUu/Y3vj7w1cNPNb2G190X2KorBlZy3jKqYysXp6v8FJR3cbHb2NADjsLsZWTkbTVOw2B8t3vDzgeWpqyuyl0Dd4FUWR4txhjC6f2M+DIRQKEQwGCAQCWC02srIyGDlyJEVFRWzfsmvAfdgkG/PmXGkmW6S2kext2MlPHr+Olq4GtuxdrxMpGqioBEK9h2zL5fAwfdxZjCqbwDsrXuS2DZcydfQsvj5nAfVtOzjlvEn9ZoC/e+st/WTmXV1dtLS0sHnzZtxuN7FYjHA4bPpIDARZlvnBXfeQlZXFsOGlKIpCfn4+xcXFDBkyhKysLPP6KIpCU1MT2W5d1RKJRIlGIjR1teKxuvSEjeRMdUaGD7fLTSgWJiujYMB9DwS/389N37+LTsnOs6/8jV/cfRdVVVUoioIgCNhstkPahYz7OS8vD0mS2Lt3L3v37uXJJx/h4otdx/w9jMaiPPHnnbhjQ7hwzFdxOBw4nU6i0QgXjjmTu967n2eeeYaLLrqI4cOHH9dZVUEU0TR5UOvqRIqAr6oaMSObl/78F2LhCLfdeTvFxcWf+BgMBQNgznoaChGj7QDgoksu5uVX/8LejnWc+dAFXDzuYm45cwFomklGfp6R2pv+eYLxe2IQPA6Hg8zMTDIyMswZ4y8zDOWMIAimr1EanxzG89HhcJjeI4lEAo/HQ2ZmJrNmzULTNJYsWcKePXvYsGEDAJmZmUyYMIETTzyR7OxsWlpaqKurY/Xq1ezcuROAeDxOU1MTu3btoq2tjVAoZP4mHS01yjg2w2PK7/fT3t5OJBIxTW+P17N1sAqGB4A7ACO3Igfo0fqeyA3AQLaT16ATBSRfP5DymvGeIcl/H7wcoCCFNGgBjF/Mw20rTTCkkUYaaXzJsHNnjJbmBIVFesGmkRwQxuJYJcPVXjTNEI8rTA9ALRmX2DdLmKok6BswC31HkHIoulmjHkmZeoQWUUy2GfRJ+vskyxqgoqoasWiMaCzKokWLaGtrw+VykZWVRVVVFTU1NZSUlJiDBmM247MlW45cIMiyTCAYSBmsDLy+QRDoRp4KmZmZVFRUUFBQcMi1SnnXYfc7vLgCi2hhT8NmXljUrqeAJNUS0Vh0wC0KgsCEkScxYeRJbK+r5c1lf+GOP1yORRL5zv9caQ7ajDaKVLO8nJwccnJyyMrKoqGhwTRBFARhQIJB0zR6enp447W3OH3iRWxbvormrn2ggWgV6PK3gaBSVFxMYWEBefm5hCJB9jTsAyAei9Hd3U1TTyt5Hp10syQlr9nZOVgsInabne6OrsNeo4Px01/9H/LkaYy67Cq6N6zloutv4If/fSOnzp6NKIrJ1I/+180oQHJzc8nNzaWxsZE1a9bQ0dHA73+vsGNHLwuuq8YqHT66Vb8g+p2xckUTq5dH+N2ZNwJ9BV5PD2Q4fJxTOYsXX3+TMWPGcOaZZ5qeD8djMGwRxb7M2yMh2YZkSRJnFo+XSyaeSMP6A9y44AYe+OODlJaW9rs/BovUGcjHHnuY1177C1lZHpNES633EokwP713Mv5AmMce/RuvP/AG15x0Fcfj2fdZF/6p5pOfJyQSCaLRqNmaZdx/BnH7ZYTxGRjpGbIspwmG4wDje2+xWHA6nWiaht/vN1N4vF4v06ZNIzMzk+bmZrZv386WLVsIBAKccMIJTJw4kQkTJjB27FhaW1vZvHkzbW1t7N692/TJaG1txel00tzcTHd3t/lZ2u32w5JhqZ+30Zbo9/vp6uoyCYZjNYw8Eo5KMAiCcA7QpmnaOkEQZg92w4IgXI7eDjHrkx9eH5KeDMf0RBIE4TrgkzchppFGGmmk8W+H36/ywQdB/uvSPo8AWVaIRCNIVgmH08Gnt1scGIqqmMZX0WjUnH07HNMvCnrsZF/ahJicWdDjAy1GHKSYNC+k70df0/TCPBwOE41G9GSE5GA8nogRCgVZvXo14XAYTdNwOp3s3LmTVatWUVhYSHFxMQcOHECyJg3cOLRtZKCBvXaY2U9NUxEGGGgOpjbYuHEjiZiC056UZwv6DK1uzNi3niwniETChCNhVFWfRezt7SUSiehvO+Qap3orDEQoCUweNYtcXxEXnXFtcl19nR8+eg2ipf/5qAfdN9Vl46kuG8+qzR/ywge/Z8yYMcTjcaxW6xFnh7xeL0OGDCEUCtHU1EReXh5Op/OQAVssFmPfvn0U5Axh2tgzB7yPIrEQ3f4OekNd9OzrpLMnQH17Kx2BLpA1YvE4LYEO8t05Zm+vx+NOFgUCMyqncN1f72bilBM4/fTTj1gsLF26lNdXrqbkJ78GwFM1CrvPR9mwYaaSZjAoLCxk8+aV3HprFW63wGOP7WH+FYu54YYqTpk55LBkl6IoNDb5eeLPTVw15goyHB7QwJZU6ESjug/GV6pmsmjXMlauXMm0adMYOnToIX4inxSiKMAxGqOKot5+5HG4+OHM73DO769hxrSZvPTKC2bk3LEgNd0iGg3zX/+VzxlnHu38PNz/uzzefbeORx//HfGYm5aWFgoLC49p3wfj81b8f9aQZZmGhga2b99OW1sboihSWFjIyJEjj3ubyOcJmqaRSCRMtUwioUcvZ2VlfaIEkTT6w1B/GaRNIpFAlmV6e3ux2WwUFxczceJEOjo62LNnD729vSxevBhBEJg2bRqVlZXk5eVRWlrKsGHDKC0tNdMfFEWho6ODd999l5aWFoYOHUpFRQVjxoxh+PDhhz0mVVXp6emhtraWjRs30traisfjwW63D0r9cCwYjIJhOnCeIAhnAw50D4bfAZmCIEhJFUMJ0Gi8QRCE04G7gVmapsWSixvpa3cg+Z4Pk8tLDlpubKvVaH0QBKEIaEvZVulh3mNC07RHgUeTx/Sf9cRMI4000viSQNNg8+YonZ0yOTn6zxWJ/vAAACAASURBVJaiyESjKna7HUVR0dT+KoLjBVlWiEZjBIMB/H6/LqFV9YLc7Oc39iuQLKL1GaDUP6vV2u9Pn4HVTA8JYxOJRIJQOKSTGtEYHf5mdjV9zLYDawhFg2zZskXfJhJWi52d23cTjYfJzs5myJAhlJSUUDVS1uWOFgEhxZtCMA819edQS5pIDlyEHm7AcbgaxFiuqiojhlYnl+lZnBZRRBQt5v/r5ysTiUQJhYMAJsEQCoVM0qVvG+ZRHfa4DFNMATHlfX3vlRUFTVMxL4qmb08njfq243Q4Oeecc0yZqzEYPxiGtN1wl9+3bx9tbW04HA5dhZGy0d7eXl577TW2bNlCY/MBWjobKMwpSR53yr7tLpx5QynOG4qqquxv2sv7q18hy5lBe6ideDxGZ6ibMfmVuN1u8vPz+ylWSrOLmVE6ifvv/iUBf4AL5l3Ajh07GD26f9RhKBTitp/eS+b8BVjsDtCg5aXnOHXCOMaPH092dvZRB5wGAfbkE09QX1/H5CmzScQT3Puz8XzwfgN/+MM2/va3Br7//bHkF7hI4Xv0a+KPctutHyOGM8hweNnYtB1RtJAtd5IRbaa7p0c3OovLTCgaxfvvf8T06dMRRbFfXvunHRjL4RCJgN/8IAyli34vCWiaimi1IofDqMleaDkcQszVFR4ep4dcVxE3Xnczv3nwV8yYMcMklwZzbAeTQOogi3zdCLKc6dNLeeP1A1x44WmcddbF3HHHXZ9I1v95VRh8llAUhYaGBmpra02CoaCg4EtPMAD9YmdlWSYnJ+czS8L5T4Oh/jLaGROJBJ2dnUSjUXJycigqKmL8+PH4/X5CoRBbtmxhxYoVBAIBSkpKqKmpISsri5KSEsrLy6moqKC1tZXOzk7C4TDBYJAlS5ZQW1tLTU0NU6ZMITc397AEg/Eb1tnZydKlS1m+fDl2u52hQ4ciiiKKopiqsOPRKnFUgkHTtLuAuwCSCobbNE27TBCEl4CvoydJXAm8llxnIvAI8FVN09pSNvVP4OeCIBhTUGcCd2ma1iUIgl8QhJPQTR7nAw8l1/lHctu/TN1HcvlNgiA8j27u2Jv2X0gjjTTS+PLC71dZvDjEvHm6qZkh85NlBVlOEIsLyIqcVAzory9d2k7DgQQdHRqyTEqxiplQYNSfqgrxhExzczOS1YYx+a9LEhWiURmPR6CwwEJhoYUMnyWlJcMgGAREUUAUhKQiIFmkaAJoAhZJZNw4LzabZP6Qx+NxEokEjR37eHvds7gdXj3uT1WJJiJsb1hLXI6Rl5/LpHETKSoqIj8/nyUfLGPy0LPxB7vY3bSF+vYd1O+vp7W1lZaWEHabHmFpKCoCAQVV0Vi/Y0kycUDCKtnQNOgNdrF9/8doKPQEdGm9JEnsqP8YRNi2bz3d/k4a2usQNIGu3nba23t4+uk9CEJKMaZBXV0H7733Ho2NjWzauZ7ntYdRVZXaXSsoHzIKv9+PqirmZxAKBUnICaIJXbEQDAZpbm6mvr6eYCjExpZVNHfUs7txMzE5yofrXzdTQ4RkgoXZfiKIoEFD6x7yfSV6tGA8QSIRR1U1EnGZUDBIV1c3kqT7dshyAlVRiEajfZJ7AQ607KVonJf333+ftrY2/H4/fr8fwIxjVFWVyspKqquryc/PJzc3F4/HgyiK7N27l0QiQU5ODolEgldfeZW/PvsSw3JHI2giPb09PL/wD3znkv+FI6hvVFXjw7Wvo6kytzz3E3KcmTgFO3u66jmzegaSVZ910lQVRVP1ayII/OCsG1m1dwOvvfIPPF4Pd918OzNPm8Mdd99pegX85qGHCFZUU1wzDk3V8O/cjn3jWr7z+4cGXZzGYjHWrFnDm6+8jmTRZ+GxCUhWibPOrmD6jBKee2433/rWMk47vYgFC0Zit/e1jrz2WiNDh9bQUt/OLz96GLfbg8ftpmLECKp91XSr3bQF2jhQX0/9gQPIKLzwwgtkZ2czefLk4zLrluH1IS98ko4Vi81lmpqi3tE0IsEgKhrR9m6IxrFb7VjiUfLH5iJJEpW5QwlYfJxZ8zVuufEOrr3hm1x9zVVIkjTo5IFUb49jsVMIBhNs29pDNCoRDHbyyisvctll8ykrKzuWy5A8Vc00YvxPgaIo7N+/n3Xr1tHS0mLG9o0dO5a8vLx/9+F9ZlBVlVgsRiQSwW634/P5cDqdR39jGscMQ6Hk9XpxOBzmb0hlZSWyLFNfX09jYyPxeFz/7dy0iaKiIkpLS8nKymLGjBkUFRXh9/vp6enR2+Samti4caPpwxAIBEwjyIEQjUbZv38/GzduZNu2bezduxev14sgCKxfv173SSouJjc3F6fT+al9R44lReJg3Ak8LwjCz9CTIx5PLv814AFeSj706zVNOy9JJNwLrEmu91PD8BH4Nn0xlW/T59vwS+BFQRCuAfYDFyeXv4UeUbkbPabyqk9xHmmkkUYaaXzOoaqwbm2EU0/1kJFhMQtURZHNmWWjQBRFkWhU4aknW7nwwvlUVuabSQupzHzq/4uiyNatW/m4Yym5X/8vQN9+LBYjEo2hSBJtLc3sbKyHjxuwtu5Di0Sw6LaTA8z4Ga0PmNvSNI3v3JxDaUn/H+6CQpFh5XEOHFjLiLzpIOgle0KOIQgiHq+bESNGcPLJJ1NTU0N1dTX79zaSnZHD6OGTqBl+Ej2BTtbt+oB9rdvZuT1AsEPCZrNiSfpEtPe2M+2UE7EO8ScTIhIEYzq5UTWqAmdhnB77Lv754UJExYbXlUlvsBtZlXn6zQeJJ6L4XNnkZxfT3tOMw+phx6rKvnNNXtPynEpee/p94vE4w3JH0d7VYp57T6ArqQJJoKpG60ccOSETjPSiahqhUIjm5mb279/PqDFVhIIhsrJELhp3jm6cKFnNGXBV1VAVFVVVUGTdpCwai1HWXkIoHGB/0x6i0QjRSISEohCJhjnQUgeqLlmVrBId3a0ImkQgFMBhs5stLR5HJptq16CqKjt37qSuro7GRl0oaRiLCoLAnDlzSCQSVFdX43Q6aWho4L9vuIWhhRUEwr34Q90AVBaP59KZ3yM/uxiLRaJ2xyqGFlUmUye0w1IMG3esZsnWfyJUj+TtDBXkdhItTageB79Z+zwv7VjIxBFjybL7yLR5yHFmkufLpTS7kJriSn7x/sO89OwL3DLrajp6e7j0vIu5/PpvMmHiBJ57+58M+Z/7QAMlHqPz6Ue494ZvUVBQYEp7j1a8h8NhfvnTn3PxuLN5dPXDZguQpunmCj4vXHXVCGbP9vP443Vc9c1l3Pzd0Zw4NZ9IVGbpkgC5uYWoEvRGg4yoGcnkyZOZO3cuc+fOJRKJEAqF+O1vf8tvf/tbFEVhzZo1nHrqqUSj0SOaaQ6E2tpaPv74Y/P7OnPmTK65+iquufrIw0ijT33B5Vdz2dCzGFta3e/1WdUn8Yclr/L1067lklNu5tnHHqeluYXb77zNvFeOpLrpty+O3PClqhp79vjZtMnPju0Kzc0JJk6cwowZZ/C9702jrKzsE5Mun1cTxs8SiqLQ2NjIxo0b6erqwmKxUFxczOjRo79USQqpn6nxOcfjceLxOD6f70uv1vh3wfguSpJkJvDEYjE0TaO8vByPx8OGDRvYsmULzc3NNDU1sWnTJnJzc3G73YwePZqTTjqJSZMmEYlECAQCtLS0sGXLFrq6umhqaiIajRIKhUyT0tT9GojH4zQ0NLBt2zb27NlDfX09OTk5SJLE1q1bkSQJVVVxOp1IkvSvJRg0TfsQva0BTdP2AlMHWOf0I7z/z8CfB1i+FhgzwPJO4LQBlmvAjYM/8jTSSCONNL7oCAQUli8LcdbZfaZbSnL2WRAEHA4nVquERbSgaSoej5vrrrt+0Nt3Op28095LwewzzGV6j2oMWVZQFNlsMeh+921qdm3i5BNPNGcdjYhFo6fV+ItGo/T29lJbu4KXXgzgcIjIsobdBg6niNUqoChglRycMekb2G027HY74XiQl1b8jquvm09BQQFDhgwhLy+PnJwc3G4XdruDjAwfTqcDr8dLbuY3WLFlIXtbtnLRjG/jcNhxOJ28suxPRNU4Tz/7DDabDVmW+53fg797iFdeepUhxaV4PT6+ecadFOcNP8xV0ti8dz0vLPwT35x7O7KsEzxGbKHhen/w/y/Z9AZb69ewYtMiZCWOomqomoIAiILE3pYtRGJBEqoFRVFYvXp1XyShoPUz9RuoeDIGVbIsE41FWL3pbdbvWJxUqfQNrFfveJe1u5LbQSAY9bOvZQcf71qqt7eIVl3ZoMjEEmG2bN5KNBIlEo1SmDkMnyuLULSXtt5GEnKMpUuX4vf7KSoqoqCggMzMTHIzC7nyjDsRRVEnPxQFSdKLYFEUaOtuwuYWaI/v5dG3/ofqIZMYUz6V/KxDUwhWbX+f7HPPo/CaGwiFQoSCQYLBIMLuHZQse5dbv30DVVVVtLS00NzcTHtrG+ta9vD3jR9QX19PKBpmW+0W7r7iWuySldlVJ/GHF5/hR/f8mMIf3ovk8QDQ+ubfmVxcyOzZs3G73YMeXL780ks4AiJzp8/h0VV/MpcLCGiChmSVcAoC1dV2fvnLbBYtOsCv/6+WyZPzOGFSHiUlVbS0dBMKhRBFkaqqKs455xyqqqpMo0y3201mZiY5OTk6QRVL8OJfXmDs2LGMGzeOmpqaQR0rwLNP/YVtq+vJzxlC7c6VhEIhbrzx6MPJVFPXgXDGmFP40WsPoagyRfmlzD/zdv7+wWNcu+M6fvXrX1BQUPCpYg5DIV2lsGlTmM2bo+TllTJz5mV8bd4pTJgw4bilHBgtP8ezFzsVfQa0n6/EC0mScDgcZq+82+0mJyfnSzejbxBloVDI9PLxer1f+pSMzxOM55rRTmmYPiqKwsKFC9m+fTvbtm3D4XBQXl5OZWUliUSCQCDA7t27qa+vp7u7m/3799PZ2YkgCGRlZZkteaFQaEDi1W63U1ZWRiAQYPPmzTQ0NODz+cjJyWHcuHGcfPLJ5Ofn4/V6j4m0PRw+jYIhjTTSSCONNP5lkGVYuTLMrNkeXC4xuUwmGo0mf7BFrJIVySqhoaAo2jEN7Pfu3YvVZsVm7xts2Ww23G73Ib3JCY+bObNnc+0112CxWMxCOhKJmP2RwWDQlNY3NzfT2dGF5nczPKOaQLgX0FBjKolQnDwRxo8uwypZcbnceDwe3JqD/Px8brnllkMyrt1uNza7Da9XJ1tCoZBZhAPE4wa5ESMcDnLRxRcSiUTMnlADuuEkZLoKOWfiAiyihWxf/mGvkaaBpiqALrnU/yLE4nES8QSKIqMo/SXWggByQqY70M7GuhV6fCYCoiCiaiqKItPae4C8jGLcdh8xOcq+3Q3s3VlPNBFCVhIHbW8gxYhxbQSskh2H1cX8OXeRGmGZWtMYb9/WsIYt+1fztWnfIp6IEpMjJOQ4sUSE11f/mfHFp5LlySPTk4vT5jHfr2oK9W27WLnjHRa98z6qkMDr9XL++efTHWgnEO4l05uFlEw5STUe2Lx3Fd+8Zj633vY9tm7dyptvvMXfXv8jkuakungyE0dOx+vKJBINs/1ALdX3PotmtSHb9XsdBLRggAy3i4KCAiZOnIgkSWiaxuxTZlEzbgx3/+xH2Gw2HvjN/RR3e7EIArKiUODNYZirEDKzyJk6DYBIUwPqhwv59q9+oUemulxHdZFXVZXOzk6eevgJ7jnlhmTiSeqHoZMMkiQhSVLSoR7OPLOUMWMyePB3u1i1qptLLrmWjRtfIBwOY7FYqKys5Ctf+YpZzNtsNmw2G9nZ2RQVFRGPxijxlSInVJ566imuv/76ft4SAxWuf3zoYR5//M9kZWbT0trMeSddw4TKk7FZjt3ITk2NYU2Bx+Em0+3lQMteRleMRwDmTb+O99a/winTTuG1N15jwoQJ/dIiDrsPRfd5+fDDJj76oI19O1RaOwKcdd5pnHXW17j77lnHzeDyYPwrCIbjGYV3PGCQRzabzSQ/3G43WVlZx9VV/1+NgxULxp8sywQCuqdQZmYmHo8nTTD8C2H4Mhlwu91MnToVj8fD7t272bx5M7t27UKWZWbOnGkqE7q7u9m6dSu1tbWEw2E6Ojro7OxEFEUyMzPJy8tDFEWTrDV+51MTJkpLS4nH45SUlJCTk4PP56OgoIAxY8YwefLkIx73saqa0gRDGmmkkUYaXxj4/SqrVoaZc6pe7Bkyz0QiocvlNT3W0WIRsNmi3HrrjfzpT0986v32j54zjN/0YsNut5tFr91ux+12k5GRYSoZ4vE4FRUV7N2zl/B+FyeOOs00MIxGo0QiYfMcJEk3FbTZbWiJBKAlZfT9jeAURUFOulIbg2OfT5+JstvteDwewuEIiqJgEey88847BAIBXC5Xv4GHoii8/o83yLEOQ4mCgkpLuCW5l5QBaso/DjTvB02gq6sLWZaRZTmpVtCPU5fJi1gsEmIyPUOySowaNol50xeYW9M0zGv050X38tUTLmdIXjk2q41EIkFbTwONnXU0dOyivn0XCTlGjq8wGW3Yd2wCAlarnQxXNnkZQxAEkeXb3jL73y0Ww3RTMBUoxljJbrMjCBoOuxO7zYFPzEIU9P7Y3IwiHA47NSNOMNsiDJ5AkWVyc3IZOWwcW+vWs2zbG/QGO1i2bBmiYKV222pGDh2Py+XG7Xab5I+maWxvXMct5zyIIAjU1NRQU1PDbbffyvr167nn7h/i39DF3GmXs27bMqS8HBwFhcTjcWxWm1nwyL09aIkEwWDQHPgFAgECHb2sWLSEzatrKSkbitoS4bK5V2CxSKBp7G7Zx6PLX2TIr+9HQ0NJyLQ89QjfnX85VVVVuFyuQRV/fr+fn/34p5w69ERGFlcQiIWOKOsXRVG/hqKFoUOzueKKKp55JozP59MVGYKAz+czv0sHY/LkycyaOYv3O6J8f9oCWsMd3Lf8CXbv3k1vby8Oh+OwzvdLlyxjTs3XGVGqC2UzPZ9O9n6461OWU8yexm2MrpiAJFlxOQWmVM9h+bp/8J1rb+SOH3+f2XPmkJGRcch7NU2jrq6OpUuXsvSDFbQ0NFOSVcSM8jO56swT+KBuFadecC4XXnjhpzr2NA6FqqoEg0Ha29uJRCKmSe9nSbT8K2EoFoxkHtCJu/z8fBwOh5kgkMa/B0acZV5enhk73djYiN/vZ/369WRkZDBkyBBkWWbr1q2sWrXKjFUNBALYbDacTqdpRhyPxw/xUdE0jUgkQnNzM3V1dQiCQHl5OWPGjGHcuHGUlpYe5uj6byNV/Xg0pAmGNNJII400vjCIxzUWLw4ybboLu11EUVRURSWRkFFUJdnbr2KxiNx0UwUffBAY9LYFQUA4mKUX+r/et1AwC3+bzXZE+TTog9h1a9exqbWJ3Nw+4zC/34+mqSiqfg4Wiy7VtVltxGU9qlJV1UNmPhOJBHF0xYAkWcyZXrvdgc1qxePxJts7EpRkVfL2uqfZ+PEmNEHFarUhiTZEwYKWVF6Mm3oGLS2th8QJDjRp0dPdg6pq9PT09IuMFAQQk1GcVqvVVEtIVimZniGRkeHTHfKT2w0Gg8TjcWQlgdvhSyo4XCQSCYoswyjIKmVixQw+2vQa0XiY0ydcMuD1NeSmeiKGxrKtb2K12XDYHdhsNiySBYtoATRz/xoaVqsN0SJht9v1pABRJ0c0TaMoexitvQ1kZ2djs9uwWW3m+cZiOjEiCCJVJeMoKxjFzqaPWbH9bXpCnbjUJWTai8jOycZqs2LVrEiSRGN7HU6vjZEjR5oEFegF+OTJk8nLKaDcXYOmaazdsQTnNF1lIFpErDYrUrIQULvaicdiZtqGIAh0d3djkSw8fOkvUFWVj+u3MnfGqUgWXd0Qiob5/iv/B3Nm464ciaZqtC1+j2FKnAvOO4/s7OyjFhoGmbFs2TI2LFnLI5f8r+71IVuOaBxgzNoZBM/SpSGuvvq/2bFjB6FQKBm16cHhcAw4w+1yudiwdC33nn0LjoREltPH6KxyFi1cxAUXXEBOTs5hCQaHw06mK5dsX3/DPgHhUOXFUSDZJBq7W6gZUnXIa5OGjWHx/q36eknlhqopeBxu7px+Lb+65+c039jMguuuQxAEgsEga9asYemHS1ixeDlaRGZkbhnXT/gGk+eNZ0hukbntmCaz4qNlnznB8J/kvWBAS3q/dHV1kUgkTOO741V0H3xNj3SNU+/7YyU3DlYsAGbbXjAYpLe31yT6UtMivgwkyhcZhmosJyeH6upqGhsbCYfDNDY2UltbiyAInHzyyXg8Hnbu3Mn69evN99ntdlOF4na7EQQBWZYPIRhUVSUajVJfX09dXR2iKFJRUcGMGTOYPHkyHo/HvGdSf5eg7/4wPKkGizTBkEYaaaSRxhcKvX6V9esinDzNDeh1jaooxGNxLKIFURBASkl3OBYMcoCtoRFMOjd7vd6jrh+LxQhHwsmCRjeGBFBVhXg8jiLLgB6VqKgysiKjqHIycWHgY1IUmbgcR7QIZrKC7m+QwGIVcHnsKIEEpXkjkCw2Rg45gWxvITbJhsfhw+vMxmn38NePfovHmZUiX4ZUZqX/+FNAtAh0B9pYu+e9ZBqDBVHUZ/itkhWrxYposVCUPZRhRZWIooBFtJBQElisFizJ66wBmqAQl6MoqozD6sJut+Hz6W0fqYZV9l0OLFaR4uIi+iV3JI+pj4DRUBSdkLHYVBJaCE2LY5H148vy5ukJH4aCwW7HZrOSm5trDqwMpUP5kNGs3/0hdrs9WRyr5r4kSQIBMjMzcDgcJBIJcnPzGFt+Eks3vcm63e8jCiKnjJ2Lpmqm+eHaHUuYPvNEQqGQOSA0sG/fPtavW0/hlDF09LSys2Ej1b/4vk7cCLoSxGj5UHt6UJL3DkBnZyc3XXsDN8+4ktJs3cthWG6fjF5WEjz0zyfY51KpuvkOBEEg2tNN6G/Pc+0tNxOPxwclXTec53//mwe5dsrFuOwOk5SJJ1QeerAWTYXUul0QNC69tJK8fDcC0NAYZt8+DZ/PR2Njo3ndJUk6rL/Gr//3V1w54XxGDqmgp6eHcDjC5RPO48cf/Z5HH32UuXPnMnv27AGPOR5P8HHDCgKhHuw2B2MqpvRt+xgJhm/fchO3f/t7uGxOZlRN6ffanFHTeG712/2WJZQ4NouV8cNquO+8H/DTxx/kjX+8gV200tLYQk1hJZMKa7h3zs2UZhfpyqSkKikVk4aP5ZGXnzdfX79+PcuXL2Hbtg3E4zHy84dw330PcTzwn0gy/CtaNxRFMSMhU434DDLDiMQ1DFaPFUYMoWHcmPr8tFqt5OXlmfv6snlLfBngdrsZN24ciqKYnjodHR0cOHCAiRMnkp2djdVqNZVwhqfCmDFjmDp1KieccAJZWVmmEiwVBom2bds2Nm3aRGFhIWVlZRQXF5stcYYqwjB6PLhtJh6P09raOujzSRMMaaSRRhppfKEQi2q8916QyVNcWK36QMxwZrZIFiySLs23Oyxs2LCWZ599mssvn3/U7TY2NlL/t9c48PfXU5ZqKf+ljxvQNLbMOolgMDioWDfD7FF3aO8r2hVFT3SQZT26sbW7gf+36MfY7TY0NIYOG1i6mJuXzT/eegxpna4OMBygG5oOEI/K/PSJ6xGEvjhFyWIjEg8ypfLUfiSCpmkklHifJDgZs2koEoy8TiOKUkhGcGqoBKJdetEr9qkrNE1FVmU0TWXF5oUEwj0AJOQEFis8+s4PEVJM3np7emlqbkLTVF5d8Sc0QcUiWbDb7HoxpcjkZRWxu34LkkXirx+EicYiKKqM2zkwsaOqCgk5ziOv36u3sqD3HkdiIS4543pOnXy+ua7RUjKQg3pV2WgWrvsr7659lab2/UTjYa47/wfYbDa21q3D7fRRPqQan6+v1UMUBapLT2D93g+prVvCjob1jC+bwcTymTjtbjbsXsrZ82+jo6MDQRD6eWIMHTqUB/94P6+89Dfue+FhxNxsbNm5+mchCoiIOjkigBoOIUuYCSr/fPsdGvYcoGbmyAGvyf1vPcaTq/9Gxf/dj82uG322vfg0Z500lZycHOLxeD+PEQMHFzqKovDkn5+giCxOKpsACFgsIpmuDOZPno//gN7uIKYUbC+sfYUTT8olL98NArz/XifTpp3B5s2baWnRU0YsFks/JVDqcaxatYq23U185Rs3Eo/F8fkyUBWVXHc2Z1WcwgvP/pWhQ4cybdo0U96eetwXX/p1Vi5fhaZ18srfX2dE6RgcNiccxsvjSJg0aRJ/ePJhbr7+v+kIdnH+xDPNfVUVDCcY8febAVTUhO4LI0kUZxVw3/l38Y1HbiIUDfPoN3/F2GHV9JNJHYYUzXJnICkCl102j337dlNSYmXkSJGTT/YgKxp//MPGYzqPNA7FZ0kwGM/hUChET0+Ped8Zyh6jzS2VaBvoWA5JHEm5fw3yzyAxDPNjSZLIzs4mMzPzc+d/kUYfnE4nNTU1uFwu1qxZw6ZNmwiHw7S3txOPx802Pd08WMVutzN8+HAmTpzIxIkTGTduXL/tGc/zVBXLzp072bZtG8OGDaOiooLc3FxsNhvxeJxIJJJMeZLJzMzE6/Wa4wKDgGhraxv0+aQJhjTSSCONNL5w6OlR2LQxwgmTdAPHhCwTjkT04tRuR9MkyoZ7OffcLFauXElBQRGnnXbagAZrmqZx5+13sWTxEmaecAZfn3V9MmXgoHWNgZmm8d7a1/AMjx7TMQ88YCSl4NcVCOPGjeWxJx8xZ7UGwm133MoFXzuf/Px8RowYYc5YPPLII6x4fSdfOfGiZFxnhFAojKIqSXVGas8HoIHD7qSwoIicjDyMGfqDTzn1fTndOWR581hw/l2mFNw41oPVDwZeX/YM4+cM5dJLLyU/P98khB555BHu/cn/UpE/kSE55azeuQinw8O8Uy4jEg3TG+rEYrEyonAsGmARBTbuRwHuEgAAIABJREFUWU17TzPjy0/m0AlofVA1vmI6p514br9j+d3zPyQSC/df+wgF5rCiCoLhAFv3bqAgq4QNO5Zz++8vxSpZ6Q12o2kqv/j20+RlFWGxWLDbbWRkZDKkqIT8/HxOnD6J2tpaVm3/Jxv3L6csfzQ9gQ4WLlzIhg0byMrKYurUqUybNg2n04nFYuG0007jtNNO48L589k2coJ5LHobkJ7WgQaEguSNKDMNTOdd+DXC4TDfe+znnDrsRC6dcj5ep+5TsqNxNy98/A5jnn4eV+kwAHo3b8SzezsLfvcAPp8Pq9VKR0dHspXFavZmH4xt27bxzCNP8sD59+BwOnRvB3TVx+1nf2vA67iiYan5b0XRWL8+wne/O83sJQbIyclh5MiRZGdnmxJfPX1D4o+/fYjLJp6HRbSYZEw4FMIiipxVNYv396zgo48+YvTo0ZSXl1NWVtZv/+eddy7nnXcuAO8tej+pRDm8WejRMGrUKJ58/mlu+fbNvPvycs6tPpU5o6axeOdqcnz5/b4/cTmGNekBIkkSGS4fb373KR5Y9DjXPfV9bj/jeuaddFbK9+bwxZ9btNHevpKf/2IyHk+fUWs0qmCzdRzzeaTRB6MIGwxZ/ElgKAcGcug/WIKuG7ke+rohpXc4HOb3IxKJ9JOtG0ouj8djtnoIgoDT6UwTC18ACIJAdnY2Z599NiUlJXR2dqJputqrt7fXnKRQVVV/nmRkkJub20+RYngrxeNxotEo4XAYv9/Pvn37TH+GDRs2EAgEmDlzJsOGDTOjK9va2ohGo+Tl5VFQUMDQoUMpKSmhsLAQgK6urkGfS5pgSCONNNJI4wuHSERj0aIg4yc4sVgEFEUmGlGx2axmjruGxqjRmSz+aCU/+MG7vP76YvOH8mC8+cbbzD/9drJ9edhtR5ePShbpmOXVoij2MyfUISTVAUmSQRCw2fXkimPpdzQgyzKaqiaLKBGn04XTeaQUDQ3JIpGTk01+dsGg9pHbWYDD4RzQrO5wUFWFnp4eent7yc3NJRqN0tPTo6dfiBKjSqcwNK+S2ZPOpbCwEIvFgqzIukGkZCG18LJKdjbWreCcWZcesh9F0U0nByJmrBYr8UT/wbuGetiiwm5zkp2Rx7xZ32R4cSWnT5lHJBbGYhERBHhnxcv8+plbGTl8Alee/V1sNjs2mx3JJuJ2u7nggguw2Wy0tLTg9/vZemA1CTnGc889h9vtxuv1Eg6HqaqqIjMzE7vdjiRJhMNharfvoHB+X3yiMWBUZL1lRguHKC4uxuPxmAXEtdct4Gtfv5A/PfQHFrz4Ay4ZczbnTjidV1a/hTpxrE4uaKDEYvQ+9zg/uvYaCgsLyc3Npbu7m46ODmw2m2kWlhoNCvrA9aH7H+Sr5adQ4MvDZrMf4tlx9PtAIxpV8Pl8+vkkfUxycnKorKwkKyvLjJ5NJBJ8/PHH+Bu6mDX9JKDP28Bmt2ORJGyCwJUT5/Hoipf5aPRHWCwWysrKDpsFH4vFkCx6gafza5+sHaC4uJi/vvoCK1eu5C9PPstTz75KS0cbE6rm9Fsvnohhk2wpHhQ6AXbLmdcyZdg47vn7fdQ2buOu827CcRQn/9OqZ/LCns39yAUAUfzPbGs4njBaTw7uPz8eMLZnsVjweDx4PJ5+rxvmefF4nN7eXgKBgHmfpLZuGOocq9WKoigkkiavgUDAJBacTiculwuXy5Vug/iCwficMzMzOe2005gyZQo7d+6ksbERm81GW1sbkUjE/L0SRRGv10tWVlZyUkV/BhitOMFgkJ6eHrq6umhvb+fAgQOEw2FkWaa2tpa6ujp8Ph+hUIiFCxeycuVKGhsbCYVCFBQUUFZWxqRJk5g4cSKKouB2u9MtEmmkkUYaaXz50dmpsG1bjDFj9IJIRUWR9ULMcAAvL3czYoSPjz+u5fLLv8all17J1Vdf3287xgAwEguRkAdXNLscXjq7G4/peEVRZOnmN4kkepk381qgTzqr+y/oyQu1tRs5f+6FRKIRysuH88dHfj/ofRxroWEQMQd7Qxz5PZ9slq9P5SCwd+9eli9fTm1tLaqiYpUkXC53it+BYQR56PlofDJjMlEQeWflS2zcsyp5nTQ6uttJqDGe/+ChQ7YpCCJxOco/lj1NSeFQVFVj2uivUlJQhiBATdlk1u9cSu3OVfyg/irOmHIhp025ALvVQTweo7q6GpvNxpgxY9iwYQPr16+nra2Nrq4u4vE4gUCAjz76CL/fT1ZWFl6vl8mTJ9Pc3IxtzETsHq/eEkEKwaDIEIthEQXKyspM2bOB7Oxs7v7xD/nG5Zfym1/8mjefv5vNu7fjnX+Zfu00jZZ/vMys0dXMnj0bh8OBKOqEiCG9Bejp6aGxsRGfz0dmZiayLPP+++/TuLmOW869Syce9Kt0TJ+BJAkmAWhkwQN4PB6KiopM0urUU+YwrmYM+/bu43snX3WIN4rT6SQnJ5tAIMi4wpEM31PEq6++SllZGbNmzTrs/aFqWtLsE71F4hhJwlSIosi0adOYNm0amzdv5qorrmX6hDP6rROLR7FZ+ggBIblfi8XCKdUn8rcbH+Xap+7kyke+y6+/cTfFmQOTfKqq8t72j9jfFEBVNERL3/mJonBcZ94/y5lu43n3eSVE/l0xmgZx4PV6TeXQwWSHMTPd3d1tLrPb7f2MUY3knINVEml8cXDw5ygIAu3t7dTX1xMMBs31NE0jGo0SCoXMv1gsRnd3N1u2bGHXrl20tLTQ0dFBKBSit7eX1tZWOjs7zQSKhQsXsmbNGurq6mhpaTEJiJ6eHurq6ohEIuzZs4dly5bhcDjYt2/foM8jTTCkkUYaaaTxhUQopLJoYYDRo+1JYz4NWZFNgsGYfRVFkXt+WMXij1pYs2b5IQQDwDnnns3iNS/hFQu47PTvHnXfXlcG+9qD5oD5cAP81FmFc845h+HDh/P7+x6lL6pRS/FlEBiSW8YN5/wUQRToCXTx2upHBuyNPxyOdWCsp24MfvvGMQuHGC0e9chMgypVVdm7dy///Oc/2bZtG6qmIll11YZuLKXHSBpRnvq16duXospJn4gByAft8D6d582czwkjZ0BK68uGnUsoGZ3JNQuu7nd+RhH0X93nmHLUN15/k10NmxmSPxwQKMgdgtft41c3PsOiVX9n6cfv8Oay55gx/qskEjJFRUVUVVVhsVh47bXXiMfj7NixwzRhi8VirF69mpUrV5KZmUlWVhaRSIS123fgmH0WgmioWwyCIUZg2UeEd27HLggMGzaMrKysAT/ziooK/vD//sSSJUu49rpvITYeIHygHhUNZcm73PL/HjXNwAyCwe12E41GiUajtLS0sG/fPoqLi7HZbPT09PD7+x7ktulX4XbqZMRg5QsCIorSpyiw2y0kEol+x+12uykoKDD7fkmonJ09jSHl8yjNLkZRVYRke5MAOBwOJMmCLCsEg0Gml57A75Y/xd69e4lEIubgPNUZ3Xivqiq6ouAwjuufBNFoFJ8zh6LcElLvS1mOI1mkvmW6hQaWpKIh25PJXxc8xL1vPMi8Bxfw5DW/pbLQaPHQ6Ax2c99bj7CycTnu7Ah/fPhkxBSTUgBREPQ0nU9xHkbxmnq9jhdpcXCiQSIZrztQ5N2xpigc7tmVShQcbjupPeqpipd/dTxlqsnjwcZ6qcdpFH5+v98kJDIyMkxT3DS+HDDuB6vVaj6fu7u7TXWBAUOp0N3dTXd3Nz6fj0AgwIEDB/jwww9ZsWIF9fX1tLe3H5bUa2lpOWQZ6OlWfr+f+vp6UxkjSZJpKjwYpAmGNNJII400vrBobZXZvTtOVZU+6yPLMpFIJNkT39dDXlDgZMQIH5s2HerBAPCrX/+CxYsX88C9jw1qv4IgkEgkCAQCdHV1DejanNpT6/f76e7uxul00t7Vypqti6kqHdvXzmGYfiGSm1moEyNYkCxWenp68Hg8/TwZjjQTqH7C2cFU88mjrImGhiwrHJ1kMDao4fV6kSSJrq4uGhsb2bdvH4FAAMkiYbfZ8Xq92Gw2VFUze4yN7acO+FVFRdOE5P77Q1FkNuxYxrqdixEFEQGB/OxispIRhYtr30SyCXrbiAaBkJ+zx3+TmTNnHvEsotEo3d3dvPfSWrNAzc8qJBINIYqW/8/eecfZVZf5/33K7W16TTIzmRTSISQgJfQSEFRklbKAriKWXdvPsiruWlhdZWEXV1dBVNauiB0EQVBaIIVU0tskmT5z5/Z+zzm/P879nrkzcyeZCQm6cD6v1zBk7infU+/3+TzP8/lw+VnXcumZb2H7/k08se4hhsND7Nixg7lz51JXV8fChQu57rrrCIfDRCIR9u7dy549ezh06BBHjhyxlOUfe+wx9oQjNL/tXYSHw3g8btxujxWYpZ54hNz+PSxasYKOjg5qamoq6oqIc3beeeex4+Wt/OzBB7nrni/RHx7hjo98iJkzZ1otGeVQVdMmtampCY/HY5V0/+vt/0IxkuVIpJfDI73IsoQkyWjG2Gug6TqKEPzUDYqGxkBkhPXr8xTy5r07PBxj/fr1bNiwgd7eXgzDYGRkhD179gCYmbhCjipvgHg2yfaePaPXv/RLlkzycGRkhJHoCAdGjlDUNZ566inS6TRLly5lyRLz+RoeHkZVVTRNI5VJ8fDzP8Xr9rNt3zoSnjYeffRRS39CBL21tbW0tbVZrQ2aplnPmwjEBbGoaRr/8z/fhLyD/d27S7agZlVSz1AX4eQIWw6ZRJphGGilFiaj5Hii6zpvXHoRj2x5knUHNtMT7efwSC/JXIpfb/stc5bo3PahZk47rRGHQzHHWPacijGFw+Gj3sPly4vf4rym02nLqjGfz5NMJhkeHp601eRY2xbLi/Mj+sHD4TDRaJRkMkkkErEIHiEmJ/RcXC7XMbPwIoObzWatd6i4NqFQiFAoNKmWiFg/kUgwMDBgifUKwdzJnqm/FkTQ6fP5rKy2sCm08dqE0FhoaGggEong9/vHPBPZbJZdu3aRz+fZv38/1dXVjIyMMDg4yMGDB+nu7iaRSLziiiHDMCxXEtHSNqXxH/cebdiwYcOGjb8yEgmdXz4Uo6ZWIZPWyeUligUJRXFy9jkNhIIeFFUBQ2J4OENDw0T/egFJkthxYBO/Uu+3SujLYZRVHURiwyj+UX/x8ZknEYAkk0krG7Bp0yZ2bd9DXUMNP3vq61BU8HlC5HI5/O4QDsVJMBSkpqYG3dCIxiMc7DrApz5xu+V2ILYdi8VY/cZLaWhoGDN56Orq4qkNv2X/kR1IJWtFc+JdZFbTXN5+6XsmHHc6m+Lbv/kSXrffOg/W71LXhNCJMDAYivQSS47woyf/E8mY2FYgSaNkhajM2LZnA/3ZPWxcv5lcLs/Bgwc4fLAHDANN1/j18/fz1NaHAKyWETMAkssE8MzBRGLD1FY1mO0C41DUNJ7a+Bu0gs7CjtPpHzlCf2Yv571lKdFYjHeddRPXXf/2MRaRU8kAOhwOOjo6iGcfR9NMu8qAL4QkyYRjA9SGGpFlhSVzV7Bk7gq+9st/pquri/r6ekvAcP78+Vag+swzz/Dkk0/icJgEUiaTIZFIsG7dOtSFywiHh3F5fYRCQRRFNe3nCgW0RBw0jbNWrKCtrY2ampqjBn9CS+Hvb7iBq668kieffJKLL74Yp9OJoigIa7vxmf5gMEgwGMQwDPbu3cujj/+CpkaFH+752mjnikTJLWL0eov/NwzDzLQDNY0FXt4WZe+eBCARHSzwhx//FhcO6vUg1c0L0HrTrP3tM2xxr0VSZGLxON/Z/Wvrnivk8xQKBSsAdDqduNwuDnUdoquri1gygT9UJJ3eytNPb2fzlqpS+wisfbaXJn8L82d2ckpwBuHubYxIEkFdI75rgJ/d9b88sfFpTp29iAXNc8hrBfYOdZE3SjaxYFU8KJJpgyvJMjKmLaokSURGInSH+wjnj9DY0GQqq0gSiUIcAirf3vmQRVZIslxyNxGBtUqhWKBtdjvrsrs4+OJviKV66Gj3c9Xbg7zpTR2WBWuxNMGXxl1jw9AYHBysmAGvdE+U9/gLgmdkZMS6/ul02nI7EATAVCsKypcXlQrJZNIiFWKxmCU8JwIXURJu2saa1UxCwPRo+xIZfbHfQqGAruu0tLRYx1ZekTBqZ2u+Z6LRKL29vSQSCQBr339LLQblGg6i0sjGax+KohAMBmloaGBwcNAi4MV9nclk2LdvHz09PWzduhVVVRkaGiIWi53QcYjvCGBaxJtNMNiwYcOGjf/T6O4u0N1tfgE6HA48Hg8rV66kuWkJbW1tNDU14XK5kCSJefMmJxhOP/10/usbd04Q16pUtjs4OEgulyMYNAmBShUMgKXyHIvF+PlPf8FwV5aVnVewrCVPIV8gl8vx/MuPsePIOtxuN5ctvYy3vX01Ho+Hnp4e7v7KXmY6TkdVFNDMcnNJknhp+4/pW97H8uXLx4yvrq6Ouqom5sxaCEimjaQkcWhgH+t3/qUiwaDIMgs7llMTMi0RxfjLAxAwCRYJiZ6hA0S0bj706XdPWE5kS8S/xdh+9lOVTK+LZhaT1jMEGjqZ4z8bh8PJ79c+wIr5F3JK+xIrODWzwxqSZKrvmxlhk2R4YduTZLXkhOx76QphGAZnLbmUS97wZhLpGN974ot84B8/8IrKniVJYubMmURTw2P+VhOs51D/PmpDY3vnXU4PmUym4nZkWaazsxNFUVi0aBGXXnop27dvZ9u2bRw5coSRQwcI3/4RnLPnkF7xBlLLz0T3eMnGomgl6zkRCE0loASzssftdnPBBRcQi8XIZDJkMhmrd1dkggUBIq5jsVjk+9//Fre8o4mzz67B0A10QzefCyTLxFW0s4j/xyjVuVTQ0fjEP/bwoTNvptlbZ+3L6XKVstYqhgS39h7gnvu+bok6lmeqwWyp8Pv9bNu2ja1bt/KDH/wAt/tlrr+hBlmSzMqLgAefz8ehPTE+uPwWLl02WqVSPiIJaPc08+iuv/Cxm74yrUm02M4ze9Zy5+P38sGP/CNvectbxpB+le678kAcsNpmkskkd975BZYuDXLW2U0Tzt3oiMdCURQaGhomdZ2ptH8RpKRSKSsbXlVlEjNCJHC82OBUtyuWF9dXHF8kEiEejxMIBCySTzwToq1tqjoChmFWReVyuTHtDqKKQRC74nlxuVxjWgqKxSL79+9nzZo19PT04HA4WLp0KW94wxuYM2fOlM6jDRsnC6I1Qcwx6urqLMI6mUySzWatik3xLhnvQHIi4Xa7aWlp4cCBA1Na3iYYbNiwYcPGawaFQoFCoUA2m6WlpcVSQR6v3F0JXq+XK664Ykr76e7uZt++ffj9fqqrq3G73UddPhqNohcNVp16BQs6Ti2phueIRKLsObKN7pG9eDweZs+ezYUXXojf76erq4sH7vsRS+eeMWGCv/XgmooTfp/Px6nzzuLyM/8ORVGtZbYfeIkjffsrjs3l9LBq2ZU01rVMKYjY172D3fFnWL169TGXFdiwbiMJ3MxuWUQ4HCbjzpD35ggGQ/i2+lk290yWzDsdXRdq7uZ6qqqUiITRcQ1H+9nds7lkkVgZZgYVgr4qZMPB4cOHaWtrm/J4K21v1qxZxBIjaIaGUtJxqA010TN4kOXzzxmzvEt1V5zsiYCyvb2d9vZ2q+z0scceszLz+Z07ySfj5F5aS2brRpL79uC95ga0wcHR4L5E4ORLmX0w+2Y//rFP8P4PvI/TTjsNGCW6RIAnnDyGhoYYGhpieHiY/v5++vv7rbYfMSZN0+jp6cbpPMT8+Q1ERiJouvl3cwxmVt0khXSLfCjPkE8gGAyAPEPRMPWuKuujfC5HPp8DJIpakWw2ayniCxE7gGQyafUhZ7NZFi9ezPLly9mwYQMHu3YCUNSKJFNJsjnz/EsYaGgoFQkpE++55O95dv86PvrzO/jvv//CtEvkX+rdzgVvupRrrrmG6urqaa0rYBiG5eRhBtolsmAK2quKohAKhaZcwQBYwX8ymRzTGjNz5szjGv+xoOs61dXVJJNJS5zQVSKWTiSKxSJ9fX309/eTSqXIZDJ4vV78fj+qqlrOK5qmcfDgQdauXUtfXx8Oh4PFixdzxRVXMHv27BM6Jhs2pgtBMOi6TlVVFTU1NTQ0NFBfX2857YgKoZNJLIix+Hw+Ojs7bYLBhg0bNmy8fjE8PMzWrVtpampi4cKFUyIYXg2IOMEsJzcV33TDtIDq6OigoaFhTGZ+coGyyp+bgeckgpPHacl3ImEGnTqUgk9FUZBKWcupFhgYhm6JHx4ZOMBDf/6Odcy6rnOwdzerllxple3PqJvDxo0bXxHBAGb5dKgqSCw5Qn1J7b8+1MKWfWvJ5tJs2fs8p847l9VnvR2H6iKTyRCPx1m3bh0rVqyoGPyJQPaUU05BkiTOOeccRkZG6Orq4sCBA7y4cSOxdWso7NiKFKpGz+WQdJ3nnnsOXdctHQXDMOjv7+fpJ59n09rthOq8rDhjOX6/37Lg0zTN0gRJp9Ok02kymQyHDnSRjaaRXDLugBdFUSxBvnS6nw99uJZCIW+dXzNAlUavV6laYULVwiTJ90BdnkPRbhY2zgHJXE4qtSDIioLqUFFUxWrjGH8NyqtqxPlbuHAh0ehThEJ+kklTfLVYKBIORxkcyBByBszMOlQUGnE5nHzj5ju47YFP8aavvYtbz7+Btyy/fOr3hqyyYtWq4yYXBCxipvyPU3guZFkil8tNuaJF3AOCcAoGgzQ2Nk7Lena6kCTJIouEi4op+nliCQZZlgmFQiiKYhHO4r7N5XIMDAwQCATQdZ1wOGxpwTidThobG+ns7KSqquqEjsmGjeOFqMxxOp20t7eTTqetaqBXA6JyYd68eVx44YU88cQTU1rPJhhs2LBhw8ZrDuFwmJdffpm5c+eSyWTQNG3KfcQnCxOFlkpl5oaO2+1mxowZ1NbWTqnMuTzIOiFjmw75cBz7HR1reWYbFEVGlihZB07t2uglbQaATXue50jfAS46/c2lcwmnzFjOos7Trf11NCzkj4/+iWuuuWba4x6PpuZmoomwRTA4VAcDA/tp8/o5vXEOmZFDfP5b76Rg6Mxe1sAf//hHfvi9n1FVHeKiSy7gqjddyRlnnGGVf4u+/M7OTtrb261/r1+/nhdeeAG3283TTz9NcrCffG8PAVeIgmqwdu1a1q9fj9PpxOl0mtUMuTxBdy1Xr7qNLQee44cP/JRsPkVBy09oWSmviGn01vLVyz/BC0c28avtjxPNJUAGRSly+eU+amokCoXiuKtTumPG/NHU6ZCEZocklhxdB2DRUicvPbedK+ZfgGh7KS+Nl2QJVZmcYBDHK0T9NE1j/vz57NhRRTDoJZ8vUMjnyRfybNgwRDZtkC57B8iTvAPqQ3X88H3/zU3f/CCf+PmXmFXbyvK2xVO6L1RZtSpJXgl03axQ0bXpPWOSxDHJhfLKBUEuxONx0un0Sa1cKIfb7cbpdDI8PEwymTxm5dfxQJZlS0MEsKo0EokEqVTKqthwOp1EIhGOHDmCpmn4/X7q6+stInK8AO/xYCpuFjZsTAVOp5MZM2ZQLBY5ePDgq7Zfl8tFW1sbp59+Oueff/6U17MJBhs2bNiw8ZpDJpNhaGiIQ4cOsWPHDnRdp6mpacoZvpMNw6BUTm6qyefzeQYHB4nFYmiadsz+Y7nU3zweR5vESkcJ4I/22YmCZP23zNu99DPl8gXGEjUSErNb5nPVeTcAWAGnsC0FiUWzV/Ct3/+WtWvXcuaZZ76iY2hpbSI6NKrDMGfmAg53zeQ7t/6H9be+kX5+8eIjPPXH54gWMrzt/PfT3jKfl/ev598+fTfdwwe48o2r+ep/fMVaJ5vNkkwmrVLumTNnWmWpjY2NbNy4kS2bt3H+4rewZucjOFQnmlEAedQG1dBh5dxLkJA5rfN85raeyvq9f2J3z0soTvD5fbjdbtxuNx6PB4/HQ9+hXq6ft5o5M9qZN6uTN512CT/a+DvW9mwhXujn7dd14HSa5M+okYMgD8rummncPlddVeRjv++iqGs4FAXDMNuTgsGgWY0hYYk5HvV+LvXuS5JEPB7H6TTwer3U10vEYjGKsSKnLfcSiSb41MNf5r3hm7nx3GtKJfKVB+13e/nXt36E7+76NV9b/wPOOLiYd59zHW7H0bPsEpWfx+lClmWcTheaPr2gVjhdHAvCHnVkZIShoSECgQCzZ89+TWfsy6skZFm2bEzFZ+NdQoQ2RSaTIZ1OT7v8vFyLRmiFeL3eY4pW2rBRCYqi4Pf7CQQCDA8Pk0gkpmUX+UrhcDioq6ujpaVlWgKjNsFgw4YNGzZecxCWaIJg8Pv91NTUnFCC4bgyW1KZiKJeysLqpo1bOBy27NLG7YmJwdDodsZuXmL34a001cxAkhRyuSy6YXCwdyeRxDAbdj4zZh2RBa6E4Wg/h/v3jRuJwaG+vaQdI9M67Ggsygub/8y+w7tIpVMUC2bJvu+Il1Q6ibD1mxoMDvbu4mePf4vNe9fQXDPa+iCcDMpdJ5wOF1euvIVPfeKz/PbhX74i3/gZM1tYt2MvKzEFA2c1z6U/PjSmOqC5pokPXPYObkxew/l33UhHy3wCvirOXnopZy+9lE27n+dPT/50zHZzuRyxWAxZlvH7/bS0tNDS0mKJhMbjcbZt2UFzzSzec/nniKSG2HrweXYPrMfj9ZDP50nradOhQJKQZYmQr4bLll9Pe+N81uz9PZ2dHVZmNxQKmXaQvQmuWXkFHpcLRVGpq6vji20f4/O/+y82Rrfywx+keONVNTgcMi9vS7J5c5aXXz6M16uMvStL9hGjt9bo+TAMA90oUUsS6AZklTz/vfZ7fPzs2zAkA4/HQ01NjelsgVGxeqEc5cr6AJs2rWXHjhFyuUZqa2otj/hisciFF/qZN7fIvd/8XxyKyjsuejuSLHPXn77AO/AfAAAgAElEQVRNJB8f9wwZdA1007ZoNt/76ff54r9+gc8+fDd3XfOZo94XOvqUBRaPBvPaqejTJBgkWZr0OS4/vlwuRyKRIBqNEolEqK+vp729/ZUMeepjrFA982rtV9heisBf2JYKcUnRRmFq4+QtlwvhejEdlDuz1NbWUltbC2Bpa4xx6bFh4xgQBINw9Umn05YOzomsYqwEIZBaX19vEww2bNiwYcOGsCzr6+tj48aN1NTUMH/+/L+qFoPZAy4ml2KCaf5bVVW8Xq/ldgGUJhEyldPDlSenS5Ys4dSztpJxHCEcDvPnp56hrqqRTDaF6pRxzIhafevCRz4YCiArE0Xt1u54kiPJbZyxcjTrLwFtDVUsPXXVtI49PBxmJDaEU/aSz+etjGFeS1kZv6niyOB+MCSS6QRzZyxlQdtp45YQgS1IkhkGz525mD1HFnHHF77EnXd95bgn9x2zO/j2/u9xzfnvBKAmWI9m6IwkI9QGasYsu2bfBuqrmgn4xva1D8Z6uOWdN1fc/njyZ+/evTz88MNs2LCBal89LfXtBINBWpRWciRpnOfl4ksvQtM0/vLnp/FnqmhpabGsRSVJoiDPozvXxrvf/W5LVM/hcPCf/34X7191E6FAEFmWkCUZRPDnVvj8F/+dVCrJo48+ZDpznPV3zJvnxuf/T269dcaYqhehd4EkLB2hUNCRJFBVeZTvKV2ejS8d5r5v7ESSZSiabQFymXjldCfOn/3sF/nBDzr4tzu+zTXXBDn1ND+NjY2WKGR7u4Obb4Nv3Pe/tFY1cfaClTy1dw13f/OeCe8ESZJobm4mGAzy8U99glvedMMx919OML0SiIz6dOMGk985+kq6rhOJROjt7cXr9TJ//nxqamqOus7JxKtNNOi6TiKRYHh4GEVR8HhMl5Ha2lrC4TD5fJ4DBw6wbt06mpubqa+vx+v1UldXd1z7Eton2WyWvr4+BgcHqa2tpbq6GkVRTgghZeO1D2FX2djYSHNzM83NzdTW1hIIBMhmsyetmkHYojY1NdHZ2cncuXOnpdFiEww2bNiwYeM1B5GZHxgYYPPmzcyePZtsNmv1OP81skdjA4Cxfbniy7ycYDj6GCsHE4sXL+aTn/o4fr+f7du3k4soXHfBP9EfPsLjO37Al/79jjHLR6NRnn/2BZRKqvkS3Pa+93D99ddP5zAroqOjg2Z5KXOalxIOD5PJZsnn8tTW1vI/v/sMijyN6YhscP6yq3jrJe+s8GH5tTUwDMkiGS47423c/8gd/OGRP/DGq95YcdP5fJ7NmzezbNmyiuJzF154IS6Pi3whj6qq5PPZipdCAtbsf4mO5oVj/m4YBpv3P8un/uM269+VLA2LxSKFQoHdu3fz6KOPko7nuGrFP+Dz+sxKHIeD6qEqOhes4IMf/CAAfn+AH933S85Qz6Opdoa1zbQWpXVGKzfccIO1/UOHDjHUM8Dpq5fi8XgqnAmzquDSSy/lpptuttZ74oknaKgP0NJy/FUgAKeeVosuH6Y71k+zzwzgpJLzRyKTwuebHhEYCoX44Ac/wsUXX8Ydd3yaJ5/ay803NVJVpZQU13O0t6tc8qYCn3/kv/hW6Mu4VCfLli2zguzJnrepvCtejWzi0SAfY/8iOx+NRunv72fBggV/M1aMYtwn+51sGAbJZJLh4WGqq6vx+XwEAgEaGhpIp9OkUikOHjzIhg0bWLVqFfPmzXtFop3FYpGBgQEGBgYsa1hJkvB6vRNaNGzYmAyKohAIBABobGykqamJ+vp6QqGQZQF7svYbDAZpbm5m9uzZdHR0HLN1sxz2XW3Dhg0bNl6zSKfT9Pf3W7Zl8Xh8Sr3KJw2TTKJVVSUYDOLxeKwJ59EcIXRDP6ZoZflnhmGYGepp4QSKSI5v5Rj3uUkCvHKMtkiIfY5u16E6ecs5t/KlL3yFvr6+Meu9/PLLfPITn2LVWRfyzptuZePGjZNsX6Khvp6R+CAAiUwMVVEmVC8AaLqGQx3bkpMv5EhnU5YgYC6XIx6Po+s6gUDAEr3bvXs3v/71r3n66adJJ3Kcu+hq5sxYgtPpxKE6kGXFLKXXRu+PW265mVs/fCMPPv81fv3sdxmJD5ljRqJYKBAOhy2Lx5aWFt7x/nfxgYf+lV+sf5h8caxAoSzJ9Pf3k0gkJhAgJ+JKKYrM7Nku1h7ZUtqutQd29u5j0eKFk657NCxYsIAf/ehXONRO1q2L4PF6qK2pJRDw43Q6OXdVgPbFCT79268wODzECy+8YOpXVAjO9+/fz51f/irxZOKY+z1RGgxGScDUmLYGA0dtkRgeHmbfvn2WoOhfs3IByis1jDJnkldv32AGUK2trSxZssSy/hsYGGDv3r0nRKFfiE22tLTQ0dFBZ2en6XJz8CBdXV0cOXKEROLY95YNGwKyLONyuaivr2fGjBnTalmYLlwuF62trXR2dlpVN9MRsrUJBhs2bNiw8ZqFEHvs6+ujt7eXSCTy1yUYrGzdaEm5hHQcFQzH/vyvmVEdj7El5OXjNkmA4xupMeHHsKwNDHR91LFCVAo0187itPaL+PAH/x8PP/wwH/3wx1lx2hu47tob2fT0Hm69/HMs6jjdFAkseYyLH2F519jURDg2gGEYeJ1+coX8qL1gmc1g0B0gnU2M2b/T4WLurMWMjIyg6zrZbNYS9vT5fJYN44YNG7jnnnt48bkNrD71ZlbMvQC3243L5URVFWTZdCDJ5/PW+GRZ5oYbbuAPj/+Oc65cwgNPfImH1/yIZCZOsagxMjJi9e+qqsqHPvph/veXP2J/YJjbfvYZ/rJzjaWFISNZgmK6rls/hmFUOu3T5qIkJKqqFSKZ2OhfSvfHrv59LDtj+cRzOoUfgE2bNhGN7uPqN7XicXuorq7G7/fjdrlRZIW3XRfAqO4mHAvzl7/8hXg8TjabnbCtDRs2MLj5MLdf9oFjHqSERCQSOSHPnK4XkeTpZfMrVVCI4F3TzGt/+PBhFEWho6Pjry7qKLQPwMz0v1rvqvFVY+UEg67rDA0NsW/fvhNGMAQCARobG5k1a5blTtHb20tPTw+9vb0kEokJApM2bFSCJJnzBLfbTUNDw7Q1EaYLh8NBfX09zc3NOJ1Oy3lmqrBbJGzYsGHDxmseQ0NDbN682eqpPdHe61PB2IqCUrhSmk8K67h8Pm9NMo812Zza58dfdmwYnPDy3fJMtdiHOcqpjHOULNAx0DSdUVJBjNmcpJtBdwFZVlBV0wUhHBugd/gQiWSCP/zlEaI9WU6ZeTrvuuRf8Lp8SFKpIkRX2Lt3L7NmzbICEqG+Pzw8jCEXefzFX3Kody86Bulcmnse+26JWNDRDLMcfWv3DnJKgFwuN/Yc6DKHDh1i7ty5JBIJYrEYoVAIh8OBw+Ggp6eHj3/sExRzBkFPDTu717OnfyNOh4MzF1/MWaGL0XXTLUNkpgWExsLfve1aLr3sEn7y45/ys59+HVkxrVtFtl7ci16vl09/7na2bNnCt+75H362+RGuW3YlkiGRyWRIJBKEw2EkSeLr993H93/2c4bCwzz0p35qQyqXnO3h5ptm43KqFXU8JoNu6CiKQWFc5YQEJAop5tXXWddzst/jiQUwxV0/+y8f5brrq/F4zAw5Eng9XqQ6iWg0RqFY4Lb3h/jJTyI8+OD3yefzXH755VxwwQWWe0U2m2Xnzp3MqGri1JkLKRaLpXtBrvhMNAXquf9r3+LFZ9ZQ1DW+/+MfoKrHN8XWdQ1FmS7BQMUKing8TiwWo1AoEAqFLBLzr9n/L0kSHo8HSTKFKWOxGD6f76QGS5WgKAozZsygUCiwZs0aDMMgHo/T399vVfqcKEiShKqqNDQ04HK5SKfTVvWSpml4vV48Ho9lw2rDRiWYLjNOgsEgNTU1J8XmVSCdTrNr1y6SySQ7d+6ktraWYrE45fVtgsGGDRs2bLzmMTQ0xJYtW2hubmbZsmX4fL4pB/KVILJOQsjrWF+8ompiTMuDMfZzMekUQZMZMEjousbmvS+g61qpLUImkYqOySyL4GL833RdQ9eLaHpxzHIC4m+FYoF8IWeJT0pIFLV8xXWOB0fbhqHr5PIZIvEwRa1AuaGErMioqmpmPGUVVVHIF3Ps79nOC9v+RDafIV/Iktcy5IsZcsUsuUKGbbtfQkK2XDo0vYgiqYT8NThUJ4qssnLupbQ3z0OSJIpFDTCvkWyodHd309XVZWVaU6mUJRA3MhKmq38PRU1DlmR0ZDZ1bSPg9iMBiqSYGWXdIJUvWYqNykJg6BJ9fX1EIhGSySSxWAyHw0EwGKRYLNLf349ShFVtpxFyB3AqDvxeP9v79/Liy4/xhiUXUdSK6JpGPJ7gyJEj1j0s1MaFiNzf33Qjl1x6Mbt27SIajVpZsPHVL21tbXz57q/w2GOPce9Pf85w/xD1Z7RZWau+vj5+/PtHaPrcnbQEAmjZLNFdO/nl73/Bb27ZzA1XV3HBha0EA2aPrqaNPltFTbeIDcMARZaIxTOkUhqpVJhDkR5yLp2kkiWXz9Md7mdpoWBltiuRCuUZ33Lxwm9+8xv0dO+ir6+Vnu4khmEep24YaBrEYnHisRTFok5dvcLwcIJvf/vr7Ny5k0gkgsfjweFwMDAwwHfuux8/Hg72Hy45YeilS2hw3ilv4Poz32Sdv7eevpqrll3MM7vXcvef7+eu/7gLr8eLrmvMmz+fK9945TSeFpnf/baffftK+ys9OrphFUCha9btBEAsVrDEKsvPlwiYnU4nVVVVeL3ev4kA1u1243A4GBkZIR6Po6rqq04wyLJMc3MzHo+H2traMRoNmUzmhO1HPGuqqlJfX091dTUDAwMMDw+TTqdJJpNUV1dbxJ8gpmy3CRvlEPeG2+22nIBO5rOczWbZt2/fGAJ7OrAJBhs2bNiw8ZpHNBpl9+7dLFq0iHQ6jdPpJJVKWSrM01WB7+vr48iRI6RSqTE+6+MhAqBkMkkylaJYZZIRui76vs1gIJ/PMzIyYpXMxuNxuru7icbDPPCH/6B7ZC9XvvEKNODJp/5EMpYhm11GNBolHA7jdDqtgCIajVq2a7uPbOaLP3wvhq6zaMlChoeHJ5wXl1vlG7/9DC6XuyQWZ35W1Apco1xEOBw+KgkzFYG7fD5vTTjGnmaDRDrON375OZwOl3XOxmwfiaJeBElnRusMMrk0zY3NeNtTNAYD+Pz1+P1+fD4fHo8Hr9fLxz/6SU6dcQnz25bi9wbwuL24nV6r9PxLD3yQbDFpZoDGXXaPy0coFGL+/PlWBUOhUCCfzxOJRMjn8+gJLzdc8CFkWeZz37mV/7f6Nha0jIrmaZrGkzue464//9S8N8r24fME8Pv91NfX4/F4cLvdhEIhvF4v0WgUp9NJe0c757QsZ2nLAlRVpba2lid2P8cvtj+LLMs4VAdOp4ugJ2CJ9RmGae8o2mxEpr2uro6mpiaGh4epqamhrq5u0nv9lltu4e1vfzsPPvgg7e3tNDQ0UFdXx1e/9t94L7+awKx267oFmluYcf5F9K15hp/89kG++5ONLJvXbJEbhmEgyzJ9vX0kEwkcqgMJCQODfKFAOpMnWdjFSxtHUGQZVVGRHCr5WJSWzZ2ceuqpY++DcZoi4rdhGFz1trchN89gJJFE987hf/6kmo4YsnDGkJFUBV1vRMMsy88e6sIT7sen+tj40tNEIi/R3l6Nx+tGlmVWXaDi92sYxjYkCWRFQpZh3944P97YNYZgAHCqDi5ZdC6arhHeFkGXsgwnImx84aVpEQzXXnsDq1ZdbAm7Ca0VcU3Lr634eyAQmCDWaRiGZUvZ1NREQ0MDXq93yuM4mRDjFmK8r1ZrwGSaD2I8Ho+HUCh0UivcJEmygkQxFpfLhWEYJBIJkslkqR3Khaqqx10JY+O1BSG6WFNTw9DQkPUd/7cK+661YcOGDRuveUSjUdLptCWsJcsy0WjUsrET7hJTxeDgIAMDA+RyOVRVnTSTICoA0uk06XQKDMZNqM1goVAoEI1GSaVSaJpmkR83vfMGnnjiCZAN3vv+9yBJEv/w7nfwzDPP4HK5rBJ7VVWtDFw8HqdYLDJr1ix+8/tfTjgP5YhEInzy058gGAzS0NBQsXS6fJ1KE/NyTBYoFItF1Arn1zAg4A1x25tvZ86shRSLBfL5Aoahm9luVUFVVAwM7nrww/zkwR9SVVVllRI7nU4cDseYIAxg9RVPEtmjMLOpY4yuhYDX7SdfyFQMJHzeAG6Xh87OzgmfJZNJAB791VN4vV5kWcLr8hHLJcZsSysWqQ1Uky/mcLmclDMMHpfPypq63W7cbjcejwePx8Phw4fZvXs30XCEhnm1qKqCx+PG6XSWbCTNoN0slXXh8/np6OioeM7LIVwEqqqqqK6uPmbry80332wREl1dXTy/aw8zv/Q+ZHXi/THrgotpPfcC9tx6Pd/73s8nBET//rkvcXloJWd1LkdRVBRFYWBggP987H5+Hkziu+6dNDU10dzcjGHo9P7wO9TU1NDd3W1d0/E/MEpsGYaB5HRT+9HbaRTuE5I44yJ7rKAoKlLJjnNgYIADv/gJS5/dyO3n3cZPtvyeJ7sfY/UVfs45pxmP24Pb0461MQxLgPG5547w4Hcnz3BfvuR86/939Ozhe/t+d9RzPR4tLS0sXbqU2traaa1XDnG9c7kc2WwWl8t1XHaLJwPjr5+oBjtZqFQBM34cYBI5brebQCBwUjPDgmAYPzZd18lkMuRyOQKBwJjxjb/vbbz+IISgheBiLpd71fSkyu+9qT6rNsFgw4YNGzZe89B1nUKhwNDQEDt37mTevHmWJ3mhUJgw6TwWvF4vuq5TW1tLR0fHpL2QYkKbSCRwu9w4HM5SAGagKKY+gCqrSJJk9X9LkkR1dTVz585l4cKF3HzzzWQymTEBwqpVqzh48CBVVVXU1tZa9lEulwtZlvH5TDvDY/Vai/0FAgFqa2tPWm+2CDoNA0t8sRzlGVoz+y1jniO1NEZoqp3JyMgIs2bNsrLkItgef93mzOvkkfXPTjoeCShOMjlzOzzEY5XV3R0OB3PmzCGaDJcmdwpul5eR5ETxq4ZADZHEEF9+4MMYhtkmEKgKEIkPc86bFgBmVsrpdFrnPR6Ps2/fPnLZHLWeKlTVYZXty5I85rxJkoymTa0ndnyQMlXous6/fPVO/G+9HtkxedAlyxKKLFNXVzfhHnJ73MhSqUJBlpFg9LpZSxlm+5AkISsKVVVVY2wUxwdX4rc4H6a7hopjfGBYIhpM7YRRixF/wI/X48HtdOHz+3nHymuZuaeF79z7A7ZuSfG2t7dSU+PH6/Hi8XoACVmSrO0dGRzggWce5G1nXIXfPXlVgCwr6PqrKyorXCPC4bDlGvFK7BZPFsqf95Nt1VgoFKz2M9FyVQmvVkWFOHZBrgii1OVyWd9HJimdtqwthcOQTTC8PiHLskVEp9NphoeHyWazJ32/iqLg9XqtucXg4OCU1rMJBhs2bNiw8ZqHEP4bHh5m586dVFdX09HRYZUhTxeKopDJZKivr6etrW1CefJ4mK0IbtRSBleSzGBbUVTqgq3IPeYkW9d1crkctbW1tLe3Wz3zqqricDisyWUqlSIQCBAKhaiurray54qioOs6fr+f6urqY5bXCqu4qS5/vFBFeXypl310Am9YHhKjAYdcatMYJWFAorFqFj09PaxateqY++vs7GQg+mMmU/53O71k8pWF3FxOD4n4cMXPnE4njY2NOFwOEukoIX81HpePkfREgmFGTSt3XvtxdAwUWeGZPetoPKOdW9/7HmbNmgWMEgzivMfjcfbs2UOwJDpp9tx6rCqN8qORJWmMTeWxcDzByR//+EcOFgxmnHnOUZeTSoRBJVLL7XIhKzJK2b01WnFiBnpmBtdAUWRkybT3a29vn/I4HU4nDoeKwzk1n3Yhduh0OvH7/aRSKc6bvYJFjXO5a829/MvmvVx/Yw3nrmrD6XKZrh2yDEjMmhVk0TIX9zx7Dy6HkxvPesuk+5ElqSRGOnWMJ0+mes3Ks+GRSIRDhw7R3t5OW1vbtPzrX01MpglyImEYBoVCgXQ6bbUjVCJSy103TmZFRTmxIvblcDgsDQohfJlImPotmqZZBLQYd/n5sgmH1wcEweB2u8nlckSj0QkCwicDDoeDmpoaq3LCJhhs2LBhw4aNcRgeHmbbtm20tLRM6PGeLsaX5R8LkiSVBYgSkmTqPsxrXcaaXY8yNDTESy+9RDAYZMWKFZx22mlWz/TJzvCddBijRIJcOl/D8V7WHXiMdDaJ1y0E6kxBPglp1NWhhMaqWWzfthOuP/buZsyYQSQ5TK6Yw+WaWF3icflIpeMV13U7PQwnkpNuW5IkmpuaicSHCfmrcTu9JDJjlxfX+cqlF6E6VEDCqTrZ5xpm/vz51nKyLI9p8chkMnR3dxN0+TEMg6yWB3n0Hsvl0wxFenE63CTSUfJS/tgnQ4xpGjZ4w8PDrFmzhtu/eAfKyrM59OMHMHQDDJ3QomXUrDiDyNZNjKx93lonnYhb1puPPPKIpWuyv+sgy1rbx45F103BS0OIk5qZY0mWMEpE23SgKIq5zWnBwFHqa3a5XHi9Xnw+P1+57NP8ef+LfP87P2bDhp38/U0zaGoKWZUkbe1VfPkrZ/KpT66jOMUKkqlCVHYIonG6PfhCK0TTNBRFsZxJ/hbfH4L0zWaz01KnP579xGIxwuEwLpeLmTNnEgwGKy4nnFPy+ak/V8cDcZ0rVUwILQhBQAgSQrSqCUvjY5HaNl6bEK08Pp/vpBKHwrGitbWV1atXs3z5ciRJ4l3veteU1rcJBhs2bNiw8bpBOBzm5Zdf5pRTTiGbzVraC9PNAk235HxUmK08wDP/Vh2s59yFV/HUlod46aWXiEQi6LrOvHnzrInmZPv5v+Kbrhs6ChKFYo4dhzbw3I4/EIkPUlUX4KxFl1JX3VRa0sDQDSSZCQRDa30bz2x50Ao+xWf5fJ7169fTdbCLHdt3USxqjITDpFJJPnf/ewh4g2NdHAydkdgwikMmY8QAKBTyHD5yBKfDSSw1guqCt11zfamXXbPuE1mWQYLNmzfx5J+fIOSvoZDPs+PgBn609mFzFyKTXHb8pptFgVtvGzs5E4GGQCaTobe3l6XB2Ty652l+f+AvhEJVXDz3LFa0LCEc7uWO+99D25y5FAp5rrvh7VM6/9MhFwDu+8a3eOK3j7GyZg7qgQhyVwwJODh4mL4/PkbNT35J/3fupfbwIEvbFgLwXNZ0Krnznq/xzV/+mqZVF4Jh0LVjO4uUNlYvvcDavm4YaIZmnSPRQqIoCkyjz1dAlmWY5jq6prGzbx+aYiA5ZWRU8pkiqWKG82avZGXrIu5+/rv8y+27uOHGBi64cBaGoVAoyOg6JBJ5aDjGuCR5rHPMFI7D4XBYBANQMWs9GfL5vKUp43K5LILhbxWFQoFsNntSe8mFNk04HKatrY2mpqZJKxjE+SsUChW2dOIgKhhgVKen/DORqQazokG4bQjSTgR/5e8Ou5Lh9QFxf/h8vhNWcSjuRzGn0XUdWZYJBALMmjWLiy++mKuuugpd122CwYYNGzZs2BiPTCbD4OAgvb299Pb24vF4CAaD0/6iHi8YdiyoqsqR/h5e3PwVAr6QGTxjBjmFYpFCsYDh8RDJ5th24CCDP/oJv3/yKU5bvJiv3PHFCervTz75OA8//CsCwQChYLCs1Fzm3e9+v2VZNxXcf993GOgfMsmMUlZNL2XWPviRD7B48eIpb2syDAwMsGHN40iqgeIEdwiyIylqa9txudxW9lm0UMioZrm8LCHYgcaaGRw82EU4HLZ6UYeHh/nnT3yGnv1DNFbNpKVmNoqsUKs3UOPdhmZoXHXOjRZZIUkgSwrPbX2UN1y2iL/7u79DkiRyuRy33fpefGoVF668moA3RMhfiyxJyLJiuR+IMca6C9T6hrnpio+UyvrNthcJs8+/WCyiaUVUxWG1tvz3r/+Z2z/32aOep1wuRyad4fnel8gV8lx09aVcf/313HfHf/PJy97HxqWP8v5ffJavfvce5s6dO+XzP11CTCtofPLy93HRwnPGPBtPb3+Bf376fnObSLzj7Gu5cdVbAbjqvnebfeW6Tst1N9F48WoA+jeupz5QM/FYtQKSbp5T3TBKzirHV5quFAsc+vSHcXq9IFwCDAND1+nt7TbvL0kqVU6Yn+mahrcYZdXdq1FlqeQwYqBpOrWuRu5981e547KP8cz+dXznf3/A+nXb2bcvQzYj4/O4KBZh/rlzjjouWZKn1SLh8Xis+zEWi+H1ei3dgGMRBYZhWJaiPp+Pjo4OQqHQlPf9auPVDOhFm5lolxCtGQLi306n86RnhsX+JqtgGA+hqaOqqkVG5PN5BgcHrUoGoUlj47UPUcHg9/uP+z4tb8NyuVw0NTXR2NhIfX09TqeT3t5estksbW1tzJs3j0KhwKZNm4jFYlPeh00w2LBhw4aN1w2y2SzZbJa+vj66u7uprq4+7kzAdEgGVVWpaW2id+58pEXLEKr0hqahIpHPZnGlUqilbUVVhVg6xb7f/Y5PfvQjE5wq1q9fg6JsY9GiepwOJ4piThi++c0uFi48lTe/+c1TPo6Hf/8HLlx8LdWOkoikJCEh8eRLv2Lnzp1TIhjK+7/F7/Jg9pQF82lsbuCMM1aydu1a1q5dy6FDhygWi+QLWTRdt86JruugGGZgX7YNh+rE6wpw7733Ul9fTzQS43e/foSlM1fx7ituG5PNKxQKDA7389Lev7B07hmWtgNIpLNJHnrmXmbOWM2KFSusdZ59/hmuu/ZGfG4/SzrPOOrxzpmxmHW7nqS5rrV074wN3DXNtCMVgQ1I+LyBCYHU+IA/l8uhOlTCeopUNsWsWbNYvHgx/ZkwvdEBZtQ0A8e2BR2PcmvDKS0vSxjFifsoTUsrr2RMrKhJ9feRPbCfK4wpJjIAACAASURBVK/+woTxSEhIrlFr0kr2gVPFU394hGw2O8bGUfw+66wlfPXOeaiqVAokQVXNKgS3W55wXrZs6efuLx3G7w+QIslZbctZ2rSAb6z9X+KxLXz18s9w7sKVVFfXWEGiZB7UhHEp8vSEOIU94dDQEMlk0gp8hW5AJZJIvINE735/fz/z58+3dD7+liGcLk5miwRgtYsIgkH8W0DcK6JV5m+BYCgXNPX5fJawsBArjsViFvkEo210J6uSoXyMU31Oba2IE49jtUgczTa6nEgThJvP56Ozs5MFCxbQ2dmJ1+vl5ZdfJhKJsHjxYtra2jAMg927d9PX1zflcdoEgw0bNmzYeN1heHiYzZs34/P5aGhoOC7f8+lkhRVFobmlhZGlZ1Cz/EwrHjVKjgrpdJpEMkEqmSKZSpoxXDxK7rHfcs8997Bq1Souuugiy75MkiSWnVrNypV1uF2uUvYVTl0zwMGD+6Z1HAFfgIUdp1MTqh9zLAf7d0x5Iil6hYUtXjnBIEkS73v/e3E4HDzzzDNks1kr0M5ms2RcaYqFIrpuCv3pmoaumE4b4wP3yEiEH3z757xhyUX43EFuPO9jNNS0VDjfMh2t8/jTxocoFAuoqloiGCCdTeH0yJy76twx61RXV/PNb3+dm6//B5pqZ9FY0zrp8daGGkhmKms4mBAChqOTPbfLSzKZPGpWWajdi0mkw+GgubmZeafM49Yf/TP/dvXHiCaiJBKVXS5OFCRJQq9w7RVZITc4yJ63vZl8MoE66wLrMxEwyZJkcRDJniPU+kJUecf2vKuqilN1IBkTRcqM42iR8Pv9k1btuFxOgkEHTufUM7xCaNLldOL1+UinU3zq/A8QzyWp8gSJxWLkcnm8Hg8erweHw1lxsi/0FKYLn89nuQxks1kr02/qRPgmLJ9KpSwL1Zqamv8T/flCa6CqqmpSF54TuZ9QKIQsy2QymYqE8qsZAB9PW55YT1VVQqEQTqcTTdOIRqN4vV7rfXGyhHoBSwRT3JOVLDTHE3yCZLUrLE4MxHeD1+sdc63F3xVFGfMdK6AoCn6/n6amJlauXMmcOXOsCobW1laampqoqqrC4XDQ0dFBNpulvr7e+r7SNG1aVXM2wWDDhg0bNl53GBoaYsuWLbS0tHDaaadNW7F9up7kqqqiiExp2SqSbGZyPV4PTqdpRZjP58jn8xSKRRLxBPfffz+ZTIZFixZZVmZglOz3xu5n7rwguez0ssDGJBnpSpmQSlksIdZWKBSsQGe8toXb7cbj8VAoFCgUClbWLpfLkc2l0bSi1R6haXrJaQIkaSzJ0FQ7iypvPVe84foJFQ7lkGWZ9tY5ZLIp0ukkfl8QQzXPV5W/Br9ayy03vZNzzjZJhsVLFvKBf3o/c+bM4WOf+gjfuvs7vPvK21GVytOkhpoW0tnKLhTmuRu9N3TdQJZN8chUqvI6Igst1O5FD72YQH7oYx/ho7d+kEWt82mpbT7pon2yLEMFguGMuafxwDvuBMyrMn9G54TjKL9vZNWJVqHtQVUUVEVFliqooCvqCc1om84qU38mJGQkWcLv9+PxuE1NjxGZQqFAlRygWCyQSBRJJEyyyDAMPF4qVhhITJ8sKc9Yx2Ix4vF4qeVGs/qvy/djGAapVIpwOIwkSf/nCAYRLE/3HTzd/WiaRqFQIJPJTDg/5YFy+XvtWJUFYrny31MdU/n+plMRoCgKgUAAr9fL8PAwsVjMap0QoqDiZ/x9MhVMVqkgqi0KhQLJZJJ0Oj1G6FgQCuU/olJEEPjTraSyMRGi0ikQCIwhVlVVJRgM4nQ6icVilmsKmNfO6XRSX1/P/Pnzufrqqzn//PMxDMMiHk70O8MmGGzYsGHDxusO0WiU3bt3s2DBAiKRCD6fD7fbPeXJbXl7xPGUi46HLMmgqPj9fmRZJpFIEI2bGfJ8Ps/GjRu57777OPvssznrrLOQZRlVZKvKCQumL/x4PEXp2WzWCoTLRRdVVaWmpsb6t3V8pYloKBRi5syZdHV1WaXfOppFFsiyjKoq5vmogDkzFrJp1wviSEt/rXReJdwuDzXBRnYd2cKKU0atLVXVwfuv+Tz7e3aADol0jF//8nd84J/eD8C1176Vvzz1NE+99CsuO6OyiGJtqIF8PktRK06aDRUZffP8yLhUt5VlHg9RuZDJZCgUCtZk0e12YxgG/3P31/nI+f+Ax+k+rms8XYyvYBD7UxWVpW0LgInOJsKG1NB1CokI2aEBCtERdL2CgJ8kmYSbCL7LWiSMo5T4HvexTCPGL39MZUlGVSEQCOBwOMhkMmQzGXL5PPnS9dJ1nUwmg6uUVfR6vaMBlyRPi9wYD/FOyufzFAoFisUiIyMj1n4E0uk04XCYhoYG6uvrJ2i2/K1ClGuD+Z47GZluSZKsADeRSFAoFCbcX+ODffGTyWTIZDLWv0ULy/hxiusjrtFk96/I5rvd7lfUlgdmNYHb7UbXdVKpFP39/ezatYv+/n6WLVvGggULCIVC+P1+a2xifOJ9bD1zFYgEQRqL81W+3HgHpUrnUxDPuq5blTfBYLBiBY6NqUNUKrS2tvLWt76Vyy67DDDvLaHHkUgkSKVSVkWhqCoU37+zZ8/G7zedioSw7ImGTTDYsGHDho3XHWKxGMlkkkOHDjE0NER1dfUEVe6jYbxn+lRU0A3DAN2oGNGLzJTX67UylClVsYLU7du3c+DAAeLxOM3Nzei6gVoS9pIwS9INRq0gReB/rHHpul4KDMHUQCj7zNDLAuSxmbpsNmtN1oWPu8PhIBAIEAgEJgQJwj4vGAzS2tpKVVWVNVHS0VEU2QrIFEWxHDcMQxoT8C2ZvZJHnvsphqFjGEqltvfys0prfQfPb3mMKn81qsNhnitzy2bwZ+goskI6nR5zLb74b5/nqtVvQVujk0zH0SmJBhoGhmFWWBSKeb79my9TX9VkiWzqhoZuGBSKWfKFAkFvNfXVzQDsO7RrUoIhm81a92Q+n6eqqora2lp8Ph8bN25k544dpAfjfG/9Q4STkZNebuz0uPivR77HN174YSniNq99oVCgf2AAh6KyomMpd153O9U+s4RWls2AZc/27UQfe4r0dx8gV8hhVLhGkgQehwujZAdo2lTqFqlxIgkGs01h6tsr37UkyyjIVqYwmUiQUFVIJimW7ulcLkemdP+LTKHQ/DCDuONzSCjXZMjlcuRyOatCSDw7IhgU9ootLS3U1dUd1/7+GijPbgtryJNFMKiqSiaTsd7ZosqqfDkYSx5ns1ni8bgViI9fRyyfz+fJZDLWdZrs/nU4HDidzrLqptEA/1jvarH/8nUcDodVyXDo0CEef/xxNmzYwFvf+lZ8Ph9tbW14vV6LvMxkMmOIHNHaVul48vk86XTaclsS1UmC/PT5fNZ3Q/n5KT+eVCpFPp/H6/Va3wtut9siKGxMH6KKacaMGZx99tnU19eXkfNmWJ9Op0kmkyQSCRKJBPF43LoOgmQ42USPTTDYsGHDho3XHcQEaGBggM2bN1sTtalmlUQfaiKRYGRkZEoaDrl8Hq2oHbX8W2SFvF4vtdXVGHW1NM9sZXBwkEQiwYYNG8jn8wwPd5NOh9F0HbdbRdN0tKLOpo1hWlvjDAwMjBEAE5O+8SXckUiklKXKl2zxRj/L5/NEIhGr5SGfz1vbkWUz6BLbEuWw5ZPnyY6vXPHcMAyKWt4K6sxVxUR/YgtIY+1MsvkMA+FemutnlAiIiVoN2XyGNdseo2toO/6gl+3hPxMKhcZafkpmMKP4ZK68evWY9WtqavjSV7/IF/71iyQGC9QEGwCTAKGUnXOoLroHD+Jzh0zyoURayLKCqrjRZYkd3S9y7dnX4PN5+aeL38uCBQsqnpfu7m62bdvGwYMH0TSN+vp6lixZQnNzMytWrOAXD/8KXddJJpM4HA4WLlw46TmuhOlU2gB86KMf5i3XXmP15or7ZtOmTdzzz1/hk+e/h7v+8G2uuPsWbj3/em49/waLvDl10RIu8pzKm5dfxrZDO/jwz74wYfu6buBSnBAzW0ZSL28l1XsIt9NJautGXs6t4PHHH0fXdRYuXHhMwcKHfvEQh7sOjSHBdM0MdnoOh/nMJyKoqkpRK3LaSj+1NW403eDUU2vxetXS+TE1UY50p4inktz79I/QDB0D00bTwCBfyJMvFMjlzUAync2QzmQIuHy86ZQLURSFXC6H2+02yTPt6M/7VCGeY0FoFItF0uk08XjcIq1mzpxJMBg82mb+piACf+GaEY/HTd2L49DDmSpEi0QymbQqCcRYYKzYqHgPmy02o4FzNpsdU9VQ/l4VvfGTvQPF9uPxuJVdFmKw5ffJ+HYRUU0gAv9yjQNd1y2B01wuRzgcZsOGDciyzOWXX05TUxO5XI5EImFVVwiiY1SEdiwE8VDeziaOU1hklme9y98v5RUWYswimx6Px5Ek6aSU5L9e4HA4qKursypCxHUotzwW8xmRtKirq7OuuxAyPdmwCQYbNmzYsPG6g5gQDQwMsGnTJmpra5kzZ471xXus7IqmaVaGJx6PT2lSXCwU0PSJGaNyiEDd4/Eg11Rj1NSwbN5ctmzZQjQaZdu2bWzdupUzz1xBodDI0JCH1tYWCoUihUIep7OBlpaZRCKRMZNAQaiISYg4vmg0iq5rFAp58vlyUSiDYqFg9XKKgEZMHoPBoFU2PiZonwTlE9Ty5XXdrAQYtScsJ0AMyoUezZLbAl63j7Uv/5lzTr0UzSii6UXS2RTpTAJN1xhJDLD9yItceOl5/PCjD5BIJJg5cybz58+fVinohRdewNZrt7Lr2WEuPP3qMZ9pWpGiViDoC3Lj6n9kPMEhxnv3gx/l8tWXMX/+fOrr6yfNzvb09PDCCy/Q1dWFrus0NDSwdOlSWlpaUFWVzs7OiuudLDgcDqqqqnC5XBOyjYZhUB+q46s3fIY1u9bz5Ue+wW82PU44G2HPnj1jtiMhU4nXMAydBzc9giOfQvrkh9ENg46aGTRW1bPpyAE2RgqwP0VfZJAll5zOZz//r0cd73/++11cMusNNAbrTHeKsr7w/3fubRiGjizJ3PvcjwkGrqa5eRYvvriGF9bsxeNxjQnYkkk3F112BXMvP33M8yKunSzLVpD57LPP8tIzzxDpj/CX/S9y82lvYVX7CoKBIH6/n7xUwNBNrRFhVzrdzK3IGgvxNhGMplIphoaGGBoaorm5mdbW1v8zrREwWqHhdDqtoP9kij2Kkn2RzRcBdvl4ACsrLzLFYky6rpNIJEgmk1bgLN5rwjZX/FTSGrA0Z7JZwuEw4XDYet8LQU+xnPgtxiQqI9LpNLlcziIGxPqiikyIPm7dupVkMsmCBQs444wzyOfzZLNZ67gEGSyO73hFJ6eCfD7PkSNHGBoaIpFIoGma1R53Mvf7WoVoQxQijuUVM+WEVLFYtEg84RoBo0REeatM+bonbJwndGs2bNiwYcPG/yEIsce2tjbOPPNMy/rpWF+2qqri8XgIBoPU1tZOiWBwlRSej1YlUT7h0g0Dp9PFtddea2Ugenp6iMVijIzEmDFjKeeccw7nnXceVVVVqKpKKvX/2Xvv+Diu8977O9sXu4ut6IUEGwiwgJ0iRVGSVWw125KLYqf4Ji5x7OvEyc3NTbuxc1+/jpM4xY6TN44jW857nbjkOrYsy5Ipi6JYxN5AggRAEr1jF4vtdeb+sXsOByBAAiySHe/v81lBWzhz5szM7nme5/f8fnGmp6fluITQl77CNFv122QyY7FYsVqtaJpKLp9neLKP/vHL3G1ZK3uP9RUnq9WK2WxecEuJcJhIJpOSNqtpGrFYjFQszLde/XvKHHZy+SypdJJsPoOGWHCrZHIZ0pkUigIGi8ap3r1M5Lqx2Qr/xmwyYSuzs2RJI1t2LuePH/kYTU1N9PT00NXVddOLJ5vdSi6fmfO9QHkNg5Pd8/5bRVFoqFhJR0cHzc3N193PyMgIx48fp7+/H1VVqa6uZtOmTdTX1/9Ui6LtXL2V/7P8Kzyz99/4yr5/5anH3om30s+H1r8LEIvWOdwojCY0BT7/+O/jMTpwOl343V7K3W7KHGWYjIXr9rlTe5hQ5p5/PSr8AZ7a/Ag1nspr3vulr/4WwVTBv93hK+fXfu1DNDc38/GPf+KmjllQyFOpFC0tLezatYtjx45x8OBBvnbmu+zvPc7HdvwiDVotKTXN+MQEhw8fZuPGjVit1luq0ItgMBaLSXaRcB15I+wV7wTEd9Ot2JQuBIqiYDabsdvtOJ1OXC4XFotFBmKiHe164o5CpV+0WUhtkqKo4o2SSMJuWDAdnE6n/M2ZnXzUJxiEJoJ4zLZj1TQNp9NJRUVFIbGVyRAMBgmHw1LUUmhdKIoik8/i9+FOwmg04vP5MJvNhMNhIpGITO6IFqASFg6h+TI+Pk5nZyc9PT2Ew2EpICyKCbPbWsTD7Xazdu1aVqxYgd/vp7y8fEFFgsWilGAooYQSSijh5xahUIhYLEZrayuhUAiv17ugNglBr3W5XAu2WbNaCl72RtONe4wL9pVgtVh45JFHMBgMDA8PSzrspUuX6OrqIpVK0dDQwJo1a6iurqa8vBy73Y7D4ZBJhzm3r6f3GgpJhnQuwanOg5ztPYRqyvDwO+7hgQceKIzdapXWeTcDQbcVFF9BB04kErjcLlZsrOZd73oXqqoyNTWF1+ulsrISp9NJeXm5tGETFGWx0BbBwnzj0i/CxXGL1xcCu91OXs3O+Z7fXUlH/9FiK8e1bRoAdb4VtJ85z5NPPjnnNsR4xsfHOXv2LPF4HE3TqKysZO3atZSXl79pCYb55mj261azhY89/AEeXncvn33ui7QPdNJXNyQ/O1fIaDKZMJvMuGxl2BUbNrMVQ1GzwGwyoZ/LhZyryVCQ/DxaBzmjxov79lBZeW3y4WYgqoJWq5W77rqLbdu2UVFRgaZpnHGc4cyZM/y3H/4Z72h5kAdX7iQUCnLo0CHq6uooLy+XrTqLFfnTV/ynp6eZmpqSVH2TyfQzxV6YDVVVryuOeLsgvrdFcA+F76AZid1ZiQ59Mla0vtwMRKAHSJq63++Xori3CrvdLq0GQ6GQZEmI79PbtZ/Fwmg04vV6KSsrIx6PMzU1RTQalcyNUoJhbohEgf4hkpvBYJCenh727t3LoUOHGBoaIhgMzvhdn11QEKyVuro6Hn30UXK5HEuXLpXsF3E+9Ne7/rXFJiBKCYYSSiihhBJ+biG0BcbHx2lvb8dkMrFq1aoF9Ycu9gfXYFDQFihnr2lqoX87nyOVSrF8+XKefvppGhsb2b9/P/39/YyNjdHZ2ck3v/lN7rvvPh566CFcLpekRF5vfMK1IBKJEA6H+NeXv0AiH+a+t+zm07/x+6xZs0ZW/CwWyw23dyOIBYrH46GpqUm2C4jFTUNDA9u2bSOdTjM0NCTFqESFTwTZov8Yrg0E5sPszy/mOBwOB5l8as7jCXiqiCejxfaOuRdgS2ua+Y+jP7nuAm0uhfZcLrcohshCsBgdhtmJmYVgRc1SnvnIX/HCiZf5qz1f4UTvWX599y/NWw0uyG0oQEEIMZPJXtM+ZFAM5Bdwz5S7XPNaipoNpkVbRS4GiqKwevVqzGYzmzdvZmBggEOHDvEfx/ewr/cIqXSKH//4xwwODtLa2kpLSwtNTU00NTXd9D5FT73H42HJkiX4/f7beERvPISYotfrveP7mn1N69tp4I1xaJndqnY7YDabWblyJTt37uTkyZMMDAzI9r+dO3eyYcOG27avm4HRaKSyshKLxUI0GmVsbAyr1YrL5XpTx/XTinQ6zfDwMIODg1y+fJnBwUEp1JlMJpmamuLKlSsMDQ3J1hMh9CgSkSKhFYlESKVSpNNpxsfH2b9/P729vXg8HlmUEEk34UThcDioq6ujqqoKp9O5aIZUKcFQQgkllFDCzy2EKvfY2Bjt7e34fD6WLl16jfjX7UAylcKwwB9oVdNQtYKyfjqdprGxkVWrVuFyuWTFLR6P09fXR29vLwBNTU0sW7aMQCAgqbr6fl79wlmonsdiMR594lGqqyt561vfSm1trWRx3M7+WLGodrvdmEwm/H6/DKRTqZTsJ9X3Lgua5+ztCCrxXO/Ptd/ZlcnFHJPL5ZozwQAKVf46Eqk4qqoWg4WZLAZVVanwVjMdjjA5OSnbXMS4xGf01SkRdHznW/+H3/iN37hha8VCsdiEgb43fPbr7X0X+cxLXwKKziXFa1VA01TWr1zLie6zfOjr/x2L0XzNvCuKAuLcaJDPq2SzGdR8Xi+7gUFRFuTQIujtc0FQ2u8UFEVh5cqVrFixQiq2u91uYrEYPT09JLIpDh06xKFDh7jnnnsIBoOFa6OiQrqvLPZeS6fTxGIxGhoaWLFixR07tjcCQptAOKgslmW0GFyPlSO+L+90q8aNxnKzsFgsrFixgh07djA6OsqlS5dob29HVVVqa2tpa2u7I/tdKMT3fllZGRcvXmRycpKKioo3ZSw/jdA7U2WzWcLhMN3d3Zw+fZoDBw5w+vRppqenJcsNrv4eCgaTEHEUGklWqxVVVRkdHSUYDJLP50kkEpw+fZoTJ04AyGRCeXk5FRUVeDwezGYzPp+PtrY2mpubCQQCeL3eRQlzlhIMJZRQQgkl/NxjYmKC06dP09DQwPbt23G5XNddiM225FoIcvk8yk1YsGUyGWmBt3z5cp544gkaGhpobGzk4sWLdHZ20t7ezrPPPsvDDz/MY489JnsqxfiSySSJREIG5aKCZrVaed/7nqa8vJxAICCVp2/3IlRUViwWC9lsViYwcrkcyWRSeq3rEwgi+aOHPlBeyPwrikIqlaKjo4OxsbEZyuaioiMeqWSadDpDKpkimUyRSqY4d74dh3LtIlhRFHzuCvJqnkhsGrfLM+P9Pcf+nUMdPwJFQdXyjI+Ps2TJEskIEYhEIoRCoaLYporVasWomDFqNp75ytf4i89/7lamXcJut+P3+2UP/41gtVpxu93X0MFbW1v56y9/4Zo517uCiOSEoiicO3eOZ//pq/zudz/Lh3a8l5balcXPGzAoRVcODTRVnWFTKWBQDGSL4qOapjE8PEwmkyEWi2G32+X5D4WnePXC61SUX6WBaxqUW8sYmxzn/PnzhEIh3XuF/Qg/+NsFMW/33HMPgUCAzs5OeY92d3dz5coVMpkMfX19HD9+nM2bN7Np0yZpT7tQzJcA+lmFCJIEBVyIWt5uXO/7YqHfKbdzLLcTJpOJhoYGUqkU+/fvR1VVQqEQ/f39hMNhstnsDAefNxMGg+G2M7R+1iGYlIJ5cvHiRSYmJhgbG2NgYIDp6emi0xPSycPj8VBdXc2aNWtYtmwZDoeDsrIyqZEk7qlYLCZ/a8bHx+nt7ZV6TvF4XCY0Cg5Vk1LgdHh4mCNHjsgWxcW0dZUSDCWUUEIJJfzcIxgMcu7cOdauXUs8Hsfn8133x1QkGBZDvXbZbIx94c8J6/ynNU0jk0gQHe7CaNIFfkX2gsXqlFZhAEuWLGHJkiXU1tZK/2shZNjZ2YnD4WDXrl2Ul5fLxYUI4qenp8lkMqiqKumQgqLqdrsXrD9xMxCJA709GnBdBsNci339+wsJBBRF4fDhw/zr179Dy/I2zAYLZpMFo8GEyVD4a1TMhecmMyajGZOxDIvJg9dk4S2r11HhrZlzuyaTCbfDR//YJdY6t8x432K0cd/99/KXf/3nsuKUTCalwJpAJBKhv7+fUCiEpmmFudEsvOfej7J/339IW7cbYbbom36cAjabTSrhzzdXs+c1k8nIcyOwefNm+fnZj9nnpK2tjfe973189Nc/yi/8w8f51Xveyyce/FX5vvikqu83VlU5lnSusOBNJBIMDg7y/qd+geryCk5cOkJFtYO6ugKlXjUH+XrH36EooFDQfRgbS5CKW7CWefntP/0rjGbLVdYEMN5/iT/9vd/iIx/5yA3ndyEQ2gwWi4WtW7eyZcsWTp48yYkTJzAajYyOjjI2NkZ/fz8dHR34fD4SiQTV1dX4fD68Xq9kNCxkX7N7pn9WIe6lgshsIcEAvCmB8BvBYJirT/52wGg0UlNTI1sRjEYj09PTsj8/Ho9L+vubCb3oIDCDXfSf4XpeLMQ1l0gk6O/v5+jRo3z3u9/lwIED8jMiMSRESoX9ZENDAy0tLTzyyCPs2LEDj8eDQ7e+ENsHSCaT9Pf309XVxdGjRzl79ixDQ0NMTEyQTCbJZDJEo1Gmp6dlIUK4At3MtVpKMJRQQgkllPBzj2w2SywWY3x8nJ6eHqxWK5WVlfMu9vV9tAvFlz7/lySTSfnvBZ577jn27v1zPvCBhsJ7hkJlf3Qkzpe+FJlzwVtZWcmmTZsYGhri1KlTqKpKLBbj/PnzPPvss+zatYtdu3ZhNBrlPr1er1wo6CvpIvC909ArrotFrgiUxP6FENV8C03BQBCCazdy/HA6nQQCAbaveYC33/3Lxc/evmP1u6sYCw2wfuXWa16fUsKYzeaCjkMxUJ99HicnJ7lw4YJkV+TzeRRNxWuvor+/n8985jMYjUa5CNULbomKq6qqUsTN5XLhdDrlnAi1+KmpKUKhkPyrn898Pi8/J7Y3MTFBf3+/DGJFIk1UugKBAIbiuTIXNTrGx8fxBwLkc/kCI0ErjDeTTjM8OMiOpk386NRe9nW8zmce/100DcLRMA5HZcG9JJcjFosBBYaD0WDEpCq8/MrLfOAXf4WTJ09SafHy2bf8Nu8e+hU++CEvy5e7CrZ3BhcGoxFxKaiqyssvD/GNH7qoe/en5QJdPCwWCxx/iXRmbgHPW4VIllRXV7Nt2zZ8Ph8bN27k+PHjnDx5knQ6TTAY5JVXXmFkZIQlS5awdOlS1q9fz/r16+/ImH5aoSgKbrcbKASYwgXnjRL/0zSNbDZLKpWSYpN3WrND/L0T37tms5na2lpaWloYHh4mnU7T1N3bggAAIABJREFU0dHBq6++ytq1a1m5cuVt3+diILR9zGYzkUgETdPkPSl6/X+eIJgLly9f5pVXXuHQoUOy7REKieH6+nqWLl1KS0sLDQ0Nkg3ocrkIBAIsW7aM8vLy6yYnzWaz1D7yeDxs2LCBaDRKIpGQjL5oNEooFKKvr49gMCh/e5LJJOl0mlwux+XLlxd0XKUEQwkllFBCCT/3EK4E4ofe6/Xi9XoZGBjga1/5KlORMJ/4zU/InvjZlduFYL7qZFlZGRaLCavt6k+ywQAWqwGDUZmz2iXUxy9dusTSpUul8JOgVRoMBtra2rDb7SQSCekqoQ/sAUmbfSOoqoI+L/zbRZJGn3AQ1mXis7MTDaI/FZBMiNkJGP3/W63WgsuErM7DXLaJc7+mH7v+MyJAgAp3DcPBPkC5Ztv5YkJAHI9wzhD/n81mGR4e5vz584yOjsrgxuOsIh6PEY1G+eIXv4jJZJKJAH37gdCwyOVyNDY2smTJEioqKggEAvIzQjF/YGCAvr4+BgYG6O/vl+0o2WyWXC6H1WqVDIdcLkcmnaGtqpmNta0YDEYUCtdM52QPp6ODWJvXYzAYMRQDdoPBQOjoD3G/7e0Yyn2FuVAUsgN9OA69zlPrHmJL/ToqNnn558Pf5le+/jvk1Tz9wWHqHJWoqgbkicdipFKpwr1iMtFQVs2vrnknalplXWsD2xrXMTk5SS6bJxqNEwxmyWYz8t5SVQ1VhWw2RyyWIJM2MTk5icVixmy2yM9pmoYGxBNxksmktBi8XRCJsIqKCmpra1m9ejWJRAKfz0ckEmFgYICBgQGOHj3K66+/zurVq2ltbUVRFJqamuQ4r1fRfaOo/HcaiqJIoblQKEQ0Gr1pp4abRS6XI51Oy3vzTiYY4M7qS4hWiXXr1pHJZOjt7eXixYsy+djY2HjHWlD0mOs7WVyz4n6LxWLyfDscDilQeKeSL4uBGLNeJweuZQ/d7G+ncImIx+P09vZy4sQJmWAQSRir1YrP56OlpYVt27bx4IMPsmnTpkW1uoh5NJvN+P1+/H7/jCSTGIewvuzv7+f48eP09PTI78rp6emCpXQqVUowlFBCCSWUUMJiIVolDAYDX//nZzl79BR31bRxoH0v1RWV/PGn/wS4Vvn/VqAoc1v5FXY098ui4tbU1MQv/dIv8eKLL/Lcc8+RSqWYmpqSvtjCjUGwAt4siJ5bsdgRYxFVK6vVKinm5eXl8t+J1wX0QlgiiEskErLyLhaC4nk2myUYDBbnKw1oiFOmoRXnV5PJAfmePLcFxcF8Pkculy9Q8BUFo9GEyWzC46jg4uCJAq27SM9HKehtxFJXldL11NZ8Pk9HRwdnz57lyJEjHD16lKGhIVS1IOhZUVNHNp8r7jd/DaVa1bUQiOfBYJBcLsfIyAh2u10u0AUVWVBfo9HojJaHnNWGobYC1ekiaTBA8VpUey+zs3ETjzTvnnF9Hu4/w6GhGLZ7HtSxeBQUxYDy/e9g3r4Lg8t9dS79FbhPnefJlocK21XzfHjr07TVtPDsie/SNdnLXY1C3V4jr6poxQAvm81iQGF7fRsKMBEP8d5vfBSzBcwWMBrLyGQK2xSL5M99bpjx8VyR3aGhBWqL9ocq2WwOQ5EZkUjEiUYiHH69m5XLl9PS0kJra+ttudbhqn5IOp0mk8lISvP27dtxu92cP3+e8+fPc+HCBbq6uhgfH5cJpL6+PrZs2SK1GX4eKrqLTdbe6r5mQ2hAiHvrTo7jTrVICFitVinoGA6H6enpYWBgoKAb4/NhsVhYtmwZDQ0Nt33fc0FVVVkBFwnMfD4vGQyZTEZe56JVQDgXvZkQzJZoNMqVK1cYGRkBCr9ZNTU1VFZW4na7r2lJWCjS6TSDg4N0dXVx4MABjhw5Ql9fn0wE1NfXs2XLFtauXUtlZSW1tbU0NjbeMY0kq9WK3++XgpHr16+XDIZ0Oi2T0S+88MKCtllKMJRQQgkllFBCESLBcOb0GbwJG199359TZrHz9LYn+OTzn+F3fu93KSsrmxHoiiryzUJVVbhmsakVesmLFTXxEJVnoalQW1tLc3Mz4XCYV199lXg8zvT0NKFQiImJCdxut6xoz6VpIBZ7+grN9TBbD2G+92cnBQQFM5vNyoeoZIkKv14bQGxHCDEKCLFKMW793AhWgFgIifdHR0ela4Y+caBpXPP/IumgqiqqdrWKmc1kyWTSUBQmtFotWK1W7BYHiVSUVColj1tRFLKZDMdPHmPPnj1s3LgRt9tNeXm5PJ9nz57l+9//vgwwxfVjNduoCyxH01SMRiNO68IECHO5nGyz0AcvsyvgJpNpRgIibrLg/NB/xVRdS9E4Eg2NyD99AcWgXG07KOZiCgkWkSAS94CoThbmySASGArkcoXznNNdWyajkZ1LNoKi8L3zP0ZRigwRreBAkddUVPXaIC8Ym8LtVvj9P6rEZCy6cBS3q6oq0UiWyckclt2fxOJv0Ok7qKg5FcgV91HsSY7GONF/iopAweJRiHAKJsOtLOLF94OohgvxtY0bN9LW1sbx48epra1FURRGRkaIxWJMTU0xMTHBiRMnSCQSVFVVSVu/udqx3uwK7+3EYl1ObmU/872uZ3fd6XHcSfaJxWKhpaUFn8/HiRMnOHjwIBMTE0xNTclks9FolG5Ds1ltNwv9Mc3+jUmlUsRisRm/MxaLRQbxZrNZJhhSqZQMbGdvV39ubvc50idwhY1zIpFgfHycEydO0NHRgaZpOBwOWltbWblyJdXV1QQCAcnIEAyz+bYv/uZyOcLhMF1dXRw+fJiXX36Z48ePF77znU4aGhrYvHkzTz/9NPfee+9tPc7ZEHMqkjrC+vZWUUowlFBCCSWUUEIRkUiEnp4eNm7ciHE0i91kBTRqPJVsqFjN3/3NF/m9P/wfsrKhaZoUdpsPN1pIjo+Pk8lmyaQz8rW8mieTzZDJZBgYGMDhcOB0OjGZTIyPj/PhD/8a733v+3j00UdZtWoVgUCAtrY2LBYL/f39tLe388wzz0hXCbPZTDwel44JggIciUSYmprC4XAQjUZnLI70izo9XVRU+fQBwezF4Gyxxo6ODk6ePEk8HieXy3HmzBkZlE9MTPDKK68QDAZnzKN+O2L7IoEgxnGjh6ZpDAwM4MzWMT4xTl7NkM3lyOezZPMZ8mqObC6DquYZmx4inoiQyafJ5dJkcpkCc0HNklcz5NUMBsWK0+bG66rEX15FIh1lLDjM/3r2gyRzE1gsZhQgr+bQyPEHf/AJAoEadu16QPaUq6rKlStX6O7ulpaFAjZrGSsb1+J2uqmqruLXP/bB6y6kxYJ4aGiI/v5+yV7JZArXjsPhkD7zmqaxbNkyVq5cKVkV//hv34SKCmw1tTO2my1zYDXaqKmpKe6/MP/eSC/mCSs1NdWomkYmXXDeUBSFaYOCw1GG0ekoMBoUhYzbUxSdqyi0WujEIN/m8/CVY9/CYDZhRCGTzqAUA2m73T7DVUEBIloSo8lEVWWgMKbisFRNw2BQyGaSqFqImlUbMRhEy8rM+0/MSzaXBcXA9HSEY8eOYTQaiUQibNiwgQ0bNnCr0AdCs1t4FEWhvr4ek8lEIBBgy5YtUpshlUoRCoXYu3cvY2Nj3HPPPdxzzz14vV48Hs+MbfxncpHQB3eCtfNGQp8MutOtJ28EW0NRFMrKyti2bRvT09McO3aMzs5OOjs7yWQyTE9PMzY2xrp1626bJoO+V1+wyPTaOm63e8a8qqqKy+Uim83idrulKwwgk3wiaSwsTIXbinBIuJ1QVZV4PC6TfBcuXJC/j0NDQ0xOTsqxnT9/Hp/Ph8PhIBAIsG7dOpqbm6mrqyMQCFx3P/F4nHPnztHe3s6pU6dob29ncHBQMiOWL1/O7t272b59O01NTbf1GN9IlBIMJZRQQgkllFBENBolHo+zfft2BlLDXB7rY3n1UhQFPnHvB/jv3/sz/rWxni3btpJMJsnlclgsFunYcCPoP5NKpfjGN77G8PAQV64McPbMqAyKVE0jncoRDsPf//1fFanuZgwGhQsXzpDPBxkdHWVoaIimpiYqKirYuHEjyWSS0dFROjs7uXLlCg6Hg3vuuQe73U4kEiEWixGLxeRCWlDny8rKZOVo9jj17gCiAqXXAtAfm9iueF8EDK+99hrPPvssiURiRiAh5uHAgQMcPHjwmoSFvnKrD0LEuBZS0dI0DavpIscu7gWUohCgbtxo5NUcdkeS1c02LFYFl1XBbAaLRcFsVjCawGhUyGU1wmGVYDBP75BKLJ7HYFUpcxt5+8MOamvNUmjQbFIIhvJ8+R8v8Y//2D1jjHqRRpFIAVhSvYrmpjWo5Cl3ufjkJz953WBEnI+zZ89y8uRJBgcHGRwcJJFIkEwm8fv90mteVVV27tzJ7t27cTgcWK1WXjhwiLjfj6OqcsZ2xxwOzKqZqspK0O07MOHHZFCoqKwsLMhjceLxOIYi26HMXobJ6cRoMGAwGEm7XCgGBb8/gNlkKiYQCu0LX/jxMySySYZi4yz3NpDJZDAYFNlO4PV6Z1yDoXyEWCzLH/3xlcJriOtSzIWKxawy+e3fKpxVXdIrn8uhlFWy9P1/TiKRJJVKEjMYSCZTtLe3k06nGR4eRlEUli9fjsVikQmhmwkE9edsdsCsKAq1tbXU1NSwZs0akskk3/zmN4lEIvT29tLf38/hw4c5evQo2WyWqqoqli5dKm3i9Javb7YjwO3Gm5VgEN9t+u+wudhYtwt3MskgtltWVsamTZuw2WxEo1EuX75Mf38/w8PDTE1NyYRudXW1TDjezJj0vw2CqSAYZKJNy+PxFLRwdBbD+XxeshYEk0GfSFZVVTLPhAuG1+uVyQir1Sp/Z26GiaFPmuv3c+nSJX70ox/x8ssvy1bD68Hv9/PYY4+RSqXQNE2OZXaSURzn+Pg4x44d4yc/+Qnt7e309vYWHIncbpqamti6dSsPPfQQO3bsWNR5+GlDKcFQQgkllFBCCUWIBWYoFKK5rZW//clX+dy7fh+n3YHT5uBTj/wWv/mF/0XrV9bg8XgoKyujqqrqugJx8y14XnzxRU6f3sO731PL9u21Mo4rUMY1NE3FYDBisQygGAwYjQbQ4OGHK/nOdxRpb2c2m1m6dCkPPvggsViMs2fPyp7J0dFRzp49y7Jly/D7/TidTslgUFWV6elp7HY7DocDr9d73YBFv5CcTQXVJwb0C/NsNksmkyksCPMmHm99hJ6pQdA0VviXYDAYuDB+mXNjXcSzyTnnTnh+W61W0uk0qVSq0J5QHLfT6ZwhfHhVG8Aw4/ns1/WPeDxOLH6ej/3XqqLNYbHFQDGgGAxomno1+FA11OL5ma2NUNiPQZ4/yGIyG7FajWQyGTkG4fogaPFWqxWTwUJL/WasVhtGo4FsNk8kEpEK+/NdW0ajkbq6OkwmEy0tLVJnIZfLXcMEqKuro6ysTFb/9OyU2dvVtMJxGua4fsVx2Oy24tyDQVEoK7NjLy+/GkDZ7WQotGYokvFSSPJ8++jzPL7mAVbVLcOUNxTtPCm2hxTOuXJ1h9itZZhMRn7zEytAQcdSKIzRZDagoGE0mTAoimznQFGYGI/zZ39+BYfTidVqI5N1kPf68C5pZHJ0mOnpaS5cuMAPf/hDhoaG2LFjBzt27Lgl8Ud9IDWfaKAQNd2+fTvl5eW8+uqrvPbaa0xOTjI1NcXJkyfJZDLSXWLlypUsX74ct9tNQ0PDDL2Sn3WI7xYRnN6pfcx1LoSLgb7VTZ/4uxPjuNMQbRCrVq3i3nvvRVEUzp07R09PDyMjIxw7dkwKA2/fvp3169djs9kWLbCpaZpMaKqqitVqpaysTP4+CKFbg8EgkxCjo6OMjIwwOjrK+Pg4sVhMstv0TBIh6ptMJslmszKxYLFY5G9vfX09a9euZdmyZYtqsxGtClNTU7z++uucOXOGUCjE6OgoFy5ckLbOglElWjrEMQg71Xg8zsmTJwkGg7z22mtUVlZSUVGBx+OR1quhUIhwOEwikWBqaorLly/T19fH1NQUNpuNxsZGmpub2bVrF9u2baOxsXFxJ/unEKUEQwkllFBCCSXooGkawWCQ1atXcyh4kK+9/h0+8Zb/AkC1u4IHmu7i/3zr33nsHY/jcrmoqKiQ1P7Zi5vrPfd4PNx9dx0PPjhbbEspVqZzxYWogtFomEEJtdrCuN1uvF4vJpOJ+vp6lixZwuXLlwkEAnLBNzo6yqlTpzCZTPh8PpxOp6Raq6padLAoCC36fL4bVkTnaoHQV+VVVeVP/vh/YjNbyefyqJrK40++ncrKShSDwi9uegdGRSGvqqhqHjQ47j3HxckrlNvLr2FECJFCkUhIJBJEo1GcTidutxu/3z+jl1gkPsRfId6mf65/CDX1cDjMyy9fprwYHBcCEa1glyiZGIUx60UkRcIhr+ZR82pBRLD4eVVVGRnNYFBMBAL+YqW/8H51dTV1dXUoikIwGMRqtWJVHLSt2lkIrBVwO3wMDQ3NCCJnX0/iHNTU1FBTU3PdczcXjEYjzBFwaYpCVl5/uv0pV8+5EOUUlX6D0YjdZsdZbMkAyJbZyRarmHooioLdauNX7nkXy+qWMjo6elUBQi0wYCxm8wz2hNVixWYzsXVb/aKP0+W0YjL34igrCLLl83nSbjfl9Q2YKCQUe3p6CIVCnDx5ElVVWbFiBS6XS14TN9OOcD3bQ70I58aNG1m3bh1QaBm6dOkSyWSSzs5OLly4wMDAAMFgEE3TqKysxGq1Ultb+4YI4emTaDc7DwuFmK87FYDrj0UPkWAQrwtq/q24Lej3MYNNU/z+0H+PzAf9/b7YZIfRaMTn81FWVkYmk8HpLOi5BINBIpEIk5OT9PX1SZvjiooKfD6fTFreqAVBzGMulyOZTBKPx6UbjQjIhT2vYAik02kikQgXLlyQj+7ubiYmJgiFQjJwv968iPvG4XDQ3NxMW1sbRqNR/hbqk82zH3DV4UXoAg0MDLBnzx6ef/55pqamiMViMxLbLpcLj8eDw+GQCQ/BdBSJ3PPnz3Pu3DnJvlq+fDkNDQ1Sr6m/v5+hoSGmpqak1pAQPvb7/axevZodO3bwwAMPsGnTpkWd559WlBIMJZRQQgkllDALwWCQrq4ufu0jH+S5f/sP3jv9GFXuAtX83Zse5cPf/kPe98vvx+FwyGBL75Kgx3wLw8ICbm5K6myqqqz8UqD0KxREskQvqqjcrFu3jg9+8IPs27ePffv20d/fz49//GO6uro4ePAgbW1tbN68GY/HIxkAYiG1EKeJ2VTv2f8fj8fZ84OX+MSuX8FoMPK/j/wHazeuZ+fOnfh8fjx+D2UWO7FYjEhkuhDEKwaqq6v45Q/9F7kgE/vSU2DFgjWdTmOxWKQFo91un8FWmO9xPVZDKBTilVe+KRfhBVHDYkBVZCTMxVqY/SjsqyCAqGkagwMZVq/ewtNPP002m5Vj6e3tlXZfmqZRZnOwY9Vj+Mr9RQYLlNu9DA4Osnz58jtmK5fNpIlcOEc+nSrsVFFQjEYyI8Ocio1zcfhSof2n+PmeyQFSk+OMvPQ8aiaDls9DsdqYDocY+t63sPsrCudQzZMYGSI5Ncb3TryE1+Hm3tV3Xb1mUKR+gsFgwGA0oAmBzSJLRNhjApgMBm4m7vz8X55g//5JonHd9WpQMBjN1NbV8QvvfpJDhw7x+uuvk0gkitfCK4RCIclkKC8vvy6TZDZmBzYLCQ6FtazVaqWzs5OLFy9y9uxZzp8/T19fH9lslomJCdrb29m8eTMbNmxAiLPdacTjcRKJBE6n86ZV86+H2ffonWIOzNWaIL67bTabZE+MjY3R1dVFdXW1bDG6WQhtglQqNcPZJh6Py+9wMY7ZyQ+9XenN3v8mk4mamhqMRiPl5eVs3LiR48eP097eTiQSYXx8nJdeeonBwUFaWlpYvXo1K1asuGEVXdM04vG41NJxOp3yeAwGA9FolCNHjnD27FlpcyjsQIPBoHxMTU3JhLhIulz9Lp35EO8JMciBgQG5vb1798rz6PF48Hg8kilWWVmJ1+sln88TjUY5deoUnZ2dxGIxgsEg7e3thMNh0um01Iuoqalh69attLa2UlZWJsWI8/k86XSacDjMuXPn6OrqYmhoSCYARbtVNBqV51Ycfzqdlte5x+Nh48aNbNiwgTVr1rB69Wpqa2uvN+U/UyglGEoooYQSSihhFoLBIBcuXGD9+vW8831P8fkffYXPvfN/YDQY8Tu91JVXMTw8LAXhxCJ1MdU9k8lUCOjmSTD09MR5ec8YH/7IsuICq/hmUc5fJBf0wad+kXL+/HlGRkYYHh7m9OnTeDweHn30UbxeL42NjbLiY7fb5XZupGdwI+TzebwuD49teACAc+NduFwuNm3aRE11NY5yJwGXDyjoXShKHkUxEAhU8NGPfhSPxzMjYJ8dcIjKokg63GicQqNAH8DM1WecSCTIZg2LtgQUGgCKMvecbd2aJhzewsc//nHd5zX+/d//ncuXL8u+b6Nm5e51D2OxWCnoQ6g4rG6GhoYkFfdOJBge2n0Prx05iq2/W1KY87k8MS3PuBrlS+3/ht1ml5IVYxMTONLjOF74C0wmpWBRSaFdYdyYpHrfMWq8lYU5RiGZThHyN/KtI8/RPniRc5/9idy3YjAUkzgiEC9YeKqaYIfkpTAkIFtPFoun3rWMxiVlPPPMgHzNoBgwmkxUeir54Ac/iMPhIBgM0tPTQzAY5MiRIxw/fly6OdTX12Oz2RZU2ZX7WGSwbDAYWLNmDS0tLVy8eJGOjg4A+vv7mZycZHh4mMuXL3PkyBEymQx1dXX4fL5rvgNuN0SAPDU1JSvHdwJ3Updg9n5mP7fZbDidTikCOjw8TEdHByaT6YYJhtlMhdnXaD6fJ5lMEo1GyefzZDIZIpEIiUQCh8Mxw9VlNsNCtCss9JrTz59MzJlM0pGktbWVcDiM1+slnU7T3d1Nf38/Bw8e5MSJE+zcuZNQKITdbqe+vn7O8yHGJloF4vE4LpdLfncKe9bR0VH27dvHc889x+joqBRJ1I9TfCcL1pn+N3SupLCe/ZHL5ZicnGR0dJQzZ85IRpXD4aCuro66ujrq6+tpbGxk1apV1NfXS7ed559/nr179xKJREgmkzMYCw6Hg9raWlpbW3nyySd58MEHZ4hKivMzOTnJiy++KO2H9c5FU1NTTE5OztASEr9ZZWVlmM1mamtrueeee3jooYdoamqiurp6Qef4ZwWlBEMJJZRQQgklzEImkyEcDjM6OsojjzzCmZNn+N6pl3jX5kcB2Fa7jlMnTrF582aSyYJ2gBCdWigURaGrM8LXv14MfDQNg0HjyadqOXhggueei5JMpjEZTSgGRWoDiHLyXNZ1gtLZ1tbGe9/7Xo4dO8apU6dkxeXw4cNMT09LoayKigqqqqpYtWoVra2tOJ3Om+45F/NmMVlIZzPktTyxdMHTXFVV7HY7iXQCnF7EQSiKAZPRJFkYgkorKlUTExOMjY1J7YXJyUlcLhe7d++moqLihkFVMpkkEonIxZ2g786G3W5HVRWyWRWzeeHnUCwoC4HnVYFHgdUtbr74hYOyIidouaOjo1y+fJlwOIzd6mBn66N43L6CC0VRhLLc7mdsdIxYLCarg7c7+Pq9T36S35vj9Z6eHnp6emhsbJS9zQDd3d381m+9i0//6bXq5h/4pX386eO/zcala695T9M0Nv7Pt814zaAo5PKFnnexwC9YtkI6lWJ6OlLQdLAXAheT0Yg2P5t8Xixb5sXttvK//3V0xutKMaFiNBqlGN7x48c5fvw4/f39DAwMcOzYMRKJBJs3b2bz5s0sWbJkQf3R+orrYkULFUWhsrJSznlTU5N0mcjlckxMTPDyyy8zMjJCW1sb69ato7GxkYaG2a1Wtxd3sjVCzJewNLwV29+F7EsPoWGybt06Ojo66O3tpaOjA4vFQnl5OS0tLTfcpjjPgqkw1z6FyKHQlxABrT6Q1o9PVMtjsdiCj0sw0eZzWVCUq+4S5eXlnDt3jvPnz3P+/HkGBgbo6upCURSWLl3K6tWrsdvt12gyiCp9NpuV+xP6FRcvXqSrq4v+/n56e3tpb29nbGyMeDw+4zgFA83tduPz+aitrZVsEY/Hcw0bTS/0m81mCYfDjI+Pc/bsWbq7u0kkEnJM8XickZERotEoQ0NDXLx4kePHj+NyuaRTRGdnp9SqEdaQ1dXVbN++nXXr1uF2u6msrKSlpWXOwoGiKDgcDjZu3Ijf7+e+++5jcnKSYLAgvnzp0iUGBwfJFi17fT4f1dXVNDc309jYiNlspry8XD6XzLn/RCglGEoooYQSSihhFkQla2xsjP7+ft77i0/zN5/+C9658a0YDUa2Nm3gi699g4989CPX+HYvFPfeey+a9hczXvv85/8fzrafp7ZmA9/61t/w7nc/xKXLsatq+ZqGhsbgwDRw7WJZLPbWrFmD0+nEarUyNjbG6OgosViMc+fOyWqPwWCgqamJ5uZm7r//fjweD4FAQIoA6rUKBESQ/PLLL8seWf1x9/X1MRYa4x1f/jCKUQGDwubBezhw4ACTwSAGg5HiYQCFINNouupIkclkiEajdHd38+yz/8iVK2doWubAZlUwmzUGB8NEIn7WrVuH3+9fUIIhFAphNptlK4XNZpuzille7iQRz+H2LJxyns8XFMiFk4jenQKgqspOJjvI8PAwdXV1koI/PDxMb28vmqZRbguwpXm3HJ/YhsflZ2R4gEQi8YbQ4PUQvdWz+6CvF2QqBgWVue8BMd//cuDf5f9PxcIFu0iuVvhUNU8eSBervMaiwJqYk2xO5cD+UVQN1HzhXgBQ8xRbWEBTteI1dvX5ZDBFOpml/9APZJIuMTlIrqUKo9HI2rVrWbNmDYFAQCYKR0dH6ejooL29nYmJCdlv7fF4ZlDXrzcn+p77hUKiFuBtAAAgAElEQVRRFAKBAH6/n9raWrZt24bT6SQUCklhvCNHjnDs2DEeeOABEokEqqri9XqvodzfLrwR7AJRoRYOAndqH7MhEgzr16+X+hednZ1MT0+zfft2+Tl965b+LyDHHY/Hr0kIGAwGysvLpc2wohTEb/P5vGxLE9+zs+dZJCwWInwpgt4buV9YLBba2tpYtWoVTU1N1NTUkEqlpNVtJBJh27ZtTExMUFFRITVW9POQzWZJp9PYbDaZXJiamuL06dO88sornD59ms7OTnn8IlkjRFOFfW5NTQ0NDQ2sXbuW1tZWmpqaqK+vn9ESMvs4BMOku7sbm80m2TWRSETOUzgcJhQKzau5IbYrBCmrqqpYvXo173znO3nrW996XRtMMR6Hw8G6deukdkoymaS/v5/Ozk7279/PmTNnJDtiyZIlrF69mre85S1s2LDhjths/rThP/fRlVBCCSWUUMItIBqNMjg4SENDA77qABdHLtNSs4JIMsrJ0yf5g099ml3btvLAAw8sets+n4+nnnpqxmtf/eqXaW29n8985nOYTCbuuus+fvxSSFZ0cvnCAqqiwj6jyjkbLpeLxsZGHnzwQRoaGujo6JDVuf7+fimkNTk5iaqqJBIJOjo68Pv9eL1eGhoaWLJkCXV1ddeIyR07dozP/N6nuXvF5sIL2tVFl6ppPNb6FnrCg5jMJjQ0XvvWj3lNg9+865ep81ZfzS4UYdRR30dHR/nSl/6GY8f28Na3OfnwR5ZjtVokM+DSJSf//JVrK4TzQb9Yn2+hKeDxeIjGsotKMCxk/ytXFirjdXV1XLp0iYMHD3Lu3Dmy2Sw+d4D7170Tp0MIXAp6MzjL3AxPnr3huN9IFKryc4/FoEBenT8Iur95B3svvI6hsCHMipneqSHupsD+cbvdsqqYz+XIZDLkdEGVBrjKKpicvLto1WgutqYU2iuMRaaPwWxAUYzy3jAaTfh8Cu/7hW5a1zQW2iOMBozGVbLFSWD58uWYzWYqKipobGzkzJkzskqaTqfp6Ohg//79MrDw+Xz4fL555wqQbJybgdVqxePxsHv3biorK2XF+dKlS/T29nLx4kWSySSXL1/m1KlTbNmyhU2bNsm2iZ8ViPvUbrfjdruvCWrvJEwmE8uWLSObzXLhwgUAqRUwO6gXiedsNiur0/pjsFgs+P3+azQerFarTNpqmiZFJQWjSrQIzG5vEAnRG4lBCugFDGdjtraDohSciLZu3Uo6ncbpdHL27FnGxsa4cOEC+/fvZ/v27Xg8HpkkE2MU7gjj4+MMDw/L6/LKlSv09vYyPj4ux+73+9m5cydtbW0zGBainaG8vFwm1DwezzX2x7MhBB1XrlzJE088wYYNG6Q95ujoqHSoEO4UiURCnjP9XJSXl7Nz5042bNhAIBCQtrGLbXXUz6lg1blcLrZs2UIulysmr8vx+/2SvXAn2UA/LfjZ+fYpoYQSSiihhDcYsViMoaEhTp06xVh4klcvvM4Hvvk/mTYbcO7Yyauean78+b/m7rvvvi37e/75l2Y8/4d/eGbG80QiURRIjBCJRObt8XY6nTidTmpqarj77rs5evQoe/fupaysjHQ6TTQaJVcM4gSddd++fXi9XgKBAG1tbWzdupV169ZJSzBRZUskEqxvWM1/e8uHr3ssivwPkn2halery1BslNAKFb0vfelv+MEPvsnOnWY+9ekllJWZCtacOhgMyqIt7BaaYHA6XSQS04va9kKwapWJI0f28cQTT3D58mVeeukluru7C4v2nMaaZZux2YT2ghw1JoPxjlVybxaKoszDUShAvc78/s0vf3rG81/9yn9DtMpYrdZiZTdDLBYjrxZ61fXn2mVzUOGp4bOf/etbOIL5ISqNjY2NVFZWsnTpUgB6e3slk+n06dO4XC4ef/xxLBYLjY2Nsndc72QyO7F1s+MRlnzbtm1j8+bNHD58mMrKSoxGIxMTE5IJ09XVRUNDA5qm0dDQIMUYZzOQfhoxO5h2uVxvKGPHaDRKxf8f/ehHwNUEg97RQlULbKVEIkEqlSKdTs/Yhtlsxu12S9eX+YLkfD6PyWSS51awn+Y6TwuZB/34hBhnOp2eM8mgqqq8rkSLnEhUO51OGaQPDAxw6tQpmpqaaG1tlVo2MtFddI7o7e3l5MmTvPDCCxw8eFDer3r3hWXLlvHoo4/yxBNPSJvJW4EQYXS73fIehcJvY2dnJ52dnXR0dNDd3U0wGCQUCs0QoxSoqqrioYce4vHHH8fr9d5ym4JwavL5fKxYseKWtvWfAaUEQwkllFBCCSXMg3Q6XbCw67pMuG+Cfxt8EfW++9j+8d9BMSjkc3kGTx6ho6ODurq6Oz4evY/7jejZejQ0NHDffffR3NzMQw89RCqVIpvN0tHRwYkTJ5iamiIej0tWw9mzZxkfH+fo0aNUVFTIhXMgEGB8fJxkKkUqlbomwTGXOKOsyhU+gaZrpB8Oj/D1U99hQh1meDjCn3yqAZfTKKnv1x7/4qjfemHHGwV6Llc5yWRwwdteKFpaPPzt3+5namqK0dFRenp6iEaj2O12VrWu4NsHv0Bb0y7u3/R2HParlpRqsR3mp4W9AIVrbr7hKAZlxrm9EcwGE9ki48FoNGK1WAosBEUptjbMrPwrKGhvUMIlEAjIfvmmpiZOnz7N6dOnSSQSTE9Pc/jwYSYmJqRavWixqK2tpaamBkVRZJXa4XDckq6JgMFgYOnSpZjNZqqrq2lra+PYsWOcOHGCTCZDX18fzz//PFeuXGHDhg20tbXJ3vafBYi2nLmYA3caIkGgr54L1oBofxAtMiIYd+nsWMX5XkjwLAJ8Yck5PT2N0+lctMDsfNs1FNuKrjeHIvEk5jwQCLB69WpaWloIh8PS1cDtdsvEAlxtOTt8+DCnTp2SIsJXrlxBVVWpZ7B582bWr19PdXU19fX1rFu3Toqk3ikI4USbzUZdXR1btmyR7hSCxaBvTSgrK5N6C290C9rPA0oJhhJKKKGEEkqYB8FQiP0nT5HK5sg47bjaNrHyAx9CMegosCuaaT9/ngcffPCG/a+3CtHTrQ/irwcxjtra2hkWWIJGu2fPHrLZLAMDA4RCISKRCNFoVFZFxfH4fD4qKipoamoqaAUk0ySTSYxGA4pikMKEmlawGCxQ0K/aZxoKXHa5aI+nEvzVgX/iVPAEd+2w87G3N7JmTQMokM/NvzAWx71QLKZFwuFwkUze/sCmqspOKtnLN77xDSmmZjKZ8Hq9vO2Rt/L+97+ff/vGN/n77/wRVrOtyPBQicTCPPjwA3LsPw1QFAVtvhaJRbJLjAaDFHkUAY/eyaTAhiiKmioF54d0Js3BAwe5e9ftYQzNBUVR8Pv9Upht+/btPPfcc7LHOh6P09HRwenTp+WY3/a2t8mqtlCJF8GMeH4jzD7Hc+mECHX8VatWsWPHDux2O2NjY0xMTDAxMcG+ffs4cOAAjz32GJqmkclkpLaHmOO5kn9zjUP/V9y3IuC+ExD2f3rBvtup+yDGPtdxiASBmB/R1iJYXoIRIObRbrfPKRa7EIhEgKIo0sVACNDezPHq2yqsVusNkxx6xkMymSSXy+Hz+bBYLLS2thKPx1mzZg3Nzc2yPUK4X0xOTnLlyhVeeOEFnn/+eVKplAzcRbtHdXU1u3fv5tFHH6W+vp6qqqqbmqfFwmw2U1VVJfcnfiuz2SyRSIRUKkV5efnPDLPnZx2lBEMJJZRQQgklzIJiL0Ox20lu2Ia2YQuVG7ZQ3dR0TaCgKAr2las5cniv9Dm/E2r/AnpV+lvxixfCW62trbznPe8hGo3K1onp6WnOnDnD0aNHZ7RSTExMkMlkCgvsuJE9Z14r7H/WEAoiexqKQvGvEYfVzlJfPU6LnT2dB/mn17/JdHacd72nnO3bHfgDtqvtFJK9oMzeNAoK6nX6/OfC7F74+YJ1jydALJqd8735IUTErr//Rx9z8dxzf8nZs2Gy2SwrVqxg8+bNNDc3U1tbyx/80e/z0Y/9ugxiFKVgnZfNZmfYu90qnvna1xgZHy8E7xoMj46C0VhI/uTzGDQNj9dDb08P4fEBVixfTnl5oVKraSrRaIxLl4b5+McmCiwD7erZGhmN8y/HvsvB/pPcvWQzd6/cct3r06AYZmgsQCFJYTIaycuAtjC/CmAxmdlavZbf/PgnePvb347RaOSTv/vbd1SBXQSC27dvx+/3MzAwwODgIEeOHOHo0aMyCO3o6CCZTHLw4EGqqqpYt24da9euJRAIEAgEFsVgEI4j13vdZrPh8/m47777qKqq4tixYxw/fpzR0VEmJiY4d+4cyWSSQ4cOUVNTQ2VlJZWVlVRXV1NVVYXb7ZatCGJsIiEhKvWiPUDTNEKhENPT0whnjNnjmmvc4vn1rgH9/ZjP5xkYGGBgYIBkMkkymZwRMM83JzNYLspVu0f9a1C4/0USVTwXYxQVf6PRiM1mk+05U1NTDA0NSZFGsf1bZaQIxoRwzxHuGSIx9UZAaEbo23ra2tqkuGhVVRXl5eUkEgnJWJiYmGBkZIRz586RSqXI5/PYbDZWrVrFqlWrWL58OStWrJB2yW+mO4KwsRwcHOTw4cP09fWxYcMG1qxZQ319/Q3tR0u4NZQSDCWUUEIJJZQwC5qm4Xj3L2PdtA2DQUGz2VA1tRAw68JeRVEob27l3Nf+P+LxOMAdXSCKgGZ2G8JiIVgAy5YtY+nSpbIyF4vFmJ6e5gc/+AGTk5OMj4+TKrZDpNNpRkZGyGQy2IxWPvXi3wKFvIDOCb4QeDKT2m5UDBgUA05rGcFEmGg6jtlMUaSvqJwubDjFv7uqeXh13MarXugLhQiKbiS25/dX0z+wyASDJjQr59lusQC/ZbODS5dGaG8v0JKXLl3K/fffz8qVKyUtey6xwFAotLjx3ACf/dsvYHv7ezAWnRm0inqMdgdGm5VcNArF66m/cw8NqXPcuzssrTMVg0JlJXzk1xsKwp6G4vmjMMff/raZpz72frxeL9945l/43vk9fPiuX6C5ZtmcYzEajKRzaY5fOUMimyLg9FJp8RbEQTNZ8loxyaCp5HMFgbnfefjDvNZ9FG1A5e8P/G+eeu+7WLVqFXB91tBcyv8LgagKr127lrVr10pHFlVV6e7ulkygnp4euru7JaPh0UcfJZvN0traisvlktT/641TMAVEoCsqrKISK6jqhTaVQmDc2tpKc3MzJpOJcDhMJpNhfHyc7u5uurq6sNvtOBwOVq5cycqVK1m9ejWrVq2SiYaysjKpryJsYvUVexHAC7E8IVA3ex7FvSW+j2Y/n++49fdkNptlYmKC0dFRGXiL1oH5vuf0/17/nahPHugZTNFolFgsNuM49C1UNpsNj8fD9PQ0qqoSDAbp6+tjxYoVt60SL9gSgNReyOVyJBKJ29ZOc6P9i7/61gCz2UxrayvLly+X48pkMoyOjrJv3z6+973vMTExQTgcnsHk8Pl8tLW1sXv3brZt28aaNWtm7OfNQi6XIxgMcuHCBZ5//nlOnDjBE088gdFoxOFwlBIMdxilBEMJJZRQQgklzEYqSbbzPJaNW1FVyOVzsrpsMplkkkFRwOIqx+Dz09HRQVtb2zW9tLlcjtdee40X9uzhU3/4hzN6dxcLIa5lNBopKyuTFdZFH14qRSKRkGwIu90ubS3dbjd33XWXrF6J6loymeTixYucPXuWeDxOLpfDZrNRVlYmF+iRSIRwOIzVapXVK1VViUQiTE1NEUpNy4V/NqsxOXG1Sqqht8MzFIOFWSyGYrCVTqcXFCzqbd9u1CLhdntIdC5S3VsRQfbc50AtBsmjY3FeejFMPu/A4XBQX1/P2rVrqampecMVxd1r23AuX3ndz4TaT7Ms1sPduyqxWa3XJHrmwmuvadx11100Nzfz+OOP8/3vfZ/P/O3fc6LrIrk56MipeAyuHObvOn5MPpslF5mmPKthMZqLlpMqiggsi9eMWkzwKShk1CydnZ1YLJYbUsNFwKynyAtV/IXMv95GMBAI8Na3vpUlS5bIirsQlxMJQMEgeO2116isrMTr9eLxeHR2pjMhEgvhcJhIJCK1HaAQGE9NTcn7ymKxyCSAOI7e3l76+vrIZrP4/X7i8TjxeFxaJ/b19RGJROjt7eXo0aM4HA75cLlcLFmyhCVLltDQ0CD72EULgaZpOBwOaUtos9nk63rMx2CA6ydVxF9VVTGbzTQ0NOByuXA6nTe03ZydOJqLwSBe1x+HXmxQJJHy+Txr1qzhoYce4vjx43R1dXHx4kX27t2LxWKZISh4OyCYEwaDgUQiQTwel3a6bwZEwiGbzXL+/Hk6OzsZGhqir69PWrUmk0lMJhOVlZXU19ezceNG1q9fT2NjI3V1dfh8PsnEeLNdTIToZSQSkboe8Xic6enpGQKdJdwZlBIMJZRQQgkllDAHcl0XrtJ3c3ky6QwmowlTkVIOgKKgKGBYsZpTp0/T2tqKpml0d3fT3t7Olf4B/vV73ycdqGTyYgfveec7Z3irL3pMxWBf9ADfbDtGKpViampqRpBVVlYmVc03btzIxo0b5eeFUNYrr7yCoiiEQiEymQwulwuv1yurlmNjYwwNDeFyuQgEAoW5y+cZHR3FbDaTTCZnVEmnI3nyebX4yGM0FpgOBoMBg9HI7ENzOExEozGy2eyCq9ELnR+3200ysdi5VOZsExEQtnE/+ME409MqVmsen89HQ0MDzc3NMpi409odArvu2s7e//ePWPbXX8bi8d727YvjMBgMPPnUk7zjne+gaf0G6j/3d8V5mnl8Nq8XQzGAbP/Mn/C2cZUPbn8v8XiMZCKJx+PB7fZQ7nRJBxRhN/eJ//hT4vE4o6Oj0jVhPoiAW1D/BSNgob3Y4r6oqKjA7/dz7733cv/999Pe3s6ZM2ewWCwEg0F5bQ8MDNDT0yOr1XV1ddTX188boItxjYyMMDY2JsUixecGBwcZGRnB4XBgt9ulW4BImIjxiQRhLpcjFotJccJEIsHQ0NCMY1IURSYYtm7dyrZt21BVlUAgMEMcFZAB+fXmS388+mSe/pqe7/8FZmvFzP6cnpmg3+fsVoy5GAxQEPaz2Wwz2F96fYrVq1dLFkhHRweXL18GYO3atXMe881AP2abzSa/F5PJ5IKSz/p5nospIlpd9AyUxSASiXDq1Cn27NnDuXPnuHLlClC4p8X1tWzZMtavX8+TTz7J/fffL7//hSbDjawm3wgIjYloNCptmROJBJFIZE6HjRJuL0oJhhJKKKGEEkqYA2oyjhqaxOivKIpcpbFYzGia9Zp40rx8Fcfbj+L+4Q/53p6fcLF/gKyq4t51H57f+SMCtfUo//8zXLp06ZYTDMlkUgYqN5tgSKfThMNhHA4HXq8Xh8Nx3UquEKtraWmRVGqh4yDEyTRNIx6PE41GpbUlXK0kxWIxQqEQwWCQkydPcuTIEU4cT1JZaeKxx8oYHx+T9prKbOZCES6XmWw2My9lejZEACGYGtdbbHs8HuLxxYopavN2RwBkMhmGh8McPxYjn4elS5eybds2mpubZ1Dd3yh8/ctfZuuDD6Plctf/oKIwj5bj9f7JNa0rBoMBs8WMPeDHYLo+9fv/svee0XGdZ57n/6bKuQoFoBAIEGAmAGZaVKBkWbIsK6st2TPubvdp7azP9nH7w0yH/dDTs+N1293bcbf3zNi97ml32y0HtWRbI1lWskSJIimSIBjAgEAUcgGogIq36tYN+6HwvrxVKIAAERh0f+dAQqi6972hLt/neZ/n/7ft6EBv3/+Ez+0FqzFQJBmMxkCVS8kAhmFgEgRqXWpmBfzFN74Np82BjJjFX/zdX2L//v1Vt+1yuVAsFstaZZaiEUAgASipGiL3X2NjI0wmE2pra3HXXXchk8kgnU6jv78f/f39iEajiMfjmJ2dRaFQAM/zC1YwqKpKWxEmJiaQTF6zTE2n08hms5AkqSxxQI6jWCwin8/TcYqiOK+aoBqSJCGdTuPixYtIJBI4f/48XnvtNVoRQloVSOBIKgto1VGVLxLIkdVufbBJKjD0511fZbTYF1CyDc5ms3RcxEpSn2BSVRXpdBq5XI7uj3zW9OMSBAE2mw0ejwd79uzB9u3b4XA4qHaApmkoFArIZDKrHpDqRSZJBRnP8zT5dD1Ipczs7Cyy2Sy8Xi91fMhms1QzIZvNQhTFRZ99lb8vFAoYGBjA0NAQYrEYTUT5/X4cOnQIe/fuRU1NDWpra9HS0kL/LSDVfbeSgCJx6iCfddLCtN5VY59EjASDgYGBgYFBFTRRhDJ8lSYYCoUCzGbLvAnZ9JF3MfG9v8XViVH0yIDz4SewsWMXmIpJTJHjcOHS5RWNiYxDVVU6eb4RJElCKpWC1WqFy+W6rvI4ER9rb29HQ0MDTCYTrFbrdUugCeScTU5OYmxsDAzDoKenB6lUAW/8Mg27ncWnDuahaVqpJ5zlqlYFsBwLt9uEbDZbVgq90P71K7HXSzC4XC5ks8u3QVxMgkGSJBw/NgVFMUFRMtiwYQM+/elPo729na76VQZRq42mafjpT3+K2dmSwGQyOgPrdewkGZbFMrU051WbEFiWw1KyFd7tOzH0g3+C1WpBJlOanpKKHZvNCoYBOJ4HNzd1/b+/+H9AlPLgWBZ/8+4/UjX89aS+vh719fXo6uqirQzxeBxHjhyB3W7H4OAgVeCfnZ1d0jZJyXwul6O/0wsLkgqOhVoHyLOhsn2KBMxkZVtVVbrSPTAwgIGBAbo9ErCTFgWS+AgEAvD7/WXJBJKw0TsvJBIJJJNJWllB7m+73U5bqvQVBOTv+t/pv8jnNxaLIZFIUEtJ4jgRCATg8/noOKanp5FIJOhxkPNAWlBItYfX60UoFIKiKKipqYGmafR5SM5XLpej53wpXK9iQNM0qmtDXisIAtVfqHym6benb/GRJIm2zpCEbi6Xw+TkJN577z288sor9DqQ91QbS7VWF7J/UrHg9/uxceNGPPbYY3jiiSfo+ZckCbIsz9OUuFUglUrk2CudagzWDiPBYGBgYGBgUI1CHsVL52Hac3AusJfoqqEUj2H4X/8HpKOvwcHn8KWHA/jRjzW4H30KrvbNVScw9tY2JMIrSzAA88uB1xNSbk0E0ZY6DkVRqGZDfX09PB4PXUVKp1W8/G8pyDLwmQctmGam4XS64HI6S6/R7YIB4HKVBO2Wo8EAzF9dr8TpdCInLi3BUJqYl75nr9Mi0dOTg6KUgkG/34+tW7ciGAyCYRgUCoW5xJV5nnbHaqEoCv7Lt76NvO74oz9/CY0v/N7Cb1K1BRMGy8VssUDJi2Cvkwzztm3GRVlCNJuYC1JKzhYl7YT518UimGERSokxh8W2ZvaJy4EEz/v370ddXR1mZ2eRSCSQTqeRTqcXTXIxDENbJQRBKAvMSZBEKiD0GhIMw9AVeb1WBNkmcG0l9+TJkzh9+jTi8TgSicS8/RNIEoK0DxB3iXg8TlfEgfIAlfyflMor9NopdPvFYhGZTGbe+BaqaKj8myiKyOfzNPFQLBahKApisRgV2SWVCqTao1Ao0PcTpx+9HkehUMAvfvELXLp0CT6fD2azuawtYCVuPZUoigJRFNHb24u+vj6qk9HU1ITGxkZq61gtaSRJEqamphCPx8taYhoaGsAwDEZHR/HBBx/g6NGjuHDhAmZmZpDP58vcQCqvdbV7kfzNbrfj0KFD2LNnD2praxEKhbBt2zaaINE0jd6ni9me3ixIsklfwUCqLIwKhrXHSDAYGBgYGBgsQLH/MjRVhaKWJsvJc92Y+fkPoI5dxq4uFz7/+yHs3x+CBhVX+nLoe/dNuNo2X9uAbr5lbWjC5bdfW9F4qpUNr3Rby0EvMkkCnKVAKi9MJhPt+9avVqbTKn72ShKppIrPPVoAAwZWa6k/mQF7LdhlAI+HQzweLytzv95xAtVX6/S43W5kM0tbqSxNXrU5N4WFAxBFUTA0JEKWS3/3er3YuHEjrFYrXSFNpVJwuVxlFSE3SuU5IcHnhdOn6LX61a9+hT/86c8X3w40MMucgy90al0uF+RcFoLLvej7GZ6Do30z3rtyHA+13U0rPOS5ILKkAlr9vewtENiQnnqLxQKv14udO3fS60FWmvWl8eQ9+v+TCgOisUI+I0Q3ggSWJBFBVmTNZjPt5xcEoeyzTcaQz+fx4x//GJlMBuFwmDpgSJJUVcOAJDoAlIkt5vP5qseuZ6EKCrJt/c/Xo/J1+hYTfQWXflwcx5VZJOq1JPQaGMTKMBKJ4MiRI2htbUVdXR2Gh4cXHMdCn9FKDQpyDvWr6JIkIZlM4syZM/jwww8Rj8eRz+fR0dGBzs5OyLJcJlpKrj9J7E5PT2NsbAwOhwMulwuNjY3w+XyYnJxEX18fXnvtNbz88sv0GWA2m2kFx/X0L/QwTMnV5r777sNTTz2Furo6+Hw+2ppDrEx5nr/hKrr1oFoFw62gD/FJwEgwGBgYGBgYLICaF5F//y1oA72wTFwBb1Lw2CM1ePxP9iMYdOpWCBns6nLg/K+PQ1X/t5L7QUWEZq0PYXh8HMVicd28zteKG01wVAb5xIVCURTk83n8+tdZTM/I+J2vmGmptsPpuLZCBgY+P0tXUgEsOsGtFmgtRKlForikpIWqqCjKxZIugEkAx3JVnSQyGRmSpNLgkJTn6q3+ZFlelu3mYpDJf+XEX58IWlJgp2lgl693WfW8uV0uzGYyS9qEZWcnPvy4G5/ddC84joOmqVCIZgIWM7NgbokKhkqKxSLVXtC3FlRCzhsJSIkKP7l/ScUCsZMkooAkEUFK0xdabSctFvv27YPD4cDg4CAGBgbQ3d2Ns2fPlo2LZVk0NDSgqakJO3bswJYtW2igVtnScL0WqWoJBj2VlRDX+yLnoTLRqq80qPY3MhZ9K4ckSbh69SqGhoZw9epVjI+PIxaLQRRF2s5CNBiIOC1J6EqQYtEAACAASURBVCwGERckiZyRkRFMT08jGo1SV4NwOIyxsTGIoghZlqlux/vvv49QKIS9e/eio6MDgUAALpeLtjo4nU5s376dPktEUcSFCxdw9OhRHDt2DOfPnwdQeia6XC7cdddd2LVrF00Q6e+RxZ5xiqLAbDajs7MTwWCQitGS9gdiBXorJxeMCoabi5FgMDAwMDAwWACTlEZ990+wZ7cZ2x6zY9MmL7xeD2w2DqqqgWMZGljt2hXEP794cS5YZFHZisoKJpj8AYyMjKCtre2mHM9qs9Qkw0KBFZkIkyAqnU6j58ws/jI6it99IYBNmzSYzeaSbz1Xmhj7fRyi0QhyuVxJRHBuxXaxMZLgYjE4joPJZEKhoMBiWXx6pMy5Q5gEE3h+bv9VhpBOlyoiSIKBjJVMcDVNK1thWymyLCOXy1GHkQW53nWbCyaXw0Kn1+1yIppdWoLBvXUHzr/5FlimJMgmyxpUVaFBIXDtnnv34lGcGy05vbxz5SNsS1UXeLyZSJKEXC5HxUtXA7LynsvlYDKZrus8QM6XIAjo6OjA9u3bMTg4iL6+PqiqipGRkTJNAIZhaCvPgw8+iE9/+tNlwRkJ0CoD1eXfL9eeCeT6ks9CtaSCviKgUp+hMsFCvioFB0kFCWnjOHHiBE6cOAFBEKgA5NTUFBWFlSQJ2WwWiUQCMzMzZaKR+mQOGR9pBUsmk4hGozh16hS6u7vR39+PcDiMVCqFbDY77/zp9+lwOPDss8+C4zhs3rwZPM8jmUwimUyisbERwWCQtlpMTEzg7Nmz+OUvf4m3336bHrPb7UZzczM+85nP4Mknn6SJKH3iqjLI1p9vkvQk13uh83mrQ64Juc+MCob1w0gwGBgYGBgYLIBF0PD0Uw5s3mQDL/BgGQaimAfLzq1kCXxJxA4MWlu8MHMyUlcuwb11O6rVdHO19StOMMiyjNdffx2hUKisEsJut6OjowPNzc03vO21Ql9FoA8Wye/27t2LQ4cO4fLlyzh+/DjGx8fxd387jRdeYNDRqcLpdMLlKlWMeLwWDIfjmJmZoaKQC61I6ZMLlfuthsvlRDYjXzfBoFd1ZOa7L9KXnDmThKKUxkFsQNdyclssFqlK/kIwDLNwNmAlaKh6HfxuN67kskvahH9HB66m45C0UkKmFHTKVOyRtAAAwH99489h86XR2OBEQkvO0xS4FSDjXqo7wFLQtJJbSzQahclkgsfjWdb7GYZBIBAAx3F49tlnsWPHDhw7dgwff/wxkskkMpkMxsfHIcsy4vE4uru7cdddd2H//v2w2+303lqtNi0AtKKHtG2Q3n5yvJXJBn1wv1hVRWUFRS6Xo9fDYrFg8+bNcDgc2LZtGx566CH09vbi4sWLGB4exsTEBHXAeeuttzA1NQWn0wmn0wmfzwePx0OrAmZnZzE7O1vmGJLL5RCJRDA1NUV1OHieRyAQoGKZDocDZrMZ4+Pj1DmkUCjg1KlTyOVyuOeee7B//3643W40NTXB6XSiWCzi0qVLuHjxIs6dO4fz58+jv78fDMPA5XKhpqYGhw8fxj333IOdO3fC4/GUJYYWum7ExaQkqnrNzvN2DcbJ/UISSkYFw/piJBgMDAwMDAwWgGUBnmfAsAx4jp/rm8+D5zmYTSawDAPWNDdp41hs3+5E75F34Nq6rWpJtxoIYmRkZEVj+tkrP8Mvf/4O7t71EJ2Yy6qM/pHzePjx+/HNb31jRdtfjJVMNkmgr1+xJ4HDjh078JWvfAVHjx6FKIpgWRaXL1/G3//9BJ58KovPPdIIi6W0elhTY8PpUxHqgqEsstqu3w+ZbC5WSu9wOJHJyvD5r388pGxf71TJzP1Hm/vD668lIYosTKZSfz7P82XngKzIyrKMYrE4z7pS38NN/k72Tc6pnnw+j0wmQ21FyUpp5TkRYzMQJ8ah6SonGMxVYTAMpEQCsGkoqTFogFa+n1RKQrGo6oI5IJMtzNMYAACPywkpFoOczlw7SSTIIe/nWDCCCSanC+aGRrx7+Sjua9kPRZEhFQB5LnECEPcEDQLP4oUXNmPP3jr88AcjqK2tXVGbxFJaY5YLsY8kYoSrAan0mZ6epgFnNcjxVKswcLvdcLvd2LBhA+6//344nU7E43Ea6MbjcUQiEVy+fBkOhwOyLGPDhg3w+XzUnlafxCKfr5VAKgbI558421Qex3K3qa+SIC0KPp8PVqsVzc3NaG5uBsMwEEURb775JkwmE60O0DQNoijiww8/xLFjx+DxeOD1etHc3IxQKETbUyYmJjAxMYHR0VFEIhEA1/RPyBdJBvl8PrS2tmLDhg2oqamB3W7H+fPnYbVaMTIygomJCVy6dAnhcJiK6XZ2dqKuro46dPT09ODdd9/FmTNncPnyZXq+gsEgtm7dikcffRRPPfUUHcdSIAmGdDoNQRCuWxlzO1CtgsFIMKwPRoLBwMDAwMBgAXI5DWOjRbS2apAVGRw4cCyHYrGIbC4LDVrJOo8rTeK6Ohzoeffkgtvj6kK4OKdQfqNoGvCZ/c/iwf1PlOk8XB2/hLMDb61o29fft1Y2aV8ODMNQUTlSjk1WeImf/ebNm/Hcc8+hvr4egiBgdHQUL/9bFMnkBL74xdIKndvFo7//Kurr62EymRa1/ysUCrT8u1gsIpvNIhaLLTjB5DgT/v7/uYTmZhdIvK1pDDSUzjvLAGA09F1KIJu5tirGzAkWkP9q0KCpGtKzGlRFnXec0WiU9mSTfm1yHPqAKpvNIp1OI5VKUU96/bWoXGFMJpN0JTUWi8Fms1FnA3INrFYrrFMTKP6/fzFXfUMSFteuKz84iFPFDL71Z2GYTNfsEFVVQyyWQzIpwx8IlF4/955slqOK/noagkGo//R9TL7xs/L7R8Nc8qIUiHMeL0wH7oaptQ3/evYNdDVux2wujtlUEtOFBIT4GGwOOxxOBwpSAclsFrIiQypIKMoy+vr6cPz48bKSehL4EjG8hahc5byRsv9qKv3ErWEpLTpLRVVVRCIRjI6OgmXZeYkL0m6kaRoVC1yoNJwIQxJthuHhYQwNDeH06dPo6emBoijI5XJ4//33EY/H0dbWhra2NuzcuRPbtm2jx000IW702UC2Mzs7i3Q6Te0syXVZiSUvQVVVTE5OIhaLQZKksutNBCl9Ph9aWlpw8eJFANdsF8kxiaJIxRojkQgNVjOZDA3OAVDbyE2bNlFr3/r6elitVthsNrjdbjidTthsNgiCgI0bN+LgwYM4deoUenp6MDQ0hJmZGZw7dw7pdBonTpxAMBikLRvhcBjDw8OYnp4Gx3Hw+/1oaGjA4cOHcffdd2PHjh03dP7z+TzS6fSyq2JuVfQJBtLiIQiCkWBYB4wEg4GBgYGBwQJIkoYPP8zi7nvskOXS71iWpW4KpZXikuYCAHR1BaH+4Py11byypW3AGmrApTc+XpOx1vmb8PKxgTXZNmElyQWGYahrgt7qTh8Qtra2YtOmTeB5ngZmvb1pvPN2Eiwr4/nnW+D1+qBppRLx1tZWZDKZBVdPidq+PHfxJElCOp1eMHD8vd/7A4yNjZVVCOiDTVmWkc1m8f7bf4WHd30JJpO5rCeZYRiwDANlbmL7fvpV9GfPggdfVkWRTCYxNjZGe52z2ey8BAPHccjn8xBFEel0mq7o6qmsUEin00gmk9QW0ePxlFmCsiyLxsZG/Ns/f3/R63jlyhUcP34coVCI2uDpx19bW4v6+vqq702lUmU/P/Tgg3jowQfpz/oKDhL4xmIx9Pb24tzFS3hzeAC9k1fwmy++AEFgoGkKGDBgOQY8z0EQeDAMYLbLcLt4FOUiBvvTeO2V7+Pd1z8Cy7BgwJTcPcAgk0vj4L278bWvL2zLSe6Ryt7+lSYY0uk0MpkMLaVfDVRVRSwWw9TUFNUD0O9XLwrpcDhgt9tp7z05Lv1YTSYTOjs70dXVhaGhIfT395dpM4iiSLUEOjo60NXVBYZhEAqFysQGyefwRisZVFWlLQaiKMJms9HKD4fDsWIbV0VRMDMzg6mpKZjNZuraQs6J2WyGx+NBY2MjnM5SO5YgCLTyiCRzSBVBLBajwSu5j3meh8/ng81mg9frxb59+3Do0CHs3LmT6ikQm1H9s5TjOIiiCL/fD7PZTM8F0cqw2Wwwm820xYNca7PZDK/Xi5aWFmzfvh0PP/wwHnnkkRs6PyTBkMlkFqyKuZ0gzxl98tCoYFg/jASDgYGBgYHBIsTjCgYHJGzabKFl7QzDQhDIZP1aUNjc7ILbCUSPvIPQZz43b1u25hb09w8sy+JxqXAsv+ZK+jdaqkx6X1OpFAYGBqiomd/vR1tbGxoaGsrOx5YtW/DMM8+gvr4eTqcTQ0NDePutUdTVTuHwYQ333WfFv/zL/4c//dM/g9frXbRFQi/Aphctq4bf76cBVLXXJBIJvPrqq9jZth93734IPM+B43iqwVB6H4tsNotMJoNjvb+Cpmn0OElQFggEIAgCDTRIIGexWGC322nwEY1GMT09jUAggNra2nn3TOU4Z2dnkUgk4HQ64fF4aK85ea3+/4uRSqXQ2dmJlpYWtLW1zWvNWOp2FoIcNwnw7HY7gsEgHn/8cXz7//wGfvrTn+CXv/wLPP9FG2ZmZqCqpfNRUxNATU0NGIZUGWjQNEDMK3ig6wt45O5n5+3r9OUPAD4Kv3/hvpeF3AlWCtmOz+eD1+td8faA0liDwSDi8Tg2bNiA1tbWsv0B1xKBRFi0MmlCriMVUJ27vn6/HyzL4tlnn8X27dtx4sQJnDx5EolEAqlUChMTE5BlGalUCmfOnMGhQ4dw4MABcBwHr9e7ogoGck+wLAuv1wuXy0Wvi94m80Yh487n8/B4PPR+YBiGaqNUCkW63W6EQiHs27cPHR0dkCQJhUKhTHMhk8mgoaEBdXV1sNvtVKfCbDYjFAqhvr4eHo8H+XyeVkpUiiySMWzdupUK3/p8Ply6dAlXr16FJEm0TQooVVa4XC4cOnQI+/btQ319PRobG7F58+aqx/5JxRB5vHkYCQYDAwMDA4NFSKdV/OrNNNrazWAYdW7Fau6fTw1zKvfXSuUfe7QGL/7kewg9+DlauaDKMqRYFIXoNOLxOMbGxm5YjJFlGXzU+wamM8NgGQZkJ4ViHkVZWoUjXhx98FVN3b9acEb6kZPJJPr6+hCJRCDLMvx+P7Zt24b6+voy68bW1la0trbCbrfTpMnU1BR+9KMZuD0K9u6rwd/+7VsYHv5fsXv3bro6vNYTx0wmg+MffYzNoV2wWMxgmPkaBwAgy0Wk02mMR4egqioCgQC2b99OhTkdDkdZyX4sFkM0GoXX60UgEChTck+lUvD7/WhpabnuKrjD4YDVaoXX66XB4o3g8Xjgdrtpz/l6r/h94QvP4Xvf+ztEoyWxx1JrjUxtH0sl89cqOgSeBRhc+1zqYFkOvNm0agH+UtGvoLrd7lVNMPj9fgSDQTQ0NKClpQXA6tz7Xq8XHo8Hra2tuP/+++HxeJBMJjEyMgJFUZBIJDA1NYUrV67gvffeQ6FQQCgUoqKFenHGGzkuUmnk9/vp52O1PtPFYhEulwvpdBoul2teG0CxWKQBKEnIEJ2KRx55BE899RQVb5ycnMTExARGRkYQjUaxc+dObN26lSZG9KKyqqqiUChQ+1i93agenufR2tqKYDBI751YLIarV6/SSg6TyQSHwwGLxYK6ujo8+OCDePrpp+HxeOByuVblPN0p6CuuDJvK9cdIMBgYGBgYGFyHoSEJ77+XwQMPOAG2FECKYql/XNVUagP2619PY2iIRX64Dxeevw+MJkMpFqEUFVgsHGw2DlxOwdmzZ284wfCZhz6D/Qf2o6GhoWwyzzDMugdRpHyf2EWazWZYLJaqCQaGYRCJRHDy5EmEw2EoigKv14v29nb4/X4ajOkrDFpbW/HZz36WCq8NDAzg+99P4A/+wIpQSMKLL76ILVu2wOFwrMuqlCiKOH+2F/c9/u90q+jzUVUNM7MTULRScqS2tha7d+9GY2PjkkrlyUTYbrfTkutPEmazGV/+8n/AB0f+Gk8/40Y6lUZRKqKQLyCZTMLlcsFsMutUVDVoWvXSfAarp39wq8Cy7JqvxFbTZjh58iTOnDkDVVWRy+XwwQcfIB6Po7W1FW1tbejq6sLOnTtveJ/6lX19Rclqsti9QD53ZL8Wi6V0r821J/E8D6vVitraWtjtdtTW1kIURQQCAXi9XhSLRUQiERrQEp0FovNANB0WcnGIxWIYHx/H8ePH8fbbb2NkZITqpng8HuzevRs7d+6E1+tFMBikLhHV2qcMym1JAUPkcT0xEgwGBgYGBgbXIZtR8ctfpmC2MLjrLjvkooxiUS5TzTeZzDh1Mov29icRHvoFHnmERdcuP7xeK5xOS0kIUgN++MNhJBIJOoEGlrdK53a7qXjYzZpYknLdTCaDWCwGQRComOBCY2IYBrFYDBcuXKD6Ax6PB21tbTTBIMty2fsbGxvR2NiIQqGA6elpKIqCM2fi+O53I3C7OFxWLmN6ehosy5aJGa4VPT098DvqYbc6Fw1+NE3DWHQQGkoT22AwiI6ODoRCoUUtNfXfcxwHq9UKt9tdNWlzJ6NpGp599jl897t/jcceN8NskZDOpFGQClBSJdE/DRoYraTEyXKlpE5VGEBb6G+3KRzH0XJ7/cr7akDuM57n0dnZiR07dmB0dBRXr16l2gy5XA6iKOL06dM4c+YMtm3bhp07d4JlWTQ0NFBxycpWgOtBEifEsnK1g8HrJZrI/kmbhMVigdPppC0UpMXKarUiEAjQbSqKAkmSqFgr0WUgIpqLtXeQMcmyjKmpKVy8eBEnT57EsWPHqBtMIBBAc3MzPv3pT+Ozn/0sgsEg3f9qs5I2l1uNygoGnuchCMIn6ll6szASDAYGBgYGBksglVLx8r8lMTws4fnnPXRyDwCWogWAhkDAhIMHDyIaHUZD4zCaGl3g+LkVKw1QVAWBAIOeno/xG7/xG1R87XaCqL3HYjFahmqz2eB0Oql3eiVkQsdxHBWGIz3OGzZsgNfrpZaM1Whvb8eTTz4JABgcHMTQUAICX8TGjZP4zne+gwceeAAPPPAArFbr2h04gI8+PI72us4F/66qCsZnwojH4xiZ7oOsFsCyLBwOB4LBIBWPq0Qv1qgoyie+T7hQKAAAHn30Czh69Bc4fNg8ZxNbsqyUiyVRRo7lwHIsWGbxCoY7CYZhqBihoigIh8MIBAKLumSsBKKJ0NbWhqeffhpbtmzB0aNHceLECaRSKWQyGdryJIoizp07h7vuugsHDhyAw+GAw+FY8nGR1X5ZljE7Owun07nuz8dKnZHrJTgURcHY2Bi1DfV4PNSWcjmVR/l8HidPnsSrr76Kvr4+CIJQVhnS0dGBTZs2IRgMwm633/DxLQRx9SkWiyu2HL0VqGyRAIwKhvXk9prVGBgYGBgY3EQyGRVne0TUBDhIkoZcTkM+XxL/4/lpSFIGd9+dgd3uRC5XhKqpYDWWukkoiormDRZ88MFp5HI5GhAvdcJDyj3JxGkpoo7VAlUy+dL3CVdDr3ZOfOolSUIymUQqlYLNZoPL5YLJZKLiZguNQa/iTVYJnU4nGhoaIAgCEokEZFkuW0EjY29paUFLSwvGxsZw9OhRjIyomJ2dxcDAAKanp8HzPLq6uqgK+2oH5+R8f/ThcXzhrq8DCwSt5wdP4hcnvgeH3YFwdJAes91uh9/vXzAwIKXZAMqE3D6pFItF5HI5PPHEM/jt3/4+RkcEjIzMojhXOeRy5+F2pcBxPFiWR+/FLA5vq77qms2n8frL/4TL/cfp7/QOJkTXYTaZx4cffLwmwdtq43Q6IQgCJiYmMDk5CZPJtGYJBrI/u92OxsZG3HfffbDZbIjFYpiYmEAkEkEqlcLMzAwGBgbw/vvvQxRFBINB1NTUlIk0VtqqVkJW/OPxOFKpFH2urBfkvtA/fxYaM3lu5vN5TE1NIRwOY/PmzQiFQrBYLMuqLiPuNGfPnsUbb7wBVVVhsVjQ3t6O++67D/fffz8OHDiwOge5wHEQzZc7uYLBsKlcPz65/3oZGBgYGBjcALOzKl56qWTFRyagDz98L77yla+gu/skampqoGksUsk8CvkCilyxTJm+IeTA9PQgJiYm4HQ6qV3aUkgkEkgmkxAEAbFYbEFBNX1AX610OpPJIJfLIZvN0ol8NYgbgiRJKBaLSCQSVIwwFApRGzyy8ng9SBmxLMu0koGsWBKV9cUcNvbu3Yuvfe1reOutt/DGG29Q28szZ87gRz/6EQ4ePIj9+/eveuuIKIo4ceIELJwdNd7auaBj/uuujHfjv37zTzE6OopvfetbYNWSQ8L1xkPs5iRJwuzsLBwOB5xOZ5lY3CcJosK/efNm/PEffxvhcBgWyyWcP38evb3nsGdPAzZ07MfBgwdx4MABvPTSSxjtFqtua2Z2EnW1Mn7rt0v3lP66MeDA8WYIggt/8J8uIpvN3hYJBlJyz3Fc2QrtWpHL5ZDL5ei9fPDgQTidTkxMTGB0dBTHjx9Hd3c3NE2DKIr48MMPEY/HUVtbi1AohD179qCzsxNWq5W6mlRjLTQXlstSnVJUVcX09DRisRg4jkNLSwv8fj8sFsuy2lUURUE0GsXQ0BDi8ThUVYXT6URdXR327t2Lw4cPo6mpaUXHtJT9JxIJsCxL3TBudyqT6IZN5fpiJBgMDAwMDAxuENJ/S0rcH3vsybmVZx6pdAFSUQIjM2Dn3AZ4QYDNZoLHI2BoaAjNzc2QJGnJEx6SGMhkMkgmk2WBq35iTFbdSAuG3poOKAXMxBoxl8tR9fbKQIX4vRP/9Xg8jmg0CqvVimAwCLfbDbPZXGZjqF/9q5ygF4tFZLNZyLJMe5MZhqE9zMVicc4GlKk6Sd+5cye2b98ORVFw/vx5jI+PI5lM4sKFC8hmsxAEATt27KCTyNUKVkRRxJu/egtttV1zq+bzr1deEhGeuoTDh/8K//iP/whRFGmVBqmqqKzMIJCe9Xg8jnQ6DZ7naWn5Jy25AJQSDBaLBTzP4/nnn8fVq1cxODiIF198Eb29vZiZmcG5c+ewd+9ebN26FaFQCKPdA1W3xbE8vF4TGhsXD5r8/qWV8t9sSC85eeboRezWApI0SCaTVHh0165d6OzsxMTEBIaHh6EoCoaHhyGKIkRRRE9PD86cOYPa2lo0NjYin8/D5/PB7XZTTQNBEMoSoHo9GvJcvRVK9SufY+TzSFweRkdHUVdXh1AoVCYIuVQURcHU1BQGBgYQi8WgqipcLheam5vR2dmJgwcPrurxEMizmiSOp6amqBvInZBgAEqVIcTiE1i8IsVgdTESDAYGBgYGBitEURRqnxcMBtHUtAHhME8rA8hEmvSMNzZaUCwWqW3hUic8JEAnE3VSUlyZQCCTx4W2SwJa4vpAxlmt/5hYRZIVO5fLhUAgUDXIliQJ+XwegiBUFSYkfb5EDJK0ARD7NUVRaC+z2+2el2QgY9q9eze++tWv4u2338abb76JVCqFcDiMgYEB9PX1obm5GcFgcNWE7/L5PD48cgyf3/W7Oos5YtWpoFDM46Pzb+Lwp++l4peKosBms6GmpmZFLhefxNU2otVBPldutxtbtmxBfX09HA4HRFHEyMgIpqenkUqlSqv4qJ6IYRhmgb9c49y5ONLpghF4LAOiodLS0oInn3wS7e3tVPRxenoa0WgU6XQao6OjeOONNzAwMEBbqoh4ZG1tLWpqasq2q2kacrkcEokEnE7nuh7TctoDSMtYPp8Hz/O0VWy5FItF9PX14ciRIxgbGwPHcaipqUFrayvcbveyt7dUyHkmFWVmsxl2u52239zuKIqCZDKJ6elpiGKpuqlYLCKfzy+ptdBgZRgJBgMDAwMDgxVCelivJRia0NcnlJUy89yc/SID1NczSCRKJcTLQZIkiKIIq9VKJ4TV2hOIPgP5qlw5N5vN1FqTJCn0kNeRbes1EwRBgNPpBMuy8ybjpAXCZrNVXckjPbEMw5SJPZpMJjidTqRSKSQSCQCo6utOVhN37NiBjRs3Ip/Po7u7GzMzMxgfH8fAwAB6e3vB8zy1b1uNsutwOIz0bAb1gaZ5Aow/ePtvMDLTB6/Xi//rj7+NqakpGvQStfnrrQguND59a816Uy3Yqvx5rcZFEnKiKKJQKMDlciEYDKKhoQGBQADxeBwTExP0q1AoAAsEhgwYqIvEEyPDGfzT/0jhj//4W2umzH8nQmwYHQ4HQqEQ7r33Xrz88suQZRn9/f0oFouQJAnRaBTT09P44IMPYDab4XA48PnPf56WrpNEoj4ZmM/nkUqlaDC4XDeKtYZod8iyTNu9lipmWYksyxgdHcW5c+cwMzNDRTVJC9paoWkaCoUCstksVFWFIAiwWq1rus/1RFEUpNNpxONx2n5HEkKkYs9g7TASDAYGBgYGBiuAZVk6wSQuCi6XC7mcBoZhwbKlIExW5LmyZg6hBgv6+88ve182m416r0ejUSiKUtaiQCAtBgu1GjgcDvh8vrJy5WpUBrjkWBmGgSRJ81bsyMR7uStEgiDA4XAgnU4jm81S/YGFICvcnZ2d+NKXvoTjx4/j448/xpUrV/DSSy8hnU7D7XajpqZmQeeG5fDWW28hk86hp+9Y6fhRShRlxBRGZ/px6swJmEwmnD9/HmfOnMHY2BhUtWSn6Ha7l+xucTMTCnoq/eMJJGG1mraIyxlHa2srHnjgAXR3d+PMmTM4ffo0/uEf/qFkbbhIkmaheymfl/HDH0bwW7/1e/jCF55bs+NYKypV8teKpWiB8DyPXbt2weFwYGpqCpFIBN3d3ejp6cHs7CySySQVMzx9+jQN/gCgrq6urJKB2EMqioJ4PE4TqTeDys+iqqqIx+OYnZ2FIAhobm5eUaUFz/NobGxER0cH8vk8EokE0uk0YrEYXXlfC/Q2nNlsXpl67wAAIABJREFU9o5b2WdZFmazGTabDTzPQ1VViKKIdDoNSZJu9vDueIwEg4GBgYGBwQ1CgkGz2Vwm2Gi1WpHPa3Mrb9xcICBD0wCGYRGqt+GDI5eWvT+r1QpBEBCJRBCJRMDzPF11VVUVvb29KBaLZT3M+u9JMJLJZBCPx9HR0YENGzYs2bGAqHADpWoF4vpA0KuRL+XcEfR6DLlcDoVCYdFghrgubNmyBU6nE7Iso7e3FyMjIxgaGoLT6cTWrVvBsixsNltZf/dyIGN4/PHHcfbMeeTdo7B4ShalmqrBxTL435/+Q5poicfj6O/vx8zMDFXPr9RguN4YbnZyAVg4oCQtCyaTaV0SDJW9+Bs2bMDhw4cRj8dx5swZ9Pb2Ynh4GIcPH0b46jScVi+gaWXtEr2DpzGRiuNbf9YDWdFQLKqQZAaqyiA2I+HZZ34XL7zw1TU/lrVAf50W0vdYzX1Vg+yP53ls374d27ZtQy6XQzKZhNfrhSiKGB8fB8dxKBQKKBQKuHTpEgYGBqj7BcMw9P/k+elyuaAoChKJBDiOuykJhmrnUlVVJJNJRCIReL1eaj97o/A8j6amJnR2diIcDqOvrw/pdBrRaHRdEgzk+0KhcEclGMh95HA4YDKZaLVGMpk0EgzrgJFgMDAwMDAwuEHIBN9kMsHj8cBut4NlWWSzWVgsDBhG76VeCsgURUZtnRmjo1fpavBSIYE1EUbUB/KXL1/GF3/j36OlfjMYhp2brLNgALAMp/sdA1VRMTh6Gfc/fDf+6m+6lrx/IjCnqioKhQJNZtwoC03grwexMTSbzWhqakJbWxu2bt2KcDiMSCSC3t5e/OAHP8BnP/tZPProo3A6nTdk+0iub1NTE775rW/A5/Mtqu1AbDfdbnfVY1vr5MFqbZ/cZ5XHWSgUkE6n4XA4Vt2pYynU1dWB4zicOHGCVtFkMhk0NDSgq2MXNOg/byV2mNtw+cg4Tvk/B/A8GF4AIwjQikVII7/Af/7P37ilyu+Xg/55sJTk1Ur3tVR4nofNZsNdd92F2tpapFIpzM7OoqenB+fOnUMkEkE8HsfZs2dpopK4F5BkLcuyiMVitIJhPVnsmaZpGvL5PDKZDAKBwA0JO+rheZ6K/Z44cQKapmF2dhZjY2NIp9M3vN2loP+s3En2lEApaR0MBtHS0oJYLIZsNotEIoFIJIJcLnezh3fHYyQYDAwMDAwMVgARyxoaGkI+n4fT6cSRI+/D5yPK1aTSgYOilCoZeJ6D08khHA6jpaVl7nXzS+MrfybK8QzDUAsugqqqaKzdiN9++I+uO2ZFUXD60gfIK2PLOlYS0EiSRAOD1YRMcq830SVilw6HAzU1NWhvb0dnZydUVUU6nUY4HMbIyAicTie6uroQDAapnkSlm4NeVbxaOfTPfvYzzMZn8ewXnqUVKpXjI9fCbDZTFXaSBCKrtsVikSrnryWrEWQS3Y1qLiCZTOaGxOxWg5qaGtTU1KCurg5ms5neB3a7Hf/pD/8jHbN+3O+++y7OsgJqv/r7ZdtSixLCx967bZMLQHmCgdi7rkWSYbmtO6Q8vaurC3v27KFB+auvvgqWZXH27FnE43FcuXIFw8PDaGpqws6dO1FfX4/a2lrY7XZYLBbE43Ekk0kEg8FVP6aFqKYzUukiUSgUkMvlqC7NSuA4DqFQCCaTiSYvU6kUJiYmkE6nqUvRal9XclyV9/9aV8KsFzzPo6amBs3Nzejr68PExARSqRRmZmbWtDLEoISRYDAwMDAwMFgGggB4PBx4gQHHMeA5YHT0KP71X/tgNptQlDX0nBkHwwg4fbofXq8FFisLm5WFw6nB6VCQy6mYjERx8uRJ2O12mEwmqoWwHFcJPaqqgmWWEyyVyvyXg17dfyXVC4sFK0s59sre/M2bN+Ppp5+mq6DDw8MYGxvDqVOn8J3vfAdNTU0IhUKw2WywWCx0/A6HA263m67WV2oLWCwW/NWf/yWkgoTnvvjcvMk4SSwkk0kkEgkMDw9jfHwcqVQKmqZhamoKPT09aGxsRHt7OxobG1FXV3fbT95vNj6fDxs3bixzK5icnITb7Z4nDmoymaBVS4Qx7ILCkLcLpIWEfAE333WE2Frmcjk4HA6abBMEAV1dXTCbzRAEgSYPcrkcjh49inw+jyeeeAIPP/xw2TGspyZJNZvd9dg30cohujHZbBbZbBbpdBqpVIqK8a4VJHFNNHTWS2NlLWFZlp5PooFzq4mF3skYCQYDAwMDA4Nl8vnHXNi//1pPsN/vQ11tHUxzgot/9k0VL7zwZ2hvbwdQ0itIp9OIRCKYmopgbGwEDaGPIcsyUqkULBYLrFYrLXPWr1hVK30mAbb+S5Kkkvgg7T9fysR4+QkGQRBQKBRocL2aLRLVbC0X+r0+wdDS0oKWlhZYLBZaVTE5OYmLFy9icHAQra2taG9vh8fjgdPppGriZDWcOFqQBA+B53moogwzw+PixYtobW2FIAh0kqooCmRZRiQSwfj4OIaGhhAOh5FIJKBpGmKxGGZnZ7FhwwZs3LiRKs5brVaYTKayihSD60POk8/nw6ZNmyDLMmZmZpBKpTA2NgaGYeYlGHieB5T5CQaG4+g9fLsGHSQgJs+DW+U4iAuE3l1BEARs3boVGzZswNTUFAYHBzE0NIR0Oo3u7m709fUhFAph3759tFWC4zjwPL+ux7VQBVVlddVqJR/INjiOg8fjQX19PUZHR5HL5TA7O4tYLAa/37+mCQZynlVVhSRJ9NzfznAcB5fLVZZgMFg/jASDgYGBgYHBMigWgbxYrhOgaRpUTYWmqQAYCCYGxWIRXq8Xfr+fBq0kECgUCsjn8wBA7R5JGXFlf2i1IJus/plMJsRiMZjNZiSTSaiaBkVR55IUwPWSDMudIBMXiZ6eHgwNDcFut8Pr9dK2gVQqBbPZjM9//vPX3dZC+9a3gCyX1tZWugKaSqUwOTmJeDyOsbExZLNZmM1mWoFBbCTtdju14awM+GOxGAKcG82eevzJn/wJtm3bVjbxJsEdWW1MJpNUAI4IaiqKgkuXLkGWZXz88cdUMb6zsxM+n29BvQb9Pj6pLBTsBYNBdHR00DL7SCSCnp4eCIKAxsbGstfyPA+myr1UOucLO0zcDpCEXzXL2FsVnudx8OBBWK1W/PznP0c0GkWxWEShUMCvf/1rSJKEhx56CPfccw/cbjc0TbtlrBP1rREOh2NB950bgYg9dnR0QBRFhMNhhMNhdHd3o7OzE16vd9X2pYdlWVgsFtjtdiiKglQqBZfLtarHdjNgWRZWq5U6JZHWFlEU7ygxy1sVI8FgYGBgYGCwTDKZKgkG3Wq+SSipcldCAlhiy0hen8/nIYoiRFFEPp8vC66rJRgymQxyuRwNbAuFAiRJQnjiCn7w9l+jztOCxpo2NNa2wG2vNjHVbiiIZ1kW4XAYf/qnX8ehQ27wPAeTSQDDAiwDZDIFfPRRbtEEA2lPEEURg4ODOHLkCNxuN1RVRTQaxeTkJHWGqCY0uBiNjY1obGxEJpPB5OQkTCYTJEmCKIpIpVJVFferrY6SlW2B4fAf9n8Rje46/OTXr+HIkSOLqunzPA+e56m7Bvm6evUqwuEw3G43fD4fHnroIQiCgJaWFqrmTpwmqlU03Mzg8WaKv+lXifVjCAaD2LlzJy5fvgwAmJ6exoULF9Dc3DxvGxzHQa1okdBUteQ2oam3dfWIPsEA3B7JKEEQsGvXLmzatAkTExNUj2F2dhYnTpxAf38/PB4POjs7YTabqd7GemgDLEXcMZ1OUyvi1RQ65XkeoVAI27dvx+DgIAYGBjA2NoYLFy4gFAqt2n4I5Fg1TYMgCLR1TBTFm2YJupqQ5yqxqdQ0jSayVls7yGA+RoLBwMDAwMBgmaQz8wPzUiBW+t5iYWiFwlIgq0Umkwl2u71solttYp3P5xGPx+HxeOD3+6nd209f+RHC4TB6L/Tipy9+FxPDY3DYXfB66rGpqQsHtj+AGm89NE0FP2cbtxzIGJqbPfh3/74eAi/AZDbR388m8jh7dnjRbUiSBLu9gPZ2FrPJ9/HSS2fBsBwYBpAKMqZnMnj//U34y7/8b3C73fB4PMsu192yZQueeeYZ3HPPPZiZmcHVq1cxODiITCYDURQhSRKKxSKcTifcbjeAa0kFIhSZTqcxG00gW8xhe207zLwJjJlDLpejiQDyepPJBLPZjJaWFrS1tWFoaAiXL1+GJEllfydtE8eOHcP09DTa2trQ1taGrq4udHV1UTcSi8VySwS+JBm03ETPasFxHA3iCoUCTeB4vV5s3LiRrupmMhlEIhFkMpl523C73cgP9uHqV7+MTL6AHf/9n3Hpay/AoilobWy8Jc7zSqiWgLkdMJlMuP/++2Gz2fDaa6/hnXfeoS1j7733HhRFwd133419+/YBKL/+a8lCIo+qqiKRSGBqagoulwsNDQ2rWllB2ntqa2ths9mgaRokSUIul1uzgJgkekkrRl1d3Zq3Y6wXJKEgSVKZPonRkrY+GAkGAwMDAwODZaJWVlhqZGJampwKApYUvOs95JczcU6n03A6nXA6nfB4PDQIczgcaGlpwf79+3HxzHn8wV2/AwtnxtmRizg5ch5/feoV8IIJHncdTIId/iYbFEWpqoWg/x35nlRf0NWvinFxHAN5rjVgMfbts+HpZ3wI+AMl5XS+FLyqiop8oYjf/9plTE9PU6Gu5dLa2orW1lZIkoRCoYDu7m4cO3YMsVgMyWSSVosQDQbi+EA0FaLRKKanpzE2Nobx7AzstpIQZ20ogEQiQdX7ZVlGsViE3W6Hw+HA7t278alPfQoffPABhoeHoaoqZFmG2+1GIBBAMplEPB5Hb28vzp49i7a2NmzevBmSJKGurg5OpxNWq7WsPHk5vd6kJWOlwSa5zqqqIpfLwWq13rQEg8lkoraopEqECHOSeyObzWJqagrZbHbeNjZu3Ijzx48BAHYevAvhf/lHdLZvxOs//tEto1lwo1RWeNxOSQae57F//35s27YN0WgU3d3dyGazyGQyOH78OHWB2bVrFz2utb5e1XQWCKqqUhcCn8+H2traVf1MkLYLv99P9XiKxSLy+fyaJBjI9kVRRCaTQTabhSAI8Hg8q76vmwFJ0IiiCFmWqZDmejj5GBgJBgMDAwMDg2XDVf3X89pk1GQqKamv5YS/WuBJVnxJAGyzWrGvpRMHNu/C/wJAVhRcHO1H99B5/Ori+4hOJRGLxeiEi6zME9HJahN6lmVxzXyi/PisVg55MX/d417IvIJhGVjMAliWQTKZhNfrXdE5JOdj48aNsFgsEEWR2kYWCgU4HA64XC66QlmpqTA1NYWffOeHCAZrYLc78NWvfpWugOkrGHieh8lkQl1dHerr6zE4OFjmdLF792488cQTGB4eRm9vLwYGBjAwMIBYLEYrHXp7e7F//34cPHgQwWAQfr+fBthLnRCLoohEIrFiUTOiq5HL5RAOh1FXV4e6uroVbfNGILaT+Xye2nwCpYof4r7CMAwkSUI2m4UkSVW3Q1Zkn3nsUYDl8NRXvnTbJxf0kPv2doPYWd57770ASpaiH330EXK5HGZmZnDq1Cn4/X5s374dW7ZsuSmBYaVFJVkNr2bjulL0tqPAtaqqtfh3hLR8iKKIQCCAmpoa+Hy+Vd/PzUJRFMRiMYyPjyObzVIRTUP0cX0wEgwGBgYGBgbLpNq8suxXjIxsNgtZlukkcTUtzxbaFrEXy2azGOwbxObO3wGny4awLIeu1u3Y3rgJ9209iP/4y2/TnuLS31kqGKevqiAijpqmlXraFa2qAcWSV9oXyDAwDAOGYyAILLLZ7IqsMIFr56OpqQlNTU3095IkQZIkGsBXWtKRBATHcfjX7/0LLA4bOlq24p577kFXV9d19/vuu+8CuBYg7NixA1/+8pdx8eJFBINBmM1mpFIppFIpDA8PIxwO491338XMzAxcLhdUVaVl0sThYinnVpIkZDIZFAqFFZ03l8sFp9OJ/v5+jI+Pw2q13rQEA8dxdBWXBNGCIFDXD4YpCaoulmAg/MU3v7kew15XbtcKBjJuk8mEAwcOoKWlBclkEufOnUOhUEAsFkNPTw80TYPFYsGmTZvWpYJBH9BXPhfIOSar4asJSVoSwVngmkvNWlxXUhVUKBQQDAbvqOQCAOowMzIyUqabYSQY1gcjwWBgYGBgYLBMnI6KiS7DgGGvTUQVRUM0GsXMzAzMZjMcDsea9dVrmobJyUm43W7YbDYMDAzg+PHjONjQCa+9vL2gtPvSRDbkrYUo5uD1eunKMJnIqqqKZDI5bz8mk2nu+4XHspTxHjtVxMhkDBZzBhbLJHi+ZLDJsQDDqBgbz0IUxWWdh+VA/N6JXaXZbC6bdKZSKUxNTcHn86G5eQNGY5No8zVjcHBwSQkGQRBgs9nmKZY3NTXh3nvvRWtrKw4fPowPP/wQ7733HtLpNPL5PM6fPw9ZlnHgwAEcPHgQDQ0NCIVCSxaTI/caEYxcCZXB1a1GZfl6sVi8LVfxV8Lt3CJBIGJ8Pp8PBw8eRCaTwalTp9Db24uJiQkAQHNzM4LBIJqbm9HQ0HDD+6pMIFT+TRRFJJNJ2t7G8zwsFktZhddafh70GgGqqs4bz2pCjmUtKjFuBQqFAvr7+3Hy5ElMTU2B4zg4nU74fL47QmPiVsdIMBgYGBgYGCwDlgWcrvLVK4ZBmXhULscAVgmpVKosUCeTObJSde39y5vg6YOJixcv4oUv/w5MnAmcwCOXzYLhWPzm7iervJOhY+V5HizDwuPx0MQB0SHIZDJIp9M0OCb7s9vtpVV5TYNGShg00PINVS0/DwuR82zA2P6HS60YFjN9s6aq0FQN+YGX1tRKjJQ6kyQDsZwj1yedTmN8fBwsy6JtcxuGo6NodtVjsG9gSdvneR5WqxUmk6ksOKivr0d9fT12794NVVVht9sxOjqK0dFRKkbZ39+PRCIBWZaxf/9+urJItDKq6WOQ+8lkMsHtdq9Y3Z5sfyFXi5uNfnzA6mlPrDZrff70Lii32rEvFYZhYDabwXEc9u7dC7vdjlwuh4GBAWrH29jYSFuGamtraSB+I/ta6HpomoZsNot4PI58Pk8TH+TZoGka1SZZq1aNyoTRWiYYgDtT9JB8DgqFAsLhMM6fP498Pg+Px0MrGIwEw9pjJBgMDAwMDAyWgdnMwOksn9wyDAOWYcEwLDQN6OmJ4/nnGhEIBGgpPAkCSS9oIBCgJd43gn4i1dGwFX/++B8hJ4kQOAGFYgEOi33Z2yQrWkTUj6wIk33JsgxZlhGZzOKttyZx3311ELwCWK00SZVl9brl/CzLQtiwEfZDh+H3+8tEHglDPR8vup3FViKXAs/zsNvtSCaTyOVytJXBYrHAYrGU2Vlu3NKO4bf6satxO169fHRJ29eXOlc7BhIc7du3DyaTCceOHcPRo0cRiUQwPT2Nq1evQlEU9PX14aOPPsKGDRvQ0tICi8VCAxzyxfM8fD4f/H4/bDYbgsEgrFbrqgQN1QKQW7GqYblaFesFcYXRi3auJvprsVa9+jfCjdwjLMuipqYGmqZh7969mJmZweDgIMbGxnDp0iUUi0VYLBbU19fD7XbD5XIte0w2m61MFFePoiiIRqO4evUqZmdnwXEcgsEgNm3aBJfLBVmW4XA4UFdXB7t9+c/WpUBEY8m1zOfzyGQyKBaLa7K/OxWif6N3keB5nlbBLPfeMVg+RoLBwMDAwMBgGbAscPTDHC5eLMBmZWGzMwgEgFCIRWODE83NDigyh8OH74fP58Pw8DDtiyeVA4qi0GoAEsTpS1WXMjl//X++jngsjqeefQo8WwrG7eaSf7mJX1pAo0Er2xcJfK1Wa1nLAAlcZmdn4XA48Ed/9A288sqL+NUbfbjvPhc+85laBGutkIoKBIEvC9CrHs/1AiFFvq6rxkpKwgVBgCAISKfTdPKuF3ok9pIA0NbehpMvH8FTgYcxeGJpFQwkUUOC82ouHRzHoaOjAx0dHXC73chms7h8+TKKxSISiQQikQh6e3vhdDrR1dWFPXv2lLlMEA0Ck8mEpqYmKIoCQRDAsizy+TwkSYLZbKbtEjcS9N0qFQzXGztZWb7VhBtJq8xaJRiA21eDoRKWZeHz+WC327Fr1y6k02nIsoyJiQkMDQ1hYmICLS0t2LRpEzZu3AiHw7Gse5pUJDidzqoJBlVVEY1GMTQ0hGQySRMMZF8le92SsKjNZlvtw6dJU6I1ondBWI8EA3UGqnDSWAp6l6Gb/azQNA2FQoFaEsuyDI7jYLfbEQwG0dDQcMslIu9EjASDgYGBgYHBMshmNZw/nwcAGuQdONAGaBvxyssfQVGG8Vu/9Xs4dOgQMpkMmpubUSwWqaOApmmQZRkjIyP0Z6/XS8X/lhokDQ1cxUzfOP7LyW5sr21fy0OmWK1W+Hw+7Nu3D4FAAABw6tRxfPObr2DTZhZ791jBctyCDhr0dzpLz2ospUJhNSazDocD9fX1NMGTTqcxPT2NRCJBVxJbW1sxnBhHwOlDPlcqWV6KdeZygvpt27bhueeew/DwMEZGRtDb24sLFy4gm80ilUrh0qVLiMfjNDFCklJk3E6nk1o3kr5xi8WCffv24cCBA7Db7Xd0WfCtWFUBgFbhrGVJPXle3EoVDCuB53ls2rQJPM8jlUphcnIS0WgUqVQKx48fhyiKeOqppxAMBmmibTksdJ8Q3ZnJyUnkcjlwHAe3241gMIhisYhUKgWr1bpgBcRqUdkmsZbXleyL7Ie0yJEE5ULoxS7J/U3awtby3CyFfD6PK1euoLu7G5OTk+B5Ho2Njdi5cydqamruWM2JWw0jwWBgYGBgYHCDWCwWeDwefOpTd+HrX/86PB4PEokE6uvrAZQCWIfDQV9PSjYnJycRiUToCkuxWKRlt3otgIUmQgzDwGGz44m7n0dnyzbki4ur568UMg6z2QyTyYRUKgWHwwGn04kXXvgqvvSl38Trr7+G73//u8jlRJw4cQKBQIAmTPTJAE3ToDHMXBHDwhPnxSbWqyVAaLPZqFuDpmmIxWKYmJhAsVik26+vr0e6kEFOyqPZ34DBwUHs2bNn0e0ud2xtbW1oa2vDzMwMpqam8M4770CWZYyOjmJiYgIzMzOYnJwEUL7SSFo7CCThQOw3M5kMamtr4ff7aWBUzTXjdudWXL1nGKbMiWWt9qEPEG8VVnI9OI7Dhg0bEAwGMTIygnA4jEuXLmF2dhbnzp3D4OAgGhsbsWvXLni9XlruvpR7udo9rxe2TaVSmJ6ehiiKsFqtcDgc8Pl8mJqaQjKZhM1mg9PpvKHjWgrVWpLWw+pYVVXaSkAcWfL5fNX3VFY4kGcO0dEgej7r/WzRtwz29fXhxIkTiEQiEAQBLS0t6OzsLLXjGdUL64KRYDAwMDAwMLhBiPq5w+EAy7LzWgsqYVkWJpMJfr8fgiDQygYiSGWz2eB2u+F0OuFwOBadDJXaG1iEPLVgmPUvDdc0DYIg0GD2K1/5HTzyyOfws5+9ApPJhL6+PrhcLrhcLtjtdlpWrGkaGI6bm6QutPHr+8yv9sRbH6w5nU5q1VgsFtHcvAEjsTG0e5px9IOj100wVLO7WwpWqxWBQACHDh1CY2MjpqamMDU1hUgkgkgkAlmWqXVdsVhEOp1GKpWCJEkoFosQRRGiKFKLz/fffx9TU1MIhUKoq6ujLRl2u/2OsmqTZZlaWX6S0LsA3CkVDARBELB3716YzWb8/Oc/p4F/oVDAe++9h3w+j8997nN44IEHVhzMktaEfD6PbDY7z1Y4n88jnU6vqZUjwzAwmUywWq208kUQBPrzWkCq6ZLJJDKZTFkF1GI6E/r7jLhdkAQFqWy4GRDdisH/n70zj4/rKs//986+LxrtkuVVtrzv8ZY4ie0kkIQALQFaChT4UcKW0lJo2SkQSmnYKWUpbaGsAQqkJE2chWAnjmNbiTdZkm3Zki1Z6+z7en9/jM7JjKzNtrwk3OcTfWLN3Ln33HvPXJ33eZ/3ebu6eOGFFxgeHsZms7Fs2TK2bt1KfX39VRnXHyM0gkGDBg0aNGi4SFgsFnw+H06nc1qZEWHM5/V68Xq9smyip6eH3t5eLBaL7HtuMBgwmUxljuljW/NdyjquNPi9kEC4VIlgNBplXTJAPB5n8+YtpFIp+vv7ZRZMyGdl9ksQDBMoGFS1MC0p60wFVGMzhg6Hg4aGBulnMGfBXE4Pn6HRVUswFp9yf6UZ5QtZbAvFx8qVK1m3bh3BYBC/38/Jkyc5ceIE6XSaXC4n+9cPDQ0xODhIMpkklUoRDAYJBAKScDh8+DCtra00NDQwa9YsIpEITqeTyspKvF4vJpNJzrGJvCKuhbrqqZDP52WAA+VZ1ssxRwTG2/d425WWMswkSp8N16KK41JgMBhYunQpc+bMoa+vj6NHjzIwMMDIyAj79u3j+PHj+Hw+Vq1ahc1mu2jCrLRmX2TuS0uRVFUlm81KAmsskTPec/RCUDpfSpUA4ncxDqFWEuTH2M+Ot7/JIP72FAoF0uk0hUJBKrrsdnuZ8m4yCNJTfP/y+fxVKUMQ9zEYDNLV1cWxY8dQVZXGxkYWL17M+vXrr3r5xh8TNIJBgwYNGjRouEhYrVaqqqpwuVyX1DbN5/PR0tJCOp0mm83i9/sZGRnB7XbLvt1C2l72eS5tEadTdHJBOBMwm814PB70er30AbBYLJjN5nLTR0Ox7dtEEga1pB74SkKMURAimUyGRCLByvWr2fWDR1hdt2Ta+xGByIUstEWgI4wBRQlOc3Mz1dXVMigQ1yaRSJBIJMhmszIDm0ql6Ovr4+zZsxw/fpzOzk4SiQTd3d08+uijnDhxQrbLXLZsGcuXL8fhcIxrXGcymcpM8cYry7gSmCp4NhgMmM1mmekV3U4EETMTuBiCQXy/LyUAngoiCL6W2nRRCURwAAAgAElEQVTOZPmNyWTihhtuwGaz8eCDD/L444/LlpJPPvkk6XSa7du3c8MNN1zU/vP5PN3d3Rw9epT+/n75PJ47dy5Op5NCoYDT6aS2thaz2Syz9dlsdtLrXVrKNFW5m+iAIp7/4vuVTqeJRqOkUinS6TSBQIBIJFJm8jpW3TDd6y7KIoQKzWQylRnHThc6nQ673S67DoXD4auikEqlUnR0dNDa2srZs2cBqKmpYcGCBbJcTyuPuHLQCAYNGjRo0KDhIiEk7S6X66IWL4KUEIqGQCDA4OAggUCAUChEKpXCYHixK4PBYJDZrEsNJhRFwWw0kUwmp72gnGrxWloy4fP5AGTgIzNwegO6mjpe1C+oxf9Kz2fUQT2TyZBOp+WCXgSMAwMDWCwWSQKIaz/WfGy6EJ/LZDJlCpJ0Ok0qlWLjxo1861++wXzPLGB6mb3SzOZ0x5LJZGRLU4fDIRUibrd7Wi1Nxbxob2/n6NGjuFwuEokEwWCQSCRCa2sre/fupb6+nsbGRkKhEHa7ncrKSjwez3lqhWw2K7tWlOJaUzSM7SJRKBRkyUg8PrXi5HJAkAulBniX4xjXYpvKS8HYwNxgMLB27VoWLVrEwMAABw8eJBqNEo/HefbZZzl58iROp5OlS5dOWaI2HlGVy+U4ffo0+/bt49y5c+h0Ompra1m4cCFut5t8Po/NZpNlC/F4XD6TJjOyHXseExHQon2myWQqU0iI8gVBOmSzWUKhEIODg2XE7cVm5UV5jdlsxu12T1uxMNH4jUYjwWCQRCIhSzuuJFKpFO3t7ezZs4fe3l4URaGhoYHFixdTVVV1QaSJhkuHRjBo0KBBgwYNFwmbzUZ1dTVut3tGJNBWq5Xq6mpcLldZ1mpkZARVVXG5XFRUVJBKpVALl17rqtcb5EL5cgSNYhErMu3xeJyc0Yitth6dokOhaPZYlNnmpKBhqL+fv/vHz2AwmlAUUHQ6dEYTOqMRndFE3D/M+/7iTdx0002EQiF57QUhYTKZLmjhnU6nSSaTjIyMMDIygtVqxe12S7WA2+3mui0beO65g6xbfv2U+xtbIjHdaysCCqHcEBlMu90+rVaHwpuhoqKClStX4vP5WLduHT09PXR3d9PZ2UlHRwfJZJKenh4ee+wxTp48KTOOImgRZFBzczOLFi3C6/UCxfnp8/kuS5u+S0E2m5VKDnhRaq7T6a5q9wxB2FwOabaYVy/HEglBKJpMJjnvjUYjN954IyaTiZ07d7Jr1y4ymQzBYJBHHnmEQCDArbfeyo033jjhfsX3ufQ65fN5enp6OHDgAAMDA5hMJhYvXsyOHTtobGyUpGYqlSKRSJBOp6moqJiWH8N0Ss8EiSKUKE6nU5YtORwOqqqqcDgcWCwWampqZDmewWCQJXcXg9L5c6nB9+UqAboQZDIZurq6aG1tZXh4GKvVyvLly7n55ptpbGy8qmP7Y4RGMGjQoEGDBg0XiVKCYSbklyIDJxam586do6enR/b09vl8MmAvFPIoSrHK4GK5gWwud1mz0WLRmUwmGRgYIBQKUSio6Gvq0Ov16HRKUclQKJDN5uR5Z7MZXB/5DPaGWZhtNnR6PZSUgwz+6N8ZHBxkaGiIVColjxOPx4nFYlitVpxO57TPLRwOy9aU+XyeeDxOMBiU8uN8Ps+c5nkceOxZUqkUkUiEfD4viYzSmmhRiz1ewDeVL4DIuguSQRxLUZQpM7PiOicSCWw2G1VVVTQ3N6PT6ejo6KC9vR23200ikcDv9xMOhzlw4AB79+6VgYow4xQlGK985StxuVyYzWasVqsM3BVFKTNUFOc/VgFxqXNrbMeM8bLDoka+lGAQZnUv95rra7VN5aWSHYJoEx4EQlW0du1aFixYQCAQ4NChQySTxbaxu3bt4ujRozidTpYtWzZhSYroklBKMmSzWXp7ezl8+DDJZBKz2UxzczPXX389er1ekpapVIpwOEwymaSiogK32z2jpSDCW0Eol4QywOv1YrVaMZlMsjXwtYSJnn1XCqXdI3p6emhrawOQ3gubNm265gjRPwZoBIMGDRo0aNBwERCLwcbGRhn4zzTcbjezZ8+WNfbZbJZYLMbw8DCpVIqhsL+sLODCxg9dPV3ce8/7+einP8ayZctmfPwCfX19PPvssxw+fBgcDkwO56j03oROUUBvwGxWpOWjzmQubuN0YjKNBvAlp2ew2HA6LVRXV2O328sCEo/Hg8PhwOv1Tjur1t/fL2utfT4fLpcLh8NBKBRiZGQEs9nM8OAQVqOZ06dP84tf/IJAIMCSJUtYs2aNzDiKQLzMb6LkvoiyD2GMVgrRJnNkZASDwYDdbicSiRCNRssk06KFZqnxm3g/HA4TCoXwer34fD5JWHk8HpYtW4bH42HNmjV0dHTQ2dnJiRMnOHnyJDqdDoPBQC6XIxKJyP0eOHCAYDBIdXU1VVVVzJs3j3nz5uHxeCSpJlQqogzAarWWmQ/OBASBINQVYwO7sSaPfywQ82AsAfNSh/g+GY1GCoUCgUCAYDBIRUUFTqeTjRs3Eo/Hee6553jhhRfI5/NEo1F27txJJBLhtttuO0/JoKqqJCSEIkeYN6ZSKTl/BJnncrmkekG8J2r5PR7PjBOz4l6WqhlEd4uXyn29GiqasSacQjEkWkSL8g0NVxYawaBBgwYNGjRME2IRKIywqqqqaGhowOv1zmgrMbF4FYskgcHBQc6ePUssFqNxbhM/2fU7Tkd6+avr30Sl0zuqZJjuwlfBZrKwzriAv3n7+1i1aS333PseFixYcMnjH7vIHBwcpLW1la6uLvQNTRgMeswW82imTidr1cXQdTo9OhWMxmKXA0VX0uUhX6Dnt7/gh6qOh3+7E7PJgtFgYiQwTG1jNf/+/e9Ic8zpkj65XI5QKERjYyPNzc3oR7tcJBIJkskkkUiE3U/8gRWV8/npQw/x+ONPYXEYedOb3sT8+fOx2+1ybghJ/nhtKoUqQUjAxXtiYR6JRKQvQjwel6UlYrtUKkUymSwjBEQAoqoqIyMjDA0NUVtbK0tqhDt8ZWUl8+bNQ1EUWltbee6557BYLMTjcdkST5TliJrvEydO0NbWhsPhwOl0sn79ejZu3EhtbS3V1dVSpi1c910ulyQeSr8PgnAQteOlSo6xhpFjyQPhjZFMJrHb7fK6Crm7uNaCtBH7K3Xbf7lCXAtx3mI+zCQEsVX6M942Yq4L88DSn9LtBMZ2XRj7u16vl0qakZER+vr65BxbuXIlDoeDZDLJqVOnZHvJZ555hvb2dioqKli9ejVmsxmTySSvkfgui1KHXC4nv4ul5oql4y3t3FBRUXFZVQTie1Pqq1E6p69lXK0SHVVVZRvRTCZTpvxwOp0vq3a8LyVoBIMGDRo0aNAwBUTQY7VacTgczJkzh4ULF7J582aqq6uxWq1XJJgRiglVVbnx5pu4865XceC5fbz/l5/ik7e+n5b6+aOZ4+mNJZlN8+VH/p2/2f52LCEr73zD27j5ju28673vpqamZsLPTUcaPLbOOZPJkEinMcxdgKLo0OuKJRLj7l+nUMhmxn0PRYF0hs+/55ejUng9hYLKY/t+xZx1btxu9yXfD/FZm82Gz+dj967dtHjmMjzip867EJfNy+Gzu8lms7jdbinBFZ0+JlIwiCy/yWQqCxrE9qFQCKvVKlvFCUJCtI0zm81YLBZJZogATBA0oie90+nE7XZLo8pMJkM4HJalAzabjcWLF1NbW8vWrVvl5/P5PLlcjr6+Pnp7ezly5AhHjhyRbeza2toIBAKylEKURBTLXXQyoJOE0ei5ud1uPB4PGzduZMuWLWQyGRkURKPRsusznpN9NBqVZSOKouD3+xkeHsbv98trbDAYZKs6USojiI+XK0KhEOFwWAb+M/UMKlVFqarK0NAQfr9/3E4VggASWf5sNsvIyAjBYJBgMCjNXoGyNoZivpaaII5VYwmzzjNnzjAwMCDnlMFgYP78+Wzbtg2bzcZzzz3H888/L8ubWltb+fWvf82aNWtYtmwZmUyGeDwuS4jOnTtHJpOhsrJS+o6YTCZJNhw7dozHHnuM5uZm5syZI8/7ckvtxXUoLXt5KSkYrgZEiUtnZyfBYBCj0UhDQwMLFy7E4/Fc7eH90UIjGDRo0KBBg4YJIDJKIrDzeDz4fD7Wrl3Lli1bWLJkCT6f74oZydntdux2uyyVsNlsvOktb2bR4hY++U9f5ZO3vJ/lsxZNW8mw6x8e4AM//QzBRIR7b7qbVyy7iQdaH+L1t/8Jr3jNHbztnW+ntrZ22uMrJR5E9q804M4rOhS7AxQReCuAOvpTUruvN1AYzfCfdwxdsVxCRS071smBQ/z5lg/jcDhksDmd1n2TZdGF5P83P/0Vn7vlA7z52x9gx7p3cHbkJIlEnHw+L2W4hUKhzHCvNHgS4yyVP48dgwhgRJbfZrORy+VIJpOScBABlxijyHCKQFoE7UJJIDpviDpyEbjb7Xaam5txOp2yA0ppcHrs2DGOHj2KwWBgcHBQqhr6+vo4ffp0We2/GMfYbDQgs7+1tbXU1tZiMBhYunSpPA9RgiI+KwI90SFE7CudTpcRNkNDQ7S1tTE4OAggJe3CiT8ej+P3++W1vBYxNmNf+vp0oKqqVLmI+zNRV4ELDVDHtlgNBoOMjIzIezN236XtUjOZDENDQwQCAfm72I8o9RLtFUV2XgTVpQSDoiiSmOzv72doaAiXy4XL5aK+vp76+np0Oh11dXXE43GOHz8ulS6HDx8mn89jtVqZO3eunP9C5SJIGYfDQUVFBQ6HA4/HQzgcJpPJ0NHRIbvhrFixYka9FiaDIOZKvz+is8S1DnGNxLwer5TpciCTydDX13cewVBqTqvhykMjGDRo0KBBg4ZxoCgK1dXVzJ49mwULFrBw4UJ8Ph9ut5va2loaGhrw+XxXNUMqWoztuOUWLFYrn/3UF/j49vewrLEFvU43ofujAmRyGf78u39NIBZk0+zVADitDt5x/Rt47erb+M5TP2bV11bz13//Qf7q7W8ry0ROBJEdVxSFRCIhTRDtdjs1NTXYzSbi8RiFfIFsLku+IGrmy8ep6HQU8uNLvdVCsa2l2WSWREowOkw8E2bRokUUCgWZ7RYBxWSmioODg5w4cYKqqqrzAm2DwcCjjzzKcl8z/nAQVaejyl1P70gXVqtNnutYc0MY30F+rOv/eNdPlD+UusQbjUZZWiACyNKFvFAQiC4jbrdbEhIiaywyx2KsiqJItcHYsVdXV7Ns2TKcTifr1q2ThprHjx+ns7NTBpqC2BClHeJ3EViKwFOYYu7du1e6u69YsQKDwYDb7ZbHFedbWmIhvE7S6TQOhwNVVeno6ODXv/417e3tACxdupRNmzaxcuVKzGYzXq8Xi8Uir+G1iJkgGMR8Fcagbrd73O/pxWTARaBfKBSIxWJEo9EJ9y+IA0EcmUwm9Ho9s2bNor6+vqxEqHS+jkfClRIbor5eqFpmzZrFrFmzcDgc6PV6OZaNGzeSSCQ4cuQIJ06cYHBwEEVR2Lt3Lzabjfnz59PY2Eh1dTV6vV6Wnnm9XoxGI4sXL2b79u08//zztLe309/fz7Fjx7j++uuvSplNKcHyUiEYAElgFgoFUqnUFVEQZTIZenp6OHr0KH6/H6PRSFNTE0uXLp3W3ywNlwcawaBBgwYNGjSMgdFoxGw209TUxLp169i0aRObN2+msrJSuulfbYj2YsKj4cYbb4R/hH/8xOf55I73sXL2YhSVCUmGZCbFSCTAd978eRbUzS17r8Lu4XWrX8mvDj3Oj8+N8N93vYa33f2n3POOd+B0Oicck9FoxGazSVM+QBIMtbW11FVX03vgWfI7Xkk2kyWfyzNe6KPo9aglCoZCoUA+mSA5MkI6EkJVC4yEBqjy1qGqKp09h7HYTDz11B941avuJJ/Pl3kJTBTIJRIJfvXLX/KbB37NJz73KebNm3feNX7wgV9z346/5Vd7H6KqYq7s2uF0FoOU0sxdKUoJhrFO6xNBZHLHkgwGg0GSAZNBmFSKMpGLNVr0eDzYbDaam5sxGAx0d3dz6tQpdu/eTS6Xw263y/pmi8XCwMAA/f39UjEgpOihUIhQKCRbgD7//PNEIhEAFixYgMPhKKtpn+jaiPsozPza29t57LHHZNZ+yZIlvOpVr2LOnDmS1ColLl6OKPWwEAG70+mccVl4Pp+XahARlE8G4WuQyWSora1l9uzZl3R8YeCXzWapq6ujrq5Ovifm6bp169DpdKTTac6ePUskEiEWi+H1etHr9ZhMJmbPno3H45EtaMVzLJPJsHDhQpLJJMPDwxw9epShoSGgWIJypQiG0meEwEvJg0GUKYmuH4lEArvdXkYUziQEmRWJROju7qajo4NQKITFYmHWrFm0tLRoCoarCI1g0KBBgwYNGsagvr6elpYW1q9fz6ZNm5g7dy5er3daQd6VgCjbENkhvV6PxWLhxhtvRP95Pfd94j4+ctO7WNm0dAolg8KaecvLTBRLodPrqX/T28jc/mp+8Ouf8/NXv5a/f889zB2tSx4rjTcYDFgsFvL5PLFYjHg8LgNDt9tNXV0datsx4s/tQdm4BX0mxeD//g9qPEohFiEfi5GPx4j399H9hU9zzm6nkEpRyGbQ6fSYjSYsRhOWvMrPdn6L97zuUwDMrm2md/AUjzz0KLfcskMGFaUtJM8791E1QcfBY9wydxP/9e3vc/3115fd3/b2djwGJw3uGp7oeIbVy16Fqqro9Dpqa2upqakZdz6MLRe4UkGK1Wqlqqpq0vOeDkKhEMPDw/h8PqqqqqRhptFoZN68ebJbhpC6JxIJ4vG4JENKO57EYjGefvppdu/eTTQapbOzk9/+9rd0dnayY8cObr311vOUI2MhjFWPHz/OkSNHaGtrI5lMytKLxYsXM2/evJc9qTAWYp6LVq0vV1PLyb5DQimh0+kIBAIkEgm6uro4e/Ys3d3dFAoFPB4PLpeL6upqKisry/w99Ho9DQ0N5HI5nnrqqbLSjKvVlaRUwfFS8WAQYxVdOYQS5XKVKKXTaY4fP87Bgwdpb29neHhY+r34fL4rWrqo4XxoBIMGDRo0aNBQAkVRqKurY/PmzWzevJkNGzZMmrW/GhClCKKNm8hwK4pSVDJ8Fj730c/wsW3vYc2cZcUChFJnfoplCEUnA1GgUO6DUETxd5O3gvq3v5vYyeP84/8+SLJ1L9/60v3nLXyFvD2RSPDJ+z7Prv0HsLs95HLFDHQ8FgOrjejP/pPYr36EooKSzVBzy+1Y3R6MTXMwuD2kzp3jDXUruXvdndR5qnBZnGVdCX76zG/4ZfvT5PPFzF6Vp46VzZt4fuARcrkcer1eEjCip3zxEpSf39DQEL1nern/LR/iB/t+xZe+8C986etfkee1+w+72Ni4klP93YzEQ8yrWU48HkOn0xONRtm/fz8Bf4ANGzewYcOGsn2LfVxJQkp4RlwqotGodO2vqqrC7S6aZ1ZWVrJ48WKpVBBqi4mubzKZJJlMYjQa6enpYWBggO7ubnp7e9m1axdGo5GVK1fKtqACY/cj5tXp06fZuXMnnZ2dZDIZqqurWbduHS0tLcyaNetlG2BPBEH6iNKalyMEuTARgaLX66mtrcXr9RIMBkkmk2SzWc6dO0d/fz9+v5/q6mq8Xi/XXXedJCNKP19dXY3JZKKiogJ4MTt+tZQD4pyvBQVD6TN+slaowmhUlEZls1lcLpfsOjLWwPJixyGMXP1+P0eOHOGZZ57hxIkThMNhqquraWhooLq6WjN4vMrQCAYNGjRo0KBhFCIDW1tby4oVK6TkeiZQWlt8qbDZbNTU1FAoFAiHw7K7hcho3nDDDXzqXz7LZ//+U3x46ztZO2c5upIssaqqqHmVglwsCsPFF1FQC+fxDY4FC7G/929pe8ufcPDgQc6ePUtvb6/0BdDpdKxduxaAZC5Hzb0fpnLNevKFAvlcnkAgwMjICPFjh4j+57ep/djn8DbNxVXhxW53SCXFyDO7mGNvYkVTy+hr5QNx25zk89kS0kHFYXOSSWdwuVyytGCqrO6+fftYXb8Yk9HEO69/Ix/8n8/zve9+j3fd8y4ADu57ntfX72D38f24nTUUCnlUFZobluN02Il0mXj8hQP0nD5bRjCUGkdeiIKhtH3j1cR4gY2qqgwPD9Pd3U1tbS319fWYzeZJy4UEubNlyxZcLhcPP/wwDz30kOw2sGvXLuLxONu3b2fHjh3S12EiDA8P09bWxsjICHq9nsWLF3PXXXcxf/78GT3/lwqm8vR4uUB4kEzWCliv18sWs+FwmP7+fkZGRohGoxw9epRsNovNZmPOnDnYbLZxO0JcC989AeHRMR2j2isBoUpLJpPneWaIZ4UwnPX5fJIgEX4Yer0ep9OJ3W6/pHEkEgkOHTrECy+8wP79+zl48CADAwM4HA5uuOEGtm/fTnNz80ycsoZLgEYwaNCgQYMGDaMQdaQVFRXMnz9fOt9fKgqFAq961SsoFAps2LAevd6A0+nF6/Wh0+lobd3LK195F1/8p/uxmG3TCEhVVBXe/Jd/xpq1a1BVVda7isB669atfPKLn+HTH/oEH8i+lesXXVc8R0BVC+SymSLpMdERhNnAeVAIjPg58eghTul1HDSZZPx/crCbd330/dx0003FLUc/r9fp0Zl0mMwmTGYT6uIVpJeuJPCrn2G45wMYEhasNht6tRis6owmsoUshgmCTbfNRS6fLgtu7VaXbNE4FcSC/dmnnmHTrNUogEFv5BOveD8f+I/PsnLVShYtWsTJjhMs3/B+7n/oO8ytWyGzcbWVs1i5ZB1Go5Gjp/aR4ux5+78YgqF0bFcTpV0whPQ5l8sRCAQkoVRVVSXJgInOT/hIrFmzhpUrVxKJRHjhhRcIBAKEw2EOHDhAa2sriqLQ0tJCZWWlDE5Kr5sIYIaHhzlx4gSqqmKxWGhubmbbtm0v2+z9VCjN7F9LwfFMw2g0YrVaJ30WGwwGZs+eTWVlJd3d3Zw+fZr29naCwSAnT55kYGCAhQsXsmLFCurq6mS7VzhfsVBK3FwNjFUwXI37Wkp2im4WiUSCaDRa1vVDmOrmcjlZOmW32zGZTPj9fiKRCKqqSjXZhRIM4txFy96BgQH27dvH448/Tnt7Oz09PVgsFpqamti0aRO33347brf7j07NdK1BIxg0aNCgQYOGURQKBTKZDLFYjGAwSHV19ZRZ2onQ29tLKBTi+PHj/OY3P0NVe9hyvROT8QnyBZVgMEdPT3HbXbv6+eUvf87i+hu4be1bplwcqWqBcCzI/f/0dd7x7jdz5513li1ChUngpk2b+Odv3s/HP/gRhmMB7lp1y6h7u0oil8ZsMIGiTGTRwHgOjEJR8Hc3vQOr1YrZ/OJC/atP/keZlPXF3RQXqlaLlarKSsLhMOlXvprwlz9HvPcMDoeDQr6AThntcGAyk0qnJzx/p9VBNlv+vsVsJRaLTnrdSpFOp3nhQCvv/tM/la9VuXx8+Ka/4uN/+xH+9M2vZ1X9EhLpBJ2Dp/iLFX8hW6+JdnI6nTKuSaVYeOfzeRkoTBdX0rNhIpQGELlcjuHhYQYGBlAUhaVLl1JZWSlLdKYDkcG84YYbsNvt7Ny5k0ceeYRMJkMul2PPnj0kk0luueUWbrnlFtmuUyASiRAMBgkEAmQyRZWKx+PB6XReE9fraqK0m8jLEaLbid1un9Z8M5lMrF+/HqvVygMPPMCZM2ek6euuXbuIxWLceeedbN++XXrqhEIhzp07RzRafH5YLBacTueMqdcuFKXtHq9WiYSqqrK9pzDMNRgMVFZWlinDstksQ0NDsmVqOBwmEAhgMBjkMzCTyaDX68lkMhc1FtHSds+ePezdu5fDhw/T0dFBMBjE4XCwcuVK1q9fz5IlS2S7Wg1XFxrBoEGDBg0aNIxCLOTS6TSRSIREInHRTtTvfvdbGRg4xeLFbhpn6XnPexehm8BMcUGzgU98rI3bt7yJWl/jJHstKhcKhQIVzhresPVevvvNr+BwOHjjG98otxIBos1mY+PGjXzl21/nQ/d+kMHoCG/f/HpgVHSgiOzz+eOaNGib8C31PMm2yosBt9lcbG+Yy+WJ1dSS2ngD4Z/+JxUfv49sNjOqINGjmIyko2lQ1XEDeJfNQXZUgSFgMlhIJBIcO3YMh8NBfX39xFdRVXnmmWeY552F02IvjrH4BitmLeY1zdv57je+zd/f9C52HduL3e7FoCu2X9PpBMGgQ1F041pXjCUYppuBvJaCZUVRZDcQv99Pb28vc+bMYcGCBZhMpjJvi6kg5sSaNWtYs2YNuVxOljoEg0EOHz7M4cOHMRqNLFy4kJqaGtk2VKfTEQwGOXXqFH6/n2w2i8PhYNasWbjd7pe1ueFUGFsicS0qGMZr13qhmE6JROm2y5Yto6mpiRMnTtDa2kooFCISifD8889z8uRJqqqqWLx4MT6fD6fTid/vp6enh3A4jKIouFyuYlvdS5TzXyzEfb0aJRLiWEKZkEgkSCaT5HI5Kisr8Xg8Zc8p4YeQTqfLiC6dTidVJ4lEQrauFM/EqRQi4tyz2SzhcJju7m6efPJJHn74Yfx+P/F4HJvNRkNDAxs2bGD79u2yM42Gqw+NYNCgQYMGDRpGYTKZRrPy5jKZ+IUinU7T29vDN/91GUbj1FJbk0mHTq9iMU3uel0oiIxWnny+QE1FA3+x/YN840tfx1fh4/Y7bi/bXizkqqur+fAn/oFv3P81Bh/5Fn+z7R1FTwaUibkCYFwJQ3HHZAo5xtoJltIBqgq5eIxEXy/paJR00I/BYERvNJJOJjEVVFzbX8Hw5z9OtPfsaI2uC5fLhWI0kcwmiotrVAr5Aslsing6SSKTpDcwQDwV40ePfJ1ILEAqFyOTjxGK9HHrjhuoqmriRz/5kTRtGxu4J5NJHvzVb8cCTggAACAASURBVFhft7xMAizwmpW34rO52TJ/DZ/45f3UVzWjqgUUBXQ6PTqleE8TqRhPvPArtr3i+vLrMLo4Fi70V8uN/mKRy+VIp9MMDg4Sj8exWCwsWrRIdlK51DatmzZtwmKx8Pvf/54nn3ySaDRKLBZj7969JBIJtm7dyo033ojP58Pr9dLb28vevXs5e7ZYilJXV8eaNWsmJZH+GFBaSnItkgszhUKhcMHPYovFwo4dO3C5XDz00EM8+eSTqKpKMpmUc+7OO+/k+uuvp7u7m/379zMwMIDJZGLZsmXccMMN57WtvRIYWx50sX+DLgXZbFaqF0RbWvG3cSxRJDpFeDwe7Ha7VB6JZ2AymWRgYIBAIIDL5ZI+DFOZ0ebzRc+evr4+9uzZw3PPPUdbWxuBQACdTkdDQwObNm3iuuuuY8mSJTQ3N2ttKa8haASDBg0aNGj4o4bwXTAYDLLFlWhldrF1uJ2dnXg8hmmRC6qq8v1/72JuzRqc1ona7AnlQjFozRfyqIUCOr2Oxpo5/MX2D/LZT/4zer2e215xm1yUioV5NpvFYrHwV++/hx9+/wd88qEv847NbyCVTfHPD36L9936l9gs55uejQ7wvJcURaGQL6CqY0oh1BeDgdn1dZz6xX8TNxjo7zyF1eAoEhq6Yhu4ZDpOUk2DUmDkK58n2rIUt9dLwmohfewIe/xh/vInHyaWihNPJzCbzdhsNuw2OyaLmZwxirnmKZoX6XE4DNgdBuy2OvKFAg8/3M8TTzzBbbfdVuZCLxAIBNi7+1lef8dH5D14kQRQURQdW5s3kMgkebzjGZYu2EowOoQyWnqijxbAmCMYHSKRC/PBD/2NzMyV1k6L7KPI6olrMxHEZ/L5vPQ9KP39StWFZzIZ4vE48XicQCDAwoULmTNnDkajccqWkpNBfG7VqlWsWrUKnU7H2bNnOXv2LJlMhra2Ng4fPkwmk6GqqooFCxZgs9k4c+YM+/bto7e3F1VVqa2tZfXq1dTV1c3kaV8Syr8HV6ZkQcyn0vlSSpRdKEpVEKWZbFHKItqPTnb/S+ewGNeFmpyOPX4qlSKZTEqp/kT7GxuMr1+/nsWLFxMIBDh69Kgksp577jk6Ojqoqqpi/vz5dHZ2cvDgQQYHBzEYDCxatIibb76Z2bNnT2vcY89hPFzId+ZqeDCMvd+pVIpsNgsUTYUn8rYRJSxOp5OqqirZvaFQKBCLxRgZGSGTyUjflWg0Ks2US7tKlBIqokzxzJkzHDlyhN/97nc8+eSTKIqC0Wikurqa5uZmbr31Vm677TZcLtdVU5toGB/TJhgURdEDB4A+VVXvVBRlLvAzwAe0Am9WVTWjKIoZ+CGwFvADb1BVtXt0Hx8B3gHkgXtVVX109PVXAF8D9MC/q6r6hdHXL/gYGjRo0KBBw3RhMBgwGo3U1dUxe/ZsFi1axOLFi1m0aBELFy6koqLiokweT548SUvL1O0CVVXlhz84xmCfifveef+Ex8rnC+TyOdRCgUJBLZomGoyjMn2o9TXyZzf9NZ/+2H0YTUY2b95MMpkkEokQCoVQFIWqqipqamq474uf55c//wUf+/mXMFnN/MezD/BE914qXMWFocjOj4QDJOJRTv/tPSh6vfxBUVAzaf76N5/BYrZiMhpHvRPg9OBZViQ3E4lEeNdfvpU3vvY1fOKjn2bj5reyecVtGPR69HoD+XyOeDLOl376d/QOdpOO9EJgmI233caf3flqrHe/BovFQlVVFU6nE6ezvE1loVBg27b13HvvIoymkqBbLWouTp400tDQQFVVFXC+gqGzs5MKs5tGX73crwIUVHVUqVBc+CYzaewOOydHDrKv4wlMJjM2mxVfZSVVlZVkslmuv34LUGztKEoHxgsuC4UCiURCyoXF/S+dC6JkwOFw4PF4CIfD+P1+crncpF1IxstiT2X8V/p+qSQbYGBggHPnzlFZWUltba00EJ1pgmPdunVYLBZ2797Nrl27GBoaYmhoiEOHDpFOp6WSoaOjg+PHj8vsZV1dHatWraK6unpGx3OpENdT3OfS8oCpuj1caBCpqqrMNAeDQUKhEA6H46LblIr2f+l0Wga2UJy3Z8+epb+/X2aehQfJeMjn84RCIaLRKIFAAJvNNm1JfCqVIpVKlRE0+XxedoSIx+NkMhnZmlNsJ74XQs4PRYl+RUUFNpuNbdu2yS4mv//974Giiumxxx6T7SyHhoZIJBLodDrcbjd1dXVXJWgtLQ0QPghXSgElCCTRLUJ02xBdgqbC2OePxWLB7XZTVVVFNpulsrISu91OPB4nGo3i8XhwOBxlz7VUKsXRo0c5evQobW1tHDt2jM7OTvR6vfw7vWHDBtauXUtLS4vmuXCN4kJWTX8NtAOu0d//GfiKqqo/UxTl2xSJg38b/X9QVdUFiqK8cXS7NyiKsgR4I7AUqAceVxRl4ei+/hW4BegF9iuK8qCqqscu9BgXeQ00aNCgQcMfGUT/eIfDgcvlYvHixaxevZrrrruODRs24PF4LimgOnz4AE1NU/+J/c//PMajv0tw7+u+XtZy8cV12osO2rlseT/xsTXwtb5ZvH7r+/jo332Kj3/m71m+fDnDw8MMDw/LQNHhcGCxWHjvve/jT+7+U8LhMLlcrsw8rTQozWazGI3GsoxkoVDgzJkz+P1+KioqqK+vR6/Xy4X+rFmziMfjhEIhPvvp+2i0L2XLilfI8RYVI0bcTg/vueuz/PcjX+OFU3+gUCgwd+5ctm3bhsPhIJPJSAJorGwYwO12kU6rWKzlgU6hoHL6VIa1a9fKEonz7s/Bw2xpWl3MpI2SJgBKIU8+XwxO9DodLouDD930Tr741HdJ5eIsaJnLpk2b2LFjB7fccgupVIpQKEQ8HpeLXL1ef95CW1xXkZkrVTKUZmxjsRjxeJxYLEY0GpU/Op0Ok8lUdv5jjyEIAkEYTGb+V5ohLfpK6OS4xThKgy1RMjQZyXExWLp0KUuXLsVkMhEOhzl27BjRaJSuri46OzvJ5/O4XC46Ojro7u6WQUttbS0tLS3XZPcIkXGPRqPymgkvCTifSBiPWBhv/oy3bTabJZvNyjkj5s3FQFVVadRXqoQQ3TsEiREOh2UGejzk83kSiQSpVIp4PE4kEinLVE92fKEwKC2JyOfz9PX1MTQ0hMVikQagZrNZPo/E3A+Hw0QiEQDZstfpdLJhwwaWL19OMBjkyJEjMoDes2cP+/fvlxnwRCKBoig4nU4qKysxm81l3VQu5FqO9Z6Y6nsztixCkIqiu0VpVwdxjJmEOFY6nSaVSpFOp3G73VN25RHPHnF/8/m8/F08N3w+H/l8Ho/Hg8ViIRqNEo/HZdcJ8fcjmUwyODhIa2srTz75JIcOHaKrqwuLxYLX62XBggWsXbuWO+64g82bN8vja7j2MC2CQVGURuAO4D7gb5Xi3dwG/PnoJj8APk0x+H/16L8Bfgl8c3T7VwM/U1U1DZxWFOUkcN3odidVVT01eqyfAa9WFKX9Qo+hvpwL0DRo0KBBw4yhpqaG+fPns2jRIlpaWpg1axb19fXU1dXhcDguSQYOcOTIAe5+/eQLs3Qqx4O/6ecf/vy/qfXNAsSiUR2VFhelscLXQJgL6nT6UWO7sXtU8bnqIGvkRz/8MR/52D9gtVppbm7G4XDgcDhksC6CtJqamos6z6qqKrq6uqipqWHBggXnubsPDAzwxX+6H7cyi1vWv05ez7GH0un0VLrqUNVisCQydSI4EAZ+443R6XQSj2dxe8qzV9FIFjAzZ86cCce/5/e7ef+KPz/v9WLJRwFVfXGhv75pBYs98zgz0IvH42HBggXSgNBms8nFtOjcIYgp0TlBlBWIBXepUmDssiWZTBIOh6moqKCyslJeV4/Hg8/nmzBInewejrc0KiU9SgkJgWXLllFbWyuDh0QiwdDQkKyhnmksW7YMs9nMrl27MJvN9Pb20tvby7Fjx8hkMpw6dYpcLkddXR319fUXPW8vN8Q9F4oPERhOJ4M/0f7GovR+ioxzJpMhk8ng9Xrx+XwXPX63210W0EIxYBREn3hmTGasWSgUiMfjpFIpvF4vXq8XvV4/Ld8Ol8slM/fiRxzfbDbT2NhIQ0ODbMc7NvB3uVyyU4EgCgQxZ7Va2bFjBzabjZ07d7Jr1y4ZVAtSJZ1Oy/aVOp1OlihlMpmycU1lxio+J8Yx1fmL54fVapXkSuk9EIoSoTCZqc4SY6+fGIfdbsfpdE5LDaMoCna7Hb1eTy6XIxQKlXksiLIGQcAWCgVcLhcOhwODwUAymcRisZBKpXjqqafYs2cPbW1tHD9+nJGREWw2G+vXr2fNmjW0tLSwaNGiSZ/tGq4NTFfB8FXgw4D4q+IDQqqqikKvXqBh9N8NUGwIrapqTlGU8Oj2DcDekn2WfubsmNc3XOQxRqZ5Pho0aNCg4Y8QBoMBk8lEU1MT1113HZs3b2bTpk1UVFRgNBpnJCOaSqU4e7aHWbMWTrpd16kQiUSBPzz/EOuX3Mjc+hYeefYBVLXAtnWvJZfLyUWmXq/HYDSg1xtGg3WxlxfVDolUjJ8+8U0amyt5xzvfTjKZpKamhsbGxvMIALPZPG3ZaynEwtbhcOB0OnG73TLDXYqvf+2b5MMWbr/5TfK6jrcgVxRwWr2YDGbS2aQ8X7HQnWwR73K5iSfOz9aaLTpSqYlbXJ45c4bISJgF1XNefFFVZbeL0ni8MBpcvGfTmxiO+ImEwsybN08SDAaDoaydooAYuyAdBMFisVjG3V4gHA7jcrlwu93SrT2Xy+HxePB6vVcsY+/1emlubmZgYICBgQHp5G4wGGTGcaog60LQ3NxMc3NzsSwlmcRgMBAKhTh79iynTp2SQVBNTQ2rVq2S3gvXEslQOhar1VoWLI53rS7097EoldILpYvT6ZxxoztxjGw2S1VVFZWVlVNuHwqFiMVici4L4u1ikMvlyGQyFAoFampqqKmpuaj96PV6Nm3aRHNzM8FgkEOHDklfB/F/VVXLSEKh2hKKjNLn02T3J5vNlnlhGI3GSVtslpKTY8mF0v9nMhlJhsxE2USpMkJRFMxmMxaLBYfDgc02gSfPOGO3WCzo9XrC4TCJRAKj0VhGMAj/HHEOdrsds9lMIpGQ193v9/P000/zwAMPEAgEiMViWCwW6urq2LBhA7fffjvz5s2joaFhihFpuBYw5bddUZQ7gSFVVVsVRbnp8g9p5qAoyl8Bf3W1x6FBgwYNGq4N1NbWsnDhQjZs2MDWrVuZP38+TqfzkhULpejo6KC+3jKlwWNNtYPb76gmkdjJN3/zc5IJhWyugMPuYs2irXidlUVjAPVF9YIy6nWgqiqJVIIjJ59jIHiGQHyAwWAvd/3JHbz1L98ss+Z2u31Gg9JYLEYwGJQmfC6Xa9zM3OM7n+C62XeUKBAm3qfD6saoN5HOJssyl1PdD5fLTSIRPO91s1kvTenGBjUP/ua3fPn+L7G6plxeX1AL5PMFFHhRzq5ALp8jlUqhR8e9m9/CF579Hn6/H4/HM63rKkoPrkUp/3TgdDplJlcoTAKBAHa7/bLUpy9atAiTyURdXR0VFRV0dnbS2dkp31+wYAGvfOUraW5uvqbIhYkgSKbxybWZGb+iKDLLfq10KylVbFyNLggTQa/X43A4WLduHcFgkP3793P48GFJAtntdnw+Hw6HA5PJJEuMjEajnO9TeaGIzwiFwdgSgok+J8reUqnUuGRTadvHmbqmY0s5hNLiQjwNVFWVJFCx/K28tFCv1+P1ejEajcTjcYaHh+W5iDKX06dPc+zYMU6ePEkwGCSdTmO1Wlm1ahVr167luuuuY+7cubhcrklGouFawnToxC3AXYqi3A5YKHowfA3wKIpiGFUYNAJ9o9v3AbOAXkVRDICbohGjeF2g9DPjve6/iGOUQVXV7wLfBVAU5dp4umnQoEGDhisOIVVvampi06ZNbNmyhc2bN1+WBcuhQweZM2fq4MFXaeEDf7MKgB/813EeeSTORz/6KX70Xz8lkYxSXVGHTqfAaCPJwUAfJ3vbCMWHWbvwRn7yxFdZsnIBa29ZwuzZ25g/fz719fV4vV4MBsOMZpgFEokEg4OD2Gw22UN+vGM0NtUzGCgKEovvT7wgd9m8o8TJi67phUJhUklxoVDAZLJx5HCAWCxLMJhBpyg4nEYG+hMEglF++MMf0ne2l2gwQiQUwT8yQldXF/4hP++7+8/kvlRebP8pPRl4ceEsWrW5LU4+dNP/43v/+u9s3bp10kxuqWfESyEQngiCSEgmk/JHeEKYzeYZVzLMnTuXuXPnYrfbsdlsFAoFenp6ZEa4sbGRjRs3XpYyjZlC6bW43Pe+1EtDlDZcCyj1BLkcwfDFQq/XY7VaWblyJWazmVAoRFtbm9y32WyWhLPwprkYpdfFYGz5hSArxBwSwf9kCqgridISFlGmA0hyW0D4uFgsFuLxOOFwGI/HI+dsNBqlo6ODZ599llOnThGJRNDr9VRUVLBq1SpuvfVWli5dSmNj49U6VQ0XgSkJBlVVPwJ8BGBUwfB3qqq+SVGUXwCvo9jl4a3Ab0c/8uDo78+Ovv+kqqqqoigPAj9RFOXLFE0em4F9FFcdzUqxY0QfRSPIPx/9zO8v5BiXciE0aNCgQcPLF5WVlTQ1NbFx40a2bt1Kc3PzjC0c8/k8fr9fLn6feeZJli6bnpO7OtrWcf/+DP/yL1+lqamJJxv+QCITLQtOzg338N9P3M9dr72Dp/7nD+x+/lFuf80O7nn3u6iurpby29Kg73JAZErtdrs0jBwvM3fTzTfyja9+iypvLRuXb59wfzqdHo+rUu4jk8kQiURkgCnOIxKJ8Ia/fBuhRIJYNEYiEWfE78cYiLCwzkyhoBKIBal2+/DZa9g+q4Xjvz5AjbOKemstNd5lVMzysN99iO//4adsaF5TEoy9mMETl63ogZElm82NbqeiKLC4ppk1Ay184bP/xHf+47sTnpdYfAtpt1h8v1Qh5pdQZORyOYaHh2WpzEyjqakJo9FIMBikr6+P/v5++vv7iUQinDt3jrq6umlLuF/OEIG8CD6vFTKrVMEwU34BMwHxjK6qqiKXy9HQ0IDX65UlELFYjMHBQdrb2zlw4ABz585l1qxZU+x15jARMXmt3NdSqKNdR0QL0Ww2K5/b45WD6HQ6HA6HNOX0+/3s37+f1tZW2tvb6ezs5Ny5cxgMBioqKpg7dy4tLS0sWbJkQrNeDdcuLq4gqoi/B36mKMrngBeA74++/n3gv0dNHAMUCQNUVW1TFOUB4BiQA96rqmoeQFGU9wGPUmxT+R+qqrZdzDE0aNCgQYOG8VBZWcnKlStZv34969atm9EFy7e//XW+/e2v4PM5AMhmU7zmtc3FuHWqdaGqMjycxGJxUlNTg8vlYtOW69i/8zhL5q5l/7E/cKL/EOeCXbz3r9/FW976ZjZt2cDffOCDrF6zSrrsX6msliAYdDodNpttXCltd3c3X7n/6+QLOXqGTrCRiQkGvV6H1WzBYrISSxXr7wOBAC6Xq8xgLJvN0nbyJHM+/zWqHE70NhuZ//oemw6c4Btv/sy0x/+hn3+ON133GvT6840WFUVBGb1huVyOZDJFJpNBLRTbVup1Oo70d/C8v4P7PvGFKY0VhRN8qSP/SxWldel6vZ5IJCK7A4j666nq0qcDcS+EmWBPTw9dXV3kcjn6+/sJhUKcPn1ampT+sUMEope7FOdCc3jCn0SQa5P5D8w0hPKo1DtClCyUdp0QHSlsNhvZbFYqdEZGRujo6KCmpoZsNiuNWUVWXszxUiJAEChms1ka6V7ovRi7X6EAudbKqwR5mssVy8eEj0KhUMBgMJxH/InzEp1wTCYTkUiEkZERnnrqKXbu3CnbkELxGlZXVzN//nyam5uZN2/eFT9HDZeOCyIYVFV9Cnhq9N+neLELROk2KeDuCT5/H8VOFGNffxh4eJzXL/gYGjRo0KBBw1jU1NSwfv16mpubZzwYr6lpZNkyF+97f5N8TVF0FNQCCpPLx/OFPCdPBmlsnE19fT0ul4u7Xn0XP/z+n6FTdKSsQ7zjA2+Q7ueBQICmpiZ++rMfk8/npUP71UA0GuXMmTMMDQ0xMDBA/7lB+vsGeG7vPl614W2Y9BY8rqkc7RX2djxGIlM0awyFQnR1dWG328vOzel0otfpMdfUyutpsDsI5eLTHu/v2/fgjwR53aZXgbwvKoV8AUbrj4W/RTKZJBgMkkwWfSGsVisvDB7jJ50P8+0ffI+1a9dexBV76UOU3jidTgwGA/l8noGBAWnmN1MQAWJ1dTVr165leHiYI0eOcObMGXbv3o3NZqOlpeWazOy+XDHdEgVhZup0Osnlcvj9fhRFuWKKk0KhwNDQEP39/Zw+fbpo6hqJEIvFpDeLoihks1mOHDlCOBwmnU7LkoRsNkt7ezuxWIznn3+empoaKisrpclqqSGjxWLBaDSSSCQoFAosWbKEhQsXyjaaF4tSgmYmiLuZhKqqJBIJksmkLGVzOp0YjcZJ/7YK/5auri4OHTrEkSNHOHHiBP39/bIlrqqqmEwmZs+ezYoVK6Y0FNVw7eJSFAwaNGjQoEHDNQ9FUaisrGTFihXMnTv3ggyspkI+n+cVr3glX/7yfbz7niPY7WacLgMupwGnU4/VBl6vwnXrK6mttYECCsqoKF8llcryu/8NcPfd/4/q6mqsViter5d0LsGBk0/w5B8el3LedDpNOBxmYGAAp9NJbW2tNOG70jh27Biv/5M3Mqu2GYfZjcPswWHx4rLP5e233orT6iabzU1JfiiKwqHTu5kzr4mOjg7C4TDd3d3U19ezYMECuV0sFiNfyBdrSkoIhkhu4m4Rpcjlcnz2wa/xnpvegsNiG124K+TzBQqFF/u2F4OoAslkgkgkUqwfB5449Sy/Hz7Al//ta6xateqCr5fIjIpA61oKGC4EQoovspGBQIBQKITJZCrzM7mU8ytVf1RXV7Nq1SoOHToEQF9fH/v27WPJkiXSmO9Sj/dSx5U491JPgFwuN6VpoSAYAoEAkUgEh8NRphSaDkrJjMnIjVIvgFQqRTwep6uri46ODlpbWzl48CAjIyOMjIzIdp6l+y+dQ+L8Tp8+TXd3t5znTU1N1NfXy/kvuvDY7XYsFgvhcHhU9ZSUPg52u11m7MV3RqiZRHeKsSoqg8FAPB4nl8uVKSaupfldKBSkF4voOmGz2aZsaZnL5RgaGqKtrY3/+7//4+mnn5ZEisFgwG63k0qlMBgM1NbWMm/evBklLTVcWWgEgwYNGjRoeNlCtKV0OBxSdj/TXRX8fj9f+MLXpZw1Go0SCoUIh8OjNb3nuO++h3jtax3csLUGg8FIoZAnkcjw3e/0UFOzmptuuknWUBcKBV5x+2287u4/paqqCoBgMEh/fz/ZbBaHw4HH48HpdE7ogXC5IBa79fX1WMx23rrjw+MeP5/PTV0eAqCAx1FFMlP0aU6lUgQCAeLxeFndtsFgwGyzo5Qcy2C3E8mmpjXuDz3wOZo89dy98Q55HjqdnmSyWHedz+dRS1q2JUdb0un0On51bCetkQ7+7T++w6JFi6a12B8bFLS2tvKtb32Lbdu2sW3bthklua4GRBZXKBlES8ZLzbaK6y++C5WVlVgsFnw+H3q9nng8Tn9/v5wjFovlJX8tZwIiwL5cXgdmsxmHw0Eul5uyi4hoS6iqqrxPmUxmWp1hxoM4t4m6ZKiqSiwWIxQK0drayvPPP09fXx+9vb0MDg4yNDQk21CWBvWlnxfkwtjXhRHh8PAwiUTiPM8Lo9GIwWCQJQLBYJC9e/dKkqGlpYXm5mbZmaKvr49z584xMjJCIBCQRIMgPerr67HZbJw6daqstONas5kTRIloZzmdEph0Ok17eztPP/00fX19kkiorq7GbrejKAonT56U91LcNw0vTWgEgwYNGjRoeFlCtB2rqKiQLRXNZvOMZoPi8TgDAwNyQWkwGKirqyORSBCNRqUkd+XKNXzrW1/kzJlu5i9wcvx4gkMH4yxevIl77rkXu90ugyq9Xs8/f/ELALJuOBwO09vbi8PhoLa2Frfbjc1mu+je8pcCnU6Hx+OhpraKcyOnaayeP+52ClJsMCEUFBY3rmPvqYdQFKWMYDh37hyPPrqTcDBMMBgmduYcR+9+Lfl0mnwmTS6bwWKcOrj88Z7/4YXuNn5yz9dRdDpUtYBaUCmMmpRFIhEymQzZbLaokBgdfK6Q58eH/peO1Bk+/PF/YMWKFedJgMXCv2gKmZMZOSjP1La1tXH27FmsVivr1q3D4XCMe+9Ks5ul2U7RJvJak0uLAD+fz5NMJsetVRcobUEqauNFEFe6zViiwmQyYTAY0Ov1MlDs7+/n7Nmz0lSuFDOhahibWZ4Kk7UtvNBjimC3tKOA1WqVypex53e5PRhEyYPL5ZIlDzqdblKCwWQyyTGVdhkQ2erJzl/8eyyxIOaE2E5k0uPxOH6/n3PnzvHEE0/wf//3f4TDYaLRKCaTCbPZjMlkmlS+X3qfCoUC8XicRCIBFL+TwWCQUChURmiVzhHxXe3q6pJlAw6Hg61bt7J582YaGxvx+XwcO3aMjo4Oenp66Ovrk+0dU6kiUbp8+XKampro6+srPo9gXFLkcmKyYwllkfjOGo3GKZUL4jsdj8c5ceIEra2tRKNRLBYLc+bMoaWlBZfLRT6fJxKJEAqFCAQC9PX1EYlEyu69hpcONIJBgwYNGjS87CB6eS9ZsoStW7dyww034HA4ZnyRIhaXVquViooKSWBks1l8Pp9cHM6bN4/m5u/x4IP/Q/uxXhYuXMEbXr+e+fPnS4npeMFBPB4nFAqRzWapqanB6XTi8/km3P5yw2AwYDQaOXfuHPMXzuXE2TYaq+cxPbnC+dDpddgsDtlDPZlM4vf7icVifPADHyLSn2dF8wZsGwIp/AAAIABJREFU5lredssHsZpt2CxO7FYHI8FB/vN3n590/w8fepJvPv4DvvrGT1HtqpJZwVSqaOiWSCTIjGY2UVUUnQ69XkdOLfBv+35MyJbkM1/4HEuWLBk3MBKLZ1HzXVVVRV1d3XnBoihv6erqYs+ePcyePVtul8/nZeAYDAaJRCIEg0H8fr/8dzqdJpFISMn1dFFmYDnO3C8NZC8WqqpK00dxnNKuBoqiyOy6cJ3v6enh9OnTxONx4vG4DJJFtrbU6E6n09Ha2opOp5Nky+7duwmHwxiNxrLsqQhgxfEvFoLkKZWyjxd4lY5Rr9fL7URANFlAXQpBJInzFgSMkOPffPPNbN26FaPROO79d7vdNDY2XpaOHoqi4HA40Ol0DA0NMTIygsPhmPJzOp0On88nvwM9PT1UVlbi803uyyK6r6TTafx+vyzBaW5uxuv1AkXT13g8ztNPP83evXsJBAKMjIxw8uRJgsEgZrOZpqYmVqxYwZIlS3C5XGVjHns/S8mLRCLBzp072b17N9lslnw+j9FoxG63s3r1apYsWVI2LzKZDL29vfT29kplgiA/2traCAaDUm3m9/sJBAJEo1FisZhUKBQKBYxG43lz7WoqF8SzTRAm4nsu/BI8Hs+0fCby+TyhUEgqN6LRKEajkerqaq6//nq2bt1KOp1mcHCQI0eOkM1mOX36NGazmSVLlrB8+fJL9rTQcOWhEQwaNGjQoOFlB7EgXLRoEbfffjsLFy6cMON2KRAKA6PRiMfjmTBLls1mpRz03LlzeDweGhoapLx0LFkgFpaJRILBwUHMZrNUYYg+7VcDer2ebDbLe999LzXeRpbP3jjJ1qVB6/gtNf4/e28eHGd+n3d++r7vbqBx3wBvEsNjhuQcnBnNxLKsxJJix47XsSoVe8v2Otmkyl5XvMnuppJssuVssqmKnfWunLLKkbOybGk0kqyxNMNrhhTPAQ8ABIgbaNx939fb+0fj92MDBEmQBI+ZeR8VakQA/fbb74Xf9/k+3+fRarQ0+FvQlAxUylWCIRaLkclkaGltxRno4kDP0U23XioVyRfvHf/4rYvf59/98I/411/6HV7o2CvJhYqikE5niMWiFIultQ6ZBt1aMZgt5fh3H/4xJa+O3/md3+Xtt9+W51WcF1EAFotFSqUSi4uLcma7rq6u+tlqzqmY/Z6enubjjz9GURQZ2SbIFY1GQyKRIJlMyk5eMpkkmUySz+fJZrPS9X4rhECtYmDjzHxtAbMdow2RSITl5WVJEIhCuDbWUsjrRTzdxYsXpYdDrQdGrbeCKNBrZeylUomPP/6Yjz/+WL6/+H3Rrd5IPNzvGG2G2nMrr5tNij6hGhDEm/g98Xm2SgiJDr94XTabJZfLYTQasVqt6HQ6Ojs7sVgscixqo2rBZrOh1WqlYWEt+bGZOqD260EQM/KFQoGVlRW8Xi/5/N3+Jxu3Z7FYqKurk+MKorN/P1QqFZlOEA6HWV1dpbOzk5aWFkqlkhw9W11d5eTJk/z5n/+5/J4ga+vq6mhvb+fNN9/kjTfewO/34/V6ZcEs3keglpCKx+Nks1nGx8cJh8PE43F0Oh1Wq5W+vj5ef/11eV0KIuHmzZsMDg4yPT2NXq+X53N+fp7Z2Vn5PuKcCVWOePZrtVosFoskQoxG41Pv2G98JggSRaRDVCoV0uk0qVQKj8cjjR0fBGHuGAqFWF1dJZvN4na7aWtr4/Dhw7z55pusrKwwOjpKMBjEaDSytLREqVTi9u3bzM7OEggEpOnvs/rbp+LhoBIMKlSoUKHiUwer1SoLepfLJbsfj9ut3YitGnCJYsPn80mDMIvFIl3574VCoUAymUSv12O32zGbzc9UKmo2m2lsbESLjn/wU/9srbN19yhEtbipflMYKm663xpo9LfxGz/zL/nf/+w310miXS4H2Vjmnvtis9oploqb/ux/+86/58c3P+T3f+73ONLdTy6XkwWbKNCqBo/VYsNms+F0uohk4/z+j/4LO1/bz8/9ws/T2tq6rgMtCpSZmRkmJiZIJpNkMhlsNhsul0uOuoiiQXTdBcQISKFQwGKxyM517eJ+M9WB8DkQaQ0PUq9sLCi3qmB41GvLarUSCARIpVKkUikp6RfFvjhuV69eZWFhgeXlZcLhsEzpqPUPqP38tXJsQTDcbx9FHKiQ5d8LDyqqa9UL95uBF/smznEtQSKUG2K+fivvV6tgEKMk6XSaDz74gOXlZSwWCxaLBbvdjs1mw+fz4fF4yGQy5HI5mpubaWxslOZ7DocDu91+lyJDFMDivtgKyuUys7OzLC4urpPFi/Mh9lf4LQAy2jQUCrG8vAyw7ucbUUuKaLVavF4vPp8Ph8NBPp/n5s2bDA8PMzs7y/T0NIODg8RiMQqFAkajkf7+fvr7++ns7KSzs5O2tja8Xi8Gg4FsNksqlSKTyUgiqvb+F6M2ZrOZ119/HZvNxve+9z1OnjxJqVQimUwyPDyM1Wrl2LFj7N27V44MNDU1cfDgQak4GhsbY3JyknA4TDQaldeE2+3G5/PR0dFBS0uLHLkQahe/349er2dpaYlbt25RKpXkMXwaEAohcZyEGkg8o8Q+WiyWTYnxzVAqlVhZWWFqaopEIoHBYKCjo4MDBw4QCATQarU4nU6amprYv38/i4uLjI2NEYlE+PGPf0wkEuHo0aP09/dTV1cnVSwqnm+oBIMKFSpUqPjUQbjaG41GcrkcyWRSjjJYLJYn1gW5l1O66Ly4XK4tOWOLhV4+n5ez91arddsjNh8Wer2eQqGA1WyTne/NIXLcqXoeVLRoNJuoGCpV2wOlpkMtijqny0kkl7znvlhMNkrlklyEA5wZucAfvP8n5PJ5vv6r/54WbwOFYpHimuRadLZr5fhQ7bTGlRT/6swf8t//09/iF37xF6q7t/ZzQQSIeLbJyUlu3LhBIpEgnU6zY8cOWlpaZBdZfG28DsQ5hWohI7rkogAXr6k1khMyeZvNhsPhwO12P5PxmPvB7XYDyI6z1WrFZrPJjrs4DmNjY7LgFsXV/Yp98bqNCgfY3CdBFOjiGN4LDyIYNnufWohCvbZAr1VuCGJlq8qQjaMh4nrI5/Pk83kGBga4cuUKZrMZs9mMx+PB6/XS3NxMU1OT9Kbo6emhq6tLmjCKkQRxnYn9EfsvpPoPgjge0WiUdDpNJBKRpG0twSCUF4K4slgsGI1GotEo8Xhc+sbc6/iL614odZxOJzabTaawXL58mQ8++ICRkREmJiak54nD4cDpdHLkyBF++qd/mu7ubtrb22UnXtx3QhkkiBehThGKN6iqYI4cOUJ3dzfz8/NcuXJFKirGxsYAOHjwIO3t7es+v0Aul+PChQtcvXqVmZkZFhYWZDRmQ0MDLS0tHDp0iD179mzqIRIOhzl9+rQkHsSo0ZOGUAeJv5e1JqqFQgFFUfB6vbhcrofy+yiXy4TDYUKhEKlUCoPBQHNzM7t27ZLmrcJbaN++fdITR5xv8cwwmUyUSqV195eK5xcqwaBChQoVKj51SCaTTE1N8aMf/Yjx8XHZ2du3bx+HDx9+IrPK2zkvG41GmZ+fp1Kp0NzcjMfjeSaGjhuRzWY5d+4cjb5O9HodOp12UyPHaqEkVAIVoMJm61FFqXZ4w/FFFKWMRmOgVCoxPT1NqVTi3I1L3Jy4TL6YXfvK89/9jX9Ib+tetFodZqOFmUgIs8HMb3z9n5LMpHljx1H+0U/9A+wWG/l8nkKhgE6nk4RTdd/W78fgwhh/PPiX/N6/+V9466231v1MuOEvLS0xPDzM4OCg9MJob2/H6XTKTqsYXxCFzUZXf5/PR29vLw0NDbJYrFXXZDIZYrGY3J5YRLtcLrxeLxaL5bk2OxNKBqG80Wq160ix3t5ejEYjx48fJxwO3yWp32huuJmyYjO3f4HtMHjcuM3N7utIJEIkEuHChQtcuHBBFuBtbW309vbS09NDd3f3Q5s81hbr5XKZoaEhhoaGZPqAoihkMhnK5TKpVIpIJMLExIQkwMbHx3G5XJLoEASPuA7FfnV0dNDW1obH49mSwgKqJIjb7SYYDBIIBPD7/cB6gqHWlFSYnmo0GlwuF42NjXJU4X4Khlo/jvHxcSYmJpienmZmZobJyUlmZmZIJBKYzWa6u7vp7u6mq6tLfnV2dkrCSxToYv/FCIXD4cDhcJBMJjdNKtDpdFgsFvr7+/niF7/Ixx9/zPDwMPF4nPn5eRKJhCRsNpLVer2erq4u7Ha7LNSFgkGQhPX19fc0egXWKbmelsmjGIEQxIIwLa4lY81m80MTHmLkJZVKSbJcKL5qx4dMJhN9fX0ymcNmszE6Osry8jLnz58nHA7T39/PgQMH2LlzpyR4VDyfeParFRUqVKhQoWKbIRZK8/PzXLhwgZaWFnp7ewHYs2fPthEMtQtYUVA+THdZFKIbJfLRaJS5uTkCgQCtra2yM/6skcvluPiTSzR5utDp9GtjEHcvNqsLUA0ajUKlomz6OyAW0nkGpy5SVspoiibKCSPXz03gsntoq9+BxWjDarZjNds5f/NHDE5cprd1LwAdjTv4zyf/lDMjF/iZvW/y2z/z65gMxrXjqKzN0Bcwm8xY147hxsXxx/ND/NeR7/GHX/9/ePnll+Xcvej6lkolZmdnmZqa4tKlS5w/f54jR47IRW5zczPlcll2m4WRWa28X8Bms9HQ0IDH48FoNMpCRizgRUfTarVKaXsul8Nut29pNOJpY6PyQiiEVldXiUajdznMt7e337cwEMdbSLOfRzKlUqkQCoUIhUIoiiKl3+l0Gr/fz86dO6Wx7MOacortQ/XeOHXqFG63m6GhIXQ6HZlMRhIMuVyObDbL0tLSXSMW4rkiIM7R8ePHOXbsGEajkaamJhkzuJVRL0VRcDqd1NXV4Xa7cTqd99x/cf+I/fJ4PJRKpXuaLdbGNQrVVjqd5ubNm5w6dYqhoSFGR0el94Hdbqe5uZkDBw7w0ksvcfjwYQ4cOACsJ5eEKkoU+CKyWIyYZLPZu0ZgxLEwm83s3r1bpvjcunWLdDrN8vKyNLsUSoha6PV6mpubaW5u3uIZv/v4CZLmacRUiuNTLBblNWW32/F6vduyfaHESafT69R4G00ijUYj7e3tkrgSIyyxWIzR0VEmJiZYXV0lkUig1+tlkpJq/vh8QiUYVKhQoULFpx65XE6mE2yWpf6oMBqN0nQxkUigKIqcw98K0uk08XhcxlmKorZQKOB2u+Ui7EFeDU8LiqJwa2iUL/S/smX/ifuhVCqRSqe5cOtHdAf38nOv/wZ+nw+Lxbqp38TUwggDo+d5ac+bNPhbCbiaePfqX/I/f+G3+KVXvix/r7pAL1LI5ykUihgMBjSaWq+I6r6fnrzI1y79OV/8Oz/LgQMHZGrHzMwM09PTLC0tsbq6KscU/H4/X/nKV+jp6WHHjh0YDAbi8TiLi4vMz88zNzdHKBTiwoULm3aGE4kE09PTNDU1yYW9kCULUzsh4a5NkTAajbIz/TyZnIn9Fp4L27G9VCqFzWbbUlLBs4KQiX/uc5/D6/Vy6tQpzp49y/T09DqPAaEYeBRotVp6enqwWCwcOXJEXg+1RqBiRGNlZYVwOIzZbMZgMBCJROTsvxjtyeVyjI+Pk81mGRkZ4eTJk7z11lucOHFCGg7eD4JA0mq161Q34r8bTQLFa4QJJiCJEfF7xWKRfD5PNBpldXWV4eFhRkdHyeVypNNpZmZmmJ2dlUkSnZ2ddHV1sWPHDnp7e2lsbKShoYFgMHjP/a5UKqRSKRmNWGvGW6sU2AidTkdjYyPlcplz585ht9vJ5XLk83kuXryIzWbjlVde4fDhww9zWreEWhXHk4aIkMxms+h0ui2nQ2wVQsGwkWAQsdEbYTQa6e7uxmw24/P56O7u5ic/+Qmjo6NMTk6Sz+eJxWJMTk5y9OhR9u7du237qmL7oBIMKlSoUKHiU49aV+zthPB6KBaLJBIJ6Qa+1SIwm82yvLxMfi0qUaQNOBwOPB6PNKh8XorKeDxOJBwl6G1ZK9a3MF9eqaBRyiiKBhAd+AqVStU9/+yNd1Eq8NqeL2E2mbE7HOh1eqlCgDsjF68f+iL58zn+zZ/8j9htTlaiS+i1ev7uy19at91SqUg2myOXz1MsFiiXzWt+EFWSoVKp8M7Qj/mza98jlkvg8XjkiMLKygpjY2Ncu3ZNSrObmpro6upi//79HDhwQBo6itGJsbExRkZGuH79OtevX5dzxBs7j9FolImJCVpaWuju7pbd7XQ6TTKZlMkRsViMcDhMMpmUSgBRGIl0hjvHZX3H9mlCxIo6nc51vhEiSQFYVyRtVpDCnTSGXC5HNBrdUtrAs4JGo5Gd65deeone3l5ZtMdiMUKh0LoUAOGMv1UlQ+2YR1tbG21tbUD1mMViMeLxOCsrK6ysrMjrZmpqitnZWel7IYgu0QmPRqPEYjEikQhzc3NcvXoVm80mizmj0bilolJ00zOZjFQHbPa1sfMuuvG13hjVuNhqxzwUCjEzM8Pp06c5d+6cJERqFQvCBPDw4cO89NJLvPDCC+v2rZoGc0ehJPalVCqRyWSk+ahQXmyMId0InU5HIBDAZDJJU8bFxUWi0SgDAwOkUimCwSAvvPDCAz0/HhbbNepzP9SqR7LZLOl0Wl7X2+lvUKlU1ikYBFElRtY2wmg00traSjAYpKmpiba2NqkME8/bpaUlQqGQJCDEWIWK5wfq2VChQoUKFZ9qCHOto0ePsnPnzm01SjSbzXi9Xhm/ZzAYHsrlWsyqi26NMEATKRMi8eB5wa1bt8hl8/z4yl+sqQLWJMiVqjy7VC6iUCVxNMBqdJmppRH0Bj06rRZqJMvK2hx5IpFgV8sRbBYHaERns0CxeEciLFzxWxu6+LUv/y7pTIqbY5fI5XP84PyfUCxWVQpi29lMllg8LsczBHljMOhBo+H/PvcNvjX0Hplybp3bv3DBb2howG63c+TIEUqlkjRXNBqNFItFrl69ysTEBPPz8ywsLMiO8crKilSkbFa0LCwscPHiReLxOKOjo3JkQBBIuVx1fwqFAvF4XG5TdIHFCIUwqxSF67NSt8RiMaampuRsuzAfFEoejUZDOp2Wvy+OsRiFEEWOx+PB4/GQy+WIx+PPLbmwEWK84LXXXsNisTwRJUMthILAbDbj9/vl8ezp6SEej8vrIZVKydl/RVGqxGAkwvvvv8/p06dlcX/y5ElWVla2HP+3MXVkM6PNjWqGjd8Tzwy4k/YhFAazs7Ok02npb9DQ0EBrayv9/f3s27ePhoYGGhoa8Pv9ZLNZOYohCnxBsIj3FCMlAA6HYx2JIpIbHpSGYDQaOXbsGFqtlu9///ucOXNGejEIVVytl8p2oJasqT2O24laBYlIiKg1wd3O9ykUCmSzWXleTSaTNPy8F3Q6HR6Ph97eXr70pS/R2dnJqVOnJLkzOTnJ0NAQbW1ttLS0UF9fv237rOLxoRIMKlSoUKHiUw+3201PTw8NDQ3b2p3R6/VYrVaZnS4Wa1tFsVgkk8lgtVrx+/1y0SU6cc/DWATcWeB2dXXxd375y7hcLtmZrVQqsjgREm2xOE4mk9hsNl566SX8fr9cuE5PTzM+Ps7Xv/51vv+dv+aV3X8Tvd6ATquDChQK1eNSVspUlAoaTdUETBwPs8nMgb6jLCzPkS3kKBYKGPR6WbBkslkZ7yncx/P5HPmSlv9w+r9wqzCN2WUlXcrK8ZR8Pi/d3oPBID09PbKgF9sVYxAXL17k1KlThEIhlpaWZNEl0g42Qize0+k0ExMThMNhBgcHOXjwIEeOHMHn8+FyuahUKrJoFeMCiURCGtOJjqsoJIUip7Zzu5miYaNp4nYhm80SjUbl529sbFznxVAoFEin0/L6ETP2ojAWx8tgMEgJeiqV2rLx4LOEmNM3mUwcOnSIzs7OeyoZxDX1KJ4MG9/PbDavS6KpndkX950oFMXPk8kkkUiETCZTJQnXRiuuXr3KpUuX7vm82YxQuFdqRy0EsbHx+7X3R20RLdIgdDodZrMZvV6P2Wxmx44d7N27l5/6qZ/ixIkT8r3E/ovUCqGeETG0Yh8ymcy6+2cjGafT6bZEMBw4cIBgMMjExIRUWIgu+tzcHPX19U+EYBDYzvu2lrQQUaVQfb4+CSXARoJBjJsJovReEMa8drudhoYGduzYQT6fZ3l5WR734eFhmpubsVqtKsHwnEElGFSoUKFCxacalUqFhYUFzp8/j8vlYufOndvWIRWRXoVCAbvdLjuMW4VYMIuFbi258DzC6/Xy5ptvygL8QYvqaDRKOByW/xaFx8jICN/73vc4c/osu1texG5zrBnOWdY6+Hk5ciIIi1pUKqAoFUwGK/lCnmQmhclsIrtGLKTTKSmZrpqM5fhw7BLfGHiXI28f42/v+nm+9rWvkc1m5Vc4HMbj8dDS0iLVAcvLy9y6dYuJiQnGx8dZXl4mHA4zNzfH7Ows+Xxe+g+IaECbzcbi4iJzc3Oy6Kuvr6e1tVV6aQhDyGQyyczMDGazmcbGRurq6mQHVowWFItFgsEgbW1tshgS1108HkdRFClx3xiPWat0eBJmaMFgEL1ez8LCAvPz8ySTSZaXl/H5fJhMJhYXF5menl7nQl/bYQ4EAgQCAcrlMvPz8/LzPI3Z8+3EVpUMIvVguyEKbCEjr6urIxAIyJ+bTCY8Hg+f//znaWxs5OOPP+batWssLCywvLwsiQuhZBDXj1CbiO8LElX8e2MMqUAikSCVSsk4QWGA63K5cDgc0nsklUqRy+WkMkFcD4FAQH6Guro62tvb77qutVotRqNRKmU0Go28xoWfjRg5E8/mRxk1qzWHDAQCNDU1EYlEyOfznD9/HoDPf/7zvPbaa4907jZ7P/H3QERoPgkVm6IocjRCmH0+Kvn1oPcRRE+hUJDEwlZH/4TSxOv18uqrr2I0GvnBD37AwMAAExMTfPzxx3R0dLBr165t33cVjw6VYFChQoUKFZ96rKyscPXqVVpbWzl69Khc4DyuW30ulyMSiVCpVOQi7WEJBlEcPM9zpLVpFxuLkPtBfD4hi85kMsTjcW7cuMF3v/tdcgmFA/texWQy4XLdiZEsrLnIG41G9IY7x0T4MoiCVYMGg87EzEoIl8NFNlvdfrFYoFJRKBTzfOvGD/mr0Y8oVkCj0/CbR1+kWCzi9/uJRqOys7uysiJHVaDanZ+enpZd3kuXLpFIJMhms7Jot9vt0i+j9mt4eJhUKiULKLfbTXt7u/RuWF5eZmFhgUqlwvLyMo2NjVISXF9fL03R4vE4BoMBs9mMzWaTihdB3AhyRJAcglAQs+vi+3a7/Z5d6o1daDFmIs638FMQ57z2+vb7/fj9fnK5HFNTU2vHvghUi8DFxUXGx8eBahHs8/nkKJBOp8Nms1FfXy9d+YWr/ycJW1EyCPIHHt6TYSvvL85dMpkkFAphsVgkwSAKbxEPevjwYX7wgx+g1+tlMoNIVhDXjbhWcrnc2mhR9ZpKJpOkUil5nTmdTpnIU5tgsbq6SjgclgSESJSor68nEAjI0YZIJEIymaSvr4/e3l7a29tpa2ujtbWV5ubmex6nWoXGRiiKIjvm4loUSqZHPb7iXm9ubqavr4+RkRGmpqYYGBhgeXmZ1tZWDh48+FjntVZZUPvcFOdjOyEUWUJJJO7F7YT4PILISCQScpxFr9c/lCeJXq/H6XRy8OBBmapy6dIlZmZmsNlsnDhxYlv3XcXj4/lcyahQoUKFChXbCEEECDnwgQMH5Eyvx+N55LEJMStvs9kIBAKykNsqxGz982LieC+IYlcU5sLU70EQC3Mh579y5Qo/+tGP+P73vk8mXuRnX/o13A4vJrMJo9GEXq8DNNX/aaqFgdlkRq+vnh8hsdfpdRhFkW9xMrk6Q5u/iXQ6Q7FYJBRf4hvXvs+1pdvUe9t4+6W/x76eF4mml/mD//hH/MN/8ht0dXXJAjAWizE+Ps7ExASZTEZ6aohIuqWlJWKxGGazWc4F79q1i2AwSCAQkAWmKKja2trwer0MDQ0xNDREJBLh9u3bvP7667z11luk02nC4bAcuYhGo4yMjBCJRFheXpYFwK1btxgcHGR8fJyRkRHm5+eZnp4mk8nIAqpUKkmFgsvlwu12y0Le6/XK8Qun0ykLSAEhTRceF0JqH4vFiEajsgBxOp1Sriw6xrVIpVIsLCxgNBqxWCwsLCzIwsDv9+P1evF6vdJEThRPLpdLmiZqtVqWlpbIZrPPrYLnQRBKhldffXWdkmFqauqpKBkeBHFO9u7di8PhIBqNShJLdMprlQJiJEd8X0RICpJRKBQ2ni9BfInrrVgsoiiKvH6ESkVcw4KYqyUsVldXZZzrw6BSqRCPxwmHwzgcDurr67cl4cRoNHLkyBGMRiPf/OY3mZqakgkYQ0NDfPTRR/T29tLR0fHI71GbLCPIEaH+2C4oirKm8kqj1+vx+Xx3xcluFzaabD7O6JMYvRJ/T6D6dykajcoxDxXPD1SCQYUKFSpUfOohpLI3btxgfn6eSCSC3W7HbDbjdDofmmConSlPpVJyxvdhF2qiy/wsjfq2gmw2Kzv8Xq8Xu92+pf01GAzYbDa5EL98+TL/7x99DavOw88d+x8IeBqrRbnJjNFoAKodUDR3iiGzuSqlFVnt2WwWi9WC3mLBYDDS4Gvn/zz9df7wo/+PZD5LtpBFo9Wxq/Uwf+9v/C5dLTswW8xYzGYC/gBTyy/y7b94h94d3fJzhMNhbt26xerqKtPT08zNzTE3N4eiKDIZxGKxSCn3yy+/zKuvvkpLSwvBYFAupIUyxu12Y7PZKBQKjI2NSad/qBr+GQwGSqUSV65cIZlMkslkGB8fv4tguHnzJgMDAwQCAebm5rh+/TqXL1+WoxcCgqgKBALU19fT1NQkjc+CwSAulwuXyyUJEFH4iZEFMS98ntDyAAAgAElEQVQvOsvRaJSlpSU56x4IBKR0W3hhiG61KFjC4bA8Z2I+vru7m76+PlpaWmhpaZHvXztjXi6XMZlMmEwm4vH4Ok8BWG8KKP69ERu9Au71e9uBje9TC71ej8Ph4ODBg/f1ZIA7SoZazxKx/UeBUJ2I/4rjtxk6OjoeqRAW5py1pOiDjrNIERBGrJs9a2s/s6IoLCwssLq6Kg0ttwJRnAtlxPLyslTvAOTz+XW/Xy6XpR+IIC4fhJ07dxIMBhkYGOCDDz6gVCqRSCQYHh6WSTROp1M+Lzbu32bmjeJ60mg0MrK21qOk9nc3JiE97DideE6l02lSqRSBQACPx/NE7hURTymUXJlMRpJSj6IarDWHFPeRGDMTZIyK5wcqwaBChQoVKj4zyOVyhMNhotEo6XT6nm7/D0JtDJuI2HsUOBwO2trasFgsz+14BFRnscXIgtVq3XK6hZiTHhkZ4fLly/zFt/4SvWLhb7/6m1jNNux2G06nC4vFjEajvSPNVypoNFr0eh1GowlFUUilUlQqlWpRptdTzamo8LeOf5WbE1fQaLR4HF68zgAupw+T0bRW0OjR6fRoNNX9ff3A3+I/fOt3mZufIRwOoygKs7OznD17Vkp5xXs5HA5cLhcvvPACL774IoFAALfbTV1dHfX19SiKIqPrIpEILS0tdHZ20tTUhE6nY3p6Wo5WZDIZzp49SyaT4cCBAzLu8uDBg0xOTjI+Pi7d9BcWFpibm2N1dZXV1VXpwRAKhdDpdHdF64nCQahMYrGYlA9brVZZ2Pv9fgKBAMFgkGAwKD+H6CSLgkdE84nzIV5fLpdZXV2VBZEoVGKxGFarleXlZVZWVmhvb2ffvn243W7pGRGLxVAUBZ1ORy6Xk1GHcKdIDYfDrK6uytEOcW+VSiXK5bJUXGx2fdaaRm41FWEjtkJkCNJps5+L7+l0Onw+H6+++ipms5nTp0/fU8nQ1dUli1yhSHmUfVMUhWg0SiKRIBqNyjjGzZ5vta/f6vOvUqnI7QtFy71MTTfu18LCAisrK1JJsxGCGBGk1dLSEuFwmEwmQzKZfOC+ietUXI/iellcXLxnd1tRFGKxmDRSrfWK2QhBrvl8PgwGA3V1dXR2drK0tEQ8HmdsbIxsNsvs7CwDAwO8+uqrHDp0SF6/tX4QhUJBEgWCeBHmuJlMhoWFBaLRKIqirEu5KJfL8m+XOGe1hJ3Yz3tBmC2KJAehBHhSRFyhUGB8fJyBgQGWlpbk8Wtubn5sDyQxQmIwGB7ZW0PFk8Xzu5pRoUKFChUqthligScKQhHl9rAQqQPC0X8zmfBWIGafn3cI2S5Ui6fFxcVNiyzRmReSaJ1OR6VSYXBwkL/+679mZnaaTL6ARlNZ8xWw43I50Wp1QLVDVygWUJRq7KVubYEtzMgEwaHT6deKvQparZ5d7QepVBSsVhsOhx3jGrkAtXnvJRSlWiDuaT7Kdz76YyraaiEiCmMxQ200Gqmvr5cF+JtvvsmXvvQlPB4PFotFGiyKUYrFxUV5TFpbW/F4PLhcLimZnpycJBQKceXKFW7evMnq6qpM4ti7dy+JRIJr166RTqdJJBKMjIwwNDQEII0qrVbruiSNWojPKLqFy8vL634uiu6mpiaam5ulRD+dTlMul8lms9KUz2Qy0d7eLkkSoSAQBaxQGWg0GplJn06ncblcMqZTjImI1xYKBWnaqdfr18UH1no/JBKJdV+CKBAEgvC+qL0uqyaeVdNM4RXwqH4mGzvKm90HgmDYrLAWr3O73bhcLg4dOkRHRwe5XI6RkRHi8fhdSob6+vp1poT36qRvhWAQozNCkr7xdZu9fqsEgyj8FxcXCQaDsgB+0HEul8ssLCwwOTkpPRg2QigjRMd+cXGRcDi85cJRHL/V1VVisZi8/+4nyxejFCIW834ye6HMsVqtuFwumpqa2LNnDxqNRvpehEIhxsbGuH79OiaTiYaGBqnMEdeKGB0plUoUCgUymQy5XA6Hw4HZbCYej7OysiKTV2o7/uL+Xl1dpVyuPh/F2NKDrltxHjKZjBxze1IEQ626b2xsjEuXLrG0tIRer6exsZHe3t6HinK+FzQazTovJRXPF9QzokKFChUqPnMQMYHBYJC+vr6HNrgSM+darZampiY53/5phcPhoLm5mVKpxI9//GN+73/65/g9wXWLU0Upk8mmsFmdVblvuYhBbyRfyLESXcDhspFOZeis34fbWZ3Lt1isaDRaad4oFASFQtVnQRS4VemzMJurkhHFougI5jGZzLIAN5qM6LQ6NBooFquFRy6XZW5pmqGpywzO/IRULk5JKVBcKz5EZ7yvr4/du3fT2tpKS0sLDocDm81Ge3s7NptNFjyrq6uMjY0xNjbG+Pi4HB1IJpMyVSSZTNLU1MRXv/pV/uqv/op3331X5s4PDQ2h0Wh4++236enpobm5mc7OTkKhEAsLCxQKBSm51mg0hEIhfvjDHzI1NSWVBg8DIa+ORCIUCgXpCeF0OmVX2eVy0draSnt7+11detExtNls6PV6KS3PZrPMzc3h8XjYuXMnTU1N7N+/n1KpxNjYGE6nE7fbLU3+YrEYyWQSp9OJz+eTBZHYnhg18vl81NfXy32oLexFsVVLqIh4QqHW2Epn/V7HSeBBoxj3K8yEwkeYvm7myZDP56UKSpgbbpTAPwwURSGRSJBMJvF4PNKsdLsgCAxhWur1eu9rtiggOvUmk4lAILDpftXGWtaOzLS1tdHc3Hzf7QspfiaTkRGegsi4n5JFURQikQipVEoSEveDMHssFArs27cPm83Ge++9R6FQIBwOSzXE3Nwc77zzDrdu3SIQCNDY2Mjhw4fZs2cPer1eelCIMRbhoaLT6XC5XBiNRqn8EZ4mwujR5/OtUywIMvR+161QNwmDRZF886Q7/xsJBqvVyqFDh/jCF75AZ2fnY2279np5HJNmFU8On97VkAoVKlSoUHEPRKNRhoeH6enpIZfLycXKVhcq6XSaxcVF/H6/HHH4NC9yRM646Fi31vfy9z//uw/8zIpSJpvL8i/+5FcZHx/HZarjjf1fwWKx4HS61ha4FblgFF09AIPegH5tASwUFNVEg+qIQKGQp1gqoigVjMaqo311sVmNsCwWqyqTG2OXeO/Sf2MxPEeumCaXz0oXc5vNJrv2Iu/+jTfeYNeuXezcuRNFUcjlcjJSD6oqjVQqRSgU4saNG1y4cIFAIEBLSwvz8/PMzs5Kb466ujr6+/uJRCJcvHiR1dVVMpkMY2NjzM/P09zczGuvvYbdbqe9vV0qBEqlEhaLRRa0CwsLjIyMSInzw0JsRygD5ufngTvEgSAWRJEvCkhhXikKHKvVitVqJR6Pk89Xo0RDoRBer5fW1lYMBgM6nY4bN24wMDCwLomi1rvAarWuk8qLOX2LxSINKgUBsRlEYVYoFOScvtlsvqcJ5bOCkLAfPnyYjo4O6ckglAyiu63RaGhpaZHX4aNAURRJiDkcDtxu97Z+lnK5jNPpJJVKSUJqo6LkXvtVLpfliI7X673v74tiuFwu09DQQEtLy6a/V+tNEI/HpVml2+2msbGRYDD4wM9jsViIx+PSEPVBn0OMOgkFUCKRYHV1lZmZGebm5kin00QiEc6ePcu5c+doaWmRcb7Nzc3y/tmM+BAkoE6nk94wgjgTnhdut3vL57X2+IhnqhjFEGTsk4J4Xk5NTTE4OEi5XKauro59+/bJKNfHgVBUbfwePDnvFRUPB5VgUKFChQoVnznE43EmJiaYmZkhEolIg8aH7ehsjPj7tEKv18titFQqsSZqB+7/uYulEplsmmKxhLZi4LW9P4vd6kKvrxIB5XKJYrGEopRRlIrMOxcz2TqdnrJSllGJFSpyPCWTyazr6lWNIMuUSlUpcDqdplgsMjF3i6X4NAUlj1Ipo9fraWtro7Ozk66uLjo7O7HZbJhMJhobG2lpacFgMLCwsMDw8DDXr1+Xfh2vvvoqb731lkySGBwcZGVlhUgkwvz8PIuLi9y8eZOuri66urpkhOH+/fv5xV/8Rc6fP8+5c+ekp8Xp06dJJBKyYGloaCAUCjEzM8PKyorscNfObm8nxEI9EonI9Ijh4WFaW1tpa2tj9+7d7NmzR6ZACITDYcbHx5menmZpaYmmpiYSiYRMiWhvb8fpdHLjxg0+/PBDrFYrNpuN3bt3s3v3bjkmIYgCkdbhdru3JKEWvg9Go5FAICCl+o+aBvOkYbVaCQQC91QyCAKmp6fnmaRLbBW1z7qHGS17ks9Icf3Oz8/j9/tpbm7e9shFuKMEEPehxWLhyJEj+Hw+aYp65swZLl68SDqdln4/5XKZb37zm9y8eZOjR49y6NAhvF7vpkTBxhQPMSLxODGVpVKJcDhMJBKR9/FWjTMf9f1WVlaYnp4mEolQLpfxer20t7fLxIrtUk6IOF+h6vq0/x3+JEElGFSoUKFCxWcO6XSadDrNzMwMs7OzOBwOgsHglhc+ojD7rCxqRMdNpDlUqFCpgEZzf5KhXCqTy2bJ5XIUS0XsZpfscoOGslKWfg2VioJ+rbtW7WIW0Ov1a4aPGvQGA1qNFqWirC3089jtVQ+Hqoy+TKFQXOusx4lGq6aCDa4uivkKOn21M2ixWNi9ezeHDh3ihRde4IUXXpDeBmLWf2lpifn5eU6fPs17771HIpGQM/6HDh2Sc/YajUaOHRgMBpkWoNfr6erqkuqInTt3YrfbKZVK3L59m1gsRjab5cqVKwwMDPCVr3yFnTt3ShO05eVlmRv/qKkCAoLksFgsUkpei9oO8OzsLIBMoxD58u3t7UC1A2o0Gkkmk9K3QhjPJRIJ2ZEXSRYjIyOMj49L1YXdbmf37t3kcjlpSCkk5qurqwQCAdrb2+95T9XGG8bjcerq6ta54D+P96I4/iaT6b5KBjEKUh0dujuF4HnCxjSEZwXhnZBIJIjFYtK49ElBpLsoioLRaGT37t3s2rVL+vpotVoikYi8LzKZDNPT08zMzHDhwgXS6TQ2m42Ojg45VrVRAVJ7DYsI2UctyKtKr4L0jPH7/Q8cBXlcCCPYqakpIpEIiqLg9/vp7OzE5/M9tnJCo9HIERph7CmeX0IxpOLZQyUYVKhQoULFZxYTExO88847JJNJPve5z21Znlwul2WcmJCBP4/FzXZDuHZrRJwkWqrNtfWffXDiCn/8nX+FBkhnU5SVMnqNnhtT56nzNqDReMnk0qzGFvC5glhM1XEAsZmq/4IZrbZKalisFikTNhgMaG1ajEYDOr0epVIhmxaKhYIcjaieE3Db/AS9LXTubuDgwYO0trYSDAapr6/H4XCQz+cZHx9ncnJSxlOKWfbZ2VnpiVAulxkbG+P06dNks1lWV1cZGhqiVCpRV1dHa2sr+/fvp7+/n66uLjo6OvB4PGi1WlwuF1qtluPHjwNw4cIFfvKTn0i3+/n5ea5cuYLb7aa/v594PM74+DjJZPKxMt5FZ//gwYMcOXKEy5cv89FHHz1QCZFKpVAUhY8++ojZ2VmpsOjp6aGnpwez2czOnTtZXFyUs/nRaBSHwyGN71KpFF6vl8997nMMDAwwMDDAtWvXMBgMtLS00NTUJLu1guC731gEIA0MNRoNPp8Pq9X6ibrvHqRkWFlZYWJighdffJEjR448693dFLWmnM+SYFAURZJcWq2Wjo6ORx4LedxrSMSTnjhxgmAwyMzMDJOTk1y8eJEbN24A1Wv3Jz/5CbFYjP3797Nv3z527ty5TrFSq5AQ231UE0Mx0pFKpbDb7XKs5UmjVsGQSCQwGAw0NTWxY8eOB46hbAVie319fYRCIZLJJJcuXcLj8XDw4EH6+vq24VOoeFyoBIMKFSpUqPjMYm5ujtOnT1NXV8fLL798Vx77g0YgRAdNmJ19koqdR4GYudZoNEwvjvHXV/8MjfZON1OpKCgVhbmFKXoDrfyLv/lPSCaTZLIZbs2P8+3hH/O1v/qX9LTuJVleprmlieX5CL/6hX+O2WipGp+Vi2i0WnTaO/4Lep0OvaWqehD7YTDoqx3FQpFUOkU0EqVUKlIuK2i1Gik31mq19He/QtGyyJe//GX6+vpkckI0GmVubo6rV69y8eJFBgcHGR4eplAobCoBHx8f58yZM7KDPzU1Rblcpr6+nv7+ft544w3eeOMNOWcNd2a9bTYbBw8exO/3A0giQ3QYR0dHOXjwIDt27OD27dtYrVZyudxjEQwiyq21tZXjx4+zurrK+fPnH0gwCLf7SCTCtWvX6OrqoqenhxdffJFyuSxn1p1OJzabTcqwHQ4HTqeT5eVlFhcX0Wg09PT0MD4+TiKRYHp6Gp1OJ13yRYxmQ0MD3d3d97x/xH0pfB/sdjsej0eORDzN+652tv1hpP/3UzLEYjHm5uZYWlpifHwcq9XKrl27pInf84JHSZ8Qv7tdZITYVqlUIhKJEAqFpBnko3SvH/faEUaDNpuNQ4cOsX//fiYmJhgeHqZYLLKysiLTPYaGhrh9+zYLCwvEYjHsdjudnZ0ybaVYLMqUCWBdTOVWIbwcCoWCVOoFAoF1Sp8nCaFgmJmZIZlMbnt6hNFopK2tjX379pHL5ZicnOTatWtoNBosFguBQGCdkuHT/jf5eYVKMKhQoUKFis8shCv9xgWccPsXHaSNixSPx0NfXx/ZbJbJyUn8fj9NTU3PVTHwJCA67pUKzK9Msv9IH7/5W78hTQC1Wi1Xrlzh/fffJ3wzRKM3SNrkIJFIYMXE4dZ9fGfox3x37CR/96u/hE6n44PVk7xz6uu8sveLVNZiJKt+DxpYK+C0Gi0arQadVotWu+a1UC6jlMuUywr5fI7SmuFjdaFpxWa3YdBXxzFc3tf409P/B0ajkbm5OQYGBrh06RKxWIxMJiPj98Lh8H3HEkKhEOfOnZMJCvF4HEVRaGxs5MiRI3R0dGCxWMjn86TTaaC6wLXZbNjtdukO/4UvfIHu7m6ZRCHi78R8svBdeFzPBWGMefXqVZLJJOPj4w+1TXEcVlZWpEfDjRs3aG5upqWlBY1Gw8svv0w2m2V5eZlwOMzAwABzc3PMzs7K+ysWi0l/i7a2Nqns8Hq9NDU1PbCzeSdqtOq8LwzwHmc2/XEgjAi3kqKwGTYqGd5//31Onz5NNBqlUqkwMDBAU1OTVI48LxA+LIAcEXsWyOfzZLNZKpUKJpNJjgA9yijBdn8GrVYrEyzMZjP9/f2cP3+eq1evsrq6SjKZZHJyEq1Wy969eyVBrdVqyWQyxGIxSSqKkaCH2UdFUYjFYiQSCfR6PW63G7PZ/NS8gkRazOLiIplMBr1eTyAQoK2tDYfD8djbt1gsHDhwQI6jzM7OsrS0xNWrV6lUKoRCIY4dO8bevXvl80fF04dKMKhQoUKFis8sameJa7tswoRPFA9Cai4WaDabjcbGRmZnZ5mdnUWj0VBfX/+pJxhElvrk4jBL8Rl++0v/+K7O8y///C/Ra23hCztflwWJRqMBzZqsORVGa9Dy3nvvEQ6HWVpawqy/hc/cQp27mfVrYA0ajTA/08k4yVKpVE2QKCvrCAmdXrcWp2jF4/bIsQqNxkPQ08L169dpbm7m3LlzfOMb35A+B1uFMCPcCL/fz+7du6mrq6NQKLC8vMzCwgJGoxGr1QogSQa73Y7X62X//v3cuHGDa9eukclkZIKD8CV41MSIWogC5datW9y6deuRtlGpVIjFYsRiMaamptBoNLS3t9PZ2cmhQ4c4dOgQoVBIGslFIhFGR0cZHR2VaR3CNLKtrY329nZpqurxeGhoaNhSEkGpVKo519p19+PThug0azSahyYYNlMyJBIJBgcHyWQyMp3E6ax6iwSDwedGySCK1Gc5HiHIt3Q6jUajkb4qjzPbv52fRafT4fV68Xg8dHV1cezYMRwOB7lcjuHhYRKJBAsLC2SzWSYmJlheXsbj8WC1WkmlUoTDYUkwbDR9fNBnEMqFeDxONBqlrq4Op9P5VMk4RVGIx+MsLy+TyWTk8WhsbMRutz/29k0mE319fdJEdnBwUMbuCiWQ2WwmEAjIVJXNmggqnixUgkGFChUqVHxmkc/niUajJJNJWcBotVopxRbdUrfbjd/vl8VEJpNhZWWFbDaLy+XCbrd/JhYwlUqFvXv38hff+Sbd3d2bFj02i41ff+2XafTUk0qlyOfza510qFQUri/cIllKkZ3PyYjQdD7B1YlT/MyRr1aNHWtGLiprHbxyuXp+SuUSFdnZA42maj5otVowGk0YjQaMJhMGg4FCIU8ikatGMXp288633+Uf/ePfQqfTSTXGdmJ6eprV1VWGh4cZHBxk165dHDx4UEp3BRYXFxkfH8doNHL06FFZHKysrDA6Osr8/Dy5XG7b9+9xIQqxcDhMqVQiHo8zODjIzp072bt3L8VikVwuh0ajYWpqCo/Hg8/no6Ojg87OTunj4HK5cDqdWCwWOSN+v0I9k8nIeW6/3//MDRBFgavX6x9rX6xWK36/nxMnTmAymfjwww85d+4ck5OT5PN5OabT3d1NV1fXdu3+I0OMAwAy6eVZQJBwTqcTv9+/LZ3xJwWz2cyRI0ewWq288847hMNhmTJx5coVnE4nr7zyCvv27SMWizE/P08qlUKr1WK327d8vYvCXkTIClLqaSt9FEUhnU4Ti8UoFotYrVYZIbtdKS9arRan08lbb72Fy+Xi9OnTDAwMkM/nGRsb49vf/jYTExMcO3aM/v5++TdaxdODSjCoUKFChYrPLAqFAolEQsYQig5puVwmn8+TSqXIZDJyoSS6ZKlUikgkgkajwePx4HA4PjNSTEHCiK+7ccfwUlnrqIlOvAaoUJGvtdlsOBwOtFotc5HbVLRlLBbHGsGgyDEIRamSDVQqVJRqcoVOp5MjFNURAydmc3X2VhARuVyeeDyOVqul2dvDB6e+JR3gHwcbO7kiVSQUCnHt2jUuXLjA+fPnSafTtLa20tDQsO71y8vL3Lhxg/7+fvbt2yf356OPPuL69etEo1Hy+fxzRzDA+tSJmZkZoCpbPn78OFarFaPRyPT0NNevX8fj8RAIBGhtbaW9vZ2Ojg46OjpwuVy4XC4SiQSJRAKj0YjNZrsry14cW5E64ff7ZXrHs0SpVCKbzUp1yqNgo5Khra2NYrHI+Pg48Xic+fl5mcoBSIXUs1IyiGv+WY1I1HpfiBEVt9uNz+d7avvwMBDXqNFoZM+ePbS0tDA3N8fo6Chzc3NEIhFu3LhBPp+nubmZ3bt3E41G1xEMLpeL+vr6+8ZuimdPsVgkHo8TDocxmUzy79XTjm4Vpq+CoDcYDHKEZTsgjqvdbuell16ira2NcrksE1lWVlY4c+YMw8PDlMtl6XNhNpvv8zdLxXZDJRhUqFChQsVnFrWLs3w+T7FYRKerxhk2NDSwuLhIIpGQeea1bt4WiwWr1SplmOrCpQoRKwmsxVlWu5zChMvldPGzX/45GVkmXNL/4D/+EQ6nFZ/bV32lLNyFHLt6rspCJq/RyqLHaDRgNJrWZPOQzWaIJxLkslkKhYIsjBrc7YyPj1NXV4ff70dRFFKp1EMXSiaTSXotZDIZ8vkqkSEUGyLKThRhG7efy+WIRCLSp0HA6/XS09PD4ODgJ+p6unbtGoVCga6uLjo7OwkGg/zKr/wKi4uLLC4urilMqj4Tfr9fnnMxciPIhY3EgYgNrVQqmM3mT62Rqt1uR6PRcOLECWw2G6dOneLDDz+USgZxbLq6up6pkuF58WCA7fdO2Op7Pup4iMlk4ujRo2i1Wt59913OnDkjTWaF70I4HCYUCpFKpdDpdPj9ftrb23E6nffdp0wmI8nwUqmE1+vF5/M9k8hGMcIiRogsFssjpWA8CBqNBr1ej9fr5c033yQYDHLp0iUGBgaYnZ0lEolw8uRJIpEIn//85zl+/DgOh+OxSEEVW4dKMKhQoUKFis8sBMGQzWYJh8P4/X6MRqPMoRcGgPl8nnA4LIsbp9OJ1+vF7XbjcrmeyALqk4hisUhoaZ7/ev7b/HDwQ8plZc2ssMCJjsOYzRYSuRSNjY0Eg0F5nLVaLWazCaezKp0XxfmdGeTq9stlhXK5LFMiqgTD3YV4Pl8gmUhSLBYpl+/4GPQ1HuTU+2d47Y1XaG5uplQqyW6oiButhSioDAaD9OHQ6XRYrVZsNhvRaJRCoUAqlWJ2dpZoNIqiKBgMBinJTSaTxGIxwuGwLJCF90QkEqkaYFqt6PV6nE4nXV1dBAIB9Hr9fQ0ntwqtVvvEZ+ZHR0e5ffs2/f39pFIpdu/eTX9/P1evXiUSiWCz2aRqQUR2CgjjRkHu1Sa3CKUAVKXmG++zjUXuxpSDB6XAPA8QRZjZbObw4cO0t7eTyWS4ffu2VDLUfvZnqWR4VILhYbwE7gdBBlcqFXmtPG086n1kNBrZv38/gUCA4eFhzp49SyqVYnFxkfn5eWZnZ5mZmWFubo5kMolOpyMQCEjFz732o1wuk06npepJr9djs9keObLzYbGRdBHqv3w+j91ux2q1PhFiUBAMLpeLI0eO0Nvbi9PplPfJ0NAQN27cIBQKUVdXR0tLC62trSrB8JSgrohUqFChQsVnFmLBOjMzw9mzZymVShw+fFjKSj0eDz09PbLYFTAajVLe/EnqND8uBCGzWXHxJ3/6p3znu+8ykUnxf535Bg2ORn7+xK+Ry2e5OHSSW+kkulyGcCLJuT//gIDLt07pMDk+SeFYgUKxQCadIZVOYbVYcTgd6HX6NVJBA9QWoZvvp9FkxOFwrMW0leS+ttX38WcffY9XThzn6NGjeL1eRkdHiUajxONxCoXCOtNHi8WCw+GgoaGBpqYmfD4fHo9HutiPjo6Sz+cZHx/nW9/6Fi0tLTQ1NbF//34aGxsplUqcPHmSkZERWlpapGz5xo0b3Lp1S8Y0NjU1UV9fLzPe/X4/NptNLtYfFQaDAZPJJE0jn5ZRDKcAACAASURBVBTE8Z2bm6NQKLCwsMDExIRUh/j9fnbu3EkgEFhXaNjtdoLBIOVyuWr2uSalFgV0Pp8nmUxK9YMoqhVFkcSEHL9Z8wfQ6XTSFPJ5MUfcKkS6xGuvvYbFYuH06dPrlAxiZKa7u5vOzs6nvn+1o0EPQzCIEZjHketXKhVSqRTpdBqtVovH49mWDv3DRo0+aqEskk/EWJjdbieXy5HNZnnvvfeYmJhgfn6ehYUF4vE4er2e+vp6urq67kkWCAIuGo0SiUTk8+lpelKIeE2h2hIqrkqlgsViwe12P5YB51ZhsVjYt28fdrudpqYmWltb+fjjj2XChNVq5e2336a+vv6J74sKlWBQoUKFChWfYQhzvYWFBS5fvozX66Wvr09GBep0Ourr62Xh8qDFpSi+xciFMEITr99ouvU8d1Y3gygs0uk05XKZcrksF92//5/+gNIrn2PXz/w8k+9+m+KVjzm052UAXtx3gmKxgE6v55/957/Pr7/yS+xq6l237e9eOomiVOW1qXSKaCRKxV3BarOi0+qoJkpo2YrVhUFvwGq1rBXVNWMaFR3Nnm4GBq5x4MB+ea6EEVkul5MLZagqVdxutzQoDAaD1NfXSwXC6uoqJpOJ2dlZxsfHOXbsGI2NjTQ2NtLe3s6VK1e4fPkyIyMjuFwuHA4HDoeD+fl5ZmZmpGfB7t27MRgMNDQ0EAgE8Pv9eDweSQw8StdUo9HgcDjwer1SQfGw26ktqGpn4DdDpVJhaWmJpaUlVlZWmJmZIRAIEAgEpNHjxkJDHPdwOEwsFpMFtDj+gsix2WzYbDa5L+VyeV3axmb7UqlU7jKWe57vt1olw6FDh2hrayOTyUgCLBQKYTabJYHi9Xoxm81PVQZfez1s1eBRJG2ITvajQowCxGIxvF4vLpfrmfjePCrJUEsw1NXV0draKp8hFy5c4OLFixiNxjVz2gIOh0P6F2i1Wkql0joViCjsBUmRz+exWq0Eg8Ht/sibQvztFMkVwrNocXFRjn45HA58Pt9TMWU1mUz09PRIgtbj8RAOh5mZmWFoaAiAPXv2PPH9UFGFSjCoUKFChYrPPESaxPz8POPj45jNZjKZjOzAeDwePB7PAxfIwl1+YmKC4eFhYrEYmUwGp9Mp5a7d3d3YbLZP7FjF7du3+bf/6Q/QGYzV4kGrwQSEiyX6Pv9FDA4ndfEYyWs35WvEKINWU7V53BRrSRHZTBZFUTCZTBiNRqleqFpEbg1V4qC0NvZw5/uFYoF2/25Ovf8OWm1Vgt/Q0EBzc7OMVDQajXLhLKT72WyWqakpRkdHZYRpJpNheXmZZDIpRyzGxsbI5/O0t7fT3t4ujejS6TRzc3PYbDacTqfswmazWZaXl9m5cyd+v1/O4ouIu3K5TDKZfGhiQK/XYzAY2LNnDy+99BIXL17k7NmzD20aKZQ6gjjbqPDYCCGhLxaLpFLVUZj6+nqcTicGg+EutY/4fbvdLuNHS6WS9LXQ6XRYLJa77juh7BCmrOJ18/PzzM/P4/P58Hq9a+M01RGXp2129zgQZMqJEyewWq2cOnWKjz76iImJCXK5HLFYjOXlZQ4cOMC+ffue2n496oiEOPePSwgIYgl4ZiMSjwuz2Sy9Nt577z3OnDkjfVtEso0Y2ztz5gyKovDiiy+yf/9+bDabJJTE72QyGbxerzRAfVoQqRWCANRoNAwNDXH+/HlmZmYwGAwEg0F6enqe6n4ZDAaam5vJZrM0NDTIUUcRm6ni6eCTubpRoUKFChUqthHCYGtqaorr16+j1WqJxWK4XC6CwSCdnZ3SK2Dj/LhYGJZKJdkpvnLlCmfOnGFhYYFkMimd9A8ePIjRaKShoQG32y3n4zd2xcT/f9YLaNEJFgW3KLqtu/fT8tv/HI1Ww+K//V/RTdxGZzZjcKyZkSmVdXyARgNarabmc60vNBVFoVAqYtSbyOWyKGVFdvN0Ou2G36+mSDxov+8ugKqFaJ2jlfBKlFu3blFfX4/H48Hv9xMIBHA6nTidTnleI5EI4XCY0dFRpqamCIVCLCws3LNQD4VChEIhEokEGo2GVCqF0WiUsXQmk4lKpSI78kLKbDAYZMeyVCrhcDjo6OggEokwOzsri6qtFnQ6nQ6DwUBjYyMHDhwgFAqh1WofmmCwWCx4vd51SoH7EQyiiBTmjcKcUVEUksnk2vnUSU8Lcd2L45FMJqVZXSwWw+12y9QI4dEgurmCgKglJkTMZ3t7uzTiFK/7pOBeSobbt29LU8BUKsXq6ipGo1F+VlHEb0bkbOe+iW1v1ddDzMpvB6EqRl+AT9Q5rYXJZOKFF16gra2NeDzO7OysTFPJZDLkcjmg+jfp8uXLLC4uyjQEt9stE4s0Gg3ZbJZSqfRUyQXxbBVkunjGKYrCrVu3OHv2LPPz8xiNRpqamujr68Pj8TyVfYPqs7S+vr76rK+rw2KxkEgkiEajMhb5efdl+TRAJRhUqFChQsVnHul0WhaOoVAIjUZDPp/HZrPh9Xo5evQoFouFhoYG7HY7Wq0WRVFYXV2VRefS0hKhUIi5uTlmZmaYnp4mlUpRKBSkO3goFGJwcJDjx4/z+uuvYzKZyOVyslOs0WjkSMXz0HEtlUoUCgU541ssFu8kZqytz8w//bPE//K/YbXWRKlV7iYBKmvf3iwtIFfKU1bKWEw2VpOra4WSGCVZb9xXff39yRedXrc28pBd82lYi9XUaNHqtPQ29ZNOrxKJRBgeHpYSbqFgEKSKkOiLz59KpbYkDRdFgSiEOzs7OX78uFQ2iOLIaDRiMpnw+XxEo1FJTJTLZXw+H263G5vNRj6fl93NrZAEogi7efMmxWKR0dHRR4q89Pl89PT0SOJMGMrdCxsL3FAoRKFQIJlMMjc3h9/vx+fzyTESoVIRMJlMUlIvzD5F/F4ikZAjJoJQEIasUL02RBxoXV0ddXV1OBwOLBbLJ7YYFZ1h4clw8uRJSVyWSiV8Ph9Go5Fdu3bR29uL3W6nrq7uvrGGj4NagvVZpEg8rPfD8wqdTofT6eStt96iubmZhYUFZmdnOX/+PAMDA8Adz4m5uTl++MMfMjw8LNNYvF4vwWCQAwcOsGvXrqc6JiPIwkwmg9FoxOv1UiwWWVpaYnJykomJCana6+3tpb+/n7q6uqe2fwIajUaSjOl0WpJTxWJRmvWqeHJQCQYVKlSoUPGZRy6XkyqG4eFh+X2TySQX6+3t7bJDW6lUyGazTExMMDg4yOjoKOPj40xNTTE1NUU6nd40AUAoJCqVCt3d3Vj+f/beOziudD3z+3XOAWjkRCQSAEkQAME45MyQM5q5nHBlyVeutVeu3bJ3a6us8rrWJbtUWsvekrfWpVprayVZWql2vVuSXL5Xkm++c+fOnUTODAOYCYAEkTMaqdE5d59u/9H4PjbABJJgmjm/GhSH6HBCB573/Z73eSwWQqGQHMUQK0PiwkhIu8Vq/rM2lBTFod/vZ3l5GYfDgdPpJHprgIX/5Z9JZYEeiEfCXPnHv1nIqU8msEaS68+y8RwEslH+i+/+Lob1CzwNGuLRMNq8ppA6kclgXD/WwmjEnWJGSPULK9cFX4Z7odfp14tXE3p9wcFcp9fJ3emo7+WL0b9Dq9UyMDCw7QaIfr8fv98vV3z37dvHgQMH6O7uprOzU67ei0J5ZWWFxcVF2byIxWLSANJkMsnfi8bHwxCNiLGxMWZnZ0mn01uemS9GmA4K8zaxcnq/Ak+r1cpjUxSFpaUlFhcX8fl8TE5OUldXR319PW1tbWQyGakYEY2WQpqIWTZ3hJeJeO21Wi1Go1GqhrRaLSaTSTZgXC6XXP0XEahGo/GlXK3cnC7R0NBAOBzm5s2bUtEg4gs1Gg0VFRUy2vBpzbwLI00h41cbDI+OOIc2m43Dhw+zf/9+ZmdnGR4eZmVlhf7+fuCOx0I4HObixYtcuHBB+jjU1dWxa9curFarNPvM5/NSGfQ0EM1dMcIUjUalD8jMzAxjY2NMTU2xuLhISUkJNTU1tLa20t7e/lw+f+I8i383RbNSxBarDYani9pgUFFRUVFRuQ/iYur27dv8+Mc/5siRIxw9ehSfzycbC2NjY6yurhIKhQiFQjKL/F4XwWJ+/9atW/zoRz8im83i8/mw2+2Ul5cTjUZZXV3FarXKi7S6ujqamppobm5+Jm7cxYTDYWZnZzEajTQ3N2O1Wmlra+Pz/fvvupD9q7/8K5a+HOeN3ce4MXWLn9z6YsPtGgqpDzmTkcr/6XcxOl0Ig4TJP/s39NJQKMg1QoKdI5e7I8dNJBKysLTbbTidLtmA2IxYSReeBlqdFoNeTyqdJpVM0Wht45P+rakBngRREPn9fsbHx3E6nZSUlGA0GmWhpigKo6OjDA0Nyf2JRCIbZL3CePJRCyvxPnzc4xQr5aKgSCQSD9wH0VARyg1x/F6vl3A4zNzcHDdv3uTy5cuUl5dTX19PXV0dbW1ttLW1ySKmr6+P8+fP09DQwI4dO6irq6O2tpZ0Os3y8rK8n3CvX1hYYGxsTI5UrKysEAgE6OjooL29/bGO/UXCZrNRWVnJsWPHUBSFS5cucfXqVebm5qTB3urqKl1dXezdu/epqZ+KUyTuFev6NHlQgs3LjFarxePxsGPHDlwulyzGdTodFRUVuFwuVlZW8Pv98j3v8/lQFIW/+7u/Y3BwUDYua2pqnppaQJhsJhIJFEWR32HJZJKLFy/y0UcfMTY2htlsZv/+/Rw/fpzm5ubnNo4gPCrC4bA0yk0mk0SjUdmoVHl6qA0GFRUVFRWV+yAKwMnJSQKBAOl0GpvNxszMDF988QXj4+Mymm8rZDIZ6QwfDoeJRqOsrKzgdDqpqKhgbW2Nubk52XDYtWsXHR0dJJNJSkpKcLvdG1IoniaTk5OyoKurq6Ourk42OO41U1tRUYG9MstrbYdJJ1P89aWf8H//5A+AQkGSJ49OoyWrZHC17MJaXi4f6y2vpCRRJpsBUFAzKEoWRSkUq8LHIJVKrs/t2+978Sqk3BaLBb1ev76yrpPpF+Sho+4Ak8Frcob/cVb4t0I+n8fn8zEyMoLFYsFkMmE2m+VYhs1mkyM0YrRBFNAijvFxi6qtjlTcD5/Ph8/ne+THZbNZOe8s1D6rq6vydqHMaWlpobW1lVAoJN/XGo2G8+fP8zd/8zccPHiQw4cPYzQaqaqqkk2XYnWC0WhkaWmJkZERqqqqSKfTzM7OMjs7i8vlYufOnfJ5X2Ylg8lkore3F4/HQz6fx+v1EgqFGBwcJB6Ps7S0hFarpb6+HofDscHjYrv3B57diETxNr4OCobN6HQ63G73euPULn159Ho99fX1NDU1MTU1xfz8vFTaxeNx6d9w8eJFTp06JZvXuVwOh8OB1WoFntzHR5xz8W9XLBaTyoBUKsXa2hqXL1/m008/laqkffv28cYbb1BfX//cYpzz+UIiUTKZlM0wMfL3tL7rVe6gNhhUVFRUVFQeQjKZJBAIcPXqVdbW1giHw8zPzxMKhWRRuBXEhVowGJRyTWHUJeLGxJy7WK1aXFyUhnkdHR20tLQ8dSVDMBjk3f/sP+fX33+HV155ZUsXicUX/Xsb2vivet4hm1fWb1uPtdPAV9yj6Fl/rE6nw2wyFeLXUknZKMjl8phMJrLZDIlknmy2IJ2HvByB2PSEcpuKkiWRyNyJdEsmMOj17Krt4sb0V7jdbsLhsDznTwOheFlZWeH69etyBvitt97i/fffp7e3l/r6+g0Xvvl84ZhnZ2cJh8MyNeFFRowFPawpIi74xXs7FosxMTGBx+PB4/EwMzNDLpdjfHycUCjEyMgIfX198naLxYLFYiGZTJLNZolEIqRSKfr7+/nyyy9lAyISiRCJRJ55nOPTQKSLaLVa3n33XXbs2MHp06f56quvCIVC3Lp1i2w2y+3bt3nrrbc4efKkNMHcLoo9GLZq8rgdiPn5fD6/IarxSXkRGxZCISJMPt99911CoRB+v5/h4WFGRkaYmJhgdnaWXC5HPB7n2rVr+Hw+PB4P9fX1vPvuu7z22mvbsj/Cc0EomLLZLG63G71ez40bN7h69SrDw8Ok02lKSkpobGykpaWFxsZGOcLzPBANZrvdTiaTQavVYrPZZJNe5emiNhhUVFRUVFQegpBiDw8PMzw8/NjPI4qvaDRKNBqVv08mk4TDYfl3MYMeCASYnJwkl8vJFcny8nJ5gfe0VmQzmQzxtML/+9NP0Wi17NixQ8qT77cqms/nSWZSBGIhTEYz/+jNvy/HIjTcScX4T7c/uWeDQaMpzO+bTGayWYVMOkFCo0Gj1WBaX6lOpvTkc2IlKoVOp0WnE5cy+aKV/zz5fI50Jk0qlSaRTJCI3xmx0Gq0VJXuwKSzYnJapfz3aRUawWCQYDDI5OSkPA8ajYbq6mpOnTpFeXk55UWKDsHAwAA2m41kMilnh19ktqqYEO8loZBYXV3l9u3bcmRidnaWbDbL3Nwck5OTTExMUF5eTnt7O7t375b+FKlUCkVRZBzs+Pg4Q0NDtLe309HRQS6XI5lMvrSRsMVoNBqcTicOhwOPx0NXV5c8R8vLy0xPT7O4uMjly5exWCzs2rULt9stZffbsZL8uDGVT4oYuQG23YvmRWsuiHNsMpno6Ojg1KlT5PN5YrEYfX19VFRUyGQWEZc7OTnJyMgIer2e8vJyPB4Pu3btkkqGx1GyFK/4RyIRQqGQ9L8R/jG3bt3ik08+YXJyknw+T1VVFXv27KGlpYWampqndJa2hjCLNZvNUsUmGg4v+vfo14GX/xtXRUVFRUXla878/Dxffvkler0ej8dDc3MzVVVVT2XWOpPJ8Dv//PdAq6fh/f+ev/vpH/Mrb74p5a73W/2pqqrih97/j89+chFgvcjPA3nI52WCRF7JsvzZL9BbbKDVoNVoiM1MQvUODAYDNpuNTDZDPB5DySqkkin0Oj0Wi369sMijKDkymSwGQxaDQSGXy6PkCtGSmXSGTCZNOpNBySrkcgqgQa/XYTQa0et1hRVus5n9O19jNnYds9mMz+d7ZgoBUdTcvHmT7373uzI9YTMXLlzA5/MRi8VkMf11JJvNkkgkmJubw+fzydg+EUspRoaSySQjIyMy1lJE+y0sLLC0tIROp6OhoYGenh6OHTtGQ0MDDofja7diKWJNT5w4gcfj4cMPP+Tjjz+W6qczZ84QiUR48803ef311+VYzpMgilRhjvcsRyTi8TjhcFhGEG5nSsbzkvA/CHGexbhEIpEgmUxKVcC+ffvwer2MjIwwMjLC8PAw09PTKIpCOBzmF7/4BX6/n7fffptXXnnlsWJCFUVhbW2NQCAgFUdOpxOz2SzTLYaHhxkfHycajeJyuThy5Ajvvfcera2tT+nMbJ3iEQnRGHmZR6VeNtQGg4qKioqKygvOysoKKysruN1uGhsbMZvNeDweedG4nRdM/+Af/ROuTq3R9Gv/I5byekztJ/nDP/5T/vxP/q1MCbiXA/d3fuM7fOc3vvPQ529pasPy0zOFfRYFyryXbEUWna5Q/BvWxx4URSGdTssIT3Gc4vc6nQ6NVktOyclVzlQqRTqdIpVOrysoNFIyL6TyhVQOI73txxn89CxNrfXcunVr287hVsjn84yNjZFIJOQK22a8Xi+BQOCpekS8CIjXThiyCcxmsxyHcLvdG8aLxDmJRCIsLy8Ti8VkI2Lv3r0cOHAAq9X6wESFexXIW/0sPa1CpXifhNKj+O/idp1Ox759++jo6CAYDDIyMiLTS27cuMHo6ChGo5GGhgYqKiooKyvbULQW/9xvP4pvFwWbSPYQfgDJZPKej98K4vyJcyliejfvh/BgEYqM7WoYFW/3RaD4tRXfCWJUTlEUampq2LlzJwDxeJyLFy/S19eHRqMhHo8TiUSIxWKcP3+eiYkJ7HY79fX1lJSU4HQ60el0D01PECq7ZDJJMBjE5/NhsViw2WzY7XYsFgtTU1MMDAwwMjKC1+ulpKSEHTt2cPDgQV5//fUXImJZmAMnk0npaVE84qPydFEbDCoqKioqKi8Jy8vLXL16FbfbTUtLi3TyfhyCwSAffvjhXWMPV69eo+m//lcY7W5yOYW6w+8y9pM/4vf+xe/ze7/7OzQ3Nz9RxFdFWTn/5NTvY7M45O/+5G9/D6PBIGethbeCmOUXRY2SVdBoCikFwvRRF9YhxiPEcZhMZiwWK3qDHoO+EN2m0+vQ6/To9Tq0Wh0aDdSU78BpKV1XNuilDPtZEQwGURTlvnPl0Wj0vokkXyeELHzzORDvh2w2i6IocrbbarVitVq5cuUKX331FZWVlTQ2NlJRUUFFRQV1dXVbbsoUF9BbLTZ1Ot0Tffa2gli5F94gwhU/kUjI/XS5XNjtdo4ePYrL5eLcuXP09fURCAQIh8N8+eWXBAIBTpw4wfHjx2WBLhoE4rxuRlEU2fQRDR+xki6c+cXnr3jUC+40DR72nhWr9GJ1XcjZDQYDmUxmw/0Ko1N3jD2/riMSUNif4veXkPaLuGKBwWCgtbUVq9VKU1MThw4d4tNPP6Wvr49cLkcwGOSjjz5ieXmZ119/nUOHDuF2ux/qi6AoCoFAgFAohKIouFwuOZpjMBgIBoNcunSJDz74gKmpKaxWK0ePHuX111+no6PjucQp3wvRmEmlUnJcQo2mfHaoDQYVFRUVFZWXhNXVVfr7+2loaODAgQPo9XosFssG+SdsbRX2D//oT/jep5dxVDUBkCcPebDseQO9xQ6AVqvDaNLR8O5/x7kP/wN//Kf/jv/zD/4PuRL0OCt/ufVCDgoF5N9++ufcmrxKV8sx8uTXGx5ajEYDqVSeTCYtCx5x8V1YYUtIrwWttrAyZzLfUSmYTeb1osS4vr2791Wn09G54ygTgUvS0+JZFhyxWIxYLPbMtveiUjx7LlZt4c4cuFjBttlstLS0yESVmzdvMjU1hdvtxuPx0NDQIBU+wWAQnU6HxWKRK++bV3BF80JsV6RSPAy9Xi9nu58m6XRaJgMoiiLjS8X+6vV6HA4HXV1ddHV1YTabicVijI2NEY/HuXnzJkNDQwB4PB4aGxul+38qlZLeMsWIcy5SA+LxuLytkOKSIhQKyTSBYu7nzbIZcb+CksggGwe5XA6j0ShX7MXrs9nccbOy43EQzSuxnc3n4V73Fx4jW7n/wyhuqimKIo8ZkK/tnYap6S5jX4PBQH19PbW1tezevZuenh6i0Shzc3MEg0Gi0SgXLlzg9u3b5PN5XC4XO3bskM2L+6kMhCpobW0Nu92Oy+XC7XZjtVrx+/1MT09z5coVzp49i06no7KykoMHD3Lq1CkqKyufu99J8XeHMFIW5+9FaHx8U1AbDCoqKioqKi8JYvb11q1bnD9/ntbWVqqrq3G73TKabisXUV6vlx/87CN2/r3/DcN6M+FBGEwWak/+Jh//8F/x20tLlJWVYbfbH29FaP0CMJvN8m++9z8Tj8f53X/4JzTW7CSXU9CYjOTyufW7RuUKlNFoJJ8vpEIkk4UxCHExaTKbsNvssrlQWBUVxeS9mwuCvc2HOP2jHxZGMwyGLUeOqmwfYoVep9Nt8AoQEv2VlRUSiQSBQIDBwUEOHjzIwYMHqaiokCZ4c3NzsoEgEln27NnD7t27mZmZYXp6mtLSUjwejyxQxUiB2+3G7XZTXV1NdXX1Q/dXFCtPUwqu0WiwWq3odDp5HtxutzT7LES12jCbzfIz39vbi8vl4sqVK1y9epWxsTGmp6e5dOkSoVCIY8eOcfToUan0EAqhzYjzI0ZRBGIkYm1tjVAoRE1NDVVVVY99fKJRIhqkmUyGRCJBNBolFAoRCASwWq1SMSFMB7eDXC4nlR6iyH/Y/f1+P9FodEv3fxharRar1YrRaCQUCrG4uEg0GpXjXyKx4WGIBkR5eTnf/va3qaur45NPPuHcuXMkEgni8Thnz57F7/dz8OBB9u/fT3NzM/X19Q/dv+LUhXA4zJkzZzh9+jQ3b95Eq9XS3NzM3r17aWtro7KyUkZjPm82R/w+q4agyh3UM62ioqKiovKSIFYUR0ZGKCkpIRAI0NLSQmVlJR6PR8pqi1eE9Xo9RqMRk8kkC7A/+4t/j2nnK1tqLgBotBrMDjc6Rxm3b9/m0KFDj220JqTo3/34/yKbUfjt3/zXWM02stnMepKEAZOxYAwpmguFYynIXC0WC4qSKxpn0GA2mQoGZBYzJuMdw8R8Pkcup9x31jybzWI3u3BZKgjHA9smoX3Sue7iVbjtfN6tbvdxb39cRDGw2URUrLD7/X5WV1eZmZlBr9eTTCZxOAojNu3t7SwsLOD1egmFQvh8PoLBIIFAAKfTicfj4fbt21y/fp2qqiqqqqpk4bGwsMDi4qJsLGg0GioqKh6YuiCaXU/LOLL49RX+E1shn8+zc+dOaWxpt9sxGo3EYjHW1tbo6+vDYDBgMBjo6OiQq9hiNOFh2xFeCIlEQh5/dXU1dXV1T3S8AiHrj8VissAOh8PYbDbi8TipVGpbZe7CEDORSBCLxe4qPjd/zhRFkd+/ohn5pIhmidfrZWJiglAohNlsxmazUVJSIsdFir0DNu+XeD+WlJRw/Phxdu/eTSKRYHFxUcZLLiwsyCadGHsRz6fVanE6nbI5kM/n5eiLxWJBr9eTzWbx+XxcvHiRM2fOkEqlqK2tpbOzk8OHD9Pa2orH43ni87EdCK+QRCJBOp2WqhhxLCrPBvVMq6ioqKiovGQsLi5y4cIFRkZGcLlc0syueEVQr9fjcrkoLS2ltbWV9vZ2ysrKiEaj/PDnH7Pj7/2LLW9PQ6HAJ5umtrYWq9X62HLTfD6PBg23pq7y68f+MalEmng0IbPfrVbremGZlat8+XyeYCiE2WTGZrOTXb/Yh4J02mA0YjabMOj1FF9/J5Mp4vE46XSadCZ9x1RynVyusNLVXLaP0dmBJ5Y9C4Tse/NxpTJ8bwAAIABJREFUF/OgRoEouDdL+MWs+oOe91G412Pv97vifdpuxKrt5gaKaASIfRKy9ps3bxKNRqXEvL29nSNHjshGWllZmRwXOHfuHGNjY0xOTjI8PEwmk5ENp2g0SiwWk8oGnU4nRyy2Wti/KOTzedbW1lhdXaWkpISTJ0/S3t7OO++8QygUIhQKMT8/z89+9jO+/PJL3G43tbW11NXVsX//frq7ux+6jacZUykUGwB+v1+OgqRSKVwuFzab7S4fgidBKBB0Oh2lpaW43e679mfz/YUfxL3u/6gkk0n6+/sZHBxkamqKmZkZAoEAe/bs4ciRIxw8eJDW1lZ8Pp/8fn+YSaNQtby5nvqTyWSIxWKMj48zPT1NJBLh5z//OZcvX6a0tBStVovL5eK9997j1Vdflc2FyclJrly5QigUIhqNyrGZWCwmFQstLS1UV1dTVVVFbW3tE52L7SSTyeD1ehkbGyMYDMp/Q7bTHFTl4agNBhUVFRUVlZcMn8+Hz+e77+2iEK2srKS2tpaDBw+SyWRobGzkL/+f72Lc+Qo6s2VdTQAPGiEA0GggGVhBn0vR0tKCxWJ5opX0cDxIKBJgYOISVe5G0ul0QU3gsKPTaWUhq9VqMVvMpFMpopEIOq22MAqiE40ELdr1Zsqd1c07+yXiLmOxOPF47L4FUU1JM1r0j1U8b/a9EIZiosFwLzXCw86dKGaEfH2zGqWY7VAdPChJAO5I5p+mgmGrhoyKojAxMcHExAQ6nQ6DwUB5ebkcbRAqhIqKCi5evMjg4CDLy8sEAgEWFhZYWFggnU6TSqVkweZyuXC5XHR3d8tUj5eRUCiE1+ultbWV5uZmoHBuV1dX8Xq9fO973+OLL74gGAwSiURoa2uTaoaGhgYsFoscUbnXe7S4wfCgBIrHQaPRSBm7SCuAQsFos9moqanZtm0B0vcgl8vhdDof2jAQ9xdxjS6X67G2K1bYY7EYt27d4mc/+xlTU1Osrq7S2dlJb28v3/72tzl58iSrq6uEQiH52RDfKfdS2IjXy2KxyBEiKKjeLl++zMWLF/niiy+4ceMGV65cIZlMotVqqaiooLS0lObmZvL5POFwmKGhIc6fP8/Y2Bjz8/NYrVZKSkro6enhwIEDvPnmmxw6dOiFSuAQZDIZFhYWGB0dJRAIoNFosFgsX8u42heZl/MbVEVFRUVFReW+iNU5v99POp0mHo8zNjaGy+Xik/NXaf77v086lZaRkw+7Roz7vCz+8s/5l7/z29hstie6qExlkvzHj/4l7//qKZYmotjtdqLRgtdCIp6AfB6dTkSKadBqDWTSabJZhUikkKqQTKZQlBwaDeQzeWnWptFoKVx3F/bPoDdIRUQ8rgHuXRBZjDZqShsZWQg80rEIpYj4U5DL5YjH449dhBUrBsTzAaRSqUdugmyHwkH8+aRz59uN8Am4ceMGqVSKgwcPcujQIRKJBNPT0yQSCez2gjdHbW0tDQ0N+P1+RkZGGB0dxe12U1JSQmVlJVVVVdTU1Dz1dIhnjUajweFwUFtby6lTp6ipqeGzzz7jyy+/ZG1tjYGBAex2O8lkkt7e3gcqGYoLyqdphire/+Iz8DS39ayN/zKZDFNTUwwNDdHf38/k5CSpVIqysjKOHj3K+++/z86dO6W3gsViIR6PE41G5ejLvUwf74fBYJBN4bKyMlpbWzl79izXrl2TDYUPPviAiYkJ9Ho9iqIwNjbG1NQU8Xgcj8fD4cOHOXDgAK2trbS0tFBfX//CNRYE6XSaqakpbty4werq6gavjxd1n7+OqA0GFRUVFRWVrxlC0hoOhwmHw3i9Xq5fv47V6cba9Q7xdBZ9IoHJVJjD1mi0aDSs/6mBwn/EfIusXfk5ysoEf/y//x7vvHOKH//ox/RduCQv2Ir9HopX8bVaDWg0LMzPs6+7k9/6rd8C4Ic//j719fXEYjF+5fVT6PQaDEaDnPHOZjOYzZZ1ubRwjc/LGehEIoGIpSzUHTmUDaMEdy5tRMpGIpFcV0bcv+DfXX+Y6ZURUpn4PW8Xx1V8kSqUEyJqT9wm4vy2qzASK/diFVWlgDgvY2NjzMzM4HA46OzslAqFdDotfQzMZjPV1dUkEgmSySTz8/PU1NTQ3NxMZWUllZWVVFdXS6+Srwti9MBisXDkyBF2795NMplkenqalZUVJicn0el0hEIhjEYj9fX18v7i8ZufD7ZfwSAQr2kmk5GKhqfVBCj+/nraiONKJBKMjY1x4cIFhoaGWFxcpKqqipaWFg4dOsSJEydkkopQ6Ii4UjH+JiI+i70ZircjfoSaqq6uTjbRGhoaCAaDTExMkEwmicfjnDt3jgsXLsg0D6HUKCsro7Gxkddff523336bqqoqysrKnvq5ehLS6TRzc3MMDQ3h9/sB5HlUUySeHWqDQUVFRUVF5RtCWtHgqt1DLBolnU5jNBgxGg3o9Qb0eh1GY2FlTKPR4J+4TqTv+/zzf/ZbHD9+jB07dgDww+//hJhXS0vdbkCsZObJFRcb+fx6EkSeC1euk9fckb+3trYCBbnvsdeOcmP8PLvrD2EwGshmsusr9Hn0ej2pdIp4LE5KJkaIpsJGslmFZDKFRqNFp7vjw1C4GC/4M1gsVll03kvJ0FC+C5PBfN8GgzBTK145FBfyIm/9zv5kn2nc5Tcdcb6vXbtGKpWip6eHnp4e2eQR8YoipaK6upr29naOHz/OsWPHpHmf2+3+2jUYijEYDDgcDl577TWcTicff/wxp0+fJhAIcPPmTZxOJ8lkkgMHDtDT03PX45+FgiGfzxOLxQgEAng8HmpqarDbt2ZG+zjbepqjP8XkcjlCoRALCwtcuXKFM2fO4PV6sVqtHDt2jLfeeou9e/ei1WqlckOkodjtdkpKSmSTIhqNyt9vTm4Q73fRoBHfV1qtFrfbTVNTE0eOHCEejzMwMMDY2NiG7zGNRoPdbqeiooKTJ09y7NgxWltbqaqqemxj32eJoijS7DWRSKDT6bDb7fKzrfJsUBsMKioqKioq3xA0Njd5o414PAHxBHqDHqPBiMFowGgwYrVmUTIplk//NVX6BP/+P/47Ojs7N6zw6XQ6etuP01q/Z0vbTKTidO67d/TfgUP7+fFffcaehsPodTqUbHZ99KGw8pZMJojHY5uk+fdOg0gmk+srVfo7SgxAp9NjNBakxgVJffqeTQqTwcyO8nYGZ84XzlVRMSX+f7N5o0g5SKfTqrLgOSLk9CMjI8zMzGCxWNizp/D+NBqNpFIpUqkUTqcTu91OZWUliUSCgwcP8q1vfYvx8XEmJiY2zGkXF53Fq8IP4nk2lYQBpvAvud/70Wg00t3dzc6dO4lGo0xOTrKyssL09DQ6nY5gMIjJZKKhoUEqGUQhLt7rolGXSqVIJpNPvO/ivGm1WrLZLPF4nHA4jNFopLy8XEZ1Ft9f/IgGwaOee0VR5HGIz/GDKI49fJRtiftms1kWFxcZGhri+vXrXL9+HbvdTk1NDYcPH+a9996TySiiuSCUBHa7HZfLJU1JRTqCUFAJJYM4H+J10ev1GxoMDocDrVZLU1MTu3btYn5+fsN+ZrNZOZpRUVEh92u7UjOeJsXv0UgkIscDhcFjRUXFS2fc+jKjNhhUVFRUVFS+CWi0mOs7N/wqp+RI59PSxT+VShG++jP2uTP8xZ/9KRUVFVJqKx+T254iamZmhj/6wz/lVNc/JJFMkM5kyK3LgvXrhXwiaUCr1T30ol64pYsiS/gi6HQFE8hCpruVbDZDIhG/73N11B9gfHGATC4pL9yLV7NzuRyx2B2zyOKiQ+X5Iwqyy5cvSxPDtrY2/H4/gUAAu92Ox+MhHA4TiUQ2NB/S6fRdHhOiaEmn06TT6bsaDff7u0A0Pu4lZd8ONr+Pg8Eg4XBYRnQW3098jrVarZztP378OA6Hg48//pjPP/+cQCDArVu3cLlcJJNJDh48SHd3t4ynDIVCBAIBvF4vKysrwJ2oxSdBfL5FUoT4LANyJKD4WIqbG8Wv36MW/pFIRDZIHvZYoUBIJBKPbPAoxtUuXLjAhx9+yPDwMEajkZ6eHo4ePUpnZycOhwODwUAulyMSiRCJRDCbzTIRRZhgarVa2RRJJBLE43GcTic2m02OlmQymXt+L2WzWSKRCP39/Xz66afMzs7e9f2uKArBYJD5+XnGx8cZHR2ltraWysrKRzrmZ00ul5PRpiI5SFEUaWC6Z8+eFyZK85uA2mBQUVFRUVH5BqA12zFWtW74nSiAhHFgxD9GYOAMznd+hYmJCbLZLCUlJXJ1zGg0yjjBJyGfz/Ov/+AP2VN7FI+9MBefzRT2QavVoNVo0Wg1aItmpB9UABQaBzmUXGH1Vm8oeCIY1r0RQLM+y/0g6buGOk8rJoOZXDYjUyn0er0s0IRkWR1/eDERPhVDQ0OMjY3JCEuRFmEymaTcPBaLFUaB1iMRY7EYiURiQ7pENpuVzYVUKiUbDrCxubD5p3h/RINhq2MX93uv3+szJwrzYu8P8R6NxWJ33U/sh8FgwGw209PTw65du4hGo0xMTLCyssLMzAw6nY5AIIDJZKK+vl5K88PhsGzWBINBrFbrtjjzi/Mkni8ajUoVhlAWFDf1ksmk9A8QvizFjYJ7navNvy8eJbDZbA9d3RYFbCqV2nLiiXhcMBhkZmaGS5cu8cknn6DRaGQqw9tvv01LSwtWq1U2esVxORyODekWRmPBM8dgMJBKpQgEAsTjcfk70XgQSoTN+x8Oh1lYWODmzZtcunQJKDRwnE4nVquVaDQq/R6WlpYYHh6Wr7/NZsNoNL6wSQxiNGJ5eZlwOCzfN3q9nqqqKpqbmzc03VSeLmqDQUVFRUVF5ZuAVoe+5P5Rb7lUnOC5vyGfCDM6Osp3v/tdmpqa5AVmOp3G4XAwPDTCm7/6DyiMKmyx0bCpXlpdXaXv3CX+mzf/V0LBEJlMmlxOJCZoiMfjBPwBEokEuZxyz5GGDU+/XgClUymUrFKUQKGVBpaALBI3cqeJYdSa2FnTxc2581JqXly4qN4KLwfF6RKRSIT6+nrq6uoKCp1wGLPZTENDA+FwmPPnzxMMBgmFQng8HtxuNx6Ph9LSUqLRKMFgcMMsO9xt9il+t5li5cBWm3KP0mDYPDYQCoUIh8PyGDbfV+yHyWRCq9ViMBiw2Wy8+uqr2O12fvnLX3LmzBmCwSBDQ0O43W4SiQTd3d10dnai1+txuVzS2HTHjh3U19dv6bgehFACLS4uMjc3RzAYJJPJsLq6yvj4+F0qEdEAKiiTbDgcDql0uN+5vluJlZOr3WVlZZSWlj50H7VarSzot0oymeTChQt8/vnnDAwMkMvlaGpqoq2tjX379tHa2orL5ZLKhWg0il6vp6Ki4i5/BYEwlC0pKcFqtZLNZlldXSWVSqEoCi6XC6fTuaEZkEwmuXLlCl999RVjY2NA4T3ldDp599136enp4YsvvpDqn1QqxfXr14lGo6ysrBAOh2ltbd2W1/tpkE6nGR8f5+rVq1Jdo/L8UBsMKioqKioq3wB0Vhea+6zg5/M5Ipd/RDa0DLmcXM1saGigublZrt5FI3Haaw5i0JnWV8lE+oS4qL/7wl5zj9+Nj49TVdqAVqMlk82SzSrroxeFIiKZTBaUFUp2SzPPxfPYW/NCuLNPd1IwNMRTERLpGDqtnqxSWAl81FhIleePUOaMjo4yPj7O0aNHcTqdMpFAjErMzc0xNzcHFN4Ha2trzM/Py/skEgmi0aj03iguYIuTUzb/iOd71sfscDiwWq13rXzfD6Fo6O7upqWlhUgkwtTUFMvLy1LJ4Pf7MZlM7Ny5E5vNRklJiRxFqampoa6u7on2W3y2M5kMKysr+P1+UqkUWq2WRCKBz+e7Sx1itVqx2WzyRySFiMbDVlAUBbPZTDgcpqSk5KHnS4wfCO+DrRxXIpFgZWWFS5cu8cEHHxAMBjEYDDQ3N3P06FF2795NbW2t/N4ScZRlZWVSObaZ4gQJg8GAxWJhZWWFYDAoG6hOpxOLxbKhWZVKpRgcHOSTTz5hdnZWejLU19fz5ptv8s477wDI8Yjl5WUmJydZWFiQyqBcLofZbMZut78wfgbFxzc5Ocn169dZXV3dcHsymSQajW7pM6GyPagNBhUVFRUVlW8AuUSY9PIExsqWu25LTlwmuTAEuUIxLS5UZ2dnCQaD0mE8Fkpx5NRv4FvzYTKa1qW5einRLTZX3LDtTZJig8FAnjx6vQGL2VxoDiTvKBUUJUs6nSeXy29oPDw5oggs/D8UmivzvkkGZ84ztTxEWkmg5LKySFV5eRFNp8nJSRKJBLt27WLXrl2UlpaSy+XQ6XR4PB5ZYOZyOVZWVqQUXcirzWYzRqPxrubB5tXyZ91U2C70ej02m43XXntNejKcPn2aUCjE7du3OX36NMlkksOHD9Pd3Y3D4aCiomLbUgWEgsFms1FVVUUikSCdTtPY2EhDQ8MG9QIgXxvxvSM8V16k85/JZLh58ybXrl1jcHAQv98vE0wOHDjAyZMnqaurI5/Py5EIkfQgvBa2cjyi0VXc9MhkMoTDYSwWCyaTSX5/+3w+ZmdniUQiMrb01Vdfpb29HYfDwYkTJygvL6evr4+rV68yOzuL3+/n9u3bRKNRpqenGRwc5LXXXmP//v1P8/Q9MiKecnh4mEAgIJsryWRSKhuMRiMVFRXPe1e/EagNBhUVFRUVlW8AStRP8Mu/xt71LSw7j8qL10zAS6T/I/KpO/GMYuU+Ho+ztLQkf99UuQclVVjlMptMGE0mTEYT5vUmgVaru2tVt7aiiR/94Hv80//hn8rnSSQSpNIJ6XKezWbJpNPkckKJkCeX2/5UhmK1RSwZYnjhOgNTZ4mlwkTjIfLb1shQeVHI5/N4vV68Xi+KomAymaSPiFarxel0ypX4paUllpeXpVlceXk5Ho9H3vfriEajkQV6sZJhcnKS5eVl5ubmuHjxoiyQGxoa0Gq1lJWVbdsqdjqdJplMYrFYqKqqkiZ926GQeNaIFAe/309/fz+fffYZIyMjxGIxqqur2b17N/v376e3t1eOXQnPDBE7aTQat/R+Kx7B0a/7zYiYy3g8jl6vl0am4XCY1dVVlpeX5evX09PDqVOnqKurw2az0dvby86dO+VohkajIRaL4fV6WVhYYGpqisnJSWpra+nu7n6k0Z+nhTiH0WgUr9fL9PQ02WwWo9EoPVQmJiYoKSmhqamJ9vb2p2a6qnIHtcGgoqKioqLyDSGXCBO5/nOyfi+OA79KPpcjfP5vycWCD32sUW9mb8NhAJRslmQ+TzqTIaFLYIgZMBgN0lSxYAZmQq/XE4ysUVu70fvh0sXLVLua1y+Otej1BsxmC1mlELOXU3LSk+HxERe+d5oGyXSChbUJhuYvM+8bJ51NoOQLvgpqc+Hrz8LCAqlUira2NnK5HHa7HYfDId34KyoqaGhowGazyYjGF6GIelYUKxmcTie//OUvNygZPvvsM2KxGL29vXR3d29LdKFIZ/D7/VitVmpra1lcXMTv92/DEW0/D4vEzGQy3L59m4GBAc6dO8fAwADBYBCHw8GxY8d4//336ejokKvrwjRSGCgKf4ytIGJD8/m8HJcQDTRFUdBoNGQyGaamphgYGGB5eRmNRoPVaqW0tJSamhrq6+ux2+3yOc1mM11dXXKMyOVyMTo6KtVsS0tL0kRRNKaeJ9lsVo5z+Hw+MpkMLpcLk8lEIFDw8Zmfn5eKjXA4jNVqxWw2P9f9/rqjNhhUVFRUVFS+QeRTcRKTl8lGVtDZSsgEvVt6nMlgobGyA8jLmVyBVluIgzToDRiNhWaBxaJgsViIJkIcOnmwsO31C/PXXn+V//AX/wmb0cWu2h40Gg0Gg76oJ5Anl9Pw+KMRd5In8vk8K6F5bs70MbJwDSWnkEhHH/N5VV5mVldXWV1dlVGNlZWVaLVaAoEABoOBXbt2sXPnzpd6hfNxGyL3UzIIT4b5+XkuXbrE2toaFotFrnQLaf6jbrM4ZSEWi+H3+3E4HJSVlcmGw4vGgxoLwmg2kUgwPDzM6dOnuXbtGpOTk7hcLurr6zl06BBvv/02Op1OKh3EaITJZJKjOVvdFxFVCciEFJEGkUwm0Wg0pFIpxsfHuXz5MktLS+h0OsrLy2lpaaGuru6ukQGj0cjOnTupqamRqoh4PM7CwgLxeByfz8fi4iKLi4uUlpY+92SGTCbD/Pw8t2/fZnV1lXw+j8fjoaysDI1Gw9LSEmtra2i1WsbGxpicnKSiooLS0lJMJtNjv39VHozaYFBRUVFRUfmGkc+mSS+No9GbQNmaiWFNaRMW471nrgs+CQqZ9YvsTKaQvpDPbzRoFAW/2WzGXWbnpxf+khLLR3TUHaDEUUF1yQ45YqHVaihYIGylyXB3UZXOJhhd6Of65JeEYj5iqcgWn0vl687S0hKZTIbW1lY0Gg0rKysMDQ2Rz+eprKyUpoHfVISS4dVXX93gyRAOhxkZGeHzzz8nkUhw5MgRent7H7s4ExJ2QMbCvsiF3oNSKkQKhNfr5datW1y5ckX6eezfv59jx47R1taGXq+XI2hCaWA2mzc0a7aCNN6NRrHb7TJGUqPRYDab5XNFo1Fu3rzJmTNnWFhYwGKxcOzYMd555x06Ojru+/xGo5Fdu3YBMDs7y9jYGJFIhFgsxldffQXAW2+9xeHDhx/lFG47iURCjqLMzc1hMplob2+no6NDJp6srq4SCoX47LPPWFlZkakozc3N1NTcP1lJ5fFRGwwqKioqKirfRPJ58pnklu5qNljZ03DoQU+27puQA7Jyxt1oNKIoORLxOH6/X2bb37p1i4mJSXI5hdGFG4wtDGLSm3HZPOyo2EVv6xvYzW40GtaNHx/cGBDeCvl8nuXgHLdm+xjz9pPIxEilE1s9IyrfENbW1lhbW0Ov12OxWKRUvb6+nra2NsrKyqSE+kUueJ8G91IyRKNRpqamWFpaYmFhQSoZTCaTlNgLw8dHOV/pdJpYLAYUkiG2Y+TiaXKv5oJommazWZaWlhgeHmZoaIjR0VHMZjOVlZX09vby9ttvs2NHoYEqfBcKSTwaOd6wFcT2FEUhnU6TTqdlQ0jcLs7j2toaExMTDA4OcuPGDTQaDRUVFXR3d3Pq1KkHbtNgMMhxoYGBAYaHh5menmZ5eZlr164RCoVobGxk//79Mo3kWSIaLMvLywwODnLp0iWi0SgOh4PW1laOHj2KwWBAq9UyOjrK3Nwc/f39TExM4PP5iMfjZDIZtFqtHJl4EUY+vi6oDQYVFRUVFRWVB2I0mGko3/UIj8iTz0M6nSKZTDI0NMRPf/pTgsEgwWCQ4eFhqqoqWVlZKVws59JklTTZfAZ/fInb89c42vYOe3ccQavRUpidyFNQKqxvoajnEEuGGV24zuDsBaKJIPF0dH0fVMWCyv1ZWlqSZoLV1dWsra3R19dHd3c3ZWVlz3v3njuicD1+/Dh2u52PP/6YM2fOEIlEGBkZ4ZNPPiEYDPLqq69y+PDhR2ou5HI5wuEwPp8Ph8NBdXW1NBd8lmzHd0Q2myUcDnPu3Dk+/PBDhoaGMBgMdHV1cfDgQQ4fPkxjYyN2u12a58ZiMaxWK1arFZPJ9Ej7m0gkSCaT6PV63G73XY/PZDIEAgE+/PBDPv74Y/r7+4FCE6ekpETGWG5FMWG1Wnn11VexWCz84Ac/YGlpiVgsxtraGisrK6ysrOByuTb4ODwLhHLh0qVLjI6OEo1GUZTCWF5lZSUdHR2UlpbS3t7O5cuXuX79OuPj46yurjI4OMja2ho3btygpaWF/fv3s2fPHsrKytQoy21CbTCoqKioqKioPJDGig6M+kc1xcqTSqeJJ6IsjY2STCWli/nq6io+n++uGEihNkiS4PTA9xmev8Jb3f8lpY4KCk2GOw0GJZdlZmWE/qmvWAzMkEhHyWTTT3ysKt8c/H6/NBesr6/H5/ORTCapqqoim80+l5XZJ2WzCeHmAro48nFz/OO9/tRqtezdu5fm5mYikQjT09MsLi4yPz+PoigsLCxgMpmoq6uToyVGo3FD8aooivysi+dWFIVQKITP58Nms+F0OtFoNGSzWenxIhRPgmJvjO1UljxOk6H4OAKBADMzM/T19fHxxx+j0Whwu910d3fz9ttv09bWRnV1NZlMhlQqRTqdJpPJYDAYcDgcj7xd4d1QfL6LlRQ+n4+JiQk+++wzvv/976PRaDCZTFRVVdHa2rpBofMwzGYz3d3dlJeXMzg4yPnz58lkMvj9fmZnZxkfH6elpeWZNRhyuRzpdBqfz8f169c5c+YMk5OTpFIpXC4XlZWV1NTU0NjYSGNjIx0dHTgcDiwWCzqdjkwmw+rqKvPz8wwODlJZWUkkEiGfz9PU1EQmk5FNn5fZi+V5ozYYVFRUVFRUVO6LXmckmY7j9U9RXbIDjWbrF1yZdIZsRmF6Zoa1tTWZ955IJB56UZ/MxJlZGeZvz/4Jh3b+Cs2Vu3HaygjFfNyeu8KtuYukMglS2fh6tGXugc+nonI/vF4v6XSanTt30traSiKRwO/3Y7PZnvnK7NNCyOof9pPL5Tb8PyBXvF955RUcDgcffvghn376KdFolOnpaX76058yMTHBvn372Lt3L/X19dTU1Mhtitn9XC4nnz+TybC2tiYjQdfW1oA7BXs4HCYQCGC32+V3hc1mk4Xi8xhdEd8zxQ2YZDLJ+fPn+fTTT+nv7yeXy9HY2EhbWxs9PT10dHRQUlKCoiiEw2EikQhms5ny8vLH9vkQjZfNkZZCufCLX/ziLuVCeXk53/rWt3jrrbfYs2fPI21Pq9VisVior6+nvb2dhYUFIpEIFy5cIJPJ8J3vfOeZxYmK2Enhc3Hz5k3W1taw2WwcPHiQ48eP09TUJO9vNpvp6OjA6XTS3t7O+Pg4V65c4datW0QiEVZWVvjiiy+Ynp7/UoFmAAAgAElEQVSmvr6elpYWDhw4QEdHBxaLRU2beEzUBoOKioqKiorKfckqaUa915lfm8BpLaGr8Ti7arqwmB5eeClKlkwmzdLSIotLj75KmMvnCMV8nBn8IX0jHxWGJDQaEuko2WzmoY9XUdkKPp9PFilVVVUEAgF8Ph8ajeaxvAWeB6L4FcW7SBcovl2YCxb/CKXA5r8XNxiMRiMul4uuri5aW1vx+/2Mjo4SCARYW1vj/PnzXLt2jTfeeENGfgoPFr1eTzQalRJ2RVFIpVJyJV+r1ZLJZIjH4wAyiSGZTMpRAlHQC0WJ+HkSivcjlUrddb42k8vlyGQyssEgRjy8Xi/nzp3jgw8+IBKJYDQaaWlp4ejRo3R2drJjx44Nr0k8HsfhcDy2FF80bVKpFNFoVPo4QMHQcX5+ns8++4wf/OAH0o9BjAycOHGCd95555HOnUajQafTYTabaWxspLu7m3Q6zerqKgMDAwQCAfbt28fhw4cfKQXjcY5bJI6Mjo5y/vx5+vv7mZ6exmazUVdXx6FDh3jjjTeoq6uT58RkMtHY2Eh9fT2dnZ0sLCxgs9nI5/PMzs7i9XoZGRlheHiYiooKWlpaUBQFo9GIx+PB7XbL+E9xPlQejtpgUFFRUVFRUXko8VSYeCqML+ylb+Qj9jQcobvpOHaL64GPU3IK+SdMb8goaTKJwviDiHhTUdlO8vk8CwsLZLNZnE4nNTU1GAwGPB7PY0c/PmuSySShUAiv10s2e3c6jDiG4uMRf4qoRHGbkIdrtVocDoccedBqtZw8eZKSkhLOnj1LX18foVCIWCzGwMAAfr+fvr4+6urq6OzsZPfu3bjdbiorK2WRGAgEiEQilJWVYbVaNxRwYqU/Ho9TUlKCx+MB7sQyhkKhDcfzOK+LKPiXl5dZW1sjHA4/NG5RNGg0Go1siJw+fZrPP/9cmh7abDYqKio2FLoiXSIWi8mIyCdNKBGqkCtXrjAzMyNVHYuLi8zNzXHjxg3y+TxGo5GysjLefvtt3nrrLZqamohEItLU8FEwm83s378fg8GAz+fj9u3bKIoiDUBv3rxJQ0MD1dXVT3Rs96M4pePKlSt8+eWXLCwsYLfbeeWVVzh69CiHDh2isbERl+vuf5PEmEhlZSUnTpygqamJ2dlZJiYmuHr1KmNjY0SjUUZHR8lkMgwMDNDc3MzOnTvp6uqipaVlWxpb3xTUBoOKioqKiorKlskqGULxNfpGf8ngzHna63rpbTmB01p6z/srOWVbt68o2/t8KiqClZUV1tbWqK2tpa6uDpvNRk1NDUajEaPR+Lx376Fks4V42HA4fFchVLz6r9fr0ev1spFgMBik2kD8GAwG+ad4rEg86OnpoaWlBb1eTywWY2FhAa/Xi8/nY35+HpPJhMPh4M033ySbzdLa2orJZMJsNmOxWEin0+TzecrLy2UDofgYRCqF0+mUxWIulyMYDBKNRuWohYi0vRf3+32xAiAUChEIBKQ64WEYjUZMJhPJZJJkMsmFCxf4/ve/TyQSIZvNsmPHDjo7O+nt7aW3t5dcLicTI6LRqFwRf9B+3w/RnMlkMrLQPn/+PH19fdjtdgwGAzMzMywuLqLVanE6nZSWltLU1MSJEyd4//335eiJVquVkZZb3Q8RW+lyubhw4QJut1uOvI2Pj3Pt2jXMZvO2NxiEekU0hIaHh+nv76e/vx+z2UxdXR1Hjhzhvffeo66ujoqKins+j2igmUwmSktL2bt3L4uLi4yOjqLX68lkMvh8PgKBAJcuXeL69eu0tbXR1dUFFJQQdrtd+jOIhtvL0Hh8HqgNBhUVFRUVFZVHJpfLEkkEuDp+muH5K7RUddLd/CrlztoNF125bW4wqKg8LUTxOT4+Lgs6YRxXVVX1vHfvoZjNZlwuFzU1NdTU1MjfF6sURFNBo9EQi8VIJpPY7XbsdvsG1ULxT3EhKkwGk8kkXV1dVFVVyUSBq1evcuPGDcLhMKFQiEuXLuH1eikvL6eiooLdu3fT3t5ORUUFFRUVj7SSr9FosFqt6PV6aWj4uAoGUfiLiMKSkpJ7rnpv3r5WqyWXy+H1epmcnGRqaopIJEImk8FisXD48GF+7dd+jfb2dvL5vCzAoeBj8STGgSI9Ym1tjatXr3L27FkGBwdZWFiQTaBIJIJGo5Er7/v27ZO+GGJ/AoEABoNBxjJuJUmiGJPJxJ49ezh58iQDAwPMzs4yODhIPp+nrq5OFuTbST6fJxKJcO7cOX75y18yOjqKyWRi9+7d9PT00NnZSV1d3SP5pej1epkyYTKZ6OrqYnx8nJGREW7evMnc3BwLCwskEgmWlpa4cOECu3fvpqOjg5aWlg2fL5W7URsMKioqKioqKo9NLq8QSQS5MXWWscV+aj0tfPvgf4tOq5O3q6i8DIjCdXZ2Fp/Ph8fjkSuypaWlL7REWnge2Gw2PB7PQ033xJiRKI4fVmALhLFhPB6npaWFgwcPEo1G8fv9uN1ucrkcs7OzzM3N4fV6mZiYwGAwyLjLcDhMZ2fnBm+LzYVusWKieJRDpCZsB4qiyDECj8dDaem9FVjFx53NZolGoywsLNDX18fMzAzJZBKn00ldXR0HDhzgjTfeQKfTSTWJSHsQK9+P+v4pToeIRqOsra0xPz/P7Ows0WgUnU4nm2FC+dHV1cXhw4c5duwYPT09QMEcUVEU0uk08Xgck8mEzWbbcoOh2NOgo6ODSCSC3+9nYmKCiYkJYrEYr732GoFAYFvNEYXvgtfr5dKlS3z++ecyMWLPnj28+uqrsmn1KOh0OpxOJw6Hg/r6enp7e7l58ya1tbXy9QuHw8zMzDA5OcmVK1c4evQooVCIXC6HwWDAbrfL96OqZNiI2mBQUVFRUVFR2QbyxJJhZldGGJg+T1NlB/FUhJnVkee9Yyoqj4SYsR8bG+PMmTPkcjncbjdut3vLhfjXFbGSHgwGZcSiyWSipKSEo0ePUltby+LiopyVv3HjBrFYjGg0Sn9/P6urq1y5coX6+nr2798vIxDFqIRolFit1qdmGPg4CO+IhYUFrly5whdffMHi4iJWq5UTJ05w8uRJ9u3bJ8cYhKGlXq+X0vxHVQvAxljKQCBALBZj//797Nq1C6/Xy8rKihzhEU2DmpoaamtrN4wr6HQ6SktLMRgMRCIR5ufnqampeeSGjcFgoLGxkUwmw6VLlzAYDCiKQjwe5/r161RWVtLT00NbW9sjH+u9SCQSXL9+nb6+PkZGRkilUpSUlNDU1MT+/fs5cOAAlZWVT7wdo9HIjh07sFqtNDQ0cOzYMQYHBxkaGmJmZoZgMMjg4CCBQIDJyUn27t3LsWPH6OzsfKyRl687aoNBRUVFRUVFZdtIZuKcu/0BfSO/KKw+pcLPe5dUVB4JURxOT0+TTCbxeDzU19eTy+Ww2WyPLXN/2jws+nW7SKVSRCIR0um0bAgYjUY6OzvZs2cPgUCAlZUV7HY76XQar9fL4uIii4uLTE1NYbPZcLvdBINBNBoNjY2NpNNpLBaL9IJwOBwvhO+FUBCI4xgYGOD69ev09/djt9upqanh2LFj/Pqv/zomk2lDQoZOp9tQ/D/qdqHQ2IjH49J/Ip/P097eTmVlJcvLywQCAeltYbPZ5BjJ5uaM8GUwmUwEAgFWV1dxOp2UlJSg0+m2/H42GAxUV1ej0+lobGyUqSuJRIJbt25hNpspKytj165dwOOv7AtTz2AwSH9/P2fOnGF6epp8Pk9tbS1dXV10dXXR0dHxWM8vEPtnNBqprq6mqqqK3bt3EwgEqK6uxm63YzQaGR0dZXV1lcXFRWZnZ5mZmcFms1FWVobL5cJms8kxGhW1waCioqKioqKyzcRTkee9CyoqT0wkEiGXy3H79m1KSkrIZDK4XK5tlepvF6IgfVZNhvttS3glCLf+xsZGpqammJiY4Nq1awwMDKAoCsFgkAsXLrCwsEBVVRU1NTV0dXWxZ88e7HY7VVVVL8w5TqVSBAIBzp49y89+9jPGxsYwGo3s27ePQ4cOsXfvXpxOJ/F4XCZGpNNpKioqKCkpeWwlhiiyfT4fy8vLlJaWUl1djdlsJpVKYbFY0Ov1soFRbMp5L8TIiRg/EaoIYV74KFgsFo4ePUoqleLMmTP09/czNzeHyWTitddeI5vNyu08DsLUcWxsjFu3bjE8PEwkEqGkpIQjR47w7rvv0tTU9FjP/TC0Wq0cNSkrK6O3t5fx8XHOnj3LwMAAsViMkZERfvKTnzA3N8eJEyfo6enBZDK9EE2xFwG1waCioqKioqKioqKyCWHSNzY2hsFgwOFwUFdXh8fj2RDp+KIgVtufRZPhftso9kooLS1l3759TExMMDo6ilarJRqNyvM6OzvL6OgoDocDj8fD2toauVyOhoYG/n/23uNJzutM9/x96b3PrCxvUAZlAFQVHA1EkCKpltSKYN+OO3czq4mY5fwRUsxqtrO8i9nd5Y3QbUndrRYp0YBwBAEUqoDy3mRlVVZ6b2dBndNZIEwVDAGQ5xeBoFiVmZ/J81F43/O8z9PR0UG1WqVUKmGxWKQC4EXeb51Oh8FgeGRBLq6vVqtxcHDA8vIy165d49NPP5VqgNHRUT766CMGBgaw2WwUCgWKxSLZbJZSqUQoFDp24d58/FKpRDabJZ1Ok81maW9vJxwOUygUKJfLMtngUYqFh3k4jtRqtUqFgPDBOI7HiMVi4cyZM5jNZjY2NuT4C8DGxgbRaBS32y3HaI5z3eLaxefOzc2xvb2Nz+eju7ubc+fOcenSpWcaOXka4pm22WwMDAzQ19cn16qIytza2iIajfLVV1+xvr6OzWbD5/MRDAalIuR19Wr5oVANBoVCoVAoFAqF4jFEIhEqlQqhUIhwOEx/f7/0CHidfAJEcfQ6NT10Oh1er5e+vj4++eQTLly4IFMn7t69y8zMDKVSif39fb7++mvW1tbw+/34/X66urro7u5meHiYgYGB59oRf9R52e32p44vFItFvvjiC/70pz9x9+5dAJxOJ21tbfT39zM8PIzD4ZBmmaFQCKPRSC6XO/ZYRDO1Wo3d3V329vawWq0MDg5it9vJ5/M0Gg3ZVHiSYuFR6PV6AoEAZrOZbDZLNpuVHgperxeXy3Wkz9HpdHg8Htra2ggGg7jdbnK5HPl8nitXrqDX6/nggw84d+7csa67eTTixo0b/OlPf2J1dRWbzcaFCxe4fPkyg4ODj20MvWg0TcNsNhMOh/mHf/gH2tra+Pzzz7l16xbpdJqdnR3+9Kc/sb29zfvvv8/Zs2fxeDzHSrT4MaIaDAqFQqFQKBQKxWOIx+MkEgna29sJBoMYjUaCweAhF/5XXdS/bs0FkWxQr9exWq20trbS19eHyWQiGo2ytbWF0+k8VEivra0xNzeH0WjEarUyOjrK2NgY5XJZejM07ww/TanRbL7X/NqH71Uul5OvaX5dvV4nmUzy5Zdf8vvf/55KpSIL9L6+Pvr6+uju7qZUKskGg8PhoF6vS2+KZ7lv1WqVfD5PPB4nFosxMDBAR0eH9HYQzYVniZnU6/V4PB7sdjs7OzuyyVAsFuW4xVE+V6/X43Q6CYVCdHV10dvby+bmJgcHB9y5c4d8Pk93dzfj4+NH8ixpVowkk0nW1tb49ttvuXbtGjqdjpaWFs6dO8dHH31EW1vbS1EvPAqdTofJZMLn83Hx4kV6e3vl97OwsMDm5ibffPMN6+vrmEwmHA4Hvb296PV6+R39FPlpXrVCoVAoFAqFQnFEGo0GKysrVKtVdDodPp+Pjo4ObDbba1PUPwvPOlJxlGaGGIUQkZgmk0mOF3R1dfHxxx9z8uRJIpEI29vb3L17l+npaXK5HLlcjqWlJRKJBMvLy/zHf/zH9xoM9XqdWu3RMbiapsnRiuZmR6PRwGKxYDKZvncN1WqVarWKpmkyLSOdTnP37l0qlQrwnffAyMgIH3zwAV1dXQDynEQR/TyjKrVajf39fQ4ODjAYDHR1dWGz2Q4ZaorC9Xl28IWyxGQyybGVeDxOJpMhFAo9NbZTYLfbee+997BYLPz+97/nyy+/JJ1Os7u7y/7+PslkErvdfmQ/jXw+z/Xr1/n888+Zn59H0zS6u7sZHR1lcHCQtra2V6YOEGv30qVL+Hw+rly5wq1bt2Rj5a9//SvRaJSf/exnnD17lo6ODgKBwCs511eNajAoFAqFQqFQKBRPYXt7m729PYLBIG1tbej1evx+/2s3KvE60Gg0ZNEqYhobjQaapuFwOKSR4+TkJHt7e2xvb+N0OqlWq0QiEaLRKNlslkQiwcLCArVa7XtpB6Ih8KhCXoxA2Gw22Vyo1WoyCUQ0hpqbDEKJIBoMwrBRNCaEciAQCNDe3k69XmdnZwej0YjJZMJisciRheOOczQaDWq1mjSUjMVitLS0EAwG0TSNarUq19mzKBceRq/X43K5sNls6HQ6arUa6XSaZDKJ2WyW1/I0hY7FYmF8fBy/38/9+/e5fv06xWKR/f19NjY2WF1dpbOz86kNBuG7IGJMP/30U6LRKGazmRMnTnD+/HkGBgYIBoPPdd3Pirh+m83G6dOn6ejoQKfTybUxPz/P9PQ06+vrNBoN9Hr9oWbWT838UTUYFAqFQqFQKBSKpyB2yxcWFtDr9ZTLZZxOJ8Fg8Ce7U/k4Go0GxWKRdDpNKBTC7XY/ssgSu8I6nY6PPvqIwcFB1tbWWFtbY3t7m52dHeLxOMlk8pAKQfzvx1Gv1ykWi9RqtUOKAlHI5/P57xXNogEhKJfL8nji97lcjmvXrrG7u4vP5yMQCDAwMEB/fz8DAwNScWAwGLBYLEe+X/V6nVQqRSqVolar4XQ6sVgsUh0h0h9etPeATqfD6XTK881msySTSZLJpPRXeFKjRNM0jEYjdrud1tZWent7iUaj0ouhUqnwySefEA6Hn3ge5XKZ5eVl7t27x9zcHNFolEajQUtLC2fPnuWDDz6go6PjhV3382K325mYmMDpdBIIBPD7/czOzrK/v88333xDKpUiFouRTqfp7++nvb39VZ/yD4pqMCgUCoVCoVAoFE9BzMevr6+TTCbxeDy0tLRQr9dxuVxvtHu8KKKfd9xDFP+VSoVyuSw9Ax4na9c0Dbvdjt1up6WlhYmJCZaXl1leXmZpaYmlpSUikQi7u7tUKhUqlcqh3f5yuXxoHKFer8vUBfF6ER9otVqxWCyHrrFZ/VCtVimXy+RyOQqFglRc2Gy2Q82C9fV1lpaWMBqNOBwOzp8/z7lz56hUKrJQN5vNchdbHEc0MB5eJ+J+pdNpDg4OMJvNuFyuQ0klQhXxomf6dTqdvD4xfrG9vU0ikcBms2Gz2eTPHzUWIxIobDYbvb29jI2NUavVWFpaYmpqimQyyfDwMGfPnsVsNn9P6SPWSy6XY2FhgevXr7O0tEQqlaKjo4PBwUHGx8eZmJh4LUaRxDlYLBYGBwcJh8NS8VGtVslkMqysrBCJRA5936Jh9Dil0+twbS8S1WBQKBQKhUKhUCiOSLFYpNFoMDMzg06no1gs4nK5cLvdR3bh/zHwqNEEMVqQyWTQNI1AIHDk+Xv4rtByOp20t7fj9/s5c+aMTCd4WI1Qq9WkqkScS6FQ4M9//jNffPGFVDC0t7fT29vL+Pg4IyMj8jgPGzru7e2xs7PDtWvXuH37tvQ8GBsbY3h4WI4qrK6usr6+Ti6XI5PJcP/+fWKxGLOzs/I4o6OjhMNh/H4/9XqdarVKMpkkm83i8/lwu92HvB7ENVarVbmWACqVChaL5ZCa4WUg0hLEebndbvL5PHNzc4TDYQKBwBNNC61WK+fPn8dsNkv/jHK5TCqVksqEnp4e2traDr2vXq+TzWbZ3d3l9u3bXLlyhZ2dHRwOB++88w4ff/wxAwMDr5WBaTOi0WA2m7HZbIRCIb799lvW19d58OABxWKRQqFANptleHiYrq4uqYp53YxZXySqwaBQKBQKhUKhUBwRsTO+tLREOp3G7XbT3t4u5fFHcc1/3RAFz3Fe/7ifZ7NZ9vf3cblc+P3+YzcYRKHb09MjC+2jnk8mk6FQKLC5uSnHK1wuFx0dHbz99tt89NFHUtHQ/N5yucz09DS3b99maWkJnU6H1WrF6/UyPj7O5cuX0el0VCoV7t69i91uJxqNsre3RzweZ2dnh7m5OXw+H/F4XI5TCAWFXq8nm82STqex2+3SE6JSqZBKpUin05RKJfR6vdwRFyMaer3+pc/wNxtIWiwWHA4HS0tL7O/vSw8Nq9WK1Wp95Po2m80MDw/j9Xq5efMmV69epVgskslkWFhY4NatWxiNRvx+/6Exj0qlQjQaZW5ujnv37jE9PY3VaqWrq4vz58/z4Ycf4na7X9vnyWQy0dnZic/nw26343a7KRQKxONxIpEIBwcHwHdrzOv10tHRccgTpNFoyPv+Y0I1GBQKhUKhUCgUimOSzWap1+vcvXsXvV7PO++8g9vtloXY607z7umLHJHIZDLs7e1hs9lwOBzHKp5EgyIWi8lGw3Ewm8387Gc/w2az8b/+1//is88+Y3t7m1qtRigUwuFwMDAwQE9Pz6FCL5PJcOPGDX7/+9+ztLSEwWBgZGSE8fFxmQqgaRq1Wo2enh7effdd9vf32dnZ4fbt2zL94uDggKtXr7K5uUlbWxtdXV1MTEwwOjqK2Wymvb0dk8kki9BkMkkul6NcLhMMBgmHw9Jw0WazYbfbf/DiU6fTYbFY6OjowOl0ks/n2djYwOPxyIjLR61v0RwaGhri3Xff5cGDB+zs7HDv3j1KpRJWqxWfz4ff78ftdlOv18lkMnz99df8+c9/ZmFhAbPZzOjoKOfOnWNwcBC3243ZbP5Br/9ZMJlMtLe3yzXi9/u5du0aS0tLLCwsYDKZOHPmjDQRrVQq7O3tkc/naW1txe/3v+pLeKGoBoNCoVAoFAqFQnFMCoUChUKB2dlZcrkcLpeL7u5ugsGgjEH8oXdejyO5bj6/4yoYHqbZa0AU2iLG87ifUygUSCaTxzLOFOcuCrnOzk7W1tb45ptvyGQypFIpgsEgNptN7pCLMYuDgwM2Nja4efMmf/vb39DpdDgcDoaHh/nwww85ffo0Q0NDh45Xq9XkLrXD4ZCJEpFIhPX1dRYXF3G73YTDYXK5HEajkdbWVlpaWqQ/RSaTIR6PUywWqdfrtLS0YLfbyWQyFItFHA7H9+5f831u/r5elMxerB+j0UgwGMTlcrGyskIsFjt0DOEj0WxCCd+NDAwNDRGPx8nlcuzu7rK+vk4ikaCzs5NwOMzJkyexWCzk83m2tra4ceMGf/3rX+U1j46OcunSJfr6+l5ZJOVxMRqNhEIh6bXgcrnY29tjdXWVSCSCpmmsr68Ti8VwOp1omkYqlSKZTMqRmDdR+fQ4VINBoVAoFAqFQqF4RhKJBLVajdu3b2OxWJicnMRms0lp+etMs4LhcWMPR0UY9hWLRbLZLOVy+UWc4rERpoMjIyP8/Oc/Z3p6muXlZRl32dPTw+TkJEajkWq1yldffcW//du/cefOHQAcDgetra0MDQ0xMTHxyN1lEbfZ1tbGz3/+cwYGBtje3mZjY4Pbt29z//59KpUKOzs7fPbZZywuLtLe3k5nZycTExMMDw/j8Xhwu90Ui0U5q7+wsCC9PB43FtFsHviyC1KDwUBLSwtWq5VcLkcqlaJSqZDP5/F4PN/zHDGZTAwMDKDT6eQISTQaJZfLcfXqVTKZDL/5zW8A5FjE3NwchUIBp9NJZ2cnY2NjTExMvLJIyudBNBoGBgYYGRlha2uLzc1Ncrkct2/fxuv1cuHCBTn+IxQPqVQKu91+rOSR1xnVYFAoFAqFQqFQKJ6RbDZLNptlZmaGWq2GxWKhtbUVr9eLwWB4JUqGo9B8XvV6/bnPsVKpUCgUKJVKVCqVJ8ZIvixEnKPVamVoaEiqAXZ2dtjb2yOVSnHx4kXW1taw2+1Uq1WuXbvG73//e0qlklQajIyMMDw8zMDAwGOPI9Ii3G43J0+eJBaLsb29jdVqpVqtEo1GicVizM/PMzMzQygUoquri0qlgs1mIxAI4PF4sFgs1Go1Njc3OTg4kMoF4fnQHMkpkkxqtZpUEAjzxRdpGCg+x2Aw4Pf7cTqdbG1tkc1m5T0V3hAGg0GaPxqNRjo6OrDb7ayurrK7u0u9XieZTHL//n12d3cJhUKYzWZu3LjBrVu3WFlZkSMso6OjjIyMPPa+v+4YDAZ8Ph+NRoOxsTGi0SjFYpGNjQ1mZmakD0UoFMJischnLp/Py+9TJIa8yagGg0KhUCgUCoVC8ZyIKEWPx4PVamV4eJjh4WFMJtNrp2R42MH+uAqGh4vZRqNBMplkf3+fWq2Gz+d7pT4Uer2enp4eDAYDiUSCdDrN6uoq0WiUzz//nIODA0KhEHa7nenpaYrFIgaDAafTyeXLl/nNb37DyZMnn3gMUeyLz7darfT29vLLX/6SkZERNjc3WV1dZWpqitnZWQqFAisrK/zpT39iZmaGjo4Ourq6GBkZ4cSJEwQCAYLBIA6HA71eL5UN+XxexmY2j0hUKhUAmfLQXOi/jPvp9/sxm81kMhny+TyxWIxMJkMoFMLn8x16vc1m48KFC7LZsr29TalUIpvN8uWXX8oox2g0SiaTwel08vbbb/PrX/+a/v7+l3INPyQ2m43JyUl0Oh37+/tsbGwQiUTQ6/WEQiE0TePEiRO0trbKNBSRJOJ2u7Hb7a/6Ep4L1WBQKBQKhUKhUCiek0QiQTKZxO12o9fr0TQNv9+Px+N5LZUMzU0CEZ33rIhd6q2tLRqNxrHjKR/Hs5pP6vV6wuEwHo+Hvb09mdKwtbXFnTt3mJ2dlVGYa2tr1Go1gsEgfX19XLp0iV//+tdPPUa5XKZQKJBOp8lkMrhcLlpaWmhtbeXs2bOsr6+ztLSExWKhUqlwcHBAPB5namqK27dv09nZSUedYgUAACAASURBVF9fH9lsVioFvF6vHDMplUry84WhaKPRQKfToWkahUKBWq12KOVB7Iq/6HWm1+txu93YbDZpUihSMUTqRXOMpdlsZmRkhEAgwOLiIouLi0QiEWKxGFNTU0xPT0tVhoh3nJyc5PLly1gsljc+ulHEV1osFr799lump6dJJBIsLS0dMi4Vo1Q6nY5SqUStVpMmsW9yhKVqMCgUCoVCoVAoFC+ARqPB5uYm5XJZSp6HhoYYGBiQkvqXeexnUSI8y/sedexEIsHW1hadnZ10dnZ+bz7/OOf0ogorkQZhNBpl4kOj0aBYLBKNRkmlUuRyORwOB++//z6/+tWvGB8ff+rn1mo19vb2pGlfa2vroV1nvV5PIBDAaDRit9uZnJxkZWWFlZUV7t27x+LiIslkkrm5ObLZrJzPDwQCnDhxgr6+Ptrb2wkGgzidTiqVyveaLdVqVfohbGxs4PP5pHrmZc3yi0aD0Wgkk8mQzWZJJBKkUina2toIBAKHvjuHw8EvfvELfD4f//N//k/+9re/SXPNRqOB2Wymp6eH8fFx2trasFgsb/x4gECn0+FyuXj77bcpl8tcuXKFhYUFlpaWSKVSzM3N0dfXx4ULFxgbG8PpdMrmZLFYlOMnbyJv5lkrFAqFQqFQKBSvIaLwtFqt6PV6Go2GjPh7WUqGZun8UXlUTOXzHD+dTrO7u0t3dzfhcPi5FQyi6fE8zQa9Xk93dzcej4dPP/0Uk8lEuVymXC4Tj8fR6XS43W7a29t59913+eSTT2QCyJMQEYuxWAy3200gEDhUGOt0Omni2NnZSalUYnFxkdnZWUwmE9VqVSof7t27x+3bt7Hb7Xg8Hs6ePcvk5CSnT5/GaDRiMpkwmUyHFAri/MrlMqurq8TjcSwWC1ar9bHmkC8CnU4nzQhFAbyzsyNjNW02G0ajEaPRiKZpWK1Wzp8/T1tbG1NTU3z++eeHmlkmk4ne3l4mJiZobW19qef+QyK+H7vdzvj4OCaTiWw2SzKZJB6Ps7m5yd27dwmFQhQKBXQ6HZ2dnXJ8wmg0ykjQ5s97U1ANBoVCoVAoFAqF4gVSr9dZX1+XSQp6vZ6BgQGMRqOUlL9IRAFynELkYZPH4zYZHn69MHkUheWz7r4+qvHxvAWWTqfDbDZjs9kO+ReYTCYmJia4dOkSIyMjUq5+lM+z2+34fD5sNpsciXkYoZYoFAr4fD7Gx8cJBoO8++67bGxssL6+ztzcHCsrKxSLRWKxGHfu3GFra4vr168TCoVobW2lvb2d0dFRBgcHD6UNGAwGQqEQDodDSuuNRuNz3aujIEwuxRoSaobZ2VlaW1ulJ0RzjKUwpBQmlfDd/e/r62NycpJQKPTSz/uHxmAwEAgEGB0dRa/X09/fz7//+7/zzTffUK1WSaVSXLlyhbW1NZxOJ4FAgL6+PgYGBjh16tQb68WgGgwKhUKhUCgUCsULRqQIiGK7Xq/jcrlkusSLnpVvLsxrtdqRlBLPavL4sGJCGB6Wy2VZfD4Pzef1vM2FUqlEJpOhVqthNBoP3RO9Xk9rayvDw8MEg8HHNgoEzeMkFosFt9t9KA3g4ddVq1Xy+Ty5XA63201raysDAwNUKhWWlpaYn5/H6/ViNpvZ39+XPg07OzsyirK3t5eBgQGy2SyNRgOv1yuPazabcTgceL3eQ/ftZSMaNiaTCYvFgsPhYHl5mf39fSwWi1RTiIaNXq/H5XIRCARIpVJks1nguwK8tbWV/v7+Q94EPxYMBgNut1uO0fT29pJIJIjFYuRyOXK5HIuLi8zMzKBpGm63m3PnzpFIJKQSRHzPZrNZNo9ed0WDajAoFAqFQqFQKBQvgXq9zurqKsViUUYODg4OMjAwIHecXxSNRoN6vU65XKZYLEp5/eN4WMHwPMcVO9LN8v1n5UWOblSrVdbW1pidnWVlZYVUKiVVJfDdiMHU1JQ0S+zv75feGY+jUqlQLpfR6XSPVWo0Gg1p0hiPx8lkMpjNZlwuF5qmYTAYCIfDWCwWWlpauHjxIqlUilgsxv3795mbmyMSiXBwcEA0GiWfz7O/v8/169fxer0Eg0EGBgbo7++nu7ub1tbWV1Z0iljQjo4OXC4XuVyOtbU1vF4vXq8Xm82GxWLh9OnTfPzxx9y8eZMHDx4AL84D5HVHjD20tLTwySefMDw8zM7ODisrK3zzzTfMz89Tr9fJ5/PMzc2RSqVYWlqir6+P/v5++ae1tfVVX8qRUA0GhUKhUCgUCoXiJdBoNNjd3WVvb0/uPgpPhmYlw/Ma2z0cXyhGFZ7WYHhWBUMz1WqVUqkEfJce8KKN6Z7lvMR9yOVyLCwscO3aNdnocTqd2O12SqUSpVKJ9fV14vE4o6OjTE5O4vV6H2tQ2Wg0ZHrEo0ZBmr+HQqFAMpkkl8vJhADR0BGxj36/nxMnTlCv18nlcsTjca5cuYLT6WRpaYnV1VXy+TzJZJJoNMo333yDw+EgEAhw7tw54vE4uVyOSqWCyWSSTSWx+y0UGS+r+SBGH3Q6HaFQCLfbzfLyMrFYTCp0xDocHh6mWCyyvb0tGwzifubz+Td2HOAoiKaSz+fj0qVLnD17ltXVVaanp+XzUyqVKBaLJJNJYrEYi4uLBAIBJicnicVilMtlmTIhVA1i7b1uigbVYFAoFAqFQqFQKF4ijUaD9fV1SqUSJpMJp9NJR0cHtVoNh8OB0+l8rs8XBYZOp5PJAk+bxX+emErx3kajIc0ORTTkiyoUn6fpUa/XicVibGxscOPGDT777DN2d3ex2WxcvnyZCxcuyPjEzc1NEokE169fx2w2895773Hx4sXHnlOxWJSxlMLU8OHXVCoVYrEYe3t7+Hw+wuHwU79jo9GIy+VicnKSjo4OkskkBwcH0qthfn6etbU1yuWybDasr6/z1VdfEQqFCIfD0q+hvb2dlpYW/H7/UxUZLxKhzLDZbDJhQq/X43Q6CYVCDA0N4fP55OsrlQqRSISlpSWMRqMc9fixYzQaCYfDGAwGPB4P7733HgcHB0QiEWZmZlheXiaTybC/v8/t27fZ2tri5s2bdHV1cerUKYaHh+np6ZGmkK8bqsGgUCgUCoVCoVC8RBqNhvRk8Pl8uFwuisWiNOkzm83PXQg2+y+IHfOnvV6n08nxiGdVCqTTaaLRKDqdjtbWVhwOx/FP/hGfK0Y+juNTId5TLBbZ2Njg9u3b3Lp1i7t37+J2u+no6ODSpUt88skn3Lp1C4/HQ7VaJRqNMjMzQy6Xw+/3Mzg4+Mi4R9E8EGMWZrP5UIEnRiNyuRyZTIZcLkd7e/tTpe3iuxBjGidPngS+84+Yn5/nwYMHeDwezGaz9GnY399nc3OTRqOB0Wikt7eX3t5ehoaGGBwcpKenh/b2dgwGwyGljPgjfm40GuXvn7VYFe8zGAz4/X6cTiebm5vEYjEKhQImkwm3201XVxculwudTifVC5ubmzx48ICWlhZ6enoOfd6PFb1ej8fjweVy0dPTQ6lUYm9vj9XVVVwuF2azma2tLaLRqBylEKqXSCRCJpOhUChQLpel54XFYnltPBpUg0GhUCgUCoVCofgBqNfrLC4uUigUyGQyGAwGSqUS9Xodt9v9WGn+cTiOJF6oEJ41RaLRaJBMJtne3sbn88kC8nloHjN4lnPKZrPs7+/z9ddf86//+q+srKxgMpk4e/Ysly5dYnJykmAwyOTkJH6/n3Q6zerqKoVCgdXVVW7evInT6eTUqVOy0BcIA8tqtYqmadLrQhR29Xqd3d1dotEodrudwcHBQzv2TzpvkTbhdDql9N1gMNDW1iY9Di5dukQikWB/f196NUSjUeLxuCw8Nzc3uXXrFi6XC4fDgcFgwGQyYbPZcDgcuN1uvF4vgUBAKh+CwaAcrXgR6PV6AoEAFouFcrlMLpeTZpgiSaVSqVAqlbh//z4AAwMDTExMvPLi+IegXq9TKpWkKWqj0cDtdjM4OIjD4WB8fJzd3V2Wl5f5+uuvmZmZkdGm3377LZFIhKtXr9LR0cGJEyfo7+9naGiIjo6O1+L+qQaDQqFQKBQKhULxA9BoNNja2mJnZwer1YrT6aRcLqPX62k0Gt9TMjxrsXCU3ejnGZFobgKk02l2d3cJBoO0trY+97jHw8c5ynmJ11UqFXZ3d5mfn+fGjRt89dVXWCwWQqEQZ8+e5Ze//CWdnZ243W7cbjdtbW3Mzc3x4MEDNjc32d3d5e7du/I78Pl82O12bDYbgPQTEP8uvBgMBoP0vkgkEsTjcQKBAJ2dnU9UYIjmjkjgKJfLsnkB3zUYgsEgwWCQwcFBarWabKAItcjy8jIbGxuyaRWPx6lWqzIOUhT1LpcLt9tNMBikpaWFrq4uurq66Ovro7OzU+6CCzVFc4qB+PlR16Ner8ftdmOz2YjFYjIBQ3g2GAwGarUa5XKZtbU1SqUSi4uL7O3tSY+MHyNindZqNYrFolQxibGpQCBAV1cX5XKZ/f19FhYWpGlrLpeTZp9bW1tSBTExMcHk5KRMjhH3T3hjvApUg0GhUCgUCoVCofgBaTQaUskgdner1aqMshQy8mdpMIhC8Cjvfd60hmZPAhGz97y74M3mgMc5r0qlQjqd5sqVK/zbv/0b9+/fR6fTcfLkScbHxzl79izd3d2HGiAmk4mLFy9iMBj44x//yBdffMHW1halUol8Ps/29jZvv/02k5OT8p6aTCY0TZPmjaIQ39vbIxaLYTAY6OnpwePxPPU7bDQa5PN52ah4lKfDw/fGYrEQCAS4ePEi3d3dJJNJEokEu7u7RCIRqaDY29vj4OCASqVCtVolm81SLBZJp9Ps7OywtLSE0+nE5XLhdDoxmUwydtJms8mG0djYGP39/YeUGkdFxFOKcZN8Pi+bKWKER6hOvvrqK/R6PZcvX2ZycvJYx3lTaI5zFc0kkSbTbBYqEko6Ozv5p3/6Jy5evEgmkyEajTI1NcXc3BwHBwekUilmZmakomVwcJD33nuPM2fOSI+QV4FqMCgUCoVCoVAoFD8gQskQiURwOBx4PB5KpZIsqoVDvMFgOBQneVSO2lwQn/s8hooiBUCn072QnefjXK8451qtxsHBAevr61y7do0///nPaJqGx+NhbGyMDz/8kLGxMcLh8KH3G41GRkdHCYVCRKNRVlZWODg4YHZ2lmw2SyQSwWw2y4QEMb6g0+lkwS5294X7f0dHh4ygfNx1iPtdrVYpFovk83kcDgc2m+2JKRyapmE2mzGbzXg8Hk6ePCm9DNbX11lbW2N1dZWVlRXW19fZ2dmhUChQLBYpl8uy2ZDJZEgkEvLfxe63TqfDZrPhdDrp7OzkxIkTlEoldDodXq8Xt9stGxFHQawJ4YuRTqcpFApUKhWpkqjVahQKBW7fvk0+n6etrY2RkRG5/n8MNCt+RApJtVo9NDLyMDqdDpfLxdtvv43JZCKTybC1tYXX68VsNrO6usr29jYHBwdsbW1x79497t27J7/Dnp4ejEbjC0mpOS4/jm9NoVAoFAqFQqF4w6jX68zPz5PP5/nZz36Gw+GQBYHNZsNqtcrowaNyVAVD8+uO21x4XuXDcT7/aceo1+sUCgW+/vpr/vKXv3Dnzh2q1Srd3d309/dz/vx5JiYmCAQCjzyO2WzG5/Px4Ycf4vV6+eyzz7h27RrpdJq5uTn+8Ic/sLm5yQcffMDbb78tGwyieZBMJqnX69TrdWlwaLVan1jUCTPIYrFIo9GQRbsoCI97r4RZqNVqpa2tjVOnTpHJZMhms9RqNdlEKJVKxONxYrEY0WhU+jeIOE1R/MfjcSnT397e5vr160xMTHD69Gl6enpoa2s71jmWy2VWVla4e/cuu7u7aJqGw+HAbDaTyWRkPOP29jbr6+tsbGwQDAZ/VKkSoqGUzWYpFApy9OZRzYVqtcrBwQHZbJa2tjZMJpP8bi9fvszAwACxWIzV1VW+/PJLpqamKBaL7O/v85e//IVYLMYvf/lLLly4IEdVfkhUg0GhUCgUCoVCoXgFNBoNNjc3iUQieDweWlpaqNVqGI3GQ7P4oqB9WvEpGgZHKVKfV8EgvAOAZyqMn4fmHeFkMkkkEuHatWv88Y9/pFAoYDab6evr4+233+bMmTMMDg4+8nM0TcNoNOJ0Ojl37px09N/f32dnZ4e9vT2uX7/OysoKFouFcDhMIBDA7XbLwj6TyUgFgtvtloXzk85bmPwVi0X0er1MADjujn2zV4PX631sQS68HsrlMpFIhO3tbdbW1lhfX5djFclkklQqRaFQIJ/Pk8/n2draYm1tjW+//ZaDgwOKxaJU2jgcDqxW6xPXpbjecrnM0tISt27dIhqNyjjLQCDAzs4O+/v7lEolotEoi4uL3L9/n1OnTuHxeA5d55uKaC6USiVKpRLVahWTyfS9xJVmL5F0Ok0ymZQjLCKhw+/3c+rUKfL5PGtra1SrVfL5PDs7O8TjcaampohGo3g8HpncIZJqxPp62fdTNRgUCoVCoVAoFIpXSK1WY3Z2lkKhwHvvvYfT6ZSxhWLnWcxqP43jKBiaGwzHodFokMvlSCQS6HQ6fD7fkc7tKBzHTLBcLnP9+nX++te/cvPmTbLZLC6Xi5aWFt566y1+8Ytf0NHRcaRjGo1GvF4vH374IeFwmL/97W9cvXqVTCbD7u4uf/7zn4lEIvz85z/nnXfewWg04vF45L0TxpGPay4IqtUqlUqFSqVCrVbDbDY/dTTiRSDk+H6/H4vFQjAY5OTJk9I8UBS/lUqFTCbDvXv3ePDgAWtra8RiMe7evUssFmNqaorBwUHZvHlUnOfDFItFFhYWuHbtGru7u9jtdt59910uXLjA3bt3mZ6eZmVlhXg8zvXr1ykWi1itVk6cOPFS78kPRa1WI5VKkc/nMZvN0pfiYR6OQRUmqmKtiYaEUN60trbyj//4j/T29nLjxg2mpqZYX1/n4OCATz/9lEgkwsWLF5mYmKCnp4eWlpYf5HpVg0GhUCgUCoVCoXiFNBoNOTPvdrsJh8Oy+DSbzZhMJjk60Zwy8TiO6sHwrDGVosHQaDTQ6XQEg8GXIsN+nLJCmCPGYjFu3rzJH/7wB+LxOJVKhXA4zJkzZzh37hznzp070nFEg8FgMDA5OcnAwAD1ep1MJsPi4iJra2vcvXuXpaUlTCYTfr+fjo4OgsEgFouFRqMhDRMfdx3in5VKhWKxKH0PRFLDy6RZ6dCcoPGo86zVamQyGdra2nA6nZjNZur1uhyXWFhYYHZ2FgCbzUYgEJANJjHK05xOks/niUajLCws8ODBAzRNIxwOMzk5ya9+9Ss8Hg9ms5l8Ps/u7i6zs7Mkk0kmJiaYmJjA4XC8sakS4tkSCSPFYhGXyyWVGc2vA6TKpFQqYTAYZMxnKpU69HzpdDpMJhOBQIBAIEB/f7+MJK1Wq8zNzclmQz6fl2tOmJIKM0+TyXQobeJFKRv0v/3tb1/IB73u/O53v/vtqz4HhUKhUCgUCoXiUTTLySORCDqdDo/HI6XUokgRxcXD7xWz9KIgs1gsT91Nb/YCsFgsT1UhiOPE43EKhQLpdBqDwUAgEKClpQW32/1c96Ber5NKpchkMtjtdumu//DufqVSYXp6mi+//JKvv/6aBw8e0Gg0cLlc/OIXv+Cf//mfGRoaeiaJvWi82Gw2Ojo6pIqkUqmQSCTIZrNsbm5Sr9dxOByYTCZZYD9NhSCaFtlsFrPZLMcpfmgTvifRbPbY2tpKT08P3d3dAKRSKarVKul0mlgsxvLyMrFYjEKhgMVikSMa4n4Xi0Wmp6e5cuUKN27cYHNzk3A4zODgIJcuXWJsbEw2ZtbX11lZWZHPgV6vl6MnD5tzvikIf5BCoQAgEzpEGkfzuhQjR8I81Gaz4Xa7ZRKJGEl5FJqmYbfb8Xg8UvFULpfJZrNy1GV2dpbp6WmWl5eJRqNUq1WMRiN6vf57jaHH8bvf/S7y29/+9r8/7bqVgkGhUCgUCoVCoXgNqNVqrKyssLm5KQs2EWUnMBgMsjB4XGF6HA+GZ1UwpFIpstksfX19crf7eRHnJNIzHj4n4cKfTqeZnp7ms88+Y35+XprhnThxgosXL/LBBx+g1+uPvSMrXm82mxkZGaG3t1fGUur1ejKZDPPz8ywvLwPf7eCfPn2a1tbWR97zZuWC2MkWfwwGw/dm8F81orlisVjo6+ujq6uL4eFhRkdH0ev1lEoldnd32d/f586dO0xPT7O5ucnBwQG1Wg29Xi/Xp8FgIJvNMjU1xVdffcXGxgaaptHR0cHp06cJh8M4HA76+/txOp3cvHmTe/fusb+/Ty6X486dO5TLZdra2jh58uQbmSoh0jMKhYIcJRHrqZlmj4ZyuUyj0cDtdmOxWDg4OKBQKMi1/yjFgdVqZWBgAK/XK6NATSYTS0tLRCIRVldXpddIT08Pg4ODxONxMpkMoVAIr9crTUablQ3P2vhSCgaFQqFQKBQKheI1QhQTkUiEUqkkoytDoZCckRceDaLIaFYwiJ3xo6RPFItFuWP6tDEHoWBIJBLE43EODg7o6OjgxIkTR9rBP8p1VyoVALlj21xY1mo1uRv7+eef8/XXX5NIJDAYDHz00Uf8t//23xgfH6elpeVYXg6PQ8y6h0IhnE4nNpuNUqnE3t6edO33er20t7ej1+vlzvTD11Sr1aRxomgsvIj79UOg1+uxWq34/X5OnDgh70WpVCKRSFAsFtnb22NtbY0HDx4wNzfH6uoqe3t7RCIRvvjiC27evEksFsNkMvHxxx/z61//mr6+PlwulxyDEaMnyWSSra0tOULS29tLa2urHBl4kxDjJs3P16NMMWu1mlQbmM1mnE4nFotFNsk0TaNUKlEoFJ6oOBDKm9bWVjo7O2lpaZENOdHgKhaLHBwcsLGxwf3797lz5w537txhaWlJKhvEM/fwfz+UgkGhUCgUCoVCoXgDqdfrrK6usrGxcWhX0+fzUalUqFar6PV6Ka9v3u0/bkzls1IsFkmn02ia9tyjEc3nJAoss9ks5eGiSC8UCqysrHDjxg2mp6dZXV3F5/PR29vLO++8w29+8xssFssLS7TQ6/V0d3fT2tqKw+HA5XJRKBRkCsPu7q7cEe7s7MRqtUo1SLN6QYxYlMtlmTTxJqDT6eTOeyAQYGxsjO7ubsLhMDqdjkqlQiqV4sGDBywsLMgoxfb2drq6urDb7UxNTbG0tISmaXK3PBwOYzQaKRQKsnEwOTmJ1+tlZWWFe/fuUSwW2dnZkd4NY2Njcp297qkS4vuvVquUy2Vp2vioBhQcHqWw2+3Sy6PRaGCxWNA0jUQiQT6fl+ol0ahoVupYrVYGBwfp7u5mcHCQvr4+GaEqYkhFSsr29rb874jD4TikbEin07S0tBAMBnG5XNhstmPdc9VgUCgUCoVCoVAoXkNEo6HZAO/MmTOMjY3JnWNRAIqdf1HcHGXk4UXs8r9oms+nuUjPZDLs7e3xzTff8K//+q9sbm5iMpnkSMTExAQWi+WFqwKEkWVHRwd6vV76TywsLBCJRLh79y42m41/+Id/IBwOS0WI+A4MBoNsBgn5+ZuKyWSit7cXu91OZ2cnb731lkwvSCQS5HI5tre3SSQSbGxsYDAY2N7elu/P5XJ8+umnxONxLly4wPj4OOFwGK/Xi8Viwefz0d/fz6lTp1hbWyORSHD16lUZPdrb2/sKr/7oiBjSXC5HLpejWCzKSNen0bz+xWiO2WzG4/FQKBTIZrMkEgmZKmO32w8pmeA/Y0uHhoYwm828++67lEolstksW1tbbG5usrKywvb2Nul0mmw2y/r6OslkkrW1NXw+Hy6Xi+7ubj7++GP5bB0V1WBQKBQKhUKhUCheQxqNBru7u0SjUXZ3d1lYWKDRaNDS0iLd4IXMvFwuU61Wv7dzDv9ZtLyIZoLwEwDkTuqLblI0KxDETvDu7i4PHjzg1q1b3Lx5E7vdTmtrK+fPn+cf//EfCQQCjzW1FPenUqnI+9M8QtLcjGnefa5UKmSzWQqFgpxfP3PmDIVCgUwmw/r6OvPz81QqFUZGRgDkLL24R6JBIRJB3kTE92s0GmltbaWlpYXBwUHGx8flWM3W1hbRaJRyuSxl+IVCQd4HgEKhwLfffsva2ppMIenr66O9vR2DwUCpVCIcDjM6OirjQe/fv08ikeDUqVNSxfC6KkCa0yByuRypVIpKpfLYZ6T5WYVHP6fiGTMYDOh0Og4ODkgkErhcLul78ajPFzGW7e3taJomxzUWFxeZm5vj3r17zM3NEY1Gicfjh0ZdKpUKRqORvr4+HA4HHo+HUCh05PugGgwKhUKhUCgUCsVrTKPRIJ1OU6vV+Otf/8rm5iZnz55lcnJSSqaz2SzpdJpgMIjRaJSO9GIHXUiqhZHiwxylSSA8EsQsuEhReJEIVYWYAxfy8atXr/LHP/6RBw8eYDAYOHXqFOfPn2diYoJQKPTEBIxarcbu7i67u7ty3KS3t5fOzs5D19Y8zhCLxdjd3eWbb75hfn6et956i4mJCVpaWrh06RKzs7NSWbG/v08+n5cGiQaD4VAagigQfyyIeE2v18ulS5fo6ekhm82SyWSIx+NEIhFu3rzJ9PS0lOULqtUqqVSKa9eusbm5idfrxev14vF48Hg8WK1W+vv7WVxclN9/Op3mL3/5C4VCgV/84hdcvHjxFV79kxHNrFgsRjwelzGej2uKVKtVqtWqXPOPG+8Rz23zH0AqJB51HuIZtVqtMhmkp6cHt9vNiRMnZCpKIpFgc3OT5eVlpqamWF1dpV6vs7u7y7/8y7+wsbHB+++/f+R78ONZ6QqFQqFQKBQKxY8UMaMtkgzq9Tp+v59QKITf7yeXy8ld93K5LAuSWq0mC5fmBsOj3OgfR/MOv5Ba6/V6KW1/kQhZuDi/WCzG1tYW169f5y9/+Qs6nQ6v18vp06f5+OOPGRoakvGIj0MUSzMzZX7McQAAE5BJREFUM2QyGSlXd7vd8ji1Wo1arUa1WqVYLLK7u8vKygpffPEFX3/9NY1GA7/fT39/P62trQSDQeC77yWZTMoiz2QyvbFKhaMiimGn08nY2Bijo6PAd/Ghu7u7LC0tSTm+iF8UyoN6vS4bPuvr68B3kv5QKERnZyfnzp0jHA7LNSvUALdu3SKRSNDZ2cnIyMiRTUxfBs1KhUqlIpVC4nfiORXmjmKtPOr95XKZUqkkVS6PSm4Qz6eIqBUjEdVqlXq9/r3EFdEsE6keYo0bjUaCwSAtLS3ydbVajXQ6zdzcHFNTU/Kz0um0vO8bGxuP9Y94FKrBoFAoFAqFQqFQvCFUq1X5F/+DgwPGx8c5ffo0NpuNlpYWSqUSKysrBAIBAoGAlPtXq1VqtZosPlwuF3a7/VjHrtfrZDIZYrGYLFSEId2LQDQXRFFUrVa5evUq//7v/87t27ep1Wp0dXUxNDTE5OQkIyMjT20uwHcKhkgkwtTUFDs7O6RSKUqlErVaTaZDCEO+UqlEsVgkm82Sy+VkAbe3t8f8/Dx+vx+PxyNHVISJnrivr5unxQ+JaDp1dHTQ0dEhPSkALl++zLlz56hUKuRyORYWFlhZWSEWi5FOp+VIRTKZxG63s76+LtcrQD6fJxaLsbCwwL179+jt7aWtre2VXatoRO3t7ZFMJoHDKgObzYbX631ifGtzeoTD4cBmsz2xkDcYDPh8PoxGI8lkklQqRSgUwu12PzLSVahx4vE4JpMJk8n0PR8Qca69vb04nU56e3t57733uHPnDjMzM6ytrZFOp7l+/fqR741qMCgUCoVCoVAoFG8IYqd9fn6elZUVCoUCAH19ffT09FAsFsnn89hsNuleL3wTarWa9Gowm81PjaVsplqtyp3ZZDJJZ2cnHR0dTyygngVRpIki/9atW/zLv/yLTBzo6+vjnXfe4dSpU/T09Dzxs5r9FISHw+rqKrFYDLfbjcvlkmoEcX+KxeKhaNBgMEh3d7c0eCyVSjIq0Gw2S78F4T1gNBqPtdv7Y0A0VfR6PU6nk0qlIhsxZrOZWq3G+Pg4n3zyiRyR+Pbbb/F4PGxsbBCJRMjlcuTzeebn5+X9b/YoKBaLxONxZmdnaWlpwWq1yjjSF5Ua8jiaFQdCJVStVsnn8zKyVYz2iFjPcDiM3+9/pJpFFP+imSU8D57W8NPr9bhcLgwGAwcHB+RyOfmzhz9fjDMJU1LRYBQNvOa0GYvFQmtrK+FwmLGxMfb39wkEAhiNRrLZLPPz8zx48ODI90s1GBQKhUKhUCgUijcMUTgvLi6SzWYZHBxkaGiIsbExhoeHKZVKMmnBbDbjcrnwer1kMhmy2awscsTu+9OOlUgkiEajJBIJarUadrudlpaWY6sgjnJd5XKZtbU1FhcXWVpaIpfLYbFY8Hq9jI+P8+GHHx5591rEW+7v70unfBGvuL+/j9/vx2q14nQ6cTgcUnYu7m9HRwcffPABpVIJTdOkYsJoNGK1WmXDJ5VKEY1GpZ/ATxlR3AqVSHP8qJDpu1wuTp06RSaTIZFIsLa2xurqKrOzs7JxJhoNgkKhwL1796hWq4TDYfr7+3+wZA7RENjb2yORSAD/WfAHg8FDBbvBYMBmsz125KFWq5FMJsnn85hMJpxO55FHjY5q2CrOw+VyYTabZSOjXC7LcYnH+YI4nU4mJyfRNI2NjQ2Wl5ePnIIBqsGgUCgUCoVCoVC8kdTrdba3t6XsP5VK4fV6OXnyJPl8nnQ6jd1up16vY7fb5Q6r0WiUhbfY1Xycy70otNPpNHt7e9L932q14vP5XujusVBalMtlNjc3mZqaYnd3F4BQKMTAwADj4+NMTk4e+TPF6IMwwazVapjNZur1upTvW61WAoHA92blAU6ePEmlUmFpaYlIJILD4UCn0+FwOPD7/TI14uDggLW1NfR6/U++wQBIeX69XpfeAWIMwGKx0NbWJpsHhUKB2dlZZmZmsNvtGI1G2QgTsZ9CWbKyskIul2N8fJyTJ0/S1tb2yO/teWhW/IgmnFDwJJNJ4vG4bCK0tLQQCoWeGPn6cLJLsViU5owOhwO3232s5+gozQX4rgFit9vlKE+xWJSqJuHD0vx68U+r1crAwAA6nY6vvvqKu3fvkkqljnx+qsGgUCgUCoVCoVC8wTQaDfb29mQM48bGBsPDw5w8eVKa4YlUBK/Xi9vtJpfLEY/HsdlsOJ3Ox+4Cl0oluZvcHE/5siIqRWGaz+fJZrNYrVa6u7v56KOP+OCDD6Sh4HHQNA2Px0NnZ6c0vezt7aWjo0MqGJ6W8iCKTGGe197ezqlTp6jVaqyvr7O6usqtW7ew2+10dXU96+X/qGje1RdKmkft6BuNRjo7O7HZbHR3d/Phhx9SLBZJpVIsLy+zuLgox1sAMpkMn332Gdlslv/yX/7LC28wCGNJkYyRz+eB74pyu92O3++XKgCHw3HkZ6BarUrlgk6nw+12YzabX/qIh2g0GAwGcrmcHPN5UmoFgM1mY2xsjJ2dHe7cuSPv/9NQDQaFQqFQKBQKheINRygY0uk0S0tLVCoVwuEwTqcTTdPIZDJy1EA0GGKxGG1tbVKJUK1WDzUNhCO+GCsQ/gIiGvNlFEZCil6r1dDpdNIj4f333+dXv/rVM8U96nQ66bcA3yU9dHV1yQaD2DV/mObZe7Gr3Wg0MBgMtLe3MzY2xvb2NsvLy2xubnL37l1OnjwpjSF/qoaP4n6HQiFSqRTVahW73f69YlrcH6PRSCgUIhgMyvsnvBru3bvHt99+K4v+TCZDoVDg9u3bHBwcMDQ0xJkzZ7BYLMdO7xAKHXE80UCrVqtkMhkymQypVIpcLodOp8NqtRIKhQiFQsdqrgkVkPBHKRQKeDwe2dg7zudUKhWKxaJs2hzlGRTnrtPpyOVy0sxUPMuPe6ZsNhvDw8Mkk0l2dnZUg0GhUCgUCoVCofipIZIPvvzyS7a2thgZGWF4eJjOzk6GhobI5/Osrq6Sy+UoFArk83kpPxeFiNVqBb4rrOPxOFtbW3g8Htra2tjf32dvb++RO9HPiyigyuUyXq+X4eFhvF4vPp+P/v7+p+64PgrhlWCz2eSYiDBvbG9vJxAI4HK5HqvgEFL5RqMhi0q9Xk9bWxujo6Oy+N3b22NxcZGDgwOq1apUefwUsVgsjI+PYzQamZmZIZFI0N7efqSiXIwk1Go1jEYj/f39OJ1OvF4vXV1dXL16lZmZGdkEmJ2d5caNGwwNDdHd3X2s8xTmkYVCgUQiQTabBQ6nmYRCIYxGo/yZ0+k89hoUHh35fF56UQhl0XHWSKVSYW9vj1QqhU6nw+fzHSsm1mAwSNWEeOY9Hg8Oh+ORr7dYLJw4cYJcLse1a9eOfpwjv1KhUCgUCoVCoVC81pRKJUqlEvfu3WNubo54PE6lUpGjBslkkkQiIXdsi8UimUxGzmQLV/56vS4jBbPZrCxEnE7nE0cqnoVmIz8xkiGOd+LEiWMXjgJhdGc2m7Hb7bjdbumaHwgECIfDeDyexxpVNhoNSqUSuVwOTdOw2WyyyREKhahUKjLRIJvNsrGxwcbGBltbW/h8Plwu109SyWAymRgYGMDn8+F0OolEInLX/2kIn4JqtYqmaYTDYfk9+f1+9vb2WFpakr4as7Oz+P1+XC4XnZ2dR7rfzcqUfD4vn4lMJgN8V4iL+FKfz4fb7T7SdQs1hFBCiOOIkZ9isYjT6cTj8RxrXYjPqVarxONx4vE4LS0teL3eY6k2xIiHwWAgn89Lk8mHx500TZMNtXA4TD6fP1YkqGowKBQKhUKhUCgUPzKEWeLCwgLpdJqdnR0WFhYYGhpiYGBAGudVKhW2t7dl+oGIoRQ7nKJYLJfL7OzsoNPpaG9vf+HxlOKcc7kcyWSSYDBIMBg8cnH3JDRNw+12097eTqPRwGQyEQwG8fl8TyzQ6vU6BwcH7O/vY7fb6enpkYZ8NpsNv99PX18fo6OjbG1tkUwmuXLlCo1Ggw8++IC33nrrJ6lk0Ol0WCwWOSbRaDSOnDai1+up1+uy0aRpGlarlWAwSH9/PydOnKCrq4u9vT2y2Sxzc3PodDpGR0ePrRypVCrEYjEODg7wer1yhEb4K5jN5mMpBETDolgsStWLaA6YTCZpcPmsDSfxTJfLZWma+SyRqMKTodFoSNNKq9V6aFxDeI04HA4sFstTI2Gb0Z4WS/NjQdO0fWD9Gd4aAGIv+HQUbx5qHSgEai0oQK0DxXeodaAAtQ4U/4laCwr48a6D7kaj8VRHzZ9Mg+FZ0TTtVqPROPeqz0PxalHrQCFQa0EBah0ovkOtAwWodaD4T9RaUIBaBy83E0OhUCgUCoVCoVAoFArFTwLVYFAoFAqFQqFQKBQKhULx3KgGw9P576/6BBSvBWodKARqLShArQPFd6h1oAC1DhT/iVoLCviJrwPlwaBQKBQKhUKhUCgUCoXiuVEKBoVCoVAoFAqFQqFQKBTPzU+2waBp2v+madp9TdPqmqY90uVT0zSLpmk3NU2b+vtrf9f0u/+hadq8pmkzmqb9f5qmGR9673lN06qapv3Xl30timfnZa0DTdP+d03T7mmaNq1p2lVN0878UNekOD4vcR1omqb9v5qmLf19PUz+UNekOD4vYB38X3//rhuapgWafu7WNO0PTe/5P36I61E8Oy9rLfz9d+9rmnb37+/54mVfi+LZeZnr4O+/V39XfAN4if/foP6u+IbxEtfCj+rviz/ZBgMwA/wz8OUTXlMCft5oNM4A48AvNU176++/+x/ASeAUYAX+T/EmTdP0wP8D/MdLOG/Fi+VlrYNV4HKj0TgF/N/8xGex3gBe1jr4FTDw9z//f3t3F2vZeMdx/PtjaNqQECaqpkokXlIpDY0SEVRDXRAXkpkKiU7TClGXLiRc9KY3DRfNECYu3JAUraZlRNHMpAwqxniZRqVSHSQTZAh3+LvY6yTbyTjstdc6++x1vp/kXKyXZ61nJb+s/Zz/fvZavwJu777r6tC0OfgncAHwv0VtrgNebdqcC/w+yYFddlyd6yULSQ4BNgGXVNX3gcu77rg61dc9wbHifOkrB44V509fWRjUeHHNrDswK1W1CyDJUvsU8FGzeEDzV822hxf2S/IssG6s6fXAA8CPOu20OtdXDqrqqbFDbOeL+dAK0+P94FLgnqbt9iSHJDmyqt7p/CI0tQ5y8MKXtC/g4Iw2HAS8D3zSYdfVsR6z8HPgwap6s9lvT5f9Vrd6zAE4VpwbfeXAseL86fGeMKjx4mqewfC1JNk/yQ5gD/BYVT2zaPsBwJXAlmb5KOAy5rzypC+aNAeLbAQe6b+X6luLHBwF/H9sl93NOs2xr8rBPvwBOAl4G3gJuKGqPuu5m1oGLbJwPHBokn8keT7JVf33Un2bNAeOFYepxf1gnGPFAWmRhUGNFwc9gyHJ34Fv72PTTVX10Nc5RlV9CpzaTGv8U5KTq+rlsV02AVuraluzfBtwY1V9tlR1S8tnRjlYOPd5jD40zm7Xe3VlljnQyrFMOVjsQmAHcD5wHPBYkm1V9eGE3VeHZpSFNcBpwE8Y/Zzq6STbq+q1CbuvjswoB44VV5gZ5WDh3I4VV5BZZmEoBl1gqKoLOjzW3iRPAhcx+v0NSW4B1gK/Htv1dOC+5gPjcODiJJ9U1Z+76osmM6MckOQHwGbgZ1X1Xld9UDszysFbwHfHltc16zQjfefgS1wN/K6Z+vh6kjcYPbPj2a76osnNKAu7gfeq6mPg4yRbgVMACwwzMqMcOFZcYWaUA8eKK9CMsjCo8aI/kVhCkrVN5Ykk3wR+Cvy7Wf4lo2+lNoxPda2qY6vqmKo6BrgfuNYPjPnWJgdJjgYeBK70m6lhaJMD4C/AVc3TgX8MfDCvv6fTyFI5WMKbjL6xJskRwAnAf/vsp/rXMgsPAWcnWZPkW8AZwK5+e6o+tcmBY8XhaZMDx4rD1PKzYVDjxVVbYEhyWZLdwJnA35I82qz/TpKFB7YdCTyZZCfwHKPf0Py12XYHcASj6Y07kty8zJegDvSYg5uBw4BNzfp/Ldc1aXI95uBhRv9Ivg7cBVy7PFekNqbNQZLfNO3XATuTbG7a/BY4K8lLwOOMpka/u3xXpkn1lYXmAWFbgJ2MZrBsXk3TZudNj/cEzZEec+BYcc70mIVBjRczmrEpSZIkSZLU3qqdwSBJkiRJkrpjgUGSJEmSJE3NAoMkSZIkSZqaBQZJkiRJkjQ1CwySJEmSJA1QkruT7EnylW8tSnJr81aTHUleS7J34vP5FglJkiRJkoYnyTnAR8A9VXXyBO2uB35YVb+Y5HzOYJAkSZIkaYCqaivw/vi6JMcl2ZLk+STbkpy4j6YbgHsnPd+alv2UJEmSJEnz507gmqr6T5IzgE3A+Qsbk3wPOBZ4YtIDW2CQJEmSJGkVSHIQcBbwxyQLq7+xaLf1wP1V9emkx7fAIEmSJEnS6rAfsLeqTl1in/XAdW0PLkmSJEmSBq6qPgTeSHI5QEZOWdjePI/hUODpNse3wCBJkiRJ0gAluZdRseCEJLuTbASuADYmeRF4Bbh0rMl64L5q+bpJX1MpSZIkSZKm5gwGSZIkSZI0NQsMkiRJkiRpahYYJEmSJEnS1CwwSJIkSZKkqVlgkCRJkiRJU7PAIEmSJEmSpmaBQZIkSZIkTc0CgyRJkiRJmtrnLdDoy/MuohUAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 1440x1440 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Calculate homlessness rates per 100_000 populatino by race. \n", "# This is an odd sort of metric, since the homeless population is much more transient than \n", "# than the population counted by the census, so really, the measure is more about the correspondence\n", "# between the r/e of the tract and the r/e of the local homeless pop. \n", "#\n", "# Also, the homeless pop is frequently located in non-residential areas, so you can get per-pop rates that\n", "# are much larger than 1. \n", "\n", "hsr = hs[hs.geoid.notnull()].groupby(['geoid','raceeth']).sum()['count'].to_frame()\n", "\n", "t = raceeth.unstack().to_frame().reorder_levels([1,0]).sort_index(0)\n", "t.index.names = ['geoid', 'raceeth']\n", "t.columns = ['total']\n", "t = hsr.reset_index().merge(t.reset_index(), on=['geoid', 'raceeth'])\n", "t['rate'] = (t['count']/t.total * 100_000).replace(np.inf, np.nan).fillna(0).astype(int, errors='ignore')\n", "hl_rates = t" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
plumbwj01/Barcoding-Fraxinus	scanfasta.ipynb	1	3754	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from DNASkittleUtils.Contigs import read_contigs, Contig, write_contigs_to_file " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "contigs = read_contigs(\"D:\\Genomes\\Ash BATG-0.5-CLCbioSSPACE\\BATG-0.5-CLCbioSSPACE.fa\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the two contig names you sent me it's simplest to do this:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Contig1131', 'Contig3182', 'Contig39106', 'Contig110', 'Contig5958']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "desired_contigs = ['Contig' + str(x) for x in [1131, 3182, 39106, 110, 5958]]\n", "desired_contigs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have a genuinely big file then I would do the following:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "desired_contigs = open('Data/BATG-selects.txt', 'r').read().splitlines()\n", "unique_desired = set(desired_contigs)\n", "len(unique_desired), desired_contigs[:10]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grab = [c for c in contigs if c.name in desired_contigs]\n", "len(grab)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ya! There's two contigs." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "assert len(grab) == len(unique_desired)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D:\\josiah\\Documents\\Research\\Barcoding-Fraxinus\n", "Done writing 5 contigs and 682,487bp\n" ] } ], "source": [ "import os\n", "print(os.getcwd())\n", "write_contigs_to_file('data2/sequences_desired.fa', grab)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Contig110', 'Contig1131', 'Contig3182', 'Contig5958', 'Contig39106']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[c.name for c in grab[:100]]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'D:\\\\josiah\\\\Documents\\\\Research\\\\Barcoding-Fraxinus'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "os.path.realpath('')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
noashin/3D_features_classification_CNN	parse_and_plot_caffe_log.ipynb	1	563417	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regexps:\n", "\n", "###### Text: \n", "02 15:11:28.242069 31983 solver.cpp:341] Iteration 5655, Testing net (#0) \n", "I1202 15:11:36.076130 374 blocking_queue.cpp:50] Waiting for data \n", "I1202 15:11:52.472803 31983 solver.cpp:409] Test net output #0: accuracy = 0.873288 \n", "I1202 15:11:52.472913 31983 solver.cpp:409] Test net output #1: loss = 0.605587 (* 1 = 0.605587 loss) \n", "\n", "###### Regexp: \n", "(?<=Iteration )(.)(?=, Testing net) \n", "Result: \n", "5655 \n", "###### Regexp: \n", "(?<=accuracy = )(.) \n", "Result: \n", "0.873288\n", "###### Regexp: \n", "(?<=Test net output #1: loss = )(.)(?= \\()\n", "Result: \n", "0.605587 \n", "\n", "###### Text:\n", "I1202 22:45:56.858299 31983 solver.cpp:237] Iteration 77500, loss = 0.000596309 \n", "I1202 22:45:56.858502 31983 solver.cpp:253] Train net output #0: loss = 0.000596309 ( 1 = 0.000596309 loss) \n", "\n", "###### Regexp: \n", "(?<=Iteration )(.)(?=, loss) \n", "Result: \n", "77500 \n", "###### Regexp: \n", "(?<=Train net output #0: loss = )(.)(?= \\() \n", "Result: \n", "0.000596309 \n", " \n", " \n", "###### Text: \n", "test_iter: 1456\n", "test_interval: 4349\n", "base_lr: 5e-05\n", "display: 1000\n", "max_iter: 4000\n", "lr_policy: \"fixed\"\n", "momentum: 0.9\n", "weight_decay: 0.004\n", "snapshot: 2000 \n", " \n", "###### Regexp: \n", "(?<=base_lr: )(.)(?=) \n", "Result: \n", "5e-05" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "imports, and setting for pretty plots." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n", "\n", "\n", "import seaborn as sns\n", "sns.set(style='ticks', palette='Set2')\n", "sns.despine()\n", "\n", "import matplotlib as mpl\n", "mpl.rcParams['xtick.labelsize'] = 20 \n", "mpl.rcParams['ytick.labelsize'] = 20 \n", "%matplotlib inline\n", "\n", "import re\n", "import os\n", "\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from scipy.stats import ttest_rel as ttest\n", "\n", "import matplotlib\n", "from matplotlib.backends.backend_pgf import FigureCanvasPgf\n", "matplotlib.backend_bases.register_backend('pdf', FigureCanvasPgf)\n", "\n", "pgf_with_rc_fonts = {\n", " \"font.family\": \"serif\",\n", "}\n", "mpl.rcParams.update(pgf_with_rc_fonts)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_iteration_regex = re.compile(\"(?<=Iteration )(.)(?=, Testing net)\")\n", "test_accuracy_regex = re.compile(\"(?<=accuracy = )(.)\")\n", "test_loss_regex = re.compile(\"(?<=Test net output #1: loss = )(.)(?= \\()\")\n", "\n", "train_iteration_regex = re.compile(\"(?<=Iteration )(.)(?=, loss)\")\n", "train_loss_regex = re.compile(\"(?<=Train net output #0: loss = )(.)(?= \\()\")\n", "\n", "learning_rate_regex = re.compile(\"(?<=base_lr: )(.*)(?=)\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_empty_regexp_dict():\n", " regexps_dict = {test_iteration_regex: [], test_accuracy_regex: [], test_loss_regex: [],\n", " train_iteration_regex: [], train_loss_regex: [], \n", " learning_rate_regex: []}\n", " return regexps_dict\n", "\n", "def search_regexps_in_file(regexp_dict, file_name):\n", " with open(file_name) as opened_file:\n", " for line in opened_file:\n", " for regexp in regexp_dict:\n", " matches = regexp.search(line)\n", " # Assuming only one match was found\n", " if matches: regexp_dict[regexp].append(float(regexp.search(line).group()))\n", " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rgb_dict = create_empty_regexp_dict()\n", "search_regexps_in_file(rgb_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/0702/rgb/log.log')\n", "\n", "hist_dict = create_empty_regexp_dict()\n", "search_regexps_in_file(hist_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/0702/hist/log.log')\n", "\n", "rgb_hist_dict = create_empty_regexp_dict()\n", "search_regexps_in_file(rgb_hist_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/0702/rgb_hist/log.log')\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.001\n" ] } ], "source": [ "print rgb_dict[learning_rate_regex][0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dates_list = ['1601', '1801', '2101', '2701', '0302', '0702', '0902', '1202']\n", "\n", "acc = [[],[],[]]\n", "for date_dir in dates_list:\n", " rgb_dict = create_empty_regexp_dict()\n", " search_regexps_in_file(rgb_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/'+ \n", " date_dir +'/rgb/log.log')\n", "\n", " hist_dict = create_empty_regexp_dict()\n", " search_regexps_in_file(hist_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/'\n", " + date_dir+ '/hist/log.log')\n", "\n", " rgb_hist_dict = create_empty_regexp_dict()\n", " search_regexps_in_file(rgb_hist_dict, '/home/noa/pcl_proj/experiments/cifar10/every_fifth_view/'\n", " +date_dir+'/rgb_hist/log.log')\n", " \n", " acc[0].append(rgb_dict[test_accuracy_regex][-1])\n", " acc[1].append(hist_dict[test_accuracy_regex][-1])\n", " acc[2].append(rgb_hist_dict[test_accuracy_regex][-1])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.797413375\n", "0.0670550883676\n", "0.663382\n", "0.0630766907324\n", "0.856194\n", "0.0637660614728\n" ] } ], "source": [ "print np.array(acc[0]).mean()\n", "print np.array(acc[0]).std()\n", "print np.array(acc[1]).mean()\n", "print np.array(acc[1]).std()\n", "print np.array(acc[2]).mean()\n", "print np.array(acc[2]).std()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "_, p_1 = ttest(np.array(acc[0]), np.array(acc[1]))\n", "_, p_2 = ttest(np.array(acc[0]), np.array(acc[2]))\n", "_, p_3 = ttest(np.array(acc[2]), np.array(acc[1]))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rgb vs. hist:\n", "0.000832760333593\n", "rgb vs. rgb_hist\n", "0.0160929192832\n", "hist vs, rgb_hist\n", "9.59061672747e-05\n" ] } ], "source": [ "print 'rgb vs. hist:'\n", "print p_1\n", "print 'rgb vs. rgb_hist'\n", "print p_2\n", "print 'hist vs, rgb_hist'\n", "print p_3" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nLiIiIiIiIiIiIiIiIiIiIqLaMczVJEVRyq4b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\ncN999+Hf/u3fisYuAijpbLXM5/PhhhtuwLvf/W5YLM2Fbc444wy43W4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WyqhWyqDb1PWnZi2iMlg82hVyArroKI44lOOdCj/x3q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\nc4p3xs8Gw1xEREREREREREREREREREREc1/cK3ONjY0FjG3fvh0ejyfeh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Z07FzU1Nbrme/LJJzF27Nig8a+++gqff/55RDE2N4WFhdiwYYPibZdddhm6detmbUBERBTzmPhH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No8pLGi+BXDoG6h1DaV4+Xi6cq0m41GxV1BZnKAUpPTBJ+gOAoB/ygWchPP59tj1ldD2wmoDB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zSO+mGc+DeOMYeNGzciW6Vo4/P5cM899+Duu+/G5s2boXCSWBoFbon4iazZ14P4Z1Q8Vpwv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\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0YewQw0T8aOH6QmBWvx5WXfJEbSn4+QJ/Ax0WCI4ZyzCcWet6Ln0iZZ9PccW/5rP69Xv3\nOytkq+3ZBcX9sPoNIyaVJHZFb3X83SzbWgRjfWX3bf2x+m2O75cmUUSsrOt51c/THM8miowSwSVt\nrqsqJw1eFDDY0mcjnDdC8S9DmmceXVQaD8e8Y1KJI1pI29j9ihL/+K8TKi8WQXwqPPbOuelQIF3N\nCtMapnRSejNtnnBCGPuiKRJD3LLfhFuJXJXVrzwCRiNIKYTDXvgfl9+CUSOKDs57WA6qNyxgWTCd\nOYf9W9ipXNAg0h4HNceXhQJUX6x7uUFp32RbLtIgTDgPEfGvpPjXH0viw9hdsCuPp40leJM8hKXZ\nM9S0rg/lpRCU7xqkSdR1XFtU/LvGPDdlRWkssTO6Htvjd0pJkwWd4x240nPV9bzBSAy/6F2MBQK6\nJtWK8DO4qnbjxbankFZiAIBPouvx8ORmrJwmwwB+W/3Sv92o0oaftj+LSSXBnfao2gEL8hQhg0Ah\nJITzT2O34YmJt2x/80L8y0NFgajlmEa/vsh2Hn88sgKPTm4WnuPSNjoFHccsjckuR+P4wbK1GIgl\noDu4ErqVVS8xj7AgDH3nOX1h+f/WBMHK7AnXd58tWf0qcvsdu1S8kCOLin/06+OXO5DO5hE1NFgu\nxGsLJECr3xkEbQl9MLLalvjHO86uzLefc3u5Vr9ai/jXgi/wh/YK4I/+6I/w/vvvwzSLDdjWrVsx\nPDyMrq4uv25Zh6NHj+Kdd94p//2Hf/iHgd27mXHnnXfizjuDm2S1cP1AaZIJHX3Q1BzP0LL6bYEX\nxDKZgud5okHxybKzKj8lxT+BYlzcEdNSmpUNy8XuzuvuwPIuYc+Kf/a4f+A89mjz+TOGcAbenUHK\nFn2NhnzFv/qguLvV7/XTF9La6LLVr8tYS5biX2kxpwAFByNrcMpYinP6Ailp192npiCF8ZtmlCh0\n010BoLIdDeNzyEaW6J7aT5oChgyULebdzvMwhxElJjSTIqQJEngA2An/3fFlaFYON2UO477Ux+Vc\niZYiu/LrVndF6nbnWAeUEI01Sn0FrRyWlGBo79Yi4VGDZIUd4dhtDNwIpJQY4taU63k5opS/o6x6\n2jjFP7Y56Cfdc3C4owexKRHirjhYFkxL5/D0K6rFpjCUh4qL2jycMRYzp11CDrrQvLtRKCrNhj3D\ngoRzgf5fbEwpZ8FYFkqxhB2di5DNOBOJzukLMSdPZ1eFUS2PF2HsO9Mk4vpuJ5QE8kTxQfGvOCM4\nEL1RSnrsYMv/xVgSqiUWD3N6R59GbyuT/oDimOrj2EasyJ4sX+En8a/gQoQ/HFklRPor5knHhJKE\nzrBxLizIQ5MWS/GCi5qzY41ofXs/8TkMaLNRgIr5+Yt4dOId5FyeVbSNoiv+Bft+S5uI93f2YiBW\nLMtOfbDbmOP6UPxrfPmuxIfxe3BD9rRrWcuQSLXVr8T5Ti28kCMtENfrCQgOnRjC+hvnMCn+NcLq\nN+gNn073422DKtPxMxY7pshz4syjZfXbgj/wrRavXr0aX//618t/5/N5/M3f/I1ft7PFkSNH8MIL\nL+CFF17A9773vUDv3UILn0U0i+IfbbxjN3gMY4dohWgBroXmAOtCSg5aMIvR5XrIf688CV4K+7MA\nt7bOD5tP53u5E6Bq8eBAP9aNuO/WtkMYLA9YYYG4BumCguwd3PaBl8+G4p8bZuyPXIh/koKppcWX\n15OP4oPEfb6Q/kr3qcWoHj4ltDSJMAV7X257Av/W8RvYnPg8pkg0gJw1Gt7IIX7X3xnFPzerX/Fn\nCNJitlGwCAmH8tI08kTH3ugt2BO9pXxMlAQQFAEjTKQ/YMb6k2W85VY/wqAewQO77xdGAkaKxJna\nl5yiySdgkMYQ/44k5jH1tSYAWEDPSDf3Pby8IxalmhmrX3aw9AcZYuCltifxcvuTHCnPIEe0phoz\nF4ga+rZFtP8P1OqX+yr/UCLUWAzf9bzLZr6wlw0WhJK8SNxJCzliID+t9mtCXv9pQUGGRDCpJKWk\nx3NnViimWLlzaiuORVbUHRtX2zCszIimsJQTUbLaNbUbm+Ofx0/ansHHsTvLatAl7Ixtulf5SAAA\nIABJREFUEEq3hGGls+n6nXAoslM2QQu2Gxf0+cgTHRZRcF5fgLcTX3S9RriN8nEzOS9Km4hTqlZx\nzP5ct7HY9aD4lw9Z3zmlxHAkstK1ncgoJatfuYp/dm2zF1VKN6vfEgaGJqfPd+9/5Cj+8RH/gp4D\nOr0z3nF2ZTp+rplnFXn9RKEB65vNpMTbgjh8rcV/8id/gi9+cWYg8dOf/hQvvfSSn7dsoYUWGohm\nUfyjLbxYIMhBw2l9EY4YKzBJwmPJVImirVIIA0UthBaqZS9nXwdCpBFHqLexSgtU/DBbin++wNVC\nNMA2R3QiL3pd2AIgNFgIpo6yQDZxxW5y7hY4uR4CcCwovRtX4p+k92FCwWVtDs4ai6Sk5wS75wnj\nAsEUiTIFwAa0ORhVO3Aosga/TNwTQM4aCwve+ga/v3Wp/Xjkcr8v+cgQA8OqmKNBM23gsUKqvHTU\nqF8w5YXIc10vwdI80ajtWplw7mp91VxzUrtnDiXxT4kxtU15os4Q/ySVTQukId91EMvwj12/i72R\nm6jnWYSge6QbiSkRJSLxd8Sj+Mdzn9KmuwIUTJCE7fx4f+RGXNLnMadZi3yTEf9MKEgr4dhAkSGG\n7VzRi+UiL0TrdqgU/6bfYVZxn8e6tT/NtGnPCWEcVwFsfbppFRepC0SR1u+YDEpJfoAnHqmaov0i\n3ztKV5AKWNqLWsIeK37W/gwORdfgkj4Pn8bW45W2x6WKH7OOY8KCPFSkQ0D8o7UNstqNfmMR0i6b\nFH1R/Ava6ne6TdnW21c+ViqTeaiYJLG6404YUGf5kMPgMK4kMar0Njobddgfucn13WeJAZMQ5EuK\nf5LmKHbxbE9Wv8Td6hcASOk5XDanAgCRYD/O65AQtDOFU7vGu95wwrhh5tomidc0wtGsmfrlFsTh\n66qhoij4zne+g7/4i7/Aiy++CAD4sz/7M1y6dAm///u/D1Vt/olaCy20MAMlfPFybphExX91fAnD\nanH3uG7lsCh3rsG5qofVoF34LTQvVEbFP0B8FyEfxIl/eaK1FP98gNu3CDIQy2J5agfRQFjYLA/c\nkAtJ+Zdu9WtjN0Mrd28nHpCcg/DCYlTzkVWWLaJgW+wOKWnRYG+zGT6klehnhmTKh+ZQ/IsW6Jsf\nePNhguDD+L04EFkjPGZqpoCb2SDbTzdc0WYWYMQ3DASjvBRGZIlOXTgp2d+69jtNtHkCKPU71W1C\nGL9phkSY8lWAyrw5gBWmixqkn8gTHR8m7sOswhD68pdtzzEtoGO8Qyh91rnCoNqLrfG7MKR2Y25+\nAPemtjItBs6omrF/CwsKdkdvwfbYHcgRHR2FUTw28TZmFYbK52yNb2ROzw5ZosOwcp7SCBIFoiKL\ncMx3ftD5VagoYE3mKD6X+gijSju2xzbguI1aFwvs21366FfU6jdM/s55UoxIHezowTwXF3M35c8w\njkl4EVbSPMu7tSwdQA4FQqT1OxZRpG1g4wJj9i3iQfFvejwldK2PVr+1uKj34Yrai9kFMReNWuSI\nHsrxlRMKRA2F1S+NsCLzfV7Veqi/i8ZWacS/fn2hUJqiKBAV23vmYlyfIdZYIPhl/G7sj9yEAtEw\nK38FT0y85U4+k6jyFTTyUPGztqcanQ1bXFV70FO4Rj0nSwwUyP/P3nlHS3LU9/5b1T1zZ24Om3OU\ndqUV0oJWAQVQIioh2QjDsfEjGZmHzcE2HMk2CANGhmP7SLbhYeNjCwzHYCx5/SzpASJIgOWVdhUQ\nkpC0K2lzDjffCd31/pg7szNzezpWV1d11+cc0N4J3TXd1RV+9a3vr9LoN3k6nLcTLdVvsE2xzJfw\nj4Pj3+x53K5b83uiM1N06ueCjpN+3n0xzir9CgVWUqbvSWJ9U8Z4u4Y/sT/FpmniC1/4Av7iL/4C\n/f39sG0bd911F972trdh69atmJqairsIGo1GEMqk+vWgLvoDahPVXU07BmRBp/rVBIXCp+OfIMjs\nSDPMLpwqDFS04x93vAR18riMuk1Ww5VRKcc/QqQISAL8AwJOE1C3YPcLXWdyPb/M1K+DV9/P0w3y\noLmI27E64dSuyLg7c4Z0pWKRkTcM0RZfvYJOUYNSzKcYJmhg8JddZ+GZwtmR+kVZF3ydYOC3sBsX\n4VMuikn1KyMlj3bNd4p5xUTRTs+ejP0OI/7Ed1VqNBauuAkwEnJeaubHg5s6vmcRA/lquEUSP9do\njPbivr7rsCe3HJO0B7vya3Bv3/W+nIBO9zv+maLd+Fn36xsbe0aNAfxH37W+0h77pSbAUAcL1NON\nSBQ1QVIOzxQ24WfdF+Pe/utDi/4Agal+IZvjn4ltI4tRMryf3Ww4/sk5DvTT7lC7Nt+0OQr/bBDs\nGIx/7jmXIKl+w272CX+v/YhIeMaGnvFw3A1CmeSVGjPLkuqXgaCMHFen2U7ncSPsXJVIdM8tUHxz\n1YaW1wbHBvBU4dzGhtmj5nz8V+9bhLuciWRfbgnGjHAbZmKHEE/hU4nUU/3y3+jUTrRUvxS2j/nT\n4emx2fOLEf7V+znXNR9BaXKd6BRTC1OOfeaS2e/KOcZqx0pkfTO9bZ3mNLGuGh44cKDx7y1btuCe\ne+7B1772NTz44IPYvXs3PvnJT+JTn/oULrzwQmzcuBHr16/HokWL0Nvbi+7ubphmtOKdOnUq6k/Q\naDQBUCXVbxpQYQFOIxcG8+/4J4IowYAqMVHSwj/uuLUpSS/+NeO2OBs2WKOSk1c9Jb0MPFM4G3tz\nS7F55mlsKj0f+Xi8Uy2kidPPpzgBhggnTKdgjowL0yVSUEqoJYro6SA9FhwiBuz8CjDcfsMJOogj\n5nzMt45h2DoJAuDpwjmRygWoE4wEZoPY0tf/kI5/juMGtw0GcrZRYfAS/vkVzso0RvSDKql+AX+C\nxApM7gthfkWHcXLCXgfgIcf3opXN+xq9nFs9x9ll3Ojz5VTDGoLZaNdvhhZx0FyEZdUD3h/2wSk6\niAFrjMuxRGAToyXdpSw8XXhN5GM411/3DXih6jyTTIBBDHxr1QZ0lbzL5LVwzlMUmxRJt7Gd8BMX\nMWad77g6/oHASiDGESjVrxVW+BcePyISnrGhcaOP27FqYh552iAvLJhSCP+qJIe/H/pfoLCxofQi\n3jj1U9DZWsSz3fDeGMc/1a9oDhd6YJNyy2tOjtHHzHk4SYdEFUs4z3Vt8P5QgnjV65rj3zQYIbA4\nZiJoruMHzEV4Kb8WO/NrIxzPX/x61/gxHJ2egJ/egYvjn4/2o/k90XGf9nKdoIP4ReEcPFMILkTf\nl1uGdZVXeBUtdirEFJ7RqR6Dt0ADZYfTqEWsI+orr7yykbO8TruFaalUwiOPPIJHHnmk5fX274XB\nj12qRqPhR1oc/1Qg+kKrJmsYkjn+1dPO2Cx4IEzWhQDVYS4pSGRqb9zqS9jAs1KOf5An1S8AnDIG\n8eOeN6DLLkU+ltP9k3VRRDQNxz+POZJqaaudAsoybmywCE3FIiNvbEIj7Qh263eA6IIiv45lTu8z\nANuK5+Px4vmN186bfhqXTj+KU8ZgpHL5KZNM2IRIsRDmRtjrGTTd+Bjtk9cxISAl0uU6vvP7/Kgm\n0He+53I+j37aQIvwd/yrwMQxc9j7gwkRyWnWo9/5qzNfi9zRCxzfezW/yvP4NgimSRce6r0iZAlP\n83zXmdyEf8fMEayo7OVyLBHI5PjHG6exvFdbHG51g0glwCgRE4z4K5PXM+5HDCU7olPp+cVP+2pY\nTcI/TnXMBq2JDkUv5ZEgjn/h6p3zfNft86fxE4eTJRtEOxWSl9JRuRNVYvhy9hUBm409PFs4C91s\nGhdNPz77Ok/HP3fCjrVk6necnr1C2XlssTe3LO7iJMY0KSZdBFe8BOcl0tXY5GRRwu05qI/HduZW\n4//1XhM5yxGD35ghw1PH9+I1w0vdP0UAyuF58rOZr/lZER2Dbz7fKO3Dvf03YJqGq7NdbGbOMWUm\nibnOgdwS/LT79ThhDGPEOoE3Tv0UK4SXQhM3sT8BjLGW/4X9Xpj/aTQasfAQ7Gr8wTjucNFkA5NV\nky5CC5RRLDqyCEemLsc/D7wn8PenSHcMpeJH0Z5KugiBUcXxz92dJly7qNaitTypfpt5oeuMyMdw\nqoN+0ttkgXoQSlZxQlicRWPy/UYbybsfyYgFI2Kq33gdLE8HhYML/44ZIy2iPwB4qngutxTYMgnq\nvWAgmJZcgBF2AcBRcO5S7/6t/x2hziMjno5/PsVkMo0R/eCY6lfS9t1PO2ERyl3498PeK7C9+Dou\nxwoLc1mSZhHqnJdYfSIXbXMNIwSPFi+MdIw6BuO3ce+YMQIZx1edsGCkV/gX0HW0JvwL3kYRJpcA\noy7W8yX880z1K2ebHQRZ5xV++p3Tjn+Uo9NsMinmAzn+hU71G2xzY7Ow1U894Rsb4reeWiY5pWIX\nFuRI9dvOrtzqxr9FpfplCN9G5aryxCqDiMTT0K90ojgrhpIVL7HcUXN+o2+qRtx42ky9jj9efF1k\n0R9QExL6zSb088Mvu8516vAcx7luMmmKpYhut5vP/UL+jNCiPwDIsUrtqiqiUZim4uc6jxXPx3Fz\nHhihOGbOw9a+twsvgyZ+0tujaTQa4Rja8U8YOtWvJigmx4UDHhRLBQxM9IMhh4kQ6SQmqdzCvz57\nIukiBMZV+CeRMM49SBquXVRp0ZqBoCKh8O8VHy4oXjgHxdW5N3HiN+WiajiJdSRao2ygnZadqTlN\nxSf8i+pgWT9+ieRdP+fUd3RK58sjzW/tnOrUJxsUMwkEJYPA0/HP7d5MSz7+DMKMh/DvpfwaAN6i\nSr8LHLLgfM8l7HjgL51lFUaj/DwWrWTBMjrPXeNN9RutLjAQPFs4K9Ix6pgcHfuPG8NKLWpbhKbW\n4T+w8I/QkOJ2EsTMLHbYrJubH/car7YvDfNDWceBfvqdZsc/Lzd6v9igiaT6DSJ0oyGFf9uLm3HU\nGGl5zS3+1NxW+xsH8LtuPEdDNeGfOlSJgZKE/c6JJgdmnrEgL6fZMHP84kw3lhxZEqVYXLFBQWyC\neSdGsGL/ciw5tNj1s2mlYEsu/PMxlyxO9c1+lnDbsMVAUIaJY+Y8LseboD2+P1swTNg+jKv4CP9m\nM265tB8tqX4FPwsVkm9kYdrWvSXSsarEVCpmHkXkyIuKR6xUoybyrRpqNBplIVr4JwybkFQF9jXx\nY0Aux7+oHMv1J10EV/rtcRzBgqSLERA3xz952hu3AFRYx5+orlIiYQCqkCfVL09sh/RrKi1Sxomd\nUuGfKikXGdGOf05Ed/xzJ2oa9hnShf/b+1a8ml/pXg6HvuPF/DrHz+7Mr41Upjoq1SclHP9CthtO\n916mMU+clGgeeVbp+P7Pu18PghQ6/jmm9pav3wH8p/oFIbBAlFpg98KiFhiAo8Y8TNMCFlcOI49a\nfY0i+vFM/R5xga3McfHE4OjYbxMDJ4whbseLmzQ7/jFQTJEiKsTEgD0OwH2+YyGcqxoBQCRr2wjj\nk+pX9fnhK7mVmAQfoQFv/LSvLal+eTn+gcBOYpkyQPGNkKl+nylswi+7zsI1kz/CmeWdALxEV02O\nf76cfzmOwzhmUKuQvLTjKycsmJ6bxZKkRPLYl+MnqnMT2tayHQS/d/mqXHFKBorFRxajb6rX87Np\n3uTJcyNJHPiJi8w7MR/jPZNcU8wfNBfhqMGvL56k3vUMqG10Lhr+hNGERa+X9fP4NVFI4lnY2vd2\n3DB+f+TjWFBM+Cd5Gm6Numjhn0aj4YbW/YkjDTtcNWKRzfEvKqNmH1yMIBJnT28eKCddimCkwfEv\n7AQvqquUSJikqX55EDQNTpaoXweVghh+UEX4B8jVDsrCIXMhds66goXB2/Ev2jV/orjZZznE10NZ\n67kTjBCpBRhTpIj/7r4o1Hed7kPYTQSqMU2KGKfurtvbC5uxqHrE9TMqbZ4AOixmSLrY5zfVb+2/\n/BwwZMCmDPf1XY/9s4vcXXYJ1008gMXVw5F+Z6dFNhsED3dfiqFDG0MfGwjm9uEFhQ2AX+LFYxwX\nN+OmRAvKuYn65cG+NzX+Pb96FNeNP+A63ymRAk4Zg6HOJVOqX6Au/PN+fr3aviqRS1wShAnSgwd6\n3+T9wYTwI6qMI9VvzfFP7lS/YR3/gNomske6L8X68i5QMNdNJlZA5yWe142n4EO1VL9lkpO2bXm6\naxN+1n0x137R7Vm3U7LpcQa96JvyNy5TbSNTEGTf1OanruWreZiWWet3OI1tXuxaz+U4wWEomnlx\njn+k7gzvlt47Occ/ADiQW4IX8tHvR5VE25gsmikJHP806USdp0Cj0UgP1co/YejFZ01Q0ub4Ry25\nhU8GldtK3wm3KadME6f2suw3F+OH3W/A93uuxL7cslDHlD0Q0oqcqX554BQYTnMALgj1II1KwXM/\nOIpuEiiHH3imUUoLL3atj7QA4VWfRV3zJJ4rXqnRRMBApE31a4Hi3/rfEfr7QVMupolfFs7Gr7rO\ndP3MDC3isOnuYK1aP91+fydJN0rWSIdPJ4ufuljfEGiTdDn+FUuFhugPAEq0C9/vuRIMUR3/nK/p\nY8Xz8cvC2ZGdNSZ8un34YXvxddiZW83tGVMpde4kSU9adTeOmvPxg94rXZ/1f++/PtzBGan9TyL8\nOv6p1q8EYWd+jdSiVj/9Tt3xz+bp+EdIQhvc/fWcFkhkB80ZWsABs5Zq1K0ONL/nS/jHsT7tyy3D\ns/kNXI5VhlqOf5NU3n7nkZ5Lubcbno5/km6KCUIJ/jdjpHn+J/vGcb9tGLXpbIr5dNyrp4/vc32f\ngY/wz5fjH2kW/iXTbu/OL498jCpMpebDOs2uJi6Etfq33XYbrrrqKlGnAwA89NBDuPPOO4WeU6PJ\nMlShBSzVUUukIieE2ZlKl5w2xz/Z0ta0YxAVhX9uqX7lCU43BwVeza3A/b1vjhwEU0lMbRMipUNh\ntz2JqYguJ451UI8tADQ5/qXsejgF7WR1LFLN1UoN4nX880t7cJPBfUGOzznlrOdO2KDSpvp9JbcK\nY0Z/6O87C87VuTci8Ep7ptIYCmh99srI4d6wohoB+Lm2td9jzQow0l13x4wBHDHmR3P869DtvMQp\njTtPxz8AeLDvzdyOpVK/MyWxAIM3e3PL0WtPdnx/JqQbCIF8jn+UUV9lSnOc7vHia5Mugiu+hH8N\nxz9+wr+a0MgQvgPM7yPCS5RY7yN8O/75eBZ4z1F/1PMGjFgnIh+nTHIIlEs5YbLU7wDeKeZVGjPw\nQPUU8m5UJYrnO+F3vcGwKaocnWaTggHYcWwPdvj4LOUyjqtvIndL9Xv6vaTGYLs4zMWqxEz1GFKj\n8YuwVcPh4WEsWxbOhSUsQ0NDQs+n0chBcoMf7fgnDplEOCoywI7ixtHvYU9uOaZINx4vvlbqXbc8\nMFIm/JMdg5SSLkJg3ARFMi3qNk9Inyy8hsuz67bbVTYqkqYf4TECaA/gqLRTL25YI1iTrrGWs+Of\nnIEa2duJKq3CtOUuYzs2Ia4Puqjx7gN9b8GZpRdx3swvsDu3Ak8Vzon9nLLWcycsGCgROZ2iHu65\nNNL3nZwX0+IiwAuvcZZqQsnm+7s7tzx0Ck0R+Hf6qcwKMNLPUXNeRMc/53EUr3owyVn4xxOVFvFl\ndZmNi1EaXsDuxuD4QCzHDYth+RP+pRnZf72fMVDd8c8ihNsC+393X4TZ7OaC8ddz2pzmgfX779aP\nWUEd/3jPlwjBq/mVkQ9TIXnZTEddkTXNb1y4OevbiqXL5EGaf69M8Xwn/M69qW3UHP+k70k98F18\nfy7JXpx2/Ot8rOb3VL6+FjFSFzPXaMKg1sqARqORGu34Jw69IBUNymz02xPYVHoeADBq9HumulId\nM2WpfmVHxeutiuNfc/sXNrVvOxMK7e4tSxqQ5FFH2sWnaQ6+BaXh+JeyIIZTUEnWQJPsjn+WYSkn\n/PNM9Svwmr/QdQZe6DpD2PlkredOTNOCtO6rUd05nASYuu8JhuyLSe0039+fd1+cYEm88TO2qv8e\nO0Wpr9yopXCKL8V8VGQW/qXZzUZ14mhHe6blq4vFmWJDNJYlGOQX/NXxMwaijILYBBah0mfi8GJw\nbBDlXAVTxSnXm8QrHkZm1Y1u7XHzPfBzP+LYxP548XWRj1EmOT2mlhi3fscClXbuFxfJpBoXg4wZ\nY5rxu4mM2hQWVd/xr5PgnKHW5htNKng+myVqx3CbJ7am+k2m3e63RjFmRNuwUpsnql4/NJroyN3q\nazQapdCOf61cMfkwniy8BqcM/u6jMolwVISQ1q2klCWytVQoaUv1Kzt7u7vRPZV0KYLhKvyTaKEo\njknoK/nV3I8ZFxXIKfyrcAgmtd/bquQBKpHUXanSFsRwEt3I6oSmgvAPlaRLERSvVL/pbQNUEuhM\nh0wzqAJObapepAyGavPS5vs7bvQlWBJv/LQTDeFfysYHnagSM9IzmrZxVBB02yYvqrWjYVlwYkHS\nRUiE9oV8mfErQqWMwiIEhuJtalelC8sPLcNE9wQOLDjY8XO8REGEsdnjuaRZbboHqm2uaIYRquM5\nEuPW72TxvskU8+aN7Kl+/Y5PDavW76gUR/HLi/l1eLR4AcZpL+Zbx3D15I8B8BH+scZ/Ox+rOQab\nVDx20B6LLvwjRmbmxBqNG+lrJTUaTWJo4V8rBrNBWTwJd2R1fFIFCqvtbzWCcFEwFHSgUxUGhgPd\n6jjI+UGmxYisL1rJmuqXh0CnPRDhln4ka7C0Ov45BO1kFf5Jn+rXUE/g71afDxkL8OPuywSWRiyy\n1nMnpkh6hX+OrqMpXEyIE9UWpVW6v76cfmY/U0t9Jfa3Van4+WWV8Hf8y8oCUZrdbFRHtXZUE4zW\ntlnupOx+nZcII+lIuThL71Qveqd6O77PTfg3e/893dYa51VnzOJEiXQlXQRNByaMzvX92a6NAksi\nB6o/a27IPsbwLTi3KaqEKB8TbR8FHDIW4Ps9V2LM6AcjFEfMBdjady1sUE6OfzXc6nhLqt+E3D4N\nFn1eWZsnpvdZ1mj8IuwpYDGJX9zo6enBkiVLsGTJEixevFj4+TWarKFT/bZCYccmKNvWfUEsx80K\n7Q5/RPLgGw+04584GGFKpkVwnQRKFChQacE2DmQV/vHg2wO/hufzp9Ouy+6wJhK7kZ5BvbbFDaeg\nnayBPNl3vluGegL/TqOvPeYy/Hv/DSjRgtDyiESlhdKo6XRlhoFihnS1iBvTvPATB6o5c6r07PlZ\nCGNN4wPRM9pyviz4jEAF/B3/svLMZ30OJTMybbLT8EfWuY0TftvDmvBPfMpFi8YX1+ye7jze5bUB\nrN5T+429qd5ul0g+6SJoQvBMYVPSRRCO7LGeOksqB0BosPRCaXH8o7aRSL/Dnbbi78qvAWtr6ydp\nDw6TpZwc/7yzxwRNMR8HPO5rFQY4aiU1GmWJtUe77777Gv9eunRpnKdy5Oqrr8bVV18t/LwaTVZR\nfD7KHcqsTDjJqQhpuy80E8I/9QQBqsIIA1OwTrlNsiZoj8CSuON3F3paSbPwDwAe6r0CXeMzWFPZ\nLb3DmkhO71pMVxTDSYAh6w5N2QOmVooc/7YXN0slOI8DppCIN82Ofw/2vQlgtQ0byyr78baJ72VG\nBJRVVLq/fsa8dfc7mxAwJva3lXMVdM8IPeVsqt/wOLW9KtWJKGR9DiUzsrvxaKJhE9LY7SL76M+v\n0IywmtOsaAFGxazAKMfzvBRKnd3peDn+0YbjX+frPNPkkqd6/1TWwj+NIqiykWnAHsOBgQPAyfN9\nf0f239YueuuEYddS/RLF28X27a/Pd53p+KlnjIth2jz62PomMTfBefKpfnmc19KOfxoNgJiFfxs3\nZs8WWKPJMoZO9dsChZ0JJzkVaRdkEpZ+gaYB9QQBqmKTdD33x4xh/Lz74qSL0aAe/EzXVfZPFlK9\nv5BfjzWV3drxr4m6059KTkV+cFrMkDVQI/sucIuqN5bpJH7bnxO/aVA0Ki3kTdP0Cv8ANFya9+WW\n4oc9b1DeXUXjjkr31087QUvzsHpPP7blDcyvjAoo1WnKOfGOf7UUTuFnAU4ilayIrlTqd7KGFmWm\nm9a5jdxRjCCOfzYhwjeyVMwKCuV4HMHdNlHxTvXrdrxfFM7B3txSXDX5sPLttnb802j4YrIqJk0D\nQbbmp8VVmNoUFqGgisdE20cBHWMtnPrX+vnc+pPmcUpSWWZ4jCeqMNR3hNRoOKD26FGj0UgFUci5\nQgQGbJAE0pxrvCGk3fFPvcXyoGjHP3EwYsu/lduBTpOsn0kk+gOAw+aCTE/m0u74BwA7u9YBkF9o\nJRLWELymq947tTuy/kbZHehsFYV/kt5rEai0kCe7SwBPduXXopKh35tFVHr2/IoU89U89phrsaP4\n2phL1EpSwj87grOho/BPoToRBZVEr1kjK+LTrKLSxi2/8x3CaEKOf/HFNd3SCPNO9evV75w0hrG1\n7+1SZd4IQ4l0dlHUaDTBMZiFyVywMUNa4qq1VL/iBedx02NPCDmP21ik+b2k5so8xko1Z/h01Q+N\nJgx61q/RaLhBteNfCzrVr7y0p/rNgjOjdvwTR5pS/TIAe3PLxRfGhWPmPPz90P/Cr/JnJF2URMiC\n8K9OVaf6bVAPXqRNKKWS45/saOGfWhwz5yVdBE0Hpojai6wad1QS/snu0JFEivkSyUdyXnLqd2QX\n9vMiKwJHFZH9WddEI41zG4KEUv3mKkLPV8fmJfybNQfwI8SukDxeza3gct6k0Kl+NRq+mLAwaQaL\nCaclk4phU1QpVWou54eqEa9RR72fdkup3NwnJSWcm6C9kY9RS/Wb3RijRlMn1atpTz31FL797W83\n/v7CF76QYGk0GkEkuOuBpmzHRVQMnepXWtoFmVkQaGrHP3EwRVP9Ogv/5GzXLWLih71XJF2MRCgj\nQ8K/lASoeHDMnIdduVUo0XhSGyWF00KYrO2OzNiwlRScazQyYmlXrFSjkuuZ2wL03/u4AAAgAElE\nQVSNDCQh/CuTPECs0NkyteOfRkayIj7NKjYhsmf4DQxhBBahwudtVTN+4R8D8GpuBSZpDxZXD2HE\nOskx1W8t9uxX7FumajvmqS78Y7BBMjJG0KiBwSyUqXedZDidiCgtmwtqqX7FC85tYoNGcBufQ9ua\n0ZSRR5zav7pDon/Hv2TqyyljMPIxqjBS5wip0YQh1cK/3bt347777gNQS0GqhX8aTbxox79WqBb+\nSUu74x/NQEpmk2nHP1EwwpQU/zk6YOggl3RkyvEvJSkpeLAntxx7JHPf5IFT8EkL/4LDKJsTQFSB\nTk6zGk2SpGVxROOMHtvygYHBJuI3z5VIF+Z3GTg6E+77zo5/2agTSS3kaTRZp3WjUzrmOTXhHxHe\np8aZ6hcgsECxte9a7M8tqb3EGC6dfpTb2LB+97PS75RUF/4RpuIUW5NiDFi+TF++NvhebCo9hwun\nt6dmc0Et1a94wXnFrKCrwlGEzVrLbxFTyAjdzX14X7EfmJz9nMLCOUaozhyk0SDlwj+NRiMW7fjX\nCmU2qF6+lBLK2lP9pt/xz4B2/BOFrWhkyKnUenFUPrIk/LP0VCX1tAeVjhtDYBlyteSFTWwlR5wq\nOc1qsoPsLmuaaOixLR9saiey0alE8rBY+Lk7c+hisiL2Vb3ud9kzqXO+1mSD5o1O1ZS0N4QR2IQ4\ntqlxUonZ8W97YfNp0R8AEIKfFy/CEA5xPU9W+h1L9Y2chOldaRqpMHyaOszQIrYXX5cqEwjDpqga\nRLhwumJW+Qr/2iC2mI7UzfGvOS6r+nxBGwhoNFD8KdZosozpvGuKLlwlthzN59YtSgsGLBDBTnKL\nKwexqMo3IJFGCGlP9Zv+mXyaJntOkAgLQLxhCbhf8MDZAUMLMGQjK8K//9v7Fkzrxb3U0xxUGqX9\nuLfv+gRLoy7pcvzT/Y5Go4kPPbblg03tRIyryiQfaUGq4iC2UH2Byy+qO0xlIWajSSfNLjtpWZA+\nnepXXLtSNaqxCs4Nm+LpwmvmvM4IxQmyxOEbwamXPiv9TlVxpzGm+x2NZJioBhp/P991ZnyFEQxl\ntJbKVfEU8/NOjaB3srfxN9c0wg7Ur5fbPKC5T3ITCKpAVtZMNBo3sjHK1GhSCL3ourkvDswHFqwQ\nX5hZiA6it1Bz/BMrAKJgKNgh896kgFKuhLJZ9vxcu8NfNhz/0i3862Le910UjDBFM7joVL8qoHq6\nFL+8ml+Fn3dfnHQxNDFTD0IxAD/puQwztJhsgRRFWcc/h7mDFv5pNJo40WNbPljUTmRB3CImyiy8\nkGA0N3ccbSkuTPCLpXjdFx1b02h4URecW6CpSblYT/Urctwet/CvZ7oHJRqfq1ON2bqQknrghfIp\nFxXcWKdJN8as6YFf84NRYyDO4ginQnLChWmVHH+n2SWHF6M4XYt9EkHWuW5C/ea+XPWNQuoL/9K9\nfqsRg9pPsQdahKRJM/S114CcseX0C90DMK/73UTrvUH1M9eMARtEcDCcwMagNSr0nElSypVqwR8w\nTBWmsG/xfl/fI22DKCrYmTEJ2tMbpw2TxZvyIwiM1uqTartDnQQYdkZSkKhEmcQdjNZoxFEPKu03\nl2BPbnnCpVGXJNIt8sDRaVYL/zQaTUCCOH+LdCdKMzZNblFi2g4vJHBK5a26IM4vqoteZXL412iC\nUB/vpim9ayLCP7Mq7Fxx0XBeUrw99ovqDpdqzrA1aWYsV3umRK83ykKF5IRvFC3l+BtNEBAsOroQ\nxCbiHP9crltzn6T6XLmivOBcz3c00fH9FJw4cQJPPPEEbNvG5s2bMX/+fM/v3HbbbZEKF5W9e/cm\nen6NJk6IYcJ8+++AXf7rYJNjIAuWg9BkgwhEC/9aoLCEO8lRZmPQzobwb6J7AvsXHgBQCzrVxVZ+\nxv/taWKy4PiX9h3yOcgj/LMVFWA4TQJ1OjT5yIrjnyYb1ANMr+RWJlwStamlXFSv73FM9av4DmON\nRiMeYhD41QRZs22Mei2mXNjUTkx0Pm2Fd3Jwnu+kR4zjRlnxOUTa4xma9FKf71QUF0E1k5TjH0ht\ngy1RfKOQlZH5juqOf6purtOklyeG5yVdhESpIC/Uka5qVFGOQfgHAPlqHn2TfbEcuxnW+G/nfrPF\n8U/x/lV9xz8939FEx9fo6x//8R9x9913o1Qq1b5kmvjQhz6E3/u933P93n333Re9hBqNxhXSNwzS\nN5x0MQAAVAtEWqDMBhHsJEfAMGidEnrOpDgyfLQh8gs6GW8X+rULAdNI2gPlOSbP7t96fWSECbNs\n54NO9asCWhSjSRP1ANMk7Um4JGpjE9U8Zms4Cv8SKIdGo1EbalDYtr/Woz621WPcaNg0ubnlDIvi\n+De338mK4x8Uj9dlIWajSSd1kYLq7mfNEBBYhArdKFpJg+Pf7OXKyhikqriwXgv/NLKxu7cXwLRw\n1ztZqBJT6G+f7pr2ZTASlt7J3vgO3mDW8c9lLaHZ5U/1/kl54R9hOiiqiYznU3zvvffiS1/6UkP0\nBwDVahVf+cpX8K1vfSvWwmk0GrWg2vGvBQN2IsHJoayk+u1Q3fwsfc8V/qVbFAekP9VvXqZUv4ra\ncjunXFR7wqfRaMQTJPW67SMIpfGm1u+oFx1yKrHqqUWygk65qJGJIBsQ621MmgQYSWATO9aFsLhw\nmttkxfFPddIez9Cox4mBk74+V4+zpKnfqTv+iewI6ql+1RZjpS/tsxuq13k1t9Zp0oyd8VCJRQyh\nwr+ZwkysfY5piWsj3dZ3LGsQp2g/APXNBpQX/ul+R8MBz6f4a1/7muPrjDHcc8893Auk0WjURTv+\ntUKZDSK4s7aIiR42KfScyRH+2hJitf4t2JkxCYyUixuDCE3iRtVApFOptRhHo9EExWCW94dmqbcx\nqqeTSBqbMihlMDuLs+Ofgj8kg/TaWZlvaFQgyAbEhvOS4unnksaadfxTbVHcabyRGcc/xWnfvKnR\nJM2xoWM1NyAP6ovtaep36sI/kXM4i9bmmKr1O82wjG16qyouwFBxfq1JN3Y9w1BGYyYWqNCNomM9\n47H2OdSO/z7W64pXnfnG4LuxrXi+8gYQFcUF51r4p+GB51O8Z8+eju8dOHCAa2E0Go3aaMe/VgxY\nwp3kKsTMzNA/ygS8XZCZCcc/l9+ocuAMACizYMC/0CRuGql+Fbuu2vFPo9HwwAzQHjcWP3RbEwlV\nnWadXEKyGsRWjS5W8v6QRiMIGqALsRqOf9lw24kLuy7AUGzDk1OqXz0GUYO0b2TUqAejDHuW7PX8\n3AljCAzqu581UxP+UaHjdsuYnWMqPFWo95hZcfxTHdXGOJr006iTGTVfsWDEIjgf6x2b89p49wSq\nuWqsfQ5l4uYgfgTnjxXPx2FzvoDSxEcFagvOofsdDQc8n/bly5d3fG/p0qVcC6PRaNRG6/5aobCF\nO8nVd5D2WeNCz6se7cK/9A+q3FLj2MoKBmpQ2FKl/jk9EU+2HEHRwj+NRsODQI5/s22M6ukkkqbm\n+KfeWMZJgKGFf2qQhU0zGnUgAQIR1qzgL00CjCSwqZptgNPcxtIiUCUQnU1Do3EjyMb3h3suw3/2\nvR3TpBBjicRCGIFNSCzj9iqtznnNJjZK+dqmE9U22DqRFcc/1dGOfxrZUDHmwxOb0Fhih8cHT+BE\n/0nYxAYDw0T3BA7POwwg3j6HCMjdXI+5+e2vj5gL4ixO7Kie6jfrz7iGD56Rrg9+8IO4/fbbHd/7\n7d/+bd8nOv/887Fs2TLfn+fB3r17sWPHDqHn1GiyjHb8a4VAfHCyvoBx8fQ2fL/3aqHnFk2ngbef\niXn7YJdIJBqLC1fHP8ogkWFeYAxmS+UAcNp6Xy0cHTB0QFKj0QTEZHMXazpRH7foVL/RUNXxz1Fw\nntHd66qhBRgamQgSh6jMhkHTlHIxCWxFU/06xQr0Ric1kGmjn0YTdLi6J7cc47QnnsIkQD3Vr1MM\nKSpjfWMYHh1ueW28Z7wWtwSU22DbjHa7VwvVxjia9JN1UVDN8Y//NWCE4ei8ozg2fKx2jUnre3FB\nBaiL66XPSr+jU/1qND6EfzfddBNOnDiBu+66C5VKpfYl08SHP/xhvOtd7/J9one+8524/vrrw5c0\nBFu3btXCP41GIFr4NxfRTnL1BfR15ZfxcnkXdubXCj2/UDpWN+9r3o3RtkOlf1DlKvxTfOJIYUm1\nEHD6eqp2XbXzkkajiU6Q1OtTtBtAdoJQcWETW8m+3KnEut9RA5nGXRpNEN1Bfb6sU/1Gw6o7/hEo\nNeXRjn/qop1mNTLRiH8H6H9OGsPeH1IEwggsSsBimMMdHzoBmzAMjg+AMILxngkcGTnSeF9tMVat\nwuhUv2qgHf80sqFizIcnNjHAYnCuqF/XhsC8GeVT/dYF59lo0NLg+JeNO6WJE1/y1w984AN4xzve\ngR07doAxhs2bN2PBgmCWn0Tv3NdoUg/Vz/kciODgZN25wICNt0z8AH87nF7hX9hgTw85ghwrt7yW\nhSCy29Opeqpf2Rz/Gs5LijWJTlMLS4txNBpNQMwAqX4nSc35Io5FoyxhOwUoFaDq4Cqrw1xqIHpz\nk0bjRpB4Y3V2McDSjn+RUNbxT893lEX3OxqZyHr8u+b4F89TycBwfPg4jg8dnz1Z2/sKX/r69bIE\nZtagzIKtBe7hyLjISiMfqmZ64IUNGku8yG0+o9pcpxNZyeikuvBPqR11GmnxHekaGRnBm970pjjL\notFoFIdox785iE/1e3oyTwAMWKMYNQaElkEYIavbYvMpsGrrl7MeRFZ9xxiFJZV4k6ma6tfhtaxM\nDFXHYFVYytvZa9KCAf+pfidmU17p9K7RYIo6/pXo3HZLC//UQPTmJo3GjSBhiPpGOe34Fw2bqrnR\nycntQgsi1IBop1mNRGQ9TEJAYJN4HP8ac5oIWV5k5XSqX3H9zqrKbrycXyPsfGkiLYIfTXqwFYz5\n8MQGjWWdwjWWRmptAVFt0jNLvd/JymbrI2YwwzLZUDGuq5GPbDztGo1GCFnf8eiE6DRY7cKPNKew\n7TQB99r96bQ7KuuLl45W5gphwAYN4DAVN7aiqX7bxRYWKP6neEFCpdEEocBKSRdBo2nC/3hwknaD\nQaf6jYqqAWDLYe6ghX9qoFP9amQiyAbESiPVr94wEQVVHf+cNhpoxz81kGmjn0aT9fg3YfGkDmRg\nnlNJlR3/6oh0/DMkipWqRhrqmiZdZF0UFJfjn3e/o/51F5nqd8v0DhTsaWHnSxNpqGua5MlMdIMx\n/cBoNHFDtePfHJIW3iV9fulgbHanTis0432E6jFLyuR0/FNNu9A+eX6o5wocNecnVBpNELpsLfzT\nyEOQgJJNDMyQghb+RYRRWznxBeAs+GSqD0oygp5jaGQiSBhihuYB6FS/UbGJmo5/TouFegyiBrrf\n0chE1uPfhNW2T/MWYPhb8Fa5LagLJgUK/ySKlSqHFmBoNFLBEkj1C6gtxjrt+Cdu3NJrT2DAHhN2\nvjShcl3TyEOska4bb7wRAEAIwfLly+M8lSMrVqxoKYNGo4kXquOlcxAdnFxSOdDyd689gZPGkNAy\niKLzQKjzNSdgjrv8ZRKNJYLiz64BW6pg1ulUv2oN1pvFFtOkCy/m1yVYGk0QurTjn0Yigi5kTNAe\nneo3IjZhSi5MOIn8tABDDTI/dtZIRZB438lcEXdu3IL85AhyEzEWKsVUjAoso+YgpPJ8p46lU/0q\nwTODI+idSroUGk0N7fhHAEK4b9jxs+Ct8qJ4veRxpKrshHbpDo9qY5y0QWBnJj2pH0yDKrfhhjcM\nNBbnOq9+ReW2IIl+h4Gg157EYWFnTA/q1jSNTMQq/LvzzjvjPLwnmzdvxubNmxMtg0aTJbIe+HCC\nCu6uN8883fL3BdM7sDc3V3h90dRj+J/u7KXwJGBgIHN2uWR997jqax0Gs6UKZqkaiGx+Lnbl16hv\nBZkhCmwm6SJoNA2CLgDV0v3qgG4UbGorOZIRmW5EwxfRcxyNxo0g3U7OyuEwFiGfz2FBfEVKNScH\nTp1eeFSsGXcSl4sUnHfZMyjRgrDzpQlbsbqWPhiUe+BjRDv+1aKo3B3/Uj6+rM+TLYgLwhrQqX7D\nolP9JgtJfYsQDMPQFbIWhRAfO1S7LRDvNAsAvfak0POlBVXXFDVyoVdYNBoNN7Sz5lxECspWlvdg\nZWVvy2uLqoexuHKo5bV+awzryzs9j1e0p7mWjzshqlvN8W/ugF0m0VgSqO7WSWFJFcxiKUh9VSG5\nBEuiCUpBO/5pJCJoQGmS9miXt4gwwpQMEDkJ/3RdUAOS8bGzRi6CxiGWHl6C3qnemEqTbg7OP4ST\ngycbf6u2JOtUWpGOfzK51KuG2ouu6pP1zbLtZFz3Nyv8m7upOircHf+YnPVWp/pVAxXn12lC9zut\nGFnveAAwRrhnCyEUnus3aWgLRKb6rTn+aWv9UKSgrmmSJ1bHP40maf7gD/4A999/f+PvLVu24Bvf\n+EaCJdJkhfpimChB2eWTP8PZpefnTOgpGK4fvx9PFM/FQXMR5lkncN7ML9Btu+dI2Wg9hlF7GaZp\nMc5ix4LbYLzu+Of0epZRfbeyAbkc/2xVU/02PRtE0gCpxhmd6lcjE0Fd3CZJj3Z+i4hN5OkDg8Ac\n0o2IDEhqwqMd/zQyEXQuQ0DQPdMdU2nSzVjfWNJFiIRTvyNSgCHTnFU10rDoqjQEOv9YE0TxGFpU\nCCNgJAbHP87PuYkqqpBpU+us459AwTll8mySVg/d6CUJI0Tfgia04QpQi0LwvQ6+MtgF6JtWl1/B\nK/nVEUrEl0aqX8EbbHu0418otMO5hgda+KdJLQ8//HCL6A/QAySNOOpCMlGCsjPKO2F2cDzLo4KL\npre3vOZWqiMjR3DFkd0Yw1KOJZQDgtrEsf3304zvgCRGTTRAmZouO5RZUt1DVRclmkudRjEsZRZW\nVvbilfyqpIvCnYKthX8aeQiaemOC9jguxGv8w2hwxz/C7MSvu1PPHTRVtCYZiETjLo3G14KNJhaC\n9D0y9DtOi4UvC5wbUIlc6tUjffNTpdACjBay7rxEZiNGvDdv+XH2DNLvmKyKqkTZLOp9kHb8UwNV\nY7upgVLoYdNpmN6gP+s0y7f99NOf+zV2GKkew+untkkm/Kv9PtFxNp3qNxyqmYho5CTVKywPPfQQ\nrrzySlx11VW46qqrki6ORiCTk5O44447ki6GJsOIFv4FXXxzG+qx2SFGGoU/AHO8Uun8rQEgwHjP\neNKlCI0BW07hn2Kx4GNdzc4n6XomVpdfwY3j/4XF1UPeH1YQ7finkYleFizAc8Scr9O7RiSM41+n\nDSMi0Y5/6qId/zQyQSgw2juadDE0HsgwX2tPD/Z01yZUSF7Y+bUAIzw61W+y6OvfStYF5/VUv+B9\nHfyk+g1wOFMyt7t62YWmmJfsGqiEbveSRV9/zRwY5S44N6h3LNKPCHjL9A68Y/y/kGdlHsXiBxEv\nOGeATvUbEi041/Ag1Y5/k5OTOHDgAADt9JY1/vqv/xoHDx5MuhiaDHNa+CcmsEu57vohqO3fTB+E\nsdkdLq2/Lispd86eeQ7PFs6a8zoZKeNw9xEMTAwkUKroUGZJFcxiVM1Uv3u7+4DZ7F1pev6Hqydw\n7cT3AACHzIUJlyYeTFZNugiaDLJqaT9e3T835d+5M7/Antxy38c5aszjWaxMwggL3HAbrIpKwg4Y\nTkFbLfxTg6yMnTVqQAnB8aET6J7uRs6Sx9knCwRZnDCYBYskG4ZmIDhBB/Hf3RfisLkQU1Rsymfd\ndoZHtbl12tCGf60ETTGfOhjBkUI31vF2/OtQyyghsOtx7yD9jgQbnZwQmmJeC85Do/udZNH9Tiva\n8A+oEoO7e7if/txrvrOyvBsXTT8OAJgkRS7l4sXpVL8ixy0EPfaUwPOlB53qV8MDba2gSR1PPfUU\nvvnNbwIA+vr6Ei6NJqvUhX98BXne5+NyLEbASC0Vj4q4XQkKBtsx1W82Zk9nlHfOua/lQgmki4FR\nhmODxxMqWTSkdfxTjGaxRZpcMJsF2Gn6Xc3IVP812WHjmhH0GK2i08WVg1hYPRrsQCQGt4gMMdYz\n3hD9BVmckEEw7JRuRAv/1CArY2eNGhAAlVwFry7bnXRRNC7IMF6toAv39V+PV/KrhYv+ADmugbIo\nOsdODXp41kLWhX/5ag7dJ1fhuMl383CnWFqenhaNB5nvyLRBGDg9z5mm4oQh2lk/ArrfSZZsN7Nz\n0LWxJvybpnw3ERmUYFXfiOtnvNZ5mmMjssZJeAsmXc8FAhMW8rbOThQUVdcUNXKhR36aVFGpVPCn\nf/qnYIxhYGAAH/rQh5IukiajECY21S/PADIBZlP9KorrAGk2RNT240Q5M/qhaE/Hduxl1QNYSXdg\nJj8Di1oY7xnH6PLjp4OWig4uKbOlck+oByJFpCU4MnwUv16+H1umt0c+VlqFf82T7jT9rmb0IqIm\nCfp68vi1JYewaeZZLKkcwPnTT+D68ft1fRRI2Szj8LzDjb+DBIlMSCD8cxhtit2JrAmLTGNnjabh\nZGDoeimaQI5/ErQbJ7EoEcFfHVndp1QgnbM4japo5yWgf7IPo9RdLBGUzsK/ptS4AaYK8rW5BDtz\na4SesceeFHq+NKEd/5JF7w1themOB4QRHC70cD0mpQSXLHRvl4MloZftPolP9VsnJ0HMUTW045+G\nB6lO9avJHl/96lfx0ksvAQA+8YlPgFKtbdUkQ8PxT1Bwm6uYhREwkFQKZEjD8a8t1a9Ev7XHnoxt\n9+eEmcMvFhk4WNzTUHcuyPWCzM6mVQ1qGLCkWEhq0Khe8V9Py7AwhHHsZfMjHyutLkvNbRlJaaBE\nJuGrJjtQQjCYt3DF1E9bXi8znWZRFLuX7gktdMlJ4PhXF/kxADOkC0VWcnQB1MiHTGNnjUYPg9RA\nhvFqCb2Jnl+Ga6Asim5STAs65WIrsocV1hWOYOfMgqSLEZiOwj/DACqznwlQE2XbkMYA/LT7YqHn\n7GIlLKwexmFzodDzpgERm7k1LujrP4eCkcOMVUm6GIlBQFCiBnhu4aGU4LJF61C2LTxy8CVMVcvY\nPLIcM1YVjx19FYD3RqfmdQfZ4iRJpPqtry/FnWXEJjYoS5f+Q/c7Gh5IIfyzLAujo6M4efIkRkdH\nYVl8duO8/PLLXI6jUYNdu3bhq1/9KgDgoosuws0334x777034VJpskp9wCdKPMc11W/jv3IFSHhA\nZkNE7VdLprTGPfYUjsVw3AcXr8LjI4twpDA7PZq90SY1Tu/5UXRwGTTV70x+BoVyIZaynOo7Fctx\nveDxvDYL/9IkApQt2BsHWfiNGvkgBI7bsNO4cUBW2gOQqqW+skHxcm4lHu6+FBNGH/qscawv70y6\nWBofyDR2ziZK+7NzR9XNS2kgmONf8v1O0ugxe3h06quE0V1OC7I7L3XR5Df4hKHTc55rTvWrcF2c\npD2YMPqEnpOB4JqJH+FfBn9D6HnTgO53EkbhZz0OGAPeuvws3Pfq00kXJTEII9yrBaUEhBBcvXQD\nrl66AYwxEELw9Re3NT4TJNWvbIYDrLHZVqBArr7mGLPwjxGWul0ptu53NBxIRPjHGMMjjzyCRx99\nFNu2bcOLL74I27Yb72k0QWGM4U/+5E9QqVRQKBTwZ3/2Z0kXKbNs3rgATz5/JOliJE5D+CeiTWN8\nJTqEETCi7vzK7YoTALaDSEGmAHxcaRjuX+psW25SqrzjH2UWaAABQ1zBm4nuCRwZORrqPMPVEzhh\nDoc4a91dNPpvag6gpinVYnM7nFZBkmw7ClXh1998Jv7tey9EPg5hNhhJ1y5DPxBCAIffndbnTAkI\nfAe+ZLhP47QfD/Zuhk1q6bvGjT48UdyccKk0fpBp7JxFapuZ0jNWi4oOI4rhZH+0DU7a7U5fgyjo\nxzxhdJfTguz9jqoG2p1ikrmWjE7+L34SqQ3dqJIklmIJhuxRnDPzLJ4pnJ3A+VVG8gc95fCXeKkN\nYwzXLN2IvRMnMZ5RvyPCCHgPSAzaejzi1IF6re8osO7gtB4aF/UrYMac6pcaBGkLS2nhn4YHQke/\ntm3jP//zP/H2t78dv/M7v4N//ud/xvPPPw/LssAY06I/TWi+9a1v4cknnwQA/O7v/i5WrFiRcImy\ny0XnLkm6CFJAOIpxvOC+8JbmVL+stkg2x/FPot/ab48LPZ9JDND67h9F59QG7ECpfuMYRB8fOIH9\niw6A0XDH3lR6LtT36veMxyJS8wKyDSPy8WShJdWvRM86T9L6u+Imn+MzFcqqAIZq4V/iRHH8k4Ej\ndGlD9JcEQTYNaFrRgvOEUXTMHhuSV8cNpeibDJKmYlRxcuDknNeDplxcU87oSuUs2vUwAnohLFF0\nt6MWVNHnhbfjn2wbWpMoDyP1lIvZTQ8aFlVj5KlBX/8WGACDUnxw46VJF8WRtT2j3I5lE+cYK2GE\n+3CQUu+K5jXfoRKvOyTi+Dd7zlzMjn99+a5Yj58Etm73NBwQ9rSPjo7ife97Hz7xiU/oFLwarhw+\nfBh/9Vd/BQDYsGEDPvCBDyRcomxT7DLxjqvXJ12MxDmd6jd+MQDvAWXdLEY2a2rfuMwACFhtANW2\ny0WmxctuewoLquJcM5sd/6RfNesAZVYg4Q1vx7/JwhSODx2fex6f3z9v5hfot8dCnr2W6o2H60uL\n8C9F7mUyPd/xwbC0ciDpQigHrx3EsgV2REEogFx+7usZvR5SoFiQaIb0JHr+IJsGZIAwG4sqh5Iu\nBgCd6jdpmKpWPjEh+7R1cVWO5zYop/pGcXzwOI6MHMGepXtQyTmIBgJURQKG18w8y6+ACpLVzSI8\nkPwxTz+622lBdvMKVaM5nYRWedq8Ucj/tRcrdPAmCQfC+tXSwvPg6FS/yaKnO21IXh37cvxEXp2e\nvTgc/3wJ/zzaAhUMB5IQnhsxC/+6cokkNI0VLfzT8EDIaHPv3r145zvfiVxzrMcAACAASURBVP/5\nn/8RcTpNxrjjjjswOTkJwzDwuc99DpTKNanLIu0WyVmkLpoTMdjjfY6KWYnluDLQKS2WTCl3GCG4\nZEpcf2lS9R3/CFige8gzeLNn8V7sW7zP2enP4zzLKvtw3fj9uHTqv0NPv2qyP8DiINRrdfxLT1+q\nwgQ8KoQBW6a3xz6pThOUEh1IjAglBDCzKfwr2tNJF6FGWx1WzfEvaVRz/Bu2TsSeLsUv2RDVSwzH\n/isN7Yb0AgyJ5ppBmCpO4tjwcZwcOIWq6dz2BLnyDATLq/tx1szzfAoI4No3rPG1YCcLhqJ1QQa0\nAEMjE7bs/Y6i4ZxOz7nZ9IMCOf5JNuFnCW6w1f1PGOR+ztOO5M2scKSf73BsbjtlaorD8c/POrb3\nGFTmdYfa7xO5zlNfX8rFHLsyDbn6eB4wRcdvGrmIvRqVy2V85CMfwe7du+M+lSaDPPDAA/jxj38M\nAPjN3/xNbNq0KeESaQA0uYdll/pimIhFsbADyoumHpvzGgPDWO84GFE31a97qWtLWzKn+rVBsax6\nAG+a+CG/g7pMDnNEfcc/gATavcpr0WC8ewLTxenQi68XTz2GVZW9qO1XC1kmAgAMFofUvGl1/Gt2\nJZLpWecJAcPy6gG8c+xebJnegZ7QDpLZwaCE23iFx/OnIoQQkNzc1ApZGAXK6tijqoA/KVRzviiw\nEkpEjnQmIlzNNZ3hOd1Og5hG8nUwZcefNvXxnIeoP0ur/Fyq168cUmrcIev4QQ3UfI7SgtYMtSJ/\nv6MmncYkRkt8yv/Fl21DayfHpVKuhJl8KZZz1uN8VLF5jwykYYysMrYc+900PuE7P+2Q6hdJOf55\nHENi4V+9NElkDDBjNicwTbn6eB6kYVOmJnlifzI+97nP4cUXX4z7NJ7IrojXBGd0dBSf//znAQBL\nly7Fxz72sYRLpKmjdX9NqX4FRMdoyPbtrNLzGLROtrx2fPAEbMNuJA9NGwS1HZ/tv0ymAHw9MDVg\njXI7ptu9NKlRc22C2oKBpBz/XM/j8X6LE13ocUrtexbhITyqVYASyeOJwrkcjicHaWzL2qn/xnnW\nCVw0/TjOtJ5OuETyYxiEmxOC1879Lnum43tvnviB63cvm/x5qDKJgBA4Ov5lAXnTnAZp7xTu9Dmh\nmvMFhS2N8E87/qUHu8PCikrIHu5TLa14HT/CvyCLE/VP8ux9CCEgCjn+qer+KAMqxypSga66Lci+\nzqSq41/RzM15bVXvcMvfQeJ5yqT6JUDViEcgURf+qTbvkQHd7ySLYub83Oi0rrpicb/YggSEq+Of\nU1YlxOP4Rztc8OaXveY7rZmG5KLeByTh+Be78M+Qq4/ngRaca3gQaxLsXbt24Tvf+c7ck5omrrvu\nOlx++eXYtGkThoaG0NvbCwDYunUrPvnJTwKoBXCef949BcT4+DiOHTuGJ598Ej/4wQ8a7m8AcN55\n5+Ef/uEf0NfXx/FXaWThzjvvxPHjx0EIwWc+8xkUCoWki6SZRbYBThI0hH9CHP/CTd572DR+bWwr\ndubX4L7FZ2OiexpT3VMAoLjwr3O5O6X6lem31lNR8CyRu/CPNgUt5bkOQWAggRbUOu0cC4qPPWEe\n329+P9y1r7c0PIR/jBDYILi/982okrkBV1VpFvaGF1jKTfszns5fyReD0tndovFzRnknnuk6e04E\nb3X5FZxR3oWT00N4rHi+43e7WFlEEUNBCQEcHP+ywHzrGCYM+eaYQRYn9EKGeo5/BrMxI4vwTy8e\nJot2/GtBdgGGvGJxd3yJQgPVRTL7/3zvl0K6P2VFoDKQhrZKZSRvZoUj+/VQdUP+yr4h7Mb+xt8E\nwFVLN2DHsT2N14JcetkyWXRyXGJgsbdxqs17ZOCCBSux/3Al6WJklqwK/15/3lL8/Mn9c14/f9Oi\nBErjH57j8Y6Of4xwX3j25/jnsb4j86Bg9ud1cpyNg/XlXQAAM/ZUv3L18TzQ8x0ND2J9Mu655545\nr23ZsgUPPvggvvCFL+Ctb30rli9f3hD9haGvrw+rV6/GTTfdhK985Sv4j//4D6xfvx4A8NRTT+HW\nW29Ftap9gdPGo48+ivvuuw8AcO211+LSSy9NuESaZnSq39PBbJlT/QJAkc3gnNJzODZ8tCH643Fc\nLw7OP4ipwpT3BzlDwGYd/0jb6wGOEfPCTX0HDs/r7yr8a0r1q6oIgCGYa6OwQbTH9eSyI2z2ixaH\nvRyMAS/k12N/bmnkY8lE63XOxgTKVvRZFkkt1a+Yc/Xb41hZ2TPn9TPLOwEAVZfnV+Z0moQAyGXR\n8c/GhlLyjvbOBHHA0A2FauI1ChtlKofwT+a2KRPwXFjp4KigEjKvtQByucsHwZ/jXxj43jBRGzl4\noFMtRkHyBz3lKDZkih3ZBecqCaKbWdU/gg9suATnjizD6+atwK1nXY4LFqxq/VCAeJ5IoYMf3ByX\n4opTNhz/dP8TmAsWrkq6CJoMsnJJP9atGGx57YyVQ1i6ILyGQgQ8+521g/McXyeMcB/3GzyEf1KP\nUQksUBw3na8pbxZUj2DAHgMgwvFPrj6eC1r4p+FAbI5/ExMT2Lp1a8trV1xxBe666y7k8/EtEG3Y\nsAHf/va3ceutt2Lbtm3Yvn07vvSlL+G2226L7ZxROHToEO644w785Cc/aXn9V7/6lfCyMMawfft2\n/OhHP8IzzzyDV199FWNjY7BtG729vVi6dCk2btyIyy+/HG94wxvQ1ZXMosPMzAw+9alPAQCGhoZw\n++23J1IOjQsp7HODUt/pIWLgx0Nc2L7rkBES224Vi1oY6xtHrpJH90w3/xO41D/CZs25I9RREiih\nUXDisN72SvVrW2o7/oEQ0ABbAbkF1DwOEyjVb+hlMwYQwOKxk5kQPNR7ZfTjSAblIbDsQLc9hSka\nQzsWkDmOf7of9oQa4lLDmayKt0z8AD/puQy7cytQZNM4b+aZxi7IiovDJo9xRJ89gZPLCcz9PZGP\n1QylJJPCv3n0JRRZ5/TNSRLI8U8P2JVbAKPMwuryq3glvyrpouhUvwlDCL9Ru071Gz9pE/5RENgI\nPn+s9zu8ex/JDJ1c0akWw6PnNwnDserW8nCofUNt2fsdRS8vpQRb5q/ElvkrO34myKWXbb7TKd7L\nCItxsb92DVTb8CQDfgRBmvgwC0CVU9jFhg0qWervTuRMire/YQ127R3F0eNTWDDSjbXLB3050yUJ\nTyOYLtNZtkIYx0nwLJ2u6znDS/GzQ7vqJ3Y9hsx3hgH4Sc9lQs4135zCdScfaPytU/0GR/LhpUYR\nYhP+PfnkkyiVSo2/lyxZgi9+8Yuxiv7qdHd34+6778bNN9+Mffv24V/+5V9www034Kyzzor93H5h\njOFb3/oW/vIv/xJTU20uWwm4pT3wwAP4u7/7O+zatcvx/VOnTuHUqVN49tln8d3vfhdDQ0P4rd/6\nLbz//e8Xck+bufvuu7F3714AwCc/+UkMDQ0JPb/GG5kHO6Los8cBiHHDiEOgF2eq37psbrx3HPNO\njcRw/M4QMDAS7ZdR2LARPa1qJ+qpKA4Xi9yO6eX4V4khvbBIknP882rt/O8Ii1IrCQPWlV/Gc10b\nQx8DAGxLDhch3rS2kfxqedGewq+N/Qe+PvhubscMS3v9kW1nu4wYlNZS1QrAZFXkUcWbJn8827+2\nUiZujn/R62yhtwcYPAVwFv4RQkDMdLYbnSjlShghL0u8o1fWcsmJasI/AzZWVF+SQvgn7zOgCUoa\nxDTSOy8pK/xzvq4mpSjbtfYzTIp53vELlTJOqFoXZCDe7Zcab/g9Z4yw2uK9wkjf7yh6eQ0/7j2B\nHP/kEgW4xWniauPqR1Vt3iMDKo0v0kjPQoLR3XyeC0YZVwF7nBiUwKAUZ6wcwhkr1Vn3phybW9N0\nPhgBf8e/TsK/TUOL0W3mMFWt+HD8k7dyWTDwUn6dkHPdOLgThSOn1bq5mIV/RhqFf9rxT8OB2J6M\np556quXvW2+9FX19fXGdbg4DAwP4+Mc/DgCwLAt/+7d/K+zcXuzatQvvec978NnPfnaO6E80Y2Nj\n+PCHP4yPf/zjc0R/hBD09PRgYGAAtK3nPnnyJO666y5cf/31eOmll4SV97nnnmukkL7kkktw4403\nCju3xj96YgRcML0DAEAFBIN4BI+dShnbgt5s9SjnyhjvnojhBJ3LTcAiX624FzpXVmrC5ocXLON2\nTLfFFZMap8UvMQ8uZ/Il7w+FggRyT+A1iPZs6bxS/fIQpM3+lmWV/eiyI17fihjbd/HEk+rXYDYG\n7DHkWIXbMcPS/oy3u7hq5mIa/FL9ej17Jk4HO5xO6er4x2EcQboKoDHY4dAMpvqdKk7BIkRa0ZN2\n/AuGas4Xu3r78fUzl2KyOJl0UZS7dho35GzPgiC5/kLZ56XTnMmkzZvgglz8er/DO9WvOvAS/i2q\nHsIbJn/K5VhurC07bw5PBL0QFpqNa/hvuI2CnYJ7KX2/o1LD2IQfR6sgl37LzBPhCxMDrMOcnEFA\nqt8AWVLCkOsg1FEZnkImTTDOXjeCXA9fwbkqqCpmMjiOyCnp7LhJbDHCP5Ma+N9nvxE9ZpenMFvm\nLneaFlxjzjxpF2U2x8LjoJNAVGVUaqs08hLbk/H00083/t3X14cbbrghrlN15K1vfSsWLFgAAHj4\n4Ydx9OhR4WVoplKp4Mtf/jJuvPFGPPFE8hOPI0eO4JZbbpmTZnjJkiX49Kc/jUceeQQ7duzAtm3b\n8Nhjj+Fv/uZvcN5557V89tVXX8Utt9yCbdu2+T7vvn37sGHDBl//u++++xrfsywLf/zHfwzLslAs\nFvGZz3wm0u/XxEfW9QbLKvuxwKq1NyIWhrmco+2m2SIWtQlwYOEBHBk+EpMA0OmUdce/kJWUsVjF\nnD32JBZVD+PZ/hHs7e7ldlz3VL+0MTCPu7ZOF6ZQMfiLpJJz/PM4j8f7rY5/Ec5BCAzYuH78fhTt\nZDcUyAhrGu7y7J5oY8e0fJMyznGQVEIp4bJRgTAb68s7XT9jegTZqzGn+qWGGcviDyEEyGXL8a+e\nWSQOt2U++C/X2aXnYyyHGqjmfHGsUMBkzsT+hQdwcP5BnOg/mVhZtGtVODauGeZzII5NkKytWRDy\nObmD/mqmxnZ3jG98KpDgvAbvOEMcG0+rRjwLVZST8IIwJiTWNL96LPZz+MUt9aemM0sX9GLhSHfk\n4xgFfvWNpSLFvNztOlFU+ecrtarPeB5hNtaWX45YIr50dPwjLL445ewp4x67p034R8C0sUUEXn/e\nktDfffMlq3DNxau4Ci9thfod2VP6doLnXmNCSMfrYDC+WbjcMsGs7Z+PL130DrxpuXuGJZkd/0Q6\n37bPTeJP9avms+JG2LHATJ5TXnRNKojtqT98+HDj3+eee67wdLBArYO45JJLANREYz/84Q+Fl6HO\nL37xC9x00024++67Uakk7wwzMTGB973vfXjllVdaXr/qqqtw//334zd+4zcwf/78xuu9vb245ppr\n8K//+q/46Ec/2vKdqakp3HrrrXj22We5l7N5gP1P//RPeP752gLVRz/6USxbFtwNS/aJeVrgbbms\nGteOP9gI7gtJ9es34EuDDYzjS/XbchKcHDyFA4sO8BuguFQ/AoYoIfLaN3kvVNT+25tjuGHxUZy8\n7Gb8w7pNsDjOcF8381TH90wizvGPEYa9S/ZhqjAFBobhgQIKheiLH2eWXgoUxBK3w9z/jjAez9si\n6wjef+rrePfotyMfK000L0hydfyTaGI/J9VvtrthXxgG5bJR4bKp/0YX83D88wh2vGbmGcfXzyy9\nyKXOEhLP4g8hBDCz5fgHMNiESCt68hskoszCOskWwpIgbucL3tTvL6MMY33jODrvKCYEbZ5pR00h\nU/K89bI1GOyPLpjmGtZQaFf5685e6Pj6lnMWCS5JMGTtM9ygLgukZss8NbjjH+8RCe8F0lKuFNvi\nCa+2k0aKavhHJoH8hYtWJ10EJaGUoNAVfZG827n5DUUa3ExkX15QVDcCw0cc1E9K3Bwr4y0TD2HA\nHse6kjzOpcxlKVZ9xz++YpykiWAZkHkKXSYWz+8J/f11K4a4j+1U6nd8pTyXEJ63zDCIsOvgJTg3\nCMW8ors5h7wbgwEb4trm9isZv/AvXYLzGuHqUhrctDX8iO3JGB8fb/x706ZNcZ3Gk7Vr1zb+vWPH\nDuHnn5qawp//+Z/jlltuaUmJOzAwgM9//vNYsiT87oco3H777di5s9Wd5MILL8Tdd9+NYrHo+t2P\nfOQj+OAHP9jy2tTUFH7/93+/5b57QQjx/F8zDz/8cOPfX/ziF305Bt5+++0tx3j88ccdP3fbbbf5\nLrfGB2qOT7lw9sxzyDXZGItYFPMjLiSbLodx88d9H5MhztvofE2CuAUEP/ppoghibEJQ5jyo/N13\nbcZ7bzgbH/yN87HoLb+GqTO2oEoN8BIYUmbhzFLnlOw5SmNKuuRMJVfB3iX7MHrWIfz2jZswPBwt\n+JQnYxiyT9UWP3ym0RI24fdK9dvs+Bd2ktj2WwiAEeskRiRyZ0ia5uDqpGFyOy4vxw4etC/86eme\nNwYHx793jf4bzi390rO/9EpvsLRyAHmHdMFnl57jMo4g8F4UX1V+FRtLvwp0XLdUv6vLrzi+PrI4\nj56imDQTnbhg6vHQ32WEiXFFjpEcq+BtE99HkYUTNYz1+J/vyY5MggY/8Bor80DlZyBp3FwFfJNR\nx78zVw2j2NU6lhseKGDxwvCLiyJQMdWv4Sr8O72AFC7FvNypfitmJbb2ltcvJ7CFLDIaEtVdLm1n\nBjEMgq58tDnw5o0LYBSzmXLxnDPmOb5+4WsWx35uk4U3jlD1ecn5cPD1ap83Tz+FD578Z6yr1DY5\nnV16jkfRuGB3SvUbo+Nf0Z4GEP/G1bQ5L2nHv/AYNFq/E4twWaFb6cv5VEJ4FjufM3wJwXngR2Tq\n9RmZ79ju/Aph56Jt/VjsqX5TKPwLPQdVaGytiZ/YnozR0dHGv0dGRnx/r31AVa1GaxwGBwcb/961\nS+wOn0cffRTXXnstvv71rzec5ggheMtb3oIHH3wQN998s9Dy1HnggQfw/e9/v+W13t5efPGLX4Rh\n+FOAf+xjH8PGja0Wt/v27cOXvvQlz+8uW7YMv/rVr/D88897/u/GG29s+a4fsWAn4aDbMTR8yfIV\nbV8EExGM9Uo9a77nUzCv+S2QQpDUsUS4TXWBoyCnE3XHv9CZfsFQ4eldDqArb2BksNhoi+r/5VVz\n3jbxPfSyyY7vm5Sebgfjdvxr+nf9nEHncYfmHUbZLMOGjcniJJbktjdup183DW4BNY/RuPdZopej\nJtKde5y0pnAMI7ZrvjrPDDoH7sOgHf/UxjRopMDUOTO/xHzrOABvAYzXLsc8qrhh/H70WTVBVY6V\n8cbJR7C0egg82glCiOviz7LKPlw38f+wuHoo2HEpAenQJ14wvQNd9mlhGYONfQv344xzexMX/m0o\ndxbDe8FQS9Ekq+jJq3+7aWwrPnjyn7C6sjv0OSZ6JmDmWutTsWDiPdduRHch/rEcT2QSNPjByeEk\nqZrod7OFZi5cHCwy6vjX35PHr7/5TJyxagjDAwWcvW4efv3NZyLnM46VFKlz/CPhHP+USfVLgLha\nVze3pyCIcvyTqe4KWv9NHQalKOTDt5HvuHo93rhlOdc4r0rCv03r5s1xiTcowboVg85f4EgUpxxV\nn5fBPj+uyO71Z551vCVWs6K6H6+bfiJiyfjQMdUv4ul1cqyCVZU9AKI7/nXZJdfraHASYAxZJ7gc\nJyoUjEuGCF4sqgSL1SQJIbW1jtDfp/wdov04hcqCKMEbbyjH9bK8aQgTQPq53l7ugzKn+hVKW6Np\nxmyYYKYsxTwQfoys0thaEz+xReab09n29fX5/l6hUGj5e3x8HENDQ6HLUS6XG/8+ePBg6OOE4Rvf\n+AYOHDjQ+HvhwoX49Kc/jSuvvFJoOZoplUq4884757z+3ve+FwsX+vftNwwDf/iHf4j3v//9La9/\n97vfxbvf/W5s2LAhclnb+cpXvhJICMoYwwMPPIDPfvazjdde+9rX4stf/vKczyaRijrNZFlM2R58\nFREo9Qz4hrgfjMQoWuxQHH7VpnO5CWOwScSAiqjqzeE8r5l5BqtngzydMImBuudf7EPE5pSrIYV/\no32jGO0fbdhSvmb/6X6ewvbl3cNrMOx9i7xS/TLHf/MoybmlZ3HcGMGzhbMiHFcshHinzMmxCkok\nWACp7i7y6MhiPD8whCVHwpawFZkFI4xEbejCY7AqLCK/+Gf5or5I45VWJ75owj+glqr7vaPfxATt\nQY891Tg+F8c/AlCXxrbuRBS033cTEy6wjuGWsX/HrvxqVJHD/asGMdnDQIm4lB2diLToQRgYIZ6b\nLmRlUfVwy0LY8so+7M0tC3SMqlHFeRcN4eiuKo6cmMLCkW5c/rrlmDdUxG/dcDb+z7ef5l3s2FDP\n8U+eeieTGEQV6qkWoyyE1eHZBMlTq7whlGDeUBHXvmFty+tlK143gaio+LzE4fgXV6pfzvvywBCf\nw6qb6CMIhNnZE/5lOM4YBRrBeWnhSDdWLx0AwDNeJ9d4xosFw91462Vr8KNtuzFTstBTzOHNl6xC\nb3f86wh5VsEM3LMydULV52Wwv+D5Ga/22altXFnZix3F14YtFjc6ib9r/Q7/5+LC6ccbc7+o8x4T\nVQxZpzq/z2mOX7RncFKC/RyyOf712+M4lM8BZf8GO0lhUIJCBMe/+lXn2+/wO1bc8E5zLAqexc7l\nqLC4oZ/r7SVCVPOO8ac9fh13qt+kY8u80ZtrNbyIbVWut7cXp07VBoNTU1OBvleHMYbjx49HEv6d\nOHF6l8jkZGfHozihlOKd73wn/uiP/gg9PcmmIPnud7+LI0daV7xzuRze8573BD7WJZdcgnXr1rWk\nDLZtG1/+8pdx9913Ry5rO811wy/taYtN02xxgdRoeDPH8U9Iql8v4d9sYCHAjImBxGYJ23GXlYCx\nWsPxL+TJiIBC8gzQ+RGM8HL8m+6aQbHkFaRrFrrVoEEH6aT1v6zpehnMQsXH4UQFmoMEJMO2FW6/\n5ZzSs0oJ/0xKULHcr0OOVVCCdzC4GQaCbSOL8M3VG9E7yfP5kkcwIpPjn8Es6YV/lBCctW4kUiCx\neUend6pff3WFAOizW+crPET4hBAYLqWs15+g7ZDX9Ruwx/HamV8AAO7ruhRAHhSEmxtAWKIsejDC\nYEPeHb1eO9nbF/DPmfllYOEfAPT253D5NWvmvN6Vk2CVJgC80m+uLb+MjaUX8F99b+VyvI449vnJ\nLJ7zECVnjXqKWi6upxl1/Os0T5JpYdYJFVP9tqdqaias41+073Qmljl6TM+FHXADUyeEOf5JVHeJ\nogvhSWNQ0hCeByWuttVWqd+hBBtWD+PMVUMYnyyjrycf6LqYJkG1Gu73ZtHxrz5Wcsf/Blu315Jg\nf26J8xuEIVfl54pfzO3F+pmXsXnmdCaQqBtXqYfgnNccv8imuRwnKuFXDuKBwgbr24njFtA93Y3u\nUjhRsAhqgvMIjn+NNQpOBQIAMEx3TaMo8XVTHZ79Tj5nuG5e5gmfVL9y9DFJQgmb88jmUHH8LC/S\nluqXwg7tTqproKaZ2J6MZpe/ZvGdF+3irl/+8peRyvH006cdB5hgZwZCCNasWYOvf/3ruOOOOxIX\n/QHAPffcM+e1Sy+9FMPDw6GOd8MNN8x57Qc/+AH2798f6niadCB53D1W2n+6HMK/EI5/EL+ozave\nuImtCGpOPWHvCpmVDoqBw3l89HsmNRqLaFHOuGfJHoz2jvr+PA3p+NdOc5nzzN+EQtwOc4+AZNPb\ncbQVqk08/czXcgge/GYgGMvlG3/xQupUvwmGKGV30CIEuPrilSjkzUiLWc2CPC9xXpRFUx7PMSHu\nO1TDC//8X7/6FaCECkvZ0Ykojn8MgE3kTfXr9uhTZs15e23lVZw3E8yhj5HOiQplF9+049VeLawe\n9nUcwpiQdtdx/JLQJZdJDKIKwwO1hSY+wj9+Nz5Ma2a7uMHFSad5AxXwIBhRBBgSjRn94lbm5qY+\nSP2pOwXx6kPPXFWLY/IfVrDY5vzzq0e5HIcIGu3LVHdVdTBLGiOC41/LJefqvCRPvfKiPrYlhKC/\ntyvQWHfhSDfedM3y0Oc2Q8Q+6qRZJ+s1BBLhzL5yST/X4zEApfwMl2OdWXoBxcIuDKO1v4m6cdWA\n5dp/8xJgdNt8rkNUZHP8M5gFRoDjw8exd+neSOPSuKGERHKtO6374zjfIQyn+v2vWWiCwzPOl8+J\nixv60Yxo4Z83G+aTOWu7cTv+iRL+iYp9RKpHCm2q0cRPbE/G/PnzG/9+9tlnfX+vXYD26KOPhi7D\n+Pg4tm/f3vi7v5/voNyLW265BVu3bsX5558v9Lyd2L59O/bsmZvyMUrq4auuumrOa4wxbN26NfQx\nNeoj08RINHNS/QoIOHgGYxszpmD3RfhdFOL4l6wgxg/1iS2PmuNnwJgj9PQ5Qw4SGZiv+9d89Po5\no47Rm4+Z8yv8k2RCxmdi6JLaWpLf6Rc/k3q/97gZRuJZGqOz4qHY0qIHoP3RtRPshyOlUY2Jk/0n\nseHCHrzl0tV4303nYNP6eQCiLSC2Pl/BXNbCnyfkMQhx/a0NB9YY24x6nTQISXxXZpT7wWZT/crQ\nvo71jM15za1/6yRWXl3eHfjcncb6qk0B3NqrDaUXcNXkT3wdh8DumLqLJ7KMXwD1xhgycOFrFgPg\nJPzzYNA6hXeN/ht6rXHvD4cY//OoiwPEf2aQOp0d/6KWxpso4xsVHTLdUv22TPwC1J/zZ54I+hUA\nwKk+54XazRsX1I7He2EwxvqUZxWsKM+NywYli45/qqa+SxpqEOTMcGOU5vEez6sfRrsu0xgoCD3d\nOZzq65we1Y1ojn8cMx0IGuCbPhM7eNUFp03sPNvLDWuGcfG5HZz7mUmYAwAAIABJREFUIjBd4CN4\nM5jt+IxF3bjq5fhnhmxn2nm5Tw5HNgK55rYUdktmjySzfHgRtf2Jw/GPARjrG8PB+YcwVQg+B9F4\nw3M9OGcakqX6dW/fZFgXSBKDVXHF2rnXyIx5fUBUbPmlVTvx8vJXYj8P8bm+6kS2a6CmndiejPXr\n1zf+vW3bNkxP+7NpXrp0aYsz3ve+9z2Mjc1d1PDDPffc03LegYGBUMcJy+WXX45cLv6grl++973v\nzXmNEIILL7ww9DHXrFmDefPm+TqXJjtIPPcQQHuq3/gDpZ6DyxDCv1gXtWOvIO5CKEYimmTEXP7G\nbeJwHj/30KA0ciAvjGCwUS0jBgSaU/36FoXx2gXjUZHEpPoFpjc49+OqTTx9Of6FCH4zkIboiOcV\nkckBo/2XJRkE9JvWViSMMPQOmDhr7QgGersar0dL9Ts3dXnnzyYt/Ks57XUmnONfEOrPoAyOf9F+\nZ83ZTQYRx3ivg6DHw/HPiaDXgxEWT1rFBHD77VdP/hh5VvZ1HAomRHDtNK5IqibK1QfKz/JFfVg4\n0g1AjPDPZFXMt47jvaPfwk1jW/GeU//a8bNhxBRR3bPzxije964LcOWFKwJ9r/OCUvzPX5TxjWpj\ncsBd+Nd8tf3+suWVfVjUcFENdj1K+RksXptvee2CcxZh8fyeOeXhQX1UFAeMELx58qHI4j/CbIjo\nAWRp6wnmpg/T+MNrsdqNuBz/QgnOJXQyOX+edx9GCQld9kj9juvcLxhRUnYGIVf0Wck8rqdjql+O\n/fDbLluDYiGci2ZHCEMpX4JFo8dSDFg4Z2Q51g7Mb309ogCj5vjn8j4nkc6hbp8K0JihhEkl/DNg\nt/SElkyFayOK8C82ofFsuzHWN4a9S/Zh95LomzA0rRgc751Ix7++nrznZ7TjnzvXjT+AnDl3rBDF\nudgPpimoHSRiXP9IJNf5bNdBTSuxCf/OOOOMxr+npqbwne98x9f3CCHYsGFD4++ZmRl8/vOfD3z+\nZ555Bn//93/f8try5eHt1dPAI488Mue1gYEBrFgRLNjazrnnnjvntRdeeAFHj/JJYaFREHnnHrHT\nHkyQK9VvAOGfn+OGpOMARojjX21hNkrYmOdO426HYJHoxXSTGI0zhg1I2rPfCyKobDj+RYwhtqT6\nhV/HPz54/1z/Ackoz1vpjPMBjoHdpDB9VIYw6Sxa/DA4Pl6GRA4Y7fVnce9gQiWR0/EPcG5bo+xI\nbRV+eTn+hX++uQj/4M/xj8RYp+uum5QQYTt3OxHlmjJSE9aKFnEsHTiKsZ4xWNRCKVfCwfmHMNEz\nObd8Lr+t0+J98OvReRFEPddv9+fC7w7lWoBOgOOf4zgtmQCfLGIQWZkqTGH1mj6sXNyPi85djHdc\nvb7xfPAQ/hkF9/teD7JTMCytHsSw7eI2FMZ5KaIAYzzfBZIvYKi/y/vDTXRseyKVxh/RxjfqBeLz\nBrCw2Dfn9U1DS1rHVB51YX3pJVw2+XNcO/5gYzwUXHAOLFrThQ/cfA6ufeNavO8dm3Dpa5e1pODk\nCwPhmE677cgosDJumHgg0nEomGt2CV5uX7K09TXnJdXGGHIQZeG8ef7ANeViAoLzOLhiyRnI0c4x\nDEJqV82t7E4xwTpRUnnyNMERJfz7/+y9d5Akx30u+GVVte/x3uzO2llvsNiFN4QjQAAitQAB0BxB\n6k4SHk+G8SQF70nxQi+o0IlxoZD4SIiSKJxEIx0l8ckcqSedQJFBSAQBEguA8G53gbWzOzuz403b\nyvujXVV3VZqqrO6e2f4igO2pysrMqspK88vv9/2inWJtTMbBlnXMD1Q/EwoABDg/MOE7Lx15HOrf\nhIRhn2MpCfXLGHdUKS+thOrT3ngo7Bw0z7hTrcDbfD1iBSrC/Fb/9ovqcefa/k3qMl9jCIpcqXJb\nIhTSfTkuCINQbBrmi0Xx5lL1Iv7lSXPa23WYgMN8aL2E+gVQn31rj3bm+eT8Fc3HaKEWgX0Z+/bt\ns/39+OOP4+zZs0LXVhPJvv3tb+NLX/qScNkvvPACHnvsMWQydg/9Q4cOCeex3jA9PY3Tp2vDOFkJ\nml6xY8cOx+PHjh3znbcMZmZm8Pzzz9v+q77nxcVFvPDCC8w0LfjHlWyQq77zeijCcMsozbwlXgtF\n/Te169FsSJMFCDmyd7DmWJmEp4TsIRDqV9N8f7PCRlhLulKZfj24rCROUcW/qK7YO9czLM/D4+um\noECyE/q9vwjo9o3kteZxpof478XL5pNV8U8lSobTZhjxqt/11o7+BtWkYBBuNlASxBgjTtz1Q6hT\np/jHegDBK/6V1Fl1okFvcKhfX8S/ogNBPRSdrTAMExcGLuLE2Emc2nAaC20VRXw7AcM9D1VkZUrq\n76QQFHhh4EWJPqSoBBk0mmnTey0qmNUTmVAGBw704MH3j+OGgyM2o7QK4l+sn0P8kyCpeXmTftti\nRZBe7rtxDzMe/PfnZ36z1ubkQEHx74Nj+239fUQz8IENu21zKtadRc1V3LP8fRxMv2pTrpJ/WxQa\nNLQnIxgf60Jnu10NSPnrJ4AWGPFPDQptikXAUFP/ZnF0IgyngxbYaEYCBgCsRuTCmjbTHKiEbR39\n+LV9d3BSsRX/QoaOseH2muOj2XO+xg6VoX7DdSBimcRE26BonT0o/qkm/il/JiVnav/11GnekYHj\ntz/XOEqzqtS5msVqT8r/k8NyrNY5TwW06tVmE4+JfohlJCDCuTWr8Y5+3Di4lXtJs7RF1UjEg1Gf\n1xUy/8J1CPVrEhOdO6mQgiuvLnUj/unNZ28HiuMDqTW8+w0xDwDt+XnXc/Uk/tWjP/Aa6nemc3bd\n9lcteENgO9979+5FR0cH5ucLH+bS0hI+9alP4YknnsCWLVuY19533334i7/4C9uxP/7jP8axY8fw\n6U9/Gtdddx00B8b36dOn8dWvfhV/93d/h1yulk185MgRH3e0tvHqq686Ht+0aZPvvN3yePXVV3Hv\nvff6zl8UTz31FH7rt36LmebNN9/Exz/+cduxo0eP4vOf/3yQVbvicGUb5BoQ6leY+CdumCj4tQWk\n+OdmyFDUbpg2ehWblIrquXtrD67aNVCbvcIPSOQdGpoOrXhTXieJtCx3LX596S41n7Lc1vctSvz7\n2PZr8L1zYs4IfiDnieyxbRYJGNr4YZAtB0CnzoJE4sh9/b+uuU1GETKQFzK1PdSvumfSLBthgEO3\nVA/PSBc0p+JfQcdWJawqK7ycfSn+KRi3CCHlft7xfPFfz/XUdMBkv/dS+GmNkCYI9evnYgqK+of6\n7YitFMuvPWdoGrLF589W/FMT6hcIMARPvcG5D1GiD6G0TkEIHd5Vg15Fs6hANStYhHO/xL9r9w/h\nrVW2IoysSlDGyCCc44c5KkEVASOIriSn52Dk1Zs6/SgvrbU5OQDooDjcN4auSBwvTp9FWNNxuG8M\nI4kqVWlGW3DrQ70o/rHWyKqJnwUnyKCIf2rm6AUCAFt5KZP13083S19PCLnC7YzeEY14JyjZCRhq\nMdc+h9hUrSOsG1Ss43WNYOeWHrx+Ytp3XiVsae8FcMr1vEbYY6amEdx8aBRTM+9gJVUYZ6JmCjet\nPItjsas912utzdUvd85ga0isPfBaAksNVRV0XYNhaMjlFPWR5deloJ3DdLQH+XXQ1AXGHRVoFpJv\ngXAu9x2dGzyP5fgyhi8Oo20lqbQ+Gs3b2nazPCcnlIjHB3f246W3LkldG1iI+SL+26F7MRTvwOTl\nFW5aSoJTgG4kQkYw9mKiqWuT4ZCmlMBuxVWrL+F81yp+ONSDm7u2CV3TLKF+87qJgKPneoIGU63k\nowWHUi/jqcQtjufWneIfTOn57lT3FDLhTFOTwVuoPwIj/mmahuuuuw5PPvlk+dj58+fxwAMP4JOf\n/CQ+9rGPYWCglvAAAHv27MGWLVvw7rvv2o4fO3YMx44dQyKRwO7du9HV1YVIJIK5uTm8++67OH/+\nvGt9tm3bdkUr/r399tuOx0dHR33nPTIy4nj8nXfe8Z23DIIL89GCLK7kN1A90auHGgZXSaj0TbT3\nAPEOYMXuKXEy6SBpTYKbtLqumeqk+Af4M6X4fSodyTA+eu8uxF02/UjND+8o3S+rhRhWxT+PZYoa\nHKypSoZIvws5L4p/qtaOmVCGk4LniWz97bVlVdQPiBECGdoCujzvM8/GQMSbrzq8hQgoSCBKTCo8\n19ShauxhhBwKGs3Y7tTT/uz3yVX880P8U6b4xzKI+FT8M8JAZpWZpPQNEkIarvjnB5QAeRKcc4QT\nBnMXEQ25b9ToREO2uJHDsku79Vmyc1VKmivskR/wyHoazILTCGd9SUBhBmTotMJpvtWoHrcZ+/rm\ngvt3Eo14N8Ndf1M/rtsyjLeeZRP/DMkdgYXkInrneoTTKyP+BdCXTHVPY0iCTCIKP/O+tdhj6kXH\nrq3tfdja3ueajtUS3J1BJMcdUCaJxTRV90fBbfRa1yQDuUlMGs52cR4Ipcx+WNVcq94Kx24gur5u\n5h71RjJeIHUf2TuIY69dlLrW/tkRqJx1LLQtgBIT7YsdaEsnwFvm+x138loev/KRw8jmTKXEPxZI\n0UmTTfwD+nvi+F9+ZjdOffUPYULHpuxpxGgKfp63yr2RvBlsP5CIv4y3u2IABMduTluoh+IfUFD9\nU0X8K230qxh6dJp3DLno13FNo/k6Ef+UZOMbBPIOKhXChvr2Vk34XwvEv51bumuIf9GIAQJgNe28\nVtECJJwDwHDRgUXk3VJCpV+lCRNacEEWyxgdSOLc5JKnazWNYN/2Xrx6XO1YqFLxLxTSEQkHQ12J\n0AxC2jJAxNe+TRPqV1PH+svqWWRCWSRScd956XAed1SAJS5gKCKxzrbPQjM1dCzV7pGnw2kA9VH8\n8zJOZ43CXmjzjggtNAKBxrq77bbbbMQ/AEilUvjKV76CJ554Alu3bsWf/umfOhLHfv7nf95VvW15\neVk6jOyjjz4qlX69wS2cbV+fuwFPFG55iIZ2VoWjR4/i6NGjdS2zBWdcyeTL6olec4T6rWy2awfe\nB/PZb9tOP903XHNJgSQRVN2d81XXatzrXQj167ckf8+ltyvuSvoDKhtgakL9opiX+z2HNL28sGaW\nSairBaZkcJAx0JTuU6XyU5jyiHjFshX1UTOds76uJzbFMI9qi0CtklcdyAdBQMRQ6CnULyGBGA8r\n6lmNX17VkM6v4HHYEUTeW5ubpa0tstsAr92+1daFnYvO/Yka4h8p9vPs8dfz5q5hALzut0T2htp+\nv96goKB1JP6NZs/j7qXv4VVyk2sau4GVsbHoYjC7khX/OvJzzPMEBSNmnmM20UCxMXvO8VxvVwzT\ns2xirCiaZSMMaB4VqGYFiyDr1ellpmMGfb1jQspXMqF+M+E0Zjpmce+GPXjz3RksLvPn0743G0tO\nKwFMWReTi2hbTiJpUVoRVRzRdYJ83vne/LT5tUiUjehiDlXeFP8kQdgr+ExWvdo0ocGsp6ztaEf6\nuGfinwY28U9VqN/L4SgGcxdx0VBPppUBIVibDNomQIn4t2tLD3761iUbUSkeNcoqc06wKf4F0l8v\nIdxD8cjIHvzz904x0/odd7LhLEIhHbl8fecvhBC2IndxTpCMh7Ejc0JZuSqVktKZ4BT9r115Dm92\npADEhK+Ri6xRgHboTuCkZOU4iIR1LK8KjpXC8D9f0OAc6tcveIp/qsJyNku4Qo0z92AigPFKhwnN\n0vhN0rxrsZKtYLgviXtu2oz/eP4sVlI59HTGcO/Nm/HtH5zAatr5WmvTVWpyqGpWIs4EXsYdUzOh\nmd6/v2v3DQmlO7J3COcmj3sqQ9cIbjk8irnFNM5eXAQAJMxlLGsJT/mV4BSh0SvChoakT5V8NxSc\nNuX23nj3Vg/hF0BtqN/VaAoXBi4gnAlh87nNvvIqh/oNACGGQ6Oq9U4mlMVqdMWR+DfdeVlJGSLw\nYjOgDr9aaCHQneG77roLsZjzxN00TZw4cQKLi4uO548ePYqDBw8qqcfBgwfx4IMPKslrrcJNDbG7\nu9t33j09zuz4iQm2F3oLLaxHVE836mHk52/UV2qlXXs/tPd9FGR4O0519eOrm3fjWI/zomKthvpl\noaz418ANa64BTmHVSgsPlmEsYYQrC15W2YxzwothS7oy8c+nQ5BpeZchwRBcrJCXophPLnAV/2QM\nkp6/N+JA8iqRfdfYpF+EDOSJ+FcI0lH4Q6FXbHOF+m0exb9mRDCKf5bfrGZF+QbjU8l2XIhWeVmG\nIsVy1JDAdda4VyzCs7HKEA8PWVD8W8M7t4TCBOGrLSvAdSvP4ejiPyFOV5mtwPo0vSj+yaJeQW3r\ngYHcFBJmraf8YtsU/nbjOAAxAhWBiXZzEf25WmLRwZ39/itahPN8qzFjvRcF3isJFOpt3/Y82Zkb\nDnPim5afcciTYr5tASDAjVeN4Ocf3IePfGAnvy5NrPhHCcX5gQmcGTqLi72TuPG2PhzeI0auaouH\nEXNQZGw3531NIdfanFyjeeg7xJ4Z687c+k/pUL9gE85VE/8ogW1zXRV0msOGbMU2uz/9Gm5Y+TEy\nRgY5PYfZ9jkc7ppn5FABT89clfLSj/sGcM3q80ry8oMC4Xm9zD7qi2S8sHHe2xXDh98/js2jHehq\nj2Df9l48+P5x5rVBO3o8sOkg/svBu9EZ5ZO+/I87pQVPfdsRV/GPFcbcT7kK7zOVDo74x6ZFOoN3\nhZONXOvbIFkKH5GwOptLachRp/inKTeA6HVT/GuOOZOXUL8lBHEHGl17in8AsHtrDx57+AD+948c\nxCc/tAd93XHmc7WvDVQ2YnteQop/Ht6kqXlfI/d3x3Fot9j8e9NIOw7s8CboQwhBJGzgobt34FNz\nf4lPzH0TPzf3l3hw4f/1lF8JKv17dV1jimb4gZd1WdMo/ikk/sGDiIcbNJcQ8wAQNVO+8maJfKhS\nOKeEIh3OYK7Nvg5bji1jOb5cThM0CKh8OQrnDy2sHwRK/EskErjnnntcz1PG5hIhBL/zO7/jShwU\nRVtbG37/938ful9mwRrHzMyM4/H29nbfeScSCUfWez6fx/y8mNGqhfWFK9seV0W+qIMaBndyafXS\nJQT6VXfAeOT/wN8fvBUv9Dh7bpsITs3GLVeZdnOpe0o6f8CqgNc48G6zTEpT8B2JhDaOGeHys2cu\nahkzhsqkVPzJlsrUfBJArCWKKv759X6OD1Fc7Lso8I7kQ5DIgoI6KP41nvjnxUASmOIfiMWbTw4s\nL1rNRcWkEaheF5IGhVKNmmpUrZQjgAW6dXxnfWsibTZPNHxpxyE81z2A6XAUb3f2QX/w17h5C9eV\nu1lKi3X1WJYubpDTQJRtCjQCFIBJ6qPobB3TWFEM7a+WpfjnEupX9l6cCOdrFAQUdy49Bd1CkkqF\nU5jsXsAP+0cxs+0q6AIhU0vP8L7Ff8VQ9gJAKcIGwbX7h7Bve6+y+jaLAgaw9ohMdQcnJHbCy0aG\nJU+u4p/DHGVX5m305exrqMudM8jr+WI4MyKkJggoJP4F1ZUQYDW2ivn2ebS1hYXDAOk6wZ5ttY6t\nW7PH4SvkokIVCE9tRxIbzOMw29qE0rL6JVfFP9nnQdihfjNZ1TaXYEL97ky/Y1OtIACuTr2E9zae\nwsmN7+JS7yWEBBV8eIp/qjbC3m7vwvm2PEZdVG3rBUKudDujd5QU/4CC+tLRO7bj547uw103bHIk\nOrtBLVG7kNctQ9vRHo4GprzkUKRSYoJIkYSw665Smc9WtsIPJkiVxCDWVE55qnoeXe2R8m+VxD+V\nVmodZiAhFzWwiX8qCRjNsOYpfb9yF8nbxkWhwcTezsoejrlGiH9A4fuLWsYbVrdnfeaBjvsBrXe8\nEP/uun4MP3vHNjzygR2IRcXGZUIIbr92I3ZvFQ9XW4KVxNZmLqPTXAABMJy7iD2pN6TzK0H1eJaI\n89c8XhwUCEzpL5R3b9V949WrL0qWIAaVxD+V/axOTYA4jzsHUy/7yjtE3ZV1lSnNEgoQYLJ3EucG\nzuNy52VM9E/g/MAEqFa/vtabjS24caeFtYtAQ/0ChRC7buQ9Qgi6urpcrx0fH8fjjz+OX/7lX0Yq\nJc8M7u3txRNPPIENG9R79aw1zM05hzGKx/3HcAeAWCyG5eXlmuOzs7Po6KiVSG1hfWO9bAZ6QbUR\nW6uD1DM/1K/z4ltjSf8TtRsU9rz9Kf6ZxMRSoladRSSfgueEWDlu8Hs9//tQr2Xvps0T1UPQiSZG\nNiTumlmii2FrqpJxt7B4UtPWWIsBK/z0UVk9i7bNBnCRn5Z3V0oU/8BQ/GvgnH+ydxKD07XE4sOr\nL+D52NWO1/AWbISanlT2rMQ/6WsJdX2Rpbo0x4hXRTpvULjnvek3MGGIhaaoJwoqSWrflCYYqluE\n+EcBLIbC+MaWPQCAznAM/9fQVuShZh5BSCkcLDvknud+yJAgIZA1Huq3pPhXF0Vn0aANlefJSuem\n+CevvMQmNK0lEFBszJ3Do3PfxMnIRvzThg2YbDPLxj0CMXXX0pw5SVfw4cVvI0XCiH/qd6B3qiP9\nAc7zrUZ59taD/LqWwVP8u+GqYfzbM6c958kb0nQHxb8oTePo4j/h3dAmPN29Bcd7dKzECoR96zct\nMl76ffvlcadOdoNYxEAyHsLSCnutoGkENx0aha5peOvUDKZWFzHftoC9Z17BxeiIjxqo+V42j3Rg\nsC+BZ18KLrrGnUs/wJ/vGsC9ohewlGYlQk6zUFiFuhcUBCmFmOraZn84jc10AodnfsgosPCPaN9K\nqMm016gKfUUJ8M3NOzG8MoXehRXMkR7EVuQ3mf2CeJh5LMeWEUlHYJiBb300NUIM4jNvI9t6ntdd\nz3TMoGc1h1gqiRDN4rrV5/Bk8i7mNaJjGlAhA3mdg+aMfLGsejL/CDSe4h/rHfgYOlSGXAwSBRut\n3Dvh2R+d+kbZ974cW0ZitTbs5U2HRsu/I2F1fUtZ8U/BfEGn+UAYUzo1mTZGZar+hCKv5Rved3tR\n/Cs/ngC6GR15W/9HfSjLBQ0eGYup+EfExx0ZVGclNJYQeduHLPEvEtaxb9y7cl9PZ1T6Ota4429v\nQu24wwr1G40Y+F+P7gUI8Md//ZJUvl5srLKKf1elXsY73UewuKpWMCCnkvincNzRYIK4zDsOpV7G\njN6NdyLbAQAZI4P+tiTmZsXEO1jrSVXK1LTkeEWA5cQylhO1PJd6mEA1ADs6xVQ/S2hZ5VpwQuAz\nqF27duG3f/u3PV9/00034Zvf/CZ+/dd/He+9957wdTfeeCM+97nPYXR0lJ/4CoATKY8QgmhUfnLg\nBCfiH6UUKysrSvJvYW1hfWwFekNNuMV6bAzzJqwukyBW2L+CcT0gxT+PxL+QoWHJWMFk1xSyIdam\nDUNNVkABjw81nsaupxVOvEtFuW0KJ4rhGUsLa1aZhLjfecnTUKbGpcl5YWHo3WBhNRKKEv+8LkVN\nYuJi3yQ2ED8bf87wFeq35ljpDhs3/V9MLKFrPo1ItuIRvSXzHvpy067X8DyE43TFk4oq9aG9yvom\nmkrxr3rsqbOh36BZ7Egfx7Wrz+Mf2j5Y17LFIB+alFATlGG4OplsR8+RB7Dhx/+TnY+H79BuCOVf\nvxxdQSLl7sxDCC/EeYnk5K3P0Mb2wJw6K5SWgChTA2gISGFzqt7EP5ZPsu3NsjYWVfVZhK4b1R0y\ntgcLE8dwJhLDk8NJXEzYnxElmpTiXwlRmqnZLFfiT9NEyg71UDVf0+Ao/u3a0oMzEwt4+9QsgMIG\nTFungfkZ9lxWdOPJQB7/NrARd02esR2P0Ax2Zd7B81EdK7EKUV92k02d8lLwnQlBQcnw2n1D+P5P\nzjDTaoRA0whuPDSCGw+N4LEffrOQB/sygTp4R2dbBLu29qCvK4ZNIx04fnrWX2UYuHX5h3huREMq\nJGMqZqj/qAr1y1H8Uw1KgEw4g9Cqf3XFI6vP44ZtA6ATp4TW96Lq5qROin8FBQyCiUQCEwkAuIzB\nSwY6lurr4F1Q/JNrA5e7LmM1ksLw5BDaVsQULK808MkZ4nllQlkQ4138bxcryi5Pwpn4V8pWNtqF\nH+LfUvecTFHqQNjKXEH1bY1yBJQFoR4snzzin1OOkvaR1UgKY11dmJ6oEBM2DrVh82il74sqVfwr\nQkFz0JEvKv7VZhYzV7GqeYuupsNkjjsqQ/1e6p3C8KXGOpRqTrZWQQSxYtMATPcMAdMFx6G1pPhX\nDTbxT3VtvJdDUQi7KaMCnZck/vklo3u53vp+cgObYEye8lUHP3VhgRXqNxrWyyqSl7qn0D8jTp4k\nMKUJ57LEvxhN45FbBvF/P3leqhwelCr+lfoQBa9NQ740Wa85p8PE3cvfx7+Ma9DzOjKhDDbnDooT\n/xh2TFVtrllCp+uE4tf234k//Onz4hcpDNncwvrBmnB72717N77zne/gH//xH/Gtb30Lr732mmO6\nZDKJG264AQ8//DBuuummOteyuZHNOhuQVXmAueXjVm4L6xxX8EBTfetewlLKl+mN+Mc2mAUZ6tc5\nX9ZcbaAnjo/dtwv/+dm/w2reR79SNio1sJFydn5VquhU3qFznolQgfgnpvjnfkp8gmxVyCr+63OS\nbv3CQhBU/POgNrUSXcGFvovIhXLi74jzXKz9g9c1BnXyQm2CUL+mbuLs8Fl0LHbg+otzGM1ewO70\nWzgV2uh6DW8hnTQdPK4EsCX7Ht4rywnIbjS6n3NTz2oEGk38+8XZrzbV86gGJfJ9a4SmkSLuRvGf\ndvdj55b90EJRkGfcQxeIeJNWp7CS9ESUX3hexYQQ5qZSuT/2Svw7eDvM5/9VLC0ha1vxD4BJCDRQ\ndOVnMKt3B1aW7X24i+7awHqDqkL9UvCIpGsHxs0P4q/Oj+CNORcZXyKmWOX4DG1EKgKqgPnnPIdu\nzFhfD1XztQzeJ2voGu69ZQuu3Z/C/GIawwNJfPeVE2ziH4c3bmeZAAAgAElEQVRMaMVLXT3oMBaF\n62vNVUjxz7dxvOQA5DMbCRzY2Y9ZuoQXn5txTROYAoaPa7eMduD6A8Plv9ssYTtVo7A2kVVecj/n\nGurXw/OoJ/EPAGY6ZxzVnmRh0Dygi5veRRX/tHoRMBzKaMTGkmgYcisoUFDraZLNvGYEP3Qdsf7B\nRF7Lu4mLu+dftlsIjDulOnh4nUuxZeTCBUcOGVvQfkEVpqt3D+CFNyZrjh/ZO1iwrHoO9eu97aoM\nuXjz1aN49qWJQNRVNZjSLnq8p+Jkh5cdQ6hmYvv+JK4f78LF6WX0dsWwfazL1rcGEepXmeKfpsHp\nSe1PvYafxI94ypcX6leZ0iyA5fgycloehhkAuVIQBcU/2YtKz0f9uKMPbkQ+VJkDlhWqmhA8cySr\ne7J9qwHO/YJSf5VV/PNbDS/zY+v4kN9/K4x/O1WpT53rwkIi5j5/tva/i8lFdM93wciLzbc1S08r\natLQuBGKajMKGer7r7ymUoBA3bijUX6I+byeLxMXRecohoDAh0YITJ+2qWYhUntZI1OHXy20sCaI\nfwAQCoXw8MMP4+GHH8bc3ByOHz+Oqakp5PN5dHV1obe3F9u2bYNhrJlbqityOWelAl1XMwC55dMi\n/l2ZWC/hv7ygIYp/PLKFy8SbNSEPVvHP+ThrfVAw9IoZe1m1Liv++Wiifg3dpjeepieU7tethcQl\nFP9YMnkVLyHezVl+ysR1Ecw0LKr4R4DpzsvonRMPUzTXPo9ciK/6YwXraeg0Z99k9bWZ2HzEPwDI\n6yZmOmdx63v/gXi+8OxYm1m8DaqkuYzR7Hm8Et0nVY/d6bdwEh4VoFlG+jIZpPGLq5qvqM7EKivp\nr9HtzhnydQrTDFJwJ/7Roge4fvB2vHEhD1xyTiemimV/XzZbp0jdeaoHBNAYqg+lMrgKwm7Xt3VD\nu/khmD/8H/y0a1zxrxDqt4CDqVfxg8StgZVlffcx3QCos0eszWjNaAuqQv3Cg+pO04JyFAdAYAjs\nYjuObZZvTtXTaiYv3no4N61pEH54MEIIerti6O2KFf9mZ2ltZdymQExme6lusTbFP17eUEekqUdf\nYi1iYDCOmY4T6J53Jm2rJErY6uBnnl9Vp7ZEcMQ/QH7GxFo/uhGn3020y5UhQXpVA4qV6CrSIbt6\nuRcYNFfYCBNs66oU/1QqLzUDvCj+VS5WW5f1BC7xz9KMeI/f1EzIRveuOIOKJKbSm9OrkVUsJZYw\n2z6HBAnbyuRB1wn2bBOzF+3c0o0X35y0EQjiUQMbh9qxYqaZ31FgDlGKxtdISMf2jZ3QNYKnjokp\nvMuAFHUc5SCv+CcfspXC0HRsH+vC9rEuxzQsNSpZlOdsCl6bDtP+8VpwdeqnWNDb8FZ0JygtONl3\ntkfx9nvuThHlfCmbcK5SadbUCo7E/dP9iKYj0Gn9CYAEkP6OghwxjYO3QaOnK2U1yfjsBH+Kf3Jr\nEq8QDjFPqNSLXUguoGNJfJ7rmyzn4XJbmeNH8OSrT+H2yTMIUYqUDydyLyILLLCIc1aiV87I4czQ\nWXTPdyOSCSOWZquaEg9UN65CstO4E4BDfrMq/rHGHcf0gu3eoDmuwynRIOR4omkEpsumbLP0p6IO\nYE5oJlthC43HmmTJdXZ24sgRb94pVyoMw3Ak/+XzagYLt3xCIXWLoBbWDtbLXqAXNIL4x50UuEy8\nmMQ/EmDdPYT6rSwa/TUuYvl/NXak38HbkXGBXPw9F57iS2lTQ82ks0R0dL7nhFHYyCi3BSb50v3O\nRetq37AsKX34e6fWia1wqF+iYa5jDsmVJKIZ0c2cSu03t/fgqQty11SjdiPM2/t2Dn3Faun1h/29\nu99nnOHNBxSIf5uyZxCiGWSJ2Kbnrcs/RLu5VG4nSjczm4j0UKv4p0NacmEdg3rYLIy4kKycsMzY\nwBZZuFcv0MtqakYYhMcWB78P5in+Vcr13qb1w3cDAJf8pxECXZEaQKNgFp/l3vSbuKT34fXo7kDK\nsRIx28IRDGpRXFxdsKW5vn8z3rSo1bEV/1yUlyQ7Rj9h1poNJuWEjyREUPHP4dupUvxTsRXULAZJ\nQJTUfOXCyzY2d5yyvn+uXZ39flzHHZF6wH9blCJ7+IS1v9IIYXr0B0XA8LOurq5TMh6cjU2jJigk\nN9cZj8yNOD0RTwLi0ywA9VX8KymLnRs6j61ntvjKy0AOkHC4Ft0yc1PxLZeraq7lmE39xyJCvMw8\nqOX/VyYSHGISfyNb/Knn9TyQFUxP7eWLrtOo5HTq3OB5mLpZLANCZQ32JtCWCOOqnf0Y6ksKlTPQ\nk8B9t27F0y+cw9xiGgM9cdxz02aEDA0kwx4z2Q4o3sFy+hJBezKMns4Yrj8wjM72KPZsC+H46Vmc\nv7TkK99qkIIXjtQ1vM1t51C/smVQ6JxnqFaBV6XyUr5oD6q9Zx0m7lx+Crd/6qPIZPNoS4TxH8+L\nETr1ein+Fb+XTDiDc8PnAAokVuMYvejRmdcjXKJWCiEIAoZhhEAsfaypNe/o5ieMvF3wr/E2B9lv\nMhVJIRVOS+w1+IOX2ZGVmGloOv5pdCv+dWgTOrJpaIuD6BIXjbfXpY7vK5+3v5dsOIvJvoLybtdc\nFzP0b8HOJldXQgiTOOZM/FP/PFQq/lX0SRWMOzClxlnRthKiOXSZs45h6jcOtRXKJgR5gXsIGRrS\nGefn1yx2Ni8tplL35riHFpoDa5L414I8QqGQI/HPNNUY7N3yaRH/rkw0wby8aVCwSwW7QcpV6PGk\n+Meu77b0SZyIbOXWzTlvt0myOwR4aZbEDKMaTExHoo7n9qTfFCT++QP3dSksq5SXW5Flxb8S2ZA1\nSWRUzEuIgco79XfHpkfFv7yex9nhs0isxGHkQsjpOQxPDbleU3oym5Ld6IkIhn1ikVmrNmSDUPxr\n1KT/Urdd+sxKPGXdZ0cyUlD0cLGSJc0l6DDxgaXv4jtt97vmc9vyvyNipjGUm0SSFsIDmx7bGdM7\nn7PhVk/UEP9EXd6uGMgTMHjEP/vYzh53ZFEygmjX/QzIM09y0/PCEhDwPCqLin8++wzSPchPAwli\nhRqulFJQYqfz7Eu/Hhzxz3rzFPjYtiN4/PWnkDUL33ZPJIH7x/bZiH9eFP8gq5jvYxOk2UAp5YQt\nJkKEWMe5uI34J1EnxrrBaUxqlGdvK9QvG15CzPO6RorKXJ3wOkhicvVwbH9JtldaVF7yO4+v94Ye\ngTcCBqH+1mh+xtdq1ZQgVXMLz0eSHOFB8U92cHd2dAoQxaJkw7U5oRByUSbUr7jiH2t2q2qzUcVm\noAp4IWBUxsfmuIdG4OBOdqha3jO1NiPe4y8oI4nVq5JnifjHT0tBpb1VrP29SFkdyTA+dt8uqTJK\nGB/rwvhYF3J506a4yQv1y14X+bAR+SBhbRxqw4ffv8N2LBLW8cBd43j8/3nRc75OIKDSthqu05vD\nHJVIEiELxD92vVQS8cs1VtB16yiF+nVHJKyXQ2WKzsU0yib+KVP8qy6DNKYXJ57mucERMDTNHgWp\nmUP98uw9LEVAmTVJnuTF1SCrBigxRydIf5OEEpwbPIehS0OIp2JIxEK4Zt8QnnrOmWDrd3rrJQS7\n9fnrxb4iq+uY1uPo98Ht1jQNPOczVcgz7ps3RmgwLesd8W9VmvgXwF6wqZnq9pkVkt0IABBnwrkT\nRG3CIZoFAXAg9Sp+HL+mUh4BDu7sL/4Wyysc0tcA8c+H4p/CerSw9hEY8S+fz2NycpKZJhwOo7e3\nN6gqtGBBPB7H6uqq7RilFKlUSkn+1XkDhU43Ho8ryb+FtYZ1shvoAc4DtLwnif8yrQlcNjAYdaJg\ne5ETmNiceQ/vhTeLVNGet1uxjIlaxdDv7znORKLIaWnHJzaSu4Dblv8dz3bcipRcRFcpmDzFP5Wb\nGsWi/n7DdsfTiSLxT2S+zapWaYIsM8ksb7QTAp3mkCf+pyQhUYWu4s2YmonFZGFVG85wjHbF6j62\n+2bMpFaEimE9D72KlOXnrde8v6JRsxEhV1cjq5hvsytS2RT/GO1f0wjMSA56yvldJM0CiW8sew7X\nrBzDc3Fn9efu/CyGcxdtx0wPi3qATajQiu+wGUe8IDwL1zJYBAyNEMd+uTd/GedCI+6ZWjcgWeOX\nADmmOkWprtqhu6BNTABzsjlU5cfZLa2MsD77DIF7JRKKf4ZBkMs2l/mCEmrpT4AoTQdWlvV9EFDs\n6BzA71x9P16bvYCYEcLuzkEkQhFb22Yq/rmF+pVQIiqUsX4U/yjlzLsIEVLtdE5j3bQQf14sAnyz\nEDCAxswx1hb4oX6rwf+urH0CDyaTFFT99mzkjnp83pVlQL2KKpbnh4DhHfN3fAL4qbdr60l4I+AR\nRh0vckX1eqcEU/qe2ONOLGJgNa1yAV94Cof7NmL+lL+cQqVQv4LgKfmV03HIMqraTfNshK0jr4M6\nQdMI9o/ziH8cBRsJ5l+BEC6H0jgp9GaJv3lQuSzBEJNeUR1mmzfuBBdi3jsJS3chjYUMDVs3dOLk\nWe7iVBiifZ5UngqUl4QU/xIKFf/KNlX/fa5OiyEXBW9ZdLzQYTLteTIh5nWd1Ch2ldAs444GP4Rz\n9dA0YnNY4zl/NhKd7c6CCyWw+lqZT3VicALDk4PQzYINmbVPVWM2FypHLsR8nuQL6rcEODd8DsQk\n+MMbHkQ8FHYl/vmdr2Wy8g7f1vUOr5+TQT0dunIu/QcgQA73pM1fGANzORebWp0U/0CAvGbCMP2H\nPy/v5anqSyRCG4uqEhsorO8Op15EnK7gRGgLItv2Ye/2fmwe7ZAqNmS4J2yWcceTja0csrk57qGF\n5kBgxL/vfe97+MxnPsPs8A8cOIC/+Zu/CaoKLVjQ2dmJy5cv1xxfWREjL/DgRPwrldvClYcr2R7X\niE0w/oakC/GPG9aDfc7rvbpNpljV0SQMgqxavZvoAHDJdYK/N/0m9t94D/7i+TwWl51JZH4ng/xQ\nv4oRieP1jh7HU9WKf9yX7gLxZ2LZsLQYXq9bPYYfxa8XzKO67MrvEBXb8HHa1OMZaCgoPjS2H92R\nBGbTomOnuAKGn+/JTfEviK7YJCY06r5YOjt0DrQq5IT1e+PdZz7qTvxLmBXXQxbZxsl4XPLmkzfE\nrRHFv+p+RaHxZn2Auo4xV+3qxwtv2J2VunMz6MnVzptr8gT/WxNR/Ksek8r9o27AuOsTwP94hXk9\nV/GPiBkU66Hg1RmOYUoTczwyQhpybobMAmNLYc3EMZKorG8iZn2IfyV0RxO4ZWhbdULLb3nlJaLL\nq1XUVXkpQPAU/wgAItDXO45tXhX/CHUdepznW40x8MnOW6a6phFLxZBcFVRNXuMoEM7lwB267Qw2\n8BT/pMq2zdXEVlwskmokpKNnJISJU879fYU3Xw/mn/3eWAopbgSMfx3eBGPWP+HEC5zqdHBnP156\n65JDan/wshHmRfFPlthAOfOY6w4M4QcuG6p+sK97BE9jylceOnIguiF0xxuT3SCzYnMkYlMrcTgv\nM+6sAaXZZgu52Cj0dcUwNetsf6coKGNSWiDg3HPzZsSi/DmeRtwpv7Zhh9M3yNnJnO0XzPwdLpOB\nyKVBDEkE7GfDVL7yU67EBnw1WE5aqomKxDIiCFsVBUgdNcc8Kf6xr4lHQyBEyPetrhBR/LNC9NHw\nSJqizn1AoY/K513W+U7ZNIDQ0Gzjjq4R23ySKlAlDgpbN7D3g5n2M0sfw3uMq5EUpofewi+9eQJx\ncxXt5gL+qPs/CdVRaLUjqWS7lFiyZUw1yu0z/Y47WRciGgvWOqm066jKa9NIe/n37q09eONkrW32\nql39rtfzFf+s6x3xOodCGlZdpunO9iD1dvmeSAKmngdUEP8U1McGifsVnUuUInsRAHvSb2FP+i0Y\nt3wQRK/QmoTDBq8B4p+XKVZz1LyFZkNgu4L//M//DKBgTHf7r4X6obu72/H4wsKC43EZLC8vO4b6\n1XW9Rfy7QrFO9gI9wTnUV7D9HZdU4LLgZ210Us5mEgEVUkBxyd0tU/fyirfgt23lBWZQRNOUhmyo\nhps3taUGtn/8gLR3w/jwb2A55OyJGisr/gkUJkL847V1Yv1Jyv8eSL2KzZn3+HVwKtuSackTiAen\nts+d5BOrR7rYy2EZCGoV/7z3E9Xvr1zPAOZap0fOwGRsmFaT/gDAtL13Rr9CCGjE/R2WFP8Adhgs\npzK8msSYRvo6hTAQQU2oX46hdzU2D7OJ6h80Ck/H+YO87sAQBnsrRJQoTeHO5R9wv0nr980krnv4\ntq3ZifXPfMU/jbHsI+Un5LNNhCLM07s6BxEzwsJqACHD/d5Dgv29G/amXvd03eG+jXhg00FLPcRC\nzHuBPdQva05mScbIT4XiXyHMWjDE8kaAq/gHsb5eKfGPSeZqHhuK7LxlMbGI80PnkQqriTjQ/FCv\n+GdTUOZlTUzmPNRNabb4BxeUsNvqcH8SW3e3ccuvv+IfZ27nUqGfdvV5UoYol+uDgOG0MXJgR185\nRJ9KkGIfD0DJLoLhovgnP4qwFf92be1Bf7e6aCOlWw9LKuI6waA5QDCfO0Z2iCv+UTZJU4ac0yyb\nXSwQIr4Gr0CWTiSTc/2f2UBPHPsYCn7TXdN45GfH8dF7d+LTHzmA8bEuoXxVhVwsBYD3AqGxgMgp\nL6VDaZttQqT9BEFGb5jin497YanfuqkdeQUBrThpKvquQrR2feZF8c/gjN2aRpCMq1H9K/dWCvpj\nneZRCLkoBnHFP3aoXxnFP1ZaR8K5cM7qQDzM5YMcd9aS4h9PDZP1PdrOcJ5/iZg3lJtEh7kgNQKJ\nv1v2cw7RDHSaw/b0CUz21jrlsPbeihURrIczvCj+BeXEqUrh7sjewfLvvdudo0VuZ8xxZMjhMnP9\ncIhhU3UKMe9j/eeG39h/J5JRRWqz5b08NdnJtGVRlX3DYTyvLke0PbOIf6x9rnrCz94gMVsCEC1U\nEEhryOVyePrpp4PIugWP2LBhg+Px2dlZ33nPzMw4Hh8eHvaddwtrFetlO1Aea0vxz/0KysmXgCLv\n8T37Ufzz37ZKMtbsVEGqT9ST965ddSdI/0bX83GjQHAUuV9WClHjlOOGpaZBh4n7lp7EJ+b+GkcX\nvoPunPO4ws1T9CKH++UZGb35g7lDpVqc2/sLoj/KhDPSNixRxT9CADPibrRImBWlRVniH0sRgwU2\n8a9Q15tWnvGUt0rIEv9mus/hxKaTeGfTcby74d2GbF7VFQyiUiRs4CMf2ImP3rsTD9y5HZ9c+UcM\n5KcEvh+riiDLcCmg+Fd1uSaxyQaoU/zz22eQke1AuDasy/PdAxiOd+BT49cBEFcDMBiGGV1Q4dUN\nu9Jve8rjg5v2I2L1LvVVCzZEydu2UL8eFP+gywcBqGcYlyBhUsr9NkTIu1zin0RLYb1Dp/nrmum9\nS34t60l6iQEvt8nfG5AgPfCM1y5OI9W/WXVhOm0R9kZXqYh6qIeGNWufTZhjphsBYzEUwfm4D7VK\nH4oPTs+opzOGj96703t9XEAoO3ysE7yMO7LfB0/xLxo28NDd43KZslC8JxXt00Ae0Njj7KGeDfj0\nrptxXf9mhIjY3IRwwnnL1J25lnfMphHKS6SpQi42giw51Jdk2/AIRTSiY6gviZAhQfphdE/W8rjE\nPx/PRHDUkSpjtsO+3+HT19UzCkqz8oRz/wX7IP4xSFnZnDzJhAUvTnLM50nziFEHVUzJcdgUUPwD\noM5pXGGfIq341xDin3uZzUJG1+DBTaF4QRB3oOtVin9N8pyqcevhUW4aFufHtiYRKM/rUxBp9gVi\noXsJ2zIn8YuzX8Uvzn4V9yx/z9ERXkU9WMhm/Sn+VcNPq5J30KjFnm29GOmvOI+N9CdtRECg0Ma6\nGOGkeetlQisOcrcNi68fwoy5lbPSrPrxvTuawGhHh5K8Ss9J2X6Apgt3msJkPSebbY0dQ6zMUMj9\n/TVLf+pnTqS3iH8tWBBIqN9XXnmFGUK2vb0de/fuxcGDB13TtKAWGzc6Ez8uXfIfHsQtDzeyYQvr\nH+tkL9AjnDzT/OgD8MEMQdY16EoAYSr+gU0AWDYMvJfohOFBtMM1V8GFHxdCkzX2ThWPFOkHJncj\nv36TzVKoX64HGsB8ZKUNNJmaWxX/Stl3mvPoNOeFlfsAb4QuJ3uU2CS/WFcFnoE1in8eGKGNUl6S\nXczblNF4BMuY87uPmSs21RBWSFJnxb9SJeSeM3szs9D3jmXPIK/loSuQ2veKWuIfuy6UkLJRKqvl\ncLHvIganBpUYapoRlOOtrWkEQ31JAEC2uOHKbauw9F8s4rrQt11luLCSZUVCX4ko/rHCwZUV/3wS\n/3QD2tV3w3z225W6GWFsv/khXLf5QPledMGNCIPhUWv4JP4N5i/hg4v/gpei+7GoJTFtOHsSNxL2\n9+H/23QjTGuGAdFht9TW1ktf0Z4Ig8yzvw2R8dlZ8bvSfuVC/TLOORIMxfNWCa/9hUavDIMkBTuM\ntBP4YRQhPI2hHFJQreKf5bfIRhjYmwReSDp+caBnFC9fPmc71haKYEOyogjBVfyr2gh7cPNV+Pv3\nfgoAWDEMJDyKvPrpM90257o7YoiEdaQz6kgYqp2Gqtc7JciWwpvHAQVHjp6OKC7Pq1MVVdGGRRT/\nHtt9c/l3RFjBnm1lWm82uWYLuUgJrTv/MWRozO+AErYyphuYawTbOd4YJR4SsTqZMOGcgUhIx8BA\nDK/PXsBCcrEQctFWpoATVADqe7Ljju1aP8ovRIPXuAesOnkJK8kCAZWuJWv+kTSXHd+0LAFDJNQv\nAIQZRAKp8ko/FDRBnZqSxD+xdBplzy1lHjFT8c9xvdMAUkZVaF0pBFBfjdh7MdokClVWdCTDTGXa\nEljP1UY4ZzTjkj3c6xgvZGfj5UEL0bA0V4Vrfjl+7So9ne4EODcEFonR5xj6wdu2YuuGzhqHtJuv\nHsW+7b2YnlvFQE+CqygpGup3R8cA+qPuCvXVYBHHHIl/ASn6RiNqKD3K58cSBHvh8LyOEVbs1wqT\nCCWVZhsBX9HAGrgn1ULzIRDi3xtvvOF4PBaL4Td/8zfxwAMPwDACKboFF+zYscPx+Llz5xyPy8At\nj/FxhR63LawprDcjowwcbz1goyBrUqDtv9X9OuaLYu8svdbRCwKCdoH6VcOT4p9WIYf5gaj3HXPS\n6HMy2EyR7mMyin8c73IxVNKxVBxlJrpeHidxEDzmh/qlQn2bTjTki4QwZqhfqi7Ubz0V/7zAqhzC\nDfUbMpEKpxDN2I0Xm7On7WkZ5mEncotZCh8j2YmwnmDJuPN6VyfODp3D4NRATb0bBcJRVDOrTi+0\nLWIxsYRIJoK8nsOWs1sCrF0DIEWQLY03vD7BYqRjDRkC32ENAYM4/3YvhEP8A2dcK17uxbuwGtq1\n94N09MI8+TJILAmy50b0Dm62pRFV/GOFYnAPH8iBZRAezU1gdGkCAPCt9qOYNAa4l9dzimttOyzy\nmfXVeglPTvSQMPHPqcy1iu6OKDrbo9AuuKchVCzULy+NKgcW5/fbmLFefo5RJI1eKZ7IRH6TWWZz\ngB/ql09et8KmNCtUAXZCnuJf6WKVIQ7vGN6B12cmkLM4xd09urvq3ghTjaO6Ptf1b8YzF0/iwuqC\nr7r52fgRDcOpAoWQi3LX1EPxD9z2VMxXMltePkoU/2iOq/hnRVhQ8U/jKf7JhPqVdYySSq0GBTKx\n7PugVf+qw1J8GR1LXqxh3mEYGtcm46XFstqK9RQvb0q8a8aIKS+x+5utGztx8FA3/u2FF13KELB5\n8ashDY2n+Helh/qlpvzCgpE8aS45HpcNuUgJFQpF6CXMpkuJxf/776905KUIGKJtUEde2bjDUpVs\nllC/emefhzVvcDXVdbszp+lBXc4LbrtmI37w3BnHcxkjg6H2diwtZTHSn8SNV40IkWGZWy6CD73S\nTgRfUtXkU2y9w1b8s66H32jvdkzDm7/6HQJ2be3BMy9N1Bzv3aZj+oTLPJzVdHzUx++6ZPNIh2se\nne0F240I+KF+TYy1dePje26VqjMz1K8j8U8tEWvHpkIbiykj/hXrTEqiNT4bo8Q4K9runRy+q9+Z\nsGrtGlCa9fYGWop/LdQiEPbd6dOnHY9/4QtfwPve974gimyBg3379jkef++993znferUKakyW1j/\nWC8qIF7QCKJNjdLT6DhAKbSd1zGJfyxDNiVslaKC4oTHe3Ul/okZHHnw/QYCVqgIzLPKA2K6hOIf\nA14myGXan8PLlVG/q045mj2HcyF2aAGntQjvHqzqXqxvx7AQ/5h1qCIJeOk7SkZBxz63dxRkunbx\n3whYVRmZ5BUU+oHJ3ksYmRyGkS9MU7tzM7h25XlbWmaoX4cyvJqneYp/52MJ/N2G7UiH0jg9egZd\nc13on+F7uKpGzThAOIp/ADYkunB2uRICiWoUqWgKpJoVuA4gZUAv3j5fqc9CymKkEiEN1VbB7uHK\nA1/xj6PmUepLFIxPhBCQXddD23W9axoWoc8KI8Toaz0q/rn1tcJhdR3GrMOrL+L52CHmdZM9l5AJ\npzF6YVR4nixOxBQ0dLmNTYYBpMVKKrU1v/OGRiNiENxzU4GQSpgbY1ToG+aF+pXBeg29XibSXCmh\nfj28Rx7JiAqOOwCQ1Qncons75eAl1C9f8Y9FWCvNYcVxcGc/8/yOzgH82v478OPJ97Caz+JgzygO\n943Z0miEMBVSqt9BeziK3zhwJ379x/8gUVPHnL1fKaiKogKEoyInC3fFP1miGT8sO6B+na2RAtHK\nT7YG8lzFPyui+24E/n2Wm47Qgq6o63kpwrl40kaBwzV2RJCj6VJiEW3Lybqq2IZ0jWPD4ytjOkGY\nXCxCzvPYmITHHRZZBGxbtEgJQYTlI4Qw+zy2rdNHKyYa4NFRihnqN69e8c+LCqwb2tyIfwEp/g31\nJXFhalkqb0cobHo6LYX6FVxzCj4bjZrMiCsyzzgowvLsLPwAACAASURBVKtKkEhM+prg6OYlxb/K\nc6uH4t/VuwfQkXRXV1uJreL9d+zBUFwu9CirzdkJ53yijmeLidCYxnZWKdkAMpqG7w6NuSXiVMzf\nt9CRjODAjj68/PZU+VhfVwwfOLAJ3zjxpuM17PmyD1ECPwrnhDDHHhnwiX8U1/RtRkSXo8awSK0q\n7UFO0DWCAzsKew05ZeOw4p5KYtyJ6CGhdCHKl9sX5bkzx6gmGZI87Q2W9jBaxL8WLAiE+Le0VDvJ\nvvrqq1ukvwait7cXY2NjNaTM48eP+8777bffrjlGCMGRI0d8593C2sR6UAHxCqcBOugNPNvGcDQB\n46HPCl3HXEBZ/u+ewo8fgkN9WPOvkuKf78YlsjAMlvhnms2zodseLnhLidyvkOIfjzxnXcAXM3Rq\nh1KKf1UV25V+m0v8EzHg1VZKzIyta5qFZSavgCFXp8I/TkYTbd8tMH/w1/7LUADrU2Ar/hX+TUVT\neG/0FGKpGH7p+IsYzl2sUffSGORKx344gBAd39i8E2c7dFvejfISqw31y27jj+2+Bdv7N+CXf/S3\nNecKag0KvP2aCUTCCFV+nwJkYIEsRUhDtYp/VgIGvwwKtodmIdQvv99jKWmqRCIWQnsijIXlDDMd\nW/HPX6jfaoiMO4mojvZEGHTOfnxf6jW8E96GBd1d+SWv57ASW8WF/ovonutGKGsgHc4gnmZtKFCX\n34wrWGRll80/IqHEX/arX8OT/fsX/z9sfOBRRHsTAPiqZCJtw9nQ6y3UL6eg5gD1sLVfJlVfIQZJ\nIkZUsl0ikIBafrPwbqINBxfdVepqFP/gYdxhKWBwx125tZ2uEeza4qykYcXW9j5sbXd3wCAelJeS\noSgGY/6UvYJS/FOhSGeFt80GD+sdD+pOYsQ/uWzdy7OSbL3QUiooKP5JEP+GNgLgE/80mBjKXXQ8\n19keCdYm14D1jifFv2LyIGpraibOD0xgZHK4buOamOKfB+KfaMhFVtmgUnOUdMi+BhCi/ZFCSW4g\nGmFm5DfKhR80RvHPe7tkKd2xQv16IUprXnpYxiNTqvgn8Ay3bejEi29MSuXtWF7pB/GvvFRQ/BMf\nd0TbPVfxT4b4x58iVqH+447mY9wJJNRvVehhsw5jsaGzQ8ybWt5TS2U7B4mtSdyIf25iAIuJRXs5\nIjUn1KGECrTuQayO34n/np7H+bhzyFheOSrGnduv3Yix4XacvbiI7vYodm7pRiTsbuMJalvKz3on\nxFDTkwU31C+l0mMCIK/4J/1yI3kgXdt3m8TEw+/fhdGBQhuLx8RIczxY9+gooSB+HTQF5x3X9m+C\nnhIrKyTg8C0c6lfQAb2R8DMlTEXSTROFqoXGI5DWHolEao7dcMMNQRTVggRuueWWmmPz8/M4c8ZZ\nrlkUL7/8cs2x8fFx9PXVX/GmhRaaEgFv0tkmlxKTStZCiwpsdHqdD7pOwBn5lSZxYkUylAqFrmcb\n3/yujxoh+Le/e6Tm2Nb2XsQNCcU/pne5D0UrJ8U/GeJfVd13Zo7j0OpLvMIdj7FMjgWSjztZsQSr\ncZAZ6hfqQv065n/wdmi3fVxpniXIGiFNS9thk6AqhixTN7GcWMbG3HnHkJ4sJSync5V2Iq8w4oaL\n8bgDobA5iL08I8aOzgGE3DYhS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+UsevNyYRG9gLXBTEC5H309vIdloNE8wh0GUgvu9Qpp\nBK5vQErxr0j8E+irSflfhrFPQCG0mkhvC7lISHE7hEFIZztW2jauV6Mp3L38FCJmbb1Y3+eBHX24\n/dqNSg2TvE0G06S42DcJzdQQXy0YsRaSizg89zyiNIt7lv4N/9J2j5K6CI07lu9Ku/5DQKIT9ORP\ngUgc+t6bgKfc1egccydgvltrnTrDrDC31l1Z1rjjRvyTn2M1wls2pGnM+1tOLiOcDUM3KxvxbflF\nfHz+b2EgV1Vjy7tkfb8Q7QscCNh1Iv41RHnJZfbFuQhAQRni7NBZdM93I5qOIBVJYbrrMrrn/KuI\nqkDp3nIaW1lJLC+563nFHe4fk/teJRgQXkLMs5VmCQgIFhILaF+uDdNLerLcsoJT/HMfl3VGqF8/\n35qfUL+svXvW8+vrimFqVk4llXhx7mMkd1P880KEFFOoJ4htzeG0PonkctKx7cmAQGJxwkJR4YIQ\nDWTHEWBHrcqfFSz/nUrd2PPLwNQrgYasQf3cTc7II5R33uC9evcATk3MY2pG7lsJon/iIWRoSGfc\nyzUDCPVr++6E1sWM9QqTHCFGOGcqzRL2uCtEbmrAprDVPki6BmE88l9gvvEsXjn1Ck6gAwJR7hzh\nh3AuMv9pT0bQnvRv1yiMO4Xfe7uGRS9CJpSpIVfFzBXEqLN6mrRiqITinwpUviACXSPwGzxcZt4h\n+mw0mNBhYlf6LbwS3Wc7t2+8r0y+FeEUNUt0Eha8rANKNtGF5CL6Zmu/D5OYuNQzBUILDo55Iy9k\ne9u6oRN6VdjdplD807w5tLMV98XWJOX7Z5LSGRBqg2zFv1jE4NjhROauAtVQDNY3mgnLqcbZ4Iv4\np1Dxj0f8o97mS2Gm4p+DfVXyeUTDFaoOn7zvv+GU2nZY15lze+sZMzKLj88/h7OhUSTNJWzInkfI\nw4gl2r+6Ri2x5sVai1ptHEIlNhr+avnRrYfx/NQZvLc4jRwtjDd63kBy1a7KzNtjaGHtI7AZ7Mc+\n9jHb388++yxWV/2F5pHF1NQUjh07hueeew7Hjh2ra9nNikgkgs9+9rM1x7/+9a9jcnJSOJ9cLoc/\n+IM/qDn+4IMPYteuXb7q2EIL6w1BL8VENiQdr2Ma5Sr/dzvv+b5cJpOs6YY6wb/inTEn34TNn/RZ\nCV6oX0XFSIEZiqsI3oI7XFQykIbDNVJt2qFI3vVeQv0WiipcJxrq14vy0nqEaKjfAndJ3Ojono+T\n4l+xLkK5W8EmgzoUzsiJwjA54VE8KEE6K/7xiH+8UL/NRfw7mP4pdI7TNMtDtGDWkeufuEQw6+Ya\ni7jgodXJd6Vsxb9wWK/q95wLYH1XmsYjxcuDRbgCAGoWjHbnhs7jxNhJHN90Ahf7L5YJbFuzp3Db\n8r8rqYvQuGPZiSeEQD/wPhgP/GcY9z0GbWwP+725fFPs0DGWc4zqWYtljzvO/YuMmlFpY64B+90I\n6WwmRNbI4uzQOSzFlhFCClsy7+KBxe8gVEP6g53EyVWeFFH8q3ry1WTeNbC55Rcyt5gL5XCp9xLO\njJzFpd4pmLrpa9xRuS9bepd5UlC4iEbcfWUjIfc26UV5ifcMd3cNCqWloEViMStNNeHc+tt/eyWk\n8G3NF73NrcjpOZD2Qn/E+v4CIf4RUlYqr1eZpXKDuJb1/O66YZN8WZbZhGiNWfMP1/BMHggYos+Q\nEGAxuYQLAxeRMbxuXtJyXqoU/+SS88usmV9W1TPIUL+NgJfXUGqbc+1zjucv9l5EPBbCRz6wEz97\nxzZM919CRjBMmlO7mE/Oy1cSwOXOy5hpn8VqZBVmp3v5IaN6Pm+H1VFRBqKqoqysy6F+PbYliZmo\n6xmNEGEyiWuahigvVdWhcwD6DT+Lf9l1BJcjLMcfHrzfi5J+TxClcac9FMX2jj5GOnud5ttqv7et\nubfd71qSCEkJhSE8wawt1avdiaD+7VB0uCjZMG9eeQbXrjyHPnMGGwbbcN+tWzA+VlDzlBmr5dAA\nwrmP17ASc3aIPz8wgfn2ecx1zCFviNn++nOXcNcNYwDs9hMeuUkFjCqyYTVMzeTadJzA9E2ynGNG\nqnIJMa+iDpUy2PnHYwbbCVigHvXsb0swGbavxcSiDzVJ3j6jOwxD6YKeuT7RYHr6wMOMtb+KtxgJ\ny6wX1PWJYY2jDVZ1c13mPPanX8eW7Oka0h/pHoQIZJRm/eRlneOGpZ5vY+BFAdna1kcSnfiNA3fi\n8RsfwWO7boKpmzg/eB6nRk7jUvclTHdN473RU1hI1tpoWlhfCIz49+EPfxhbt24t/51Op/G1r30t\nqOJakMD999+Pu+66y3ZseXkZn/3sZ5HLiTG0v/jFL+LNN9+0HRsZGXEkFbbQwpUEx1C/ARMobBuS\nEhNXttGQE+qXwLOnt5dQv6WFpoixjqnQxb0aAMfA7/dtikuq19HYJlIUx8iscUKrFFPWHHF61qKK\nf22hKN43tEMoLa9MgP2tWr1hWItke6g09zqIeC3x4CcsUyrs7AkdBKwmA7bin0gbKoBFnHTytCuT\nDyU/K+bmheM59jvRKWWHO/ZCEnO6hmfY5jyHZgn5tTFzBj+z+C84kHkZvGha7NAQ8op/PMIerSRl\nPk6eIkspL/s1pOo8v/48T2SRcE9MUmoABsnS2J7VnaUs+voqm12mboJqtfULUzkZDLd7FLo7znfF\nd6iQg72ubNJ05Q95xT+5jbDGKf4ZmsYepwlFOpLG+aHz2Kc9ifuWvot20yVUmk3djH0vIv1yjUp2\n1fe2BjgVUnC6H14XwevH/KyXYoZ8qB43lMbofPGGutrd1WyiEdbAJK8kwG2Lgqp8XjbCNMG8K4nY\nChgl5aWV+AomeybLiiDpUBpnh85VnLoYZQUy7oAw680u0wc5lWjoyHsjBUkIN9rgpl7ILAseQi6y\n1juuIebl6+ZlY3m6+7KnOW05rD3E1yZM6HLBdoQU/2rGHXtFdYmQac0y72fBDxFmOb6MnG63dee0\nPBaTSwAKhLoto51Y6lhC1hCbVzpNnWY6Z5HT5FVPcnoeU71TODNyFtjgHj3BMDQu6dvLd8LqKmyC\nfxx7EA9M51uB90sJe23eluCH1uShEfO1oEIusuyrccziYq97iOt6ElE0aiIZiuJX994mFZ54pmMW\nl7qnkA6lkQqnMNU9hV1Z94gpMgqIJQcK8ecg5xDqmEN5XkTUOVgJ5iOqvFRaE2mguCb1Ih7J/Cse\nunsHdmyqqHaLPjNpBUap1Grg6TsoXpKKpDBXRU5dTCw6EgJZ9/bQ/D/gkeRriEcLa516c8QMna/4\n5ynUL4ukLeCwCljXO+y9Lddy3KtnzYFpX4nHQmxbYNMqzTLOaRRT3VPeMm4SxT+ATYz1YncH2E5/\nKsQd2LYFAUjaU0rfUEjTOWsBMbskAGh7bnRsB9rNH7b9LdpUhBT/WMrVlnPWscqK6r66GbBri0Q0\nDNs2ZHE/g1jCkBMgHUljtnMOl7tmkAlnPDvqtLB2EBjxT9d1fOELX0AsVtmw+cpXvoKTJ08GVWQL\nEvi93/s9bNmyxXbsJz/5CT7zmc9gZcXd0EApxR/90R/hiSeesB2Px+P44he/iLa2tkDq20ILawWO\nxL96likl5+9+joJHevB+V26TSebGj2L1Ba6uW0CbPoBY2AOgvmo6VgOxGyEs2uV+fZn4x1Nvcjro\nkfjXHoriV/a8DzGjdiOFd73b++WSvIrnI6zNG1seDAKGB2U3lViNpnBq5HRdyhJV/CulEAFTmczh\nIzM9GyDYZFApEMCgHMU/AZKYQ7a1xxhGEyJARmgWxb9bV36ETdkzhTGJ0ymGWIp/Mob7MtGc/wyI\nZSHtBhHFvxriX3VdeYQawk4TjRhCG4FMYm4Ag1Lpncx0ztSci0Q19HSLhI9S01aF3jdnjuVF8S/E\n8GwWVr+1FMxU/HMLuSjxaq2bUvUGT/HPLpDIqZ/lXfJC/XblnZWC7Ok4fbfM85J9tE0TctFfm2CF\nX+VB9xHOrhqlvsBEifgXdU0bCbvPBymR3/vgJbfnJ0LAYKSpKVuO+Ecd8rDCqrw01zGPE2MncWLs\nJE5tOF0IIVUaa4U3/tSAcMZLd1BfTZxoGg6vvuj1aka+7udkiF/l/CzjjmjPwpoP6y6hn2RCEJYI\nGOLtoZJuMbmIM8NnhctyyklJO+SNX1XQDH6ZtWsG+zVBEncasUrwdDvFa/J6HmeHzmExvoSsnsVS\nbAlnRs74UktyaheZcKZA3vMB1qcRMtjKSzLKmFaoUfwr/itdeqkckVRsAkZPZ5StvCRQSCOUl5gE\nDD/jDqMxRbCM+XZ3tZc6RrgFAcVju2/BhiTD4Oh8IWY7Z3Fqw2mcHj2Dmc5ZZiOVGneK7ay+652K\ncxWr3JXksrAdSttywPE4Gdhk/1vwPmvsGg6NGJu8iAAAIABJREFUV9iRV/UkOQB4ef/ld0OAyd5J\nnBk6i6nuKZwbOI+J/gsuxjv399lpzkHff0slqaCjuSoYBtsBwus4KswD5+xbWf9lpXEuh/8AC4p/\n7rkkoiH2WoZbQrD9zEBP3PH4lg0dzOvmOuZxZsjDfIZ5K4Sposp25JYHq216VfyTU+STR5RhWxCB\n9BysHOqXHbKaSnQ7JJqAdsPP2g/2bYC25yZ7OqWKf4xzlpOJWAjbNnba89cIFpqJ+Fes7oEd/RIq\nmJU3L+qw05jVXAv1hL/ehIPx8XF86Utfwq/8yq8glUohlUrhk5/8JL7+9a/b1ADXMx5//HF8+ctf\nlrqGUoqdO3e6nj969Cg+//nP+6pXW1sbvva1r+HRRx/FqVOnyse///3v4/7778cv/MIv4M4770Rf\nX0FmfWlpCc8++yz+/M//HC+99JItr3g8ji9/+cvYu3evrzq10MJ6gNOQGvRQ6oUoUriON4Hgqx15\ngtuiVmDh53s9JKKAQYIl3R3ZKyZ7XU/LhnUyONc+j8Fp+yZnV2cEoVja9foC8U+TMhBWlLJqL2IR\nMP7rVR/Aaj6LsWQ3IroBJ3cCPvHP+Tib7FRR/Nvc1oO4EcZKzh6K50jfGE4uTFmucIcSxT+fTaRa\n+YAHXvgiN4OI9Shb8U+81cuH+iXFusj2XKyOqfYQ753olLKJf9T08Ok7GVxZIQj4m0LNQvwrkWko\nCFf9RJniH8SIf5RUqEVsvpfIGF21WStwRU19GPWNRQyskoqCydl4EtuWar9n0jUgWEM10EukkPZ5\nRNNRdCwVDJA5LYfrrxmCLqRSKAe39F69fkWxp2sYt49vxXC8A6/MnMd0ahm7Ogfx3YlzWMm6ECNs\nTDYFin+uyktrQ/EvpOtsw77l3pmqMoBtIsDsDynFtsy7+AG9BTnirirHD/XLro6tSPGkjYPD/fid\no/sZd1Q6CJWJf0UV4u4Od+IfT/FPWnmJ22xFCRj89U4t4dy5HGYOnHdWvXGX1ytz3zpyCmzghpgP\n7APUsDPzDi6kB/FGZJfUlV5FsrwQWAio/PqCkd513JH4ZkvzG6+EnFQ0hfnkfHmOIVoqACk1ciZ4\nIbSqIELa5I076y/Ur5dKVp5RJpzBxOCEwBWChBqX55sNySlRl0otgXWfhZCLjFw8jqPMEGnCBAwB\n5SVGHVQQzrs7YiAMJwKhcacB34KrDcXneORnrl7vUL+qiC+s9ic17hCKI31jEiX73wkou2wQ9veQ\nC+cwMXABI5PD/Fptvxr4/l8BVXZL7fA99r8FH031uON0mWqn/UbC//4DsBpbxWpslZmM1VKMuz4F\nbdc15b/rTU42dA15k+EMXRQBkAV73CGW34xMSmMec2xikcEZeVtyYOVfUPxj2bj5hQT5yRzeO4h/\n/vd37eVpBONjfDWxVMRLlCD3kVbTCP77DQ/h8ZM/dTyvXPGPMR8glHr6wMMh9/m8Cnti0MTCapRq\nPBhrx5xCK5R+zX0gw9tAz74F0tEPsvUASMROQhV9/K4RPGx5MfqTqg/s3lu24NmXJnBqYh5t8TAO\n7R7A/3nibbHK1AWF+g73J/Hw3Tvw2vFprKSyOHHG3RnZ+uastg5mRJom2fNpITgEbm+7+eab8bWv\nfQ1DQ0MAgOnpaTzyyCP4+te/DtP0L4Hagnf09/fjW9/6Fm699Vbb8YmJCXzuc5/D/8/em8dIct1n\ngt+LyLsys6qy7vvsqr4v9sWjeVOUSImyJFuyLQuyBa+hHY/hgbGwF5gFFgPvrrEw7PHAGuwKO8Bi\nvGvZ6/ExlGellca2DosixUOieLVINvu+u6u67iuP2D+yMjMiM+L3jniRmdXMT4BYnfHivV+8ePGO\n3/ve9zt9+jSOHj2KEydO4NixY/it3/qtGtLf2NgY/uIv/gL3339/PU1voYWmhas6S8CDqVPxT3zi\nSjsNaTeRBSg/l2eoX+KeykRNcCPKFxjGh9w3BjYiG76cgabJsGtM7CRrPZfz9oX1YmoRcx1z5UXS\nenQdjzw0yD1dbgoo/rluFLuscqlcRpKdmGnvpVX3lEE5NCp2GczAMyP7HNejZghPDM4Kn8D0DLko\nBX9tXXaiv5TyPhUOwPN5HYp/mnZUqXzcSIEFxQ9KZ/dtwcIHSXrTUSnUr1tdSCqLVEN5ERhLisUn\nE0TpVLnFwCUWU6fh7N+vKITaqsDQJKT4V3V/dX/LDZHJ2aiPRUOOPP/fwUnXdObpX/DMIwgfc5n4\nzYAbvTdxdvQDXBi6iA/GzqGzM+o57jj2IKX7FK/0/j92apz8+Nh+TKV7EA9FcLJ3As+O7sdkuptW\nWbHb1MXf3AF4in/6Qi42QBAFYcMU7pS5qQxBxT/LQgh5PLD2EpldLQGjKtRvgPXFe9ZSiFWdcHsc\n7jNyrheakPgHAB0+FP8UCidhf0xqTBQirtS8MDFSYbkMRs8VGGddINLvBPLd8NphYCEXi/OBJ1a/\nh19d+L/w6aXnsXtTbJOBJP4RD2QqbKCphPpVUfyTmpVtJ1UJYVqCKpmRp7wkDMm5scg0nnf4U6Zf\nlFcxr/9mkcprULJSsBxDFykUzvZJvbei8hLtk1EK9Suo+Ec9cJlwrkrAELGbWQjlvMfdZCLcnOMO\nB0ERzmVC21ajnuQtkWgEoqDan0x9GAbDL07dJ1OyRFoP2MYdmkhlYaVtBXMdc/wsw1GYz/0mEK6o\n6BtHniwSAm0QJW7FCjSBDRBfV8q2sUaEpFdqlUrzf2IeNeN8V/U+fBcyDWRzxHxDwc8G0O1Eetwh\nyvF7WN5iFtn2EvEQPe4IlBHkO50Z68TBmZ7yvw2D4dmHJ2sIZgPxtJbyeGuWMDEfFlc3EwM/1K8C\n8U+zjdWgfAtBoPQN3d83QVeHrY/aElzTGMOzMO//JIy999eQ/gCxcac/ewMJiz/uiBKJgWKfdvq+\nYXzhE/vwc0/swuiAnrYfBPq72/Dk/WN47rFpzE4QZF1Hl2knTreIfx9m1KU3OXz4ML7+9a/jD//w\nD/G3f/u3WFlZwR/8wR/ga1/7Gp577jk8++yzGB8f115uI0IQVaOtrQ3d3d1a89QZTjedTuOrX/0q\nvvGNb+ArX/kKzp1zngLwCvvb2dmJL3zhC/j1X/91RCIRbfa00MLOR+3A6XehwYNj01tCtYVyClrY\nVp7yLFTdteG1aCou/N2vlfpzoaoknY0CicAwOdyOaMTE5pZzs/Ru+wIS6+5S6TwUQnl85rE9aIt7\nK8ZUmVE3VJPV7mTmcKdzDkbBQMEsoC1xiJb3ZwUYzFDylLotcqXIT25cW56qnIedvImv/b6nhveg\nJ5bE63NX0BaO4lTvhEt4Ekp5ScNGvK82YglP9GOFdezfeh3vxruUSirYDOWdhhR1Osoq/sluYgLA\nUhuH6OgKok4Z8FZHN05evU4lkYbb81IKXiLfl+wi0PzEvwDyObDRPcBf/whaeK2ojG8WAIOjfkKF\n+gUk1gXl8UZE8Y/Z8vYYvxRGy5rx2Tt7u0WeV2JRE+ZWpU18kGzH2+ku7FuqbBiw4Vmwsb3AD96Q\ntlcV1d97PpRHPlTsGxkEyOQAdOmj6TihSyoveWxumCLEv7Z2sAFvtXx7PVHfrue8Tir01XZedd50\nAEA6igEncUyb4t/2pUObb2PNSODVuPsmIFfxT6q+9Dnhrvdcx1JqGamVFAZvDWjL133coZ+RS2A2\n1J+b+o5kUcqpdGCgM+0dcpxS/LMUFGx47cSZn4AjV2hNVPuLqN0k0Zj5VyUJJNQvp46tgBgYhq2f\nTRVWkSqs4v2IWBQUmshClKnwXTDbl7qvU7DPIIrxWu+oKP75aw/qykt+upeDG28W/5AN9Sui+Ffd\nVqvGcpkQ6NlwFrEtb5JzM6Be/n3RlhIUMYoBmBhux/krTmXuscE0TIOv+KdSTTTxz2mbd0L5g05e\n5XjeDyCSJdSXGU2MFJmLNWIfyWus9K825oP456PwkGkglxd3BqiGXHRDgXrHEvWRjkSRDPvrE+Vn\nE6Wxjm6Hpe/IS42rOtqAMbYP7Mt/AuvWRbCOXrBELclBRGVqOHsFkRoyf+1TinaN1PzaFY0g5Srd\no/rm3VHdFurdR5mmgZ5M3PVaNlRSuFWYbwqOByQFtkz8o/a2BAmGnvcDhkWETQ+byOW8I+mIkdr5\nSVTBGMOT94/hxIF+zC9uYKCnzZVc9tz4QXz1zA8cvykRg0g/NA3tin9kqF9aydEL1KGqHBF1RwSM\nAZFwfTXxS+84GY6iLRxBVkC0+lq8DQvhCDqyTiVZNn1Eqmze95corOLJ1e8I5uV9baep0Hr1WeT8\n37HbbfdzUve0cK8jUOLfV77yFce/e3t78fTTT+Ob3/wmCoUCLl68iD/90z/FV77yFaRSKczOzmJ4\neBjJZBJtbW0IhfyZ9+67jZfp/NKXvoQvfelLjTaDi2eeeQbPPPMMXn75ZXznO9/BG2+8gYsXL2J5\neRn5fB7JZBKDg4PYs2cPTp8+jcceewzRqOQkvYUWPgRwJ5zUsUyJ+QzpTODerf5UXosHkVBF/ikA\n/JPIjAGGaeCTj0/j+X88i81sccNicjKFd633kFh3X/RSWI2twZpawdig+EmSem6qu9Y9AwpmYdsW\ngyS2AMV3xFdVc9kodiva56YbSS4T2Sh1u+Yy/T7cPYLD3SPO/Jn9Hm9oCfXr937OQv7Axls4uf4q\nYtYG5iMxAA8ollP5m1I/c2uHb3R04+DCnZrfyXxcCdisdFEIyUQY5zJzyCzwQyA4yuFczxomvt8z\nDLifq1AiH7kSMHw6TWQdlsb0UaF0p9ZexkuJE/yE27CH+uX5UShHkbx+jdi7KBP/iDQkiX4b1SXV\nKv5xDSGtiIZNGNnK9YJh4P+YPoDjczcwtrqExOA0Tjz8ObCQ92GeIJzM1KYSFV7PXh+6QvRyQ8Tv\n5fd/IvOYmt+JjfmSTeYzv8E5Fc81rZiPx7gjty+og4Chhggn1K+9ZXC/OttDU75A+zA5kLvhmc6o\nIWBUb9LwDPIBIae83tVIEM/TdKF+t/vUjpT3xm8kTAxMTH4/QYKvyiF/bP9XsnxRO8q5+1D8E1nn\nBNHOeGQGIpqZL7gdyBBRBC7e632NavvKxD/G0BdPYbitQ+geWvGPUpoVrOztx2gE4Zyn+Lce3UB8\n072PMKw8dm++V8xHkoBjhvjPWnMIquoWmfe/kFpEarX2gPntztvCeQQNpbcveVMxuVi71LuP6CRe\nP3p8BLfn17CyVtyBbYuH8ejxkfJ1z1yYmoKNqOKfiHKIymG76nK8E1kI5+lDtKpE6cr9wWE+fReZ\npdroH4dme1xSa1ACFCCPed7qo4E/emIE//DiReH0xZCLysWJlyMxqZAfP13SS2ZRJpyDCa3NVhKr\nAPIAnHPRB9ZrVcpZKAw2OO2ZV6ad9nG35xfxxOp3+UZBvJ73THbhlbdu1LTzO521vr9GISg1TpeS\nPK9UN8V6z4QMBsQiIXR2RXB3zkn0WUgvlNPI5ys27tD7VnzFPx4Y47xnZsHIe6/3dCic1yN8czoZ\nRTrpvY9/qGsYJ3rG8fLtC5Uf2bbIA0F8rIGPZzEFDr14Yba9D+8u3nT8Rob6RQG6v6YcExT58ABP\n1TkYbPv2wJCOxDC35k4od6z1GMM3ByfwSxdtvJtwFMbRj0iVzHvUzy9+DTFBwQyZUL9NDw9zRQ8S\n26vCIIK9thT/7n3UlfjnBcuysLS0hFdeeQWvvPJKkCa1wMGJEydw4oT4hmwLLbTghOsw3KShfmnn\nOeNvppObPcTiyes+YgOgvC/udxIsdHsx0XBfCl/+3CHcml9DezKKW7klfPN1zq0eyIWyCFgR3B2C\nTY+aDAIC1c4AEwy8CnYzx22CrovI4QbeKXnvG/XaoUXxz289cRbyppVH3NrYTuqD7Gs/ccQhZVYv\nZl7N9LkS/+h8alFZ8tPPMT6Uxr6pbgz1t+G1V9+W777JE1XFzK60pTDgQfzTpl5GKGOJEGv9KdV6\n57976128Fd2LFTNZ/i0SNbC16e6UsYf65QmXhChHEYcY50xbIvPx6qmiqlFstx7jl4D8YfUmWe2i\nnm+LQTCv3dQ0c4aBF3sG8WLPIB4fnMXJcP0P9JAhGVz6gxKYx98i8Gr/5IyobxzmI5/l563g9CVJ\nG5YF8xO/CWN4llt2CdQ4ZnqQUGUJCUD9Nx2AkqobRdC3/83d0az8yZkDVdLJEM6riX+NcTgKEcAs\nSwvDym8Wvoh/Guu3OtRvOGSgMx3F3aVNRzrTYEi1eZOli8pLcnbxiX+CvZ+QAkbVvyWr32KcQwJM\nfFOPyEI7eHkWPJh/FnzO0NzWOwIHA4q3EvVI3KeihGlYFrpjbfjkvke19FueIeY5/bkdpXYm+p3r\n6Q7sRCzvVKuJFRgFhmjWOYcayN7AifVX0JdXI86JbH7yxh2Zja5cKIe76bvotJGStkJbWEy5q583\nYqtIpT0qhYYUXS5o3Ei0W2kwhs50DL/6c/tx+foyLFgY6U+VFXpIVVKm1m/SfbVYHiJjOC8Fj4Bh\nAZjrmEPXQm0Ugv27ipGWeB5OHoKcry0nl9G51OGwI5Ey0d0pf7BYCOSzMDzQN4m5c+5X/TTvPRMZ\nvHt+HpdvLAulL/ZljXCWEmjEvN3mVyDHO5uqs5F8H1jehcL2CcmR7GUc2HhHuuh4jN4e/pXFvxQ+\nrCA69sSiIZw8OICXflqJhtGRjuJsatE1fSNC/dYN1OuuagvVbeNOxxy6XfpEXYhsq0Eeuq8T3/j+\necQ34rCYhcXUIubb75aslM6XPjAplk5N4bzaDkaOqxaAUIGn5KY2T6/YIJAoYJjMwK/N3o/X5y5j\nq1DZq7CYpW3Sx/PP+FH8e2p4dw3xj4omYGjyf9gRLWzyExEQUfvmQtKfUvIViSrNlvBCzxDmIzEc\nWriNh0cPwNh9Eqx3VKps8jAJLM/DY655EeOOzsgQdUHKvT8X3csUVfwLmqvQQuNR38DhLbTQQgv3\nONw2BXtiSayt12tAFZ8o8yZ1FFmBt/lhMIa8x+LJkw8osdhVhVX1X54hpmlgoKdIUGHLxd9VCDEq\n99Rzk5hXlAEGxnkIgxkCin8uZbv+JvO9uKidcTYivXOiFvyW4IaT3UtBLDRdvq/9G2/jrdg+gTJq\ni1IFtZC3q0P5WRMIq3O5PM+PM33o2lzHUzcuIZG3h1CQM6i0gc+7a/90N2bGM8gXSu9HtpL99fUq\nr1Q25KI9BOZUugcfLNVuSlrE6UiuPckMsOReDzcSYRjtb2IuNINwNoK1+Bo+MjmDN152d/DalZcY\ntWqxLESJsAgWZEJfiRH/HF0iyTmUbxM1tnJstwAYHIek3029QJSXfKpyFCFZv+EoLMZqCYCEw9f8\npX8tqETifcmLuEo7oiwg1iZQrFhlec7rFF5uI4hsjDFyPmV3eHHnXYIKNg7yB9FGuKF+A6wuoS+A\napsooAC50DTuczd/D+lr3NFYv6X3XLBlun9XN/75tauOdNOjHYiEOKF+JeuEF6La3lbpVivSKvyR\n8oqKf95XDY4ChhAC+HC4NgWl+GfUbpmLHAwAipvjnvlqVziw8KXdD8GI16q/ed7BLGTNHMJ5p50d\n+QXP2pZqG+VNKfX2ILsWL4e1Z7T6RsEo4PLAFXQudqJ71cB9y+dwZON1xKwtz3tEoKT4V9XCZDe6\nbnXdxnpsA4n1OLbCWSwll5AP6Tiopgf1mnaIdgHF/liTUXb3wfY/ImETU6O1qpu8cOQq34mo4p+Q\n8hLpa6FtKx49pp7PwlpsHbVbohaO7+/n2ihG/OMmUcZGbANX+6+h624G4WwYa/F13Hd8hPPOfAxK\nRL5xM4xfmjmFP37xVdfrfg5UhMMmPvXkLly8toS7ixsY6kvip+/exjsfzLmXJbVOrw90E8jFUlvl\nsskNftvfLHoXX7z857gaHkCqsIK+3C1hgl41wjEg6y725J2ny8+iQw9jwAOHhzDQncSl60tIJ6OY\nHe/Ej159UyyDOoDX37reo+A4JQXnWPW/nT/Md8wjsRFHYiMhXS4P/d1tiG2TzuOxMC4PXoGRN4oh\nVF3GLRmQfbVgI6occBPzfbqWxSuEo/gHcKIHNJhwLgODMYwlu/D+0q3ybwVmyXkJqAOlnMcM+VDL\n2N85iI8M78G3r5wp5sUM7O7qw7WVdQ9TLL5BHlhILaBj2Tk/ixfW0Ze/5XFH/SDb+5THHR7h3AVn\n2rtwpr0Lj53+BclSi+AXxyD6ROQ32CTflzAS7pHiRJ/C/h4pf/s9TaZvAUDTHalpoYUWWtjpqB04\n+9rEw7v6hsR8hpoYiZ6/98ybyDzoUL8i1quEIPEvNiiXgY6pqZzj2huM8cNNmJyNkWJG7nlXI1lY\nofPhgLJCNdSv8Ol/e35EOjfFvwfXXsJIMueS2h0WLOzrHBBO7wZK/l5WlTBvuOdl3zyniRPubfG/\nDozjvz/8EDaSFRUKWX9aQfADLrXHspKbzrWQkAkKJDEfC7anh/e4W+GnA4p6Ox3/3e6jeKW3C3cy\nc7jedx2L6UWhUKdFxT9vo9KFZa7in3AfvP3ya0J3umbrbC9uEHG+V9d3ta3cHBhgFuhOmlJ2bUT4\nPMD/5lwxnVz7Z4aJrY/8mlQ+os4ilQMMpOIf5E8iU+OY4RXqV0nxr/5txmAGxGeovPmITfFP8FHo\nEPPVIReriX/i9VVRT3Dibtr9dxr8Iy+i5CM7XIl/PD4VZ0D1o/in9yS3jXC+/aT37e3HiQP9iEVN\nhEwDsxMZfOSBcb5Cn2TJQv7v0p/EZ1sJuUil4RQlYDzlMGaMXms2KtQv71ssBBTXTTXUb2c6io6U\ntyIv9ThKin8cQqe7EcBSslYZrhTm1r0g8XGnrPjna9xRf688AkY+lMedrjuIpN7G/esv+yb9AUBX\nP1+FmafgLUP8tLbf+3JyGTd7buFux10O6a/+m0Wlb3fahQzXCOgNHWZXmOT0UeoceU8IHw6ivgUN\n4w6Xl82Atfga7m6HlyyWZyE6kkdnOsbNQuygU7Dz29XEKi4NXcYH4+dwve86QmG6vJzpg3zr41lK\nKl+qCJkGpkY6cGx/PwZ6kuR4JErAONU77ssmGTRkZVzyP4HnV618Ze8nOxDBOma2PsBA7mZ5XsHG\nJA4TbyOS0LNFLEraKs2xJ4bb8cjxERzZ04tEnAiVucO4G3IQ90NUV4NlWLg8cEW7RdGwiUdPjJT/\nXRonCmahxgjtoX7tf/sdd3iG8MYd8P1sVCY7jnRUBT8H82ThR/GPMYbPTBzBH578FH7nwBP4o/s/\ng6FUu3d61LZjUcx33EXWzFZ+sCw8tPZD311UQ9rKdpHFg05UOv3zflqsQNLHS+S14xT/vJw8CnuS\nMiqOLdx7aCn+tdBCCy1ohNu4qTP8lBscm5sSZZEEKHBC/TK+yoNn3h4TOLFwUPzn42wrcu/nQyUP\nFTJP/WZhvLIMxhBJMOTMHEJVag4r8dViHkLKHmL1cGjjLfw4fqTm94eODgndrwr6hLqYcovoWzNd\nCBgRZPGZ3Tn8yavi07OH+qeE07ohF8ojnHd3sIVsNhqwkAhF8FD/FGb7h/HtH16sSX+r2/2Em3Co\nX0pRgxnIRWI2e+QcEGUbhDe6xfscZzn+IBKGt+YeH2UcyAzhgb5J/PBmJc5PfzwttdB+8v4x5w+S\nC2vav1B8zxYYiOjF6MzfBWMjntctGbr3dsIQsmQyu4ogGeJVgVRTPR6aBoMHZ6tii4cNpaxMoTHW\nG0E4okQdvhSkrWJwnSupqiM4s5avY+5GmMZ69wz1q+AMa4SfyGR0wVKKfzbSCak8aWsW1PhV0344\nmzQU1mPryJpZx9hswcJS0itkGqVWzEsBGJa641snCj4cyj2ZBK7dXtViR3WoX6BI8Hjo6DAePDKE\nQsGCub0xwXNY6w71K6z4V4/QV4z+zpKJCE3uFhl3AmiYvDzDXmoTPgmBbsQ/3vzANBgePU6HTSKj\nfqqouaIAlfPhdzJ3AGYhtZJGbzyJPVMZ3PdPP9ZiW6k9C4f6Fc6ZLHU7Lw4Bw3ZJxzyihGjMwGp8\nFW3r3qq/NeVV/VNqo0uB7FlvlF7D8f39uHR9CVtZgfl1gHYG5eLjZRsEOZk8hCI77viodCFPHyv6\nHBbSC4huRbAR2cSRwcFKHk2s+KeCxfSijzCe3n15zCiGJJwZ78R7F5wHS2JRE4PbUU90wSQIHUUC\nBr/i92UGkQhFsJbzT67mQce6V1V5yWC8UL+VP7OmiZ909uLEvDPEpbH3AcnSgUgbw+q87F21Tyk8\nVktWcUudqAjXfkxjv/Wx0xPI5y2MDaaRaouUfzfJg4IK801KGE6UcV5qEz6+12JZ9N7XZmQT8c3a\nkOylgzmkP1PIBoFEDQIVLtcVPh7GD/GvhHQkjnSk+K4813QQJ5y7IRvO4uLQJaTWkjDzJj594xXs\n3qrdH5FFI9pBae7GW+8E0fvqfF5yHrvTiH8qYO7/IOe9rVC/9zxain8ttNDChwaNGuzrOnmTUG3h\nLch5akfkJiIvdJ3rTVR+xf8KVSW5Kez8r/v9dCmqoX6l20Ed2w1X8W87zd0qFRoLVvk3kxlc8Qa3\nN++24Z60VrF344zjt3Qygj2TLoFdZOeqgqQB11slV+1Ufl4EDIQi7r+7oCeexNFuekOQh5zprTBo\nV/xrD8fwR6c+jc9MHMHkcAeMsPPZtkJbWIuvueZj/2Z46lyiTpO4tY5UvpYE0Ze7WfMbgHIYap7D\nsFpdVHotRG6yiyjIyUOWLGgPd2Ewhi/sOol/feSj+OLMKfyb+57Fx0b3CRP/Um0RTI9UKW9I1hlX\n8Qx84l8mf5e/MSvJ/IsVPGLd2JIJKf4JvJ9qWmJ1dokQcfqdg0i4WHFkOECNpGZdEHfFyTssgjqQ\nQSsXe/yuQQHDDhXFPznlpSKo9jQ73ul5zQ+YIa7vyFf8c/aDVE7lWyjievV3XqP4R5vjKJFZuDx4\nBWuxNViwsBXewvXe69iIcfokClTbVDnrwb0zAAAgAElEQVSc4tLO/G6S+lH8O7Knz/X3m13ucwIK\nbsS/8jXGHJvX9Ml46aK59zjLo0mHgJrCuaApACzkQu4E+UjYxORwu2812SC66lKX66WguW+6m7jb\nj2pcbT9LKeI9cmwYX3huHyaGvdUqACiFoaPAoFjvDLiTmcP50fP40qcP4NTBQXpOr0BK8xXqVzZ9\nuUw94eBlwRjD1b5rWEl4q+DziKON8n1lzRwWk0tYSC1qzbc05x7oSeKXntmDU4cGcHSve99fQpBE\nEZ1zSftamddvFgr6n4k8hMLc/65GJeQikYbzbLxv3F5PW5EtLCdXkI04xyHyMIfAK2uEojWFXCiH\nxaTit8QYHlh7yfXSaOQaAODonr6a8IrH9w9o7z8oZX5miR10Chsm/tX+x9EVLRKio+a9q2VCK806\nv7Kvje/Bd3qHMReJAb2jMJ/6IozdJ6XLTHa6O1s2InLrDx1K+X6xdzqjJZ/SsD4+JBHFKeAuJOge\nas9kF/bv6naQ/gDeIVL5cujDQZW/yUhVAmtH7kEngfur90FKeORY8eCx34NOiZi6ry9oUBGCXOHj\nw+7urCVX+kHJD+oGlcgaduRDeSykFzHXOY92S5ox3TSwh5inh/0ADp2QRENZxT+1cu5FGI7+U18d\nt7DzULdZ8uTkJLq6VE8pqeHOnTs4f/58XctsoYUWmhf1GOtdiU31nGToUvxjAF9BhNhcJkP9etwj\nsFhq5HStVF/qkyM565tJ8S9ihMAY25Y0zyG1mkTBKGAxtYT1+DqA0vtTWfG7//z42veQ7Y0j1H0f\nOtNR7JuudT6ogONO9r7Cglf8AwCEwiClvWxoC/PDQPGQCxHEvyo7jO1Ny0Q8jI79Fi68t4zYVgwb\n0Q3cztxBwSPUr7jiHzh9mJ2AARzcfAsvJO53pDi48RbXBgqVUL9l6p9n2k+OHcI/Xv0ZVnKbrjaq\nQC1sr78yDcYwmsxgNFl0kF5ZXeD2c4wBh2Z7cWxfHx2SRQA0X297tGE0r70zv4A8NaYJKnaWSgWA\nMHIwrDwKzN1h5Kgj8hkUwmhWVUrUDGEDauoG0e1QTX4V/+oN0bFWOtQvg2tj8hMyu5K3fB1ziX91\nUPyTKkPAwb53uhvvVqmHpNsi+OxHZ/Gf/+ks7txdFy/PBl4IEofiHycvOwmHVuy0/02MXzXXqol/\ncu8xG87i8uAVIQYsfZilnMoziVKoX4/5B2Pqwmh+iH+ptjBOHOjHy2/eKP/W392G95PL6JujiSHV\nsBP/eG+NnrGoECrFSRH0pjAfYgQMei243LaCzEIGhmX7ngwLH39kEulkFHc2vJVzRdbHQYxMpbe6\nmFpC+3K7w/ZMb0TLWsO94Nqn6SgsoTd3C7dCvY7fP/n4NKaqD1V4QDcHqDhn8V/z3D5P4rCillC/\nircWFTC8r9u/c5GDHuLlFlVWrvfewK4L065paknbzn/LEXckDxER1+Y75rHQvoDoZhQdyzRxVQb2\nJtPVEccDh4uRAD64tIDFlU2Pu4JBX1diO9pBAOBkGoTiH0XEdYw7AoRzX3YoVqi9ZHpdKTDuNN+S\nCDd6bmIjuiE9nwFj2L35Ht6O7sGiWfkW926cQby9OD4P9ibxuY/O4p0P5rCxmcfUaAdmxvQf4KEU\n/2QOoIylMvifjz+Hu5traI/E8S9e+Esd5tVASzuQzcOmvET231WXcoaBvxmdwd+MzuCrp39ZstAK\nkhmjRnEcKCqRe8KlPxIdeqT9D4LJN8NbGBtM452z/sk4pTF+/64eXLi65Du/nQyTOCioMhqSTVx0\nvaNl3OERzi2sJtawGd5ENFtZ/7J4vkwIFVqKA7j/8CBefP1aTZr7OIcYGgnpOhb4ro/v78crb91w\n/JZORtDXlZAri4NwmGizlpjSbD2hxycrKwogqnAewNyTc3BfpkTykPu9ovgnWCH2/ljkwE4L9y7q\nRvz78pe/jOeee65exQEAnn/+efze7/1eXctsoYUWmhcGY8g3gNGeSga0gVCG2mhNM/85oX5hkZME\nkvjnR51HaCJM2138f3KVSWevNOGV19xgAPIsD9MiZK40gXrkEDMQNUNl+5dTy1hO1SqtmcyAIVE3\nZaUsj5phAPrjd3H89ASZT8TUVz9cxT+hXMRS2dX0HAhFAIiRIvysC0tPKqr4V/1dRRIM1/uci3Uv\nFGx28oh/1CNtJdJom6+UeXTjp4gWNvF+dApn2jvx8zd/iF3Zc673uin3eNng+DeR9pnRffjoyF78\n/o+/gWtrxZP4ZAsSMkGBKCB9Bw2TGdx+7pef3YO+Lo/wY5IG0Yp/pVC/gEEoBWQKd3GH13WLGsYq\n/4lZm1hj3g4oPj1UbDOjljIkRxqi+q7SSVe/YXWbzC9WhvyQ7O7U0kH8o+DVzkmVFcuS/p5oxT8v\n4p/eIAATQ+144uQoXvzpNaxt5NDXlcCzDxeJQF/4xF782z97TSlfU2KTnezzq55XOCQVsdle0358\nKP5VsQ19gndUR5GcTEmwKsLP5o3BGB48MoTRgTQu31hGpj2GqZEOfO+ln0jnVXqXecbAe0rREKDi\nZfPKE0tcUfwTR/X6jNtmWVFx6Wr/VXTP9yCcDWEzuonR2QTGh4oEA1J5ScSoAAae0jvbjG7i8sAV\ndC52IpwLYy2+igcPz3jeZwH+vkePZ3lu+Rv4et8nMZ/vQjIRxvH9/cKkPyAAxT8L+uqdYAIrhSH2\nZZe05l+xTF65tkubGtWnSmVSfhOewqBMqN+doPng6TPQ2k0IEL4ZcOLAgM5C4TzcRlvQmYppLlvP\n4aBSW6V8hLxNRl5RYn5Ef8/SlOsdBiy0LyKSjaBzSYKUxxjarDV8Zul5nInOYMHswFD2GnZvvYez\n2F9O1tfV5r2u1wQqhGPxoJP4WoQxhkwsWHuDOXpAo9S6DUa31aBUegzDwK3u2xi41V8+FLEZ3sRc\n5zxwQSafYOqOeu7lxDLCuTA2ohu40zmHWGSvrkIBADNjnfj4I5N4473b2NjM49a8e6STexl+fUk1\n91BEHcEMdXwJIn1+wSjg8uAVdC52ILoZw2Z0A33j0TKhWTTE/J6JDF4/cwvrmxUf/PhQGh1p/wf6\ng0JBI/GvlNOJAwO4cnMZ12+vAij6Kz/20IT2w8hcxb8mgxban2ImDPQhkCBqS0bZlgeqf9ppxD+v\nJxetEft3ZBCh9XQQp1tobtSN+NeMShIttNDChwv1UN5zo3cdnOnBa2/Lh5pSgcxJK16/TE6EOcWo\nyLHTEzWhYov5U9fEdpncf2X8/D3L9cyVsIIxzHfOo2e+R6FEOVCTwVQ4tn2indNeOEo8AFwZGqJK\nO14YTLRrW4RwJ76SfQgZ6tdT8U+c+KejTyOJf3YbqzaXZPoaUcW/7XNmnlev73sQnVecYdH2bf0M\nb/ZbuNo7hKmrFzzvLYjSNqvrlNNpGIwJh5kJLNSv5mV4yDC430I4pI8oJNIHWGDwEN4DUFT8m6Oc\nTMyS+HwrCaPWBtbgQfxjtp6d2hP2Ullz2MexyMe33pkubhAaxIaKWP7Ns5ZzWqJHVUuP4p/8NX6o\nX33fmjfxT/+7PbS7Fwdne7CVLZRVJ4tFMbQno0rqPIzRJ5HtfSxNwnbmIUz886P4J/X91NcJp6JS\n5VVlxWNDHvnxNvRlQwnZYBjFtjE6kMbogEQ4LheUFf8gQvzzvqa2MUvfY2+rZJsSIP75JWCUcl+L\nr+PS0KXyr1Ntu2x5UJk0hoBhz3IjtoHrsevlf5sa5za1Bbs/TNzawPH0Wez62JNKGxO6xb8YNCpg\nMAPwOOjEJELMl1DPKAqlamU8pVnbV/azdCe2DAORgrMvY3sfkC5fRN2wZkyvagxS7Um6auu/WeT5\nHvx1M1LJD832YHY8g+H+lFzGHMjUZk8mjkQihLU15xp+rl1d3YriXNnrnfpsK2tHap5G1zB3ruQ5\n9+Am4V4rp2nivau8R3QFT2w/S5u1hmMbrzsu1fsLNqlQvwoHUIKGHjE6tVrmKc0GtSRnjGGlbQUX\nhi8ivhFHgRWwmliFZcg9hzBZWGMjvN57o2wnA61OJwO7iTPjGcyMFyNk/KdvvYvLN2oPw9/LMH37\nkqrvoa7Z1jvkABW80mxpbMubedzJzJV/7zcHpfPvSMfw2Y/O4idnbuHu0gaG+1I4fqC/qccd+fU5\n/1miEROf++hu3JhbxfpGDkO9ScSi+ikqlM+aKe3SeWWmKx892cigrPjHGGcfV3/ZMsq2PFCvYIfx\n/jwhOmaKKv7tjGNfLfhBgJ6tFlpooYXmgmZBE1e4ObM60zHcv/5y8IVDbpOJGv/FdCIIVRmFCRzt\ncAzScS2abYngoTA5YlBaDMy338VSm3pIga52sRPhlGnJ7XBuXKIoY0obMn7XSBSZRRY8tTadTcvw\nIAOxkL+wqbIgQ/3aN+uqKkfmXTsV/2hQ2a5nBnCx0xkG4XY0jtc7+eTYkg1cVceq8qXfuUD/QFMf\nVRT/9C7YTMYn/lGn9mXNESJ0M8AMuZ/aTOWXEbM2+SESRd+mLZ9YwZucZKHiRKByFjtNWp2DpPIS\ngNuZ266/H9/fv20HteHSGAIGBcsSC2cuHeoXgOsSWMNnRJ9cd79GKv6hoNUR7NkWA5ogM8YcpL+y\nHYrFFcldVIGVPy0JxT+SfGzzsFEhcbnhciVeo3xT9L5DrPdRafz6OwRpRQEbdH4nJTNElYJ1gh/q\nt/I36ZwvvVMB1QXPsjjvWORtkYe6BKo3iDcgqsrhZstCatFHwcSJe3DC+hHQHfZTd4h574IkBgIN\n5iiIA2//hxf6qvJnzjDx3d4R5/VwFMahR2VLL4Oaj/P67kAVLgQ2knQLSTTDvvgTp8a0k/4ASLVx\nxhgeOTWEgm0zfj26jvkOdeIfORcVlJottVVqNsRtEoKEczILnw2lGdqZFwqyxD+drFifoHwHhlWn\ncUcCsta4tUz5cac0d+NF5wkGpRKz4SyWUktYSa4IkP5qr4sMPSHTQIQIw+kHOv3DQSNqKJCdAvxW\ndk9mPK/VU/HPfonetxL4Gnz7CPllyPjZujriePL+MfzC07O4//Ag7VdtAhQkib+i7dMwGAZ7kpga\n6QiE9AfQin91W+9IoJHmcNc7AYw8bXHvva9QPiTVsZBRhHYa889nVTv9Rfx5ewv3Luqm+NdCCy20\n0GjU85R4NY5uvoG7Rjt+Fp3VnrdjqJZw/tMTADrUrwX6xAftqHDPlz6hoevdCVAaOWWpTY3kQ/0W\nbQGu993ArdxthPImeuZ70LYuFtYiEjbLobZ4oOo3FYmVTCFRPAHooWK3Dbe6k1PBCRgcU8RstTz+\ndoJW/BODjs+CJP7BO9SvzDcp3vo5X5fB8PzBhzB69nVMrSziWrwN3+8dxkq4WGcWY55ZiNqg0tfY\n7/G/dNoZxD+din8ir8YCAzMMzI6348337ziu7ds8s20UQRKTea124p+1wcmTbf9Jk7d4qK7tQtUP\n3E0sZmEpuYzOxc6ik2QboZSFvq6iYqFfAka9YcFCIhTBZKob55bveKaTJ+fCNYyFFsU/6ppH/dOK\nf6gTAaO+DUB1U5avLByA4p9tXk0q/nHm34FWcQO+30CU2ALI81TvBO6ek7SjpPgnNDaRGUmD29WL\nqqiIKP4pKisJX+ckESOc628U5OYcSaYE1uJryJk5xzirA35UFHihfh89MYLvvnxZOL+6KWBIrSPk\nkI7EXcqTzKREXmO8duG07utDk7gVi2P/whyOju6Dsf8hsL5x2cKF2j6PcC4X6leulsX6Pr3fbzMr\n4tQbu4e78ee7XsL6goW8mcdGbAMWszDT3quUn3DIRbJT16G8xPHD1WVK3LztTJr41wDymBdoxb9m\nJGDU357SOzG4in/BvD1dzyySz96pLunyREkKpuKBdClbfNz7ucn7sJHPwWDA4a5hfP3im7h2c0v4\n/iCf7NCs94Fq3Yp/JJFQeL0jksjfGt2rDEFOfLN1bdLQq/hX38rgKv412ctpyN7YdpG89U4QpmUE\nhUpEQM5jm9HZTcB7TSY28th9HVSbanbScQv+8aF5wzyHWAsttHDvox6Ld69NFAMWnlz9Dj699Dwy\nOfXTuHwDZBT/aEcQHfpL/YS514KdPPFl8MkVIraVrqg4DUu3qJyK8DsC5UN5bEZpZ0B/d4UQaDCG\npx8cF57gUvWaklD8MwOU+PeGbOgJ9bxkH49qZ6ZH6CsZ4p+WhWrIeyFv2lUJq8NHyWzYiTpuLB6B\nCiiYIfzDwBi+uusg/n54CouRCtmLKkfUhhp/llBnIVgXQpvkjZ+vhgyDuwEYChEBECWbpUi/XnoN\nj50cxf74HCKFTSQKqzi2/hru2/gJ2MgeGGHvE4MWZEL9VhCzKMU/24hP5C2m+Febux1chySKJN5L\nA5exkFrAWmwNcx1z6NhbUYyjT2k3nzOkVAO/sutEeRwCgN64U2lFRfGPuSyBgw716zUc08Q/+ZCL\nSic3JZQRdPRQqs63UjhXL9i7a5LUVCU5KKrmJBfqt+q6zHuUnWv4zEtJaTaALiMIp+zJ3nHpe+yh\nfrnQPGTvytAKxo52RJGRNFQll4Ahorzkcyes3gRTkTCTlwcuYzMsvkErVLCPMZi39N8/3Y2h3qRw\nflo3wkTjh3Ih96E9NjDjO49Sar4CRhUYw0vdg/gP0wdgPvErSqQ/QCzUbw3h3E+oX40oWSGvTkaj\nyfZnAQDdMfFvi4asP4PhI+O7sZJcwXp8HRazYIDhyaHdSqXT6tOVa9RssUw4F5ynuZbFfcfu9aRz\nKG6mZlY9zsoT/3T1wf5BbTBrJZw3CDqtL4aY3yH14fLx8caefdNdePTECJlG2gzb+tdghjYxe885\nluC+y2Sq2/Hvo90jeGRwF54Z3YePjuxDf6IdHx3Z69NKNRya7SkropkGw6PHRzDU661oSxL/FMoX\nFX7wq0JWreCekPG3E2XYmwDtw94h37IHpH1LTdR30cS/5oNs1Z3oGddWtgGaMB0Er4YxhsyI+2G6\nrdCW1NyOGnZ2GvHPC6L14QiVTrT0J4fV1gwt7BwEqvg3O1tUtmKMIZ1OB1mUZ/m/+Zu/WbahhRZa\n+HCj0YM9AzCUu44Dm2/je6HTwRSiSfFvOzPl60qhfgU2zEW6ct8n0D0LKRmhsqke/ILvsx+dxZUb\ny1hZy2JkIIX2JD9EYglUWyiH+hXIQ2Wsrf9CmCa8qt4rC9NLpUEi1K8fa0zGMJ7M4Lmxg/jWhWuu\nafIO54468U/GRa0ago1rgzA3j1X9W+QeMQhtkit1L/VX/AsRp/alzRFgKJRIPCHTwFOfeBCP/s0f\ng92+VLyY7ob51BeBmxzijTBBs5IuViAU/4qZVt9Sm0RgXK5OUf1v0e8tG8niZs+t8r/3mJ22PPyd\n0m7UUmqorQO/f+w5vLd4E2HDxHS6B1sv/BdbCgXSkkusWS3fkcABhmqYRNxbxsnTDXkzj83wJqJZ\n5xxgbOuS9011V/xTu89khrCiNNmtVH0LpAKY3alPfMtc4h951QlZ5aV65eWE/jaj6q+ZGev0vLa3\ncwD/H67K2bFdZwuRGJI88ptmJ3giGi42Oo8G7OD9keQKAcW/qvurH0X1O7XfRirNqmXvG+TmnMBD\nb0WyuDB8AbPn3chlVMFUqF918EL9RsImPv3UDC5fX8LC8iaG+1M4c24Or7190zU9ffBPIwIMxZeJ\nteFEzxhevn2x/FvUVHN9M3BUkQMaPstNkcxf/SBmPbAV3kIkZmBrQw8BsBk3z3e196AtFMVqzvug\nkAhUvrqHB3YhHY7h1TuXEDZMnOqdwGxHn1L5XnPU4jXb36QfReQp6Hfo1zvJg8j9HWl9CjQikHmm\ngqzyUjMp/pHk0iZUXpI0ZzLdjX+89m5VJnJ5OMlr/DmebogQzmtRawtVd//t5w4jHgs2+JyplTjp\nr66/OHMSdzZWcXFlDiNtGezLDNQQ6IbbOtEdS2Jr1VdR0pgYbsdjJ0Yxt7COjnSMG9GDOuSvMj7T\nwg9iediXS2eT7ZheWXRcvx5LYL3Kv/756ePCdlSX4QVymthkfZssVEL9duTvYsGsXZ93GXOarBID\nV9FM07vR1SPLmtMTT2E8mcGFFbu4jJo1XMJ5QM24ZzyMO1e2YFjOd7Ue4/jhq+BX8c8AQ0Gw7nJG\nHqECIYQQFARfragvJkKJObRwTyDQ2dbzzz8fZPZc7N69G7t3t9irLbTQQhH1mW8LbJ5rdrM4VE00\nKv6RoX4ZvQAiN6M88qVDjOhduKtsz5ZMUHt7VuALvpBpCIf2rQat+BfjpgEAAwbAc0banFQi+xli\nNaaxXjlONNktKsoB7qn4R4Ur1YjHBmfw6JFRAMC34E78s+xPXK0iIVHvcop/NOh26H2trNzDKaDa\n1MXUEhIbiZp0UyMd7jb5dMQqKS9p3qQtEv+8r4dCBtmfDfencPXWSs3vWyEFhZxt2J+QxZMIf/5/\nAG5fgZXPgvWNgxkmcOO2dwZMraeIEop/YJXvgGqXvFBsxQyc91dv5PNDkHiMq3Y7fI9B9XVahm0e\n33gojENdw+V/21uSilVM+0xsO1/CGK/6JxX/rAJUnvBO5xwGbw2U22XYZDi28WPvG6TUjPzXnGpb\n5IYgsYFUa6vKRNSeuin+1RlNo/inkGlnOopHjutVDWGwsBSK4FyyHcOctLr7EcYY2mJhrK7nXK87\nFDBIuyyBVBxbuAkEDjNQm3pChPP6EkzpOnUmzBt5mDJO/4D6AEtgihEOGZi0zVvfu0BFH7D0kfJo\nWXeprErrUVH86uz9mEh1493Fm+iNp5COdeDNOYkNx9KygTENZCcV8OvH8xDZNqT6VOnmSdKKy3mO\nzSTw/hu1awIV1G0YlXilBjPw+enj+A8/e6G8YTgQVxA9UHy2w90jONztfwwUJnQQXYMI4by6xVaX\nyu3zBdY7onjo6BB+8GPnwQDGgP27uj3uaDx0hvqtN0yCgGGgUO9lpnYc6hpG1Ahhs2Cfv6mND9xQ\nvwGNO9rEfomMyMOj3IzFkplMjcLoBr9uNpMZ2J8ZxP7MoGcagzFMpLvx7pxYZChdJHjGGAyDoSdT\n6+t0g47QvML52Z6RzNk2Jvxj/ygmzr4F0/Z9vDQy60jeHUtib+eAMwuen03k8HYT9bW6IR3qlzGc\nXH8N30o+WZWRhSHjij7DBBCL8mgvzfbe5O35l/sexX98/yX8bOEmOiJxPNQ3i3euLiuUTI87Qa13\nIhEDt7puo/9O5eBK3shjIX1XKh+qPxGJTPaL08fwtbOvCJV1s/smhm7V9un7Zrrw9nvBkVuVFP+I\nR+/sjMIwGAqFeh8FaaFeCPaYRQsttNBCE0EfecwbjRguw7A7F8Qn5fQpQiYQnpDYAFXg5pAigTKh\nfgPiNlR+ln/LFpVxE4Cq+3KIRY75JnMLnuiErKuWDNXHyVUF3Jxk+xAiuQmvUL8Sin9+mpTt5snh\ndpy74jwdCcvCQO6G4992SIX6VTKwFgy8ZyZKYvYv2PLsS6pJOCuJFRRYoeYE2pE9vW5Za9A6kEe9\nFf/CnJOTh2Z78KM3rtf8fifjvgi2BMatavIoYwbQO+qoS74zQpTcU6lRXqjfUp4k4Uvk+Xg2KROl\nREm3Ig5NJRO4OJgZwhvzzo23RCiCsVRG6H7pUL+MgRm1d/XnbuJN7K9JH+KcfnfaQpXr/jsd6pe4\nkcBKcgWXQpeRXE3i2fF92NUbQsf/c8MzPQtQecm9PLXGZBo06dju/JdR/KOtsalvUErXXOIfedkn\n6qGB40QQjyPilLXjs0/Por+njX+aXxIFAP9pdJfQoYUgxNHiBPHPqfhHZFIOueidxO+443W/g6jv\ns6U08z5agRVgQg/xz09oZhXVSZqAIXAKRxQ+lYbtON0/JZXeZAYeH5rF40PFTd8fLsopf9rnlNQQ\nGZTvx17kcmIFqTVnSNmR7BUu8U+mjqU39ASzHhxO4FD/IP762+/J5e9WpNfzNHi/6r6eUQy1deCd\nu9fRHoljb+cA/re3fyqZS+3hyHqCVPyzjztEHiLEP7/Qmff+6W6888Ec5hcrqjInDgwgziUK6IXM\n+9ZJ/BM9mKkLtOJf5f+bBbJjVNgw8eu7H8RXz/wzctuM/IlUN7Leyy8XWOWyG0MkUijTZQ5Crm19\nKtEuJ5aRWkvV/ObgJ28T2poDov6n+kO2TL63XzI/DWdD7K3vzY4e/OnsYRyfuwnTKuDBh38RTw1M\nYvPSm7i4PI/RZAYfH9tfE+qXTzgXs+VeRUH60CfD9NYHuLA5inej2+roloUnVr+LcMx9fRsUImET\nI10RXJ5zHkCf2vpg29Tmerkq5qQiMfzLfY8ibxVgMgN37q7jHbytVDa9RyxvmygWU4vIhrJIraaQ\nN3NYSi5jKyInGkCt1UTGnZM94/jRrfP4YOkON+1aYg1boS1EcpW+ZCu0hbHBtBTxb+9UF975oDb9\nwZke9xuEFf8qz2sQ/XYkbOLhY8P47suXxTJuYcehRfxroYUWPjSox+K53g4Uw8pjfOuCzQDxe2lV\nPl5mAZz3IBX/9BRRmqyqBWRjjjxk0VxLCieoRXxJYYF3btJghpIjhyR8BlBpatuYIvfKwYsMxIz6\nyG3bn+XArp4a4t9E9gLilre8uhzxT5B8RBDyANRlYV7dLxbMAq72XcPgrQGYBRMWLCx0zWN0oKLm\noDP0lBqJT29vHDJo4h+PCJVMRPDIsWF879XKic5dY51413Df9BOR1K9Q7DiJCAg3H1tCivgnmrfI\nO61OUT3CqjZ90bbZCMJqCY8NzuCtu9ccKoePD87SoYntf6swb1zynt46h+9YDyPHnOTrh44MCWdL\nWeI156I2wooKGGo1vxHbwEZsAw8cHoK1ugjSzVrvuatqe+Y5JB1/k6dJquwh2potU1Lxj9MOpdYg\nkvXjdwRQGne8nodB2SDZDaXh/hQ/kQL+74nduNIuaot+igNFOnCc4KYOzoiE+uXYoaNboEP9NvPK\niI+CUYDXGR5XqJyKE7FDhfjHCxDPEF4AACAASURBVLmo691IELAppCIxfGLsgE9T1J7J4BEwAgq5\naC/zVtctxLaiCOeK85O2wioeWf1nl7vqx4Aj+xZbdZmMYbhPT18dxFRlf5Xyjyr6E2n0JxSU/rbR\naK0Nci5qu0aHmC+nItLQL5H7jn22AfvtiXgYn/vobrx/8S4WVjYx0p/ChGL0jHpBnvhX34M9FLhE\nrHuAgHGwawj/68lP4eziLXTFksguMPzn988K31/6hhjo+mquUL8u+ZDKcP7yvtl9C5HrEUSzxcPp\nm+FN3Oy+5UhjMs5BMQl4TbHElZe0mBEIZOtIt5CGaGhOkfVOCWdTnTibKoaYPT2+H50AvrDrJG0H\nx06/O19N3ASEoKL4Z8DCU6v/hCMbP8VdswMDuRtIFVbxPsYDsZHCo/vS+NvvXsSq0QYASOeXcHrt\nxbrbIQI//VZ1CHHpssEjnAe43mFFMt1aYq3GKlGQin8CzsdYKIzf3v843py/ihtrS/jnG2exsLXu\nmrZgFHB58Aq67mYQ24xhI7qBuc55xGP7hO0FgH3bB1AcdkRNjA36m4vaH5d6pSZjOLqnFwPdbbh8\nQ14lsoXmB5f4t3v3bqGOx7KKYQzffvttGEbzLC5aaKGFFkpolq5JVZ1p33Q3fnZuDnmbDO/ezZ8h\nYt/OFYn3U7aDvsq1U/MKhno/pXFIbB4sUL80S4Nzr8oGqZ5JcmCqAsQzJwVDz5pMdUuFd/qXc3dH\nHz+Rf1O2L8s9IfXadXw+ughnU6MdeOb0BF575yZWbt/C+NYlnF77oTNRdehRiQ36gqiZHJERBvqZ\n9dRpLdYSazg79gGiWxFshbOIhJzkTHFCWfE/edN7p1gp5KL0HTS4in8CCmj37evH+FA7rt5aQVdH\nDAPdSfyXF37kmjYU4j9BgRN6ABAgMSjUVLRAh/qt/E04LoXG5ar7qx6Gq7zk8b5ECVZ2xSBXBVAA\n02OdYplJYm/nAH57/2N44cY5rOe3cLhrBA/2TQZSVgW1FRNCHk+ufAffSj1VJo2NDaZxwOvEpQtU\nOIh8RQINXzjv45GcIB/KiJMh3aCq9lBUwKASVP4kX4VZ3YeLOTnpUL8c5SXyanWJGpWXRMKxauT9\n+cFIsgNTox04e2lBf+YSuBNNAPA++GBHIIp/JPFPLA+xTWFnZrWEc96kWKBtUdcEHqaZQ2c1i/KS\nShvkEv+01Tu13hYvoyvWRhK0hSzx8Uj13wZzzllz4RzOD19AfCMOBobff+ebTv9PE4M7bkvm5X5B\nPc9nRp1Kz03c5QQK8b6WOnzBV5rl5cdXmnX/4kS/w+p08VgIB2fF5/lBQKYP0TruyOXkG3VZ72iE\nqjXJcLQcfvviUu16moZd8U/RAB/Q11d7X/NLHsuH8rgwfBHRrW3iX2Sz5mWZWuvP35ciTKbcAe87\novmAurD7gbTTf0/GJ5w3mprfWEgr/pX27gD05OfQk68Qm0QFAXSiOx3Bryz8Ja6FB8BQwFD2OkKl\nk1tNNunTYY5qHgZjDVE4r8cbEJ3jRs0QjvWMAQBuri/j5dsXPNPmQjnc7HGSzkOSPtWR/hSevH8M\nP/jxFWxs5tGZjuLZh6eI/Raxt+BU/KOI+MVrAz1JDPQkPdO1sHMhpPgnGj5CJcxECy200EK9UJ/N\nA48yNPSPw31J7Jvuwk/fvY2V997EePYSDm+8oVwOT8bZIPIqOvSI6yqb36TiX+maABGdvKaua1T6\nVVWPS6cqmG5Q30ZJ8Y/3/ag6cejbBDYFu4eAjl5g4RY3LS9L/eonAb9zjdnvnuzC7skuZP/tH7sn\n8BXqVyytYdCnzBiP/CU6XyWUBalNpc2ou9y87Le9nlhHJGxgK+t03h/YeBs3Qr0ed3lDnixI2xsy\nDLKvFCH+AUBXRxxdHXFuumjCREc6ioUlJ8luZvP98t86QgsIZ2FrAxGLF2LA4uatovhXrYKo/vhi\nd9pL2zPVVUP86+9uQ3tSjASugt0d/djd0a90r3yoX4B5OGV2Zc8BnT8F2/8ptCej6O9ukwsVpJn4\n50fxzwl9m22MMTw9sseXNX42fWjFv8oLIIk0VSF+hPcdiDGmsaF+vSE262084RwAPjF6EJHuKM5f\nWXQccqo37KQ53nMGE+pXUPGPdM6XFP/otR4F7rNzrgMc1ReB+5t35aRAwNAcIq0E3aF+9Sr+kaf6\n9JQhCOniWGl+1yjFP+e/LcMqK2F4kv78dEhaX0fFDq5iogQ8sxF87IlUF84vVzagH+qfwkSq25Hm\nubFD+P4VubDQWtBgYgGp+OdQmiUg8Az1UJq9l1FQUF5SuhYAuHP/Znv5dVl/OWFX/KPHHXWLaOjq\nq/lEA38FAJtR78OZJjO0+d49+yzBLjuIPah6EDTdYBoG9nUO4O271x2/H+kaUSxfbNyhoCX8KJdw\n/uGGZej1NdcdzEAEWYxnL7lcay5b69CcybJF/Ww6oa2v1myeir8yRKyvvXBwpgcHdnVjfSOHRDxM\nphVXmmWuf1dDt4prC80Hrd6nZj6R20ILLbRQj0EtyEUBY8BwXwrPPjyJTy//PY5u/LR2o1FitkNK\nptv+n06jD+TJQKNyakgHONuz5M+qoRWaeYik2kJqW/GPZ74hEFLBre50KLiZH/tvgGiFYPRBm6I0\nNufdyiv+NfFLl4YP4l9V2pHs5Zo0iVgImfYYNy8tC0Oyr5G/X9YigzEcPeQk+LUVVnF44w1FAobe\n3thkjA71q7CgpVCwLDx2fBSmrcy2wipOrL9a/rcIeZS7+S34otjkofLfUcFQv1TmPEIQ5/biZWWF\nNNGUFRtnxzN49MQIkokwTINhfCiNn3tiWqn8oGCvUd3tP25msWeyC4O9STnSHwCVmRE39EQ9PIAS\np1On0t2YSvtTR/EzHxf9Fsg3EXYS/8i5iy0jStWPr5Lt/0XOtHsRw/1+Awrjjs/nGW7rcPz7YGYI\nE+kujA6k8ZmnZrBnMtP0IfeAYJzgMUHFPzr0lX879KgO0LT4etigD853rZOA4acVqXBkqbGNARp3\nlalr4uNO8G2xFqVqZZzy66H4Vw/I92Vi6XX63/yOO1+cOYXfOfAEfn7iCH7nwBP4/PSJGvuOdo80\nZCOs0cQCam7lGHeob6HkKyN9jM5r1Sl57d5rfdpUQ4UkTCmfij4CRtMp/jXXgN9gAgYjVfuDImAo\nuhuaDgYztEV7kvk+3dDMVaoyz/jl6ePoiraV/90bS+JzU/eplU8SRG1/E3no+BbUFf+EaTgS1jQf\nVEL9eubVdNKWAROEZdEAwnnlLvHIGjpB9QMy9ap7XJRdczAw5XDLjDEu6Q+AOOHcrvhHPIff8NAt\nND+EFP9E0VL8a6GFFpoZ9fAldOXnA8xd4AEkQv3SpzkEloGa65PczJUpS2CySo5Wnry/0gWVmGjN\nPT6GiAlfzCxOQHktQlnxT0NDMvonwH79D2FdfR+IJ/En7/0QM+fl89H9lrbCWyiwAgzLWb8ZtqIl\nf199ms9ql1P8c+LQxpu4EhqCZWt3R/f2FcOa+LBJj4NWpJ9VuKcq/dHdffiray9jcCGMx26fw/TW\nOSStVe0kJnfQZZjMpIl/gop/oshbBUwMt+PzBwo499KPELG2ML51EW3WejmNxYCoyTkBx6k60VAr\nxv7TKPzweQAWEjYbKNCH8vnjcrXp1WsqnuVejg7RZ66+++iePhzZ3YtCwSKVgRoHn99JQA5JlaUw\nX9BBQ/1zVTbEy4hxvkMR+PExGQzwCpZu77coxT9WrfgnqEpGh/rVqPjnkfZ0/zTeWxRUNxbLknst\nKPz2/sfw+twVXF65i/FUF072jpedj8P9KQz3pwAAf/Wtd3HlxrKvsk4eHMCP3rjOT1iG+IdMffOT\nVUpSoqAU/xxtlVRu3z7C5YewpHCYpyYLihQv0vDqTASQ6cJ1hlz08xWq+GDrprykSW2qkVumjLGG\nEMF0voJ6u+ntxRkatQb8VokBhtmOPsx29HmmaY/EMZXuwY1Vsfn/vQKK3OJc51LzZwHFv1qmX1VZ\n3Cx2LI73jOGV2xcdvxlgONw1LJ4JAzbDm4hmBVXYAyKcq4B70KnZ0ABzS98Q4ymlBmabQsZNuA9s\ncqKIuOG+fX147e2bNb8f2es9XoggGIEcPXmqmNYdS+LfHPs4zi3dAWMMk6kuhBRDAJPbPw6l2WC/\nBb6f7cMN+VC/iteCQh36Ul2ExsbR/gTGnYDQrDMD2dWLyZg2wrkXxBX/bH+TQivNWvst6II08e/P\n/uzPPJ1LjDEYQbfyFlpooQVFyCu3yKE9v4huT+Jfpd+MFTaU8heag2lS/AO2Q8x5FwTdSyCRSYca\nhcHtmnxbKNP+7sG5kWkYmG3vw7uLTmfHfd2j5ck/bxFgMs4JIQ/Q6zCJTaFIDGziAADAev9FeUOK\nd9JlSD6fZVhYSawgvZp2/L6XXZE1TDukX1V1qF+JHApVFTeRvYTnlr+BM9FZZFkYMyePYN+BgaJd\nnJOfZKkaHI8qi12VLiFmhjHe34G13Ls4fOVNhRzs5esNvxAyDHJDXzTUryhKYW0zCYb26vD1Npzq\nnfBZktibYm3tMD/yReS//R8RtbYwkL2O6+EBR5qVhJO8SyrWCrTLamdRdahf/ia9x8+iYVJcbGSM\nwTSbf8CTbf/RsEkzz3w8skoPxH1HeqSO9JWhwRx/oX4NT+KfHSQlJ+QkL9JzDXvYV4L4x/nOdTjW\nTvSOY25zFd++cgZruUoYcvowiwg5S0XxT/oWB2JmGA8P7PKXiSBmxjvx2ts3kcuLEbWk5vlE1Z3q\nUxuz4sKKf94oE/+oNJw2qbxxYPub/tZ1rfsag4Js6KuACBhKxD/eNE7bxo+mUL8a7JHOohzqlzNO\nBxXqV9vGPgvkgD7ZT9qu6VX883u/WAZtoSiAxhH/GrLxKvipkpZtfwsFH+bzHl018kYz4IG+Sbx2\n+5JjjXdfzyjiVYdReFhML6J3zksBugqNkO/xQDTCIQcpngra29GPdxZuVLLR5KPW8R3KjzuV/+hS\nQVIofsdDJBJNNXZPZPD6mVvI22SUezJx9HTGibv4aOY6VW3jYcMkCfQ6yhf2YWlR/KPL8h53mvnt\nqqO6TuXHXe++vDEjePOMgzzoCoUuiq1wxZ/EwBGHCWq9o2nOq3upI2uXaciPO0HBvldH0SD8Ktq2\n0PyQIv4xxnDixImgbGmhhRZaCBTBnta28LGVbwulnMheRMjKIsecG4+z4xm8e8FbMVD3JIJ0JnCd\nbgGE+hVKxE8lQvtTCvW7/bvK4tJC85+m+OXp4/ijN/4BS9kiMbUnlsSnxishL3nWG8xQesagT/DV\nZEnkyWv3Is9X3Tpu9NwEGJBcTSIZjeDArh4cfuvv+YYKoK5tqpr450PxDwBGc1cwmisSIEOjp8u/\nc7ehNfSD1DesRl6Vu6m0EPvizCn85Z2rzrwUVqwq91DghfrVTvwr2+9dj6lIAskwrW7A29iUeU3G\nvofAxg/AunEOj7Au/PkL12EWihsWOTOH25nbxTJLeZMn6SRVgdwzCRQ7dxtNfoPn4WMjALtD5qiK\n5lVe4pUhEXLRpyWAv/ksvelc+ZNS/EPI2ZeQ78D2Tqmw3bXtsEq1U9M3/LGRfXh6eA/+z3dfxMsl\n5RjK/O3/ZkM5PQZsw++aRPh+DeNbT2cCn3pyF1766TXML25guC+FkYEU/uHFiwJ3czaDCPseUSQ2\n0sQ/myNXiLCqPsfmvyK/70aAlNpES6fqV61T8c/PoTKVUL9125gg99rEbdAzCsrlUv6COAfbgiNg\nKDy16yEO4QJr8PMTR/DX538ib4ejfJ3EP5/jTjP7YhpMaKPU2MQJGOU7iDSclb7iO94J65i9nQP4\n8t7T+M6197C4tY79mUF8cuygdD530wsAgPRyGoZlIJIliINNNIimk1F0pCJYWN5y/D6xdcFXvg/0\nTTqIf7qgiXqtdhdrVKjfRj61PpjMkG76fV1t+LknpvGjN67j7tImhvtTePzESFOOO42uX10gD85K\nEs592aF8504YefyjWRTOlaGBYFov1Nuc0nxCpPygxh1tj6zZPBmhC6A47gStEC/qa3Yqpnr7eRuh\naN9CfaE11G8LLbTQQjODDCXrAoMxGyGBxq+nXkV8fs47gS0bEwUcW/8JXkpUiNQ5I48je3o5xD8B\nQ7SG+uU8u+Y5gsik23eRrOq/7obQPysZ0fyLwv5EGv/T8efws4UbCBsmJtPdjpB+vPdjCCn+uWxI\nKNjaKKg4bizDwvXeG4AF/O+nfwmMMWTf0LQBX8/K80P84xJb7IsRikDF3YIXM4h5J1VZ/Mi2i1IR\nndEEfmPPaRRe/57DNPny9cJgBnriSc/rukP9lsdZou4H2tq1likC1tYONnUEgwDOX3wRbettACys\nJFZRMJ1jLem41BDql9cu/Yf6bf4xSgc60lEM9yeBO97zNX/KS/L3cLucuin+1a8N+FHgFu2jyacJ\ni4f6tYMiWfO+c51+NYMZ0goxW+EtpJMRLK04N1wPbLyFmyFB5RgB+OCYuEJXqxzpT2Gkf7b87w8u\nLxCpxRHEV0OF+nW0I2IoFlP8o//NV8AgL3NhLy8WDWFjs3ZuPNjrPRdpNApMltQfzKTdUmD+8Qnn\nmuZ5ZD4SZdRhGPQumhf6KigFDD35GIwhL2BjeySGhWxF5S5qhHCoa5gg/on1LjpXDP4V//TYEQQa\nPQun5mXC8y6Bcaca1WtoXkk7WfEPAA51DeOQTGhfNzDgbvsC7rYX5zHTF6bKh9NcE3ugEVFMHjjY\nh2/+4CKs7bEhbG3h2PqPixcVP9DjvePIWxa+f+N9rGW3kArHgPVVXSb7guwTlUP9csedJkIzhvpl\nTMmnNzbYjrFBvT6nZn6PjTaNVPwT9BVoaX3Kin/3JvZ2DuDs0u3yv6WfvwFqpSTqQUTUlY2O9Y5A\nGgsWFtILWKgh/tW/U6D2UmTai25/trTinwLhPCg43EXUGdBmMbiFwNAi/rXQQgsfGsjuMxoGQyEv\nNnkwJZ3/xzd+jEx+HhfCY/h+fy+Wkkvo6jhG3iNkvlSoX9512qGre2KVK4gEb/MLeYdkbQ4Kin+s\n8QtrEUTNkKczkme+yRg3kd3BKBJCOJgqoxYWAS5Fme1Zc1ldWdYRehX/HLBtDJLRtMDh7Gp4fUqK\nfz7uMIxquojKQ+hvtyf7xvHW2WXXa2FTL/EvVHr/5GlMfpm8WpA9tWdHPpTHUmrJOwHZbgXeT9X9\ntQQMufsr9wk6TXewP5NXv9G4gUQ4goGeNjx0dAjhkImC1q1of2iOUL8GIBRAV4+DSCWLzfAmABni\nH5FOItQvc/wto/hXtZmtW7Xbkb9YH/PUA+N4/p/OIpcrjjqZ3DxOrL+Gv099TL58j8cR7Uoa7WjU\ndaK9tyuhwRonKMU/e/snqUh12jjQhVOHBvDdly87fotGTIxr3oDlQmIwbBYFDCXCuQ/ytVxB5G6D\neDYaTJFHJdQvqbwUkHE6Q/2K4F8deBx/de41nFu+g6G2Tnx6/BB64ymlMu1NsqX4tzNAza3sl/yS\nYKvDANfkpviO79U363d9RvrZGrD2mx3rQNs3/h3ORcYRsnKY3jqHTGGbeODj+z7VN4FTfRMAgOyZ\nV3WYWpflV+0Nlf/QUUqaO+Rioz/I5iJgNIkhLmjmtZjjEqmYrSPUr+8s7imc7p/C1y++Uf63JXvQ\nqdkqVNNapB7Q42ej8yiwAs6OfQDLqP12GqE0S9orUR3aQ/1KK/41D2Hfbjv1HKaug34tNC1axL8W\nWmjhQwNpxr7BkBPkolHhv7wwlb2AqewF/F3mcQAC+7Ii9kvMdmjFv/qv11eyW9w0QpMvYvFXCfUr\nP/kvl624uGzmRb8I6I1x5iPUr/pVFVA5bsQ2XH/PmUUVEm1S2Hm9IfcaAZl3XZAgtpApGU39kw35\n6V4E/7mqF73SoX4dTJIqcojCM8jeI5L6ZN843sKbrtdUFf9O9Y7jpVsXHL8ZYGLKB0LjHy8Pfhaq\noNqAITAuV49J1Yp/qk4E0bt2suIfL9T1niNpPL5r2nkPuZlS37GaX5yOnSfON1vnZ1YZS5eSRSKy\n6NhDkTFYSELxL5Wp3CdF/Ku+rhf2cYQuuXJ1bCCNL31qPy5dX0bom/8ew9lrCCOnOO74Q6PnxKJO\nZl5THehuQzIRxsqa80DHQ0eHlG2jiH8OCGyEUf1ZzVqoui9VVJoVhb24QzM9uHJjGWcvFQkAoZCB\nTzw65UsdNGjIE/+IftjHY4pGJ7CDW631GBMMGcW/4DfCqlEaQxhjDdkY1LV5JJpNfyKN3z7wOCzL\n0rpxpTOMlFdO4oRzXZYEgcbOw6m+lgmu00vfjJ/ZEP8d7dz1SvOhAXXJDPTnb6F//ZbbRU2F6Hku\nLfNUySzKin+MNWT+o3ZIsvm+SaMOBAzd446MtbqerNFjIk04F/QPawCvHvySC5t66uGCdCSOz0+f\nwJ+ffRkAUNBING6E0qw2FfM6oB7jTjaUdSX9gaM0G9xBp+aE7PrFZEbg47bokt9uOvUcfoQRWtgZ\naBH/WmihhQ8NZAduw2SAoDCXjuFSy5ArpfhHTeq4OwI0wU5hE2I1twnesKSD+6GKUtFq+TefQ0QW\nVN2HSps3Co1Y1wkfHdiKbGEzvIloNur4/W77Xb0F5TUp/vnx1vj09Mgp/okT/2hinx4HFbVZXQ8H\nmKPvrSH+qeRXpcZ44lmFXJygFq1hReLfg/3TePX2JeRsIelP9k0gXlLfIjfFRRT/eFtN+l9uJSQP\nVa5CqN/qPHgOSY9n/zAo/vHgWgMBOQBV5j51UfzjGlFfh6js2LXUtoT5jnkAxTC3IqAV/ySIf0ef\nBN7+AQAe8Y+G6DNvxNf5iSBPgCylTyYi2DvVhWz2ki0vfe1W9M0Kt4CA+iZ9m2YMH390Cn/3X9/H\nZrZ4WmxiqB1H9/Yp5xkOm8i0xzC/6DyM0t0Zd9Q79SXoIHOrKs2Ko2KjaRaJfneXNrC0soXB3iQi\nYa/Qhc0BnYp/9R6CuRsT2lR/qHmdeBla/CyKmTDwFP+CeXtKo7LLHER07BFR4ncURU98yzA0Kiz7\nrelGE84pNGQz3Aaa+CeWR/lb8NHX8YryXU87bMEjVPc76ZF2kPKSHv6FWiZFnxf/cEcL7qiL4p9g\nXxLEuBM1w/xEAmj0mEgenLVNHSg7Q2ENB0P4I4/vMnYaHh6Yxv7OAXywdBvLm1t4/RIR+aQapK+m\nAW1uB407WpRmOde9xo9YKMRRhG9upVnd1kkT/wxDOspgUHAo/lH9bJO1/xb0o0X8a6GFFj40kGXf\nmxLpdShNSXFjvGCJb0LwFP+4RQmXJIbV3Bb4w5LPiUlJAYNM41HG9s9Kjhama4nTuEUnLRG9vVnA\nzcVlQ0KxzKBwte8ahm8MIZIrEgMWk0uY3yb+aTs5qinUrx94PgljQo4sGVlwfm5iC5N6QKV8+ZNS\n3kRH34p/kRiM2RPCpXvBNCnin9pm/Ex7L35r/6P47rX3sbi1jv2ZQXx0ZK+YYSJKjJyqC6Rllfa4\niCQiisDcjbDAQ1/tLIem8wviED7d6k5GBUsCKrXIdbjUI9aUhPKSDnNkhJ4A4HbXHUy39xTLB4NK\nODkHqoh/pKJxZz/mR/cgc+kMDBSQyi9j2XSGP+zIL7jcWa3aybO4uO5Y6nLLy8Uue4YCeWsfWv1O\nx5t4rLeT5kSsHOxJ4jc+ewjXb6+gLR5Gpj3m+/mO7u3DP7x40fnbHieZkJqGiaxVeASKoF9RLcGd\nIdMeR6Y9HmzBmlCQDH1FtgkfQ/DYYBoXr9VuyI0NpD3v4Tv6dRH/BNlhfrIRz0UyfUV5qRH9lVqZ\ncmG7Skgm9JAI3OzQuqnkc6ra2uDyhrjyEp+M5Oc18du9e+47axUjjkB5io0gQTbRgV8etHQXknmU\nvqHiuKOhfEk0em6uC/VQ/BNFEGbMtPciZoax4fMweaOriC7feXE5sYLUWtLxW87MYbgrjatrBSxl\nnYelHD5GX3Y0npjfKGRibcjE2nB+cQ6vQ4L4R6AhY3UdiH+6nqsexLFUJFbz26neiaJinZ5lmxR0\nZatyAJuCfKhfo4nGnYod1F5Va11076NF/GuhhRY+NJAd0+SIf5Kn/j1yIa9qPvLZ6BNe1VjNbqK3\nowtzC7VqJz0dejaBRM/l6YYFNH5lHSBMViQC8VWp3FDneuEUl41kcX7kAiLZCPJmHnmzEu9bm6VN\nHepXjFQhs6jRFeqXbf/PN4gslBbbkvdQoX5V3AYslQFiI2AdvTCOPQ3WTYcYFCmBGv9UQ/0CwO6O\nfuzu6Pe4Gqynwc9CfDSZwaWVecdviVAYPfGiA5I6HWmIzA+qbKsO3ae6Ry/6vRR28JaZloMXzgzV\noXIugFuejj6vuUL9qnyLjwzsAgCkIzEsga+KRxI4w+KKf4wxnH/w5/BtFDC1sgDDvAXkncS/3Zvv\nce3hPfOxfX3YPdGFP3jvHCAwRXDmxm94ukN57PQZrSgfKRmudY67IRwyMEoQrWRxcKYHsYiJn52f\nh2UBe6a6MDPWKXy/CAGDr7xEv+WdHCJeB6QV/wLCiQP9rsS/U4cGPO+pm+Jfo8vwUZx9o5e+t7m/\nA5HxdnYiw02jCp2bSn77nOYet5o51K/z32uxNSQ2Eo7fcmYO2VCRhEKOO9VK9zX/pu38sBIwKDSb\nT5eEJjJ4PaCjXlVzYOAc0m8mAoab0qxvS/yBS2CpI8TXX+IGm4aBjwzvxtcvvln+LWIoHMxtcB2R\nISirLt3tmEfbegKGVfFpzHXMYchI4EuzD+Dfv/M9ZAvbyuupLjw9LE784+HDvt6JyB76brZ9rx00\n7uioO968vzvehlO943j19iUUYOFQZhifnz7OvVc3sa4E8kCJzPvR7RKW5Q/UgXCuFl3G+1qL+Hfv\n454m/s3NzeHcuXPlfx8/fryB1rTQQguNhnSoXxkFFG4KESKNv+vFYsQnArTin0BhhJoEFZ6pK9qG\nuc3Vmt93d/RhYKoL33/tgQargQAAIABJREFUiuP3XWOdCG/np21aopBRaUKqGlphp0+pqPZSCvXL\nD1noli+VvEG1xophf11+DhaSDptA5umMCS2aZEgE3OxspBRyWc7UCCMyW7Mi+deo1Ei2DN2hfo3O\nfoQ/8j8q3EnkSYx/ITOgsKA+T2MGKVxwqne8hvh3vGdcKOwoEzDMr+Kf31C/O9mfyVNUdK0C4r35\nqQoVx3BdQv1yy5BR/PNvj+x8/PPTx3GidxwAMBBP44oQ8Y+AjOIfGAzDxA96h/CD3iHAyqNr4Q7S\nK2lYsPDI4us4tvETrj3UIz94ZBAnDw5y83DmJ1qHFfUQ7xTy77RZTjSrQtT+0/3TAVvijZnxDGbG\nvQk55NxDSHmJpo/WSxSumSDTg+sl/qmPPEO9KeyZzODMucocZe9UFwZ6kp73qKzXlECG+pXxs+jY\nCFO7zwC9kRMcAUMhY5dmRD13LGpi90QXTh8dli+LaLP2K6XnmBrpwAeXxRRtg0PzdlqNJrRRxD/H\nnI0BC6nFGuLfQmpRqHqDWm4IV98On7vohMj6VHuZzRYCkkIDzeEr/tWfgLGTUBcChmC6oOx4ZmQ/\nemIpvD53BclwFA/0TeJvz56XyqPR75ueIlZsYwxYj23g0uBlpFdSMAomVtpWsJpYxTDasKezH//L\n8U/i3YUbSEfimEx3IyzhV6/bnHiHIiQbqoHyszVGyrT+ZSqiHqaaBsOvzT6Az0+fQAEWYrbQ4VT5\njw7N4u9uvlbz+8dHD/iyR9czNzzULzOko5oEgc6oc35O+zmbwOAWAsU9Tfx74YUX8Lu/+7sAigP5\nmTNnGmxRCy20IArG9G/iyy66qFCHtXn7n2jw91dEvGkSin8+ndhUSQdmenD5xnLN70f39CI92Yev\nnvnnmmvHesbQP5rGxlYOb7x3B7lcAZPD7fjIg+N8Y+ygiHnljTA/BBOVN+0neGDzIySxeVOL+taM\nn9J0OW6Mxz+Pwj/9ee3vj/6ilvztmBhux/krizW/e6o7CD6jzEJIRvGP94Z0vAHqC1Z5xfa6kCYG\n1xD/1EKJ60ZQin8kfIdh4FEc1Cvq8cFZ5KwCXrhxDluFHO7rHsWnJw4LmSek+FeFagKZatcj+sw7\n7SSz3VrTynumA+p7klFl3loPck1dyIUSkC3ucHeFlGAKetQoxzILOcMakm2k+hID5jrnMddZJNn8\ndze9SH/ipO50Mkpc9crdNu4Q6Urzed2Kf/VCYCQBojpK/eFD/VNIu4TDaRbQz1BOJZDGK381wrko\nglIOqBdkiH+BLC23YRgMTz84gT2TXbg1v4beTAJjg2ny/aW4oV01zfN0hdeqxzhIlN2IPUNdfQ/1\n3F/+7GG++qMHRJtsaXw9vLsX568uolBQb+x+u4xmUYBqRtDKx7a/ASynlnGNWWhfbgezGJbbVrCQ\nrpA6Ka9Xo8edhoS3bUEQzfVudHQXsuNOOdQvGN03Kx5E50EfSa2xnW0x5GJDTQgcjDGc6B0vH4wr\nQpL41+A6otqb2yp6M7qJ29HNqjyK/01HYjjuqAsZO+jrO81PphthJqv4F4wd6qAYps1lbD0OOpXG\nlohZSwmi5oJ7OnvxjTshbNqiV4WYgRM9Y2qGbkMbAflDEOpX5AmPdo04/k3Z1FoX3fu4p4l/O92Z\n2EILH2YwxrR/w7LcJJlQv1qgY19WRvGPykY4F3e0JyMYH0rjwtVK6KGQaWDPVBcynTEcygzhp/NX\ny9c+MrwH/fHiJsVDR4fx4JEhFAoWzCplKZHJl5+wVhRKJaucyC7es7NnVdSE0Swr/inku8PrRQXG\n+AEUQhEgZ1MVDEdhTB+VykdkYXFgV3cN8S/THkNPp7/w2TJkGr7iH3P7szaZYlup3YDwtkikTqkU\nm5FN5IwcQgXnFHslvlL+26mcUJWbZvl2VVDO5lBgY6O/DWJe1fkl/T49vNczbAhFqhEhc1anqA71\ny/3ePC7fq4J/9scyQRP/XKsgqJAfChXJ70t1EW0ZPA2UUl7yD1migUofR4f6dRLtyA1vjtqTKKg8\n0smI5zXv/PSVr6b4J31LU4Gy/2DXIPb09DdU7c8vyqF+Jd6TtOLfPQjqkas3/gqsORT/gGKfOj7U\njvGhdqH0HekYMukI5pec6uZ7N7YPStdj71+igTWiLZbeNwPjREkIZgbVHUtiIJ7G9fXaMM7eqLWF\nGm5VSX9c2LIt1d3YYBqfeWoGb5+9g43NHM65HEoLGsKHYT6E+wh0qF/bOn27DpeTK1hOrnjcoG7H\nh1FptnFotnbeXPbomfsr3scpv+HKmU0Oow4EDFE088GrRtcRTTi3jzvBImjFv+ZtAWIIGQbupu/i\n/2fvzaMkOep7329k1l7VVV3V3dX7vkxPz9qzj0YzkmZGu0BoQ4CMwEbXBgTY+GH8rt/zM1yfZzj2\nwWax8D2Xc98B4YUDMiCDZAOSkIQALaN9QTOa0ez70j09vS9V74+e7q4tIzMyIzMjq+Jzjo6mI7Mi\nfhkZGfGLiF/8fsmRpMFf0NaRTYSktgqvQ0gO4IQ4RvW9QqoCYfzxymvw3X3P4eTECGpDMXygez3q\nI3FL8vB6ZCE8/tn9AikPGVR82FDXjjs6BxkyFKv9S/hT1oZ/EonEu9gx/LCH+nV2ENRbjDRm+Mfi\nfcDa5jbNsxUhBO+5uhu/eeUEjp68hHgsgPUr6lFfEwUA/NHAdrw1dBInx0fQHa9FV1Vt/uSOkJIe\nFx1xA61RyJJ8Jj3+cTk2yiEPG4pWF40G9AQsrjv6PEwsRZSX102SqIX6vs9g7pf/Bpw/DtQ0Q919\nL0jU2IYdCz1tSVy9qRXPv3YSE1OzaKyL4abtnZS6NVbnTIZ/DN6mdPtBMx9B4U9om2BGDMyKsss1\n5AOGEkOoG6rLuT+LocRw6ftRaNzMjtPfiX0bhRYN/yxkbx3tzI15/NNr93Sse16y9HPHmR9z5utV\nz+NfyRdvyUutNmaqUX+Tk1PDpbnSdtjzEqs+bqaPoxo8FYX6pZQNs5s2xltDPGrN458R8Xh326Lp\nZ6zQ5L+3bwsiIT2PaO6jULxfGvE+rKeb6b5hi03AY8NOEXxD/TrP7vUN+NET+zFD5vvD6rlhbJ54\nYf4itzGST6hfHjB3WSTnfzadFdDjkyuuwj+++SROT1yCShRc2dCNp06+w5SHbX01Ndulrzt3vG9t\nqEJrQxUA4Lv/8SbODk2wlanVaRjsTEQet9z2KETfCGbLi9YzFh40KJqim/T45/XxRAu324XEeeov\nH4an6+2ie/xzF5XQDfadxKgYbojrdg1RI2YYNPzj4iFN53qljTuF+BQVZ1PnEJgJIDoR1b2f1o+0\nxTSiDtmJXQd+bcDuw6YAPXqG3nJ8d7wOX9hwCyZmZxD28Vkr4TXudDYn8PLvzhSl16XMObxgXf9T\nFeKqHelXtt7BFOIcAAKqC4a4EkeRhn8SiURMbBgwWY0VmDz+ObJrzsfoaAGaIqPn/cPIPrXfp+Kq\nDa0lr6tEwapUM1almvXEtAW9gJDU34o1N3AM2qR6IdSvvpv8JRYm817y+MdTkVda+6Hc+0VkZ2eK\nwg7ylmfd8noM9qcxM5tBwK+j3BvMlCnUr64706XJp27fYrjUJVh6Z1NeKwt+cyE5hFnfLGJjVcgo\nGYxUXcR4OGdzK/f+gt+aCfXr9BfkjuGf/gZxJKg3rbGvpmjiEQPjcuG4UhTq1/aDCN5ausztf3Q9\n/pU0/NO+X9dQmYoZj53GDaMtQYi2eEwe/zgsSFrw+GdUzaWOOwVjLjXUEKf6n57Rbqcx3bCbxeRL\npe/r2ku6Vh42za/s3kRyApqUC2OIFS/otodcFBCmJyLAlH8KwRl2w93igp2vy+Z0DB8e/jcc87cg\nkJ1G88xxBDCr/0MWqB+a8e+Mz8auuTyIjgGBnd9BOlyFL66/BcPTE/ARBVWBkI7hX6kDds73Z7lS\n8OxPrda1N3p2d+Q06nnJGJTvRU/lNZ+1xOuoYh244NF1Mvd/BNje0H25fNq6iAWh6MWzI+DpQcWB\nUL9GH9toG+jvTOGtA+eL0rta+B8MX8Tl/pQ67hhcmuDxCNLTLB0fUZBVsjjWeBy+GR+UrILmU80I\nzLL32VEfe5QDy9jQl65MNuGNoRPmfkzBCd8ztLV82jeZe42X0R9A7yNZRpe2xjjCQR8mpvLnsusH\nGszJZcLjn/2hfrVrhGb0t62+C78+/W5e2vrathwHLpJyRb5hiUQiJHYMmKxZ0k5C2AGXvV+GiTc1\nfIChurIWLtMMxibO2nItesAwofwvJJtZdM4S72wkamEo1K9uJkv/vLqxT/c3Zmvs5taV2nlefo7b\nOtYy52vHOzRr9Aew1Q8hRN/ob/5GQ/kpDCqkbn9isExCYKpRsBn+sRdQql2MVF3CiYYTOJU+lW/0\nhwKj66LJlg2GQ5xx5TS4gTL7u1Il28eFxIX5LGztgymLJCZC/RYO5dLsL59cZ1YKsiAUb8cl686m\nNmxm74OXMbYuvMKccBCHdVHTlMc/2kUGj39myy9kZGxa85qpcSfnN0aMu3h7vhDEkYZp6MaeDgpi\nF5efgXaQq7jdODvwlIPh4HDcWLhSIeeAhCCancCy6XfQOXM43+ivDMcd9kdaap9u9gmEECSDEVQF\nQiZ/z1mgRYx9v6J4XQKMf4eq6sI2idsGGJRHztXZrK3E6XjNh3571Ro3xGllHsIlgy3SVbwGRxq7\nQfwcjOg54kao3w90b8Du5n7d8u3Sn4TUVUww73lJjGcxKkdbYxyRUPEh1tXL6krczQe364g6F8tb\nL+WkS5qQAyiP+YoVcsPzzvpnMR3QXtPQw5UWZ0P72ZzuyPtbz3GKUZzwYGnWw7NtDsQ55asoBLft\n7l3sRwkB1i1PY3mXOS+TrO+C1YjuynXOOcF5f9d6LK9eMoDsidfhnp5NjpUvcY+y9vjnthIjkUjM\nY8fXyxzqt0So2VJ0tSSAETMS5aPXZ/E2/KOfXjdQlPOHEA1hxGjRnMc/Qr+sA486mQxMITYeK0ov\nFRaZN7QSljz+GZOjo6oG9eEq/d+YHMe31HdiP/ZS79nZ1Ie3h0/hd8OnAMyfkNlW34UnKZ4UxFsM\nc08eplC/uh7/SM4/6QtAZt4Br4m4Fqy55z1iwfOa8fjnNGFdz3omsXjiLBjwobbVj3NHZhbTZpVZ\nDFfNb8zbOS2gGTIRQ6F+8yny+Ke3IGkgrCMA1IViODs5WpTeX23uJKQoqJjDrIYxcum+ihIi04Ic\nZn7b0hBDwEcwPZv/656p/fP/MNBwjS1I8xlreXxGrHN0M98uzXMj8ReeNtfRv9mLL2KUYvhnBsNj\n4aLqytnwzyH9Y9OqRvz4if1F6esH6i3l68bCNm/oJ+QXDjpZyJ/TuCMi1YEwhqeLw4x2VNUw5TOc\nGEaWZBEfrQLJEoSnzIUTcgUnPgKqO2QWT7McRGHMJM9rHbWuBPoOSohi15q40afmafhn1U7JaF1s\nXNmAdw4PFaVvXt1oTQCBoXpzz603I3VIvYXQ/9Q9Ca1fvERs1O13Yvb0QWDssuF8MAL1mg+5K5Qg\nXNPUt/hv6iEpoQwwSg08ViWxhhe9GCkKwZ3XLcNPnjyAoZFJBPwKtqxpQldLtW1luj3fMaqGuvAp\nGKJShqNSepz5Z3dDZ+bfgjalOzA1N4tfntiH0dkpJAJhAMOm8sqTxgGDc1pkO1pUDvtsbPjl21Ab\nxR/etQbnhydQFQsgFDC/b8E6f2EZd6JhP/o72Q0Szc6DQj4//mTVTgxPjSODLFJB/ZDdkvKA6QvI\nZrO499577ZKFO+fOnXNbBIlEIhCsioqRgT4YULF1bRPwtFmpjGPV2x1LfvqGMnpGih6eBmmIvpBs\nbqMry2VmPRwfRu1w8abUNZvaLOetB629sCi5m9MduLtr/WIbsWMin75sVEgjoPrwmZVX4+joMIam\nx9Ebr8M7I2fphn+iNWs75DH4Llm+cf0vhpT4F0cKM+W83mApBFKR4Z+Z8k38yAD9nSm8ffBCXlo8\nFkBNtTmvI5Yw2C6be4N4feIIohNRzPhmcbHqImb884aAdn6+tP5RMWD4VzjmRgo9kpkUvnBx4MbW\nFXjwnefy0kKqD6tSTeYKcIuCClGzc5glGt5TS9UddeCxsJRpYjVGVRSs667Cs3uXTpCo2Vmsnnpj\nQSDT8uRB9XDGsEHCQRzWhbS8PtZgFTN5/NO1TTfVM+f9NT3LbgBMwwmvifT8uGanSetlDxjjk0ve\nyBRC5j28WoBvWEOXMKBAUz0vFTxnsSem8mVXcz/+/eDLeWnd8VokgxHN32jV5cX4RVy87Plv2bt9\npW/SrUwXNsKo7dyBt89icM7lm2TtNHPL177N6Td3Z+cgsOcJw/c7ETasiJy1Ei39WLSt31zqayKo\nTYZxbmjJONjnU7Csw9q4Q8Ntj0L08G5L/7a6Gmn1Kd2up7LCLY9/qQb4PvxFZI+8BczNgbQPgEQ5\nhjPl9FhuDDv55Rs43MEZ8Q45m8OLhn8AUJsM4/dvW4nR8WlEQn66QTYH3H7btDZu97Pny6FzTeO6\nHI1M4IryZ898Z3tjD7Y39gAAZg+/gyxOms5rURouzZ6eidlQv3Ytj/DurRWFoC6lPZ83Cuvzqjo/\naKyLIhT0IRkPYe2yOsRjzns5rqasc0jKE2bT1+eff94OOSQSicR2WCcPel7Utq5pwkBPDRKxYG5w\nHNvgrWhZPYFNW3AQ1w20+ZnGQtmmQv2Cz8R6zjeHc8lzqB2qXUxrro+ZOi3CCq3uF0P96jzk+7vW\n4ar2noJ83VtyUIiC9qoU2jFff24vfrDiiJGcBgpD6Rl9qwpDApjz92f/+gKrVPmTW+tWiXZ9Q5tW\nNeLwiRFMTM1eLgfYvr7Fvm+Wg/cZRSEYqbqEkapLxVnY+YXTRDexsXJzW364cl51vrW+EwdGzuLX\np98FAARVHz4xsAN+xUAYcIFRMad5reR7p2wIWOovTP54y7JqxF78Md4NdCCUmcKKqd+hYe7M/EXB\nQi7ykIYWUq5kmSYKpXn8Kw71q3OYhb14FDaG9Svq8eTzR4vusuq5DgCMeJyijdlmvOI6pbv5fQru\nun4ZfvbrQzh9fgyJqiC2r2tBfY3Fk8p8Pgdh4bEpbHc9uGRzAADY3dyP85OjeObUAcxmM+isqsEf\nLd/unkCubITRvPGJNe7wwEpxdI9/5vM1w4a6dsrV4oZkn85uTAquHv8s/t5oXRBCcNd1fXhqzzEc\nO3UJyUQIW1Y3ojZpo0dPl8cdeui33AN6RgQ1/72YPuhk7mcVjZtNjoRjIMvEDjPHx+7PfC5Uwz8C\nWxq9qe9PPId/QoWYN0MsUuiZ3h7cPuhktHjad8QlNCrV8N3bbUk83DjoRJvvOCeGEbh8kzpZmDXu\ns+tbcLsf0oJlvwtY2hPVYv1APfqsHiCSyq6EkbIO9SuRSLyLHWM/s8W+jqHg4EDakutgAEV+gAjR\n3gjJU4hoNxqE9nTUDVMDGdi3xmztnPFCGGBzoX6NXeb2Gw3OJy9ge28nUjNxpBIhdDQn4PfZf6qR\nVvc+o6cqWetBsEmAcKdgXbT8Ywr1q2v4t9R+nPC8FFR9yGjbCbHnzihSUV+ee82M4R/zL4xRmwzj\nnluWY/+RYUzNzKGrJWHd2IIGhw1i6jdq4+dLUxcMefzL+X1bLFUUblD38Y0a7BIF9/ZtwS3tq3Bu\ncgwdsRQCqgengwX6j5rVrmP28H7mG4pZrYyoKpZP78Py6X0lLjpggMFkiWfvAnspzCw0Ut+Fv9Dw\njw6Psb+jKV5SdV+7PG0qv5nMUpune9eZvyqYOsVETXUYH7p5OWbnMvCpfPRd6iaSRyqLJmVGmW8f\ntP5Mr78yH+rXaP25t3qtEIIP9mzEHZ2DmJybQTxgb4he/RoRzeMfryIU7Sdz2CMP69PmGs/SdDyn\nPZAlgxHMMNzvdnemNX6bEsvGta9CwiE/briy01J5XoJqZMRY7XSdpHDea1wOwHqI+XLcM50ITSA6\nUTw/b6zTm7OXY22A33zfgZCLNNzw1irmEVt2vOrxz2lE1Q/0ruXCxU6KukTijTmh3axINuLNIese\n7Vw59EVdY+bUV3BqJjyam14WRg96sFyzC0P70jbB+rxOjDvS67WEFQ/u9EgkEok5WDcOFZ2NUDtU\nEAKiPZjnFkgUIGvNcsVOxU04A6kiaPJpXZtPN6dsZbnWSVXSh80NjdzyMwJN+gXDP702VaoOFBBN\nsxinW5FeH+H24kgh9tj92WD4x1Am9csk5p45t/x0KIaQ6sc4Rz+t7N+2tuGfmSe0s13GY0Gs4+GR\nyhAcDP9oixW29ii0cvXHjBXJJsyEwuiO1+GWtlVFCwf0BRrjUi6QCkaRCtpoxOkwNI9/Jfsquz4a\n05Z/ZnQSd8rgs8BuxbjSWCVTDTiLQv3S5clwWGRLJcK44cpOPPHcEUxNz8HvU3D9tg4kTIb5mJ5j\nG8N4n9J2Qx/iZfQH6OwBcCvFXggIRqKXEB+ryksfC48hq+i3WX3DPwvCeYSA6jNu/G7nWrtwHv8c\nOFDG4mnWjXGHYPEQhleMgUu1I7s2rY2OxVoeM0ytplj8TkR+jyJv5vl8bPXGFuq3wBBQp6iNde14\n4dzhovTtDT0l7q4MzldfKGn4d8XaZhekKR/c7i0Ipe/Okqw9Hv9cf2o+6IVc5EHWTbfVnHB7TDRq\nj0Wfs3Ew0LX7MJjAuodRdjf3Y+/wacxSDtuKitvtnAUnPP7RHNxQvQHaNDUU1asms8c/nQriMWR0\nNidw5GRxZCNbPZJLPI00/JNIJBUDq0Kh5/HPluUAAs1JfF5pHJQjmiJjRJ2nLlDapLsZyTZLvSmr\nf4/GNaJzXa9UnlXihmpMm4QYDfVb8rqYen5JnFoMW9lbizfeOWfgTnvMj43AZvinZ1CZa/inZ7jF\n/sxB1YeP9G1BRPVjWXUDHjz4FnMeNFglypsSFtRjxsw7ZRwPrHg0sxXqcxhbaaC3H/ugnhQ2sDJ/\nQ+sK3NTSp3mdpo94aTHLLlTWgxA2hfwwvXHrRMhF2oOxxt61CKsdgpk2TtXzfH6mvOYyfBa5l3fV\noK8jiZFL00hUBS0ZZEwbdVtLFv5H2UA0ZXDu7X6HHubGO892puYMQtNBBGbmjVln1Bmcrj2zeJ0+\nJyq4WLAibbfO6/0tU36Y8fZsvVDvWL/y+CbZs8hiZ1Of7m+teiCzG7f7M2c29Iy9A+8Ytbgj50B3\nDd46cD4vrbu1Oi98Ge11+ogybxRA+150ZNB7R1fUdxUZ/iUDEfQk6nRyLl8mQhMYiY0gPhpfTOtr\nT6K1oYryK5TvIMzpudy296H+1qZ35yH1l4r0+GcMt1+3ewd2C+XQvubwEomwDCQb8aerd+HZ0wcx\nOTeLyRNBTM2aWR8RbOARzICXy3xH59sxe6BcVAM9u2D3+Ee/n4ex+EB3DZ5+8VhRs92y2lmnMBLv\nIA3/JBJJxcA6/1NVPWMVC8Jo5UktL/cqjwVwE9ZvBm+xTSm0mG+24P8ahVDTTW3sm7NX8gwLiyt6\nk4ySjpccNtSxcmLQqVfY35kyZPhnx2emXnk75h57sChd2XZb/t8MhWfoHVvB3/S8zDwzIQquqO9a\n/NvqpKvw96yTQlqoXzOtrBLm4EbrmGZHY+fmJzXUr5GTsTqyqZRVR50lBv2yywCaxz/W926lxkx3\nLU4YIXEIpQ3wGQdpXiz0MFrHKsVYjyhq/t86TzVj0cN2LqqiIJkIWc5nitHjn9vGH8LB53NwFQJg\nzjeHgy2HEJ4MgwCYCE7mefujel6yqm9ZrKfKGJ2M4nxtWF4HKHM+2rcVm9LtAMz1n+7UoJ4/NYfI\nMYYUaewRR5ISCCDc1ZtacfHSFI6fGQUApFMRXHtFe8Fd2oKurWnBqxeOY3bO/OFgPVV1INWID/du\nwn8cfh0j0xNoi6Xwsf4rKtvQhwAn607hznWrMHJxGvU1UXQ2JwwcLpGjMB37DTDs+q3rZbrcn7Hu\nRazqrbVJErFxe3g2aoBk934BTU+htaWoz5zXfq/SHa9Dd3zeyP5bb7yGKUybyEW0cYeXPHw+Jj7t\nmX7dfKhfsxLREfVQDrPjID2Pf1aEuUw45Mf12zrxs18fXFwTXdlbi972JIfcJeWINPyTSCSCwn/w\nZw/1K5YCkm/3Z102Wn3obQbpYZ9S6EAGGsIvJJs72c831K8b0NqLb8EoRW8xt8QNYpjaXS5Ntzh9\neXgo9G2Ncdy4vRO/eeUELl6a4pCjcUj3WuDp7wPTk0uJqg9K74a8+1hcn1O9CRVMkPQWdky1CLdX\ntajkP78Zj3+sfYsrnmWMwMELm2v9LPXEsvX6NrtAUynQPP6VrB3RDB6ceIdG4+no5mNdFCdODPsZ\nQtHoiTPLyeMfT6YzOYZ/FL104bAKa7gSPbze7dDPI3jk4RbkJMBEeELrJsPZ3di2siB75xf/KxaX\n1DLSvxnZt5/LT1u2id83wMmTht0bu6VYXbsUJtPLzd2Kob1Zct86z/He6sEtsft29+dmoYAPd9/Y\nj+FLU8hkskjGg0V1RqtBv+rDbR1r8IMDL2reU7gmUHT8zcBBmCsberCtvhtTc7MIMXpwLleqAiEM\ndNQyfW8ifw0iwKW7sJCH7lTVllC/5YHCaAi8oscew79VqSZb8uWHu2+c7mnPmGx2j+uEENzesRY/\nPPRK0bUbWgdsLVtkyiHUNQBu/aiyfAvmju0tSiedq5nycUJPpX1bbhyip+5L21KiMVj3FWgH9QF+\nziUHumvQ0RTHibOjqEmEUV1CV5dIFmAy/COE4LHHHvNMB//YY4/hy1/+sttiSCQSE9gxbDG76tWZ\nbNgnY+k+Nk/xcMAFNP23dM93dikeRnKle+S7HOrXlHGNebwxappnyeMfnZIe/wRSUnXdojso6kJI\nwK999yXNe2zpgyJxqLf/KeZ+8R3g/HEg2QD16g+AJOvz7mMK9UvfYc//kyqcadO/fHks55APa1+a\nV3cFz28u5CLb/eKzEeUBAAAgAElEQVSG+qVdM7aASw3fyCgOC7S8FfDw+CcNMGhQPf6VajrCeQZx\n2/CPpXx7D57o0d1ajRfeOFWU3lAbRe5yq5/BWK86EEHMF8TobL6hfWt0/vTsrNGwug4ynePxz8iY\nxlvX0sxN9keOYWxORLu2lENI9WFNqplydz70b9igluWRNUUuCPpdqNd8CHMjF5A98Q4AgDT1QN15\nD7f8TXnJL4EbIRdzdXtq/ynSuy3xTdk2dzV4ENLrBx+dQqTesLpK24uRni3Srub+ed3pxV8aKstf\n4IGZRv70mUijvxw2pzuYdetYhXmrYqUSey5ec4Vo2N1v06gH0GjYj+3rm9GUjnGXIeLz473tbEY/\nTuP2GpIRQ+/5f9sth/Y1RSHYWNeOx46/jZGZpYPx/dX1aIokjOVvVcByQiRlhyOkdz3wxL8CczN5\n6crgLrZ8eMx3dK5T15XlmvMi9eEqpvv1xx1+jT8S9qOnTXr5k+jD7PGvudn4gqDbJJPyI5BIJEvo\nGOAXoWexn6dR8Vo4Mbovy2HTmqa46XqdysKlGYy1Qg2pWpoVQ/L+xwrP6nLDWI62cL9o+Kcjlwhz\nBWt1J8IT5GCTOEpjF5R7v4jszBSIv3TfxlKPdEOzorP+1Ly4PDLnBQfWTTWaEXfWM+HEbICDYRLV\nY6SN/SZ14dKYWQ71qvT4R0eleHcr2SYoVWbV47EpHHmHNI+axnVKLguSFlTYxrooqqIBXBrLDy+z\ntj+Np04s/e1j8PinEILN9R14/Hj+Se0t9Z0AgFmGvJximtEYkYOZVkGG5hvCgkGlq5RBv2nVoCYR\nCOMiMqgPV+GjfVsRLdD37HaMWqZ7P6Zoc+mbIKEofHf/ObIj54FsFiQharg75w88Wu8ixOhj3NYR\nuXr845aT/ezY0IKn9xxzWwz+GDhg1Vddj5lkAzCUf0jjdDBSNM/9YE9+RAGqAUYZjNus0Np8QziO\nmcwcNta149YOdgMjo0YrFQuPg/Yea7O8pF27LI0X3zxdlH7FWmc84Kk69X7rNT1oqo8hFFBteUd3\nda3D2poW1Ib4GxTyxO3mSfUuZjAPHo9AD/ULpEJRfG7Nbjx+fC9OjY+gJ1GHG1oGPPd9S0rB6YBS\nMAL11k9j7pF/AqYmAEWFsvW9UNpXAAC2r2/Br17U1wm56Dk6WVAPypsMe20FUQ8I9SbSJQ8Ga6E3\n7lTSeUeJOMhQvxJP09/fb+n3Tz31FOrr6/VvlJQFrIqKqurdv3Rd2Xgj5vYXh7VQtt7KVCa1RKLx\nb5NQw34ZyJ/u8c+EQLwwsFFlZmPf0iORLNeJYUh1/hQlTfyFUL8Bv85uPruFlKPoTTqcbtb6kyB7\nJdIy+gMAhWJEQjBvDLpgLJFhMOjSdaph5jsq8qrHGwvvgYPHP9dX7bhh3crALacshBCMRkYRG89f\n4G2fPmysXJ2bzHr8K9/1hfwnUzGrcZ+GriNcqF+Xy3C4D7GycEgIwe27e/Efv9yPoZEpqCrBhoEG\nLO9KAbmGf4zhee/sHIRfUbHn7BH4FRXb6ruwq2kZAA+E+qVxuap5L9ZayW5zuoObHKYp89XXNalm\nvHrhOHW+898Hr8dEMIqoP1Dyuv2L/956B1a81+np81UUfdsJSLzG1fJ14bKzy3h7majWMZs8L9G/\nBntCzHuJ3vYknn/9FCanlsbqYEDF1LSG0X5ZVFXO2uiqHcg8/f28q3vqW/P+TgYiWF7dkJ+DyQ3p\nSuSLG26x9Hs/66n4CsMJz0s03FBTad9YBtQjZHkkqoLo60hi36GhxbRo2I/l3c7oGnqel7LIIhy0\nbzt8d7O1fUqncLtPNezxj8MaIVUO2rXLa3D14Tg+1LPRclnlgvn+SbS5Hz95lPYBkI9/FTh/AkjU\ngQRCi9d62qrx21dOYHaOvqbE55u0cKCclqtdhn+CqnYKIfhw32b8r989gzkDh4B1xx3Rmr6kIpCG\nf5KKxW0lV6KDDa+H9Z3TFKL5/HL+Xd8Gkm5H9szhpcRAGEoffXLAYuSRP+Gx9wSiVZ1EdKWQ+nxa\nhVgo22x9bqvvwq9Pv5uX5ldUrEo5c1oyFyMe/wIBergW0TcA9NqXaOOGm+LQN30JPtSzEQ++8xwA\nnfZfsOhM9dgGk59hkax8Z13M4cNcDvUrLsYNRLVzcMegiwA4U3MGwekg/LPzm6xVc5dw1fgzDDlo\no1AOIpTP+zePmqV4Pyupy2kvzNixJqP7jvwhnRt4CMHJ4x8PUSw22prqMH7/tlUYGZ1CJOyHTy2W\n/1wwzJSnQhTc1rEWt3WsLbqWCDjwfhgZqG7EkdEh3fsWjJV4941m3mDEF8COhh7PbIh5mUQgjHU1\nrThW4MUyF0IUTaO/+eva+RNCsCXdiWfPHCy6dmVDD5OslYDPR+9j5TBOh8+4w3r/0g+yntmtKZZz\n/YoGvHngfFH69vX2RfJZMDjmbXDumdcAIBEL4q7r+/D8a6dw/uIEmupiWL2sDv/8k7dK3p9rWCyy\nXm/0DImy7lpA9SH79rNAFiDLt2BjzyCOH34NR0eH0F6Vwp2dgwj7App50PKvFCrwkYWBS93b9AKt\nHESgwdPz0o3bO9FUF8PRU5dQHQ9iTV8dEjFnDjlUondQM7hdS9QDRgaXJrgY6FLykG1JwgJRVKCu\ntSg9GQ/htt09eHrPMZw+P679e5vbM68yeCKqxz8AWFvTgi+uvwW/Gz6FRCCE/733N5iaK3341ojB\nuUTiNNLwT1IWsG4geWfxzrsUnvASAdZ2QvOwU5y3AvWOP0XmmR8ic3wfSKoRysabQFIN+j82KGOe\nrUhtC7LH9xXfVJUyXBbNCEvP+GTaP8PDRsMmtL9vY8pWaeGtKqRmfn1VYx9ePHcEkznK5e7mfvgV\nuoGdHdCe37dg+OfTkUsjizPBMNJTE0XpF+vaDMvHA+E8/ok7B6K7iAewrrYVT57chyOjQ/TwtYUe\n/6ilmv0KC4zrOKsArFIVmjrmUsmGf9Qx2rDhn7lrPJjxz+JgyyGEJ8P41L5X0Dh7Cn6KJ7o8dISj\nevwTeLHEKVTQQv0aTZzHHsM/nZ5N9YF0rUX23Vfy0weu4CmEuWtF91oXhdX5rxZxygbSvngSE6qK\n8Fy+Uaiymd1DymBNKx4kzyFTMHhc17Ic2PNE6R/ZPNXcWt+F/zo2b0CQJfqF8T64YCa/r2y5HYqV\nOM+SPPQM8z7WfwWeymaBN541lz/tIAaZ99xYaPjXGEmgJVptKP9yXI05WXcKjWeL5/67tzg7nyk3\n+HheYsukXDSrmuoQOprjOHR8ZDEtGvajv9N+z0u8x510KlIyXdS+pC4Zwc1XdS3+fWls2kVpnIUQ\nAnXtTmDtzsW0ZgD3r7iK/jvKtUo0wLC1bZfpnojS1IPM+ePFFxJ1bBlxGXe8BU95VUXBuoF6rBtw\nPsKWT8dqjEfTL4vPx+U+lR7ql5T8d/F9HJCeZp2jLD4cc7Q2xHHPLQP4n//xEsaHSq9dcjnopHNd\nNF3KToc0PKgLx1AXnj/UGFR82oZ/OuOOaPUuqQyk4Z/E8xBC8Pjjj6OpyXkPWBJt1ixLC2f4xxrN\nQG/gLhy2SSgKdfeHYcUky/Ap2iveh7kf/G3RPepVdxsvy4DisWHiJewJr8v/HWYwHh4HocSPsssQ\nwUi+xpRDZ7ybWM2hvSqFz62+Fr85fQAj05NYmWrClnSnZUnMYCTUr6IQZJUMSKb0t6P1/p6ob8UH\njuQbsp4KRTBWzbhAZxH9NySWsu6mNNSJCwHCvgA+u2oXnjtzEErmJQBvat9M+TPvEtG5gSKPnTD3\nd7w9/gnWLk1DbVPWj/3auXC38A6yShbjkXG0zR5jzoGGQtFH5BoC3eNfyUVlqvc76/KYyVK99l7M\n/vA8cPbo/G+aeqBe9QGOQtCOs7N4/LNeQXoetXmQIQr+rb0fH333zSVj67pWKGt3MecV8vlxQ8sA\nHj26NI5VB8LY0dDLR1gTNETiuLltJR458oah+2ljtqkQ8yYQyuivLDpO+kaYT1Gxs3mZtvm5nkGy\njmHhQLIRH+3bgkePvokLk2PoS6Txkb4thsfactz7GYuMYVadhW9uaZk1FvGjrSnuolQuwu0lc5iF\nM6vqHuwjSlQ3IQTvvaYHL755GsdOX0IqEcJgfxpVUW1vn4YwYHBudpPr6o2tePKFo3lpqkKwrCNp\nKj9RMFod62qKvcWIAk0H5BLZweAhaAkPynAQxry3yczrTxWlq1fewZQPj/mO58YRj4mrhZ7nJWmA\nMY/b1WDU6QU1D5v1QxkRvczxOeOFNBf6nq/9FufEgXU4FsSShg7t/aiXry3rSGHvoQsFvwO624wd\njJRIeFLWhn+1tbXYuJEeZlMikdhDa0MVdm5uwzMvHcP0jLYnFidhneDpbkzacRzD6Kmnpm6QjpXI\nHlra8CON82nGi6ZsAl6+tHbyVRzyt+GcrxYAoGTnEAvvBUiAulZk1yTSULYGXD6ZCvVrBZI1nW1r\nLIm7Yxv4ymMCI6F+5//IQsv5klYOv65rRv3kOLafPQ5fNotToQj+Z89q7LbhXVBztLAJ6gouykNb\n3F9oKxFfANc0LUNm9BLm8LjGzcZD/QJm30HBj3h7/GPdTMz7I//5qd4ROZUvLBwexLWqsFywnuGf\nPIlMQ4W24R97/bhj+Ucicfju+X+AoVPzq8yJNOd3q2tV7RhOtdmXUvU4FqnC6tFh3LFqJ0jbAIjJ\nsL3vbV+NtlgKbw2dRCoUxeZ0B1LBKGa0fuDAI76nbRUGa1rx+rFTePv4GFUO3uLIbsd9dLQlA3dZ\nMfyb///W+i5sre/CXDaju9FaCcypczjaeBQ7yRqcH5pEfU0U29e3IBTQW3YtTwMMXrjR31gtU6Qu\n0qcq2Ly6EZvR6FCJ8+3ZrCHYyt5a7D8yhGOnRxfTdm9th99f+nitSHVNg+rV5LIhZcTnx4qUuAfa\n7f4W9QzOJRwp12EnWQ9l/XXIvPjzxSTSuRqkaw1TNpXY3Ao9m3uVBX20p60a+48M519TCNqaqtwQ\nSzjcbuNc+nsu64cUg3a3K0nChVKRNZBqBIm5YIxlfmpuMHuddWXB2rSXnBjQJF0Yd1b01GDf4Qt5\nZ99625MG1gIkEv6Udavbtm0btm3b5rYYEknFsrY/jVV9tRgdm8HLb5/BS2+dNvxbW2yvWEP9qnpb\nKvyE7I4b8GyWUxxRVKjvuR+ZN3+N7OmDIHVtUAauAPEbP7FC9/4xTzg7hTtHfoxj/iaMKVG0zhzH\no/FGAM3u7CFbzNhYqF9JKYwoufN/ZKG1E661J5glBP/e1oefNHchNjuDC8GwaTl1oZ3o0/2pWMYR\nbk6S9EL95t9M866Vfzd9HkxMPnP+b3j3AsyhfvM8/uVfy+i2wgrFcKhfd74Jq6XqPR411C/1t5Ux\n5tE8/pXEaUM3gy2EEAKkbNqY5+FRUycbozi54HgmFMFvYgnc1bNO/2YKhBAM1rZisFYcTzyEELTG\nkgimg3gbb5W8Z0HvLaxzsnIHsm88balsLxOL+EumBwNW/LY7C+0NLA0Z1BNl9Pwp77iwPZkz+ivP\n8Wk6MIMbt3Yi5Cvdxkriwc32+nAc+y6ecVsMw3i9zzKGc+2IVtLCAVKzY33Ar+L2a/tw/PQoLo5O\nobW+CsmEttG+V74eveqI+0P45MAO+BXvjEO58PjGqAYYgnmpcYL3tK3C/3r7maL0G1tXcMjdK18O\nG4QQKNvvmjf0OHkApLZ5/uCPWtbbn1yYyWjPp+e9gzvTZtbVtuKlc0f1b9RgQSdd2VuLA0eH81Ss\n5d018Pu82cfyxm2DG+qYkbtcan4qY1AO7WuVOO7YikvzHXXLLZg9vg+YGp9PUFRmL7BO4MRcRbTp\nkGjymGVh3OloTuA9V3fj5d+dwdjEDDqbE9i2rtll6SSVitR8JRKJraiKgkRVUAgbfvZQv/ZLvaD2\n7m5eBoDt1BPx+aGuuRrA1abKppWVG/bLj1l0zhxZ/DuDpsv3aCvtdimsvHKletXSuGb1mUT4BqxA\ne35f7sdFXUeh18K06sMFVxflrHubK88l1GKYNnMYvjfaAhSBye+o4EdZzgsO7Kejica/zVE2m5nU\n8KtiG0RaXzil/55u+Fcm798CNI9/JfsqSnsy43VTFxFeESfDPx44/zmL8AKMwi6rkV8U9lHqmqsx\nu+8FYHqCubxywKcqWNaZwt6D+aFY1iwzcBBLFKh9Fcn7H/UeU1etU9b6spe6HJNc29yPX53aX5R+\nT09OxBVO+jaXyFeMeUjdyjpWjPx9qoL2MguRTWtTf7X+JjREEsJ5YnEaqgFGBVbNQLIRUV8QY7NT\ni2k+omBjXbvlvD1ob24YQghISx/Q0mcpD+tyWM7CUWYzlEhNztn9YVt9tzXDv8tr010t1XjP1d14\nde9ZTEzOoqs1gS2rxfWo6jReaZ9668PW86dc80olOUws4sfYRLG3B1ENJUl9B3wf+r+R2f8SMDMF\npXsQJN3mjiwuGrIC4r0j+jq6d2TNPbTT05ZET1vSCZEkEirS8E8ikTiDAOM1q9LulEJ0c+tKDNbM\nexChTmo4i2MkVKdZ3HzdVK9+LgmWdbNwTtCkz/P0oWjXv2BzjCL0vjG3T0UW4uY6BK0/Lb7GYGhr\ny7zP3oqaycwy3Z9n9lfw/FkTslbEelS5P6TO89FD/fIWxnvQPP6xVk9rlP8ijRCvyMSBh9K3Wn+a\nitjYdnJD1UB1Fnn8S7fB9/7PI/PGr4DjIVBsZ0sXWQav8Nqt7Ziby+DgsYsgCsFAVw2uWOudE9nG\n1CUL3z3N45/oCr3LMIc49aAFRjpcha31Xfjt6XcX01qi1Vhfa90gpRjZ3gwhWDuizZsFE9URaK24\nMZLwhIGB7QYYDIegK4Gwz48/Xb0T3933HA6PDqEhXIU7ugbRHOURnrACP0KH8VqbpXn8c5KVqSZ8\nqHsjfnLkdVyamWT+vZpT79IAozTtTXHPtc/S2HsyRE53SnPluhb8+y/2FaVfd0WH88IYhFSnoW64\nwW0xqPA56KSzrkzdx7FePiue6oYosvo86q1bUt5Iwz+JROII9vivYIN1k0Lvfh4KikoUvLdjdU6m\nlPKsF5efH0MIp1wWwrd46GDGIgtGgfRlrtLC2+vPSXxo7UVVCkL9auXBXCr/BUl6s+XxlipjEZVp\nU5PBk5ve+zG1QMV5Nln4hmdop6NLQOtfM0KMli7BwTDJrYUDu4ulevwrnxZgGhXa32DJPoPSUBLB\nCA+RjBbnILTvy2GPf2JUSNlgpA8odQ+pa4V6zYeAH78BXGTbWCuHfifgV/Hea3owMzsHhRCoqtie\nZVlY/MaMeAXUzMPsL8uTytDujUMIwb29mzBQ3YD9I2fRGIljU10Hov6ADWXxyKMSW611djT04OkS\nnh2NfBEVYeTPQtkbtVl/BraDhZVBSzSJ/z54A2Yyc5zDQMtRjYbIzc2uNxdS/Y6XqcVVTb3Y3tiD\nqbkZfO/AHjx75pDh36oOzGupzgYEQyEE4ZBv0UNbNOzHNZvc8XZmmJzqpc5k7LX7kwedNGiuj6G+\nJoLT58cX05LxIDpbEi5K5RFc1gVFG9uoh4SEk1UbP2uIQYnEAaThn0QicQQRFmpYZVAdGLgZ/GJx\nr0Oa4U6WMsEx5JHKpnkwr41GqngaRVguW4BvwC58OYsrhLIeSQRXhvXekNzAWIK2oFbUt9DeO0Oo\nX9MUetXj3D+xevyjkQX7NyLC+MoFLh7J3LL8s3d8MOvxr1I8qviy2t9gacM/430SD4QwkuLm8c+6\nKE6rAgLUvq1Q+wAy3wnw1l/KZdgBAL+v/E5oG3o95h3+cdE7smU8QLHXjzfrQiEKNqU7sCndoXGH\nOM9VTn2Wk2xOd+CZ0weQKfheaZtxCwYRjs1PxGlmZY/doXhpWVS6/QVfoz/I70YHuz1YikhfIo2Q\n6sPkHL+1LSsohCDsCzDPo30OTDTbmxI4e2GiKL0uGba9bFaq40F88KZ+HD11Cdks0NZYhWBAmgMs\nYPd8R4h1IM74VAV3XNeHPW+cwqlzY6hLRrB+RT3CQZ12VcZzP6O43R5EM2Z1uz5YoMnqo22ESiQu\nIUd6SVmQzWbx2muv4ZFHHsErr7yCQ4cOYWxsDMFgEMlkEh0dHdi8eTOuvfZadHR0uC2uxCVY9Rua\nhx3A+8YWdI9/2pNlI6q6XV4zeGxk6d+kaflnCW+3FrqhaK7hH1+Pf3bgLRcmhGjPj93sg6hlF1k0\nGzc4oWfLyycj3wUHVo9/3E+1Mf7GTDhhZxDHIxkr9nv8035+j6siXFApcUpLfm9O15kI74jauSrY\nsqYRz7560iFRRKgQm3HwEY1Up1PizH9vclHfCegakZE3rjPPpYV0rIBP2ArM1VOuG2GcHstujy4S\nbXoSaXxyYAd+dvR3OD81iuXVDbiraz3eOH4avzp+ivpb2tqB0++jLhRztsAyxfq4o5M/zeOfYJvV\n3qdMxx1O8DH4Mc6aZXWWy7OKqii4qW0lfnjwlcW0iC+A8dlpV1sLswMHB9aN1i6rw543isfArWub\nbC/bDMGAT8iQx1pr3dGIP/8mrd/zGHcoeciD/9qEAj5cua6F8Vdy3KHBq7mJuodUCsHEMQ33wxkS\nCQek4Z/E82SzWXzuc5/Dyy+/vJi2MJCNj49jbGwMx44dwzPPPIN/+Id/wE033YQ/+7M/Q319vVsi\nVySsg7kdYz+rgqOozmsg9PAWnMuiXjNmmDISG0F8NJ53vbu12r5TJIay1Z5MLHg+oRahUdFWn8jz\n+izlAXKNUogqeOADHcMykz91BTcnSWyLICybBHp9oImHLpSVu8c/baOjUlBDqUuPf9ay4CCGuXKt\nWobTf0/3+Fcm798Calbb+La0zaS9G5dFOQrxiugG2P2dKbz81hlMzdD7Mx6P4vwiuhAvwEb0n4/W\nT5ixOZL9jvsYOlNhwdMnLw8YEZ8f47MzRemd8VrDeYgAi4dCL3kvsBc+CrfdG7tlg00GpKtSzViV\nas5La4+l8CtoGP5drmrqfIenqAZe7ZZ0J8cCzeEro3DypeDxhXFyTi2RaBLwq5jWmevww1ijDfgV\nrOrLN/xzK1DK9S0DaAjH8dqF46jyh7Al3Ym/evGngIsHe1jHbycM/+KxIK7a0IKn9hxbTBvorkFX\nS7XtZZcT6wbq8eKbp/PSetqq88ZLu1dtZKhfiaNwml+bRTRjVi/Nz2hV54SnWYmEFWn4JykLXn75\nZaiqiuuuuw433ngjurq6kEgkMDQ0hFdffRU//OEP8fLLLyOTyeCnP/0pnn32WXzjG9/A4OCg26JX\nDMyDuQ1jP6uC44hCxBReja88TB67csj1FnWm5gyCM0EEp4IAgJpECLu2tPESsYRY+nVg33JEZZv+\n0eo+b3HFw/quvuGfC8bAINw91NlN0eaoP6B5LwlF8//WydtcN8j3vRW+D1bDP3re7LJ6u2fJgbqq\nZ+wpvVsXdMlpHogFW7txBZrHv1LQ9Snz/a1CSFE4PAAIB/0l7hYHQghSiTDuvL4Pe944jb2HLtBu\n5lCe5SzEx8Fh20h90uc07MJWxDv0MFwMpTgdRru9YxD/vP/5/N+DYGu6y6xoQtMRS1G99JakXD3+\niYTss/hiedxxhqDqw9Z0F25qW+G2KPCpCprTMRw/M5qX3tWScEkiztj8ukVoT2VFhY47Oze34b+e\nOah7H5fmZiCPVX21WNOXRjoVyUvvaqku6bWptz2JdzmIRmNNTQvW1LB68rIP1nfhhOEfAKxf0YCu\nlmqcODuKmuow6msi8mAUI1cONmN8YgZ7Dw4hk82ivSmO67d15N1DXyIUZ74jkRjBiePHhBDNA2uy\nTZuHtr4iPf5JRITJ8C+bzWLXrl2a1wgheOyxx6SiI3GcxsZG/P3f/32RIV9dXR36+vpw11134dvf\n/ja+/OUvAwDOnTuH++67D9///vfR3d3thsiVhwDdAusaPG1xya5uTpRTrkaNT+bUDI63HMMXVrwX\nmWwWdclw2Y4Bls3+PF4tNCU3L9QvRbMQ/TSP3jsS7R0KJs4ihVNMUtcK+ALA7HTRvaRzVWEKNW8e\nz8x7mZvV8I/qecmM4Z+oDYEZyiBtdAHXpcqwu1iaB+JyHXNZULLa36CTG4XXXtGOn/36UFH6VRtF\n2DzR7/nqa6K4+aouHDx+UdMbBhePf/L0vOOIrn9J2KGG4l38h/se/7bUd+KV88fwxtCJxbQP9WxA\nlHIoxKv4iIL3tK92W4yyg0uoX+tZCF6gs9Afb17foIX6DQY4bpBR1Jt/2HInuyGujVyzuQ0P/Xwv\nJqfmdaxo2I8dG1pdlso49HHHbgOMMv+oJI7Q01aNprooTpwdo9/owEGn9+3sQVdraQ9xoaAP3a3V\n2H9kOC99oKsGPztqWTRm3DQTpY0lpXDS81IyEUIyEXKsvHJDVRXcuL0Lu7bMYS6TRThYajPB3r5f\nevyTiAIvPYeWC61Nl/7+7MVLTi5o9SoN/yQiwvxFHz9+XPPagkWxnJBJnKK+vh6RSATf/OY30dlJ\nD9/w0Y9+FBcvXsQ//dM/AQDGxsbwqU99Cj/5yU/g80nnl3Zjplvobq3GgaPD+jcaloFNCNG6Mkc3\n7WiGKYXXCFCbDNss0GJRrmFtbPOOMqsF7fFzF9QVJavpe0mEb4p+wko8j3/UqBoiVGgJCj1eEX8Q\n6u4PY+7n3wYWjeQIyLKNUDbfkncvbW1lvv5NPDPnUL+F7YA51C/lmjmPf2K2A2Y87fHPYsk6z0f1\n+Ef5nfdHHg3CVcDo0OKfNI9/TnaTXS0JxKMBjIwtGTnXJEJobahyTggtGBqD3VXmtNeWMukhzXH5\n4ekG5yayFVT/kMyz9H7MvycOQzKA+YXwTw7swIFL53BmYgS98TTqI3HTcrlFyOfHyMxkyWs3t67E\nHDJYX9uGtljKYckERiAlxEqfJdBj0HHSgxelOhekoNX5tsEm/OBn+4rSr9nE1whOJKM/AEinIvjo\nrStx+OQIFCE3UzEAACAASURBVELQ1hR3ZaPVDrg4SLP5ELSXNphtp0I9/gX8Km6/tg/7Dg3h5785\npHmfEw7/9MalG7d34ek9R3Hw+EVEw34M9qfR3VYNuGD45yas47cq5yieI+A3ZzRj95sOmpRLokGF\njjtGcSbonHYhTekYwkEfJqZm89JX9NTYJs9cNmNb3tyh1J0M9SsRkfKYYUoqlqeeeorp/k984hN4\n+OGHceLE/InzgwcP4kc/+hHuuusuO8STWGTdQBqHjl/EXIaPcsi84KtrgMIfcdyMG98g1HIhbQeG\n3qETvrNN4HXjHLrHv6UJMfGwZuHXVdadf4eCNmcqpfoEZflWkOY+ZI++Dfj8IA1dIInaEr8274HG\nsHwWF/0Lfz/LHOqXr8c/YRsCKzSvfoIv4NpuKEUN9St23diBes0HMfeTby79TfH4RxwK9wMA4ZAf\nd92wDM+/fhJnzo+joTaKrWua4PeJsGjMyfKPh+elCmyzbsPqLUMP+Qbdx/JnpOvxT/s6q/Guqijo\nS6TRl0gz/U4k3t+1Dv/4ZvHa04d7N+HKhh5rmcuNMCp8QrlxEESyCHVd4/IlWj/RVBdDbTKMc0MT\ni2mRkA89bUleIgpLJOzH8i77NlHthJcnWM08KNdYxp2bWlfg0aNvFqXf3FYYaUBSiQT8Klb21uLV\nvWdw+vx4yXv4jBn0TPSWHv0+Bbu2tPMQxNMw65wOzv0l9kN//fZ6mg1xMMyX+qckF0eivFG9WNLK\nJ9i2rhmP/fbwYlo46MP6gQZOghVT6DQiF9Fmx1SPf0SE9V6JJB+u2/NOGn+UE6dOncIXvvAFPPnk\nk3npb7/9tuOyZLNZ7NmzB0888QRef/11HDp0CCMjI8hkMojFYmhubsby5cuxY8cOXHXVVQgGg47L\naIVAIIDbbrsNDzzwwGLagw8+KA3/HIDZ2x6A1oY47rp+Gd46cB5T07PoaUti/9Fh7D14wZQMrAb4\nRhY0eWPznIYLpgxTnETAoShLhHl9pqF7/Mu5qGi/AFbjR6fVioBKV4vkpN0YWoZ1JF4DsmIb9bd6\nHmZMGdDa/OKmmUP9al8r8qhqMb+ywbDHP3cqw3KpVjz+VcL7L4C0DQDJemDoNAAdj3+smVsceBKx\nIK7d2mEpD3vgdIiGQx5OH4h15RsR7LvkXgeCPV8lYjnUr95LdGJjwkP0JeqRDlfhzMSlxbS4P4RV\nqWYOuQs4eeWCOM/l9QN4xnDyMKb+PTRjDVVVcNd1ffj1yydw4uwoapNhbF7ViKpo+YUALyfs/o54\nHYJeV9uGnx/7HWZzPMnUBKPorip16LBCkft39PbGxaDIfPnCQeDakM76LqThX+XA4xOiZVEuHnmF\nQY47VHjpWITSYeuNO6v76pCMh/Du0WGEQz70daRQXWWf7cecp9qEdt3JUL8SEdEdwf7mb/7GcGaE\nECjStaVhstks/vVf/xVf+cpXMD6ef8rIjQnAo48+igceeAAHDhwoeX14eBjDw8N488038dBDDyGZ\nTOLee+/Fxz72MQQC3lmg2bFjR57h3/79+3Hu3DnU1spFADsx26Kb0jE0pWOLf++3EPqX9aQY9SSG\naSl0cnLBy2ApaMYnXlLLJPyge/zLCfUrur5L+YyCio7hn2ibRoKJs4ClPkKnDzT3yPm/4j23TAYj\nOD81Zvh+mueljJlQv15aNKbhyPFHm7Dueol6lTa/Kpv3zwAJhOC788+Qee6nyJ4+hPPBJmBU415n\nRSt/uHheqoC3IpiyTB09TchaEe/Qw/DxkCY9zeYSVH3401W78PDh13Bo5BxaYknc0rYKiUDYbdEE\nhlfUBjHykLCh52k2HPJj91bpzapc4LJOwsmjYGssiU8M7MB/HH4NpyZG0BOvwz09m4QL/ewugimq\nomGzQRHAvj+xgBtOV96NJtB/aaj4QrrN9rJZq0l+5/l4yq6mBIYOOlnJn5IJD49/klw83hhthtdc\nhZaPkXGntaEKrQ1VfITRIeOhUL+0mpOhfiUiojuC3X777U7IUXEcOHAAf/mXf4mXXnrJbVEwMjKC\nz3/+80UeB4H5yXUkEoHP58OlS5eQySx1yENDQ/ja176Ghx9+GN/4xjfQ29truMxjx45h9+7dhu79\n0pe+hNtuu81w3noUypnNZvHKK68YlkfiLlb0IGavgy6sEOt5u3IMqkcq58QoxEjRWSLmZMLrm2Q0\n6dUct9YKxQ5chCqgiaDr8c9A/twXVpx3PGoYOw7+6ocjFM/j301tK/D1N540/gOqYbUJwz/mXwiK\nFc9ERrKwEese/+iX9Tz+aX2LbbHyDZlGYtVQd/0eAOD4794Anp8seR8tTHJFwRLp1+Zxx+xGl8Q8\nvOtcvkGxMeTxTy/UL+1ahTaAZDCCj/ZtcVuMisPt5manpwuvQvVeflnh8Prah4QNPgYY2rmw6jEr\nU01YmWpCNpuVbVFSEtvPHOp5/POQncB/NnWif2+x4Z+6zf49YwVsFaW/nijxEuZnKwbzp4b6Fd2r\ngcRzcDrgYBYvrcOJFm1OK7IVID3NSsREtkqHmZmZwTe/+U28733vE8Lo78yZM7j77ruLjP6amprw\nV3/1V3j66afx4osv4rnnnsPzzz+Pb3zjG1i7dm3evYcOHcLdd9+N5557zhYZeQ98kUgEkUgkL+3C\nBXOhYyXGEWGxhafHP/v0Dz7hLaxj3DDFWTM7/UrIKllMBCeK0sfCxj1y8UZUY0QWaN9w7ukWxU8w\nFZgqumc0PCqex7wCfERxJcQ3Dbo8btens+UTmHxkm8XsjafRGjVuYEUTx5Thn9vNgBsit3W70VnS\npG2wZoHbOweL0sOqHytTTVYF8wSKSvGIyJqZ14/Fa8LyXPZ+b85/zuXef+hD0yXMtPiy75I9AF2n\nJgX/Z8fvk55mHaNsxx1OuOxptrM5UfJ72Lyq0YpIplF23KWR/n7HZDAypxd93i9hh/pGbfaQZnZP\nVY5XGshxxwHobc9LBhjvxhJ4MZnOS3s71QDS2m972azVJL/5ykF6/PMYctih908OePwTzT6tq6o2\nL4KYyMxmtL0TynFHIiLe+LLKhNdeew233347vv71r2NmZsZtcTA6Ooo/+IM/wMGDB/PSd+3ahUce\neQQf/OAHUVdXt5gei8Vw7bXX4nvf+x4+/elP5/1mfHwcn/jEJ/Dmm28yyUAI0f3PDmKxWN7fw8Pm\nw8dKjMFtsmahSTDLQL1mj0ZGP3Uohsu/Ql2dduqBN0ar4GzqHDJkSSmbU+ZwNnUu5w7nZJ5RZzDt\nn/b80jfd4x/Ju+9ssqD+yRzOpc7bJxwnCCEIqtqn+kQ7Peq2NHZ0SfR+zmyBnAUt6D4Cqg+fXbUT\n17UsR288jasb+/DpFVdr/pzWjswZ/rndEjjhpRWKAizrBHoeAWheIrNZXN3Yi4HqhsU0H1FwX/+2\nijl5SPeIyFoHckWSBo/uRnphdB7+Hv/4z9Mk/Fj8xCy47QsGtPXhclE7hCEUdVsCe9AZTvs7U4ay\nsXtjVw9VVbBzc344w5rqEAaXpzV+YS9K7wbAV+Bi3x+E0lN8CMQ2DNSnU/MTqbU5CC0EPBfPS7Rr\ncuCR8IWuItnbnnmV4RRZQvCdrgF8q3slft7Qjv/dtRIP9W8A0YmYwgPR1mAlDmOzQw7a2BWWhn8S\nB+F3YIaf92S7Cag+bKxrL3ltWhHL4+ash8ISSySAgVC/EuuMj4/jq1/9Kr773e8im3OqKpFI4POf\n/zweeOABnDhxwnG5/uIv/gL79+/PS9u8eTO+/vWvQ6UYPgDA/fffj8nJSXzrW99aTBsfH8cf//Ef\n40c/+hGqquix4FtaWvD222+bF94i09PTeX+HQiGXJJE4CddQv3bpSoI4G6OG8y285uBqq9E6mAhP\n4FDLYXy0fjt+cPAljEZGMeuftVW2BTIkAyW7ZGhwofrCZcHFUrBZoU1CckP9EkIwFh3DkaYjiI7H\nAJLFpegoZvzuG7zPQ38PAcWHyTln2oohBG42CggyDhv+iuo9IuoP4o4cr2vHxygHCmje2wSbiAuD\nwXpxq31Yf23mM8hm5xdMPr3yahwZHcKFqXH0JeoQ81eObks1/HNQDqFhCfVrnxTz+Tvcz8k2oFPn\nplz+mRZFwgn6Z2TI8o+afzCgvVTopc1qkVA23oTMC48Wp6++ygVp3GdVXx3ePuhM5A2rTXZFTy3S\nqQgOnxhBLBpAZ3Oc/o3Y2EmSeA3U930Gc0/8C3DhJFDTBHXn74HEjHsftyyDgYuylyg/bLa/4Brq\nV6KDrE8dQ1YO2etc99pBqAxR8GoyjVcve/5L6+wb8kLqnFbxtnk8TZ/iYhQqPf45iLfbot3w6upo\n2YioS/1e7yZEfAG8cv4Yov4grm7sxb/ufwE/bO3BJ/a/VnS/svMeF6QEZjNzrpQrkZilMtxAuMhv\nf/tb3HLLLXjwwQcXjf4IIbjhhhvwn//5n7jjjjtckevRRx/Fz3/+87y0WCyGv/3bv9U1+lvgT/7k\nT7B8+fK8tGPHjuHv/u7vuMlpB9lsFqOjo3lpyaRzi2SVigi6BbvXQco1a6KYytfRCS/Nu5DgoX4X\nmPHPYMOKBgwnhh0z+gPJ4mjjMQzFh3ExdhHH6o9jOHFx/pIA34AlKPLnhvpduG0qOI0LyQu4UD20\naPQngtGW3vpakHJq1Y1FJwvOWmzHjvrQ21AwVaJRozGH6pMecrGSPf7RpiVl8oyamH++zOX5hUIU\ndFTVYF1ta0UZ/QGAqnJU2OR6JP0QCof+RsQFx3KHvujD3ujlK3Qfmi5Biv5Bu6k0QT/FA7Z8/6ZQ\nBq4o8u5H+jY6arDlLPS+pbWhCru2tFG9SwK8+hvrmdSlItiwsgH9nSmq0Z8TKK398H/kr+H71APw\n3/s/oLT0OSsA9aUs6KWyo6gk7Pf4Zzn7ikRZf33JdFUjvZKwfZ1N1+MfhzJcxCnxpcc/iTb2to2Q\njn4qkbDiyD6zx3Qpn6Li/d3r8TebbsX/NXgDtjV0AwDeqUribDB/XfuSzw+la40bYkqPfxLPIQ3/\nbOa73/1unje/+vp6PPDAA/jqV7+KVMpYaAveTE1N4ctf/nJR+kc+8hHU19cbzkdVVXzuc58rSn/o\noYds9+b3X//1X9i5cyd27tzJHDb56NGjmJ3NNwJqby/tVlbCE/e1C64e/2zD/XoC9AxT3INf7dhU\nz1lgMjSJM7VncCp9GmPRMbtLdAzagosvx2BHFCOkqza0lEzfrpG+QECheW+Q5GJHfdA3DQi10MnB\n3SXT1c23WBPKBGZP2W2YeLlk+kB3jXZZ5dIwORgbuVUXdof6pZHNSEs1VdGe0rJvFpRrfRp/Lts9\n/skVCE1sO1hEPdAjKTeW3rd5y78ALdSv1IhNQVIN8N31eShrrgFpXwFl+51Qb/iY22LZh4HOZc2y\nND5x91r4fJSBwYGQi16F+IPulEu5ll28p0wrvYIx5GjWJqQhqTlI30ag8GBrbTNQR18TqwhsbtB6\nfaD327Qz8nu+mhziirVNJdO3DTY7LAlfqAedOLSNqWltL16qKhctuCIXHajw2kujZeMlT7PTqoqv\nLVuHPak0zgVCeLW6Ft/oGwSpcseeZjYjDf8k3kKOYA6hKAo+8IEP4NFHH8XOnTtdleWhhx7CmTNn\n8tL8fj/uuYfdVeq2bdvQ09OTl5bJZPDNb37Tkox6jI+P48SJEzh58iTOnz/P9NtXX3017+9YLIaV\nK1fyFE9SAhEmazxlsOt5RDnlStPHi0NROhnr13glNEYSNgpiBgE+AgvQ3nLuopUoHuq6W6vhL9hE\nqooG0FpPD0UfoHi9FW0Dw2157Fis1MuS9swzvesApeD9BSMgHauMlW2wPo31eObqpm/6HSjZ/MUn\nQoBVvbWcSxIRQQZAV7Di8Y+jGB7F5ytdf3PKHMrpC7FElk9D4fEpen+jSyyM1CZvbxmiHPKoZIyZ\n9NE2y+jvkOblQhrvmofUNkPdeQ98t38W6oYbQCiexisFRSGoivg1rzvgeEnCiJEhQA4T5YgBT7MW\n8FGMLObkhMcUSkMH1Pd+CqSpBwhXgfRugO+O/wNEDuS2r1vq5SHnQ8ZgWfOs5Drt60hCLTDqCQd9\naG+KuySRN6AZ/kl4I8dxKg64/PPKGs5CSxkOhPDtrpX4wuor8K2e1TgRibkm05z0+CfxGFLTtxlC\nCLq6uvDggw/iC1/4AqLRqP6PbOY73/lOUdqVV15p2gPhrbfeWpT2i1/8AsePHzeVHwvZbBYvvfQS\n029++tOf5v29c+dOKBRPIRI+8FItrOTD7vHPLknM4aSCVmzcl3NN529RuLNz0IVS7V0MdZMMxWgg\nt23S2qmThmrV8RDuuLYPLQ1VCAVVdLYkcNf1y+CnhC4D9EL98pZSH2qZLjcqe94n/RuiepioaYZ6\ny8eB6vR8Ql0rfHd+DiQYtlo0M3QX/toXE5lLuGH0FwhmJgEAAb+KG7d3IVWt/QxembzrwsHy3S1j\nWMuvwEIGWU4GXV5GVRQMV10sSh+JjZioWlmfdsPSZzXXW1/cK5s+UoNEVRDhULHuMhGcWPw3tQ5M\nNPnyrlGPYLO+LT3+SZzF5jZV5uOAUJCF/zlT5xtWlI5as7Y/7Uj5lYTd66NNaW2dz0teakRD6VgJ\n393/J/wf/wf4bvk4SEQaAgFwfVwgHm/TTknPMo9TK9igNZUI49adPahNhkEI0FgXxZ3X9yEY8PYB\nE7PrqkaZmp7Vv8kCcs4kMQqvIYmWjVd0qayH1mQr2eBcIjbeHv09wN13342tW7fC79c+weoke/bs\nwZEjR4rSrXgh3LVrF77yla/kpWWzWTz88MP45Cc/aTpfo3zve9/DTTfdZOjeV155BU899dTi36qq\n4uMf/7hdokkEg2Usbm2o0jFgKm+8Huq3N57GsmrjocsdweONJmPwdItIj9mUjuH91y9j+k2QEupX\nrKdzHzuMKfT2E/RCPSjdg1C6B5GdmWIOf2X0aYzcR51s62TQPXMIncPfweg1/w3J1euhKIS+IFU2\nzdK7Hv/cXNCjGWVXCoQQnKk5AyVDEBuLAQS4FL2EMzVn3RbNk9htwM9y3mqQw8a9K1+nz7m5PyEE\nq3pr8fzrp/LSL+YYw/LuowTvkiWL78f8+BCkGf7J9y8xBEOIeZtVQNlm+WJkDuiU0X1fRwpPv3gM\nc3P57W0lxVu6hD883nZTXQwKISXnNrQxSSIxA93jn/UWrZeHR+wvNHFKfBajCrXCB/uO5gQ6mhOY\ny2SgVoCDEx5ve1J6/HMQuW5ptyHr5YxMlS8UHmoqfiL1U4mYlL8W4DI7duwQxugPAH72s58VpRFC\nsHnzZtN5dnV1oba2eFGlVFl28Pzzz5f0YljImTNn8Od//ud5affddx+6urrsEk2SgwjeNlgkWNFT\n64qnLVH277OCeTs0UnJjOI4t6U7c3bUen155NfyFIT9dxusnveYMNk6qYRYvYWwkQPP456AcRkp1\nu1u1o03rGpwYnCizGv1dzsAQ1kP96hekIItkeOlEniieNG1FZPeWdmPJ4x9HOTyKAoKsksXJ+lPY\n33EA+9sP4FT6NEBMhDgt1/rk1VA4fIpGNnBUhWDz6kb0tietF2gjyq4Pl0xXr/6Ao3JsG2zGtsFm\nTAYmMRGcwKnaU7gYH1m8zntzUWtMKvOeWijoIyYHj38+seZRksqFR3uWfZNzLHjpcKrOo2E/btvV\ni2h4ft09GFBxw5WdSKciDklQOdAP4PE4GEJw2+6ektdaOHiAlkiM4kT/ZfabEWaq6tBiZHXAYPQO\nAEoFe/zLpZyM/qg6IIcmaLfHP4kkH/v3d+hOCLwxIxJmnDNAOfW3kvJCevyrMJ5++umitEQigba2\nNkv5rlmzBo8//nhe2t69e3H27FnU1dVZytsIX/rSl3Dw4EHcf//9ReVls1k8+eST+Ou//mucOHFi\nMf26667DZz/7Wdtlk5jDDl1Eb2JNCBAK+rB+oB4D3TWYmHRgAiCs0qUtV8ZVkbULv6Z5Ga5q7HVE\niiyyzJsQor5p3lC/M2Hb+xIBisGoGwbMIteYHaeU9Uy/7JzE8jSg432CnJ4fc3ZiQu07jE2mU0F3\nNvlc9fiX8dKyiD3kfvtZpaA+mF+NrE8aPFo6rQ/cvr4ZPa1JxKJ++D1geKR0rUHmNz8GJi4tJcZr\nQVr7HZWDEIJNqxrw/138ZenrDnnyll+Pc9htKk8LBTQ9a8wLuKTCYTA4t/vMoQiHUMsJI9XppG7c\n1hjHH961GpfGphGLBDwTyqyc4FXj7U0JbF7diOdeO7mU1hgX/iCIxHs4sWxJiPZQ6PV+yinp19e2\n4Z/feR6zBiLQVLrHv0qDh54RCflxHpMcpJHoIhcKdMYdPv0X1WmAZ/pI7zQWnzQ4lwiKNPyrIM6d\nO4fDhw8Xpff19VnOe9myZUWGfwDwwgsvGA7Dy8Lq1auxZcsWPPvss4tp3/ve9/CDH/wAAwMD6Ojo\nQDgcxoULF/Dqq6/i7NmlUF+qquKP/uiP8JnPfIa7XBIK3HQL8xnR9Jt1y9PYNtgMn09ZVITcsV8S\nQ7kRVRnkJ5Yb9SxmnRqlKZpAWPVjYm4mL70vkR+Gj+dGZNaF9+Snhvp1ASdcwZvEnvLNm/4J9YVR\nhDE1LTQRer5n+gD2B7pLpL8LwLynZ9vgMOg2RBJIh6twJtcIB8COhtLeI4TBksc/MfQGN6EZ/brd\nT4qDV9oJQTIRclsIw5BYNXx3fg5zv/0xsmeOgjR2Qb3yDpCAuWcYSDbihbPF8/XuuP5BOicXeQWd\nJlQY7i3qz8zIsFgSCY1y7yKNhfp1QJC88gjiMRMe3yWG0TlGza2cK9Y2obM5gWOnL6EmEUZ7cxw+\nVW6sSsoLj9v9OUZA9eHmtlV4+PCruveq0gBDwsj6FfU4eupSUfrure0uSCOpZJzQm73inM4rK5cA\nhIs2J5EsINjutsROXn/99ZLpHR0dlvPWyuP111+3xfCvp6cH3/72t3Ho0CE88sgj+OUvf4m33noL\nmUwGr7/+etGzEkIQj8dx/fXX47777rPs4VDCDi/9xYoipOeJzO9XC5NouZkXxAPQlKysoM8uplRL\neH2TVCUKrmjowuPH9+alX9mQb1hED0kqPgHVQ0q7yxVqx8aynmc7O0Pb8Xwcuiz510jnamQPvlZw\niwLSsXLxT9rCsNZ7GJx4taTh3+D0GxTZBIXh5Xy4dzP+8Y0nMZWZ99rbEI7jlvZVdkkGgEfbMZ+B\ndPjH2bhP1qdLB1+8C6lthu8993PJ67qW5SUN/25pW1nibuM4NVrL5uEcbn6L0zPS45+EMzaPO3Ls\ncpDLda3IEaH8cEg/JISgKR1DU1qG95XYh92hqxfy0TqkJ+phexG5qW0FWmPVeGvoJKqDEfzw4Csl\n7/NKGEuJcajrEhzyb62vQioRwoWLS17/omE/etukl1n+yIU2Gk70XnLc4Y8M9SsRFWn4V0Hs3bu3\nZHpLS4vlvJubm0um79u3z3LeNDo6OnD//ffj/vvvx9jYGPbt24fDhw/j4sWLmJycRDQaRXV1NXp6\netDf72zIJYl4sE4Y3DBgEkUNphn3uRnqVxTPPWZC/ZYDd3auQ9QXxEvnjiCk+rG9oQeb051593i9\nXrx0WsftmrbF359Opk64xtdHv6fWM2DMRVm+BXMFhn+kaw1ITthauuF66eTGrTuw/sWX8GJ43WLa\nxok9qN96FUU6F6Gd0GZ4t32JNP5643vwu6FTiPgC6KtOI6T6OQiojZv9XlZa/ul4/GOlTOuTm2dI\nt0cedrykl7RGk9jVtAyPn1iat29Jd6K/ut5SvrQxxJzX0DL9TsqERYMbmzyfTEuPfxLu2Ou5V250\n8YVWmwse+2Wdlx9UQykH5ZBIuMAzVImJbLwf6tdZ+VelmrEqNb/3qG34Jw0wyg27DXT9fhV3XteH\nZ189iZNnR1GbjOCKwSaEQ9Jkgjty+QCpUATncbHkNX4G59rXpHE0f2SoX4moyFGsgigV5hcA6ur0\nQwfpoZXH0aNHLedtlGg0isHBQQwODjpWpsQ+bPEkxWg44YrDP0EUYUJdhHBPUXQqvIgek6FJRCYj\nRekByqFkL208a6EQgpvbVuJmiucZUd6RWQKChfoVeV5mj8c/vY0/+zYGnarrQg8YyrJNwPQk5l5+\nHJgYAelaA/XqDxqWTeua2rcBW3/7MJZP7cVZXx3qZs+iWpmA2vd+q4/gAmwvJxEIY0t9p/6NomCh\n8WVkqF+64Z/InagHkdVpL4QQ3NW1Duvr2nDo0nm0xlLoidfKjSxJEUZ0HhIMA8kGYOhUfnpjsTdg\nFmZmpcc/iQEY9BPq2MLD45/1LCQ5GNEFymHtQ8KCfN+S8sEZz0sOFGIjIsqviiiUxDZ4ve1YJCBD\n+0ocIeILaF5zovvyehcpovg+DzkPkVQWYu1uS2zl+PHjJdNTqZTlvGtqakqmnzhxwnLeknLB/eGZ\n2WGSEx7/PKh1SRMD4FzyPNpOFhv+VXcowHjp33jvTZuD6inTA5UQ8JDS7nZ9Oh3CiW72Rw+H6zxs\nwiirdkBZtQPZbJbZUElrY40kauG79dNIPvEvSA7vB5L1UHf9N5Aq63qfLXg4vqib0knDP84eSMq1\nPpkMMMT+3sodQgi643Xojls/nLeYZ8VooZWDUc/C6obrMfeL7+RdVNZda6ls6fFP4iQ8ei85rPHG\ngOGxrPOyg+55yUFBJBIOOBFJYj6f0nMws56XsnJFXhNVHpQqO7zuWECSi+y76A5p+LRn2rKf99f5\nxJNfevyTiIo0/KsgLly4UDI9Ho9bzjsajUJRFGQy+ae/5+bmcPHiRSQSCctlSLyNCLoFddOrhIBu\nyCyKGkwL9ZsV4WWWwEmpJkITuBS5hKrxqsW0juY4kBzTNPwTUD+1BZ6PmQiEOeZmjIAqmuGfuJ6s\n7Che3zOrjR7/OLZeswvJWteoYRopI4fSPgDl9/9fZKcm5j3/iIyHDf8sy2fh9+Vqp8YCfeNE8LYj\nsR/Z2951UAAAIABJREFUBKhdjOxDyo9cfUZZuR0Ix5DZ+wKgqFCWb4HSvsJQPqpCMCfDyUscwKgh\nq/kC5EDAFenxT1KAfN8SSQkcMC6ULCHDWFYW8m1Lygle7Tlbxos7In7zPkUa/knERLbMCmJ4eLhk\neiRS7LXKDOFw6Q3loaEhLvlLJFZh9fhHX4C2Sd0QRT+jbRA6J0URwiyOEOBE/UmcSJ/EhcQFVPcC\n772mB4pU+GDGMGt3c39Rml9RsaamhZtURvEL5vFPkBZfEjsW+OnmO8Tek9kOVbYbGyPCG/0BoK+M\ni923Wn+jFkL9SqMMeqhfB+UQGhaPf7RrskI9CW3cqY4HS6YH/GLpQ5ICqPPa/ItK9yB8N/0hfDd8\nzLDRHyDbgMRBqGMLBy1Ljl1cMfK6pAFG+cEcQUUiERi3PVh6pYu8rWONRvpahyXRRxF8zUhiBulp\ntmwoY4M0ozhx1r2sa1nAb16G+pWIitSIKoixsbGiNEIIQqEQl/xLGf5ls1mMj2u5v5JInIU1BKkz\nRmaFZQiiolEmzIJIWITjkz4CXIpdwtmac4jUAz5VoYY+rZRT0LRwq1qXrmnqQzKYb4R+a/tqV4zw\n/IpgzpAFbjZ29JF63wnXkJ4mMdIHOrqQLGqnzIqHPf5Zlk7sxxMert+bBxcknRXZe43VexI7y7a1\nzSXTd29p0/yNBz+TsoPuyZ5PGQG/XC6UWIFPR8FDBZTjgPPIOpdIJELDcIDCLFnKAT1hDrXrsLam\npWhdNhmIoC+RdkkibVSP1KnEOPRXKt+3xFvoOTrgQTmv04i4rypD/UpERbDdbYmdzMzMlEzn5aFK\nKx+tciUSp2GdAzozZ/SeRuZmqF/xVLx8vLJ4YydmNiJrQzH8+ZrrsOfsYQxPT2BlsgnLkw32CKhD\nwEOnddxubjRDV9PoZGmnJ1ae9enEhL7c8Hb/aVV2/d8TUt6LOFage/zzcrviCUPjkR5dyg7aeN2Y\njqK1oQpHT11aTKtLhdHZUu2EaBIb4NXvreqrwzMvHS9KX91XxyV/SZnD5GnW3tHFaR3T2zotL2Qd\nlBv0gzbyfUu8hRP2RLZM3R1eD2iIJPCZFVfjx4dfxfGxYXTF6/Ch7g0IqOJtKavSAKPsoK+rSryF\nXMykbTpw8/gnF40dRRr+SURFPC1NYhuzs7Ml01WVj5GDVj7S8E8CsCvkWvdb0YPooSnYci7/dS1R\nH1DsaZ8Zb3flBtWzJqUWksEIrm1ZbodITFg1/Mtynszq+b9zE3s8/ulco9xg1BAxFFQxOTVXlJ6I\nlQ53aArvOq+TmKDwnX6/rQ/vP7KPIQP9WxRCMCcXcUpCDycnPzgAvOz+ZHV6Fcp7UxUF79vVg9f2\nnsXJc2OoS0awuq8OwYB3DkJUIk54QF7ZW4tfv3y8yH6rryPJqQSJRB/p8c+b0NZFJBKJRGR4dV/l\nYoDRV12Pz1dfh2w2K7SRrwwxX454Y51na30Xfnv63aL0q5t6XZBGUMqkP7SCIwbnZVzN4nzxS6ic\nHGpJJLyRLbOC8PlK23nOzRVvfptBKx+/388lf4nH4WX5Z0kENqss2oSWnyKVX4Yw+pmgE2ZBxVps\nD/RNOEGF54zXn9LPyRjeCdyuazvK1/MkwCO03c7N7SXTt69vMZaBAZzsC4QZNySL7EnV41g4lpdG\nGrspvzDi8c/tL15caEa/7NUmvyhae/RiK6wU/YuGXg34fSrWr2jALVd1Y/PqRoRD5s6HpmsiJdNT\niZCp/CTaUA+0cWrykZAfN1/VDfWyBQ8hwLbBJrQ1xvkUIJFcxvZQbgwfRX9Xynp5Ejn2Vhi2RAKQ\nSGyEemCZkyJliwGGi5+a6OsR0vCv/KBHfHFMDF12NPQUpQUUFYM1bS5II/Ei/MYduZ7pJD4PRQ2T\nVBbS418F4ff7S3r9y2QyXPLXykca/klEwYkNEssIop/RqsNNEc2c9dre0INfndqfl3YyHC19c5W1\nhX7qQoMobcxmRF8M0iOgeEg1crmq3djQ4RFCt7M5gdpkGOeGJhbTmuqiaErHKL/ih9wIM4Hg/Urh\nOx33+fG1ZYNYf+E0dobiaOzbCJKow+y//A/NHPRQFAB8zuqUHXRPs5J5+GhvXh/jKxWnxp1ta5vx\n/Z/tLUrfwdGwXqIPz/fd155E5wfX4uyFCaQSIYSCHtKTJWUBj9bMMnQt76rhUGJ5Y6Q+pb5QfshX\nKpEIgCB7BiKiyJCLFYVI66pd8Vr8Xs8mPHTwZUzOzSARCOO+ZVcg6g+4LZpEIJzYMiznIULEuYVf\njjsSQZGrdhVEJBLBxMREXlo2m8Xk5CSX/AvzBuY75Eik9Ml/SWXBTSG3kI0zHvwYKRBJFAVNPFVq\nAXbJNv3/7N15nB1Vnf//d917e7u973tn786eACFBs4kkgDEYGECU+RLmizK/8eE281AzjvPz8YPH\nzE+dcRiFEJdh8KHiw4XB8Qczxi+jUXFQhkUkRAQMgYQABrJvna277+8PpOFyb1ff27eqzqmq1/Ov\n9Enfup+uqlPn1KlPndM2Wffv2ZG1BGtDTaM07Sxpx2+yfjcx/20lRkfyQdhnPWypzJ/8VVdmZrYa\nmxNa/LjnKuXvLTScivKkrrywX7956hXtPXBCHS1pnTWrXamkdzdsYXkzNTzs3mn5ojuRKtP9bT3q\nH3iLutumKLN3d3EbePOvcOKMydOlfnlD1vbqhgkI6vLR1Vajab0N2rH70GhZX2et+rqYIc5rQfap\ny1LJwF6OQIR41J4GsdRvMuGoprpcS+Z1akp3felfGHEV5SnV11To8LFTWeUny18fW2bmpehxn5k/\nwEAAn3E+h1OSAxc9IZpbYXnndL21Y6oOnhpUc0U143fIFcRMsyOMZwaJpX5hKxL/YqShoUH79+/P\nKR8cHPRk+/kS/177XsC6HnmJIj91coRuUPrr2/S+gbfoP5//rfadPKYZ9W1a379EqflJDf+f25XZ\nuU1KlSkxd7kS515c0ne5d9Sjs0/dhD3hqSKZ0sLmHj22/4Ws8iVtk80EZDE/Hjq7b3Gc/y3iBKu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Uqn00qlUjpy5EjebUyaNEm33HKLBgYGggoboVRcDyM2HRKL/86D0xbmJP491NyhEcfgKvEW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\nmIa6Sv3vy+bqw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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2f6448e990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#csfont = {'fontname':'Comic Sans MS'}\n", "#hfont = {'fontname':'Helvetica'}\n", "\n", "fig2, axs2 = plt.subplots(1,1, figsize=(40, 20), facecolor='w', edgecolor='k', sharex=True)\n", "spines_to_remove = ['top', 'right']\n", "for spine in spines_to_remove:\n", " axs2.spines[spine].set_visible(False)\n", "axs2.spines['bottom'].set_linewidth(3.5)\n", "axs2.spines['left'].set_linewidth(3.5)\n", "#axs2.set_title('Test set accuracy and loss', fontsize=20)\n", "axs2.xaxis.set_ticks_position('none')\n", "axs2.yaxis.set_ticks_position('none')\n", "axs2.plot(rgb_dict[test_iteration_regex], rgb_dict[test_accuracy_regex], label='RGB', linewidth=8.0)\n", "axs2.plot(hist_dict[test_iteration_regex], hist_dict[test_accuracy_regex], label='FPFH', linewidth=8.0)\n", "axs2.plot(rgb_hist_dict[test_iteration_regex], rgb_hist_dict[test_accuracy_regex], label='RGB+FPFH', linewidth=8.0)\n", "axs2.legend(loc=4, fontsize=60)\n", "axs2.set_ylabel('Test Accuracy', fontsize=70)\n", "plt.yticks(fontsize = 60)\n", "axs2.axes.get_xaxis().set_visible(False)\n", "'''for spine in spines_to_remove:\n", " axs2[1].spines[spine].set_visible(False)\n", "axs2[1].xaxis.set_ticks_position('none')\n", "axs2[1].yaxis.set_ticks_position('none')\n", "axs2[1].plot(rgb_dict[test_iteration_regex], rgb_dict[test_loss_regex], label='rgb')\n", "axs2[1].plot(hist_dict[test_iteration_regex], hist_dict[test_loss_regex], label='histograms')\n", "axs2[1].plot(rgb_hist_dict[test_iteration_regex], rgb_hist_dict[test_loss_regex], label='rgb+histograms')\n", "axs2[1].legend(fontsize=18)\n", "plt.ylabel('Test Accuracy', fontsize=18)\n", "plt.xlabel('Iterations', fontsize=18)'''\n", "#plt.xlim(0,3000)\n", "plt.show()\n", "\n", "\n", "fig2, axs2 = plt.subplots(1,1, figsize=(40, 15), facecolor='w', edgecolor='k', sharex=True)\n", "for spine in spines_to_remove:\n", " axs2.spines[spine].set_visible(False)\n", "axs2.spines['bottom'].set_linewidth(3.5)\n", "axs2.spines['left'].set_linewidth(3.5)\n", "axs2.xaxis.set_ticks_position('none')\n", "axs2.yaxis.set_ticks_position('none')\n", "axs2.set_yscale('log')\n", "axs2.plot(rgb_dict[train_iteration_regex], (np.array(rgb_dict[train_loss_regex])), label='RGB', linewidth=6.0)\n", "axs2.plot(hist_dict[train_iteration_regex], (np.array(hist_dict[train_loss_regex])), label='FPFH', linewidth=6.0)\n", "axs2.plot(rgb_hist_dict[train_iteration_regex], (np.array(rgb_hist_dict[train_loss_regex])), label='RGB+FPFH', linewidth=6.0)\n", "#axs2.set_title('Training set loss (log-scale)', fontsize=20)\n", "axs2.legend(fontsize=60)\n", "plt.ylabel('Train Loss', fontsize=70)\n", "plt.xlabel('Iterations', fontsize=70)\n", "plt.yticks(fontsize = 60)\n", "plt.xticks(fontsize = 60)\n", "\n", "plt.show()\n", "#plt.xlim(47800,48000)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
andreww/theia_tools	Viscosity_profiles.ipynb	1	110830	{ "metadata": { "name": "", "signature": "sha256:9f0914e8b199793dc4d5120c50fc96632b08c92af513b6c5bf8ce58567c259cc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Upper mantle viscosity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What function can we reasonably use in order to parameterise the upper mantle viscosity (and test Theia)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def sinha_butler(nu_j, lam, z_j, z):\n", " \"\"\"Smoothed jump \n", " \n", " This is the function used by Sinha and Butler \n", " (2007; JGR 112:B10406 http://dx.doi.org/10.1029/2006JB004850)\n", " \"\"\"\n", " z_j = z_j / np.max(z)\n", " z_norm = z / np.max(z)\n", " nu = ((nu_j-1)/2)np.tanh(lam(z_j-z_norm))+((nu_j+1)/2)\n", " return nu\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This can be used to give something that looks quite like the function used by Sinha and Butler" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(1.0, 0.0, 1000)\n", "lam = 50.0\n", "z_j = 0.77\n", "nu_1 = sinha_butler(1, lam, z_j, z)\n", "nu_10 = sinha_butler(10, lam, z_j, z)\n", "nu_100 = sinha_butler(100, lam, z_j, z)\n", "plt.plot(nu_1, z, nu_10, z, nu_100, z)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[<matplotlib.lines.Line2D at 0x110833690>,\n", " <matplotlib.lines.Line2D at 0x110833910>,\n", " <matplotlib.lines.Line2D at 0x110833fd0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x102fc6310>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, because we are normalising inside the fucntion, we can use real numbers.\n", "(However, the lambda parameter is a bit unclear)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(6346.0, 3480.0, 1000)\n", "lam = 50.0\n", "z_j = 6346.0 - 670.0\n", "nu_1 = sinha_butler(1, lam, z_j, z)\n", "nu_10 = sinha_butler(10, lam, z_j, z)\n", "nu_100 = sinha_butler(100, lam, z_j, z)\n", "plt.plot(nu_1, z, nu_10, z, nu_100, z)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[<matplotlib.lines.Line2D at 0x110860110>,\n", " <matplotlib.lines.Line2D at 0x110860390>,\n", " <matplotlib.lines.Line2D at 0x110860a50>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x110775350>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Play around with hypobolic tangent to see what it does" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(0.5np.tanh(np.linspace(-2np.pi, 2np.pi)), np.linspace(-2np.pi, 2np.pi),\n", " 0.5np.tanh(10(np.linspace(-2np.pi, 2np.pi)-2)), np.linspace(-2np.pi, 2np.pi))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[<matplotlib.lines.Line2D at 0x110d1e590>,\n", " <matplotlib.lines.Line2D at 0x110d1e810>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1108482d0>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(sinha_butler(0.1, lam, z_j, z), z)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "[<matplotlib.lines.Line2D at 0x110f06550>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x110764d50>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As long as the tapers fall off quickly, we can create a low viscosity zone" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def sinh_notch(z, z_j1, nu_j1, lam_j1, z_j2, nu_j2, lam_j2):\n", " z_j1 = z_j1 / np.max(z)\n", " z_j2 = z_j2 / np.max(z)\n", " z_norm = z / np.max(z)\n", " nu_j2 = (nu_j2/nu_j1)\n", " nu = ((((nu_j1-1)/2)np.tanh(lam_j1(z_j1-z_norm))+((nu_j1+1)/2)) \n", " (((nu_j2-1)/2)np.tanh(lam_j2(z_j2-z_norm))+((nu_j2+1)/2)))\n", " return nu\n", "\n", "z = np.linspace(6346.0, 3480.0, 1000)\n", "lam_j1 = 1000\n", "z_j1 = 6346.0 - 100.0\n", "nu_j1 = 0.05\n", "\n", "z = np.linspace(6346.0, 3480.0, 1000)\n", "lam_j2 = 1000\n", "z_j2 = 6346.0 - 670.0\n", "nu_j2 = 11\n", "\n", "plt.plot(sinh_notch(z, z_j1, nu_j1, lam_j1, z_j2, nu_j2, lam_j2), z,\n", " sinh_notch(z, z_j1, 0.05, 1000, z_j2, 12, 100),z,\n", " sinh_notch(z, z_j1, 0.05, 1000, z_j2, 13, 50),z,)\n", "\n", "plt.xscale('log')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x110f1a690>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But this isn't great if the lower jump is too smooth" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(sinh_notch(z, z_j1, 0.05, 1000, z_j2, 20, 20),z)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "[<matplotlib.lines.Line2D at 0x110fd4610>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x110d36490>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How about just doing a smooth lower boundary, and making the top arbitary:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def visc_profile(nu_lid, z_lid, nu_j, lam, z_j, z):\n", " \"\"\"Smoothed lower viscosity jump for base of the asthenosphere\n", " \n", " The upper boundary is defined by a lid nu_lid times more \n", " viscous than the asthenosphere with a base at depth z_lid\n", " \n", " For the lower boundary we use the function of Sinha and Butler \n", " (2007; JGR 112:B10406 http://dx.doi.org/10.1029/2006JB004850)\n", " which is parameterised by:\n", " * z_j: the depth (in km) of the viscosty jump at the \"base\" \n", " of the asthernosphere\n", " * lam: a parameter giving the width of the jump (numbers \n", " between 20 and 200 give good values), larger is smoother\n", " * nu_j: the radial viscosity factor: how much more viscous is the\n", " mantle below the jump, compared to the manlte above the jump.\n", " \"\"\"\n", " z_j = z_j / np.max(z)\n", " z_norm = z / np.max(z)\n", " nu = ((1-nu_j)/2)np.tanh(lam(z_j-z_norm))+((nu_j+1)/2)\n", " \n", " for i in range(len(nu)):\n", " if z[i] < z_lid:\n", " nu[i] = nu_lid\n", " nu[-1] = nu_j\n", " \n", " return nu" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(0.0, 3480.0, 1000)\n", "z = np.linspace(0.0, 1000.0, 1000)\n", "\n", "\n", "nu_1 = visc_profile(20.0, 100, 50.0, 100.0, 670.0, z)\n", "nu_2 = visc_profile(20.0, 100, 50.0, 25.0, 670.0, z)\n", "nu_3 = visc_profile(20.0, 100, 50.0, 10.0, 670.0, z)\n", "\n", "nu_4 = visc_profile(20.0, 100, 50.0, 100.0, 400.0, z)\n", "nu_5 = visc_profile(20.0, 100, 50.0, 25.0, 400.0, z)\n", "nu_6 = visc_profile(20.0, 100, 50.0, 10.0, 400.0, z)\n", "nu_7 = visc_profile(20.0, 100, 50.0, 5.0, 400.0, z)\n", "\n", "plt.plot(nu_1, z, nu_2, z, nu_3, z, nu_4, z, nu_5, z, nu_6, z, nu_7, z)\n", "plt.gca().invert_yaxis()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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LERkZydNPP82JEye4//77+c1vfsPgwYP597//jdPpbG3zLojFMoZR\no7ZjMFjZuXMUZWXfN7gOnVlHsKPh724fjr88HqfXwZmyZlwZJwTcfDOsW6f1+x88qG3ofvfd2jnV\nDaRQtGmMRiP/8z//w+7du3n77bdZuHAhiYmJvPXWW1RUVLS2eXWi14fQr997xMc/z969yRQULGvQ\n8zqjDn0jOijaheMXCMZ1u4EVx1a0zAtHj9a+/DMytEifjz0G/frB88/DsWMtY4NCoWgUQghuvPFG\nVqxYweLFi1m3bh0JCQm8+uqrlJc3Yj1QCxATcw+DBy/l8OGHyM7+R72f0wXrCOqojh9gStxtLDy4\nsGVfGhEBP/+51g00f742/3/8eG0w+M034ezZlrVHoVA0iNGjR7NkyRJWrlzJ9u3bSUhI4M9//nOb\nFACr9WpGjNhIVtYrnD79dr2eEfrGBYdrN47/5vg72J69ncPnWmEZtxCas3/rLcjOhj/+EXbvhgED\n4Prrtc3ejx9vebsUCkW9GDp0KJ999hnffPMN+/bto2/fvrz99tu4XK7WNq0GISF9GDZsLVlZfyEn\n51/1esbdiH1i2o3jN+lDeeyqx5i7dm7rGmIwQHIyfPghnDkDc+fCoUNaS2DIEPjd72DHDmjDswoU\niiuVAQMGsGDBApYvX05qair9+/fno48+wtuGFnOGhPRm2LC1ZGT8nsLCS0cY9nZUx18Z6vrX435N\nen46H+39qHUNqsRkgmnT4J//1FoC770HTifce68WH2jOHG0z+DMtG65VoVBcnOHDh5OamsoHH3zA\nu+++y4gRI1i5cmVrmxUgNDSRgQM/5+DB/8FuP3LRe32N6O1ptOMXQkQIIRYKIQ4KIdKFEFcJIaKE\nEKuFEEeEEKuEEBHV7n9GCHFUCHFICJHcmHeaDCYW/WARj698nA2ZGxprevOg18O4cdo00EOHtK/+\nG26Ar77SQkQMGwZPPaUtFCsra21rFQoFMGHCBDZt2sTzzz/Po48+yowZMzjWRiZwRERcR3z8c6Sn\n33PxIG8t6fiBN4FUKeUAYChwCJgLrJZSJgFr/ccIIQYCdwEDganAu0KIRr17cJfBfDrrU+78/E4W\nH1x8GeY3M3FxWkC4hQshPx/+/nctYNyLL0K3btruYE89BcuWQcllxiFSKBSNRgjBbbfdxoEDBxg/\nfjxXX301zzzzTJsYAI6N/RlGY1cyM/90wXvqv+9WFY1yvkIIK3CdlPI/AFJKj5SyBJgBzPPfNg+4\nzV+eCSyQUrqllBnAMaBB+yJWn0Y/KWESy+Ys4/GVj/OL1F9Q5mzjX9AGg9Ya+MMf4JtvNCF45RUw\nm+G116B7dxg1Cv73f7UNZFTXkELR4gQHB/P000+zd+9esrOz6d+/P59//nmLbKpyIYQQ9Ov3b3Jy\n/o7Nll7nPS3m+IHeQL4Q4gMhxC4hxL+EEGYgRkqZ678nF4jxl2OB6sF2TgPd6/uyurazHNN9DN//\n7HvKXGX0f6c/H+z+AI+v7e/cA2hf/hMnwrPPaovDCgq06aHR0Vr46MGDtRbDXXfBG2/Ad99pYwcK\nhaLZiY2N5b///S+fffYZf/jDH5g5cyanTzdhrLAGEhzcjV69fsPx479usjob6/gNwEjgXSnlSMCG\nv1unEv+u6ReTysuW0QhTBB/e9iFf/uBL5u2ZR5+3+vD6ltcpcbSzrpPgYG2h2G9/q3X9nDunRRCd\nPh2OHIFHHoGoKLj6avjVr7RdxY4cUTOHFIpmZPz48ezatYtRo0YxYsQI/vGPf7RaDKDu3R+houI4\nhYXnbybVmC9+0ZhmjBCiK7BFStnbf3wt8AyQANwgpTwrhOgGrJdS9hdCzAWQUr7kv38F8KyUcmut\neuWzzz4bOJ44cSITJ04MbKTVq9fF7dqWvY3XtrzGimMrmJo4lXuH3MuUxCkY9cYG/8Y2h80GO3dq\nX/9bt2rB5QoLtcByI0dqXUUjR2orjPX61rZWoehQHDhwgIceegij0ci8efOIj49vcRvOnp3H2bPz\nGT58DWlpaaSlpQFw6OUMPnPMQ8r6S0CjHD+AEGID8JCU8ogQ4jkg1H+pQEr5st/ZR0gp5/oHdz9B\n69fvDqwBEmWtlwshap8CNIf/7bda70d9KLAX8EX6F3y09yP25+1nUsIkbk68mZsTb6a7pd49TG2f\nwkJNAHbt0kRh1y5tNfHQoVVCMHSoNqvIZGptaxWKdo3X6+X111/nlVde4Y033mDOnDkt+n6fz8XW\nrX0YPHgJ4eGjAueXdErj9oIbWszxDwP+DRiB48CDgB74HOgFZAA/kFIW++//DfAjwAM8JqU8b9Ls\nhRx/XJwWPr++jr86ueW5rDy+kuXHlrPq+Cq6mLswvud4xvUcx/ie40mKTkLUNYjQXikuhu+/rxKC\nffvg6FGIj9dEoDINGaL9QTvSb1coWoDdu3czZ84cRo0axTvvvIPVam2xd2dmvoTDcZx+/apW9S7u\nnMYd51rI8TcHzeH4q+P1edmXt49Npzax+fRmNp3ahM1tY1S3UQzvOpxhMcMY1nUYSdFJGHSGy3tZ\nW8LlgsOHtZhD1VN5uSYAQ4ZUCcKgQVqMIoVCcUHsdjtPPPEEy5cvZ+HChYwaNerSDzUBTmc227cP\n4ZprstHrQwDl+BtFdmk2u87sYk/uHvbk7uH7s9+TU5bDwM4DGdR5EEnRSfSL7kdSdBKJUYmEBIU0\nvRGtRUGB1iKoLgYHD0J4uNY9NGCAlleWO3dWLQSFohoLFy7k4Ycf5i9/+QsPPPBAi7xzz54pdOv2\nI7p0uQuAL7ukMStfOf7LptxVzr7cfaTnp3Ok4AiHCw5zuOAwJ4tO0jWsK/069aNvVF/iI+JrpOiQ\n6PbfbeTzwenTkJ6uiUB6elXS62sKQmXevbsSBMUVS3p6OrfffjuTJk3ijTfewGhs3skkOTn/pLj4\nGwYO/BiARTFp3JnXAR3/0KFaePwWak1dEI/PQ0ZxBkcKjnCk4AiZxZlklGRoeXEGLq+LuIg4TQis\n8cRFxNE9vDux4bF0t2h5mDGsdX9EY5EScnNrCkJlbrdrIjBggLZ5TVKSNrsoMVFbs6BQdHBKSkq4\n//77KSgoYMmSJURHRzfbuxyOLHbsGMH48bkIoWdR1zTuzO2Ajv+BB7RZi7/6Vcvb1BBKnaUBEcgo\nziCzJJOcshyyy7K1vDQbg84QEIGAKIR3p1t4N7qYuwRSpCmy/bQeCgs1ATh8WFtfUJmfOAFdu9YU\ng8q8Z0817VTRofD5fMydO5elS5eSmppKQkJCs71r+/ahJCX9E6v16o7r+HfsgFtugdRUbYZie0VK\nSYmzhOzS7PME4Uz5GfLt+eSW55Jny8PuttPZ3LmGGMSYY2ocV08mQxucrunxQGZmlRBUF4Vz56BP\nnyoxqC4MnTq1tuUKRaN55513eOGFF0hJSWHMmDHN8o6jR3+ByRRPz56/ZmHXNGZ3RMcPWqyzhx+G\nyZO1SAaTJmljkB0Vp8dZQwiqp1zb+eeMeiNRIVFEh0YTHRIdyKNComocVz8fGRKJrnGx8i6f8nJt\nG8vaonD4MOh0WjdRXalLFzWeoGjzpKSk8NBDD/H5559zww03NHn9ubkfc+7cEgYN+qJjO37QgljO\nnw9LlmgLWHv10rqA+vbVPh4TEiA2Vgt+eSV1LUspKXOVUWAvoLCikIKKAgrsBRRU+I/95drHZc4y\nrCZrQBCiQqKICokiIjiCCFMEkSGRRJgiAinSVHVsCbag1zVDV42UWmvg2LGa6fhxLXc6LywK3bpp\noqFQtAHS0tKYPXs2CxYsYPLkyU1ad0XFcXbvnsC4cac7vuOvjtutdSvv2VPlE44f1wJbnj2rLVTt\n1k2bgRgVBZGR5yerFcLC6k5BjdjVpr3h9XkpchRVCYO9gGJHMUWOIoodxYF03nFFEWWuMsKN4TWF\noVIoLiEclmAL4cHhjWttFBVV/QevnUpLtS+AukShRw81pqBocb799ltmzZrFf//7X6ZOndpk9Uop\n2bgxkquuOsbSXvuvHMd/MaTU/MPZs5CXp5UrU3FxVbmsTOtxqMwrU1mZ5iPCwrTIySaT1oKoTNWP\n6yqbTFrcNaPx/HSh8xe6r636Kq/PS5mrrIYYXFQsqh2XOEqwuW2Yg8xYTVYswRaswf7cZMVitJx3\nPnCt1r0hhpCqQfCysguLwrlz0Lu3JgLVxaFPH21V85Wg9IpWYcuWLcycOZPPP/+ciRMnNlm9u3Zd\nQ0LCK6zp71WOvymQUlvsWikEDgdUVFSl6scXKrvdWh0ul9Y7UVm+WKp9n9OpdWdfSiCCgrRkMJyf\n16fcXPfWLlcmvR50eh8V3jLKXKWUOEsodZZS4tDyUmetc66qa7XvdfvcVcJQXTyqHwdbiZImYvMd\nxJwtIzqnGGtWHqGnzhKckYXuzFlEjx5VglA9T0iA0NBL/ptRKC7G+vXrueuuu1i5ciUjRoxokjoP\nHfoRFsvVbB6ZpBx/R8PrvbRAuFya0Hg8WrpUubnures5t1v7DZXXK1PlOZ3ufFGoLRQXO68LcoGx\nDBlcgjSWIoNL8AWV4gvSco++FK+hBI+hFLe+BI+uFJeuBLeuFJcowUkpPm8xCcV6+heF0K/QSGKx\nIKHQS3yhk9giO6XmEHI7RXOuSwzFXWIp7RZHRfcEnD37ERLRnfBgC9ZgC+HBFoKD9A2yPyCEOjVm\n3dFZtGgRv/jFL9iwYQOJiYmXXd+pU3/B5crh+2tnNtjxd6CANB0Tvb6qK6mjIaW2UPhColBXOv+a\nEY8nGo8n+oLPXao+t0/iCHdgCy3hbNdSTnhLqZAlOGQpLm8RlrJTdCnNJLY4m9isDHru30lcSQnx\nJTYcBsHxSD3HoiTbotyciDByMtzCyfAIzhkiEa4IhMsCTgvCaUU6LcgKKz6HBV+FBZ/ditduAYcV\nvceCwWvB4AsjyKBrlBC29LXmqK+jCuCsWbPIz89n+vTpbN26lYjLjIkVEpJAaenmRj2rHL+i1RBC\n+z+6Xq91WbWiJUCIP3Wt/2NSEpabS6fjx7nq2DHksWN4jhxCHjyK7mQGuHOw9+xGSc/OFHazkRdn\nI7tLIac6Gzll9lLiKavRbVWZ2z12dEFhhAZZCDdaCQuyEBZkxay3EGqwYNZbCdFbCNVZCdVZMAkL\nJmHFJCwEYyVYWjBKCwYZitcrGiSGDkdDRLfpr3m957cCawtDUFDdY2aV5fqeq891k0nr6auegoMb\nL04/+9nPOHDgAHPmzOGrr75CfxmDeMHBPXA6TzfqWdXVo1A0F9VnINXOi4rOH2z2596ePSiTjpqi\n4Kw2/lHXuTrGSVxeV40xj/MGy2uNiViCLYGZV5EhkUSaIgkzhrXoCnIpNed/MbFwu7VU2eVZvevz\nYucac71y3M5ur0oul9YCry0IF0tWqzaTMCICwsPd/P73Uxg5cgwvvPAyERGNm1vgdGazc+docu5Y\noPr4FYp2gc2mhbSoSxRycrTpp3WIAgkJ9e73c3ldlDnLLi0a1QbQK2dgVc7ScngcNabmRpoitXJw\nlThUF4rqZavJ2rHCm/vxejVBqC4GdaVKwbDZtNmElTMKi4shL+8cBw6MIiTkHez2WzCZNGHo0kX7\nT9+zZ808KUmbnl4dKb1s2BBC/pxlzM5JVo5foWjXuFyQkVG3KGRkaCEt6pqW2qeP9mnZlKZ4XTWm\n7FZOy71g2S8aRY4iSp2lmIPMNUQjOjSaTiGdtDy0E9Eh/ty/orxTaCesJmvrrShvQTZu3Midd97J\nrl27CQ/vRlGRFgfx9GnIytLyynJ6utbFNHIkXHUVzJ4N/fvDli09OfPjvzDrwD3K8SsUHRavV/MG\ndYnC8eNaa6CulkJioiYYLdht45M+Sp2lgdZD9cWC5+znKLAXcK7Cn9vPBRYRlrvKA+FHaoiDP+8U\n2omYsBhizDHEhGnxq9rrvtrPPfccmzZtYuXKlegusupcSjh1SttUb8MG+OwzrafwpZdGcO7Jh7lj\n60+V41corkgqQ2dXX8RWXRQ8Hi0QXmXq31/L+/ZtU3syu71uCisKA2JQKRKV5coYVrm2XHLLc8m3\n5xNuDK8hBl3NXWscB86HdW1TIuHxeJgwYQL33HMPP//5z+v9nNcL8+aB3X4jPZZM5/a1TyjHr1Ao\n6qCwsCoQ3qFDVfnJk1qQq+piUFnu2rXNz6/0SR+FFYU1xOBs+Vmt7D+uzPNseUSFRNHD0oMelh70\ntPQMlHtYetDT2pPu4d0JNrTcNLNDhw5x3XXXsWvXLnr27NmgZ1NS7oT3B3PbV39Qjl+hUDQAt1tz\n/rVF4fBhbRSzuiC00VZCffH6vOTacjldeprTpafJKsnSymVV5ZyyHCJMEfS09qSXtRcJEQn0iepD\nQmQCfSL7EBcR1+Sthueff54dO3aQkpLSoFlUu3f/hKxXwpj56evK8SsUiiaispVQXQwOHdIGmXv2\nhEGDqtLgwdr0k9ZdlHHZ+KSPPFseWSVZZJZkcqLoBMcLj3OiWMuzy7LpFtaNPlF9SIxMZEDnAYE9\numPDYxs1/dXlcjF8+HBefPFFZs6cWe/njh+fS/pLBcz497+V41coFM2MywVHj8KBAzVTRoYW9K62\nIPTt22EC4bm9bk6VnOJ40XGOFR4jPT+d9Px0DuQfwOFxBERgcJfBjI4dzYiuIzAbzZesd8WKFTz2\n2GPs37+foHr+rTIzX2L/G99zyxufKcevUChaCadT21Rn//6agpCVpc0wGjRI20R7+HAtxca2+TGE\nhnDOfi4gBHvO7mHnmZ3sz9tPn6g+jI4dzehuo7ku7joGdxlc55TVKVOmcOutt9Z7oPf06b+x92/r\nmf7KYuX4FQpFG6OiQusm2r8f9u6F77/XkpSaAAwbViUG/fp1mNYBaGsh9uftZ0fODrZlb+ObzG8o\ndhQzIW4CN8TfQHKfZPpG9wVg3759TJ48maNHj2KxWC5Zd07Ov9nzjy+Z9sflyvErFIp2gJTaphmV\nIlCZsrJg4MAqQRg1CkaM6FCRCk+Xnmb9yfWsz1jP8mPLiQ6J5vb+t3PHgDt47cnXGDx4MHPnzr1k\nPbm5n7D7X+9z8+/WKcevUCjaMTYb7NunicDu3bBzp7Z0tV8/GDMGxo7V8kGDtMht7Ryf9LH19FYW\nH1rMwvSFGAuM5LyTw+Gjh+kW1e2iz+bnL2Hnh3/h5qc2K8evUCg6GA6Hts/q9u2wbZuWZ2VprYLq\nYtCnT7seM/BJH+tPruf+e+6nKKaIXz32K3497tdEhUTVeX9h4Sq2f/Qbpj62s0GOv+MHxFAoFO0f\nk0kLUvPzn8N//wsHD0J2Njz/vLbIbOFCmDhRi2R2553wxhuwY4e2WrkdoRM6JiVMIuXdFCJ2RZBb\nlsHdI2wAAAyiSURBVEvS35J4e9vb+KTv/Pt1oQijs8HvUV/8CoWi45CZCRs3VqXMTK01cO21Wrrm\nGm0j7XbAuHHjePrpp0kal8SPv/oxQgg+v/NzuoVXdf+Ule3iu4V3kfyjY+qLX6FQXKHExcEPfwh/\n/7s2TpCZCY8/rk0zfe45rXVw/fVaS2HzZm3VchvlkUce4d1332VA5wFseHADyQnJjP33WNLz0wP3\n6HTBCEPDf4P64lcoFFcONpvWElizRksnT2pCMHmylgYMaDNjBA6Hg169erFp0yb69tWme87fM5/f\nrPsNGx7YQO/I3tjth9m8ZDI3/fC0GtxVKBSKepGfD+vXayKwapV27tZbtTRhQquHn3jiiScIDg7m\nhRdeCJx7fcvrfLL/Ezb/aDMe1yk2Lb2Om+4+oxy/QqFQNBgptWmjX30FS5dq5cmTNRGYPl3bz6CF\n2b17N7NmzeL48eOBGEBSSqZ9Mo0b4m/gl6PuZtPXY5g8O0/18SsUCkWDEUJbGzB3rtb/f+QI3HKL\nJgKJiTBlCnzwgbZ3YgsxfPhwgoOD2bp1azUzBX9N/iuvbn6VcncF6Bs+c0k5foVCoaiLLl3ggQdg\n0SJtH+SHHoKvv9YGkGfMgE8+0TbVbUaEEMyZM4dPPvmkxvkBnQcwvtd4vjq8HKH3Nrhe5fgVCoXi\nUoSGahvdLlqkLRz7wQ9g/nwtNPWjj2orjJuJH/zgByxatIja3eD3DrmXhQe/BF0LOn4hxDNCiANC\niH1CiE+EEMFCiCghxGohxBEhxCohRESt+48KIQ4JIZIb+16FQqFoVSwWuPdeWL5cCyvRtSvcfru2\nE/p77zV5K6Bfv36Ehoby/fff1zg/JXEKW7N3gKGFunqEEPHAj4GRUsohgB64G5gLrJZSJgFr/ccI\nIQYCdwEDganAu0LUEZNUoVAo2hM9e8LvfgcnTsDLL2tiEB8P//d/cOZMk71m+vTpLFu2rMa5MGMY\nfaL7IVrwi78UcAOhQggDEArkADOAef575gG3+cszgQVSSreUMgM4Boxt5LsVCoWibaHTwU03wZIl\n/P/27j02q/qO4/j7Q4FSGIKNUJFx03VCvUwgYCMTAQc6MWxqMoXMkOAGCQbZsixDTRyJGFnm3CXG\nGTbmxG0szGXetrnhZsPwDwiIo6OiAkMuwTLBMkDLcHz3xzltnyK354G1fXo+r+Sk5/zO5Tn9pvn2\nl99zvr/Dq68mXwBfdhnMmpX8UzhLU6dO5cUXX/xY++X9PwNtNcYfEfuB7wE7SBJ+Q0SsBCoioj49\nrB6oSNcvAnblXGIXMLCQzzYz69AqK+Gxx2DLFhg8OJkyYvbspIq4QOPHj6euro79+/e3ar+476dQ\nycfn8DmdguY0lXQJ8DVgKHAA+I2kL+ceExEh6VQP5Z9w38KFC5vXJ0yYwIQJEwq5RTOz9lVenkwT\ncc898OijyXcAs2YlQ0Nn8JKVXKWlpVRXV7N69WqmTZtGTU0NNTU11NbX8tbh/G+toAIuSbcDkyPi\nK+n2nUA1MAmYGBHvShoAvBIRwyUtAIiIxenxLwHfjog1x13XBVxm1jnV18N99yXfAzz0EMycmQwR\nnaFFixbR0NDAI4880tz28raXKdl2I5Mm/7dNCrg2A9WSypSUk30OqANeAGamx8wEnk3XnwfukNRd\n0jCgElhb4GebmRWfigpYuhSeew6eeAKuvz6ZK+gMjR8/nlWrVrVqS+bpz39uoULH+P8OLAPWARvT\n5iXAYmCypLdIev+L0+PrgBUk/xz+CMx1197MMmnMmKQy+KabkvH/JUuS6SJOY+zYsWzatImDBw82\nt5WXlcOZd/Sbea4eM7P2UlcHM2YkTwAtWXLadwWMGzeORYsWMXHiRAD2fbCPjasHMOmGo56rx8ys\nKFRVJb3/bt2guvq0Qz+jR49mQ06VcGnXUtpsqMfMzM6Rnj2Tyd9mz4Zrr03eLXwSo0aNYv369c3b\npSWlBQ31OPGbmbU3CebNSx77nDw5KQI7gVGjRvHaa681b3ft0pU8RniaOfGbmXUUTZO/3XLLCSd+\nGzFiBO+88w6HDh0Cktk73eM3Myt2N9yQvDN46lTYurXVrm7dulFVVUVtbW1LoxO/mVkncNttcP/9\nSc//cOvS3OHDh/Pmm2+e1eWd+M3MOqK5c2HkSJgzp1XzxxK/e/xmZp2ElFT4rlsHzzzT3HzppZey\nefPmluOc+M3MOpGysmSah3nzYN8+wD1+M7POb9w4uPXWZKZPoLKykm3btvHRR+mbt5z4zcw6oYUL\nYfly2LqVHj160K9fP3bv3p3udOI3M+t8+vWD+fPhwQcBGDx4MO+kL3YppICroBexmJlZG7v7brjk\nEtizhyFDhrBjx46kvYB5Ld3jNzMrBuXlMH06PP5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"text": [ "<matplotlib.figure.Figure at 0x112b0a3d0>" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "z = np.linspace(0.0, 3480.0, 1000)\n", "z = np.linspace(0.0, 1000.0, 100)\n", "\n", "nu_4 = visc_profile(10.0, 100, 10.0, 100.0, 400.0, z)\n", "nu_6 = visc_profile(10.0, 100, 10.0, 10.0, 400.0, z)\n", "\n", "plt.plot(nu_4, z, nu_6, z)\n", "plt.gca().invert_yaxis()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1125e4850>" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "def write_viscosity_file(filename, header, z, nu):\n", " f = open(filename, 'w')\n", " f.write(header)\n", " for zi, nui in zip(z, nu):\n", " f.write(\"{:10.3f} {:10.3f}\\n\".format(zi, nui))\n", " f.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "write_viscosity_file(\"square_viscosity_profile.dat\", \"Profile with 10 100 10 100 400\\n\", z, nu_4)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "write_viscosity_file(\"smooth_viscosity_profile.dat\", \"Profile with 10 100 10 10 400\\n\", z, nu_6)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
dssg/wikienergy	proto/dataset_adapter_notebooks/get_pecan_street-not_class.ipynb	2	19079	{ "metadata": { "name": "", "signature": "sha256:68bae724dda65dbc747549e2dfefadc2c5c8c9cd97e882fed773a2202586e500" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "sys.path.append('../../')\n", "import pandas\n", "from disaggregator import utils\n", "import disaggregator.PecanStreetDatasetAdapter as pecan\n", "import os" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(pecan)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "<module 'disaggregator.PecanStreetDatasetAdapter' from '../../disaggregator/PecanStreetDatasetAdapter.pyc'>" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(utils)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 83, "text": [ "<module 'disaggregator.utils' from '../../disaggregator/utils.py'>" ] } ], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "user_name = 'USERNAME'\n", "pw='PASSWORD'\n", "host = \"db.wiki-energy.org\"\n", "port = \"5432\"\n", "db = \"postgres\"\n", "db_url = \"postgresql\"+\"://\"+user_name+\":\"+pw+\"@\"+host+\":\"+port+\"/\"+db\n", "\n", "table = {'curated':'\\\"PecanStreet_CuratedSets\\\"','raw':'\\\"PecanStreet_RawData\\\"','shared':'\\\"PecanStreet_SharedData\\\"'}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "pecan.set_url(db_url)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "pecan.eng" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "Engine(postgresql://stomkins:**@db.wiki-energy.org:5432/postgres)" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "schema = 'shared'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "tables= pecan.get_table_names(schema)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "[i,a] = pecan.get_table_dataids_and_column_names(schema,str(tables[3]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "query = 'select from \"{0}\".{1} where dataid={2}'.format(pecan.schema_names[schema], tables[3],i[0])\n", "df = pecan.get_dataframe(query).fillna(0)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "temp = pecan.clean_dataframe(df,schema,[])\n", "test = pecan.get_month_traces(schema,tables[0],i[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "#sys.path.append('../')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "homes_with_cars = pecan.get_dataids_with_real_values(schema,tables[3],'car1')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "cars_and_ev = pecan.generate_traces_for_appliances_by_dataids(schema, tables[3], ['car1','air1'], homes_with_cars)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=26\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=624" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=661" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=1714" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=1782" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=1953" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=2470" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=2638" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=2769" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=2814" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3044" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3192" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3367" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3482" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3723" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=3795" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4135" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4352" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4373" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4505" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4526" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4641" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4767" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4957" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=4998" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=5109" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=5357" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=6139" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=6836" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=6910" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=6941" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=7850" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=7863" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=7875" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=7940" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=8046" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=8142" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=8197" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=8645" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=8669" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9484" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9499" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9609" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9729" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9830" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9932" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "select car1,air1,localminute from \"PecanStreet_SharedData\".validated_05_2014 where dataid=9934" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "utils.pickle_object(cars_and_ev,'ev_and_air_{}'.format(tables[3]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "print os.path.relpath(os.getcwd(),'data/')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "..\n" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "utils.pickle_object(cars_and_ev,'3923')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "air = pecan.get_app_traces_all(schema,tables[3],'air1',i)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "utils.pickle_object(test_all,'ev_{}'.format(tables[0]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "test_all[0].series" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'test_all' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-bd72202c1ac4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'test_all' is not defined" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "os.path.relpath('data/ev_validated_01_2014.p')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ "'data/ev_validated_01_2014.p'" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "os.getcwd()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "'/Users/sabina/wikienergy/proto/dataset_adapter_notebooks'" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "os.path.abspath(os.path.join(os.path.dirname( '' ), '../..','data/'))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "'/Users/sabina/wikienergy/data'" ] } ], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
fonnesbeck/PyMC3_Oslo	notebooks/6. Model Checking.ipynb	1	26142	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Checking\n", "\n", "After running an MCMC simulation, `sample` returns a `MutliTrace` object containing the samples for all the stochastic and deterministic random variables. The final step in Bayesian computation is model checking, in order to ensure that inferences derived from your sample are valid. There are two components to model checking:\n", "\n", "1. Convergence diagnostics\n", "2. Goodness of fit\n", "\n", "Convergence diagnostics are intended to detect lack of convergence in the Markov chain Monte Carlo sample; it is used to ensure that you have not halted your sampling too early. However, a converged model is not guaranteed to be a good model. The second component of model checking, goodness of fit, is used to check the internal validity of the model, by comparing predictions from the model to the data used to fit the model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convergence Diagnostics\n", "\n", "Valid inferences from sequences of MCMC samples are based on the\n", "assumption that the samples are derived from the true posterior\n", "distribution of interest. Theory guarantees this condition as the number\n", "of iterations approaches infinity. It is important, therefore, to\n", "determine the minimum number of samples required to ensure a reasonable\n", "approximation to the target posterior density. Unfortunately, no\n", "universal threshold exists across all problems, so convergence must be\n", "assessed independently each time MCMC estimation is performed. The\n", "procedures for verifying convergence are collectively known as\n", "convergence diagnostics.\n", "\n", "One approach to analyzing convergence is analytical, whereby the\n", "variance of the sample at different sections of the chain are compared\n", "to that of the limiting distribution. These methods use distance metrics\n", "to analyze convergence, or place theoretical bounds on the sample\n", "variance, and though they are promising, they are generally difficult to\n", "use and are not prominent in the MCMC literature. More common is a\n", "statistical approach to assessing convergence. With this approach,\n", "rather than considering the properties of the theoretical target\n", "distribution, only the statistical properties of the observed chain are\n", "analyzed. Reliance on the sample alone restricts such convergence\n", "criteria to heuristics. As a result, convergence cannot be guaranteed.\n", "Although evidence for lack of convergence using statistical convergence\n", "diagnostics will correctly imply lack of convergence in the chain, the\n", "absence of such evidence will not guarantee convergence in the chain.\n", "Nevertheless, negative results for one or more criteria may provide some\n", "measure of assurance to users that their sample will provide valid\n", "inferences.\n", "\n", "For most simple models, convergence will occur quickly, sometimes within\n", "a the first several hundred iterations, after which all remaining\n", "samples of the chain may be used to calculate posterior quantities. For\n", "more complex models, convergence requires a significantly longer burn-in\n", "period; sometimes orders of magnitude more samples are needed.\n", "Frequently, lack of convergence will be caused by poor mixing. \n", "Recall that mixing refers to the degree to which the Markov\n", "chain explores the support of the posterior distribution. Poor mixing\n", "may stem from inappropriate proposals (if one is using the\n", "Metropolis-Hastings sampler) or from attempting to estimate models with\n", "highly correlated variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import seaborn as sns; sns.set_context('notebook')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import exp, Normal, Binomial, sample, Model\n", "\n", "# Samples for each dose level\n", "n = 5 * np.ones(4, dtype=int)\n", "# Log-dose\n", "dose = np.array([-.86, -.3, -.05, .73])\n", "deaths = np.array([0, 1, 3, 5])\n", "\n", "def invlogit(x):\n", " return exp(x) / (1 + exp(x))\n", "\n", "with Model() as bioassay_model:\n", "\n", " # Logit-linear model parameters\n", " alpha = Normal('alpha', 0, 0.01)\n", " beta = Normal('beta', 0, 0.01)\n", "\n", " # Calculate probabilities of death\n", " theta = invlogit(alpha + beta * dose)\n", "\n", " # Data likelihood\n", " deaths = Binomial('deaths', n=n, p=theta, observed=deaths)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import Metropolis\n", "\n", "with bioassay_model:\n", " step = Metropolis(scaling=0.0001)\n", " bioassay_trace = sample(1000, step=step)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import traceplot\n", "\n", "traceplot(bioassay_trace[500:], varnames=['alpha'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Informal Methods\n", "\n", "The most straightforward approach for assessing convergence is based on\n", "simply plotting and inspecting traces and histograms of the observed\n", "MCMC sample. If the trace of values for each of the stochastics exhibits\n", "asymptotic behavior over the last $m$ iterations, this may be\n", "satisfactory evidence for convergence. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with bioassay_model:\n", " bioassay_trace = sample(10000)\n", " \n", "traceplot(bioassay_trace[9000:], varnames=['beta'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A similar approach involves\n", "plotting a histogram for every set of $k$ iterations (perhaps 50-100)\n", "beyond some burn in threshold $n$; if the histograms are not visibly\n", "different among the sample intervals, this may be considered some evidence for\n", "convergence. Note that such diagnostics should be carried out for each\n", "stochastic estimated by the MCMC algorithm, because convergent behavior\n", "by one variable does not imply evidence for convergence for other\n", "variables in the analysis. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "beta_trace = bioassay_trace['beta']\n", "\n", "fig, axes = plt.subplots(2, 5, figsize=(14,6))\n", "axes = axes.ravel()\n", "for i in range(10):\n", " axes[i].hist(beta_trace[500i:500(i+1)])\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An extension of this approach can be taken\n", "when multiple parallel chains are run, rather than just a single, long\n", "chain. In this case, the final values of $c$ chains run for $n$\n", "iterations are plotted in a histogram; just as above, this is repeated\n", "every $k$ iterations thereafter, and the histograms of the endpoints are\n", "plotted again and compared to the previous histogram. This is repeated\n", "until consecutive histograms are indistinguishable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another ad hoc method for detecting lack of convergence is to examine\n", "the traces of several MCMC chains initialized with different starting\n", "values. Overlaying these traces on the same set of axes should (if\n", "convergence has occurred) show each chain tending toward the same\n", "equilibrium value, with approximately the same variance. Recall that the\n", "tendency for some Markov chains to converge to the true (unknown) value\n", "from diverse initial values is called ergodicity. This property is\n", "guaranteed by the reversible chains constructed using MCMC, and should\n", "be observable using this technique. Again, however, this approach is\n", "only a heuristic method, and cannot always detect lack of convergence,\n", "even though chains may appear ergodic." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with bioassay_model:\n", " \n", " bioassay_trace = sample(1000, njobs=2, start=[{'alpha':0.5}, {'alpha':5}])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bioassay_trace.get_values('alpha', chains=0)[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(bioassay_trace.get_values('alpha', chains=0)[:200], 'r--')\n", "plt.plot(bioassay_trace.get_values('alpha', chains=1)[:200], 'k--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A principal reason that evidence from informal techniques cannot\n", "guarantee convergence is a phenomenon called *metastability. Chains may\n", "appear to have converged to the true equilibrium value, displaying\n", "excellent qualities by any of the methods described above. However,\n", "after some period of stability around this value, the chain may suddenly\n", "move to another region of the parameter space. This period\n", "of metastability can sometimes be very long, and therefore escape\n", "detection by these convergence diagnostics. Unfortunately, there is no\n", "statistical technique available for detecting metastability.\n", "\n", "### Formal Methods\n", "\n", "Along with the ad hoc* techniques described above, a number of more\n", "formal methods exist which are prevalent in the literature. These are\n", "considered more formal because they are based on existing statistical\n", "methods, such as time series analysis.\n", "\n", "PyMC currently includes three formal convergence diagnostic methods. The\n", "first, proposed by [Geweke (1992)](http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1177011446), is a time-series approach that\n", "compares the mean and variance of segments from the beginning and end of\n", "a single chain.\n", "\n", "$$z = \\frac{\\bar{\\theta}_a - \\bar{\\theta}_b}{\\sqrt{S_a(0) + S_b(0)}}$$\n", "\n", "where $a$ is the early interval and $b$ the late interval, and $S_i(0)$ is the spectral density estimate at zero frequency for chain segment $i$. If the\n", "z-scores (theoretically distributed as standard normal variates) of\n", "these two segments are similar, it can provide evidence for convergence.\n", "PyMC calculates z-scores of the difference between various initial\n", "segments along the chain, and the last 50% of the remaining chain. If\n", "the chain has converged, the majority of points should fall within 2\n", "standard deviations of zero.\n", "\n", "In PyMC, diagnostic z-scores can be obtained by calling the `geweke` function. It\n", "accepts either (1) a single trace, (2) a Node or Stochastic object, or\n", "(4) an entire Model object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import geweke\n", "\n", "with bioassay_model:\n", " tr = sample(2000)\n", " \n", "z = geweke(tr, intervals=15)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.scatter(z['alpha'].T)\n", "plt.hlines([-1,1], 0, 1000, linestyles='dotted')\n", "plt.xlim(0, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The arguments expected are the following:\n", "\n", "- `x` : The trace of a variable.\n", "- `first` : The fraction of series at the beginning of the trace.\n", "- `last` : The fraction of series at the end to be compared with the section at the beginning.\n", "- `intervals` : The number of segments.\n", "\n", "Plotting the output displays the scores in series, making it is easy to\n", "see departures from the standard normal assumption." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A second convergence diagnostic provided by PyMC is the Gelman-Rubin\n", "statistic [Gelman and Rubin (1992)](http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1177011136). This diagnostic uses multiple chains to\n", "check for lack of convergence, and is based on the notion that if\n", "multiple chains have converged, by definition they should appear very\n", "similar to one another; if not, one or more of the chains has failed to\n", "converge.\n", "\n", "The Gelman-Rubin diagnostic uses an analysis of variance approach to\n", "assessing convergence. That is, it calculates both the between-chain\n", "varaince (B) and within-chain varaince (W), and assesses whether they\n", "are different enough to worry about convergence. Assuming $m$ chains,\n", "each of length $n$, quantities are calculated by:\n", "\n", "$$\\begin{align}B &= \\frac{n}{m-1} \\sum_{j=1}^m (\\bar{\\theta}_{.j} - \\bar{\\theta}_{..})^2 \\\\\n", "W &= \\frac{1}{m} \\sum_{j=1}^m \\left[ \\frac{1}{n-1} \\sum_{i=1}^n (\\theta_{ij} - \\bar{\\theta}_{.j})^2 \\right]\n", "\\end{align}$$\n", "\n", "for each scalar estimand $\\theta$. Using these values, an estimate of\n", "the marginal posterior variance of $\\theta$ can be calculated:\n", "\n", "$$\\hat{\\text{Var}}(\\theta \| y) = \\frac{n-1}{n} W + \\frac{1}{n} B$$\n", "\n", "Assuming $\\theta$ was initialized to arbitrary starting points in each\n", "chain, this quantity will overestimate the true marginal posterior\n", "variance. At the same time, $W$ will tend to underestimate the\n", "within-chain variance early in the sampling run. However, in the limit\n", "as $n \\rightarrow \n", "\\infty$, both quantities will converge to the true variance of $\\theta$.\n", "In light of this, the Gelman-Rubin statistic monitors convergence using\n", "the ratio:\n", "\n", "$$\\hat{R} = \\sqrt{\\frac{\\hat{\\text{Var}}(\\theta \| y)}{W}}$$\n", "\n", "This is called the potential scale reduction, since it is an estimate of\n", "the potential reduction in the scale of $\\theta$ as the number of\n", "simulations tends to infinity. In practice, we look for values of\n", "$\\hat{R}$ close to one (say, less than 1.1) to be confident that a\n", "particular estimand has converged. In PyMC, the function\n", "`gelman_rubin` will calculate $\\hat{R}$ for each stochastic node in\n", "the passed model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import gelman_rubin\n", "\n", "gelman_rubin(bioassay_trace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the best results, each chain should be initialized to highly\n", "dispersed starting values for each stochastic node.\n", "\n", "By default, when calling the `forestplot` function using nodes with\n", "multiple chains, the $\\hat{R}$ values will be plotted alongside the\n", "posterior intervals." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import forestplot\n", "\n", "forestplot(bioassay_trace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autocorrelation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import autocorrplot\n", "\n", "autocorrplot(tr);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bioassay_trace['alpha'].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import effective_n\n", "\n", "effective_n(bioassay_trace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Goodness of Fit\n", "\n", "Checking for model convergence is only the first step in the evaluation\n", "of MCMC model outputs. It is possible for an entirely unsuitable model\n", "to converge, so additional steps are needed to ensure that the estimated\n", "model adequately fits the data. One intuitive way of evaluating model\n", "fit is to compare model predictions with the observations used to fit\n", "the model. In other words, the fitted model can be used to simulate\n", "data, and the distribution of the simulated data should resemble the\n", "distribution of the actual data.\n", "\n", "Fortunately, simulating data from the model is a natural component of\n", "the Bayesian modelling framework. Recall, from the discussion on\n", "imputation of missing data, the posterior predictive distribution:\n", "\n", "$$p(\\tilde{y}\|y) = \\int p(\\tilde{y}\|\\theta) f(\\theta\|y) d\\theta$$\n", "\n", "Here, $\\tilde{y}$ represents some hypothetical new data that would be\n", "expected, taking into account the posterior uncertainty in the model\n", "parameters. Sampling from the posterior predictive distribution is easy\n", "in PyMC. The code looks identical to the corresponding data stochastic,\n", "with two modifications: (1) the node should be specified as\n", "deterministic and (2) the statistical likelihoods should be replaced by\n", "random number generators. Consider the `gelman_bioassay` example, \n", "where deaths are modeled as a binomial random variable for which\n", "the probability of death is a logit-linear function of the dose of a\n", "particular drug." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import Normal, Binomial, Deterministic, invlogit\n", "\n", "# Samples for each dose level\n", "n = 5 np.ones(4, dtype=int)\n", "# Log-dose\n", "dose = np.array([-.86, -.3, -.05, .73])\n", "\n", "with Model() as model:\n", "\n", " # Logit-linear model parameters\n", " alpha = Normal('alpha', 0, 0.01)\n", " beta = Normal('beta', 0, 0.01)\n", "\n", " # Calculate probabilities of death\n", " theta = Deterministic('theta', invlogit(alpha + beta * dose))\n", "\n", " # Data likelihood\n", " deaths = Binomial('deaths', n=n, p=theta, observed=[0, 1, 3, 5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The posterior predictive distribution of deaths uses the same functional\n", "form as the data likelihood, in this case a binomial stochastic. Here is\n", "the corresponding sample from the posterior predictive distribution:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with model:\n", " \n", " deaths_sim = Binomial('deaths_sim', n=n, p=theta, shape=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the observed stochastic `Binomial` has been replaced with a stochastic node that is identical in every respect to `deaths`, except that its values are not fixed to be the observed data -- they are left to vary according to the values of the fitted parameters.\n", "\n", "The degree to which simulated data correspond to observations can be evaluated in at least two ways. First, these quantities can simply be compared visually. This allows for a qualitative comparison of model-based replicates and observations. If there is poor fit, the true value of the data may appear in the tails of the histogram of replicated data, while a good fit will tend to show the true data in high-probability regions of the posterior predictive distribution. The Matplot package in PyMC provides an easy way of producing such plots, via the `gof_plot` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with model:\n", " \n", " gof_trace = sample(2000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pymc3 import forestplot\n", "\n", "forestplot(gof_trace, varnames=['deaths_sim'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise: Meta-analysis of beta blocker effectiveness\n", "\n", "Carlin (1992) considers a Bayesian approach to meta-analysis, and includes the following examples of 22 trials of beta-blockers to prevent mortality after myocardial infarction.\n", "\n", "In a random effects meta-analysis we assume the true effect (on a log-odds scale) $d_i$ in a trial $i$\n", "is drawn from some population distribution. Let $r^C_i$ denote number of events in the control group in trial $i$,\n", "and $r^T_i$ denote events under active treatment in trial $i$. Our model is:\n", "\n", "$$\\begin{aligned}\n", "r^C_i &\\sim \\text{Binomial}\\left(p^C_i, n^C_i\\right) \\\\\n", "r^T_i &\\sim \\text{Binomial}\\left(p^T_i, n^T_i\\right) \\\\\n", "\\text{logit}\\left(p^C_i\\right) &= \\mu_i \\\\\n", "\\text{logit}\\left(p^T_i\\right) &= \\mu_i + \\delta_i \\\\\n", "\\delta_i &\\sim \\text{Normal}(d, t) \\\\\n", "\\mu_i &\\sim \\text{Normal}(m, s)\n", "\\end{aligned}$$\n", "\n", "We want to make inferences about the population effect $d$, and the predictive distribution for the effect $\\delta_{\\text{new}}$ in a new trial. Build a model to estimate these quantities in PyMC, and (1) use convergence diagnostics to check for convergence and (2) use posterior predictive checks to assess goodness-of-fit.\n", "\n", "Here are the data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r_t_obs = [3, 7, 5, 102, 28, 4, 98, 60, 25, 138, 64, 45, 9, 57, 25, 33, 28, 8, 6, 32, 27, 22]\n", "n_t_obs = [38, 114, 69, 1533, 355, 59, 945, 632, 278,1916, 873, 263, 291, 858, 154, 207, 251, 151, 174, 209, 391, 680]\n", "r_c_obs = [3, 14, 11, 127, 27, 6, 152, 48, 37, 188, 52, 47, 16, 45, 31, 38, 12, 6, 3, 40, 43, 39]\n", "n_c_obs = [39, 116, 93, 1520, 365, 52, 939, 471, 282, 1921, 583, 266, 293, 883, 147, 213, 122, 154, 134, 218, 364, 674]\n", "N = len(n_c_obs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write your answer here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science. A Review Journal of the Institute of Mathematical Statistics, 457–472.\n", "\n", "Geweke, J., Berger, J. O., & Dawid, A. P. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In Bayesian Statistics 4.\n", "\n", "Brooks, S. P., Catchpole, E. A., & Morgan, B. J. T. (2000). Bayesian Animal Survival Estimation. Statistical Science. A Review Journal of the Institute of Mathematical Statistics, 15(4), 357–376. doi:10.1214/ss/1177010123\n", "\n", "Gelman, A., Meng, X., & Stern, H. (1996). Posterior predicitive assessment of model fitness via realized discrepencies with discussion. Statistica Sinica, 6, 733–807.\n", "\n", "Raftery, A., & Lewis, S. (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science. A Review Journal of the Institute of Mathematical Statistics, 7, 493–497.\n", "\n", "[CrossValidated: How to use scikit-learn's cross validation functions on multi-label classifiers](http://stats.stackexchange.com/questions/65828/how-to-use-scikit-learns-cross-validation-functions-on-multi-label-classifiers)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }	cc0-1.0
aylward/ITKTubeTK	examples/Demo-ImageRegistration.ipynb	1	3033	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is intended to demonstrate how registration methods of ITKTubeTK operate.\n", "\n", "There are many other (more effective) registration methods available in other pages (e.g., NiftiReg, ANTS). However, the method in ITKTubeTK is often sufficient and is easy to control. So, if it works for your problem..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import itk\n", "from itk import TubeTK as ttk\n", "\n", "from itkwidgets import view\n", "\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "im1 = itk.imread(\"Data/MRI-Cases/mra.mha\", itk.F)\n", "im2 = itk.imread(\"Data/MRI-Cases/mri_t1_sag.mha\", itk.F)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ImageType = itk.Image[itk.F,3]\n", "\n", "reg2To1 = ttk.RegisterImages[ImageType].New(FixedImage=im1, MovingImage=im2)\n", "reg2To1.SetReportProgress(True)\n", "reg2To1.SetExpectedOffsetMagnitude(5)\n", "reg2To1.SetExpectedRotationMagnitude(0.005)\n", "reg2To1.SetRegistration(\"PIPELINE_AFFINE\")\n", "reg2To1.SetMetric(\"MATTES_MI_METRIC\")\n", "reg2To1.Update()\n", "im2Reg = reg2To1.ResampleImage()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "im2RegMath = ttk.ImageMath[ImageType,ImageType].New(Input=im2Reg)\n", "im2RegMath.IntensityCorrection(100,4,im1)\n", "im2RegInt = im2RegMath.GetOutput()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e67d7644e304b389c8cd7fec1570e0a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cb12 = itk.CheckerBoardImageFilter[ImageType].New(Input1=im1, Input2=im2RegInt)\n", "cb12.Update()\n", "im12 = ImageType.New()\n", "im12 = cb12.GetOutput()\n", "view(im12)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
camilogavo/Colombian_Energy_Forecasting	Revisar Corridas.ipynb	1	3122834	null	mit
letsgoexploring/teaching	winter2017/econ129/python/Econ129_Class_09.ipynb	1	15117	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Class 9: The Solow growth model\n", "\n", "The Solow growth model is at the core of modern theories of growth and business cycles. The Solow model is a model of exogenous growth: long-run growth arises in the model as a consequence of exogenous growth in the labor supply and total factor productivity. The Solow model, like many other macroeconomic models, is a time series model.\n", "\n", "## The Solow model without exogenous growth\n", "\n", "For the moment, let's disregard population and total factor productivity growth and assume that equilibrium in a closed economy is described by the following four equations:\n", "\n", "\\begin{align}\n", "Y_t & = A K_t^{\\alpha} \\tag{1}\\\\\n", "C_t & = (1-s)Y_t \\tag{2}\\\\\n", "Y_t & = C_t + I_t \\tag{3}\\\\\n", "K_{t+1} & = I_t + ( 1- \\delta)K_t \\tag{4}\\\\\n", "\\end{align}\n", "\n", "Equation (1) is the production function. Equation (2) is the consumption function where $s$ denotes the exogenously given saving rate. Equation (3) is the aggregate market clearing condition. Finally, Equation (4) is the capital evolution equation specifying that capital in yeat $t+1$ is the sum of newly created capital $I_t$ and the capital stock from year $t$ that has not depreciated $(1-\\delta)K_t$.\n", "\n", "Combine Equations (1) through (4) to eliminate $C_t$, $I_t$, and $Y_t$ and obtain a single-variable recurrence relation for $K_{t+1}$:\n", "\\begin{align}\n", "K_{t+1} & = sAK_t^{\\alpha} + ( 1- \\delta)K_t \\tag{5}\n", "\\end{align}\n", "\n", "Given an initial value for capital $K_0 >0$, iterate on Equation (5) to compute the value of the capital stock at some future date $T$. Furthermore, the values of consumption, output, and investment at date $T$ can also be computed using Equations (1) through (3).\n", "\n", "### Simulation\n", "\n", "Simulate the Solow growth model for $t=0\\ldots 100$. For the simulation, assume the following values of the parameters:\n", "\n", "\\begin{align}\n", "A & = 10\\\\\n", "\\alpha & = 0.35\\\\\n", "s & = 0.15\\\\\n", "\\delta & = 0.1\n", "\\end{align}\n", "\n", "Furthermore, suppose that the initial value of capital is:\n", "\n", "\\begin{align}\n", "K_0 & = 20\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize parameters for the simulation (A, s, T, delta, alpha, K0)\n", "\n", "\n", "# Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0\n", "\n", "\n", "# Compute all capital values by iterating over t from 0 through T\n", " \n", "\n", "# Print the value of capital at dates 0 and T\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Store the simulated capital data in a pandas DataFrame called data\n", "\n", "\n", "# Print the first five rows of the DataFrame\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create columns in the DataFrame to store computed values of the other endogenous variables\n", "\n", "\n", "# Print the first row of the DataFrame\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the last row of the DataFrame\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a 2x2 grid of plots of capital, output, consumption, and investment\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(data['capital'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Solow model with exogenous population growth\n", "\n", "\n", "Now, let's suppose that production is a function of the supply of labor $L_t$:\n", "\n", "\\begin{align}\n", "Y_t & = AK_t^{\\alpha} L_t^{1-\\alpha}\\tag{6}\n", "\\end{align}\n", "\n", "The supply of labor grows at an exogenously determined rate $n$ and so it's value is determined recursively by a first-order difference equation:\n", "\n", "\\begin{align}\n", "L_{t+1} & = (1+n) L_t \\tag{7}\n", "\\end{align}\n", "\n", "The rest of the economy is characterized by the same equations as before:\n", "\n", "\\begin{align}\n", "C_t & = (1-s)Y_t \\tag{8}\\\\\n", "Y_t & = C_t + I_t \\tag{9}\\\\\n", "K_{t+1} & = I_t + ( 1- \\delta)K_t \\tag{10}\\\\\n", "\\end{align}\n", "\n", "Combine Equations (6), (8), (9), and (10) to eliminate $C_t$, $I_t$, and $Y_t$ and obtain a recurrence relation specifying $K_{t+1}$ as a funtion of $K_t$ and $L_t$:\n", "\\begin{align}\n", "K_{t+1} & = sAK_t^{\\alpha}L_t^{1-\\alpha} + ( 1- \\delta)K_t \\tag{11}\n", "\\end{align}\n", "\n", "Given an initial values for capital and labor, Equations (7) and (11) can be iterated on to compute the values of the capital stock and labor supply at some future date $T$. Furthermore, the values of consumption, output, and investment at date $T$ can also be computed using Equations (6), (8), (9), and (10).\n", "\n", "### Simulation\n", "\n", "Simulate the Solow growth model with exogenous labor growth for $t=0\\ldots 100$. For the simulation, assume the following values of the parameters:\n", "\n", "\\begin{align}\n", "A & = 10\\\\\n", "\\alpha & = 0.35\\\\\n", "s & = 0.15\\\\\n", "\\delta & = 0.1\\\\\n", "n & = 0.01\n", "\\end{align}\n", "\n", "Furthermore, suppose that the initial values of capital and labor are:\n", "\n", "\\begin{align}\n", "K_0 & = 20\\\\\n", "L_0 & = 1\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize parameters for the simulation (A, s, T, delta, alpha, n, K0, L0)\n", "\n", "\n", "\n", "# Initialize a variable called labor as a (T+1)x1 array of zeros and set first value to L0\n", "\n", "\n", "# Compute all labor values by iterating over t from 0 through T\n", " \n", " \n", "# Plot the simulated labor series\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0\n", "\n", "\n", "# Compute all capital values by iterating over t from 0 through T\n", "\n", " \n", "\n", "# Plot the simulated capital series\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Store the simulated capital data in a pandas DataFrame called data_labor\n", "\n", "\n", "# Print the first five rows of the data_labor\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create columns in the DataFrame to store computed values of the other endogenous variables\n", "\n", "# Print the first five rows of data_labor\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create columns in the DataFrame to store capital per worker, output per worker, consumption per worker, and investment per worker\n", "\n", "\n", "# Print the first five rows of data_labor\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a 2x2 grid of plots of capital, output, consumption, and investment\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a 2x2 grid of plots of capital per worker, outputper worker, consumption per worker, and investment per worker\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### An alternative approach\n", "\n", "Suppose that we wanted to simulate the Solow model with different parameter values so that we could compare the simulations. Since we'd be doing the same basic steps multiple times using different numbers, it would make sense to define a function so that we could avoid repetition.\n", "\n", "The code below defines a function called `solow_example()` that simulates the Solow model with exogenous labor growth. `solow_example()` takes as arguments the parameters of the Solow model $A$, $\\alpha$, $\\delta$, $s$, and $n$; the initial values $K_0$ and $L_0$; and the number of simulation periods $T$. `solow_example()` returns a Pandas DataFrame with computed values for aggregate and per worker quantities." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def solow_example(A,alpha,delta,s,n,K0,L0,T):\n", " '''Returns DataFrame with simulated values for a Solow model with labor growth and constant TFP'''\n", " \n", " # Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to k0\n", " capital = np.zeros(T+1)\n", " capital[0] = K0\n", " \n", " # Initialize a variable called labor as a (T+1)x1 array of zeros and set first value to l0\n", " labor = np.zeros(T+1)\n", " labor[0] = L0\n", "\n", "\n", " # Compute all capital and labor values by iterating over t from 0 through T\n", " for t in np.arange(T):\n", " labor[t+1] = (1+n)labor[t]\n", " capital[t+1] = sAcapital[t]alphalabor[t]*(1-alpha) + (1-delta)capital[t]\n", " \n", " # Store the simulated capital df in a pandas DataFrame called data\n", " df = pd.DataFrame({'capital':capital,'labor':labor})\n", " \n", " # Create columns in the DataFrame to store computed values of the other endogenous variables\n", " df['output'] = df['capital']*alphadf['labor']*(1-alpha)\n", " df['consumption'] = (1-s)df['output']\n", " df['investment'] = df['output'] - df['consumption']\n", " \n", " # Create columns in the DataFrame to store capital per worker, output per worker, consumption per worker, and investment per worker\n", " df['capital_pw'] = df['capital']/df['labor']\n", " df['output_pw'] = df['output']/df['labor']\n", " df['consumption_pw'] = df['consumption']/df['labor']\n", " df['investment_pw'] = df['investment']/df['labor']\n", " \n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `solow_example()` defined, we can redo the previous exercise quickly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create the DataFrame with simulated values\n", "df = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100)\n", "\n", "# Create a 2x2 grid of plots of the capital per worker, outputper worker, consumption per worker, and investment per worker\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(df['capital_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital per worker')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(df['output_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output per worker')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(df['consumption_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption per worker')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(df['investment_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Investment per worker')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`solow_example()` can be used to perform multiple simulations. For example, suppose we want to see the effect of having two different initial values of capital: $k_0 = 20$ and $k_0'=10$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df1 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100)\n", "df2 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a 2x2 grid of plots of the capital per worker, outputper worker, consumption per worker, and investment per worker\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(2,2,1)\n", "ax.plot(df1['capital_pw'],lw=3)\n", "ax.plot(df2['capital_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Capital per worker')\n", "\n", "ax = fig.add_subplot(2,2,2)\n", "ax.plot(df1['output_pw'],lw=3)\n", "ax.plot(df2['output_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Output per worker')\n", "\n", "ax = fig.add_subplot(2,2,3)\n", "ax.plot(df1['consumption_pw'],lw=3)\n", "ax.plot(df2['consumption_pw'],lw=3)\n", "ax.grid()\n", "ax.set_title('Consumption per worker')\n", "\n", "ax = fig.add_subplot(2,2,4)\n", "ax.plot(df1['investment_pw'],lw=3,label='$k_0=20$')\n", "ax.plot(df2['investment_pw'],lw=3,label='$k_0=10$')\n", "ax.grid()\n", "ax.set_title('Investment per worker')\n", "ax.legend(loc='lower right')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
jorisvandenbossche/2015-EuroScipy-pandas-tutorial	solved - 03b - Some more advanced indexing.ipynb	1	34013	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced indexing" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "try:\n", " import seaborn\n", "except ImportError:\n", " pass\n", "\n", "pd.options.display.max_rows = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset is borrowed from the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!). You can download these data from here: [`titles.csv`](https://drive.google.com/file/d/0B3G70MlBnCgKa0U4WFdWdGdVOFU/view?usp=sharing) and [`cast.csv`](https://drive.google.com/file/d/0B3G70MlBnCgKRzRmTWdQTUdjNnM/view?usp=sharing) and put them in the `/data` folder." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Suuri illusioni</td>\n", " <td>1985</td>\n", " <td>Homo $</td>\n", " <td>actor</td>\n", " <td>Guests</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Gangsta Rap: The Glockumentary</td>\n", " <td>2007</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Menace II Society</td>\n", " <td>1993</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Lew-Loc</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Porndogs: The Adventures of Sadie</td>\n", " <td>2009</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Bosco</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Stop Pepper Palmer</td>\n", " <td>2014</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type character n\n", "0 Suuri illusioni 1985 Homo $ actor Guests 22\n", "1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", "2 Menace II Society 1993 Too $hort actor Lew-Loc 27\n", "3 Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n", "4 Stop Pepper Palmer 2014 Too $hort actor Himself NaN" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cast = pd.read_csv('data/cast.csv')\n", "cast.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Rising Son</td>\n", " <td>1990</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Ashes of Kukulcan</td>\n", " <td>2016</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>The Thousand Plane Raid</td>\n", " <td>1969</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Crucea de piatra</td>\n", " <td>1993</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>The 86</td>\n", " <td>2015</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "0 The Rising Son 1990\n", "1 Ashes of Kukulcan 2016\n", "2 The Thousand Plane Raid 1969\n", "3 Crucea de piatra 1993\n", "4 The 86 2015" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titles = pd.read_csv('data/titles.csv')\n", "titles.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting columns as the index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why is it useful to have an index?\n", "\n", "- Giving meaningful labels to your data -> easier to remember which data are where\n", "- Unleash some powerful methods, eg with a DatetimeIndex for time series\n", "- Easier and faster selection of data\n", "\n", "It is this last one we are going to explore here!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the `title` column as the index:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = cast.set_index('title')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Suuri illusioni</th>\n", " <td>1985</td>\n", " <td>Homo $</td>\n", " <td>actor</td>\n", " <td>Guests</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Gangsta Rap: The Glockumentary</th>\n", " <td>2007</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Menace II Society</th>\n", " <td>1993</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Lew-Loc</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>Porndogs: The Adventures of Sadie</th>\n", " <td>2009</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Bosco</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>Stop Pepper Palmer</th>\n", " <td>2014</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year name type character n\n", "title \n", "Suuri illusioni 1985 Homo $ actor Guests 22\n", "Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", "Menace II Society 1993 Too $hort actor Lew-Loc 27\n", "Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n", "Stop Pepper Palmer 2014 Too $hort actor Himself NaN" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of doing:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 476 ms, sys: 16 ms, total: 492 ms\n", "Wall time: 495 ms\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11638</th>\n", " <td>Macbeth</td>\n", " <td>2015</td>\n", " <td>Darren Adamson</td>\n", " <td>actor</td>\n", " <td>Soldier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>20153</th>\n", " <td>Macbeth</td>\n", " <td>1916</td>\n", " <td>Spottiswoode Aitken</td>\n", " <td>actor</td>\n", " <td>Duncan</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>23106</th>\n", " <td>Macbeth</td>\n", " <td>1948</td>\n", " <td>Robert Alan</td>\n", " <td>actor</td>\n", " <td>Third Murderer</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>24080</th>\n", " <td>Macbeth</td>\n", " <td>2016</td>\n", " <td>John Albasiny</td>\n", " <td>actor</td>\n", " <td>Doctor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>34024</th>\n", " <td>Macbeth</td>\n", " <td>1948</td>\n", " <td>William Alland</td>\n", " <td>actor</td>\n", " <td>Second Murderer</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3288130</th>\n", " <td>Macbeth</td>\n", " <td>1998</td>\n", " <td>Jessica Werbin</td>\n", " <td>actress</td>\n", " <td>Lady Macduff</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3298214</th>\n", " <td>Macbeth</td>\n", " <td>2014</td>\n", " <td>Finty Williams</td>\n", " <td>actress</td>\n", " <td>Lady Macduff</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3301599</th>\n", " <td>Macbeth</td>\n", " <td>2006</td>\n", " <td>Jamie-Lee Wilson</td>\n", " <td>actress</td>\n", " <td>Female Constable</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>3302941</th>\n", " <td>Macbeth</td>\n", " <td>1998</td>\n", " <td>Dawn Winarski</td>\n", " <td>actress</td>\n", " <td>Lady Macbeth</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3311842</th>\n", " <td>Macbeth</td>\n", " <td>2006</td>\n", " <td>Edwina Wren</td>\n", " <td>actress</td>\n", " <td>Malcolm's Girl</td>\n", " <td>35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>329 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " title year name type character n\n", "11638 Macbeth 2015 Darren Adamson actor Soldier NaN\n", "20153 Macbeth 1916 Spottiswoode Aitken actor Duncan 4\n", "23106 Macbeth 1948 Robert Alan actor Third Murderer NaN\n", "24080 Macbeth 2016 John Albasiny actor Doctor NaN\n", "34024 Macbeth 1948 William Alland actor Second Murderer 18\n", "... ... ... ... ... ... ..\n", "3288130 Macbeth 1998 Jessica Werbin actress Lady Macduff NaN\n", "3298214 Macbeth 2014 Finty Williams actress Lady Macduff NaN\n", "3301599 Macbeth 2006 Jamie-Lee Wilson actress Female Constable 39\n", "3302941 Macbeth 1998 Dawn Winarski actress Lady Macbeth 2\n", "3311842 Macbeth 2006 Edwina Wren actress Malcolm's Girl 35\n", "\n", "[329 rows x 6 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "cast[cast['title'] == 'Hamlet']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can now do:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 188 ms, sys: 4 ms, total: 192 ms\n", "Wall time: 195 ms\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>2015</td>\n", " <td>Darren Adamson</td>\n", " <td>actor</td>\n", " <td>Soldier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>1916</td>\n", " <td>Spottiswoode Aitken</td>\n", " <td>actor</td>\n", " <td>Duncan</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>1948</td>\n", " <td>Robert Alan</td>\n", " <td>actor</td>\n", " <td>Third Murderer</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>2016</td>\n", " <td>John Albasiny</td>\n", " <td>actor</td>\n", " <td>Doctor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>1948</td>\n", " <td>William Alland</td>\n", " <td>actor</td>\n", " <td>Second Murderer</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>1998</td>\n", " <td>Jessica Werbin</td>\n", " <td>actress</td>\n", " <td>Lady Macduff</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>2014</td>\n", " <td>Finty Williams</td>\n", " <td>actress</td>\n", " <td>Lady Macduff</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>2006</td>\n", " <td>Jamie-Lee Wilson</td>\n", " <td>actress</td>\n", " <td>Female Constable</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>1998</td>\n", " <td>Dawn Winarski</td>\n", " <td>actress</td>\n", " <td>Lady Macbeth</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Macbeth</th>\n", " <td>2006</td>\n", " <td>Edwina Wren</td>\n", " <td>actress</td>\n", " <td>Malcolm's Girl</td>\n", " <td>35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>329 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " year name type character n\n", "title \n", "Macbeth 2015 Darren Adamson actor Soldier NaN\n", "Macbeth 1916 Spottiswoode Aitken actor Duncan 4\n", "Macbeth 1948 Robert Alan actor Third Murderer NaN\n", "Macbeth 2016 John Albasiny actor Doctor NaN\n", "Macbeth 1948 William Alland actor Second Murderer 18\n", "... ... ... ... ... ..\n", "Macbeth 1998 Jessica Werbin actress Lady Macduff NaN\n", "Macbeth 2014 Finty Williams actress Lady Macduff NaN\n", "Macbeth 2006 Jamie-Lee Wilson actress Female Constable 39\n", "Macbeth 1998 Dawn Winarski actress Lady Macbeth 2\n", "Macbeth 2006 Edwina Wren actress Malcolm's Girl 35\n", "\n", "[329 rows x 5 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "c.loc['Hamlet']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But you can also have multiple columns as the index, leading to a multi-index or hierarchical index:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c = cast.set_index(['title', 'year'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Suuri illusioni</th>\n", " <th>1985</th>\n", " <td>Homo $</td>\n", " <td>actor</td>\n", " <td>Guests</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Gangsta Rap: The Glockumentary</th>\n", " 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"mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
lisa-1010/smart-tutor	code/student2w2skills.ipynb	1	190327	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import scipy as sp\n", "import six\n", "from matplotlib.pyplot import " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2.\n", "'''\n", "data11 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(50, 55)\n", "last 0.0138820153256 threshold 0.0148\n" ] }, { "data": { "text/plain": [ "(0.013, 0.015)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uewhRVvWU/cyCor0dPvww2iTWro2geOmlaLQ+//woVZx9djzmdPz4OGYWFOUlCoPi4BkI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\nSf3h/wMIWnCGR29A9wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faa756c5310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.0148\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "xlim(30,40)\n", "ylim(0.013,0.015)\n", "\n", "# looks like epoch 54 is good for 0.0139\n", "# looks like epoch 30 is good for 0.0148" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2.\n", "'''\n", "data11 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(50, 47)\n", "last 0.000100714282317 threshold 0.001\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7faa75a24fd0>]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DjI1Bd3fWUUhSNkqf5LdujX75iYmsI5Gk5Vf6JL9lS8yVf/HFrCORpOVX+iRfqcRXAi7B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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faa75aafc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] * 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.001\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "#xlim(15,30)\n", "#ylim(0.0,0.002)\n", "\n", "# looks like epoch 46 is good for 0.0001\n", "# looks like epoch 20 is good for 0.001" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2.\n", "'''\n", "data11 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 61)\n", "last 2.86438517734e-05 threshold 0.001\n" ] }, { "data": { "text/plain": [ "(0.0, 0.002)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Be/s5ZlvonMuADwiKytcBl2N1iB/5502kQMhSqYrQe/bAgw/awnqTJ1swLFoUTIjr7bVw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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7faa75c40e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] * 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.001\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "xlim(15,20)\n", "ylim(0.0,0.002)\n", "\n", "# looks like epoch 43 is good for 0.0001\n", "# looks like epoch 20 is good for 0.001" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2.\n", "'''\n", "data11 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "data21 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 55)\n", "[[ 0.09441637 0.0821061 0.06651635 ..., 0.01403089 0.01401687\n", " 0.01400377]\n", " [ 0.09528936 0.0877762 0.07626525 ..., 0.01350397 0.01348733\n", " 0.01347177]\n", " [ 0.09361646 0.0820771 0.06622386 ..., 0.01369305 0.01367851\n", " 0.01366495]\n", " ..., \n", " [ 0.09256435 0.0779258 0.06302304 ..., 0.01383118 0.01381435\n", " 0.01379868]\n", " [ 0.09070774 0.07706053 0.06221853 ..., 0.01358032 0.01356731\n", " 0.01355519]\n", " [ 0.09316752 0.07823799 0.06175289 ..., 0.01400177 0.0139882\n", " 0.01397556]]\n", "scores shape (50,)\n", "[ 1. 0. 1. 1. 1. 0.84375 0. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 0. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. ]\n" ] } ], "source": [ "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f36517afd10>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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aiSaLgQOAp4l3JCxk7TepFcGagiS1aThqCmuIgADwFPDsQFcmSRrZWrn66FHi9Zm3APsC\nywrNkSSpY1qpYqwHzAV2JG5eu5h4Y1rRbD6SpDYNR/PRbkSN4hTi1Zo7DXRlkqSRrZVocifwAeIZ\nSNsClxHNSEWzpiBJbRqOmsILREAAeIToeJYkjUGtdDT/HjibuDR1d+B/Cs2RJKljWqlibAicBOxA\n9SmpzxeZqcTmI0lqU9HNR28h7ku4APhNGh6OK48kSR3QLJp8guhg3pu4T+G1VF+wc2rB+QJrCpLU\ntiIfiLcY2Id4QupjwPbEY7RvA/Yc6ArbYFCQpDYV2Xz0FPAS0YS0jAgIg1qZJGlkaxYU+ojO5TnA\nT9K07bFPQZLGrGZn/W8DvkI0HR0N7Ap8CziCaEIqms1HktSm4XrJDsD6RP/CCwNdWZsMCpLUpsEG\nhVZuXqsYjnsTJEkd1MpjLgaa7oXAIuKlPNNz816VplX+VhBPYZUkdVgrQeGQmvH3t/Cd2cAkYC/g\nDOI+h4rHgP3S32eBu4AFLaQpSSpYs+ajQ4gb144iDu5dRBA5DLhyHenuDVybhm+n/us7u4DzU/p2\nHkjSCNAsKCwBNiMebfEAcRBfA1zRQrpTgNW58TVEQOnLTTsUuBd4qI38SpIK1Cwo/AG4FPgmERC6\niBrDfS2kuxqYnBuvDQgQl7l+uVkiPT092XCpVKJUKrWwakkaP3p7e+nt7R2y9Fq5bOk84umo2wC7\nEH0Cx67jO4cTNYE5xNvazgQOrllmGf07oGt5SaoktWk4LkndnXgAXi9QAm5s4Ts/BA4Ebk3jc4Aj\ngY2JTuXNgVXtZVWSVLRWgkI38Z7mR4kb2CY3XxyIjuOP1Ex7MDf8OHGHtCRpBGklKHwT+Bpxtn8u\n8ZIdSdIY1Gq701RgGtEP8HRhuenPPgVJatNw9Cm8F/hcWva7xFVE/zrQFUqSRq5WoskiYH/gGqLz\n+A6Gpz/AmoIktanodzRD3Hj2XBp+ieFrPpIkDbNWgsIviLuYtyI6mX9ZaI4kSR3TahXjIGAn4ia2\nn6xj2aFi85EktanIl+xMBCYQtYQPUH0g3tVEH0PRDAqS1KYirz46HvgM8GrigXgQVx7dMtCVSZJG\ntlaiyVzg4tz4y4kX4xTNmoIktanIq4+2AF5P1Bh2SH87AtcNdGWSpJGtWfPRTOJBeK+n+miLPgwK\nkjRmtVLFmAX8tOiM1GHzkSS1aThuXnuRuCT1YOAR4uU4kqQxqJWg8HnisdcfJ969fFKhOZIkdUwr\nQeEZ4C9EjeHPrP1aTUnSGNFKUFgNXAtcCZxMBAhJ0hjUSmfEBsC2wH3Am4GHgOeLzFRiR7MktWk4\nOpo3B84igsK/EPcvSJLGoFaCwgLgW0Qn82XAJYXmSJLUMa0EhQ2AHxOPtvgRsF6hOZIkdUwrQWEC\nsHMa3gmwoV+SxqhWOiN2IZqQtgD+BJwA/KrITCV2NEtSm4p8nwLAFOIVnM8MdAWDYFCQpDYVefXR\nKcASYCnwroGuQJI0ejQLCkcTT0idCZw2PNmRJHVSs6DwLPAC8ARecSRJ40KzoJBvk2rlKiVJ0ijX\nrDPiL8ANaZn9gZvS9DJwVMH5AjuaJaltRV59VCICQO0yZeDmga6wDQYFSWpT0ZekDlQ38FXiprfn\ngQ8Dy3Lzdwfmp/X/D/D3RP9FnkFBkto0HA/EG4jZwCRgL+AMIgBUdAEXA8cB+wI3Aq8rKB+SpDYU\nFRT2Jt7BAHA7MCM3bwfgSeATQC+wCfBAQfmQJLVhYkHpTiFezlOxhghAfcBmRA3iZKJJ6b+BO4GF\ntYn09PRkw6VSiVKpVFB2JWl06u3tpbe3d8jSK6pPYT6wGPhuGv8D8Jo0/AbiLW6Vh+ydRtwH8X9r\n0rBPQZLaNFL7FG4FZqXhmcSjMioeATYGpqfxfYF7C8qHJKkNRdUUuqhefQQwB9iNCAYLgP2Ac9Jy\ntwKn10nDmoIktWmkXpI6FAwKktSmkdp8JEkahQwKkqSMQUGSlDEoSJIyBgVJUsagIEnKGBQkSRmD\ngiQpY1CQJGUMCpKkjEFBkpQxKEiSMgYFSVLGoCBJyhgUJEkZg4IkKWNQkCRlDAqSpIxBQZKUMShI\nkjIGBUlSxqAgScoYFCRJGYOCJCljUJAkZQwKkqSMQUGSlDEoSJIyBgVJUqaooNANXAgsAhYC02vm\nnw7cm+YtBHYoKB+SpDZMLCjd2cAkYC9gD2B+mlaxK3AMcE9B65ckDUBRNYW9gWvT8O3AjJr5uwGf\nBW4BzigoD5KkNhUVFKYAq3Pja2rWdQVwIrA/sA9wcEH50Agydy6USjBrFqxc2enctGc0570d42U7\n1VhRzUergcm58W6gLzd+HtWgcTWwS/rsp6enJxsulUqUSqUhzqaG04MPws03x/DcuXDllZ3NTztG\nc97bMV62cyzp7e2lt7d3yNLrGrKU+jscOBSYA8wEzqRaG5gKLAXeCDwDXAlcQrW5qaJcLpcLyp46\nYdYsuOYamDEDrr8eNtmk0zlq3WjOezvGy3aOZV1dXTCIY3tRQaEL+CqwcxqfQ/QjbAwsAI4krkB6\nHrgBOKtOGgaFMWblyjj7vPji0XewGc15b8d42c6xbKQGhaFgUJCkNg02KHjzmiQpY1CQJGUMCpKk\njEFBkpQxKEiSMgYFSVLGoCBJyhgUJEkZg4IkKWNQkCRlDAqSpIxBQZKUMShIkjIGBUlSxqAgScoY\nFCRJGYOCJCljUJAkZQwKkqSMQUGSlDEoSJIyBgVJUsagIEnKGBQkSRmDgiQpY1CQJGUMCpKkjEFB\nkpQxKEiSMkUFhW7gQmARsBCY3mC5i4F/KygPY0Zvb2+nszBiWBZVlkWVZTF0igoKs4FJwF7AGcD8\nOsucCLwZKBeUhzHDHb7KsqiyLKosi6FTVFDYG7g2Dd8OzKiZvxfwNuAioKugPEiS2lRUUJgCrM6N\nr8mtawvgn4BTMCBI0ohS1EF5PrAY+G4a/wPwmjT8MeBY4Cng1cBGwJnAN2vSeJjGfRGSpPqWAdt1\nOhO1Dge+kYZnAlc3WO5Y7GiWpBFjYkHp/hA4ELg1jc8BjgQ2BhbULGtHsyRJkqSqPYh7GOrZiKhl\nvD6Nt3rfw2jVTlkA3J2WXwhcUmzWhl2jsjiS6Kf6BfA1oj9svO4X9coCxud+8XfAHcRVjh9P08br\nflGvLGCU7BefApYS/7RaM4A7gT8BO6RphwNfT8N7AD8qOoPDqN2y2ID4J49FjcpiQ+LCgw3S+LeB\nQ+nfdzVe9otGZTEe94sJwIPAZCIQ/BbYlPG5X9Qri1cwgP2iU4+5eJj4x9W7+mkScfPbA7lp67rv\nYTRrtyzeQtQergNuJHb6saJRWTwH7Jk+IfrCniP2i2vStPGyX9Qri2cZn/vFGuANxJWMmxMHxhcY\nn/tFo7IYVfvFNOC2JvMXUj07XgC8Kzfvd4yt5zZNo/WyeDPwoTS8PbGTjKey+BjVq9nG+36RL4vx\nvF8cTtSmLya2eTzvF7Vl0fZ+MVoKajVRLaroBvo6lJdOexD4zzT8EPAkcUPgWNcNfBF4J9F2CuN3\nv6hXFuN1vwD4AbAVsD7w94zf/QLWLou294vREhRuBWal4ZlEm9p4NYfqs6S2JO4e/3PnsjNsLiJ2\n9PdQbToZr/tFvbIYj/vFFOBmopm1DPyVaEYZj/tFo7Joe78o6j6FVlXuUWh0D0NFvfsexppWy+IS\nohPt52l8DmPvLKi2LO4Ejie2+aY078uMz/2iUVmMx/1iAXA5sc0vAkvSOIy//aJRWUxg7O8XkiRJ\nkiRJkiRJkiRJkiRJktTMF4nHetxPPJ5gIXBli9/9NLB7k/n/TvUNgANxHMW/FGoD4P8B/5CbNo21\nH2twEjAvDb+cuE+hl7g2/wriJiVJGjOOBc7udCZqDMebAo8mgte9VB96No21g8KJxLvOIR4WeVhu\n3mlEYJDa1uk7mqVm8k+CvJR4FPArgHcDXwC2Jp7j8mPiPd+XEgfDLYjHHGxIPEv/XOAy4kz6ROIu\n0GnAK4FtgNOBnwGHAGcBq4AVxOMRzmohn0cDpwLPE8+XmQtsS9xJ+iLxOJmj0vzvpO3agDjbX1KT\n1odSWq9M29DoVbakdF4LvAq4Kjf9fOBlLeRbWstoefaRVCYe/bsP8bCz24gnYe5BHFwry1Q+pxDv\nGXg3cEad+c8RB91TiaDQDZyX0tyfeBx1K6+K3RToAfYD9gVWEoHnAOJFOAcQzTxTiaatJ4CDgJNZ\n+8C9fZr2ayKgnLyOdZeJ59k8WjO9j3iEstQ2g4JGk8p7JVYQB9jLgS8RD4er9av0+UeqL6RpNn9z\n4umaj6fpt1D/HRe1tgV+QzyADOIZM28i2vhXEU07pwAvEc/4v5U4q/9n1n4GzYeJoHAN0aewD1HT\nebbONk4GngF+T9SY8tYjaiZS2wwKGk0qZ+7HEWfkHySCwkZNll1XWhV/IQ60m6XxPVvM06PAG3N5\nKBHB6zAisBwAfI/oBC8RT6j8W+Dz9O8zWQ84gggEBxE1lnOAjwKPpbztmJadkNL9JfHs/CeIGlHF\nqTXjUsvsU9BIVnvgrozfQLyGcjfiCqU7iWaURt+tFyBq55eJM/qfEmf43cSz6GsdSxyQK9/bj2ge\nWkic+T9EvDJxa6If44WU1unEWf1/AR8hfnv5/opD03aszE27FLgH+BwRCL+e1rEe8YrJm9NyxwAX\nELWLScSLVE6ok3dJUhvOIA6qAN8iaiLSuGJNQap6iugcfoZoFvpOZ7MjSZIkSZIkSZIkSZIkSZIk\nSaPO/wd4fGr3NL6C4QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f36517c5210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2. With the simpler model\n", "'''\n", "data11 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "data21 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 47)\n", "[[ 5.60354423e-02 0.00000000e+00 3.45983439e-02 ..., 1.07799362e-04\n", " 9.98436733e-05 9.25175496e-05]\n", " [ 5.61487071e-02 4.63366595e-02 3.68188272e-02 ..., 9.92860048e-05\n", " 9.18873619e-05 8.50844727e-05]\n", " [ 5.51633144e-02 4.41024173e-02 3.34944126e-02 ..., 1.16312240e-04\n", " 1.07566351e-04 9.95353181e-05]\n", " ..., \n", " [ 5.55556385e-02 4.53986116e-02 3.51159690e-02 ..., 1.21000005e-04\n", " 1.12096910e-04 1.03894910e-04]\n", " [ 5.49687211e-02 4.39238912e-02 3.30713904e-02 ..., 1.29236685e-04\n", " 1.19730405e-04 1.10972159e-04]\n", " [ 5.55256202e-02 4.51044651e-02 3.49070457e-02 ..., 1.15971312e-04\n", " 1.07432622e-04 9.95662725e-05]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1.\n", " 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 1.]\n" ] } ], "source": [ "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fae11912310>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GaB/Yut2dSZJ6W5Fosgh4B/HE0ObAhUQ1UtksKUhSizpRUniaCAgAvyUaniVJE1CRp49+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\nSZIkSZIkSZIkSZIkqcH/AaXl+iTDkX0dAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fae119122d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2. With the gru model\n", "'''\n", "data11 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runA.npz')\n", "data21 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 44)\n", "[[ 1.24660005e-01 9.74883808e-02 6.46064837e-02 ..., 1.90614390e-04\n", " 1.73033830e-04 1.57141048e-04]\n", " [ 1.26972781e-01 1.00285856e-01 6.67210233e-02 ..., 9.77403937e-05\n", " 9.03463540e-05 8.35759406e-05]\n", " [ 1.18413929e-01 8.01361284e-02 4.64666935e-02 ..., 8.62135920e-05\n", " 7.91471312e-05 7.27472454e-05]\n", " ..., \n", " [ 1.22993569e-01 9.04669706e-02 5.20611677e-02 ..., 1.20912361e-04\n", " 1.11792422e-04 1.03415427e-04]\n", " [ 1.31132155e-01 1.02064132e-01 6.27080469e-02 ..., 1.24872571e-04\n", " 1.15411477e-04 1.06727092e-04]\n", " [ 1.26553115e-01 1.02315676e-01 6.59910226e-02 ..., 1.15525539e-04\n", " 1.06497150e-04 9.82490103e-05]]\n", "scores shape (50,)\n", "[ 0. 0. 1. 0. 0. 0. 1.\n", " 0. 1. 0. 0. 0. 0. 0.\n", " 0. 1. 0. 0. 1. 0. 0.\n", " 0. 1. 0.92708333 1. 0. 1. 0.\n", " 0. 0. 0. 0. 0. 0. 1.\n", " 0. 0. 0. 0.83333333 0. 0. 1.\n", " 0. 0. 0. 1. 0. 1. 1.\n", " 0. ]\n" ] } ], "source": [ "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f3fa586bb50>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3fa586ba10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 31)\n", "[[ 0.08936516 0.0754681 0.06283438 ..., 0.01514173 0.01501562\n", " 0.01490227]\n", " [ 0.09201896 0.07820546 0.06347355 ..., 0.01518269 0.01508005\n", " 0.01498646]\n", " [ 0.0960233 0.08372398 0.07006697 ..., 0.015364 0.0152562\n", " 0.01515773]\n", " ..., \n", " [ 0.08906623 0.07618408 0.06147273 ..., 0.01456837 0.01447097\n", " 0.01438174]\n", " [ 0.0943704 0.0821831 0.06846968 ..., 0.0154057 0.01529267\n", " 0.01518957]\n", " [ 0.08907868 0.07548768 0.06275198 ..., 0.01502329 0.01490513\n", " 0.01479774]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 0. 1. 1. 1. 1.\n", " 1. 0.01041667 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 0.\n", " 1. ]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7ff244d22b90>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff214b8f8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2. With fewer training epochs.\n", "'''\n", "data11 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 21)\n", "[[ 0.05621311 0.04638603 0.03557363 ..., 0.00123461 0.00109884\n", " 0.00098196]\n", " [ 0.05526491 0.0446969 0.03436239 ..., 0.00126352 0.00110778\n", " 0.00097658]\n", " [ 0.05540264 0.04436184 0.03365188 ..., 0.00126147 0.00111623\n", " 0.00099174]\n", " ..., \n", " [ 0.05540877 0.0454841 0.0356551 ..., 0.00119104 0.00105837\n", " 0.00094481]\n", " [ 0.05590601 0.0456485 0.03532783 ..., 0.00121171 0.00108016\n", " 0.00096674]\n", " [ 0.05541185 0.04434226 0.03342759 ..., 0.00134842 0.00119605\n", " 0.00106492]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1.\n", " 0.92708333 1. 0. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 0.98958333 1. 1.\n", " 0.96875 0. 1. 1. 1. 1. 1.\n", " 0. 1. 1. 1. 1. 1. 1.\n", " 1. 0. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1.\n", " 1. ]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7ff2455bec50>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pnyUS4bfTe4wlet2t/LbCB4GzidLFOOInJY+sE7skqQUnUf3xl+8RJRFptWJJ\nQap6hmgc/gdRLfTDxotLkiRJkiRJkiRJkiRJkiRJkmr8L5nVIezldJd3AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff212671110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2. Simpler model with fewer training epochs.\n", "'''\n", "data11 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 21)\n", "[[ 0.1333752 0.11080759 0.07659189 ..., 0.00111848 0.00095859\n", " 0.0008297 ]\n", " [ 0.12815169 0.09953441 0.06451445 ..., 0.00127968 0.00112063\n", " 0.00098746]\n", " [ 0.13361075 0.1095274 0.07571035 ..., 0.00124767 0.00107896\n", " 0.00094073]\n", " ..., \n", " [ 0.125384 0.09691066 0.06426334 ..., 0.00125629 0.00109703\n", " 0.00096482]\n", " [ 0.12904907 0.10344235 0.0708679 ..., 0.001012 0.00088422\n", " 0.00077725]\n", " [ 0.12629925 0.10087278 0.06796972 ..., 0.00175584 0.00152009\n", " 0.0013258 ]]\n", "scores shape (50,)\n", "[ 0.94791667 0. 1. 1. 0. 1. 1.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 1. 0. 1. 1. 0. 0.\n", " 0.83333333 1. 1. 0. 0.25 1. 1.\n", " 1. 1. 0. 1. 0. 0. 1.\n", " 1. 1. 0. 0.91666667 0.35416667 1. 0.\n", " 1. 0. 0. 1. 0. 1. 1.\n", " 1. 1. ]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7ff244d22c90>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { 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BsKLdHbbAoCBJLepE30cziWqmo4j2gS3a3ZkkqbfliSYLgY8QTwxtBFxIVCMVzZKCJLWo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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff21253d050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 2 and testing length 2. GRU model with fewer training epochs.\n", "'''\n", "data11 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l2-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 61)\n", "last 0.0113193088135 threshold 0.012\n" ] }, { "data": { "text/plain": [ "(0.011, 0.013)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f885ef10a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Double LSTM\n", "Trying to find a training epoch to stop training.\n", "'''\n", "data11 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] * 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.012\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "#xlim(30,40)\n", "ylim(0.011,0.013)\n", "\n", "# looks like epoch 40 is good for 0.012" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 61)\n", "last 4.15097025594e-05 threshold 0.001\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f885ec45f10>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f885eee6890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Single LSTM\n", "Trying to find a training epoch to stop training.\n", "'''\n", "data11 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] * 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.001\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "#xlim(30,40)\n", "#ylim(0.0,0.002)\n", "\n", "# looks like epoch 25 is good for 0.001" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 61)\n", "last 3.51745995667e-05 threshold 0.001\n" ] }, { "data": { "text/plain": [ "(0.0, 0.002)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f885ec5a050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Double GRU\n", "Trying to find a training epoch to stop training.\n", "'''\n", "data11 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runA.npz')\n", "\n", "vloss = data11['vloss']\n", "#scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_(vloss.shape)\n", "figure()\n", "x_avg = np.mean(vloss,axis=0)\n", "x_serr = np.std(vloss,axis=0) / vloss.shape[0] * 1.98\n", "plot(x_avg, color='#0000ff')\n", "plot(x_avg + x_serr, color='#ddddff')\n", "plot(x_avg - x_serr, color='#ddddff')\n", "\n", "x_end = np.mean(x_avg[-2:])\n", "x_thres = 0.001\n", "six.print_('last {} threshold {}'.format(x_end, x_thres))\n", "plot([0,60],[x_end,x_end],color='#ff0000')\n", "plot([0,60],[x_thres,x_thres],color='#ff0000')\n", "\n", "#xlim(30,40)\n", "ylim(0.0,0.002)\n", "\n", "# looks like epoch 25 is good for 0.001" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 41)\n", "[[ 0.11973911 0.08873725 0.062083 ..., 0.01210291 0.01204618\n", " 0.0119935 ]\n", " [ 0.12763282 0.10679469 0.07714722 ..., 0.01184324 0.01179229\n", " 0.01174514]\n", " [ 0.12275101 0.10102007 0.07785704 ..., 0.01197197 0.01191723\n", " 0.01186645]\n", " ..., \n", " [ 0.11925535 0.08805766 0.06705227 ..., 0.01219413 0.01213706\n", " 0.01208434]\n", " [ 0.12563111 0.09619637 0.0692479 ..., 0.01184236 0.0117885\n", " 0.01173862]\n", " [ 0.11884481 0.0890677 0.06535447 ..., 0.01169011 0.01163844\n", " 0.01159064]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1. 0. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 0.21875 1. 0. 1. 1.\n", " 1. 1. 1. 1. 1. ]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f99b5d914d0>]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f99b5acc950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Double LSTM model.\n", "Looking at correlation between training AUC and actual performance\n", "'''\n", "data11 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 26)\n", "[[ 0.07038667 0.05296684 0.04162331 ..., 0.00086331 0.00077806\n", " 0.0007032 ]\n", " [ 0.0744532 0.06084078 0.04826403 ..., 0.00063225 0.00057206\n", " 0.00051893]\n", " [ 0.07082326 0.05457835 0.04520417 ..., 0.00087264 0.00078487\n", " 0.00070826]\n", " ..., \n", " [ 0.07223552 0.05409869 0.04126726 ..., 0.00088338 0.00079667\n", " 0.00072036]\n", " [ 0.07215533 0.05550894 0.0463388 ..., 0.00082692 0.00073836\n", " 0.00066132]\n", " [ 0.07042608 0.05536843 0.04656875 ..., 0.00083239 0.00074515\n", " 0.00066937]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f99b65f7a90>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f99da763ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Single LSTM model.\n", "Looking at correlation between training AUC and actual performance\n", "'''\n", "data11 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2simple_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vloss shape (50, 26)\n", "[[ 0.16523354 0.13730846 0.08717519 ..., 0.00076391 0.00067873\n", " 0.0006053 ]\n", " [ 0.15543124 0.11512258 0.07533636 ..., 0.00139091 0.00122447\n", " 0.0010836 ]\n", " [ 0.14960701 0.10779101 0.06741117 ..., 0.0006807 0.00061247\n", " 0.00055265]\n", " ..., \n", " [ 0.16040278 0.11989488 0.06712871 ..., 0.00071317 0.0006422\n", " 0.00057992]\n", " [ 0.1577603 0.11754873 0.06949968 ..., 0.00063443 0.00056828\n", " 0.00051057]\n", " [ 0.1591086 0.10939669 0.06788692 ..., 0.00104207 0.00093177\n", " 0.0008365 ]]\n", "scores shape (50,)\n", "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f99b37d2550>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zHNGwvRtxn8WLqD346NiS42rGkoUkFVRmR4ILgd2JHmfvBV5KdFd+NbBLuxscAyYLSSqo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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f99b36d0f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "Analyzing results of student2 with 2 skills, with training length 3 and testing length 2.\n", "Double GRU model\n", "Looking at correlation between training AUC and actual performance\n", "'''\n", "data11 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/stats-runB.npz')\n", "data21 = np.load('experiments/test2_model2gru_tiny-dropout10-shuffle0-data-test2-n10000-l3-random.pickle/mcts-rtype2-rollouts1000-trajectories100-real1-runB.npz')\n", "\n", "vloss = data11['vloss']\n", "scores = data21['scores'][:,0]\n", "#initialq = data51['qvals'][:,0]\n", "#opts = data61['opts']\n", "#qfuncs = data61['qs'][:,0,:,:]\n", "\n", "six.print_('vloss shape {}'.format(vloss.shape))\n", "six.print_(vloss)\n", "six.print_('scores shape {}'.format(scores.shape))\n", "six.print_(scores)\n", "\n", "xs = np.sum(vloss,axis=1)\n", "ys = scores\n", "title('Training Loss AUC versus Actual Performance')\n", "xlabel('Training Loss AUC')\n", "ylabel('Posttest Score')\n", "plot(xs,ys,'.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
bcdev/snap-cawa	src/main/resources/demo/demo_olci_complete.ipynb	1	26782011	null	gpl-3.0
oudalab/fajita	otherHelperCode/english_to_arabic_dictionary/Actors_to_Arabic _extension.ipynb	1	608708	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import re\n", "import json\n", "import pickle\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def name_formatter(name):\n", " name = re.sub(\"_\", \" \", name).strip().title()\n", " return(name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "need to do pip3 install lxml and then reload the jupyter file" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def ar_lookup(name):\n", " \"\"\"\n", " Convert names from English to Arabic using European Media Monitors and \n", " code modified from Phil Schrodt.\n", " \"\"\"\n", " name = name_formatter(name)\n", " base_url = \"http://emm.newsexplorer.eu/NewsExplorer/search/en/entities?query=\"\n", " url = base_url + name\n", " \n", " try:\n", " page = requests.get(url)\n", " soup = BeautifulSoup(page.content, \"lxml\")\n", " name_url = soup.find(\"p\", {\"class\" : \"center_headline\"}).find(\"a\")['href']\n", " except Exception as e:\n", " #print(\"Couldn't get page of results back: \", e)\n", " return []\n", "\n", " try:\n", " base = \"http://emm.newsexplorer.eu/NewsExplorer/search/en/\"\n", " name_url = base + name_url\n", " name_page = requests.get(name_url)\n", " soup = BeautifulSoup(name_page.content, \"lxml\")\n", " # check to make sure in list???? Take in alt names???/petrarch2/petrarch2/data/dictionaries\n", " names = soup.find(\"td\", {\"colspan\" : \"1\"}).find_all(\"p\")\n", " names = [i.text for i in names][1:]\n", " names_en = [i for i in names if re.search(\"\$.?Eu\|\\(.?en\", i)]\n", " names_en = [re.sub(\"\\s+?\\(.+?\$\", \"\", name) for name in names_en]\n", " #print(\"Found match. Matched English name: \", names_en[0])\n", " names_ar = [i for i in names if re.search(\"\$.?ar\", i)]\n", " names_ar = [re.sub(\"\\s+?\\(.+?\$\", \"\", name) for name in names_ar]\n", " return names_ar\n", " except Exception:\n", " traceback.print_exc()\n", " return []" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['أنجيلا ميركل', 'أنجيلا ميركيل']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar_lookup(\"ANGELA_MERKEL\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def clean_line(line):\n", " # Take out extra space, underscores, comments, etc.\n", " cleaned = re.sub(\"_ .+\", \"\", line).strip()\n", " cleaned = re.sub(\"_$\", \"\", cleaned, flags=re.MULTILINE)\n", " return cleaned\n", "\n", "def ingest_dictionary(dict_path):\n", " \"\"\"\n", " Read in the country (or other) actor dictionaries.\n", " \"\"\"\n", " with open(dict_path) as f:\n", " country_file = f.read()\n", " split_file = country_file.split(\"\\n\")\n", " \n", " dict_dict = []\n", " key_name = \"\"\n", " alt_names = [] \n", " roles = []\n", "\n", " for line in split_file:\n", " if not line:\n", " pass\n", " elif line[0] == \"#\":\n", " pass\n", " elif re.match(\"[A-Z]\", line[0]):\n", " # handle the previous\n", " entry = {\"actor_en\" : key_name,\n", " \"alt_names_en\" : alt_names,\n", " \"roles\" : roles}\n", " dict_dict.append(entry)\n", " # zero everything out\n", " alt_names = []\n", " roles = []\n", " # make new key name\n", " key_name = clean_line(line)\n", " # check to see if the role is built in\n", " if bool(re.search(\"\\[[A-Z]{3}\\]\", line)):\n", " roles = re.findall(\"\\[(.+?)\\]\", line)\n", " elif line[0] == \"+\":\n", " cleaned = clean_line(line[1:])\n", " alt_names.append(cleaned)\n", " elif re.match(\"\\s\", line):\n", " roles.append(line.strip())\n", " return dict_dict \n", "dp = \"./Phoenix.Countries.actors.txt\"\n", "dict_dict = ingest_dictionary(dp)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'actor_en': 'MANSUR_BIN_RAJAB',\n", " 'alt_names_en': [],\n", " 'roles': ['[BHRGOV 070101-100831]']}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict_dict[1123]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar_lookup(\"Mohammad_Najibullah\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#dict_dict here is just one object, not a list of object\n", "def eng_to_ar(dict_dict):\n", " \"\"\"\n", " Update an English language dictionary entry with Arabic names\n", " \"\"\"\n", " ar_names = ar_lookup(dict_dict[\"actor_en\"])\n", " if not ar_names:\n", " #print(\"No ar name match found for \"+ str(dict_dict[\"actor_en\"]))\n", " raise Exception(dict_dict[\"actor_en\"])\n", " #return dict_dict\n", " dict_dict['actor_ar'] = ar_names[0]\n", " if len(ar_names) > 1:\n", " dict_dict['alt_names_ar'] = ar_names[1:]\n", " return dict_dict" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'actor_ar': 'أحمد الجلبي',\n", " 'actor_en': 'AHMAD_CHALABI',\n", " 'alt_names_ar': ['أحمد شلبي', 'أحمد جلبي'],\n", " 'alt_names_en': [],\n", " 'roles': ['[IRQELI 620101-030901]',\n", " '[IRQGOV 030901-030930]',\n", " '[IRQGOV 031101-040630]',\n", " '[IRQGOV 050601-060531]',\n", " '[IRQELI]']}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eng_to_ar(dict_dict[7777])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def grabAllTheEnglishNamesThatNoArName(dict_dict):\n", " noFindList=[]\n", " #count=0\n", " for item in dict_dict:\n", " if(item['actor_en']!=\"\"):\n", " try:\n", " temp=eng_to_ar(item)\n", " if(temp['actor_ar']==''):\n", " noFindList.append(item['actor_en'])\n", " except Exception as e:\n", " noFindList.append(item['actor_en'])\n", " else\n", " #print(e)\n", " return noFindList " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "count=0\n", "for item in dict_dict:\n", " if(item[\"actor_en\"]!=\"\"):\n", " count=count+1\n", " " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18389" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 27min 40s, sys: 13.6 s, total: 27min 54s\n", "Wall time: 4h 34min 55s\n" ] } ], "source": [ "%%time\n", "anotherNoFind=grabAllTheEnglishNamesThatNoArName(dict_dict)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15949" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(anotherNoFind)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 27min 37s, sys: 12.3 s, total: 27min 49s\n", "Wall time: 4h 30min 43s\n" ] } ], "source": [ "%%time\n", "notfind=grabAllTheEnglishNamesThatNoArName(dict_dict)\n", "#then dump the data to pickle\n", "# try:\n", "# with open(\"noFindWord\", 'wb') as f:\n", "# pickle.dump(notfind, f, pickle.HIGHEST_PROTOCOL)\n", "# except:\n", "# print(\"failed to save the result to disk\")\n", "# pass\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15949" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(notfind)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "try:\n", " with open(\"notFindFromPreviousMethod\",'wb') as f:\n", " pickle.dump(notfind,f,pickle.HIGHEST_PROTOCOL)\n", "except:\n", " print(\"failed to save\")\n", " pass" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'AZAM_DADFAR'" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notfind[33]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2441" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dict_dict)-len(notfind)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hack_wiki(eng_name):\n", " base_url=\"https://en.wikipedia.org/wiki/\"+eng_name\n", " try:\n", " page=requests.get(base_url)\n", " soup=BeautifulSoup(page.content,\"lxml\")\n", " name=soup.find(id=\"firstHeading\").contents\n", " ar_url=soup.find(\"li\",{\"class\":\"interwiki-ar\"}).find(\"a\")['href']\n", " #print(ar_url)\n", " ar_page=requests.get(ar_url)\n", " ar_soup=BeautifulSoup(ar_page.content,\"lxml\")\n", " ar_name=ar_soup.find(id=\"firstHeading\").contents\n", " print(ar_name)\n", " #print(\"name \"+name+\" url: \"+str(ar_url))\n", " except Exception as e:\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def eng_to_ar(dict_dict):\n", " \"\"\"Screenshot from 2017-06-29 08-36-04\n", " Update an English language dictionary entry with Arabic names\n", " \"\"\"\n", " ar_names = ar_lookup(dict_dict[\"actor_en\"])\n", " if not ar_names:\n", " print(\"No ar name match found.\")\n", " return dict_dict\n", " dict_dict['actor_ar'] = ar_names[0]\n", " if len(ar_names) > 1:\n", " dict_dict['alt_names_ar'] = ar_names[1:]\n", " return dict_dict" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test=eng_to_ar(dict_dict[7777])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dic=pickle.load(open('countrycode.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "countrycode=[key for key,value in dic.items()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Format stored in our db. { \"_id\" : ObjectId(\"5772026ca78a30ce5acbec43\"), \"sentenceId\" : \"5771f20f0dfcd69f645102fa\", \"word\" : \"President Obama\", \"countryCode\" : \"USA\", \"firstRoleCode\" : \"GOV\", \"secondRoleCode\" : \"LEG\", \"dateStart\" : \"Sun Jun 01 2008 00:00:00 GMT-0500 (CDT)\", \"dateEnd\" : \"Wed Jun 01 2016 00:00:00 GMT-0500 (CDT)\", \"confidenceFlag\" : true, \"userId\" : \"577201cea78a30ce5acbec41\", \"userName\" : \"guest\", \"taggingTime\" : ISODate(\"2016-06-28T04:51:56.092Z\"), \"__v\" : 0 }" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'actor_ar': 'أحمد الجلبي',\n", " 'actor_en': 'AHMAD_CHALABI',\n", " 'alt_names_ar': ['أحمد شلبي', 'أحمد جلبي'],\n", " 'alt_names_en': [],\n", " 'roles': ['[IRQELI 620101-030901]',\n", " '[IRQGOV 030901-030930]',\n", " '[IRQGOV 031101-040630]',\n", " '[IRQGOV 050601-060531]',\n", " '[IRQELI]']}" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "haha='[IRQELI 620101-030901]'\n", "hahaha=haha.split(\" \")" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'6'" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hahaha[1][0]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pymongo import MongoClient\n", "client=MongoClient()\n", "client=MongoClient('mongodb://portland.cs.ou.edu:23755/')\n", "db=client['lexisnexis']\n", "db.authenticate('boomer', 'burritos_for_breakfast')\n", "secondroles=db.secondroles\n", "firstroles=db.agents\n", "secondrolelist=[]\n", "for item in secondroles.find():\n", " secondrolelist.append(item['id'])\n", "firstrolelist=[]\n", "for item in firstroles.find():\n", " firstrolelist.append(item['id'])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def transferStringToDateTime(inputtime):\n", " if(str(inputtime)!=''):\n", " dd=datetime.datetime.strptime(inputtime,'%y%m%d').date()\n", " #this is for 62 return 1962 not 2062:\n", " if dd.year>2017:\n", " new=dd.replace(year=dd.year-100)\n", " return new\n", " else:\n", " return dd \n", " else:\n", " return ''" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.date(1962, 1, 1)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transferStringToDateTime(\"620101\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#if it is first role it will return 1, if it is in secondRole list it will reture 2, and it it does not exist in both it return 3\n", "def checkBelongToWhichRole(code):\n", " if code in firstrolelist:\n", " return 1;\n", " elif code in secondrolelist:\n", " return 2;\n", " else:\n", " return 3;" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def checkIfCountrycodeExist(code):\n", " if code in countrycode:\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[AFGGOVMIL >050101] this format of stuff needs to be handled" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strtest=\"IRQELI 620101-030901\"\n", "strtest.split(\" \")\n", "len(\"yan\")" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'RQE'" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strtest[1:4]" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'actor_ar': 'أحمد الجلبي',\n", " 'actor_en': 'AHMAD_CHALABI',\n", " 'alt_names_ar': ['أحمد شلبي', 'أحمد جلبي'],\n", " 'alt_names_en': [],\n", " 'roles': ['[IRQELI 620101-030901]',\n", " '[IRQGOV 030901-030930]',\n", " '[IRQGOV 031101-040630]',\n", " '[IRQGOV 050601-060531]',\n", " '[IRQELI]']}" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#extract the roles and parse the string of each role to get the role format we like for our fajita website\n", "def extract_actor_roles(dict):\n", " roles=[]\n", " #it could be ['actor_ar'] is not written since we did not find the translation\n", " if 'actor_ar' in dict:\n", " wordlist=[]\n", " wordlist.append(dict['actor_ar'])\n", " if 'alt_names_ar' in dict:\n", " for name in dict['alt_names_ar']:\n", " wordlist.append(name)\n", " for word in wordlist: \n", " for item in dict['roles']:\n", " temp=[]\n", " splitresults=item.split(\" \")\n", " #if it has both firstRole and secondRole \"[\" will be include in teh str\n", " len1=len(splitresults[0])\n", " country=''\n", " if(len1==4):\n", " country=splitresults[0][1:4]\n", " role1=''\n", " role2=''\n", " elif(len1==7):\n", " country=splitresults[0][1:4]\n", " role1=splitresults[0][4:7]\n", " role2=''\n", " elif(len1==10):\n", " country=splitresults[0][1:4]\n", " role1=splitresults[0][4:7]\n", " role2=splitresults[0][7:10]\n", " temp.append(country)\n", " temp.append(checkIfCountrycodeExist(country))\n", " temp.append(role1)\n", " temp.append(checkBelongToWhichRole(role1))\n", " temp.append(role2)\n", " temp.append(checkBelongToWhichRole(role2))\n", " if(len(splitresults)>1):\n", " timerange=splitresults[1]\n", " if(timerange[0]!=\">\" and timerange[0]!=\"<\"):\n", " temp.append(transferStringToDateTime(timerange[0:6]))\n", " temp.append(\"startdate\")\n", " temp.append(transferStringToDateTime(timerange[7:13]))\n", " temp.append(\"enddate\")\n", " elif(timerange[0]==\">\"):\n", " temp.append(transferStringToDateTime(timerange[1:7]))\n", " temp.append(\"startdate\")\n", " temp.append(\"\")\n", " temp.append(\"enddate\")\n", " elif(timerange[0]==\">\"):\n", " temp.append(\"\")\n", " temp.append(\"startdate\")\n", " temp.append(transferStringToDateTime(timerange[1:7]))\n", " temp.append(\"enddate\")\n", " else:\n", " temp.append(\"\")\n", " temp.append(\"startdate\")\n", " temp.append(\"\")\n", " temp.append(\"enddate\")\n", " temp.append(word)\n", " roles.append(temp) \n", " return roles\n", "#ok, the format will be\n", "#[\"contry\",'1',\"role1\",\"1\",\"role2\",\"2\",\"starttime\",\"something\",\"endtime\",\"something\",\"word\"]\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "midresult=extract_actor_roles(test)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#generate a list of json object for each element in the list\n", "def generateFajitaObjectsList(midresult):\n", " finalResult=[]\n", " for item in midresult:\n", " tempjson={}\n", " #insert in all the deault stuff first\n", " #should be false, the flag is false means we are confident about the word being tagged in fajita.\n", " tempjson[\"confidenceFlag\"]=False\n", " tempjson[\"sentenceId\"]=\"10000\"\n", " tempjson[\"userName\"]=\"existEnglishDictionary\"\n", " tempjson[\"userId\"]=\"existEnglishDictionary\"\n", " tempjson[\"countryCode\"]=item[0]\n", " tempjson[\"firstRoleCode\"]=\"\"\n", " tempjson[\"secondRoleCode\"]=\"\"\n", " tempjson[\"taggingTime\"]= datetime.datetime.now().strftime('%Y-%m-%d')\n", " tempjson[\"dateStart\"]=str(item[6])\n", " tempjson[\"dateEnd\"]=str(item[7])\n", " tempjson[\"word\"]=item[10]\n", " if(item[3]==1):\n", " tempjson[\"firstRoleCode\"]=item[2]\n", " elif(item[3]==2):\n", " tempjson[\"secondRoleCode\"]=item[2]\n", " else:\n", " pass\n", " if(item[5]==1):\n", " tempjson[\"firstRoleCode\"]=item[4]\n", " elif(item[5]==2):\n", " tempjson[\"secondRoleCode\"]=item[4]\n", " else:\n", " pass \n", " finalResult.append(tempjson)\n", " return finalResult" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "final=generateFajitaObjectsList(midresult)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '1962-01-01',\n", " 'firstRoleCode': '',\n", " 'secondRoleCode': 'ELI',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد الجلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-09-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد الجلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-11-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد الجلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2005-06-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد الجلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': '',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد الجلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '1962-01-01',\n", " 'firstRoleCode': '',\n", " 'secondRoleCode': 'ELI',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد شلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-09-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد شلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-11-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد شلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2005-06-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد شلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': '',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد شلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '1962-01-01',\n", " 'firstRoleCode': '',\n", " 'secondRoleCode': 'ELI',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد جلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-09-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد جلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2003-11-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد جلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': 'IRQ',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '2005-06-01',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد جلبي'},\n", " {'confidenceFlag': False,\n", " 'countryCode': '',\n", " 'dateEnd': 'startdate',\n", " 'dateStart': '',\n", " 'firstRoleCode': 'GOV',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'أحمد جلبي'}]" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#now need to insert the value back to the database" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for item in final:\n", " db.sourcedictionaries.insert_one(\n", " {'confidenceFlag': True,\n", " 'countryCode': 'FG',\n", " 'dateEnd': '',\n", " 'dateStart': '',\n", " 'firstRoleCode': '',\n", " 'secondRoleCode': '',\n", " 'sentenceId': '10000',\n", " 'taggingTime': '2017-06-24',\n", " 'userId': 'existEnglishDictionary',\n", " 'userName': 'existEnglishDictionary',\n", " 'word': 'حامد كرزاي'}\n", ")" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#filter out the items that does not have roles defined in there:\n", "\n", "def iterativeInsertAll(dict_dict):\n", " insertRecordCount=0\n", " insertDistinctPersonCount=0\n", " for item in dict_dict:\n", " try:\n", " #print(insertRecordCount)\n", " temptest=eng_to_ar(item)\n", " tempmidresult=extract_actor_roles(temptest)\n", " finalresult=generateFajitaObjectsList(tempmidresult)\n", " for insertrocord in finalresult:\n", " try:\n", " db.sourcedictionaries.insert_one(insertrocord)\n", " insertRecordCount=insertRecordCount+1\n", " except:\n", " pass\n", " insertDistinctPersonCount=insertDistinctPersonCount+1\n", " except Exception as e:\n", " print(e)\n", " pass\n", " return {\"recordinsert\":insertRecordCount,\"distinctPersonCount\":insertDistinctPersonCount}\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[AFGGOVMIL >050101]\n", "fromat like this need to handled,\n", "maybe have two actors and have the time format with > or < there." ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 07\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 03\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "time data '1-3501' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '5-2013' does not match format '%y%m%d'\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "time data '1-6810' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '6-2013' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '2-2010' does not match format '%y%m%d'\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 10\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 06\n", "time data '3-2013' does not match format '%y%m%d'\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '0-2014' does not match format '%y%m%d'\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "time data '5-2014' does not match format '%y%m%d'\n", "unconverted data remains: 01\n", "unconverted data remains: 01\n", "time data '1-2013' does not match format '%y%m%d'\n", "time data '4-2009' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '2-2012' does not match format '%y%m%d'\n", "name 'traceback' is not defined\n", "time data '5-2009' does not match format '%y%m%d'\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "time data '7-2011' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 09\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "time data '193001' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 2\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "time data '193004' does not match format '%y%m%d'\n", "unconverted data remains: 01\n", "time data '193001' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '0-2008' does not match format '%y%m%d'\n", "time data '9-2009' does not match format '%y%m%d'\n", "time data '9-2013' does not match format '%y%m%d'\n", "No ar name match found.\n", "time data '1-7112' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 6\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "time data 'Countr' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "unconverted data remains: 03\n", "unconverted data remains: 01\n", "No ar name match found.\n", "unconverted data remains: 01\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 09\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "time data '1-3610' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "local variable 'role1' referenced before assignment\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "local variable 'role1' referenced before assignment\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "time data '190001' does not match format '%y%m%d'\n", "time data '1-4501' does not match format '%y%m%d'\n", "time data '1-5301' does not match format '%y%m%d'\n", "name 'traceback' is not defined\n", "unconverted data remains: 01\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "time data '1-4101' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "unconverted data remains: 01\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '1-5301' does not match format '%y%m%d'\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 04\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "unconverted data remains: 1\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 1\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "unconverted data remains: 01\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '1-5301' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "time data '1-4701' does not match format '%y%m%d'\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match 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match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match 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'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "name 'traceback' is not defined\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "No ar name match found.\n", "CPU times: user 29min 16s, sys: 30.9 s, total: 29min 47s\n", "Wall time: 5h 46min 29s\n" ] } ], "source": [ "%%time\n", "totalinsert=iterativeInsertAll(dict_dict)" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5696" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "totalinsert[\"recordinsert\"]" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17567" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "totalinsert[\"distinctPersonCount\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
scjrobertson/xRange	tracking/tracking.ipynb	1	491579	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Two Object Tracking\n", "\n", "### Summary of notebook\n", "* <b> Kalman filter: PGM implementation </b>\n", " * Nearly identical to standard implementation.\n", " * This section is just a basis for comparison.\n", "* <b> Simulation of the two object tracking </b>\n", " * It tracks the objects, but the likelihoods seem incorrect." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import numpy as np\n", "from matplotlib import pylab as plt\n", "from mpl_toolkits import mplot3d\n", "from canonical_gaussian import CanonicalGaussian as CG\n", "from gaussian_mixture import GaussianMixtureModel as GMM\n", "from calc_traj import calc_traj\n", "from range_doppler import \n", "from util import \n", "\n", "np.set_printoptions(precision=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Target information" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names, p, v, w = load_clubs('clubs.csv')\n", "\n", "cpi = 40e-3\n", "T = 12\n", "t_sim = np.arange(0, T, cpi)\n", "\n", "t1, p1, v1 = calc_traj(p[0, :], v[0, :], w[0, :], t_sim)\n", "t2, p2, v2 = calc_traj(p[-1, :], v[-1, :], w[-1, :], t_sim)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sensor_locations = np.array([[-10, 28.5, 1], [-15, 30.3, 3],\n", " [200, 30, 1.5], [220, -31, 2],\n", " [-30, 0, 0.5], [150, 10, 0.6]])\n", "\n", "rd_1 = range_doppler(sensor_locations, p1, v1)\n", "pm_1 = multilateration(sensor_locations, rd_1[:, :, 1])\n", "vm_1 = determine_velocity(t1, pm_1, rd_1[:, :, 0])\n", "\n", "rd_2 = range_doppler(sensor_locations, p2, v2)\n", "pm_2 = multilateration(sensor_locations, rd_2[:, :, 1])\n", "vm_2 = determine_velocity(t2, pm_2, rd_2[:, :, 0]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Kalman Filter Model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "170\n" ] } ], "source": [ "N = 6\n", "if pm_1.shape < pm_2.shape: \n", " M, _ = pm_1.shape\n", " pm_2 = pm_2[:M]\n", " vm_2 = pm_2[:M]\n", "else:\n", " M, _ = pm_2.shape\n", " pm_1 = pm_1[:M]\n", " vm_1 = vm_2[:M]\n", " \n", "print(M)\n", "dt = cpi\n", "g = 9.81\n", "\n", "sigma_r = 2.5\n", "sigma_q = 0.5\n", "prior_var = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Motion and measurement models" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = np.identity(N)\n", "A[0, 3] = A[1, 4] = A[2, 5] = dt \n", "\n", "B = np.zeros((N, N))\n", "B[2, 2] = B[5, 5] = 1\n", "\n", "R = np.identity(N)sigma_r\n", "\n", "C = np.identity(N)\n", "Q = np.identity(N)sigma_q\n", "\n", "u = np.zeros((6, 1))\n", "u[2] = -0.5g(dt*2)\n", "u[5] = -gdt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Priors" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Object 1\n", "mu0_1 = np.zeros((N, 1))\n", "mu0_1[:3, :] = p1[0, :].reshape(3, 1)\n", "mu0_1[3:, :] = v[0, :].reshape(3, 1)\n", "\n", "prec0_1 = np.linalg.inv(prior_varnp.identity(N))\n", "h0_1 = (prec0_1)@(mu0_1)\n", "g0_1 = -0.5(mu0_1.T)@(prec0_1)@(mu0_1) -3np.log(2np.pi)\n", "\n", "#Object 2\n", "mu0_2 = np.zeros((N, 1))\n", "mu0_2[:3, :] = p2[0, :].reshape(3, 1)\n", "mu0_2[3:, :] = v2[0, :].reshape(3, 1)\n", "\n", "prec0_2 = np.linalg.inv(prior_varnp.identity(N))\n", "h0_2 = (prec0_2)@(mu0_2)\n", "g0_2 = -0.5(mu0_2.T)@(prec0_2)@(mu0_2) -3np.log(2np.pi)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2.89]\n", " [ 0. ]\n", " [ 0.56]\n", " [ 72.86]\n", " [ 0. ]\n", " [ 14.03]]\n" ] } ], "source": [ "print(h0_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Kalman Filtering\n", "\n", "### Creating the model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z_t = np.empty((M, N))\n", "\n", "z_t[:, :3] = pm_1\n", "z_t[:, 3:] = vm_1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R_in = np.linalg.inv(R)\n", "P_pred = np.bmat([[R_in, -(R_in)@(A)], [-(A.T)@(R_in), (A.T)@(R_in)@(A)]])\n", "M_pred = np.zeros((2N, 1))\n", "M_pred[:N, :] = (B)@(u)\n", "\n", "h_pred = (P_pred)@(M_pred)\n", "g_pred = -0.5(M_pred.T)@(P_pred)@(M_pred).flatten() -0.5np.log( np.linalg.det(2np.piR))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Q_in = np.linalg.inv(Q)\n", "P_meas = np.bmat([[(C.T)@(Q_in)@(C), -(C.T)@(Q_in)], [-(Q_in)@(C), Q_in]])\n", "\n", "h_meas = np.zeros((2N, 1))\n", "g_meas = -0.5np.log( np.linalg.det(2np.piQ))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "L, _ = z_t.shape \n", "\n", "X = np.arange(0, L)\n", "Z = np.arange(L-1, 2L-1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "C_X = [CG([X[0]], [N], h0_1, prec0_1, g0_1)]\n", "C_Z = [CG([X[0]], [N], h0_1, prec0_1, g0_1)]\n", "\n", "for i in np.arange(1, L):\n", " C_X.append(CG([X[i], X[i-1]], [N, N], h_pred, P_pred, g_pred))\n", " C_Z.append(CG([X[i], Z[i]], [N, N], h_meas, P_meas, g_meas))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Kalman Filter algorithm: Gaussian belief propagation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "message_out = [C_X[0]]\n", "prediction = [C_X[0]]\n", "\n", "mean = np.zeros((N, L))\n", "\n", "for i in np.arange(1, L):\n", " #Kalman Filter Algorithm\n", " C_Z[i].introduce_evidence([Z[i]], z_t[i, :])\n", " marg = (message_out[i-1]C_X[i]).marginalize([X[i-1]])\n", " message_out.append(margC_Z[i]) \n", " \n", " mean[:, i] = (np.linalg.inv(message_out[i]._prec)@(message_out[i]._info)).reshape((N, ))\n", " \n", " #For plotting only\n", " prediction.append(marg)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p_e = mean[:3, :]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AVCl/VaMfefoabE3TVDabVU1NjWpqaghEqHj+CuBIJKJEItFqpbBp\nmqGPwsXjLjqLwvF4PHTXBgAAUI2IwAAAVCE/6DiOU5LxD9lsVrZtq7GxMVglCVSbrsZHVEMUjsfj\nisViobs2AACAasBdGgAAVcZ1XVmWFWx81pdQUygUlMlkFI1G1dTURPQBilRbFLYsS5ZlBY8ThQEA\nAMoHERgAgCrRdvO3vox/8DxPpmkql8uprq5OiUQiVIGnOxvDAaVWjVHYNE1J28bN+FE4Fov1+QdQ\nAAAA6BkiMAAAVaB446q+xhfXdZXJZOS6rlKpVKvwA6D72kZh13WDKGzbtjzPq/go7M8UJgoDAAD0\nLyIwAAAVrjgo9TW02LatTCajeDyuhoYGog1QQoZhBHFU6joKh201bXEU9lfh+1HYf2VC8UZzYbo2\nAACAMCACAwBQoUo9/iGfzyufz6u+vl6JRKKEZ1oZGDGBUusqCudyOUlqtVI4LOHUP8eOorA/U7ht\nFO7L1y8AAAAQgQEAqEiu68q27ZKNf0in05KkpqYmYgwwSLqKwn48JQoDAACgI0RgAAAqiL/RlG3b\nktTnUGJZljKZjGpqalRTUxOKoNQT/ogMIIyKo7Dnea02miMKAwAAoBgRGACACuF5nmzbluM4fY49\nnucpm83Ktm01NDQEKw8rRRhCGNAT/v/zPYnCYQmnRGEAAIC+IwIDAFABXNeVZVkl2fzNcRyl02kZ\nhqFUKkVMAUKo2qKw53ntonA8HlcsFgvVtQEAAPQXIjAAACFWys3fJMk0TWWzWdXW1iqZTLJiFqgQ\nHUVhf6ZwoVCQaZqKRCKhjcLFX6v8KGyapkzTlEQUBgAAIAIDABBS/sq3Umz+5nmeMpmMCoWCGhsb\nFYvxLQJQyYqDrySicEiuDQAAoLe4wwMAIIT8zd9KMf6hUCgonU4rFoupqamJ1b9AFepOFDYMo91G\nc2HQnSgcjUaDecKxWCw01wYAANBdRGAAAEKklOMf/AiSy+VUV1enZDJZwjMtf5FIJIjoAFrrKgrb\ntq18Pl9RUdh1XeXz+eBtRGEAAFBpiMAAAISE67qybbsk4x9c11Umk5HrukqlUkHoQe/5URmoRETh\naKvxEWG5NgAAAB8RGACAMud5XhBapPbBoqds21Ymk1EikVBDQwMxoxN8XoDOEYWJwgAAIFyIwAAA\nlLG24x/6uvlbLpeTaZqqr69XIpEo4ZkCqGbVGoX9r9E1NTVEYQAAUNaIwAAAlCnXdWVZVkk2f3Mc\nR5lMRpLU1NTUp1nCALA9HUVhx3HkOI4sy5LruhURhf3QzUphAABQ7ojAAACUmeLxD33d/E2SLMtS\nJpNRTU2NampqiBH/hxm+wMCJRCKKxWKKxbbdflRSFJbU6uu0v1I4l8sF10AUBgAAg40IDABAGfE8\nLwgipRj/kM1mZdu2Ghsbg/gCAIOtO1G4OAgbhhGacOp/7fbDMFEYAACUA+4GAQAoE6Ue/5BOpxWN\nRpVKpRj/0Av+nwOA/tdVFDZNkygMAADQR0RgAAAGWdvN3/oSbP2VxNlsVrW1tUomk8QEAKETlijc\nmx8WdScK+9dOFAYAAKVCBAYAYBC5rivbtksy/sF1XWWzWTmOw/gHABUlLFG4NzqKwsVz4aVtUTge\nj4dyXjIAACgP3B0CADAIim/yJfU5ABcKBaXTacXjcaVSKQJBN5R6Yzg2mgMGTjVG4UKhELyPH4Vj\nsViorg0AAAweIjAAAAPMH9mQTqdVX1/f583f8vm88vm86uvrlUgkSnimYC4wEA5to7DrunJdV4VC\nQbZty/O80EdhX3EU9r9GEYUBAMD2EIEBABhA/vgHP0z0dfxDJpOR53lKpVKKRqMlPNPq5kcXVvYC\n4WQYhgzDaBWF/ZXClRyF/ceLN5oL07UBAID+QwQGAGAAtJ3x2JfN3yTJtm2l02klk0nV1tZygw8A\nXfCjcDwel9Q+CktqFYX7OqJnIHUnChdvNEcUBgCgOhGBAQDoZ/74h+LN33q7wtTzPOVyOZmmqYaG\nhiBoAAC6r6sobFmWpMqKwv6rT/zHicIAAFQfIjAAAP3IdV1ZlhXMbfRvtHsTgh3HUTqdlmEYampq\n6vNqYohxDwAkEYWJwgAAVD4iMAAA/cC/yS4UCiUZ/2CaprLZrGpqalRTU8MNegnwOQTQmeIo7Hle\nMGKhbRQO4+zwjqKwbdtEYQAAKhwRGACAEuto/ENfjpXNZmXbthobG4NNjgAAA8P/Ot5RFPY3mctk\nMu02mgsLfyM5X2dROB6Ph24TPQAA8B/cSQIAUEJtX2Lb2Y2y/3Z/TERnx/LDQlNTEzfdAFAGiqNw\nJBKRZVmqqakJNmMzTTMIq5UahePxuGKxGFEYAIAQIQIDAFACbcc/9HX1r2mayuVyqqurUyKR4AY7\nBLoT9gFUHj8IF68U9mcKV2oUtiwruC6iMAAA4UAEBgCgj1zXlW3bJRn/4LquMpmMXNdVKpVqdeON\n0gvbLE8A5a84+ErqNAr70dSfLRwWXUVhSUEM92cKh2kTPQAAKhkRGACAXiqeCSmpxyu7/A2F/Jtj\n27aVyWQUj8fV0NDATXM/4/MLYCB0FYVt21Y+n5dhGK1WCofp61N3o7A/U5goDADA4CACAwDQC/6M\nRMdxSjL+IZ/PK5/Pq76+XolEooRnCgAoJ9UUhf1XW1iWJcuyJG2LwsUbzRGFAQAYGERgAAB6yHVd\nWZYVrOLt6/iHbDYrSWpqagrVnEgAQN9VchT2z7MnUZh/BwEA6B9EYAAAuqnt5m+luFHdunWrampq\nVFNTE5qb+mrhj+sAgIFEFCYKAwDQH4jAAAB0gz/jsBSbv3mep1wuJ8/zVF9fr2QyWcIzBQBUks6i\ncKFQCP5dIgoDAIDtIQIDALAd/urfUox/cBxH6XRahmEEu8NjcLDSF0BfFW/uOVA6isKO48hxHKIw\nAADoFHeeAAB0otTjH0zTVDabVW1trZLJpLZs2UKEBAD0if8DRf+Hil1F4VgsFvwQMgw6isL+K3OK\no3A8HlcsFiMKAwDQBSIwAAAdcF1Xtm2XbPxDJpNRoVBQY2Mjq38rmL+6OCyBBUDl6SoKm6Yp13Vb\nrRIOWxQuPlc/CpumKdM0JRGFAQDoDHehAAAU8W+WbduWpD7fPBYKBaXTacViMTU1NYXmRhsAUBmI\nwkRhAAAkIjAAAAHP82TbthzHKcnqX9M0lcvlVFdX1+nmb4yDAAAMpGqPwtFoNJgnHIvFQnNtAAD0\nFREYAABtG/9gWVZJNn9zXVeZTEau6yqVSgWzDNvixnNwsTEcAFRfFHZdV/l8PnibH4X9lcJhuTYA\nAHqKCAwAqGql3vzNtm1lMhklEgk1NDRwMwkACJWuonA+n5fneURhAABCiAgMAKha/g7jpdr8LZfL\nyTRN1dfXK5FIdPvjAAAoV8VROJlMynXdIArbtk0UBgAgJIjAAICqVHzz2tcA7DiOMpmMJKmpqanb\nq4m5kQQAhI1hGMFma5K6jML+zN2w/HtHFAYAVDIiMACgqpR6/INlWcpkMqqpqVFNTQ03gwBQRfwf\nJFazrqJwLpeTpFYrhYnCAAAMDiIwAKBquK4r27ZLNv4hm83Ktm01NjYGsxMRHtvbGI4beQDoua6i\nsGVZkiovCudyOTmOo0gkokQiQRQGAJQl7lgBABXP39TGtm1J7W/iespxHKXTaUWjUaVSqV6vJt5e\nhET48GcKAK0VR2HP81ptNNdRFO7rK3QGkv/9hGEYchxHkoIo7H+fwUphAEC5IAIDACqa53mybTtY\nodPX1b+WZSmbzaq2tlbJZJKbOQAAuqk4mlZiFJb+E72l1iuFicIAgMFGBAYAVCzXdWVZVkk2f3Nd\nV9lsVo7jMP4BAIASqKQo7Hleu3Mrvj7/fdpG4VgsFvwiCgMA+hN3sACAilM8/qEUm78VCgWl02nF\n43GlUqmS3aAxOgAAgP/oKAr7M4ULhYJM01QkEglFFO5IR1G4+PsVaVsUjsfjrWYmAwBQCkRgAEBF\n8Uc2lGrzt3w+r3w+r/r6eiUSiRKeKQZbdyK8v4ocADDwioOvpKqJwoVCIXgfPwrHYjEZhsG/SQCA\nXiMCAwAqRqnHP2QyGXmep1QqFdyAojoUz5L2b7wBAIOrO1HYMIxWUThM0bTt9y7FUdj/3oYoDADo\nLSIwACD0PM9ToVBQoVAoyfgH27aVTqeVTCZVW1vbbzdYjIMoT57nKZPJBH+fLMsKdVQA0H/4Gj64\nuorCtm0rn88P2Nfv/njlSFdR2H+8eKYwURgA0BUiMAAg1FzXlW3bJRv/kMvlZJqmGhoaFI/HS3im\nCAN//nMsFlN9fb0cx1EkEhm0qACg/PH/f/kopyjcHzqKwoVCQbZtB48ThQEAnSECAwBCqXgzFan9\njVFPOY6jdDotwzDU1NTEy/+rjD9LOpvNqq6uTslkstVKq7ZRoXjnetd1ZRiGYrFYsLM9AGDwEYWJ\nwgCA/yACAwBCp+34h77e0JimqWw2q5qaGtXU1AzYDRLjIAaXv8LXH//gOE635j8X31RLraOwv5lg\n202KuOkGgMFHFCYKA0A1IwIDAEKl1OMfstmsbNtWY2NjEPVQPVzX1ebNmxWLxZRKpXr196n4ptp1\nXcViMUUiETmOo3w+L8/zgpjgP8ZNNwAMvu6+0qM7Ubgcf6jbURS2bZsoDABVirtdAEAoFI9/KMXm\nb4VCQZlMRtFoVE1NTdz0VJni1VH19fVKJpPt3qe3fyeKb6qTyWSwysxxHOVyOUlqt1IYADD4unql\nR3eicLl/L+FHb19nUTgej/NKFgCoQERgAEDZ8+e1lmr1r2mayuVyqqurUyKRGLQbHH8cAQaWP/7B\ntm3F4/EOA3ApGYYhwzAUj8eDmcHFUUEiCgNAOepOFPa/dodxJnx3onA8HlcsFiMKA0AFIAIDAMqa\n67qyLEue5/U5ALuuq0wmI9d1uzX7FZWnUCgonU4rFouppqZmwCO8/3e4OAr7K4ULhYJM0wz1PEoA\nqGSVPhO+oyhsWZZM0wz+/SIKA0B4EYEBAGWp7eZvfV0dadu2MpmM4vG4GhoauGmpMv6NbDabVV1d\nnZLJZDCvdzBV+iZFAFDJiqOw4zhKJBKSJMdxZJpmq5XCYYymXUVhScEPNP3PATPvAaC8EYEBAGWn\n1OMf8vm88vm86uvrgxu0chCJRAY9QlYDf/yD4zitVoCX441qZ1G4UCi0mkfJKiygPPivUgE8zwvG\n/3S0UrjSo3DxSmF/pjBRGADKCxEYAFBW/M26pPa7WveU67pKp9OSpKamJmatVqHi8Q+pVCp0N6Nd\n7VxfCUEBqAT8P4fOdDU+ohK+hhdHYf+H2pZlBfPu/SBOFAaA8kAEBgCUhbbjH/p6k2BZljKZjGpq\nalRTU8NNR5XpaPxDJegqKPjjLfyYwEtzAaC8tP0a7o//8UcAFX8ND1sU9s+zJ1GYH84DwMAiAgMA\nBp3rurJtu2TjH3K5nCzLUkNDg+LxeAnPtLQYB9E/Ohv/MBAG+s+0OCgkk8lWQSGXy0lSu6AAACgP\n/vgI/3sVojD/RgFAfyICAwAGjb+K0R//0Ndv/h3HUTqdlmEYSqVS3ExUoZ6Of6i0CF8cFDzPa7VS\n2L/pJgoDQOmVYj709qKw1PpreJhe7UEUBoDBRwQGAAwKz/Nk27YcxynJTYxpmspms6qtrVUymQzN\nTRFKozfjHyr974j//1VxFPaDQqFQkGmaMgyjXVAAAJSHrqJwRz/YC3sU9v8tJwoDQP8gAgMABpzr\nurIsK1g109fxD5lMRoVCQY2NjcGcvbCotJWog2Ewxz+ESUebzPlBwbZt5fN5ojAAlLGevtojbFG4\n+Fw7i8LxeFyxWIwoDAC9EK47ZQBAqLXd/K2v37wXv/S/qakpNDc6vrCdbznq6fgH/EdnUbhQKMiy\nLLmuG6zCCtssSgCodB292qOSRgB1FoVN05RpmpKIwgDQU0RgAMCA8FdzlGrzN9M0lcvluv3Sf1SW\n3ox/6KlqC54dRWE/JpimKdd1Q7tBEQD0p3J4VQ9RmCgMANtDBAYA9Dt/9W8pxj+4rqtMJiPXdSvi\npf/lcOMYNox/GBiRSESxWCwYsVIcE/L5fKtd62OxWKhedgyUSik2A0PlKKe/C92ZC1/8w7+wRdPu\nROFoNBrME/b/nQKAakYEBgD0m1KPf7BtW5lMRolEQg0NDaH/Zj7s5z8YSjn+IRKJEOF7oDgKJ5PJ\nVhsU5XI5SeFdYQYAla6rufCVGoVd11U+nw/e5kdhf6Uw34cBqDZEYABAv3BdV7Ztl2z8Qy6Xk2ma\nqq+vVyKRKOGZIgwGYvwDeqanGxSFKSYAQKXrThQO82ahRGEAaI8IDAAoKT8E2bYtSX0OP47jKJPJ\nSJKampoISVUoTOMfqnV1cXdedhzmmAAAla6rKGzbtvL5fKi/jhOFAYAIDAAoIc/zZNu2HMcpyXxQ\ny7KUyWRUU1OjmpqaivtmvFqDYU+UcvwDBk6lxwQAKFaJs6Er/es4URhANSICAwBKwnVdWZZVks3f\nPM9TNpuVbdtqbGwMNqZC9WD8Q2XpLCYUCgVZliXXdWUYRqsd3bnZBoDyQRQmCgMIP+6qAQB9UurN\n3xzHUTqdVjQaVSqVYvxDFRqo8Q+sxB48HcUEf56waZpyXbfdPGFutgGgfFRrFM7lcsHbicIAwoYI\nDADoNX+1Zqk2f/NXftbW1iqZTFb8N9NEyPYY/1CdIpGIYrFYsOq/qygci8VKMm4GAFA6Xf1wr/gV\nH2GPwv7iBKIwgDAiAgMAesVf6VGq8Q/+yk/GP1Qnxj+gWNso7K8ucxxHuVxOktqtFAaAwcIPdNvr\n6od71RKFI5GIEomE4vF46K4PQGXiLhsA0COlHv/gr/yMx+Os/KxSAzX+AeFlGIYMw1A8HpfUOgpb\nliWJKIzBUYkbgqF3+HvQtZ5E4VgsFroxQB1F4VwuJ9u2g2v2r5+VwgAGCxEYANBtruvKtu2SjX/I\n5/PK5/Oqr69XIpEo4ZmGA+Mgyn/8Q7mdD7YpjsLFcygLhYJM0wz16jIAqAY9GQMUxtnw/vfJ/vn7\n12fbdnAdsVgsWCXMv1UABgIRGACwXcXfuErtN8voKdd1lclk5HkeKz+rVDmMfyDCV4ZK35wIAKpB\nJUbh4lcKdLRS2P/hpc+PwmFcCQ0gHIjAAIAueZ4n27blOE5JNmOybVvpdFrJZFK1tbV8g1uFKn38\nA2F5cHUWhQuFQquXHPsvx+VGGwDKTyVG4WJtv6cujsJ+PCYKAyg1IjAAoFOu68qyrJJt/pbL5WSa\nphoaGoLZntXM/3xW00zJch//0FesLi4/Xe1YXwkhAcDgq6Z/xwdLNUdh//HimcJhuz4A5YEIDABo\np6yi7iMAACAASURBVO3csr5usvT/2bvXWFfO+773P3KRQ3KRazFtLMcCWm3t1IEdJy5qF25T+BKp\nqaMiRWqcIlBqHLvSgRSgaNLTokjhFJUQGJIjp/Cb9LgFWiuBYrg1KiByXKBwXNdwT5JXEhAZbeNL\nG1iX+FhuZLTYe3E492fOi61nNOQiubnIGXIu3w9gQJa0t2ZtksOZ3/yf35MkiWazmbrdrqbTKZs2\ntVAV6h92wY1981wlSOj1eoWsgAAAFGv5XG6MyVZ9RFGkNE2PHgrvcw2xKhS2P5v954TCAK6KEBgA\nsMCGdUVs/iZJQRBoPp9rOBxqOBxygdpCda5/4P3afKuCBBsKe54nSZeCBABAtdgNQ1edy6sSCu+D\nUBhAEQiBAQCZ/IVyEfUP8/lcURTp7OwsuyjHIlsf0NQL9SrXP9yuuqFKx4rDsUGCrazJBwlhGEpa\nDIUBANWz6VyeD4VtP3zdVn2sCoWjKCIUBrARd+QAgGyaII7jQuof4jiW67o6OTnRdDrlorOF6lr/\nACzLBwlpmi6cL4MgWPh3bJAAoF2a/DC3Ka76gK+IUPiQ7wvbf5//bxMKA1hGCAwALWeMURRFhdQ/\npGmqIAjkeZ5OT0/lOA4XmC1U5/oHYBN7jnQcR9Kt97rv+5KkKIrk+34WBueDBDQX4R9QT8cIhQ9p\n21C43+/Xsh4DwG4IgQGgpfKbv0mXl5VdlTFGruvKGEPwdwW3qySomyrXPwBFsysnbDCcpmkWJNhu\ndRsKM3kFANW1atXHplD4dqvmqnZtt00o3O/3s3oMvq+AZiIEBoAWWq5/2PciL4oiua6rfr+vyWTC\nRWMLUf8AvH6TfXJykoXCNkQIgkDGmFpvTAQAbWCvjYsIhat6jl8VCodhqCAIsp+fUBhoHkJgAGgZ\nY4zCMCxs8zff9+X7vsbjcbZEGu1S1/qHsqawqzb9g+PJdzBKIhQGGorzfrNtCoVtP3z+IWAdq4A2\nhcKSsp/dfqfVrR4DwC2EwADQEvmLOcdx9t78zRij2WwmSZpOp3v/fm1V9zoI6h8Wtf3nx2bLofDy\nbvXS1ZYbA6gOzv/tsSoUtufzfCgs3VotV8fz+baTwrZTmFAYqAdCYABoAXvhFoahfN/fe6l+GIZy\nXVfD4VDD4ZCLvhai/gHY31U3JqpbiAAAbZCfApZeX/Xh+/6lSWFbr1C3a+d8KGyHF+y9hXTr+yy/\n0RyhMFBNhMAA0HDL9Q/7SNNUnucpDENNJpMsuEC71LX+Aai6VRsT2f52GyLYAKGOIQIAtEF+09DR\naLQwKRxFkXzfzzYNreP53B7rVUJhHmIC1UAIDAANtbz5W7fblTFm598vSRLNZjN1u12dn59zMVeg\nOtVBUP8AHIadorJd600LEQCgyfLDF6smhZt0PicUBuqDEBgAGsgu1TfGLCzH2rV/NggCzedzjUYj\nDQaDWl2YVl1d/iybXP9QxJQ8ULZNIYI939sQodfrscncgXD+gMT7AFdDKEwoDBwLITAANIjtILMb\nDO3bx2WX/cdxrLOzs2wzI7RLU+sf6nRDBSzLhwiO42Tn/yRJFASBjDGX+oR5zwNA9bQxFM7vVyIR\nCgOHwt08ADTEcv3DuovDbSeB88v+p9NprS4266bKdRDUPwD1YPuC7cM6QmEAOJ59psOvsvKjrqFw\n/njXhcL9fj/rwScUBopBCAwADWCMURRFl+oflm1zgZimqYIgkOd5jVv2X0VVvWhvcv0D0AbLobAN\nEPKrRZZDYQBA9Wxa+dHkUDgIAgVBIIlQGCgKITAA1Nhy/cO+F0TGGLmuK2NMo5b942qaWv9wCLv2\nbgNl63a72U20tBgK28krQmFgN2ma8pnBwWxa+UEoDGATQmAAqKk0TRVFkZIk2br7d1NAFUWRXNeV\n4ziaTCa1ulhEcah/ANohHwrbG2xbKRQEQRYy1DFAAIBjO+RmgduEwnWuA9omFD45Ocn6hHu9Xq1+\nPuCQCIEBoIaMMQrDMLvAvOqFTv7CNE1TeZ6nIAg0Ho/lOE4Zh4w1qjI52tb6B/vnz80C2sx+j9jz\nf9M2JQKANml6R/yqUNgYI9/3s79nQ2H7MLNOPx9QJkJgAKiR5c3frrr0afkCKEkSua4rSZpOpyyl\nainqHwDkXWVTol6vV7sAAQDahFCYUBiwCIEBoCbspObtNn/bVhiGcl1Xw+FQw+GQi6GWov4BwO1s\n2pSoCQECsK8qrOhBtVR5lRGhMKEw2osQGABqII7jbPO3IgJg13UVx7HOzs6yC0Acx7HqINpa/wBg\nf00PELZF8Ie8Jr7H0Q5NP6cTCgOv484fACpsuf5h3wuSJEmy3/f8/Jz6h5ai/gFAkZYDBFsdYTuF\nJV3qE27SDXaTfhYAuN05PU1TQmGgpgiBAaCijDGKoqiQ+of81Gen09FoNCIAbinqHxZVZWM+oEm6\n3a663a76/b6kxQAhDENJuhQgAECTVLkO4qo2ndObHAp7npf9fUJhNAUhMABUjF2CZaen9r05zk99\nnp2daTabceFSIYcKIdM0VRAE8jyP+gcAB5UPENI0XVjlEgRBNnWWnxQGAFTT7UJhqd6rP+zx2nsw\nQmE0CSEwAFRImqaKokhJkhRywWSnPvv9fjb1yeRj+1D/cFh8voD17PeQ4ziSXr+5tuGB7/vqdruX\nAgSgypo09Qlc1VVXfzQxFLb1GYTCqDpCYACoCGOMwjDMbiT2rX/wfV++72s8Hmc322gf6h/2c9U/\nL/58gavpdDpZMCAthsJhGMoYk4XCvV6vdsuMAbRTmqatrbpZtfqj6aGwfZBJKIyqIwQGgCNb3vxt\n3wtGY4xc1802f2Pqs9rKmsym/gFAHeVDYcdxGrdLPQC0ST4w3TYUrlt4vm0o3O/3WeGCoyMEBoAj\nshu2FbH5myRFUaTZbKbBYKDRaLTy96MOovmof9genweg2pZ3qScUBoD6anMoHMdx9u/YTuF+v8/3\nFg6KEBgAjiS/o24R9Q+e5ykIAk0mk6yTC+2zqgcaAJpiORRu2oZEqCc6gbGM98R2VoXC9rye3zy0\nCaGwla/t6/V62fea3WiOUBhlIgQGgAMruv4hSRLNZjN1u11Np9Pb/n5MPlZLp9ORMWbv34f6h3Jw\nEwdU21U3JCoyPOD8AADF2tQT37RQ2Ia/y5PC+YedhMIoGiEwAByQMUZRFBVW/xCGoVzX1XA41HA4\n5AKhpah/AIBbVm1IZB+82vDAbtRDLyOAsjBwUYxtQmG7eWidzuv5jQNXTQrHcZytbiEURpEIgQHg\nAPIbBEja+4l1mqaaz+eKokhnZ2fZsthtMAncLNQ/AMBq9sbacRxJi+FBFEXyfb+W4QGAeuB8UrxN\noXBTzuubQuFvfvOb+tznPqePf/zjRzxC1BkhMACULE1TRVGkJEkKmf6N41iu6+rk5ETT6bR2FzZY\ntGsoT/1DcXgoArTDpvDAbtJa9/AAx8H3CHAcdQ2Fr1InlL9/vLi4UBAEZR4aGo4QGABKZIxRGIaF\nbf6WD/0cx9np92MSuP6ofyjONp8hej+BZsqHB47jXNqh3hhzqXeScwHW4b2BPK4djqOuofC2PM/T\neDw+9mGgxgiBAaAERW/+ZoyR67oyxhD6tRz1D4djH9xc5aEJD1mA+sr3LkpaCIWDILgUCgMAqq2q\nofCuDwlc19Xp6WkJR4S2IAQGgIKlaZpNEBVR/xBFkVzXVb/f12QyKeTChJCqOrYNDal/AIDDWg6F\nbXBgwwN77o6iKAsOeDAHANW1KhReXgFS5Ulhex8A7IoQGAAKlL8xLKL+wfd9+b6v8XicbWyzrypd\nyGA71D8AwPF1u111u131+31Jt1Zm+L6fhQeSLtVHAGgn6iDqYdMKkDJD4V3fH/P5nDoI7IUQGAAK\nUEb9w2w2kyRNp1NuJFuM+ofyMRkPYBe2I3g4HCpN0yw8iONYQRBk4UIVp8lQHPsdwusL1N+2obA9\ntx+6K34+n+tP/ak/dbD/HpqHEBgA9mSMURRFhdU/hGEo13U1HA41HA4Lv7Cgs7R6Vr0e1D8cBjft\nAHaVn+Sy3/92UrgqvZMAgN1dtSt+21B410lgz/M0Go2u/OsAixAYAHZkLwKiKJKkQuofPM9TGIaa\nTCbZclM026r3DPUPAFBvVd2MCED5GLZorrJC4W1RB4F9EQIDwA6W6x/2/XJPkkSz2Uzdblfn5+el\n1j90Oh0ZY0r7/bEf6h8AoHm22YyozOAAwOHxGW6+XULhfXieRwiMvRACA8AVGWMUhmEhm79JUhAE\nms/nGo1GGgwGXDC2kO2SpP4BANrh2NNkKA4bgAGwtj23S8r6ha9y/pjP5zo9PS3l2NEOhMAAsKV8\n/UMRm7/ZJf9xHOvs7Cy7WCgbncDVYl8P6h+ag88XgKtaDg5sdUS+dmq5OoLgEagmHgzAWnVuj+NY\nSZLI932laXqlB352WATYFSEwAGwhTdNsuWYRN152yX+v19N0OuVCscWSJMneV9Q/HF7RD0V4/QAU\nodvtZpvMSYuhcBiGklToEmMAQPm63a56vZ6iKNJ4PL70wM+Gwv/jf/wPdTod/ciP/MjC+X0+n2sy\nmRzxJ0DdEQIDwG0UWf9QhSX/TAJXQ/690Ol06PcCAKyVD4VthVCSJIrjWEEQZJ3DvV6PTeYAoCbW\nPfB79tln9Wu/9muazWZ697vfrfe+97163/veJ9d1rzwJHASB3ve+9ykMQ8VxrJ/5mZ/RL//yL+uj\nH/2oPvWpT+mNb3yjJOlXfuVX9Nf/+l8v/GdEtXRuEwSQEgBoraI3fzPGyHVdGWM0mUyOtuQ/DEMF\nQaCzs7Oj/PehhfqH0Wik+Xyu7/u+7zv2YbWS3YRv3QOZ/AOgbdjXlimN9rABnOM4xz4UHJjteDz2\n0tw0TRemyZIkUbfbvVQfgXIYY9isCQuqcm5ANV3l/fHSSy/pd3/3d/V7v/d7+v3f/33NZjP95E/+\npH7iJ35C99xzj37oh35oq/O77RJOkkTvfve79c//+T/XF77wBZ2dnekf/aN/VMSPhWpZ+6ZgEhgA\nVjDGKIqiwuofoiiS67pyHEeTyeToN2NMAh+PrQLp9/s6Pz+XMebYh4QNmJwHsE5Vzg12Ctg+XM6H\nwlEUyfd9QuES0f8KoCzXrl3Thz/8YX34wx9Wmqa677779P73v1+/+7u/q8cee0xpmuqee+7Rvffe\nq3vuuUc/+IM/uPJ8ZAPnIAiyASepOt9jOBzKowAgx07/2t1bi6h/mM/nms1mOj091enp6dFvFI79\n32+rNE3l+74uLi40Go00Ho95LQCg5qp4HrehsOM42feNnVQPw1Cu68rzPIVhqCRJCAEA4IB2fXBk\nz+0PPfSQPv3pT+vll1/Wf/7P/1k//uM/ri9/+ct6z3veo2vXrumBBx7QU089pZdeein7tcYYveMd\n79Cb3vQmvf/979e73vUuSdInP/lJ/YW/8Bf08MMP68aNG4X9jKguQmAAeI0NgKMoUqfTue3urLeT\nJIkuLi4Ux7Gm0ylLhVvMVgQEQaDz8/OF6gEmTY+LP38ATWd3px8MBjo9PdV4PM66hYMgIBQGCsZ0\nOMpg++CtTqejN7/5zfq5n/s5/dt/+2/1ne98R1/60pf0V/7KX9EXvvAF/aW/9Jf0sY99TNKt7uHn\nn39e3/72t/Xss8/qa1/7mv7e3/t7+ta3vqWvfvWretOb3kQtREtQBwEAKr7+wU7aDIdDDYfDSl0I\nEnod1nL9Q5XeCwCA9rGhcK9361ZweXd6SZeqI/juAoBi7PuQYN2v7XQ6estb3qK3vOUt+rt/9+9m\nqxDzzs/Pdc899+h3fud3FkLfn/u5n9NP//RP73xMqA8mgQG0Wr7+IU3Tvad/7cTnfD7X2dmZRqMR\nN04tRf0DAKAO7M70w+FQ4/FYo9FIJycnSpJEnudpPp/L9/3sYTkWMfUJ4FCucq7pdDoajUb63ve+\nl1U9eJ6nL33pS3rrW9+q7373u9m/+8wzz+hHf/RHCz9eVA+TwABaK01ThWFY2PRvkiSazWY6OTnR\n+fm5ut1qPmdjErh89mFAkiQ6Pz/PNutZhdejmQgFANRVt9vNgmG7/DhJkuyhue2l7PV6bDIHrMA1\nADbZ5/2xyz3DK6+8ogceeEDGGBlj9LM/+7P6qZ/6Kf2dv/N39NWvflXdbld33323/tW/+lc7HRPq\nhRAYQCsZYxSGYfYlvO/0bxiGms/nGo1GGgwGXPi1GPUP7cbrDaBJ7DVSPhS29RFRFMn3fXW73Uv1\nEQCAYkVRpH6/f+Vf9/a3v11/8Ad/cOnvf/rTny7isFAzhMAAWsXWP8RxnN3U7Pv72YnPs7OzrF+v\nypg8LYfdYMfzPJ2eni5s/obq4vMAANuzU8B2hQuhMHAZ1xXYZNdJYNd1NR6PSzgitEn10woAKEjR\n9Q9MfMK6Sv3DMvu+YekgAKBuVoXCdpM5e82VD4T33Xuhivj+xiq8J1A0z/M0Go2OfRioOUJgAK0Q\nx3G243UR9Q++78v3fY3HYzmOU9RhHgSTj8XiYQAAtBPfpZd1Oh31er1sZVQ+FA6CoBWhMABsYjcj\nv6r5fK7T09MSjghtQggMoNGW6x/2vdEwxsh1XaVpeuWJTzQL9Q8AAALMzVaFwnEcZ/URaZpmG8zZ\n6gj+TFF3TIejDITAKAIhMIDGMsYoiqLC6h+iKNJsNtNgMNBoNKr9xR0XqLvbp/5hFTudzesBAGiy\nTqejfr+fbW5k+4RtfYSkS5PCANAku17zz+dzOoGxN0JgAI1jlx7a+ociNn/zPE9BEGgymey0K2uV\nEDTuh/qHZtlUj2In1owx6vV6vNYAULBut6tut6t+v680TbNruDiOFQRB1jlsp4U5DwNoKyaBUQRC\nYACNkqapoihSkiSFTP8mSaLZbKZut6vpdMpESotR/9Audto7jmNJUhAE6na7WRBBjyUAFMtet+VD\nYTspHEWRfN9Xt9tdmBSuwnmYlTxYtmvnK9phn0lgQmDsixAYQGMYYxSGYfbFuu8FeRiGcl1Xw+FQ\nw+GwURf41A9cTdH1D8vYrK9a7MOfk5MTjcdjGWOyv79qcyM7JcznCQCKY6eA7XduXUJhACiD53nU\nQWBvhMAAam9587ci6h/m87miKNLZ2Vm2mQnaifqHdrEPf0ajkQaDgZIkkXR5c6N8j6XneZJ0Kazg\nvQIAxVkVCuf7hPMP51ixAaBpmARGEUg2ANRamqbZhX8Rk3hxHMt1XZ2cnGg6nTb25oHJ09uj/qFd\nrvrwZ1OPpXRrWmN5x3sAQHGWH87lQ+HlFRuEwjgkHgRjk33qIL7/+7+/hCNCmxACA6gtO/1bRP3D\ncuDnOA4Xby1Wdv3DMkL54zLG6OLiQp1OR+fn55dWE9zuYn25x3I2m2kwGMgYc2nJcq/XI4gAgBKs\nC4XjOFYURUrT9NLDuSLOxQR+AA7B8zyNRqNjHwZqjhAYQO0UXf9gjJHrujLGHCTwqwJCx/Wof2gX\n2yU+Go0K6/625yWm04BmI/yrtk01PmEYStKlczEAlG3X7w7P8zSZTEo4IrQJITCAWrGTdUXVP0RR\nJNd11e/3NZlMuJlrMeof2iVNU/m+rzAM1e/3S52suN10mkQQAQBlW1fjYx/Q2c5hOy3MNSF2xQMi\nrLPPEI7runQCY2+EwABqwV6o28CkiM3ffN+X7/saj8dyHKeIw6wNJoEXHbr+Yd0x4DDs9H+aphoM\nBgf/s992Oo0gAgDKsVzjk6Zpdi5ervGh2x1A0XadBCYExr4IgQFUXpqmiqJISZIUMv1rjNFsNpMk\nTadTpu5argr1D9xYHo59vR3H0Wg0UhAESpLkqMe0PJ1GEAEAh2WngO1D4Kuci5n6BHAInudpPB4f\n+zBQc4TAACrN9nUWsfmbJIVhKNd1NRwOC+v/rKu2T55S/9Au+dd7efq/Sp+FdUFEHMcKw5A+YQA4\ngFXn4vyKjfy5uErfIagGHgxgnX3eG4TAKAIhMIBKKnrztzRN5XmewjDUZDJRv98v6Ejrqe0XplWo\nf1h1TCjHpte76p+FTUFEfpO5fHVE1X8mAKibdd3uy//jAR2AstAJjCIQAgOonDRNsymLIgKNJEk0\nm83U7XZ1fn5O/UPLVaH+YVkVjqGp7Of/5OTkYK93mZ3b7HYPAMeXPxenaZrV+tgNP9M05QEdgEuY\nBMaxEQIDqBTbvVZU/UMQBJrP5xqNRhoMBlyAv6aNG8NR/9A+9vO/z+td9XPGqt3u7SqKIAjoEwaA\nA+ABHfKog0AZ7OovYB+EwAAqoYz6B9d1Fcexzs7OsotytFMV6x9QnjRNNZ/PFUVRqz7/9sGZ7TvO\nb2xkV1d0u91sMo3lygCwv1WB36oHdPkqH1v1k58UBtB8+zwg4OECitCOuyIAlWaMURRFhdU/2OX+\nvV5P0+mUL8sV2jQJXMX6h2Vtej3KRv3L6/J9wo7jrO0TtkEEy5WBq+GGHNuw59Z8KGwf0EVRJN/3\nWbXRIFzPoQy8r1AUQmAAR2MDiSiKJGnvAILl/sjj/dA+YRjKdV0Nh0MNh8OtzidtCuA3LVf2PE8S\ny5UBoGyrNvwkFG4eXjOsss/DQx7WowiEwACOIk1TRVGkJEkK+UIzxsh1XRljWO6/haYHX9Q/tEua\npvI8T0EQaDKZqN/vH/uQamHdcmX6hAHgcFaFwvk+4fyqDap8gPZq8r0bDocQGMDBGWMUhmFhm79F\nUSTXdeU4jiaTCRfGLVeH+odlTQ/ly2SM0Ww2kyRNp1OmV3d0leXKvV6PEAIAXlN0Lcjyqo1NVT6E\nwtXD9RxuZ5fPK+8rFIUQGMDB5Osfitr8zU7/jcfjbDMk3F4TQ0fqH9rHBv6O42g0GnETXKBNk2mE\nEABwOOtC4TiOFUWR0jS9tMkc5+Pj4s8f6+x6/xVFEfe6KAQhMICDSNM0W9JWxMVpkiRyXVcS03+g\n/qFt8oF/VR8ANe0hy+1CCIk+YQA4hE397mEYSuJ8DDSN67oaj8fHPgw0ACEwgNIVXf+wy+ZPuKwp\nIVUd6x+WNXEyuyxFB/5l/NnX8T14VduGEPnJNABA8db1u9uVG/mVHb1ej/NxyYquB0Gz7Pr+mM/n\nGo1GJRwR2oYQGEBp0jRVHMeK47iw+of5fK4oinR2dpaFD7i6JlycUv/QPk0I/JtqOYRgp3sAbVGl\nh7ib+t3Z9BOoL3u/A+yLBAVAKYwxiqKo0PqH2Wymk5MTnZ+fs7St5ah/aJ8gCDSfzwn8a2BVn7Ax\nRnEcs9M9gEaq6jls3fmYh3TAcaRputN97Hw+pw4ChSAEBlCoNE2zi8p+v793AGy7hO0SmMFgwMVp\nAepcP9DEadA6vx5lO/YKgCa8v45t0yZzvu+zqRFqa9ebeeBYNp2PeUhXDOogUAbXdZkERiEIgQEU\nxtY/hGEo3/f3ntbLT3tS/wDqH9rHrgDodrusAGiQfJ/wYDDYuKkR/ZUAUJ51m37aPmFCYaBYuz4k\noA4CRSFRAVCI5fqHfacamzjtWSV1mzyl/qF97AaQZa8AqNtnoYlWbWpk++TprwSAw1kXCsdxrCiK\nWLkBHAl1ECgKITCAvdiLwyiKso0o9glU0jSV7/vyfV/j8ViO4xR4tKijNjwQ6HQ6MsYc+zAqIU1T\neZ6nMAw1mUzU7/ePfUg4IBso2HN/vr/SLlXudrtZAMFUGoBja/Ly/+VQeNPKDXtObrsmvx+wPyaB\ncWyEwAB2Zvt6lzd/23Wyzhgj13WVpinTniWrw/Qj9Q/tY4zRbDaTJOofIGmxv9JxnI1LlW11BDff\nAFCOVSs38ufk/DmbUBgojuu6TAKjEITAAHZijFEYhtnTzPxN9y4BYxRFms1mGgwGGo1G3MS3HPUP\n7cM5ANvYNJXmeZ4kptIA4BDs9X8+FLbnZOp8gNV2nQT2fV9vfOMbSzgitA0hMIAryXc12gu/fX8/\nz/MUBAFLvw+oypPAbah/WFbl16NsTayAafPreWjrptIIIADgsPJTwNJinU8URfJ9vxXnZOogUIb5\nfE4dBApBCAxga+vqH5bZv3+7i6AkSTSbzdTtdjWdTpnYajnqH9rHVsAYY4428U1g2xybptKWA4he\nr0efMIC98f2x3qpQON8nnK/zoeMdbbHrQwI2hkNRCIEBbMXuCixpYwC8rTAM5bquhsOhhsMhF30H\nVrXgi/qH9slPfE8mE84BKNymAGK5T5gAAruo0vcojotzx+0t1/lwTga2x8ZwKAohMICNlusftr0Y\nsyHj8r+fpqnm87miKNLZ2Vl2IYj2amP9w7KqhfJlC4IgW9bGxDcOZV0AkX/ISZ8wrqqN31lAEW53\nTk7TNFu5Yasj6vB5ow4C6+xzrc8kMIpC+gJgLWOMoii6bf3DKqtCrTiO5bquTk5ONJ1OuUBqOeof\n2oeHQKiSTZvMhWEoSZcCCABAObY9J/OgDnW3y/UEk8AoCndfAC6xT+LtZFQRm7/lwz7HcbiZPrJj\nT55S/9A+tgO8jg+B6nSs2N3yJnNt3NAIwNW0aRXPoa3b+JNQGG3EJDCKQggMYEGapoqiSEmS7LXs\nyoaMVdj4CdVC/cNqTb6RtB3go9FIg8GgUq/5sR+IoJrW7XIfx/GlDY14/wDtVqXvtKbatPFnHMcK\ngqAyD+rSNCWQxkr7VIXYGjVgX4TAADLGGIVhmH1B7XvxFMexfN9n46cKOkbwRf3Dek39bKRpKs/z\nFIYh9Q+otU2bzMVxLOnWd2jduisBoI7WPahj9QaayvM8JoFRCO7GAFza/K2I+gcb/ozHYzmOU9CR\noq6of2gfY4xms5kk6fz8nKkYNMpyd6V0a+nyqmXKvV6P8AEASrTpQd3y6g1bHVHWeZnVIVhnn0lg\nYwzX0igEITDQcmmaZhdHRUwu2eAnTVONRiMC4Iqyr/MhdjCm/mE7TbppiKJIs9lMw+FQw+Gw9q/5\nVT8nVEy0T6fTUb/fX+iutA9Xq7RMGQDaYPlBXT4UDoKg9FCYczzKwPsKRSAEBlrM3qAWVf9gJaFh\nqgAAIABJREFUez+Hw6Gk/TeUQ71R/7C9plzUpWkq3/fl+74mk4n6/f6xD6kQTXl9cBj2+9Q+BM0v\nU7YPXbvdblYdUeZEGoByHeJhOva3KRT2fV9pmmYrN6j0QVl2PV9wnkGRCIGBFiqj/sH2ftrgJ0kS\nJuEqzk4rlnFRQf1D+9R5E0jOVShTfpmy4zgbJ9JsdQQ3e/XAuQOop3woPBgMsgd1qyp97MO6bXFe\nQFm4NkARCIGBljHGKIqiwuofkiTRbDZTt9ul9xOSqH9oo/xrXrdNIG93rHX6WVAPyxNp+fDB8zxJ\nu4cPODzOEUD9dbtddbvdhUqffUJhzgtYZZ/hGx4uoCiEwEBL2IuZKIokFVPVEASB5vO5RqORBoPB\nwpcanZjVV/RrRP3D7ur6eeE1B/a3LnygTxgADs8OyeTPy/ZhHedlHAN1ECgSITDQAmmaKooiJUlS\nyPSvXeofx7HOzs4Wdke36hpqYTfUP7QPrzlQvE3hQxRF8n0/Cx96vR59wsCREc40X77SR9LG8/LJ\nyUm22hJYtuv5IgxDBi1QGEJgoOGMMQrDsLDN3+yy716vp+l0ykUOqH9oIVsDc3JywmsOlGhV+HDI\nHe4BAIs2nZdtdYTv+2z+icK4rqvT09NjHwYaghAYaKgyNn+7yrJvJoGrb9/XiCqA4tTp8xKGoVzX\nXVkDU2dMc6EO1u1wH8dxVvdEnzAAHM7yedkORhhjeFiHBbtea9r6RaAIhMBAA6VpqjAMC9v8zRgj\n13VljGHZNyRRBdBGaZpqPp8riqK1NTB1xI0Y6mzTJnP5zYzsRBrvdwAoX6/Xy863+Ulh3/eVpmkW\nCNt/j3MzNvE8T+Px+NiHgYZoxh0cgIztpyqq/iGKIrmuK8dxNJlMtv79Op2OjDF7/bdRrl2nT6l/\naB9jjGazmTqdjs7Pz5kuvI06TXajWZY3mdvUW0koDADlyz+sGwwGCw/rPM+TxAqOtkjTdKfXdz6f\nUweBwhACAw1RRv2D53kKgkDj8ViO41zp1xOCNA/1D+Wp8ucliiLNZjMNh0MNh0NCI6Am1m1mFMdx\ntlqIJcrAfqgSwlUtP6xb1SlMKIw8OoFRJEJgoAGMMYqiqLD6hyRJ5LquJGk6nXLx0VBXCR6pf2if\nNE3l+75839dkMlG/3z/2IQHYw6bNjOwSZVsbQfCwHQJAAHn2uvoqKyft8M7yCo44jhUEASs4GmTX\n7ww7gAMUgRAYqDF7A2c3gykiALabPu079VflyUZcDfUP7WN7wNM05UEQ0FCbligvT6Pl+y0BAOVY\nt4KDWp92oxMYRSIEBmoqTVNFUaQkSQoJf5u66RPWu11QT/3D4VVhqsyG/o7jaDQaHf14DsF+Ftrw\nswLrrFqibGummEYDgMPbptan2+1mqzio9Wkm6iBQJFIeoIaMMQrDsLDN35Ik0Ww208nJSWGbPjEJ\nXG/UPxxWFS7Y86H/Lj3gAJrDXlvY80B+Go0+YeB1PEBEXtnvh021PkEQcG6uuF3fH/P5XGdnZyUc\nEdqIEBiokXz9Q1Gbv4VhqPl8rtFopMFgUNiFAiFw9a17jah/aB9CfwCb5IMHx3E2Bg+2OoLvDgAo\nV77WR1rd9c65uf5839edd9557MNAQxACAzVhA9uiNn/Lhz7UP0Ci/uHYjlVJsLwSgJsDALezHDzk\n+4Q9z5PE7vYAcGibut45Nx/fPpPAo9GohCNCG5H6ADVgp3+Lqn84xKQnk8D1YF8jJkHbKQgCzedz\nnZ6eynEcAmAAO1nVJ8zu9gDapmr1IOvOzas2ACUUrq75fM7GcCgMITBQYflNWYqqf/B9X77v0/mJ\n7CKV+of2YSPIRUU/tOp0OjLGFPb7AXViH1bng4d1u9v3ej06K1FrVQv9gHU2nZt5YHcYu54v7CpN\noAjtvusDKswYoyiKCqt/MMbIdV2laXqQSU8mgavPTgP4vk/9QwUc6jNj6x+63W5hG0ECwDpsZAQA\n1bPq3LzugR2h8HExCYwiEQIDFZPf/E1SIQFwFEWazWYaDAYajUYH+QInBK42O2VujKH+oUXCMJTr\nuhoOhxoOh1zMAzi4dRsZxXGcXfvUZXkyU6AA8up8TlgXCsdxnO1L0+121ev1eGC3A3tfvGsnMJPA\nKAohMFAhy/UPRWz+5nmegiDQZDJRv98v6EhRZ7b+odPpyHEcAuAW4FwAoKo2bTJnOytt6MAkGgAc\nBqs4qsPzPCaBURhCYKAijDEKw7Cwzd/yS76n0+nRJmnq/ES8adI0VRAEWa+UvZhDNZQ1PW+M0Ww2\nk6SjngsAYBvLGxmxPBlAXTR5FeS6VRy2Wi5N0+y83Ov1CrmfbZJ97ompg0CRCIGBI8vXPxSx+ZtU\njSXffOlXS5qmcl1XSZJk9Q++7x/7sFAyO/XtOM7BqmDqiPoaoJq2WZ7MJBqOKU1THq5iQVvOQflQ\neDAYLKzi8DxPUn2qfarO8zyNRqNjHwYaghAYOKI0TbObmKLqH+bzuaIo0tnZWfak9lhssNKWi6Gq\nskFgv9/X+fl59noQfDVXfup7PB7LcZxjHxIA7G3T8mQ7iZavjiB0AIDDWF7FkT8/22qfNofC+9wT\n87AJRSIEBo6k6PqHOI7luq5OTk40nU4rEbxW4RjabLn+YTAYHPuQcBtFhPKrpr4BoIk2TaIthw52\neTIAlIXhl1vsve2qap84jhUEAdU+V8SfD4pCCAwc2PLmb/s+1VsO+hzHqdSXBJOmx7FNEMgkcLUU\n8bldN/WN/fDnCNTDqkk0e81F6AAAx7Gu2qdNfe+7PiDgwQKKRggMHFDR9Q/GGLmuK2NMJSf+CBmP\ngyCwnYIg0Hw+Z+q7Ajj3Acdnr7NsHU4+dKBPGEUhoAGubpu+9263m9X7tP38zHkGRSIEBg4gv/mb\npEIC4CiK5Lqu+v2+JpMJXwzYqf6BoKpadnk9qtYFXld8FoBmy4cOjuMs9FUGQbAQCm+zsz3nDADL\nCOt2s6nvffn8XOdQuI7HjObhThEo2XL9QxGbv/m+L9/3K7/hE9Nwh7NLDywXItWyy+uRJIlms5m6\n3a7Oz8/ZNGJHfBaA9sn3CUvaeWd7zh8AUKzl8/OqTUCv8tCuCna9J2ZTOBSNEBgokTFGURQVWv8w\nm80kSdPptBZfCITA5aP+oZ3CMJTruhqNRhoMBrzuJWKyB2i+dTvbr+sTBgAcxqZNQK/y0K6OfN+n\n5g2FIgQGSrBc/1DEF5ENfIbDoYbDYS0CiTocY53tUv+Qx6R2PaVpKs/zFIahJpOJ+v3+sQ8JABpl\n0872+U2MpFsrMuq6NBn74zoKeUxtHkb+oZ20uJIjDENJ1QuFdx0osPt9AEUhBAYKlqapoihSkiSF\n1T/UNfAhZCzPLvUPqLZtPi/51QDUPwDAYazrq/R9v1F9ldgNrzVwXMsrOWwovG4lR50+s4TAKBoh\nMFAgY4zCMMye9O37BUPfJ1Ypqv6BkL5eoijSbDbTYDDQaDSq1QVsHfBZALAtGwpL0unp6UJ1hF0F\nVrUpNABog1UP7Vat5Dh0KLzrJLBd8QkUhRAYKMDy5m9FXOwHQaD5fF7rvk9CxmLtW/+AeqrTZpB1\ntc35lV5gAOts2mTOLk3u9Xq1nEIDsD2uFapnXSgcx7HCMJQxJguFe71e5VZyuK5LCIxCEQIDe0rT\nNPsCKar+wXVdxXGss7Oz7IYC7VZG/QMhfbWsej2MMXJdV8YYaj+OxJ7X+awA2Na6pcnHnEIDAKyv\n90mSpNR6n137oqmDQNFIl4A95Jf9FREA22X+vV5P0+m09jcFBCfFKKr+AfWSf90nkwmve41w7gNg\nbTOFRp9wfTH5CdTb8kqOTaFwr9cr5J7/KjzP03g8Pth/D81HCAzsYLn+oYjp3yYu8ycI2U/Z7wte\nn+qydTBNOh8AQJPs+v25aQrN932labpQHUGfMFAfPBSov031Pp7nSdqt833X9waTwCgaITBwRcYY\nRVFUWP0Dy72xShn1D6i2TqeTnQ+iKKIOBgAqroiwJx84DAaDlX3Cy1NoAIDDyNf7SKs738vcCJQQ\nGEXj7hLYkp3UsPUPRZzgoyiS67pyHKeRy71tqIWrOXT9A1ML1WAfMPX7/UbUwdQJU/EAqmK5Tzi/\n+iwIAvqEAeCIdj1H7zMJ/H3f931F/xhoMUJgYAtpmiqKIiVJUlj9g+d5CoJA4/FYjuMUdKSos0PX\ngnDjWB1hGCoMQ/X7fY3HY14bAEB2zWmvE/ObzNEnXA08SEce74d22XSOXt4I1AbGV+X7Pp3AKBQh\nMHAbxhiFYZh9qe/7xZ4kiVzXlSRNp9NGd70xXbc96h/ayT4QCsNQg8Hg4JtNAADqI98n7DhO5TYw\nAoA2W7cRaJIkStN0IRTu9XpbPbhzXVej0egQh4+WIAQG1lje/K2IsDYMQ7muq+FwqOFw2PgLc0Lg\n7Ry6/iHPvkZNfy9WkTFGs9lMknR+fq4gCPi8AAC2VtYGRgCA/eVD4SiKNBwOs4d3yw/uoijSYDC4\ntB+I53lMAqNQXAkAK6RpqjAMswC4iPoH13U1n891dnam0WhE6IbsifDFxYVGoxE1AC0SRZFu3Lih\nfr+vs7MzbswBAHuzPZXD4VCnp6cajUY6OTlRHMeaz+eaz+cKgkBxHPPQESgBgxVYJ01TdbvdbBPQ\n09NTjcfjrFv4c5/7nK5fv677779fn/zkJ/Xf/tt/kzFmpxA4CAL95b/8l/WOd7xDb3/72/XRj35U\nkvS///f/1k/+5E/qLW95i+677z7duHGjjB8VFcddJ7DElrobYwqrf7h586bSNNX5+fmlp3tNxiTw\nevbBQBAEOj8/L73/dx1eo8Oy9Q+z2Uzj8XjhgRCvxfHwZw+gaewqtn6/nz1otrVDdmNiGwrbpcq4\nGv7MAOzDruYYDAb60Ic+pGeffVZ/62/9LX3ta1/TBz/4Qb35zW/WV7/6VX3hC1/Qt771ra3POYPB\nQF/5ylf0/PPPZ7/+2Wef1cc//nH9tb/21/TNb35Tf/Wv/lU98cQTJf+EqCJCYOA1dvO3KIokae/N\nNewmXzdv3tRgMNB4PGbaD5JuPWi4ceOGOp0O/b8tYusfwjDU+fk5G0I2GKEygKqxy5Idx8lCYfs9\nFASBXNfNOuoJha+GyU9IPBTAetu+N+6880797b/9t/Uv/+W/1H/9r/9VX/7ylzWdTvX888/r3e9+\nt65fv66HHnpI/+bf/Bt95zvf2fh7nZ6eSlK2+qPT6ejzn/+8HnjgAUnSAw88oN/+7d/e7wdDLZFI\nAXp987ei6x9839fZ2Vkr+n9XIQhZVMX6B16jw4jjWDdv3lS32yX4B4AGqPuy7/wEWn5ZsjFGvu9r\nPp/L931FUSRjzLEPF6iNOp8XUK6rvjeuX7+uXq+nf/2v/7W+853v6Atf+ILe+c536rd+67f0oz/6\no3rb296mX/iFX9Azzzyj//W//tfCrzXG6B3veIfe9KY36f3vf7/e9a536X/+z/+pH/iBH5AkvelN\nb9Kf/MmfFPazoT4IgdFqdvM3W/+w7/SvdHnKs031D8sIGF9XlfoHHNZVgn8+L9XFDR2AprOh8HA4\nzOqKTk5OlCSJ5vN5dg1DnzAAHM58Ptfp6ak6nY5++Id/WD//8z+vZ555Rq+++qo+85nP6Nq1a/rU\npz6lu+++W3/xL/5FfeYzn5F0a1Xz888/r29/+9t69tln9Yd/+IeXrme5vm2n9qZTaD1b/5AkSWHT\nv77vy/f9hSV2QBzHms1m6vf7Oj8/r9QXLsFjeWzwnyQJ078AgFrpdrtZp3CapjLGKEkSRVEk3/fV\n7XazXe1PTk4qdW0DHEPdVwegPPu8N3zf12g0uvT3T05O9M53vlPvfOc79Y//8T9WGIZ69tlnLw0a\nnZ+f65577tHv/M7v6Ad+4AeyaeDvfve7euMb37jTMaHemARGKxljso0wigiAbddnFEV0fea0PWCs\nYv0DDsNuCCmJALgG2n6uAoBNVvUJ26AhDMPW9gkT+gEoW5qmW+0r5DiO3vOe9+hd73qXvve97+nG\njRuSJM/z9KUvfUk//MM/rL/5N/+mnnrqKUnSb/7mb+oDH/hAmYeOimISGK1i6x9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bAAAg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\nfFZ++tOfwjAMfPzjH8db3/pWHD9+3Ps14tm6urqKxcVFPProo22/l8KPm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oJy/HrgxAnE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E3ZNZEsXvISL9UisDzPJWuCc6ZaBL3eZW8R9nqfRGFFv3C5oH1EPp+P5HVHtcuTYqXZ2Vls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\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seXEH4NDQUJhMpsZeDtE1cQfg8PBwPmF1nTg3vvFoFYBPnDiBzMxMvPHGG+jZs6eGKyQi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\n2bKMHj36jJ7T2NiYRYsWZcWKFenatesfjv3www9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f268861f208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(25, 25))\n", "ax = plt.axes(projection='3d')\n", "\n", "ax.plot(p1[:, 0], p1[:, 1], p1[:, 2])\n", "ax.plot(p_e[0, :], p_e[1, :], p_e[2, :], 'or')\n", "ax.set_xlabel('x (m)', fontsize = '20')\n", "ax.set_ylabel('y (m)', fontsize = '20')\n", "ax.set_zlabel('z (m)', fontsize = '20')\n", "ax.set_title('Kalman Filtering', fontsize = '20')\n", "ax.set_ylim([-1, 1])\n", "ax.legend(['Actual Trajectory', 'Estimated trajectory'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "D = 100\n", "\n", "t = np.linspace(0, 2np.pi, D)\n", "xz = np.array([[np.cos(t)], [np.sin(t)]]).reshape((2, D))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gaussians = message_out + prediction + C_Z\n", "ellipses = []\n", "\n", "for g in gaussians: \n", " g._vars = [1, 2, 3, 4]\n", " g._dims = [1, 1, 1, 3]\n", " \n", " c = g.marginalize([2, 4])\n", " \n", " cov = np.linalg.inv(c._prec)\n", " mu = (cov)@(c._info)\n", " \n", " U, S, _ = np.linalg.svd(cov)\n", " L = np.diag(np.sqrt(S))\n", " \n", " ellipses.append(np.dot((U)@(L), xz) + mu)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in np.arange(0, M):\n", " plt.figure(figsize= (15, 15))\n", " \n", " message_out = ellipses[i]\n", " prediction = ellipses[i+M]\n", " measurement = ellipses[i+2M]\n", " \n", " plt.plot(p1[:, 0], p1[:, 2], 'k--', label='Trajectory')\n", " plt.plot(message_out[0, :], message_out[1, :], 'r', label='After measurement update')\n", " plt.plot(prediction[0, :], prediction[1, :], 'b', label = 'Recursive prediction')\n", " plt.plot(measurement[0, :], measurement[1, :], 'g', label='Measurement')\n", " \n", " plt.xlim([-3.5, 250])\n", " plt.ylim([-3.5, 35])\n", " plt.grid(True)\n", " \n", " plt.xlabel('x (m)')\n", " plt.ylabel('z (m)')\n", " plt.legend(loc='upper left')\n", " plt.title('x-z position for t = %d'%i)\n", " \n", " plt.savefig('images/kalman/%d.png'%i, format = 'png')\n", " plt.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/kalman/kalman_hl.gif\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two object tracking" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AFXMjG4bDoXq93tKbv/m+L9/31ev11G63czxSMBcYKIfJKGyMkTFGcRwriiJZ\na0sfhZ1sFHbnKKIwAAA4DBEYAIAVcuMfXJhYdvyD53my1qrf76vRaOR4pJvNRRdW9gLlVK/XVa/X\n90Rht1K4ylHY/Xp2o7kyPTcAAHB8iMAAAKzA5IzHZTZ/k6QoijQcDtXpdLS1tbWWG3wiKYCycFG4\n1WpJ2h+FJe2JwsuO6FmleaJwdqM5ojAAAJuJCAwAwDFz4x+ym78dNZ5aazUejxUEgXZ2dtKgAQCY\n36woHIahpGpFYffuE/frRGEAADYPERgAgGNkjFEYhuncRnejfZQQnCSJhsOh6vW6BoPB0quJIVYy\nA5BEFCYKAwBQfURgAACOgbvJjuM4l/EPQRBoNBqp2+2q2+0W5ga9zBG1KH+HAIonG4WttemIhcko\nXMaxONOicBRFRGEAACqOCAwAQM6mjX9Y5rFGo5GiKNLu7m66yVEREAUAbAJ3Hp8Whd0mc57n7dto\nrizcRnLOQVG41WqVbhM9AADwvOLcSQIAUAGTb7E96EbZ/bwbE3HQY7mwMBgMuOkGgALIRuFaraYw\nDNXtdtPN2IIgSMNqVaNwq9VSs9kkCgMAUCJEYAAAcjA5/mHZ1b9BEGg8Hmt7e1vtdpsb7BKYJ+wD\nqB4XhLMrhd1M4apG4TAM0+dFFAYAoByIwAAALMkYoyiKchn/YIyR53kyxqjf7++58S6iss3CnFT2\n4wdQPNngK+nAKOyiqZstXBazorCkNIa7mcJl2kQPAIAqIwIDAHBE2ZmQkhZe2eU2FHI3x1EUyfM8\ntVot7ezsFP6muejHd5iyHz+AcpgVhaMoku/7qtfre1YKl+n8NG8UdjOFicIAAKwHERgAgCNwMxKT\nJMll/IPv+/J9X71eT+12O8cjBQAUySZFYfduizAMFYahpAtROLvRHFEYAIDVIAIDALAgY4zCMExX\n8S47/mE0GkmSBoNBqeZEAgCWV+Uo7I5zkSjM90EAAI4HERgAgDlNbv6Wx43q+fPn1e121e12S3NT\n77hxFlVV9ecHoJiIwkRhAACOAxEYAIA5uBmHeWz+Zq3VeDyWtVa9Xk+dTifHIwUAVMlBUTiO4/T7\nElEYAAAchggMAMAh3OrfPMY/JEmi4XCoer2e7g6P9WClL4BlZTf3XJVpUThJEiVJQhQGAAAH4s4T\nAIAD5D3+IQgCjUYjbW1tqdPp6Ny5c0RIAMBS3AuK7kXFWVG42WymL0KWwbQo7N6Zk43CrVZLzWaT\nKAwAwAxWOAjmAAAgAElEQVREYAAApjDGKIqi3MY/eJ6nOI61u7tbmdW/rKTdz/2dlCWwAKieWVE4\nCAIZY/asEi5bFM4eq4vCQRAoCAJJRGEAAA5SjbtQAABy4m6WoyiSpKVvHuM41nA4VLPZ1GAwKM2N\nNgCgGojCRGEAACQiMAAAKWutoihSkiS5rP4NgkDj8Vjb29sHbv7GStricjGhLDEEAOax6VG40Wik\n84SbzWZpnhsAAMsiAgMAoAvjH8IwzGXzN2OMPM+TMUb9fj+dZTiJG8/1mjXOIgxDDYfDPRswsYIM\nQBVtWhQ2xsj3/fTnXBR2K4XL8twAAFgUERgAsNHy3vwtiiJ5nqd2u62dnZ1K30xWcSawtVaj0UhR\nFGl7ezsNBm6DJUlEYQCVNisK+74vay1RGACAEiICAwA2ltthPK/N38bjsYIgUK/XU7vdnvv3oRiS\nJNFwOFS9Xle/31eSJOkKuFarlb6t2L1oEARBGktcDAGAqslG4U6nk74w5ubnE4UBACgHIjAAYCNl\nb16XDcBJksjzPEnSYDCYe3UoN5LFEYahPM/T1taWOp2OarWakiTZ8zHu68QF/uwq4SiK0mgQhiGx\nAEBl1ev1dLM1STOjsJu5W5ZzIVEYAFBlRGAAwEbJe/yDi4fdblfdbpebwZKx1srzPEVRpN3d3fTt\nz/PIzgvOPlatVkujcL1e37NCjq8PoFrYPHJ2FB6Px5K07zxYlr8zojAAoEqIwACAjWGMURRFuY1/\ncLNjF42HVVH2mcDWWvm+r2azqX6/v+8FgUW/PtzXVKvVUr1e3zNH040dKetbpgFgXrOi8LTZ6lWI\nwuPxWEmSpO8WIQoDAIpo8+5YAQAbx8W4KIok7b+JW5SbHdtoNKbGw3mVPaKWWRAECsNQrVYr1w38\nsp/TWZsrBUFAFAawEbJR2M1WnxWFy7ThprueqNfr6QghF4Xd+ZyVwgCAoiACAwAqzVqrKIrSFTrL\nrv4Nw1Cj0WjP7FiUR3YFd7vdXukN+UFROI7jPXM0s6GAry8AVZKNplWMwtLz0Vvau1KYKAwAWDci\nMACgsowxCsMwl83fjDEajUZKkmRjxz+U3eQK7uxMx3WYjMKHvWW6bCEEAA5TpShsrZ06Vii7/8C0\nKOy+DxCFAQDHjTtYAEDlZMc/5LH5WxzHGg6HarVa6vf7xzI6oIzc30MZNkYKgqDwK7ir/JZpAJjH\ntCjsXiCL41hBEOzZlLNs58JpUTh7vSJdiMKtVosNRQEAuSMCAwAqxY1syGvzN9/35fu+er2e2u12\njkeKVZi1gd88EX5dgXueEFKv1/dtrgQAVZINvpI2JgrHcZx+jIvCzWaT2fEAgKUQgQEAlZH3+AfP\n82StVb/fT29AUR7Z8Q+DwaDUN86zQkgURfJ9nygMoPLmicJlPhdOXrtko7C7tiEKAwCOiggMACg9\na63iOFYcx7mMf4iiSMPhUJ1OR1tbW8d2g1X2cRBFVobxD8s4KITEcZyuhHchhFAAHB/O4etVpBfI\njuOdI7OisPv17ExhzvUAgFmIwACAUjPGKIqi3MY/jMdjBUGgnZ0dtVqtHI+0mlzILspN56zxD1U2\nLYS4ecJBEMgYs+/t0kX5nAFlx7+l4ihSFD4O06JwHMeKoij9daIwAOAgm3FnBAConOxmKtL+G6NF\nudEB9Xpdg8GgVDMFcUGVxj8sKxsCJKIwgM1EFCYKAwCeRwQGAJTO5PiHZW9o3OiAbrerbre7shsk\nxkHk5yjjH2q1mowxuR9LET+nk1HYRZDsCymTEYRQAKBqiMJEYQDYZERgAECpGGM0Ho81Ho+1s7Oz\n9PiHTRwdUCXWWnmepziOC/E5LMvNdL1eV71eT0eeZKNwGIaStG+lMABUzaxROpPz1Q+LwkV9AXAy\nCkdRRBQGgA3F3S4AoBSy4x/cyp1lblTiOJbneYwOWNI6VzMf9/iHTfqayEZha+2ezYeCIEhDSbPZ\nLN3KOACY16xROvNE4aKfG9253DkoCrdaLUYFAUAFEYEBAIVnrU1vvvLY/C0IAo3HY21vb6vdbq/t\nBue4xhFsAjf+Yd2fwypy/8ayUbhKb5cGgHnNE4XdedC9gFYm80ThVquVvgBIFAaAciMCAwAKzRij\nMAxlrU3j1FFXnxpj5HmejDHq9/t7bnxQDkUb/7AJqj5DEwDmNSsKW2vl+36pN92cFoXDMEzfEUIU\nBoBy484JAFBIk5u/LTuTNIoieZ6nVqu19Cxh7LWqlU9xHGs4HKrZbOY2/qFsq7aKYJ4ZmmWOIAAw\nr2wUTpJE7XZb0oVxRUEQlP58OCsKS0rfMeL+DthUFACKjQgMACicvMc/+L4v3/fV6/XSG7QiWOc8\n3bys4mbPfT248Q+dTieXx+VGNR+zVsZVIYIAs7h3qQDW2nS+elXPh/OuFHYzhYnCAFAsRGAAQKHE\ncbxnFt20m4d546kxRsPhUJI0GAyWXk2M1XPjH5IkYYRHScyKwr7vy1qbBhBWjqEK+PrFQar+Ilk2\nCrvrsjAMFYahJKVBnCgMAMVABAYAFMLk+IdlbxLCMJTneep2u+p2u9x0lFB2/EO/3+dzWFLZCNLp\ndNJ5wkmSaDweS9K+CAIAVTQZhbPnwyiK9rxIVrYo7I5zkSjM+R4AVosIDABYO2OMoiiae/zDrJXA\n1lqNx2OFYaidnR21Wq3jOORcVGEchJT/XF1rrYIg0Hg8znX8wypU5XN6nNzbpd2/zWwEcaGAKAxg\nE8w6HxKFAQB5IwIDANbGvS3SjX9Y9uI/SRINh0PV63X1+31uJlYg75vRVY9/INiuXzaCWGv3vCvA\nzZl0O9G7txMDQNHkMR/6sCgs7X2RrEzjFYjCALB+RGAAwFpYaxVFkZIkOfJNTPaGKwgCjUYjbW1t\nqdPplOamCM9z4x9ardZKxj/wNVI87lzgNnC01qYRJIoi+b6ver2+L4IAQBUt+s6Jskdht9EcURgA\njgcRGACwcsYYhWGYRtxFb1iyH+9WjsZxrN3d3XTOXlmwErXc4x9wvNymQ9lIQBQGsKmmvXOiSlF4\n8vpuWhRutVrpu0OIwgCwmHLdKQMASm1y87dlL97jOJbneWo2mxoMBqW50XHKdrwHWSZkr3r8A8pt\nWhTOBhBjTGnnZwLAIlw0XSQKlymaHhSFgyBQEASSiMIAsCgiMABgJdxqjnk3fzvssSRpOByycnTN\nlvk8rnr8w6KKdjzYz80Ldu8AyAaQIAiIwgBWogjv6iEKE4UB4DBEYADAsXOrf486/iHLGCPP8yRJ\nOzs76Zy8sirCjeOqMf4Bx+WgKBzHsaIokrVWjUYjDQRleqs0iimPzcBQHUX6WpgWhd04nezGm1WO\nwo1GI50n3Gw2C/X5AYB1IAIDAI5N3uMfoiiS53lqt9uKoqj0owM28WbEGKPRaFSI8Q+1Wm0jI/wm\nmYzCh22qVKYAAgCLmDVjvapR2Bgj3/fTn3NROPtCIABsEiIwAOBYGGMURVFu4x/G47GCIFCv11O7\n3U43lsN6LRJSiz7+AdW36KZKZQogALCIeaJwmTfeJAoDwH5EYABArlxUiaJIkpaOKEmSpOMfBoMB\nUaaENmn8A6uLy2Oet0qXOYAAwCJmReEoiuT7fqnPiURhACACAwByZK1VFEVKkiSXWZthGMrzPHW7\nXXW73X2PV/bYtgnB0M1wNsasffwDMEvVAwiA41PF2dBVPycShQFsIiIwACAXxph0REMe4x9Go5Gi\nKNLu7m46zzOLC/Hiy45/2NnZ4XOGUpkVQMIwlDEmfWdCvV6vZAQCAIcoTBQGUH5EYADAUvLe/C1J\nEg2HQzUaDfX7fcY/FNy01czZ8Q9uhnMRbcJKbOQnG0Da7XY6+iYMw3RszeQ8YQIBgKra1Cg8Ho/T\nnycKAygbIjAA4MistemKuDxW/4ZhqNFopK2tLXU6nZmPV4WAV4XnMInxD9gUtVpNzWYzHX/TarXS\nTeaCIJAxhigMYGNMi8LZjTfduyfKHoXd4gSiMIAyIgIDAI7ErfTIa/yD53lKkuTA8Q8oPsY/YJO5\nKOzOX25FXHajzMn4wb8RoBqq9oJuHibPiZsYhWu1mtrttlqtVumeH4Bq4i4bALCQvMc/ZMNhv99f\n6AKZm65isNbK9/3Cj38AVqler6ter6vVaknaG4XDMJSkfSuFUT7MgobD18Fsi0ThZrNZundPTIvC\n4/FYURSlz9k9f1YKA1gXIjAAYG7GGEVRlNv4B9/35fv+kcJhFS6cqzIOIggC1Wq1So5/qMLXGYoh\nG4WttWkAieM4/Tfk4gdxAEDVzYrCVRip466T3fG75xdFUfo8ms1mukqY8z6AVSACAwAOlb1wlfZv\nlrEoNzfWWlvJcLgpXLxym/iV7ealKhEe5ZNdMeaicJU2VAKARVUxCmffKTBtpbB7IdBxUbiMK6EB\nlAMRGAAwk7VWURSlmx8te0EaRZGGw6E6nY62traO/HgEvPXJruJ2NyvcqDyPr0ssatqGSkRhAJus\nilE4a/KaOhuFXTwmCgPIGxEYAHAgY4zCMMxt87fxeKwgCLSzs5POydxk7u+zTDMl3SpuY4z6/b6C\nIFj3IRUKL04gD9Oi8LTZmW50BHEAWK8yfR8vq02Owu7XszOFy/b8ABQDERgAsM/k3LJlNyxKkkTD\n4VD1el2DwYANkEoqiiJ5nqd2u62dnZ30hqXK0ZMbexRB1eMHACxq8rxojJExRnEcK4oiWWvXfl5c\n5hpiWhR2z839OlEYwKKIwACAPay16UqzPMY/BEGg0Wikbrerbreb2wVq1eNjkSy7iV+ZcUOFIpoV\nhX3f3xM/3LgWvpYBVJnbfDMbhd15sShReBlEYQB5IAIDAFLZC+U8xj+MRiNFUaTd3d30ohx7uZhd\n1Av1yfEPVdrE77AXEor6OQEmZW/+O53OnvgxHo8laV/8AIAqc1HYjR87KAq7sTple7FsWhSOoogo\nDGAm7sgBAOlqgjiOcxn/EMexPM9To9HQYDA4totOVgIfr2njHwAU36z4EYahJKIwkKciv5iLCxY9\nL+YRhVf5deFmyWf/bKIwgElEYADYcMYYRVGUy/gHa62CINB4PNb29rba7faxXWBy4Xp8Fhn/UKvV\nZIxZ4dEBWFQ2flhr97zwFwRBGgey8QPzIf4B5bSOKLxK80bhVqtVyvEYAI6GCAwAGyq7+Zu0/21l\ni6ry2IDjVLTZxu7zaK3l8whUkDvXuxd3rLVp/IiiSL7vq16v74sfAFBl014sW+YdFEW6tpPmi8Kt\nVit9QZAoDFQTERgANtDk+IdlL/Lc2IBWq7WysQFFi6dVkB3/sLW1xcU/sAFcGHBxgCgMYNO5a+M8\nonBRz5fTonAYhum7Q4jCQDURgQFgwxhjFIZhbpu/zTs2AMW1qZ/H43ohgRcnUGbTonA2fBhj9oUP\nwgA2Hef9apsVhbNjdcr8YtmsKCwpfe5upnDZxmMAuIAIDAAbInsx1263l94IyBij4XAoSRoMBmvZ\nWKgKN13rXtG87Odx3cdfNNwQoWqymwlJe6NwEAREYeB/8XW/OaZFYfcOimwUli68y6qMG3DOu1LY\nzRQmCgPlQAQGgA3gLtzCMJTv++p0Oks9XhiG8jxP3W5X3W53LRd9XGguL4oiDYdDdTodxj8AmMtB\nUTiOY0VRJGutGo1G+hZiwgCAqjvoHRS+7+9bKZw9N5ZJNgq7F//dvYV0YaVwdqM5zv1AMRGBAaDi\nJsc/LMNaq/F4rDAMtbOzk+6ojHLZ1PEPAPI3GYXdarijbqYEAGXnVgnXajVtbW1Vbta6O9ZFojDn\nfqAYiMAAUFGTm7/V63UZY478eEmSaDgcql6vq9/vr/1irkpjCFb5PIowxgNAddXr9Vw2UwKAMssu\nvqj6BpxEYaA8iMAAUEFu/IMxZs/bsY4aToMg0Gg00tbWljqdTqkuTItulX+XxzH+oQoxPo9V8gD2\nm2duZpnDh8T5AxfwdYBFEIWJwsC6EIEBoELciqsoiiRp6Xlc1lp5nqc4jrW7u5u+3Rflkh3/wBiP\n55XphgqogqqHDwA4iqqfG6dF4ex+JRJRGFgV7uYBoCImxz8cdHE476rNOI41HA7VbDY1GAwKd7FZ\nhRWoznE+D8Y/ACiqWeHDvZvFhY9ms5nO2ASAoltmdfgi58ayRuHs8R4UhVutVrqRHtevQD6IwABQ\nAcYYRVG0b/zDpHkuEK21CoJA4/FY29vb6nQ6eR8uMo7zov04xj9MU5UYD2C9suGj3W7vmSccBIGM\nMfvmCZcpfADAUcw6N1Y5CgdBoCAIJBGFgbwQgQGgxCbHPyx7QWSMked5Msao3++nKxCKqEorgfNm\nrdV4PFYQBMc+/qFMNxmrwNclkJ9araZms5mOIiIKo6istUQprMyscyNRGMAsRGAAKClrraIoUpIk\nc8/+nRWooiiS53lqt9va2dkp1cUinsf4BwBVNRk+3Nujsy+GTkYPvpcBWIdVbhY4TxQu8wtm80Th\nRqORzhNuNpulen7AKhGBAaCEjDEKwzC9wFz0Qid7YZpdNdrr9dRut4/jkHGAPFeOuvEP3W5X3W6X\nC+A5uL9//q6A8qnX6+lqMGlvFHZzJSfDBwBUXdXfRTEtChtj5Pt++nMuCruVwmV6fsBxIgIDQIlM\nbv626A3t5AVQkiTyPE9S+VaN8rb7561y/MNBfz4ArFs2CruVYtnwkZ2ryUoxAJuCKEwUBhwiMACU\nhNs197DN3+YVhqE8z2PVaMmte/wDXzcAish9n8xGYbdSOI5jBUFQ6pmZKBZeDMWkIr/LiChMFMbm\nIgIDQAnEcZzOO8wjAHuepziOtbu7m14AllEVbrqWWdFMyAeA+WRXAUvaE4WjKJLv+wtH4Sp8D0J+\n+B6MsiIKE4WxOcp75w8AG2By/MOyFyRJkqSP2+/3SzX+YdImX5yte/wDAJTdtCg8uZFSvV7fs/P8\nQd93Nvn7EYDqOWwTTmstURgoKSIwABSUMUZRFOUy/sGNkhiNRqrVatra2ip1AN5k6x7/MKnss5nL\nfvwA8nGUlXAAcJAij4NY1KxNOKschcfjcfrzRGFUBREYAArG3Xi68Q/L3mhaa+V5npIk0e7urobD\nYSUuXKoS7xZ5Hox/AIDVmDcKS8ptVj8AlMFhUVjSvtE6ZTo/ZmfKS0RhVAsRGAAKxFqrKIqUJEku\nF0xxHGs4HKrVaqnf76ePWYV4uknc+IcwDBn/UAL8+wKqZ9bbo8fjsaRyRw8sp0qrPoFFzYrCYRhK\nKvf5cZ4o7L4/EIVRdERgACgIY4zCMExvJJYd/+D7vnzfV6/XU7vdzvFIsUpJksjzPNVqtdLPcS6j\nRf8dctEPbAb3ducgCNTr9Q6NHpy7gc1ird3Yf/fZKGyt3TdzXapeFHaroInCKDoiMACs2eTmb8te\nMBpj5Hleuvmbe7tq1VRlRfOs51GG8Q9V+TwAwDKmRQ/3vT0IgnQlcTZ6AEDVZYPpvFG4bPF83ijc\narX4HoC1IwIDwBq5DdvymicYRZGGw6E6nY62tramPh7RrvgY/7A6/HsAkDf3/dy9C8e9ddhFAd/3\nVa/X962EA4Cq2+QoHMdx+jFupnCr1SrdRnooNyIwAKxJdkfdPMY/jMdjBUGwcdGwanP4kiTRcDhU\nvV5n/AMAVECtVktjhkQUrqKqXYtgeXxNzGdaFHbnx+w7KaoQhZ3s2L5ms5m+U8RtNEcUxnEiAgPA\niuU9/iEbDQeDwaGPV5WVj1W5OKrVajLGSCrH+IdNw00cgIMc9fwwLQpnV8EZY/YFD85DADbBrBfN\nqhaFXfydXCmc3YiUKIy8EYEBYIWMMYqiKLfxD0TDarDWajQalXL8Q1VeVACAdcne8Et7o3AQBERh\noIS4NsrHPFG4jO+kyG4cOG2lcBzHiqIo/XWiMPJCBAaAFchuECBp6VesXTSMoki7u7vpjeM8qhTt\n3HMp84WQe2Gg2Wwy/gEAcGAUdlHAWqtGo7Fn1/kyfx8Eqop/l/nbhPE6s6Lwd7/7XX3+85/Xhz70\noTUeIcqMCAwAx8xaqyiKlCRJLjdqcRzL8zw1Gg0NBoPSXdjgeWEYyvd9NRoN7ezs8Llck6q8KAKg\nmiajsAseVdlEqQr4PgKsR1mj8CKLWLL3j+fPn1cQBMd5aKg4IjAAHCNjjMIwzG3ztyAINB6Ptb29\nrXa7feRZhFW5WSnrc8mu5O52u+l4EKzePH/vZV9tDqBa6vX6nk2UJmcKS0ThdeD7BLK4dliPskbh\neY3HY/V6vXUfBkqMCAwAxyDvzd+MMfI8T8YY9fv99MIG5ZPdyK/f76czosvKXThX8WbHvXCzyAsN\nZX1hAkA5ufNUNgpXYV4mAOShqFH4qNfNnudpe3v7GI4Im4IIDAA5s9amu3vnMf4hiiJ5nqdWq5Xb\nyAAi1Xq4jfy2trbU6XSOFBmxOu7FHHcDAQBFV9TgAQBFMO0cmX0nhTGm0OdI945Q4KiIwACQI3eT\nldf4B9/35fu+er2e2u12LsdYpAuZZZUloC6zkR/Ww62+j+M4/fc8uRETABTdrCg8GTzYdR7ITxXf\nIVVFB23EedxR+KhfH6PRiHEQWAp3oQCQg+MY/zAcDiVJg8GAeX4lNjn+gc9l8Uy+kBDHsYbDoVqt\nlra3t/fM3GQlHYAyy0bhdru9J3gEQSBjzL55wpzfZnPfQ/h7Aspv3ijsFgWs+hw5Go108cUXr+zP\nQ/UQgQFgScaYdK5rHuMf3MiAbrerbreb+4VFWVbPzqvIz2Xa+Idpivwc5uG+psp4A5w95uzmi271\nvdtkyUVf93HGGMVxnN4QZGNw2T+fAOZT1vNe1qzgQRQGsOmO6xx51O8f4/FYW1tbC/8+wCECA8AR\nZVcGSlo6AFtrNR6PFYahdnZ21Gq18jrUyirqjegi4x+K+hw2jbVWnucpSZJDN1+cNU/OnQ/G43H6\n9uo8XhwCgFWYDB5udET2/Db5LgjOb8BevBhcXet+4YxxEFgWERgAjmBy/MOy39xXOTKgVqvJGHNs\nj7/pGP9QPsYYPffcc2o2m+r3+wv/e87eELRaLY1GIzWbTSVJovF4LEn7bggAoAzq9brq9Xr6wnQ2\nCrt3SnB+A6bjBZLqO0oUXoZ7txpwVERgAFiQMUZhGOay+ZskBUGg0Wh06MgA7Fe0t94f9XNZpOew\naYwx6aqKTqeTy2PWajW1Wi21Wq0984TjOFYQBOkNA/OEAZRNNgpnz28ueGTfKeHeCVF1VRgLAiAf\n80ZhSel84UXOH6PRSNvb28dy7NgMRGAAmFN2/EMem7+5t5/HcXzoyIA8FS2cVsEi4x8mVeHGsYxf\nU+5zliRJGu3zfGzHvVCUjSZuJR2bzAEos1nnN/eiF+c3bCJeGIAzbcROHMdKkkS+78tau9D4iPF4\nTATGUojAADAHa226AVQeq3/jONZwOFSz2dRgMOBCscTc+IdGo8H4h5LIjuxotVq5fs4O+7c8a57w\n5CZzbMIEoEymnd940QsAnlev19VsNhVFkXq93r656y4Kf+9731OtVtPLXvayPdepo9FIOzs7a3wG\nKDsiMAAcIruaJY/N34IgSF/FzXP14bzKuGrzIOt+LozyKJ8wDOV5Xvo58zxvrccz622D2RUibnwE\nmzABKIuDonAcx+mLXi6I8KIXgE100Nz1Rx55RHfffbeGw6F+9Vd/Vb/2a7+mX//1X5fneUdaCWyM\n0Q033KDLL79cX/ziF3X27Fn9zu/8jp544gm9+MUv1mc/+1kNBoO8nx4KiOVKAHAAa62iKNJ4PJbn\neUvfnBhjNBwOFQSB+v3+WgIw8uFGeYzHY+3u7qrb7R75a2PdIXtTuPEPo9Fo6c/ZcXJRuNPpqNfr\naXt7e88mc6PRSL7vK4oiNngEUCouCnc6HW1vb6vX66ndbqcvkLvvq2EYKkkSvjeitBgHgVlmfX24\nIPzud79b//Ef/6GvfvWreuMb36h///d/12/91m/pkUce0enTp/XJT35Sjz322Nznybvvvlu/9Eu/\nlP74Qx/6kF7/+tfru9/9rl772tfqz/7sz3J5big+IjAATOE2f4vjOJeLuCiKdO7cuXRkgFsVsy5V\nubFaR0BNkkTnzp2TtVaDwWBls5yLrOgh2xij8+fPK0kS9fv9Us1sdjcD3W5X29vb2traUr1eVxzH\nadQOgkBxHBf6cwBUEf/mlpN90ctFYTdbuExRmOAH4LicOnVK73jHO/TJT35S3/72t3XNNdfoDW94\ng77xjW/oda97na644grdeuut+vM//3N9//vfn3qefPLJJ/XlL39Z7373u9Of+8IXvqB3vvOdkqR3\nvvOd+od/+IeVPSesF3euAJCR3fxNen7Tk6PeeFhrNR6PFQRBuuJl3bhROTrGP5RPFEUaDofqdruF\nXf07L3c+cueRWfM2m80mb60GVoB/Y/mZtoFSdlampH3zhPn7B1A2R33hyL2b4rbbbtO73/1uWWv1\n/e9/Xw899JD++Z//WXfccYdarZZe85rXpP+dOnVK733ve/XhD39Yzz33XPpYP/nJT/TCF75QknTy\n5En99Kc/ze35odiIwADwv6y1iuM4Xf3rvjkf9QYjSZJ03uhgMGDDsBJzowSiKNLu7m6uq3+Lvoq2\nrKy18n1fvu9rZ2cnnbU2qcx//7M2mQuCgE3mAJTaQbMy3Uaakvad44AiYHU4joO1ds81a61W01VX\nXaWrrrpK73nPe2St1WOPPaaHHnpI//iP/6g//MM/1Bvf+Ea98IUv1C//8i/rq1/96oGPzdfr5iAC\nA4Au3Fi4GZuTK0uOEonc5lNFXH1Y5ug1aRXPJUkSDYdDNRoNDQaDQn0ui6RIX1PGGHmel47s2JQw\ncNAmc3EcpztOuw2YCCYAyiYbhV0McS/eB0GQngOzK4UBoGiWfZHgoN9bq9X00pe+VC996Ut1+vRp\nWWv1B3/wB/rMZz6jL3/5yxqPxzp//rze8Y536OTJk+lq4KefflqXXHLJkY8H5cLVP4CN5m4ggiCQ\ntSHC1EkAACAASURBVHbplXJuwzC3+dTW1hY3ISUWBIHOnTuXbtLF53K6Iv29xHGczt/e3d3d6NDp\ngki32003mWs0Gukmc57nsckcgFKq1Wqq1+tqt9va2tpSr9dLX3SPoii9FlvFzHRWfQJYlUXONbVa\nTR/+8If13//93/rBD36gv/3bv9VrX/ta/fVf/7Vuuukm3XfffZKkv/zLv9Rv/uZvHtMRo2hYCQxg\nY1lrFYbh1NW/WfOuNs2uGO33+4WNT1VaCSwdzwpUF/PjOM59/MOkqn0+1sVtJDQejwsxf7uIUWBy\nFZ17a7V7IczNE2YVHYCymTYe56CZ6ZzjcNyKeA2A4ljm6yOve4Y/+qM/0m//9m/rL/7iL3Tq1Cl9\n9rOfzeVxUXxEYAAbyRijMAzTb8LLrv4Nw5ANw9bgOAIq4x/Kx1qr4XAoY4z6/X4aAdahLF8vs4KJ\ne3GsXq+nb61mnjCAMpk1M92d45iZDqBMoig6cI+Ledx444268cYbJUknTpzQV77ylbwODSVCBAaw\nUSY3f1tkte60V23ditEkSY59xWheWHl6sCAINBqNtL29rXa7zQ3hAtb1NRXHsYbDoVqtlnZ2dhb+\nnPHv4YJsMGm322wyB6BSDpqZzjkOx4HrCsxy1JXAnuep1+sdwxFhkxS/VgBATuYd/zDpoI/Lxqd+\nv8/NwhrUarVcZpmucvzDJPd1U+a3Dq7ruLPRvtPprOUYquqwTeYk7QsmAFAW82yk2Wg00ndDzLpu\nLPP3bxwfviaQt/F4rK2trXUfBkqOCAxgI2TDxVHGP7jVgu5/fd+X7/uFmD26KFY+7uVifrPZZPxD\nSWSj/brHP2yKyWDiRke4t1ZL2hdMgKrje2l1zHuO44UvAMtym5Evyi18AJZBBAZQaZPjH5YNE8YY\neZ4nay3xqeSys5xZSVoezGwuhoM2mWMDJmwavrarafIcNzlTWHo+CvNiACaxOhzHgQiMPBCBAVSW\nMUZRFC08/mGaWq2mMAw1Ho/V6XS0tbVV+ou7KlygHnVVc3aWcxFifnaleVmt4iY4DEN5nscGjAVz\n0CZzcRyzyRyA0nPXkNNe+IrjWEmSSLowoogXvgAc5qjX/KPRiJnAWBoRGEDluNUabvzDsm/Zcxf7\n4/FYOzs7S+3KWgSbfmOSHf/ALOd8HPffobVWo9FIURTlPrP5sBcS+PpY3LQofNAGTM1mM5d3aQDA\nqkye48IwVJIkqtVqvBsCwLFhJTDyQAT+/+zdfYwk5X0v+m/3dHf123RbwbBgx7OzdnJiOCbXKLGC\nEju2gbyYEFaRAw5HiUFafCLFWPcey4k39u7lcJnA3lwf3T+CZUeGiORaBwtHkTdyMDp2ID6KDg4r\nGdsxEJzAzE7IAXvJiae36/3t/jH71Nb0dPf0S1XX8zz9/UgWGJbdqqnq6qpf/Z7vj4i0EscxfN9P\nbsbnvekWS88BoNVqKV8A1s00ncBxHMN1Xdi2zfgHhYjPYLlcRqfTWXgOIx/c5zcua9O2bQDM2iQi\ntZXL5WRGRLpTOL0aQrz44moI/c2a+UrLYZ5OYBaBaV4sAhORNqIogud5yRfrvDfYYul5vV6H67pa\n3czpED8wDdniHwZxWN9w6c9gvV4v5HzlccneuKxN13WTojE76IhIRelO4VqtNnY1BCNyiGhStm0z\nDoLmxiIwESlvcPhbFvEPg0vPfd9nMUhRIv6hWq0y/iEnWRex4ziGbdtwXVfJCJZle8kyj3FZm1xW\nTUQ6GFwNwaIwEc2CncCUBRaBiUhpcRwnS+2y6P4NggCmaWJlZQXdblfbm3BdOk/H7QfjH9QURVES\nwdLtdrXqwKeDTTJkjsUSIlLZuIgc0XQwuBqC1zm18EUwjTNPHMQll1ySwxbRMmERmIiUJbp/s4h/\nGCwY1mq1Pb+fLkXTZSF7/MMgnl+7fN9Hv9+HYRhoNBoLe4A66GfPh7nicMgcEclu3u+IdEQOsLco\n7HkeAOamE9FuHESj0Sh6M0hxLAITkXKyjn+IogimaSKKIiUKhlnQpeg4bD8Y/6CeOI7hOA4cx0Gr\n1UqG6ywCzw+1cMgcyYQviCgPk+Sm8zpHpK5Zvzts20a73c5hi2iZsAhMREqJogi+72cW/+D7PkzT\nRLVaRbvdHvn76VI01RnjH4ozz+djGV/CUHZGFUuCIOCQOSJS3rjcdHGdY266fPiCiEaZ53nSNE1m\nAtPcWAQmIiWIB3vf9wEgk+Fv03Ye6lQE1q2oHUURLMtSJv5hGJ2Ox6TSXdvjXsIQTYJD5ohId6Ny\n03mdI1LLrJ3ALALTvFgEJiLpxXEM3/cRhmEm3b+zDJ7iDbScSqUSoihCr9dTOv5BxW2eB7u2aRHG\n5QlzyBwRZaXIrs9JhmmWy+VkRQSvc0Tqsm0brVar6M0gxbEITERSi6IInudlMvwNADzPg2maqNfr\nqNfrU/1+unVqqr4/opAYxzELiQqRcWif6p8FmsxgnnC6KOw4DuI4TgbMie45FkuIaBKyXCumGabJ\nonB+GAdBo8xzbrAITFlgEZiIpJT18Lc4jmHbNjzPQ7vdTiYwT0q3GznV90fkyIZhCABaFIBVLkRO\nGi8ShiH6/T5WVlak6dqWYRuoGOmisGEYe4bMeZ4HgEPmiEht415+sShMpBZmAlMWWAQmIunEcZws\nYcuiE0sUnsrlMjqdzkwP8rpl6KosnSPbbDbR6/WK3qS5LcMDl+u6sCwLzWYTtVpN633m9UJNw4bM\niZeRHL5ERDoYLAqnX36JuRuD1zle64iyw05gKhqLwEQkFXETmlX8gyg8NRoNGIbBG9kLVCxSpXNk\nxTC/KIqK3iw6QBzHsCwLvu9jdXU1efBUBa8Zy0l8/4ihoeOGL4nMTSIi1aRffgHgioiMMA6C8iA6\n94nmodaTGBFpK4/4B9M0EQRBJoUnMYCMiiHiH6Io2pMjq2Ixe5lk0YVPJINxOZvif+ll1VxSTbQ8\ndCr4DVsREYZhsiJCXAvT2elENLl5rhc6XWuoOCwCE1HhoiiC7/uZxT+IuIBKpYJut8svyyFUKp6m\n4x/a7baWx1Ol4zHMsJckYggju/BJR4NLqoHdbrkgCIYuqeYLED3xgZx0Ju7J00XhUSsiGJNzkcr3\ncyQvnleUFRaBiagwortAPDDPWwBOxwU0m81Mh4WpXqRT0bD4h3G/lg8ecph3COOi8bNNWRiXszm4\npLpSqfB6RUTKGbYigkXh0ZZ532m0eZ5ZmNFNWWARmIgKEccxfN9HGIaZfKGNigug4WQvfE16PHkj\nJJcoitDv9wGA8Q+01AaXVItCCYfMEZEuxsXkiAHPgysieK0jmp3Mz26kDhaBiWjhoiiC53mZDX/z\nfR+maaJWq+UWFyB70VQnyxD/MEiH8yuKIuzs7KBer6Nery/FcSOaxLjuOVEoKZfLScYmCyVEauFq\npF2DKyLSRWHXdZemKKz6/Rzlb5bznucVZYVFYCJamHT8Q1bD32zbhuu6B8YF0F4yFh3jOIbjOHAc\nZ6rjKfZFxwcJFYiuft/3sbq6Kn38A1HR0kXhWq22tIUSItLbuKKw4ziI43jfkDldrnW67Adlb9bn\nL9/3+axLmWARmIgWIo7jpOMpi5u8MAxhmiYAoNvt5r7sXMaiqU4Y56EmcdzCMES1WmUBOIXXC5rU\nsEJJEAR7MvM5ZI6IVJe+1hmGMTY7ndc6or1M00Sr1Sp6M0gDLAITUe6yjn/wPA+maXLZ+ZxkKVIt\nIs5Ddiq+ZBCxHbVaDdVqFUEQFL1JM8njZ7+M5zBlp1Qq7Xmpki6UuK6bdBKnu+eIiFQzmJ0+mCkM\nqFkU5uo0GmfW88OyLDQajRy2iJYNi8BElBvRzRQEQWbxD5ZlJcvORdfUIqhYpBtHhpvTWeMfBul2\nbGQXxzFc14Vt28lxc1236M0i0taoIXO+78NxHA6ZIyoY70HmJ5pEOFCTaDjbttFsNoveDNIAi8BE\nlIsoiuD7fqbxD/1+HysrK+h0Osp0A9BwjH9QUxzHSfxD+rixEE+0GKOGzAVBkEQuMU+YaPH4OcvW\nuIGafAFGKovjeKbnWMuyGAdBmWARmIgyJYZEOY6DarU6dwFYZAmLJTCGYRRyk6dbkavI/ck6/kGH\nY6PCPoj4h2q1ik6nszQPW8uyn6SmYYWSUUPmKpWKVoOXZDDrwzwRTWdcUVi8ABNF4UqlUugLMMZB\nUB5M02QnMGWCRWAiyoyIf/A8D47jwDCMuX8/0XW46PgHyl5W8Q+0eK7rwrIsNJvNuT/XRJSfwSFz\n6Txh27YBqJmxSUSUli4K12q1sS/AuCqCZDLrSwLGQVBWWFEhokwMxj/M29UoW9ehCp2a01j0/oj4\nhziOGf+gkCJzuBdFt882UdqowUsiY1MUjbmcmohUNvgCjEVh0g3jICgr+j3NEdFCiZss3/eTgQ7z\nFFRk7hZloWg26fiHRqOR+U23DkW8UqmEKIqK3ow90jnc3W537HHT4RgQ6W7c4CVmbBLNjsv/5TNu\nVYTv+4jjeN8LsKyOIc8HGoedwFQ0FoGJaGYir3dw+NusBSGZu0V1u5lbRNFO5oI+jed5HkzTLDSH\nm4jyNS5PmEPmiEgn6VURwN6isOd5ABiVQ3IzTZOdwJQJFoGJaCZRFMHzvORtZvrBcJYCo+/76Pf7\nMAwjl27RrPDt/mSiKEK/3wcAdLvd3G+m2YWajTiOYds2PM/TNv6BiIYbt5zacRzEcZwMXcq6c46I\naJFGReWI+Ij0SzIWhSlLsz5LOo6Dyy67LIctomXDpzsimooY/hYEQbKsdN7fz7ZtuK6LdrudvKGX\njW4Punl2Ai+6oK/DsZEhTkEU7kulEjqdztSf7aK3XyYyHE+ieaWLwoZhsHOOiLQ0LipH5KdPE5XD\nhhHKgxjQTDQvFoGJaGKj4h8GiX9+0E2QyBwtl8sL6Radlyjs8MZuOMY/qEsU7uv1Our1+tTnuMqf\nCRZsiSYzrHNOvBSetkhCpAt+f+hnWFQO89MpK7M+S3IwHGWFRWAimkgQBPB9HwAyWQIqMkdnLTrR\nfLIufC06/iGNRbzZqdKJT0RyEfcB4mXfuCJJpVLRNk+Y3z0k6Hh+065RReEgCJLmmHK5nETl8LpA\neeBgOMoKi8BENNZg/MOkN7mjumbjOIZlWfB9X7nMURYbh1Mlz1lmRZxbRRbuiUgv44bMua6r9ZA5\nXfaDiCYzyfVOPDPpdr2j+cxzr89OYMqKOtUXIlq4KIrg+/6B8Q/DDCtqBUEA0zSxsrKCbrfLGyLF\npeMf2EWqFt/3YZomarVaZoV7viAhImGSIXOia45D5ohIZYPXO8dxAOxe93R/CUazmeX4sxOYssIi\nMBHtIx7WRPxDFsPfXNdNvrxqtZqSNz86dQLPuy8ydZHqdFzylldus4qf52novn9EeeOQOdIV7z9o\n0GBcTvp6J56tBvOEeZ9BB2EnMGWFRWAi2iOOY/i+jzAM57opEYW5KIpgmiaiKEKn00mWTpG6GP+Q\nj7wfJPlZHI4vEYgWb3DInCiScMgcqYjnJ6XFcbznRVb6egeAL8GW2DwDxi3LYicwZYJFYCJKRFEE\nz/OSL6h5b2qDIIDjOKhWq2i328rfJOtULJplX2QeIqb6ccn7sxEEAfr9vjafRSLSx6ihS6JAMjh0\niUupiUhlgy/BxApM8RJMXBPFNY/XOwJ24yDYCUxZYBGYiPYNf8si/kEUDLNcck7FkSn+YRBvjkcb\njGIxDCOXP0enFyREVKx0UbhWqy3VkDkiUt8090Oi6WbYygjf9+E4DldGaGSeTmDxQpRoXiwCEy25\nOI6TTpssun9FsTCOYzQaDa0KwDoVusRxnuRmRMQ/1Ot11Ot13nzmJOtzK45jmKaJMAwZ/zCnaW/a\ndbpWEBVt1JC5IAiG5mvyIZmIijZPnN6olREsChOPNWWBRWCiJSa6f7OKf/A8D6Zpol6vA5h/oBwV\nS+b4hzQdCm5Z39SFYYh+v4+VlRV0Oh3eNM6JPz8ieQwWhUfla3IpNS3CPJ19RAcZVhROX++4MkIt\ns14veJ2hLLEITLSE8oh/sG0bnuclxcIwDJUvzA3SodiYJvZn2E2FzPEPNJ7rurAsC41GA4Zh8KZx\nAjp9romWzaghc4vomuO1g4gG5XldGLUygnE5y4HHkrLAIjDRkomiCL7vZxb/IDoOy+UyOp0Oi4Ua\nSHd0qxL/wAfx3Z+BZVnwfR+rq6vJA8IiqPyC5KDzW4Xzn4h2FTFkjtcIIhq0qOvCuKKw4ziI4zi5\nJlYqlUye/Wh283T0qnqfTfJhEZhoSYibApGfl0WxdlzHocpFoVF026fB/VEl/mGQDjez855bfBlD\nRLTfNEPmWCAhItWli8KGYeyJy7FtGwAz1FXEOAjKEovAREsgjmP4vo8wDDN5wBEDp4IgGNlxqFvB\nVHeMf1CXip3bRERFGJcnzAIJzYLFGRok0zkxGJczmCkM8Jq3SLOeG57nwTCMHLaIlhGLwESai6II\nnudlNvwtCAL0+31UKhV0u11pbnIWQdfCtupFRF2Py0GGZXETEdHkxhVIXNdNisZ55AkTES2SeA4c\nlqEeBAFc1801Q51mZ5omms1m0ZtBmmARmEhTeQx/c10Xtm2j2Wwe+DZyWQtzqrFtG0EQsIhYsGk/\nL+nObZniH2TqfpmWyttORPMbVyAZNWSOiEhV4zLUFzFYcxnNeq8p4heJssAiMJGG4jhOBqBk0f0b\nRRFM00QUReh0Okv74KNTYTsMw+RGT6YiIh3M9330+30YhoFGoyHFDbkM2zArlbediPIzqkASBEFy\njwVgz6wFXk+ISNWXypMO1mRRePFs20ar1Sp6M0gTLAITaUa8vc0q/sH3fZimiVqthna7PfHvVyqV\nkgcknehQBBbxD+VyGY1GQ/kCsE7F+XHiOIbjOHAcB61WC7VarehNohGW5ZwkWibDCiSWZSUrpThk\njoh0Mm6wpigKD+YJ85o3XhzHMz13WZbFOAjKDIvARJrII/7Btm24rjtTwUnHIojqNzaDGbK2bSu/\nT7o46PMiuvHjOF7qbnwiIlmIIm+tVsPKygqHzC0pVbs+iaY1OFhzMEOdReH8MBOYssQiMJEGoiiC\n7/uZxT+EYQjTNAEA3W6XDy4pqha2wzBEv99HuVxO4h8cx1F2f9J0fOGQJoYxVqtVNJtNaW+oxXGQ\ndfuIiLKWvuaNGjInBi5xyByR/sT96DJ8vgeLwukXYWJV6uA1bxl+LuPMep8sZvIQZYFFYCKFiQcM\nkUeXxZeriAqo1+uo1+sz/346FuZUvXHJ6pjSYqWHMTL+gYhIHbMMmeN3MxGpLP0iDNhbFPY8DwBX\nR8yKmcCUJRaBiRQVxzF830cYhpkUf0W2ne/7WF1dTd7q0kWqFbYH4x/ETZmg2v4sA9EhEMcxTNNM\nBvcx/iFf7GImojwNyxNmtiYR6WzU6ggRH5G+LrIoPB7jIChLrPIQKSiKIniel9nwNxEVsLKykkQF\nzIsFxmINi3/QlQ7nWvoznI5/6HQ6LAQQEWlmXLamiGoSA+a4jFpufIFIaTwfhhu3OkJE5izD6ohZ\nzw/LsrC6uprDFtEyYhGYSCHp+Ieshr95ngfLstBoNGAYRmZfuDoU5gapsk+Txj+osj/LRMQ/NJtN\nGIZR9OZMhecTEdFs0kVhwzC4jJqItDZsdQQjc0ZzHAdXXHFF0ZtBmmARmEgRomCb1fC39HJzxj/o\nYZkjPVQvPortdxxn6Y4dERHtNWwZdRAES9UxR0TLY1RROAiC5Pm3XC4nqyNUjcyZpxO40WjksEW0\njPiUSaSA9JTVLArAi1hurmNXoMz7NGv8g6z7Mw0VbwLTxLEDgNXVVeb/EhFRQtz3ieGg4zrmKpWK\nssURIh0wDiIb43LUXddduhx1y7I4GI4ywyIwkcTSnR9ZxT84jgPHcdBqtZIHClKbiH+YNtJD55sl\nVaSPnWVZPCYFGfeCRyzHnqbjrlQqIYqirDeTiIjFEcmw6EeUv3E56ipd92a9XoioOKIssAhMJKko\niuD7fmbxD1EUwTRNxHGMTqeTe7ehzF2zs5Jtn5Y5/iFNtuMyiTiOYds2PM9Ljp0YBqQqFY/DQUSR\nfmVlJVmOqMJDBhEtj1HFkSAIklVkYgk184SJSAeD1710jrrv+wCwLzJH5fs1dgJTlpazYkAksfTw\nNwCZfGn5vo9+vw/DMNBoNBbyJahjQUgms8Y/pPEYFSOKIvT7fZRKpZmPHeVrcNWE6NxId56Ioj2L\nK0TLS8Yu0HHFkcEhc5VKRbrtJ1KZjNeEZZDOUQfGX/eKul8Tz1yzZgKzE5iywiIwkUQG4x+yGP5m\n2zZc10W73U6+GGk2shRNZ41/0JkMx2US4oVMvV5HvV7nsZNQetVEt9tFqVRKHiDSxRXDMEY+ZADq\nnJNEpLfBIXPiusUhc0Skq2HDNdPXPRGrI17ky37ds22bncCUGRaBiSQRRRE8z8ts+Fu6U7Tb7RbW\nocY34tnJOv5Bl9xSFc6vdGfpqBcysrxkWGbpoZnNZvPAYzKquCKWYFuWxWFNRCSNYXnC4rolIm/K\n5XJSGOF1i2g6vI+Tj3iuHnW/lh6umefLsHmeiRkHQVliEZioYOn4hyyGvwEXO0WL7DbU8aGhyCJd\nFvEPVIzBzlJdj53KRexSqQTXdeG6LprNJgzDmOn3SD88BEGAarU6dGiJWIKt43WSiNSRvm7VajVl\nhy0VKY5jbb/XaTb8jMht3MuwRRaFp2HbNhqNRqHbQPpgEZioQCKuQSzxyCL+QaZBYaIoVPQXZ9YW\nvU+u68KyrMzjH1Qu2g0j47nm+z5M00StVltYHjdNR9z8e56X6dDMccOabNsGUHw+Xd62t7bwyMYG\ndl56Cf/4L/+CmuuiEsfwGw1cccklOLezg0u7XZzb2cGbDx1C48gR3HbiBNbW14vedKKlNOy6FQTB\nyGFLOl63iGi5DCsKp6O+snoZNs9zCl82UZZYBCYqiCg6RFGUZADPIwiCZIq9yLEsmgzbkKVF749s\nRX1ZyXiexXEM13WTFzy1Wm3i/44WR3TYx3GMdrudWQF4mHRxZVg+nWxdJ7MYLPpe/r/+Fz7uuvh/\nAVwF4EMAHgJw7N/+DQ/9z/+J/3Th//8nAI9ub8M/cwa//1d/hY988Yu49p3vLHBPiAjYvW5Vq9Wh\nw5ZUzNUkypuMDQk0nVEv8YteIcHzirLCigLRgg0Of1tZWZmr8JMuNjWbTdRqNam+JFjUmo0oTuVZ\n1NetE1gWIv4hiqKpOktl+tzOSqXzKT2kTxQzRsn62IzLp8uy62RRtre28Nnjx9F/4gn8nuPgIQDv\nAHAcwKcAHEr9/T0Dfz2G3ULwPQBeA/CgaeJzN92Ex375l3HnqVPsCiaSyLgcdBmXUBMRzWtcUdhx\nHMRxPFHc16wvCPhigbLGIjDRAsVxnDzgp78gZi2czFpsWhQdi4yLiLjIK/5BZ7JEj6QHi7Xb7cK3\nZ5FU2ddhQ/o8z8v0z5j22jdJLqfotJNtCfY3/vZv8V9vuQU/apo4hYsF3j8E0AIQASin/n7wr4/i\nYgH4j8TfRxEefOwx/MFf/zUOXX89i8FEEhqVqxkEgZIvs2Ylw70HES1OuihsGMaeFRJ5xX3xOkNZ\nYhGYaAHSw98ADH1DOO3FXWSNLmOxSVdFxD/oVqQvkijezzpYDODxyNu4IX0y/ewHu07SDxiiYC3D\nEuztrS380S234BHT3FP0bWG38Gte+GuQ+vvBv/oXfr0oHoti8DEAj7ou/Mcew+9//euMiCDpyHTN\nkMG4XE0Ox6RlwWLd8hlcITGYKQyAKyNIKvK0khBpSsQ/+L6fLP9NfwlM+4Ughsn1+300m81MBsrl\nRedO4KyFYYher5cUpxZRAJb1vJlFkedaHMfo9/uwbRudTmfmArBOx0NGQRCg1+uhXC5jdXV1TwFY\n9p+9eLio1+toNpuo1+sol8vJy0DLsuC6LsIwXNjnYHtrCyduugk/aZr7ir4mgDsA3A3gVgDfB3Dy\nwt/fPfDX71z49aJ4/DAuRkR8DLs5wv/bhYiI//M3fgPbW1sL2T+iScl+/SiKeJllGAaazSaazSYq\nlQqiKIJt27AsC47jwPd9RFFU9OYSEc0tHfUl7tkajQbK5XJSGDZNE67rIgiCie7ZOBSOssZOYKIc\nRVGU3Nwe1PEwyZvjKIrQ7/cBYF8Xm6x0KwID2e8T4x/UtYjsZprfvF3aMnX2TNNtl8cS7HT+7793\nHFSxt+h77MJf7wHwEQAPAvhBrYaXul18PAxRiWP8XqOBKy65BJ/Y2UG7Xsd/fOklvDkIkmIwIyKI\n9DOqW04MxxRFY+YJE5EuxPO/mNkTBAFqtdrILPUoivYNk3YcZ+YGE6JhWAQmysFg/MNBxdpJbnQ9\nz4NpmqjX66jX60rcHKuwjdPKcp+KiH9I07FTe5HEZ5LF+10ynk9Ff8YWYdTAErECBcgum257awsP\nHT2K121uJvm/oqs3XfT9BwC/8YY34N+98Y1oHDmCPzhxYmzBVhSW73riCfyo4yDE8IiIewC0XBfm\nY4/h5PPP49jp0ywEEylo3HBM1YbMyfa9R8Vi1yaNIs6NYVnq4kX+tddei8suuwzvete78O53vxvX\nXntt0sQwDdd18fM///PwPA9BEODXf/3Xcffdd+Oee+7B5z73OVx22WUAgPvuuw+//Mu/nPm+ktz0\nexoiKlgcx/B9H2EYTpx3Nq54IuIfPM9LhhipQsaiUBay2Cd2kGZrkedaXoVFXT8vRRGfsXK5vFSf\nsXRReFi33ayFFRH/8Pnt7ST/9w5czO/9FHbzfb/TauF/nzK/d219Hfd94QtJMfgfH38cZhQlERHp\nYvCncCE6YnMTnz1+HPd94QsT/zlEJKdxKxxUGDIn07YQkToGBwN/4xvfwFNPPYWvf/3ruOeee/DC\nCy/g7W9/O6Iowje+8Q389E//9ETPHYZh4Mknn0Sz2UQYhvi5n/s5vO997wMAfPSjH8VHP/rRZPwq\nzwAAIABJREFUvHeNJMYiMFGGoiiC53nJ0uFJbwpHFX/SRYxOp8M3yxLI4kY/vTRdLA8qAouO0+Nn\nUg2+76Pf70+1ckLHz8K4brtJCyuD8Q/p/N/D2O3+fRi7BeBn19Zw/5e/PHN3rigGf+Nv/xZ33XIL\nftQ0k4iIPd3AF/78u554AttbW+wGJtLMqBUOYRjCcRzEcbxvOCYLsUQks0mixVqtFm644QbccMMN\nAIAf/vCH+Mu//Et87nOfw2//9m9ja2sL73rXu3D99dfjuuuuw9VXXz3yWUR0D4v8YfFn63i/S9Ph\n0ytRBkT3r+u6AJBJh4Lruuj1ejAMA+12W8lik45Fxnn2KT1AbHV1lRECGcv7XPM8D71eD7VaTdnP\nZN6K/rynB2e22200Go2JV2NM8nurTnSc1Gq1ZLCoKA67rgvTNGHbdpJln8Q/PPYYHhiS/ysKwR8D\nYB05go05CsBp177znfjYU0/hhzfeiLvqdUTYjZkQBeCz2O0IfqPj4MRNN3FYHJHm0kPmWq1WMmQu\nDEMOmSOpyDRDgNT3ute9Dm9729vwzne+E9/+9rfxT//0T7j99tvxve99D7fccgsuu+wy3HLLLfjM\nZz6DF154Yc+9ahRFuOaaa3D55ZfjF37hF/COd7wDAPDAAw/g7W9/O+68807s7OwUtWtUID7BEs0p\njuMkb2fWToR0YXGwUKhK/i+NF4Yher0egN2hfjJkk+pUpM/zMyLiH0zTnKqwOC3Vj0fR1ykxONPz\nPHS73cyic3TuMEsXVprN5p7CyvdeeAGf/JVfwb2bmyjjYvzD3QBej90O4FMA/kO9jhM33ph5Pq/o\nCv7Y00/jhzfeiBdKpaQA/EfYLTxvAPj89jYeOnqUhWCiJSJWN9TrdTSbTTQaDZTLZQRBAMuyYFlW\n0v2m8vcqEelj1hcE6UzgSy+9NCn6fu9738MzzzyDX/3VX8Xf/d3f4YYbbsCb3vQmfPCDH8T58+dR\nLpfxzDPP4OWXX8bTTz+N5557Dr/zO7+Dl156Cd/61rdw+eWXMxZiSbEITDSHIAjgOA6iKJq7UBDH\nMYIgSN7IyVIonIfqRa1hZtkn0dVdr9fRarW0LSjpKIoinD9/HkEQZFpYpGwFQYBerydNTIeq1z5R\nWPnBq6/ikd/4Dbztn/95aPzDpwB8DsB319bwyaefxv/1hS/kFskgisFvfN/7YGI3emKwI7i5ucmO\nYKIlJSJvarUaGo0GWq1WstLK932YppkUhcMwzPzazM5PIsqTbdtotVpD/50o+j788MPY3t7G3/zN\n3+Dd73432u128ms6nQ7e85734PHHH8ell16aXK8+9KEP4cyZMwvZB5ILi8BEMxDxD77vJzef894A\nep6H8+fPo9FooN1ua3FDqWohJCuyxz/odHzy2Bff97Gzs4NKpYLV1dXCC4s0nOu6ybWTL1my8cjG\nBu7d3Bwb/9A/fBgn/vzPcfkb3pBLYWXQnadO4eSRI/ABdgSTFHT5/tRNOvZGfC/UajUA2BN743ne\nQq5dtFz4UoBGyaITeJxSqYQf+7Efw7Fjx/Cv//qvSWOZbdv46le/ire+9a149dVXk1//F3/xF3jb\n29429faQ+tRuMyQqQBRFSeZYFsuExZCeKIrQ6XSSycgkp0mLjUEQoN/vo1KpoNvt8oZQIXEcw3Ec\nOI6z5+ExbzoU5Re5/SKmw/d9rK6uKr9yQib25uae+Id7cDH+4cV6HYeuuw533n8/3vimNyEIAvi+\nDwD7hsxlaW19HcdOn8aJm26Cub2ddAS/ht1u4AhAa3MTnz1+HPd94QuZ/tlEo/C7XX6TDJkTA+Y4\nZI6IZDNpETjtlVdewe23344oihBFET7wgQ/gxhtvxAc/+EF861vfQrlcxvr6Ov74j/84p60mmfGJ\niWhC4qZRPOxm8YArlqmJZWy6FYBLpdLSDegQGdHiC9swjKI36UDsWrgoiiKYpsmXMjNY5DkkYjqy\nin/QoQCfle2tLfzjc8/ti3/wATy7trZv+JsorIgXmkEQwHVdlMvlPUXhLM6PtfV1bHz5yzh59Cia\nm5t4DbvdwCIewgRw1xNPYHtrK7d4CiJSW7oobBhGcu0KwxCe5wHI94UWES2neTqBX/e6103131x9\n9dX45je/ue+f/9mf/dnUfz7ph99qRBMYFv8w7+9nWRb6/X4yiIdFODWMKxbFcQzTNOE4DjqdjvQF\nYJ3OuSyKeIO5siwAy0nEdNRqNbTbbT6gZ2h7awsnbroJ/7dp7ot/+JdWa18BOE3kCQ9mcnqel2Ry\nZrH8WnQEP7u2hgexPx/4jY7DfGAimti8Q+b4Ip3SeD5Q1sTKRKKssBOY6ABRFMHzvORLfd4v9jAM\nYZomgN3hb+JGU8cutGXqrkvHP3Q6Hd4AKiSOY7iumwxeWFT8w6Bl+rzMIh3T0W63OaQvY9tbW3jo\n6FH8++1tXImLHcARdjsG2ldeOXF3rcjkFLmc6eXXrusiiiKsrKwkS7CnLeSLjuA/eMc70HLdJB84\n6Qje3sbJo0dx7PRpdgQT0cTEfb64D4jjOOkU9jwPURQlqxwqlQpfQhLRxGZ9QWCaJhqNRg5bRMuK\n31xEI4juX9d1ASCz4W+9Xg/VanXfoCkdiz86FrUG90kUplQd6qfLMZp1P0T3tuu66HQ6hRWAdZDn\nuRRFEfr9PjzPQ6fTYQE4Y6IDOD0M7jAu5gF/DED3zW+e+fcXy68Nw0Cz2UxWwIRhCNu2kxUU07wQ\nXVtfx6Hrr4cJJPnAok+mBeDezU08srEx8zYTEaWHzDWbzaFD5oDdFSocMkdEeRBNKkRZYScw0RAi\n1zWr4W8HDTDSpRC3bEQBMQxDxgcoKAxDnD9/nt3bkhNd9tVqVbmXLCpIdwAPDoMTObsnjxzBsRMn\nMvszy+VysgQ73Wnn+z4cx5k4T/jOU6dw8vnn0bwwyA7YjYV4GLsdzM8++STzgYkoM8OGzIk5AosY\nkEnyYxwEjTLrucEiMGWN30xEA8RQm6wKwGEYotfrIY5jdDqdoRPsdS0C67hfYp+CIMDOzg5KpZLS\nBWAdj9EkXNdFr9eTqnt7WY/FOK7rJl32rVYrt+O0zD/7RzY29nUAiyiIEwB+c20t11iFdKedOM6i\n007kCdu2PTRPOJ0PbAJJLMTHsFvE/vy5c3jo6FHmAxNRrgzDQKvVQqPRwMrKylyrHIiI0kzTRLPZ\nLHozSCMsAhNdkB7+Bswf/yByRnu9XnJzOKojYJkLEKoR2ZaLKEzR5Cb9DImuHdu2sbq6Kv3wvmWl\n+nFS5Zq+vbWFs08+uacDOD0MzjpyZOwwuDwMRke0Wi1Uq1VEUQTHcWBZFhzHge/7iKIoyQc+eeTI\nnkFxAGMhiGgxxH3g4JC5er2OcrkM3/eTAZnjhsyR2nhMaZR5zg3btlkEpkwxDoIISJZxZRn/IGIC\nhsU/LAtVCiGTSseEqNz9m6bbMRonDEP0+32Uy2V0Oh0u08xYqVRCFEVz/z4i/1d02fM45UPEQKyf\nO7evA9gH8OzaGjYkGKw2uPxaREeIVTvlchmHrrgCd/zFX+D/+cVfROvcuT2REGUAOy+9VNwOkLa4\n7JvGSQ/IBEYPmUsPyOT5pAceRxpl1jiIdrudw9bQsuKTFS01saxfxD9kcQM2GBMwSQFY10KcTvuV\nPq7ihp3kMu5cE0MZDcNAu92WsrCo0+dlVr7vY2dnJ8n/leU46fhAJ2Ig7oQcHcCTEp12YiWGYRgo\nlUq4/A1vwBvf+U48j72REB8D0H/+eUZCEFGhRg2ZEysHx0XfENHysiyLncCUqeVsTyTCxfiHMAwz\n6/51HAeO4+zJNJzmvyf5iJtzsRSnVCrBdd2iNyszuhQeR+1HHMfJQ1W73Ua1Wi1g6+gg6esnj1P+\n0jEQLVzsAI4AfOfSS6XoAJ7EYKfdb/7n/4zf+2//DV8wTbRwcUjcG00TJ266SdrCNhEtn1GrHMSQ\nTIBD5lTD1QE0yjznhuM4aDQaGW8RLTMWgWkpRVEEz/OSC/K8X9hRFME0zWT427RdorreMKheYBTH\nNR3/IM4bkp+IFQDAWAGJxXGMfr9faMyK6teqaQzGQLSw2wEsuoHvf+97lS2UHj5yBP/uqqvQOnMm\nGRInMoLN7W188uab8cEvfhFH3vIWrKysaPvdS0SLkWXRr1wuJysd4jhOZlCEYQjXdZOXXmI1Gq9f\nRMshjmM+w1CmeDbRUhHdv6KTM4v4B7F8eWVlBaurqzMVMJapAKGKIAjQ6/WS/FhxXHW86dbh3Bv8\nDKVjBVZXV5W4eVL9OjDL9odhiJ2dnX2fM8rPsBgIXPjrySNHcNuJE8VtXAYaR47AxG4H8OCQuD/Y\n2sKfnzrFIU1EJLVSqbRnyFyr1eKQOSKFzfPCSMdnTyoWO4FpaaSHemUV/2DbNlzXnXv5surFn1FU\n3K/B+AfDMIb+Gl3odmPBWAF1uK6b5JwN+5xR9nSJgRjnthMncPLMGTQ3N5MCcHpQ3PP//b/j3Pe/\njzcdPowoihAEQXJvMLj0WrfrIxGpadSQOV6/5MI4CMqaWBVAlCUWgWkpiHytrOIfwjBEv99HuVxG\nt9tVosuwSKrcFA2Lfxikwn4sKxErEMcxP5cSi+MYlmXB932srq5ONDyT5qdzDETa2vo6jp0+jRM3\n3QRzexuvYSAW4tw5nDx6FMcuFLzTRZUwDBEEAfM4iUhqw4rC6eiIdFG4Uqlk8uxDRLOb91mYn1/K\nEu9oSWsi/sHzPADZxD94noderwfDMDKbXq9ix+wkVPrCGhX/MEi3Y6XL/oicbxHLomrBRodjMU4U\nRTh//nzyooUF4MXRPQYibW19HRtf/jJOHjmCB7E/FuLezU08srGx578RQ5rE0utGo4GVlRUEQQDL\nsrj0moj2kaXJQVy/DMNAs9lEs9lEpVJBFEWwbRuWZcFxHPi+jyiKit5cIpqCDNcY0gufvkhbURQl\nNztZxT/k1b2mSyFuGLFvsn6BpeMfWq0WarVa0ZtEUxDHz3EcVCoVNJvNojdpZrJ+RiZ10HXM9330\n+33U63XU63Wp9jfra7Bs1/RliIEYJDqCT11/PVrnzu35dy0A8SuvjP3vB4c0RVGUrCpyHAflcjkZ\n0CTzdxwRLZ9RQ+aCIEiGzInrF4fMZYffBTTKrOcGzynKA4vApB1xoyOWc2ZRAA6CAKZpYmVlBd1u\nN7eLMS/0izVJ/MMg2Yo781J5f+I4hmmaCMMQzWYz+cyTXPiipVjLEgMxzNr6Og6/970wH30Ur+Fi\nLnAEoLe6OvHvM8nSa3GvIQoq/C7XH+/ZSAXienTQS610/A3PayI52LaNer1e9GaQZlgEJq3EcYwg\nCBAEQWbdv+khYbVaLZcbI51vtmQtMgZBgH6/j2q1ina7rfUx0FH6+HU6HRaAJZUu1E/6ooWyJWIg\nXsNu4TfJxsVuDMQxjWIghrntxAn8H//jf2D15ZdxLy7u+/HvfAfbW1szFcBFF51YEeQ4TlIYFvFT\nIouTBRUiksm4l1ocMkeUj1lfGooBykRZYhGYtCEyQbMa/jZLl+g8ZI9NmJVsReB5uxJl2595qbg/\nrusmN0WGYRS9OZlR8ViME4Yhzp8/nxTqdbu2qWAZYyAGra2vo/2TP4l7X355Ty7wqZdfxv0bG/j4\ngw/O/WeILrtarcYuO6IlosN39uBLrXRRWLzgGnypxWvYcDo+x1GxLMtCq9U6+BcSTYFFYFJeOv5B\nPIjNy/d9mKbJLlHNLLqwT9kal8utWwFVRelj4HkeTNNEo9HgMraCLHMMxKDm+fNJAfgsLsZCPPvk\nkzN3A48yrMsuiiIEQcAuOyIN6fb5TReFDcNIXmoNrnRIX8OIaLx5OoEbjUYOW0TLjEVgUlocx8lD\nVVbxD47jwHGchWdX6lrEkmW/so5/0OVtvyzH5yBhGKLf7+eey03zEYV6z/MyH6CZNxU+B9NY9hiI\ntNIVV8AE8BqAP0LqZ3HuHE4ePYpjOXZEj1p6HQRBEmPDggoRyWrYkDkRvee6Llc6EOVIRFISZUmd\npzOiAXnEP/T7fQBAt9td+IOYKsU41WRd2OfN7eKlu0oNwxh6DHT4/Ki+D6JbqFwuo9PpKFXMOuhz\nXS6XEUXRgrYmG/Erryx1DETabSdO4OSZM2htbiYFYFz4672bm5nFQkxicOl1usuOBRUikpl43hL3\n0uPibyqVytKtdNClQYSyF8fxTPfFpmkyDoIyxyIwKWdw+FsWhQZRZKrX66jX6/wCz1CRha284h90\ny2+WtfA4Lv6B5OL7PizLQqlU0jJCR9bPyCjbW1t47uzZpY+BENbW13Hs9Gmcuv56tM6d2/PvWtgt\nmBdlsMtuWEFFZHEuW0GFiOQ2bsic67qMvyGaEwfDUR74RE1KiaIIvu9nGv9g2zY8z0O73Ua1Ws1o\nS6enehfgKEXtl8h1rtVqWhalsiLrzyUMQ5imiVKppFxX6TJJD1qs1+vwPE/ac2pZiCzg+7a3lz4G\nIm1tfR2H3/temI8+ihYuZgP7AJ47ezbzbOBZTFpQ4YAmIjno1BCQhVFD5kT8TRzHyfWL8TdEB2Mc\nBOWBRWBSQhzHSVdMtVrN5MFHZIzKsnRZ1yLwoi0i15nHKl+zdObrcExU24c4jmGaJsIwRKfTSTLa\n6eLLlSIKBCILOB0D4QN4dm1t6WIgBolYiA9tbuIhpArk29u5ZwPPYlx0xOCApkqlwmIUEUll2a5h\nfClAo8wzGI5FYMoaX7+R9ET8g+d5sG07k6VEruui1+vBMAy02+3CC8A6W2RhS+Q6+76PTqez0MF+\nqpKp8CjiH0zTRLvdRqPR4M20pMIwRK/XA4BMo1ZofiILGLgYA7EB4KrDh6UqcBZBxEJ8Ym1taDbw\nIxsbBW7dwURsRL1eR7PZRKPRQLlcRhAEME0TlmXBdV0EQSDNdV1nLPgQTWfwGlav13kNIxrDsiy0\n2+2iN4M0w05gklo6/qFcLs99QyA614IgkC5jVKZinIrS8Q95Fw95rLKXxWBGHpPFmGRQn2om+Uyr\nUPAZzAIWTAClK64oaKvksra+jqsOH0ZrezuJhIiw2xWx89JLhW7bNEYNaBIvzZnFSUQyGxZ/IzqF\nxTWMmeiki1nvIW3bRqPRyGGLaJnJUwEjShEZUr7vJ8Pf5p3MHgQB+v0+KpUKut2udDcSuhYW896v\nRcQ/6EyG8y6LAr5sn+dZyHAsxklnqA97iSb79uuOWcCTK11xBZ4H9kZCALjr+eelyAaexag8YZHF\nCWBfUZiI5qPCy0FVpK9htVpNySFzcRzz2kqZsiwLrVbr4F9INAUWgUk6IldycPibKDBMe8OVHlzU\nbDZhGEZem04j5FUYiqIIpmkijuOFLklnsSsbLOCrI92pLUOGOu3HLODJ3XbiBD7+V3+FR0xzTyTE\nA6aJ+zc28PEHHyxy8zIxLovTdd3k34uCimzFFCJabsOGzAVBkDQJAXyxReqYpxOYRWDKGovAJJUo\niuB5XnKhTF8sZ7lwiiJhFEXS51bqWljMa78WGf+gs6LOu6w/m7p+fmTAz5r8tre2cPbJJ/dlAQPA\nJ5kFvM/a+jp+/Kqr0DpzZs8/b2E3U1lH5XI5yeNML7sWQ3e57JqIZFYqlVCtVlGtVgGMHjInrmNF\nXMPYGU5ZsyyLcRCUORaBSQri7W4QBEn8wzCi0DPJF2y6cNFut6X/Uta1iFUqleaO8kiToXtU12O1\nKCKapVqtKvHZXDRZHiLSqygm/azxc7F4IgZi/dw5ZgFPoXHkCMwzZ9ACkmxgH8BzZ88qGwkxqVHR\nEYPLrtPFFBmuSUREwiQvttKdwryGUZHYCUwyYRGYpCAKhVk8aIjcStd1ucRcM1kMD6O9FlnQXkQ0\niywF1FnItN1iiGYYhhN3asu0/dNS+cWOiIF4DWAW8BRuO3ECJ8+cwYc2N/dmA29v4+TRozi2RBEa\n46Ij0h12ojCs8medKEuqfm/oZtSQOQ7KJNVZloV2u130ZpBmWEEhaUzyhXzQg3oYhjh//jyCIEC3\n21WqAKxyEWKcrPbL933s7OygUqlgdXW10AKwrscqT6Ko6LouOp1O5gVg3sxnJwxD9Ho9AJA+Rod2\n4wta2I2AEFnAdwP4zUsvXapC5rTW1tdx7PRpfGJtLSkA48Jf793cxCMbGwVuXbFEd129Xkez2USj\n0UC5XEYQBDBNE5ZlwXVdBEHA70Jaerz/kI8oChuGgWaziVarlXQMu64L0zSTQbdRFGV2HVO5EYHy\nM8/5JRpniLLETmBSyrjim+d5ME0T9Xod9XpduS9hXQuL8+5XOv6h3W4nWWBF0/FY5SUMQ/T7fays\nrKDT6Sj32VykaSJv8iCuo41GA4Zh8FhdMMvPYRHHcntrC8+dPZvEQIgsYBPA/e99LwvAB1hbX8dV\nhw+jtb2955/rnA08LbFCS7xUZ4fdRbwPIFLDuNUOtm0D4JA5yt8s342WZTEOgjLHIjApZVhBMY5j\nWJYF3/exurqafMGT+mSNf9DpATfvlw+u6yZDDfIuKhZdQFWZiNHxPG/m66iuL7JkJbKA79veZgzE\nHEpXXJEU0ZctG3gW4/KEHccBsFzFFH7fEKlnME9YXMeCIIDruknRmHnClIV5nk0cx8klPo+WG6tl\nJI1ZLo6DHYYqP2zoWkCZdb9830e/34dhGGg0GtLdgOl4rLLElzPqSL9sUf06ukxEFnALF2MgfADP\nrq1hgzEQE2M28HzSHXaGYezpsGMxhYhkJ1Y7ZDFkjo0IlDVxbhJliWcUKUUUFEWmU6/Xg2EYaLVa\nyl8gdS0CT0t0JPb7fbTbbTSbTeluqGTbnnnkcd6JTNkoitDpdFgAnsKirwNBEKDX60mRtV0kFa+/\nIgsYuBgDsQHgqsOHWbScArOBs5XOE261Wkk8l+/7SZ6w53kIw1C5zxzRIBb99CNWO9RqNTQajT1D\nxkVkllg5xesYTWKe6wTPL8oDn8xJKaVSCVEUJVPr2WEov2mKK7LGPwxSsWC0KEVmc/O4TE68SLNt\ne88DDqkjHWMgmBf+OU2H2cD5GBcd4brunjzhSqWSdOQREcliME941HWsUqkkjUpEWeC5RHlh9YyU\nIrpEa7WadgOmlr2AJXv8g66yOu/SmbIyDfCj/eI4Tl6kdTqdpECT5e/Pz2++tre2sNPv4656HQ84\nDrOAM8Ciev6mGc4kisJERDIZdx0DdjNclykXnQ42z30xX45SHlgEJmmMu8DFcQzHceD7Pmq1mpZT\nMnUtAh+0X6J46LquMsVD0ZFOu2TJlNXlM5TnPgzmqGd5Y8mb1MUQA+FObW7iNQCnALxYr+PQddfh\nzlOnGAUxo3Q28KPYzVf+TquFj9xxR8Fbpq9hw5mCIEiGM02aw0lEVJT0dazf76Ner+8ZMsfrGBHJ\nhkVgkp6If4jjGIZh8I2qYsYV5lSJf1gGs76lZgd3tvL8+YmojkajAcMweKwUlR4I1wJwLwDTcXB/\nu80C8BzW1tfxvk9/Gn94yy14wDR3u6tNEyc//GG8gcPhcie6nUQ0jRjOFAQBPM/bEx0huut4DaOi\nceULCeJZRxR9xT8bNWSuUqnwOkZjRVHEZ2PKBc8qkprv+9jZ2cHKykoytEiHTr9hdOlinJQ4ttVq\nVbmBVDodq3kGFcg+wI92xXEMy7JgWRba7fbCs5pVoNJnOj0QTmB2bTa+/vDDSQEY4HC4Iok8YcMw\n0Gw20Wq1ko5hx3FgWVayQowrc4hIFun7q3FD5lzX5ZC5JTHryyLLslCv13PYIlp27AQmKY2KCNB5\nGb5KRYhpDO6XivEPuhPHaNIblHR3vkwd3Lp8hrLch0VHdUx7Luksj/Nxe2sLz509y+zanLDALq90\nDqdhGHtyOF3XTf79Ipdc6/B9Q0SLM27InOM4iON433WM91PLSwxvJsoai8AkDfElJzIry+WyVAWm\nRdG1gCLy/nSIf9Cl2DiLIAjQ7/dRrVbZ/ZuDLH+e4liJDhQeK7WJLOD7trdxN4B7AA6Ey1h6ONxZ\nAA9jNxv4ubNnsb21xUgIiQzmCQ9bci2KKXkuueZ1lYhmNe7llud5AMAhcxqY9dleRLgRZY1FYJLK\nQZmVOhffdH2QEPvl+z5M00S9XudydAkd9LmK4xiu6yZvpcVyNtnoen2YhirHSiUyXK/SWcAfAfAp\n7BYon11bwwYzazORHg73EFKF9u1tnDx6FMf4c5aSWHKdzuFMdwmn84QrlQq76ygzvOcgIYsmHg7L\npDTbttFsNoveDNIQXyeRNDzPg2VZWF1dHVkk1LkIDOi5f2J/TNNEu93WoiNRt+N00PGI4ximacJ1\nXXQ6HWmLiqqfV1lQ5VjR9NJRBYcB3A1gA8BVhw+zMJmRtfV1HDt9Gp9YW0sKwACzgVUjuutEnnCz\n2USlUkEURbBte0+esE7f5VQM3ntQHkqlEsrl8p48YdEgJRprLMuC67rME5bcrC8IGAdBeWEnMEmj\nVquh2+1OVJAiNaTzSFdXV5MMLFJHOv6h0+nwYSdn87xgEFE6KysrhR0rVV+QqLDd6agCgVnA2Vtb\nX8dVhw+jtb29558zG1hd7K4jItVNs+Ih7xgcWgzTNNkJTLlgRYakMcnyPN2/zFQoRExKRHvU63WE\nYajVsdPpOAGj98d1XViWhWazCcMwCtiy6eh2XKZxUJQOqW17aws7/T7uqtfxgOMwCzhnLLjrS9xr\nilUSIk84CAJ4nsdCChHNZNEzXUYNmQuCAL7vA2CesCziOJ7p5y+ewYiyxiIwKUX3Io8O+xfHMWzb\nhud5aLfbqFarcF236M2iKcRxDMuy4Ps+O7glJz5vrusmnzfSixgId2pzE68BOAXgxXodh667Dnee\nOsUoiByks4EfxW728ndaLXzkjjsK3jLK2rjuOsdxALCQQkTyGywKjxoyJwZm8uWW/JgJTHnhkz0p\nRYciqc7CMIRpmiiVSuh0OsnDkm7HTef9SUcKTBLPQtma5tyKogimaSKOY3S7XRYnNJUP3y8jAAAg\nAElEQVQeCNcCcC8A03Fwf7vNAnBO1tbX8b5Pfxp/eMsteMA0dzuvTRMnP/xhvIHD4bSWLqQYhrGn\nkOK6bvLvV1ZWFt75R/LhOUCCbM8FgzE44lrm+z4cx2EMzgLNep2wLIuZwJQLPjGSNCa9OMr2JZsl\nlYuLnueh1+uhWq2i3W7vKUipvF/LRBxDwzDQarWUuyFcpvMsCAL0ej2srKxgdXVVmgLwMh2DRUkP\nhBOYT5u/rz/8cFIABjgcblmJIkq9Xker1UoGF/u+nyy75mAmIgLkjS0UKx6GDZkTcWKWZcHzPF7L\nJMIiMOWFncCkFFm/XLOiYgFlWPyD7lQ8TgdxHAdhGDL+QQEiq7nVaiW5ljSfPD7TWfye21tbeO7s\nWebTFoDFdxo0GB1h23byAm5wMFOlUplo1gUR0aKlr2W1Wo1D5nI2ayewbdtoNBo5bBEtOz7pk1QO\nemjWsfimMhEdUC6X98Q/DNLxuOmyP2J52EHHUAU6nGfj9iGd1dzpdJJCBOlJZAHft72NuwHcA3Ag\n3AKlh8OdBfAwdrOBnzt7FttbW4yEIJRKpaRTGNibwWnbNgDsKwoTkZ5UjgYZlyfMIXPFsW0b7Xa7\n6M0gDbEITEpS+Yt2HJWKWGL5UL1eT5ZHLgtd9tX3/aSIbxgGb+okxqzm5ZPOAv4IgE9htwj57Noa\nNphLm7v0cLiHkCrCb2/j5NGjOMZjQAMGMzjjOEYQBAiCAK7rMoNTQ7o+j9ByS1/LAAzNRueQucnN\nkwnMwXCUBz7xk1LEBVSVQum0VCgCx3GcZEe12200Go0Dv9hU2K9ZqLpPIsKj3++j3W7zBk5yKmU1\n6/pZL0I6juAwgLsBbAC46vBhFh8XYG19HcdOn8Yn1taSAjDAbGCajOgSHszgBC6+RBdRWszgJCKZ\nDctGL5fL8H0/eSZ0XRdBEPBaliEWgSkv7AQmqUxSQJC5AKK7SeMfBulWGFL5HIyiCP1+HwDQ7XZR\nLpfhuq4Wx0eH8yy9D3Ecw3EcOI6zNHnbMlv05z4dRyAwC3ix1tbXcdXhw2htb+/558wGpmkN5gmn\nMzgdx0Ecx0lXHZdbE6lnWbrCh13LoihCEATwPI95wiPMmgnMIjDlgUVgUo4OhZ5RZN430bnSaDSS\nibLLTBwrlX4O4o296EwS267SPiyLKIpgmibiOE6K9ZQvma6921tb2On3cVe9jgcch1nABWIxnvKQ\nzuA0DGPPcmvP8wBgT1GY39NEJKNxL7g4MHPXrPeXYtUtUdZYBCblyFwonZeM+5YeRrW6upoMDZiG\njPu1TOI4huu6sG0brVYLtVqt6E3KjQ7nWRRF6PV6qFaraDabS3ezXASZfsZiINypzU28BuAUgBfr\ndRy67jrceeoUoyAWTGQD33vheDwI4EXDwKF+nwPiKDODecKiKOz7PhzHSfKEK5UKO+skocP9BlHW\nxg2ZGxyYuQyrHua5TjAOgvLCIjApiTdeizFr/MMyUKWwLTpKoyhCp9NJ3tSnqbIvB9HhoVg89Kfz\nI1Wiy7k0yPM8eJ63kIeW9EC4FoB7AZiOg/vbbRYcCyCygY8fP47+E0/sdma7LszHHsPJ55/HsdOn\ncYhdwZQhdtaphT97ApYnDmJawwZmhmGYDMwURWPdVz3MGgfRarUO/oVEU2JFh6QyyQVS1y8HQK4C\nSnoYVbvdnqvoIdN+LZMgCNDr9ZIi/rACMMlBDFwMggC1Wk3JArCOxHERnXhhGMKyrFyHoKQHwgnM\noC3W2vo6uu12Es0BcEAcLY4okhiGgWaziVarhUqlgiiKYNs2LMuC4zjwfR9RFBW9uUREQ4mBmdVq\nNRmYWa/XUSqVOGRuCM/ztF69ScVhJzApR+eCogz7lkX8w6BSqaTdg4kMx2qUOI7heV6yjOiggqLM\n+zINVfcj3XHP4q88xBDFUqmEdruNIAhQLpf3LNUeNwRl1vORGbRyYnGeBhXV+VcqlVCtVvd01gVB\nkHTWiegI3TvriEht41Y96DJkbp7viTiOuQqXcsEiMClH1UKPChj/oL50Rym7f+Xn+z76/T7q9Trq\n9Tpc10UYhkVv1tILggD9fj8Zopi+iU8/tNRqtT1LG33fB7CbdxdF0dTXUA6EkxeL8yQj8cJJdItN\n+pKKiLLFgt38BvOE00Vhx3EQx3ESgyNecPF6RjQ9FoGJJFJkgdvzPJimiUajAcMwMv1S1bFwL+M+\niSL+ysoKut3uxMdQx05t2cVxDMdx4DgO2u02qtVq0ZuUCRk/F5MQn5V0F/2kQxTHDUHxPA9BEEzU\nlceBcHITA+I+tLmJRwH4AL7TauEjd9xR8JYRXTTqJVW6iJLO32TRanbMgCXKV/r+yjCMffdXgPxD\n5ma9Tqh4L03qYBGYpDJpJrCuF8Yi9i2P+AdaPNd1YVlWLkV8VahybRDD+uI4RrfblfKmdVll0UUv\nhqCEYZgsyw6CYF9X3uBAJw6Ek9va+jre9+lP4w9vuQUPmOZul7Zp4uSHP4zXf/GLPEYkpUmKKMsw\nlImI1DdsyJzOUTjsdKa8sNpDylGl0KOCRcU/6HjMZNmnLIr4suzLMhAxA9VqFc1mc9/NHY9FMUQn\nvCjMZ3XTPSrvLggC2LYN4GIXCzNn5ff1hx9OCsDAxeFwG/ffj4/98R8XuWlEExksooiisO/7yQBM\n8ZKK0RFEk2Nn+GKNi8KR6Xo2byYwUR5YBCbl6FwkWeS+LbJzVOdjViRmOO8l+3kmPnOTDOujxRG5\nzADQarVyvxaKrrzBLpbw9a9n5qzkRhXq8eqrBWwN0XzGDWVyXXfsygUiIplMcz1TIR89DEPOdaHc\nsAhMUpH5YrwIiyhiMf4hG0UXHEWGsxgoNs9np+h90d0yfeZUOpfiOIbrurBtG61WC/1+f+znKOsu\nn3QXy/bWFkzX3TcQ7sT6Ou44fhxRFLEAI4FRw+Fw+eXFbBBRhoYNZQqCAGEY7lu5IGv+5iKx85NI\nXqOGzIkhvovKR5/1OiGaRojyoO+TKGmLQ6xmN+vgsHmpVBiSXRzHsG0brutqNVBMV7N0a/Ozkr84\njmGaJsIwTPJ/xXVq1HUxr+vluIFwd2xs4A0/+qN7CjDpqdi0WGI43L0XjtWDAF40DFxqmvjns2fx\nlh//8aI3kSgzpVIJ1Wp1afI3iebBlwJyO2iIL4B9Kx+KxCIw5YlFYFKOzgXFPPeNg8OyVcR5GEVR\nsmydA8X2k+3aIGIGpunW5ucyf+mXYZ1Op/Cf+biBcG/+sR8DMDrrThSEZV/WqIu19XUcO30ax48f\nR/+JJ3a7tl0X5uOP48Q//APu/Mu/5IC4JSLT903exuVvDg695DWJiGQ3Kh8965dc83QCNxqNmf5M\nooOwCEzKka3Qk6U89k2Gpeg6H7NFEQVFwzDQaDQyX5bO45OdOI7hOA4cx1nKbm2Zz6VZCvOzmvRz\nNclAuEmz7tJFYcrH2vo6uu02Tl2I7QB2j9fG1hbu39jAxx98sMjNowVb1kJn+ppUq9X2XJMcx1nY\nUmsionkNu8cafMm16BfvIqqMKA8sApNUlvVmelBWS4qKin8YpGORcVH7lC4otlqtpAuH9pPhPIui\nCKZpIoqimbu1i96Hech6DU/n/8pWmB+VMztuINwkyxrTBRhZj4uqJincEy2T9DXJMIyRS611irNR\n+buassc4CH2Me8k17ZA5ZgKTjFgEJuXIUOjJS5Y3D4x/yF/e52G6oChyS/Og82dqkYIgQL/fR7Va\nRbvdnukzx89p9obl/8oknTMrBsKdPHIEx06cmPj3GLWskdER+ZilcE+0TCa5JomisMrXJFW3m4gm\nN25opu/7ALIfmskiMOWJRWBSju4Fq4OGEx1EhviHQToes7z3KYuCIi2OeOnSbDZhGEbRm0MXTJP/\nW8R1antrC49sbMC/5BL8ZhjizYcOoXGhADxrtuy46Agu087GsML9ifV13DlF4Z5oWUwaZ5MeyMR7\nHlKNbs85NFp6aCYweshcpVJhJzBJqfjqENGUdCwoZkWW+IdRdFsqlcd5mF62vqj4B10+U0Xsh4wv\nXYomy7kk8n9lXQ2xvbWFh44e3dsBvLKC2x56KNPhYuOWaWc5/GSZpAfEvXzmDFpxjEve+taiN4tI\nCeO66mzbBpB9Vx3RIvD7czmNW/kQhmHyfDLNfRYzgSlPfFolqfDLc/ZCVroTsVarSfWzlGlbspLH\nPsm+bJ32iqII/X4fpVIJnU4nkwdV1QvyMmy/KoP5HtnYSArAwG60wL2bm7kPFxv1sCKGn0yac0dA\n9fnn8flz53aL+I8/jpMvvIBjp09nWsQn0l26qy6O46RTOAgCvqgiIqUMrnywbTt5PpjmPss0TXYC\nU274apWUI0ORIU/T7l8cx+j3+7BtG6urq1J2vOko6/MwDEPs7OwAwMILwDp9pha1H77vY2dnJ4nr\nYKeSHMT10PM8dLtdaQvAgBzDxcTDSq1WQ7PZRKvVSgoxjuPAsiw4jgPf9xFF0cK2SwWjiviPbGwU\nuVlESiuVSslLqkajgVarldzXep4H0zRh2zY8z0MYhoXeu+i2uo1mp8s9NOVjZWUFhmHsu89yXRff\n/va3ceutt+LTn/40nn/++eRea5ZOYNd18TM/8zO45pprcPXVV+Oee+4BAPzbv/0bfvEXfxE/8RM/\ngV/6pV9KnjdpefGplaRz0A2VTgWreQVBgF6vBwDodrtSL0XncRvNdV30ej00Gg3m/85hET+3OI5h\n2zb6/T7a7TYajQaPlyTCMESv10O5XM6sMztPYrhYWtHDxcQybcMw0Gq10Gg0sLKygiAIYFkWLMuC\n67oIgmDpr+cyFPGJdHfQiyrTNPmiiqTB+0E6SPo+q9lsYn19He9///vx3e9+F+9///vx1re+FceO\nHcOLL76YxONMyjAMPPnkk3jmmWfwrW99C1/5ylfw9NNP49SpU7jhhhvwwgsv4LrrrsP999+f096R\nKuR+QiIaQ9cH0EmLpa7r4vz586jX62i1WtLfeOhWBM5if0T8Q7qLuwi6HZu8pLtMO51OLl2mqh+L\norbf8zz0er2keDnL9XCR2769tYWdfh931etJIdgEcPLIEdwm0XCxYR15APZ15EVRpPR5OwsZi/i0\nWMt2zstg8EVVs9nEyspKkicsisJ8UUVEMjhoxcDrXvc6fOADH8BnPvMZfPe738VXvvIVXHvttfj7\nv/97fOxjH8OVV16Ju+66C1/60pfwwx/+8MA/T0RIiBf2pVIJp0+fxu233w4AuP322/GlL30pm50j\nZcnbNkg0guzFznkdVIgQhcMgCDiISmFiiJ8qXYsqyWOJZhAE6Pf7SfyD7tchVYjObNd1pcz/HXYt\nFwPhTm1u4jUApwC8WK/j0HXX4c5Tp6TNkx3MuUvndg4Oc6pUKtp/Rm47cQInz5zBvReO44MAXjQM\nHOr3sb21Je1xpGzpfp7LbtxAJsdxkjzhSqXCjHPKDaNBKAvlchlvectb8Ja3vAXb29vY2NhAp9PB\n1772NXz2s5/Fb/3Wb+Gqq67C9ddfjxtuuAE/+7M/i3q9vuf3iKIIP/VTP4UXX3wRH/7wh/GOd7wD\n3//+93Ho0CEAwOWXX44f/OAHReweSYTVI5LOJN1Y4tcs2xeuKERVKhV0u12l9l/1DsdB8+yP6KJr\nNBpSZTir/pnKa9vTQxeL6tam/aIogmmaiOMY3W5Xuhcpo64R6SzZFoB7AZiOg/vbbaUKh6Ijr1Kp\nJMOcgiDYN8xJ1+LL2vo6jp0+jePHj6P/xBN4wHHQcl2Yjz2Gk88/zwFxRAs26kVVGIZwXXfPQCbx\nomqe65Lq90xElL9ZrxO2baPdbuOaa67BNddcg9/93d+F67p46qmn8Nd//df45Cc/ie9+97u49tpr\ncf311+NXfuVXcPXVV6NcLuOZZ55Br9fDr/3ar+HZZ5/d9+fzukVyPTERTUi3gmLasH0T4fHnz59X\nOjdW12M2qTiOk1zNdruNer0uxXGUYRtkVERch87Xtiyl839XV1elKwCPo2OWrBjmVKvV9kVHuK6r\nbXTE2vo6uu32bgH4wj/jgDgiOQxmb7ZaLVQqFURRBNu2OfiSiKQ1bDCcYRh4z3veg3vvvRdPPfUU\nXn75ZXzkIx/BK6+8gq985St7fm2n08F73vMePP744zh06BC+//3vAwBeffVVXHbZZQvbD5ITO4FJ\nSToXSgb3TRSiwjBEp9NJOhxUo1uhcdpzMIoi9Pt9lEolxj/kKKtVAjxes1nEtVl00qvamS2yZNO3\n9rplyQ7ryAuCIMntBJB0462srCj9/aBjUZ9IR6VSCdVqNYmOSEfapFcviP+pfF2ixWJXOI0y67lh\nmua+IvCgbreLm2++GTfffDMA4LXXXkO1WkW324Vt2/jqV7+K48eP4+abb8bDDz+Mj3/84/jTP/1T\nHD16dKZ9IX2wCEwksXQOaafTUfoGQ+fC/UF830e/30e9Xpem+3fQskasDKPC8VpGIv/X87zc8tAX\ncY1KZ8m2cHEg3DGJBsJlbVjxJQgCLXI7l6GoT6QbEQUxLE9YrFhIF4RVuy4Rkdps206GvE3qlVde\nwe23344oihBFET7wgQ/gxhtvxLXXXotbb70Vf/Inf4LDhw/j0UcfzWmrSRUsApN0JrnJ0rmgWCqV\nEEURHMdJvgBU7HbT3STnYBzHcBwHjuNIObRKR/NcG2Q4Xjpf2+YhOrMB5NaZvYgH/O2tLTyysQH/\nkkvwm2GINx86hMaFAvCy5MeK4kutVgMwOrdTdAnL3oV/24kTOPH009jY2lqaoj6RbtKrF2q1mvLX\nJSIq3jz38yITeBpXX301vvnNb+775z/yIz+Cr33tazNvC+mHRWBSku6FEs/zkmXoqsY/DNL9mA2S\nfWjVoGU7PoPiOEa/30cURVp97nQgVkSIvFlVu7G2t7bw0NGjezuAV1Zw20MPLU0BeJj0gDkASTee\n6MgDkBReZFyivba+jg/++Z/jD+6/H+fPnsXLP/gB3vz61+ORjQ3ctkTFfSKdTHJdEp3EXEVFPAdo\nnFnODd/32TxEuZG7KkE0ho4FK5FLplsBWEfjiqZBEKDX62FlZUW5oVXLKAxD7OzsoFwu83M3p6xf\nJqQHYjabzUIfsub9sx/Z2EgKwAAHiI0ilmfX63U0m80kksX3fZimCcuy4HkewjCU5j7gTYcP49ZP\nfALVf/1XfH57G//lzBn8/qOP4qGjR7G9tVX05hHRnIZdl4Dd+wdxXXJdV6rrEhEVa56XA3yxQHli\nZYKUpNtFUSxDP3/+PKrVKiqVinb7uAydpunj2Gw2Cy9aTUOX4zPtfriui16vh0ajgVarJc3x0uFY\nzEMMxLRtG6urq1pE4nCA2PTEEm3RBd5qtZKl2q7rwjRNOI4D3/cRRVGh2/roffexyL9k+JC+nNLR\nEdVqNbkuAUiuSyK/nkVhIiKSDeMgSDrLlgksih1hGKLT6STDcnSj0zED9u8P4wTUEscxLMuC7/u5\nDRmbBQsKF/N/xYqIRXbSZ3mNEvnuyf/nALG5jVuiLVbRFBYd8eqrLPITLaHB65LIE07fzw8OmSO9\n8IUQDTPrecHzifLGbyFSki4FxSAIsLOzw/gHhYkp9zrECejwmQIO3o8oinD+/PmkYC9LAZguRqlU\nKhW02+2FPiznfcN924kTOHnkCMwL/18MELuNA8Rmll6i3Wq1kugIz/MW3413+eXJsRVY5CfS16hC\njSgKi+iIRqOBlZUVBEEAy7KS6IggCLS57yKibLEQTHniky8pS+UbJ7GU1bZtNJvNPUuddSlwD9Jt\nv8QX86jjqBpdbjQO2g/f99Hv91Gv15OCkWzEZ0XGbTvIPJ9z13VhWdaepbU62N7awiMbG4hfeQX+\nlVfixJVXonn+PEpXXIFjHByWmfQSbREZIbqEHccBkG833q2f+AROfvObewf/HTmCYyzyEy2tUqmU\nDJCrVquI4zhZweB5HqIo2nddUvG7n4iI1MEiMElH9ziIKIpgmubI2ACV922ZiGPkOI5UcQI0XPrF\ni25FRtWlozlk7qSfpTD/z2fP4vO33rqvMPgfTp9m8Tdn6SXahmEkhRcxgLVcLu8pvsxbeHnT4cM4\ndvo0jh8/jpfPnEEbwOuvvDKbnSEiLYx7WeW6blIUFrE2jI5Qg6ov7ilfs54XQRDwuZJyxW8WUpKq\nhVKx1Fn12IBZqHrMhgnDEL1eDwC0KQDrcnyG7YfI3XZdF51OhwVgiQxGc+h2TfziqVMcFiYJ0Ykn\nBswZhpFLdET1+efx+XPn8P+dO4eNxx7DQ0ePYntrK7sdISJtiJdVhmEkA4UrlQrCMIRt28nwS0ZH\nEC0Py7LQbDaL3gzSGIvApCTVClZxHMNxHJw/fx7NZhOtVmvkm0HV9m1SuuyX53no9XpJ9APf/Mst\nDEPlcrd1+awcxPd97OzsoFqtLjz/d2E4LExKohuvVqsl38nVahVRFMFxHFiWBcdx4Pv+VJ/FRzY2\nWPQnWhJ5dH+mc86bzSbq9TrK5TJ834dpmkme8EJyzmliPBY0zKzXCBaBKW/qt6/R0lLlC/eg+IdB\ny1IAUk16ybro/nUcR5tjpeN5Jzr8Go0G6vV60ZuzFCY5j2SN5sjlM3BhWFi6EMxhYfJJR0fEcZws\n0Z42OiJ+5RUW/YkoE+noCAAjoyOYJywH/uwpK7Zto9FoFL0ZpDEWgUk6k2YCqyAIAvT7/aTTTZXt\nzoPKRcYwDNHv95MYj3THoqr7pKtSqYQoimBZFjzP0yauQxcimiMMQ2U6s4GLw32m7eq45fhxDgtT\nzKhBTkEQ7BvkVKlU9pwPpSuuYNGfiHKRfln1/7N352FylXW+wL+n9r3BBKEb7VS3epFBuTAooDiI\nIItBaCGAJgSIdNDIpuJgWCpQmIohLjiCKEICzBgTQAVbvUEvIOrVUdKjwszc4c5ip9JqegCdId11\n9u3+kbwnVZWq7lpOna1+n+eZ5xnMdk6fc9469Xt/7/cFUDNZpaoqgN5ufkkIaU83ncDpdP2UMiH2\noW/GxJe8XlDsptPN6+fWb1g3aSKRQCKRqP3CH6CiflDuOxa9Eg6HDyrY+0kQrkW9+smUID0/jfx+\n92586847oS5ahJW6jtHDD0dyfwGYNoXzj0bdeJqmWZmdTDgcxoduvRXrJiep6N8H2Bgd9HGMeFd9\nUbjXm1+S5mhjOGInnucpDoL0FBWBiS95uWDVbvxDPS+fWzf8dl6maUIURciyjEwmg2g02vT3EW9Q\nVRWqqiISifi6896vxz0fVVVRqVQaTqYE0XS5jIeXLUOpuhgYDmP5li1UAPY5juMQjUatLmE28aTr\nOhYddhhWPPooSps2Ya5cxh9feQWjixdje6mE5VT8J4T0UCgUOmgFg67r1gqGUCiESCRC0RE9Qj9P\nUq/TyQFRFKkITHrKny1ShHiUpmmYnZ21Ot26WeoctOKin4rAhmFgbm4OmqZhYGCgaQE4SC98fro+\n9VgRplKpIBKJIBaLBera+En9fVR9bTKZDJLJZF9cm+2lklUABmiDsKBisRHhcBjRaBTpdBpveOMb\nccnNNyPy5z9j6/Q0vjg5iZsfewxbxsYwXS67fciEEJt4ufuz0eaXsVjMWqnI8zxEUYSqqjAMw+3D\nJYRUoTgI0mtUBCae02omsJcKVqxrdG5uznrZ6vTF0KsvlP1CVVXMzs4iEokgm83OGyfgtfuwH7GM\nWVmWfZUxu5Ag3Ff116bZZIpX2Pk80wZh/Yktz/7O5z6HDeXyQZMA3ygWIUkSNE0LxDNOCPEHNjbF\n43GkUimkUilEIhEr1obneRqbuuDlCQHiHuoEJl5FcRDEl7xUfOs2/qGRTjYg8jovXbNGWMeiJElt\n5zgHhZevTyMsY5bl/1Zv3uVnQXjuG12bfkIbhPW3ZpMAoZdfRigUgqqqkCSJlmcTQlzRLDqiemyi\nPGFC3EGZwKTXqBOYeNJCLxteKfSwrlE74h+IewzDQKVSgaIoyOVyLReAvXIf2sFvL/iKomB2dhbx\neLyrzntiP9M0+/7aLC8UUBgZAb//v9kGYctpg7C+wCYBqvEAQkNDiMViSCaTtDybEOIJ1dER1WMT\ncGBzZFEUoSgKdF0PzHsvIb3WTScwxUGQXqJOYOJrbnXL9rprNEjFRcar56RpGiqVCqLRqK83E+sX\nLHpFURRks1lrV+z630Ocx4pZpmkim816Pv6hV6bLZWwvlaC+5jW4VNPwhiOOQHJkBOO0MVjfWF4o\nYN3kJNZXbwy4/x5g2PJsNoaxTjy2kRMAq0uYOvEI8Z6grdhj6scm0zSh6zo0TYOqqgBQ0yU8X2xa\nvwjqvUDcQXEQpNeoCEx8yc0P2l7EP9TzasG0G147J1awYh+08Xi87b/Da+fUDY7jPN99xjq2ASCX\nyzX84hGEa+LHc2D5v7quA0BfF4C3jI3VFv8iESzfsoUKwH1kOJ/H+MQEbrrpJvxhchIZAIuPPnre\nP7PQ8uz6ogsVHAghTmg2YaVpGmRZpugIQprodHKA53nqBCY9RVN3xJO8ujkci39gOZcU/+BP9RtW\ndVIAJs7SNK3lDfuIs3Rdx+zsLAD4upvejs+U7aWSVQAGDmwItr1U6vr4iP9EX3wRW195Bd945RWU\nduzAlrExTJfLC/65RsuzWXFYkiRrEyeKjiCEOI1NVrGxKR6Pg+M4KzpCEASKjiCkC9QJTHqNvkUT\n33KyCMyWoFcqFWtX3V4WOvzYCbgQ9vNy+7yqC1bdFvKDdJ28ei6s6DE3N9fSs+fV8wiqRtnM/fzz\nb7YhmDkz48bhEBfZOSHAOvHYc5ZKpRAOh6HrOgRBgCAIkGUZmqb19fPnJFr+TUjthFUqlerbrHMa\nD0gzlAlMvIjiIAhZAIt/ME3Tse7ffi+k9IosyxAEAclk0upcIN5VHTHQT533fnj+q3PRM5lM38Y/\n1GMbglW/uvP7/3fSX3o5IdAsOkJRFBiGQdERhDjA65/Tbmgl6zwcDlt55zQ2kTK7OWEAACAASURB\nVCDrdIwQBIE6gUlPURGY+JYThRJVVcHzvLUkk15WusOumdM/R9M0IQgCVFVtuplYJ/xQrGuV185F\n13VUKhUreqWde8ZL5xFE1RNjAwMDFM1RpZUNwUh/cGpCgHXisW482sSJEOfQ94L5LZR1TnnChBxM\nEATqBCY9RUVg4kluZwJXd7mxpU1O8lpBzi5unBcrJoZCoaabiRFvYblynXRs0xeI3tJ1HXNzc4hG\noz2PxfGahcav6XIZ20slqIsWYaWuY/S1r0V0eBjjxSJtCteH3JoQoE2cCCFeVD1hBcAqCmua5vtV\nDBQHQRrp9L6gTGDSa1QEJr7Vq4KiF7rcgloEdhorJiYSCSQSCdtf0DiOC0zGmRfuOZa9rSiKrR3b\nfuOFa9EIe55SqdSCmyn69QtRpz/36XIZW8bGagt+4TAu+upXqQDcp4bzeYxPTGBjqYS9U1P4w8sv\nY3TxYmwvlbC8UHDsvmjUideo6BKJRMBxnC+fW0KI/zQqCtMqBhIU3bzHUyYw6TUaTQmpoqoqZmdn\nEQ6Hkc1m6YXDZk4Vt1j8gyAIyGQyFOXhA4ZhYG5uDpqmIZfLdVwA9moB1c+qn6dsNjtvAdjPz1k3\nx95sE7Bv33mnLcdG/Gk4n8fyQgHRP/8ZW6en8cXJSdz82GPYMjaG6XLZ8eNhRZd4PG5t4hSNRmEY\nBkRRhCAIkCQJqqrSOErIPOj5sB9bxZBIJJBOp5FMJhEOh6Fpmqc3wPTSsRDv6eTdUlVV2muD9BRV\nuIgnOR0HwToQK5UKUqmU68ucqZDVufpiYi8/RIN2ndw6F03TMDs7i0gkQpMvHmMYBiqVStfF+aBr\ntgkY99JLbhwO8ZBmEwTbSyU3DwtAbdEllUohmUwiFApB0zTwPG8VXXRdD9RnHSF28fPEp9exFQzJ\nZBLpdNqKB2P7tQiCAEVRPDM+0b1A7ET3E+kl+jZHfMuuAhwrcgDwzCZHQSsuMr0+L1VVUalUehb/\nEGRu/KxM04Qsy9ayJ6ezt73KK8+/pmmoVCp9mf/brmabgJmHH+7SERGvaDZBYM7MuHE4TbEoCDYO\ns6XZuq5DlmWKjiCEuKZZdET9+BSJRKyscxqfiJu6iUWje5f0GhWBiW/ZUShhRcN4PO65yAAvFIHs\n1qviVvVGfplMxrElNF4p1vmRaZrgeR66riOXy1kv9t2ia2IPWZYhCEJL+b+N+DUTuFPNNgFbcdNN\nbh8acVmzCQJucNClI2pN/QZzpmlC0zToug5RFAHgoKJLP6HPGULc02wDTF3XoSgKgP4en4h/mabZ\nd+/QxHlUBCZ9ya2iYato4G+d2xv5BeWLoJPFU13XUalUEA6Hkcvl6H73EDs25/Pz9ezkGZgul7G9\nVIK6aBFW6jpGDz8cyZERrLrlFhx+xBE9OEriJ80mCMYLBbcPrS0cxyEajdZsMKfrOlRVhSRJCIVC\nVsElFAr5ehxoVT+cIyF+0GgDzPrxqXqTOTufXSrYkUboviBeRkVg4km9zAT2YvxDvaB2M9p9Xmy5\neiwWc6WTmz7c26coCnieRzKZtPLd7BSEZ8etc6geG3O5nCfHxl7q5F6cLpexZWystsAXDmP5li04\n8vWvhyzLth8n8ZfhfB7jExPYWCrBnJkBNziI8UIBw/m824fWsXaXZvfbWEKCjwo83tVofDIMA5qm\nQVGUmmibfpq0Iv5B9yPpNXorI77VSaFEVVXs3buXNqDyOdbJPTc35/pGfn4vODK9Ljyapmnt7pzJ\nZCiz2WNoc77OzLfpVxAmJIg9hvN5rN28GSvuvRcAsO2aa7Bp9WpMl8vuHphN2NLseDxufSZHIhEr\nOoLneciyDE3T6JkghDiKFYXZ+JROpxGNRmEYBiRJgiAIkCQJqqrCMIy2/34a00gjnU4UqapKmzCT\nnqM7jPhWO1+w2RJnWZY9Gf9QL6jFAzvOq1dZsp2gImZr+r3D1OtY/q9dm/MFdfxqxC+bfhH3Newa\nn5zE+MSEr7uCG2llaXa/RUcQQryhWZ6wpmmQZbmj6Agaw4hdeJ5HKpVy+zBIwNE3cRJ4hmFgbm4O\nmqZhYGDA8wVgINhFlG7OS9M07N27FxzHuV4ABoJ1nXp1Lk53mAbhmjh1DmxCRRRFZLNZWwrA/YZt\n+lXND5t+EefN1zUeZKwLj8U2sckm0zQhyzJ4nu+qC48QQrrBJqzY+MSiylRVBc/zEAQBiqJA13Xf\nv18S53TaCSyKIpLJZA+OiJADqBOYeJJdmcCqqqJSqSAej7uSGduNIL5odPPzZ92KqVQK8XjcxqMi\nvcC+4IuiaFuHKbEP685mEyrUnd2ZoGz6RXqPusb3adaFx/KEe7mBEyF2CeI7Omk/7zwIjQfEW9j3\nJkJ6iYrAxLfm++D1W/xDvaB+6enkZYllyaqqimw266mcpCC+/Nmx2YmbkR1BvCZ2c3tDRa9a6N5p\n9HOab9Mv6mok1VjXePVXO+oabx4dQRs4Ea+jezH45pu0UhQFwL4xzDRN2iyQ1Oj0fqA4COIE71RT\nCOlAoy/s1fmjAwMDvuxwo0LWPrquo1KpIBwOY2BggF6uesiun231NcvlcnTNOtDL51+SpJ53Z/fD\n+DVdLmN7VeF3+f7CLyHNBKlrfG4O+I//CKFcDmHPHg7/+Z8hvPQSh717OezdC8zNcVBVQNM46DoQ\njZpIJIB4HMjlTCxebGLRIhODgwaGh00sWWJgdNTAIYcc6MJjkRGs4CJJEgAcVBQmhBCn1E9amaYJ\nRVGs5gdayUC6xVa9EtJLVAQmntRpCD+Lf0gkEkgkEvTh6zEcx7XcHacoCnieRzKZtPK5vCZoxS52\nPp3+rP1wzfpVdUe9F/K0/ayfNvgi9mFd4zfddBP+MDmJDIDFRx/t9mHNyzSB3/2Ow/PPh/HCC2G8\n8EII/+//hbB3L4c3vMHAyIiBwUETQ0Mm3vxmHYccAgwMmMhmTcRiQCRiIhTaVwyWJECSgFdf5fCn\nP3H48585zMyEsHMnh927Q/jd70IYGDDxF39h4Nhjdbz97QZOPFHHYYft68SLx+O2bOBk388mOJ/9\nhJD2cRxnxUcAsMYoTdNoJQPp+DOC4iCIE6gITHyrugDn9/iHekErLjKtnBe7loqieC7+gTTmpecv\nqM9ON9jmmKFQiDrqbdBsg6+NpRLWbt7s5qERH4i++CK2vvLKvgmEHTuw7sUXPTOBYJrAv/5rCD/+\ncRi//OW+/4tGgRNO0HHssQauvlrBMccYOPLIfcXdNv7mBX+HYQDT0xz+5V9CeOGFMDZvjmLNmgQW\nLzbxnvdoOOMMHX/1VxpyOe9ER9BYSghhmuUJa5oGVVUB0EqGftPJZwR1AhMnUHWFeFYr+YzsS4Df\n4x/q9Wshy6+bVfVzDphhGOB5HqZpBub58wK7nn9aHWG/Vjf46tdxnDTnxQkEUQSefTaMJ5+M4Omn\nIwiHgdNP1/D+92vYsEHG8LAz93AoBOTzJvJ5HUuX6gD2FYb/+Z9DePbZMO6/P4qrrkrg5JN1jI1p\nOPdcDYsXcwdFRzQquEQiERr7iK36+b2PtGahTTDZr1N0BKlGRWDiBCoCE99iX7D37t0b2AJH0F4y\n5yuK+LFY5YdjbEe7RSsvbzDm52fHjuM2TROyLPc8/7cRvxY/W12pQBt8kU61OoHQa5IE/PCHEXz3\nuxE880wEb32rjqVLNVx3nYg3vcmAV4bOUAg49lgDxx5r4OMfVzE3B/zv/x3B974XQaEQxzveoePS\nS1Wcc46GePxAwYVldWqadlB0RCQSoWXZhBDbtPq+2WwTTFVVIUkSQqGQVRSmMcr/TNPsqDGGisDE\nCVQEJr7Elp8DcLzA4YR++uD3UpRAJ7rN0fUrWZYhCILnnr9+uw6NsA1KdF2n/N8eCNIGX8RZbk4g\nmCbwq1+FsX17BN/9bhTHHafjwgs1fP7zMg47zB+TNtkssGyZhmXLNFQqwPe+F8EDD0TxiU/EsXKl\nhtWrFSxZYlpZneyziRVcWEG4OjqCdQnTZwchxCnNoiNYlzAbo1hRmMao/iGKIhYtWuT2YZCAoyIw\n8R1d18HzvPVh6LeiYauCWFys77QLYpSHn7XaCUkbjHmXruuoVCoIh8PI5XKBGj+8gm3wtbFUgjkz\nA25wEOOFgicyXYm3uTGB8F//BWzfHsVDD+17V7r0Ug2//CWPI4/0R+G3mUwGWLFCw4oVGqamOGze\nHMOpp6bxzndq+MQnFJx00oFNaBsVXDRNg67rVkNBfcGFEEKcMl90hKIoACjexm86/Q4vCAKSyWQP\njoiQA6gITDyrUUFKURTwPG9FBrz66quBK5QGHbumqqqC53lPRgm0w69L3ztRXWD08gZjfp9A6fSe\nYpEqyWQS8Xjct+fvZdO7d+PRDRus4u+Ke++l4i9pmZMTCC++GMK990YxMRHFWWdpuPtuGe94h+6Z\nqAc7jY6a+OxnZdx6q4xt26JYvTqJ4WEDn/qUgve85+Bz5jgO0WjUWpbNisLVy7IpOoLMx8/vGMRe\nvbgX6qMjmsXbUJ5w8LAYN0J6iYrAxBdYZICiKAdFBgS1ABfE4iI7J1EUIUmS56IE+t1891z9BAy9\ncHqHaZqQJAmSJHkiUiWIYxcATJfLeOgDH6jt4pycxPjEBBWCScuG83ms3bwZ0+UytpdK2HbNNeAG\nB7HchmKwae7b5O3uu2P4538O4aqrVPz2tzwWLw7e89hIOg1cdZWKVatUfPvbEXz603EsXmyiWFRw\n8sl6wz/TKDpivmXZtGKJEOKk+eJtFEWpibehPGHv6KYTmIrApNeoCEw8rzr+IZfL1byAB/lDLqiF\nFMMwoChKYKIEgnqdGK8VGEktyv+1z0LP8rb1660CMLAv13X9rl3YWCph7ebNjhwjCYbpchlbxsZs\nm1AwDOCHPwzjc5+Lg+eBj39cwSOPaEgk7D5yf4hGgeXLNVxyiYZHHolgfDyBY44xsH69jKOOMub9\ns60sy2a/FuTPfkKINzXLE2arGQAcVBQm/iGKIm0MR3qORgXiaYqiYHZ2FtFoFJlM5qAPsn4owAWF\npmlW/i8Vq7ypWWazqqoYGBjwTQHY7+NCq8ev6zpmZ2cB0DPlBHPPHtT3ZqQBmDMzbhwO8bHtpVLD\nCYXtpVJbf49pAj/4QQTvelcKGzfG8clPKnjuOQErV/ZvAbhaOLwvA/k3v+Fx6qkazjkniZtvjuPV\nV1v/O9iS7EQigVQqZa2E0TQNhmFAEAQoigJd1339uUMI6Yzb0SBs4iqRSCCdTiOZTCIcDkPXdQiC\nAJ7nIcsyNE2jMcpB3XQCUxGY9BoVgYlnCYIAQRCQyWSaZsb6vdgzn6B0ObNO0rm5OevLU1DODQju\nPahpGmZnZxEKhZDNZqmTwGPYBFk8Hkc6nQ7UM+VV3NAQ+Lr/jQfADQ66cTjEx8yZma4mFEwT+PGP\nw3jPe1LYuDGGdetk/OxnAsbGNNBQfbB4HLj2WhU7dwqoVIC3vS2NRx6JoN2PbtaBF4vFEIvFrP/f\nNE3Isgye5yFJElRVhWHM33FM/C2I730kGKonrtLptPXdi+3FQhNX3kaZwMQJFAdBPIu9ZC9UfArq\nB1gQiov1S9U5jrN24g4Sv18nht1zsixbM9HxeNztw2pbEJ6dZvwQzxHUn/+lt92GdTt31i7hHxnB\neKEw759zu0uIeA83OAgeqCkEtzqh8OKLIdx0UxzT0yEUCjIuuIAKv6067DAT99wj49e/VnHddQk8\n+mgUX/qShHy+s/FqvugIWZatX6fNm4KJrifxumbREfWZ5yz3PGiNOm6iTGDiZfTaSDwrGo0uWACm\nDyrvarRUPYjFoSDdg6wALIoicrmcLwvAQdDsOTFN05fxHEGwu1zGNz/zGaiveQ1WDg/jU29/OzZe\ncsm8Ga5BGhuIvZYXClg3MmJ1lrMJheXzTCj8138BN94Yx7nnJvG+92nYuZPHsmVUAO7ECScY+OlP\nBZx6qo7TTkvh/vujbXcFN7JQBx7b4Jg68AgJDj9N9LKJqXg8jlQqhVQqhUgkAsMwIIoiBEGwVjPQ\nGOUOioMgTqBOYOJZrXygBrGoyPj53FgnaTKZRDweP+ha+umFqV+wTSVCoRAGBgbo+niMruuYm5tD\nNBpFKpWi69MDzcbc3eUy7lu6FOunpg50AIfDWL5lS0ebeBEynM9jfGICG0slmDMz4AYHMV4oNLyf\nNA146KEo7rwzhvPP1zA5KWDRIn++G3hJNAp88pMK3v9+FVddlcSOHRF89asShobs+dnO14EnSRIA\n2ryJEOKuUChkTV6ZpgnTNKFpGjRNgyzLCIVCNeMUvXu2ppvv77quW6tLCOkVeuMgvubnQulC/Hhu\nLP5BFEVks1mrC4YJ4suDH69TPVVVrfzfWCzm++sUhGtSjeX/JpNJyv91wdZi0SoAA51v4kVIteF8\nHssLBXCDgzBnZrC9VMJ0uVzze3760zDe9a4UJiYimJgQ8aUvyVQAttmb3mTi6acFnHyyjne9K4Xv\nf783X76rO/CqN2/SNM3aA4M2byKEuIXjOOt7AHvfZCsCFUWh1Qwd6OR9nSI5iBNomoH4XpA/hPx0\nbrquo1KpIBQKIZfLNe1qYQW6oHzA+bngWJ8vqyhKYK6Ln7F7yjRN64U7m836pjPAz89EI+aePV1t\n4kVII9PlMraMjdVmTE9OYnxiAqnMCD796TgmJ8PYsEHGeedpoKG5dyIR4KabFJxxhoYrr0ziZz8L\no1SS0ctEpPoOPJYnrChKTU4n6xKmz2ZvCdJ7LOlOUO8FWs3QuU7viSC9OxNvo6eV+FoQP3QZP50b\n61SMxWLIZDL0IuADhmEclC8bpOKd38+D5f9qmoZcLuebAnAg7d/EqxoPQD/sMOrcIx3bXipZBWDg\nQIf5pqs24OSTUxgcNPHcczzOP58KwE55+9sN/OxnPPbs4XDmmSns3u3MD54VW2KxGFKpFNLpNKLR\nKAzDgCRJNTmdhmE4ckyEEFKt2WoGXdchCAJ4nqd3IhsEdVKBeAtVaohnUSaw98/NNE3rgz+TySCZ\nTC543fxwXu3w4/lommbFP2Sz2cAV7f3+8qTrOgAE9vr4iaZpGPvUp1BosInXittuA8dxBy2TNAzD\nd2MCcZ45M9Oww/zlf3oJjzwiYsMGGbQ3jPMOPRTYulXCJZeoOOOMFH7yk7Djx8CKLWyDOYqOIMS7\n+vUZXGgjTEEQKDqCEI+i1iLiaxzHBborwssfmqyTFAAGBgZaLlT5sWgaJGzTvlQqZWV9MXRt3Meu\nDwCk0/UlItJL9fc/uxZHvfnN+NiTT+LO22+HsWfPQZt4xWIxa5mkpmkQRREAapZQ+n1igtiP299h\nXv2U8wD+8n2vxdveFtz3Gj/gOODaa1Uce6yBK69M4BOfUHDNNWpNR7aTz3Wj6AhN0w6KjohEIpQn\nSYgL+v2Zmy86Qpblvhunuvl8CPLPhXgHFYEJ8SgvfwioqopKpYJ4PN5S92+Q+aVwyrq2VVX1Vb5s\nJ/xyTapV5/9mMhnMzc25fUgd8+PPvxp7VqZ+9ztMfPGLwMwMuKEhXHrbbRg88siGE16scy8SidTk\newKAIAgIhUKIRCKU70ksywsF3PTLSdz5+6pM4JERjN9ecPvQyH6nnqrjxz8WsGJFEv/6ryHcdZeM\naNTdY2pWbKmfgGLjDY01vWGaJq3SIaSJ6nciANY7ka7rNeNUdVGY7ItXjLr9IUP6QnCrAKQv+L3Y\nMB8vnlv1RmLpdBqxWKztv8OL5xV07WzaF+TOeq+q7qrP5XL0Muwi0zQxNzeH3+/ejW0f/GDtpl3P\nPYcPf/e7yI+MzPt3VBdpFEVBKpWyvgDRZiqE+YffvBHb5p7CnrcWcNTAHxEaqu0wJ94wPGzihz8U\nMD6exIUXJvF3fyfi0EPdPqoD6iegTNOEpmlQVRWSJCEUClmFFpqAIsR+tNpnYfWrGdg4pWkaZFm2\nxin2f37/eXZ6T/A8jxTlQBEH0DcP4ln9ngkMeCsOghWqFEVBLpfrqAAcRF6/B1VVpU37PIzlM0ci\nkYPyf718XwURy62LRCL47he/2HDTrkdKpbb+TvY51mgzlfp8T8rN6w+VCnDNNXF85jNxPP7dI/Dw\nL+7HpV+9FwCw7ZprsGn1akyXy+4eJKmRyQDbtok45hgDZ56Zwu9/780CBcdxCIVCiMViSCaTNZP1\nsixb2eW0wRwhxC2NxikWT1e/x0K/vReJokhFYOII6gQmvub1Alw3vDQLqmkaKpUKotEoMplMV8cW\n5GvmJdVd25lMpqXlRUG5Nn45j2b5zF569jvlh59/NfbFg+M4pFIpmHv2NNy0y5yZ6frfapbvWZ2b\nVx0dQYLj+edDuPLKJE46Scf/+T88sllgulzGlrGx2q7zyUmMT0xQV7CHhMPAnXfKuPfeKM4+O4VH\nHtFw1FGa24c1r/mWZCuKAoCiIwgh7povT9ivq6c67QRm3wkI6TUqAhPiUV4oZJmmCVmWIYpix/EP\n9bxwXnby4vkYhgGe52EYRlub9hFnBD2f2YvPRDPVWcypVMralI8bGmq4aRc3OGjrv1//5adRkYYV\naKhI41+GAdx7bxRf+lIMn/ucjIsuOlA83F4qNew631gqYe3mza4cL2nummtULF5s4oILcnjooVmc\neqrbR9S6RhNQuq7XREdQdnlr/PIZR3qL7gP7VU9exePxmvciWZatXw/iexEVgYlTgvXNkwQKxUG4\n+3JhmiZ4noeu68jlclaRgnibnV3bfuXlcYHFqnAct2A+M+XM9ZZpmqhUKjBNE7lcrubXVhaLWLdz\nJ9ZPTdVs2vXhQm837WpWpFEUpe921w6Kl1/m8JGPJFCpcPjxjwXk87Vjkzkz07Ouc9IbH/yghlxO\nxRVX5PDQQxJOO013+5DaNl/3Ha1KaA2Nv4She6F3Wpm88lruOXUCE6+jIjDxNS8Xe7rl5ocYKyRG\nIhHbN6oK2jXz0vk0ixdolZfOJYjYc8Vy0LzwotqvdF3H3NwcotEoUqnUQZsiLsnnsWbHDmwqFmHO\nzIAbHMRHb7sNRwwNOXaM1UWaWCxmFWk0TavZXZuWcnvXzp0hXHFFEsuXq7jlFgWNmv65wUFHus6J\nvc44Q8WDD87iyitz+NrXJJx9tv8KwdVaiY4IavcdIcQfWp288utkOVt5S0ivURGYeNpCRakgF63c\nOrduC4nEeUGPFwgC9lzZFavidV4el1n+bzKZRCKRaPr7luTzuPXhh63/NgzDKoa4obpIU727Ni3l\n9h7TBB56KIpSKYavfEXC0qXNC4TLCwWsm5yszQQeGcF4j7vOSXdM08Q736nhkUdEfOhDSdx9t4z3\nv9/bGcHtWKj7rj6jk8Yb0o9oxZa75pu8qp4sry4KO6HT+4LneeoEJo6gSgEhHuZkIcWpQmLQCvdu\nn0+r8QKtcPtc7OKl86h+rtqNVfHKObTLq1+IWt0scXe5jK3FIsw9e8ANDWFlsYglHtugi3W3sAmF\n+o1UTNOs6dqjpdzOkSTgU5+K4x/+IYwf/UjAm940/3M8nM9jfGICG0slq+t8vFCgTeF8gOM4nHii\ngccfF7FsWRKhkDlvwd+vFtq4icYbQogX1E9esclytvkui47w6ooGURSpCEwcQZ/SxNe8VOyxm5Mf\nTLquY3Z21tpIrJedpEG+Zk5TVRV79+618n/pi5e3GIZR81y1UwD22oup37GMc0VRkMvlmhaAfz89\njfuWLsXaRx7B+p/9DGsfeQT3LV2K3eWyswfcJtYNE4/HkU6nkUqlEA6HoWkaBEGAIAiQZRmaptH4\n20PT0xzOPjsFnufwzDMLF4CZ4XweywsFcIODMGdmsL1UwrTH7zlywHHHGfjWt0Rce20CP/pR8PdP\naDbe6LreN+MNdYAS4m0cxyEUClkRbOl02lrhylaEsY2BdV23dazqJhOY4iCIE6gTmPgaG2CD+DLm\nVLG0eml0PB4P3M+x19woarfa0diuIBXo3T4PVVVRqVSQSCSQSCTouXKRruuoVCoIh8PzZpxzHIfv\n3HmntRkcsC+ndf3UFDYVi7j5wQcdO+ZuNVrKrWlazQZztOGTvX760yg+9rEUPv5xBddeq6KdR366\nXMaWsbHaSIjJSYxPTFBHsE/85V8aVjTE5s0STj89eB3BzbS6oSVFR5CgCeL3z6Dyw4oGURRx2GGH\nOf7vkv5Db/7E01r5YA1S4aqRXp0bW6YuCAKy2axjhaqgX69eM00TlUplwY7GfubmCzkr0FcqFaTT\n6a42gPPrc+KlZ1xVVczOziIWiyGdTi98LV56CfU9GGkA5sxMR/++F34W7ItPPB5HKpVCOp1GJBKx\nMvN4ng98114vmSZw330JXH11Bg89JOG669orAAPA9lLJKgAD+ycfdu3C9lLJ7sMlPXTiiQa++U0J\n4+MJPPdcf37FYuNNLBazxhtWHJYkCYIgQJIkqKpasxknIYQ4aaEVDd28G3XTCZxMJtv+c4S0izqB\nCfGoXhay7MyR7XdOFnk0TUOlUrHiH+y+R7xQsPIzFjmg63rb+b/1qLOkO6ZpQpZla6flljfjO/xw\n8EBNIZgHwA0O9uAo3cFxHKLRKG34ZANVBW68MY5f/SqEH/1oL0ZHO3utNmdmbJ18IO55xzt0fP3r\nElasSOJ73xNxzDH9Xeis3rgpHo9b441fMjoJIf1hoc0w2VgViUR69m4kiiIymYztfy8h9agITHwv\nyIUrdm52ftC4vUw9yNerl2RZhiAISKVSVqYVacyNe6zVyAHSe2yVg6ZpbRfjl61di3UvvGBFQvAA\n1o2OYk2x2KvDdVWz5ZGapkFVVQDu7KztB3v3ApdfnkQkAvzgB3uRy3X+d3GDg4GffOgnZ52l4847\nZSxblsSTTwoYGaF3HqaVqJrq8carYw7FABCA7oOgmi86QpblBceqTr+DsO95hPQaFYGJp1EchH1L\nwnuVI9uuoF2vXp8PK2ipqopsNkub9nmQ2xMr5ID6VQ7tXovXDQ9jzY4dzgVABAAAIABJREFU2FQs\nwpyZATc4iDXFIpbk8zAMI/DXtrprb76dtXvZCeMH5TKHSy5J4tRT9xX7dN0E0PnPYnmhgHWTk7WZ\nwCMjGC8U7Dpk4rCLL9bw3//N4aKLknj6aQGHHur2EXnPfJNQoigCoEkoQoj7qt+NAFjvRixWC0DN\nigb2Z9pFRWDiFCoCk0AIauHKrhdewzDA8zxM08TAwADFP9isV/cfxXZ0zokxoZcTK34uyLt17Cwu\nhe0E3e74Ob17Nx68+WZE/vxnhIaGcNnXv44lfbwpF+tsYVEajTph3N5ExQ3PPRfCZZclccMNCtas\n2dctrXe5B9hwPo/xiQlsLJWsyYfxQoE2hfO5j3xExa5dIaxcmcQTT4hoNZWmX9EkFCHED+pjterH\nKmBfg4hpmm3F3IiiSEVg4ggqAhPfC/JLoB3FFFVVwfN8x4URu/m5uNVIr36e1F3aOSd+Vnbm/5Lu\nsbiUtvJ/q+wul3Hf0qW1MRA7d2LNjh19XQiuVt8Jw/LyWFG4H7I9v/3tCG68MY6vfU3COed0Wfmt\nM5zPY+3mzbb+ncR9pZKMlSsTuP76BL72NantTQP7VaNJKBYd0cpybEKcQHEQpNFYxfM8OI47KOZm\nob0WKBOYOKU/2jZIoAWtqFiv03NjXYqVSgWpVAqpVIpeVHrIztgOURRRqVSQTqcdLdwH/Vmyi67r\nmJ2dBQAqALuMvWyLoohsNttRARgAthaLVgEY2JfNun5qClsDmgVsB5brmUgkkE6nraxyRVGsa8K+\nAPmdaQJf+EIMt98ex/e+J9peAK42XS5j0+rVuPPcc7Fp9WpMl8s9+7dI51ot/oTDwObNEl58MYTP\nf55agTvFoiPi8ThSqRTS6TQikQgMw4AoihAEAZIkQdM0eo8hhLiGFYWj0ag1VrGOYUmSwPM8nn32\nWTz00EPYvXt3zZ/leZ46gYkjqBOYeFq/ZwJ3WvwzTROVSgWGYXiuSBW062VngdYr183vnQ29vMdY\ngSuZTCIej/fs5xS056QXWFwKgK7jUsw9e2o25QL2FYLNmZna3+fzZ6NXGmV71uflVUdH+OlnaBjA\n2rVx/PznYTz9tIDBwd49l9PlMraMjdVmA09OYnxigqIhfCydBh59VMQZZ6QwMmLg4os1tw/J9+qX\nY7OVCaqqQpIkhEIha8zpRXQEfRYQQpqpHh+qV1HF43Frf4mf/OQnuOOOO3DooYfi1FNPxemnnw5V\nVdsqAv/hD3/A5ZdfjpdeegmhUAgf+chHcN111+GOO+7AAw88gNe+9rUAgM9+9rM455xzenKuxJ+o\nCEx8L8jFkk7OjeViRqNRZDIZz72kBvF6sXPq5met6zrm5uZcvW5eu1e8xCsbK/qBE894t/m/9bih\nIfBATSGYB8ANDtb+vg7+naCNd61YqEDT6tJItykK8NGPJjAzw+HJJwUcckhv/73tpZJVAAb2d6Tv\n2oWNpRJFRfjcEUeYeOwxEeedl8TrXmfiHe/oXTd5v2m2wVx1fjmLjein/HLSezQZQOot9M4XCoVw\n2mmn4bTTToOu6/jHf/xHPPPMM3jggQfw3HPP4d3vfjfOPPNMnHnmmTjppJPm/b4RiURw11134bjj\njkOlUsEJJ5yAM888EwBwww034IYbbrD13Ehw0KcgIR7XTgFBlmXMzc0hmUwinU7Ti4lPKIqC2dlZ\num4exTq0FUXBwMAAFYBdpiiKNc7ZFXOzsljEutFR8Pv/mwewbnQUK6viIDr5d+hZPlCgYQX7+qWR\nbBk320TFKyoV4JJLkpAk4IknxJ4XgIF9neetdKQTfzrmGAP33y/hsssSKJdpbOgV1nnHoiNSqRQi\nkYi1MoHneciyTNERhJCeaS0uKIzjjz8ef/3Xf40f/OAHOP7443HHHXdAkiRcf/31WLx4Mc4//3zc\nfffdePHFFw8ar4444ggcd9xxAIBMJoOjjz4af/zjHwH0ZwMCaR0VgYnvBbGzlGm1gMCKVKIoIpfL\nWdmMXhTE69XpOZmmCUEQIAgCstmsJ65bEK6Pneeg6zr27t2LUCjUdeRAO4JwHezWy+dlST6PNTt2\nYP2FF6Jw6qnY9KEP0aZwPVJdoGG55+FwGJqmged5CIIAWZah67prz8Cf/8zhvPNSOPJIA9/4hoRk\n0pl/lxsctCYimEYd6cS/3vteHTfcoOCyy5LYn5RCeqw6vzyVSlmb7bKNkwVBgKIoro45xJ/ofiF2\nMU0ToVAI733ve7Fp0yb85je/wX/8x39gxYoVeOGFF3D22WdjeHgYH/7wh7Ft2za8/PLLNX++XC7j\n+eefx0knnQQA+MpXvoLjjjsOq1evxt69e904JeJh3AKDF41sxFWGYUBV1Xl/jyiKME0zkEHqPM8j\nHA4jkUg0/T26rqNSqSAcDvuii9QwDOzduxeHHnqo24dim1dffRXZbLatDF+WZ8pxHNLptGeWJ/73\nf/83BgYGPHM8nWDFpIGBga7+nur83/mewV5gkS5emBhol6qq1oSUXdhEl2mayGQytt2fu8tlbC0W\nYe7ZA25oCOd98pN4y1vf2vDvN00TiqK0NcaKoohoNIpIhNK3WlG9jFvXdVeWcU9Pc7jggiTOP1/D\nbbcpaOVyy7Jcszt4x/92o0zgkRHKBPYgFmvSycoQ0wSuvDKBRAL46lellu4x0huNxpzq/PJmnwU8\nzyOTybhwxMRL7Br7SXCwzSrT6fp1PfMzTRPve9/78Itf/KLpr//bv/0bnnrqKTz11FP4yU9+gh07\nduCUU05BpVLBaaedhnXr1mFsbAyvvPIKFi9eDI7jUCgUMDMzgy1btthxesRfmr5d0LcSEghBnomd\n79yc2qTKTkHscGz3nFRVRaVSQSKRsDpSvCII16fbczBNE6IoQlEUZLNZKuC1ye57qDov2674B2Bf\nAfi+pUuxfmrKKrjd+qtf4WM7diA/OmrLv0HaU72BCgArS5hle4ZCoZo8YbvHzhdfDOHCC5O47joF\nV189/wR0Lwzn8xifmMDGUgnmzAy4wUGMFwpUAA4YjgPuuUfCGWek8PDDUXz4w87fa2Qft8ccQghh\n5htfOI7DUUcdhaOOOgrXXnstNG3fBqOapuGiiy7CZZddhrGxMQDAYYcdZv25q666Cuedd15vD5z4\njn9bvQjZL8gvZM3OjXUhsGXRXisktsLvhcZOsAzMSqViLYP223ULOtahrWkacrmcawXgIBTj7cDy\nshOJhO0rHbYWi1YBGNiXvbqhXMbWO+6w7d8g3alexp1Op63JTjYByiZrDMPo+nn57W9DOO+8JIpF\n2ZUCMDOcz2Pt5s1Yce+9AIBt11yDTatXY7pcdu2YiP0yGWDrVhHr18fw61/T1zGvaHXMAfrzPZbU\noo3hSL1O74l2xxM2eXXllVfiL/7iL/Dxj3/c+rX//M//tP7/xx9/HG95y1vaPh4SbNTeRDytlUE0\nyMWSRufG4h+czii1SxBfllq5B1nhXtd15HK5tqIjnBTk52khmqZZMQx2dpyS9rEJE0mSkMlkerIZ\nn7lnD23C5SNsgzm2yRxbxq1pGsT94arV0RHtPL/PPRfC8uVJ3HOPjHPP1Xp1Ci1rGAsxOUmxEB5i\nR/HnTW8y8eUvy7j88iR+9jMBixb152evV8035gCAIAg1XcJ+ex8nhHiHoihtx4r84he/wDe/+U28\n9a1vxfHHHw+O4/DZz34W27Ztw/PPP49QKIR8Po+vf/3rPTpq4ldUBCa+F/SiVfW5sU4EL8YItINd\nM78ef7uqc5tzuVzfnLdbOhkT2LOVSqV8mcMbJE5NmHBDQ+CBmkIwbcLlH9XLuE3TtJZxq6oKSZIQ\nCoVqsoSbjbu/+EUYl12WwH33STjrLN3hs2hse6lkFYCBfffo+l27sLFUwtrNm908NGKz887T8A//\nEMKVVybw+OMiPDo/THBgzAmFQtA0Dclk0ioKU3QEIQTofIJQEIS2c4RPOeUU6PrB7y3nnHNO2/8+\n6S80ZUkCIahFYPYhYpomBEGwNqKgGAFvma/oyJazx+NxZDIZX1y3oD5PjbBni0WreKUA7OfJrW6O\nXdd1zM7OAkDPO+ZXFotYNzoKfv9/8wBuzedx6W239ezfJL3BOvZisRiSySTS6bTVuSdJEgRBgCRJ\nUFUVhmFYf+7ZZ8NYuTKBBx/0TgEY2NeNTl3q/WPdOgWGAWzYQJtL+QXHcVZ0BBtzmkVH6Lru289z\nMr9+amghvSUIApLJpNuHQfoEdQIT3wvyhy/HcTAMA3NzcwCAgYGBQCw383OBq1V+3VwsCM9Tq/cX\ny/8F4MtolaBxesPEJfk81uzYgU3ForUJ18obbqDl9gFQ3SUcj8etLuHqjr0f/ziB669PY+tWEaec\nYiz8lzqIGxykLvU+EokADz4o4V3vSuHd79bx7nd7Z0KCtGa+6AhV3Zcxzn49EokE4l2LEHKwbjqB\nU6lUD46IkIP5oypB+la/ZwIbhgFVVZFIJALV/Ru0a1Z/Pqy4yHEcFRc9iuX/ss7BoDxbfiVJEkRR\ntDo4nbIkn8etDz9s/ferr75q698ftLHOr0KhkNW1Z5omvv/9MD75yST+9m9fxf/8nzJEMVwTHeG2\n5YUC1k1O1mYCj4xgvFBw+9BIjxx2mImvfU3CmjUJ/PznPBYtcvuISDfq42pM04SmaQdFR7B4CXoH\nIaS/URGYOImKwMT3gvglu3pTpHA4TB8KPqKqKnie921xMYjPUz1Zlq2XLa/EP9Trh+sAHIjj0DTN\n0Q0Td5fL2FoswtyzB9zQEFYWi1iSz/fNz72fPf54FGvXxvGd74g47rgoDCMMXdeh6zoURQEAqyDs\nVq7ncD6P8YkJbCyVrC718UKButQD7vTTdVxwgYbrr09g61YJPnt9IE1wHAeO46wJTpZhzgrChmEc\n1CXst3fHfkVxEKQRpzKBCekUFYGJ57XypTxIX9oNwwDP8zAMA6lUCrIsu31ItgtaoYXFdrjVzUhq\nVWdpV7+IsYKjqqq+iujwm3bjOFjHvFNfpHaXy7hv6VKsn5o60GW5cyfW7NiBQw491JFjIO741rci\nuPXWOCYmRBxzzL4IiPouYRYdoSiKq8WZ4XyeNoHrQ7ffLuP001P427+NYtUq1e3DIQ10W/irjo5g\nf5+madB1HaIoAoA15tAGc4T4S6ffb6kTmDjJ/TVvhHQpSC9HmqZhdnYWoVDIihEIUrE0qEzThKIo\nkGUZuVzO1wXgoBXoGZatbRgGcrkcFYBdxsa6SCTi+IaJW4tFqwAM7MtdXT81ha3FomPHQJz3xBMR\n3HJLbQG4XvUGc6lUCul0GtFoFIZhQBRFa4M5TdMcGSeny2VsWr0ad557LjatXo3pcrnn/yZxVzwO\nbNkioViM4d//PTjvt6Q5juMQjUaRSCSQSqWQTCYRCoWslWWCIECWZdpgjpAAE0WRisDEMfQtmPhe\nEIpWpmlClmXrA4AtUQ9SgbtaEK4Zo+s6VFW1CvdBvWZ+5sf8X9ZdHkQsjsOtjnlzzx7UL7hLAzBn\nZhw/FuKM738/ghtvjOOJJ0QcfXTrz1WzXE9VVSFJEkKhECKRCAzDsD1LeLpcxpaxsdpc4MlJjE9M\nUCxEwL35zQYKBQXj40k8/bQAH88rkzY1io5gcTXV0RFeyjDvZxQHQep1szEcxUEQp9AnB/G8VgdS\nvxYVTdMEz/NWF2l1RmmQiqVBpCgKZmdnA7Xbc1DuOXYesixjbm4OqVQKqVQqENfIr1gchyiKyGaz\nrnXMc0ND4Ov+Nx4ANzjoxuGQHnvyyTA+8Yk4vv1tEW99a+cTKxzHIRQKWZNJbBKDFWlYYVhVVVsm\ncLaXSlYBGNjfsb5rF7aXSl3/3aR7vS7+jI+rGBoysH69N3PriTPYRFQ8HrfeYyKRiBUdwb4/OLU6\ngRDSGxQHQZxERWDie34u6ui6jr179wJAw02RglKQq+f382LFLEEQkM1mEY1GfX0fBlF1wdHvER1+\n0+j5ZnEcbAM4N+M4VhaLWDc6ahWCeQDrRkexcn8chJ/HJlLrqafCuOaaBB59VMRxx9nbWV9dnIlG\no4hGowiHw9A0zfp86KY4Y87MUMd6H+M44CtfkfHYYxH89KfObJhJWuNm9yfLL2fREYlEAhzH1URH\nKIpC0RGEuKTT8YHtKUOIEygOggQCKzr4qRDHlkSnUinEYjFfHXu3/FwEZptZAbBym1VV9e35NOL3\nc2FdeIZhYGBgwLfPlt+vA+O1OI4l+TzW7NiBTcUizJkZcIODWFMsYkk+j9nZWVePjdjn2WfD+OhH\nE9i+XcTb3tb7aBWW61m9wZymaTUbzLWzhJsbHAQP1BSCqWO9vyxebOKeeyRce20Cv/wlj0zG7SMi\nXtJog7n66Ag25lB0RG/47bsn8S5BEJBMJt0+DNInqAhMAsFPRUXWoaiqKrLZ7LwdcX46r37QrJgV\npBdAv5+LqqqoVCrgOM7X8Q9+Pe56iqKA5/marHO37C6XsbVYhLlnD7ihIazcX/glwfPzn4dx5ZUJ\nbN0q4aSTnM/WblSc0TQNuq5DURQAqCnONHrelxcKWDc5WZsJPDKC8ULBwTMhbjvrLB2nnKKjWIzj\nC1+Q3T4c4mHVGebAvonw6qIw+/X5xh1CSHe66QTO0EwfcQgVgYnntTKQ+qVYqus6KpWKtYlYq7Py\nQZtp9sv1Yqo37mu0mZXfzmchfjyX+mskCEKgnhk/YstSF5rscsLuchn3LV2K9VNTBwpqO3dizY4d\nPS0EB21s8INf/SqMyy9P4OGHJZxyiu724QBo3CXMCsKsS7i6W4/jOAzn8xifmMDGUsnqWB8vFGhT\nuD702c9KeMc70rjwQg3vfKc37mnifaFQyIqPqB53WH55o3GHtI4+24mdKBOYOImKwIQ4hHXEJZNJ\nxOPxlovbxF1s4z5d1xvmNgeNH4tWja6RKIq+O496fj9+VVXbmuzqpa3FolUABvZvsjU1hU3FIm59\n+GEXj4zY6fnnQ1ixIoEHHpDw7nd7s1hW3SVcvbGcpmlQVRUArF9//ZIlWLt5s8tHTNz2mtcAd90l\n4+qrE/j7v+dBdQJ3+fGzeb7oCEmSAOCgojBpDX1XI9U6bdyiIjBxEhWBSSB4uXBlmiZEUYSiKMhk\nMohGo239eT/mHS/Ey9erGuvcDofDyOVyTa+BX84niFq9Rn7j1/Ng1wMAstmsZ75Imnv2tLzJFj3L\n/vTv/87h4ouT+Ju/kXHGGd4sADdSvYTbNE0rOkLTNMiyjFAohJk//hHf2rgReOklcIODWE4dwX3n\n3HM1fPvbEWzYEMeGDRQL4Ta/fkYz1eNOPB63uoSrx53qorDfz5cQr6ON4YiTqAhMAsGrRbhGm4i1\ny6vn1g2O46zNu7yqk87tIPDDtWFY/m8ikbB2yK4WtOfG66qfGa/FcXBDQy1tsuWlYyat+/3vOXzg\nAykUizLOP19z+3A6xnEcOI6zIodM00R5agp/u2wZSuWyFWVS2LkTV05MID8y4urxEmd94QsyTj45\nhbExFSee6I/PaeIPzaIj5ousIYQcrJtMYCoCE6d4o0WHkHn4NRNYVVXs3bsX0WjUUx1xZH5s4z5B\nEJDNZhsWF+t58f4LMtM0IUkSKpUKMplMzSZ9DH1BcQ5b7cDzPDKZDBKJhNuHdJCVxSLWjY6C3//f\nPIB1o6NYWSy6eFTEDq+8wmFsLIWrr1Zw6aX+LQA3wnEcHtu40SoAA/smMkrlMr5ZLEIQBMiyDE3T\n6DOoDyxaZOJzn5NxzTUJ7F/BT4jtWHRELBZDKpVCOp1GNBqFYRiQJAmCIECSJKiq6pumgV4I2ipN\n4i62iTIhTqBOYEJsxgpUkiR1FP9QL4gFRq+ekx2d237n1WvD9EtGs9evA9PsengtxmZJPo81O3Zg\nU7FobbK1pljs6aZwpPf27gUuvDCJZctUXHON6vbh9IQ5M9MwyiT8pz8hHo9D07SDuvUikUhffn45\nya3x7QMf0PCd70SwaVMMt9+uOP7vk/5THR0BgKIjCJlHp58NkiRREZg4horAJBC8UjAxDAM8z8M0\nTQwMDNjyJcwr5xZ0mqahUqkgFos17CydD10jZ7ST/0vXpPf8lse8JJ/vehM4r59jPxEE4JJLkjj5\nZB233BLcYhg3ONg0yqR+oydN06DrOkRRBABEIhEqzAQMx+3bJO7kk1NYtkzDW97Sv52YbvHSBKcb\nGkVHNJuMYhE3QUTvmMROpmnS5C1xDN1pxPP8EgehaRpmZ2cRDocp/mEBXrheDOvcnpubQyqVQiqV\nCuwLayu8dG2qqaqK2dlZxONxpNPpvr5GXsCuRywW8/z12F0uY8OqVSiddRY2rFqF3eWy24dEuqQo\nwGWXJbFkiYlNm2R4+Pbr2vJCAetGRmqjTEZGsLxQqPl9HMchGo0ikUgglUpZUUaqqoLneWuDWl3X\nPTnGk9a99rUmCgUFn/xkAn28Gp94AIuOiMfjB0VHiKJoRUcENbLGy+8+xHnd3OP9PrlEnEWdwCQQ\n3CxcmaYJWZatQHe2oYtdvFqU64ZXzsmuaAGvnE8Q2R2v4hdevqckSWppvPPC8e8ul3Hf0qVYPzVl\nbaq1budOrNmxo2EUhJd/7mQfXQc++tEEolET994rIejzrcP5PMYnJrCxVLKiTMYLBQzPE2XCCjPV\nXcK6rkPXdUj7w2Tru/WIv1xxhYpvfCOKbdsiWLkyWFnYxL+qoyNM07RWKKiqCkmSrOgIFllDYw8J\nonbva3rvJE6jIjAhXXAin5SKEr3ht6XsTvHS/WaaJiqVCgzDaDtexUvnERRs00RN0xYc77zyPG0t\nFq0CMLBvSf36qSlsKha7joZoBd2H9jJNYO3aOF5+mcN3viOiT+aEMJzPY+3mzR3/+erCTDweb5rp\nSYUZ/wiHgbvuknDRRUm8730aFi1y+4gIqcWiINhkcfVklCzLVnQEi63x2wpK6twkdgpydArxHioC\nk0DgOM7xHWpZhmw0GqUiYpvcLowoigKe55FMJhGPx7u+dm6fTxCxIn0kEkEmk6Hny2Vs00SO43w1\n3pl79jTcVMucmens76Mvfa760pdi+Pu/D+PJJwUkEm4fjbOmy2Vsr+oGXr5AN/B86jM9g1SY6SfH\nH2/gggs0fOYzcXz5y7Lbh9M36HOgM802mNN1HYqyL9e9euyhnzHxm27GBvoeSZxERWDieV7MBJZl\nGYIgIJVKIR6P9/TfogKjfUzTtHIR+ylaoF1u3292FOn9/tx46fi72TTRbdzQUNNNtYi/bNsWwYMP\nRvHUUwIGBtw+GmdNl8vYMjaG9bt2HYg1mZzE+MREx4VgZr7CTHWXMG0w502FgowTT0zj0ktVnHgi\nBQQT/2i0wZyu6zXREdWTUTT2kKCiiSXiNJreJ4HgVMGExT+IoohsNtvzAjDgrWKQXdw4J8MwMDc3\nZy1lt7MAHKRr5OZLCCvS8zyPTCZjbWxE3CPLckebJnrlmVhZLGLd6Gjtplqjo1hZLLp4VKRdTz8d\nxrp1cXznOyIGB92/r5y2vVSyCsDA/liTXbuwvVSy/d9iRZlEIoF0Om1NxLHJOTaRahiGJ57xfnfI\nIUCpJOOGGxLQKBqY+BTLMWeTzWzPAbbvCht7VFV1fOVnM1S4I/U6vSdkWUai35Y3EVdRJzAhLarO\nkB0YGKAP/i44XSByqpORXgg7ZxgGeJ6HaZpt5/8S+1V3zWezWatL0G+W5PNYs2MHNhWL1jL6NcVi\nw03hAO8Ur8kBv/1tCB/5SALbtkk46ihvfPl3mjkzY2usSauqN5hjBRmWJSyKIgBavu0FF1+s4e/+\nLooHHojiYx9T3T4cQrrWSnQEG3do7CF+x1YXE+IUf36rI6ROr7+4250h2w4qSnRHkiSIomh1FfRC\nkF4+3bjfdF3H3NwcotFoW92m8/H7c+Pm8bP8XwDI5XK+L8gvyedt3QSOJnucMzXF4YMfTOLuu2Wc\nfLLu9uG4hhsc9ESsSXVhpt+Xb3tpHOA44ItflHHOOUlccIGGI47w72efH3jp2vcLio4gftDp2MBq\nDIQ4xd/f7EhfcDMTmMU/CIKAbDbryvJ0vxezGnHinEzTRKVSgSzLyOVyPSsAk+4oioLZ2Vlr+R+9\nuLtL0zTMzs4iEokgm836tgC8u1zGhlWrUDrrLGxYtQq7y+Wu/07audlZr7zC4cILU/j0pxW8//39\nvc59eaGAdSMjtbEmIyNYXii4dkzzLd+WJAmCIECSJKiqGrh3GC866igDV1yh4rbbeh9TRoI1+e83\nC409PM9bY08voyNoMoDYRRRF6gQmjqJOYBIIvSgq6roOnufBcVwguuG8qFcvUNXRHblczpGXNHYP\n+v2F0Ml87V7GDQRx8qTX2IoHOza8dPPnv7tcxn1Ll2L91NSBTbR27sSaHTuaxkAQb+F54OKLk7jw\nQhWrV9Py9uF8HuMTE9hYKlmxJuOFQtebwtmpuks4Ho9bnXqaptVsMBeJRKhTr0c+9SkFJ5yQxq9/\nHcIJJ/RndArpP83GHtrckjit0++BgiAgna4PfSKkd6gITHzB6YICK4YkEgnXN6cKYjGrlz9PN6M7\nSGuq839pgqUxJ5/7oOT/MluLRasADOzfRGtqCpuKRVtjIUhvaBpwxRVJHH20gXXrFLcPxzOG83ms\n3bzZ7cNoWaPl26wgbBhGTZYwfQbYI5sFCgUFN98cx49+JIJef0g/ahYdwTa0rC4I04QU8QLWgEGI\nU/z9TY+Q/ewqmLBiiCzLyGQyiEajNhxdd4JYBAbs75x1+9oF5Tr1+jyc2qSPtIbFpgSpIG/u2dPx\nJlp23/8cx3lmJ3M/ME3gxhvj0DTg7rslKmI1MF0uY3tVR/Byj3UEN1K9wRyAgzr1WCcfdep179JL\nVdx/fxRPPBHBhRf2d4wKIc02t9R1HZIkAcBBReFWBWH1H7FXp/cExUEQp1ERmASCHV/cqzdDGhgY\nCEQxpF9Ud5a6de2CUgTuJVmWrR1wu40bWAgV3xbGYlMikYhtG/KevvixAAAgAElEQVR5ATc05IlN\ntEj77r03il/9Kowf/UiAB+ZgPWe6XMaWsTGs37XrQNTJ5CTGJyY8Xwiu1mqnXiQSoSzuNoXDwMaN\nMq6+OoGlSzUkEm4fUfBQ8c+/Wo2toQkp4iS2gTkhTqEqF/GFVj+EOy3Cqarq2c2QglpctOu82EZW\n4XDYc9fOz+y850zThCAIEEUR2Wy25wXgIOnVs8825IvH4z3ZkM/NcWtlsYh1o6O1m2iNjmJlsejK\n8ZDWfP/7EdxzTwyPPSYil3P7aLxpe6lkFYCB/VEnu3Zhe6nk5mF1pXqTp1QqhXQ6jWg0CsMwIIqi\ntcGcpmmBfBfqhb/6Kx3HHqvj3ntpQ1xC5sMmo9gGc+z9lEXLsagswzBo/CEL6iYTmDqBiZOoE5gE\nQqcFDLaTrCRJ1u6yXhPUIrAdWGepF65dUK6T3cXA6g57p+MG/Hw9etV9Uj3meSXyxm5L8nms2bED\nm4pFa8n8mmKRNoXzsF//OoTrr4/j8cdFvP71/n1ue82cmek46sQvqjv1TNOEaZrQNA2qqkKSJIRC\noZosYerUa+wzn5FxxhkprFyp4vDD6ZkiZCH1sTUsOkLTNIiiCAA1qxRM06TGE2ILnucxMDDg9mGQ\nPkJFYBIY7WbMsggBwzCQy+WsD33ijG6KpqyzVFVVunYe5mb+LxUGDmaaJnieh67rgX9uluTztAmc\nT0xPc1ixIomvfEXG8cdThMt8uMHBvoo6YVEQbJK3Ps/TNM2aLGGnCjJ+mGB8wxtMXHqphlIphnvu\nkd0+HEJ8p9mEFIuOAGAVgmlCigDoeGJAkiQMDQ314IgIaYymr0hgtFNUZBECoVDI88WQoHSY1uv0\nvHRdx+zsrJX/65VrF6TrZMe5yLKMubk5pFKpQOXNOsnOe4o9NwA8P+Z1Y3e5jA2rVqF01lnYsGoV\ndpfLbf8dQXmO/WDvXuDii5O4/noF555Lm1gtZHmhgHUjI7VRJyMjWF4ouHlYjmFFGRZjk0qlEA6H\noWkaBEGAIAiQZdmx6Aivf67deKOMJ5+M4B//kb7u2YkygfsPx3EIhUJWU0M6nbbeo2RZpugI0hWK\ngyBOo05g4gt2ZQKbpglZlq1dOP2QTRqk4mK3WEZXIpFAIpGgl3AP8kqXNj03B6iqikql4uhz48bP\nf3e5jPuWLsX6qakDm2bt3Ik1O3a0HAVB941zVBW4/PIk3vUuHVdfrbp9OL4wnM9jfGICG0slK+pk\nvFDw1aZwdmq0wZymaTUbzFVHR/SbQw4BbrpJwS23xPH974ugVyZC7MHeo6LRqNUprGkadF2viY5g\n4w99X+kP3WQC08ZwxElUBCaBsdCg209Lof2gnWKLH3JMg1Q86vRcWP4vx3GO5/+SxiRJsnYddjs3\nu9e2FotWARjYv2nW1BQ2FYsUDeExpgnccEMc0SiwaZNMxak2DOfzWLt5s9uH4TmN8jxZUUZRFACo\niY7ol6LMqlUq7rsvimeeCeO979XdPhxCAonjOESj0ZoJKV3Xa7LMWVGYoiNIPdacRohTqAhMAmO+\nwpWu66hUKgiHw8jlcr768A1ScbETLLuZxT9QYdGb3Og27QeUm906c8+ewG+aFRRf/nIMv/1tGD/8\noYAIvYm2bbpcxvaqbuDlfdwN3Eyzokx1l3B1lnBQP7MiEeC22xTccUccp58ugF6hCLFHs/ezZhvM\n6boOWZZplQI5CHUCE6fRqzcJjGbFUlmWIQgCkskk4vG4b1/0g5ZB1kpxm20sFo1GPZ8rG6Rifbvn\n4sVu0yBcj07v9+qO7IGBAU8/N3bihoZ6vmlWu/dVEO5Du/2v/xXZ35koIJNx+2j8Z7pcxpaxMazf\ntetA7MnkJMYnJqgQ3ER1USYWi1lFGU3ToKr7okiCvHT7vPM03HVXDE88EcGyZZS93S0a0wnTylhR\nvcEcAGtCqt9XKQRRp9/VqROYOI2mnogvdJIJzOIfRFFENpv1bXeiH4+5FQsVR9jGYmwDhqD+HPyM\nPWOyLCOXy3mmANzP2KaXkUgEmUzGtefGjeLnymIR60ZHazfNGh3FymKxrb+HvuD3zj/9UwjXXhvH\nN78p4sgj6efcie2lklUABvbHnuzahe2lkpuH5SusKJNIJJBKpZBMJhEKhaCqKniehyAIUBQFuq4H\nYjzgOKBYlLF+fRwqxW/bgt5JSadYjjkbf9j306COP2Rh1AlMnEadwCQwql/IWPxDKBQKRDYpK6j0\nw0tn9TL2bDZrzZx7XZA6/lo5F8MwMDc3Zz1jXrs3g3Q9WsVWPXipI9tJS/J5rNmxA5uKRWuZ/Jpi\nseVN4QD6Yt9LL7/MYfnyJD7/eRknnGC4fTi+Zc7MUOyJjTiOA8dx1pjZaOl2dZeeX98nTztNx5Il\nBr7xjSiuvJIqwYR0y453zFaiI4Iw/vSLbjaGo05g4iR/VFcIaQEr+iiKAp7nA5VNGsSCVqNzqi7e\n99Mydr+h/F9ntPrcm6YJURShKIqvJk56YUk+T5vAeZAsA5demsSHPqTiootoOXo3uMHBnsee9LP5\nlm7Lsmxt8AT4L6brtttk6zmkegMh3bP7+V9o/GG/TtERwSJJEhWBiaNoOon4QqsfcoqiQBAEZDIZ\nJJNJ+nD0EVVVMTs7i1gs5uoy9k4FqVDf7FxM04QkSahUKvSMeQTryNY0Dblcri8LwLvLZWxYtQql\ns87ChlWrsLtcdvuQSBXTBK6/PoEjjjBwyy2K24fje8sLBawbGamNPRkZwfJCwc3DCqzqpdvpdBrx\neNz6fGSRY2yzOa874QQDb3+7jvvv77+VIoT4Uf34wxovWMMTG38oOsIbOp0Y9NuEIvG//vu2SALJ\nMAwoigKO4wIR/1AvSAVGhp0TKyxKkoRMJoNoNOr2oZEGWP6vruvI5XJWJ5RXBfGZqcc2TozFYp4r\nyDv1899dLuO+pUuxfmrqwCZZO3dizY4dbcVAkN65++4o/uVfQvjhDwUE7KPZFcP5PMYnJrCxVLJi\nT8YLBdoUzgFs6TbHcdB1HalUCpqmQdd1iKIIwPsbPK1bp+Dss5O44goFhx7q9tH4ExVsCOD8fdBs\ng0td1yFJEgBYv07REf5DYwpxEhWBie+xpelB/tALakHLMAxUKhWYpomBgQFfXzuO43zRCdQJFtMR\nDoc9mf8bVPM996wLJJVKIR6PO3xk3rG1WLQKwMD+TbKmprCpWKRYCA948skwvvrVGJ55RgDteWKf\n4Xweazdvdvsw+h7HcYhGo4hGozBN01q6raoqJEk6qCDjhc/O//E/DJx7roa7747h9tupM58Qv6qO\njojH49b4o2laTXSNlyelgoS9r7f7c3Z7UqnT4yb+RkVg4lv1HaRsNpT4g2maUFUVsVgMqVSKPnw8\npLr4yCZZkskk4vG4b65TUCdOKP+3lrlnj62bZAX1vnHD//2/IVx9dQKPPirida+jn2kvTJfL2F7V\nEbycOoJdM98GT/VdepFIxNXP0ptuUnDKKWl89KMqjjiCnk1CgiAUClnxEdWTUiyuxouTUuQAp6+H\nrusHTQ7IsgxZlq0IErpHgqu/vz0S36gfhAzDAM/zME3TWppuGEZgv7wHrTAhy7LVJZMOSHtY0K4R\nKzZSTId3mKZpdc77IfbGieeBGxpybJMswzAOWvJNL8iN/elPHD70oSTuvFPGiScGc4WE26bLZWwZ\nG8P6XbsORKFMTmJ8YoIKwR7QqEtP07SDuvQikYjjBZnXvc7EihUq7rorhs99Tnbs3yUkKLz+vt8s\nOkLTNKiqCsA7k1JB0U1Hr5P3Eyv+hsNhzM3N4bnnnsPU1BR27dqF2dlZiKKIQw45BK9//evxtre9\nDW984xvxmte8pq9XHQYRFYGJ77AczGg02jcdpEEpMJqmCUEQoKoqkskkdW57mCzv+2Loh/zfoKp+\n7lkkRyQS8cW459SYtbJYxLqdO2szgUdHsaZYtPXfYZ87rPDb6nLvoIzd7VAUYOXK/8/euYfHVZX7\n/7vnfsuEKqVNgGYSReSAqIBYUA9QblLEeh4QKFZbmnKollsRKegEBxpaqj9QEBCl5YAU68Frjv4C\ngnqAHvXQigdR9ICaTAskBeQHzczsvWdff3+ka3cnmUlmJjOz917zfp7H5zGhSdaatfa71/qud33f\nCM49V8UFF2hON4dbtvX3WwIwsM8KZXgYG/v7ySrChfh8PoRC4wXZ7FnCxWLRytJjh0vNOOC76ioF\nH/hAHFdfrVA2cBW0WjwnpsftazGG/VCK1WMh6wh3YJpmU5M6/H4/hoeH8eCDD+Kpp57C9u3brYOB\nSCSCUCgEWZahKON2QXPnzsXSpUuxZs0a9PT00J6QE0gEJjyDaZooFouQJAnxeNxaTDNacbPtJQzD\nQC6Xg8/nQzKZtDJieIGX+cf8DNk4eXUhyMt4APv9f71mydEMulIprB4cxKZMxroSvzqTqWtROLv/\nss/ng2EY02bWtHqW8Lp1YSSTwA03kN9oIzFHR+tqhUI0D7sgA8C6ts1E4WYIMvPmjWcDf+1rIWza\nRNnA1dKq8Z3wPoIgQBCEKdYRmqZNsY5gWcI032em1kxgWZYRiUQa0KKpaJqGdDqN73//+xgeHsax\nxx6LSy+9FF1dXTj44IMRiUQQDoexd+9e7NmzB3/84x/xm9/8Bvfeey/uuOMOrFixArfccgsOOuig\nprSXaBwkAhOegNk/6LpeNjORJ9FnMl7vG/OVjUQiEzyGvNynUni9P0zsouth7sA0TSiKAlVVyZJj\nGrpSqYYUgbP7zjP/ZSb2AqUza5ggzLKEWex2uvBHs7j//iCeesqPX/1KhMvdSjyP0NHRNCsUorFU\n6uVZb0HmqqsUHH98HGvXUjYwQVQDT+/0Un7mmqZB13XLAosOuBsHSzJoBm+99RZuv/12rFy5EosX\nL8ZRRx2Frq6uGX/ut7/9LbZs2YJt27ZhcHAQP/rRj3DiiSc2ocVEoyARmPAELCsiHo+Xffl4XSid\nDq/2bXLxPruIxdsiwsv9mTxOqqp6uj92vLpQZ0KAYRieteRodMzalc1iayYDc2QEQmcnltUhA9gu\n2rKDx/b2duuq3nTvH0EQplz3VlXVssHhfRP19NM+3HRTCD//uYj2dqdbwz9L02n07dw50RO4uxu9\n6bTTTSNmwXRenvUWZObPN7F0qYqvfz2EW26hbGCCIMZjUDAYtA6lJh9wO+ln7mZq3W+Iotg0ETiZ\nTOLRRx/FP//zP09IyDIMw/p6cqKWIAg44YQTcMIJJ+Cmm27Chg0bkM1mSQT2OCQCE55AEAREo1Gn\nm0FUAcveLidieVXYng4v9sdebIyJXZqmebIvdry8KGX+v8C4P5cXBeBGf/67slncs3jxRC/gHTuw\nenBw1kKwaZoYGxuD3++fYolSab9YlrAgCJBlGdFodMomyu7/6eX5CgAjIwI+85ko7r5bxmGHeTt2\neIUFqRR6Bwawsb/fskLpTaepKFwTaObh4uQbByxLuF6xZO3a/dnA8+bRs0sQxH7KHXA76WfOG5Ik\nNU0EDoVCOOmkkwCM79PZO8OeBQ7sH3cG+35nZyfuvPNOz+8RCRKBCQ8xk2jIo6jI8Frf7MX7EomE\n5wWOSvBiH3VdRy6Xa6kii27Hbp2iaRqNSRm2ZjKWAAzsK4o1NIRNmcysrCFYxl00Gp1gXcOoJQ4z\n771SmyhZlgF4+6qlLAOf+lQUq1ap+OhHqdhnM1mQSlERuBai1LXtUrGkGkun+fNNXHjheDbwxo2U\nDTwTXr1dRNSXVp0H0/mZs0JiXl7POEEzM4GB/XPXLtiz70132226rwnvQSIwwQ1eE0qrQRAEGIbh\ndDMqolgsWi+0cDhc9t/xNl5e64+92NXkcfLSfJsONiZeWazIsjyh8GWhUPDUnGom5shI3YtiFYtF\ny8u3kTdP7JuocDhsFWTxYpawaQJXXx3BoYcauOYaKgTnFLuzWWyzZQQvpYzglqBULGEHWfYCczNd\n2167VsEHPxjHVVdRNjBBEJVTzs/ci+uZ2VLrfqOZnsBAaQFXEAS8/vrryOVy0HUdwWAQoVDIsgVh\n7xiqTcIPJAIT3OA1EY43mO+lqqpWESXCfZimCUmSoCgKjZNLsD87XvX/LUUj47HQ2Vm3olj2Z4Jl\nYDeTSrKE3Vqs8dvfDuJ//seHxx8X4bKmtQy7s1lsWbJkojfwzp3oHRggIbjFKCXIMEF4umvbHR0m\nLrhAxe23h7BhA2UDEwRRPdPdVGAxiMUfso7YTzPtIErx6quv4oEHHsB//dd/4aWXXkKhUEAoFEIo\nFEI4HEYsFoOiKDj11FORyWQsGwnC29AIEp6h0s0vj0Kw2wVuwzCQy+Us/99KhEW396lavNAfwzCQ\nz+ehaVrF40Q0Fvuz097ezo0A3Gixclkmg76eHhT2fV0A0NfTg2WZTFW/h3li25+JmZ7jhorb+zL7\nwuEw4vE4otGo5dNdKBQgiiIURYGu647Hm+3b/fjqV0P47nclJBKONqWl2dbfbwnAwD5rlOFhbOvv\nd7JZhMMwQYZt4mOxGAKBAHRdhyiKKBQKKBaLVg2AtWsVbN0axGuv0WkOQcyEl26ZOYV9PcNikN/v\nLxuDvE6tc4LdAHSC119/HV/4whdw3XXX4Q9/+AOSySTmzZuHSCSCYrGI1157DX/729/w61//Gi++\n+CIAcHFTlKBMYIIjeH4Zu1lgtHuYlvLQLIeb+8Qjdp/mmfx/eRkbt/eDjUkoFEI0Gi3pueXm9jtJ\nVyqF1YOD2JTJWFfgV2cyVRWFYwX4AoGA9Uzoen09bWc7hm7NEt69W8DKlRHce6+M7m6ao05ijo7W\n3RqF4I9y17YVRYFhGJgzx49PftKP228Por+/yPWaejaQ+EcQtTGTdQRbz7SCdYSdZttBAPuLwv3m\nN7/B1q1bce655+Kmm27CO9/5Tsvywb7mVBTFSlKhBCI+oFEkuMJrHqBexjRNyLIMWZaRSCRq9gni\nZbzcLNhV6tNMNA82Jsz/l6ierlSq5iJw7PAqGo0iHA57IgZV4v/ZDO89SRovBHfllQpOOYUKwTmN\n0NFRN2sUojWwX9sOhULWZn/NGhEnnzwHn/3sWzjwQB8VdyIIoiFUU+TSK9YRte5nRVHE2972tga0\nqDxsv/rSSy8hFArh0ksvxRFHHDEhy3fympPgC/c/UQRRBW4W4maD2/rFrlArioJkMlmTAEybisbD\nvGYlSUJbW1vFL3G3zbdacWM/Jo8JrwKwGz97hizLyOfzSCQSVd1ecBssoyYajVqHCexwThRFyLIM\nVVXrOg6mCaxdG8G73mVgzRq1br+XqJ2l6TT6ursnWqN0d2NpOu1kswgPwTb7PT0hfOxjGrZubYfP\n54Oqqq6zoSEIN8BLAotbKGWFZbeOEEWRK+sIO7IsO+YJHAwG0dHRYWX3ss+Xt8+YmAplAhOeoZVf\ntm4SVOy2AolEoqXHxQ77HNyyMGT+vwCQTCY9cYrOO9WOiZuee7ewK5vF1kwG5sgIhM5OLKvCAoLX\nAnxA87KEt2wJ4g9/8OEXv6BCcG5hQSqF3oEBbOzvt6xRetNpKgpH1MSVV6pYvDiKK65QEYtNzdAz\nTZOKOxEE0VBmsq9xo3WEaZo1xcNCodB0T2C2/l20aBHuvvtu/PSnP8XJJ5/MbWIKMRUSgQmuINGk\nsdTbVoDsOxrDTF6zM0HPUf2pxpOZKM2ubBb3LF6M9UNDiGNfxuOOHVg9ODijEMwEeEEQphXgeZn7\nkzdQ5Sp0V+Ml/PTTPmzYEMLjj4twqIYJUYYFqRTWbd7sdDNaBp7XLYcfbmDhQh0PPhjEpZeqFR0w\nsVjiFjGmkfDwfiBmD82D5lHOvkbTNKjq+I0kr1lH2JEkybFM4MMOOwx9fX144IEHcM899+CUU05B\nPB5HOBy24r79s+c9vrcSJAITXMHLBn4yTvfLnkHX1tZWN1N4p/tVb9wgapPX7H7cMr8URbEKP5Cv\nVu1szWQsARgY90BdPzSETZnMtN7Auq4jl8vVfCjidewiDoCaRJzXXhOwYkUUd90l4x3vcP6ZIgii\ncVx1lYKLL45i5UoVk92+SmXosVjCMvTstw54pNXeIURpaB44Q6VrGva/Zo1Trfs/p0RgVhzumGOO\nwa233orPfe5z6OnpwSGHHGLtVyKRCOLxOAqFAq644gqccMIJju9zifpAIjDBFW4RfeqNk/2qNIOO\ncJZ6CvW8PkfNxjRNSJIERVFqGhNBECYUafAa9Z5D5sgIJiegxgGYo6Nlf4YE+KlUmiXMRBxVBZYv\nj2DZMhVnnUWF4NzK7mwW22yWEEvJEoKokQ98wEAqZeCHPwzgwgu1sv+uVHEnTdOsa9sAJsQTEg4I\ngqg35Q6mJltHsJtPbotDLHGnmTDrihdffBGXXnopnn76aRxxxBGIx+N444038Morr6BYLEJRFJim\niddffx1LlizBCSecYH2mhLchEZjwDG4L2q2AqqrI5/OIRCINKaDEm9joVH9IqC+PU/OLFU80TbMl\nx6QR8Vro7EQBmCAEFwAIHR1T/i0rkCbLMhKJRE3FK0u2gbP3UCUZNZlMEtGoieuuKwLgq/+8sDub\nxZYlS7B+eHi/VcrOnegdGCAhmKiJtWsVfPGLYZx/voZKX1+CICAYDHrKx5MgaoGnvQtPlDqYYmsa\nSZIAYIooXC9qzZB1QgRm8fjee+/Fk08+iTVr1uDiiy9GZ2cnfD6flRygqio0TUOhUEBPTw8AkADM\nCSQCE1zBm6jIaHa/7AIK2Qq4m9n6/5aCl+fIqQ2mruvI5/MIBALk/1tHlmUy6NuxY6IncE8PVmcy\nE/6daZooFArQdZ27AnCNZnJGzfe/78ejj4bwyCNvQJJ0KgjlUrb191sCMLDPKmV4GBv7+8krmKiJ\nRYt0hELAz3/ur+kGQKU+niym0HuS8Bo0Z92P/aDbNE3rtoKbPM2d9AR+9tlncfjhh+OGG27A3Llz\nHWkD4QwkAhNcwYt4VY5m+PA0U0Dhbbya3R9ZliFJEgn1LoJlz0ejUYTD4Vk/rzw9H7OlK5XC6sFB\nbMpkrCvvqzOZCUXhJmfFO10U0csx7i9/8WPduigGBiQcfHC05QtCuRlzdLRqqxSCmA5BGPcG/trX\nQjjrLKkOv6+0GKOqKmRZhs/nm+Al7NZ4Qn6YBOFNmBUE2y+VssOafDBVzbM+G0/gRCJR9c/VA5/P\nh0MOOcRap2qaNmHfT7GOX0gEJjxDpYHIqxvu6WhWEGYFlILBYE0CSrV4WSBxErv/b6OEeh7GpZnz\nqxH2A15efDXqs+9KpcoWgWtEVnyr8tZbwEUXRbFhQxFHHz3uSz1TQSjKEnYOoaOjYqsUgqiUJUs0\n3HRTGL/9rR8nnFA/P/BKxBiKJ4SbYZ6qhHeZbIdVytO8GbcVJElqes0Ktm+8+OKLcfXVV+Pll1/G\nQQcdVLfC74T7oehFcAXPm/5GC1qKomBsbAzRaBTxeJzrz7JRNEN0NAwDY2NjMAwD7e3tDRGAaeyr\ng2XPK4qCZDJZN/9ZYj+7slncvGIF+s84AzevWIFd2eyE/64oCnK5HGKxGFlwzBLDAFavjuC00zQs\nXVq6KBS76h0Oh63P3O/3Q9d1iKIIURRRLBah6zoXB0puZ2k6jb7ubhT2fV0A0NfdjaXptJPNIjxO\nIABceaWC225r7E0jJsbMFE80TaN4QhBEQ2Ce5pFIBLFYDJFIZF9xXBWFQgGiKEJRlLLrmtncEnDq\nQGHhwoU4/PDD8ZWvfAXbt2/Hnj17MDY2hkKhAEmSUCwWoaoqdJ2KAvMGyf0EV/CcWdqovpmmCUmS\noCgK2tramnoKyON4NbI/jS7UR1QP8//1+/1NyZ5vRXZls7hn8eKJXsA7dmD14CAWdHU5Fr945fbb\nQ3j9dR++8x2x4p+ZKUvYnk1D2VP1Z0Eqhd6BAWzs77esUnrTaSoKR8yaiy5SsWFDCH/5iw9HHGE0\n5W+WiyeTC8wxKxqCIIh6Uq7AXDnriNnEISf2DUywvvLKK/Haa6/hySefxC9+8Qscd9xxmDdvHqLR\nKCKRCKLRKGKxGEzTxDXXXOOYdzFRf2i3RHCFIAgwjOYsUnlgsn8mLaZnR6Ne5KZpolgsNs3/lxdx\nvtH9aLQoz8s4zJatmYwlAAP7il4NDWFTJoMrvvENmKZJ8atObN/ux113BfHEEyJqDTOTN0/MS5ht\nnshLuDEsSKWoCFyTaCVf2EgEWLVKxTe/GcQddxSb/vdLiTHsyrYkjXsV260jmlE3o1XGnigPzYPW\nYrJ1hH1dw6wjgHFbskAg4HoLS9Y+VVWRSCRw8skn46233sLf/vY3/OlPf4KiKFBV1bp9IYoiLr/8\nchKBOYJEYMIzuD2gNpp6C0JMwAqHw475Z/IoctW7P80s1Ffqb9MitzRUlG966vlsmyMjJYteqS+9\nBEEQkEgkaJ7WgVdfFbBqVQTf+paMQw6pXxyjLGGC8Da9vSqOPTaOG25QcOCBzq7Z2JVtezzRdd0q\nMMfEYLcXmCMIwrtMXtfoug5ZlicUzq200KWTe60tW7ZYgi8TtVVVtf7H/pskSWhvb3ekjURjIBGY\n4AqeF3v1ElWanVU6HbyJwPWef05ZDfDyHDVifjWjKB8xEaGzs2TRK19HR939y3mLSZWiacDKlREs\nX67i1FMb5/1WaZZws7L6eGR3NottNluIpWQLQcySuXNNfPzjKrZsCWLdOmXmH2gS013ZlmUZACZY\nR1A8IQii3rA4BADRaHRKHDJNE4FAAM899xw6Ojpw6KGHOtZWXdcn7Fs6qHhsy0IpFwRXtOoGvlJY\nVmmxWEQymaQMxjpTz/mnqirGxsYQCoWoUJ9LMAwDuVyuoUX57FA8G2dZJoO+np4pRa+W9/fTc1En\nbr45hEAATRd4WCZNJBJBPB63KmQrimIVJlFVlWyeKmR3NostS5bg+ocfxs3bt+P6hx/GliVLsHtS\nIUWCqJY1a1Rs3hxEsfmOEBVjLzAXj8cRjUbh8/mgaZpV2IpFXoIAACAASURBVIkKVhL1gG7KEXbs\n82FyHGKFLn/605/iwx/+MI477jhce+21ePTRR5HL5areS7z88stYtGgRjjzySLznP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xDg9jY39/w4rGVYPT17uJxnLKKTouu0zAX/7iwxFHUKY30Rzsh02hUMgqMKdpGiRJAoAJsaXS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nrpZxvR6/Xc0Cm2jZgs1BFjWRYajQbfPwbQr1sDiCfks1+4nGwfEYUVw7h4W8Jt20a32x05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+9V/bvxcNQu4j7G9zFZdK+LACYMdxcPbZZ2PBggX49Kc/HYvvnYhiiZXARLJu/8HtthJY\n13U0Gg2Uy+Wew6ywBsCJkMJ13dRfU/ZX3IgQQdd19leNCFHlxz6V/WlVcSOeRbxOH54kDNeMq6Bn\nuWVZqNfrbI3SAgcWxov8LAcQWD2Z1DZX4iYQA+DxWrjQwf/9v8PdhymKMuvmXtwq4UWRTRgB8Cc/\n+Um89KUvZQBMRGPDSmBKJdd1YRhG2/9NtVpFPp9vG+qKTYGu66hUKj1fuQwrAGZI0Z1W/VXjVImQ\nBAwphou9ssPDSfXRJJ7lYp2zNQoHFiaRXD2ZtEp4BsDR8fTTCt7whgI2bmyO/Gu3qoSP0iG2eNez\nbXvgAPi8885DuVzGxRdfHInvjYgSjYPhiGTdhMC1Ws178W/1a9Trddi2jcnJyZ5fuMIaAMeeqv1J\n60TjceJQrdGTe2VzOn1vGFLEh3iWp/E6PcBnaxrIlfBxHxIqnq1sBRUNrgvss08Rv/lNE3vsMd7P\nIgfC4hB7nH2EwwyAP/e5z0FRFFx66aV8RhPRKLAdBFGv2g2GEz3MFEXB1NRUXwPgwgiA2VO1f/6B\nW2LjyYFbw+HvpcYN8Gj4r2CyNUp3+GyNl7Repwd2DimS+D3S7NYorYaEij1NlPcsDICjR1GAAw90\nsGGDij32cMb6WeQhof49y6hv78mHa4MMg3VdFxdffDFM08SXv/xlPqOJaOxYCUyppet62x+v1+te\newWZbduoVqvI5XJ9tV4QmxoAA20EOPl7eOQKYQ7cGpyomlcUhUO1IoKtUVpjv+rkSHolfFiDiije\n5NYotm1Htic8A+DoOvfcLHbf3cXHP26N+6MEkvcslmUN/eAjrPY6ruvi0ksvxXPPPYevfe1rifh3\nh4hig5XARL0K2lCYpolarYZisYhCodDzr2nbdigD4OSNCSv4wtdq4Faz2WQg3CPRU1VUXPP3LBr8\nlfDi5UoMl0xrJTwP15KlXSV83A8+xDBYAAyAU07sWfL5/Kye8GLPMs7r9AID4GhbuNDBHXdE931C\n3rPIfYSHNQw3rAD4y1/+MjZt2oQrr7ySATARRQYrgSm1DMNo2e4BgHfFrlQqAYC30ahUKj1Xh4U1\nAE6u+imVStxQjJjYdIpqG7HhjFq1TVTIPVVzuRx/j2JAfrlKWyU8BxamR9x7wssBMG9XUCvywYdl\nWbNaAI3y4EPsBXi7Iro2bFCwYkUed9+tjfuj9Mw/DHfQGx+apoUSAF9xxRX44x//iKuuuop7CiIa\nBw6GI/LrFAKLHnulUgnNZhOGYaBSqfRcHSZCFdu2B9p0O46DRqPhtajgS994+TedSe872Sv2VE0G\n/8GHeLFKUiAc1rVPiqe4HXyI9jrcC1AvxnXwIfYCDICjzTCA+fOLeOqpJnxd8GLFf/DR642PsALg\nK6+8Evfeey+uvfZaBsBENC4MgYn8ugmBRe9e27YxOTnZ84YgrAFwtm17153y+Txf+iIm6X0neyWu\nofJKfbIETe2O+8GHPPiFATAB0e6vyvY6FIZRHXwwAI6X//N/CvjGNwwcfPB4h8OFpdc+wrquwzCM\ngQPgq6++Gr/61a9w/fXXcw9MROPEnsBEfoqitA2BgR1hVjabxdTUVF8D4MIIgFlRGX2t+k5qmpa6\nQJhX6pPLP7VbBAjNZjOW61xurzPI5G9Klk79Vcd18CECYB4G06AURWk5+6DRaISyzhkAx8/ChQ4e\neEDBwQeP+5OEQ+4jDLx4wOfvIzwxMQHTNEMJgK+77jrceeeduPHGGxkAE1Fk8elEFMC2bTSbTSiK\n0tfAFREEAhjoRZFDiuJHvnrWbhBR0gJSuaKyUqnEJgik/rQbuCX3nYzqOudQLeqGqqqz1rmoKJMP\nPiYmJoa+zuUAuJ+htETtyAd8/oOPftY5A+B42hECqwDscX+UoWh3wAcA+Xy+71/bdV3ccMMNuOOO\nO7By5UqueyKKNKZKRD6maaJWqyGfz3v9pHph23YoA+Dka0lRDVKoPX8gLK6l1ev12E+ml8kVlbxS\nnz5BBx+WZUV2nbO/OvUj6OBjFOtcHrA5SEhB1A3/wUfQQbYIhIPWuQiAWbwQP4sWObj9aEzJJAAA\nIABJREFU9nSEl2KdizVeLBZhWRZqtVpX69xv1apVWL9+PW6++Wbe2iSiyGNPYEot0zThOLP7Xokr\nQpVKBQDQaDQwZ86crn490WMtjABYBGqlUomBWgLFfTK9TAwpUhSFU+ppllb9+Ma5znmlnsI2zHUu\nAmC2g6Jx66a/KucBxNtzzwGLFhXx9NNNpOGfRnHbUi62EetcHH6Iln533nkn/vZv/xbFgKl5t9xy\nC2666SasXr2aNzWIKEo4GI7Iz7Is2PaOK08ieDUMA5OTk8hkMl6FTzchsAiAbdseqApIVKgxUEuP\noAEt4w7KusUhRdStUQ0iaocVlTRs8jq3LAuO4/S9znmlnqJMDoRt20Ymk4Ft2yiVSlyvMfbylxfw\nk5/o2GefZEcAQQFwENu28eSTT2LFihV46KGH8MY3vhFHHXUU3va2t2G33XbDrbfeim9961tYs2YN\nSqXSCL8DIqKOGAIT+YmNq+u6qNVqcF13Vi9T27ZRrVaxyy67tP11whoAZ9u2N5CDgVp6yRXC4wjK\nuiUHarlcLlKfjaJPrrSxbds7+BjWOueATRoH+eDDtu2uB24xAKY4MQzD6x8s1rlY67zNFi/velce\nK1ZYWLYsmX2Bge4DYL9nnnkG3//+93H77bfjzjvvxKte9Sps27YNN998MxYvXjzET0xE1BeGwER+\nIoCoVqvIZDI7DQdyHAfbtm3D3LlzW/4aYQXADCgoiD9AGHZQ1i2uVwqTXDlpWVYok+ll4ooyAzUa\nJ3kQkWVZ3sAtf1DGK/UUJ/71KvcRtixr1qDQqPSFp9Y++9ks8nkXn/60Ne6PMhT9BsB+69evx1VX\nXYU999wTP/zhD7Hrrrvi6KOPxjHHHINDDz2Uhx9EFAUt/8Hl7pJSy7IsbN++Hblcrq/hQGKjC2Cg\nf+zFhoQBBfn5J3aL4SyiYlwECKN8qRLrlQEFhUVe567regcf8mT6fivKuF4pKtoN3BJBGbBjNkG5\nXOZ6pcgLCtQ6DcSN6/yDtFi40MEttyRzGLVpmqEEwD/96U/x9a9/HevWrcOcOXPgOA5++9vfYt26\ndTjllFPw/PPPY9myZTjmmGPw5je/ObCPMBHROLESmFKrVqvBcZyWvSFd18XMzAx23XXXnX7Mtu1Q\nBsDpug7DMAbekFC6+CvKwq6cDOK6LgzD8AIKrlcatqCKMnlidye6rnO9UuSJoEzTNNi2PStEY1BG\nUdVrRWUU+sJTZ3/4g4K///s8HnxQG/dHCZU4WB50P/Bf//VfuPjii3Hrrbe2vCn6P//zP7j11ltx\n66234t5778Wb3vQmHH300Tj66KPxkpe8pO+vTUTUI7aDIPITPSnb2bJlC+bOnettTsUmNowAuNls\nwnEclEolXhuivsmVk+2uGA/6NTRNg2VZKJfLXK80ciIQFqGwHJT5rxiLAzbTNLleKRbEgYUYCOsf\nFCp6rDIooygI40p9v/2yabhsG9hjjyIefbSJqalxf5pwhBUA//KXv8QFF1yAdevWdR3mPv/88/je\n976HdevWYfny5Tj22GP7/vpERD1iCEzk100IPDMzgzlz5kBVVS8Atm17oL5mrut61+LECx9RGOTK\nSdM0QwmEXddFo9GA67o79c0mGgf5irFlWV5QJta5rus8sKBY6HRgETRAUYRlfBbTOITVU1XWbb9s\nGo2/+Zs8Pv95E69/vTPujzKwsHqs//rXv8ZnPvMZrFu3DrvttluIn5CIaGjYE5jIr5cXKHkA3CAB\nsOM4qNfrmJiYQKFQ4Eschcrfi0/uOem/YtwNx3HQaDSgqioPLCgyRF9JMYRIVJSJ2xWKovTV551o\nlLq5YZHJZJDJZLy+8OKZ3mw2WTlJIzeMABho3y+73a0PGo6FC1088IAa+xA4rAD47rvvxnnnnYc1\na9YwACaiRGAITNSGoihwnB2bINd1B2oBYVkWGo2GNwCJaJg6DWfp9FLFAwuKA0VRkMlkoKqq1091\nYmLCG6Ao1jmv0lOUiADYtu2uK9b9QZmomgxjgCJRJ6Pqsd5q7yJuJLFf9vAtXOhgw4Z4P0fEs3HQ\nAPjee+/FJz/5SaxevRp77LFHiJ+QiGh8GAITtSF68w1aaSOqJ4rFIrLZbIifkKgzuXKyUCjMCoQB\n7PRSZds26vU6DywoFlpVrIsKYcMw0Gg0WDlJkSDPBOi3xY6iKKycpJERAXClUhnps7PVrQ9N0+A4\nDgfLDcnChQ5uuim+w1RF0c2gAfADDzyAs88+G6tXr8aee+4Z4ickIhov9gSm1BKbyVZs20az2YRh\nGH1X2Yh+f4ZhcEI9RU7QtO5MJgPLslAsFpHL5cb9EYnaEhXr2WwW+Xy+ZRDg7zkpggVWTtIoiR7r\nAIbSYqddv2xWTlI/5D1slJ6VYu8iBobykC8827YBr3hFEZs2NRG315awAuCHHnoIZ5xxBlatWoUF\nCxaE9wGJiEaHg+GI/FzXhWEYgf/dcRyvtySAWeGBqqpdBcKi2qeX655E46RpGnRd9wYhssqGoqzf\nivVWAxQnJiZ4UEdDM+wAOOjr+Q/5+EynXmia1nJoYZTI7VH4TA/Ha15TwG236Xj5y+MTBYQVAD/8\n8MM47bTT8J3vfAf77bdfiJ+QiGikOBiOqBvipcm27VnXKFsN2xKBsH+j6bqu13u1UqnwZYsiTa5Y\nr1QqyGQyXnjA3qoUReJlr1Ao9FyxHjRA0bKsrvtlE/VK7AlUVR3Z0ELRLzuTyaBQKOz0TBeBcDab\n5TqnncQlAAaC26PIz3Sx1lkN370DD3Tw4IMqXv5ye9wfpStiT1AsFgcKgB955BGcdtppuPHGGxkA\nE1FisRKYUstfCSyuUXY7AE6+dmmaphceZDIZaJrGgVoUC91MqJeryWzbZnhAYyUmfofdYz3omc7w\ngAYlelaLMDYK64jtUagVcSgclwC4HfFMF2ud1fDdu/DCLFQV+MxnWrfNiwo5AB5kT/DYY4/h5JNP\nxvXXX49Xv/rVIX5CIqKxYDsIIj85BO41AA76tWzbhq7rsCzLq0pgNRlFmbie7Lpu1wOKgsID9uGj\nURFDNge97tlJ0FV6sc4ZHlC3RM/qKB8KyzecRMsr+fCD0iNJAXAQORDmgXZ7q1dnsGpVBitX7tw2\nL0pEW6hBA+AnnngCJ554Ir71rW/hgAMOCPETEhGNDUNgoiC6rnsvQP2EvzJRnVYoFKCq6k4VwgyE\nKUpEddog15NFHz4RHvQ7QJGoG2JC/TiGbMrhAafSUze6HVoYJa1uOImWV3H4Hqg/3dwKShL/gTb3\nL7P98Y8K3v3uPB58UBv3R2kprAD46aefxnHHHYerrroKixYtCvETEhGNFUNgoiBicNsgAbCoKA4K\nJ4KuonFSN43bMMKJVsO2+EJFg4padRrbo1An/Q4tjJJW+xdWwydP2gJgP381PIs3ANsG9tijiEcf\nbWJqatyfZmfiGdvPXADZpk2bMD09jW984xs4+OCDQ/yERERjxxCYyO++++7Dueeei6OOOgrLli3D\nbrvt1lcbiG43zq2uFzMQplEaRTjR7oWK14upF1EPJ9hblfzCCieihtXwySSesbZtd90WKsn81fAA\nUrtX/+u/zuOyy0wcdpgz7o8yS1jP2M2bN2N6ehpXXHEFDj300BA/4c4WLFiAOXPmeAPFf/Ob3wz1\n6xERgSEw0c5c18UzzzyDNWvWYO3atbAsC0cddRSOOeYYzJs3r+NGb9u2bTj33HPxz//8z9hzzz17\n3hjKm0wGwjQKwxqo1U6r68VprrCh7riui2azCcdxYhFOtKqGF1fpKfnEgKKkBcB+4kDbsiz2ho8x\nBsDtycUblmWl7vDj9NNzOOQQBx/4gDXuj+IJKwD+3//9Xyxfvhxf+tKX8LrXvS7ETxhsv/32wz33\n3IO5c+cO/WsREb2AITBRO67r4vnnn8fatWuxZs0aNBoNvP3tb8fRRx+Nl73sZTtt9B5//HG8+93v\nxute9zp8+ctfHjhQEyFZGjeZNBqjGqjVjhwIW/+fvXsPb6rK1wf+JmnTtCkWgQoI/EB0FGQcPaiA\nMEUYoIClTfAyXDrqI3AGr6iMgheOUn1QwOMojuJ4Y5xRBw9N2lJKLXCKVCpTpahTQWEUGbQdtUzB\nCklz2dnr94dnxzSmpW12kp3k/TyPz4z2sldgdTf7XWt9v5LExQ/qkNK0EAAyMjLibm7weHHyUatD\nfbxRasMr852lgOJDvC2yaUGyLX6sX5+Cf/xDh6ef9sZ6KADUC4BbWlowZ84crFmzBjk5OSqOsGPn\nnHMO6uvr0bdv36hcj4gIDIGJuuf48eMoLy9HSUkJWltbMX36dFgsFgwbNgz19fWYO3cubr/9dtx5\n552qv/ELLBnBQJjCpdRT9Xg8MWmo1RGWR6GOqNG0UEtC7YZX7uuc64lBOWURy0U2LeDiR3xgABy+\njhY/Eunkxzvv6PHII6n43/91x3ookGUZp06dCjsAPnHiBObMmYNHH30UkydPVnGEnRs+fDh69+4N\ng8GA3/72t/jP//zPqF2biJIWQ2CinmptbUVFRQXsdjs+++wzNDU1oaioCAsWLIj4G+fgBkTKwxQD\nYeoK5UFPOeqp5Z0q3A1PQGSaFmpJR4sfbLYVvxgAh8baqtrEAFh9yuKHEggrix9KIByvf8YOB/Dp\np3pcdllsawIrAXC4vSy+++47zJ07Fw899BCmTp2q4ghP7+uvv8bAgQNx7NgxTJs2Dc8++yx++ctf\nRnUMRJR0GAIThUMIgaeffhpPPPEElixZgg8++ACNjY2YMmUKrFYrRowYEfVAmB3pqTPxfJyeix/J\nKRpNC7WGzbbim1JmR0unLLQo1OIH53r0KQGwECLu3hfEC2XxQ7mvc6EvPGoFwN9//z3mzp2L++67\nDzNmzFBxhN1XVFSEXr16YenSpTEdBxElPIbARD0lSRLuvPNO1NTUYOvWrRg6dCgAwOl0oqqqCjab\nDUeOHMGVV16J2bNnY9SoURHfcRmqIz0DYVIk0nH6jmrwca4nlmRpqNUZLvTFF7fbDbfbzQC4BzjX\noy+eF4bjWWAgrCxqK/OdfwedU04GGY3GsALgU6dOYe7cuVi6dClmzZql4gi7xul0QpZlZGZmwuFw\nIDc3Fw8//DByc3OjPhYiSioMgYl64uTJk5gzZw4kSUJxcTGysrJCfp7L5cKOHTtgs9lw8OBBTJw4\nEVarFRdffHHEA2GlLllwIJyojSqocz6fD06nMyGP03e0+MG5Ht+U4/TJ1lCrM6HmujLfOddjz+Vy\nwev1ar7MTjzgXI88BsDawLnedWoFwA6HA/PmzcNtt92G2bNnqzjCrjty5Ahmz54NnU4HSZJQWFiI\n++67LyZjIaKkwhCYqLsaGxsxa9YsjBkzBs8991yXwwmPx4OdO3fCZrOhoaEB48ePh9VqxWWXXRaT\nQJhvMJOHspsyGY7TsyN9YlCO07OeascCm21xrseW0miTAXBkBDeW0+v1CddsK9oYAGtTqLke+H49\nmf+eAnsDmEymHn+ftrY2zJ8/H4sWLcJ1112n4giJiOICQ2Ci7vjwww9RUFCAJUuW4J577unxmzFJ\nklBTU4Pi4mLs27cPY8eOhcViwbhx4yL+QMPgILkk825KBgfxicfpuy94risNiBgcRJ4QAi6XC5Ik\nMQCOgkRtthVNDIDjA5so/kitANjlcuE3v/kNrr/+esybN0/FERIRxQ2GwERd9dFHH2HatGl4/vnn\nce2116r2fX0+H2pra2Gz2VBXV4dLL70UVqsV48ePj/gOOAbCiY27KX/UUXDAkEw7uJtSHcHBgU6n\n8+8kS7bgINKUhlqyLMNsNvPPNso6arbFud4xJQDW6XRx3xsgmSRzE8XAADiccmZutxs33ngjrr32\nWlx//fUJ/WdGRNQJhsBEXeX1enHw4EFcdNFFEbuGz+dDXV0d7HY7amtrcdFFF8FqtWLixIkR38XJ\nXZOJIzBMy8jI4N9fkFAhGQPh2OJuysgIFRwwJFMHd1Nqj3JflyQJsiwnTUjWVUIIOByOhGgOm+w6\naqKYaL0Q1AqAPR4PbrrpJsyaNQsLFizg3CeiZMYQmEirZFlGfX09bDYbampqMGLECFgsFkyePDni\ndV07C8kYKGpb4M60jIyMhHoYiATuJIs97qaMHoZk6mAArH1KSKacAEnUkKyrGAAnruDGcsqpvnjf\nxKHMWYPBAJPJ1OM56/V6sWjRIkyZMgWLFy/m3CeiZMcQmCgeyLKMhoYGFBcXo7q6GsOHD4fVasWU\nKVOQnp4e0Wtz12T8YDARHu6ajD7O2djpaCdZamoq/x46wTAt/gQ3x0220lecs8mjo/rw8VYzW60A\nWJIkLF68GBMmTMBtt90WN6+fiCiCGAITxRshBPbv3w+bzYYdO3Zg0KBBsFqtyM3Nhdlsjvi1GQhr\nkyzLcDqdfMhTEXdNRhbnrHYE7yRL9l2THVHmbLjBBMVOsjVRVCtMo/gT+J5dkiT/wnZKSoqm38eo\nNWd9Ph9uvfVWjB49GnfddZdmXy8RUZQxBCaKZ0IIHDp0CDabDdu2bUN2djYKCgowc+ZM9OrVK+LX\n5jF6bfD5fHA6nWHXTKOOBe+aVOa6lh+ktEytOn+kPmXXpHJvT7Zdkx1R5mxKSgrDtASR6E0UGQBT\noMD37Mr7GGW+a2VuqBkAL1myBCNHjsS9996rmddHRKQBDIGJEoUQAocPH4bdbkdlZSWysrJQUFCA\nq666Cr179474tXmMPjYkSYLT6YTJZILRaIz1cJJCR7UmtfQgpWU+nw8OhwNpaWkRr29O4Um2XZMd\n4aJF4gv1PiaeT39w0YI6E+r0hzLfY7XYp1bZElmWcffdd2Po0KF48MEHOfeJiNpjCEyUiIQQOHr0\nKOx2O7Zu3QqTyYSCggLk5eWhT58+EX9DFLizhoFw5Hi9XrS1tSE9PR2pqamxHk5S4jH67uGiRfzq\nqBxQvNWa7C4uWiSnUKc/tLZrsiMMgKk7ghf79Hp9u0A4GvNHzQB42bJlyM7OxsqVKzn3iYh+iiEw\nUaITQqCpqQklJSUoLy+HXq9Hfn4+8vPzkZ2dHbVAmHVV1eV2u+F2u5GRkYGUlJRYD4fAY/Snw0WL\nxJEs5YCUAJiLFslNi7smO8Jd6xSOWJRIURrE6nS6sAPgBx98EBkZGXjsscc494mIQmMITJRMhBD4\n9ttvUVpairKyMkiShFmzZsFisaB///4Rf8MUuLOGgXDPCCHgdrvh9XqRkZEBg8EQ6yFRCKF21gTu\nmkw2Ho8HLpeLixYJKhEX+7hrnUIJvLcHLvZp4d7OAJjUFI0SKWoGwEVFRRBCYO3atZpbnCEi0hCG\nwETJSgiBlpYWlJWVobS0FE6nE1dddRUKCgowePDgqAbCbLTVNUIItLW1QZZlZGRk8E1unFBCAyU4\nSLa6qsqudbPZHPOQhCIvno/RK5QAmLvWqTMd3dtjUSIlMAA2mUxRuy4lj+B7e7jlr5QAGAAyMjJ6\n/PMihMCqVavgcDjw1FNP8b0xEVHnGAIT0Q+OHz+O8vJylJSUoLW1FdOnT4fFYsGwYcNiEgjHW2gQ\naWq9WabYSqa6qoG71s1mMx/MklA81sxWypZw1zp1RyxLpCgBsNFoZN1qiorge3t3y1+pGQCvXbsW\nx44dw7PPPqvZ3ytERBrCEJioq6qqqnDXXXdBlmUsXLgQy5cvj/WQIqa1tRUVFRWw2+1obm5Gbm4u\nLBYLzjvvvKgEwqFCg2QOhNnkJTElcl1VIQRcLhckSWIATADio2Y2A2BSQ+Ax+kiXSJFlGadOnWLj\nQoqZ4PJXpzvtpGYA/NRTT+Ho0aN44YUXNPN7hIhI4xgCE3WFLMs4//zzUV1djbPPPhuXX3453nzz\nTYwYMSLWQ4u4kydPorKyEna7HY2NjZgyZQqsVitGjBgR8ZBKCQ3iaReZ2pTGRMoOn3gOBqljoWrv\nKXM93kqkBJYtMZvNcTV2io7OQoNYlQxR6lazbAmpTe1j9IHflwEwaUngaSdJkn6yuA0AbW1tEEKE\nHQD/4Q9/wMGDB/HKK6/wnk1E1HUMgYm6oq6uDkVFRXjrrbcAAKtXr4ZOp0vo3cChOJ1OVFVVwWaz\n4ciRI7jyyisxe/ZsjBo1KuKhbDIGwmxMlLwCdwjHU6Mtli2h7tJCiRTWraZoCfcYvUJZIGYATFoV\nvCPe5/NBp9NBp9OFdUJICIEXXngBH374IV599VXes4mIuqfDN9Y8A0cUoKmpCUOGDPH/++DBg/H+\n++/HcESxkZGRgauvvhpXX301XC4XduzYgfXr1+PgwYOYOHEiLBYLLrnkkoiEsjqdDkajEUajsd0u\nsra2Nk0eKw6X8trYmCg5GQwGGAwGpKWl+R+i3G43nE6nZmtmy7IMp9MJvV4fVpdvSi46nc6/491k\nMvkXQJTdYpEukaIEwJmZmQnz+4O0S6/Xh3wv43a7u9w0VAmAuUBMWqbT6fzvZZQFYiUIPnnypP++\n35337kIIvPLKK9i7dy9ee+01BsBERCpiCExEnTKZTMjPz0d+fj48Hg927tyJV199FQ0NDRg/fjys\nVisuu+yyiAXCyoNS8EOUXq+P+0CYu9IokF6v9+/2CtxF1tbWppma2YGd6Vm2hHoqMBAOXABRAmE1\nd8QHNi5kAEyxEPxeRtkR73A4/D8LwQsgDIAp3iglooQQ6NWrF3Q6Xbsa8S6XCwaDASkpKZBlGRkZ\nGR1+n7/85S945513sHHjRtZtJyJSGe+qRAEGDRqEL7/80v/vjY2NGDRoUAxHpC1GoxEzZszAjBkz\nIEkSampq8Oabb+Lee+/F2LFjYbFYMG7cuIgEml0JhJVjxVoX2EyLoQSFEryLTDlCrwTC3d1VowYe\nS6ZICNxFZjKZVN0Rz8aFpDXBO+KDF0CU+7rL5UJ6ejoDYIoLyr02uEdAqNN9kiShqKgIVVVVmDlz\nJgoKCjB27Fj/TuK//vWv2L59O/7nf/6HJ+SIiCKANYGJAvh8PlxwwQWorq7GwIEDMWbMGGzcuBEj\nR46M9dA0zefzoba2FjabDXV1dbj00kthtVoxfvz4iK/gd1RnMpaNhzoT2EwrIyODoQR1S+ACiNfr\njVqJFNatplgIrqvanRrxbFxI8cbn88Hj8cDj8QCAZksCEQVSAmCfz9fle60sy6ivr0dFRQW2bt2K\nlpYWzJgxA0OGDMGHH36I0tJSLjYTEYWHjeGIuqqqqgp33nknZFnGwoULcd9998V6SHHF5/Ohrq4O\ndrsdtbW1uOiii2C1WjFx4sSIr+h3Fgh3VncvWoQQ/uOfbKZF4QoMhCVJitiOeNatJi0IPFZ8ugUQ\nBsAUj5TTFunp6TAYDD1eACGKlp4EwKF89tlnePHFF7FlyxZ8//33mDp1KqxWK/Ly8nDmmWeqPGoi\noqTAEJiIok9Z6bfZbKipqcEFF1wAq9WKyZMnR3yFX2uBsFJLVTkCylCC1BSp+e7xeOByuZCRkcG6\nfKQZoRZAAkukOJ1OAOBiG8UN5bRFqMW2jhZA4qUEFiWm4NJm4dxry8vL8ac//QmlpaVwOBzYunUr\nysrK8Pbbb+Oyyy6DxWKBxWLB0KFDVXwFREQJjSEwEcWWLMtoaGhAcXExqqurMXz4cFgsFkydOhXp\n6ekRvbYSkCkPUNHoRB8osJaq0WhkKEER1VEgrAQGXZ1/bFxI8SB4vgshoNfr/bspeb8lressAA4W\nWFdVCwvclJwCG26GW2+9srISf/zjH1FWVobMzMx2H3M6ndixYwc2b96MLVu2YPDgwbBarbBYLLj4\n4os534mIOsYQmIi0QwiB/fv3w2azYceOHRg0aBCsVityc3NhNpsjfm2lEUs0AmHWUqVY6sl8V/Ph\njihahBA4deqUv9GcJElRX/Aj6q7uBMDBAhdAON8pmlwulyrvEXbs2IF169ahrKwMZ5xxRqefK0kS\n9uzZg82bN6OsrAw+nw/19fXo169fj69PRJTAGAITkTYJIXDo0CHYbDZs27YN2dnZKCgowMyZM9Gr\nV6+IXz94B5maD1A8Sk9a0lEgrHSq1+l07Y53MgCmeBGq3E6o+R5YV5UBGcVaOAFwsMD5LkkSZFnm\nfKeIUCsAfvvtt7F27VqUl5cjKyurW18rhMDBgwcxYsQIzm0iotAYAhOR9gkhcPjwYdjtdlRWViIr\nKwv5+fnIy8tD7969I379wB014T5A8Sg9aV1giRRlvvt8PgAIu74fUbQoAXBqairS0tI6nLeBgbDP\n5/Pf21NTUznXKeqUADhSi8TB852N5UgNagXA77zzDlatWoXy8nI2fiMiigyGwEQUX4QQOHr0KOx2\nO7Zu3QqTyYSCggLk5eWhT58+EX9oD3yA6k4gzJ2UFI+UutXKewIGZBQPAuutd6fZqCzL7RrLMSCj\naPJ6vWhra4vaKaHg+a40llPqCBN1hdvthsfjCfu97bvvvouVK1eivLwcffv2VXGEREQUgCEwEcUv\nIQSamppQUlKC8vJyGAwGzJo1C/n5+cjOzo5qIKzsIAsVCAsh0NbWBlmWYTabGZ5RXJBlGU6n099M\nS2k8xICMtKynAXAwIYT//s6AjCIt2gFwsOD7u16v99/j2ViOOqJWAPzee+/hwQcfxObNm5Gdna3i\nCImIKAhDYCJKDEIINDc3o6SkBGVlZZAkCbNmzYLFYkH//v1jEggrNVUDgzQ+SFE8ON1R+uCATJnr\nDMgolpQAWO2Gm50FZCzrQ+GKdQAcLLCxnNfrhU6nazff+T6GgB/Lm2VmZob1e3/fvn1YtmwZSktL\nMWDAABVHSEREITAEJqLEI4RAS0sLysrKUFpaCqfTiauuugoFBQUYPHhwVAJhSZLg8Xjg8/mg0+lg\nMpl4hJ7iQnd3UgYGZF6vlzsmKSbUbKbVmVABWeB85z2eukNrAXCwrjQOpeSjVgD80UcfYenSpSgp\nKcHZZ5+t4giJiKgDDIGJKPEdP34c5eXlKCkpQWtrK6ZPnw6LxYJhw4ZF7AFGCdIsutSMAAAgAElE\nQVSMRiP0ej2P0FNcUIK0nu6k5I5JigUlSIt0ABwsMBCWJMkfkHHHJHWFMm/jqVFsYOPQzspgUeLy\neDxwuVxhz9v9+/fj9ttvR0lJCQYPHqziCNW3YsUK7NmzB1dccQXmzJmDIUOGsHEdEcUrhsBElFxa\nW1tRUVEBu92O5uZm5ObmwmKx4LzzzlPtAaajIC3UEXoGwqQVagdp3DFJ0aCVnZShdkx2tXEoJR+1\ngrRYUua7JEl8T5Mk1Jq3n3zyCW699VZs2rQJw4YNU2+AEXDixAmsX78eW7ZswZEjR/Dvf/8bOTk5\nuPHGG1FYWKhq6SEioihgCExEyevkyZOorKyE3W5HY2MjpkyZAqvVihEjRvT4gV15g3y6QIJH6ElL\nujpve4o1JikStBykdbVxKCUfLc/bnlIWuZX3NXxPk3jUmrcHDx7E4sWL8eabb+Lcc89VcYSRd+DA\nAbzxxht45ZVXcOzYMeTn56OwsBDXXXcd7+tEFC8YAhMRAYDT6URVVRVsNhu++OILTJo0CbNnz8ao\nUaO69AAjyzLeeOMN5Obmol+/ft16g8xAmGJJqe0XrUCioxqTDISpO6I9b8PBHZOkSMQAOFhwWSCe\nAol/as3bzz77DIsWLcIbb7yB888/X8URRpbP52v3uvfs2YM33ngDf/nLX2A2m3H77bfj/vvvT9if\naSJKKAyBiYiCuVwu7NixAzabDQcPHkROTg6sVisuueSSkA/skiThnnvuwe7du1FRUYGBAwf2+Nqh\naqoqYQHfXJKahBBwu93wer0wm80xC6MCdwjzCD11RTwFwMGCywJx0S95JEMAHIx1s+OfWrWrv/ji\nC9x000147bXXMGLECBVHGD1CCP+cPXHiBLZt24a77roLzc3NuP/++1FUVKTJBo9ERAEYAhMRdcbj\n8WDnzp2w2WxoaGjA+PHjYbVacdlll0Gv18PpdOKmm27C8ePHsWnTJlUbRSiBsBIKB+6mSZYHSIoM\nIQRcLhckSYppABwssOmQLMsMhKkdrSxcqIWLfskjnhcu1MK62fFHrQD4yy+/xA033IA//elPGDVq\nlIojbK+qqgp33XUXZFnGwoULsXz58ohdS/Huu+9izpw5OHHiBFavXo3bbrst7n83EVFCYwhMRNRV\nkiShpqYGxcXF+OCDD/CLX/wCH374IYYOHYqXX34ZJpMpYtdmky1SixACTqcTQgiYzWbNzp3gmqpK\nWJCamqrZMVPkaHXhQi2dLfrxHh/fGACHxnu8tqkVADc1NaGwsBAvv/wyfvGLX6g4wvZkWcb555+P\n6upqnH322bj88svx5ptv9njXsSzLIX/PBO4GVlRVVeHGG2/EwIED/dcM9XlERBrQ4Y3JsHLlys6+\nsNMPEhElIr1ej+HDh2PWrFmYMmUKHnroIfTr1w/Hjx/Hp59+CpPJhMGDB0cknNDpdP5dYkajEQaD\nAT6fDy6Xy7+jRqfT+f8hCkUIAYfDAZ1Op+kAGIC/eZzRaITRaIROp/M/lPp8Pv/naPk1kDqEEGhr\na4Msy8jMzEy4ABj4YS4rpSEC7/FKgCjLMu/xcUj5+8vMzGQAHOR093ghBBdAYkT5ewi3WezXX3+N\nwsJC/PGPf8Qll1yi4gh/6r333sPHH3+M2267DQaDAd999x0OHTqEX/7yl93+XkqpHgA4fPgwWltb\nYTAYkJaWBp1O578fK4YMGQKj0YjXXnsNOp0OM2fO5LwlIq0q6ugDiffumohIJR999BEmTpyIpUuX\nYvfu3airq8P111+PHTt2YOrUqbjjjjtQXV0Nj8cTkesrD07p6eno1asX0tPT/bs7T548iba2Nn/t\nPSKFLMtwOBwwGAzIyMiIqwcUvV4Po9EIs9mMM844A6mpqfB6vTh58iQcDoc/JKPEExgAa33hQi2B\n9/jMzEz/625ra8PJkyfhdDr9i3+kXW63Gx6PJ2EXLtQUfI83Go3w+Xw4deoUTp06BZfL5V/8o8hS\nKwD+5ptvUFhYiD/84Q8YPXq0iiMMrampCUOGDPH/++DBg9HU1NTt76PsSgeA3/zmN7jiiivws5/9\nDFdddRVeeeUVAD/M18D5mJaWBqvVipycHLz44ovYvXt3mK+GiCj6WNGciCiEHTt2YP78+Xj++edx\n7bXXAgAMBgMmTJiACRMmQJZl1NfXw2az4dFHH8WIESNgtVoxefJkpKWlqT4eJSxISUlpV2+vra2N\nDVjITwmAU1NT/TtZ4pVOp/PvHAusqepyudhkK8Eoi1sAkiYADqbsEDYYDDCZTP662W63G06n0z/f\nWVNVW5RTOolYuiTSAkuhBJbCUk6xBNbN5pxXlyRJqgTAx44dQ2FhIZ566imMGTNGxRFGnrID+Lrr\nrkNZWRmuuOIKDBkyBKWlpdizZw8OHz6Mxx57zH9aQ/n84cOHY/ny5cjPz0ddXR1ycnJYEoKI4gpD\nYCKiIK+99hruuecelJSUICcnJ+Tn6PV6jBkzBmPGjIEsy2hoaEBxcTHWrl2L4cOHw2KxYOrUqUhP\nT1d9fIFhQVpaGgNhAvDDrhaHw4G0tLSILETEUnBYoATCbrcber3eX2OSx7Djj1K6RK/XIz09nfes\n/xPqHu/xeOB0Ots12WLwGDsMgNUTuNAduAgS+L5G+TjvEeGRJAlOpzPsALilpQXz58/H2rVrccUV\nV6g4ws4NGjQIX375pf/fGxsbMWjQoB59r7fffht79+5FUVERbrnlFpx55pmoqanB3XffjdWrV8Pt\nduPJJ5/0B8FK2ZIZM2YgLy8PL774IhYsWIC+ffuq9fKIiCKOjeGIiP6PEAKrV6/GCy+8gMrKSlx4\n4YU9+h779++HzWbDjh07MGjQIFitVuTm5sJsNkdg1O0FNpVjR+7koTzUmUwmGI3GWA8nathIMb4x\nAO4+IYR/viv1LLkrPrqEEHC73QyAo0QJhL1eL2RZ5vuaMKgVAJ84cQJz5szBo48+ismTJ6s4wtPz\n+Xy44IILUF1djYEDB2LMmDHYuHEjRo4cedqvDW4C9/rrr6OoqAh1dXXo27evf8fvvn37cPvtt+O9\n997D7bffjmeeecZ/bWWx+ZlnnsGyZcvw8ccf42c/+1lkXiwRUc91+AuSITAREX54Y3fHHXfg3Xff\nxVtvvYWzzz477O8phMChQ4dgs9mwbds2ZGdno6CgADNnzkSvXr1UGHXnAjty88EpcSm7wNPT05Ga\nmhrr4cRMqEA4cIcw57y2KKVLlJ1//PvpvsBd8ZIk+ZuKKkfoSX0MgGNLeV8jSRIkSeKu+G5QKwBu\nbW3F3LlzsWLFCkybNk3FEXZdVVUV7rzzTsiyjIULF+K+++477dcEBrgnTpyAEAJbtmzBn//8Z+zc\nudO/qKb8Lvroo49w++23Y8+ePbj55puxfv16APDPu2+//RYLFy7EsmXLMHHixMi9WCKinmEITETU\nGYfDgQceeACPPPIIsrKyVP/+QggcPnwYdrsdlZWVyMrKQn5+PvLy8tC7d2/VrxcsMBD2+XysL5kg\nPB4PXC5X2A91iSawbrayK55lUrQjkWpXa4USCCuhMHfFq08IAZfLBUmSGABrAHfFd50SAIe7WHzy\n5EnMnTsXy5Ytw8yZM1UcYWQF7gC+9957sXXrVvzzn//E0KFDkZqair///e/Q6XTtgmIA+Pjjj7Fk\nyRLU1NTg6quvhs1m839MkiTs27cPo0ePTuoFeCLSLIbARERaIYTA0aNHYbfbsXXrVphMJhQUFCAv\nLw99+vSJ+MN6qEBY2U3DoCB+uN1uuN1umM1m7vo7DZZJ0Q4lADYajQlXu1orAnfFS5LERRAVKAGw\nz+dDRkYGQ0aNCd4Vz0WQH6kVAJ86dQpz587F3Xffjfz8fBVHGD2LFy/GSy+9hJ///OcYMGAA3n77\nbfh8PixZsgRPP/00gB93DCvN3j755BPMnTsXWVlZ2L17d8jvy8ZwRKRBDIGJiLRICIGmpiaUlJSg\nvLwcBoMBs2bNQn5+PrKzs6MSCAc+OCnhGANh7eJx5PBwV3zsJHLzQq3qaFc8m2x1XWAAbDab+Wem\ncSwN9CPlnhtuAOxwODB//nzccsstuPrqq1UcYWQF7uytq6uD1WrFTTfdhCVLlmDgwIH429/+hjlz\n5qCxsRHLly/H448/3u7rlHC3sbERgwcPBvDTusJERBrFEJiISOuEEGhubkZJSQnKysogSRLy8vJg\nsVgwYMCAiD+4BB+tZK097eFuNHUFB8JcBIkcJYxItuaFWsMmW93DADi+hVoESZY5r1YA3NbWhsLC\nQixYsAC//vWvVRxh9OzduxfNzc343e9+h23btmHo0KH+97kffPAB5s6di88//xxLly7Ff//3fwPA\nT0pDAAyAiSiuMAQmIoonQgi0tLSgrKwMpaWlcDqduOqqq1BQUIDBgwczEE5CQgg4nU4IIRhGREBH\nu+I558On1nFkUldHTba4CPIDIQTa2togyzLvuQkiWcphqbXo5nK5cP3116OwsBDz589XcYTq+9e/\n/oWzzjrrJ/0Rli9fjieeeAJjx45FRkYGqqur4fV6/Z+n0+nw8ccfY86cOTh48GC70hAMfYkojjEE\nJiKKZ8ePH0d5eTlKSkrQ2tqK6dOnw2KxYNiwYVEJhJVwzOv1svlKDAgh4HA4oNPpkJGRkVAPq1rE\nhkPqYQAcH0ItgijhWDLOeQbAiS9R57xaAbDb7caNN96Ia665BjfccIOmfwa+/PJLjBw5Elu2bMGv\nfvWrdh+rra3FtGnT4Ha7cfHFF+PDDz8E8GPAq/yvUvt3//79mD9/Pl5//fVYvBQiIrUwBCYiShSt\nra2oqKiA3W5Hc3MzcnNzYbFYcN555zEQTkCyLMPpdMJgMMBkMmn6QSwRcc73nNfrRVtbGzIyMn6y\nO4u0q6M5n5KSkhRNKBkAJ5/gxnJ6vT4u57xaAbDH48GCBQuQl5eHBQsWaP5n4LPPPsPYsWOxfPly\nLF++3F/KQfnfjz76CJMnT0ZrayvuvfderFmzBsCPJR+UIPgf//gHJk+ejL59++LDDz+Mq797IqIg\nDIGJiBLRyZMnUVlZCbvdjsbGRkyZMgUWiwUjR46MaiAc+NDEcEw9sizD4XAgNTUVaWlpmn8QS3Sd\ndaDnw2J7Ho8HLpeLAXCcU+a8Mu8D57xer0+4e5ISAAsheOoiSXU055VAWKtzQq0A2Ov1YtGiRfjV\nr36Fm2++WbOvN9iUKVNw/Phx7Nu3D3q93t/UTQl6Dxw4gIkTJ+LEiRN45JFHsGLFCgA/DYIbGxvR\nr18/mEwmloMgonjGEJiIKNE5nU5UVVXBZrPhiy++wKRJkzB79myMGjUq4m9iOwsKGI71jPJAl5aW\nhrS0tFgPh4KE6kCfyOFYdygBsNls5s9/Agk155Xj81oOx7pKqbsOgAEwAfhxzivvbYQQ7d7baGWO\nyLKMU6dOhR0AS5KExYsXY/z48bj99ts18/o6owS1zzzzDO666y48+eSTuPvuu9t9jhL0fvrpp8jJ\nycHx48fx0EMPYeXKle2+R2DoG6oxHBFRHGEITESUTNxuN7Zv3w6bzYaDBw8iJycHVqsVl1xySVQC\nYYZj4VHqqIb7QEfRkejhWHe43W643W4GwAlOCNGuyZYSjil1VeNtzjMApq5Q7vOSJEGW5XYNRGM1\nZ5QAONwFY5/Ph1tvvRX/8R//gbvvvjvufgb+9a9/4dJLL8W5556LP//5zzj33HP9u4GBH0Pdzz77\nDDk5OWhubsaDDz6IRx55BDqdjrt+iSjRMAQmIkpWHo8HO3fuhM1mQ0NDA8aPHw+r1YrLLruMgbAG\nKXVU2UgrPnUUjiV6ICyEgNvthtfrhdls5sN0kgncLamVcKyrGABTTyj3eeUUVOCcj9b9T80A+M47\n78QFF1yAZcuWxd3PgBLwrl27Fvfddx/uv/9+rFq1CgBC7u794osvkJOTg6+//hq33nornnnmGf7O\nIqJEwxCYiIh+2GFaU1OD4uJi7Nu3D2PHjoXFYsG4ceMivmsvMBCWJAkAkna3ZEdYRzXxBC6CCCHi\nKhzrKiEEXC4XJEliAEztFkF8Pp9/zqempmpuzisBsE6nQ3p6uubGR/FBCOGf85IkRaWBqNIzwGg0\nhhUAy7KMpUuX4v/9v/+HBx98MK5/Bg4cOIDrr78eH330EdavX4+bb74ZQOgg+KuvvsL555+Pfv36\n4eDBgzCbzbEcOhGR2hgCExEFGzZsGLKysvwNzd5//32cOHECc+bMwdGjRzFs2DBs2rQJWVlZsR5q\nRPh8PtTW1sJms6Gurg6jR4+G1WrFhAkTIh5AJutuyc7wGH3iCw7HlDkfz4GwEgD7fD5kZGQwAKZ2\nZFlu10xRKRehhQaiQgg4HA7o9XoGwKSazhqIqnUCSs0AePny5ejbty+KiooS4megpKQE1157LQDg\n5ZdfxoIFC37yOUoQ/O2330Kn0+Gss85qVzqCiCgBMAQmIgo2fPhw7Nu3D2eeeab/vylvhpctW4Y1\na9bgxIkTWL16dQxHGR0+nw91dXWw2+2ora3FRRddBKvVipycnIjXpE32QJjH6JNTPO2W7IgQAm1t\nbZBlGWazOW7GTbERGI55vd6o7JbsbCwMgCnSIlEvXgmAU1NTYTKZejw2WZaxYsUKpKen47HHHov7\nn4HAEPdPf/oTFi5cCABYs2YN7r33Xv/nKbuCAxu/sQkcESUghsBERMHOOecc1NfXo2/fvv7/NmLE\nCNTU1KB///745ptvMGnSJBw8eDCGo4w+WZZRX18Pm82GXbt2YcSIEbBarZg8eXJYO066KhmOzyu4\ni5KA0Lslo11bsrtYR5XCEY3dkp1dmwEwRVuoBe/uvr9RMwAuKiqCLMt44oknNPt7prsCyz5s3LgR\nv/3tb+FwOFBYWIhFixbhiiuugNFo5K5fIkoGDIGJiIINHz4cvXv3hsFgwOLFi7Fo0SKceeaZOHHi\nhP9z+vTpg+PHj8dwlLElyzIaGhpQXFyM6upqDB8+HBaLBVOnTkV6enpUrq88MMVbs6HTUUI0IQR3\nUZJfLGpLdhcDYFJTJHZLdnYth8MBg8EAk8nEuUsxE9hMUSkPpMz7UPMyMABOS0vr8dwVQmDVqlU4\ndeoUnn76ac38XlFLYBC8a9curFu3Dtu2bUNmZiamT5+OBx54AGeffXbClnojIvo/DIGJiIJ9/fXX\nGDhwII4dO4bc3Fw888wzsFgs7ULfvn37oqWlJYaj1A4hBPbv3w+bzYYdO3Zg0KBBsFqtyM3NjUpD\njUSqp6oEETqdjiEadSh4t6RSvzyWgTB3UVIkRbI8EANg0qrTnQZRMwBeu3Ytmpub8dxzz8VVAKzs\n3m1ra0N6enq7sLejzwV+eK9/4MABrFq1Cnv37oXb7caFF16IhQsXYv78+ejXr180XwYRUbQwBCYi\n6kxRUREyMzPx8ssvY9euXf5yEJMnT8ann34a6+FpjhAChw4dgs1mQ1VVFbKzs2GxWDBz5kz06tUr\n4teP50BYlmU4nU4GEdQtnR2fj1YtQ85dirbA3ZLhnAbh3KV4oZwGUea9wWDwz/1wFt6EEHj66adx\n5MgRvPjii3EVACs2btyIdevWoaamptvlySRJwqFDh7Bt2zbU19dj2LBhuO222zBo0KAIjZaIKKYY\nAhMRBXI6nZBlGZmZmXA4HMjNzcXDDz+M6upq9OnTB8uXL0+qxnDhEELg8OHDsNvtqKysRFZWFvLz\n85GXl4fevXtH/Pod7aDRYoMttXbzUHILdXw+0vVUOXcp1nraTFGZuykpKQyAKa4ocxf4cXdrT+71\nQgg8++yz+OSTT7Bhw4a4a4KmnBCYPn06du7cibfeegvTp0/vcm3f4F3DkiQBAFJSUiI2ZiKiGGMI\nTEQU6MiRI5g9ezZ0Oh0kSUJhYSHuu+8+HD9+HL/+9a/x1VdfYejQodi0aVNUgsxEIYTA0aNHYbfb\nsXXrVphMJhQUFCAvLw99+vSJ+MO3lhts+Xw+OBwOpKWlRaXBHiWHwEBYkiRVj88r1GpGRKSWUPd6\n5X4feK/n4gXFq+DyJQB6dK8XQuDFF1/Evn378Oqrr8Z18Llr1y7MmDEDixYtwrPPPtvtr2dDOCJK\nIgyBiYgouoQQaGpqQklJCcrLy2EwGDBr1izk5+cjOzs74m/EgxtsxTIQliQJTqcTJpMJRqMxqtem\n5BGJeqpcvCCtCyyVohyfV+Z8W1sbA2CKO6erXx14r1c2MvTt2xcFBQWYNm2av0+DEAIbNmzAu+++\ni9dffz2uA2Dgh/q+s2fPxt69e1FTU4Nf/vKXsR4SEZFWMQQmIqLYEUKgubkZJSUlKCsrgyRJyMvL\ng8ViwYABA6ISCIcKCaLRYMvr9fobmaSmpkb0WkSBAneN9aSeqhIAc/GC4kXwvV6n08FoNEa0VAqR\nmnrSfPPo0aMoLy9HRUUF/v73vyMnJwezZs2Cx+PB7t27sXHjxrh7/6Hs2g0u5bBhwwYsWrQIq1ev\nxrJlyzptEEdElMQYAhMRkTYIIdDS0oKysjKUlpbC6XTiqquuQkFBAQYPHpxQgbDH44HL5UJGRkbc\n78Ch+NbdZorK7nUuXlC8CawBnJqa2i4QVv6bWqVSiNTUkwA42LFjx1BZWYlNmzZhz549GD9+PK65\n5hpYLBYMGTIkAqNWlxLqtra2Iisry//flRNd3377LaZOnYp///vf2Lt3LwYPHhzD0RIRaVaHv0AM\nK1eu7OwLO/0gERFRd+l0OmRkZGD06NEoLCxEQUEBGhsb8cwzz2DDhg3497//jf79+6N3794ReUjX\n6XT+4DctLQ16vR6SJMHlcvmP0KuxY8ztdsPtdsNsNjMApphTAjCj0Qij0egvl9LW1gafzwcA/nnP\nAJjilRIAG41GmEwm6PV6pKamwmg0IiUlBbIs++/NsixDp9P5/yGKJSEEnE5nWAEwAJjNZvzjH//A\n4cOHUVtbi4EDB2Lnzp343e9+h+LiYhw7dgz9+vVDv379NDnvdTodamtrMXPmTDQ3N6N///7Iysry\nn0bJzMzE559/jurqapxzzjkYM2aM/2eZiIj8ijr6AHcCExGRZrS2tqKiogJ2ux3Nzc2YNm0arFYr\nzjvvvKjtEFZ2CQd24e5OJ20hBNxuN7xeL8xmM48pkqYFN9jS6/WQZRkZGRkMgCmuyLKMU6dOdal+\ndbilUojUpATAOp0urAAYAEpLS/H666+jpKQE6enp/v8uSRJ2796N0tJSlJWVwWQyYfbs2bBarRg7\ndqxm3qsIIbBmzRqsXLkSHo8Hffr0wejRo3H//fdj6NChGD58OP71r39hwoQJGDx4MHbv3h3rIRMR\naRHLQRARUXw5efIkKisrYbfb0djYiClTpsBisWDkyJFRCYSVkCA4EO5sl7AQAi6XCz6fDxkZGZp5\nqCLqCo/Hg7a2NhgMBvh8vqjWziYKRzgNDINLpSiBcGpqKgNhijglAAaAjIyMsObcli1bsGHDBpSW\nliIjI6PTa37wwQf+QLilpQVWqxXPPvtstxa9I+nIkSOor6/H+vXrsXv3buh0OlxwwQW46aabkJub\nizVr1mDjxo144YUXsGjRolgPl4hIaxgCExFR/HI6naiqqoLNZsMXX3yBSZMmYfbs2Rg1alTEw6mu\nBsLKg5wQAmazmeEBxRWlfrXZbIbBYGhXO1vZIcxAmLQonAA4WPDO+MAdwpz3pDY1A+C33noLzz//\nPMrKypCZmdmtr/3ss8+wZ88e3HjjjT2+fiS988472LZtG1555RU0Nzdj5MiR8Hq9+Pzzz3HzzTdj\n/fr1sR4iEZHWMAQmIqLE4Ha7sX37dthsNhw8eBA5OTmwWq245JJLohoIS5IEAP6QoK2tDXq9PuwH\nOaJoC6xfHWoXWHAg3NWd8USRpgTAJpPJXzNULUrdbGXuc2c8qUnNAHjHjh14+umnsXnzZpxxxhlq\nDTHmlCZxik8//RQff/wxfv/73+PLL7/EN998AwB4++23ceWVV8ZqmEREWsQQmIiIEo/H48HOnTth\ns9nQ0NCA8ePHw2Kx4PLLL49KICzLMjweDzweDwDAaDSy8zzFjZ7Ur+5pqRQitUUyAA7GhRBSkxAC\nbW1tEEKEHQC//fbbWLt2LTZv3ozevXurOErtEEK0+zNyuVzYu3cvtm7dirVr1+Luu+/Gk08++ZPQ\nmIgoiTEEJiKixCZJEmpqalBcXIx9+/Zh7NixsFgsGDduXMRq3Cmd6JXdwEpIIIRo11SOAQFpjRoN\nDIN3xnPeU7REMwAO1tFCSEpKCuc9nZaaAfA777yDVatWYfPmzejTp4+Ko9QupV698v9zcnLQ1NSE\n+vp6ZGdnx3h0RESawRCYiIiSh8/nQ21tLWw2G+rq6jB69GhYrVZMmDABKSkpql2jozqUgQGBEoyl\npKSw8zxpQiQaGCo744PnPQNhUlssA+BgSiDMBUDqCiUAlmU57N4Be/bswcMPP4zNmzejX79+Ko4y\nPihh8D333IPf//732LJlC/Ly8mI9LCIirejwF4w6T8JEREQaYjAYcOWVV+LKK6+Ez+dDXV0d7HY7\nHn74YVx00UWwWq3IycnpcYAgSRKcTmeHIYTBYIDBYIDJZPIHY263G21tbe0aDTEgoGhTM4QIpNPp\n2s17ZSHE5XJBlmXOe1KFEgCnp6cjNTU11sOBTqfzL/Bx3lNn1Lz3vv/++3jooYdQVlaWlAEwAP9u\n4DPPPJN1uomIuoE7gYmIKGnIsoz6+nrYbDbs2rULI0aMgNVqxeTJk7vcVb6yshJ9+vTBpZde2u0Q\nInCnpM/n8+8YY0BA0aBmI6Lu4LwnNSiLb1oJgE8n1LxXQmHO++QSePoi3AB43759WLZsGUpLSzFg\nwAAVRxl/3n//fVxzzTXIzMzEO++8w3IQREQ/YjkIIiKiQLIso6GhAcXFxaiursY555wDq9WKqVOn\nIj09PeTXvPbaa/iv//ovvPnmmxg3blzY12cwRtESqwA4mDLvJUmCJEn+UN1KrC8AACAASURBVIzB\nGHUm3gLgYLIst2ssF7hDmDsYE5uaAfDf//533HXXXSgtLcXZZ5+t4ijjjyzLqK6uxiOPPIKXX34Z\nF1xwQayHRESkJQyBiYiIOiKEwP79+2Gz2bBjxw4MGjQIVqsVubm5MJvNAICnn34azz33HMrKyjBq\n1ChVr99RQMBgjNQghIDD4YBer0d6erpm5hSDMeqKeA+Agwkh/HPe6/XCYDD47/ec94lFzQB4//79\nuOOOO2Cz2TBkyBAVRxm/2traIEkSevXqFeuhEBFpDUNgIiKirhBC4NChQ7DZbKiqqkK/fv2QmZmJ\nuro6bNmyBUOHDo3o9RkIk5pkWYbT6fTX6tXqHGIwRqEkWgAcTAjR7n6v1+v993yl5inFJyUAliQJ\nmZmZYd17P/nkE9x6663YtGkThg0bpt4giYgoUTEEJiIi6i5JkvCb3/wGf/vb33D++ecjPT0d+fn5\nyMvLQ+/evSN+fSUY405J6glZluFwOPxNq7QaAAcLFYwp857BWPJQAuCMjAykpCR+L2shhL+xnNfr\nhU6nazfv4+Xnl374u3S73fB6vTCbzWH9vj506BAWL16MjRs34txzz1VxlERElMAYAhMREXWHy+VC\nYWEhWltbUVpaiszMTBw9ehR2ux1bt26FyWRCQUEB8vLy0KdPn4g/oHOnJHWHEgCnpqYiLS0tbgMk\nJRBW5r0SjCnzPl5fF3XO6/Wira0taQLgYEogrMx7IYR/3jMQ1j6Xy6VKAPz5559j4cKFeOONN3D+\n+eerOMLQioqK8NJLL+Gss84CADz22GOYMWNGxK9LRESqYwhMRETUVd9//z2sViv69euH1157DWlp\nae0+LoRAU1MTSkpKUF5eDr1ej/z8fOTn5yM7OzsqgbASDjAQpmA+nw8OhwNpaWk/mbvxjDslk0Oy\nB8DBhBDtGokKIdqdCuG81xa1AuAjR47gpptuwp///GeMHDlSxRF2rKioCL169cLSpUujcj0iIooY\nhsBERERd0dzcjJkzZ2LMmDF49tlnT3v8XAiB5uZmlJSUoKysDJIkIS8vDxaLBQMGDIhqIBx4dJ6B\ncHJSAmCTyQSj0Rjr4UQMd0omJgbApxcYCPt8Pn8YzLrxsadWAPzVV1/h+uuvx4YNG/Dzn/9cxRF2\nrqioCJmZmfjd734XtWsSEVFEMAQmIiI6nX/+85/Izc3FvHnzsHLlym4/UAsh0NLSgrKyMpSWlsLp\ndOKqq65CQUEBBg8eHPVAOPDoPGupJj6lhmqiB8DBQu2UVIIx7pSMHwyAu6+jRqKsGx99brcbHo8n\n7AC4qakJhYWFeOmll3DxxRerOMLTKyoqwquvvoqsrCxcdtllePLJJ5GVlRXVMRARkSoYAhMREXVm\n//79mDFjBpYvX4477rhDle95/PhxlJeXo6SkBK2trZg+fTosFguGDRsWlUA41NF51lJNTEoAnJ6e\njtTU1FgPJ6YCdwjLssyj83FACYDNZjMXrHqIdeNjR60A+JtvvsG8efPw/PPPY/To0SqO8EfTpk3D\nt99+6/93IQR0Oh1WrVqFcePGoV+/ftDpdFixYgW+/vprvPLKKxEZBxERRRRDYCIios6sXbsWQ4YM\nwbx58yLy/VtbW1FRUQG73Y7m5mZMmzYNVqsV5513HgNhCgt3UHaMR+e1z+PxwOVyMQBWUagyQcq8\n55+xutxuN9xuNzIzM8MKgL/99lvMmzcP69atw9ixY1UcYc8cPXoU+fn5aGhoiPVQiIio+xgCExER\nacXJkydRWVkJu92OxsZGTJkyBRaLBSNHjoxJIBwYDjAYiy8MgLuuo6PzDIRjhwFw5HERMHLUCoCP\nHTuGuXPn4sknn8T48eNVHGH3fPPNNxgwYAAA4KmnnsLevXvx17/+NWbjISKiHmMITEREpEVOpxNV\nVVWw2Wz44osvMGnSJMyePRujRo2K+BHejmqpMhCODwzQek45Os9aqrHD+Rt9bKioHrXmb0tLC+bO\nnYvHH38cEydOVHGE3XfDDTfgo48+gl6vx7Bhw/DCCy+gf//+MR0TERH1CENgIiIirXO73di+fTts\nNhsOHjyInJwcWK1WXHLJJQyEqR1lBxoDtPCxlmr0MQCOvVD3fNbP7hq15u+JEycwd+5cPPLII5g8\nebKKIyQioiTHEJiIiCieeDwe7Ny5EzabDQ0NDRg/fjwsFgsuv/zyqARTgceHlUA4JSWF4YAGuFwu\neL3esJsQ0U+FqqWqzH2GlergAoY2sX5216gVALe2tmLu3LlYsWIFpk2bpuIIiYiIGAITERHFLUmS\nUFNTg+LiYuzbtw9jx46FxWLBuHHjohKiBIYDsixzt1iMCCHgdrsZAEeJEggroTBrqYZPrRqqFFkd\n1c9O9nIpagXAJ0+exNy5c7Fs2TLMnDlTxRESEREBYAhMRESUGHw+H2pra2Gz2VBXV4fRo0fDarVi\nwoQJUWkMxkA4NoQQcLlckCSJAXAMdNRcS9khzLl/egyA41Nw/exkLZeiNOEMNwA+deoU5s2bh7vu\nugv5+fkqjpCIiMiPITAREVGi8fl8qKurg91uR21tLS666CJYLBZMnDgRRqMx4tcPdXyYgbD6hBBo\na2uDLMswm838s40xNtfqPrfbDY/HwwWMOBeqXIqyEJjIpT3UCoCdTifmzZuHW265BVdffbWKIyQi\nImqHITAREVEik2UZ9fX1sNls2LVrF0aMGAGr1YrJkycjLS0tKtcPdXyY9STDowTAQghkZGTwz1Jj\nOmqoyPrZP2IN68TU0e74RCuXolYA3NbWhsLCQixYsAC//vWvVRwhERHRTzAEJiIiShayLKOhoQHF\nxcWorq7GOeecA6vViqlTpyI9PT0q12cgHD4hBJxOJwAwAI4TgTuEk71cCmtYJ4/AQFiSpITZHa8E\nwBkZGWGVW3K5XLj++usxf/58FBYWqjhCIiKikBgCExERJSMhBA4cOACbzYbt27dj0KBBsFqtyM3N\nhdlsjsr1A+tJssFQ1wgh4HA4oNfrkZ6eHrchSjILVS5Fmf+J/vfJADh5hdodH4+LIWoFwB6PBzfe\neCOuvvpq3HDDDXHz+omIKK4xBCYiIkp2QggcOnQINpsNVVVVyM7OhsViwYwZM3DGGWdE5fpKGOz1\nepO2wdDpyLIMp9MJg8EAk8nE0CABJNPueDYxpEDxWDtekiQ4nc6wA2Cv14sFCxZg5syZWLhwoWZf\nLxERJRyGwERElNwWLlyIiooK9O/fHw0NDQCAEydOYM6cOTh69CiGDRuGTZs2ISsrCwDw+OOPY8OG\nDUhJScG6deuQm5sby+GrTgiBw4cPw263Y+vWrcjKykJBQQHy8vLQu3fvqFxfCcUYCP9IlmU4HA6k\npKQwAE5QoXbHK6FwvM99BsDUGSUQliRJsydD1AqAJUnCokWLMGnSJNxyyy28lxMRUTQxBCYiouRW\nW1uLzMxM3HDDDf4QePny5ejbty+WLVuGNWvW4MSJE1i9ejU++eQTFBYWYu/evWhsbMTUqVPx2Wef\nJexDnBACR48ehd1uR0VFBdLT05Gfn49Zs2ahT58+EX/doTrOJ2MgrATAqampSEtLS9j5Rj9KpMUQ\nJQD2+Xwwm82cv9Sp4MUQLcx9NQPgm2++GePGjcMdd9zBnwUiIoo2hsBERERHjx5Ffn6+PwQeMWIE\nampq0L9/f3zzzTeYNGkSDh48iNWrV0On02H58uUAgJkzZ2LlypUYO3ZsLIcfFUIINDU1oaSkBOXl\n5dDr9cjPz0d+fj6ys7NjEggru8XC6cyudT6fDw6HA2lpaUhLS4v1cCgG4nnuMwCmcHS0EJiSkhK1\nua9WAOzz+XDbbbfh4osvxtKlS/mzQEREsdDhL5+e/4YjIiKKc83Nzejfvz8AYMCAAWhubgYANDU1\n4YorrvB/3qBBg9DU1BSTMUabTqfD4MGDsWTJEtxxxx1obm5GSUkJFi9eDEmSkJeXB4vFggEDBkTk\n4Van0/l3gwV2nHc4HO0+pvVQrDuUANhkMsFoNMZ6OBQjXZ37er1eU8GSEAJtbW2QZZkBMPVI8NxX\nykVEa+4rAXB6enpYAbAsy7jzzjtx4YUXMgAmIiJNYghMRET0f/jA1p5Op0P//v1xyy234Oabb0ZL\nSwvKysqwZMkSOJ1OzJw5EwUFBRgyZEjEAmGlXqrJZIqbUKw7lPCBATAFOt3cD9whHMu5zwCY1BZ4\nbw+c+06nE0KIdguBasy3wAA4NTW1x99HlmUsXboU55xzDpYvX86fBSIi0iSGwERElLT69++Pb7/9\n1l8O4qyzzgLww87fr776yv95jY2NGDRoUKyGqQk6nQ79+vXDokWLsGjRIhw/fhzl5eVYtmwZvvvu\nO8yYMQMWiwXDhg2LWSCsHB2Ol4dvtcIHSmzBc19prtXW1haRUKyrGABTpAXOfSFEyLmvfLwn88/n\n86kWAC9fvhwDBgzAihUr+LNARESaxZrARESUNP75z38iPz8fH3/8MYAfGsP16dMHy5cvD9kY7r33\n3kNTUxOmTZuW0I3hwtXa2oqKigrY7XY0Nzdj2rRpsFqtOO+886JSQ1gJBrxeb0xDse5QggwGwNRT\noea+skO4p6FYd66tBHEZGRma/TmjxOXz+fx1hGVZ7vbcV8rwqBEA/9d//RfS0tLw2GOPxV1DRyIi\nSkhsDEdERMlt/vz52LVrF1paWtC/f38UFRXBarXiuuuuw1dffYWhQ4di06ZN6N27NwDg8ccfxyuv\nvILU1FSsW7cOubm5MX4F8eHUqVOorKyEzWZDY2MjpkyZAovFgpEjRzIQ/j9KABxuAyKiQOGGYl0l\nhIDT6QQABsCkCcp9X6klHDj3Q4WyatVhF0KgqKgIPp8PTzzxBANgIiLSCobAREREFF1OpxNVVVWw\n2Wz44osvMGnSJMyePRujRo2KysOyUjJCS4Gwx+OBy+WC2WxOqOZ2pC2BiyE+n88fiKWmpoY19xkA\nk9YJIfxzX5IkGAyGdvXj1QyAH3vsMXz//fdYt24dA2AiItIShsBEREQUO263G9u3b4fNZsOnn36K\niRMnwmq14pJLLolaIByNXZKdcbvdcLvdDIApqmRZ9s/9ruyS7AgDYIo3Qoh2c1+n00GWZaSlpcFk\nMoX1fZ944gl88803WL9+PQNgIiLSGobAREREpA0ejwc7d+6EzWZDQ0MDxo8fD4vFgssvvzwqD9OB\nuySjFQgrAXBmZiYDA4qZ4F2SSlMtZZdkZ1/HAJjimSRJcDgcMBgMkGXZ31BUmftdndNCCKxbtw6H\nDx/Giy++yAU9IiLSIobAREREpD2SJKGmpgbFxcXYt28fxo4dC4vFgnHjxkXl4TrUsXk1A2EhBNxu\nN7xeL8xmMwNg0ozAXZJer/cnx+YDP8/pdEKn0yE9PZ0BMMUdWZZx6tQpfwkIIYS/XJAkSV0uFySE\nwHPPPYcDBw5gw4YNDICJiEirGAITERGRtvl8PtTW1sJms6Gurg6jR4+G1WrFhAkTotJAraNj8z2t\noyqEgMvlgiRJDIBJ04KPzev1ev8uYZfLBb1ezwCY4pISAKelpSEtLe0nHw9sKOrxeFBQUIALL7wQ\nFosFkyZN8n+NEAIvvfQS6uvr8eqrr7KpJxERaRlDYCIiIoofPp8PdXV1sNvtqK2txUUXXQSLxYKJ\nEyeG1cynq8INhIUQaGtrgyzLMJvNDM8obii7JD0eD7xeLwDAaDTCaDR269g8UaydLgAO5dChQygv\nL8eWLVtw+PBhTJs2Dfn5+WhpacHf/vY3vPHGGwyAiYhI6xgCExERUXySZRn19fWw2WzYtWsXRowY\nAavVismTJ3f5wT4coeqodtZYiwEwxTshBBwOB3Q6HYxGo39BRKfT+ed/Z8fmiWJNlmU4HA4YjcYe\n/55obGxEeXk5bDYbPvzwQ8yYMQPXXnstZs2ahTPPPFPlERMREamGITARERHFP1mW0dDQgOLiYlRX\nV+Occ86B1WrF1KlTkZ6eHvHrK4GwEooF11FlAy2Kd0oAbDAYYDKZ/HM48Ni81+vtch1VomhTIwAG\nfpjzGzduxJYtW/DHP/4R27dvR2lpKXbu3Ilx48Zh9uzZsFqtGDhwoIqjJyIiChtDYCIiIkosQggc\nOHAANpsN27dvx6BBg2CxWDB9+nSYzeaoXD+4sZYsyzAYDAyAKS51FACHojTWUgLhwB3ynPsUK0oA\nnJqaCpPJFNb3Ki4uRnFxMWw2W7vvderUKWzbtg0lJSWorKzEhRdeiNmzZ2P27Nk499xzw30JRERE\n4WIITERERIlLCIFDhw7BZrOhqqoK2dnZsFgsmDFjBs4444yIX18JHpT3VXq9vt0OYSKtU+ZwSkrK\naQPgUF+rBMKyLDMQpphQMwAuLS3F66+/jpKSkk5PmXg8HuzcuRMlJSXYvHkzBgwYgKuvvhoPPPAA\nUlNTwxoDERFRDzEEJiIiouQghMDhw4dht9uxdetWZGVloaCgAHl5eejdu7fq1wsOzwC0ayqn1+vb\n1VEl0ppwAuBQ30sJhH0+X7ebKhL1RGAAnJaWFtZcq6iowMsvv4yysjJkZGR0+et8Ph/27NmDmpoa\nrFixosfXJyIiChNDYCIiIko+QggcPXoUdrsdFRUVSE9PR35+PmbNmoU+ffqEHUqdLngQQrQ7Nq/T\n6drVUSWKNTXDs1DfO3BB5HRNFYl6Qs05XFVVhfXr16O0tBS9evVScZRERERRwxCYiIiIkpsQAk1N\nTSgpKUF5eTn0ej3y8/ORn5+P7OzsbgcHsizj1KlTSEtL61Lzoc4CYb1ez12SFHWRDICDKU0VlUA4\nuKkiUU90p4716fzv//4vnnrqKWzevDkqZYSIiIgihCEwERERkUIIgebmZpSUlKCsrAxerxezZs2C\nxWLBgAEDThskHDhwAEuXLkVxcXGPwoKOAuGUlBQYDAYGwhRxatZP7a5QTRUZCFN3qRkA79q1C2vW\nrMHmzZsjUjaIiIgoihgCExEREYUihEBLSwvKyspQWloKp9OJmTNnoqCgAEOGDPlJsLBv3z5ce+21\nKCoqwg033KDK9QPrqAoh2pWMYCBMaotlABwsMBCWJIklU6hL1AyAd+/ejUcffRTl5eXo06ePiqMk\nIiKKCYbARERERF1x/PhxlJeXo6SkBN999x1mzJgBi8WCYcOG4d1338X8+fPx1FNP4ZprrlH92gyE\nKdKUANhoNHapjEk0sWQKdYWaAfCePXvw8MMPY/PmzejXr5+KoyQiIooZhsBERERE3dXa2oqKigrY\n7XYcPnwYX331FdasWYP58+dHJZAKDMQYCFO4ulvHOpZCBcJKYznO/+SlBMB6vR7p6elhzYP3338f\nDzzwAMrKynDWWWepOEoiIqKYYghMRERE1FObN2/GokWLsGTJEnz88cdobGzEr371K1itVowcOTJq\ngbBybF6W5f/f3p0HRlXf+/9/zZadRRRICWiophKF3jQoqPgFDYEETDIDIrKIK1wrFUrFYu3iruG6\n4AUtVkVFrlSBSQgREcJSqVgRlItcMRfZpCS9kAoCWWc9vz/4zTRhE8hMlsnz8RfMJHM+CROSeeZz\n3icYxKxWK0EMP8jn86m6urpVBOATsUMeUmgD8JYtW/TrX/9aS5cuVWJiYghXCQBAsyMCAwAAnI93\n331Xv/rVr7R8+XJdddVVkqSamhqtXLlSTqdTe/bs0Q033KARI0boyiuvbJILW9UPYgRh/JBAAI6J\niVFUVFRzL6fRTtwhz/M/8hmGoZqaGplMpkYH4C+//FLTpk1TYWGhkpKSQrhKAABaBCIwAACtic/n\nkyQujNTMXnvtNT3++ONatWqVevfufcq3cblcKikpkdPpVGlpqQYOHCiHw6G0tLQmD8I+ny+4Q5Ig\nBinyAvCJeP5HvlAG4K+++kr333+/CgoK1KNHjxCuEgCAFoMIDAAAcC5eeOEFvfTSS1qzZo0uu+yy\ns3oft9utdevWyel0atu2bbruuutkt9t19dVXN2kQ9nq98nq9wR2SNpuNINYGRXoAPtGJQZjnf+sX\nCMCSFBcX16h/x6+//lqTJ0/WokWL1LNnz1AtEQCAlua03yzD/2oEAACckwceeEC33Xab9u3bd8r7\n/X5/E6+obTEMQ4899phee+01ffzxx2cdgCUpKipK2dnZmjdvnv72t78pNzdXixYt0o033qgZM2Zo\nw4YNwV3e4WA2mxUdHa34+Hi1a9dONptNHo9Hx44dU3V1tdxut35gAwAiRFsLwNK/nv8JCQmnff7z\n/2frEcoAvGPHDt1333169913wxaAnU6nevfuLYvFoi1btjS4Lz8/XykpKUpNTVVJSUlYjg8AwA9h\nJzAAAC3MunXrlJmZqRkzZmjmzJnNvZw2xTAMPfjgg1q9erVWr16trl27huRxfT6fNmzYIKfTqY0b\nNyo9PV0Oh0MDBgyQ1WoNyTHOxDCM4A7J+juErVZrk+xQRtNqiwH4TE58/lssluAOYZ7/LVMoA/Cu\nXbt0zz33aOHChfrJT34SqiWeZMeOHTKbzbr33nv1/PPPKz09XZJUWlqqcePGafPmzSorK1NmZqZ2\n7tzJ7nQAQLiwExgAgNYiPT1dl19+uUpKSoKn9UtSbW2tFi9erOXLlzfzCiOX3+9X+/bt9dFHH4Us\nAEvHZzsPGjRIL730kjZu3Kjbb79dq1evVmZmpqZMmaI1a9bI7XaH7HgnMplMioqKUnx8vNq3bx/c\nIVlZWamqqiq5XC52SEYIr9er6upqxcbGEoD/fyc+/6Ojo+Xz+Xj+t1CGYai2tlZS4wPw3r17dc89\n92jBggVhDcCSdPnllyslJeWksy2WLVumMWPGyGq1Kjk5WSkpKdq0aVNY1wIAwKkQgQEAaGE6duyo\nIUOGaOvWrfr0009ltVq1d+9ePfTQQxozZoxGjx6t+fPnn9VjBV6MFhUV6dJLL9WCBQvCuPLWz2Kx\n6NFHH1WnTp3CeowBAwZo1qxZ2rhxo+69915t2LBBQ4cO1c9//nOtXLlSLpcrbMcniEUur9ermpoa\nxcbGymazNfdyWiSTySSbzaa4uLgGz/+qqipVVlaqrq4urCNbcGaBAGwYRqMD8P79+3XXXXfprbfe\nUmpqaghXeW7Ky8sbXIQuKSlJ5eXlzbYeAEDbFf7zDwEAwDlzOBx65ZVXtGLFCh05ckS/+tWvtGfP\nHo0ePVozZ85UcnKypOMvmM/0Itnr9aqkpERjx47Vz372M1133XVN9BHgbJjNZvXr10/9+vWT3+/X\ntm3btGTJEj377LPq2bOnHA6HMjMzFRsbG5bjB4KYzWaTYRjyer3yeDxyuVwym82cMt+KEIDP3YnP\nf5/PJ4/Ho+rq6gb3mc1mTt1vAoEA7Pf7FR8f36jPeXl5uSZMmKDXX39dvXv3DtkahwwZooMHDwb/\nHvge/PTTTys3NzdkxwEAIByIwAAAtECXX365Lr30Uv3xj3/Uq6++KovFolmzZmnatGkN3u50L5ID\nL0zff/99TZw4UVdddZUWLlyoiy++WH6/n6jXApnNZqWlpSktLU1PPfWUtm/fLqfTqdmzZ6tbt25y\nOBzKyspSfHx8WI7/Q0E4MEfYYrGE5fg4fwTgxjOZTLJarbJarYqJiQkG4ZqaGhmGEfzasFgsBOEw\nCGUAPnDggG677Ta98sor+rd/+7cQrlJavXr1Ob9PUlKS9u/fH/x7WVmZkpKSQrksAADOCq8AAQBo\nIerPEdyyZYs6dOigqqoqXXXVVVqwYMFJAfhMTCaTvvrqK/3mN79RTEyMnn32WV188cUyDCMYgA3D\nOGl2IVoGk8mk3r1767HHHtMnn3yip556St9++61GjBih8ePHa/HixTp27FhYjx84Zb5du3aKiYmR\nYRiqrq7mlPkWJhCA4+LiCMAhEgjCsbGxSkhICEbJ2tpaVVZWqra2Vl6vl/8/Q8QwDNXV1YUkAB88\neFDjxo3TnDlz1Ldv3xCu8tzUf27k5eXpvffek9vt1t69e7Vr1y7169ev2dYGAGi7TD/wwws/2QAA\n0IQ8Ho8effRRzZw5U/Hx8aqurtazzz6rBx98UNIPj38I3H/kyBFNmzZNCxcu1Ny5czVp0qRTvt0P\n3YaWxTAM7d69WwUFBfrggw/UoUMH5eXl6aabblLHjh2b5PiBHZIej4dT5ptZ/QBstXKCX1MIPP+9\nXq/8fn9wh7zVauX5fx4CAdjn8zU6AP/zn//U2LFj9dxzz2nAgAEhXOXZKSoq0pQpU/Tdd9+pY8eO\nSktL04cffihJys/P1xtvvCGbzabZs2dr6NChTb4+AECbcdpvpkRgAABaiA0bNujJJ5/U6tWrlZWV\npalTp2r69OnyeDzauXPnWUVan88ni8WihQsXasKECRo+fLjeffddtWvXTtK/Qm9VVZXmz5+v2267\nrUE89Pv9MgyDU/5bAcMwtG/fPhUUFGj58uWKjY1Vbm6ucnJy1KlTp7AHqdMFYavVyinzTcDj8ai2\ntpYA3Iz8fn/w+e/z+YK/ECEIn51QBuBDhw5pzJgxys/P18CBA0O4SgAAWp3TfkNlHAQAAM3M4/Ho\nkUce0ahRo7R69Wr9/ve/15/+9CcNGzZMN954o/bu3as1a9ac1QvkwKiHt956S5I0depUtWvXLnhq\nqt/vlyTNnz9fU6dO1bPPPiuPx6MVK1boH//4h8xmMwG4lTCZTEpOTtb06dO1bt06vfbaa/J4PLrr\nrrs0YsQIvfHGG6qoqAjbKev1T5lv166d4uLiJIlT5psAAbhlMJvNio6OVkJCgtq1ayeLxSK3261j\nx46purpabreb5/9pBAKw1+ttdAD+/vvvNW7cOD355JMEYAAAzoCdwAAANLPvvvtOt912m3bs2KFX\nX321wWmiy5cvV15enmbOnKkZM2accTdw4L79+/frkksuUXJysvbs2dPgbQI7hYcOHapPP/1Ul156\nqWw2m/7+97/r8OHD+n//7/9p5syZJ80rDOz6PN0Oz71796p9+/a6xaZASgAAIABJREFU8MILQ/AZ\nQWMYhqGKigoVFhaqqKhIHo9HOTk5stvtSkxMbJIdwvV3SHJRrdAiALd8fr8/eGFFr9fbYGQEF+U8\n/n+Ey+WSx+NRfHx8oz4nR48e1ZgxY/S73/2OEQsAABzHOAgAAFqLQGyVpOrqal122WXq3Lmztm3b\ndlbv9/rrr+vee+/VL37xC7300ksNHi/wmIHxEGPGjNEtt9wil8ulzz//XC+++KIyMjK0ZMkSdezY\nUUePHlVdXZ26du16xmPPnz9fd999t/7+97+re/fujfwMIFQMw9ChQ4dUVFSkpUuXqqamRsOGDVNe\nXp569OjRJEG2/sgIgnDjEIBbH8Mwgs9/r9cri8XSYI52W1RXVxeSAFxZWamxY8fqwQcf1PDhw0O4\nQgAAWrXT/oDNT48AADQzv98fvMCQpAbBNi4uTnfccYeeffZZLV26VCNGjDjt4wReTG/cuFGSdNNN\nNzW4PxCDi4qKJEmjR4/Wn//85+D9t9xyi44dO6Z58+aptLRUy5Yt02effaZvv/1WsbGxmjRpkiZO\nnBgMyNK/dh//9Kc/lc1m01//+leNGzeukZ8RhIrJZNJFF12kiRMnauLEiTp8+LCKi4s1Y8YMHTly\nRNnZ2bLb7UpOTg5bkLVYLLJYLIqJiZHP55PX61VdXR0X1TpHbrdbdXV1io+PZ2RLK2IymRQVFaWo\nqCgZhhHcIexyuWQ2m9tcEA5VAK6qqtK4ceP0y1/+kgAMAMBZahs/bQAA0IKZzebT7uozmUyaOXOm\nVqxYoaSkJEn/mut7qretra3V0aNHZbPZ1Lt3b0k6KRi9/fbbkqSJEydKklwul9xutywWS/CK6rfc\ncos2btyoa6+9VhMmTFBCQoKmT5+uefPmnXRMSWrfvr06duyonTt3nnGNaF6dOnXSnXfeqeLiYr3/\n/vu65JJL9Ic//EFZWVl67rnn9M0334R1hqnFYgnOUE1ISJDFYpHL5VJlZaVqamqCu4XREAE4MgQu\nnhgXF6d27dopOjpaPp9PVVVVqqysDF4kLVKFKgDX1NRo/Pjxmjx5sux2ewhXCABAZCMCAwDQCmRn\nZwfn9J7pxXNgR2X37t3ldrsb3GexWFRXV6c1a9bommuu0XXXXSdJio6ODoalTz75RJJ01113afny\n5XrmmWf0xBNP6M0339Qll1yiP/7xj6qqqjrpuN27d1dVVZUOHTokn8/Xone13XPPPeratat++tOf\nBm97/PHH1b17d6Wnpys9PV0rV64M3pefn6+UlBSlpqaqpKSkOZYcFh06dND48eNVWFiolStXKjU1\nVc8884yGDBmiZ555Rl9//XVYg2z9i2rVD8LHjh0jCNdDAI5MJwbh2NhYGYah6urqBkE4Ur4GQjUD\nuLa2VrfddpsmTpyom2++OYQrBAAg8rXcV2gAACDobEKA3++XzWbT999/H/x7QGB3WWAUxA033KC4\nuLjg41osFvn9fq1fv14xMTGaOnWqEhIS5PP55PP51Lt3b1177bU6ePCgSktLT1pXeXm5YmNjtXPn\nzhYfqu666y6tWrXqpNsfeOABbdmyRVu2bFF2drYkqbS0VIsXL1Zpaak+/PBDTZ48OWKiTH0JCQka\nPXq0Fi9erDVr1ig9PV2zZs3S4MGD9cQTT+h//ud/wrq7u34QbteunSwWi9xut44dO6bq6mq53e6I\n/Lz/EAJw22AymWS1WhUbG9sgCNfU1KiyslK1tbXyer2t9msgcLZJYwOwy+XSHXfcoQkTJujWW28N\n4QoBAGgbmAkMAEArcDbzUs1mswzDUFJSkj7//HP96Ec/OultFixYIJPJpMGDB0s6HnENw5DZbNZf\n//pX7dy5Uw6HQ507d5bf728Qnvbu3Sur1aoePXqctK6KigqZTCZ16dIl+Lgtdcbr9ddfr3379p10\n+6kCy7JlyzRmzBhZrVYlJycrJSVFmzZtUv/+/Ztiqc0iLi5OI0eO1MiRI+VyuVRSUqK5c+eqtLRU\nAwcOlMPhUFpaWth2eweCcHR0tPx+f3CGam1tbXCGsM1ma7HPr1BxuVxyuVwE4DYmEIStVqsMw5Df\n7w8+/1vjhRUDz+OEhIRG/Z/hdrt11113adSoUcydBwDgPLETGACACBEIr5dffrmsVqs8Hk/wvsDO\nyrVr16p///7BiBkIx9Lx4CkpOGMxECAkafv27dq5c6cuvvhiJSYmnhRM/X6/6urq1LFjR7lcrlYR\nJ0708ssvKy0tTRMnTtTRo0clHd/hXD96JyUlqby8vLmW2OSio6OVm5urt99+Wxs2bFBmZqbmz5+v\njIwMPfzww/rss8/CvkM4KipK8fHxat++vWw2mzweT4MdwpE4f5oADOl4EA5cVLFdu3aKj4+XyWRS\nXV1dq5ijHaoA7PF4dM899ygnJ0d33HFHq/z+AgBAS0AEBgAgQgReGHfr1k2VlZXavHmzpH/tcP3L\nX/4ij8ejPn36KCEhocEoCElatWqVOnbsqGHDhklqOHt4/fr1Onz4cHAG44kXL9q1a5eqq6vVvn17\nRUdHh/GjDI/Jkydrz5492rp1qxITEzV9+vTmXlKLExUVpezsbM2bN09/+9vflJeXp0WLFunGG2/U\njBkztGHDhrBe1MpkMp0yCFdWVqq6uloulysignD9cEYARn2BINwa5mi73e7gLzIaE4C9Xq8mTZqk\nwYMH65577iEAAwDQCIyDAAAgwmRlZSk7O1sXXXSRpOPxzDAMZWVlaceOHcGwFAhmFotFn3zyib75\n5hvl5OSoS5cuwV3FgRfca9askSSNHTtW0skXp/v4448lSb169Qr/BxgGnTt3Dv550qRJys3NlXR8\n5+/+/fuD95WVlSkpKanJ19fSWK1WDR48WIMHD5bP59OGDRvkdDr1u9/9Tunp6XI4HBowYICs1vD8\nqBkIwlFRUTIMIzgyoq6uThaLJXjKfEu+QOGpBGanNnbnJCLfiWNTPB6P3G63ampqmn1sSqhmWXu9\nXv385z/X9ddfr/vuu48ADABAIxGBAQCIMD/60Y+0cOFCeb3e4G2BEJySkhK8zWKxBHeNLVq0SH6/\nX0OHDpV0PBCbTCaZzWbt2LFDf/vb33TZZZcpJSUlOEM4YN++fVqxYoWSk5PVr1+/JvooGycwCzng\nwIEDSkxMlCQVFhaqd+/ekqS8vDyNHz9ev/rVr1ReXq5du3a1mo+xqVgsFg0aNEiDBg2Sz+fTxo0b\nVVBQoEcffVR9+vSR3W7XwIEDFRUVFZbjm0ymYPCqH4RdLpfMZnOrCcJ1dXXyeDyN3jmJtuds5mhb\nrdYmeV6FKgD7fD5NmTJFffv21ZQpUwjAAACEABEYAIAIlJCQcNJtp3oRHbjtd7/7nX7yk5/olltu\nkXQ8KgR2Cv/1r39VRUWFJk6cKOn4i/PADk+3263ly5frwIED+vnPf94gMrdU48aN00cffaRDhw7p\n4osv1uOPP66//OUv2rp1q8xms5KTk/Xqq69Kkq644gqNHj1aV1xxhWw2m+bOnUuMOAOLxaIBAwZo\nwIAB8vv9+vzzz+V0OvXUU0+pV69estvtysjICNvIkLMJwlartcWNWSAAI1QCc7QDu+Q9Ho+8Xq9q\na2vDvks+VAHY7/dr2rRpSk1N1QMPPMD/uQAAhIjpB+ZGtYyhUgAAoNlkZmZq3bp12r59u1JTU4Nx\n2Gw2a/Pmzfr3f/93VVdX609/+pMyMjKaebVoifx+v7Zt26YlS5Zo7dq16tmzpxwOhzIzMxUbGxv2\n4xuGIZ/PJ4/HI4/H0yAWN2cQNgxDLpeLAIywq/9LEa/XG/JfigR2HociAE+fPl1JSUn6wx/+QAAG\nAODcnfabJxEYAAAERyOc+ILb7/frnXfeUXFxsZxOZ3BWcMCdd96pBQsW6KWXXtKkSZPCdso/Iodh\nGNq+fbucTqdKSkrUrVs3ORwOZWVlKT4+vkmOf7ogbDabmyw6EYDRXAJBOBCFG/s1EMoA/Jvf/EYX\nXHCBnnjiCQIwAADnhwgMAAAaz+fzyWKxyO/3q6CgQLfeeqsyMjKCF44DzoVhGNqxY4ecTqd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f26878ec240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(25, 25))\n", "ax = plt.axes(projection='3d')\n", "\n", "ax.plot(p1[:, 0], p1[:, 1], p1[:, 2])\n", "ax.plot(p2[:, 0], p2[:, 1], p2[:, 2], 'or')\n", "ax.set_xlabel('x (m)', fontsize = '20')\n", "ax.set_ylabel('y (m)', fontsize = '20')\n", "ax.set_zlabel('z (m)', fontsize = '20')\n", "ax.set_title('', fontsize = '20')\n", "ax.set_ylim([-20, 20])\n", "ax.legend(['Target 1', 'Target 2'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "L = 10\n", "\n", "X_1 = np.arange(0, L).tolist()\n", "X_2 = np.arange(L, 2L).tolist()\n", "Z_1 = np.arange(2L, 3L).tolist()\n", "Z_2 = np.arange(3L, 4L).tolist()\n", "\n", "z_1 = np.empty((M, N))\n", "z_1[:, :3] = pm_1\n", "z_1[:, 3:] = vm_1\n", "\n", "z_2 = np.empty((M, N))\n", "z_2[:, :3] = pm_2\n", "z_2[:, 3:] = vm_2" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "C_X = [CG([X_1[0]], [N], h0_1, prec0_1, g0_1)CG([X_2[0]], [N], h0_2, prec0_2, g0_2)]\n", "\n", "for i in np.arange(1, L):\n", " C_X.append(CG([X_1[i], X_1[i-1]], [N, N], h_pred, P_pred, g_pred)\n", " CG([X_2[i], X_2[i-1]], [N, N], h_pred, P_pred, g_pred))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "C_Z = [None]\n", "\n", "Z_11 = CG([X_1[1], Z_1[1]], [N, N], h_meas, P_meas, g_meas)\n", "Z_11.introduce_evidence([Z_1[1]], z_1[1, :])\n", "\n", "Z_22 = CG([X_2[1], Z_2[1]], [N, N], h_meas, P_meas, g_meas)\n", "Z_22.introduce_evidence([Z_2[1]], z_2[1, :])\n", "\n", "C_Z.append(Z_11Z_22)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in np.arange(2, L):\n", " Z_11 = CG([X_1[i], Z_1[i]], [N, N], h_meas, P_meas, g_meas)\n", " Z_11.introduce_evidence([Z_1[i]], z_1[i, :])\n", " \n", " Z_22 = CG([X_2[i], Z_2[i]], [N, N], h_meas, P_meas, g_meas)\n", " Z_22.introduce_evidence([Z_2[i]], z_2[i, :])\n", " \n", " Z_12 = CG([X_1[i], Z_2[i]], [N, N], h_meas, P_meas, g_meas)\n", " Z_12.introduce_evidence([Z_2[i]] ,z_2[i, :])\n", " \n", " Z_21 = CG([X_2[i], Z_1[i]], [N, N], h_meas, P_meas, g_meas)\n", " Z_21.introduce_evidence([Z_1[i]], z_1[i, :])\n", " \n", " C_Z.append(GMM([0.5(Z_11Z_22), 0.5(Z_12Z_21)]))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "predict = [C_X[0]]\n", "\n", "for i in np.arange(1, L):\n", " marg = (C_X[i]predict[i-1]).marginalize([X_1[i-1], X_2[i-1]])\n", " predict.append(C_Z[i]marg)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "D = 100\n", "\n", "t = np.linspace(0, 2*np.pi, D)\n", "xz = np.array([[np.cos(t)], [np.sin(t)]]).reshape((2, D))\n", "\n", "ellipses = []\n", "norms = []" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0.]\n", " [ 0. 1.]]\n" ] } ], "source": [ "i = 0\n", "for p in predict:\n", " \n", " if isinstance(p, GMM):\n", " mix = p._mix\n", " else:\n", " mix = [p]\n", " \n", " time_step = []\n", " \n", " for m in mix:\n", " m._vars = [1, 2, 3, 4]\n", " m._dims = [1, 1, 1, 9]\n", " \n", " c = m.marginalize([2, 4])\n", " \n", " cov = np.linalg.inv(c._prec)\n", " mu = (cov)@(c._info)\n", " \n", " if i == 0: \n", " print(cov)\n", " i = 1\n", " \n", " U, S, _ = np.linalg.svd(cov)\n", " lambda_ = np.diag(np.sqrt(S))\n", " \n", " norms.append(c._norm)\n", " time_step.append(np.dot((U)@(lambda_), xz) + mu)\n", " \n", " ellipses.append(time_step)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in np.arange(0, L):\n", " plt.figure(figsize= (15, 15))\n", " \n", " plt.plot(p1[1:, 0], p1[1:, 2], 'or', label='Trajectory 1')\n", " plt.plot(p2[1:, 0], p2[1:, 2], 'og', label='Trajectory 2')\n", " \n", " for e in ellipses[i]:\n", " plt.plot(e[0, :], e[1, :], 'b')\n", " \n", " plt.xlim([-3.5, 25])\n", " plt.ylim([-3.5, 15])\n", " plt.grid(True)\n", " \n", " plt.legend(loc='upper left')\n", " plt.xlabel('x (m)')\n", " plt.ylabel('z (m)')\n", " plt.title('x-z position for t = %d'%(i))\n", " \n", " plt.savefig('images/two_objects/%d.png'%i, format = 'png')\n", " plt.close()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<img src=\"images/two_objects/two_objects_pd.gif\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
fivetentaylor/rpyca	RPCA_Testing-3d.ipynb	1	136458	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Robust PCA Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Robust PCA is an awesome relatively new method for factoring a matrix into a low rank component and a sparse component. This enables really neat applications for outlier detection, or models that are robust to outliers." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make Some Toy Data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.random.randn(100) * 5\n", "y = np.random.randn(100)\n", "z = np.random.randn(100)\n", "points = np.vstack([y,x,z])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add Some Outliers to Make Life Difficult" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "outliers = np.tile([15,-10,10], 10).reshape((-1,3))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pts = np.vstack([points.T, outliers]).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute SVD on both the clean data and the outliery data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "U,s,Vt = np.linalg.svd(points)\n", "U_n,s_n,Vt_n = np.linalg.svd(pts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Just 10 outliers can really screw up our line fit!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXd4U+fd9v2coWXZyHuCDcZhbzB7hhGyIYM2s0napG26\n", 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randrange(n, 23, 32) \n", " ys = randrange(n, 0, 100)\n", " zs = randrange(n, zl, zh) \n", " ax.scatter(xs, ys, zs, c=c, marker=m)\n", "\n", "ax.set_xlabel('X Label')\n", "ax.set_ylabel('Y Label')\n", "ax.set_zlabel('Z Label')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now the robust pca version!" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rpca" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'rpca' from 'rpca.pyc'>" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(rpca)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "logger = logging.getLogger(rpca.__name__)\n", "logger.setLevel(logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Factor the matrix into L (low rank) and S (sparse) parts" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:rpca:Number of iterations: 212 to achieve eps = 0.000000\n" ] } ], "source": [ "L,S = rpca.rpca(pts, eps=0.0000001, r=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run SVD on the Low Rank Part" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "U,s,Vt = np.linalg.svd(L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And have a look at this!" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x107c55810>" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ 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the factored components..." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x108078690>" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGVVJREFUeJzt3X+QXeV93/H3Rz8QK8SyWpFBWJCKDBAsQmNDhnYGWu/U\n", "EVLaFFvxOGnHnoofxc547LW9UgKIOqjGMDixFkVmHDsEsBrbZMikYNE4tuQOO3U7jV1+WraQhWzw\n", "GARKgiQWJCEJ7bd/PM9q765W0u79sefcs5/XzB3Oec6993znsPvVs9/znOdRRGBmZtU1o+gAzMys\n", "tZzozcwqzonezKzinOjNzCrOid7MrOKc6M3MKq6hRC/pfEmPS/qxpB9J6s3t3ZK2SNohabOkruaE\n", "a2Zmk6VGxtFLWggsjIhnJM0DngTeD1wP/FNE/LGkm4H5EXFLUyI2M7NJaahHHxGvRsQzeftN4Dlg\n", "EXAtsDG/bSMp+ZuZWQGaVqOXtBh4N/B94JyI2J0P7QbOadZ5zMxscpqS6HPZ5m+AT0bEG7XHItWG\n", "PM+CmVlBZjX6BZJmk5L8X0bEo7l5t6SFEfGqpHOBfxjnc07+ZmZ1iAhN5v0NJXpJAu4HtkXE+ppD\n", 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0x107afb310>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.ylim([-20,20])\n", "plt.xlim([-20,20])\n", "plt.scatter(L)\n", "plt.scatter(S, c='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### It really does add back to the original matrix!" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10818d990>" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGz5JREFUeJzt3X+QXeV93/H3ZwVWkLAi7cJIGKuI1FAbqqmsZFw60Ho7\n", "tpCcTm2rHsfthAnFFNtD6o0sOQVkpygRpXZqqVRNHWwFEzW1yThDATGxQSLDTt1O49QSsmVAAWJg\n", "+GHJQT9GgLAE2m//eM7VPXv37u79dfbee/bzmjmjc8+555zH15fvffZ7vs9zFBGYmVl5DXS7AWZm\n", "ViwHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JrK9BLWirpUUmPS/qxpJFs+6Ck3ZKekrRL0sLO\n", 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"display_data" } ], "source": [ "plt.ylim([-20,20])\n", "plt.xlim([-20,20])\n", "plt.scatter(*(L+S))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ryan-leung/PHYS4650_Python_Tutorial	notebooks/Feb2017/Astronomy/Astropy - Load fits.ipynb	2	21410	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from astropy.io import fits\n", "from astropy.utils.data import download_file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://chandra.harvard.edu/photo/2013/vela/fits/vela_2.0-8.0_flux.fits [Done]\n" ] } ], "source": [ "image_file = download_file('http://data.astropy.org/tutorials/FITS-images/HorseHead.fits', cache=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can open the fits file by fits.open() and check the info of the fits file by .info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /home/yanyan/.astropy/cache/download/3bc6e348e5bca65cae417559bd2c6b85\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 189 (588, 472) float64 \n" ] } ], "source": [ "hdu_list = fits.open(image_file)\n", "hdu_list.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get the data by the following command" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 3.57891936e-09 8.34289603e-10 0.00000000e+00 ..., 0.00000000e+00\n", " 1.13029266e-08 1.66015553e-08]\n", " [ 0.00000000e+00 9.58613079e-09 6.73792224e-09 ..., 7.05635584e-09\n", " 2.39556844e-09 4.19313275e-10]\n", " [ 3.72970966e-09 7.14046017e-09 6.35789505e-09 ..., 8.96702983e-09\n", " 4.60270655e-09 4.86753610e-09]\n", " ..., \n", " [ 1.13655557e-09 3.81179035e-09 3.45669998e-09 ..., 0.00000000e+00\n", " 1.00146203e-09 1.23558768e-08]\n", " [ 0.00000000e+00 2.70082504e-09 1.06594018e-09 ..., 4.79216329e-09\n", " 2.36669205e-09 7.71180067e-09]\n", " [ 4.77691706e-09 8.34896416e-09 6.02902179e-09 ..., 5.10113207e-09\n", " 2.29898113e-09 0.00000000e+00]]\n" ] } ], "source": [ "image_data = hdu_list[0].data\n", "print image_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get the header by the following command" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<bound method Header.items of SIMPLE = T / file does conform to FITS standard \n", "BITPIX = -64 / number of bits per data pixel \n", "NAXIS = 2 / number of data axes \n", "NAXIS1 = 588 / length of data axis \n", "NAXIS2 = 472 / length of data axis \n", "EXTEND = T / FITS dataset may contain extensions \n", "COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy\n", "COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n", "HDUNAME = 'vela_2.0-8.0_flux.fits' / ASCDM block name \n", "ASOLFILE= 'pcadf399999303N003_asol1.fits' \n", "THRFILE = 'acisD2005-07-01evtspltN0002.fits' \n", "ORIGIN = 'ASC ' / Source of FITS file \n", "CREATOR = 'dmimgcalc - Version CIAO 4.4' / tool that created this output \n", "ASCDSVER= 'CIAO 4.4' / ASCDS version number \n", "MJD_OBS = 5.5021583397856E+04 / Modified Julian date of observation \n", "DS_IDENT= 'Merged ' / dataset identifier \n", "TLMVER = 'P009 ' / Telemetry revision number (IP&CL) \n", "REVISION= 3 / Processing version of data \n", "CHECKSUM= '5aAA8S845YAA5Y53' / HDU checksum updated 2012-11-07T22:42:04 \n", "DATASUM = '1188128169' / data unit checksum updated 2012-11-07T22:42:04 \n", "CONTENT = 'EVT2 ' / What data product \n", "HDUSPEC = 'ACIS Telemetry Products: Level 0 to ASC Archive ICD Rev 2.11' / ICD r\n", "HDUDOC = 'ASC-FITS-2.0: McDowell, Rots: ASC FITS File Designers Guide' \n", "HDUVERS = '1.0.0 ' \n", "HDUCLASS= 'OGIP ' \n", "HDUCLAS1= 'EVENTS ' \n", "HDUCLAS2= 'ACCEPTED' \n", "PIX_ADJ = 'EDSER ' / Subpixel adjustment algorithm \n", "RAND_SKY= 0.0000000000000E+00 \n", "SUBPIXFL= 'acisD1999-07-22subpixN0001.fits' \n", "RAND_PI = 1.0000000000000E+00 \n", "DATE = '2012-11-07T22:42:04' / Date and time of file creation \n", "DATE-OBS= '2009-07-09T14:00:05' / Observation start date \n", "OBS_MODE= 'POINTING' / Observation mode \n", "DATE-END= '2010-09-05T02:46:25' / Observation end date \n", "TIMESYS = 'TT ' / Time system \n", "MJDREF = 5.0814000000000E+04 / [d] MJD zero point for times \n", "TIMEZERO= 0.0000000000000E+00 / [s] Clock correction \n", "TIMEUNIT= 's ' / Time unit \n", "DATACLAS= 'OBSERVED' / default \n", "RADESYS = 'ICRS ' / default \n", "BTIMCORR= 0.0000000000000E+00 / Correction applied to Basic Time rate (s) \n", "TIMEREF = 'LOCAL ' / Time reference (barycenter/local) \n", "TASSIGN = 'SATELLITE' / Time assigned by clock \n", "CLOCKAPP= T / default \n", "SIM_X = -6.8282252473119E-01 / [mm] SIM focus pos \n", "SIM_Y = 0.0000000000000E+00 / [mm] SIM orthogonal axis pos \n", "SIM_Z = -1.9014258036517E+02 / [mm] SIM translation stage pos \n", "FOC_LEN = 1.0070000000000E+04 / [mm] HRMA focal length \n", "TIERRELA= 1.0000000000000E-09 / default \n", "TIERABSO= 5.0000000000000E-05 / default \n", "TIMVERSN= 'ASC-FITS-2' / Timing system definition \n", "TSTART = 3.6353520557476E+08 / [s] Observation start time (MET) \n", "GRATING = 'NONE ' / Grating \n", "DETNAM = 'Merged ' / Detector \n", "RA_TARG = 1.2883625000000E+02 / [deg] Observer's specified target RA \n", "DEC_TARG= -4.5176583000000E+01 / [deg] Observer's specified target Dec \n", "DEFOCUS = 1.4449365687057E-03 / [mm] SIM defocus \n", "TSTOP = 4.0004198513585E+08 / [s] Observation end time (MET) \n", "STARTOBT= 0.0000000000000E+00 / On-Board MET close to STARTMJF and STARTMNF \n", "TIMEPIXR= 5.0000000000000E-01 / default \n", "TIMEDEL = 3.2410400000000E+00 / [s] timedel Lev1 \n", "ACSYS1 = 'CHIP:AXAF-ACIS-1.0' / reference for chip coord system \n", "ACSYS2 = 'TDET:ACIS-2.2' / reference for tiled detector coord system \n", "ACSYS3 = 'DET:ASC-FP-1.1' / reference for focal plane coord system \n", "ACSYS4 = 'SKY:ASC-FP-1.1' / reference for sky coord system \n", "GAINFILE= 'acisD2000-01-29gain_ctiN0006.fits' \n", "CTI_CORR= T \n", "CTI_APP = 'PPPPPBPBPP' \n", "CTIFILE = 'acisD2002-08-01ctiN0007.fits' \n", "MTLFILE = 'acisf12075_000N003_fptemp_egti1.fits' \n", "TGAINCOR= 'T ' \n", "TGAINFIL= 'acisD2010-08-01t_gainN0006.fits' \n", "GRD_FILE= 'acisD1996-11-01gradeN0004.fits' \n", "CORNERS = 2 / num adjacent side pix > threshold to include co\n", "GRADESYS= 'ASCA ' / grade system: ASCA, ACIS, or USER \n", "BPIXFILE= 'acisf12075_000N003_bpix1.fits' \n", "MISSION = 'AXAF ' / Mission \n", "TELESCOP= 'CHANDRA ' / Telescope \n", "INSTRUME= 'ACIS ' / Instrument \n", "READMODE= 'TIMED ' / Read mode \n", "DATAMODE= 'FAINT ' / Data mode \n", "RUN_ID = 1 / Science run index \n", "FSW_VERS= 48 / ACIS flight software version number \n", "STARTBEP= 315212112 / BEP timer value at TSTART \n", "STOPBEP = 253043056 / BEP timer value at TSTOP \n", "FEP_ID = 0 / Front End Processor ID: 0-5 \n", "CCD_ID = 2 / CCD ID: 0-9 \n", "TIMEDELA= 3.2410400000000E+00 / Inferred duration of primary exposure (s) \n", "TIMEDELB= 0.0000000000000E+00 / Inferred duration of secondary exp. (s) \n", "FLSHTIME= 0.0000000000000E+00 / [s] \n", "EXPTIME = 3.2000000000000E+00 / [s] \n", "DTYCYCLE= 0 \n", "FIRSTROW= 1 / Index of first row of CCD (sub)array readout \n", "NROWS = 1024 / Number of rows in (sub)array readout \n", "FLSHTIMA= 0.0000000000000E+00 / Inferred duration of flush before primary fram \n", "FLSHTIMB= 0.0000000000000E+00 / Inferred duration of flush before secondary fr \n", "CYCLE = 'P ' / events from which exps? Prim/Second/Both \n", "HISTNUM = 632 \n", "TITLE = 'The Unique Dynamical Vela Pulsar Wind Nebula' / Proposal title \n", "OBSERVER= 'Dr. George Pavlov' / Principal investigator \n", "OBJECT = 'Vela PWN' / Source name \n", "OBS_ID = 'Merged ' / Observation id \n", "SEQ_NUM = 'Merged ' / Sequence number \n", "ONTIME = 8.8108228969980E+05 / [s] Sum of GTIs \n", "ONTIME7 = 4.4054114484990E+05 / [s] Sum of GTIs \n", "ONTIME2 = 0.0000000000000E+00 / [s] Sum of GTIs \n", "ONTIME5 = 0.0000000000000E+00 / [s] Sum of GTIs \n", "ONTIME6 = 0.0000000000000E+00 / [s] Sum of GTIs \n", "ONTIME3 = 0.0000000000000E+00 / [s] Sum of GTIs \n", "ONTIME8 = 0.0000000000000E+00 / [s] Sum of GTIs \n", "LIVETIME= 8.6992549522356E+05 / [s] Livetime \n", "LIVTIME7= 4.3496274761178E+05 / [s] Livetime \n", "LIVTIME2= 0.0000000000000E+00 / [s] Livetime \n", "LIVTIME5= 0.0000000000000E+00 / [s] Livetime \n", "LIVTIME6= 0.0000000000000E+00 / [s] Livetime \n", "LIVTIME3= 0.0000000000000E+00 / [s] Livetime \n", "LIVTIME8= 0.0000000000000E+00 / [s] Livetime \n", "EXPOSURE= 4.3497374761178E+05 / [s] Exposure time \n", "EXPOSUR7= 4.3496274761178E+05 / [s] Exposure time \n", "EXPOSUR2= 0.0000000000000E+00 / [s] Exposure time \n", "EXPOSUR5= 0.0000000000000E+00 / [s] Exposure time \n", "EXPOSUR6= 0.0000000000000E+00 / [s] Exposure time \n", "EXPOSUR3= 0.0000000000000E+00 / [s] Exposure time \n", "EXPOSUR8= 0.0000000000000E+00 / [s] Exposure time \n", "DTCOR = 9.8733739787229E-01 / Dead time correction \n", "ASPTYPE = 'KALMAN ' \n", "BIASFIL2= 'acisf399998116N003_0_bias0.fits' / bias file used: CCD 2 \n", "BIASFIL7= 'acisf399998116N003_1_bias0.fits' / bias file used: CCD 7 \n", "BIASFIL5= 'acisf399998116N003_2_bias0.fits' / bias file used: CCD 5 \n", "FP_TEMP = 1.5344601440000E+02 / [K] Focal Plane Temperature \n", "BIASFIL6= 'acisf399998116N003_3_bias0.fits' / bias file used: CCD 6 \n", "BIASFIL3= 'acisf399998116N003_4_bias0.fits' / bias file used: CCD 3 \n", "BIASFIL8= 'acisf399998116N003_5_bias0.fits' / bias file used: CCD 8 \n", "AIMPFILE= 'telD1999-07-23aimptsN0002.fits' \n", "GEOMFILE= 'telD1999-07-23geomN0006.fits' \n", "SKYFILE = 'telD1999-07-23skyN0002.fits' \n", "TDETFILE= 'telD1999-07-23tdetN0001.fits' \n", "SHELLFIL= 'telD1999-07-23sgeomN0001.fits' \n", "FLTFILE = 'acisf12075_000N003_flt1.fits' \n", "MASKFILE= 'acisf12075_000N003_msk1.fits' \n", "PBKFILE = 'acisf399999425N003_pbk0.fits' \n", "DY_AVG = 8.6558815057000E-01 / [mm] Mean DY during observation \n", "DZ_AVG = 9.1717584578000E-01 / [mm] Mean DZ during observation \n", "DTH_AVG = -3.0294623866000E-03 / [deg] Mean DTHETA during observation \n", "OCLKPAIR= 8 / # of pairs of overclock pixels per output \n", "ORC_MODE= 0 / Output register clocking mode \n", "SUM_2X2 = 0 / On-chip summing. 0:None; 1:Sum 2x2 \n", "FEP_CCD = '275638 ' / CCD to FEPID mapping, fep0 is left most digit \n", "CALDBVER= '4.5.0 ' \n", "WCSTY1P = 'PHYSICAL' \n", "WCSTY2P = 'PHYSICAL' \n", "HISTORY TOOL :dmimgcalc 2012-11-07T22:42:04 ASC00622\n", "HISTORY PARM :infile=vela_2.0-8.0_image_clean.fits ASC00623\n", "HISTORY PARM :infile2=vela_5.0_image_expmap.fits ASC00624\n", "HISTORY PARM :outfile=vela_2.0-8.0_flux.fits ASC00625\n", "HISTORY PARM :operation=div ASC00626\n", "HISTORY PARM :weight=1 ASC00627\n", "HISTORY PARM :weight2=1 ASC00628\n", "HISTORY PARM :lookupTab=/Users/depasq/LocalApps/ciao-4.4/data/dmmerge_ASC00629\n", "HISTORY CONT :header_lookup.txt ASC00630\n", "HISTORY PARM :clobber=yes ASC00631\n", "HISTORY PARM :verbose=0 ASC00632\n", "MTYPE1 = 'SKY ' / DM Keyword: Descriptor name. \n", "MFORM1 = 'X,Y ' / DM Keyword: Descriptor value. \n", "CTYPE1P = 'X ' \n", "CRVAL1P = 3.9572430458512E+03 \n", "CRPIX1P = 2.9400000000000E+02 \n", "CDELT1P = 1.0000000000000E+00 \n", "LTV1 = -3.6632430458512E+03 \n", "LTM1_1 = 1.0000000000000E+00 \n", "CTYPE2P = 'Y ' \n", "CRVAL2P = 4.1919555257836E+03 \n", "CRPIX2P = 2.3600000000000E+02 \n", "CDELT2P = 1.0000000000000E+00 \n", "LTV2 = -3.9559555257836E+03 \n", "LTM2_2 = 1.0000000000000E+00 \n", "MTYPE2 = 'EQPOS ' / DM Keyword: Descriptor name. \n", "MFORM2 = 'RA,DEC ' / [degree] \n", "CTYPE1 = 'RA---TAN' \n", "CRVAL1 = 1.2882334000000E+02 \n", "CRPIX1 = 2.9400000000000E+02 \n", "CDELT1 = -1.3666666666667E-04 \n", "CUNIT1 = 'degree ' \n", "CTYPE2 = 'DEC--TAN' \n", "CRVAL2 = -4.5176306000000E+01 \n", "CRPIX2 = 2.3600000000000E+02 \n", "CDELT2 = 1.3666666666667E-04 \n", "CUNIT2 = 'degree ' >\n" ] } ], "source": [ "image_header = hdu_list[0].header\n", "print image_header.items" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get individual header items by calling it as dictionary" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "128.82334\n", "-45.176306\n" ] } ], "source": [ "print image_header['CRVAL1']\n", "print image_header['CRVAL2']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
ChadFulton/statsmodels	examples/notebooks/contrasts.ipynb	3	15612	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Contrasts Overview" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This document is based heavily on this excellent resource from UCLA http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.\n", "\n", "In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model's intercept. On the other hand, a set of contrasts for a categorical variable with `k` levels is a set of `k-1` functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding isn't wrong per se. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context.\n", "\n", "To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let's load the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "url = 'https://stats.idre.ucla.edu/stat/data/hsb2.csv'\n", "hsb2 = pd.read_table(url, delimiter=\",\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2.groupby('race')['write'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Treatment (Dummy) Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import Treatment\n", "levels = [1,2,3,4]\n", "contrast = Treatment(reference=0).code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we used `reference=0`, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let's look at how this would encode the `race` variable." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2.race.head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(contrast.matrix[hsb2.race-1, :][:20])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sm.categorical(hsb2.race.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a bit of a trick, as the `race` category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won't work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import ols\n", "mod = ols(\"write ~ C(race, Treatment)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. Patsy doesn't have the Simple contrast included, but you can easily define your own contrasts. To do so, write a class that contains a code_with_intercept and a code_without_intercept method that returns a patsy.contrast.ContrastMatrix instance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import ContrastMatrix\n", "\n", "def _name_levels(prefix, levels):\n", " return [\"[%s%s]\" % (prefix, level) for level in levels]\n", "\n", "class Simple(object):\n", " def _simple_contrast(self, levels):\n", " nlevels = len(levels)\n", " contr = -1./nlevels * np.ones((nlevels, nlevels-1))\n", " contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels\n", " return contr\n", "\n", " def code_with_intercept(self, levels):\n", " contrast = np.column_stack((np.ones(len(levels)),\n", " self._simple_contrast(levels)))\n", " return ContrastMatrix(contrast, _name_levels(\"Simp.\", levels))\n", "\n", " def code_without_intercept(self, levels):\n", " contrast = self._simple_contrast(levels)\n", " return ContrastMatrix(contrast, _name_levels(\"Simp.\", levels[:-1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2.groupby('race')['write'].mean().mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "contrast = Simple().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(\"write ~ C(race, Simple)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sum (Deviation) Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import Sum\n", "contrast = Sum().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(\"write ~ C(race, Sum)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This corresponds to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2.groupby('race')['write'].mean().mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Backward Difference Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import Diff\n", "contrast = Diff().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(\"write ~ C(race, Diff)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, here the coefficient on level 1 is the mean of `write` at level 2 compared with the mean at level 1. Ie.," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res.params[\"C(race, Diff)[D.1]\"]\n", "hsb2.groupby('race').mean()[\"write\"][2] - \\\n", " hsb2.groupby('race').mean()[\"write\"][1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helmert Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name 'reverse' being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import Helmert\n", "contrast = Helmert().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(\"write ~ C(race, Helmert)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grouped = hsb2.groupby('race')\n", "grouped.mean()[\"write\"][4] - grouped.mean()[\"write\"][:3].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = 4\n", "1./k * (grouped.mean()[\"write\"][k] - grouped.mean()[\"write\"][:k-1].mean())\n", "k = 3\n", "1./k * (grouped.mean()[\"write\"][k] - grouped.mean()[\"write\"][:k-1].mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Orthogonal Polynomial Coding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coefficients taken on by polynomial coding for `k=4` levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order `k-1`. Since `race` is not an ordered factor variable let's use `read` as an example. First we need to create an ordered categorical from `read`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hsb2['readcat'] = np.asarray(pd.cut(hsb2.read, bins=3))\n", "hsb2.groupby('readcat').mean()['write']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from patsy.contrasts import Poly\n", "levels = hsb2.readcat.unique().tolist()\n", "contrast = Poly().code_without_intercept(levels)\n", "print(contrast.matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = ols(\"write ~ C(readcat, Poly)\", data=hsb2)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, readcat has a significant linear effect on the dependent variable `write` but not a significant quadratic or cubic effect." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
flaxandteal/python-course-lecturer-notebooks	04 - interactions.ipynb	2	352093	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<table style=\"float:left; border:none\">\n", " <tr style=\"border:none\">\n", " <td style=\"border:none\">\n", " <a href=\"http://bokeh.pydata.org/\"> \n", " <img \n", " src=\"http://bokeh.pydata.org/en/latest/_static/bokeh-transparent.png\" \n", " style=\"width:70px\"\n", " >\n", " </a> \n", " </td>\n", " <td style=\"border:none\">\n", " <h1>Bokeh Tutorial — Adding Interactions</h1>\n", " </td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bokeh.io import output_notebook, show" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.0.min.js'];\n", " \n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.0.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.0.min.css\");\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", " </script>\n", " <div>\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span>BokehJS successfully loaded.</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "output_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Layouts\n", "\n", "In order to add widgets or have multiple plots that are linked together, you must first be able to create documents that contain these separate objects. It is possible to accomplish this in your own custom templates using ``bokeh.embed.components``. But, Bokeh also provides simple layout capability for grid plots, vplots, and hplots (than can be nested). \n", "\n", "An example using ``gridplot`` is shown below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"3d2b8383-c9ae-44df-9a23-2cb050b45637\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " 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\"notebook_comms_target\": \"16cb9fd3-1aea-477a-bc21-2db10b7abccf\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<bokeh.io._CommsHandle at 0x7f0a96001588>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bokeh.plotting import figure\n", "from bokeh.io import gridplot\n", "\n", "x = list(range(11))\n", "y0, y1, y2 = x, [10-i for i in x], [abs(i-5) for i in x]\n", "\n", "# create a new plot\n", "s1 = figure(width=250, plot_height=250)\n", "s1.circle(x, y0, size=10, color=\"navy\", alpha=0.5)\n", "\n", "# create another one\n", "s2 = figure(width=250, height=250)\n", "s2.triangle(x, y1, size=10, color=\"firebrick\", alpha=0.5)\n", "\n", "# create and another\n", "s3 = figure(width=250, height=250)\n", "s3.square(x, y2, size=10, color=\"olive\", alpha=0.5)\n", "\n", "# put all the plots in an HBox\n", "p = gridplot([[s1, s2, s3]], toolbar_location=None)\n", "\n", "# show the results\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# EXERCISE: create a gridplot of your own\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bokeh also provides the ``vplot`` and ``hplot`` functions to arrange plot objects in vertical or horizontal layouts. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# EXERCISE: use vplot to arrange a few plots vertically\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Linked Interactions\n", "\n", "It is possible to link various interactions between different Bokeh plots. For instance, the ranges of two (or more) plots can be linked, so that when one of the plots is panned (or zoomed, or otherwise has its range changed) the other plots will update in unison. It is also possible to link selections between two plots, so that when items are selected on one plot, the corresponding items on the second plot also become selected. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linked panning\n", "\n", "Linked panning (when mulitple plots have ranges that stay in sync) is simple to spell with Bokeh. You simply share the approrpate range objects between two (or more) plots. The example below shows how to accomplish this by linking the ranges of three plots in various ways:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"1188174b-3d52-4143-93b5-079ad964a668\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being 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\"attributes\": {\"callback\": null}, \"id\": \"ada7db98-8127-4dfb-96d5-61c70950b541\"}], \"root_ids\": [\"89c190dd-e5be-4d4d-b2a7-b8ee2c72ed7d\", \"a14cb3fc-27a7-49ad-a626-4c44a792ffdc\"]}, \"version\": \"0.11.0\", \"title\": \"Bokeh Application\"}};\n", " var render_items = [{\"modelid\": \"a14cb3fc-27a7-49ad-a626-4c44a792ffdc\", \"elementid\": \"1188174b-3d52-4143-93b5-079ad964a668\", \"docid\": \"27554472-2b25-49ad-8d97-d0178f7d8c41\", \"notebook_comms_target\": \"8dc3ff35-005a-4340-8d2e-46ba9d343e84\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<bokeh.io._CommsHandle at 0x7f0a95fae438>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_options = dict(width=250, plot_height=250, title=None, tools='pan')\n", "\n", "# create a new plot\n", "s1 = figure(plot_options)\n", "s1.circle(x, y0, size=10, color=\"navy\")\n", "\n", "# create a new plot and share both ranges\n", "s2 = figure(x_range=s1.x_range, y_range=s1.y_range, plot_options)\n", "s2.triangle(x, y1, size=10, color=\"firebrick\")\n", "\n", "# create a new plot and share only one range\n", "s3 = figure(x_range=s1.x_range, plot_options)\n", "s3.square(x, y2, size=10, color=\"olive\")\n", "\n", "p = gridplot([[s1, s2, s3]])\n", "\n", "# show the results\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# EXERCISE: create two plots in a gridplot, and link their ranges\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linked brushing\n", "\n", "Linking selections is accomplished in a similar way, by sharing data sources between plots. Note that normally with ``bokeh.plotting`` and ``bokeh.charts`` creating a default data source for simple plots is handled automatically. However to share a data source, we must create them by hand and pass them explicitly. This is illustrated in the example below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"f8cd5828-6eb2-442e-a4fe-4c07b270eec0\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"f8cd5828-6eb2-442e-a4fe-4c07b270eec0\");\n", " if 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\"Bokeh Application\"}};\n", " var render_items = [{\"modelid\": \"138b48d6-1eec-45b4-a56a-53bbc414c1b8\", \"elementid\": \"f8cd5828-6eb2-442e-a4fe-4c07b270eec0\", \"docid\": \"f1a079d7-7b76-43a5-96cd-bd98a30f33c8\", \"notebook_comms_target\": \"7c9a69b8-a13f-40dd-b41c-20efa46a5966\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<bokeh.io._CommsHandle at 0x7f0a95f51c88>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bokeh.models import ColumnDataSource\n", "\n", "x = list(range(-20, 21))\n", "y0, y1 = [abs(xx) for xx in x], [xx2 for xx in x]\n", "\n", "# create a column data source for the plots to share\n", "source = ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))\n", "\n", "TOOLS = \"box_select,lasso_select,help\"\n", "\n", "# create a new plot and add a renderer\n", "left = figure(tools=TOOLS, width=300, height=300)\n", "left.circle('x', 'y0', source=source)\n", "\n", "# create another new plot and add a renderer\n", "right = figure(tools=TOOLS, width=300, height=300)\n", "right.circle('x', 'y1', source=source)\n", "\n", "p = gridplot([[left, right]])\n", "\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# EXERCISE: create two plots in a gridplot, and link their data sources\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hover Tools\n", "\n", "Bokeh has a Hover Tool that allows additional information to be displayed in a popup whenever the uer howevers over a specific glyph. Basic hover tool configuration amounts to providing a list of ``(name, format)`` tuples. The full details can be found in the User's Guide [here](http://bokeh.pydata.org/en/latest/docs/user_guide/tools.html#hover-tool).\n", "\n", "The example below shows some basic usage of the Hover tool with a circle glyph:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'utils'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-6-288e9f9df650>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;31m# Also show custom hover\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_custom_hover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgridplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mget_custom_hover\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: No module named 'utils'" ] } ], "source": [ "from bokeh.models import HoverTool\n", "\n", "source = ColumnDataSource(\n", " data=dict(\n", " x=[1, 2, 3, 4, 5],\n", " y=[2, 5, 8, 2, 7],\n", " desc=['A', 'b', 'C', 'd', 'E'],\n", " )\n", " )\n", "\n", "hover = HoverTool(\n", " tooltips=[\n", " (\"index\", \"$index\"),\n", " (\"(x,y)\", \"($x, $y)\"),\n", " (\"desc\", \"@desc\"),\n", " ]\n", " )\n", "\n", "p = figure(plot_width=300, plot_height=300, tools=[hover], title=\"Mouse over the dots\")\n", "\n", "p.circle('x', 'y', size=20, source=source)\n", "\n", "# Also show custom hover \n", "from utils import get_custom_hover\n", "\n", "show(gridplot([[p, get_custom_hover()]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ipython Interactors\n", "\n", "It is possible to use native IPython notebook interactors together with Bokeh. In the interactor update function, the ``push_notebook`` method can be used to update a data source (presumably based on the iteractor widget values) to cause a plot to update.\n", "\n", "Warning: The current implementation of ``push_notebook`` leaks memory. It is suitable for interactive exploration but not for long-running or streaming use cases. The problem will be resolved in future releases.\n", "\n", "The example below shows a \"trig function\" exporer using IPython interactors:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"plotdiv\" id=\"59e863ab-6092-4d71-8475-d884bf4f0057\"></div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " 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\"fb68bfff-3971-4a6b-a980-b4e36ecc6122\"}, {\"type\": \"ResetTool\", \"attributes\": {\"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"ad86661b-979b-4d8e-8200-8d405fef95ad\"}}, \"id\": \"81388f5d-db35-4437-ba5e-59f1a6bc3ad0\"}, {\"type\": \"LinearAxis\", \"attributes\": {\"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"b482907c-8b5d-4d78-a359-8ee82acef199\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"10c75425-9e2f-4c44-9a88-94785f30ef63\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"dcfc1b76-82ee-460b-b538-48fa63befe33\"}}, \"id\": \"5ad675a0-ee18-48a8-8714-8dbe9c1752c1\"}, {\"type\": \"ResizeTool\", \"attributes\": {\"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"cc2163bd-1831-4446-b72b-46484a682182\"}}, \"id\": \"920701c1-3efa-46b3-852f-7cff13f8a140\"}, {\"type\": \"GlyphRenderer\", \"attributes\": {\"hover_glyph\": null, \"selection_glyph\": null, \"glyph\": {\"type\": \"Square\", \"id\": \"2010b1ec-18f4-4835-b0e5-254c0fa8e6b2\"}, \"data_source\": {\"type\": \"ColumnDataSource\", \"id\": \"73ca7b37-ddcf-4dae-ba34-d900c0bc1621\"}, \"nonselection_glyph\": {\"type\": \"Square\", \"id\": \"e105abac-8f2e-472e-bf5b-c08e7164af41\"}}, \"id\": \"480598a2-370d-48c9-8e91-437ec6170816\"}, {\"type\": \"LinearAxis\", \"attributes\": {\"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"726aa092-eb20-47c8-8e72-6fa83545e0d5\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"08b98c10-2138-42cf-b6a4-5200a3c72d81\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4cceac60-e60a-4d41-8adb-c66f4a67e048\"}}, \"id\": \"b7d2bdd8-856e-422f-bb75-72555241ad51\"}, {\"type\": \"ColumnDataSource\", \"attributes\": {\"data\": {\"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"y\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}, \"callback\": null, \"column_names\": [\"x\", \"y\"]}, \"id\": \"a8485387-a2e8-4bc9-91f6-fd48173264a2\"}, {\"type\": \"BasicTicker\", \"attributes\": {}, \"id\": \"af860fad-9649-48d0-bf5a-e2449b770651\"}, {\"type\": \"BasicTickFormatter\", \"attributes\": {}, \"id\": \"5fdb9d05-9004-4064-a56a-22f3209e94eb\"}, {\"type\": \"ColumnDataSource\", \"attributes\": {\"data\": {\"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"y\": [5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5]}, \"callback\": null, \"column_names\": [\"x\", \"y\"]}, \"id\": \"73ca7b37-ddcf-4dae-ba34-d900c0bc1621\"}, {\"type\": \"LinearAxis\", \"attributes\": {\"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"45981811-2677-4f3e-8ffc-538101de8532\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"1aad3397-219c-4119-8e04-99f9a7487390\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"cc2163bd-1831-4446-b72b-46484a682182\"}}, \"id\": \"53157547-ea46-4a4d-a5b6-861f6aeadae1\"}, {\"type\": \"DataRange1d\", \"attributes\": {\"callback\": null}, \"id\": \"ada7db98-8127-4dfb-96d5-61c70950b541\"}], \"root_ids\": [\"89c190dd-e5be-4d4d-b2a7-b8ee2c72ed7d\", \"a14cb3fc-27a7-49ad-a626-4c44a792ffdc\", \"138b48d6-1eec-45b4-a56a-53bbc414c1b8\", \"c97c4b99-6a7f-4953-b44a-dec208ccffd4\", \"cc2163bd-1831-4446-b72b-46484a682182\"]}, \"version\": \"0.11.0\", \"title\": \"Bokeh Application\"}};\n", " var render_items = [{\"modelid\": \"cc2163bd-1831-4446-b72b-46484a682182\", \"elementid\": \"59e863ab-6092-4d71-8475-d884bf4f0057\", \"docid\": \"da04c363-3398-41a1-9778-8d6f4226290c\", \"notebook_comms_target\": \"d8f6d264-42a2-4ad3-bb35-470b319dcc66\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<bokeh.io._CommsHandle at 0x7f0a95e96710>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from bokeh.models import Line\n", "\n", "x = np.linspace(0, 2np.pi, 2000)\n", "y = np.sin(x)\n", "\n", "source = ColumnDataSource(data=dict(x=x, y=y))\n", "\n", "p = figure(title=\"simple line example\", plot_height=300, plot_width=600, y_range=(-5, 5))\n", "p.line(x, y, color=\"#2222aa\", alpha=0.5, line_width=2, source=source, name=\"foo\")\n", "\n", "def update(f, w=1, A=1, phi=0):\n", " if f == \"sin\": func = np.sin\n", " elif f == \"cos\": func = np.cos\n", " elif f == \"tan\": func = np.tan\n", " source.data['y'] = A func(w * x + phi)\n", " source.push_notebook()\n", "\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:17: BokehDeprecationWarning: bokeh.models.sources.push_notebook was deprecated in Bokeh 0.11.0; please use bokeh.io.push_notebook instead\n" ] } ], "source": [ "from ipywidgets import interact\n", "interact(update, f=[\"sin\", \"cos\", \"tan\"], w=(0,10, 0.1), A=(0,5, 0.1), phi=(0, 10, 0.1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bokeh supports direct integration with a small basic widget set. Thse can be used in conjunction with a Bokeh Server, or with ``CustomJS`` models to add more interactive capability to your documents. You can see a complete list, with example code in the [Adding Widgets](http://bokeh.pydata.org/en/latest/docs/user_guide/interaction.html#adding-widgets) section of the User's Guide. \n", "\n", "To use the widgets, include them in a layout like you would a plot object:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", " Bokeh.$(function() {\n", " var all_models = [{\"type\": \"Slider\", \"id\": \"60799281-8da5-416d-bb20-231b10d327fb\", \"attributes\": {\"start\": 0, \"id\": \"60799281-8da5-416d-bb20-231b10d327fb\", \"value\": 1, \"doc\": null, \"step\": 0.1, \"callback\": null, \"end\": 10, \"title\": \"foo\", \"tags\": []}}, {\"type\": \"VBoxForm\", \"id\": \"73fe436a-a4d6-4794-9bad-2c30e84c1d3d\", \"attributes\": {\"id\": \"73fe436a-a4d6-4794-9bad-2c30e84c1d3d\", \"tags\": [], \"children\": [{\"type\": \"Slider\", \"id\": \"60799281-8da5-416d-bb20-231b10d327fb\"}], \"doc\": null}}];\n", " Bokeh.load_models(all_models);\n", " var plots = [{'elementid': '4f636e1c-33e3-4d42-a730-d03275f34392', 'modeltype': 'VBoxForm', 'modelid': '73fe436a-a4d6-4794-9bad-2c30e84c1d3d'}];\n", " for (idx in plots) {\n", " \tvar plot = plots[idx];\n", " \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n", " \tBokeh.logger.info('Realizing plot:')\n", " \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n", " \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n", " \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n", " \tvar view = new model.default_view({\n", " \t\tmodel: model,\n", " \t\tel: '#' + plot.elementid\n", " \t});\n", " \tBokeh.index[plot.modelid] = view;\n", " }\n", " });\n", " </script>\n", "<div class=\"plotdiv\" id=\"4f636e1c-33e3-4d42-a730-d03275f34392\"></div>\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.models.widgets import Slider\n", "from bokeh.io import vform\n", "\n", "slider = Slider(start=0, end=10, value=1, step=.1, title=\"foo\")\n", "\n", "show(vform(slider))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# EXERCISE: create and show a Select widget \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Callbacks" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", " Bokeh.$(function() {\n", " var all_models = [{\"type\": \"ToolEvents\", \"id\": \"e1822e27-644a-4a6f-b43f-c435f7f2ab08\", \"attributes\": {\"id\": \"e1822e27-644a-4a6f-b43f-c435f7f2ab08\", \"doc\": null, \"tags\": [], \"geometries\": []}}, {\"type\": \"LinearAxis\", \"id\": \"2069a5b5-2fa6-48ad-88ad-5a36fc01c8a0\", \"attributes\": {\"id\": \"2069a5b5-2fa6-48ad-88ad-5a36fc01c8a0\", \"doc\": null, \"tags\": [], \"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"1ba0a57f-be42-4d0c-8a7d-fb539a85eec0\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"a9ca9aa7-1f83-4c7c-adb0-72914bf672ea\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}}}, {\"type\": \"Grid\", \"id\": \"90daa407-74a0-4c19-8ee3-9683c39f2417\", \"attributes\": {\"doc\": null, \"id\": \"90daa407-74a0-4c19-8ee3-9683c39f2417\", \"dimension\": 1, \"tags\": [], \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"a9ca9aa7-1f83-4c7c-adb0-72914bf672ea\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}}}, {\"type\": \"BasicTicker\", \"id\": \"a9ca9aa7-1f83-4c7c-adb0-72914bf672ea\", \"attributes\": {\"id\": \"a9ca9aa7-1f83-4c7c-adb0-72914bf672ea\", \"num_minor_ticks\": 5, \"tags\": [], \"mantissas\": [2, 5, 10], \"doc\": null}}, {\"type\": \"TapTool\", \"id\": \"68bcb879-96b0-489b-a670-e160a1f4d5cf\", \"attributes\": {\"doc\": null, \"id\": \"68bcb879-96b0-489b-a670-e160a1f4d5cf\", \"renderers\": [], \"callback\": {\"type\": \"CustomJS\", \"id\": \"54a76baf-0180-4601-8fb9-0e93aa562a5d\"}, \"tags\": [], \"names\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}}}, {\"type\": \"Grid\", \"id\": \"a26ae277-45ef-4d2b-9950-0e61417bd451\", \"attributes\": {\"doc\": null, \"id\": \"a26ae277-45ef-4d2b-9950-0e61417bd451\", \"dimension\": 0, \"tags\": [], \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"849f29b8-0e08-4458-b63f-27bc343ad5bd\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}}}, {\"type\": \"Circle\", \"id\": \"f3020026-6e84-4b21-acc6-220fe444b257\", \"attributes\": {\"size\": {\"value\": 20, \"units\": \"screen\"}, \"fill_alpha\": {\"value\": 1.0}, \"tags\": [], \"line_alpha\": {\"value\": 1.0}, \"id\": \"f3020026-6e84-4b21-acc6-220fe444b257\", \"doc\": null, \"y\": {\"field\": \"y\"}, \"x\": {\"field\": \"x\"}, \"fill_color\": {\"value\": \"#1f77b4\"}, \"line_color\": {\"value\": \"#1f77b4\"}}}, {\"type\": \"BasicTickFormatter\", \"id\": \"1ba0a57f-be42-4d0c-8a7d-fb539a85eec0\", \"attributes\": {\"tags\": [], \"id\": \"1ba0a57f-be42-4d0c-8a7d-fb539a85eec0\", \"doc\": null}}, {\"type\": \"DataRange1d\", \"id\": \"3f7984df-419f-43ee-bc15-129ee1b63d46\", \"attributes\": {\"id\": \"3f7984df-419f-43ee-bc15-129ee1b63d46\", \"renderers\": [], \"callback\": null, \"tags\": [], \"names\": [], \"doc\": null}}, {\"type\": \"BasicTicker\", \"id\": \"849f29b8-0e08-4458-b63f-27bc343ad5bd\", \"attributes\": {\"id\": \"849f29b8-0e08-4458-b63f-27bc343ad5bd\", \"num_minor_ticks\": 5, \"tags\": [], \"mantissas\": [2, 5, 10], \"doc\": null}}, {\"type\": \"Plot\", \"subtype\": \"Figure\", \"attributes\": {\"tools\": [{\"type\": \"TapTool\", \"id\": \"68bcb879-96b0-489b-a670-e160a1f4d5cf\"}], \"extra_x_ranges\": {}, \"renderers\": [{\"type\": \"LinearAxis\", \"id\": \"c1b25bf0-50eb-41fb-9b58-061eddb57d5c\"}, {\"type\": \"Grid\", \"id\": \"a26ae277-45ef-4d2b-9950-0e61417bd451\"}, {\"type\": \"LinearAxis\", \"id\": \"2069a5b5-2fa6-48ad-88ad-5a36fc01c8a0\"}, {\"type\": \"Grid\", \"id\": \"90daa407-74a0-4c19-8ee3-9683c39f2417\"}, {\"type\": \"GlyphRenderer\", \"id\": \"6b5d6765-4279-4357-9f5b-6b6e7ce63f96\"}], \"below\": [{\"type\": \"LinearAxis\", \"id\": \"c1b25bf0-50eb-41fb-9b58-061eddb57d5c\"}], \"x_range\": {\"type\": \"DataRange1d\", \"id\": \"eb7140ea-291e-458a-bf4f-548fc7527eed\"}, \"tags\": [], \"right\": [], \"left\": [{\"type\": \"LinearAxis\", \"id\": \"2069a5b5-2fa6-48ad-88ad-5a36fc01c8a0\"}], \"above\": [], \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\", \"y_range\": {\"type\": \"DataRange1d\", \"id\": \"3f7984df-419f-43ee-bc15-129ee1b63d46\"}, \"plot_width\": 600, \"plot_height\": 300, \"extra_y_ranges\": {}, \"doc\": null, \"tool_events\": {\"type\": \"ToolEvents\", \"id\": \"e1822e27-644a-4a6f-b43f-c435f7f2ab08\"}}, \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}, {\"type\": \"Circle\", \"id\": \"f4748c0f-4377-4914-ae83-35d375a34776\", \"attributes\": {\"size\": {\"value\": 20, \"units\": \"screen\"}, \"fill_alpha\": {\"value\": 0.1}, \"tags\": [], \"line_alpha\": {\"value\": 0.1}, \"id\": \"f4748c0f-4377-4914-ae83-35d375a34776\", \"doc\": null, \"y\": {\"field\": \"y\"}, \"x\": {\"field\": \"x\"}, \"fill_color\": {\"value\": \"#1f77b4\"}, \"line_color\": {\"value\": \"#1f77b4\"}}}, {\"type\": \"GlyphRenderer\", \"id\": \"6b5d6765-4279-4357-9f5b-6b6e7ce63f96\", \"attributes\": {\"glyph\": {\"type\": \"Circle\", \"id\": \"f3020026-6e84-4b21-acc6-220fe444b257\"}, \"doc\": null, \"data_source\": {\"type\": \"ColumnDataSource\", \"id\": \"2ab17120-74f6-456a-98f8-4989666ee405\"}, \"nonselection_glyph\": {\"type\": \"Circle\", \"id\": \"f4748c0f-4377-4914-ae83-35d375a34776\"}, \"selection_glyph\": null, \"id\": \"6b5d6765-4279-4357-9f5b-6b6e7ce63f96\", \"tags\": []}}, {\"type\": \"ColumnDataSource\", \"id\": \"2ab17120-74f6-456a-98f8-4989666ee405\", \"attributes\": {\"data\": {\"y\": [2, 5, 8, 2, 7], \"x\": [1, 2, 3, 4, 5]}, \"column_names\": [\"y\", \"x\"], \"id\": \"2ab17120-74f6-456a-98f8-4989666ee405\", \"callback\": null, \"tags\": [], \"selected\": {\"1d\": {\"indices\": []}, \"0d\": {\"indices\": [], \"flag\": false}, \"2d\": {\"indices\": []}}, \"doc\": null}}, {\"type\": \"CustomJS\", \"id\": \"54a76baf-0180-4601-8fb9-0e93aa562a5d\", \"attributes\": {\"id\": \"54a76baf-0180-4601-8fb9-0e93aa562a5d\", \"code\": \"alert('hello world')\", \"tags\": [], \"args\": {}, \"doc\": null}}, {\"type\": \"LinearAxis\", \"id\": \"c1b25bf0-50eb-41fb-9b58-061eddb57d5c\", \"attributes\": {\"id\": \"c1b25bf0-50eb-41fb-9b58-061eddb57d5c\", \"doc\": null, \"tags\": [], \"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"81b3a074-9e0c-4e8e-8df0-0d8dd1f353f5\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"849f29b8-0e08-4458-b63f-27bc343ad5bd\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce\"}}}, {\"type\": \"BasicTickFormatter\", \"id\": \"81b3a074-9e0c-4e8e-8df0-0d8dd1f353f5\", \"attributes\": {\"tags\": [], \"id\": \"81b3a074-9e0c-4e8e-8df0-0d8dd1f353f5\", \"doc\": null}}, {\"type\": \"DataRange1d\", \"id\": \"eb7140ea-291e-458a-bf4f-548fc7527eed\", \"attributes\": {\"id\": \"eb7140ea-291e-458a-bf4f-548fc7527eed\", \"renderers\": [], \"callback\": null, \"tags\": [], \"names\": [], \"doc\": null}}];\n", " Bokeh.load_models(all_models);\n", " var plots = [{'elementid': 'd5cc8bd4-54fd-4005-9540-caf42811c771', 'modeltype': 'Plot', 'modelid': '4faf3140-60a7-4e2c-b8ed-1ecc8a5c18ce'}];\n", " for (idx in plots) {\n", " \tvar plot = plots[idx];\n", " \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n", " \tBokeh.logger.info('Realizing plot:')\n", " \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n", " \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n", " \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n", " \tvar view = new model.default_view({\n", " \t\tmodel: model,\n", " \t\tel: '#' + plot.elementid\n", " \t});\n", " \tBokeh.index[plot.modelid] = view;\n", " }\n", " });\n", " </script>\n", "<div class=\"plotdiv\" id=\"d5cc8bd4-54fd-4005-9540-caf42811c771\"></div>\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.models import TapTool, CustomJS, ColumnDataSource\n", "\n", "callback = CustomJS(code=\"alert('hello world')\")\n", "tap = TapTool(callback=callback)\n", "\n", "p = figure(plot_width=600, plot_height=300, tools=[tap])\n", "\n", "p.circle('x', 'y', size=20, source=ColumnDataSource(data=dict(x=[1, 2, 3, 4, 5], y=[2, 5, 8, 2, 7])))\n", "\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lots of places to add callbacks\n", "\n", "* Widgets - Button, Toggle, Dropdown, TextInput, AutocompleteInput, Select, Multiselect, Slider, (DateRangeSlider), DatePicker,\n", "* Tools - TapTool, BoxSelectTool, HoverTool,\n", "* Selection - ColumnDataSource, AjaxDataSource, BlazeDataSource, ServerDataSource\n", "* Ranges - Range1d, DataRange1d, FactorRange\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Callbacks for widgets\n", "\n", "Widgets that have values associated can have small JavaScript actions attached to them. These actions (also referred to as \"callbacks\") are executed whenever the widget's value is changed. In order to make it easier to refer to specific Bokeh models (e.g., a data source, or a glyhph) from JavaScript, the ``CustomJS`` obejct also accepts a dictionary of \"args\" that map names to Python Bokeh models. The corresponding JavaScript models are made available automaticaly to the ``CustomJS`` code. \n", "\n", "And example below shows an action attached to a slider that updates a data source whenever the slider is moved:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", " Bokeh.$(function() {\n", " var all_models = [{\"type\": \"BasicTicker\", \"id\": \"803f7d5b-3ed9-4e6d-8390-6572f3baf504\", \"attributes\": {\"id\": \"803f7d5b-3ed9-4e6d-8390-6572f3baf504\", \"num_minor_ticks\": 5, \"tags\": [], \"mantissas\": [2, 5, 10], \"doc\": null}}, {\"type\": \"Grid\", \"id\": \"15215351-30be-458b-9ebe-c655a4d73725\", \"attributes\": {\"doc\": null, \"id\": \"15215351-30be-458b-9ebe-c655a4d73725\", \"dimension\": 1, \"tags\": [], \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"be4d1167-fb61-47ec-a43f-c41a2648e6d3\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"PanTool\", \"id\": \"e22b3954-3de0-4233-8020-361f0c5b8348\", \"attributes\": {\"id\": \"e22b3954-3de0-4233-8020-361f0c5b8348\", \"doc\": null, \"tags\": [], \"dimensions\": [\"width\", \"height\"], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"Grid\", \"id\": \"0fbd1492-414f-4a87-ac4b-7655291e6d56\", \"attributes\": {\"doc\": null, \"id\": \"0fbd1492-414f-4a87-ac4b-7655291e6d56\", \"dimension\": 0, \"tags\": [], \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"803f7d5b-3ed9-4e6d-8390-6572f3baf504\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"Line\", \"id\": \"7dc3f3ed-49ac-49d0-8a5d-d9e62f810b8a\", \"attributes\": {\"line_alpha\": {\"value\": 0.6}, \"id\": \"7dc3f3ed-49ac-49d0-8a5d-d9e62f810b8a\", \"doc\": null, \"y\": {\"field\": \"y\"}, \"x\": {\"field\": \"x\"}, \"tags\": [], \"line_width\": {\"value\": 3}, \"line_color\": {\"value\": \"#1f77b4\"}}}, {\"type\": \"DataRange1d\", \"id\": \"7af5780d-58cd-4b73-adfd-829e7a8cdcf3\", \"attributes\": {\"id\": \"7af5780d-58cd-4b73-adfd-829e7a8cdcf3\", \"renderers\": [], \"callback\": null, \"tags\": [], \"names\": [], \"doc\": null}}, {\"type\": \"ColumnDataSource\", \"id\": \"638ed839-20fb-439b-bad8-4009d852aa77\", \"attributes\": {\"data\": {\"y\": [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04, 0.045, 0.05, 0.055, 0.06, 0.065, 0.07, 0.075, 0.08, 0.085, 0.09, 0.095, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.17500000000000002, 0.18, 0.185, 0.19, 0.195, 0.2, 0.20500000000000002, 0.21, 0.215, 0.22, 0.225, 0.23, 0.23500000000000001, 0.24, 0.245, 0.25, 0.255, 0.26, 0.265, 0.27, 0.275, 0.28, 0.28500000000000003, 0.29, 0.295, 0.3, 0.305, 0.31, 0.315, 0.32, 0.325, 0.33, 0.335, 0.34, 0.34500000000000003, 0.35000000000000003, 0.355, 0.36, 0.365, 0.37, 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\"671f384f-0cb4-4ced-845d-0682f9a97311\", \"attributes\": {\"line_alpha\": {\"value\": 0.1}, \"id\": \"671f384f-0cb4-4ced-845d-0682f9a97311\", \"doc\": null, \"y\": {\"field\": \"y\"}, \"x\": {\"field\": \"x\"}, \"tags\": [], \"line_width\": {\"value\": 3}, \"line_color\": {\"value\": \"#1f77b4\"}}}, {\"type\": \"HelpTool\", \"id\": \"40300e85-4530-4c3c-ba57-c79006c3fc6c\", \"attributes\": {\"id\": \"40300e85-4530-4c3c-ba57-c79006c3fc6c\", \"doc\": null, \"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"GlyphRenderer\", \"id\": \"ef6694da-e1da-40cc-b069-9d87db4217ad\", \"attributes\": {\"glyph\": {\"type\": \"Line\", \"id\": \"7dc3f3ed-49ac-49d0-8a5d-d9e62f810b8a\"}, \"doc\": null, \"data_source\": {\"type\": \"ColumnDataSource\", \"id\": \"638ed839-20fb-439b-bad8-4009d852aa77\"}, \"nonselection_glyph\": {\"type\": \"Line\", \"id\": \"671f384f-0cb4-4ced-845d-0682f9a97311\"}, \"selection_glyph\": null, \"id\": \"ef6694da-e1da-40cc-b069-9d87db4217ad\", \"tags\": []}}, {\"type\": \"BasicTicker\", \"id\": \"be4d1167-fb61-47ec-a43f-c41a2648e6d3\", \"attributes\": {\"id\": \"be4d1167-fb61-47ec-a43f-c41a2648e6d3\", \"num_minor_ticks\": 5, \"tags\": [], \"mantissas\": [2, 5, 10], \"doc\": null}}, {\"type\": \"VBoxForm\", \"id\": \"72f1a5c2-4a5f-4fb3-8213-f3d41e9c293f\", \"attributes\": {\"id\": \"72f1a5c2-4a5f-4fb3-8213-f3d41e9c293f\", \"tags\": [], \"children\": [{\"type\": \"Slider\", \"id\": \"d90b501a-acd3-4fee-bf56-7fd9c8ec7067\"}, {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}], \"doc\": null}}, {\"type\": \"LinearAxis\", \"id\": \"abb11a6d-3e68-424a-b5c9-b653e08bf0c2\", \"attributes\": {\"id\": \"abb11a6d-3e68-424a-b5c9-b653e08bf0c2\", \"doc\": null, \"tags\": [], \"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"7daef8b2-6c5b-4920-87b0-85395949a0b9\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"803f7d5b-3ed9-4e6d-8390-6572f3baf504\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"ResetTool\", \"id\": \"3d6c55f8-6644-4e64-ad34-d8519acc5924\", \"attributes\": {\"id\": \"3d6c55f8-6644-4e64-ad34-d8519acc5924\", \"doc\": null, \"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"Plot\", \"subtype\": \"Figure\", \"attributes\": {\"tools\": [{\"type\": \"PanTool\", \"id\": \"e22b3954-3de0-4233-8020-361f0c5b8348\"}, {\"type\": \"WheelZoomTool\", \"id\": \"a0f15b95-8f1b-498d-b8fa-4d017491e672\"}, {\"type\": \"BoxZoomTool\", \"id\": \"eda4c2a9-1550-4ac0-963a-57bf73f7c3e3\"}, {\"type\": \"PreviewSaveTool\", \"id\": \"298d73b3-8754-412a-ab95-d5fe5cb0d558\"}, {\"type\": \"ResizeTool\", \"id\": \"5d6bc1f1-701e-4598-a3ae-ab6879ba160f\"}, {\"type\": \"ResetTool\", \"id\": \"3d6c55f8-6644-4e64-ad34-d8519acc5924\"}, {\"type\": \"HelpTool\", \"id\": \"40300e85-4530-4c3c-ba57-c79006c3fc6c\"}], \"extra_x_ranges\": {}, \"renderers\": [{\"type\": \"LinearAxis\", \"id\": \"abb11a6d-3e68-424a-b5c9-b653e08bf0c2\"}, {\"type\": \"Grid\", \"id\": \"0fbd1492-414f-4a87-ac4b-7655291e6d56\"}, {\"type\": \"LinearAxis\", \"id\": \"940ca555-5548-450f-988d-3ab8c3c6dcf7\"}, {\"type\": \"Grid\", \"id\": \"15215351-30be-458b-9ebe-c655a4d73725\"}, {\"type\": \"GlyphRenderer\", \"id\": \"ef6694da-e1da-40cc-b069-9d87db4217ad\"}], \"below\": [{\"type\": \"LinearAxis\", \"id\": \"abb11a6d-3e68-424a-b5c9-b653e08bf0c2\"}], \"x_range\": {\"type\": \"DataRange1d\", \"id\": \"7af5780d-58cd-4b73-adfd-829e7a8cdcf3\"}, \"tags\": [], \"right\": [], \"left\": [{\"type\": \"LinearAxis\", \"id\": \"940ca555-5548-450f-988d-3ab8c3c6dcf7\"}], \"above\": [], \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\", \"y_range\": {\"type\": \"DataRange1d\", \"id\": \"b783326b-f811-48c7-9d9d-bce27248f23a\"}, \"plot_width\": 400, \"plot_height\": 400, \"extra_y_ranges\": {}, \"doc\": null, \"tool_events\": {\"type\": \"ToolEvents\", \"id\": \"dfe2ca23-259c-4f99-af69-003579c68f91\"}}, \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}, {\"type\": \"WheelZoomTool\", \"id\": \"a0f15b95-8f1b-498d-b8fa-4d017491e672\", \"attributes\": {\"id\": \"a0f15b95-8f1b-498d-b8fa-4d017491e672\", \"doc\": null, \"tags\": [], \"dimensions\": [\"width\", \"height\"], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"BoxZoomTool\", \"id\": \"eda4c2a9-1550-4ac0-963a-57bf73f7c3e3\", \"attributes\": {\"id\": \"eda4c2a9-1550-4ac0-963a-57bf73f7c3e3\", \"doc\": null, \"tags\": [], \"dimensions\": [\"width\", \"height\"], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"CustomJS\", \"id\": \"006fff8a-feb9-4006-a778-17764f875567\", \"attributes\": {\"id\": \"006fff8a-feb9-4006-a778-17764f875567\", \"code\": \"\\n var data = source.get('data');\\n var f = cb_obj.get('value')\\n x = data['x']\\n y = data['y']\\n for (i = 0; i < x.length; i++) {\\n y[i] = Math.pow(x[i], f)\\n }\\n source.trigger('change');\\n\", \"tags\": [], \"args\": {\"source\": {\"type\": \"ColumnDataSource\", \"id\": \"638ed839-20fb-439b-bad8-4009d852aa77\"}}, \"doc\": null}}, {\"type\": \"DataRange1d\", \"id\": \"b783326b-f811-48c7-9d9d-bce27248f23a\", \"attributes\": {\"id\": \"b783326b-f811-48c7-9d9d-bce27248f23a\", \"renderers\": [], \"callback\": null, \"tags\": [], \"names\": [], \"doc\": null}}, {\"type\": \"BasicTickFormatter\", \"id\": \"7daef8b2-6c5b-4920-87b0-85395949a0b9\", \"attributes\": {\"tags\": [], \"id\": \"7daef8b2-6c5b-4920-87b0-85395949a0b9\", \"doc\": null}}, {\"type\": \"BasicTickFormatter\", \"id\": \"a83afa06-2ddf-43aa-89f8-d475fecdf9c7\", \"attributes\": {\"tags\": [], \"id\": \"a83afa06-2ddf-43aa-89f8-d475fecdf9c7\", \"doc\": null}}, {\"type\": \"LinearAxis\", \"id\": \"940ca555-5548-450f-988d-3ab8c3c6dcf7\", \"attributes\": {\"id\": \"940ca555-5548-450f-988d-3ab8c3c6dcf7\", \"doc\": null, \"tags\": [], \"formatter\": {\"type\": \"BasicTickFormatter\", \"id\": \"a83afa06-2ddf-43aa-89f8-d475fecdf9c7\"}, \"ticker\": {\"type\": \"BasicTicker\", \"id\": \"be4d1167-fb61-47ec-a43f-c41a2648e6d3\"}, \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"ResizeTool\", \"id\": \"5d6bc1f1-701e-4598-a3ae-ab6879ba160f\", \"attributes\": {\"id\": \"5d6bc1f1-701e-4598-a3ae-ab6879ba160f\", \"doc\": null, \"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"PreviewSaveTool\", \"id\": \"298d73b3-8754-412a-ab95-d5fe5cb0d558\", \"attributes\": {\"id\": \"298d73b3-8754-412a-ab95-d5fe5cb0d558\", \"doc\": null, \"tags\": [], \"plot\": {\"type\": \"Plot\", \"subtype\": \"Figure\", \"id\": \"a45ad774-274e-4bbe-b6a7-820275c2d7ea\"}}}, {\"type\": \"ToolEvents\", \"id\": \"dfe2ca23-259c-4f99-af69-003579c68f91\", \"attributes\": {\"id\": \"dfe2ca23-259c-4f99-af69-003579c68f91\", \"doc\": null, \"tags\": [], \"geometries\": []}}, {\"type\": \"Slider\", \"id\": \"d90b501a-acd3-4fee-bf56-7fd9c8ec7067\", \"attributes\": {\"start\": 0.1, \"id\": \"d90b501a-acd3-4fee-bf56-7fd9c8ec7067\", \"end\": 4, \"doc\": null, \"step\": 0.1, \"callback\": {\"type\": \"CustomJS\", \"id\": \"006fff8a-feb9-4006-a778-17764f875567\"}, \"value\": 1, \"title\": \"power\", \"tags\": []}}];\n", " Bokeh.load_models(all_models);\n", " var plots = [{'elementid': '987c415f-753b-4087-8c57-1dbcc134baf0', 'modeltype': 'VBoxForm', 'modelid': '72f1a5c2-4a5f-4fb3-8213-f3d41e9c293f'}];\n", " for (idx in plots) {\n", " \tvar plot = plots[idx];\n", " \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n", " \tBokeh.logger.info('Realizing plot:')\n", " \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n", " \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n", " \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n", " \tvar view = new model.default_view({\n", " \t\tmodel: model,\n", " \t\tel: '#' + plot.elementid\n", " \t});\n", " \tBokeh.index[plot.modelid] = view;\n", " }\n", " });\n", " </script>\n", "<div class=\"plotdiv\" id=\"987c415f-753b-4087-8c57-1dbcc134baf0\"></div>\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.io import vform\n", "from bokeh.models import CustomJS, ColumnDataSource, Slider\n", "\n", "x = [x0.005 for x in range(0, 200)]\n", "y = x\n", "\n", "source = ColumnDataSource(data=dict(x=x, y=y))\n", "\n", "plot = figure(plot_width=400, plot_height=400)\n", "plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)\n", "\n", "callback = CustomJS(args=dict(source=source), code=\"\"\"\n", " var data = source.get('data');\n", " var f = cb_obj.get('value')\n", " x = data['x']\n", " y = data['y']\n", " for (i = 0; i < x.length; i++) {\n", " y[i] = Math.pow(x[i], f)\n", " }\n", " source.trigger('change');\n", "\"\"\")\n", "\n", "slider = Slider(start=0.1, end=4, value=1, step=.1, title=\"power\", callback=callback)\n", "\n", "layout = vform(slider, plot)\n", "\n", "show(layout)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calbacks for selections\n", "\n", "It's also possible to make JavaScript actions that execute whenever a user selection (e.g., box, point, lasso) changes. This is done by attaching the same kind of CustomJS object to whatever data source the selection is made on.\n", "\n", "The example below is a bit more sophisticaed, and demonstrates updating one glyphs data source in response to another glyph's selection: " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script type=\"text/javascript\">\n", " Bokeh.$(function() {\n", " var all_models = [{\"type\": \"BasicTicker\", \"id\": \"db7ef45c-d2c1-456b-ad2c-d9b46094b669\", \"attributes\": {\"id\": \"db7ef45c-d2c1-456b-ad2c-d9b46094b669\", \"num_minor_ticks\": 5, \"tags\": [], \"mantissas\": [2, 5, 10], \"doc\": null}}, {\"type\": \"Plot\", \"subtype\": \"Figure\", \"attributes\": {\"tools\": [{\"type\": \"LassoSelectTool\", \"id\": \"6904edaa-cc74-4ac0-912b-fea5af5c5d59\"}], \"extra_x_ranges\": {}, \"renderers\": [{\"type\": \"LinearAxis\", \"id\": \"40a9ef71-932f-4960-a7fe-e8bc05d7d44a\"}, {\"type\": \"Grid\", \"id\": \"7d0e7117-8f26-44af-a871-1d8eb698ec4e\"}, {\"type\": \"LinearAxis\", \"id\": \"a66055ca-c365-4e59-b851-d01131a50fdc\"}, {\"type\": \"Grid\", \"id\": \"363044df-b185-4fe0-bcb2-0659eda81f67\"}, {\"type\": \"GlyphRenderer\", \"id\": \"ba6fa92a-1c79-4680-9400-151adc2bd556\"}, {\"type\": \"GlyphRenderer\", \"id\": \"076c082a-0bed-4729-b9a5-2fb9c2137dd5\"}], \"right\": [], \"x_range\": {\"type\": \"DataRange1d\", \"id\": \"74effb81-cb83-4a60-9d23-6a6a4dd12010\"}, \"tags\": [], \"title\": \"Select Here\", \"plot_width\": 400, \"left\": [{\"type\": \"LinearAxis\", \"id\": \"a66055ca-c365-4e59-b851-d01131a50fdc\"}], \"above\": [], \"id\": \"34edb3ce-4963-4b0a-80f0-8322f9782872\", \"y_range\": {\"type\": \"DataRange1d\", \"id\": \"25a454f1-f6d8-4f2b-8169-4521237113c7\"}, \"below\": [{\"type\": \"LinearAxis\", \"id\": \"40a9ef71-932f-4960-a7fe-e8bc05d7d44a\"}], \"plot_height\": 400, \"extra_y_ranges\": {}, \"doc\": null, \"tool_events\": {\"type\": \"ToolEvents\", \"id\": 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\"34edb3ce-4963-4b0a-80f0-8322f9782872\"}}}];\n", " Bokeh.load_models(all_models);\n", " var plots = [{'elementid': '31fd489f-f4e5-4cb9-a61b-93ec606f17d6', 'modeltype': 'Plot', 'modelid': '34edb3ce-4963-4b0a-80f0-8322f9782872'}];\n", " for (idx in plots) {\n", " \tvar plot = plots[idx];\n", " \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n", " \tBokeh.logger.info('Realizing plot:')\n", " \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n", " \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n", " \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n", " \tvar view = new model.default_view({\n", " \t\tmodel: model,\n", " \t\tel: '#' + plot.elementid\n", " \t});\n", " \tBokeh.index[plot.modelid] = view;\n", " }\n", " });\n", " </script>\n", "<div class=\"plotdiv\" id=\"31fd489f-f4e5-4cb9-a61b-93ec606f17d6\"></div>\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from random import random\n", "\n", "x = [random() for x in range(500)]\n", "y = [random() for y in range(500)]\n", "color = [\"navy\"] len(x)\n", "\n", "s = ColumnDataSource(data=dict(x=x, y=y, color=color))\n", "p = figure(plot_width=400, plot_height=400, tools=\"lasso_select\", title=\"Select Here\")\n", "p.circle('x', 'y', color='color', size=8, source=s, alpha=0.4)\n", "\n", "s2 = ColumnDataSource(data=dict(ym=[0.5, 0.5]))\n", "p.line(x=[0,1], y='ym', color=\"orange\", line_width=5, alpha=0.6, source=s2)\n", "\n", "s.callback = CustomJS(args=dict(s2=s2), code=\"\"\"\n", " var inds = cb_obj.get('selected')['1d'].indices;\n", " var d = cb_obj.get('data');\n", " var ym = 0\n", " \n", " if (inds.length == 0) { return; }\n", " \n", " for (i = 0; i < d['color'].length; i++) {\n", " d['color'][i] = \"navy\"\n", " }\n", " for (i = 0; i < inds.length; i++) {\n", " d['color'][inds[i]] = \"firebrick\"\n", " ym += d['y'][inds[i]]\n", " }\n", " \n", " ym /= inds.length\n", " s2.get('data')['ym'] = [ym, ym]\n", " \n", " cb_obj.trigger('change');\n", " s2.trigger('change');\n", "\"\"\")\n", "\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# More\n", "For more interactions, see the User Guide - http://bokeh.pydata.org/en/latest/docs/user_guide/interaction.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
google/applied-machine-learning-intensive	content/06_other_models/01_k_means/colab.ipynb	1	22645	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "<a href=\"https://colab.research.google.com/github/google/applied-machine-learning-intensive/blob/master/content/06_other_models/01_k_means/colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "copyright" }, "source": [ "#### Copyright 2020 Google LLC." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "DIk2MioaatFI" }, "outputs": [], "source": [ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "daX5oL8aDZLn" }, "source": [ "# k-means\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "e5jEmmM1wQBC" }, "source": [ "k-means clustering is an unsupervised machine learning algorithm that can be used to group items into clusters.\n", "\n", "So far we have only worked with supervised algorithms. Supervised algorithms have training data with labels that identify the numeric value or class for each item. These algorithms use labeled data to build a model that can be used to make predictions.\n", "\n", "k-means clustering is different. The training data is not labeled. Unlabeled training data is fed into the model, which attempts to find relationships in the data and create clusters based on those relationships. Once these clusters are formed, predictions can be made about which cluster new data items belong to.\n", "\n", "The clusters can't easily be labeled in many cases. The clusters are \"emergent clusters\" and are created by the algorithm. They don't always map to groupings that you might expect." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ytHWSs1qG3e5" }, "source": [ "## Example: Groups of Mushrooms\n", "\n", "Let's start by looking at a real world use case involving mushrooms. The University of California Irvine has a [dataset containing various attributes of mushrooms](https://www.kaggle.com/uciml/mushroom-classification). One of those attributes is the edibility of the mushroom: Is it edible or is it poisonous? We want to see if we can find clusters of mushroom attributes that can be used to determine if a mushroom is edible or not." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1NUs6PhaH9J7" }, "source": [ "### Load the Data\n", "\n", "For this example we'll load the [mushroom classification](https://www.kaggle.com/uciml/mushroom-classification) data. The dataset attributes about over `8,000` different mushrooms.\n", "\n", "Upload your `kaggle.json` file and run the code block below." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "-IXUekcf9kLk" }, "outputs": [], "source": [ "! chmod 600 kaggle.json && (ls ~/.kaggle 2>/dev/null \|\| mkdir ~/.kaggle) && mv kaggle.json ~/.kaggle/ && echo 'Done'" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "90kxf9D89sh-" }, "source": [ "And then use the Kaggle API to download the dataset." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "yA7gddSDBP0B" }, "outputs": [], "source": [ "! kaggle datasets download uciml/mushroom-classification\n", "! ls" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XloYj7BNNzs9" }, "source": [ "Unzip the Data." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Cs3D-ZBXBXTy" }, "outputs": [], "source": [ "! unzip mushroom-classification.zip\n", "! ls" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oKcL398EN2Vt" }, "source": [ "And finally, load the training data into a `DataFrame`." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Bo2GFoXNBfFN" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv('mushrooms.csv')\n", "data.sample(n=10)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ShvO0AWGOAX3" }, "source": [ "### Exploratory Data Analysis" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xcvUECVCODNV" }, "source": [ "Let's take a closer look at the data that we'll be working with, starting with a simple describe." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "6Vlqf1a3GaZv" }, "outputs": [], "source": [ "data.describe(include='all')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YZE93JydGaEq" }, "source": [ "It doesn't look like any columns are missing data since we see counts of `8,124` for every column.\n", "\n", "It does look like all of the data is categorical. We'll need to convert it into numeric values for the model to work. Let's do it for every column except `class`. We aren't trying to predict class, but we do want to see if we can get pure clusters of one type of class. So we don't want it included in our training data. Also, it is the only feature that isn't observable without having dire consequences!" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "UxVI_cMiBvtx" }, "outputs": [], "source": [ "columns = [c for c in data.columns.values if c != 'class']\n", "id_to_value_mappings = {}\n", "value_to_id_mappings = {}\n", "\n", "for column in columns:\n", " i_to_v = sorted(data[column].unique())\n", " v_to_i = { v:i for i, v in enumerate(i_to_v)}\n", " \n", "\n", " numeric_column = column + '-id'\n", " data[numeric_column] = [v_to_i[v] for v in data[column]]\n", "\n", " value_to_id_mappings[column] = v_to_i\n", " id_to_value_mappings[numeric_column] = i_to_v\n", "\n", "numeric_columns = id_to_value_mappings.keys()\n", "data[numeric_columns].describe()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pxHbDQi5RehL" }, "source": [ "### Perform Clustering" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-ZTI-QWeRcXE" }, "source": [ "We now have numeric data that a model can handle. To run k-means clustering on the data, we simply load [k-means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) from scikit-learn and ask the model to find a specific number of clusters for us.\n", "\n", "Notice that we are scaling the data. The class IDs are integer values, and some columns have many more classes than others. Scaling helps make sure that columns with more classes don't have an undue influence on the model." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "R1kKMdvzC27A" }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.preprocessing import scale\n", "\n", "model = KMeans(n_clusters=10)\n", "model.fit(scale(data[numeric_columns]))\n", "\n", "print(model.inertia_)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PMOPqimXSlaQ" }, "source": [ "We asked scikit-learn to create 10 clusters for us, and then we printed out the `inertia_` for the resultant clusters. Inertia is the sum of the squared distances of samples to their closest cluster center. Typically, the smaller the inertia the better.\n", "\n", "But why did we choose `10` clusters? And is the inertia that we received reasonable?" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Asbd5pyUSp2G" }, "source": [ "### Find the Optimal Number of Clusters\n", "\n", "With just one run of the algorithm, it is difficult to tell how many clusters we should have and what an appropriate inertia value is. k-means is trying to discover things about your data that you do not know. Picking a number of clusters at random isn't the best way to use k-means.\n", "\n", "Instead, you should experiment with a few different cluster values and measure the inertia of each. As you increase the number of clusters, your inertia should decrease." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "1WlVhQkWC_iK" }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.preprocessing import scale\n", "import matplotlib.pyplot as plt\n", "\n", "clusters = list(range(5, 50, 5))\n", "inertias = []\n", "\n", "scaled_data = scale(data[numeric_columns])\n", "\n", "for c in clusters:\n", " model = KMeans(n_clusters=c)\n", " model = model.fit(scaled_data)\n", " inertias.append(model.inertia_)\n", "\n", "plt.plot(clusters, inertias)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UGrJ_QGkS3EF" }, "source": [ "The resulting graph should start high and to the left and curve down as the number of clusters grows. The initial slope is steep, but begins to level off. Your optimal number of clusters is somewhere in the [\"elbow\" of the graph](https://en.wikipedia.org/wiki/Elbow_method_(clustering) as the slope levels.\n", "\n", "Once you have this number, you need to then check to see if the number is reasonable for your use case. Say that the 'optimal' number of clusters for our mushroom identification is 15. Is that a reasonable number of clusters to deal with? If we have too many, we can overfit and make the model poor at generalizing. And what are the purposes of the clusters? If you are clustering mushrooms and want to find clusters that are definitely safe to eat, 15 or more clusters might be perfectly fine. If you are clustering customers for different advertising campaigns, 15 different campaigns might be more than your marketing department can handle.\n", "\n", "Clustering the data is often just the start of your journey. Once you have clusters, you'll need to look at each group and try to determine what makes them similar. What patterns did the clustering find? And will that clustering be useful to you?" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cVmJq7-ZTho3" }, "source": [ "### Examining Clusters" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2_IFLvUbTlDq" }, "source": [ "Let's say that `15` is a reasonable number of clusters. We can rebuild the model using that setting." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "xfLmrYcwGKII" }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.preprocessing import scale\n", "\n", "model = KMeans(n_clusters=15)\n", "model.fit(scale(data[numeric_columns]))\n", "\n", "print(model.inertia_)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AjHeNiyETwLZ" }, "source": [ "Now let's see if we have any 'pure' clusters. These are clusters with all-edible or all-poisonous mushrooms." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "_Q5DkPXBGs_I" }, "outputs": [], "source": [ "import numpy as np\n", "\n", "for cluster in sorted(np.unique(model.labels_)):\n", " num_edible = np.sum(data[model.labels_ == cluster]['class'] == 'e')\n", " total = np.sum(model.labels_ == cluster)\n", " print(cluster, num_edible / total)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "irNyQCzzT_el" }, "source": [ "In our model we had clusters `0`, `1`, `6`, and `10` be `100%` edible. Clusters `2`, `4`, `7`, and `12` were all poisonous. The remaining were a mix of the two.\n", "\n", "Knowing this, let's look at one of the all-edible clusters and see what attributes we could look for to have confidence that we have an edible mushroom." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "5WhvIM6CMRp0" }, "outputs": [], "source": [ "edible = data[model.labels_ == 1]\n", "\n", "for column in edible.columns:\n", " if column.endswith('-id'):\n", " continue\n", " print(column, edible[column].unique())\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-URbeexwVRtT" }, "source": [ "The mapping of the letter codes to more descriptive text can be found in the [dataset description](https://www.kaggle.com/uciml/mushroom-classification)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "t2ySDMeeP-6J" }, "source": [ "## Example: Classification of Digits\n", "\n", "Clustering for data exploration purposes can lead to interesting insights in to your data, but clustering can also be used for classification purposes.\n", "\n", "In the example below, we'll try to use k-means clustering to predict handwritten digits." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QePSEGmwQY2W" }, "source": [ "### Load the Data\n", "\n", "We'll load the digits dataset packaged with scikit-learn." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "C9pGH4rfQUgA" }, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "\n", "digits = load_digits()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5OTRl2plQgDi" }, "source": [ "### Scale the Data\n", "\n", "It is good practice to scale the data to ensure that outliers don't have too big of an impact on the clustering." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "t0aBmYGPQnri" }, "outputs": [], "source": [ "from sklearn.preprocessing import scale\n", "\n", "scaled_digits = scale(digits.data)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dwQ9Df4zQ_a_" }, "source": [ "### Fit a Model\n", "\n", "We can then create a k-means model with 10 clusters. (We know there are 10 digits from 0 through 9.)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "gZaOn2gRQzsu" }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "\n", "model = KMeans(n_clusters=10)\n", "model = model.fit(scaled_digits)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "M0bCgYN_SCw5" }, "source": [ "### Make Predictions\n", "\n", "We can then use the model to predict which category a data point belongs to.\n", "\n", "In the case below, we'll just use some of the data that we trained with for illustrative purposes. The prediction will provide a numeric value." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "mpLDPTMyRm7i" }, "outputs": [], "source": [ "cluster = model.predict([scaled_digits[0]])[0]\n", "\n", "cluster" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "R_yDe1QZSaBj" }, "source": [ "What is this value? Is it the predicted digit?\n", "\n", "No. This number is the cluster that the model thinks the digit belongs to. To determine the predicted digit, we'll need to see what other digits are in the cluster and choose the most popular one for our classification.\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "RzccT4gMSxi2" }, "outputs": [], "source": [ "import numpy as np\n", "\n", "labels = digits.target\n", "\n", "cluster_to_digit = [\n", " np.argmax(\n", " np.bincount(\n", " np.array(\n", " [labels[i] for i in range(len(model.labels_)) if model.labels_[i] == cluster]\n", " )\n", " )\n", " ) for cluster in range(10)\n", "]\n", "\n", "cluster_to_digit" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LtBucdm7j9vQ" }, "source": [ "Here we can see the digit that each cluster represents." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Y1aVtOwEVdQf" }, "source": [ "### Measure Model Quality\n", "\n", "If we do have labeled data, as is the case with our digits data, then we can measure the quality of our model using the [homogeneity score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html#sklearn.metrics.homogeneity_score_) and the [completeness score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score)." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "r85vjdc0_l6S" }, "outputs": [], "source": [ "from sklearn.metrics import homogeneity_score\n", "from sklearn.metrics import completeness_score\n", "\n", "homogeneity = homogeneity_score(labels, model.labels_)\n", "completeness = completeness_score(labels, model.labels_)\n", "homogeneity, completeness" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X5IZSU1OvPMN" }, "source": [ "# Exercises" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sCkXnAZHXEUL" }, "source": [ "## Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Pzwm9vnEkoAf" }, "source": [ "Load the [iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html), create a k-means model with three clusters, and then find the homogeneity and completeness scores for the model. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "abvbIkWfvSl3" }, "source": [ "### Student Solution" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "s3rLeWvIXhoo" }, "outputs": [], "source": [ "# Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qqSceBD3HDkU" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fjo4qeMNYu1T" }, "source": [ "## Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5VetIjZ1kqNk" }, "source": [ "Load the [iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html), and then create a k-means model with three clusters using only two features. (Try to find the best two features for clustering.) Create a plot of the two features.\n", "\n", "For each datapoint in the chart, use a [marker](https://matplotlib.org/api/markers_api.html) to encode the actual/correct species. For instance, use a triangle for Setosa, a square for Versicolour, and a circle for Virginica. Color each marker green if the predicted class matches the actual. Color each marker red if the classes don't match." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6ewsx_qWvXM8" }, "source": [ "### Student Solution" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Q-tgFdNHYuI5" }, "outputs": [], "source": [ "# Your code goes here" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "eiHWlzVxHge1" }, "source": [ "---" ] } ], "metadata": { "colab": { "collapsed_sections": [ "copyright", "exercise-1-key-1", "exercise-2-key-1" ], "include_colab_link": true, "name": "k-Means", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
shantnu/data_mine	sql3.ipynb	1	5688	{ "metadata": { "name": "", "signature": "sha256:dffb00b66d42e4e3b7f4be0521b406b2bdd4ebf44b3e5b5bb61c6fed22d83370" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sqlite3\n", "\n", "input_file = \"npidata_20050523-20150111.csv\"\n", "counter = 0\n", "db_list = []\n", "with open(input_file) as infile:\n", " for line in infile:\n", " #print line\n", " line = line.replace('\"', '')\n", " data = line.split(\",\")\n", " name = data[6] + \" \" + data[7] + \" \" + data[5]\n", " business_name = data[4]\n", " city = data[22]\n", " state = data[23]\n", " if counter > 0:\n", " db_list.append((name, business_name, city, state))\n", " counter += 1\n", " #if counter == 10:\n", " # break" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "len(db_list)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "4476420" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "db = \"./npi_data.db\"\n", "conn = sqlite3.connect(db)\n", "conn.text_factory = str\n", "c = conn.cursor()\n", "\n", "c.executemany(\"INSERT INTO npi_table VALUES (?,?,?, ?)\",db_list)\n", "conn.commit()\n", "\n", "conn.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "db = \"./npi_data.db\"\n", "conn = sqlite3.connect(db)\n", "conn.text_factory = str\n", "c = conn.cursor()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where State='TX' and lower(city)='houston'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where State='TX' and lower(city)!='houston'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where city like 'Z%'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where city like 'Z___'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where name like 'RAHUL%'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where name like 'RAHUL%' and state='MA'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[('RAHUL HARDAS RATHOD', '', 'BROOKLINE', 'MA'), ('RAHUL K SHAH', '', 'BROOKLINE', 'MA'), ('RAHUL J SAWANT', '', 'FRANKLIN', 'MA'), ('RAHUL C DEO', '', 'BROOKLINE', 'MA'), ('RAHUL CHATURVEDI', '', 'HYANNIS', 'MA'), ('RAHUL K PATEL', '', 'GREENFIELD', 'MA'), ('RAHUL KAKKAR', '', 'BOSTON', 'MA'), ('RAHULKUMAR SINGH', '', 'SPRINGFIELD', 'MA'), ('RAHUL ANIL SHETH', '', 'CHESTNUT HILL', 'MA'), ('RAHUL N. SOOD', '', 'WORCESTER', 'MA'), ('RAHUL CHANDRABHAN GUPTA', '', 'BRIGHTON', 'MA'), ('RAHUL SRINIVASA VEDULA', '', 'BOSTON', 'MA'), ('RAHUL MODI', '', 'BOSTON', 'MA')]\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "c.execute(\"SELECT * from npi_table where name like 'RAHUL%' and state='MA' and lower(city)='boston'\")\n", "result = c.fetchall()\n", "print result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[('RAHUL KAKKAR', '', 'BOSTON', 'MA'), ('RAHUL SRINIVASA VEDULA', '', 'BOSTON', 'MA'), ('RAHUL MODI', '', 'BOSTON', 'MA')]\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "conn.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
zacwentzell/BIA-660-C-Spring2017	In-class Lectures/April 13/Sample Code for Algorithms Covered in Class.ipynb	1	97475	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 1)\n" ] }, { "data": { "text/plain": [ "(100, 1)" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a sample data set for regression\n", "X = (np.random.uniform(0, 1, size=100)).reshape(100,1)\n", "y = 2 + 3*X + np.random.normal(0,0.25, size=100).reshape(100,1)\n", "\n", "print X.shape\n", "y.shape" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f04c901c310>]" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f04c91095d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X, y, \"k.\")" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.linear_model import SGDRegressor\n", "\n", "knn3 = KNeighborsRegressor(n_neighbors=3).fit(X, y)\n", "knn15 = KNeighborsRegressor(n_neighbors=15).fit(X, y)\n", "lr = LinearRegression().fit(X, y)\n", "sgd = SGDRegressor(loss='squared_loss', penalty='none', learning_rate='constant', eta0=0.1, n_iter=200).fit(X, y)" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f04c9251a50>" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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E8grtuLlYz3l+bEwMaa+/Qf3MDFosXozFw4MagwcDMPW35XiGzOaPI9toV7sdz3V+jnre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gS35PNft/N2ZAyr/GpSNR/Xjtmfdb5mWkohIiIiJUzB2At4dFc0ayH+H0jaffylhAV/MmXpTEL2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\ngrGIiIiICAD/D6rLPVBLL+dBAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f04c9393650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,9))\n", "plt.plot(X, y, \"k.\")\n", "knn_x = np.linspace(0, 1, num=80).reshape(80,1)\n", "line_x = np.array([[0], [1]])\n", "plt.plot(knn_x, knn3.predict(knn_x), label='3NN')\n", "plt.plot(knn_x, knn15.predict(knn_x), label='15NN')\n", "plt.plot(line_x, lr.predict(line_x), label='LR')\n", "plt.plot(line_x, sgd.predict(line_x), \"--\", label='SGD')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 2)\n" ] }, { "data": { "text/plain": [ "(100,)" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import make_classification\n", "\n", "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=2, \n", " n_clusters_per_class=2)\n", "\n", "print X.shape\n", "y.shape" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f04ca763ad0>" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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v+rh90AOsWbaWilIf/vIAQV+Q7z+YxqfPfJGyaxiNg7j6x2Y5VadhcO4bd/jo\n4YfhdFdN7labld0O6IG3IDV1gpxuJ+13aNdkkzqYxN6kaHgpBH4gfiVoEC1PzZSwlUtWsWTu33Gr\nGP3lAcY9OSEl1zAaEeeB4Nh3s+RuBVyxmTGW+EHYQVcfxx6H74bT7cDljXWZtO3ampEvXZzWsHNd\nSrpiRKQ/8Aixn+ozqnpPKto1UiyyNLZJQlzfegTCv6fkEkF/CIs18f1C9Z3ajewnYoHmj8V2zfJP\nAMmLFY2z71DlPFUFXY/V5uH290ey6Ncl/DFjIa07F7HbAT1SMrip4QVo+cuxMtqOfRDPKbFZXk1Q\n0oldRKzA48DhwN/ANBH5UFXnJtt2rtHQH2jZ/RCcCZZW4D0fcR+fvhF7W9cESR3ADvbdU3KJdtu3\nIa+5d+Osh41XcNo5cPA+KblGNgkFQ8yb8gciQo+9d0iqvG5jJSLg3B9x7p/w8ajvEyj9b2wGDVbU\nM5jOO19Ll11qKHddD+r/srLCagiIQPBHtOIlKHwv4SeHXJeKO/Z+wAJVXQggIm8AxwMmsW9Gw4vQ\ntYMqa6coRIqhdBQaXV6lbnlDEmtr1H0M+D5hU4lhqZxieFZKrmGxWLjulUu56Zi7iYQjhAJhXF4n\nrTsVMeiqY1NyjWwxY+LP3Dn4IaLR2OCi1W7l1neuYbcDemQ4svr7a85S3v+/T/j3r1XscdhuHH3e\noTWXHAA08AOsv45Nv28hqHgT1QDS7PaUxKQaQTffmhJiX0dXoeXPxK+haAKSLtsrIgOB/qp6buX3\npwN7qmqNnWa5WLZ3a6LFV4N/HPEzCNxI6ymIpGegRzUS27y44mWIloGjL1JwXWyxSQqtXLqa8c99\nwb9/raTXIbty4Cn7JCz9mqvWrVzPGduNwF9R9ZOLK8/F60ueJK95+kvKJmvKuBncOeRBQoEw0UgU\np9tBQWE+T8y4r8aKhtE1QyH0Y4JHnMg2P6Skq0TDf6JrTgatiH/Q2hlLUc1jO8v+/JeFvyymXdc2\nbLdb459m2+jK9orIcGA4QMeOqfsIljVCs0g8LSyC+icj7vQU5I9NMbwA8i5o0Ots06GQM0ad0qDX\naMy+euO7jXfqVajyzdgpDDg3u0o8RyIR7j/nf1U2bA74gqxbsZ437n2f80fXUDI3siTxcbFCdC2k\nog9cvKAJNuMGkMTth0Nh7h76KFM+mo7NYSMSjrLdbp24+9MbtvgJJFukYlbMP0CHzb5vX3msClUd\no6p9VLWN7ud8AAAgAElEQVRPUVFRCi6bZaw1vZkFYf2VRNedl7FSp0bqla4rIxiI33MzFAxTurYs\nAxElZ9mCf6tsoL1BOBjm+/en1fxE+67UWN3T2iYlsYm1TWxDbqqPX7gR75kJn/PmfR8wddwMgv4Q\nFSU+AhUBFvy0kIcuGJOSmDItFYl9GtBNRLpIbG+zIcCHKWg3p8T60avvC7pBAAJT0bKn6tSmRsvR\nyJr61Uk3GtQeh+6KyxNfRMtmt7HHYdlXC8dT4CESTrwQydu85vnnkncp8b/3bvCOSOlWiNL8UbB1\nqawcmgc4wHMKuI5LeP5HT0yIm6UVCob57r0fE74hZ5ukE7uqhoGLgc+AecBbqjon2XZzjTj6QrP7\nQAprOMMPvjdr1ZZGS4muuwRd2Q9ddSC6+lA08H3qgjWStvO+O9L7iN2rbHDs8jrZ98R+dNtjuwxG\nVj+t2rage9+uWG1VU4bL6+Sky46u8Xli7460er1yrns+WLeDgtuw5J2X0vjEug3S6mOk5ctIs/uQ\nos+xFNxY44wzf3n8qlgAjUYJB8MpjS0TzJ6naRaNrEdX7YWQoE9QmmFpvYWPtRvaWHNaZZ/95ncW\nbqTwnZQPghr1F4lE+PqtH5jw4ldYLMKRZx3C/ifvicWSnesC1/67juuOvJPlC1dgsVoJBUIce8Hh\nXPDgsKwrsnXnkIf4duwPRKstpOu8Swee/uXBDEW1dbUdPDWJPc1++/EP7BWD6LJj1RF8xYa4jsXS\n/N4tPl/DC9HVJ1B1aheAFdwnY0nzXpNG06KqLJi5iNX/rGWHPl2TqqMeCobQqOLYQv2Y+lq3ophJ\nr37L2uXr6HnQzvTp37NKnaIVi1dxUd+R+Mv8BP0hbA4bNoeN+ybewk57dkt5PKliEnsjpKqc1vki\nmjVfwuixf2K1K06X4q+wgOTh7vgJYq15ZyEADXyLFl8OWhr/oL0fllavNFD0hpEaxavW8+B5T/Lj\npzPRqNK97/Zc9eyFdNqpfUra//nrOdx0zN1EI1GC/hDuPBfb9+rCPRNurjLldv3qEj4eM5E538+n\nU48OHD+iP607Ne6JHSaxN0J/zVnKJXtdj788QIuiEEedtoaO3QLMneZh4e+9eGjyQ1ttQyMr0VWH\nEF8q1QHec7HkX94gsSeMpfJ3J9s+hhuZE41GOXeXK1m24F8i4Vh3pAh4m3t58Y//o6BlflLtRyIR\nhrQ/n+IVVUsAOz0Ozrn7NE68ZEBS7WdabRN7dnb2ZanN89+6VXZee7gN94zoxIcvFBHw127xjli3\nAfdJwOYLmiwgHsRzekrjrUnpujLuOeP/ONpzKv0dQ7i+f6zf1TC25uev5rD6nzUbkzqAKoQCISa+\n9HXS7S+avYRAefy0zEBFkEkvf5N0+9nCJPY06rhTewoK4+9InB4n/c+u3YIV9X8GwenE9ph0xGYa\nuI5FCt9DrK1SG3Ci66ty9SG38vVb329cgfjTpF+4ZK/rKS9JsPIvS5WsKaVkbYLuriw194f5XH/U\nnZzW+UJuOeFeFsxMXZnmuli24F+ikfhegkBFkMVz/066favNSk2dELlYp6cmJrGnkYgwauzVeAs8\nuLxOLFYLLq+TXfffiQHnbT2xRyveRouvgcgCYl0xQdAQ4hmCWLdt8PgBfvl6Lsv/XFFlSlg0qvgr\ngkx6JfvviJb89g8X9RnJ4G2HM7jdcC7Z63r+WbA802ElZdr4mVx7+O1M/+xnVi5ZzZSPpnP5/jcx\n94f0bxreZbdOJOq5c3mddO/TNen2O+/cgWZF8TdPLq+TAecdlnT72cIk9jTboXdXXlv6JCMeOZth\ntw/hrk9u5K5PbtjqZruqUSi9n/jZMH60dHSDxVvd0vnLiEbiF6oEKgIs+mVx2uJoCL4yH1fsfxML\nZi4iHAwTDoaZP/1PLt/vZoL+7F0V/Phlz1cpBaAau0N+8qoX0x7LTnt2Y/teXXC4NnU9Wm0W8pp7\nOeS0xNUh60JEuO29a8lr4cWd78LutOP0OOg3YA8OP+OApNvPFmZrvC1QDUHgCwgvBNt24DwEkeQL\nWXny3fQ/+5A6BlMCWsNS9BTVUq+NTj3aIwnqrbu8Trru3jltcTSEr9+eQjAQrrKSV6NKoCLA5Hen\ncsipySeedAuHwixb8G/Cx9LRHaOVtXHeeegjStaUsefRezDypUv44PHxTHjxK8LBMHsf15fz7h2K\n21vTyuy66bp7Z974+ym+/2A661YUs9sBPdi+V5eUtJ0tTGKvgUbWoGtPiRUqUh+IGywtoeVbaenL\njiN5lZtkJFjubG2XtjB22W9HOnRvx6LZSzZ2x1gsgsvr4tCh2X1HtHLxKvxl8SsSA74gKxavzkBE\nybParLjzXFSU+uIeK6ihImMqvTDqTd59aNzG+vwf/u8zvnrze56e/UDNhcNSwOl2cvCQ+K35mgrT\nFVMDLbkDIstBy4Fo7L+R5WjJbRmJR8QGnmFUnQ0D4K6sx5GuOITRn4/i8DMOxOV1YnPY2POY3jz+\n49148rN7j8luvbfDnRd/1+h0O9ihT/aVAYDYz+vEywbg9FRdBOTyOBky8oQGvXbJmlLeHv1hlU1X\nwsEwZevK+PB/nzXotZs6c8dek8AkoHrNiDAEPs9ENABI3iUoAhXPgwZjM2Lyr0FcR6Q1Dm+BhyvH\nXMCVYxq29G+69RvQi7ZdW7P0t2WEKgtBWe1Witq3ouchu2Q4uvo7fdQgKkp8fDxmIlablWhUOemK\nozl+RP8Gve6CmYtwuOwb/19uEPSHmDHhZ4beNLBBr9+UmcReo8ZXMVHEguRfiuZdFNtUQPJie04a\nKWG1Wnnomzt4+fa3+fDx8QT9IUSElUtXc0Gva7hv0ihabNMs02HWmdVq5aKHz+KsO4ew+p+1FHUo\nTFh5MtVatWtBOBRfUEss0uhXeGY7kxVq4jyU+Pc9W+XxzBKxIZYCk9QbgCffTYvWzRFLbE5eOBjG\nXx5g6fxl3HfG/2U4uuS489x06L5tWpI6QKceHejUowNWe9X54w6XnZMur7kipJE8kxlqIAW3gLV1\nbHcWiP3X2hopuDmzgRkN7qMnPqsyPRAgEoow66s5lK8vz1BU2em/H1/Pzvt0x+60485zkdfCyzXP\njWCH3snPWTdqZrpiaiDWQij8LNanHv4TbF3BeWhKNwcwGqfqSX0DkVj/sDf7emMypnlRMx748jZW\nL1tLeXE57Xdo16RWgGaKSexbIOIA11GZDsNIs32O78P4578kEqpaM791pyKaZ2Efe2NQ2K4lhe1a\nZjqMJsN0xRhGNWfeNpgW2zTbOEXQ7rDhynNxzfMjTCVLIyuYO3bDqKZF6+Y8M+chJrz4FbO/mUeH\n7u04+vzD2aZDTdsaGkbjYuqxG0aOmfrxDF667W1WLF5Ftz2246w7h5jByhxR23rs5o7dMHLIhJe+\n4tGLnt44ADz9s1nM/nYeD3x1W0qqJxrZwSR2IydEwhG+fut7Pn9tMk63g6POPZQ+R+zepPrEo9Eo\nY655OW5WT6AiwNWH3Mpex/Rm8LXHs33PplUQqykyid3IetFolJuPu4fZ387bWJdk2viZHHvhkQy/\nLz27SjUGJWtKqSiJL/YF4C/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jkw/LqA9VZdaXv8aVJNCoMnPSbHrstQNzvp9f5TER2P2g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\nMDMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f04c9459f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X[:, 0], X[:, 1], c=y)" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import AdaBoostClassifier\n", "\n", "from sklearn.metrics import accuracy_score\n", "\n", "knn = KNeighborsClassifier(n_neighbors=5).fit(X, y)\n", "lrc = LogisticRegression().fit(X, y)\n", "svm = SVC().fit(X, y)\n", "net = MLPClassifier(hidden_layer_sizes=(15, 10), activation='logistic', learning_rate_init=0.1).fit(X, y)\n", "rf = RandomForestClassifier(n_estimators=20).fit(X, y)\n", "ab = AdaBoostClassifier(n_estimators=20).fit(X, y)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNN Training Accuracy: 0.9\n", "Logistic Regression Training Accuracy: 0.89\n", "SVM Training Accuracy: 0.9\n", "Neural Network Training Accuracy: 0.91\n", "Random Forest Training Accuracy: 1.0\n", "AdaBoost Training Accuracy: 0.97\n" ] } ], "source": [ "print \"KNN Training Accuracy: {}\".format(accuracy_score(y, knn.predict(X)))\n", "print \"Logistic Regression Training Accuracy: {}\".format(accuracy_score(y, lrc.predict(X)))\n", "print \"SVM Training Accuracy: {}\".format(accuracy_score(y, svm.predict(X)))\n", "print \"Neural Network Training Accuracy: {}\".format(accuracy_score(y, net.predict(X)))\n", "print \"Random Forest Training Accuracy: {}\".format(accuracy_score(y, rf.predict(X)))\n", "print \"AdaBoost Training Accuracy: {}\".format(accuracy_score(y, ab.predict(X)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
BrownDwarf/ApJdataFrames	notebooks/Patten2006.ipynb	1	65141	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "`ApJdataFrames` 007: Patten2006\n", "---\n", "`Title`: Spitzer IRAC Photometry of M, L, and T Dwarfs \n", "`Authors`: Brian M Patten, John R Stauffer, Adam S Burrows, Massimo Marengo, Joseph L Hora, Kevin L Luhman, Sarah M Sonnett, Todd J Henry, Deepak Raghavan, S Thomas Megeath, James Liebert, and Giovanni G Fazio \n", "\n", "Data is from this paper: \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tables define the value and error as a string: \n", "`val (err)` \n", "which is a pain in the ass because now I have to parse the strings, which always takes much longer than it should because data wrangling is hard sometimes.\n", "\n", "I define a function that takes a column name and a data frame and strips the output." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def strip_parentheses(col, df):\n", " '''\n", " splits single column strings of \"value (error)\" into two columns of value and error\n", " \n", " input:\n", " -string name of column to split in two\n", " -dataframe to apply to\n", " \n", " returns dataframe\n", " '''\n", " \n", " out1 = df[col].str.replace(\")\",\"\").str.split(pat=\"(\")\n", " df_out = out1.apply(pd.Series)\n", " \n", " # Split the string on the whitespace \n", " base, sufx = col.split(\" \")\n", " df[base] = df_out[0].copy()\n", " df[base+\"_e\"] = df_out[1].copy()\n", " del df[col]\n", " \n", " return df\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table 1 - Basic data on sources" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names = [\"Name\",\"R.A. (J2000.0)\",\"Decl. (J2000.0)\",\"Spectral Type\",\"SpectralType Ref.\",\"Parallax (error)(arcsec)\",\n", " \"Parallax Ref.\",\"J (error)\",\"H (error)\",\"Ks (error)\",\"JHKRef.\",\"PhotSys\"]\n", "\n", "tbl1 = pd.read_csv(\"http://iopscience.iop.org/0004-637X/651/1/502/fulltext/64991.tb1.txt\", \n", " sep='\\t', names=names, na_values='\\ldots')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallax (error)(arcsec)\n", "J (error)\n", "H (error)\n", "Ks (error)\n" ] } ], "source": [ "cols_to_fix = [col for col in tbl1.columns.values if \"(error)\" in col]\n", "for col in cols_to_fix:\n", " print col\n", " tbl1 = strip_parentheses(col, tbl1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>R.A. (J2000.0)</th>\n", " <th>Decl. (J2000.0)</th>\n", " <th>Spectral Type</th>\n", " <th>SpectralType Ref.</th>\n", " <th>Parallax Ref.</th>\n", " <th>JHKRef.</th>\n", " <th>PhotSys</th>\n", " <th>Parallax</th>\n", " <th>Parallax_e</th>\n", " <th>J</th>\n", " <th>J_e</th>\n", " <th>H</th>\n", " <th>H_e</th>\n", " <th>Ks</th>\n", " <th>Ks_e</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> GJ 1001A</td>\n", " <td> 00 04 36.4</td>\n", " <td> -40 44 03</td>\n", " <td> M3.5</td>\n", " <td> 1</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 8.60 </td>\n", " <td> 0.01</td>\n", " <td> 8.04 </td>\n", " <td> 0.03</td>\n", " <td> 7.74 </td>\n", " <td> 0.04</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> GJ 1093</td>\n", " <td> 06 59 28.9</td>\n", " <td> +19 20 53</td>\n", " <td> M5.0</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.12880 </td>\n", " <td> 0.00350</td>\n", " <td> 9.16 </td>\n", " <td> 0.02</td>\n", " <td> 8.55 </td>\n", " <td> 0.02</td>\n", " <td> 8.23 </td>\n", " <td> 0.02</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> GJ 1156</td>\n", " <td> 12 18 59.5</td>\n", " <td> +11 07 33</td>\n", " <td> M5.0</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.15290 </td>\n", " <td> 0.00300</td>\n", " <td> 8.53 </td>\n", " <td> 0.03</td>\n", " <td> 7.88 </td>\n", " <td> 0.03</td>\n", " <td> 7.57 </td>\n", " <td> 0.03</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> GJ 1002</td>\n", " <td> 00 06 43.4</td>\n", " <td> -07 32 19</td>\n", " <td> M5.5</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.21300 </td>\n", " <td> 0.00360</td>\n", " <td> 8.32 </td>\n", " <td> 0.02</td>\n", " <td> 7.79 </td>\n", " <td> 0.03</td>\n", " <td> 7.44 </td>\n", " <td> 0.02</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> LHS 288</td>\n", " <td> 10 44 21.3</td>\n", " <td> -61 12 35</td>\n", " <td> M5.5</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.22250 </td>\n", " <td> 0.01130</td>\n", " <td> 8.49 </td>\n", " <td> 0.01</td>\n", " <td> 8.05 </td>\n", " <td> 0.04</td>\n", " <td> 7.73 </td>\n", " <td> 0.02</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name R.A. (J2000.0) Decl. (J2000.0) Spectral Type SpectralType Ref. \\\n", "0 GJ 1001A 00 04 36.4 -40 44 03 M3.5 1 \n", "1 GJ 1093 06 59 28.9 +19 20 53 M5.0 2 \n", "2 GJ 1156 12 18 59.5 +11 07 33 M5.0 2 \n", "3 GJ 1002 00 06 43.4 -07 32 19 M5.5 2 \n", "4 LHS 288 10 44 21.3 -61 12 35 M5.5 3 \n", "\n", " Parallax Ref. JHKRef. PhotSys Parallax Parallax_e J J_e H \\\n", "0 NaN 1 2MA NaN NaN 8.60 0.01 8.04 \n", "1 1 1 2MA 0.12880 0.00350 9.16 0.02 8.55 \n", "2 1 1 2MA 0.15290 0.00300 8.53 0.03 7.88 \n", "3 1 1 2MA 0.21300 0.00360 8.32 0.02 7.79 \n", "4 1 1 2MA 0.22250 0.01130 8.49 0.01 8.05 \n", "\n", " H_e Ks Ks_e \n", "0 0.03 7.74 0.04 \n", "1 0.02 8.23 0.02 \n", "2 0.03 7.57 0.03 \n", "3 0.03 7.44 0.02 \n", "4 0.04 7.73 0.02 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl1.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table 3- IRAC photometry" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names = [\"Name\",\"Spectral Type\",\"[3.6] (error)\",\"n1\",\"[4.5] (error)\",\"n2\",\n", " \"[5.8] (error)\",\"n3\",\"[8.0] (error)\",\"n4\",\"[3.6]-[4.5]\",\"[4.5]-[5.8]\",\"[5.8]-[8.0]\",\"Notes\"]\n", "\n", "tbl3 = pd.read_csv(\"http://iopscience.iop.org/0004-637X/651/1/502/fulltext/64991.tb3.txt\", \n", " sep='\\t', names=names, na_values='\\ldots')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.6] (error)\n", "[4.5] (error)\n", "[5.8] (error)\n", "[8.0] (error)\n" ] } ], "source": [ "cols_to_fix = [col for col in tbl3.columns.values if \"(error)\" in col]\n", "cols_to_fix\n", "for col in cols_to_fix:\n", " print col\n", " tbl3 = strip_parentheses(col, tbl3)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Spectral Type</th>\n", " <th>n1</th>\n", " <th>n2</th>\n", " <th>n3</th>\n", " <th>n4</th>\n", " <th>[3.6]-[4.5]</th>\n", " <th>[4.5]-[5.8]</th>\n", " <th>[5.8]-[8.0]</th>\n", " <th>Notes</th>\n", " <th>[3.6]</th>\n", " <th>[3.6]_e</th>\n", " <th>[4.5]</th>\n", " <th>[4.5]_e</th>\n", " <th>[5.8]</th>\n", " <th>[5.8]_e</th>\n", " <th>[8.0]</th>\n", " <th>[8.0]_e</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> GJ 1001A</td>\n", " <td> M3.5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.05</td>\n", " <td> 0.04</td>\n", " <td> 0.01</td>\n", " <td> 1</td>\n", " <td> 7.45 </td>\n", " <td> 0.03</td>\n", " <td> 7.40 </td>\n", " <td> 0.03</td>\n", " <td> 7.37 </td>\n", " <td> 0.01</td>\n", " <td> 7.36 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> GJ 1093</td>\n", " <td> M5.0</td>\n", " <td> 4</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.02</td>\n", " <td> 0.09</td>\n", " <td> 0.02</td>\n", " <td> 1</td>\n", " <td> 7.86 </td>\n", " <td> 0.03</td>\n", " <td> 7.84 </td>\n", " <td> 0.02</td>\n", " <td> 7.76 </td>\n", " <td> 0.01</td>\n", " <td> 7.74 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> GJ 1156</td>\n", " <td> M5.0</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.08</td>\n", " <td> 0.06</td>\n", " <td> 0.02</td>\n", " <td> 1</td>\n", " <td> 7.24 </td>\n", " <td> 0.03</td>\n", " <td> 7.16 </td>\n", " <td> 0.02</td>\n", " <td> 7.10 </td>\n", " <td> 0.01</td>\n", " <td> 7.08 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> GJ 1002</td>\n", " <td> M5.5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.05</td>\n", " <td> 0.04</td>\n", " <td> 0.02</td>\n", " <td> 2</td>\n", " <td> 7.07 </td>\n", " <td> 0.01</td>\n", " <td> 7.01 </td>\n", " <td> 0.01</td>\n", " <td> 6.97 </td>\n", " <td> 0.02</td>\n", " <td> 6.95 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> LHS 288</td>\n", " <td> M5.5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.06</td>\n", " <td>-0.03</td>\n", " <td> 0.07</td>\n", " <td> 2</td>\n", " <td> 7.31 </td>\n", " <td> 0.03</td>\n", " <td> 7.25 </td>\n", " <td> 0.04</td>\n", " <td> 7.27 </td>\n", " <td> 0.01</td>\n", " <td> 7.20 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Spectral Type n1 n2 n3 n4 [3.6]-[4.5] [4.5]-[5.8] \\\n", "0 GJ 1001A M3.5 5 5 5 5 0.05 0.04 \n", "1 GJ 1093 M5.0 4 5 5 5 0.02 0.09 \n", "2 GJ 1156 M5.0 5 5 5 5 0.08 0.06 \n", "3 GJ 1002 M5.5 5 5 5 5 0.05 0.04 \n", "4 LHS 288 M5.5 5 5 5 5 0.06 -0.03 \n", "\n", " [5.8]-[8.0] Notes [3.6] [3.6]_e [4.5] [4.5]_e [5.8] [5.8]_e [8.0] \\\n", "0 0.01 1 7.45 0.03 7.40 0.03 7.37 0.01 7.36 \n", "1 0.02 1 7.86 0.03 7.84 0.02 7.76 0.01 7.74 \n", "2 0.02 1 7.24 0.03 7.16 0.02 7.10 0.01 7.08 \n", "3 0.02 2 7.07 0.01 7.01 0.01 6.97 0.02 6.95 \n", "4 0.07 2 7.31 0.03 7.25 0.04 7.27 0.01 7.20 \n", "\n", " [8.0]_e \n", "0 0.01 \n", "1 0.01 \n", "2 0.01 \n", "3 0.01 \n", "4 0.01 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl3.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.options.display.max_columns = 50" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "del tbl3[\"Spectral Type\"] #This is repeated" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>R.A. (J2000.0)</th>\n", " <th>Decl. (J2000.0)</th>\n", " <th>Spectral Type</th>\n", " <th>SpectralType Ref.</th>\n", " <th>Parallax Ref.</th>\n", " <th>JHKRef.</th>\n", " <th>PhotSys</th>\n", " <th>Parallax</th>\n", " <th>Parallax_e</th>\n", " <th>J</th>\n", " <th>J_e</th>\n", " <th>H</th>\n", " <th>H_e</th>\n", " <th>Ks</th>\n", " <th>Ks_e</th>\n", " <th>n1</th>\n", " <th>n2</th>\n", " <th>n3</th>\n", " <th>n4</th>\n", " <th>[3.6]-[4.5]</th>\n", " <th>[4.5]-[5.8]</th>\n", " <th>[5.8]-[8.0]</th>\n", " <th>Notes</th>\n", " <th>[3.6]</th>\n", " <th>[3.6]_e</th>\n", " <th>[4.5]</th>\n", " <th>[4.5]_e</th>\n", " <th>[5.8]</th>\n", " <th>[5.8]_e</th>\n", " <th>[8.0]</th>\n", " <th>[8.0]_e</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> GJ 1001A</td>\n", " <td> 00 04 36.4</td>\n", " <td> -40 44 03</td>\n", " <td> M3.5</td>\n", " <td> 1</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 8.60 </td>\n", " <td> 0.01</td>\n", " <td> 8.04 </td>\n", " <td> 0.03</td>\n", " <td> 7.74 </td>\n", " <td> 0.04</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.05</td>\n", " <td> 0.04</td>\n", " <td> 0.01</td>\n", " <td> 1</td>\n", " <td> 7.45 </td>\n", " <td> 0.03</td>\n", " <td> 7.40 </td>\n", " <td> 0.03</td>\n", " <td> 7.37 </td>\n", " <td> 0.01</td>\n", " <td> 7.36 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> GJ 1093</td>\n", " <td> 06 59 28.9</td>\n", " <td> +19 20 53</td>\n", " <td> M5.0</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.12880 </td>\n", " <td> 0.00350</td>\n", " <td> 9.16 </td>\n", " <td> 0.02</td>\n", " <td> 8.55 </td>\n", " <td> 0.02</td>\n", " <td> 8.23 </td>\n", " <td> 0.02</td>\n", " <td> 4</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.02</td>\n", " <td> 0.09</td>\n", " <td> 0.02</td>\n", " <td> 1</td>\n", " <td> 7.86 </td>\n", " <td> 0.03</td>\n", " <td> 7.84 </td>\n", " <td> 0.02</td>\n", " <td> 7.76 </td>\n", " <td> 0.01</td>\n", " <td> 7.74 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> GJ 1156</td>\n", " <td> 12 18 59.5</td>\n", " <td> +11 07 33</td>\n", " <td> M5.0</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.15290 </td>\n", " <td> 0.00300</td>\n", " <td> 8.53 </td>\n", " <td> 0.03</td>\n", " <td> 7.88 </td>\n", " <td> 0.03</td>\n", " <td> 7.57 </td>\n", " <td> 0.03</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.08</td>\n", " <td> 0.06</td>\n", " <td> 0.02</td>\n", " <td> 1</td>\n", " <td> 7.24 </td>\n", " <td> 0.03</td>\n", " <td> 7.16 </td>\n", " <td> 0.02</td>\n", " <td> 7.10 </td>\n", " <td> 0.01</td>\n", " <td> 7.08 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> GJ 1002</td>\n", " <td> 00 06 43.4</td>\n", " <td> -07 32 19</td>\n", " <td> M5.5</td>\n", " <td> 2</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.21300 </td>\n", " <td> 0.00360</td>\n", " <td> 8.32 </td>\n", " <td> 0.02</td>\n", " <td> 7.79 </td>\n", " <td> 0.03</td>\n", " <td> 7.44 </td>\n", " <td> 0.02</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.05</td>\n", " <td> 0.04</td>\n", " <td> 0.02</td>\n", " <td> 2</td>\n", " <td> 7.07 </td>\n", " <td> 0.01</td>\n", " <td> 7.01 </td>\n", " <td> 0.01</td>\n", " <td> 6.97 </td>\n", " <td> 0.02</td>\n", " <td> 6.95 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> LHS 288</td>\n", " <td> 10 44 21.3</td>\n", " <td> -61 12 35</td>\n", " <td> M5.5</td>\n", " <td> 3</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2MA</td>\n", " <td> 0.22250 </td>\n", " <td> 0.01130</td>\n", " <td> 8.49 </td>\n", " <td> 0.01</td>\n", " <td> 8.05 </td>\n", " <td> 0.04</td>\n", " <td> 7.73 </td>\n", " <td> 0.02</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> 0.06</td>\n", " <td>-0.03</td>\n", " <td> 0.07</td>\n", " <td> 2</td>\n", " <td> 7.31 </td>\n", " <td> 0.03</td>\n", " <td> 7.25 </td>\n", " <td> 0.04</td>\n", " <td> 7.27 </td>\n", " <td> 0.01</td>\n", " <td> 7.20 </td>\n", " <td> 0.01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name R.A. (J2000.0) Decl. (J2000.0) Spectral Type SpectralType Ref. \\\n", "0 GJ 1001A 00 04 36.4 -40 44 03 M3.5 1 \n", "1 GJ 1093 06 59 28.9 +19 20 53 M5.0 2 \n", "2 GJ 1156 12 18 59.5 +11 07 33 M5.0 2 \n", "3 GJ 1002 00 06 43.4 -07 32 19 M5.5 2 \n", "4 LHS 288 10 44 21.3 -61 12 35 M5.5 3 \n", "\n", " Parallax Ref. JHKRef. PhotSys Parallax Parallax_e J J_e H \\\n", "0 NaN 1 2MA NaN NaN 8.60 0.01 8.04 \n", "1 1 1 2MA 0.12880 0.00350 9.16 0.02 8.55 \n", "2 1 1 2MA 0.15290 0.00300 8.53 0.03 7.88 \n", "3 1 1 2MA 0.21300 0.00360 8.32 0.02 7.79 \n", "4 1 1 2MA 0.22250 0.01130 8.49 0.01 8.05 \n", "\n", " H_e Ks Ks_e n1 n2 n3 n4 [3.6]-[4.5] [4.5]-[5.8] [5.8]-[8.0] \\\n", "0 0.03 7.74 0.04 5 5 5 5 0.05 0.04 0.01 \n", "1 0.02 8.23 0.02 4 5 5 5 0.02 0.09 0.02 \n", "2 0.03 7.57 0.03 5 5 5 5 0.08 0.06 0.02 \n", "3 0.03 7.44 0.02 5 5 5 5 0.05 0.04 0.02 \n", "4 0.04 7.73 0.02 5 5 5 5 0.06 -0.03 0.07 \n", "\n", " Notes [3.6] [3.6]_e [4.5] [4.5]_e [5.8] [5.8]_e [8.0] [8.0]_e \n", "0 1 7.45 0.03 7.40 0.03 7.37 0.01 7.36 0.01 \n", "1 1 7.86 0.03 7.84 0.02 7.76 0.01 7.74 0.01 \n", "2 1 7.24 0.03 7.16 0.02 7.10 0.01 7.08 0.01 \n", "3 2 7.07 0.01 7.01 0.01 6.97 0.02 6.95 0.01 \n", "4 2 7.31 0.03 7.25 0.04 7.27 0.01 7.20 0.01 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patten2006 = pd.merge(tbl1, tbl3, how=\"outer\", on=\"Name\")\n", "patten2006.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert spectral type to number" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import gully_custom" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "patten2006[\"SpT_num\"], _1, _2, _3= gully_custom.specTypePlus(patten2006[\"Spectral Type\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a plot of mid-IR colors as a function of spectral type." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sns.set_context(\"notebook\", font_scale=1.5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1096e3650>" ] }, 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"<matplotlib.figure.Figure at 0x1096ec250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for color in [\"[3.6]-[4.5]\", \"[4.5]-[5.8]\", \"[5.8]-[8.0]\"]:\n", " plt.plot(patten2006[\"SpT_num\"], patten2006[color], '.', label=color)\n", " \n", "plt.xlabel(r'Spectral Type (M0 = 0)')\n", "plt.ylabel(r'$[3.6]-[4.5]$')\n", "plt.title(\"IRAC colors as a function of spectral type\")\n", "plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the cleaned data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "patten2006.to_csv('../data/Patten2006/patten2006.csv', index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
noammor/coursera-machinelearning-python	ex7/ml-ex7-pca.ipynb	2	250645	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Exercise 7 \| Principle Component Analysis " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.io\n", "import itertools" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Load Example Dataset \n", " We start this exercise by using a small dataset that is easily to\n", " visualize.\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ex7data1 = scipy.io.loadmat('ex7data1.mat')\n", "X = ex7data1['X']" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb9272be470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_data(X, ax):\n", " ax.plot(X[:,0], X[:,1], 'bo')\n", " \n", "fig, ax = plt.subplots()\n", "plot_data(X, ax)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def normalize_features(X):\n", " mu = np.mean(X, 0)\n", " X_norm = X - mu\n", " sigma = np.std(X_norm, 0)\n", " X_norm = X_norm / sigma\n", " return X_norm, mu, sigma" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_norm, mu, sigma = normalize_features(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Principal Component Analysis \n", " You should now implement PCA, a dimension reduction technique. You\n", " should complete the following code.\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def pca(X):\n", " #PCA Run principal component analysis on the dataset X\n", " # [U, S] = pca(X) computes eigenvectors of the covariance matrix of X\n", " # Returns the eigenvectors U, the eigenvalues in S\n", " #\n", "\n", " m, n = X.shape \n", " \n", " # You need to return the following variables correctly.\n", " U = np.zeros((n, n))\n", " S = np.zeros(n)\n", " \n", " # ====================== YOUR CODE HERE ======================\n", " # Instructions: You should first compute the covariance matrix. Then, you\n", " # should use the \"scipy.linalg.svd\" function to compute the eigenvectors\n", " # and eigenvalues of the covariance matrix. \n", " #\n", " # Note: When computing the covariance matrix, remember to divide by m (the\n", " # number of examples).\n", " #\n", "\n", " \n", " \n", " # =========================================================================\n", " \n", " return U, S" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0.],\n", " [ 0., 0.]])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U, S = pca(X_norm)\n", "U" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Draw the eigenvectors centered at mean of data. These lines show the\n", " directions of maximum variations in the dataset." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb92728fd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def draw_line(a, b, ax, args):\n", " ax.plot([a[0], b[0]], [a[1], b[1]], args)\n", "\n", "fig, ax = plt.subplots(figsize=(5,5))\n", "ax.set_ylim(2, 8)\n", "ax.set_xlim(0.5, 6.5)\n", "ax.set_aspect('equal')\n", "plot_data(X, ax)\n", "ax.plot(mu[0], mu[1])\n", "draw_line(mu, mu + 1.5 * S[0] * U[0, :], ax, '-k')\n", "draw_line(mu, mu + 1.5 * S[1] * U[1, :], ax, '-k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top eigenvector should be `[-0.707107, -0.707107]`." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0.])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Dimension Reduction \n", " You should now implement the projection step to map the data onto the \n", " first k eigenvectors. The code will then plot the data in this reduced \n", " dimensional space. This will show you what the data looks like when \n", " using only the corresponding eigenvectors to reconstruct it.\n", "\n", " You should complete the code in `project_data`.\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def project_data(X, U, K):\n", " #PROJECTDATA Computes the reduced data representation when projecting only \n", " #on to the top k eigenvectors\n", " # Z = projectData(X, U, K) computes the projection of \n", " # the normalized inputs X into the reduced dimensional space spanned by\n", " # the first K columns of U. It returns the projected examples in Z.\n", " #\n", " # You need to return the following variables correctly.\n", " Z = np.zeros((X.shape[0], K))\n", " \n", " # ====================== YOUR CODE HERE ======================\n", " # Instructions: Compute the projection of the data using only the top K \n", " # eigenvectors in U (first K columns). \n", " # For the i-th example X(i,:), the projection on to the k-th \n", " # eigenvector is given as follows:\n", " # x = X[i, :].T\n", " # projection_k = x.T.dot(U(:, k));\n", " #\n", " \n", " \n", " \n", " \n", " \n", " \n", " # =============================================================\n", " \n", " return Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Projection of the first example: (should be about `1.49631261`)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K = 1\n", "Z = project_data(X_norm, U, K)\n", "Z[0,0]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def recover_data(Z, U, K):\n", " #RECOVERDATA Recovers an approximation of the original data when using the \n", " #projected data\n", " # X_rec = RECOVERDATA(Z, U, K) recovers an approximation the \n", " # original data that has been reduced to K dimensions. It returns the\n", " # approximate reconstruction in X_rec.\n", " #\n", " \n", " # You need to return the following variables correctly.\n", " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n", " \n", " \n", " # ====================== YOUR CODE HERE ======================\n", " # Instructions: Compute the approximation of the data by projecting back\n", " # onto the original space using the top K eigenvectors in U.\n", " #\n", " # For the i-th example Z(i,:), the (approximate)\n", " # recovered data for dimension j is given as follows:\n", " # v = Z(i, :)';\n", " # recovered_j = v' * U(j, 1:K)';\n", " #\n", " # Notice that U(j, 1:K) is a row vector.\n", " # \n", " \n", " \n", " \n", " \n", " \n", " # =============================================================\n", " \n", " return X_rec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Approximation of the first example: (should be about `[-1.05805279, -1.05805279]`)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0.])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_rec = recover_data(Z, U, K)\n", "X_rec[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Draw lines connecting the projected points to the original points" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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omw/6GK6S12u73Wz08XDHrl4CbidcN1cAbTcxsz2APsBDkj4mCNIoOtBX0gRg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\n4IIgqFlC4IIgqFn+HzG5PqhRBE2CAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb9272ae470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(5,5))\n", "ax.set_ylim(-3, 3)\n", "ax.set_xlim(-3, 3)\n", "ax.set_aspect('equal')\n", "plot_data(X_norm, ax)\n", "ax.plot(X_rec[:,0], X_rec[:,1], 'ro')\n", "\n", "for x_norm, x_rec in zip(X_norm, X_rec):\n", " draw_line(x_norm, x_rec, ax, '--k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 4: Loading and Visualizing Face Data \n", " We start the exercise by first loading and visualizing the dataset.\n", " The following code will load the dataset into your environment, and later display the first 100 faces in the dataset.\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5000, 1024)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = scipy.io.loadmat('ex7faces.mat')['X']\n", "X.shape" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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pSCRiKBTjwJyBckH87CXkujgakvY4I/7b5V1xYLwf6jGS4SB55gYjiPN1nwt+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w3FuDEWIq537mS6GCJNH88rxolgeBwwDo7UEuH3h2V8fNzgE7ifDE6AQ3v5T+8oxsfHrdnGBNz5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pUGWnjBRBN42B769BACkePp6alKpZLh7FNTU0okEsaqwBADFQDbYXSGzw7v5uUNWfVR1OXl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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb927293ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def display_faces(X, example_width=None):\n", " example_size = len(X[0])\n", " if example_width is None:\n", " example_width = int(np.sqrt(example_size))\n", " num_examples = len(X)\n", " figures_row_length = int(np.sqrt(num_examples))\n", " \n", " fig, axes = plt.subplots(nrows=figures_row_length, ncols=figures_row_length, figsize=(6,6))\n", " fig.subplots_adjust(wspace=0, hspace=0)\n", " for i, j in itertools.product(range(figures_row_length), range(figures_row_length)):\n", " ax = axes[i][j]\n", " ax.set_axis_off()\n", " ax.set_aspect('equal')\n", " example = X[i*figures_row_length + j].reshape(example_size//example_width, example_width).T\n", " ax.imshow(example, cmap='Greys_r')\n", " \n", "display_faces(X[:100])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 5: PCA on Face Data: Eigenfaces \n", " Run PCA and visualize the eigenvectors which are in this case eigenfaces\n", " We display the first 64 eigenfaces.\n", "\n", " Before running PCA, it is important to first normalize X.\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAFvCAYAAABTmZBxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb927301b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_norm, mu, sigma = normalize_features(X)\n", "U, S = pca(X_norm)\n", "display_faces(U[:, :64].T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 6: Dimension Reduction for Faces \n", " Project images to the eigen space using the top k eigenvectors " ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5000, 100)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K = 100\n", "Z = project_data(X_norm, U, K)\n", "Z.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 7: Visualization of Faces after PCA Dimension Reduction \n", " Project images to the eigen space using the top K eigen vectors and \n", " visualize only using those K dimensions.\n", " Compare to the original input." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5000, 1024)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_rec = recover_data(Z, U, K)\n", "X_rec.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display_faces(X_rec[:100])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
mne-tools/mne-tools.github.io	0.12/_downloads/plot_eeg_erp.ipynb	1	12395	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n.. _tut_erp:\n\nEEG processing and Event Related Potentials (ERPs)\n==================================================\n\nFor a generic introduction to the computation of ERP and ERF\nsee :ref:`tut_epoching_and_averaging`. Here we cover the specifics\nof EEG, namely:\n - setting the reference\n - using standard montages :func:`mne.channels.Montage`\n - Evoked arithmetic (e.g. differences)\n\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "import mne\nfrom mne.datasets import sample" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Setup for reading the raw data\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nraw = mne.io.read_raw_fif(raw_fname, add_eeg_ref=True, preload=True)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Let's restrict the data to the EEG channels\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.pick_types(meg=False, eeg=True, eog=True)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "By looking at the measurement info you will see that we have now\n59 EEG channels and 1 EOG channel\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "print(raw.info)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "In practice it's quite common to have some EEG channels that are actually\nEOG channels. To change a channel type you can use the\n:func:`mne.io.Raw.set_channel_types` method. For example\nto treat an EOG channel as EEG you can change its type using\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.set_channel_types(mapping={'EOG 061': 'eeg'})\nprint(raw.info)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "And to change the nameo of the EOG channel\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.rename_channels(mapping={'EOG 061': 'EOG'})" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Let's reset the EOG channel back to EOG type.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.set_channel_types(mapping={'EOG': 'eog'})" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "The EEG channels in the sample dataset already have locations.\nThese locations are available in the 'loc' of each channel description.\nFor the first channel we get\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "print(raw.info['chs'][0]['loc'])" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "And it's actually possible to plot the channel locations using\nthe :func:`mne.io.Raw.plot_sensors` method\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.plot_sensors()\nraw.plot_sensors('3d') # in 3D" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Setting EEG montage\n-------------------\n\nIn the case where your data don't have locations you can set them\nusing a :func:`mne.channels.Montage`. MNE comes with a set of default\nmontages. To read one of them do:\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "montage = mne.channels.read_montage('standard_1020')\nprint(montage)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "To apply a montage on your data use the :func:`mne.io.set_montage`\nfunction. Here don't actually call this function as our demo dataset\nalready contains good EEG channel locations.\n\nNext we'll explore the definition of the reference.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Setting EEG reference\n---------------------\n\nLet's first remove the reference from our Raw object.\n\nThis explicitly prevents MNE from adding a default EEG average reference\nrequired for source localization.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw_no_ref, _ = mne.io.set_eeg_reference(raw, [])" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "We next define Epochs and compute an ERP for the left auditory condition.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "reject = dict(eeg=180e-6, eog=150e-6)\nevent_id, tmin, tmax = {'left/auditory': 1}, -0.2, 0.5\nevents = mne.read_events(event_fname)\nepochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,\n reject=reject)\n\nevoked_no_ref = mne.Epochs(raw_no_ref, epochs_params).average()\ndel raw_no_ref # save memory\n\ntitle = 'EEG Original reference'\nevoked_no_ref.plot(titles=dict(eeg=title))\nevoked_no_ref.plot_topomap(times=[0.1], size=3., title=title)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Average reference: This is normally added by default, but can also\nbe added explicitly.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw_car, _ = mne.io.set_eeg_reference(raw)\nevoked_car = mne.Epochs(raw_car, epochs_params).average()\ndel raw_car # save memory\n\ntitle = 'EEG Average reference'\nevoked_car.plot(titles=dict(eeg=title))\nevoked_car.plot_topomap(times=[0.1], size=3., title=title)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Custom reference: Use the mean of channels EEG 001 and EEG 002 as\na reference\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw_custom, _ = mne.io.set_eeg_reference(raw, ['EEG 001', 'EEG 002'])\nevoked_custom = mne.Epochs(raw_custom, epochs_params).average()\ndel raw_custom # save memory\n\ntitle = 'EEG Custom reference'\nevoked_custom.plot(titles=dict(eeg=title))\nevoked_custom.plot_topomap(times=[0.1], size=3., title=title)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Evoked arithmetics\n------------------\n\nTrial subsets from Epochs can be selected using 'tags' separated by '/'.\nEvoked objects support basic arithmetic.\nFirst, we create an Epochs object containing 4 conditions.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "event_id = {'left/auditory': 1, 'right/auditory': 2,\n 'left/visual': 3, 'right/visual': 4}\nepochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,\n reject=reject)\nepochs = mne.Epochs(raw, epochs_params)\n\nprint(epochs)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Next, we create averages of stimulation-left vs stimulation-right trials.\nWe can use basic arithmetic to, for example, construct and plot\ndifference ERPs.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "left, right = epochs[\"left\"].average(), epochs[\"right\"].average()\n\n(left - right).plot_joint() # create and plot difference ERP" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Note that by default, this is a trial-weighted average. If you have\nimbalanced trial numbers, consider either equalizing the number of events per\ncondition (using ``Epochs.equalize_event_counts``), or the ``combine_evoked``\nfunction.\nAs an example, first, we create individual ERPs for each condition.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "aud_l = epochs[\"auditory\", \"left\"].average()\naud_r = epochs[\"auditory\", \"right\"].average()\nvis_l = epochs[\"visual\", \"left\"].average()\nvis_r = epochs[\"visual\", \"right\"].average()\n\nall_evokeds = [aud_l, aud_r, vis_l, vis_r]\n\n# This could have been much simplified with a list comprehension:\n# all_evokeds = [epochs[cond] for cond in event_id]\n\n# Then, we construct and plot an unweighted average of left vs. right trials.\nmne.combine_evoked(all_evokeds, weights=(1, -1, 1, -1)).plot_joint()" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Often, it makes sense to store Evoked objects in a dictionary or a list -\neither different conditions, or different subjects.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# If they are stored in a list, they can be easily averaged, for example,\n# for a grand average across subjects (or conditions).\ngrand_average = mne.grand_average(all_evokeds)\nmne.write_evokeds('/tmp/tmp-ave.fif', all_evokeds)\n\n# If Evokeds objects are stored in a dictionary, they can be retrieved by name.\nall_evokeds = dict((cond, epochs[cond].average()) for cond in event_id)\nprint(all_evokeds['left/auditory'])\n\n# Besides for explicit access, this can be used for example to set titles.\nfor cond in all_evokeds:\n all_evokeds[cond].plot_joint(title=cond)" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.11", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	bsd-3-clause
sivakasinathan/incubator	make_2DRs/fourier.ipynb	1	8279347	null	mit

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