**Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Set the batch size higher if you can fit in in your GPU memory\\n\",\n \"batch_size = 10\\n\",\n \"codes_list = []\\n\",\n \"labels = []\\n\",\n \"batch = []\\n\",\n \"\\n\",\n \"codes = None\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" # TODO: Build the vgg network here\\n\",\n \"\\n\",\n \" for each in classes:\\n\",\n \" print(\\\"Starting {} images\\\".format(each))\\n\",\n \" class_path = data_dir + each\\n\",\n \" files = os.listdir(class_path)\\n\",\n \" for ii, file in enumerate(files, 1):\\n\",\n \" # Add images to the current batch\\n\",\n \" # utils.load_image crops the input images for us, from the center\\n\",\n \" img = utils.load_image(os.path.join(class_path, file))\\n\",\n \" batch.append(img.reshape((1, 224, 224, 3)))\\n\",\n \" labels.append(each)\\n\",\n \" \\n\",\n \" # Running the batch through the network to get the codes\\n\",\n \" if ii % batch_size == 0 or ii == len(files):\\n\",\n \" \\n\",\n \" # Image batch to pass to VGG network\\n\",\n \" images = np.concatenate(batch)\\n\",\n \" \\n\",\n \" # TODO: Get the values from the relu6 layer of the VGG network\\n\",\n \" codes_batch = \\n\",\n \" \\n\",\n \" # Here I'm building an array of the codes\\n\",\n \" if codes is None:\\n\",\n \" codes = codes_batch\\n\",\n \" else:\\n\",\n \" codes = np.concatenate((codes, codes_batch))\\n\",\n \" \\n\",\n \" # Reset to start building the next batch\\n\",\n \" batch = []\\n\",\n \" print('{} images processed'.format(ii))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# write codes to file\\n\",\n \"with open('codes', 'w') as f:\\n\",\n \" codes.tofile(f)\\n\",\n \" \\n\",\n \"# write labels to file\\n\",\n \"import csv\\n\",\n \"with open('labels', 'w') as f:\\n\",\n \" writer = csv.writer(f, delimiter='\\\\n')\\n\",\n \" writer.writerow(labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Building the Classifier\\n\",\n \"\\n\",\n \"Now that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# read codes and labels from file\\n\",\n \"import csv\\n\",\n \"\\n\",\n \"with open('labels') as f:\\n\",\n \" reader = csv.reader(f, delimiter='\\\\n')\\n\",\n \" labels = np.array([each for each in reader if len(each) > 0]).squeeze()\\n\",\n \"with open('codes') as f:\\n\",\n \" codes = np.fromfile(f, dtype=np.float32)\\n\",\n \" codes = codes.reshape((len(labels), -1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Data prep\\n\",\n \"\\n\",\n \"As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!\\n\",\n \"\\n\",\n \"> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the labels. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"labels_vecs = # Your one-hot encoded labels array here\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use [`StratifiedShuffleSplit`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) from scikit-learn.\\n\",\n \"\\n\",\n \"You can create the splitter like so:\\n\",\n \"```\\n\",\n \"ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\\n\",\n \"```\\n\",\n \"Then split the data with \\n\",\n \"```\\n\",\n \"splitter = ss.split(x, y)\\n\",\n \"```\\n\",\n \"\\n\",\n \"`ss.split` returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use `next(splitter)` to get the indices. Be sure to read the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) and the [user guide](http://scikit-learn.org/stable/modules/cross_validation.html#random-permutations-cross-validation-a-k-a-shuffle-split).\\n\",\n \"\\n\",\n \"> **Exercise:** Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_x, train_y = \\n\",\n \"val_x, val_y = \\n\",\n \"test_x, test_y = \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print(\\\"Train shapes (x, y):\\\", train_x.shape, train_y.shape)\\n\",\n \"print(\\\"Validation shapes (x, y):\\\", val_x.shape, val_y.shape)\\n\",\n \"print(\\\"Test shapes (x, y):\\\", test_x.shape, test_y.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"If you did it right, you should see these sizes for the training sets:\\n\",\n \"\\n\",\n \"```\\n\",\n \"Train shapes (x, y): (2936, 4096) (2936, 5)\\n\",\n \"Validation shapes (x, y): (367, 4096) (367, 5)\\n\",\n \"Test shapes (x, y): (367, 4096) (367, 5)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Classifier layers\\n\",\n \"\\n\",\n \"Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.\\n\",\n \"\\n\",\n \"> **Exercise:** With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])\\n\",\n \"labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])\\n\",\n \"\\n\",\n \"# TODO: Classifier layers and operations\\n\",\n \"\\n\",\n \"logits = # output layer logits\\n\",\n \"cost = # cross entropy loss\\n\",\n \"\\n\",\n \"optimizer = # training optimizer\\n\",\n \"\\n\",\n \"# Operations for validation/test accuracy\\n\",\n \"predicted = tf.nn.softmax(logits)\\n\",\n \"correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))\\n\",\n \"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Batches!\\n\",\n \"\\n\",\n \"Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_batches(x, y, n_batches=10):\\n\",\n \" \\\"\\\"\\\" Return a generator that yields batches from arrays x and y. \\\"\\\"\\\"\\n\",\n \" batch_size = len(x)//n_batches\\n\",\n \" \\n\",\n \" for ii in range(0, n_batches*batch_size, batch_size):\\n\",\n \" # If we're not on the last batch, grab data with size batch_size\\n\",\n \" if ii != (n_batches-1)*batch_size:\\n\",\n \" X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] \\n\",\n \" # On the last batch, grab the rest of the data\\n\",\n \" else:\\n\",\n \" X, Y = x[ii:], y[ii:]\\n\",\n \" # I love generators\\n\",\n \" yield X, Y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Training\\n\",\n \"\\n\",\n \"Here, we'll train the network.\\n\",\n \"\\n\",\n \"> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the `get_batches` function I wrote before to get your batches like `for x, y in get_batches(train_x, train_y)`. Or write your own!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"saver = tf.train.Saver()\\n\",\n \"with tf.Session() as sess:\\n\",\n \" \\n\",\n \" # TODO: Your training code here\\n\",\n \" saver.save(sess, \\\"checkpoints/flowers.ckpt\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Testing\\n\",\n \"\\n\",\n \"Below you see the test accuracy. You can also see the predictions returned for images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\\n\",\n \" \\n\",\n \" feed = {inputs_: test_x,\\n\",\n \" labels_: test_y}\\n\",\n \" test_acc = sess.run(accuracy, feed_dict=feed)\\n\",\n \" print(\\\"Test accuracy: {:.4f}\\\".format(test_acc))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from scipy.ndimage import imread\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Below, feel free to choose images and see how the trained classifier predicts the flowers in them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'\\n\",\n \"test_img = imread(test_img_path)\\n\",\n \"plt.imshow(test_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Run this cell if you don't have a vgg graph built\\n\",\n \"if 'vgg' in globals():\\n\",\n \" print('\\\"vgg\\\" object already exists. Will not create again.')\\n\",\n \"else:\\n\",\n \" #create vgg\\n\",\n \" with tf.Session() as sess:\\n\",\n \" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\\n\",\n \" vgg = vgg16.Vgg16()\\n\",\n \" vgg.build(input_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with tf.Session() as sess:\\n\",\n \" img = utils.load_image(test_img_path)\\n\",\n \" img = img.reshape((1, 224, 224, 3))\\n\",\n \"\\n\",\n \" feed_dict = {input_: img}\\n\",\n \" code = sess.run(vgg.relu6, feed_dict=feed_dict)\\n\",\n \" \\n\",\n \"saver = tf.train.Saver()\\n\",\n \"with tf.Session() as sess:\\n\",\n \" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\\n\",\n \" \\n\",\n \" feed = {inputs_: code}\\n\",\n \" prediction = sess.run(predicted, feed_dict=feed).squeeze()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.imshow(test_img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.barh(np.arange(5), prediction)\\n\",\n \"_ = plt.yticks(np.arange(5), lb.classes_)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n },\n \"widgets\": {\n \"state\": {},\n \"version\": \"1.1.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":4092,"cells":{"repo_name":{"kind":"string","value":"deepmind/arnheim"},"path":{"kind":"string","value":"arnheim_3_patch_maker.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"8665"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"9G-3dxY01xJd\"\n },\n \"source\": [\n \"# Arnheim 3 - Segmented Patch Creator\\n\",\n \"DeepMind, 2021\\n\",\n \"\\n\",\n \"## Intructions\\n\",\n \"This Colab is to support the creation of segmented patches for collage creation using Arnheim 3.\\n\",\n \"\\n\",\n \"The Colab uses [PixelLib](https://github.com/ayoolaolafenwa/PixelLib) and is pretty basic but good enough to get one started creating patches from JPG images.\\n\",\n \"\\n\",\n \"The process is\\n\",\n \"\\n\",\n \"1) Provide source images\\n\",\n \"\\n\",\n \"* Upload images using this Colab to either Google Drive or the temp folder\\n\",\n \"* Alternatively use a Google Drive folder that already contains images\\n\",\n \"\\n\",\n \"2) Create segmented patches\\n\",\n \"* The patch file is save to Google Drive. Be sure to copy the location of the file in the Arnheim 3 Colab.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"cellView\": \"form\",\n \"id\": \"-X9f5OnKJ1-O\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Installations\\n\",\n \"!pip3 install pixellib\\n\",\n \"!pip3 install tensorflow==2.0.1\\n\",\n \"!pip3 install Keras==2.3.0\\n\",\n \"!pip3 install h5py==2.10.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"K41eEXVbu1k6\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Imports\\n\",\n \"import glob\\n\",\n \"from google.colab import drive\\n\",\n \"from google.colab import files\\n\",\n \"import io\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import pathlib\\n\",\n \"import pixellib\\n\",\n \"from pixellib.instance import instance_segmentation\\n\",\n \"import requests\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"A_k0GI-du3E1\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Function definitions\\n\",\n \"\\n\",\n \"def mkdir(path):\\n\",\n \" pathlib.Path(path).mkdir(parents=True, exist_ok=True)\\n\",\n \"\\n\",\n \"def upload_files(target_path):\\n\",\n \" \\\"\\\"\\\"Upload files to target directory.\\\"\\\"\\\"\\n\",\n \" mkdir(target_path)\\n\",\n \" uploaded = files.upload()\\n\",\n \" for k, v in uploaded.items():\\n\",\n \" open(target_path + \\\"/\\\" + k, 'wb').write(v)\\n\",\n \" return list(uploaded.keys())\\n\",\n \"\\n\",\n \"\\n\",\n \"def download_from_url(url, force=False):\\n\",\n \" \\\"\\\"\\\"Download file from URL and cache it.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" cache_dir = \\\"/content/cache\\\"\\n\",\n \" mkdir(cache_dir)\\n\",\n \" cache_filename = f\\\"{cache_dir}/{os.path.basename(url)}\\\"\\n\",\n \" cache = pathlib.Path(cache_filename)\\n\",\n \" if not cache.is_file() or force:\\n\",\n \" print(\\\"Downloading \\\" + url)\\n\",\n \" r = requests.get(url)\\n\",\n \" bytesio_object = io.BytesIO(r.content)\\n\",\n \" with open(cache_filename, \\\"wb\\\") as f:\\n\",\n \" f.write(bytesio_object.getbuffer())\\n\",\n \" else:\\n\",\n \" print(\\\"Using cached version of \\\" + url)\\n\",\n \" return cache \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"cellView\": \"form\",\n \"id\": \"hpsPBACa4gps\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Authorise and mount Google Drive\\n\",\n \"ROOT = \\\"/content\\\"\\n\",\n \"MOUNT_DIR = f\\\"{ROOT}/drive\\\"\\n\",\n \"drive.mount(MOUNT_DIR)\\n\",\n \"# ROOT_PATH = f\\\"{MOUNT_DIR}/MyDrive/Arnheim3\\\"\\n\",\n \"# \\n\",\n \"# mkdir(ROOT)\\n\",\n \"# IMAGE_PATH = f\\\"{ROOT}/source_images\\\"\\n\",\n \"# SEGMENTED_PATH = f\\\"{ROOT}/segmented\\\"\\n\",\n \"# \\n\",\n \"# print(f\\\"\\\\nUsing base directory: {ROOT}\\\")\\n\",\n \"# print(f\\\"Source images directory: {IMAGE_PATH}\\\")\\n\",\n \"# print(f\\\"Segmented directory: {SEGMENTED_PATH}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"cellView\": \"form\",\n \"collapsed\": true,\n \"id\": \"f2Dhp16ptHuF\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Source images and target file locations\\n\",\n \"#@markdown Source images can be stored temporarily with the Colab, be already on Google Drive, or can be uploaded to Google Drive.\\n\",\n \"use_google_drive_for_source_images = True #@param {type:\\\"boolean\\\"}\\n\",\n \"#@markdown Source images (if stored on Google Drive)\\n\",\n \"GOOGLE_DRIVE_PATH_SOURCE_IMAGES = \\\"Art/Collage/Images\\\" #@param {type:\\\"string\\\"}\\n\",\n \"#@markdown Target segmentation file will be saved to Google Drive for use with the Arnheim 3 Colab.\\n\",\n \"SEGMENTED_DATA_FILENAME = \\\"fruit.npy\\\" #@param {type: \\\"string\\\"}\\n\",\n \"GOOGLE_DRIVE_PATH_SEGMENTED_DATA = \\\"Art/Collage/Patches\\\" #@param {type:\\\"string\\\"}\\n\",\n \"\\n\",\n \"data_path = MOUNT_DIR + \\\"/MyDrive/\\\" + GOOGLE_DRIVE_PATH_SEGMENTED_DATA\\n\",\n \"data_file = data_path + \\\"/\\\" + SEGMENTED_DATA_FILENAME\\n\",\n \"\\n\",\n \"if use_google_drive_for_source_images:\\n\",\n \" IMAGE_PATH = MOUNT_DIR + \\\"/MyDrive/\\\" + GOOGLE_DRIVE_PATH_SOURCE_IMAGES\\n\",\n \"else:\\n\",\n \" IMAGE_PATH = f\\\"{ROOT}/source_images\\\"\\n\",\n \"mkdir(IMAGE_PATH)\\n\",\n \"mkdir(data_path)\\n\",\n \"\\n\",\n \"print(f\\\"Source images directory: {IMAGE_PATH}\\\")\\n\",\n \"print(f\\\"Segmented data will be saved to: {data_file}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"cellView\": \"form\",\n \"id\": \"Ylun-hVm5iGq\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Run this cell to upload a new set of images to segment\\n\",\n \"empty_target_dir_before_upload = False #@param {type:\\\"boolean\\\"}\\n\",\n \"\\n\",\n \"if empty_target_dir_before_upload:\\n\",\n \" !rm {IMAGE_PATH}/*\\n\",\n \"\\n\",\n \"upload_files(IMAGE_PATH)\\n\",\n \"print(f\\\"Images uploaded images to {IMAGE_PATH}\\\")\\n\",\n \"\\n\",\n \"!ls -l {IMAGE_PATH}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TEmqp9LOdv9P\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Segment images and save patch file\\n\",\n \"\\n\",\n \"# https://pixellib.readthedocs.io/en/latest/Image_instance.html\\n\",\n \"segment_image = instance_segmentation()\\n\",\n \"segmentation_model_file = download_from_url(\\n\",\n \" \\\"https://github.com/ayoolaolafenwa/PixelLib/releases/download/1.2/mask_rcnn_coco.h5\\\")\\n\",\n \"segment_image.load_model(segmentation_model_file)\\n\",\n \"\\n\",\n \"imagefiles = []\\n\",\n \"for file in glob.glob(f\\\"{IMAGE_PATH}/*.jpg\\\"):\\n\",\n \" imagefiles.append(file)\\n\",\n \"\\n\",\n \"print(imagefiles)\\n\",\n \"print(\\\"num images to process = \\\", len(imagefiles))\\n\",\n \"\\n\",\n \"segmented_images = []\\n\",\n \"for imagefile in imagefiles:\\n\",\n \" print(imagefile)\\n\",\n \" try:\\n\",\n \" seg, _ = segment_image.segmentImage(\\n\",\n \" imagefile,\\n\",\n \" extract_segmented_objects=True,\\n\",\n \" save_extracted_objects =False,\\n\",\n \" show_bboxes=False,\\n\",\n \" output_image_name=str(imagefile) + \\\"______.tiff\\\")\\n\",\n \" except:\\n\",\n \" print(\\\"Error encounted - skipping\\\")\\n\",\n \" continue\\n\",\n \"\\n\",\n \" if not len(seg[\\\"extracted_objects\\\"]):\\n\",\n \" print(\\\"Failed to segment\\\", imagefile)\\n\",\n \" else:\\n\",\n \" for result in seg[\\\"extracted_objects\\\"]:\\n\",\n \" print(result.shape)\\n\",\n \" segmented_image = result[..., ::-1].copy()\\n\",\n \" segmented_images.append(segmented_image)\\n\",\n \"\\n\",\n \"with open(data_file, \\\"wb\\\") as f:\\n\",\n \" np.save(f, segmented_images)\\n\",\n \"print(\\\"Saved patch file to\\\", data_file)\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"MakeSegmentedPatches.ipynb\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":4093,"cells":{"repo_name":{"kind":"string","value":"probml/pyprobml"},"path":{"kind":"string","value":"deprecated/arhmm_example.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"832622"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"view-in-github\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"![\\\"Open](\\\"https://colab.research.google.com/assets/colab-badge.svg\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"5918355f-c759-41e8-9cc9-64baf78695b3\"\n },\n \"id\": \"WeQdPp8KfQJ7\"\n },\n \"source\": [\n \"# Autoregressive (AR) HMM Demo\\n\",\n \"\\n\",\n \"Modified from\\n\",\n \"\\n\",\n \"https://github.com/lindermanlab/ssm-jax-refactor/blob/main/notebooks/arhmm-example.ipynb\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook illustrates the use of the _auto_regression_ observation model. \\n\",\n \"Let $x_t$ denote the observation at time $t$. Let $z_t$ denote the corresponding discrete latent state.\\n\",\n \"\\n\",\n \"The autoregressive hidden Markov model has the following likelihood,\\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \"x_t \\\\mid x_{t-1}, z_t &\\\\sim\\n\",\n \"\\\\mathcal{N}\\\\left(A_{z_t} x_{t-1} + b_{z_t}, Q_{z_t} \\\\right).\\n\",\n \"\\\\end{align}\\n\",\n \"$$\\n\",\n \"(Technically, higher-order autoregressive processes with extra linear terms from inputs are also implemented.) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git\"\n ],\n \"metadata\": {\n \"id\": \"-ne60jsthTe6\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"b40d03b1-1f3f-4388-c622-84b8f75b1034\"\n },\n \"execution_count\": 4,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Collecting git+git://github.com/lindermanlab/ssm-jax-refactor.git\\n\",\n \" Cloning git://github.com/lindermanlab/ssm-jax-refactor.git to /tmp/pip-req-build-2x6xx8nv\\n\",\n \" Running command git clone -q git://github.com/lindermanlab/ssm-jax-refactor.git /tmp/pip-req-build-2x6xx8nv\\n\",\n \"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.19.5)\\n\",\n \"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.4.1)\\n\",\n \"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.2.2)\\n\",\n \"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.2)\\n\",\n \"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (4.62.3)\\n\",\n \"Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.11.2)\\n\",\n \"Collecting jax==0.2.21\\n\",\n \" Downloading jax-0.2.21.tar.gz (756 kB)\\n\",\n \"\\u001b[K |████████████████████████████████| 756 kB 15.4 MB/s \\n\",\n \"\\u001b[?25hRequirement already satisfied: jaxlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.1.71+cuda111)\\n\",\n \"Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.1.0)\\n\",\n \"Requirement already satisfied: jupyter in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.0)\\n\",\n \"Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (7.6.5)\\n\",\n \"Requirement already satisfied: tensorflow-probability in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.15.0)\\n\",\n \"Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (1.0.0)\\n\",\n \"Requirement already satisfied: opt_einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (3.3.0)\\n\",\n \"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax==0.2.21->ssm==0.1) (1.15.0)\\n\",\n \"Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->ssm==0.1) (1.5.2)\\n\",\n \"Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ssm==0.1) (1.0.2)\\n\",\n \"Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ssm==0.1) (0.2.0)\\n\",\n \"Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ssm==0.1) (5.5.0)\\n\",\n \"Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ssm==0.1) (3.5.2)\\n\",\n \"Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->ssm==0.1) (5.1.3)\\n\",\n \"Requirement already satisfied: traitlets>=4.3.1 in 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jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (21.4.0)\\n\",\n \"Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (0.18.1)\\n\",\n \"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (4.10.1)\\n\",\n \"Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (5.4.0)\\n\",\n \"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (3.10.0.2)\\n\",\n \"Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->ssm==0.1) (3.7.0)\\n\",\n \"Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0->ipywidgets->ssm==0.1) (0.2.5)\\n\",\n \"Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (5.3.1)\\n\",\n \"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (2.11.3)\\n\",\n \"Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (5.6.1)\\n\",\n \"Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (1.8.0)\\n\",\n \"Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.13.1)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ssm==0.1) (2.8.2)\\n\",\n \"Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ssm==0.1) (22.3.0)\\n\",\n \"Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.7.0)\\n\",\n \"Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib->ssm==0.1) (2.0)\\n\",\n \"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (2.0.1)\\n\",\n \"Requirement already satisfied: jupyter-console in /usr/local/lib/python3.7/dist-packages (from jupyter->ssm==0.1) (5.2.0)\\n\",\n \"Requirement already satisfied: qtconsole in /usr/local/lib/python3.7/dist-packages (from jupyter->ssm==0.1) (5.2.2)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (1.3.2)\\n\",\n \"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (3.0.7)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (0.11.0)\\n\",\n \"Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.7.1)\\n\",\n \"Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.8.4)\\n\",\n \"Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.5.0)\\n\",\n \"Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (4.1.0)\\n\",\n \"Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.3)\\n\",\n \"Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (1.5.0)\\n\",\n \"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (21.3)\\n\",\n \"Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.5.1)\\n\",\n \"Requirement already satisfied: qtpy in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter->ssm==0.1) (2.0.0)\\n\",\n \"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (3.0.0)\\n\",\n \"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (1.1.0)\\n\",\n \"Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn->ssm==0.1) (1.3.5)\\n\",\n \"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn->ssm==0.1) (2018.9)\\n\",\n \"Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (1.3.0)\\n\",\n \"Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.4.0)\\n\",\n \"Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.1.6)\\n\",\n \"Building wheels for collected packages: ssm, jax\\n\",\n \" Building wheel for ssm (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for ssm: filename=ssm-0.1-py3-none-any.whl size=75282 sha256=bde077b314fa6664e8403f9bed8b69a2ae93fde31ed7843c500b456c71f7a819\\n\",\n \" Stored in directory: /tmp/pip-ephem-wheel-cache-s91lg85w/wheels/78/93/24/866323c03bb6444c9ad2485bc0abe61ad5e6828d66c2c2fda3\\n\",\n \" Building wheel for jax (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for jax: filename=jax-0.2.21-py3-none-any.whl size=869303 sha256=dc33be4778a51583c3656c00dad98a420e13b2905773a4f7fd321de9ac478eb7\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/5c/69/0d/3784dd6d281be0837d8cef1db0c8b37d108c8bff727b961178\\n\",\n \"Successfully built ssm jax\\n\",\n \"Installing collected packages: jax, ssm\\n\",\n \" Attempting uninstall: jax\\n\",\n \" Found existing installation: jax 0.2.25\\n\",\n \" Uninstalling jax-0.2.25:\\n\",\n \" Successfully uninstalled jax-0.2.25\\n\",\n \"Successfully installed jax-0.2.21 ssm-0.1\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import ssm\"\n ],\n \"metadata\": {\n \"id\": \"TR3R3yM0hftZ\"\n },\n \"execution_count\": 5,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"346a61a3-9216-480d-b5b8-39a78782a8c3\"\n },\n \"id\": \"eWi_LAC1fQJ_\"\n },\n \"outputs\": [],\n \"source\": [\n \"import copy\\n\",\n \"\\n\",\n \"import jax.numpy as np\\n\",\n \"import jax.random as jr\\n\",\n \"\\n\",\n \"from tensorflow_probability.substrates import jax as tfp\\n\",\n \"\\n\",\n \"from ssm.distributions.linreg import GaussianLinearRegression\\n\",\n \"from ssm.arhmm import GaussianARHMM\\n\",\n \"from ssm.utils import find_permutation, random_rotation\\n\",\n \"from ssm.plots import gradient_cmap # , white_to_color_cmap\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"346a61a3-9216-480d-b5b8-39a78782a8c3\"\n },\n \"id\": \"gFuWYNd_fQKA\"\n },\n \"outputs\": [],\n \"source\": [\n \"sns.set_style(\\\"white\\\")\\n\",\n \"sns.set_context(\\\"talk\\\")\\n\",\n \"\\n\",\n \"color_names = [\\\"windows blue\\\", \\\"red\\\", \\\"amber\\\", \\\"faded green\\\", \\\"dusty purple\\\", \\\"orange\\\", \\\"brown\\\", \\\"pink\\\"]\\n\",\n \"\\n\",\n \"\\n\",\n \"colors = sns.xkcd_palette(color_names)\\n\",\n \"cmap = gradient_cmap(colors)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"id\": \"--VbhuWHfQKA\",\n \"outputId\": \"1771fbef-cb26-46a9-b8a9-376460008735\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 321\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"5918355f-c759-41e8-9cc9-64baf78695b3\"\n },\n \"id\": \"WeQdPp8KfQJ7\"\n },\n \"source\": [\n \"# Autoregressive (AR) HMM Demo\\n\",\n \"\\n\",\n \"Modified from\\n\",\n \"\\n\",\n \"https://github.com/lindermanlab/ssm-jax-refactor/blob/main/notebooks/arhmm-example.ipynb\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook illustrates the use of the _auto_regression_ observation model. \\n\",\n \"Let $x_t$ denote the observation at time $t$. Let $z_t$ denote the corresponding discrete latent state.\\n\",\n \"\\n\",\n \"The autoregressive hidden Markov model has the following likelihood,\\n\",\n \"$$\\n\",\n \"\\\\begin{align}\\n\",\n \"x_t \\\\mid x_{t-1}, z_t &\\\\sim\\n\",\n \"\\\\mathcal{N}\\\\left(A_{z_t} x_{t-1} + b_{z_t}, Q_{z_t} \\\\right).\\n\",\n \"\\\\end{align}\\n\",\n \"$$\\n\",\n \"(Technically, higher-order autoregressive processes with extra linear terms from inputs are also implemented.) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git\"\n ],\n \"metadata\": {\n \"id\": \"-ne60jsthTe6\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"b40d03b1-1f3f-4388-c622-84b8f75b1034\"\n },\n \"execution_count\": 4,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Collecting git+git://github.com/lindermanlab/ssm-jax-refactor.git\\n\",\n \" Cloning git://github.com/lindermanlab/ssm-jax-refactor.git to /tmp/pip-req-build-2x6xx8nv\\n\",\n \" Running command git clone -q git://github.com/lindermanlab/ssm-jax-refactor.git /tmp/pip-req-build-2x6xx8nv\\n\",\n \"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.19.5)\\n\",\n \"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.4.1)\\n\",\n \"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.2.2)\\n\",\n \"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.2)\\n\",\n \"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (4.62.3)\\n\",\n \"Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.11.2)\\n\",\n \"Collecting jax==0.2.21\\n\",\n \" Downloading jax-0.2.21.tar.gz (756 kB)\\n\",\n \"\\u001b[K |████████████████████████████████| 756 kB 15.4 MB/s \\n\",\n \"\\u001b[?25hRequirement already satisfied: jaxlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.1.71+cuda111)\\n\",\n \"Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.1.0)\\n\",\n \"Requirement already satisfied: jupyter in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.0)\\n\",\n \"Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (7.6.5)\\n\",\n \"Requirement already satisfied: tensorflow-probability in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.15.0)\\n\",\n \"Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (1.0.0)\\n\",\n \"Requirement already satisfied: opt_einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (3.3.0)\\n\",\n \"Requirement already satisfied: six in 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terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.13.1)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ssm==0.1) (2.8.2)\\n\",\n \"Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->ssm==0.1) (22.3.0)\\n\",\n \"Requirement already satisfied: ptyprocess in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.7.0)\\n\",\n \"Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib->ssm==0.1) (2.0)\\n\",\n \"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (2.0.1)\\n\",\n \"Requirement already satisfied: jupyter-console in /usr/local/lib/python3.7/dist-packages (from jupyter->ssm==0.1) (5.2.0)\\n\",\n \"Requirement already satisfied: qtconsole in /usr/local/lib/python3.7/dist-packages (from jupyter->ssm==0.1) (5.2.2)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (1.3.2)\\n\",\n \"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (3.0.7)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->ssm==0.1) (0.11.0)\\n\",\n \"Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.7.1)\\n\",\n \"Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.8.4)\\n\",\n \"Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.5.0)\\n\",\n \"Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (4.1.0)\\n\",\n \"Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.3)\\n\",\n \"Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (1.5.0)\\n\",\n \"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (21.3)\\n\",\n \"Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.5.1)\\n\",\n \"Requirement already satisfied: qtpy in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter->ssm==0.1) (2.0.0)\\n\",\n \"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (3.0.0)\\n\",\n \"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (1.1.0)\\n\",\n \"Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn->ssm==0.1) (1.3.5)\\n\",\n \"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn->ssm==0.1) (2018.9)\\n\",\n \"Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (1.3.0)\\n\",\n \"Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.4.0)\\n\",\n \"Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.1.6)\\n\",\n \"Building wheels for collected packages: ssm, jax\\n\",\n \" Building wheel for ssm (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for ssm: filename=ssm-0.1-py3-none-any.whl size=75282 sha256=bde077b314fa6664e8403f9bed8b69a2ae93fde31ed7843c500b456c71f7a819\\n\",\n \" Stored in directory: /tmp/pip-ephem-wheel-cache-s91lg85w/wheels/78/93/24/866323c03bb6444c9ad2485bc0abe61ad5e6828d66c2c2fda3\\n\",\n \" Building wheel for jax (setup.py) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Created wheel for jax: filename=jax-0.2.21-py3-none-any.whl size=869303 sha256=dc33be4778a51583c3656c00dad98a420e13b2905773a4f7fd321de9ac478eb7\\n\",\n \" Stored in directory: /root/.cache/pip/wheels/5c/69/0d/3784dd6d281be0837d8cef1db0c8b37d108c8bff727b961178\\n\",\n \"Successfully built ssm jax\\n\",\n \"Installing collected packages: jax, ssm\\n\",\n \" Attempting uninstall: jax\\n\",\n \" Found existing installation: jax 0.2.25\\n\",\n \" Uninstalling jax-0.2.25:\\n\",\n \" Successfully uninstalled jax-0.2.25\\n\",\n \"Successfully installed jax-0.2.21 ssm-0.1\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import ssm\"\n ],\n \"metadata\": {\n \"id\": \"TR3R3yM0hftZ\"\n },\n \"execution_count\": 5,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"346a61a3-9216-480d-b5b8-39a78782a8c3\"\n },\n \"id\": \"eWi_LAC1fQJ_\"\n },\n \"outputs\": [],\n \"source\": [\n \"import copy\\n\",\n \"\\n\",\n \"import jax.numpy as np\\n\",\n \"import jax.random as jr\\n\",\n \"\\n\",\n \"from tensorflow_probability.substrates import jax as tfp\\n\",\n \"\\n\",\n \"from ssm.distributions.linreg import GaussianLinearRegression\\n\",\n \"from ssm.arhmm import GaussianARHMM\\n\",\n \"from ssm.utils import find_permutation, random_rotation\\n\",\n \"from ssm.plots import gradient_cmap # , white_to_color_cmap\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"346a61a3-9216-480d-b5b8-39a78782a8c3\"\n },\n \"id\": \"gFuWYNd_fQKA\"\n },\n \"outputs\": [],\n \"source\": [\n \"sns.set_style(\\\"white\\\")\\n\",\n \"sns.set_context(\\\"talk\\\")\\n\",\n \"\\n\",\n \"color_names = [\\\"windows blue\\\", \\\"red\\\", \\\"amber\\\", \\\"faded green\\\", \\\"dusty purple\\\", \\\"orange\\\", \\\"brown\\\", \\\"pink\\\"]\\n\",\n \"\\n\",\n \"\\n\",\n \"colors = sns.xkcd_palette(color_names)\\n\",\n \"cmap = gradient_cmap(colors)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"id\": \"--VbhuWHfQKA\",\n \"outputId\": \"1771fbef-cb26-46a9-b8a9-376460008735\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 321\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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Tg4GAIhUKYmJggLi4Oubm5WLdunbLKIoS0ElUPT3mU+uKWDRs2IDQ0FPHx8SgsLIRAIEBERARsbW2VWRYhRMHYLq3NMEwrVKMYSg1XLS0tBAQEICAgQJllEEKUgM3IlcKVEEIaiaYFCCFEAdhMC9TU1LRCJYpB4UoIUQoauRJCiAJoaGgouwSFonAlhCgFjVwJIUQB1P3xVwpXQohS0MiVEEIUQN1Hrur96Qgh76zWeLdAc1aYvnDhAubMmYNhw4ZhyJAhmDlzJo4fP866bwpXQkir43A40NDQkPuruQHb1BWmz507h/nz56O6uhqfffYZlixZAi6XC39/f4mXTTWEpgUIIUqh6DnXuhWmg4KC4O3tDQBwdXXF1KlTERwcjH379tV77L59+2BoaIjo6GhoamoCADw8PDBhwgTEx8dLLKxaHxq5EkKUQtGrvzZnhemSkhLo6emJgxUANDU1oaenx3qVFBq5EkKUgu3Itbi4GHZ2dg3uc/XqVak2NitM17fiydChQ7Fjxw6Ehobigw8+AADExsbi0aNHCAoKYlW32oWrnp4e7t27p+wyWNHX11d2CY2Sl5en7BIahdZhe7cp+m6B5qww7efnh8ePHyM8PBzbt28HAHTo0AHbtm3DqFGjWPWvduFKCFENbEeuurq6Mkem8jRnhWlNTU2YmZnByckJjo6OqKmpwa+//oqlS5ciKioKVlZWcvuncCWEKIWi3y3QnBWm16xZg5s3b+LQoUPiEfbkyZMxdepUrF27Fv/973/l9k8XtAghSqHo+1ybusK0SCTCoUOHMG7cOImpCx6PhzFjxuDmzZuorq6W2z+FKyFEKRR9t0BTV5guKChAdXW1zHfJVldXo7q6mtUKCRSuhJBWx2bU2tzRK9sVpnNzc5GVlSXep0uXLujUqRNOnTolMa1QWlqKc+fOgc/ny5zLfRvNuRJClELRDxGwXWE6ICAAly9fRmZmJoDXc8Hz589HaGgoZs6cienTp6O2thaHDh3C8+fPWa/5R+FKCFGK1nhZdlNXmP7kk09gbGyMPXv2YOvWrRCJRBAIBNiyZQscHR1Z9c1hVHl5xbfU3WjclNs2lIHuc1Usus9VMZr798zOzg6lpaXo16+f3H3v3bsHHR0dlfk7/aZGj1zLyspw48YNvHjxAiNHjsR7772niLoIIWpO3d/n2qgLWr/88gv+9a9/Yf78+QgICBA/CfXy5UtYWlri119/VUiRhBD1o+i7BZSNdfUnT57E6tWrMWzYMHz33XcStyJ06dIFY8aMwenTpxVSJCFE/bTG+1yViXW47tq1C8OGDcPWrVsxYcIEqe0DBw5UmWf6CSHKR+H6j7t37zZ4lczQ0BAvX75skaIIIepP3acFWF/Q4nK5qK2trXd7fn4+2rdv3yJFEULUn6qPTOVh/U+Dubk5zp8/L3NbbW0tEhMTYWlp2WKFEULUG00L/MPT0xN//PEHQkNDUVhYCABgGAYPHjzAkiVLcP/+fcyZM6dRnefn5yM4OBhz5szBoEGDIBAIcOnSpcZ9AkKIymmNx1+VjfW0wJQpU5CZmYnw8HBEREQAAHx8fMAwDBiGweLFizF27NhGdf7w4UNERkbC1NQUAoFA7qJhhBD1ocrByUajHiLw9/fHxIkTcfToUTx48AAMw8DU1BQuLi5NmhKwsLDAxYsX0blzZ5w+fRqLFi1q9DkIIapJ1S9YydPoJ7QsLCxgYWHRIp137NixRc5DCFE96h6urD/dhAkTcObMmXq3nzt3Tub9r4QQIos6z7cCjRi5Pn36FGVlZfVuLy8vR25ubosURQhRf6oenvK02CsHX7x4AW1t7ZY6HSFEzbXpcL1y5YrErVGnTp1Cdna21H6FhYU4fvw4+vfv3/IVEkLUUpsO10uXLmHLli0AXv8gkpKSkJSUJHNfU1NTBAUFtXyFhBC1pO4XtBoM17lz58LNzQ0Mw8DBwQErVqyQumjF4XDQoUMHlXvxMyFEudr0yFVXVxe6uroAgD179uD999+HgYFBqxRGCFFvbTpc3zR06FCFFLBt2zYAEK++GB8fj9TUVHTq1Amenp4K6ZMQonwUrm+orq7G6dOnkZaWhqKiIqm3ZHE4HKxdu7ZRBWzevFni94cPHwYAGBkZUbgSosYoXP9RUFAALy8v3Lt3DwzDgMPhiFcjqPvvpoRr3XK2hJC2g+1DAqocwKwv14WGhuLBgwf47rvvcOrUKTAMg127duH48eNwdnaGpaUlvdGKEMKaur8sm3X1v//+O1xdXfHhhx+K3wnA5XLRp08fBAcHQ1tbGyEhIQorlBCiXtT98VfW4SoUCsVvvmrX7vVsgkgkEm+fMGECzp4928LlEULUlbqHK+s5V319fZSXlwMAdHR00K5dOzx79ky8ncfjoaioqOUrJISoJVUPT3lYj1zNzMxw//791wdxuRgwYADi4uIgEolQXl6OI0eOoFevXgorlBCiXtR95Mo6XEeNGoWTJ0+KpwK8vb2RlpaGoUOHYuTIkbh16xbmzp2rsEIJIepF3cOV9bSAn58fFixYAE1NTQCvl31p164dEhISwOVy4eTkhClTpiisUEKIelH1uwHkYR2uHA5HHKx1Jk6ciIkTJ7Z4UYQQ9afqI1N5WP/T4eXlhZSUlHq3X7x4EV5eXi1SFCFE/bXGtIBIJMLGjRsxevRoWFlZwcPDo8Ece9vRo0fh7u4OGxsbDB06FJ6enkhPT2d1LOtwvXz5Ml68eFHv9levXuHKlStsT0cIaeNaI1wDAwMRHR2N6dOnY+XKleByufD19WW10vSmTZsQGBiIfv36YeXKlVi0aBF69eoFoVDIqu8WW4mgqKhIatqAEELqo+g51/T0dBw7dgxBQUHw9vYGALi6umLq1KkIDg7Gvn376j322rVr2LFjB8LCwuDo6Nik/hsM1zt37uDOnTvi31+9ehU1NTVS+xUUFGD//v3o27dvk4oghLQtHA6HVbg2Z/SamJgIHo+HGTNmiNu0tLTg7u6OTZs2IT8/H127dpV57J49e2BpaQlHR0fU1taivLwcOjo6jeq/wXA9ffq0xEoEBw4cwIEDB2Tuq6Ojg5UrVzaqc0JI26XoC1oZGRno3bu3VChaWVmBYRhkZGTUG64pKSlwdnbGjz/+iJiYGJSVlcHIyAhLly7F9OnTWfXfYLi6ublh6NChYBgGc+fOxcKFCzFq1CiJfepWInj//fehpaXFqlPyWkFBgbJLaBRVW20iLy9P2SWw1hb/7rAN1+LiYtjZ2TW4z9WrV6XahEIhunXrJtVuaGgIAMjPz5d5rsLCQhQUFODYsWPQ0NDA8uXLoa+vj3379uE///kP2rdvz2qqoMFwNTIygpGREQBg3bp1sLOzo6ewCCEtQtEj14qKCvB4PKn2un/IKisrZR5XVlYG4PXg59dff4W1tTUAwNHREY6Ojti6dWvzw/VNbm5uMttfvXqFoqIimJmZsT0VIYSwDlddXV2ZI1N5tLW1UVVVJdVeF6r1fVuoazc2NhYHKwBoampi0qRJ2LNnD0pLS+XOwbK+XHfkyBF8/fXXEm0hISEYNWoUJk+ejFmzZqGkpITt6QghbZyib8UyNDSU+dW/7laq+uZb9fX1oampiffee09q23vvvQeGYVhlHetw/e9//4vq6mrx72/evInIyEjY2dlhxowZuHnzJqKiotiejhDSxin6Zdnm5uZ4+PAhSktLJdrT0tLE2+urq3///jLn7J8/fw4NDQ3o6enJ7Z919Y8fP4ZAIBD/PjExEXp6eti1axdWr14Nd3d3nDhxgu3pCCFtnKJHrk5OTqiqqsLBgwfFbSKRCLGxsRg8eLD4Yldubq54gdQ3j3327BmSk5PFbSUlJThx4gQGDRoEbW1tuf2znnMtLi4WL7MNvL5VYeTIkeIHBwYOHIiEhAS2pyOEtHGKfojA2toaTk5OCA4OhlAohImJCeLi4pCbm4t169aJ9wsICMDly5cl1vObPXs2Dh48iM8++wze3t7o1KkTDh8+jOLiYixbtoxV/6zD1dDQENnZ2QBeX8S6c+cOPvzwQ/H2srIyaGhosD0dIaSNa40Xt2zYsAGhoaGIj49HYWEhBAIBIiIiYGtr2+Bx7du3x549e7Bhwwbs3bsXFRUVsLCwwM8//yz32Dqsw3XYsGHYt28f9PT0cOnSJXA4HIwdO1a8/eHDhzLvKSOEEFlaI1y1tLQQEBCAgICAeveJiYmR2W5oaIiNGzc2uW/W4bpkyRJcv35d3Nknn3wCY2NjAEB1dTWSkpLo9YOEENbofa7/6N69O44dO4b79+9DV1cXPXv2FG+rqKjA6tWr6736Rgghb1P397k26q1YGhoaEncM1OnYsSMcHBxarChCiHpjezeAKgdwi71ykBBCGkOVg5MNCldCiFJQuBJCiAJQuBJCiAKo+33xFK6EEKVQ95Er6xvNgoKCxC88kCU9PR1BQUGsO05PT8eqVaswZcoU2NjYYNy4cfD39xc/BUYIUW+tsUChMrEO17i4ODx+/Lje7Tk5OThy5Ajrjnfu3IlTp05h5MiRWLlyJTw8PHD58mW4urpKvUSBEKJ+1D1cW2xaoKysDO3asT+dt7c3goODJVaMnTJlCqZNm4bIyEisX7++pUojhLyDVD085WkwDXNzc/H06VPx7x88eIArV65I7VdYWIj9+/fD1NSUdceDBw+WajMzM0O/fv1o5EpIG9CmwzU2NhZbtmwRD9HDw8MRHh4utR/DMOByuVi7dm2zimEYBi9evKDHaAlpA9r0uwUcHBxgZGQEhmGwYsUKeHh4YNCgQRL71K3+amlpiR49ejSrmISEBOTl5cHf379Z5yGEvPva9MjV3NxcPIrMzc3FxIkTwefzFVJIVlYWVq9eDVtbW7i4uCikD0LIu4HD4bAauapyALO+ArV48WKFFSEUCrFw4ULo6elh8+bNav91gRCi2sHJRqPuFmAYBhcuXMCjR49QUFAAhmEktnM4HCxatKhRBRQXF8PX1xfFxcXYv38/DA0NG3U8IUQ1Ubj+49GjR1i0aBEePHggFap1GhuulZWV8PPzw6NHjxAVFYU+ffqwPpYQotro8dd/rFmzBo8fP8by5csxfPhw6OvrN6vjmpoaLF26FDdu3MC2bdtgY2PTrPMRQlQLjVz/kZqairlz52LBggUt0vH69etx9uxZ2Nvbo6CgAPHx8eJtOjo69PJtQtQches/NDU1xWtmtYQ7d+4AAM6dO4dz585JbDMyMqJwJUTNUbj+Y/To0bh27RpmzZrVIh3Xt+IiIaRtUPdwZX3PU2BgIG7cuIHdu3dDJBIpsiZCSBvA5XLl/lJlrEeus2fPRnl5OTZu3IiQkBB07dpV6sNzOBycPn26xYskhKgfVQ9PeViH65tLaRNCSHOp+7QA63ClOVJCSEuhx18JIURBVDk42Wh0uF65cgXnz5/Hy5cvMW/ePPTt2xelpaW4ffs2BAIBOnXqpIg6CSFqhsL1HzU1Nfjiiy9w8uRJMAwDDocDZ2dn9O3bF+3atcOiRYswf/58+Pn5KbJeQoiaUPdwZX25LjIyEklJSQgMDMTx48cl3i+gpaUFBwcH/P777wopkhCifjQ0NOT+UmWsw/XIkSNwcXHB3Llz0blzZ6ntffv2xZMnT1q0OEKI+mqNBQpFIhE2btyI0aNHw8rKCh4eHkhJSWn0eXx9fSEQCPD999+zPoZ1uD59+lRqFYI3derUCYWFhaw7JoS0ba0RroGBgYiOjsb06dOxcuVKcLlc+Pr64vr166zP8b///Q9Xr15tdN+sw1VHRwcFBQX1bs/OzoaBgUGjCyCEtE2KDtf09HQcO3YMy5cvx5dffomZM2ciOjoaPXr0QHBwMKtziEQirFu3rkkvrGIdrra2tjh69KjMd7kWFhbi8OHDGDZsWKMLIIS0TYp+/DUxMRE8Hg8zZswQt2lpacHd3R2pqanIz8+Xe449e/agoqKiSeHK+m4BPz8//Pvf/4aXlxc++OADAEBmZiays7MRERGB8vJyfPzxx40ugBDSNrENz+LiYtjZ2TW4j6yv7RkZGejduzd0dHQk2q2srMAwDDIyMtC1a9d6zykUCrFt2zb83//9H9q3b8+q1jexDldLS0uEhQfx8ucAABloSURBVIXhq6++QlBQEADghx9+AMMw6NKlC7Zs2YL333+/0QUQ1dHQtNC7qLkvdG9NeXl5yi6h1Sn6ViyhUIhu3bpJtdctJSVv5Prjjz+id+/eTV4wtVEPEYwbNw5nz55FcnIysrKywDAMzMzMMHr06CYlOyGk7WIbrrq6uk26oFRRUQEejyfVrqWlBeD1MlP1SU9Px5EjRxATE9PkfwRYhWtpaSk++eQTTJs2DTNmzIC9vT3s7e2b1CEhhLTGuwW0tbVRVVUl1V4XqnUh+zaGYfD9999j4sSJcqcjGsJq0kNHRwc3b95scieEEPI2Rd8tYGhoKPOrv1AoBIB651tPnTqF9PR0zJ49Gzk5OeJfAFBSUoKcnBxUVFTI7Z/1tED//v3x4MEDtrsTQkiDFD3nam5ujpiYGJSWlkpc1EpLSxNvlyU3Nxe1tbWYO3eu1LbY2FjExsYiMjIS//rXvxrsn3W4fvbZZ1i8eDHGjh2L4cOHsz2MEEJkUvTjrU5OTti9ezcOHjwIb29vAK/vW42NjcXgwYPFF7tyc3NRXl6Ovn37AgDGjx8vc73ARYsWwd7eHu7u7rCwsJDbP+twTUhIQM+ePTFv3jyYm5vDzMwM2traEvtwOBysXbuW7SkJIW2Yokeu1tbWcHJyQnBwMIRCIUxMTBAXF4fc3FysW7dOvF9AQAAuX76MzMxMAICJiQlMTExknrNXr16sF09lHa5xcXHi/87IyEBGRobUPhSuhJB3yYYNGxAaGor4+HgUFhZCIBAgIiICtra2Cu+bdbjWLYVNCCEtoTVeOailpYWAgAAEBATUuw/bVVbqRrZssbpboLS0FEFBQThx4kSjTk4IIfVpjRe3KBPrW7GOHz+OkpISRddDCGkj1D1cWU8L9O3bF0+fPlVkLYSQNkTdl9Zm/el8fHywf/9+PHz4UJH1EELaCBq5/uPBgwfo0aMHpk2bBnt7e5iamsq8FWvRokUtXiQhRP2oenjKwzpct2zZIv7vU6dOydyHwpUQQl5jHa5nzpxRZB2EkDaGRq7/MDIyatGOb968ifDwcNy+fRsvX76Erq4uzM3NsWjRIgwePLhF+yKEvFta461Yytao97m2pCdPnqCmpgYzZsyAoaEhiouLcfToUXh6eiIyMhKjRo1SVmmEkFagysHJButwrVt9oCGNefx1ypQpmDJlikTb7Nmz4eDggD179lC4EqLmKFz/8ea7BerT3HcLtG/fHgYGBigqKmryOQghqoHC9R+y3i1QU1ODJ0+eYPfu3bh79y527tzZ6AJKSkogEolQUFCAI0eO4O7du3THASFtgLqHa7MekdDQ0ICZmRlWr14NfX19bNy4sdHnWLFiBUaMGIHJkydj9+7dmDVrFvz8/JpTFiGEKF2LPX82ZswYJCUlNfq4RYsWYffu3Vi7di0GDx4MkUgkc90bQoh64XK5cn+psharvqCgAGVlZY0+TiAQYNSoUfjwww+xa9cu/PXXX6wunhFCVJu6P/7a7HAtKipCYmIioqOjWS190BAej4cJEyYgKSmJ1QJghBDVpe7hyvqClrm5eb0flmEY6OnpITAwsNkFVVRUgGEYlJaWSr27gBCiPlQ9POVhHa6urq4yfxj6+vowMzODs7MzOnbsyLrjV69ewcDAQKKtpKQEJ0+eRI8ePdClSxfW5yKEqB4K13+sX7++RTteunQptLS0MGjQIBgaGuLZs2eIjY3F8+fP8eOPP7ZoX4SQd4+qX7CSR2mPv06fPh3x8fGIiYlBUVERdHV1YWNjgw0bNmDo0KHKKosQQloE63Ddt28fTp06haioKJnb58+fj4kTJ2LWrFmszufu7g53d3e23RNC1Iy6TwuwHpfHxsbC1NS03u1mZmY4fPhwixRFCFF/6n63AOtwzc7OBp/Pr3f7+++/j+zs7BYpihCi3tgEq6oHLOtpgerqaohEonq3i0QiVFZWtkhRhBD1p8rByQbrkauZmRmSk5Pr3X7+/HmYmJi0SFGEEPVHj7/+w9nZGcnJyQgNDZUYwVZVVeGnn35CcnIypk6dqpAiCSFE1bCeFvD29sYff/yB8PBw7N+/H3369AHwelXYwsJC2NnZYd68eQorlBCiXtR9WoB1uPJ4POzevRtRUVH47bffkJGRAeD1dMHHH38MLy8v8Hg8hRVKCFEvFK5v4PF48PX1ha+vr6LqIYQQtaC0J7QIIW1ba4xcRSIRNm/ejPj4eBQVFcHc3Bz+/v4YMWJEg8clJSXh+PHjSE9Px8uXL9GjRw/Y29vj008/ha6uLqu+KVwJIUrRGncDBAYGIikpCV5eXjA1NUVcXBx8fX0RExODQYMG1Xvc119/ja5du8LFxQU9e/ZEZmYmYmJi8Oeff+Lw4cPQ0tKS2zeFKyFELaWnp+PYsWMICgqCt7c3gNdv95s6dSqCg4Oxb9++eo/96aefMGzYMIm2gQMHIiAgAMeOHcMHH3wgt3/VvpGMEKKyFP10VmJiIng8HmbMmCFu09LSgru7O1JTU5Gfn1/vsW8HKwA4ODgAALKyslj1T+FKCFEKRYdrRkYGevfuDR0dHYl2KysrMAwjvuOJrRcvXgAAOnfuzGp/tZwWUJXHcNnM25CmKygoUHYJrOnr6yu7BNYMDQ2hp6fXav0VFxfDzs6uwX2uXr0q1SYUCtGtWzepdkNDQwBocOQqS2RkJDQ0NDBx4kRW+6tluBJC3n2KvqBVUVEh8977ukFNYwZhR48exaFDh7Bw4ULWj/lTuBJClILt135dXV2ZI1N5tLW1UVVVJdVeF6psvzlevXoVK1euxLhx47BkyRLW/dOcKyFELRkaGsr86i8UCgEAXbt2lXuOO3fu4JNPPoFAIMCmTZugoaHBun8KV0KIUij6gpa5uTkePnyI0tJSifa0tDTx9oY8fvwYPj4+MDAwwI4dO9ChQ4dG9U/hSghRCkWHq5OTE6qqqnDw4EFxm0gkQmxsLAYPHiy+2JWbmyt1e5VQKMT8+fPB4XCwa9cuqZWq2aA5V0JIq2Mbns0JWGtrazg5OSE4OBhCoRAmJiaIi4tDbm4u1q1bJ94vICAAly9fRmZmprjNx8cHT548gY+PD1JTU5GamireZmJi0uDTXXUoXAkhStEa7xbYsGEDQkNDER8fj8LCQggEAkRERMDW1rbB4+7cuQMA2Llzp9Q2Nzc3CldCyLurNcJVS0sLAQEBCAgIqHefmJgYqbY3R7FNRXOuhBCiADRyJYQoBb0smxBCFIDClRBCFIDClRBCFIDClRBCFEDdw5XuFiCEEAWgkSshRClo5NqKIiMjIRAI4OLiouxSCCEKpuh3CyjbOzNyFQqF2L59e6PfPEMIUU2qHp7yvDPhGhISgoEDB4JhGBQVFSm7HEIIaZZ3YlogPT0dCQkJCAoKUnYphJBWou7TAkoPV4ZhsGbNGri6uqJ///7KLocQQlqE0qcFjhw5gvv372Pr1q3KLoUQ0opUfWQqj1JHriUlJQgJCcHHH3/Maj0bQghRFUoduW7fvh08Hg/z5s1TZhmEECVQ95Gr0sI1Pz8f0dHRWLJkCV68eCFur6ysRFVVFXJycqCrqws9PT1llUgIUSAKVwV5+fIlqqqqEBwcjODgYKntEyZMgK+vL5YvX66E6gghpHmUFq7GxsYyL2KFhoairKwMK1asgJmZWesXRghpFTRyVRBdXV04ODhItUdHR0NDQ0PmNkIIURVKvxWLENL2tMbS2sr2zoWrrJUYCSFE1bxz4UoIaRtUeVTKhtIffyWEEHVEI1dCiFLQyJUQQkij0ciVEKIU6j5ypXAlhCiFuocrTQsQQogC0MiVEKIUNHIlhBAVJRKJsHHjRowePRpWVlbw8PBASkoKq2Pz8vKwZMkS2NnZYfDgwfj000/x5MkT1n1TuBJClKI11tAKDAxEdHQ0pk+fjpUrV4LL5cLX1xfXr19v8LjS0lJ4eXkhNTUVfn5++Pzzz3H79m14eXmhsLCQVd80LUAIUQpFTwukp6fj2LFjCAoKgre3NwDA1dUVU6dORXBwMPbt21fvsb/88guys7MRGxuLAQMGAADGjBmDadOmISoqCkuWLJHbP41cCSFqKTExETweDzNmzBC3aWlpwd3dHampqcjPz6/32JMnT8LGxkYcrADQt29fjBgxAidOnGDVv1qNXEtKSsAwDEaNGqXsUghpFENDQ2WXwBqHw0FJSUmzzlFcXIwhQ4aw2g8A7OzsGtzv6tWrUm0ZGRno3bs3dHR0JNqtrKzAMAwyMjJkrt1XW1uLzMxMzJw5U2qbpaUlkpOTUV5ejvbt2zdYk1qFK5fLRW1trbLLIKTRVGk5o5KSEnC5Tf/Sq6ur26h9mxrkQqEQ3bp1k2qv+4esvpFrQUEBRCKRzH/wDA0NwTAMhEIhTExMGuxfrcL19u3byi6BECLHuXPnWqWfiooK8Hg8qXYtLS0Ar9frk6WuXVNTs95jKyoq5PZPc66EELWkra2Nqqoqqfa68KwLyrfVtYtEonqP1dbWlts/hSshRC0ZGhrK/OovFAoBQOZ8KwDo6+tDU1NTvN/bx3I4HFZz5BSuhBC1ZG5ujocPH6K0tFSiPS0tTbxdFi6XCz6fj1u3bkltS09Ph6mpqdyLWQCFKyFETTk5OaGqqgoHDx4Ut4lEIsTGxmLw4MHii125ubnIysqSOHbSpEm4ceOGxHWcBw8e4OLFi3BycmLVP4dhGKYFPgchhLxzlixZgjNnzmDu3LkwMTFBXFwcbt26hejoaNja2gIA5syZg8uXLyMzM1N8XElJCdzc3FBeXo558+ZBQ0MDUVFRYBgGR44cQefOneX2TeFKCFFblZWVCA0NxdGjR1FYWAiBQIBly5Zh5MiR4n1khSsAPH/+HGvXrkVycjJqa2sxbNgwrFy5Er169WLVN4UrIYQoAM25EkKIAlC4EkKIAlC4EkKIAlC4EkKIAlC4EkKIAlC4NqA5S0SQ+qWnp2PVqlWYMmUKbGxsMG7cOPj7+yM7O1vZpamdyMhICAQCuLi4KLuUNoduxWrAsmXLkJSUBC8vL5iamopvQI6JicGgQYOUXZ7K+vzzz3Ht2jU4OTlBIBBAKBRi3759KCsrw6FDh9C3b19ll6gWhEIhJk2aBIZhYGJigvj4eGWX1KZQuNYjPT0dM2bMkFgiorKyElOnTkXXrl0bXCKCNOzatWsYOHCgxCvdHj16hGnTpsHZ2Rnr169XYnXqIzAwELm5uWAYBkVFRRSurYymBerRnCUiSMMGDx4s9a5MMzMz9OvXT+oZb9I06enpSEhIQFBQkLJLabMoXOvBZokI0nIYhsGLFy9YPbNNGsYwDNasWQNXV1f0799f2eW0WRSu9RAKhTLf9yhviQjSNAkJCcjLy8PkyZOVXYrKO3LkCO7fv4+lS5cqu5Q2jcK1Hk1dIoI0XlZWFlavXg1bW1u6qt1MJSUlCAkJwccff1zvy6BJ66BwrUdTl4ggjSMUCrFw4ULo6elh8+bNzVr4jgDbt28Hj8fDvHnzlF1Km6dWCxS2pKYuEUHYKy4uhq+vL4qLi7F//36VWl76XZSfn4/o6GgsWbIEL168ELdXVlaiqqoKOTk50NXVVamVZlUZDRPq0dQlIgg7lZWV8PPzw6NHj7Bjxw706dNH2SWpvJcvX6KqqgrBwcGYMGGC+FdaWhqysrIwYcIEREZGKrvMNoNGrvVwcnLC7t27cfDgQfF9rrKWiCCNV1NTg6VLl+LGjRvYtm0bbGxslF2SWjA2NsbWrVul2kNDQ1FWVoYVK1bAzMys9Qtroyhc62FtbQ0nJycEBwdDKBSKl4jIzc3FunXrlF2eSlu/fj3Onj0Le3t7FBQUSNzcrqOjAwcHByVWp7p0dXVl/uyio6OhoaFBP9dWRuHagA0bNiA0NBTx8fHiJSIiIiLEa++Qprlz5w4A4Ny5czh37pzENiMjIwoBohbo8VdCCFEAuqBFCCEKQOFKCCEKQOFKCCEKQOFKCCEKQOFKCCEKQOFKCCEKQOFKCCEKQOFKCCEKQOFKWtzp06cRFhbW4ue9dOkSwsLCUFRU1KzzZGRkICwsDDk5OS1UGSHSKFxJizt9+jS2bNnS4ue9fPkytmzZ0iLhumXLFjx9+rSFKiNEGoUrIYQoAIWrmomNjYVAIEBKSgp27doFBwcHDBw4EJMmTUJcXJzMYy5cuID58+fDzs4OlpaWmDZtGvbv3y+xz9KlS9G/f39cunRJov3PP/+Eubk5vvzySwDAnDlzxP0IBALxr9jY2AbrvnbtGnx8fDBq1ChYWlpizJgx8PX1xY0bNwC8Xia6bjQ8YcIE8Xnrph/y8vKwfv16uLi4YMiQIbC0tMSUKVMQERGBmpoacT9hYWHiFVG9vLzE5wkMDBTvIxKJEB4eDmdnZ1haWsLOzg5+fn64fft2wz98Qt5Ab8VSU5s2bUJFRQVmzpwJTU1N7N+/H4GBgTAxMZF4q9eBAwfwzTffwMbGBn5+fmjfvj0uXLiAb7/9Fo8fP0ZAQAAAYM2aNbh58yb+85//4MiRIzAwMIBQKERAQABMTU3xzTffAAD8/PxQW1uLq1evYsOGDeJ+Bg8eXG+tDx48wPz58/Hee+/By8sLXbp0wcuXL5Gamoo7d+7AxsYGM2fORElJCU6dOoWgoCDxKrECgQAAkJmZiaSkJDg6OsLExARVVVX4888/ERISgpycHKxevRoA4OjoCKFQiAMHDsDPz0/8km4TExMAQFVVFRYsWIDr16/DxcUFH330EUpKSvDrr79i9uzZ2Lt3LywtLVvqj4moM4aolcOHDzN8Pp9xcXFhKisrxe3Pnz9nLCwsGH9/f3FbXl4eM3DgQGbZsmVS51mzZg1jbm7OPH78WNx248YNxsLCglm4cCFTU1PDeHt7MxYWFsytW7ckjg0ICGD4fD7rmqOjoxk+n8+kpaU1uN9PP/3E8Pl85smTJ1LbysvLmdraWqn25cuXM+bm5kxeXp64re5ndPHiRan9f/75Z4bP5zN//PGHRHtxcTEzduxYxtPTk+3HIm0cTQuoqX//+9/Q1NQU/75bt27o3bs3Hj16JG47efIkRCIR3N3d8erVK4lf48ePR21tLS5cuCDe39raGkuWLMG5c+fw0Ucf4cKFC1i+fDksLCyaVauuri4A4MyZM01eVVdbWxscDgfA66/1BQUFePXqFUaPHo3a2lrcunWL1XkSEhLQp08fWFhYSPw8RCIRRo4cidTUVFRUVDSpRtK20LSAmurVq5dUm76+vsQV8qysLAAQL2Mjy5sL3QGAj48P/ve//+Hq1asYPXo05s6d2+xanZ2dkZCQgPDwcERFRcHa2hqjR4+Gs7MzjIyMWJ2juroaERERiI+PR3Z2Npi3XlPM9g6DrKwsVFRUYMSIEfXu8/fff6NHjx6szkfaLgpXNcVmieq6APrhhx/qXc327ZDOyclBZmYmACA7OxulpaXo2LFjs2rV1NTEzz//jPT0dPz555+4evUqfvrpJ2zZsgUhISFwdHSUe47169cjJiYGU6ZMgZ+fHwwMDMDj8fDXX38hODgYtbW1rGphGAZ8Pl980UsWAwMD1p+NtF0Urm1Y3WJ1nTt3xsiRI+XuX11djS+++AI1NTX46quv8P333+Pbb79FcHCwxH51X88by8rKClZWVgCAZ8+ewdXVFaGhoeJwbei88fHxGDJkCDZt2iTRnp2dLbVvQ+cxNTXF33//jeHDh7P6B4qQ+tD/PW3Y5MmToampibCwMJnziMXFxRCJROLfh4aGIi0tDV9//TXmzJmDefPm4ejRo1K3eHXo0AEAUFBQwKqOV69eSbV1794dBgYGKCwslDrvm211uFyu1FRAWVkZoqKipPZt6Dyurq4QCoX4+eefZdb69jQJIfWhkWsb1r17d3z77bf46quvMGXKFEyfPh1GRkZ49eoV7t69i9OnT+PYsWMwNjZGcnIydu7cialTp+KDDz4AACxbtgxXrlzB6tWrMWjQIPFI2NraGnv37sWqVaswduxY8Hg8WFlZyZwHBoDt27cjOTkZ48aNg7GxMRiGwblz5/DgwQP4+PiI97O2tgYABAcHY9q0adDS0kK/fv3A5/MxadIkHDhwAEuXLsXIkSPx4sULHD58GPr6+lL9WVpagsvlIjw8HIWFhejQoQOMjY1hbW0NLy8vXLhwARs2bMDFixcxfPhwdOzYEbm5ubh48SI0NTURExPTwn8SRB1pfPvtt98quwjScjIyMnDmzBm4ubnB2NhYYltcXByKi4slLkINGDAAI0aMQE5ODk6fPo2kpCTcvXsXmpqamDVrFoYPH47CwkIsWLAAXbp0QXh4uPguBA0NDQwfPhwHDhzApUuX4ObmBg0NDfTr1w/l5eX4/fffkZCQgMTERPTv3x/9+/eXWbOWlhaePXuG5ORkJCUlITU1Fdra2vjss8/g4+Mj/hrfs2dPaGtrIzk5GUePHsWJEyfQpUsXDBs2DMOHD4dIJEJycjISExPx9OlTeHh4wM3NDUeOHIGDg4O4f11dXRgZGeHy5ctISEjAiRMnUF1dDQcHB2hoaMDZ2RmdO3fGjRs3kJiYiPPnz+PZs2fo168fPD09xffEEtIQWv2VEEIUgOZcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAShcCSFEAf4fpgi8QIa0qOwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"# Make a transition matrix\\n\",\n \"num_states = 5\\n\",\n \"transition_probs = (np.arange(num_states) ** 10).astype(float)\\n\",\n \"transition_probs /= transition_probs.sum()\\n\",\n \"transition_matrix = np.zeros((num_states, num_states))\\n\",\n \"for k, p in enumerate(transition_probs[::-1]):\\n\",\n \" transition_matrix += np.roll(p * np.eye(num_states), k, axis=1)\\n\",\n \"\\n\",\n \"plt.imshow(transition_matrix, vmin=0, vmax=1, cmap=\\\"Greys\\\")\\n\",\n \"plt.xlabel(\\\"next state\\\")\\n\",\n \"plt.ylabel(\\\"current state\\\")\\n\",\n \"plt.title(\\\"transition matrix\\\")\\n\",\n \"plt.colorbar()\\n\",\n \"\\n\",\n \"plt.savefig(\\\"arhmm-transmat.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"564edd16-a99d-4329-8e31-98fe1e1cef79\"\n },\n \"id\": \"nNiT5hX4fQKC\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Make observation distributions\\n\",\n \"data_dim = 2\\n\",\n \"num_lags = 1\\n\",\n \"\\n\",\n \"keys = jr.split(jr.PRNGKey(0), num_states)\\n\",\n \"angles = np.linspace(0, 2 * np.pi, num_states, endpoint=False)\\n\",\n \"theta = np.pi / 25 # rotational frequency\\n\",\n \"weights = np.array([0.8 * random_rotation(key, data_dim, theta=theta) for key in keys])\\n\",\n \"biases = np.column_stack([np.cos(angles), np.sin(angles), np.zeros((num_states, data_dim - 2))])\\n\",\n \"covariances = np.tile(0.001 * np.eye(data_dim), (num_states, 1, 1))\\n\",\n \"\\n\",\n \"# Compute the stationary points\\n\",\n \"stationary_points = np.linalg.solve(np.eye(data_dim) - weights, biases)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"print(theta / (2 * np.pi) * 360)\\n\",\n \"print(360 / 5)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"fzFKiyptoevv\",\n \"outputId\": \"68275575-c151-4605-f618-a2be195c61df\"\n },\n \"execution_count\": 34,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"7.2\\n\",\n \"72.0\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"HManGRbPfQKC\"\n },\n \"source\": [\n \"# Plot dynamics functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"id\": \"RbPFU_M4fQKD\",\n \"outputId\": \"43a07dff-331f-49c3-9d28-1e502a0f1bc8\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 234\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"if data_dim == 2:\\n\",\n \" lim = 5\\n\",\n \" x = np.linspace(-lim, lim, 10)\\n\",\n \" y = np.linspace(-lim, lim, 10)\\n\",\n \" X, Y = np.meshgrid(x, y)\\n\",\n \" xy = np.column_stack((X.ravel(), Y.ravel()))\\n\",\n \"\\n\",\n \" fig, axs = plt.subplots(1, num_states, figsize=(3 * num_states, 6))\\n\",\n \" for k in range(num_states):\\n\",\n \" A, b = weights[k], biases[k]\\n\",\n \" dxydt_m = xy.dot(A.T) + b - xy\\n\",\n \" axs[k].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[k % len(colors)])\\n\",\n \"\\n\",\n \" axs[k].set_xlabel(\\\"$y_1$\\\")\\n\",\n \" # axs[k].set_xticks([])\\n\",\n \" if k == 0:\\n\",\n \" axs[k].set_ylabel(\\\"$y_2$\\\")\\n\",\n \" # axs[k].set_yticks([])\\n\",\n \" axs[k].set_aspect(\\\"equal\\\")\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \"\\n\",\n \" plt.savefig(\\\"arhmm-flow-matrices.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"colors\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 75\n },\n \"id\": \"_GJxepDyzZwP\",\n \"outputId\": \"eac5fbe3-2039-4601-f83c-8ff684ff8deb\"\n },\n \"execution_count\": 62,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"[(0.21568627450980393, 0.47058823529411764, 0.7490196078431373),\\n\",\n \" (0.8980392156862745, 0.0, 0.0),\\n\",\n \" (0.996078431372549, 0.7019607843137254, 0.03137254901960784),\\n\",\n \" (0.4823529411764706, 0.6980392156862745, 0.4549019607843137),\\n\",\n \" (0.5098039215686274, 0.37254901960784315, 0.5294117647058824),\\n\",\n \" (0.9764705882352941, 0.45098039215686275, 0.023529411764705882),\\n\",\n \" (0.396078431372549, 0.21568627450980393, 0.0),\\n\",\n \" (1.0, 0.5058823529411764, 0.7529411764705882)]\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 62\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"print(stationary_points)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"9lzdZ70NzVp8\",\n \"outputId\": \"6b108b7f-55f3-49d5-9ab2-99833a634e62\"\n },\n \"execution_count\": 61,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"[[ 3.9209812 -1.9056123 ]\\n\",\n \" [ 3.0239954 3.140209 ]\\n\",\n \" [-2.0520504 3.8463693 ]\\n\",\n \" [-4.2922325 -0.7630231 ]\\n\",\n \" [-0.60069436 -4.3179426 ]]\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"JCk26lnYfQKE\"\n },\n \"source\": [\n \"# Sample data from the ARHMM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"tags\": [],\n \"id\": \"XkWFWd_DfQKE\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Make an Autoregressive (AR) HMM\\n\",\n \"true_initial_distribution = tfp.distributions.Categorical(logits=np.zeros(num_states))\\n\",\n \"true_transition_distribution = tfp.distributions.Categorical(probs=transition_matrix)\\n\",\n \"\\n\",\n \"true_arhmm = GaussianARHMM(\\n\",\n \" num_states,\\n\",\n \" transition_matrix=transition_matrix,\\n\",\n \" emission_weights=weights,\\n\",\n \" emission_biases=biases,\\n\",\n \" emission_covariances=covariances,\\n\",\n \")\\n\",\n \"time_bins = 10000\\n\",\n \"true_states, data = true_arhmm.sample(jr.PRNGKey(0), time_bins)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"0feabc13-812b-4d5e-ac24-f8327ecb4d27\"\n },\n \"id\": \"AWNcIC9rfQKF\",\n \"outputId\": \"abb93ea8-dcad-40bf-b968-a79cb7fbfeb9\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 517\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(8, 8))\\n\",\n \"for k in range(num_states):\\n\",\n \" plt.plot(*data[true_states == k].T, \\\"o\\\", color=colors[k], alpha=0.75, markersize=3)\\n\",\n \"\\n\",\n \"plt.plot(*data[:1000].T, \\\"-k\\\", lw=0.5, alpha=0.2)\\n\",\n \"plt.xlabel(\\\"$y_1$\\\")\\n\",\n \"plt.ylabel(\\\"$y_2$\\\")\\n\",\n \"# plt.gca().set_aspect(\\\"equal\\\")\\n\",\n \"\\n\",\n \"plt.savefig(\\\"arhmm-samples-2d.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"fig = plt.figure(figsize=(8, 8))\\n\",\n \"for k in range(num_states):\\n\",\n \" ndx = true_states == k\\n\",\n \" data_k = data[ndx]\\n\",\n \" T = 12\\n\",\n \" data_k = data_k[:T, :]\\n\",\n \" plt.plot(data_k[:, 0], data_k[:, 1], \\\"o\\\", color=colors[k], alpha=0.75, markersize=3)\\n\",\n \" for t in range(T):\\n\",\n \" plt.text(data_k[t, 0], data_k[t, 1], t, color=colors[k], fontsize=12)\\n\",\n \"\\n\",\n \"# plt.plot(*data[:1000].T, '-k', lw=0.5, alpha=0.2)\\n\",\n \"plt.xlabel(\\\"$y_1$\\\")\\n\",\n \"plt.ylabel(\\\"$y_2$\\\")\\n\",\n \"# plt.gca().set_aspect(\\\"equal\\\")\\n\",\n \"\\n\",\n \"plt.savefig(\\\"arhmm-samples-2d-temporal.pdf\\\")\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 517\n },\n \"id\": \"K2BUiXsrrW34\",\n \"outputId\": \"790c4ec7-ee96-4733-d0b3-60785456dfaf\"\n },\n \"execution_count\": 53,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"print(biases)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"v74obcLSwF8V\",\n \"outputId\": \"6394c27e-9d0d-404b-cfd4-24b0d3a33b17\"\n },\n \"execution_count\": 57,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"[[ 1. 0. ]\\n\",\n \" [ 0.30901697 0.95105654]\\n\",\n \" [-0.80901706 0.58778524]\\n\",\n \" [-0.80901694 -0.5877853 ]\\n\",\n \" [ 0.30901712 -0.9510565 ]]\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"print(stationary_points)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"mIVS3eU3wESp\",\n \"outputId\": \"6fafaacc-de1a-44fa-fb5f-d4f25d97fd4a\"\n },\n \"execution_count\": 56,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"[[ 3.9209812 -1.9056123 ]\\n\",\n \" [ 3.0239954 3.140209 ]\\n\",\n \" [-2.0520504 3.8463693 ]\\n\",\n \" [-4.2922325 -0.7630231 ]\\n\",\n \" [-0.60069436 -4.3179426 ]]\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"colors\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 75\n },\n \"id\": \"BfiRdzsDwUuf\",\n \"outputId\": \"74d339ba-61f2-409d-e940-e6a8264e0706\"\n },\n \"execution_count\": 58,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"[(0.21568627450980393, 0.47058823529411764, 0.7490196078431373),\\n\",\n \" (0.8980392156862745, 0.0, 0.0),\\n\",\n \" (0.996078431372549, 0.7019607843137254, 0.03137254901960784),\\n\",\n \" (0.4823529411764706, 0.6980392156862745, 0.4549019607843137),\\n\",\n \" (0.5098039215686274, 0.37254901960784315, 0.5294117647058824),\\n\",\n \" (0.9764705882352941, 0.45098039215686275, 0.023529411764705882),\\n\",\n \" (0.396078431372549, 0.21568627450980393, 0.0),\\n\",\n \" (1.0, 0.5058823529411764, 0.7529411764705882)]\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 58\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"a58c7a02-2777-4af8-982f-e279bd3bbeb6\"\n },\n \"id\": \"22ZahXInfQKF\"\n },\n \"source\": [\n \"Below, we visualize each component of of the observation variable as a time series. The colors correspond to the latent state. The dotted lines represent the stationary point of the the corresponding AR state while the solid lines are the actual observations sampled from the HMM.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"lim\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"ssmnDQ3_uZRb\",\n \"outputId\": \"fcec599a-1fbe-4597-97ae-4099408df14b\"\n },\n \"execution_count\": 52,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"DeviceArray(4.7118726, dtype=float32)\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 52\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"1ec5ac27-2d23-4660-8702-4156f8ffdf39\"\n },\n \"id\": \"eLu1Bi-5fQKF\",\n \"outputId\": \"6deca2a4-c65d-4ea7-ef05-7eadf850a623\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 424\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"# Plot the data and the smoothed data\\n\",\n \"plot_slice = (0, 200)\\n\",\n \"lim = 1.05 * abs(data).max()\\n\",\n \"plt.figure(figsize=(8, 6))\\n\",\n \"plt.imshow(\\n\",\n \" true_states[None, :],\\n\",\n \" aspect=\\\"auto\\\",\\n\",\n \" cmap=cmap,\\n\",\n \" vmin=0,\\n\",\n \" vmax=len(colors) - 1,\\n\",\n \" extent=(0, time_bins, -lim, (data_dim) * lim),\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"Ey = np.array(stationary_points)[true_states]\\n\",\n \"for d in range(data_dim):\\n\",\n \" plt.plot(data[:, d] + lim * d, \\\"-k\\\")\\n\",\n \" plt.plot(Ey[:, d] + lim * d, \\\":k\\\")\\n\",\n \"\\n\",\n \"plt.xlim(plot_slice)\\n\",\n \"plt.xlabel(\\\"time\\\")\\n\",\n \"# plt.yticks(lim * np.arange(data_dim), [\\\"$y_{{{}}}$\\\".format(d+1) for d in range(data_dim)])\\n\",\n \"plt.ylabel(\\\"observations\\\")\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"\\n\",\n \"plt.savefig(\\\"arhmm-samples-1d.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"data.shape\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"r1aHBVjbxZTj\",\n \"outputId\": \"65ccb5f1-ffa5-40cc-8470-2ead0a3005ce\"\n },\n \"execution_count\": 59,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"(10000, 2)\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 59\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"data[:10, :]\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"Hupky83Hvteg\",\n \"outputId\": \"32706a81-56d2-4b64-9814-51197efc2211\"\n },\n \"execution_count\": 55,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"DeviceArray([[-0.8169615 , 0.55239207],\\n\",\n \" [-1.423961 , 1.1161395 ],\\n\",\n \" [-1.8636721 , 1.6114323 ],\\n\",\n \" [-2.194045 , 2.0294168 ],\\n\",\n \" [-2.3697448 , 2.4288142 ],\\n\",\n \" [-2.4682324 , 2.7841942 ],\\n\",\n \" [-2.4781866 , 2.9988554 ],\\n\",\n \" [-2.4822226 , 3.1525304 ],\\n\",\n \" [-2.489001 , 3.346456 ],\\n\",\n \" [-2.4774575 , 3.4129188 ]], dtype=float32)\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 55\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbpresent\": {\n \"id\": \"759699ce-fffa-4667-90af-267122e39f01\"\n },\n \"id\": \"2QEsdj8DfQKG\"\n },\n \"source\": [\n \"# Fit an ARHMM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"id\": \"a9CK4IevfQKG\",\n \"outputId\": \"f6fd2a62-5c16-4dc2-82dc-ec9e6d850002\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 85,\n \"referenced_widgets\": [\n \"e6f056311e3a47ebbe9bc981d8861de3\",\n \"9ae7f4c7c9234082afc36c57cae36036\",\n \"01d4fa61844e45758f177a2c28ed70c4\",\n \"d0115936959a4d11af7afad22cbd1887\",\n \"48c34ee72aa544f897d0260017f3b689\",\n \"7202fcfcb71c497588d4ce4b69ec435a\",\n \"6f2018c97c344ddd84b8048bc036271f\",\n \"6929d58b80ba4b758429134af3a89f56\",\n \"9587c289109e4a3996b5599937ea3e74\",\n \"0902dddb283c4f6aba2d959cc12119a5\",\n \"ec5013b082484a7ebb485bafab2e5846\"\n ]\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Initializing...\\n\",\n \"Done.\\n\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"e6f056311e3a47ebbe9bc981d8861de3\",\n \"version_minor\": 0,\n \"version_major\": 2\n },\n \"text/plain\": [\n \" 0%| | 0/100 [00:00\"\n ]\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"qz-kwUfRfQKH\"\n },\n \"outputs\": [],\n \"source\": [\n \"# # Find a permutation of the states that best matches the true and inferred states\\n\",\n \"# most_likely_states = posterior.most_likely_states()\\n\",\n \"# arhmm.permute(find_permutation(true_states[num_lags:], most_likely_states))\\n\",\n \"# posterior.update()\\n\",\n \"# most_likely_states = posterior.most_likely_states()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"id\": \"ENBLYZJnfQKH\",\n \"outputId\": \"7f1ea2ad-1e21-4073-a93b-ececc978a2e1\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 425\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"if data_dim == 2:\\n\",\n \" lim = abs(data).max()\\n\",\n \" x = np.linspace(-lim, lim, 10)\\n\",\n \" y = np.linspace(-lim, lim, 10)\\n\",\n \" X, Y = np.meshgrid(x, y)\\n\",\n \" xy = np.column_stack((X.ravel(), Y.ravel()))\\n\",\n \"\\n\",\n \" fig, axs = plt.subplots(2, max(num_states, test_num_states), figsize=(3 * num_states, 6))\\n\",\n \" for i, model in enumerate([true_arhmm, arhmm]):\\n\",\n \" for j in range(model.num_states):\\n\",\n \" dist = model._emissions._distribution[j]\\n\",\n \" A, b = dist.weights, dist.bias\\n\",\n \" dxydt_m = xy.dot(A.T) + b - xy\\n\",\n \" axs[i, j].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[j % len(colors)])\\n\",\n \"\\n\",\n \" axs[i, j].set_xlabel(\\\"$x_1$\\\")\\n\",\n \" axs[i, j].set_xticks([])\\n\",\n \" if j == 0:\\n\",\n \" axs[i, j].set_ylabel(\\\"$x_2$\\\")\\n\",\n \" axs[i, j].set_yticks([])\\n\",\n \" axs[i, j].set_aspect(\\\"equal\\\")\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \"\\n\",\n \" plt.savefig(\\\"argmm-flow-matrices-true-and-estimated.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"if data_dim == 2:\\n\",\n \" lim = abs(data).max()\\n\",\n \" x = np.linspace(-lim, lim, 10)\\n\",\n \" y = np.linspace(-lim, lim, 10)\\n\",\n \" X, Y = np.meshgrid(x, y)\\n\",\n \" xy = np.column_stack((X.ravel(), Y.ravel()))\\n\",\n \"\\n\",\n \" fig, axs = plt.subplots(1, max(num_states, test_num_states), figsize=(3 * num_states, 6))\\n\",\n \" for i, model in enumerate([arhmm]):\\n\",\n \" for j in range(model.num_states):\\n\",\n \" dist = model._emissions._distribution[j]\\n\",\n \" A, b = dist.weights, dist.bias\\n\",\n \" dxydt_m = xy.dot(A.T) + b - xy\\n\",\n \" axs[j].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[j % len(colors)])\\n\",\n \"\\n\",\n \" axs[j].set_xlabel(\\\"$y_1$\\\")\\n\",\n \" axs[j].set_xticks([])\\n\",\n \" if j == 0:\\n\",\n \" axs[j].set_ylabel(\\\"$y_2$\\\")\\n\",\n \" axs[j].set_yticks([])\\n\",\n \" axs[j].set_aspect(\\\"equal\\\")\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \"\\n\",\n \" plt.savefig(\\\"arhmm-flow-matrices-estimated.pdf\\\")\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 241\n },\n \"id\": \"VT-JvKEZmU9k\",\n \"outputId\": \"17b0e47c-30ab-4d7c-e0c7-aee78f24d54e\"\n },\n \"execution_count\": 22,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"id\": \"4PEtU6ICfQKH\",\n \"outputId\": \"ae8729d6-2634-4fb3-e7ba-9edd9de781e8\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 280\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"# Plot the true and inferred discrete states\\n\",\n \"plot_slice = (0, 1000)\\n\",\n \"plt.figure(figsize=(8, 4))\\n\",\n \"plt.subplot(211)\\n\",\n \"plt.imshow(true_states[None, num_lags:], aspect=\\\"auto\\\", interpolation=\\\"none\\\", cmap=cmap, vmin=0, vmax=len(colors) - 1)\\n\",\n \"plt.xlim(plot_slice)\\n\",\n \"plt.ylabel(\\\"$z_{\\\\\\\\mathrm{true}}$\\\")\\n\",\n \"plt.yticks([])\\n\",\n \"\\n\",\n \"plt.subplot(212)\\n\",\n \"# plt.imshow(most_likely_states[None,: :], aspect=\\\"auto\\\", cmap=cmap, vmin=0, vmax=len(colors)-1)\\n\",\n \"plt.imshow(posterior.expected_states[0].T, aspect=\\\"auto\\\", interpolation=\\\"none\\\", cmap=\\\"Greys\\\", vmin=0, vmax=1)\\n\",\n \"plt.xlim(plot_slice)\\n\",\n \"plt.ylabel(\\\"$z_{\\\\\\\\mathrm{inferred}}$\\\")\\n\",\n \"plt.yticks([])\\n\",\n \"plt.xlabel(\\\"time\\\")\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"\\n\",\n \"plt.savefig(\\\"arhmm-state-est.pdf\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"id\": \"vFlh-AmVfQKH\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Sample the fitted model\\n\",\n \"sampled_states, sampled_data = arhmm.sample(jr.PRNGKey(0), time_bins)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"id\": \"dujqSI13fQKI\",\n \"outputId\": \"a8c85dac-3880-45d9-c0e8-1688925cc97b\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 517\n }\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"![](\\\"images/ipython_logo.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# IPython Documentation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This directory contains IPython's notebook-based documentation. This augments our [Sphinx-based documentation](http://ipython.org/ipython-doc/stable/index.html) with notebooks that contain interactive tutorials and examples. Over time, more of our documentation will be pulled into this format.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Topics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* [IPython Kernel](IPython Kernel/Index.ipynb): IPython's core syntax and command line features available in all frontends\\n\",\n \"* [Notebook](Notebook/Index.ipynb): The IPython Notebook frontend\\n\",\n \"* [Interactive Widgets](Interactive Widgets/Index.ipynb): Interactive JavaScript/HTML widgets and `interact`\\n\",\n \"* [Parallel Computing](Parallel Computing/Index.ipynb): IPython's library for interactive parallel computing\\n\",\n \"* [Customization](Customization/Index.ipynb): How to configure IPython and customize it with magics, extensions, etc.\\n\",\n \"* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\\n\",\n \"* [Builtin Extensions](Builtin Extensions/Index.ipynb): Extensions we ship with IPython\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":4095,"cells":{"repo_name":{"kind":"string","value":"zhangh87/AmazonPricePredictor"},"path":{"kind":"string","value":"Exploratory Analysis/Baby Dataset Preliminary - New Image Features Count Vectorized.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"148088"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import struct\\n\",\n \"import gzip\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn\\n\",\n \" \\n\",\n \"def parse(path):\\n\",\n \" g = gzip.open(path, 'rb')\\n\",\n \" for l in g:\\n\",\n \" yield eval(l)\\n\",\n \"\\n\",\n \"def getDF(path):\\n\",\n \" i = 0\\n\",\n \" df = {}\\n\",\n \" for d in parse(path):\\n\",\n \" df[i] = d\\n\",\n \" i += 1\\n\",\n \" return pd.DataFrame.from_dict(df, orient='index')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"reviews = getDF('reviews_Baby.json.gz')\\n\",\n \"meta = getDF('meta_Baby.json.gz')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"img_feat = pd.read_csv('rcnn_image_features_stephan_1.tar')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"reviewsDF = reviews.set_index('asin').groupby(level = 0)['unixReviewTime','overall'].agg(np.average)\\n\",\n \"del reviews\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"meta = meta[['price','asin','title']].set_index('asin')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"df.set_index('time')['price'].groupby(pd.TimeGrouper(freq='6M')).count().plot(kind='bar')\\n\",\n \"plt.ylabel('Frequency')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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97s+/nuu+/WzJkzdeGFF9pWGAAAGDi/vya3Z88e3+Ndu3bJ6XTaUhAA\\nADhzfo3gf/7znys/P19ffPGFJCk+Pl5FRUW2FgYAAAbOr4C/4oortHXrVh09elSRkZGM3gEACHJ+\\nTdEfPnxY9957r7KysvT1119r9uzZOnTokN21AQCAAfL7drH33XefRo0apXPPPVd33nmn8vPz7a4N\\nAAAMkF8B39LSouuvv16S5HA4lJmZKa/Xa2thAABg4PwK+BEjRuhf//qXb7GbP/3pT4qIiLC1MAAA\\nMHB+XWS3ZMkS/eQnP9E///lPTZs2Tf/5z3/09NNP210bAAAYIL8Cvrm5Wa+99po+/fRTnThxQvHx\\n8YzgAQAIYn5N0a9evVrh4eEaN26cxo8fT7gDABDk/BrBjx07VkuWLNGVV16pESNG+LZPnz7dtsIA\\nAMDA9RnwR44c0fnnn6/o6GhJ0gcffNDteQIeAIDg1GfAz507V1u2bNHKlSu1YcMG5eTkDFVdAADg\\nDPT5GbxlWb7Hb731lu3FAACAwdFnwJ/63rvUPewBAEBw8/t2sd8OewAAENz6/Az+448/1o033ijp\\n5AV3px5bliWHw6Gqqir7KwQAAKetz4B/++23h6oOAAAwiPoM+AsvvHCo6gCAXuWs2hnoEoCzjt+f\\nwQMAgLMHAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgA\\nAAxEwAMAYCACHgAAAxHwAAAYqM/bxQ5UZ2enCgoKdPjwYR0/flzz5s3Td7/7XS1evFgOh0Pjxo3T\\n8uXLFRISosrKSlVUVCgsLEzz5s3TlClT1N7erry8PDU3N8vpdKqoqEgxMTF2lAoAgJFsGcG/+eab\\nGjNmjMrKyvTrX/9ajz/+uFauXKnc3FyVlZXJsixVVVWpqalJpaWlqqio0Pr161VcXKzjx4+rvLxc\\nCQkJKisr0/Tp01VSUmJHmQAAGMuWEfytt96q9PR0SZJlWQoNDdXBgweVnJwsSUpNTVVNTY1CQkI0\\nceJERUREKCIiQnFxcaqvr1ddXZ3uv/9+374EPAAAp8eWgHc6nZIkr9erBx98ULm5uSoqKpLD4fA9\\n39raKq/XK5fL1e04r9fbbfupff0RHT1KYWGhg3w2gy821tX/TsMUvekZfUF//vs1wmumd8OlN7YE\\nvCR9/vnnmj9/vjwej6ZOnarVq1f7nmtra9Po0aMVFRWltra2bttdLle37af29UdLy9eDexI2iI11\\nqanJvzcsww296Rl9gT++/RrhNdM703rT15sVWz6D/+KLL5STk6O8vDxlZGRIki6//HLV1tZKkqqr\\nq5WUlKQJEyaorq5OHR0dam1tVUNDgxISEjRp0iTt3r3bt29iYqIdZQIAYCxbRvDPPfecvvrqK5WU\\nlPg+P3/00Uf1xBNPqLi4WPHx8UpPT1doaKiys7Pl8XhkWZYWLlyoyMhIud1u5efny+12Kzw8XGvW\\nrLGjTAAAjOWwLMsKdBGD5WyYdjFtemgw0Zue0RcpZ9XOQJcQ9DYsTvM95jXTO9N6M+RT9AAAILAI\\neAAADETAAwBgIAIeAAAD2fY9eADA0OnvQsRvX4SH4YERPAAABiLgAQAwEAEPAICBCHgAAAxEwAMA\\nYCACHgAAAxHwAAAYiO/BAwAk8V160zCCBwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIL4m\\nByDg+vt6Fs4cPR5+CHgAtiNcgKHHFD0AAAYi4AEAMBABDwCAgQh4AAAMRMADAGAgAh4AAAMR8AAA\\nGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLg\\nAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAA4UFugAAgBlyVu3s8/kNi9OGqBJINo/gP/jgA2VnZ0uS\\nGhsb5Xa75fF4tHz5cnV1dUmSKisrNXPmTGVmZmrXrl2SpPb2di1YsEAej0dz5szR0aNH7SwTAADj\\n2Bbw69at09KlS9XR0SFJWrlypXJzc1VWVibLslRVVaWmpiaVlpaqoqJC69evV3FxsY4fP67y8nIl\\nJCSorKxM06dPV0lJiV1lAgBgJNum6OPi4vTss8/qkUcekSQdPHhQycnJkqTU1FTV1NQoJCREEydO\\nVEREhCIiIhQXF6f6+nrV1dXp/vvv9+1LwANA4PU3BY/gYlvAp6en69ChQ76fLcuSw+GQJDmdTrW2\\ntsrr9crlcvn2cTqd8nq93baf2tcf0dGjFBYWOohnYY/YWFf/Ow1T9KZn9AUmCJbXcbDUYbchu8gu\\nJOT/fxrQ1tam0aNHKyoqSm1tbd22u1yubttP7euPlpavB7doG8TGutTU5N8bluGG3vSMvsAUUxe9\\n0efzQ3ERnmn/nvp6szJkX5O7/PLLVVtbK0mqrq5WUlKSJkyYoLq6OnV0dKi1tVUNDQ1KSEjQpEmT\\ntHv3bt++iYmJQ1UmAABGGLIRfH5+vpYtW6bi4mLFx8crPT1doaGhys7OlsfjkWVZWrhwoSIjI+V2\\nu5Wfny+3263w8HCtWbNmqMoEAMAIDsuyrEAXMVjOhmkX06aHBhO96ZkJfeHiLPiDKfrTFxRT9AAA\\nYOgQ8AAAGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADDdlStQAA9KW/FQ+H\\nYqU7kzCCBwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAA\\nBiLgAQAwEAEPAICBuNkMgDPW301CgMHAzWhODyN4AAAMRMADAGAgAh4AAAMR8AAAGIiABwDAQFxF\\nD6BfXCUPnH0YwQMAYCACHgAAAzFFDwAwwpl+lGTaQjmM4AEAMBABDwCAgQh4AAAMRMADAGAgLrID\\nwPfcAQMR8MAwQIADww9T9AAAGIiABwDAQEzRAwCgwfkoK5gWy2EEDwCAgQh4AAAMxBQ9cJbjCnkA\\nPQnagO/q6tJjjz2mjz76SBEREXriiSd08cUXD2kN/f3HGUyftQAAAi+YciNoA37Hjh06fvy4Xnnl\\nFe3fv1+rVq3S2rVrA10WMOgYgQOwQ9AGfF1dnVJSUiRJV111lf7yl78EuCIMRDC9mx2oMz0HAhxA\\nIDgsy7ICXURPHn30Ud1yyy2aPHmyJOmGG27Qjh07FBYWtO9JAAAIGkF7FX1UVJTa2tp8P3d1dRHu\\nAAD4KWgDftKkSaqurpYk7d+/XwkJCQGuCACAs0fQTtGfuor+b3/7myzL0pNPPqlLL7000GUBAHBW\\nCNqABwAAAxe0U/QAAGDgCHgAAAxEwAdIQ0ODEhMT1dHREehSgkZra6vmzp2rH//4x5o1a5bef//9\\nQJcUUF1dXSosLNSsWbOUnZ2txsbGQJcUFDo7O5WXlyePx6OMjAxVVVUFuqSg09zcrMmTJ6uhoSHQ\\npQSN559/XrNmzdLMmTP16quvBrqcIcH3zgLA6/WqqKhIERERgS4lqPzmN7/Rtddeq3vuuUd///vf\\ntWjRIm3ZsiXQZQUMqzn27M0339SYMWO0evVqffnll5o+fbpuvPHGQJcVNDo7O1VYWKgRI0YEupSg\\nUVtbq/fff1/l5eU6duyYNmzYEOiShgQj+CFmWZaWLVumhx9+WCNHjgx0OUHlnnvuUVZWliTpxIkT\\nioyMDHBFgcVqjj279dZb9dBDD0k6+e8pNDQ0wBUFl6KiImVlZem8884LdClB449//KMSEhI0f/58\\nzZ07VzfccEOgSxoSjOBt9Oqrr+rFF1/stu2CCy7Q7bffrvHjxweoquDQU2+efPJJTZgwQU1NTcrL\\ny1NBQUGAqgsOXq9XUVFRvp9DQ0P1zTffDPsFn5xOp6ST/XnwwQeVm5sb4IqCx+bNmxUTE6OUlBS9\\n8MILgS4naLS0tOizzz7Tc889p0OHDmnevHnatm2bHA5HoEuz1fD+n8Jmd911l+66665u226++WZt\\n2rRJmzZtUlNTk3JycvTyyy8HqMLA6ak3kvTRRx/p4Ycf1iOPPKLk5OQAVBY8WM2xd59//rnmz58v\\nj8ejqVOnBrqcoLFp0yY5HA7t3btXH374ofLz87V27VrFxsYGurSAGjNmjOLj4xUREaH4+HhFRkbq\\n6NGjOueccwJdmq3432KIbd++3fc4LS1t2HwW5I9PPvlEDz30kH75y18O+xkO6eRqjrt27dLtt9/O\\nao7f8sUXXygnJ0eFhYX6/ve/H+hygsq3BwvZ2dl67LHHhn24S1JiYqJeeukl3Xvvvfr3v/+tY8eO\\nacyYMYEuy3YEPILGmjVrdPz4cf3f//2fpJMj2OF8UdnNN9+smpoaZWVl+VZzhPTcc8/pq6++UklJ\\niUpKSiRJ69at46Iy9GrKlCnat2+fMjIyZFmWCgsLh8W1G6xkBwCAgbiKHgAAAxHwAAAYiIAHAMBA\\nBDwAAAYi4AEAMBABDwxTtbW1ys7O7nOfgwcPavXq1X7/ziNHjmjOnDmnXUtXV5fmz5/fbWEfAGeG\\ngAfQq5UrV55WYJ9//vlat27daf+dkJAQZWZm6le/+tVpHwugZwQ8MMxlZ2frF7/4hWbNmqWbb75Z\\nu3fvliTt3btXsbGxvhW/rr32Wi1ZskRTp05VVlaWDh06JOnkioy5ublKT0/XgQMHlJaWJkk6fPiw\\nZs+erTvvvFMZGRmqr6+XJL3++uuaMWOGpk2bpoKCAt8tk6+//npt375dXq93qFsAGImAB6DOzk69\\n8sorWrJkiZ5++mlJ0s6dO5WUlOTbp6WlRcnJyXrrrbd0xx136IknnvA9l5qaqrffflsxMTG+bStW\\nrFB6erp++9vfasGCBVq7dq0+/vhjVVZWqqKiQm+88YbOOeccrV+/XtLJm+lcdtlleu+994borAGz\\nsVQtAN9taceNG6cvv/xSktTY2Khrr73Wt09kZKSmT58uSZoxY4aKi4t9z1155ZX/8zv37dvn22fy\\n5MmaPHmyNm7cqMbGRmVmZko6+cbi8ssv9x1zwQUXqLGxcZDPDhieCHgAioyMlKRut88MCQnpdve6\\nkJAQ3/NdXV3d1vI+dfy3fftYy7LU0NCgEydO6LbbbtPSpUslSW1tbTpx4kS3Y0JCmFgEBgP/kgD0\\naOzYsTp8+LDv52PHjmnnzp2STt53PDU1tc/jk5KStHXrVknSnj17tGzZMl1zzTXavn27mpubZVmW\\nHnvsMb344ou+Yw4dOqS4uDgbzgYYfgh4AD1KS0tTbW1tt23btm3T1KlT9e6776qgoKDP4wsLC/XO\\nO+9o2rRpevbZZ/X4449r/Pjx+ulPf6q7775bd9xxh7q6uvTAAw9Ikk6cOKG//vWv+sEPfmDbOQHD\\nCXeTA9Ajy7LkdrtVUlKimJgYXXbZZfroo49s+3s7duxQXV2d8vPzbfsbwHDCCB5AjxwOhwoKCgb0\\nvfbT1dXVpddee03z58+3/W8BwwUjeAAADMQIHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCA\\ngf4f7vlv7ehni9kAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"np.log(df['price']).plot.hist(bins = 50,)\\n\",\n \"plt.xlabel('ln(price)')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(33378, 4)\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = df[df['time'] > '2013-01-01']\\n\",\n \"df.shape\\n\",\n \"#33378\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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cnJdT7uEwAA1K/eM+3Dhw/rpz/9qaRvXpT27deWZcnlcqmoqKj+jbvd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\\nAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"df[df.apply(lambda x: 'sock' in x['title'].lower(), axis = 1)]['price'].plot.hist(bins = 20)\\n\",\n \"plt.xlabel('Price')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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pubVVtbq/j4eDtGAgCgX7Jlj/w3v/mNPvnkE73wwgt64YUXJElPPfWU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\\nIQcAwGCEHAAAg/0XMw8YJL+AejYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"df[df.apply(lambda x: 'stroller' in x['title'].lower(), axis = 1)]['price'].plot.hist(bins = 20)\\n\",\n \"plt.xlabel('Price')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"360191\\n\",\n \"344911\\n\"\n ]\n }\n ],\n \"source\": [\n \"import nltk\\n\",\n \"from nltk.collocations import *\\n\",\n \"from nltk.corpus import stopwords\\n\",\n \"words = nltk.word_tokenize(' '.join(df['title']))\\n\",\n \"print(len(words))\\n\",\n \"stopset = set(stopwords.words('english'))\\n\",\n \"bigram_measures = nltk.collocations.BigramAssocMeasures()\\n\",\n \"filtered_words = [w for w in words if not w in stopwords.words('english')]\\n\",\n \"print(len(filtered_words))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"293700\\n\"\n ]\n }\n ],\n \"source\": [\n \"import string\\n\",\n \"filtered_words = [x for x in filtered_words if x not in string.punctuation]\\n\",\n \"print(len(filtered_words))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('Bedding', 'Set'),\\n\",\n \" ('Car', 'Seat'),\\n\",\n \" ('Diaper', 'Bag'),\\n\",\n \" ('2', 'Pack'),\\n\",\n \" ('Carter', \\\"'s\\\"),\\n\",\n \" ('Crib', 'Bedding'),\\n\",\n \" ('Jojo', 'Designs'),\\n\",\n \" ('Sweet', 'Jojo'),\\n\",\n \" ('Cloth', 'Diaper'),\\n\",\n \" ('Summer', 'Infant'),\\n\",\n \" ('One', 'Size'),\\n\",\n \" ('BPA', 'Free'),\\n\",\n \" ('Safety', '1st'),\\n\",\n \" ('Changing', 'Pad'),\\n\",\n \" ('Crib', 'Sheet'),\\n\",\n \" ('Lambs', 'amp'),\\n\",\n \" ('amp', 'Ivy'),\\n\",\n \" ('3', 'Pack'),\\n\",\n \" ('Gift', 'Set'),\\n\",\n \" ('4', 'Piece')]\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"finder = BigramCollocationFinder.from_words(filtered_words)\\n\",\n \"finder.nbest(bigram_measures.raw_freq, 20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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QBYWvwffPABnn32WQQFBaG6uhpRUVH48ssvrb7mvn150Go1jtgdAMCl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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"word_fd = nltk.FreqDist(filtered_words)\\n\",\n \"word_fd.plot(35,cumulative=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"13978\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"words_only = [w for w in filtered_words if w.isalpha()]\\n\",\n \"unique = set([w.lower() for w in words_only])\\n\",\n \"len(unique)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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l26KCEhIfz6o48+amlhsRLw+zTo7FZ6f95GvfqfPJ3ePjvyNwEAYKGI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\\nDOEMAIDD/H9DusvJdw2tEwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"word_fd = nltk.FreqDist(filtered_words)\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(list(range(len(word_fd))),np.log(sorted(word_fd.values(),reverse = True)))\\n\",\n \"plt.xlabel('# Words')\\n\",\n \"plt.ylabel('Log Frequency')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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bC1jm9LoLGNLo1v9GhsIyOqFxCWl5dz//33U1BQwI033siRI0dISUkB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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"word_fd = nltk.FreqDist(filtered_words)\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(list(range(len(word_fd))),np.array(sorted(word_fd.values(),reverse = True)).cumsum())\\n\",\n \"plt.xlabel('# Words')\\n\",\n \"plt.ylabel('Cumulative Frequency')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df = img_feat.set_index('rcnn_image_features.csv').merge(df, how = 'inner', left_index = True, right_index = True).dropna(how = 'any')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"price = df['price']\\n\",\n \"rating = df['overall']\\n\",\n \"df = df.drop(['price','overall','time'],axis = 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"del img_feat\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(33378, 4097)\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n \"countvect = CountVectorizer(analyzer = 'word', tokenizer = nltk.word_tokenize\\n\",\n \" , stop_words = 'english', max_features = 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.sparse import hstack\\n\",\n \"allFeatures = hstack((df.drop('title',axis = 1).values, countvect.fit_transform(df['title'])))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.ensemble import GradientBoostingRegressor\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import r2_score\\n\",\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"from sklearn.neighbors import KNeighborsRegressor\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(allFeatures,price, test_size = 0.2, random_state = 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gb = GradientBoostingRegressor(random_state = 1, n_estimators = 100)\\n\",\n \"param_grid = {'learning_rate': [0.01, 0.1, 1, 10],\\n\",\n \" 'max_depth': [3,5, 7]}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fitting 3 folds for each of 12 candidates, totalling 36 fits\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=3)]: Done 2 tasks | elapsed: 10.2min\\n\",\n \"[Parallel(n_jobs=3)]: Done 7 tasks | elapsed: 51.2min\\n\",\n \"[Parallel(n_jobs=3)]: Done 12 tasks | elapsed: 61.0min\\n\",\n \"[Parallel(n_jobs=3)]: Done 19 tasks | elapsed: 110.7min\\n\",\n \"[Parallel(n_jobs=3)]: Done 26 tasks | elapsed: 149.9min\\n\",\n \"[Parallel(n_jobs=3)]: Done 36 out of 36 | elapsed: 201.3min finished\\n\",\n \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\model_selection\\\\_search.py:672: RuntimeWarning: overflow encountered in square\\n\",\n \" array_means[:, np.newaxis]) ** 2,\\n\"\n ]\n }\n ],\n \"source\": [\n \"gs = GridSearchCV(estimator = gb, param_grid = param_grid, scoring = 'r2', cv = 3, n_jobs = 3, verbose = 10).fit(X_train, y_train)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pickle\\n\",\n \"pickle.dump(gs, open('gsgd','wb'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"gs = pickle.load(open('gsgd','rb'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\\n\",\n \"\\n\",\n \"You can get the `` word id by doing:\\n\",\n \"```python\\n\",\n \"target_vocab_to_int['']\\n\",\n \"```\\n\",\n \"You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Convert source and target text to proper word ids\\n\",\n \" :param source_text: String that contains all the source text.\\n\",\n \" :param target_text: String that contains all the target text.\\n\",\n \" :param source_vocab_to_int: Dictionary to go from the source words to an id\\n\",\n \" :param target_vocab_to_int: Dictionary to go from the target words to an id\\n\",\n \" :return: A tuple of lists (source_id_text, target_id_text)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" source_split, target_split = source_text.split('\\\\n'), target_text.split('\\\\n')\\n\",\n \" source_to_int, target_to_int = [], []\\n\",\n \" for source, target in zip(source_split, target_split):\\n\",\n \" source_to_int.append([source_vocab_to_int[word] for word in source.split()])\\n\",\n \" targets = [target_vocab_to_int[word] for word in target.split()]\\n\",\n \" targets.append((target_vocab_to_int['']))\\n\",\n \" target_to_int.append(targets)\\n\",\n \" \\n\",\n \" #print(source_to_int, target_to_int)\\n\",\n \" return source_to_int, target_to_int\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_text_to_ids(text_to_ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Preprocess all the data and save it\\n\",\n \"Running the code cell below will preprocess all the data and save it to file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"helper.preprocess_and_save_data(source_path, target_path, text_to_ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Check Point\\n\",\n \"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import numpy as np\\n\",\n \"import helper\\n\",\n \"import problem_unittests as tests\\n\",\n \"\\n\",\n \"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Check the Version of TensorFlow and Access to GPU\\n\",\n \"This will check to make sure you have the correct version of TensorFlow and access to a GPU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"TensorFlow Version: 1.1.0\\n\",\n \"Default GPU Device: /gpu:0\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"from distutils.version import LooseVersion\\n\",\n \"import warnings\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow.python.layers.core import Dense\\n\",\n \"\\n\",\n \"# Check TensorFlow Version\\n\",\n \"assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\\n\",\n \"print('TensorFlow Version: {}'.format(tf.__version__))\\n\",\n \"\\n\",\n \"# Check for a GPU\\n\",\n \"if not tf.test.gpu_device_name():\\n\",\n \" warnings.warn('No GPU found. Please use a GPU to train your neural network.')\\n\",\n \"else:\\n\",\n \" print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Build the Neural Network\\n\",\n \"You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\\n\",\n \"- `model_inputs`\\n\",\n \"- `process_decoder_input`\\n\",\n \"- `encoding_layer`\\n\",\n \"- `decoding_layer_train`\\n\",\n \"- `decoding_layer_infer`\\n\",\n \"- `decoding_layer`\\n\",\n \"- `seq2seq_model`\\n\",\n \"\\n\",\n \"### Input\\n\",\n \"Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\\n\",\n \"\\n\",\n \"- Input text placeholder named \\\"input\\\" using the TF Placeholder name parameter with rank 2.\\n\",\n \"- Targets placeholder with rank 2.\\n\",\n \"- Learning rate placeholder with rank 0.\\n\",\n \"- Keep probability placeholder named \\\"keep_prob\\\" using the TF Placeholder name parameter with rank 0.\\n\",\n \"- Target sequence length placeholder named \\\"target_sequence_length\\\" with rank 1\\n\",\n \"- Max target sequence length tensor named \\\"max_target_len\\\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\\n\",\n \"- Source sequence length placeholder named \\\"source_sequence_length\\\" with rank 1\\n\",\n \"\\n\",\n \"Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def model_inputs():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\\n\",\n \" :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\\n\",\n \" max target sequence length, source sequence length)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" #max_tar_seq_len = np.max([len(sentence) for sentence in target_int_text])\\n\",\n \" #max_sour_seq_len = np.max([len(sentence) for sentence in source_int_text])\\n\",\n \" #max_source_len = np.max([max_tar_seq_len, max_sour_seq_len])\\n\",\n \" inputs = tf.placeholder(tf.int32, [None, None], name='input')\\n\",\n \" targets = tf.placeholder(tf.int32, [None, None])\\n\",\n \" learning_rate = tf.placeholder(tf.float32)\\n\",\n \" keep_probability = tf.placeholder(tf.float32, name='keep_prob')\\n\",\n \" target_seq_len = tf.placeholder(tf.int32, [None], name='target_sequence_length')\\n\",\n \" max_target_seq_len = tf.reduce_max(target_seq_len, name='target_sequence_length')\\n\",\n \" source_seq_len = tf.placeholder(tf.int32, [None], name='source_sequence_length')\\n\",\n \" return inputs, targets, learning_rate, keep_probability, target_seq_len, max_target_seq_len, source_seq_len\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_model_inputs(model_inputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Process Decoder Input\\n\",\n \"Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def process_decoder_input(target_data, target_vocab_to_int, batch_size):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Preprocess target data for encoding\\n\",\n \" :param target_data: Target Placehoder\\n\",\n \" :param target_vocab_to_int: Dictionary to go from the target words to an id\\n\",\n \" :param batch_size: Batch Size\\n\",\n \" :return: Preprocessed target data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\\n\",\n \" dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['']), ending], 1)\\n\",\n \" return dec_input\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_process_encoding_input(process_decoder_input)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Encoding\\n\",\n \"Implement `encoding_layer()` to create a Encoder RNN layer:\\n\",\n \" * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\\n\",\n \" * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\\n\",\n \" * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"from imp import reload\\n\",\n \"reload(tests)\\n\",\n \"\\n\",\n \"def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \\n\",\n \" source_sequence_length, source_vocab_size, \\n\",\n \" encoding_embedding_size):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create encoding layer\\n\",\n \" :param rnn_inputs: Inputs for the RNN\\n\",\n \" :param rnn_size: RNN Size\\n\",\n \" :param num_layers: Number of layers\\n\",\n \" :param keep_prob: Dropout keep probability\\n\",\n \" :param source_sequence_length: a list of the lengths of each sequence in the batch\\n\",\n \" :param source_vocab_size: vocabulary size of source data\\n\",\n \" :param encoding_embedding_size: embedding size of source data\\n\",\n \" :return: tuple (RNN output, RNN state)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" embed_seq = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)\\n\",\n \" def lstm_cell():\\n\",\n \" return tf.contrib.rnn.LSTMCell(rnn_size)\\n\",\n \" rnn = tf.contrib.rnn.MultiRNNCell([lstm_cell() for i in range(num_layers)])\\n\",\n \" rnn = tf.contrib.rnn.DropoutWrapper(rnn, output_keep_prob=keep_prob)\\n\",\n \" output, state = tf.nn.dynamic_rnn(rnn, embed_seq, dtype=tf.float32)\\n\",\n \" return output, state\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_encoding_layer(encoding_layer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Decoding - Training\\n\",\n \"Create a training decoding layer:\\n\",\n \"* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \\n\",\n \"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\\n\",\n \"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \\n\",\n \" target_sequence_length, max_summary_length, \\n\",\n \" output_layer, keep_prob):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create a decoding layer for training\\n\",\n \" :param encoder_state: Encoder State\\n\",\n \" :param dec_cell: Decoder RNN Cell\\n\",\n \" :param dec_embed_input: Decoder embedded input\\n\",\n \" :param target_sequence_length: The lengths of each sequence in the target batch\\n\",\n \" :param max_summary_length: The length of the longest sequence in the batch\\n\",\n \" :param output_layer: Function to apply the output layer\\n\",\n \" :param keep_prob: Dropout keep probability\\n\",\n \" :return: BasicDecoderOutput containing training logits and sample_id\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" training_helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length)\\n\",\n \" train_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, training_helper, encoder_state, output_layer)\\n\",\n \" output, _ = tf.contrib.seq2seq.dynamic_decode(train_decoder, impute_finished=False, maximum_iterations=max_summary_length)\\n\",\n \" \\n\",\n \" return output\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_decoding_layer_train(decoding_layer_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Decoding - Inference\\n\",\n \"Create inference decoder:\\n\",\n \"* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\\n\",\n \"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\\n\",\n \"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\\n\",\n \" end_of_sequence_id, max_target_sequence_length,\\n\",\n \" vocab_size, output_layer, batch_size, keep_prob):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create a decoding layer for inference\\n\",\n \" :param encoder_state: Encoder state\\n\",\n \" :param dec_cell: Decoder RNN Cell\\n\",\n \" :param dec_embeddings: Decoder embeddings\\n\",\n \" :param start_of_sequence_id: GO ID\\n\",\n \" :param end_of_sequence_id: EOS Id\\n\",\n \" :param max_target_sequence_length: Maximum length of target sequences\\n\",\n \" :param vocab_size: Size of decoder/target vocabulary\\n\",\n \" :param decoding_scope: TenorFlow Variable Scope for decoding\\n\",\n \" :param output_layer: Function to apply the output layer\\n\",\n \" :param batch_size: Batch size\\n\",\n \" :param keep_prob: Dropout keep probability\\n\",\n \" :return: BasicDecoderOutput containing inference logits and sample_id\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" start_tokens = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_tokens')\\n\",\n \" inference_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id)\\n\",\n \" inference_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, inference_helper, encoder_state, output_layer)\\n\",\n \" output, _ = tf.contrib.seq2seq.dynamic_decode(inference_decoder,\\n\",\n \" impute_finished=True, maximum_iterations=max_target_sequence_length)\\n\",\n \" return output\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_decoding_layer_infer(decoding_layer_infer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build the Decoding Layer\\n\",\n \"Implement `decoding_layer()` to create a Decoder RNN layer.\\n\",\n \"\\n\",\n \"* Embed the target sequences\\n\",\n \"* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\\n\",\n \"* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\\n\",\n \"* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\\n\",\n \"* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\\n\",\n \"\\n\",\n \"Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def decoding_layer(dec_input, encoder_state,\\n\",\n \" target_sequence_length, max_target_sequence_length,\\n\",\n \" rnn_size,\\n\",\n \" num_layers, target_vocab_to_int, target_vocab_size,\\n\",\n \" batch_size, keep_prob, decoding_embedding_size):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create decoding layer\\n\",\n \" :param dec_input: Decoder input\\n\",\n \" :param encoder_state: Encoder state\\n\",\n \" :param target_sequence_length: The lengths of each sequence in the target batch\\n\",\n \" :param max_target_sequence_length: Maximum length of target sequences\\n\",\n \" :param rnn_size: RNN Size\\n\",\n \" :param num_layers: Number of layers\\n\",\n \" :param target_vocab_to_int: Dictionary to go from the target words to an id\\n\",\n \" :param target_vocab_size: Size of target vocabulary\\n\",\n \" :param batch_size: The size of the batch\\n\",\n \" :param keep_prob: Dropout keep probability\\n\",\n \" :param decoding_embedding_size: Decoding embedding size\\n\",\n \" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" #embed_seq = tf.contrib.layers.embed_sequence(dec_input, target_vocab_size, decoding_embedding_size)\\n\",\n \" \\n\",\n \" dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\\n\",\n \" dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\\n\",\n \" \\n\",\n \" def lstm_cell():\\n\",\n \" return tf.contrib.rnn.LSTMCell(rnn_size)\\n\",\n \" rnn = tf.contrib.rnn.MultiRNNCell([lstm_cell() for i in range(num_layers)])\\n\",\n \" rnn = tf.contrib.rnn.DropoutWrapper(rnn, output_keep_prob=keep_prob)\\n\",\n \" output_layer = Dense(target_vocab_size,\\n\",\n \" kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))\\n\",\n \" \\n\",\n \" with tf.variable_scope(\\\"decode\\\"):\\n\",\n \" training_output = decoding_layer_train(encoder_state, rnn, dec_embed_input, \\n\",\n \" target_sequence_length, max_target_sequence_length, output_layer, keep_prob)\\n\",\n \" \\n\",\n \" with tf.variable_scope(\\\"decode\\\", reuse=True):\\n\",\n \" inference_output = decoding_layer_infer(encoder_state, rnn, dec_embeddings, target_vocab_to_int[''],\\n\",\n \" target_vocab_to_int[''], max_target_sequence_length, target_vocab_size,\\n\",\n \" output_layer, batch_size, keep_prob)\\n\",\n \" return training_output, inference_output\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_decoding_layer(decoding_layer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build the Neural Network\\n\",\n \"Apply the functions you implemented above to:\\n\",\n \"\\n\",\n \"- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\\n\",\n \"- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\\n\",\n \"- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def seq2seq_model(input_data, target_data, keep_prob, batch_size,\\n\",\n \" source_sequence_length, target_sequence_length,\\n\",\n \" max_target_sentence_length,\\n\",\n \" source_vocab_size, target_vocab_size,\\n\",\n \" enc_embedding_size, dec_embedding_size,\\n\",\n \" rnn_size, num_layers, target_vocab_to_int):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Build the Sequence-to-Sequence part of the neural network\\n\",\n \" :param input_data: Input placeholder\\n\",\n \" :param target_data: Target placeholder\\n\",\n \" :param keep_prob: Dropout keep probability placeholder\\n\",\n \" :param batch_size: Batch Size\\n\",\n \" :param source_sequence_length: Sequence Lengths of source sequences in the batch\\n\",\n \" :param target_sequence_length: Sequence Lengths of target sequences in the batch\\n\",\n \" :max_target_sentence_length: Maximum target sequence lenght\\n\",\n \" :param source_vocab_size: Source vocabulary size\\n\",\n \" :param target_vocab_size: Target vocabulary size\\n\",\n \" :param enc_embedding_size: Decoder embedding size\\n\",\n \" :param dec_embedding_size: Encoder embedding size\\n\",\n \" :param rnn_size: RNN Size\\n\",\n \" :param num_layers: Number of layers\\n\",\n \" :param target_vocab_to_int: Dictionary to go from the target words to an id\\n\",\n \" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" _, enc_state = encoding_layer(input_data, rnn_size, num_layers, keep_prob, \\n\",\n \" source_sequence_length, source_vocab_size, \\n\",\n \" enc_embedding_size)\\n\",\n \" dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)\\n\",\n \" training_output, inference_output = decoding_layer(dec_input, enc_state, target_sequence_length, \\n\",\n \" max_target_sentence_length, rnn_size, num_layers, \\n\",\n \" target_vocab_to_int, target_vocab_size, batch_size, \\n\",\n \" keep_prob, dec_embedding_size)\\n\",\n \" return training_output, inference_output\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_seq2seq_model(seq2seq_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Neural Network Training\\n\",\n \"### Hyperparameters\\n\",\n \"Tune the following parameters:\\n\",\n \"\\n\",\n \"- Set `epochs` to the number of epochs.\\n\",\n \"- Set `batch_size` to the batch size.\\n\",\n \"- Set `rnn_size` to the size of the RNNs.\\n\",\n \"- Set `num_layers` to the number of layers.\\n\",\n \"- Set `encoding_embedding_size` to the size of the embedding for the encoder.\\n\",\n \"- Set `decoding_embedding_size` to the size of the embedding for the decoder.\\n\",\n \"- Set `learning_rate` to the learning rate.\\n\",\n \"- Set `keep_probability` to the Dropout keep probability\\n\",\n \"- Set `display_step` to state how many steps between each debug output statement\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Number of Epochs\\n\",\n \"epochs = 10\\n\",\n \"# Batch Size\\n\",\n \"batch_size = 128\\n\",\n \"# RNN Size\\n\",\n \"rnn_size = 254\\n\",\n \"# Number of Layers\\n\",\n \"num_layers = 2\\n\",\n \"# Embedding Size\\n\",\n \"encoding_embedding_size = 200\\n\",\n \"decoding_embedding_size = 200\\n\",\n \"# Learning Rate\\n\",\n \"learning_rate = 0.01\\n\",\n \"# Dropout Keep Probability\\n\",\n \"keep_probability = 0.5\\n\",\n \"display_step = 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build the Graph\\n\",\n \"Build the graph using the neural network you implemented.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"save_path = 'checkpoints/dev'\\n\",\n \"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\\n\",\n \"max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\\n\",\n \"\\n\",\n \"train_graph = tf.Graph()\\n\",\n \"with train_graph.as_default():\\n\",\n \" input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\\n\",\n \"\\n\",\n \" #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\\n\",\n \" input_shape = tf.shape(input_data)\\n\",\n \"\\n\",\n \" train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\\n\",\n \" targets,\\n\",\n \" keep_prob,\\n\",\n \" batch_size,\\n\",\n \" source_sequence_length,\\n\",\n \" target_sequence_length,\\n\",\n \" max_target_sequence_length,\\n\",\n \" len(source_vocab_to_int),\\n\",\n \" len(target_vocab_to_int),\\n\",\n \" encoding_embedding_size,\\n\",\n \" decoding_embedding_size,\\n\",\n \" rnn_size,\\n\",\n \" num_layers,\\n\",\n \" target_vocab_to_int)\\n\",\n \"\\n\",\n \"\\n\",\n \" training_logits = tf.identity(train_logits.rnn_output, name='logits')\\n\",\n \" inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\\n\",\n \"\\n\",\n \" masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\\n\",\n \"\\n\",\n \" with tf.name_scope(\\\"optimization\\\"):\\n\",\n \" # Loss function\\n\",\n \" cost = tf.contrib.seq2seq.sequence_loss(\\n\",\n \" training_logits,\\n\",\n \" targets,\\n\",\n \" masks)\\n\",\n \"\\n\",\n \" # Optimizer\\n\",\n \" optimizer = tf.train.AdamOptimizer(lr)\\n\",\n \"\\n\",\n \" # Gradient Clipping\\n\",\n \" gradients = optimizer.compute_gradients(cost)\\n\",\n \" capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\\n\",\n \" train_op = optimizer.apply_gradients(capped_gradients)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Batch and pad the source and target sequences\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def pad_sentence_batch(sentence_batch, pad_int):\\n\",\n \" \\\"\\\"\\\"Pad sentences with so that each sentence of a batch has the same length\\\"\\\"\\\"\\n\",\n \" max_sentence = max([len(sentence) for sentence in sentence_batch])\\n\",\n \" return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\\n\",\n \" \\\"\\\"\\\"Batch targets, sources, and the lengths of their sentences together\\\"\\\"\\\"\\n\",\n \" for batch_i in range(0, len(sources)//batch_size):\\n\",\n \" start_i = batch_i * batch_size\\n\",\n \"\\n\",\n \" # Slice the right amount for the batch\\n\",\n \" sources_batch = sources[start_i:start_i + batch_size]\\n\",\n \" targets_batch = targets[start_i:start_i + batch_size]\\n\",\n \"\\n\",\n \" # Pad\\n\",\n \" pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\\n\",\n \" pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\\n\",\n \"\\n\",\n \" # Need the lengths for the _lengths parameters\\n\",\n \" pad_targets_lengths = []\\n\",\n \" for target in pad_targets_batch:\\n\",\n \" pad_targets_lengths.append(len(target))\\n\",\n \"\\n\",\n \" pad_source_lengths = []\\n\",\n \" for source in pad_sources_batch:\\n\",\n \" pad_source_lengths.append(len(source))\\n\",\n \"\\n\",\n \" yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Train\\n\",\n \"Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forums to see if anyone is having the same problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0 Batch 10/1077 - Train Accuracy: 0.2447, Validation Accuracy: 0.3402, Loss: 3.6887\\n\",\n \"Epoch 0 Batch 20/1077 - Train Accuracy: 0.3355, Validation Accuracy: 0.3878, Loss: 3.0551\\n\",\n \"Epoch 0 Batch 30/1077 - Train Accuracy: 0.3789, Validation Accuracy: 0.4357, Loss: 2.7872\\n\",\n \"Epoch 0 Batch 40/1077 - Train Accuracy: 0.4238, Validation Accuracy: 0.4869, Loss: 2.5160\\n\",\n \"Epoch 0 Batch 50/1077 - Train Accuracy: 0.4480, Validation Accuracy: 0.5170, Loss: 2.3008\\n\",\n \"Epoch 0 Batch 60/1077 - Train Accuracy: 0.4963, Validation Accuracy: 0.5352, Loss: 1.9147\\n\",\n \"Epoch 0 Batch 70/1077 - Train Accuracy: 0.4556, Validation Accuracy: 0.5071, Loss: 1.6960\\n\",\n \"Epoch 0 Batch 80/1077 - Train Accuracy: 0.2562, Validation Accuracy: 0.2859, Loss: 1.3617\\n\",\n \"Epoch 0 Batch 90/1077 - Train Accuracy: 0.3539, Validation Accuracy: 0.3707, Loss: 1.2548\\n\",\n \"Epoch 0 Batch 100/1077 - Train Accuracy: 0.4859, Validation Accuracy: 0.5195, Loss: 1.0572\\n\",\n \"Epoch 0 Batch 110/1077 - Train Accuracy: 0.5828, Validation Accuracy: 0.5419, Loss: 0.9233\\n\",\n \"Epoch 0 Batch 120/1077 - Train Accuracy: 0.5113, Validation Accuracy: 0.5724, Loss: 0.9251\\n\",\n \"Epoch 0 Batch 130/1077 - Train Accuracy: 0.5573, Validation Accuracy: 0.5810, Loss: 0.8014\\n\",\n \"Epoch 0 Batch 140/1077 - Train Accuracy: 0.5119, Validation Accuracy: 0.5874, Loss: 0.8533\\n\",\n \"Epoch 0 Batch 150/1077 - Train Accuracy: 0.6101, Validation Accuracy: 0.5913, Loss: 0.7517\\n\",\n \"Epoch 0 Batch 160/1077 - Train Accuracy: 0.5906, Validation Accuracy: 0.6140, Loss: 0.7538\\n\",\n \"Epoch 0 Batch 170/1077 - Train Accuracy: 0.5648, Validation Accuracy: 0.6048, Loss: 0.7696\\n\",\n \"Epoch 0 Batch 180/1077 - Train Accuracy: 0.5934, Validation Accuracy: 0.6108, Loss: 0.7277\\n\",\n \"Epoch 0 Batch 190/1077 - Train Accuracy: 0.6254, Validation Accuracy: 0.6140, Loss: 0.6937\\n\",\n \"Epoch 0 Batch 200/1077 - Train Accuracy: 0.5789, Validation Accuracy: 0.6140, Loss: 0.7119\\n\",\n \"Epoch 0 Batch 210/1077 - Train Accuracy: 0.6250, Validation Accuracy: 0.6151, Loss: 0.6736\\n\",\n \"Epoch 0 Batch 220/1077 - Train Accuracy: 0.5983, Validation Accuracy: 0.6374, Loss: 0.6787\\n\",\n \"Epoch 0 Batch 230/1077 - Train Accuracy: 0.6168, Validation Accuracy: 0.6286, Loss: 0.6376\\n\",\n \"Epoch 0 Batch 240/1077 - Train Accuracy: 0.6395, Validation Accuracy: 0.6250, Loss: 0.6000\\n\",\n \"Epoch 0 Batch 250/1077 - Train Accuracy: 0.6286, Validation Accuracy: 0.6250, Loss: 0.5548\\n\",\n \"Epoch 0 Batch 260/1077 - Train Accuracy: 0.6347, Validation Accuracy: 0.6151, Loss: 0.5610\\n\",\n \"Epoch 0 Batch 270/1077 - Train Accuracy: 0.5961, Validation Accuracy: 0.6200, Loss: 0.6164\\n\",\n \"Epoch 0 Batch 280/1077 - Train Accuracy: 0.6336, Validation Accuracy: 0.6371, Loss: 0.6046\\n\",\n \"Epoch 0 Batch 290/1077 - Train Accuracy: 0.6254, Validation Accuracy: 0.6406, Loss: 0.5982\\n\",\n \"Epoch 0 Batch 300/1077 - Train Accuracy: 0.6164, Validation Accuracy: 0.6175, Loss: 0.5605\\n\",\n \"Epoch 0 Batch 310/1077 - Train Accuracy: 0.6348, Validation Accuracy: 0.6428, Loss: 0.5610\\n\",\n \"Epoch 0 Batch 320/1077 - Train Accuracy: 0.6770, Validation Accuracy: 0.6499, Loss: 0.5327\\n\",\n \"Epoch 0 Batch 330/1077 - Train Accuracy: 0.6508, Validation Accuracy: 0.6435, Loss: 0.5100\\n\",\n \"Epoch 0 Batch 340/1077 - Train Accuracy: 0.6451, Validation Accuracy: 0.6548, Loss: 0.5102\\n\",\n \"Epoch 0 Batch 350/1077 - Train Accuracy: 0.6434, Validation Accuracy: 0.6388, Loss: 0.5007\\n\",\n \"Epoch 0 Batch 360/1077 - Train Accuracy: 0.6844, Validation Accuracy: 0.6751, Loss: 0.4801\\n\",\n \"Epoch 0 Batch 370/1077 - Train Accuracy: 0.6778, Validation Accuracy: 0.6705, Loss: 0.4667\\n\",\n \"Epoch 0 Batch 380/1077 - Train Accuracy: 0.6863, Validation Accuracy: 0.6776, Loss: 0.4595\\n\",\n \"Epoch 0 Batch 390/1077 - Train Accuracy: 0.6648, Validation Accuracy: 0.6982, Loss: 0.4892\\n\",\n \"Epoch 0 Batch 400/1077 - Train Accuracy: 0.7043, Validation Accuracy: 0.6818, Loss: 0.4613\\n\",\n \"Epoch 0 Batch 410/1077 - Train Accuracy: 0.6780, Validation Accuracy: 0.6950, Loss: 0.4672\\n\",\n \"Epoch 0 Batch 420/1077 - Train Accuracy: 0.7242, Validation Accuracy: 0.7021, Loss: 0.4154\\n\",\n \"Epoch 0 Batch 430/1077 - Train Accuracy: 0.6770, Validation Accuracy: 0.6982, Loss: 0.4376\\n\",\n \"Epoch 0 Batch 440/1077 - Train Accuracy: 0.6844, Validation Accuracy: 0.6971, Loss: 0.4465\\n\",\n \"Epoch 0 Batch 450/1077 - Train Accuracy: 0.6988, Validation Accuracy: 0.7028, Loss: 0.3913\\n\",\n \"Epoch 0 Batch 460/1077 - Train Accuracy: 0.7363, Validation Accuracy: 0.6921, Loss: 0.4315\\n\",\n \"Epoch 0 Batch 470/1077 - Train Accuracy: 0.6998, Validation Accuracy: 0.7237, Loss: 0.4263\\n\",\n \"Epoch 0 Batch 480/1077 - Train Accuracy: 0.7533, Validation Accuracy: 0.7184, Loss: 0.3890\\n\",\n \"Epoch 0 Batch 490/1077 - Train Accuracy: 0.7230, Validation Accuracy: 0.7234, Loss: 0.3788\\n\",\n \"Epoch 0 Batch 500/1077 - Train Accuracy: 0.7469, Validation Accuracy: 0.6950, Loss: 0.3724\\n\",\n \"Epoch 0 Batch 510/1077 - Train Accuracy: 0.7535, Validation Accuracy: 0.7351, Loss: 0.3604\\n\",\n \"Epoch 0 Batch 520/1077 - Train Accuracy: 0.8132, Validation Accuracy: 0.7390, Loss: 0.3276\\n\",\n \"Epoch 0 Batch 530/1077 - Train Accuracy: 0.7344, Validation Accuracy: 0.7202, Loss: 0.3627\\n\",\n \"Epoch 0 Batch 540/1077 - Train Accuracy: 0.7941, Validation Accuracy: 0.7273, Loss: 0.3168\\n\",\n \"Epoch 0 Batch 550/1077 - Train Accuracy: 0.7254, Validation Accuracy: 0.7692, Loss: 0.3366\\n\",\n \"Epoch 0 Batch 560/1077 - Train Accuracy: 0.7539, Validation Accuracy: 0.7468, Loss: 0.3176\\n\",\n \"Epoch 0 Batch 570/1077 - Train Accuracy: 0.7656, Validation Accuracy: 0.7447, Loss: 0.3313\\n\",\n \"Epoch 0 Batch 580/1077 - Train Accuracy: 0.8032, Validation Accuracy: 0.7610, Loss: 0.2912\\n\",\n \"Epoch 0 Batch 590/1077 - Train Accuracy: 0.7451, Validation Accuracy: 0.7724, Loss: 0.3086\\n\",\n \"Epoch 0 Batch 600/1077 - Train Accuracy: 0.8080, Validation Accuracy: 0.8136, Loss: 0.2810\\n\",\n \"Epoch 0 Batch 610/1077 - Train Accuracy: 0.8059, Validation Accuracy: 0.8050, Loss: 0.2945\\n\",\n \"Epoch 0 Batch 620/1077 - Train Accuracy: 0.8387, Validation Accuracy: 0.7965, Loss: 0.2629\\n\",\n \"Epoch 0 Batch 630/1077 - Train Accuracy: 0.8254, Validation Accuracy: 0.7947, Loss: 0.2463\\n\",\n \"Epoch 0 Batch 640/1077 - Train Accuracy: 0.8010, Validation Accuracy: 0.7649, Loss: 0.2506\\n\",\n \"Epoch 0 Batch 650/1077 - Train Accuracy: 0.8219, Validation Accuracy: 0.7859, Loss: 0.2525\\n\",\n \"Epoch 0 Batch 660/1077 - Train Accuracy: 0.8176, Validation Accuracy: 0.7983, Loss: 0.2428\\n\",\n \"Epoch 0 Batch 670/1077 - Train Accuracy: 0.8317, Validation Accuracy: 0.8082, Loss: 0.2429\\n\",\n \"Epoch 0 Batch 680/1077 - Train Accuracy: 0.8203, Validation Accuracy: 0.8061, Loss: 0.2372\\n\",\n \"Epoch 0 Batch 690/1077 - Train Accuracy: 0.8602, Validation Accuracy: 0.8171, Loss: 0.2182\\n\",\n \"Epoch 0 Batch 700/1077 - Train Accuracy: 0.8742, Validation Accuracy: 0.8189, Loss: 0.2064\\n\",\n \"Epoch 0 Batch 710/1077 - Train Accuracy: 0.8398, Validation Accuracy: 0.7898, Loss: 0.2257\\n\",\n \"Epoch 0 Batch 720/1077 - Train Accuracy: 0.7903, Validation Accuracy: 0.8018, Loss: 0.2420\\n\",\n \"Epoch 0 Batch 730/1077 - Train Accuracy: 0.8367, Validation Accuracy: 0.8026, Loss: 0.2275\\n\",\n \"Epoch 0 Batch 740/1077 - Train Accuracy: 0.8477, Validation Accuracy: 0.8164, Loss: 0.2046\\n\",\n \"Epoch 0 Batch 750/1077 - Train Accuracy: 0.8668, Validation Accuracy: 0.8121, Loss: 0.1949\\n\",\n \"Epoch 0 Batch 760/1077 - Train Accuracy: 0.8359, Validation Accuracy: 0.8292, Loss: 0.2232\\n\",\n \"Epoch 0 Batch 770/1077 - Train Accuracy: 0.8415, Validation Accuracy: 0.8388, Loss: 0.1953\\n\",\n \"Epoch 0 Batch 780/1077 - 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Train Accuracy: 0.9480, Validation Accuracy: 0.9521, Loss: 0.0527\\n\",\n \"Epoch 3 Batch 980/1077 - Train Accuracy: 0.9344, Validation Accuracy: 0.9407, Loss: 0.0572\\n\",\n \"Epoch 3 Batch 990/1077 - Train Accuracy: 0.9433, Validation Accuracy: 0.9435, Loss: 0.0654\\n\",\n \"Epoch 3 Batch 1000/1077 - Train Accuracy: 0.9423, Validation Accuracy: 0.9137, Loss: 0.0563\\n\",\n \"Epoch 3 Batch 1010/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9091, Loss: 0.0487\\n\",\n \"Epoch 3 Batch 1020/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9308, Loss: 0.0484\\n\",\n \"Epoch 3 Batch 1030/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9265, Loss: 0.0721\\n\",\n \"Epoch 3 Batch 1040/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9240, Loss: 0.0622\\n\",\n \"Epoch 3 Batch 1050/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9364, Loss: 0.0500\\n\",\n \"Epoch 3 Batch 1060/1077 - Train Accuracy: 0.9621, Validation Accuracy: 0.9354, Loss: 0.0420\\n\",\n \"Epoch 3 Batch 1070/1077 - 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Train Accuracy: 0.9527, Validation Accuracy: 0.9286, Loss: 0.0684\\n\",\n \"Epoch 8 Batch 930/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9098, Loss: 0.0490\\n\",\n \"Epoch 8 Batch 940/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9233, Loss: 0.0605\\n\",\n \"Epoch 8 Batch 950/1077 - Train Accuracy: 0.9312, Validation Accuracy: 0.9460, Loss: 0.0504\\n\",\n \"Epoch 8 Batch 960/1077 - Train Accuracy: 0.9353, Validation Accuracy: 0.9251, Loss: 0.0587\\n\",\n \"Epoch 8 Batch 970/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9212, Loss: 0.0705\\n\",\n \"Epoch 8 Batch 980/1077 - Train Accuracy: 0.9262, Validation Accuracy: 0.9386, Loss: 0.0515\\n\",\n \"Epoch 8 Batch 990/1077 - Train Accuracy: 0.9268, Validation Accuracy: 0.9315, Loss: 0.0590\\n\",\n \"Epoch 8 Batch 1000/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9375, Loss: 0.0550\\n\",\n \"Epoch 8 Batch 1010/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9190, Loss: 0.0537\\n\",\n \"Epoch 8 Batch 1020/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9038, Loss: 0.0550\\n\",\n \"Epoch 8 Batch 1030/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9261, Loss: 0.0774\\n\",\n \"Epoch 8 Batch 1040/1077 - Train Accuracy: 0.9396, Validation Accuracy: 0.9386, Loss: 0.0691\\n\",\n \"Epoch 8 Batch 1050/1077 - Train Accuracy: 0.9430, Validation Accuracy: 0.9276, Loss: 0.0558\\n\",\n \"Epoch 8 Batch 1060/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9339, Loss: 0.0528\\n\",\n \"Epoch 8 Batch 1070/1077 - Train Accuracy: 0.9629, Validation Accuracy: 0.9286, Loss: 0.0507\\n\",\n \"Epoch 9 Batch 10/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9233, Loss: 0.0643\\n\",\n \"Epoch 9 Batch 20/1077 - Train Accuracy: 0.9418, Validation Accuracy: 0.9382, Loss: 0.0429\\n\",\n \"Epoch 9 Batch 30/1077 - Train Accuracy: 0.9352, Validation Accuracy: 0.9478, Loss: 0.0473\\n\",\n \"Epoch 9 Batch 40/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9290, Loss: 0.0640\\n\",\n \"Epoch 9 Batch 50/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9315, Loss: 0.0598\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 9 Batch 60/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9126, Loss: 0.0545\\n\",\n \"Epoch 9 Batch 70/1077 - Train Accuracy: 0.9235, Validation Accuracy: 0.9119, Loss: 0.0744\\n\",\n \"Epoch 9 Batch 80/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9148, Loss: 0.0523\\n\",\n \"Epoch 9 Batch 90/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9187, Loss: 0.0562\\n\",\n \"Epoch 9 Batch 100/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9137, Loss: 0.0525\\n\",\n \"Epoch 9 Batch 110/1077 - Train Accuracy: 0.9566, Validation Accuracy: 0.9052, Loss: 0.0543\\n\",\n \"Epoch 9 Batch 120/1077 - Train Accuracy: 0.9621, Validation Accuracy: 0.9158, Loss: 0.0609\\n\",\n \"Epoch 9 Batch 130/1077 - Train Accuracy: 0.9647, Validation Accuracy: 0.9059, Loss: 0.0420\\n\",\n \"Epoch 9 Batch 140/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9212, Loss: 0.0631\\n\",\n \"Epoch 9 Batch 150/1077 - Train Accuracy: 0.9524, Validation Accuracy: 0.9158, Loss: 0.0691\\n\",\n \"Epoch 9 Batch 160/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9272, Loss: 0.0366\\n\",\n \"Epoch 9 Batch 170/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9141, Loss: 0.0598\\n\",\n \"Epoch 9 Batch 180/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9098, Loss: 0.0485\\n\",\n \"Epoch 9 Batch 190/1077 - Train Accuracy: 0.9324, Validation Accuracy: 0.9261, Loss: 0.0489\\n\",\n \"Epoch 9 Batch 200/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9233, Loss: 0.0605\\n\",\n \"Epoch 9 Batch 210/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9137, Loss: 0.0624\\n\",\n \"Epoch 9 Batch 220/1077 - Train Accuracy: 0.9564, Validation Accuracy: 0.9066, Loss: 0.0492\\n\",\n \"Epoch 9 Batch 230/1077 - Train Accuracy: 0.9323, Validation Accuracy: 0.9421, Loss: 0.0700\\n\",\n \"Epoch 9 Batch 240/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9332, Loss: 0.0599\\n\",\n \"Epoch 9 Batch 250/1077 - Train Accuracy: 0.9347, Validation Accuracy: 0.9368, Loss: 0.0501\\n\",\n \"Epoch 9 Batch 260/1077 - Train Accuracy: 0.9435, Validation Accuracy: 0.9212, Loss: 0.0426\\n\",\n \"Epoch 9 Batch 270/1077 - Train Accuracy: 0.9141, Validation Accuracy: 0.9293, Loss: 0.0566\\n\",\n \"Epoch 9 Batch 280/1077 - Train Accuracy: 0.9266, Validation Accuracy: 0.9162, Loss: 0.0579\\n\",\n \"Epoch 9 Batch 290/1077 - Train Accuracy: 0.9187, Validation Accuracy: 0.9268, Loss: 0.0779\\n\",\n \"Epoch 9 Batch 300/1077 - Train Accuracy: 0.9478, Validation Accuracy: 0.9300, Loss: 0.0602\\n\",\n \"Epoch 9 Batch 310/1077 - Train Accuracy: 0.9398, Validation Accuracy: 0.9304, Loss: 0.0586\\n\",\n \"Epoch 9 Batch 320/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9112, Loss: 0.0747\\n\",\n \"Epoch 9 Batch 330/1077 - Train Accuracy: 0.9246, Validation Accuracy: 0.9141, Loss: 0.0652\\n\",\n \"Epoch 9 Batch 340/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9070, Loss: 0.0607\\n\",\n \"Epoch 9 Batch 350/1077 - Train Accuracy: 0.9484, Validation Accuracy: 0.9023, Loss: 0.0568\\n\",\n \"Epoch 9 Batch 360/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9215, Loss: 0.0408\\n\",\n \"Epoch 9 Batch 370/1077 - Train Accuracy: 0.9658, Validation Accuracy: 0.9350, Loss: 0.0449\\n\",\n \"Epoch 9 Batch 380/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9425, Loss: 0.0458\\n\",\n \"Epoch 9 Batch 390/1077 - Train Accuracy: 0.9219, Validation Accuracy: 0.9279, Loss: 0.0696\\n\",\n \"Epoch 9 Batch 400/1077 - Train Accuracy: 0.9383, Validation Accuracy: 0.9205, Loss: 0.0571\\n\",\n \"Epoch 9 Batch 410/1077 - Train Accuracy: 0.9120, Validation Accuracy: 0.9471, Loss: 0.0769\\n\",\n \"Epoch 9 Batch 420/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9354, Loss: 0.0544\\n\",\n \"Epoch 9 Batch 430/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9389, Loss: 0.0524\\n\",\n \"Epoch 9 Batch 440/1077 - Train Accuracy: 0.9156, Validation Accuracy: 0.9173, Loss: 0.0600\\n\",\n \"Epoch 9 Batch 450/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9055, Loss: 0.0587\\n\",\n \"Epoch 9 Batch 460/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9258, Loss: 0.0616\\n\",\n \"Epoch 9 Batch 470/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9180, Loss: 0.0553\\n\",\n \"Epoch 9 Batch 480/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9219, Loss: 0.0495\\n\",\n \"Epoch 9 Batch 490/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9084, Loss: 0.0598\\n\",\n \"Epoch 9 Batch 500/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9055, Loss: 0.0485\\n\",\n \"Epoch 9 Batch 510/1077 - Train Accuracy: 0.9074, Validation Accuracy: 0.9304, Loss: 0.0664\\n\",\n \"Epoch 9 Batch 520/1077 - Train Accuracy: 0.9773, Validation Accuracy: 0.9379, Loss: 0.0534\\n\",\n \"Epoch 9 Batch 530/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9265, Loss: 0.0660\\n\",\n \"Epoch 9 Batch 540/1077 - Train Accuracy: 0.9469, Validation Accuracy: 0.9222, Loss: 0.0586\\n\",\n \"Epoch 9 Batch 550/1077 - Train Accuracy: 0.9211, Validation Accuracy: 0.9155, Loss: 0.0610\\n\",\n \"Epoch 9 Batch 560/1077 - Train Accuracy: 0.9668, Validation Accuracy: 0.9300, Loss: 0.0502\\n\",\n \"Epoch 9 Batch 570/1077 - Train Accuracy: 0.9252, Validation Accuracy: 0.9222, Loss: 0.0680\\n\",\n \"Epoch 9 Batch 580/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9201, Loss: 0.0371\\n\",\n \"Epoch 9 Batch 590/1077 - Train Accuracy: 0.9235, Validation Accuracy: 0.9187, Loss: 0.0622\\n\",\n \"Epoch 9 Batch 600/1077 - Train Accuracy: 0.9368, Validation Accuracy: 0.9418, Loss: 0.0643\\n\",\n \"Epoch 9 Batch 610/1077 - Train Accuracy: 0.9268, Validation Accuracy: 0.9421, Loss: 0.0636\\n\",\n \"Epoch 9 Batch 620/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9379, Loss: 0.0544\\n\",\n \"Epoch 9 Batch 630/1077 - Train Accuracy: 0.9332, Validation Accuracy: 0.9396, Loss: 0.0639\\n\",\n \"Epoch 9 Batch 640/1077 - Train Accuracy: 0.9431, Validation Accuracy: 0.9322, Loss: 0.0517\\n\",\n \"Epoch 9 Batch 650/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9261, Loss: 0.0661\\n\",\n \"Epoch 9 Batch 660/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9428, Loss: 0.0592\\n\",\n \"Epoch 9 Batch 670/1077 - Train Accuracy: 0.9361, Validation Accuracy: 0.9347, Loss: 0.0715\\n\",\n \"Epoch 9 Batch 680/1077 - Train Accuracy: 0.9308, Validation Accuracy: 0.9279, Loss: 0.0622\\n\",\n \"Epoch 9 Batch 690/1077 - Train Accuracy: 0.9320, Validation Accuracy: 0.9134, Loss: 0.0562\\n\",\n \"Epoch 9 Batch 700/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9027, Loss: 0.0542\\n\",\n \"Epoch 9 Batch 710/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9467, Loss: 0.0396\\n\",\n \"Epoch 9 Batch 720/1077 - Train Accuracy: 0.9260, Validation Accuracy: 0.9531, Loss: 0.0593\\n\",\n \"Epoch 9 Batch 730/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9432, Loss: 0.0708\\n\",\n \"Epoch 9 Batch 740/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9293, Loss: 0.0544\\n\",\n \"Epoch 9 Batch 750/1077 - Train Accuracy: 0.9129, Validation Accuracy: 0.9347, Loss: 0.0561\\n\",\n \"Epoch 9 Batch 760/1077 - Train Accuracy: 0.9469, Validation Accuracy: 0.9389, Loss: 0.0656\\n\",\n \"Epoch 9 Batch 770/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9407, Loss: 0.0580\\n\",\n \"Epoch 9 Batch 780/1077 - Train Accuracy: 0.9273, Validation Accuracy: 0.9226, Loss: 0.0808\\n\",\n \"Epoch 9 Batch 790/1077 - Train Accuracy: 0.8980, Validation Accuracy: 0.9300, Loss: 0.0665\\n\",\n \"Epoch 9 Batch 800/1077 - Train Accuracy: 0.9641, Validation Accuracy: 0.9421, Loss: 0.0573\\n\",\n \"Epoch 9 Batch 810/1077 - Train Accuracy: 0.9501, Validation Accuracy: 0.9283, Loss: 0.0404\\n\",\n \"Epoch 9 Batch 820/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9471, Loss: 0.0577\\n\",\n \"Epoch 9 Batch 830/1077 - Train Accuracy: 0.9195, Validation Accuracy: 0.9254, Loss: 0.0792\\n\",\n \"Epoch 9 Batch 840/1077 - Train Accuracy: 0.9566, Validation Accuracy: 0.9251, Loss: 0.0586\\n\",\n \"Epoch 9 Batch 850/1077 - Train Accuracy: 0.9286, Validation Accuracy: 0.9393, Loss: 0.0859\\n\",\n \"Epoch 9 Batch 860/1077 - Train Accuracy: 0.9315, Validation Accuracy: 0.9233, Loss: 0.0609\\n\",\n \"Epoch 9 Batch 870/1077 - Train Accuracy: 0.9280, Validation Accuracy: 0.9105, Loss: 0.0579\\n\",\n \"Epoch 9 Batch 880/1077 - Train Accuracy: 0.9695, Validation Accuracy: 0.9190, Loss: 0.0539\\n\",\n \"Epoch 9 Batch 890/1077 - Train Accuracy: 0.9609, Validation Accuracy: 0.9276, Loss: 0.0528\\n\",\n \"Epoch 9 Batch 900/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9297, Loss: 0.0740\\n\",\n \"Epoch 9 Batch 910/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9442, Loss: 0.0579\\n\",\n \"Epoch 9 Batch 920/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9315, Loss: 0.0549\\n\",\n \"Epoch 9 Batch 930/1077 - Train Accuracy: 0.9496, Validation Accuracy: 0.9311, Loss: 0.0494\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 9 Batch 940/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9407, Loss: 0.0485\\n\",\n \"Epoch 9 Batch 950/1077 - Train Accuracy: 0.9487, Validation Accuracy: 0.9506, Loss: 0.0396\\n\",\n \"Epoch 9 Batch 960/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9371, Loss: 0.0470\\n\",\n \"Epoch 9 Batch 970/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9251, Loss: 0.0629\\n\",\n \"Epoch 9 Batch 980/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.9403, Loss: 0.0514\\n\",\n \"Epoch 9 Batch 990/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9332, Loss: 0.0625\\n\",\n \"Epoch 9 Batch 1000/1077 - Train Accuracy: 0.9513, Validation Accuracy: 0.9336, Loss: 0.0561\\n\",\n \"Epoch 9 Batch 1010/1077 - Train Accuracy: 0.9727, Validation Accuracy: 0.9460, Loss: 0.0514\\n\",\n \"Epoch 9 Batch 1020/1077 - Train Accuracy: 0.9594, Validation Accuracy: 0.9471, Loss: 0.0489\\n\",\n \"Epoch 9 Batch 1030/1077 - Train Accuracy: 0.9508, Validation Accuracy: 0.9339, Loss: 0.0553\\n\",\n \"Epoch 9 Batch 1040/1077 - Train Accuracy: 0.9511, Validation Accuracy: 0.9339, Loss: 0.0593\\n\",\n \"Epoch 9 Batch 1050/1077 - Train Accuracy: 0.9680, Validation Accuracy: 0.9304, Loss: 0.0481\\n\",\n \"Epoch 9 Batch 1060/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9290, Loss: 0.0606\\n\",\n \"Epoch 9 Batch 1070/1077 - Train Accuracy: 0.9637, Validation Accuracy: 0.9400, Loss: 0.0504\\n\",\n \"Model Trained and Saved\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def get_accuracy(target, logits):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Calculate accuracy\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" max_seq = max(target.shape[1], logits.shape[1])\\n\",\n \" if max_seq - target.shape[1]:\\n\",\n \" target = np.pad(\\n\",\n \" target,\\n\",\n \" [(0,0),(0,max_seq - target.shape[1])],\\n\",\n \" 'constant')\\n\",\n \" if max_seq - logits.shape[1]:\\n\",\n \" logits = np.pad(\\n\",\n \" logits,\\n\",\n \" [(0,0),(0,max_seq - logits.shape[1])],\\n\",\n \" 'constant')\\n\",\n \"\\n\",\n \" return np.mean(np.equal(target, logits))\\n\",\n \"\\n\",\n \"# Split data to training and validation sets\\n\",\n \"train_source = source_int_text[batch_size:]\\n\",\n \"train_target = target_int_text[batch_size:]\\n\",\n \"valid_source = source_int_text[:batch_size]\\n\",\n \"valid_target = target_int_text[:batch_size]\\n\",\n \"(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\\n\",\n \" valid_target,\\n\",\n \" batch_size,\\n\",\n \" source_vocab_to_int[''],\\n\",\n \" target_vocab_to_int[''])) \\n\",\n \"with tf.Session(graph=train_graph) as sess:\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \"\\n\",\n \" for epoch_i in range(epochs):\\n\",\n \" for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\\n\",\n \" get_batches(train_source, train_target, batch_size,\\n\",\n \" source_vocab_to_int[''],\\n\",\n \" target_vocab_to_int[''])):\\n\",\n \"\\n\",\n \" _, loss = sess.run(\\n\",\n \" [train_op, cost],\\n\",\n \" {input_data: source_batch,\\n\",\n \" targets: target_batch,\\n\",\n \" lr: learning_rate,\\n\",\n \" target_sequence_length: targets_lengths,\\n\",\n \" source_sequence_length: sources_lengths,\\n\",\n \" keep_prob: keep_probability})\\n\",\n \"\\n\",\n \"\\n\",\n \" if batch_i % display_step == 0 and batch_i > 0:\\n\",\n \"\\n\",\n \"\\n\",\n \" batch_train_logits = sess.run(\\n\",\n \" inference_logits,\\n\",\n \" {input_data: source_batch,\\n\",\n \" source_sequence_length: sources_lengths,\\n\",\n \" target_sequence_length: targets_lengths,\\n\",\n \" keep_prob: 1.0})\\n\",\n \"\\n\",\n \"\\n\",\n \" batch_valid_logits = sess.run(\\n\",\n \" inference_logits,\\n\",\n \" {input_data: valid_sources_batch,\\n\",\n \" source_sequence_length: valid_sources_lengths,\\n\",\n \" target_sequence_length: valid_targets_lengths,\\n\",\n \" keep_prob: 1.0})\\n\",\n \"\\n\",\n \" train_acc = get_accuracy(target_batch, batch_train_logits)\\n\",\n \"\\n\",\n \" valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\\n\",\n \"\\n\",\n \" print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\\n\",\n \" .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\\n\",\n \"\\n\",\n \" # Save Model\\n\",\n \" saver = tf.train.Saver()\\n\",\n \" saver.save(sess, save_path)\\n\",\n \" print('Model Trained and Saved')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Save Parameters\\n\",\n \"Save the `batch_size` and `save_path` parameters for inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"# Save parameters for checkpoint\\n\",\n \"helper.save_params(save_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Checkpoint\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import helper\\n\",\n \"import problem_unittests as tests\\n\",\n \"\\n\",\n \"_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\\n\",\n \"load_path = helper.load_params()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sentence to Sequence\\n\",\n \"To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\\n\",\n \"\\n\",\n \"- Convert the sentence to lowercase\\n\",\n \"- Convert words into ids using `vocab_to_int`\\n\",\n \" - Convert words not in the vocabulary, to the `` word id.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tests Passed\\n\"\n ]\n }\n ],\n \"source\": [\n \"def sentence_to_seq(sentence, vocab_to_int):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Convert a sentence to a sequence of ids\\n\",\n \" :param sentence: String\\n\",\n \" :param vocab_to_int: Dictionary to go from the words to an id\\n\",\n \" :return: List of word ids\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement Function\\n\",\n \" sentence = sentence.lower()\\n\",\n \" sentence_to_id = [vocab_to_int[word] if word in vocab_to_int.keys() else vocab_to_int[''] for word in sentence.split(' ')]\\n\",\n \" return sentence_to_id\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"tests.test_sentence_to_seq(sentence_to_seq)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Translate\\n\",\n \"This will translate `translate_sentence` from English to French.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:tensorflow:Restoring parameters from checkpoints/dev\\n\",\n \"Input\\n\",\n \" Word Ids: [52, 28, 109, 153, 105, 79, 230]\\n\",\n \" English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\\n\",\n \"\\n\",\n \"Prediction\\n\",\n \" Word Ids: [294, 318, 149, 301, 338, 353, 22, 186, 1]\\n\",\n \" French Words: il a vu un vieux camion jaune . \\n\"\n ]\n }\n ],\n \"source\": [\n \"translate_sentence = 'he saw a old yellow truck .'\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\\n\",\n \"\\n\",\n \"loaded_graph = tf.Graph()\\n\",\n \"with tf.Session(graph=loaded_graph) as sess:\\n\",\n \" # Load saved model\\n\",\n \" loader = tf.train.import_meta_graph(load_path + '.meta')\\n\",\n \" loader.restore(sess, load_path)\\n\",\n \"\\n\",\n \" input_data = loaded_graph.get_tensor_by_name('input:0')\\n\",\n \" logits = loaded_graph.get_tensor_by_name('predictions:0')\\n\",\n \" target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\\n\",\n \" source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\\n\",\n \" keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\\n\",\n \"\\n\",\n \" translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,\\n\",\n \" target_sequence_length: [len(translate_sentence)*2]*batch_size,\\n\",\n \" source_sequence_length: [len(translate_sentence)]*batch_size,\\n\",\n \" keep_prob: 1.0})[0]\\n\",\n \"\\n\",\n \"print('Input')\\n\",\n \"print(' Word Ids: {}'.format([i for i in translate_sentence]))\\n\",\n \"print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\\n\",\n \"\\n\",\n \"print('\\\\nPrediction')\\n\",\n \"print(' Word Ids: {}'.format([i for i in translate_logits]))\\n\",\n \"print(' French Words: {}'.format(\\\" \\\".join([target_int_to_vocab[i] for i in translate_logits])))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Imperfect Translation\\n\",\n \"You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\\n\",\n \"\\n\",\n \"You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\\n\",\n \"## Submitting This Project\\n\",\n \"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \\\"dlnd_language_translation.ipynb\\\" and save it as a HTML file under \\\"File\\\" -> \\\"Download as\\\". Include the \\\"helper.py\\\" and \\\"problem_unittests.py\\\" files in your submission.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":4098,"cells":{"repo_name":{"kind":"string","value":"jakubholynet/lodash-fp_issues"},"path":{"kind":"string","value":"mutating_with_seamless-immutable.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"3623"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Experiences with seamless-immutable\\n\",\n \"\\n\",\n \"On our data the \\\"dev\\\" version (that actually does freeze data etc.) was 2-3 slower than the (nearly \\\"noop\\\") prod version - f.ex. 2200 ms x 800 ms.\\n\",\n \"=> it is great there are both the dev and prod versions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Modifying data with seamless-immutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"var Immutable = require('seamless-immutable');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Imutable arrays\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Push, unshift => concat\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ 2, 3 ]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"var original = Immutable([2]);\\n\",\n \"original.concat([3]);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ 1, 2 ]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Immutable([1]).concat(original);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Pop => slice\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Immutable([1]).slice(0,-1);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Other: splice OK, sort :(, ...\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Impractical\\n\",\n \"Mutating nested structures is cumbersome. F.ex. here we want to add a new \\\"event\\\" to a log at the last path:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[ { path: '/', log: [] },\\n\",\n \" { path: '/last', log: [ 'new event' ] } ]\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"var report = Immutable([{path: \\\"/\\\", log: []},{path: \\\"/last\\\", log: []}]);\\n\",\n \"// Update to [{path: \\\"/\\\", log: [\\\"new event\\\"]}]; It would be easy if we had updateIn and a curried concat:\\n\",\n \"// var updatedReport = updateIn(report, [report.length - 1, log], concat([\\\"new event\\\"]))\\n\",\n \"var oldEntry = report[report.length-1];\\n\",\n \"/*var updatedReport =*/ report.slice(0,-1).concat([\\n\",\n \" oldEntry.merge({log: oldEntry.log.concat([\\\"new event\\\"])})\\n\",\n \"]);\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Javascript (Node.js)\",\n \"language\": \"javascript\",\n \"name\": \"javascript\"\n },\n \"language_info\": {\n \"file_extension\": \"js\",\n \"mimetype\": \"application/javascript\",\n \"name\": \"javascript\",\n \"version\": \"0.10.38\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"cc0-1.0"}}},{"rowIdx":4099,"cells":{"repo_name":{"kind":"string","value":"Olsthoorn/IHE-python-course-2017"},"path":{"kind":"string","value":"exercises/Mar07/readingText.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"46666"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \" ![](\\\"../../logo/logo.png\\\")
\\n\",\n \" \\n\",\n \"\\n\",\n \"# IHE Python course, 2017\\n\",\n \"\\n\",\n \"## Reading text files\\n\",\n \"\\n\",\n \"T.N.Olsthoorn, Feb 27, 2017\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reading and writing files is one of the essential functions of computers as this is the connection between volatile data in the computer's memory and permanent data on disk, in the cloud.\\n\",\n \"\\n\",\n \"The three forms of reading text files are shown, and we'll illustrate handling its contents. We'll take the README.md file as an example as it is readily available on this site.\\n\",\n \"\\n\",\n \"## Finding the file in the directory tree\\n\",\n \"First steps are finding the file in the directory structure. We'll start with an illutration how this can be done using some functions in the os module, which deal with directories.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['.DS_Store',\\n\",\n \" '.ipynb_checkpoints',\\n\",\n \" 'dealingWithStrings.ipynb',\\n\",\n \" 'introspection.ipynb',\\n\",\n \" 'readingText.ipynb']\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.listdir() # make a list of the files in the current directory, so that we may handle them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Turn the relative refference of the current directory, '.' into an absolute path.\\n\",\n \"\\n\",\n \"Then specify the file name that we're looking for.\\n\",\n \"\\n\",\n \"And in a while-loop look upward in an ever higher diectory in the directory tree until we found that with our file.\\n\",\n \"\\n\",\n \"Walking upward in the tree is done by cutting off the tail of the path in each step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Starting in folder ,\\n\",\n \" to look for file ...\\n\",\n \"\\n\",\n \"I'm searching: ...\\n\",\n \"/Users/Theo/GRWMODELS/Python_projects/IHEcourse2017/exercises\\n\",\n \"/Users/Theo/GRWMODELS/Python_projects/IHEcourse2017\\n\",\n \"... Yep, got'm!\\n\",\n \"\\n\",\n \"Ok, file is in folder: /Users/Theo/GRWMODELS/Python_projects/IHEcourse2017\\n\",\n \"\\n\",\n \"Here is the list of files in this folder:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"['.DS_Store',\\n\",\n \" '.git',\\n\",\n \" '.gitignore',\\n\",\n \" '.ipynb_checkpoints',\\n\",\n \" 'exercises',\\n\",\n \" 'IMG_20170221_155447496.jpg',\\n\",\n \" 'IntroPythonCourse.pptx',\\n\",\n \" 'LICENSE',\\n\",\n \" 'logo',\\n\",\n \" 'mbakker7-exploratory_computing_with_python-ca15088',\\n\",\n \" 'photos',\\n\",\n \" 'README.md',\\n\",\n \" 'sandbox']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"apth = os.path.abspath('.')\\n\",\n \"\\n\",\n \"fname = 'README.md'\\n\",\n \"\\n\",\n \"print(\\\"Starting in folder <{}>,\\\\n to look for file <{}> ...\\\".format(apth, fname))\\n\",\n \"print()\\n\",\n \"\\n\",\n \"print(\\\"I'm searching: ...\\\")\\n\",\n \"while not fname in os.listdir(apth):\\n\",\n \" apth = apth.rpartition(os.sep)[0]\\n\",\n \" print(apth)\\n\",\n \" \\n\",\n \"if fname in os.listdir(apth):\\n\",\n \" print(\\\"... Yep, got'm!\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"... H'm, missed him\\\")\\n\",\n \" \\n\",\n \"print(\\\"\\\\nOk, file <{}> is in folder: \\\".format(fname), apth)\\n\",\n \"print('\\\\nHere is the list of files in this folder:')\\n\",\n \"os.listdir(apth)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reading the file (at once)\\n\",\n \"\\n\",\n \"When we know where the file is, we can open it for reading.\\n\",\n \"\\n\",\n \"We have to open it, which yields a reader object, by which we can read the file.\\n\",\n \"\\n\",\n \"`reader = open(path, 'r')`\\n\",\n \"\\n\",\n \"`s = reader.read()`\\n\",\n \"\\n\",\n \"`reader.close()`\\n\",\n \"\\n\",\n \"Problem with this, is that when we are exploring the reader, we may easily reach the end of file after which nothing more is read and s is an empty string. Furthermore, when we experiment, we may easily open the same file many times and forget to close it.\\n\",\n \"\\n\",\n \"The with statement is a solution to that, because it automatically closes the file when we finish its block.\\n\",\n \"\\n\",\n \"With the with statement we may read the entire file into a string like so.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"with open(os.path.join(apth, fname), 'r') as reader:\\n\",\n \" s = reader.read()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It's the read that swallows the entire file at once and dumps its contents in the string s.\\n\",\n \"\\n\",\n \"Check if the reader in, indeed, closed after we finished the `with` block:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"reader.closed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then show the contenct of the sring s:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"# IHE-python-course-2017\\n\",\n \"Material for the extracurricular python course given at UNESCO-IHE from 21-Feb-2017\\n\",\n \"\\n\",\n \"Upon request of the virtually entire student community studying at UNESCO-IHE in Delft in 2017, an extracurricular course on Python was given as it was realized that learning python is an essential asset for future engineers and scientists.\\n\",\n \"\\n\",\n \"Next to my own exercises, we'll borrow much of the exercises Prof. Mark Bakker's tutorials for all students at the faculty of CEGS at TUDelft, which are availalbe on github. Other material from the internet may also be used.\\n\",\n \"\\n\",\n \"The focus is on the needs of scientists and engineers.\\n\",\n \"\\n\",\n \"The students are supposed to install the Anaconda packages on their laptop and the lessons will be on Tuesdays 16:45-17:30 as from Feb 21, 2017 in the auditorium of IHE at the Westvest in Delft.\\n\",\n \"\\n\",\n \"For your own practicing I advice the exercises in the book (2015) **\\\"Automate the Boring Stuff with Python\\\"** by Al Sweigart. The book can be read free on the Internet. The excercises are as simple as possible, systematically aranged, in each step explaining one new aspect, everything in it is extremely clearly explained and the book is even relaxed reading. Each chapter ends with a set of questions and one or two practical projects that are interesting and useful by themselves. In one word, the book is a wealth. I assume that students will do such exercises themselves, so that we can taken Python a step further in class.\\n\",\n \"\\n\",\n \"Theo Olsthoorn, Prof. groundwater\\n\",\n \"Delft Feb. 20, 2017\\n\",\n \"\\n\",\n \"**Feb 21:**\\n\",\n \"Introduction, see ppt presentation on this site.\\n\",\n \"MB's first notebook was used as a start thereafter. Basic plotting of polynomicals using matplotlib.pyplot and numpy. The resulting notebook is on this site.\\n\",\n \"\\n\",\n \"**Feb 28**\\n\",\n \"This time we'll heavily focus on tuples, lists, dicts and sets. We will read data from an excel workbook, turn this into dicts. We'll make heavily use of the list, dict and set comprehensions to generate the lists and dict that we need to order, join, analyze and visualize the results from the data that we have read in from different sheets of the excel workbook. Hence there will be quite some advanced working with sequences, iterators and generators so that afterwards you should feel comfortable with them.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Counting words and phrases\\n\",\n \"\\n\",\n \"Now that we have the entire file read into a single string, `s`, we can just as well analyze it a bit, by counting the number of words, letters, and the frequency of each letter.\\n\",\n \"\\n\",\n \"just split the sting in workds based on whitespace and count their number.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"There are 362 words in file README.md\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"There are {} words in file {}\\\".format(len(s.split(sep=' ')), fname))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We might estimate the number of sentences by counting the number of periods '.'\\n\",\n \"\\n\",\n \"One way is to use the `.` as a separator:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"We find 24 phrases in file README.md\\n\"\n ]\n }\n ],\n \"source\": [\n \"nPhrases = len(s.split(sep='.')) # also works without the keyword sep\\n\",\n \"\\n\",\n \"print(\\\"We find {} phrases in file {}\\\".format(nPhrases, fname))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We could just as wel count the number of dots in s directly, using one of the string methods, in this case s.count()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"There are 23 dots in file README.md\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"There are {} dots in file {}\\\".format(s.count('.'), fname))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Counting the non-whitespace characters\\n\",\n \"\\n\",\n \"Now let's see how many non-whitespace characters there are in s.\\n\",\n \"\\n\",\n \"A coarse way to remove whitespace would be splitting s and rejoining the obtained list of words without any whitespace like so:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"#ihe-python-course-2017materialfortheextracurricularpythoncoursegivenatunesco-ihefrom21-feb-2017uponrequestofthevirtuallyentirestudentcommunitystudyingatunesco-iheindelftin2017,anextracurricularcourseonpythonwasgivenasitwasrealizedthatlearningpythonisanessentialassetforfutureengineersandscientists.nexttomyownexercises,we'llborrowmuchoftheexercisesprof.markbakker'stutorialsforallstudentsatthefacultyofcegsattudelft,whichareavailalbeongithub.othermaterialfromtheinternetmayalsobeused.thefocusisontheneedsofscientistsandengineers.thestudentsaresupposedtoinstalltheanacondapackagesontheirlaptopandthelessonswillbeontuesdays16:45-17:30asfromfeb21,2017intheauditoriumofiheatthewestvestindelft.foryourownpracticingiadvicetheexercisesinthebook(2015)**\\\"automatetheboringstuffwithpython\\\"**byalsweigart.thebookcanbereadfreeontheinternet.theexcercisesareassimpleaspossible,systematicallyaranged,ineachstepexplainingonenewaspect,everythinginitisextremelyclearlyexplainedandthebookisevenrelaxedreading.eachchapterendswithasetofquestionsandoneortwopracticalprojectsthatareinterestingandusefulbythemselves.inoneword,thebookisawealth.iassumethatstudentswilldosuchexercisesthemselves,sothatwecantakenpythonastepfurtherinclass.theoolsthoorn,prof.groundwaterdelftfeb.20,2017**feb21:**introduction,seepptpresentationonthissite.mb'sfirstnotebookwasusedasastartthereafter.basicplottingofpolynomicalsusingmatplotlib.pyplotandnumpy.theresultingnotebookisonthissite.**feb28**thistimewe'llheavilyfocusontuples,lists,dictsandsets.wewillreaddatafromanexcelworkbook,turnthisintodicts.we'llmakeheavilyuseofthelist,dictandsetcomprehensionstogeneratethelistsanddictthatweneedtoorder,join,analyzeandvisualizetheresultsfromthedatathatwehavereadinfromdifferentsheetsoftheexcelworkbook.hencetherewillbequitesomeadvancedworkingwithsequences,iteratorsandgeneratorssothatafterwardsyoushouldfeelcomfortablewiththem.\\n\"\n ]\n }\n ],\n \"source\": [\n \"s1 = \\\"\\\".join(s.split()).lower() # also make all letters in lowerface()\\n\",\n \"print(s1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## All characters in a list\\n\",\n \"\\n\",\n \"If we convert a string into a list, we get the list of its individual characters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['#',\\n\",\n \" 'i',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" '-',\\n\",\n \" 'p',\\n\",\n \" 'y',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" '-',\\n\",\n \" 'c',\\n\",\n \" 'o',\\n\",\n \" 'u',\\n\",\n \" 'r',\\n\",\n \" 's',\\n\",\n \" 'e',\\n\",\n \" '-',\\n\",\n \" '2',\\n\",\n \" '0',\\n\",\n \" '1',\\n\",\n \" '7',\\n\",\n \" 'm',\\n\",\n \" 'a',\\n\",\n \" 't',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'i',\\n\",\n \" 'a',\\n\",\n \" 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'a',\\n\",\n \" 'p',\\n\",\n \" 't',\\n\",\n \" 'o',\\n\",\n \" 'p',\\n\",\n \" 'a',\\n\",\n \" 'n',\\n\",\n \" 'd',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'l',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 's',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" 's',\\n\",\n \" 'w',\\n\",\n \" 'i',\\n\",\n \" 'l',\\n\",\n \" 'l',\\n\",\n \" 'b',\\n\",\n \" 'e',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" 't',\\n\",\n \" 'u',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 'd',\\n\",\n \" 'a',\\n\",\n \" 'y',\\n\",\n \" 's',\\n\",\n \" '1',\\n\",\n \" '6',\\n\",\n \" ':',\\n\",\n \" '4',\\n\",\n \" '5',\\n\",\n \" '-',\\n\",\n \" '1',\\n\",\n \" '7',\\n\",\n \" ':',\\n\",\n \" '3',\\n\",\n \" '0',\\n\",\n \" 'a',\\n\",\n \" 's',\\n\",\n \" 'f',\\n\",\n \" 'r',\\n\",\n \" 'o',\\n\",\n \" 'm',\\n\",\n \" 'f',\\n\",\n \" 'e',\\n\",\n \" 'b',\\n\",\n \" '2',\\n\",\n \" '1',\\n\",\n \" ',',\\n\",\n \" '2',\\n\",\n \" '0',\\n\",\n \" '1',\\n\",\n \" '7',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'u',\\n\",\n \" 'd',\\n\",\n \" 'i',\\n\",\n \" 't',\\n\",\n \" 'o',\\n\",\n \" 'r',\\n\",\n \" 'i',\\n\",\n \" 'u',\\n\",\n \" 'm',\\n\",\n \" 'o',\\n\",\n \" 'f',\\n\",\n \" 'i',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 't',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'w',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'v',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'd',\\n\",\n \" 'e',\\n\",\n \" 'l',\\n\",\n \" 'f',\\n\",\n \" 't',\\n\",\n \" '.',\\n\",\n \" 'f',\\n\",\n \" 'o',\\n\",\n \" 'r',\\n\",\n \" 'y',\\n\",\n \" 'o',\\n\",\n \" 'u',\\n\",\n \" 'r',\\n\",\n \" 'o',\\n\",\n \" 'w',\\n\",\n \" 'n',\\n\",\n \" 'p',\\n\",\n \" 'r',\\n\",\n \" 'a',\\n\",\n \" 'c',\\n\",\n \" 't',\\n\",\n \" 'i',\\n\",\n \" 'c',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" 'i',\\n\",\n \" 'a',\\n\",\n \" 'd',\\n\",\n \" 'v',\\n\",\n \" 'i',\\n\",\n \" 'c',\\n\",\n \" 'e',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'e',\\n\",\n \" 'x',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'c',\\n\",\n \" 'i',\\n\",\n \" 's',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'b',\\n\",\n \" 'o',\\n\",\n \" 'o',\\n\",\n \" 'k',\\n\",\n \" '(',\\n\",\n \" '2',\\n\",\n \" '0',\\n\",\n \" '1',\\n\",\n \" '5',\\n\",\n \" ')',\\n\",\n \" '*',\\n\",\n \" '*',\\n\",\n \" '\\\"',\\n\",\n \" 'a',\\n\",\n \" 'u',\\n\",\n \" 't',\\n\",\n \" 'o',\\n\",\n \" 'm',\\n\",\n \" 'a',\\n\",\n \" 't',\\n\",\n \" 'e',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'b',\\n\",\n \" 'o',\\n\",\n \" 'r',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'u',\\n\",\n \" 'f',\\n\",\n \" 'f',\\n\",\n \" 'w',\\n\",\n \" 'i',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'p',\\n\",\n \" 'y',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" '\\\"',\\n\",\n \" '*',\\n\",\n \" '*',\\n\",\n \" 'b',\\n\",\n \" 'y',\\n\",\n \" 'a',\\n\",\n \" 'l',\\n\",\n \" 's',\\n\",\n \" 'w',\\n\",\n \" 'e',\\n\",\n \" 'i',\\n\",\n \" 'g',\\n\",\n \" 'a',\\n\",\n \" 'r',\\n\",\n \" 't',\\n\",\n \" '.',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'b',\\n\",\n \" 'o',\\n\",\n \" 'o',\\n\",\n \" 'k',\\n\",\n \" 'c',\\n\",\n \" 'a',\\n\",\n \" 'n',\\n\",\n \" 'b',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'd',\\n\",\n \" 'f',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'e',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 't',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'n',\\n\",\n \" 'e',\\n\",\n \" 't',\\n\",\n \" '.',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'e',\\n\",\n \" 'x',\\n\",\n \" 'c',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'c',\\n\",\n \" 'i',\\n\",\n \" 's',\\n\",\n \" 'e',\\n\",\n \" 's',\\n\",\n \" 'a',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 's',\\n\",\n \" 's',\\n\",\n \" 'i',\\n\",\n \" 'm',\\n\",\n \" 'p',\\n\",\n \" 'l',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 's',\\n\",\n \" 'p',\\n\",\n \" 'o',\\n\",\n \" 's',\\n\",\n \" 's',\\n\",\n \" 'i',\\n\",\n \" 'b',\\n\",\n \" 'l',\\n\",\n \" 'e',\\n\",\n \" ',',\\n\",\n \" 's',\\n\",\n \" 'y',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'e',\\n\",\n \" 'm',\\n\",\n \" 'a',\\n\",\n \" 't',\\n\",\n \" 'i',\\n\",\n \" 'c',\\n\",\n \" 'a',\\n\",\n \" 'l',\\n\",\n \" 'l',\\n\",\n \" 'y',\\n\",\n \" 'a',\\n\",\n \" 'r',\\n\",\n \" 'a',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" 'e',\\n\",\n \" 'd',\\n\",\n \" ',',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'c',\\n\",\n \" 'h',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'e',\\n\",\n \" 'p',\\n\",\n \" 'e',\\n\",\n \" 'x',\\n\",\n \" 'p',\\n\",\n \" 'l',\\n\",\n \" 'a',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" 'o',\\n\",\n \" 'n',\\n\",\n \" 'e',\\n\",\n \" 'n',\\n\",\n \" 'e',\\n\",\n \" 'w',\\n\",\n \" 'a',\\n\",\n \" 's',\\n\",\n \" 'p',\\n\",\n \" 'e',\\n\",\n \" 'c',\\n\",\n \" 't',\\n\",\n \" ',',\\n\",\n \" 'e',\\n\",\n \" 'v',\\n\",\n \" 'e',\\n\",\n \" 'r',\\n\",\n \" 'y',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'i',\\n\",\n \" 't',\\n\",\n \" 'i',\\n\",\n \" 's',\\n\",\n \" 'e',\\n\",\n \" 'x',\\n\",\n \" 't',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'm',\\n\",\n \" 'e',\\n\",\n \" 'l',\\n\",\n \" 'y',\\n\",\n \" 'c',\\n\",\n \" 'l',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'r',\\n\",\n \" 'l',\\n\",\n \" 'y',\\n\",\n \" 'e',\\n\",\n \" 'x',\\n\",\n \" 'p',\\n\",\n \" 'l',\\n\",\n \" 'a',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'e',\\n\",\n \" 'd',\\n\",\n \" 'a',\\n\",\n \" 'n',\\n\",\n \" 'd',\\n\",\n \" 't',\\n\",\n \" 'h',\\n\",\n \" 'e',\\n\",\n \" 'b',\\n\",\n \" 'o',\\n\",\n \" 'o',\\n\",\n \" 'k',\\n\",\n \" 'i',\\n\",\n \" 's',\\n\",\n \" 'e',\\n\",\n \" 'v',\\n\",\n \" 'e',\\n\",\n \" 'n',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'l',\\n\",\n \" 'a',\\n\",\n \" 'x',\\n\",\n \" 'e',\\n\",\n \" 'd',\\n\",\n \" 'r',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'd',\\n\",\n \" 'i',\\n\",\n \" 'n',\\n\",\n \" 'g',\\n\",\n \" '.',\\n\",\n \" 'e',\\n\",\n \" 'a',\\n\",\n \" 'c',\\n\",\n \" 'h',\\n\",\n \" 'c',\\n\",\n \" 'h',\\n\",\n \" 'a',\\n\",\n \" 'p',\\n\",\n \" 't',\\n\",\n \" ...]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(s1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The unique characters in a set\\n\",\n \"\\n\",\n \"By turning the string into a set, we get the set of its unique characters:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'\\\"',\\n\",\n \" '#',\\n\",\n \" \\\"'\\\",\\n\",\n \" '(',\\n\",\n \" ')',\\n\",\n \" '*',\\n\",\n \" ',',\\n\",\n \" '-',\\n\",\n \" '.',\\n\",\n \" '0',\\n\",\n \" '1',\\n\",\n \" '2',\\n\",\n \" '3',\\n\",\n \" '4',\\n\",\n \" '5',\\n\",\n \" '6',\\n\",\n \" '7',\\n\",\n \" '8',\\n\",\n \" ':',\\n\",\n \" 'a',\\n\",\n \" 'b',\\n\",\n \" 'c',\\n\",\n \" 'd',\\n\",\n \" 'e',\\n\",\n \" 'f',\\n\",\n \" 'g',\\n\",\n \" 'h',\\n\",\n \" 'i',\\n\",\n \" 'j',\\n\",\n \" 'k',\\n\",\n \" 'l',\\n\",\n \" 'm',\\n\",\n \" 'n',\\n\",\n \" 'o',\\n\",\n \" 'p',\\n\",\n \" 'q',\\n\",\n \" 'r',\\n\",\n \" 's',\\n\",\n \" 't',\\n\",\n \" 'u',\\n\",\n \" 'v',\\n\",\n \" 'w',\\n\",\n \" 'x',\\n\",\n \" 'y',\\n\",\n \" 'z'}\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"set(s1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The number of occurences of each non-white character in the file\\n\",\n \"\\n\",\n \"To count the frequency of each character we could use those from the set as keys in a dict. We can generate the dict with the frequency if each character in a dict comprehension that combines the unique letter as a key with the method count(key) applied on s1, the string without whitespace:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'pprint' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mccnt\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m{\\u001b[0m\\u001b[0mc\\u001b[0m \\u001b[0;34m:\\u001b[0m \\u001b[0ms1\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcount\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mc\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mc\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mset\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ms1\\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[0mpprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mccnt\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'pprint' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"ccnt = {c : s1.count(c) for c in set(s1)}\\n\",\n \"pprint(ccnt)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets order the letters after their frequency of occurrence in the file:\\n\",\n \"\\n\",\n \"We can do so in one line, but this needs some explanaion.\\n\",\n \"\\n\",\n \"First we generate a list from the dict in which each item is a list of 2 itmes namely [char, number]\\n\",\n \"\\n\",\n \"Second we apply sorted on that list to get a sorted list. But we don't want it to be sorted based on the character, but based on the number. Therfore, we use the `key` argument. It tels that each item has to be compared on the second value (lambda x: x[1]).\\n\",\n \"\\n\",\n \"Finally, this yields the list that we want, but with the largest frequency at the bottom. So we turn this list upside down by using the slice [::-1] at the end.\\n\",\n \"\\n\",\n \"Here it is:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 159,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[['e', 236],\\n\",\n \" ['t', 186],\\n\",\n \" ['s', 140],\\n\",\n \" ['a', 131],\\n\",\n \" ['o', 122],\\n\",\n \" ['n', 120],\\n\",\n \" ['i', 118],\\n\",\n \" ['r', 102],\\n\",\n \" ['h', 78],\\n\",\n \" ['l', 77],\\n\",\n \" ['u', 56],\\n\",\n \" ['c', 56],\\n\",\n \" ['d', 55],\\n\",\n \" ['f', 46],\\n\",\n \" ['p', 36],\\n\",\n \" ['w', 33],\\n\",\n \" ['m', 32],\\n\",\n \" ['b', 29],\\n\",\n \" ['y', 28],\\n\",\n \" ['g', 24],\\n\",\n \" ['.', 23],\\n\",\n \" [',', 19],\\n\",\n \" ['k', 17],\\n\",\n \" ['v', 15],\\n\",\n \" ['x', 14],\\n\",\n \" ['*', 12],\\n\",\n \" ['2', 11],\\n\",\n \" ['1', 11],\\n\",\n \" ['0', 8],\\n\",\n \" ['-', 8],\\n\",\n \" ['7', 6],\\n\",\n \" [\\\"'\\\", 5],\\n\",\n \" ['q', 4],\\n\",\n \" [':', 3],\\n\",\n \" ['z', 3],\\n\",\n \" ['5', 2],\\n\",\n \" ['j', 2],\\n\",\n \" ['\\\"', 2],\\n\",\n \" ['#', 1],\\n\",\n \" ['4', 1],\\n\",\n \" ['8', 1],\\n\",\n \" ['3', 1],\\n\",\n \" ['6', 1],\\n\",\n \" ['(', 1],\\n\",\n \" [')', 1]]\"\n ]\n },\n \"execution_count\": 159,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted([[k, ccnt[k]] for k in ccnt.keys()], key=lambda x: x[1])[::-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reading the file and returning a list of strings, one per line\\n\",\n \"\\n\",\n \"For this we would read reader.readlines() instead of reader.read:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"list\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"with open(os.path.join(apth, fname), 'r') as reader:\\n\",\n \" s = reader.readlines()\\n\",\n \"\\n\",\n \"type(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Pretty printing has been turned OFF\\n\"\n ]\n }\n ],\n \"source\": [\n \"pprint(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From this point onward, you can analyse each line in sequence, pick out lines, etc.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reading a single line and lines one by one\\n\",\n \"\\n\",\n \"Often you don't want to read the entire file into memory (into a single character) at once. It might blow up the computer's memory if the file size were gigabits, as can easily the case with output of some models. And if it wouldn't crash the memory, your pc may still become very slow with large files. So a better and more generally applied way to read in a file is line by line, based on the newline characters that are embedded in them.\\n\",\n \"\\n\",\n \"In that case you can read the file in line by line, one at a time, not using reader.read() or reader.readlines() but reader.readline()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 191,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"str\"\n ]\n },\n \"execution_count\": 191,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"with open(os.path.join(apth, fname), 'r') as reader:\\n\",\n \" s = reader.readline()\\n\",\n \"\\n\",\n \"type(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 192,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"# IHE-python-course-2017\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Which yields a string, the first string of the file in this case.\\n\",\n \"\\n\",\n \"The problem is now, that no more lines can be read from this file, because with the `with` statement, the file closes automatically as soon as the python reaches the end of its block:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 193,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"I/O operation on closed file.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0ms\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mreader\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mreadline\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m: I/O operation on closed file.\"\n ]\n }\n ],\n \"source\": [\n \"s = reader.readline()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Therefore, we should not use the `with` statement and hand-close the file when we're done, or put anything that we do with the strings that we read inside the `with` block.\\n\",\n \"\\n\",\n \"We may be tempted to put the reader in a while-loop like so\\n\",\n \"\\n\",\n \"s=[]\\n\",\n \"while True:\\n\",\n \" s.append(reader.readline())\\n\",\n \" \\n\",\n \"But don't do that, becaus the while-loop will never end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 194,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['# IHE-python-course-2017\\\\n',\\n\",\n \" 'Material for the extracurricular python course given at UNESCO-IHE from '\\n\",\n \" '21-Feb-2017\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" 'Upon request of the virtually entire student community studying at '\\n\",\n \" 'UNESCO-IHE in Delft in 2017, an extracurricular course on Python was given '\\n\",\n \" 'as it was realized that learning python is an essential asset for future '\\n\",\n \" 'engineers and scientists.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" \\\"Next to my own exercises, we'll borrow much of the exercises Prof. Mark \\\"\\n\",\n \" \\\"Bakker's tutorials for all students at the faculty of CEGS at TUDelft, which \\\"\\n\",\n \" 'are availalbe on github. Other material from the internet may also be '\\n\",\n \" 'used.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" 'The focus is on the needs of scientists and engineers.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" 'The students are supposed to install the Anaconda packages on their laptop '\\n\",\n \" 'and the lessons will be on Tuesdays 16:45-17:30 as from Feb 21, 2017 in the '\\n\",\n \" 'auditorium of IHE at the Westvest in Delft.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" 'For your own practicing I advice the exercises in the book (2015) '\\n\",\n \" '**\\\"Automate the Boring Stuff with Python\\\"** by Al Sweigart. The book can be '\\n\",\n \" 'read free on the Internet. The excercises are as simple as possible, '\\n\",\n \" 'systematically aranged, in each step explaining one new aspect, everything '\\n\",\n \" 'in it is extremely clearly explained and the book is even relaxed reading. '\\n\",\n \" 'Each chapter ends with a set of questions and one or two practical projects '\\n\",\n \" 'that are interesting and useful by themselves. In one word, the book is a '\\n\",\n \" 'wealth. I assume that students will do such exercises themselves, so that we '\\n\",\n \" 'can taken Python a step further in class.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" 'Theo Olsthoorn, Prof. groundwater\\\\n',\\n\",\n \" 'Delft Feb. 20, 2017\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" '**Feb 21:**\\\\n',\\n\",\n \" 'Introduction, see ppt presentation on this site.\\\\n',\\n\",\n \" \\\"MB's first notebook was used as a start thereafter. Basic plotting of \\\"\\n\",\n \" 'polynomicals using matplotlib.pyplot and numpy. The resulting notebook is on '\\n\",\n \" 'this site.\\\\n',\\n\",\n \" '\\\\n',\\n\",\n \" '**Feb 28**\\\\n',\\n\",\n \" \\\"This time we'll heavily focus on tuples, lists, dicts and sets. We will read \\\"\\n\",\n \" \\\"data from an excel workbook, turn this into dicts. We'll make heavily use of \\\"\\n\",\n \" 'the list, dict and set comprehensions to generate the lists and dict that we '\\n\",\n \" 'need to order, join, analyze and visualize the results from the data that we '\\n\",\n \" 'have read in from different sheets of the excel workbook. Hence there will '\\n\",\n \" 'be quite some advanced working with sequences, iterators and generators so '\\n\",\n \" 'that afterwards you should feel comfortable with them.\\\\n']\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open(os.path.join(apth, fname), 'r') as reader:\\n\",\n \" lines = []\\n\",\n \" while True:\\n\",\n \" s = reader.readline()\\n\",\n \" if s==\\\"\\\":\\n\",\n \" break\\n\",\n \" lines.append(s)\\n\",\n \"\\n\",\n \"pprint(lines)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 195,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"reader.readline?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Of course, there is much, much more, but this is probably the most important base knowledge about file reading. File writing of textfile is straightforward. You open a file with open( fname, 'w') for writing or open(fname,'a') for appending and you can start writing lines to it. Don't forget to close it when done. Still better, use the `with` statement to make sure that the file is automatically closed when its block is done.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":40,"numItemsPerPage":100,"numTotalItems":47214,"offset":4000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc0NzM0Nzg0OCwic3ViIjoiL2RhdGFzZXRzL2NvZGVwYXJyb3QvZ2l0aHViLWp1cHl0ZXIiLCJleHAiOjE3NDczNTE0NDgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.bpEYAJie1VYeFS6PC6JlEyHHtfw4xpndSu3YXkTXPdRS7d0YtLgQeS60QbuQcyUsVuhDcCRMqOf6aogZpiG_Dg","displayUrls":true},"discussionsStats":{"closed":2,"open":0,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# IPython Documentation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This directory contains IPython's notebook-based documentation. This augments our [Sphinx-based documentation](http://ipython.org/ipython-doc/stable/index.html) with notebooks that contain interactive tutorials and examples. Over time, more of our documentation will be pulled into this format.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Topics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* [IPython Kernel](IPython Kernel/Index.ipynb): IPython's core syntax and command line features available in all frontends\\n\",\n \"* [Notebook](Notebook/Index.ipynb): The IPython Notebook frontend\\n\",\n \"* [Interactive Widgets](Interactive Widgets/Index.ipynb): Interactive JavaScript/HTML widgets and `interact`\\n\",\n \"* [Parallel Computing](Parallel Computing/Index.ipynb): IPython's library for interactive parallel computing\\n\",\n \"* [Customization](Customization/Index.ipynb): How to configure IPython and customize it with magics, extensions, etc.\\n\",\n \"* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\\n\",\n \"* [Builtin Extensions](Builtin Extensions/Index.ipynb): Extensions we ship with IPython\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":4095,"cells":{"repo_name":{"kind":"string","value":"zhangh87/AmazonPricePredictor"},"path":{"kind":"string","value":"Exploratory Analysis/Baby Dataset Preliminary - New Image Features Count Vectorized.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"148088"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import struct\\n\",\n \"import gzip\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn\\n\",\n \" \\n\",\n \"def parse(path):\\n\",\n \" g = gzip.open(path, 'rb')\\n\",\n \" for l in g:\\n\",\n \" yield eval(l)\\n\",\n \"\\n\",\n \"def getDF(path):\\n\",\n \" i = 0\\n\",\n \" df = {}\\n\",\n \" for d in parse(path):\\n\",\n \" df[i] = d\\n\",\n \" i += 1\\n\",\n \" return pd.DataFrame.from_dict(df, orient='index')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"reviews = getDF('reviews_Baby.json.gz')\\n\",\n \"meta = getDF('meta_Baby.json.gz')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"img_feat = pd.read_csv('rcnn_image_features_stephan_1.tar')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"reviewsDF = reviews.set_index('asin').groupby(level = 0)['unixReviewTime','overall'].agg(np.average)\\n\",\n \"del reviews\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"meta = meta[['price','asin','title']].set_index('asin')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" price title \\\\\\n\",\n \"asin \\n\",\n \"0188399313 69.99 Lifefactory 4oz BPA Free Glass Baby Bottles - ... \\n\",\n \"0188399518 15.95 Planetwise Flannel Wipes \\n\",\n \"0188399399 10.95 Planetwise Wipe Pouch \\n\",\n \"0316967297 109.95 Annas Dream Full Quilt with 2 Shams \\n\",\n \"0615447279 16.95 Stop Pacifier Sucking without tears with Thumb... \\n\",\n \"\\n\",\n \" unixReviewTime overall \\n\",\n \"asin \\n\",\n \"0188399313 1.369613e+09 5.000000 \\n\",\n \"0188399518 1.382789e+09 3.500000 \\n\",\n \"0188399399 1.365466e+09 5.000000 \\n\",\n \"0316967297 1.371168e+09 4.500000 \\n\",\n \"0615447279 1.348464e+09 4.333333 \"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = meta.merge(reviewsDF, how = 'inner', left_index = True, right_index = True).dropna(how = 'any')\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df['time'] = pd.to_datetime(df['unixReviewTime'], unit = 's')\\n\",\n \"df = df.drop('unixReviewTime',axis = 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Tp58qQcDodWr16tnj17trsHAABM5NPQLysr835dW1ur9957T7t37253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\\nRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"| \\n\",\n \" | price | \\n\",\n \"title | \\n\",\n \"unixReviewTime | \\n\",\n \"overall | \\n\",\n \"
|---|---|---|---|---|
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\"\n ],\n \"text/plain\": [\n \" mean_fit_time mean_score_time mean_test_score mean_train_score \\\\\\n\",\n \"0 566.670623 0.114586 2.786318e-01 3.243149e-01 \\n\",\n \"1 986.255134 0.119795 3.309793e-01 4.812772e-01 \\n\",\n \"2 1485.886665 0.145834 3.545946e-01 5.994504e-01 \\n\",\n \"3 562.740034 0.114584 4.494290e-01 6.398482e-01 \\n\",\n \"4 989.149054 0.139613 4.848307e-01 8.158985e-01 \\n\",\n \"5 1452.639853 0.145834 4.886593e-01 9.063788e-01 \\n\",\n \"6 565.015646 0.120319 1.792066e-02 8.619998e-01 \\n\",\n \"7 970.525649 0.140625 -1.198566e-01 9.623524e-01 \\n\",\n \"8 1368.443473 0.145834 -1.649937e-01 9.907511e-01 \\n\",\n \"9 563.856970 0.104178 -1.796564e+190 -1.918447e+190 \\n\",\n \"10 981.543549 0.119794 -2.579831e+190 -2.902438e+190 \\n\",\n \"11 1519.095625 0.130208 -4.126538e+190 -3.936789e+190 \\n\",\n \"\\n\",\n \" param_learning_rate param_max_depth \\\\\\n\",\n \"0 0.01 3 \\n\",\n \"1 0.01 5 \\n\",\n \"2 0.01 7 \\n\",\n \"3 0.1 3 \\n\",\n \"4 0.1 5 \\n\",\n \"5 0.1 7 \\n\",\n \"6 1 3 \\n\",\n \"7 1 5 \\n\",\n \"8 1 7 \\n\",\n \"9 10 3 \\n\",\n \"10 10 5 \\n\",\n \"11 10 7 \\n\",\n \"\\n\",\n \" params rank_test_score \\\\\\n\",\n \"0 {'learning_rate': 0.01, 'max_depth': 3} 6 \\n\",\n \"1 {'learning_rate': 0.01, 'max_depth': 5} 5 \\n\",\n \"2 {'learning_rate': 0.01, 'max_depth': 7} 4 \\n\",\n \"3 {'learning_rate': 0.1, 'max_depth': 3} 3 \\n\",\n \"4 {'learning_rate': 0.1, 'max_depth': 5} 2 \\n\",\n \"5 {'learning_rate': 0.1, 'max_depth': 7} 1 \\n\",\n \"6 {'learning_rate': 1, 'max_depth': 3} 7 \\n\",\n \"7 {'learning_rate': 1, 'max_depth': 5} 8 \\n\",\n \"8 {'learning_rate': 1, 'max_depth': 7} 9 \\n\",\n \"9 {'learning_rate': 10, 'max_depth': 3} 10 \\n\",\n \"10 {'learning_rate': 10, 'max_depth': 5} 11 \\n\",\n \"11 {'learning_rate': 10, 'max_depth': 7} 12 \\n\",\n \"\\n\",\n \" split0_test_score split0_train_score split1_test_score \\\\\\n\",\n \"0 2.746436e-01 3.206852e-01 2.756077e-01 \\n\",\n \"1 3.311018e-01 4.812723e-01 3.247332e-01 \\n\",\n \"2 3.550735e-01 6.018290e-01 3.471884e-01 \\n\",\n \"3 4.584442e-01 6.370217e-01 4.378416e-01 \\n\",\n \"4 4.922831e-01 8.141078e-01 4.711908e-01 \\n\",\n \"5 4.976960e-01 9.064331e-01 4.791010e-01 \\n\",\n \"6 4.932588e-02 8.617218e-01 -3.529163e-02 \\n\",\n \"7 -1.098837e-01 9.611571e-01 -1.242398e-01 \\n\",\n \"8 -1.382360e-01 9.904348e-01 -2.061176e-01 \\n\",\n \"9 -1.592175e+190 -1.868740e+190 -1.839157e+190 \\n\",\n \"10 -2.521610e+190 -2.819105e+190 -2.814671e+190 \\n\",\n \"11 -3.475897e+190 -3.940914e+190 -5.155596e+190 \\n\",\n \"\\n\",\n \" split1_train_score split2_test_score split2_train_score std_fit_time \\\\\\n\",\n \"0 3.257986e-01 2.856449e-01 3.264610e-01 0.880525 \\n\",\n \"1 4.761615e-01 3.371035e-01 4.863977e-01 1.923422 \\n\",\n \"2 5.952919e-01 3.615226e-01 6.012302e-01 7.834503 \\n\",\n \"3 6.420067e-01 4.520016e-01 6.405162e-01 0.943872 \\n\",\n \"4 8.157892e-01 4.910190e-01 8.177984e-01 0.990014 \\n\",\n \"5 9.076069e-01 4.891809e-01 9.050965e-01 4.112546 \\n\",\n \"6 8.642543e-01 3.973017e-02 8.600232e-01 0.675001 \\n\",\n \"7 9.622157e-01 -1.254470e-01 9.636842e-01 0.890323 \\n\",\n \"8 9.913164e-01 -1.506260e-01 9.905020e-01 5.178201 \\n\",\n \"9 -1.942958e+190 -1.958380e+190 -1.943643e+190 1.106056 \\n\",\n \"10 -2.901231e+190 -2.403191e+190 -2.986977e+190 0.944962 \\n\",\n \"11 -3.937756e+190 -3.748079e+190 -3.931695e+190 18.480392 \\n\",\n \"\\n\",\n \" std_score_time std_test_score std_train_score \\n\",\n \"0 7.364516e-03 0.004974 0.002581 \\n\",\n \"1 7.367271e-03 0.005051 0.004179 \\n\",\n \"2 7.365808e-03 0.005862 0.002951 \\n\",\n \"3 7.366033e-03 0.008606 0.002089 \\n\",\n \"4 1.433218e-03 0.009659 0.001509 \\n\",\n \"5 7.365639e-03 0.007600 0.001026 \\n\",\n \"6 7.765183e-03 0.037831 0.001738 \\n\",\n \"7 3.893359e-07 0.007069 0.001036 \\n\",\n \"8 7.365527e-03 0.029516 0.000401 \\n\",\n \"9 7.351815e-03 inf inf \\n\",\n \"10 7.363448e-03 inf inf \\n\",\n \"11 7.365415e-03 inf inf \"\n ]\n },\n \"execution_count\": 67,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame(gs.cv_results_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gb = GradientBoostingRegressor(random_state = 1, n_estimators = 100)\\n\",\n \"param_grid = {'learning_rate': [0.01, 0.05, 0.1, 0.5],\\n\",\n \" 'max_depth': [8, 10, 13]}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fitting 3 folds for each of 12 candidates, totalling 36 fits\\n\"\n ]\n }\n ],\n \"source\": [\n \"gs2 = GridSearchCV(estimator = gb, param_grid = param_grid, scoring = 'r2', cv = 3, n_jobs = 3, verbose = 10).fit(X_train, y_train)\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":4096,"cells":{"repo_name":{"kind":"string","value":"vencejo/conecta4"},"path":{"kind":"string","value":"Programacion Interactiva/conecta4_PasoDe_ipynb_A_py.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"6345"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Writing conecta4.py\\n\"\n ]\n }\n ],\n \"source\": [\n \"#%%writefile conecta4.py\\n\",\n \"\\n\",\n \"## Representacion de los datos\\n\",\n \"# La representacion interna del tablero sera la de una lista compuesta por seis listas de numeros, estos numeros\\n\",\n \"# seran 0, para representar el espacio vacio, 1 para representar ficha roja y -1 para representar ficha amarilla\\n\",\n \"# Las coordenadas del tablero seran del tipo (fila,columna) y tendran su origen en la esquina superior izquierda \\n\",\n \"#, creciendo las columnas hacia la derecha y las filas hacia abajo \\n\",\n \"\\n\",\n \"# !pip install ipythonblocks\\n\",\n \"# Vease http://ipythonblocks.org/ para mas informacion\\n\",\n \"\\n\",\n \"import time\\n\",\n \"from IPython.display import clear_output\\n\",\n \"from ipythonblocks import BlockGrid, colors\\n\",\n \"\\n\",\n \"FILMAX = 6\\n\",\n \"COLMAX = 7\\n\",\n \"\\n\",\n \"def obtenNuevoTablero():\\n\",\n \" \\\"\\\"\\\" Crea un nuevo tablero \\\"\\\"\\\"\\n\",\n \" tablero = []\\n\",\n \" for i in range(FILMAX):\\n\",\n \" tablero.append([0] * COLMAX)\\n\",\n \"\\n\",\n \" return tablero\\n\",\n \"\\n\",\n \"def reseteaTablero(tablero):\\n\",\n \" \\\"\\\"\\\" Limpia el tablero \\\"\\\"\\\"\\n\",\n \" for col in range(COLMAX):\\n\",\n \" for fil in range(FILMAX):\\n\",\n \" tablero[fil][col] = 0\\n\",\n \"\\n\",\n \"def dibujaTablero(tablero):\\n\",\n \" \\\"\\\"\\\" Dibuja el tablero que se le pasa en formato grafico\\\"\\\"\\\"\\n\",\n \" grid = BlockGrid(width=COLMAX, height=FILMAX, block_size=25,lines_on = True)\\n\",\n \" \\n\",\n \" for fil in range(grid.height):\\n\",\n \" for col in range(grid.width):\\n\",\n \" if tablero[fil][col] == 1:\\n\",\n \" grid[fil,col] = colors['Red']\\n\",\n \" elif tablero[fil][col] == -1:\\n\",\n \" grid[fil,col] = colors['Green']\\n\",\n \" else:\\n\",\n \" grid[fil,col] = colors['Black']\\n\",\n \" \\n\",\n \" grid.show()\\n\",\n \" \\n\",\n \"def estaEnTablero(fil, col):\\n\",\n \" \\\"\\\"\\\" Devuelve verdadero si fil,col estan en el tablero \\\"\\\"\\\"\\n\",\n \" return fil >= 0 and fil < FILMAX and col >= 0 and col < COLMAX\\n\",\n \"\\n\",\n \"def esUnMovimientoValido(tablero, colJugada):\\n\",\n \" \\\"\\\"\\\" Devuelve falso si el movimiento a la coord colJugada no es posible,\\n\",\n \" y en caso de ser posible devuelve las coordenadas [fil,col] \\n\",\n \" de donde queda la ficha tras el movimiento\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if not estaEnTablero(0, colJugada):\\n\",\n \" return False\\n\",\n \" \\n\",\n \" for fil in range(FILMAX-1, -1, -1):\\n\",\n \" if (fil == FILMAX-1 and tablero[fil][colJugada] == 0) or \\\\\\n\",\n \" (tablero[fil][colJugada] == 0 and tablero[fil+1][colJugada] != 0) : \\n\",\n \" return [ fil, colJugada]\\n\",\n \" \\n\",\n \" return False\\n\",\n \"\\n\",\n \"def daLosMovimientosValidos(tablero):\\n\",\n \" \\\"\\\"\\\" Devuelve una lista [fil,col] con los movimientos validos del tablero\\\"\\\"\\\"\\n\",\n \" movimientosValidos = []\\n\",\n \"\\n\",\n \" for col in range(COLMAX):\\n\",\n \" movimiento = esUnMovimientoValido(tablero, col)\\n\",\n \" if movimiento:\\n\",\n \" movimientosValidos.append(movimiento)\\n\",\n \" return movimientosValidos\\n\",\n \"\\n\",\n \"def obtenCopiaTablero(tablero):\\n\",\n \" \\\"\\\"\\\" Devuelve una copia del tablero \\\"\\\"\\\"\\n\",\n \" copiaTablero = obtenNuevoTablero()\\n\",\n \"\\n\",\n \" for fil in range(FILMAX):\\n\",\n \" for col in range(COLMAX):\\n\",\n \" copiaTablero[fil][col] = tablero[fil][col]\\n\",\n \"\\n\",\n \" return copiaTablero\\n\",\n \"\\n\",\n \"def hazMovimiento(tablero,ficha, colJugada):\\n\",\n \" \\\"\\\"\\\" Pone la ficha en la columna, y actualiza el tablero. \\n\",\n \" Si el movimiento no es posible devuelve falso\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if ficha not in (-1,1):\\n\",\n \" raise TypeError(\\\"El valor de la ficha ha de ser -1 o 1\\\")\\n\",\n \" \\n\",\n \" movimiento = esUnMovimientoValido(tablero, colJugada)\\n\",\n \"\\n\",\n \" if movimiento == False:\\n\",\n \" return False\\n\",\n \"\\n\",\n \" tablero[movimiento[0]][movimiento[1]] = ficha\\n\",\n \" \\n\",\n \" return True\\n\",\n \"\\n\",\n \"def daLaVictoria(tablero,tipoFicha, filJugada, colJugada):\\n\",\n \" \\\"\\\"\\\" Analiza si una ficha en las coords. fil y col da una \\n\",\n \" situacion de victoria, en cuyo caso devuelve una lista con las coordenadas de\\n\",\n \" las fichas que forman la linea victoriosa, en caso contrario devuelve false \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" if not estaEnTablero(filJugada,colJugada):\\n\",\n \" return False\\n\",\n \" \\n\",\n \" for cdirection in [0,1,-1]:\\n\",\n \" for fdirection in [0,1,-1]:\\n\",\n \" if cdirection == 0 and fdirection == 0:\\n\",\n \" continue\\n\",\n \" listaFichasGanadoras = [[filJugada, colJugada]]\\n\",\n \" c, f = colJugada, filJugada\\n\",\n \" c += cdirection # Primer paso en la direccion\\n\",\n \" f += fdirection \\n\",\n \" while estaEnTablero(f,c) and tablero[f][c] == tipoFicha:\\n\",\n \" listaFichasGanadoras.append( [f,c])\\n\",\n \" c += cdirection\\n\",\n \" f += fdirection \\n\",\n \" if len(listaFichasGanadoras) >= 4:\\n\",\n \" return listaFichasGanadoras\\n\",\n \" \\n\",\n \" return False\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.4.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":4097,"cells":{"repo_name":{"kind":"string","value":"hvillanua/deep-learning"},"path":{"kind":"string","value":"language-translation/dlnd_language_translation.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"160359"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Language Translation\\n\",\n \"In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\\n\",\n \"## Get the Data\\n\",\n \"Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import helper\\n\",\n \"import problem_unittests as tests\\n\",\n \"\\n\",\n \"source_path = 'data/small_vocab_en'\\n\",\n \"target_path = 'data/small_vocab_fr'\\n\",\n \"source_text = helper.load_data(source_path)\\n\",\n \"target_text = helper.load_data(target_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Explore the Data\\n\",\n \"Play around with view_sentence_range to view different parts of the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Dataset Stats\\n\",\n \"Roughly the number of unique words: 227\\n\",\n \"Number of sentences: 137861\\n\",\n \"Average number of words in a sentence: 13.225277634719028\\n\",\n \"\\n\",\n \"English sentences 0 to 10:\\n\",\n \"new jersey is sometimes quiet during autumn , and it is snowy in april .\\n\",\n \"the united states is usually chilly during july , and it is usually freezing in november .\\n\",\n \"california is usually quiet during march , and it is usually hot in june .\\n\",\n \"the united states is sometimes mild during june , and it is cold in september .\\n\",\n \"your least liked fruit is the grape , but my least liked is the apple .\\n\",\n \"his favorite fruit is the orange , but my favorite is the grape .\\n\",\n \"paris is relaxing during december , but it is usually chilly in july .\\n\",\n \"new jersey is busy during spring , and it is never hot in march .\\n\",\n \"our least liked fruit is the lemon , but my least liked is the grape .\\n\",\n \"the united states is sometimes busy during january , and it is sometimes warm in november .\\n\",\n \"\\n\",\n \"French sentences 0 to 10:\\n\",\n \"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\\n\",\n \"les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\\n\",\n \"california est généralement calme en mars , et il est généralement chaud en juin .\\n\",\n \"les états-unis est parfois légère en juin , et il fait froid en septembre .\\n\",\n \"votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\\n\",\n \"son fruit préféré est l'orange , mais mon préféré est le raisin .\\n\",\n \"paris est relaxant en décembre , mais il est généralement froid en juillet .\\n\",\n \"new jersey est occupé au printemps , et il est jamais chaude en mars .\\n\",\n \"notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\\n\",\n \"les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\\n\"\n ]\n }\n ],\n \"source\": [\n \"view_sentence_range = (0, 10)\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"DON'T MODIFY ANYTHING IN THIS CELL\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"print('Dataset Stats')\\n\",\n \"print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\\n\",\n \"\\n\",\n \"sentences = source_text.split('\\\\n')\\n\",\n \"word_counts = [len(sentence.split()) for sentence in sentences]\\n\",\n \"print('Number of sentences: {}'.format(len(sentences)))\\n\",\n \"print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\\n\",\n \"\\n\",\n \"print()\\n\",\n \"print('English sentences {} to {}:'.format(*view_sentence_range))\\n\",\n \"print('\\\\n'.join(source_text.split('\\\\n')[view_sentence_range[0]:view_sentence_range[1]]))\\n\",\n \"print()\\n\",\n \"print('French sentences {} to {}:'.format(*view_sentence_range))\\n\",\n \"print('\\\\n'.join(target_text.split('\\\\n')[view_sentence_range[0]:view_sentence_range[1]]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Implement Preprocessing Function\\n\",\n \"### Text to Word Ids\\n\",\n \"As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `| \\n\",\n \" | mean_fit_time | \\n\",\n \"mean_score_time | \\n\",\n \"mean_test_score | \\n\",\n \"mean_train_score | \\n\",\n \"param_learning_rate | \\n\",\n \"param_max_depth | \\n\",\n \"params | \\n\",\n \"rank_test_score | \\n\",\n \"split0_test_score | \\n\",\n \"split0_train_score | \\n\",\n \"split1_test_score | \\n\",\n \"split1_train_score | \\n\",\n \"split2_test_score | \\n\",\n \"split2_train_score | \\n\",\n \"std_fit_time | \\n\",\n \"std_score_time | \\n\",\n \"std_test_score | \\n\",\n \"std_train_score | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 1 | \\n\",\n \"986.255134 | \\n\",\n \"0.119795 | \\n\",\n \"3.309793e-01 | \\n\",\n \"4.812772e-01 | \\n\",\n \"0.01 | \\n\",\n \"5 | \\n\",\n \"{'learning_rate': 0.01, 'max_depth': 5} | \\n\",\n \"5 | \\n\",\n \"3.311018e-01 | \\n\",\n \"4.812723e-01 | \\n\",\n \"3.247332e-01 | \\n\",\n \"4.761615e-01 | \\n\",\n \"3.371035e-01 | \\n\",\n \"4.863977e-01 | \\n\",\n \"1.923422 | \\n\",\n \"7.367271e-03 | \\n\",\n \"0.005051 | \\n\",\n \"0.004179 | \\n\",\n \"
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sot/aca_stats | fit_acq_model-2019-08-binned-poly-binom-floor.ipynb | 1 | 1027282 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fit binned-floor acquisition probability model in 2019-08\n",
"\n",
"This is the acquisition and probability model calculated in 2019-08. \n",
"\n",
"**It is NOT promoted to flight due to lack of change from 2018-11**.\n",
"\n",
"Copied from the 2018-11 notebook, modified slightly and re-run. \n",
"\n",
"### Changes\n",
"\n",
"- Factored out the date and model name to single values at the top.\n",
"- Made the data start time be a fixed 4.5 years before end time.\n",
"- Fixed the calculation of probability confidence intervals in the\n",
" comparison to flight data at 120 arcsec box size.\n",
"- Added another temperature bin in the flight comparison plot to\n",
" show higher temperature data.\n",
"- Used chandra_aca.star_probs to compute the new model instead of a\n",
" local replica of that code.\n",
"\n",
"### Key features of the model for color != 1.5 stars (with good mag estimates)\n",
"\n",
"- Incorporates ASVT data for `t_ccd >= -10 C` in order to provide reasonable estimates\n",
" of probability in regimes with little or no flight data.\n",
"- Fits a quadradatic model for `p_fail` (probit) as a \n",
" function of `t_ccd` in a series of magnitude bins. The mag bins are driven by magnitudes \n",
" of ASVT simulated data.\n",
"- Model now includes a `floor` parameter that sets a hard lower limit on `p_fail`.\n",
" This is seen in data and represents other factors that cause acquisition failure\n",
" independent of `t_ccd`. In other words, even for an arbitrarily cold CCD there will\n",
" still be a small fraction of acquisition failures. For flight data this can include\n",
" spoilers or an ionizing radiation flag.\n",
"- As in past models, the `p_fail` model is adjusted by a `box_delta` term which applies\n",
" a search-box dependent offset in probit space. The `box_delta` term is defined to\n",
" have a value of 0.0 for box `halfwidth = 120`.\n",
"- The global model (for arbitrary `mag`) is computed by linearly interpolating the\n",
" binned quadratic coefficients as a function of `mag`. The previous flight model\n",
" (`spline`) did a global `mag` - `t_ccd` fit using a 5-element spline in the\n",
" `mag` direction.\n",
" \n",
"### Key features of the model for color == 1.5 stars (with poor mag estimates)\n",
"\n",
"- Post AGASC 1.7, there is inadequate data to independently perform the binned\n",
" fitting.\n",
"- Instead assume a magnitude error distribution which is informed by examining\n",
" the observed distribution of `dmag = mag_obs - mag_aca` (observed - catalog). This\n",
" turns out to be well-represented by an `exp(-abs(dmag) / dmag_scale)`\n",
" distribution. This contrasts with a gaussian that scales as `exp(dmag^2)`.\n",
"- Use the assumed mag error distribution and sample the `color != 1.5` star\n",
" probabilities accordingly and compute the weighted mean failure probability.\n",
"- Flight data show a steeper falloff for `dmag > 0` (stars observed to be fainter\n",
" than expected) than for `dmag < 0`. As noted by JC this likely includes a\n",
" survival effect that stars which are actually much fainter don't get acquired\n",
" and do not get into the sample. Indeed using the observed distribution gives\n",
" a poor fit to flight data, so `dmag_scale` for `dmag > 0` was arbitrarily\n",
" increased from 2.8 to 4.0 in order to better fit flight data.\n",
" \n",
"### Model details\n",
"\n",
"- In order to get a good match to flight data for faint stars near -11 C, it\n",
" was necessary to apply an ad-hoc correction to ASVT data for `mag > 10.1`.\n",
" The correction effectively made the model assume *smaller* search box sizes,\n",
" so for the canonical 120 arcsec box the model `p_fail` is slightly increased\n",
" relative to the raw failure rate from ASVT.\n",
"- The `mag = 8.0` data from ASVT show a dependence on search-box size that is\n",
" flipped from usual. There are more failures for smaller search boxes,\n",
" though we are dealing with small number statistics (up to 3 fails per bin).\n",
" This caused problems in the fitting, so for this bin the `box_delta` term\n",
" was simplied zeroed out and a good fit was obtained in the automatic fit\n",
" process. Since `p_fail` is quite low in all cases this has little practical\n",
" impact either way.\n",
"- Fitting now uses binomial statistics to compute the fit statistic during\n",
" model parameter optimization. Previously it was using a poisson statistic\n",
" which is similar except near 1.0. The poisson statistic is built in to\n",
" Sherpa and was easier, but the new binomial statistic is formally correct\n",
" and behaves better for probabilities near 1.0.\n",
"\n",
"### Paradigm shift for production model implementation\n",
"\n",
"- This model is complicated, and the `color = 1.5` star case is computationally\n",
" intensive.\n",
"- Instead of transfering the analytic algorithm and fit values into \n",
" `chandra_aca.star_probs` for production use, take a new approach of generating\n",
" a 3-d grid of `p_fail` (in probit space) as a function of `mag`, `t_ccd`,\n",
" and `halfwidth`. Do this for `color != 1.5` and `color = 1.5`.\n",
"- The ranges are `5.0 <= mag <= 12.0`, `-16 <= t_ccd <= -1`, and\n",
" `60 <= halfwidth <= 180`. Values outside that range are clipped.\n",
"- This separates the model generation from the production model calculation.\n",
"- Gridded 3-d linear interpolation is used in `chandra_aca` and is quite fast.\n",
"- The gridded value files are about 150 kb, and make it easy to generate\n",
" new models without changing code in `chandra_aca` (except for a hard-coded\n",
" value for the default model)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"from itertools import count\n",
"from pathlib import Path\n",
"\n",
"# Include utils.py for asvt_utils\n",
"sys.path.insert(0, str(Path(os.environ['HOME'], 'git', 'skanb', 'pea-test-set')))\n",
"import utils as asvt_utils\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from astropy.table import Table, vstack\n",
"from astropy.time import Time\n",
"import tables\n",
"from scipy import stats\n",
"from scipy.interpolate import CubicSpline\n",
"from Chandra.Time import DateTime\n",
"from astropy.table import Table\n",
"from chandra_aca.star_probs import (get_box_delta, broadcast_arrays, \n",
" acq_success_prob, grid_model_acq_prob)\n",
"from chandra_aca import star_probs\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"MODEL_DATE = '2019-08'\n",
"MODEL_NAME = f'grid-floor-{MODEL_DATE}'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SKA = Path(os.environ['SKA'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get acq stats data and clean"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Make a map of AGASC_ID to AGACS 1.7 MAG_ACA. The acq_stats.h5 file has whatever MAG_ACA\n",
"# was in place at the time of planning the loads.\n",
"# Define new term `red_mag_err` which is used here in place of the \n",
"# traditional COLOR1 == 1.5 test.\n",
"with tables.open_file(str(SKA / 'data' / 'agasc' / 'miniagasc_1p7.h5'), 'r') as h5:\n",
" agasc_mag_aca = h5.root.data.col('MAG_ACA')\n",
" agasc_id = h5.root.data.col('AGASC_ID')\n",
" has_color3 = h5.root.data.col('RSV3') != 0 # \n",
" red_star = np.isclose(h5.root.data.col('COLOR1'), 1.5)\n",
" mag_aca_err = h5.root.data.col('MAG_ACA_ERR') / 100\n",
" red_mag_err = red_star & ~has_color3 # MAG_ACA, MAG_ACA_ERR is potentially inaccurate"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"agasc1p7_idx = {id: idx for id, idx in zip(agasc_id, count())}\n",
"agasc1p7 = Table([agasc_mag_aca, mag_aca_err, red_mag_err], \n",
" names=['mag_aca', 'mag_aca_err', 'red_mag_err'], copy=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"acq_file = str(SKA / 'data' / 'acq_stats' / 'acq_stats.h5')\n",
"with tables.open_file(str(acq_file), 'r') as h5:\n",
" cols = h5.root.data.cols\n",
" names = {'tstart': 'guide_tstart',\n",
" 'obsid': 'obsid',\n",
" 'obc_id': 'acqid',\n",
" 'halfwidth': 'halfw',\n",
" 'warm_pix': 'n100_warm_frac',\n",
" 'mag_aca': 'mag_aca',\n",
" 'mag_obs': 'mean_trak_mag',\n",
" 'known_bad': 'known_bad',\n",
" 'color': 'color1',\n",
" 'img_func': 'img_func', \n",
" 'ion_rad': 'ion_rad',\n",
" 'sat_pix': 'sat_pix',\n",
" 'agasc_id': 'agasc_id',\n",
" 't_ccd': 'ccd_temp',\n",
" 'slot': 'slot'}\n",
" acqs = Table([getattr(cols, h5_name)[:] for h5_name in names.values()],\n",
" names=list(names.keys())) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"year_q0 = 1999.0 + 31. / 365.25 # Jan 31 approximately\n",
"acqs['year'] = Time(acqs['tstart'], format='cxcsec').decimalyear.astype('f4')\n",
"acqs['quarter'] = (np.trunc((acqs['year'] - year_q0) * 4)).astype('f4')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create 'fail' column, rewriting history as if the OBC always\n",
"# ignore the MS flag in ID'ing acq stars.\n",
"#\n",
"# CHECK: is ion_rad being ignored on-board?\n",
"# Answer: Not as of 2019-09\n",
"#\n",
"obc_id = acqs['obc_id']\n",
"obc_id_no_ms = (acqs['img_func'] == 'star') & ~acqs['sat_pix'] & ~acqs['ion_rad']\n",
"acqs['fail'] = np.where(obc_id | obc_id_no_ms, 0.0, 1.0)\n",
"\n",
"# Re-map acq_stats database magnitudes for AGASC 1.7\n",
"acqs['mag_aca'] = [agasc1p7['mag_aca'][agasc1p7_idx[agasc_id]] for agasc_id in acqs['agasc_id']]\n",
"acqs['red_mag_err'] = [agasc1p7['red_mag_err'][agasc1p7_idx[agasc_id]] for agasc_id in acqs['agasc_id']]\n",
"acqs['mag_aca_err'] = [agasc1p7['mag_aca_err'][agasc1p7_idx[agasc_id]] for agasc_id in acqs['agasc_id']]\n",
"\n",
"# Add a flag to distinguish flight from ASVT data\n",
"acqs['asvt'] = False"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Filter for year and mag\n",
"#\n",
"year_max = Time(f'{MODEL_DATE}-01').decimalyear\n",
"year_min = year_max - 4.5\n",
"acq_ok = ((acqs['year'] > year_min) & (acqs['year'] < year_max) & \n",
" (acqs['mag_aca'] > 7.0) & (acqs['mag_aca'] < 11) &\n",
" (~np.isclose(acqs['color'], 0.7)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filtering known bad obsids, start len = 62138\n",
"Filtering known bad obsids, end len = 62138\n"
]
}
],
"source": [
"# Filter known bad obsids. NOTE: this is no longer doing anything, but\n",
"# consider updating the list of known bad obsids or obtaining programmically?\n",
"\n",
"print('Filtering known bad obsids, start len = {}'.format(np.count_nonzero(acq_ok)))\n",
"bad_obsids = [\n",
" # Venus\n",
" 2411,2414,6395,7306,7307,7308,7309,7311,7312,7313,7314,7315,7317,7318,7406,583,\n",
" 7310,9741,9742,9743,9744,9745,9746,9747,9749,9752,9753,9748,7316,15292,16499,\n",
" 16500,16501,16503,16504,16505,16506,16502,\n",
" ]\n",
"for badid in bad_obsids:\n",
" acq_ok = acq_ok & (acqs['obsid'] != badid)\n",
"print('Filtering known bad obsids, end len = {}'.format(np.count_nonzero(acq_ok)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get ASVT data and make it look more like acq stats data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"peas = Table.read('pea_analysis_results_2018_299_CCD_temp_performance.csv', format='ascii.csv')\n",
"peas = asvt_utils.flatten_pea_test_data(peas)\n",
"peas = peas[peas['ccd_temp'] > -10.5]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Version of ASVT PEA data that is more flight-like\n",
"fpeas = Table([peas['star_mag'], peas['ccd_temp'], peas['search_box_hw']],\n",
" names=['mag_aca', 't_ccd', 'halfwidth'])\n",
"fpeas['year'] = np.random.uniform(2019.0, 2019.5, size=len(peas))\n",
"fpeas['color'] = 1.0\n",
"fpeas['quarter'] = (np.trunc((fpeas['year'] - year_q0) * 4)).astype('f4')\n",
"fpeas['fail'] = 1.0 - peas['search_success']\n",
"fpeas['asvt'] = True\n",
"fpeas['red_mag_err'] = False\n",
"fpeas['mag_obs'] = 0.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combine flight acqs and ASVT data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_all = vstack([acqs[acq_ok]['year', 'fail', 'mag_aca', 't_ccd', 'halfwidth', 'quarter', \n",
" 'color', 'asvt', 'red_mag_err', 'mag_obs'], \n",
" fpeas])\n",
"data_all.sort('year')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute box probit delta term based on box size"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Adjust probability (in probit space) for box size. \n",
"data_all['box_delta'] = get_box_delta(data_all['halfwidth'])\n",
"\n",
"# Put in an ad-hoc penalty on ASVT data that introduces up to a -0.3 shift\n",
"# on probit probability. It goes from 0.0 for mag < 10.1 up to 0.3 at mag=10.4.\n",
"ok = data_all['asvt']\n",
"box_delta_tweak = (data_all['mag_aca'][ok] - 10.1).clip(0, 0.3)\n",
"data_all['box_delta'][ok] -= box_delta_tweak\n",
"\n",
"# Another ad-hoc tweak: the mag=8.0 data show more failures at smaller\n",
"# box sizes. This confounds the fitting. For this case only just\n",
"# set the box deltas to zero and this makes the fit work.\n",
"ok = data_all['asvt'] & (data_all['mag_aca'] == 8)\n",
"data_all['box_delta'][ok] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_all = data_all.group_by('quarter')\n",
"data_all0 = data_all.copy() # For later augmentation with simulated red_mag_err stars\n",
"data_mean = data_all.groups.aggregate(np.mean)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Model definition"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def t_ccd_normed(t_ccd):\n",
" return (t_ccd + 8.0) / 8.0\n",
"\n",
"def p_fail(pars, \n",
" t_ccd, tc2=None,\n",
" box_delta=0, rescale=True, probit=False):\n",
" \"\"\"\n",
" Acquisition probability model\n",
"\n",
" :param pars: p0, p1, p2 (quadratic in t_ccd) and floor (min p_fail)\n",
" :param t_ccd: t_ccd (degC) or scaled t_ccd if rescale is False.\n",
" :param tc2: (scaled t_ccd) ** 2, this is just for faster fitting\n",
" :param box_delta: delta p_fail for search box size\n",
" :param rescale: rescale t_ccd to about -1 to 1 (makes P0, P1, P2 better-behaved)\n",
" :param probit: return probability as probit instead of 0 to 1.\n",
" \"\"\"\n",
" p0, p1, p2, floor = pars\n",
"\n",
" tc = t_ccd_normed(t_ccd) if rescale else t_ccd\n",
" \n",
" if tc2 is None:\n",
" tc2 = tc ** 2\n",
" \n",
" # Make sure box_delta has right dimensions\n",
" tc, box_delta = np.broadcast_arrays(tc, box_delta)\n",
"\n",
" # Compute the model. Also clip at +10 to avoid values that are\n",
" # exactly 1.0 at 64-bit precision.\n",
" probit_p_fail = (p0 + p1 * tc + p2 * tc2 + box_delta).clip(floor, 10)\n",
"\n",
" # Possibly transform from probit to linear probability\n",
" out = probit_p_fail if probit else stats.norm.cdf(probit_p_fail)\n",
" return out\n",
"\n",
"def p_acq_fail(data=None):\n",
" \"\"\"\n",
" Sherpa fit function wrapper to ensure proper use of data in fitting.\n",
" \"\"\"\n",
" if data is None:\n",
" data = data_all\n",
" \n",
" tc = t_ccd_normed(data['t_ccd'])\n",
" tc2 = tc ** 2\n",
" box_delta = data['box_delta']\n",
" \n",
" def sherpa_func(pars, x=None):\n",
" return p_fail(pars, tc, tc2, box_delta, rescale=False)\n",
"\n",
" return sherpa_func"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model fitting functions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calc_binom_stat(data, model, staterror=None, syserror=None, weight=None, bkg=None):\n",
" \"\"\"\n",
" Calculate log-likelihood for a binomial probability distribution\n",
" for a single trial at each point.\n",
" \n",
" Defining p = model, then probability of seeing data == 1 is p and\n",
" probability of seeing data == 0 is (1 - p). Note here that ``data``\n",
" is strictly either 0.0 or 1.0, and np.where interprets those float\n",
" values as False or True respectively.\n",
" \"\"\"\n",
" fit_stat = -np.sum(np.log(np.where(data, model, 1.0 - model))) \n",
" return fit_stat, np.ones(1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fit_poly_model(data):\n",
" from sherpa import ui\n",
" \n",
" comp_names = ['p0', 'p1', 'p2', 'floor']\n",
"\n",
" data_id = 1\n",
" ui.set_method('simplex')\n",
" \n",
" # Set up the custom binomial statistics\n",
" ones = np.ones(len(data))\n",
" ui.load_user_stat('binom_stat', calc_binom_stat, lambda x: ones)\n",
" ui.set_stat(binom_stat)\n",
"\n",
" # Define the user model\n",
" ui.load_user_model(p_acq_fail(data), 'model')\n",
" ui.add_user_pars('model', comp_names)\n",
" ui.set_model(data_id, 'model')\n",
" ui.load_arrays(data_id, np.array(data['year']), np.array(data['fail'], dtype=np.float))\n",
"\n",
" # Initial fit values from fit of all data\n",
" fmod = ui.get_model_component('model')\n",
"\n",
" # Define initial values / min / max\n",
" # This is the p_fail value at t_ccd = -8.0\n",
" fmod.p0 = -2.605\n",
" fmod.p0.min = -10\n",
" fmod.p0.max = 10\n",
"\n",
" # Linear slope of p_fail\n",
" fmod.p1 = 2.5\n",
" fmod.p1.min = 0.0\n",
" fmod.p1.max = 10\n",
" \n",
" # Quadratic term. Only allow negative curvature, and not too much at that.\n",
" fmod.p2 = 0.0\n",
" fmod.p2.min = -1\n",
" fmod.p2.max = 0\n",
"\n",
" # Floor to p_fail.\n",
" fmod.floor = -2.6\n",
" fmod.floor.min = -2.6\n",
" fmod.floor.max = -0.5\n",
"\n",
" ui.fit(data_id)\n",
"\n",
" return ui.get_fit_results()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting and validation"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_fails_mag_aca_vs_t_ccd(mag_bins, data_all, year0=2015.0):\n",
" ok = (data_all['year'] > year0) & ~data_all['fail'].astype(bool)\n",
" da = data_all[ok]\n",
" fuzzx = np.random.uniform(-0.3, 0.3, len(da))\n",
" fuzzy = np.random.uniform(-0.125, 0.125, len(da))\n",
" plt.plot(da['t_ccd'] + fuzzx, da['mag_aca'] + fuzzy, '.C0', markersize=4)\n",
"\n",
" ok = (data_all['year'] > year0) & data_all['fail'].astype(bool)\n",
" da = data_all[ok]\n",
" fuzzx = np.random.uniform(-0.3, 0.3, len(da))\n",
" fuzzy = np.random.uniform(-0.125, 0.125, len(da))\n",
" plt.plot(da['t_ccd'] + fuzzx, da['mag_aca'] + fuzzy, '.C1', markersize=4, alpha=0.8)\n",
" \n",
" # plt.xlim(-18, -10)\n",
" # plt.ylim(7.0, 11.1)\n",
" x0, x1 = plt.xlim()\n",
" for y in mag_bins:\n",
" plt.plot([x0, x1], [y, y], '-', color='r', linewidth=2, alpha=0.8)\n",
" plt.xlabel('T_ccd (C)')\n",
" plt.ylabel('Mag_aca')\n",
" plt.title(f'Acq successes (blue) and failures (orange) since {year0}')\n",
" plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_fit_grouped(data, group_col, group_bin, log=False, colors='br', label=None, probit=False):\n",
" \n",
" group = np.trunc(data[group_col] / group_bin)\n",
" data = data.group_by(group)\n",
" data_mean = data.groups.aggregate(np.mean)\n",
" len_groups = np.diff(data.groups.indices)\n",
" data_fail = data_mean['fail']\n",
" model_fail = np.array(data_mean['model'])\n",
" \n",
" fail_sigmas = np.sqrt(data_fail * len_groups) / len_groups\n",
" \n",
" # Possibly plot the data and model probabilities in probit space\n",
" if probit:\n",
" dp = stats.norm.ppf(np.clip(data_fail + fail_sigmas, 1e-6, 1-1e-6))\n",
" dm = stats.norm.ppf(np.clip(data_fail - fail_sigmas, 1e-6, 1-1e-6))\n",
" data_fail = stats.norm.ppf(data_fail)\n",
" model_fail = stats.norm.ppf(model_fail)\n",
" fail_sigmas = np.vstack([data_fail - dm, dp - data_fail])\n",
" \n",
" plt.errorbar(data_mean[group_col], data_fail, yerr=fail_sigmas, \n",
" fmt='.' + colors[1], label=label, markersize=8)\n",
" plt.plot(data_mean[group_col], model_fail, '-' + colors[0])\n",
" \n",
" if log:\n",
" ax = plt.gca()\n",
" ax.set_yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mag_filter(mag0, mag1):\n",
" ok = (data_all['mag_aca'] > mag0) & (data_all['mag_aca'] < mag1)\n",
" return ok"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def t_ccd_filter(t_ccd0, t_ccd1):\n",
" ok = (data_all['t_ccd'] > t_ccd0) & (data_all['t_ccd'] < t_ccd1)\n",
" return ok"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def wp_filter(wp0, wp1):\n",
" ok = (data_all['warm_pix'] > wp0) & (data_all['warm_pix'] < wp1)\n",
" return ok"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define magnitude bins for fitting and show data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mag_centers = np.array([6.3, 8.1, 9.1, 9.55, 9.75, 10.0, 10.25, 10.55, 10.75, 11.0])\n",
"mag_bins = (mag_centers[1:] + mag_centers[:-1]) / 2\n",
"mag_means = np.array([8.0, 9.0, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"m0=7.20 m1=8.60 mean_mag=8.05 vs. 8.0\n",
"m0=8.60 m1=9.32 mean_mag=8.98 vs. 9.0\n",
"m0=9.32 m1=9.65 mean_mag=9.48 vs. 9.5\n",
"m0=9.65 m1=9.88 mean_mag=9.76 vs. 9.75\n",
"m0=9.88 m1=10.12 mean_mag=9.99 vs. 10.0\n",
"m0=10.12 m1=10.40 mean_mag=10.23 vs. 10.25\n",
"m0=10.40 m1=10.65 mean_mag=10.50 vs. 10.5\n",
"m0=10.65 m1=10.88 mean_mag=10.75 vs. 10.75\n"
]
}
],
"source": [
"for m0, m1, mm in zip(mag_bins[:-1], mag_bins[1:], mag_means):\n",
" ok = (data_all['asvt'] == False) & (data_all['mag_aca'] >= m0) & (data_all['mag_aca'] < m1)\n",
" print(f\"m0={m0:.2f} m1={m1:.2f} mean_mag={data_all['mag_aca'][ok].mean():.2f} vs. {mm}\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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U83urmvMDG1KJQJpUHqMkBuf0TJNtdFxGbjI2cbVZwyPWJSgu4RqzhvioyUy3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TAAAg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lJsxFMvvIjWtZ5tbqi71Qk/+C+gHREm1El1TT/ylKCju5sQCMaWiHUuoTeVadnWPb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T+jZ+Fp61lwo/lkMDyx1xcyOE2IvzjTMy4BPPXikBQnYN5jXit2JMpNITwUWYoHwjehG4Ws\n1HcfAqLPtZCKzhmmAhZX+l53tKtGv5PhQvO+6ZILVZhqIS1o9pUNrSAu/hNeKLjb3ifUF0HjliWY\nPa0ElRv2xTjWvEot2Xi3OTUefUMXfdZtr9sumci1mkfCKW/por5lQkQ9E/qQSUjakRZC3Afgh9bP\nfADfARC7dp8cPgigXkrZI6UMAzgM4EZlm6UAnpAmjgKYIIS4ZJjtXRDgDgOBR3UbtywBYEZtVUeQ\nO3jC2ma8VUI0FRyctCKGkzcgDbQd2e7qzNGbPamK8EqCxFNWncWGlhD2+eajsbUDL5dcj5ObF2sd\nyo7esDaC4JaNTdvyao0cZETdIGE6+Xw/OgfdvnR9iG5Sf6rNvhbxDDvXuj6hiYKr/ydasUhHdIWu\nB02MTbIUCyb7cWDdPJv24VbRcrigCZS/FPJ72y2CP2Icq0VrT9SOPgNDjkPXx75iR8culCiWx/b6\ngoE6wdP9qdpV/ozrnGZ+n41EvcMtIS8eVP50N/IRkvnYFQnGBDrIGV7jf9ahuNEoS+NSPIDEiYxu\nSOSAkxNfHm2xHczGXz6CnojP3keVIO3sDcMQArOnlQCAnQfCX37uPdIT47TqEsr5dzraoI7uoZtj\n+GdujqPuRU1LOfQQPF+GoObNuOXaeAG+muv1uSV6oTnwyD1o/KcqHHjkHk/bHSlSiUjfBOA6AG9J\nKf8GZiS5eJjtHgcwVwhRIoQoBLAYZsEAjnIAZ9j/Z63P3rVQHS8BaB94Dp1zRdNCR2/YTsJI1p0+\n2drtmETI2P64+9qYtmmSWOPbbX9HRorTGnbWn7YNJKGqLIB73vmMI/nELUpMNJF/GL8Pjf9Uhe2b\nv+x4kHmiGDm7PMLBv6f+xXPIdP0gHWz1GqlVD/m+ukgtGV6avNcae/B87jrMbXnCMTapRnm9dmiB\noYh6hWjFG8YlmDcpZEeFTiicYk9x5H5H9IojnvrLcLFx93F8p/Vq9PT2OVdictfhdv/PL9SkJS/t\n9QUD1SFyWxVqaAnFdZBJ/aNmztQRqXe4JeTFA6dzAMD/6n8UswYetYMbxSKE1cazWGvsieFEk+IG\nyeU9Grk+qUTGZCPmgDknlKADxSKUkMdNtDnVaaeVQnqeeA4OBUlUNHdLBxVPl7MTL9lbdc6msURt\n6kdESvszniPidky3FzUOr/MrbrOu1W1s7FUqoNtcOlzw8eLHriwt8jQyTc9v4MUf4PTXTYe59mgT\n1vh2Y9WxG3FV8w4MwkBV825P2vMKqZQI/42U8mohxMswIxxdAP4kpfyLYTUsxCoAtwPoBvAKgH4p\n5V3s+2cBbJFS/tr6/yCAv5dSvqQcZw1M6gfKyso++tRTTw2nO54jFAohEIh11p54pR91Z8MIXubH\nFz6Up/2fROcXTPbHLGfx72mb185H0Nztfh0f/5T5sG//XQgvtAjkG0CPy9xAbd7yXOIM9rrcdRiE\ngRxEsCLne/jARMM+l7qzYXu5dMFk5//lRQLfmluIDYdDeLM3sYvvE8Bji4pQcWiN3R4tMS6Y7HeM\nB50rBz8X9XvdeRb6h8anvEjYY6ueRzwU+oEHPxHbF/X68TGkc6LzBZz30d/u73a07ROw/+f7eAXq\nKy0fRyoX4k8ln0IgEIgZN924DweTm57BtMadGDSK8OZAgT0mKuh83Z6zVKC7B36btxpF6ENI5mPW\nwKPaZ9ENI+3T/PnzvSgR7rW9/ipMOp4A8KiU8nvxth9WiXAPoK5U6QquJEJVWQCzp5XEpXKpcKNY\nDKfwSV3uOlwqzgEA3pCTHM8A/y4k89GJgCM6HQEwRZzDC9EZuGVwQ1Lt0XFVO+TV9gImBUwtckMr\nrJWs8AowlO+S7HXjBbQSXTM6Nt/OEAInNy+O6YcbGrcsse9ddZ+qsgBOtIS0K8tu6iSpYOHWw9j2\nzmoMwkChEUX5fa+hrq5Oa8NofL1AorGh8xvpM00BNX5/7YoEcbf/GbTLQvhFFB0ygIbyaiy89Ttx\nj+WFfUlHifCXhBATADwK4GUAIQD/d5j9g5RyO4DtACCE+DbMiDNHM5xR6susz9TjbAOwDTCNd6ZU\n1XG7iKv270NUAs+fCePw2YhdqfDQmTDKy8vx2B3xI5HBIByTw+GzkbjbCwDBYBAbdx/HkbfMh60n\nHPvAU2S79mgTnj8TNhUyFCk4FbyyYfOAxBvdYftcaH8+oalUjJb9ex3HM6wS3rzvEsCK2VMRDM7E\ngdeqUdW82xFdeeyORfbDV1UWQDBoJiTy9qrKhB2BWrW/x9En8z1uCMUFfkcEir+gHDoTRs2cqUkZ\n+P6I0F7/YNC5rMzHkM6Zzhdw3kcr2p1OgYPXKYGD7ZM85SsfbD8OnGmyy8nXFE/FdYFzCAaDqFH6\n4tlz9/270GWMQ/5gF3ZFPu26GY2RJ8byub32c0COTx4Gze+s97xDZ8J47I5FSR0vHQ7iMOCZvVZy\nWgYAPCeEeFZK+WevOusVSJ+YwHMkCG5FqooL/Pj9fYu0hVoSgdMduMNMz04q2BUJ2pUO1Ugy/04I\nYFCabVJk2aR1lKFCtA6rzTw5gN/mrca28PVx+63aLTdQxVsAjjEtLvCjcsM+LJ89xU4mlhhe8jLt\nS/Y+3rWjaOdvxv0a27uvxYOW3jSsdulYyUJtr6ElZCew8s+9WjFsaAlhl2GO/ZMDQTyyYR8uTqM6\n/IFH7kFV826s8cV/EfRqpU43Ly4z6tAhCzFB9OCB8E04OGkFDtw6L8GRRhepqHbcLqVsl1I+DOCT\nAL5oLRkCAIQQH0qlYSFEqfV7Ckx+9E5lk58D+IKl3jEHQIeU8s1U2shE0BIxLWXxh1Yn9aPjDKkV\nELnmMtdYpglDt1R2srXbThijQjK8ymJDSyjhw6FmbfNzof11jh3xubgjSAmDvDIZyRvRMRbe+h1U\n/HMDduZ+FsAQd1FXhIEv8dL3ob5IjJ6sinjLuJSImAxn0m3sVPrHwy6Z77r9dtaf1mbA0/2k6maP\nFDQ+tOwbePEH2oIsnvKWZ9WgbSAPD4Rvch2TVHn/icCTVlfmHsYgDAyKXLwhJ2Fb+Hp7uwspscZj\ne51MTktGQrfE7eYo6fIqkuVHD5dvrMNDkaUxdA7dd9vC12OS6MB4DFEstMniSSY5dsgA/CKKIvQl\n5HK7KXaooLHmtraqLOCwxSRlR8mf9afaUuLeSgxFmpNRv1pm1KFkfBHuKf2NY37RSa7yPpON4NDN\nb+TU821PeES1qLIqCgcHtuLByFJbX1tFKipbcdtr3u3gw+ueBy+tMR2L318Vn7wVIRTac8Jo6r0n\ni6SpHQkPJMQxKWXSEnVCiCMASgAMArhbSnlQCHEbAEgpHxZCCAA/AvApAD0A/kaldahIZjlxtJAo\nKsUjpvzNlS9TbaqeaS+p8KUhHoFVJeGmrd/rKNLidnVr5kxF/ak2O1Ib6ougsrTInkTcojZuS5VV\nZYGYSYv3mZ8HAMeyGi21qY43j9ySwD5gJlJSPyo+eSsw92uu41tZWmRHxgE42onHy9YhmSg9gSIx\nqqFVl8hqNNEaPm66JUR+barKAljS8R9YGj2EXZEgtkWrPVlCBMwx3HG0Cc8ry7j8Gnq1ZGnjyP1o\n/OUjVoJjKwbGT0Fu52ns8S3A1r7P2Jt5tZRI2Lj7OMa9+AN8yXgWQgAvXroSR8q+4Hpd4mGkfUp2\nOXEkSMVeCyE+CGAPgP8PZlGXgwBeklLeqWyXEs3OC1pOIiRDUTMhUegX6IsghrrFKVSZhkQUC7fv\ndXZ8rbEHq637P1FEerjgVDnASV/k10ql7MUDP8a9R3rQ3C3hF0BYOml6hLXGHtySV4fQ1IU4M/Um\n+/N494pK7aJ7l9pLBl7R34gOekmhwJs9Evk+iZ6I0531qq1zv3kKV4UOJaQmeUW1U6mPgHkut/93\nN74ozXv2pcACTLr6cwmP5YV9SZZmN/x05Fik9GIipZyr+exh9rcEcIcH/cpIqEuQKseLJ7dxHVRg\nKIGGfhPVgxysZHl5tD+PxHBKhrrMZQiBlbmH0RMZWsJUHVyOQL7hWL7j50EOiu6cCapyxhBVI4Bl\n75hLqW1HtqPEcqTJ8ZMYipISfaX2aJMd9aZtU3Gi6QWHO8EqDYSD2gScUQv+ssK/4w6bLprN96Me\nUJb70mOH7KhB6GNfSfqcEoFWKdRlXPWlxFNYBXWu6XsV7+RfhvKuY2hEGZZGD2ErTEc6HUl/O+tP\n42BOHdpQjBwZwdrGIJaXObe5gBINk0HS9lpK+SchxL8AOACTC/U7ADGcslRpdqNCgXlub+JtADz+\nqQBW7e+JKWAFZK4TDSSmWLh970ZF6UQAu8LJ87lTgSEE3mBOpyEEysvLsWq/aUeKC/rR0RtGjiHw\nvIsTrQZ4qsoCeOyOoWBSs3W9w9ZGn70qlor3UGQptvVW4+QXF6OSH5tRvFQcPhvBY3cE7f/p3m1m\n91eiedere53ooc3dEjVzpmLcSz/AzbnOdj17rjQ8cDrPpyNBPJn7WXT0hvH+UpNWOdJnOhh0+hE1\nc0wK36tBAN+/FzCKURE5CgQfdjuEjdGk2KWqIx0PGWxuMhtcjkdVB9BJ9ajSY5ySIWAaycKCfHyp\n6Neube6wNHpVcEqGWlAkIiV2DMxzLB3Gq+RH0mW0DT8PKskdTxGBUwa4nF5DS8hewtzefS2AoZcJ\nuglJeojflKq0UbIgSofav47ecAytQV1S01FqdOCUFt1yId+PxpleQmgsDhYs9LxQyfLZU+xltm3R\najz+qSL7JTAdhVGooE7O++ehtNCHU+NmxSyZp6Pd5bOn4OnIkG40jS2hRrO6cIEjJXstpdwupfyo\nlPLjAN4B0JCebnkLtyVuHT2IP9/CZZtMQyKKhZsyh46Kwp3rVFQ8dOCqN6QFXVla5Ljp1CBKqM98\nN1NLi3PwzwUQt0gXAFe+tGrLN+4+HveBSEZDXicL6EYJGQl432uPNuFmX2y7XsrfqWO1zKhDRWkx\n7in9DTqtQJJX1BUADu1wALYm97+0Xo3G1g5s77nWs7a8gpcR6fc8OF3jugnJbadOzmqkmraniPPK\nOVMdEWlerpq+ryi5FThWC8xahZo2fXKchDsfmCLDOqdPTZ4R7HymW9FsolME8g27Dd4H+vvQmbAd\nsdYtmXMjqWbf837cg1iHlUfW6ZxIqodHyHkfgaGJV0cL2VQ9M2YseXSZnGA1uZKDqDw8CUc9Vx3c\n6C8AsK2+GqGPfSUtjh4dc+hcznnehgNzv+ag6lRZvw9uPQy4FK/xApuqZwLV2wCY9xPBbczfaxBC\nlEopW1lOy5yx7lMy2FQ9E0+9eNqhFkFUthxDYDAikWMI/O3+bqyYPQk1c6baq1q66PSFDF5+/IHw\nTTFUEDXBSxexTqWtQRhYkVOH8upHbJoigYITgHNli+yuWtlWdyV0dQ3iXTE3RQ1ql0NNFiTes2oH\naKUyKqU9bk9H56f1xZuPG2+XvxR5KX+nzntPR4K4J/IbYFYNpBU8TteTQi9aDS0hNMCa8weAVWlq\nb7jwkiN9VEo5psZ1rDnSnAe8fVGh67KCjvesAzljKidXJ/dDx1IdONUwuRklFTrOsw6c8qCej07W\nyI1fzJNF3KBSR6h9ncyVashUPrROGijeUpDbteD995QrnESfxgLZ/iTGBcKRTsle63Ja4m0/VvJ3\nOnC7Ec8GAaZdSFYGLRMRj16w1thjy4h1ozCubN1wZPvc9t+Z+1n8/r5FMQEhbp/V4JJqr1XHVgA4\npbHhV35jv2uAiF6cgFh77TZv8sAQn5/Ue1cns5jMnDYSxJN21OVPDQtH7geO1WJ7z7XY1PHpmGPz\nZ4vLAo4Ebs8f3VMHCxZi1YYfJTzOaMrfpVLZcJbmp1II4QeAsXaixwpcVSPZog3qdomqJukWGKNM\nJB5w0jxoqYz+PtnabS9Tuk0PajZuvBLatG2OIewyzrrzpvOk5S2uLgKYiSe6KohuUJfHigv8tpFS\ny3qrEQb1fNzaSXQtCFVlAbs/w5FsyiKLdMJrey2lnCulnCGlvDKRE51p4LYtnoNMq2uZEIkeLrUi\nXtVBtfx4PCSrypHM/uTYbqqeicYtS3CKKWWQvaW5jJSrVCdwU/VMLJg8dB3drtANV5Y7qgHzOYOv\nSqj2mlPV+LxplzhPQCfbVD3TQQWiOS2dlVB1xzaEiFGwGhGO1QJGDq7rPWB/pKMZekmCcqs+vDL3\nMCpKi7Gq0J2yOlZIhSP9IICjMJNJHoWpSfoTAK8JIRamoW8XBNREuWT4m+p2uvKkgPNh55X6dKXE\n6eYmZzUipe2EE5XBDSRHlAw4JYQMU0dvWFuiFUCMIeLLcW/2SLusOEF1pnlFJcDJW+zoDTu25Q/4\n8tlTHE6xSglwM3A760/bVZS2b/5yTMVEMtAH1s3TTgy8315WfMoiixTxnrTX/LmjvzuTrEy40lpd\nG3s3OnEZbjckkuEbqYMcD/Gc/4VbDw/ZwyP3A9//CHDk/pgABR97mvPo9xc+lBfDn1WhzqU8wJJj\nmHNHosqxw61iyucIktRMZ4CF2lsw2W+Pi+ftzaoBIoNoKK8GEBs0ovPU0WyGC7X6cFVZAI1blqB8\nwRogMmj2KcOQSmXDnwHYKKV8xfp/BoB/hkkp/JmU8iNp62WSGAtqhxvfORX5OwAxvFk36gbtqy7p\ncG6vSqeg/S7fMKTdzB86zhWmql6pFCTQyefxNtyWzOZdZthFLvg5cZqK7jy5mog6Lm7SgdQP3TId\noa6uDgfbJ2HVsRtRWJCPnt4+e/mTU0G4OghRSFRJwmTpO4mQadSFbH8SIxOoHWNtr8eK2hFPZlO1\nT5y+5hPA65uXDKsSYjowUmrFWCCZaoeGEDh58T8ARg4QGcTGqTti5iyytSTlSvvx+cIN8fKPvEQm\n2Z1sX/TISGoHgCoyygAgpXwVwF9IKV8fTgffLRiueoEayaaiGpySQY4f4KyMpC4jkTOn4/By+gg5\n0fRWWX+qzV5S4xJ4atERXREQAi90otIn+ItA7dEmuy1aMuO6nCSkrzr4unFbOWeqY1uKWtcebXIY\nUB5ZUJfr3K7Xpo8x0KIAACAASURBVOqZqPjkrSgt9OFgwUL7/NU+SPY3jRv9ToXmk0UWacJ70l5z\ne8mfPZ194ohKaFe9xgrpjBynCxQNb5SldmRapQxWlhbhO2/PRltntyOyqFO34DNZRErUnXWuLOhW\n/ZKZj7OrhVl4jVQc6VeEEA8JIeZZPw8CeFUIkQdY9XSzSBqqo8X/p795BEWVl1ElcDbuPq4tQ7qz\n/nRMyVty2HUTC00hPGGjoSWEytIi1wmGc6HpGOTcq44kP+cnXum3aRsbdx+PMYIqxxqAlkajc2yB\n+LKCHGRYn3il3/xg7teAr/4Oqzb8SMs341xvcuQ5UqH5ZJFFmvCetNdc0UgNOHC4JVNTNHPsXekL\nD+T8V4hWm5aiKiI1tITwYPgGfLTzX7GxbZE2wEGVbwlk/4OXOZ1yN0pkDBiVJKX9ssgiSaTiSN8C\n4M8A7rJ+Xrc+GwQw3+uOvduhOlr8f/qbc35VAo5aCnVn/WmtXJ1bJIYKm6ggaon6HTnTqephco4a\nj05s3H0ch86E7fOqPdqEaZZDzfflHGs6jvrCoDq2bv3QOba0lKuLeMQ7J16+nBvkbOJhFhmCW/Ae\ntNfqC7MuuAAAs6eV2H9zu0T76QiPI9VWzkSk45zi8bT5Cwq9tKgBDm5PDSFs+89XMIEUuMxWwhyO\n1aa2XxZZJImkHWkpZa+U8n4p5V9ZP9+VUvZIKaNSSu9EC7OwoU4CauSTO9NRKbUFVgwhYpbXCCdb\nu10d4wPr5sV8R7QPtU/kiPLKezwCfmDdPAcFhOgrKihSrgOvRKi+MKiObSrg7eUbQ+LvqSz9cYPs\nEyIbhc5izPFetdfqCzMPIhQX+G36AH/uO3vDWDDZr30x5hhuAmAmIx3n5EZLIVoeD3rQ9QLgUL8C\nPAxKWAlzRCWJF1TJUj6yGA5Skb+bLoR4RgjxqhDidfpJZ+fey9CVsN5ZfzrmYef8alU/s6osgJOb\nF2sVOcj5VRNrbJrIhn22FBE/HjcyAk6HmTvsuqVRHgnQRYrcDKeaAKR7wRiuAeTt9YSHIvipLP3p\n+N1ZZDGWyNrrWIT6Ig6qF0ECqDsbtu2IWxQ7kSLGhYhE5/R4zmacyKvB4zmbR9wWOc66oIeaM+Sm\nhsSRiKJhzwtti4Cv/s5R6Gk4x8siCzekUtnw/wC4D8BWmEuDfwNvS4yPDa5KT92Dj3Z1AePGDXv/\nm5s78NfW3/l+H/rDUUwsysX5xwfw1wDE4wC+WQwA+FV7L853D8CnKzbwZDF+0dKFvnA0+cb/HXbb\nBAHgivJi/JH1i383sSgX57sHHEuizc/cg/IJBfb/m6yf5p/04q+7BxzHKCnKNbf9bwDfZMdoN7fl\nbQrrvAj2WD0O/GGtcjwFzdZYTbS+3wTgNuuzHB8wGAXy2Hjjl//kMkj6Ppb8JBf4Zmy7w8VI7yOv\nke1PYjj6NHYFot6d9jpFFBf47QAD5TOQQhLxpAWASwqF/bLe0BJy7EdQq7peKIinAJLonK7xvYoo\nBK7xvTqiPhgJVupUdadk1DfUfVSoznkiJDpeFlm4IRVHukBKeVAIIaSUTQC+LoR4GUBiTyOLlDGx\nKBdtlrNZlOdHVdmQc3a+ewB5fh/+2NxhOnsWdMUGmtt70Z+CE53v92md7jy/L6Zfum34vue7B2xn\nljuw592caGU7AI62qG/S2o7aIMeXnz1vG8rnUvm+fEIByicUoKurC+OG4ZTx83FrN4ssRhlZew3Y\nq3GUdEjOsoTpMNMq0pP1Tdr9hoNMk64bSbnvF6IzcI3vVbwQnTHs9pOhaFBuEEGNDu+sP415lxng\nambqPipSdYwTHS+LLNyQiiPdL4TwATghhPgygGYAsdlqSUIIsQ7Al2DatD8C+BspZR/7PghgD4BT\n1kc/k1L+83Dbc0WaIkYvp6hhqL6BlwO4Rim/CQDl1o+bXirgLANOE4VOG5WXMOXH0znkpIdcDiB4\n7z5HlSj6nveDjn2FZZg+rtF0nneZgfLycsd5f9ylPCjxtfl58L7WzJmK+lNtNh3GrUTq02ycr2BG\nc+Pu43iyvgkrZk9NypjG0wG/wkNjnOp9lG5k+5MYGdInT+31hYrls6dghwuNDYD286qyAFo6+1xL\nTSfCSBzXZJCqo74rErS3TxW3DG4YRg+dmF4WwA5L/pTUnBKB6/7T9Uk2GZyQdYyzGC2k4kh/FUAh\ngK/AXKFfAOCLw2lUCFFuHWeGlLJXCPE0gM8BeFzZ9IiU8vrhtDFa8EoAXrcMRQ6xG9+YDA0tT8La\nXirb0fGerG+ytaTdjsc1nLlDG5HSlqhTnWgecXAbC3LmqfwuRYGiZ0wjWXu0yVEwJSqlfR5kfKla\nIm+TjOzO+tM4uXmx/ULQ0BJC5YZ9MX1xM647608jKpH0MiC/XlmZuywyEJ7Z61FDGmh2m2BSv5Kt\nVkgUtj80dwy7zTy04xLRhQH48Ro+i/NyHN7GhGEfT8UHxBlICNyFJ/Bp+XxS+3QC+DSex6eR3Pbp\ngvh32JREHWhFssbvw83KKuP4HAH88r609zEZZBKlLNsXPWL6kkaaXSqqHS9KKUNSyrNSyr+RUt4o\npTw6grb9AAqEEH6YBv+NERxrzOBFgoJZLMU0GZWlRXbiXDLlN7lSBm3Py4mTg7fjaJOjqiEAR7/V\nTGZdVLj2aJND35PANZzdXih0RWdUp57ridI585cIns290nKu1SQ/SmikMum6BE0dls+eAp9IvATJ\nE5KyyYVZZCrSYK8vWHD6W0lRLkqKciGsv/P9zilQAnjljc4Rtfc2JuA1ORm5CENCYKLoGtHxVJyX\n4yAgcV5mhsOSCFzLWwL4Q3OHTc1TQdS7PuZE07UqKbCOE2oBWl81f2eRRQYgYURaCPHzeN9LKW9I\ntVEpZbMQ4rsATgPoBXBASnlAs+lfCiH+AHNZ8u94pa5MgRcJClRQxBACJ1u7YyKd5LxxJ1WNGAvo\no60UMVerRFWVBexS2br+ENyoIYYQdtRYILnEDj5WnIahtgXrXNRj8vNbuPUwKtbvtZVJCLQNXylI\npm+bqmfiugnnEAzGjyyrBQSyyCKTkA57PWpIQ8RIpWDxl/1UyoHrSownwlpjD1Ybz0II4Bfh+RnB\nlx4LcKohgaiCKoh6RyuT/FrZ1LumlYBxsSlp99WxSebNEPoWgGxf3DCafRFSE3l0bCDE2wDOAPgP\nAPVQmAZSysMpNyrERQB+CmAZgHYAPwHwjJRyB9tmPIColDIkhFgM4PtSyumaY60BsAYAysrKPvrU\nU0+l2p20IBQKIRBIjpL4xCv9qDsbtis30d8kQP+3+7sRlYBPAI8tKrL3OXRmiDO2YLI/RrCe72vC\nSRQp9JuSb+r+/Nj0+b1HetDcPXSvLJjsx2vnI2juligvEvjARMPRb905XVIo8GaPRPAyP+rOhh0R\n6fIigW/NLYwZF7dzvOW5IS3pxz9VFLOfW1/inWMy10x3rHQilftoNJDtT2KMtE/z589/WUo5LJ5D\nOuz1cHDVVVfJlxI4xnVpnujcHGVy4ipd8jG8RF3uOgzCQA4iCA5sTWtbmY4cQ9i0QLccFjfwHJ6T\ni/9kFleZVZNQ0i5dSPe9mwqyfdHDi74IIZKyxclwpC8G8EkAnwewHMBeAP8xwujwJwCcklK+bXX2\nZwD+EoDtSEspO9nf+4QQDwohJkkpz/EDSSm3AdgGmMZ7rC8iRUDmXZaDx+5Iri8H249DnD2N8vJy\nALD/pujo+397GA0tIby/NIBg0DQ+weCQcREADp+NoLx8UkzEdUW72Z9AvhGTPNPD/j18NmL3l4aQ\nzqW8fBJe2OiM8tafakNztxmdeaNb4oWNixzHXrV/H6LSPC4ARCVsR/zw2QhWzJ6aMLkvGASmrd9r\nRzOojyZFw3Skq8qGxkTX9mN3BKG7JVbtH+JbHzoTxqEzYZQX+fDCRufGKgd+tG+vTDJMQLY/yWCM\n+5QOe31BQq04SnaEVz0kqbuuvrBti1KJVCfCSBL9EiHT1EESgefW6KrwxgPl1CyfPQWYu3jMHOgs\nstAhIUdaShmRUj4npfwigDkwy87WWZngw8VpAHOEEIVCCAHgOgB/4hsIIS62voMQ4mqrr20jaHNU\nQEv/8TKMVc4upx7wvzfuPo6K9XtdK/oRH1gCCXnaiTLQOcWDF2ShrHZyKKkdvsSpi+lQUYPK0qIY\n+khlaRE2Vc+0o+vT1u9FhVIeXHdsrgNLONnaHbNfvBKwG3cfx7T1e+1IFA/X8Yg7jQEVrsmK9Gdx\nISBN9vqCBNmBmjlTMb3MXCGgvBFe8Eq1jVQQxAu4VfnzAhdCpUXS6wZMTW9e1VBFvFwWmi+ySd1Z\nZCKSSjYUQuQJIW6EGTG+A8APAPzncBuVUtYDeAbAMZjSdz4A24QQtwkhbrM2uwnAcSHE7632PicT\n8VAyAGS8idKgg5qgyB0/MjBRjSyTanwoQTBeVT1yhuNBFcun/vG9yKHUaYLqVEVOWJMUTVY84YS/\nEHD+du3RJpv7fOU39qNywz5UlQXsyVAt38sTCjl44iS9jEyzHHXeniEETm1ZYht6YKgMe60ybm7V\nzmifbGnZLDIFXtvrCxXcDpDNod+qzSDFHg5eqVX3/0iw1tiDutx1WGvsGfYxLoRKi9OtXBzA1ObW\nVTUk0Lyz42gTKjfsw8Kth4dtV7M2OYvRREJHWgjxBID/C2AWgG9IKT8mpdwkpWweScNSyvuklH8h\npZwppayRUvZLKR+WUj5sff8jKeWHpJRXSinnSCn/ZyTtjRbIeMfjz6oRU27wuboFN7bx9DdVxQ0O\n7gwvmOx3qHoUF/hjHHCuIKIDqW7wSUVVFdm4+7ijXaJGkCsdtaT0aCw4eJSIJ/XRufEo00rlBUI1\nnjx6LWG+VPB+0H4H1s2zHX23yHO8pchsadksMgXpstcXOlSby+1AVVkgRrFn4+7jjki1rtLhSOBF\nNDmd0e7hQBdQ4Qnt3O6r2Lj7uB24oBXWhpbQsO1q1iZnMZpIJiK9EsB0mLqk/yOE6LR+uoQQI9MJ\neo8inuMLDBn9z+eYxnZl7mHXbRO9eVO0umbOVNSdNZ3TUF8EjVuW4Pf3LbIzp+kYFLEVGDKM3Gmm\nKDCfVHjfdPxConKc2rLEIYGn7suhc/JVqOOoGk+1H9LahxxwDtXY8+VIWOc8TUM/oQkgmepdWWQx\nCsjaawayjwActq7+VBt8QqCqLIATLaEYKU7VdozUiVYj0ImiyW4Ray8i2elAcYFfK9NKtp/sPtEE\nAefcpa40Gta1Ga7EKH9xykans0g3kuFI+6SU46yf8exnnJRy/Gh08r0GchCfCs9HDiJ4cjAYs43K\nY6blMG4sVOknmiwiSmSAO6BkgKaXBWyHutOaREjTWnVCOXZoknQaWkKOCLRqHMnZ5zQOcvJVR1sX\naXDTdlZ7SdUR6RjcqFM7ZOwPrJuHxi1LcMOV5fb+/AWA9wcAfAo9JossxgJZe+2ErtQ0j3Y2tIQc\nOvjpghqB1kWTuZPsFrHOVF50R284ZqUSGKL4cXuv5tzUHm1CZWmRba9XzpmKk5sX48C6ecMudsWD\nLNnodBbpRtIFWbIYfXRddSeuG/weuq66M+Y7zmNWEw7VRLlaqzyruj+BljkjUqL+lJnPyQu9UBuk\nXHFy82I7Yqvyi91IIW6FX+iz5bOnOHRDCcSZXrj1sN1X1RGnl4kTLSFbN5oK2pBx5tQYzndOlJ2v\nGl/aV3XeeSGdLLLIIjNA9oKez0C+AWBoxYvbL24X+Oe6z1KNDCfDZ+ZOstv2mcyLVlcqAXPuoIq4\nNSxiTc4z4WRrdwx/2qtIcrzk8yyy8ALeZU9k4TniFVhRBesXbj1sR1niOYek5cmNGH9rJwealyfn\nCSPUh5Ot3Q7nVEfpqJkz1f6MO6A7jjZBwuRsH2wfikwAsSW6G5SkRR0k+03t1R5tck1q4eciMDSm\npKvNJ0xe0IXvqxZmISlCaj8bnc4ii7EHPYf0XJKjF+qLOKge9CwTZk8ribE5/H/u9CbDUX4osjTh\ndslI5SVznEzDjqNNrna4Zs5U14JmaiT5yfpurGg/nrJt5dQ//n8WWXiFbET6AoPqwJFRcEuG41EA\nYEjL84RFt6DsaHIUKVIz3aJZrJwzNcZB1y2VqZHbHMOkOlBEmJb4uGpG3dmwfSzixcWLGqjLgRSp\n5jxwas+dfOJMNFppGfKIlOiLAI1bljgKBbgpo6gcPJ6gmV1CzCKLzIHueeQJyvTsUtR64dbDcYMR\nAumJDHO6R6ZSONzAec0qKCqtXgc7OOFC3+A2dmf9aa2ySrLI0juySCeyjnSGINllLN0yFc945iDN\n1MYtS1AzZyp8zLskvi+PQgNDahnceVadU64RrfaLQA47jxbTdoSohE2NIF6cuqzHwTnchIaWkE0N\n2Vl/2vEC4AZKfKSIdTKShSolhf9PEXYg8ctAFllkMbpQpSu53Ce92PuEGYxQ7aEOEsCPUW07vTlG\nvNd2d8Sjh2QyhUMHCdO2z55Wov2e226hfJ5o7qs/1WbPb6nYVn7cLL0ji3Qi60hnCNzemFUjQw4c\ngJiMZ4rK0m9dCVYeMQjkG3YUId5U4DeEwzlVNVk5aFKhpBPeH11U4kRLSOtA6/SveQRD5WirkXo+\nPokMdTKShfHAeznc5JgsssgiPVAdY100OniZ37FSlQi8Sl84ImNW/pJBvKhzpknbJYPao03aZHOe\nX8MdWQHzJcet6JUa6PEJd1qGWi+A7090waxtziJdyDrSGQK3N2Y3B1untBGvWlTt0SZEJWKiz5Qd\nTRFackz5hDIYkVotZ94e9YcmmI7esG3QqLiMzjlW4+i6YjDq94DJYTSEsCMgap/cqkW6jc9IElqS\noZNkkUUWowP1mdY9l5TTQYnUAOz/U4UE8NSLsbZFR3PguNCizslAN348AZxWAKgYFg/GqDrTZNMp\nKHNJobuFJdvOlZWyUegsRgtZRzpD4PbGzGkUbktVPErNlToWbj3s4CD7ROykwo/Jkwh9isQdN3C6\nvroli3BONP3mpWKLC/xaVQ43k0njoZZSpxcKnbPPVUlUhzlV7pyu6AtPyswqd2SRxdiBHGT+TKs0\nL7JLhOWzp6DubGKd6HiynzxCTUhEEbkQo87DgSpzx1VU6H9KbufXheaZUF8EAPBmj/trDpc8pWMD\n2RXCLEYHWUc6w8FpFImWqlRnsKElZDue08sCeGxREaazKAllTKuRW1WaiB9bdbzp703VM2MiMJWl\nRdrl0o7esO1UUxZ9Q0vIYfy4dJ1uPNREFLvfrD+8WiS9GKhjlGrUQqdLC5g60sSxzCa0ZJHF2EB1\nkAHYBUEIZJeIcgbAUZBFx3muKgtkI5vDBB83erk4YeXgEBVPrVKr7p9MDkvjliWO5PisHc5itJB1\npDMcamQ1ntOnLoVVMdk69Tcl3KjHJ6jRFDUSTNrU3IGdPa0EjVb1QjrGzvrTWDlnalIcQm78uPML\nmJMbj2QAQ9EGHsV2M6BuY0f77zjahFue63ZEmqet34sKpZqhrtQw9Yv4lokmXOLz8Ug8fZ5JEW1d\nf554pT+j+pgOcDWbd/u5vtvAHWS3FbOTrd0xBTs4dNFlCmRkYYKKZ/H/dTZeR2/h9Q/4y44uesxX\nG5PJYdG9SGWRRbqRdaQzDG7JhRRZVRPpOOj7G64st/nDbo4fN2AU1QWGkgPVmAxVJ+SRapI84g4s\nL5nNP9cloehABRMAZ7Y98bTVqC9JVan7qePDx1FNTOH0E6q6xfmSuuVGVb3jZGu3Y/t4zhcfC/7C\nkgzNRHXy0unsUX92HG2yXyoOnQkn1Cr3BEfuB77/EeDI/aPu2KrV70hqMetQZz7cHDIqCKJ7mU7G\n4eIvygBiKvi913CytVu7aqk6zg0tIYetcAR5kqBd8Mq9ycDtRWo0kGmBkHRhNM7zQhvLrCOdYUjG\nmdKVuHb7HoCrbJuuTfqeltq4Yaw92uRw/EiyjralDGzApDoQjSSQbziSUMjQPf6popgoBq+MpRZO\nAWJfBKg/nC6SqDANT0yp1SRAqvv9P/bePUyuqkrYf1dVdac73SE3SIAkJCGAPwU/MGQMKjENKCA4\ngn7feAGCCg5eGEYZHI1iQI2jqIOMoqODA4MEEP3mJ9ERJFGwQ5RJFAJIGBUISUhCSCTk1rck3bW/\nP/bZVbtOn1O3ruo6naz3eeqpqnNdZ59z1lln7bXXCj9ofW+1M+78h0qxcxOu/ji2NZPbnnsBiXuw\n+/GffonjSh40leDkcNUtw9TVsF2zBNJN7Fh566BjrrdnMNyz46j7y4NSV8Iv034Y2JnTBlc6dKSD\nsC3/HnB6aqgDjCutkJgUwmkCfX0Ux4LTpheE8hXD6UR/fM3HftXNjIX3cfy19w/qzXP4jqFyMjb5\n+wsvV6kxd6fXQ1tvIzOcocSfV28D1Hew1NuJ4zvM4s55EmiYIS0iV4vI0yKyVkR+KCItofkiIt8S\nkedE5A8iMrtRsg4nzngJj2COWgaKFxuImx/GGYH+YDz30Fl+9fzYsIw7vQGNrsS3L4P77xvHQqFh\nH5bPN1584/SwYLpTlEsCL6lbPspD5KoWhmO6w56UYrhc3Hjb8vNGF3uARLV9eNru3v6C7aW8HLfl\nbM9RTbaBUoRjS8PU1bCdvQAGDnBrz7xBs4aryzZc7lizshw8hB/UnZvz+fPD6UAHjInM0w9Dv+9G\nWuGVavBj0aPwQ91cz09UBpWe4HZ0oTdxxnhc+GGxF//w4HWX6cqte/IXlpU0Gn15nZFZD2eDe6F3\ngzP9cDtf5nrhHA2G+FDKoeAXiHO9y+VUOG4kDTGkRWQK8PfAHGPMSUAaeG9osbcBxwefK4DvDquQ\nDcIZL+ERzOFl4ropXaiCi2Hzq3Vdtqw78g02HF4QJ1MYQ6ExFe5WizJ4XJUrsPG24QeUG6Edlss3\nanyPclffAAtOm86e3sGj7v2qhb5SdQ/Lcti2p2+QYi334Rl1/M6I9w1/f3sDxsS+dRfLc9uUlrp4\nB/xz6or6uGvLHUMlLyblsmjHOczY+k/8a/87gML2qqfHx4UKRRlOxYr8HOyUcnwkgUq8j+78trek\nmfWZ+wtSq63b3j1s57qaFHhJ9GIXc2i4bEpxz7Oo1K5RZMp4k/Vzg4cp9uLv9Fx7Szry/neFyooZ\njf4zxRmZ9XA2hF/y/HC7Wqdi9XtfXU/Az57cwrqvnJc711HZsIaC/9x3g1IdY1sziQz5aGRoRwZo\nFZEMMBp4MTT/AuAOY1kFjBORo4ZbyEZQTiaJcBeWI1yYxK/WlTWDjZCoLBZ+TKq7icKx0T7hhPvO\nWwz5mGtfyfheoKhjj5LL3bT+Q9CFQfgxzg7nSY5rw3LfbHf3Di5jXq6SivIsOy99V99A7HaKyZaK\nCDkA66Wph3dg8YUn5R4uLhe5u7bcS09cefpq8L3+Pi7u31Gv8I6otk+L5KpgHoqU6fhoOOWmsvSv\nLWcgbe0xOV3lh6jVm2pS4CXRi72nt5+0yKD7FMgNRnfPEIECD3VUzHqUo6M/pOSjlol6FvjLxz1X\n3bMi3AvlGNuaKfpMDjt+fGqdy9qFUhabV6sXQb89/RoR/jfUL+wtHBJazgtNI2jIiAljzBYR+Wfg\nBaAXWG6MWR5abAqwyfu/OZi21V9IRK7AeqyZPHkynZ2d9RK7Irq6uqqW5axxcNY5o4GXi27jrtXd\nZA3ctXojZ417GYD5U9O5N9TLvrOM+VPTdG7uL0jvFF6+c3M/HVMzwajol7lsWfeg4i3Pbe/izGmZ\n3LLAoPU6Ozu5a7U1qlz88e3ntnHWOaP5wAP5bc2fmqazs5M3TTasfKlQIZw1zm7HHYdjd28/l31n\nWcG0/zi3DXiZLaFlwcp+2XeWcemJo/j/M/luwWo4cjRs7SF3rHc8vW/Q/qI49jP3ee1jce09f6od\nHNm5uZ+jRgtbuvMnKCPkzrt/HbnzHafsnay1vgcK92cYMDBj4X1MaRO29uTPZy1wxxhmyaqNnDkt\nk2t3t8+h3GelcPsbMIYZC+/jzGmZsjIH1FOmBuIcHweIdnw0HPdSXcpo8S8vl9lo/tR0bkD3jMAJ\nkFR+NNDBe9KdiSrk4jywS1Zt5ITJ7TyzrYuxrRm6+gbIBqEx67Z3s+GG8wet69o9zKzP3B8bUgP5\nAfD+urMmtUUatE1pyU13L1LhfVZrnLmQijjiPORDIW6bq9fvqOl+nGPMJy6Mst7410I9ekGHQkMM\naREZj/U4zwR2Af9XRC4xxtxZ6baMMbcAtwDMmTPHdHR01FLUquns7KTesly8K58aqKPDDqBZsTmv\nDB7a1M+C06Yjm1/ghMlWwQhw3KR2Ll/WzUVzj+G2KwcrMLfd9pZ0zoi6eO70gljhu1e/UDDNcdzj\nK3IKSyDfBg/kH063XXlO8KuTbY/nFdyC06bT0WG319ExWJGu2DxQsK8ve+ueMLmduTMnFtz0KzYP\ncNuVHexbdj/+49Mp+nJ5qQee/0r+AdDRkW+DVCo6XRbY3LQPbepnypQpuQFOKzbbdgMK2tB/ePeb\nfLv515Fr27GtmVhj+qUean7djV2xjN29/YxtzbC3L/9S9mK3ISXClClTcudtqFy8K/6hNGXKFE7Y\nv4NntnWxeX8LHR3za3OfrbyRLQ/dwp3752PVUn5/6c0v5K5Bdz2VYjju/eGkTMdHxU6NWr9wRDkg\n7nh6X+iFv5DntnfRMTXDu6YfqIksrrBIPfnuwAWJLuLidOvevn5uO6ctdw4qfeEOO1PIlb7K4zuF\nAJ6L6R3zdbRz8vjr+WGGGbE62H1D3png1gs7OIoRtb+hsGRVeH/5dnFtX6v9bdmyb9C0Uw+3TiKf\no9uk7o6NlOTzvT+3vfR+htOh0agcPm8B1htj/gIgIj8B3gj4hvQWYJr3f2ow7aAjqjJfOYTf5MMG\niHjTntnWxe3nttHR0ZEzUF2WjrjtunzN6dAAuHBWEH+e380fLv7iBtX5XgQ/rzVY4zkcJuII/w+P\nGl9+9XxWjmAMXAAAIABJREFUr9+Rm+6n/Itq33I9T/7bb9j7MODZ9r6B6/927eyPdvbT7UUR9rQA\nkYM3If8SUY43rlIWLV0b2ZUHFAw2qVXYg9tOVLu49oMaDzpZs4SegRTvSXcWGCiuPd35OlTz0pbr\n+KjUqTEcLxyXL7ufrCl8CVoQOAoGjMnNu/TE9pws7sWxGuptRI8EbBVdyTl4qj3FX358BeDf54P9\nnlkDH3zAxrQvvvCkAmdDV99AgTMojH/tXb7s/txvZzyHQ0n89fxrN+7lP/xSVbNr3XNKxTmFCpxY\nQ+CDDxQ+I10Rt3CvoXPg1OSefiD6uZw1+R4kd20VYzgdGo2KkX4BOE1ERouIAGcBfwwt8zPg0iB7\nx2nAbmPM1vCGDgYqLVPtCA+uCauZw7xS3ADXruwpKMtazDDw80EXy7saFWPt8G9wv8CKG9F8x9P7\nIisU+kVZnIHtjPm40d8nTG4fFKdWyrgrp0gM5A3YUl14vsLe3ds/KCbPH+3sE2c0hil2vlzqpVoT\n94Co12DDYm3sH1+5g0XLYXnL2bkBX03pfDz/gDE17yodoeQcH8aYA4BzfCQev2BSeJBSVMys/+Lo\nI2ju6HLJpKUgDVw5eeCjBoqW+7LsD8z3x6AUi3kOPyvjdGs5IQuLLzwpcrnjvewv5T5rShFuu7g2\nqlWMdPiJcqdX5t2nlk4GX7eH77lntnVV7HAcDhpiSBtjVgP/CawBngrkuEVEPiIiHwkWux94HngO\n+D7wsUbIOhwUU/bFCKfsCbO7t7/ghtrSbXIDD0tdjH7p66jl/LzOPr5SiVJWftqccJyxb1T7qerC\nBWR8peQGgoXDOsKDG6NeVFxZ2VJGWdwASB9XgMbHDcoLt185RmCUYooz6p7Z1lXSw10NcdehO3/u\nIVXLwYZxbey3bVqE5VfPr9k+P7qhIzfg68DA4Py4UYV5DjHKcXwkEr9gkl8casAYng30oEshBvHn\n+PjJ7bnMQO5FEooPPj5UDW8/jOLOVRsHFTiKSwtarjMp3N6+s8dP5VpMF4YNzThjOBUyGON09yWn\nTY8sYuYqONbK8CunfWpZjCZ8vC5Tl+88GduaqalhO3fmxFyCgq6+gUEy1KNmwlBpWNYOY8z1xpj/\nzxhzkjFmgTFmnzHme8aY7wXzjTHmSmPMLGPMa40xjzZK1noTpezLIezNdTetu/Bc5go3Gn1KW/lZ\nD+KMez/HZ5yCiBs57I7TVzhRxWD8MAhnjEK+muPiC0/KHaNfutwhUGBoxWVBccZ6Kc+HO0a3nShS\nEZ7m8P78Yworh/DLQVS7+nLGeThqOeijmFHrvyzV0hsRty3/QVXrEItyt3eohnbEOT4aKlSZRL2Q\n+0U+XKozl0GoWJYffz1fp8T1A1UbHjKSCRcy8tuqWNYMp8d9Ay1Oxx0WZNBYcNp0NtxwPuu9jDrl\nvNQXe26F8XvBws8VyOfAjsp77ahlj11csah64doz6iXB4bI31Yrwi1f4+ZzE8CmtbJggykl7B/nc\njktWbSyIJ3YluefOnMiGG85n+dXzC+KvXzUhXbYxEmfc+8aVXzTG774LxyP7uShnLryP4wOFeua0\nTOwxh6f7ieb9EA53o6e8KznOgI8ape0ryilt1ru94Ybzc0rcFXXxtxOl4MOjqKO8Av4xuUI3ft7t\nuNzgjvALUnheLdMeOXmjDPNLTpue89QUKyBTDXHhO4svPIn1wbmpdbdeqcIzUFuv0kgkyvHRaJnK\nIfxC7t9n/hl3mYgqOcdue3FeypFQwKfWBtmAMQU5/YW83pw7c2Lsek6P+4awf678Ni6WAs3pzmJt\nH6dfi537ON1ajtOrlj12ri2LGa+17Dlzz6xiz5VaZ9DwX7yiSOJ9pYZ0gogz+ML4uR39eGJn3Pg3\nkj8w8KFN/TlPMhBZZjQcqxY2aH3jyo9NK9Z958trINel6rxAUccczpPt3zx+1467if3uxHBsXjkV\nIgG29hRWdowrjBOWNRz3HNe1Fj6/pf6HWX71/NwLktuPM8SXXz2/rGunEnzj1X1uP7ctd25qnR/V\n36//IlOr+MJi+A9g91LiV7g7VL3RI51i1+nx3nnu3Nyfu/f9B3hcbLTLjbxo6dpYQymJnrMw9fCa\nu+MWCp0a4fANXz/HnSe/wu6Z0wrPQ1w+aOelXhAKtzhhcnvJF3F37sPnPE63+jI43eHvs9Y9dlH7\nhUJ5a7k//5kUp4dr+aLg79Ovquzr5SQWxhJThwFKjWLOnDnm0UeTEQFSzxGji5auzWUS8I02f6DW\nCZPbCzzSWWMKFFw40fm6r5xXsL6b5rbrZ4QIxyIvv3o+Z99UOFp61qS23OhaoCBThdv+gDEF+wnj\np7+LS/nm1j/5C4Uj7d3LQql9hNsn7rhd+jq/HaLapNi+qiFpqdRUntIMVSYRecwYM6d2Eg0/5eji\nRp07p1fcvRr+H7VMsQw/xdJRHipEtUFYF4b1aVS7F6Ozs5MHdx0eqYPr0VtULJtWkvROI2RxtoJ7\nWXLtc7C1S7m6+NAcDdFgqk1354hLYL/4wpMK0t35yy5aupa7VtvKdL5BK9gyueEHhRvw5xuZ7rdP\nuEtud1Dh6tkgpvDu1S/klKQzpv2UPa4ktvOy+m3jJ9ePe1A5hfrk9ecUGNNRxm5Uu4fbx3+bD7ez\n791fcNr0Qcq/HunnFEWpLa6na9akNmYuvC+nD/17N1zcxRmKUXmio9JR1jQ9Y8L5aPqnfKjpN/z7\n/tML0keGu/zD+tRv43Kfif42SqVxHSpxz1lF2yaMhnY0gGrT3ZWDH0frs/jCk7jtnELFtuC06ay/\n4fxII9Uf8Oc8teGBNX63lT8QL7wO5MM70iKDuoLcQ8e95bq2CS8Xjo1y3T6ue9A/Dnej+11yce3u\nlHjH1OKjj4ul/Ss3LEdRlMbgQgmA3PgPp89SUhiy5d/Pvm4p1X8r1DjH+TDQlB5a1Ol70p280mcG\nlSx3lQfLoZpnYj3DyxSlEtQj3QDC3o5a4eL1yk1/497k4zwocYVMwtNcWEd4O77H1t+WK5riKke5\nHNDh4ituX84jngrCQcLH4BSwX5rWjdRft707l5TfhZ3EZdNwMdtx+Aa5y2ZSyoNSLDxEjW5FGT7C\nGYIumpsv8HTUaCkawlYuIzFQMq4ya7kUK1m+ZNVG7ly1saD7HwrDEF2bh73TfnhgXA+s6lAlCahH\nugHUy3tZzlu976l2SutZr8y28004T0LWy5QRlj2cPu6ZbV0FA+DicJ7mLETmgPaP56K5x+QqJIa7\nCt18l6bPDwPxBz76lfl8gzY80MWN3C9GVDaTYoMaw+fE5bCNyoVZanCkoijVE/Zg+tlatvaYgns1\nrEOLZREY6Qw1c8d3By7I5WGPwqUZdLotymni9CrkMzQVyzutKElCPdIHEc6YLJaOJpwH04+N9r3J\nTnn52UFKpY9zadmiXhD8+OITJrfz7LYusoaCGEWHK0MaVqLO2+wM41mT2nJx189GeNRdOVG/TKw7\nDn/77sWgs7Mzss2icG3tl/yOaqNw74OfvcThPDCuLesV86cohzJh3bRo6drcmI+OqRmmTJkS6ZEe\n25rhyevPAQoHQPvZi0YytRwoGdW76drJ15WOcO+pr+/9ctCKkmTUI51wKvFShgf+FVvXJZKPwnlt\n/FR3UcrMeXhcYnx/wGB4v75x/8y2rlyqvnDM9YYbzgfyifBnTWor2LdT+v6AxvB2gIKUcE9ef86g\nHM1h79SipWu5bFl32d5gP347HA/uiArjiKr66IxoidmOoii1xx+3cemJowp621watQ03nJ8zoqFQ\nDxry936Yg9V7XQxXsCTcGq6dgKJGNBQ+U4rlnVaUJKGGdMKpZBBG2Dgstm6x7TkvKhBbBKNYrK/v\nfY7KteoPUvRzbxpvu45127sHJeYvVVEMbAjFjFCObJ+ogYhZk18vnF87qo2c/C6DR1w7+MfjV30M\nV127JGY7iqJURzFnQtxgtah1/IGKvuG84LTpkS++I23AYS0wWI99nF52BrJfhCqMr5frOShfUWqJ\nGtIJp5KRyWHjsNi6cduLC6sIUywDRtbr+nTL+Ps7rNVmx7j93DbWB55s56FesmpjznvtG9xucJA/\n+KRYHHa4YI1fGTEKt0/fc1Ls+P3E/3GGr5PdrwAZ9aDwq64pilI73D12p/dS74gbqxLlCLjT0x+u\nt8yFsh2Khl6Ux93pe/d7wWnTCwpq+LHQUbquVDEwRUkqakgnnKEMTAwPDPRDF5wh6OPKIEcZgGF8\nJecrQL+71Hlfw17r3b39g+TxleW67d2RRqr/gJtZpEACMCgkJSqkwsd5zP351ZQ+dW1x8heW5Yx2\n3yh3ITUzFt6Xe+jog0JR6oPTUwYq6tnz43qXhOJ6HU5ntLekARtLncTyxfUgyuPuV5xLBc8S95y5\nJHDQFOvlCztnynn26QBtJQmoIT3CKVeRuNCFJas2cvZNK3LrhD0GULw8tiOuC843sMOK0M9x7csz\nY+F9rF6/Y1Acc/g4/YGNbiR4HIcFDzV3vIbBJWv9bTsvujP+obrSp64twlUW/XAbH7cvfRgoSu1x\nOsjpFpe2Mu5e850BpRgwhhkL7yvICnTYEDNgjGT8ELxwO5fTy1mNB7rc8A81uJV60hBDWkReJSJP\neJ89IvKJ0DIdIrLbW+a6RsiadOK6LqO6yRwu24QzRBecNj0XWuGI8zhHUcx49tddfvX83KDEi+Ye\nQ8rb5TPbuiJT6/mKGOI9ymF29/bnHobOe+K8JD4uFZPNU50PISn3oVtsUKXDfwEIPyRcLttiYSeK\nogyNqLSVYfyCUNUyEkuFfzT9Uzqbr+aj6Z8OaTtn37Qi50x51nvGRPW+RenOanpfyzW+Nd5aqScN\nMaSNMX82xpxijDkFOBXoAe6NWHSlW84Y88XhlTLZhAeqhbsuo7rJzpyWGZQP1eVMjVMyq9fvKKg2\nGEUxBRi3bVdp0fdSh0NE/PWcwrwkyBBSyah4f4BilHwOl0e6nIdu3LHFebH98xCWvVTYiaIotaGY\n4RW+z10WHZ9SOfKHmpO5Ebwn3ckB0oMqE0ZlIymG76CJehVJe46MWhm25RrfGm+t1JMkhHacBawz\nxqg7rgLCA9VKpXcDuPTEUaz7ynksv3p+wfLOi+p7U932/Vi4sDfBDxGJo5QCmztzImkR5s6cWKBc\nnSwDxnD2TSsKEvbP+sz9ufXChB9yaZFcGrwoZeunW7r0xFEF86LapdSx+fGVfoGbcI+A4+7VLxRk\n8lAUpX4UM7zc/exevC85bfqge3/WpLaCnqOw4dzVN1AfwevIjwY6aGJgUGXCSseIlHJu+M8Pl+5z\nuAzbehVBUxQAMUPoxqqJACK3AWuMMd8OTe8AfgJsBrYAnzTGPB2x/hXAFQCTJ08+9Z577qm7zOXQ\n1dVFe3v9cone8fQ+Ojf30zE1M8gArFSmy5Z1kzU2tOG2c9py239oU2E3ZUqs17Zzcz9ZUzj9tnPa\nCmQCSsrX1dXF3/9Wcvt2247ax5nTBk+L4vZz8/IPtX2i2qVSouS4dmUPW7rtgZw5rbh89b6OKkXl\nKc1QZTrjjDMeM8bMqaFIw86cOXPMo48+WnSZzs5OOjo6hkegEnR2dvLgrsMHlaauNNwqqiBJUvCL\nWQ2FBadNZ/X6HYOO0zkxotrMT3fninAJNr1quZR7vRRLzVorknbtqiyDqYUsIlKWLm5oP5SINAPv\nAD4TMXsNcIwxpktEzgOWAseHFzLG3ALcAlZ5H0wnsRilNh2lTOJkunjX2twD5PJl9gFy25UnDari\nlTWwYvMAF8+dPuiB09FxEpcvuz+3DN7yt105eJ+Llq7lrtXdjGmxyv24Se3cdmW+6uLZN60oUNS/\n3pSPeXY5p6P4wAPdLDhtOrddGZ1eqZiC7ezs5MuPC89s6+KEye0cN8k+FI+b1E5Hx/xBy5ezfb9N\nXDv8dnBzxJIkxQQqTzkkUSalNOFeuGrGLCTViIbaxW+7MSzhqrFLVm1kww3nD2q3dGhsSlwRrVrh\n92w6WettWCuHNo0O7Xgb1hu9LTzDGLPHGNMV/L4faBKRw4dbwJFKqRg0Px45LibYTx3nRqP73X1z\nZ06MzVvtUkK57yj5siav3J/Z1lUQIhKONXYp9YrFJ/rbjhrMUk5cnnsQPrOtK1d2PKr8eNx+w9vX\n2DxFqT+1yMrgh3b4RE1z+OFbYZrSB+eoh7NvWpEbNxM2zqPSkoYHrPvjYupBJYXJFKUWNNqQfh/w\nw6gZInKkiA2CFZHXY2XdMYyyJYJqHxBRBtwdT++LHMx39k0rmLHwvpzR6/JH+2nwnMJct717kGLy\nq345w9pPCRUnX5g7PU9GeL5fPdD3ZEQ95GZNaquJUVup5yRq+xqbpyj1p5ix5OvQYvrU3avLry7s\nfcqGxoqA1TsbQsWkwhwYaGzYZL0o5nUPH7HAoBSpzknivmvxEhTlGCqnMJmi1IKGhXaISBvwVuDD\n3rSPABhjvgf8H+CjItIP9ALvNY0O6C4DZ6TOn5ouGX5RDuFuqnLxR0c7XMyz6+Zy4RlOMe7u7Sct\nUrA/f7l127uZNakt56ENv/EvWbWxYKAg2Lg8l1bKccLkdtZt72ZKm/BSTz49nH9yffn9LrlFS9fm\nQjtczF9YsbvQDBd24m+zVBsuOG36oK7Jcj0n5WxfUZTa4/RUXDYO38h26ULjuvvPvmlFwX9fLzWl\nhWy28EXf16EjjWripsPrOF3rng3Gm+YPfvfPT/i3n/6zGh1a7DmpelmpNw0zpI0x3cDE0LTveb+/\nDXw7vF7ScTd05+baxKM5JV1Nlb2wgnK4h8fiC0/KeZLBKj+XPcMvzuIrIX+giP/Gb0M1zKBMH119\nA4O8RG7+lu68Rzls9Pry+8rRr5wYLnriP9Ce2daVq9zoDHlXkKWYUg2nZ4LqCrMoijJ8FDOWooy4\ngUBXRRlevv5yusnpgmyWXPYgx0jWD7t7+yseINnVN1Cwjvtet707dvBg+Pz4v/1BnZU6jPxtxL1I\nKUq9aXRox0GH60ZymSuGSrgbrBpZ/I7HBSFD0k//tvzq+QVe6KiutqhwB9eV5tK4+XGD7oEVxqWu\nc4a3M+4XLV3LzKCEtsuRHU4v56Y5T7FLQTV35sTInMzOkHfVEEt1I4aroVWinLWClqIkC7+rv5x7\n24/hdakz3fLtLWlmLLyPk7+wLHefj2TjTah8gORFc4+JXMcVsIpLixqnG/2KiOUUAItCQ+iURqKG\ndI1xN3S5KddKMZT4rrCBe+a0YMCgp6SiFFCxeEOn8IoN+ps7cyLrbzi/IM+zW8d9r/vKeUxpy8/3\nC8k4s9t5qX2j3pfXVUrs6hvIyRuVkzncduUOPKlGOevAFkVJLlHjOcL4FVjDy/tjP3yPdjmDoJNI\nVKxksSGSzhETFe7mPPeuMEtYBxbTjb6uVR2qjDTUkE44tXjTdl7mzs393FmiSiEUN97DZbxnLLyP\nmQvvY9HStbltO6/vrEltOaXsBgC2t6RZsmojZ9+0IpdL2e3TJer3p8UNbPS9HsVKlDuZF3jecsgP\nqKw1OrBFUZJD2LtZqZEWXj5cgGWgTnqkkfjGdSZkVTvdvW57d4ExPbY1QzbU8xgORyxXN6oOVUYa\nI6+eqVIVLt0cUFJJRcUbRsUZ+yETvicZyFVddDFzLie1n+4uvE/n+UmLFMQhRsU3uvXvXv1CWS8a\n/jE5WaLi8Yaac1QHtihKcgiPsyg25iTq3g8vH1W5cMmqjRWX0x4p9Ee4rMOx0RCdnSmcNtTpRvdy\nE6djVYcqIw31SI9Aqokhu2juMaSkMI1cJYSNZrdNIFfq1ff6xsU2O4/OCZPbOXNapiBMxC3jYu2i\nji9cxjfuhaBYGxXzeGi3oqIcPITv9WJjTqLu/bDR6LYXzhEdNQ5kpCJUn+PZ99jHtUg1OnbR0rVc\ntqz7oPP+KwcHDS8RXkvKKUs7XNSzupnzqIY9t1H4Xpazxr0cK5Nbzq9W6BvblWS+KEeWxReeFNtG\n/vEBZR+rvw//weaHfRTDLxMctXwjKmQlrUqeylOaocpUblnaJFNVifA59T/kLbt6eaV7P6MyKfb1\nZ5nQ1syUca3s3buXrT3Q15/NpQAN87+mjM1tY0f3/rrLWi9aMin6+rNFl3GvCXHWwcS2Zrr39Q/a\nTrjqrGsziG/7cnhqy+5ctqjXettsFHv37mXMmDGNFgNQWeIYJEsVtmG5ulg90iME38NaSQxZXIxx\n+M3eD5mI8hYsvvAkNtxwPutvOL9qI7JcT4Q7rqwxkZk7iuHitH0q8X4Ui0lXb7WijGymjGvltVPG\nsq8/iwFe8QxiZxSG9YdgDUfHSDaigZJGNFhjOM6IbslYs2FfxHYmtDUzsa15UJuBbWsTrDehrZlX\nuvezZVcvYI3sp7bszv2P2q4E3+UsryjDiXqk6+QFqfWbWbVv5M4LMKGtmcPS/WzYk43cTpS3AMit\nW47nwN9X1PLh+eE28uc7pevkLLVtxx+27C4q48Qi65c6Z2EZypVpKCTpDR9UnnIokKmOXpAkU5VH\nehiJ6h374APdkcajy0d/9k0rYlPFReVirqbYSRJpSkvJKo1jWzM8ef05RXvt/HnOKeF6GsvpZfWv\nl0p6ZetBknrCVJZoaiGLeqQPMsJv5LXejvPUnDB5DK+dMpYp41pzxuwrZXpgSi3v9hFndPrrOzlH\nZVI8tWU3O0LbfmbbXv6wZTfPbNtbsA3nDWnJpHJeEQntw6cSz0ZY/krbR1GUxuFn/Iky9i45bfqg\n1G9+rHCcEZ0WYfnV8wcNOBwpRnS41kCYckqd7+7tLwirK5XiLtyrWmmmDs3soSQJzdpRp5jqx2r8\nZjYl+EBlsbpv9t7cbz1nNB0dHbntQN7L4ooP+PzY289rywjnqHT5cBv564PLNGJynmn3fUmojPeG\nG86PjOF27dTeki54qPlFaaLap1x8eX/M4HLmtaDW19FQUXlKk0SZDmWcHnC6xM/44/TEXau7uXju\n4aS8+GiBnE70Q+FcBVingwaMYebC+8ikhYHBST0STTne5jjCHvclqzbmPPMuvWgxr7TvSa40U0fc\n8o0Yx6Io6pFOOFExzZXE6pZ6c/dHpUdVnKokw0d4+bNvWsGMhfdx9k0rKl7fHaMrB+4KrbisIX71\nMYjOKuK2EfYM+e1Wi4I3WkRAUZJLWJeEM/641KD+S7x7Ife3AXnvc7gIi6E8z23SGIrMUR73ddu7\nC/R0mHrrSdXDSiNQQzqB+MZzlGKoxPirxBiutfKJyjfqc8fT+3LlwMNGvF+6PKo70K8+5paHfCo+\nfxvhVE5+MZZalZbVrkblYEZEXiUiT3ifPSLyiUbLVQ5hXeLKfrt73qUGdV5Mf1C108WuuJRfgMXv\nFYPC1G9DSSGXNMo5jgVBWMyAMTmPfpQuHIqevOPpfSXTvqoeVhqBhnYkEN94dgMzfMVQy4T1C4Iw\nCd8ArRWumy9OEXdu7s8N7vEfSlFdf+UMYvFDNnzmzpzI3JkTC7p3o4qxDAUtIqAczBhj/gycAiAi\naWALcG9DhSqTUvfm4gtPYsuWLTlHgr+s08Xrtnfnwj6WrNo4yOkQpXtcgSlI7sBDF6Zy9+oXSKUG\ne6hTQq5UukuP+uy2roJBmU6/h33btS628tAm235LVm2M3YbqYaURNMQjXY53QyzfEpHnROQPIjK7\nEbI2glIlr13IxMlfWFZxYZYwcR6YWiS+D3uNw3RMLXyPu3v1C7Fdc8VKhS8JlT138/xy6K4dXYhI\nuS8NUe1RyzZSlBHIWcA6Y8zGkkuOEDo390fqHV8Xu9/O8wrkPN1RL/cDxiBYIzuqIuJwEC5p7sJa\n3DeQ05/ZrB1v4rzLAFnDID0XNpjXbe+O7M0spR99PVqJTj04a0gqI5mGGNLGmD8bY04xxpwCnAr0\nMNi78Tbg+OBzBfDd4ZWycZQKN3ChErt7o5X/UHBKNdxtWQ8uPXFUgbfaf1iFDd3w9HCpcLeMP8/F\nRIa9+ZWEckQZ9hqHpxzivBf4YaOFqCUdUzORVVV9fRF+GS9WJdbphpRXVKoRvOPkKQWG57rt3az7\nynm5egG+/mxvSTNj4X2sXr+D9TecnzO0fQdHVJhee0s6p599SulHX48W06nOyJ7SJrnxMuF56tRQ\nGkkSQjvivBsXAHcYm+h6lYiME5GjjDFbh1/EZOFCJsa2ZujqGxhySIYfNuFnxxgOXKnetEjugeR7\nxuNGX7uQl6hKjH44TC1in8OhNVHTFOVQQESagXcAn4mYdwXW6cHkyZPp7Owsuq2urq6SywwX75p+\ngEtPbOeyZV1kjXUo3LV6Ix1TM1x64qiCZc8aB2edMxp4OVb++VPTdG7u58jRg2Oph5O7Vm/kjGmZ\nXFjE/KnpSJlHZ/KDB5/ZZs/L/KlpOjcdYH7Qc9i5uZ+jRgtbugtfCnb39ufaCvIhGAPGcNl3luXW\nDbfl/KlpHtpknUFT2oStPdHy3bW6m6yBrd2G285tx293N++u1Rs5a9zLQ2qrSkjStauyRDOcsjS8\nIIuI3AasMcZ8OzT958ANxpjfBP8fBD5tjHk0tJyvvE+95557hkfwEnR1ddHenqzBJnEyfeCB7tzv\nM6dlIpVeveT5ycamyP2FZXLKGWzcXrny3fH0vrKPZySds0ah8pRmqDKdccYZiS3IIiIXAFcaY84u\ntlzSC7KEcbL4A7yBIRf8cIVD6kVUIZhwej4YHMMdTgkYtd1127s5cjS81EOkUyJcoMZvK79gChBb\nPKWcwipO1vlT09x25TmR84Y73V0Sr90kcLDJUm5BloZ6pIt5N8rFGHMLcAtY5X0wncRaEyeTPHBf\nzgsdVlTVUK5y6+zs5LYrB8sDwAP35X76RjTYuL0Vmwci1w3v+/Jl9xddPizPSDlnjULlKU0SZaoh\n7+MgC+vwcSEcvh4phzidN2tSW0Hvoctp7wzVco1sl+d/0dK13LlqI4a8cRzet/vv4w+uDh+b37Pn\n5HHiCgXVAAAgAElEQVQG8pbuwes7/BzbpXrt4tqynN49d06ivIs6uFBJAo1Of/c2rDd6W8S8LcA0\n7//UYJpSY1zMnx97NhRqEUO8wItD9MNMwjlgS+1b0yEpSm0QkTbgrcBPGi1L0ojTeS50ratvgHVf\nOY93nDyFtAhzZ06sSCet296diwNOhQY4utjt1et3MGPhfblxLkI+RM/fV5Ssc2dOZN1XzsvpXadn\nXVxyKUM3HCseFVsel8WjFulHFaWRNDpGuph342fA34nIPcBcYLfGR9eHWr/V1yKGOCxTud134X1H\nHZtWv1KUyjHGdAMTGy3HcOAbm+XoiDidF+WZddv1DUjf0zylTXiph4KKrL7hGydXOMyi1PgS9+1v\nL6wvD/LeFUWpCQ0zpD3vxoe9aR8BMMZ8D7gfOA94DpvV44MNEFOpglob5pVsr5xlK31IKopyaFGp\nMyBO74Snx23Xr466tcfw/FfOz82LCsWIksvP2x+XcjRKJh04rShDo2GGdJR3IzCg3W8DXDnccikH\nP5p1Q1GUYtQr9rbYdp1emj81XXSduPWLGc/VyKMoSnk0OrRDUYYdfXgoipI0ig2qUxQluTR6sKGi\nKIqiKIqijEjUkFYURVEURVGUKlBDWlEURVEURVGqQA1pRVEURVEURakCNaQVRVEURVEUpQrElFmi\ndCQgIn8BNjZajoDDgZcbLUSIpMmk8pQmaTKpPKUZqkzTjTFH1EqYRlCmLk7SuVNZolFZolFZojnY\nZClLFx9UhnSSEJFHjTFzGi2HT9JkUnlKkzSZVJ7SJFGmJJKkdlJZolFZolFZojlUZdHQDkVRFEVR\nFEWpAjWkFUVRFEVRFKUK1JCuH7c0WoAIkiaTylOapMmk8pQmiTIlkSS1k8oSjcoSjcoSzSEpi8ZI\nK4qiKIqiKEoVqEdaURRFURRFUapADWlFURRFURRFqQI1pIeAiPyNiDwtIlkRmeNNv1hEnvA+WRE5\nJWL9z4vIFm+58+okzwwR6fX2872Y9SeIyC9F5Nnge/xQ5Ckh01tF5DEReSr4PjNm/WFpo2DeZ0Tk\nORH5s4icE7N+zdsotP0fece6QUSeiFluQ9B2T4jIo7WUIbSfstpfRM4N2u05EVlYR3m+LiJ/EpE/\niMi9IjIuZrm6tk+p4xXLt4L5fxCR2bWWYSRQ7HwN9/2WJH2dJF2dJB2dVP2cJL2cJJ2cBH2cCF1s\njNFPlR/g1cCrgE5gTswyrwXWxcz7PPDJessDzADWlrH+14CFwe+FwFfrKNPrgKOD3ycBWxrcRq8B\nngRGATOBdUB6ONqoiKw3AtfFzNsAHF6vfVfS/kA6aK9jgeagHV9TJ3nOBjLB76/GtX8926ec4wXO\nA34BCHAasLre5yqJn7jz1Yj7LUn6Okm6Okk6eiTo50br5STp5Ebr46ToYvVIDwFjzB+NMX8usdj7\ngHsSJE8xLgB+EPz+AXBhvWQyxjxujHkx+Ps00Coio4a6v2rlwR77PcaYfcaY9cBzwOtjlqtpG0Uh\nIgK8G/hhPbZfY14PPGeMed4Ysx97vV9Qjx0ZY5YbY/qDv6uAqfXYTwnKOd4LgDuMZRUwTkSOGm5B\nG02R8zXs91uS9HWSdHWSdHTS9fMI0svDopMToI8ToYvVkK4/76H4TXdV0N1wW626oWKYGXSrrBCR\neTHLTDbGbA1+vwRMrqM8Pv8bWGOM2RczfzjaaAqwyfu/OZgWZrjaaB6wzRjzbMx8A/wq6HK9ok4y\nOEq1f7ltV2suw3oaoqhn+5RzvI1qkyTjn6+k3W+OJOjrJOrqRuvopFwvSdHLSdTJjdDHidDFmVpu\n7GBERH4FHBkx61pjzE9LrDsX6DHGrI1Z5LvAYuxFthjbZXRZHeTZChxjjNkhIqcCS0XkRGPMnrj9\nGGOMiJSVG3GIbXQitkvo7JhFhquNKqaSNvIpU773UfyBfroxZouITAJ+KSJ/MsY8XKkspeShivYf\nKuW0j4hcC/QDd8VspmbtoxSnRuerJOXcb0nS10nS1UnS0UnVz0nSy0nSyaqPS6OGdAmMMW8Zwurv\npchNZ4zZ5n6LyPeBn9dDnsCLsC/4/ZiIrANOAMJB/9tE5ChjzNag62N7mduvqo1EZCpwL3CpMWZd\nzLaHpY2ALcA07//UYFqYqtqoEvlEJAO8Czi1yDa2BN/bReRebBdXVYqp3PYq0v7ltl1N5BGRDwBv\nB84yxkQ+KGvZPhGUc7w1bZMkU+X5qsv9liR9nSRdnSQdnVT9nCS9nCSdnHB9nAhdrKEddUJEUthY\nqth4u1CczjuBOE/IUGU5QkTSwe9jgeOB5yMW/Rnw/uD3+4GaeQciZBoH3IcdGPLbIssNSxthj/29\nIjJKRGZi2+h3McvVu43eAvzJGLM5aqaItInIGPcb6ymq17VTTvv/HjheRGaKSDPWIPlZneQ5F/gU\n8A5jTE/MMvVun3KO92fApWI5DdjtdTkfMhQ5X0m63xKjr5OkqxOmo5NwvSRCLydJJydAHydDF5s6\nji492D/Yi3gz1oOwDVjmzesAVkWs8+8Eo5GBJcBTwB+Ck31UPeTBxrc9DTwBrAH+OkaeicCDwLPA\nr4AJ9Woj4HNAdyCT+0xqVBsF867FjgD+M/C24WqjCBlvBz4SmnY0cH/w+1js6OQng/N6bR2v8cj2\n9+UJ/p8HPBO0Xz3leQ4b7+aume81on2ijhf4iDtv2BHi3wnmP0VMloiD/RN3voJ5w3q/lbj3OxhG\nfR0nCw3Q1UVkGXYdXeIcNVQ/kxC9HNfeNEAnkwB9HHWcDLMu1hLhiqIoiqIoilIFGtqhKIqiKIqi\nKFWghrSiKIqiKIqiVIEa0oqiKIqiKIpSBWpIK4qiKIqiKEoVqCGtKIqiKIqiKFWghrSiKIqiKIqi\nVIEa0spBh4hMFJEngs9LIrLF+99cw/18XkQ+GTPvEyJyqff/kyLyp0CG37t5InKPiBxfK5kURVGS\ngupi5VBAS4QrBx3GmB3AKWAVLNBljPnn4dp/UEr2MmB28P8jwFuB1xtj9ojIYdjCAwDfxVaG+tvh\nkk9RFGU4UF2sHAqoR1o55BGRS0XkDyLypIgsCaZNFpF7g2lPisgbg+nXisgzIvIb4FUxmzwTWGOM\n6Q/+fxb4qDFmD4AxZo8x5gfBvJXAWwKFryiKcsiiulgZiegFoxzSiMiJ2FK4bzTGvCwiE4JZ3wJW\nGGPeKSJpoF1ETgXei/WwZLAlfB+L2Oyb3PTA4zHGGPN81P6NMVkReQ44OWZbiqIoBz2qi5WRinqk\nlUOdM4H/a4x5GcAY84o3/bvBtAFjzG5gHnCvMaYn8Gj8LGabRwF/qUCG7cDR1QivKIpykKC6WBmR\nqCGtKLWnF2gB23UIdInIsUWWbwnWURRFUWqH6mKl7qghrRzqPAT8jYhMBPC6Ex8EPhpMS4vIWOBh\n4EIRaRWRMcBfx2zzj8Bx3v+vAN8JuhYRkXZ/FDlwArC2VgekKIoyAlFdrIxI1JBWDmmMMU8D/wSs\nEJEngW8Esz4OnCEiT2Hj5V5jjFkD/Ah4EvgF8PuYzf4CeLP3/7vAr4Hfi8ha7KCWLNiBNECvMeal\nmh6YoijKCEJ1sTJSEWNMo2VQlIMOEbkX+JQx5tkSy10N7DHG3Do8kimKohw6qC5W6o16pBWlPizE\nDnQpxS7gByWXUhRFUapBdbFSV9QjrRwSBHF3D0bMOisoGqAoiqLUGdXFysGGGtKKoiiKoiiKUgUa\n2qEoiqIoiqIoVaCGtKIoiqIoiqJUgRrSiqIoiqIoilIFakgriqIoiqIoShWoIa0oiqIoiqIoVaCG\ntKIoiqIoiqJUgRrSiqIoiqIoilIFakgriqIoiqIoShWoIa0oiqIoiqIoVaCGtNIwROQcEVnq/Tci\nclzMsh8Qkd/UYJ+jRORPInJEkWVERP5DRHaKyO/K2OYvROT95copIr8VkddVLn3jEJHJIvJHERlV\nZJnbReRLwymXoihDQ/XwyEH1cDJRQ7qGiEhncNPHXuRKAf8E3DCcOzTG7ANuAxYWWex04K3AVGPM\n68vY5tuMMT8oZ/8i8tfAXmPM4+UsnxSMMduAXwNXNFqWOETkDBH5tYjsFpENRZabHxgLXwpNv0hE\nNopIt4gsFZEJdRdaqTmqhytG9fAI4WDRwyLycRFZH+jaP4rICd68EaeH1ZCuESIyA5gHGOAdDRVm\nBCAifwWMNcasasDu7wbeX+RBOx3YYIzprsO+PwIsqWZFEcnUWJZKuQv4cINlKEY39uH8j3ELiEgT\n8E1gdWj6icC/AQuAyUAP8K91k1SpC6qHK0P1cOWoHi5JUT0sIh8CLgfOB9qBtwMvB/NGpB5WQ7p2\nXAqsAm4H3u/PEJFWEbkxeMvaLSK/EZHWYN6CYPoOEblWRDaIyFuidiAi54nI/4jIXhHZIiKfDKYP\n6sbyu+dK7P90EXlERHaJyCYR+UAwfZSI/LOIvCAi20Tke946h4vIz4N1XhGRlSKSCuZ9OpBtr4j8\nWUTOimmvtwErIqafJyLPi8jLIvJ1t93Qsc0Iji/jTesMblD3/7LgTXeniCwTkelunjFmM7ATOC1i\n25cD/w68QUS6ROQLIjI+ON6/BNv7uYhMjdt3HCLSDJzpH3fQzv8iIi8Gn39xDxYR6RCRzUGbvgT8\nR5myLBbbbblXRJaLyOHe/Eu9622Rf72JSEpEForIumD+j0PegNXAsX5bRnC4iPwy2PcKf1kReaOI\n/D64Bn8vIm8Mpk8IjvOvg//tIvKciFxaqk19jDG/M8YsAZ4vstg1wHLgT6HpFwP/ZYx52BjTBSwC\n3iUiYyqRQWk4qodRPVwMUT3cMD0cXEfXA1cbY/7HWNYZY14JFhmRelgN6dpxKfZN8S7gHBGZ7M37\nZ+BU4I3ABOBTQFZEXgN8F/v2dTQwEZhKPLcCHzbGjAFOAh4qU7a4/U8HfgHcDBwBnAI8EaxzA3BC\nMO04YApwXTDvGmBzsM5k4LOAEZFXAX8H/FUg4znAhhiZXgv8OWL6O4E5wGzgAuCyMo8xh4hcEMj0\nrkDGlcAPQ4v9ETg5vK4x5last+K/jTHtxpjrsffJf2A9JMcAvcC3K5ULOB7IBg8Qx7XYB8kpgTyv\nBz7nzT8Se86mY7vzypHlIuCDwCSgGXAP+tdg3+4vBo4CxmLPq+Mq4EJgPvZ63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TLu/stm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\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m72NIE+H10ghF9eNGoqWdP0EW13AHiwc4tO/t/h17ZTXjaYGJs3K7XxeX3p5qEnUjH7ap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qXrdOqrqoVWaAcTyFq1aAZLtYhwkFGiWlC2/iqzHcwol/ZtBpx86L1ugKxqq2MzlzI2Lthgz2Pp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\niIiIiCgCBtJERERERBH8Px2kj0ldP4iLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109abf710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10, 14))\n",
"for subplot, halfwidth in enumerate([60, 80, 100, 120, 140, 160, 180]):\n",
" plt.subplot(4, 2, subplot + 1)\n",
" ok = (data_all['halfwidth'] > halfwidth - 10) & (data_all['halfwidth'] <= halfwidth + 10)\n",
" plot_fails_mag_aca_vs_t_ccd(mag_bins, data_all[ok])\n",
" plt.title(f'Acq success (blue) fail (orange) box={halfwidth}')\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Color != 1.5 fit (this is MOST acq stars)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# fit = fit_sota_model(data_all['color'] == 1.5, ms_disabled=True)\n",
"mask_no_1p5 = ((data_all['red_mag_err'] == False) & \n",
" (data_all['t_ccd'] > -18) &\n",
" (data_all['t_ccd'] < -0.5))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7.2 8.6\n",
"16850\n",
"WARNING: imaging routines will not be available, \n",
"failed to import sherpa.image.ds9_backend due to \n",
"'RuntimeErr: DS9Win unusable: Could not find ds9 on your PATH'\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 594.808\n",
"Final fit statistic = 489.751 at function evaluation 640\n",
"Data points = 16850\n",
"Degrees of freedom = 16846\n",
"Change in statistic = 105.057\n",
" model.p0 -2.82214 \n",
" model.p1 1.89806 \n",
" model.p2 -0.967379 \n",
" model.floor -2.6 \n",
"WARNING: parameter value model.floor is at its minimum boundary -2.6\n",
"8.6 9.325\n",
"19168\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 1917.9\n",
"Final fit statistic = 1742.39 at function evaluation 670\n",
"Data points = 19168\n",
"Degrees of freedom = 19164\n",
"Change in statistic = 175.511\n",
" model.p0 -2.55334 \n",
" model.p1 2.35371 \n",
" model.p2 -0.705211 \n",
" model.floor -2.14837 \n",
"9.325 9.65\n",
"10188\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 3025.98\n",
"Final fit statistic = 2260.44 at function evaluation 652\n",
"Data points = 10188\n",
"Degrees of freedom = 10184\n",
"Change in statistic = 765.535\n",
" model.p0 -2.26195 \n",
" model.p1 3.87921 \n",
" model.p2 -0.999994 \n",
" model.floor -1.66518 \n",
"9.65 9.875\n",
"6953\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 3921.12\n",
"Final fit statistic = 2161.78 at function evaluation 598\n",
"Data points = 6953\n",
"Degrees of freedom = 6949\n",
"Change in statistic = 1759.34\n",
" model.p0 -0.784509 \n",
" model.p1 2.28222 \n",
" model.p2 -5.83471e-07\n",
" model.floor -1.52313 \n",
"9.875 10.125\n",
"6602\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 5693.39\n",
"Final fit statistic = 2489.26 at function evaluation 602\n",
"Data points = 6602\n",
"Degrees of freedom = 6598\n",
"Change in statistic = 3204.13\n",
" model.p0 -0.190854 \n",
" model.p1 1.97771 \n",
" model.p2 -0.248602 \n",
" model.floor -1.50779 \n",
"10.125 10.4\n",
"4641\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 8210.43\n",
"Final fit statistic = 2053.26 at function evaluation 585\n",
"Data points = 4641\n",
"Degrees of freedom = 4637\n",
"Change in statistic = 6157.17\n",
" model.p0 0.810736 \n",
" model.p1 2.10795 \n",
" model.p2 -0.885583 \n",
" model.floor -1.29356 \n",
"10.4 10.65\n",
"2767\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 8480.61\n",
"Final fit statistic = 1139.14 at function evaluation 831\n",
"Data points = 2767\n",
"Degrees of freedom = 2763\n",
"Change in statistic = 7341.47\n",
" model.p0 1.48231 \n",
" model.p1 1.95199 \n",
" model.p2 -1 \n",
" model.floor -0.901686 \n",
"10.65 10.875\n",
"2034\n",
"Dataset = 1\n",
"Method = neldermead\n",
"Statistic = binom_stat\n",
"Initial fit statistic = 7731.07\n",
"Final fit statistic = 548.938 at function evaluation 707\n",
"Data points = 2034\n",
"Degrees of freedom = 2030\n",
"Change in statistic = 7182.13\n",
" model.p0 1.84089 \n",
" model.p1 1.29966 \n",
" model.p2 -0.684655 \n",
" model.floor -1.4808 \n"
]
}
],
"source": [
"mag0s, mag1s = mag_bins[:-1], mag_bins[1:]\n",
"fits = {}\n",
"masks = []\n",
"for m0, m1 in zip(mag0s, mag1s):\n",
" print(m0, m1)\n",
" mask = mask_no_1p5 & mag_filter(m0, m1) # & t_ccd_filter(-10.5, 0)\n",
" print(np.count_nonzero(mask))\n",
" masks.append(mask)\n",
" fits[m0, m1] = fit_poly_model(data_all[mask])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ll1m7dm0YLJeMNX1Lvn2BQmNjIxDMBb366qtD0qd9MzkHDhwAIjdzpbr9OD9s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cJiIecf/6Bi/Xoip08j+F7XLGsLYBd2pv05jc05m5l+w3QGtxgst6QmhxDCKTWphRCHAJfMX5KD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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119fbcb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors = [f'kC{i}' for i in range(9)]\n",
"\n",
"plt.figure(figsize=(13, 4))\n",
"for subplot in (1, 2):\n",
" plt.subplot(1, 2, subplot)\n",
" probit = (subplot == 2)\n",
" for m0_m1, color, mask, mag_mean in zip(list(fits), colors, masks, mag_means):\n",
" fit = fits[m0_m1]\n",
" data = data_all[mask]\n",
" data['model'] = p_acq_fail(data)(fit.parvals) \n",
" plot_fit_grouped(data, 't_ccd', 2.0, \n",
" probit=probit, colors=[color, color], label=str(mag_mean))\n",
" plt.grid()\n",
" if probit:\n",
" plt.ylim(-3.5, 2.5)\n",
" plt.ylabel('Probit(p_fail)' if probit else 'p_fail')\n",
" plt.xlabel('T_ccd');\n",
" plt.legend(fontsize='small')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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swgSW3ZNH9hwblpjQrF3e2lra/ukn9LzzDjIQIGbtWmyPPYpp5swJ/gSThh/4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OAAAg\nAElEQVSQHU4ad4ql3W1JhEYr6yzIRiOFM2eh/eYbPAIDiX31FULvvBOpmnwHnMLJt2nf8vGuj/HR\n+PDaja8xuMFgPCqpAE51oyJ9GhYKIe4HbhRCbAeMlOUzVBvE/7F3nuFRVVsDfs+kF9IrIZBA6L2j\niPQWpCmK2D9UpAiIKCAKoqAiTQRBpQpyLVwFpVcJJfQSWgKEBNJ7nySTafv7McC1IGLI5JxM5n2e\nPGTOtJfJzOyz9t5rLSHIz8/HaDTi4+PzwMlsSqL4ZBq6tGJTMx57ZXzIo3Zv40bUGXqMHI13rdpy\n61Q6pUWmgEGdp2HghFYEKKAjt1GrJXn8BAx5edTZsAG7B0i4VyKF2kImRUzCzd6NBV0XYKeqfoGq\nDFT4Uo4QQn9rpXs3ppKra4QQlyv6eW49F1qtlqKiIiRJqpDVBSUhDEYyl5/HUKjFa0QjnFv6yupT\nmJ3F9iXzSb0aTfMefej+4ijs7tIUz9IoK9ERtS+J8/uT0GkNNOjgT/vwUDz85Q0WhBAUbtlCxvwF\nGHJy8HjySXwnvYFtBXUYVwIp6hTeO/IepzNO82itR5n10Cx8neX9HCid+1lpaCtJUk1gpCRJ6/lT\nF08hRK5ZzCqRkpISysrKcHNzu2cznqqGoVhHwe4EHOq649RcGT0ZcpITObRhLaGt29GqzwC5dSod\njVrHr4ujKMou5bHXW1IzTP5GSUII0t+fRem5cwQt/gynZk3lVqpQjMLI9MPTSStOY23ftfg4KeOz\nYKlIkuQqhFALIRLudZvyPr4QYgewo7z3vx9urzxrtVocHBzw8PCwmNUFAKNGj6FIh9AZ8HutBfbB\n8m6NjDtzkl3LP8Og1xM+4W0ad+4qq09loNXouRiRzLk9iZSV6KnXxo8Oj4UqohCGJiaG9NlzKD17\nFscWLQj+cjlOzZvLrVVhCCHYFLuJeafmAfDBwx8wNGyoxW3HNQf3EzR8BewH6gJn+GPQIG4dr7Lo\ndDoKCgpwcHDAxUX+D2tFUrj7JqJMj8fgeor4MOh1OrYvmY+dkxN9R09UhFNloinW8evn58jPKGHA\n2BYENVTGjE3uN+so2LwZn7Fjcetnefv8V1xYwcHkg0zvOJ1Wfq3k1qkO/CpJUhTwK6ZiGcUAkiTV\nxbSt9SlgJfCTfIr3RqUy9VpwdHSskCp6SkEIgVGtw1BQhqSS8Hu9tazltw16HYe/W8eZ7b/gG1KX\ngW9MxTMwSDafykCvNZB9VbBh+zFKi3SEtPChw8BQfGUO3AAMBQVkfb6EvB9+wMbdncA5s3F//HEk\nC9p9kVWSxaxjsziUfIj2Ae2Z3Xk2Qa6W/Z6rSO6nudsSYIkkSV8KIcb83e0kSfIUQuRVqJ2ZMRqN\n5OXloVKp8PDwsJiBAUCbXETxqXRcOwcpJvk58sdvyUq4wZApM3HxUMYJc2VRVqpn65IoctOKCR/d\nguAmyqgxrj54kMz586nRpw8+r4+TW6fCOZx8mOVRyxlYdyBPN3xabp1qgRCipyRJ4cBrQGdJkjwB\nPXAV0wrBi0KIdDkd/wlJkvDy8kKtVlvMuCCEwJBfhrFYh+Roi6qGnawBQ0FmOts+n0f69Wu06juA\nrs+9jK0FrfT/GYPeSExkKqd33KS4QBDc2JUOg+oSECr/9lRhNOIYGUncO9MxFBTgOWIEvhPGY+Mu\nv1tFsvPGTuYcn0OZoYxpHaYxotEIa+7Cv+S+E6HvFTDcYj/Q5sF0KpfCwkL0ej1eXl4WtfR8O/lZ\n5WKHWy9l5AwkXjrP6W2badm7P/XaWk7zl/tBq9GzbWkU2Ulq+o1uTp1myuisXBYXR8rkt3Bo2JCa\ncz+xqNkkMO1XnXZ4Gg08GzDjoRkWc/JXFaiMLUTmxpLeL8JgRJ+rQZQZUNWwx8bNHilLvv9f7Imj\n7P7qcwCL7+xsNBi5eiKdU9tuUpSrITDMHd+2OgY8pYzKQ6UXL5E+ezbuFy5g36YNATPew7FxY7m1\nKpR8TT5zTsxh983dNPdpzkePfESoe6jcWlWSijxLqFLfsKWlpZSUlODq6opjBSVbjRw5Ej8/P5o1\na3bnWG5uLr1796Z+/fr07t37TuO433P16lU6d+5Mq1ataNWqFW5ubixevBiAWbNmERQUdOe622X3\n7kXJ2Qy0SUW49w9F5fhvCmSZB41azc5li/AMDKLr8y/LrVOp6MoMbF92gYybRfR5tSmhLZSxn16f\nl0fSmLFIDg4EL/sClbO8SXcVjc6oY8qhKRiFkc+6fYaTreUksFYVJEn6S+3Pux2rrpR3vIiNjb0z\nHtzPeGHUGdBnlSK0Bmw8HbF1d5AtINLrdOxf8xVbFn2MZ2BNnpv7ucUGDMIouHYqne8+OMFv66/g\nVMOOgeNbMnRyG1z85D9d0uflkTZjJjefegpdaioFL71Enf9ssLiA4VDyIYZuGcr+xP2Mbz2e9f3X\nWwOGB6AigwZRgY9lVvR6Pfn5+djZ2d21N0J5eemll9i1a9cfjs2dO5eePXsSGxtLz549mTt37l/u\n17BhQyIjI4mKiuLMmTM4OzszdOjQO9dPmjSJqKgooqKiCA8Pv6eDsVRPwc6b2Ndxw7m1MirgHPjm\na4rz8wh/fTJ2DpZfDeM2eq2B7csvkHY9n94jm1BPIX8PodORMulN9Glp1Fq6FLuaNeVWqnC+OPcF\nF7IuMOvhWQS7KaPUcHVBkiRHSZK8AB9JkjwlSfK69ROCqaOzFco/XtSvX//OePBP40W/Hn1MAYNR\nYOvjhI2LfFXD8tJT+X7GW0Tt3kbbAYN5+sN5ePgHyOZjLoQQxJ/L4oc5J9m7OhpbOxX9Rzdn2LR2\n1G7qLfsKljAYyPv+e+L69Sd/0ya8XnyRert2ounUUXa3ikStVfP+0fcZt38cno6efD/ge0a1GIWt\nSv6J1KpMtXv1bpdXBSo8we3RRx/l5s2bfzj266+/3mnr/eKLL9KtWzc+/fTTv32M/fv3U69ePerU\nqVMuh8K9CRhLdHgMaoakkv8LIPbkUaIPH+ChYSMIqFdfbp1Kw6AzsvPrS6Rcy6PXi42p385fbqU7\nZHwyl5Ljxwn85BOLas5zm8iUSNZcWsOTDZ6kb0hfuXWqI68BbwA1gbO/O14IfCGLkQIx93hhUGsx\n5Jch2amw9XZCspVv++GVo4fYu2IpKpUNg9+eQVi7jrK5mAshBEkxuRz/JZ6sxCI8/J3p80pTwtr4\nKWIsBig5d4702bMpi47BuWNHAt57F4f6ljcun0o/xXtH3iO9JJ2Xm73M2FZjsbex3HyZyqQigwZl\nfCqAnTt3kp5+9zw7nU5naoVta/uv+jEEBATQv3//f+2SkZFBYGDgncfIyMi45+1/+OEHRowY8Ydj\nS5cuZf369bRr146FCxfi+Td1koXBiPpYKi4dA7EPkr9Zd0lhAXtXLsMvpB4dhw6XW6fSMBiM7F51\nicTLOXR7tiENOwXKrXSHvB9+IO+77/AaORKPoUPk1qlwskqymH5kOmEeYUxpP0VunWqJEOJz4HNJ\nksYLIZbK7fNP3Gu8MBgM5cp3k3u8WLd2HW2btWLeR5/iWzNQtpNWnbaMiG9WcmH/LgIbNOKxCVNw\n81XGimtFkn6jgOO/xJFyNZ8a3o70fLExDTr4o7JRRp6YPjubzAULKfjlF2z9/QlatJAa/ftb1MoC\ngEav4fOzn7MhZgN13Oqwrt86a8W8CuYf39G3lprfkCTpC0mSXpMk6e8CjZ4V7FbhCCEQQqBSqWRp\n4CZJ0j0/pFqtli1btvDkk0/eOTZmzBji4+OJiooiMDCQyZMn3/W+QgiMJXpUTra49ynfKkVFIoRg\n38plaEuK6T9uEja21WNRy2gwsnd1NDfOZ9NleAOadlHObozi4ydIn/MRLl0fxW/ym3LrVDgGo4F3\njrxDia6EBV0X4GhbfbbCKQlJknrc+jVFkqTH//wjq1wVojzjxejRo7l26jKndh4hsFZNpn70rmwB\nQ05KEt+9O5kL+3fRftATDH9/rsUFDDmpanZ8eYGfPz1DbmoxXYbX59lZnWj0UKAiAgah15O7fj1x\n/fpTsH073q++Sr0d23ELD7e4gCE6J5ontz7JhpgNjGg0go2PbbQGDGbgfs7k1gE64DDQH2gCTPzz\njZTU5O3vZnhycnLQarX4+/tXWtDg7+9PWloagYGBpKWl4XePTrs7d+6kTZs2+Pv7/+H+t3n11Vd5\n7LHH7npfY6keoTfi1jcElbP83W6vRB4k9uRRujzzEj61Q+TWqRSMRsH+9THEnc3k4SfCaNG9ltxK\nd9AmJpIycSL2deoQtHAhkgVVC7vN6kurOZF2gg8e/oB6HvXk1qnOdAV+Awbe5ToBbKpcnXtzrxWB\noqKiCs17+yceZLwQRoG3jRuizICtpxOjXh/NwIF3+xOYn+hDv7Fv1XJs7e15fNosQlu3k8XDXBRm\nl3Jq2w2unEjH3sGGjoNCadEjGHsFFB65TfHJk2TMnkNZbCwunTvj/+67ONS1vARgIQT/ifkPC88s\nxNvRm5V9VtIpsJPcWhbL/bzDmwghmgNIkrQaOGleJfPh6elJUVFRpa4yDBo0iHXr1jFt2jTWrVvH\n4MGD//a233///V+Wmm8PIACbN2/+Q6WN2wijMDXrsVHh0l7+xDJ1bg7713xJYINGtBs49J/vYAEI\nITj4nytcO5FBx0F1ad1bGaVuAQxqNUljxwIQvHwZNq7yb12raM5mnGV51HL6h/ZnaFj1eM8pFSHE\n+7f+/T+5Xaoa5R0vhMGIPruU1ORUajUOwcbZjl9W/XLX8cKcGPQ6DnyzkvN7dxDUqCkDJr5NDS9l\nVIyrCEoKtZzZeZNLh1KQVBKtetWmbd86OLrKP1F3G11GJpnz5lG4fTt2NWsStHQJNXr1sriVBTCV\nUp1xdAYRSRF0C+7G7Idn4+HoIbeWRXM/QYPu9i9CCH1VfuOZe1vSiBEjiIiIIDs7m1q1avHBBx8w\nbdo0nnrqKVavXk2dOnXYuHEjAKmpqbzyyit3SqgWFxezd+9evv766z885pQpU4iKikKSJEJCQv5y\nPYChSAsGgcrZVvaEKyEEe1YsxaDT02/MJFQqy5vR/jNCCNLPCnJj02gXHkK78BC5le4gDAZSJ7+F\n9mYCtVetwr6cCfZKpqCsgKmHp1LTtSYzO820yMGxKiJJkjfwPvAIphWGI8CHQogcWcUUQkWNF0ad\nAX22BoyCdxfO4vzFC/ccL8yFtljNxg+mk3othnYDH6fLiBdRWciKprZUz7l9iUTtS8KgM9L44UDa\nDwjB1VM5WyCFVkvutxvIXrYModfjM3Ys3q++gsrJMstNn8k4w9RDU8nR5DC1/VSebfys9bu/Erif\noKGlJEmFt36XAKdblyVACCHczGZXxfj+++/venz//r+WJq9Zs+Yfei64uLiQk/PXsfTbb7+953Ma\ndQaMai0qZzukIvn3UF46sJcb507T/aXX8KqpnP385kIIQeTP18mNhVa9a9NhoLKWfzPnzUd98CAB\n78/EpZNlViyZETmD7NJsNvTfgKu95a2iVGF+AA4BT9y6/CzwI9BLNiMFURHjhbFMjz6rFABbXyc2\nfPcf88j+AylXoon56Vsw6BkwcQqNHn5UFo+KRq8zcDEihbO7EtAU6whr60fHQXXx8FdWX5vio0dJ\nn/MR2vh4XLt1w3/6O9jXVs5qd0ViMBpYdXEVy88vp5ZrLTaEb6Cpd1O5taoN/xg0CCEsY6rAAhFC\nYMgvA0nCxt0e7l1ow+wUZGZwYN1Kgpu2oHXfAfLKVBIntsRzfl8SXvXh4cfrKWqmw+lABLk//ojn\n88/j+adtb5bCd1e+40DSAaa0n0JTH+vAoTAChRCzf3d5jiRJ1aeMmpkxlurQ55Yh2UjY+shTUlUI\nwfk9OziwbiV2Lq48/cGnFpHDZjQYuXI8nVPbbqDOK6N2Ey86Dq6LXx1lzZHqUlPJmPspRXv2YFe7\nNrW++pIa3brJrWU2skqyeOfwO5xIP0F4aDgzOs2wThRVMsrJ2rHyrxEaPaLMgI2HA5LMlRqE0cju\nrz5HkqDv6IlIMlSnqmxO77jBmZ0JNHmkJgSlKSpgKDpwgBobN+Laowf+06bKrWMWonOiWXh6IV1r\ndeW5xs/JrWPlr+yRJOlpYOOty8OA3TL6WAx3ejDY22Dr7SjL979eq2XfquVcPriP0NbtcGvdqcoH\nDLcbsx3/NZ78jBL8Q93o9VITghrevcy5XBi1WnLXrCH7K9P2M9+JE/AaORKVg4PMZuYjMiWS6Uem\nU6Ir4cOHP2RI2BBFjbnVBbMHDZIk9QM+B2yAVUKIv7a4NN2uPXAMeFoI8VN5nksIUW3eRMIo0Odr\nkexUqFzsEELehtzndm8n6fIF+rw2AXc/5TQyMxdn9yRwYssNGnYMoNszDTl46O513uWg9PJlUt6c\njD44mKAF8y2yUlKxrpgph6bg6ejJ7M6zq83nviogSVIRphwGCVOTtw23rlIBauAtmdT+QFUcL4QQ\n2GjAUFaG5GiLrZdjufLYHnS8KMzOZMvCj8mIv06nJ0bw8LARHDx06IEeU25SY/M4uimOjBuFeAa6\n0H90c0Jb+ijuPaI+eJD0jz9Gl5BIjT598J86Bbsgy90KbBAGFp1ZxNpLawnzCGNN3zXW6ngyYtag\nQZIkG2AZ0BtIBk5JkrRFCBF9l9t9Cuwp73M5OjqSk5ODt7f8bdorA4NaCwYjNl6mJKecnBwcHeVJ\nyspNTeHwd98Q2rodzbr3lsWhMjn/WxLHNsUR1s6PHi80kj35/Pfo0tJIHj0GG08P8seNReWsrL23\nFYEQgjnH55BUlMSavmvwdFTWLGB1RwhReTVKy0lVHC+EEBjyyrAtA5WzHTaeDuVyF0I80HiReOkC\n2xbPxaDXMfit9whrX7XLW+akqjm+OY6bF3Nw8XCgxwuNaNgpEJWCvtcBtElJZHz8CeoDB7APDSV4\n1SpcH+kst5ZZSVGnsDh9MTcTb/JkgyeZ0n6Ktf+OzJh7paEDcF0IEQ8gSdIPwGAg+k+3Gw/8DLQv\n7xPVqlWL5ORksrKy7nk7jUYj28n1vfg3XsIgMBSVIdnZYFNkKvXm6OhIrVqV3xfAaDSwa/kibO3s\n6DNqfJUZgMvLpUMpHNkYS2hLH3r9XxNFNPC5jUGtJum10RhLSwlZ8x0pKSlyK5mFLXFb2Ba/jXGt\nxtHWv63cOlbugSRJg4DbWbERQohtcvrcpqqNF0IIjMU6hM6I0Rbsazg+UA5becYLIQRntv/Cof+s\nxTMwiMFvvYtXTeX0ovm3qPPKOLktnitH07BztKXTkLq07BGMrb2yVmaNGg05K1aSs2oV2Nri99Zk\nvF54AcneXm41s7I3YS/vR76PTq9jQdcF9A3pK7eSFcwfNAQBSb+7nAz8oYSLJElBwFCgO/cIGiRJ\nGgWMAlMDnIiIiHIJqdVqXBVYp/7feAWcVeGaAwldjBjU/zuekJBQ4U7/9DqnnztBWuxVQnsN4PSF\nixX6/A/iZQ7ybwhSTghcA8GxQQ6HD/9vOV4upzsYDHgsW4b99evkj3+dtJQU+Z3+hgfxStelMz9t\nPvUd6tMgt0GF/f8s8bWSG0mS5mL6Tr9d0meiJEmdhRDvyKgFgJ2dHaGh/1zpLCIigtatW1eC0d9j\nUGvJ/uYyuhQ1HkPCOF1ylW4dKtdJp9Gw++slXD16iPodHqbf2Dewd6qaq5hlpXrO7k7gwv4kjELQ\nokcw7fqHKKrXApiCNPX+/WR8MhddSgpuAwbgN+Vt7Pwte/tvmaGM+afm8+PVH2nm3YwnHJ6wBgwK\nQgmJ0IuBqUII471mqYUQK4AVAO3atRPdylkhICIigvLe15zcr1fplVxyMi/j3j+E2l2DZXXKSrxJ\n1MrFNOjYmcdeGV1pqwxy/A2vnUon+mQ0tRp5MmBcC2zt/jgbJef7SghB+syZ5EfHEPjRHJo88YTs\nTveivF5lhjKe2f4MLvYufDXoK/yc/75bbmU5mRulet0n4UArIYQRQJKkdcA5QPagoaqgzykle80l\n9AVavJ9vglMTb4i4WqkO+elp/LrwI7KTEnhkxIt0GDysSq4oG3RGLh1K4fSOm2iKdTTo4E/HQXVx\n81FeH4OyGzfI+PgTig8fxqF+GLXXrcOlYwe5tcxOfEE8bx98m2t513ip6UtMaD2ByMORcmtZ+R3m\nDhpSgN+f2da6dez3tAN+uPUl5AOES5KkF0L8Yma3KofQGynYGoetjxOuneVNfDLodexa9hn2zi70\nfGVslRxE7pe4c5nsWxtDYJgH4WP+GjDITc7KVeT/9ye8X3sNjyee+Oc7VFHmn5rPtbxrLO+5vEID\nBitmxQPIvfW7u5wiVQ1tchHZay+DEPi+2hwHGcp93og6w/Yl85CQeOKdDwhp2abSHR4UYRTEns7g\n+K/xFOVoCG7syUNDw/CtrbzUG2NJCdlffkXON9+gcnDA/51peD7zDJKdslZBzMGuG7uYeXQmjjaO\nLOu5jEdrWUavD0vD3EHDKaC+JEmhmIKFp4Fnfn8DIcSdNWJJkr4BtlkDhrtTdDgFfY4Gn5HNZKnJ\n/XuOb9pI5s04Br31Ls5ulnsuEHc2kz2rLuMfUoMB41pg56CsgKFwxw6yFi3CLTwc34kT5NYxG3sT\n9vLj1R95qelLdKnVRW4dK/fHJ8A5SZIOYKqk9CgwTV4l5SMMguIz6RRsi0flbIfPyGbY+VXuViBh\nNHLil/8SuXEDvrVDGDT5XTz8AyrVoSJQpwv+O/c0WYlF+AS70v3ZVgQ38ZJb6y8IISjatYuMT+eh\nT0/HfcgQ/Ca/ia2vr9xqZkdv1LP4zGLWRa+jlW8rFnRdgL+LZW/BqsqYNWgQQuglSXodU21uG2CN\nEOKyJEmjb13/lTmf35LQ55dR9FsiTk29cWwgb7WY9LhYTmz+kSZdulO//UOyupiTuLOZ7L4VMAwc\n3wp7RyXs5vsfJWfPkjrtHZzatiXwk48ttjdGijqF9yPfp7lPcya0ttzAyJKQTEuPR4BO/C9XbaoQ\nQjm1iRWGEAJNTC4Fu26gzyzFvo4b3s82wsatcmvv6zQadi5bROzJozTq3JU+r43HzkH+ZPB/Q1ZS\nEcc2x5EULajhpaPX/zWhQXt/RVW6u03Z9eukz/mIkuPHcWjSmKBFi3BuI28eTWWRq8llysEpnEg/\nwfCGw5nafip2Npa/qlKVMftZkBBiB7DjT8fuGiwIIV4yt09VJX9LHADuA+rK6qHXatm5bBEuHp50\nf+k1WV3MyfUzmexZfRn/EDcGjm+JvZOyAgZtQgLJY8dhFxhIrS+WWmxTH51Rx5RDUxAIPn30U+uA\nUkUQQghJknYIIZoDW+T2UTpliYUU7LiB9mYhtj5OeD/XGMemlV8Otignm83zPiQ74SbdXniFNuGD\nq9TW06JcDSe2xHP1RDoOzrb4t5YYOrITNnbKm1AxqNVkf7GM3A0bUDk74z9zBp7Dh1tkX527cTn7\nMpMiJpFTmsPszrMZEjZEbiUr94GyzoSs3JXSy9loonNw7x+CrZe8Mz6RGzeQm5LEE+98gKMCq1BV\nBLGnM9i7JpqAUDceG99ScSsM+rw8kkaZArbgFV9j62m5fQqWnVvGhawLLOi6gOAa5k38t1LhnJUk\nqb0Q4pTcIkpFl1VC4e6blF7KQeVqh8eQeri0D5Clw3P69Wv8smAOOk0pQ6fOJLR1u0p3KC9ajZ6z\nuxKI2p8EAtr0qU2bvnU4djJScQGDEALHEyeImzEDQ3YOHsOG4TvpDWy9lLdtylxsjt3MnONz8HLy\nYn3/9TT1aSq3kpX7RFlnQ1b+grFMT/6WOOwCnHF9RN7k55Qr0ZzetpkWvfoR0soy6+PfCRjquvHY\n68oLGIxlZSS/Ph5dWhq1v1mLfZ06ciuZjcPJh1l9aTXDGgyzltyrmnQEnpMk6SZQjCmvQQghWshq\npQAMRVoK9ydSfDIdyVbCrVdtXLvUQiVTztTVY0fYtWwRzh6eDPvwQ3xqh8ji8W8xGoxER6Zxcms8\npUW3KiINroubt/IqIgForlwhffYc3M+cwa55c4KXLcOpRfX5OOgMOj499Sk/Xv2RjgEdmdd1Hl6O\n1SdYsgSUdUZk5S8U7knAUKjF65nGssw+3Uan0bBr+We4+/rR9bmRsnmYE6UHDMJoJG36u5SeOUPQ\nooU4t6l6lUzul/TidKYfmU5Dz4ZMbT9Vbh0r5cMa6f0JY5kB9eFkig6lIPQGXDoE4tazNjY15GnU\nJYTgxKYfidy4gZoNGjP4rXdxdveQxeXfIIQg4VIORzfFkZdWTGCYOwPG1cc/pPIrTN0PhsJCspYs\nJe+777Bxc6PwuWdpNH26xeah3Y3MkkwmR0wmKiuKl5q+xMQ2E7FVKWuMtfLPWP9iCkabokZ9NBWX\njoGylNv7PQf/s5b8zHSemvlxlW3qcy9iT2Wwd81lAuq5KzJgAMhasoTC7dvxnTQJt/BwuXXMhs6o\n4+2Db6MzmjqBOtpWrSTM6o4kSY7AaCAMuAisFkLo5bWSF2EQFJ9Op3BvAka1Dqem3rj1C8HOV77v\nUr1Wy56vlxBzJILGXbrTZ9R4bKtAl+Hs5CIif7pO8pU83P2c6D+6OaEtfRSZeyGMRgo2/0LmwoUY\n8vPxfHo4vhMmcDgqqloFDGczzjL54GSKdcXMf3Q+/UL7ya1kpZwo78zICmCqLZ23KRaVqx3ufUNk\ndYk7c5Lze7bTdsBggps0l9XFHFw7mc6+tdEEhnkwYFwLRQYM+T//TM5XX+Px5DC8R70qt45ZWXJ2\nCVFZUczvOp8Q9xC5daz8e9YBOuAw0B9oAkyU1UgmhBBoonMo2HUTfZapIpL7801knwQqKcjn1wUf\nkXoths7Dn6fj0KcUedL9e9R5ZZzYGs+VY2k4OtvRZXh9mnYJwkbm8uN/R+mly2TMnk3p+fM4tW5N\nwKqVODZpIrdWpSKE4IerPzDv5DwCXQNZ0XsF9T3ry61l5QFQ3tmRFQDUx1LRpVC87wcAACAASURB\nVKjxGtEIlYyVe9R5uez+cjG+dUJ5ZMRLsnmYi6sn0tn/jSlgeOz1lorrwwBQfPQoae/PwqVzZwJm\nzlT84P4gRCRF8M3lbxjecDj9QqyzUVWUJreqJiFJ0mrgpMw+5UKfXYpdsenfct0/v4zCvQloEwqx\n9XXC+/kmODbxkv3zm514k83zZlNSkM/ASdNo0OkRWX3+Ca1Gz7m9iUTtTcRoFLTuVZu2/evg4KzM\nSmr6vDyyFn9O/saN2Hh7Ezj3E9wHV60qVBWBRq9h9vHZbInbQpegLsx9dC5u9srcPmYRaAqQjAaz\nP401aFAg+oIyCncn4NDAE6cWPrJ5CCHYuWwRurIyBkyYgq2FdaW8HTDUrO/BgHHKDBg00dEkT5iI\nQ2goQYs/s+jOoCnqFN498i6NvRozpf0UuXWslB/d7V9u9eqR06XcpC86TR2jDemHT5f7MVQ17PAY\nGoZLuwAkG/lfh/hzp9j++TzsHJ0YPmsuAfWUO+trNAquHE3jxJZ4Sgq11G/nR6ch9XDzUWaSszAY\nyP/vT2R99hkGtRqvF57H5/XXsamhvM7T5iZVncobB94gJjeG0S1HM6blGFSSMleEqix6LSSfgvgI\n00/KGWq0+gjoadantQYNCiR/SxwIgeeQMFlnJzLOnyblYhS9R72Ody3LKnd5J2Bo4MGAscoMGMqu\nXyfx5VdQudUgeMXXFj346AymPAYhBAu7LcTeRvl7q638LS0lSSq89bsEON26fLt6UrmmGyVJehKY\nBTQGOgghyn82fx94PdmQ6JgYGjduXK77SzYSjo28UNnL/90ihODczi1ErF+Nb51QhkyZQQ1v+Sak\n/onEyzlE/nyd3NRiAuq6039McwJC3eXW+ltKo6JInz0HzeXLOLdvj/+M93Bs0EBuLVk4nnactw++\njd6oZ0n3JXSv3V1uJcvAaITMy/8LEhKOgq4EJBUEtYUub6ItM3/5dWvQoDBKo3PQXM7BrZ+8PRky\n4q+TeuIw9Ts8TPMellUE5erxNPatiyGogScDxrXATgGD+p/RJiSQ+H8jwdaGOmvXYhcYKLeSWVl0\nZhEXsy+yuNtiaz+GKo4QwlwfqEvA48DXZnr8P+Dc2g91QTQurf0q4+nMhkGv58A3X3N+707C2nci\n/PW3sHNUZnGB3LRiIn+6TuLlHNx8neg3qhl1W/sqdmuPPieHzIWLKNi0CVs/P2ouXIBbeLhifc2J\nEIJ1l9fx2dnPCHELYXH3xYS6h8qtVbXJT/xfkBB/EEqyTcd9GkDr56BuN6jTGZxMFc80ERFmV7IG\nDQrCWGYg/9c4bP2dqdFFvp4MWk0p25fMw9bJmd6vjbeoL8Arx9LYvz6GWg09CR+rzIBBl5JCwv/9\nH0Kno8636y26FwPAvoR9bIjZwHONn6NnHfMurVqpugghYgCL+j4yNxq1mq2ffULipfN0GDyMR55+\nQZFVezRqHSe33+DSwRTsHGzoPCyM5t1qKTbJWej15H33PVlLl2LUaPB+5WV8xoxB5eIit5os6Aw6\nZh+fzebrm+lVuxdzHpmDi131fC0eiJJcuHn4f4FCbrzpuKs/hPU0BQmhXcFdvvNDa9CgIAr3JmAo\nKMP3mZay9mQ48M1K8tLTaDDwSZxcLWdLTMzRNH77VuEBQ2YmCSNHYixSU2fdNzjUV+6e44ogqTCJ\nGZEzaO7TnDfbvim3jhULQZKkUcAoAH9/fyLKOQOnVqvLfV9zcb9OmoI8ru/YjLYwnzrd+2GoGcLB\nQ4dk9/o9wijIvQ5ZlwQGHXjWA7/mBvJt4zl8JF4Wp3/CLjaWGj/8iF1KCmWNG1M0/CkyAgLg1P03\nPq/K76s/U2woZnXWamLLYunr3pdwwjkVWXFN4C3ptfoLQuBSfBOf7JN455yiRtF1JAR6G0fyPZqT\nF9aDPM+WlDgHgyRBPnAuFog1r9c9sAYNCsG+ENTHUnDpGCBrOb6rx45w6cAeOg59Cn1Abdk8KpqY\no6n89u0Vght5Ej6mBbYKDBj0eXkkjhyJPiub2qtXWXx5vjJDGZMPTkYlqZjfdT52Npab5G3l/pAk\naR8QcJer3hVC/Hq/jyOEWAGsAGjXrp3o1q1buXwiIiIo733Nxf043Tx/lu3ffo0kSTw142NqNWmm\nCK/b3G7OFvnTdfIzSghu7EnnYfXxDnKVzemf0GVkkrlgAYVbt2JbMxD/JZ9To3fvcq18VdX31Z9J\nKExg3P5xpOpS+fiRjxlYb6AivMzNAznptZBwBK7uNP0UJAGSKS+h7TSo2w3boLb42Njxb7OOKuO1\nsgYNCkAYBX6XVKhc5O3JUJiVyd4VSwkMa8hDw57h8JEjsrlUJJcPpxDx3VWCG3sRPrq5IgMGQ2Eh\niS+/jC4pmeAVK3Bu3VpuJbMz/9R8YnJjWNpjKUGu8i23WlEOQohecjtUZYrz84hYv4orkQfxCgpm\n6JSZeAQoKx8qJ1XN0Z+ukxidi4e/MwPGtqBOc2/FbjsTOh25678le9kyhE6H95jR+IwahcpJmVWc\nKotT6ad448AbqCQVq/qsoo1/G7mVlEtJLsTuhas74Pp+0BaBrRPU6w5dp0D9vlDDX27L+8IaNCiA\n4uNpOBZKeIyoi0qm2tNGo4EdXyxACCPhE97GxrbqvzWEEJzZmcCJLfHUaeZNv1HNFBkwGIuLSRr1\nGmWx1wle9gUuHTvIrWR2dt3YxY9Xf+Slpi/RLbib3DpWrFRpjEYD5/fs4MgP32LQaen0xAg6DBmG\nnb2D3Gp3KFVrObX1BpcOp2LvaMMjT9anWVflNmcDKD52jPQ5H6GNi8O1a1f8p79j8Tlm98Pm2M18\neOxDgt2CWdZjGcFu1uIVfyEn7n+rCYnHQBhMuQnNHoeG/U25CfbydYQvL1X/zLCKYygoo2D3TYp9\nBEEtfGXzOLF5IylXoun/+mQ8/O+2O6BqIYyCI/+N5cKBZBp09KfHC42xkTFP5O8wajQkjR1H6cWL\nBH22CNdHH5Vbyexk6jJZeHQhrXxbMaHNBLl1rFQRJEkaCiwFfIHtkiRFCSEsq7RbOUiPi2XfqmVk\nxF+ndvNW9Bw5Bq+aylm5M+iNXDqYwqntN9BqDDTrUpP2A0NxclVuWWVdWhoZn86jaNcu7IKDqfXl\ncmp0t5YONQoji88uZu2ltXQK7MTCbgutDdtuYzRA8mnTasLVnZB91XTcryk8MgkahkPN1qDAQgT/\nBmvQIDP5W+MQBkFWEyMNZVqeTbkaw7GfvqfxI91o0qXqfzEa9Eb2r4sh9lQGLXsG0/mJMCSV8pa+\nhVZL8sSJlJw8Sc15n+LWp4/cSmZHo9ewOms19jb2pjwGlTWPwcr9IYTYDGyW20MpaIrVRP74LVF7\nduDi4cmAiVNo+FAXxWzzEUKQcNHUbyE/o4TgJl50HhaGd82KzVuoSIxaLblrvyH7q6/AaMRnwni8\nX34ZlYNyVmzkokRXwvQj09mfuJ8nGzzJOx3fsX5/G42QfBIubYLLm6E4E1S2EPIItH8ZGvQDT8ta\nmbIGDTJSGp1D6SVTTwY9cbI4lJUUs2PpAtx8fOn58lhZHCoSXZmBXV9fJDE6l4eG1qN1n9qKGUR/\nj9DrSXnrbYoPHiLgww9wH1jxCWRKZO7JuaTqUlneczkBLlV/RcuKlcpGCEHM4QNEfLua0sJCWvd7\njM5PPYeDs3JKXOakqIn8KZakmDxT3sK4FtRppty8BQD1oUOkf/QRuoREavTuhd/UadjXUs6KjZxk\nlmQy/rfxxOTE8Ha7t3m+yfOK/luaFSFwLboOe/bBpc1QmAy2jlC/DzQZDPV7g6NyGxE+KNagQSaM\nWgP5W37Xk+Fw5QcNQgj2rVpOUU4WT38wDwfnqre/7vdo1Dq2LTtP5s1Cuj/fiCada8qtdFeE0Ujq\n9OkU7dmD/zvT8HzqKbmVKoWtcVv5OfZnerv1pkutLnLrWLFS5chJSSJ26385m5JIQFgDHp82C/+6\nYXJr3UFTrOPk1htcOpiMvZOtKW+hW5Ait4beRpucTMYnc1Hv3499SAjBK1fi2uURubUUQ0xODK//\n9jpF2iKW9FhSfXPQMqLh0s9w6Wfa5d0AlZ2pd0Kv9005Cg6WU57+XliDBpko3JeAIb8M3zHy9WSI\nPvQbVyIP0nn489Rs0EgWh4qiKFfD1iVRFGZr6Pdac+q2ki8/5F4IIUj/4EMKt2zF942JeL34otxK\nlUJ8fjyzj8+mrX9bBjgMkFvHipUqhU5bxolNGzm15WckGxt6vTKW5j37olIpo7CD0SjIvS74z9bj\nlJXoaPpoEB0H1sXRVbnbV4waDTkrV5GzahXY2OA7+U28X3wRyV65uRaVzYHEA0w9PBU3ezfW919P\nI6+qfZ7wr8mJM209uvQzZMWApILQrlzxe4xGQyaDk6fchpWONWiQAW2qGvWRFFw6yNeTIS89lf1r\nvqJW42Z0GDJMFoeKIjetmK1LotCW6hk4oSVBDZT5QRZCkDn3U/J//BHvUaPwGT1abqVKoURXwuSD\nk3GydWLeo/OIPhktt5IVK1WG+HOn+G3NVxRkZtCkS3ds6zaiZe9wubXukBqbx6EfY8lJFtSs70KX\n4Q3wqaXcvAUhBOrffiPj40/QpaTgFt4fvylTsAuwbpe8jRCC9dHrWXh6IU28m7C0x1J8nZU5EVfh\n5Cea8hMu/Qxp503Haj8M4QtM249c/UiPiKBRNQwYwBo0VDrCKMjbfB2Vsx3u/UJkcTDo9exYMh+V\njYr+r09WzGxVeUi/UcC2L86jslExZHIbfIOVu0SYvXQpuevW4fn88/hOekNunUrjoxMfEZcfx9e9\nv8bP2Y9orEGDFSv/RFFONgfWrSD2xFG8goJ5aubHBDdtoZjuuEW5Go5tuk7s6UxcPR2o9bDEoOdb\nK3qvu/bmTdI//pjiQ4exD6tH7W++waVTR7m1FIXOqOPjEx/z07Wf6F2nNx898hFOthbek6I0zxQk\nXNgISSdMx4LaQt+PockQcLfmttzGGjRUMsUn0tAlFeH1dEPZejIc/e9/SI+LZeCkabj5VN3Zg8TL\nOez8+iLObvYMmtgKd1/l5mRkr1xJ9vIvcR/2BP7vTFP0wFqRbI7dzJa4LYxpOYaHaj4kt44VK/fN\nt1MnUlRUSOL2n2R5/vyMdIQQPPL0C7QbOBQbW2Vs9dHrDETtTeTMrgSEgPYDQmjdtw6RRw8r9nvN\nWFJC9tcryF2zBsneHr9pU/F69lkkO2W8pkqhoKyAyQcncyLtBK80f4XxrcejkpSbj/JAGI1wIwLO\n/QditoKhDPyaQM+Z0PRx8AqV21CRWIOGSsRQWEbBrps41PfAqaU8J+uJly5w8tefaN6jDw06Vd1k\nr2un0tm/NgbPmi4MHN8SF3fllsTLXf8tWQsX4fbYYwR+8AFSFa/TfL+czTjL7OOz6RjYkddavCa3\njhUr/wp3P3+0koS7j48szx8Q1oCOQ5/C3U8Z22aEENyIyiby51gKszXUa+PLw4+H4eaj4FloISjc\ntYuMuZ+iT0/HffAg/N56C1vfqjtZZi5y9bm8sPMFEosSmd15NkPChsitZB7ybkLUd6afgiRw9IC2\nL0KrZyGwJSg08FUK1qChkhBCkL81HmEQeA4Jk2VGprSokJ3LFuIZGET3F0dV+vNXFDnXBJfPRlOz\nvgfhY1vg4KTMt7EQguwvlpG9bBmuvXpS85OPkWyq7lawf0NCYQITD0wkyDWIhV0XYlOFt8BZqZ4M\nmjydiIgIunXrJreK7OSmFnN44zWSr+ThVdOFwZNaU6uhsvd0l8XF4fH556RcuYpDo0YELVyAc9u2\ncmspkuicaBamL0SoBCt6r6B9QHu5lSoWbYlpNeHct3DzMCBBve7Q+wNoOADsHOU2rDIo82zLwhA6\nI3m/XKf0YjZufUOw9a78mRkhBHu+XkJJQQHPTJmJnWPV+5AIITi59QbpZwWhLX3o80pTbO2UeTIq\n9HrSZs2i4KefcX/8cQI/mFVtlsLzNfmM2z8OCYnlPZfj7mC5NautWLFkykp0nNx2g4sRKdg72tBl\neAOaPVoTlYJLqBry88lesZLc9euxs7fDf8Z7eA4fjmRrPd25G0dSjvBmxJs44sia/msI81ROCd8H\nQghIOWMKFC5tgrJC8AyB7u9By6fBI1huwyqJ9VNkZgyFZeR8G4M2qYgaPWtTo2stWTyidm/j+qnj\ndH3+ZfxD68ni8CAYjYKD318l+nAqHnWh36hmih24jCUlpEx6E/XBg3iPGY3vhAmK3etb0WgNWiYe\nmEiaOo1VfVcR7Gb9YrZipaphNApiIlM5/ms8ZcU6mnYJosOgUJxclVmO1KBWo96/n8IdO1FHRoJe\nj/uwJ4jt0IGmgwbJradYNsVu4sNjH1Lfsz7POT1nGQGDOhPO/wDnNkD2VbB1gqZDoPVzpipI1WR7\nsLmwBg1mpCyxkJxvYxBleryfa4xTM3n2xt6MOsOBdSup26Y9bcMHy+LwIOi1BvatjSbuXBZt+tVB\n456o2IBBn5tL0ugxaC5dImDW+3g+/bTcSpWGEIKZR2dyNvMs8x6dR2u/1nIrWbFi5T4wGozkpBaT\ncaOQjPgCUq/nU5itITDMnS7DGyiyKp2xpAT1wYMU7tiB+uAhhFaLbc1AvF58AfdBg3Bs2JBrCqk0\npTSEECw/v5yvzn9F55qdWdhtIaciT8mtVX6MRojbD6fXwrVdIAxQqwMMXAJNh4KjPKXtLRFr0GAm\nik+nk7f5OjbuDvi+3Aq7ABdZPLISb7J18Vx8guswYMLbVS4JtyhXw86vLpKVWETnYWG06lWbiIgk\nubXuijYpiaRXXkWXnk6tpUuo0bOn3EqVypfnv2R7/HbGtx5P/9D+cutYsWLlbygp1JIeX2AKEm4U\nkJFQhL7MAIBTDTsC6rrTaUg9wtr6KWqV1KjVUnz4MIXbd1B04ACitBQbXx88hg/HrX9/nFq1rHJj\nXGWjM+iYdWwWW+K2MDRsKDMemoGdqopunS3NM1U/OrUK8m6Aiy88NM60quDbUG47i8QaNFQwwiAo\n2BGPOjIVhzAPvEY0wsZFng9kcX4emz/9AHtHJ4ZOfR97J+WWJL0byVfz2L3yEka9kfCxLQhtIc9K\nzf1gm5jIzfdmgE5H7bVrcW5TvWbZt8Zt5cvzXzK43mBebf6q3DpWrFi5hUFvJDtJTfqN/wUJhdka\nAFQqCZ9gV5o8HIh/XTcCQt2p4e2oqEBB6HQUHztG4Y6dFO3bh1GtxsbDA/dBg3ALD8e5XdtqU2Di\nQVFr1UyKmMTxtOOMbTmW0S1HK+pvfd+kX4STK019FfSlENwJerwHjQeBrTK30FkK1qChAjEU68j9\nLoayuAJcO9fEPbwuko08H0hdmYZf5n1IaVEhT8/6lBreyj3h/jNCCC78lkzkz9fx8HOi/+jmeMq0\nUnM/qI9E4rlwESpvb4LXr8Ohbl25lSqV0+mnmXl0Jh0COvD+Q+9XzUHIipU/cX5/EtnXBVH6RLlV\n/kB23P05qfPLyIgvJCuxCIPeCICrpwP+oe4071YL/1B3fINdsbVX3gm3MBgoOXXKtKKwdy+G/HxU\nNWpQo3dv3MLDcenUsdoUlqgoMoozGLt/LPH58Xz48IcMrT9UbqV/h14LV7aagoXEY6ZchRZPQvtX\nIbCF3HbVBmvQUEHo0ovJXh+NobAMzycb4NLWXzYXYTSy84tFpMdfZ/Bb7+Fft+okN+m0BiI2XOHa\nyQxCW/rQ66Um2Cu0pCpA/i+/kPbeDAwBAdT5z3+w8/eTW6lSuVFwg4kHJhJcI5hF3RZhZ2MdyK1Y\nBpEbryIkFRnnrsut8hfux0ll1OGmzSC4LA0PbSoeZWk4JqghynR9CZBQgU7eJSXEOVfMarYhPx9D\nXh6SszM1evTALbw/Lo88gsreOotcHmLzYhmzbwxF2iKW9VzGw0EPy61039iX5cCBT+DMWlBnmCog\n9fkIWj8LTsou+2uJmP1sTJKkfsDngA2wSggx90/XPwtMBSSgCBgjhDhvbq+KpORiNnn/vYrkYIvv\nqBY41JY36ebQd98Qe/Io3V54lbB2HWV1+TcUZpey8+uLZCer6TioLm371UFSKXPWWghBzspVZC1a\nhPNDnbj51FPVLmDI0+Qxbv84bFW2LOu5zFpa1YpFEe6yj6zMDHwV1ggsKyvrvpxsMKByFXcugXkr\n9xVmZuLoVzHfgZKDI65dH8W1a1dUTgpuHlcFOJl2kjcOvIGjrSPr+q+jkVcjuZX+GSFMqwknV9Ap\negsII9TvbVpVCOtlrYAkI2YNGiRJsgGWAb2BZOCUJElbhBDRv7vZDaCrECJPkqT+wAqgSpzpCqOg\ncF8CRb8lYV+7Bt7PNcbGTd7OxBf27+L01k207DOANuFVp9RcUkwue1ZdxmgUDBjbgpDmyt1OJQwG\nMj76mLzvvsNtwABqfvIxN44elVurUikzlDHxwEQySzJZ3Xc1wTWspVWtWBYhi+ZyMyKC9gpr7qZE\nJ4DYiAhaK9CrOrMtfhszImcQ4hbC8p7LCXQNlFvp3miLTXkKJ1dC5mVwdCcl6DGCh74P3lWvVLwl\nYu6Vhg7AdSFEPIAkST8Ag4E7QYMQ4vdnW8cx93RIBWHU6Mn98SqamFyc2/rjOTQMyVbe6PfmhXPs\nW7Wc0FZt6fHSqCqxt1wIQdS+JI5tuo5HgAvho5vj4a/chG1jWRmpb71N0d69eI0cid9bk6tdtQ6j\nMDLjyAzOZZ5jQdcFtPRtKbeSFStWrFi5hRCC1ZdW8/nZz2kf0J7F3RfjZq/gsqNF6XDiKzi9BjQF\n4N/cVC61+ZPEHT1JsDVgUAzmDhqCgN/Xx0zm3qsILwM773aFJEmjgFEA/v7+RJSz/rJarS73fW9j\nVwwB51TYF0N2Y0GBTyocSX2gx3xQr9LcbK5s/g5HDy/c2z7MocOHH8inIpz+CaNekHJSUJgIbrXA\nv2MJUTEnIUZer79DKi7G48uvsIuLQ/3kMDI6tCfm0CFZne6FuZy25W9jd8FuBnkMwuGmAxE3/91z\nVKfX6kFRqpcVK1aUid6o55MTn7Dx2kbCQ8OZ3Xk29jYKzQXJugZHl8CFH8Goh8YDodNYCO4IVWDS\nszqimAxTSZK6YwoaHrnb9UKIFZi2LtGuXTvRrZzLoBEREZT3vgCaa3nkfHcFSQVeLzcmOMyj3I9V\nUV7F+Xl8995knJxdeGb2PNx8KmZf6YO+VveiIKuUnV9dpDBVTachdWnTt859r4yY0+vv0KWmkjhq\nFLqERGouWohb/z/2IZDD6Z8wh9Mv139hd8JuHq//OLMemlWu1azq8lpVBEr1qg4cTj7M5dLLqJKV\ntZKoRCdQppcSncC8XhuvbuRg8kFebvYyE9pMQCUp7/9P4nGIXAJXt4OtI7R+3tRfwbqioHjMHTSk\nAL/f7Fzr1rE/IElSC2AV0F8IkWMumZKoTLyvShSU3ijX/Y2leopPpWPn74L3C02w9XKsYMN/j05b\nxq/z51BSUMDwWXMrLGAwJ4nROexZdRmAx15vSZ2m3jIb3RvNlSskvTYaY0kJwatW4dKxg9xKsnAi\n7QQfHP2AjoEdea/Te1Vi+1uVRa+F+APUKLwJdJNZpnoy/rfxGIQB9sttcheU6ATK9FKiE5jNSyWp\neK/jewxvNNw8T1BejEa4ugMiP4fkk6bKR12nmpKbXZVVbMDK32PuoOEUUF+SpFBMwcLTwDO/v4Ek\nSbWBTcDzQohr5pTRXM3DPUGiKOkvcct949TCF8/H66NykL+2tTAa2fXFItLirjFo8nQC6tWXW+me\nCCE4tyeR47/E4VXThf6jm+Puq9z8BSEEed9/T+an87Bxd6fOhg04Nmwgt5YsxOfHMyliEnXc6phK\nq1bVDqJKxqCHm4fh0s8QsxU0+dTy6wq8JrdZteTb/t9y5uwZ2rZpK7fKH1CiEyjTS4lOYF4vLycv\nglyDzPLY5UKngQs/wNGlkHMdPGpD//mmkqn2yu2/ZOXumDVoEELoJUl6HdiNqebbGiHEZUmSRt+6\n/itgJuANLL81c6kXQrQzh4/X8IZc8E+jW7dHzfHwlc6RH9Zz7UQkXZ8bSf32D8mtc0+0Gj2/rb9C\n3NlMwtr60eOFxtgpIPD6O/R5eaRNfxf1gQO4dOlCzU8+xtZHuRWdzElOaQ5j94/FTmXHsl7LlJ1Q\nV9UwGiHpuClQiP4VirPA3hUaDYBmT3AlWYV8HV+qN819m5PjkENz3+Zyq/wBJTqBMr2U6ATK9apQ\nSvPg1Go48TUUZ0JgSxi2BhoPBhvF7Iy38i8x+19OCLED2PGnY1/97vdXgFfM7WFpXDywh5O//kSL\nXv1o+5iyOztmJhSy75sY8tOLeejxerTuXVvRW1uKjx4ldeo0DPn5+E9/B8/nnqt2FZJuo9FrmHBg\nAtml2aztu1ZZM1hVFSEg5awpULi8GYpSTd1NG/SFZk+Y6pHbmWrTi9QIWVWtWLFi5V+RnwTHl8OZ\ndaArhno9ofNECH3UmtxsAVjDvSpIwsUo9q1cRp0Wrenxf6MVewKu1xk4te0m5/Ym4lzDjoHjWxHc\nxEturb9FaLVkfv45uavXYF+vHsErV+DYqAo0wjETJboS3jz4JhezLrKw20LLnxkzJ0JA+kW4vAku\nbYL8BLCxNzUqajYbGvQDB1e5La1YsWKlfGTHwqEFcPG/psvNh8HD4yHAOm5YEtagoYqRk5zI1kWf\n4BkYxMBJ07CxVeafMD2+gN/Wx5CXXkLjhwPpPCwMB2fl7oMvu3GD1LfeRnP5Mh5PD8d/6tRq3Yn0\ndrfnyzmXef+h9+ldp7fcSlWTrKumFYVLmyAnFiQbqNfdlADYaAA4VUz1NStWrFiRhaxrcGie6XvO\nxgE6vmYqm+phbfhpiSjzjNPKXSkpyGfT3A+wsbPj8WmzcHBWXhKRTmvgxJZ4zu9PwtXTgYETWlK7\niXKrIwkhKNi0ifQ5H6Gyt6fWF0up0auX3FqykqJOYfTe0aQVp/FZt8/oL7HVPQAAIABJREFUUbuH\n3EpVi/xE0wB68SfIuARIEPIIPDTWtJ/XRbmfBytWrFi5L7KuwsFbwYKdEzz0Ojw8wVoJycKxBg1V\nBK2mlF/mz6akIJ+n3v8YN1/llVZNjc3jt/VXKMgqpdmjQTw0tB72Tsp9ixkKCkh7fxZFu3bh3LEj\nNed9ip1/9U47vZp7lTH7xqAxaFjRewVt/NvIrVQ1UGdB9C+mpfmkE6ZjtdpDv0+h6RCoESCvnxUr\nVqxUBJlXbq0sbAI7Z+g8AR4abw0WqgnKPaOzcof4c6fYv/pLCrOzGDhpGoFhDeVW+gNajZ7jv8Rz\nMSIZNx9HBk9qTa2GnnJr3ZOS06dJeXsK+qwsfCe/iffIkUg2yq3mVBmcSj/FxN8m4mTnxLp+66jv\nqewSvrKjKYQr20yBQvxBEAbwawI9Z5oSmj1D5Da0YsWKlYohM8a0snB5861gYaIpZ8GlelYVrCi0\neiMFpToKSrWU6Y0YjAKdQWAwCvQGI3qjuHXMdJ3eKNAbjehv3UZnFPx/e3ceXWV973v8/d1Dhp2E\nDEAGCEOQScAJA4oKxhnn2un2rHu1zrWtrfW0tlq77tF7l11arN4O9qin9dae2x7FVlvrLGqwVhkC\nQhAIYRIShgxkIMnOsIff/eN5IEEhTMl+fmR/X2vttfMMO/mw2clvf/fzG2Luebmd8UHPq0WDxTpa\nmnnv2f9gw4fvkzd6DP/tgYcpnjrd61gHqFnfxHv/r4q2pi5OvbCYs689yeqpVE00SuNvfkPjk08R\nHFPM+P/6E+mn6ECtRdsW8aP3f8TorNE8dfFTFGUWeR3JTpFO2PiWUyhUvwWxbmfe8fO+BzO+DAXT\nvE6olFIDp26dc2Vh7V+ddRXO+55zZUG7WR6gOxqjqaOHlnCElrBTBLSEI7R0Hrjdun87Qku4h46e\n2IBl+EHp4C84rEWDhUw8zpr33ub9Pz5DtLubc77635l1zZcJBO0ZSNzdGeXDv2xi3Qc7ySkI8cXv\nz6Root2DOntqa9n5g3voXLWK7Ouuo+D++/Fn2jcuJNEWbljIQ0sfYsaIGTxx4RPkpNn9/5hwsShs\nLYc17qJrPW2QkQ+lNzmFQnGpTiWYACKyALga6AE2AzcZY1q8TaXUEFW3DhY/4nS7TMmE8+52xi0k\nabHQ2RNjR0uY2uZO3t0eYekbVdQ2d7Kj2dlX39Z9yMcG/UJOKIWc9CA5oSCjctKZNmrY/u3sUArZ\n6UFSAz6CfsHv8xH0CX6fEPALAZ/vgK8D7rGg3+feO9vLPvxg0J8HLRoss2dHDYv+4wlq139C8bQZ\nXHLbneSNKvY61gG2fbKH8j9W0dHSzRmXjmX2VSUEUuy9ugDQ+vdX2P3ggyDCqJ8/SvaVV3odyXPG\nGJ5c/SS/Wf0b5hXPY8G8BYSC9q7QnVDxuDM2Yd9aCuFGSM2Gadc6UwmOn6sLFCXe28B97qKhjwD3\nAT/yOJNSQ0vdWrdY+BukZMHcH8Ccb0PI3unSB0JXJMa2PWFqm8PsaOl0C4LO/duN7T0HnB+o2sKo\nnHRG56Rz/uSRjM5NJz8rjdxQkOxQkJz0FHJCTlGQHvQnZGp8v2/wf4a2epaIx6J89Of/YulLzxNM\nTePSO77LjLJLrFqDoasjwgcvbGTDkt3kjcpg/u2nUFBi9+rA3Rs3Uv/oz2lfvJj0mTMZ9bOfkVKs\nC5TF4jEeWvoQL1S/wDUnXcMD5zxA0GfPlSxPGAO1Fc5aCmv/6i66luasoXDKV5xF1wKpXqdMWsaY\nt/psLgG+7FUWpYaUeIy8PSvgT09C9etOsTDvHmfq1CFWLMTjhprmMFW729iwu42q3Xup2t3Gp40d\nxE3veSkBH8U56YzOda4KjM5Jpzg3xOjcdGqqVnHtpRck5E26bbRosEDt+k9Yv/APdLU0MfXc8ym7\n4VYycuwZSGzihk0r6/lg4UY62yOUXjGe0svH4w/au0pypK6Ohl/9itYXX8KXkUH+PfeQ9/UbEEvX\ntUik7lg3975/L4u2L+KWGbdw18y7rCpOE8oY2LWKCZt/Dx9/B1q39y66Nv1/wZT5kJrldUr1eTcD\nz3sdQqkTWkcjfPyfUPF/ObVlG2SMdNaQOeuOIVEsNHX0ULV7r1Mc7Gqjqq6NjXVthPuMIxg3PMSU\ngiyuOqWIiQVZFOemU5ybzoiMVHyHKAo6PvUlZcEAWjR4qqujnX/88fdUvvMGKVnD+OJ9D1Jy+ple\nx9rPGMPWykaW/X0LjTXtjBiTyVV3nsbIsfa+iYq1t7Pnt7+l6ffPQixG3g03MPwbtxPItacI89Le\nnr18993vsqJuBT+c9UOun3a915ESzxhn/YS1LznTBjZvpVj8MPEiuODHMPUKSMv2OmVSEpFFwMHm\np73fGPM395z7gSjwx36+z+3A7QAFBQWUl5cfU5729vZjfuxgsTET2JnLxkzgcS5jyG5dz6idrzOy\n4UN8JkpL9gw2T7iT9uIyjARhWaU32Q7iSJ4rYwy7OgxbWmPUtMWpbYtT225o7e69dJAZhDFZPs4t\n8lGcmUJxlo/RmT7SAgK0O7fmXexthnUDkMkLicilRYMHjDFUL/mA937/NOHWVkqv/iLRgmJrCgZj\nDDXrmtj6tmFdUyXDRqZz8Y0nM2l24SErb6+Znh6aF75A4xNPEGtuZthVVzHye3eRUmzXeBAv1Yfr\nuWPRHWxt3cojcx/higlXeB0pseqrnK5HfVdnLpkHc/+VD/fkct4lV3udMOkZY/pdWVFEbgSuAi4y\nxphDnWeMeRp4GqC0tNSUlZUdU57y8nKO9bGDxcZMYGcuGzOBR7m69kLl81DxDNSvg9RhMOsWKL2Z\nnPyptJ1Az1VXJEZlbSsV25pYua2ZFduaaQ5HAEgN+JhUkMnF44YxtTCLKYVZTC3MYmRW6oBdUU/m\n15UWDQm2t6Ged575d7asXE7BhIlc96N/o2DCRGuq1toNzSx7eQu7NrcSDMEF109lytmF+P12dkUy\nxtD25pvUP/44kW3bCZ11Fvn33EP6DLumpvXa1tat3PH2HbR0t/DERU9wzqhzvI6UGI2b3DEKLzkN\n5QGrM1+zf47xqCW/f+rQRGQ+8EPgfGNM2Os8Sp0Qdq+B5b9zponuaYei0+DqXzoTOqScGLMHNrR1\ns2JbEyu2NVOxrZlPdrQSiTmfGUwYkcHFJxdQOj6XmWNzKRmRQcDS9ytDQVIVDds/WU3jukoqY4ee\nGmswtTftoeLvL2IwlN1wK2fMvxqfJQuK7drUwtK/b2HHhhYyslM4/18mUx/dyLRzR3kd7ZDCFRXU\nLVhA1+pKUidNYszTT5Exd27y9s8/hDUNa/jWO9/CJz6emf8M04cP4YLKGNj5MVS96twa1jv7x86B\nyxc4sx9lJfeq3yewXwOpwNvu7/gSY8wd3kZSykKRLmeq1OW/g9plzoQOM74EpbfA6JlWTxEdjxs2\n1re7BUITH1SFqX9jEeAMTj51dDY3n1dC6bg8Zo7NYXimTk6RSElVNFQueoNtH/2DbYvfOvzJg6Tk\njFIuvuVbDBuZ71mGvuo+3cuyl7ewfV0T6VlBzvvKJKbPHUUgxU95+Sav4x2Uf9cuar71bdrffZdA\nQQFFDz1E9heuTfoVnT8rbuIs3LCQRyseJS8tj6cueYpxw8Z5HWvgxSKw7Z+9hcLeHSA+GHcunPmw\nc0UhW2fMOtEZYyYm9Aeu+D1FOzdAxdaE/tjDKdpZbV0msDOXjZlgkHPt2QSr/gSdTZB3Elz2Uzjt\nX6wd2GyMYWtjB4urG/jHxkYqPm1ib1cUgOEZKYzL9HFr2STOHJfLjNHZpAa0nfdSUhUNF93yTVIm\nTmPOnDme/Hyf308oO8eKT8IbatpY9vetfFrZSFpGkDnXncQpZcVWr+Ycqa+n8ddPMPyFFwhnZDDy\n7rvJu+F6fOnpXkezTmVDJY/ufpSa7TWUFpSy4PwFjEgf4XWsgdPTAZsWOUVC9RvQ1QqBdGcw84U/\ngUmXJe0iRGqAvPKvTDExqPY6yIGmgHWZwM5cNmaCQc4lfmcyh9JboOR88NnXVaejO8pHm/ewuLqB\nxdUNbG9yehuWjMjgylOLOHNcHmeOy2X88BCLFy+mbN5JHidW+yRV0ZCeNYyUzCyyhg+hN09Hac/O\ndpa/spXNKxtIDQU465oSTr1wDClpdr4UTCxGd3U1e998k6Zn/4CJRglfUMbpDz2kMyIdRFNXE79Y\n+Qte3Pgiw/zDeHjuw1xRcoUVhepx62iEDa87hcKW9yDaBem5MOVKmHolnHQhpOjidGqA3L2WDz/6\nkHPm2DX+x8ZMYGcuGzPBIOdKCVk3+5sxTpej8g31LK5uYPnWZnpicUIpfs45aTi3zS3h/Mn5jB2u\nf79tZ+c7RTVg4rE4exu7aK4Ls3F5HRsr6gim+im9YjynXzyG1JBdC3qZSISudesIV1QQXrac8MqV\nxNvaAMi6fD75d9/Nh1u2aMHwGbF4jIXVC/nVx7+iM9LJ16d9nelt07l8wuVeRzs+TVug6jWnUKhZ\nAiYO2WPgzBth6lXOWAVdmVkNhmFF9KQOh2FFXic5gI2ZwM5cNmYCe3MNpL1dET7c1Ej5Budqwq7W\nLgAmF2Ry47njOX/ySErH52p3oxOMtrZDRFdHhJa6MM27w7TUdbj3YVobOom7swwEUnzMvHQsZ1wy\njrRMO4qFeHc3natXE66ooLOigvDHqzCdnQCklJQwbP58QrNKCc2aRbDI/SO7ZYuHie2zqn4VDy19\niKqmKmYXzubHZ/2Yk3JOsmZGrqPStRc+/Qdsfhc2vQPNbr/fghnOCqVTr4TCU60eyKeUUsnGGEPV\n7jberapn8YYGVmxvJhY3ZKUGOHfiCO66aCTzJo9kVI52Jz6RadFwAul71aBld5jmug5a6pzioLMt\nsv88n1/IHplOTkGIktNGklMQIrcwRF5RBinp3v6Xx9o76Fy1inDFcsIVFXStrsREIiBC6pQp5Hzp\nS4RKSwmVnklgRPJ2IzsSjZ2NPL7icV7e/DL5oXwWnL+Ay8ZddmJ1RYrHnNmONr/r3GqWgYlBMANK\n5jork06+FPImeJ1UKaVUH8YYNtS18WrlLl6t3MWWxg4Apo8axjfmTaBsSj5njM0hqFOgDhlJVTS8\n+uDrbNsZYP1zi7yO8jnGmMPmiiNA7xvCVF8PWYFOCgKdZA3rJCsQJjPQSYa/C58ADe5tjXN+01Fm\nytqxg93vv3+Ujzo4E43RtX49XevWQSwGfj9p06eTe/31TpEw8wz8OTkD8rOGumg8yvMbnueJj5+g\nM9bJzTNu5hunfoNQ8MToD5ra1QArnnWKhC3l0NXiHCg6Hc69yxmbMGY2BHQqPaWUsk11XRuvVO7i\n1cqdbG7owCcw56Th3DK3hEumFZCfleZ1RDVIkqpoGNm+idjuevwWTs0Zi8UOm0tMjFB3E6HuBjK6\nGwnGuj53jsFZEH0gpEUi7K1cM0DfDVJOmsDw224lVDqL0Bmn48s4MRaWscmKuhX8dOlPqW6uZk7R\nHO476z5Ksku8jtW/7nZnStRN78Dmd5mzZ6OzP6uodwDzhLL9C60ppZSyy6b6fYXCLjbWt+MTOKtk\nODedW8L8GYWM0PUSkkJSFQ2zF3wnqZf/Plo2ZkpWDeEGHlvxGK9seYXCjEIeK3uMi8debGdXpPYG\nZ0GhmqVOd6PaCohHnClRx5/Lppy5TLzsdhg5VccmKKWUpTbVt/PaGqdQ2FDXhgjMHp/H/752OpfN\nKNQrCkkoqYqGJbuW8M+2f9JY3eh1lM/Z0LbBulw2ZgI7cw1mpoZwA8+ue5aeWA+3nXIbt55yqz1d\nkeIxqF/fWyDULO0dvOwLQtFpcPY3nfUTxpwNwTRqy8uZmH+yt7mVUkp9zpYGp1B4pXIXVbudQmHW\nuDwevGY6l88oJH+YFgrJLKmKhr9U/4U3mt6Aj7xOcgg25rIxE9iZaxAznTf6PO6dfa/3Kzp3tsCO\nit4CoXYF9DhT4pIxEsacBaU3OfdFp0NQGxillLLZ3q4I726P8NivP6CythWA0nG5/NvV07h8RhGF\n2fp3XDmSqmj4ydk/4dzIuZ6tCN2fjz76yLpcNmYCO3MNZia/z8/wtOGJ74oU7YHGati1uvdKQkMV\nYEB8kD8dTv2qUyCMmQ2547W7kVJKnQCMMSz/tJnnlm/ntTW76IrEmVqYyk+uPJkrTy2iKFunRlWf\nl1RFQ3ZqNjmBHAoyCryO8jk25rIxE9iZy8ZMRyweh5ZPoW6d09Wofq1zv2cTxKPOOWnZUDwLZnzR\nKRBGnwmpWZ7GVkopdXTq27p4ceUOFi6vYUtjB5mpAb44s5iJvnpuumaunePklDWSqmhQKqkZA+11\nUL+uT4Gwzrl6EAn3npczDgqmOzMb5U9zFlYbMRl8Q3eubWMMXZEYXZEYnZEYnT0xuiJxuqIxItE4\nkZghEou7N+frnn3b7vF929E+x2NxQ9wYjIG4ATDE42AwxI3zX2Jwjhvj7gPizgFGxiOUefvUKKVO\ncNFYnMXVDTy/vIZ3quqJxQ2zxufyrQsmcsUphYRSApSXl2vBoA5LiwalhgpjoKsV2nZD+24Kdr8H\nH6yC1treAqGzz2odGfmQfzKceaNznz8dRk6B1EzP/gn9iccN4UiMcHeUjp4YHd1Rwu59R0+UcHfM\nud+3zz2vbzHQFYm7BUGfAsHdz5tvDEhOv08I+oWg34ffJ/hEnHVTcO5FwCfirLoignxmn08E3O1A\nlhmQTEqp5LNtTwcLK2r484pa6vZ2MyIzhVvPK+ErpWOYmG/n33llNy0alLKdMdDZ7FwlaNsFbe59\n3+323U6xEO1du+NkgCogNdspBqZd41w5yD/ZuU/Augg90Tgd3VHa+9wqG6K0V+6kvcvZ7uiO0d4d\nob075m6753Y5xUBHd4ywWwwcqaBfyEgNEAr6SUvxkx7sveWGUkgL+kgP+kkL+klP8VO3o4Ypkyb0\n7gv6SQv6SA36SfH7CPp9+wuBlEDv9v5jAR8BX2+hMFDKy8sH7HsppYa+rkiMN9fu5rllNXy0ZQ8+\ngfMnj+TBa8Zy0cn5ujqzOi7JVTT85VbmffISvG/fJbh5xliXy8ZMYGeuQc1k4mAO8oY5dRhkFkBW\noTPeIKsQMgud+6xClq7bzlkXXXNUVw6isbj7aX5s/6f37fs/tY/2vqnvitLe7Xyi397jvsHvUxh0\nuMVATyx+8B+04uMDNtODfjJSA2SlBchI9ZOZGmBUThoZqQHnluJ37wOEUv3O/b597vHQvvuUACmB\no2sYy8vrKCubeFSPUUopLxhjaO2MsL0pvP9W0xSmpqmTytoW9nZFGZOXzvcvmcyXS4t1ULMaMINe\nNIjIfOAXgB/4rTHm4c8cF/f4FUAYuNEYs3JQwkyeT02rYdzYsYPy7Y9Hzfbt1uWyMRPYmWtQM4lA\naDhkFRLPKKA7PZ9wygg6Jc3pd39AP3y3u01DjDUNnXy8pI7Onp2Ee5yuOvu69oTdT/A7ew7s0tMd\nPcSb/IPISPGTmea8ac9039AX54b2v/HPSA2QmeIeTwuQ5b7Br15Xybw5s53HuG/0A/rpl1JK7dcT\njbOzpfOAoqBvkdDWFT3g/OEZKYzJC3HZ9EK+cMZo5kwYjm8Ar3oqBYNcNIiIH3gCuASoBZaLyMvG\nmHV9TrscmOTezgL+3b0fcL+sP43XGzPIjeUOxrc/Ls3NzdblsjET2Jmrv0z7BrvG484A15gxxOPO\noNd9A2Xj7iDYuLvtnOOeHzd0R+N0RmL0RFuBVmDjkQVbXwVAyP0EPiPVuQ+l+MlKC1A4LK33k/tU\nP6Fg7zn77/t8op/lFgmhoP+YG6T4Tj+TC3TmJWW3//Hbpexp6uTpjUu8jnKA5mb7MoGduWzMBP3n\nisYMO1o62dXa6U6e4EgJ+CjOTWdsXogzx+UyNi/EmLzQ/vvM1OTqOKK8MdivstnAJmPMFgAReQ64\nFuhbNFwL/MEYY4AlIpIjIkXGmF0DHSYWN8QMRA7VZcJDNuayMRPYmetwmUQEnw+CPp87MFbcQbIc\nsC1Cn8GzznG/T0gL+kl1++Gnu/3w0wIH9tdPC/r299FPD/pZsWwJF5fNIzXg00+clDpKkVj8hPxb\n4xUbc9mYCfrP5RNhdkne/oJg3y0/K1X/jivPDXbRMBqo6bNdy+evIhzsnNHAAUWDiNwO3A5QUFBw\nTAMEzwjCpOkxMjN7jvqxg6293b5cNmYCO3MlNJMBut1bh7MrCrS7t32kp4OlH/4jMZmOQnt7u3UD\nfG3MBPbmSgbPf2MO5eXllJWd43WUA9iYCezMZWMmsDeXUodzwlzPMsY8DTwNUFpaasrKyo7p+zi/\nrMf22MFkYy4bM4GduTTTkbMxl42ZwN5cSimlks9gjz7cAYzps13s7jvac5RSSimllFIeGeyiYTkw\nSURKRCQF+Brw8mfOeRm4QRxnA62DMZ5BKaWUUkopdWwGtXuSMSYqIncCb+JMufqMMWatiNzhHn8S\neA1nutVNOFOu3jSYmZRSSimllFJHZ9DHNBhjXsMpDPrue7LP1wb49mDnUEoppZRSSh0bXVFJKaWU\nUkop1S8tGpRSSimllFL90qJBKaWUUkop1S9xhhScWESkAdh2jA8fATQOYJyBYmMuGzOBnbk005Gz\nMZeNmeD4co0zxowcyDAnoiHYXtiYCezMZWMmsDOXjZnAzlw2ZoIEtBcnZNFwPESkwhhT6nWOz7Ix\nl42ZwM5cmunI2ZjLxkxgb65kYePzb2MmsDOXjZnAzlw2ZgI7c9mYCRKTS7snKaWUUkoppfqlRYNS\nSimllFKqX8lYNDztdYBDsDGXjZnAzlya6cjZmMvGTGBvrmRh4/NvYyawM5eNmcDOXDZmAjtz2ZgJ\nEpAr6cY0KKWUUkoppY5OMl5pUEoppZRSSh0FLRqUUkoppZRS/UqaokFEviIia0UkLiKlnzl2qoh8\n5B5fIyJpXmdyj48VkXYR+UEi8hwul4hcIiIr3OdohYhc6HUm99h9IrJJRDaIyGWJynSQjKeLyBIR\nWSUiFSIy26ssfYnId0Skyn3+fuZ1nr5E5PsiYkRkhAVZFrjPU6WIvCQiOR5mme++njeJyL1e5UhG\nNrYVh8vlHk94e2FjW9FfLveY5+2FrW0F2Nte2NRWQBK3F8aYpLgBJwNTgHKgtM/+AFAJnOZuDwf8\nXmbqc/zPwAvADyx5rs4ARrlfzwB2WJBpGrAaSAVKgM2J+v87SMa3gMvdr68Ayr3I8ZlMFwCLgFR3\nO9/rTH2yjQHexFl4a4QFeS4FAu7XjwCPeJTD776OJwAp7ut7mtfPT7LcbGwr+svV53jC2wsb24rD\n5LKivbCxrXCzWNle2NZWuJmSsr1ImisNxpj1xpgNBzl0KVBpjFntnrfHGBPzOBMi8gVgK7A2EVn6\nOlQuY8zHxpid7uZaIF1EUr3MBFwLPGeM6TbGbAU2AV59amOAYe7X2cDOfs5NlG8CDxtjugGMMfUe\n5+nrceCHOM+b54wxbxljou7mEqDYoyizgU3GmC3GmB7gOZzXuUoAG9uKw+TyrL2wsa3oLxf2tBc2\nthVgb3thVVsBydteJE3R0I/JgBGRN0VkpYj80OtAIpIJ/Ah40Oss/fgSsHLfHxcPjQZq+mzXuvu8\n8D1ggYjUAI8C93mUo6/JwFwRWSoii0VklteBAETkWpxPH1d7neUQbgZe9+hn2/SaVr2sayvghGgv\nbGkrwJ7fLRvbCrCwvTgB2gpIovYiMFjf2AsisggoPMih+40xfzvEwwLAecAsIAy8IyIrjDHveJjp\nAeBxY0y7iAxEjIHKte+x03Eux11qS6ZE6S8jcBFwtzHmLyLyVeB3wMUeZwoAecDZOK/xhSIywbjX\nNT3M9WMG+PVzJI7kNSYi9wNR4I+JzKYSx8a24jhyPcAgthc2thXHmysRbGwrjiCXJ+2FjW0FaHtx\nMEOqaDDGHMsvXS3wvjGmEUBEXgNmAgPSEBxjprOAL7uDkHKAuIh0GWN+PRCZjiMXIlIMvATcYIzZ\nPFB5jiPTDpz+jvsUu/sGRX8ZReQPwF3u5gvAbwcrx1Fk+ibwovtHf5mIxIERQINXuUTkFJz+xKvd\nNznFwEoRmW2M2e1Fpj7ZbgSuAi5KRGF1CAl9TScjG9uK48g1qO2FjW0F2N9e2NhWgJ3thY1tRX+5\n+uS7kSRrL7R7kjO45hQRCYlIADgfWOdlIGPMXGPMeGPMeOD/AD8dyILhWLmzA7wK3GuM+afXeVwv\nA18TkVQRKQEmAcs8yrIT5/UDcCGw0aMcff0VZ3AbIjIZZ6BUo5eBjDFrjDH5fV7jtcDMRDQC/RGR\n+Tj9Zq8xxoQ9jLIcmCQiJSKSAnwN53WuvGVdWwF2theWthVgT3thY1sBlrUXtrYVkLztRdIUDSJy\nnYjUAnOAV0XkTQBjTDPwGM4Tvwqn7+WrXmbyWj+57gQmAv/TnSpulYjke5nJGLMWWIjTeL8BfDuR\ngxM/4zbg5yKyGvgpcLtHOfp6BpggIp/gDJD6uoefiNju10AW8Lb72n7SixDu4Lo7cd6krgcWuq9z\nlQA2thX95fKSjW1Ff7ksai9sbCtA24ujkZTthejrQSmllFJKKdWfpLnSoJRSSimllDo2WjQopZRS\nSiml+qVFg1JKKaWUUqpfWjQopZRSSiml+qVFg1JKKaWUUqpfWjQopZRSSiml+jWkVoRW6niJyHB6\nV3gtBGL0roY52xjTM0A/5wGg3Rjz6EB8P6WUUomjbYVKRlo0KNWHMWYPcDroH2ullFIHp22FSkba\nPUmp4yAiN4hIpYisFpH/dPcViMhL7r7VInKOu/9+EakWkQ+AKZ4GV0oplTDaVqihQK80KHWMRGQ6\n8BPgHGNMo4jkuYd+CSw2xlwnIn4gU0TOBL6G88lUAFgJrPAit1JKqcTRtkINFVo0KHXsLgReMMY0\nAhhjmvrsv8HdFwNaRWQu8JIxJgwgIi97kFcppVTiaVuhhgTtnqTX55xOAAAA1ElEQVSUUkoppZTq\nlxYNSh27d4GvuLNo0OeS8zvAN919fhHJBt4HviAi6SKSBVztRWCllFIJp22FGhK0aFDqGBlj1gIP\nAYtFZDXwmHvoLuACEVmD0xd1mjFmJfA8sBp4HVjuQWSllFIJpm2FGirEGON1BqWUUkoppZTF9EqD\nUkoppZRSql86e5JSh/GZlT/7ushd4EcppVSS07ZCDXXaPUkppZRSSinVL+2epJRSSimllOqXFg1K\nKaWUUkqpfmnRoJRSSimllOqXFg1KKaWUUkqpfv1/6Ni2MszjrmQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119f26c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# This computes probabilities for 120 arcsec boxes, corresponding to raw data\n",
"t_ccds = np.linspace(-16, -0, 20)\n",
"plt.figure(figsize=(13, 4))\n",
"\n",
"for subplot in (1, 2):\n",
" plt.subplot(1, 2, subplot)\n",
" probit = (subplot == 2)\n",
" for m0_m1, color, mag_mean in zip(list(fits), colors, mag_means):\n",
" fit = fits[m0_m1]\n",
" probs = p_fail(fit.parvals, t_ccds)\n",
" if probit:\n",
" probs = stats.norm.ppf(probs)\n",
" plt.plot(t_ccds, probs, label=f'{mag_mean:.2f}')\n",
"\n",
" plt.legend()\n",
" plt.xlabel('T_ccd')\n",
" plt.ylabel('P_fail' if subplot == 1 else 'Probit(p_fail)')\n",
" plt.title('P_fail for halfwidth=120')\n",
" plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZAfdvgA3vw/r3IelPGHgXwWmnIEUPVpMm5IdjIX2PdoxHS/AO0BbaRoK0wtZP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\n4LGMDR6LxWphd+Zu1qauZe2xtbyx5Y2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H9vkuGBkn5GSTPGMGjU3nmDZ3Xm9J90x3uNcT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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119daeb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mag_bin_centers = np.concatenate([[5.0], mag_means, [13.0]])\n",
"fit_parvals = []\n",
"for fit in fits.values():\n",
" fit_parvals.extend(fit.parvals)\n",
"\n",
"fit_parvals = np.array(fit_parvals).reshape(-1, 4)\n",
"parvals_mag12 = [[5, 0, 0, 0]]\n",
"parvals_mag5 = [[-5, 0, 0, -3]]\n",
"fit_parvals = np.concatenate([parvals_mag5, fit_parvals, parvals_mag12])\n",
"fit_parvals = fit_parvals.transpose()\n",
"for ps, parname in zip(fit_parvals, fit.parnames):\n",
" plt.plot(mag_bin_centers, ps, '.-', label=parname)\n",
"\n",
"plt.legend(fontsize='small')\n",
"plt.title('Model coefficients vs. mag')\n",
"plt.xlabel('Mag_aca')\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define model for color=1.5 stars\n",
"\n",
"- Post AGASC 1.7, there is inadequate data to independently perform the binned\n",
" fitting.\n",
"- Instead assume a magnitude error distribution which is informed by examining\n",
" the observed distribution of `dmag = mag_obs - mag_aca` (observed - catalog). This\n",
" turns out to be well-represented by an `exp(-abs(dmag) / dmag_scale)`\n",
" distribution. This contrasts with a gaussian that scales as `exp(dmag^2)`.\n",
"- Use the assumed mag error distribution and sample the `color != 1.5` star\n",
" probabilities accordingly and compute the weighted mean failure probability."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examine distribution of mag error for color=1.5 stars"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_mag_errs(acqs, red_mag_err):\n",
" ok = ((acqs['red_mag_err'] == red_mag_err) & \n",
" (acqs['mag_obs'] > 0) & \n",
" (acqs['img_func'] == 'star'))\n",
" dok = acqs[ok]\n",
" dmag = dok['mag_obs'] - dok['mag_aca']\n",
" plt.figure(figsize=(14, 4.5))\n",
" plt.subplot(1, 3, 1)\n",
" plt.plot(dok['mag_aca'], dmag, '.')\n",
" plt.plot(dok['mag_aca'], dmag, ',', alpha=0.3)\n",
" plt.xlabel('mag_aca (catalog)')\n",
" plt.ylabel('Mag err')\n",
" plt.title('Mag err (observed - catalog) vs mag_aca')\n",
" plt.xlim(5, 11.5)\n",
" plt.ylim(-4, 2)\n",
" plt.grid()\n",
" \n",
" plt.subplot(1, 3, 2)\n",
" plt.hist(dmag, bins=np.arange(-3, 4, 0.2), log=True);\n",
" plt.grid()\n",
" plt.xlabel('Mag err')\n",
" plt.title('Mag err (observed - catalog)')\n",
" plt.xlim(-4, 2)\n",
" \n",
" plt.subplot(1, 3, 3)\n",
" plt.hist(dmag, bins=100, cumulative=-1, normed=True)\n",
" plt.xlim(-1, 1)\n",
" plt.xlabel('Mag err')\n",
" plt.title('Mag err (observed - catalog)')\n",
" plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MRVEUpU5S6+ccicgIYARAmzZtWLBgQXINcikqKqo1tkDtsqeytqwviLBxT4TG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\nL9lNU5pwGIGkHJup9h+0iIrQmXQ+Zy3tyWQn+2m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9jrIK6Wut0+JDGT5skG30wNoWooxc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MIIqg65rW7fePdqepUM55PWBz/j2m0felkjcbLOQMMFRVoajB8Pr74Kq1cnOhqDwWC44DCZo4uY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\nIiIiIoCKIxEREREREQD+P2FLzBeW1qgqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109ad2908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_mag_errs(acqs, red_mag_err=True)\n",
"plt.subplot(1, 3, 2)\n",
"plt.plot([-2.8, 0], [1, 7000], 'r');\n",
"plt.plot([0, 4.0], [7000, 1], 'r');\n",
"plt.xlim(-4, 4);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define an analytical approximation for distribution with ad-hoc positive tail"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define parameters / metadata for floor model\n",
"FLOOR = {'fit_parvals': fit_parvals,\n",
" 'mag_bin_centers': mag_bin_centers}"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calc_1p5_mag_err_weights():\n",
" x = np.linspace(-2.8, 4, 18)\n",
" ly = 3.8 * (1 - np.abs(x) / np.where(x > 0, 4.0, 2.8))\n",
" y = 10 ** ly\n",
" return x, y / y.sum() "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"FLOOR['mag_errs_1p5'], FLOOR['mag_err_weights_1p5'] = calc_1p5_mag_err_weights()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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rZXZGU/MOGzYsQ2s9pKHnNeVGsE7A7mMe5wFDT/ZkpdQFwDjAximOALTWrwCv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\nEEIID/X/AKLJuUNQJS+4AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119cd3438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.semilogy(FLOOR['mag_errs_1p5'], FLOOR['mag_err_weights_1p5'])\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Global model for arbitrary mag, t_ccd, color, and halfwidth"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def floor_model_acq_prob(mag, t_ccd, color=0.6, halfwidth=120, probit=False):\n",
" \"\"\"\n",
" Acquisition probability model\n",
"\n",
" :param mag: Star magnitude(s)\n",
" :param t_ccd: CCD temperature(s)\n",
" :param color: Star color (compared to 1.5 to decide which p_fail model to use)\n",
" :param halfwidth: Search box size (arcsec)\n",
" :param probit: Return probit of failure probability\n",
" \n",
" :returns: acquisition failure probability\n",
" \"\"\"\n",
"\n",
" parvals = FLOOR['fit_parvals']\n",
" mag_bin_centers = FLOOR['mag_bin_centers']\n",
" mag_errs_1p5 = FLOOR['mag_errs_1p5']\n",
" mag_err_weights_1p5 = FLOOR['mag_err_weights_1p5']\n",
"\n",
" # Make sure inputs have right dimensions\n",
" is_scalar, t_ccds, mags, halfwidths, colors = broadcast_arrays(t_ccd, mag, halfwidth, color)\n",
" box_deltas = get_box_delta(halfwidths) \n",
"\n",
" p_fails = []\n",
" for t_ccd, mag, box_delta, color in zip(t_ccds.flat, mags.flat, box_deltas.flat, colors.flat):\n",
" if np.isclose(color, 1.5):\n",
" pars_list = [[np.interp(mag + mag_err_1p5, mag_bin_centers, ps) for ps in parvals]\n",
" for mag_err_1p5 in mag_errs_1p5]\n",
" weights = mag_err_weights_1p5\n",
" if probit:\n",
" raise ValueError('cannot use probit=True with color=1.5 stars')\n",
" else:\n",
" pars_list = [[np.interp(mag, mag_bin_centers, ps) for ps in parvals]]\n",
" weights = [1]\n",
"\n",
" pf = sum(weight * p_fail(pars, t_ccd, box_delta=box_delta, probit=probit)\n",
" for pars, weight in zip(pars_list, weights))\n",
" p_fails.append(pf)\n",
" \n",
" out = np.array(p_fails).reshape(t_ccds.shape)\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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5ZgjOUc7kzs8lJSSFzRdtpnpJNVqHdR/DQfn4wP/+B3l58Pjj8kf4lFNg/Hj4\n9VfVOqMoymG56PVc6OPDZ9HRlI8dy7dDh3KOlxd/A2ebG5RmZmSwrKbG6n/ne5POTofXFC+GLBzC\n2JKxDHxtIDp7HTvu2EFKQApbLt5C1c8q29QJExgohwF/950cPvXzz/Dvf0NaGlxzjRyn/tNPBz6v\nsVHef9118mQsPFxmaNx/iElBAdx8M8yfL/epHBUVXPQgBwfZg/HQQ/DHHzLYWL4c/vMfOWz+1Vfl\nUCqTCRIT4ZlnDh9sWxMHnY5p3t58Eh1NeVISX0RHM9bdnQXFxSStW0f4qlXMy8khx8rHNOrsdPic\n78OIX0cwJmsMwXcFU7uslo2TN7Imcg0FzxbQWmWDmSo8POCBByA3F158EXJy5BCq0aPlZDmVYUpR\nlH/gpNcz3dubD6Oi+AZYPGwYZ3t58WlZGRM3bCAkJYU52dlst6W0ib3AYDQQeFMgsamxctjULYHU\nLK1h09RNpISmkDM3h92Zuy1dzL7D0VHWb6++Knvv09Pl8Kn4+AO3LSqSQ0xOO03erqmBwYMhM3Pv\nNitWyBM3Pz+Z1eqJJ/YNPlTv3T9SwUUvcnSU8zTmz5dpnGtr5d8HH4S2Nrj3XrmMwVVXQXKy7TQy\nO+v1XOzry9dDh1KelMRHkZFEubjwZH4+EatXc8aGDXxdUWH143adBzgT8XQEiYWJRH0chcHXQPY9\n2SQHJrPtqm3UpdTZ3hhaZ2eYPRuys+Htt2U32WWXyTR/Dz0ExcWWLqGiKDbAHjjby6trLt7C6Gji\n3Nx4sbCQqNRUTl23jo9KS2my8oQfvc11uCsDnh/A2OKxDPlqCK4jXMl/Kp81g9ewdtxait8upq3e\nRtdkskZCQEyMHDLi5XXg4yUlsvdi+HB5u6wMGhqgf/+92yxZAhERMGcOPPywDD4++kg+tm2bPIkb\nPFgOrdplwxkoe1CPBhdCiHeFEOVCiM3d7jMJIX4XQmSZ/xoP8dxcIcQmIcR6IUSf7JNydIQJE+R3\nIDUVNm+GmTNlL19SkpynsWCBbX123e3suNLfn5+HDycvIYFHwsLYvns3F23ZQnBKCg/YQG+G3lGP\n3xV+xKyIIW5THAE3BFD5fSXrxq4jfXQ6Je+X0N5sYxWovb3sBs7IkN3Ho0fL9LWhoTKn8po1li6h\ncpJT9YXtcNbrudTXl2+HDaMwMZGnw8MpaWlhxvbtBJiHx262pYqrF+jsdfhc6MPwH4eTWJBI+FPh\ntFW1kXmKJHi8AAAgAElEQVRDJsn+yWy9cqscjquGTfWsujoZYDg7y+tZWXL4SGjo3m3CwuQCts7O\n8vbSpXL4FMi1N5yd4ccf5dyN7nMay8vlRenxnov3gbP2u+9+4A9N0wYCf5hvH8pETdNGapoW10Pl\nsypDhsi5RcXFcm6upsnFKgMD4bbbZOZRWxLk6Mj8sDB2JiSweNgw4t3dedrGejNch7oy8OWBJBYl\nMvC1gXQ0dZBxTQargleRMzeH5oKjyE5hDTozTP3wg/xRvfVWOfEnPl6OzVu4UC1YpFjK+6j6wub4\n2tszJySEzDFj+HPECM4ymXizuJhhaWkkrV3LR6WlVp++vLc5BDgQcm8Io7eOJmZ1jFyk78dqNk7e\nyKr+q8iZp4ZN9RgPj709Gr/9Bn/9BeedJ293fk6nTZNDimfOlI1wGzbI4OO332QGq1mz5LoEfn5y\nbgbAqlVyNMAppzBy9mzbO2E7wXo0uNA07S9g/0Ss04EPzNc/AM7ryTLYIldX+Zlevx5WrpSf8wUL\nZPAxfrxMhNDQYOlSHjm9EJzt5cV3w4YdtDdjXk4OeUeTQs4C7FztCLwpkNGbRzPijxF4jPMg/6l8\nVoWtYvOFm6lZVmN7Q6YiIuD55+UY1JdekovwXXaZ7B5+4gm1WJHSq1R9YduEEEw0GvksOpqixESe\nCQ+norWVGdu3E5iSwt07dpC5W50wdyeEwH2MO4NeG0RiSSLRn0fjMsyF/CfNw6aS1lL8VjFtdWrY\n1AmTlCSHBfv7wwcfwC23wMiR8jG9XjaueXnBW2/BhRfK4OHii2W2ni1bICpK9nTs3g0jRuxdvPaB\nB+Qk24wM6ocOPekngYuePiESQoQBizVNG2q+Xatpmqf5ugBqOm/v97ydQB3QDrypadqCQ+x/JjAT\nwM/PL3bhwoU9cRj72LVrF66urj3+Ot3V1hr4+Wd/Fi8OoLjYCUfHdk45pYIzzyxj5Mga9Ppj268l\njgXkP3UNsBhYZb4vHnkmMZqjj3otchylwPfAj0A90B956jMZcDr23Vrqf0JHB16rVxP49deY0tNp\nt7enbPJkii68kMbu41EPw27XLoypqZjS0ig+91xKAgMtcywnmMX+Jz1g4sSJ6dbauq/qC+t1LMfR\nAawDfgBWIP85o4BzgSTAcILLeKSs/n9SCSwBfgHykBNexgFnArFAt/re6o/lCFniOAx1dbR6eOC9\nfDmO5eUUT5+OfvdudM3N7PH3ByD6v/+lPiqK4mnTCH/7bRoGD6Zs8mQcyssJ+O47GiMiaPb3Z9Cz\nz5L2zjsAOKxcyYBff2XLf//bq8fTE465vtA0rUcvQBiwudvt2v0erznE8wLNf32BDcCp//RasbGx\nWm9YunRpr7zOwXR0aNqKFZo2c6ameXhoGmhaUJCmPfigplVUHP3+LHksnfKamrR52dma34oVGkuX\nav1TUrSn8/K02tbWI96HJY+jbXebVvxusZY6KlVbylLtb8+/tay7srRdW3Yd0/6s4X+ibdqkaTfc\noGmOjvJDdvrpmvbxx5pWXX34582YoWnXXqtpjz6qaRMmaGmvv9475e1hVvE/OUGANK2Hf/eP9aLq\nC+t1vMdR0tysPZabq4UmJ2ssXar5rVihzcvO1kr37DkxBTwKtvI/6ejo0OrW1GkZt2Rofxv/1pay\nVFvZb6WWdXeWVremTuvo6LCZY/knFj+Opib5d/16TYuM1LT4eE27+GJNmzdv78lVbKympafL63/+\nKU/E8vM1bfZsTbv77q5dZd1yi6add5680d7eiwdx4h1rfWGJbFFlQoh+AOa/B539omlakflvObAI\nGNNrJbRiQshevTfflHOSFi6EYcPksMCwMNkz19lLZytCHB35X3g4+YmJLIyOJsTBgXtzcghOSeGe\nHTsosPIhU3onPf2u6UdseiyjVozCeKaRopeKSB2SSnpCOsULbLBbe+hQORavsFCul7FtG1x5Jfj6\nypzJB7NkiUzr98QTMgWajw9OhYXyMRmi7L2uKEdG1Rd9hL+DA3NDQ8lOSODHYcMY4+7O4/n5hK1a\nxc2ZmexQQ6YOIITAfbQ7g14ZxNiSsQz5eghuY9woeqmItWPWsjp8NbyJWg38RHB0lH9HjJD13Wuv\nwbXXynWjvL3lcKmAgL3zMl56SQ6RCg6Wk7ovvrhrV94rVsA558gbQvTygVgHSwQX3wNXm69fDXy3\n/wZCCBchhFvndeAMYPP+253snJzg0kvlejBbtsC558JTT9lukGGv03Gpry/LRo0iPTaWc7y8eKGw\nkPDVq5mxbRsbrTz7iBACjyQPhiwcQmJRIhHPRtC+q53MGzNJ7ifT2dYsrbGtxfm8vOSHKT9fTlib\nM0dmmtpfc7N8fMwYGYA0NMDIkeg6J4cLIS+5uTBvHsyYAd9806uHotgkVV/0MXohmOrlxffDhrF9\nzBiu8PXlnZISBq1Zw8VbtrCmvt7SRbRKOgcdPhf4MOzbYYwtG8vg9wbjHOUMX0J6XDqrB6wm54Ec\nGtYdXaDRuL2RoleL2FOkVqjeR0yMTH7S+V4aDHDTTfJEa+xYuWDt7bfLui4sbG9wUluLU1GRXCkc\n9gYXK1dCXJyc4/HhhzKhSh8OCHs6Fe1nQAowWAhRKIS4DngSmCyEyAJON99GCBEghOhcTtEPWCGE\n2IAcmv+jpmm/9GRZbV1UFHz66b5BRv/+sgG5psbSpTt6MW5ufBodTXZCArcGBvJNRQUj0tI4ff16\nfqqqsvqVYe197Qm+K5jRm0YTsyYG/6v9qfyhkg2TNrA6YjW5/82lOc+6e2T2odPJjFKPPy7zJ++v\nqkoGDklJ8nZZ2YFLzxcUwH33yTR+kyfD66/LxY4UBVVfnIwGOTvzdmQkuQkJ3B8SwpKaGuLXrmX8\nunUsrqy0+t95SzEYDfT7dz+G/zQcvoHB7wzGaaAT+c/kkx6TzuqB5kBj7eEDjbJPy8icmUn96no2\nnrWRmj9s8GShp3XveZgyRQ4Z+fhj2YMvBLi5waRJ8MYbcj2BJ5+kKjFRTvrung2zo0NmqvroI7j6\nahg0SGabOu88ua+VK2UjXR9h15M71zTtskM8dNpBti0Gppqv5wAjerBofVZnkPHgg3Il8Mceg1de\ngbvukumZ3d0tXcKjE+royPMDBjA/NJQFJSW8XFjI2Zs2MdjJiTuCgpjh74/zsc5m7wWd3druo92J\neC6CykWVlLxbQu7DueT+JxfjaUb8r/PH+zxv9I6HP449pXuoT6nHeJoRO/ce/eoevbY2WLdOrpAK\ncj2Nmhrqx43bu83y5eDpKfMr+/jID+Pbb0NsrHx+aqpM9XfFFTLNn3JSUfXFyaufgwOPh4fzQEgI\n75SU8HxhIdM2bybK2Zl7goO5ws8PB51a8/eg3KHfuf3od20/WqtaqVhUQcWXFeQ/k0/+k/m4J7gT\nkxJzwNN2Z+6m8rtKgu4IwucCH/Iez6PmjxqMpxnROjSE7uQcznNEOte86HTaabB6tRw6fN115CQm\nEgD7BiannAJ//CGHVW3bJtfIWLlSrpj8nblD1t5e1odJSbJ3ZOxYGYDYIPVt7aOio+GLL2R65okT\n5SKT/fvb7oKSRoOB+0JC2JmQwCdRUbjq9dyUldWVytYWRoDpnfT4Xe7HyCUjid8ZT9jDYezO2s22\ny7aREpBC1uwsOtoOvu5H/ep6tpy/hbIPy0iLSaPqFytLE1tVJcelOjjI8Xg//ACDBrE7JGTvNpGR\n8sPXmRHk00/3Llz0xBMyEi4tlT/Q3RcmUhTlpOBmZ8cdwcHsiI/n46go7IXguowM+q9axZN5edSo\nNXgOy+BlIOD6AEb8OoKxpWMZ9NYgfC/3PWA7rUOj5s8aDD4GfC7wAUDnqMPgZUDT9gYWDesbyJmX\nQ/Wv+2eIVvYRGSmHOq1fD7fcQpuHh7z/YPMt9Ho5p/GGG+S6ApmZsqd/0SI5zEoIOZ/jggtkutyB\nA+Gaa+Cdd+S2NtKbp4KLPm74cPmZTUuTa6Q98IAMup97Dqx8oeyDMuh0XO7nR2psLH+NHMl4T0+e\nyM/nMmDGtm2ss5EFQJzCnAh7OIyEnASG/zYc4xlGGjc1orM78Cu5p3gPxQuK8bnIh6GLhhI6L5Sq\n760suBg2TE5si4iQXb6ennLlx/230evlj+b//rd3Kfq1a+UHdN48OVQqPl625nTavFmuhqooyknB\noNNxhZ8f6+Li+G34cIa6uPDAzp2ErFrF7Kwssm2x8upl9t72BFwfQNBtQQc81lbfxu7tu3GPl0MZ\nWipa0No0Opo7EN1OiN1GuuES7UL+M/msP209OfNyaG9st615g73tWHrYfH3l8Kinn5a9GfX18u8z\nz8gFzhYvhuuvh8GDZcBx4YWyriwqOvHlP0FUcHGSiI2Vn8+UFLlezN13y5En330XQEuLpUt39IQQ\nnOLpyTdDh5IVH8+5wDcVFcSkpzNx/Xq+r6yk3QYifKETmCabGLJwCCP+OHBkh6bJFiahE/S7sR8A\nHU0dGLwNXY8DNOc3U/ZpGS3lFvpnGgxyiNOXX8oxeE8+CStW4L5pk3y8uVn2anz4Ibz4ohy3eskl\nsss3JUV2qyUmyi7j4OC9k+Nef13+wD79tGztWbvWMsenKEqvE0Iw2WTitxEjWB8Xx/ne3rxRXMzA\n1as5f/Nm/qqtVVmSjoHQC+pT6vGcIJeMacpsojm3Gbc4N4B93lO/K2Rvu88FPuQ/kU9zfjNCJ9i1\n2QaHQNgKBwc5JOqee+Dbb6G8XA6lWrBATjJfuxZuvhmCgvbOhdy61ap6NVRwcZJJSJDD2pctk+dz\nL7wwiMGD5XmhLQYZABFOTtwGFJpXhc1uamL65s0MXL2a5woKqLWRrvSDjXFtb2yncXMjrqNcsXO1\no62+TbYamX9DOluZ7Dzt2L19N+snrSfjxgxqV9T2ZtH3iomR408BQkKwa2yU13fsgM8/l9dDQ2Wm\njEmTZDaqrCyZZQrkj2hjowwuiopkcHHnnXK+xiWXyB9QRVFOOiNcXfkwKorchATmhoTwd20t49ev\nJy49nY9LS2npOPiQUuVAewr3IAwCx2DZiFP9azV6dz0ep8jhPJ31Sucw3bKFZTRuayTimQhcolzY\ntXkXaSPSWJu0lvz/y6el0kZPHmyFEHLo1Q03yFXFc3Jk9p7HH5ePz5snezgiI+X1dessHmio4OIk\nNX48/P03PPnkRry95Wd20CC5fsYeG81I52kwcE9ICDnx8XwRHU2ggwN3Z2cTlJLCLZmZbO880bUh\nOnsddSvq9rYw7WiiKasJl6EuwN4WJjt3O/r/tz9x6+Kw97Vn/anrqfnTwpk/Bg6kOiFh7+2XXpK9\nD1dfDaefLv86O8sUtomJcpvsbJlV6vTTZfaNmBjZ1QYyGl6ypPePQ1EUqxHg4NC1LtKbgwbR1NHB\nVdu3E7ZqFY/n5VFlI41JluQ0wAmnAU6kJ6Sz7aptNO1oIui2IPTO+n2GPOnsdLRWtZJzXw4eYz3o\nN1P2nhe/XkzIfSFEvh9J46ZGds7daalDOTkJISfWPvCAnEheWCiTqQQHy1ShMTFyaMq998KaNRYJ\nNFRwcRITAuLjq1mzRq6V4e8Ps2bJ+UOvvWa7WdHsdDou9vXlb/N6GRf7+vJ2SQlRqamctWGDTaSy\n7dSc14zWquESLYOJmiU1CL3AeLpxn+06WmQL0+6tuxEGgf+1/hgnGWlvaid1RCrZ92ZbNvXt0KFy\nDOmff8o0ZvfeKz+AxcXyh69/f7ndDz/IoVLh4fDJJ3DrrXv3sWiRHHMK+6b4UxTlpOOs1zMzIIDN\no0fz87BhDHNxYd7OnQSnpDArI4NtNtiY1Ft0Bh1R70cRfE8wxtONDH57MG11bbRUtCB0oqvHomFd\nA7mP5mI83Yjf5X7YuckshRVfVWA604TzQGeiPohi4GsDAVkPVf9WTcELBbRWqSCv1wQGymFSS5bI\npChvvy1bi59/Xg6bCguTIwBWruy1ulMFFwpCyPTNKSnw668y+L3lFhn4vvyy7QYZINfLeC8ykoLE\nRB4NC2NjYyNnb9pE5Jo1vFxYSH2bda+c7RjmiHOUMxvO2EDmLZnUrayj3w39ZFaPDq2r+1pnL7/K\nGddnABA6T2ZhKnq1CDtPO/RuejaevZGMWRmWOZBOvr6y67ZT51yLESNkT8aOHXD//TKVrdEoFx0C\nGYAsWwbXXSdvq7SUiqIAOiE4y8uLX0eMYPPo0Vzh58f7paVEp6ZyxoYNar2Mw/C9yBf/q/3RO+vZ\nU7SHqh+q0Dq0rsQi2fdkYzAaCHs4rOs5e4r3YJxkZMfdO8i6PYvWmtau7XPm5lD+RTkNqQ2sO2Ud\nDettI8FKn+LtLevJn3+Ww4w/+EDWr6+9BuPGyXkat94Kf/3Vo4GGqqGVLkLAGWfILKBLlsjG49mz\n5d8XXrDN7FKdfO3teTAsjNyEBD6LisLbYGD2jh0EpaRwa2am1bZydbYw+Vzog/NgZyLfjUTvpqej\nrQOhE2jt5gndec3kPpqL3l1P2PwwnPo7AVD2QRkDnhtA2ENhjNk8hsBbA7v23VrbSntTu0WOq4tO\nJz9cTz4pF9Z77TW5KFFzM4watTeyfeMNOTzK13ffH8R582TrjA2l6FMUpWcMcXHhrcGDKUhM5LH+\n/dna2Mi0bvPvqtWQqUMyTTbR79p+CJ2grb6NvCfyaK1sJezhMBxDHLu2cwhwIPqzaOLS4mgpbaF+\ntVxRveKbCupX1hPxVATRn0Tjc4kPTZk2fNLQFxiNMGMGfP89VFTI0QCJiTKt7fjxsiX5jjvkYrYn\nuP5UwYVyACHknNy//oKlS+VIlDvvlFlGX3gBdu+2dAmPnb1Ox7/8/EiOiWFNTAzneXvzVkkJ0amp\nnL5+PYsqKmizwiE3ATcGEDQ7CIOXgYbVDZS+W4rWriH0suciZ24ObdVtRDwT0fWcppwm2urayHss\nr2tdDNehco2Jyh8qyZmTQ9qINDJvzqS1ttWyWVemTJHrW3QuGDRihFxa/oYb5IqQq1fLQAL25g5v\nb4dffpHZqQYPluP5Zs+W99lyJKwoynHxsbdnbmgoOxMS+Dw6mn729tydnU1gSgrXbN/OdksX0MrZ\nudsRcGMA0Z9FA3Q1YrU37tsYpXfWsydPTtIs+L8C/Gb4YfCSmQx19jrq19T3YqmVw3J3h8svh6+/\nloHGp5/KRCqvvy5HCAwdKudrFBaekJdTwYVyWBMmyABj2bK9QUZoqExSUFdn6dIdn9Hu7nwYFUVh\nYiKP9+9PZlMTF2zZQvjq1TyRl0eFlabP8rnQB/9r/BF62cJU8EIBdX/XEfZoGG4xbl3bOYU7MWb7\nGPyu8qP49WIat8jemV0bd5H/RD7e53kTszqGPcV7aMpq2ie/uVV46y0ZZNTXy56NzixUneXU62WL\nS06OnMzWmfZsyhSZheqcc2RPyE412VBRTkYGnY5LfH1ZERPD+rg4rvbz48vycm4C4tLSeKekhN3t\nFu69tVIGk6Frrp/QCzRNI+9/eWTemklLZQt1yXVobRoGXwMtFS007WgiYFZA1/PLPinDOFHODVTp\ngq2Mqytcdpmcx1haKjP5GI1ySHJIiEyo8uGHx7XisgoulCMyfrwMMlasgNGjZSNyaKhsVK48guWx\nrbkh2cfengdCQ8mJj2fRkCEMcnJi7s6dBKWkMGPbNtbUW1/ri84gv7p27nZ4nupJ5HuR2LnadbUw\ndWb80Dvr8Tnfh449HexaL38ocv+Ti+cET7zO9sJgNOCe4E7tMgulrj0ck0nm+X7pJZlt4FAVVP/+\ncjLbjz/KlcJ//lkuOLRtm5w8FB4uM2vcc4+cUG6lQaOiKD1nhKsrbwweTPHYscwGmjs6uD4jg4Dk\nZGZnZbH5OE6kTgZCCAJmBWDnZseGiRvIfzIfj3Ee+JzvQ83vNRjPMHY1UNWvqaetrg2vs726nqtY\nKaMRZs6UJ3c7dsDDD8sGuauvloHGMVLBhXJUkpJkZqn0dBncPv64DDLuuksuT3AwL74IN90kF2he\ntqxXi3tU7HQ6zvPxYcnIkWwdPZqZAQF8W1lJ/Nq1jElP5/2SEpqssJXLLcYN42myhaizhan4zeKu\nVLTN+c04Rzqjc9LRvrud2qW1BM8J7np+zZKaAxbls0pHUkE5OclFhl56Sf5QZmTIORnBwTI7wWmn\nyV6N88+H996TE94URTlpuNvZcT6wafRolo8cyVQvL94sLmZYWhpj167lfdWbcUiOoY6EPxHO6E2j\nGfT6IAJulD0VTgOc0LvqAWhvbqfwxUL6XSfT1nY2eAFsv247xQuKaa1Wc1+sUkSEDC527JDBxhNP\nHPOuVHChHJOYGPjqK7mOy4UXyoX5HB333UbTZGbRBQtg7lz5OX3hBTncz9pFubjw8sCBFCUm8urA\ngexqb+eajAwCU1K4a8cOMqx44okQAqEX7Jy3k3UT1pF5cyZCyPS1FV9X4DHeA4NRBhPNec3sWrcL\n/6v9u57bZwgh0/HdcYdMg1ZVJSe2XXklpKXBtdfKHpGkJDnWdNs2NSlcUU4SQghO9fTk0+hoihIT\neTYigurWVq4x92bclpXFJtWbcUgOgQ5d150inGjc3MiGMzeQcX0G9r72BN8lG7A65wW2VrdSt7KO\nzBszSfZPZtO5myhbWEb7bhXIWR0hZL14443HvAu7E1gc5SQUFSWH5u3ZI4fAd5edLefWzpkjz/Hc\n3OQSBw4OB92VVXKzs+PmwEBuCgjgr7o6Xi8q4pWiIp4vLGSSpyfX+Psz3dsbNzvr+ioFzAwgYGYA\nNUtr0No0jKcZZf7yPR24jdo7LyP/6Xx8LvSRmac6NIRO0FrVytYrtuI9zRvPiZ44Rzn3jaDD1RWm\nTZOX116D9etl9Pv993Ks6f33y/zLU6dCbKyc7xEVBfb2li65oig9yNvenruCg7kzKIi/6+pYUFzM\nW8XFvFJURIK7Ozf068fFPj5W9ztvLQxeBmJWxFD+VTl0gO8lvgduYzIwZtsYdq3fRfmn5ZQvLKfq\nhyp0Ljq8z/OGcGiJbsHeV/3e9gXqm3KUGlsaWVezDm2n9bdwRpgiCPE49jFzR2P/gEHTZLYpgEsv\nlX+3bIFTT4XaWpm4AGRQ8scfcqRKfHyvFPWYCCEY7+nJeE9PylpaeKekhAXFxVy1fTtOOh3neHlx\nma8vU0wmHPePsiyoc0JdJ/d4dzJnZuKe4E5TdhNNmU0Mftu8MJ05fmjKaaJpRxNZt2YBYPAz4Dne\nU14m9JFgQwiZ6nbUKJg/X2bIWLxYBhoLFuxNgWswQFQUkX5+srdj+HAZdHRmtVIUpc/o7M041dOT\nF1tb+ai0lAUlJVyXkcHsrCwu8vHhKn9/Jnh6orf138Ae4HvRgUFFd0II3Ea54TbKjfCnwqn7u46y\nT8qo+KoCaiD50WRcRrhgPN2I8XQjnqd4onexnvpUOXIquDhKBfUF3LXxLtho6ZIcmTDPME4NPZVT\nQ05lfNh4IowRvXJiKIScXzFpkhwGX10tR50IAQF7E0qQnw+5uXKCeFiYHJ0yaFCPF++4+JnTHN4f\nEkJyXR0Ly8v5oqKCLysqcNfrOc/bm4t8fJhsNFpVoKFpGq7DXOk3sx95j+XhnuhOxLMROIY6oml7\nF+RzH+1OfFY8zTubqV1aS82fNdQur6XiCzmezeBjwONUj66Aw2WoC0Jn4xVtUJBcnn7WLGhrg6ws\n2LCh62JMTYXff9+7vZ/f3kCj829kpOrlUJQ+wstg4I7gYG4PCmJVfT3vlJTwZUUFH5SVEWBvz+V+\nflzp58cIV1dLF9UmCZ3oqkMGvT6I5W8tp39Nf2qW1FD0chGFzxYiDAL3se4YTzdimmzCLc6ta5iV\nYt1UcHGUQjxCeH7E84waOcrSRTmsdq2dzeWbWZ63nJ+yfuLDDR8C0M+1nww2Qk9lfOh4OrSeWdOh\npkau+P3WW/L26tVybu2ZZ4KdnVwHTaeTSxMMHCiT/cybB999J4dR2QKdEIzz9GScpycvDBjAn7W1\nLCwv55uKCj4sK8NVr+dsk4kLfHyYajLhauEu9c7god81/eh3TT86Wju6sk7tH3AKIXAKd8Ip3Il+\n1/VD0zSac5qpXV7bdan8WqYJszPZ4XGKR1fPhutwV9uuAOzs5HCoqCj4178ASFm2jAlDh8KmTXuD\njo0b5STxPTLPOwaDzFoQGysnJcXGytv7T0ZSFMVmCCFI9PAg0cODlwcOZHFVFR+XlfFCYSH/V1DA\nMBcXrvTz43JfX4LUd/2YCL2ASAidEEroA6G0726nbmUdNUtqqFlSQ+78XHIfysXOaCd7Nc4wYjrD\ntM/ifop1UcHFUXI2ODPScyTjw8Zbuij/aFL/ScyOn42maWyv3M7yvOX8lfcXy/OW8/mWzwFwt3Nn\nStUU5o+fT7RP9Al7bVdXOQTq//5PrtPy4oswfbpcggBkYNGpM9CYMAEeeURmDBUCGhpkj0do6Akr\nVo+x0+k4w2TiDJOJNwYNYmltLd9UVPBtZSWfV1TgIARnmkxc6OPDNC8vjAaDpYvcFVgcCSEEThFO\nOEU40e9amQWkOW/fYKPqO7lQn52nHR7jPeSQLFfQTtVsv2cDwNsbJk6Ul05tbXJ18A0b5ByOtWtl\npoPOqFqvhyFD9gYcMTGyl8PFxTLHoCjKMXPS67nY15eLfX2pbGnhi4oKPi4r476cHO7PyWGCpydX\n+vlxoY8PHmp+xjHTO+sxTTZhmmwCoKWihZo/aqj5rYbqX6up+FL2ojtHOncFGp4T1BAqa6I+/ScB\nIQRRPlFE+UQxK24Wmqaxs3Yny3OX88XqL/hlxy98tfUrboy9kUcmPoK3s/dxv6bBIOfHzpkDmzfL\n5QamTTv4tjodrFwpM0mdc44MLL76Cj7+WDYUx8buXePFFtjrdJxpMnGmycRrgwaxsq6Obyoq+Kay\nku+rqrATgomenlzo48N0Ly/8bWmGezeOoY74z/DHf4bMNLWnaA+1y2qpWVpD7dK9wcbK+1bKXo2J\nniJou1wAACAASURBVBgnGnGO7gNzNjrZ2ck1NKKj5aJEICcc5eXJQCM9Xf5dvFimvgX5gY+KkgvG\ndF6GD7etTAfKEdmzRzaeKH2Pt709NwcGcnNgINlNTXxSVsbHZWVcl5HBzZmZnG2eh3e2lxdOVjQ8\n1hbZ+9jj9y8//P7lh6Zp7N66m+rfqqn5rYaSt0ooeqkIYRB4jPPAdKYJ45lGXEe49p16xgap4OIk\nJIQg3BhOuDGc/nX9GTpmKP9Z9h/eSHuDTzZ9wtxT5nLrmFtxNjgf1+sMHAjffivnxjo6wrvvykWU\nk5Lk401NciX65GQ5V/aGG+QlPV3OqZ01Cy64AK64Qs7fOP/84z/23qbvNkHw+QEDSGto4OuKCr6u\nrGRWZiazgHg3N6Z5e3OWycQoGx6/6xDogN8VfvhdISc7N+c3s+q1VXiVelG7tJbKb+QwKoOvAc8J\nnrgnuuMW54brSFfsXPvQT5EQcgJRWJj8AIMMOIqK5Ic7PV1+4Bcvhvffl48bDDLAiIuTgcrgwXIO\nR3Dwvt18ik158UV47LEkJkyQvbaXXmo7jSTKkYtwcmJ+WBgPhYaS2tDAx2VlfFFezjeVlbia5+H9\ny9eXyUYj9ur7fFyEELgMccFliAvBdwbT3txO3Yq6rl6NnPtz4H6w97fHeIYRz1NlXeMc6dw3etBt\nRB+q0ZVj5e3szStTX+Hm0Tcz5/c53LfkPp5LeY4Hxj3AzNiZOBmcjmv/ncNQzzhDrua9a5fMBPr+\n+3K9s/PO27vqfHGxzA46evTe87LW1n2XH0hLAx8f2xgu1Z0QgtHu7ox2d+eJ8HC2NDbyXVUV31VW\n8uDOnTy4cyc+BgMjgcLSUs4wmfC14QnCjiGOcBZETYiSczZy5QTx2qW11C7bO0EcIbu33WLdcI11\nlX9H9cGAIyhIXqZPl/dpmsxokJoqP9SpqbBwIdTV7X2e0/+zd95hUV3pH/+cmaHMMEyhFxUQsCEa\nQbErxm6sifvbFBNTTNk03eyuSTab3fRoyppsuptmmimb2GLXqLEr2DuIiooU6U3q+f1xhkFTLSDt\nfp7nPMPcuTP3XAbuud9z3vf7GpVK79ChVnC0b69aExaiLYWYGOjT5yz79weycKEquTJunCp+O2yY\nWvjSaD4IIYizWIizWJgVEcFaRx7et47wKbvBwAQfH/7o58e1NhsGTWhcMXp3PV5DvPAa4kX4S+GU\nnSlTQmNZDtmLs8n4JANQ4bqWXhYsvR2tpwWDRfsHrC+036yGk06+nVh882I2pG7gX2v/xbTl03hp\n00v8vd/fmRIzBTfDlYVt1NxbZWWpUKn0dGU/O2xY7T4pKco9ato09fzwYWjbVtXIyM2Fl15SrlMH\nD6pyBDNnqknfpoYQgs5mM53NZp4ICSGzvJwVOTksy8lhcWYmKw8dAiDWbGaEI8Sqp8XSZGe9hBAY\nw4wYw2pzNsrOlFGYWEhRYhGFiYXk/pBLxmcZjjeAqb1JiY3unqp182xeMbVCKIUcEgITJ6ptUkJG\nhvrDP3wYDh1Sj4mJKlbw/Bib4OALBUenTiq/IyDg4qqZa9Q7Q4aAwXCYgQMD2blTTah88QV8/bX6\nmm69VQmNqKiG7qlGXaMXgsF2O4Ptdt6KjGRlbi5fZWbyTVYWH6an4+PiwkRfX27w8WGAzdZkr+2N\nDbdANwImBxAwOQApJaVHSsnfnE/BpgIKNhdw/KnjIAEdeER7YO1nxdrXirWfFffWWoJ4XaGJC42f\n0a9NP1bftpq1x9fy5JoneXDpg8zcOJMn+j/BHd3uwFV/ZbPpvr6q8N727Srh++mnVXhUx44qPF2n\ng2uuUfdZBw6oe62+fVWit5QwZ44KqXrwQRXT3IQn9534uboyKSCASQEB/JCZiTU2lmUOsTEjNZXn\nU1Px0Onob7Nxrc3GYLudrmZzk/Zadwt0w220Gz6ja3N8ytLLnGKjMKGQvDV5ZH6eqV7UgUcnj1qx\n0d0Tj64e6N2bmeAICFBt4E9MI8rKIDn5QtFx+DB8/vmFqx1eXupuNSoKOneu/dnX9+qei4YTIWrz\n+V95BRYvVkJj1ix4+WUVDXf99eoxNlZ9hRrNB1edjuu8vbnO25tzVVUsy8nhq6wsPklP5920NCx6\nPSO8vBjj7c1Ib2+8m+KMWSNECIGpvQlTexOBt6tJrcr8Sgq2FVCwqYD8jflkzMkg7a00ANxauymx\n4RAcHp09mrbzYQOiiQuNXyU+NJ4fb/+RVSmreHLNk9y3+D5e2PACj/V9jDu73XnFKxk9esBXX6lJ\n2Y4d1ba9e2vvqTZtUha2/fure6e5c1UUiaenKsLn4qJMevr1u8ITbWTogFhPT2I9PXkiJIS8igrW\n5OXxQ14eq3NzmZ6SAoDdYGCQzca1djuDbTbam5p+orRbgBtu17nhfZ23c1vNCkdhgmrZS7JJ/zgd\nAGEQeESfJzh6eOLR2eOSnLCaDG5utULhfGpWOw4cUJUq9+1Tj3PnXig6fH2V2NBoUFxdVf7YhAmQ\nmalWMubMgb//vXafsDAlMmJjleCIidEER3PBXa9nvK8v4319KamqYlVuLouys/k+O5uvs7LQAX2t\nVsZ4ezPG27tZXNcbEwar4QInqurKaor3FJO/MZ/8Dfnkrcsjc66a0NJb9Fh6W7ANUDbrnt090bk2\nw7GlHtDEhcZvIoRgaPhQhrQdwrLkZTz747Pcv+R+nlv/HNP7TOfu2LuvOPE7Nrb253HjYPp05fA5\ne7ZKgLz9dpXoPXmyEhag7qM2b1aDcnPH5uLCBF9fJjhmns+UlTmFxurcXL47qxKlg1xdudZuZ5DN\nRrzNRpi7e7MYlH66wiGlpOxUmVNsFG4vJOt/WZz57xkAdO46zDFmLHEWPOM8scRZcG/bPH4Xv8j5\nqx3XXlu7XUqVxHS+4Ni/v+H6qfEz/PxUCOi0acp2u8ZgrCbn/3//q903LEwJje7d1dccE6Pl+jd1\nTHo9Y318GOvjQ7WUJBYWsig7m0XZ2UxPSWF6SgoRRiNjvL0Z7e1NP6tVC5+qY3QGHZ4xnnjGeNLq\noVYqP/DEOfI35FOwsYC89Xkce+KY2tekw9rXii1eExu/hyYuNC4KIQQjI0cyImIEPxz7gWd/fJZp\ny6fxwoYX+Evvv3Bf9/uwuFmu+DijR0NVFfzwAzz8sHKKys5W0SCfflq736OPqvpmrq4tz+ox0M2N\nW/z9ucVf2fKlnDvHDw6hsTwnh88yVN5CKzc34m02BlqtDLDZiDQam8UNthAC99buuLd2x3eCElw1\nRf4KthdQuL2Qwm2FpL2XRvVr6o/D4GVQYsPhTmW+xox7mHvzdg8RQuVlBAdfmNjUDP4GmiNeXipH\nY8iQ2m3nC46EBNW++Ua95u0NQ4fCDTcom2/NybhpozvP8OOZsDBOnjvH9w6h8fbp08w6dQpPvZ7B\ndjujvLwY5e1NsPal1zlCCIyhRoyhRgImKZv18rPl5P+YT95aZUZygdhwhFHVrJ67+jaDOO06QBMX\nGpeEEILBbQczuO1g1p9Yz7M/Psujqx7lhfUvcH+P+5nacyr+Zv8rOsa4cbWGOgAmk4oEqSmEvG6d\nCoeaP189r5nIOXsW7rgDJk2CsWOV0U5zRwhBuNFIuNHI3UFBSCk5WFLC2rw81uXlseI8sRHo6soA\nh9AYaLPR0WRC10xuNM8v8ud/o/r7q66spmR/CQXbCijcVkjB1gJyVuZAlXqP3qzHo4uHEhtdVfPo\n7NG8ksY1mjS/JDgyMmDVKlixApYvVwZjdruabJk8WRUtbSb/1i2a1u7u/Ck4mD8FB1NUWcnqvDyW\nZmezNCeH+Y7V6m5mM50BY0EBPTw9m831vLHh6uOK7/W++F6vJrN+KjaO/+u4ShIH3ELcIBRSR6Uq\nwRHricHa8m61W94Za9QZ/UP6s+LWFSSkJTBz40xmbJjBvzf/mzu73clfev+FcK/wOjmO0ajCpO68\nU7lLZWTAU08pG8ea6t6gVjd271blAzw8lJvUxInqsaW4dgoh6OThQScPD+4PDkZKyeGSEn7Mz2ed\nQ3B8laUsYL0NBvpYrfS1WulrsdDd0xP3ZlTsSWfQOUUDd6ttVaVVFO8vpmhXEcW7iynaXUTGZxmk\nva0S+hBgjDQ631ezyoH89eNoaFxN/P3Viu4tt6hV3lWrVHjoRx/BO+8o47DJk9UkS+vWDd1bjbrA\nbDAwzseHcT4+SCnZX1zM99nZLM7J4XPg0x078HVxYZSXF9d5ezPUbsemJYXXGz8VG5UFlRTtLHKu\nnGdtyCLl0RTn/sb2Riw9LM68QHM3M3pj8xlrfwlNXGhcMd2DuvPNH77hSPYRXtn0Ch/s/IB3E95l\nWPgw7o65mzHtx1yxw9Ts2Sr+eO9eeOGF2kHz/PDTXr2Uje26dSp04Lvv1KO7u6qnMXGiCruyWq+o\nK00KIQQdPDzo4OHBPY6VjWPnzrEuL4/1+flszM9nUbaqpO0qBLGenvSxWJTgsFqbdJ2NX0Jv1GPp\nbsHSvTaEr6YGR9HuWsFRmFhI1jdZtW+0w+643XjGqIHB3M2Msa2xeYdVaTR69HoYPly1/Hx1jaxJ\nDn/iCRgwQIVNTZigbMA1mj7n25g/FhLCgrVrKe7Yke+zs1mYnc2cjAx0QA9PT4bY7Qyx2+ltteKm\n5WrUGwaLAdtAG7aBNgDWrl1L3+i+FCYUKsGR8BOrdT2Yo82q3kYv1YyRzSNsuQZNXGjUGe282zF7\nzGyein+K9xLe48NdHzLxm4n4efgxuetk7up2F+192l/250+ceGE5gF/6P9TpYNAg1d54AzZuVDa3\n336rwqhcXVWc8sSJKnSqpTmwCCFoazTS1mjkjkBlzZdVXs6mggI2OcTGG6dP8+qpUwBEGI30tVjo\nY7XS22Khk4dHk7a//SXOr8HhO77WrrWyoJKiPUUU7SwieUkyFekVnHz5JLJSLWPoLXrM15hrBUeM\nGVMHEzqDNohrXH2sVrjrLtVSUpTd9zffqNy1hx9Wq77XX69aRERD91ajrrAC4/z9udnfn8rqarYU\nFLAiN5dVublOG3OjTkd/q9UpNrqazVoIVT3j4u2C13AvvIbX3mSUpZU5TUgKthaQ8XkGae+oVXOD\nl8EpNCy9LFjiLE06nKrp9lyj0RLkGcTTg57mnwP/yfKjy3l/x/vM2jKLlze9zICQAUzpNoWJnSZe\nUeXvi7ku6vVq5m7AAOUnv3Wrmtn79lvlM28wKBEyerQKnWqpA66vq6tzyR2grLqaxMJCNjrExpKc\nHOY48jY89XriPD3p7RAbvSwWvJrp8rvBYsDWz4atn43k6GS6x3enuqya4v3FFO4opGinEh5p76VR\nXaoSx3UmHeZu5gtqcZjambQVDo2rStu2KnT0qadUSZR589RK7qOPqhYdrVY0rr9euRNr95nNA4NO\nRz+bjX42G8+EhVFQWcm6vDxWOcRGjY25t8GgLMztdgZarZrd7VXCLcgNt7Fu+Ix1OB9WSUoOlVCw\npYCCLQXkb84nZ2mOCsMVYOpkwtLLgrWPqrthbNd0Vjc0caFRb+h1ekZFjmJU5CjSi9KZs2sO7+98\nn9vm38ZDSx9iUpdJ3BN7D138u9R7X3Q66N1btVdeqS14PH8+TJ2qWrt2SmSMH6+K9hla6H+Hm05H\nH6uVPlYrf0OFDR0tLWVzQQGbCwrYUlDAiydO1ORF085opLcjlKqfY6BqrrNiOrda28IaZJWk5EjJ\nBbU4zvz3DKdfPw2A3lOPOcYhOGJUgp8xUgup0rg6dOgAjz+u2okTtULj6aeV+IiIUELjzjvVNVCj\n+WAxGBjj48MYx8RRWlmZ08J8ZW4u3zjy73xdXOhntRLvKNDaSRMbVwWhF3hEeeAR5UHgXecV+duu\nxEbB5gLOzjtL+geqrpPB2+AUGpY+KoejseZutNDbJ42rTYA5gEf7Pcr0vtNZd2Id/93xX97f8T5v\nbX+L2MBYJnWZxE2db7pip6mLQYhav/gZM+DoUVi6FJYsUQmRr72mbB7HjFFCY+hQ5VjVUhFCEGEy\nEWEycWuAsuYrqqwkobDQKTgWn7e6YTMY6G2x0MdiwQjEVlbi2YyVmtALPDp64NHRw2ldWF1ZTcmh\nktpaHAmFnH7zNLLMEVJl1qsVjli1umGONWsrHBr1TkhIbV2NjAxYsEAJjVdegZkz1UruvfeqHI1m\nlm6lAQS5uXFrQAC3BgQgpeRIaSnrHfl36/PzmedwoQpwdWWwQ2gMtttp4+7ewD1vORisBryGeOE1\nRIVTyWpJyeES8jequhv5G/PJXqTyJIVBqJpOvWtXN9yCG4c9cfMd8TUaJUII4kPjiQ+N542Rb/Dp\n7k/5ZM8n/Hn5n/nrir8yLHwYk7pMYnyH8VdcnO9iCQ+HBx9UrahI2TvOn6/axx8rt6rhw1X41LBh\nmgMLKPeSeLudeLsdUKsbSaWlbMzPZ7Mjf2NZTg4SmL5hA9EeHmo1xGKht9VK22ZS4O/X0Bl0mDub\nMXc2E3i7mpGqrqim5KBjhSOxkKLEItLeTaP6nAqpOl9wmGPVo6mdCaFvvr8njYbD3x/uuUe19HT4\n8EP473+Vpa2vr7L1vucedX3UaH4IIWhvMtHeZGJKUBAAx0tLWe0o0LoyN5fPM1Wl6gijkWttNgY5\nbMwDtfoaVw2hq528CpqivqfyrHK1srGpgPxN+ZyZXbtS7h7m7qy9Ye1vxdShYVah6lVcCCE+BEYD\nmVLKzo5tXsBXQChwHPg/KWXuL7x3BPA6oAfel1LOqM++alx9vIxeTO01lam9pnIg6wCf7fmMz/d+\nzi3f3YLZ1cz1Ha/n1i63Mih0EHrd1Vn6M5tViMANN0BFhXKeqhEaNXU1OnZUImP4cJXP4eFxVbrW\nqBFC0M5kop3J5EwUz6uoYPbGjZSEhLCpoIDPMjJ4J00lr/m5uNDLYnHmbXT39MTcjFc3AHQuOsxd\nzJi7mAm8wyE4KmsFR1Gicqk6P4dDb9bj2cNTJfj1tODZ0xO3gOY5sGvjRcMREKAcph57TNXPeO89\nePVVeOkltXJ7771gtWoit7kTajRyl9HIXYGBSCnZV1yswqjy8pibmcnsM2cA6GAyEW+zOYu0Bmhi\n46ri6uuKzxgffMaocLfqimqKdhWRvzGf/A355CzPIeNTFUlg8DY4xYatvw1zN/NVqSpe36P5x8Cb\nwCfnbXsMWC2lnCGEeMzx/NHz3ySE0ANvAUOBU8B2IcRCKeWBeu6vRgPRybcTLwx+geeufY71J9bz\n6Z5P+ebAN3yy+xOCPIO4ufPN3NLlFrr6d70oFZ6ck8za42sZ027MZYdaubjUFrB64w3Yv7+2cNV7\n78Hrr6vQgf79a8VGly5acmQNNhcX4oD4sDAAqqTkQHGx05lqS0EBCx02uDog2sOD3lYrvRyCo10z\nqSj+W+gMOszRZszRZrhdbasJqSpKdPimby28wKXKPdQdz561gsPczYzevXHG3V4iH6ONFw2KTqds\nu0eMgLQ0+OADtZoxcSJ4efXivvtgyhRw/EtrNGOEEESbzUSbzUxr3ZrK6mp2FRWxJi+PtXl5fJ6R\nwbuOyaKO54sNmw1/LabuqqJz0WHpYcHSw0Lraa2RUlKaXEr+eiU28tfnk73AMdYadVh6WbDF27AN\nsmGJs6Bzq3uxUa/iQkr5oxAi9CebxwHxjp/nAGv5yWABxAHJUsoUACHEl473aYNFM0cndAwMHcjA\n0IG8MfINvj/yPZ/u+ZTXtr7GK5tfIdQWyth2YxnbfiwDQgbgov+5U9G8g/N4ZfMrhFhDmLVlFq8N\nf42h4UOvqF9CKFeVzp3hkUegtBTWr68VGzUuLP7+aqavxg5XG4Rr0Z83WN3rWIbPrqhg23mJ4l+c\nN2DZDQan0OhlsRDn6dkiCkOdH1IVMFnlcFSVVlG0o4iCrQXO5fCsr1QypnARmLs54m77OuJug5re\nTKI2XjQugoLgySfVisayZfDCC4XMmOHGiy+qCZcpU2DcONAmrVsGBp2O7hYL3S0W/tamDZXV1ewo\nKmJtXh5r8vL49LyV6XZGI/0dBh/9rFbCW8BEUWNCCIEp0oQp0kTgnWqVvCy9TAmNDfnk/5jP8aeO\nw78cYqOPBfsgO7ZBNjx7eKJzuXKxIaSs39KzjsHi+/OWufOklDbHzwLIrXl+3nsmAiOklFMcz28F\nekopH/yFz78HuAfA398/9ssvv6zHs1EUFRVhbiYln5vKueRX5LP+7Ho2Z28mITeB8upy3ur2Fp0s\nnYDa8zhdepoPj31Ib+/eDPEfwlcnvyK3PJf7wu9DSllvF7izZ11JSLCzfbsXO3bYyctTMzf+/ue4\n5po8+vfPIi4uBxeX3/9/ayrfycVwqedSDaSi7gpr2nFqC2SHAB2BAag7yqs1X98ov5OzwEFH2w8c\nBsocr/kDnYFhQA/gvD/7QYMGJUopu1/Nrl4s2njReCkqKqK01JulSwNYsiSQjAx3LJYKhg1LZ/z4\n0wQHn2voLl40zeU7gcZzLpXAEWA3sBfYBxQ6XvMCooGeQH/gl3rbWM6jLmgS51IA7AF2AruAmoLi\n7qgvqzcwFAaNuczxQkp5WQ1oDfztIvYLBfad9zzvJ6/n/sJ7JqLiZmue3wq8+XvHio2NlVeDNWvW\nXJXjXA2a4rkUlxfLRYcXyarqKue2NWvWyKrqKjk7Ybb80/d/ktXV1VJKKWdtniVnrJ9xwfsrqyrl\nyqMr5YYTG+qlf9XVUu7bJ+Wbb0p5ww1SenlJCerxvvuk3LBB7fNrNMXv5Neoi3PJr6iQq3Jy5HPH\nj8vRe/ZI+/r1kjVrZODGjXJ6crI8WFR05R39HZrCd1JVXiXzt+bL1Fmpct/EfXKD7wa5hjUyoWeC\nPLvkrPN/AkiQl3ndv9ymjRdNn/PPo7JSyuXLpZw4UUqDQUohpBw9WsoVK3772tZYaC7fiZSN91yq\nqqvlvqIi+c6pU/KW/ftlq02bJGvWSNe1a+X4vXvlVxkZsriy0rl/Yz2Py6EpnktZVpnM/F+mPPzA\nYbm141a5hjVyvW39ZY8XlxQWJYTwBf4A3AQEAfMuVcwAGUKIQCnlGSFEIJD5C/ucRg1GNbRybNPQ\nwORiYnS70T/bXlhWyMGzB4kLjkMIQXZJNpXVlZRXlV+wX35ZPvsy9/HI8ke4N/ZeZg6dicXNUmf9\nEwKiolR74AGVGL5iBXz2GcyZA+++C6GhcMstcPPN0KlTnR26WWIxGJyWiADl1dUsyc7mo/R0Xj15\nkpdOnqSXxcIdAQHc6OeHpZknhv8aOhcdljhV2ZVpUF1WTfrH6Zx44QR7R+3FM86T0H+GXrX+aONF\n80WvV3lmw4ap3Ix331Vt2DBlePHww3DrrZrZRUtGJwRRHh5EeXhwX3AwUkq2FxYyNzOTrzIzmX/2\nLGa9ngk+Ptzs50fzD3ht3Lj6uOJ7gy++N/gCULhDuRqqdd5L53cDq4QQnkKIyUKI5cA2IBwIk1KG\nSyn/ehnHXAhMdvw8GVjwC/tsByKFEGFCCFfgRsf7NDR+FYPOwKaTmxgYMhCApJwkjuUeIyYwBoBq\nqRx4vIxe3Nb1Np6Kf4o21jb1bnnr4gLXXQdz5ypv+TlzVLGqF19UAqRLF3j+eUhOrtduNBtcdTrG\n+/qyIDqaU71783LbthRUVnLvkSMEbtrEXYcOsSU/v2YWu8Wic9MRdG8QPZN60u6/7ajIrGDv6L31\nekxtvGh5BAXBM89Aaqq6thmN8Kc/QatW8Je/qDpCGhpCCOIsFmZFRHCyd29+6NqVG/38WJSdzci9\ne5kIPJSUxGbt2t0o8IzxJOjuoMt+/8VkbWQCdwLPAW2llH8Byn/7LQohxFxgM9BeCHFKCHEXMAMY\nKoRIAoY4niOECBJCLAGQUlYCDwLLURHFX0sp91/SmWm0OE4WnEQndITZVRb1iqMrMLuaGRAyAFDJ\n4jUXrT0Ze8guySYmMAaDTs1055/L553t7xD/cTzvJrxbLxc4T0+47TaVBH7qFPznP2CxwD/+AZGR\nqrDfV1+1JjW1zg/dLAlwc+Ovbdqwr0cPtsTEcLO/P19lZtJ75066JCTwn1OnyKmoaOhuNig6Vx1B\nU4KIOxJHh0861PfhtPGiheLurq5tCQmwcaNyz/vPf9R1bfRolRReXd3QvdRoDOiFYJDdzn/btye9\nTx/mRUXRFXj/zBn67NxJ+Nat/CMlhQPFxQ3dVY3L5GLExeOAG/A28LgQ4qJL6kgpb5JSBkopXaSU\nraSUH0gps6WUg6WUkVLKIVLKHMe+aVLKUee9d4mUsp1jxuv5Sz0xjZZHuD2c9j7t6fthX+5YcAf7\ns/bzQNwDeLp5OlctahK6E9ISsLpbuSbgGgBKKkqYvnI6W05v4en4p1l4eCELD9fv5GdgIDz0EGzY\nACdOqCq5QsC774YTEgJ9+6rB2WEtrvEbCCHoabHw3/btOdOnD++1a4e7TsfU5GSCNm1i0oEDrMvL\na9EzYjoXHQG3BtT3YbTxooUjBPTpA19+qa5rTz6pBMfIkdChg7qm5ec3dC81GgtujpXop4CMPn34\nuEMHIo1GXkxNJWr7dq7Zvp2XU1M5da7pGAZoXIS4kFK+JqXshbL2A5gPBAkhHhVCtKvX3mloXAIu\nehc+GvcRD8U9RL/W/fhg7AecqzxHbmkuOqGjqroKgP2Z+zlVcIpIr0j8zf5IKVmdspq8sjyeG/Qc\nA0MHEm4P53D2Yednbzu9jf8d+F+99b1NGxVCsH07fPbZFp5/XlULnzoVgoPh2mvh/fe1Qfli8DQY\nuCcoiO2xseyMjeWuwEAWZWcTv2sXHbZt45XUVLJb+GpGfaGNFxrnExQETz+tRMbnn4O3t7qmtWoF\n998PO3Y0dA81GhMWg4HJAQEs79qVtD59eD0iAnedjukpKbTZsoVrd+3iozNnKKisbOiuavwOF21m\nK6VMkVK+IKWMBroDFmBJvfVMQ+MyubHzjdwVcxdmVzNHc47y7cFvqZbVzlWL1PxUjAYj0f7RMKOd\nIwAAIABJREFUAJwpOkPimURiAmJobW1NUXkRYfYwZy7Gs+ue5a3tb/FOwjt0fbcrB7Lq1z4/OPgc\nf/877N4NBw7AP/8Jp0/D3XerSro33QRLl4J2ff19rvH05K127TjjmBHzcXHhbykptNq8mTsPHWJn\nYeHvf4jGJaONFxrn4+amzCs2b1YTKDfcAB9+CLGx0K2bKlKak3Nxn3X2rLr+lZbWb581GhZ/V1ce\nbtWKLbGxJMXF8a/QUE6WlXHn4cP4b9rETQcOsDQ7m0ot1q5RcjEJ3RFCiL7nb5NS7gOWAiPqq2Ma\nGnXByMiRTImZgk7o2JC6Ae+XvHls9WO46l2dIVHnKs+RnJPMwFCVCH664DSnCk4Ragtl88nNLDyy\nkGfin2H1basZHj6cozlXL0OxY0d46ik4dAi2bYO77lLOU6NGQevW8Ne/wt76zdFtFpj0eiYHBLAx\nJoY93btze0AAX2VmEpOYSL8dO/gqM5MKbZC6YrTxQuP36N4dPv5YhXu+9ZZynnr4YRUmeuONsHLl\nr+dm7N8PY8fC228rl70lmlxtEUSYTPwrNJQjcXFs7taNOwMCWJGTw6i9e2m1eTN/Tk5mZ2Fhiw57\nbWxczMrFa6hyGz8lH5hVt93R0Kg/BoQMYNWtqxjQZgAzN87kqbVPAVBcXszO9J30atULgH2Z+8g/\nl8/QtkN5fv3z3Bh1IyG2EAC8jd6sT11/1fsuBPToAW++qQbl776DXr3g9deV21S3bjBrlnKj0vht\nos1m3mnXjtO9e/Pv8HDSy8u58cABQrZs4Znjx0kvK/v9D9H4NbTxQuOisNtVaFRCAuzaBffdp4TF\nsGFq20/JzFTXv+HDYdEi5bA373LMjTWaLEIIelmtztXoeVFR9LVaeev0aWISE4nevp2XUlM5o13D\nG5yLERf+UsqfzY06toXWeY80NOqRboHdeGPUGxQ+XsjkrpOd2zv6dARg66mtfHvwW64Nu5ZzlefY\ncmoLU3tNde43d99cpwhpqFkSV1eYMEENrGfOqJACgwEeeUTlZ4waBV98AZrRxm9jc3Hhz61bc6Rn\nTxZHR9PVbOZfx4/TZssWJh04wNaCX7pH1vgdtPFC45Lp2lVNlJw+DQsWqImUn/LDD1BerkwwAAoL\nwctL/Vyz0lFYCFu3gpb72/ypsST/tnNn0vv04Z3ISCwGA486wl6v27OH/2VmUqatSDcIFyMubL/x\nmrGuOqKhcTVx0bs4LWs7+nYkzBZG4KuB/HPtP7k27Fpu6XILCw4vYFj4MKdV7a70XWQUZ3B9x+uB\nWuephlyK9fGBBx9Uccz796swqX37VIE+f39VyGrZMi0/47fQCcEob2+WdunC4bg4/hQUxMLsbHrt\n2EFcYiKfZ2Sg/fouGm280Lhs3N1V2JPj0uqkpAT27FGrtF5eyuxCSjWpUlUFOsedzKFD8OmncM01\nKtTq7Nmrfw4aVx8vFxfuCw5mU0wMh+PieLRNG3YXFfGHAwcI3LSJB48cIVELm7qqXIy4SBBC3P3T\njUKIKUBi3XdJQ+PqYtAZeHnYy6Q8nMLbo95mSswUAFpZWuFrUtUqq2U1s7bM4rYutwE4nacApq+c\nzpBPhvDRzo8oLGu4BOFOnWDGDDh+HNatUwmU33+vLCCDg2HaNJUkrvHrtDOZeD0yktO9e/NWZCSF\nVVVMOniQm4CXUlPJ11Ta76GNFxp1jouLsuyOj1fPjx6FI0eUta1eX7ty0amTCp3auFEJkOXLG6zL\nGg1EO5OJF9q25UTv3izr0oXhXl68f+YM3RMT6ZKQwMupqZzQlrbqnYsRF9OAO4QQa4UQrzraOuAu\nYOrvvFdDo8lgdDES7lVry9/RpyMbTm7gtnm3cf/i+3HRuTC973QA9Dq9c79WllacyD/BnQvvJODV\nAG6bdxurUlZdIECuJjodDBgAs2dDerrKz+jfH955R83oxcaqRMrc3AbpXpPA02Dg/uBg9vfoweLo\naFoDj6ak0GbzZh47elTLy/h1tPFCo845cUK5Q3Xtqp6vXq0ExbBh6nnNSoeHh1rJ8PZWkypz5tR+\nxqFDKpG8pOSqdl2jgdALwXAvL+Z26sQZR9iUyWFrG7plC7137ODt06c1W/J64mLqXGRIKfsATwPH\nHe1pKWVvKWV6/XZPQ6PhCPQM5IfbfuCagGvoHtSdN0e9ibfJ+2dLq1N7TeXIg0fYdOcmJkVPYuHh\nhQz9dChtXmvD9JXT2Ze5r4HOQFlATpgA//ufys/4z3/U4Pvgg7XuLMuXq20aP6cmZOrfQEJsLMO9\nvHj55ElCt2zh3sOHSdLuVC5AGy806oOQEIiKguuugz//WeVf3HGHCv2srr4wjEqvh4MH4ZNPasXH\na6+pa978+ao46eHDng1zIhoNgt0RNrU1Npbknj15MSyM4qoqHkhKInDTJibs28e8rCwtP6MOuZQ6\nF2uklG9IKd8AtgohJgkhFtdj3zQ0Ghyru5VHej/ClJgpuBvcgdpci/MRQtC7dW/eG/Me6X9N5+uJ\nXxMbGMusLbOIfieabu9149+b/016UcPdX3l5qWTIXbsgMVHVzVi5EkaMgNBQeOIJSEpqsO41emI9\nPfk6KorDcXHcHhDAnPR02m/bxh/27ydBS/6+AG280KhLXFzggw/UtSo4WP3s769e0513F5OUpFZl\nb74ZBg1SQiQxUYmRJ59U4mLgQNizx+p8T3n5VT4ZjQYl3GjksZAQ9vTowa7u3Xk4OJgtBQVcv38/\ngZs2cf+RI2zJz9fyM66QixYXQghXIcQEIcQ3wBlgMPBuvfVMQ6OJ4m5w5w9Rf2DhTQtJeySN/4z4\nDy46F/6y4i8E/zuYkZ+PZO7euZRUNNysd0yMcplKS4Ovv1aJkjNmQLt2KoTqww+V84rGz4kwmXi3\nfXuO9+rFo23asCInhx47djB0925+zMtr6O41CrTxQqOu0evV5Mhf/6qExdq18N576rX0dPXaY48p\nx6l331Xuefn5sGqVqhc0cKBaoTWbwWRSS7Vbt8Ljj6vaG6+/rl7X7ilbDl3NZl6JiOBkr14sjY5m\npJcXH6en03vnTtpv28azx49zTKvWeFlcTBG9YUKIj4BjwA3AJ0COlPIOKeWi+u6ghkZTxtfDl4d6\nPsS2u7dx8IGDPNb3MQ5kHeDm724m4JUAbp9/OyuOrqCyumEShd3c4A9/gMWL4eRJJTCyslSxvoAA\nmDxZDeLaavHPCXBz48W2bTnZuzcvtW3L3qIiBu7aRfzOnfyQm9siZ7608ULjanHjjSo0Ki8P7rxT\nrVhMngwvvAA9e6p9srIgORmGDFHPT51SIVQuLtWcOQOPPqqSwN9+W+Wm7dv3c6cqjeaPQadjhLc3\nn3fqRHqfPnzYvj2t3Nz45/HjtN26lUG7dvFlRoYWNnUJXMzKxTKgLdBPSjnJMUBov2ENjUukg08H\nnh/8PMemHmPt5LX8X9T/Mf/QfIZ/NpzgfwfzwOIH+Hr/12SVZTVI/4KC1GB78CBs2gSTJqkwgkGD\nIDIS/vEPlUippRlciMVg4G9t2nCsVy9ej4ggqbSUwbt303/nTj5NTye3ZSUMauOFxlXD1RVsNjU5\n8tFHSmB07w5Ll6rXz52DAwfg2mvV80OHlMCIi8vhhRegc2c1kRIXp/IzNm9uuHPRaBxYDAbuCAzk\nh2uu4XivXjwXFsaJc+e46eBBWm3ezF+Sk1mTm0u5JjR+E8NF7BMD3AisEkKkAF8C+t9+i4aGxq+h\nEzoGhg5kYOhA3hz1JkuTlvL53s/5aNdHvJ3wNgBtDrahb+u+9Gndh76t+xLtH+2st1HfCAG9e6s2\na5Yq1vfBB/Dii6oqrouLKnI1cKBqffuqUIOWjlGv5+FWrbgnMJAP09OZmZrKbYcOYRCCeJuNCT4+\njPfxIcjNraG7Wp9o44XGVUcItWoxebIKg/LzU9uPH1fGFXo9pKYqi+727cHTs4IvvoC955V73LZN\nFSAFFRqlrWBohLi780RICI+3acPK3FzeS0vjjdOn+fepU5j1egbbbIzw8mKElxehRq2Mz/n87t2K\nlHIXsAt4TAjRB7gJcBFCLAXmSSln13MfNTSaLe4GdyZ0nMCEjhOoqKpgV/ou5qyZQ6ZbJutOrGPu\nvrkAmFxMxATG0COoh2rBPQi3h/9icnldYjKpgny33AIFBco/ft061V5+WQkOvV7Z29aIjX79wGr9\n/c9urrjr9dwfHMx9QUFsLyxkXlYW886e5YGkJB5ISqKnpycTfX35Pz8/2ri7N3R36xRtvNBoaGpC\noEBZcr/6KowZo0I7o6JULsbTT/sTF6dWawEyMmD9epirLreasNC4AJ3D1na4lxdFlZX8kJfHspwc\nlubksCA7G4AOJhMjvLwY7e1NywuI/TmXNBUqpdwEbBJCTAWGoGaoZgMIIaKklPvrvosaGi0DF70L\nPYJ7UNyqmPj4eKSUpOansvHkRrac2sL2tO28vf1tyqpUjQW7u53uQd3pEdRDPQb3INgzuN4Eh8Wi\nvONHjlTPi4pUGMHatUpsvPaaEhxCqHoaAwaAl5cPUVHg61svXWrU6ISgp8VCT4uFF9u25WBJCfPO\nnuW7rCz+lpLC31JS6G2x8Ec/P/7g69vsVjS08UKjobHb1fVpzhy1ujphgqrqnZPjysCBtfvNnKmu\nayaTEiE1DlTr1imzC7u9Qbqv0QgxGwyM9fFhrI8PUkqOlJayNDubZTk5vHP6NK+dOoUHcN3+/Yzx\n9maUtzdeLi4N3e2rzmXFWUgpq4EVjlbDp6glcQ0NjTpACEGILYQQWwg3R98MQEVVBfuz9rP99Ha2\np6k2c+NMqqRyPwkwB9A9qDsxATHEBsUSGxhLkGdQvQgOsxmGDlUNVC7Gli1qBvDHH1URv9LSzvzr\nX7VuLQMGqBYcXOfdadQIIejk4UEnDw+eCAkhuaSEb7Ky+Cozk2nJyfw5OZl+Vit/9PNjoq8v/q6u\nDd3lOkMbLzQaEp1OJX6fT3R0HgsXwk03KceorVtVzgbUrlqUlsLo0VBWpq5xf/gDjBunCQ2NWoQQ\ntDeZaG8yMa11a4qrqliVm8vsfftYl5fH11lZ6IC+VitjvL0Z4+1Ne5Op3iMOGgN1GcTd/H9bGhoN\njIvehWsCruGagGu4O/ZuAEorStmdsdspOBLPJLIkaQnVUiWc+Xv4ExsUe4HgaGVpVecXOJNJJU7W\nJE+Wl8Ps2TsoKorhxx/h88+VRSRA27ZKbMTHq9amTZ12pdETYTLxeEgIj4eEcLikhK8zM/kqM5MH\nk5J4OCmJeJuN//Pz4wYfH3yakdA4D2280GgQpISwsBK6dlV1MwYPhmeeUTbc5+dauLsrA4tvvlFt\nyRIVAhofD+PHK6HRunWDnopGI8NDr2ecjw9WYECfPiQUFrIoO5tFZ88yPSWF6SkpRBiNjPH2ZpyP\nD/2sVvTNVGjUpbjQwsw0NBoAo4uRXq160atVL+e24vJidmfsJjEtkcQzqi1LXuYUHL4mX2KDYukR\n1IOewT2JC47D16NuY5dcXaFz5wLi45X/fFUV7N6tVjXWrYMFC2pnC8PC1KDdp49KJO/Y8cLiWM2Z\n9iYTT4aG8mRoKPuLi/nKITTuO3KEB5OSGG63M9bHh6F2O2HNJ2lQGy80GgQhwGisYuZMZb1dUgIe\nHrWvnb9fXJxqL70E27crc4v581VNjYceUrlm48fD2LEQHa3lamjUohOCOIuFOIuFZ8PCSD13ju+z\ns/k+O5u3T59m1qlT+Li4MNbbmyF2O4NsNgKaUWjs1bGf0dDQuKp4uHrQp3Uf+rTu49xWUlHCnow9\nTsGRkJbAiqMrnIIjzBZGz1Y9nWKjW0A3jC51dzOr16vifTExMG2aim3et0/FRK9de6HYsFiUV32v\nXkps9OypKow3d6I8PHgmLIynQ0PZU1zM3IwM5mZmsjgnB4C27u4MsdsZYrdzrd2OdwuM5dXQqCuE\nqBUWv7dfjdB48UVlabtggRIbTz6pWkiIEhljx6rQz+a54KhxubRxd+f+4GDuDw6mqLKSZTk5zDt7\nlm+zsvgwPR1QSeHxNhuDbDbibTb8mvAfUV2Ki/I6/CwNDY06xuRi+tkKR1F5EYlpiWw7vY2tp7ey\nIXUDX+77EgCDzkAX/y70DO5JTGAMXfy70NmvMyYXU530R6dTyZJdusDDD6uQhKQklSS+ZYtqzz9f\nW8CvXTslNmpadLRKzmyOCCHoajbT1WzmxbZtOVxSwqrcXFbl5vJlZiazz5xBAN3MZobY7Qy12+ln\nteKubzKur9p4odFk6dBBtUcfVdXBFy+GhQvh/ffhjTfU5MiQISrsasQILXxK40LMBgMT/fyY6OdH\nlZTsLCxkTV4ea/Py+Dwjg3fT0gDoZDIxyGZjsGNlw9aEJpMuemgWQvxS8l0+cEJKWSml7PULr2to\naDRizK5mZ82NGtIK09h2eptTcHy25zPeSXgHUDU6Ir0i6eLfha7+XdVjQFdaW1pfcQ6HEEpAtGun\n/OpBOVJt364SLrdsgWXL4JNP1GsmkyqYdb7gCAy8oi40SoQQdPDwoIOHBw+2akVldTUJhYVOsTHr\n1CleOnkSo05H/Hm+65FGY4MlDmrjhUZLISBAFeK76y4VYrV6NSxapK5V332n9unUqVZo9O+v8jk0\nNAD0QtDdYqG7xcLf2rShsrqaxKIi1ublsSY3l4/S03krLQ0d0N3Tk8GOles+Fkujnky6lHm/t1Hu\nHntQyXidgf2AVQjxJynlit96s4aGRtMgyDOI8R3GM77DeACqZTXHco+xO2M3ezL2qFyOM4l8c+Ab\n53ts7ja6+Hehi18Xuvh3Ido/ms5+na+4L2azqhA+aJB6LiWcOFG7srFliyr0V1MEu3VrJTJqQqpi\nYqD5pCkoDDodvaxWelmt/CM0lOKqKn7My2N5Tg7LcnKYmpwMQJi7u1NoDLLZ8Ly6yzzaeKHR4jCZ\nVE2NMWPUtergQSUyli2DN9+Ef/9bXY8GDVLWtyNGQEREQ/daozFh0OmcFuaPtmlDeXU1WwsKnJNJ\nL6Wm8mJqKu46Hf2sVobY7Qy22ejm6XnRyeGp584x/+xZ1uXlMa1VK/rbbHV/HpewbxpwV403uRCi\nE/AMMB34jgttBjU0NJoJOqEj3CuccK9wru94vXN7QVkB+zL3sTu9VnR8vPtjisqLnPsEuQfRI70H\n0X7RTtER4RVx2dXGhYDQUNVuvFFtO3cOdu1S4VQ1tpLfOHSPwQBdu9aKjZ49ITKyeSVeeuj1jPT2\nZqS3NwDHSkudQuPTjAzeSUvDRQj6Wa2MuHqJK9p4ASSmJbI0fSkuqS60826Hj8mnRdhQaqhrTKdO\nqj3yCBQXKyOLGrGxZInaLzRUOVYNGaKc9mqqi2toALjqdPS32ehvs/F0WBgFlZX8mJfH6rw8VuXm\n8lhKCgB2g4FrbTZnsb/fKtB656FD9LZaGWy389yJE7xqMNDZbK7Tfl/KCN/u/KJHUsoDQogOUsoU\n7WKpodHysLhZfpY0Xi2rOZF3gj0Ze9ibuZdV+1ZxOPswi44sciaOu+ndiPKL4hr/a+gf0p8hbYfQ\nytLqsvvh7l4bFlVDRkZtKNXWrSqU6u231Wt2uxIZAwfCsGGq4F9zcqYKMxq5LziY+4KDKa+uZlN+\nPsscYuNRx0B0FdDGC+Dbg9/y0uGXeOnwS4AqfNnOu52zRXpFEukdSaRXJJ5ung3cW436xMMDRo1S\nDeDoUVi+HFauhG+/hQ8+UNujo5XYGDlSJYZrIVQa52MxGBjt48NoHx8A0svK+MEhNFbk5PDt2bMA\nzGzblum/4PH+bVYWFoOBp0ND0QnBgrNnSSsvpzNQLSU6IThXVUVmRcVvCpTf41LExX4hxDvAl47n\nfwQOCCHcgIrL7oGGhkazQSd0hNnDCLOHMa7DOPpV9yM+Pp7SilIOnj3I3oy97M3cy56MPSw4vIAP\nd30IQHvv9gwOG8yQtkOID43HbryySlX+/rXOLaBscA8erBUbmzfD44+r5uenimQNH67Ehr//lf4W\nGg+uOh3xdjvxdjszwsNJKyvjKtUv1MYL4JlBzxBVHoU9ws6R7CMcyT7C4ezD/HDsBz7d8+kF+/p7\n+BPhFUGEVwTh9nDnzxFeEVf8/6DR+AgPh/vvV62qCnbsUPkaq1fDO+/Aa6+pMKuaEKqRI1V9IA2N\n8wlwc+Nmf39u9vdHSsmBkhJW5OQQ/wuhToWVlewsLGSgzYZOCLLKy+lrtZJZrvw1dI6Jn93FxYze\nu5eQK7DGvRRxcTtwPzDN8Xwj8FfUQDHosnugoaHR7DG6GIkJjCEmsDbPt1pWsy9zH6tSVrEqZRVz\nds/h7YS30QkdsYGxDGk7hMFhg+nbpi/uhiubvtProXNn1aZMUdvS09Ws4fLlsGKFKvIHaiVj+HDV\n+vZtXpaSQVfPR/12tPECg85AsDGY+Mh4RkWOuuC1kooSknOSScpOIiknieScZJJzkll9bDVzds+5\nYF+7u/0C4RHuFe58DDAHoBPNaOmtBaLXQ48eqj32mEoMX7sWli5VbfFitV94uAqfGjpUhVBp1cI1\nzkcIQZSHB1G/4q98tqKCU2VljHesemSUl5NbWYnxvMTw8upqDpWUMNHXl6nBwXS8zL5ctLiQUpYC\nrzraTyn6hW0aGhoav4pO6FQSuH8XHun9COVV5Ww9tZVVKatYfWw1L218iRc3vIib3o0ewT3o36Y/\n/dr0o0/rPtjcrzwBLSAAbr1VtepqlbexfLlqr74KM2eqUIaBAyE0tBXe3kqctKConstGGy9+H5OL\nyfn3/1NKK0pJyU1xCo7knGSO5h5ly6ktfLX/K2eIIYC7wZ229raE28Odj+Fe6ue29ra46puROm4h\nmEwXhlAlJak8jZUr1STIe++pUM7u3aFduzCEUPWAmtNEiEbdU1Zdzb7iYrpbLAAcKS2lpKqKOM/a\nkMy8ykoWnT3LtXY7HS6mCMyvcClWtJHAi0AnwDmNKKXUFuo0NDSuGFe9K/1D+tM/pD9PD3qagrIC\nfjzxI2uOrWHDyQ28vOllXtzwIgJBtH80/Vr3o3+IEhxXkrMBaqCuKfD3+ONQWAhr1iihsXo1LFkS\nwdtvqxCqmuTLIUPgF0JaNdDGiyvF6GIkyi+KKL+on71WXlXOibwTpOSmcDT3KEdzjpKSl8LRnKOs\nPraakooS576uele6+HchNjCW2MBYugd1J8ovShMcTYzISNUeekg5423dqoTGypXwxRdt+OwzJUgG\nDlSrGkOGQFRU88ol07hyCquq8Hco0BPnzrEoO5s4T09au7sjpUQIQYWUuOp0vHbqFHuLiy/7WJcS\nFvUR8C9gFmpZ+w5A+9PV0NCoFyxuFka3G83odqMBKC4vdhb625C6wRlGBRBqC6Vfm37O1Y2OPh2v\nyJXH0/PCnI2vv95MSUlvVq1SYmPuXLU9IqJWaAwa1DKqiF8k2nhRT7jqXVUSuHfkz16TUpJZnOkU\nHXsz95J4JpEv933Je4nvOd/fxb8L3QO7ExukREdnv8646JtOga6WjIsL9Oun2tNPw/ffb6Cysj8r\nV8KqVSqMCsDHp9bGe9AgaN9eW3Vt6VxjNhPs5kbrzZvp6CjQd29QEIBzvAx2c+OLTp0orqriH8eO\nXfaxLkVcGKWUq4UQQkp5AnhKCJEI/POyj66hoaFxkXi4enBt2LVcG3YtAJXVlexO38361PVsSN3A\niqMr+GzPZwB4G73p26YvQ9sOZVz7cbS2XlmJXD+/MuLj4fbblX/9gQNqIF+1Cj77DN59Vw3c3bsr\n7/oJE1TuRgsezLXxogEQQuBv9sff7H+Bi5uUkqO5R0lMSyTxTCIJaQnM3TeXdxPfBVRo1ZC2Qxjf\nfjxj24/F18O3oU5B4xIxm6uIj4fxqiwRJ06oVdcfflCPNbbcgYFKZFx3nWpWa4N1WaOBcNHpmN2+\nPcklJZytqKCX1cr3Dnep0T4+FFVWYnbUQ/LQ67FeQZG+SxEXZUIIHZAkhHgQOA3UrTGuhkYLYVny\nMqYum0pVdRVTYqbwWL/HLnhdSsnUZVNZkrQEk4uJj8d/7EyGDn0tFE83T/RCj0FnIOGeBABySnP4\n4//+yPG844TaQvl64tfN2mXGoDOomdegWKb1moaUkuScZKfY+PHEjyw8vJCHlj5EbGCsszBglG/U\nFa1qCKFCDqKiYOpUFaawbZsKUVixAp5/Hp59VvnXX3+9ar17t7gQBW28aEQIIZwJ4X/s/EdAGSqk\n5KaQmJbIxpMbWXh4Id8f+R7d9zr6t+nPhA4TGN9hPCG2kAbuvcalEBKiJkFqJkKOHlUiY80ater6\nxRdq9WPQICVIxo6F4KtkIafROIgwmaip3djDYiGpRIVSfpmZSUZFBZP8/Wnt5sY8h/C4HC5luJsK\nmICHgVjgVmDyZR9ZQ6OFUlVdxQNLHmDpLUs58MAB5u6by4GsAxfsszR5KUk5SSQ9lMTsMbP50+I/\nXfD6mslr2HXfLqewAJixYQaDwwaT9FASg8MGM2PDjKtyPo0FIQSR3pHc2e1OPhz3IckPJ3PwgYPM\nGDwDF70LT655kuh3ool4I4JHlj/CqpRVlFWWXfFxXVyUq9RTT8GmTXDmDPz3v6p41htvqPCF4GD4\n05+U+KhoGUas2njRyNEJnVNs/Gfkfzg29Rg77tnBE/2f4GzJWaYtn0bo66HEzo7luR+f43jxcaSU\nDd1tjUtACBW6effdSlSkpSkb7j//GY4dUza4rVqpuj8vvKBWZLWvuGXh7+pKP4dt7SC7nTNlZdyw\nbx8j9+xhwBVU7r4Ut6jtjh+LUPGzGhoal8G209uI8IqgrV3ltt4YdSMLDi2gk28n5z4LDi3gti63\nIYSgV6te5J3L40zhGQI9A3/1cxccXsDayWsBmNx1MvFz4pk5dGa9nktjp4NPBzr068Cj/R7lTOEZ\nFh1ZxPxD83lr+1vM2jILDxcVajUqchQjI0bWySytn5+yu50yBfLzVSXe776DTz9V4VNvcz8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+WXwd8fqlc3St1u2CCjtpYmyYUQ4pFUw6sGM1rN4Jdjv/DqsldNXlGmSukqJHZPpFKpSrT8qiXj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\nIYRJSHIhhBBCCCGEMIn/B12+SONkZhFxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119eb0080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mags, t_ccds = np.mgrid[8.75:10.75:30j, -16:-4:30j]\n",
"plt.figure(figsize=(13, 4))\n",
"for subplot, color in enumerate([1.0, 1.5]):\n",
" plt.subplot(1, 2, subplot + 1)\n",
" p_fails = floor_model_acq_prob(mags, t_ccds, probit=False, color=color)\n",
"\n",
" cs = plt.contour(t_ccds, mags, p_fails, levels=[0.05, 0.1, 0.2, 0.5, 0.75, 0.9], \n",
" colors=['g', 'g', 'b', 'c', 'm', 'r'])\n",
" plt.clabel(cs, inline=1, fontsize=10)\n",
" plt.grid()\n",
" plt.xlim(-17, -4)\n",
" plt.ylim(8.5, 11.0)\n",
" plt.xlabel('T_ccd (degC)')\n",
" plt.ylabel('Mag_ACA')\n",
" plt.title(f'Failure probability color={color}');"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8, 11)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NatE8afGFHNuYTGl+FYGRbgyYEoKje/N1g8SaGoqWLKFw2XIUzQSj\nfw2y8MvI/A84u2Mz53dvJ2LoXXQcNKxVrPiWUHBJ4lL8ddr1aMOdD4WjqBV9Sa8n85lnqU5MxHfR\nImy6dQWgyljFM4eeodJQydLhS3G2qvV3Z5yBn16F0JEw6KW68ZcdTeHglXwWjOtIZ7/6rhtJkvgo\nLZeP0/IY4GTHNxGBOKoblqD8/L1cuvwcVlY+dOm8Emtrv0b3dPz4cfbv309wcDDe3t4tFn1DXgXa\ntVcw5FZi198bx5FBTRZVy0xMYPtH7yIIAlPe+A8+YU03bQEoya/k2MZkNPFFOHnYcPfTnQno2LJv\nIRWnTpH71nz0Gg0OY8fi8X+vgFvDp5d/LbLwy8j8wVw+epijP3xrzqV/7KlmT3DeLlXlejSXiki5\nWEB+vERobw/uuL/DDdEXRbJfe52K48fxeudt7IcNBUCURF4/9jpXtFdYNGwR7ZzbmQcsz4MN94Oj\nL0xYArXrP5umZeHeJMZ08uL+vvXr7tSIInOvZLApr5ipni58GOaLRSP7zshcw9WrC3Bw6ELnyKVY\nWDTcZEWSJA4cOMDx48fp2LEjEyZM4NixY80+D0mSqDidQ8muVBRWSlwf6oh1WNONXBKPHmbv15/h\n0MaTia+8hZNn05lB+moj53/UEHMwHaVSQb+JIUQO861zqTWFUasl/4OFlG7fjtrfH79vlmPXv3+z\n9/0aZOGXkfkDSYu7yN6vPsU/IpIRjz/3u4i+JElosytIiy8kLa6I3NRSkMDW0QK3cLjjgfB6+eH5\nH35E2c6duD/3LE6TJ9e9viR2Cfs0+3ih+wsM9htsftFkgE0PmUssP3IArM1WfZGuhqd+uICfszXv\nT+pUzz9ebDAyKyGVkyUVvBLkyXMBHg36zyVJ4nrKx2g0X+HmdicRHT9FqWw4b95kMrFz505iYmLo\n0aMHo0ePbrJOft19FQaKN12lOlGLZagzLlNCUdo3XmdHkiRObVnHiQ3f4xsewbi5r2Ft13jmjSRJ\nJJ/N48Tma1SU6gnr40nfCW2xdWw+uCxJEqVbtpK/cCGmykpc58zGbc6cuoyq1kQWfhmZP4i81Ovs\n+Pg/uPr4MW7uay3KAGkpJoNIVnIxaXFFpMUXUl5UDYC7vz09xwQRFOmGm58d0dHR9US/aMVKtCtX\n4jxjBq6zZ9e9vjdtL1/Gfsm4tuN4sOODNyY6MN/cTGXiMnP6JuZ6+s+tj6G40sCKJ3rWC+ZqqmqY\nEZdCepWeL8MDmOjRcGqkKOpJvDKP3NyteHtPIyx0AQpFw/JkMBjYtGkTSUlJDB48mCFDhrQofbL6\nWjHa9VcRKw043h2MXT/vJjtimYwG9i1ZxOUjhwgfNIy7Zj+NUtX4/1lBejlH118l53op7v72jJzd\nCc/g5k/rAtSkpJD71nwqz57Fuls3vBbMx7Jduxbd+1toFeEXBGEFcDeQL0lSRGuMKSPzd6I0P5et\n78/Hys6Oia8uwNKm8QBiS6ks06NJMAt9xmUthhoTKrUC3w4udB8ZQGAnN2ydGrc0S3fsIH/hQuxH\njsRj3qt14nmp6BKvH3udLu5deKvvWzdENWEznPwCes2GyH/VjbP48DWOJhfy3sROdPS+IXQXyiqY\nGZeKKEms79KWvk4NxzCMRh3xCU+h1R4lOOh5AgOfbFTIq6qqWLt2Lenp6YwePZpevXo1+5wko0jZ\nfg3lRzJRuVnj9lBHLLybjqdU6crZ+fF/yLgcT78pM+gzaVrja9LpOb09hUvHsrG2UzN0Zns69PVq\nts0i/By8XUrhsmUorK3xfPvfOE2a9Lu7/1rL4v8W+AJY3Urjycj8bagsK2Xzf97CZDAw5Y3//OZm\nG5IkUZRVQVpcIWnxheSllZldOE6WhPb2JLCTK75hznUnb5tCd/Qo2fNew6Z3b7wXfoBQW6+moLKA\nZw49g7OVM58M/QQLZa0bJD8Rtj8Nfn3grnfqxjl+rZBPDlxlQlcfpvW8EYDNrTEwMy4VO6WCHzoH\n07aRFok1NQXExj6MruIKHdp/gLf35AavA/OhrO+++46CggImT55MRETzNqahoBLtuiQMWTpse3vi\nOCa42SYpJbk5bPlgAWX5uYx6ai7hA4c2eJ1oErl0NJvTO1LQV5uIHOpLr7uDsGwgk6khbgnevvIy\nqlYK3jZHqwi/JElHBEEIbI2xZGT+Thhqqtn24duUFeYz+fV3cPVtPDulIYwGE1lXS+rEXqetAaBN\ngD297g4isJPZhfNrmnFXxceT+exzWIaE4PvFIhQWZnGvNlbz7OFnKdeXs2bUGtysa0WoutRccdPS\nDqZ8Cyrz9fll1Ty77iJt3e14554bp2KNosTjl9OoNIls7RrSqOhXVqZyMeZB9PoiIiOX4uY6pNE1\na7Va1qxZg06nY8aMGbStTTVtDEmSqDyfR8mO6wgqBa4zO2DdsXlRzUpKZPuHbyNJEpNfewff8IY/\nXKorDOxcFEt+Whk+Yc4MnNoO12a+RfzMHxG8bQ7Zxy8j8zshmkzs/vxDcpKTGPf8qy2q3QJQUVpj\nduHEFZJxpRhjjQmVhQK/Di70HBNEQIRri4KFDaHMyyPj1XmonJ3xW7oEpb05UClJEm+deIv4wng+\nHfopYS5htZsQYdsTUJwGD+wEB3M2i9Ek8vTai1TUmFj7aDdsLW9IycdpuZwsqeDzDv6E2jYs+qWl\nF4mNexQQ6N7tBxwcIhtdc05ODt999x2iKPLAAw/g6+vb5B7FKiPFW5OpiivEMtgRl6lhKFvwvK6c\nOMJPX36CvasbE16Z32hxteoKAzs+i6EoW8fwh8Np16PhYPUvkSSJ0q3bzMFbne5XBW9rTDUsjlnc\n7HUtRWitY8C1Fv+uxnz8giA8BjwG4O7u3n3Dhg2tMu+fEZ1Oh10LTvL9Vfk776+19iZJEulHDlB4\nORa/AcNo06lbk9dWl4AuG8qzJKpqO+ypbcDOG+y9BWw9QKG8vUqNiuJinBZ+iNJgQPvSi5g8POre\n21u6l10lu7jb6W5GOI6oe91fs4ng1DUkhzxClu/Yutc3XdWzK8XAo50s6O9zw7URL6n4D7YMQs/j\nQlUj+41BlJYATiiE5xAEjwavA3NLxISEBFQqFZGRkdjaNh0bkbKqCEy2RVUDRe0kSoIkaOaxSZJE\n7sUzZJ8+ip2nD21Hjkdl3fBhKZNeIi1KoqYE/AYI2Hu37P9EmZuLw/c/YJGcjL5tW8pmTMfk7d2i\ne1NrUvm+8HvyjHkkPJhwXpKkHi26sQn+MItfkqSlwFKAsLAwaciQIX/U1H84UVFRyPv7a9Jaezu1\nZT2Fl2PpNX4yA6c/2OA1ZYVVXNyXjia+EF1xDQjgEehA5AA3AiNdcfX5dS6chpAkiarz5ylet57y\nvXsxKRQEf7eGjp1uND0/qDnIrqhdjA4azX8G/ufGnNcPQfT3EDGJdpM+ol3t64eT8tn101mm9fTj\ntUk3LPX8GgNPn0uinUrFih6R2DZQ5z4rax1XkhZjb9+RLp2XY2HRuPslMTGRo0eP4uzszMyZM5vu\njiVJlB/OoDQ+DZWrNa4Pt8fPr/mCZyajkQPLvyT79FHa9x/MiDnPorJoOL1TX2Vkx+cx6EvLGTWn\nE0GRzbuOxJoaipYuo2jpUgRra9r8ewFOkye3KHhbY6ph8cXFrEpfRRubNiwZuoT+tI5LSHb1yMi0\nMglRBzi+fg3hA4cy4N4HGrymorSGbZ9cpKpMj39HV3qNdSUgwg0bh+Z7t7YEU1kZpdt3ULx+Hfpr\n11HY2+M0dSrJ7ULqiX6SNolXj71KJ7dOLOi34Ibol6TDpofBvT2MW1RXcTO7pIoX1sfQ3tOe+eNu\nuK5MksTjlzXojCY2dml7i+hLkkRq6uekpn2Oq+tgIjouQqVq3Hq/cOECO3fuxMfHh+nTpzdZO18S\nJUp2XqfiZA46L4mwOV1RWDYvbdUVOnb+9z3SE2LpM2ka/abMaPSDVl9tZOeiGAo05Yx4LKJFol9x\n6jS58+ejT0vD4e678fi/V1ocvI0tiOWN42+QWprK5NDJzO0+FzuL1vuW3VrpnGuBIYCbIAiZwFuS\nJH3TGmPLyPyVSL14jn1LPicgsit3zXmmQSHRVxnZ9UUsVToDE17sRpuAhvvJ/haq4uMpXreOst17\nkKqrsYqMxOvdd3EYPQqFtTVXbirLXFhVyFOHnsLewp7Phn6GlarW12yoNlfcFI3mipsWZoE2mESe\n+uECBpPElzO6YaW+Ie6fpOVxvETHJ+39aG9b/9CVKBpJSnqD7JwNeHlNpn3YOygUDWe+SJLEsWPH\nOHjwIG3btmXq1KlYNGKBA0gmkeJNyVRezMdukA/XrNPp0ALRL83PY+sHCyjOyWbkE8/TcfAdjV6r\nrzb/f+WllTPikY4Ed3FvcmxjcbE5eLttG2o/P/yWL8duQMss9Tor/3KtlT98Cf28+7Xo3l9Da2X1\n3Nsa48jI/JXJvZ7Mzk/ex90/iHEvvNrgYR+TUeTHJfFosyoY/WRkq4i+WFFB6Z49lKxbT/WlSwjW\n1jiOHYvT1KlYRzQcUNab9Dx/+HlKqktYNWoV7jY3idmPL0FODExbC643smcW/nSFC+klfDG9K8Hu\nN6zPY8XlfJyWy2QPZ6Z51i99YDJVEp/wNEVFUQQGPklw0PONWtWiKLJv3z5OnTpFp06dGD9+PCpV\n4xIlGUSKfkikOlGLw4gA7If4QXR6s88r51oS2xa+jcloYNK8f+Mf0Xhg2VBjYvfiOHJTyrjr4Y60\n7dam0WtvCd7Ono3b4y0/eft7W/k3I7t6ZGRagZLcHLZ+sABrB0cmvjofiwaCg5IocWh1IplXirnj\ngQ4tLtbVGNVJVylZv57SHTsQdTos27XD4803cBw7ti5bpyEkSWLByQXEFMTw0eCPCHcNv/Hm+VVw\nYTUMfBHaj657ed+lXJYdTeWBvgHcHXkjKFmgN/DEZQ0hNpZ8EOpbT9T1+iJiYx+hrDyBsLC38fWZ\n3uiaTCYT27dvJy4ujt69ezNixIgmSzCINUaKVl2mJrUUp/FtsevbskBp8ukT7Fn0EbbOzvzrrfdw\n9Wk8vdagN7H7y1hyrpUwfFZHQro3Lvo1Kankzp9P5ZkzWHftite/F7T45G21sZovY7783a38m5GF\nX0bmNqksK2Xze28imkxMmrcAW6eGyxKc3Hqdq2fy6D0+mPZ9m2//1xBiTQ3le/dSvG49VRcuIFhY\n4DBqJE5Tp2HdtUuLgsHfXvqWHdd38ETnJxgReCODh6zzsOdFaDsMhs6rezm9qJK5G2OJ9HVk3pgb\nFSlNksSTlzWUGU2s79y2Xo9ck6mGmNiHqKi4RmSnL3F3H97oevR6PRs3biQ5OZlhw4YxcODAJvdh\nqjBQuDIBQ7YOl6lh2HRpXJB/RpIkzu3aypHvV+IVEso9L7/ZZPNzo97Eni/jyL5awh0PhtOuZ8OZ\nR6JeT9GSpXXBW88FC3Ca0rLgLZit/NePvU5aWdrvbuXfjCz8MjK3gaG6mq0fLEBXVMSUN9/Fxbvh\nHPPYgxlc3J9OxGAfuo8MaPCaptCnpVG8fgOlW7ZgKi3FIiCANq+8guM941E5N94a8JckVCaw9PxS\nRgSOYE7nOTfeqCiE9feDnSdM+gZqewDUGE08+cMFBGDx9G5Y3iTun2vyOFKs4+MwPzrY1ffrJ1/7\nD+Xll4jstAR39zsbXU9lZSVr164lMzOTu+++mx49ms5UNJXWUPBNAkZtNa4zw7Hu0Py3JtFk4uCK\nr4g78BOhfQYw8snnUVs0ntdvNJjY83U8mUnmb2ZhvT0bXvu5c+S8/oY5eDtmjDl46960//9nqo3V\nLI5ZzOrLq/8wK/9mZOGXkfmNiCYTuz77gLzr1xg3dx7eoQ3XZ08+l8exTckEd3Vn4NTQFqdoSgYD\n5QcPUbx+HZUnT4FKhf2dd+I8bSo2vXv/6lTPhMIEvi38lg6uHXi7/9s37hdNsGkWVBTAw/vA5oaf\n/t3dicRnlbLs/h74udxwX50o1vFhai6TPJyZ7lXfr5+Xt5usrO/w93u4SdEvKytjzZo1aLVapkyZ\nQnh4eKPXAhiLqihYHo9YacR9Vkcsgxtu1XgzNZWV7Pr0fdJiL9DrnikMmDqzSWvcZBD58et4MhK1\nDJvZnvZ9Gv5mVnn+POkPzULl4YHfsmXYDWy8M9gv+V9Z+TcjC7+MzG9AkiQOLF9MyoWz3PnIk4T0\n7NPgdZlJxRz49jJebR0Z/lD9csiNYcjOpnjjRko2bcJUUIjK28tcMnnSpBZblDdzveQ6X8d+zd60\nvTgoHfh86OdYq26y0A+9DanRMH4xeHepe3lnbDarT2p4dGAQw8NvuDoK9AYev5xGkPWtfv3KylQS\nr8zDwaErbdveaNDySwoLC1mzZg1VVVXcd999BAUFNbkHfU4FhSviwSTh/mgnLFrQlLyssICtHyxA\nm5XBXbOfodOwu5q8/v/ZO+vwqq6sD79x94RggSQ4SdDi7i6lQJUWCsWKtcVanOJ0ihS3AoUWCa6F\nQoJ7gBjxEHf3XNnfH7dIyLk3l8J8087c93nmmSFnn3PPSeauvc/aa/1+CpmSc9sCiA3KpMsn9WnQ\nVnrfoDQujvhJkzGqWhXXgweeexJXxBut8kvy4Pxs7cZqgS7w69DxF7jl/RsBly/Qesj7NO7RR3JM\nenw+5zb7Y+NkTt8JjTSKpwmFgvxr18g+cJD8q1dBCCw7dcL2g/ex7NDhuYja6xCVE8WWx1s4H30e\nU0NTRnuNpnZWbZwtXspXPzkF19dA85HQ9JMX56blM/uIP81q2DKzd/3nP1cKweTgWLLlCn5tXAvL\nV/L6AYFT0NMzxMtzvdqSzYSEBPbv3w/AyJEjqVpBB2tJTC7pPwehb6yP4zgvjdaIz0iJiuDYqsXI\niosZMnsRNRs10TheIVdyfnsgMQEZdPqoHg3bS9+TIjeXuPETEEolLlu3aB3032iVH38fjoyB7Bjt\nxmuBLvDr0PGa+F/6nVvev+LRqTtth38iOSYvs5jTPz3CyNSQAZMbY2ohHQTlaWlkHzlC1qFDyBOT\nMHByxGHcWOyGDsWomrRWTEVE50Sz1X8r56LPYWJgwijPUYz0GImdqR2+L9Xxkx4OxyZAtebQZ9Xz\nHxfLFEzc74exoT4bPmqGkcGL1MiG2FR8s/JYXa86HuXy+kvIzw+mcaPtmJpKB86oqCgOHDiAubk5\nI0aMqNAMvTg8i4y9wehbG+M02gtD+4pLIyPu3+HM+lWYW9sw9PvVOLpo3lNRKJRc2BHEU/90On5Q\nF8+O0r93IZORMO0rSmNjqbFjB8aurhXey8urfGdzZ7b12Eabqm0qPA9QpeCu/Qi+y8G6Gow8Cwvf\nzsLB5YcAACAASURBVD6ALvDr0PEaRD64yx87NuLapDk9xk6SzLMXF8g4tf4RslIlQ6Y3w0pNsMrc\n+wspq1aBXI5F2zY4z5qNVdcu6P1Fg5aY3Bi2PN7C2eizmBiY8FnDzxjpORJ7UwlbwZJ8leKmoTEM\n3wuGLzY7F5wIIiQ5j92jWlDV9kVwv52dz4qoJAZXsuWTKmUDdnLySRISfqVGjS9wdOwqeX9BQUEc\nPXoUBwcHPvnkE6ytNfcwFAWmk/FbCEZOZjiO9tLolPWM1AA/HtzwobJ7bQbPnK+2wuoZCoWSizuC\niHqURof36+DVWXpzXghB8tKlFNy8SZWlS7FoVbEPwBut8rNi4Ng4iL0FXsOg37/AVDtTF23QBX4d\nOrQkKTyU02tXUsnVnQFfzcZAorlIXqpq+MlJL2LglCY4VJP+ouee/52UZcuw7NwZ59mztFo9qiM2\nN5at/ls5HXUaY31jRjQYwSjPUTiYqVlNCwEnvoT0MBhxXOWd+yfeD+I5eD+OSV1q07neizLJjFI5\nE4JjqGlmzOp6LuXy+iGhc7GxaUYt928kP/LevXucOXMGFxcXPvroI8zMpC0Vn1FwP4WsI2EYu1jh\nONID/Qo07oUQ3Dl6kLjrl6n1Tmv6TZmOkYnmtwOlQskfu4KJfJhG+2F1aNRFfU1/1t69ZB84iMMX\nY7B9b4jG677RKh/A/zCc+Vr1v4dsL2N687bQBX4dOrQgKymBYysXYWFnx7uzFmBsWj5wKZWCCzuD\nSI7OodcYT6rVlV5tFj16ROKsWZg1bUq1tWv+sqdqXG7c84BvqG/IJw0+YZTnqBc6+uq4tRGCj0P3\nReDe6fmPw1LymHs8gNbu9kzr/qL5SCkEk5/EkFEq50zzOliVyesXExA4CX19Yzw91pXL6wshuHLl\nCr6+vtSpU4dhw4ZplGAAyLueQM7pKEzq2OIwomGFxilCCK7u/5n7p45iX7chA7/+Fv0K9kSUSsEf\nu58Q8SCVtu/VpnE39UE/z8eHlBUrserRHaevvtJ43Tda5RfnwJnpEHBIZXgzZCvYuWp37muiC/w6\ndFRAQXYWR5YvAOC9b6UbtIQQXD0QRvTjdDq8X0dtl2dpbCxxE7/E0NmZ6ps2/qWgH5cXx3b/7ZyM\nPImhviEf1v+Q0V6jKw74gG1WAPgvgAYDoN3UF89YImfifj8sTYxY/0FTDF/K62+KTeVyZh4r6lbH\ny6psR3JY+Pfk54fQuNGOcnl9pVLJ+fPnuXv3Lo0bN2bgwIEYaAjIQghy/4gl71IsZh4O2H9YHz1D\nzY1QSqWCSztVNfpNevVD362+VkH/0p5gwu+l0ObdWjTtUUPt2OKQEBK+mY5pgwZUXblSbSnoG6/y\nY27B0bGQmwBd5kD7r8Hg3xeedYFfhw4NlBYXcWzlIgqysxg+fxl2VaQ3/h6ce0rQ1QSa9aqhNmWg\nyM4mbuw4UChw2brltRqvABLyE9jmv42TESfR19Png/ofMNpzdFmdHU3kJNAweLVKf2fQpueKm0II\n5hwLICotn31jWlHJ+sVkdDc7n+XRSQxwsuWzquXz+omJB6hZYxyOjmXtCYUQnD59Gj8/P9q0aUOP\nHj00SjAIpSDndBT5NxMxb+6M3ZA66FXgP6CQyzm/aQ0hN66oavQ/+JQrV65oPEcoBT57nxB2R9VB\n3ayX+o1fWWoqcRMmYmBlRfXNm9FXoxD6Rqt8hQyurIJrP4BtDfj8d3Bpod25b4Au8OvQoQaFXM6p\nNStIfRrF4BnzqFK7nuS44BuJ3DkZTb1WlWk9WNoSUFlaStykScgSEqix+2dMKqhbf5nE/ES2+W/j\nRMQJ9PT0GFZvGKM9R5cty9SEUgEPf4FLi9FXlqgUN01fbKweuBfH8UeJfNOjLm1rvXhryJTJGR8c\nQ3UTY/5Vv2xev6AgipDQOdjYNMfd/etyH+nn54efnx/t27ene3f1TVwAQiHIOhJGoV8qlu2rYdPX\nrUKjcnlpKafXrSTy/h3af/gZrQYPq/DXIJQCn30hhNxOpuUAN97p46p2rLKoiPgvJ6HIzsZ1/z6M\nnMu/wb3xKj8zCo58AQn3ocnH0GclmFTcn/A20AV+HTokEEJwcdsGnj56QI+xk3FvJr0KexqQju/+\nUFwa2tPl0/qSVT5CCJK+m0PR/QdU/dcPmDdvrtU9JOUnsT1gO8cijqGHHu/VfY8xXmOobCEtISBJ\n3D2V/k7SI6jRloeV3qeF04sJLCgxhwUng+hY14kvu9R+/nOlEEx5Ekt6qZxTzetgXSavX0Rg4CT0\n9U3/zOuXDSMJCQmcPXuWWrVq0bWrdIXPM4RMScZvIRQHZ2DdoyZWXV0q7EguLS7ixOolxAY+ptvn\nE2jSq1+FvwahFPj+GsqTm0m808+VFv3UT7xCqSR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vpXasvFRB3DVBcXYRAyert01UlpYS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/VV/sGROhf+ERZeD+ppRv6Mwn93NvJlRQf3r55HgmH0D7F9JivPN3dyfVw4cwNH2kA1NY/j4xNE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\neOyKPC7Mi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AzWrv3D7ZnAaHm1HpE16L+HZ0JwLEgSBQUFrJxGrZ7ZRghBj8XhXa03eG4H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\nJxj/H3Gx6BuNE9jYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1146190b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mags = np.linspace(8, 11, 301)\n",
"plt.figure()\n",
"for t_ccd in np.arange(-16, -0.9, 1):\n",
" p_fails = floor_model_acq_prob(mags, t_ccd, probit=True)\n",
" plt.plot(mags, p_fails)\n",
"plt.grid()\n",
"plt.xlim(8, 11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare to flight data for halfwidth=120\n",
"\n",
"Selecting only data with `halfwidth=120` is a clean, model-independent way to\n",
"compare the model to raw flight statistics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup functions to get appropriate data"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# NOTE this is in chandra_aca.star_probs as of version 4.27\n",
"\n",
"from scipy.stats import binom\n",
"\n",
"def binom_ppf(k, n, conf, n_sample=1000):\n",
" \"\"\"\n",
" Compute percent point function (inverse of CDF) for binomial, where\n",
" the percentage is with respect to the \"p\" (binomial probability) parameter\n",
" not the \"k\" parameter.\n",
" \n",
" The following example returns the 1-sigma (0.17 - 0.84) confidence interval\n",
" on the true binomial probability for an experiment with 4 successes in 5 trials.\n",
" \n",
" Example::\n",
" \n",
" >>> binom_ppf(4, 5, [0.17, 0.84])\n",
" array([ 0.55463945, 0.87748177])\n",
" \n",
" :param k: int, number of successes (0 < k <= n)\n",
" :param n: int, number of trials\n",
" :param conf: float, array of floats, percent point values\n",
" :param n_sample: number of PMF samples for interpolation\n",
" \n",
" :return: percent point function values corresponding to ``conf``\n",
" \"\"\"\n",
" ps = np.linspace(0, 1, n_sample)\n",
" vals = binom.pmf(k=k, n=n, p=ps)\n",
" return np.interp(conf, xp=np.cumsum(vals) / np.sum(vals), fp=ps)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.55463945, 0.87748177])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binom_ppf(4, 5, [0.17, 0.84])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.78004095, 0.84058371])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n = 156\n",
"k = 127\n",
"binom_ppf(k, n, [0.17, 0.84])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calc_binned_pfail(data_all, mag, dmag, t_ccd, dt, halfwidth=120):\n",
" da = data_all[~data_all['asvt'] & (data_all['halfwidth'] == halfwidth)]\n",
" fail = da['fail'].astype(bool)\n",
" ok = (np.abs(da['mag_aca'] - mag) < dmag) & (np.abs(da['t_ccd'] - t_ccd) < dt)\n",
" n_fail = np.count_nonzero(fail[ok])\n",
" n_acq = np.count_nonzero(ok)\n",
" p_fail = n_fail / n_acq\n",
" p_fail_lower, p_fail_upper = binom_ppf(n_fail, n_acq, [0.17, 0.84])\n",
" mean_t_ccd = np.mean(da['t_ccd'][ok])\n",
" mean_mag = np.mean(da['mag_aca'][ok])\n",
" return p_fail, p_fail_lower, p_fail_upper, mean_t_ccd, mean_mag, n_fail, n_acq"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mag=10.0 mean_mag_aca=10.00 t_ccd=f-14.92 p_fail=72/1056=0.07\n",
"mag=10.0 mean_mag_aca=10.00 t_ccd=f-14.08 p_fail=83/1131=0.07\n",
"mag=10.0 mean_mag_aca=10.01 t_ccd=f-13.00 p_fail=120/1248=0.10\n",
"mag=10.0 mean_mag_aca=10.02 t_ccd=f-11.93 p_fail=87/671=0.13\n",
"mag=10.0 mean_mag_aca=10.03 t_ccd=f-11.05 p_fail=63/333=0.19\n",
"mag=10.0 mean_mag_aca=10.02 t_ccd=f-10.11 p_fail=48/170=0.28\n",
"mag=10.3 mean_mag_aca=10.26 t_ccd=f-14.92 p_fail=74/612=0.12\n",
"mag=10.3 mean_mag_aca=10.26 t_ccd=f-14.09 p_fail=84/642=0.13\n",
"mag=10.3 mean_mag_aca=10.26 t_ccd=f-13.00 p_fail=163/745=0.22\n",
"mag=10.3 mean_mag_aca=10.26 t_ccd=f-11.93 p_fail=144/440=0.33\n",
"mag=10.3 mean_mag_aca=10.25 t_ccd=f-11.08 p_fail=76/182=0.42\n",
"mag=10.3 mean_mag_aca=10.26 t_ccd=f-10.08 p_fail=59/106=0.56\n",
"mag=10.55 mean_mag_aca=10.50 t_ccd=f-14.93 p_fail=53/223=0.24\n",
"mag=10.55 mean_mag_aca=10.49 t_ccd=f-14.08 p_fail=61/231=0.26\n",
"mag=10.55 mean_mag_aca=10.50 t_ccd=f-12.98 p_fail=132/292=0.45\n",
"mag=10.55 mean_mag_aca=10.50 t_ccd=f-11.96 p_fail=96/185=0.52\n",
"mag=10.55 mean_mag_aca=10.47 t_ccd=f-11.11 p_fail=43/60=0.72\n",
"mag=10.55 mean_mag_aca=10.44 t_ccd=f-10.11 p_fail=16/27=0.59\n"
]
}
],
"source": [
"halfwidth = 120\n",
"pfs_list = []\n",
"for mag in (10.0, 10.3, 10.55):\n",
" pfs = []\n",
" for t_ccd in np.linspace(-15, -10, 6):\n",
" pf = calc_binned_pfail(data_all, mag, 0.2, t_ccd, 0.5, halfwidth=halfwidth)\n",
" pfs.append(pf)\n",
" print(f'mag={mag} mean_mag_aca={pf[4]:.2f} t_ccd=f{pf[3]:.2f} p_fail={pf[-2]}/{pf[-1]}={pf[0]:.2f}')\n",
" pfs_list.append(pfs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare model to flight for color != 1.5 stars"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"def plot_floor_and_flight(color, halfwidth=120):\n",
"\n",
" # This computes probabilities for 120 arcsec boxes, corresponding to raw data\n",
" t_ccds = np.linspace(-16, -6, 20)\n",
" mag_acas = np.array([9.5, 10.0, 10.25, 10.5, 10.75])\n",
"\n",
" for ii, mag_aca in enumerate(reversed(mag_acas)):\n",
" flight_probs = 1 - acq_success_prob(date='2018-05-01T00:00:00', \n",
" t_ccd=t_ccds, mag=mag_aca, color=color, halfwidth=halfwidth)\n",
" new_probs = floor_model_acq_prob(mag_aca, t_ccds, color=color, halfwidth=halfwidth)\n",
" plt.plot(t_ccds, flight_probs, '--', color=f'C{ii}')\n",
" plt.plot(t_ccds, new_probs, '-', color=f'C{ii}', label=f'mag_aca={mag_aca}')\n",
"\n",
" if color != 1.5:\n",
" # pf1, pf2 have p_fail, p_fail_lower, p_fail_upper, mean_t_ccd, mean_mag_aca, n_fail, n_acq\n",
" for pfs, clr in zip(pfs_list, ('C3', 'C2', 'C1')):\n",
" for pf in pfs:\n",
" yerr = np.array([pf[0] - pf[1], pf[2] - pf[0]]).reshape(2, 1)\n",
" plt.errorbar(pf[3], pf[0], xerr=0.5, yerr=yerr, color=clr)\n",
"\n",
" # plt.xlim(-16, None)\n",
" plt.legend()\n",
" plt.xlabel('T_ccd')\n",
" plt.ylabel('P_fail')\n",
" plt.title(f'P_fail (color={color}: new (solid) and flight (dashed)')\n",
" plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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6xm7WGTSpYhBCfIKSWzdECJGBknjEA0BK+TZKRqzJKKn5KoBZTSmPyrkzf/58XnvtNY4c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\nEv4Pmochb5si1i8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x100a706a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_floor_and_flight(color=1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare model to flight for color = 1.5 stars"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+OZZpX+vpyN6T5wkP8KBrO2+CvFyJCPDguhBvuoV4Ex7oQZivB74eztd82Ng2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QIhpkKCv8iKhJGgN9zLvsg+nt///Ei4D3ZktNoPzz4xzGaxUemupNJdycllJ3PRoouozK+kyl1F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\nB+ag9emjtc76BfPN9CBm634n8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"text/plain": [
"<matplotlib.figure.Figure at 0x10ae935c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(13, 4))\n",
"plt.subplot(1, 2, 1)\n",
"for m0, m1, color in [(9, 9.5, 'C0'), (9.5, 10, 'C1'), (10, 10.3, 'C2'), (10.3, 10.7, 'C3')]:\n",
" ok = data_all['red_mag_err'] & mag_filter(m0, m1) & t_ccd_filter(-16, -10)\n",
" data = data_all[ok]\n",
" data['model'] = floor_model_acq_prob(data['mag_aca'], data['t_ccd'], color=1.5, halfwidth=data['halfwidth'])\n",
" plot_fit_grouped(data, 't_ccd', 2.0, \n",
" probit=False, colors=[color, color], label=f'{m0}-{m1}')\n",
"plt.ylim(0, 1.0)\n",
"plt.legend(fontsize='small')\n",
"plt.grid()\n",
"plt.xlabel('T_ccd')\n",
"plt.title('COLOR1=1.5 acquisition probabilities')\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plot_floor_and_flight(color=1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Write model as a 3-d grid to a gzipped FITS file"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def write_model_as_fits(model_name,\n",
" comment=None,\n",
" mag0=5, mag1=12, n_mag=141, # 0.05 mag spacing\n",
" t_ccd0=-16, t_ccd1=-1, n_t_ccd=31, # 0.5 degC spacing\n",
" halfw0=60, halfw1=180, n_halfw=7, # 20 arcsec spacing\n",
" ):\n",
" from astropy.io import fits\n",
" \n",
" mags = np.linspace(mag0, mag1, n_mag)\n",
" t_ccds = np.linspace(t_ccd0, t_ccd1, n_t_ccd)\n",
" halfws = np.linspace(halfw0, halfw1, n_halfw)\n",
" mag, t_ccd, halfw = np.meshgrid(mags, t_ccds, halfws, indexing='ij')\n",
"\n",
" print('Computing probs, stand by...')\n",
" \n",
" # COLOR = 1.5 (stars with poor mag estimates)\n",
" p_fails = floor_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=False, color=1.5)\n",
" p_fails_probit_1p5 = stats.norm.ppf(p_fails)\n",
"\n",
" # COLOR not 1.5 (most stars)\n",
" p_fails_probit = floor_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=True, color=1.0)\n",
" \n",
" hdu = fits.PrimaryHDU()\n",
" if comment:\n",
" hdu.header['comment'] = comment\n",
" hdu.header['date'] = DateTime().fits\n",
" hdu.header['mdl_name'] = model_name\n",
" hdu.header['mag_lo'] = mags[0]\n",
" hdu.header['mag_hi'] = mags[-1]\n",
" hdu.header['mag_n'] = len(mags)\n",
" hdu.header['t_ccd_lo'] = t_ccds[0]\n",
" hdu.header['t_ccd_hi'] = t_ccds[-1]\n",
" hdu.header['t_ccd_n'] = len(t_ccds)\n",
" hdu.header['halfw_lo'] = halfws[0]\n",
" hdu.header['halfw_hi'] = halfws[-1]\n",
" hdu.header['halfw_n'] = len(halfws)\n",
"\n",
" hdu1 = fits.ImageHDU(p_fails_probit.astype(np.float32))\n",
" hdu1.header['comment'] = 'COLOR1 != 1.5 (good mag estimates)'\n",
" \n",
" hdu2 = fits.ImageHDU(p_fails_probit_1p5.astype(np.float32))\n",
" hdu2.header['comment'] = 'COLOR1 == 1.5 (poor mag estimates)'\n",
"\n",
" hdus = fits.HDUList([hdu, hdu1, hdu2])\n",
" hdus.writeto(f'{model_name}.fits.gz', overwrite=True)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing probs, stand by...\n"
]
}
],
"source": [
"comment = f'Created with fit_acq_model-{MODEL_DATE}-binned-poly-binom-floor.ipynb in aca_stats repository'\n",
"write_model_as_fits(MODEL_NAME, comment=comment)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# Fudge the chandra_aca.star_probs global STAR_PROBS_DATA_DIR temporarily\n",
"# in order to load the dev model that was just created locally\n",
"_dir_orig = star_probs.STAR_PROBS_DATA_DIR\n",
"star_probs.STAR_PROBS_DATA_DIR = '.'\n",
"grid_model_acq_prob(model=MODEL_NAME)\n",
"star_probs.STAR_PROBS_DATA_DIR = _dir_orig"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/aldcroft/miniconda3/envs/ska3/lib/python3.6/site-packages/chandra_aca/star_probs.py:298: UserWarning: \n",
"Model grid-floor-2019-08 computed between t_ccd <= -16.0 <= -1.0, clipping input t_ccd(s) outside that range.\n",
" .format(model, name, val_lo, val_hi, name))\n",
"/Users/aldcroft/miniconda3/envs/ska3/lib/python3.6/site-packages/chandra_aca/star_probs.py:298: UserWarning: \n",
"Model grid-floor-2019-08 computed between halfw <= 60.0 <= 180.0, clipping input halfw(s) outside that range.\n",
" .format(model, name, val_lo, val_hi, name))\n"
]
},
{
"data": {
"image/png": 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6PfR82kDt7lJK1ToKdKXEhEZx1013ED87dcJHzcer4KbTZqO65DCVhw5Qeegg\n7fV1AITHWph+xTVkzp1Pes5cQiL8Vxn7UmHSBQ33vetbqvLS9S9NsCWSicLpdA7IS2hs9I1mm0wm\npkyZwpIlS5gyZQpm8/Alz/qzZcsW3L2KCS0tLWzZsmVYoyVeqwtbYTO2A42463tAo2CaGUPoTQmE\nzDIPmXQWzKoTnU0NeFoqUB1Wfvvv99LdesYRmCIiic/IJPfa64nrDQ4GSxgLBG11tX3bQogB+8HA\nNT1TJtqESY/0F5KxIISgsrKSoqIijhw5gsvlIjo6muXLlzN37ty+waRDhwar7OI/vN0uuj+qo/uT\neoTDw3vhB+jQ+BJ32x1dvL7jH3wjO/Aj+hdiLEtZh0KoKvUnj1NZdGbJker1ojMaSZ+Ty7zr1pGR\nOx9z8sQHTsHOpAsaxorX62Xjxo3U1taSlZXF5s2b+2TOKioquPzyy5kzZw4Ar732GnFxcRNprgTf\n36yzs5OdO3dSVlZGdXU1qqqi1WrJyMjg2muvZcqUKSQmJvpl7Wj/0ZELjZYIt5fweoWWl4p960lV\n0KeGE33zVEJyLWjDh7cEKthUJzwuFyf3fcLh7e9TVVwEgGIII23+5X0zB3Ej1KL2N+bkFFprqn22\nKQrm5JQJsUNy6SL9xeSkvb2doqIiCgsL6ejoQK/XM2fOHObOnUtGRkZAcxT642m1Y91dS09BI3hV\nQubEErE8jY4Xt/edE8gR/ZEynKWsw6WruYmKQweoPFTIqYMFHHA6QFFIyJrKoptu711yNBud/tIo\nPjdeTKqgodpaTXFLMQ6vg1vfvJXnVj1HWkTaiNp44403yMrK4pVXXuGpp55i69at3HnnnX3Hly9f\nzuuvv+5v0yUjxOVyYbVasdvt/PSnP8XlcgG+BLPFixczZcoU0tLS0AfggmGxWGhubgYGHy05XUWz\n50AT9sMtJDo1uKNsRCxLI3RBPPr4kStGBIvqRHNlOYd3vM/RXTtw9HQTGRfP4ju/wP6Ckyh6Ezc8\nGDwFrG599DFeffgRbO7OgGtbjwbh9U60CZMa6S8mD/4QzXA6nRw5coSioiIqKysByMrKYsWKFcye\nPXtccuBO46rrxrqzBvuhZtAohC1IIHxZCvpev3AhHzVRnL2UdSS47L4lRxVFB3uXHPlmjsPNsURn\nTeOqNWtJv2weoZFRfrd7MjGpgoYHP3wQh9eX1FreWc6DHz7Im7e+OaI2Tp06xbx58wBYsGABb775\n5gAn8NHMJW45AAAgAElEQVRHH7F06VKWLl3KE088Iae6xhm3201BQQF79uyhp6cHnU7HvHnzsNvt\n3HjjjYSGBv5mesOGDTz//PO43e4BScnuZhu2A03YDjbh7XCiGLSEXGahVFPHFbddPqbEM8vGbEqf\n3uPLaTir8EygcdpsHPtoJ8U73qfhVClanY5pl19Nzso1ZOTMRdFoOFAUfEWsohMSWZp+BwBzfnzr\nBFtzLnua3gVgPnde4ExJIJD+QnIhVFWlvLycoqIijh49itvtxmw2s3LlSnJzc4mOjh43W4QQOMs6\nse6swXmiHcWoJXxpKhFLktFGDlzuuWHDhqAVzhguquqlqewUFYd8QULdiaN9S47Ssi9j3pobyMhd\ngDkllZ07dzLrmuUTbfIlwaQKGiq6Kvq2VdQB+8MlOzubd999l/Xr17Nt27a+Yl4ASUlJnDx5ktDQ\nUL761a+ydetW1q9f7wfLJRfC4/Fw4MABdu/ejdVqJSsrC5vNhslkYt26deTn549LwAC+0ZLk5GQA\n7rnzi9gPNdN0oBBXtRUUME6PIer6TEzZsWgMWuz5dWNWqtDFhrA7/DgA93/rm2P+DBdCCEHt8RKK\nt7/P8b178DidWNIyWLHxq8xeumJAAlkwqxQVG/cCMIfgChqaKrswhd+ODveFT5YEBOkvJOejpaWF\nwsJCDh06RFdXF0ajkdzcXObNm0dq6viuixeqwFHSinVnDa5qK5pwPZHXZRJ+VRKakMFv8cxmM/GG\nGADuHwd1In/R1dJE5aFCKg4dpOpwIY5uKwDxWVNZtO42MnIXkDwzMEuOgtmPjSeTKmjIjMzkVOcp\nADRoyIzMHHEbp29AV65cyZw5c0hMPPOjMRqNGI2+iP72229n79690gkEGK/XS2FhIbt27aKzs5P0\n9HTWr19PZGRk32j/pk2bmDJl/JJKhUcl3hpGUlcE9T/+FLwCfWIYUTdkETovHm3k+E1T+5uejnZK\ndm3n8I4PaK+rQW8KYfaSPC5buYbEqTMGdZZvPvV9VJcNheBTKWqp7p5oE/rwelROHWzi8I4aGsq6\nQDGhFXKJ0kQh/YWkP3a7neLiYoqKiqipqUFRFKZOncqaNWuYOXPmqJe6jlZtT3hUbAebsO6qwdNs\nR2s2EX3rNMIWxqPoh64y7Gm1s7R7JmGqkYZn9mPZmN0n3x1MuBx2akqKfbkJRQdpq/MVvAuPMTN1\n4ZVkzJ1PxjgtOQp2tT1VHZ9+JlXQ8Nyq57j977fj8DrIisriuVXPjbgNRVF4+umnAXj88ccHVPy1\nWq19+sq7d+9m9uzZ/jFccg5er5fDhw+zc+dO2tvbSUlJ4eabb2bKlCkoisKmTZsGKBj19PSwdu3a\ngNvlbrLR9pejzG1Iwqn1EL44mdD58RiShyfXGoyoqpeKogMc/vB9yg58hur1kjwzmyse+D/MuHoJ\nBtPQzqatrravWHUwqhRNND2dTo7squXI7jpsXS6i4kJY8rnpFL+6Bw1ios2btEh/IfF6vZw6dYqi\noiKOHTuG1+slLi6O1atXk5ub65d6CiNV21M8YN1VQ/eeWrxdLvRJYZg3zCIkxzLsImzBrLanuuyo\n1mb+9/vfpfb4UVSvB53BSGp2DrnXXk9G7nxiU9PHfSlfsKvtLbWPz8DopAoa0iLSyLHkAKOX0Gto\naGDDhg1oNBpWrVrFsmXLeOihh3jyySfZs2cP3/ve9wgNDSUrK4sf/vCH/jRfgm8NaXFxMTt37qS1\ntZWkpCTuvvtupk+fPuAicraCkc0WeDWhnv2NdLx5EsWgoSi5nubwHu69cUXA+w0UnU0N1H62h9/9\n9SW621oJiYxiwQ23kJO3mtjU4SeESpWicxFC0FjexaEdNZw60ITqFaTPiSV3RSrp2WYUjULJqzJg\nmEikv5i8dHd3895773H48GG6u7sJCQlh4cKFzJs3j6SkJL/esA5Xbe+0bGrmbg2dnnKMU6KIuWMG\nxunRI7Yn2NT2nDYbJ/bu4cjObbjKS3yvhU9l4bpbycydT/KM2ejGMZF8MKQf8zGpggZ/kJiYyI4d\nOwa89uyzviqBa9euHZfR7MmIqqocPXqU/Px8mpubiY+P56677mLWrFmDXjDPVocICQnc1Kvq8tLx\n91PY9jdiyIpEeAS1nU3oI8IC1udg3Bq7bMxteFwuSvd9QnE/qdSseQtZce/9TF14BVrdyKfgb330\nMf7wyDfwuJxBqVI0nnjcXkr3NXE4v4bmKisGk5bLlqeSszyF6ISzcm7EOM03SwKG9BcXD16vl717\n91JUVER9fT0ajYbp06czb948pk+f3ieV628upGR0tmyqPR6m3DEPQ9roZzmCQW1PVb1UHS7iyM4P\nOblvr88/JKeis2ShjUzgS09+b9xtGgqptudj0gUNskjPxYUQguPHj7Njxw4aGxuxWCzccccdZGdn\nD6l1fbaCUaByGtyNPbT++RieZhsRK9OIXJVB8+8Pc2vsMuLvyw1In4GgubKcw9vf5+jugVKpVmMY\na9bdPKa2oxMSSZw2HYC7HvuJP8y96LC2OSjeVUvJnjoc3W5iksJYvmEGM65MxGA69zLceqQCm1uP\nwxTLX76/lxv/fS5RccG35vhSR/qLSxtVVamoqKC5uRmbzUZNTQ1JSUlMmzaN2267jbCwwA/8nE9t\n72zZ1ND58UQsT+Xkkc+YNYaAASZWba+1ppojuz7k6O4ddLe1YgoLZ87yVcxZvorEaTP49WP/M262\njASptudj0gUNkosDIQQnT55kx44d1NXVYTabuf3228nJyRlWYZz+Ckb33Xcf+fn5frfPVtBIxz9O\noRi1WL6cg2l6jF/7GCnNlWUAxDO8YOW0VOrh7e/TWHZGKvWyldeRnpOLotH4/XubTAghqDvRwaH8\nGsoLfSOJmbkWclekkjIz5rxLCqzbtvHPPzXjMMaBoqGjwcZbvy7i7seuGk/zJZJLlp6eHgoLC9m/\nfz9tbW1oNBoiIiL44he/SEJCAvn5+eMSMMBAX3XvvffiLOuk+c1in2yqQUv40hQirklBG2W8QEvD\nZ7zV9uzdVo5/tIsjuz6k4eQJFI3GN4O98atMWXjlRVNgLVjV9gDMYWtQCPzstAwaJEGFEILy8nK2\nb99OTU0N0dHR3HLLLeTm5qLVDq0IMV6oTg8db5zEVtiMcWoU5s/PQhtxcSgijUQqVTI6VI+geFct\nh/NraKvrwRimY97qdHKWpRBpOf9sgep00vTUz2j/85+xLX8OFF9wLAR0NEzsmmOJ5GJHCEFVVRUF\nBQWUlJTg9XpJT09n+fLl7N+/H0VRSEhImCDjIK47jOZfF/lkU8P0RF6XQfiVSWhCL44b6rPxejxU\nFB3gyM5tlO3/DK/HQ1x6Jsu/9BVmL8kjLPrcQTYpazoyXA4PZQebOf5pAx2aOCLUtoD3KYMGSdBQ\nWVnJ9u3bqaysJDIyknXr1jFv3ryArSUdDa66btr+cgxPq53I1RlErEgbc40FfzHUKMhgUqnZS1aQ\ns3L1eaVSJwPJs7/st7Y6m+0c3lnD8Z2Co+7jWNLCWfGlWcy4PAGdYeiA11lWTu3DD+M8ehTzxo2E\nlrbRozODRgOqSqg38M5AIrkUsdvtFBUVUVBQQEtLC0ajkYULF7Jo0SLi4+Npa2vjX//61wDJ0/FC\nqALbwSaurkgn3GXAa3YTfetUwhYmXFA2NVhpqiijZNeHHN2zE1tnByGRUcxdfQNz8q4lPnPoZcLB\nLM8NwSHR7fWqVB9p4/hnDVQUteBxq0RaTJhEDxoCn9cQPHdj40Tll+4BIONPf5xgSySnqa6uZseO\nHZSVlREeHs7atWtZsGDBqHWvA4EQgp5PG+j41yk0IXos/3YZpqnjV+1zNKjeXqnU7aOTSpVcGKEK\nqo+2cSi/hsriVjSKQngqrLpzAUlTo4YVjHW8+SYNP/ghGoOB1Od/TcSKFeQuXE5R9lexhSYQam8k\nt+R3ICtDjzvSX1ycCCGora2loKCA4uJiPB4PKSkp3HLLLcyZMwdDPyWewSRP58yZE3D7HCfa6Xy7\nHE+jDWEUHEpqYO037xi2bGowYevsoLFoP398+280V5aj0eqYuvAKspevImveQrTDHPiT8tyDc1pt\n78SnDZTub8LR7cYUpmfW4iRmXplIQlYkr96/ZVxsmXRBw1jxer1s3LiR2tpasrKy2Lx5c99I+Cef\nfMJ3v/tdAOrq6rjxxhv5+c9/Tl5eHl6vF61Wy1e+8hW+9KUvTeRHCBrq6urYsWMHpaWlhIaGsmbN\nGhYtWjTggh4MqA4P7VtLsR9qwTgjBvOdM9CGD26jp9WOq8YKbnXCiuZ0NDZQvOMDjuzcNiap1Eud\nzmY7TRVdeNzqiJONXXYPx/bWczi/lo5GGyERehatzWTO0hQKij4hedqFA0q1p4eGH/yAzr//g9DL\nLyf5f36Gvnd5RFRiBFfu+zEKAjQaDFlZY/qskolB+ovxxel0cujQIQoKCmhsbMRgMDB37lwWLVpE\nUlLSoO8ZruSpv3DVdtP5dhnOU51oY02Y757FBwX/AoWLKmDwuN2UHfiMIzs/pKJwP6rXS8KU6ay8\n72vMumb5qJa6SlnTgXQ02jj+WQMnPmukq9mOVq8ha66FGVckkp5tRqu7cH6nv5FBwwh54403yMrK\n4pVXXuGpp55i69at3HmnbwTw6quv7kscvffee7n11jPLRN555x3Cwy/eAl/+pKGhgfz8fI4dO0ZI\nSAirVq3iiiuu6KuOGky4aqy0bjmGt91B5PWZRCxLHXI5UssfSsDtS0Ya76I5LruNzqZGXviPf0NR\nNGTOnc/Ke7/GlIWXj0oq9VLnrV8X4en9Ww032bi9oYfDO2o4trcBt9NLQlYk196XzbQF8Wj1w7+A\nO0pKqP3Pb+GqrsbyzW9i+foDKP1ydtJ+8zzFt9yNydaKMSuLtN8EzxS9ZPhIfzE+1NfXU1BQwKFD\nh3C73SQmJrJu3Touu+yyC/qVC0me+gtPu4Ou9yuxHWxCE6oj6qYphF+ZRPMLxdzIQuK/Nr5qe6OR\n6BZC0HiqlCO7PuTYR7twdFsJizGz8MZb6QmJYO3td4zNJinPja3LRWlBIyc+baCp0goKpM6MYdHa\nTKbOj8MQcu5te0ddF1Y1DLcuPOBqe5MqaHBVV2M/fBjhcHDqxnWk/eZ5DGkjG3k9deoU8+bNA2DB\nggW8+eabfU6grx+Xi88++4wXX3wRAI1Gww033EB0dDTPPfccGRkZ/vlAFxlNTU3k5+dTUlKC0Wgk\nLy+Pq666CpPJNNGmnYMQgp6P6+h4uxxtuJ64+3MxZl64VP2AIjnjWDSndN8nNFWUo9FqWXznF5iz\n/FoiLXHj0vfFSkfjmb+NEAP3+6OqgsriVg7vqKb6aDsancL0hQlctiKVhMyRjaYJIWj/0ys0/exn\naGNiSH/5JcKuuOKc8wxpaRxf+HUANvzu7hH1IfEP0l8ENy6Xi+LiYgoKCqirq0On05GTk8OiRYtI\nSUkZdp7WYJKnhw4d8pudqt1D145quj/2LbWJWJ5KRF4amkFu/saTkajtWdtaOLo7nyM7P6Stthqd\n3sDUy69izvJVZFw2D41W6xelvckqz+12eikrbObEZ41UH21DqAJLWjiL109j+qIEwmMGD3yF203H\n1jf4+7sCtz4WFCXganuTKmiofuDrCIcDAFd5OdUPfJ2pb/1rRG1kZ2fz7rvvsn79erZt20Z7e/s5\n52zbto1Vq1b1SYO+9tprxMbGsnPnTh588EH+8Y9/jP3DXES0traSn5/P4cOHMRgMLFu2jKuvvjqg\nBdfGgmpz0/a3UhxHWjHNMhPzuRlow4Y3Uq+LC8XT1HvzOU5Fcw6+9y+2v/Rb9EYTlvQMrl4/fol8\nwyUYHUB0Qijt9b6/laJwTnE1R4+box/VU7yrhq4WB2HRRq68eQrZS5IJjRz5EjpPezv1//U9urdv\nJ3z5cpJ+8iS6mImV6b0YURTlRWAd0CSEyAlUP9JfBCeNjY3s37+foqIinE4ncXFxrF27ltzc3FH5\nlLPluf2F8Kh0763Hur0K1e4hdH48kWsy0EUH3yDZYLhdTk7u20vJzg+pPFSIECrJM7NZff83mXHV\nEkxhciZsLKheFWu94IOXjlBW2ILH6SXcbGT+mnRmXJFAbPL5v1/h8dD593/Q8vzzuGtqxlVtb1IF\nDa6KijM7qjpwf5isW7eO/Px8Vq5cyZw5c0hMPFcO7LXXXhtw8YmNjQVg+fLlPPzwwyPu82Klvb2d\nnTt3UlRUhE6n45prrmHx4sXjpn89GgfgqrbS+pejeDtdRN2QRfiSlBGpI1k2ZtPw7AFwq+jiQgNa\nNEeoKrv+8jIF/9xK+mVzqTt+lPoTx3j54a9LqbphcOO/z+XVH3yKx60SnRjKjf8+F4CWmm4O76jm\nxGeNeNwqydOjufq2aWTNs6DVjm4Nqa2ggNpHvo2ntZWE7/5fYu65Z9IqVvmBl4FfAQHNTpb+Inhw\nu92UlJRQUFBAdXU1Wq2W7OxsFi1aRHp6elD9LwkhsB9qofO9CrxtDozToom6IQvDEDeBE8Fganun\nJblLdn7I8U/24LLbiLDEceVtnyN72UpikiZ3jsFY1faEEDRXWTn+aQOl+xqxWwXG0FZmXJHAzCsS\nSJoaPeT9hvB66XrrLZo3bcJdWYUpJ4fE/++/Cf1DAz3a8VHbC3jQoCjK9cAvAC3weyHET846HgW8\nAqT32vM/QoiAlOE0ZGbiOnXKt6PRYMjMHHEbiqLw9NNPA/D444+zcuXKAcfdbjf79u3jhRde6Hut\nq6uLyMhISkpKiJkEI4udnZ3s2rWLgwcPoigKV155JUuWLAnqNbpCCLr31NL5TgXaSANxD+RiTB95\nIpcuNgRDqq9aZyDXqHrcbt799c85/vEu5q65keojh/C4XEBwStUFI1FxIcT3Li+65aF5lBW2sP2P\nR6kr7UCn1zDjCt8SJEvq6KuvCq+X1s2baX7uV+hTU8ncsoWQnMAqs1zqCCF2KYqSGeh+pL+YeFpa\nWti/fz+FhYXY7XbMZjNr1qxh7ty54zb4NBKc5Z10vF2Ou9qKPjGUmC/nYJwefd6gJhiEMwC6mps4\nsutDSnZtp6OhHr3RxIyrriF72SrSsnNQhlFQ9VJnLMIZnc12TvQmNHc02tDoFDIvs+AOa+HGu5Zc\nMB9OqCrWd9+l+VebcJWVYZw1i9RfbyJ8xQoURSH3P8dPbS+gQYOiKFpgE7AaqAH2KYryDyFESb/T\nvgGUCCFuUhQlDjiuKMqfhRAuf9uT9pvnKbvpZoTDgWGUyYUNDQ1s2LABjUbDqlWrWLZsGQ899BBP\nPvkkISEhbNu2jZUrVw6oWrxy5cq+adNNmzb57fMEG1arldLSUnbv3o0QgoULF7J06VIiI4O7YJi3\nx037aydwHGvDlB2L+Y7pQV1Qx9Hdzd+f/hE1JcUsvfteLr95PT+/+5a+41KqbvioXkFPp5M/fe8T\nutudRMSaWHz7NGZfk4RpmEvSzoe7sYm6Rx/F9umnRK5bR+Ljj6EN4sBZMhDpLyYGj8fDsWPHKCgo\noKKiAo1Gw6xZs1i0aBGZmZkDvqtgwd1ko/OdchxH29BGGoi5YzqhCxIuOEs9kcIZqurF3tXF/37/\nu1SXHAYgbU4uV93+eaZfuVhKcp/FSIUz7N0uThY0ceKzRhrKOgFInh7N/NXpTJkfhylMT35+/pAB\ng1BVrNu20fLcr3CWlmKcPo2UX/yCiNXXDgjkxlNtL9AzDVcAJ4UQZQCKorwK3AL0DxoEEKH4QvFw\noA3wBMIYQ1oaIZddBoxedzsxMZEdO3YMeO3ZZ5/t2167di1r164dcLygoGBUfY0HL73km9QZy1rO\n7u5uPvroI/bt24fX62X+/PksW7aM6OjgrmMA4Kzsou0vx/B2u3xqFouTg2qq+2y6mpvY+pPHaa+v\n44YHH2H2kjxAStWNhrKDzTRVdqF6BWmzY1j2+RlkXGZB44difd27dlH3f7+LareT9MQTRN1+W1D/\nri41FEW5H7gfICEhYUCSZlRUFFar9cKNREejz/YtL4z/7W9wAs7zvM/r9Q7aZlhY2ICcBKvVyg9/\n+EM8Hg9Wq5UlS5awZMmSAe89278My1bA4XAMmoza3d3tlyTVQNDfNrvdTn19PfX19bjdbkwmE1lZ\nWSQmJmI0GqmqqqKqqiogdnR0dAD02TLc70zrBPNJhcgaBaGB9umCjkw7ovsY7Dp2wfdPbdagnK5M\nIMDd3HPBfsf691S9XlqOFFF34jioKrbuHpIvvwbzjGyMkVE0C2je++mI2/XX7+zsv4U/8Idt7Q1q\n37YQ0N5gO6dN1SOw1kFnhcBaDwgwRkH8XIWodDCEddHk6aJp34mh7RICw+HDhP/zn+ira/AkJND9\nlS/jXLiQKo0Gdu0acLr23o2E/fiXmOyteBMSaLl3I9UB+p8PdNCQAlT3268BrjzrnF8B/wDqgAjg\nLiGESoCQRXr8h81m4+OPP+bTTz/F4/GQm5uLyWQ6J2gKRoQqsO6qoev9CrTRJuK/PrdvWVGw0lRR\nxtafPI7H6eSO//oBaXPOLH+SUnXDp6fTya5XT1B2sBmdQYM5OYyb/898v7QtXC7C//Y3qj/YhnHG\nDFJ+/gzGqVNH1dZix7u9W1I9aaQIITYDmwEWLVok8vLy+o4dPXqUiIjh/a9H/OXPwzrParUOu81A\nYTKZmD//3N9xfn4+/T9/MLF9+3YSEhIoKCjg1KlTKIrCjBkzWLRoEVOnTh23WYXy8nKAvu/pQt+Z\n6vLSvasG60e1CI9K2FWJRK5KJ/089XvOR8OB/QOEM/RxYeTlDT3TMNq/pxCC0s8+Zvdf/kxHQz3G\nsDAiLfF86ae/9MuAhr9+Z407fdc9f/5m/WFb3c69A4UzEkPJy7sKVRXUnmjnxKcNnDrYjNvhJSzK\nwLxrE5l5ZQKxKeHn/X7PtksIQc+ePTT/8jkchw+jT08n7qc/IXLdugGS3IOx5X1fgcJAq+0FQyL0\ndUAhsBKYCnygKMpuIURX/5P8MnI0Qs43ejRejMfI0WiierfbTU1NDTU1NXi9XuLj48nMzCQ0NDRo\nR7X626VxQcIhDWEtCt0JgqacHo6f3A8n/dTZTN9TyTC/h+F8Z51V5ZS9/w+0RhPTb/ocp5rbOHXW\ne0y9EquZN91F4dFjcPTCI13+sG0iGI1dQgg6yqChUCC8EJ+rYK1TsTn88xm1zc1EvfAiYRUV2JYt\no/GO9VRVV0N19YXfPAgxvf+b5UH4/UskY6Gzs5MDBw6wd+9eXC4XERER5OXlMX/+fKKiLixt7W+G\nO9MuVIGtoJHODypRrS5C5sQSeX0m+lGq5I2XcEbdiWPs/NML1J04SmxqOtc98B98+OJvaa4s5w+P\n/HtQCWcEo9IenCuccfVtU/no9VJK9zXS0+lCb9IydUE8M69IIHlGzIhmrIUQ2D75hOZfPoe9sBB9\nSgpJT/yIqFtuQRlmNe3xItDW1AL9ha1Te1/rz33AT4QQAjipKEo5MAv4rP9J/ho5GgkTPXo0HiNH\nZ4+wDIXT6WTv3r3s378fh8NBdnY2eXl5xMfHB8Q2f3LaLmdZJ62vHkO1uYm+dQopVyYxa4KXjVzo\nOyve8QEH3nkDS2o6t333cSLMgxcfCtYRmkAwUrs6Gm3k//kYdSc6SJ4ezYovziI6IZQtX/0LAHl5\nYxud6XrnHep/+hQoCm33f5Wrv/WtMbXXa9TY27gEURRlC5AHWBRFqQEeE0K8MPS7JBONqqqcPHmS\ngoICSktLEUL0JTZPnz4d7QVGUicSIQSO4+10vlOOp9GGIT2CqC/MGlbtnqEItHBGR2MDu7f8gROf\n7CYsOobV9z9ITt61/PHRb+JxOQEpnDFcouJCiE0Jx2Z1oSgKb//6MBqNQnpOLEuuTCTzslh0hpH/\nhns++4yWXz6HraAAXVISid//PtG33YpiGLms93gQ6KBhHzBdUZQsfMHC5zl3rr0KWAXsVhQlAd84\nbVmA7ZKMgNPFhz766CPsdjszZ84kLy+PpKSkiTZt+Ajo+rCKrm2V6GJDsNw7J+gk8M5GCMEnr2/h\nk9f/QkbufG76z+9iDA183YdLCa9XpfCDKvb9qwKtXsOKL85i9uKkEcnoDoVqt9P44yfpeO01QubO\nJfnpp6k/WeqXtiWDI4QIvkIkk4TR5MBZrVYOHjzI/v376ezsJCwsjCVLlrBgwQKKioqYNWtWoMz1\nC64aK51vl+Ms60QXa8L8hdmE5MQGdY6SvdvKp1tf5eC7b6HRabn6jg0suun2vuTm/kIZUjhjaLxu\nlbKiZo5+XE9jhW8BTNLUKJbfPZNpC+IxhY9OMMN24CDRzz5L1bHj6OLiSPjv7xH9uc+hCdJg4TQB\nDRqEEB5FUb4JvIdPcvVFIcQRRVEe6D3+G+CHwMuKohwGFOA7QoiWQNolGR5ut5uCggL27NlDT08P\n06ZNY8WKFaSkXFxJtl6ri+QCDV2tlYTMiyPmtmlojME15Xc2Xo+Hbb/fRPGOD5izfBWr738QbZBN\nUwY7TZVdbP/TMVprupk6P46ln59BWNTglTVHg7O0lNpvfQtn6Uliv/pV4v7jQRS9HmTQIJnkqKpK\neXk5BQUFHD9+HFVVycrKYs2aNcycORPdRXAt87Q56Hq/AlthM5owHdE3TSHsyiQUXfCpN53G43ZT\n+N6/2Lv1VZw2Gzl5q7nmzi8Qbo4dcJ4UzrgwrbXdlHxUx4lPG3H0uAmPMRJuNhIaaeD2b49e4cp+\n6BDNz/2Knt270UVEkPDd/0v0XXehMY2t6N945cAF/D9XCPE28PZZr/2m33YdsCbQdpzmjacPAHDb\nwwtG3UZnZyerV6+mpKSEvXv3kpOTw549e3j00UfRaDQ8//zzXNar0nSaCx0PJjweDwcOHGD37t1Y\nrVaysrJYsWIF6enpE23aiHGcbKft1eOYbBCzfjqhixKCeoQIwGW38c+f/4SKogNctX4Diz93d9Db\nHHYHqxsAACAASURBVEy4nV4++2cZRR9WExJpYO3XLmPK/LhBzx3NhVYIQcdrr9H44yfRhIWR9vvf\nE77kGj9YLgk2pL8YGT09PRQWFlJQUEB7ezshISFcddVVLFy4sK9oXbCj2tzEHlNo+KAAFIWIvDQi\n8lLRmAJzu+SPZUlCCE7s3cPuv7xMZ1MjmfMWsuwL9xGXnjno+VI4Y3Ccdg+l+xo5+lEdTZVWNDqF\nKXPjmL04idTZZv76tS14mgEuH3HbjpISmn/5HN35+Wijo4n/9iMcTk0l57rr/GL7eIn8BH+4H4SE\nhoby1ltv8e1vf7vvtf/6r//irbfewmq18sADD/D22wPipAseDwa8Xi+FhYXs2rWLzs5O0tPTWb9+\nPZmjKGo00QhV0PVhFdbtVegsIdTMdZB+eXAkeg1Fd3sbb/zk+zRXlbP6/gfJXeWfC8pE44+br+FQ\nfbSN/D8fo6vl/2fvzMOjKs8+fJ/ZsieTPSGEQNghIIsoAgKKWATZXBBQAdtPQdDP1orW2ha12K/a\n1qVIQCsColCqBVRWQSTsi+yBEAiQPSHLTDKTzD7n/f4IBMKakExmJsl9XVzkzJlz5plk5jzn977P\n+3ssdLu3FQPGt8enAXtuOI1GCufMwbB+AwED7qHVu++iiry+IGmhBWi6+eISQgiysrL4+eefSUtL\nw+l00qZNG+677z66du2KWu25PW+uRDhkKvYUYNiajdYs4d83iuAHE1A14OykK8g9dYLtyz6nICOd\nyDZtefT3b9P2jptfZ7XRMcR06Ah47sLjxkIIQf6ZMtJ2FXD2UBEOu0x4XACDHu9Ip7uj8aujI9bV\nWNJPU/LxPIybt6AICSHy178m9KmnUAYGgBeaXDQr0VCfjn5XolaribziRsFsNqNUKgkNDSU0NBSd\nrmYL71vtdzdCCI4cOUJKSgp6vZ64uDjGjBlDYmKiV45wOw1WSlekYztfjn+fKLTjOnBq9w53h3VL\nSnNzWPXXOZgNBsa/+ifa9b7T3SF5DZYKO7v+e4ZTewoJifJj3Mu9ievUsN10zceOkffyb7EXFBD5\n8suE/8+vWjqlNmFa8sXNMZvNHD16lJ9//pmSkhJ8fX2588476du3bw1zDE9HCIH5WDHlm7Jw6iz4\ndNRyJrKUAWM6uTu0m6IvyGPH8qWc2b+bwNAwfjHjJboNuR+FwnMXlHsSlWVWTu0tIG1XAeXFZjS+\nSjr3j6HrwFZEJQRd996nLjPT1owMiufPx7hhI4rAQCJeeIGwqVNQutmaub40K9FQ145+tUWv19fo\neqxSqbDZbGguLmi51X53YjKZ0Ov1ZGdnExsby+TJk+nYsaNXigUAy2k9upXpCJuT0Mc7EdA32t0h\n1YrctFTW/O3PKFVqnnjzr0QndnB3SF6BEIKMg0XsWHkaa6WDviMSuHNUW1TqhkucQpbRLVlK0fvv\no4qKJGHZMvz7NExfhxY8l5Z8cS1CCGw2G6tXr+bEiRM4HA7i4uIYO3Ys3bt394gY64L1XDll689h\nz61AHRNA6C+T8O0UygkPHgF2mE1sXfwJRzevR6lSM3DCU/QdNQ51PWviPQVXzko7nTJZx0o5uTuf\n7NRShKjq0txvVFsS+0Shvg33o6uxZWZSPD8Zw9q1KPz8CJ8xnfBnnkHpBithV9CsREPZBVP1z0LU\n3K4PWq0Wg+FyWwmHw1Hj4nmr/e4iLS2N4uJi1Go1TzzxBF26dPFasSCcAsPmTIzbclFF+xM+uQfq\n6AB3h1UrdBmn+OZfGwmOiuHR198kJOr2yqia2zSzUWdh+4p0Mo+XEpUQxJiXuhDRwA36HDod+b/7\nHZXbdxA0/AFi585tMhf/Fm5OS76oyZkzZ6q7Net0Onr16kXfvn29y0XvIvYiE+UbzmNJ06EM0RD6\neCf8e0c1mKuaK3DYbBza8B3Hv1mOsDvoMexBBjz+JAHahp1RbYpYDYJd/80gfW8BZqMd/xANvX+R\nQNcBsWijGsaR0JaTQ0nyAsq/+w5JoyH8V78k7Fe/QhXatP4+zUo0aKP9a3b0i26YD4u/vz8Oh4Oy\nsjKMRiNhYWF12u8Ozp8/zzfffINGoyE6OpquXbu6O6TbxlFmRbfiFLYsAwH9YggZnYiiAUYMXI0Q\ngoNrV3N+81riunRj7Ow/4hfo3VOXjYGQBaVnBCtW70MIwcDHOtDz/vg6NdOBqou8+fhxhMXC2VEP\nE79wAZr4y21lKvfuI3/2bJzl5UT/6Y+ETprktaK6hbrTki+qKCkpYdOmTZw5cwaVSkVYWBjTp0/H\nx8eza/2vh9Now7Ali8oDhUhqJcEj2hI0sBVSA85MNjRCljm1ezs7VizFWFJMSEIi4//3FcJb18+Y\npKkPMtksDjIOFpG2q4DCcwKFIoe2PSPoOiCWNt3DUCjrVlp6o3xhz8ujZOEnlK1ejaRUEvbUU4Q/\n+z+oIq7fT8nbaVai4eqOfqNm3nHb5xo5ciRHjhwhPT2d6dOnM3fuXEaOHIkkSSQnJwOwceNGzGYz\n48ePv+5+d5Gfn8+KFSsICwvDx8cHhRfXZZtP6dD/Jx3hEIRN7Ix/L++opZVlJ9uWfsbhjd8T2r4T\nj70xF5UHjCZ6Orr8Sn768hSF5wTxXYMZ+mQXgiPqXmcOkDPjeYTFAoDt/HlyZjxP+3VrEQ4HJcnJ\nlCxYiKZtW+L/9Sm+Hu4l30LD09zzhdlsJiUlhf3796NWqxk4cCD79u1Dp9Px2WefMWnSJLcLmtoi\n7DLGHbkYt+UgHILA/q0Iuj8eZT0XubqanBPHSPnycy6cyyCqbXtGPP9rzpXo6y0YmipCCArPGUjb\nlc+Zg0U4rE5CY/yJ7iUxcvJA/INv/+99db7IfvZZAgcMQP/1N0hA6MSJhD/7LOpo77gHuV2alWgI\nifQjqm1VrWh96+Wu52axe/fuGtsjRoyo/nnw4MHX7HcHJSUlfPnll/j5+TF69GiWLVuG3W5n/vz5\nXpUEAEzHS9AtT0MdE0DY5C6oI72j8ZndZmX9P/9OxoE99B01DhHfvkUw3AKnXebgpiwObsxE7aMk\n7m6J0dN61Wvk35aZeXlDlrFlZmIvKCBv9mzMPx8kZPx4Yv74BxQtDfWaJc01XzidTg4dOsTWrVsx\nm8306dOH+++/n6VLl+JwOICqPLJixQpmzZrllhhrixACy4lSytadw6m34ts9nJCH2qG+zYGGxqI0\nL4cdy5dw9ud9BIZH8NCsl+k6aCiSQsE5D15vUV9u13zAZLCRvreQtN356AtNqHyUdOwbRdeBrYhJ\nDCYlJaVeggGuzRf2zCz0efloH32EiOnTUXthmd7t0KxEA7je8tGTKS8vZ9myZQA8/fTTrFy5Ervd\nDnhPEriENcuAbmU6mvggIv6nh1eUIwGYDOWs+dufKTiTzn1Tn6XPyLFsa8JJoCEoPFfO1mWn0BdU\n0rFfNIMe78j+Q7vrXSqkadsW29mzVRsKBaqoSM6PG4+w22n13ruEjBnTANG34M00t3xx9uxZNm3a\nRFFREQkJCYwYMaJ6zUJJyeWeq0KIGtueiL2wkrLvz2I9W44q2h9VTACyyeHRgsFUXsbur5dz7MeN\nqH18GDRpKn1GjkGt8b5SsNuhLuYDslMm+4SOk7vyyTpeiiwLYhJDuO/pLnToG4WmgftqqOPjsV8h\nHBRBQbRbvRpN6+bVGK/ZiYbmislkYtmyZZjNZqZNm0ZERITXJYFL2ItNlC49gUrrQ/jU7l4jGMoK\nC1j11zkYS0oY/Zvf0enu5tEQ7HZHj2wWB3u/PcfxbbkEan0YNasnbXs0XJ1o/MIFnBs9BmGxoAgK\nwlFQiG+3bsS9/w80XtibpIUWbpfS0lJ++OEH0tPT0Wq1TJgwga5du9YQ5hERERQXFwNVXYQjPLRm\n21lpr1q3sLcAhZ8K7dj2BNwVS/Fnx90d2g2xWy0cWv8d+7/9GrvVyh3DH+KeRyfhH6J1d2iNSm3M\nB8qKTKTtLiB9TwGV5Tb8gtT0HBZP1wGxhMU2vPmJQ69H9/li7IWF1Y+p27ShzaLPmp1ggBbR0Cyw\nWq189dVX6PV6nnrqKVq1agV4TxK4EmeFjZLFJ0CSiHimO6VfpgEN01XTlRRmnGbVu28hZJnH/jCX\nuC7d3B1So3E71pWZx0tIWZ5ORZmVHkNb039sYoOPHGni4/Hp1AlrRgZyeTmhU54m6pVXULSUirXQ\nTLBYLGzfvp29e/eiVCoZNmwY/fv3v25DtkmTJrFgwQLsdjsRERFMmjTJDRHfGOEUVO4roHxzFsLq\nIKB/LCHDE1A0YHPHhkbIMid3/MTOlcuoKC2h/Z39GfzkNMJatXZ3aG7hRuYDdpuTs4eqFjXnnylD\nkiAhKZzBA1uR0CMcZR0XNdcGp8GAbskSdEu/QDaZCB45EltWFgo/v0brvuyJtIiGJo7D4WDlypXk\n5+fzxBNP0K5du+p9np4Erka2OSlZehLZaCPi2R6owj13mvlKzh7cz9qP3iUgRMsjr7/V7BJCXawr\nTQYbO78+w5kDFwiNDeDR2UnEJLrG4tSWk4P11CmE00nr5PkE3X+/S16nhRY8DVmWOXz4MFu3bqWy\nspJevXoxbNgwgm7SeCosLKx6wOmZZ55prFBrhSWjjLLvz+K4YMKnfQja0e1Rx1wedXaUmrHlGsEu\nU/j+QSKmdnN7/sg6foSULz+nOPMcMe07MuqFV2jdLcmtMbmbq80H7h7bnm1fneLMgQvYLE5CIv3o\nPy6RLv1jCdC6pmTLWVGB7osv0C1egmw0EvSLXxAxaya+nTqR9fQUl7ymN9EiGpowsiyzatUqzp07\nx9ixY+lylQOMJyeBqxGyQLfiFPZcI+FPdcOnTfCtD/IAjm5ez4+LFhLVrj3jX/tTs/TUro11pRCC\n9H2F7Pz6DHaLk7tGt6PPgwko1a5x9rLl5JA1ZSrC6cS3S5cWwdBCsyEzM5ONGzdSWFhIfHw8kydP\nJi7OO8ssHKVmytafx3KiFGWYL+FPdcW3e/g1651Klp6Ei7OdjmITJUtPEvNyX3eEjL4gj21ffMa5\nQwcIjoxi5P/Opss997Z0l6fKfCAiPhCTwY5CIbFx4XFUagXt+0bRbWAssR20LrO9lisr0X21HN2i\nRTjLywkcNozIF2bhe4UdfXOeYbhEsxMNK9/6HVA/j+Ly8nKGDx/OyZMn2bt3L0lJSezcuZNXX30V\nhULBggUL6NGjR41jRo8ejV6vB2DevHn07t2bJUuW8M477xAXF0dcXBxfffXV7b+xqxBCsG7dOk6e\nPMmDDz5I797e28FWCEHZd2expOnQjm2PX/dwd4d0S4Qss/PfX7D/229I7NOPh196rcl07Kwrt7Ku\nNJSY2fbVKXLS9FUL2Z7qQlgr1zXmqxYMJhO+Xbq0uCO1cEOaUr7Q6/Vs3ryZkydPEhwczKOPPkpS\nUpJX9h6RrU6M23Iw7shFUkgE/yKBoEGtkW4wyOAovmJ2U1y13UhYTSb2rvo3h9Z/h0qj5t7J0+jz\n0JgW57yLFGUZSE3J48J5A0JAVEIQQyZ3pmO/aHz8XHerKpvN6Ff8m9LPPsOp0xEwZDCRL7yIX4/m\nPetzI5qdaGgI/P39WbduHbNnz65+7I033mDdunUYjUZmzJhxjcXeRx99RGJiIunp6fz2t79l7dq1\nALz00ku88MILDR7j1q1bOXjwIIMGDWLAgAENfv7GpGJ7HpV7CwgcHEfgPa3cHc4tcTrsbFrwEWk7\nt9Fz2AiG/ep5FErvWKztCm5kXSnLgmNbc9j33TkkSWLwxE4kDY5zaVfWKwVDmyWLufCX/3PZa7XQ\nArg/XzgcDrZs2cKePXtQKBQMHTqUAQMGeESX6boihMB0pJjyDeeRDTb8e0cRMqItypCbl6qoIv1x\nFF0UClLVdmNxad3CjuVLqCzT033oA+jyczh/5GfuGvtYo8XhiThsTjIOFnE8JY+iTAMqHyV+QRoC\ntD48/no/l762bLVStvI/lPzrU5zFJQQMGEDEiy/g76UDrM9srKoWWTxisUtfp1mJhrILhRRmnMFh\ns7Lkt88z7tU5aKNj6nwetVpNZGRk9bbZbEapVBIaGkpoaCg6ne6aYxITEwHQaDQ1mqklJyezcuVK\nZs2axcSJE2/jXV3Lnj172LFjB3369GHYsGENck53YTpaRPmG8/j1jCBkRLtbH+BmrKZKvvvHO2Sn\nHmPQxCncNe5xrxzJczUluRX8tCyNoiwjbXuEM3hSZ4LCXDsTc7Vg8PXiLugtuB5PyBcVFRXVvRGg\nbvlClmWOHj3K/v37sdls9OjRgwceeICQENesEXI1thwjZd+fxZZtRN06kPAnu+KTULsy1Yip3Sj8\n8BDYZVSR/kRMbRwjioKMdH5a/CkFGenEdujM2Nl/wC8ohKWvzKr358qbKS82kbo9n7Td+VgrHYTG\n+HPvE53o3D/GpbMKAMJmo+y//6Vk4Sc4LlzAv18/It9/H/9+rhUpTYVmJRrWvPcWDpsVAF1eLmve\ne4tp/1hQ7/Pq9XqCgy9fvFQqFTab7bojOa+88gqvvPIKAOPGjWPKlClUVlYybNgwhgwZUu2Jfbsc\nOXKETZs20bVrVx5++GGvvmG1nitH95/TaNoGE/Z452tGoD1tcZuhpJjVf30TXX4uD816mW6DW+rk\nr8Zhd/LzukwO/5CNT4CKB/+nOx36Rrn8c9oiGFqoK56QL+bMmcPMmTOBuuWL7OxsNm7cSH5+PkFB\nQTz99NPEx8fXO3Z3rH1zGm2Ub8zEdPACikA1oY91wr9PVJ1mJFXhfrT+c+NZXFeW6dmxfCknUrYQ\noA1lxMzf0O3e+5AUCpb89nmXfK48HVkWZKWWkpqSS/YJHQqFRLtekfQYEkerTq5bq3AJYbdTtmYN\nJQsW4MgvwK93b1q9+1f8777bq++TGptmJRp0+XnVPwshamzXB61Wi8FgqN52OBw3TAD9+/dn8ODB\n1ccBBAUFMXToUNLS0uolGtLT0/n2229p164djz76aI0ZDW/DXmSi5IuTqMJ8iZjS7bq1qp60uK00\nN4dv3vkDNrOZR15/i4QevdwShydjNTlYOfcAZRdMdLknhoGPdsQ30PV2iDcTDC0L21q4EZ6QL+68\n887q8tLa5IuysjK2bNlCamoqQUFBjB8/Hp1O1yCCobERDpmKXfkYtmYjHDKBQ1oTfF88iga2Xm5I\nnA47hzZ8z97/rsBhs9NvzKPcPf4JfK5YN+Wqz5WnYjLYSNudT+r2PCp0VgJCNNw1uh3dBrZymQPS\nlQiHA989ezj7zl+w5+Tg26MHsW+9TcCggU1GLOQYcjhafBS7bGfcmnHMGzaP+CDXfOc999vnAsJa\nxVGamwNU9SUIa9UwjhH+/v44HA7KysowGo2EhYVd85wlS5aQm5vLokWLqh8zGAwEBwfjdDrZt29f\n9YjS7ZCZmcnXX39NbGwsEydORKXy3j+t02Cj5PNUJJVExDNJN/TZ9oTFbVDVxXPVX99EdjqZ+Na7\nRCZ4fhlVY+K0y5QVmTCV2wiO8GXM//Yivtu13xFX0DLD0MLt4gn54t13361+7Gb5QpZlLBYLH3/8\nMQCDBw9m0KBBaDQar+s4L4TAkqajfN05HKUWfLuGETIq0aM7OQOcP/wzPy39F/qCPBL79GPI0/9z\n3c+Mqz5XnoQQgsKz5RxPyePsoSJkp6B1l1AGPd6Rtj0jXNJX4ZoYnE4M6zdQMn8+IZmZKLp1pfWC\nZAKHDm0yYgFgf8F+Zv44E7tsB+B8+Xle/PFF1oxb45LX8947y9tg3KtzqmsJw+JaM+7VObd9rpEj\nR3LkyBHS09OZPn06c+fOZeTIkUiSRHJyMgAbN27EbDYzZswYnnvuOfr168fQoUNp164dixcv5oMP\nPmDDhg0IIZg0aRJtb7MLbUFBAStWrCAkJIQnn3wSHx/vbTkvW52ULD2BXGkncnpPVDepc3fn4rZL\nOGw21vx9LqbyMp6Y838tguEqTAYbGxYew1RuI0Drw8Q/3o3ap3EWhbcIBvchSVJrYCJwL9AKMAOp\nwDpggxBCdmN4tcIT8sXYsWNp06YNK1asuG6+EEJgNpsxGAxYLBY6d+7M8OHDq2clvA17kYmyteew\nntajivQj4pdJ+HbybJvqKy1UQ2NbMf53c0jsfeP6+Ib8XHkaNouD0/svkJqSR2leBRo/FUlD4kga\nHEdojOsc8a5EyDLGH36g+OOPsWWcxadTJ8qmT6f/r19qUmLhZOlJPjr0Ebvzd9d4XEYm05Dpstdt\nVqJBGx1DTIeOQP0s9IBr3C4Adu+u+ccbMWJE9c82m+2a58+ZM4c5c+p3wSgtLeXLL7/Ex8eHKVOm\nEBDQOF9MVyCcAt3yNOz5FYRP7Y6m9Y0bDYH7FrddQgjBpoUfUXD6FGNe/j0xHTo16ut7OsU5RtYn\nH8NSYSc0xh+/IE3jCYbsbLKmTvNqwdBYbhgNjSRJi4E4YC3wLlAE+AKdgBHAG5Ik/U4Isd19Ud4a\nd+cLh8NBcXExQgiKiop44403auQLm81GeXk5drsdtVpNYGAgjz/+eL3idBey2YFhSxYVewqQNApC\nHk4k8J5YpEYYkb5dbGYTe1et5OC6b1Fp1Ax+6pf0eWg0StXNSy4b8nPlKZTmV1BwUGbJml3YLU4i\n4gO576kudOwX3WjXfCEEFT/+SPG8j7Gmp6Np3564D94n6Be/IHv79iYjGLIMWXx8+GM2Zm4kxCeE\nV+58hVVnVnGu/BwAChS0DW7rstdvVqIBPONLWlJSAkBERES9zmMwGFi2bBmyLDNt2jSvdcWAi70Y\nvs3Akq5HO74Dfl1uXb6iCverFhZR03u6OsRr2PPNCk7tSmHQpKkc2vgdhzZ+5xGfL0/g3OFiNi8+\ngY+/mkdm9yWyzc0FYEPSFASDl/MPIUTqdR5PBVZJkqQB2jRyTLeFO7/POp0OIQRQte5Bp9MRFRWF\n0+nEYDBgNptRKBRotVr8/Pyq84pXIaBiXwGGHzKRTQ4C+sUQ/GACykDPtYO9xkJ1yAPcO3lqnRp3\nNoU84XTKnD9SQmpKLnmny5AU0KlfJElD4ohuF3zbN+mXui7Xdr2ZEIKKlBRK/jkPy8mTaBISaPW3\n9wgeORKpCVmdF5mKWHh0IavOrEKj1DC953Smdp9KkCaI+9vczyPfPoLFaaFdSDvmDZvnsjianWho\nKphMJr788ktMJhNTp06tYennjRi35VC5v5Cg++IJvLt+DlKNQdquFPZ8s5zuQx/grrGPkXn0oLtD\n8giEEBzcmMW+b88R3S6Yh2b0IOAWHuoNSQ3BsHQJvld1QW/B9dxAMFy53wZkNFI4XsuVVquXto1G\nIxUVFQghCAwMJDAw0GsNL6znyonfraDMmIGmbTDa0e3RxAW6O6ybUphxmq2LP6EgI52YDp0Y+8of\niO3Y2d1hNSoVegsnduZzckc+JoONoHBf7hnfHp04xwMjGm+2XwhB5a7dFM/7J5ajx1C3bk3sX/5C\nyJjRSF68pvNqyq3lfJ76OcvTluMQDiZ0nsBzPZ8jwu/yoHN8UDxJEVXN6Fr6NLRwDU6nk+XLl1Na\nWsqTTz5JXJx3L6SqPFyEYVMW/r0iCX4wwd3h3JK89DQ2LfiQ1t2SGP7srCYz7VlfHDYnW5ed4syB\nC3S6K5r7nu6CSt14Iz0tgsEzkCTpOCCutwsQQojGnxb0QlQq1TXCwWg04uvrS3BwsNeaXTjKLJSv\nP4/5WAkKXwib3AW/HhEefR2tLNOzY8VSTmzbgn+ItoaFqrey+h+HgJoNN2+EEILcU3pSt+dx/mgJ\nQggSksJJGhxHm+7hKBQS27adr3dMtpwczMePIywWzo56mPiFC9Bcx/mrcu8+iufNw3zwIKrYWGLe\nfgvt+PFIate78TUWZoeZr9K+4vPUz6mwVTAqcRQze828oStSY5WxeudVpxnjcDg4ceIEer2exx9/\nvLoJkLdiyShD/81pfBJDCH2sk0cnDoDyokK+/ftcgiMiGfPy729Zv9pcqCy3sn7BcYoyDfQfl0if\nXyQ06t+yRTB4FA+7O4CmgFarrVFypFQq0Wq1Xmt0IducVGzPxZiSixAQNKwNRxTnadfTc2fJnQ47\nhzd8z56LFqp3jn6E/o9MrGGh2pSxmuyc2lNI6vY8yi6Y8A1U03t4PN3vjSPYBW5WOTOeR1gsANjO\nnydnxvO0X7e2er/p4EGK/zkP0759qKKiiP7TH9E+9hgKL+xufiPssp3VZ1az8OhCis3FDGk9hBd7\nv0jnMM+Y0WoRDV6ELMusWbMGnU7H6NGj6dat/lOB7mjWcwl7YSWly06iivAj/OluSKq6j9o05loG\nq6mS1e++jXA6GffqHPyCqho0NVTnWG+lKMvA+gXHsZodPDSjB4m9GvcmoEUweBZCiCx3x+BtCCFw\nOBzYbLbqf06ns3p/SEgI/v7+Hj+ocj2EEJiPlVC+/jzOcit+PSMIGdkOldYX0QCj067i/OGf+emL\nz9Dn59Ku950MnfJsk7FHLS82U5RpwGGXWf7WXkbNvIOQyMsioDjbSGpKLqf3X8Bhl4lJDOaBZ7rR\nvk+kS2ePbZmZlzdkuXrbfPQoxf+cR+WuXSgjIoj+/etoJ0xA4Xtjd0VvQxYyP2T+wLzD88g2ZtM7\nqjd/H/J3+kTfeiaoMWl2oqHok2NA/W42y8vLGT58OCdPnmTv3r0kJSWxc+dOXn31VRQKBQsWLKBH\njx41jhk6dChOpxOlUsmECROYMGFCnV5TCMGGDRtITU0lMTGRvn3d08SsoXCWWylZnIrkoyTime4o\nXNw6vr7ITifff/BX9AV5PPr7P9dIHq7qHOsNZBws4sclJ/ENUvPo7D5E3MLxqqFpEQyehyRJO4UQ\ngyRJMlKzTOlSeVLwDQ6t7flHAB8BSuAzIYRLVpWWlJRg/XcWarW6wfPF9u3bee211wB477336NCh\nQ/WiZ4VCwWOPPYYsy0iSxJNPPlmvHj7uxJZXQdn3Z7FlGlDHBhD2RGd8Ej3bsENfkEfG+lUcffAS\n0gAAIABJREFUzDpXZaH62hwS+9zYQtUbWZd8FMfFxqhlhSbWJR9lwu/7kXGwiNSUPC6cN6DSKOh0\ndwxJg+MazchC07YttrNnqzYUCtSxseRMn0FFSgpKrZao2a8QOmkSiiY00yOEYHf+bj469BFpujQ6\naDvw8f0fM7j1YI8cJPDsOzUPxd/fn3Xr1jF79uzqx9544w3WrVuH0WhkxowZ17XY27BhA4GBgbfl\ncrFt2zYOHDjAgAEDUHt53Z5scVCy+ASy2UnkjJ6otJ49WiCEYOviT8g6dpgHZ/wvbZJq3kA0tw6f\nAEIWHFifyYG154ltH8KI6T3wD27cKeKmLBhyjDmklqRicVpc3uGzoRFCDLr4f4PfaUiSpATmA8OB\nXOCAJEnfCSFONvRrNRT+/v58++23vPrqqxiNRoqLi3nttddYtGgRlZWVvP7663zzzTdoNBo0Gg1K\npRKVSsXatWuxXCzV8DacFTYMP2RReaAQhb8K7fgOBPSLQVJ43k3QJWxmE3tX/4eDa9eAQmLwk8/Q\nZ+SYJlmCWnbhciNUIUBfYGLp73ZjqbSjjfZn0ISOdOkfg88NGqu6iviFCzg3egzCYkHy88Oem4vT\naCTy178m9KmnUAZ6r6X89ThvPc8XP3zBgcIDxAXG8ZdBf2Fku5EoFZ7r+tSsRIOj1Iwt1wh2mcL3\nDxIxtRuq8LrX5anV6hpuRWazGaVSSWhoKKGhoeh0umuOUSgUjBw5Eq1Wy1tvvUX8dRb33Ih9+/aR\nkpJCr169GD58OCkpKXWO2VMQTpnSr9KwF1USMS0JTSvPdssAOLzhO45uXk+/MY/S474Hr9nfHDp8\nXond5uTHJWmcPVREl/4xDH2yC0p14y4IVBYXk/XW201SMAC88OMLWJxVN4yu7vDpaiRJiqKqTwMA\nQojsepzuLiBDCHHu4rn/DYwFGlw0yGU2RKEZm8NUp3whhMBut2O327FardjtdoQQWK1WzGYzFosF\ntVpNYmIiGo2GioqKa5qxXcoX/v7+/N///V+97bkbC+GUqdhTgGFLFsImEzigFcEPJHj0THK1heqK\npVTqdXQfMgxlQkf6jWq6S3O00f7oC0w1HovrpCVpSBxxnUPdNsItrFYUfn44LRYkSSL8hRcImzoF\nZVDjzmC7mrNlZ/nnoX+ytXArYb5hvH7X6zze6XHUSs8XqJ77TXYBJUtPwsUpOUexiZKlJ4l5uf5l\nPnq9nuDgyzPuKpUKm82G5orFOV9//TXh4eGkpKTw+uuv8+WXX9bq3MeOHWPDhg107tyZ0aNHe+R0\nVW0RQqBflYH1TBmhj3X0+E6fAOcOHWDbF4vo0O8e7p009brPacodPq+mQm9h/YLjFOcYGfBIB3oN\nj2/0z6QtO5vQ9z9AyHKTFAxCCM6XX671dnWHT1chSdIY4B9UdYQuAhKANKB7PU4bB+RcsZ0L3F2P\n890Q++occFzsk3CTfCHLMg6HA4PBgM1mqxYJUHXzr9FoCAgIwNfXl8jISJRKJWFhYfherMe+Vb6Y\nM2cO3333nSveYoNiOa2n7PuzOIrN+HQKRftwIuoozy4jKcw4zdYln1BwJp2Y9h0Z+9s3iO3YmW3b\ntrk7NJfgtMucPlCI03G5atA3UM3I53sS2959ZWPW8+cpmZ+MYd06kCRUsbEkrlmN0ot7T12PgooC\nko8m893Z7/BT+TEqZBR/GvUn/NWe/T25EpeLhtrUn0qSNBT4EFADJUKIIa6IxVF8hbIWV23XA61W\ni8FguPw6DkeNBAAQHh4OwJAhQ3jppZdqdV673c6aNWtISEjgscceQ+nljUoMW7IxHbxA0LA2BNzp\n+QuFi7POs/aj94hs246RL/z2hvZ6TbHD5/UoPF/OhgXHsducjJrZk7Y9Gn/005adTdaUqUg2G22+\n+rLJCQaA9w++j7hiKYCrO3y6kD8D/YEtQojekiTdBzzVGC8sSdJzwHMA0dHRNW4CQ0JCMBqNtzyH\n0Nmu2KjKFwaDASEETqez+p8sy9VPUygUqFQqlEolSqUSSZKQJKn6GJPJRFRUFHq9vjoGq9Va/e8S\nGo0Go9FInz59yM3NrVW8Fovluje7FRUVLr0JVldCxCkFAcUSNn9BSR8ZU2QJnCy55fyPq2O7EXZT\nJXn7dlB6KhWVnz8J940gvHN30vMKSM8rcFtcteF2YnPaBLoM0J0WOCzgqwVNECh9IPEBJ+k5h0nP\nufV5GjouZXExAevW47tvH6hUmIYPp/LB4YjAQPIOH65fQPWMrSGpcFbwQ/kP7DDuAGBI0BAeDHkQ\nzLB/1363xXU7uFQ01Kb+VJIkLZAMjBBCZF+cynYJqkh/HEUXhYJUtd0Q+Pv743A4KCsrw2g0EhZ2\nbTdjg8FAcHAwJ0+evGYq+npYrVZMJhPR0dFMmjTJ69cxVB4oxPhjNv59owl+wPObwVbodax+9218\n/P0Z/+qfUDchl4bb4fT+QrZ+cYoArYYxv+5FuBvKyi4JBmGxoP/Nr5ukYPg89XOWnFjCw+0eZkv2\nlkbp8OlC7EKIUkmSFJIkKYQQP0mS9GE9z5kHXFnb2friYzUQQnwKfApw5513iqFDh1bvS0tLI6gW\n5Q6GMA2i9KJwkEAK88FkMlWLBEmSUKvVaDQanE4nISEhN220plarCQgIIDo6ulpEGI1GIiMjr4nn\nynwRHh5eq3h9fX3p3bv3NY9v27aNK99/QyFbHBi25lCxOw9JqSD4oXgCB8aRWAcXPFfFdiMuW6j+\nG4fNdkML1caOqy7UJTZDqZljW3M5vTMfu9VJfNdQeg9PoHXXhi9Bqktc9rw8ShZ+Qtnq1UgKBaFT\nnib82WdRuagMz11/T5PdxNKTS1l6Yilmh5mxHcby/B3PExsY69a46oOrZxpqU386GVh1qc5VCFHk\nqmAipnaj8MNDYJdRRfoTMfX2LUtHjhzJkSNHSE9PZ/r06cydO5eRI0ciSRLJyckAbNy4EbPZzPjx\n47n//vvx86uqh507d+5Nz22329HpdNXOGb5efsNqOa1Hv/oMPh21hD7SweNLrOxWC9/+7c+YKwxM\nfOs9AsPC3R2S2xBCsPfbsxzckEWrjlpGTE/CL7DxPbGvFAxtliwmv7Cw0WNwNavPrOaDgx8wou0I\n3rn3HQo3Vb3Hxmra4wLKJEkKBLYDX0mSVARU1vOcB4COkiS1o0osTKQqhzQ40uhYxJfZVSVKoWoU\nY2KrFytrNBrUanX1tcxoNN5UMNQnX8yfP98Vb++2ubDwKHKFHWFxIFfY8e8bTciItiiDPNsr//yR\ng/y09F9VFqq9+jJ06rOEtWrt7rBcQnG2kcObs8k4WIQEdOgXRe/hbRrd3e5q7BcuUPrJJ+i//gYJ\nCJ0wgfDp01FHu2ys2C3YnDa+Pv01nx77FJ1FxwNtHuDF3i+SqPXuvlrgetFQm/rTToBakqRtQBDw\nkRDiC1cEowr3Q3PxS1Nff//ruSPt3r27xvaIESOqf/7555+BqtKl4uJibDYbRUVFhIWF1ejs6XA4\nKC0tRZIkAgMDCQz0/IXCN8OWX0Hpl2moowMIf7IrktKzO2gKWWbj/A8oPJfB2Ff+QHS79rU6rimW\nJdksDnJ2CYy5WXQbGMvgSZ1R3kYvjXrHcZVg8O3SBZqYaNiavZU397zJgFYD+Mugv6CQPPt7cjMk\nSfIRQlipGiCyAL8BngRCgLfrc24hhEOSpBeATVSVvH4uhDhRz5Cvi0KrQY72RaVSEvFsj3p1YL7d\nfOFpmNN12LMMVUa6KgXhT3XFL8mzF2nrC/PZ9sVnnDu4H21MbJO0UIWqAZ6ckzoOb84m95QetY+S\nnve35o774wkKc+/Ao6O4mJJ//Yuyf69EyDLaRx4hYsZ01K1auTWuhsYpO1l/fj3zj8wnryKPu2Lu\n4qU+L9EzsvH6SbkaT1gIrQL6AsMAP2CPJEl7hRCnr3xSQ9SoAvhNbgdQq+dfmj5uSCorK6sXyTkc\nDkpKSggIqLIRk2UZk8mEEAJ/f39sNptbalTrw5WxqczQeq8CoYBznQ2k7d3pEXHdjLx9Oyg8tI/W\n9wwht8JMbiP8nj3x72mrFOTsEFjKBDG9FdC6kB07L9z2+UL/8T4A+t++XKfjlMXFhL7/AZLNhv43\nv66aYSgs9Mjf2SXqGtsZyxmSLyTTRtOGR5SPsGvHLgDKysoAPPZ93oQ9QB9goRDi6YuPLW2okwsh\n1gPX3oU3MCqVCtWktl7jXORKhFPGuC0Xw+Yr+vY5Zcp/yPI40VB2obDamMI3KAibyYRSreHeydPo\nM3IsKi8v9b0ap0PmzM8XOLI5m9K8SgJCNNwzvj3d723V6JapV+PQ6ShdtAj9V8sRdjshY8cS8fwM\nNHVwj/QGhBCk5Kbw0aGPyCjLoGtYV/70wJ+4p9U9Hl9ZUVdcLRpqU3+aC5QKISqBSkmStgN3ADVE\nQ0PUqNYVo9HY4Oe9WoTIskxQUBCyLFf3b4iIiECj0TR6jWpDcCk22eygaOFRnFiJmnEH7WLc669c\nm9/ZiZQfOXhoHz2G/YLhz77QaF92T/t7FpwtZ8PCY8gOQcIQJw9Puq/e58xa9DkAd9Thfdqys8l6\n8y2EENcseva039mV1CW2tNI0Xt/0Om1C2rB0xFK0vpfXOy3dWHWf7anv8yZoJEmaDAyQJOmRq3cK\nIVa5IaYWbhNbfgX6r09jL7iqsqwBzUQaktVXNNu0GI1o/P155v2FBIZeu9bQm7GaHZzYkcexrblU\nllkJaxXAsKld6dgv2i0zwlfiLCujdPES9MuWIZvNBD/8MJGzZqJp29atcdWXZzY+A9QsGT144SAf\nHvyQI8VHSAhO4G9D/saDCQ969WzxzXC1aKhN/em3wMeSJKkADVXlSx+4OC63oVKpcDgcNbZlWUan\n0+FwOAgLC7vGecnbEA6Z0mUncZSYiXgmCbWbBUNtyD2Zyg+fzKNN0h0M++XzTW50oLac2lvAT1+e\nIijUl1GzenL01AG3xHHdkqQmRrYhmxlbZhCoCeST4Z/UEAxezgyqypG0wOir9gmgRTR4AcIhY9ia\njXFbLgp/FeFPdaX8hyyXmIk0FIVnz6DLrWkDZLdYmpRgsJsEu/6bwYkdedgtTuI6h3LfU11o0z3M\n7XnLaTSiW7IU3dKlyBUVBD00gshZs/Dp0MGtcbmCdF06Hx36iB15O4j0i+RP9/yJcR3GoVY0rZms\nq3GpaLhR/akkSTMu7l8ohEiTJGkjcAyQqbJlTXVlXO4kLCyM4uJihBCoVCpCQ0PR6/XYbDZCQ0O9\nftEzAvTfnMZ6rpzQJzrj28Hzb4T0hfl8+493CImOYfRvXkdZj9plb0WWBXvXnOXwD9nEdQ5lxHNJ\n+Aao4VTjx9IcBEOxqZjnNj+HLGQ+Gf4JMQGeb0FcW4QQO4GdkiT9LIRYdKPnSZI0XAixuRFDqzUO\nh6O638L11p41dWw5RnRfn8ZRZMK/TxTahxNR+KtRxwY0mJlIQ2IqL2Pnv7/g+E+bkRQKxBUOV02l\n2WZJbgVHNmdzer9AknLo0CeSXsPbEJUQfOuDXYyzohL/DRvIeO13yOXlBD4wjMgXX8S3c2d3h9Zg\n5BhzSC1JxeK0cM/ye6iwVxCkCeI3fX/DpC6T8FPVvVGwN3LLq6AkScfhCtPwK3YBQghx0xUe16s/\nFUIsvGr7b8DfbhltE0ClUlXbp4aHh1NWVobVaiUkJKTaLcObCTsjYTpXTPCDCQT09nxHBEtFBav/\n+hZIEo+8NgdfL154vvofhwAY/9s+dTrOZnGwedEJMo+XkjQ4jkFPdETppgXrzUEwGGwGZmyZgc6i\nY9GDi0gM8X5HjetxM8FwkXcBjxQNOp2uxtoznU5HVJTnX8/qi7A7Kd+cTcWOXJTBGsKndcevy+VR\nelW4H63/PNCNEdbE6XBwZNM69nyzHLvVQt+RY+k2+H7Wz/sbuvw8wlrFeXWzTSEEuaf0HNmcTfZJ\nHSofJWEdYdSU/gRHuP9+QTab0S9fTulniwjS6/EfMoSIF1/EL6k+/Rs9k1lbZmFxWgCosFeg9dGy\ndvxaQnyaVgO6W1GboZMm1Ut98eKqWrRnnnnmts9RXl7O8OHDOXnyJHv37iUpKYmdO3fy6quvolAo\nWLBgAT169KhxzNixYykvL0eWZY4dO0ZGRgYLFizgb3/7G61bt6ZNmzZ89dVX9Xpv7qZiXwFh5xQE\n3BVD0H2ev9DJ6XDw/Qd/obzoAo//cS7amFh3h9ToGErMrEs+hr7QxOCJnegx1H0WhLasLLKmTmvS\ngsHsMPPijy9yrvwcycOS6RHZ44bP9WKr1drisTWAl0pIv//+ewBGj766yqr21DdfHD9+HL1ez5Il\nS3jnnXeIi4sjLi6uwfOFNbMc/TdncJSYCbgrhpCR7VD4eu7sSuaxw/y05FN0eTkk9OzNfdOeIzyu\nKu9M+8cCN0dXP5xOmbMHizi8OZuSnAr8gjXcPTaRpMFx7D2wy+2CQbZaKVu5kpJP/4WzpISAgQPJ\nGTiArr/8pVvjchW78nZx3nC+xmNGm7HZCQaohWgQQmTd6jnNDX9/f9atW8fs2bOrH3vjjTdYt24d\nRqORGTNmXGOx9+233wJViyQ/+eQTnE4nVquVmTNn8sorr7i9FrG+mE/pKFuTQWWkIG6s5/diEELw\n46JkslOPMWLmb2jdNcndITU6+Wf0bFiYihCC0f97B/Fd3Ff32xwEg122MztlNoeLDvPekPe4p9U9\n7g7J3VxvBtsjuN7as9ulvvli6dLLxlMvvfQSL7zwwm3Hcj1kqxPDpkwq9uSj1PoQ8askfDuGNuhr\nNCRlFwpJWfYZGQf2EhIdw9jZf6R937s8PufUBpvFwcmd+RzdmkOFzkpojD/3Pd2FTndFo1Ir3R0e\nss1G2TffULrwExxFRfjfdReRH36A/513cs77XN5uSZmljPcOvMf3575HrVBjl+0AKFDQNrite4Nz\nE7UpT9ophBgkSZKRmhf5S+VJ7i+oqyU6nY78/Hzsdjvz589n0qRJ1+3efCvUajWRkZHV22azGaVS\nSWhoKKGhoeh0uhse+/XXXzN69GicTicajYZ//etffPfdd8yaNYuJEyfe1vtyN7ZcI7qv0lC3CqSw\nazmdlZ5/8f557WqOb/2Bu8c/Qfchw9wdTqNzclc+KcvTCY7wY9TMnmijXbeg0ZaTg/n4cYTFwtlR\nDxO/cEENy70agmHpkiZVB3sJWci8uftNUnJT+MPdf2BE2xG3PqgFtxEWFsbZs2cpLi7G4XDwn//8\nh8mTJ7slX0yYMKF6Ozk5mZUrVzZYvrBk6NH/9wxOvZXAAa0I/kVbFD7uvzm9HnaLhf3ffs2B71ch\nKRQMmjiFvqPGofJy4xCAyjIrx37KIXV7Pjazg1YdtQyZ2JmEpHAkhfvzqbDbKVu9mpKFC3HkF+DX\npw+t3nuPgP5Xt91qGggh2Ji5kb/u/ysGq4Hnej7HyHYjmbh2IhanhXYh7Zg3bJ67w3QLtZlpGHTx\nf/e2EmwAVqxYgd1epRRLSkpYsWIFs2bNqvd59Xo9wcGXtZNKpcJms13jgmS1WtmyZQuvvvoqkiQx\nefJknn/+eSorKxk2bBhDhgwhNta7SmQcOgslS06gCFATMa074uDuWx/kZs4c2MP2rxbTqf8gBk54\n0t3hNCqyU2b3qrMc/TGH+G5h/OJ/urvcyztnxvMIS1UtqO38eXJmPE/7dWurtpuBYBBC8P7P7/Pd\n2e+Y2WsmT3R5wt0heQqZ7g7gRqhUKjZu3Fg921BaWtro+UKWZX766Sc+/PBDAMaNG8eUKVMaJF/I\nFgfl689Tub8QVYQfkdN74tPOM0sthBCk79lBypefU1FaQpeBQxj85DMEhXtWf4jbQZdfyeEt2Zze\nV4iQBYm9I+k9PIHodp4xFiscDsq/X0tJcjL2nBx8e/Yk9q23CRg0sEnM7FyPwspC5u6dS0puCknh\nSXw6/FM6h1XlpaSIqoqEZlA6ekPqPOcqSVIUUG3xI4TIbtCIXMilPghQdSG6crs+aLVaDAZD9bbD\n4aiRAGw2G0ajkW3bttG7d298fHyqR5oAgoKCGDp0KGlpaV4lGmSTnZLFqQiHIPK5JJRBnj/ic+Fc\nBuvn/Z2Y9h0ZMes3SIqm6aV8PaxmBz98lkr2CR0972vNwMc6oGiEBc+2zMzLG7Jcvd0cBAPA56mf\ns/TkUiZ1mcSMnjPcHU6jIUmSLzATGETVLPVOYIEQwgIghLimh4Mncam5HjRuvrjEjh076N+/f7Vx\nhlZb5URX33xhTtdRtuoMToONwMFxBD+QgELjmbMLRZnn+GnJp+SmpRLVtj2j/nc2rbt49yJbIQT5\np8s4vDmbrNRSVGoF3Qe14o4H4gnxEAtb4XRiWL+BkvnzsWVm4tOtK60XJBM4dGiTFQuykPk6/Ws+\nOPQBTtnJK3e+wlNdn0KpuPzdaM5i4RK1Fg2SJI0B/gG0AoqABCAN8JpvcEREBMXFxUCVFVtDdfr0\n9/fH4XBQVlaG0WgkLCwMIQRWq5WKigpsNhuSJLFp0yamTJlSXR9rMBgIDg7G6XSyb98+Zs6c2SDx\nNAbCLlPyxUkcOguRv+qBOsozLnY3w6grYc17b+MXFMy42X9ErfFxd0iNRlmRifXJxygvMjP0yc50\nv7fxbAg1bdtiO3u2akOhqNpuJoJh1ZlVfHjoQx5q9xC/u+t3TTbh3oAvACNwaR5/MrAMeNxtEdUB\nrVaLXq8HXJ8vrsfVpUn1zReyyU7Z2nOYDhWhivIn8vmu+LTxjBHtqzEbDexa+SXHtmzEJzCQ4c++\nQNL9w1EoPFPc1AbZKXP2cDFHNmdTlGXEL0jNXaPbkTQkDr/Axh9wy3p6CgAJy76ofkzIMsYfNlP8\n8TxsGWfx6diRuHn/JOiBB5r0tet8+Xne3P0mh4oOcXfs3cy5Zw7xQZ5v5uIO6jLT8GegP7BFCNFb\nkqT7gKdcE5ZrGDFiBCtWrMDhcBAREcGkSZNu+1wjR47kyJEjpKenM336dObOncvIkSORJIn333+f\nkpISfvjhB6xWKxMmTMDX15fdu3eTnJxcPYL1wQcfsGHDBoQQTJo0ibZe0i1RyALd1+nYMg2ETeqM\nT6JnTmtfidNuY827f8ZqNjPp7fcI0HruQr/bobzYTFGmAYddZvlbexk18w5CIqscNnJP6dj4aSqS\nJDHm172I69S47z1+4QLOjR6DsFjQtGtHzB//0CwEw49ZP/LWnrcY2Gog7wx8p8l2CL0JSUKIK438\nf5Ik6aTboqkjDz/8cKPki+TkZAA2btyI2Wxm/PjxyLLMtm3bqkuToH75wnyiBP2aDORKO0H3xxN8\nfxskN3cNvh6y08nRLRvYvfJLrGYTvUaMYsBjT3q1FbbN4uDwpmx+3pAJgEIpcdeYdvR+oA0qD5nh\nEUJQsXUrxfM+xnrqFJrEROLe/wdBI0Y06dl4u2xnSeoSFh5diI/Kh7cHvM24DuOatECqL3URDXYh\nRKkkSQpJkhRCiJ8kSfrw1od5DiEhIURFRaFWq+tluQpc43YhhGDLli1UVFTgcDiQZZlx48bh7+9f\n/QFMTa3Zs27OnDnMmeN9HtLlG89jPlZCyEPt8L/D873LZdnJ+S3rMWSdZ9xrfyQyoZ27Q2pw1iUf\nxWGvamhUVmhiXfJRJs/pT+r2PHb8+zQh0f6MmtmzWkg0Jpr4ePwuWkrGzv1zsxAMBwoP8Or2V0kK\nT+L9oe+jVjbtLqE34JAkSf2FEHsBJEm6G/jZzTHVGlfmC4Ddu2uu/xox4vLieIVC0SD5wllhI/qI\nRGlhGurYACKmJaGJ88wb8JwTx9i65FNKsjNpk9ST+6Y+R0Sbtu4O67ap0Fs5vi2HEzvysZouO3EJ\nWXDmwAX6jXRfHrrSnOLMffejCAzEduYM6oQ2tHrvXYJHjUJSeoagcRUnSk8wZ9cc0vXpDE8Yzu/v\n/j0Rft6/TsbV1EU0lEmSFAhsB76SJKkIqHRNWK7jkUceabBpZqharGY2m6moqMDpdKJSqdBqtfj5\n+TVJtVqxO5+K7XkE9I8lcLB3dNrcsXwp5ZkZ3DftORJ793N3OC6h7IKp+mchqra3//s0x7flkpAU\nzoO/6o7Gz72e67LF0iwEQ44th/lb59M6qDXzh83HX+35pXsuoi+wW5KkS+ve2gDplxqG3qoxqCfQ\n0PmisRBCYD5WTNl3Zwk0SQQPTyBoaGskNzVtvBmGkiJSvlzM6T07CI6MYvTLr9PxrgFemz9Lco0c\n2ZzDmZ8vXFzcHMW5w0Vc7BVYfX12J1eaUzgKCkClIvaduYSMHYvUxDufmx1mFhxZwNKTSwn3DefD\noR8yLKH5OSjeLrWxXPURQliBsYAF+A3wJBACvO3a8DwXWZYxmUxUVFQgyzJqtZrg4GB8fX299mJ3\nK8wnSin7/iy+XcPQjmnvFe/z2I+b+Pn7VUR270XvEbffoMnT0Ub7oy+4mIgkUKoUHN+WS68H4rnn\nkQ4o3GzbJ1ssWE+dQhEQ0KQFQ5YhiwUXFhDsF8wnwz9B66t1d0jupMVX1g04DTb0azKwnCxF3TqQ\nzF5W4oe1cXdY12C3Wcn/eTdHF80DIRjw+JPcOeYRr1xrJoQg+6SOI5uzyT2lR+WjJGlwHHcMiyc4\nwo/lb+2tvj5LEi61uL5pnLKMccuWy2vMqncItI8+6paYGpP9Bft5c8+b5BhzeLTjo7x858sEazxz\nXY+nUhtJuQfoAywUQjx98bGlN3l+k8bpdFJZWUllZSVCCDQaDUFBQWg0Gq+4ib5drNkGdP8+hbp1\nEGGTuniEd/StyDp+hB8XJdP2jj6E9W+6rg8Ao2bewb/f3ofDLqNQSDgdMvdP6ULXAa3cHRq27Gys\np04hhGjSgqHIVMT0zdMRCD4Z/gkxATHuDsktSJIUKISouFlj0Iuz1i00MJUHL1D2/TmEw0nIQ+0I\nHBRH2o4Ud4dVAyEEZ/bvJmXZIgzFRXTqP4ghT/2S4EjPL3W9Gqdd5vSBQo5syUGXX4n+XWONAAAg\nAElEQVR/iIb+4xLpfm8cvgGXSxJHzbyDdclHKbtgQhvtz6iZdzRqnMJup3ztOko/+6xKMKhUcKl5\n4UVziqaMSTbx5u43+e+Z/xIfFM+iBxdxV+xd7g7LK6mNaNBIkjQZGCBJ0jUWeUKIVQ0fVt0xm80u\nHeV3Op1UVFRgMpkQQuDj41MtFupKbaa7nU7n7YTpEhylZkqXnkARpCFiajePtee7ktK8HL7/4P8I\njY3j4V+/xp79B9wdkksJifQjJMoPXUElPv4qRkzvQasO7h/ltuXmkTVtGkKW8e3SpckKhnJrOdM3\nT0dv0TMrahbtQpreupk68K0kSUeAb4GDQohKAEmSEoH7gAnAv4Bv3BWgq/NFYyKEwFxpQjba0K86\njSYhmNDHOqL2EPvOKynJyeKnJZ+QnXqMiDZt6TRmAqOfnOLusOqMpdJO6vY8jv+Ui8lgIzwugGHT\nutLxzmiU11lgHhLpx+Q5/Rs9Ttlioeyb/1L6+SIc+QX4dOpEq7//Hd/u3Tg//pFqc4r4hQsaPbbG\n4sesH3kn/x0q5Aqe6f4Mz/d6Hj9V46/tayrURjTMoKocSQtcXd8hALeLhtjYWPLy8qobt92IiooK\ngGrb1VthsVjQaDRYLBZsNhsAGo2mus9CUVFR/QK/BbfTfbShcVbaKVl8AgREPNMdpRus4eqKyVDO\nmnffRqFUMf61P+HjH+DukFxOxsEiSvMrUakVPPbanQRHuP+iaC8oIHvaNOSKSnw7d0bh73k3MQ2B\n2WHmxa0vkmXIIvmBZCzpFneH5FaEEMMkSRoJTAcGSpIU+v/snXd8FNX6h5/ZTe89IY2E3jsCgoIg\n4KWocEXk2htiQa965f5UhCtiL/cqKkWxYQEbKKAgxQgoTSEEEkgCIaS3TbKbtnXO749NQgKhZzOT\nsA+f/WTOmdmZ7+6yO+c973nfF7ACqcBPwJ1CiAKl9DnifmE0GvHw8DjncY5ANtmQyi14pBjxn9wB\nn2GRqvMEGysr+ePbL0jcuB53Ty9G3zOLvtf+jW3btyst7YLQF1dzYHM2h3fmYzXLxPYIot+1sUR3\nD1SVAWqrqKDsq5WUfvopNp0Oz/79iXjuuUZ1Frol7ldYpWMpqSnhpd0vsenEJqJco1j2t2X0DG41\nFQJUy/lUhN4B7JAk6U8hxPIzHSdJ0lghxKZmVXeeBAQE1Be+ORsff2wvzHE+mTAKCwv57rvvKC4u\nRqPR0K9fP4YPH66KgXxLISw2dJ8mYy03Enp/H1XOXJ2K1WLhxzdfoqK0hJvnvYR/WNtfIpK8PZeE\nL1NxddcSHOmtDoOhsJATd96Frbyc2I8/xrN3L6UlOQSLbOFfv/2LxKJE3hj5BkPbDSUhNUFpWYoj\nhPgJu4GgOhxxv6gr3NmSWHU1lH2fjumYHvcO/gRO74dLsPLf/YbIso1DWzexY+VnGCsr6XPtdVx5\n8614+ak/TXdD8o/pSdycRUZiMRqNRJcrwul3bSzBKstEZdXpKP1sBWVffIFcWYn3iBGEPDATz0GD\nVGXUOBIhBGuOruGNP9/AaDXy2IDHiC+JdxoMzcR5h8mfzWCo5VVAEaOhOcnJyWHHjh0cOXIEjUbD\n0KFDGTZsGH5+l1ewjJAFupWpmLMrCPpHd9zbq//1CyHYtGwRuUeSmfDoU0R26a60JIcihGDfxhPs\nWpNBbM9gzEar4gHPAJaiIrLuvAubTkfs8g/brMEgC5n5v89nW842nhv6HOPixiktSVVIkrRFCDHm\nXH1OLgwhC6p25qHfkAkaiYApnfAeHKE670LukRS2fryUosxjRHXryei7HyAsroPSss4bWRYcTywm\ncXMWBRkG3L1cGDC+PX1GReMdoK5gbUteHrqPPqb8228RJhO+48YRfP/9ePa6vAbK2RXZLNi5gF35\nuxgQNoD/XPkf4v3jSUhIUFpam6E5c2up6xfrAhBCkJmZyfbt28nIyMDDw4ORI0ditVoZO3as0vIU\nQb8+A2OyDv9JHfDq3TpSDu5Z8w0p27Zy5bRb6T58pNJyHIqQBb9/d5QDW7LpckU4o+/szo//S1Ra\nFtaSErLuvgdLURGxH36AZ79+SktyCEII3vjzDdZmrOWRfo9wc9ebz/2kywRJkjwALyCkdmlS3b3B\nD2gdeZo5Pw9DS2Mprqbs23TMJwy4dwkkcGonXAKUWRZ1JipKS9j+xScc3pGAT3AIEx+bQ9dhV7Wa\nmW6z0YouTfDF5p0YSoz4hXhw1fQudBsWgZuHutKRmjIy0H3wIfq1awHwv/56gu+7F/cOrcc4aw5s\nso3PD3/Oe4nvoZE0PDf0OW7qctPlWFDT4TTnN0A047laBFmWSU9PZ/v27eTk5ODt7c3YsWMZNGgQ\n7u7ul611WrE9l8rf8/AZHonviNZxj0/duYMdKz+j+4hRDP37LUrLcSg2m8yvK46QuquA3tdEc9W0\nzkgaiSlPDlBUl7WszG4w5OYSs2wpXgOU1eNIlh9azoqUFdza/VZm9pmptBy18QDwTyAS2Neg3wC8\nq4iiVo6wCSp35KLfdALJRUPgtC54DQhT1UDcarHw17rV7F79NbJsY+jU6VxxwzRcFYr1uFCq9CaS\nfs0heVsupmpBeLwbV07tRHy/UFV4cBtScygZ3bJlVGzahOTuTuAttxB8z924RiqfLa+lSStLY/7v\n8zmkO8TI6JHMHTr3ss1c1xKoy2xuIWw2GykpKWzfvp2ioiICAgKYOHEi/fr1w9X1sqzcWk/1wWL0\nP2Xg2TMY/4mtY7Yi/2gqG957i8gu3Rn3wKOqupE2N1azjY0fHCLzoI4rJsczaEKcKl6vrbycrHvu\nxZyVRczSJXhf0XbT2X2X9h1v73ubCfETmDN4jirefzUhhHgbeFuSpNlCiEVK62ntWAqrKP0mDUtO\nJR49ggm8sRNaP/UkpBBCkLFvDwmffkh5YT6dBg9j5O33EhDeOgZuutxKEjdnkbanEFkWdOgXiggu\nYcJNg5SW1gghBNV79xLwzjtkphxG4+tL8MyZBN1xOy7BwUrLa3HMNjNLk5by0cGP8HP347WrX+O6\nuOucv8cO5ryNhlqX80PACOxehR3AYiFEXaqQzGZX18wIIaisrOTdd9+lrKyMkJAQpkyZQq9evdC2\n8ZLp54MpU0/pqlTcYv0IuqWr6tbINoWhpIg1r72Ad2AgNzw1F5eLSIHbWjBVW1j/fhL5x/SMnNGF\nXiOjlZYEgM1gIOve+zAfPUr04sV4D2351IItxeYTm1mwawHDo4azcPhCp/u7CSRJGi2E2ArkqjlN\nt5oRFhvm3EqMR8qo2J6Dxl1L0IyuePYJVdWgSJebTcKnH5B5YB9BUTH8/dkXiOvTsgHhF4MQgpzD\nZSRuziIrpRQXNw09R0TSZ0wMAWFeqlplIISgMiEB3dJl1CQm4uLrS+gTTxA44xa0vr5Ky1OExKJE\n5v8xnwx9BpM7TGbO4DmXeyHNFuNCPA2fARVA3czRP4AVwDQAIcRpNwe1UVZWRkVFBe3atWP69Ol0\n7doVjcZ50y9amoQw27CVGXEJ8CD4jh5Iruo3osw11ax+dQFWs5mb573U6jJyXAhVehNrFx2gLL+K\ncff2pPOgcKUlAfbUfln33Y8xLY2YdxfhM2K40pIcxp78PczZNofeIb15a+RbuGovb6/kWRgJbOX0\nFN2gkjTdakIIga3chDnLgPlEBaYsA5b8KrDZV/xqvF0Jf3yAoumuywsLWPPa85Tm5RIUGcWE2XNI\n2b6V/T//iIubO6PuuJ9+4yeidVH34gWbVSZ9byGJm7PR5Vbi6efGkBs60OvqxsXY1ICwWjFs2Ihu\n2TJMaWm4RkYS/txckkJD6TXu8ky6UGWp4u19b7PyyEoivCNYfO1iRkSNUFrWZcWFfMN7CSF6NGj/\nKklSSnMLciS+vr54enoyc+ZMVc3WKI2wyVgKq9C4u9hrMajsx7MpZNnG+ndeR5eTxdSnnyc4OlZp\nSQ5DX1zDj+8kUq03MfHhPsT2UIcr2lZZRfb9MzGmpBD9ztv4jGy7wecpuhQe/fVR2vu1570x7+Hl\nqv70w0ohhJhf+1d9kcQqQFhkzLkVmLMqMJ8wYMqqQK6w1wGSXDW4RvvgOyIKbaAH5esykKssFC87\nSMidPRRLqbrmtefR5WQDoMvN4fOnH0MIQe9rxjLiljvw8lf3LK+xykLydnsxtiq9maBIb0bf0Y0u\ngyPQuqpr4lA2m9GvXoNu+XIsWVm4dexIu1dexn/iRCRXV1CRF6Ql2Z6znQW7FlBYVcg/uv+DR/s/\n6vwdVoALMRr2SZI0VAixC0CSpCHAn46R5RhcXV1xdXV1GgwNEFYZa2E12ATBCt6UzodVz/8fANPn\nv8Jvny0nY99err3voVbhDr9YSnIqWftOIjabzA3/7E9EB3V4U+TqarJnPUDNwYNEvfUWvqNHKy3J\nYZwwnODBzQ/i7+bPkmuX4O+ujs9A7UiSFAzMp/GS1gVCCJ2iwloQIQQ2vQnziQrMWXYDwZJXWe9F\n0AZ54N7RH/dYP9xifXFt542ktQ9iC976C6wyANbiako+TSHiiYGKvI7SvNyTDSEQQnDrS/8lomNn\nRfScL4aSGg5sySblj3ysJhvR3QK55o7uxPYIUt04QK6qomzV15R+8gnWoiI8evUibNE7+I4Zg3QZ\nr4goM5bx6t5XWZ+xno7+Hfnsb5/RL6xtZuVrDVyI0TAQ+EOSpKzadiyQKknSQUAIIfo0uzonDkUI\ngW7lEYTJBkDZt+mKzmadL4kb17Pv5x8ZMOEG+o6doLQch5F/tJz17yfh4qZl6j8HEhSpjsrWck0N\n2Q8+RM2+/US98Tp+49uuq7youogHNj2AEIKlY5cS7q2OZWGthJXANuDvte1bgVXAtYopcjCNvAi1\nRoJsaOBFiPLBZ0QU7rG+uMX6ofU985Ija3F1gxOf0m4hTNXVJP+2GUmjQch2AwZJIigyWtUGQ8Fx\nPYmbssjYX4wkSXQeHE7fa2MIjVFfDIC1rIyyz7+g7PPPsen1eA0ZQuQrL+M1bJjqDJuW4O4Ndgfl\nR+M/4qfjP/HqnlepsFTwYN8Hua/3fbhp227cYmvgQoyG6xymwokiVP6eh/HQyUk/pWezzgdjZQVb\nP1lKhwGDGXn7PUrLcRiZB0vYuOwQPkEeTH60L34qMeRkk4mchx+hes8eIl99Bb8Jbddo05v0PLDp\nAcqMZXx03UfE+ccpLam10U4I8UKD9kJJkqYrpqaZaehFCDksUZiceLoXoYM/7jG+uLX3a+RFOB9c\nQr2wFtUaCpK93VLocrNJ3LiO5N+2YjHWEBrXgRqDnqryMoIio7hxzvwW03K+yLIg80AJiZuzyD+m\nx83Thf7jYuk9KgafQHUVYwOwFBZR+sknlK1ahaiuxmf0aEJm3t9ma9tcCGabmYe3PMz23O30CenD\nf678D50D1WukXk5cSEXoE44U4qRlMaaVoV+f0bhTodms86G8sID89FRsFgtaF1dG3HIHGo36g7Uv\nhtTdBWz59DAh0T5Mnt0Xz7PMRrYkstlMziOzqdq5k3Yvvoj/9dcrLemSqZvV+vi6jxv111hreGTL\nI5wwnGDxtYvpGXx5VVZtJn6RJOkW4Ova9k3Axos9mSRJ04D/AN2BK4QQDl0eW7Q0CYCwB+xOdGGR\nMedVYj5hOM2L4KeRkGKl8/YinA8hd/ag4H/7wCLjEupFyJ09zv2kS0CWbRzf/yf7N6zjRNJ+tC4u\ndL3yavpfN1nVXgVTtYXDf+RzMCEHQ4kR3yAPRkzrTPfh7VRXjA3AnJWF7sPl6FevRths+E2cSPD9\n9+HRpYvS0hTnhP4EicWJWGUrEhIze8/koX4PoW2j9/rWiPq+UU4cjqWkBt2XR3AN90LYBNbiGvuO\nFp7NuhC+f3keNosFAJvNyvp3XuOuNxcrrKr5ObA1mx1fpxPVNYAJs/rg5qmOr6gwm8l99DGqtm8n\n4oUFBEydorQkh2GRLTyR8ARJJUm8MfINhrQborSkVoUkSRXYYxgk7EXePq/dpQEqgX9d5KkPAVOB\npZeq8XwQVhnZaKV87TH7cqOGXoRAd9zj/e0GQns//kj7i1Gj+zbr9V2CPYl+wfHZyIyVlRz69RcS\nN/2EvrAAn6Bghk+/nT5jxqs6wLmsoIqkrTkc2ZWP1SwT2t4XTZmJilIjyTtyiesToiqjwZiaim7Z\nBxh+/hlJq8X/71MJvvde3GJilJamCpJLkrnj5zuwytb6vi1ZW5g9YLaCqloRH0+0/717vUMvo55v\nlJMWQTZa0X2ajKSB4Dt6ghAtOpt1MVQb9JTl553sEKJxYF4bQAjBnrXH+fOnTDr0C2XsvT1wUUna\nW2GxkPvkk1QmJBAxfx6B06YpLclhyEJm3u/z2JG7g/nD5jO2/VilJbU6hBAOWTguhDgMtNg6b0t+\nFVhlKstMuEX74DM8qt5IOM2LcLRFJDUrJVmZ7N+4jpTtv2I1mYjq1pOrZtxFp8FDVZs6VciCE8k6\nkn7NITulFI2LRJfB4fS5JoZNHycj1xp15QXVrH//AP+Yr2zNGLm6mootW9H/8ANVO3ag8fIi6O67\nCLrzTlzDwhTVphYqzBUs2r+IlUdWIhD1/QJBpiFTOWGtidLjkPsXWGvgvSEwYyUExTvkUur8ZXDi\nEIQsKF2ZilVnJOTeXrgEeQDgFm2/x9e54dWEqbqK719uvH5WkiSCIqMUUtT8CFnw21dpJG/Lpfvw\ndoz6R1c0F7D22aHYbOQ+NYeKTZsJf+YZAmfMUFqRwxBC8Pre11mXsY5H+z/KTV1uUlpSq0eSpOuB\nq2ubCUKIdS103ZnATIDw8PCLKtYV46FBSJAzzAKaMqAMSrA/TqGyslJVBcEa0lCbkGXKM49RfHAf\nFXnZSFoXgjp3J6x3f7xCwsg3WcnfsUMRbWfDZhGUH4fSdIG5Alw8IKy3RGBH0HoUkZxRRFmBXH+8\nEFBWUH3Rn8klfZ42G24pKXjs2YvHgQNIZjO2wEBqJk+ietQo8r29ISXF/mhpbQ7kQnUJIdhXvY/v\ny76nwlbB1b5Xc7jmMEXWIgAkJEJdQpvltbaV9+xUXCwGgkr30SVtKVpbDRIgF6dS8+Fk9l7xbrPp\nbHRNh5zViSoxbMzEeKSUgBs74tFRvW7nOixGI9+/8jzFJ44z7oFH2frxUqxmE0FR0aoMxLsYbFaZ\nnJ0CQ3YuA8bHMvTGjqrJmCFsNvw++ZSKvXsJmzOHoDtuV1qSQ/nw4Id8fvhzbut+G/f1vk9pOa0e\nSZJeAQYDX9R2PSZJ0nAhxNNnec5mIKKJXc8KIX4432sLIZYBywAGDRokRo0add666yhKtcc0jBp9\n7smUhIQELuYaLUFCQgJDBg7g4NZfSPxlPRUlxfiGhHLVP+6i9+hxePr6KartbO9beVE1BxNySPsj\nH4vRRni8H31ujqZj/zC0Lo0nVvJ+20V5QTVCgCRBQIQXo0ZdnKfhQj9PIcvU7NuHft06KjZsxFZe\njtbfH98pU/CfNBHPgQObLW2qWv+vXYiubEM2L+5+kd9LfqdHcA/mDZ1Hz5CeZFdkM/WHqRhtRjr4\nd2DRmEXE+F768q228J4Bdmu4IAnSf4G0XyD3TxByo0M0CLxr8hz2eh1uNEiSdB3wNqAFPhRCvHKG\n4wYDO4FbhBDfOlrX5Ub1/iIqfsvBe0gEPkMjlZZzTqxmM2veWEh+2hEmPjaHrsNGkLJ9K2Cv09AW\nMButbFh6EEM2XDm1E/3HqadAnbDZyH/mWTz37iX0iScIvqdt1+n6Ju0b3tn/DpM6TOKpwU+pxnBr\n5UwA+glhv6tJkvQpsB84o9EghGiz6ViVoCgzg8xfN3Lgw3ewWszE9OzDNXfeT8eBQ9Bo1bH88VSE\nEOQcLiPp12wyD+nQSBIdB4bRZ3Q0EfFnrpEy8aG+rH//AOWF1QSEezHxoeaNMWlKpyktDcO6dejX\nr8eal4/k4YHvmDH4TZqIz/DhSG5umLOzSR0wEGE04taxIzFLFl+2cQxmm5mPD33MBwc/wEXjwtNX\nPM30rtPrA51jfGPYe9tehVWqDFMFZCRA2kZI3wSVBfb+yAFw9RzoMg5WPwglqfZ+SQMhjktc4FCj\nQZIkLfAeMBbIAfZKkvSjECKlieNeBX5xlJbS0lLy8vKwWCy89957zJgxg6CgIEddTlWYsyso/S4d\nt3g/AiZ3PG2/2pYl2axW1r39KlkHE7nuocfpOsxeJr6tGAsAxkoLa989QHFWBZFXSOoyGGSZ/Hnz\n0P/wA5WTJ9N95v1KS3II2RXZHCo5hNFm5M/CPxkUPogFwxegkVSyNKxtEACU1m63mqp4Vl0N5pwK\nsMgUvPVXq6hfU4fNauXo3l3s37CW3CPJaFxc6DXqWvqPn0RIbJzS8s6IxWQjdXcBSVuzKSuoxtPX\nlUET4uh1VRTeAedOmeof6tkiMQzmnFwM69ZhWL8OU/pR0GrxHjGcsMcfx3f0aDTejevpZM96EGE0\n2p97/DjZsx6k4/oWWaWnKvYW7OWFXS9wXH+c8XHjmTN4DmFezriO0xACdEdrjYRf4MQfIFvA3Q86\njobO46DzWPBp8N79YxUsHgaWGgjpYo9pcBCO9jRcARwVQmQASJK0ErgBOHUx32zgO+yubIfw1Vdf\nYanNvlNSUsJXX33Fww8/7KjLqQabwUzJihS0Pq4E39odyUXdAyJZtvHze29x7M/djL5nFj1HjlFa\nUrNTWWbkx7cTMZQYuW5mL7LKk5WWVI8QgoLnF6D/7ntCHnqQwj7qMiibk9lbZmO0GevbOqMOV42r\ngoraHC8D+yVJ+hV7JqWrgf+72JNJkjQFWASEAuslSUoUQoxvFqWnUPJpCljUUY35fKk26EnavIED\nm36islSHf1g4I2+7h3I3L64dr94yS4aSGg7+lsvh3/MwVVsJjfVlzF3d6TwwHK2rOu5XVp0Ow4YN\nGNatp2b/fgA8Bw4kYv48fMePx+UsE5DmzMyTDVlu3L4MKDWW8uafb/LjsR+J9olm8bWLGRE1QmlZ\n6sJihMwddiMhfSOUZdr7Q7vD0Aehy3iIGQLaM9yfguIhsvb3qZVnT4oCshu0c4BG+QslSYoCpgDX\ncBaj4VID24qLi+u3hRAUFxef8xytPXhGskHUHg1uVZAzVCb1zz9Uo60phBCcSPgF3ZGDRA29Gr27\nT7O9/2r5LE0GwYkEgc0MsVdLZJUnq0YbQuC76mu8EhKoGj+ewt691aPtFJpDV4a+cZ2SE/oTbTro\nriWR7Ou7dgBDOfm7/m8hRMHFnlMIsRpY3QzyzokaqjGfLwXH0tm/YS2pf2zDZrXSvk9/rr3vIeL7\nD0Kj0ary/6IQgrz0crJ2yKSs2gmSRId+ofQdHU1ER39VLA+0VVZRuXUL+rXrqPrjD7DZcO/ShdAn\nnsBvwgTcos8vGYdbXBzmY8fsDY0Gt7g4x4lWEbKQWZ2+mrf+eotqazX3976fmX1m4uHiobQ0dVCe\nTWTuz/DlYsj4zZ75yMUT4q+GK2dDp7EQ2F5plaehhkDo/2G/mchn+6G41MC25OTkesNBkiRCQkLO\nGSjSmoNnhBCUfZNGtb6I4Nu6E9srRDXamkIIwa+fLkN35CBDp05n+PTmDbpVw2dZdMLA2kUHcNHC\nlDn9CI31VY02IQRFr7xKaUICQXfdRbd/z0GSJFVoa4pL1ZVTkYMmS4NN2ADQoCHeP75ZXqta37OW\nRAghJEn6SQjRG/hRaT0XipLVmM8Hm9VC2u4/2L9hLflpR3B196D3mPH0Gz+J4Cj1rpe3mm2k7S0k\n6dccdDmVaN2g/7j29BoZhW+Q8oNJYTZTuWMH/h99RPo/H0cYjbhGRhJ87734TZyIR9cLL8AWs2Qx\n2bMexJyZiVtcHDFL2l59oVNJL0vnhV0vsL9oPwPDBzJv6Dw6BHRQWpay2CyQvcfuSUjfBEUpdAEI\naA8DbrcvO4obAa4XuQzSwR6GOhxtNOQCDX/Bomv7GjIIWFlrMIQAEyRJsgoh1jSnkBkzZrB48WIs\nFgshISHMaMOpIwEqd+RSva8Iv2tj8Wwhg+FS+H3V5+z/eS0DJtzAlTffprScZicntYyf3k/Cw9uV\n6x/rR0C4egYhQgiK33yT0k8/JfD22wmrNRjaKqXGUmZtnoWniycWmwWTbCLeP55FYxYpLa2tsU+S\npMFCiFYX2djS1ZiborywgE//9TBWs4ng6BhunDMfV3d3Dmz6maTNP1NVXkZARDuuufN+eo66Fncv\n73OfVCEqy4wc/C2XlO15GKssBEd5c81t3SgwpjLs2tPj7C4Uc3Y2GZOvv6hgYyHLVP/5J4Z16zFs\n3Iis1+Pm40PA1Cn4TZqEZ79+l5T5yC0m5rKJYai2VLMkaQkrklfg4+bDwuELub7j9W36fnJWKovh\n6Cb7sqOjW8GkB40LtL8Sxi1kT1kgV0y41Z7uq5XgaKNhL9BZkqR47MbCLcA/Gh4ghKivQCFJ0ifA\nuuY2GACCgoKIjLRnDbr77radCcaYVob+p+N49grGd7R6AmzPxO4137B79Sp6jx7HqDvua3M/MMf2\nF/HL8mQCwryYPLsfPoHnDuprKYQQFL/9NroPlxMw4xbCn3m6zb3/Dam2VPPIlkcoqCrgg3Ef8M6+\ndwD4+LqPFVbWJhkC3CZJUiZQhT2uQQghVB8o4xLsqXj9mjWvPY/VbAJAl5vDin8/itVsRrZZie83\nkP7XTSau74BmS+XZ3AghKMgwkLQ1m2P7ixFCEN8nhD6jY4jqEoAkSRQlpDXLtS402FgIgenwYfTr\n1mNYvx5rYSGSlxe+Y8bgP3kSf1os9BzT9uLpHMmh6kO8/MPL5FXlMbXzVB4f8DgBHupP7d6syDLk\n77d7EtI2Qt5+QIBPOPSYDJ3HQ4dR4GFPc1ydkNCqDAZwsNEghLBKkvQIsBF7ytWPhBDJkiTNqt2/\nxJHXvxyxFFej+/IwruHeBE7riqRR93/I/RvWsuOrT+k2fCTX3v9wmxuwpuzII4iAVloAACAASURB\nVOGLI4TH+zHx4b54eKsr0LbkvffRLVlKwLSbiHjuuTb3/jfEIlv412//IlmXzH9H/Zf+Yf2VltTW\ncUiQsloQQiBkGdlmw2YxY6yqrG/Lsg1hO7kt2+yPk/tlRMN9sg3Z1rivNDen4cUw11Qz4G/X02/8\nRALbqbe4pc0ic/SvQg5szaE4qwI3Txf6jo6m96ho/EIck4HqfIONzVlZGNavR79uvT3OwMUFn6uu\nwm/OU/hecw0ar1oPsArjQNRKQVUBr+x5hS3FW+gU0IlPr/uUAeEDlJbVctSUQ8av9roJRzdBVTEg\nQfQguOYZ+7KjiD6gUuP+QnF4TIMQ4ifgp1P6mjQWhBB3OVpPW0ausaL7LAVJKxF8Rw807urMw13H\noV83sfXjpXQcNJTrHnocjUbdei8EIQT7f8li5+pjxPYM4rqZvXFV2edRsmQpJe++i/+UKUQ8/7xq\nZyybAyEEz//xPNtztzNv2DxGx45WWlKbRZIkD2AW0Ak4CCwXQliVVXXhWG1m1ux8C+kvzclBfoNB\nvZAbF1VK/PAdB6qRCIqM4pq7ZjrwGpdGld5E8rZcDm3Po8ZgJjDCi5EzutBlSARuHo4dapwt2Nha\nXIzh5w3o16/DeMBesM9r0CCCnn8e33FjcQkMdKi2topVtvLl4S95L/E9ZCFzfcD1/GfSf3A9U4af\ntoIQUHzkZN2ErJ0gbOARAJ3G2L0JncaAt/qXhV8MagiEdtIMCFmg++oIVp2R0Pt646KCoLKzkbpz\nO78sXUT7Pv2Z9M9/o3VpO/8VhRD88d1REjdn03lwOGPu7H5a9VKl0S3/iOL//Q+/yZNpt/CFNm0w\nACzav4gfjv3AQ30fYlqXaUrLaet8CliA7cDfgB7AY4oquggkSUNUcBe8eoag0WrRaLRIWm39tkar\nsfdpNBzPzKRzly5Imtr9tfs0Wvv+xs8/ua/u+RrtKcdqtVSWlrLpg3fRFxUQFBnFjXPmK/p+6Itr\nWLlgN1aLTGA7e/E0/1BPCjPtS5CO/lWEbBO07x1Mn2uiiekW1GKe7lODjSPffIPy1WswrFtH1c6d\nIMu4d+9O2FP/wm/CBFzbtWsRXW2VpOIkFuxcQGpZKldHX80zQ54h/c/0tmswCAF5+2DlrVBdCjb7\nskHCe8Hwx+zehOjBoG0745gz0fZf4WWCfkMmprQyAqZ0wr2DumsoHftrDz8teoPIrt244clncXFt\nOz80sk3m18+PcGRnAb1HRXPVzZ1Vt0Ss9NNPKXr9dfwm/I3Il19CUmll2Obii8Nf8MHBD7ipy03M\n6jur0T5nLIND6FGbNQlJkpYDexTWc1FoNS4M6z6VsPvPHdNQk5DAwGbOmBUUGc29by9r1nNeCuvf\nP4C1tnZFeUE1q9/ch2+QOwUZBlw9tPS6Ooreo6IVSfLgFhND/HffUrVjB/q16zgx/RaEyYRrdDTB\nM+/Hf9Ik3Dt1anFdbQ2D2cA7+97h69SvCfUK5b+j/suY2DFIkkQ66UrLa37KTsDBr+HAKtClAxJ4\nBsKYV+2Ggr96lwk6CqfR0Aao2ldI5bYcvIe2w2eIumdQThxMZO1/Xya0fTxT/j0fVw91e0QuBKvZ\nxsYPk8lMKmHwpHgGT4xTXYxA6RdfUPjyK/iOG0fkq68itSEPT1NszNzIq3teZXTMaOYOmau6z6ON\nYqnbqI1rU1KLk2aivPBkrQohoKrchIurhhE3d6b7sHa4eTr+t0TYbFjy8jBnZmI+non5xAn7dmYm\nlrw8EAJtcDAB06bhP2kiHn37Or/zzYAQgp+O/8Tre1+nzFTGbT1u4+F+D+Ptqt6MXRdNTTmkrLEb\nClm1ta1ir4S+t8C2N6CmFHYvsQc0X4a07RHDZYApy0DZ9+m4d/AnYLK68yDnph7mh9cXEhgRyd+f\nWaDqFIEXiqnGyk/vJ5F3tJyrb+lC71HRSks6jbJVX1P4wkJ8Ro8m6o3XkdqQh6cp9hbs5entT9Mv\nrB+vXv0q2jYUM6Ny+kqSZKjdlgDP2nZd9iQ/5aQ5aYiQBaZqK9UGM9UGE9UVZmoMFnu7wkyNwUy1\nwUxNhRnROIQDn0B3bn1+aLN7UoUQ2EpKMGdmYqo1CMyZduPAkpWFsNTbpGh8fHCLi8Ozf3/8p0zB\ns18/vIcOafOTIS3JCcMJFu5ayK78XfQK7sXiaxfTPbi70rKaF6vZHsSctApSN9iXHwV3htFzoffN\n9iJr7w2xF2ADKEmDr26Bh3crq1sBnN+sVozNYEK34jBaP3eCbu2OpFXvuvTCjKOsfuU/eAcGctPc\nhXj6tp1xQ7XBzNpFiZTmVjHunp50HhyuiI4Tt98BQPsVn522r/y77yiYPx/vkVcT9b//Irm5tbS8\nFiW1NJVHtz5KrG8si0YvclYhbUGEEE7rTEGELDBWnRz4l2cKEjdn2Qf/tX112zUVFmRZnHYOjUbC\n09cVTz83vPzcCY7yxrVSx7HdOVR7huBtLWPijEGXZDDYKipwOXEC/dp19d6CuodcVVV/nOTqiltc\ne9w7xOM7+hrc4uLsj/bt0QYHOz0JDsJsM7P84HI+PPgh7lp35g6Zy01dbmo7ky9CQM6fkLQSDn1v\n9yB4hcDAu6DvdIgc0DgdakmD5VdCbty+jHAaDa0UYbFR8lkKwmQl9N5+aFWWyrMhupwsvntpHm5e\nXkx77kW8A9pGtorVb+7DarFhqrJSpTcx4eE+tO8ZrLSs09D/8AP5c5/De/hwot95B00bNxjyKvN4\ncPODeLl6sWTsEvzd1R3j40SdKFWfoSlkWWCstDQ58K/zCFTVGQKVFsQphkAuR9FoJbz83PD0dcPb\n352QGF+8/Nzw8nWz9zfYdvdyOc0gODZxEqENMhTpC+IJOUfRMtlsxpKVVW8MNPQc2EpKCAbyACQJ\n16go3OLi8O/f/6RhEBeHa7uINh93pTZ25e/ixV0vkmnI5G/xf2PO4DmEeLaRbEClGZD0td2rUJoB\nLh7QdYJ9+VHH0XCmYO6QzvasSQCSxt6+DHEaDa0RAWXfH8WSU0nw7d1xjVDvMp/ygny+WTgXjVbL\ntLkL8QsJU1pSs2E2WinNq8LVXcsN/+xPhAoD0PXr1pP39DN4DRlC9HvvonFXT2E5R1BuLOeBTQ9g\ntBn57LrPiPCOUFqSk1bM2ncSKc6uONnRYOax4ZDabJY5/vOO0/obHX+mCXGp4ebJhizLVJWbz6pP\n66LB088VL183fAPdCWvvi5dvrQFQ+zh0JJGRY0bYDYFLmJU/Uy0EYbNhyS84zVtQH2fQIDWtNiQE\nt7j2+IwaiXtcHGmVlQyYNAnXmJg29duUXZHN1B+mYrQZ6ejfkUVjFhHje34VqluKuzfYi9w2TAZR\nUlPCG3++wfqM9cT6xrJ07FKujLxSKYnNhoulAvYutxsK2bsBCeJGwIgnoMf14HEe9+4ZK2HxMLDU\nQEgXe/syxGk0tEICMiWqU4vwG9sez57qtf4NJcV8s/BZbBYL0+e/rOqCRBeCEII9645Tkl0JgLuX\nC56+6pu9N2zYQN6//43XgAHEvP8emjYUdN4U1ZZqHt76MHmVeSwbt4xOgc5sKU4uHltlJeV5ejRC\n0L53iH05Qy2N5vEFFBQUEBHhf1p/Uw0hmj6m8eGCzOSyRno8fVwYfn00nl5avLy1eHprcXWrNTNk\nuda7IOyDdNmGENUgqvCqLEDKy8QkC/uyitqidDQ4XsiyXYCQ7W0hTtkvcAkLw1pQUP8CNB4eZEye\njPlEFsJ80rjReHnhFh+PZ9+++N9wQwOvQXu0vr6NXpMpIeGSsxqpcYA+e8tsjDZ7herj+uPM3jKb\nNTeuUVTT2ZCFzLdp3/K/ff/DaDUyq+8s7ut9H+7aVmzIWU32WgpJq7gydQMIK4R2gzHzoc/N4H+B\ncYdB8RA50L599/rm19tKcBoNrYyaI6UEp0p49g7Bd7S6Zi4aUlVexrcL52KsrOTmeS8REhuntKRm\nwVhl4dcVR8hILK7vq9AZWf/+Af4xf6iCyhpTsXkzuf96Cs++fYlZuuRkpdM2ilW28tS2pzhUcoi3\nRr3FwPCBSkty0spJGzKUQTabvXH2VTg4Imdd2sh3QDq5LKemwoQ8cyJVQNWZn3YaIUBGc4sDtMHB\nuMa2x/vqq3GLi8O91jjQhoS0aJyBGgfomYbM+m0ZuVFbbaSWprJg1wKSipMYEjGEZ4c+S7x/vNKy\nLg4h7J6EAysheTUYy8E7jNyoCcRMeBLa9T2Ly8/J+eA0GloRlqJqSr86gtkXoqZ1UW0AWE1lBd++\n+BwVpSX8/ZkFhHdoGzO+BRl6fvkwmapyU20eGHu/EI3TESqBOTubmoMHEUYj6deMxlpcjEfPHsQs\nW4rGW73L15oDIQQLdi5gW842nhv6HGNixygtyUkbIOypf/H7qu8BGH7z1LMee+zYUTp2bN7fOb8D\nNgw1kn39NAI/L5nwuXNBwl6MUdLYB0AaqbY4owQajT0WQap9nkYi5fBhevbqVXt87XPrj5fs95H6\n8zU+t6Sp2yeRay7mycz/csKUT5x/HO+OeVfxGX1Q5wA9zi+OY3p7/IcGDXF+ccoKOoXsimwOlRzC\naDNy09qb8Hfz56URLzGpwyTlxxUfT7T/vZDZfN0xu6GQtArKT4CLJ3SfBH1ugQ6jOLZ9BzGR/S5d\n22XsYajDaTS0EuRqC7rPUpBcNOQPkOnops7AMJvZxPcvzaMsL4cp//4P0d16Ki3pkhGyYP+mLHb/\nkIFPkDtTnxrIls9SKMu3GwqShCIFjRqSPetBhNE+22bNz0dydyf2gw/Q+vgoqqsleDfxXVYfXc2s\nvrO4uevNSstx0kZwmTid7J0BgA8bs3zqKyA3xcGEBIKbubjb9cU1rH//AOWF1QSEe5/1+mfD5O2N\nXzNoe2rNjRw35QKQqc9UxYw+qHOAvmjMImZvmU2mIZM4vzgWjVnk8GsarUbKjGWUmkopM5bZt421\n26YG28YycipykDkZaxLoEcjkjpMdrrFZqdLBoe/shkLun4AEHUbCqP+D7pPB3fecp3By4TiNhlaA\nsAl0K1OxlhkJvb83aZn7lZbUJBaTkaM/raaqMI/rn3yW9n2awbJXmJoKM5s/SSEruZSOA0K55rZu\nuHu5MvGhvqxcsBurRSYgwouJD/VVVGejIEVAWCxo/dpOWtszsfLISpYlLePvnf/OQ30fUlqOkzbE\n+vcPAD4gaSgvqG7xJYj+oZ6qWvKoxhl9UGaAfi5ifGMuyaASQlBlqTrNCCgzlZFUlsSmHZsa9ZUa\nS6mpqyFwCi6SCwEeAQR6BBLkHkSP4B5kV2Q3OubUtmqxGCHtZ3vhtaObQLZCWE8YuwB6TwO/SKUV\ntnmcRkMrQP/zcUxpZQRO7Yx7nD9kKq3odKwWCz+++RKV+TlMfPQpOg0aorSkSyY3tYxNHyVjrLIy\n8h9d6XlVZL3r1j/Uk7A4+6B8ypMDlJSJMJvR+Pgg6/X2DknCLb6Vrkm9ADaf2MxLu19iVPQo5g51\nVnt20ryU5uVhNqxByGVImkBK5RuVlqQoapzRh0sfoLcEspCpMFc0mu0/m0eg3FiOWW46c5ar5Eqw\nLZhA90CCPIKI84+zGwQeQQS6B57c9rBv+7r6nvbbmF6WrsrPktLjkPeXPUPRe0PsGYoC2tsrMyet\nguQfwKQHnwgY+qB9+VFEL6VVX1Y4jQaVU/VXIZU7cvG5MhLvK9SZPlK22Vj/9mtkHthH+1Hj6TZ8\npNKSLglZFvz5UyZ/rj+Of5gXk2b3JST6dFen0sYCgDknh9wnnrQbDFot2Gy4dehAzJLFSktzKOnG\ndJZsW0Kf0D68NvI1XDTOnzInzYu1+keEXAYIhFyGpfoHkku6IgsZgUCuLZEsEBw1HsWnwAeBQAhB\n3T9Z2LMS1W032i+EfYnIqftr92nzSgh+7HU8qyxYYsLJm3cX1nYn68CI2qAq0Sir0+l9RyqPUJpe\nevKY2n31xzbxnKaOHR83nq9Tv0Zn1BHkEcTY9mP54vAXjV5P3bnqntvw9TS1L6M8gyMHjpzcV/te\nnPo+NXx9Dc978mkn389G7+8p73ndZ3bqZ9ioLaCguIC1CWvPep5ztoXAYDZQZiyj3FSOTdia/H/m\n7epdbwCEe4XTNbBro0H/qcbA7h27ueaaa5o81/myaMyi+oxT8f7xqvDOAPYqy5a6qsup8OEYcPUG\nfZb9b/fJ9sJr8SOhrRSZa2U477QqxpRloOz7dNw7BeA/sYPScppEyDIb3v8vR/fu5Jo778fgpb5a\nBRdCVbmJTR8lk5tWTtehEVx9SxfcPNT5NTFs/IX8uXMBiHr7bco+/xxouiJ0WyKtLI0Pij4g2i+a\n98a8h6fLha/zduLkXNgsOk4mQhVYzSXcsv6WMz9hY/Ne/80PrLhXgSRAm12I9v9eZc79F/lb9Efz\naisxlrAkaUnznCzxzLsk7IHadfUrJCR7sHbdv4b7arc1kubkPklCg6Z+X13bHgOuqT8eGrerzdXo\ny/XnPE/dvtOui4RWoyXWN5a+oX0bGwHuJ7cDPQIvOK1pc3hUY3xj6BVin6FvWKdBcRpVXRZQrYOO\n/WDMc9BtIri17aQerQF1joacYNWb0K1IQevvTtCMbkha9S29EEKwefn7HN6RwPDptzNgwg0kJCQo\nLeuiOZGsY/PHKVjNNsbc2Z1uwxyRSPHSkU0mil59jbIvv8SjTx+i3noTt+joeqOhLZNfmc+Dmx7E\nTePGkmud1Z6dOA6/iAj0+fn2Iakk4RUWzLuj5zQaONb9TTqQRL9+/eoHksBpg8imBp5N/a17nunV\nyfa6CYBWQEyphvVT7Nlb6ovA1f9pWEROatS3a9cuhg0d1uRgs+GA+0znOduxdbrr+hsO7psa0J/6\n3G3btjFy5MjT9ym81DAhIYFRzRzY7uQM6HMh5Qd7itRG3hgJgjvC7d8rJs3J6TiNBhUiLDZ0n6Ug\nTDKh9/VG632GsuYKIoTgtxXLSdq8gStuuIkhU1pv1hqbTWb3Dxns/yWL4Chvxt3Xi6B26pzRMGdm\nkvPEE5hSDhN0992EPf5PJDf1FZZzBOXGch7Y/AA11hoeCXuEdj7qNOqctA2mPf0CH/3zQYRsITgq\nmhvnzCcgvOklouY0M0PbNW/Q8rH4eMzH7OvO0Whwi48n1i/2gs8T5BKk2u+KVtI6lxYqhKIeBkM+\nHP4RDn0P2bvsfRG94crZsOcDsBohtOtlW3VZzTi/rSpDCEHpt+lY8ioJvr0HruHqHLzu/PZL/lq/\nhn7jJzFixp2KzwxdLIaSGn5ZnkzhcQM9r45ixE2dcFFpOlv9uvUUzJuH5OpK9OL38T1lXWtbXpZU\nY63hka2PkFuRy5KxS6g6ciHlrZw4uXACwiNw97ZXsb/rzXdb/PoxSxaTMfl6hNGIW3x8m49TctLG\nqSi0GwrJq+HEH4CwZz66Zi70vBFCOtuPy63NDumsiXBBTF+6E4BVDwxz6HWcRoPKqPgth5oDxfiN\nj8OzR/C5n6AAe9d+z85vv6LnyGsZfdfMVmswZOwvZuuKwwhZMO6+nnQeFK60pCaRa2oofOklyr/5\nFs8BA4h68w1c26lz5tARWGUrc7bNIak4iTdHvcngiMEkHElQWpaTy4CQGOXqnLjFxNAtUZ3ptZ04\nOS8qixsYCr/bl9uFdoNRT9sNhdCuSit0coE4jQYVUXNYh2FjJp59Q/EdFa20nCZJ/OUntn3+EV2G\nXcW4WbNrK5G2LqwWG398d4yDCTmEtfdl3H29LqpoUktgOnqU3Mcfx3T0GMEPPEDo7EeQXC6fr60Q\ngoW7FpKQncDcIXMZ236s0pKcOHHipPVzMZWXz4cq3UlDIXO73VAI7gxXPwU9p0BY97M/X8Uehpaa\nzW8Kq01GV2Wm0GCk0GCi0GCkqMJEkcFIocHIwVw98SGOX5ly+Yw+VI6lsIrSlam4RvoQ+PfOqpy9\nT9m2lS3L36fDgMFMeOQJNK0w5Vl5YTUbPzxESXYlfcfEMGxKR7Qu6jN8hBDov19NwQsvoPHyIuaD\nD/AZMVxpWS3O+wfe57v075jZZybTu01XWo4TJ06cODmV6lIi8jfBirch4zd7QHNQR7jqyVpDoQeo\ncEyjBmyyQFdloqiBIVBnGBQZjBRWGCkymCipNCGfkhVZkiDEx51AL1eMFhvJeQbGvvUby+8cTGyw\nl0P0XlZGw9133620hCaRqy3oPktBctUQfHsPNCpcU5+2+3c2vP8/Ynv1YdLj/4fWRX3B2ecibU8B\nCV+konGRmPBQH+L7hCgtqUnkqioKFixA/8OPeA0ZQuTrr+EaFqa0rBbn69SvWXJgCVM6TeGRfo8o\nLcfJZcj0+a8oLcHJ2Sg9bs/tX5JuXxM/YyUEKVDYsvQ4LB5mrzEQ2k05HS1JTRkc+QmSv4eMBLrJ\nVgiMg+GP2Q2FiN6XtaEgy4LSavMpHoEGBkGFkeziagy//IztVGsACPFxI8zXg3A/d3q28yfcz50w\nPw/C/ex9Yb4ehPi44aLVMPat3+oNimPFldz76V42PeGYelmXldGgRoRNoPvyCNZyE6Ez++AScGE5\nm1uC4/v/ZP3brxPRuQs3PPUcrm7q03g2LGYbuXtkkjNSaNfJn7H39MQ3yENpWU1iPHKE3H8+jjkr\ni5DZjxAyaxaSVn1GpKPZcmILL+5+kZHRI5k3bJ4qPW9OWhZJkl4HJgNm4BhwtxCiXFlVjiO7Iru+\nAFdH/44sGrOIGN8YpWWpi69ugeIj9u2SNHv74d3K6KgvSqagDkdj1NcaCqvh2FaQLRAQC8Me4U9j\nLIMm3dPmDQUhBKVV5kZegaImlgwVVZiwNmEMBHm7EebrTrifB/4hWvp2iWtkEIT5uhPq646r9vxX\nQGQUn0wMIovG7ebGaTQojP6nDExHywn8e2fc2/spLec0spOT+PHNlwiJac/U//sPbh7qXPt/JnR5\nlWz8IJnyfBj4t/ZcMSkezQV8GVsKIQTlq1ZR+NLLaP39if34Y7yHXKG0LEXYV7iPOdvm0CukF6+P\nfN2ZktFJHZuAp4UQVkmSXgWeBv6tsCaHMXvLbIw2IwDH9ceZvWU2a25co7AqldGoGJjcuH056jhf\nSo9D3l92Q+e9IWf3jBgNkLbBbigc3Qw2M/jHwNBZdo9C5ACQJCoTEtqcwVBttpKSZyApR8+uDB17\njpcigAEvbDrt2AAvV8J9PQjzc6djaAjhfu4nvQINjAF3l5OTgPZ6IJceDN4h1Jv0okoANJK97Sic\nd2MFqdpbQOXvefgMj8R7cNP5v5UkPz2V1a+9gF9YOH9/dgEe3splErlQhBAc/j2f7avScPV0of0o\niaE3dFRaVpNINTXkPv4EFRs24H3VVUS+8jIuwerMnOVojpYd5ZGtjxDpE8m7o991Vnt2Uo8Q4pcG\nzV3ATUppaQkyDZn12zJyo7aTWkI6n/Q0SJqTaTsvVx3ny7k8I6bKk4ZC+iawmcAvCgbfD72mQtTA\nNmcgGC02DucbOJirJylHz8EcPelFFfXLfrQaqb4+vARE+HuwaEZ/wv08CPV1x8NVuRUBy+8czLj/\n/YbRItMx1Ifldw522LWcRoNCmDL1lK05invnAPwndFBazmkUZWbw3cvz8PL3Z9rchXj5tZ7Ku+Ya\nKwlfppK+t5DoboFce3cP9u7fqbSsJqk5eIigF1+ioqyM0CefIPjee1tlRqrmoKCqgFmbZ+Gh9WDp\n2KUEegQqLcmJerkHWKW0CEcS5xfHMb29uJsGDXF+ccoKUiMzVp4e03A56zhfmvKMmKsg/Rd7wbX0\nX+wF1nzbwaB77B6F6MHQRu5NJquN1IKKeuMgKVdPWmFFfWxBiI8bvaP8Gd8rgj5R/vSO9ufKl7fW\nP18ARQYTg+KCFHoFjYkN9qJvdADgrNPQJrGWm9B9fhiXAHeCZ3RD0qrLYtflZvPti8/h6uHJtLkv\n4hPUema9i7Mq2PjBIQwlNQy5vgMDrmuPRqOu9xfsnpCyFSsofP0NJB8f2q9YgdeA/krLUgy9Sc8D\nmx6gylLFJ9d9QqRPpNKSnCiAJEmbgabcrs8KIX6oPeZZwAp8cZbzzARmAoSHh5OQkND8YhtQWVnZ\n7Ne4zfs2llYvpchSRJhrGLd533ZR13CEtuaiWbT1fPXkdtIJ4MSlnY+L1OUAHU3RHO/ZYM9IvKqz\nkQCBhE3jhvRKPFrZhMktkOLwMRSHDkfv393uOcmogYxtDtflCKyyIL2oioQVm8jUy2QaZLIrZGy1\nbgMfV4jz1zIhzoU4fw3x/hoC3SUkqRqohqI8DhdBuBfk1YYKSNjbl/p6m/M9Ky+3e44c/Rk4jYYW\nRjbb0K1IQVhkgmf2QeOlrixE+qICvl04F0mSmDZ3If5h6ix4dipCCJJ+zeGP74/i5evGjU8OILJT\ngNKymsRWXk7eM89SuXUrPqNHkzFxAr0uY4PBaDUye+tssiuyWTp2KV2DnAV/LleEENeebb8kSXcB\nk4AxQojTowxPnmcZsAxg0KBBYtSoUc2o8nTsa5Ob/xo3NcMKLEdpaw7Uqk2tugB+27qFkUMH2JcX\nWaob/K0+vc/cRJ+lBsI7QmYuCBkJgYubp33ZUc8puMcOI1qj5UIrRanhPbPaZNKLKjmYe9KDcDjf\ngNkqAWb8PFzoEx3E3wb40zvK/ogO9DyvRBsre1fXLwHqFObTLGlNm/M9W5xqX00xalQr9zRIknQd\n8DagBT4UQrxyyv5bsQezSUAF8KAQ4oCjdbUkQgishdXUJOuoSMi2Gwx39cQ1zDF5dC+WitISvnnh\nWawmEzfPf5mgSHUWmDsVY5WFrZ8d5viBEuJ6BzPmzh54+KjLGKujet9+cp98EmtJCeHPPE3g7bdz\n7LfflJalGHXVnhOLEnl95OsMjnDcWkwnrZvae8kcYKQQotrhFyw4BCtubKigTshpfcPMZvjLvfFx\njY49V99pGxfw3NP1NOwbXF0Nh7zPedxZ+xpuShr7A+nktqSxP+/U7XMc1EPrMgAAIABJREFU06NE\nB0UfX+J5ah8aF/tD6woa7cm2xqW27XpKu+5Yl9OO9zWkQV7A6efQNnEOTYNzyNYGg/W6gXrDQXtV\nE31nGtw3PeAfaTPD2Sf9T0frDq6e4OoFbl72bTdvcPWGqcsgboRdvwo43wJqNlmQUVxpX2KUqycp\np5yUfANGiwyAj7sLvaL8uOvKOLT6XG4ZN4zYIK+LzsTXkkuA1IxDjQZJkrTAe8BYIAfYK0nSj0KI\nlAaHHcd+EyiTJOlv2GeHhjhSV0sgZIE5y0BNso6aFB02ndH+u+emRRvqiWc3dayFq6PaoOfbF+ZS\nbTAw7bmFhLZvHTmmCzL0bPzwENV6M8Nv6kTfMTGqTM8pZBnd8uUU/+9tXCMjifvySzx791JalqII\nIXhx94v8mv0r/3fF/zE+brzSkpyom3cBd2BT7Xd8lxBilsOu5uEH3Sfbt+udGg2cGw36dPn5RLZr\n16CvwbGN/CHilOc2U99ZNFYVF+MdGnLO407jjNcW9nXwQra367cbtOUm+uoeiPo+76oKECVnPeac\n55Fttde02lOANgMDAfY1y6nOjYtH7YDeu/Zv3eDeB3zCG/e5enI8p4j4Lj0a9TUyBhr21W03ZRDU\nVYTu4Jh8/s2JLAuO66rs3oMcPYdy9RzK01NttgHg5aalV6Q/tw5pT59ouwchLti7fmlyQkIh7YMd\nXy35csDRnoYrgKNCiAwASZJWAjcA9UaDEOKPBsfvggv2iqkGYZUxHi3HmGI3FORKC2gl3DsG4Ht1\nNJ7dg9F9dURpmfWUFxbw6b8exmo2oXV1BSRuemYB7Tqpf3mIkAX7N2Wx64cMfIPcmfrUQMLj1Jey\nFsCq05H37/+jascOfMePp93CF9D6+iotS3GWJC3h27Rvua/3fdza/Val5ThROUKITi15vY232pcG\njV+/55zHpiUkEKnS5SwpCQmENYc2BxQw29vcS1rqjRZrg4cNbJbT+2TLKe3abZuFpAP76dOzx7mP\nrz9v7X6taxODdq9TjIEGfS6eFxxcfCIhgfgrm/E9UxlCCExWmbUH8uo9CIdyDVSarAB4uGroGenP\nzYNi6B3lT59ofzqE+qBVYexiS9JS3g9HGw1RQHaDdg5n9yLcC/zsUEXNjGy0YkwtpSZZh/FIGcJs\nQ3LX4tE1EM+ewXh0DULjYX+brboazDkVYJEpeOsvQu7sgUuwcikl17z2PFazCQCbxYJvSCjRPdQ/\n+11tMLPlkxSyUkrpNDCMUbd1w91TneE5VXv2kPfkv7Dp9UT8Zz4B06er0hPS0nyT9g3vJ77PDR1v\n4NH+jyotx4kTJ+eiNRQwkySQtLUz6xdfhLQ0RwvdRjWbLCdnp9Bg5P/bu+/4uKoz4eO/c2dGvVnF\nkizLlnvHGNzAEBuMqQE7lASSLCQhZCFAkjcQCCELm81mSWhhl1CWQAjJJpB4s9gkNqEbYhvjiivu\nklVsVVujOppyz/vHHUmjNlafK+v58tFn7j23zKPR4HOee+85Z8PhStYfrmRHUTW+gObuV3cQ5TSY\nlp3EF+bkMGu0lSBMzEjAacO5loYL27S0lFIXYSUNF3SxfVBHw4Cue7Y7PBBfrogvV8RVgdIKf5Sm\nfqSmPlPTkBYAowxOlVn3ToJy/2EQ5QOFwldez7Fnt1B0odlvcfVUVUlxm/XaqkpbjQbQmfoyTfHH\nmoAXsucqoiZU8PEnlRGPqwPTJP7NN4n/2xoCIzNw3/cDSrOyoJP+C3YddQIGJrZdDbt4seJFpsdO\n5yLfRXzYiz4dw+0zEyLihtoEZsK23I0+Nh2tYmMwUTgSnMF4RJyLxBgXSTFOnvnKOUzOTOzRzMhi\n4A100lAChM57PzpY1oZS6izgReAKrXVVZyca7NEwoG3Pdl9lI569lTTurcJbVAsaHGkxxF6YRuyM\ndKJyE1GnuT1W/NY/WpYViugGenVrtq897qtLT7Dp9T+1eWZVKUVqzug+3yoeqBEUTFOzZU0+e9cV\nMCIzjku/OZP00d2fbG4wR3bwlZdz/L77adi0iaRrrib74Ycx4rt+ntIOo050pb9i+/rfvw7Ad875\nDr97+3fMTJ/Ji5e+SJyrd4MBDIfPTAhbSZ9k3WHQ5tCYwEyE9/U1g/ZWHl+AbcdOseFwJRuOVLG7\nuBpTQ6zLwfxxqXxpXi6LJqYTH+Xk8v/8iJP1Xr732qf9MkJRfxrOHaCbDXTSsAWYpJQah5Us3Ah8\nOXQHpdQY4P+Af9JaHxzgeLpNa020G9xvFdC4twp/uTVYhysngaRLxhI7Iw1nZs964jsz4lrOg7LW\nB1N1WSmfvP4n9n74HobDwfTFF3Nw43r8Pi+pOaNZcd/DgxpPd9WdauKd3+zl+KFqpi7M4sIbJxMV\nY5ubZG3UbdjA8fvux6yvJ/tnPyP52i/I40hBjf5G7nrvLrLjs3lm6TO9ThiEGGhFtUU8dpmHikTN\nc6tW8PTSp8lNzD39gWeyoTaBmYiYgKnZU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3+37iYc/QD8HohOgknLrMeOJl4CsdZMpp51\n6yIbqzgjtL+KfusrW3jn+4sjHJW9jM+I51B5HQCGstbF6cl3S/TWsEkanGmxRAWfrx/5z2dFJIZV\nj/4Ev7cJgKriIl576D4Mh4MZS6zHkOyaLDTWetm9rpjd60rw1PvIHJfEousmkTc7HcMYmE6w3e1s\nrH0+6v6xHvfq1dS9/z7a5yNq4gQy7vk+yVdfjSsry9pPGnJhaa15M/9Nntj2BOUN5Vwz4Rq+d873\n2Lt57/BMGLS2Ri9qHu2oeCugIXkMnHMLTL0SxpwPztM/uiJEb9jtKnqb/gMjE2xxdfqlW+a1xDQh\nw4opUobS1Xu7fbfE0DFskoZmFe4iarY0YJoBtGliBlpfTdNEm4GWsrKDB9nsrrS2mwHMgBmy3O74\nkPN1VqZNk6rionbRKL7x1Au2TRbcFQ18+m4R+zeewO8zyTsrnTmXjiF7QvKAj5gTrrOx1hrPnj24\nV79BzZo1BE6dwpGaSspNN1r9FGZMlxF9emD/yf088skjbC/fzvS06Tyx+AnOHnl2pMMafAG/1Xm5\nef6EU8EhNEfNgYsetPonZM7o2MNViAFgt6vodrw6PSYtjv0/vSKiMTSz4+fTFbt9t8TQMeyShl0F\n73H88YPd3r/44w/brCvDwDAMlMOBYRgYhsMqc7S+digLrjtdUfh93uCJFGk5oyOaMLgrGjvMuJyc\nEUv5sRp2vF3Ike3lKEMxZUEWZy8bQ2r24P3DEpWXhzc/H0yzpbOx7/hx3H/9G+7Vq/EePYqKiiJh\n6cUkL19OwqJFKNcwvCLeB6c8p/jVjl/xv4f+l5ToFH5y/k9YMXEFhhpGo3w0nIT8D2H/Wmt4VE81\nOKJh/GJY9B2YfDkkjYp0lCICqqI1Iy5sIrdpLwX/NhPXV1eSM37aoL2/na6ig1ydPp2h9PnY7bsl\nho5hlzTMm/R5En8w1mrwt2vUhzb8lWGwceNGPrd4McpwYDgMlDL6dAW7uqyUVY/+hJPHS0gdlcOK\n+x7ux9+s59Y8u5NTJ6zO4dWlDaz65XaSM2IpOVBNVIyDOZeO4ayLcolPGfwp5Vs6G+fn40xPx0hI\n4PDSS0BrYueeS9bX/42kyy7DkZQ06LENdX7Tz8qDK/nVjl9R76vny1O/zB1n30FS1DD4LN0l1t2E\nYxutn4rPrPLYVKtvwpQrYMLFEJ0Q2ThFxI3I3sIYoxyH0uQGiin6nxvgoT2D9v52uooOcnX6dIbS\n5zMmLY7Zo60+WHbvtC3sZdglDUlx6YwcN6Fb+zqionFFd2NM9W5Kyczia0/YZ6Sd6rLWYWe1hrqT\nTWgTzr9uIjMuGEVU7MB/PQJ19fjLy/CXluIrLcNfXoavtBR/aRkqOgoVFYW/vBwVG0P63XeRfM01\nRI0ePeBxnam2lG7hkc2PcOjUIRZkL+CB+Q8wIaV7/z8MOVpD1REo3AjHPoZjG6D6mLUtKhGyZoHh\nAtMH8Rmw+D5rfgQhgFyjDEdwcjWH0owOlEQ4osiSq9PhyecjhoNhlzScabSp8TYFaKr30dTgp6mh\n+dWPJ2S5pby+tQy/HzCsMeS1SVyCk3/69/NwdDHtfI/i0ppAdTX+stYkwEoIgglCeRn+0jLMuroO\nxzpGjMCZmYkrM5P4efNIuuIKYmbPln4KfXCi7gRPbHuCtwreIichh18u+SVLxyw9sz5TMwDl+1rv\nIhzbCPXl1ra4NBhzHiy4HcaeB5mz4PlFVsIAUHUIXr0R7uzfWWDF0FXsyCE3UIxDaQJaUezIIS/S\nQUWQ3e582I1cvRfDgSQNNqBNjdfjb9vYr2+bALQu+6goMyl672OaGnx4G/xo3fW5laGIjnMGf1zE\nxLtIzoglOs5F3Es/Zs+o5TTEZRLXUM7cE2txOJecPt5AAH9lZacJwYgDBzj8H4/gLy1Fe71tDzQM\nnBkZODMziR43nvjzzseVORJnZhaurEycmdaPET34j0OdqTx+D7/d+1te2v0SAHeefSdfm/E1Ypz9\ndwctYvxeOPGpdQfh2MdQuAma3Na2pNEwfgmMPd/6SZ/csQNz5aHWZW22XRfDnuurKyn87QpyKafY\nkYPrqysjHZIQQkTUsEoaRv7zWRzcUsqGJ7Z32NbhgquC6lMmq3Zsb1t4uuM60byP3xug9GgNWoPD\naRCb6MTXZOJtDN/wNwxFdLzV6I+Oc+KMhszRSW2Sgeg4JzHB19B9XdGOLq8mf/azz1hYEvKMrsOB\n6fXiLysLJgRl+MtK8ZVZdwV8ZaX4y8rxV1RAIND2d3S5cGZlQXQ0sbNm4Vx2Ca7MzLYJQXo6ytm9\nr1xRbRF3v3c3BTUF5CXl8fTSp8lNDD9Pw0Aqqi3i2tXX4gl4mJA8IeLxnI7WmvcL3+exrY9RUlfC\nZXmXcc+595CdkB3p0HrPWw9Fm6HwY2bvXAvrD4O/0dqWPhlmrLAmWBt7HqR0Yxqs9EnWsKoAyrDW\nhQjKGT+NvTEZHCAjIpO7CSGE3QyrpKFm7Vr2v3oQd/Ro4hJbx1fvqr1uNDXRVNv8+EzHhneYdn6n\nTnli0aYCpQj4A3jcXsYmniIq2o/L8BNlBHApP1GGH5fyWsvKj4MACm09o92kKSkpYZTOtgLQwXI0\nOrjsNTVeraltUx6yb7DciInBrA8Z4UFrDpw1u+PnEBeHMzsbV+ZIos87D2dWZjAhyMSVlYUzMxPH\niBEopVi3bh2zlyzp4SfT0d3v3c0R9xEA8t353P3e3axasarP5+1LPJ6AxzbxdObrf/86AD9e+GN+\nvvnnbDqxiUkjJvGby37DvKwh+Hxtw0ko+qT1TsKJT8H0gzJwxo+Dc79m3UUYcx4kZPT8/De9Bs+d\nB75GK+m46bV+/xWEEMOHPJYkznTDKmkofedjqphKgyMZXVnO7H0vEOup6nJ/rXW/PvP9/gW/tK5o\nAqDwBxR5a39m3YoI+VHBV1/wp6UcQCli/H5q90SFHAOKkHMYRseyTvY9mZOEp6KBjGpNxQiDlAsW\nMzpvJq7MrNbEICsLR8LgjyRTUFPQsmxitlmPBDvFo7XGa3pp9DXS4G+g0d9IvjufXRW78JpeVqxe\nQYIrgR8t+BE3TL4Bp2GD/81fvsp6/fqarvepORHstBzsuFy+1yp3REHOuXD+d6w7Cbnz2bZpO0v6\nmpymjoMHS/t2DjFolFI/BZYDJlAOfE1rfTyyUQkhxPBhg9bE4NkUdz6n/K+iq0/RZIxgz+U/5Oaf\ndd2xa926dX1vmITY8ZNNVJc2oLXVfk/JTmTq7l09Pk9/xXXPqhUccVcAYGAwLvk4q1Y82+fz9oe8\npDzy3fmYmBgY5CXlDbl4TG3S6G+k0d9Ig6+hw3Jzg3+3ezefffpZm7I2+3dSFtCBsO+dHpvOTVNv\n6qffvo9O5sPxbdYV/WcWWFf0R+TByaNWgtA8BGrzZGqueBizAGZ8wbqTkHMuuM6APhiirx7TWv8L\ngFLqO8BDwO2RDUkIIYaPYZU0VB59FW2eAjTaPEXl0VfZVZGDDj5opEM6Fmg0RzxHSCpLarNdhzyU\n1Ly/Rne+j6a1HE3Wokqa/thEgyuVWO9JMhdF8VHxRx3i1O06OOjkMUj6AAAUe0lEQVR2D0LtbtiN\nLtRdbm+/3nHVKsh357eUmZjku/N5u+Dttr9jyO/Sviz092+2r24fdUfrOvwOnR3XVRnA1ROu5g+f\n/YHKxkrSYtO4avxVrDy4su2xuvmT7/rvo4OPZx2uOcyxvcc6xN28vbPjQpcXjlrISc9JqpuqSYxK\nZOKIidz30X1WI97XeeO++XGmbqmGGEcMsc5Y4lxxxDpjrWVnHFlxWcS6rOWW8uA+cc44Yl2x3P/h\n/ZiYLacrqm0/+3gEvXqjlTAAVByA/74QXHFQV2aVxaZaycG8b1qvWWeBY1j90yS6QWtdE7IaT8+f\nEBVCCNEHw6pmbk4YgmuY5im+svYr4Q/6e/+9/xO/9jOvChwaAgqOH4Tv3dbLP8EH/RdXMxOTez68\np+8n+kffTxGqorGC/9rxX30/0da+n8JQBn7tZ3vZ9jaN94SoBEbGjezYoO+ikR+6bdumbSxbsgyH\n4eh1XM9/+nxLH5CI3JkJ+KH2OFQXgbsY3IXWa3VRa2djADQ01VozLY85z3rcKH2y9UidEKehlPoZ\ncDPgBi6KcDhCCDGsDKukITlrFO7S5gl6FDHpqTyz9AcoVEvfBRX8D2DXrl3Mnm11DO5se2h/hw7n\nCFluFvWLr6C0dTXYoWH0SYM/XvnHDudqf1ywoMW2rduYO3du2P1Pez6grL6MRzY/wvH644yKH8UD\nCx4gOz67w/6d/S7Niy2fRfB18+bNLFiwoOM5Wg/o1vnDfcbtt3X292tznFJsWL+BCy64oMM5mrd3\n+h0IiTX0uP6e2yDGiOlTwgDw9NKnO4w21a+aaluTAHfzT3FrklB73Bq2NFRcOiSPhqgE8AYHFFCG\nlSRc92L/xifOCEqpd4GsTjY9qLVerbV+EHhQKfUAcBfwcBfn+RbwLYDMzEzWrVvXq3gecTwIwAOn\nOb6urq7X7zHQJLaes2tc0H+xVVdbd3/76/ccDp9Zf7NrXOEMq6Th+h/9hFfuvRO/t4m00aNZcd/D\npGR2Vj9ZvIe8nDeq/0ZD2D82l0DBMQwNpgLH2FymZ8zq8XkqoiuYnja9z/FMSZ3C53I/1+fzhCpw\nFTA2aWzvT3Ay33qcpfKQNQTmTa/1yyy9sUYsiVGJEY9joOQm5vZ+NCfTtCZBcxdDdfAOQTApmFv8\nGWw6BZ7qtscYTkjKgeRcGHehlRwk51qvKWOsbVFx1r4n88+4UYq01jT5TZp8Jh5/AI8vgMdn4vEF\nOHAygHGwwirzW2VNwe1N/tb9PP7msuC6L9ByvppGH8eqGtDApJHW7LJj0uIi/WsPOK31Jd3c9Q/A\nWrpIGrTWLwAvAMydO1f3tg/Ycwc+BmDJkvD1QH/3f+tPElvP2TUu6L/Yuvvd7q7h8Jn1N7vGFc6w\nShpSMrP47u//ErH3/8X1Bje9DKOq4HgavHq9wcsRi8amXr2x9XGWyoORm6XXLnF0V2jDPGNq2yTH\n54GaEisRaHl8qKh1vaYEAu0m4otOguRcmqLTSJiwNCQpyIWUXEjIhO7eHYnAKEVaaxq8Aeqb/NQ1\n+alvCgRf/dR7rbLiUw38+qN8/KYmOdbJeRPScTmMDg14jy8Q0rA3W9bD2ry5y02GghiXw/pxGsS4\nHES7HMS4DGKcDlLjozhSUdfyIOWRijpufWUL73x/cf99QEOQUmqS1rp5Br7lwP5w+wshhOhfwypp\nAKB8f8cGUhcSao9CaVoPTh7+sZUdrhK2hvRhcKgSKNvb43PH1x2Dsn2n2b0bj9C4S2DtvVB9DFLG\nwpVPQMroDu/Xeq7Oytq+Z0zjCasB261jOynrbJbe2lJrX6U6eaXjeif7GoEmq/Hc6TlChqRt1lkc\nAb+1rE3QgdZlMxCc/yKk3AzZHvrTptxaTnIfgMLYvp333X9t29n4+QshY7KVIDR3OG79sCExy0oE\nRs2BaVdbdweaE4OUXIhJBmD3IF0Jab5yXx/awPf6Wxv6TX7qmgItywfzm1hVuqO1rM2+Aeq94SdM\nbM/d6OfdfWXkpsYRHWzIx7gMUuOjWtedwYZ9SCM/2tna2G8+5sC+PSyYO6dlW3TIthiXA5fj9P03\nJjywtmXZ1HC0oj7M3sPGz5VSU7CGXD2GjJwkhBCDasCTBqXU5cB/Ag7gRa31z9ttV8HtVwINWGNv\nd5yyuY+8RUUc/dZtBAqOUZIKr64w+bemCnL9XQ9dORdgW//FkJeTRb7LhakUhtbkeT3w3Pk9Ps88\n6JdOvW2cyoc/XNvn0ywE6M8L8joAT0zp82k+Bz3ooK3oOORUAH7akwSy+84B2NGfZ9TgrYXoRJh0\naWsi0JwUJOWAM+r0p2l/1mDDvtEboNEX/PEGaPIHaPSaLWWekO0eX4CyGg9/3lpMwNTERzuYlp1E\nwNRtE4QmP36ze638aKdBlGEyor6a+GgnCdHW1fncEXHERzuCZU7igz8J0Q7io9qXOVn08/cJtBmN\nCz64d0mPP5f2HGWfce7Y1D6dY3xGPIfKrX4ghrLWhzut9XWRjkEIIYazAU0alFIO4BlgGVAMbFFK\nvaG1Dr1MfgUwKfizAHgu+Nqvim6/o6U/waiTcNMqg9vvnMWaOT/o8pg9e3Yzc+bM7r1BNy5r/rjk\nEA8VvEyJS5Pjgx/n3QaLJvX43Hv37mXGjBnhDjj9OQH9v99o6ZgNoJWBuu7Fzn+XNmW6y7LPPtvH\ntKnTTrtfV2UlFSfxbXqB0bqUYpWFa8E3yUlPsfbRocfqjq9hth09coTx48eFlBP+HI2n8O5+HYfn\nJEfMbH7qvJufzfMwJt4PymF16DWCr8oIlqkuypvLVNuyYPmu3Xs4a/ZsUA60UmgcmMF9TQw0Blq1\nvpootHKgUdZ2pYj985dw1RzDgSaAojFpAgcWv9y2MV8VwFMaoNFb1KGB7/GZLY380KSgurYB/dHb\nLdt7IzQFq28KsKfYzbxxqWQmxrQ26kMa812WRTmJi7au1PfHs6DjM+I5UlGHqe3XMH/plnnc+soW\njlbUMz4jnpduGYIzep8BZIZfIYRoNdB3GuYDh7XWRwGUUq9hPYsamjQsB36nrcHzNymlUpRS2Vrr\nE/0ZiLegACPYcnEEE4dibxVL/tZ1Q6GxcTaxBbH9FsOva1/ir+o4DqUJaMXRot+wOPGZ0x7Xvg3f\n2DgnbFwd5mnowm8C2YwPjcfM5htrR3Tr2K54GpOIKez9RFwnqjPwm4+3rDv/oRiVEtvhaajQ1Y4j\nRXVcaWiYQlxlXJfHtX/YSikoqL0YbyD4WXph6XrF6BFxaK0xg3NwmGbzXA5gao3W1uMkoWWmaS3r\nYLmpg/sCWgcwzanorU1080J7p3LVd3nJ9Tjj1QmO6mxurbiTouc+DntMtNMgNsp67CY2ynqEJtZl\nlaXEuYhxOXBHNZGXOypke+s+zesxLuv49suxLgfRToNJD77Z5oq+L6D5/a39fl2gx+zcMB+TFjfs\n+zAIIYSwl4FOGnKA0Fmmiul4F6GzfXKAfk0aovLy8Bw9jKGVNUdCKuT5/OTlpnR5TFlZE5mZXW/v\nqXGfncChrMaTQ2nGcYI5Yd4/VGgDt7SsiazM8I377gwKeuun9/Ki63HGYzU0v+m7l3l9fKyitKyM\nrMzen+P1kyVt1gOm5pwxbT+jNvcowsxjFzoRXHm5h4yRSZ3u2GFyvODqobK6NuX+gGZmTjJGcDRW\nQwWHYVUEyxSG0TyEq7XdULTso7DWDcPa3lxeVFjIuLyxLWVG6DlbllXIezbvF4zDUDz8Blzqfawl\nVkPB774xv7WhH2WELDuIdjpwGKf/llhX9Lt5t60Ldr2iLw1zIYQQovuGTEfovo677fjaLYx4/CHM\nGs3xVMWrKzT3lxl4J7m7PKYuwU9CQtfbe6pwfzZjdeuV/UKVzYqsnp+/Lt5PQkL16Xc8jU1xWVxW\n/xgaq/GZHa+4JrNv562L9/Uptk3xihP1uk1MvfmMOsSV5Cchoeb0O4bYmd8xluuy++/70GzGKC8J\nUafJkXW713ay49rGmhWnMI/vpQGro1Bv9cc40rdNNXmqXlHaoMmKU9w2NdDnc9p5fGs7xyaEEEL0\n1kAnDSVAbsj66GBZT/fpl3G3S86die9/buDiQAmTG3NwfXUlOeOndbl/f4+hWzLmLxT9zw2MDpRQ\n7Mgh+qsrWRLm/Qc6rtdmNXR4PKOvY8H3NbaBiKm3cQ1ULP0RW3t2+tw688Ur+3yKNuw8vrWdYxNC\nCCF6a6CThi3AJKXUOKxE4Ebgy+32eQO4K9jfYQHg7u/+DM1yxk+Dh/YAkDcQb2Dz92/Pjo9n2Ckm\nO8VyOkMpViGEEEIMPQOaNGit/Uqpu4C3sIZc/Y3Weq9S6vbg9uexZvW8EjiM9STF1wcyJiGEEEII\nIUTPDHifBq31WqzEILTs+ZBlDdw50HEIIYQQQggheuf0U5MKIYQQQgghhjVJGoQQQgghhBBhSdIg\nhBBCCCGECEuSBiGEEEIIIURYkjQIIYQQQgghwlLW4EVDi1KqAjg2CG+VDlQOwvv0lF3jAvvGZte4\nQGLrDbvGBfaJbazWOiPSQUTaINUXdvmbd0Zi6zm7xgX2jc2ucYF9Y7NTXN2qL4Zk0jBYlFJbtdZz\nIx1He3aNC+wbm13jAomtN+waF9g7NjEw7Pw3l9h6zq5xgX1js2tcYN/Y7BpXOPJ4khBCCCGEECIs\nSRqEEEIIIYQQYUnSEN4LkQ6gC3aNC+wbm13jAomtN+waF9g7NjEw7Pw3l9h6zq5xgX1js2tcYN/Y\n7BpXl6RPgxBCCCGEECIsudMghBBCCCGECEuShnaUUjcopfYqpUyl1Nx2285SSn0c3L5bKRVjl9iC\n28copeqUUvfaIS6l1DKl1LbgZ7VNKXXxYMYVLrbgtgeUUoeVUgeUUpcNdmztYjlbKbVJKfWpUmqr\nUmp+JOMJpZS6Wym1P/g5PhrpeNpTSt2jlNJKqfRIxwKglHos+HntUkq9rpRKiXRMYmBIfdF/cUl9\n0e0YbVtXgL3rC7vVFTD06gtJGjraA1wLfBRaqJRyAv8D3K61ngEsAXx2iC3Ek8CbgxdOi67iqgSu\n1lrPAm4Bfj/YgdH133M6cCMwA7gceFYp5Rj88Fo8CvxEa3028FBwPeKUUhcBy4HZwe/94xEOqQ2l\nVC5wKVAY6VhCvAPM1FqfBRwEHohwPGLgSH3Rc1Jf9I0t6wqwd31h07oChlh94Yx0AHajtf4MQCnV\nftOlwC6t9c7gflWDHFq42FBKrQDygfpBDqvLuLTWO0JW9wKxSqlorXVTpGPD+ofttWAs+Uqpw8B8\n4OPBiq0dDSQFl5OB4xGKo707gJ83/8201uURjqe9XwL3AasjHUgzrfXbIaubgOsjFYsYWFJf9JzU\nF31m17oC7F1f2K6ugKFXX8idhu6bDGil1FtKqe1KqfsiHVAzpVQCcD/wk0jHEsZ1wPbBrABOIwco\nClkvDpZFyveAx5RSRVhXZ+xytWEycKFS6hOl1IdKqXmRDqiZUmo5UNLcMLOpbxCZq7kisqS+6Bup\nL7pm17oCbFpfDJG6AoZAfTEs7zQopd4FsjrZ9KDWuqss1AlcAMwDGoD3lFLbtNbv2SC2fwV+qbWu\n6+yqUgTjaj52BvALrKtvtoptMIWLE1gK/D+t9V+UUl8EXgIusUFcTiAVWIj13f+zUmq8HqRh104T\n248YoO/U6XTnO6eUehDwA38YzNhE/5L6YtDiaj522NcXdq0ruhFbxOoLu9YVcGbVF8MyadBa9+Z/\nsGLgI611JYBSai1wDtCvlUAvY1sAXB/sdJQCmEopj9b6VxGOC6XUaOB14Gat9ZH+iidUL2MrAXJD\n1kcHywZMuDiVUr8DvhtcXQm8OJCxhDpNXHcA/xf8R3+zUsoE0oGKSMamlJoFjAN2Bhs+o4HtSqn5\nWuvSSMUVEt/XgM8DSwcrwRIDQ+qLQYtL6osgu9YVYN/6wq51RbjYmg2l+kIeT+q+t4BZSqm4YCe3\nxcC+CMcEgNb6Qq11ntY6D3gK+I/+rAB6KzgKwBrgh1rrDZGOp503gBuVUtFKqXHAJGBzBOM5jvWd\nArgYOBTBWEKtAi4CUEpNBqKwOixGlNZ6t9Z6ZMj3vhg4Z7AqgXCUUpdjPTt7jda6IdLxiIiQ+qKH\npL7oNrvWFWDD+sLOdQUMvfpCkoZ2lFJfUEoVA+cBa5RSbwForU9hjTaxB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"text/plain": [
"<matplotlib.figure.Figure at 0x11a0140f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Remake standard plot comparing grouped data to model, but now use\n",
"# chandra_aca.star_probs grid_model_acq_prob function with the newly\n",
"# generated 3-d FITS model that we just loaded.\n",
"\n",
"colors = [f'kC{i}' for i in range(9)]\n",
"\n",
"plt.figure(figsize=(13, 4))\n",
"for subplot in (1, 2):\n",
" plt.subplot(1, 2, subplot)\n",
" probit = (subplot == 2)\n",
" for m0_m1, color, mask, mag_mean in zip(list(fits), colors, masks, mag_means):\n",
" fit = fits[m0_m1]\n",
" data = data_all[mask]\n",
" data['model'] = 1 - grid_model_acq_prob(data['mag_aca'], data['t_ccd'],\n",
" halfwidth=data['halfwidth'],\n",
" model=MODEL_NAME)\n",
" plot_fit_grouped(data, 't_ccd', 2.0, \n",
" probit=probit, colors=[color, color], label=str(mag_mean))\n",
" plt.grid()\n",
" if probit:\n",
" plt.ylim(-3.5, 2.5)\n",
" plt.ylabel('Probit(p_fail)' if probit else 'p_fail')\n",
" plt.xlabel('T_ccd');\n",
" plt.legend(fontsize='small')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Max difference is 0.067\n"
]
}
],
"source": [
"# Check chandra_aca implementation vs. native model from this notebook\n",
"mags = np.linspace(5, 12, 40)\n",
"t_ccds = np.linspace(-16, -1, 40)\n",
"halfws = np.linspace(60, 180, 7)\n",
"mag, t_ccd, halfw = np.meshgrid(mags, t_ccds, halfws, indexing='ij')\n",
"\n",
"# First color != 1.5\n",
"# Notebook\n",
"nb_probs = floor_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=True, color=1.0)\n",
"# Chandra_aca. Note that grid_model returns p_success, so need to negate it.\n",
"ca_probs = -grid_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=True, color=1.0, \n",
" model=MODEL_NAME)\n",
"\n",
"assert nb_probs.shape == ca_probs.shape\n",
"print('Max difference is {:.3f}'.format(np.max(np.abs(nb_probs - ca_probs))))\n",
"assert np.allclose(nb_probs, ca_probs, rtol=0, atol=0.1)\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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xQ0TsFhG7kTWn7NUqcYOTt5lV2fB6m8wDDpB0OzAtDSNpZ0kLACJiNXAEcAmw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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1136c62b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d_probs = (nb_probs - ca_probs)[:, :, 3]\n",
"plt.imshow(d_probs, origin='lower', extent=[-16, -1, 5, 12], aspect='auto', cmap='jet')\n",
"plt.colorbar();\n",
"plt.title('Delta between probit p_fail: analytical vs. gridded');"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8, 11)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SOiooN8iRWaePE79pLXovLxo//myZxiRbFc5sALkQPIOgenc0W+Joysmh0tx5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\niUQimWBI4ZdIJJIJhhR+iUQimWD8f8wUENAICpAIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11377a9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mags = np.linspace(8, 11, 200)\n",
"plt.figure()\n",
"for ii, t_ccd in enumerate(np.arange(-16, -0.9, 2)):\n",
" p_fails = floor_model_acq_prob(mags, t_ccd, probit=True)\n",
" plt.plot(mags, p_fails, color=f'C{ii}')\n",
" p_success = grid_model_acq_prob(mags, t_ccd, probit=True, model=MODEL_NAME)\n",
" plt.plot(mags, -p_success, color=f'C{ii}')\n",
"plt.grid()\n",
"plt.xlim(8, 11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate regression data for chandra_aca\n",
"\n",
"The real testing is done here with a copy of the functions from `chandra_aca`, but\n",
"now generate some regression test data as a smoke test that things are working\n",
"on all platforms."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"array([-2.148, -2.148, -2.148, -2.148, -1.77 , -1.483, -1.665, -1.665,\n",
" -1.665, -1.503, -0.948, -0.661, 0.377, 0.932, 1.218, 1.519,\n",
" 2.074, 2.36 ])\n",
"array([-1.623, -1.506, -1.433, -1.301, -1.038, -0.869, -1.155, -0.965,\n",
" -0.867, -0.697, -0.385, -0.207, 0.369, 0.741, 0.92 , 1.121,\n",
" 1.466, 1.63 ])\n"
]
}
],
"source": [
"mags = [9, 9.5, 10.5]\n",
"t_ccds = [-10, -5]\n",
"halfws = [60, 120, 160]\n",
"mag, t_ccd, halfw = np.meshgrid(mags, t_ccds, halfws, indexing='ij')\n",
"\n",
"probs = floor_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=True, color=1.0)\n",
"print(repr(probs.round(3).flatten()))\n",
"\n",
"probs = floor_model_acq_prob(mag, t_ccd, halfwidth=halfw, probit=False, color=1.5)\n",
"probs = stats.norm.ppf(probs)\n",
"print(repr(probs.round(3).flatten()))"
]
}
],
"metadata": {
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
michaelaye/iuvs | notebooks/2015-03-30 inbound.ipynb | 1 | 100632 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"570"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(io.l1b_filenames('inbound', iterator=False), columns=['fname'])\n",
"df.size"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"df['basename'] = df.fname.map(os.path.basename)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['fname', 'basename'], dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# removing echelle for now\n",
"df = df[~df.basename.str.contains('-ech')]\n",
"# then focusing on FUV data\n",
"df = df[df.basename.str.contains('-fuv_')]\n",
"# remove data with mode3001\n",
"df = df[~df.basename.str.contains('-mode3001-')]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"141"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.basename.size"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fnames = df.basename.copy()\n",
"fnames.sort()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_4_to_3(width):\n",
" return width, width*3/4\n",
"plt.style.use('bmh')\n",
"plt.rcParams['figure.figsize']= calc_4_to_3(9)\n",
"plt.rcParams['image.aspect'] = 'auto'\n",
"plt.rcParams['image.interpolation'] = 'none'\n",
"plt.rcParams['lines.linewidth'] = 1\n",
"plt.ioff()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l1b = io.L1BReader(fnames.iloc[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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sCdZ5OlJ7V7q/l2SOPxOb/wcrx1PdWzYAbmtrJW+UzwBaVPWjqnqvqj6K7aQ7\nmvGdnmz53Qec3kW97FPVXdgPVdafzIUVpnsx8HW1HTkrsfVm2ZGESup1BbDAje4BICIzsPVj2R/W\nilHVjkyeW44grt3Y4utsu7sEW6ztdyNtxUZ2Qs6m9PlzmMpeVld2kZ7f3FAxIjIWW793CjZl+WRO\nmGHY+q2XYGtvlmfDYNNjc11Yj982vqE3NkUisWNz5JS8RbkfrF8CH3CHVgNniMjfisjJIvIebG1F\n9u3rFmyh5SPBg/IW4Ergz9r7Laxp/Kq6DvgV8F8i8kwRWYAt2p1I6YNRcuzK8jhwiogscH4nct+i\nReTTIvJiEZnv/Ppcga1P2RgEm+psOkVEng98Avimqh5Q1b3Y6MbVIvJ2F89pIvJKN5zty/oHwNdF\n5NWufBeJyLuDNNYDl4rILBGZ1lWmXIfzf1xcz3E2/Ts2bP8FF+wGbNHj90XkDDcKcw22NqCn0YnV\nwHQReb2IzBbzrfO2Hq4ZbFqw/D5XRGaKyGR3/KPAi0TkSyKy0JX75WI+jvzUyZeA94rIP4jIXBF5\nL7Z+rLM8mTLWAFeIyOkishAbDWigtG2uB54hIie6dpjXbn+I7cr6iYicLSLnYiM/m4Cf9KokKsTd\nFwuxnWwjReQsV0bdja58BniXiLzRldVbgLdi7d/zR+AVIvJsdy98BZuay5bJua59TZOuXRh8BniJ\niHxQROaJyMuxF6gv9eY5IyITgN9jHcVXumMz3We0+z4MK/MXYi97rUGYcUF0XwSmY/ffKSLyLHfs\nv11HORKpnMFe5DOUP9h25j/kHL8IW6vwHOwN6pvY/PEu7MfxHWS2UGJvs53AV4JjL3DxfLACW3ra\nFTUF++Heh70BfxybGvllEOZW4NuZeD9M6Tbfydj22Z10v93737C1Intc2Fsp3YZ7K7aI8PPYD+hu\n4NuUb/d+A7Yl+wC2c+Nu4C3B+eFYh2g9xa2r4c6q51Lc1hpu9z6cY/MEV1d+u/dfCbbhuzALnQ0H\nsR/gl2LTL+/rqi6C459wZb8X83X0Sle/jV3ZhXUIs23FX9fgvjd1VxcuzGJ3zXF534NwJYutsY71\n4+542A6egS1W3e3yswqbQvVbhsPt3nuwTsa/AruDOD5GsIg+OH46xa3kj2M/8iULY7HRxvtdmA6K\n2707Mu1sHuXbvWcH5/PK/AQXT5fbpbsp5/UE26SDvz1tKPDbvQ9jmwfenTk/HlvDssO1t49irhlu\nCcKchE1zMdUeAAAgAElEQVR37+nJfmwRvr8vNmHrAxu6K5cu2lSYzzI3EEHbzAuTXfx9KXbPHXDl\n+Dm62QkaP/HT1UdUj3SKPjIUcW9Sq4FfqOr7ByH9W7FdI28e6LT7ExEpYA/hF6rqbwbJhkuxjtIC\nVd0wGDZUgohcC5yhqosG25ZIJFK7xMXDdYKIXIzt8ngQG5l4H/aWe91gmcQAebTtT0TkCmzx6Hps\nUePnsTUAfd2Z1R88H9suvGEQbShBRGZha8Zuxd7WX4iN/rxjMO2KRCK1T+zY1A/DsGmlOdi0wnLM\nr83Kbq+qHsrQdDk/BVsgfjw2LXAn8BJVbRssg1T1nwYr7W7owKbpPoFtI14HvFVVrxlUqyKRSM0T\np6IikUgkEonUDDUzYrN06VJdtmwZCxcuHGxTIlUm1nN9EOu5Pjja6nnJkiVVnSJfunRpv44mVNve\noUjNdGwA7vr5tzmu5RTaxHYsDtPibvbJTAFgf44/sE63A3WKTE+P7dTtFkfgKmM/5ihzt1gcJ+hx\n6bnd2I7EhmAH/Whs5+tBisK43o42ijMXnZhPqslijk53BW4xxrgdkYcDcd0JbtfyCBmbHmtw7h/G\njp5nB8Y3pef2nmzuIA43jcpmneGbinZMXGt+8Dp3mouPg21PpOc69BAA7YFPuwNuF+aBoEzHORcv\nob0dhD63ivkF2MteM5fxZbY9IVuK8WK2j9ZR3HXHaoZvLzrrbVT7PyxTXw8jAh9mPi1/LKwrf+1B\nOZQem6TH2LGg/nbKHnduQpAfaz8dUr6T2ed9N0Wh9YmMAWCKTi4Lv02s3c0LHONuYB0Ah4P8DXO2\ndwS7p6frVJc/24EftnXfjp+UremxqTqx5BzAbtlbEhcUy2a/WB4mB3a3SEuZHd62Noo7hw+4OHw9\njgzqZYTaYygs+2k6jfvueJiZLbcVw2XqrTNI09sdpjldp5SF8/Xh6xFgLnOBYpvdFfiCG5HjA2+P\nSytktI4qSStszzuktSRMSGjbRI7JnOsoCxe2cf9/eN1eivlKbXPPId+2Jui4sjAh47G2Heb9kLsH\n/D15OCjnGWpunML279uIbx+WrpXJDrHyneKuu+eOVcxsKXd4nPdM2CzmiHs+p6THEsw/qm/HYTn7\ndhG2t93uOV7QolugTWI+Bect+XBZmtVgyZIl/RLP0qVL+yWeWqOmOjbbdu3vOVBkyBPruT7Yuqu8\nAxGpPbbuynM+Xdt0xiUgVSU66ItEIpFIpAZwDitvFZGVIrIi46w0G3aRiLSLSJfaXkOVmhqxueSM\n3mrkRYYisZ7rgyVn9EmPMTLEeNYZTYNtwoATTkH2M22Ys9BlIjIeuF9EblbVR8JAzo/Z5zA19ppb\no1NTIzYLCl16zI/UELGe64MzCn0RlY8MNU4rTO85UI3RXx52c+LdoqrL3P97gUeA48oCwruAGzHP\n4DVHTXVsViV91qKLDCFiPdcHy5PmwTYhMgCsTGryt3XQEZEmTCj1nszx44EXAd9wh2puwU9NTUVF\nIpFIJHK009nHvsTtt93On26/Pf0+eeKk3B1WbhrqRuA9buQm5KvAv6iqOvHYmpuKGjIdGxG5HKuQ\nYcB3VfVz2TBxiqI+WFCYxtZgS26kNjmjMCN1oxCpXU4rTOcQh3oOWEN0at/W2Fx8ycVcfMnF6fe7\nbruzLIxTkv8ZcIOq/iInmnOBH1ufhmnA80SkTVV/2SejjkKGRMfGLXT6T+AyTKfnXhH5ZXZBVCQS\niUQiRzt9HbHpCTcCcw2wSlW/mhdGVWcH4b8H/KqWOjUwdNbYnA88qqobnCbPj7E5whLi2ov6INZz\nfRDX2NQHcY1Nv/J04ArgWSLyoPs8T0TeIiJvGWzjBoohMWKDCQ4+EXzfBFwwSLZEIpFIJNJnquWg\nT1XvpBcDFqr6uqoYMsgMlY5Nj63gxhtvZOXyjaza2EKndDJm1AhOOvaYdN3Nw4m5Ap9TMHfh/i3B\nbzVcmWxjorRxVuG4kvNnFsxd/4pkKwc5mMa3NmmlVYelW1L9KMLphWPT7yMZWfId4OkFc/Pu30b9\n9cuTZiZIG2cXTig5f37T7BL7vT0PJk8wXEZxTqEJgPuTDQBcPN8kFe599AEAFs05B4AHlt9J27aR\nnL3I5mcfvPcOOz/rQjv/8J2Me+IJFs09165fv5rDHds418X/QJJY+o2z0vQP6T7OKswqsffCpom5\n9ublF+Ckwrguz2+TVuYWzDX76mQHAAsbZ7GgMI0/u/zOLUxK66ed9pL6BFjo6nN50swBDqT1tzLZ\nRgMNaXhfP7ObJqTpjddDZfV3nJOl8N99fKuSFjpFObVgUgSPJE4awdm3LtlZYu/qZAcT9HBZecxs\nGu7KdxNA2h4eSbbTTjvzXfvx5eHjW5PsYIu2p/E9nGzhEAfT/K1ITEphchPOnlaa9XBJeQCc2DQm\ntz5WJts4KIfS/Pnzs5qGpekDJfa108F8V38+/wtdfL58fHyrkhYOSxunZO4Pz6qkheEML6nfTjrT\n7z792QVrf2uTVrZqR1l78PGvTUzmYJ6z76FkM216OG2vvn59+84+L3z5+/h8+Kz9vvxWJzsYqSNK\n2kv2+nEcLGkPnXSk3339+fJcmWyjnfY0vuL9NLYkfn9+RbKVnbI7vT6v/YbflyfNDGcYZ2bu70lN\nVh+Puvqc49pftv1n28fqZAdj9UBQX61M1DZOK0zntMJ0HkyeLLNnDPvK7o+pLv1lLvzCwvFp/A00\npOln23O2va1LWtntpDyWb2xm/S4r35dNXdZvcgfdUa2pqIgxJNS9ReRC4CpVvdx9/1egM1xAvHTp\nUt36x68BRK0oqGmtKICtUlw8HLWijFrTigJKFg9Hraja1Iqy+MsXDw+WVtRAiGCed8lF/RLXfbff\nHUUwcxgqa2zuA+aKSJOIjAReAZQtdoprL+qDWM/1QVxjUx/U4xqbajnoixhDYipKVdtF5J3A77Ht\n3tfEHVGRSCQSGYpUTVAhAgyRjg2Aqt4E3NRdmOjHpj6Ifmzqg+jHpj6oRz82keoyZDo2kUgkEonU\nAtXaFRUxhsoam4qIay/qg1jP9UFcY1Mf1OMam07VfvlE8qmpjk0kEolEIpH6pqY6NnGNTX0Q67k+\n8D5MIrWN921TT3Si/fKJ5BPX2EQikUgkMoB0xj5JVampEZu49qI+iPVcH8Q1NvVBPa6xiVSXOGIT\niUQikcgAEhf+VpeaGrGJay/qg1jP9UFcY1Mf1OMaG+2nTySfmhqxOQbTHtmrppcSap20YRpHXk9k\neHDO6xsd1KK2zjQx8bdWLWrrjMW0mcY7zZNDgYbQVOzHNtRq8RpDoRZPKyaeN11mpsc61TRh2jM2\nQlGbKbRXnYZMm+5Pj41pKGr7ACDFODpHuP+HBacbcm6LTqdD4+yR4IJDWq6P47VsvDYRwGZMvHGs\n00OCog6M134J9W5myYklcQHsUBuanhnoFfk4vGbOsYHOzE4xJ24nUiiLI7TN1992p90zNdA88mW+\nM6g/H2+oP3ScOj0v2V2MV11etTQuKOoajaaoX+PbaR4TXdvaRXG6zV97khT1cTbpo2VpeU2iFqeJ\ntVeKul4zXbyTtai/s8lpBx1XUg5mr9dFg2K5TVBLy+uXAUzStrLwB5zm0ySnRQVwgtMf8jpMoUbS\nHqe9FpaR1+7x5R3ir31SilMYI105zNTij6TXhQo1wbweVRiv1zrzbdBrKwGMcv/754fl65iS+EOb\nvIbZCC3er167yOfT4rCy2SdFDbHDrs69TlbotC5PsyrvmH+uNATtYpfTvZvlNNVCp4e+rkItpXZX\np62uPUFRd2+0Cxdqg/k85OmFzQjKORHTnvMae+Gz0uvvTQ3ua1/2Jc9Zd34HxefyMPesS3XZAi0v\nn69QP8o/m9qD59DeoH4jQ5+aGrHxitKR2iausakPvAJ3pLapx/u5U/vnE8mnpkZsIpFIJBI52olr\nbKpLTY3YnFmY2XOgyJAnrrGpD+YVJvccKDLkifdzpL+JIzaRSCQSiQwgcRqputTUiE1cY1Mf1OOc\nfD0S19jUB/V4P8ddUdWlpjo2kUgkEolE6puamoqKa2zqgzgnXx/MK0yOr6V1wILCtBI3C/VAnIqq\nLkNmxEZErhWRZhFZPti2RCKRSCTSV+J27+oyZDo2wPeAy7sLENfY1Af1OCdfj8Q1NvVBPd7Pqv3z\nieQzZDo2qnoHEJ90kUgkEolEuiSusYkMOeIam/ogrrGpD+Iam0h/UzMdmxtvvJF1d97JjGPGc5jD\njB01nDkzpnF64VgAlifNQFFwzX/3QnvLkic5rAfTztGyxDSPCo2mNbQiMW2SML4OOtL4Hk6eAmB2\nwbR4VibbaKc9/RFemZiuzSmFKQA8lJgWzlmF49z3pxgmw1lYON7FZ9NqC4PzFt40rB5MNiE0cHbB\ntJbu3/A4AE+f3wTAvevuB2DR3HMBeODhO+nYMoyzz7/Yrv/rHQCcN/2i9Py4jRtYNOdsu37DGto6\nWjm3cFJJeSwsnJB+P6wHONPZ4+09rml4SX59+axKWhjDvrS8/fmLm47NLY+VyTYaaCi5Hoqdmuz3\n1ckOdjCi5HqACwoTUvvaOJzW35pkBxO1Lf3u63dakwA2DTKSEWl9rXHTIjMazZ5HEtPfObUwNTe/\nPj7fnrL2+vLy5/332QXTYcq2z5XJNrbI8LS+fXzh+YNyKLX3sWQXB6SNpsLEND8A5zWOS6/fLns5\nuXBMSXxzmkq/e3uXJ80ompaXr6/ZjWNzw69JdjBWDwT3h+WvUBjdRXgrb1+e3t7jGmfklseKZCvN\nspO5hUkl4Wc2Fsu/k840fV8/JzWNz41vedJMJx0l8QOcW2jsMv28+Kc0NZR8D9vvfjnIfOd00J8/\nsWlMWl4NNKT1tzxp5hAHy9pT9vrs8+z8ptmAPc+A9HmyItnKMIan9vvyP6nJ7o9se16RbGW/HEi/\nZ8OvS0wTy5f/mqSVTjpTp4o+/NNdfh5OttAsren55UkzB9if1r+vv4saJ6X5G8f+9Pni8zevUDwf\n5v+RZDuddAb3q2nyZZ83YX15DbEVG7fy2C473zB1GUuWLKHaxGmk6iI6hEpYRJqAX6nqGdlzS5cu\n1Xuv+TxnFmam4mqhSJxvxF6sricRzLHuWCiCmY0rFHMc70T+QmE3fz4UKvRCbXkimMNlZJkdPq1Q\n1G602MM5FKkcM8weQKNHNdmBCSen53bPnw9Ae2OxH+tFMIetLwo8Tly90uzZtQqAQ+3N6bl9neXl\ncNAJY4aChi1iD7RUGJLuRTCnyLEujnIRzFC8MBQEXJW0pD+wUBQ2zBPB9PVicZiYXp4IpmdLIKw4\n2glohiKYM5zIYq4IpiNPBDMUiexOBNO3nzFOeBLggLt2lpyUHssTwdwnFq47EcyJWhSw3OzKIRTB\nPCxWN6WigWaLr+cJUrR/j+4sy58XVpwYCG6O70YE04sohiKY+zjA2qSVxY1F4U+Pv3ajFNfUdSeC\nGbLfpXVMINDp7y3fBkPb8kQw/fk8EcynxAtOFgVcfbg8EcxQsNELR3oRzANBmfpnWXjveMK24gVK\n80Qw/TOvJxHMCa7e9oS2ORFMH35fcD+OyRHG9O1sMlPSY12JYK5KWjiuyeo+FMGciHWyQxFM/xwI\nnw37UxHOjjI78kQw/bXhvfOoWEfwnCUfZ8mSJUIVWbp0qc4459x+iav5gfurbu9QpGZGbCKRSCQS\nGQrEqajqMmQWD4vIj4C7gHki8oSIvC4bJq6xqQ/iGpv6IGpF1Qf1eD/HXVHVZciM2Kjqqwbbhkgk\nEolEIkc3Q2bEphKiH5v6oB79XtQj0Y9NfVCP93McsakuQ2bEJhKJRCKRWiCusakuNTViE9fY1Af1\nOCdfj8Q1NvVBvJ/7j56kh0Rkmoj8TkSWicgKEXntAJs4INRUxyYSiUQikaOdKk5F9SQ99E7gQVVd\nCCwGviQiNTdzU1Mdm7jGpj6oxzn5eiSusakP6vF+VpV++ZTH26P00FOAd+I0Ediuqu3dhB+S1FxP\nLRKJRCKRSC7fAW4Rkc3ABODlg2xPVRiQjo2IXApsUNXHRWQW8DmgA/hXVe23YZa4xqY+iHPy9UHU\niqoP6lErqq87mu6/6w7uv+vO9Psps6b3VgLiQ8AyVV0sIicDN4vIWapaUxUwUCM2Xwee4/7/Mva4\nage+DfxtfyXiXW97l9mhbIJngtiCxJagP3VAzXX5FCm6Y8+TUtgp5op8pI4oSSdM20sEALSruWH3\nUgkAh9Xcf08aVnT/v7MjKQmfx0gpuu0/THkcI4aZK3IZaa7cO4cV867DbchShhXvJj1sx0bsKbor\nlzZz+d7RaeVxoHN7ei7rdh6KrtnDcghd1XsmOgmBA1j8oStzH1+Yd+823rtUBxiLdxVvabWyIz03\nSY8piR+KdZkX7xjnXn1/jsv6SVqUYGgTG6E9GLjT9y7dD4eu7d2IsD/WqI3pqe1sK4nf7DjsjhXb\nxX5nu5ceCN3IbxeTIRilm8ts205R2mGyc4U/WczF/jE6Osif3epjA/f7h538RUsYh5O92B5IRnRq\np7PNzu3T4jl/T8zSWekxfz+F7v99/ny8MwPJgQbnrt/nCWAc1t73UnTr720/6Mrm2MD9vpcrCCUx\nvLxCKA3gaQ7CHXZt8FgnLRHa7fM3IZCH2OJkEyYGZbk7aEtQKnPg75NJwb3h3f+PC+Q4fP593Ydy\nC8e58vX2QLHMw+fLEyQu/kBWQKzttbqyHBO0RS8REspIeEZo8efBt98R7tk3kWJ5+PIaFUhieJmD\nQ1psx14uod2F91Iolj/TdAqlIHw57JDizIqXRgjlLLydoSSHZ6drb2MZU3YujOOEoD0OBFpe3BVx\nzoUXc86FF6ff9zxyX2+jeBrwaQBVfUxE1gPzgV5HdDQzUGtsjlPVjSIyAngu8BbgrcDT+zMRLxQX\nqW284GiktvHCjJHaZnWyo+dAkf5iNXAZgIjMwDo1jw+qRVVgoEZsdovITOA0YKWq7hGRUZAzpBKJ\nRCKRSA1TLed6TnroEmCaiDwBfAz3O6uq3wKuBr4nIg9hAxsfUNWa61kOVMfmP4C/AqOA97pjTwce\n6c9EQsXnSO1yZmFWyVRUpDY5tTA1rrGpA04pTKm7es7b0dQ/8XYvPaSqLcALq5L4UcSAdGxU9XMi\n8gugQ1UfdYc3AW8ciPQjkUgkEonUBwPmx0ZV1wSdGlR1rarmekfsK3GNTX0Q19jUB3GNTX1Qj2ts\ntLN/PpF8qjZi4+b3ekJVgy0kkUgkEonUOnU29TbQVHMq6soqxp1LXGNTH8Q1NvVBXGNTH9TjGptI\ndalax0ZVb+vP+ETkROD7wLHYbfBtVf1af6YRiUQikUi1qdbi4YgxIGtsRGS0iFwtIo+LmMckEXmO\niLyzF9G0Ae9T1dOAC4F3iMipYYC4xqY+iGts6oO4xqY+iGts4hqb/magFg9/BTgdeDWkLi5XAm+v\nNAJV3aKqy9z/e7Gt4sf1s52RSCQSiVQX7adPJJeB8mPzYmCOqu4VEQVQ1SdF5Pi+RCYiTcDZwD3h\n8bjGpj6Ia2zqg7jGpj6Ia2wi/c1AdWwOZdMSkelAr/XqRWQ8cCPwHjdyA8CNN97I6jvv4dhjxqEo\nY0eNYN6MYzmjYBoky5NmAC5sMr2WlYlpxZxWmJ5+nyhtnFWwQSA/reU7SyuSreyVfXYTAquSFhpo\nSAUZffxPazK9mWXJk3RoO2cVZqXfARY02vX3bzAv1uc2zQbgoWQzndqZhn84MS2rhc6eZckm9/2E\nNPz4ho70+ns3rAXgogUn2/d1Jv2xaO55ADzw0B3o5gbOvsB0Rh689w6zd/xCs2flXYza8hjnn3wW\nAPdteJT9HVs426X3oEu/mJ9NtGpLSfmF9vry8OX/UPIUhziQfvfle1HTJJdfm14608W/KmlhHAfT\n8NnyWJVY0/HlvyLZyjCGB+mZrtJpjdPS+PexL7U3e723P6zfdumwH1dgbWJ6NRc1mr1r3PD5fBf+\nkWQ77bSn3x9y+TmhMLwkfp/+8qSZ4YxIhVv9+UWFcSXl49vf2qSV7VosTz9Nc3zBNHDWJa2M14Np\nftYkO+hEmV+w9uiH+y9sPCaNf0PDLmYXjnHXmx7V+YXxaXwAc931q5IWxrCv7H46vmlkWr5QrL8V\nyVbaaS9rH1ObGnLLI1vePn+nNxbPj2Z0Wh7Z+lvj7J3j8rM2aaVFNQ3v4/Px+/C+fNYlrWzT9rL2\nMafpmFx71yWtjOVAGp8vL1+e2fa/KmlhOMO7bH/Z/K9MtrFX9qXtybf/GU22NmN1soNtWixv396n\nOPm4vPa5hwPMLfj2a/bOcOWb97w7zOGy+yO0LyyPVUkLIxmZfvflfX7j+DS+sD1kn6e+vM4Knh+d\ndKTl59vvwsbi8yFbfg00lLW345tG5drrvwOs3LiNLbtMA/JF05b1VlSyT8RppOoiWi3fzmEiIl8E\n5gD/CNwPLAC+Cjyqqh/uRTwjgF8DN6nqV8NzS5cu1buu+RSnF45Nxc1GMbosjnFiHZuWHFHxUARz\nj+4sO9+dCKYX6PMim9C9COa04XOL8ToRzE51gpCBCJ9PY7QURefanYhingjm8LHWsekcW8zL7lPc\nsROL9noRzDGPFEVdx2542Gzcaw6h97RvIEsogrlVnyyxEUrFHj1jnIClF6kMBfcmiBPGC8Qqt7v+\nbiiC6YUzG2jg4eQpTiwUBe9Gu3oORU/HyLiyeL0YYShymLU7FMbzooT7nOgoFIX8QoHCke7aSkUw\nU0HBHBFML/YZimB6YUcvFAiwy4n7heKLXgTzgBMXDMvZi2B6wVCAVQ0bARivRTu8CGZevOMD4UOP\nFygsEcGkXATTtxEvIBmKYPpwoQgm2I+j79hAuQhmiBfBDMUfuxPBPEhRgLE7EczDYv9XKoLp05+t\nJwbnrN2FbSBsZx6f/7FOGHNnIETqRTCbpTk9VrEIJn0TwQzL2dueF647Ecyw3Hw4H5d/nq5OdvC0\nRntGNeQI5OaJYOYRiut6fBlOCQRT8/C2HXvZu1myZElVV/YuXbpUR514Qb/EdeiJe6pu71BkoNbY\nfBhYDzwMHAM8CjwFfKLSCEREgGuAVdlOTSQSiUQikQgMUMdGVQ+p6vuACcBMYIKqvhdyuv9d83Tg\nCuBZIvKg+1weBohrbOoDP/weqW38tEektvHTUfVE3BVVXQZkjY2I/BF4japuBra6Y2cB1wNnVhKH\nqt7JAEpARCKRSCRSFeJi6aoyUB2F+4GHROQVItIgIv8C3Ap8vT8TiX5s6oPox6Y+iH5s6oN69GMT\nqS4Dpe79QRH5NTZC8zlgM3B+KIoZiUQikUhdEKeRqspATu3MBiZiW7zHA2P6O4G4xqY+iGts6oO4\nxqY+qMc1NnT20yeSy0BJKtwIfAi4XFXPA74F3C4iHxiI9CORSCQSidQHAzVisw1YqKp/BVDV/8L0\nnl7Sn4nENTb1QVxjUx/ENTb1QT2usVHtn08kn4FaY/O2nGNrReRpA5F+JBKJRCJHDXEaqapUrWMj\nIleq6vXu/zdQvsFN3LFr+yvNuMamPohaUfVB1IqqD6JWVKS/qeaIzauwXVAAV9J10+23js04JpR8\n3xn8+Hk33jvUpqtC99/HiOmNDAtc3O/BJBVCt+LeHb13Dx+6xB/rpBpGBdIHB5wr9eFBuAYxt98t\n7evSYxMavFtzcyEeukhP49Jd6f9jxOxo6yzKIXhJBZwsgw4ruk3XBvO4LYHHce/cqaEtcGPvrh3W\nYK7iR0ppeWbx3UgvEwFF2YTQNfpIsXXiY5xEwg7dnJ7bqTbdELpDn4wtJvQyA+F5L5PhXegDjHAS\nF6GExgG186E8hXdj76UBvLwGwEi3lv1JXZ8eS923Bw7LJznbGrQ4i7tDdro827ENsiE95125h+70\nvYv4kyjKamzGpteGqeUzbMsnq/0fyll0YHF4GYAwfS/tELq/b8Pq2cszABzn2nPofn+E2iPheIov\nCf4e8PFtk+IUkb92jTyeHvNyAqHUhpcaGe/yEkpG+Hh93qF4f4bu9Mdr6cx5mL9xToYglOHwdR/K\nJ6T5DB59e935rS6t2YEkxmgncfG4FDdwTlVrN8cwKQhn9+JBKU/LS1x4SQMLb+1hYiA54PPv21PI\nLvc8CiUK0s598GQd5m7y8H7y7Xick4fYHUiETHJ5z5NFCdusj8+X+fbAxkmuTkPpilACxbNPTHZi\nimuzBynKnWRlVyBfEmenWDmPy9l7MlmnuDiKciBN2gTAbor2epmFGVps43lyDFUljthUlap1bFT1\nb4L/F1crnZCHkqdSkcZI7fJwsoUpTYNtRaTaLE+aU5HNSO2yNmnl5Maew9UUcYSqqgyUujciMgl4\nATAL82PzW1Vt7f6qSCQSiUQikcoZqO3elwIbgHcBi4B3AxtE5LL+TCeO1tQHZxZmDrYJkQHgjMKM\nngNFhjzzCpN7DlRrRD82VWWgRmz+C3izqv7UHxCRlwH/CZwyQDZEIpFIJDLoSOyUVJWB8mMzC/hZ\n5tgvMKXvfuOh6N+kLng42TLYJkQGgOVJ82CbEBkA1iZxRUKkfxmojs31wDszx95GcddUJBKJRCL1\nQZyKqioDNRV1DvBWJ6HwJHA8tlv4HhG5w4VRVX3mkSQS19jUB2cWZrKJJwfbjEiVOaMwgx3Et/la\nZ15hcv3tEqq3/A4wA9Wx+Y77dEes6kgkEolEIkfEQEkqXHck14vIaOB2YBQwEvg/Vf3XbLjox6Y+\niH5s6oPox6Y+qEs/Np3xPb6aDJgfmyNBVQ+KyLNUdb+IDAfuFJFnqOqdg21bJBKJRCK9Ie6Kqi4D\ntXj4iFFV7yd7JDAMysWC4mhNfRD92NQH0Y9NfVCXfmwiVWXIdGxEpEFElgHNwK2qumqwbYpEIpFI\npNdUaVeUiFwrIs0isjwvWRF5tYg8JCIPi8ifReTMfs7ZUcGQmIoCUNVOYKGIHAP8XkQWq+pt/vyN\nN39B8OQAACAASURBVN7Ivb/7Haceb8Jm40aNZMaMEZxWMIHJFYmJX55amAoUfWT4t8JlySYaGMHZ\nhRNLws8pmODdqqSFkYxMFcSXJ80MZ0Q6erAs2QTABU3mb/CBJOGQ7mNh4XgAHnTn/ajSw87nzpnu\n+wPJRlQ70/QfSBIAzikUXPy2C8jH90CykQYa0vP3blgLwEWnNtn3tfcCsGjeIkt/2R3wlHD2BRfb\n93ttM9ozxpwOwH2r7mbktsc5/+QzXXxrONS+lXMKTS69Dc6e4vfDupeFhROAog+hea68eipff/6U\nwpSS8vbl+3CyhUMcLKu/cwuNPJxsYZsTLJzv3vZWJtsYxZ60Pnz8pwbxH+YwCwrT0vNjZC9nFY4r\nqZ9j3Vz/qqSFBhrS9B9JTPhxZuOsND0gPb8maaUBYb5Lb43zzXFR46Tc8lid7GAXm9LyW5PYAOTT\nGieXlKdvLw8lT6F0pO1ltQt/buPY1J7Q3tXJDtrpSMvH2zOnYCKRa5NWRjIiLX+f/jmNx+fWR7b9\nZ+Nbl5jI4NxCMb+ddAT1b+13QeOU3PLIlufypJn1za0844JieU3Qw2n4VUkLnXSm9bkqaQHgQmfP\n8qSZDtrL7PXl4cvP5//RZCcjGMZcd977Sjq/6eSS8vH1uyppYRwH0/bm7T+5Kb/9r0l2cJj2NH4f\n36LG8bn5z9rr8+fbw8pkG+20l+V/bpOVv2+v/nm3MtnGfjmQ2v+oq68TGqd3Wf4ddJTdf94e73tm\nXmDfYWlLy9Onf2FjsTz2yN70/PKkme3SyrzCZNYmrezV8ufHSEaWtfemwvjc/D2SbGeLFu8Pb+8F\nhQldlO8Otqqtc1m+sZktu0wY8wXTzmfJkiVUG6neGpvvAf8BfL+L848Dz1TVXSJyOfBt4MJqGTNY\niGp1ClhEPontdJJugqmqfrQPcX8EOKCqX/THli5dqg9c8+WS6ag8dW+v8Bwq1+ape7eo/dCF6t6j\nndpsxereWq7u7RWaD2pRCTdV99bK1L19GqGC9tgR1mEYPqbJ8jnxxPTc7vnW+aGxWBWd++z/cSuK\n22lHb7JOfscBy/v+tifK7Ag5qPZw7End2ys7+/IN1b3bnAJzqK7rVX27Uve2xcNFpWuvoD3WKQRD\nsZ5DdW9fl2OxzkBP6t4+zVCxeaZa+wrVgrPq3qHqdKXq3o+yBoCpTvk4q1Rv8Rbz/JTYD8E4HZse\n8+l7lWWv6B0SKnmPdvURHvMq2aMDZWVfbv5Ynrr34cA2r+4d2uvbgG8robr3oRz17VGMKls87FWh\n9zv15s6cV9Y8de/QXk+o7t3q2tlId6xE3VucujdFdW9fRqG6996MuvfkQHW9WexH9mDQFvPUvT1e\nfTpkgo5zdhfbUVsQX3pMyuvcq3vvdu0iVPc+Qe3Zk6fuHcbv7wV/LE/dO7xPfPhjtHiP+Xbv63Gz\nWOdvbdLK8xrPBfLVvX1bh6IieJ6697FOrTtU9/b3UU/q3r6tTLzszSxZsqS736wjZunSpTqaRf0S\n10HuLbNXRJqAX6nqGd1dKyKTgeWqekK/GHMUUc0RmxPpfgu39HC+GFBkGtCuqjtFZAzwbODj2XBx\njU19EP3Y1AfRj019UJd+bI4O3gD8drCNqAZV69io6mv7MbpZwH+LSAO2Luh6VV3aj/FHIpFIJDIg\n9HUq6oGH7uDBh4qbgRcsnN6nqTMReRbweuDpfTLkKGdA19iIyARgGsH0lKo+3tN1qroc817cLdGP\nTX0Q/djUB9GPTX0Q/dhUzjlnPINzznhG+v3A8Pt7HYdbMPwd4HJVrckh0QHp2IjIAuAHwFmZUwrB\n4opIJBKJRCJVQUQagf8FrlDVR3sKP1QZqBGbbwC3Ac8C1gMnAVcDd/dnInG0pj6Ia2zqg7jGpj6o\nxzU21doVJSI/Ai4BponIE8DHwFaDq+q3gI8Ck4FviAhAm6qeXxVjBpGB6ticBVymqm0i0uAWAb8f\nWEFU+I5EIpFIPVElz8Oq+qoezr8ReGN1Uj96GCgHfQcg3fO8TUQKLu2p/ZmI9/0RqW28j5FIbeN9\nwURqG+8TJxLpLwZqxOZO4GXAdcCNwE3AIeCWAUo/EolEIpGjgio66IswcOreLwu+fhhYCYyna++I\nfSKusakP4hqb+iCusakP6nKNTZUc40aMAZdUUNUO4rqaSCQSiUQiVaBqHRsR+Y6qvsn931VHRlX1\nNf2V5qqN21lYOIH9ai6zx1B0N+/du3s33d4FOsBTutGFKa7o8q7ZxwcuzL3bbe8afJYWR4h8HGMD\nF/fejfz+QA7BSw1Maih6sW4QO9bhXMuHrujbnAv6PYG7Ae+WfpxMKYbrMJfhw9uL7tI9fthTNfC8\n7V4YpD1wwe6uPdTe7NIplpE4u/dpUabCuy4P3f/78h0dSEvsUtOx8a7zJ1G0u9PVw/CgnA+l0gdF\niYTWQB5jRbKVGU1FLwFZuQwo1m/ogr4Yv9mxX4trdfaJ5T10ST/Ru7HX4m2yUaye9wUyALPUlop5\nd/MtOS7x9wdu3o93HsyfZEN6bAyjSsJvo7i+ZDqmN7SdlqK9Lv1pTEuPZV33z9Gm9Jxvd6GUga+/\nUIZgN7tcHMVw3lV+Iqa3MyVo49PV6nKblLeLkP1qI2xeliGsq1Eu76EL/0McYmWyjZlNxfrb5crV\n10dz4NZ/smsr4whc+KuVh5dAADjg8lJaf5YHX/ePuzoGmOokASZSbM/e9jAP/lmz1Y0wHZZiXuax\nACi9r9ezriwO//zx9o4K2kReOx7v7rvwueVHMqdrcfniTrE6HenimKHFeL3dLVJsWyPV3U9SjNeX\nuS+j0YFMjJdPGB3E6489IVuCY+LyYtcep9aulyfNFApWXmEb8M/ZaVps4/6+nsr09JiXYfD3R2l7\ntjYSPkv8sy+sj7AeBoTOKq0ejgDVHbEJHe89Rr5uVByPi0QikUhdEdfYVJdqSip8Jvj6LVUt27Ik\nIv26KMYrJUdqm9MLx7KNcmHDSG1xWmE62wPxwkht4hXQI5H+YqDW2KyBYIy4yEoI5iUikUgkEqlx\n4ohNdRkoPzZlMvAiMpF+dlO0LNnUn9FFjlJWJFsH24TIALAy2TbYJkQGgLr0V9Sp/fOJ5FLVERvn\n0hlgbPC/Zyrwo2qmH4lEIpFIpL6o9lTUle7vTcAVFEduFGhW1dX9mVhcY1MfxDU29UFcY1Mf1OMa\nG9G4K6qaVLVjo6q3AYjINFXdV820IpFIJBIZCsQ1NtVloDwP7xORs4GLsSkoCc59tNJ4RGQYcB+w\nSVVfmD2/LNkUR23qgKwfm0htkvVjE6lNlifNnFM4cbDNiNQQA9KxEZE3A18B/gD8DfBb4DnA//Uy\nqvcAqyDwCBeJRCKRyFAiOuirKgO1K+qDwPNU9cXAfvf3pUB795cVEZETsE7Rd8nZZQVxjU29cHrh\n2ME2ITIAnFaY3nOgyJCnLtfYdHb2yyeSz0B1bKar6p/c/51uSul3QNl0Ujd8BXg//bxFPBKJRCKR\nSO0wUA76NonISaq6HlgHvAhogUCwpRtE5AXAVlV9UEQW54W58cYbuf/3v2HB8TNp00OMGzWSphmT\n0rcB7yvBz+V6Hxn+rXBV0kInnSwoTCsJH17fQXs6WrA62cE2hTOdoviqxHRKzis0puHHyn4WFo4H\n4OHEHC/7UaUHkg3OniYA7k820KmHOadQcOdNr+aMxum59jycPMVo2cfZLj/3bzAFi2ecMh+Ae9fe\nC8CieYssvofugC0NnH3BxQA8eN8dAFw84hQA7nvkL4zYtpbzZ5/u7FnPoc7dnO3sfTCx3frzGsel\n6R/iUFoeD7n8neG+e59CPr/Lk2baaEvL24efUxifm78VyVZGMJIzCzPLyndFspWdTjdofmFyen0D\nw9LrfXiv+L4y2UY77Wn9rky2ldT3msS0jmYXzI/k2qSVsXogPe/jKzRZ/tcltltnbmFSbvz+/PGN\n00uu9+cfSp5iJzvK4vftp6fw6xLT0Sk0Hp/mv1VamV+Ykp7fr6PS9unL+5SgvA5xMK0P3z6bCmNz\n01+d7KBV9jG7cIwrL0v/osZJaXmBU2om//4COMe1h+x5u7860u8rk21saN7JhRdMT+MfzX5OLUxN\n49suu5njyn+1q79CYTYAy5In2aM7y9I/qWlCSfnNdfauSlpol440fn9+auPELvMzhv1lz5eJTbjy\nMXt8fTyYbELpSO8Hb+/CxuLzI2yPq5IWRjKypHyg9Hk0kpFp/T6cmCbTFJe+9/UUPq8aaCgpP4AL\nXX16e05x9q5KWugULQs/t2lSbvjs/eTLa3rT8LQ8G5C0fTycbKGdNs4ozGB50pxqxc0pTEiv3y8H\n0vh9/k9y7dO3Z39/L0+aOcCBsuf3PJc/Xz7+ebIy2ZbqWa3YuJUtu0yD6oXTLmLJkiVUm6juXV1E\nB6CAReR12Pbu34rI84CfASOBd6vq1yu4/mps63g7MBrzYvyzUEBz6dKluvza/ywRwSwVmLP/vQBb\nKILpw+WJYIZ4EUwvKheKYHqRxrGUi2B60UooihFObChem4pgatcimF5IEopCk6EI5phh9gAaM9o6\nNh1T5qXn9sy1H0stFAfoOvfZbN74h4pO0EZtegCA/QdWAXCgs7ilulIRTG97pSKY+3JEMH05+3xC\naflmFw/PUHv4+7KFfBFML7DnH2hh+0hFMCkXwWwIBjZ9uEpFMI93toXifl7ALxS69Pj2E4pmehHM\nMHyr7AWgoMenx/pTBDMsGy+C2ezyFYpgTlH7oQtFMKe6YyE+vjwRTF9HYRl10lm2eHgcJg7pBRmf\nCtL0IpgFZqfHvAjmoaCuvAjmwbA+nNClF3jcHZS9PzcsaFve9vE5S/0S8WKfRbu7E8EMhSM7M4PR\nPYlg+vujRARTuhbB9HkYpsX27Ou+tyKYYf2Nc+0hPObT2iqtwTF75hTUXsbaOAyULh7eT3Hz7B7X\nxisVwdzL3pI8Qb6grk8j79kw+bK3sWTJktylDv3F0qVLdeoTJ/VLXNtPXF91e4ciA7Ur6nvB/zeJ\nyGRgpGogH9399R8CPgQgIpcA/5ynCh7X2NQH/5+9M4/Po6wW//c0SZsmbdN9b9OWLlAopCwFpXiF\n4hVRQUVFFFG8inLdr9dr7/Xq9a7W9YeKCy7gLmIVAZE1oBBAtpLSfaNNW0rXpFuWZju/P55n3kze\nvEnmTd83efPO+fYzn2Zmnpk5c2Z5zzznPOdYHpt4YHls4kEcY2wseDi79JcrCm/MvBGYAuzBjYzq\nK9aPZxiGYRhGF/oleFhELgG2Ax8HzgM+AewQkUvT3Zeq/lVVr0i1zmpFxQOrFRUPrFZUPIhjrSjR\n9oxMRmr6q8fmu8ANqnpHsEBE3gHcDJzaTzIYhmEYxsBjrqis0l/DvafgAobD/BGYnMmDWIxNPLA8\nNvHA8tjEg1jG2BhZpb8Mm18AH0tadqNfbhiGYRixQdrbMjIZqekvw+Zs4Osi8rKIPCMiLwPfABaL\nyON+eqyXffSKxdjEA4uxiQcWYxMPLMbGYmwyTX/F2PzITz1hI50MwzAMwzgp+iuPzU/74zgWYxMP\nLI9NPLA8NvEgljE2FjycVbLqihKRc0VkUWh+ooj8WkReFJFbRELpaQ3DMAwjDmh7ZiYjJdnusbkJ\n+E9gjZ//ETAV+CFwDfA1XBBxRnhox3OJWiHQObV2kG47nCY8YIy60gd1ofTfgWMsnII+SLs91rcP\npzIPUoiHU+Gf8CUSwqm7g/T8R9s3JJZNlOl+fy4YLCgJEW4/Rjq+aorEpZZv1ebEsqEFHecNINoR\nWKaFLuN2e0so87bPMj+kLVRgvcjZmUOaXNmJAhmaWNXmjxUuldDuH6zWUHr6oDRC53NwuhnuywU0\nhtKml4lL/X5Q9yaWTRRXJmC/vpxYFpQyGKNjebFmLxfMOi2xrg4XcxNcY+jQeZCWHWCkOtmD61ZG\nR+r/Ml8GICjdANAs7rwm6aQu7WpkV2JZjXSO+RlPh46C++JwqMzCfp9OPyi3ECaQt0SHJ5YdwsWa\nHJUOvTX70g/h+zOg1Kfi3xmSsdnfWxP9vQsdpRIO09GuzetmQSgLw25qABjh0/8Xp0j1P0E7ymQE\n5xouTxGsD56hcJmK4P5oCOmonkY219QlahW5c3DbjvT6nRI6ZlCqYR8d90yQ/j+syyJfLqA0lP5/\nxoWXE6Yt5BUv9HJqaFmjvy9GhvabSMnvywYUhM4vkK01pI95nNGlXaD74FoNo5ANT64EYGiKkgrF\n/v1yNHSPHwnuB+no0RyppZ22C54ld2Ku3MI07ejtPuyfgXo6rkeR/6kIyiaEr213JTGg415051rg\n27l3Sa1/326sqeWCmbO77CMo1RCUqQAY5d8hB7QjLicoPRE8H8NCZSoa/LnWhnr+gnMpDZUGSVVC\nxxi8ZNuwOQ14HBKZhy8HzlDVTSJyF/AUGTRsDMMw+sL6J3+X+Ls5ZPQENZ/CHzG14j5epoaMxHrc\nD2hgqBSFXq2j1f1oNoWM0GNS36VdYCwc918d57/6vX0+HyO3sRFN2SXbhk0BHRW8zwf2quomAFXd\nJSJdq+WdBOHeGiN/CSr0GvlNUAnayG9OLR8bv6Ej5kbKKtke7r0eeKf/+13Aw8EKEZkGFhloGIZh\nGEbmyLZh8y/ALSJSB7wJ+Epo3dXAE5k82Pqag5ncnZGjvFizt/dGxqBnc01d742MQc/GmtreG+UZ\nom0ZmVLuW+QyEdkoIltE5HPdtHmtiLwgImtF5C/ZPNeBIKuuKFWtEpGZwHxgk6oeC62+F7g9m8c3\nDMMwjJwjSzE2IlKAq8F4KfAy8KyI3K2qG0JtRuPqN75eVXeLSN7FcGQ9j42qHgWeS7F8U6aPZTE2\n8cBibOKBxdjEg1jG2GSPJcBWVd0BICK3A1cCG0Jt3g38XlV3A6hq3rk6+qukgmEYhmEYkM08NtMg\nlL8BdvtlYeYBY0XkURF5TkTybvhdf5VU6BfW1xy0XpsY4PLYjBpoMYwsk5zHxshPXB6bmOWR6SY+\npjee2VrNs9uqE/On6JksW7as054j7KYIV79xGVACPCUif1PVLX0SKgcZVIaNiOwAjgJtQIuqLhlY\niQzDMAyjf1gyt4IlcysS83sruvTavAzMCM3PwPXahNkFHFTVRqDRF6A+CzDDZoBQ4LWqmjKM3npr\n4oHF2MQDi7GJB7GMsclY8LAkL3gOmCcis4A9uNHH1yS1uQu42QcaD8PlmPtmhgTKCQabYQMprqRh\nGIZhDBa6G6qdPp1/wlW1VUQ+BjyAS5D7E1XdICIf9utvUdWNInI/8CLQDvxIVddnSKCcYLAFDyvw\nsA94+lDySstjEw8sj008sDw28SCOeWyyiarep6oLVHWuqn7ZL7tFVW8Jtfm6qp6uqotU9dsDJ212\nGGw9Nheq6isiMgF4SEQ2qurjACtXruSJp7ayYacr/lYyrJCFk6axqNwVMAyMngWzXOG8DTWuXRCc\nuKZmH8fkWKd5gPnlLqhtXc0BWmlNuLvW1OyjkKKEW2RdjStUeGr52MTxhjCE08tdocO1Na5QYnL7\nYP0LNbtR2qgod8Xogh/vivKpifUAi/36VTU1tGkri8udO/XZHW70/AXzXu3mN7sR9ufNP9dtX/04\n7aOHsHjJRW7++ccB+LviuQA8t/FpCo7sYckpZ7n5Hdto1qOcXT6z0/HPKp8CQHXNbpq0IXE+gbxn\ne3mC+WB9oM/gegTzr541JqU+qmt2U6cHE/Ob/I/c9JnTEuudfqYn9NtEU+L6BPubNcsVANxYU0uJ\nNib2t67mAMOp7yL/jHJX+HN9zUFapS1xPZPPJ/jRDdwl22pcMcFT/P0SvKyDoMhNfn6B39+WmsMc\nVjrJAzB71oiU+lhfc5Dj0pA43tYal7R75sxJifbHpT6x/401tbTSxgLffouXd+LMDn03S0vifg/0\nO9fLX13zstev0/eGmkM00MQ8v7/g+blg5qiU8m7yxw/kDdYHx0tun/z8ba6pY9e+Y53kG4Ikzm9t\nzX6aaU5c7+D5mls+KqGvVmnrcrxgPngfBNsH55+sr0XlE1Ouf6nmCEe0vZO84f0H1//UbuQN9neG\nP//k67W15jBFIzcRlNlMfl9tqDnEUC3qpG+AieXFKeXter6ufcVM9zyvrnkF6Hi+3f3U0GX7ObNG\nptx/8vMXtJ87q+N5GMKQpPft8U76gc7XL/z+DK7HOeUlKc9nXc0BDsvRLvdb8Dwly7uxppZidfW6\n1u88yOEjrvjmZePPSA7GzQ5Z6rExHKI6OJ2bIvIfwHFV/QZAZWWl7n34pk5twhVbj+J+eKJW9w6W\nparuXeIrzBbRUf36OC73YFuogF5QzTZVde9wFdso1b1LpONcUlX3Lhu6wB2zyL042secklh3ZOEc\nt2xiqIPOp0octe6Vjv0efAmApmMu6r6pvaNCcFDduz10fmE5A4Lq3uGK32GdJO9jpDg991bdO6gY\nPV3duhLpGBVVp+6lGK6sHui8XjqWJVf3HsHILvL3Vt07IFzd+zjNndaFq3sHBRAPhe6tel8+LVV1\n76AacUmKytGHpSO/ZaNfNjMkW6CjgsQ91lGBuafq3k2Jcm49V/c+6vUbVFgGKNNRnWQMy5Gquneg\n+3B172G++nVdJx11LioZ3macP4dwUcmggnZ4Warq3sHx25Kqe+dqEcyeq3sHVa07qnvX+WtU5vUB\nPVf3Hu73MYHQfeSfgXBF+mAfwT3T+dq6d074+QsIniHoeB8G90xt6Hqn0lHQfn+oXXDvFWj4/nHn\ncEBqvTwdVd+D5yl8j/dU3bvo0mtZtmxZVsMdKisrdfKzXXXVF/aeV5J1eQcjg8YVJSIlIjLS/10K\n/D2wZmClMgzDMIw00bbMTEZKBo1hA0wCHheRauBp4E+q+mC4gcXYxAOLsYkHFmMTDyzGxsg0g8ZB\np6rbgYpeGxqGYRhGLmO9LVll0Bg2UbA8NvHA8tjEA8tjEw9imcfGDJusMphcUYZhGIZhGD2SV4aN\nxdjEA4uxiQcWYxMP4hhjo9qWkclITV65ogzDMAwj5zGjJKvkVY+NxdjEA4uxiQcWYxMPgiR9hpEp\nrMfGMAzDMPoT67HJKnnVY2MxNvHAYmzigcXYxIN4xti0ZmQyUpNXPTaFFFJEEdOGuPpH9e0dD8wI\nnyb9AK5MQGkozXpQbqE5lBb+oDgjKUhbDh0p2vdxoMs+AsbQ0a0apG0Pp4ofr85dVhoqCbBXd3oZ\nR3baDuAIriZQo3ak4C5Ul159/JAZHQeWgk5ytA3tkE2HuIzbMqRjTKX6IunSFno4Wlxq9iHizrk9\n9FUhPr15s3akbw/KSEyWmSRTp/tC7dw2QdryIXTI2qRBavmOlPFH1Ok+nKY/SKd/nGM00sBBDZWC\n8NuOCJUyOCDuOo/WDj0f86nnp6irhxNOk79POuQNCEopvCQ7E8vG+f0Vh8ppBH/X4c7lKB0p64NU\n+KO0Q7bgPgoTyFKk7pEMn3vAaO0oATHd3yvBvQsd6fHr/Hk2h9Lej/JVhw7J4S5yh+/jYNutbE4s\nC1LQB+UYwin8T/hU9cNDZRZafEr+Ujr2u12cMRqk+g9fl5d8eYpwKYPhFNGOJko8hAmubbgsw2F/\nL4avS3CdD9HxwZOqpEor7TTSwix1ddl2SYfhHOimIHTPHgml/U+08+calJ0IlzEIymmEyyIE51Uc\n2m9wjGD/rbTT5O+DQI7iUKmEvXKgixxjgpIDId0EZRACwroPnp2doXs8KMswKVSSJpkDviSE20fX\nn5GgbEi4/EYywXNSz4lEKZaCFPd9KR3v4Dr/LiFURGAUnd/DJ0LlE4J3evjeSlXWptGfc9fCFcZg\nJK8Mm6BgmpHf2HWOB/MsxiYWzCsfHbs8Noq5orJJXhk2hmEYhpHzWIxNVsmrGJugVL2R39h1jgdb\nLMYmFmypOdx7I8NIA+uxMQzDMIx+xJLrZZe8Mmws9iIe2HWOBxZjEw/iGWNjI5qySV65ogzDMAzD\niDd5ZdhY7EU8sOscDyzGJh7EMcbGakVll0HjihKR0cCPgdNxHZcfUNW/DaxUhmEYhpEeNtw7uwwa\nwwb4FvBnVX27iBQCpckNLPYiHth1jgcWYxMP4hhjY2SXQWHYiEgZcJGqvg9AXS7pIz1vZRiGYRi5\nh7mRsstgibGZDRwQkdtEZJWI/EhEuuTqttiLeGDXOR5YjE08iGOMTTttGZmM1AyKHhucnGcDH1PV\nZ0XkJmA58MWgwcqVK3nyqU2s33mQkbKPEcXDmDFhOGeVu3oxa2pcLaDJs9wpBwUzF5aPT8w3SwsL\nyl2tp6AwW8XMKYn1rdLGaeXjANhUU0ex1nfaHuBCv/2LNXtpp51F5a7e0IYaV9/mopmu/eqaPQCc\nVe7q06yrOcBw6jmzfHIneWeXO/ttbc1+AM4on5hYXzaknbPLywF4dvtGAC6YdwEAz216GoBzF5wP\nwAsvPA5jYPGSi9z8848D8NqiWW77Tc9QULeDJXPOcNvv2Epj234Wl7t6VC/UuHo+p84c5eV/heMc\nTbiFXqjZDcDi8umd5A/Of33NQUppSswH6yv8+ac6v0YaE/rd5K/HeTNHJPQFHW6p5OMF7c/38q6r\nOUCDNHJq4vq8Qjua0HdwvU8NXf992rE++JEd5/eXqv0xGplbPtq3dy/r8aHjh+XdXFPHfm1LzAfr\ng/2luj/D88H5lpcXp1y/paaOFtoS8gT6mFM+KnH8oRR1Od6UWcW+vTvfBd4dtLGmlmKtT8ibfD2S\n9b+5po5iGhLPy7Ya18F6tpc3WR9bag7TSjunlJcl5N+973hCvs01dQxhSEKeTTV1DEESz2tQMPNM\nf/yNNbUcUDizvOP5BVgwy7UPnsdAvi01hzmmBQn5k693srzbao5QS2ti+2B/08qHp7wem2pqKaQw\n0T6Qd7FvnyzPtpojFI3cnHhBB/s72z9fa2v2UyuHme/1EewvkC/V/RmeD84neP6C6xnoM/n8Jr5r\nZAAAIABJREFUguPPmeVqlL3kr+ccf72S9x/o7xz//ko+vzU1+9grh50bio77Z275yJT6DvY/1esr\neL6C7TfW1NJMS0IfgbyzvbyBPPMS908tQ33NvQ07D1F3pBmAN41fwrJlyzAGN6Ka+85NEZkMPKWq\ns/38UmC5qr4paFNZWamHHv4uQMoimI2+QGFQQC9c+C8oQNgYKhYXFJFLVQSzyRdZO5kimEOlo5hd\nrbqHOFURzOOJ4n4d7YOCceEimKVD3TkXFLoHvWXSGYl1x+a7lxcTO+TUI66KXNmLuzr2u389AM0n\n3LLjrR3rAhq1wwN42Bc7TLcIZpig8N+JUGHBjoKXHcUWg2sTFJMMF0AsSlG6LttFMJMLC0JHEcxw\nAcLxvn1YxqAQ5QTtuFcCWdoifoWNSFEEMyFHD0UwW0LLgoKRwQs+vO3QUHHGoMhhqbofqXARzOB6\nhItg7hZntIeLYO7zcqYqgvmKv1bJRTABCkLVDguSOpjD90BwXn0pgjnz1W9i9ZO3pyyCGVzvcBHM\nGnFGeKoikUERzHGh86sXV+wxXASz3heALKXrOyTQ1ZJXX8uaJ3/baX+9FcEMrlWyrsKMDBVkDe7L\n4HmB1EUwg+KswX2/Rzruu6naVQ89FcEs8+eyw98nAHPUvcuCYpSd9iUdy8IFZgOCe7sJZ5yMChUg\nDd4bTaGCl8X+nIeH3u0lXs5Rl97AsmXLQiU2M09lZaWOfPTxjOzr2MUXZV3ewcig6LFR1b0isktE\n5qvqZuBSYN1Ay2UYhmEY6WKjorLLYImxAfg48CsRWQ2cCfxfcgOLvYgHdp3jgcXYxINYxthoW0Ym\nIzWDoscGQFVXA+cNtByGYRiGYeQug8awiYLlN4kHdp3jgeWxiQdxzGNjrqjskleGjWEYhmHkOjZU\nO7sMphibXrHYi3hg1zkeWIxNPIhjjI2RXfLKsNmxzx6QOGDXOR7s3ne890bGoOflGF7nbCboE5HL\nRGSjiGwRkc910+bbfv1qEVmc1ZMdAPLKFdVworX3Rsagx65zPGi06xwL4nidszWiSUQKgJtxKVFe\nBp4VkbtVdUOozeXAXFWdJyLnA98HLsiKQANEXvXYGIZhGEaMWQJsVdUdqtoC3A5cmdTmCuBnAKr6\nNDBaRCb1r5jZJa96bPYfqR9oEYx+wK5zPDh0uKn3RsagpzaG1zmLo6KmAeGU8buB8yO0mQ50Tb8+\nSMkrw+aK93+ScRUVieT8BaF1I5L+70+6JhXvzLg+btvQzd+O3R1/tvi/X+66j4NzQjNzRvs/gv8X\ndWkf1mkgd0uXVp31HEXnw1Is6267K8ZXM6aiosf9pdq2p8HDXYtCdHBaj0eKto8wPV3vdEl1f0w+\nif1F2bY3+eenWDatD/t757hq5vdynaPQ2zm1AWcsS5Sd6/V693T+PZ1nVIJ9tAKnLfv3btuN6nZN\n+oSfl9kR2ke913siKPpy9bhqSvx1TnU/Z/J5yRX6OiqqumYPq3d2lKI4a9yc5NpWUQfOJ5dhyKsB\n93lj2CxbtkyseFk8sOscD+w6xwO7ztGpKJ+aKFzqFnQx/F8GZoTmZ9DpKzdlm+mk/OwdvFiMjWEY\nhmH0I20Z+peC54B5IjJLRIYCVwN3J7W5G7gOQEQuAA6rat64oSCPemwMwzAMYzCQqQR9BUnzqtoq\nIh8DHvCrf6KqG0Tkw379Lar6ZxG5XES2AvXA9RkRJocww8YwDMMw8gRVvQ+4L2nZLUnzH+tXofqZ\nvHFFRUlKZAxORGSHiLwoIi+IyDN+2VgReUhENovIgyIyurf9GLmDiNwqIvtEZE1oWbfXVET+1T/b\nG0Xk7wdGaiNdurnOXxKR3f55fkFE3hBaF4vrnM0EfUaeGDahpESXAQuBa0Qk6mAWI/dR4LWqulhV\nl/hly4GHVHU+UOnnjcHDbbjnNUzKayoiC3GxAgv9Nt8Tkbx4d8WAVNdZgW/653mx72GI1XU2wya7\n5MtNEyUpkTG4SR6emEgy5f9/S/+KY5wMqvo4kFwMqrtreiXwG1VtUdUdwFbcM2/kON1cZ+j6PINd\nZyND5IthkyrhUCZSShi5gQIPi8hzIvIhv2xSKJJ/H5BXmTNjSnfXdCqdh6za8z34+bivU/STkMsx\nNte5PUP/jNTki2GTV8mFjC5cqKqLgTcAHxWRi8IrVVWxeyCviHBN7XoPXr6PywNYAbwCfKOHtnl5\nnbM43NsgfwybKEmJjEGKqr7i/z8A3Inrnt4nIpMBRGQKsH/gJDQyRHfXNO8TisUJVd2vHuDHdLib\n7DobGSFfDJsoSYmMQYiIlIjISP93KfD3wBrc9X2fb/Y+4I8DI6GRQbq7pncD7xKRoSIyG5gHPDMA\n8hkZwButAW/FPc8Qo+tsrqjskhd5bLpLSjTAYhmZYRJwp4iAu19/paoPishzwB0i8g/ADuCdAyei\nkS4i8hvg74DxIrIL+CKwghTXVFXXi8gdwHpcCaV/9F/7Ro6T4jr/B/BaEanAuZm2A0HyuNhcZ3Mj\nZRfJ0/vGMAzDMHKOyspKPVT53Yzsa9yyj7Js2bJUI8xiTV702BiGYRjGYMHcSNnFDBvDMAzD6EfM\nFZVd8iV42DAMwzAMw3psDMMwDKM/aTNXVFaxHhvDMAzD6EcyNdz7q1/9KiJyT6bl8xmhv5Tp/Z4M\nInKFiDwfpa0ZNoZhGIbRj2SmBGY7Pg1GRoc2i8gC4Crg/4WW/UVE2v10QkT2iMh9IvKeFNvv8O2W\nJi3/UrjKe2j5u0Tkhd7kUtW7gUIReUdvbc2wMQwDABH5qYj8dzfr3i8ij/ew7Z9F5L3Zk84wjGR8\nupZMD/f+R+AeVT0SPhRwKzAZVw7jzcBTwC0icmdSFXYFmoCvRDzelbiM8lH4BfDR3hqZYWMYGUZE\nlorIkyJyWEQOiUiViJyb5WPuEJFLTnI3fa65paqXq+ovTvL4hhELMtVjE0ZEhonITSKyV0QaReQp\nEbkwqc0bRWSTX/+oiFzte1dmhpp1l7m/wZfD2KOqz6vqfwFvwxkm1yW1/SGwWETe2pMeRKQIuAy4\ny8+/RkT+JiLH/PvzaRE5PbTJ3cBrkrJXd8EMG8PIICIyCvgT8C1gDK468X8CJ7J8aKWHLzcRiTpQ\nwJJ9GUaWaZP2jEye4GPkq7hs3dfjCoyuAe4P1V+bCfwBuAc4E7jZb5P4mBGRU4GJwLNRzkNVH/TH\nuSpp1S7gO8CXRaSgh11cDBxR1dX+HXUX8JiXbwnOHRYeG78FOIzLZt0tZtgYRmaZjytO/Vtf569J\nVR9S1TWQcOk8ISLf8V8kG8I9LSJS5gP39ojIbhH573A3r4h8SETWi8hREVknIotF5BfATOAe/6Xz\nz75uWruIfEBEaoCH/fa/E5FX/LH/KiIL0zg36UHuv/hSCME5VonI10SkVkReEpHLQm3fLyLb/Dm8\nJCLv7qOuDcPA1dQDbgT+RVXvU9VNwEeAfXS4bm4EtqrqP6vqFlX9PfADOn/MzPP/70zj8BuAOUnL\nFPgyMAH4YA/bXklHTbhRQBnwJ1XdrqqbVfV2Vd2Y2Knzve0KyZkSM2wGCd7VsEFEqkVkjYhc3Yd9\nFPlYiNUi8k0R+bCIfMqve7+I/C7DMt/oZX5eREZkct99xf8AvzGLh9gEtPl4lctEZEzo2OW4L40l\nwFZgHK52zh9EZLRv9lOgGTgFWIwr+vlBv/07fPv3quoo4ArgkKq+F/ciepOqjlTVr4fkeR1wKvB6\nEfkw0ALMxb1wVgG/Sj4BL3sqP/b5SXLf5e/HF4DzgPeLyHDfdgmw0bf9KvATv+9S4LvAI/4cXgVU\np1KkiJSLyIdSrcsmInKliJwXmj9HRH6Z5WP+s4hsFJG2vt6fIjLOu0BfEJHPJK2b710PG/z741YR\nKe5tnV8/VkR+I86FsVZEvnByZ2tkwRV1Ci59yxPBAlVtx8XBnOYXnUrXnpjkIqOjgBN+26gIdB2/\nrqqHccbNf3jDKxVvxruhVLUW9/57QET+JCKfFpEZKbY5ijOAusUMm8GDAlepagXwXuA2ERkbbhDB\n3XA2MFNVz1LVf1LVW1T1ptD+M83HgWtV9RxVPd5dozTcJJmgz3EkkXauegxY6o/xI2C/iNwlIhNx\nQXd/B+xX1W+papuq3oEzht4kIpOANwCfVtVGVT0A3AS8y+/+g8BXVPV5f6xtqtrTl5UAt/p9nfDX\n+92qWq+qLTgX2Vniq6eHT4PUOkqWewPwTVVdjHtBDsd9FQLUqOpP/BfWz4EpXgfgDLf7RGS4qu5T\n1fXdyD8buKGH8+sTvXSNg6s4vSSY8fEE12ZajiT+AlyO64bv6/15KVCrqotV9RtJ604An1LV03Dd\n/CXAP0dYB+7H5ilVXaCqZ+DiJ4yTIBsxNt0gdL6fenM1HwGGSedg4N5YCLzUzbrv4D6m/ilJDsTF\nHY4A/hosU9UP4D6gHsN9uG0Skb9P2uconDuqW8ywGYSoajVwDJjjv65/LCKP4a1vEfmc//IKvr5K\nxQ3h+yUw23/RvVPc8Luv+d12uuFF5H3igrieE5FKEZmfShYROU9ckNpq/7V4rl/+W9xXxC9Tfe12\nI/evRORZEXlRRBK9GP5r8e3+738R5woRP79eROZ2pysRWSguAG2tuErD4S/Rv4jIV0XkcXGukS/3\npHcRWeDPNeg1+0w3Tf8dOAOoAx4FZuAMlO/iYm5Gi6tiHFADTAXeCAwDjvsv9+O4ruLFIvIj4CLg\ncyLyQ3FBd4jIu0Xkb377H0iHeyjocfm2v96nich/el1sFZFjwAHcdX9eRD6ZfLopzuvlpPkduOrr\nQfsiYL+f3xs0UtUG/+cIVa0Hfgx8D9jj9blBRH7g76FqcX5+vL4Wevnv8Oe7QFyv4zO+7fsTAotc\n5fe1SkT+TZwrrsSvaxeR/xCRZ4AvisgZIvKYuN7EdcH5i8jrcV+Ry/1x3ysirxWRZ0PHuc7fo6v9\nfTrBL3+/iDwoIrf7+63KG6u9oqrPqWp3Pw4JRKRARL4eer6/JiJDRORiXM/YhV7uTkNtVbVGVVf7\nvxX35V7e2zoRmQcsUtVvh/a1L8o5Gf3KNtwHQ+K6izPgX4Wrlg7uQyR5EMOSpPmt/v+ZRMA/L6cD\nK1OtV9UTwBeAz+J6icNciXM7tSdt86KqflVVL8YZ/O8LHU9w79MtPcllhs3gIvgxvxj3Axhc3DOB\n16vq2SLyBuBa4FWquggoAL7gfa4fBNb7L7o76ObLXEQuAt4BvEZVzwW+jhvql9xuKPB74N9U9Szc\nDfx7ESlU1auBPbhepu6+dhNy+/lPqOp5qnom7mH8nF/+MLDM/70MWAssERcZX6qqW+meXwA3+y/N\nm3AukwAFZqjqRTi3zwdF5JQe9vWPwF2qWuF1+5Nu2n0ydB7PAAdxhs4/4oyDw6r6zlD7cr/8HbiX\n01BVLQCmqWoZrqv2PKAK+JpvH/Rk3K+qF/jt/wf4mV8elA9+u7/eG7wMp+B0OAVnDCluZMMNuC+h\nTojIuSJyr5+dlrS6HGgQkWrcC7VFVaO4bNYCj+OGju4EFgDf9/fQHTjDEJy+gvv1neJ69n6N69Fa\ngjP0lntjZxJwC84ddzbQQFcaVHWJqv4Hzii7VFXPwX0h3iAiC1T1AdzIiy/743Ya6SUiZ+C611/n\n5V2L+yoNOBf4jL/f1uN6LTPJDcBZuPv1bP//Dar6KPBF4GEvd1V3OxDnLrwe7wLoZd1CYLf/CHle\nRO6V9OKyjBS0oRmZPOI/Hr4PfEVE3iAip/n5CbiPCHAfSad4Y3iBiATPffh3YBPugyfZ4BGgVEQm\ni8h0cR+0/4F7//8R99HcHb/APW8fSFp+JaF7UFxc4AoReZU4N/TFuN+IdaFt5gOjce+PbjHDZvAg\nwEpx8QxfwhkMR3A35EpVbfTtLgV+E3L9/NAvC/aRar/JvBn38nzaH+/LwPQU7Rbg/LGPAqhqJe6H\neUGE80mWG+B94nqIXgSuwUX2AzwCXOoNqWmhc1rm16VE3Ail04MfJ1V9GhfBH+Z3ft1R3BdNt70/\nuC7TD4rIf4nIxd6HnHzMBcCt/mv+RZyReS7O1x3oeqKIfEJczNM7cL7vPwP349wCj4vIa4Fj3tCa\nCPzWn/dnvByXiOupulBEHsQZCf8DTJYOl08yQ3EjDGpxL7wqL9MdOCNnXPIGvichiPlIJfevvXv0\nCWCIiCzvQX942c7GGdwtQCPO4FjtmzyNM76g67053x/zdn9fPubP6VSccbJKVbf5trelOPzPQn+X\n4q7Ti14PU3H3fELUbk7hYuDeUK/FLXQ8XwBPqGrQs/W30LlkimXAbara6t2Jt9Hz890JbxzeDlSq\n6p8irCsALvDHPAfX25ZqKLCRBllK0Pc53HviNuAF3IfMZcG96t3WV+FcPNXAJ4H/wt03Tb6N4u6B\nK5JEVpzBuwfXO3Q37pn7sKq+zW+XEr/uc7jecpd4R2QOLgD4/lDTBr/sdzgD66c4gymcD+cK4DFV\n3dOTfq1W1OAhiLFJFY9Qn9Qu/ILr6/DdW/2XbTZJyO17iT6C62k6JG6kzIcAVHW7OJ/vu4AnccZM\n8BVQeZIyNIX+bsO9yFOiqn8QkSeB1+N6Cj6gLnA3zGnAJThX4SicodKIM0iCbuCncQ/wAZzL5ipV\nrQO+JSKP4HoAHsBdy/U4v7eo6koRCQJ3J/h9jMG5nW722xUCHyNFDxuwGmcEvIy7L/6KM+TehPu6\nG0738R2K+6FOJTe44MFngAtx3dLJ+wnmh3j9zQEOAdv9FNBG9+8lAQ76mJ7OK0Te3M02YcJxXv+H\ne0lfp6rtIvIAITdlCvnDy3t6vsL3UzspzkVczEDwsv5liniY3ujT8+1dE7/CBZx/MuK6GmCnqj4B\noKp3isgvRWSsumBPYwD57Gc/y/33338FgKo2A5/2U0pU9V4g6IHFu2CPqIvnC/ge7qN2dPDx5t1C\nkVDV2SmW3U/njpQrcQZ0fajNfroOG0/g3VDvBVImEQ0zKHpsxI1S+KH3Xb9uoOXJcR4GrhaREf5G\n+CDwYDdtu3sp3gNcJyLTIOHXPydFu03AUN+7gLj4jkK/PF3KcD/gtSIyjK7dlo/ggl0fVtXduN6F\nv6cHw8b3wqwRn/ZbRJYAi5KapfPDcAougPZnuC+d5O5agFbcaKBpXsYXgM2+B+0oLjBTVfXjqjpa\nVU9V1WAo9nxVXaOqr/Xn/xfvVqkB3iEuXuQnwIs4d9eZOGNkh7p04/+N++H9saru8O3CowdagT+q\nG430APCgd3mV4Nw6P1bVL6Y6d1X9mapeFJYb2OavFThjpQx4xuvnGhHZENq+QFVfUtW9wAqcS28M\nLqiwidQkj37YhHN9JVybInKquODnZ4Cz/ZcghPzy3VAG7PZGzRn+/MPHHZ16MxfkG4qd+RDdP18p\nUdUHvbsoVZCv0PM9+TCuZ7NQXJzV+6Ic338Y/BR3D3ww6jrcyLn6wP0kIq/BGT9m1JwEGXZFRUZE\nPioiS0Rktohcg3P7/jTcRl3Ywkpcj0622I3zBKTDm3Hu7l5H7w6KHhtVvQs3tHQ0Lt7joQEWKddI\n3OGqer+InIlzfYALBPyfUDtN2k6T/1bVx0Xk88Dd/ktuKM5d0akAmao2i8hVuADVUtwX8dtVtTVd\nuXFdktcCm3ExKY/ROR6mEtcVGrieHgcuUdVXejnGdbgRZMtxbqjk4Y3pvB3eCbxHRJr9dp9I0aan\n81gNvIL7Ab4jKc4G4OPer9yM+7EP4jOCgM4HcW6pR+kYmfIp4I8iUuePfTC0v2/jzr0eeA+dr/f/\nAL8Ql3tmM6GRCaFjBiMX/jPkjgrzalwwczvuI+khOl5WU3GuplSkug9TrVuNGxWxBtjg42zeDNwk\nIp/F9a7tBd6pqvtE5CPAn/353ot7CTakOEZv5/8L4Kfe3fZNXBxQ8Gys9ffSQyKiuG75D/dwXpHu\nL38+nwDG+2M3Agu162jCH+J62YLaOvfjRt/1drw34O6BNbhgcYAqVf14T+tUVUXketx9NAzXy/q2\nKOdkdM8AVvc+BfhX3EfXblxP7X8lN1LVnnLPnDRRjJMU29xNRDeo9OAayyoicituFMh+dYGYwfLL\ncEGeBbgvyK+E1n0d13WbMu+FYeQ6IvI+4B9U9TVpbHMb8JyqfrfXxjmCiHwa2Keqv+7HY44IDAH/\nY3x9Ono2jP6gsrJS//bIv/feMAIXXPI/LFu2zLKFJzGQPTa34WICfh4s8L0DN+OC4V4GnhWRu3Fd\n+yuA+8yoMQYz3k3zs14bptg007JkE1X9f723yjif8L0shbj4nX5P7mcYUeiLG8mIzoAZNt7dMStp\n8RJcyucdACJyOy7IKBgBM0pE5qrqLf0oqpHjiMjlwP+mWPWvPmgt3f3dRdc8DjWq+pa+yHeyqOr1\nA3HcwYaq/h8uKNgwchozbLJLrsXYTMPVgQjYDZzv/cDfSb2J48Ybb9Rt27YxefJkAEpLS5k7dy4V\nFW7EcHW16+jJt/lgWa7IMxDzDz/8cHfr76usrEzMb926lbe//e193V/FN77xDc2F8+2P+ZUrV8bi\n+cnEfPKzONDy5Pp8LukLYPXq1ezd6/JJvv71r+czn/mMuXYGOQMWYwMuIQ9wTxBj4wNRL1PVD/n5\na+kwbHqksrJSzz777N6a5R0rVqxg+fIeU4cYHtNVdExX0TFdpUcu62vVqlVZj1mprKzURx/514zs\n6+JLvmwxNinIteHeL+PSJQfMwPXa9Ep1dTUrVqygqqrbhJt5yc6d6RRhjTemq+iYrqJjukqPXNRX\nVVUVK1as6NSTk03aRDMypYO4YqYPichmcaU/RnfTbrSIrBRXnmS9iFyQkZPuR3LNFfUcMM/35OwB\nrsZloO2ViooK4thjYxiGYZwcS5cuZenSpaxatWqgRckmy4GHVPWrIvI5P5+q6+xbwJ9V9e0+G3Vp\nfwqZCQasx0ZcQcIngfkisktErvf5Tz6GSx62Hvituho3Rje8+93vHmgRBg2mq+iYrqJjukoP09eA\nJei7go4RmT8D3pLcQETKgItU9VYAX7rjyMmc60AwkKOiUvbEqOp9wH3p7q+6upoHH3wwYXnHhTid\n68liuoqO6So6pqv0yEV9VVVVUVVVxcSJE1m2bFnvG5wkAzQqapJ21DjbB6SqPD8bOOBzZ52FS8r6\nyVCiy0FBrsXY9JmKigqWL1+ekw9NNolbTNHJYLqKjukqOqar9MhFfS1dupTly5cnRk3lKrtqjvHk\nY3sSU3JMkI+hWZNi6lTU0hemTGVdFeKK1H7Pl3OpJ7W7KqfJtRgbwzAMw8hr+tpjM7V8BFPLRyTm\nkw0xVe22lqKI7BORyaq6V0SmAPtTNNuNq6H2rJ9fySA0bPKmxyauo6Li1kN1MpiuomO6io7pKj1y\nUV/9PioqQ1Oa3E1Hcdj3AX9MbuCL1O4Skfl+0aXAuvQPNbDkTY+NjYoyDMMw+kJ/j4oaoBibFcAd\nvvDrDlxRX0RkKvCjUKHbjwO/EpGhuCKvgy7zed702MSVuPVQnQymq+iYrqJjukoP09fAoKq1qnqp\nqs5X1b9X1cN++Z6QUYOqrlbV81T1LFV9m42KMgzDMAyjR9qsVFRWyRvDxoZ7G71huoqO6So6pqv0\nyEV9xWS4d2zIG8PGYmwMwzCMvhCTzMOxwWJsBjnmr46O6So6pqvomK7Sw/Q1YKOiYkPe9NgYhmEY\nxmDAjJLsYj02g5xc9FfnKqar6JiuomO6Sg/Tl5Ft8qbHJq7Bw4ZhGMbJ0f/Bw0Y2yZseG6sVZfSG\n6So6pqvomK7SIxf11d+1oizGJrvkjWFjGIZhGIZhhs0gJ50eqv+t3M6G/fVZlCa3iVtv3slguoqO\n6So9TF8uQV8mJiM1ZtjEiI0HGqjafnigxTAMw4g15orKLnlj2MS1unfU821XpbahhWd3H82yRLlL\n3O6Nk8F0FR3TVXrkor76u7q3kV3yZlSUZR7umSNNrQwrHEJtQwv7jzczccTQgRbJMAwjJ+j/6t5G\nNsmbHpu4EtVffai+hYkjijhn+iiei2mvjfn2o2O6io7pKj1MX9CeoclIjRk2MeFAfQvjSoZy3vRR\nPLsrnoaNYRiGkf+YYTPIieqvPtTQwvjSIs6ZPpLqV47T0hY/ez8Xffu5iukqOqar9DB9QZtKRiYj\nNWbYxIQD9c2MLy1izPAipo0axvp98R32bRiGMZDYqKjsYobNICedGJvxpS5g+LwZo2I5Osp8+9Ex\nXUXHdJUepi8j2+TNqCirFdUzB+pbGF9SBMBpE0u4c+2BAZbIMAwjN+jvWlHt5kbKKnnTY2O1onom\niLEBGF1cxJGm1myKlZOYbz86pqvomK7SIxf1ZbWi8ou8MWyMnjnoY2wARhUXcPRE/AwbwzAMI//J\nG1dUXInSQ1Xf3EabwoihBQCUFRdypDF+hk3cevNOBtNVdExX6WH6MldUtjHDJgYc8vE1Iu5hKi4c\nggJNre0UF1qnnWEYRn9ibqTsYr9qg5wo/uqDDR1uKAARYVRxIUdjFmeTi779XMV0FR3TVXqYvlyP\nTSYmIzVm2MSAg/UtnQwbgNHFhRyOmWFjGIZh5D/mihrkRPFXHwzlsAmIY4+N+fajY7qKjukqPUxf\nFmOTbcywiQEHG1ooH13caVlZcSGHYxhAbBiGMdC0YYZNNjFX1CAnUoxNfXMXV1RZcWHshnybbz86\npqvomK7Sw/RlZJu86bGxzMPdkyrGZlRMh3wbhmEk0/+Zh7N+iFiTN4ZNRUUFZ5999kCL0e/0NcZm\ndHEhWw81ZEusnMQM3uiYrqJjukqPXNRX8EG8atWqfjmexdhkF3NF5Tktbe0cb25jdHFnG3ZUcUHs\ngocNwzCM/McMm0FOb/7qQw0tjBleSMGQzl8IcRzubb796JiuomO6Sg/Tl+WxyTZm2OQ5hxpaGFdS\n1GW5G+5t+S8NwzD6m4EwbETkHSKyTkTaRCRl3IaIzBCRR327tSLyiYyccD9jhs0gpzcdbCILAAAg\nAElEQVR/dV1DK2NTGDZlwwpjV+E7F337uYrpKjqmq/QwfQ0Ya4C3Ao/10KYF+LSqng5cAHxURE7r\nD+EySd4EDxupqW1sYezw1D02x0600tauXdxUhmEYRvYYCDeSqm4EEjUDu2mzF9jr/z4uIhuAqcCG\n/pAxU1iPzSCnN391XWMro4d3tV8LhgilQwuob46PO8p8+9ExXUXHdJUepi9QlYxM2UREZgGLgaez\neqAsYD02eU5tQwvzxpekXFfmA4hHFdttYBiGMdgRkYeAySlW/Zuq3pPGfkYAK4FPqurxTMnXX9gv\n2iCn1xibRjcqKhWjhsWrXpT59qNjuoqO6So9TF99d0XV7z5Cw8tHEvPVM6s7JRRU1dedrGwiUgT8\nHvilqv7xZPc3EJhhk+fUNaYOHoaOHhvDMAyj/+irYTN82miGTxudmK+oqOirCCkFEBeA8xNgvare\n1NedDzQWYzPI6T3Gpvsem7KYVfg23350TFfRMV2lh+lrYBCRt4rILtxop3tF5D6/fKqI3OubXQhc\nC1wsIi/46bIBErnPWI9NHqOq1DW0MibFqCiAsuKC2A35NgzDGGiyHfib+ph6J3BniuV7gDf6v6vI\ngw6PQWHYiMhs4PNAmaq+Y6DlySV68lcfb25jaOEQhhWmvk/Ligs52NCSLdFyDvPtR8d0FR3TVXqY\nvqxWVLYZFJaZqm5X1Q8OtByDDddb073tOipmrijDMAwj/xkww0ZEbhWRfSKyJmn5ZSKyUUS2iMjn\nBkq+wUJP/uraxpZu3VAAo4fHK3jYfPvRMV1Fx3SVHqavwZHHZjAzkD02twGdgpJEpAC42S9fCFwz\nGNM55wp1jS2M7anHZpjVizIMw+hvrAhmdhkww0ZVHwfqkhYvAbaq6g5VbQFuB64UkbEi8gOgwnpx\nOtOTv7q2oZUx3Qz1BhdjE6fgYfPtR8d0FR3TVXqYvoxsk2vBw9OAXaH53cD5qloLfKSnDVeuXMmP\nf/xjZs6cCUBZWRmLFi1KPERB92ec5p/fcJBF513Q7fqmlnaONJXljLw2b/M2b/P9OR/8vXPnTgDO\nPffcTgnvsoW5kbKLqOrAHdzVorhHVRf5+auAy1T1Q37+Wpxh8/He9lVZWalnn52yEnteU1VV1e0X\n0Nf+WsOZU0bw+vnjUq5XVd5022r+cN2Z3Y6cyid60pXRGdNVdExX6ZHL+lq1ahXLli3LqtVRWVmp\nH/jt/2VkX7de/W9Zl3cwkms9Ni8DM0LzM3C9Nr1SXV3Ngw8+yNKlS3P2oelvekrOB67Ka+COmjhi\naD9KZhiGkTtUVVVRVVXFxIkTrccmD8g1w+Y5YJ7vydkDXA1cE2XDiooK4thj01uMzdgeRkUBjCkp\npLahJRaGjRm80TFdRcd0lR65qK/gg3jVqlUDLYqRAXr0P4jIBBH5jIg8IiKHRKTV/18pIv8sIhP6\nemAR+Q3wJDBfRHaJyPWq2gp8DHgAWA/8VlU39PUYcaeusaXH4GGAcSVFHIpRkj7DMIyBxoZ7Z5du\nDRsRWQGsAhYAPwZeB5zm/78VmA+s8u3SRlWvUdWpqjpMVWeo6m1++X2qukBV56rql6Pur7q6mhUr\nVsQuR0J359vWrhxtamV0cc+dcuNLhsbGsInbvXEymK6iY7pKj1zUV1VVFStWrKC6urpfjqftkpHJ\nSE1Pv3q7gbmqeiLFulXAr0SkGMiJjMBxdUV1x5GmVkYMK6RgSM83/9jSIg7Vx8OwMQzDSIW5ovKL\nbg0bVb25t41VtQmXUM8YILrzV/eWnC9gfEkRa/Yez7RYOUku+vZzFdNVdExX6WH6suDhbBNpjK+I\nXCIic/zfU0Tk5yJym4hMzq54Rl+pa+w5OV/A+FKLsTEMw+hPzBWVXaImL/keEKSo/Saup0eBH2ZD\nqL5gMTadqW2I1mMzriQ+rqi43Rsng+kqOqar9MhFffV3jI2RXaIO956qqjtFpAh4PVAOnABeyZpk\naWIxNp2pa2ztsQBmgI2KMgwj7vR7jI25orJKVMPmqHc7nQ6sU9VjIjIM6P2X08gq3fmraxtbmFDa\ne26akcMKaG5rp6m1neI8zz5svv3omK6iY7pKD9MXaPtAS5DfRDVsvgM8AwwDPuWXXQjkTI4Zyzzc\nmdr6FhaML+m1nYh4d1Qz08qK+0EywzCM3KK/Mw8b2SXSJ7qqfgWXv+ZCVf2NX7ybHBnqDc4VtXz5\n8tgZNd35q2sONzFjdDRDZVxMAohz0befq5iuomO6So9c1NfSpUtZvnw5FRUV/XI8S9CXXXrssRGR\nXcB9wJ+Bh1S1PlinqpuzLJvRR1ra2tlz9ATlUQ2bkiIOxiSA2DAMY8CxEU1Zpbcem/NxLqjrgB0i\n8rCIfFpEFmRfNCMKqXqodh0+waQRQxkaMWZmfEwCiOPWm3cymK6iY7pKD9OXkW167LFR1T24cgo/\n9iOiXgNcDtzpg4fvxfXm/MUn6zNygO11jcwZOzxy+3ElRRyIgWFjGIaRC5gbKbtEHgajqi2qWqmq\nn1HVhcClwGbg434aUCyPTQfbaxuZlY5hUzqU2hi4ouJ2b5wMpqvomK7SIxf11e95bNozNBkpiToq\nqguquh1XTiEnSipYHpsOttc28abTxkduP66kiIPWY2MYRkyxWlH5RW/Bw9t72V5VdU4G5THSJJW/\n2vXYRB+6HZeyCubbj47pKjqmq/QwfWHBw1mmtx6bD6VYpsA5wOfoKLNg5AhHm1ppaGlj0ojek/MF\nBNmHVRURe+AMwzCyiepAS5Df9Bhjo6oPhydgD3Ajzqj5BmC9NQNMsr96R10js8YMZ0gaBsqwwiEU\nFw7h2Im2TIuXU+Sibz9XMV1Fx3SVHqYvI9tEre49R0R+ATwJbATmqOr/hfPaDDRxDR5O5qXaJman\n4YYKsFw2hmHElf4PHpbMTEZKeouxmQ58AbgG+BEwV1UP9odg6RLX4OFkf/X22kZOGRd9RFSACyBu\nZk4fth0smG8/Oqar6Jiu0iMX9dXvwcM2oimr9BZjswWoB74OvAxcEYrBEFzw8K3ZE89Il+21jVw6\nb2za27kAYguZMgzDMAY3vbmingbWAhcD1wLvDU3BvDGAhF1v7arUHG5i1pi+uaIO1TdnUrScI+5u\nynQwXUXHdJUepi9AJTOTkZLeMg+/tp/kMDLAK0ebGTG0gJHD0k9PNGHEUNbvy5mQKcMwjPzFXFFZ\nJdIvoIiMAZYAY4Fa4BlVrcumYEY0wv7qLQcbmDe+pE/7OW1CKStf3J8psXKSXPTt5yqmq+iYrtLD\n9IUZNlmm11FRIvJF3DDvPwHfxNWHekVEvpRd0Yx0ORnDpnxMMUeaWqmLQaI+wzCMuCEi7xCRdSLS\nJiLdjrQRkX/17daIyK99XchBRY+GjYi8E/gYLp6mRFWnAMP9/EdE5F3ZF9HoibC/evPBBub30bAp\nGCIsnFTK2jx2R5lvPzqmq+iYrtLD9MVA1YpaA7wVeKy7BiIyC5eY92xVXQQUAIPud743V9QNwD+p\n6u+DBaraAqz0VtyHgNuzKF9kqqurefDBBxPD9uKGqrL1UCPzxvd9uPYZk0tZu/c4F80enUHJDMMw\ncpuqqiqqqqqYOHEiy5Yty/4BByDzsKpuBHrLLn8UaAFKRKQNKMGNiB5U9GbYVAB/7mbdfcC3MytO\n34l7Hps9R5spKRrC6OFFfd7Xokkj+O5TuzMlWs4RR4O3r5iuomO6So9c1JcVwXSoaq2IfAPYCTQC\nD/iqA4OK3mJshqlqbaoVfnn0gkRGVjkZN1TAvAkl7D5ygvrm/C6tYBiGMaD0MdNw88FD1G/ZlJiS\nMyWLyEM+NiZ5enMUsUTkFOBTwCxgKjBCRN6T6dPPNr0ZNuLLKaSaTsEl6TMGkMBffTKBwwFDC4Yw\nd/xwNuzPzzgb8+1Hx3QVHdNVepi+gHbt0zR09DhKZy9ITBUVFZ12q6qvU9VFKaZ7Ikp2LvCkqh5S\n1VbgD8CrM3z2Wac3w6YE2NrNtMWvN3KATBg24NxRa/cez4BEhmEYRo7SXafERuACERkuLhjnUmB9\n/4mVGXqr7j2kt6m/BDVSs3TpUtozEDgccMbkEazdm589Nrno289VTFfRMV2lh+kLpD0zU1rHFHmr\niOwCLgDuFZH7/PKpInIvgKquBn4OPAe86Df9YabOu79IP0WtkXO8cvQEpUNPLnA4YOGkUjYfbKCl\nrZ2iArNbDcMwMs7AjIq6E7gzxfI9wBtD818FvtqPomWcbn+5ROROEVnS08YiskREuijK6D+qqqrY\nfLDxpAOHA0qHFjB55FB21DVlZH+5hPn2o2O6io7pKj1MX0a26anH5gfA90RkFPAXYBNwDBgFzAf+\nDjgCfD7LMhq9kKn4moD540vYnOF9GoZhGB4rqZBVuu2xUdUHVPVc4D3AbuB84O3AeUAN8C5VXaKq\nD/WLpEZKli5dyrp9x1k4sTRj+5w/oYTNBxoytr9cwXz70TFdRcd0lR6mLwYq83Bs6DXGRlWfBZ7t\nB1lOirhmHm5qbWd7bRMLMmjYzBtfwv2bDmVsf4ZhGLlMv2ceNrJK1Orec7pZdQJ4RVUH3HaMa+bh\n3/7pYWaPLae4MHOBvnPGDmfX4SaaW9sZmsH9DjRVVVWxMnpPBtNVdExX6ZGL+ur3zMPtAxA9HCOi\njora2sO6dhG5G7hRVfdlQCYjDbbXNXH6whEZ3eewwiFMH13MttpGTstgT5BhGIaR/lBtIz2ifo7f\nAPwamIer7j0f+AXwj8AinIH0vWwIaPTMickLWTQ5s4YNuADiLQfzK84m174ScxnTVXRMV+lh+jKy\nTdQemy8B81S10c9vFZEbgc2q+gMReR899+oYWaCtXdmwv57lF8/K+L7nTyhhw778TNRnGIYxoKi5\norJJ1B6bIbiiWGFmAgX+74bQ30Y/sb22Ed29lrLizOdZDIZ85xOWPyM6pqvomK7Sw/SFjYrKMlF/\nEW8CHhGRW4FdwAzgeuBbfv3lwFOZF8/oiTV7jzN7bHFW9j1rTDGvHD1BY0sbw4vMZjUMwzAGB5F6\nbHyK5euBKcCV/v8PqOoKv/5OVX1D1qQ0UrJuXz1vuvTirOy7qGAIs8YO56VDjYlluw43cduzewZt\nT4759qNjuoqO6So9TF8g7ZqRyUhNZB+Gqt4P3J9FWYw0aGlrZ+3e4/zDkqlZO8a88SXc/NRuJpQW\ncbixlX3Hm5k1Zjh7jp3g85fMztpxDcMw8hqLsckqkXpsRGSoiPyXiGwXkRMi8pKfH5ptAQcTre3K\nd57YRWNLW5/3cexEK7f8bTefvXcL1/12HTdV7ezSpqG5jS88+BILJ5WytfqZkxG5R96zeDLXnT2F\nNywYz/XnTuVX15zB5y+ZxbO7jlLf3PdzHCjMtx8d01V0TFfpYfoysk3U4OGvAMuADwNnAR8BLmGQ\nVwDNNA9tqeWeDQep3FoXeZtndx3lgc2H2HKwgce3H+aG32/kRJtyTcUkvnTpHB7ffphD9S2J9ocb\nW/jsn7cwacRQPn/JbEQkG6cCwLiSIl5VXsarystYPG0khUOEUcWFnDV1JFU7DmftuIZhGHlNu2Zm\nMlIS1RX1TuAsVT3o5zeKyCrgReBTWZFskNHarvymei/XnTOFP647wBtPHder0aGqfPuJXcwdN5w/\nrNmPCPzbJbM65aV57Zwx3LvxINedMwWAbz6+k0WTR/Dh86chIgPir7507ljuXn+A188fl9Z2x0+0\nMmJY5kdwRcV8+9ExXUXHdJUepi8sPibLDIp8+SJSKiI/E5Efisi7M73/w40tfLtqF//wu/VoH32f\nj2ytZdKIobynYhJDBF7Yc6zXbXYfOUFbu/LFS2dzy1Wn8YO3ndYl2d4VC8fz540HaWlr5+mdR9h1\n+AQfOG9qVntqeuP8GaN4qbaR/cebI2/zwOZDvPNXa9lz9EQWJes7e4+5a2EYhmEMbqIaNr8D7haR\ny0TkNBF5A3CXX94fvA24Q1VvAK7I5I6fqjnCh36/kaIC4diJNvYfb+l9oyTa2pVfV+/lvWdPRkR4\ny+kT+OO6AwD8becRPnX3Zn67eh9Hmlo7bffc7qOcN2NUj0ZK+ZjhlI8ppnJrHd//224++urpDC3o\nuGwD4a8eWjiEpbNG8+i23l1uqq4n65er9nLqhBLW7j3eDxKmpjtdvXL0BDfeuanf3WvtqjS35WYy\nCouDiI7pKj1MX7jg4UxMRkqiGjb/AjwM3Aw8D3wHeMQv7xMicquI7BORNUnLLxORjSKyRUQ+5xdP\nw+XPAcho1OrKNfv55IUzuPFV0zl1YgmbDqSfbfcvL9UxrmQoZ04ZCcAlc8eyYX8D33xsJzc/uYsr\nFo5n95Emrr9jPVXbO348n919lHOnj+p1/1eePoHvPLGL2WOGR2rfH7xu3lju3Xiw1yDiyq11PLK1\njpvePJ/XnjKGtXtzK5txc1s7//vIDsqKC9le29j7BhnkyR1HWPFoTb8ec6A40tTKsROtvTc0jDhg\nMTZZpVvDRkSWicglInIJsBT4Ky54+M3+/78AF57EsW8DLks6ZgHOeLoMWAhcIyKnAbtxSQF7lDld\n6pvb2HqogXNnOGNhwYRSNh5IP0fLPesPctWiCYn54sIhvOX0CRw90cr33nIql8wdy2deU84XLp3N\nrc/toa1dOdHazvp99Sye2nudp/NnlHFBeRkfuWB6l3UD5a8+Y/IIlswYxf8+sr1HF84Dmw9x3TlT\nGFdaxOmTSlm7b+B6bFLp6kdP72F8aRHXnzuFHXVN/SrPjrpGdh3p32NGJdP31Xef3MXX/jo4jbgd\ndT0bvBYzkh6mL8ywyTI9RXL+BIiiuT4lNFHVx0VkVtLiJcBWVd0BICK34xICfhu4WUTeCNzdl+Ol\nYtXLxzh9UinFhc5WWjChhNur0ytQvu1QA/uPN3P+jLJOy9+zeHKXthVTRjBqWCGPba9jxNBC5owb\nHimYtmCI8IVluZc35sYLpvPvD2zjB397mY++uqvRdbC+mZdqGznfG46zxgyntqGFw40tjB5e1N/i\ndmHzgQaeqDnMLW87lbqGVm6r29Ovx3/56An2HmtGVQc0ZirbNLe28+zuYwwrFDbsrx9UFeOr9xzj\nX/68ld9duygrpUsMw8g83T6pqjqrH+UICLucwPXUnK+qDcAHetpw5cqVfOXb36diwSmIQFlZGYsW\nLUp8HQR+3fD8yhf38drXXJSYr29uY8uh0bS1K089+USX9qnmV1HOG04dF7n9exafyQ+feZmyAxsp\nKy7AFUrvvn1v88Gyvm5/svOfv+QCPnH3Zn7w+/s5Y9KITuv/sq2OV88+k6GFQxLtF06azLp99eju\ntf0u75o1a7jxxhsT83etO8Bl517AyGGFvPD0U2xbvY3Gt57K8KKCbvfXPHkhr5kzhr9FvN49zT//\n9C5OjDuVw02trHv+6U7rH/3rY3zriV3Mr1jCgvEllBzYwIyy4n7T1/e///1en5+o88+/fIzS/eup\nmDqSnz5XzFcunzdg92s68+3tyq8PTmBYgXDPQ39h1pjU+k9+FnNF/lyd705fjS1tzFp0HvPGl2T0\neBv31/Pzux7i8tPGp3x/VlVVsXOnyxd27rnnsmzZMrKNWHxMVpG+jgLKyMFdj809qrrIz18FXKaq\nH/Lz1+IMm4/3tq/Kykq9bVcpB4438803z2fqqGE9tldV3vObdXzl8rnMGN1Rb+n6O9bzxUtnM3vs\n8F7lb2hu49rb1/HDq05lfGm0XIWqysfv2sz22kZuumI+88aXRNquO6qqqga8a7dqx2Fur97Hd66c\n36nn4cY7N3LD+dNYPHVkYtmvXthLfXMbN5w/rf/lDOmqrV15z+1r+erl85jpr/9H/rCRf7poJvMn\npL4mjS1tvPXnL/Kfr5vD+TPLUrZJh6t+8SLFhUP492Wzu/RiVG0/zB/W7uddFZPYuL+BP288yHkz\nRnH9uVMZW5L93q5M3ldf/WsNC8aX8MbTxvPBlev51NKZVITuiVzlvo0HeWhrLRNKh3Lu9JG8bl7q\n9Aa58AwOJrrT1z3rD/DLF/bys6tPT/SiZ4Kv/7WGR7fVcce1iygd2nPdu1WrVrFs2bKsdp9WVlbq\ndTd+OSP7+vn3/zXr8g5Gcm2498t0xNLg/94dZcPq6mqmbbiT6ce28kiE0To76pooLBCml3U2gOZP\nKGFTxDibR7bVcdaUEZGNGgAR4T2LJzOquJBTxvVuPPVGLrxQX11eRlNrO6te7hjiXlPXyOHGVs5M\nGr5+xqRS1vUSZ9PaR9+xqvY4XD+sqzV7jzNmeFHCqAFX+LOneIotBxtpVyLdX71xtKmVtnbl1Iml\n7D3Wddj8X1+qY9m8sSyZUcZ150zhJ+9YyMhhhXzsj5v6nJIgHTJ1XwVpCi6cVUbhEOHaxVO49dk9\ntOToaLCA+uY2fvb8K3zkgulMGzWM3Ue6T1OQC8/gYKI7fb1yrJljJ9q4Z/2BxLJfvbCX21fvjbzv\no02t/GHt/sT8idZ2nqw5winjhvNUzZFut6uqqmLFihVUV1dHPpaRu+SaYfMcME9EZvlyDVcTMaam\noqKC5cuXc92Vr+OJCMN2n919lPOmdx1qfeqEEjZHMGz2HD3ByjX7eONp46OI14lXlZfxk7efxpA8\niasYIsLVZ03k9tUd8UmPbK3j4lPGUDCk8zkumFjKS7VNNLWm/mF7suYw7/71Wo53M4Jm26GGbg2f\n3764j4/dtYndEQJyH91Wx8VzxnRaNmtscY8BxJsPNvB3s0fzzK6jJ1U2A9z9M3XUMCaPGMreY51/\nNBtb2nh291GWzhqdWFY6tIAbzp/GkCGw52j0/EEDTfWe48woK04Y/xefMoYxw4v48qM7+mzA9gcP\nbanlzCkjmD++hGllw9jTjWGz9WDD/2/vzuOjrM4Fjv+e7PtC9j0Bwi4EZBGMuICoqFDb6nVp/WgX\nl1q7uLRSbzeXilWrdrHeW2+9eq1at6ogVgSVRUFUiCAECJCQhISE7CvZ5tw/ZjKZJDPJBLLn+fLJ\n55N555133nkyzPvMOc85h8qGvk8Roborqmni6lkxvLq7lMaWNj4vrOGlrONsP1rj9jF2FNTw9PZj\n7C+tt99Oj/Rn5fQoNh1x/oWkpc1CZmYm99xzDxkZGf3yWnplLP3zo5wassRGRF4CPgEmiUiBiNxo\njGkFfgi8B+wD/mmMye7LcafHBFJe30JxLxPBfVbgfKi1dWSU6yHJxhjWHyznx28fZOW0KM5MOLUm\n9YBemkTdNVzmhDh/wjiO1zbzeWENf/+siLX7y7gwfVy3/fy8PEgL9+OgkxiX1jXzxJYCEkJ97fMA\nOdp2tJrb3jzA9vzu37xqTrby2u5SFiSF8tM1OWw8VNFtn/ZYtbRZ+DivivMmdElswv17bLE5eKKe\neUkhTIsO7PHbnzuO1TSREOpLbLBPtxab7fnVTIsJdFqsOikyYFBWVz+d91WrxVDZ2IIxhq15VWSm\ndnTbeXoI9y5JpbnN8PBHeQMyKWJuRSPFtac3EeTRykZm2FobE0N9Oebi8+SxLfn8+lnn372+Ol7H\n41u6r/U21vU0n9SilFAy4oP43y+KeXTzUVadn8qRika33yd7S+oYP86f53cWA/Dh4QrOnzCOs5JD\n2XO8rtuUAy1tFq5+8Ss2555+K2yf6KioATVkiY0x5hpjTLwxxtcYk2SMeda2/V1jzGRjzERjjNsd\nkVlZWaxevZptn3zMotRQtvTQanOkvJHD5Y1kOBlqPSHCn4KqkzQ5aVGobWrlwQ/yeG1PKb9fPpEr\nZkSP6tEsfeHlIVw5M5r/fO8wVY2t/PWKKS7rlGbEBrE1r3Ni0Gox/O6DPL55RjR3LU7hrX1lnVpt\ndh2r5Q9b8lmQFMohJxf2V3aXkJkWxvVnxrH6kgn8Y9dxHtt81GnLyhfHakkK8yM6qHMXYmq4H3kV\nPbXYNDIpKoALJob3uTtqa24VjzoMdz5W3URCiC+xwb7dEpuPjlRxXpfWpHbpkQHkDEJiczre2nuC\na1/8iiue383GQxWcnRbW6X4fTw9+tSSN6pOtvLDLdTdD14tZS5ul2ySXzjy5tYC71uZQVn/qLVuF\ntr8PQEKINbHp2gV4rLqJsvoW9hyvo8JJq82mI1VszasalK7D4ayuqZVXd/c82tQYQ1FtM3Ehvnxr\nThxv7T3BJZMjOTs1jMhA716H3LfbW1LP7WcnUlDVxKf51ew8VktmaiiBPp7Mjg/mky5fSHIrTuLt\nKdz3v2u465f3a1fUKDHcuqJOWXtXVGZmJpmpYfbuKIuthaXG9oFY19TKfRtzuW1RIv7e3VtNfL08\nSArz41B554vH7uI6bnljP+MCvPnTysluFRcPhuHUv3/Z1EheuHo6dyxO7pY0OPr6jCg+K7A2M4N1\n8rZHNh0lwMeDb86MJiHUl/lJIby59wTGGD48XMHvPszjl0tSWTZpHDllnT/kyhtaePdAuX2I/YSI\nAP7ytcm0WayF2gVV1mQlMzMTYwxv7zvBBRO6Jw7RQT7Ut7Q5nUiutqmVqsYWkkL9WJQSyt6Seqoa\n3euC+Lywhic/LmBLXpX92I4tNiV1Ha0BdU2tfFlUy9mpYU6PNSnSva7S05V2xlzu35jbaWbknLIG\nnv2s9yHxeZWN/PDsJJ7/j+k8dcUU4oK7F/L7eHnws3NTWLPvBPlV3ZPJ578oZtW/D3Xa9tqeUla9\ne6jHROFY9UmKa5tYPiWSe/99+JRXoS+sbrIPKgjy9cLH04OKxs7vi825lZyTFsYVF13AmuyyTvcZ\nY9hRUE1Tq2XYLiPiSkVDC7e9uZ+Xso73S1K2KbeKv+0ootL2/yUzM5Ps0noe2Jjb8ZyNrfh5eRDo\n40lymB+PXz6Jb9n+P7tbHlDX1EpJXTOTowK5bnYsD32Yx+z4YPuUGovHh3frjtpXWs+CpFDu/85K\njk29QruiRolRk9g4mhUXRGF1E8W1Taz+MI8Xs0q46Y1stuZV8cjmfOYmBrNkYghVmywAACAASURB\nVPduknaLUkJ5fU9HV0hpXTP3bTjCjzOT+MHCRHz7sWJ/NPEQcauQOjLQh0cvS2dDTgUPfpDL917L\nJszPi3svSLPXHV2bEcube09w73uHeTmrhPuXjWdmXLC9xcLxA/elrONcmD6OKIfn9vf25GfnpbJi\nWiS/XH/EfoH76EgVZfUtXDy5+wgXDxFSwvw46qTO5uCJBiZEBODpIfh7ezI/KYQtub3Xcu0rqefh\nj47y66VpzIgJsq8h1l5jExPkw4m6FnvrxLb8ambFBbscvZEeGcCh8gYsA9gKYIzhia0F7C2p40Vb\ni0pLm4VHNh3ljb0neq2NKahqIinUjxA/r07F2V1FBvpw3exY/vRxQae/5/qD5Ww4VEFOWWOn+pVt\nR6spqmnis0LXNRcbbLVd12bEMDMuiIc+zHPzVXdobGmjrqmVyMCO0WcJIb4c61K7tSW3isVpYXz9\njCjWZpd1auUtrG6ixWKYnxRKdmn/J6ID1QpU3tDC3e/kkBEXzEeHK3l6+zGa2yx8cKiCVe8eYs2+\nE33uPtx0pJIQX0++KOwYXPDR4Up2FNTYj1Vc00R8SMf/36nRgfb6vEluTpy6r7SeSZEBeHkIF6aP\nIyLAmwsndXzOn5Ucwr6SevuXXIDs0nqmxQSyMCWUK2dG9+l1nQ6xmH75Uc6Nyiu0t6cHC5JD+cnb\nB2lqNfzX16fwnxek8cyOIiobWri5l6HGV82MIa+ykY9tzch/+riAldOjmJ90+kN8+9twqbHpq4gA\nbx69LJ1gXy9+v3wity5M7HQxTwj1ZcW0KGbGBfGXK6YwxTYcOirQGwOU2S54za0WNuRUcNXMGKfP\ns2JaFHPig/nDlnze+2AT/7W9kJ+ek4y3p/O3vrXOxkliU9bApMiOVrpFKaHsKOi9qPGZHcf4wcIE\nZsQGMTcxmM8LajHG2LuifLw8CPHzotz2ej7Nr2Fhiuv3WYifF6F+Xt1G6bRZDB/nVbksuu6L93Mq\nOLL7M/64YjLv7C/ncHkDL2WVEBvsQ1ywD4fLXV9kjDEUVJ8kOazn6RbarZgWRX1zG2/vK+NweQMb\nD1XwzI4iHrhoAmcmBLPdFuOKhhYKq5u4bVEiL+4qcXphtxjDhpwKLkwfh4hw81mJ7Cup73Nxb2G1\nNel0LO5PCPXlmEPMi2us3VBnxAaRt+dzpkYHsMGhrmtHQQ3zk0KY6uYyLcYYSmqbex0xaIzh+S+K\n+f7r++0tIKersrGFTUcqeWV3CXetzWHJxHF8f0ECj16WzsGyBq56YQ/vHijn/AnhbM6t4rY397vd\nHVrZ0EJOWSPXzY61J6Rbtmyxdwm1x7Sopslpyx5YJ051p65sb4k1SQFrLdfTX5/CopSOlk9/b0/m\nJISwzaFGL7u0nqlR1sd8fcbgJTZqYI2axKa9xqb9Qn/51EiWTBzHr5am4evlwYzYIP77G1P4/aXp\nLi9q7Xy8PPhJZjJ/+aSQNdlllNY1c/Us5xdOderC/b350dlJLrv1rj8zjqtnxeLlMLJKREiP9Ld/\nsO4+XkdquH+Pc7vcclYCxTVN/PnjAs4dH97jzLep4/zYUVDNX7cV8t1X9/GF7cP44IkGJkV1PG5W\nXBBfldT3+O3VumRHo/3DdV5SCJ8X1lDT1IYBe3GwtYDYurr4rqJa5vWyHphjd5TFGNbsO8ENr+zj\nia0FvLantMfH9qaqsYVndhRx5cxoooN8+O68eB78II812WX86OwkZsQGsaeH9b6qbN+G3Z2l19ND\n+Ok5yazNLuORTUd5J7uM/1ySRnKYHwtTQtl21Noq9mlBDWcmBHPBhHFUn2zly+LuCcBXx+sI8PZg\nvO395OUhZMQH8YXDNATuKKxuIrFLS1NiaOch35tzqzg7NdTeqvDNM2L455cl9pquHQXVzE8KYUp0\n760NW3OruO6lvdz+1gHuWXfIZSLWajE8tjmfHQU1nJkYzK/WH3FrdF5jSxtPby90OcT+ia0FrNlX\nRmVDCzfOjeNaWxdQsK8XD18ykb98bQqPXJrOskkR/H75RJZNiuBvO471+rwAW/KqmJ8UQmZaGJ8X\nWltoimqa8PQQ5ieF2BOW9hZMZyaM86fQRd2jo73H65ke0/F/1Nnn/Dlpofb1+iobWqhraiMxzHfw\nh3trV9SAGjWJjWONDVibMm9akNBpuLGPp4fbEz/NjAtiQXIIT20r5I7Frr/hD7XhVGMzWKzdUdY6\nm0/za1iQ3HMi4OPlwS+XpDFnwSJumBvX477TY6zzygT6eHJNRiyPbD5KZUMLB8samOwwcV+Yvzcx\nQd49fpPcc7yOKdEB9q7LhBBfvDyFT/KqSAjxtReet4+M2l9aT1SgDxGBPU/Alx7VUUC8bn857+wv\n557zU/jDZems219Ocy8XgJ48/8Vxzp8YztWXLgXgoknjSBvnz80LEogM9OGM2ED29rBCe0HVSZJC\n/fpUVJ8eGcDfvjmVp78+lT9cPomZcdai/vlJIewurqOxpY3t+dUsSLYmEtdkxPCPXd3rP97PqWCp\nrbWm3dzEkB67rpw5Vn2SxC4X2fYC4nbWbihrnVZmZiYz44KYFRfE09uP0dDcxv4TDcyOt3ad5lU0\n9vg3+UfWcX6wKJF/XjeD+cmhLs/3ya35VJ1s5ZFLJ3LLggSSwvx46MM8cisaKW9ocZlk//mTQt7c\ne8JlC+OBE/XcdW4yN5+VyOIuRes+Xh4kOMz1JSJcMjmCAycaOiVVm49UsrZLnZF1u7UQPirQh8gA\nbw6caKApdjqLUkKt/49trX/Ftc0uExsfF3WPjlothoNlDUzrZbmO+UnW0VH1zW1kn6hnSnQAHiKD\nP9xbR0UNqOF5tR4mvj8/gQcumsDkqJGzts1YkB7RUWfzaUE1Z7kxC3BciC+/XTbeacG4o8lRgfz3\nN6Zy/ZlxLE0fx0WTIvjthlxOtlqIC+5cPzQrPpisItetATuP1TLHYToAEWFuYghv7TvR6WLRPjJq\nR2EN8xJ7nz6gfch3fXMbL+ws5u5zk5keE0RSmB+TogJOeQLB6pOtfHSkkqsduvVErOuULbUN3Z8e\nY22pclXjkV/V1GNdTV8E+3oxOSqA7fk1fFlUy3zbmmMXTBzHyVYLt791kE/zq8mvPMk/dh1na141\nF0zoXDs3NzGEncdq+1STVFDd1OnvA527og6XN1BW32xPwNrdelYiWUW1PLWtkClRgfh7e+Jnvyg7\nH9VzuLyBuqY2FqWEIiIsSAphe373BKS8voWP86pZdX4q/t6eiAg/zUwi2NeL332Qxy1v7Oem17O7\nzYm08VAF2aX13Lwggfdzuk+BUN7QQkubIaaHYv+u/L09mRwV0KnV7NU9pfzX9sJORb7l9S3kVjZy\npu09PT/JmmR+crSKs1NCO7W8FtU0ERfi+hwm91JAfKisgbhgn17X3gv08eSM2CB2FFSTXTKy1i1T\n7hs1iU3Xrqj+EODj6XSum+FkpNbYnI72AuL8qpO0WQyp4e5dSE8lVt+eE2d/zq6tEBlxPSc2u47V\nMie+8/tnbmIwRypO2ocSQ0dX1GcFNcxL6v39lh4ZwOHyRl7YWcy8pBAmRHS0JH1tehT/+qr0lIpL\n1+0v4+zUUMIDvF3GKjrIB18vcTmvS0H1SZLcrK9xx8KUMP7+WRETIgIIsXVveXkIT66YxFWzovn7\nZ0X8/N1DVDW28ruLJ3Rr7YoO8iHUz4tDZe4NFwZr3UdiaOf3VHyIL8W1TViM4W87irh2dqy9Nbg9\nVgE+nvzsvBQ2HKro1Io4pYe5sd47aK0Jaq/nmZcYwq6i2m7dRusOlHHe+PBOdWjenh7cfW4Kf/vm\nVF791hlcNjWSO9bkcLjcmvTuKKjm6e3HuPeCVJZNiuDL4rpuI/kOlTUwMaL7e7s3cxOs3apgTUpK\napv5yTnJrP4oj8aWNizGsHZ/GQuSQ/GxtXbPSwrhvYPlHPryM6ZEBzIxIoAj5Y1YjLEWD7uosQHr\nF46uM8JbjGHb0Wp2F9fxaUEN02O6T9/hTGZaGFtyq8kubbAnNmOhK0pEHhGRbBH5UkTeEBGX3whF\nxFNEdonImtN+rUNg1CxXm5GRwZw5c4b6NNQgiA7yps1iWHegnAXJoQM6l5CXh/DrpWlUOynKnRkX\nxOqP8mhuteDTpYuzrL6ZisaWbstmZMQF4yl0anaPDfLhnycaqGxsZZobH86BPp5EBnrz7oFy/ufK\naZ3uOzMhmKcNZBXXdVqjqzctbRbe3lfGAxeN73Xf6TFBfHW8vtvFH6xdUXP6cR2ohcmhPLWtkBXT\nOs/w7SHC4rRwFqeF97o6+tzEYD4rrHG5BpgjYwyF1Se7LbXi7+1JsK8X7x4op7SumeVTnM84Pj0m\niF8vHd+p1mNKdIA9CXDU0mbhw8OV/HHFJPu28ABvEkN9+aqk3v73a7UY1u0v53cXT+jx3K+YEU1E\ngDd3rs3BYqxzct22MNGe+C5ICuHDw5Vc4VAkm9OlKN5dcxNDuM82XHvTkUoy08JYMnEcnxfW8MDG\nPErrmvH2FO4+N8X+mGkxQTQ0tzE9xjriKcTPixA/Lw6caKC5zRDm7/pyNDkqgFe6zIXzwaFKnvui\nmIgAb0rrmrn97CQXj+5sYXIof91WiMVYh5KDtTsxMzOTnTt39jUUp2Zo6mPWAz83xlhEZDWwCrjH\nxb4/xjpJ7vBf1M2JUdNiM1aNxRobawFxAO9kl3FWL/U1jk41VuEB3qSGd//wD/TxJCXMj32l3b+N\n7yqqJSM+uNuSEgE+niyfEsnU6I6LbJxtLaLZ8UGdCqV7MisuiGtnxxLRpWhaRLhiRhTPfV7ca2Hp\nl0W1rM0uo7nVwubcKpLCfO0XwZ5i1dN6XwVVTZ0WlT1dMcE+XDI5gnPSnM/rA/Sa2M5NDLEXgber\nb27jrrU53Ub3VDW24mW76HaVGOrL09uP8d158Z3+Tl1jtTAltNPjp0QHOh3yvT2/hpQwP+K61JYs\nSAphh8PInU/yqogP8XVr7qzF48N54erp/Ov6mTx++aROs2svmzSuW3dUTlnjKS3EmzbOj5OtbRTV\nNLHpSKV9QskfLkoiKsibWxcm8JevdZ7vy8tDWDk9ipu/cbF9W3pkAJuPVBLvUHPmTHKYHydbLPa/\nY3Obhee+KOZn56XwxIpJvHjtjB5HEzoK8bN2cUYH9d51NZoYY943xp5RfQokOttPRBKB5cAzwIic\ngVYTGzUipUcGIMCsuKH9QpFhq7NpabPw+p5S3jtYjsUYdh6rddlicvvZSSQ4tHZEBHjj5SG9joZy\n9KOzk1wOcb94UgSJob6sevew08kGwdqi9OAHeWzJreKGV/fx3BfFbg93nRFrbbHp6mSrharGlj7V\na7jjp+ckE9tDN0VvzogN4nBFo30ofJvF8MDGXIprm/j3gfJO+xY46YZqFx/iS3qEP4vcvIC2Swz1\npb65jfL6zt1A6w+Ws2xS9/m05ieH8qlDoe/b+8q6tVj1JMjXq1tCDdb/K1UnW8mt6OiWyylrOKXE\nRkSYmxDCv74qpfpkGzNirS1UgT6e/CQzmTkJ3dfhA7hxbrx9SDZAeqQ/m3KrOs1h44ynh7X159HN\n+VQ2trA2u4zUcD/OiHWv+6mrpenj7DVbQ8FYTL/8nIbvAOtc3Pc4cDcwYoddaWIzwo3FGhuwdgNl\npoX1abLEgYjV7PhgNh2p4tZ/HWCXrQXkrndy+Lyw1u11xDw9hHPSwpjvRhF0u56+3Xp6CHcsTmZy\ndAB3rs3hr9sKeWJrPq/sLqGp1UKbxfDwR0dZOT2Kh5dP5DdLx3P+hPBOH/Q9xSol3I+aplYOnOhc\nRFxYdZL4EF+nF9Wh5OvlQUZ8ML96/wjrD5bzp08KEIHfL5/I5tyqThMOHnPSDdXumowYVl2Q2i32\nvb2vPES4YGI4L2Z1LB+xt6SOnPIGpy1REyP8aWhuY/3Bch7YmEtJXbPLmaj7wtNDWJY+zj56qbKh\nhZOtFmKDTy0RPTMxhLf3lbF4fFifFvR1jFd6RABl9S0u57BxNDshmAvTx/HwR0d5OauE78yLP6Xz\nBrgwPYKbepnPbECdYk1NU1M1NbWF9p+uNUEi8r6I7HHyc7nDPvcCzcaYF7uelohcBpQaY3YxQltr\nYBTV2GRlZbF+/Xp7X6ka3eYmhgyLwu5pMYFEBnqzYloUmamhWAy8s7+MzwpqunUx9GTV+an9el4e\nItyyIIEPD1dS0diKr6ewq6iOt/ftY3pMEMZgn5tpUlSAW/Unjse+bnYs92/MRRC+Nj2Kb5wRbSsc\n7r9uqP70i/NT+TS/mg2HKmhotvDbZeMJ9PEkIcSXncdq7JNvFjoZEdXudFqNrp8Tx/dey2b5lAiS\nw/x4YmsBt57lfFkXDxHOSQvntT2lXDY1kp+ek+x2F2VvVk6L4nuvZ/OtObHklDcwMdL/lGvU2hN3\nV+uauaO9tcjd/yvXnxnHHWsOMi8ppF+Xtdm6dStbt24lOjqaJUuW9Ntx+5uvTzC+Ph1fmLoOTzfG\nXNjT40XkBqzdTK5e5CJghYgsB/yAEBF53hhz/Wmc9qCT0bJA28aNG40WDyvVs93Fdby5t5RbFyZ2\nWoLiVBhjyK04yW82HOHGuXHkVzUhWC8+I8Xb+06wt6Tenlj+ev0RlqaP67Gm51St21/G+zkVzE0M\nYX9pPfctGz8ki+j+8eMCgn098fb0oLG5je+fRsvFobIGJkScenIE8O2X93LHOcnMdrOFs7GlDU8P\nsY+26k87d+5kyZIlA/pH2bhxo7n+2rv75VjPv/iI2+crIhcDjwHnGmO6TzrUff9zgbuMMZf3tu9w\no11RSo0hM+OC+NXS8aed1IC1O2x8hD+/WTqep7YdY9vR6n4d6j0YFqeFsaOghsaWNioaWsgpb3DZ\nFXW6LpoUQXObhVd3l/DDRUlDktQAXHlGNO9kl7G7uPaU6mscTXQyDUJf/fbC8d3mBOqJv7fngCQ1\ng8pi6Z+fvvkTEAS8bxvK/RSAiMSLyDsuHjMiWz5G+LtDjdUam1OhsXJfX2I1PsKf2xclcqSikSQX\nhbfDVZi/N9OiA/nLJ4Xc8sZ+Lkwf5/a8SO3cjZWnh/Czc1NYdX4qMadY19If4kJ8OTMxhKyiutNO\nbE5F13iNj/AfdnVZo5ExJt0Yk2KMmW37+YFte5Ex5lIn+28yxqwY/DM9faOmxkYpNXQWjw/nySCf\nbvP2jASXTIngH7uOc/9F4wd8lvGUcH9SnEwdMNiumhnN/tL6XkcjqYFhhmYemzFj1NTYPPbYY6a0\ntFSLh5VSyg1tFqMtJTaOxcN33nnngNfYfPuqH/fLsf7vlScHvCZoJBo1LTY687BSSrlPk5oOY2Tm\n4TFDa2xGOK0bcZ/Gyn0aK/dprPpG46UG2qhpsVFKKaVGAq2xGVia2IxwWk/kPo2V+zRW7tNY9Y3G\nC+2KGmDaFaWUUkqpUUMTmxFO+6vdp7Fyn8bKfRqrvtF4Wbui+uNHOaddUUoppdQg0qRkYI2axGas\nLoI5ll7r6dJYuU9j5T6NVd8Mx3iNlEUwlXtGTWKj89gopZQ6FTqPzeiiNTYjnPZXu09j5T6Nlfs0\nVn2j8dIam4GmiY1SSimlRo1R0xU1Vg3H/urhSmPlPo2V+zRWfaPx0uLhgaaJjVJKKTWINLEZWNoV\nNcJpf7X7NFbu01i5T2PVNxovNdC0xUYppZQaRNpiM7A0sRnhtL/afRor92ms3Kex6huNFxg0sRlI\n2hWllFJKqVFj1CQ2WVlZrF69esz1346113s6NFbu01i5T2PVN8MxXlu3bmX16tVkZWUNyvPpPDYD\na9R0RenMw0oppU7FYM88rEnJwBo1LTZjlfZXu09j5T6Nlfs0Vn2j8VIDbdS02CillFIjgbbYDCxt\nsRnhhmN/9XClsXKfxsp9Gqu+0XiBBdMvP8o5TWyUUkopNWpoV9QIp/3V7tNYuU9j5T6NVd9ovLQr\naqBpYqOUUkoNIk1sBpZ2RY1w2l/tPo2V+zRW7tNY9Y3GCyz99E85p4mNUkoppUYN7Yoa4bS/2n0a\nK/dprNynseobjZd2RQ00TWyUUkqpQaRDtQfWiOiKEpE0EXlGRF4d6nMZbrS/2n0aK/dprNynseob\njZcaaCMisTHG5BpjvjfU5zEc7dmzZ6hPYcTQWLlPY+U+jVXfaLzAYiz98tMXInK/iHwpIlkislFE\nklzsd7GI7BeRHBH5eb+84EE2qImNiPxdREpEZE+X7SM+kEOlurp6qE9hxNBYuU9j5T6NVd9ovMD0\n078++r0xZpYxJgN4E/h11x1ExBP4M3AxMA24RkSmnu7rHWyD3WLzLNaA2bkKpIh8W0QeF5H4QT5H\npZRSalQxxtQ63AwCypzsNh84ZIzJM8a0AC8DKwfj/PrToBYPG2O2iEhql832QAKIyMvASmPMauD/\nbNvGAb8DMkTk58aYhwftpIe5/Pz8oT6FEUNj5T6Nlfs0Vn2j8aLP3Uj9RUQeBL4NNABnOdklAShw\nuF0ILBiEU+tXw2FUVK+BNMZUALf0dJCsrCyee+45++1Zs2aRkZHRj6c5PM2dO5edO3cO9WmMCBor\n92ms3Kex6pvhFK+srCy+/PJL++1Zs2axZMmSAX/eDRs2nNLjup5vVlZWp/MVkfeBWCcP/YUxZo0x\n5l7gXhG5B3gcuLHLfqNiuJYYM7ivw9Zis8YYc4bt9jeAi40x37fd/hawwBhz+6CemFJKKTUGiEgy\nsM4YM6PL9rOA3xhjLrbdXgVYRlovyXAYFXUMcKzOTsLaaqOUUkqpfiAi6Q43VwK7nOz2OZAuIqki\n4gP8B/D2YJxffxoOic2oCKRSSik1jD0kIntEJAs4D7gTQETiReQdAGNMK/BD4D1gH/BPY0z2EJ3v\nKRvUrigReQk4F4gASoFfGWOeFZFLgCcAT+B/jDEPDdpJKaWUUmrUGNQWG2PMNcaYeGOMrzEmyRjz\nrG37u8aYycaYiZrUdHA274+IPCIi2baJlt4QkVCH+1bZ5gLaLyLLhuash4aLWLmckGosxwpczyll\nu+9OEbHYRiO2bxuz8XLx3vqNiBSKyC7bzyUO92msus9Vdrvtc+srEXnYYfuYjZUaOMOhK0q51m3e\nH2A9MN0YMws4CKwCEJFpWLvxptke85SIjKW/r7NYOZ2QSmMFOI8XtuTvQuCow7axHi9nsTLAH4wx\ns20/74LGCudzlZ0PrABm2opVH7VtH+uxUgNE30TDmDFmC1DZZdv7pmNp2E+BRNvvK4GXjDEttjmB\nDmGdI2hMcBErVxNSjelYgfN42fwB+FmXbWM6Xj3ESpxs01h1j9WtwEO2Cd8wxpywbR/TsVIDRxOb\nke07wDrb7/F0Hk1WiHWOoDFNRB4UkXzgBqC9m1Nj5YSIrAQKjTG7u9yl8XLudltX5/+ISJhtm8aq\nu3RgsYhsF5GPRGSubbvGSg0ITWxGKBG5F2g2xrzYw26jYrKl02GMudcYk4y1ifyJnnYdpFMalkQk\nAPgFndePcdYi0W5Mxwv4K5AGZADFwGM97DvWY+UFhBtjzgLuBl7pYd+xHivVDzSxGYFE5AZgOXCd\nw+au8wEl2rYpqxeBebbfNVbdTQBSgS9FJBdrTL4QkRg0Xt0YY0qNDfAMHV0oGqvuCoE3AIwxnwEW\nEYlEY6UGiCY2I4yIXIz1W89KY8xJh7veBq4WER8RScPa/LtjKM5xuOhhQiqNVRfGmD3GmBhjTJox\nJg3rxWiOMaYEjVc3IhLncPMKoH0UkMaquzeBCwBEZBLgY4wpQ2OlBshwWCtKueAw70+kiBRg7SZY\nBfgA74sIwDZjzA+MMftE5BWskyq1Aj8wg71exhByEavlIjIZaAMOYy1iZKzHCjrPKWWL16/ap1+w\nscdjrMfLxXvrPBHJwBqnXOBm0Fg5e18Bfwf+bhsC3gxcDxorNXAGfa0opZRSSqmBol1RSimllBo1\nNLFRSiml1KihiY1SSimlRg1NbJRSSik1amhio5RSSqlRQxMbpZRSSo0amtgoNczZ1tf57hA9t0VE\nxru57/+KSLOIHBmgc1kqIrUi0iYiSwbiOZRSI58mNkq5ICKrRGRdl205LrZdNYCnYhiENXT6IYEy\nwGpjjFuJUJ8PbswGY0wwkI+uKaSUckETG6Vc2wQsEtsUz7Zp9L2ADBHxcNg2Adg8ZGfZf/ojWehp\n4UyllBpwmtgo5drngDfWFZwBzgE+BA522XbIGHNcRG4UkX0iUiMih0XkpvYDiUi2iFzqcNtLRE7Y\npuVHRM4SkU9EpFJEskTkXFcnJSLfsT1PhYj8W0SSHe6ziMjNInLQdqw/O9znISKP2Z73iIj80La/\np4g8aHstf7Z19/zR4SkvdHY8d9hage4Tka22uLwnIhG2+1Jtz3+DiOSLSLmI3CIi80Rkt+35/tSX\n51NKKU1slHLBGNMMfIp17RuAxcAWYKvt9/Zt7a01JcClxpgQ4Ebg8fbEBevq4tc4HP4ioNQYkyUi\nCcBa4D5jTDhwF/B6ewLgSERWYl0v7Aog0nY+L3XZ7VJgLjATuEpELrJtvwm4GJgFzAG+hq2byxhz\nr+1Ytxljgo0xP3LjeO66BrgBiMa6ztldXe6fD0wErgaeBH6BddHE6bbnW4xSSrlJExuleraJjiQm\nE2sSs8Vh2zm2fTDGrDPG5Np+3wysd9jvJWCFiPjZbl9LR0LyLWCdMebftsduwNpaZG/hcXAL8JAx\n5oAxxgI8hLVrLMlhn9XGmBpjTAHWFqZZtu1XAU8YY4qMMVW2x3btOnLWldT1eBlO9nHFAM8aYw7Z\nVqN/xcnj7zfGNBtj3gdqgReNMWXGmCKssZ7dh+dTSo1xmtgo1bPNQKaIhANRxpjDwDastTfhWFsV\nNgOIyCUist3WpVIJLAciAIwxh4BsrMlNAHA51lYcgBTgSlvXS6XtsWcDsU7OJwV40mG/ctv2BId9\njjv83gAE2X6PAwoc7it0cnxndTaujucux8c3Onl8SZf7u97u6/MppcYwRFa3AwAAAe5JREFUr6E+\nAaWGue1AKPB94GMAY0yNiBRh7dopMsYcFRFf4HWsrS9vGWPaRORfdG4BeQlrt4wnsM8Y0z4sOh/4\nP2PMTfQuH2sLR9fuJ3cUA44tO0ld7h+uI42G63kppYYhbbFRqgfGmEas3UJ30Hnk01bbtk222z62\nnzLAIiKXAMu6HO5lrLU1twD/cNj+AnC5iCyzFfL6ich5ttqbrp4GfiEi0wBEJFREruzhJQgdydUr\nwI9FJF5EwoCf0zlpKME6wqsnvY16cpaEnO5IKR1ppZRymyY2SvVuExCFNZlptwVr8e5mAGNMLfAj\nrMlDBdaWmbccD2KMOQ58AiwE/umwvRBYibVothRrq8ydOLmgG2PeBB4GXhaRamAP1mTJvkvXhzhs\n+xvWup/dwBfAO0CbrVYHrIW737SNtnrCRSx6m1PHWRJiuvze9XZvtMVGKeU2MUY/M5Qai2ytSn81\nxqT20/H+G2tCd9wYk94fx+xy/CXAa1hbxpYbYzb18hCl1BikiY1SY4RtRNYFWFttYrDWBH1ijLlj\nSE9MKaX6kSY2So0RIuKPtVttCtbRRmuBHxtj6ob0xJRSqh9pYqOUUkqpUUOLh5VSSik1amhio5RS\nSqlRQxMbpZRSSo0amtgopZRSatTQxEYppZRSo8b/A1DMqfwTGZMTAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb81e688470>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1b.plot_raw_overview()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def process_fname(fname):\n",
" import os\n",
" l1b = io.L1BReader(fname)\n",
" fig = l1b.plot_raw_overview(-1, save_token='1', imglog=True, \n",
" proflog=False, prof_plot_hist=True)\n",
" scaling.do_all(l1b, -1, log=False)\n",
" plt.close('all')\n",
" return \"{} done.\".format(os.path.basename(fname))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'mvn_iuv_l1b_inbound-orbit00238-fuv_20141112T135825_v01_r01.fits.gz done.'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"process_fname(fnames.iloc[0])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0 % done.\n",
"0.7092198581560284 % done.\n",
"1.4184397163120568 % done.\n",
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"65.95744680851064 % done.\n",
"66.66666666666667 % done.\n",
"67.37588652482269 % done.\n",
"68.08510638297872 % done.\n",
"68.79432624113475 % done.\n",
"69.50354609929079 % done.\n",
"70.2127659574468 % done.\n",
"70.92198581560284 % done.\n",
"71.63120567375887 % done.\n",
"72.34042553191489 % done.\n",
"73.04964539007092 % done.\n",
"73.75886524822695 % done.\n",
"74.46808510638297 % done.\n",
"75.177304964539 % done.\n",
"75.88652482269504 % done.\n",
"76.59574468085107 % done.\n",
"77.30496453900709 % done.\n",
"78.01418439716312 % done.\n",
"78.72340425531915 % done.\n",
"79.43262411347517 % done.\n",
"80.1418439716312 % done.\n",
"80.85106382978724 % done.\n",
"81.56028368794327 % done.\n",
"82.26950354609929 % done.\n",
"82.97872340425532 % done.\n",
"83.68794326241135 % done.\n",
"84.39716312056737 % done.\n",
"85.1063829787234 % done.\n",
"85.81560283687944 % done.\n",
"86.52482269503547 % done.\n",
"87.23404255319149 % done.\n",
"87.94326241134752 % done.\n",
"88.65248226950355 % done.\n",
"89.36170212765957 % done.\n",
"90.0709219858156 % done.\n",
"90.78014184397163 % done.\n",
"91.48936170212765 % done.\n",
"92.19858156028369 % done.\n",
"92.90780141843972 % done.\n",
"93.61702127659575 % done.\n",
"94.32624113475177 % done.\n",
"95.0354609929078 % done.\n",
"95.74468085106383 % done.\n",
"96.45390070921985 % done.\n",
"97.16312056737588 % done.\n",
"97.87234042553192 % done.\n",
"98.58156028368795 % done.\n",
"99.29078014184397 % done.\n"
]
}
],
"source": [
"import sys\n",
"no_detector_dark = []\n",
"darkmean_smaller = []\n",
"interesting = []\n",
"for i,fname in enumerate(fnames):\n",
" print(\"{} % done.\".format(100*i/len(fnames)))\n",
" sys.stdout.flush()\n",
" l1b = io.L1BReader(fname)\n",
" rawmean = l1b.detector_raw.mean()\n",
" try:\n",
" darkmean =l1b.detector_dark.mean()\n",
" except AttributeError as e:\n",
" no_detector_dark.append(fname)\n",
" continue\n",
" if darkmean*100 < rawmean:\n",
" interesting.append(fname)\n",
" print('{}, {:.1f}'.format(i, rawmean/darkmean))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for fname in fnames[:20]:\n",
" process_fname(fname)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['mvn_iuv_l1b_inbound-orbit00676-muv_20150204T060224_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00676-muv_20150204T060228_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00676-muv_20150204T060232_v01_s01.fits.gz']"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interesting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['mvn_iuv_l1b_inbound-orbit00241-muv_20141113T001547_v01_r01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00247-muv_20141114T035244_v01_r01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00341-muv_20141202T034550_v01_r01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00389-muv_20141211T075233_v01_r01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00451-muv_20141223T035444_v01_r01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00899-muvdark_20150318T030704_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00903-muvdark_20150318T211052_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00907-muvdark_20150319T151438_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00911-muvdark_20150320T091823_v01_s01.fits.gz']"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_detector_dark"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"112"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(darkmean_larger)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['mvn_iuv_l1b_inbound-orbit00894-fuvdark_20150317T075924_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012428_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012445_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012502_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012519_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012537_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012555_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012612_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012629_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012646_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012704_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012722_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012740_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012756_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012814_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012832_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012850_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012906_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012924_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012940_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T012958_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013017_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013034_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013051_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013108_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013126_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013144_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013201_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013218_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013235_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013253_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013311_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013328_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013345_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013402_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013420_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013438_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013455_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013512_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013529_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013547_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013605_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013622_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013639_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013656_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013714_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013732_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013749_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013806_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013823_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013841_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013859_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013917_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013933_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T013951_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014009_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014027_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014043_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014101_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014117_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014135_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014154_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014211_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014228_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014245_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014303_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014321_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014338_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014355_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014412_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014430_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014448_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014505_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014522_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014539_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014557_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014615_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014632_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014649_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014706_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014724_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014742_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014759_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014816_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014833_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014851_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014909_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014926_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T014943_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015000_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015018_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015036_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015053_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015110_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015127_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015145_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015203_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015220_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015237_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00898-muvdark_20150318T015254_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00900-fuvdark_20150318T110514_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00904-fuvdark_20150319T050901_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00904-fuvdark_20150319T050935_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00908-fuvdark_20150319T231248_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00912-fuvdark_20150320T171630_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00912-fuvdark_20150320T171704_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00948-fuv_20150327T114841_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00948-fuv_20150327T114841_v01_s02.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00956-fuv_20150328T235556_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00960-fuv_20150329T180023_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00964-fuv_20150330T120448_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00968-fuv_20150331T060909_v01_s01.fits.gz']"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"darkmean_larger"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"regular_darklarger = ['mvn_iuv_l1b_inbound-orbit00948-fuv_20150327T114841_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00948-fuv_20150327T114841_v01_s02.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00956-fuv_20150328T235556_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00960-fuv_20150329T180023_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00964-fuv_20150330T120448_v01_s01.fits.gz',\n",
" 'mvn_iuv_l1b_inbound-orbit00968-fuv_20150331T060909_v01_s01.fits.gz']"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mvn_iuv_l1b_inbound-orbit00948-fuv_20150327T114841_v01_s02.fits.gz\n",
"Dark: 3927.27678571\n",
"Light: 3876.74461828\n",
"Ratio: 1.01303469081\n",
"mvn_iuv_l1b_inbound-orbit00956-fuv_20150328T235556_v01_s01.fits.gz\n",
"Dark: 3942.03472222\n",
"Light: 3857.40467411\n",
"Ratio: 1.02193963436\n",
"mvn_iuv_l1b_inbound-orbit00960-fuv_20150329T180023_v01_s01.fits.gz\n",
"Dark: 3898.37896825\n",
"Light: 3845.37731774\n",
"Ratio: 1.01378321193\n",
"mvn_iuv_l1b_inbound-orbit00964-fuv_20150330T120448_v01_s01.fits.gz\n",
"Dark: 3967.89781746\n",
"Light: 3859.84685349\n",
"Ratio: 1.02799358837\n",
"mvn_iuv_l1b_inbound-orbit00968-fuv_20150331T060909_v01_s01.fits.gz\n",
"Dark: 4125.79563492\n",
"Light: 3851.95024758\n",
"Ratio: 1.0710926595\n"
]
}
],
"source": [
"for i,fname in enumerate(regular_darklarger[1:]):\n",
" print(fname)\n",
" l1b = io.L1BReader(fname)\n",
" rawmean = l1b.detector_raw.mean()\n",
" try:\n",
" darkmean =l1b.detector_dark.mean()\n",
" except AttributeError as e:\n",
" continue\n",
" if darkmean > rawmean:\n",
" print(\"Dark:\", darkmean)\n",
" print(\"Light:\", rawmean)\n",
" print(\"Ratio:\", darkmean/rawmean)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for fname in regular_darklarger:\n",
" process_fname(fname)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.parallel import Client\n",
"c = Client()\n",
"dview = c.direct_view()\n",
"lbview = c.load_balanced_view()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%px\n",
"def calc_4_to_3(width):\n",
" height = width * 3 / 4\n",
" return (width, height)\n",
"from iuvs import io, scaling, plotting\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('bmh')\n",
"plt.rcParams['figure.figsize']= calc_4_to_3(9)\n",
"plt.rcParams['image.aspect'] = 'auto'\n",
"plt.rcParams['image.interpolation'] = 'none'\n",
"plt.rcParams['lines.linewidth'] = 1\n",
"plt.ioff()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ret = lbview.map_async(process_fname, df.fname)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141802_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-muv_20141014T141914_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1443-muv_20141014T142730_v01_r01.fits.gz done.\n",
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"mvn_iuv_l1b_APP1-orbit00087-mode1444-fuv_20141014T142818_v01_r01.fits.gz done.\n",
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"mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141838_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1442-fuv_20141014T142446_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1442-fuv_20141014T142258_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1442-muv_20141014T142522_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1443-fuv_20141014T142730_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141914_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-muv_20141014T141802_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1443-muv_20141014T142648_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1444-muv_20141014T142818_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1443-fuv_20141014T142702_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1442-muv_20141014T142146_v01_r01.fits.gz done.\n",
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"mvn_iuv_l1b_APP1-orbit00087-mode1443-fuv_20141014T142722_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1444-muv_20141014T142826_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-muv_20141014T141950_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T142026_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141650_v01_r01.fits.gz done.\n",
"mvn_iuv_l1b_APP1-orbit00087-mode1444-fuv_20141014T142832_v01_r01.fits.gz done.\n"
]
}
],
"source": [
"for res in ret:\n",
" print(res)"
]
},
{
"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
}
| isc |
makeyourownalgorithmicart/makeyourownalgorithmicart | blog/generative-adversarial-network/08_synthetic_gan_deconv_conditioned.ipynb | 1 | 1115144 | null | gpl-2.0 |
srom/chessbot | estimator/train/grid/grid.ipynb | 1 | 3466 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import logging\n",
"import math\n",
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_grid():\n",
" path = os.path.join(os.environ['GOPATH'], 'src/github.com/srom/chessbot/estimator/train/grid/grid_conv2.json')\n",
" print 'Loading grid from %s' % path\n",
" with open(path, 'r') as f:\n",
" return json.loads(f.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grid = load_grid()['grid']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sorted_best_scores(grid):\n",
" scores = []\n",
" for sq_nb, square in enumerate(grid):\n",
" square_score = get_best_square_score(square, sq_nb)\n",
" scores.append(square_score)\n",
" return sorted(scores, key=lambda s: s['loss'])\n",
"\n",
"\n",
"def get_best_square_score(square, sq_nb):\n",
" best_score = None\n",
" for i, l in enumerate(square['test_losses']):\n",
" if best_score is None or l < best_score['loss']:\n",
" best_score = dict(\n",
" loss=l,\n",
" iteration=i, \n",
" learning_rate=square['learning_rate'], \n",
" epsilon=square['epsilon'],\n",
" step=sq_nb,\n",
" )\n",
" return best_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scores = sorted_best_scores(grid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = range(20, 50)\n",
"plots = []\n",
"best_params = [(b['learning_rate'], b['epsilon']) for b in scores[:5]]\n",
"for grid_square in grid:\n",
" params = (grid_square['learning_rate'], grid_square['epsilon'])\n",
" if params not in best_params:\n",
" continue\n",
" test_loss = grid_square['test_losses']\n",
" learning_rate = grid_square['learning_rate']\n",
" epsilon = grid_square['epsilon']\n",
" l, = plt.plot(x, test_loss[20:], label='l: {}, e: {}'.format(learning_rate, epsilon))\n",
" plots.append(l)\n",
"\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.legend(handles=plots)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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 |
SylvainCorlay/bqplot | examples/Interactions/Mark Interactions.ipynb | 2 | 11875 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from bqplot import *\n",
"import numpy as np\n",
"import pandas as pd\n",
"from ipywidgets import Layout, Dropdown, Button\n",
"from ipywidgets import Image as ImageIpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter Chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Scatter Chart Selections\n",
"\n",
"Click a point on the `Scatter` plot to select it. Now, run the cell below to check the selection. After you've done this, try holding the `ctrl` (or `command` key on Mac) and clicking another point. Clicking the background will reset the selection."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_sc = LinearScale()\n",
"y_sc = LinearScale()\n",
"\n",
"x_data = np.arange(20)\n",
"y_data = np.random.randn(20)\n",
"\n",
"scatter_chart = Scatter(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, colors=['dodgerblue'],\n",
" interactions={'click': 'select'},\n",
" selected_style={'opacity': 1.0, 'fill': 'DarkOrange', 'stroke': 'Red'},\n",
" unselected_style={'opacity': 0.5})\n",
"\n",
"ax_x = Axis(scale=x_sc)\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"\n",
"Figure(marks=[scatter_chart], axes=[ax_x, ax_y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scatter_chart.selected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternately, the `selected` attribute can be directly set on the Python side (try running the cell below):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scatter_chart.selected = [1, 2, 3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Scatter Chart Interactions and Tooltips"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"x_sc = LinearScale()\n",
"y_sc = LinearScale()\n",
"\n",
"x_data = np.arange(20)\n",
"y_data = np.random.randn(20)\n",
"\n",
"dd = Dropdown(options=['First', 'Second', 'Third', 'Fourth'])\n",
"scatter_chart = Scatter(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, colors=['dodgerblue'],\n",
" names=np.arange(100, 200), names_unique=False, display_names=False, display_legend=True,\n",
" labels=['Blue'])\n",
"ins = Button(icon='fa-legal')\n",
"scatter_chart.tooltip = ins\n",
"line = Lines(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, colors=['dodgerblue'])\n",
"scatter_chart2 = Scatter(x=x_data, y=np.random.randn(20), \n",
" scales= {'x': x_sc, 'y': y_sc}, colors=['orangered'],\n",
" tooltip=dd, names=np.arange(100, 200), names_unique=False, display_names=False, \n",
" display_legend=True, labels=['Red'])\n",
"\n",
"ax_x = Axis(scale=x_sc)\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"\n",
"fig = Figure(marks=[scatter_chart, scatter_chart2, line], axes=[ax_x, ax_y])\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def print_event(self, target):\n",
" print(target)\n",
"\n",
"# Adding call back to scatter events\n",
"# print custom mssg on hover and background click of Blue Scatter\n",
"scatter_chart.on_hover(print_event)\n",
"scatter_chart.on_background_click(print_event)\n",
"\n",
"# print custom mssg on click of an element or legend of Red Scatter\n",
"scatter_chart2.on_element_click(print_event)\n",
"scatter_chart2.on_legend_click(print_event)\n",
"line.on_element_click(print_event)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Changing interaction from hover to click for tooltip\n",
"scatter_chart.interactions = {'click': 'tooltip'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding figure as tooltip\n",
"x_sc = LinearScale()\n",
"y_sc = LinearScale()\n",
"\n",
"x_data = np.arange(10)\n",
"y_data = np.random.randn(10)\n",
"\n",
"lc = Lines(x=x_data, y=y_data, scales={'x': x_sc, 'y':y_sc})\n",
"ax_x = Axis(scale=x_sc)\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"tooltip_fig = Figure(marks=[lc], axes=[ax_x, ax_y], layout=Layout(min_width='600px'))\n",
"\n",
"scatter_chart.tooltip = tooltip_fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image\n",
"\n",
"For images, `on_element_click` returns the location of the mouse click. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i = ImageIpy.from_file(os.path.abspath('../data_files/trees.jpg'))\n",
"bqi = Image(image=i, scales={'x': x_sc, 'y': y_sc}, x=(0, 10), y=(-1, 1))\n",
"\n",
"fig_image = Figure(marks=[bqi], axes=[ax_x, ax_y])\n",
"fig_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bqi.on_element_click(print_event)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line Chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding default tooltip to Line Chart\n",
"x_sc = LinearScale()\n",
"y_sc = LinearScale()\n",
"\n",
"x_data = np.arange(100)\n",
"y_data = np.random.randn(3, 100)\n",
"\n",
"def_tt = Tooltip(fields=['name', 'index'], formats=['', '.2f'], labels=['id', 'line_num'])\n",
"line_chart = Lines(x=x_data, y=y_data, scales= {'x': x_sc, 'y': y_sc}, \n",
" tooltip=def_tt, display_legend=True, labels=[\"line 1\", \"line 2\", \"line 3\"] )\n",
"\n",
"ax_x = Axis(scale=x_sc)\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"\n",
"Figure(marks=[line_chart], axes=[ax_x, ax_y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding call back to print event when legend or the line is clicked\n",
"line_chart.on_legend_click(print_event)\n",
"line_chart.on_element_click(print_event)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bar Chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding interaction to select bar on click for Bar Chart\n",
"x_sc = OrdinalScale()\n",
"y_sc = LinearScale()\n",
"\n",
"x_data = np.arange(10)\n",
"y_data = np.random.randn(2, 10)\n",
"\n",
"bar_chart = Bars(x=x_data, y=[y_data[0, :].tolist(), y_data[1, :].tolist()], scales= {'x': x_sc, 'y': y_sc},\n",
" interactions={'click': 'select'},\n",
" selected_style={'stroke': 'orange', 'fill': 'red'},\n",
" labels=['Level 1', 'Level 2'],\n",
" display_legend=True)\n",
"ax_x = Axis(scale=x_sc)\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"\n",
"Figure(marks=[bar_chart], axes=[ax_x, ax_y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding a tooltip on hover in addition to select on click\n",
"def_tt = Tooltip(fields=['x', 'y'], formats=['', '.2f'])\n",
"bar_chart.tooltip=def_tt\n",
"bar_chart.interactions = {\n",
" 'legend_hover': 'highlight_axes',\n",
" 'hover': 'tooltip', \n",
" 'click': 'select',\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Changing tooltip to be on click\n",
"bar_chart.interactions = {'click': 'tooltip'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Call back on legend being clicked\n",
"bar_chart.type='grouped'\n",
"bar_chart.on_legend_click(print_event)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Adding tooltip for Histogram\n",
"x_sc = LinearScale()\n",
"y_sc = LinearScale()\n",
"\n",
"sample_data = np.random.randn(100)\n",
"\n",
"def_tt = Tooltip(formats=['', '.2f'], fields=['count', 'midpoint'])\n",
"hist = Hist(sample=sample_data, scales= {'sample': x_sc, 'count': y_sc},\n",
" tooltip=def_tt, display_legend=True, labels=['Test Hist'], select_bars=True)\n",
"ax_x = Axis(scale=x_sc, tick_format='0.2f')\n",
"ax_y = Axis(scale=y_sc, orientation='vertical', tick_format='0.2f')\n",
"\n",
"Figure(marks=[hist], axes=[ax_x, ax_y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Changing tooltip to be displayed on click\n",
"hist.interactions = {'click': 'tooltip'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Changing tooltip to be on click of legend\n",
"hist.interactions = {'legend_click': 'tooltip'}"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Pie Chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up a pie chart with click to show the tooltip."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pie_data = np.abs(np.random.randn(10))\n",
"\n",
"sc = ColorScale(scheme='Reds')\n",
"tooltip_widget = Tooltip(fields=['size', 'index', 'color'], formats=['0.2f', '', '0.2f'])\n",
"pie = Pie(sizes=pie_data, scales={'color': sc}, color=np.random.randn(10), \n",
" tooltip=tooltip_widget, interactions = {'click': 'tooltip'}, selected_style={'fill': 'red'})\n",
"\n",
"pie.selected_style = {\"opacity\": \"1\", \"stroke\": \"white\", \"stroke-width\": \"2\"}\n",
"pie.unselected_style = {\"opacity\": \"0.2\"}\n",
"\n",
"Figure(marks=[pie])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Changing interaction to select on click and tooltip on hover\n",
"pie.interactions = {'click': 'select', 'hover': 'tooltip'}"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
StartupsPoleEmploi/labonneboite | ROME_NAF/groupby/groupby_DPAE_ETT.ipynb | 1 | 63140 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import bisect\n",
"import pickle\n",
"from collections import Counter\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Charge les référentiels\n",
"cf `referentiels.py`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ROME_df = pd.read_csv('../referentiels/referentiel_ROME/20150921_arboprincipale28427_ROME.csv', index_col=0, sep='|', dtype=str)\n",
"OGR_df = pd.read_csv('../referentiels/referentiel_OGR/20150921_arboprincipale28427_OGR.csv', sep='|', dtype=str).set_index('OGR')\n",
"NAF_df = pd.read_csv('../referentiels/referentiel_NAF/naf2008_liste_n5_nouveau_header.csv', sep='|', encoding=\"utf-8\").set_index(['NAF'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tranches_effectif = ['00', '01', '02', '03', '11', '12', '21', '22', '31', '32', '41', '42', '51', '52', '53', 'NN']\n",
"seuils_tranches_effectif = [0, 1, 3, 6, 10, 20, 50, 100, 200, 250, 500, 1000, 2000, 5000, 10000]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def trouve_tranche_effectif(effectif):\n",
" index = bisect.bisect(seuils_tranches_effectif, effectif) - 1\n",
" return index, tranches_effectif[index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Charge les DPAE"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filename = '../LBB_ETT_ETT_20160430_20170530_20170530_162434_clean.csv'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv(filename, sep='|', dtype=str, keep_default_na=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Histogramme sur la durée"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"duree_liste = list(df.dn_nbjcaltotalmission)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"duree_liste = [int(duree) for duree in duree_liste if duree != 'NULL']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"comptage_duree = Counter(duree_liste)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"365"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"duree_max = max(comptage_duree.keys())\n",
"duree_max"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(15339856, 1066312)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"histo = [comptage_duree[i] for i in range(duree_max + 1)]\n",
"sum(histo), sum(histo[30:])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6c9bf67fd0>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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cCmyMzp9mX7Jqbc19/gvAgAHh0dMVmL/9LTy/+WbPnldERKQUip0DMwRw4J3o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\nxszMzErHCYyZmZmVjhMYMzMzK51/AVnFe9NEsMcFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6c99a47f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(histo[:50])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6c9be8e5c0>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rgv13sG4dQ4PD0EBERH74bp4oEteskbNPRG1tLaqqqjo8VlNTg5qamkA7VY1v84QqsHEj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6c9bf72be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"volume = [histo[i]*i for i in range(duree_max + 1)]\n",
"plt.plot(volume[:100])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(152336029, 62580705, 0.4108069864417957)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(volume), sum(volume[30:]), float(sum(volume[30:]))/sum(volume)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Array : ROME x NAF x effectif"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ponderation_duree(nbrjourtravaille, dn_nbmission):\n",
" return 1 if (int(nbrjourtravaille) * int(dn_nbmission) >=30) else 0\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_NAF = len(NAF_df)\n",
"n_ROME = len(ROME_df)\n",
"n_tranches = len(tranches_effectif)\n",
"\n",
"array_ROME1 = np.zeros((n_NAF, n_ROME, n_tranches))\n",
"array_ROME2 = np.zeros((n_NAF, n_ROME, n_tranches))\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
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"16000000\n",
"16100000\n",
"16200000\n"
]
}
],
"source": [
"for i, row in df.iterrows():\n",
" (dn_nbjcaltotalmission, dc_nafinsee700_id, dn_nbmission,\n",
" dc_nafrefv2_id, dc_trancheeffectif_id, dc_romev3_1_id,\n",
" dc_romev3_2_id) = row\n",
" \n",
" if ((dn_nbjcaltotalmission != 'NULL') and (dc_nafrefv2_id in NAF_df.index) and\n",
" (dc_romev3_1_id in ROME_df.index)):\n",
" \n",
" NAF_index = NAF_df.index.get_loc(dc_nafrefv2_id)\n",
" ROME_index = ROME_df.index.get_loc(dc_romev3_1_id)\n",
" \n",
" try:\n",
" effectif = int(dc_trancheeffectif_id)\n",
" except ValueError:\n",
" tranche = 'NA'\n",
" tranche_index = 15\n",
" else:\n",
" tranche_index, tranche = trouve_tranche_effectif(effectif)\n",
" \n",
" poids = ponderation_duree(dn_nbjcaltotalmission, dn_nbmission)\n",
" \n",
" array_ROME1[NAF_index, ROME_index, tranche_index] += poids\n",
"\n",
" if dc_romev3_2_id in ROME_df.index:\n",
" ROME2_index = ROME_df.index.get_loc(dc_romev3_2_id)\n",
" array_ROME2[NAF_index, ROME2_index, tranche_index] += poids\n",
" \n",
" if i % 100000 == 0:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('../array_ROME1_ETT.pickle', 'wb') as f:\n",
" pickle.dump(array_ROME1, f)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('../array_ROME2_ETT.pickle', 'wb') as f:\n",
" pickle.dump(array_ROME2, f)"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| agpl-3.0 |
JelleAalbers/xeshape | notebooks/extraction/extract_s1s.ipynb | 1 | 34813 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"from multihist import Histdd, Hist1d\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import hax\n",
"from hax import cuts\n",
"hax.init(pax_version_policy='6.8',\n",
" minitree_paths=['./sr1_s1shape_minitrees/', \n",
" '/project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/',\n",
" '/project/lgrandi/xenon1t/minitrees/pax_v6.8.0/'])\n",
"\n",
"from pax import units, configuration\n",
"pax_config = configuration.load_configuration('XENON1T')\n",
"tpc_r = pax_config['DEFAULT']['tpc_radius']\n",
"tpc_z = -pax_config['DEFAULT']['tpc_length']"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Select clean 83mKr events"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"KR83m cuts similar to Adam's note: \n",
"https://github.com/XENON1T/FirstResults/blob/master/PositionReconstructionSignalCorrections/S2map/s2-correction-xy-kr83m-fit-in-bins.ipynb\n",
"\n",
" * Valid second interaction\n",
" * Time between S1s in [0.6, 2] $\\mu s$\n",
" * z in [-90, -5] cm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([4584, 4596, 4643, 4628, 4657, 4615, 4635, 4633, 4667, 4679])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get SR1 krypton datasets\n",
"dsets = hax.runs.datasets\n",
"dsets = dsets[dsets['source__type'] == 'Kr83m']\n",
"dsets = dsets[dsets['trigger__events_built'] > 10000] # Want a lot of Kr, not diffusion mode \n",
"dsets = hax.runs.tags_selection(dsets, include='sciencerun0')\n",
"\n",
"# Sample ten datasets randomly (with fixed seed, so the analysis is reproducible)\n",
"dsets = dsets.sample(10, random_state=0)\n",
"dsets.number.values"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:hax.minitrees] You're mixing blind and unblind datasets. The blinding cut will be applied to all data you're loading.\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_2107_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_0741_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1735_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1030_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1030_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1030_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_1140_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_2107_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161115_1600_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1202_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1202_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1735_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_0741_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161115_1600_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1202_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161114_2107_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1141_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1141_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161115_1600_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1141_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_1734_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_1734_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_1734_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161116_1735_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161118_0538_Basics.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161118_0538_DoubleScatter.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161117_0741_Corrections.root\n",
"DEBUG:hax.minitrees] Found minitree at /project2/lgrandi/xenon1t/minitrees/pax_v6.8.0/161118_0538_Corrections.root\n",
"DEBUG:hax.minitrees] Removing weird index column\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(log(cs2/cs1)/log(10) > exp(-0.720893+(-0.032622)*cs1) + 1.883038 + (-7.185652e-04)*cs1) | (cs1 > 200) | (s2<150) | (largest_other_s2>200) selection: 62417 rows removed (94.06% passed)\n",
"int_b_x>-60.0 selection: 670140 rows removed (32.19% passed)\n",
"600 < s1_b_center_time - s1_a_center_time < 2000 selection: 244036 rows removed (23.30% passed)\n",
"-90 < z < -5 selection: 11517 rows removed (84.47% passed)\n"
]
}
],
"source": [
"# Suppress rootpy warning about root2rec.. too lazy to fix. \n",
"import warnings\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" \n",
" data = hax.minitrees.load(dsets.number, \n",
" 'Basics DoubleScatter Corrections'.split(),\n",
" num_workers=5,\n",
" preselection=['int_b_x>-60.0',\n",
" '600 < s1_b_center_time - s1_a_center_time < 2000',\n",
" '-90 < z < -5'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Get S1s from these events"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from hax.treemakers.peak_treemakers import PeakExtractor\n",
"\n",
"dt = 10 * units.ns\n",
"wv_length = pax_config['BasicProperties.SumWaveformProperties']['peak_waveform_length']\n",
"waveform_ts = np.arange(-wv_length/2, wv_length/2 + 0.1, dt)\n",
"\n",
"class GetS1s(PeakExtractor):\n",
" __version__ = '0.0.1'\n",
" uses_arrays = True\n",
" # (don't actually need all properties, but useful to check if there's some problem)\n",
" peak_fields = ['area', 'range_50p_area', 'area_fraction_top', \n",
" 'n_contributing_channels', 'left', 'hit_time_std', 'n_hits',\n",
" 'type', 'detector', 'center_time', 'index_of_maximum',\n",
" 'sum_waveform',\n",
" ]\n",
" peak_cut_list = ['detector == \"tpc\"', 'type == \"s1\"']\n",
" \n",
" def get_data(self, dataset, event_list=None):\n",
" # Get the event list from the dataframe selected above\n",
" event_list = data[data['run_number'] == hax.runs.get_run_number(dataset)]['event_number'].values\n",
" \n",
" return PeakExtractor.get_data(self, dataset, event_list=event_list)\n",
" \n",
" def extract_data(self, event):\n",
" peak_data = PeakExtractor.extract_data(self, event)\n",
" \n",
" # Convert sum waveforms from arcane pyroot buffer type to proper numpy arrays\n",
" for p in peak_data:\n",
" p['sum_waveform'] = np.array(list(p['sum_waveform']))\n",
" \n",
" return peak_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/pandas/computation/align.py:98: RuntimeWarning: divide by zero encountered in log10\n",
" ordm = np.log10(abs(reindexer_size - term_axis_size))\n",
"DEBUG:hax.minitrees] Minitree 161114_1140_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4584: Making GetS1s minitree: 100%|██████████| 1881/1881 [00:49<00:00, 38.14it/s]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4584\n",
"DEBUG:hax.minitrees] Found minitree at ./sr1_s1shape_minitrees/161114_1140_GetS1s.root\n",
"/home/aalbers/root_numpy/root_numpy/_tree.py:271: DeprecationWarning: root2rec is deprecated and will be removed in 5.0.0. Instead use root2array(...).view(np.recarray)\n",
" DeprecationWarning)\n",
"DEBUG:hax.minitrees] Minitree 161114_2107_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"DEBUG:hax.minitrees] Minitree 161116_1735_GetS1s.root not found\n",
"Run 4596: Making GetS1s minitree: 0%| | 0/7815 [00:00<?, ?it/s]DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4596: Making GetS1s minitree: 0%| | 1/7815 [00:00<22:10, 5.87it/s]DEBUG:hax.minitrees] Minitree 161116_1030_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4628: Making GetS1s minitree: 0%| | 0/18 [00:00<?, ?it/s]/s]04it/s]DEBUG:hax.minitrees] Minitree 161117_0741_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4643: Making GetS1s minitree: 0%| | 36/18617 [00:01<24:01, 12.89it/s]DEBUG:hax.minitrees] Minitree 161115_1600_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4628: Making GetS1s minitree: 100%|██████████| 18/18 [00:09<00:00, 1.99it/s]/s]]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"Run 4657: Making GetS1s minitree: 2%|▏ | 347/20573 [00:09<13:16, 25.38it/s]DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4628\n",
"Run 4643: Making GetS1s minitree: 2%|▏ | 390/18617 [00:10<07:11, 42.21it/s]DEBUG:hax.minitrees] Minitree 161116_1202_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4635: Making GetS1s minitree: 100%|██████████| 1670/1670 [00:36<00:00, 45.18it/s]]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4635\n",
"Run 4596: Making GetS1s minitree: 28%|██▊ | 2166/7815 [00:50<03:09, 29.82it/s]]DEBUG:hax.minitrees] Minitree 161116_1141_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4633: Making GetS1s minitree: 100%|██████████| 1397/1397 [00:31<00:00, 48.90it/s]]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4633\n",
"Run 4596: Making GetS1s minitree: 47%|████▋ | 3646/7815 [01:24<02:14, 31.11it/s]]DEBUG:hax.minitrees] Minitree 161117_1734_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4667: Making GetS1s minitree: 100%|██████████| 2663/2663 [01:18<00:00, 33.80it/s]]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4667\n",
"Run 4643: Making GetS1s minitree: 36%|███▌ | 6672/18617 [02:47<04:10, 47.75it/s]DEBUG:hax.minitrees] Minitree 161118_0538_GetS1s.root not found\n",
"DEBUG:hax.minitrees] Not found in non-preferred formats either. Minitree will be created.\n",
"Run 4615: Making GetS1s minitree: 100%|██████████| 7940/7940 [02:54<00:00, 45.60it/s]]\n",
"Run 4643: Making GetS1s minitree: 38%|███▊ | 7011/18617 [02:55<04:03, 47.70it/s]DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4615\n",
"Run 4679: Making GetS1s minitree: 100%|██████████| 57/57 [00:14<00:00, 2.90it/s]it/s]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4679\n",
"Run 4596: Making GetS1s minitree: 100%|██████████| 7815/7815 [03:05<00:00, 47.80it/s]]\n",
"Run 4643: Making GetS1s minitree: 40%|███▉ | 7432/18617 [03:05<03:36, 51.58it/s]DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4596\n",
"Run 4643: Making GetS1s minitree: 100%|██████████| 18617/18617 [07:48<00:00, 39.76it/s]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4643\n",
"Run 4657: Making GetS1s minitree: 100%|██████████| 20573/20573 [08:55<00:00, 38.40it/s]\n",
"DEBUG:hax.__init__] Extraction completed, now concatenating data\n",
"DEBUG:hax.minitrees] Retrieved GetS1s minitree data for dataset 4657\n"
]
}
],
"source": [
"s1s = hax.minitrees.load(dsets.number, GetS1s, num_workers=5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Save to disk"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Pandas object array is very memory-ineficient. Takes about 25 MB/dataset to store it in this format (even compressed). If we'd want to extract more than O(10) datasets we'd get into trouble already at the extraction stage.\n",
"\n",
"Least we can do is convert to sensible format (waveform matrix, ordinary dataframe) now. Unfortunately dataframe retains 'object' mark even after deleting sum waveform column. Converting to and from a record array removes this."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"waveforms = np.vstack(s1s['sum_waveform'].values)\n",
"del s1s['sum_waveform']\n",
"s1s = pd.DataFrame(s1s.to_records())"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Merge with the per-event data (which is useful e.g. for making position-dependent selections)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"merged_data = hax.minitrees._merge_minitrees(s1s, data)\n",
"del merged_data['index']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"np.savez_compressed('sr0_kr_s1s.npz', waveforms=waveforms)\n",
"merged_data.to_hdf('sr0_kr_s1s.hdf5', 'data')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Quick look"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"134920"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(s1s)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAECCAYAAAD9z2x7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/bna1t0iAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2b4ccdecc400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pax import units\n",
"plt.hist(s1s.left * 10 * units.ns / units.ms, bins=np.linspace(0, 2.5, 100));\n",
"plt.yscale('log')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"S1 is usually at trigger."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2b4cce1fd668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(s1s.area, bins=np.logspace(0, 3, 100));\n",
"plt.axvline(35, color='r')\n",
"plt.yscale('log')\n",
"plt.xscale('log')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.92600059294396675"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(s1s['area'] > 35)/len(s1s)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Single electron contamination is not so severe."
]
}
],
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
wizmer/NeuroM | tutorial/plotly.ipynb | 1 | 596048 | {
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from neurom.view.plotly import draw\n",
"import neurom\n",
"from pathlib import Path\n",
"\n",
"path = Path(os.path.dirname(neurom.__file__),'../test_data/valid_set/Neuron.swc')\n",
"neuron = neurom.load_neuron(path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D plot"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"line": {
"color": "#1f77b4",
"width": 2
},
"mode": "lines",
"type": "scatter3d",
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"source": [
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},
{
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"metadata": {},
"source": [
"# 2D plot"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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-48.782438608, -49.5973501854, null, -49.5973501854, -50.0453682892, null, -50.0453682892, -50.8053605771, null, -50.8053605771, -51.0312723514, null, -51.0312723514, -51.4701139599, null, -51.4701139599, -51.6330830305, null, -51.6330830305, -52.0466040172, null, -52.0466040172, -52.7993330767, null]}], {\"title\": \"Neuron 3d\", \"autosize\": true, \"scene\": {\"yaxis\": {\"showbackground\": true, \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"rgb(230, 230,230)\", \"zerolinecolor\": \"rgb(255, 255, 255)\"}, \"zaxis\": {\"showbackground\": true, \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"rgb(230, 230,230)\", \"zerolinecolor\": \"rgb(255, 255, 255)\"}, \"camera\": {\"eye\": {\"z\": 0.71, \"x\": -1.7428, \"y\": 1.0707}, \"up\": {\"z\": 1, \"x\": 0, \"y\": 0}}, \"aspectmode\": \"manual\", \"aspectratio\": {\"z\": 0.7, \"x\": 1, \"y\": 1}, \"xaxis\": {\"showbackground\": true, \"gridcolor\": \"rgb(255, 255, 255)\", \"backgroundcolor\": \"rgb(230, 230,230)\", \"zerolinecolor\": \"rgb(255, 255, 255)\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw(neuron, plane='xy', inline=True)"
]
}
],
"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": 2
}
| bsd-3-clause |
google/eng-edu | ml/cc/exercises/estimators/sparsity_and_l1_regularization.ipynb | 1 | 945 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "g4T-_IsVbweU"
},
"source": [
"# Sparsity and L1 Regularization"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "g8ue2FyFIjnQ"
},
"source": [
"The \"Sparsity and L1 Regularization\" Colab is deprecated. For the list of current Colabs, see the [Programming tab on the Exercises page](https://developers.google.com/machine-learning/crash-course/exercises#programming)."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"JndnmDMp66FL"
],
"name": "sparsity_and_l1_regularization.ipynb",
"provenance": [],
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 2",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
luwei0917/awsemmd_script | gremlin/GREMLINtoRnative.ipynb | 1 | 13494 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#read in median distances for pairwise interactions (obtained from analysis of the pdb)\n",
"directory='/Users/weilu/opt/gremlin/'\n",
"distancesCACB=pd.read_table(directory+'CACBmediandist.dat', delim_whitespace=True, header=None)\n",
"distancesCACA=pd.read_table(directory+'CACAmediandist.dat', delim_whitespace=True, header=None)\n",
"distancesCBCB=pd.read_table(directory+'CBCBmediandist.dat', delim_whitespace=True, header=None)\n",
"distancesCACB.columns = ['i', 'j', 'dist']\n",
"distancesCACA.columns = ['i', 'j', 'dist']\n",
"distancesCBCB.columns = ['i', 'j', 'dist']\n",
"#if you want to filter the gremlin data, adjust the parameters below\n",
"filter_threshold=0.1\n",
"column=6\n",
"name='gremlin'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11ed4b5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#make sure that there is a sequence file for the protein and the downloaded gremlin data in the proper directories\n",
"\n",
"# pdbID='2bg9'\n",
"pdbID='1j4n'\n",
"directory = \"/Users/weilu/opt/gremlin/protein/\" + pdbID + \"/\"\n",
"f = open(directory+pdbID+\".seq\",\"r\")\n",
"n=len(f.readlines()[0].strip())\n",
"f.close()\n",
"#load downloaded gremlin file\n",
"gremlin_data=np.loadtxt(directory+\"gremlin.\"+pdbID+\".dat\", dtype=bytes, skiprows=1)\n",
"rnative_matrixCACB=np.ones([n,n])*99\n",
"rnative_matrixCACA=np.ones([n,n])*99\n",
"rnative_matrixCBCB=np.ones([n,n])*99\n",
"for pair in gremlin_data:\n",
" i=int(pair[0])\n",
" j=int(pair[1])\n",
" irestype=pair[2][-1:].decode()\n",
" jrestype=pair[3][-1:].decode()\n",
"# print(irestype, jrestype)\n",
" if float(pair[column]) > filter_threshold:\n",
" if sum((distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype))>0: #check if pair is in correct order\n",
" well_centerCACB = distancesCACB[(distancesCACB['i']==irestype)&(distancesCACB['j']==jrestype)]['dist'].values[0]\n",
" well_centerCACA = distancesCACA[(distancesCACA['i']==irestype)&(distancesCACA['j']==jrestype)]['dist'].values[0]\n",
" well_centerCBCB = distancesCBCB[(distancesCBCB['i']==irestype)&(distancesCBCB['j']==jrestype)]['dist'].values[0] \n",
" else:\n",
" well_centerCACB = distancesCACB[(distancesCACB['i']==jrestype)&(distancesCACB['j']==irestype)]['dist'].values[0]\n",
" well_centerCACA = distancesCACA[(distancesCACA['i']==jrestype)&(distancesCACA['j']==irestype)]['dist'].values[0]\n",
" well_centerCBCB = distancesCBCB[(distancesCBCB['i']==jrestype)&(distancesCBCB['j']==irestype)]['dist'].values[0]\n",
"\n",
" rnative_matrixCACB[i-1, j-1] = well_centerCACB\n",
" rnative_matrixCACB[j-1, i-1] = well_centerCACB\n",
" rnative_matrixCACA[i-1, j-1] = well_centerCACA\n",
" rnative_matrixCACA[j-1, i-1] = well_centerCACA\n",
" rnative_matrixCBCB[i-1, j-1] = well_centerCBCB\n",
" rnative_matrixCBCB[j-1, i-1] = well_centerCBCB\n",
" \n",
"plt.matshow(rnative_matrixCACA)\n",
"\n",
"directory = \"/Users/weilu/opt/gremlin/protein/\" + pdbID + \"/gremlin/\"\n",
"os.system(\"mkdir \" + directory)\n",
"np.savetxt(directory + 'go_rnativeCACB.dat', rnative_matrixCACB, fmt='%10.5f')\n",
"np.savetxt(directory + 'go_rnativeCACA.dat', rnative_matrixCACA, fmt='%10.5f')\n",
"np.savetxt(directory + 'go_rnativeCBCB.dat', rnative_matrixCBCB, fmt='%10.5f')"
]
},
{
"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": 1
}
| mit |
MatthewDaws/OSMDigest | notebooks/Geopandas.ipynb | 1 | 175421 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Allow to import without installing\n",
"import sys\n",
"sys.path.insert(0, \"..\")\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import geopandas as gpd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to build a GeoDataFrame\n",
"\n",
"We firstly explore how to do this by using the GeoJSON schema.\n",
"\n",
"- See https://gist.github.com/sgillies/2217756 for the \"`__geo_interface__`\".\n",
"- But this basically copies GeoJSON, for which see https://tools.ietf.org/html/rfc7946\n",
"\n",
"It's then as simple as this..."
]
},
{
"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>geometry</th>\n",
" <th>prop0</th>\n",
" <th>prop1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (102 0.5)</td>\n",
" <td>value0</td>\n",
" <td>value1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry prop0 prop1\n",
"0 POINT (102 0.5) value0 value1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"point_features = [{\"geometry\": {\n",
" \"type\": \"Point\",\n",
" \"coordinates\": [102.0, 0.5]\n",
" },\n",
" \"properties\": {\n",
" \"prop0\": \"value0\", \"prop1\": \"value1\"\n",
" }\n",
" }]\n",
"\n",
"point_data = gpd.GeoDataFrame.from_features(point_features)\n",
"point_data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"101.0 -0.5 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><circle cx=\"102.0\" cy=\"0.5\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.point.Point at 0x15de54fd438>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"point_data.ix[0].geometry"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>geometry</th>\n",
" <th>prop3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LINESTRING (102 0.5, 104 3, 103 2)</td>\n",
" <td>value3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry prop3\n",
"0 LINESTRING (102 0.5, 104 3, 103 2) value3"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line_features = [{\"geometry\": {\n",
" \"type\": \"LineString\",\n",
" \"coordinates\": [[102.0, 0.5], [104, 3], [103, 2]]\n",
" },\n",
" \"properties\": {\n",
" \"prop3\": \"value3\"\n",
" }\n",
" }]\n",
"\n",
"line_data = gpd.GeoDataFrame.from_features(line_features)\n",
"line_data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"101.9 0.4 2.1999999999999886 2.7\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,3.5)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.054000000000000006\" points=\"102.0,0.5 104.0,3.0 103.0,2.0\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x15de54fda58>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line_data.ix[0].geometry"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>geometry</th>\n",
" <th>prop1</th>\n",
" <th>prop4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POLYGON ((102 0.5, 104 3, 102 2, 102 0.5))</td>\n",
" <td>value1</td>\n",
" <td>value4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry prop1 prop4\n",
"0 POLYGON ((102 0.5, 104 3, 102 2, 102 0.5)) value1 value4"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polygon_features = [{\"geometry\": {\n",
" \"type\": \"Polygon\",\n",
" \"coordinates\": [[[102.0, 0.5], [104, 3], [102, 2], [102,0.5]]]\n",
" },\n",
" \"properties\": {\n",
" \"prop4\": \"value4\", \"prop1\": \"value1\"\n",
" }\n",
" }]\n",
"\n",
"data = gpd.GeoDataFrame.from_features(polygon_features)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15de5663198>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x15de279bd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.plot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"101.9 0.4 2.1999999999999886 2.7\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,3.5)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.054000000000000006\" opacity=\"0.6\" d=\"M 102.0,0.5 L 104.0,3.0 L 102.0,2.0 L 102.0,0.5 z\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x15de56630f0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.ix[0].geometry"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>geometry</th>\n",
" <th>prop0</th>\n",
" <th>prop1</th>\n",
" <th>prop3</th>\n",
" <th>prop4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (102 0.5)</td>\n",
" <td>value0</td>\n",
" <td>value1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LINESTRING (102 0.5, 104 3, 103 2)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>value3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POLYGON ((102 0.5, 104 3, 102 2, 102 0.5))</td>\n",
" <td>NaN</td>\n",
" <td>value1</td>\n",
" <td>NaN</td>\n",
" <td>value4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry prop0 prop1 prop3 prop4\n",
"0 POINT (102 0.5) value0 value1 NaN NaN\n",
"1 LINESTRING (102 0.5, 104 3, 103 2) NaN NaN value3 NaN\n",
"2 POLYGON ((102 0.5, 104 3, 102 2, 102 0.5)) NaN value1 NaN value4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = []\n",
"features.extend(point_features)\n",
"features.extend(line_features)\n",
"features.extend(polygon_features)\n",
"gpd.GeoDataFrame.from_features(features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notes\n",
"\n",
"Some things that jumped out at me as I read the GeoJSON spec:\n",
"\n",
"- Coordinates are always in the order: longitude, latitude.\n",
"- A \"Polygon\" is allowed to contain holes. The \"outer\" edge should be ordered counter-clockwise, and each \"inner\" edge (i.e. a \"hole\") should be clockwise.\n",
"- If a polygon contains more than one array of points, then the first array is the outer edge, and the rest inner edges.\n",
"- Lines crossing the anti-meridian need to be split. (I wonder what OSM does?)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Via using `shapely`\n",
"\n",
"Under the hood, geopandas uses the `shapely` library, and we can alternatively build data frames by directly building `shapely` objects"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(shapely.geometry.point.Point,\n",
" shapely.geometry.linestring.LineString,\n",
" shapely.geometry.polygon.Polygon)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(point_data.geometry[0]), type(line_data.geometry[0]), type(data.geometry[0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import shapely.geometry"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>geometry</th>\n",
" <th>key1</th>\n",
" <th>key2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LINESTRING (0 0, 1 0, 1 1)</td>\n",
" <td>value1</td>\n",
" <td>value2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry key1 key2\n",
"0 LINESTRING (0 0, 1 0, 1 1) value1 value2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pts = shapely.geometry.LineString([shapely.geometry.Point(0,0), shapely.geometry.Point(1,0), shapely.geometry.Point(1,1)])\n",
"df = gpd.GeoDataFrame({\"geometry\": [pts], \"key1\":[\"value1\"], \"key2\":[\"value2\"]})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.0216\" points=\"0.0,0.0 1.0,0.0 1.0,1.0\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x15de5ad95c0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.ix[0].geometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support in the library"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import osmdigest.geometry as geometry\n",
"import osmdigest.sqlite as sq\n",
"\n",
"import os\n",
"filename = os.path.join(\"..\", \"..\", \"..\", \"Data\", \"california-latest.db\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"geometry LINESTRING (-122.244191 37.819027, -122.244287...\n",
"osm_id 33088737\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db = sq.OSM_SQLite(filename)\n",
"way = db.complete_way(33088737)\n",
"series = geometry.geoseries_from_way(way)\n",
"series"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15de5b8d940>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ZmZmsmhfFqF7jAbjtiW50vLstoTcGaeFQqoxJv5jOb18sZfrYH/G/rhn9xvTG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15de5aa3240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gpd.GeoDataFrame(series).T.plot()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"addr:city Piedmont\n",
"addr:housenumber 344\n",
"addr:postcode 94611\n",
"addr:street Highland Avenue\n",
"amenity bank\n",
"building yes\n",
"geometry LINESTRING (-122.2314666 37.8246631, -122.2313...\n",
"name Wells Fargo\n",
"osm_id 285549437\n",
"dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"way = db.complete_way(285549437)\n",
"series = geometry.geoseries_from_way(way)\n",
"series"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>addr:city</th>\n",
" <th>addr:housenumber</th>\n",
" <th>addr:postcode</th>\n",
" <th>addr:street</th>\n",
" <th>amenity</th>\n",
" <th>building</th>\n",
" <th>geometry</th>\n",
" <th>name</th>\n",
" <th>osm_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Piedmont</td>\n",
" <td>344</td>\n",
" <td>94611</td>\n",
" <td>Highland Avenue</td>\n",
" <td>bank</td>\n",
" <td>yes</td>\n",
" <td>LINESTRING (-122.2314666 37.8246631, -122.2313...</td>\n",
" <td>Wells Fargo</td>\n",
" <td>285549437</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" addr:city addr:housenumber addr:postcode addr:street amenity building \\\n",
"0 Piedmont 344 94611 Highland Avenue bank yes \n",
"\n",
" geometry name osm_id \n",
"0 LINESTRING (-122.2314666 37.8246631, -122.2313... Wells Fargo 285549437 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = gpd.GeoDataFrame(series).T\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15de5bb5f98>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Ge2PMuZebm8uXE2ZwWZA02uezxHIWfvw6lSatGtEhLiLQoRhjKqDFX6XQKKwB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\n2MuFi3U8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15de5b6cda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# For relations\n",
"\n",
"We can build a geo data frame with the raw data from a relation."
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>barrier</th>\n",
" <th>geometry</th>\n",
" <th>members</th>\n",
" <th>name</th>\n",
" <th>note</th>\n",
" <th>osm_id</th>\n",
" <th>place</th>\n",
" <th>population</th>\n",
" <th>role</th>\n",
" <th>source</th>\n",
" <th>wikidata</th>\n",
" <th>wikipedia</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>[(node, 2155598516, admin_centre), (node, 2155...</td>\n",
" <td>Palm Springs</td>\n",
" <td>Areas listed as city by county, not city's sph...</td>\n",
" <td>2866485</td>\n",
" <td>city</td>\n",
" <td>44552</td>\n",
" <td>NaN</td>\n",
" <td>RCTLMA_GIS</td>\n",
" <td>Q488004</td>\n",
" <td>en:Palm Springs, California</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>POINT (-116.540303 33.8246269)</td>\n",
" <td>NaN</td>\n",
" <td>Palm Springs</td>\n",
" <td>NaN</td>\n",
" <td>2155598516</td>\n",
" <td>city</td>\n",
" <td>44552</td>\n",
" <td>admin_centre</td>\n",
" <td>Wikipedia</td>\n",
" <td>NaN</td>\n",
" <td>en:Palm Springs, California</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>POINT (-116.540303 33.8246269)</td>\n",
" <td>NaN</td>\n",
" <td>Palm Springs</td>\n",
" <td>NaN</td>\n",
" <td>2155598516</td>\n",
" <td>city</td>\n",
" <td>44552</td>\n",
" <td>label</td>\n",
" <td>Wikipedia</td>\n",
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"text/plain": [
" barrier geometry \\\n",
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"\n",
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"0 [(node, 2155598516, admin_centre), (node, 2155... Palm Springs \n",
"1 NaN Palm Springs \n",
"2 NaN Palm Springs \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 NaN NaN \n",
"7 NaN NaN \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN NaN \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 NaN NaN \n",
"20 NaN NaN \n",
"21 NaN NaN \n",
"22 NaN NaN \n",
"23 NaN NaN \n",
"24 NaN NaN \n",
"25 NaN NaN \n",
"26 NaN NaN \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 NaN NaN \n",
"30 NaN NaN \n",
"31 NaN NaN \n",
"32 NaN NaN \n",
"33 NaN NaN \n",
"34 NaN NaN \n",
"35 NaN NaN \n",
"36 NaN NaN \n",
"37 NaN NaN \n",
"38 NaN NaN \n",
"\n",
" note osm_id place \\\n",
"0 Areas listed as city by county, not city's sph... 2866485 city \n",
"1 NaN 2155598516 city \n",
"2 NaN 2155598516 city \n",
"3 NaN 301016136 NaN \n",
"4 NaN 301016103 NaN \n",
"5 NaN 301016104 NaN \n",
"6 NaN 301016134 NaN \n",
"7 NaN 301016125 NaN \n",
"8 NaN 301016128 NaN \n",
"9 NaN 301016110 NaN \n",
"10 NaN 216274692 NaN \n",
"11 NaN 301016127 NaN \n",
"12 NaN 301016116 NaN \n",
"13 NaN 301006748 NaN \n",
"14 NaN 301006782 NaN \n",
"15 NaN 301006724 NaN \n",
"16 NaN 301006719 NaN \n",
"17 NaN 301006739 NaN \n",
"18 NaN 301006733 NaN \n",
"19 NaN 301006721 NaN \n",
"20 NaN 301006716 NaN \n",
"21 NaN 216274693 NaN \n",
"22 NaN 295884024 NaN \n",
"23 NaN 295884029 NaN \n",
"24 NaN 295884048 NaN \n",
"25 NaN 295884062 NaN \n",
"26 NaN 216302383 NaN \n",
"27 NaN 216302381 NaN \n",
"28 NaN 216302370 NaN \n",
"29 NaN 216302379 NaN \n",
"30 NaN 216302388 NaN \n",
"31 NaN 216302362 NaN \n",
"32 NaN 216302387 NaN \n",
"33 NaN 216302374 NaN \n",
"34 NaN 216302384 NaN \n",
"35 NaN 216302369 NaN \n",
"36 NaN 216302386 NaN \n",
"37 NaN 216302382 NaN \n",
"38 NaN 216302375 NaN \n",
"\n",
" population role source wikidata wikipedia \n",
"0 44552 NaN RCTLMA_GIS Q488004 en:Palm Springs, California \n",
"1 44552 admin_centre Wikipedia NaN en:Palm Springs, California \n",
"2 44552 label Wikipedia NaN en:Palm Springs, California \n",
"3 NaN outer NaN NaN NaN \n",
"4 NaN outer NaN NaN NaN \n",
"5 NaN outer NaN NaN NaN \n",
"6 NaN outer NaN NaN NaN \n",
"7 NaN outer NaN NaN NaN \n",
"8 NaN outer NaN NaN NaN \n",
"9 NaN outer NaN NaN NaN \n",
"10 NaN outer NaN NaN NaN \n",
"11 NaN outer NaN NaN NaN \n",
"12 NaN outer NaN NaN NaN \n",
"13 NaN outer NaN NaN NaN \n",
"14 NaN outer NaN NaN NaN \n",
"15 NaN outer NaN NaN NaN \n",
"16 NaN outer NaN NaN NaN \n",
"17 NaN outer NaN NaN NaN \n",
"18 NaN outer NaN NaN NaN \n",
"19 NaN outer NaN NaN NaN \n",
"20 NaN outer NaN NaN NaN \n",
"21 NaN outer NaN NaN NaN \n",
"22 NaN outer NaN NaN NaN \n",
"23 NaN outer NaN NaN NaN \n",
"24 NaN outer NaN NaN NaN \n",
"25 NaN outer NaN NaN NaN \n",
"26 NaN outer NaN NaN NaN \n",
"27 NaN outer NaN NaN NaN \n",
"28 NaN outer NaN NaN NaN \n",
"29 NaN outer NaN NaN NaN \n",
"30 NaN outer NaN NaN NaN \n",
"31 NaN outer NaN NaN NaN \n",
"32 NaN outer NaN NaN NaN \n",
"33 NaN outer NaN NaN NaN \n",
"34 NaN outer NaN NaN NaN \n",
"35 NaN outer NaN NaN NaN \n",
"36 NaN outer NaN NaN NaN \n",
"37 NaN outer NaN NaN NaN \n",
"38 NaN outer NaN NaN NaN "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"relation = db.complete_relation(2866485)\n",
"geometry.geodataframe_from_relation(relation)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>[(way, 160333986, ), (way, 340366203, ), (way,...</td>\n",
" <td>J-Church: Outbound to Balboa Park</td>\n",
" <td>...</td>\n",
" <td>3433311</td>\n",
" <td>NaN</td>\n",
" <td>J</td>\n",
" <td></td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td>Balboa Park Station</td>\n",
" <td>NaN</td>\n",
" <td>route</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.3958183 37.7937502, -122.3969...</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Muni Metro</td>\n",
" <td>...</td>\n",
" <td>160333986</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>yes</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.3969324 37.792879, -122.40120...</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Muni Metro</td>\n",
" <td>...</td>\n",
" <td>340366203</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>yes</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\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>LINESTRING (-122.4259714 37.769864, -122.42610...</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Muni Metro</td>\n",
" <td>...</td>\n",
" <td>160307216</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4271777 37.769586, -122.42861...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni Metro</td>\n",
" <td>...</td>\n",
" <td>216045932</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.428928 37.7694799, -122.42899...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160281860</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4289627 37.7672876, -122.4288...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272608</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4284093 37.7614511, -122.4284...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272584</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4277519 37.7546832, -122.4269...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272598</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4265677 37.742319, -122.42655...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272626</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.42342 37.7420698, -122.423704...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272619</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>yes</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4320798 37.7332618, -122.4326...</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160272602</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4326883 37.7329691, -122.4328...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>309010756</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4351319 37.7316664, -122.4351...</td>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>309010747</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>yes</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4354583 37.7311016, -122.4354...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>309010759</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.4385742 37.7298829, -122.4389...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>160279678</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
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" <td>LINESTRING (-122.4447109 37.7230433, -122.4447...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni J</td>\n",
" <td>...</td>\n",
" <td>159791122</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>contact_line</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1435</td>\n",
" <td>LINESTRING (-122.447138 37.7226667, -122.44716...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Muni Metro</td>\n",
" <td>...</td>\n",
" <td>159785610</td>\n",
" <td>light_rail</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>38 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" bridge colour electrified frequency from gauge \\\n",
"0 NaN #FAA635 contact_line 0 NaN 1435 \n",
"1 NaN #FAA635 NaN NaN Balboa Park Station NaN \n",
"2 NaN NaN contact_line 0 NaN 1435 \n",
"3 NaN NaN contact_line 0 NaN 1435 \n",
"4 NaN NaN contact_line 0 NaN 1435 \n",
"5 NaN NaN contact_line 0 NaN 1435 \n",
"6 NaN NaN contact_line 0 NaN 1435 \n",
"7 NaN NaN contact_line 0 NaN 1435 \n",
"8 yes NaN contact_line 0 NaN 1435 \n",
"9 NaN NaN contact_line 0 NaN 1435 \n",
"10 NaN NaN contact_line 0 NaN 1435 \n",
"11 NaN NaN contact_line 0 NaN 1435 \n",
"12 NaN NaN contact_line 0 NaN 1435 \n",
"13 NaN NaN contact_line 0 NaN 1435 \n",
"14 NaN NaN contact_line 0 NaN 1435 \n",
"15 NaN NaN contact_line 0 NaN 1435 \n",
"16 NaN NaN NaN NaN NaN NaN \n",
"17 NaN NaN NaN NaN NaN NaN \n",
"18 NaN NaN contact_line 0 NaN 1435 \n",
"19 NaN NaN contact_line 0 NaN 1435 \n",
"20 NaN #FAA635 NaN NaN Embarcadero Station NaN \n",
"21 NaN NaN contact_line 0 NaN 1435 \n",
"22 NaN NaN contact_line 0 NaN 1435 \n",
"23 NaN NaN NaN NaN NaN NaN \n",
"24 NaN NaN contact_line 0 NaN 1435 \n",
"25 NaN NaN contact_line 0 NaN 1435 \n",
"26 NaN NaN contact_line 0 NaN 1435 \n",
"27 NaN NaN contact_line 0 NaN 1435 \n",
"28 NaN NaN contact_line 0 NaN 1435 \n",
"29 NaN NaN contact_line 0 NaN 1435 \n",
"30 NaN NaN contact_line 0 NaN 1435 \n",
"31 yes NaN contact_line 0 NaN 1435 \n",
"32 NaN NaN contact_line 0 NaN 1435 \n",
"33 NaN NaN contact_line 0 NaN 1435 \n",
"34 NaN NaN contact_line 0 NaN 1435 \n",
"35 NaN NaN contact_line 0 NaN 1435 \n",
"36 NaN NaN contact_line 0 NaN 1435 \n",
"37 NaN NaN contact_line 0 NaN 1435 \n",
"\n",
" geometry layer \\\n",
"0 None NaN \n",
"1 None NaN \n",
"2 LINESTRING (-122.446833 37.7209604, -122.44679... NaN \n",
"3 LINESTRING (-122.4458288 37.7219202, -122.4457... NaN \n",
"4 LINESTRING (-122.4446761 37.7230291, -122.4443... NaN \n",
"5 LINESTRING (-122.4385522 37.7298607, -122.4384... NaN \n",
"6 LINESTRING (-122.4353891 37.7311316, -122.4351... -1 \n",
"7 LINESTRING (-122.4350813 37.7316453, -122.4350... NaN \n",
"8 LINESTRING (-122.4326384 37.7329369, -122.4320... 2 \n",
"9 LINESTRING (-122.4320284 37.7332331, -122.4312... NaN \n",
"10 LINESTRING (-122.423347 37.742122, -122.423337... NaN \n",
"11 LINESTRING (-122.4265292 37.7423202, -122.4265... NaN \n",
"12 LINESTRING (-122.4277179 37.7546853, -122.4277... NaN \n",
"13 LINESTRING (-122.4283697 37.7614531, -122.4284... NaN \n",
"14 LINESTRING (-122.4289267 37.7672859, -122.4289... NaN \n",
"15 LINESTRING (-122.428885 37.7694495, -122.42884... NaN \n",
"16 LINESTRING (-122.4271746 37.7695516, -122.4264... NaN \n",
"17 LINESTRING (-122.4259714 37.769864, -122.42613... -1 \n",
"18 LINESTRING (-122.3969324 37.792879, -122.40120... -1 \n",
"19 LINESTRING (-122.3958183 37.7937502, -122.3969... -1 \n",
"20 None NaN \n",
"21 LINESTRING (-122.3958183 37.7937502, -122.3969... -1 \n",
"22 LINESTRING (-122.3969324 37.792879, -122.40120... -1 \n",
"23 LINESTRING (-122.4259714 37.769864, -122.42610... -1 \n",
"24 LINESTRING (-122.4271777 37.769586, -122.42861... NaN \n",
"25 LINESTRING (-122.428928 37.7694799, -122.42899... NaN \n",
"26 LINESTRING (-122.4289627 37.7672876, -122.4288... NaN \n",
"27 LINESTRING (-122.4284093 37.7614511, -122.4284... NaN \n",
"28 LINESTRING (-122.4277519 37.7546832, -122.4269... NaN \n",
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"30 LINESTRING (-122.42342 37.7420698, -122.423704... NaN \n",
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"37 LINESTRING (-122.447138 37.7226667, -122.44716... NaN \n",
"\n",
" members \\\n",
"0 [(relation, 2877693, ), (relation, 3433311, )] \n",
"1 [(way, 247320917, ), (way, 159785611, ), (way,... \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 [(way, 160333986, ), (way, 340366203, ), (way,... \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"30 NaN \n",
"31 NaN \n",
"32 NaN \n",
"33 NaN \n",
"34 NaN \n",
"35 NaN \n",
"36 NaN \n",
"37 NaN \n",
"\n",
" name ... osm_id railway ref \\\n",
"0 J-Church ... 63222 light_rail J \n",
"1 J-Church: Inbound to Downtown ... 2877693 NaN J \n",
"2 Muni Metro ... 247320917 light_rail NaN \n",
"3 Muni J ... 159785611 light_rail NaN \n",
"4 Muni J ... 159791118 light_rail NaN \n",
"5 Muni J ... 160279679 light_rail NaN \n",
"6 Muni J ... 309010742 light_rail NaN \n",
"7 Muni J ... 309010734 light_rail NaN \n",
"8 Muni J ... 159798877 light_rail NaN \n",
"9 Muni J ... 160268072 light_rail NaN \n",
"10 Muni J ... 160272613 light_rail NaN \n",
"11 Muni J ... 160272583 light_rail NaN \n",
"12 Muni J ... 160270943 light_rail NaN \n",
"13 Muni J ... 142843532 light_rail NaN \n",
"14 Muni J ... 27145509 light_rail NaN \n",
"15 Muni Metro ... 27145527 light_rail NaN \n",
"16 NaN ... 252221514 light_rail NaN \n",
"17 Muni Metro ... 160306978 light_rail NaN \n",
"18 Muni Metro ... 340366203 light_rail NaN \n",
"19 Muni Metro ... 160333986 light_rail NaN \n",
"20 J-Church: Outbound to Balboa Park ... 3433311 NaN J \n",
"21 Muni Metro ... 160333986 light_rail NaN \n",
"22 Muni Metro ... 340366203 light_rail NaN \n",
"23 Muni Metro ... 160307216 light_rail NaN \n",
"24 Muni Metro ... 216045932 light_rail NaN \n",
"25 Muni J ... 160281860 light_rail NaN \n",
"26 Muni J ... 160272608 light_rail NaN \n",
"27 Muni J ... 160272584 light_rail NaN \n",
"28 Muni J ... 160272598 light_rail NaN \n",
"29 Muni J ... 160272626 light_rail NaN \n",
"30 Muni J ... 160272619 light_rail NaN \n",
"31 Muni J ... 160272602 light_rail NaN \n",
"32 Muni J ... 309010756 light_rail NaN \n",
"33 Muni J ... 309010747 light_rail NaN \n",
"34 Muni J ... 309010759 light_rail NaN \n",
"35 Muni J ... 160279678 light_rail NaN \n",
"36 Muni J ... 159791122 light_rail NaN \n",
"37 Muni Metro ... 159785610 light_rail NaN \n",
"\n",
" role route route_master to tunnel type \\\n",
"0 NaN NaN light_rail NaN NaN route_master \n",
"1 light_rail NaN Embarcadero Station NaN route \n",
"2 NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 NaN NaN NaN NaN NaN \n",
"5 NaN NaN NaN NaN NaN \n",
"6 NaN NaN NaN yes NaN \n",
"7 NaN NaN NaN NaN NaN \n",
"8 NaN NaN NaN NaN NaN \n",
"9 NaN NaN NaN NaN NaN \n",
"10 NaN NaN NaN NaN NaN \n",
"11 NaN NaN NaN NaN NaN \n",
"12 NaN NaN NaN NaN NaN \n",
"13 NaN NaN NaN NaN NaN \n",
"14 NaN NaN NaN NaN NaN \n",
"15 NaN NaN NaN NaN NaN \n",
"16 NaN NaN NaN yes NaN \n",
"17 NaN NaN NaN yes NaN \n",
"18 NaN NaN NaN yes NaN \n",
"19 NaN NaN NaN yes NaN \n",
"20 light_rail NaN Balboa Park Station NaN route \n",
"21 NaN NaN NaN yes NaN \n",
"22 NaN NaN NaN yes NaN \n",
"23 NaN NaN NaN yes NaN \n",
"24 NaN NaN NaN NaN NaN \n",
"25 NaN NaN NaN NaN NaN \n",
"26 NaN NaN NaN NaN NaN \n",
"27 NaN NaN NaN NaN NaN \n",
"28 NaN NaN NaN NaN NaN \n",
"29 NaN NaN NaN NaN NaN \n",
"30 NaN NaN NaN NaN NaN \n",
"31 NaN NaN NaN NaN NaN \n",
"32 NaN NaN NaN NaN NaN \n",
"33 NaN NaN NaN yes NaN \n",
"34 NaN NaN NaN NaN NaN \n",
"35 NaN NaN NaN NaN NaN \n",
"36 NaN NaN NaN NaN NaN \n",
"37 NaN NaN NaN NaN NaN \n",
"\n",
" voltage \n",
"0 600 \n",
"1 NaN \n",
"2 600 \n",
"3 600 \n",
"4 600 \n",
"5 600 \n",
"6 600 \n",
"7 600 \n",
"8 600 \n",
"9 600 \n",
"10 600 \n",
"11 600 \n",
"12 600 \n",
"13 600 \n",
"14 600 \n",
"15 600 \n",
"16 NaN \n",
"17 NaN \n",
"18 600 \n",
"19 600 \n",
"20 NaN \n",
"21 600 \n",
"22 600 \n",
"23 NaN \n",
"24 600 \n",
"25 600 \n",
"26 600 \n",
"27 600 \n",
"28 600 \n",
"29 600 \n",
"30 600 \n",
"31 600 \n",
"32 600 \n",
"33 600 \n",
"34 600 \n",
"35 600 \n",
"36 600 \n",
"37 600 \n",
"\n",
"[38 rows x 25 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geometry.geodataframe_from_relation( db.complete_relation(63222) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Looking at relations\n",
"\n",
"These are harder to compute automatically, because the exact interpretation of the sub-elements depends upon context. However, most relations which have \"interesting\" geometry (as opposed to giving contextual information on other elements) are of [\"multi-polygon\" type](http://wiki.openstreetmap.org/wiki/Relation:multipolygon), and can be recognised by the presence of ways with the \"role\" of \"inner\" or \"outer\".\n",
"\n",
"I found that using the `shapely` library itself was the easiest way to conver the geometry.\n",
"\n",
"There are some cases of geometry which shapely cannot handle. For example:\n",
"- http://www.openstreetmap.org/relation/70986 (A lot of self-intersection, I think).\n",
"- http://www.openstreetmap.org/relation/184199 (Ditto).\n",
"- http://www.openstreetmap.org/relation/1483140 (Adjoining polygons)."
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Relation(22601 -> [Member(type='way', ref=25774607, role='outer'), Member(type='way', ref=25790915, role='inner')] {'name': 'Charles W. Davidson College of Engineering', 'type': 'multipolygon', 'source': 'survey', 'website': 'http://www.engr.sjsu.edu/', 'building': 'university'})\n"
]
},
{
"data": {
"text/plain": [
"building university\n",
"geometry POLYGON ((-121.8819651 37.3363975, -121.881993...\n",
"name Charles W. Davidson College of Engineering\n",
"osm_id 22601\n",
"source survey\n",
"type multipolygon\n",
"website http://www.engr.sjsu.edu/\n",
"dtype: object"
]
},
"execution_count": 264,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gen = db.relations()\n",
"for _ in range(15):\n",
" next(gen)\n",
"\n",
"relation = next(gen)\n",
"print(relation)\n",
"series = geometry.geoseries_from_relation(db.complete_relation(relation))\n",
"series"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15de9b8bf98>"
]
},
"execution_count": 265,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NG1OyjCurfvvPE3mcce/xXeZsms7gX/vgVK4QV65eYcrUKWTOnNno0KJU5syZ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\npvkfdScwAlzJf0cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15de9a77320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gpd.GeoDataFrame(series).T.plot()"
]
}
],
"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 |
google/starthinker | colabs/bigquery_query.ipynb | 1 | 7098 | {
"license": "Licensed under the Apache License, Version 2.0",
"copyright": "Copyright 2020 Google LLC",
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "BigQuery Query To Table",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-001"
},
"source": [
"#BigQuery Query To Table\n",
"Save query results into a BigQuery table.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-002"
},
"source": [
"#License\n",
"\n",
"Copyright 2020 Google LLC,\n",
"\n",
"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.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-003"
},
"source": [
"#Disclaimer\n",
"This is not an officially supported Google product. It is a reference implementation. There is absolutely NO WARRANTY provided for using this code. The code is Apache Licensed and CAN BE fully modified, white labeled, and disassembled by your team.\n",
"\n",
"This code generated (see starthinker/scripts for possible source):\n",
" - **Command**: \"python starthinker_ui/manage.py colab\"\n",
" - **Command**: \"python starthinker/tools/colab.py [JSON RECIPE]\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-004"
},
"source": [
"#1. Install Dependencies\n",
"First install the libraries needed to execute recipes, this only needs to be done once, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "50b4b5d5-005"
},
"source": [
"!pip install git+https://github.com/google/starthinker\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-006"
},
"source": [
"#2. Set Configuration\n",
"\n",
"This code is required to initialize the project. Fill in required fields and press play.\n",
"\n",
"1. If the recipe uses a Google Cloud Project:\n",
" - Set the configuration **project** value to the project identifier from [these instructions](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md).\n",
"\n",
"1. If the recipe has **auth** set to **user**:\n",
" - If you have user credentials:\n",
" - Set the configuration **user** value to your user credentials JSON.\n",
" - If you DO NOT have user credentials:\n",
" - Set the configuration **client** value to [downloaded client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md).\n",
"\n",
"1. If the recipe has **auth** set to **service**:\n",
" - Set the configuration **service** value to [downloaded service credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_service.md).\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "50b4b5d5-007"
},
"source": [
"from starthinker.util.configuration import Configuration\n",
"\n",
"\n",
"CONFIG = Configuration(\n",
" project=\"\",\n",
" client={},\n",
" service={},\n",
" user=\"/content/user.json\",\n",
" verbose=True\n",
")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-008"
},
"source": [
"#3. Enter BigQuery Query To Table Recipe Parameters\n",
" 1. Specify a single query and choose legacy or standard mode.\n",
" 1. For PLX use user authentication and: SELECT * FROM [plx.google:FULL_TABLE_NAME.all] WHERE...\n",
" 1. Every time the query runs it will overwrite the table.\n",
"Modify the values below for your use case, can be done multiple times, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "50b4b5d5-009"
},
"source": [
"FIELDS = {\n",
" 'auth_write':'service', # Credentials used for writing data.\n",
" 'query':'', # SQL with newlines and all.\n",
" 'dataset':'', # Existing BigQuery dataset.\n",
" 'table':'', # Table to create from this query.\n",
" 'legacy':True, # Query type must match source tables.\n",
"}\n",
"\n",
"print(\"Parameters Set To: %s\" % FIELDS)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50b4b5d5-010"
},
"source": [
"#4. Execute BigQuery Query To Table\n",
"This does NOT need to be modified unless you are changing the recipe, click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "50b4b5d5-011"
},
"source": [
"from starthinker.util.configuration import execute\n",
"from starthinker.util.recipe import json_set_fields\n",
"\n",
"TASKS = [\n",
" {\n",
" 'bigquery':{\n",
" 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Credentials used for writing data.'}},\n",
" 'from':{\n",
" 'query':{'field':{'name':'query','kind':'text','order':1,'default':'','description':'SQL with newlines and all.'}},\n",
" 'legacy':{'field':{'name':'legacy','kind':'boolean','order':4,'default':True,'description':'Query type must match source tables.'}}\n",
" },\n",
" 'to':{\n",
" 'dataset':{'field':{'name':'dataset','kind':'string','order':2,'default':'','description':'Existing BigQuery dataset.'}},\n",
" 'table':{'field':{'name':'table','kind':'string','order':3,'default':'','description':'Table to create from this query.'}}\n",
" }\n",
" }\n",
" }\n",
"]\n",
"\n",
"json_set_fields(TASKS, FIELDS)\n",
"\n",
"execute(CONFIG, TASKS, force=True)\n"
]
}
]
}
| apache-2.0 |
KwatME/match | code/7.ipynb | 1 | 5309 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:57:13.593506Z",
"iopub.status.busy": "2021-06-05T22:57:13.592614Z",
"iopub.status.idle": "2021-06-05T22:57:13.607174Z",
"shell.execute_reply": "2021-06-05T22:57:13.607743Z"
},
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-07-10T08:51:35.021387Z",
"start_time": "2018-07-10T08:51:32.209467Z"
},
"execution": {
"iopub.execute_input": "2021-06-05T22:57:13.611785Z",
"iopub.status.busy": "2021-06-05T22:57:13.611028Z",
"iopub.status.idle": "2021-06-05T22:57:14.320835Z",
"shell.execute_reply": "2021-06-05T22:57:14.321393Z"
},
"tags": []
},
"outputs": [],
"source": [
"from __init__ import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:57:14.325477Z",
"iopub.status.busy": "2021-06-05T22:57:14.324802Z",
"iopub.status.idle": "2021-06-05T22:57:14.369230Z",
"shell.execute_reply": "2021-06-05T22:57:14.369741Z"
},
"tags": []
},
"outputs": [],
"source": [
"bi_co_sa = pd.read_csv(\"../output/01_comparison_sample.tsv\", \"\\t\", index_col=0)\n",
"\n",
"sc_se_sa = pd.read_csv(\"../output/score_set_sample.tsv\", \"\\t\", index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:57:14.373834Z",
"iopub.status.busy": "2021-06-05T22:57:14.373135Z",
"iopub.status.idle": "2021-06-05T22:57:14.405536Z",
"shell.execute_reply": "2021-06-05T22:57:14.406032Z"
}
},
"outputs": [],
"source": [
"sc_se_sa.isna().sum(1).sort_values()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:57:14.410167Z",
"iopub.status.busy": "2021-06-05T22:57:14.409481Z",
"iopub.status.idle": "2021-06-05T22:57:15.257428Z",
"shell.execute_reply": "2021-06-05T22:57:15.257952Z"
},
"tags": []
},
"outputs": [],
"source": [
"na_se_fe_ = {}\n",
"\n",
"pa = \"../input/set/\"\n",
"\n",
"for na in os.listdir(pa):\n",
"\n",
" na_se_fe_[na] = kraft.gmt.read([\"{}{}\".format(pa, na)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:57:15.266451Z",
"iopub.status.busy": "2021-06-05T22:57:15.265749Z",
"iopub.status.idle": "2021-06-05T23:12:53.024963Z",
"shell.execute_reply": "2021-06-05T23:12:53.025480Z"
},
"tags": []
},
"outputs": [],
"source": [
"fu = kraft.information.get_ic\n",
"\n",
"funa = fu.__name__\n",
"\n",
"ke = {\n",
" \"tyta\": \"binary\",\n",
" \"st\": SETTING[\"st\"],\n",
"}\n",
"\n",
"for co, ta in bi_co_sa.iterrows():\n",
"\n",
" ta.dropna(inplace=True)\n",
"\n",
" pa = \"../output/compare_set/{}/{}/\".format(kraft.path.clean(co), funa)\n",
"\n",
" kraft.path.make(pa)\n",
"\n",
" st = kraft.function_heat_map.make(\n",
" ta,\n",
" sc_se_sa,\n",
" fu,\n",
" n_jo=SETTING[\"n_jo\"],\n",
" n_sa=SETTING[\"n_sa\"],\n",
" n_sh=SETTING[\"n_sh\"],\n",
" n_pl=SETTING[\"n_ex\"],\n",
" title=\"All ({})\".format(funa),\n",
" pa=pa,\n",
" **ke,\n",
" )\n",
"\n",
" if 0 < len(SETTING[\"se_\"]):\n",
"\n",
" kraft.function_heat_map.make(\n",
" ta,\n",
" sc_se_sa.reindex(SETTING[\"se_\"]),\n",
" st,\n",
" n_pl=None,\n",
" title=\"Peek ({})\".format(funa),\n",
" **ke,\n",
" )\n",
"\n",
" plot_peek(st[\"Score\"], SETTING[\"se_\"], pa)\n",
"\n",
" for na, se_fe_ in na_se_fe_.items():\n",
"\n",
" pa2 = \"{}{}/\".format(pa, na)\n",
"\n",
" kraft.path.make(pa2)\n",
"\n",
" se_ = sc_se_sa.index.intersection(se_fe_)\n",
"\n",
" if 0 < len(se_):\n",
"\n",
" kraft.function_heat_map.make(\n",
" ta,\n",
" sc_se_sa.loc[se_, :],\n",
" st,\n",
" n_pl=SETTING[\"n_ex\"],\n",
" title=\"{} ({})\".format(na, funa),\n",
" pa=pa2,\n",
" **ke,\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": "3.8.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
Roos12005/time-series-dtw | knn_Coffee_0.01band-for.ipynb | 1 | 16240 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KNN & DTW"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# -*- coding: utf-8 -*-\n",
"class Dtw(object):\n",
" \n",
" def __init__(self, seq1, seq2,\n",
" patterns = [(-1,-1), (-1,0), (0,-1)], \n",
" weights = [{(0,0):2}, {(0,0):1}, {(0,0):1}], \n",
" band_r=0.005):\n",
" self._seq1 = seq1\n",
" self._seq2 = seq2\n",
" self.len_seq1 = len(seq1)\n",
" self.len_seq2 = len(seq2)\n",
" self.len_pattern = len(patterns)\n",
" self.sum_w = [sum(ws.values()) for ws in weights]\n",
" self._r = int(len(seq1)*band_r)\n",
" assert len(patterns) == len(weights)\n",
" self._patterns = patterns\n",
" self._weights = weights\n",
" \n",
" def get_distance(self, i1, i2):\n",
" return abs(self._seq1[i1] - self._seq2[i2])\n",
"\n",
" def calculate(self):\n",
" g = list([float('inf')]*self.len_seq2 for i in range(self.len_seq1))\n",
" cost = list([0]*self.len_seq2 for i in range(self.len_seq1))\n",
"\n",
" g[0][0] = 2*self.get_distance(0, 0)\n",
" for i in range(self.len_seq1):\n",
" for j in range(max(0,i-self._r), min(i+self._r+1, self.len_seq2)):\n",
" for pat_i in range(self.len_pattern):\n",
" coor = (i+self._patterns[pat_i][0], j+self._patterns[pat_i][1])\n",
" if coor[0]<0 or coor[1]<0:\n",
" continue\n",
" dist = 0\n",
" for w_coor_offset, d_w in self._weights[pat_i].items():\n",
" w_coor = (i+w_coor_offset[0], j+w_coor_offset[1])\n",
" dist += d_w*self.get_distance(w_coor[0], w_coor[1])\n",
" this_val = g[coor[0]][coor[1]] + dist\n",
" this_cost = cost[coor[0]][coor[1]] + self.sum_w[pat_i]\n",
" if this_val < g[i][j]:\n",
" g[i][j] = this_val\n",
" cost[i][j] = this_cost\n",
" return g[self.len_seq1-1][self.len_seq2-1]/cost[self.len_seq1-1][self.len_seq2-1], g, cost\n",
" \n",
" def print_table(self, tb):\n",
" print(' '+' '.join([\"{:^7d}\".format(i) for i in range(self.len_seq2)]))\n",
" for i in range(self.len_seq1):\n",
" str = \"{:^4d}: \".format(i)\n",
" for j in range(self.len_seq2):\n",
" str += \"{:^7.3f} \".format(tb[i][j])\n",
" print (str)\n",
"\n",
" def print_g_matrix(self):\n",
" _, tb, _ = self.calculate()\n",
" self.print_table(tb)\n",
"\n",
" def print_cost_matrix(self):\n",
" _, _, tb = self.calculate()\n",
" self.print_table(tb)\n",
" \n",
" def get_dtw(self):\n",
" ans, _, _ = self.calculate()\n",
" return ans"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import csv\n",
"import random\n",
"import math\n",
"import operator\n",
"import numpy as np\n",
"\n",
"def loadDataset(filename, data=[]):\n",
" with open(filename, 'rb') as csvfile:\n",
" lines = csv.reader(csvfile,delimiter=' ')\n",
" dataset = list(lines)\n",
" for x in range(len(dataset)):\n",
" dataset[x] = filter(None, dataset[x])\n",
" dataset[x] = list(map(float, dataset[x]))\n",
" data.append(dataset[x])\n",
"\n",
"def euclideanDistance(instance1, instance2, length):\n",
"\tdistance = 0\n",
"\tfor x in range(length):\n",
"\t\tif x == 0:\n",
"\t\t\tcontinue\n",
"\t\tdistance += pow((instance1[x] - instance2[x]), 2)\n",
"\treturn math.sqrt(distance)\n",
" \n",
"def getNeighbors(trainingSet, testInstance, k, pattern, weight):\n",
"\tdistances = []\n",
"\tlength = len(testInstance)\n",
"\tfor x in range(len(trainingSet)):\n",
"# z-normalization\n",
"\t\tnew_testInstance = (np.array(testInstance)-np.mean(testInstance))/np.std(testInstance)\n",
"\t\tnew_trainingSet = (np.array(trainingSet[x])-np.mean(trainingSet[x]))/np.std(trainingSet[x])\n",
"\t\td = Dtw(new_testInstance[1:], new_trainingSet[1:], pattern, weight)\n",
"\t\tdist = d.get_dtw()\n",
"# \t\tdist = euclideanDistance(testInstance, trainingSet[x], length)\n",
"\t\tdistances.append((trainingSet[x], dist))\n",
"\tdistances.sort(key=operator.itemgetter(1))\n",
"# \tprint \"dist >>>> \",distances\n",
"\tneighbors = []\n",
"\tfor x in range(k):\n",
"\t\tneighbors.append(distances[x][0])\n",
"\treturn neighbors\n",
"\n",
"def getResponse(neighbors):\n",
"\tclassVotes = {}\n",
"\tfor x in range(len(neighbors)):\n",
"\t\tresponse = neighbors[x][0]\n",
"\t\tif response in classVotes:\n",
"\t\t\tclassVotes[response] += 1\n",
"\t\telse:\n",
"\t\t\tclassVotes[response] = 1\n",
"\tsortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)\n",
"\treturn sortedVotes[0][0]\n",
" \n",
"def getAccuracy(testSet, predictions):\n",
"\tcorrect = 0\n",
"\tfor x in range(len(testSet)):\n",
"\t\tif testSet[x][0] == predictions[x]:\n",
"\t\t\tcorrect += 1\n",
"\treturn (correct/float(len(testSet))) * 100.0\n",
"\t\n",
"def knn(train_data, test_data, k, pattern, weight):\n",
"\t# prepare data\n",
"\ttrainingSet=[]\n",
"\ttestSet=[]\n",
"\tloadDataset(train_data, trainingSet)\n",
"\tloadDataset(test_data, testSet)\n",
"# \tprint 'Train set: ' + repr(len(trainingSet))\n",
"# \tprint trainingSet\n",
"# \tprint 'Test set: ' + repr(len(testSet))\n",
"# \tprint testSet\n",
"\t# generate predictions\n",
"\tpredictions=[]\n",
"\tfor x in range(len(testSet)):\n",
"# \t\tprint \">>\",testSet[x]\n",
"\t\tneighbors = getNeighbors(trainingSet, testSet[x], k, pattern, weight)\n",
"# \t\tprint \"neighbors >>\", neighbors\n",
"\t\tresult = getResponse(neighbors)\n",
"# \t\tprint \"result >>\", result\n",
"\t\tpredictions.append(result)\n",
"# \t\tprint('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][0]))\n",
"\taccuracy = getAccuracy(testSet, predictions)\n",
"\treturn accuracy\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Main"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PATTERNS_1 = [(0,-1), (-1,-1), (-1,0)]\n",
"WEIGHTS_SYM_1 = [{(0,0):1}, {(0,0):2}, {(0,0):1}] "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"COUNT = 5\n",
"weights = []\n",
"for i in range(COUNT):\n",
" for j in range(COUNT):\n",
" for k in range(COUNT):\n",
" weights.append([{(0,0):i}, {(0,0):j}, {(0,0):k}])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"TRAIN_DATA = 'dataset/Coffee_TRAIN'\n",
"TEST_DATA = 'dataset/Coffee_TEST'\n",
"OUTPUT_FILE = 'acc_coffee_0.01band_for_ijk.csv'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"92.85714285714286"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn(TRAIN_DATA, TEST_DATA, 1, PATTERNS_1, WEIGHTS_SYM_1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"i: 0 j: 0 k: 0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/envs/py27/lib/python2.7/site-packages/ipykernel/__main__.py:42: RuntimeWarning: divide by zero encountered in double_scalars\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"i: 4 j: 4 k: 0\n",
"92.8571428571\n",
"i: 4 j: 4 k: 1\n",
"92.8571428571\n",
"i: 4 j: 4 k: 2\n",
"92.8571428571\n",
"i: 4 j: 4 k: 3\n",
"92.8571428571\n",
"i: 4 j: 4 k: 4\n",
"92.8571428571\n"
]
}
],
"source": [
"with open(OUTPUT_FILE, \"w\") as myfile:\n",
" myfile.write(\"i,j,k,accuracy\\n\")\n",
"for weight in weights:\n",
" i = weight[0][(0,0)]\n",
" j = weight[1][(0,0)]\n",
" k = weight[2][(0,0)]\n",
" print \"i:\", i, \"j:\", j,\"k:\", k\n",
" acc = knn(TRAIN_DATA, TEST_DATA, 1, PATTERNS_1, weight)\n",
" print acc\n",
" with open(OUTPUT_FILE, \"a\") as myfile:\n",
" myfile.write(str(i)+\",\"+str(j)+\",\"+str(k)+\",\"+str(acc)+\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:py27]",
"language": "python",
"name": "conda-env-py27-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 |
Fifth-Cohort-Awesome/NightThree | three_agd.ipynb | 1 | 22940 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goal One"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Injest a csv file as pure text... (just 500 chars)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'budget,genres,homepage,id,keywords,original_language,original_title,overview,popularity,production_companies,production_countries,release_date,revenue,runtime,spoken_languages,status,tagline,title,vote_average,vote_count\\n237000000,\"[{\"\"id\"\": 28, \"\"name\"\": \"\"Action\"\"}, {\"\"id\"\": 12, \"\"name\"\": \"\"Adventure\"\"}, {\"\"id\"\": 14, \"\"name\"\": \"\"Fantasy\"\"}, {\"\"id\"\": 878, \"\"name\"\": \"\"Science Fiction\"\"}]\",http://www.avatarmovie.com/,19995,\"[{\"\"id\"\": 1463, \"\"name\"\": \"\"culture clash\"\"}, {\"\"id\"\": 2964, \"\"name\"\": \"\"'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"with open('tmdb_5000_movies.csv','r') as f:\n",
" rtext=''\n",
" for line in f:\n",
" rtext += line\n",
"rtext[:500]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then as a list of lines... (just one line)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'budget,genres,homepage,id,keywords,original_language,original_title,overview,popularity,production_companies,production_countries,release_date,revenue,runtime,spoken_languages,status,tagline,title,vote_average,vote_count\\n'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('tmdb_5000_movies.csv','r') as f:\n",
" lines = [line for line in f]\n",
"lines[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then as a data frame... (just Avatar)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>budget</th>\n",
" <th>genres</th>\n",
" <th>homepage</th>\n",
" <th>id</th>\n",
" <th>keywords</th>\n",
" <th>original_language</th>\n",
" <th>original_title</th>\n",
" <th>overview</th>\n",
" <th>popularity</th>\n",
" <th>production_companies</th>\n",
" <th>production_countries</th>\n",
" <th>release_date</th>\n",
" <th>revenue</th>\n",
" <th>runtime</th>\n",
" <th>spoken_languages</th>\n",
" <th>status</th>\n",
" <th>tagline</th>\n",
" <th>title</th>\n",
" <th>vote_average</th>\n",
" <th>vote_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>237000000</td>\n",
" <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
" <td>http://www.avatarmovie.com/</td>\n",
" <td>19995</td>\n",
" <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
" <td>en</td>\n",
" <td>Avatar</td>\n",
" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
" <td>150.437577</td>\n",
" <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
" <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
" <td>2009-12-10</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" budget genres \\\n",
"0 237000000 [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam... \n",
"\n",
" homepage id \\\n",
"0 http://www.avatarmovie.com/ 19995 \n",
"\n",
" keywords original_language \\\n",
"0 [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":... en \n",
"\n",
" original_title overview \\\n",
"0 Avatar In the 22nd century, a paraplegic Marine is di... \n",
"\n",
" popularity production_companies \\\n",
"0 150.437577 [{\"name\": \"Ingenious Film Partners\", \"id\": 289... \n",
"\n",
" production_countries release_date revenue \\\n",
"0 [{\"iso_3166_1\": \"US\", \"name\": \"United States o... 2009-12-10 2787965087 \n",
"\n",
" runtime spoken_languages status \\\n",
"0 162.0 [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso... Released \n",
"\n",
" tagline title vote_average vote_count \n",
"0 Enter the World of Pandora. Avatar 7.2 11800 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"tmdb_5000_movies.csv\")\n",
"df.query('id == 19995')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goal Two"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Right now, the file is in a 'narrow' format. In other words, several interesting bits are collapsed into a single field. Let's attempt to make the data frame a 'wide' format. All the collapsed items expanded horizontally."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### References:\n",
"#### https://www.kaggle.com/fabiendaniel/film-recommendation-engine\n",
"#### http://www.jeannicholashould.com/tidy-data-in-python.html"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"df = pd.read_csv(\"tmdb_5000_movies.csv\")\n",
"\n",
"#convert to json\n",
"json_columns = ['genres', 'keywords', 'production_countries',\n",
" 'production_companies', 'spoken_languages']\n",
"for column in json_columns:\n",
" df[column] = df[column].apply(json.loads)\n",
"\n",
"\n",
"def get_unique_inner_json(feature):\n",
" tmp = []\n",
" for i, row in df[feature].iteritems():\n",
" for x in range(0,len(df[feature].iloc[i])):\n",
" tmp.append(df[feature].iloc[i][x]['name'])\n",
"\n",
" unique_values = set(tmp)\n",
" return unique_values"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def widen_data(df, feature):\n",
" unique_json = get_unique_inner_json(feature)\n",
" \n",
" tmp = []\n",
" #rearrange genres\n",
" for i, row in df.iterrows():\n",
" for x in range(0,len(row[feature])):\n",
" for val in unique_json:\n",
" if row[feature][x]['name'] == val:\n",
" row[val] = 1\n",
" \n",
" tmp.append(row)\n",
" \n",
" new_df = pd.DataFrame(tmp)\n",
" new_df[list(unique_json)] = new_df[list(unique_json)].fillna(value=0)\n",
" return new_df"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"genres_arranged_df = widen_data(df, \"genres\")\n",
"genres_arranged_df[list(get_unique_inner_json(\"genres\"))] = genres_arranged_df[list(get_unique_inner_json(\"genres\"))].astype(int)\n",
"\n"
]
},
{
"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>Action</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Comedy</th>\n",
" <th>Crime</th>\n",
" <th>Documentary</th>\n",
" <th>Drama</th>\n",
" <th>Family</th>\n",
" <th>Fantasy</th>\n",
" <th>Foreign</th>\n",
" <th>...</th>\n",
" <th>production_countries</th>\n",
" <th>release_date</th>\n",
" <th>revenue</th>\n",
" <th>runtime</th>\n",
" <th>spoken_languages</th>\n",
" <th>status</th>\n",
" <th>tagline</th>\n",
" <th>title</th>\n",
" <th>vote_average</th>\n",
" <th>vote_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>[{u'iso_3166_1': u'US', u'name': u'United Stat...</td>\n",
" <td>2009-12-10</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{u'iso_639_1': u'en', u'name': u'English'}, {...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" Action Adventure Animation Comedy Crime Documentary Drama Family \\\n",
"0 1 1 0 0 0 0 0 0 \n",
"\n",
" Fantasy Foreign ... \\\n",
"0 1 0 ... \n",
"\n",
" production_countries release_date \\\n",
"0 [{u'iso_3166_1': u'US', u'name': u'United Stat... 2009-12-10 \n",
"\n",
" revenue runtime spoken_languages \\\n",
"0 2787965087 162.0 [{u'iso_639_1': u'en', u'name': u'English'}, {... \n",
"\n",
" status tagline title vote_average vote_count \n",
"0 Released Enter the World of Pandora. Avatar 7.2 11800 \n",
"\n",
"[1 rows x 40 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genres_arranged_df.query('title == \"Avatar\"')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Goal Three"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>budget</th>\n",
" <th>genres</th>\n",
" <th>homepage</th>\n",
" <th>id</th>\n",
" <th>keywords</th>\n",
" <th>original_language</th>\n",
" <th>original_title</th>\n",
" <th>overview</th>\n",
" <th>popularity</th>\n",
" <th>production_companies</th>\n",
" <th>...</th>\n",
" <th>revenue</th>\n",
" <th>runtime</th>\n",
" <th>spoken_languages</th>\n",
" <th>status</th>\n",
" <th>tagline</th>\n",
" <th>title</th>\n",
" <th>vote_average</th>\n",
" <th>vote_count</th>\n",
" <th>genre</th>\n",
" <th>genre_val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19212</th>\n",
" <td>237000000</td>\n",
" <td>[{u'id': 28, u'name': u'Action'}, {u'id': 12, ...</td>\n",
" <td>http://www.avatarmovie.com/</td>\n",
" <td>19995</td>\n",
" <td>[{u'id': 1463, u'name': u'culture clash'}, {u'...</td>\n",
" <td>en</td>\n",
" <td>Avatar</td>\n",
" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
" <td>150.437577</td>\n",
" <td>[{u'name': u'Ingenious Film Partners', u'id': ...</td>\n",
" <td>...</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{u'iso_639_1': u'en', u'name': u'English'}, {...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" <td>Science Fiction</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38424</th>\n",
" <td>237000000</td>\n",
" <td>[{u'id': 28, u'name': u'Action'}, {u'id': 12, ...</td>\n",
" <td>http://www.avatarmovie.com/</td>\n",
" <td>19995</td>\n",
" <td>[{u'id': 1463, u'name': u'culture clash'}, {u'...</td>\n",
" <td>en</td>\n",
" <td>Avatar</td>\n",
" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
" <td>150.437577</td>\n",
" <td>[{u'name': u'Ingenious Film Partners', u'id': ...</td>\n",
" <td>...</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{u'iso_639_1': u'en', u'name': u'English'}, {...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" <td>Fantasy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52833</th>\n",
" <td>237000000</td>\n",
" <td>[{u'id': 28, u'name': u'Action'}, {u'id': 12, ...</td>\n",
" <td>http://www.avatarmovie.com/</td>\n",
" <td>19995</td>\n",
" <td>[{u'id': 1463, u'name': u'culture clash'}, {u'...</td>\n",
" <td>en</td>\n",
" <td>Avatar</td>\n",
" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
" <td>150.437577</td>\n",
" <td>[{u'name': u'Ingenious Film Partners', u'id': ...</td>\n",
" <td>...</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{u'iso_639_1': u'en', u'name': u'English'}, {...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" <td>Adventure</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62439</th>\n",
" <td>237000000</td>\n",
" <td>[{u'id': 28, u'name': u'Action'}, {u'id': 12, ...</td>\n",
" <td>http://www.avatarmovie.com/</td>\n",
" <td>19995</td>\n",
" <td>[{u'id': 1463, u'name': u'culture clash'}, {u'...</td>\n",
" <td>en</td>\n",
" <td>Avatar</td>\n",
" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
" <td>150.437577</td>\n",
" <td>[{u'name': u'Ingenious Film Partners', u'id': ...</td>\n",
" <td>...</td>\n",
" <td>2787965087</td>\n",
" <td>162.0</td>\n",
" <td>[{u'iso_639_1': u'en', u'name': u'English'}, {...</td>\n",
" <td>Released</td>\n",
" <td>Enter the World of Pandora.</td>\n",
" <td>Avatar</td>\n",
" <td>7.2</td>\n",
" <td>11800</td>\n",
" <td>Action</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" budget genres \\\n",
"19212 237000000 [{u'id': 28, u'name': u'Action'}, {u'id': 12, ... \n",
"38424 237000000 [{u'id': 28, u'name': u'Action'}, {u'id': 12, ... \n",
"52833 237000000 [{u'id': 28, u'name': u'Action'}, {u'id': 12, ... \n",
"62439 237000000 [{u'id': 28, u'name': u'Action'}, {u'id': 12, ... \n",
"\n",
" homepage id \\\n",
"19212 http://www.avatarmovie.com/ 19995 \n",
"38424 http://www.avatarmovie.com/ 19995 \n",
"52833 http://www.avatarmovie.com/ 19995 \n",
"62439 http://www.avatarmovie.com/ 19995 \n",
"\n",
" keywords original_language \\\n",
"19212 [{u'id': 1463, u'name': u'culture clash'}, {u'... en \n",
"38424 [{u'id': 1463, u'name': u'culture clash'}, {u'... en \n",
"52833 [{u'id': 1463, u'name': u'culture clash'}, {u'... en \n",
"62439 [{u'id': 1463, u'name': u'culture clash'}, {u'... en \n",
"\n",
" original_title overview \\\n",
"19212 Avatar In the 22nd century, a paraplegic Marine is di... \n",
"38424 Avatar In the 22nd century, a paraplegic Marine is di... \n",
"52833 Avatar In the 22nd century, a paraplegic Marine is di... \n",
"62439 Avatar In the 22nd century, a paraplegic Marine is di... \n",
"\n",
" popularity production_companies \\\n",
"19212 150.437577 [{u'name': u'Ingenious Film Partners', u'id': ... \n",
"38424 150.437577 [{u'name': u'Ingenious Film Partners', u'id': ... \n",
"52833 150.437577 [{u'name': u'Ingenious Film Partners', u'id': ... \n",
"62439 150.437577 [{u'name': u'Ingenious Film Partners', u'id': ... \n",
"\n",
" ... revenue runtime \\\n",
"19212 ... 2787965087 162.0 \n",
"38424 ... 2787965087 162.0 \n",
"52833 ... 2787965087 162.0 \n",
"62439 ... 2787965087 162.0 \n",
"\n",
" spoken_languages status \\\n",
"19212 [{u'iso_639_1': u'en', u'name': u'English'}, {... Released \n",
"38424 [{u'iso_639_1': u'en', u'name': u'English'}, {... Released \n",
"52833 [{u'iso_639_1': u'en', u'name': u'English'}, {... Released \n",
"62439 [{u'iso_639_1': u'en', u'name': u'English'}, {... Released \n",
"\n",
" tagline title vote_average vote_count \\\n",
"19212 Enter the World of Pandora. Avatar 7.2 11800 \n",
"38424 Enter the World of Pandora. Avatar 7.2 11800 \n",
"52833 Enter the World of Pandora. Avatar 7.2 11800 \n",
"62439 Enter the World of Pandora. Avatar 7.2 11800 \n",
"\n",
" genre genre_val \n",
"19212 Science Fiction 1 \n",
"38424 Fantasy 1 \n",
"52833 Adventure 1 \n",
"62439 Action 1 \n",
"\n",
"[4 rows x 22 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genres_long_df = pd.melt(genres_arranged_df, id_vars=df.columns, value_vars=get_unique_inner_json(\"genres\"), var_name=\"genre\", value_name=\"genre_val\")\n",
"genres_long_df = genres_long_df[genres_long_df['genre_val'] == 1]\n",
"genres_long_df.query('title == \"Avatar\"')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
csaladenes/blog | hodlon/binance-nov.ipynb | 2 | 1235378 | null | mit |
crhaithcock/RushHour | RushHourPy/analysis_prototypes/state_construction_pytables_prototype.ipynb | 1 | 10127 | {
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{
"cells": [
{
"cell_type": "code",
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"input": [
"import os\n",
"import tables"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Initialize Data\n",
" * Create hdf5 file\n",
" * Load Constants\n",
" * Create fixed data (e.g. list of all car positions, list of red car positions)\n",
" * Spin up working data structures\n",
" \n",
" \n",
"* Create and record states based on input value for #cars and #trucks\n",
" * For each red car position:\n",
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" \n",
"* Close hdf5 file\n",
"\n",
"\n",
"_Create States_\n",
"* Place red car on board\n",
"* Recursively place each non-red car on the board until all cars are gone or board is empty\n",
"* Upon reach base case, record state in hdf5 file\n"
]
},
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],
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},
{
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{
"metadata": {},
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"text": [
"2"
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{
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"import pandas as pd\n",
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"prompt_number": 40
},
{
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"a = collections.OrderedDict\n",
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"a['B'] = [4,5,6]\n",
"a['C'] = [2,3,4]"
],
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{
"ename": "TypeError",
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}
],
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{
"cell_type": "code",
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"input": [
"df_a = pd.DataFrame.from_items( [ ['A',[1,2,3]] , ['B',[4,5,6]], ['C',[2,3,4]] ] )\n",
"df_b = pd.DataFrame.from_items( [ ['A',[1,2,4]] , ['B',[4,6,3]], ['D',[2,3,4]] ] )\n",
"df_a"
],
"language": "python",
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"</table>\n",
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"collapsed": false,
"input": [
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],
"language": "python",
"metadata": {},
"outputs": [
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} | cc0-1.0 |
nkmk/python-snippets | notebook/pathlib_dir.ipynb | 1 | 6006 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pathlib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"p = pathlib.Path('temp')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"temp\n"
]
}
],
"source": [
"print(p)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pathlib.PosixPath'>\n"
]
}
],
"source": [
"print(type(p))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p.exists())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"p.mkdir()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(p.exists())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(p.is_dir())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"pathlib.Path('temp/dir').mkdir()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(pathlib.Path('temp/dir').is_dir())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# pathlib.Path('temp/dir/sub_dir/sub_dir2').mkdir()\n",
"# FileNotFoundError: [Errno 2] No such file or directory: 'temp/dir/sub_dir/sub_dir2'"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"pathlib.Path('temp/dir/sub_dir/sub_dir2').mkdir(parents=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(pathlib.Path('temp/dir/sub_dir/sub_dir2').is_dir())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# pathlib.Path('temp/dir').mkdir()\n",
"# FileExistsError: [Errno 17] File exists: 'temp/dir'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pathlib.Path('temp/dir').mkdir(exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"pathlib.Path('temp/dir/file').touch()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(pathlib.Path('temp/dir/file').is_file())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# pathlib.Path('temp/dir/file').mkdir(exist_ok=True)\n",
"# FileExistsError: [Errno 17] File exists: 'temp/dir/file'"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"p_sub_dir = pathlib.Path('temp/dir/sub_dir/sub_dir2')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(p_sub_dir.is_dir())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"p_sub_dir.rmdir()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p_sub_dir.exists())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"p = pathlib.Path('temp')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# p.rmdir()\n",
"# OSError: [Errno 66] Directory not empty: 'temp'"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"import shutil"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"shutil.rmtree(p)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p.exists())"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
peterewills/NetComp | tests/20170614_speed_tests.ipynb | 1 | 404640 | {
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import netcomp as nc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Speed Tests\n",
"\n",
"We're going to test the timing (absolute, and complexity) for the algorithms we've got implemented so far.\n",
"\n",
"We'll do this in the following manner. We'll set up a script for running multiple timing runs in parallel, then we'll take the average time (or maybe average of $n$ lowest?) in order to start examining the complexity. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The line_profiler extension is already loaded. To reload it, use:\n",
" %reload_ext line_profiler\n"
]
}
],
"source": [
"# Import things we'll use to analyze the timing\n",
"from sklearn.linear_model import LinearRegression\n",
"%load_ext line_profiler\n",
"\n",
"def complexity_plot(log_range,times,label=None):\n",
" \"Make a logarithmic complexity plot, report best-fit line slope.\"\n",
" logtime = np.log(times).reshape(-1,1)\n",
" logn = np.log(log_range).reshape(-1,1)\n",
" regr = LinearRegression()\n",
" regr.fit(logn,logtime)\n",
"\n",
" slope = float(regr.coef_)\n",
" fit_line = np.exp(regr.predict(logn))\n",
" \n",
" plt.figure();\n",
"\n",
" plt.loglog(log_range,times,'o');\n",
" plt.loglog(log_range,fit_line,'--');\n",
" plt.xlabel('Size of Problem');\n",
" plt.ylabel('Time Elapsed');\n",
" if label is not None:\n",
" plt.title('Complexity of ' + label)\n",
" print('Best fit line for {} has slope {:0.03f}.'.format(label,slope))\n",
" else:\n",
" print('Best fit line has slope {:.03f}.'.format(slope))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_dict = pickle.load(open('graph_distance_timing.p','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time each of our metrics to discover\n",
" computational complexity. \n"
]
}
],
"source": [
"print(data_dict['description'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = data_dict['results_df']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Edit</th>\n",
" <th>Resistance Dist.</th>\n",
" <th>DeltaCon</th>\n",
" <th>NetSimile</th>\n",
" <th>Lambda (Adjacency)</th>\n",
" <th>Lambda (Laplacian)</th>\n",
" <th>Lambda (Normalized Laplacian)</th>\n",
" <th>Edit</th>\n",
" <th>Resistance Dist.</th>\n",
" <th>DeltaCon</th>\n",
" <th>...</th>\n",
" <th>Lambda (Adjacency)</th>\n",
" <th>Lambda (Laplacian)</th>\n",
" <th>Lambda (Normalized Laplacian)</th>\n",
" <th>Edit</th>\n",
" <th>Resistance Dist.</th>\n",
" <th>DeltaCon</th>\n",
" <th>NetSimile</th>\n",
" <th>Lambda (Adjacency)</th>\n",
" <th>Lambda (Laplacian)</th>\n",
" <th>Lambda (Normalized Laplacian)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.00028348</td>\n",
" <td>0.222237</td>\n",
" <td>0.00820446</td>\n",
" <td>0.155957</td>\n",
" <td>0.000599384</td>\n",
" <td>0.0318468</td>\n",
" <td>0.00213671</td>\n",
" <td>0.000197411</td>\n",
" <td>0.0945263</td>\n",
" <td>0.0390024</td>\n",
" <td>...</td>\n",
" <td>0.000494242</td>\n",
" <td>0.00204062</td>\n",
" <td>0.00241899</td>\n",
" <td>5.55515e-05</td>\n",
" <td>0.00218225</td>\n",
" <td>0.00351286</td>\n",
" <td>0.0290842</td>\n",
" <td>0.000563383</td>\n",
" <td>0.00170541</td>\n",
" <td>0.0019412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4.57764e-05</td>\n",
" <td>0.302673</td>\n",
" <td>0.00330758</td>\n",
" <td>0.972329</td>\n",
" <td>0.000780106</td>\n",
" <td>0.0020709</td>\n",
" <td>0.00264382</td>\n",
" <td>5.57899e-05</td>\n",
" <td>0.301902</td>\n",
" <td>0.00316405</td>\n",
" <td>...</td>\n",
" <td>0.000786543</td>\n",
" <td>0.0181258</td>\n",
" <td>0.00707316</td>\n",
" <td>5.4121e-05</td>\n",
" <td>0.0881832</td>\n",
" <td>0.00327969</td>\n",
" <td>1.43964</td>\n",
" <td>0.0011487</td>\n",
" <td>0.0091393</td>\n",
" <td>0.00272584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>8.96454e-05</td>\n",
" <td>0.183244</td>\n",
" <td>0.165371</td>\n",
" <td>8.45824</td>\n",
" <td>0.186596</td>\n",
" <td>0.0797551</td>\n",
" <td>0.0687363</td>\n",
" <td>8.10623e-05</td>\n",
" <td>0.472628</td>\n",
" <td>0.204094</td>\n",
" <td>...</td>\n",
" <td>0.120481</td>\n",
" <td>0.24446</td>\n",
" <td>0.503008</td>\n",
" <td>0.000215054</td>\n",
" <td>0.0893686</td>\n",
" <td>0.130405</td>\n",
" <td>9.89184</td>\n",
" <td>0.197206</td>\n",
" <td>0.150302</td>\n",
" <td>0.154846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>0.000606298</td>\n",
" <td>1.16293</td>\n",
" <td>0.159863</td>\n",
" <td>25.7166</td>\n",
" <td>2.63298</td>\n",
" <td>3.79907</td>\n",
" <td>3.31286</td>\n",
" <td>0.000564814</td>\n",
" <td>0.608195</td>\n",
" <td>0.133687</td>\n",
" <td>...</td>\n",
" <td>5.36296</td>\n",
" <td>6.76249</td>\n",
" <td>6.377</td>\n",
" <td>0.000378847</td>\n",
" <td>0.409895</td>\n",
" <td>0.090127</td>\n",
" <td>26.4921</td>\n",
" <td>0.725003</td>\n",
" <td>0.584409</td>\n",
" <td>0.812854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>0.0154662</td>\n",
" <td>9.41187</td>\n",
" <td>1.00786</td>\n",
" <td>155.145</td>\n",
" <td>32.3615</td>\n",
" <td>42.97</td>\n",
" <td>43.1355</td>\n",
" <td>0.00598025</td>\n",
" <td>5.61302</td>\n",
" <td>1.06776</td>\n",
" <td>...</td>\n",
" <td>12.0492</td>\n",
" <td>17.0299</td>\n",
" <td>15.4275</td>\n",
" <td>0.00331473</td>\n",
" <td>1.94809</td>\n",
" <td>0.34633</td>\n",
" <td>156.875</td>\n",
" <td>5.04571</td>\n",
" <td>4.75076</td>\n",
" <td>4.89122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3000</th>\n",
" <td>0.369191</td>\n",
" <td>299.135</td>\n",
" <td>3.47908</td>\n",
" <td>273.29</td>\n",
" <td>564.239</td>\n",
" <td>477.134</td>\n",
" <td>560.69</td>\n",
" <td>0.370803</td>\n",
" <td>187.179</td>\n",
" <td>19.5115</td>\n",
" <td>...</td>\n",
" <td>472.193</td>\n",
" <td>186.07</td>\n",
" <td>123.262</td>\n",
" <td>0.0430229</td>\n",
" <td>29.1903</td>\n",
" <td>2.08132</td>\n",
" <td>1974.56</td>\n",
" <td>282.375</td>\n",
" <td>145.252</td>\n",
" <td>59.3902</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 700 columns</p>\n",
"</div>"
],
"text/plain": [
" Edit Resistance Dist. DeltaCon NetSimile Lambda (Adjacency) \\\n",
"10 0.00028348 0.222237 0.00820446 0.155957 0.000599384 \n",
"30 4.57764e-05 0.302673 0.00330758 0.972329 0.000780106 \n",
"100 8.96454e-05 0.183244 0.165371 8.45824 0.186596 \n",
"300 0.000606298 1.16293 0.159863 25.7166 2.63298 \n",
"1000 0.0154662 9.41187 1.00786 155.145 32.3615 \n",
"3000 0.369191 299.135 3.47908 273.29 564.239 \n",
"\n",
" Lambda (Laplacian) Lambda (Normalized Laplacian) Edit \\\n",
"10 0.0318468 0.00213671 0.000197411 \n",
"30 0.0020709 0.00264382 5.57899e-05 \n",
"100 0.0797551 0.0687363 8.10623e-05 \n",
"300 3.79907 3.31286 0.000564814 \n",
"1000 42.97 43.1355 0.00598025 \n",
"3000 477.134 560.69 0.370803 \n",
"\n",
" Resistance Dist. DeltaCon ... \\\n",
"10 0.0945263 0.0390024 ... \n",
"30 0.301902 0.00316405 ... \n",
"100 0.472628 0.204094 ... \n",
"300 0.608195 0.133687 ... \n",
"1000 5.61302 1.06776 ... \n",
"3000 187.179 19.5115 ... \n",
"\n",
" Lambda (Adjacency) Lambda (Laplacian) Lambda (Normalized Laplacian) \\\n",
"10 0.000494242 0.00204062 0.00241899 \n",
"30 0.000786543 0.0181258 0.00707316 \n",
"100 0.120481 0.24446 0.503008 \n",
"300 5.36296 6.76249 6.377 \n",
"1000 12.0492 17.0299 15.4275 \n",
"3000 472.193 186.07 123.262 \n",
"\n",
" Edit Resistance Dist. DeltaCon NetSimile Lambda (Adjacency) \\\n",
"10 5.55515e-05 0.00218225 0.00351286 0.0290842 0.000563383 \n",
"30 5.4121e-05 0.0881832 0.00327969 1.43964 0.0011487 \n",
"100 0.000215054 0.0893686 0.130405 9.89184 0.197206 \n",
"300 0.000378847 0.409895 0.090127 26.4921 0.725003 \n",
"1000 0.00331473 1.94809 0.34633 156.875 5.04571 \n",
"3000 0.0430229 29.1903 2.08132 1974.56 282.375 \n",
"\n",
" Lambda (Laplacian) Lambda (Normalized Laplacian) \n",
"10 0.00170541 0.0019412 \n",
"30 0.0091393 0.00272584 \n",
"100 0.150302 0.154846 \n",
"300 0.584409 0.812854 \n",
"1000 4.75076 4.89122 \n",
"3000 145.252 59.3902 \n",
"\n",
"[6 rows x 700 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"labels = df.columns.unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_dict = {}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>4.62532e-05</td>\n",
" <td>7.51019e-05</td>\n",
" <td>0.000561237</td>\n",
" <td>0.0184975</td>\n",
" <td>0.370901</td>\n",
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" <td>0.00029397</td>\n",
" <td>4.52995e-05</td>\n",
" <td>7.9155e-05</td>\n",
" <td>0.00056529</td>\n",
" <td>0.00492525</td>\n",
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" <td>0.000118732</td>\n",
" <td>4.52995e-05</td>\n",
" <td>7.82013e-05</td>\n",
" <td>0.000560999</td>\n",
" <td>0.0178421</td>\n",
" <td>0.408601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000142336</td>\n",
" <td>4.72069e-05</td>\n",
" <td>9.91821e-05</td>\n",
" <td>0.000623703</td>\n",
" <td>0.00508285</td>\n",
" <td>0.322326</td>\n",
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" <td>0.000145674</td>\n",
" <td>4.48227e-05</td>\n",
" <td>0.000103474</td>\n",
" <td>0.00056982</td>\n",
" <td>0.0406191</td>\n",
" <td>0.510803</td>\n",
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" <th>Edit</th>\n",
" <td>0.00028491</td>\n",
" <td>4.62532e-05</td>\n",
" <td>8.7738e-05</td>\n",
" <td>0.000611305</td>\n",
" <td>0.00784707</td>\n",
" <td>0.253818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000200033</td>\n",
" <td>3.52859e-05</td>\n",
" <td>8.65459e-05</td>\n",
" <td>0.000569105</td>\n",
" <td>0.00455499</td>\n",
" <td>0.379067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000190973</td>\n",
" <td>4.50611e-05</td>\n",
" <td>7.4625e-05</td>\n",
" <td>0.000567913</td>\n",
" <td>0.0204105</td>\n",
" <td>0.313604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000103235</td>\n",
" <td>4.81606e-05</td>\n",
" <td>0.000126362</td>\n",
" <td>0.000588417</td>\n",
" <td>0.0201089</td>\n",
" <td>0.502235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000200033</td>\n",
" <td>4.55379e-05</td>\n",
" <td>0.000131845</td>\n",
" <td>0.000624895</td>\n",
" <td>0.0173266</td>\n",
" <td>0.336385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000199795</td>\n",
" <td>5.84126e-05</td>\n",
" <td>9.03606e-05</td>\n",
" <td>0.000575304</td>\n",
" <td>0.0579138</td>\n",
" <td>0.302057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000113487</td>\n",
" <td>2.98023e-05</td>\n",
" <td>0.000117302</td>\n",
" <td>0.000631094</td>\n",
" <td>0.00911355</td>\n",
" <td>0.340617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000103235</td>\n",
" <td>5.03063e-05</td>\n",
" <td>7.86781e-05</td>\n",
" <td>0.00058794</td>\n",
" <td>0.0154772</td>\n",
" <td>0.3358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000117779</td>\n",
" <td>5.84126e-05</td>\n",
" <td>0.000117064</td>\n",
" <td>0.000590086</td>\n",
" <td>0.0203865</td>\n",
" <td>0.31888</td>\n",
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" <td>0.000203609</td>\n",
" <td>3.55244e-05</td>\n",
" <td>0.000110626</td>\n",
" <td>0.000581026</td>\n",
" <td>0.0177763</td>\n",
" <td>0.360284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000222921</td>\n",
" <td>4.76837e-05</td>\n",
" <td>6.8903e-05</td>\n",
" <td>0.000571251</td>\n",
" <td>0.00486159</td>\n",
" <td>0.409907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000100851</td>\n",
" <td>6.41346e-05</td>\n",
" <td>0.000112534</td>\n",
" <td>0.000606298</td>\n",
" <td>0.0599113</td>\n",
" <td>0.117487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.05583e-05</td>\n",
" <td>8.10623e-05</td>\n",
" <td>0.0001266</td>\n",
" <td>0.000300646</td>\n",
" <td>0.0341403</td>\n",
" <td>0.148152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.62668e-05</td>\n",
" <td>6.48499e-05</td>\n",
" <td>0.000183344</td>\n",
" <td>0.000627756</td>\n",
" <td>0.0193818</td>\n",
" <td>0.230133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.05583e-05</td>\n",
" <td>0.000118256</td>\n",
" <td>9.46522e-05</td>\n",
" <td>0.000367165</td>\n",
" <td>0.0267262</td>\n",
" <td>0.218837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>8.03471e-05</td>\n",
" <td>0.000126362</td>\n",
" <td>0.000107527</td>\n",
" <td>0.0161557</td>\n",
" <td>0.0543747</td>\n",
" <td>0.379593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>9.58443e-05</td>\n",
" <td>0.000174761</td>\n",
" <td>0.0376918</td>\n",
" <td>0.000381947</td>\n",
" <td>0.039829</td>\n",
" <td>0.372713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>7.36713e-05</td>\n",
" <td>8.03471e-05</td>\n",
" <td>0.00021863</td>\n",
" <td>0.000302315</td>\n",
" <td>0.0811725</td>\n",
" <td>0.256876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.81877e-05</td>\n",
" <td>6.69956e-05</td>\n",
" <td>8.46386e-05</td>\n",
" <td>0.00110316</td>\n",
" <td>0.0713711</td>\n",
" <td>0.452341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.26905e-05</td>\n",
" <td>6.74725e-05</td>\n",
" <td>9.799e-05</td>\n",
" <td>0.000343323</td>\n",
" <td>0.00329924</td>\n",
" <td>0.234745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.91278e-05</td>\n",
" <td>9.39369e-05</td>\n",
" <td>9.82285e-05</td>\n",
" <td>0.0168324</td>\n",
" <td>0.058032</td>\n",
" <td>0.264047</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>Edit</th>\n",
" <td>5.79357e-05</td>\n",
" <td>6.67572e-05</td>\n",
" <td>0.000139952</td>\n",
" <td>0.000785828</td>\n",
" <td>0.0182071</td>\n",
" <td>0.263054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000189781</td>\n",
" <td>0.00015831</td>\n",
" <td>9.91821e-05</td>\n",
" <td>0.000528336</td>\n",
" <td>0.0174797</td>\n",
" <td>0.0825362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.36578e-05</td>\n",
" <td>9.89437e-05</td>\n",
" <td>0.000165462</td>\n",
" <td>0.000321865</td>\n",
" <td>0.010603</td>\n",
" <td>0.221422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.22272e-05</td>\n",
" <td>6.41346e-05</td>\n",
" <td>9.36985e-05</td>\n",
" <td>0.000463247</td>\n",
" <td>0.0279763</td>\n",
" <td>0.259271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.53131e-05</td>\n",
" <td>6.67572e-05</td>\n",
" <td>8.96454e-05</td>\n",
" <td>0.00028348</td>\n",
" <td>0.00350404</td>\n",
" <td>0.0781689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.03199e-05</td>\n",
" <td>6.7234e-05</td>\n",
" <td>9.84669e-05</td>\n",
" <td>0.000288248</td>\n",
" <td>0.0303786</td>\n",
" <td>0.108067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.96046e-05</td>\n",
" <td>7.15256e-05</td>\n",
" <td>0.000112295</td>\n",
" <td>0.000309944</td>\n",
" <td>0.0295844</td>\n",
" <td>0.0876651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.57899e-05</td>\n",
" <td>6.10352e-05</td>\n",
" <td>0.000103235</td>\n",
" <td>0.0447133</td>\n",
" <td>0.046418</td>\n",
" <td>0.0760965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.88894e-05</td>\n",
" <td>9.34601e-05</td>\n",
" <td>9.58443e-05</td>\n",
" <td>0.000294209</td>\n",
" <td>0.0291274</td>\n",
" <td>0.0407164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.36442e-05</td>\n",
" <td>6.34193e-05</td>\n",
" <td>9.94205e-05</td>\n",
" <td>0.000397682</td>\n",
" <td>0.0187783</td>\n",
" <td>0.0356209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.07967e-05</td>\n",
" <td>7.22408e-05</td>\n",
" <td>0.000258207</td>\n",
" <td>0.000315666</td>\n",
" <td>0.0341394</td>\n",
" <td>0.186528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.53131e-05</td>\n",
" <td>0.000107765</td>\n",
" <td>9.77516e-05</td>\n",
" <td>0.00032115</td>\n",
" <td>0.0276186</td>\n",
" <td>0.147884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>7.7486e-05</td>\n",
" <td>8.46386e-05</td>\n",
" <td>0.000612974</td>\n",
" <td>0.000521898</td>\n",
" <td>0.0254531</td>\n",
" <td>0.221212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>7.08103e-05</td>\n",
" <td>7.41482e-05</td>\n",
" <td>0.00010848</td>\n",
" <td>0.000302792</td>\n",
" <td>0.0469444</td>\n",
" <td>0.182513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.8903e-05</td>\n",
" <td>6.74725e-05</td>\n",
" <td>0.000104904</td>\n",
" <td>0.000324726</td>\n",
" <td>0.00325584</td>\n",
" <td>0.237404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.86646e-05</td>\n",
" <td>8.03471e-05</td>\n",
" <td>0.000146627</td>\n",
" <td>0.000370979</td>\n",
" <td>0.08916</td>\n",
" <td>0.227283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.93798e-05</td>\n",
" <td>8.29697e-05</td>\n",
" <td>0.000107527</td>\n",
" <td>0.000365734</td>\n",
" <td>0.0677176</td>\n",
" <td>0.314513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000149727</td>\n",
" <td>9.41753e-05</td>\n",
" <td>0.000114441</td>\n",
" <td>0.000779629</td>\n",
" <td>0.046108</td>\n",
" <td>0.368344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.38826e-05</td>\n",
" <td>6.65188e-05</td>\n",
" <td>9.65595e-05</td>\n",
" <td>0.00188541</td>\n",
" <td>0.0161653</td>\n",
" <td>0.342402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.48499e-05</td>\n",
" <td>6.31809e-05</td>\n",
" <td>9.32217e-05</td>\n",
" <td>0.0003829</td>\n",
" <td>0.0215144</td>\n",
" <td>0.120729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000106335</td>\n",
" <td>7.1764e-05</td>\n",
" <td>0.000112772</td>\n",
" <td>0.000779629</td>\n",
" <td>0.0222611</td>\n",
" <td>0.186024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.76973e-05</td>\n",
" <td>9.39369e-05</td>\n",
" <td>9.77516e-05</td>\n",
" <td>0.000374317</td>\n",
" <td>0.0146379</td>\n",
" <td>0.190964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>0.000102043</td>\n",
" <td>0.021872</td>\n",
" <td>0.000219584</td>\n",
" <td>0.000350952</td>\n",
" <td>0.0381646</td>\n",
" <td>0.1529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>8.17776e-05</td>\n",
" <td>7.7486e-05</td>\n",
" <td>0.0131552</td>\n",
" <td>0.0205743</td>\n",
" <td>0.0205081</td>\n",
" <td>0.281375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.53267e-05</td>\n",
" <td>7.48634e-05</td>\n",
" <td>9.27448e-05</td>\n",
" <td>0.000552177</td>\n",
" <td>0.0180812</td>\n",
" <td>0.27703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.98431e-05</td>\n",
" <td>8.53539e-05</td>\n",
" <td>9.41753e-05</td>\n",
" <td>0.000318289</td>\n",
" <td>0.0241232</td>\n",
" <td>0.208313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.60284e-05</td>\n",
" <td>0.000104666</td>\n",
" <td>9.39369e-05</td>\n",
" <td>0.000702381</td>\n",
" <td>0.0276973</td>\n",
" <td>0.0578537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.74589e-05</td>\n",
" <td>7.22408e-05</td>\n",
" <td>0.000216246</td>\n",
" <td>0.000584364</td>\n",
" <td>0.025069</td>\n",
" <td>0.0741835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>6.7234e-05</td>\n",
" <td>7.51019e-05</td>\n",
" <td>0.000115871</td>\n",
" <td>0.000340462</td>\n",
" <td>0.0193155</td>\n",
" <td>0.0608673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Edit</th>\n",
" <td>5.55515e-05</td>\n",
" <td>5.4121e-05</td>\n",
" <td>0.000215054</td>\n",
" <td>0.000378847</td>\n",
" <td>0.00331473</td>\n",
" <td>0.0430229</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" 10 30 100 300 1000 \\\n",
"Edit 0.00028348 4.57764e-05 8.96454e-05 0.000606298 0.0154662 \n",
"Edit 0.000197411 5.57899e-05 8.10623e-05 0.000564814 0.00598025 \n",
"Edit 0.000216246 4.43459e-05 7.98702e-05 0.0258429 0.0320714 \n",
"Edit 0.000275612 4.52995e-05 6.91414e-05 0.000577211 0.00949001 \n",
"Edit 0.000202894 4.62532e-05 7.51019e-05 0.000561237 0.0184975 \n",
"Edit 0.00029397 4.52995e-05 7.9155e-05 0.00056529 0.00492525 \n",
"Edit 0.000118732 4.52995e-05 7.82013e-05 0.000560999 0.0178421 \n",
"Edit 0.000142336 4.72069e-05 9.91821e-05 0.000623703 0.00508285 \n",
"Edit 0.000145674 4.48227e-05 0.000103474 0.00056982 0.0406191 \n",
"Edit 0.00028491 4.62532e-05 8.7738e-05 0.000611305 0.00784707 \n",
"Edit 0.000200033 3.52859e-05 8.65459e-05 0.000569105 0.00455499 \n",
"Edit 0.000190973 4.50611e-05 7.4625e-05 0.000567913 0.0204105 \n",
"Edit 0.000103235 4.81606e-05 0.000126362 0.000588417 0.0201089 \n",
"Edit 0.000200033 4.55379e-05 0.000131845 0.000624895 0.0173266 \n",
"Edit 0.000199795 5.84126e-05 9.03606e-05 0.000575304 0.0579138 \n",
"Edit 0.000113487 2.98023e-05 0.000117302 0.000631094 0.00911355 \n",
"Edit 0.000103235 5.03063e-05 7.86781e-05 0.00058794 0.0154772 \n",
"Edit 0.000117779 5.84126e-05 0.000117064 0.000590086 0.0203865 \n",
"Edit 0.000203609 3.55244e-05 0.000110626 0.000581026 0.0177763 \n",
"Edit 0.000222921 4.76837e-05 6.8903e-05 0.000571251 0.00486159 \n",
"Edit 0.000100851 6.41346e-05 0.000112534 0.000606298 0.0599113 \n",
"Edit 6.05583e-05 8.10623e-05 0.0001266 0.000300646 0.0341403 \n",
"Edit 5.62668e-05 6.48499e-05 0.000183344 0.000627756 0.0193818 \n",
"Edit 6.05583e-05 0.000118256 9.46522e-05 0.000367165 0.0267262 \n",
"Edit 8.03471e-05 0.000126362 0.000107527 0.0161557 0.0543747 \n",
"Edit 9.58443e-05 0.000174761 0.0376918 0.000381947 0.039829 \n",
"Edit 7.36713e-05 8.03471e-05 0.00021863 0.000302315 0.0811725 \n",
"Edit 6.81877e-05 6.69956e-05 8.46386e-05 0.00110316 0.0713711 \n",
"Edit 5.26905e-05 6.74725e-05 9.799e-05 0.000343323 0.00329924 \n",
"Edit 5.91278e-05 9.39369e-05 9.82285e-05 0.0168324 0.058032 \n",
"... ... ... ... ... ... \n",
"Edit 5.79357e-05 6.67572e-05 0.000139952 0.000785828 0.0182071 \n",
"Edit 0.000189781 0.00015831 9.91821e-05 0.000528336 0.0174797 \n",
"Edit 6.36578e-05 9.89437e-05 0.000165462 0.000321865 0.010603 \n",
"Edit 6.22272e-05 6.41346e-05 9.36985e-05 0.000463247 0.0279763 \n",
"Edit 5.53131e-05 6.67572e-05 8.96454e-05 0.00028348 0.00350404 \n",
"Edit 6.03199e-05 6.7234e-05 9.84669e-05 0.000288248 0.0303786 \n",
"Edit 5.96046e-05 7.15256e-05 0.000112295 0.000309944 0.0295844 \n",
"Edit 5.57899e-05 6.10352e-05 0.000103235 0.0447133 0.046418 \n",
"Edit 5.88894e-05 9.34601e-05 9.58443e-05 0.000294209 0.0291274 \n",
"Edit 5.36442e-05 6.34193e-05 9.94205e-05 0.000397682 0.0187783 \n",
"Edit 6.07967e-05 7.22408e-05 0.000258207 0.000315666 0.0341394 \n",
"Edit 5.53131e-05 0.000107765 9.77516e-05 0.00032115 0.0276186 \n",
"Edit 7.7486e-05 8.46386e-05 0.000612974 0.000521898 0.0254531 \n",
"Edit 7.08103e-05 7.41482e-05 0.00010848 0.000302792 0.0469444 \n",
"Edit 6.8903e-05 6.74725e-05 0.000104904 0.000324726 0.00325584 \n",
"Edit 6.86646e-05 8.03471e-05 0.000146627 0.000370979 0.08916 \n",
"Edit 6.93798e-05 8.29697e-05 0.000107527 0.000365734 0.0677176 \n",
"Edit 0.000149727 9.41753e-05 0.000114441 0.000779629 0.046108 \n",
"Edit 5.38826e-05 6.65188e-05 9.65595e-05 0.00188541 0.0161653 \n",
"Edit 6.48499e-05 6.31809e-05 9.32217e-05 0.0003829 0.0215144 \n",
"Edit 0.000106335 7.1764e-05 0.000112772 0.000779629 0.0222611 \n",
"Edit 5.76973e-05 9.39369e-05 9.77516e-05 0.000374317 0.0146379 \n",
"Edit 0.000102043 0.021872 0.000219584 0.000350952 0.0381646 \n",
"Edit 8.17776e-05 7.7486e-05 0.0131552 0.0205743 0.0205081 \n",
"Edit 6.53267e-05 7.48634e-05 9.27448e-05 0.000552177 0.0180812 \n",
"Edit 5.98431e-05 8.53539e-05 9.41753e-05 0.000318289 0.0241232 \n",
"Edit 5.60284e-05 0.000104666 9.39369e-05 0.000702381 0.0276973 \n",
"Edit 5.74589e-05 7.22408e-05 0.000216246 0.000584364 0.025069 \n",
"Edit 6.7234e-05 7.51019e-05 0.000115871 0.000340462 0.0193155 \n",
"Edit 5.55515e-05 5.4121e-05 0.000215054 0.000378847 0.00331473 \n",
"\n",
" 3000 \n",
"Edit 0.369191 \n",
"Edit 0.370803 \n",
"Edit 0.239238 \n",
"Edit 0.244957 \n",
"Edit 0.370901 \n",
"Edit 0.302423 \n",
"Edit 0.408601 \n",
"Edit 0.322326 \n",
"Edit 0.510803 \n",
"Edit 0.253818 \n",
"Edit 0.379067 \n",
"Edit 0.313604 \n",
"Edit 0.502235 \n",
"Edit 0.336385 \n",
"Edit 0.302057 \n",
"Edit 0.340617 \n",
"Edit 0.3358 \n",
"Edit 0.31888 \n",
"Edit 0.360284 \n",
"Edit 0.409907 \n",
"Edit 0.117487 \n",
"Edit 0.148152 \n",
"Edit 0.230133 \n",
"Edit 0.218837 \n",
"Edit 0.379593 \n",
"Edit 0.372713 \n",
"Edit 0.256876 \n",
"Edit 0.452341 \n",
"Edit 0.234745 \n",
"Edit 0.264047 \n",
"... ... \n",
"Edit 0.263054 \n",
"Edit 0.0825362 \n",
"Edit 0.221422 \n",
"Edit 0.259271 \n",
"Edit 0.0781689 \n",
"Edit 0.108067 \n",
"Edit 0.0876651 \n",
"Edit 0.0760965 \n",
"Edit 0.0407164 \n",
"Edit 0.0356209 \n",
"Edit 0.186528 \n",
"Edit 0.147884 \n",
"Edit 0.221212 \n",
"Edit 0.182513 \n",
"Edit 0.237404 \n",
"Edit 0.227283 \n",
"Edit 0.314513 \n",
"Edit 0.368344 \n",
"Edit 0.342402 \n",
"Edit 0.120729 \n",
"Edit 0.186024 \n",
"Edit 0.190964 \n",
"Edit 0.1529 \n",
"Edit 0.281375 \n",
"Edit 0.27703 \n",
"Edit 0.208313 \n",
"Edit 0.0578537 \n",
"Edit 0.0741835 \n",
"Edit 0.0608673 \n",
"Edit 0.0430229 \n",
"\n",
"[100 rows x 6 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Edit'].T"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 100\n",
"for label in labels:\n",
" df_temp = df[label].T\n",
" df_temp.index = range(100)\n",
" df_dict[label] = df_temp"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_total = pd.concat(df_dict,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th colspan=\"4\" halign=\"left\">Edit</th>\n",
" <th>...</th>\n",
" <th colspan=\"4\" halign=\"left\">NetSimile</th>\n",
" <th colspan=\"6\" halign=\"left\">Resistance Dist.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>10</th>\n",
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" <th>100</th>\n",
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" <th>2</th>\n",
" <td>0.0255492</td>\n",
" <td>0.00326967</td>\n",
" <td>0.134615</td>\n",
" <td>0.2121</td>\n",
" <td>1.35013</td>\n",
" <td>20.7121</td>\n",
" <td>0.000216246</td>\n",
" <td>4.43459e-05</td>\n",
" <td>7.98702e-05</td>\n",
" <td>0.0258429</td>\n",
" <td>...</td>\n",
" <td>3.04235</td>\n",
" <td>18.7939</td>\n",
" <td>69.3679</td>\n",
" <td>424.551</td>\n",
" <td>0.0662394</td>\n",
" <td>0.329336</td>\n",
" <td>0.366627</td>\n",
" <td>0.60622</td>\n",
" <td>5.28801</td>\n",
" <td>286.318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.00254083</td>\n",
" <td>0.0031991</td>\n",
" <td>0.0675135</td>\n",
" <td>0.289525</td>\n",
" <td>1.2284</td>\n",
" <td>18.9496</td>\n",
" <td>0.000275612</td>\n",
" <td>4.52995e-05</td>\n",
" <td>6.91414e-05</td>\n",
" <td>0.000577211</td>\n",
" <td>...</td>\n",
" <td>3.46615</td>\n",
" <td>15.0995</td>\n",
" <td>76.0705</td>\n",
" <td>1164.6</td>\n",
" <td>0.0367355</td>\n",
" <td>0.289481</td>\n",
" <td>0.161982</td>\n",
" <td>0.386847</td>\n",
" <td>7.01957</td>\n",
" <td>286.356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.00373173</td>\n",
" <td>0.00378919</td>\n",
" <td>0.123849</td>\n",
" <td>0.139059</td>\n",
" <td>0.71292</td>\n",
" <td>6.24403</td>\n",
" <td>0.000202894</td>\n",
" <td>4.62532e-05</td>\n",
" <td>7.51019e-05</td>\n",
" <td>0.000561237</td>\n",
" <td>...</td>\n",
" <td>7.13728</td>\n",
" <td>29.5192</td>\n",
" <td>131.814</td>\n",
" <td>284.094</td>\n",
" <td>0.108705</td>\n",
" <td>0.351261</td>\n",
" <td>0.372429</td>\n",
" <td>0.796469</td>\n",
" <td>7.88605</td>\n",
" <td>310.052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.010268</td>\n",
" <td>0.00323176</td>\n",
" <td>0.145001</td>\n",
" <td>0.394451</td>\n",
" <td>0.889508</td>\n",
" <td>10.9971</td>\n",
" <td>0.00029397</td>\n",
" <td>4.52995e-05</td>\n",
" <td>7.9155e-05</td>\n",
" <td>0.00056529</td>\n",
" <td>...</td>\n",
" <td>3.69945</td>\n",
" <td>15.4404</td>\n",
" <td>71.9671</td>\n",
" <td>962.901</td>\n",
" <td>0.0287809</td>\n",
" <td>0.286721</td>\n",
" <td>0.0342119</td>\n",
" <td>0.643994</td>\n",
" <td>9.9891</td>\n",
" <td>401.197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.00732493</td>\n",
" <td>0.0032835</td>\n",
" <td>0.142652</td>\n",
" <td>0.122106</td>\n",
" <td>1.71077</td>\n",
" <td>7.25871</td>\n",
" <td>0.000118732</td>\n",
" <td>4.52995e-05</td>\n",
" <td>7.82013e-05</td>\n",
" <td>0.000560999</td>\n",
" <td>...</td>\n",
" <td>9.35148</td>\n",
" <td>13.8124</td>\n",
" <td>171.157</td>\n",
" <td>383.63</td>\n",
" <td>0.0609736</td>\n",
" <td>0.316966</td>\n",
" <td>0.204251</td>\n",
" <td>0.376051</td>\n",
" <td>4.84392</td>\n",
" <td>219.087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.0634875</td>\n",
" <td>0.00327587</td>\n",
" <td>0.134998</td>\n",
" <td>0.148662</td>\n",
" <td>0.628493</td>\n",
" <td>6.32107</td>\n",
" <td>0.000142336</td>\n",
" <td>4.72069e-05</td>\n",
" <td>9.91821e-05</td>\n",
" <td>0.000623703</td>\n",
" <td>...</td>\n",
" <td>5.67684</td>\n",
" <td>42.9957</td>\n",
" <td>156.593</td>\n",
" <td>826.862</td>\n",
" <td>0.0581741</td>\n",
" <td>0.284809</td>\n",
" <td>0.333027</td>\n",
" <td>0.493491</td>\n",
" <td>5.38635</td>\n",
" <td>272.664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.00247455</td>\n",
" <td>0.00315833</td>\n",
" <td>0.238209</td>\n",
" <td>0.128114</td>\n",
" <td>1.12096</td>\n",
" <td>13.2293</td>\n",
" <td>0.000145674</td>\n",
" <td>4.48227e-05</td>\n",
" <td>0.000103474</td>\n",
" <td>0.00056982</td>\n",
" <td>...</td>\n",
" <td>5.68893</td>\n",
" <td>19.5079</td>\n",
" <td>90.1442</td>\n",
" <td>707.171</td>\n",
" <td>0.0392709</td>\n",
" <td>0.346845</td>\n",
" <td>0.408748</td>\n",
" <td>0.451287</td>\n",
" <td>6.31324</td>\n",
" <td>338.808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.0185492</td>\n",
" <td>0.00331211</td>\n",
" <td>0.220097</td>\n",
" <td>0.123091</td>\n",
" <td>1.45278</td>\n",
" <td>19.683</td>\n",
" <td>0.00028491</td>\n",
" <td>4.62532e-05</td>\n",
" <td>8.7738e-05</td>\n",
" <td>0.000611305</td>\n",
" <td>...</td>\n",
" <td>7.40615</td>\n",
" <td>19.6082</td>\n",
" <td>74.8941</td>\n",
" <td>376.752</td>\n",
" <td>0.064425</td>\n",
" <td>0.360898</td>\n",
" <td>0.166249</td>\n",
" <td>0.574641</td>\n",
" <td>9.71514</td>\n",
" <td>308.576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.00246906</td>\n",
" <td>0.00330091</td>\n",
" <td>0.105686</td>\n",
" <td>0.354446</td>\n",
" <td>1.63971</td>\n",
" <td>15.1695</td>\n",
" <td>0.000200033</td>\n",
" <td>3.52859e-05</td>\n",
" <td>8.65459e-05</td>\n",
" <td>0.000569105</td>\n",
" <td>...</td>\n",
" <td>3.81637</td>\n",
" <td>13.9831</td>\n",
" <td>104.885</td>\n",
" <td>728.491</td>\n",
" <td>0.0466452</td>\n",
" <td>0.257225</td>\n",
" <td>0.223319</td>\n",
" <td>1.30637</td>\n",
" <td>7.14932</td>\n",
" <td>306.006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.0235674</td>\n",
" <td>0.00328159</td>\n",
" <td>0.0674219</td>\n",
" <td>0.138284</td>\n",
" <td>1.06574</td>\n",
" <td>11.6472</td>\n",
" <td>0.000190973</td>\n",
" <td>4.50611e-05</td>\n",
" <td>7.4625e-05</td>\n",
" <td>0.000567913</td>\n",
" <td>...</td>\n",
" <td>4.37711</td>\n",
" <td>18.2324</td>\n",
" <td>102.386</td>\n",
" <td>296.851</td>\n",
" <td>0.0717518</td>\n",
" <td>0.343324</td>\n",
" <td>0.149315</td>\n",
" <td>0.475544</td>\n",
" <td>7.3124</td>\n",
" <td>346.756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.0299931</td>\n",
" <td>0.00333881</td>\n",
" <td>0.320308</td>\n",
" <td>0.140428</td>\n",
" <td>0.753508</td>\n",
" <td>6.85036</td>\n",
" <td>0.000103235</td>\n",
" <td>4.81606e-05</td>\n",
" <td>0.000126362</td>\n",
" <td>0.000588417</td>\n",
" <td>...</td>\n",
" <td>3.32854</td>\n",
" <td>36.7943</td>\n",
" <td>163.731</td>\n",
" <td>1323.01</td>\n",
" <td>0.0829215</td>\n",
" <td>0.34898</td>\n",
" <td>0.219229</td>\n",
" <td>0.673227</td>\n",
" <td>5.08562</td>\n",
" <td>226.097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.0198274</td>\n",
" <td>0.00331926</td>\n",
" <td>0.132601</td>\n",
" <td>0.450442</td>\n",
" <td>0.869783</td>\n",
" <td>16.4244</td>\n",
" <td>0.000200033</td>\n",
" <td>4.55379e-05</td>\n",
" <td>0.000131845</td>\n",
" <td>0.000624895</td>\n",
" <td>...</td>\n",
" <td>8.0428</td>\n",
" <td>36.0131</td>\n",
" <td>75.1092</td>\n",
" <td>393.946</td>\n",
" <td>0.0961423</td>\n",
" <td>0.294921</td>\n",
" <td>0.134118</td>\n",
" <td>0.448892</td>\n",
" <td>5.25793</td>\n",
" <td>305.277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.0398979</td>\n",
" <td>0.00331473</td>\n",
" <td>0.282356</td>\n",
" <td>0.167487</td>\n",
" <td>1.78895</td>\n",
" <td>11.1853</td>\n",
" <td>0.000199795</td>\n",
" <td>5.84126e-05</td>\n",
" <td>9.03606e-05</td>\n",
" <td>0.000575304</td>\n",
" <td>...</td>\n",
" <td>3.26578</td>\n",
" <td>12.8642</td>\n",
" <td>82.2807</td>\n",
" <td>318.312</td>\n",
" <td>0.0905228</td>\n",
" <td>0.319452</td>\n",
" <td>0.465786</td>\n",
" <td>0.714469</td>\n",
" <td>6.24104</td>\n",
" <td>406.607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.00579095</td>\n",
" <td>0.00381613</td>\n",
" <td>0.133854</td>\n",
" <td>0.243878</td>\n",
" <td>0.53689</td>\n",
" <td>9.99625</td>\n",
" <td>0.000113487</td>\n",
" <td>2.98023e-05</td>\n",
" <td>0.000117302</td>\n",
" <td>0.000631094</td>\n",
" <td>...</td>\n",
" <td>2.9404</td>\n",
" <td>42.9733</td>\n",
" <td>106.815</td>\n",
" <td>299.544</td>\n",
" <td>0.0158229</td>\n",
" <td>0.260389</td>\n",
" <td>0.349355</td>\n",
" <td>0.965806</td>\n",
" <td>7.55051</td>\n",
" <td>289.761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.00652194</td>\n",
" <td>0.00316167</td>\n",
" <td>0.117872</td>\n",
" <td>0.178213</td>\n",
" <td>1.41665</td>\n",
" <td>12.0191</td>\n",
" <td>0.000103235</td>\n",
" <td>5.03063e-05</td>\n",
" <td>7.86781e-05</td>\n",
" <td>0.00058794</td>\n",
" <td>...</td>\n",
" <td>3.70223</td>\n",
" <td>33.8596</td>\n",
" <td>114.352</td>\n",
" <td>377.378</td>\n",
" <td>0.0421722</td>\n",
" <td>0.26601</td>\n",
" <td>0.194582</td>\n",
" <td>0.730792</td>\n",
" <td>8.13302</td>\n",
" <td>253.171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.0037322</td>\n",
" <td>0.00333786</td>\n",
" <td>0.105466</td>\n",
" <td>0.197984</td>\n",
" <td>1.59124</td>\n",
" <td>5.5716</td>\n",
" <td>0.000117779</td>\n",
" <td>5.84126e-05</td>\n",
" <td>0.000117064</td>\n",
" <td>0.000590086</td>\n",
" <td>...</td>\n",
" <td>3.16932</td>\n",
" <td>27.4833</td>\n",
" <td>132.772</td>\n",
" <td>342.497</td>\n",
" <td>0.0762393</td>\n",
" <td>0.308653</td>\n",
" <td>0.146093</td>\n",
" <td>1.46753</td>\n",
" <td>5.81154</td>\n",
" <td>272.882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.00249028</td>\n",
" <td>0.00324774</td>\n",
" <td>0.158101</td>\n",
" <td>0.287137</td>\n",
" <td>1.50268</td>\n",
" <td>2.5454</td>\n",
" <td>0.000203609</td>\n",
" <td>3.55244e-05</td>\n",
" <td>0.000110626</td>\n",
" <td>0.000581026</td>\n",
" <td>...</td>\n",
" <td>8.05726</td>\n",
" <td>16.3483</td>\n",
" <td>183.262</td>\n",
" <td>312.721</td>\n",
" <td>0.0249312</td>\n",
" <td>0.292686</td>\n",
" <td>0.303927</td>\n",
" <td>0.731482</td>\n",
" <td>8.13812</td>\n",
" <td>257.194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.0350316</td>\n",
" <td>0.00318265</td>\n",
" <td>0.0870342</td>\n",
" <td>0.229768</td>\n",
" <td>0.977381</td>\n",
" <td>10.9638</td>\n",
" <td>0.000222921</td>\n",
" <td>4.76837e-05</td>\n",
" <td>6.8903e-05</td>\n",
" <td>0.000571251</td>\n",
" <td>...</td>\n",
" <td>4.04973</td>\n",
" <td>19.8607</td>\n",
" <td>140.327</td>\n",
" <td>753.634</td>\n",
" <td>0.0936012</td>\n",
" <td>0.30969</td>\n",
" <td>0.13713</td>\n",
" <td>0.598171</td>\n",
" <td>7.03823</td>\n",
" <td>261.466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.00315642</td>\n",
" <td>0.0509071</td>\n",
" <td>0.252518</td>\n",
" <td>0.565023</td>\n",
" <td>1.85396</td>\n",
" <td>11.4247</td>\n",
" <td>0.000100851</td>\n",
" <td>6.41346e-05</td>\n",
" <td>0.000112534</td>\n",
" <td>0.000606298</td>\n",
" <td>...</td>\n",
" <td>40.9044</td>\n",
" <td>176.153</td>\n",
" <td>488.329</td>\n",
" <td>763.664</td>\n",
" <td>0.00217104</td>\n",
" <td>0.105391</td>\n",
" <td>0.585435</td>\n",
" <td>1.49593</td>\n",
" <td>15.6418</td>\n",
" <td>175.558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.0703032</td>\n",
" <td>0.00404811</td>\n",
" <td>0.578033</td>\n",
" <td>0.353467</td>\n",
" <td>1.28807</td>\n",
" <td>10.9359</td>\n",
" <td>6.05583e-05</td>\n",
" <td>8.10623e-05</td>\n",
" <td>0.0001266</td>\n",
" <td>0.000300646</td>\n",
" <td>...</td>\n",
" <td>37.767</td>\n",
" <td>153.539</td>\n",
" <td>417.211</td>\n",
" <td>773.88</td>\n",
" <td>0.00197649</td>\n",
" <td>0.186705</td>\n",
" <td>0.258148</td>\n",
" <td>1.91176</td>\n",
" <td>17.2154</td>\n",
" <td>168.516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.0383575</td>\n",
" <td>0.041707</td>\n",
" <td>0.431826</td>\n",
" <td>0.37448</td>\n",
" <td>1.12623</td>\n",
" <td>8.54068</td>\n",
" <td>5.62668e-05</td>\n",
" <td>6.48499e-05</td>\n",
" <td>0.000183344</td>\n",
" <td>0.000627756</td>\n",
" <td>...</td>\n",
" <td>33.3566</td>\n",
" <td>157.035</td>\n",
" <td>385.872</td>\n",
" <td>683.128</td>\n",
" <td>0.0373538</td>\n",
" <td>0.0551326</td>\n",
" <td>0.266732</td>\n",
" <td>1.73766</td>\n",
" <td>20.8298</td>\n",
" <td>252.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.0401249</td>\n",
" <td>0.00505853</td>\n",
" <td>0.271244</td>\n",
" <td>0.377284</td>\n",
" <td>1.34504</td>\n",
" <td>10.9792</td>\n",
" <td>6.05583e-05</td>\n",
" <td>0.000118256</td>\n",
" <td>9.46522e-05</td>\n",
" <td>0.000367165</td>\n",
" <td>...</td>\n",
" <td>32.383</td>\n",
" <td>125.333</td>\n",
" <td>379.993</td>\n",
" <td>619.949</td>\n",
" <td>0.0577505</td>\n",
" <td>0.0818896</td>\n",
" <td>0.181272</td>\n",
" <td>1.90132</td>\n",
" <td>19.3298</td>\n",
" <td>225.051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.0363102</td>\n",
" <td>0.00368142</td>\n",
" <td>0.123279</td>\n",
" <td>0.290556</td>\n",
" <td>1.66722</td>\n",
" <td>11.3701</td>\n",
" <td>8.03471e-05</td>\n",
" <td>0.000126362</td>\n",
" <td>0.000107527</td>\n",
" <td>0.0161557</td>\n",
" <td>...</td>\n",
" <td>31.3307</td>\n",
" <td>120.558</td>\n",
" <td>367.725</td>\n",
" <td>618.596</td>\n",
" <td>0.00286388</td>\n",
" <td>0.0667305</td>\n",
" <td>0.18289</td>\n",
" <td>1.63502</td>\n",
" <td>24.8362</td>\n",
" <td>191.847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.00356483</td>\n",
" <td>0.00354505</td>\n",
" <td>0.272037</td>\n",
" <td>0.299258</td>\n",
" <td>1.08178</td>\n",
" <td>11.4604</td>\n",
" <td>9.58443e-05</td>\n",
" <td>0.000174761</td>\n",
" <td>0.0376918</td>\n",
" <td>0.000381947</td>\n",
" <td>...</td>\n",
" <td>32.3313</td>\n",
" <td>118.203</td>\n",
" <td>369.642</td>\n",
" <td>608.396</td>\n",
" <td>0.047744</td>\n",
" <td>0.0994029</td>\n",
" <td>0.531896</td>\n",
" <td>1.50364</td>\n",
" <td>15.9397</td>\n",
" <td>170.523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.0383987</td>\n",
" <td>0.112523</td>\n",
" <td>0.164367</td>\n",
" <td>0.362127</td>\n",
" <td>1.11254</td>\n",
" <td>6.63539</td>\n",
" <td>7.36713e-05</td>\n",
" <td>8.03471e-05</td>\n",
" <td>0.00021863</td>\n",
" <td>0.000302315</td>\n",
" <td>...</td>\n",
" <td>29.5611</td>\n",
" <td>113.711</td>\n",
" <td>354.963</td>\n",
" <td>645.559</td>\n",
" <td>0.00248456</td>\n",
" <td>0.153606</td>\n",
" <td>0.200012</td>\n",
" <td>1.93583</td>\n",
" <td>17.4286</td>\n",
" <td>222.579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.0267434</td>\n",
" <td>0.0327902</td>\n",
" <td>0.10284</td>\n",
" <td>0.238473</td>\n",
" <td>0.864369</td>\n",
" <td>7.71238</td>\n",
" <td>6.81877e-05</td>\n",
" <td>6.69956e-05</td>\n",
" <td>8.46386e-05</td>\n",
" <td>0.00110316</td>\n",
" <td>...</td>\n",
" <td>27.774</td>\n",
" <td>115.812</td>\n",
" <td>313.698</td>\n",
" <td>663.518</td>\n",
" <td>0.0411949</td>\n",
" <td>0.078248</td>\n",
" <td>0.158036</td>\n",
" <td>0.908624</td>\n",
" <td>21.2324</td>\n",
" <td>186.154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.00316405</td>\n",
" <td>0.0187602</td>\n",
" <td>0.169974</td>\n",
" <td>0.137836</td>\n",
" <td>1.00438</td>\n",
" <td>11.2765</td>\n",
" <td>5.26905e-05</td>\n",
" <td>6.74725e-05</td>\n",
" <td>9.799e-05</td>\n",
" <td>0.000343323</td>\n",
" <td>...</td>\n",
" <td>25.0697</td>\n",
" <td>103.779</td>\n",
" <td>327.332</td>\n",
" <td>722.827</td>\n",
" <td>0.0395265</td>\n",
" <td>0.0964274</td>\n",
" <td>0.351207</td>\n",
" <td>0.849531</td>\n",
" <td>14.3489</td>\n",
" <td>167.171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.0454853</td>\n",
" <td>0.0173705</td>\n",
" <td>0.118016</td>\n",
" <td>0.218685</td>\n",
" <td>1.57731</td>\n",
" <td>5.00993</td>\n",
" <td>5.91278e-05</td>\n",
" <td>9.39369e-05</td>\n",
" <td>9.82285e-05</td>\n",
" <td>0.0168324</td>\n",
" <td>...</td>\n",
" <td>23.0361</td>\n",
" <td>104.01</td>\n",
" <td>363.297</td>\n",
" <td>685.268</td>\n",
" <td>0.00203586</td>\n",
" <td>0.0399907</td>\n",
" <td>0.14689</td>\n",
" <td>1.34373</td>\n",
" <td>23.6519</td>\n",
" <td>181.885</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>0.0221875</td>\n",
" <td>0.00478601</td>\n",
" <td>0.203532</td>\n",
" <td>0.195059</td>\n",
" <td>0.755858</td>\n",
" <td>7.37219</td>\n",
" <td>5.79357e-05</td>\n",
" <td>6.67572e-05</td>\n",
" <td>0.000139952</td>\n",
" <td>0.000785828</td>\n",
" <td>...</td>\n",
" <td>20.1595</td>\n",
" <td>95.0164</td>\n",
" <td>441.813</td>\n",
" <td>575.968</td>\n",
" <td>0.00234365</td>\n",
" <td>0.0498929</td>\n",
" <td>0.149518</td>\n",
" <td>1.5206</td>\n",
" <td>9.44375</td>\n",
" <td>166.049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>0.0266867</td>\n",
" <td>0.0034945</td>\n",
" <td>0.122632</td>\n",
" <td>0.200907</td>\n",
" <td>0.729664</td>\n",
" <td>6.03421</td>\n",
" <td>0.000189781</td>\n",
" <td>0.00015831</td>\n",
" <td>9.91821e-05</td>\n",
" <td>0.000528336</td>\n",
" <td>...</td>\n",
" <td>25.1566</td>\n",
" <td>84.6385</td>\n",
" <td>436.033</td>\n",
" <td>734.326</td>\n",
" <td>0.00241542</td>\n",
" <td>0.0651419</td>\n",
" <td>0.127522</td>\n",
" <td>1.64205</td>\n",
" <td>11.9583</td>\n",
" <td>153.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>0.0216017</td>\n",
" <td>0.0241156</td>\n",
" <td>0.0987835</td>\n",
" <td>0.135259</td>\n",
" <td>1.18199</td>\n",
" <td>6.2178</td>\n",
" <td>6.36578e-05</td>\n",
" <td>9.89437e-05</td>\n",
" <td>0.000165462</td>\n",
" <td>0.000321865</td>\n",
" <td>...</td>\n",
" <td>19.4583</td>\n",
" <td>79.4992</td>\n",
" <td>445.041</td>\n",
" <td>617.057</td>\n",
" <td>0.0363936</td>\n",
" <td>0.0648901</td>\n",
" <td>0.0928342</td>\n",
" <td>1.43396</td>\n",
" <td>12.1885</td>\n",
" <td>135.448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>0.0126171</td>\n",
" <td>0.00331187</td>\n",
" <td>0.102618</td>\n",
" <td>0.461809</td>\n",
" <td>0.64116</td>\n",
" <td>3.95158</td>\n",
" <td>6.22272e-05</td>\n",
" <td>6.41346e-05</td>\n",
" <td>9.36985e-05</td>\n",
" <td>0.000463247</td>\n",
" <td>...</td>\n",
" <td>16.5918</td>\n",
" <td>74.4532</td>\n",
" <td>437.51</td>\n",
" <td>1011.45</td>\n",
" <td>0.0020082</td>\n",
" <td>0.0479052</td>\n",
" <td>0.141966</td>\n",
" <td>0.872335</td>\n",
" <td>7.55138</td>\n",
" <td>142.963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0.00331283</td>\n",
" <td>0.00361419</td>\n",
" <td>0.136445</td>\n",
" <td>0.0892792</td>\n",
" <td>1.07894</td>\n",
" <td>4.61254</td>\n",
" <td>5.53131e-05</td>\n",
" <td>6.67572e-05</td>\n",
" <td>8.96454e-05</td>\n",
" <td>0.00028348</td>\n",
" <td>...</td>\n",
" <td>16.6964</td>\n",
" <td>75.106</td>\n",
" <td>419.636</td>\n",
" <td>723.292</td>\n",
" <td>0.015305</td>\n",
" <td>0.0511754</td>\n",
" <td>0.122267</td>\n",
" <td>1.42161</td>\n",
" <td>10.6903</td>\n",
" <td>144.138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0.0207899</td>\n",
" <td>0.00347638</td>\n",
" <td>0.377512</td>\n",
" <td>0.11098</td>\n",
" <td>0.87001</td>\n",
" <td>5.01065</td>\n",
" <td>6.03199e-05</td>\n",
" <td>6.7234e-05</td>\n",
" <td>9.84669e-05</td>\n",
" <td>0.000288248</td>\n",
" <td>...</td>\n",
" <td>19.1428</td>\n",
" <td>82.8759</td>\n",
" <td>415.194</td>\n",
" <td>820.765</td>\n",
" <td>0.0175529</td>\n",
" <td>0.0731101</td>\n",
" <td>0.132956</td>\n",
" <td>1.27241</td>\n",
" <td>12.1347</td>\n",
" <td>68.5179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0.0200582</td>\n",
" <td>0.00362062</td>\n",
" <td>0.0971103</td>\n",
" <td>0.203827</td>\n",
" <td>1.42324</td>\n",
" <td>3.61205</td>\n",
" <td>5.96046e-05</td>\n",
" <td>7.15256e-05</td>\n",
" <td>0.000112295</td>\n",
" <td>0.000309944</td>\n",
" <td>...</td>\n",
" <td>20.0881</td>\n",
" <td>86.6134</td>\n",
" <td>388.782</td>\n",
" <td>1320.35</td>\n",
" <td>0.0177104</td>\n",
" <td>0.0815868</td>\n",
" <td>0.141449</td>\n",
" <td>1.44218</td>\n",
" <td>19.2873</td>\n",
" <td>98.2618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0.0229545</td>\n",
" <td>0.00340271</td>\n",
" <td>0.120472</td>\n",
" <td>0.542338</td>\n",
" <td>1.68286</td>\n",
" <td>3.79592</td>\n",
" <td>5.57899e-05</td>\n",
" <td>6.10352e-05</td>\n",
" <td>0.000103235</td>\n",
" <td>0.0447133</td>\n",
" <td>...</td>\n",
" <td>25.9848</td>\n",
" <td>86.4087</td>\n",
" <td>386.427</td>\n",
" <td>1551.21</td>\n",
" <td>0.00232625</td>\n",
" <td>0.0898879</td>\n",
" <td>0.162069</td>\n",
" <td>1.24732</td>\n",
" <td>20.606</td>\n",
" <td>79.3052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0.00324655</td>\n",
" <td>0.0245507</td>\n",
" <td>0.223871</td>\n",
" <td>0.184385</td>\n",
" <td>0.995938</td>\n",
" <td>3.82391</td>\n",
" <td>5.88894e-05</td>\n",
" <td>9.34601e-05</td>\n",
" <td>9.58443e-05</td>\n",
" <td>0.000294209</td>\n",
" <td>...</td>\n",
" <td>19.4843</td>\n",
" <td>82.8619</td>\n",
" <td>308.528</td>\n",
" <td>1552.65</td>\n",
" <td>0.00199056</td>\n",
" <td>0.0793159</td>\n",
" <td>0.137465</td>\n",
" <td>1.26285</td>\n",
" <td>14.7202</td>\n",
" <td>75.4733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0.0399075</td>\n",
" <td>0.0200801</td>\n",
" <td>0.103044</td>\n",
" <td>0.212389</td>\n",
" <td>1.15255</td>\n",
" <td>2.37331</td>\n",
" <td>5.36442e-05</td>\n",
" <td>6.34193e-05</td>\n",
" <td>9.94205e-05</td>\n",
" <td>0.000397682</td>\n",
" <td>...</td>\n",
" <td>21.3017</td>\n",
" <td>89.5381</td>\n",
" <td>259.461</td>\n",
" <td>1821.54</td>\n",
" <td>0.00201917</td>\n",
" <td>0.0216198</td>\n",
" <td>0.109902</td>\n",
" <td>1.69047</td>\n",
" <td>27.8197</td>\n",
" <td>75.3377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0.035527</td>\n",
" <td>0.00375414</td>\n",
" <td>0.532439</td>\n",
" <td>0.404772</td>\n",
" <td>1.51675</td>\n",
" <td>8.31719</td>\n",
" <td>6.07967e-05</td>\n",
" <td>7.22408e-05</td>\n",
" <td>0.000258207</td>\n",
" <td>0.000315666</td>\n",
" <td>...</td>\n",
" <td>39.2255</td>\n",
" <td>160.007</td>\n",
" <td>635.21</td>\n",
" <td>940.132</td>\n",
" <td>0.0020864</td>\n",
" <td>0.0531466</td>\n",
" <td>0.228098</td>\n",
" <td>2.30744</td>\n",
" <td>32.4833</td>\n",
" <td>134.867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.0354512</td>\n",
" <td>0.0463622</td>\n",
" <td>0.212068</td>\n",
" <td>0.271919</td>\n",
" <td>1.53248</td>\n",
" <td>10.7588</td>\n",
" <td>5.53131e-05</td>\n",
" <td>0.000107765</td>\n",
" <td>9.77516e-05</td>\n",
" <td>0.00032115</td>\n",
" <td>...</td>\n",
" <td>28.7231</td>\n",
" <td>154.394</td>\n",
" <td>607.673</td>\n",
" <td>865.007</td>\n",
" <td>0.0272379</td>\n",
" <td>0.103535</td>\n",
" <td>0.192799</td>\n",
" <td>1.63943</td>\n",
" <td>12.7567</td>\n",
" <td>258.762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0.0584714</td>\n",
" <td>0.0430498</td>\n",
" <td>0.171326</td>\n",
" <td>0.289474</td>\n",
" <td>0.847046</td>\n",
" <td>9.27352</td>\n",
" <td>7.7486e-05</td>\n",
" <td>8.46386e-05</td>\n",
" <td>0.000612974</td>\n",
" <td>0.000521898</td>\n",
" <td>...</td>\n",
" <td>38.3272</td>\n",
" <td>144.591</td>\n",
" <td>564.026</td>\n",
" <td>826.382</td>\n",
" <td>0.0439725</td>\n",
" <td>0.0781062</td>\n",
" <td>0.134982</td>\n",
" <td>2.04452</td>\n",
" <td>18.0174</td>\n",
" <td>267.903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0.0792615</td>\n",
" <td>0.00361729</td>\n",
" <td>0.206428</td>\n",
" <td>0.275465</td>\n",
" <td>1.38957</td>\n",
" <td>10.6554</td>\n",
" <td>7.08103e-05</td>\n",
" <td>7.41482e-05</td>\n",
" <td>0.00010848</td>\n",
" <td>0.000302792</td>\n",
" <td>...</td>\n",
" <td>35.1668</td>\n",
" <td>136.899</td>\n",
" <td>527.727</td>\n",
" <td>901.435</td>\n",
" <td>0.00212622</td>\n",
" <td>0.294862</td>\n",
" <td>0.206567</td>\n",
" <td>1.52487</td>\n",
" <td>13.4985</td>\n",
" <td>131.153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0.00346994</td>\n",
" <td>0.00500035</td>\n",
" <td>0.179486</td>\n",
" <td>0.737461</td>\n",
" <td>1.34554</td>\n",
" <td>8.63438</td>\n",
" <td>6.8903e-05</td>\n",
" <td>6.74725e-05</td>\n",
" <td>0.000104904</td>\n",
" <td>0.000324726</td>\n",
" <td>...</td>\n",
" <td>31.7289</td>\n",
" <td>134.146</td>\n",
" <td>504.352</td>\n",
" <td>799.212</td>\n",
" <td>0.0021987</td>\n",
" <td>0.0955625</td>\n",
" <td>0.176665</td>\n",
" <td>2.14557</td>\n",
" <td>18.8681</td>\n",
" <td>191.115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0.0411093</td>\n",
" <td>0.00357485</td>\n",
" <td>0.206932</td>\n",
" <td>0.223555</td>\n",
" <td>1.36813</td>\n",
" <td>8.43188</td>\n",
" <td>6.86646e-05</td>\n",
" <td>8.03471e-05</td>\n",
" <td>0.000146627</td>\n",
" <td>0.000370979</td>\n",
" <td>...</td>\n",
" <td>30.1444</td>\n",
" <td>125.013</td>\n",
" <td>522.019</td>\n",
" <td>818.913</td>\n",
" <td>0.00361514</td>\n",
" <td>0.0801191</td>\n",
" <td>0.188461</td>\n",
" <td>1.97422</td>\n",
" <td>18.1579</td>\n",
" <td>234.733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0.0232716</td>\n",
" <td>0.0298278</td>\n",
" <td>0.140514</td>\n",
" <td>0.464055</td>\n",
" <td>1.62925</td>\n",
" <td>8.13316</td>\n",
" <td>6.93798e-05</td>\n",
" <td>8.29697e-05</td>\n",
" <td>0.000107527</td>\n",
" <td>0.000365734</td>\n",
" <td>...</td>\n",
" <td>26.4046</td>\n",
" <td>121.995</td>\n",
" <td>510.979</td>\n",
" <td>736.415</td>\n",
" <td>0.0291243</td>\n",
" <td>0.0895164</td>\n",
" <td>0.182347</td>\n",
" <td>1.12704</td>\n",
" <td>19.7729</td>\n",
" <td>218.865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0.00319791</td>\n",
" <td>0.0296748</td>\n",
" <td>0.128548</td>\n",
" <td>0.654692</td>\n",
" <td>1.30826</td>\n",
" <td>6.3271</td>\n",
" <td>0.000149727</td>\n",
" <td>9.41753e-05</td>\n",
" <td>0.000114441</td>\n",
" <td>0.000779629</td>\n",
" <td>...</td>\n",
" <td>29.0845</td>\n",
" <td>121.213</td>\n",
" <td>487.977</td>\n",
" <td>761.874</td>\n",
" <td>0.00220513</td>\n",
" <td>0.0892253</td>\n",
" <td>0.165562</td>\n",
" <td>1.5611</td>\n",
" <td>21.3043</td>\n",
" <td>168.555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>0.0320525</td>\n",
" <td>0.0337968</td>\n",
" <td>0.170508</td>\n",
" <td>0.236835</td>\n",
" <td>1.12896</td>\n",
" <td>6.49818</td>\n",
" <td>5.38826e-05</td>\n",
" <td>6.65188e-05</td>\n",
" <td>9.65595e-05</td>\n",
" <td>0.00188541</td>\n",
" <td>...</td>\n",
" <td>33.0258</td>\n",
" <td>122.565</td>\n",
" <td>513.899</td>\n",
" <td>853.68</td>\n",
" <td>0.0527539</td>\n",
" <td>0.0590501</td>\n",
" <td>0.167497</td>\n",
" <td>2.0806</td>\n",
" <td>22.177</td>\n",
" <td>167.309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>0.0188746</td>\n",
" <td>0.00455403</td>\n",
" <td>0.17303</td>\n",
" <td>0.250048</td>\n",
" <td>0.83953</td>\n",
" <td>6.67258</td>\n",
" <td>6.48499e-05</td>\n",
" <td>6.31809e-05</td>\n",
" <td>9.32217e-05</td>\n",
" <td>0.0003829</td>\n",
" <td>...</td>\n",
" <td>22.0971</td>\n",
" <td>86.8921</td>\n",
" <td>505.164</td>\n",
" <td>866.656</td>\n",
" <td>0.0261919</td>\n",
" <td>0.0771041</td>\n",
" <td>0.156201</td>\n",
" <td>1.34541</td>\n",
" <td>11.719</td>\n",
" <td>161.827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>0.0298221</td>\n",
" <td>0.0544572</td>\n",
" <td>0.267794</td>\n",
" <td>0.236287</td>\n",
" <td>1.06179</td>\n",
" <td>6.83444</td>\n",
" <td>0.000106335</td>\n",
" <td>7.1764e-05</td>\n",
" <td>0.000112772</td>\n",
" <td>0.000779629</td>\n",
" <td>...</td>\n",
" <td>18.4411</td>\n",
" <td>84.4593</td>\n",
" <td>508.354</td>\n",
" <td>784.863</td>\n",
" <td>0.0274999</td>\n",
" <td>0.0698869</td>\n",
" <td>0.409035</td>\n",
" <td>1.80838</td>\n",
" <td>11.9296</td>\n",
" <td>117.131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>0.0192378</td>\n",
" <td>0.021419</td>\n",
" <td>0.131151</td>\n",
" <td>0.22551</td>\n",
" <td>0.733673</td>\n",
" <td>9.10781</td>\n",
" <td>5.76973e-05</td>\n",
" <td>9.39369e-05</td>\n",
" <td>9.77516e-05</td>\n",
" <td>0.000374317</td>\n",
" <td>...</td>\n",
" <td>19.6784</td>\n",
" <td>84.7171</td>\n",
" <td>485.348</td>\n",
" <td>710.333</td>\n",
" <td>0.023351</td>\n",
" <td>0.0490839</td>\n",
" <td>0.121492</td>\n",
" <td>1.42748</td>\n",
" <td>12.9422</td>\n",
" <td>146.072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>0.00487614</td>\n",
" <td>0.0207679</td>\n",
" <td>0.211021</td>\n",
" <td>0.222272</td>\n",
" <td>1.8271</td>\n",
" <td>4.85606</td>\n",
" <td>0.000102043</td>\n",
" <td>0.021872</td>\n",
" <td>0.000219584</td>\n",
" <td>0.000350952</td>\n",
" <td>...</td>\n",
" <td>20.4142</td>\n",
" <td>91.615</td>\n",
" <td>503.385</td>\n",
" <td>965.821</td>\n",
" <td>0.0189078</td>\n",
" <td>0.0106602</td>\n",
" <td>0.378006</td>\n",
" <td>1.6321</td>\n",
" <td>25.0955</td>\n",
" <td>154.101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>0.00327659</td>\n",
" <td>0.00739121</td>\n",
" <td>0.295464</td>\n",
" <td>0.416258</td>\n",
" <td>1.11788</td>\n",
" <td>3.95077</td>\n",
" <td>8.17776e-05</td>\n",
" <td>7.7486e-05</td>\n",
" <td>0.0131552</td>\n",
" <td>0.0205743</td>\n",
" <td>...</td>\n",
" <td>20.0036</td>\n",
" <td>87.3383</td>\n",
" <td>521.452</td>\n",
" <td>879.857</td>\n",
" <td>0.0200162</td>\n",
" <td>0.0557923</td>\n",
" <td>0.136731</td>\n",
" <td>2.37452</td>\n",
" <td>20.3286</td>\n",
" <td>152.069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>0.0204937</td>\n",
" <td>0.0255022</td>\n",
" <td>0.319885</td>\n",
" <td>0.155387</td>\n",
" <td>1.03905</td>\n",
" <td>4.48649</td>\n",
" <td>6.53267e-05</td>\n",
" <td>7.48634e-05</td>\n",
" <td>9.27448e-05</td>\n",
" <td>0.000552177</td>\n",
" <td>...</td>\n",
" <td>19.0064</td>\n",
" <td>83.9567</td>\n",
" <td>475.22</td>\n",
" <td>830.873</td>\n",
" <td>0.00249386</td>\n",
" <td>0.0521882</td>\n",
" <td>0.172211</td>\n",
" <td>1.29354</td>\n",
" <td>16.7002</td>\n",
" <td>146.748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>0.0187821</td>\n",
" <td>0.00358224</td>\n",
" <td>0.153645</td>\n",
" <td>0.164125</td>\n",
" <td>0.867605</td>\n",
" <td>3.67033</td>\n",
" <td>5.98431e-05</td>\n",
" <td>8.53539e-05</td>\n",
" <td>9.41753e-05</td>\n",
" <td>0.000318289</td>\n",
" <td>...</td>\n",
" <td>20.1804</td>\n",
" <td>88.4705</td>\n",
" <td>479.735</td>\n",
" <td>1031.1</td>\n",
" <td>0.018559</td>\n",
" <td>0.0536823</td>\n",
" <td>0.130244</td>\n",
" <td>1.32246</td>\n",
" <td>16.1566</td>\n",
" <td>113.799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>0.0196774</td>\n",
" <td>0.00339079</td>\n",
" <td>0.390221</td>\n",
" <td>0.272635</td>\n",
" <td>0.812143</td>\n",
" <td>4.70923</td>\n",
" <td>5.60284e-05</td>\n",
" <td>0.000104666</td>\n",
" <td>9.39369e-05</td>\n",
" <td>0.000702381</td>\n",
" <td>...</td>\n",
" <td>18.5417</td>\n",
" <td>79.1443</td>\n",
" <td>276.093</td>\n",
" <td>1520.78</td>\n",
" <td>0.00216317</td>\n",
" <td>0.0201104</td>\n",
" <td>0.383921</td>\n",
" <td>1.6588</td>\n",
" <td>13.3962</td>\n",
" <td>99.488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>0.00361419</td>\n",
" <td>0.0187836</td>\n",
" <td>0.203906</td>\n",
" <td>0.150442</td>\n",
" <td>0.959997</td>\n",
" <td>3.22343</td>\n",
" <td>5.74589e-05</td>\n",
" <td>7.22408e-05</td>\n",
" <td>0.000216246</td>\n",
" <td>0.000584364</td>\n",
" <td>...</td>\n",
" <td>16.694</td>\n",
" <td>71.8677</td>\n",
" <td>221.063</td>\n",
" <td>1512.7</td>\n",
" <td>0.0235848</td>\n",
" <td>0.109781</td>\n",
" <td>0.104036</td>\n",
" <td>1.31046</td>\n",
" <td>20.5147</td>\n",
" <td>88.9696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>0.0141919</td>\n",
" <td>0.0243754</td>\n",
" <td>0.165395</td>\n",
" <td>0.220182</td>\n",
" <td>1.1008</td>\n",
" <td>3.67256</td>\n",
" <td>6.7234e-05</td>\n",
" <td>7.51019e-05</td>\n",
" <td>0.000115871</td>\n",
" <td>0.000340462</td>\n",
" <td>...</td>\n",
" <td>15.9247</td>\n",
" <td>67.543</td>\n",
" <td>233.497</td>\n",
" <td>1717.78</td>\n",
" <td>0.0161595</td>\n",
" <td>0.0676987</td>\n",
" <td>0.169985</td>\n",
" <td>1.40358</td>\n",
" <td>15.3727</td>\n",
" <td>73.6181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0.00351286</td>\n",
" <td>0.00327969</td>\n",
" <td>0.130405</td>\n",
" <td>0.090127</td>\n",
" <td>0.34633</td>\n",
" <td>2.08132</td>\n",
" <td>5.55515e-05</td>\n",
" <td>5.4121e-05</td>\n",
" <td>0.000215054</td>\n",
" <td>0.000378847</td>\n",
" <td>...</td>\n",
" <td>9.89184</td>\n",
" <td>26.4921</td>\n",
" <td>156.875</td>\n",
" <td>1974.56</td>\n",
" <td>0.00218225</td>\n",
" <td>0.0881832</td>\n",
" <td>0.0893686</td>\n",
" <td>0.409895</td>\n",
" <td>1.94809</td>\n",
" <td>29.1903</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" DeltaCon \\\n",
" 10 30 100 300 1000 3000 \n",
"0 0.00820446 0.00330758 0.165371 0.159863 1.00786 3.47908 \n",
"1 0.0390024 0.00316405 0.204094 0.133687 1.06776 19.5115 \n",
"2 0.0255492 0.00326967 0.134615 0.2121 1.35013 20.7121 \n",
"3 0.00254083 0.0031991 0.0675135 0.289525 1.2284 18.9496 \n",
"4 0.00373173 0.00378919 0.123849 0.139059 0.71292 6.24403 \n",
"5 0.010268 0.00323176 0.145001 0.394451 0.889508 10.9971 \n",
"6 0.00732493 0.0032835 0.142652 0.122106 1.71077 7.25871 \n",
"7 0.0634875 0.00327587 0.134998 0.148662 0.628493 6.32107 \n",
"8 0.00247455 0.00315833 0.238209 0.128114 1.12096 13.2293 \n",
"9 0.0185492 0.00331211 0.220097 0.123091 1.45278 19.683 \n",
"10 0.00246906 0.00330091 0.105686 0.354446 1.63971 15.1695 \n",
"11 0.0235674 0.00328159 0.0674219 0.138284 1.06574 11.6472 \n",
"12 0.0299931 0.00333881 0.320308 0.140428 0.753508 6.85036 \n",
"13 0.0198274 0.00331926 0.132601 0.450442 0.869783 16.4244 \n",
"14 0.0398979 0.00331473 0.282356 0.167487 1.78895 11.1853 \n",
"15 0.00579095 0.00381613 0.133854 0.243878 0.53689 9.99625 \n",
"16 0.00652194 0.00316167 0.117872 0.178213 1.41665 12.0191 \n",
"17 0.0037322 0.00333786 0.105466 0.197984 1.59124 5.5716 \n",
"18 0.00249028 0.00324774 0.158101 0.287137 1.50268 2.5454 \n",
"19 0.0350316 0.00318265 0.0870342 0.229768 0.977381 10.9638 \n",
"20 0.00315642 0.0509071 0.252518 0.565023 1.85396 11.4247 \n",
"21 0.0703032 0.00404811 0.578033 0.353467 1.28807 10.9359 \n",
"22 0.0383575 0.041707 0.431826 0.37448 1.12623 8.54068 \n",
"23 0.0401249 0.00505853 0.271244 0.377284 1.34504 10.9792 \n",
"24 0.0363102 0.00368142 0.123279 0.290556 1.66722 11.3701 \n",
"25 0.00356483 0.00354505 0.272037 0.299258 1.08178 11.4604 \n",
"26 0.0383987 0.112523 0.164367 0.362127 1.11254 6.63539 \n",
"27 0.0267434 0.0327902 0.10284 0.238473 0.864369 7.71238 \n",
"28 0.00316405 0.0187602 0.169974 0.137836 1.00438 11.2765 \n",
"29 0.0454853 0.0173705 0.118016 0.218685 1.57731 5.00993 \n",
".. ... ... ... ... ... ... \n",
"70 0.0221875 0.00478601 0.203532 0.195059 0.755858 7.37219 \n",
"71 0.0266867 0.0034945 0.122632 0.200907 0.729664 6.03421 \n",
"72 0.0216017 0.0241156 0.0987835 0.135259 1.18199 6.2178 \n",
"73 0.0126171 0.00331187 0.102618 0.461809 0.64116 3.95158 \n",
"74 0.00331283 0.00361419 0.136445 0.0892792 1.07894 4.61254 \n",
"75 0.0207899 0.00347638 0.377512 0.11098 0.87001 5.01065 \n",
"76 0.0200582 0.00362062 0.0971103 0.203827 1.42324 3.61205 \n",
"77 0.0229545 0.00340271 0.120472 0.542338 1.68286 3.79592 \n",
"78 0.00324655 0.0245507 0.223871 0.184385 0.995938 3.82391 \n",
"79 0.0399075 0.0200801 0.103044 0.212389 1.15255 2.37331 \n",
"80 0.035527 0.00375414 0.532439 0.404772 1.51675 8.31719 \n",
"81 0.0354512 0.0463622 0.212068 0.271919 1.53248 10.7588 \n",
"82 0.0584714 0.0430498 0.171326 0.289474 0.847046 9.27352 \n",
"83 0.0792615 0.00361729 0.206428 0.275465 1.38957 10.6554 \n",
"84 0.00346994 0.00500035 0.179486 0.737461 1.34554 8.63438 \n",
"85 0.0411093 0.00357485 0.206932 0.223555 1.36813 8.43188 \n",
"86 0.0232716 0.0298278 0.140514 0.464055 1.62925 8.13316 \n",
"87 0.00319791 0.0296748 0.128548 0.654692 1.30826 6.3271 \n",
"88 0.0320525 0.0337968 0.170508 0.236835 1.12896 6.49818 \n",
"89 0.0188746 0.00455403 0.17303 0.250048 0.83953 6.67258 \n",
"90 0.0298221 0.0544572 0.267794 0.236287 1.06179 6.83444 \n",
"91 0.0192378 0.021419 0.131151 0.22551 0.733673 9.10781 \n",
"92 0.00487614 0.0207679 0.211021 0.222272 1.8271 4.85606 \n",
"93 0.00327659 0.00739121 0.295464 0.416258 1.11788 3.95077 \n",
"94 0.0204937 0.0255022 0.319885 0.155387 1.03905 4.48649 \n",
"95 0.0187821 0.00358224 0.153645 0.164125 0.867605 3.67033 \n",
"96 0.0196774 0.00339079 0.390221 0.272635 0.812143 4.70923 \n",
"97 0.00361419 0.0187836 0.203906 0.150442 0.959997 3.22343 \n",
"98 0.0141919 0.0243754 0.165395 0.220182 1.1008 3.67256 \n",
"99 0.00351286 0.00327969 0.130405 0.090127 0.34633 2.08132 \n",
"\n",
" Edit ... NetSimile \\\n",
" 10 30 100 300 ... 100 \n",
"0 0.00028348 4.57764e-05 8.96454e-05 0.000606298 ... 8.45824 \n",
"1 0.000197411 5.57899e-05 8.10623e-05 0.000564814 ... 7.54827 \n",
"2 0.000216246 4.43459e-05 7.98702e-05 0.0258429 ... 3.04235 \n",
"3 0.000275612 4.52995e-05 6.91414e-05 0.000577211 ... 3.46615 \n",
"4 0.000202894 4.62532e-05 7.51019e-05 0.000561237 ... 7.13728 \n",
"5 0.00029397 4.52995e-05 7.9155e-05 0.00056529 ... 3.69945 \n",
"6 0.000118732 4.52995e-05 7.82013e-05 0.000560999 ... 9.35148 \n",
"7 0.000142336 4.72069e-05 9.91821e-05 0.000623703 ... 5.67684 \n",
"8 0.000145674 4.48227e-05 0.000103474 0.00056982 ... 5.68893 \n",
"9 0.00028491 4.62532e-05 8.7738e-05 0.000611305 ... 7.40615 \n",
"10 0.000200033 3.52859e-05 8.65459e-05 0.000569105 ... 3.81637 \n",
"11 0.000190973 4.50611e-05 7.4625e-05 0.000567913 ... 4.37711 \n",
"12 0.000103235 4.81606e-05 0.000126362 0.000588417 ... 3.32854 \n",
"13 0.000200033 4.55379e-05 0.000131845 0.000624895 ... 8.0428 \n",
"14 0.000199795 5.84126e-05 9.03606e-05 0.000575304 ... 3.26578 \n",
"15 0.000113487 2.98023e-05 0.000117302 0.000631094 ... 2.9404 \n",
"16 0.000103235 5.03063e-05 7.86781e-05 0.00058794 ... 3.70223 \n",
"17 0.000117779 5.84126e-05 0.000117064 0.000590086 ... 3.16932 \n",
"18 0.000203609 3.55244e-05 0.000110626 0.000581026 ... 8.05726 \n",
"19 0.000222921 4.76837e-05 6.8903e-05 0.000571251 ... 4.04973 \n",
"20 0.000100851 6.41346e-05 0.000112534 0.000606298 ... 40.9044 \n",
"21 6.05583e-05 8.10623e-05 0.0001266 0.000300646 ... 37.767 \n",
"22 5.62668e-05 6.48499e-05 0.000183344 0.000627756 ... 33.3566 \n",
"23 6.05583e-05 0.000118256 9.46522e-05 0.000367165 ... 32.383 \n",
"24 8.03471e-05 0.000126362 0.000107527 0.0161557 ... 31.3307 \n",
"25 9.58443e-05 0.000174761 0.0376918 0.000381947 ... 32.3313 \n",
"26 7.36713e-05 8.03471e-05 0.00021863 0.000302315 ... 29.5611 \n",
"27 6.81877e-05 6.69956e-05 8.46386e-05 0.00110316 ... 27.774 \n",
"28 5.26905e-05 6.74725e-05 9.799e-05 0.000343323 ... 25.0697 \n",
"29 5.91278e-05 9.39369e-05 9.82285e-05 0.0168324 ... 23.0361 \n",
".. ... ... ... ... ... ... \n",
"70 5.79357e-05 6.67572e-05 0.000139952 0.000785828 ... 20.1595 \n",
"71 0.000189781 0.00015831 9.91821e-05 0.000528336 ... 25.1566 \n",
"72 6.36578e-05 9.89437e-05 0.000165462 0.000321865 ... 19.4583 \n",
"73 6.22272e-05 6.41346e-05 9.36985e-05 0.000463247 ... 16.5918 \n",
"74 5.53131e-05 6.67572e-05 8.96454e-05 0.00028348 ... 16.6964 \n",
"75 6.03199e-05 6.7234e-05 9.84669e-05 0.000288248 ... 19.1428 \n",
"76 5.96046e-05 7.15256e-05 0.000112295 0.000309944 ... 20.0881 \n",
"77 5.57899e-05 6.10352e-05 0.000103235 0.0447133 ... 25.9848 \n",
"78 5.88894e-05 9.34601e-05 9.58443e-05 0.000294209 ... 19.4843 \n",
"79 5.36442e-05 6.34193e-05 9.94205e-05 0.000397682 ... 21.3017 \n",
"80 6.07967e-05 7.22408e-05 0.000258207 0.000315666 ... 39.2255 \n",
"81 5.53131e-05 0.000107765 9.77516e-05 0.00032115 ... 28.7231 \n",
"82 7.7486e-05 8.46386e-05 0.000612974 0.000521898 ... 38.3272 \n",
"83 7.08103e-05 7.41482e-05 0.00010848 0.000302792 ... 35.1668 \n",
"84 6.8903e-05 6.74725e-05 0.000104904 0.000324726 ... 31.7289 \n",
"85 6.86646e-05 8.03471e-05 0.000146627 0.000370979 ... 30.1444 \n",
"86 6.93798e-05 8.29697e-05 0.000107527 0.000365734 ... 26.4046 \n",
"87 0.000149727 9.41753e-05 0.000114441 0.000779629 ... 29.0845 \n",
"88 5.38826e-05 6.65188e-05 9.65595e-05 0.00188541 ... 33.0258 \n",
"89 6.48499e-05 6.31809e-05 9.32217e-05 0.0003829 ... 22.0971 \n",
"90 0.000106335 7.1764e-05 0.000112772 0.000779629 ... 18.4411 \n",
"91 5.76973e-05 9.39369e-05 9.77516e-05 0.000374317 ... 19.6784 \n",
"92 0.000102043 0.021872 0.000219584 0.000350952 ... 20.4142 \n",
"93 8.17776e-05 7.7486e-05 0.0131552 0.0205743 ... 20.0036 \n",
"94 6.53267e-05 7.48634e-05 9.27448e-05 0.000552177 ... 19.0064 \n",
"95 5.98431e-05 8.53539e-05 9.41753e-05 0.000318289 ... 20.1804 \n",
"96 5.60284e-05 0.000104666 9.39369e-05 0.000702381 ... 18.5417 \n",
"97 5.74589e-05 7.22408e-05 0.000216246 0.000584364 ... 16.694 \n",
"98 6.7234e-05 7.51019e-05 0.000115871 0.000340462 ... 15.9247 \n",
"99 5.55515e-05 5.4121e-05 0.000215054 0.000378847 ... 9.89184 \n",
"\n",
" Resistance Dist. \\\n",
" 300 1000 3000 10 30 100 \n",
"0 25.7166 155.145 273.29 0.222237 0.302673 0.183244 \n",
"1 20.9183 116.988 437.527 0.0945263 0.301902 0.472628 \n",
"2 18.7939 69.3679 424.551 0.0662394 0.329336 0.366627 \n",
"3 15.0995 76.0705 1164.6 0.0367355 0.289481 0.161982 \n",
"4 29.5192 131.814 284.094 0.108705 0.351261 0.372429 \n",
"5 15.4404 71.9671 962.901 0.0287809 0.286721 0.0342119 \n",
"6 13.8124 171.157 383.63 0.0609736 0.316966 0.204251 \n",
"7 42.9957 156.593 826.862 0.0581741 0.284809 0.333027 \n",
"8 19.5079 90.1442 707.171 0.0392709 0.346845 0.408748 \n",
"9 19.6082 74.8941 376.752 0.064425 0.360898 0.166249 \n",
"10 13.9831 104.885 728.491 0.0466452 0.257225 0.223319 \n",
"11 18.2324 102.386 296.851 0.0717518 0.343324 0.149315 \n",
"12 36.7943 163.731 1323.01 0.0829215 0.34898 0.219229 \n",
"13 36.0131 75.1092 393.946 0.0961423 0.294921 0.134118 \n",
"14 12.8642 82.2807 318.312 0.0905228 0.319452 0.465786 \n",
"15 42.9733 106.815 299.544 0.0158229 0.260389 0.349355 \n",
"16 33.8596 114.352 377.378 0.0421722 0.26601 0.194582 \n",
"17 27.4833 132.772 342.497 0.0762393 0.308653 0.146093 \n",
"18 16.3483 183.262 312.721 0.0249312 0.292686 0.303927 \n",
"19 19.8607 140.327 753.634 0.0936012 0.30969 0.13713 \n",
"20 176.153 488.329 763.664 0.00217104 0.105391 0.585435 \n",
"21 153.539 417.211 773.88 0.00197649 0.186705 0.258148 \n",
"22 157.035 385.872 683.128 0.0373538 0.0551326 0.266732 \n",
"23 125.333 379.993 619.949 0.0577505 0.0818896 0.181272 \n",
"24 120.558 367.725 618.596 0.00286388 0.0667305 0.18289 \n",
"25 118.203 369.642 608.396 0.047744 0.0994029 0.531896 \n",
"26 113.711 354.963 645.559 0.00248456 0.153606 0.200012 \n",
"27 115.812 313.698 663.518 0.0411949 0.078248 0.158036 \n",
"28 103.779 327.332 722.827 0.0395265 0.0964274 0.351207 \n",
"29 104.01 363.297 685.268 0.00203586 0.0399907 0.14689 \n",
".. ... ... ... ... ... ... \n",
"70 95.0164 441.813 575.968 0.00234365 0.0498929 0.149518 \n",
"71 84.6385 436.033 734.326 0.00241542 0.0651419 0.127522 \n",
"72 79.4992 445.041 617.057 0.0363936 0.0648901 0.0928342 \n",
"73 74.4532 437.51 1011.45 0.0020082 0.0479052 0.141966 \n",
"74 75.106 419.636 723.292 0.015305 0.0511754 0.122267 \n",
"75 82.8759 415.194 820.765 0.0175529 0.0731101 0.132956 \n",
"76 86.6134 388.782 1320.35 0.0177104 0.0815868 0.141449 \n",
"77 86.4087 386.427 1551.21 0.00232625 0.0898879 0.162069 \n",
"78 82.8619 308.528 1552.65 0.00199056 0.0793159 0.137465 \n",
"79 89.5381 259.461 1821.54 0.00201917 0.0216198 0.109902 \n",
"80 160.007 635.21 940.132 0.0020864 0.0531466 0.228098 \n",
"81 154.394 607.673 865.007 0.0272379 0.103535 0.192799 \n",
"82 144.591 564.026 826.382 0.0439725 0.0781062 0.134982 \n",
"83 136.899 527.727 901.435 0.00212622 0.294862 0.206567 \n",
"84 134.146 504.352 799.212 0.0021987 0.0955625 0.176665 \n",
"85 125.013 522.019 818.913 0.00361514 0.0801191 0.188461 \n",
"86 121.995 510.979 736.415 0.0291243 0.0895164 0.182347 \n",
"87 121.213 487.977 761.874 0.00220513 0.0892253 0.165562 \n",
"88 122.565 513.899 853.68 0.0527539 0.0590501 0.167497 \n",
"89 86.8921 505.164 866.656 0.0261919 0.0771041 0.156201 \n",
"90 84.4593 508.354 784.863 0.0274999 0.0698869 0.409035 \n",
"91 84.7171 485.348 710.333 0.023351 0.0490839 0.121492 \n",
"92 91.615 503.385 965.821 0.0189078 0.0106602 0.378006 \n",
"93 87.3383 521.452 879.857 0.0200162 0.0557923 0.136731 \n",
"94 83.9567 475.22 830.873 0.00249386 0.0521882 0.172211 \n",
"95 88.4705 479.735 1031.1 0.018559 0.0536823 0.130244 \n",
"96 79.1443 276.093 1520.78 0.00216317 0.0201104 0.383921 \n",
"97 71.8677 221.063 1512.7 0.0235848 0.109781 0.104036 \n",
"98 67.543 233.497 1717.78 0.0161595 0.0676987 0.169985 \n",
"99 26.4921 156.875 1974.56 0.00218225 0.0881832 0.0893686 \n",
"\n",
" \n",
" 300 1000 3000 \n",
"0 1.16293 9.41187 299.135 \n",
"1 0.608195 5.61302 187.179 \n",
"2 0.60622 5.28801 286.318 \n",
"3 0.386847 7.01957 286.356 \n",
"4 0.796469 7.88605 310.052 \n",
"5 0.643994 9.9891 401.197 \n",
"6 0.376051 4.84392 219.087 \n",
"7 0.493491 5.38635 272.664 \n",
"8 0.451287 6.31324 338.808 \n",
"9 0.574641 9.71514 308.576 \n",
"10 1.30637 7.14932 306.006 \n",
"11 0.475544 7.3124 346.756 \n",
"12 0.673227 5.08562 226.097 \n",
"13 0.448892 5.25793 305.277 \n",
"14 0.714469 6.24104 406.607 \n",
"15 0.965806 7.55051 289.761 \n",
"16 0.730792 8.13302 253.171 \n",
"17 1.46753 5.81154 272.882 \n",
"18 0.731482 8.13812 257.194 \n",
"19 0.598171 7.03823 261.466 \n",
"20 1.49593 15.6418 175.558 \n",
"21 1.91176 17.2154 168.516 \n",
"22 1.73766 20.8298 252.76 \n",
"23 1.90132 19.3298 225.051 \n",
"24 1.63502 24.8362 191.847 \n",
"25 1.50364 15.9397 170.523 \n",
"26 1.93583 17.4286 222.579 \n",
"27 0.908624 21.2324 186.154 \n",
"28 0.849531 14.3489 167.171 \n",
"29 1.34373 23.6519 181.885 \n",
".. ... ... ... \n",
"70 1.5206 9.44375 166.049 \n",
"71 1.64205 11.9583 153.42 \n",
"72 1.43396 12.1885 135.448 \n",
"73 0.872335 7.55138 142.963 \n",
"74 1.42161 10.6903 144.138 \n",
"75 1.27241 12.1347 68.5179 \n",
"76 1.44218 19.2873 98.2618 \n",
"77 1.24732 20.606 79.3052 \n",
"78 1.26285 14.7202 75.4733 \n",
"79 1.69047 27.8197 75.3377 \n",
"80 2.30744 32.4833 134.867 \n",
"81 1.63943 12.7567 258.762 \n",
"82 2.04452 18.0174 267.903 \n",
"83 1.52487 13.4985 131.153 \n",
"84 2.14557 18.8681 191.115 \n",
"85 1.97422 18.1579 234.733 \n",
"86 1.12704 19.7729 218.865 \n",
"87 1.5611 21.3043 168.555 \n",
"88 2.0806 22.177 167.309 \n",
"89 1.34541 11.719 161.827 \n",
"90 1.80838 11.9296 117.131 \n",
"91 1.42748 12.9422 146.072 \n",
"92 1.6321 25.0955 154.101 \n",
"93 2.37452 20.3286 152.069 \n",
"94 1.29354 16.7002 146.748 \n",
"95 1.32246 16.1566 113.799 \n",
"96 1.6588 13.3962 99.488 \n",
"97 1.31046 20.5147 88.9696 \n",
"98 1.40358 15.3727 73.6181 \n",
"99 0.409895 1.94809 29.1903 \n",
"\n",
"[100 rows x 42 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_total"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best fit line for Edit has slope 2.340.\n",
"Best fit line for Resistance Dist. has slope 2.024.\n",
"Best fit line for DeltaCon has slope 1.159.\n",
"Best fit line for NetSimile has slope 1.065.\n",
"Best fit line for Lambda (Adjacency) has slope 2.183.\n",
"Best fit line for Lambda (Laplacian) has slope 2.227.\n",
"Best fit line for Lambda (Normalized Laplacian) has slope 2.177.\n"
]
},
{
"data": {
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99hqam5sRFhaGDz/8EAsXLkRAQIDhGoVCgeHDh+Phhx/Gli1bkJiY2K6NzZs3Q6fTQaVS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\niNo+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1139e7898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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VBTJw7zV9584dfPPNNzh58mSrcw8cOBAAVApk4N4MNJmZmdi7dy/279/f4vHq+ifn5ubi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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115663ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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6FRkZGRg9ejSsrKxaXNv+TpSVlTXrn0NEZMqsrKxERyAiMgkmUST7+flh0aJF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\nQkkGAAAAWqEkAwAAAK38P6NAljZLXNcPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114599fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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8fHxQXFyM/Px87N+/H9OmTcP169dx7do1DB06FDY2Njh27JjRx9RoNDXG2xAR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114a2a550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Zs0fjue7du6NDhw7q+bfrIyYmBnPmzIGjoyNCQkLU05Q15jxo09hzlJOTg1df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\nP3/e6889VVpamukI6AW+Of2bjD1GtyktLdXgwYNNxwCAoOf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gTrJhDU0N2rR7k/YX7tfs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\nz3kgG1R0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113e1ae48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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vvvsO+fn5mD17Nvr27QtLS0sUFBTg9OnT2LFjh+YqR2vy8vKwbNkyTJ8+HRMn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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113b09630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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i097eHnPmzIGtra1028GDB6UiS98fpho1aqBXr17STA9KqVmzps6Kbvlpp9e5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\nUJIBAACAdv4v32ftw7a+h6UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1141191d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ran = [10,30,100,300,1000,3000]\n",
"for label in labels:\n",
" complexity_plot(ran[2:],np.array(df_total[label].median())[2:],label=label)\n",
" plt.title('Complexity of ' + label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hrmm... I don't really trust these results, because a lot of them don't look that linear. We'd better take a look at the statistics, and the code, to make sure taht we're not accidentally taking any unexpected algorithmic shortcuts here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
asedo/FoodFilter | foodfiltertest.ipynb | 1 | 1880651 | null | gpl-3.0 |
margulies/topography | sandbox/macaque/Untitled0.ipynb | 2 | 181 | {
"metadata": {
"name": "",
"signature": "sha256:97f38b5a4cb8784a3c9f6c35df68276405bd34648124baa53cf5d58c6ecd74ff"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": []
} | mit |
dnx4015/pbrt-importance-sampling | pbrt-v2-master/outputs/createPBRTS.ipynb | 1 | 3042 | {
"metadata": {
"name": "",
"signature": "sha256:766c3ad4f19b9df4350c187c035c88c60b2aa2a8f31de3f4c6681fc61e3074d5"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"BRDFS = [\"nickel\", \"blue-metallic\", \"plastic\"]\n",
"BIN = \".binary\"\n",
"def geomProgression(init, size, step):\n",
" l = [1]*size\n",
" n = init\n",
" for i in range(size):\n",
" l[i] = n\n",
" n *= step\n",
" return list(reversed(l))\n",
"\n",
"maxSizeRange = [i for i in range(5, 31, 5)] #6\n",
"minPDistRange = geomProgression(0.1, 5, 0.1)\n",
"minRDistRange = geomProgression(0.1, 5, 0.1)\n",
"mRDist = 0.01\n",
"mPDist = 0.01\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"FOLDERS =[\"06OP\"]\n",
"from jinja2 import Environment, FileSystemLoader\n",
"env = Environment(loader=FileSystemLoader('.'))\n",
"template = env.get_template('template.pbrt')\n",
"for brdfFilename in BRDFS:\n",
" for compression, folder in enumerate(FOLDERS):\n",
" #for mSize in maxSizeRange:\n",
" for mPDist in minPDistRange:\n",
" for mRDist in minRDistRange:\n",
" fname=\"minPDist\"+\"{0:.5f}\".format(mPDist) +\\\n",
" \"_minRDist\"+\"{0:.5f}\".format(mRDist)\n",
" render = template.render(filename=folder+\"/exr/\"+\n",
" brdfFilename+\"/\"\n",
" +fname+\".exr\",\n",
" brdf=brdfFilename+BIN,\n",
" comp=5,\n",
" maxSize=mSize,\n",
" minPDist=mPDist,\n",
" minRDist=mRDist)\n",
" with open(folder+'/pbrt/'+brdfFilename+\"/\"+\\\n",
" fname+'.pbrt', 'w') as output:\n",
" output.write(render)\n",
"print \"Done\"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Done\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
karst87/ml | dev/pyml/2001_使用sklearn做单机特征工程.ipynb | 1 | 102248 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用sklearn做单机特征工程\n",
"http://www.cnblogs.com/jasonfreak/p/5448385.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 1 特征工程是什么？\n",
" 2 数据预处理\n",
" 2.1 无量纲化\n",
" 2.1.1 标准化\n",
" 2.1.2 区间缩放法\n",
" 2.1.3 标准化与归一化的区别\n",
" 2.2 对定量特征二值化\n",
" 2.3 对定性特征哑编码\n",
" 2.4 缺失值计算\n",
" 2.5 数据变换\n",
" 2.6 回顾\n",
" 3 特征选择\n",
" 3.1 Filter\n",
" 3.1.1 方差选择法\n",
" 3.1.2 相关系数法\n",
" 3.1.3 卡方检验\n",
" 3.1.4 互信息法\n",
" 3.2 Wrapper\n",
" 3.2.1 递归特征消除法\n",
" 3.3 Embedded\n",
" 3.3.1 基于惩罚项的特征选择法\n",
" 3.3.2 基于树模型的特征选择法\n",
" 3.4 回顾\n",
" 4 降维\n",
" 4.1 主成分分析法（PCA）\n",
" 4.2 线性判别分析法（LDA）\n",
" 4.3 回顾\n",
" 5 总结"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 特征工程是什么？\n",
" 有这么一句话在业界广泛流传：数据和特征决定了机器学习的上限，而模型和算法只是逼近这个上限而已。那特征工程到底是什么呢？顾名思义，其本质是一项工程活动，目的是最大限度地从原始数据中提取特征以供算法和模型使用。通过总结和归纳，人们认为特征工程包括以下方面：(参考im ages/2001_1.png)\n",
" \n",
" 特征处理是特征工程的核心部分，sklearn提供了较为完整的特征处理方法，包括数据预处理，特征选择，降维等。首次接触到sklearn，通常会被其丰富且方便的算法模型库吸引，但是这里介绍的特征处理库也十分强大！\n",
" \n",
" 本文中使用sklearn中的IRIS（鸢尾花）数据集来对特征处理功能进行说明。IRIS数据集由Fisher在1936年整理，包含4个特征（Sepal.Length（花萼长度）、Sepal.Width（花萼宽度）、Petal.Length（花瓣长度）、Petal.Width（花瓣宽度）），特征值都为正浮点数，单位为厘米。目标值为鸢尾花的分类（Iris Setosa（山鸢尾）、Iris Versicolour（杂色鸢尾），Iris Virginica（维吉尼亚鸢尾））。导入IRIS数据集的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.1, 3.5, 1.4, 0.2],\n",
" [ 4.9, 3. , 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.3, 0.2],\n",
" [ 4.6, 3.1, 1.5, 0.2],\n",
" [ 5. , 3.6, 1.4, 0.2],\n",
" [ 5.4, 3.9, 1.7, 0.4],\n",
" [ 4.6, 3.4, 1.4, 0.3],\n",
" [ 5. , 3.4, 1.5, 0.2],\n",
" [ 4.4, 2.9, 1.4, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 5.4, 3.7, 1.5, 0.2],\n",
" [ 4.8, 3.4, 1.6, 0.2],\n",
" [ 4.8, 3. , 1.4, 0.1],\n",
" [ 4.3, 3. , 1.1, 0.1],\n",
" [ 5.8, 4. , 1.2, 0.2],\n",
" [ 5.7, 4.4, 1.5, 0.4],\n",
" [ 5.4, 3.9, 1.3, 0.4],\n",
" [ 5.1, 3.5, 1.4, 0.3],\n",
" [ 5.7, 3.8, 1.7, 0.3],\n",
" [ 5.1, 3.8, 1.5, 0.3],\n",
" [ 5.4, 3.4, 1.7, 0.2],\n",
" [ 5.1, 3.7, 1.5, 0.4],\n",
" [ 4.6, 3.6, 1. , 0.2],\n",
" [ 5.1, 3.3, 1.7, 0.5],\n",
" [ 4.8, 3.4, 1.9, 0.2],\n",
" [ 5. , 3. , 1.6, 0.2],\n",
" [ 5. , 3.4, 1.6, 0.4],\n",
" [ 5.2, 3.5, 1.5, 0.2],\n",
" [ 5.2, 3.4, 1.4, 0.2],\n",
" [ 4.7, 3.2, 1.6, 0.2],\n",
" [ 4.8, 3.1, 1.6, 0.2],\n",
" [ 5.4, 3.4, 1.5, 0.4],\n",
" [ 5.2, 4.1, 1.5, 0.1],\n",
" [ 5.5, 4.2, 1.4, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 5. , 3.2, 1.2, 0.2],\n",
" [ 5.5, 3.5, 1.3, 0.2],\n",
" [ 4.9, 3.1, 1.5, 0.1],\n",
" [ 4.4, 3. , 1.3, 0.2],\n",
" [ 5.1, 3.4, 1.5, 0.2],\n",
" [ 5. , 3.5, 1.3, 0.3],\n",
" [ 4.5, 2.3, 1.3, 0.3],\n",
" [ 4.4, 3.2, 1.3, 0.2],\n",
" [ 5. , 3.5, 1.6, 0.6],\n",
" [ 5.1, 3.8, 1.9, 0.4],\n",
" [ 4.8, 3. , 1.4, 0.3],\n",
" [ 5.1, 3.8, 1.6, 0.2],\n",
" [ 4.6, 3.2, 1.4, 0.2],\n",
" [ 5.3, 3.7, 1.5, 0.2],\n",
" [ 5. , 3.3, 1.4, 0.2],\n",
" [ 7. , 3.2, 4.7, 1.4],\n",
" [ 6.4, 3.2, 4.5, 1.5],\n",
" [ 6.9, 3.1, 4.9, 1.5],\n",
" [ 5.5, 2.3, 4. , 1.3],\n",
" [ 6.5, 2.8, 4.6, 1.5],\n",
" [ 5.7, 2.8, 4.5, 1.3],\n",
" [ 6.3, 3.3, 4.7, 1.6],\n",
" [ 4.9, 2.4, 3.3, 1. ],\n",
" [ 6.6, 2.9, 4.6, 1.3],\n",
" [ 5.2, 2.7, 3.9, 1.4],\n",
" [ 5. , 2. , 3.5, 1. ],\n",
" [ 5.9, 3. , 4.2, 1.5],\n",
" [ 6. , 2.2, 4. , 1. ],\n",
" [ 6.1, 2.9, 4.7, 1.4],\n",
" [ 5.6, 2.9, 3.6, 1.3],\n",
" [ 6.7, 3.1, 4.4, 1.4],\n",
" [ 5.6, 3. , 4.5, 1.5],\n",
" [ 5.8, 2.7, 4.1, 1. ],\n",
" [ 6.2, 2.2, 4.5, 1.5],\n",
" [ 5.6, 2.5, 3.9, 1.1],\n",
" [ 5.9, 3.2, 4.8, 1.8],\n",
" [ 6.1, 2.8, 4. , 1.3],\n",
" [ 6.3, 2.5, 4.9, 1.5],\n",
" [ 6.1, 2.8, 4.7, 1.2],\n",
" [ 6.4, 2.9, 4.3, 1.3],\n",
" [ 6.6, 3. , 4.4, 1.4],\n",
" [ 6.8, 2.8, 4.8, 1.4],\n",
" [ 6.7, 3. , 5. , 1.7],\n",
" [ 6. , 2.9, 4.5, 1.5],\n",
" [ 5.7, 2.6, 3.5, 1. ],\n",
" [ 5.5, 2.4, 3.8, 1.1],\n",
" [ 5.5, 2.4, 3.7, 1. ],\n",
" [ 5.8, 2.7, 3.9, 1.2],\n",
" [ 6. , 2.7, 5.1, 1.6],\n",
" [ 5.4, 3. , 4.5, 1.5],\n",
" [ 6. , 3.4, 4.5, 1.6],\n",
" [ 6.7, 3.1, 4.7, 1.5],\n",
" [ 6.3, 2.3, 4.4, 1.3],\n",
" [ 5.6, 3. , 4.1, 1.3],\n",
" [ 5.5, 2.5, 4. , 1.3],\n",
" [ 5.5, 2.6, 4.4, 1.2],\n",
" [ 6.1, 3. , 4.6, 1.4],\n",
" [ 5.8, 2.6, 4. , 1.2],\n",
" [ 5. , 2.3, 3.3, 1. ],\n",
" [ 5.6, 2.7, 4.2, 1.3],\n",
" [ 5.7, 3. , 4.2, 1.2],\n",
" [ 5.7, 2.9, 4.2, 1.3],\n",
" [ 6.2, 2.9, 4.3, 1.3],\n",
" [ 5.1, 2.5, 3. , 1.1],\n",
" [ 5.7, 2.8, 4.1, 1.3],\n",
" [ 6.3, 3.3, 6. , 2.5],\n",
" [ 5.8, 2.7, 5.1, 1.9],\n",
" [ 7.1, 3. , 5.9, 2.1],\n",
" [ 6.3, 2.9, 5.6, 1.8],\n",
" [ 6.5, 3. , 5.8, 2.2],\n",
" [ 7.6, 3. , 6.6, 2.1],\n",
" [ 4.9, 2.5, 4.5, 1.7],\n",
" [ 7.3, 2.9, 6.3, 1.8],\n",
" [ 6.7, 2.5, 5.8, 1.8],\n",
" [ 7.2, 3.6, 6.1, 2.5],\n",
" [ 6.5, 3.2, 5.1, 2. ],\n",
" [ 6.4, 2.7, 5.3, 1.9],\n",
" [ 6.8, 3. , 5.5, 2.1],\n",
" [ 5.7, 2.5, 5. , 2. ],\n",
" [ 5.8, 2.8, 5.1, 2.4],\n",
" [ 6.4, 3.2, 5.3, 2.3],\n",
" [ 6.5, 3. , 5.5, 1.8],\n",
" [ 7.7, 3.8, 6.7, 2.2],\n",
" [ 7.7, 2.6, 6.9, 2.3],\n",
" [ 6. , 2.2, 5. , 1.5],\n",
" [ 6.9, 3.2, 5.7, 2.3],\n",
" [ 5.6, 2.8, 4.9, 2. ],\n",
" [ 7.7, 2.8, 6.7, 2. ],\n",
" [ 6.3, 2.7, 4.9, 1.8],\n",
" [ 6.7, 3.3, 5.7, 2.1],\n",
" [ 7.2, 3.2, 6. , 1.8],\n",
" [ 6.2, 2.8, 4.8, 1.8],\n",
" [ 6.1, 3. , 4.9, 1.8],\n",
" [ 6.4, 2.8, 5.6, 2.1],\n",
" [ 7.2, 3. , 5.8, 1.6],\n",
" [ 7.4, 2.8, 6.1, 1.9],\n",
" [ 7.9, 3.8, 6.4, 2. ],\n",
" [ 6.4, 2.8, 5.6, 2.2],\n",
" [ 6.3, 2.8, 5.1, 1.5],\n",
" [ 6.1, 2.6, 5.6, 1.4],\n",
" [ 7.7, 3. , 6.1, 2.3],\n",
" [ 6.3, 3.4, 5.6, 2.4],\n",
" [ 6.4, 3.1, 5.5, 1.8],\n",
" [ 6. , 3. , 4.8, 1.8],\n",
" [ 6.9, 3.1, 5.4, 2.1],\n",
" [ 6.7, 3.1, 5.6, 2.4],\n",
" [ 6.9, 3.1, 5.1, 2.3],\n",
" [ 5.8, 2.7, 5.1, 1.9],\n",
" [ 6.8, 3.2, 5.9, 2.3],\n",
" [ 6.7, 3.3, 5.7, 2.5],\n",
" [ 6.7, 3. , 5.2, 2.3],\n",
" [ 6.3, 2.5, 5. , 1.9],\n",
" [ 6.5, 3. , 5.2, 2. ],\n",
" [ 6.2, 3.4, 5.4, 2.3],\n",
" [ 5.9, 3. , 5.1, 1.8]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"\n",
"# 导入IRIS数据集\n",
"iris = load_iris()\n",
"\n",
"# 特征矩阵\n",
"iris.data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 1, 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, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 目标微量\n",
"iris.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 数据预处理\n",
" 通过特征提取，我们能得到未经处理的特征，这时的特征可能有以下问题：\n",
" \n",
" 不属于同一量纲：即特征的规格不一样，不能够放在一起比较。无量纲化可以解决这一问题。\n",
" \n",
" 信息冗余：对于某些定量特征，其包含的有效信息为区间划分，例如学习成绩，假若只关心“及格”或不“及格”，那么需要将定量的考分，转换成“1”和“0”表示及格和未及格。二值化可以解决这一问题。\n",
" \n",
" 定性特征不能直接使用：某些机器学习算法和模型只能接受定量特征的输入，那么需要将定性特征转换为定量特征。最简单的方式是为每一种定性值指定一个定量值，但是这种方式过于灵活，增加了调参的工作。通常使用哑编码的方式将定性特征转换为定量特征：假设有N种定性值，则将这一个特征扩展为N种特征，当原始特征值为第i种定性值时，第i个扩展特征赋值为1，其他扩展特征赋值为0。哑编码的方式相比直接指定的方式，不用增加调参的工作，对于线性模型来说，使用哑编码后的特征可达到非线性的效果。\n",
" \n",
" 存在缺失值：缺失值需要补充。\n",
" \n",
" 信息利用率低：不同的机器学习算法和模型对数据中信息的利用是不同的，之前提到在线性模型中，使用对定性特征哑编码可以达到非线性的效果。类似地，对定量变量多项式化，或者进行其他的转换，都能达到非线性的效果。\n",
" \n",
" 我们使用sklearn中的preproccessing库来进行数据预处理，可以覆盖以上问题的解决方案。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 无量纲化\n",
" 无量纲化使不同规格的数据转换到同一规格。常见的无量纲化方法有标准化和区间缩放法。标准化的前提是特征值服从正态分布，标准化后，其转换成标准正态分布。区间缩放法利用了边界值信息，将特征的取值区间缩放到某个特点的范围，例如[0, 1]等。\n",
"\n",
"#### 2.1.1 标准化\n",
"\n",
" 标准化需要计算特征的均值和标准差，公式表达为：\n",
" x' = (x - mean) / std\n",
" \n",
" 使用preproccessing库的StandardScaler类对数据进行标准化的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ -9.00681170e-01, 1.03205722e+00, -1.34127240e+00,\n",
" -1.31297673e+00],\n",
" [ -1.14301691e+00, -1.24957601e-01, -1.34127240e+00,\n",
" -1.31297673e+00],\n",
" [ -1.38535265e+00, 3.37848329e-01, -1.39813811e+00,\n",
" -1.31297673e+00],\n",
" [ -1.50652052e+00, 1.06445364e-01, -1.28440670e+00,\n",
" -1.31297673e+00],\n",
" [ -1.02184904e+00, 1.26346019e+00, -1.34127240e+00,\n",
" -1.31297673e+00],\n",
" [ -5.37177559e-01, 1.95766909e+00, -1.17067529e+00,\n",
" -1.05003079e+00],\n",
" [ -1.50652052e+00, 8.00654259e-01, -1.34127240e+00,\n",
" -1.18150376e+00],\n",
" [ -1.02184904e+00, 8.00654259e-01, -1.28440670e+00,\n",
" -1.31297673e+00],\n",
" [ -1.74885626e+00, -3.56360566e-01, -1.34127240e+00,\n",
" -1.31297673e+00],\n",
" [ -1.14301691e+00, 1.06445364e-01, -1.28440670e+00,\n",
" -1.44444970e+00],\n",
" [ -5.37177559e-01, 1.49486315e+00, -1.28440670e+00,\n",
" -1.31297673e+00],\n",
" [ -1.26418478e+00, 8.00654259e-01, -1.22754100e+00,\n",
" -1.31297673e+00],\n",
" [ -1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
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" 1.44795564e+00],\n",
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" 9.22063763e-01],\n",
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" 1.05353673e+00],\n",
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" 1.44795564e+00],\n",
" [ 6.86617933e-02, -1.24957601e-01, 7.62758643e-01,\n",
" 7.90590793e-01]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# 标准化，返回值为标准化后的数据\n",
"StandardScaler().fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.2 区间缩放法\n",
"\n",
" 区间缩放法的思路有多种，常见的一种为利用两个最值进行缩放，公式表达为：\n",
" x' = (x - min) / (max - min)\n",
" \n",
" 使用preproccessing库的MinMaxScaler类对数据进行区间缩放的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"# 区间缩放，返回值为缩放到[0, 1]区间的数据\n",
"MinMaxScaler().fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.3 标准化与归一化的区别\n",
" 简单来说，标准化是依照特征矩阵的列处理数据，其通过求z-score的方法，将样本的特征值转换到同一量纲下。归一化是依照特征矩阵的行处理数据，其目的在于样本向量在点乘运算或其他核函数计算相似性时，拥有统一的标准，也就是说都转化为“单位向量”。规则为l2的归一化公式如下：\n",
" x' = x / ((sum(x[j] ^ 2)) ^ 0.5)\n",
" \n",
" 使用preproccessing库的Normalizer类对数据进行归一化的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 0.73089855, 0.30454106, 0.58877939, 0.1624219 ],\n",
" [ 0.72766159, 0.27533141, 0.59982915, 0.18683203],\n",
" [ 0.71578999, 0.34430405, 0.5798805 , 0.18121266],\n",
" [ 0.69417747, 0.30370264, 0.60740528, 0.2386235 ],\n",
" [ 0.72366005, 0.32162669, 0.58582004, 0.17230001],\n",
" [ 0.69385414, 0.29574111, 0.63698085, 0.15924521],\n",
" [ 0.73154399, 0.28501714, 0.57953485, 0.21851314],\n",
" [ 0.67017484, 0.36168166, 0.59571097, 0.2553047 ],\n",
" [ 0.69804799, 0.338117 , 0.59988499, 0.196326 ],\n",
" [ 0.71066905, 0.35533453, 0.56853524, 0.21320072],\n",
" [ 0.72415258, 0.32534391, 0.56672811, 0.22039426],\n",
" [ 0.69997037, 0.32386689, 0.58504986, 0.25073566],\n",
" [ 0.73337886, 0.32948905, 0.54206264, 0.24445962],\n",
" [ 0.69052512, 0.32145135, 0.60718588, 0.22620651],\n",
" [ 0.69193502, 0.32561648, 0.60035539, 0.23403685],\n",
" [ 0.68914871, 0.33943145, 0.58629069, 0.25714504],\n",
" [ 0.72155725, 0.32308533, 0.56001458, 0.24769876],\n",
" [ 0.72965359, 0.28954508, 0.57909015, 0.22005426],\n",
" [ 0.71653899, 0.3307103 , 0.57323119, 0.22047353],\n",
" [ 0.67467072, 0.36998072, 0.58761643, 0.25028107],\n",
" [ 0.69025916, 0.35097923, 0.5966647 , 0.21058754]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import Normalizer\n",
"\n",
"# 归一化，返回值为归一化后的数据\n",
"Normalizer().fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 对定量特征二值化\n",
" 定量特征二值化的核心在于设定一个阈值，大于阈值的赋值为1，小于等于阈值的赋值为0，公式表达如下：\n",
" x = 1 if x > threshold else 0\n",
" \n",
" 使用preproccessing库的Binarizer类对数据进行二值化的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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" [ 1., 1., 1., 0.],\n",
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" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 0., 1., 0.],\n",
" [ 1., 1., 1., 0.],\n",
" [ 1., 0., 1., 0.]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import Binarizer\n",
"\n",
"# 二值化，阈值设置为3，返回值 为二值化后的数据\n",
"Binarizer(threshold=3).fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 对定性特征哑编码\n",
" 由于IRIS数据集的特征皆为定量特征，故使用其目标值进行哑编码（实际上是不需要的）。使用preproccessing库的OneHotEncoder类对数据进行哑编码的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<150x3 sparse matrix of type '<class 'numpy.float64'>'\n",
"\twith 150 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"# 哑编码，对数据的目标值，返回值为哑编码后的数据\n",
"OneHotEncoder().fit_transform(iris.target.reshape((-1,1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 缺失值计算\n",
" 由于IRIS数据集没有缺失值，故对数据集新增一个样本，4个特征均赋值为NaN，表示数据缺失。使用preproccessing库的Imputer类对数据进行缺失值计算的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.84333333, 3.054 , 3.75866667, 1.19866667],\n",
" [ 5.1 , 3.5 , 1.4 , 0.2 ],\n",
" [ 4.9 , 3. , 1.4 , 0.2 ],\n",
" [ 4.7 , 3.2 , 1.3 , 0.2 ],\n",
" [ 4.6 , 3.1 , 1.5 , 0.2 ],\n",
" [ 5. , 3.6 , 1.4 , 0.2 ],\n",
" [ 5.4 , 3.9 , 1.7 , 0.4 ],\n",
" [ 4.6 , 3.4 , 1.4 , 0.3 ],\n",
" [ 5. , 3.4 , 1.5 , 0.2 ],\n",
" [ 4.4 , 2.9 , 1.4 , 0.2 ],\n",
" [ 4.9 , 3.1 , 1.5 , 0.1 ],\n",
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" [ 4.8 , 3.4 , 1.6 , 0.2 ],\n",
" [ 4.8 , 3. , 1.4 , 0.1 ],\n",
" [ 4.3 , 3. , 1.1 , 0.1 ],\n",
" [ 5.8 , 4. , 1.2 , 0.2 ],\n",
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" [ 4.8 , 3.1 , 1.6 , 0.2 ],\n",
" [ 5.4 , 3.4 , 1.5 , 0.4 ],\n",
" [ 5.2 , 4.1 , 1.5 , 0.1 ],\n",
" [ 5.5 , 4.2 , 1.4 , 0.2 ],\n",
" [ 4.9 , 3.1 , 1.5 , 0.1 ],\n",
" [ 5. , 3.2 , 1.2 , 0.2 ],\n",
" [ 5.5 , 3.5 , 1.3 , 0.2 ],\n",
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" [ 4.4 , 3. , 1.3 , 0.2 ],\n",
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" [ 4.5 , 2.3 , 1.3 , 0.3 ],\n",
" [ 4.4 , 3.2 , 1.3 , 0.2 ],\n",
" [ 5. , 3.5 , 1.6 , 0.6 ],\n",
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" [ 4.8 , 3. , 1.4 , 0.3 ],\n",
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" [ 5.5 , 2.3 , 4. , 1.3 ],\n",
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" [ 6.6 , 2.9 , 4.6 , 1.3 ],\n",
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" [ 5. , 2. , 3.5 , 1. ],\n",
" [ 5.9 , 3. , 4.2 , 1.5 ],\n",
" [ 6. , 2.2 , 4. , 1. ],\n",
" [ 6.1 , 2.9 , 4.7 , 1.4 ],\n",
" [ 5.6 , 2.9 , 3.6 , 1.3 ],\n",
" [ 6.7 , 3.1 , 4.4 , 1.4 ],\n",
" [ 5.6 , 3. , 4.5 , 1.5 ],\n",
" [ 5.8 , 2.7 , 4.1 , 1. ],\n",
" [ 6.2 , 2.2 , 4.5 , 1.5 ],\n",
" [ 5.6 , 2.5 , 3.9 , 1.1 ],\n",
" [ 5.9 , 3.2 , 4.8 , 1.8 ],\n",
" [ 6.1 , 2.8 , 4. , 1.3 ],\n",
" [ 6.3 , 2.5 , 4.9 , 1.5 ],\n",
" [ 6.1 , 2.8 , 4.7 , 1.2 ],\n",
" [ 6.4 , 2.9 , 4.3 , 1.3 ],\n",
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" [ 5.7 , 2.6 , 3.5 , 1. ],\n",
" [ 5.5 , 2.4 , 3.8 , 1.1 ],\n",
" [ 5.5 , 2.4 , 3.7 , 1. ],\n",
" [ 5.8 , 2.7 , 3.9 , 1.2 ],\n",
" [ 6. , 2.7 , 5.1 , 1.6 ],\n",
" [ 5.4 , 3. , 4.5 , 1.5 ],\n",
" [ 6. , 3.4 , 4.5 , 1.6 ],\n",
" [ 6.7 , 3.1 , 4.7 , 1.5 ],\n",
" [ 6.3 , 2.3 , 4.4 , 1.3 ],\n",
" [ 5.6 , 3. , 4.1 , 1.3 ],\n",
" [ 5.5 , 2.5 , 4. , 1.3 ],\n",
" [ 5.5 , 2.6 , 4.4 , 1.2 ],\n",
" [ 6.1 , 3. , 4.6 , 1.4 ],\n",
" [ 5.8 , 2.6 , 4. , 1.2 ],\n",
" [ 5. , 2.3 , 3.3 , 1. ],\n",
" [ 5.6 , 2.7 , 4.2 , 1.3 ],\n",
" [ 5.7 , 3. , 4.2 , 1.2 ],\n",
" [ 5.7 , 2.9 , 4.2 , 1.3 ],\n",
" [ 6.2 , 2.9 , 4.3 , 1.3 ],\n",
" [ 5.1 , 2.5 , 3. , 1.1 ],\n",
" [ 5.7 , 2.8 , 4.1 , 1.3 ],\n",
" [ 6.3 , 3.3 , 6. , 2.5 ],\n",
" [ 5.8 , 2.7 , 5.1 , 1.9 ],\n",
" [ 7.1 , 3. , 5.9 , 2.1 ],\n",
" [ 6.3 , 2.9 , 5.6 , 1.8 ],\n",
" [ 6.5 , 3. , 5.8 , 2.2 ],\n",
" [ 7.6 , 3. , 6.6 , 2.1 ],\n",
" [ 4.9 , 2.5 , 4.5 , 1.7 ],\n",
" [ 7.3 , 2.9 , 6.3 , 1.8 ],\n",
" [ 6.7 , 2.5 , 5.8 , 1.8 ],\n",
" [ 7.2 , 3.6 , 6.1 , 2.5 ],\n",
" [ 6.5 , 3.2 , 5.1 , 2. ],\n",
" [ 6.4 , 2.7 , 5.3 , 1.9 ],\n",
" [ 6.8 , 3. , 5.5 , 2.1 ],\n",
" [ 5.7 , 2.5 , 5. , 2. ],\n",
" [ 5.8 , 2.8 , 5.1 , 2.4 ],\n",
" [ 6.4 , 3.2 , 5.3 , 2.3 ],\n",
" [ 6.5 , 3. , 5.5 , 1.8 ],\n",
" [ 7.7 , 3.8 , 6.7 , 2.2 ],\n",
" [ 7.7 , 2.6 , 6.9 , 2.3 ],\n",
" [ 6. , 2.2 , 5. , 1.5 ],\n",
" [ 6.9 , 3.2 , 5.7 , 2.3 ],\n",
" [ 5.6 , 2.8 , 4.9 , 2. ],\n",
" [ 7.7 , 2.8 , 6.7 , 2. ],\n",
" [ 6.3 , 2.7 , 4.9 , 1.8 ],\n",
" [ 6.7 , 3.3 , 5.7 , 2.1 ],\n",
" [ 7.2 , 3.2 , 6. , 1.8 ],\n",
" [ 6.2 , 2.8 , 4.8 , 1.8 ],\n",
" [ 6.1 , 3. , 4.9 , 1.8 ],\n",
" [ 6.4 , 2.8 , 5.6 , 2.1 ],\n",
" [ 7.2 , 3. , 5.8 , 1.6 ],\n",
" [ 7.4 , 2.8 , 6.1 , 1.9 ],\n",
" [ 7.9 , 3.8 , 6.4 , 2. ],\n",
" [ 6.4 , 2.8 , 5.6 , 2.2 ],\n",
" [ 6.3 , 2.8 , 5.1 , 1.5 ],\n",
" [ 6.1 , 2.6 , 5.6 , 1.4 ],\n",
" [ 7.7 , 3. , 6.1 , 2.3 ],\n",
" [ 6.3 , 3.4 , 5.6 , 2.4 ],\n",
" [ 6.4 , 3.1 , 5.5 , 1.8 ],\n",
" [ 6. , 3. , 4.8 , 1.8 ],\n",
" [ 6.9 , 3.1 , 5.4 , 2.1 ],\n",
" [ 6.7 , 3.1 , 5.6 , 2.4 ],\n",
" [ 6.9 , 3.1 , 5.1 , 2.3 ],\n",
" [ 5.8 , 2.7 , 5.1 , 1.9 ],\n",
" [ 6.8 , 3.2 , 5.9 , 2.3 ],\n",
" [ 6.7 , 3.3 , 5.7 , 2.5 ],\n",
" [ 6.7 , 3. , 5.2 , 2.3 ],\n",
" [ 6.3 , 2.5 , 5. , 1.9 ],\n",
" [ 6.5 , 3. , 5.2 , 2. ],\n",
" [ 6.2 , 3.4 , 5.4 , 2.3 ],\n",
" [ 5.9 , 3. , 5.1 , 1.8 ]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from sklearn.preprocessing import Imputer\n",
"\n",
"# 缺失值计算，返回值为计算缺失值后的数据\n",
"# 参数missing_value为缺失值的表示形式，默认为NaN\n",
"# 参数strategy为缺失值的填充方式，默认为mean(均值）\n",
"Imputer().fit_transform(\\\n",
" np.vstack((np.array([np.nan, np.nan, np.nan, np.nan]),iris.data)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.5 数据变换\n",
" 常见的数据变换有基于多项式的、基于指数函数的、基于对数函数的。4个特征，度为2的多项式转换公式如下：\n",
" (x1',x2',x3',...,xn')\n",
" =(1, x1, x2, ..., xn, x1^2, x1*x2, x1*x2*x3, ..., )\n",
" \n",
" 使用preproccessing库的PolynomialFeatures类对数据进行多项式转换的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 5.1 , 3.5 , ..., 1.96, 0.28, 0.04],\n",
" [ 1. , 4.9 , 3. , ..., 1.96, 0.28, 0.04],\n",
" [ 1. , 4.7 , 3.2 , ..., 1.69, 0.26, 0.04],\n",
" ..., \n",
" [ 1. , 6.5 , 3. , ..., 27.04, 10.4 , 4. ],\n",
" [ 1. , 6.2 , 3.4 , ..., 29.16, 12.42, 5.29],\n",
" [ 1. , 5.9 , 3. , ..., 26.01, 9.18, 3.24]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"# 多项式转换\n",
"# 参数degree为度，默认值为2\n",
"PolynomialFeatures().fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 基于单变元函数的数据变换可以使用一个统一的方式完成，使用preproccessing库的FunctionTransformer对数据进行对数函数转换的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 1.96009478, 1.28093385, 1.88706965, 0.87546874],\n",
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" [ 2.01490302, 1.38629436, 1.82454929, 1.09861229],\n",
" [ 1.97408103, 1.48160454, 1.85629799, 1.19392247],\n",
" [ 1.93152141, 1.38629436, 1.80828877, 1.02961942]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import FunctionTransformer\n",
"\n",
"#自定义转换函数为对数函数的数据变换\n",
"#第一个参数是单变元函数\n",
"FunctionTransformer(np.log1p).fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6 回顾\n",
" 类\t功能\t说明\n",
" StandardScaler\t无量纲化\t标准化，基于特征矩阵的列，将特征值转换至服从标准正态分布\n",
" MinMaxScaler\t无量纲化\t区间缩放，基于最大最小值，将特征值转换到[0, 1]区间上\n",
" Normalizer\t归一化\t基于特征矩阵的行，将样本向量转换为“单位向量”\n",
" Binarizer\t二值化\t基于给定阈值，将定量特征按阈值划分\n",
" OneHotEncoder\t哑编码\t将定性数据编码为定量数据\n",
" Imputer\t缺失值计算\t计算缺失值，缺失值可填充为均值等\n",
" PolynomialFeatures\t多项式数据转换\t多项式数据转换\n",
" FunctionTransformer\t自定义单元数据转换\t使用单变元的函数来转换数据"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 3 特征选择\n",
" 当数据预处理完成后，我们需要选择有意义的特征输入机器学习的算法和模型进行训练。通常来说，从两个方面考虑来选择特征：\n",
" \n",
" 特征是否发散：如果一个特征不发散，例如方差接近于0，也就是说样本在这个特征上基本上没有差异，这个特征对于样本的区分并没有什么用。\n",
" \n",
" 特征与目标的相关性：这点比较显见，与目标相关性高的特征，应当优选选择。除方差法外，本文介绍的其他方法均从相关性考虑。\n",
"\n",
" 根据特征选择的形式又可以将特征选择方法分为3种：\n",
" Filter：过滤法，按照发散性或者相关性对各个特征进行评分，设定阈值或者待选择阈值的个数，选择特征。\n",
" \n",
" Wrapper：包装法，根据目标函数（通常是预测效果评分），每次选择若干特征，或者排除若干特征。\n",
" \n",
" Embedded：嵌入法，先使用某些机器学习的算法和模型进行训练，得到各个特征的权值系数，根据系数从大到小选择特征。类似于Filter方法，但是是通过训练来确定特征的优劣。\n",
" \n",
" 我们使用sklearn中的feature_selection库来进行特征选择。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Filter\n",
"##### 3.1.1 方差选择法\n",
" 使用方差选择法，先要计算各个特征的方差，然后根据阈值，选择方差大于阈值的特征。使用feature_selection库的VarianceThreshold类来选择特征的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 4.7],\n",
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" [ 4.2],\n",
" [ 4.2],\n",
" [ 4.3],\n",
" [ 3. ],\n",
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" [ 6. ],\n",
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" [ 5.6],\n",
" [ 5.8],\n",
" [ 6.6],\n",
" [ 4.5],\n",
" [ 6.3],\n",
" [ 5.8],\n",
" [ 6.1],\n",
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" [ 5.5],\n",
" [ 5. ],\n",
" [ 5.1],\n",
" [ 5.3],\n",
" [ 5.5],\n",
" [ 6.7],\n",
" [ 6.9],\n",
" [ 5. ],\n",
" [ 5.7],\n",
" [ 4.9],\n",
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" [ 4.9],\n",
" [ 5.7],\n",
" [ 6. ],\n",
" [ 4.8],\n",
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" [ 5.8],\n",
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" [ 5.1],\n",
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" [ 5.2],\n",
" [ 5. ],\n",
" [ 5.2],\n",
" [ 5.4],\n",
" [ 5.1]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_selection import VarianceThreshold\n",
"\n",
"# 方差选择法，返回值为特征选择后的数据\n",
"# 参数threshold为方差的阈值\n",
"VarianceThreshold(threshold=3).fit_transform(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.1.2 相关系数法\n",
" 使用相关系数法，先要计算各个特征对目标值的相关系数以及相关系数的P值。用feature_selection库的SelectKBest类结合相关系数来选择特征的代码如下："
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" [ 1.4, 0.3],\n",
" [ 1.5, 0.2],\n",
" [ 1.4, 0.2],\n",
" [ 1.5, 0.1],\n",
" [ 1.5, 0.2],\n",
" [ 1.6, 0.2],\n",
" [ 1.4, 0.1],\n",
" [ 1.1, 0.1],\n",
" [ 1.2, 0.2],\n",
" [ 1.5, 0.4],\n",
" [ 1.3, 0.4],\n",
" [ 1.4, 0.3],\n",
" [ 1.7, 0.3],\n",
" [ 1.5, 0.3],\n",
" [ 1.7, 0.2],\n",
" [ 1.5, 0.4],\n",
" [ 1. , 0.2],\n",
" [ 1.7, 0.5],\n",
" [ 1.9, 0.2],\n",
" [ 1.6, 0.2],\n",
" [ 1.6, 0.4],\n",
" [ 1.5, 0.2],\n",
" [ 1.4, 0.2],\n",
" [ 1.6, 0.2],\n",
" [ 1.6, 0.2],\n",
" [ 1.5, 0.4],\n",
" [ 1.5, 0.1],\n",
" [ 1.4, 0.2],\n",
" [ 1.5, 0.1],\n",
" [ 1.2, 0.2],\n",
" [ 1.3, 0.2],\n",
" [ 1.5, 0.1],\n",
" [ 1.3, 0.2],\n",
" [ 1.5, 0.2],\n",
" [ 1.3, 0.3],\n",
" [ 1.3, 0.3],\n",
" [ 1.3, 0.2],\n",
" [ 1.6, 0.6],\n",
" [ 1.9, 0.4],\n",
" [ 1.4, 0.3],\n",
" [ 1.6, 0.2],\n",
" [ 1.4, 0.2],\n",
" [ 1.5, 0.2],\n",
" [ 1.4, 0.2],\n",
" [ 4.7, 1.4],\n",
" [ 4.5, 1.5],\n",
" [ 4.9, 1.5],\n",
" [ 4. , 1.3],\n",
" [ 4.6, 1.5],\n",
" [ 4.5, 1.3],\n",
" [ 4.7, 1.6],\n",
" [ 3.3, 1. ],\n",
" [ 4.6, 1.3],\n",
" [ 3.9, 1.4],\n",
" [ 3.5, 1. ],\n",
" [ 4.2, 1.5],\n",
" [ 4. , 1. ],\n",
" [ 4.7, 1.4],\n",
" [ 3.6, 1.3],\n",
" [ 4.4, 1.4],\n",
" [ 4.5, 1.5],\n",
" [ 4.1, 1. ],\n",
" [ 4.5, 1.5],\n",
" [ 3.9, 1.1],\n",
" [ 4.8, 1.8],\n",
" [ 4. , 1.3],\n",
" [ 4.9, 1.5],\n",
" [ 4.7, 1.2],\n",
" [ 4.3, 1.3],\n",
" [ 4.4, 1.4],\n",
" [ 4.8, 1.4],\n",
" [ 5. , 1.7],\n",
" [ 4.5, 1.5],\n",
" [ 3.5, 1. ],\n",
" [ 3.8, 1.1],\n",
" [ 3.7, 1. ],\n",
" [ 3.9, 1.2],\n",
" [ 5.1, 1.6],\n",
" [ 4.5, 1.5],\n",
" [ 4.5, 1.6],\n",
" [ 4.7, 1.5],\n",
" [ 4.4, 1.3],\n",
" [ 4.1, 1.3],\n",
" [ 4. , 1.3],\n",
" [ 4.4, 1.2],\n",
" [ 4.6, 1.4],\n",
" [ 4. , 1.2],\n",
" [ 3.3, 1. ],\n",
" [ 4.2, 1.3],\n",
" [ 4.2, 1.2],\n",
" [ 4.2, 1.3],\n",
" [ 4.3, 1.3],\n",
" [ 3. , 1.1],\n",
" [ 4.1, 1.3],\n",
" [ 6. , 2.5],\n",
" [ 5.1, 1.9],\n",
" [ 5.9, 2.1],\n",
" [ 5.6, 1.8],\n",
" [ 5.8, 2.2],\n",
" [ 6.6, 2.1],\n",
" [ 4.5, 1.7],\n",
" [ 6.3, 1.8],\n",
" [ 5.8, 1.8],\n",
" [ 6.1, 2.5],\n",
" [ 5.1, 2. ],\n",
" [ 5.3, 1.9],\n",
" [ 5.5, 2.1],\n",
" [ 5. , 2. ],\n",
" [ 5.1, 2.4],\n",
" [ 5.3, 2.3],\n",
" [ 5.5, 1.8],\n",
" [ 6.7, 2.2],\n",
" [ 6.9, 2.3],\n",
" [ 5. , 1.5],\n",
" [ 5.7, 2.3],\n",
" [ 4.9, 2. ],\n",
" [ 6.7, 2. ],\n",
" [ 4.9, 1.8],\n",
" [ 5.7, 2.1],\n",
" [ 6. , 1.8],\n",
" [ 4.8, 1.8],\n",
" [ 4.9, 1.8],\n",
" [ 5.6, 2.1],\n",
" [ 5.8, 1.6],\n",
" [ 6.1, 1.9],\n",
" [ 6.4, 2. ],\n",
" [ 5.6, 2.2],\n",
" [ 5.1, 1.5],\n",
" [ 5.6, 1.4],\n",
" [ 6.1, 2.3],\n",
" [ 5.6, 2.4],\n",
" [ 5.5, 1.8],\n",
" [ 4.8, 1.8],\n",
" [ 5.4, 2.1],\n",
" [ 5.6, 2.4],\n",
" [ 5.1, 2.3],\n",
" [ 5.1, 1.9],\n",
" [ 5.9, 2.3],\n",
" [ 5.7, 2.5],\n",
" [ 5.2, 2.3],\n",
" [ 5. , 1.9],\n",
" [ 5.2, 2. ],\n",
" [ 5.4, 2.3],\n",
" [ 5.1, 1.8]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_selection import SelectKBest\n",
"from scipy.stats import pearsonr\n",
"\n",
"# 选择K个最好的特征，返回选择特征后的数据\n",
"# 第一个参数为计算评估特征是否好的函数，该函数输入特征矩阵和目标向量，\n",
"# 输出二元组（评分，P值）的数组，数组第i项为第i个特征的评分和P值。\n",
"# 在此定义为计算相关系数\n",
"# 参数k为选择的特征个数\n",
"SelectKBest(lambda X, Y: tuple(map(tuple,np.array(list(map(lambda x:pearsonr(x, Y), X.T))).T)), k=2).fit_transform(iris.data, iris.target)"
]
},
{
"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 |
caganze/wisps | notebooks/simulations3.ipynb | 1 | 180941 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2404 sources from /Users/caganze/research/splat//resources/Spectra/Public/SPEX-PRISM/ to spectral database\n",
"Adding 145 sources from /Users/caganze/research/splat//resources/Spectra/Public/LRIS-RED/ to spectral database\n",
"Adding 89 sources from /Users/caganze/research/splat//resources/Spectra/Public/MAGE/ to spectral database\n"
]
}
],
"source": [
"#imports\n",
"import splat\n",
"import wisps\n",
"import astropy.units as u\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import glob\n",
"import seaborn as sns\n",
"\n",
"import splat.photometry as sphot\n",
"import splat.core as spl1\n",
"import splat.empirical as spe\n",
"import splat.simulate as spsim\n",
"import matplotlib as mpl\n",
"from tqdm import tqdm\n",
"\n",
"\n",
"from astropy import stats as astrostats\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#constants \n",
"grid=np.sort(np.random.uniform(1000, 4000,1000))\n",
"\n",
"#best_dict={'2MASS J': {\\\n",
"# 'spt': [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39], \\\n",
"# 'values': [10.36,10.77,11.15,11.46,11.76,12.03,12.32,12.77,13.51,13.69,14.18,14.94,14.90,14.46,14.56,15.25,14.54,14.26,13.89,14.94,15.53,16.78,17.18,17.75],\\\n",
"# 'rms': [0.30,0.30,0.42,0.34,0.18,0.15,0.21,0.24,0.28,0.25,0.60,0.20,0.13,0.71,0.5,0.12,0.06,0.16,0.36,0.12,0.27,0.76,0.51,0.5]},\n",
"# '2MASS H': {\\\n",
"# 'spt': [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39], \\\n",
"# 'values': [9.76,10.14,10.47,10.74,11.00,11.23,11.41,11.82,12.45,12.63,13.19,13.82,13.77,13.39,13.62,14.39,13.73,13.67,13.57,14.76,15.48,16.70,17.09,17.51],\\\n",
"# 'rms': [0.30,0.31,0.43,0.35,0.23,0.21,0.25,0.29,0.3,0.30,0.62,0.31,0.20,0.73,0.5,0.18,0.15,0.24,0.40,0.24,0.37,0.78,0.5,0.5]}}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#functions\n",
"def flux_calibrate_spectrum(row):\n",
" try:\n",
" #calibrate using absolute magnidtude\n",
" sp=splat.getSpectrum(filename=row.DATA_FILE)[0]\n",
" spt=splat.typeToNum(row.SPEX_TYPE)\n",
" #use optical types for early dwarffs\n",
" if (np.isnan(spt) | (spt <=15)):\n",
" spt=splat.typeToNum(row.OPT_TYPE)\n",
" #no need to flux calibrate, reject high uncertainty in classification types\n",
" #absmag=row.J_2MASS-5*(np.log10(row.DISTANCE)-1)\n",
" #sp.fluxCalibrate('2MASS J', absmag)\n",
" return [spt, sp]\n",
" except :\n",
" return []\n",
" \n",
"\n",
"def make_mamajek_fit(spt):\n",
" \n",
" js=mamjk.M_J.apply(float).values\n",
" jminush=mamjk['J-H'].apply(float).values\n",
" hs=js-jminush\n",
" \n",
" spts=mamjk.SpT.apply(wisps.make_spt_number).apply(float).values\n",
" \n",
" hsortedindex=np.argsort(hs)\n",
" jsortedindex=np.argsort(js)\n",
" \n",
" hval=np.interp(spt, spts[hsortedindex], hs[hsortedindex])\n",
" jval=np.interp(spt, spts[jsortedindex], js[jsortedindex])\n",
" \n",
" return ((jval, 0.4), (hval, 0.4))\n",
"\n",
"\n",
"def absolute_mag_best(spt, flt):\n",
" #\n",
" mags=wisps.best_dict[flt]\n",
" spts=np.array(mags['spt'])\n",
" if (spt < spts.min()) | (spt> spts.max()):\n",
" return np.nan\n",
" else:\n",
" vals=np.array(mags['values'])\n",
" rms=np.array(mags['rms'])\n",
"\n",
" sortedindex=np.argsort(vals)\n",
"\n",
"\n",
" val=np.interp(spt, spts[sortedindex], vals[sortedindex])\n",
" rmsv=np.interp(spt, spts[sortedindex], rms[sortedindex])\n",
" \n",
" vals=np.random.normal(val, rmsv, 1000)\n",
" return vals.mean(), vals.std()\n",
" \n",
"\n",
"def get_abs_mag(spt):\n",
" \n",
" spt=wisps.make_spt_number(spt)\n",
" \n",
" if spt < 37:\n",
" (j, junc), (h, hunc)= make_mamajek_fit(spt)\n",
" \n",
" if (spt >= 37):\n",
" h=wisps.absolute_mag_kirkpatrick(spt, '2MASS H')\n",
" (j, junc), (_, _)= make_mamajek_fit(spt)\n",
" hunc=0.7\n",
" corr0=splat.photometry.vegaToAB('2MASS J')\n",
" corr1=splat.photometry.vegaToAB('2MASS H')\n",
" return [[j+corr0, junc], [h+corr1, hunc]]\n",
" \n",
"def schn_flux_calibrate(row):\n",
" sp=row.spectra.splat_spectrum\n",
" spt=splat.typeToNum(row.Spec)\n",
" sp.fluxCalibrate('MKO J',float(row.J_MKO))\n",
" return [spt, sp]\n",
"\n",
"def get_colors(sp, flt):\n",
" #measuring filtermags in for two filters and comparing that to target filters\n",
" #remember to include euclid filters\n",
" #using splat filtermag\n",
" mag, mag_unc = splat.filterMag(sp, flt, ab=True)\n",
" #calculate the mag of the standard in J and H\n",
" \n",
" magj, mag_uncj = splat.filterMag(sp,'2MASS J', ab=True)\n",
" magh, mag_unch = splat.filterMag(sp,'2MASS H', ab=True)\n",
" #calculate the offset between HST filters and 2mass filters but add the uncertainty\n",
" \n",
" offsetj=magj-mag\n",
" offseth=magh-mag\n",
" \n",
" unc1=(mag_unc**2+mag_uncj**2)**0.5\n",
" unc2=(mag_unc**2+mag_unch**2)**0.5\n",
" \n",
" #offsetj=np.random.normal(offsetj, unc1)\n",
" #offseth=np.random.normal(offseth, unc2)\n",
" return [[offsetj, offseth], [unc1, unc2]]\n",
"\n",
"\n",
"def get_abs_hst_mag(color, mag0):\n",
" return mag0-color\n",
"\n",
"\n",
"def k_clip_fit(x, y, sigma_y, sigma = 5, n=6):\n",
" \n",
" '''Fit a polynomial to y vs. x, and k-sigma clip until convergence'''\n",
" \n",
" not_clipped = np.ones_like(y).astype(bool)\n",
" n_remove = 1\n",
" \n",
" #use median sigma\n",
" #median_sigma= np.nanmedian(sigma_y)\n",
" \n",
" while n_remove > 0:\n",
"\n",
" best_fit = np.poly1d(np.polyfit(x[not_clipped], y[not_clipped], n))\n",
" \n",
" norm_res = (np.abs(y - best_fit(x)))/(sigma_y)\n",
" remove = np.logical_and(norm_res >= sigma, not_clipped == 1)\n",
" n_remove = sum(remove)\n",
" not_clipped[remove] = 0 \n",
" \n",
" return not_clipped\n",
"\n",
"def fit_with_nsigma_clipping(x, y, y_unc, n, sigma=3.):\n",
" not_clipped = k_clip_fit(x, y, y_unc, sigma = sigma)\n",
" return not_clipped, np.poly1d(np.polyfit(x[not_clipped], y[not_clipped], n))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#load spectra, ignore binaries, objects with high uncertainty in mag and objects without parallaxes\n",
"splat_db=splat.searchLibrary(vlm=True, giant=False, young=False, binary=False)\n",
"splat_db['SHORTNAME']=splat_db.DESIGNATION.apply(lambda x: splat.designationToShortName)\n",
"#sml=splat_db[~ ((splat_db.H_2MASS_E > 0.1) | (splat_db.J_2MASS_E > 0.1) | (splat_db.MEDIAN_SNR <20) )]\n",
"sml=splat_db[~ ((splat_db.H_2MASS_E > 0.3) | (splat_db.J_2MASS_E > 0.3) |\n",
" (splat_db.SPEX_TYPE.apply(splat.typeToNum) <15))]\n",
"\n",
"#sds=sml[(sml.METALLICITY_CLASS=='sd') | (sml.METALLICITY_CLASS=='esd') ]\n",
"sml=sml[~((sml.METALLICITY_CLASS=='sd') | (sml.METALLICITY_CLASS=='esd') \\\n",
" | (sml.MEDIAN_SNR <20))]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"mdwarfs=sml[ (sml.SPEX_TYPE.apply(splat.typeToNum) <20)]\n",
"ldwarfs=sml[ (sml.SPEX_TYPE.apply(splat.typeToNum).between(20, 30))]\n",
"tdwarfs=sml[ (sml.SPEX_TYPE.apply(splat.typeToNum).between(30, 40))]\n",
"\n",
"#tighter_constraints on m dwarfs \n",
"mdwarfs=mdwarfs[(~mdwarfs.PARALLAX.isna()) & (mdwarfs.MEDIAN_SNR >100)]\n",
"ldwarfs=ldwarfs[ (ldwarfs.MEDIAN_SNR >70)]\n",
"\n",
"def choose_ten(df):\n",
" if len(df) >10:\n",
" return df.sort_values('MEDIAN_SNR', ascending=False)[:10]\n",
" else:\n",
" return df\n",
"ls=ldwarfs.groupby('SPEX_TYPE').apply(choose_ten).reset_index(drop=True)#.groupby('SPEX_TYPE').count()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#get y dwarfs\n",
"def get_shortname(n):\n",
" return splat.designationToShortName(n).replace('J', 'WISE')\n",
"schn='/Users/caganze/research/wisps/data/schneider/*.txt'\n",
"schntb=pd.read_csv('/Users/caganze/research/wisps/data/schneider2015.txt', \n",
" delimiter=' ').drop(columns='Unnamed: 14')\n",
"schntb['shortname']=schntb.Name.apply(get_shortname)\n",
"spectra_schn=[]\n",
"from astropy.io import ascii\n",
"for f in glob.glob(schn):\n",
" d=ascii.read(f).to_pandas()\n",
" shortname=(f.split('/')[-1]).split('.txt')[0]\n",
" s=splat.Spectrum(wave=d.col1, \n",
" flux=d.col2,\n",
" noise=d.col3, \n",
" name=shortname)\n",
" #measure snr \n",
" mask= np.logical_and(d.col1>1.0, d.col1<2.4)\n",
" snr= (np.nanmedian(d.col2[mask]/d.col3[mask]))\n",
" spectra_schn.append([s, snr])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#schn_merged=(schn_merged[schn_merged.snr1>10]).reset_index(drop=True)\n",
"smlf=pd.concat([mdwarfs, ls, tdwarfs]).reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def make_spt_number(spt):\n",
" ##make a spt a number\n",
" if isinstance(spt, str):\n",
" return splat.typeToNum(spt)\n",
" else:\n",
" return spt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def get_file(x):\n",
" try:\n",
" return splat.getSpectrum(filename=x)[0]\n",
" except:\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"jupyter": {
"outputs_hidden": true
},
"scrolled": true
},
"outputs": [],
"source": [
"%%capture\n",
"templs=smlf.DATA_FILE.apply(lambda x: get_file(x))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"schntb['spectra']=[x[0] for x in spectra_schn]\n",
"\n",
"schntb['snr']=[x[1] for x in spectra_schn]\n",
"\n",
"schntb=schntb[schntb.snr>=2.].reset_index(drop=True)\n",
"\n",
"all_spectra=np.concatenate([templs,schntb.spectra.values ])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"spts=np.concatenate([smlf.SPEX_TYPE.apply(make_spt_number).values,\n",
" schntb.Spec.apply(make_spt_number).values,\n",
" ])\n",
"\n",
"#remove nones\n",
"nones= np.array(all_spectra)==None\n",
"all_spectra=all_spectra[~nones]\n",
"spts=spts[~nones]\n",
"assert len(spts) == len(all_spectra)\n",
"#assert len(spts) == len(all_spectra)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from astropy.io import ascii\n",
"mamjk=ascii.read('/users/caganze/research/wisps/data/mamajek_relations.txt').to_pandas().replace('None', np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#combined calibrated spctra\n",
"#combcal=np.append(calbr, calbrschn)\n",
"#specs=np.array([x for x in pd.DataFrame(combcal).values if x])\n",
"specs= list(zip(spts, all_spectra))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[-0.5709339437262742, -0.4242192636729918],\n",
" [0.11667832715031544, 0.17366490501192167]]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_colors(all_spectra[-1], 'WFC3_F110W')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"output = open(wisps.OUTPUT_FILES+'/validated_spectra.pkl', 'wb')\n",
"pickle.dump(specs, output)\n",
"output.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
" 0%| | 0/336 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['WFC3_F110W' 'WFC3_F140W' 'WFC3_F160W' 'EUCLID_J' 'EUCLID_H']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 336/336 [08:09<00:00, 1.46s/it]\n"
]
}
],
"source": [
"#specs\n",
"\n",
"#compute colors for different filters\n",
"colors=[]\n",
"uncolors=[]\n",
"fltrswfc3= ['WFC3_{}'.format(k) for k in ['F110W', 'F140W', 'F160W']]\n",
"fltrseucl=['EUCLID_J', 'EUCLID_H']\n",
"\n",
"fltrs=np.append(fltrswfc3, fltrseucl)\n",
"print (fltrs)\n",
"for pair in tqdm(specs):\n",
" c={}\n",
" uncclrs={}\n",
" for flt in fltrs:\n",
" x=pair[1]\n",
" sptx=pair[0]\n",
" color, uncc=get_colors(x, flt)\n",
" c.update({flt: color})\n",
" uncclrs.update({flt:uncc})\n",
" uncolors.append(uncclrs)\n",
" colors.append(c)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"assert len(spts) ==len(colors)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"sp_grid= spts\n",
"#sp_grid=sp_grid0[~nans]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"colors_df=pd.DataFrame(colors)#[~nans]\n",
"uncolors_df=pd.DataFrame(uncolors)#[~nans]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"colors_df['spt']=sp_grid\n",
"uncolors_df['spt']=sp_grid"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"colors_polynomials={}\n",
"for k in colors_df.columns:\n",
" if k != 'spt':\n",
" clrs=np.vstack(colors_df[k]).astype(float)\n",
" uncs=np.vstack(uncolors_df[k]).astype(float)\n",
" \n",
" mask0, pc0=fit_with_nsigma_clipping( sp_grid,clrs[:,0], uncs[:,0],6, sigma=5.)\n",
" mask1, pc1=fit_with_nsigma_clipping( sp_grid,clrs[:,1], uncs[:,1],6, sigma=5.)\n",
" \n",
" x0, y0, yunc0= sp_grid[mask0], clrs[:,0][mask0], uncs[:,0][mask0]\n",
" x1, y1, yunc1= sp_grid[mask1], clrs[:,1][mask1], uncs[:,1][mask1]\n",
" \n",
"\n",
"\n",
" colors_polynomials.update({k+'_J': {'pol': pc0, 'mask':mask0, \n",
" 'color':clrs[:,0], 'unc': uncs[:,0], \n",
" 'scatter': 5.*np.abs(pc0(x0)- y0).mean() }, \n",
" k+'_H': {'pol': pc1, 'mask':mask1, 'color': clrs[:,1] , 'unc': uncs[:,1] ,\n",
" 'scatter': 5.*np.abs(pc1(x1)- y1).mean() }})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"two_mass_values=np.array([ get_abs_mag(x) for x in sp_grid])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc53433d370>]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(sp_grid, two_mass_values[:, 0][:, 0], '.')\n",
"plt.plot(sp_grid, two_mass_values[:, 1][:, 0], '.')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"polynomial_relations={}\n",
"\n",
"for k in colors_polynomials.keys():\n",
" \n",
" if k.endswith('J'): #use j-offset for j offset for h\n",
" #take the median centered around the uncertainty \n",
" two_mass_to_use=two_mass_values[:, 0][:,0]\n",
" two_mass_uncer= two_mass_values[:,0][:,1]\n",
" \n",
" else:\n",
" two_mass_to_use=two_mass_values[:, 1][:,0]\n",
" two_mass_uncer= two_mass_values[:,1][:,1]\n",
" \n",
" mask= np.logical_and.reduce([(colors_polynomials[k])['mask'], \n",
" ~np.isnan((colors_polynomials[k])['color']),\n",
" ~np.isnan((colors_polynomials[k])['unc']), \n",
" ~np.isnan(two_mass_to_use)])\n",
" \n",
" #add values and propagate total uncertainty\n",
" total_uncer=(two_mass_uncer**2+ (colors_polynomials[k])['unc']**2)**0.5\n",
" \n",
" vals0= np.random.normal(two_mass_to_use+ (colors_polynomials[k])['color'], total_uncer , \n",
" size=( 1000, len(mask)))\n",
" \n",
" vals=vals0.mean(axis=0)\n",
" uncs=vals0.std(axis=0)\n",
" \n",
" #only fit masked area \n",
" x=sp_grid[mask]\n",
" y=vals[mask]\n",
" yunc=total_uncer[mask]\n",
"\n",
"\n",
" maskn, p=fit_with_nsigma_clipping(x,y,yunc,6, sigma=5.)\n",
"\n",
"\n",
" polynomial_relations.update({k:{'x': x, 'y': y, 'pol': p, 'yunc': yunc, 'mask':maskn,\n",
" 'scatter': 5*(abs(p(x[maskn])-y[maskn])).mean()}})"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.67"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wisps.kirkpa2019pol['scatter']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"RMS_BEST={'J', np.array((wisps.best_dict['2MASS J']['rms'])).mean()**2 + 0.4**2, \n",
" 'H', np.array((wisps.best_dict['2MASS H']['rms'])).mean()**2 + 0.4**2}"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"RMS_DAVY=wisps.kirkpa2019pol['scatter']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['WFC3_F110W_J', 'WFC3_F110W_H', 'WFC3_F140W_J', 'WFC3_F140W_H', 'WFC3_F160W_J', 'WFC3_F160W_H', 'EUCLID_J_J', 'EUCLID_J_H', 'EUCLID_H_J', 'EUCLID_H_H'])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polynomial_relations.keys()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"final_pol_keys=['WFC3_F110W_J', 'WFC3_F140W_J', 'WFC3_F160W_H']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['pol', 'mask', 'color', 'unc', 'scatter'])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colors_polynomials[k].keys()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.3222664721637294\n",
"0.3685143906206988\n",
"0.3965506236229219\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#visualize \n",
"fig, (ax, ax1)=plt.subplots(ncols=3, figsize=(12, 8), nrows=2, sharey=False)\n",
"\n",
"for idx, k in zip(range(0, 10), final_pol_keys):\n",
" \n",
" pc=colors_polynomials[k]['pol']\n",
" p=polynomial_relations[k]['pol']\n",
" \n",
" masked=colors_polynomials[k]['mask']\n",
" maskedpol=polynomial_relations[k]['mask']\n",
" scpol=polynomial_relations[k]['scatter']\n",
" scolor=colors_polynomials[k]['scatter']\n",
" \n",
" print (scpol)\n",
" ax[idx].plot(np.linspace(15, 42), pc(np.linspace(15, 42)), c='#001f3f', linewidth=3)\n",
" ax1[idx].plot(np.linspace(15, 42), p(np.linspace(15, 42)), c='#001f3f', linewidth=3)\n",
" \n",
" ax[idx].fill_between(np.linspace(15, 42), pc(np.linspace(15, 42))+scolor, pc(np.linspace(15, 42))-scolor, alpha=0.5 )\n",
" \n",
" ax1[idx].fill_between(np.linspace(15, 42), p(np.linspace(15, 42))+scpol, p(np.linspace(15, 42))-scpol, alpha=0.5 )\n",
" \n",
" ax[idx].errorbar(sp_grid[mask], (colors_polynomials[k]['color'])[mask], yerr=(colors_polynomials[k]['unc'])[mask], fmt='o', mec='#111111')\n",
" \n",
" ax[idx].errorbar(sp_grid[~mask], (colors_polynomials[k]['color'])[~mask], yerr= (colors_polynomials[k]['unc'])[~mask], fmt='x', mec='#111111')\n",
" \n",
" \n",
" ax1[idx].errorbar(polynomial_relations[k]['x'][~maskedpol], polynomial_relations[k]['y'][~maskedpol],yerr=polynomial_relations[k]['yunc'][~maskedpol], fmt='x', mec='#111111')\n",
" ax1[idx].errorbar(polynomial_relations[k]['x'][maskedpol], polynomial_relations[k]['y'][maskedpol], yerr= polynomial_relations[k]['yunc'][maskedpol], fmt='o', mec='#111111')\n",
" \n",
" \n",
" #ax[idx].set_xlim([15, 42])\n",
" #ax1[idx].set_xlim([15, 42])\n",
" \n",
" ax[idx].minorticks_on()\n",
" ax1[idx].minorticks_on()\n",
" \n",
" \n",
" ax[idx].set_xticks([15, 20, 25, 30, 35, 40])\n",
" ax[idx].set_xticklabels(['M5', 'L0', 'L5', 'T0', 'T5', 'Y0'])\n",
" \n",
" ax1[idx].set_xticks([15, 20, 25, 30, 35, 40])\n",
" ax1[idx].set_xticklabels(['M5', 'L0', 'L5', 'T0', 'T5', 'Y0'])\n",
" \n",
" ax[idx].set_xlabel('Spectral Type')\n",
" ax1[idx].set_xlabel('Spectral Type')\n",
"\n",
" \n",
" \n",
"\n",
" \n",
"#ax[0].set_ylim([-0.75, 0.0])\n",
"#ax[1].set_ylim([-.25, 0.5])\n",
"#ax[2].set_ylim([-.75, 0.25])\n",
"\n",
"#ax1[0].set_ylim([8, 27])\n",
"#ax1[1].set_ylim([8, 27])\n",
"#ax1[2].set_ylim([7, 27])\n",
"\n",
"#ax1[0].set_ylim([8, 27])\n",
"\n",
"for a in ax1:\n",
" a.invert_yaxis()\n",
"\n",
"ax[0].set_ylabel('2MASS J - WFC3 F110W')\n",
"ax[1].set_ylabel('2MASS J - WFC3 F140W')\n",
"ax[2].set_ylabel('2MASS H - WFC3 F160W')\n",
"\n",
"ax1[0].set_ylabel(r'$M_\\mathrm{F110W} $ (AB)')\n",
"ax1[1].set_ylabel(r'$M_\\mathrm{F140W} $ (AB)')\n",
"ax1[2].set_ylabel(r'$M_\\mathrm{F160W} $ (AB)')\n",
"\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(wisps.OUTPUT_FIGURES+'/abs_mag_relations.pdf', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['WFC3_F110W_J', 'WFC3_F110W_H', 'WFC3_F140W_J', 'WFC3_F140W_H', 'WFC3_F160W_J', 'WFC3_F160W_H', 'EUCLID_J_J', 'EUCLID_J_H', 'EUCLID_H_J', 'EUCLID_H_H'])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polynomial_relations.keys()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"291"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(maskedpol)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"rels={'abs_mags':{'F110W': (polynomial_relations['WFC3_F110W_J']['pol'], polynomial_relations['WFC3_F110W_J']['scatter'] ),\n",
" 'F140W': (polynomial_relations['WFC3_F140W_J']['pol'], polynomial_relations['WFC3_F140W_J']['scatter'] ),\n",
" 'F160W': (polynomial_relations['WFC3_F160W_H']['pol'], polynomial_relations['WFC3_F160W_H']['scatter'] ),\n",
" 'EUCLID_J': (polynomial_relations['EUCLID_J_J']['pol'], polynomial_relations['EUCLID_J_J']['scatter'] ),\n",
" 'EUCLID_H': (polynomial_relations['EUCLID_H_H']['pol'], polynomial_relations['EUCLID_H_H']['scatter'] )},\n",
" \n",
" 'colors':{'j_f110': (colors_polynomials['WFC3_F110W_J']['pol'], colors_polynomials['WFC3_F110W_J']['scatter'] ),\n",
" 'j_f140': (colors_polynomials['WFC3_F140W_J']['pol'], colors_polynomials['WFC3_F140W_J']['scatter'] ),\n",
" 'j_f160': (colors_polynomials['WFC3_F160W_J']['pol'], colors_polynomials['WFC3_F160W_J']['scatter'] ),\n",
" 'h_f110': (colors_polynomials['WFC3_F110W_H']['pol'], colors_polynomials['WFC3_F110W_H']['scatter'] ),\n",
" 'h_f140': (colors_polynomials['WFC3_F140W_H']['pol'], colors_polynomials['WFC3_F140W_H']['scatter'] ),\n",
" 'h_f160': (colors_polynomials['WFC3_F160W_H']['pol'], colors_polynomials['WFC3_F160W_H']['scatter'] )\n",
" },\n",
" \n",
" 'snr':wisps.POLYNOMIAL_RELATIONS['snr']}\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"rels0=wisps.POLYNOMIAL_RELATIONS\n",
"rels0.update({'abs_mags': rels['abs_mags'], \n",
" 'colors': rels['colors']})"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"output = open(wisps.OUTPUT_FILES+'/polynomial_relations.pkl', 'wb')\n",
"pickle.dump(rels0, output)\n",
"output.close()\n",
"\n",
"def interpolated_templates(s):\n",
" try:\n",
" s.normalize()\n",
" #s.toInstrument('WFC3-G141')\n",
" wv= s.wave.value\n",
" fl= s.flux.value\n",
" fl[fl < 0.0]=np.nan\n",
" #s.toInstrument('WFC3-G141')\n",
" return interpolate.interp1d(wv, fl,\n",
" bounds_error=False,fill_value=0.)\n",
" except:\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"from scipy import interpolate\n",
"df= pd.DataFrame()\n",
"df['spt']=spts\n",
"df['name']=[x.name for x in all_spectra]\n",
"df['spectra']=all_spectra\n",
"df['interp']=df.spectra.apply(interpolated_templates)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"df=df[~df.interp.isna()]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fc53667e1f0>]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(df.spt, '.')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"#d"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"output = open(wisps.OUTPUT_FILES+'/validated_templates.pkl', 'wb')\n",
"pickle.dump(df, output)\n",
"output.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#splat.filterMag?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"#"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"#"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/TransientFlowToAWell.ipynb | 1 | 112144 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transient flow to a well\n",
"\n",
"## The Theis' well function (a well in a confined aquifer)\n",
"\n",
"The Theis will function is perhaps the most famous, and most often used and practical analytical solution in groundwater science. It describes the transient flow to a fully penetrating well in a confined aquifer after the well starts pumping at time zero. The solution is also used for unconfined flow, but then it is an approximation that is good as long as the thickness of the aquifer does not change substantially, not more thabn 20%, say, from it's initial value.\n",
"\n",
"\n",
"\n",
"Figure: The situation considered by Theis (confined aquifer)\n",
"\n",
"\n",
"\n",
"Figure: The situation considered by Theis (unconfined aquifer, s<<h)\n",
"\n",
"In cases with wells that are only partially penetrating the aquifer, we can add the influence of that separately as we will see.\n",
"\n",
"Although the solution was derived for a uniform and unchanging ambient groundwater head, it can still be applied in much more general situations, because we can use superpositioin, that is, we can add the influence of different and indiependent actors that change the groundwater level in space and or in time separately. Therefore, if we can, with a solution like that of Theis, compute the effect of a single well everywhere in the aquifer of any time, we can do for an arbitrary number of wells, simply, by adding their individual effects. Not only this, we can also superimpose other effects that are not due to wells, if we have their analytical solution available."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Governing partial differential equation solved by Theis\n",
"\n",
"Theis solved the following partial differential equation\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figure: Situation to derive the partial differential equation\n",
"\n",
"Continuity for a ring of width $dr$ at radius $r$, see figure, yields:\n",
"\n",
"$$ \\frac {\\partial Q} {\\partial r} = \\frac \\partial {\\partial r} \\left(-2 \\pi r kD \\frac {\\partial \\phi} {\\partial r} \\right)= - 2 \\pi r S \\frac {\\partial \\phi} {\\partial t} $$\n",
"\n",
"For convenience, use drawdown $s$ instead of head $\\phi$\n",
"\n",
"$$ s = \\phi_0 - \\phi $$\n",
"\n",
"$$ \\frac {\\partial} {\\partial r} \\left( 2 \\pi r kD \\frac {\\partial s} {\\partial r} \\right) = 2\\pi r S \\frac {\\partial s} {\\partial t} $$\n",
"\n",
"$$ kD \\frac {\\partial} {\\partial r} \\left( r \\frac {\\partial s} {\\partial r} \\right) = r S \\frac {\\partial s} {\\partial t} $$\n",
"\n",
"Which yields the governing partial differential equation for transient horizointal flow to a well that starts pumping at a fixef flow $Q_0$ at $t=0$:\n",
"\n",
"$$\\frac 1 r \\frac {\\partial s} {\\partial r} + \\frac {\\partial^2 s} {\\partial r^2} = \\frac S {kD} \\frac {\\partial s} {\\partial t}$$\n",
"\n",
"Which was solved by Theis (1935) subject to the initial condition $s(x,0) = 0$ and boundary conditions $s(\\infty, t)=0$ and $2\\pi r kD \\frac{\\partial s}{\\partial r} = Q_0$ for $r \\rightarrow 0$. (This solution can be readily obtained by means of the Laplace transform).\n",
"\n",
"The dradown according to Theis is mathematically descrbided, by hydrologists, as\n",
"\n",
"$$ s = \\frac Q {2 \\pi kD} W \\left( \\frac {r^2 S} {4 kD t} \\right) $$\n",
"\n",
"Where owercse $s$ [L] is the transient drawdown of the groundwater head due to the well, $Q$ [L3/T] is the well extraction, $kD$ [L2/T] the transmissivity of the aqufier, $S$ [-] the storage coefficient of the aquifer, $r$ [L] the distance to the well center and $t$ [T] time since the well was switched on.\n",
"\n",
"$W(u)$ is the so-called Theis well function, which is a function of only one dimensionless parameter, $u$ that is a combination of $r$, $t$, $S$ and $kD$ as shown."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The name Well Function was given by C.V. Theis (1930). The well function turned out to be a regular mathematical function that already was available under the name `exponential integra1` at the time that Theis developed his formunla. It's form is:\n",
"\n",
"$$ W \\left( u \\right) = Ei \\left( u \\right) = \\intop_u^\\infty \\frac {e^{-y}} y dy $$\n",
"\n",
"The function has been tabled in many books on groundwater hydrology and pumping test analysis, among which the book\n",
"\n",
"Kruseman, G.P. and N.A. de Rider (1994) Analysis of Pumping Test Data. ILRI publication 47, Wageningen, The Netherlands, 1970 to 1994. ISBN 90 70754 207.\n",
"\n",
"The print of the book of the year 2000 is available on the internet: [KrdR 2000](http://www.hydrology.nl/images/docs/dutch/key/Kruseman_and_De_Ridder_2000.pdf)\n",
"\n",
"For verification of self-implemented well functions here is the table of its values form page 294 of the mentioned book:\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to get the well function?\n",
"\n",
"In the past we used to look up the well function in a table like the one given. Nowadays, with computing power everywhere, we only use such tables to verify our version of the function when we programmed it ourselves or use one from a scientific library. This is what we'll do here was well.\n",
"\n",
"One way is to see if the function is already available on our computer. Well if you have Maple, Matlab or Python.scipy it is in one form or another. If you don't know where, then searhing the internet is always a good start.\n",
"\n",
"This shows that we have to look for the function `expi` in the module `scipy.special`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.special import expi\n",
"\n",
"#help(expi) # remove the first # to show the help for the function expi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The reveals that we have the function\n",
"$$ expi = \\intop_{-\\infty}^u \\frac {e^y} y dy $$\n",
"\n",
"By just changing the sign of y to -y we obtain\n",
"\n",
"$$ W(u) = \\intop_u^\\infty \\frac {e^{-y}} y dy = - \\intop_{y = -\\infty}^{y = u} \\frac {e^{y}} y dy $$\n",
"\n",
"Replace $y$ by $-\\xi$ the $W(u)$ becomes\n",
"\n",
"$$ W(u) = - \\intop_{\\xi = \\infty}^{\\xi = -u} \\frac {e^{-\\xi}} \\xi d \\xi = - expi(-u) $$\n",
"\n",
"So that\n",
"\n",
"$$ W(u) = -expi(-u) $$\n",
"\n",
"according to the definition used in `scipy.special.expi`.\n",
"\n",
" Notice that diferent libraries and books may define the exponential integral differently. The famous `Abramowitz M & Stegun, I (1964) Handbook of Mathematical Functions. Dover`, for example define the exponential function exactly as the theis well function.\n",
"\n",
"We can readily check the expi function using the table from `Kruseman and De Ridder (2000) p294` that was referenced above. Verifying for example the values for `u = 4, 0.4, 0.04, 0.004 etc to $4^{-10$` can be done as follows:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" u wu \n",
" 4.0e+00 3.7794e-03\n",
" 4.0e-01 7.0238e-01\n",
" 4.0e-02 2.6813e+00\n",
" 4.0e-03 4.9482e+00\n",
" 4.0e-04 7.2472e+00\n",
" 4.0e-05 9.5495e+00\n",
" 4.0e-06 1.1852e+01\n",
" 4.0e-07 1.4155e+01\n",
" 4.0e-08 1.6457e+01\n",
" 4.0e-09 1.8760e+01\n",
" 4.0e-10 2.1062e+01\n"
]
}
],
"source": [
"u = 4 * 10** -np.arange(11.) # generates values 4, 4e-1, 4e-2 .. 4e-10\n",
"print(\"{:>10s} {:>10s}\".format('u ', 'wu '))\n",
"for u, wu in zip(u, -expi(-u)): # makes a list of value pairs [u, W(u)]\n",
" print(\"{0:10.1e} {1:10.4e}\".format(u, wu))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which is equal to the values in the table.\n",
"\n",
"It''s now convenient to use the familiar form W(u) instead of -expi(-u)\n",
"\n",
"We can define a function for W either as an anonymous function or a regular function. Anonymous functions are called lambdda functions or lambda expressions in Python. In this case:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.special import expi\n",
"W = lambda u : -expi(-u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, alternatively as a regular one-line function:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def W(u): return -expi(-u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or in full, so that we don't need the import above and we directly see where the function comes from:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy\n",
"W = lambda u: -scipy.special.expi( -u ) # Theis well function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can put this well function immediately to use for answering practical questions. For example: what is the drawdown after $t=1\\,d$ at distance $r=350 \\, m$ by a well extracting $Q = 2400\\, m^3/d$ in an confined aquifer with transmissivity $kD = 2400\\, m^2/d$ and storage coefficient $S=0.001$ [-] ?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" r = 350 m\n",
" t = 1.0 d\n",
" kD = 2400 m2/d\n",
" S = 0.001 [-]\n",
" Q = 2400 m3/d\n",
" u = 0.01276 [-]\n",
" W(u) = 3.7969 [-]\n",
" s(r, t) = 0.30215 m\n"
]
}
],
"source": [
"r = 350; t = 1.; kD=2400; S=0.001; Q=2400\n",
"u = r**2 * S / (4 * kD * t)\n",
"\n",
"s = Q/(4 * np.pi * kD) * W(u) # applying the theis well function according to the book\n",
"\n",
"print(\" r = {} m\\n\\\n",
" t = {} d\\n\\\n",
" kD = {} m2/d\\n\\\n",
" S = {} [-]\\n\\\n",
" Q = {} m3/d\\n\\\n",
" u = {:.5g} [-]\\n\\\n",
" W(u) = {:.5g} [-]\\n\\\n",
" s(r, t) = {:.5g} m\".\n",
" format(r, t, kD, S, Q, u, W(u), s))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above we computed $u$ separately to prevent cluttering the expression. Of course, you can define a lambda or regular function to compute like so"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u = lambda r, t: r**2 * S / (4 * kD * t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The lambda function $u$ now takes two parameters like $u(r,t)$ and uses the other parameters $S$ and $kD$ that it finds in the workspace at the moment when the lambda function is created. So don't change $S$ and $kD$ afterwards without redefining $u(r,t)$.\n",
"\n",
"Try this out:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.012760416666666666"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u(r,t) # yields u as a function of r and t"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.7969115073331832"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"W(u(r,t)) # given W(u) as a function of r and t"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.30214861743728766"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Q/(4 * np.pi * kD) * W(u(r,t)) # gives the drawdown that we had before"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's now straight forward to compute the drawdown for many times like so:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"t = np.logspace(-3, 2, 51) # gives 51 times on log scale between 10^(-3) = 0.001 and 10^(2) = 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This given the following times:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" 0.001 0.00126 0.00158 0.002 0.00251 0.00316 0.00398 0.00501 0.00631 0.00794 \n",
" 0.01 0.0126 0.0158 0.02 0.0251 0.0316 0.0398 0.0501 0.0631 0.0794 \n",
" 0.1 0.126 0.158 0.2 0.251 0.316 0.398 0.501 0.631 0.794 \n",
" 1 1.26 1.58 2 2.51 3.16 3.98 5.01 6.31 7.94 \n",
" 10 12.6 15.8 20 25.1 31.6 39.8 50.1 63.1 79.4 \n",
" 100 "
]
}
],
"source": [
"for it, tt in enumerate(t):\n",
" if it % 10 == 0: print()\n",
" print(\"%8.3g\" % tt, end=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these times we can compute the drawdown for all these times in a single strike without changing anything to our formula:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.66884951e-08, 2.85176796e-07, 2.83106391e-06,\n",
" 1.82356839e-05, 8.31967240e-05, 2.88203801e-04,\n",
" 8.01032536e-04, 1.86616447e-03, 3.77281387e-03,\n",
" 6.80385595e-03, 1.11871477e-02, 1.70653500e-02,\n",
" 2.44886132e-02, 3.34252649e-02, 4.37821437e-02,\n",
" 5.54271204e-02, 6.82090511e-02, 8.19730658e-02,\n",
" 9.65709268e-02, 1.11867155e-01, 1.27741957e-01,\n",
" 1.44091964e-01, 1.60829602e-01, 1.77881714e-01,\n",
" 1.95187846e-01, 2.12698459e-01, 2.30373226e-01,\n",
" 2.48179485e-01, 2.66090892e-01, 2.84086266e-01,\n",
" 3.02148617e-01, 3.20264351e-01, 3.38422599e-01,\n",
" 3.56614690e-01, 3.74833708e-01, 3.93074143e-01,\n",
" 4.11331609e-01, 4.29602614e-01, 4.47884381e-01,\n",
" 4.66174701e-01, 4.84471817e-01, 5.02774335e-01,\n",
" 5.21081143e-01, 5.39391360e-01, 5.57704286e-01,\n",
" 5.76019363e-01, 5.94336150e-01, 6.12654295e-01,\n",
" 6.30973518e-01, 6.49293599e-01, 6.67614359e-01])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = Q / (4 * np.pi * kD) * W(u(r,t)) # computes s(r,t)\n",
"s # shows s(r,t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a nicer print print t and s next to each other"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" time drawdown\n",
" 0.001 1.67e-08\n",
" 0.00126 2.85e-07\n",
" 0.00158 2.83e-06\n",
" 0.002 1.82e-05\n",
" 0.00251 8.32e-05\n",
" 0.00316 0.000288\n",
" 0.00398 0.000801\n",
" 0.00501 0.00187\n",
" 0.00631 0.00377\n",
" 0.00794 0.0068\n",
" 0.01 0.0112\n",
" 0.0126 0.0171\n",
" 0.0158 0.0245\n",
" 0.02 0.0334\n",
" 0.0251 0.0438\n",
" 0.0316 0.0554\n",
" 0.0398 0.0682\n",
" 0.0501 0.082\n",
" 0.0631 0.0966\n",
" 0.0794 0.112\n",
" 0.1 0.128\n",
" 0.126 0.144\n",
" 0.158 0.161\n",
" 0.2 0.178\n",
" 0.251 0.195\n",
" 0.316 0.213\n",
" 0.398 0.23\n",
" 0.501 0.248\n",
" 0.631 0.266\n",
" 0.794 0.284\n",
" 1 0.302\n",
" 1.26 0.32\n",
" 1.58 0.338\n",
" 2 0.357\n",
" 2.51 0.375\n",
" 3.16 0.393\n",
" 3.98 0.411\n",
" 5.01 0.43\n",
" 6.31 0.448\n",
" 7.94 0.466\n",
" 10 0.484\n",
" 12.6 0.503\n",
" 15.8 0.521\n",
" 20 0.539\n",
" 25.1 0.558\n",
" 31.6 0.576\n",
" 39.8 0.594\n",
" 50.1 0.613\n",
" 63.1 0.631\n",
" 79.4 0.649\n",
" 100 0.668\n"
]
}
],
"source": [
"print(\"{:>10s} {:>10s}\".format('time', 'drawdown'))\n",
"for tt, ss in zip(t, s):\n",
" print(\"{0:10.3g} {1:10.3g}\".format(tt,ss))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And of course we can make a plot of these results:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt # imports plot functions (matlab style)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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fDbfeCqtWRZ3GFBJfChFjCpUVIiZvBgxwd+YdNCjqJKaQ+HJqxphCZYVIDI0e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\nkcSoICIiIiKJUUFEREREEvMfQ1hJF0cagKAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e8ebe48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"\n",
"# Drawdown versus log(t)\n",
"ax1 = fig.add_subplot(121)\n",
"ax1.set(xlabel='time [d]', ylabel='drawdown [m]', xscale='log', title='Drawdown versus log(t)')\n",
"ax1.invert_yaxis()\n",
"ax1.grid(True)\n",
"plt.plot(t, s)\n",
"\n",
"# Drawdown versus t\n",
"ax2 = fig.add_subplot(122)\n",
"ax2.set(xlabel='time [d]', ylabel='', xscale='linear', title='Drawdown versus t')\n",
"ax2.invert_yaxis()\n",
"ax2.grid(True)\n",
"plt.plot(t, s)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"1. Show the drawdown as a function of r instead of x, for t=2 d and r between 0.1 and 1000 m\n",
"2. For the 5 wells of which the lcoations and extractions are given below, show the combined drawdown for time between 0.01 and 10 days at x= 0 and y = 0."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"well_names = ['School', 'Lazaret', 'Square', 'Mosque', 'Water_company']\n",
"Q = [400., 1200., 1150., 600., 1900]\n",
"x = [-300., -250., 100., 55., 125.]\n",
"y =[-450., +230., 50., -300., 250.]\n",
"Nwells = len(well_names)\n",
"x0 = 0.\n",
"y0 = 0."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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yZArMgsQoJflmgVS4bwkmk23cLq1N+jKmdVZWdn6+y2kt9nwpKbBsK7LSDpS+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Mx9wCLgU8CLJAlMe3ddsgNatGhR1k2Ydxzz9nPM288xbw5J5PN99Pb2TbvTc6m0gaSvoPnmRCIS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\ns8w4ETEzM7PMOBExMzOzzPw/rPJ8n1I4/qIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111c20470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t = np.logspace(-2, 2, 41)\n",
"s = np.zeros((Nwells, len(t)))\n",
"for iw, Q0, xw, yw in zip(range(Nwells), Q, y, x):\n",
" r = np.sqrt((xw-x0) ** 2 + (yw - y0) **2)\n",
" s[iw,:] = Q0 / (4 * np.pi * kD) * W(u(r,t))\n",
" \n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.set(xlabel='time [d]', ylabel='drawdown[m]', title='Drawdown due to multiple wells')\n",
"ax.invert_yaxis()\n",
"ax.grid(True)\n",
"for iw, name in zip(range(Nwells), well_names):\n",
" ax.plot(t, s[iw,:], label=name)\n",
"ax.plot(t, np.sum(s, axis=0), label='total_drawdown')\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Show the drawdown for the case that the wells start at different times as given here:\n",
"(hint: compute use tw = t - ts[iw] for each well and deal met times tw < 0). ts = [0., 5., 2. 8. 1.5.]\n",
"5. There is a vertical imermeable boundary at x = -500 m. Show the head as a function of time at x = -10 m and y = 0 m for both the situation\n",
" 1. with \n",
" 1. and without \n",
"1. There is a fixed-head boundary (e.g. a fully penetrating river) at x = -500 m. Show the head as a function of time at x=-10 m and y = 0 m for both the situation \n",
" 1. with\n",
" 2. and without\n",
"1. With the fixed-head boundary. Show the head as a function of time at x = -10 m and y = 0 m for all three cases, where time runs as in t = np.logspace(-2, 2, 41)\n",
"1. Show the heads between r = -500 and r = 500 for time = 5 d the three cases, that is\n",
" 1. without impermeable or fixed-head boundary at x = -500 m\n",
" 2. with the impermeable boundary at x = -500 m\n",
" 3. with the fixed head bounary at x = -500.\n",
"1. Two vertical impermeable boundaries: Consider not only an impermeable boundary at x = -500 m but also at y = + 200 m. Then using an extraction of Q = 1200 m3/d and times between 0.01 and 100 days, compute the drawdown as a function of time at pooint x= -100.m and y = + 50 m.\n",
"1. Consider a fixed-head boundary at x = -500. m and one at y = 200., with an extraction of Q = 1200 m3/d at x= -100. and y = 50 m, compute the drawdown for times between 0.001 and 100 days.\n",
"1. Extraction from two different aquifers: Compare two situaations where both aquifers have the same transmissivity kD [m2/d] but different storage coefficient. The extracton from both aquifers is the same, Q = 1200 m3/d. Plot de drawdown as a function of time on a logarithmic scale and determine the shift of the two curves along the time axis. Given an explanation for that shift.\n",
"\n",
"**important** Don't forget to rerun the lambda expressions above, if you change the kD and S, or redefine them so that they take kD and or S as input parameters."
]
}
],
"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": "3.7.7"
}
},
"nbformat": 4,
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}
| gpl-3.0 |
knightman/MSPA-PREDICT400 | Wk3/Linear Equations using Simplex.ipynb | 1 | 4487 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Linear Equations using the Simplex Method\n",
"Version/Date: Oct 3, 2017\n",
"\n",
"### Exercise\n",
">PREDICT_400-DL_SEC56\n",
">Week3 Module\n",
"\n",
"### File(s)\n",
"Linear Equations using Simplex.ipynb\n",
"\n",
"### Instructions\n",
"Present a model involving minimization that contains three or more equations (ideally a system that you have come across professionally or personally) and solve the system using the Simplex Method and again using Python. Be sure to share your Python code and output.\n",
"\n",
"### Reference\n",
"I will be using a modification of my example from Week2. See details here:\n",
"<a href='http://andrewdavidknight.com/projects/mspa-predict400/wk2/Wk2LinearEq.html'>Wk2 Linear Eq Example</a>\n",
"\n",
"### Description\n",
"The code below shows an example of three product models and an attempt to minimize the cost."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Goal: Cost minimization of the objective function: C = 18y1 + 20y2 + 25y3 \n",
"This function represents the total cost for three different products manufactured and sold by our company.\n",
"\n",
"The constraints for this linear program example:\n",
"\n",
"> 120y1 + 160y2 + 200y3 >= 75000\n",
"\n",
"> yy1 + y2 + y3 >= 450\n",
"\n",
"> y1 + 2y2 >= 300\n",
"\n",
"> y1, y2, y3 >=0\n",
"\n",
"The first constraint function gives the total revenue target (at least $75,000 USD). The second limiting function gives the minimum total number of units we want to produce with this batch (at least 450). Also given demand for the first two models is high, we must make at least 300 between y1 and y2 units. Based on past sales, we expect we'll need about twice as many y2 as y1 models. Obviously, y1, y2, y3 all must be >= 0.\n",
"\n",
"#### How many of each units should we produce to limit minimize our costs?\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-120, -160, -200], [-1, -1, -1], [-1, -2, 0]]\n",
"[-75000, -450, -300]\n",
"Optimization terminated successfully.\n",
" Current function value: 9375.000000 \n",
" Iterations: 4\n",
" fun: 9375.0\n",
" message: 'Optimization terminated successfully.'\n",
" nit: 4\n",
" slack: array([ 0., 0., 450.])\n",
" status: 0\n",
" success: True\n",
" x: array([ 0., 375., 75.])\n"
]
}
],
"source": [
"# Cost objective function is:\n",
"# C = 18y1 + 20y2 + 25y3\n",
"# Rewrite Constraints:\n",
"# Revenue: 120x1 + 160x2 + 200x3 >=75000\n",
"# Numbers: x1 + x2 + x3 >= 450\n",
"# Condition: x1 + 2x2 >= 300\n",
"# x1,x2,x3 >= 0\n",
"\n",
"# coefficients of the objective function\n",
"z = [18,20,25]\n",
"\n",
"# left-hand coefficients - tableau matrix\n",
"A = [[-120,-160,-200],[-1,-1,-1],[-1,-2,0]]\n",
"print(A)\n",
"\n",
"# right-hand coefficients - \n",
"b = [-75000,-450,-300]\n",
"print(b)\n",
"\n",
"from scipy.optimize import linprog as lp\n",
"\n",
"x1_bounds = (0,None)\n",
"x2_bounds = (0,None)\n",
"x3_bounds = (0,None)\n",
"\n",
"res = lp(z, A_ub=A, b_ub=b, bounds=(x1_bounds, x2_bounds, x3_bounds), method='simplex', options={\"disp\": True})\n",
"print(res)\n"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"# Based on these calculations, if we want to hit our total revenue target of $75,000, meeting the other constraints and \n",
"# minimizing cost, we should produce zero y1 units, 375 y2 units and 75 y3 units. The min cost would be approximately $9,375."
]
}
],
"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": 2
}
| mit |
aflaxman/SmartVA-Analyze-Mapping-Example | 01_example_mapping_in_python.ipynb | 1 | 22789 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np, pandas as pd"
]
},
{
"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>module</th>\n",
" <th>question number</th>\n",
" <th>caption</th>\n",
" <th>data type</th>\n",
" </tr>\n",
" <tr>\n",
" <th>field name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sid</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>What is the study ID number?</td>\n",
" <td>text</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_2_4</th>\n",
" <td>General</td>\n",
" <td>1.1</td>\n",
" <td>Address of/directions to household</td>\n",
" <td>text</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_3_1</th>\n",
" <td>General</td>\n",
" <td>2.1</td>\n",
" <td>Did respondent give consent?</td>\n",
" <td>yes = 1, no = 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_5_0</th>\n",
" <td>General</td>\n",
" <td>3.1</td>\n",
" <td>What was the name of the deceased?</td>\n",
" <td>text</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_5_1a</th>\n",
" <td>General</td>\n",
" <td>3.2</td>\n",
" <td>What year was the deceased born?</td>\n",
" <td>integer</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" module question number caption \\\n",
"field name \n",
"sid NaN NaN What is the study ID number? \n",
"gen_2_4 General 1.1 Address of/directions to household \n",
"gen_3_1 General 2.1 Did respondent give consent? \n",
"gen_5_0 General 3.1 What was the name of the deceased? \n",
"gen_5_1a General 3.2 What year was the deceased born? \n",
"\n",
" data type \n",
"field name \n",
"sid text \n",
"gen_2_4 text \n",
"gen_3_1 yes = 1, no = 0 \n",
"gen_5_0 text \n",
"gen_5_1a integer "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load codebook\n",
"fname = 'https://github.com/aflaxman/SmartVA-Analyze-Mapping-Example/raw/master/Guide%20for%20data%20entry.xlsx'\n",
"cb = pd.read_excel(fname, index_col=2)\n",
"cb.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Minimal example\n",
"\n",
"Generate a .csv file that is accepted as input to SmartVA-Analyze 1.1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# SmartVA-Analyze 1.1 accepts a csv file as input\n",
"# and expects a column for every field name in the \"Guide for data entry.xlsx\" spreadsheet\n",
"\n",
"df = pd.DataFrame(index=[0], columns=cb.index.unique())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# SmartVA-Analyze 1.1 also requires a handful of columns that are not in the Guide\n",
"df['child_3_10'] = np.nan\n",
"df['agedays'] = np.nan\n",
"df['child_5_7e'] = np.nan\n",
"df['child_5_6e'] = np.nan\n",
"df['adult_2_9a'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.loc[0,'sid'] = 'example'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# if we save this dataframe as a csv, we can run it through SmartVA-Analyze 1.1\n",
"\n",
"fname = 'example_1.csv'\n",
"df.to_csv(fname, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>sid</th>\n",
" <th>cause</th>\n",
" <th>cause34</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>example</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sid cause cause34 age sex\n",
"0 example NaN Undetermined 0 NaN"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here are the results of running this example through SmartVA-Analyze 1.1\n",
"pd.read_csv('neonate-predictions.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example of simple, hypothetical mapping\n",
"\n",
"If we have data on a set of verbal autopsies (VAs) that did not use the PHMRC Shortened Questionnaire, we must map them to the expected format. This is a simple, hypothetical example for a set of VAs that asked only about injuries, hypertension, chest pain:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>sex</th>\n",
" <th>age</th>\n",
" <th>injury</th>\n",
" <th>heart_disease</th>\n",
" <th>chest_pain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M</td>\n",
" <td>35</td>\n",
" <td>rti</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M</td>\n",
" <td>45</td>\n",
" <td>fall</td>\n",
" <td>N</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>F</td>\n",
" <td>75</td>\n",
" <td></td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M</td>\n",
" <td>67</td>\n",
" <td></td>\n",
" <td>Y</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>91</td>\n",
" <td></td>\n",
" <td>Y</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex age injury heart_disease chest_pain\n",
"0 M 35 rti N N\n",
"1 M 45 fall N N\n",
"2 F 75 Y Y\n",
"3 M 67 Y N\n",
"4 F 91 Y "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hypothetical_data = pd.DataFrame(index=range(5))\n",
"\n",
"hypothetical_data['sex'] = ['M', 'M', 'F', 'M', 'F']\n",
"hypothetical_data['age'] = [35, 45, 75, 67, 91]\n",
"\n",
"hypothetical_data['injury'] = ['rti', 'fall', '', '', '']\n",
"hypothetical_data['heart_disease'] = ['N', 'N', 'Y', 'Y', 'Y']\n",
"hypothetical_data['chest_pain'] = ['N', 'N', 'Y', 'N', '']\n",
"\n",
"hypothetical_data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# SmartVA-Analyze 1.1 accepts a csv file as input\n",
"# and expects a column for every field name in the \"Guide for data entry.xlsx\" spreadsheet\n",
"\n",
"df = pd.DataFrame(index=hypothetical_data.index, columns=cb.index.unique())\n",
"\n",
"# SmartVA-Analyze 1.1 also requires a handful of columns that are not in the Guide\n",
"df['child_3_10'] = np.nan\n",
"df['agedays'] = np.nan\n",
"df['child_5_7e'] = np.nan\n",
"df['child_5_6e'] = np.nan\n",
"df['adult_2_9a'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# to find the coding of specific variables, look in the Guide, and \n",
"# as necessary refer to the numbers in paper form for the PHMRC Shortened Questionnaire\n",
"# http://www.healthdata.org/sites/default/files/files/Tools/SmartVA/2015/PHMRC%20Shortened%20VAI_all-modules_2015.zip\n",
"\n",
"# set id\n",
"df['sid'] = hypothetical_data.index\n",
"\n",
"# set sex\n",
"df['gen_5_2'] = hypothetical_data['sex'].map({'M': '1', 'F': '2'})\n",
"\n",
"# set age\n",
"df['gen_5_4'] = 1 # units are years\n",
"df['gen_5_4a'] = hypothetical_data['age'].astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# good place to save work and confirm that it runs through SmartVA\n",
"fname = 'example_2.csv'\n",
"df.to_csv(fname, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>sid</th>\n",
" <th>cause</th>\n",
" <th>cause34</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
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" <td>35</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>75</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>91</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sid cause cause34 age sex\n",
"0 0 NaN Undetermined 35 0\n",
"1 1 NaN Undetermined 45 0\n",
"2 2 NaN Undetermined 75 1\n",
"3 3 NaN Undetermined 67 0\n",
"4 4 NaN Undetermined 91 1"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here are the results of running this example\n",
"pd.read_csv('adult-predictions.csv')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# map injuries to appropriate codes\n",
"# suffered injury?\n",
"df['adult_5_1'] = hypothetical_data['injury'].map({'rti':'1', 'fall':'1', '':'0'})\n",
"# injury type\n",
"df['adult_5_2'] = hypothetical_data['injury'].map({'rti':'1', 'fall':'2'})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# _another_ good place to save work and confirm that it runs through SmartVA\n",
"fname = 'example_3.csv'\n",
"df.to_csv(fname, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>sid</th>\n",
" <th>cause</th>\n",
" <th>cause34</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>Road Traffic</td>\n",
" <td>35</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
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" <th>2</th>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>75</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>Undetermined</td>\n",
" <td>91</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sid cause cause34 age sex\n",
"0 0 30 Road Traffic 35 0\n",
"1 1 14 Falls 45 0\n",
"2 2 NaN Undetermined 75 1\n",
"3 3 NaN Undetermined 67 0\n",
"4 4 NaN Undetermined 91 1"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here are the results of running this example\n",
"pd.read_csv('adult-predictions.csv')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# map heart disease (to column adult_1_1i, see Guide)\n",
"df['adult_1_1i'] = hypothetical_data['heart_disease'].map({'Y':'1', 'N':'0'})\n",
"\n",
"# map chest pain (to column adult_2_43, see Guide)\n",
"df['adult_2_43'] = hypothetical_data['chest_pain'].map({'Y':'1', 'N':'0', '':'9'})"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# and that completes the work for a simple, hypothetical mapping\n",
"fname = 'example_4.csv'\n",
"df.to_csv(fname, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sid</th>\n",
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" <td>2</td>\n",
" <td>3</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>gen_5_2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_5_4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gen_5_4a</th>\n",
" <td>35</td>\n",
" <td>45</td>\n",
" <td>75</td>\n",
" <td>67</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>adult_5_1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>adult_1_1i</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>adult_2_43</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"sid 0 1 2 3 4\n",
"gen_5_2 1 1 2 1 2\n",
"gen_5_4 1 1 1 1 1\n",
"gen_5_4a 35 45 75 67 91\n",
"adult_5_1 1 1 0 0 0\n",
"adult_1_1i 0 0 1 1 1\n",
"adult_2_43 0 0 1 0 9"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# have a look at the non-empty entries in the mapped database:\n",
"df.T.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>sid</th>\n",
" <th>cause</th>\n",
" <th>cause34</th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>Road Traffic</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>Falls</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>IHD - Acute Myocardial Infarction</td>\n",
" <td>75</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>17</td>\n",
" <td>IHD - Acute Myocardial Infarction</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>IHD - Acute Myocardial Infarction</td>\n",
" <td>91</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sid cause cause34 age sex\n",
"0 0 30 Road Traffic 35 0\n",
"1 1 14 Falls 45 0\n",
"2 2 17 IHD - Acute Myocardial Infarction 75 1\n",
"3 3 17 IHD - Acute Myocardial Infarction 67 0\n",
"4 4 17 IHD - Acute Myocardial Infarction 91 1"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# here are the results of running this example\n",
"pd.read_csv('adult-predictions.csv')"
]
}
],
"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 |
basnijholt/holoviews | examples/reference/containers/matplotlib/NdOverlay.ipynb | 1 | 4514 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
"<dl class=\"dl-horizontal\">\n",
" <dt>Title</dt> <dd>NdOverlay Container</dd>\n",
" <dt>Dependencies</dt> <dd>Matplotlib</dd>\n",
" <dt>Backends</dt> <dd><a href='./NdOverlay.ipynb'>Matplotlib</a></dd> <dd><a href='../bokeh/NdOverlay.ipynb'>Bokeh</a></dd> <dd><a href='../plotly/NdOverlay.ipynb'>Plotly</a></dd>\n",
"</dl>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('matplotlib')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An ``NdOverlay`` is a multi-dimensional dictionary of HoloViews elements presented overlayed in the same space. An ``NdOverlay`` can be considered as a special-case of ``HoloMap`` that can only hold a single type of element at a time. Unlike a regular ``Overlay`` that can be built with the ``*`` operator, the items in an ``NdOverlay`` container have corresponding keys and must all have the same type. See the [Building Composite Objects](../../../user_guide/06-Building_Composite_Objects.ipynb) user guide for details on how to compose containers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ``NdOverlay`` holds dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the ``sine_curve`` function below, we can declare a dictionary of ``Curve`` elements, where the keys correspond to the frequency values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"frequencies = [0.5, 0.75, 1.0, 1.25]\n",
"\n",
"def sine_curve(phase, freq):\n",
" xvals = [0.1* i for i in range(100)]\n",
" return hv.Curve((xvals, [np.sin(phase+freq*x) for x in xvals]))\n",
"\n",
"curve_dict = {f:sine_curve(0,f) for f in frequencies}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have a dictionary where the frequency is the key and the corresponding curve element is the value. We can now turn this dictionary into an ``NdOverlay`` by declaring the keys as corresponding to the frequency key dimension:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndoverlay = hv.NdOverlay(curve_dict, kdims='frequency')\n",
"ndoverlay"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the ``NdOverlay`` is displayed with a legend using colors defined by the ``Curve`` color cycle. For more information on using ``Cycle`` to define color cycling, see the [User Guide]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ``NdOverlay`` is multi-dimensional"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By using tuple keys and making sure each position in the tuple is assigned a corresponding ``kdim``, ``NdOverlays`` allow visualization of a multi-dimensional space:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"curve_dict_2D = {(p,f):sine_curve(p,f) for p in [0, np.pi/2] for f in [0.5, 0.75]}\n",
"ndoverlay = hv.NdOverlay(curve_dict_2D, kdims=['phase', 'frequency'])\n",
"ndoverlay"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ``NdOverlay`` is similar to ``HoloMap``"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other than the difference in the visual semantics, whereby ``NdOverlay`` displays its contents overlaid, ``NdOverlay`` are very similar to ``HoloMap`` (see the [``HoloMap``](./HoloMap.ipynb) notebook for more information).\n",
"\n",
"One way to demonstrate the similarity of these two containers is to cast our ``ndoverlay`` object to ``HoloMap`` and back to an ``NdOverlay``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hmap = hv.HoloMap(ndoverlay)\n",
"hmap + hv.NdOverlay(hmap)"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
aflaxman/AI4HM | Week_1/Exercise_1.ipynb | 1 | 6386 | {
"metadata": {
"name": "",
"signature": "sha256:057063db0b1ebb24990f9e2ee7df4db748a3c11628d237ffb771f39ba58117e3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"internals": {
"slide_type": "subslide"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Exercise 1.0: Learning Python\n",
"\n",
"* From http://software-carpentry.org/v5/novice/python/index.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# an easy way to get the data necessary for following along with part 1 of the python intro\n",
"\n",
"import numpy, pandas as pd\n",
"df = pd.read_csv('inflammation-01.csv',\n",
" header=None)\n",
"data = numpy.array(df)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.0.1. Draw diagrams showing what variables refer to what values after each statement in the following program:\n",
"\n",
" mass = 47.5\n",
" age = 122\n",
" mass = mass * 2.0\n",
" age = age - 20\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.0.2. What does the following program print out?\n",
"\n",
" first, second = 'Grace', 'Hopper'\n",
" third, fourth = second, first\n",
" print third, fourth\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.0.3. \"Adding\" two strings produces their concatention: `'a' + 'b'` is `'ab'`. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original:\n",
"\n",
" print fence('name', '*')\n",
" *name*\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.0.4. If the variable `s` refers to a string, then `s[0]` is the string's first character and `s[-1]` is its last. Write a function called outer that returns a string made up of just the first and last characters of its input:\n",
"\n",
" print outer('helium')\n",
" hm\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.0.5. We previously wrote functions called fence and outer. Draw a diagram showing how the call stack changes when we run the following:\n",
"\n",
" print outer(fence('carbon', '+'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"internals": {
"slide_type": "subslide"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Exercise 1.1: Predicting Weather"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"df = pd.read_csv('weather-numeric.csv')"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# use TAB to explore commands"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## Abie's dumb predictor"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def predict(s):\n",
" if s['outlook'] == 'sunny':\n",
" return 'no'\n",
" else:\n",
" return 'yes'"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"predict(df.loc[1]) # .loc[1] means \"location = row 1\""
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## How good is this dumb predictor?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"i = 0\n",
"predict(df.loc[i]) == df.play[i]"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in df.index:\n",
" # count how many predictions are correct\n",
" pass"
],
"language": "python",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"internals": {},
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## How much better can you do with a single rule?"
]
},
{
"cell_type": "markdown",
"metadata": {
"internals": {
"slide_helper": "subslide_end"
},
"slide_helper": "slide_end",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Homework:\n",
"\n",
"* Find the best \"length-two decision list\" for `weather`\n",
"\n",
"* Think about machine learning projects you might do for this course (related to your IHME research?), and about elevator pitches\n",
"\n",
"* *Read*\n",
"\n"
]
}
],
"metadata": {}
}
]
} | mit |
djfan/yelp-challenge | data_processeing/1.2-Cuisine_Types.ipynb | 1 | 20839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Combine Cuisine"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## load all cat from pickle\n",
"#df = pd.read_pickle('../data_all_cities/all_cities_preprocess.pkl')\n",
"# df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function that creates a cuisine type feature"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False):\n",
" \"\"\"\n",
" This function creates a columne with spefic cruisine types\n",
" \n",
" Attribute:\n",
" path_df: imported dataframe with businesses and a column of lists of categories\n",
" tar_column: the columne in the df used to compare with our lists\n",
" cuisine: name of the created cuisine type column\n",
" path_culture: file path of the text file with a list of cultural words\n",
" path_cuisine: file path of the text file with a list of selected cuisine words\n",
" save: change it to \"True\" if save the output to pickle file\n",
" \n",
" Returns the original dataframe with a new cuisine column\n",
" -- 2 if the business category belongs to the selected cuisine\n",
" -- 1 if Not the selected cuisine (but with other region/culture related words)\n",
" -- 0 if it has no cultural labels\n",
" \"\"\"\n",
" # load dataframe\n",
" df = pd.read_pickle(path_df)\n",
" \n",
" # load saved txt file\n",
" list_culture = open(path_culture, 'rw').read().split('\\n')\n",
" list_cuisine = open(path_cuisine, 'rw').read().split('\\n')\n",
" \n",
" # assign numbers to each business\n",
" type_cuisine = df[tar_column].apply(lambda l: 2 if len(set(l).intersection(list_cuisine)) > 0 \\\n",
" else 0 if len(set(l).intersection(list_culture)) == 0 \\\n",
" else 1)\n",
" df['cuisine_{}'.format(cuisine)] = type_cuisine\n",
" \n",
" if save:\n",
" df.to_pickle('Yelp_Cuisine_{}.p'.format(cuisine))\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Cuisine-- Chinese \n",
"Chinese food, non-Chinese food, no-labeled"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# parameters\n",
"path_df = '../data_all_cities/all_cities_preprocess.pkl'\n",
"path_culture = 'cat_culture.txt'\n",
"path_cuisine = 'cat_culture-Chinese.txt'\n",
"cuisine = 'Chinese'\n",
"tar_column = 'categories'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create column for Chinese cuisine type\n",
"df_chinese = create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>address</th>\n",
" <th>attributes</th>\n",
" <th>business_id</th>\n",
" <th>categories</th>\n",
" <th>city</th>\n",
" <th>hours</th>\n",
" <th>is_open</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>name</th>\n",
" <th>...</th>\n",
" <th>RestaurantsDelivery</th>\n",
" <th>RestaurantsGoodForGroups</th>\n",
" <th>RestaurantsPriceRange2</th>\n",
" <th>RestaurantsReservations</th>\n",
" <th>RestaurantsTableService</th>\n",
" <th>RestaurantsTakeOut</th>\n",
" <th>Smoking</th>\n",
" <th>WheelchairAccessible</th>\n",
" <th>WiFi</th>\n",
" <th>cuisine_Chinese</th>\n",
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" </thead>\n",
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" <th>EDqCEAGXVGCH4FJXgqtjqg</th>\n",
" <td>979 Bloor Street W</td>\n",
" <td>[{u'Alcohol': u'none'}, {u'Ambience': {u'roman...</td>\n",
" <td>EDqCEAGXVGCH4FJXgqtjqg</td>\n",
" <td>[Restaurants, Pizza, Chicken Wings, Italian]</td>\n",
" <td>Toronto</td>\n",
" <td>[Monday 11:0-2:0, Tuesday 11:0-2:0, Wednesday ...</td>\n",
" <td>1</td>\n",
" <td>43.661054</td>\n",
" <td>-79.429089</td>\n",
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" <td>GDnbt3isfhd57T1QqU6flg</td>\n",
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" <td>1</td>\n",
" <td>33.586710</td>\n",
" <td>-111.835410</td>\n",
" <td>Taco Bell</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <td>1</td>\n",
" <td>45.502346</td>\n",
" <td>-73.573807</td>\n",
" <td>La Prep</td>\n",
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"<p>3 rows × 98 columns</p>\n",
"</div>"
],
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" address \\\n",
"EDqCEAGXVGCH4FJXgqtjqg 979 Bloor Street W \n",
"GDnbt3isfhd57T1QqU6flg 11072 No Frank Lloyd Wright \n",
"a1Ba6XeIOP48e64YFD0dMw 2000 Mansfield Street, Suite 104 \n",
"\n",
" attributes \\\n",
"EDqCEAGXVGCH4FJXgqtjqg [{u'Alcohol': u'none'}, {u'Ambience': {u'roman... \n",
"GDnbt3isfhd57T1QqU6flg [{u'Alcohol': u'none'}, {u'Ambience': {u'roman... \n",
"a1Ba6XeIOP48e64YFD0dMw [{u'Caters': True}] \n",
"\n",
" business_id \\\n",
"EDqCEAGXVGCH4FJXgqtjqg EDqCEAGXVGCH4FJXgqtjqg \n",
"GDnbt3isfhd57T1QqU6flg GDnbt3isfhd57T1QqU6flg \n",
"a1Ba6XeIOP48e64YFD0dMw a1Ba6XeIOP48e64YFD0dMw \n",
"\n",
" categories \\\n",
"EDqCEAGXVGCH4FJXgqtjqg [Restaurants, Pizza, Chicken Wings, Italian] \n",
"GDnbt3isfhd57T1QqU6flg [Tex-Mex, Mexican, Fast Food, Restaurants] \n",
"a1Ba6XeIOP48e64YFD0dMw [Sandwiches, Breakfast & Brunch, Salad, Restau... \n",
"\n",
" city \\\n",
"EDqCEAGXVGCH4FJXgqtjqg Toronto \n",
"GDnbt3isfhd57T1QqU6flg Scottsdale \n",
"a1Ba6XeIOP48e64YFD0dMw Montréal \n",
"\n",
" hours \\\n",
"EDqCEAGXVGCH4FJXgqtjqg [Monday 11:0-2:0, Tuesday 11:0-2:0, Wednesday ... \n",
"GDnbt3isfhd57T1QqU6flg [Monday 10:0-22:0, Tuesday 10:0-22:0, Wednesda... \n",
"a1Ba6XeIOP48e64YFD0dMw [Monday 6:30-17:0, Tuesday 6:30-17:0, Wednesda... \n",
"\n",
" is_open latitude longitude name \\\n",
"EDqCEAGXVGCH4FJXgqtjqg 1 43.661054 -79.429089 Pizza Pizza \n",
"GDnbt3isfhd57T1QqU6flg 1 33.586710 -111.835410 Taco Bell \n",
"a1Ba6XeIOP48e64YFD0dMw 1 45.502346 -73.573807 La Prep \n",
"\n",
" ... RestaurantsDelivery \\\n",
"EDqCEAGXVGCH4FJXgqtjqg ... False \n",
"GDnbt3isfhd57T1QqU6flg ... False \n",
"a1Ba6XeIOP48e64YFD0dMw ... NaN \n",
"\n",
" RestaurantsGoodForGroups RestaurantsPriceRange2 \\\n",
"EDqCEAGXVGCH4FJXgqtjqg False NaN \n",
"GDnbt3isfhd57T1QqU6flg False NaN \n",
"a1Ba6XeIOP48e64YFD0dMw NaN NaN \n",
"\n",
" RestaurantsReservations RestaurantsTableService \\\n",
"EDqCEAGXVGCH4FJXgqtjqg NaN NaN \n",
"GDnbt3isfhd57T1QqU6flg NaN NaN \n",
"a1Ba6XeIOP48e64YFD0dMw NaN NaN \n",
"\n",
" RestaurantsTakeOut Smoking WheelchairAccessible WiFi \\\n",
"EDqCEAGXVGCH4FJXgqtjqg NaN False NaN NaN \n",
"GDnbt3isfhd57T1QqU6flg NaN False NaN NaN \n",
"a1Ba6XeIOP48e64YFD0dMw NaN NaN NaN NaN \n",
"\n",
" cuisine_Chinese \n",
"EDqCEAGXVGCH4FJXgqtjqg 1 \n",
"GDnbt3isfhd57T1QqU6flg 1 \n",
"a1Ba6XeIOP48e64YFD0dMw 0 \n",
"\n",
"[3 rows x 98 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_chinese.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Check\n",
"# df_chinese[df_chinese.cuisine_Chinese == 0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(27314, 98)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_chinese.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cuisine_Chinese\n",
"0 8458\n",
"1 16163\n",
"2 2693\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_chinese.groupby(df_chinese.cuisine_Chinese).size()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_chinese.to_pickle('Yelp_Cuisine_Chinese.pkl')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# if save as .csv\n",
"df_chinese.to_csv('Yelp_Cuisine_Chinese.csv', encoding=\"utf8\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Japanese Cuisine"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# parameters\n",
"path_df = '../data_all_cities/all_cities_preprocess.pkl'\n",
"path_culture = 'cat_culture.txt'\n",
"path_cuisine = 'cat_culture-Japanese.txt'\n",
"cuisine = 'Japanese'\n",
"tar_column = 'categories'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create column for Chinese cuisine type\n",
"df_japanese = create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#df_japanese.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(27314, 98)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_japanese.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cuisine_Japanese\n",
"0 8458\n",
"1 17231\n",
"2 1625\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_japanese.groupby(df_japanese.cuisine_Japanese).size()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# save\n",
"df_japanese.to_pickle('Yelp_Cuisine_Japanese.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# American Cuisine"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(27314, 98)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# parameters\n",
"path_df = '../data_all_cities/all_cities_preprocess.pkl'\n",
"path_culture = 'cat_culture.txt'\n",
"path_cuisine = 'cat_culture-American.txt'\n",
"cuisine = 'American'\n",
"tar_column = 'categories'\n",
"\n",
"# create column for Indian cuisine type\n",
"df_american = create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False)\n",
"\n",
"df_american.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cuisine_American\n",
"0 8458\n",
"1 13477\n",
"2 5379\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_american.groupby(df_american.cuisine_American).size()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save\n",
"df_american.to_pickle('Yelp_Cuisine_American.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Indian Cuisine"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(27314, 98)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# parameters\n",
"path_df = '../data_all_cities/all_cities_preprocess.pkl'\n",
"path_culture = 'cat_culture.txt'\n",
"path_cuisine = 'cat_culture-Indian.txt'\n",
"cuisine = 'Indian'\n",
"tar_column = 'categories'\n",
"\n",
"# create column for Indian cuisine type\n",
"df_indian = create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False)\n",
"\n",
"df_indian.shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"cuisine_Indian\n",
"0 8458\n",
"1 17528\n",
"2 1328\n",
"dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_indian.groupby(df_indian.cuisine_Indian).size()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save\n",
"df_indian.to_pickle('Yelp_Cuisine_Indian.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spanish Cuisine"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(27314, 98)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# parameters\n",
"path_df = '../data_all_cities/all_cities_preprocess.pkl'\n",
"path_culture = 'cat_culture.txt'\n",
"path_cuisine = 'cat_culture-Spanish.txt'\n",
"cuisine = 'Spanish'\n",
"tar_column = 'categories'\n",
"\n",
"# create column for Indian cuisine type\n",
"df_spanish = create_cuisine(path_df, tar_column, cuisine, path_culture, path_cuisine, save = False)\n",
"\n",
"df_spanish.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cuisine_Spanish\n",
"0 8458\n",
"1 16323\n",
"2 2533\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_spanish.groupby(df_spanish.cuisine_Spanish).size()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save\n",
"df_spanish.to_pickle('Yelp_Cuisine_Spanish.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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": 0
}
| mit |
utexas-ghosh-group/Experiments | GAN_Exp/dcgan_KITTI.ipynb | 1 | 6010 | {
"cells": [
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#imports\n",
"# Set Up image Ordering as theano\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Reshape\n",
"from keras.layers.core import Activation\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.layers.convolutional import UpSampling2D\n",
"from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
"from keras.layers.core import Flatten\n",
"from keras.optimizers import SGD\n",
"from keras.datasets import mnist\n",
"import numpy as np\n",
"from PIL import Image\n",
"import argparse\n",
"import math\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import keras.backend as k\n",
"k.set_image_dim_ordering('th')\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as img\n",
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define the Networks\n",
"\n",
"def generator_model():\n",
" model = Sequential()\n",
" model.add(Dense(input_dim=100, output_dim=1024))\n",
" model.add(Activation('tanh'))\n",
" model.add(Dense(128*7*7))\n",
" model.add(BatchNormalization())\n",
" model.add(Activation('tanh'))\n",
" model.add(Reshape((128, 7, 7), input_shape=(128*7*7,)))\n",
" model.add(UpSampling2D(size=(2, 2)))\n",
" model.add(Convolution2D(64, 5, 5, border_mode='same'))\n",
" model.add(Activation('tanh'))\n",
" model.add(UpSampling2D(size=(2, 2)))\n",
" model.add(Convolution2D(1, 5, 5, border_mode='same'))\n",
" model.add(Activation('tanh'))\n",
" return model\n",
"\n",
"def discriminator_model():\n",
" model = Sequential()\n",
" model.add(Convolution2D(64, 5, 5,border_mode='same',input_shape=(1, 28, 28)))\n",
" model.add(Activation('tanh'))\n",
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
" model.add(Convolution2D(128, 5, 5))\n",
" model.add(Activation('tanh'))\n",
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
" model.add(Flatten())\n",
" model.add(Dense(1024))\n",
" model.add(Activation('tanh'))\n",
" model.add(Dense(1))\n",
" model.add(Activation('sigmoid'))\n",
" return model\n",
"\n",
"\n",
"def generator_containing_discriminator(generator, discriminator):\n",
" model = Sequential()\n",
" model.add(generator)\n",
" discriminator.trainable = False\n",
" model.add(discriminator)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/achanish/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:21: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), padding=\"same\", input_shape=(1, 28, 28...)`\n",
"/Users/achanish/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:24: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (5, 5))`\n",
"/Users/achanish/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1024, input_dim=100)`\n",
"/Users/achanish/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:12: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), padding=\"same\")`\n",
"/Users/achanish/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:15: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), padding=\"same\")`\n"
]
}
],
"source": [
"#Load and Serialize data\n",
"(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
"X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
"X_train = X_train.reshape((X_train.shape[0], 1) + X_train.shape[1:])\n",
"\n",
"\n",
"#\n",
"discriminator = discriminator_model()\n",
"generator = generator_model()\n",
"discriminator_on_generator = generator_containing_discriminator(generator, discriminator)\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#SVG(model_to_dot(discriminator).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#image = img.imread('data_road/training/image_2/um_000001.png')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#image.shape"
]
},
{
"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 [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": 1
}
| mit |
clemsos/mitras | doc/Visualize memes from clean csv.ipynb | 1 | 661229 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Analyse Meme from data set"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import csv\n",
"import lib.tweetminer as minetweet\n",
"import datetime"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"init tweet entities regex\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from lib.memes import list_to_csv\n",
"from lib.visualizer import create_bar_graph"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Connecting to MongoDB... \n",
"Connected successfully MongoDB at localhost:27017\n",
"\n",
"\n",
"Connecting to MongoDB... \n",
"Connected successfully MongoDB at localhost:27017\n",
"\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Load CSV"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"meme_name=\"The_Voice\"\n",
"\n",
"# name files\n",
"meme_csv=meme_name+\".csv\"\n",
"gephi_nodes_path=meme_name+\"_nodes.csv\"\n",
"gephi_edges_path=meme_name+\"_edges.csv\"\n",
"words_file=meme_name+\"_words.csv\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Extract conversationel user graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open(meme_csv, 'rb') as csvfile:\n",
" memecsv=csv.reader(csvfile)\n",
" memecsv.next() # skip headers\n",
" \n",
" edges=[]\n",
" nodes=[]\n",
" \n",
" for row in memecsv:\n",
"\n",
" # extract text\n",
" t=row[1] \n",
" \n",
" # regexp extract tweet entities\n",
" mentions,urls,hashtags,clean=minetweet.extract_tweet_entities(t)\n",
" \n",
" # add timestamp to generate a dynamic graph\n",
" d=datetime.datetime.strptime(row[9], \"%Y-%m-%dT%H:%M:%S\")\n",
" timestamp=datetime.datetime(d.year,d.month,d.day,d.hour,d.minute,d.second)\n",
" \n",
" # if row[0] not in nodes : nodes.append(row[0])\n",
" \n",
" for mention in mentions:\n",
" edges.append((row[0],mention,timestamp))\n",
" # if mention not in nodes : nodes.append(mention)\n",
" \n",
" # retweeted_uid\n",
" if row[7] != \"\" : \n",
" edges.append((row[7],row[0],timestamp)) \n",
" # if row[7] not in nodes : nodes.append(row[7])\n",
" \n",
" print \"User diffusion : %d nodes, %d edges\"%(len(nodes), len(edges))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"User diffusion : 0 nodes, 34374 edges\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plot Graph"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"import networkx as nx\n",
"\n",
"G=nx.Graph()\n",
"G.add_edges_from([(e[0],e[1]) for e in edges])\n",
"G.name=\"diffusion graph\"\n",
"# nx.draw(G)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'edges' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-3536d4d12669>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mG\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_edges_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[1;32min\u001b[0m \u001b[0medges\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"diffusion graph\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;31m# nx.draw(G)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'edges' is not defined"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Extract time series"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open(meme_csv, 'rb') as csvfile:\n",
" memecsv=csv.reader(csvfile)\n",
" memecsv.next() # skip headers\n",
" \n",
" dates=[]\n",
" values={}\n",
" \n",
" for row in memecsv:\n",
" \n",
" d=datetime.datetime.strptime(row[9], \"%Y-%m-%dT%H:%M:%S\")\n",
" day = datetime.datetime(d.year,d.month,d.day,d.hour,0,0) # round by day\n",
" i=day.strftime(\"%s\")\n",
" if day not in dates : # collect values \n",
" values[i]=0 \n",
" values[i]+=1\n",
" dates.append(day)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Plot time series"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# prepare data\n",
"graph_title=meme_name+\" Volume of Tweets - Time Series\"\n",
"\n",
"# sort values by time\n",
"vy=[values[v] for v in sorted(values.keys())]\n",
"vx=[datetime.datetime.fromtimestamp(float(v)) for v in sorted(values.keys())]\n",
"\n",
"# Create a figure\n",
"w=20 # width of the canvas\n",
"h=10 # height of the canvas\n",
" \n",
"fig = plt.figure(figsize=(w,h))\n",
"\n",
"myplot=fig.add_subplot(111)\n",
"myplot.plot_date(x=vx, y=vy, fmt=\"r-\",color=\"#74c476\", fillstyle=\"top\")\n",
"\n",
"myplot.set_title(graph_title, fontstyle='italic', fontsize=18)\n",
"myplot.set_ylabel(\"Number of Tweets\")\n",
"myplot.grid(True,linestyle='-',color='0.75')\n",
"\n",
"fig.autofmt_xdate() # Auto-adjust and beautify the labels\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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wtM2vEDJV5MljaRsAAAAQQrU9kiZOnKhf/epXKikp0Ykn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TBjGNtrdxAVFsnM3JmnfYETal2tqlurWV+5gcYOJ3azHVBUtVbh7Ggi055Jlj2TIy2l1HpqGR4/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\nUpYcP+IAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7452d10>"
]
}
],
"prompt_number": 74
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Extract keywords"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pytagcloud import create_tag_image,make_tags\n",
"from lib.nlp import NLPMiner\n",
"nlp=NLPMiner()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"init NLP toolkit\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import Counter\n",
"\n",
"with open(meme_csv, 'rb') as csvfile:\n",
" memecsv=csv.reader(csvfile)\n",
" memecsv.next() # skip headers\n",
" \n",
" words=[]\n",
" meme_urls=[]\n",
" meme_hashtags=[]\n",
" \n",
" for row in memecsv:\n",
"\n",
" # extract text\n",
" t=row[1] \n",
" \n",
" # regexp extract tweet entities\n",
" mentions,urls,hashtags,clean=minetweet.extract_tweet_entities(t)\n",
" \n",
" # store this stuff\n",
" meme_hashtags+=hashtags\n",
" meme_urls+=urls\n",
" \n",
" # segent sentence\n",
" dico=nlp.extract_dictionary(clean)\n",
" \n",
" # remove stopwords and store clean dico\n",
" clean_dico=nlp.remove_stopwords(dico)\n",
" words+=dico\n",
" \n",
"print \"Content : %d words, %d hashtags, %d urls\"%(len(words),len(meme_urls), len(meme_hashtags))\n",
"\n",
"words_count=Counter(words)\n",
"\n",
"tags = make_tags(words_count, maxsize=150,colors=((59,76,76), (125,140,116), (217,175,95), (127,92,70), (51,36,35)))\n",
"print tags\n",
"create_tag_image(tags, _name, size=(900, 600), fontname=\"Chinese\",background=(0, 0, 0), )\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"Building Trie..., from /home/clemsos/Dev/mitras/lib/dict/dict.txt.big\n",
"loading model from cache /tmp/jieba.user.3946248680419969172.cache\n",
"loading model cost "
]
},
{
"ename": "IndexError",
"evalue": "string index out of range",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-12-e1de743870e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[0mwords_count\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mCounter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mtags\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_tags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords_count\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m59\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m76\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m76\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m125\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m140\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m116\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m217\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m175\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m95\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m127\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m92\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m70\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m51\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m36\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m35\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[0m\u001b[0;32m 35\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mtags\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[0mcreate_tag_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m900\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m600\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Chinese\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbackground\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\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;32m/usr/local/lib/python2.7/dist-packages/pytagcloud/__init__.pyc\u001b[0m in \u001b[0;36mmake_tags\u001b[1;34m(wordcounts, minsize, maxsize, colors, scalef)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0meither\u001b[0m \u001b[0mchosen\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mrgb\u001b[0m \u001b[0mtuples\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mprovided\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m \"\"\"\n\u001b[1;32m--> 119\u001b[1;33m \u001b[0mcounts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtag\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtag\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mwordcounts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: string index out of range"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Content : 1205847 words, 2898 hashtags, 4686 urls\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
" 2.53493189812 seconds.\n",
"Trie has been built succesfully.\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print words_count"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Counter({u' ': 83110, u'\\uff0c': 69403, u'/': 65163, u'\\uff1a': 48747, u'\\u7684': 37488, u'\\uff01': 33601, u'\\u597d': 33556, u'\\u58f0\\u97f3': 32628, u'\\u4e2d\\u56fd': 26241, u'\\u3002': 25411, u'[': 24016, u']': 24001, u'\\u4e86': 21998, u'@': 19138, u'\\u300a': 15501, u'\\u300b': 15482, u'\\u771f\\u631a': 11354, u'\\u52c7\\u6562': 11201, u'\\u4e50\\u89c2': 10389, u'\\u79ef\\u6781': 10374, u'\\u4e2d': 9600, u'\\u90a3\\u4e9b': 9226, u'\\u751f\\u547d': 9128, u'\\u4e1c\\u897f': 9122, u'\\u6700\\u6709': 9090, u'\\u4ef7\\u503c': 9082, u'\\u63d0\\u70bc': 9077, u'\\u5f69\\u8679': 8627, u'\\u6211': 7264, u'ukn': 5543, u'\\u8282\\u76ee': 5115, u'~': 4977, u'\\u542c': 4865, u'\\u662f': 4773, u'\\u3001': 4160, u'\\u4f60': 4157, u'\\u52a0\\u6cb9': 4060, u'\\u5531': 3946, u'\\u4ed6': 3909, u'\\u5728': 3832, u'\\u5f20\\u73ae': 3797, u'\\u8d64\\u5cf0': 3628, u'\\uff1f': 3574, u'\\u770b': 3551, u'\\u5f88': 3468, u'\\u8fd9': 3424, u'\\u8fd9\\u4e2a': 3421, u'\\u554a': 3344, u'\\u90fd': 3281, u'\\u951b': 2864, u'!': 2795, u'\\u5427': 2642, u'\\u65b0': 2635, u'\\u6709': 2628, u'\\u4e5f': 2612, u'\\u65e9\\u5b89': 2586, u'\\u201c': 2392, u'\\u201d': 2371, u'\\u559c\\u6b22': 2287, u'\\u8d5e': 2222, u'good': 2195, u'\\u6211\\u7231\\u4f60': 2162, u'\\u4e0d': 2116, u'\\u8fd9\\u4e48': 2060, u'\\u5c31': 2005, u'\\u4eba': 1989, u'\\u597d\\u542c': 1920, u'\\u5c31\\u662f': 1882, u'\\u5417': 1843, u'\\u4e00\\u8d77': 1806, u'\\u5fae': 1764, u'\\u8fc7': 1720, u'\\u53ef\\u7231': 1709, u'\\u8bc4\\u59d4': 1706, u'\\u771f\\u7684': 1638, u'\\u81f4\\u656c': 1616, u'\\u2026': 1583, u'\\u9f13\\u638c': 1563, u'\\u4f60\\u4eec': 1560, u'\\u6027\\u611f': 1545, u'\\u5feb\\u6765': 1527, u'\\u5176\\u5b9e': 1407, u'\\u4e00\\u5468': 1372, u'\\u751f\\u6d3b': 1366, u'uGL4HNKOZ': 1362, u'\\u4e0d\\u9519': 1358, u'\\u611f\\u52a8': 1352, u'\\u52aa\\u529b': 1346, u'\\u4e00\\u5929': 1317, u'\\u5e73\\u51e1': 1306, u'\\u8eab\\u8fb9': 1303, u'\\u81ea\\u5df1': 1303, u'\\u4e00\\u4e2a': 1285, u'\\u97f3\\u4e50': 1285, u'\\u592a': 1241, u'\\u6cea': 1240, u'\\u88ab': 1234, u'\\u3010': 1224, u'\\u8ba9': 1212, u'\\u3011': 1201, u'\\u6d93': 1192, u'\\u548c': 1162, u'\\u5979': 1133, u'\\u54ed': 1100, u'\\u80fd': 1094, u'\\u9009\\u624b': 1087, u'\\u6ca1\\u6709': 1073, u'\\u6700': 1048, u'\\u771f': 1048, u'\\u5230': 1024, u'\\u652f\\u6301': 1016, u'\\u9286': 964, u'uZ32OGRGW': 963, u'\\u6b4c': 958, u'\\u89c9\\u5f97': 957, u'\\u4e0d\\u662f': 943, u'\\u7231': 922, u'\\u5bfc\\u5e08': 911, u'\\u56db\\u4f4d': 896, u'\\u8bf4': 887, u'\\u8fd8': 880, u'\\u770b\\u5230': 869, u'\\u53c8': 869, u'\\u4f1a': 849, u'\\u54c8\\u54c8': 841, u'\\u53bb': 837, u'\\u9009\\u79c0': 832, u'\\u771f\\u5fc3': 820, u'\\u5f97': 805, u'\\u5218\\u6b22': 784, u'\\u771f\\u646f': 782, u'\\u6a02\\u89c0': 782, u'\\u7a4d\\u6975': 782, u'\\u6765': 779, u'\\u5fc3': 776, u'\\u60ca\\u8273': 770, u'\\u9e21\\u76ae\\u7599\\u7629': 765, u'\\u7ed9\\u529b': 761, u'\\u5e73\\u5b89': 757, u',': 756, u'\\u536b\\u89c6': 751, u'\\u6211\\u4eec': 730, u'\\u6d59\\u6c5f': 730, u'\\u591a': 728, u'\\u8981': 721, u'\\u6d5c': 703, u'\\u6fc2': 683, u'\\u5927\\u5bb6': 680, u'\\u60f3': 674, u'\\u628a': 671, u'\\u624d': 670, u'\\xb7': 660, u'\\u66f4': 659, u'\\u4e00\\u9996': 653, u'\\u8f6c': 649, u'\\u77e5\\u9053': 642, u'\\u5ebe\\u6f84\\u5e86': 640, u'\\u4ec0\\u4e48': 635, u'\\u7d1d': 632, u'\\u5173\\u6ce8': 632, u'\\u5531\\u5f97': 625, u'-': 625, u'\\u786e\\u5b9e': 624, u'\\u5a01\\u6b66': 623, u'\\u7ed9': 616, u'\\u597d\\u770b': 614, u'\\u6b4c\\u58f0': 611, u'\\u85c9': 605, u'\\u6f14\\u7ece': 598, u'\\u9707\\u64bc': 592, u'\\u8fd9\\u6837': 589, u'\\u6447\\u6eda': 589, u'\\u4e0a': 587, u'\\u91cc': 582, u'\\u4e3a': 577, u':': 576, u'\\u6b63': 568, u'\\u5462': 556, u'\\u542c\\u5230': 548, u'\\uff5e': 539, u'\\u592a\\u68d2\\u4e86': 531, u'\\u89c6\\u9891': 531, u'\\u9428': 530, u'\\u4ee5': 524, u'\\u59d1\\u5a18': 521, u'\\u6b91': 520, u'I': 519, u'\\u6768\\u5764': 515, u'\\u7740': 510, u'\\u90a3\\u82f1': 510, u'\\uff10': 508, u'high': 507, u'\\u9009\\u62e9': 503, u'\\u6bd4': 493, u'\\u53ef\\u4ee5': 489, u'\\u5531\\u6cd5': 487, u'\\u5531\\u6b4c': 485, u'\\u8d77\\u6765': 484, u'\\u8fd9\\u662f': 481, u'\\u68d2': 478, u'\\u6d57': 478, u'\\u8001\\u5e08': 472, u'\\u600e\\u6837': 470, u'\\u5b69\\u5b50': 469, u'\\u54e6': 467, u'\\u770b\\u770b': 463, u'\\u8fd8\\u662f': 463, u'The': 462, u'\\u5c0f': 456, u'\\u52a0': 452, u'\\u68a6\\u60f3': 444, u'\\u7406\\u89e3': 444, u'\\u63a8\\u8350': 442, u'\\u611f\\u89c9': 440, u'\\u5168\\u65b0': 439, u'\\u5566': 437, u'\\uff08': 436, u'you': 435, u'\\u6ca1': 434, u'\\u4e00': 434, u'\\u975e\\u5e38': 432, u'\\u77e5\\u9752': 431, u'\\u50cf': 429, u'\\u5440': 423, u'want': 420, u\"'\": 420, u'\\u901a\\u4fd7': 416, u'\\u8303\\u56f4': 416, u'\\u4e2a': 415, u'\\uff09': 415, u'\\u7528': 415, u'\\u5766\\u8a00': 409, u'\\uff02': 409, u'\\u4f46\\u4ed6\\u5374': 408, u'\\u725b': 408, u'\\u6309\\u4e0b': 405, u'\\u5e2e\\u5230': 402, u'\\u4eec': 401, u'\\u4eca\\u5929': 400, u'\\u5b8c': 399, u'.': 387, u'\\u6b4c\\u624b': 387, u'\\u2014': 387, u'\\u773c\\u6cea': 382, u'#': 381, u'\\u5f88\\u68d2': 380, u'\\u4f46': 376, u'...': 375, u'\\u8ddf': 375, u'\\u7d12': 368, u'\\u90a3\\u4e48': 364, u'\\u4e0d\\u884c': 362, u'\\u5609\\u5bbe': 361, u'\\u5389\\u5bb3': 356, u'\\u6700\\u540e': 353, u'\\u771f\\u662f': 353, u'\\u4e00\\u5b9a': 353, u'\\u90a3': 351, u'\\u6fb9': 349, u'\\u8fd8\\u6709': 349, u'\\u5feb': 348, u'\\u7edd\\u5bf9': 348, u'\\u581d': 345, u'\\u7176': 342, u'\\u90a3\\u4e2a': 341, u'\\u30bd': 341, u'\\u4e0d\\u706b': 332, u'\\u70b9': 332, u'\\u4e48': 329, u'\\u4e0b': 327, u'High': 325, u'\\u5973\\u5b69': 324, u'\\u771f\\u6b63': 322, u'\\u518d': 320, u'\\u7537': 319, u'\\u51fa': 316, u'\\u5b9e\\u529b': 315, u'Voice': 312, u'\\u4e3a\\u4ec0\\u4e48': 312, u'\\u8f6c\\u53d1': 312, u'\\u8d77': 311, u'\\u89c2\\u4f17': 311, u'\\u771f\\u68d2': 310, u'\\u8bdd\\u7b52': 310, u'\\u6b4c\\u5531': 309, u'\\u542c\\u542c': 308, u'\"': 306, u'\\u5f00\\u59cb': 305, u'1': 304, u'\\u6fb6': 302, u'\\u5bf9': 302, u'\\u65f6': 302, u'\\u942a': 301, u'\\u5077\\u7b11': 300, u'\\u771f\\u706b': 297, u'\\u53c2\\u52a0': 297, u'\\u5e94\\u8be5': 296, u'\\u5c0f\\u59d1\\u5a18': 294, u'\\u4f34': 293, u'\\u771f\\u5b9e': 292, u'\\u73b0\\u5728': 292, u'\\u4ed6\\u4eec': 292, u'\\u4e09\\u5473': 290, u'\\u5927': 290, u'\\u7248': 289, u'\\u671f\\u5f85': 286, u'\\u6700\\u597d': 284, u'\\u8c01': 282, u'\\u5b66\\u5458': 282, u'\\u4e0e': 281, u'\\u903c': 280, u'\\u6708': 280, u'\\u5c06': 277, u'\\u771f\\u8bda': 276, u'\\u8d85': 275, u'\\u4eca\\u665a': 274, u'\\u5e0c\\u671b': 273, u'\\u6253\\u52a8': 273, u'\\u4e00\\u6b21': 271, u'\\u65f6\\u5019': 270, u'\\u93c8': 269, u'\\u4e00\\u4e0b': 269, u'\\u5fae\\u535a': 268, u'\\u5f81\\u670d': 268, u'\\u505a': 266, u'\\u7b2c\\u4e8c\\u671f': 264, u'\\u563b\\u563b': 264, u'\\u5c7d': 264, u'\\u9996\\u6b4c': 262, u'\\u6628\\u665a': 261, u'voice': 261, u'\\u4eba\\u5bb6': 259, u'\\u6240\\u6709': 258, u'\\u5f90\\u6d77': 258, u'\\u7cbe\\u5f69': 258, u'\\u54c7': 258, 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"prompt_number": 13
},
{
"cell_type": "code",
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"language": "python",
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"metadata": {}
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} | mit |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-aerchem/land.ipynb | 1 | 173544 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Land \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: EC-EARTH-CONSORTIUM \n",
"**Source ID**: EC-EARTH3-AERCHEM \n",
"**Topic**: Land \n",
"**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n",
"**Properties**: 154 (96 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?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 16:53:59"
],
"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', 'ec-earth-consortium', 'ec-earth3-aerchem', 'land')"
],
"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](#1.-Key-Properties) \n",
"[2. Key Properties --> Conservation Properties](#2.-Key-Properties--->-Conservation-Properties) \n",
"[3. Key Properties --> Timestepping Framework](#3.-Key-Properties--->-Timestepping-Framework) \n",
"[4. Key Properties --> Software Properties](#4.-Key-Properties--->-Software-Properties) \n",
"[5. Grid](#5.-Grid) \n",
"[6. Grid --> Horizontal](#6.-Grid--->-Horizontal) \n",
"[7. Grid --> Vertical](#7.-Grid--->-Vertical) \n",
"[8. Soil](#8.-Soil) \n",
"[9. Soil --> Soil Map](#9.-Soil--->-Soil-Map) \n",
"[10. Soil --> Snow Free Albedo](#10.-Soil--->-Snow-Free-Albedo) \n",
"[11. Soil --> Hydrology](#11.-Soil--->-Hydrology) \n",
"[12. Soil --> Hydrology --> Freezing](#12.-Soil--->-Hydrology--->-Freezing) \n",
"[13. Soil --> Hydrology --> Drainage](#13.-Soil--->-Hydrology--->-Drainage) \n",
"[14. Soil --> Heat Treatment](#14.-Soil--->-Heat-Treatment) \n",
"[15. Snow](#15.-Snow) \n",
"[16. Snow --> Snow Albedo](#16.-Snow--->-Snow-Albedo) \n",
"[17. Vegetation](#17.-Vegetation) \n",
"[18. Energy Balance](#18.-Energy-Balance) \n",
"[19. Carbon Cycle](#19.-Carbon-Cycle) \n",
"[20. Carbon Cycle --> Vegetation](#20.-Carbon-Cycle--->-Vegetation) \n",
"[21. Carbon Cycle --> Vegetation --> Photosynthesis](#21.-Carbon-Cycle--->-Vegetation--->-Photosynthesis) \n",
"[22. Carbon Cycle --> Vegetation --> Autotrophic Respiration](#22.-Carbon-Cycle--->-Vegetation--->-Autotrophic-Respiration) \n",
"[23. Carbon Cycle --> Vegetation --> Allocation](#23.-Carbon-Cycle--->-Vegetation--->-Allocation) \n",
"[24. Carbon Cycle --> Vegetation --> Phenology](#24.-Carbon-Cycle--->-Vegetation--->-Phenology) \n",
"[25. Carbon Cycle --> Vegetation --> Mortality](#25.-Carbon-Cycle--->-Vegetation--->-Mortality) \n",
"[26. Carbon Cycle --> Litter](#26.-Carbon-Cycle--->-Litter) \n",
"[27. Carbon Cycle --> Soil](#27.-Carbon-Cycle--->-Soil) \n",
"[28. Carbon Cycle --> Permafrost Carbon](#28.-Carbon-Cycle--->-Permafrost-Carbon) \n",
"[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n",
"[30. River Routing](#30.-River-Routing) \n",
"[31. River Routing --> Oceanic Discharge](#31.-River-Routing--->-Oceanic-Discharge) \n",
"[32. Lakes](#32.-Lakes) \n",
"[33. Lakes --> Method](#33.-Lakes--->-Method) \n",
"[34. Lakes --> Wetlands](#34.-Lakes--->-Wetlands) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Land surface key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of land surface model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.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 land surface model code (e.g. MOSES2.2)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.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": [
"### 1.3. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.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": [
"### 1.4. Land Atmosphere Flux Exchanges\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Fluxes exchanged with the atmopshere.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"water\" \n",
"# \"energy\" \n",
"# \"carbon\" \n",
"# \"nitrogen\" \n",
"# \"phospherous\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Atmospheric Coupling Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Land Cover\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Types of land cover defined in the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_cover') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"bare soil\" \n",
"# \"urban\" \n",
"# \"lake\" \n",
"# \"land ice\" \n",
"# \"lake ice\" \n",
"# \"vegetated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Land Cover Change\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.land_cover_change') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.8. Tiling\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.tiling') \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 --> Conservation Properties \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Energy\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Water\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Carbon\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.conservation_properties.carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestepping Framework \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Timestep Dependent On Atmosphere\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is a time step dependent on the frequency of atmosphere coupling?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \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": [
"### 3.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Overall timestep of land surface model (i.e. time between calls)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.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": [
"### 3.3. Timestepping Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of time stepping method and associated time step(s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \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 --> Software Properties \n",
"*Software properties of land surface code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.software_properties.repository') \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. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.software_properties.code_version') \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. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.key_properties.software_properties.code_languages') \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": [
"# 5. Grid \n",
"*Land surface grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of the grid in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.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": [
"# 6. Grid --> Horizontal \n",
"*The horizontal grid in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the horizontal grid (not including any tiling)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.horizontal.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": [
"### 6.2. Matches Atmosphere Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the horizontal grid match the atmosphere?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_grid') \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": [
"# 7. Grid --> Vertical \n",
"*The vertical grid in the soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the vertical grid in the soil (not including any tiling)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.vertical.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": [
"### 7.2. Total Depth\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The total depth of the soil (in metres)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.grid.vertical.total_depth') \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. Soil \n",
"*Land surface soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of soil in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.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": [
"### 8.2. Heat Water Coupling\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the coupling between heat and water in the soil*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_water_coupling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Number Of Soil layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of soil layers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.number_of_soil 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": [
"### 8.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the soil scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.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. Soil --> Soil Map \n",
"*Key properties of the land surface soil map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of soil map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.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": [
"### 9.2. Structure\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil structure map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.structure') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Texture\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil texture map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.texture') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.4. Organic Matter\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil organic matter map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \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. Albedo\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil albedo map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.albedo') \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. Water Table\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil water table map, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.water_table') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.7. Continuously Varying Soil Depth\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the soil properties vary continuously with depth?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \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": [
"### 9.8. Soil Depth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil depth map*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \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. Soil --> Snow Free Albedo \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Prognostic\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is snow free albedo prognostic?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \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": [
"### 10.2. Functions\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, describe the dependancies on snow free albedo calculations*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation type\" \n",
"# \"soil humidity\" \n",
"# \"vegetation state\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Direct Diffuse\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe the distinction between direct and diffuse albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"distinction between direct and diffuse albedo\" \n",
"# \"no distinction between direct and diffuse albedo\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.4. Number Of Wavelength Bands\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If prognostic, enter the number of wavelength bands used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_bands') \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. Soil --> Hydrology \n",
"*Key properties of the land surface soil hydrology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of the soil hydrological model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.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": [
"### 11.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of river soil hydrology in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.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": [
"### 11.3. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil hydrology tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.tiling') \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. Vertical Discretisation\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the typical vertical discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \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. Number Of Ground Water Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of soil layers that may contain water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_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": [
"### 11.6. Lateral Connectivity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe the lateral connectivity between tiles*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"perfect connectivity\" \n",
"# \"Darcian flow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.7. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *The hydrological dynamics scheme in the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Bucket\" \n",
"# \"Force-restore\" \n",
"# \"Choisnel\" \n",
"# \"Explicit diffusion\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Soil --> Hydrology --> Freezing \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Number Of Ground Ice Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *How many soil layers may contain ground ice*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_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": [
"### 12.2. Ice Storage Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the method of ice storage*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \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. Permafrost\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the treatment of permafrost, if any, within the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \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. Soil --> Hydrology --> Drainage \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General describe how drainage is included in the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.drainage.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": [
"### 13.2. Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Different types of runoff represented by the land surface model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Gravity drainage\" \n",
"# \"Horton mechanism\" \n",
"# \"topmodel-based\" \n",
"# \"Dunne mechanism\" \n",
"# \"Lateral subsurface flow\" \n",
"# \"Baseflow from groundwater\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Soil --> Heat Treatment \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General description of how heat treatment properties are defined*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.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": [
"### 14.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of soil heat scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.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": [
"### 14.3. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the soil heat treatment tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.4. Vertical Discretisation\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the typical vertical discretisation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.5. Heat Storage\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the method of heat storage*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Force-restore\" \n",
"# \"Explicit diffusion\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.6. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe processes included in the treatment of soil heat*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"soil moisture freeze-thaw\" \n",
"# \"coupling with snow temperature\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Snow \n",
"*Land surface snow*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of snow in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.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": [
"### 15.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the snow tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.tiling') \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. Number Of Snow Layers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of snow levels used in the land surface scheme/model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.number_of_snow_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": [
"### 15.4. Density\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow density*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.density') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"constant\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Water Equivalent\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of the snow water equivalent*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.water_equivalent') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.6. Heat Content\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of the heat content of snow*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.heat_content') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.7. Temperature\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow temperature*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.temperature') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.8. Liquid Water Content\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Description of the treatment of snow liquid water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.liquid_water_content') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.9. Snow Cover Fractions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify cover fractions used in the surface snow scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"ground snow fraction\" \n",
"# \"vegetation snow fraction\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.10. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Snow related processes in the land surface scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"snow interception\" \n",
"# \"snow melting\" \n",
"# \"snow freezing\" \n",
"# \"blowing snow\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.11. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the snow scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.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": [
"# 16. Snow --> Snow Albedo \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe the treatment of snow-covered land albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_albedo.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"prescribed\" \n",
"# \"constant\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. Functions\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation type\" \n",
"# \"snow age\" \n",
"# \"snow density\" \n",
"# \"snow grain type\" \n",
"# \"aerosol deposition\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Vegetation \n",
"*Land surface vegetation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of vegetation in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.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": [
"### 17.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of vegetation scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.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": [
"### 17.3. Dynamic Vegetation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there dynamic evolution of vegetation?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \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": [
"### 17.4. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the vegetation tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.5. Vegetation Representation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Vegetation classification used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"vegetation types\" \n",
"# \"biome types\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.6. Vegetation Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List of vegetation types in the classification, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"broadleaf tree\" \n",
"# \"needleleaf tree\" \n",
"# \"C3 grass\" \n",
"# \"C4 grass\" \n",
"# \"vegetated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.7. Biome Types\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *List of biome types in the classification, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biome_types') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"evergreen needleleaf forest\" \n",
"# \"evergreen broadleaf forest\" \n",
"# \"deciduous needleleaf forest\" \n",
"# \"deciduous broadleaf forest\" \n",
"# \"mixed forest\" \n",
"# \"woodland\" \n",
"# \"wooded grassland\" \n",
"# \"closed shrubland\" \n",
"# \"opne shrubland\" \n",
"# \"grassland\" \n",
"# \"cropland\" \n",
"# \"wetlands\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.8. Vegetation Time Variation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How the vegetation fractions in each tile are varying with time*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed (not varying)\" \n",
"# \"prescribed (varying from files)\" \n",
"# \"dynamical (varying from simulation)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.9. Vegetation Map\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.vegetation_map') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.10. Interception\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is vegetation interception of rainwater represented?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.interception') \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": [
"### 17.11. Phenology\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation phenology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.phenology') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic (vegetation map)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.12. Phenology Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation phenology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.phenology_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": [
"### 17.13. Leaf Area Index\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation leaf area index*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prescribed\" \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.14. Leaf Area Index Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of leaf area index*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.leaf_area_index_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": [
"### 17.15. Biomass\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation biomass *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biomass') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.16. Biomass Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation biomass*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biomass_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": [
"### 17.17. Biogeography\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Treatment of vegetation biogeography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biogeography') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.18. Biogeography Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation biogeography*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.biogeography_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": [
"### 17.19. Stomatal Resistance\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify what the vegetation stomatal resistance depends on*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"light\" \n",
"# \"temperature\" \n",
"# \"water availability\" \n",
"# \"CO2\" \n",
"# \"O3\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.20. Stomatal Resistance Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of the treatment of vegetation stomatal resistance*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.stomatal_resistance_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": [
"### 17.21. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the vegetation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.vegetation.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": [
"# 18. Energy Balance \n",
"*Land surface energy balance*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of energy balance in land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.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": [
"### 18.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the energy balance tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.tiling') \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. Number Of Surface Temperatures\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.4. Evaporation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Specify the formulation method for land surface evaporation, from soil and vegetation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.evaporation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"alpha\" \n",
"# \"beta\" \n",
"# \"combined\" \n",
"# \"Monteith potential evaporation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.5. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Describe which processes are included in the energy balance scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.energy_balance.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"transpiration\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 19. Carbon Cycle \n",
"*Land surface carbon cycle*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of carbon cycle in land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.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": [
"### 19.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the carbon cycle tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of carbon cycle in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.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": [
"### 19.4. Anthropogenic Carbon\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Describe the treament of the anthropogenic carbon pool*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"grand slam protocol\" \n",
"# \"residence time\" \n",
"# \"decay time\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.5. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the carbon scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.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": [
"# 20. Carbon Cycle --> Vegetation \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 20.3. Forest Stand Dynamics\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the treatment of forest stand dyanmics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \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. Carbon Cycle --> Vegetation --> Photosynthesis \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \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. Carbon Cycle --> Vegetation --> Autotrophic Respiration \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Maintainance Respiration\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for maintainence respiration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Growth Respiration\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the general method used for growth respiration*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Carbon Cycle --> Vegetation --> Allocation \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the allocation scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. Allocation Bins\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify distinct carbon bins used in allocation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"leaves + stems + roots\" \n",
"# \"leaves + stems + roots (leafy + woody)\" \n",
"# \"leaves + fine roots + coarse roots + stems\" \n",
"# \"whole plant (no distinction)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Allocation Fractions\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how the fractions of allocation are calculated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"fixed\" \n",
"# \"function of vegetation type\" \n",
"# \"function of plant allometry\" \n",
"# \"explicitly calculated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 24. Carbon Cycle --> Vegetation --> Phenology \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the phenology scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 25. Carbon Cycle --> Vegetation --> Mortality \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general principle behind the mortality scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Carbon Cycle --> Litter \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.4. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the general method used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 27. Carbon Cycle --> Soil \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. Number Of Carbon Pools\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Carbon Pools\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the carbon pools used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.4. Method\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the general method used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Carbon Cycle --> Permafrost Carbon \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. Is Permafrost Included\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is permafrost included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_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": [
"### 28.2. Emitted Greenhouse Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the GHGs emitted*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 28.3. Decomposition\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List the decomposition methods used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 28.4. Impact On Soil Properties\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the impact of permafrost on soil properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 29. Nitrogen Cycle \n",
"*Land surface nitrogen cycle*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of the nitrogen cycle in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.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": [
"### 29.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the notrogen cycle tiling, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 29.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of nitrogen cycle in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.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": [
"### 29.4. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the nitrogen scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.nitrogen_cycle.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": [
"# 30. River Routing \n",
"*Land surface river routing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of river routing in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.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": [
"### 30.2. Tiling\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the river routing, if any.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.tiling') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of river routing scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.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": [
"### 30.4. Grid Inherited From Land Surface\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the grid inherited from land surface?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \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": [
"### 30.5. Grid Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *General description of grid, if not inherited from land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.grid_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": [
"### 30.6. Number Of Reservoirs\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Enter the number of reservoirs*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.7. Water Re Evaporation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"flood plains\" \n",
"# \"irrigation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.8. Coupled To Atmosphere\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Is river routing coupled to the atmosphere model component?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \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": [
"### 30.9. Coupled To Land\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the coupling between land and rivers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.10. Quantities Exchanged With Atmosphere\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.11. Basin Flow Direction Map\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *What type of basin flow direction map is being used?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"present day\" \n",
"# \"adapted for other periods\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.12. Flooding\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the representation of flooding, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.flooding') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.13. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the river routing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.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": [
"# 31. River Routing --> Oceanic Discharge \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Discharge Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify how rivers are discharged to the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"direct (large rivers)\" \n",
"# \"diffuse\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Quantities Transported\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Quantities that are exchanged from river-routing to the ocean model component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Lakes \n",
"*Land surface lakes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of lakes in the land surface*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.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": [
"### 32.2. Coupling With Rivers\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Are lakes coupled to the river routing model component?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \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": [
"### 32.3. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Time step of lake scheme in seconds*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.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": [
"### 32.4. Quantities Exchanged With Rivers\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"heat\" \n",
"# \"water\" \n",
"# \"tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.5. Vertical Grid\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the vertical grid of lakes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.vertical_grid') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *List the prognostic variables of the lake scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.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": [
"# 33. Lakes --> Method \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Ice Treatment\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is lake ice included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.ice_treatment') \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": [
"### 33.2. Albedo\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe the treatment of lake albedo*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.albedo') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"prognostic\" \n",
"# \"diagnostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.3. Dynamics\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which dynamics of lakes are treated? horizontal, vertical, etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.dynamics') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No lake dynamics\" \n",
"# \"vertical\" \n",
"# \"horizontal\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.4. Dynamic Lake Extent\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is a dynamic lake extent scheme included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \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": [
"### 33.5. Endorheic Basins\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Basins not flowing to ocean included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \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": [
"# 34. Lakes --> Wetlands \n",
"*TODO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Description\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe the treatment of wetlands, if any*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.land.lakes.wetlands.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": [
"### \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 |
mne-tools/mne-tools.github.io | 0.17/_downloads/8b68ef11c9dcc68ed3cd0ccec9a41a34/plot_decoding_unsupervised_spatial_filter.ipynb | 1 | 3819 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Analysis of evoked response using ICA and PCA reduction techniques\n\n\nThis example computes PCA and ICA of evoked or epochs data. Then the\nPCA / ICA components, a.k.a. spatial filters, are used to transform\nthe channel data to new sources / virtual channels. The output is\nvisualized on the average of all the epochs.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Jean-Remi King <[email protected]>\n# Asish Panda <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.decoding import UnsupervisedSpatialFilter\n\nfrom sklearn.decomposition import PCA, FastICA\n\nprint(__doc__)\n\n# Preprocess data\ndata_path = sample.data_path()\n\n# Load and filter data, set up epochs\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'\ntmin, tmax = -0.1, 0.3\nevent_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)\n\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.filter(1, 20, fir_design='firwin')\nevents = mne.read_events(event_fname)\n\npicks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n exclude='bads')\n\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,\n picks=picks, baseline=None, preload=True,\n verbose=False)\n\nX = epochs.get_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform data with PCA computed on the average ie evoked response\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pca = UnsupervisedSpatialFilter(PCA(30), average=False)\npca_data = pca.fit_transform(X)\nev = mne.EvokedArray(np.mean(pca_data, axis=0),\n mne.create_info(30, epochs.info['sfreq'],\n ch_types='eeg'), tmin=tmin)\nev.plot(show=False, window_title=\"PCA\", time_unit='s')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform data with ICA computed on the raw epochs (no averaging)\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ica = UnsupervisedSpatialFilter(FastICA(30), average=False)\nica_data = ica.fit_transform(X)\nev1 = mne.EvokedArray(np.mean(ica_data, axis=0),\n mne.create_info(30, epochs.info['sfreq'],\n ch_types='eeg'), tmin=tmin)\nev1.plot(show=False, window_title='ICA', time_unit='s')\n\nplt.show()"
]
}
],
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
mlmurray/TensorFlow-Experimentation | notebooks/5 - User Interface/loss_visualization.ipynb | 1 | 417558 | {
"cells": [
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Loss Visualization with TensorFlow.\n",
"# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n",
"\n",
"# Author: Aymeric Damien\n",
"# Project: https://github.com/aymericdamien/TensorFlow-Examples/"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import numpy\n",
"\n",
"# Import MINST data\n",
"import input_data\n",
"mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Use Logistic Regression from our previous example\n",
"\n",
"# Parameters\n",
"learning_rate = 0.01\n",
"training_epochs = 10\n",
"batch_size = 100\n",
"display_step = 1\n",
"\n",
"# tf Graph Input\n",
"x = tf.placeholder(\"float\", [None, 784], name='x') # mnist data image of shape 28*28=784\n",
"y = tf.placeholder(\"float\", [None, 10], name='y') # 0-9 digits recognition => 10 classes\n",
"\n",
"# Create model\n",
"\n",
"# Set model weights\n",
"W = tf.Variable(tf.zeros([784, 10]), name=\"weights\")\n",
"b = tf.Variable(tf.zeros([10]), name=\"bias\")\n",
"\n",
"# Construct model\n",
"activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n",
"\n",
"# Minimize error using cross entropy\n",
"cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent\n",
"\n",
"# Initializing the variables\n",
"init = tf.initialize_all_variables()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a summary to monitor cost function\n",
"tf.scalar_summary(\"loss\", cost)\n",
"\n",
"# Merge all summaries to a single operator\n",
"merged_summary_op = tf.merge_all_summaries()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Launch the graph\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
"\n",
" # Set logs writer into folder /tmp/tensorflow_logs\n",
" summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)\n",
"\n",
" # Training cycle\n",
" for epoch in range(training_epochs):\n",
" avg_cost = 0.\n",
" total_batch = int(mnist.train.num_examples/batch_size)\n",
" # Loop over all batches\n",
" for i in range(total_batch):\n",
" batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
" # Fit training using batch data\n",
" sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n",
" # Compute average loss\n",
" avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n",
" # Write logs at every iteration\n",
" summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})\n",
" summary_writer.add_summary(summary_str, epoch*total_batch + i)\n",
" # Display logs per epoch step\n",
" if epoch % display_step == 0:\n",
" print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n",
"\n",
" print \"Optimization Finished!\"\n",
"\n",
" # Test model\n",
" correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n",
" # Calculate accuracy\n",
" accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
" print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the command line\n",
"```\n",
"tensorboard --logdir=/tmp/tensorflow_logs\n",
"```\n",
"\n",
"### Open http://localhost:6006/ into your web browser"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
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ZIqGo7qCpZ50RvWYS8hqGPMwpz1A/RSIT1hxxH72ga14V8KvK0W/kKy3RayCS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9aNepBOAtFigGuSN76JzY9sFKfRihsdgOuWkeJ49+tb3CXMT648z218+f+Ronl/bZFQR8w9fvCz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\nSzvI6IRKo2w24VV1e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69lxgnjMyePceHjt228L6Ndjk/X8pC/18PjMnhm5o2usz3X1br1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\nPuKLbCnxIKXxsb+AK6d3N74YDYJH+5+Id6KHFRvhfacN3FLp1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Tz+04Yp+hXtfb8IeSVu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\n8r9utuN6mdN1sa7beSsvJ3VVrMmFW9Su3uDwVWX1UKqkP5pUyd1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3oZymF/L8Yt1duWXL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X2LB7oUHpk7d250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AECBAgQIECAAAECBAgQIECAAAEC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MLiqDmHJqKrJshtPdbOtyDguyfD6YlH+QznOn36Oa61McONdicyLNimLWD4ZOEe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\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/vqS9iYnXP/zhxemEe5RbrXVbKY/BCJSZm/aT02ef/R8dOxqJWv/x9fPSyYuNNtpgUpnY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\nykeZIvGKlmtvv/31/wfSJn1/ECSR6QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Loss per minibatch step"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "IPython (Python 2.7)",
"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": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
abhi1509/deep-learning | intro-to-tflearn/TFLearn_Digit_Recognition.ipynb | 1 | 20447 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handwritten Number Recognition with TFLearn and MNIST\n",
"\n",
"In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \n",
"\n",
"This kind of neural network is used in a variety of real-world applications including: recognizing phone numbers and sorting postal mail by address. To build the network, we'll be using the **MNIST** data set, which consists of images of handwritten numbers and their correct labels 0-9.\n",
"\n",
"We'll be using [TFLearn](http://tflearn.org/), a high-level library built on top of TensorFlow to build the neural network. We'll start off by importing all the modules we'll need, then load the data, and finally build the network."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import Numpy, TensorFlow, TFLearn, and MNIST data\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tflearn\n",
"import tflearn.datasets.mnist as mnist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieving training and test data\n",
"\n",
"The MNIST data set already contains both training and test data. There are 55,000 data points of training data, and 10,000 points of test data.\n",
"\n",
"Each MNIST data point has:\n",
"1. an image of a handwritten digit and \n",
"2. a corresponding label (a number 0-9 that identifies the image)\n",
"\n",
"We'll call the images, which will be the input to our neural network, **X** and their corresponding labels **Y**.\n",
"\n",
"We're going to want our labels as *one-hot vectors*, which are vectors that holds mostly 0's and one 1. It's easiest to see this in a example. As a one-hot vector, the number 0 is represented as [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], and 4 is represented as [0, 0, 0, 0, 1, 0, 0, 0, 0, 0].\n",
"\n",
"### Flattened data\n",
"\n",
"For this example, we'll be using *flattened* data or a representation of MNIST images in one dimension rather than two. So, each handwritten number image, which is 28x28 pixels, will be represented as a one dimensional array of 784 pixel values. \n",
"\n",
"Flattening the data throws away information about the 2D structure of the image, but it simplifies our data so that all of the training data can be contained in one array whose shape is [55000, 784]; the first dimension is the number of training images and the second dimension is the number of pixels in each image. This is the kind of data that is easy to analyze using a simple neural network."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading MNIST...\n",
"Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
"Extracting mnist/train-images-idx3-ubyte.gz\n",
"Downloading MNIST...\n",
"Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
"Extracting mnist/train-labels-idx1-ubyte.gz\n",
"Downloading MNIST...\n",
"Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
"Extracting mnist/t10k-images-idx3-ubyte.gz\n",
"Downloading MNIST...\n",
"Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
"Extracting mnist/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"# Retrieve the training and test data\n",
"trainX, trainY, testX, testY = mnist.load_data(one_hot=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(55000, 784)\n",
"(55000, 10)\n"
]
}
],
"source": [
"print(trainX.shape)\n",
"print(trainY.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize the training data\n",
"\n",
"Provided below is a function that will help you visualize the MNIST data. By passing in the index of a training example, the function `show_digit` will display that training image along with it's corresponding label in the title."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10c00c3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualizing the data\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# Function for displaying a training image by it's index in the MNIST set\n",
"def show_digit(index):\n",
" label = trainY[index].argmax(axis=0)\n",
" # Reshape 784 array into 28x28 image\n",
" image = trainX[index].reshape([28,28])\n",
" plt.title('Training data, index: %d, Label: %d' % (index, label))\n",
" plt.imshow(image, cmap='gray_r')\n",
" plt.show()\n",
" \n",
"# Display the first (index 0) training image\n",
"show_digit(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building the network\n",
"\n",
"TFLearn lets you build the network by defining the layers in that network. \n",
"\n",
"For this example, you'll define:\n",
"\n",
"1. The input layer, which tells the network the number of inputs it should expect for each piece of MNIST data. \n",
"2. Hidden layers, which recognize patterns in data and connect the input to the output layer, and\n",
"3. The output layer, which defines how the network learns and outputs a label for a given image.\n",
"\n",
"Let's start with the input layer; to define the input layer, you'll define the type of data that the network expects. For example,\n",
"\n",
"```\n",
"net = tflearn.input_data([None, 100])\n",
"```\n",
"\n",
"would create a network with 100 inputs. The number of inputs to your network needs to match the size of your data. For this example, we're using 784 element long vectors to encode our input data, so we need **784 input units**.\n",
"\n",
"\n",
"### Adding layers\n",
"\n",
"To add new hidden layers, you use \n",
"\n",
"```\n",
"net = tflearn.fully_connected(net, n_units, activation='ReLU')\n",
"```\n",
"\n",
"This adds a fully connected layer where every unit (or node) in the previous layer is connected to every unit in this layer. The first argument `net` is the network you created in the `tflearn.input_data` call, it designates the input to the hidden layer. You can set the number of units in the layer with `n_units`, and set the activation function with the `activation` keyword. You can keep adding layers to your network by repeated calling `tflearn.fully_connected(net, n_units)`. \n",
"\n",
"Then, to set how you train the network, use:\n",
"\n",
"```\n",
"net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')\n",
"```\n",
"\n",
"Again, this is passing in the network you've been building. The keywords: \n",
"\n",
"* `optimizer` sets the training method, here stochastic gradient descent\n",
"* `learning_rate` is the learning rate\n",
"* `loss` determines how the network error is calculated. In this example, with categorical cross-entropy.\n",
"\n",
"Finally, you put all this together to create the model with `tflearn.DNN(net)`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:** Below in the `build_model()` function, you'll put together the network using TFLearn. You get to choose how many layers to use, how many hidden units, etc.\n",
"\n",
"**Hint:** The final output layer must have 10 output nodes (one for each digit 0-9). It's also recommended to use a `softmax` activation layer as your final output layer. "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Define the neural network\n",
"def build_model():\n",
" # This resets all parameters and variables, leave this here\n",
" tf.reset_default_graph()\n",
" \n",
" #### Your code ####\n",
" # Include the input layer, hidden layer(s), and set how you want to train the model\n",
" \n",
" #Input layer\n",
" net = tflearn.input_data([None, 784])#len(trainX[0])])\n",
" \n",
" #Hidden layers\n",
" net = tflearn.fully_connected(net, 392, activation=\"ReLU\") #ReLU -> f(x)=max(x,0)\n",
" net = tflearn.fully_connected(net, 196, activation=\"ReLU\") #ReLU -> f(x)=max(x,0)\n",
" \n",
" #Output layer\n",
" net = tflearn.fully_connected(net, 10, activation=\"softmax\") #ReLU -> f(x)=max(x,0)\n",
" net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')\n",
" # This model assumes that your network is named \"net\" \n",
" model = tflearn.DNN(net)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Build the model\n",
"model = build_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the network\n",
"\n",
"Now that we've constructed the network, saved as the variable `model`, we can fit it to the data. Here we use the `model.fit` method. You pass in the training features `trainX` and the training targets `trainY`. Below I set `validation_set=0.1` which reserves 10% of the data set as the validation set. You can also set the batch size and number of epochs with the `batch_size` and `n_epoch` keywords, respectively. \n",
"\n",
"Too few epochs don't effectively train your network, and too many take a long time to execute. Choose wisely!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Step: 9899 | total loss: \u001b[1m\u001b[32m0.47010\u001b[0m\u001b[0m | time: 8.023s\n",
"| SGD | epoch: 020 | loss: 0.47010 - acc: 0.9651 -- iter: 49400/49500\n",
"Training Step: 9900 | total loss: \u001b[1m\u001b[32m0.42476\u001b[0m\u001b[0m | time: 9.056s\n",
"| SGD | epoch: 020 | loss: 0.42476 - acc: 0.9686 | val_loss: 0.07993 - val_acc: 0.9771 -- iter: 49500/49500\n",
"--\n"
]
}
],
"source": [
"# Training\n",
"model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=100, n_epoch=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing\n",
"After you're satisified with the training output and accuracy, you can then run the network on the **test data set** to measure it's performance! Remember, only do this after you've done the training and are satisfied with the results.\n",
"\n",
"A good result will be **higher than 95% accuracy**. Some simple models have been known to get up to 99.7% accuracy!"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 0.9791\n"
]
}
],
"source": [
"# Compare the labels that our model predicts with the actual labels\n",
"\n",
"# Find the indices of the most confident prediction for each item. That tells us the predicted digit for that sample.\n",
"predictions = np.array(model.predict(testX)).argmax(axis=1)\n",
"\n",
"# Calculate the accuracy, which is the percentage of times the predicated labels matched the actual labels\n",
"actual = testY.argmax(axis=1)\n",
"test_accuracy = np.mean(predictions == actual, axis=0)\n",
"\n",
"# Print out the result\n",
"print(\"Test accuracy: \", test_accuracy)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:tflearn]",
"language": "python",
"name": "conda-env-tflearn-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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
eneskemalergin/OldBlog | _oldnotebooks/Inferential_Statistics.ipynb | 1 | 145740 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inferential Statistics\n",
"\n",
"Let's say you have collected the height of 1,000 people living in Hong Kong. The mean of their height would be __descriptive statistics__, but their mean height does not indicate that it's the average height of whole of Hong Kong. Here, __inferential statistics__ will help us in determining what the average height of whole of Hong Kong would be, which is described in depth in this chapter.\n",
"\n",
"> Inferential statistics is all about describing the larger picture of the analysis with a limited set of data and deriving conclusions from it.\n",
"\n",
"### Distributions Types\n",
"\n",
"#### Normal Distribution\n",
"\n",
"- Most common distribution\n",
"- \"Gaussian curve\", \"bell curve\" other names.\n",
"- The numbers in the plot are the standard deviation numbers from the mean, which is zero.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__A normal distribution from a binomial distribution:__\n",
"\n",
"Let's take a coin and flip it. The probability of getting a head or a tail is 50%. If you take the same coin and flip it six times, the probability of getting a head three times can be computed using the following formula:\n",
"\n",
"$$\n",
"P(x) = \\frac{n!}{x!(n-x)!}p^{x}q^{n-x}\n",
"$$\n",
"\n",
" and x is the number of successes desired\n",
" \n",
"In the preceding formula, n is the number of times the coin is flipped, p is the probability of success, and q is (1– p), which is the probability of failure."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Calling the binom module from scipy stats package\n",
"from scipy.stats import binom \n",
"# Plotting Function\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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gUpgOzCkCyYNU7qIZAcwBiIiLgHGZOaWYPw14CphfrL8/cBbwlaoxvwrcExFnArcBTVQu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\nJEmlMYhIkqTSGEQkSVJp/j9ANUnaTVOelQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86ebb9cd50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = list(range(7))\n",
"n, p = 6, 0.5\n",
"rv = binom(n, p)\n",
"plt.vlines(x, 0, rv.pmf(x), colors='r', linestyles='-', lw=1, label='Probability')\n",
"plt.legend(loc='best', frameon=False)\n",
"plt.xlabel(\"No. of instances\")\n",
"plt.ylabel(\"Probability\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+9TTkrATe3Un5u4Hnej4cSZKk6ujpxOPvADMiYnfgN3nZQcCFZK92kCRJqqme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\nhWTIkSRJhfT/ATPZ1Oth0YP6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eba6b410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = range(1001)\n",
"n, p = 1000, 0.4\n",
"rv = binom(n, p)\n",
"plt.vlines(x,0,rv.pmf(x), colors='g', linestyles='-', lw=1, label='Probability')\n",
"plt.legend(loc='best', frameon=True)\n",
"plt.xlabel(\"No. of instances\")\n",
"plt.ylabel(\"Probability\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Poisson Distribution\n",
"\n",
"- Independent interval occurrences in an interval.\n",
"- Used for count-based distributions.\n",
"\n",
"$$\n",
"f(k;\\lambda)=Pr(X = k)=\\frac{\\lambda^ke^{-k}}{k!}\n",
"$$\n",
"\n",
"Here, ```e``` is the Euler's number, ```k``` is the number of occurrences for which the probability is going to be determined, and lambda is the mean number of occurrences.\n",
"\n",
"__Example:__\n",
"Let's understand this with an example. The number of cars that pass through a bridge in an hour is 20. What would be the probability of 23 cars passing through the bridge in an hour?\n",
"\n",
"```Python\n",
"from scipy.stats import poisson\n",
"rv = poisson(20)\n",
"rv.pmf(23)\n",
"# Result: 0.066881473662401172\n",
"```\n",
"\n",
"With the Poisson function, we define the mean value, which is 20 cars. The ```rv.pmf``` function gives the probability, which is around 6%, that 23 cars will pass the bridge."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bernoulli Distribution\n",
"- Can perform an experiment with two possible outcomes: success or failure.\n",
"\n",
"Success has a probability of p, and failure has a probability of 1 - p. A random variable that takes a 1 value in case of a success and 0 in case of failure is called a Bernoulli distribution. The probability distribution function can be written as:\n",
"\n",
"$$\n",
" P(n)=\\begin{cases}1-p & for & n = 0\\\\p & for & n = 1\\end{cases} \n",
"$$\n",
"\n",
"It can also be written like this:\n",
"\n",
"$$\n",
"P(n)=p^n(1-p)^{1-n}\n",
"$$\n",
"\n",
"The distribution function can be written like this:\n",
"\n",
"$$\n",
"D(n) = \\begin{cases}1-p & for & n=0\\\\1 & for & n=1\\end{cases}\n",
"$$\n",
"\n",
"__Example:__ Voting in an election is a good example of the Bernoulli distribution. A Bernoulli distribution can be generated using the ```bernoulli.rvs()``` function of the SciPy package."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
" 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
" 1, 1, 0, 1, 1, 0, 0, 1])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.stats import bernoulli\n",
"bernoulli.rvs(0.7, size=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### z-score\n",
"\n",
"- Expresses the value of a distribution in std with respect to mean.\n",
"\n",
"$$\n",
"z = \\frac{X - \\mu}{\\sigma}\n",
"$$\n",
"\n",
"Here, X is the value in the distribution, __μ is the mean of the distribution__, and __σ is the\n",
"standard deviation of the distribution__.\n",
"\n",
"__Example:__ A classroom has 60 students in it and they have just got their mathematics examination score. We simulate the score of these 60 students with a normal distribution using the following command:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"class_score = np.random.normal(50, 10, 60).round()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JkooZJCRJUjGDhCRJKmaQkCRJxQwSkiSpmEFCkiQVM0hIkqRiBglJklTMICFJkooZJCRJ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\nM0hIkqRiBglJklTMICFJkor9f/BwSaN8wGRBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86ec49acd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(class_score, 30, normed=True) # Number of breaks is 30\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The score of each student can be converted to a z-score using the following functions:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.59403678, -0.31404492, 1.16158785, 1.61562871, -0.42755514,\n",
" 1.04807764, 0.707547 , 1.04807764, 1.38860828, -0.20053471,\n",
" -1.56265727, -1.22212663, -0.31404492, -0.88159599, 2.06966956,\n",
" -0.76808578, 0.36701636, 1.16158785, 0.13999593, -0.54106535,\n",
" -1.90318791, 0.48052657, 0.82105721, -2.01669813, -0.42755514,\n",
" 0.48052657, 1.84264913, -0.65457556, -0.65457556, -0.76808578,\n",
" 1.27509807, -0.31404492, -0.54106535, -0.42755514, -0.65457556,\n",
" -0.42755514, -0.88159599, 0.25350614, 0.02648572, -2.01669813,\n",
" 0.13999593, 1.72913892, 1.16158785, 1.61562871, -0.76808578,\n",
" -0.65457556, -1.44914706, 0.48052657, -0.54106535, -0.65457556,\n",
" 0.13999593, -0.42755514, 1.72913892, -0.0870245 , -0.0870245 ,\n",
" 0.82105721, 0.59403678, -0.20053471, -0.76808578, -1.33563685])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy import stats\n",
"stats.zscore(class_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, a student with a score of 60 out of 100 has a z-score of 1.334. To make more sense of the z-score, we'll use the standard normal table.\n",
"\n",
"This table helps in determining the probability of a score.\n",
"\n",
"We would like to know what the probability of getting a score above 60 would be.\n",
"\n",
"The standard normal table can help us in determining the probability of the occurrence of the score, but we do not have to perform the cumbersome task of finding the value by looking through the table and finding the probability. This task is made simple by the cdf function, which is the cumulative distribution function:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.091101928265359899"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob = 1 - stats.norm.cdf(1.334)\n",
"prob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cdf function gives the probability of getting values up to the z-score of 1.334, and doing a minus one of it will give us the probability of getting a z-score, which is above it. In other words, 0.09 is the probability of getting marks above 60. \n",
"\n",
"__Let's ask another question, \"how many students made it to the top 20% of the class?\"__\n",
"\n",
"Now, to get the z-score at which the top 20% score marks, we can use the ppf function in SciPy:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.8416212335729143"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.norm.ppf(0.80)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The z-score for the preceding output that determines whether the top 20% marks are at 0.84 is as follows:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"58.166881798674652"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(0.84 * class_score.std()) + class_score.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We multiply the z-score with the standard deviation and then add the result with the mean of the distribution. This helps in converting the z-score to a value in the distribution. The 55.83 marks means that students who have marks more than this are in the top 20% of the distribution.\n",
"\n",
"The z-score is an essential concept in statistics, which is widely used. Now you can understand that it is basically used in standardizing any distribution so that it can be compared or inferences can be derived from it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ### p-value\n",
"A p-value is the probability of rejecting a null-hypothesis when the hypothesis is proven true.\n",
"\n",
"If the p-value is equal to or less than the significance level (α), then the null hypothesis is inconsistent and it needs to be rejected.\n",
"\n",
"Let's understand this concept with an example where the null hypothesis is that it is common for students to score 68 marks in mathematics.\n",
"\n",
"Let's define the significance level at 5%. If the p-value is less than 5%, then the null hypothesis is rejected and it is not common to score 68 marks in mathematics.\n",
"\n",
"Let's get the z-score of 68 marks:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.9561593469610463"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zscore = ( 68 - class_score.mean() ) / class_score.std()\n",
"zscore"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.025223192906655423"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob = 1 - stats.norm.cdf(zscore)\n",
"prob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One-tailed and two-tailed tests\n",
"The example in the previous section was an instance of a one-tailed test where the null hypothesis is rejected or accepted based on one direction of the normal distribution.\n",
"\n",
"In a two-tailed test, both the tails of the null hypothesis are used to test the hypothesis.\n",
"\n",
"\n",
"\n",
"\n",
"In a two-tailed test, when a significance level of 5% is used, then it is distributed equally in the both directions, that is, 2.5% of it in one direction and 2.5% in the other direction.\n",
"\n",
"Let's understand this with an example. The mean score of the mathematics exam at a national level is 60 marks and the standard deviation is 3 marks.\n",
"\n",
"The mean marks of a class are 53. The null hypothesis is that the mean marks of the class are similar to the national average. Let's test this hypothesis by first getting the z-score 60:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zscore = (53-50)/3.0\n",
"zscore"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.84134474606854293"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob = stats.norm.cdf(zscore)\n",
"prob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Type 1 and Type 2 errors\n",
"__Type 1 error__ is a type of error that occurs when there is a rejection of the null hypothesis when it is actually true. This kind of error is also called an error of the first kind and is equivalent to false positives.\n",
"\n",
"\n",
"\n",
"Let's understand this concept using an example. There is a new drug that is being developed and it needs to be tested on whether it is effective in combating diseases. The null hypothesis is that it is not effective in combating diseases.\n",
"\n",
"The significance level is kept at 5% so that the null hypothesis can be accepted confidently 95% of the time. However, 5% of the time, we'll accept the rejecttion of the hypothesis although it had to be accepted, which means that even though the drug is ineffective, it is assumed to be effective.\n",
"\n",
"- The Type 1 error is controlled by controlling the significance level, which is alpha. Alpha is the highest probability to have a Type 1 error. The lower the alpha, the lower will be the Type 1 error.\n",
"- The Type 2 error is the kind of error that occurs when we do not reject a null hypothesis that is false. This error is also called the error of the second kind and is equivalent to a false negative.\n",
"\n",
"This kind of error occurs in a drug scenario when the drug is assumed to be ineffective but is actually it is effective.\n",
"\n",
"These errors can be controlled one at a time. If one of the errors is lowered, then the other one increases. It depends on the use case and the problem statement that the analysis is trying to address, and depending on it, the appropriate error should reduce. In the case of this drug scenario, typically, a Type 1 error should be lowered because it is better to ship a drug that is confidently effective.\n",
"\n",
"### Confidence Interval\n",
"A confidence interval is a type of interval statistics for a population parameter. The confidence interval helps in determining the interval at which the population mean can be defined.\n",
"\n",
"\n",
"\n",
"Let's try to understand this concept by using an example. Let's take the height of every man in Kenya and determine with 95% confidence interval the average of height of Kenyan men at a national level.\n",
"\n",
"Let's take 50 men and their height in centimeters:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"height_data = np.array([ 186.0, 180.0, 195.0, 189.0, 191.0,\n",
" 177.0, 161.0, 177.0, 192.0, 182.0,\n",
" 185.0, 192.0, 173.0, 172.0, 191.0, \n",
" 184.0, 193.0, 182.0, 190.0, 185.0, \n",
" 181.0,188.0, 179.0, 188.0, 170.0, 179.0, \n",
" 180.0, 189.0, 188.0, 185.0, 170.0, \n",
" 197.0, 187.0,182.0, 173.0, 179.0,184.0, \n",
" 177.0, 190.0, 174.0, 203.0, 206.0, 173.0, \n",
" 169.0, 178.0,201.0, 198.0, 166.0,171.0, 180.0])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GYCFJkjpjsJAkSZ0xWEiSpM4YLCRJUmcMFpIkqTMGC0mS1BmDhSRJ6ozBQpIk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\nkjpjsJAkSZ0xWEiSpM4YLCRJUmf+f0H+fHMRuK5CAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eb872cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(height_data, 30, normed=True, color='r')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"183.24000000000001"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The mean of the distribution\n",
"height_data.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, the average height of a man from the sample is 183.4 cm.\n",
"\n",
"To determine the confidence interval, we'll now define the standard error of the mean.\n",
"\n",
"The standard error of the mean is the deviation of the sample mean from the population mean. It is defined using the following formula:\n",
"\n",
"$$\n",
"SE_{\\overline{x}} = \\frac{s}{\\sqrt{n}}\n",
"$$\n",
"\n",
"Here, s is the standard deviation of the sample, and n is the number of elements of the sample.\n",
"\n",
"This can be calculated using the ```sem()``` function of the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3787187190005248"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.sem(height_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, there is a standard error of the mean of 1.38 cm. The lower and upper limit of the confidence interval can be determined by using the following formula:\n",
"\n",
" Upper/Lower limit = mean(height) + / - sigma * SEmean(x)\n",
"\n",
"For lower limit:\n",
"\n",
" 183.24 + (1.96 * 1.38) = 185.94\n",
"\n",
"For upper limit:\n",
"\n",
" 183.24 - (1.96*1.38) = 180.53\n",
"\n",
"\n",
"A 1.96 standard deviation covers 95% of area in the normal distribution.\n",
" \n",
"We can confidently say that the population mean lies between 180.53 cm and 185.94 cm of height.\n",
"\n",
"__New Example:__ Let's assume we take a sample of 50 people, record their height, and then repeat this process 30 times. We can then plot the averages of each sample and observe the distribution."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eb1ca6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"average_height = []\n",
"for i in range(30):\n",
" # Create a sample of 50 with mean 183 and standard deviation 10\n",
" sample50 = np.random.normal(183, 10, 50).round()\n",
" # Add the mean on sample of 50 into average_height list\n",
" average_height.append(sample50.mean())\n",
"\n",
"# Plot it with 10 bars and normalization\n",
"plt.hist(average_height, 10, normed=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can observe that the mean ranges from 180 to 187 cm when we simulated the average height of 50 sample men, which was taken 30 times.\n",
"\n",
"Let's see what happens when we sample 1000 men and repeat the process 30 times:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAFkCAYAAAB4sKK5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfeBqYB/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"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eb72e5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"average_height = []\n",
"for i in range(30):\n",
" # Create a sample of 50 with mean 183 and standard deviation 10\n",
" sample1000 = np.random.normal(183, 10, 1000).round()\n",
" average_height.append(sample1000.mean())\n",
"\n",
"plt.hist(average_height, 10, normed=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" As you can see, the height varies from 182.4 cm and to 183.5 cm. What does this mean?\n",
" \n",
"It means that as the sample size increases, the standard error of the mean decreases, which also means that the confidence interval becomes narrower, and we can tell with certainty the interval that the population mean would lie on.\n",
"\n",
"### Correlation\n",
"\n",
"In statistics, correlation defines the similarity between two random variables. The most commonly used correlation is the Pearson correlation and it is defined by the following:\n",
"\n",
"$$\n",
"\\rho_{X,Y} = \\frac{cov(X,Y)}{\\sigma_{x}\\sigma_{y}} = \\frac{E[(X - \\mu_{X})(Y - \\mu_{Y})]}{\\sigma_{x}\\sigma_{y}}\n",
"$$\n",
"\n",
"The preceding formula defines the Pearson correlation as the covariance between X and Y, which is divided by the standard deviation of X and Y, or it can also be defined as the expected mean of the sum of multiplied difference of random variables with respect to the mean divided by the standard deviation of X and Y. Let's understand this with an example. Let's take the mileage and horsepower of various cars and see if there is a relation between the two. This can be achieved using the pearsonr function in the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mpg = [21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,\n",
" 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5,\n",
" 15.2, 13.3, 19.2, 27.3, 26.0, 30.4, 15.8,19.7, 15.0, 21.4]\n",
"\n",
"hp = [110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180,\n",
" 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264,\n",
" 175, 335, 109]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.77616837182658638, 1.7878352541210664e-07)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.pearsonr(mpg,hp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The first value of the output gives the correlation between the horsepower and the mileage \n",
"- The second value gives the p-value.\n",
"\n",
"So, the first value tells us that it is highly negatively correlated and the p-value tells us that there is significant correlation between them:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JklSYwUGSJBVmcJAkSYUZHCRJUmEGB0mSVJjBoQtVKpV2d6EjOA4Zx+EwxyLj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\nIWsLeuOVkAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eba964d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(mpg, hp, color='r')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look into another correlation called the Spearman correlation. The Spearman correlation applies to the rank order of the values and so it provides a monotonic relation between the two distributions. It is useful for ordinal data (data that has an order, such as movie ratings or grades in class) and is not affected by outliers.\n",
"\n",
"Let's get the Spearman correlation between the miles per gallon and horsepower. This can be achieved using the ```spearmanr()``` function in the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SpearmanrResult(correlation=-0.89466464574996263, pvalue=5.0859694309244122e-12)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.spearmanr(mpg, hp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the Spearman correlation is -0.89 and the p-value is significant.\n",
"\n",
"Let's do an experiment in which we introduce a few outlier values in the data and see how the Pearson and Spearman correlation gets affected:"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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z/vf/Kvfd95VJ7+NuopI0WalUolQqTSo7ePDgsX3TlFLhB9mvjO+uOD47L/tX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f86eb616490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mpg = [21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8,\n",
" 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4,\n",
" 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26.0, 30.4, 15.8,\n",
" 19.7, 15.0, 21.4, 120, 3]\n",
"hp = [110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180,\n",
" 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245,\n",
" 175, 66, 91, 113, 264, 175, 335, 109, 30, 600]\n",
"\n",
"plt.scatter(mpg, hp)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the plot, you can clearly make out the outlier values. Lets see how the correlations get affected for both the Pearson and Spearman correlation\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.47415304891435484, 0.0046122167947348462)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.pearsonr(mpg, hp)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SpearmanrResult(correlation=-0.91222184337265688, pvalue=6.055168165798113e-14)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.spearmanr(mpg, hp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can clearly see that the Pearson correlation has been drastically affected due to the outliers, which are from a correlation of 0.89 to 0.47.\n",
"\n",
"The Spearman correlation did not get affected much as it is based on the order rather than the actual value in the data.\n",
"\n",
"### Z-test vs T-test\n",
"\n",
"We have already done a few Z-tests before where we validated our null hypothesis.\n",
"\n",
"\n",
"\n",
"A T-distribution is similar to a Z-distribution—it is centered at zero and has a basic bell shape, but its shorter and flatter around the center than the Z-distribution.\n",
"\n",
"The T-distributions' standard deviation is usually proportionally larger than the Z, because of which you see the fatter tails on each side.\n",
"\n",
"The t distribution is usually used to analyze the population when the sample is small.\n",
"\n",
"The Z-test is used to compare the population mean against a sample or compare the population mean of two distributions with a sample size greater than 30. An example of a Z-test would be comparing the heights of men from different ethnicity groups.\n",
"\n",
"The T-test is used to compare the population mean against a sample, or compare the population mean of two distributions with a sample size less than 30, and when you don't know the population's standard deviation.\n",
"\n",
"Let's do a T-test on two classes that are given a mathematics test and have 10 students in each class:\n",
"\n",
"\n",
"To perform the T-test, we can use the ```ttest_ind()``` function in the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=-5.4581950568484077, pvalue=3.4820722850153163e-05)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class1_score = np.array([45.0, 40.0, 49.0, 52.0, 54.0, 64.0, 36.0, 41.0, 42.0, 34.0])\n",
"class2_score = np.array([75.0, 85.0, 53.0, 70.0, 72.0, 93.0, 61.0, 65.0, 65.0, 72.0])\n",
"stats.ttest_ind(class1_score,class2_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first value in the output is the calculated t-statistics, whereas the second value is the p-value and p-value shows that the two distributions are not identical.\n",
"\n",
"### The F distribution\n",
"\n",
"The F distribution is also known as Snedecor's F distribution or the Fisher–Snedecor distribution.\n",
"\n",
"An f statistic is given by the following formula:\n",
"\n",
"$$\n",
"f = {[{s_1^2}/{\\sigma_1^2}]}{[{s_2^2}/{\\sigma_2^2}]}\n",
"$$\n",
"\n",
"Here, s 1 is the standard deviation of a sample 1 with an $n_1$ size, $s_2$ is the standard deviation of a sample 2, where the size $n_2σ_1$ is the population standard deviation of a sample $1σ_2$ is the population standard deviation of a sample 12.\n",
"\n",
"The distribution of all the possible values of f statistics is called F distribution. The d1 and d2 represent the degrees of freedom in the following chart:\n",
"\n",
"\n",
"\n",
"### The chi-square distribution\n",
"The chi-square statistics are defined by the following formula:\n",
"\n",
"$$\n",
"X^2 = [(n-1)*s^2]/\\sigma^2\n",
"$$\n",
"\n",
"Here, n is the size of the sample, s is the standard deviation of the sample, and σ is the standard deviation of the population.\n",
"\n",
"If we repeatedly take samples and define the chi-square statistics, then we can form a chi-square distribution, which is defined by the following probability density function:\n",
"\n",
"$$\n",
"Y = Y_0 * (X^2)^{(v/2-1)} * e^{-X2/2}\n",
"$$\n",
"\n",
"Here, $Y_0$ is a constant that depends on the number of degrees of freedom, $Χ_2$ is the chi-square statistic, $v = n - 1$ is the number of degrees of freedom, and e is a constant equal to the base of the natural logarithm system.\n",
"\n",
"$Y_0$ is defined so that the area under the chi-square curve is equal to one.\n",
"\n",
"\n",
"\n",
"The Chi-square test can be used to test whether the observed data differs significantly from the expected data. Let's take the example of a dice. The dice is rolled 36 times and the probability that each face should turn upwards is 1/6. So, the expected and observed distribution is as follows:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"expected = np.array([6,6,6,6,6,6])\n",
"observed = np.array([7, 5, 3, 9, 6, 6])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The null hypothesis in the chi-square test is that the observed value is similar to the\n",
"expected value.\n",
"\n",
"The chi-square can be performed using the ```chisquare``` function in the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Power_divergenceResult(statistic=3.333333333333333, pvalue=0.64874235866759344)\n"
]
}
],
"source": [
"stats.chisquare(observed,expected)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first value is the chi-square value and the second value is the p-value, which is very high. This means that the null hypothesis is valid and the observed value is similar to the expected value.\n",
"\n",
"---\n",
"\n",
"The chi-square test of independence is a statistical test used to determine whether two categorical variables are independent of each other or not.\n",
"\n",
"Let's take the following example to see whether there is a preference for a book based on the gender of people reading it.\n",
"\n",
"The Chi-Square test of independence can be performed using the ```chi2_contingency``` function in the SciPy package:"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(28.362103174603167,\n",
" 6.9382117170577439e-07,\n",
" 2,\n",
" array([[ 136.95652174, 97.39130435, 45.65217391],\n",
" [ 313.04347826, 222.60869565, 104.34782609]]))"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"men_women = np.array([[100, 120, 60],[350, 200, 90]])\n",
"stats.chi2_contingency(men_women)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The first value is the chi-square value,\n",
"- The second value is the p-value, which is very small, and means that there is an\n",
"association between the gender of people and the genre of the book they read.\n",
"- The third value is the degrees of freedom. \n",
"- The fourth value, which is an array, is the expected frequencies.\n",
"\n",
"### Anova\n",
"Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means.This test basically compares the means between groups and determines whether any of these means are significantly different from each other:\n",
"\n",
"$$\n",
"H_0 : \\mu_1 = \\mu_2 = \\mu_3 = ... = \\mu_k\n",
"$$\n",
"\n",
"ANOVA is a test that can tell you which group is significantly different from each other. Let's take the height of men who are from three different countries and see if their heights are significantly different from others:"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"F_onewayResult(statistic=2.9852039682643414, pvalue=0.05307967881268609)"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"country1 = np.array([ 176., 201., 172., 179., 180., 188., 187., 184., 171.,\n",
" 181., 192., 187., 178., 178., 180., 199., 185., 176.,\n",
" 207., 177., 160., 174., 176., 192., 189., 187., 183.,\n",
" 180., 181., 200., 190., 187., 175., 179., 181., 183.,\n",
" 171., 181., 190., 186., 185., 188., 201., 192., 188.,\n",
" 181., 172., 191., 201., 170., 170., 192., 185., 167.,\n",
" 178., 179., 167., 183., 200., 185.])\n",
"country2 = np.array([177., 165., 185., 187., 175., 172.,179., 192.,169.,\n",
" 167., 162., 165., 188., 194., 187., 175., 163., 178.,\n",
" 197., 172., 175., 185., 176., 171., 172., 186., 168.,\n",
" 178., 191., 192., 175., 189., 178., 181., 170., 182.,\n",
" 166., 189., 196., 192., 189., 171., 185., 198., 181.,\n",
" 167., 184., 179., 178., 193., 179., 177., 181., 174.,\n",
" 171., 184., 156., 180., 181., 187.])\n",
"country3 = np.array([ 191.,173., 175., 200., 190.,191.,185.,190.,184.,190.,\n",
" 191., 184., 167., 194., 195., 174., 171., 191.,\n",
" 174., 177., 182., 184., 176., 180., 181., 186., 179.,\n",
" 176., 186., 176., 184., 194., 179., 171., 174., 174.,\n",
" 182., 198., 180., 178., 200., 200., 174., 202., 176.,\n",
" 180., 163., 159., 194., 192., 163., 194., 183., 190.,\n",
" 186., 178., 182., 174., 178., 182.])\n",
"stats.f_oneway(country1,country2,country3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first value of the output gives the F-value and the second value gives the p-value. Since the p-value is greater than 5% by a small margin, we can tell that the mean of the heights in the three countries is not significantly different from each other.\n",
"\n",
"## Summary\n",
"\n",
"In this tutorial we have seen various probability distributions. We also covered how to use z-score, p-value, Type 1, and Type 2 errors. We gained an insight into the Z-test and T-test followed by the chi-square distribution and saw how it can be used to test a hypothesis."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
exowanderer/SpitzerDeepLearningNetwork | Notebooks/tensorflow_DNNRegressor_Spitzer - ELU.ipynb | 1 | 2718380 | null | mit |
macks22/gensim | docs/notebooks/FastText_Tutorial.ipynb | 4 | 24742 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using FastText via Gensim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial is about using the Gensim wrapper for the [FastText](https://github.com/facebookresearch/fastText) library for training FastText models, loading them and performing similarity operations and vector lookups analogous to Word2Vec."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## When to use FastText?\n",
"The main principle behind FastText is that the morphological structure of a word carries important information about the meaning of the word, which is not taken into account by traditional word embeddings, which train a unique word embedding for every individual word. This is especially significant for morphologically rich languages (German, Turkish) in which a single word can have a large number of morphological forms, each of which might occur rarely, thus making it hard to train good word embeddings. \n",
"FastText attempts to solve this by treating each word as the aggregation of its subwords. For the sake of simplicity and language-independence, subwords are taken to the character ngrams of the word. The vector for a word is simply taken to be the sum of all vectors of its component char-ngrams. \n",
"According to a detailed comparison of Word2Vec and FastText in [this notebook](Word2Vec_FastText_Comparison.ipynb), FastText does significantly better on syntactic tasks as compared to the original Word2Vec, especially when the size of the training corpus is small. Word2Vec slightly outperforms FastText on semantic tasks though. The differences grow smaller as the size of training corpus increases. \n",
"Training time for FastText is significantly higher than the Gensim version of Word2Vec (`15min 42s` vs `6min 42s` on text8, 17 mil tokens, 5 epochs, and a vector size of 100). \n",
"FastText can be used to obtain vectors for out-of-vocabulary (oov) words, by summing up vectors for its component char-ngrams, provided at least one of the char-ngrams was present in the training data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the following examples, we'll use the Lee Corpus (which you already have if you've installed gensim)\n",
"\n",
"You need to have FastText setup locally to be able to train models. See [installation instructions for FastText](https://github.com/facebookresearch/fastText/#requirements) if you don't have FastText installed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastText(vocab=1762, size=100, alpha=0.025)\n"
]
}
],
"source": [
"import gensim, os\n",
"from gensim.models.wrappers.fasttext import FastText\n",
"\n",
"# Set FastText home to the path to the FastText executable\n",
"ft_home = '/home/jayant/Projects/fastText/fasttext'\n",
"\n",
"# Set file names for train and test data\n",
"data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data']) + os.sep\n",
"lee_train_file = data_dir + 'lee_background.cor'\n",
"\n",
"model = FastText.train(ft_home, lee_train_file)\n",
"\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hyperparameters for training the model follow the same pattern as Word2Vec. FastText supports the folllowing parameters from the original word2vec - \n",
" - model: Training architecture. Allowed values: `cbow`, `skipgram` (Default `cbow`)\n",
" - size: Size of embeddings to be learnt (Default 100)\n",
" - alpha: Initial learning rate (Default 0.025)\n",
" - window: Context window size (Default 5)\n",
" - min_count: Ignore words with number of occurrences below this (Default 5)\n",
" - loss: Training objective. Allowed values: `ns`, `hs`, `softmax` (Default `ns`)\n",
" - sample: Threshold for downsampling higher-frequency words (Default 0.001)\n",
" - negative: Number of negative words to sample, for `ns` (Default 5)\n",
" - iter: Number of epochs (Default 5)\n",
" - sorted_vocab: Sort vocab by descending frequency (Default 1)\n",
" - threads: Number of threads to use (Default 12)\n",
" \n",
"In addition, FastText has two additional parameters - \n",
" - min_n: min length of char ngrams to be used (Default 3)\n",
" - max_n: max length of char ngrams to be used for (Default 6)\n",
"These control the lengths of character ngrams that each word is broken down into while training and looking up embeddings. If `max_n` is set to 0, or to be lesser than `min_n`, no character ngrams are used, and the model effectively reduces to Word2Vec."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastText(vocab=816, size=50, alpha=0.025)\n"
]
}
],
"source": [
"model = FastText.train(ft_home, lee_train_file, size=50, alpha=0.05, min_count=10)\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Continuation of training with FastText models is not supported."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Saving/loading models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Models can be saved and loaded via the `load` and `save` methods."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastText(vocab=816, size=50, alpha=0.025)\n"
]
}
],
"source": [
"model.save('saved_fasttext_model')\n",
"loaded_model = FastText.load('saved_fasttext_model')\n",
"print(loaded_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `save_word2vec_method` causes the vectors for ngrams to be lost. As a result, a model loaded in this way will behave as a regular word2vec model. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Word vector lookup\n",
"FastText models support vector lookups for out-of-vocabulary words by summing up character ngrams belonging to the word."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n",
"[-0.47196999 -0.17528 0.19518 -0.31948 0.42835999 0.083281\n",
" -0.15183 0.43415001 0.41251001 -0.10186 -0.54948997 0.12667\n",
" 0.14816 -0.065804 -0.21105 -0.42304999 0.011494 0.53068\n",
" -0.57410997 -0.53930998 -0.33537999 0.16154 0.12377 -0.23537\n",
" -0.14629 -0.34777001 0.27304 0.20597 0.12581 0.36671999\n",
" 0.32075 0.27351999 -0.13311 -0.04975 -0.52293003 -0.2766\n",
" 0.11863 -0.009231 -0.66074997 0.018031 0.57145 0.35547\n",
" 0.21588001 0.14431 -0.31865999 0.32027 0.55005002 0.19374999\n",
" 0.36609 -0.54184002]\n",
"[-0.4256132 -0.11521876 0.20166218 -0.34812452 0.30932881 0.02802653\n",
" -0.18951961 0.4175721 0.41008326 -0.09026544 -0.50756483 0.07746826\n",
" 0.09458492 0.01440104 -0.17157355 -0.35189211 0.00103696 0.50923289\n",
" -0.49944138 -0.38334864 -0.34287725 0.18023167 0.18014225 -0.22820314\n",
" -0.08267317 -0.31241801 0.26023088 0.20673522 0.07008089 0.31678561\n",
" 0.31590793 0.16198126 -0.09287339 -0.1722331 -0.43232849 -0.26644917\n",
" 0.10019614 0.08444232 -0.57080398 0.07581607 0.50339428 0.28109486\n",
" 0.05507131 0.10023506 -0.17840675 0.18620458 0.42583067 0.00790601\n",
" 0.2036875 -0.4925791 ]\n"
]
}
],
"source": [
"print('night' in model.wv.vocab)\n",
"print('nights' in model.wv.vocab)\n",
"print(model['night'])\n",
"print(model['nights'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The word vector lookup operation only works if atleast one of the component character ngrams is present in the training corpus. For example -"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'all ngrams for word axe absent from model'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-108339380400>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Raises a KeyError since none of the character ngrams of the word `axe` are present in the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'axe'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/word2vec.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 1304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/keyedvectors.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;31m# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/jayant/Projects/gensim/gensim/models/wrappers/fasttext.pyc\u001b[0m in \u001b[0;36mword_vec\u001b[0;34m(self, word, use_norm)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mword_vec\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mngrams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# No ngrams of the word are present in self.ngrams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'all ngrams for word %s absent from model'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minit_sims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'all ngrams for word axe absent from model'"
]
}
],
"source": [
"# Raises a KeyError since none of the character ngrams of the word `axe` are present in the training data\n",
"model['axe']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `in` operation works slightly differently from the original word2vec. It tests whether a vector for the given word exists or not, not whether the word is present in the word vocabulary. To test whether a word is present in the training word vocabulary -"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n"
]
}
],
"source": [
"# Tests if word present in vocab\n",
"print(\"word\" in model.wv.vocab)\n",
"# Tests if vector present for word\n",
"print(\"word\" in model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Similarity operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarity operations work the same way as word2vec. Out-of-vocabulary words can also be used, provided they have atleast one character ngram present in the training data."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n"
]
},
{
"data": {
"text/plain": [
"0.97944545147919504"
]
},
"execution_count": 7,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"print(\"nights\" in model.wv.vocab)\n",
"print(\"night\" in model.wv.vocab)\n",
"model.similarity(\"night\", \"nights\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Syntactically similar words generally have high similarity in FastText models, since a large number of the component char-ngrams will be the same. As a result, FastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided [here](Word2Vec_FastText_Comparison.ipynb).\n",
"\n",
"Other similarity operations -"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'12', 0.9912641048431396),\n",
" (u'across', 0.990070641040802),\n",
" (u'few', 0.9840448498725891),\n",
" (u'deaths', 0.9840392470359802),\n",
" (u'parts', 0.9835165739059448),\n",
" (u'One', 0.9833074808120728),\n",
" (u'running', 0.9832631349563599),\n",
" (u'2', 0.982011079788208),\n",
" (u'victory', 0.9806963801383972),\n",
" (u'each', 0.9789758920669556)]"
]
},
"execution_count": 8,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"# The example training corpus is a toy corpus, results are not expected to be good, for proof-of-concept only\n",
"model.most_similar(\"nights\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.97543218704680112"
]
},
"execution_count": 9,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"model.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'lunch'"
]
},
"execution_count": 10,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"model.doesnt_match(\"breakfast cereal dinner lunch\".split())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'against', 0.94775390625),\n",
" (u'after', 0.923099935054779),\n",
" (u'West', 0.910752534866333),\n",
" (u'again', 0.903070867061615),\n",
" (u'arrest', 0.8878517150878906),\n",
" (u'suicide', 0.8750319480895996),\n",
" (u'After', 0.8682445287704468),\n",
" (u'innings', 0.859328031539917),\n",
" (u'Test', 0.8542338609695435),\n",
" (u'during', 0.852535605430603)]"
]
},
"execution_count": 11,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"model.most_similar(positive=['baghdad', 'england'], negative=['london'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'correct': [], 'incorrect': [], 'section': u'capital-common-countries'},\n",
" {'correct': [], 'incorrect': [], 'section': u'capital-world'},\n",
" {'correct': [], 'incorrect': [], 'section': u'currency'},\n",
" {'correct': [], 'incorrect': [], 'section': u'city-in-state'},\n",
" {'correct': [],\n",
" 'incorrect': [(u'HE', u'SHE', u'HIS', u'HER'),\n",
" (u'HIS', u'HER', u'HE', u'SHE')],\n",
" 'section': u'family'},\n",
" {'correct': [], 'incorrect': [], 'section': u'gram1-adjective-to-adverb'},\n",
" {'correct': [], 'incorrect': [], 'section': u'gram2-opposite'},\n",
" {'correct': [], 'incorrect': [], 'section': u'gram3-comparative'},\n",
" {'correct': [(u'BIG', u'BIGGEST', u'GOOD', u'BEST')],\n",
" 'incorrect': [(u'GOOD', u'BEST', u'BIG', u'BIGGEST')],\n",
" 'section': u'gram4-superlative'},\n",
" {'correct': [(u'GO', u'GOING', u'SAY', u'SAYING'),\n",
" (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n",
" (u'RUN', u'RUNNING', u'SAY', u'SAYING'),\n",
" (u'SAY', u'SAYING', u'LOOK', u'LOOKING')],\n",
" 'incorrect': [(u'GO', u'GOING', u'LOOK', u'LOOKING'),\n",
" (u'GO', u'GOING', u'RUN', u'RUNNING'),\n",
" (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n",
" (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n",
" (u'RUN', u'RUNNING', u'GO', u'GOING'),\n",
" (u'RUN', u'RUNNING', u'LOOK', u'LOOKING'),\n",
" (u'SAY', u'SAYING', u'GO', u'GOING'),\n",
" (u'SAY', u'SAYING', u'RUN', u'RUNNING')],\n",
" 'section': u'gram5-present-participle'},\n",
" {'correct': [(u'AUSTRALIA', u'AUSTRALIAN', u'ISRAEL', u'ISRAELI'),\n",
" (u'INDIA', u'INDIAN', u'ISRAEL', u'ISRAELI'),\n",
" (u'INDIA', u'INDIAN', u'AUSTRALIA', u'AUSTRALIAN')],\n",
" 'incorrect': [(u'AUSTRALIA', u'AUSTRALIAN', u'INDIA', u'INDIAN'),\n",
" (u'ISRAEL', u'ISRAELI', u'AUSTRALIA', u'AUSTRALIAN'),\n",
" (u'ISRAEL', u'ISRAELI', u'INDIA', u'INDIAN')],\n",
" 'section': u'gram6-nationality-adjective'},\n",
" {'correct': [],\n",
" 'incorrect': [(u'GOING', u'WENT', u'SAYING', u'SAID'),\n",
" (u'GOING', u'WENT', u'TAKING', u'TOOK'),\n",
" (u'SAYING', u'SAID', u'TAKING', u'TOOK'),\n",
" (u'SAYING', u'SAID', u'GOING', u'WENT'),\n",
" (u'TAKING', u'TOOK', u'GOING', u'WENT'),\n",
" (u'TAKING', u'TOOK', u'SAYING', u'SAID')],\n",
" 'section': u'gram7-past-tense'},\n",
" {'correct': [],\n",
" 'incorrect': [(u'BUILDING', u'BUILDINGS', u'CHILD', u'CHILDREN'),\n",
" (u'BUILDING', u'BUILDINGS', u'MAN', u'MEN'),\n",
" (u'CHILD', u'CHILDREN', u'MAN', u'MEN'),\n",
" (u'CHILD', u'CHILDREN', u'BUILDING', u'BUILDINGS'),\n",
" (u'MAN', u'MEN', u'BUILDING', u'BUILDINGS'),\n",
" (u'MAN', u'MEN', u'CHILD', u'CHILDREN')],\n",
" 'section': u'gram8-plural'},\n",
" {'correct': [], 'incorrect': [], 'section': u'gram9-plural-verbs'},\n",
" {'correct': [(u'BIG', u'BIGGEST', u'GOOD', u'BEST'),\n",
" (u'GO', u'GOING', u'SAY', u'SAYING'),\n",
" (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n",
" (u'RUN', u'RUNNING', u'SAY', u'SAYING'),\n",
" (u'SAY', u'SAYING', u'LOOK', u'LOOKING'),\n",
" (u'AUSTRALIA', u'AUSTRALIAN', u'ISRAEL', u'ISRAELI'),\n",
" (u'INDIA', u'INDIAN', u'ISRAEL', u'ISRAELI'),\n",
" (u'INDIA', u'INDIAN', u'AUSTRALIA', u'AUSTRALIAN')],\n",
" 'incorrect': [(u'HE', u'SHE', u'HIS', u'HER'),\n",
" (u'HIS', u'HER', u'HE', u'SHE'),\n",
" (u'GOOD', u'BEST', u'BIG', u'BIGGEST'),\n",
" (u'GO', u'GOING', u'LOOK', u'LOOKING'),\n",
" (u'GO', u'GOING', u'RUN', u'RUNNING'),\n",
" (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n",
" (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n",
" (u'RUN', u'RUNNING', u'GO', u'GOING'),\n",
" (u'RUN', u'RUNNING', u'LOOK', u'LOOKING'),\n",
" (u'SAY', u'SAYING', u'GO', u'GOING'),\n",
" (u'SAY', u'SAYING', u'RUN', u'RUNNING'),\n",
" (u'AUSTRALIA', u'AUSTRALIAN', u'INDIA', u'INDIAN'),\n",
" (u'ISRAEL', u'ISRAELI', u'AUSTRALIA', u'AUSTRALIAN'),\n",
" (u'ISRAEL', u'ISRAELI', u'INDIA', u'INDIAN'),\n",
" (u'GOING', u'WENT', u'SAYING', u'SAID'),\n",
" (u'GOING', u'WENT', u'TAKING', u'TOOK'),\n",
" (u'SAYING', u'SAID', u'TAKING', u'TOOK'),\n",
" (u'SAYING', u'SAID', u'GOING', u'WENT'),\n",
" (u'TAKING', u'TOOK', u'GOING', u'WENT'),\n",
" (u'TAKING', u'TOOK', u'SAYING', u'SAID'),\n",
" (u'BUILDING', u'BUILDINGS', u'CHILD', u'CHILDREN'),\n",
" (u'BUILDING', u'BUILDINGS', u'MAN', u'MEN'),\n",
" (u'CHILD', u'CHILDREN', u'MAN', u'MEN'),\n",
" (u'CHILD', u'CHILDREN', u'BUILDING', u'BUILDINGS'),\n",
" (u'MAN', u'MEN', u'BUILDING', u'BUILDINGS'),\n",
" (u'MAN', u'MEN', u'CHILD', u'CHILDREN')],\n",
" 'section': 'total'}]"
]
},
"execution_count": 13,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"model.accuracy(questions='questions-words.txt')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7756597547632444"
]
},
"execution_count": 15,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"# Word Movers distance\n",
"sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()\n",
"sentence_president = 'The president greets the press in Chicago'.lower().split()\n",
"\n",
"# Remove their stopwords.\n",
"from nltk.corpus import stopwords\n",
"stopwords = stopwords.words('english')\n",
"sentence_obama = [w for w in sentence_obama if w not in stopwords]\n",
"sentence_president = [w for w in sentence_president if w not in stopwords]\n",
"\n",
"# Compute WMD.\n",
"distance = model.wmdistance(sentence_obama, sentence_president)\n",
"distance"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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"pygments_lexer": "ipython2",
"version": "2.7.6"
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},
"nbformat": 4,
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} | lgpl-2.1 |
napjon/moocs_solution | Data_Science/DataScienceUdacity.ipynb | 1 | 66426 | {
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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"This is how we used the Linear Regression with Python"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Specifically, this is for Udacity Class Introduction to Data Science"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile udlr.py\n",
"\n",
"import numpy as np\n",
"import pandas\n",
"from ggplot import *\n",
"\n",
"def normalize_features(array):\n",
" \"\"\"Normalized the features in the data set\"\"\"\n",
" array_normalized = (array-array.mean())/array.std()\n",
" mu = array.mean()\n",
" sigma = array.std()\n",
" \n",
" return array_normalized, mu, sigma\n",
"\n",
"def compute_cost(features, values, theta):\n",
" \"\"\"\n",
" Compute the cost function fiven a set of features/values,\n",
" and the values for our thetas\n",
" \"\"\"\n",
" m = len(values)\n",
" H = np.dot(features, theta)\n",
" cost = (np.square(H-values)).sum().(2*m)\n",
" \n",
" return cost\n",
"\n",
"def gradient_descent(features,values,theta, alpha, num_iterations)\n",
" \"\"\"\n",
" Perform gradient descent given a data set with an arbitrary number of features\n",
" \"\"\"\n",
" m = len(values)\n",
" cost_history = []\n",
" \n",
" for i in range(num_iterations):\n",
" J = compute_cost(features, values, theta)\n",
" cost_history.append(J)\n",
" H = np.dot(features, theta)\n",
" GD = (alpha/m)*np.dot((values-H), features)\n",
" theta = np.add(theta,GD)\n",
" \n",
" return theta, pandas.Series(cost_history)\n",
"\n",
"def plot_cost_history(alpha, cost_history):\n",
" \"\"\"\n",
" Viewing plot for our cost history\n",
" \n",
" For function called only in the function it self, print this return function\n",
" \n",
" \"\"\"\n",
" cost_df = pandas.DataFrame({\n",
" 'Cost_History': cost_history,\n",
" 'Iteration': range(len(cost_history))\n",
"})\n",
" return ggplot(cost_df, aes('Iteration', 'Cost_History')) + \\\n",
" geom_point() + ggtitle('Cost History for alpha = %.3f' % alpha )\n",
" \n",
" \n",
"def predictions(features,values,alpha,num_iterations):\n",
"\n",
" \n",
" \n",
" m = len(values)\n",
" \n",
" features,mu,sigma = normalize_features(features)\n",
" \n",
" #create one features with one. This acts like constanta, bias unit.\n",
" features['ones'] = np.ones(m)\n",
" \n",
" #If we look here, features and values is turned into np object array\n",
" #So we can do vectorize computation, without even to use (np.add, np.subtract, etc)\n",
" features_array = np.array(features)\n",
" values_array = np.array(values).flatten()#return a copy of the array collapsed into one dimension\n",
" \n",
"\n",
" \n",
" #Init theta, perform gradient descent\n",
" theta_gradient_descent = np.zeros(len(features.columns))\n",
" theta_gradient_descent, cost_history = gradient_descent(features_array,\n",
" values_array,\n",
" theta_gradient_descent,\n",
" alpha,\n",
" num_iterations\n",
" )\n",
" \n",
" plot = None\n",
" #Uncomment to see\n",
" #plot = plot_cost_history(alpha, cost_history)\n",
" predictions = np.dot(features_array, theta_gradient_descent)\n",
" return predictions, plot\n",
" \n",
"def separate_data_from_predictions(dataframe):\n",
" \"\"\"\n",
" dataframe itself is a pandas dataframe called weather_turnstile in the Udacity Class\n",
" We use the predictions function to predict ridership NYC subway using linear regresion with gradient descent\n",
" \n",
" Separate the input from predictions to encapsulate it\n",
" \"\"\"\n",
" \n",
" dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\n",
" features = dataframe[['rain','precipi', 'Hour', 'meantempi']].join(dummy_units)\n",
" values = dataframe[['ENTRIESn_hourly']]\n",
"\n",
" #Set MANUAL alpha, num_iter\n",
" alpha = 0.1\n",
" num_iterations = 75\n",
" \n",
" return predictions(features,values,alpha,num_iterations)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting udlr.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Visualize turnstile_weather"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pandas import *\n",
"from ggplot import *\n",
"from datetime import datetime\n",
"\n",
"def plot_weather_date(filename):\n",
" data = pandas.read_csv(filename)\n",
" get_day = lambda d : datetime.strftime(datetime.strptime(d, '%Y-%m-%d').date(), '%a')\n",
" data['DAYSn'] = data['DATEn'].apply(lambda d: get_day(d))\n",
" grouped = data.groupby(['DAYSn'], as_index = False).mean()\n",
" print grouped\n",
" plot = ggplot(grouped, aes('DAYSn', 'ENTRIESn_hourly')) + geom_bar(aes(weight = 'ENTRIESn_hourly'), fill = 'blue', stat = 'identity')\n",
" return plot\n",
"\n",
"print plot_weather_date('turnstile_data_master_with_weather.csv')\n",
"\n",
"\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" DAYSn Unnamed: 0 Hour ENTRIESn_hourly EXITSn_hourly \\\n",
"0 Fri 70410.623803 10.959636 1333.800773 1070.734344 \n",
"1 Mon 67947.979363 10.931706 1084.888769 869.015152 \n",
"2 Sat 75044.070127 10.879585 809.925317 681.034775 \n",
"3 Sun 63713.827882 10.894261 604.620120 514.766136 \n",
"4 Thu 66082.544485 10.932870 1305.176382 1044.300695 \n",
"5 Tue 57099.915715 10.840182 1307.073259 1046.467860 \n",
"6 Wed 61748.322656 10.825944 1335.901803 1071.181540 \n",
"\n",
" maxpressurei maxdewpti mindewpti minpressurei meandewpti \\\n",
"0 30.004952 56.706608 47.692308 29.874937 51.952485 \n",
"1 30.093767 58.551704 48.727722 29.883075 53.745032 \n",
"2 29.975392 58.279873 50.782411 29.855360 54.278374 \n",
"3 30.071598 56.155649 48.369604 29.945723 52.365292 \n",
"4 30.030086 55.476910 45.222617 29.922571 50.724646 \n",
"5 29.995784 58.210458 49.469167 29.831124 53.214550 \n",
"6 30.024700 57.268625 47.498356 29.924539 52.507541 \n",
"\n",
" meanpressurei fog rain meanwindspdi mintempi meantempi \\\n",
"0 29.944951 0.000000 0.251643 3.743859 55.966728 63.960328 \n",
"1 29.983292 0.201376 0.598957 6.408911 56.323442 63.313146 \n",
"2 29.915342 0.000000 0.250750 4.747578 58.513033 67.020761 \n",
"3 30.003730 0.198771 0.198771 4.801229 56.178632 63.573329 \n",
"4 29.977579 0.250236 0.250236 5.752265 55.484968 64.488024 \n",
"5 29.935652 0.247116 0.250753 7.509008 56.464962 64.716738 \n",
"6 29.979618 0.248668 0.500170 5.744529 54.265223 63.267717 \n",
"\n",
" maxtempi precipi thunder \n",
"0 71.451966 0.060394 0 \n",
"1 69.699802 0.111195 0 \n",
"2 74.528489 0.017552 0 \n",
"3 70.366531 0.176906 0 \n",
"4 72.990220 0.017517 0 \n",
"5 72.721398 0.168005 0 \n",
"6 71.521714 0.667847 0 \n",
"<ggplot: (277609837)>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"This is how we visualize the data, based on the weather how much subway ridership"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pandas import *\n",
"from ggplot import *\n",
"from datetime import datetime\n",
"\n",
"def plot_weather(filename):\n",
" data = pandas.read_csv(filename)\n",
"\n",
" data['WEATHERn'] = 'usual'\n",
" list_weather = ['fog','rain','thunder']\n",
" for e in list_weather:\n",
" data['WEATHERn'][data[e] == 1] = e\n",
" grouped = data.groupby('WEATHERn', as_index = False).mean()\n",
" plot = ggplot(grouped, aes('WEATHERn','ENTRIESn_hourly', fill = 'WEATHERn'))+geom_bar(aes(weight = 'ENTRIESn_hourly', stat = 'identity')) \\\n",
" + ggtitle('The number of rider based on weather') + xlab('Weather') + ylab('The number of of ridership')\n",
"\n",
" return plot\n",
"\n",
"print plot_weather('turnstile_data_master_with_weather.csv')\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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SpHvuuUc1a9aUJN17771q0KCBvvvuuyK3NWbMGDkcDtWqVUudO3fW9u3by7TW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\nEAQ7AAAAQxDsAAAADPE/2VdYFbcb2goAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10a34b350>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<ggplot: (279137253)>\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"This is mapper and reducer function for subway station"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile riders_per_station_mapper.py\n",
"\n",
"import sys\n",
"import string\n",
"import logging\n",
"\n",
"from util import mapper_logfile\n",
"logging.basicConfig(filename=mapper_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def mapper():\n",
" \"\"\"\n",
" The input to this mapper will be the final Subway-MTA dataset, the same as\n",
" in the previous exercise. You can check out the csv and its structure below:\n",
" https://www.dropbox.com/s/meyki2wl9xfa7yk/turnstile_data_master_with_weather.csv\n",
"\n",
" For each line of input, the mapper output should PRINT (not return) the UNIT as \n",
" the key, the number of ENTRIESn_hourly as the value, and separate the key and \n",
" the value by a tab. For example: 'R002\\t105105.0'\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" \n",
" The logging module can be used to give you more control over your debugging\n",
" or other messages than you can get by printing them. In this exercise, print\n",
" statements from your mapper will go to your reducer, and print statements\n",
" from your reducer will be considered your final output. By contrast, messages\n",
" logged via the loggers we configured will be saved to two files, one\n",
" for the mapper and one for the reducer. If you click \"Test Run\", then we\n",
" will show the contents of those files once your program has finished running.\n",
" The logging module also has other capabilities; see \n",
" https://docs.python.org/2/library/logging.html for more information.\n",
" \"\"\"\n",
" i = 0;\n",
" ##UNIT = 1\n",
" ##ENTRIESn_hourly = 6\n",
" for line in sys.stdin:\n",
" #i+=1\n",
" #logging.info(line)\n",
" #if i == 10:\n",
" # break\n",
" \n",
" data = line.strip().split(\",\")\n",
" if data[1] == 'UNIT':\n",
" continue\n",
" print \"{0}\\t{1}\".format(data[1],data[6])\n",
" \n",
" \n",
" \n",
"\n",
"\n",
"mapper()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing riders_per_station_mapper.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile riders_per_station_reducer.py\n",
"\n",
"import sys\n",
"import logging\n",
"\n",
"from util import reducer_logfile\n",
"logging.basicConfig(filename=reducer_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def reducer():\n",
" '''\n",
" Given the output of the mapper for this exercise, the reducer should PRINT \n",
" (not return) one line per UNIT along with the total number of ENTRIESn_hourly \n",
" over the course of May (which is the duration of our data), separated by a tab.\n",
" An example output row from the reducer might look like this: 'R001\\t500625.0'\n",
"\n",
" You can assume that the input to the reducer is sorted such that all rows\n",
" corresponding to a particular UNIT are grouped together.\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" '''\n",
" old_unit = None\n",
" en_hour = 0\n",
" for line in sys.stdin:\n",
" \n",
" data = line.strip().split(\"\\t\")\n",
" if len(data) != 2:\n",
" continue\n",
" \n",
" this_unit, this_count = data\n",
" if old_unit and old_unit != this_unit:\n",
" print \"{0}\\t{1}\".format(old_unit, en_hour)\n",
" en_hour = 0\n",
" old_unit = this_unit\n",
" en_hour+= float(this_count)\n",
" \n",
" if old_unit != None:\n",
" print \"{0}\\t{1}\".format(old_unit, en_hour)\n",
" \n",
"\n",
" \n",
"reducer()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing riders_per_station_reducer.py\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile ridership_by_weather_mapper.py\n",
"\n",
"import sys\n",
"import logging\n",
"\n",
"from util import reducer_logfile\n",
"logging.basicConfig(filename=reducer_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def reducer():\n",
" '''\n",
" Given the output of the mapper for this assignment, the reducer should\n",
" print one row per weather type, along with the average value of\n",
" ENTRIESn_hourly for that weather type, separated by a tab. You can assume\n",
" that the input to the reducer will be sorted by weather type, such that all\n",
" entries corresponding to a given weather type will be grouped together.\n",
"\n",
" In order to compute the average value of ENTRIESn_hourly, you'll need to\n",
" keep track of both the total riders per weather type and the number of\n",
" hours with that weather type. That's why we've initialized the variable \n",
" riders and num_hours below. Feel free to use a different data structure in \n",
" your solution, though.\n",
"\n",
" An example output row might look like this:\n",
" 'fog-norain\\t1105.32467557'\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" '''\n",
"\n",
" riders = -1 # The number of total riders for this key\n",
" num_hours = 0 # The number of hours with this key\n",
" old_key = None\n",
"\n",
" for line in sys.stdin:\n",
" # your code here\n",
" data = line.strip().split(\"\\t\")\n",
" if len(data) != 2:\n",
" continue\n",
" riders+=1\n",
" #logging.info(riders)\n",
" this_key, this_hours = data\n",
" if old_key and old_key != this_key:\n",
" print \"{0}\\t{1}\".format(old_key, num_hours/float(riders))\n",
" num_hours = 0\n",
" riders = 0\n",
" \n",
" old_key = this_key\n",
" num_hours += float(this_hours)\n",
" \n",
" \n",
" #if old_key != None and old_key == 'nofog-norain':\n",
" #logging.info('last')\n",
" print \"{0}\\t{1}\".format(old_key, num_hours/(riders+1))\n",
" \n",
" \n",
"\n",
"reducer()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing ridership_by_weather_mapper.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile ridership_by_weather_reducer.py\n",
"\n",
"import sys\n",
"import logging\n",
"\n",
"from util import reducer_logfile\n",
"logging.basicConfig(filename=reducer_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def reducer():\n",
" '''\n",
" Given the output of the mapper for this assignment, the reducer should\n",
" print one row per weather type, along with the average value of\n",
" ENTRIESn_hourly for that weather type, separated by a tab. You can assume\n",
" that the input to the reducer will be sorted by weather type, such that all\n",
" entries corresponding to a given weather type will be grouped together.\n",
"\n",
" In order to compute the average value of ENTRIESn_hourly, you'll need to\n",
" keep track of both the total riders per weather type and the number of\n",
" hours with that weather type. That's why we've initialized the variable \n",
" riders and num_hours below. Feel free to use a different data structure in \n",
" your solution, though.\n",
"\n",
" An example output row might look like this:\n",
" 'fog-norain\\t1105.32467557'\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" '''\n",
"\n",
" riders = -1 # The number of total riders for this key\n",
" num_hours = 0 # The number of hours with this key\n",
" old_key = None\n",
"\n",
" for line in sys.stdin:\n",
" # your code here\n",
" data = line.strip().split(\"\\t\")\n",
" if len(data) != 2:\n",
" continue\n",
" riders+=1\n",
" #logging.info(riders)\n",
" this_key, this_hours = data\n",
" if old_key and old_key != this_key:\n",
" print \"{0}\\t{1}\".format(old_key, num_hours/float(riders))\n",
" num_hours = 0\n",
" riders = 0\n",
" \n",
" old_key = this_key\n",
" num_hours += float(this_hours)\n",
" \n",
" \n",
" #if old_key != None and old_key == 'nofog-norain':\n",
" #logging.info('last')\n",
" print \"{0}\\t{1}\".format(old_key, num_hours/(riders+1))\n",
" \n",
" \n",
"\n",
"reducer()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing ridership_by_weather_reducer.py\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile busiest_hour_mapper.py\n",
"import sys\n",
"import string\n",
"import logging\n",
"\n",
"from util import mapper_logfile\n",
"logging.basicConfig(filename=mapper_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def mapper():\n",
" \"\"\"\n",
" In this exercise, for each turnstile unit, you will determine the date and time \n",
" (in the span of this data set) at which the most people entered through the unit.\n",
" \n",
" The input to the mapper will be the final Subway-MTA dataset, the same as\n",
" in the previous exercise. You can check out the csv and its structure below:\n",
" https://www.dropbox.com/s/meyki2wl9xfa7yk/turnstile_data_master_with_weather.csv\n",
"\n",
" For each line, the mapper should return the UNIT, ENTRIESn_hourly, DATEn, and \n",
" TIMEn columns, separated by tabs. For example:\n",
" 'R001\\t100000.0\\t2011-05-01\\t01:00:00'\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" \"\"\"\n",
" ##UNIT = 1\n",
" ##ENTRIESn_hourly = 6\n",
" ##DATEn = 2\n",
" ##TIMEn = 3\n",
" for line in sys.stdin:\n",
" data = line.strip().split(\",\")\n",
" if data[1] == 'UNIT':\n",
" continue\n",
" ans = \"{0}\\t{1}\\t{2}\\t{3}\".format(data[1],data[6],data[2],data[3])\n",
" #logging.info(ans)\n",
" print ans\n",
"\n",
"mapper()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing busiest_hour_mapper.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile busiest_hour_reducer.py\n",
"import sys\n",
"import logging\n",
"import datetime\n",
"from util import reducer_logfile\n",
"logging.basicConfig(filename=reducer_logfile, format='%(message)s',\n",
" level=logging.INFO, filemode='w')\n",
"\n",
"def reducer():\n",
" '''\n",
" Write a reducer that will compute the busiest date and time (that is, the \n",
" date and time with the most entries) for each turnstile unit. Ties should \n",
" be broken in favor of datetimes that are later on in the month of May. You \n",
" may assume that the contents of the reducer will be sorted so that all entries \n",
" corresponding to a given UNIT will be grouped together.\n",
" \n",
" The reducer should print its output with the UNIT name, the datetime (which \n",
" is the DATEn followed by the TIMEn column, separated by a single space), and \n",
" the number of entries at this datetime, separated by tabs.\n",
"\n",
" For example, the output of the reducer should look like this:\n",
" R001 2011-05-11 17:00:00\t 31213.0\n",
" R002\t2011-05-12 21:00:00\t 4295.0\n",
" R003\t2011-05-05 12:00:00\t 995.0\n",
" R004\t2011-05-12 12:00:00\t 2318.0\n",
" R005\t2011-05-10 12:00:00\t 2705.0\n",
" R006\t2011-05-25 12:00:00\t 2784.0\n",
" R007\t2011-05-10 12:00:00\t 1763.0\n",
" R008\t2011-05-12 12:00:00\t 1724.0\n",
" R009\t2011-05-05 12:00:00\t 1230.0\n",
" R010\t2011-05-09 18:00:00\t 30916.0\n",
" ...\n",
" ...\n",
"\n",
" Since you are printing the output of your program, printing a debug \n",
" statement will interfere with the operation of the grader. Instead, \n",
" use the logging module, which we've configured to log to a file printed \n",
" when you click \"Test Run\". For example:\n",
" logging.info(\"My debugging message\")\n",
" '''\n",
"\n",
" max_entries = 0\n",
" old_key = None\n",
" datetimed = ''\n",
" \n",
" fmt = '%Y-%m-%d %H:%M:%S'\n",
"\n",
" for line in sys.stdin:\n",
" data = line.strip().split(\"\\t\")\n",
" if len(data) != 4:\n",
" continue\n",
" this_key, this_entries, this_date, this_time = data\n",
" \n",
" if old_key and old_key != this_key:\n",
" print \"{0}\\t{1}\\t{2}\".format(old_key,datetimed,max(max_entries, float(this_entries)))\n",
" max_entries = 0\n",
"\n",
" old_key = this_key \n",
" maxed = max(max_entries, float(this_entries))\n",
" \n",
" if max_entries < maxed:\n",
" max_entries = maxed \n",
" datetimed = '{0} {1}'.format(this_date, this_time)\n",
" elif max_entries == float(this_entries) and datetimed:\n",
" d1= datetime.datetime.strptime(datetimed,fmt)\n",
" d2 = datetime.datetime.strptime('{0} {1}'.format(this_date, this_time),fmt)\n",
" datetimed = max(d1,d2).strftime(fmt)\n",
"\n",
" if old_key != None:\n",
" print \"{0}\\t{1}\\t{2}\".format(old_key,datetimed,maxed)\n",
"\n",
" \n",
"reducer()\n",
"\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing busiest_hour_reducer.py\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile creating_pandas_dataframe.py\n",
"\n",
"from pandas import DataFrame, Series\n",
"\n",
"\n",
"def create_dataframe():\n",
" '''\n",
" Create a pandas dataframe called 'olympic_medal_counts_df' containing\n",
" the data from the table of 2014 Sochi winter olympics medal counts. \n",
"\n",
" The columns for this dataframe should be called \n",
" 'country_name', 'gold', 'silver', and 'bronze'. \n",
"\n",
" There is no need to specify row indexes for this dataframe \n",
" (in this case, the rows will automatically be assigned numbered indexes).\n",
" '''\n",
"\n",
" countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',\n",
" 'Netherlands', 'Germany', 'Switzerland', 'Belarus',\n",
" 'Austria', 'France', 'Poland', 'China', 'Korea', \n",
" 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',\n",
" 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',\n",
" 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']\n",
"\n",
" gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
" silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]\n",
" bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]\n",
"\n",
" # your code here\n",
" d = {\n",
" 'country_name' : Series(countries),\n",
" 'gold' : Series(gold),\n",
" 'silver': Series(silver),\n",
" 'bronze': Series(bronze)\n",
" }\n",
" olympic_medal_counts_df = DataFrame(d)\n",
"\n",
" return olympic_medal_counts_df\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing creating_pandas_dataframe.py\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile np_mean_pandas_columns_with_conditions.py\n",
"\n",
"from pandas import DataFrame, Series\n",
"import numpy\n",
"\n",
"\n",
"def avg_medal_count():\n",
" '''\n",
" Compute the average number of bronze medals earned by countries who \n",
" earned at least one gold medal. \n",
" \n",
" Save this to a variable named avg_bronze_at_least_one_gold.\n",
" \n",
" HINT-1:\n",
" You can retrieve all of the values of a Pandas column from a \n",
" data frame, \"df\", as follows:\n",
" df['column_name']\n",
" \n",
" HINT-2:\n",
" The numpy.mean function can accept as an argument a single\n",
" Pandas column. \n",
" \n",
" For example, numpy.mean(df[\"col_name\"]) would return the \n",
" mean of the values located in \"col_name\" of a dataframe df.\n",
" '''\n",
"\n",
"\n",
" countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',\n",
" 'Netherlands', 'Germany', 'Switzerland', 'Belarus',\n",
" 'Austria', 'France', 'Poland', 'China', 'Korea', \n",
" 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',\n",
" 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',\n",
" 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']\n",
"\n",
" gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
" silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]\n",
" bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]\n",
" \n",
" df = DataFrame({\n",
" 'country_name' : countries,\n",
" 'gold' : gold,\n",
" 'silver' : silver,\n",
" 'bronze' : bronze\n",
"})\n",
" #print df[df['gold']>=1]\n",
" avg_bronze_at_least_one_gold = numpy.mean(df[df['gold']>=1]['bronze'])\n",
" ##column first then dataframe could be right also\n",
"\n",
" return avg_bronze_at_least_one_gold"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting np_mean_pandas_columns_with_conditions.py\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile avg_medals_countries.py\n",
"import numpy\n",
"from pandas import DataFrame, Series\n",
"\n",
"\n",
"def avg_medal_count():\n",
" '''\n",
" Using the dataframe's apply method, create a new Series called \n",
" avg_medal_count that indicates the average number of gold, silver,\n",
" and bronze medals earned amongst countries who earned at \n",
" least one medal at the 2014 Sochi olympics.\n",
" '''\n",
"\n",
" countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',\n",
" 'Netherlands', 'Germany', 'Switzerland', 'Belarus',\n",
" 'Austria', 'France', 'Poland', 'China', 'Korea', \n",
" 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',\n",
" 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',\n",
" 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']\n",
"\n",
" gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
" silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]\n",
" bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]\n",
" \n",
" df = DataFrame({\n",
" 'country_name': countries,\n",
" 'gold' : gold,\n",
" 'silver': silver,\n",
" 'bronze': bronze\n",
"\n",
"})\n",
" avg_medal_count = df[['gold','silver','bronze']].apply(numpy.mean)\n",
" \n",
" return avg_medal_count\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing avg_medals_countries.py\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile numpy_medals_point_based.py\n",
"\n",
"import numpy\n",
"from pandas import DataFrame, Series\n",
"\n",
"\n",
"def numpy_dot():\n",
" '''\n",
" Imagine a point system in which each country is awarded 4 points for each\n",
" gold medal, 2 points for each silver medal, and one point for each \n",
" bronze medal. \n",
"\n",
" Using the numpy.dot function, create a new dataframe called \n",
" 'olympic_points_df' that includes:\n",
" a) a column called 'country_name' with the country name\n",
" b) a column called 'points' with the total number of points the country\n",
" earned at the Sochi olympics.\n",
" '''\n",
"\n",
" countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',\n",
" 'Netherlands', 'Germany', 'Switzerland', 'Belarus',\n",
" 'Austria', 'France', 'Poland', 'China', 'Korea', \n",
" 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',\n",
" 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',\n",
" 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']\n",
"\n",
" gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n",
" silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]\n",
" bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]\n",
" \n",
" df = DataFrame({\n",
" 'country_name':countries,\n",
" 'gold':gold,\n",
" 'silver':silver,\n",
" 'bronze':bronze\n",
"})\n",
" #print df.shape\n",
" df['points'] = numpy.dot(df[['gold','silver','bronze']], [4,2,1])\n",
" #df[['gold','silver','bronze']].apply(lambda x: numpy.dot(x,[4,2,1]))\n",
" \n",
" olympic_points_df = df[['country_name','points']]\n",
" \n",
" return olympic_points_df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing numpy_medals_point_based.py\n"
]
}
],
"prompt_number": 7
}
],
"metadata": {}
}
]
} | mit |
david-hoffman/scripts | notebooks/CurveFitTest.ipynb | 1 | 602651 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Curve fitting test"
]
},
{
"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"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x4e99898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Pull in all the normal math functions and set up matplotlib as inline plotting (i.e. Mathematica like)\n",
"%pylab inline\n",
"#import the curve_fit function\n",
"from scipy.optimize import curve_fit\n",
"from peaks.gauss2d import Gauss2D\n",
"import scipy.optimize.minpack as mp\n",
"curve_fit = mp.curve_fit\n",
"\n",
"set_cmap('gnuplot2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we define a simple exponential decay function of the form\n",
"$$y(x)=a e^{-b x}+c$$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Here's a simple test function of an exponential decay\n",
"def func(x, *params):\n",
" #this is the important part, if you want to pass an unspecified number of parameters\n",
" #you need to unpack the parameters list in the function definition and then you need\n",
" #to specify initial guesses when using curve_fit\n",
" return params[0] * exp(-params[1] * x) + params[2]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x911bb70>]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x8d43a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x8d43b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Here we generate some fake data\n",
"xdata = linspace(0, 4, 100)\n",
"y = func(xdata, 2.5, 1.3, 0.5)\n",
"\n",
"#add gaussian white noise\n",
"ydata = y + 0.1 * random.normal(size=len(xdata))\n",
"\n",
"#perform the curve_fit, NOTE: you have to give guesses so that curve fit can determine\n",
"#the correct number of parameters for the function func\n",
"popt, pcov = curve_fit(func, xdata, ydata,p0=ones(3))\n",
"\n",
"#generate a nice fit\n",
"x_fit=linspace(0,4,1024)\n",
"fit = func(x_fit,*popt) #you need to use the '*' operator to unpack the array specifically\n",
"plot(xdata,ydata,'.',x_fit,fit,'--r')\n",
"figure()\n",
"plot(xdata,ydata-func(xdata,*popt),'ro')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Let's try writing a function that fits lorentzian peaks\n",
"def lor(x, *p):\n",
" toReturn = zeros(len(x)) #initialize our returned array\n",
" toReturn += p[0] #first parameter is the offset\n",
" if (len(p)-1)%3 != 0:\n",
" #Here's where we should raise an error\n",
" pass\n",
" \n",
" for i in range(1,len(p),3):\n",
" toReturn += p[i]/(((x-p[i+1])/p[i+2])**2+1)\n",
" \n",
" return toReturn"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x94e7898>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5rqHBlvCbP59oXuOLdOOEC0OzROk2W1s7fBWqRuJzmCuogB+hyAfcrbAjBbDP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\nA0868L/5BkCSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x94b83c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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CaAJllDM/r/rhD52zTP1QUV3N9tJS1tmHeqi2TsV+YNJ2FwFbhICeHiLA/VlZ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\nEVGoJICIiEIlAUREFCoJICKiUP8Ho7uDGB9/M4kAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x94b8d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Here we generate some fake data\n",
"xdata = linspace(0, 100, 2048)\n",
"p = [1,1,24,5,2,56,10]\n",
"y = lor(xdata, *p)\n",
"\n",
"#add gaussian white noise\n",
"ydata = y + 0.2 * random.normal(size=len(xdata))\n",
"\n",
"#perform the curve_fit, NOTE: you have to give guesses so that curve fit can determine\n",
"#the correct number of parameters for the function func\n",
"popt, pcov = curve_fit(lor, xdata, ydata,p0=p)\n",
"\n",
"#generate a nice fit\n",
"fit = lor(xdata,*popt) #you need to use the '*' operator to unpack the array specifically\n",
"plot(xdata,ydata,'.',xdata,fit,'--r')\n",
"figure()\n",
"plot(xdata,ydata-lor(xdata,*popt),'ro')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#2D Fitting\n",
"\n",
"Here I've taken the code from [here](http://stackoverflow.com/questions/21566379/fitting-a-2d-gaussian-function-using-scipy-optimize-curve-fit-valueerror-and-m).\n",
"\n",
"There's a better definition of a skewed gaussian available [here](http://mathworld.wolfram.com/BivariateNormalDistribution.html)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet at 0xa5df5c0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WpvFRewfsatocyirHNiq2bV5hUxbIyiIoPgW2qs5QF+Uuo1qVWavAV7ufnLrp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\nKHFxcxxnlPwL0rqaJats9zAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x952cd30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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wjAsJ/U07fULDUWteT1f6HnVtkGaO/e2cdzVWIXB2Rp2y8G0Ror83Ybb5jj2q\ncnA+lDD9lwmLHWgbd8IOPVf9PP66wOmnzfqsTVet+fu5Jz+dfc7h/8XPqL0+VaZuZSMbxc/8oDKM\n3xCbfEYgQZXePimI/bgA8gv51oj6fLYjfx8Tku1vY1XDDyltptpeBXtNqI8jT2v480ozSn0hmV8U\nm70jx/2DbAc3rYlNPSwCmDoCqb4qovhWsu/KPlGp94/mun5YMKiLYvM+nb8r781bQoP4hkC2gdBg\nTgjEXBUM6ELO5YSwz3zXIY9bt8MN3xYIugM/ln0+pKo1EcxhOdczqz7ClcJmsiXck5UH5kDC7pg6\ndP23tP1xA0ND3835VKnWnxCu3B/LNS/lHrZyvJaQ3qfUSVBP5r5cy3cLwcR3aHnFCR/xltf0vWjS\nx22Z0922A10WTOS7QnN7KeG9LHCq8nJVBXRG1QyjOr411EbDDQ2lHT6nre+2pYz6HKYn707CYJfC\nk8YV1v2maQ/bcEVvO+5hPMc7K7TI/bjjoN2WnLVikPh1VjD+XTnXr4mjyZZg9KMiq7ih4Wx626qc\npec0/LjSjsTx2m0e31e2rSO5dxVfuKXWCv+rH1SG8QUB4Kp61ZjY/HWBUF2BBGcFAh4URLOsjsmo\nSuSN6PhoFrBZVRu/BgrjOuZ1PY9/ItT2r2Bcxyc03LHp19VHnKbg1kti08fV7rD3C2ZRaSWrajfi\n7pzbOcEYZth2rX5caBcHVdmVdYGfKpfhdr5bqut5tPPvNwRjeUlVK2LETUMHbTmpDiv/kGBolTTZ\nr/YadATiPCyQqUKyMQ1zOlpK17NoTmUkruoqnFH4EKQr/KaQhnOCuY6rHW4b6johDwgm8hz+qCCO\nLcFkzqtjOCpvxkDhtFGbNl1XOqvpfqUL6U07nWNdUAfE/ROh0s+pjcCRQdrwjHF/ypotpZEcc92U\nA9rmM7v4Oyi1HLff4665rOdsrqNKAhtJOMxres7AA1puGyoNt92ct9QayMO57geMeEHfWBYHqgz4\n/Vx3FDMKgfGw2lg8yP1cFKH/LSEsfjWfWco+dmZ/VQRsxYT+rtBq9qizeiu725/8QY30XBVEMKWu\nr3i/+hy2Lz+rzulVGG+VWrxLcOY2rul7XekI2yXRKqmyqO+XBUDfJ5DgFkb03VZsG1srC/MBQfwv\n5hz3CTBtChWzqhFxWTCQOzm3nWIz94rNnNPwSUP/V67hYPZxPf+vSrktqtXtQzmH6+r8gDMajpr0\nqBWvpmTUcTUyAAAgAElEQVQZ07UmjgJTOfZedS7FTpWbLua0JTSPa2rvxr9VqfOlZVvbNUGqs32B\nDSN+SM9vZJxHxaj3J8xeEwR1SjDh4+IIcENdwq/IMafz+4Vcb1U17Fo+HyHhpSUbRlXBa1Hl7O3s\nZyiEzGl17MsHEl7VcaYi9AmlfbreTAPlt3K8vk1X9Y1oOaClbUNXX9Mt19Mg3ElYbCYORLGbhjUz\nPmDRDX27sKmV3qH+tsa1X0j6fbhj6B5tM0b19C3puopXNNwx5aBl/8akj9kwlVGf53K8w7nOO8JO\nVBV3ejp/H0hc2Zs4cE4I3rMJo5O5VzcSL2bVDPX3395DhnFbbHSlLh8TR5O+AFLlqz+oCiyKxQ+E\nylUFw/wSfsjQiNiomwKYbVX679CcCQ9bd1EkkH0GVw2tq2tjHhIIeEudgbipDi+uirZ+Kud7MH+3\nxIZUQVyLOf7lzA+oDKVVMFVErTY1tR2y6c18f6DpXkNnlBnqXK23dMiWRXXRoCMCAc4JAt0nJOxe\noapLWL6d3x3OcW8pfEPTf6NvIeF3UemEgbY60OeUQK4vGNgnNL6qklRPXWf1aMLgjmD2swm/6fx+\nRRDQIOdZGVcvqwl7QyB+R52ZWkXwdlS5IzGfmdz3tuoo23C/oefUBW2WVFnDpf3plvy3gvBHMWvL\nQN/bmu4obOR6R6xvH0VXE55VQleUSyzd0vUZY8ZseNnQWNbsGBEEuow3FA7b7Zihrk0zei7oKzOy\nODSR0qaei0Y9qe9oRh+/nWubUwebrQkG28m57VC7sDu5Ly2h1VUa2/vUkcP7Be5Wtqd3197DEn19\nAbx1QQRbqurUhQlth8UG7xFAKPOdyope1dK8mP/fm59VyFVFc66JnJC96hTymXz3glBTK4vyCW0H\nFa7glLYPajkuCPFAjv22Ki+iLpe2pqmlZVZkQM5rmsow6CrS8iFxRh/TcUCpmXaUe4QnqAp2qrI2\nX1bYUnifUmnDQMfjCicEElQBXE21RnEuYVGFVlch0IN8/pjQKBZFcFbUIg24rKrjP+az39sZp7Gs\nNhB23zHGLsEoqsjNcSEVHxXaRBWxOqbK1Zh1xIgHUrN7VSDxhiCKm7iibbfCujmHjRqgY8JRO4wp\nXNHa1i5GlLZMO6ZjtzoqdoSMX6hiXqY8rLUdCDaSOsyWgVvGjCj0PWqX8cwLativYTL7u0doLOtW\nXTI0VHnQhu4YbhNnXSks9r9ry1VbLuta0HdNMO0DGh6z4YLSLZtuGTGlbYdg8NVeXleY0DGh8AZ2\na/icYrva+jH/bszFpDokv5m/j6qDzaII9Ltp76GGUal+1VFjSVUzIgxMd8RCXxMcd14g6Go+O40Z\nTT9toBCAq5JsCoHEmyLM+Zp15wVgrwjkrTwTbXXi1FmFhwQB7lDYrWGf0rzBdr2DqphNZQwdVZjV\nyNTl2JQNhaP57IsCCXaJGo9dhduGbum6KTSWSXzDwIuaWYFsuK2m7881z2sYKrQ1TRta13BYoWnL\nvNqguN+Uh6xbzVyPqjhQIRDxUGbI7kfPiFP6Gqn6j2ia1NKypW3o47lPrwsCqcLxd6pV3SdUxXIb\nOkpt5XbtyCrcvrpKYoeO2xp2qT1NVWHjQpTBG1ek67SjpZmxOS27tGxpWjVqwmoaZxs2tB3Q85a6\nmFDkXDT0jWtac0BLRGjEnGka1/aYwm3NDAictkfTMWP2K03pWVPVSI2j4aRZfbedyTySqVz/RK5z\nU2FOMy0ua84YupmwqKrUHxDVuWbwaobf79C0ZLBtzF9Wu+RLDQ8kbTyhyKjWclvoXc2VNUyasWY1\nR59WHwerdIZR77a9hxpGFf8eiVi1hNundEjPV3BY21IGWa2wHc0XFasL39HwiLrIblWR+ojghVW+\nxJIAGLat3lNCcjwqCGAZL+h5TmkW5/U8Y6iX478ukOcxcY48JdTiIR7SN5oVnG4rvaa/XZS3YjQR\noNOzrOtbghlMC8ZT+d/XNBxQ+An8IaU5pZdUFbs2PG3opqa2wqy2+1KrqSz1OzVNm3JDw7pghLMJ\ns/NCYn1I21TC6m0d8xrbnoMtzayJEQzwQ7ik6UcyQrM6tj0vDJnXVGHJoz6ntR2Feyu/C4Yd6eJz\nJhSued2GMxqmNNyj7ZCOA+raJx/Q8wWl0hUXrWV9j2XTbmc8ScNNbQfR0HG/RZs2vYEXjLpu0kCk\nmt9r1imF8xY9a2sb3Qur+hasGJq07rbSpqeUVu0wpq/hrI5N4wqFS5p2abjPnHENparid8CkcvVf\nU3hLy2F9d1KTq4TMomAU9ytd1/crgplH8tyKf5WG+Q3BXOJqhNJ3bPqCMoPLBl41NJFjvqm6d6Qw\nY9KiUR/UyArncewvBMOrktHeXXsPNYyqAtJlVU3E+HtBEPQJLBj309ZdNfRqPjebP9eVXrPlFU2f\nNXQjw4OruPxJcaHPmzoe0DUlDGRnc/yqHN6LguBW8T+oK2eFb31rOynumDj2DERgTkslbQMxmupa\nDlUiWpU5uTvXdEUQ0h11IlBLfcnSSVu6qsrR9Z0qVQ7KB3BV1xsYs7HtPo6Cs4UvavsbrvprSvcL\nprYo7BpX8ITSfbq+nXN61Irbghm+iFLP/Xp2J0yOaDhlxL26+qKM/7dzblV91N0Kl+102E1/H4cV\nrik1FFlxqjSi6ZDdbrtowlDHiHv18zjQNOuOrxr6NUM/hh/VMpf5Fy+LI8LDCqP6ns/SPZ+36Q2l\nedzWMqd03U6zpn3UeRv6rhlo4AEtawauKNKOMdTDTYUFI04rfFdhzqaORW8pXbLPfdp2uuySjtM2\nrHjVN3Q8puFK1gxZVntIppQmbLiWeHRVeIRmBDMZE3U8JnJfflIU7VkVR7uqtueMIPT1/Lyh6bqh\n55Ten99XRX2iRu3QrKu+Y84jtjxq6LraYzVQp1O8u/YeulUvC1XuFzFUeFCVxhxBLP+dwpTSi0L6\njauTvY4L6Rl1NGc8btXXs5ZFVc16B14x5pgjftLrfktpTn3REXXF8Y/lZ8cVziu3g8oeVfuzPymq\neBfpMagqlvdzvFGFGzivtC6OUScFI3g2+6iK/VT5KQ+Ks2oVm/HxfKZ6v0p3rmw2V4QLbibfqc7O\np4Ske0kwlRcSricFwq3meo9qGzHjUTf9fD47gW+KW90+ICzyr4j8hR8zasSm18RdHAs571gvFxTG\nUz2OawJGzOjrGNjSUmjoZVX0Jn7BqP9CYTo1ud1KtHQ17bDpTD53wrxdaRpcNtwmpJuqsPqG/Y46\n4aKeLc+ad8SWc5ZsmfKEnfouOmvcaWteMe9eS7o6xgwsZQ3ZcMV2vOkxT3jGqxoO67uRuaHVRVcX\nc80TeNhxLVe8ZVM3P2/g25rOGfGfWbcqjPHXc4+rzNiGQkthaOgfiEC5e3C/wqIw0ld1RK4LzeET\nSk07jVvxVX0n1BXoDyQ+3U48O5fzPaK+XmCQON7Knx/YwK1nRATmLkyYdtiIOTc9LbjhnDE/outf\nG25zyEtikw8IDv4EvqNwRZSCr+7JeAC0NI35k1Z9I63QH8sxqVTlIPxviGSwKTP+oGW/aZBX04Xk\nvYPHNRzU8aRNq/ibIjCrKxBrn3F7jNi06MvZ538rgsqOCEK8R30r1+vq+01+SqiWz2Z/n811hIoe\nDKCq8/iIYJz/ShwZqqPR/eorDqvCRBfEOXgu39tjxJy95pz3tGBAH0XLSKaRdy0lnP+ZqFT2qjpa\n9IRAwNC+2u435ajbhkKLaapTq4vcpz3qO2Oaucb71e7A6ny9nvvxBfx1UW/0BbVBu8h1hluZIwor\n4k6aGYUtu5xCw4K3RO7FvDg6jYnkrb4D9urZcjNd6/J41nDI0LdzvEp7quxeVVDeqBAMp0TW85Vc\nRxxJxuwyp+mify7iKh4Q8T6Foa9k+sNR9Z0nnxCXBv6cKcf0fFvXswm/ezV90KiWNX/PmD+v6x8a\n2itoZk4dkbslGFNV3et5td3sjhCyVQ7NT/+gMoy/Lc7wIR0bNhSuiJvDDuKKwkFR5HW3uhT+UGzk\nHUFMT6nTqldUF89wyIjPmjRjwbOCSBfFhhXqbL/XheT4EVGT4Q1DS/i6lj+qVBo4K5hHmRoGYfB8\nUSDSBwTSRlm1obcEk9iL/1Uwhh/NuU8JzeE7Aik/LGIEjmu4T+kryrTkB0JUFbxDra3zL0YF4W4a\n836THnPTAv5n/EUh9S6oXaBRdTyuCTyk74tCJX4JJ9OY1sqxXxFG1kfNm3TLywaumPA+ffO6volf\nVxhq+GQy1yqX41rCeFJdBOmA+v7a6rKkVbW2MoMbxvWdcMJLFkWS2C/itFFPKk3qZinDhktGPWTd\nrwltM6pWNdN7NNA3ZeCoMS96wYQHdY0YUxpYU2oa2NSzoYqXiHmfw25TThk1bt1b1vLOkrZd5pxy\n1TX3OuSir9vQV9+JuqZwPX1fx9Fy1Kiehtv6ul5Sbge5LWuZc9CDLvinhj6s5aihW4bbAW2RVtDw\nTUNthb1KLSEAZtVhBTfUV2V+UDgJqmp2lYZa0c5Z/OwPauDWlJYPi4t41rOa0OsCufaLKwKrQjVV\n8Zy+kLpVcNPfFRmaPXWxmQoZ2bJpefs6wE+LOqLL6tLum2TJ+cowFXdhTmHKYDsIqnLR7dLylMN+\nzjltg+2Los8JW8ZB5XaU5E6RGPfDgugPC0kbVvH4/lmhaX0cTyt9VemY0FgW8p15VZm9Ol5kf65/\nF4Z6rlvyCwontPyljD14K+F2RBBED+8TZeSqUPJ/LJjtVaVd6ujaY6LOxq9Y9NHM6J21qaP0LcEY\nPqM0ZZD1QEJFvkcg6gaOarlf0y3dvGyovke14wnHXdNzw4iG63oWtHzIpXRf8n/nWu/RMqHU17Vu\nRMOMB93eDvuvCvFG+b62LSN2GFGatKXQtNOYG87ZsGCY5RpHdEw4bENh1E7zxlzSNjBvaEJfy9BR\nTXs0XNL3nEUHTDvgin9ijw+5ba8VYWGgb+C8/vb6H3PdG4YmbFlPDXgHvq3wkhF/1YS2whM4Y2BL\nqSmKTk/kseV1Q7uwV+k3hXCtLpWq8nCawobVEqHxVQbrFfWR6HOa9hhx2rqf9W7ae8gwOoauifTh\nCuGq4jK/ItLLb6jva7iqzgGoArt2C+K6Ioirqi3QMW6XMTssbN+x2ROAPCTU82V1sdyq3+PiaFDq\n+LyB0mA75b6JGQOTFlxIX/zxnFdVw6JSWysPUNSPCEK6Ljj8ivps+YBgdJFXEhXBJtRBT7eFpvWA\ntp16flltt9gSSDMwcN4g0/qHDtincMuvpleg8sH/ljhXf1x9qfVIrjcSxTqOaRi1uZ0bM6/nBcFU\nTxr4qpZJcf3fqI6j1jwv1OPjuV8Pq8Lrh0aULicMlgXCR9zKBSPWTBsYNbSktMOWCWtWjZu3YX+e\n58/afMexoG/Sihv69gjtIDS/Kix7YFPpjoENZ+zBCSvpEF3RS2PnNT3jGuaV1m1pWtRIxshmkmlH\n0w1tpaHSup4pQ11b3q9wTVdXaY+hURGPPG3cYUs28YYNyzp2KpzTNIdDBllCoOtpl+3TcdimSWUK\niaZ5hb0GOhp2Ze7OfYIpvpXrPKNOBAzvWGiQD6kDH6cTPiVeMXQrq3i9u/aeMYwARpWLUcXun1RH\nDr6zhNsudXEcgmD64uhSqImu8ihMGLil56L65qee2rhZRRnuzP6qsmmXBZAZmkwNZyCYTAPLhp5w\nR1edc1Ixi746KvCohiW7/YSbftVg++qEW/nelDrsvSKoKjpSPrtswqIpj7hu0VBfSIvZfLeZsLqq\njq68YqBn035DhwRCFQKZRlR30dZ3oQ7Ul/EMDG1mMFllYf+ssE1sqO5cHeZ9peGYrErCreGbxj2q\nq22Q97FM6hq12428ACn27Qq2XEs1uUj3c+GggRsmNW36W/gpO01bt6ab+RxFVslad0WdZBXxNB0j\nhsZN6Bix6ZZVyw6LfNLvGtVM42lDJ0P5e5o6GnYqXPesER/R0tD1tk1zBvpKF5TOCzvHTfvsdtkD\nVrwt7AdrSgczLvlU4upl1fWaQ7eU2+EDl7GkNKfvJXd0tLfTzncKL8ttPKu0paFh1HGbvqMq4Rc2\nGeqSC6u5h98RjGWgLtazSxX/U2rZct67be9hHEao+LHI6ojQFGfe44KLXjfmgKaN/H63INKj6nqU\na2rtoqO6rKbrqhU3xTFgXH0j/KJac1lT3Y8aRPWUKl+i76Lh9g3kDUHEUzl2ZSyryq1VRFjVspwR\n5/cJoc3cVhdjrSz+VYWtnQLxjqsv053L33uUdim1MkowtKGGhrgOoKnQz/W8Kkq8jVl0NUO6++LY\n81zO9yO5vkvqAKxTOdZQ36ahHTruz+dvqy9VZsbDxhw1tGHghm7GFsS8ItNyTEfLulrjqtLcd6pr\nSIwk/K9qWtM2pmWHCfLosap0QGmfYPgR2dk2acqUHeaEBjajzi+J8nRTWmaN5F4s4yUD56x53sC6\niA6NIMCGjtZ21OS4cSMaCmxZNlBqmjGmZR6H08AahZfGHNfajh15QzDrUV1XzRlTpIu/b8PQrKHb\nhl5Qe/oOo6GV8UTx+YpS19AizijdVpjLSNQldSDWEaFhVCUfKqFWCaHq2sXd6jwn6qTJ3397zzSM\n4XZxj9uq26jr6lB7BJLv0zJt6x2BT9Gq+IM7gigPZl9RtLeW+Ls0bBh3wup2/ENVgfy8li0N92XZ\nt+cFY6gyAatQ4yrfpCmYy6bQaO4V+RGT73huKfu4aegF1zwlXLNLamZWZTZWtUh/WCDLdH43leON\nWTNlbVt7KIWEuqnI+zTDTTxkO8/koMIDSs8mjF5Xh3JXeRqb6rycKrS4QuIZDbPaiWg9r+l40pYZ\nhQWjppTbR7nqHH0zf3/QujWTRjTyeLlmysY206BOhov0ub6uwry4hLlr0g4XvS7sUjOW9ezUMaFh\n1abClo5WwuISGDWhZ1TPbazZSGYxNNC3blIX91h3NqMzm5mXs6Fph4G269bxgKiAdt3ApFJLYcSI\ne2w6YeCAhtvO596NaeubVmnEUQh4w4gVcw647aLSho4jBtoGXtDW0/aQroaBKYXvajmku11j9Fbi\n7/txxtDAptdM+LjI9I3AxmASKwobCltpl/mA2iB6QZ1pW2Wr3lEn1f3+23towzgkEP11Udj2Q4Y6\nYtEdschVK54TmZ+bojL0pgDICVUId3grKknOO8PNm46aNmXN+9JecsSIKUMXtYxo6us5r7Ch4VMG\n3hCEu19Vd7JO0d4lJEMVfj6rvkC5ikIdCsl3VKiLr6nV/6voazqiYcqW74rErupek7cEs+gJn/0e\nhW56bWYFUztmYENI/qri9RMY0dAW5eCqqNmmkO5V0ZeVHOt9+D9FsZVbaqS6qm9CYZ+4sPmwUaW+\nJQ373NAwdEbtCaks7zsFAy2tbpe926VlWsuILVcFg6rcpLuMu1fXhHXr4qbxsaxMdUNcllzaZdIx\nrNnnktKGlmVrttxQWDHmikkPWja0ZcnQigXd1LxOaJqy20k9lOZsmNawkC7RgYYJXT2VIT1yVjdN\naOhqum1Tw1Ajzakd789AvlUrtgSzWBECY16B0mFLbisdEpG0HT3rBiaMOmLccQO3DAyVzlhRmPKo\nDXv1rYvyh7sS/84obVrdvuA7iutUGcWFGQ2PiOS7N4X3cJf62owtkYVdJWsOvdv2HjKMtwTS78oE\nr1m9vIIvkPtnxJWEx3BOx+NKO3QNhftoTn0nw14hTUeExItISNiy4Ipdguh/EZ8362EbFi36NZHJ\nOKJQmvC4FWdFxOgrquzG2uZxJ+fzuDjbR/WmhpdEUZMZwRTuFxrKTUFQDcFoQlvoeNCoSYvaIkjt\ntxX+jnK74MyyOHrs1zSq56qwXzwlpM/9Cb9FIXEKhRuaPpORkU2h2TSFNI77VQKRqopY+7O/c+pL\nfjqpkrOaN7st+6fYmRGY7Xyv0uKqit1zOe8xdf2ONV1D3e0q41dy7J/FqgVXjdirZY+eQldfz9CI\nUwYOK/Wc1LWu6VraMVomFcaMeUDPTccccDstG12nbbqs701NY9p2KJ3R8343MeuAgXU7HNAzrquv\nY9qyRW3LumbN2mnTbkcVbhq4bGDg9XR1fi7FxIyG7xraUhrFCUXe6dr0lo6HXfVNNLR9wpqLyoxk\n3jSp5xVdXxcGyn1oO+qoC65ZsqiO8FxRFw/uvgP/+qqaKUMzKUAHwlFQxeEM8IfyGFNdr1FFK7+7\n9h4X0HlDcL7TgqjCQxEEcUmdfXc5vz+ivvK+K87nfw5/R2gX44I4WgI4h4TK38Zfyv+fNOKHDIzr\nWxLaygVN77fTYQt+O6NLq0jA+1T1GEPN/zL+lgiR7uE+kw4YeMGGN1X2gPjZl8+9s6jMcfWdHTsE\nAtzU8YfzvFtJhqlcc2UsK3Ouj6vLAozkmscFQzqJ/z3n3E14flJoA9UlSIU6UrA683464zDeVm73\neS7HeFkwgv9YnezUFVrMmzmHw+goHEvpPZJr21DoKRzMMOqqMtWvaviQcOcOxQVHLeNW3GO/MwY2\nvI2e0i57zCqNuGXT0Iqmllk7PaB0VWHU0AUrFq1h1KQd5jR0lZY0bBiY0rTu6x5z0IodXrOMi6ac\nc9LPeF4vPV+bdpvMqJpreVn3pNK+vEaiNK1wx9ds2S/uUmmL8gRb5jVccElpynGzrvlqRpVGvEdh\nVdTneE0kHlZH2XO5T23BXF8XBZj2iKsoV96Bi38vv79HbRu6IOJq9oqYo71G3IMXdX058ek+/NgP\nahzGgnArDvNIctzQBwRgvi0A+KRgAB8UavBN9VUB88KF97xYxn3GfVrf83m1wC1xbHlDbMqDYmMO\necS0BW96a/tiosMGFixsx+r/I0Egc4JIJgSBDPBX1LkiF/C6wuc0nBIS4Bkh3Y+Ko9KT+DUhmasC\nOqO5/oqol/Q8nPMcy/Utq28635Xr/MtC87mkrqpVwaRyKct+70uYPadK8a6Z6qpg0v3t8Xc6aeiQ\nO86qC+NURtyfEEbV+3BM06hCz8AhhWOGfhO3HfGkG0rr25c77zVlb94F/5xgXhdxv6G3TDhpyiE9\nt9x23YajXnLO0FMaPm/SuA3n7bRsYMYN13Ep7R4znrJiyqQ1L/9/1N13lG3nXR/8z96nz5k+c3uv\n0r3qlmTLxpaFMbhgQwDTwQQwEFrsAKEnJkAIONQs27wQICG0lxKDYxuDuyW5yEWSJV3VK+le3Ta3\nTC+n7rPzx/PsOQqLrDdBL0uLvdasOXPm7H12eZ7f8yvf3/cbcxgVjGvpu2BE5owpxwyU1NCxxb1q\nSkaUVdVMmHaLixKHVT3pbpnrDJRd8C4Vo457Jfoe0VYzsOqchp1WHMeagzqaas7qWrDgvJZJey15\nv1Nuk2+SQk0Zca2KS5adNOQsDR5oxa0CBWJRkSryewV58mgcW4/EcRy8ucRRiSMRpfrJ+FxDx3Rn\nsyO7gAZMeK7b82gwQq6i4kUq9um7HHsOxhRkM6NusqFiYK+wsi8IiMlcAPYclvo2A0s4pyuNCaCb\nDbkbi3LeYWGCDTzoT2URjBSOGYSSgtTBfcLE3C08pPsN9TurAtvWbsGIHcS91p2I19SM5150oV5R\n8EYOdUIKcaFikl9nKIp0SRhER9gkp/m0sGrUBNToESFfcCgiG8/JfF6Y2B8RyI4/aaj9UYgv3SeV\nKvmOyLv0TkFY+lrssOyUoWGZNmSfLnoo2qomDJyObfPXyDXl7hE8opuct6jvETMO66lZ0beuoW1d\n8IzOCt7NdmRaUuRKZpVNSJTM2G7O1TIVk1Jb7FaRWov8E9P2u8YWFyXmjapL7XHEusycy3q1kubI\ntIlyanaw3xOddd31NYv5msys7UZ1XbYgM7BTR8cVJ81bN3AN2tbV9W2114hJJQ9YNXDamgPyWCgN\nCeLTnjGQWtGzLJfou9uKCbmb1U3pekzTjJ5E20rMmcwJk/fuOFYnNY1pu6IdBaoaJozb4aJPCgb+\nv8Tn0xAWwlAizwUscnjGo4bsZY8IC8dhYdF6JI7h57Y9jwbjdcR00sBdMQzoCi7YfqTa3vssF/nL\niS5nuIH78aIYn/0tbpD5WMQRVAzFh/bggIrr9d2jZK+ufTEuXRWMwGnB4j8sGJj9Ql27EB0qP+tY\nmSHHxUfQNNgMk4qKzXr8/pItrrLkrN5mkqwVv6OATBcCzgUdfMlQjb3wBh4WVve9wsQLQLeQwkoM\nWaGPGsrl3WuYGG4KvCBt/U3xnO+Lnwmt81nUTimbVbFHy0mpZft8mbPep2evvlrM1VwxFDAOgkEN\nh2O+4oXWTMpiRSegFw+oq0QA1pTtttlnh2csueiyhmnb1FUNXJCpuVpX1UGJFVVnnbIUaeYqU6Mm\nr6u4+Xjm2JGSHQdb9uyp2r6jZnp2X8gAbNDtZUppRa1WUquPWFqade5c7vzTqccenXHPF1o+8+lF\nl04NbLXbir7jJjyhp6Qidb1OLENuUbFh0qQRXVV3KPmkRfMmdWOCloqq1IRXW5EILJslfammmo5U\ny7l4z4r+k1sVjHJ9dUHVflEqkZmytElNuRif4XFD/eBEoFBcNO6Yy7YaEhktGIIbi16ng/H1c9ue\nR4MxIqA9TwkXOmvIznRBUAgvYvzEcJLtMWxiqguUe4EcNt9k7toirPKnhbzHnIEg1huEn7tC5mYy\nfudGPHaRS3lMyBvcGM+1QKA+I/FqddNGbbGoKuhpLmuYlkhsbCaqSkI576LMVc+67keFyb1H8Bo+\nIBiS7zYUKCog57nhAy8IX1cU8ox5LEXWfLF5lww1XM8bqm91FKXqACDqCsbu4zhm1FYdZ/W0UDOQ\nR37PY3J9S1Zl9uJoBD4Fns66vobjFk3gcT3jcinqJqW6xi1L1cyoqlvXwaPqjtkw56wRa0oxcVjW\n17NuTWbEtUZ1cT5h5/WPe9PLRr3wi3a44UUVUzOJBx8cePhE2+knEg98IveFZzL5hcTSlQ1rnTB1\nAyGOOoYAACAASURBVFNY24KBidIux6a72rs69hwccd2xsm98Q9Ov/Eoq63H3X6fu/gve97GW+qCm\nbcW0mtGYOJ/U87QVbXtsVXfSqhlVbdt1JGblSsoumNO1YYvrnLNow5MyY5adkSkpR+KcsHjMxXsZ\n8nYtWVwcxwXBp3m586qu0vURZbfLbJdvlkgXJWYM7LTutCFp8zNs8q0sCAvUqjDVT3mu2/NoME4I\nk7noE9luSBxbNEv1hdW+cO8L4eOaIenNJ+LvQtOhQEw2BHdvGx6QRSHkQKe2ED9fJAoL0Z9CB6OA\nO18wJErJlCxpqOla0DMaS2eBTn9gUuJCPHZTAc1d92k2rf82wUitCitLUZYsqOAeVbR3h8nfiscv\niGsKmvwzCir/TFXPvng9JxVI18TLngU0Ctn4IdK0oDZ8Sawx9FVtkUv1nIkQ6R3yqHlWgOGCIQoe\nzcBAZlTV9bpWJRoC8KiiKpD056oyo3ryiFqcNtCxqm0lTo5EKtO1rqKl5NBs6hWvuew1Xz7jBa9M\nzV/Z7547ez74N4lfeGvPE08kFvKCB3aHHWo6yurWZPrqGsZV9c1Yl9lm3sWsZfFyw8nLiRP3p+6W\nGiA3cPTq1B2v7/nBt1X89Na6P/9tfvU353Xmxy3pWpMZVbfTtBUDq85YM62yyQ63rGtNXaphyrpF\nTYkZY1ZsyNGKdZyqemQzOxefb0XwUrdHdGxfWATX5E4LjYzb0VR3QMsFmfvjfNmGXTKzQv9TEXom\nhtqyA0Pg4U6px55zYfV5TnruMuSlLJCSA0Phl2ckbosDf0yYhHNCsucrVDR03SOUTIsGrUKLIovf\ns2xYlSjYjHYYMypXt7Y5uQuVrXnBgOxQsEUXDF+Btu+UrgcEPYqiiWy7jlbcd9YQm9HUtFtLXxDi\nPRT3KaoofaG9PI3neCq+vzfek1q8R8vxvcAEntgq6Gye13Ixsmx3BO/qkpBfKZi383gvivxBUzDU\nN6Gp7ZJMVdlsrJQUodohQ5GljiGrV+B26JrT97Cya4RW7F0qLutILenKopveM9DTU1GROCwxr8Bo\njBjVc9aOHZlv+NoDXveGccev46Mfafjb97X8+A/XrZyrWpVY1DOmZIuyNVVdZVRciOvusq5UVUNj\nEw2TKanZasN6VIapS+V6cqHneMODj7Z89tEx7/mPFTdfn/jyH+S+xw74nd9c9x9+acFgrSTRVFaN\njCd9+zRsxAzSgrZFLXVTSqZlWDBnTFch1Fx10ECmJFe2LCwSNykU3MN42yKVydXlUjW7jGha9KSQ\nJO4LmrZFJW5U7pRQeSnGfkHEdGscW8XiyJBE+Lltz6PBmDTUZwgIvFQmNaGvLXVUELSbkTkSQ4h1\nRRIxdGZu1d102Qti1kK7ZEmI9XfF38cMKxPTamblcmubFPVlocx1WYgVnxEmVsF7MCEzExmqthpy\nISSCJ3LWsJmtyKFsUbVNx7KBXfHYFwyZr4u8yJRgrPYJSdXifLZLjar6grZU8MqCPGFiQu5RQ0mC\nAg4c2LdyDwuDJzdkQh/Ez40KntuinnV1+2Q6epse3tln3benDPEbB1TNKunpS/Rc1o2Ykb4VTWPK\n+ro6eiaUNZUNYpKUnkzNNj0LtjXrvukNJV/5xq2O31h113sSv/UfePeHB8a6o86bi+nuEbtVjKso\nlFYXNJXMRpB5HammUWXpJjts0QB+CjS0pZE6KdOIfs2SlhkbyrYZ4P4HuOe7GP25njf/XMVDj+z2\nY9+/4W/+x7ola0ZjN/SKdVuMRHhgU8u4RFnLmlFlLcsuu6xmWRCR7qFiENGgqUTNkpYH4nNoCoRF\ns6gIdDfbNCVWlfSdt2Fc06iOsZiH6gl+XFm22cVa97/mwgpOlBo2Yjj+3LbnOYfxJNpSR4gOcc21\nUnMx31CXahvYIo8Y/qI5LHdGZ1OsJREMyaTAK/lJfQ8Lk3FdyE0UVY4wya44b0gI82j87GGhMnCX\nIfjriKJMVbTdhwmdxtf3xuOGhxJWj0IHZKtFHzWErI8oNE5JpO43YtWaLwhJ4MJwFII4A2Wpphu0\nfSB+74sjEKjHZk/FUjy3DwmrS4Aq1WyTORPzLFvi+11hGh0VKk/XGjema8OqBYFQebfgyT0ZXxd5\nmVxDWd2oNTU9s1LTmnJrTlmz1VazyqpaEa85piSNALIrcjffwrf9i1Ff/dVVn7+LP3xHwyffR9op\nGFnT6NtNWrFuQtVBpc1unCByGNg6C3P7eVRjhiATWr5yA6PKDgjaZkW7ogjKLqko2eJqPGzDSuQL\n71jXeWbDT35b3R0vq/j532t6xWu7fuDNNRudgKftWNRRdZ++3iY6Y0GqbdIWmYZUw4gJbQ1tCwLt\nQUfbuXiu1+m4GKt6q5iTWVSIcG2o6VpW8Rp9/xkjJu22qmrNaYNNTM+TcfwdksoNPCUkyNcN+WZG\nBc+2kKD4h2/Po8HYKkzW06oCJXrPQiyMjrrsvRjV9bhhv8SU4G5flPqEETdYs0/uT4Rya8uYHTIv\ntOITQqhyneDFXFAwV4efrcIEXjBkiloTGMv/TO4IzkVI+qEIz/6sgP0IbnWqKTSHFZR9fcN2675g\ngApi3ILFeV0g2qmpyR3ycg+Ykm/qmlytgGnzmK7cvA8JD76g6ZsVpldgAU+sSE3INLCm5A4DiS3G\nrZqzvJn0CuzeIZl7XqAQeNiKlro9agY2LAgyCQNlDe3N6k9gqV52xXJENTCnarejuF9X05QFVV2n\nTKgbs1XDwO5qySu+PvOlP8DOLfz2b6VecGygfbEUA4MQJuRxYp/CTomOCWNK5g3plscF//GSIf3O\nNJ6y5oqKgaYNPTVtEyZMCSnVFyqZkyipbYYsqcQZG9Y844WudgX3O6Vql3MS77+rY+4FI37od1Mf\n+ujA13xFrnElUbPLaamWdRW5NUvKNozY6awn5ebNui2yiwfqwnKkLehZ17Xoslzda7R9Mibrm0LY\nuSigRnerusO6RaG35n7nvNu4V6tqartsKCzVjWHTq/X8afTGQxNa4qDEuIE7/f/RS/I8Ij2fFsqF\njfj7hN1uddhrfMyHhTInwa1qCSvqmqE4S1uiq+r1sVfgtLAqFnTqQaQn8D/8vBAzvpbYeBSG5bKh\n0PNfCsbodZqOaHtUJjdjL86Yj+i5cIyP4/dN+EUbnonNcbmhJsY9AkipsOhFN2xHsPwn4nm8XvBU\n3iMkaK8VDE5VwH/cJYQZLxU8gs/E/Z4Q6N3CfWjabdIW53wS+21zzILTepvJ4iLsOSWRSb02ZnhO\nCULLdxiKJG1RNWG7SYe1fcx/NfByw76ZlXithfEqKjgNW4xqSbW0pEqmJjd81/cmvu8HJj300MB/\n+k8P+8L7r1IaVDQVlM55bMpONqV4HseSe2Sm3WiXbUYUYgWFxlfBOXYIHzOspa0IpvGIYK4fkbvP\naTfZKVeNLXbB8FTi60JBduFZVzcaPxOI8HJf/vO5r/nqxFtekfjo3Jw8VqRCajHT142F8MSSEwpm\n+lH79Y3omIshZCGXuCgscleEStlhwQDUBBOYGJILF/KJIaxNHRAS7XcLmJurDUPH8XisQrrzPkPZ\nz7P4kX+qFH0fEB7RQ0Ic9zKpTNlndV0R4LAbAh6i4KnYLQyZQiZwt9DwU6yghTv/bNarQvQn8Guk\ntsu9X+6KhlsE5YsPCw/rBF4tENuuoCJ1tcAV8ahgtApCn6bEDUJD2wPxfI4LE/mUMBQfEh707YYw\n7yKbfVIYNMcEr+Vi/LvgM8iFwdURXM8dwtRYEQbFAYXaWmIgkcW+gmulThq4Kh6jICAudDH2xnMt\n4x12eYsl89blGCgZ1fBCFYl1HV1/JijFhXJezQ6J7brOSj2l4UZrPmLa66zI9J21dduaH/uhI/75\ndzZ96r25v/zl1Lse6lr0sOtc64xyRBFc0nPWjZpe4Cqf13XCFdvsdLuep6VaUr0Y3e+NV/+YMEU+\nHkfOsuE0K5aVoqf1WkG/bVbJLkNW2EDNI3o4gY/tXpxxUVfNDqMmlC1ru6KjasLbfjr3yq9P3PHS\ngbnlebuMKalbFlAuW/EZgwgAPIUL6m7Tl+pLldWl5nQ9KhjgPYIxSIRw8qCh2lk7Pqc1bNf0Ii3/\n1cBuQTlvj0RF35Nx3BXEOTuFBfGioQj4mKHuz3ODhj+PBuN3iYi5IaNVoeBEGB4PGIrwpFgw7qiG\nV7notLCSF0m644Ibv2jY1VeUmQ4K1vmMYHQmiO1MiVGZQhLxYYGQ9//F9cqujTWGtjBxiyTSF+Md\nAiHPNcIkP2NYWz8Qz2NV8GAKsptdGBH4m5NIjHJt/N7DgiE4o2qncfsw74qzcf8HhcH0V0J/TFHJ\nKdiWluI9+7gwQOaISbQhcU6hLpYLYckXVG3XN2GggqckViVuVXfUXrMec7+ya1SlRmVSFWue0DBn\nu5ucENTDysbMbJ31Ez9a8a3fnnrPH/X96S9XPfGMGMDkNrRNqnvaJSUTdinr6upL7VXXlOvq+4KK\nqwwL0R1csKKsb8a0tXgnEsF43KgoJg+FFWexng586ciGX19v6ueJbYbBXEEpVARXQYPvvG0mNdXU\nBNaRVQNLglDCNL7vP3H0KK95bUs+OIkZmVkVAyOReasqs6QbEa59+4za0HVZS0VVWd+GUwod3op9\nMp8xsEuhvJdZVLJf1yLeG/MTU1g15WYDu61YFMitC0LhShyHlwzZ51NNB426ykX34Fv/qRqMPzPk\ngBhRsk3FtMSK1qYr3zIEOC3hERW7lXyJtnPCJChg2XOC07pNwQNQNqFhj9VNfoxHhLWpcGALfEHh\nDD+qYCIPGIWDeCCWrw4LD/hhYZIW+qqFBsp83H9JMBK3G1YvloTwZBbXSZSlrsj0JR406hXW/WGc\ntC+VmlCJdGo9V5Tt1/XeeNyCzbwolxbSkAUiNo+vi3JdoOUf8iMUjF9VYXAtYZeSPYJAT1duUmLc\nVlXnnZfoudEOa+qWsO6yzBUjJmWmfelk5uZ/3fNd3zPq3X808Gu/mJi7kCgrSRSJyiE90qi2FRXb\nI3HdhrBMNIWi7lWC71ngbIPMck9NbkF1c4p8tRCkFVmoQka7oIkZJLmxUuahfnmzabzgt1qOd6on\nOPefsWJcxaiG66Sb2a1CI68f7+ZSif/yocwHP7Tml/79usSUzIaqjkk7JVYt+piuCamb9XUjG0hF\nWypXUo1VkmXzgvzhkkADeFmqjq1yecRhFP0kI8q2KutKXdZzTs9aHLdZvFN1IfTuCLDzU4Jq3CFl\nY4LK2r/6x20+S5Lk9wRc9qU8z6+L7/0M3sSmDtxP5nn+/vi/n8B3xKv4l3mef+DvO27TrdqelikL\nZDCPyEwJk/CMYChmhJtwRRHZ9tT0fEoYhluENeb1woScMURv9gzM6MolvmDSV1j2eCwtTRsOh2JS\nrwsRcV1YubfJzQkGYs5Qg7QQFSowJEWys20o/bjXkMl5PF5PXzCAF+Tqsk2ZwO26qnI7BcOTGrgQ\nYclVibYgoXfEUE/1hDBQpuI+W4Vpd8JQZKkgox1INvsOuvF/Ce7U8Ep9iaamrrJO3K+kbVRdQ27M\njDVnzGnpq2gryU3rK9morXnz9yf+9Y81/NX/SN144znNs1u0NTYpYUqKAC7cidDLGhRT5xTmbuCS\nnnF9+zQ38wrbcHVz3al+WbtT29ShnzDs0ClQO4Wy66hgIlNs5Imz/bKSYbJ0zZBO6LKCVyyxXc3t\nSc1d+TmPGlFR11TRULWqb0VHTdNy1vfd3zxw531jPvyehpMPVHVj0nbNJUHhfb85a3H8pDomBUOc\nqhs3rm15k5iZgacV/JyDqE8S5BDXFITKM260ak7fWQOrBnqGjYqBUKjsOpkV+f8CEmzr6+pvlu2f\n2/Z/QtH3XwQO/mdvOX41z/Ob4k9hLI4LqffjcZ93Jkny935HaHcuVvmmgYGeK3rOGyqhF8xQXeHR\n7hKMxIgwsT/K5g2+SqiIFPRw0waqOuZRj1iAZcFQTCtuZhiyDWHIFbygRYR7BqtSW6Wb3KCJYYKz\n8JAKdvGrhfCnEc+rLIQ6BVvSRYUznBhVNiZ3Ucdn4mTeZugxBO2K3FmZL0g2cSQM2dMLjtOCrvBo\nfB2qSoFqpRrv3Yw08lcVvQkBDHRBpiNzRaDBD5QxhBx7XUNiwnkX9KyqCEnAb/yahscf2e2VLy95\nzR2Jn/muxMzZGeXIiZVYsWE5+o9h2KfxqpYMuxo6AndUSWKvNIKhwhOoYmOQauTJpmkux6d3WAgO\nC8zFTkNtuYJr7JxhK13PUD66uFsFLG5CYq+GxTyRq6gpG5EqSYwIfOTtqO2+JHHm/CW/9FM9v/qO\n1JS2LUZUjEuVravrRb2bsrLUiNFIshfY8R+Tu1sz9qCMqEhNxDPdLvCl9iRmBCMS4ASB0WtF3xWD\nzRxdQejURV/uYEz+t+K4LD5TcGoUjHX/8O3/08PI8/yuJEn2/z3/+vvcmq/En+R53sOpJEkKtaFP\n/90Ptjabw0YM1bvawuM+Jkys0M0YLn6LMBGKjtACxXY47jNlSApbTOjQop07YmWzxj1jyMhd6Lpu\nCA+r6EXJhKFXFgzTbBw6F+L3tYRy8C36Ho1Q6kK7clUwDIUAT8G4VTXUiChJVCT2CUnJpw2NV5G/\nTw3DicdigrUjsUfoTgw9JeEKFmVOCI5ggL0HLdDcQFkenf6yKTVJJOQ5qBPV0lcjCUvJVoltBkas\nWbZuSdWooFVyUcmka66d9PNvT01OVv2b7+j4/MdCg1VNT8NAIjBxXjJwUaAu7hqS3weuq65U2bTU\nHoFYP1F1rZAlWhP8zFaZC7sbDh1i1x4mtjE6TaVJo8zSgFaLwSIr5zn9NCcepn92qAtXaJhX00w9\nTy3mSUyN960Z2FC1N462pyR6ttsWR1uBES5LbVeLpIQliZ4//91zvuP793jVVybe925qqkZNR83W\nRbkliR6Rsyz0zHS1PSP3lFm3S5xTMSJxlbYl2SawqhAqGo3Pf8SS8/GZV1FSVpOYiH0pIdwcdi3v\njsdZMtQm2ZBEpbnnsj0XHMYPJknyRiHg/+E8z4sU7bONQwEZ/Hu2gto/8DoXehXhpxAuWhZuXNET\n0TLk/iwIahiyPDUNpRALIp2CB+JWIeSYEdafMwol7fCZ44I3c1wgIAnM3ewUiFUKPdLt8bs+r2J3\n7DAMIjP5po7ljNRMDH8mDaskM/G8QzPcQA1fhc9qmtTWiMCpqjC9kvidS0IL/6QkQuTzuNYn6lID\nA5elcpnTuE3JhJ4vKNmmZEToZUgitCn0u9Ts1rTXmlMRLlRW0lGS6jmpKlOOrdP7xsf9u5+d9uXf\nyC+8Nfeff3tdb9A1pm633BMWnFb2YjW7lVVMGhPM7wlDfvcaWrG7pCI1W7yXMHqc217KzS/i8M25\nLUcSly9w4cnc3DOsXEhcPsu5NWo9yiWmRqhNceAFvOxr2X0tec7JO/no+/nk/2CwwKF04LEscUHw\nVtZl5vWNq9oeR9ohwbyfkykJMk2B/KBsxsgmYcCaA67kD/itf9vy3T8z7vffHZarER0lK2qmbNgm\ntyS3bHlT5S0ES13HXZLLtW1YjuTDp2XuwnG5SanHlb1czw1SGwLD/pqCo7VsWiLV26R3nBOAeLfG\nOx0oJlNBd22gJLVzs2HiH7r9Qw3Gb+Jn4+ufw68I1Fd/3/a/MWpvNayQ7DPUfrxKWN1Dd2gwCgVY\n6ilJ5IQMEgDPSKzLvcJQGq4ipM5OCDftWjZjxH3xf0VPyZowpK+L79XQU1HTtyBoRRwTDMkZIR9/\nTtBfvdG6u/ElKg7ILUREZVviiJJJA3exqYuyz7ADNUjcJc6r+Fpdt5nFRQ/JNmXtCkX6Qnj3Aj5i\n4NoIgF6Te1pmTOYFUq9WtU0rdo92Y0/KqO0Cs/hA3WgEAt0klOq22WvdaVfrxxxNVVnDvGUXjHmd\nnXjNGxb82q/f6L73l3zl8Z5759eQmzJrp4HTVmL35qxzypt39dkwuUyYlNtQ1tTB3mluei3XvYbr\nX0lrlXvuZO3TvP+dvO8h2m2OlvvBnxyUjCS5J7PSJti/HkfLFkPuqcpepl6Re9nr+YFfTzz2N/zF\nr1U07glPIUVDTUXNUWG5SBTZAp7WkynpxNBsSuZh3YhyDSOo6noffi8/8Eu8/HZO30liXcWTmm7F\nVg37rHjSwEDFCk7p2cBVsfJW6MHWDMPuukAzMK5uXV9f2WE9mdz2OA43tDcRxi8QKmh3CwvSaLzC\noDKXekzuE3JdmT/7u5Pw/3r7P6qSxJDkPUXS83/3vyRJfhzyPP/F+L+/wVvzPL/n7+yTV/1CTPlc\nK0zm0wJuYU0IU/4tfkO4iYcVlG8l0xIz+u6T2FDxZXr+Wu5GQ57JomrQF5KFRXixKPSLPCrk1x8T\n3LYbBNzHp7DbVndYdkrPTrmpCLh5TNDVXFHxdbruFByoI4aw63XDPEvR07JDqLE/IBjDLcJqQMVx\nW13rvPcJ0Pc74jk+HPfdKkyLSUFLNShqNdykpxUTWUXkvioQ4lwUgF0z8btHhFzPhiAz8JQAiutJ\njCu5U2afRlRaT5TUrZp3wd7dR73jHRw5zFu+m899grZLyhixVflZZ7cF78IxmUsS3ZjvHwhmuyzg\nHF40zcvewGu/jr238MhHuP+vc5/+YG7smcT788RL4t0riPRPYrTUd2u9LS9lvrAyYTRepSR3Lk/s\nSIK44mSeBGLHUt/1jbZ7S6O+5Z8P3PqW1OMP8zM/xMrjub2QJ5HsIBiblrA0FH0oRUF/AhcFar57\n4zWtGJiVeNObE9ffzI+8MYztstxpXcfUPImej8s0bXcQLXPWhYb4E3Lb4pX2hIrGuBDGPh3H59E4\nVm4WcD+poDwXDFkij8cYCAvl5wybFQv+lDNxTBwVFqIb/vHLqn/XYCRJsiPP8wvx9b/CrXmef1NM\nev6xkLfYJcyUw/nf+ZJQVv0hYVK8XHD0HhE8ihFDHsPPC1FoKkykNH72sjABv07gN/wuIUw4ITzu\nGwRD8wSbJdHr4ukUOYyCJ+KKIeJ0lzDJZoVVeFxon8+FKXGnYHAG8XyvkzootxCTTcvCsAtw8mF4\nU8SVwS0tGZXaGqs9uTCRlw1Fc3txn0cEI/qV8VyeFAbF6+KdXBKm4kVhYH2TYehThHATgmEr1Ngu\nx2s4remY3crmLFh2WtWknQ4as+HFb2r7978w7c/ezm/+IpVuMA4r8cgJHpa7ZKCh5CWKbpan7DDl\niClJvJJaicOv5pXfzo2v5MG/4TN/ypm/YaqTmam3rZUyn14d30TjlHBVMtDNE41SCO7SWtvhkZb3\nXJm1M/5/6/iKu9dHXTWyod0rO90akRhKKM8KJvXRCq/+fr7lJ7n3N/ru+pW+B9r1TRnom+MIekJw\n6ufjUxwTQpSzOg4aUYl39/MGuhLXb0387WO5PTsyebusq63rpJrjulL75RbMW7YgGOuW1IuVjev6\nTHz2TwigvGOC6SpaDNaEUHgvblQ1oe9OAxsqjkts6PpbwdP4RiEkKYrLBR1j0YR5VEgDv/wf12Ak\nSfInhrP6ohBL3CH457lgDr8nz/OL8fM/KZRV+3hznud/+/ccM+cnDLspC+apK4J79Xg8/JiQEvln\nwnAcU/BmJsZUfImuXxeg07NsCjd/kTBknhEmzAnhoewRjFE9nkmRGL0oPKyThuzaOw2l6WaEdajg\n7ih0RM8LxqnyrH3OEhm3w7AvSHRHDHEPs4J+yDXCpC4QCpV43ncJIciEIZ/FLsH43CoYwJX4+aZg\nAIuybg9HvNCMC0ouekTXQMU+JbnUgsP2ecCqbcat+rCaq8zari1V2933zt9JzUxX/LtvT3zyRLjS\nZ9P6DHRNydyiYVww3QUtT1PfyYhq/ZKdPa94Ezd9Z8WVC7nOny/7/d8d115KzeOaxoY0Tz3cqVlO\ncs1B6jj+Bt86tuL8xojttY40K7vYqZlLcoM0c0u97frdZ338iaOexv2D1O3VrqlBaqVf3kRvjscn\ndSjNPD5InZbYvoef/MNcd5Xf/8ZcZWPg7qwcVWp5cXPdQ52ac/2ycza0DWw1KpWbjYjTE4YGaR5/\n9LHML//Hlr9+X1nQCO6ZdEhNasV/17YeE+PFONgm0RAIc+4y4iU6kas01zaUkVgWPOZHcEziVrnP\nCbmrAAsIY2hXfAqvFDzhS8KClwme6ePC4sVzxWE8j8CtbxGSgTcRlZ8St8cs/ccEI3BF0SYefn9a\nmOy3CIxDtwqOaNFvMsomV+cJwYDsFybx1wreyKuE8mdFmFwFmHjVsK29Y1hxrgqT+fH491WCE/UG\nQ/q+y8IQKqLhRjznQgpgVFgpRgV06DYlPRWHYyx6Me4ziOeQCHa4Y0i087eCATkuUN7VDVzRja3T\n4XtqQlL2vDGrdjrmisy8lsRJibOqXme/aVdkakrmLeo7q2mrN37zNm/91dx//Y2BX3pbyWh/aMIu\naZlQcVhZFtN5TbnlpO0baqk7O3UP5QHqds1VfNUPc/sbcg+/i798e+KzX8i9qtLzZK9iPg8Tb1s6\nMFlvaY5s6A5S5xantfPEM6iUMrdPLbiyMeJiu+GpQaolmOfZUqZcb9vfXPPo/Ky1QWq23LM8SHWz\nku2Vvn6574lWQwkLSS7Jk00w2EKZN7+DG2/M/dVrB07Ol2RxdB0vZSYGqVN5gE1lcqtKglLMwKor\n6mb0lLScM2PEd/34uJHtuR9/y8CENRfdr+wlymq6PiS13YTdBgYWo/9Ut+4GuzxtyZKangfj2Nup\n6riKDWvuEwK+eWGRaOKkUbsMlCK7W1Haf4hNdvIFAW08ISzIpw1pHt/wT5U1vGgRXxNW4xlBxKXg\nsHgSl6S+LJal2sLkmRfChKrcq4TJer8QBVUFq7yB81I1ZS/W9aAhTPu0YRb5jHCjm4Ln0TLksqjF\n12vCRL9GMDxlwYFdi595KB5vR/y7wDSejMe/NV7vvcZcbWDGuscMtPRkhl7WxwTvqFDlLmLb11dW\ndwAAIABJREFURPBsvhifscMui3I95+WyWJxs6loXQrirlR20Ztl5fV0TgjzgiNSEkoZz5lXNWPSA\nnl1GJ+r+4DcnXHM9P/iqxCP3lzaBVgXbyHEVLalLQjQ9JnHFogsecbL3Qht54o038lU/zZGX8tfv\n5N8fTZjPJWlmPC+Z71ZNC+axhNFyz8YgtbI2qi7RzsNd3YJmntjZaDnTbmiU+q4rJTr9kj2Vnka1\n5+x60zODVGmQms4TSVZWywOC4UpWkueJU6J5TQbW89ImF9tGn7d9Dz/xa4nXvjv1B6/kqXZYRs5n\nJcuC874mtUUILgNaM/G0MXPSCCmftGDR5z/Oz7x9XG5B2axpV1n0oJ2uc9lVuiZtWFGSmbJLOcL2\ntkrNmbYUYQOBZLmp7ymDTeX1QoqxiRljjsqdj2M6VwhIhfF8WcjPBaaOmjHsjIC8JwxxO//w7Xk0\nGLcautAFZq8l95AQjgRrmdsqEAQXCcSdhp7HQ8KkLgvO8mnDJq9MycvVHdR1UXCaC5GkxwzbjnKi\nzEHY/3PCJL2RTW3WAuCcCobsGsPmsaeE/MgBQ9awlmDlJxVkwByM4OYz6MltkTkjDM0Jk14qMWvN\ngt5m9LwajzUVr7GtZV6mF4FomZAT2aZmm06kEsxjo12AxBf6p7MqEbo1EpkX2lq+6CUV/+2P9vnI\ne7p+9La+rLNkXtuE3ZvQn5aQty+qH4HPLLMksSXfoXWs7N/9LLe8kPf+Mj/5RkY2Yn9rpeeGkQ31\nbtXWaler1VApZabKoaZ0plPTldhSa2vU22Ynln3+9D43Tc+TleyodM31S/Je1Z5Kz0S9bblXNVtr\nu7/VcNv4sk6rYb1fkeVJ6B4dpAaD1FbBOT+aJ3Y311zq1J3pl43i6pENn/rZjtF3TPmyd/L73xFq\nbHOGDJhtfad09aSOqqtKpBrPEuNs6hn47L2JQ1en6tUR3W6mZ9U+W7TiZwbWdAyk6ioydaklHSc8\nasFhmVRuWaYqNy/zmGyTRb4ANwaEcc82mSl90/HpzChyI8NqY4C9DaxHMF4BR/jfIBz+L7bn0WDc\nZAhmOiF4BseEwV0gMSfjBLtg6BkcFUKSM0Im+bjgaj0a960pSpkD3VjGKjoW9ghr2Hj83Tfs+ix4\nQQvdkGVcUjMjMaXtUtxnzFBQODFUVJ+L7xVt31OGALCwtTeh6NvjPkOk5ogv0nZeMGAF3UsBuqkK\nw3innlqMk0MZNJcb6EV04C3KOpGfe6euRQPr8bwrEluVZPoWdJKet/7EMd/9g2N+5LvmfeC9JdeU\n60aS1JKSCUOfK5Dhhasr4N5tvGDfiB/5+Uk3vZL//jZ++5vYnnfckgxklTJJrlTtuCRx9eia6dE1\n95zfaX+to1ntWmk11AapyVrHbKNlkCemkoGD48t2Ty1aaY24anbVwmLJympqqrFhMEi1emV5uW9n\nc91SwvaxNdV2zVynrpozVe1Jk1yl1XA/Bnmqk6faeWKs1DeZJ2bzRDpI/fab+NXPc+vX8ak/Gyqc\nFs78RaEdr2iBv2SIEa6jbsx6h9OncoeuGvP4g11lFZnt5j2qb6s0dlJnetb1JQaa6p5yKY69irqa\nvrpskzmtKPQWre17saztaWFx3WWY3J6P42Rd8M+6aOs5q8hvJZpS1zxvOIznvNXt0Y1x+LC7dEy4\ngZ8TJnJBbFs25GJYNewWKEC/BRT76vj6GSG3fUlrsyGrLxia9wmuf7F6F1wVPeGhXGNIlrtXapsg\nfnxWCIWm4r6j8Vyvjed1LxHENaQE/ILwsCcUD7Ea1TbaVoQHfwR16wbWPaK/SQhcyBwWtPHbsV/d\nfj0dBWdmyZSqMYnzetalqkbs0daNoJ7ZeG86Elv19LRmF/zVH203Uq/4spsTzs8aw+U+NdObpnDc\nkDqnoA7OUR3njT9V8vrvLPng2/nZ76W3FkzsbZMtjTyx0U5VkoEreerR9YYDpb40HVjoVxxrXtHv\nVax1qyqN1IGXpA68sK55uKa2d8YX7R+ozR6SjqTyHq9ISEr0lipWz3H58dy5L5Rt3NNx151NR2bn\njFe6StWuwSA1WspIcovtugOlTLlfttSuS7G/0pMNSjr9MPR3tQfe9c8T3/ouPvn+RHl1WD/boqwm\nsHCekVmypmnCZcFcjz/rKZ16nCNHMg892LXdQU/ZMPCIEVsEIuQn5Hq6dutYt8cxSyYUPKypA5LN\nVO1YfGZdwTDcZQhWvBR/b43jrPg7N2yT2B2PW9AlhB7eJApYP5fteTQYNZlSXAGPG676Z4Vk5R3C\nRAudp+EmBNaiYfvu7cLN+Kwh/2aPzXb0kpBhvmBYJl32v+YoiirFWPz7xYKLtw/7opZEgeOYNsyN\nzxh2Ch4ybJArIOdThmHPC+P3nlE1pmxE2zPxeiGPalhVwXgVJd5UYmkzxGDGuiv6dhoayVzFmLot\n1typo6NqTEsWjUqAtgeu9aoX3pb5jT+9xvv+KPfOf8NG1lGLicyALS0bkZsQONC3xLsJzYRXfxvf\n+At8/v38wLVszIWrLXozNrLUpW5N3q2A+Tw1Xcq0ehULa6O2jvQce3VP6eZJd7y4YuJw6vKJutYj\nud4Ta078edO+/nmzgyvOnBl3ZWVMu1fRy8sGzZrSjrKxqzj6oszk20Z86a7Ehb+e9tjvte05taKc\n5JY2RrSzkkal63iau5CVbKt09QepkVJmWSItZcZrHWm3av4ziVMfznz9j5b8zr9JTBv2ulQVpDqZ\nCSsS47oyk0r6kmhI+y6cShzc18cZgT4vUbE3BiRzAmNbFalcxWCzbzNgdloGQi6uEPJ+RPCqC0rH\nh4Qw/kr8fxEeFwziTwoh++NxrmTRs2FgTe6S/mZD5D98e94MxpJPCIpMBcqt6DhoCIbgc2wyHxQt\n5TcINycz5HZ4mcA6NG2o+bE7/hRyAkW3aKE9cgO6Uku4YuAhieuUbNM3ZtjGfsaQj3NaYr/EOUH/\n9DWK5rRgXE4p2omDcXhJ3Lfol7lPeHTrwsAo9FIrwsN+VOLrYnI3hGiJ6yVeJHdZ4VllLshtUTIr\nkcmNWJNb0RQ8qHusm5PYJ1GRm5eYVDfj+76v7C1vLfnON53xofd0HHBAli5YzCe08r6eVKKpLzcT\n17sHBZPz8uv5od+kXsr9wVdnPvM5ullJpZzZ0S+rlftqSe5spx5aAyt9y4NUmg8cn9zwoq9dN/Il\ns77iS0vWn9rh4kfbLr7tjLs/XXH/mW22jHe8bPc5lcUpndGOtUZZJc0c2XZRLyu5sDRpfjVXWh+o\nnt/w9EcSl9dGjewu2f01TS/944aVBxou/fo5vUfaeq0Re8dWLayPmkwzaZJbykpqjZaNhHvXm2yM\nOBhHxtv/bdmvfI73/xITa0ORi6YA/D+r5KA9TqBtQ0PTqoA4vazjibmSmW251CUXbFNW0nRNFLEq\nsDmBMW5D6glPq6vpqMrtE8Qc69HMXxTCi1lhsdoTZ03hdVyM/+sJi9uLhaT457BTQ1emHMfOmq6P\nCL0kx55zL8nzWFb9bwpi3kQH5+Wbgka7BFt2o1Ds2hC8kJZgeWtCkrJYeYtW9aeE1b5oY7pHsMqp\nYIR+27BsecGIa5RcZdXnldVt8WXmLMck6zOG8gM59ikZUXeT9c3QpCcYqieFCb1VEfmG67hfKOeW\nJU6DwDUxK5EY+IiAKXm13LqKL5K5N3IgTMe7dSle31Uo2e3FFjxixA5sj70sPQubieCeSddKla36\nrL6yidoBv/HOCTffyvd9VeJTT3JzhKi/vrnhA52a+X5FT/DFCk7xk5ho8MNv5Wu/nf/npzj/B10v\nHlt2CQ+vTHjx+LL3zM/65u0XNMqZSqkvSXLyRHJwzN5vatr/VVWrj/Y8/ue5/FMXvPuBHQ4110ym\nA/kgVav0ZIPUaqth/5bL9s5ecXZ+xnqnZrnV0OlVjNbbju48b+vkkgdP7TdS6/jIE0cdnblibmlK\nc7zrNT+1Yvu3b/HE25f8yVun7W2uuWX/KSfO73RycdpcVoqpdZaSXCXJw/qe5FYHqR/988TnPsiH\nfmtYWK/grK6HbNhn0nZBsrolEPJsE6b0q7+Hl7yAX/iewPu9IHHOe3XdJJdFjGhL7pBQERl1xBaP\nWpU5j0zdToFj/bRcJ46hp5V8ROZbDWUmC26Ti8LC+RIhtA1VtVtMuiJ13pKuz+G01HVK9ujZ/U+1\nrLoo4OAbEf7TsOFpAa32B4JVzYXV9nZh0nxcgG9ngndynbD6dwQjMRDcvncLZaRXC0N/IBiPLxKs\n8CVlX6nraOzyXNbXN+femGTtCOvqM4LXMIUxmQXrRg3DkMcNQTKFZuZi3K8givskjhh1nZ5FbUty\n8zF7fUliu7rbtT2qpyusFn1hMDwTr6fAcXydpqqmF7jgolVPSEwKTnQo6SY+bsJVlpQN3GTf9sSf\nvit16Wzim29jZSPwT3zGFazore82p2JSMEnXxrM+gBe9nJ/+He77LL9+HQ9eYkrVB7qznhGqD4/N\nzwat+E7NQ5cnVJKSQ2/gpT+YG9uVuOv3+PNbM7vn2yZGNhzcsuq1uzLvvbDTS3aeCz3IeeK6PWdc\nWR1Tr3Xs2j7nobO7TTRaapWeuaVJTyxMO7025vDEUsijlDJHpufVyn2DJHf2StPHf3Hd5B8vecE7\np73x6r6/+PaaUilzemlKMxnYn3JpkLoisb+x4aXjK6qlzPhIy8mLW/3x7417008mPvpbwyzXY7oe\ntWZc343x3nzemrYRGxGf0dazuEbSrFg0cM6aijHbvMYlD2kbM+WVEnXzghpd3+XITTqCI/HdtppU\n3YiWk+iqOOhqd3hES9+7DBk9xjbHJX8Yx8kG/oV7jcmdkm8SJQ0M1A2elYD/h27PM6dnEP5J9FVs\nldivs9nfcYjoAIaJPhf/LjgvCzbuEQEq/ZTg5i8JhqYAMe/HBwWSnbH4mQ1JJOgJbce7DCsjj7BZ\nrZgRvIcRIZR50JALqi54QNW4T1fqeqlU34cFsuD3xnOdktotNyvokhZ4j48LqL/vlPslXK3qpQae\n0d/UJ9kutUPD9TacM+0qLb+v7bCBY4KHVVPwL+yWu2RcJnH7jfzuu3Pv/53MR36u7HFDqaeTMvsM\ntJStSewX1q6z+tYbmbf9h5pb3sC7v5e/fE8wJofGVuT9isutRqhS1NryQUqe2L1jxb5varj6e0e0\nzvR95u2Z8x9s2zO2aGm9aba57oFn9rr14FNq5b6lTs2uySXlJHdldUwpHYT3Ww23HHzKoNr16Kn9\nLi5OWVgbNVJvO7T1kqmRDSuthnIpMzmy4QMPXqdZ6+hkJYutEbumFh3dddnWnzusO6g496MnPT23\nxcLaqEGeONurWshK8mSgUgrTtZ9mjle72tXct50c8/3HEunFQm0m1zGIqrmlSME8MCq14LyC//Ur\nvq7ka75ml2/4+p6uCxKXXedaT2tZtawUk5bZJgXQskRX7oCQQ7tT6FXaKzQWBg83cUDVn+o6IHdJ\n2c0GLhvIhJzFXBz7swJc4FpDkqejcWw+IBAr3YCdz8nD+D8h0PlH2gqQySNymZ6nDdytbo8wcT8g\n2Plr42nuFryKA/HvUWEY94QKxWOK/EXF7aruEAzBitDtelRw80MfSa4mNyl4H48JbMxTgnTdecFj\nqAn0sNOmPei4LzUs/V4vJF4/EK8lEchoLhsqv3cF7+N6A7vkBnGfzylZU/cGzMo9hQeM2SHRkTkr\nGIut2C/ID1XNOGTNY1oOGmzKKa4rO2PauP3qLlrWcr+v/YqOP/4AP/NDubf9XOoeK55wcZPx9AYl\nB1TcJvFKoT7UxNe8MPHR+yqmZ3nzDQPNz1x0XSmzjmdaI7qDxI7GhiOjq3aPr5ia7Lj5LalXfWqL\nyVsb7vsXl538zifV7znv0MS8bc11B2fm1Ss9Lzz4lKt2XNDPSmZHNozVOpY2RpxfmtTNSkbHVn3i\n4nbValc5Kzm6+6zbXnCvA3vO2D6+YvfUomrsXN19zQmnrszaOzMvTQcm6m1XbbtovNa2sDjixL+c\nkyUlE//ykKmRDf1BSbPWsbXcCz3MzQ0vmFgylpWM9ao2OjWXVkac+BjXfQkTaeZAOjAmkSqZVVJR\nGNwgijRlJjKAjhsko/p5oAgYtVXFEesqMheFkmpbVcV4fGYNp7zcThUpm7q4G3L3RS93m7Cg3a1j\nh9CpekhmUb7ZFjAuhObXKjnigG9RtkMIw4u8WUvqi5UdErzr57Y9jyHJeWHyBSaG0H570WCTBP6c\ncDMKReoLggtWENVsFSb6DYIXMKVQd88MYv76gCFpzhPCtChCioJzItQPwjmcEyC424QI/gFhBW/a\nsOiiqiHb8zY8repmmYmYrHomfs92iQ+q+gpdo7ExbY9gBALOYmBZP8CKhMrQP9OxJLMhtNXvN4S6\nN/RdsG4a24xEtumuBcGzGLHhlIGylk96y1te5cd+pOpbXstjn0uNo69uS8RXTMerKMhqKjha73rl\nD5fc8YOpv35z7sN/kbltbMXp9qi5QeKGqQXNPLHcbljLSipZ1Y5vrLjhX9dd/lzfJ75hXuuJjnIp\ns9aeNdepadY7DuWJPE90+mUTjZax8RWDue2WN0YsrI3a6FaNN1p2TC3aOrHs9n2nLayOmfyf1J13\nlGVXdeZ/N74cq+pV7Krqrs5B3a2ckACBEJJtECBgRoBtZDwGY8xgj22CB9skM2CSDcxgsJAAg4mS\nMBIIBSSUUyt0rg5V1ZXr5fzeTfPHvrces2ZmLRv90ct3rV7Vr+q+d++75+zv7L3P3t8Xa5AvZUgn\nq2zuK+DZOqrqguYwsvE0i7MThHSbqbF51opZlkoZQrpNLNSh1IgxX8xy9G1dXv9IhPJjcYZ+XCHf\niBIzLKKaQ9PRKDRjtD04hMKkbeB6Cqd+CedfAtPfVqnTq4YIyuciCGiEAJeQHzjHiZkVrE6RCbIs\nEMKhyzIH6BDiHHLoxJinRdknwbGY5CR5HFaIs4k2TWx0ekSCMcQ70JHQexpZlAJNYpAcmQn049Kh\nyN24XENPSOFZ4DgeBUxchtj9ojdWzyJgTCFfvIi49wnc9W1KFclvtOgVNwVkqDq90KOMPJgxevmL\nOu56NrmDhC5NenqraXot5KtI5agwTHq+7F6PVSuoPCjTxqTNPJKEvQIJjxq4DOOt39tOZKBdPBQc\nBpHWePl+cn8bgAQeKWwUFA4Q5xIa5OhSplcilSbsCwGVeAq4gBYmOnMMMOmvWRYacRRitFhDUePc\n+pmrOP+qDG+4BA6fqdOHQYyQX6doUsLhOG0uILYur7R5yOPd39LxdIX3na/QXIBNhkWza7LSjIke\niKdguiq26jB2tc5LPxnGKVg8/Dtlai906No6mahFNNSh2ozi2To6HRxXxdBtpnKreJbIHW7aX0bZ\nlEKdjGP3JzEHTGIDg+hRhYs9cNqb0Oot+mY99DMlEqcXaByp02nESCZqmOE2tmVwph5ne7SJ5ql0\nuiatrkmtFUHRHKbG5plZGeT+97a57n9mOfnTCsVOiIQhKjJdPKKqR1mBYU32xiq2zvQBuPR18n0D\nQsagrM8BlvxwJEKweQk1dJJxlXqjTYMaXfJ+hXKaMGlsUnRQaeL5JfxlbOKc8TV0bBqo9KHQ9r2H\nEpLgDHYJF/wr9RNiHw5xbAx6AtltPMapsOTbx73AFBp7ABuHPDYVOoz8W43z/3ucRcDQkdirgxio\nkP2qdDEYpoOB9EbY9Ehta4hBBc1WQSeqTo/azqZH0xcBv/tBgCAo3Q5Uw+IIMEzTU00PtjUXkbAj\nyPEEauoherUWKWwOIgO3DZMJdGK0OITGRdg8TVDglSCMRYU2CuI19PufcTdwARpJUgzSpEmH45gk\n/PCs5H8nB4UENg5tKrTRcFAIFMHDoTjf+mY/o33wmsthtdJj3K4Cg6pDWvU4ZisUcTgHgbDXXW1x\n48069/8vlZ993EX3XFRUcFVONmMEJImtrok9bvCbn1UY2Aqn/naF2Ttdlispto8UaFsGhuaQirSg\nr8AGIGxYKIpHdqzL+HUG7t4hlJ3nMOB5dI7W4UyZ5sk5CvdpVAseKa2O4yp01DD920rYoVH0LQm0\n111A0tSw7luAf52lOB+lP1FjzdZZKWbxLAMFCBkWlqMRMbtsHFyhUk2y9kSIpYcd9r5bZ/mvbWrd\nEC3VIWZ2Sek2uVCbpK1jaA4FR2PmqELf1h61klS/9LyKYO9KZlKVWQq0iZLJpCiUYY0qcTx/+3wC\nDZ05KnTQsOggxr+C9COJzEabNhpxhLpxExKaBO0MHkIUdaHveQaLYJUed2dQd7od8YqDftpdBF51\nF5PCujDzr3+cRcA4gBhDIFCkIoK0GjodOhxGcgMBj1JAtRdGPJOAH0Oy/T3jFsYi+Tnp/z0QLA5a\n0s/Qo+xYQEDFBMYIsZ8uh3yvIU9PaT3jf+bL/fP3IkBzCDH+GBplQth4JHAwgGlivJk2Cxgs4TKL\nDHigzp7HI0ONJ9H5DcKodOggXJ1hbEya5NB4FQ7TKAzhEfsVrZIsGirZZJubb4+xuuLy0mtUjK7A\noUKUjl9PYSgeadUlgYFOlJYCb3k/vO7dOl9+m8vp+2FYdTBRqXkKNUen3+yy1jWJpRtc8icq57xT\n58Q/Vmj/1SnoqChKPxsHV9i26RRz82Mko00M1SVsWISzLomrM4SuymFOxbGeKVB5PE/779YwWquU\nSxniuk1yeIk+1cWrJknE6xTWBsiXQ+TSz2Efa7P03QxRs0t2f5fQq8fgi1egfT9P9wdH2Dw2z+KZ\nDSzk+3FclXi4LfUgqkunGSVidqlUk5z8+zIv+2GGI39fZHotg4IilaaWSc6wKHd0CgrYngLLEE7C\nfAicjpRVlQhCN4/dqMwBJWyWaROhTBoYzg1wbMZBoUiIceoco4mKgYFJHQUDMexAwy2o4pVchEMF\n2eXKAjl00picoAmInloKE7CZw1lnYWuishGdlO+ddpCdtd2IZ3GC3o5KC2FYfXHHWQQMG4MMDtsQ\ngRbhf3CYoMG/Ig/zSsSF1/xbfZ6eXlXC/10K2foMmrEC/e6gsq6ElIO/jx5fddy/XheFJLAbT+r/\niLAPmzGc9TXlWVhvIw5K2AOeii6SJ9EBF4smBiphtlGiDEySoEmXEMX1+9aRRFQSnVW6xAEXmxOU\niOOho5KmTRgFGx2NCFEs+nGxsFlEp4ZNCId+crkot/3U45ePFHjPe1w0N0oYjRFgjkDMWaHq6JiO\nZIXaCZ33fgOSA3DDeQqpJY19oTYx1aXUNaWYXbc5f2CN/MQgb/wng8osPHjNGpPKLI6rUqglmBpc\nIRZpEc2UWD28k80ji6jbM6jXThK+NEv32TW45znm3hOmUgmz1Izykl3TqEMV+mMNnK5Jo5JCMywS\nA2tYmkPcU2isDOJ6ColIC7UTAtWldczGPjWN/r3TJN62D/2LF7L8/qOMZ4ocmR8jYnZp2wb1Vph0\nrEE62kRTPPb0FaiciFI+7jJyjcHKd9ucakbporFZc3C6JjYe867kegaBxhqM5KBwRoDX9mfYs3ic\n79dfPIaFQYKt7MHCZWTM47GHdaIo5FnBJIZFGQuLJIMIY3oMqZmoItv8h/3XNoGWjMccJhYJxtDZ\nQdPPz7mcIMwuWqRxfO9WyJ5HCNOiy2FkF+8SZAFeRHZQAm87kBB9ccdZBIyLSLCLFh6tdV2PErLz\nEbTsBnICjyJs2SAPoIvkONrIg19E3K8Z/2+LSCIxUP/6CySceRhJtG5FwOMUClFULsZmDY97KDOJ\neA5j/rW2orAXOITHQ4ik4xgudyM7KI8g4HEOcUaJ0mSRo8juSIJlHkUmRCCeVAOWMNFIcQErnADS\nGPyMfl6PzQbq9NHFxqWNikaaMCoDdGjS4nISaFRpkxpr87N7VO7+jsr7/yqH7pfE9xFhAQUPnS49\nyd4TwMhm+MztsPgAfOSNHq2uwnbDwlAgGeqQCHU5Xk7jRW0u/hudxHUq8/9jhcdvjtCyogzv9Mhl\nSjTaYWqtCCHdZm12I4PXRon80aUYGYX2HfOc+tQ8ofQpQtkiM8tXMjGwxs59z6Jniyw8t5eop5AY\nWSSTLuPV43QrKWpA3/lPsbUVQV0b4Ey+n1yiRjJRY2ZlkEYrwuaBk3Q/6aG/Icvol/fQ+PMDjCzl\nGU6XcSyDkyuD2I5G19ZZqaSot8NYrsbi7U3SVydR/qXFQLhDx1WZjDRxgBPlDJeEWhxrRVE9hWYV\n0klJpVcRPzSNbI5XsDmChk6ECkUexsahRW4yzalZg00M8DwPkuJlFKni0KCw3iNiAWsoiKqayQQd\nxv3cXcD+vUwajQRDnOQQAiSnUIhQYQ25q6C1wcbiSSxivt1MIYtqhd7GQYueOmDu32mj//dxFusw\n5oE7EaS8GFnFDyFD8yTwB8AsGntxCaOh4PEwDj9HgGIUeSg5emKzTf/1QeTBDSMP7FL/Go8jQBTo\nmQQsXi4SqfYjg3EPPZo+FYNRNEZocx8qGgneQpWvI4T6AWNYCRmgQEAmyJ1s9L9PC3Fwt/vXewgJ\neWLAhUyxmyWKNPEYJU2GKEU6lOjQj8oZ7gd+zkY+gMIQY5vg1nscbv37Nnd8NrY+HRy/D+Rc4C4k\nDRvsNW27Ct71LfjSfwflH11CmsMhR+f6zdMcqaZwbJ1ho8vqUI7fvVWhOK1w4M+q6PUWyUiL8b4C\nyUgLU7eJRVqUu2Fmt57H+X+hs7YUYelra2y076K2nCOkeOSyRbAMMCzsVoTVlUGGskVU1WV2bYDk\n1uOy21DMEu/Pg9mFdBn6iuTvuYpUuowRbeK1w8zNjZMvpxnPrZJOVVhazTH2mhDK23fDR2/j2Uc3\ncvzkFGPZIuN9BY4vD6EoHhP9ecZzq/x0/mJefnc/XxjyyIRbOJ5C2zaImh0WG3HSms2So5MC3vIM\nfO4mOHxA5mvGnxUODg+wyJWMcACFLApbkeTonSXYutkjXxANM42DKAz43aE1VPr8USh2uby2AAAg\nAElEQVRhciFRXLbjcgiVGis463xmwksuJQcqshgNEeEyOjyHy5I/v4fpiXafi2yZ7ke2VE/7tpD1\nZ8URhHF1E/Cx/6iMW/+ArOQuUpi1gf+z/uFWoMAwt1AiTBsHAZUZ8PPc8mCvQbpCbcRzOEVPeHaV\nXrIzkPMd8t9/mB5SB4nIWSQBa/rXCRHkx6UOfzNS5j2HaH3M0NMR1xAASiL5mUkEmF5Br0y8hZhw\nFNkS7iBu5PvZSB/LPEuLBCoD6H4WwqKORsD4PY3CKJdvTfLP92h8+aMed39FGtcl1etwkiJT9GGi\nUkLWnGHg1b8P1/81fOdNcP+DPTmnLHBJbgXP1tFUl91/YLDlj5O88JEaT3/VIOe3mk/05zmxMki5\nGSWXrLL1tV0iv7eZwmqU2HdfwDx9Ci3ahEYMxbDQxudQQh3c+TFaK4OUGjFG+vOo6TKkKriqi1Lo\ng/E5bM3BPbyT0OQM2Dq4Kp6toxSzMLJI/swGKGXIJKuohoWSqmABhx+/iF3/LYK6qx/7A4/z+PQW\nlsppkpEWjqty4aZTrNYSLJfTpKJNdt29nYffkOfgoQgna0nwFHTFo9+TTfiD/ki99wB87nfhkWdl\nOdmGwPsJYDuuz2fRy6JtHoQfHYSNA6KXdxqLS9EYROExRPgphUqJRerUyDLGLM+hcQiHnQyxnxKn\naVMBFv0+oXP8ZjGZxwpjeBxDPGTFn9sB98oM4nnsReDtID2B5uP+PAsWx/+wjFvnIStzsGtRoNc6\nfgZ4J3CCNaZxeBxZmUGGKeu/p4EkeeZJ8FranMBa9zpAzGXRPzeQCAxYtVMYXOinGZ/3zx/wzx1F\ngOMwAgjnI9rcxxFSnwlkCp0LfA8xv1305AzPoQckRWTwdtFrRgta8adR+a+4PM8CGgl24LFCmzw6\nY+hodHGwKdOkhEeSPdtj/PM9Cp//oM39t+jr9awZQEHjtWSYRIQIfgloCrzpE3DJ9R7febnF8SMm\nIWTaNRFH+HSxj3ha4bqvKfRtcDh64ykWno9h2VlioQ5bdxzBrSXQNYfRfRbb/nIUc9Cg8KU57IeK\nhFIVQhvWoJjFtXWURA3F0aARQ01WiYzNY67mUD0Fwm1oJVG3rUCmCVYGvdkW72I1hxNpsXBkB6Oj\nC2gji9CIkUlVIFsUHo5GjNLxreRSFYaTVbxvL6J8IYd5TZrNrXn64nUUxaNQj/ODZ84jrNnkEjUO\nzY8xesghvVMnc9RhKtTG8xQqnTCWP/pRYFuqTCKcZIsFJVSWEb8x68/YSVQO+TOsRINVHC7dHeb4\nIZMmDgu0yRDlKAovUKVOB4iRR8PhKC7HaXMFHjo2ewGLNQo465pvHg4GCmcQEBgEykgv8TgCDoFU\nRbCXM4J4F7f653QQD2XKn3cNf7Tv/3/Y4b/vOIuAEbTuLKGwGZEFPAjoaGwhwyUUfV1IGaoorDen\nacjKvx/JNQzRIunTmrn06Gq3I70nMeTBmUiycwV4AYcVHHYiIn3bUOlgcQsCXtf45z+NAEzAqaHR\n83Dy/jW6sE4jK0xJCjvReSs2BTzaGEziUsThJD12ibTfcNfCZR8iTlMG/04cwCDq81wU+OPtO/jz\neww+8Bez/OKbYSYYYpvmUFdcnrQN9gKL6EzikxCa8Mc3w8gE/NVlECvqRMwuV/evsbQ2QMU2sDyF\ny15Z4JKvZFi+u81972zT6qZYbEXZnVtlsj9PvZil6SbZ+ZcpYtcMUL35DCs/yGMoFn3JGmamhF3o\nQ4u2UDeeljCkE5JhHoijbtuGes0oDPfDcBxCYei2sdsaagjUsA6lCizPo84don/oSdS5LJQy4Kpo\nhgWuCrYudMu5VdSRRcL1OO22SfRbB1DffgHVbx6lbRlkYg3ChsVktsD08hBh3cbQHBanVZyhCKba\noq16zLfNdVmsBWBHrI7uqugJhYMV1ouvgzKqMhJcRvyZoBImhsv2/W2eerZDiDAjhGkAa7RxCOPR\nxaOKtb4NGsJhGYWN6PRh8T1sVHr0DklkWzUQ3S4hwBEsZgEtg/Q7GWwjTJwaeUSe+hTiSWwkxBge\n83TXWx0uBr74bzXQ/+dxlis9o/SSQRqSz1jAJUmTRUSrI6BvMZHhayIGHdDu54B+bI4Q1Oj3+DBb\n9AxfuC3F+KUzQJTHTCCKywoeJ5DpcAYJIQKpOpHgUejzB/khpJU8KMraSaDpJfe6BY8BXJJ4/j66\nSwrPbzHqhVN5hMd0BJcyTbI4DJFCNMxq5FFpo2Kyf+tG/vyeMH/9Fwv87Js2GRJ0gLarkFQ0dtGr\nFHkIuDrb5e/+xaBSVfjQK6DSVoiisN/RWG7E6Y81cKtJXvbeKud8qI/lT8wxf4eFZ6dRPJVzhpYY\nirakZ+NlEQbfsRv3uSL5336a4oJJLNolm1vFTJdRbB11ZBGlExJPYXgE9uyCcyYhbMDJM3CoCk9M\nA8dhUQPLoLY8hLbxNMnJRejuhrERlB0XEH3Za+DBY/CDY9C1IdQBzQFPodMJMb8wSjffz6inENIc\nOo+10N+oEtqfIXxwhUSkhaK65DIlTq3mOGd8jkMLo1TnVTIDOm3LIK467EhX0GyNeiNGn6fQtkzh\nzOiHpTWFAr1g1fJnwyySCVjyZ2KbNpdeaPLQHUK0V6BFmzigk0OjRNhPP5tYtOj4/LEaGjkSLHEu\nLs/7c8igJ64csKq5CFgEPUNdeiIIopTXpd8/dw8CfZNIg1sTWZSn/M97sXxbZxUwTiL1FCMIQcwa\n8jAUPGya63zVJoKagSMt0oE9WcWn6OmrjiLDGUWG1kZQtYQkfqpoTOFiIMraQaHvKTw2IM3Og0jT\nWNAIPYBMmyqy7RXoPTiE2IzFDIofEEgfieZ/D9UnsJFqPIcuOpuIsgePBo11LithI3fZINWaDODS\n9b0l2QLeuknj9nsyfOFD8LVvluljgi4xHGDBc6l6DgYhCv43rvbB7/5U5/Qz8M/vhJAr33RYcQl5\nCkvVJJ1Ei0s/p7L9uhgLv3+U6rRDIuYQiTZpNWJsHlrGSEHsPZsx9qSpfv5ZIgdXMVsRMv1V4oka\nhurSasSIGxZqvw3bz4cL94qG4XOH4Hs/hLUzEGlBPQ6OBokapAxoh8nsPyCv17Kw1obqY/Dc05LO\nuvA6+OS18JX7YKEkoNE10bomkUQNw1UJjyxK9all0LxnmcyrUlQProDmEE9WmV8ZJBlpsVpNUqzH\nGWzYxLa4dF0VU4GRSJNi1+RQPU7MUzjcNZnog04TYh0xyTl/FvQhS0MDl3lUlllDIUoKjf2XGHz6\n/Q06NGmikiZOF5cONVxWUWj5CfIRf25U8dBos4bHFuAuNPbhEvF3ArP+uRUgjcoen/KgSY++UVoW\nXBQ6KLAuySSiRQopXEp465q9MVgPvn794ywCRty//Cy9GrpAAMhEDE3zz0shRt9EjCxo544CjxMh\nhUKIDnUcTMTIV5DQYQZx0WaQh3YJkiQNGLkLKHTRGPXp8QIgy/n3EQjKNPEwsCkig/ULFK5Hob2+\ngqjEsPwyH4U6OjlslpFW9kFA3FgX038GATv4GSRkUn1WUI8IOgkSZEYHuPMelS9+rModXz/JKOO0\niNIGvylN6HUSCLxcPAyf+rnHI3eo3PFhiynDJWrrpByNLZpDLNRBMWK88fshWh24+9UFdiVb6JpG\nONIinajRtXWyl5oYf3Q+7UcLlG96guTADEY8RDjUEQAwLOx2WNSR37gBLn0DHJmBWx6BygvCAtyM\ngqFCvC5hSiMGXRMyJfl/qAOeIkCi2wIengLHRmHhx7B1G/zhb8E374TTp8DsEgq32bD5BCieeDON\nGM1ShurDZUb+boyK6tJuh9HNLiulDFuHlnn+zAb6EzUUEoSiHqFQl+V6HK0Zpe0pzLjCkh7Cw5tU\nWJrpSUAFtcJJRO2sQoE6aRwKbEBnYsJB0Q3uP7mAQgYVFZUyLhFcPDy6WJRQSPtzuQ2s4KBSQEcW\nv+1EGKRLxS/ACgMZNIbQiWMz6d/JQ/7c3EIvhxGEMZY/d8fpZV2W6HnjQXXxizvOImBsQYw4j3gO\nI8hDiCNDdDc9qbcxelukJ5EHsBFxs6aIch4OVSzuxllPNgbdogcQkBAPRbavlhGwSSG7Hmk/TXgc\n2Y7t988Z8z8rkBoYQty654G8T9pbACDBVjQS5H15BGiiU/I1S3OAgUudOm2f9Eb3P3cfCmESpHBI\nYWASJkqMFLkBj1vuUfnKF9v87f9aIM0c57KZeTQUAu15g5x/p5lx+Ny9cPCfPL79t7A7ZDOiuXQ1\nB82wSJtd9KEwN/wLVB5ucf97dAZiOp2Igap42F0TTVEZfPcIXDlJ9ZPHKD1SYyyXR4/XxUjDbfmZ\niqH/3g7ie7fA0wfgMzdDqwTtMIyuCQgU+iT30DXhzAbZMm2HZSfEU2B2QsAkVZHz8v0CAvW4AMyT\ny1C7Dd5yPXz+u1BdgWxR8iONGBSz2M0ozVKGtbUQIxEdI6egrKo0q0miZpfhTImHlobZl1tFjRpo\n6Ji6RdvWWGrEpJnNsFixDCYVyG2BxWmZlUJNJOuz+MBSnTlAnAIuNh67X9rhgQeKaH4rgkuMOgWg\nRIQx2gxhkkXHpsMC4KLQj0YImwQhFuiyExUbhToBxbKCh0I/BhF/EaogpQjSJyvLRaBoN05P5OsK\n4G48ngYUVCZRSONQRxbbF3ecNcBQKPpJzosR9DwM64pPbXrENHcggLIf1mn5Ax6IHNCiyRIdjuAy\nh4QkQS1EnZ7q2XYCXk0x/CpBmbbLGl0eR0BrxL92gV6/SQfxVlaQHRAVyWHUEG9lPwk0QqQok8Xm\nAB45WtyByBeeC0yjkcJjm89jugzkUNhEigz9OKxiYyG6VyQsvnSXyW3fr/PJv8uTZpwxtiOsmxLt\nDvh3sABkJ+Fz98FXPw/Tn1fZpXiU2hEsYFuywvahJSK7Q2z7Qj+P/61H50clcvEQhuZguyqq4hEf\n1Ih/4gLcrkX7/Xcx/8wGJsbyqAFY9BUgpMMVl8Fle+CpA/D1j4O5DK1J+fvagICC6sprwxIASFZh\n8wkopwU4LEPOcbSet7Cag/487H1OJsnKIBxR4P4H4K2vhC9/A5JV2aqdGycW6mB5Cv2ZEoqn0Dhh\nUU2MMlA6TbEeR9McarbOsW6IrfU4lw1XqTWz1OpxcqZFzVVY6IbYHGnhWQZznsJFu6B+WEb9jD9L\nXIJaTAVIsQOTw4RZA654RT8P3+tioNBhAZ0tJMjR5ics+7KXw1h0mKbqS2YqpDHYhEORLHvJ80Oq\nOPQqlxN4KNg8TZ3NCBAE2iQOAhRBR2oehRIqi4jA8/O+/Qipts5GVCL+TuOLP84aH4bCk/Q0RGRr\nVDDdQPB9PwIKv42ELbfTExLqINi/AMRo8S1cXMRdayLDLEzJ8rDbqOucGLp/zQH/c075d5Skp2kZ\neD27kIGxkAH7HgoPEqbjm61HwEuqUiCCR4xRRNpxC1DGoIFCFxjHwsRmiV69SQaNA+ygzjxlTH+d\n0swat92mM/NEgw/85UMkgL3EWELhOf8pVYCw4rJZdblwAj5/P3zj03DP5wXWDnpCClgEmq5KcecY\nO748yRN/2uCZf4BTqzl2j82zfWSRDdkiky9z2fD1XVhPFCn+6WG8M2G2bz3O6blxuoU+PN2GKwfh\nz94OfXH4wtfgjkchrwpANGJi+Lot4BBtCjAMroj3kFuV1xvOyOOONmF0QX6nur3QZHJGQpN8v3gz\n8Tqc+hmEHNiyC05totsOM1PM4iZqRDafQIs1GUhVoNAiMe5gRpuoikc01GFqcoY3Dy+yd2iZdjhG\nfjFEsRmjaemYnorpaNxXj0vbouIxvM/jked71EyB0HQIGMVDp8XzeMTYRE7NcvWrTA79zMQkh8EU\nDouUUdC5GsWfr0vc67cGZIAiHidoUURlFR0blRGUdSLqBaSOwkMhTE8aYxRZzIJO7CRQRuFuNB4j\nynUIaVNQCn4u0EeXw74nnEMqnV/ccdY8DJcmsuonEIPcgOQMugix74eB/4F4Cy9FVvIT/ntyiFLY\nRiSs2YZkhtvIA1eQBzsPmKicoY8IRY7jrO9TP+Zf86X+9Sf8a+QRkWcb8UyGES9oK/B7hNnEpcCD\nzGJxB4GCWpkuNTJUmEVAaA64kS2MMYNFE41ezKmjMOh3n7Z5lB8Av4mDRr+i8k+3ZsgX2/zOu4ts\n5dXEEJ/nXIQqKBI8LU/FGIOP3A8/+hTMfqlX9D6BrEUesPMGlSs+GebnNzZ5/udxOqrLYKQFQCrS\nIvmGHPqNU7Q+cQjvQAE9YhEfn4Nqkj37nqXc3Ib59htQ+kJwy11wsAKJNkydEo9h37MCAPl+AYH+\nvHgWXVO8hnZYfl/Myu92HoZDu8SrUF0JUWINmDopIcez+8Qr2X5UgGRxGO57Gq44Fw4dJ5xbZVdu\nVa4xPwbJCoriYrabqFkPxdZJRZsMm11On9iMqngslNNkI2nysxqZSIuJvjwd3eax05vWZ992o8ue\nC0zu/UOPnA/OIPsXHSCFxgQTzOCQx+E/X6CytmJxYG6FCGM0iBBlnCZLbGOME4zQJYMsHiANiikM\nhuhyF1Hexjx5MlyIxwt0eRJJr+5CpQ+NSax1pu8jBOrrsii2gCvQuYwwIWoco5fzG/VtooBAn+fb\nxe7/nzn+m4+zmMN4GeJ+yfaQGF5Q52AjhnonQl12FfLQNyBf/HGkNuNpRIvyal/34XnEkdyJfLWj\nwOW49FHghz45jzAYwJRfK9GPSpEORxEAug9ZCbqImdbQ2YdKki4P0uYEv+QCX/NjEEH6K6jQQuGn\nqCTQeCkWx4B5jhMjRT8OL9ChRJRJVFzq3IfHGAojJHgjdWws6vzNp2OMDGl86FUmm90N/nrkkfD5\nwOIIpF0WbeINqPzJz8Pc+/cu5a845BSNeU9lGBgMdUiHWwy+OcYlH45w+/V1Th/Q2D8+i+upGKrD\n5NaTtN7wUtgfY+kdBwnla6iKTsNVyaiueAsX7CF14x6UXzwL33gMzCakTTHyYla2O5+4EDae9o23\nC1smIb4XwrsgPgjhFGi+pEO9CivLYB2B4gOwkpLPCbyPdBk2nYLjWwUsuia0InB4Gu8117JW2E5u\ncAVsHffodikG6yuA5hDS2tTsMIemtzCVWwXAcjSem5vgTa+5HTZP4Xyvw2imSMiwyDdijBsW16bK\nTOcHGJ6CtgU/mxP/MRCujCHLS+DbumikgfN+C+78sY7JCFI216FOEUhSAMIM+bU+LQzGgAgeVVRy\n6FzPKArTJCgzi8cZn57RwKaEi45LDZWDpLiaCk/gsgX4NuJF7AbOw6Ifm2XEox5HqPhuRxbNRWTx\nOgfx2O/69c3VP84iYBxCQCKDhBBBtWrQSPYSBCD2Il7EGYQs9UJUatgcJtB0sLjFr8TsIOg6gJjW\nqwl2OVzuBF5NkstoE6bLDB42GkXC2HSoonAxMQZp8iguCYItMIdZHDYB+/A46uc7Bv371AgzgItH\nl3GEPOcgEkbtxOZBKmxlmDHqOFTpkmAjObKs+vUYFhE8jvKhd2/kpdeofOQy6HRUppHgKYHCdgQq\nk8DlRhc1YfC+f9V54OvwD59VmVQUNnkKTcXj1Rvm8CyDgTdnOee9Gve8poI7YzER97Bsnf5EjR1T\nK0T+7ApMG9rve4xoDSxPQ4vXGdwyDbuW4PLfgWgO5a8egNAz4lWYXTFkW5dwwbCgmoXQNXDjBTC+\nHUoLkjmceQSKeShYEMtDtgXOBOQmYN/5kHotPHYXPHFAPqeYlfDFMmDbMUludkICIv15mDtDdns/\n3uGdWLUExsCagFoxS7mYxa6ncOwW502dwuoanFzNoakuquLy4COXcN0XDApHXTrVJGOZEgOxBsrq\nICfKGeaAK16icupByXBF6O2xPQv/R0VQ0MX0mtfD225sUKCLdByvIDSMOgWW8VhCVOoS2L46Kxi0\nOYOHwQzHcXkej0GESjhoNgsjgeUpXFxqDOJyEeIpvAzxHFaQxVT3a31eiZQYlHybCUSP+v3PbNJj\n6vr1j7MIGDsQEwgYuhOItxGILZ+DYPw84m2UkYcDDhEEEJaBbf4D6yIPqoQMdR8ytAcRRDYAkxYt\nHL9OAipYvOBvc7bxaNEhg0eSEJuwiWGgoWDRYg5BcBDH/zn/dRKLkyTpI8YoJWbxmEYG51lgEzZD\nFMigE0ZnHps5f1Uq4ZGjg821143yrveHuOrSGuWyhYVJkzDDSAVnPwKxFrAQ1vn07QoHf6Lw44/7\nwpKewhnF5dxYAwPYcFOE8ZtMbrnKJV212NKfJ5moEUtWaXpDxD57Ht7sCvotT6A5CfSkTSzWQInX\nMbb0i67A9LPw/e9DNw3JjmyHuqrkKwwLBhzY+xKYuhoqeTjxMDxyC3Qa4nmYXclrRDXIFsS4rSpM\nz8DJ+0HfCVfdCJNjcP8tEG4IQFRSUuVZzEqIApDvR1lcRT/PobtYZf7odjZGm5At0mhGsTsh9JhG\nt2HTbIeoWwZ9owvc/ugljGVKhNMGRsRj0FqGPlizdQ6uDKGqLoPhNrV6nMmXKzx5r8cQCivI0rVC\nFZsGOxjmCOKfasCGvWAYcPBpHYsCgXqeTpgMKfJ08IgihpvA4wQGJipb6LAERGgTpVdL2sZjK2Lc\nJXo0DBF/u/8UKhcgjPNBT5LunztETyFP9W3hGLJYBuJGQTL0xR1nETBU5IuMIuARsGXtABoodMiQ\no0LD56YoIKu3EO3IrdcQQDkXeIEsu+mQpUGZXkhRR8xtDHmASQREJHHkoOGsV18WfT3KSZ+j0/HB\nqYAEArtgXfpGSEmgH4c4LYpoaGiEcejzv0eKYDWwiOOi4wAOTWx/e1dlmCv3qNx8c5y3/KZNc9Yg\nhEaLLiZ1wmQoIFOhDzjHgA9/T2XlBXjiw20uzTawPQWrEcdI1Eh0TPa8Qyfz1iSPvHGVREmnL9Ek\nHW1iaA7JUZORj23G/sUM/PQp0B2Mvi6qq6IOL8HVW+HiV8AP7oK1xyFhQTUsbn/XFCAYrcPeV8H2\nK2HpcTj5LigsweIIWFFI1STJCZLj0G3JUZzaJFuoui1eSmsafv43cOV74eIb4NQXBJAWR+R6Zlf+\nuaokQJ0zkN2C5qqkB1cg1qC1MoiaqhCPtNDGduH8sspqPU7T1gmhENMdHFfF3pag/IIwcrW6Jgv1\nOIVGjIjqsmgZRFUYebnGY/9NQCEgIwgh/CDC7WnhMU2ZUV7xn6Lc9S8acUJoZFEwKeLi4tJBA55E\nZw8OBTyfAMrFxqRGmBgVHkdhghT7qPEADhuQBTJMhBwGbV+c6gBi9Gk8jvlNjyYw5deSztFhFlm8\n+hBgKPlz0/Tnadif9/+hKfqCZhqDHvvQHAEhLxgIe/YYvRLsKL0chIkYJQQlNg4LvxJK1JBhTyEP\n61IghcdJBKiCXs3AVQzqJBVgDptBRAF7DtbVNH91JyfweoQTqU0XjRYaoziMIUUzw/57K3jMYRHD\n8T0pnSLDjBAa0Pn6HSrvfU+RJx4PESdKHJVBNGycdWcyigz3u78Gbhu+9QcwFbUYNbt0bZ1GuEXb\n1om/KULuphD3v6ZIY95jc3+eoVSFjmVgDfQz+alxCjcvEXpomlAKUF10swteCG64Bib64cu3wowH\n45qEHdmi7FxUM7D/IjjvN2DhGbjrQ2AtCjhYhpyjeOIlNKPihXiK71kYvfb1SkqSmn0FCW0e/gJc\n+wk4/RKoHJQkaTss71M8SYxqDlgNiITRUhWyrgqdEN1ShlhuFT1ex85FyJ/UaXZ0TN1G65iMJ6rk\n63E2XqSx+LjLWjXJTDvMTCeECziuSr0TYvBSKC4pLJ5hvXSqD1AIUyTMItIcUCOEqaq86UaNP7rG\nQUfHIeoH1A1cPGo8isfTeOwlxzbaJKmTxaHgF3FFgAyi2J71S+/6kM7lIVyGcVmlV7x4HNiEx08Q\nz2If0ntV9kOgw4jXG6i5BzwyBd8WfrUE7cUdZxEwIkg8No+EDMKWrNEmRD9NLCocQowzhoBDGXmI\nUcRh3I/GFA6ngBgVThNwHiqMorARd70/RJjCbU7SE6htIR5Iy7+nIhIKBYASVKP2IwNR8/9pBCTB\nEXRslrGYwaOL61d0ymfF6BWReYTQcEhgoZJCY4cxwUd+4PKNbzrc/h0QUj25YoSwX+4lxxgeL/mo\nQv9m+OxVEHE8dMWl2QlRb4cJmV36X5Xg2o/CL28okm0UyHcHSafLDKTLFPtGGPv8JJ2bj1L/YRMj\nGcbLFlFUF8w4vOuV0KnBP34dKgaoYanENLswsgiZSbj2JrCacOdnoTgvYOLpvUKqZFUAYjUnNRTJ\nqnyG6gp4BN6FbvfAxNFk/I98F7a+Du6ZkWuW0+JZZEryuhWBegQURV5rDpQyGIBSTUK2jtoXoXFS\nIRptSMVqM0q9E8LUbTZd6XHwMx364nWKuoPpatiOhuWqpIBt18PPb7Pw0FhCJYpkqYbosbiOodNk\nije/AlaXbZ495NFklTYuOhF0PGwsPA5gsgWLNaLsw6WOQht8XVZhe58APGrri1HQMN+hg4ONhUmY\nLmP0FrMs4iULPYJDA2e9Mnre/5n153QFWbSG6CXxg9n06x9nETAayJdT6IkTGagohNhOk2foVVxq\n/jlrSBwWR/g/Ja4zeYgOVdz1VnkLhT5U6rjMI+XmP0PEjMYRY7eQaVDxy7v34jKDhEhZBDSSyOAE\nVaFBjUewubmTEP0Il/QaOjHfZWygMIhBBot5YiTxyGESwaWNQZw4o/zxFxyKBYuP/fcIcTJEUJlE\npkcdjzGEX+EMFpe83eTyN8EnL3PJtGWN6joaFSS4G7lS54p/UDnx+6dw5nScqEoi1MH2FKrDA2z4\n9Db4xlO4v8wzOehJ147iwZgB77wOnp+Be++GWEuaT7IFCSNUDXa/DnZcAc98G449LuCgG+I1uKqU\niscaYshBJWgjJr9PVQRQUhXZRWlFxBMZWpZt2GpSzpt+Cq6/ye+9KoiHYRm9oqEcAYUAACAASURB\nVC+zKyDWRMCoE4Jwm4hhoQCdvmGM5QabssvYqosabrO4mqPZNVETCsldJuUny6TidXaaXSLtEGfa\nYeaF8phXvQF+9zVNXGIkUGkhS09Auxv2f6aB178dfnizRx6NLmWfjz2JQxSHGLAJg8uxOc0KdSwO\n42Kishl3vYR7CalAfh5pGivwq+G4xh4Mar4uauAxvPpXbCGBLEgJ/30t35ZEpDmEgYOL7XOFCuzt\n+Xfa6P99nEXAsJBt0WeRZKUIrVic8vkwhxH3SwW+izyQLcgDKgIb8GhhsUSWqynyXVxUBBAsXMq4\n6wAjVaQGVWz6/QayOXq8nFE0diHyBA/593aaHp+oVPjJZ0367EkWLi3KlFEJE+IaIkziUqeLgsIc\ncaYokyVHhDXqlHGIEiWFwetvgu1XaPyXi6PEPcmejKJzEJUOoODi4DGPS+7KNtd8PMR7r4DNJUm7\nLaMy146wMVbn0suaXPnVLM/8wRonfpFlx8giCz49XTEywY5P9lH64nH6Z48Q29DGbcRoFPow0qMo\n//VieOYemL4HRqWLlHjdN/ghuPomqLbhzg+AfgYGXNmxqMfFCzC7UEvA8FKvbFu35aety/9bkd5u\nR1DVGW4LcIB4G6ESlI5A7FwoHJD35FYFVGwdNAcvlIKSjRJr4K0NoAwto2gOOBrtid24hxqomkO7\nGWV5aZhCPc5kf57y+ROUn+lQrhgcqE2hqw6G6tFwpSFg8FKgCRufTzGDmOek4oEHx1GYQYDDAa4e\ngPOu9vjgf2kRIkKXKdpUqDOHgoPCFC4X0/DnT9NnuzcZAxS663U6IGAxg5Rznybok/JYocuzdNfD\niqCLdRXxGPqAJDoFdFZoM4DUIQWavyZxzqXDGnUO0CsjOMCLpcs6yzmMk8hQzCKAEPSI/CaybXQE\ncd36CZp2BEAyCN7vBM6wwgtIx2rQoRo02wTiRtuBPBm2U6ZDFweJA6XS06OMxQsIKGxEQOeryEBt\n8u+tBoRRsYlyJQ5tOrRxCRNiApfTFHnGv78mLrsochhQOMUDwG5CbMKhw9h5Fn/y8QQ3XAG5Gug4\naEjd3g56rUcpoLURvvQdk1v/M9SOwxoGXaQqcVe0wbWXz7Ht61v5yfta7JidIxsfxHZVrjr/KYra\nBoa/uIfVL57BeG4ab6KO4imoVzxIVtsnK/od98Ps0/Kswm0JISwDzh2FK98Kh38EoU/BhNkzfJ92\nD0+RcANgeUh6RzxF/mWLUswVbMEW+sRriNd7NRy67Yc1/uc05mAsBg+Ny+umn19yNLB1vEQf3qyD\nWkvQbUYJrQyuhznRvSnW7u/QqsdBdTk4P0azEyIba/DKN7eYvVNjuppEd3TW0EGzSZoWa50QL3sr\n3PtNGFVchjyVg0BOdVn1wHM1pvxZNABsvAlu+6HFM5WniLIdA5MWxwETj514zPvzSnR2dSZw2U8X\nE/Fa5xCPoAj+dmwvhHjSn3tRejk+25+H+5FixRoCGiXSdMhwEdOcIeilDeRHC8z7n73Ht4U0GmMv\nmjf8rAGGxoW43IM4x0FhVpOeXskGRG19MzCGysvxiKLiouL4hVHbkCa1tv//IcQ7mPV/JpAsswLs\nYJXDCIis0kuGTiLxY0D3+qv0fEEL0qB/fylcP9ARpq1rgCItHP8zl5FwKWhaq/i/uxow6HCabZk0\n3//eOB94J9SOwSwuyyywhRE8FCx6TFiRGHzqdnjko1C+r+fzbNZtBuM1KhGTkU/tZP4f80y9sETT\nCbFtaJlCPc58bYrxr26n+qOjjJbup5vI0Dg5RXx0AVpXwG/fAN++F04dgoQnYBAY8XX74dzz4b7P\nQeEE1K6UqstETQx/fkwM9dJHBEB2HZKfd/wWbD3eAx3D6oHQ0rCEL52QeA27DsluiKPB+Jx4KYsm\n2EOw8QEJQ+bGZWdleAmiTdQNSZh9DGoJQv15AZuVQYi0Mfb20fjMUU4tjjCQqHHJ5hMcXRxhx8QS\n8cv3c+xPGmRQ6E+XGbR1nq4lmLNhPAI7boAf7pU1/GHgMVwcR8VAWe+JbgBpDd7xTvjQa03O52Ws\nomACNTTm1/s3NiFGLVIAOxhhmUXWmPXnQ2LdAqSS+AJkkWohBh4hYKGXBbMPIYEa818n/Dl7hDzP\nUWDMv+ZjCFA9gFBeXoQQVC/51zro82O8uOOsAYbDC4jGx69S7dVhHQM/DVxP0L0qhVcmDhtx8Qho\n9uBeTD6OhYnHkwggXIgY/aNIRegKcBvwQYTGLOmfYyHJomMICueBncQ5hzYxv9N0hZ7E4VEkMbXR\nv8cX/L9Lo498rk6PJXwSyV4XgSqKMsjnbhnj3h/Boz/E7x9UGWOEBjoDCOwdpcUa8LGbIzz5BHz0\ni/IkXqc5vHzLcZaLWRpWmNfeEqL+VIPFW0scXt7IuWNnmF4ZZP+2eWKfOQ/7F4uk7jrNUm0PdrTJ\nhskZGMvhve1a8h89STa/ipaUXACRFgwV4FVvhMQI/PPnodiBdHg9wcjIongOO45IeNGKwJOXgXEZ\nbOmH124F703QrYLyS3B+DCfGxfvY+xycnPL5MCq9WovcqoDQU+fDlR5kygIem06JJ6J4Aj7FPhga\nhuMNGC3KtavCy8nEANQ7pFur9MUNqq0IpUaM1WqSred6WCcarM1rpKINnmqHMRyNCcPCtAzOe5NL\n/imPuQWNGVReALairnNdzfijvwvY8DqXk7MWPzugo6NR4DAdUiQYpp8QeUr+2Uv++HscJYqDDgyj\nsQ2Fps/VuZEep8sdSLJ9Elm4XkAA42IkRL4IWbh2IQvQ8wgovAKPjm83dwHvQBaqFKyLeetI0WMc\nAZ8Xd5xFD2PKJ5xZQYwx2JUQvVDYj8G52Mz4JCNCkCrFW5PIw1wG3oXBADZzhNmDzRwWT9DjzjyE\nJIf+EHnQ19GLSG16pCY/RwrJ9tLiaVx0wmzF5T66vADsxSBGnFdQAsQ9vBSdPbgcwkVHVoEaGnmi\n7ELH8zdbF8iR5ab3pUn3K9z0ell/VGxmKLORPjR6FMc6Id75pzA6CV9/CUQUj2yozWi6TLkep9KK\nsOevYziqwt3v1hiIp9g3Os+ZQh8v2XkM9YPnY003aHx1HnO4S//QMt1WhK6xhdC7LofbfkKykkfN\nlaQc2zIgqsJv3AReHR7+IET/N3XvHWZZXeV7f3Y6OVadqlOhK3R1dabphm5ocmgFQVARlWiAUTFi\nQh3UkTHOCDoiine8ijoKBtRxJIxXRATJDXQTO1d3V3fleHLc6f1j7V3H+77OnVHf5/LMfp56Kp2z\nzzl7/9b3t8J3fZcCtXY48Sl44mQJJwpJ2fltDcqnQsdlcNF5UB8H63EIPgKzMdA7IPEWCH0KKp8C\ndwScAJwyBWMuOC6MDkrviGnAvtUCIv0FqDryGrvXSSK1GRAA6U1BvSbrJVWRSkw1wrMjwwxfnCay\nfZp0vESzfYGFfIpCI8h4LULovBAz/zbLYJvDYjlGj61y0NKYd1XiwNZ3Kdz2JRkueC4C+Wn8JvOW\nfHQQl2s+4vKlG59hll4ULCxUQkRwUXEIEcegQgNnaT5qBwGShNCoUSGBTpwshwlgMea9Uhwxfr/B\nsUJrZt3DyIYXQvzLPOLvpL21O+Wt3TqyMVnIptfp/W/SW5OrEAAq/Fk2+qeOlw0wXNLIBTqEOOFJ\nWi3kvYgG9tO4zONXxMW4DVrEqyxQpAG47MGkF4caLdHTBfyYTqjmLuJN+NwPG5VlqGzGooJc0HlP\nhGcSk0kvQSrxoU0vVS/hCa8Cyqh0IkpdVfx8usJadDSqzOCSpZ9OTthi8OGPa7ziBHBMUGmSp0SC\nGBUUXO9dxYCNp6tcdR18/kQIWTavCDXINQMoxSR5W+XEK2Hla1S+uxU69CaRQIOZfIqVXdMkr11O\nM2Rj/vQhTKWTWi1MW7CBPmShfPpk+O19KOM7CW5alDChHgIjAq++BnJH4aVbIVyBWLNFsDr2Ba8y\nYgHHwNC1EF4F4/8KY++A5F7JOfRMQrMD9g+Cei+sWA5DN4H5PlnHZQ0yR2A2I+dSHfEgwjXxJiIZ\naDwl4creNeKZOKrkMrqPgf0euPmdrIZJX98YwTPXsP+6UdoW2zBNg0Wvc3bNqiLLzojz+CcbVOpx\nJmthLMvAcBQWURjYAm1dCk/fo7AMWBloMmsaFF2pQjWAPE0cTLacrtPWDk/c3c0g7YxRxyFHkyIW\nDWREVZgoMUpkEAOP06SEzREsDIp0U2PSq8atQja8A/5dR8IRDYGtAgIEjwEXIhvpBloskQVkgwvS\nSt4/SYgzaHKAOKtp0kGNPCzptoz9Rbb6x8fL2K36NPLBhxFjO+q9nShycUZxlhqMHe+7nyhKIxch\nCkx7zTdZT0xVKuiKp5chfR39wB3gzdrW6cGhjMMYLpMEaCfDqUzzAHLxpZ4tFHLRoIAcDvu9gbY1\n2tlIgRHCODRYwEVDox0XCwebGhWE1mvixmxu/EmQL77XQTmqkQF0VOqE0AnQQWtMdFcGPvZjuOVq\nl/A4xAybldEqj9YiLFR1Tj29wBk3xbj/dQU6XJflmTl6UnnKpsGqdznom9uxPv4wieQChEsEG0GI\n66gf3wbbH4W9O8EMi6EGGwIWl/8NzOyDHbdDpijhRqQqRjzZI15IvQ26Pwpdr4TF2yD3Acg5QEx2\n++ERCCqQqEhLu2GCPQqLX4fBv4GZf4TgPMSKUAvKa891tMqmiguRFXDgXjgy6CVBo/I9XoJNy+CB\nXQIWroIdK7Nrz1qWbQ6DaRMcnyccM5kvJJnKpwgaJhs+qLP4myLjkwkMzSasOSyY0BloYihw3keC\nPPoNMBzJAjxnq0vjvv1p7Qoq45S5+BNhbr4JljlDTDONRgKVGU+kWupaNgbuknaFdDqbjHgCOHGs\nJdX4AnAYhW7aaCPPS9iMozGICx53yK8U5hG0HaClBzuNeM4lJBnai+TvjhJmGJt9NKlhL4U4s96n\n8pXe/vLjZaySPAQME2M1FiWvZz+EeA3TiJEvQ7yLWVoXK4lE+u0IYg4icd6pgMw8d4hSIgQYGEzQ\nxlZmOYpI5a1CPI4E4mg2sJnGJUaA5TSXpPtWIt4OQBsqRYLMEkbk5F1mcVGxOIhDCYMBNNppejqO\nJtBJL7OU+fuvB3j4IYd/+1fFk+ADB51BdLoRtcc6YClw7Q/g/tvh4H2wFpeo4hAJNFmRzhFPuJz3\nLwkmb5qgc7FMJB0EV2GxHCN1ooF2WT/Njz5ByClR90YQagEXPnwGjIzASw9AqiFGVwtDpgav+QAU\nX4Spr8MyR3ZuR5UwIFaWPIO9BU69AXIj8Pg7oX2f/C8UlstTD4kXUFSgqgtYGKZ4Ec0fQebjENIE\nJIoJSXx65dClMquVhnA3zM7A0eUSogQbcv5gEvo6YddvQbexgNmxPvl829ZRvHuezsw8QU9XQ1cd\nanaQ5W+NcOh9hzGtDJajEXAU2kJ1dNXB6DHYcC584t2yVW0Edto6vfgDofyMWpO1my3WbAjzh4t0\nGpSYoYiypKspfUwhAoRQyC/NqhHJRVmz/qY3gWxIPoPzeByvj0T6inw5PcV7fMR7N77A9BFvPU57\nz/H7r+qIoFMYk6dxqFAjBexDJ4bKCppLHI2/7ngZAUPGBuiYuEvoJxQYhWEcNFr9/X7Y0oUUt3z2\n5W7Ea/A5EgIRNiO43rQOhSYhqkg7sJBYhO1p4lPDG0wyS5kgpyMuoN9iLzdQIemRtVcQYIE6SSrU\ncShjUsYmg04YhzyWNz4AFggCl10c4LTTDF53nIiz2Ug4skiTHmJ0eexODTj9g5BMuzx1A3S6CgUU\nFFtjqh7kuGSeLd9KM/KAy+wvLDoiNqrikqtECWddNn6pk7FPH8V5KcRAr47dCGI2gkQ+MQDNOtz7\nECQCLeZlqgLnfBAWp2DXHXIpdFuMuRQXAFi3G/peCb3XwtiXYXQ7BDog1AZqUR7ri+Pk0i0NDDsI\nZgjCZYiWwT4C6U4oFgSodEsep1vC5CzFIbUJ8vsgtgjmKvEu/F6S9SfDroMQXoSYjT3XQW26i/5N\nR4memeLQ1yYwnDgR3cK2NRTFpf+iIIURh4mdMkByoZjAdhTsSJW6rXPetUEeug1mi2JGvgjjJFV0\nApSwOUyDOlVuvqGdb99o0NV0eJASLiZQwaCLOGBTQiNAHIU8dcQrDQE7CdDt9Svl8cvzChYqw9gc\nJbekEufiUEehA4Wsl8w0YKnR8nlag8Z1VFYjIsCHEG9mM1D3eBe93oqaRKUDlZRnQ/+tAWM1kPEu\n8AHvb0nARKUHh0lYotH4Lb99tOI3FYU+QiSpswqXXUAb89SBIxiYGCSoEuUIDyIA0OWdbxRJMfqz\nq6q4DFLnKOLJtCG5DiFqKSRwqVHmsHdDdOJciMUiAQZpYtCkjEIBjRg6YSyqpLLw1W/qXHqRQ7Mi\nGgpdQIMmE1RwiLHgfapVx8Iln4Svb3VJ2ArTyN5k2zovFVKc8LYA4UGNP7w9j17M4KY14pEq2fYc\nG29bhvHwCMn9RxlzU7jhGrF0Dk5fB+va4H/cDqUoVMItgz37CrBN2P59COktkpWttajX/e+G6Pmw\n/+2wMAeJOvTGIJaBakG8DN8TqYWlgmIaEHEgl4SZNDhxSDcgaoNVk8d3zEnJVLPlK1KFVWtheod4\nHOWYfPdmkbB5Izxwt8jzLbRjjfUJi3XDSZhPzKFXqxye7ScZrmE5KnXTYN37Q+z8bJ0Xx/uwgg1c\nzSLvGuyrRunudtl6pcvXjnHoDVpYjeBSQbxMhSM0qWJTpsppW2JsPD7ERy+xCC5NW88Dq4gSI4NG\nhRA5GuRxEGNdRDQ9q4TooErTA4wQ0IaCiUYKm5/RGmfoJ83bvef7FIO0t1a78UlZ0PBAIISD7dmP\nn//LIv7SI0CfN4LgUYTztO4vtNXW8TICxk4kidMHqGhe6s9iyqsXn4gAxQS+pLp4CA3EFVtAZZwM\nHUySxuYpYByV84ELiWGRxGCUKJIDPxYx1zpyUf05EBO08iIvIDdsAHH/pL3YYR6YR6VEiCwui5Q5\nhMoyKozg0ofOEDoKQRRiyMjdm75l873vzfPS9hhpot7AAQgTYxkxwsjyGg/A9Xe4/PxjLvsPK6RV\nB1VzCKkObaqLNRBiwydibL9kmldlJ6m3iWhvb9c0qav6QLOo33GQaKbAscsPexWHTnjjRrj5F1Bx\nPJq3A8c9C+svhOggPPA5WLNPvINl46KKVUhCPg2D10NkGBpnQcoCS3IHlPeAPgSxYQg8KB6CbsPs\namlOa8tDUwO9KiHLVAaGMqDMCjjolryeH8a0L0A2D10nwZOfham1MOC53lPdkFkLTgieX8SudFA/\nvJyiaVByg6y4JMvIe0fJ51PkqxGKtTDD2RlOurKAZafZd69G0Ggy3D5PqRJlqpRgwNZ46/UOz9wB\n8/kGA6kid890EfLu+tl08CDjxIkRoo+PfcHlxi/a7G+UMdmLyxCi7ikJynnK1JjDpEydKJBE5Xc4\nQIaLKRPyhKd3oXhT+xxUmuyhFYJPISG4i8zGmUE86A5aSuD+z0UghoWGnzvB83OEjHgYllRfTUSl\nrojI950GfPAvMdal42UEjEsRj+IwChoJNhBkiGn+gNShQWEClXNxGMFlN5LLSCF5CwubFGMcpDXU\nOeV1uM6Qo0aOaRR2o7LSE0E9H0HzMnIhS8glGEI8iiqCzo8hXow/yFboVFF2sp4vYwHPYZFEpUwV\nkyIOu4mQoY1+cmi85Yr1dK2w+NylGqcSJADswmEKhU4U2mFpqMHbPuNQO+RSu7NJRQvQHS1zVrRC\nLNhANVyOuWM5O77YYOJFg1BnmL62RQ5Md5Ho6yNz/nIWP/p7DowMkUnlWXHsC6DG4YrXwe9/jjtR\ng0oHSiovBKnVy2HgtfDLm8Sofc/AN+LMvICFuxZ2vReG8mAnZcef6pYEZGMCInXor0E1ALM9cEIW\nxqakx6SgC7arDmTroLdBcR4mlrWGEsXK4s2U4hC7EObGIXAQFo8Tr2L1PgGUC1fB7t+Bo1I8PEQ+\nn2Rw5QGyrxjCPFQhPLFAV3eNgcw8+6e6KTeCrH13ll/coFAyA9Qdld3jfWS9u76q32LDmzXetk6h\nUItwoBYhiLBx8FZFhGUsAJvOgqGVLrd+dwoHnRhbKPIHVE4nSMTLcTxBCJ0IG6kyDzgEeSM1nmKK\nGjb7cdkD5NDYiMpWmkwjXu5KJB/xClpjASaRzWsDkuzMImDxByRn56vTRWgNXA4iXI4cwsXQkc0x\n452vCgyhcPi/MzX8ewgnIkaKs1EwmOFBZHL6VhRMUlxMkUdxvUllcnEyiEfwPJLA/Dyih7EDGRUH\nEq9JwijMAGs4j2d5yWOVHqCliulrauxB3MC1yI00kTLsPHjJU2hQ5iye5lFcFBz6WeR5XBSWcxxN\nL6NRAfTOGl/8qsEFF0yxptlLEp0GsMZravIHLSaA954IV16tcu2xoNTDJIHRUoJ8KUEHLqfdALVF\nh1XPbuek03Lc+9SJhAyTzmyZvs+uxv7eU6TVCdYuMzjiMzUvPg8OjMCOUZS+pngXjSAkkjD8fnj4\nFnAmxeXvmpZqhaOKAXe/F8KrYc97oKIIoORT8r9V+wUETAMaATg4ICBU1+HI4/DECXDcc5IH1Gwp\nl4bOgPpOKEWEjDXWJ57KrvWSYM3OwLptMPpLyVus3ichyY7NkMlAdghu/1+guGiJAu5chvnx5XSc\newL6Tc+xd6qHhVKc3nSO5R1zLGwaoGyGKfy6wersAk9O9ZBEgogJ4KJP6fzi27B7Ru6+L/Vc9O58\nA4c+FNYrCjffBN/6lINpppHO0kXgBBwWUdAxCOCylTrzxKmQJc0Uv6PG/cCNbKWN3cyS8xS1LPZ5\n76LovfIcwsaMe2t7J61u1IeRsN0FvoCUVpuIHxT0PpGJpGzHgMuQMYhPI55E2vsaBJ5F53nCXEDp\nv2yff/r4TwFDURTfsmdd193g/a0NuNN796PAJa7r5r3/fQL4G8RH+oDrur/902cWPjwUKDIOHMFl\nEklOPozLLEU2YXMEQWF//tSY9/N6BESuQdywNyL79WbkhswBb6BJmIkl+s0IsvUtet/TQIYAPcQ4\nj0WeR7yNcXyt0SB9KBSpU8dlDTYmvu6SdKAOMkUbbSjEcDBp8IOvhfnxv9js2JFhLTr7kNvb7n3F\ngDbd4vh4g/O+G+UfPwRPzUmqzAaSrkKPbrFpa5Vt10a5+4wivXaW0fkMjqMyX4qz/vo0jRdzRPfu\ng0iV2NAhVi0/DFtWQW873PYzqYb0jUkyEg1eczk88wgc9CL2Uly0OBtB8S5WngiZc2D2tRCpwMpZ\nCG6VkMTeBVUZV4jjJc98YtViG2T3wJZnvASmN0agGoHuc6F5lyRG4yXxUkpxCUVyabBPB60TdoyD\n4lVdfOA48dXw5BMQKJA/spLGfEaGGJ03RONwHX1PjpVZjbU9k+yf6mb/XBcnf6SdFz6/QLHRzsNz\nnZhAv2pzwFF539nTnH9xJ5/bYBIjxKx3p9sVl2uMJneaAQ66FUxCDFyiUVQsvnvnBH0MIFNzQ56A\nU5gaDwIZj0So08DEJYRsMNuAHDsZ8USAV3jruox4EToKFkFeR4MhXI56a3zes40GYVSSnM40zyJe\nxtMImPibm4nKMagci0Ue2UDPQMDkLsQT9+fvtGHToMI//GlT/DOO/4qH8X3gGwin2j+uB+53Xfcm\nRVH+1vv9ekVR1iGxxjokPP+doiirXNd1/r+nLSJ9Hhr2kj5FJ4Kar0XCi4NIXLaHVuIni5hVEHG3\nJK4LcCkWMa9M1Y6EKI9hM8MCcU8uzRe/SSMILwwIiwVK2Agn5AXkYk8CGuZSCasDjS7CHumrwgwu\nGXSSWDRpoKEyyknnV1h94haufrvOeiLkUUgCFSxKqARQRffI1ljzgTBzh+Hgna1RNClgY8ccQc3l\npFsyPPnpBrVJiA/UURWXrSsOkjjVIPbKbo5cuQ9NX0b/ygOobYsEh6pwyeXw0x+CVoHuihivYcKm\n88GyYd9dkC7Lzj7XIQCQLEBHFobfB0euArcqRh06CnMToFhgB8Dxyq6u0iqNosKGURjrgUJCACpa\nEaN3esA4G/beAk5KSqqdszLAKFIVD+SUd8Kue0BrSv7E175QNsCqHqoffQotfyxR08DQbCrlLLHz\nNrP/XZPM713DjsV2ju+YpVQP0X5xmvKsirN9nguOmeTuvWt5DGi4KgsoHPPRTu66yeXu2QANZMuq\nYfKUW8S10qxxFaKEmQot8skb23nrWxoU3ARNCgQIAYdIsY4iGjrHEyVAnXGCKAQIM89uoAuVKkP0\nMEsnDX6BxvG4GDiYKKxEIYTDCE1+i8ux+FPbW6pYCZpMk6OArIwFxGvu9Nb2H4Cmpwj3DBJOC9lR\nAGKV9xjFM8UKLi96peC/7vhP6yyu6z4CHhu6dbwW+IH38w+QQRwArwN+4rqu6bruKLKln/inz+zv\ntTGE57ABjXUIGGxFPrhftfD1m2VkoYCFX/UYAbZhk8RlHyFcggyjMIxCDy41LI4gYchKZB+HVpPa\nIzg8jskP8bU/I2xEYwjoxSGNqI2nvGRVCJMwsJckSVzmsKgRxmAw3MU3bz2Oz7/HJVeDRVQMBAR6\nUUl5ZdUcwEqFs96vcv/73aVBkf5kinolypq3R3AqDju/Y+C6ikjLmQapVJ3Yh9bj/vMLhBsN3HKM\n2kSvGP9Jb4ZdT8D4rOQcDNPrEWmD48+EF24GS5NEpS/9X4lCMw6rPgNjX4fyEXlM1zTUDKiXIZyH\nGW9cgD/9bKnL1IbEHFQ9nYtQXXIQ1QjEr4HC3aDNy2PnM8LeHOuTc6U3QbQLdr4gYY9mi8cSqcKF\nZ8ADT+LORaAcwwjXCPdMEn5zD42dBTLOcxiazVC8SDLYoL2zxpa/C/DQJywen+7mieluVFtlreKS\ndBXOPBcSKzW+fYsOjrokjRRDQ1PCrDWaTCsuOXTe9dEUTz/lsuuREO2oTheZXgAAIABJREFU1Cli\nUsdlL3WmSKKgk6FOE5NRdAoYdOIwgKi6OSxwJ3UquF6JVLyIg7jsxfWqgo5Xl2lxKtYjoXIYG4OG\nN+65pZSl4uvBqKxDJY1MeV/m2cRLyAYa9P425D3vEDKz+OWrkmRd1/VZTX4tBwQen/yjx40j0Pen\nToFUPspABJXVqCjYzCJ5hjICCGUgToqNWMxRxkKyAH7D2DJgEdurQTiYuHQh8d4oUoYyEc6GL9Kv\nIK6h3zQ2412KIiGOQYT65xGx+aO4Hg/TpUmTMVSCxFiGQwiTSbIECGHw5k+289xTsP1+E4sFGnSy\nBvFn+hAV8GlMorjceGuA274Ih47Ku1kRaLDPMlhwVJR0hPUfdnnkNfOkYzpFS6ctWkHXLebP30TX\nSJnAzimiwQgBQAs2YEsWsp1w53eg2tGiW4cacOI7YMddwoMIezNA/N3cUaH/HVAfg7lfyyxUn7Jt\n6gIA82kwPE5EMdHSs2isg8S5MHK7gI9mt7pRjW5oex08+zZQA/I6o4Pi2UQrEG7AcW+Bx+6Bmu6x\nTk15z0PrIJmCB35HMBoTzVLATicwzl3J+Jt30RWwCRkmbbZGqRoh+6Eujj7g8OITBlNmBAPQVIct\niQIztSSX3aLwvevAbUpU73OLg6jECfOc02Sv22SoL8B7Phhg65bd2KgotGFT8NoPU9Spo1HG9BoI\nRM8z7q0V0eZyGSVHFCFfZQmQxOEoOioGZYr8DqnahWgN3Qp5a9UXAG4A/w68CQlo13lr2A9sI4gY\ncAoJ0Yve80veuu5ENskqGhk0+rGovPxJT9d1XUVR/k/v4z/43x3e9zhwHi4mMrG8G0HUeQRQat5j\nulEwaCUl22FpHsMLCAAlaXq8ClkOTyAJ0R7vfPPe89d435v4Q5AUNC9f0UGDcWSSmk1rgnzT+5rH\npUwbr6SBS5MMMWJ0D9tc8W6NV20UMTZpD7Kx0Kh4n9TnBr7mjQqZLPz2GzJFs8v7pADpWJm3fEPn\nxe+51EZNjls+xng1ytF8muNOmCd4RYri9b8hkygS1GyCiksgZsEbz4C7fg2qBxSFlKhm9Z0F6LDn\nYUjY4gVMd7V0NpcFoOciGLlY/ubzMSZ6hS+x2CbgoFoQ7ADHFg86HwWtF9SEeAX9R6Xr1DQENIau\ng7kfgzMNqAIymfmWoO/y86FZhYPPCqXcn/C+0AtvvQTu+g00NCqNIHY9RNDSMd5/Agt3zLMwEsSO\nDlBrBshVooSGAiy/PMx3NjqouAzEyjiOykgjSDjQ5Ph3wcIheOYeuYO+aUe81aW6Cr83DWzK/MvN\nKrd/Q2XhiMzBlTSoiU2YKGupsODdzzEMdIRElUCjjGw8irfkfXZnOy0+RRhvZh0CAsfQKou63vrM\nodKDSgcWu2jNjfcbMwF60NDRacdCxeQRIE2AbZg8hUIa0fsMAXtw2A5EvHD9rzv+UurXjKIoXQCK\novgWDpIC7vujxy3z/vYnjlcjJcxrUDgBh5ew2IEg7Frk1j6FmFmVPLOUmEUMfQFBaF/TQsYSCqoO\n0dLcjOEvDZULEGcnSWsGyqL3ehkUmugEqVPCZAIXBZcFFGJotCOLQVTC5eIrhLGJ04NOgk/frHDL\njRbFSdAxiNAJnpSrjbs0o/70sM71X9G55VrI2LKEVMBRBDTOOb/EipNUmj+cQNdsQoEmJw8e5uhC\nG8Y1q2n+4ghqoUzTVQikcwTSOThnPYwvwJ4JMTrNllAh2YRjL4XtP4JkXsqnhtmqipgGdL4f5u8A\nbUw8i6bnDbgGOO0CPNkZMNohmIa2hjerMSm9IsVvynmrEfE+FBf6T4HQIEzcIeGPL+Q7OCrVltpK\nWHkFPPnDpSYyHFXCkhNeBQcLOE8WcTWbRjGBq7jYW3pQepPkbp+mZhqML7RTbQQZrYU58Z+iHLyl\nQLRSIhWqkTSaBHUTy1EppeOc9DG48wOyomq0aFExxFEvYKFS4ILzdNZvVPn+jXlCrER6kSoESaGR\nIOI1vSfQ0XGpMoFJgyYGVTwaO23esp9G8m5BGkxh4lIjQBEXidKHaYlY+1Rw0axVKaESRRocn8Lv\nE2kpzhUwSBBiOarHkhZJyo3AclTm/6id8VRcrsDmDIRW8Ncdf6mHcTcy9PRG7/uv/ujvP1YU5auI\nda5EPvGfODrxhxgJS1Ok8kSyrxMx+iBygXyqdh0JURYQl20CuBZxw/bS6motIjdu2DvvJBonePHk\nY8iy6UcQPg6ouFSocwCXAQwGsEnhoKNjoFJDIYBF06OJD7CATpMcXWi86lyT4dUB3nixTsD7FHO4\nbPXUsdYroLuyQK/4KIxvh7GH5Sq0AasCTYbiRRYqcbbckGL66xOk9RJu1GLHkUG2GQc48/Xz6MPL\nMb/yDOVCFjVURx8cRc2acN56+M73JK+w0C6JznQONp0Li/uguFfay+shAQRf1yLVBsGzoPAqqHoT\n04MN8TK65iCZhvRzoDjQ5QnN5vMQDcJxOQg5sBAUQ/fBJtQN3dfDC38LizHpLfHnmJTi8phTr4Ln\n/wClcZmI1vQmqSWXw7ZhrE/eizOTxcjO0JHO0Uy6aO89m8PXT1KvapTqISKBJg1Lp/fKEHrE5eiP\n8oRDYQ5VoryUayOE+KFbvxLm9q/BowfFHH1PbhHf53QJYBKJzPH1b67gi++BuUYVnRgJOqkSJ0CU\nKjUsT/OkjTZMurCZQyXokb1nES/Bn+S3ltYw8Ly3znxB3jnEy30RvGYBWR0dwBwWjyH5tY3AvyGj\nO6cQr0XCaZsidQ7SYB8iEd1Gnb3AIBbPeI+1wPOC5Of/C+3tiqL8BDgTyCiKMgbcAHwJ+JmiKG/H\nK6sCuK67W1GUnyHBlgW813Xd/0O4EqXV3rsSQdHDSMU25522gngF/jDaIBJq7PX+thsfyeV87d7P\nRRR0FGperuE+/JKrgYtDxOtGrSETtTcTJk2ZPcTJUMPGJotNDo0QEY6lRAWNDEFUglRRyaBrKtd8\ntc4nr5vDMLOenLHsWhsw5J26CiUgmYWTPqDw8RNdLNWhz1WY0y3WpRepNwO8/goL1dF49vYA8WCC\n4a5pUqE6i5UoA+/r5/EbbI4xyvStO0R9thPT1giefgI8dQRmRUJwSTA3Amx6BTz3YVGs8qXzZrLy\nez0EQ+eA8iNwmmAFBGSiFalSzCdgUoXlhngFhUlohCAW8wYUCVGOYkWeN9ELYRV6vgm5W0F9Bpyu\n1jiBfErAKHa1qJS/+O8Q8qotC+2SeH3Pq3Hv+QOVfUnibYu48xkc1cF+zcnUHinR2FlkrpRkqphk\nc/8R3LYQ531O598vqvLskT4WbZ1F8Ga/wHmXuiSXK3z3DbIiprBJopDyiNWa13HUS5j3fmaY7U/O\n8avfGvSTJYPGKDVK5DFpYLCcOE3yJBjlCAGWo+JiMY9CE5WDOOxCQuMzkA2tgngaQTS6gDg2ISSL\nMu6t9bNhSRvDRcDmVFpe8pkItL0aUaDrBSwaS8lNYY8KKO1EEqfDXk4l5+XfdPyw/q89/lPAcF33\n8v/gX6/8Dx7/D/BfKfjWgHsQF/88ZIUnEOpqBfgOCnfi8k7kgpoIYvsXKguchsJORGDH72T1h7jo\naGQJs5ISdyLexkvA+fQxTJmHmeUlxMtYwOE7lLgOqLDIw3RzEmESLFIjT4U6i7hMYbEVmygVHifK\nVi6+Os7cnMFv7kkstcqtRMHC4BEEAqeQ2/XBG+C+H0JsqsG2eIOdlSiXphfZP5uloim8/lM2xZv2\n0Z10aVo6pq2RiFTpeZWOHVbpe+l57EAH1tAhwoYJWhpOXg+3fE9CjkZQypqqAz1vgPk9MLMAlR4B\ni0BTyppd05Kf0N8EtfdAvAmBsoCFabS0OYMN+WrEIFUEvHmrjQiUTbAPQUYTgFm3D2J3QG4f7HoI\n8stbLFLVE8WZOwe2nQU/uxlycYh6XbHzGbjwTJgrorz4LImOfpRKlGI5RnNTD21b0uSv3kG12UZ7\nrMyydI5yPcS6f2hn920Wv3kyQsTWl6hKOtCWtbn8aypvex2cZcp9OMQsfcQ4hrhHdxLG7crjHS55\nK5yyoZ0CEzzHJBF6cEjRywDLUNhOg6NLAr570HkTDmPABGFWobGaEruQnH8nCitwsRAC4CGSXtiw\nwAIKFi4ZuZ5oiIfha4GGEO/i9wjsbUOYzG1IiPIrxMtoAjMoxLwu1z4kMdoDVAkziE2ZBhNIABan\npRT3lx8vI9OzgKhm9SKxlk8y6QJ2o3AZbZxKgZ9j0QZLScgmAhgXoOGQ5Rpm+Dvspayz3wtiYpGj\nzD7gLIQJKjWLUV4CLAK8CpXV1LGQiz2H5EByzDCGZB9U4mSIkqHEKhx0UijMcCb9EZ2Pfgaufp1K\njBBdiJ/k16C78cYYAsYQnHsJvGc1BL1O0j5X4fF54UJsuwqUmQo77k4CkI5WODzbSTxco+eSfuwf\njZAM1UhEquizncKdePUw7DgMB4Mw6HkCqiOGetwp8Ph3xVDjJdnpO2clH7HQDtYmuZbPLkA0LEOU\nfcXvVfsl76C4MhR5/eth7n4I7PXCjtNEuKPyCzjQCY0wtH8V0CH/ERm+HK0IKKmOnGPNDJz5Abjr\nfijNQrqJ/cKxKKU46tY0bB6Gf/wZdrLC1NF+uodHWLS76L9+BZUv7CI/EyEZLxGLVnjo2eNY8eYA\n6T6b315ucXK8yvPVKLlGEBvZXj7+DY0H7nBZ7wXE08AqsuzH5hlsQmg0KVIz5rn7e0Ncd53F4bmn\nvV4RX9bf5ShjjDONRQ8C/acBp1PjHlyPalfjF9663QCcgEoHfaxmkuWY3gaX85KiMTpIEmCCnQj4\n/BJhI+xFNrwBJCT3ZSij3nr/BXAd4n0cBGYIcSZBeihwC9Levh74DrCcGrd63kUS2SxfwX+YHfgz\njpe5vf0UBAAWEK8ih0EfSbYwz0EKhLCZQeJBlRYvXgXuwaHOHKdg82okKTmFXGjLO5+NSy9ygY9D\nZQiXgleNSWDSQMqzgwg5rJsQbazkdEYpUpLiHBUWqbGDAGU6uZgpxhmkjzddO8ejj0V4YoeCTnRJ\npiSI4Lwv96oAb/17+OWtkCrY9GourquQCjQYr0Wo6HDKp+Bfrw5SyOlEdYugYdIWrRA/OYYaUpm9\nr0pb1EFVXBYnegnX0oS3boJ7vwRnjsLIsCQQC0lIbwC1AaUXwEy2ZnzolgBHKg/KiZDfBYotNG/b\n8xTKMQGVjjl5891TYNzeal03DajshZIKTgTCChz7KWim4cXrYHxYcimmIR2pyQIMTMCyW+DoDtjz\nIiQD1J/fiKY6GJvm4c1vhNt+B1YBbd9qsokiqmXQ//k+eHyExWdLlGsdVApJCmN99K6vc+LnYuy9\nepQj5QECls7mtgVmGyHGigkuuxxWr4evvkVhwltpwrBRcVG8dDQcpspnPx1g/Cj87kcax3AsIzyF\nyWqaVLCZIkKQMKvJ0yDFCdSIUOUQLn0IB6gblx5aLOLjcCgwxUtYS/L/D+J6o7SrRGksmd02xBu+\nD5UzcenF5TmkWdJGkpTr8ZsgxQMpeF9ZGvR7a3gYoZJrCEVqBy5bkE1wAvFYRjEIYf7Zdvq/Hy8j\nYFSRmOsokqRsB5Zjo1BiApjEYhCVS5Gk5CKyVzeRMmcHLmVMUohpxoAqOsuABhYmOr0EaaPCIlIf\nH8BdYm4uQ0YFgZh0GuH9HeUoARxSaOyljRgKKrOEcVnNAjkidNBIHub91/Vz7mmLxMhQ9/C8gcJe\nbAxq9BNjGNg4DKe/Gt69AjRHpQykdQvDMJmtwevfYlM+DCtmDrIvkmU+n6RiGvR3zHP+u6I49+xD\nwSUSrqF0zBELNtA3HwNH5mC/AtFO8SJSXiVk3SVw4EmplgQb4inESwICtTAcWAndPcBBAYo/bgRr\nBrxKSwS6l4P2LLyQAL0BPYtgN2GhDk0FImHo+6rgc/FKUe8aKMvrLrZJsnOhHdZ+EIwKPP4rcCWv\nYaTyqATgTZfC04/DoTEIOLi2hm1rVE4+mVgwytw39hPWHTpSeWbH+pirJtl2q07u+xO8+FiK4WSe\n53Np8sUkeUfFWgav+Rr8w/nwRMNhhDrnEGYDCgeQFiwfQI7d3MY11+hs3SQ5J5so69lAjjhTLFBF\nI0ySFAoLTFJiFJsKLsuRnX8MKKNwItDugcIkcBdNrkbya/3IhrcSaKChECTmeR4Wos61BpcRXE8H\nVNjNY55dPABLKdya986PRzpbFz02iIV4I2/wfj4X8ZZVBER6gEFsnv3rTJaXFTCmkGrsgvd7O2Dg\nUKFBE3Hst+PSg0uVAB24DHgsSwOfTy80WAUDBRsbhwPeudtxwLsxTaQas4fWMBhfxaiJgFYSYU7M\nUWAAnaMkiKDQSYUSEEGGLiukCfOOD5o8+Osq8/uTuOg4uCSQSNLG4QXK1KmylQzbPqHyxK2iTqeh\nENEtstEy6ViZjlKSk69T+f11DrFynIhmszwzTzJco29TA32ok70fsrHrIZ46sJI1rkK6ZxL1FUPw\nh+0CALYmoUekCvUwDGyEX/2TTDDTPTqw3wOSygsPw4pAZF5+93Up0jkwBiBwIszvglAOJroFLCoR\nmFYhkRfOhDYE3d+E8vOw72ZIaHL+uQ4JbfzXXPsGSK6B334OFj0qeqKIpjpw7qWwWKb5b/uw8iki\nPZO49RDVlV0kLuln8u0v0SxEqZkGNdNg0dZZd0MAO9fkvi/GURWXfD1M3dEomxo1FT5xO/zqZhjd\nKdJHazCYRqEGjFOkkyCLNDFCLl+5PcHffwgK0yZ1DtIkgoGCQxTHmzjjUGWOaVw0GkwCK2mjw9Pc\nWvdH6zCNgUaMFLklfdd+/AFFMt3ExaFCkwSywf0GCVpLiOL9Flqt7F3eOu5Fcm9hYIgkW2iSokYe\nnSYaARoMId5EDvGdliGb4pxnX9LZ6vz/QA1/GQHD9ww6vJ91xH2aRaGKzomYjCHKQxFcbA+B/Q7S\nMILgh/HR3SWAyxiSMU7hYNJkDAlPZoA2sgxSokyVBVpT4P1AwkZ2CBOLGhHW49KGSQGwMIAkUbRE\nk/dfu4J3ntQkQZgpLDpQWI3mEbZUxrz3XO6F9RfBu4YlWAoCi65ChxnAqsQ49lxwLJh6yCQWSBHS\nTbrbFulrXyB7ZR+Lv1xkcSHOUN8YpbE+1OwMJNqhIwWH90CsKnmCYkI8hYEkmHXQ97WYlz6d21XE\ne0gWwJkDPdsqd8ZLAjhaAdk9G/B8BgwDVh6Q6ooJFGKQuRA6Pgbj34LST0A15P/tCxKK+AC0/mwY\n3Ap33wjTiVaOxDBh6+nQ3Q033YVaTKB66uBWe5DYp9dz5NNjTO6JEjJMwoEmYd2i71UG69+o89j5\nC7y42MW6VI5HKhFWBJs0TYMzrtexXOnct5CtYRCDMeCgl6maRyGIxo1fNhh51uEHP60iwxJzOFSZ\nQiNLEoMaDUapYNIg6q1RKWtaHELEqTWkAU04FC6up1MR9NZngNaMkRmggU0Te4nN2YPk1cLIxU0h\niX0byWOs8u7FKAIw09jkcLwparJRxRCwmKI1uNxvqQghwGN5f4v8JYb6vx0vI2A4CFj4c06F5Sah\nQd0jnrTjz480mUMMvOI91696PAlsI0qDGvM0lxrHDe/xvhhJGxpDaOgEECVPeykfkkEuqo24fAEg\nTg0TlRKKp0uuoRIlxBXvs/jN/7IZPxiihtzuNu8Mi0AcjSESVBAVvN/9AKZzLfn6mq0xW4mi1UJc\n+WGbw7fVWJas4LgKrqtQMw2KVpTBczqZuXI3Hcka3cvGKeVTRDvm0E5aC88eglIYEqYkGMETpFkL\nUwfEW/Cnh+m2p5spfRR0zEHgPgh9G8xbpORpGvJ/tQTKXshnpVS6dbucI1yDWA8kvwB6O+x/Nxwp\nyRq1Nal0BBuS8wAIvQX63wD3fRFyJlTaBKgW2mHZSbDlVPjqz6HmotTCqJYOhSz6Z09i4seLKDvn\nqDWzKIpLNNjA7Qpz8k1pfnxpg5HDSSKhOpVGkAVXIaM4ZE6HC94Hl2yGhtfquA8Xy/MuSjj0E6WB\nxrkXBDjtNS5v2lgmjsOMt/MatGFhYnsq4BLv1727ZuInxIuM0OJbdHp3fg6LBiVMxCuY954TBBLe\nSI05WvNrikgXq4Kkx/uRXEMR2VoqsJRo9VmjNmXmvfUps1NtVO//E569zAEhNAY89vQ0Ah5p/lsP\nYxbtzTrOkvy5hgBIHJd1NLkPKZ0uA5oohIBez/F3ESTuxqfWhonSZBQx+g6ESj7t8dI3ohJHpYMp\n7idEHxpdntiOSJtppInTRoEk0rKcY3FJYEdDJYUOhMIKH/qgzvlnG0vKBv3eZRxB4G0Yr5ksBq/5\nG3j/8bLUoqpDe6BJV6iOptrEeg26t8SY+tgkiXCAgBc+lOohtJM6aOyrkKwsEOyogOJSawZw5zpg\n0wDc+4x4FbYmu7nf9NXWD0dm5OdAsyW5Z+mttvKuabDnoDINmbfBzL8LaCiu0LdtTTyRvjEBgcLx\n0H8ZpLbB9Pdh4l9B8cq6itt6H0cGoe8otF8OPVfAT74Nk3HxKKoRAaq2DbDtlfD1e6GSAyVOLZ8i\nV46R+ewKgtNVOh/aQSGcIhGuYdoaR0pZTvpBlhe+Umf2GZsjjSivTuYZr8ToRCEXi/KpH8KnroLc\npGwBQaRPpIxPCQwRRiHZDZ+7Dd71pgYvFix6iGNRYcHTQbOokKeMhY3CSmRDm0CMtBMxupJ35+O0\naN4HvFcaRKol497a9GWfa8g7C3nrt4Dk8aLEKVGjicWCNyAz4nkhvlqWgQDGGu8cBe98FWSL6vJe\ndy8CUgYqnZ5SXJ1WGv6/saZniNdSYz8OBuJ6tSFkq4MeB95ANACCgIrKABBFppBWEOQtI8SWfUxz\nxHtOFuhFIeOBTBaXIBrtyHT3g9Qo09IHNYAgOim66KdIDpcCARYwyGKSwiRCCJUESS69ymbXEyqT\ne+TWyLup0a9CliBHHZW93qc55y2w4yFQjsoSiag2vdEyw52zpAJNAlcNceCnFm7DpWHpdCSKJGNl\nxhfa6X9DJ8oTh2mqDpl0Dtc06MvMYySB7gzsm4Gg6YURthhtpAodcXhpr4QJkz2SxOyeksfUQ9Ip\nGi+JAdtfgS3/BJu7Yf+vITQhFZJiAo4rQOhUUG6E/iGY+RVMng0LCphRCHp8vMkeAYRkQXIjwb+F\nrpPh118SO1LC8tqpPLhb4J3nwP98EPtAFYU2lFqYhqXDlatodsRwbngYNVpj4vByVNVhfK6DLf+c\nZvZFh6f+2WaxEiOruLhAzlHJa3Djj+Hu78Mj94lZ2N7qiOKSwaQEBNBYVBXuuAN+8M/wxKMNytSY\nJUYvURbpJ0CDGAEsXFxmABObLGKMPst4AtkSHqelNevP/jBRGCdAigYbkeqcz0w2kE1MRWU/NmPI\nNnIKGeLMci8WTQyyKNSpMYkYuD+EfBgJskK0xLHnEZAIIpuujng3UUxmaKnvR5ECw+Cfb6j/r+Nl\nA4wSv0JqzquQC5NHJYnGNkyeRuYtNBA3K4/NNOJRVBG361jkJlSQ5jMFuTgxoIDLYcQz2YDLDzC5\nGkHsFcjHjtBqKovQJMABijhkgCN0sJVOOpnBZh6dGAaaYnLlhzTe8fYKGYIE0WmgMME4kaBLr9LL\nnmp0Sa7nre+Fb30AHNVh2lWo2DrTi+3sK8dYG65z8cUxdr17ipGZLJ2JIqlIle6eSZS4RWLrCuxf\n7iLbIwpXSvsCGUeF7iE4NA/JWckJRCviFeTS4jlYPbDsGShUxPB1S76X4hJWBJoyyey0RyH1MOx+\nMwxeCpu/DEoIKEE8DW4e5nZD6H/Cs3shOSMhTj0jwOMrfu9bLedd7IQzLpdxhg/+HegL0K/D4eVS\n6k31w5VvgCdvgzGH0vRqjEiVcLhG5LXtJF4XY+7dL6AWQyyMrVkS6Vn3kQjRAZVvn6miWmGyoRqT\nKLTFyqxXXDZ8og3TVrjrM+Lo+3PDNCCEzdMsYNNEJc2Xb4igoXLLFxyajBJhghjbeAELjTAzHMYP\nTeOsw0GhwhFgCoUgKg42e4AsKjWvVT2N7PDHI5NJhuikl3GewKUf0XyJeWuzioFLkNMp8UskXJ7g\nMCEU1gO7qfMbb132enYx5NnGAA4V71z+aNEYrcHlfg5wAfHKjyJgIgxTWfuhv9he/eNl5mGEEPR7\nGBjDoRuHPFIeitAazHwysp9PIi7YOIKulyJCIYPgDYuRc04AJi6rcBlFLv5+4HaEJONrYnidlKjo\n3E8P1zBGGYc1TPAiBVZjE6RJnlnCXHhBgkIhyiOPTgA6AfowCBBiJTtrQosxkL2g/WT55cCD8PqO\neX5bitOvW1iuymglylmvMAmZNdY29rLLOYGZQhJds9FUh2Xna1gjJQ4+vpa2WJmOrmnUwVEox+Gk\nbpg6IgDgS/z7Q3+e2QKXKfD0Jk9Yd1rEarzKBLYmANM7IdUM1QG7COO3wszPIRCH/hy4izAV87pU\nHdBU2LNWBIOzM62pZGv2yus/cCl86BSom7D949iPnYSy70LUvjEvl3IMXPNK2PtN+JGQzhabAcy5\nDnpfbxO7aiXlj26nMBWjN92kGa7x6+c2Mfh6g7Ovctl+0VEiZCnZAToDTU5bNs5PX9rAusvgotfD\neSfAkCPbiz8nLw800FlGljEO8sZzQ1z6DpfNmw8w4wTw5+CW2AP0Y/Okd+dKQJjSEuchB4QJ0Ec3\nWY6wA5dZMlxMiQVqjABz6PQR5BSgyBj3Isn4XbTGGso40CYTNHkB2RAPAV8FutF5Jw4JbE7zVtFO\nZDPbAWwkRhcVKtg89/9Q9+bRcpz1mf/nreqqrt6Xuy/SvdpX25IXOd7kHTvG4ADBxCQEPOQHIYbD\nkN+QBEIYkpBtCGFCJgxLksEOZGENYbWJwbFl40W2JUvWrqurq7tvvXdXVdcyf3yrb5MzMwkJJ6Mz\ndU6fK93bXVVd7/s+73d9HrpWzlYk8P9tpGbDiOb6YwhsbkcyL3PFmhjlAAAgAElEQVSItXPHj748\n/w/HRQSM7XTFlzfAWpBzBGm4+Tkk33wMKa7SkYd/WfR+heIgBq+nzXcJ6UcskHWIQzCDAMIqYlWU\no58DiD+oAJMCMXrYyhxjbIwMyk59vsRF4rgkULi8850hH//4CWA7GTSazBKjl1wUfR6g22f7xvvh\n4b+Q/SVY6WWXCtFck7QecJnhMvZaHZ6aYqmWwYviEH6gYcUd1GUbCF9YIJ2tcn6lh4HRaSmCqmXA\n2gKnX5Z6i5gnv7MtETt24uC9QoqpSgUBlLgjYOLFhBk8U5O4w/SovKdQEpch1QC9ChVHOlSduFgO\nyaZYCLYFL++UNG6mJhmZh++EzHZ4/2tg4VH4/vcglUIfnJchLpRgZIjwVXcS/Pnz6NY5Fmf3UEg2\nMXWf5L4kyQc2U//AC8y/lKDlmvzVD67B1H3G9vvc9Mdxnnh9iRde7mXRidMfd2iqkM+e2EH/5XDf\nx+Ejt0Hfqnj6JToJ9G595BKwbl2Mjz9o8MY3eLQXNpJlhmo07xRtTDJo3IbNcpRlO4mARQrFpSg2\n4DLDBSDkeiDLMgcRx8hBsY2ATbhUsDARq6CAlHHfS1cb9QICa50U6FHEFlW0+ToWt6LzcqTluwvZ\nNDcBt9DEiiQWO7GM9dE5msAvIADSoTK+hI7Qo1xzCwAap/nfUN/9q46LCBgd/YVOPYaD+Gvz6FTo\nIcUyHyEg4nmkiOwfx+i4EyEHaPMNQt6BTA0/OleDruxiERnAhehnhRgWAW0CEtSAFkdx2cjzNNDp\nI8AnZBiHNB7L6ATs29zPlXs07rmnAtRoUiLPAA4WBl2KvTqgWXD56+DvdsvQZQINhxAHhQqlIXno\nFQb/cF8vzCcZLa5SbibxfJ0g0PB29DPxG3P0Gm12bTktlsFKjwQN18Xxv+ajLY6giquycDO1yBLw\nwV6GdBHKEcdEqSA/N07I36dHuyQ2ILGFWkZeuUpXtWxgQeITy71inXSUzIy2FGWt9sC+6+GyO+Dg\nZ2DqBVjYBLGCgFTPCoxsJbznjfCnB9EWnoXqFRTGJ5k8voOB2+okfmkzJ98zAy8ZLFWzAKTjDlfc\nsMSGP9tM5XeOs3SySM0usilXQTMdXl4tsn0Q3vJ38JW3Q/gSTNFghhoDpBiO6mtryNLKxjUe/PJ6\nPvmHIU89/iQeyagyU2GyhEUfbS5wLWM8QUjIXsbZQ4lZlgkJ6UNDx2QMmwXEKmgzzGWRVrsBmPgc\nJWSCJrcjm9b3kdaHaWAdJruBcVxqyMbWcVW2RHN/My5nCDmFuCSiqCZz/fP4KEIyCH9LjS63rUM3\nP3cY2ViHor/50f0CjBGu/fvfflxEwDgcXf5FxKUYQKyCUQJKVAkIKSGxii2k6MMnjU2IPJwJ4CpC\npsmzgToNPGZRbAIWCTmC7PeXIw9YlKhgV8SklCZFDzoNGqwSx6DGMnlGqZJhIykqLLDCAr0Uuf9t\nOp9/MEA5w4wAcQxWSOKhkUWGpozkZ666SwqHmnMyfGLnKLboPlmg/xJF4CiO/KAA8RR7B+bJWjaZ\nRIuGStE/GsecXiI/uoKZq0jgsn9RFnAmjVZuCHlvpiYLvZoVK2BwHlbOw+4UPKHkd73LsuBtS94X\nqq6VMLVeACXmSWwDBJhWegSA1k3DTAQaiZZYLMkmpPvh8gdElPTZd8H5MAoaOLDUK5bFlZvgujcQ\n/On3cZ4ISfbn8ZZ78XSfoUstUu+8hvofvUBxskVVT1FuJZivZRndaLPhExs59uEKziM6fckmo6k6\n9WaCum3hmAav/mrIw/8d/vGr4lDeZgWc9VI0PAsLCU92aiI/8gmYPKfx+x+dYxfjVEkzTRyfkCw6\nFjZzvMTLtPGYBbaxwBQuKeTsM4j40DBdjhWfVb6ESw/BWtYuT0g/Pp8Efh6Jz12O9ICs4FFASPkc\ncmSZIY+wac0AafrZRpMWdZIQ1R7JvB0GThGiR/NehzUqywvRPXqIu+4j1nctesWjp/AiYBOu0db8\n24+LmFbdiMeFKCOSpStdOEjID7B5DNb8uRptvH9SlCUomgYMHF6KUkgLdNOqe4iTI4NimUeAa0mz\nlRYWPmdRjOLTwOc4GklSbCNHjioOARUqaNSpkyVFKlbklW+Cm24sA0UaNOkng4aiF7Fb6sjQuMBP\n3AvPfUGGsYP1NcAIRUNtzx0hc496bBlYpidfJhZopOIOQaCR2mYRXKiSSJc5NTlOLF1n+8YJKBVw\nSwUMK45KzHcLraBLeuPrcOgCvOY+8B6RTEiniay4KjwYi/3y+0CT7EmpIIDS0VQNlSz4uSH528CC\n1GOYrlSObrgHNr8WXnwEjnwX2mmxOlQIm85ArA1X3gaXXQuf+iLqOLjuCGePXMJQuk72aoX1a5fD\nfztI7XEX14mjqxBNC3BTFjd8IcGxT7k8/Bcmo/E+6igutE3KbhxDg3d+FU4cVfze78lXHwTW+xaV\nQGcJjZA2GgFp4vz0u+CSK0PecK0iQGOBPAZpkmg0cWhSw2EFnw3M04hGq0J1raS6TIw2McwoZW8g\nFsGjNClE7+/MyWG6pQEdHdRrkABkExFY1mmToL7GKdVxEBK0SNJmAnEzknTluduI7boH2SRX6JIG\nd6qApqLZ16lPOkS3o7UQfb4DIj/ecdEAQ/w/hSBiDvHFhOxGLI7TSMpUZOAkShzHogeDDdTWlLB3\n0eIJOvGPEBtB3R2E1PB5GXFTqgQkSZDARiNLSAGTJiaLtHFYACq0SBLiRaW/LgYprv7JFGfOhEyc\n0ojjU2GZKuvIRIBR4oey5XHYcyd8+V1dSj4NedBZ3Wc8V2HL7Rme/jMwVciGfJmD5zYw3rdEuZHC\n2hojtrhEOl+mvlpEdUhxq1mUZYOmIFOKFMry4lqkGmJBrBahOQ1VB7bshbnHJX7RkUeMeWI5zA+K\nK9O3JC5Jx4rpWCAxT6wKJx5JHzahsB+uuFc6Tb/0X2C5Bl66m9IF6Xi9/eehpx/+7M9h0kQFWeKD\n85iAtmMA49e2U/ndE7Sftzm/MEQi0SJhujhGkjf8vc7Cd+oc+q8Kx7FY9WJMtg1qvk4TePOfgJaE\nh35agOJZZNmYbWMtfuGgaKC4+7aQB94P11yzgt2YJ6SHaUKSBBjECKlHqcs0rDUoGohrm0DslGVC\nAkQTdwFxME/S1fj1MIkD9ahgEGTBLtHVBBb+zY72r4OBQy2a73PRz3lqJBFCnYXoHHXEndmJxD9e\nGa2TDmC0olkl6V6FTYZ91PEIMBG3aBVxz63oHnp+pLX5zx0XDTA8jiHIJz0k4rfZKCYxeC1JJqiw\nGAFAE8UuNDaiUUengqSJ9iDIfhZ5gBvp5q5P4+Lg4iMR6SZNpkizFcUABkUSOLRJ4lOnwTyRAg8m\nvSRJ0iRDBZ3XvQn+6kEwyNCDwsIgjgyfjwx5A5lmV9wMM0dCjEqbeUwy0bccNNoMJxvkkk36riwy\n9/812KT7+KGiZluUGynSlo0xnMJfWCWXapAZn8RPtCQmEPMwEi1wfQmSOFq3ctOLycs1xS35+mPw\nptfDI0ehFbkSKz0CHjFPwMayu1KFtYwUanXO00x2CYLDu+D6W6UZ7YWHYOqYfD40xbLQfSH37U/g\nXfMetJk62me+CQmPEJPAi6EnWmy9B8I37uDou2epPhMnlyjSsC1UzMNLGOz/2wLLT7d55kMeM14C\nW4UsOXEmQ+FI/blfh43XhLz9Ro/NbQOLLld2hwkljMq/t2yz+KPPhbz+9TXOTZZJ0CBgAGjQpIVO\nPqre7bB1r0Tzbw6B+U5xloGPwmcVjTgWKZpMIJm2I0AdjT5CeqI516S7SG9CshUdxlYjul6neEsB\nwygaCIv48wjQdIKVTSQouumH7q2CWOMdKaxi9P4RYCnqV3kOsTQydDM0nQrT/6fTqinkATaQL98A\nQhR5YhRIcwlVvkWIASRpsYiQAPs0OYOkrHbT6XRVTEYuyxZiJICz+AQorkOEneV9Yg5uZ5kYy5wl\nYAKNIXSypOingY+JQQ6FxyixtMdtr9B44O1LBGgk6aXAcKT7LkM5hMQpFoG9d8L5b4VkjTbzrsk6\n08VoG4xYNvGYx2yxl+YqbPZX2TI0TxgqejM1FqpZhgolVLEX70xbzPRMDS1blcXcUTVr1IX1qrws\n8Ym2IZZGut5d5Csn4IgOt7wfvvunsBjxUli29HwY7Yj9qt4VIgo1IeEJNGj3wObrYeOdgohP/SOc\nf1YEnfMZcT9qGbleLQMbd8H911L76/NYjx5B84toiQRa3MF24qgr9mLct5HwQ89w4nsb0LwcYajw\nA42lVj/X/HkPy4favPiBOmdWe3guKrnrQ5bJHW+Hm++HX73epdF0mcVgCUkqushysIFVHPI9ii9/\nw+J3ftXnwBM2SQrE2YgHBJwkiYdPDRsXWbwLyAIbgzW+rk5J3gCdsmwNnST9NLkSAYczgIFNHdm0\neumGWjvA4CILtTeaf8no1ekXGUNhEaKQtL9Lt6/KQFKieaRlvYl0rubpblXQyfqFhJR4DKm92BTd\nQyfgP49YKf8XGLf+/Y5h5Ess090nCgRsoMkhWqQQ8aEUsizPE3ICCYJ2+kieQEy2l4jxOnwOEHAa\nk11o/ARNDHQApghYT5x1BJQjmjQpy9W5CpPN+LjYTKMYp84CW9BoEXD7PSYHnnAolc6SpocVeski\n02EKGQIbwXEN2HkbfPrNUmvRC1yeavC0bVEPodpM0r/LZOpQSKmRYrac58xyL8qLkU81WKjkGE3F\noSoxgVD3UW1D4g6WLbt5aQU2mvAPRVnsxdUuke5yr8QcTBcOvABBGe5+L5z4AUx8B7RypCUSdbGe\n2C4WhhaA1Q/p22HoKujbDRNn4NnPw+SEWBTttNRudGIiKz1QbME9t8DGHfDQVyhMzYI1yNL0KAYQ\n8wx49T6sW3uZ+8Vj9NVr3H3pYZ46sZ35cp4gabL/r3JMPgtPvNcjnVDEVEg75rHFi6GpkDe/QXHv\nbyh+5QZILOvcYxgc8KUdSwjq2oTEsFFoVpK//Dud730h4EsPuujkyBGwEMWloEAvWWyexGESsTz7\nMKjgRkICRLTN4ip3KjvG8GlQoUy3D2QIWeTFSHGmgKjiuShKCK/FViR/5iHwl0KsmaMIaMxjMIbL\nNCFbEXc8F30uQABIR8BD5rmcoxcBug7F5FFgFI1ZAorR2hqIvssCshFvROf6H7tf9aIBhs4iAfuj\nbtROEKiA+H4H0XgLAS8T8ixdgZdeut17TUSH9SSwiRTQZAiXWZocRAZ2BzCKybXYfJVhRmkzxjJT\nJIAQk0ZEZ5Ihxj7W8zAhPkUO4wMer3udxVe/GGOUa9gLzBMyj2IUGfppJBY+DjT7oW8YTr4YkkCh\nA6dKBe4emqXeShAzXfZeHuCd0QlDRcWJc7iWZbfVQlMhhVQD3erHq+u0SwU03UfPVWSR25b0dkzO\nQ3EHOMdk0dYyYmk0k4RGGwUCGCe3gToL534TLr8F7vogtJswvwTmNODBJTnRW01sgECHuZNw5hA8\n+BjEF6RV3sx1+1HaRtfS6d0Eb72BcGYR9ed/Qui2UJYBWkA22UQFKfjFPQSFFOffchJ7BaxcgqmV\nHkYLJZK9iq2f6eXY9+E773Moxtp8b7WHjVrAO3uWObrUz57XeNz0MZMHboXT52CcGAteLNJIl7Di\nAiWS5NmqTH7jwTaT023e/YEWDZYIGWWe81GG4TngRs4TABYGu9DZS4hHHzrTtNBpEnAeAQkjWsTQ\n0bRp82Q0P3fSoYpUURBVZ5UKR1FsweCWqDjrOGIJL9EJ6Herl3uitrOrmeUELqsISK0gG2GGrsZq\nH1KY9Wok49EpS7+EjhyTYiNx7sPmhSgb4iNOcoAUGSbIof3YeRL1z3L0/jsdSqlwC6vM8vUodtDx\n6VIIMIgpaDJMm8MoNhAyG7UR1xHLZBvy0B5C/EWp3JMGoX5gGZ0FDDZh8zlgDwqTPJfQ5BEclhAk\n7lTMraCzQJHdrFJmhAyaZXJ4IeSqcYdCycLD42Xq7CDPbHS19YgB6ALXvyZk/y+EXP9KhytIcLfu\nUfFjKBUylq3ScE32f8Zi4XGb+KMzxI02Pek6s6UCybjDUjXLTV/OsfrZOTgxw7rRaVmccUeAIebB\n6Ag8cBX8/oOy05sujE7jGW0a37mT3Bv+VuIQB6+E8UnJpvg6ZOow0APBLggHYHwKpoqQPA2NC3Bg\nLErb1iQOMnZeYhNXPyMuUSUnVaNBFq67A/Zsx//Uy7QeWyadruO0Epg9K6hEi7Y2gvrl/TTPOJz7\n4BxtW5E0XYrpOi+c20A1k+eVX0uw+O06Zz5aIQRWnDgHFwbJq5ChZIOeGxPc/hca73ul4rnnWUs2\nCqeVLIUEUufrAe/6GOzc63PHHUvoTow0PdRQJGhT4suE7EHOcg5IMcRWcpQ4wfMoLiPkJGPcwjx1\nLFK4TNHiUaT3+A0YxLCpIjy01yBE1FkSXIEEy1+Mzr+dbgq/J7peBbFeDGTT245EYD6NwiTkumge\nTiLuQ0cDL4O4GGeR4P/1wFXRdZ6IVtM64ANI1fMWZAM9hlDydXg5akiI+F7gZwnDsKOz8a8+LpqF\nMck38bCBzWxgFwqbc5RRbCHgWaSl/TuE3I4wGZ1GkL8f2c/nUKzD4P20OYQos/cTY4CQCXyO4FMn\n4NsIuIwRkqZCiOhZtonRg4aHyyEs1rGT7RxhAZ+TzDLPW26+iZOHBimX4lQAhc44Wa6I7mAW2RPO\nINPi1n2Kw0/D3VjEVUhc98kEGhdCjbFYm6qvUxzzubDgcrqa5ZUbJ3jkyCXsGTvPjiueJ3N6C0Er\ng5UJsCybZiXH6moRIwKW2Pgk+HMQuwyuzMLhFWglYbmX2MACmXu+JsBSKggvZzUr4NG3BOkaVFpQ\naoil8veXQvKEgFC2KVZKcbVbal4qyOvglQIc6Tpcuh3uugWOz8L7v46+pJEaroLuY4aKl47uZnC/\nTu8Ht+F8cYrYVycoxtNMVPqxXRPHi6HGUtz91yle+HiA++VVVgKTp+cHKUfM6nuA9a9QXPNJjf/6\nasXS8+LJGxDlsbocbVuBC5R4x69kuO02nZ+5QUM5IS2W8DCIY1LmScK1mEEBcRyXWWSCZbIY7KdA\nnEWeZ4Zfx+cO2qiocrgXqONzhIDnkU1pczQPi8BObObp8E8o2ijGoxT/rUjF8iRikWSjuQtiIVwN\nrCfJT9LiGAF/H91bHYHCTUj+bRPicrwquuZBupWkp6PPvBPZ/KpIJfTG6G8dIaURpBr08n/lKv1f\nj4sYw7gNIlr2ObIoWoS0EYTdDDyGsC7PwFqbeQYJTHmIGO3v4vH2KNh5JTCERoKQdfjkAQgpI+j+\nMqKVWgN60RmmnwxxZjnHMi4pJqiyl3VcYC/LTHLzT+Z57FslHMrEGKLKMg1CnmU92ehuytHdjAOX\nXw5f/2PFgAoZNlxScYc5z2AxhHPVHFlCMr0KfwFUK8nUSg97xs5TSDWwahlq9TTNRbD6YqSMNn6g\nEdd9Kq5J//YTsoBHpwm+fhR1+ytQZ/5CHmUoRVqa7ku8w4lLXMO2BEA60ocdAHlxL+w+KlWfrYS4\nGfODUpzVSEmWpLgq52qkZEPdf5+Q6Xz2ETi5BH1LhBkL//QWglBhaiEjb+sl//oBSr91EvtglZX6\nIHXbYiBbJW60OdW7k1v+R4onf83l0IMx8qle4qbD1kSLpUaKEuDfqbj2k0n+008pVp4RG9JG9s2z\nQAyfEg4DJHkG+LX7s7zxHRp3XtfmXPk0bQJMNpHExKcddUNvRxaQg9iCFXwK+ChMVlCMAUUMfoGA\nBAGHkXhBRxLAIIwybTIXHSQcu0CCSwkxabEYZUs+iSxuhQQoNyBujB7dQ0c24xxwOTYFwrWixRYC\njeloZj2PgM0VZLiBJo9HHC5pugprDSRge56u0NEUAr1ZBNyENFjs4R/vuGiA4XMQnW0E9GJzHhmI\nBiGPIv5agFC0F5FClA4BcBNBzCuRhrUXgWsxWIfPWby1/LQG0bJWTJHjUqpMEUTAE1ClgYtHLxZ7\n8ElgE3KeReos4aNz1Ss0vvgzDXrowcNgJwXmCGkiw9uL7BnDyNTYujskM9FkJkxS9mO0bA0rhKsy\nVVp2AlMLMbOK5mqMYrzFQLbKTKnAup4VJmdGSOg+/qxNbDSOFndQMY9csin9JX1L4jJMbEQtHIIb\nN8KN++HRA10Zwk5gtJqVYqyeFbEgXFMyKfW0gMjAQrfXZLlXPpdqyGejNK5XyVFN76DwQA/0DcI/\nPIk6fAhWClSX+nHraXpHZlD5MnZzI7V3X4llBbz4mgukGm10LU6llWCumuX4ci+XvCHG/t9Mc/CB\nVcpPtCmmEyzbCSq2hRdCJVTkXg3v/jS859WKp56R/XCGDj20jGiBNtNRxuHO18JbPgyvu7nNxGwL\nhzyKFh6TeKTIMUKdjWwlwzxVbJK41KIGR+GJ8ChRZha4QJtilJU7g2wDO5AFvIxJmiF2MMVzhFQg\nOodLAXGhr0J4Y2ejudqpAO1FdvkEAkCraKSIsQ6XlxAt4Y57fDZ6z3qgjIXFIFcyyRQOpQj8diHg\ncBRxTXZGidqfoMmzdK2ZLDCNSRyNm7HXmOp+vOOiAYYwAe1ENBU6aN5Dh1uzy7dZIs1GXDRcTiMI\nOk5XtEjoygKqmGQiPdMGgugJFDEMhmgzRUgOiz484uRoo6HhoTFIkQusEtLPCgFxAoaHoNgb48Dh\nBEksFDFWSAOSs0kg1K0uSji+0hDPwZlJnSSKwNejDI1iU7pOOeYz30qgmWAEbXwVEoSK04sD6FrA\nuuIqQ/ky9kQK44YiLdfEDzTSPSuYXgz/7CZ025LgZrZK+PFn0T54J6rRglPfFdBwza7CejMpBVn5\nslgRWgDZWrd2Y3ZYwCTQugQ8rQTUcrBtM9plt5Av5lGPP43/hWNo2WUqjXEaq0WUFpBONmmXCtjb\ndpL5xcuY+lwd+0vHMSpZHGIEocIyPFKWy+ADA2x/Ezz5+mWOPJekrJuEnsGcFyPwdRLA1vvg5/4I\n3n0XnH0hUrFHltxK9G+LORa4gMcGLr3D5b98wuQNd9qcPxVHp0wxosVbYYkWPjGmSDBElUcIuZmQ\naiRBOB7Nv3kCBnCoAlvw8IAvIhB1NbIxzaJwUKynwQVCJohxHX4khuxFUtsWaSzOU2YF2dQqdJi+\nkxTw6cfhMIoGGpsJmIA19fYSAgKDyAZpI7bUFuqRfKJQTUoA02AIMGijIT1Vy5F732lGS9LhGQ3I\nIaRTLaR25Mc7LhpgxNiNT4UQDVn8FeQBXke3nPUoMByZg53ctomAyzJSSXcTcDpSmsgjxTCrhCzR\nUQgJKdJgJrqOho6ORgwPDw8HHYuhiILepweXHm693uaZA4pGmImgp8VZGgzSSwyBtR2Io2NoAYXN\nsHhOY962sAkZiWoJ/FCx2kpiaT4pow2hSdJyWPENjs4Oo6mAmm3RdE1yySbl5x2GfzeHo5loQaQx\nEnWdBm2DWiNFwmij+w589BvwnjthtAe+901oeWJZlPPiYhgRfV+gCZhkavIq5yVFGvMg2cRf7EcN\nptF+YhdcuRsqNurvz6Fe/jYkq/jtcVQ9jaqnaTdSJHuXyYzYeHe/gvjYIOVff5nKUxr92YC2r+O0\nDdKJFslswIY/KhDkPf77dZBvGJRbSaZCeTadIuhb3wG3vx/ecyucPCZ6XRKj6qVFiwoGBglcNGZw\nue2mCv/toV4+dA8cOdREYZAggY6PG3U1e3iUmSdBnTkcMggBr6RXOzyuJSQD0RHc7rBkbaO7cNtA\nGo9BliMeT3EhOp/vQRatT0gOsQCEQk9iCjVkQ8wjboFBSBGfJxEXwkccL4VYKh05jTRthlleKwQ7\njyj3CYN4yKZoPpcJCaKszKXR+Tp1H3l8BqN7mEWsoh/vuGiAoRhB2LVSsDYATvRzHAlpHUR6Nzq8\nFVuRBzFLl9x0FUhikMXjfORHquhcdULatNGBdSgS2LQxMShhoxMSI8EiVYZJoFA0qJAmwy3XWRx6\nEgrEMQGo04vDOrqUrmVJYlJQIZl1irkpGcoLhAQo8HXigFPOM5pooWJtvBYEZkx4KlXIeM8Ko8VV\nGk6cSjNJq6GwjzWJ7+vFPDQB9TSh6eJZttQopOskMjX04io04vDhr8LP7IO3/Sd48gjUvwizUdv7\n/KAEMUEAo/MzVFAegj1xgl3DaDs3Qt6Ewyfg098inF4mXBhA9Tr4oSIwXdB9stmqEBbuH4K33k7w\n+Bynf/UEeaNGuTmEoftUWwlcX6dnu8/6j25g+tmAl962zNJCP3O+xJUsoD/m0Qg07vqAxk1vgl/d\nD7VzAvMeISWaNGhH9ptCAwIGuPF6i8//bYJfvjfk8acVFg5lbBQ5PJo01youm4Sko8rM6wmJEaIQ\nTZo0UECnjUETmzLi9iaRKs5ZZOdvAi1CBhBZzQ1AFZ8VZFNboMPU7eDhYCBWRQvFVZF7XYvuQdjg\nQsYjN6SBuD6bo79tQ4CmjqRJC4RrxNQeMInitYQ0aXMEAbRMtC46101GvzOie6/TkUKQ9dD7I6zM\nf/64aIDRZgoxkxbolnWbwAEEpfuiVw3xZDciX7pTmeciCbYG0EOMNCEKb62Ovzd6jxCixFglwEBj\ngDbLGLTIUAQUq0wzgc4wu5lhkatJsOfqGF/5VSH3U0CdNMOk6UGGZAKR4u1B4fg68T5YWBBW6stR\nVOmE10DTfZxA4fo6tWVoWBnS+iI7RmaoNJMUUg0M3cfxYpgxj8a3Fim+aiPu07PYThy9maQ8PUoh\nV6Gw4Rx6LYPXTEIrgV7LoP7yCdjyA9h7A+z/TdEGnJmD+QbYTdBciOsQT0O2ACNZyPXC/ALhC1XC\nv3wafX4CHANPC3DiAaqWIRkqfF/HS7Qwkk3qqVGs916BVrBofPAIS88FNGoZ1o3V8XydpmsSN9r0\n35lk02+P8Owf1PmbP8owHvYzGvM45esRdR6sj3u8+WMG68DbQ40AACAASURBVK6A914HziKkVUg5\nhD1oXGAz07QZIUEamyo2e6+zeOgrWX7uPpsj/6hI4DDGCC9Rp4JNlhRJ2rjMYWCT5RJWGAd8alSR\nWEGA7PZpYrSxyGIzE/3tTmS3P4ss5DziEJXoCl7a0aj2012IndiALGyooLg0iod0NHd0xMHqlHV3\nfkY0AzjRK4fiEhQ5Ql5C3IgcsB6FCRwjpEqXKKpJV5P1Bwj5lBA8aSSAE1Gl83r+r4gx//sdJ+jW\nX3SQsJMCSiEovxvWGoRmEBfEQpFE8Q0CfgEdHx+XFifpaEcqRJFEClgkA5NgFw3+DoPr8fHIkgMK\n1KPHqjjOdgIqFDmp6Wy6RPHYiw1CUqTRMKI7mUfgzMZFI05WRVMmr2iVJAkWqpBMqNioAs6EGpvi\nNoOJFoO5CuHiMNaIwfShAqbRJpVsUnPinJofJGvZWKbL9NdsCr+YpjoyxsRjJgO5CnUvRmupj5FQ\nYaXrtBb7iSVaxDqt6y8H8Pzz8K1vQngJwT4HVcxJBaeuUIELcz6cm4DnJmC6Bhf60UMldRdBFmyL\nRi3D0uwImWSDRKaG2b/I2dNXsek/ZrFuGKP02Vm8r51kZjnPci1D3GhzbnqUUiPFdDPPvt9MsuEu\nndPvOM/84ym2mHEWWgn8tpD5F5HhffXfWHgG/MqN0K7LVF4ItbX+T+F915lFobPA9TekeOhLFm/+\nWYcfPNrkBky+S5lFhjDIrOmau1EGLU2MneR4ghW6rd6dxW0AFRxKUdleh8ZuEjHbNaRKcj0CHLNI\nViNLl67/JAIQvSgGUdhROvUSWEvDFtBYT8hS5CIX6JaRbwceRqQSr0LjnqjWKEZIlpAaXY6L/cCu\nqLfVQYrQcgjA7ULA7EK0rk5Gf1sgxg7galwOR+eyf5SF+c8eFxEwlpAv1rmFeWRgepD4xDSKWUJ2\nIuAyhLgqVXTWYXIjNgfJcTtlXowetPAxaqRR5PGxCIkT8gw1YiguwcbFpIcyijgt+kgQkmKGzfwj\nJW6hiL1ZZ3EhoFpzGEcnR3ItmeUjkZV9JDgOjBkOp3ydLakYNEDXAi7VPZY8g4G4ww8ci2XbYnPP\nCrGYhz/VYtPlPivf9chlanz+1DY+8pPfYma1iB9o5JNNetJ1mp/SSL97G+ETUySTTYbHJ5k5thM9\nU0OpkGTcQXUyI/ODUty155BUf56t0HopR9ysEktG7FcdMWbThbwtzWPNJMQdwtNbQAtQQMy20DSf\n1VqGfE+T+I2b2fx7lxAcvAAf/Aorh4aZmxsiZbps6FvCMtqcWRggs0Xj6j8pkFiucuzeGV48PsyF\nSp7pUPEssBIqRoC7R+Dnvg5zz8On3gELnsj4GIQkUcRRa8lLD9hEyC03j/Ohv1W89WfgyPfa1DjH\nt2mQ4lryhPTioxGyjIrkLVOUcHmCJaTa8jJgCcV6QurRhiLAodHCX6Pi24piIyGDwHEULyOaOHFk\nF38aeC0CHH+JpC4zhDyJbHqXIlbyDBI81YjzG7S5Ho8X6UpYxJAFnkeyK+tIoePSwok0WCVmYQM/\n/UP/rwP7EJLrOIoMAY8jkbQepIirhiJPyOW4xFBcQKFHdSXH/i0L9Z8cF63SUx7+k0iQU0PQvRfJ\nelSAPClGafFMlDrtRWIdvYgL8wgwHtVv3Ekfw9SYx0YhokM6MUJaPI64Li+Q4TW0KONxhhhjgI5F\ngzQ7qRGwD8VTLPDb9/RzxS9o3PMqMCJDsEP5toik+6qIsboLCZW96zeh34fv/5ZA3wqS+O2QptVU\nyL7CKje/HgbvSfPd+1pYRhsv0NgxPMsL5zZwxYZzAFRbCZKmy+5P9KPXHc59aI6lepqf2H2UoJXA\nSNfRiquS8XDiAgZOvEvHZ7QJbQs1sCAXd80us3fcER3WQgksG3tynGYlh2m0RY0s7uClwb72Uvre\n1Acnl7D/bIqlozGOTo+Siio2a7bFaj3NhVKRXW+Lcfn7TOb/dI7lL5R5bHYESwUoFfJcqcgsMtr3\nXgUf/Qo8/HF46COyXVjAcRocpIQiRoxB8nQIic7xijvjfPbBQd7xeo1HH/ep4KIiHvcGx4GjKC4D\npggpEjKGmPkdGn8PWXgGKfbSZhaLFIoqcS4wzlU8y7eR4PkIBWI0eBqXNkU2E6PJIqeQzUpDiq72\n0W1cGyXOenSO0+QRxFJpRPO4F7FmOlR5IQIyGxEr4Wo66meK8wjbVoBsjhvpUFHKeY5FvzuAWER6\n9L06LXgZpM4jR5Y92LyIyypJdqKTpMZJxMLZ9/9mpadEjjsNyjUEPW06wkVwgBbrCDiOPPRLkYcp\nTUQirmyT5R6q/COrVCLOwzFEnWoVb41IeAG4CosENt8A+oiRY4gccQIWUJg4PMOLuKxD3wLHT4U0\n8Siisw6dEaCFzSnmeJoYP8sIp9A4hQyf4UHJkG8VA3wtYFtxlfpqkUyoWF9cZTBdI3ayxcC1edCU\nBDnbJk1nhaF8mflKjlTcIR11tq7+/klGPrGL8ff2kfzjGaamRxnIyB4aLvdiN5OoTA3PdCnNDtOX\nreK6JplCCTU6zdGju+mNOwxuOY3r69SP7paFXs6zd/sJyFaJp+tMzg2RNV0SQzrhXeNkf6oP56km\nL94/SzjRYOe2OWrtjZyu5BhM11nXs0LCaBP0pLjy8zGacZOD984wezTO4cYG8ppPIzDQ9YDN2QoX\nqjk+8ia476PwwbfC8tdlBpxHluEOEsQQecWjCJBUgNe9zuKP/7Sfe15d5tgzJiZp4hjYBDSBONtw\nIqpFk30EmPgYKBoEnIjmkgLG2cUYMxyiyVk88kjncp4qqwhYPANspM1uAjYAs1Q4HM3VLHCaGEl2\n8zqOUcdlGfg2FjcAaXQsBrmHeQ7QaYvrKpudif5dQMDjTDRrTkevIiE3IxtbhxQnhmRonke2Hg8B\ni73R789F97ZKl0n8BuAJ6jyFaBEPRfGZFcQ1X+bHNQ8uImBYyMP5It2y3TF0xkgAdaYjyv9rkekj\n6tdixsWBvYS8SCNKKklTjjwkGQCXjupUhxR4kA0Y3EwdiwwZNGIsY1PjFIpztNkHxCls0HjpZVhH\nDAtFARnCI5gMM0AcxVxk2F5CRAnXhMFh2QtshF3rbDVLI1SMxG3mGklCQtSEYuycz9D+GOceUWwd\nnGOpliEddwgi4G+5puR5SjGOvXWKoT/cRs/vZAg/8QJUQrxGilhxFTNqNnOdOC0nzmI5z9mFAa6N\nO7gX1jFSXCWpBdhzQ7Rdk4QWYKQaBB1SnnIeZYSMvwrUzduJXZKn/u0V5v7DUdRCk1gjhR/oHDq2\nE881GU42abYSvDg1zt5fgqsf6OGlP2nzrQ+HbM7FOVnNEbYNZrUAQkVLhcwbIW//GFz/Srj/Jkge\nk2dURraJaSCLRguNBcRgbwCveavNh35rgPvu8Hj+cAyfAJ0qHjPAJCFJQq7HYgcOL+BxhpD+6Pce\nwqfZRnb2DZxHRbUcVxCg4zADlCLBoNHoqtO0OE4QbU7S1RwgC24ZnzoTxPDYg9iYO3CjwHmbBC5t\nhJl7DsnwTSAL26RLFFVA7NQaXemrcSS4mY7uxUTc8gaSSh1Esjg+3Vb8ImLL1oE2Gtcg2qlpAk5F\na0yL0siTCIXgzf/CmvyXj4sIGJsRVI/RiVpLoPJlXGJ0uQlayIOW1mF5YEJ+GpKgTRt54CYyOJkf\nukYn9dQEFLMskmYIxTeoYFJlHw5aNAkdOhHroXUJDjysCKN0Xim64no0Vkiu4X+ntWeQkLAE2V2K\nFJEUdKhwXRMdCAKNkWyVsmdwvpah+IU2Q/dZPPqNELuWZd/APBOL0mfQk66TMF3stoHtmsR8l6df\nv8TVv22Q+9R+gi8dJ/j2LOfOj5FO18lZNl6oGNxxHLXSw4ZAw3XinJgbYtO2k8SLq5QXBvAaKVKF\nEqbpUgwt2L4Bd/cWjOv6MGYaON+ep/rhE2h6BTPucGZlF0vVLAt2gv5Ek2KySTLmkdsR58qPFNEC\njxM/f47ZF5MY3jCPVfKkPCnYMn1dOiKG4GNfCGnXfH5rn875sgDsIgIUPl3eeKhRZpH1bOJdvwY/\n9bY4r7vxLC+egTF6UaSYphoFNQeACRJAk3lC6oRrcJOky0WxQIdNq848knVrR7uvEc2PjmCxZDF8\nvOhzHcu3FN1hPyF7qdNLwDeieRVEaVoHAJ9UNE8PRN9sQzRTOprAQXQdDQGIZTTuJsCn23TWkVhM\n0bUcOkJJ+xGLaZIuoYKJhoNFliYVBFxWkCxOm246NRc9tx/vuIiAkUByzx1SXwvFJkJ82pSJcRcB\nYwRMIags9GQxkmgUcTlLl2l8AbiCFD20sXEpwVp5VQtJN13OCgoXHwcNlxUsljEYQqOPIsPMc571\nXEr/YMjinGipglAK+8AmRPG1SZcDo03kb88qsqNiB3VgTUV3seQZZNsGuq+jGyEzX65z13sTjG12\nWDibpm9LFT/QODo3hFfLMlZcQdcCKq0EGctmQ36RhY/CzJcbbHjPOlL3bWfoqRm8ZxZxDjewqyGp\nmAe+Tk+mhpFsUsjUsKtZzjdSGEWT4p4UwbYhwh29ZLfnCc9VaH2/SvOvnuXckSwp0yVu6JS8ftox\nj4xl03JNDs8PMZho4iYSbHlfD+teZXL2D1eZ+CsH2xUXqi9Tw7Ac2pUcM65JI1SM3QrveAgOfDLk\n4T/wedmVis4qcBKHWQwMmpjo9JMgySJoT/H+j23i6pvgvutavDBXoUWBJnGyGPSQxGAYFx2bKi7T\ntJmJivU7PBUOsgF1YgzDiNm/AoxHVcDzsNYk30LAIU+X7UohGRIdARQXKdNLE/JCNAPMaJSXovMl\nonN0dO8uRazdFxBw8REruHOufqQVwgPmibGJgBUCmgh5gkbIVHSNATpl5fI95qJ7yyOFYIP4LCOz\nr0zXKupBMo2dzzz7Ly3Kf/G4iIBRA7ZgkSDgUGTO9f/Q33bTDRhNIl++QLcoy0eiwuLAiMm2A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a2JE799E1yZcu+tRfbHXtptM+d2K3S/3UVJq5ZcuczWPLLq2Murw6Yq09ZLVTdfW2OZdnJl17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\nfrvfvuP2/wNh8EfHh9FvegAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa5399b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#define model function and pass independant variables x and y as a list\n",
"def twoD_Gaussian(xdata_tuple, amplitude, xo, yo, sigma_x, sigma_y, theta, offset):\n",
" (x, y) = xdata_tuple \n",
" xo = float(xo) \n",
" yo = float(yo) \n",
" a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2) \n",
" b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2) \n",
" c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_y**2) \n",
" g = offset + amplitude*np.exp( - (a*((x-xo)**2) + 2*b*(x-xo)*(y-yo) \n",
" + c*((y-yo)**2))) \n",
" return g.ravel()\n",
"\n",
"# Create x and y indices\n",
"x = np.linspace(0, 200, 201)\n",
"y = np.linspace(0, 200, 201)\n",
"x, y = np.meshgrid(x, y)\n",
"\n",
"#create data\n",
"data = twoD_Gaussian((x, y), 3, 100, 100, 20, 40, pi/4, 10)\n",
"\n",
"# plot twoD_Gaussian data generated above\n",
"plt.figure()\n",
"plt.imshow(data.reshape(201, 201),origin='bottom')\n",
"plt.colorbar()\n",
"\n",
"# add some noise to the data and try to fit the data generated beforehand\n",
"initial_guess = (3,100,100,20,40,0,10)\n",
"\n",
"data_noisy = data + 0.2*np.random.normal(size=data.shape)\n",
"\n",
"popt, pcov = curve_fit(twoD_Gaussian, (x, y), data_noisy, p0=initial_guess)\n",
"\n",
"#And plot the results:\n",
"\n",
"data_fitted = twoD_Gaussian((x, y), *popt)\n",
"\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"data_noisy.shape = (201, 201)\n",
"ax.imshow(data_noisy.reshape(201, 201), origin='bottom',\n",
" extent=(x.min(), x.max(), y.min(), y.max()))\n",
"ax.contour(x, y, data_fitted.reshape(201, 201), 8, colors='w')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0xa9614a8>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x9102c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x9102c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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U8rVax+NrVbsCRWe022q9vEtg9nBB3FALB7/zPYmNfQAWGnVMxWXBTYN5j6M/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\nXFRqXEbMhQsXlZr/B5aRpo4jE2BjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa6dcf28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create x and y indices\n",
"x = arange(32)\n",
"y = arange(32)\n",
"x, y = np.meshgrid(x, y)\n",
"\n",
"#create data\n",
"data = twoD_Gaussian((x, y), 10, 14, 17, 5, 10, pi/12, 0)\n",
"\n",
"# plot twoD_Gaussian data generated above\n",
"plt.figure()\n",
"plt.matshow(data.reshape(32, 32),origin='bottom')\n",
"plt.colorbar()\n",
"\n",
"# add some noise to the data and try to fit the data generated beforehand\n",
"initial_guess = (3,16,16,5,5,0,10)\n",
"\n",
"data_noisy = data + np.random.poisson(4, size=data.shape)\n",
"\n",
"popt, pcov = curve_fit(twoD_Gaussian, (x, y), data_noisy, p0=initial_guess)\n",
"\n",
"#And plot the results:\n",
"\n",
"data_fitted = twoD_Gaussian((x, y), *popt)\n",
"\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"data_noisy.shape = (32, 32)\n",
"data.shape = (32, 32)\n",
"img = ax.matshow(data_noisy.reshape(32, 32), origin='bottom',\n",
" extent=(x.min(), x.max(), y.min(), y.max()))\n",
"ax.contour(x, y, data_fitted.reshape(32, 32), 8, colors='w')\n",
"colorbar(img)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def _general_function_mle(params, xdata, ydata, function):\n",
" # calculate the function\n",
" f = function(xdata, *params)\n",
" # calculate the MLE version of chi2\n",
" chi2 = 2*(f - ydata - ydata * np.log(f/ydata))\n",
" # return the sqrt because the np.leastsq will square and sum the result\n",
" if chi2.min() < 0:\n",
" return nan_to_num(inf)*ones_like(chi2)\n",
" else:\n",
" return np.sqrt(chi2)\n",
"\n",
" \n",
"def _weighted_general_function_mle(params, xdata, ydata, function, weights):\n",
" return weights * (_general_function_mle(params, xdata, ydata, function))\n",
"\n",
"\n",
"def _general_function_ls(params, xdata, ydata, function):\n",
" return function(xdata, *params) - ydata\n",
"\n",
"\n",
"def _weighted_general_function_ls(params, xdata, ydata, function, weights):\n",
" return weights * _general_function_ls(params, xdata, ydata, function)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 9.59574172 14.08161948 16.93461961 5.23862105 9.40713411\n",
" 4.08501096]\n",
"[ 9.66514812 14.17445881 17.03352482 5.12652155 9.29181709\n",
" 4.14315514]\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0xaa23ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0xaa23b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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tbCjo1vy0QtLILiay4zD/AZ1G7rFTYNKZ8NorUO/n+goRcUVnz0aY4cAj3dOA\nmk3+tyeZdyRS6E3k07v9X/MDhalGjdnIRx+NYOjQ7orVqUyT7afkQ/WcQltjR8LUiy6K48Ybk5g3\nr7NzhrwlaDbygfQfnPGdbNtbK0/xe/z21oi+Gy0XMSKc5l3GSq24ZMgYAitXald4CQEjr4Edr2l4\nWMaGwawslmmNcTfb4My74ftdfskeEmxdDauWw3U3QJS25X3R8zUknx9N2AD1rq8Vn9Qz7gow6wiC\n27athchIM4MGhWnv3APs2NHK6NF2Q8dUg5AT3ppsAY+Eq9rYDDfjZ8LmH8Dt1r7CyZ0IrmaolLFN\n+MVZ2bCimMZSjYNNudKXkvUrLYMZgHVrYeMGuOJqnyuvSrhqPZS9X8+A69Wb+ZxFLmoOQra8QFbE\n0qX1TJtmrOZu//42BgywYbP1rVrSISe8fZCNlgP688fpxdhpsHW5vr6nXgD7ZFbtfmEWMCcbvtaY\nGDN7IqQOh5W9U7m217FiGVRWwKzZmrqVvlVHyoXRmCPUk2HXFzDyPK0T9GHNmkamTOmlsjkq4fFA\nUVE72dnGriwCoU8QvvWg8YQfc2oPCD8P9n+mYWVwciqUt0Chhu1DRAJMvQaWvgDuENZDDoRvFsKo\n0ZDqp3CFDJylbupWt5BysXqpu/MLGKGT8KtXNzJ5srGEB9i/38ngwf2E7wJ7jo02gwnviIKMYZCv\nwzKQNsinbS/R4hU4d5A26S5MMPNW2PYVVBdonaKxaG2Fxd/BOeejJZ6q5PVa0q5W78hTshlsEZA4\nVPsUN21qYvhwO3a7sT/3AwfayMkx1PsmIEJP+EE2Wg8Ym3B91BTI3wBuHeb3U+fBqi9Qn5VmQARk\nR8NKDYkxx80DlxO2f6t9gqHA5k2+HHbj1Reea9jQBl6J6InqFVt6l/VOp8SOHS1MmKA9IUdP0Bcl\nfJCzBPi3o3yw51j6owcz4ePvI6naH3hCwxRMF4umyv9wslZ19Qs689QIFq4QLKeJqxVMbyv8HJsw\nD/77FLwkyRN4/6cXHn1/6ePgfh4++XAeECCiYPdwSIyDYWfCE/+Fxk6FDVrkU0DPHbtVti05WmEb\ncctL8m33Bkh1MPfrrp93PQOzfw+ez1n59lWy3U6b3qkm3YbR5N2RAK/4jrlK5J1yDlQk07rUysm/\nCKf+1a6+Ex/XypdqHt1hetu3Bi6cMpb6Tv/UVeyR7bdKJgErQBRnybbBsfxWBw7AtGlwLOWpfGLM\nRfxWti02qWA8AAAgAElEQVTme6WAiwUKbd0RUglvtUNUCtQUGDvutGlWli/Xvo2IjoUxE2G5vFm0\nC6zhMOU6WKYl7dXZ0+HHdb4All6ESLBhmZyAKSVIS8y6Qji0EsbKk70bVu2FcQPBIR/62hllP7qI\nH2/BGqVd8711tfG55vbvh8GDjR0zEEKTB6gDCTk+sh9f8TOYEAJOPtnKmjXa1/OnzIL1y33JFtVg\n7PlwaKOGbXhaEmSlwzvHV83VCRNYJsUTdl46lrxYPPuaMGc6wGbCs78Jwh8Bz1bfy7sX+dhqldjx\nPzj7n9hzqmlVY3lpcsLOEjhpECyTl7ZH4GmFqnVuUk6zULRQ2/9v+zq4o4ehyFpx8CBkZxs7ZiCE\nVMLHZxsv3QcNMlNfL1GjoyjjpOmwZqn680+aD+v95wDxjxmTYPl6/TGzHbAkW0m4JY2cT8dgvyEH\n15pq6q9YSdN9m6ifv4KGG1fT9m4hSNVgnQsRb0J0Pljm9GhcXK2wdyHpt8rnUe+GDQdhQrbq0ytW\nukieql1OHd4P8cngMHAb39DgU20YHbyjhJASPi4LanUEkfQEeXkWNm/WR6hJ02GtyiQdYREwYjZs\n/kTd+SkDgdwcWK0tGKfLmEPspD8xhOx3RmFOsFJ8/z4ab19P+5cl0HpMeku1LtzrasD5JLTcBI0T\noflKcDwFYffTo+xl+74mbkYEYZkqnXG2HYYhKWBXd37FKjfJU7S73Hm9cHAXDDW4onNREWRoz9gd\nNJyQhN+yRftyPioasobAdpW5LkafCwdWQYvKJIzzfwWs3wZt+iwWYbkOMp4dRvPqevafu5WKxw7h\n3KMhC69nLTTOAusccLwO4Tp3e65Wyt6uI+NnKqV8mwvyy2DMQFWnV210EzvcjEWHpN67zfjEFIcP\n9xP+KOKyjF/S+wivXcKfdCpsWQsulc+KcRfBpo/UnRsVB3Ovx7ec14GwXDsZ/xxK+V8LqXuvEqlN\nZ4y8VAZN5/mW+r8/H5J1hMECpW/UED8nCluayofGRvXLeq8Tqre4SZqkXcrv3QbDxmru1iMcOgRZ\n2rJ7BRUhUdo9ic9MNj8rjn8XNrGKYyzKJkW2Xz6XybZdsep92bZzO72fkgcLHwg/ekzJCvxk2jHT\n28hzovBskngyzRfxtLFU/rE9f+IWRpw7msgXd5GX1/XhMmbgoe4dLhsKGyP4w4O3y17zsjz/6YDC\nc+0MfW4gvLGc9KpCuuVebFAg7U652sb/8fVbMBe+fB8O+FGoKdSlm3TOm7BzDhMfs8DXC7u0vfev\n7pGLtmWCc9YlkF/TgNTmX7dyzfz3jn2omUjKLSZIXAtA3uopsnP5y5ZjySFbN8DE+cdu0Y1meRX6\nfZ4C2bbuKViP4SW6um/m56eTm2sDDgLy84SlCm0yYYIATJQ57r82YEglfFaWmcJC41T0jmhfDfIy\nHfUeEqaEUb1GnSkvYlIkbftbcVerWEnYTHDeIPhY+6TCh4WT/WwOvLEa1vfy3mjzWvjoDTj7Ypg6\nU3v/VSthzFiIDLz2bq+VqN3sJmqqyhVFfgkMSw983nE4uBEyx4DZQDG3e3crubl9J2ouZIS3WiE+\n3kRpqQEpmjqQNRoO7/QpcLRAWCFmlIXajerW89HTo2lYqtJv/tQBsL8eDmtLxGFJtpL9r8GUPFbU\n+2Q/guJCeO1fMCIPxspJEhk0N8G2rTBRXfrW4oVOomaqJPyBckiPB6s25rY1QfUhSJdb2AQB+fmt\nDBvWT3hSUkxUVHjlqgoHBWlDoFhjsQiA6GEWWgo9eFrVTTZiYhRNa1QSeGYGfH9Y03xEmCDryUFU\nvV1Jw6IgJwJsaoQvP4DpZ0GYxh/uls0wSp2WrGJ5O5EnqdTEuT1QVgcZGsx/HTi8HTIMJHxhoZPM\nzDBEH4mSDRnh09JMlJUZJ90BBgyFEvnaA7KIGWWlfqc66R6RDNYkC21qNOSxYTA0FtaVaZpP+oOZ\ntB9yUvVqhaZ+ulFeDHt3wGlnautXWuJLSZwSOJKuId+DyWHCmqZSGVdYCVlJ2uYDFO80VsK3tXmp\nq3OTmqrOmzDYCBnhU1PNxhN+CJTu094veqSV+h3qCJ89A5o3NoOarzY9HdaUQbv6+5BweSLhQ8Mp\n+qMf5V8w8cO3MGKsrz6WFuzaASPVJXdr3tBMhFopf0gf4YsMJjxAQUFbn4mLDyHhTZSVGZvlJm0I\nlOggfMwoK/U71JnyBs2E5vUqq9jMSIcfilXPwxRlJvm2VA79ulBWmx00tLXAskUw5wJt/XZuV034\npvVN6glfWAkDdRB+h7FLeoCCAidZWX2D8EHVV87Cv+lqMV+QmjqUsjIBdN1Uj0U+/nEK8qa3dxVS\n4y/oqMSRORSS9natNfd/FMn229Fhevt6BPxlcRi1nfIuPpxV4LfPyNkD+MXl6ezZ4l+L/HlH9Jo5\nw05cvIOqZS7w+OLCk8PkE10kRzcQ+bNsXCsria2thE76rX2y5jUY8qCCA7lCsklq/ESgLdkPo0+B\nrGlQobCdyOmcyfcAhF0ME9xQd4j5nc1rxyPRAzOvIX5X93PeeeLeLp9NVrjknjg+Wngpbx2Wr1d3\nPM2K90JSNmBFcRWWoRARp+wLeWW3I4WFvyU7u4FsfiPbq4DVsm0vRPxetu225qdk2/whhBI+jLIy\n4+LgHYm+IJ1WjeWHUzLB2UYXssvBlmgiLNnMXhXeseGzk2lbXKk6XsUUZ8U+L42mVw1eyneGJMEn\n38E5M3yhgGpRuBayVGjrKyt85VqiAjufe11Qv8dN3GhtMsvdDpWFkKYjkYZeFBQUkZ3dN9ztQkr4\n8nLjMt3ED4YaHcv5IXmwT6V7e9zJ4dStc6oy+4XPSqLtOxVPkQ44rsigbVEF3gpjk4V0w6ESyC+A\nMWdr6LMWMk9WcaIEhwsgM1vVZWu2uInP075ILd5ltKa+iKysE5zw8fE2qqqMI3xMJtRrs34Bvm3A\nocCRmwBEjbZRvy0wIU3JYZiiLbh3qzPdmWxgPzuFlg/U7/eDiu9XQe5MsKjUPNccBHsM2FWktCot\nhlR1xeQa8j1EDdFe2bb8QMey3iCUllaQmqpd3xAMhJDwVmpqjCN8dDo0asgydQQZQ6FYpRNc1Agb\njTsDfydbXgztW+tVp8kafIEJ175mPCV9JJllTR1U7IUh/osgdIMkQWU+JOcGPre8FJLVEb5xn4do\nHYSvKoTkbM3ddKO8vOqnQXghRKYQYokQYocQYrsQ4q6O4/FCiEVCiHwhxLdCCM1lRX2E11fTTQ+i\n0qFRh4AcOEyDhB9po3FX4O9kzYuhfYt6h5lRN5tp/UKbrT7o2P4tjJqD6lDa8l2QrKK6YkUppKiU\n8Ps9RA3WTvjKQkg0MKClsrKahIQ4TCHPIBlYwruAeyRJGoXP8/+XQogRwG+ARZIkDQO+7/isCfHx\nNkMlfNQAaNBB+KzhULg78HnWeBNmh4m2osDmO1teDC6VhI/JEcSPNOFcVqXqfMNQng+uNshUGX5W\nvhtS5MuIHUVjgy9rb0Rg81zLYS/hiSZsGh0AKwsgyUDCu91u6uoaiNPuGNjrUNR4SJJUBpR1vG8S\nQuwC0oF5cDT742v4Qn26kX4xn/q9bnj4fzCZ5tDa2r1w/WcKaR4n8GuF2d4n2/IZMC8dvi6Gdce1\nbRpTI9vv4YIMomKhpairKQ+g6Diz1YCxZqq2eimqiUfp/5oy+QAkTSbBth5Gdl3T3+GvDtx1I2CF\nwHHty/LXVEhwyXCFp1Wkgr/ArQGKZC51Q8WbMPFssH7cte2ji7ufbxJwehI05ECD/wf9H571RdLd\nMAWWL/wd+zol7X1bZhon7YdTh0CZTGEef1qG8EJIzoL7PEopReUdjGoUIjqH4V9RVFsO7amNFFT6\n1+i+HOWfKwBvNSo9/ORSKmX7Pap6kSGEyAbGA2uAlE4lostB4Q74QVxcFDU1+qq26kVyOlRolPAZ\nuVCUjyp///gxZmq2qbCxDU6D/WXqLmoRcEYmfBtCU5wSKpeCYyBEqMjU6JXgQAmMkreZH0HZFkjN\nUzeFg/mQOEzduUfQVg9IEBenfTugF1VlvviRUEPVDIQQkcCHwN2SJHVhquQrP6vJ7Ss+PoraWmMJ\nn5QOlRqVdpm5UKRy/x4/xkTNNhX2uKFpsE9lgbmJKb666sUqPfeMhuSG0i8hVWUuvH3FMCrwurZs\nC6RoIHySCl3g8agrhKws4/zbq8shNTX0ETQBCS+EsOIj+xuSJB3J0FYuhEjtaE8DZNyu3un0Orbm\nio52UF/fu2mYlRDu8GWrbdHIm+QsKFNZMCY6x0TDPhWEz0iAQyr345NSYbkO04KRqFkHseMCnwdw\nuAIGBXaqqc6HBJWOMSWFPpOrVjSVQmqqcYHxtdUQFxdMwq8Cnuz08g/FbyyEEMDLwE5Jkjr78H0G\nXA881vFXJlVjdzdDgMhIO01NxpmYElKgWoeSOzEDCrarOzcyy0RjoQrCp8VBqUp3v7wk+FBHeJ+R\naMz3LevNDvDIZ8EBoLQaMgPHvNcehLhB6oYvL4HouerO7YymCkhKMo7wjXUQGxtMwk/teB3B037P\nCiThTwWuAU4XQmzqeM0FHgVmCyHygVkdn1XDR3gNCRZ7iMRU35JKc790qFax7zfbwRYjaClT3tlE\nJwAWM9SpWN1kRPr2+cXGrYR0QXJB4x6IVuG61tgKSL6wYAU0lYE1wldLLhAqSiBavmCNLFoqITnZ\nOMI31AVbwqtDIC39cuQfChqDo48hKspYwsen6CR8BlTKx9YcRWSmieYib0BNRtYo1Ev38UmwSb3r\nbUhRvw1ixkCtiiSchxphYDTUKX+3uoMQOwgqAqywyot9TlVa0VxprIRvqIXUMX2c8D1F02z/BtIH\nIkfT2BgLHXW/OuMyBdPb+/xMYTT5RUZqKtSW+TfR/GWbvB35vRwnz45cCgO6u8vuLDrmGx05NRpz\nZTJThvic9W+6/x/+LzjkTLYsuZFv/3Or3+arTll59H3CqHSavyqnrcP8l66QNJIMhadSVYdWPPl8\nSDkfKr6Cyq/A2wYbJ8j3K5A3VwIwc+mx97bvIfY28PqsCZ98cKlstwuvz4fpNeDoHh12WUH20ffh\n1YM474xqGs2+6MJ1W/zrCUzlvsCobWb/FYy+uu15/xMZMZxiz0zMJPtt9siYlAFa5HawQD43+D2+\nuy6BSbGJgP+H4s2NShYemd8TIJ/E0j9CYieIjLTR1GRg4EwK1GiU8BYbEGGB+sC+8dZ0G+3FKoJa\nYjOo3qHCoGEV2PKiad/QS+mrMm6AjOug5B2InQQnfQRZt0OUZgdJ/2jbBGFj8MWcBkBVOSQGtuK2\nF7VjywgcQ+71QEMVxGkyDANNrSQZ6O1aV+chNjakld2AEBE+KspYwiekalfaxQ0Aap2qDI7WAWG4\nSlR8n+h0qncGPs02Ohp3YSvehp6VnAJg0D0QPwO23QbVS2DPb2HrLSAscN29cP61ENtDFzCpEVyF\nEKYi0UWlSsIXt2NLV2c2qy6BBK3L+sZWkv0L96Cgrs5NXJz2XPq9jZAQ3uGw0tpqnB99dDw0BFih\nduuThCrpDmBJtOKuVPF9IpOoUxGIYxseSft2lVlvlZA8A2Imwo5fgqvTDXCWQME/4cU/Q3kRXHUn\nDFdpWpND+06wqXCdrauG2LiAp7kq2rEmqSNIfSXEBPbn6YoWJzG9tMBRg8ZGN5GRJ6iEDwuz0NbW\nC9JLJSKioVkjfyLjgCZ1DyVLjBlPnYrvEx5LkwqfG0uWHVdhD5Wa1hjI/SXs+7O8uazdCWuXwPv/\ngTMvBkeU/vFchWBV4aDe3Ax2h89fXgGeWg9mlUvg5jpwaC3Y2OYiqgdfVytaW73Y7cZ59skhRIQ3\n43QaWIAiSjvhHbFAszrCm2MseOoDEN4aAV43bhU8tmQ5cBcEsGkHQu4dUPYdNKnYQ1SWwP6dkNuD\nOkyuQrCoqA8neaG1BSKUbW7uOjeWWHUEaa6HSK3S2ulSUyOj19Da6sXh+Im41vY2fITv2xI+QiPh\n3fUBHmD2WGjzExzjB5aMcNxFPZDwSadC1FDY/6r6Pnu2QG4PlvWuQ2BVVxCSpkbfP0UBnjq3eglf\nr0PCO91ERGBYvviWFk+fkPBB3VRELvqr3+Nf3LKEaGce2VzUrU0+TSU8ZX1Wtu0Zl7yCxxLlYlVj\nCQV0f8hcJvy7dOXGwRdbsnn382y/7W9e1inRYvxohsbnQ5jvAfHzq7vHduXOgnMjoUhBCRjraIFw\nMya7mejWeugcBLdZgYzxnfbn4XaYfC989gYUjzwuoeRxuL+zN5YVOAz3f+T7+8q18v0A3pvf9XOY\nHW4fDO/N58K3r5Lt9s6lHzD9pGj2LbqOkm+7PlDb3cd+jqZiyI2xsLHDVHdRhLxftL0uktgY8BdD\nI16Qr0foetzLRznleFq6/1MuKj1Jtl8e8pl7t+Bf7+N0gs1mQgh1cVNd8Y5Cm/z384eQSHhbmG/7\naBSio000NGjLgR8WK2hW4yNjEhBmgRbl1UBMKtSr2L+LVDtSRQ+k++kXwN6tUFygsaMLn4e0vA1d\nEc5WXw0ve2D3uLZyL/ZU5Z+e1wXuVrCqqD7VWu/LoKUV7iYJS6RxzjBtbaHfx58QhI+KMtHYqI3w\n4XHQomYFHmmDlvaA5ruYNHWEN6Xa8ZbpJHzWUEgfBMu+0tef94D5Ac+SRV01xPlJb30cWsu82FWE\nijprvITHBz6vrV5durzj4W6WsEQYR3jfsj60+/iQjG6xgtsgq5y1w7Kjtq77EdgifMUHA8JhDSjd\nASISoElFkJyItyFV6XwaDhoOW1aBS6+Pww/AKLruJTSgsUFVimlntZcwFURub5CwxgQmpLNJnd/9\n8fC0SpjtxhHe6fQSHn4CSnizGTwGKenNFnC5tFdpMdnAo+YhYTGBK/DqwRruywgVEOFmaNN5cxyR\n2rWTXeABivElNdKBdidYAzvLeJwSprDARPM6wazC98bjArMOnxbJIyHMxhHe45EwGzieP4SE8CaT\nzzpjBCxWcOswCJitGgjvDvxlLOHgVkF4EWZG0k34KF+Z5h6hB4R3tR9bUinA0wZmFZWXPO2+B2/A\n8/QS3g0mA31hTljCC5NxEt6iV8L3NuHDNEh4vT4KjkjtWT66oSeEd6mW8GY1Er5dwmQNfJ7H5ft/\naYXXA8LAFXZfIHxQn28ZLPF73GWqo9i7jwL8BYfIe0N84Jos27afx/weT7Y4MLnvZBb+867Zbf5Z\naA2zUuQyI2fUuuN9n3Jr8DSYdw48+f4xZdeNI7o7uwxOTicloZErz94q+x3MJi/CAVJjOxbbcfvw\n4z93xsCOnHdRdojbA/ZO5gWTwsPIX3iZKAJpABwKQNzsgu7HbDUQ54Uw+bpzV877DPIyISuXgfO+\n69L273ev6PLZ5TThNltoaTfxRqu8XuE+k5PIMCtDw7rfo/ppm2T71bScyd66JAr9KlPflO23Bfl0\nPG8nyOf383jAbN4KdFfmvBAhHyX6drM8J36QbfGPEC3pBV6vMdVPLRYTHh1LepNKxaLZ5qtXFvB6\nYQKvU8V3DrfolPACwqKgvYc++FIRCJ1lkdxO31ImEFwesAYWrZ52CZNNxUrApW9p7jV4Se/1SphM\nJ+CS3mwWquqv9QasVn2EFxahisgWm2+vGQimMIHUroLwYTqX9LZI357Bn9TWhCJARwoZ0EB4rzrC\nO9Xtzb16l/SG7+G9mM0noFnO521kjIQ3m0269AUmM6oeSiaLOo4Js0DyqIm1Vaf1794vHFWO+gFR\nBwS2pfuFx+0ziwQ8z+vTfQSA5PFF8ao6T8deXPL6/s9G4YSV8EZ+cbfbq+o3eDy8LpW/3XaVUqhd\nQqhYntLm9kl5rXA2QJgKt7SASMenuNMBa5hPygc8z+xb1geA2QZeNasnq+//pRUmi0/KGwWf8DFo\naSuDEBEew+psuVxeLDqWe552SY3CWfUq1uv0YgpT8aVbPaDH/dLt9Iksq8a6S92QBRTq62oJA5cK\nwttUEj5M5XbJqu7B0K2fRaUlppdgNgs8alZ5QURICG+kecLl8ugivNeFqn7udrWElzCFq5Tw4To3\nlq21EK5zOX4EIhukAn19rSqdDawWaA8sWk02gUeFotNsE7r0NGbriSfhg6qyKOILv8fH2Wfy2rAY\nWpq6Dz92i/yTX6kYyXLC/R53uaxYrP4TWAJcN2253+P2qLE4bMnIycsbRvnSqToywkmJSeeGUcdS\n2Xy8Y3S38y8og9LaWAZPf0b2OwCQbEOkmeH4eU3YKN+noWMp76z1xYk2d6pv5lV4pj9xb/djl54G\n6yWY8LnyPC1+mBItQVMlDXf9U7ZbdGoZmMKgTYL2rv+V7MSu5qqIyGSSwuoxJzrJOSwfertXsjGs\nDdY5u/8GFnwvn1x5zZ9hjxvy/bQ9ZZWPKciIl0+fdGm5vJQ4YInE7b4Gn0djV9zWrBS4sUChLUi1\n5XoTkheEQXt4l8urywsLt1fVkl7t3tzVqnK17Wr3RRfpQWsN2AOnj1KEnnxgR2C2q1Mc2syqJbxX\nhWXDbFVnGu12fcOX9JyYS3q8kmEj693DSy5JVT+pXULYAn8Zd6vPvTYgVPqj+0VbrS/MTzcERMf6\nkqjrgUUL4QPv4dUS3qI27uH4flZ0bQX0wsgYEjmETsIbtof3EV5zZhO3F5sKgnpVOoe4WlRGdLU7\nIUzNk8EPWqogTkUlVznExEFbq77gAwBLBLhVpOYKU7eHN4erc1ayqHR+6nZ9te7TvQSLRf+t7S2E\nhvDtXoQKH+neQmsThGsMn5Sa3ThUWLk89W7M0YG16k3lEKkmd3pDnU/K6kHBEogbAmny2VoUMWws\nHFCRA08OjhRoVVEAIMYO9YGVe9ZYE+11gZVc9mhfEgytsEf6fhtGwW4XtLaegEt6Sa1NupfQ0oAq\n8naG1Oj2Za4NAG+rhDARUAPfUAJRahzY6mpUJZHwC3crrP8XTLgNbDps8iMnwE4F5aASTBYIS4BW\neT/6o4izQ22AlYDwEd5VG5jwjliVyUqOQ3gUtBpYtdxuF7T4SadlJNSUi35FCFEuhNjW6djDQoii\n4wpMqobU7sWkYt/bW2iuhwiNKZCkZjdRKrfD7loPlgAJFxvVFj1sbvJtLvUq7qp2waFlMMF/OStZ\nJKb58tIVqayPfTzsqdBW4XN7C4RYB9Qq7/WtMSY8LRKSiiWwXsLbI40jvKXj5xHqJb0as9x/gWeA\n1zsdk4AnJEl6Qs+gH5U5WVHexitb/K3Ddsv224VSYsG7ZdtKG+rZEp3PGroHlvyfjNlmQirMuaKO\njFH+67aFdTJLeerd2BNAVPmO/fmNa7p3CI+B4X9h360Xy87zCDIv8VCx+TKcu49JwSG/USjQ+4/7\nu34+vAquuRscF8Po38j3i+50P4ZMhJpvYFxHdNnWACmrS457eo3MgWQnLJ5F9DkL5fsVZENCGEil\nEN/1/9HYdkx3ERVhpq3Ke/SY0uPBFguVu/2fI7e1t9tBuL38frj/LcxzfkyrR/Cr8n0Ks/Ff7dfh\nsNDacgZp+N/XlaJUtfRXCm3da/QpIaCYlSRpGeBPbat7Td7W5iU83DgJX1/vJjpam8tBSy1YVOzN\nATy1bixxAc51Nvi0dpbAt639QCu2QT3wmPO4YeE7MHMemFUsKyyRkHoGlH4X+Fw5JMZBlQrtvgBi\nIqBeuQx2WKIJZ7U6J5UIHRI+Kgo8zcY5wTgcJlp7WGqgN9AT1t0phNgihHhZCKFJy+R0SoSpcTPt\nJTQ0eIiJ0Ub41lpUKePAVzQhYA51SYLWBizxgW197QdbseX00EW2sgTW/wgJj6P4bI4bD1P+A+VL\noblA/3iJcVClgnURdnC2g1t56R+WIHBWq9vvOmJ91We0IDIKPC1GEt5Mm3EV0mWhl3X/BgYB44BS\n4HEtnY2W8A0Nbu2Er1NPeE+dG0uciuu31mJJCWxjbz/Qim2wzkSSnbFuKYgwiPGzJLQOgxH3wagH\nYOfjsPeFno2lVsKrkO4AYQkaJHwctGjU0kdFgbfZwOpHfYTwulxrJUk6qooVQrwEyPhhdi7IMKbj\nBc3NHiIijCN8ZWU7iYnavG8aS8GapO72uMpdWFNVOMs0lmEdmEbbNmVbUNu2JpIXDAKzgJ54Zkle\nqPoFJL0Opjio/T+wz4KoG8A6FAq/htW3gruHmishIDMFilSY5FLioCKwOHZkmGkpVkfI2FSo01gd\nOCHRp2w1CjExFhp7qfq3f+wAAptUdRFeCJEmSdKRxEAXAdv8n+m/AklTk4fkZONK55aVtZOVpc2Z\npbXO5xxkijDhDbDXay9uJ2aUColcV4QtJyfgaZ5qF65iJ+GjI2jb0kNDsaccyi+FxOdh4H5wrofG\nV6HlKzjgv+qOZgxIgvomaFYhwtISobQ64GmR2WZKFqlL1x0/AGo0RvQmJYOrxjiVeWyslTqdDozq\nMKrjdQQf+j0rIOGFEO8AM4BEIcRh4CFgphBiHD5t/UHgNi1Ta2z0EBlpXOaBsrJ2Jk/WXpqkvcyF\nLcVK2wHlH157kVNdLfO6ImyD1e3NW1bV45ga03PCg69+e+X1YIoAbxDETE4GHFDSMnfCgARYuyvg\naRFZZpoLAkvgyHhob4N2jcvlpGRwG0j4uDgLDTpMh72NgISXJOlKP4dfUXPxbM7ze7yxcTdRUVag\nO+kfN98se71Yh3y+th8a5R1NpLLRDEqFYX5MIlePkU8quWf/WD5xDmXTju5tVycdczCxtQgGDghn\nV4ep6rNr/SdAjMmGO7bUMeTSD/wPeKhTRFh9EVw8jfiNa32f//OS7DyZoGAmelTBLDd6u3xbS4AV\nS+f2rGzYuO/osQvuk7fWflzSgmtlEVJx90pw88/rFF057CrOGPo1pPo88lb/9ya/10tLh+JiWCc3\nTTb4PR6bPICndzn46w7/+ubCqStlv8OKVdNk2+QU8dmxcH7kIc6a7d/sHLlokew18VMT8QgSeNLv\n8Vrp3YUAACAASURBVGqu8Hs8JJ52TU0eoqKMk/CVpZCcpr1fdREkqMjn2F4n+TzDAlRJqS/EV+xR\nja/8wTJIioHInia0CDIEMCQN9gWuo2WPBBEXhlQawD5lt4LFDI2B3W9j0qFcR4Ke5GQrFRUG7uHj\nfM5coUZICO9b0hs3dFU5JKbq6HcYEjPVndtc6CEyK8BDTDoyGRVO9R4v5BfBcJUTCBVS46C1HeoC\na94zR4F0uAkCKd+ToqBSnSIxJh3KdBHeQkWFcWa52DiQGg2M1JFBCCW8celCG+og3KGqKEoXVBVp\nI3xEIMIDVJVBksrlxs5DMDJL3bmhwtABsLdE1alZo0EqUKGTSNRGeD0SPiXFaijho2OBE1XC19W5\niYkxtqheTQUkqIlW64TyA5CqMtq0Ya+H6FwV36msGNJUPkW2HYRRWRBmnEVDM04aCjsOqTo1dyp4\n81UoDdNjoUydcjEhB4oKVJ3adYh0KyUlxi3p4xJBqtdb5LP3EBLC19S4SUgwMCE4PimQorGCUtFu\nyBiu7ty67W7ixqgg5uEDkBnYNAdAQwvsLYaTuyu4+gSykiE+CraoC7gZczp4t6rIppOZAIWBTXcA\nyblwYI+qU4/CZhMkJlooVmnn7w0kp4JUY2CNdBmEhPANDR7Cw01YDYyJLyuCVI0FVaqLIczh89UO\nhLrtbmJHqXiIVZf7IuFUlFUGYPl2mCYfyBFSnJEHS7aoSuCfNBDCI0EqVLGkHxgPh9URPkkH4QcO\ntFFU5DKsGApAUipItaGX8EEVswV8I9MSQW2tm7g4CxUVXRUZCzzyU2pRUHqcrDCPPMBTBOPT4XiH\nrF3HR311wmigZjdMHw6HAwQlNRV4sMUJbLGCSweulz/x0EDILwfHybDjuPSJyX5iyWsqwTENnKfB\nYRlNeJH8k6x2t/wSpXr9RNm2qHBlDXnKzd/B6AGw4QUY2fXcuwZ1r8iXenEkznUOXvlOPqnkux4L\nEdHw5T9h1mOXdiGkv19FXBK4JdhY5d/JBGABl3Q7NjgbRAGAvMTNWqUUnqz00Ope41AIQWLKH4j+\n4CucTv/7+Bn8XfaKCsZT5ESBXM25kNW9qalxEx9v3LK+plidie14VO6GJDXLegnqdriJHa3iO+0t\nhiFq9xcSbNwAU8apPN8g5M2EnStVlsSFuCl2alcH9o4ZOhb2b1NX9WdgLhzSKN0BYrOhtkB7P72I\niwunudklS3YjccIQXq1NvVu/vZAgXyy0C+q2qyT8vmIYoqF+2+aNMGoo2HUmxehliEgLDJ8MW9TX\nLo2bEk7t6sAPh2HjIX+zumsOzIVCnYSvK9DeTy9SU6MoKzMwl5YCQkb46mo3CQnGaZ9riiFOR9nz\nKi2E3+YmVo3irqQaHOEQK18GuAtaWiD/IEzoG3t5+7xUKNihOiY1PMOCySZo2R/YDj1snDbC65Lw\nWUYTPpKyMgNzaSkgZISvqnKRlGQc4asOQXK2jn67IXmkunNrNrtIOElN9UNgZyHkqdTWA6zYAKdN\n9OU6DiFEtAXH/AzYpD5ZRuLpDmpWqHN2Hz0Zdvv3hu2GnNFQoCPnZsJQqO2uZgga0tOjKSk5wQlf\nVtZOaqqxhI9O9tU71IKKnRA3SF1O+frdHqzRJixqIgHX7YFJueonUlgC5VUwRan+TvAReesgnEsq\noVqdsw1A8jkRVCwM7IkXnwxJ6eol/IiTYZecE70CkkZChZ/4iGAhOzuWgoI+EDlDCAlfWtpOqpoY\n8l6C1wOVBZCiMW27xwVVeyB1jIqTJahc1Y5jYlTgc/OLfEv6FA3Jgr5eBrOmQJhx960zLCOiCDsl\nnqaXC1T3sSWbicy1Ub0scH6nCTNg8zJ1xRpSs3x1K6sCu/B3QcxAX7axtqDGpndFdnYsBw8GNTZW\nNYKsNZvi9+hlxDCoDE6eDpfRVXn1PvKbsnORl4hKzqovXvCJ703jFJ64phDWHPuViE/Plu3nOdcX\nvSWq8rjt2lpIPuZRtuBL/5GA4V9Dxkk5fLLB/3J9QSfTm+n7chg6Be8qn3nOGqsgBeJrwFkDhflw\nyVT4+uNjbTny69O4SHllUdxO+b3KO190/X7CBHOeiWXtglYK1uaSV+Y/eg2guumYbmLI5XaKv2mn\nqsZ37N6HH5bt903CI+xf8v/bO+/4pqr3j79v0kEXndCWUroYLUv2piwrQxkKiICK/BwMUUFEHOAA\n4Ss4wIULByggoiggQ0aBFmTKLrRAB6ulhZYuupP7+yOMUnJvk5s0SbHv1yuvpjk5956mee459zzP\n83lAXxzivPb77/jdpY8nTmd9WNr+DPMO3O16u8lPFX7v2QyOxuled0P66i+3vyt3c5LCpLteCw72\nZPnyUPTVlLvJTtmKvdI+4Z0slOl3N1ab4a+l6ZRKLEpaPvgbWZECEJNyEMIM03lPioaQ3oYdVxt9\nCVWvesbJgW7+EwKCoI3+i2lVETamFmUFIimrjIsWCxziyIU1hvVp1hNO7DDsuLUiXCg+VfltQkUa\nNYXTFlzOA4SEqEkxILffEljN4LMvg4eClFWTSLsO9QzcGS9PUi6EGhYZdyVeVz7aI9iQ4+ZBoQah\nmRH14EpK4Lel0KUPBJlQVsoIHH0EWrzqzMFXjXMtOfmrqN3IjvSdBkSYubri7gvnjho4pqauFJ9U\nYPDN4KwJxXWMRa2GgAAVFy78xw3eKjN8qrIZnuRcCHYz+NNK3gEhvQx7r26WN9JfmJMF636BQY+B\nu4n14A2gzWwXUlYVk3PKuC9t4BBHUjcVozUkKzQolFMxhgXcIIBjuDNFCmb4xs0sO8MHBKjIyNBS\nYv2oWsCKBl98Xaex6Gy88pRyUvMhwIANtYoUlEFWMQQa1jdpGzR8wLBDa7enInTzA2cjt1POJcKu\nbTDi/8Cpaoxe7Qydv3bFLUzN8fnGiaoLamg41pmkZQZqT4WEcTzasLfaB9dCm12GNse4yDWVCsIi\n4IwFDT40VE1ysm3M7mBFgwfdrrlPg0rfZsYTFoKjGtwV7HLHZUFzwwwrYR007Kdb2ldKZhHiwSuo\nBij4IA7vhaP7oefbZjd612AVURs8EMtg26AcyvKNU89tMLQWBZc0ZB4wwChVKmgczv4/DTu2c9va\nFB4y3q/dMEKnfpQnrZRmdpo0sSM+3vohtTexrsGfgzrBFj5pcg4EG7+sEE9kIjTzNui91zMg/RiE\n9jHs2JpVSagGB+tknYxlXwwkbYWe74CTYeOrlBYBRG3wIPHnIvY+n4/GsHD5WwhqaDrFmbgPDVxy\nB4VAVhZXLxj2dqd2tSn813irva8DHFXgtzeF8HA18fG2M8NXsVtOvzthFX0BiEyBS8GwqlzbQntp\n6eTJpdLO04X20tpvLdYMufX81Z6QUeDDj2t0v990velDoy13PTx2DbsxEbdei/CUzusOr5eKeo83\n/Z6pRfi5O+VYCkv0rC5OF+N0Nh/7pu1gr4Tid30ZVdjPiuGBc9BzHnzyN6Tf/pxiYyIlu3VvWmH3\nSgCGNYb+IdT6dDvtUq7QTiK5bdv+DpLHde/rQd5lFXEbnYA7/y+eM9+76/0LvoLEhbBL8oiw+IDu\nfIIAM1rCd096kXtJt2kpZ8PlnaOd7oPLh2+/lsJYyX6n+ErmqDskW6bc+G7f5P4QOLATpuAmITd5\nE7l0TDnXm1TdORsSsbxJSgoEB1v2nAlHoImSYLX0QijTQoBhm37Xtubi3tMNwcBLaskvKdC7ve4b\nrYTNx2H9EXhrCEy8H5rXN+5YTnbwWkdo6wev7IT4K8rGoYJ2r9mxf45hy1i1Gh58GNZKZ7jeQZ0I\nXdBMrgJZq5AWkCQtUlwleATCNQNXLpbgP2fw8UegicJMU/FEFkILw+6VS9JKKT5Xglsnw9yAmuPZ\nkFcALQ3M1NHH7tMwdTmcuAhD2sL7j9JgkhcOfndfdQQ7IKg29AyEsc1hQS+4VgQzdkGWkWv4cvgO\ncKH4GlyMNkxdonN3uHQBzqcYdvyQSEiOUTa20JaQLLGAqirc6+sUx20Fy+pMVeDcOcsbfOJJqB+q\nU4ouNvJ7rT2eiaqFN5pNhl2yszbl4NXPndxdBvqvo/fDA53h6OnK3ytFUSnExOsegd7YN+xFm7VB\n5B0pJHtvIc5hDrhEOOIc5gCZ/ro9jaQc+OwQxBmmMiOJAMGTPImeavgm1eBhsFZCpl8fIZFwZrPx\nQ/Py09XzzDKyJJUp2DmCkzvk69E1sRZWNfiUFAgxU7UjQykr1UWnNmoBJ4zcwBGPZyGMNlxf7trf\nOdSbUAdVLQFtkQG73CeTYEA3iAiBU4bpxMlyIZPEZRkkz7uCT3833FrWIv9EEZdX5XA9oZiuIeb1\nT/kOckVToOW8XE2FcqjV8NAj8FAPw88R2hM2v2n82MLus8LsHgA5qboLja1g1SX91au6D6NOHcue\n9+geaN1VQcfUAijWQEPDwmxLr5SRf7gArwEGJsiIwIbd8FB3UJlP709bJJLxRy6J72aQtiKHvCNF\naAvN+y1Uuwk0nO7F6dmGrxIeeBBSEiHxjGHvD2ijS3zJUnAtDO8I8fsrf5858Q5TNtaqxKoGDxAf\nD+EGKsOai0Ox0Fq6WpAs2j3pqDobrnedsSKLOo8Z4SOPS4TcfLi/o4LRWY+wl73I3F5A7mHDY+2f\neg6WfGv4OSIGwimJOsWV0bQTnNyjrK9SfMLgqkwVMGtgSDHJ74EHgQxRFFvceM0LWAkEASnAo6Io\n6kn1kloz33Zd3DT42Fjd783l3E/J0lPBzNK+km15FcrX5+xyY+rCxznBl4StnyrZb5ie14Jz4JEv\n4fFBP0r2i/2ny63nhTtKCHxDjV1EbfKOFlF71luS/Urfm6F7MjsRu0+7ookWEE/o0irttdLX5uQT\n0ko43RdNlGxjs3Q44PdfjZfuB7QJTrn13CnCCb8HnYkfchpvVw3bi6XFA6Ju/PQOhM6dYOnw26+1\nF6Q3+taLKsYOhEUvQ8Vwe7mMgtUkIgjQsGMw3zx1gYxyGWtqfpDsp5ERquyH9HdtAbe/v/XC3DmQ\nqGUBuiCh3noENW8SLZstJ+V6g26M0Pv6LhPccj8A/Sq89hqwRRTFxsC2G78rwhoz/IULeRQWltG4\nsRFJKzc4txdcfYE6BibhiJC2Igf/x43Ie88qRrPwOOpXWoGbDRehAFBB4MwA0hZeRpNreIBJ34kQ\n85PhVV996oFfMJzYbfwQGze2Jztba9FacgBhYXYkJtpOlB0YYPCiKMYCFbP3BwFLbjxfAgxBIadO\nQUSE0t7K2bXrEt26GS9yJ2rh5DqgteGhsOm/5+DV0wV7L8Mj6cSDV9DGpKGe0tLoMVoSn0e90ZaI\nZK0zXODB0Rl6PwMbPzX8PJ0fgv2bDBPHuKtv51rsNUBA09yEhamrn8FL4CuKYvqN5+mgpw6zgVhj\nhgeIjb1I9+4KVC2BuDVAG8MNvixHS+bmfHyHG7bZdxPt0gTwdEQ1yDbry7m0dcF3vC8X3rmo23A0\nkMgnIT4WMozY0OoyEPYovH/v1MmRvXstX/UlNLQazvCVIYqiiFH/7jtJTgZ/f3CycFXkXbsuERmp\nQLcaOLMVXXUUN8MF8lKXZeP/mIcu0NxQykQ0846gGtkQGii+plYJjiGOBH/QgHOvnac4xXBjElTw\n4GRYLx9negf2LtAyUjfDK8EaM7yvr4rCQpG8PBvyyaHc4NMFQfADEATBH5AILYgu99B/OddodMv6\nFoZoxpmRuLhMHB3VhCgIbCsrAo5egI6GBxFcP1lM4bkSqDPAuJNdLkCz8Dg8PRC8jFshVBXujVSE\nfRNK6kdp5O81ThQj8nHIyYBTsYb3afwIHIuBfAU6dHXrqmnQwI7DRngPzEHTpvacOmW52T2bHZzj\nnVsPKZQa/FpgzI3nYwCJxMbe5R7SxvHvv9CmjcKRmMCGDcn0elBh512J0LWhUV3OLcyEwLEgGLcR\nJ+7LgG0H4bnBVi9GUbuhin5rnUn77DLX1hunxGrnAMPfheVGbvE2GyOwUXpDXZaoKCeiowsps/DK\nunlze06csFw9eA96EsQ7tx5SGOKWWwH0AHwEQbgAvAW8D/wqCMLT3HDL6eu73Fu/ayepQmxG/UPQ\nrq1OOPD+ZOlL/0+eAyXbxsnsGc1Ev+vN5S947EXIlUhGkttA/nRmT8aOtGfN0dFkxt25bHtp0Fr9\nnbTA6faQ8h7svDvK7TMZN9nLc9uB+lV4ow0kPQ/i7W9wSGOZUNxvnpNsKsqVXjFsybu7zTcMZq6B\nn2fCPz8Eol9uEqTiqHqPh8K4MgKPFhGoJwfp3PW7PR+eweDTAhb+BVKiMXLiQkP6OrJ5cw76/psa\n2fw86dTeozJuOfgEgGbNhnL0aCpw2/kfzYeSvbxl7oozZTLpdrFSZix3Y8gu/UhRFOuJouggimKg\nKIo/iKKYJYri/aIoNhZF8QH9PnjDSTsEflaY4ZO2QWAHcFQggiNqIX65lognjFwkrTsA/duAvYLc\n90sfgiYPwr4Be8vqg9UNgTe3werZsEPBbOvoCn1fh/2zjNN6av0kHPsFRRJRggBRUS78/bflyzw1\nb+7HiRMWDNw3EKtH2gFkHAXvcFBbWG695Dqk7ILGBspRVeTkUg3hI1WojMlIOH8VUjIgspmCM2oh\nZRrkxkDjZeAuXYnVnPgEwYxoWPs+RBsRGVee+1+GU5vhWpzhNZoFAVqPgUMKl/MhLSEvT0tKiuWW\n1jdp3tyXuLj0yt9oYWzC4MuK4Foi1LFC6bRT6yFC4X189mnISREJesDIuPe1B+CBVuCk5AonQsaP\nkPQi1HsRAt/S3RxXAYIA3R6Hd3fDXx/CVjlNCBlcfaDXC7DubeP6BXWHknxIPazsvG0ewCqze/36\n7hQUlJKVZZwOoCWwCYMHSPsX/Nta/rw3DV5Q+EmcXKKl6VNGLs9Ts+BoCgyWVo2plMI4SHhMtwE4\n/DWoY15xwIhImL0P+k6ChcNh8xfKj9X/Tdi/HDJTjOvXdiwc+lH5edv2hc2ba5bz5bEZg0/dBwGd\nLX/eaymQdxmCFJ779K9aAroLuBlrb3/ug/uCIVxZ8A8A2gI4PxMOrIcHJ0KnwaA2LRRXaFQb+znt\nGPc9bFwIb3WGMyYknQS0gPajYOPdylayOPtAxGA4XLF0jIHU9oZG7SA62ngpa1Np0yaAI0cMr71n\nSWzG4M/HQANp+bUqJW4NNBukrG9pPpz8UUurSUbO8gXFsHQHPNkLnE10tZ39F1bO0Wl+P/MhDJgA\nLXuBl+GVPoRgV+xntMLh7TZod6czNQJ2Lzctl1sQYPTXsHYG5BmpmNVhHMT9DgVXlZ276yNwcBNc\nv274noG56NixAfv2na/8jVZAEKsoO18QBBFmS7Tq3xbPyHiWtR3syJfQK/uwVPpeVVpSUi7XCPKB\nwLbwxM8wt0JM/xvdpbWUFsfevjq51YdnjsCiUF2+9pvPfiPZb9vWOzfaGs3wxtFPzYlJGfSZIT0N\nJshkvTWpV242qe0ALXzgPh+4rw4FKicu7dCQukND1iktrgECrg1UuAUJhDQvwM7fAXt/R7QFGrKX\nXSbnt3TEYpGGcq4+4HpGXcm29Ud0GmINn3aiwSO1iH7o2q1YzCevSPe7WTzL3gFWJMO0vpB8Qvfa\nTqQDZ57n7gvmC9sg5nPY+YeeDjdYJnNMufQQfzZKtqUxjbS0t+jQ4VMuXLjTefU8H0j2+0ImW+4z\nR+koy5kSmYnZCIiieNfmklUVbyoSG5tKQPdgEn6x7FX54iGda65uY8hQoC6VdxGSNkGrZ2HfR5W/\nvzyJ87NovyYAv0cUlMDSR24J7E7VPYA1iX2o11NN/T5qmo2zJ/+iSN45LblJIrlnMyhNK6YstQTt\ndfNmknm1tqPpyy53GLuh3D8ako7fNnZjqe0Hga3hpLRdVhkNGnggiuJdxm4r2JTB79x5iWe6h5Dw\ni2XPK4pwYi00HwTR0rERsuz7CIb9CQc+Ma6ftkQkbkoGrX/yh+g6xq99KyHvnEjCkjISltwdala3\nadV8KR08BTp/686/03LJN7LqiiDAyFdhgUwaf2W0HgbH10Gp5RPkbHo5DzZ0Dw8QE3OJgO7WGdKJ\ntdBisPL+lw/rXIsRw43vmx9fQspX2RD5jHJ3ga2ggo5f1ubCuiIubTA+WqbLQCjMh8PblQ+hzWNw\nyLgANLPRoUMD9u+3IV3qCtjUt+vYsas4+4Kz9G1elXE6GvxbgIuP8mPs+wg6SgvoyHLh+xzQlEJL\npcH9toH9qFDsnASOv6dsd3zkdFg+T/n5PQPBLxziDRTSNDcdOwbWzPCGotWKpO4WCYi0/LA0JXBq\nI7RWMEPf5OwGUDsCLfTHmMsiAjHfQUQv8LeCIogZUPfwxa5/ffY8m4uoYEugQ1+dOy12tfIxtB8N\nR37XXTstjaMjtGpVjwMHamZ4g0nbo8Wvg/kUW41h/xLo8JQJBxBhx+vAIx2VVZApuAY7voEez4FH\nPRMGYnlULTxxfD6CohmHKMowftNVpYLxH8A30w0sGS1BhzGwV2Eorqm06wInTqSTl2d5sQ1DqWK3\nnFSFT2k99AMjgwib7MGBR++OVFpwLliyX28ZAcRFovR17UCPHbd/UYHnys7kTjuKJqWAXjt7Svab\nLlNb7qG/nUj7NZf0P+7++zNkMtQ6NdRJnDpH+eD5YjBXXounJE4XKdagm3Rm18Zf9AsWAgT5SDuy\n69aWLsh4MUteaffD47elt+pFwJvb4cvH4cRWkPM3SEmURv0fPDJG5Jee+r+Pn8r8Dy9Mm697Ut8P\nRg2E+bcD/oUP5OJyn5FsqY90JVCpygRPz4FETS5vvSX1ucoJ8nWSaZPOlvOQELGUcsvZ3Ayfe7wY\nt6YORgnDmA0tFG++jGN/wwNW9JE0P5OQyV6oHJStVAq2XCVz7lnqzI+gVkcjxC+tgIcfTNsAv7yq\nM3YlODrD47NgxzQTJ582zeCQBYu/V6B1H9i2zXZnd7BBgy/LEylO1+DSyDpqrcWbLlMryhfUym8r\ncg8XkRdXTL0njC9LfZOiPdlceS0e75kNcY4yYSexCqkXAW/ugO3fQuxS5cd5eCrExcJlU0o5q1TQ\nKhwOnaz8vVWAqwcENYU9e2oM3mhyjhTj3tI6yi6aC4VoUgtx6GhE8Qg9JH+USYNxnkYp1Vak5Hge\nGS+exOP5IOjS2qTxmJv2Q2HGTlj7P1g7V/lxPHxh0Euw5HUTB9QkBK5egyzrBLzc11Mnoa0kb9+S\n2KbBHyumdivrSTkVbbyMYz/TBCYKEktJX51L6KveJh2nNKmA9PEnoGtbiFJSH8vMqMD/RT9GfwQf\n9IfYJZV3kWP0u7DtR0hPMXFcbZpZbXYHaNMHDm+z2ukNxjYN/kgxHq2tZ/Al2zOwb+2BrwmJbAAp\nn2Xh2dUJj86mSfJqLhfDouUQEQbD+4OddQIk7erYEfZNKM4tnJjZDpL/Ne14oa2g88O6vB+TcHGC\nRsFwNN7EAymnXV84pHAPw5LYpMHnnSihVoAd9l7WGZ5YoKH473RGvWDacTTXRRLeuEL4vLrYuZn4\nt1wvgK9WgJ0apj0NrZuCBb2Xbt3caPJLI/L355M4Lpk8hVlsN1GpYNLXsOQ1yDe8hoV+OreG4wlQ\naIVYWqBBODjUgrNHrHJ6o6hSt9x0iayJechFZfwIwLp1USxZcobffku51TKFpyV7RfpL5x9PTJP2\naUvtx9cLhpUH4cMQKNbjXYwKk64SuCjxTjXbp78AJzf4/ElYVN4NWIFEmQw0teq229G1nTP1J/uh\nclZx6ZN0Pl8kXaji1a7S7jw5t5zHRp2cdv1AeOkVGPQw/N9o2H1DY/T62O8l++IsrfTi+cUkACZP\nh959YVDvcuORPiJSNWkdHeF0isjKPiKZp+5uf1WUzlB7kOmSbetZIzOaO/MSpk9vTGCgM5MmHQHa\nyfRTxjiML0TydXVxy90kOjqNXr2sF3ySmgJnt0Jb6WuMwfw8DRp2hI7StQSNIv9gAfGPJ3Hp03QC\nXvLlpV0Q1t08xwZAgKi+sPJP+OeGG7tL69vGbiptO8CEKTBxTOXvrYyhoyD9EHqN3VIMHOjP2rVp\n1huAEdiwwafSu7dp/nBT2fUhdJmMcSKVeigugC+e1M30gpf59OdyduRxcthZdn8Fo5fAuPUQ/gA4\nG18jU0dte1TDQrFb3IN35sLGvyC8AUx7CTINL/suf4ra8O1ymDoBLpohAnXCy3BwgfWqu7i4qGnV\nyoOYGPNmOVYVNpUeW55jx7Lw8alFvXrOpKZaRwzw0kG4lgzNh+mkkk3hzD7Y9g0MnNaE3NePm2eA\nAFo4+DMcXgldnoOoN6B+a8jLgAsHwTUzgJL4fEoT8hELNKAGlYcDah97hMBa4OmI4OWI0MAVoW0d\nxD3paOYfoevCqvEIfLgIdmyBv2SEKQylZ5QuDPecFXfHO3Xy5vDhbIqKLK+sowSbNXhRhB070ujV\ny59lyxKtNo7dH0Hvt003eIDfZsPgRx1wfMif4r/MuwTUlELsF7qHoNKJeQS2g8aDHKkd6Y19QxfE\nIi0qNzXanDI0mSWocgogqxjxWjHaI5mIX8RBfhVmnXRoQvNW0Ke9eQ434WX4agGYV77TOCIjfYiJ\nMXEH04LYrMGD7j4+KirAqgafsB76fQChvSEp2rRjaUohb+4p3D9pRdmxHDTnq2blImohPV736J2c\npHtRDSp3e7Q5pdzcM5XbtDM7fl7wcDee6QKFBtaEl6NZS2h+HzwxGKaYfjjF9Ojhw/vvJ1hxBMZh\ns/fwAOvWnefBBwOxs7NO9hzoVhrb34M+75rneJpzBVz/Nhm3d5shuFrweqsBbdZtY7coLrVg3IOw\nOpaTCmWrKjL9XfhsvnUj29zc7GjTxqNazfBV6pYLlnDLyYWhnOLOb8S+faG8/no60dHXqYt0pYoM\npMXkekjUltP1k+bmlqFKBYuPw6IpcHCz7jW5yV7OvTTuxs9+H0FgF1gSpSu2ANBGxrUYL+NaZV4D\nwQAAEMFJREFUlIvefiVqs2Sbi4d0GOq0VXrLBd5C7oak/GeqtoP/bYaE/fDda/KfzTL+kmzrzEO3\nnoe3hff/hEcbQUmRdE4mgJwY8VxJZx/0RjpCMhqdS2TIkO5MmDCEvn3Lf7+k3XLeSNfIzpStEafk\nNusJ87vlBEFIEQThmCAIhwVB2G/KsaRYvTqXRx6xbplkrRaWvANjpUR4FbBpKlw+CqPXgp1+4dF7\ngomfQNF1+OEN8x3z2dmwZI7O2K1J//6d2Lhxn3UHYSSmLulFoKcoiq1FUTShjIo0q1fnMmRIbUV6\nEuYk5jewd4TOD1X+XkP5ayLkpsJjv5tcP8ImGf4KtOwJ748yTdSiPPd1g6BwWPedeY5nCv36dWTT\npv+WwUMVB3ieOVPCtWsaOnQwLR7dVEQRlrwNY2YpE7PRe0wt/PEUlBXDsOVYRwOginjiHej3NLzR\nFwrk1txGMm4uLH4byqwgYVWepk2D0Wq1xMdL68nbIuaY4bcKgnBQEIRnzTEgffzxh/WX9QC714BW\nA12laxQYjbYMVj2m08Vv/ZGHRePjq4rxH0OXwTA1Eq5ISdwooFM/qO0Fm5eZ75hK6devY7VbzoPp\nBt9VFMXWQH/geUEQzBngeYvVq3MZOlS5mIQ5+fEteGoWqM04G2tKYMXD4NxAzX3vu1dbo1fbwbPf\nQZOOMK0XZJsx+EylgvFz4ZsZ5rs9MIUBAzpXu+U8mGjwoiim3fh5BfgDuOM+/hrv3HoUskPxeY4c\nKaKkREv7LqaM1jzs3whZl2GiCYUS9FFaCHufzMKtsR2tP/aodsv7Wq4wdR24+8LrD0C+mXUoHpkI\nedkQ86d5j6sEX18v2rRpzJYtpkj0mJtTwOpyD/0odssJguAMqEVRzBMEwQXYDLwriuLmG+0ivCPR\nW07mUH/bq6/ex1MR7dgxUb/jdWKh9GXfTeZ8i2TEKJ+4pt9N1qSJA/EnfODsw1B2t2tnXb9Nksec\nf6m+9Plc8rFzhvuX1qKWt0D0M0XkJur+Pxqt9LX5UKGzZJu0/CFMcZMOvNmZJ38L1dv9tkU7+Qr0\n+dWFq4c07HulkAlZ0jp8BUjXz9sepl+M08FXTfM/Q5jdHdL0xLjI14jbIdkSRV/Jti38Ltn24ou9\naNvWgTFj9Ln15D5xaaFKD5mMuGyUBJ41NLtbzheIFQThCLAP+OumsVc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"text/plain": [
"<matplotlib.figure.Figure at 0xa9b1400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"##Here's my modifcation to the above code\n",
"\n",
"#define model function and pass independant variables x and y as a list\n",
"def gaussian2D(xdata_tuple, amp, x0, y0, sigma_x, sigma_y, offset):\n",
" (x, y) = xdata_tuple\n",
" g = offset + amp*exp( -((x-x0)**2/(2*sigma_x**2)+(y-y0)**2/(2*sigma_y**2))) \n",
" return g\n",
"\n",
"#create a wrapper function\n",
"def gaussian2D_fit(*args):\n",
" return gaussian2D(*args).ravel()\n",
"\n",
"def gaussian2D_sym(xdata_tuple, amp, x0, y0, sigma_x, offset):\n",
" (x, y) = xdata_tuple\n",
" g = offset + amp*exp( -((x-x0)**2+(y-y0)**2)/(2*sigma_x**2))\n",
" return g\n",
"\n",
"#create a wrapper function\n",
"def gaussian2D_sym_fit(*args):\n",
" return gaussian2D_sym(*args).ravel()\n",
"\n",
"# # Create x and y indices\n",
"# x = arange(32)\n",
"# y = arange(32)\n",
"# x, y = np.meshgrid(x, y)\n",
"\n",
"# #create data\n",
"# real_params = array([10, 14, 17, 2, 4, 0])\n",
"# data = gaussian2D((x, y), *real_params)\n",
"\n",
"# plot twoD_Gaussian data generated above\n",
"plt.figure()\n",
"plt.matshow(data,origin='bottom')\n",
"plt.colorbar()\n",
"\n",
"# add some noise to the data and try to fit the data generated beforehand\n",
"initial_guess = (10,12,15,5,7,8)\n",
"\n",
"data_noisy = data + random.poisson(4, data.shape)\n",
"\n",
"mp._general_function = _general_function_mle\n",
"mp._weighted_general_function = _weighted_general_function_mle\n",
"popt_mle, pcov_mle = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess)\n",
"\n",
"mp._general_function = _general_function_ls\n",
"mp._weighted_general_function = _weighted_general_function_ls\n",
"popt, pcov = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess)\n",
"\n",
"#And plot the results:\n",
"\n",
"data_fitted = gaussian2D((x, y), *popt)\n",
"\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"ax.matshow(data_noisy, origin='bottom', extent=(x.min(), x.max(), y.min(), y.max()))\n",
"ax.contour(x, y, data_fitted, 8, colors='w')\n",
"print(popt_mle)\n",
"print(popt)\n",
"#[ 2.97066005 31.99547047 31.96779469 4.97361061 9.97955038 1.20776079]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With my method of using a wrapper function to prepare the data for fitting I avoid the need to reshape the data for plotting and later analysis, but it means I need to `ravel` the y-data."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# define jacobians for timing experiments.\n",
"def myDfun( params, xdata, ydata, f):\n",
" x = xdata[0].ravel()\n",
" y = xdata[1].ravel()\n",
" amp, x0, y0, sigma_x, sigma_y, offset = params\n",
" value = f(xdata, *params)-offset\n",
" dydamp = value/amp\n",
" dydx0 = value*(x-x0)/sigma_x**2\n",
" dydsigmax = value*(x-x0)**2/sigma_x**3\n",
" dydy0 = value*(y-y0)/sigma_y**2\n",
" dydsigmay = value*(y-y0)**2/sigma_y**3\n",
" return vstack((dydamp, dydx0, dydy0, dydsigmax, dydsigmay, ones_like(value)))\n",
"\n",
"def myDfun_sym( params, xdata, ydata, f):\n",
" x = xdata[0].ravel()\n",
" y = xdata[1].ravel()\n",
" amp, x0, y0, sigma_x, offset = params\n",
" value = f(xdata, *params)-offset\n",
" dydamp = value/amp\n",
" dydx0 = value*(x-x0)/sigma_x**2\n",
" dydsigmax = value*(x-x0)**2/sigma_x**3\n",
" dydy0 = value*(y-y0)/sigma_x**2\n",
" return vstack((dydamp, dydx0, dydy0, dydsigmax, ones_like(value)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1024,)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myDfun(popt, (x, y), data_noisy.ravel(), gaussian2D_fit)[0].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing timing\n",
"Comparing using a Jacobian vs not using one"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 5.21 ms per loop\n",
"100 loops, best of 3: 8.41 ms per loop\n"
]
}
],
"source": [
"# With MLE fitting\n",
"mp._general_function = _general_function_mle\n",
"mp._weighted_general_function = _weighted_general_function_mle\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess, Dfun=myDfun, col_deriv=1)\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 3.42 ms per loop\n",
"100 loops, best of 3: 5.09 ms per loop\n",
"100 loops, best of 3: 7.84 ms per loop\n",
"100 loops, best of 3: 5.02 ms per loop\n"
]
}
],
"source": [
"# With least squares fitting for a non-symmetric model function\n",
"mp._general_function = _general_function_ls\n",
"mp._weighted_general_function = _weighted_general_function_ls\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess, Dfun=myDfun, col_deriv=1)\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess)\n",
"\n",
"# With least squares for a symmetric model function\n",
"mp._general_function = _general_function_ls\n",
"mp._weighted_general_function = _weighted_general_function_ls\n",
"initial_guess2 = (10,12,15,5,8)\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_sym_fit, (x, y), data_noisy.ravel(), p0=initial_guess2, Dfun=myDfun_sym, col_deriv=1)\n",
"%timeit popt, pcov = mp.curve_fit(gaussian2D_sym_fit, (x, y), data_noisy.ravel(), p0=initial_guess2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 15 ms per loop\n",
"100 loops, best of 3: 4.37 ms per loop\n",
"100 loops, best of 3: 4.63 ms per loop\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet at 0x8d65d68>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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eRTzkXDlwABw95iTsAH7mk/Q2WqLObIUdAF9/GDkONq+wE3Y5eJov0LH4ee4u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\n0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t8gPx2baSApGg4uhWbvgLuCPFYm2r4M9RpCk+pZ3xcwMYFhzOULksmfFjw9elQiIUHH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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xab076d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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2Fi1u03viJZ1GSKWfEeaP8JYvdffty6B//0rwQioHqrXADxjgyb59pk1KK5Ip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\n550xsVizm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"text/plain": [
"<matplotlib.figure.Figure at 0x8d70cf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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hy5Il9WnWLLrIVXUlbktfmuFYHtbujafyvkW8dfyTfDE7wFssIwg3DtBDtoxD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\n9UsNtK4Bu87D6K4w9AdQ6f+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l43UkO7xKqlWNXD2nMBVg4wlW7mDtAZYuelVVSiJcvw03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\nG18R3wL+G9/4ivgW8N/4xlfE/wGHmfPjNuB/7gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x947cba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Testing my class, notice its slower, but there's a lot more\n",
"junk_g = Gauss2D(data_noisy)\n",
"\n",
"\n",
"junk_g.optimize_params(modeltype='full')\n",
"%timeit junk_g.optimize_params(junk_g.guess_params)\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"ax.matshow(junk_g.data, origin='bottom')\n",
"(y, x) = indices(junk_g.data.shape)\n",
"ax.contour(x, y, junk_g.fit_model, 8, colors='w')\n",
"ax.contour(x, y, data, 8, colors='r')\n",
"\n",
"junk_g.optimize_params(modeltype='norot')\n",
"%timeit junk_g.optimize_params(junk_g.guess_params)\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"ax.matshow(junk_g.data, origin='bottom')\n",
"(y, x) = indices(junk_g.data.shape)\n",
"ax.contour(x, y, junk_g.fit_model, 8, colors='w')\n",
"ax.contour(x, y, data, 8, colors='r')\n",
"\n",
"junk_g.optimize_params(modeltype='sym')\n",
"%timeit junk_g.optimize_params(junk_g.guess_params)\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"ax.matshow(junk_g.data, origin='bottom')\n",
"(y, x) = indices(junk_g.data.shape)\n",
"ax.contour(x, y, junk_g.fit_model, 8, colors='w')\n",
"ax.contour(x, y, data, 8, colors='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lorentzians"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"##Here's my modifcation to the above code\n",
"\n",
"#define model function and pass independant variables x and y as a list\n",
"def lor2D(xdata_tuple, amp, x0, y0, sigma_x, sigma_y, offset):\n",
" (x, y) = xdata_tuple\n",
" g = offset + amp/(1+((x-x0)/(sigma_x/2))**2)/(1+((y-y0)/(sigma_y/2))**2)\n",
" return g\n",
"\n",
"#create a wrapper function\n",
"def lor2D_fit(xdata_tuple, amp, x0, y0, sigma_x, sigma_y, offset):\n",
" return gaussian2D(xdata_tuple, amp, x0, y0, sigma_x, sigma_y, offset).ravel()\n",
"\n",
"# Create x and y indices\n",
"x = arange(64)\n",
"y = arange(64)\n",
"x, y = np.meshgrid(x, y)\n",
"\n",
"#create data\n",
"data = lor2D((x, y), 3, 32, 32, 5, 10, 10)\n",
"\n",
"# plot twoD_Gaussian data generated above\n",
"plt.figure()\n",
"plt.matshow(data,origin='bottom')\n",
"plt.colorbar()\n",
"\n",
"# add some noise to the data and try to fit the data generated beforehand\n",
"initial_guess = (4,0,30,25,35,8)\n",
"\n",
"data_noisy = data + 0.2*randn(*data.shape)\n",
"\n",
"popt, pcov = curve_fit(lor2D_fit, (x, y), data_noisy.ravel(), p0=initial_guess)\n",
"\n",
"#And plot the results:\n",
"\n",
"data_fitted = lor2D((x, y), *popt)\n",
"\n",
"fig, ax = plt.subplots(1, 1)\n",
"ax.hold(True)\n",
"ax.matshow(data_noisy, origin='bottom', extent=(x.min(), x.max(), y.min(), y.max()))\n",
"ax.contour(x, y, data_fitted, 8, colors='w')"
]
},
{
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
febert/DeepRL | PolicyGradient/mcpg_regular_deterministic_tests.ipynb | 1 | 1136378 | null | gpl-3.0 |
nkmk/python-snippets | notebook/scipy_sparse_slice.ipynb | 1 | 12647 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.sparse import csr_matrix, csc_matrix, lil_matrix"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"l = [[1, 0, 0, 0],\n",
" [0, 2, 0, 0],\n",
" [0, 0, 3, 0],\n",
" [0, 0, 0, 4]]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"csr = csr_matrix(l)\n",
"csc = csc_matrix(l)\n",
"lil = lil_matrix(l)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t1\n"
]
}
],
"source": [
"print(csr[0, :])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 0 0]]\n"
]
}
],
"source": [
"print(csr[0, :].toarray())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 0 0]]\n"
]
}
],
"source": [
"print(csr[0].toarray())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t1\n"
]
}
],
"source": [
"print(csr[:, 0])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n"
]
}
],
"source": [
"print(csr[:, 0].toarray())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t2\n",
" (1, 1)\t3\n"
]
}
],
"source": [
"print(csr[1:3, 1:3])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2 0]\n",
" [0 3]]\n"
]
}
],
"source": [
"print(csr[1:3, 1:3].toarray())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t1\n",
" (2, 1)\t3\n"
]
}
],
"source": [
"print(csr[:, ::2])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0]\n",
" [0 0]\n",
" [0 3]\n",
" [0 0]]\n"
]
}
],
"source": [
"print(csr[:, ::2].toarray())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csr.csr_matrix'>\n"
]
}
],
"source": [
"print(type(csr[0]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csc.csc_matrix'>\n"
]
}
],
"source": [
"print(type(csc[0]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.lil.lil_matrix'>\n"
]
}
],
"source": [
"print(type(lil[0]))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csr.csr_matrix'>\n"
]
}
],
"source": [
"print(type(csr[:, 0]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csc.csc_matrix'>\n"
]
}
],
"source": [
"print(type(csc[:, 0]))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.lil.lil_matrix'>\n"
]
}
],
"source": [
"print(type(lil[:, 0]))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csr.csr_matrix'>\n"
]
}
],
"source": [
"print(type(csr[1:3, 1:3]))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.csc.csc_matrix'>\n"
]
}
],
"source": [
"print(type(csc[1:3, 1:3]))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'scipy.sparse.lil.lil_matrix'>\n"
]
}
],
"source": [
"print(type(lil[1:3, 1:3]))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"csr_slice = csr[1:3, 1:3]\n",
"csr_slice[0, 0] = 100"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 0 0]\n",
" [0 2 0 0]\n",
" [0 0 3 0]\n",
" [0 0 0 4]]\n"
]
}
],
"source": [
"print(csr.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[100 0]\n",
" [ 0 3]]\n"
]
}
],
"source": [
"print(csr_slice.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"csc_slice = csc[1:3, 1:3]\n",
"csc_slice[0, 0] = 100"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 0 0]\n",
" [0 2 0 0]\n",
" [0 0 3 0]\n",
" [0 0 0 4]]\n"
]
}
],
"source": [
"print(csc.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[100 0]\n",
" [ 0 3]]\n"
]
}
],
"source": [
"print(csc_slice.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"lil_slice = lil[1:3, 1:3]\n",
"lil_slice[0, 0] = 100"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 0 0]\n",
" [0 2 0 0]\n",
" [0 0 3 0]\n",
" [0 0 0 4]]\n"
]
}
],
"source": [
"print(lil.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[100 0]\n",
" [ 0 3]]\n"
]
}
],
"source": [
"print(lil_slice.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"lil[0] = [10, 20, 30, 40]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t10\n",
" (0, 1)\t20\n",
" (0, 2)\t30\n",
" (0, 3)\t40\n",
" (1, 1)\t2\n",
" (2, 2)\t3\n",
" (3, 3)\t4\n"
]
}
],
"source": [
"print(lil)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[10 20 30 40]\n",
" [ 0 2 0 0]\n",
" [ 0 0 3 0]\n",
" [ 0 0 0 4]]\n"
]
}
],
"source": [
"print(lil.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"lil[1:3, 1:3] = np.arange(4).reshape(2, 2) * 100"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t10\n",
" (0, 1)\t20\n",
" (0, 2)\t30\n",
" (0, 3)\t40\n",
" (1, 2)\t100\n",
" (2, 1)\t200\n",
" (2, 2)\t300\n",
" (3, 3)\t4\n"
]
}
],
"source": [
"print(lil)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 10 20 30 40]\n",
" [ 0 0 100 0]\n",
" [ 0 200 300 0]\n",
" [ 0 0 0 4]]\n"
]
}
],
"source": [
"print(lil.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"lil[:, 0] = csr[:, 3]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 1)\t20\n",
" (0, 2)\t30\n",
" (0, 3)\t40\n",
" (1, 2)\t100\n",
" (2, 1)\t200\n",
" (2, 2)\t300\n",
" (3, 0)\t4\n",
" (3, 3)\t4\n"
]
}
],
"source": [
"print(lil)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 20 30 40]\n",
" [ 0 0 100 0]\n",
" [ 0 200 300 0]\n",
" [ 4 0 0 4]]\n"
]
}
],
"source": [
"print(lil.toarray())"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# lil[1:3, 1:3] = [10, 20, 30, 40]\n",
"# ValueError: shape mismatch: objects cannot be broadcast to a single shape"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/site-packages/scipy/sparse/_index.py:127: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
" self._set_arrayXarray(i, j, x)\n"
]
}
],
"source": [
"csr[0] = [0, 0, 0, 100]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 0)\t0\n",
" (0, 1)\t0\n",
" (0, 2)\t0\n",
" (0, 3)\t100\n",
" (1, 1)\t2\n",
" (2, 2)\t3\n",
" (3, 3)\t4\n"
]
}
],
"source": [
"print(csr)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 100]\n",
" [ 0 2 0 0]\n",
" [ 0 0 3 0]\n",
" [ 0 0 0 4]]\n"
]
}
],
"source": [
"print(csr.toarray())"
]
}
],
"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 |
childresslab/MicrocavityExp1 | tools/Find taget mode.ipynb | 1 | 18022 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find target modes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"import os\n",
"manager.startModule('logic','cavitylogic')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cavitylogic.start_full_sweep()\n",
"cavitylogic._get_scope_data()\n",
"cavitylogic._save_raw_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#cavitylogic.load_full_sweep()\n",
"cavitylogic._get_ramp_up_signgals()\n",
"cavitylogic._fit_ramp()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit scan"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ppad/ygZCAOA1Y9y3o1+5YZu279qt+vXclzuPO3o2Iu7QG1/VkAcmOrb1cdgW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\n"
},
"metadata": {
"image/png": {
"height": 361,
"width": 539
}
},
"output_type": "display_data"
}
],
"source": [
"# fit plot\n",
"plt.plot(cavitylogic.time_trim,cavitylogic.volts_trim[2])\n",
"plt.plot(cavitylogic.time_trim, cavitylogic._ni.sweep_function(cavitylogic.time_trim, *cavitylogic.popt), 'r-', linewidth = 3, label='fit')\n",
"#plt.xlim(0.888,0.9)\n",
"#plt.ylim(-3.33,-3.21)\n",
"plt.show()"
]
},
{
"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": "Qudi",
"language": "python",
"name": "qudi"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": "3.6.0"
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/cmip6/models/sandbox-2/atmoschem.ipynb | 1 | 102065 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Atmoschem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NIWA \n",
"**Source ID**: SANDBOX-2 \n",
"**Topic**: Atmoschem \n",
"**Sub-Topics**: Transport, Emissions Concentrations, Gas Phase Chemistry, Stratospheric Heterogeneous Chemistry, Tropospheric Heterogeneous Chemistry, Photo Chemistry. \n",
"**Properties**: 84 (39 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmoschem?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 16:54:30"
],
"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', 'niwa', 'sandbox-2', 'atmoschem')"
],
"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](#1.-Key-Properties) \n",
"[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n",
"[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n",
"[4. Key Properties --> Timestep Framework --> Split Operator Order](#4.-Key-Properties--->-Timestep-Framework--->-Split-Operator-Order) \n",
"[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n",
"[6. Grid](#6.-Grid) \n",
"[7. Grid --> Resolution](#7.-Grid--->-Resolution) \n",
"[8. Transport](#8.-Transport) \n",
"[9. Emissions Concentrations](#9.-Emissions-Concentrations) \n",
"[10. Emissions Concentrations --> Surface Emissions](#10.-Emissions-Concentrations--->-Surface-Emissions) \n",
"[11. Emissions Concentrations --> Atmospheric Emissions](#11.-Emissions-Concentrations--->-Atmospheric-Emissions) \n",
"[12. Emissions Concentrations --> Concentrations](#12.-Emissions-Concentrations--->-Concentrations) \n",
"[13. Gas Phase Chemistry](#13.-Gas-Phase-Chemistry) \n",
"[14. Stratospheric Heterogeneous Chemistry](#14.-Stratospheric-Heterogeneous-Chemistry) \n",
"[15. Tropospheric Heterogeneous Chemistry](#15.-Tropospheric-Heterogeneous-Chemistry) \n",
"[16. Photo Chemistry](#16.-Photo-Chemistry) \n",
"[17. Photo Chemistry --> Photolysis](#17.-Photo-Chemistry--->-Photolysis) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Key properties of the atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmospheric chemistry model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.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 atmospheric chemistry model code.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.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": [
"### 1.3. Chemistry Scheme Scope\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Atmospheric domains covered by the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.chemistry_scheme_scope') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"troposhere\" \n",
"# \"stratosphere\" \n",
"# \"mesosphere\" \n",
"# \"mesosphere\" \n",
"# \"whole atmosphere\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Basic approximations made in the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables Form\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Form of prognostic variables in the atmospheric chemistry component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.prognostic_variables_form') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"3D mass/mixing ratio for gas\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Number Of Tracers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of advected tracers in the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.number_of_tracers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Family Approach\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Atmospheric chemistry calculations (not advection) generalized into families of species?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.family_approach') \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": [
"### 1.8. Coupling With Chemical Reactivity\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Atmospheric chemistry transport scheme turbulence is couple with chemical reactivity?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.coupling_with_chemical_reactivity') \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": [
"# 2. Key Properties --> Software Properties \n",
"*Software properties of aerosol code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.software_properties.repository') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.software_properties.code_version') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.software_properties.code_languages') \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": [
"# 3. Key Properties --> Timestep Framework \n",
"*Timestepping in the atmospheric chemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Mathematical method deployed to solve the evolution of a given variable*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Operator splitting\" \n",
"# \"Integrated\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Split Operator Advection Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for chemical species advection (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_advection_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Split Operator Physical Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for physics (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_physical_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Split Operator Chemistry Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for chemistry (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_chemistry_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.5. Split Operator Alternate Order\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_alternate_order') \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": [
"### 3.6. Integrated Timestep\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Timestep for the atmospheric chemistry model (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.integrated_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.7. Integrated Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the type of timestep scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.integrated_scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Explicit\" \n",
"# \"Implicit\" \n",
"# \"Semi-implicit\" \n",
"# \"Semi-analytic\" \n",
"# \"Impact solver\" \n",
"# \"Back Euler\" \n",
"# \"Newton Raphson\" \n",
"# \"Rosenbrock\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Timestep Framework --> Split Operator Order \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Turbulence\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for turbulence scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.turbulence') \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. Convection\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for convection scheme This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.convection') \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. Precipitation\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for precipitation scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.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": [
"### 4.4. Emissions\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for emissions scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.emissions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.5. Deposition\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for deposition scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.6. Gas Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for gas phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.gas_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.7. Tropospheric Heterogeneous Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for tropospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.tropospheric_heterogeneous_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.8. Stratospheric Heterogeneous Phase Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for stratospheric heterogeneous phase chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.stratospheric_heterogeneous_phase_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.9. Photo Chemistry\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for photo chemistry scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.photo_chemistry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.10. Aerosols\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Call order for aerosols scheme. This should be an integer greater than zero, and may be the same value as for another process if they are calculated at the same time.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.timestep_framework.split_operator_order.aerosols') \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 methodology for atmospheric chemistry 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.atmoschem.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. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.global_mean_metrics_used') \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": [
"### 5.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.regional_metrics_used') \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": [
"### 5.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.key_properties.tuning_applied.trend_metrics_used') \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": [
"# 6. Grid \n",
"*Atmospheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe the general structure of the atmopsheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.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": [
"### 6.2. Matches Atmosphere Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### * Does the atmospheric chemistry grid match the atmosphere grid?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.matches_atmosphere_grid') \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": [
"# 7. Grid --> Resolution \n",
"*Resolution in the atmospheric chemistry grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. 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. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.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": [
"### 7.2. Canonical Horizontal Resolution\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\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.atmoschem.grid.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": [
"### 7.3. Number Of Horizontal Gridpoints\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\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.atmoschem.grid.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": [
"### 7.4. Number Of Vertical Levels\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.resolution.number_of_vertical_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": [
"### 7.5. Is Adaptive Grid\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.grid.resolution.is_adaptive_grid') \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. Transport \n",
"*Atmospheric chemistry transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview of transport implementation*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.transport.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": [
"### 8.2. Use Atmospheric Transport\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is transport handled by the atmosphere, rather than within atmospheric cehmistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.transport.use_atmospheric_transport') \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": [
"### 8.3. Transport Details\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If transport is handled within the atmospheric chemistry scheme, describe it.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.transport.transport_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": [
"# 9. Emissions Concentrations \n",
"*Atmospheric chemistry emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview atmospheric chemistry emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.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": [
"# 10. Emissions Concentrations --> Surface Emissions \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of the chemical species emitted at the surface that are taken into account in the emissions scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Vegetation\" \n",
"# \"Soil\" \n",
"# \"Sea surface\" \n",
"# \"Anthropogenic\" \n",
"# \"Biomass burning\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Methods used to define chemical species emitted directly into model layers above the surface (several methods allowed because the different species may not use the same method).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Climatology\" \n",
"# \"Spatially uniform mixing ratio\" \n",
"# \"Spatially uniform concentration\" \n",
"# \"Interactive\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and prescribed via a climatology, and the nature of the climatology (E.g. CO (monthly), C2H6 (constant))*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_climatology_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.4. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and prescribed as spatially uniform*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.prescribed_spatially_uniform_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.5. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and specified via an interactive method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.interactive_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.6. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted at the surface and specified via any other method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.surface_emissions.other_emitted_species') \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. Emissions Concentrations --> Atmospheric Emissions \n",
"*TO DO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of chemical species emitted in the atmosphere that are taken into account in the emissions scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Aircraft\" \n",
"# \"Biomass burning\" \n",
"# \"Lightning\" \n",
"# \"Volcanos\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Method\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Methods used to define the chemical species emitted in the atmosphere (several methods allowed because the different species may not use the same method).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Climatology\" \n",
"# \"Spatially uniform mixing ratio\" \n",
"# \"Spatially uniform concentration\" \n",
"# \"Interactive\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere and prescribed via a climatology (E.g. CO (monthly), C2H6 (constant))*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_climatology_emitted_species') \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. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere and prescribed as spatially uniform*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.prescribed_spatially_uniform_emitted_species') \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. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere and specified via an interactive method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.interactive_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.6. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of chemical species emitted in the atmosphere and specified via an "other method"*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.atmospheric_emissions.other_emitted_species') \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. Emissions Concentrations --> Concentrations \n",
"*TO DO*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Prescribed Lower Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the lower boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.concentrations.prescribed_lower_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Prescribed Upper Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the upper boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.emissions_concentrations.concentrations.prescribed_upper_boundary') \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. Gas Phase Chemistry \n",
"*Atmospheric chemistry transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview gas phase atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.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": [
"### 13.2. Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Species included in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"HOx\" \n",
"# \"NOy\" \n",
"# \"Ox\" \n",
"# \"Cly\" \n",
"# \"HSOx\" \n",
"# \"Bry\" \n",
"# \"VOCs\" \n",
"# \"isoprene\" \n",
"# \"H2O\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Number Of Bimolecular Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of bi-molecular reactions in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_bimolecular_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Number Of Termolecular Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of ter-molecular reactions in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_termolecular_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Number Of Tropospheric Heterogenous Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_tropospheric_heterogenous_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.6. Number Of Stratospheric Heterogenous Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_stratospheric_heterogenous_reactions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.7. Number Of Advected Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of advected species in the gas phase chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_advected_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.8. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of gas phase species for which the concentration is updated in the chemical solver assuming photochemical steady state*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.number_of_steady_state_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.9. Interactive Dry Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.interactive_dry_deposition') \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": [
"### 13.10. Wet Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is wet deposition included? Wet deposition describes the moist processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_deposition') \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": [
"### 13.11. Wet Oxidation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is wet oxidation included? Oxidation describes the loss of electrons or an increase in oxidation state by a molecule*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.gas_phase_chemistry.wet_oxidation') \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": [
"# 14. Stratospheric Heterogeneous Chemistry \n",
"*Atmospheric chemistry startospheric heterogeneous chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview stratospheric heterogenous atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.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": [
"### 14.2. Gas Phase Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Gas phase species included in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.gas_phase_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Cly\" \n",
"# \"Bry\" \n",
"# \"NOy\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Aerosol Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Aerosol species included in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.aerosol_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sulphate\" \n",
"# \"Polar stratospheric ice\" \n",
"# \"NAT (Nitric acid trihydrate)\" \n",
"# \"NAD (Nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particule))\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.4. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of steady state species in the stratospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.number_of_steady_state_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.5. Sedimentation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sedimentation is included in the stratospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.sedimentation') \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": [
"### 14.6. Coagulation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is coagulation is included in the stratospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.stratospheric_heterogeneous_chemistry.coagulation') \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": [
"# 15. Tropospheric Heterogeneous Chemistry \n",
"*Atmospheric chemistry tropospheric heterogeneous chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview tropospheric heterogenous atmospheric chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.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": [
"### 15.2. Gas Phase Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of gas phase species included in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.gas_phase_species') \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. Aerosol Species\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Aerosol species included in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.aerosol_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sulphate\" \n",
"# \"Nitrate\" \n",
"# \"Sea salt\" \n",
"# \"Dust\" \n",
"# \"Ice\" \n",
"# \"Organic\" \n",
"# \"Black carbon/soot\" \n",
"# \"Polar stratospheric ice\" \n",
"# \"Secondary organic aerosols\" \n",
"# \"Particulate organic matter\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Number Of Steady State Species\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of steady state species in the tropospheric heterogeneous chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.number_of_steady_state_species') \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. Interactive Dry Deposition\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is dry deposition interactive (as opposed to prescribed)? Dry deposition describes the dry processes by which gaseous species deposit themselves on solid surfaces thus decreasing their concentration in the air.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.interactive_dry_deposition') \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": [
"### 15.6. Coagulation\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is coagulation is included in the tropospheric heterogeneous chemistry scheme or not?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.tropospheric_heterogeneous_chemistry.coagulation') \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. Photo Chemistry \n",
"*Atmospheric chemistry photo chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview atmospheric photo chemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.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": [
"### 16.2. Number Of Reactions\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *The number of reactions in the photo-chemistry scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.number_of_reactions') \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. Photo Chemistry --> Photolysis \n",
"*Photolysis scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Photolysis scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline (clear sky)\" \n",
"# \"Offline (with clouds)\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 17.2. Environmental Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any environmental conditions taken into account by the photolysis scheme (e.g. whether pressure- and temperature-sensitive cross-sections and quantum yields in the photolysis calculations are modified to reflect the modelled conditions.)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.atmoschem.photo_chemistry.photolysis.environmental_conditions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \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 |
dpsanders/Interact.jl | doc/notebooks/04-Animations.ipynb | 1 | 86686 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Animating"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<script charset=\"utf-8\">(function ($, undefined) {\n",
"\n",
" function createElem(tag, attr, content) {\n",
"\t// TODO: remove jQuery dependency\n",
"\tvar el = $(\"<\" + tag + \"/>\").attr(attr);\n",
"\tif (content) {\n",
"\t el.append(content);\n",
"\t}\n",
"\treturn el[0];\n",
" }\n",
"\n",
" // A widget must expose an id field which identifies it to the backend,\n",
" // an elem attribute which is will be added to the DOM, and\n",
" // a getState() method which returns the value to be sent to the backend\n",
" // a sendUpdate() method which sends its current value to the backend\n",
" var Widget = {\n",
"\tid: undefined,\n",
"\telem: undefined,\n",
"\tlabel: undefined,\n",
"\tgetState: function () {\n",
"\t return this.elem.value;\n",
"\t},\n",
"\tsendUpdate: undefined\n",
" };\n",
"\n",
" var Slider = function (typ, id, init) {\n",
"\tvar attr = { type: \"range\",\n",
"\t\t value: init.value,\n",
"\t\t min: init.min,\n",
"\t\t max: init.max,\n",
"\t\t step: init.step },\n",
"\t elem = createElem(\"input\", attr),\n",
"\t self = this;\n",
"\n",
"\telem.onchange = function () {\n",
"\t self.sendUpdate();\n",
"\t}\n",
"\n",
"\tthis.id = id;\n",
"\tthis.elem = elem;\n",
"\tthis.label = init.label;\n",
"\n",
"\tInputWidgets.commInitializer(this); // Initialize communication\n",
" }\n",
" Slider.prototype = Widget;\n",
"\n",
" var Checkbox = function (typ, id, init) {\n",
"\tvar attr = { type: \"checkbox\",\n",
"\t\t checked: init.value },\n",
"\t elem = createElem(\"input\", attr),\n",
"\t self = this;\n",
"\n",
"\tthis.getState = function () {\n",
"\t return elem.checked;\n",
"\t}\n",
"\telem.onchange = function () {\n",
"\t self.sendUpdate();\n",
"\t}\n",
"\n",
"\tthis.id = id;\n",
"\tthis.elem = elem;\n",
"\tthis.label = init.label;\n",
"\n",
"\tInputWidgets.commInitializer(this);\n",
" }\n",
" Checkbox.prototype = Widget;\n",
"\n",
" var Button = function (typ, id, init) {\n",
"\tvar attr = { type: \"button\",\n",
"\t\t value: init.label },\n",
"\t elem = createElem(\"input\", attr),\n",
"\t self = this;\n",
"\tthis.getState = function () {\n",
"\t return null;\n",
"\t}\n",
"\telem.onclick = function () {\n",
"\t self.sendUpdate();\n",
"\t}\n",
"\n",
"\tthis.id = id;\n",
"\tthis.elem = elem;\n",
"\tthis.label = init.label;\n",
"\n",
"\tInputWidgets.commInitializer(this);\n",
" }\n",
" Button.prototype = Widget;\n",
"\n",
" var Text = function (typ, id, init) {\n",
"\tvar attr = { type: \"text\",\n",
"\t\t placeholder: init.label,\n",
"\t\t value: init.value },\n",
"\t elem = createElem(\"input\", attr),\n",
"\t self = this;\n",
"\tthis.getState = function () {\n",
"\t return elem.value;\n",
"\t}\n",
"\telem.onkeyup = function () {\n",
"\t self.sendUpdate();\n",
"\t}\n",
"\n",
"\tthis.id = id;\n",
"\tthis.elem = elem;\n",
"\tthis.label = init.label;\n",
"\n",
"\tInputWidgets.commInitializer(this);\n",
" }\n",
" Text.prototype = Widget;\n",
"\n",
" var Textarea = function (typ, id, init) {\n",
"\tvar attr = { placeholder: init.label },\n",
"\t elem = createElem(\"textarea\", attr, init.value),\n",
"\t self = this;\n",
"\tthis.getState = function () {\n",
"\t return elem.value;\n",
"\t}\n",
"\telem.onchange = function () {\n",
"\t self.sendUpdate();\n",
"\t}\n",
"\n",
"\tthis.id = id;\n",
"\tthis.elem = elem;\n",
"\tthis.label = init.label;\n",
"\n",
"\tInputWidgets.commInitializer(this);\n",
" }\n",
" Textarea.prototype = Widget;\n",
"\n",
" // RadioButtons\n",
" // Dropdown\n",
" // HTML\n",
" // Latex\n",
"\n",
" var InputWidgets = {\n",
"\tSlider: Slider,\n",
"\tCheckbox: Checkbox,\n",
"\tButton: Button,\n",
"\tText: Text,\n",
"\tTextarea: Textarea,\n",
"\tdebug: false,\n",
"\tlog: function () {\n",
"\t if (InputWidgets.debug) {\n",
"\t\tconsole.log.apply(console, arguments);\n",
"\t }\n",
"\t},\n",
"\t// a central way to initalize communication\n",
"\t// for widgets.\n",
"\tcommInitializer: function (widget) {\n",
"\t widget.sendUpdate = function () {};\n",
"\t}\n",
" };\n",
"\n",
" window.InputWidgets = InputWidgets;\n",
"\n",
"})(jQuery, undefined);\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div id=\"interact-js-shim\">\n",
" <script charset=\"utf-8\">\n",
"(function (IPython, $, _, MathJax, Widgets) {\n",
" $.event.special.destroyed = {\n",
"\tremove: function(o) {\n",
"\t if (o.handler) {\n",
"\t\to.handler.apply(this, arguments)\n",
"\t }\n",
"\t}\n",
" }\n",
"\n",
" var OutputArea = IPython.version >= \"4.0.0\" ? require(\"notebook/js/outputarea\").OutputArea : IPython.OutputArea;\n",
"\n",
" var redrawValue = function (container, type, val) {\n",
"\tvar selector = $(\"<div/>\");\n",
"\tvar oa = new OutputArea(_.extend(selector, {\n",
"\t selector: selector,\n",
"\t prompt_area: true,\n",
"\t events: IPython.events,\n",
"\t keyboard_manager: IPython.keyboard_manager\n",
"\t})); // Hack to work with IPython 2.1.0\n",
"\n",
"\tswitch (type) {\n",
"\tcase \"image/png\":\n",
" var _src = 'data:' + type + ';base64,' + val;\n",
"\t $(container).find(\"img\").attr('src', _src);\n",
"\t break;\n",
"\tdefault:\n",
"\t var toinsert = OutputArea.append_map[type].apply(\n",
"\t\toa, [val, {}, selector]\n",
"\t );\n",
"\t $(container).empty().append(toinsert.contents());\n",
"\t selector.remove();\n",
"\t}\n",
"\tif (type === \"text/latex\" && MathJax) {\n",
"\t MathJax.Hub.Queue([\"Typeset\", MathJax.Hub, toinsert.get(0)]);\n",
"\t}\n",
" }\n",
"\n",
"\n",
" $(document).ready(function() {\n",
"\tWidgets.debug = false; // log messages etc in console.\n",
"\tfunction initComm(evt, data) {\n",
"\t var comm_manager = data.kernel.comm_manager;\n",
" //_.extend(comm_manager.targets, require(\"widgets/js/widget\"))\n",
"\t comm_manager.register_target(\"Signal\", function (comm) {\n",
" comm.on_msg(function (msg) {\n",
" //Widgets.log(\"message received\", msg);\n",
" var val = msg.content.data.value;\n",
" $(\".signal-\" + comm.comm_id).each(function() {\n",
" var type = $(this).data(\"type\");\n",
" if (val[type]) {\n",
" redrawValue(this, type, val[type], type);\n",
" }\n",
" });\n",
" delete val;\n",
" delete msg.content.data.value;\n",
" });\n",
"\t });\n",
"\n",
"\t // coordingate with Comm and redraw Signals\n",
"\t // XXX: Test using Reactive here to improve performance\n",
"\t $([IPython.events]).on(\n",
"\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n",
"\t\t if (md && md.reactive) {\n",
" // console.log(md.comm_id);\n",
" toinsert.addClass(\"signal-\" + md.comm_id);\n",
" toinsert.data(\"type\", type);\n",
" // Signal back indicating the mimetype required\n",
" var comm_manager = IPython.notebook.kernel.comm_manager;\n",
" var comm = comm_manager.comms[md.comm_id];\n",
" comm.then(function (c) {\n",
" c.send({action: \"subscribe_mime\",\n",
" mime: type});\n",
" toinsert.bind(\"destroyed\", function() {\n",
" c.send({action: \"unsubscribe_mime\",\n",
" mime: type});\n",
" });\n",
" })\n",
"\t\t }\n",
"\t });\n",
"\t}\n",
"\n",
"\ttry {\n",
"\t // try to initialize right away. otherwise, wait on the status_started event.\n",
"\t initComm(undefined, IPython.notebook);\n",
"\t} catch (e) {\n",
"\t $([IPython.events]).on('kernel_created.Kernel kernel_created.Session', initComm);\n",
"\t}\n",
" });\n",
"})(IPython, jQuery, _, MathJax, InputWidgets);\n",
"</script>\n",
" <script>\n",
" window.interactLoadedFlag = true\n",
" $(\"#interact-js-shim\").bind(\"destroyed\", function () {\n",
" if (window.interactLoadedFlag) {\n",
" console.warn(\"JavaScript required by Interact will be removed if you remove this cell or run using Interact more than once.\")\n",
" }\n",
" })\n",
" $([IPython.events]).on(\"kernel_starting.Kernel kernel_restarting.Kernel\", function () { window.interactLoadedFlag = false })\n",
" </script>\n",
"</div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"using Interact, Reactive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possible to create interactive animations using Reactive's [timing functions](julialang.org/Reactive.jl/api.html#timing)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions like `fps`, `fpswhen`, `every` etc, let us create periodically updating signals. This, combined with the other functions in Reactive provide for declarative ways to define animations. Let us now take the n-gon compose example from interactive diagrams notebook and animate it."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"Options{:ToggleButtons,ASCIIString}([Input{ASCIIString}] yellow,\"color\",\"yellow\",\"yellow\",OptionDict({\"yellow\",\"cyan\",\"tomato\"},{\"cyan\"=>\"cyan\",\"yellow\"=>\"yellow\",\"tomato\"=>\"tomato\"}),None[],None[])"
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"output_type": "display_data"
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"Slider{Int64}([Input{Int64}] 11,\"n\",11,3:20)"
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"data": {
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"reactive": true
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"output_type": "display_data"
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"<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
" version=\"1.2\"\n",
" width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
" stroke=\"none\"\n",
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]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Colors\n",
"using Compose\n",
"\n",
"@manipulate for color=[\"yellow\", \"cyan\", \"tomato\"], n=3:20, t_dt=timestamp(fps(30.))\n",
" t, dt = t_dt # current time, time since last frame\n",
" compose(context(), fill(parse(Colorant, color)),\n",
" polygon([((1+sin(θ+t))/2, (1+cos(θ+t))/2) for θ in 0:2π/n:2π]))\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's often advisable to give your animations a pause checkbox. Here is a bouncing ball that you can pause and resume"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"Checkbox([Input{Bool}] false,\"paused\",false)"
]
},
"metadata": {},
"output_type": "display_data"
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"0.0"
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},
"metadata": {
"comm_id": "b9ebee68-3782-46e4-99b0-3f2418a81db8",
"reactive": true
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Compose\n",
"\n",
"@manipulate for \n",
" paused=false,\n",
" dt = fpswhen(lift(!, paused), 30), # stop updating time when paused.\n",
" t = foldl(+, 0., dt), # add up the time deltas to get time\n",
" gravity = 0:0.01:5, # some sort of gravity\n",
" color = [\"tomato\", \"cyan\"] # color the ball\n",
"\n",
" compose(context(0.5, 1-abs(sin(t*gravity)), 0.1, 0.1), fill(parse(Colorant, color)), circle())\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a captivating animation made with tiles of varying colors."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
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"Checkbox([Input{Bool}] true,\"unpaused\",true)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(1.442375779021515e9,0.0)"
]
},
"metadata": {
"comm_id": "46a33ba2-d0cc-47c0-9eba-b120967a9400",
"reactive": true
},
"output_type": "display_data"
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" [Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Property{FillPrimitive}([FillPrimitive(RGBA{Float64}(0.0,0.7354124629959939,1.0,1.0))]),ListNode{ComposeNode}(Form{RectanglePrimitive{P<:Point{XM<:Measure{S,T},YM<:Measure{S,T}},M1<:Measure{S,T},M2<:Measure{S,T}}}(RectanglePrimitive[RectanglePrimitive{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}},Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0))]),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0)] [Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Property{FillPrimitive}([FillPrimitive(RGBA{Float64}(0.6472146429704387,0.6219166916076609,1.0,1.0))]),ListNode{ComposeNode}(Form{RectanglePrimitive{P<:Point{XM<:Measure{S,T},YM<:Measure{S,T}},M1<:Measure{S,T},M2<:Measure{S,T}}}(RectanglePrimitive[RectanglePrimitive{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}},Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0))]),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0)] \n",
" [Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Property{FillPrimitive}([FillPrimitive(RGBA{Float64}(0.0,0.7338497560569025,1.0,1.0))]),ListNode{ComposeNode}(Form{RectanglePrimitive{P<:Point{XM<:Measure{S,T},YM<:Measure{S,T}},M1<:Measure{S,T},M2<:Measure{S,T}}}(RectanglePrimitive[RectanglePrimitive{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}},Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0))]),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0)] [Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Property{FillPrimitive}([FillPrimitive(RGBA{Float64}(0.7334266726955249,0.5980562443362892,1.0,1.0))]),ListNode{ComposeNode}(Form{RectanglePrimitive{P<:Point{XM<:Measure{S,T},YM<:Measure{S,T}},M1<:Measure{S,T},M2<:Measure{S,T}}}(RectanglePrimitive[RectanglePrimitive{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}},Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0))]),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0)] ,1:8,1:8,[0.125,0.125,0.125,0.125,0.125,0.125,0.125,0.125],[0.125,0.125,0.125,0.125,0.125,0.125,0.125,0.125],nothing,{},UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),0,false,false),ListNull{ComposeNode}()),0,false,false,false,false,nothing,nothing,0.0)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Colors\n",
"using Compose\n",
"\n",
"@manipulate for unpaused = true, x=timestamp(fpswhen(unpaused, 30.))\n",
" gridstack([compose(context(), rectangle(), fill(ColorTypes.LCHab(70.0, 60.0, 100*x[1]+i*j)))\n",
" for i in 1:8, j in 1:8])\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, particles in a box."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1.442375813705306e9,0.0)"
]
},
"metadata": {
"comm_id": "1380a4ee-b400-406c-8b57-48a854163e88",
"reactive": true
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"Button{Nothing}([Input{Nothing}] nothing,\"Add particle\",nothing)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1-element Array{Any,1}:\n",
" [0.942597,0.413408]"
]
},
"metadata": {
"comm_id": "75708cde-117a-4b48-bda6-5ad6beee1abe",
"reactive": true
},
"output_type": "display_data"
},
{
"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",
" 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 fill=\"#00FFFF\" id=\"fig-178e2070bae74201b1348623b36bfb5e-element-1\">\n",
" <circle cx=\"84.13\" cy=\"50.41\" r=\"3.54\"/>\n",
"</g>\n",
"</svg>\n"
],
"text/html": [
"<div id=\"pwid26739\"><script>new Patchwork.Node(\"pwid26739\", {\"t\":\"svg\",\"p\":{\"width\":\"141.4213562373095mm\",\"stroke-width\":\"0.3\",\"font-size\":\"3.88\",\"fill\":\"#000000\",\"height\":\"100.0mm\",\"stroke\":\"none\",\"viewBox\":\"0 0 141.4213562373095 100.0\"},\"c\":[{\"t\":\"g\",\"c\":[{\"t\":\"g\",\"p\":{\"fill-opacity\":1.0,\"fill\":\"#00FFFF\"},\"c\":[{\"t\":\"circle\",\"p\":{\"cx\":84.1318059190359,\"r\":3.5355339059327378,\"cy\":50.407304763793945},\"n\":\"svg\"}],\"n\":\"svg\"}],\"n\":\"svg\"}],\"n\":\"svg\"});</script></div>"
],
"text/plain": [
"Context(BoundingBox(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),1.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,1.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Context(BoundingBox(Measure{Float64,MeasureNil}(0.0,0.569901704788208,MeasureNil(),0.0,0.0),Measure{MeasureNil,Float64}(0.0,MeasureNil(),0.47907304763793945,0.0,0.0),Measure{Float64,MeasureNil}(0.0,0.05,MeasureNil(),0.0,0.0),Measure{MeasureNil,Float64}(0.0,MeasureNil(),0.05,0.0,0.0)),UnitBox{Nothing,Nothing,Nothing,Nothing}(nothing,nothing,nothing,nothing,Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.0)),Rotation{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}}(0.0,Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5))),nothing,ListNode{ComposeNode}(Form{CirclePrimitive{P<:Point{XM<:Measure{S,T},YM<:Measure{S,T}},M<:Measure{S,T}}}(CirclePrimitive[CirclePrimitive{Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}},Measure{MeasureNil,MeasureNil}}(Point{Measure{MeasureNil,MeasureNil},Measure{MeasureNil,MeasureNil}}(Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.0,0.5)),Measure{MeasureNil,MeasureNil}(0.0,MeasureNil(),MeasureNil(),0.5,0.0))]),ListNode{ComposeNode}(Property{FillPrimitive}([FillPrimitive(RGBA{Float64}(0.0,1.0,1.0,1.0))]),ListNull{ComposeNode}())),0,false,false,false,false,nothing,nothing,0.0),ListNull{ComposeNode}()),0,false,false,false,false,nothing,nothing,0.0)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Compose\n",
"\n",
"box(x) = let i = floor(x)\n",
" i%2==0 ? x-i : 1+i-x\n",
"end\n",
"\n",
"colors = [\"orange\", \"cyan\", \"gray\", \"tomato\"]\n",
"\n",
"dots(points) = [(context(p[1], p[2], .05, .05), fill(parse(Colorant, colors[i%4+1])), circle())\n",
" for (i, p) in enumerate(points)]\n",
"\n",
"@manipulate for t=timestamp(fps(30.)), add=button(\"Add particle\"),\n",
" velocities = foldl((x,y) -> push!(x, rand(2)), Any[rand(2)], add)\n",
"\n",
" compose(context(),\n",
" dots([map(v -> box(v*t[1]), (vx, vy)) for (vx, vy) in velocities])...)\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you used Interact to come up with something you think people will be wow-ed by, do let us know by commenting on [this issue](https://github.com/JuliaLang/Interact.jl/issues/36). :)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.3.11",
"language": "julia",
"name": "julia-0.3"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.3.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
probml/pyprobml | notebooks/misc/early_stopping_tensorboard_tf.ipynb | 1 | 24142810 | null | mit |
febert/DeepRL | easy21/sarsa_lambda.ipynb | 1 | 6419 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"0.1\n",
"0.2\n",
"0.3\n",
"0.4\n",
"0.5\n",
"0.6\n",
"0.7\n",
"0.8\n",
"0.9\n",
"1.0\n",
"(21, 10)\n"
]
}
],
"source": [
"import cPickle\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.metrics import mean_squared_error\n",
"from math import sqrt\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"import os\n",
"os.environ[\"FONTCONFIG_PATH\"]=\"/etc/fonts\"\n",
"\n",
"\n",
"#\n",
"# import time\n",
"#\n",
"# def procedure():\n",
"# time.sleep(2.5)\n",
"#\n",
"# # measure process time\n",
"# t0 = time.clock()\n",
"# procedure()\n",
"# print time.clock() - t0, \"seconds process time\"\n",
"\n",
"from easy21 import *\n",
"\n",
"pkl_file = open('Qtable_monte_carlo_1e6.pkl', 'rb')\n",
"Q_table_mc = cPickle.load(pkl_file)\n",
"\n",
"pkl_file.close()\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def compare_qtables(Qtable,Q_table_mc):\n",
" #np.sum(((Qtable-Q_table_mc)**2).flatten())\n",
" return sqrt(mean_squared_error(Qtable.flatten(), Q_table_mc.flatten()))\n",
"\n",
"\n",
"\n",
"def runepisode():\n",
"\n",
" #initialize the state and acion randomly at beginning of episode\n",
" state = np.random.randint(low = 1, high=10, size=None),np.random.randint(low = 1, high=10, size=None) #player, dealer\n",
" A = policy(state)\n",
"\n",
" terminated = False\n",
" while( not terminated):\n",
"\n",
" reward, successor, terminated = step(state[0],state[1],A)\n",
" #print(\"successor\" , successor)\n",
" if not terminated:\n",
" A_prime = policy(successor)\n",
" Qsprime_aprime = Qtable[successor[0]-1,successor[1]-1,A_prime]\n",
" else:\n",
" Qsprime_aprime = 0\n",
"\n",
" delta = reward + Qsprime_aprime - Qtable[state[0]-1,state[1]-1,A]\n",
" episode.append((state,A,reward))\n",
"\n",
" #counting state visits\n",
" Nsa[state[0]-1,state[1]-1,A] += 1\n",
" Esa[state[0]-1,state[1]-1,A] += 1\n",
"\n",
" for s, a, reward in episode:\n",
" alpha = 1/Nsa[s[0]-1,s[1]-1,a]\n",
" Qtable[s[0]-1,s[1]-1,a] += alpha*delta*Esa[s[0]-1,s[1]-1,a]\n",
" Esa[s[0]-1,s[1]-1,a]*= lambda_\n",
"\n",
"\n",
"\n",
" if not terminated:\n",
" A = A_prime\n",
" state = successor\n",
"\n",
"\n",
"def policy(state):\n",
" # print(Nsa)\n",
" # print Nsa.shape\n",
" Ns = Nsa[state[0]-1,state[1]-1,0] + Nsa[state[0]-1,state[1]-1,1]\n",
" N_0 = 100\n",
" epsilon = N_0/(N_0 + Ns)\n",
"\n",
" explore = np.random.choice([1,0],p=[epsilon, 1-epsilon])\n",
" if not explore:\n",
" return np.argmax(Qtable[state[0]-1,state[1]-1,:])\n",
" else:\n",
" return np.random.choice([1,0])\n",
"\n",
"numiter = 1000\n",
"\n",
"\n",
"error_lists = []\n",
"\n",
"mse_1000 = []\n",
"\n",
"for lambda_ in np.linspace(0,1,11):\n",
"\n",
"\n",
" mse = []\n",
"\n",
"\n",
" Qtable = np.zeros((21,10,2))\n",
" Nsa = np.zeros((21,10,2))\n",
"\n",
" print lambda_\n",
"\n",
" # policy = np.argmax(Qtable[state])\n",
" for i in range(numiter):\n",
" episode = [] #just one episode\n",
" Esa = np.zeros((21,10,2))\n",
" #print i\n",
" runepisode()\n",
"\n",
" #compare q tables\n",
" if i%1000 == 0:\n",
" mse.append(compare_qtables(Qtable,Q_table_mc))\n",
"\n",
" mse_1000.append(compare_qtables(Qtable,Q_table_mc))\n",
"\n",
" error_lists.append(mse)\n",
"\n",
"opt_Valuefunction = np.max(Qtable,2)\n",
"print(opt_Valuefunction.shape)\n",
"\n",
"\n",
"## save to file\n",
"save = False\n",
"if save:\n",
" output = open('Qtable_monte_carlo_1e6.pkl', 'wb')\n",
" cPickle.dump(opt_Valuefunction, output)\n",
" output.close()\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"X, Y = np.meshgrid(range(1,11), range(1,22))\n",
"# print(X.shape,Y.shape)\n",
"ax.plot_wireframe(X,Y, opt_Valuefunction)\n",
"ax.set_xlabel(\"dealer\")\n",
"ax.set_ylabel(\"player\")\n",
"ax.set_zlabel(\"value\")\n",
"\n",
"\n",
"fig = plt.figure()\n",
"opt_policy = np.argmax(Qtable,2)\n",
"plt.imshow(opt_policy,cmap=plt.get_cmap('gray'),interpolation='none')\n",
"plt.xlabel(\"dealer\")\n",
"plt.ylabel(\"player\")\n",
"\n",
"fig = plt.figure()\n",
"plt.plot(mse_1000)\n",
"plt.xlabel(\"lambda\")\n",
"plt.ylabel(\"mean squared errror from 1e6 monte carlo\")\n",
"\n",
"\n",
"fig = plt.figure()\n",
"for i in range(11):\n",
" line1, = plt.plot(range(len(error_lists[i])), error_lists[i], label=r'$\\lambda$'+\"=\"+str(np.linspace(0,1,11)[i]))\n",
" \n",
"plt.legend()\n",
"\n",
"plt.xlabel(\"1000 episodes\")\n",
"plt.ylabel(\"Mean squared error to Monte Carlo 1e6 \")\n",
"\n",
"plt.show()\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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
patryk-oleniuk/emotion_recognition | temp/main_emotion_recognition_shortened.ipynb | 1 | 366793 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Network Tour of Data Science, EPFL 2016\n",
"# Project: Facial Emotion Recognition\n",
"students: Patryk Oleniuk, Carmen Galotta\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The project presented here is an algorithm to recognize and detect emotions from a face picture. \n",
"\n",
"Of course, the task of recognize face emotions is very easy for humans to do even if somethimes is really hard to understand how a person feels, but what can be easily understood thanks to human's brain, is difficult to emulate by a machine.\n",
"\n",
"The aim of this project is to classify faces in discrete human emotions. Due to the success of Neural Network in images classification tasks it has been tought that employing it could be a good idea in also face emotion.\n",
"\n",
"The dataset has been taken from the kaggle competition and consists of 48x48 grey images already labeled with a number coding for 7 classes of emotions, namely: \n",
"\n",
"0-Angry<br>\n",
"1-Disgust<br>\n",
"2-Fear<br>\n",
"3-Happy<br>\n",
"4-Sad<br>\n",
"5-Surprise<br>\n",
"6-Neutral<br>\n",
"\n",
"The faces are mostly centered in the image."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1200\n"
]
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import csv\n",
"import scipy.misc\n",
"import time\n",
"import collections\n",
"import os\n",
"import utils as ut\n",
"import importlib\n",
"\n",
"importlib.reload(ut)\n",
"\n",
"# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
"# rather than in a new window.\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (20.0, 20.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"#Data Visualization\n",
"# Load the shortened raw CSV data, it contains only 300 pictures with labels\n",
"emotions_dataset_dir = 'fer2013_shortened.csv'\n",
"\n",
"#obtaining the number of line of the csv file\n",
"file = open(emotions_dataset_dir)\n",
"numline = len(file.readlines())\n",
"print (numline)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the data from *.csv file \n",
"The first step was to load the data from the .csv file. <br> The format of the csv line is<br>\n",
"class{0,1,2,3,4,5,6},pix0 pix2304,DataUsage(not used)<br>\n",
"e.g.<br>\n",
"2,234 1 34 23 ..... 234 256 0,Training<br>\n",
"The picture is always 48x48 pixels, 0-255 greyscale.\n",
"# Remove crappy data\n",
"In the database there are some images thar are not good (e.g. some images are pixelated, unrelevant, from animations).\n",
"It has been tried to filter them by looking at the maximum of the histogram. If the image is very homogenous, the maximum value of the histogram will be very high (that is to say above a certain threshold) then this image is filtered out. Of course in this way are also removed some relevant information, but it's better for the CNN not to consider these images."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded 17% of dataset(221/1200). Filtered images: 21\n",
"loaded 34% of dataset(435/1200). Filtered images: 35\n",
"loaded 52% of dataset(655/1200). Filtered images: 55\n",
"loaded 71% of dataset(869/1200). Filtered images: 69\n",
"loaded 90% of dataset(1089/1200). Filtered images: 89\n",
"1108 are left after 'strange images' removal.\n",
"Deleted 92 strange images. Images are shown below\n"
]
}
],
"source": [
"#Load the file in csv\n",
"ifile = open(emotions_dataset_dir, \"rt\")\n",
"reader = csv.reader(ifile)\n",
"\n",
"#preparing labels(Y) and images(X) data vectors\n",
"rownum = 0\n",
"num_data = numline;\n",
"\n",
"#preparing arrays\n",
"emotions = np.zeros(num_data)\n",
"images = np.zeros((num_data,48,48))\n",
"strange_im = np.zeros((num_data,48,48))\n",
"\n",
"# for image pre-filtering\n",
"num_strange = 0; #number of removed images\n",
"hist_threshold = 270 # images above this threshold will be removed\n",
"hist_div = 100 #parameter of the histogram\n",
"\n",
"#parsing each row\n",
"for row in reader:\n",
" #(column0) extract the emotion label\n",
" #!!!! convert 1 and 0 together !!!!\n",
" if( (row[0] == '0') or (row[0] == '1' ) ):\n",
" emotions[rownum] = '0';\n",
" else :\n",
" emotions[rownum] = str(int(row[0])-1)\n",
"\n",
" #(column1) extract the image data, parse it and convert into 48x48 array of integers\n",
" images[rownum] = np.asarray([int(s) for s in row[1].split(' ')]).reshape(48,48)\n",
" \n",
" #stretching contrast of the image\n",
" images[rownum] = ut.contrast_stretch(images[rownum])\n",
" \n",
" #calculating the histogram and erasing \"strange\" images\n",
" y_h, x_h = np.histogram( images[ rownum ] , 100 );\n",
" if y_h.max() > hist_threshold : \n",
" # if img is 'strange'\n",
" strange_im[num_strange,:,:] = images[rownum,:,:]\n",
" num_data = num_data - 1;\n",
" images = np.delete(images, rownum, axis = 0);\n",
" emotions = np.delete(emotions, rownum)\n",
" #print('deleted:' + str(rownum))\n",
" num_strange += 1; \n",
" else:\n",
" rownum += 1\n",
" if not rownum%200:\n",
" print(\"loaded %2.0f\" % ((float(rownum ) /num_data)*100) \n",
" + '% of dataset('+ str(rownum+num_strange)+'/'+ str(numline) + '). Filtered images: ' + str(num_strange) )\n",
"ifile.close()\n",
"\n",
"print(str( len(images) ) + ' are left after \\'strange images\\' removal.')\n",
"print('Deleted ' + str( num_strange ) + ' strange images. Images are shown below')\n",
"\n",
"# showing strange images\n",
"#for i in range(0,int(num_strange)):\n",
"# plt.subplot( int(np.ceil(num_strange/5)),5, i+1)\n",
"# plt.imshow(strange_im[i])\n",
"# y_h, x_h = np.histogram( strange_im[i,:,:] ,hist_div);\n",
"# strange_im.sort(axis=0, )\n",
"# plt.axis('off')\n",
"# plt.title('max(hist) = ' + str(y_h.max()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore the data\n",
"Plot some random pictures from each class."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lj3L/7iL4a6xx57bFL+7Gc/O7TpCJOSv+nE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\nRi5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1pveW+K1Sg268SZGQdRfEO5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b4z3qWY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\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JwiVdEfeOXVNdvh+JiufYkc8noc7sOwmhUl1HbD7ryOeTP+iISNN1OeZkv3mv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7mOaNzvvvvu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\nkgMAAAAAAFgKDiTeqb+AqygAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5fe40fd3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classes = [0,1,2,3,4,5]\n",
"str_emotions = ['angry','scared','happy','sad','surprised','normal']\n",
"num_classes = len(classes)\n",
"samples_per_class = 6\n",
"for y, cls in enumerate(classes):\n",
" idxs = np.flatnonzero(emotions == y)\n",
" idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
" for i, idx in enumerate(idxs):\n",
" plt_idx = i * num_classes + y + 1\n",
" plt.subplot(samples_per_class, num_classes, plt_idx)\n",
" plt.imshow(images[idx])\n",
" y_h, x_h = np.histogram( images[idx], hist_div );\n",
" plt.axis('off')\n",
" plt.title(str_emotions[y] )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare the Data for CNN\n",
"Here the initial data have been divided to create train and test data. <bv>\n",
"This two subsets have both an associated label to train the neural network and to test its accuracy with the test data.\n",
"The number of images used for each category of emotions is shown both for the train as for the test data."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of clean data:1108 48x48 pix , 0-255 greyscale images\n",
"orig train data (1044, 48, 48)\n",
"orig train labels (1044,)from 0.0 to 5.0\n",
"orig test data (64, 48, 48)\n",
"orig test labels (64,)from 0.0 to 5.0\n",
"TRAIN: number of 0 labels 186\n",
"TRAIN: number of 1 labels 146\n",
"TRAIN: number of 2 labels 271\n",
"TRAIN: number of 3 labels 172\n",
"TRAIN: number of 4 labels 103\n",
"TRAIN: number of 5 labels 166\n",
"TEST: number of 0 labels 13\n",
"TEST: number of 1 labels 9\n",
"TEST: number of 2 labels 13\n",
"TEST: number of 3 labels 14\n",
"TEST: number of 4 labels 6\n",
"TEST: number of 5 labels 9\n"
]
}
],
"source": [
"print('number of clean data:' + str(images.shape[0]) + ' 48x48 pix , 0-255 greyscale images')\n",
"n_all = images.shape[0];\n",
"n_train = 64; # number of data for training and for batch\n",
"\n",
"# dividing the input data\n",
"train_data_orig = images[0:n_all-n_train,:,:]\n",
"train_labels = emotions[0:n_all-n_train]\n",
"test_data_orig = images[n_all-n_train:n_all,:,:]\n",
"test_labels = emotions[n_all-n_train:n_all]\n",
"\n",
"# Convert to float\n",
"train_data_orig = train_data_orig.astype('float32')\n",
"y_train = train_labels.astype('float32')\n",
"test_data_orig = test_data_orig.astype('float32')\n",
"y_test = test_labels.astype('float32')\n",
"\n",
"print('orig train data ' + str(train_data_orig.shape))\n",
"print('orig train labels ' + str(train_labels.shape) + 'from ' + str(train_labels.min()) + ' to ' + str(train_labels.max()) )\n",
"print('orig test data ' + str(test_data_orig.shape))\n",
"print('orig test labels ' + str(test_labels.shape)+ 'from ' + str(test_labels.min()) + ' to ' + str(test_labels.max()) )\n",
"print('TRAIN: number of 0 labels',len(train_labels[train_labels == 0]))\n",
"print('TRAIN: number of 1 labels',len(train_labels[train_labels == 1]))\n",
"print('TRAIN: number of 2 labels',len(train_labels[train_labels == 2]))\n",
"print('TRAIN: number of 3 labels',len(train_labels[train_labels == 3]))\n",
"print('TRAIN: number of 4 labels',len(train_labels[train_labels == 4]))\n",
"print('TRAIN: number of 5 labels',len(train_labels[train_labels == 5]))\n",
"print('TEST: number of 0 labels',len(test_labels[test_labels == 0]))\n",
"print('TEST: number of 1 labels',len(test_labels[test_labels == 1]))\n",
"print('TEST: number of 2 labels',len(test_labels[test_labels == 2]))\n",
"print('TEST: number of 3 labels',len(test_labels[test_labels == 3]))\n",
"print('TEST: number of 4 labels',len(test_labels[test_labels == 4]))\n",
"print('TEST: number of 5 labels',len(test_labels[test_labels== 5]))\n",
"#plt.imshow(train_data_orig[0,:,:])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Convert, normalize, subtract the const mean value from the data\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1044, 2304)\n",
"(64, 2304)\n",
"-0.026113\n",
"-0.0591093041003\n"
]
}
],
"source": [
"# Data pre-processing\n",
"n = train_data_orig.shape[0];\n",
"train_data = np.zeros([n,48**2])\n",
"for i in range(n):\n",
" xx = train_data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" train_data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
"\n",
"n = test_data_orig.shape[0]\n",
"test_data = np.zeros([n,48**2])\n",
"for i in range(n):\n",
" xx = test_data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" test_data[i] = np.reshape(xx,[-1])\n",
"\n",
"print(train_data.shape)\n",
"print(test_data.shape)\n",
"print(train_data_orig[0][2][2])\n",
"print(test_data[0][2])\n",
"#plt.imshow(train_data[0].reshape([48,48]))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1044, 6)\n",
"(64, 6)\n"
]
}
],
"source": [
"# Convert label values to one_hot vector\n",
"\n",
"train_labels = ut.convert_to_one_hot(train_labels,num_classes)\n",
"test_labels = ut.convert_to_one_hot(test_labels,num_classes)\n",
"\n",
"print(train_labels.shape)\n",
"print(test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model \n",
"\n",
"In the first model it has been implemented a baseline softmax classifier using a single convolutional layer and a one fully connected layer. For the initial baseline\n",
"it has not be used any regularization, dropout, or batch normalization.\n",
"\n",
"The equation of the classifier is simply:\n",
"\n",
"\n",
"$$\n",
"y=\\textrm{softmax}(ReLU( x \\ast W_1+b_1)W_2+b_2) \n",
"$$\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def bias_variable(shape):\n",
" initial = tf.constant(0.01,shape=shape)\n",
" return tf.Variable(initial)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wcl= (10, 10, 1, 64)\n",
"bcl0= (64,)\n",
"x_2d= (64, 48, 48, 1)\n",
"x2= (64, 48, 48, 64)\n",
"x3= (64, 147456)\n",
"Wfc= (147456, 6)\n",
"bfc= (6,)\n",
"y1= (64, 6)\n",
"y2= (64, 6)\n",
"y3(SOFTMAX)= (64, 6)\n"
]
}
],
"source": [
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# CG inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
"#d = tf.placeholder(tf.float32);\n",
"\n",
"# Convolutional layer\n",
"K0 = 10 # size of the patch\n",
"F0 = 64 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"Wcl0 = tf.Variable(tf.truncated_normal([K0,K0,1,F0], stddev=tf.sqrt(2./tf.to_float(ncl0)) )); print('Wcl=',Wcl0.get_shape())\n",
"#bcl0 = tf.Variable(tf.zeros([F0])); print('bcl=',bcl0.get_shape())\n",
"bcl0 = bias_variable([F0]); print('bcl0=',bcl0.get_shape()) #in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\n",
"\n",
"x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d=',x_2d0.get_shape())\n",
"x = tf.nn.conv2d(x_2d0, Wcl0, strides=[1, 1, 1, 1], padding='SAME')\n",
"x += bcl0; print('x2=',x.get_shape())\n",
"\n",
"# ReLU activation\n",
"x = tf.nn.relu(x)\n",
"\n",
"# Dropout\n",
"#x = tf.nn.dropout(x, 0.25)\n",
"\n",
"# Fully Connected layer\n",
"nfc = 48*48*F0\n",
"x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
"Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"y += bfc; print('y2=',y.get_shape())\n",
"\n",
"# Softmax\n",
"y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy\n",
"\n",
"# Optimization scheme\n",
"#train_step = tf.train.GradientDescentOptimizer(0.02).minimize(total_loss)\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Runing the computational graph"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.171875 , loss= 1.7911\n",
"test accuracy= 0.203125\n",
"\n",
"Iteration i= 50 , train accuracy= 0.296875 , loss= 1.62821\n",
"test accuracy= 0.1875\n",
"\n",
"Iteration i= 100 , train accuracy= 0.578125 , loss= 1.14282\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 150 , train accuracy= 0.6875 , loss= 0.906755\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 200 , train accuracy= 0.71875 , loss= 0.733014\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 250 , train accuracy= 0.8125 , loss= 0.550771\n",
"test accuracy= 0.34375\n",
"\n",
"Iteration i= 300 , train accuracy= 0.953125 , loss= 0.297246\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 350 , train accuracy= 0.9375 , loss= 0.264587\n",
"test accuracy= 0.34375\n",
"\n",
"Iteration i= 400 , train accuracy= 1.0 , loss= 0.119752\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 450 , train accuracy= 1.0 , loss= 0.0723346\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 500 , train accuracy= 1.0 , loss= 0.0651125\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 550 , train accuracy= 1.0 , loss= 0.0368162\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 600 , train accuracy= 1.0 , loss= 0.0329836\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 650 , train accuracy= 1.0 , loss= 0.0255295\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 700 , train accuracy= 1.0 , loss= 0.0219603\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 750 , train accuracy= 1.0 , loss= 0.0158333\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 800 , train accuracy= 1.0 , loss= 0.0128005\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 850 , train accuracy= 1.0 , loss= 0.00994784\n",
"test accuracy= 0.265625\n",
"\n",
"Iteration i= 900 , train accuracy= 1.0 , loss= 0.00801104\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 950 , train accuracy= 1.0 , loss= 0.00927464\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 1000 , train accuracy= 1.0 , loss= 0.00780998\n",
"test accuracy= 0.28125\n"
]
}
],
"source": [
"# Run Computational Graph\n",
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(1001):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) \n",
" idx = [indices.popleft() for i in range(batch_size)]\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" #print(batch_x.shape,batch_y.shape)\n",
" \n",
" # Run CG for vao to increase the test acriable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y})\n",
" \n",
" # Run CG for test set\n",
" if not i%50:\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" acc_test = sess.run(accuracy, feed_dict={xin: test_data, y_label: test_labels})\n",
" print('test accuracy=',acc_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As it is possible to see from this result, the model overfits the training data already at iteration 400, while getting a test accuracy of only 28%.\n",
"In order to prevent overfitting in the following model have been applied different techniques such as dropout and pool, as well as tried to implement a neural network of more layers. \n",
"\n",
"This should help and improve the model since the first convolutional layer will just extract some simplest characteristics of the image such as edges, lines and curves. Adding layers will improve the performances because they will detect some high level feature which in this case could be really relevant since it's about face expressions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/\n",
"\n",
"# Choosing Hyperparameters\n",
"\n",
"How do we know how many layers to use, how many conv layers, what are the filter sizes, or the values for stride and padding? These are not trivial questions and there isn’t a set standard that is used by all researchers. This is because the network will largely depend on the type of data that you have. Data can vary by size, complexity of the image, type of image processing task, and more. When looking at your dataset, one way to think about how to choose the hyperparameters is to find the right combination that creates abstractions of the image at a proper scale.\n",
"\n",
"# ReLU (Rectified Linear Units) Layers\n",
"After each conv layer, it is convention to apply a nonlinear layer (or activation layer) immediately afterward.The purpose of this layer is to introduce nonlinearity to a system that basically has just been computing linear operations during the conv layers (just element wise multiplications and summations).In the past, nonlinear functions like tanh and sigmoid were used, but researchers found out that ReLU layers work far better because the network is able to train a lot faster (because of the computational efficiency) without making a significant difference to the accuracy. It also helps to alleviate the vanishing gradient problem, which is the issue where the lower layers of the network train very slowly because the gradient decreases exponentially through the layers (Explaining this might be out of the scope of this post, but see here and here for good descriptions). The ReLU layer applies the function f(x) = max(0, x) to all of the values in the input volume. In basic terms, this layer just changes all the negative activations to 0.This layer increases the nonlinear properties of the model and the overall network without affecting the receptive fields of the conv layer.\n",
"\n",
"Paper by the great Geoffrey Hinton (aka the father of deep learning).\n",
"\n",
"# Pooling Layers\n",
"\n",
"\n",
"After some ReLU layers, programmers may choose to apply a pooling layer. It is also referred to as a downsampling layer. In this category, there are also several layer options, with maxpooling being the most popular. This basically takes a filter (normally of size 2x2) and a stride of the same length. It then applies it to the input volume and outputs the maximum number in every subregion that the filter convolves around.\n",
"\n",
"Other options for pooling layers are average pooling and L2-norm pooling. The intuitive reasoning behind this layer is that once we know that a specific feature is in the original input volume (there will be a high activation value), its exact location is not as important as its relative location to the other features. As you can imagine, this layer drastically reduces the spatial dimension (the length and the width change but not the depth) of the input volume. This serves two main purposes. The first is that the amount of parameters or weights is reduced by 75%, thus lessening the computation cost. The second is that it will control overfitting. This term refers to when a model is so tuned to the training examples that it is not able to generalize well for the validation and test sets. A symptom of overfitting is having a model that gets 100% or 99% on the training set, but only 50% on the test data.\n",
"\n",
"# Dropout Layers\n",
"\n",
"\n",
"Now, dropout layers have a very specific function in neural networks. In the last section, we discussed the problem of overfitting, where after training, the weights of the network are so tuned to the training examples they are given that the network doesn’t perform well when given new examples. The idea of dropout is simplistic in nature. This layer “drops out” a random set of activations in that layer by setting them to zero in the forward pass. Simple as that. Now, what are the benefits of such a simple and seemingly unnecessary and counterintuitive process? Well, in a way, it forces the network to be redundant. By that I mean the network should be able to provide the right classification or output for a specific example even if some of the activations are dropped out. It makes sure that the network isn’t getting too “fitted” to the training data and thus helps alleviate the overfitting problem. An important note is that this layer is only used during training, and not during test time.\n",
"\n",
"Paper by Geoffrey Hinton."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 2\n",
"\n",
"The difference from model 1 is that the size of the filter has been decreased to 5 (i'm changing it to 10) and it has been applied a dropout technique to prevent overfitting."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wcl= (5, 5, 1, 10)\n",
"bcl0= (10,)\n",
"x_2d= (64, 48, 48, 1)\n",
"x2= (64, 48, 48, 10)\n",
"x3= (64, 23040)\n",
"Wfc= (23040, 6)\n",
"bfc= (6,)\n",
"y1= (64, 6)\n",
"y2= (64, 6)\n",
"y3(SOFTMAX)= (64, 6)\n"
]
}
],
"source": [
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# CG inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
"#d = tf.placeholder(tf.float32);\n",
"\n",
"# Convolutional layer\n",
"K0 = 5 # size of the patch\n",
"F0 = 10 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"Wcl0 = tf.Variable(tf.truncated_normal([K0,K0,1,F0], stddev=tf.sqrt(2./tf.to_float(ncl0)) )); print('Wcl=',Wcl0.get_shape())\n",
"#bcl0 = tf.Variable(tf.zeros([F0])); print('bcl=',bcl0.get_shape())\n",
"bcl0 = bias_variable([F0]); print('bcl0=',bcl0.get_shape()) #in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\n",
"\n",
"x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d=',x_2d0.get_shape())\n",
"x = tf.nn.conv2d(x_2d0, Wcl0, strides=[1, 1, 1, 1], padding='SAME')\n",
"x += bcl0; print('x2=',x.get_shape())\n",
"\n",
"# ReLU activation\n",
"x = tf.nn.relu(x)\n",
"\n",
"# Dropout\n",
"x = tf.nn.dropout(x, 0.25)\n",
"\n",
"# Fully Connected layer\n",
"nfc = 48*48*F0\n",
"x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
"Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"y += bfc; print('y2=',y.get_shape())\n",
"\n",
"# Softmax\n",
"y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy\n",
"\n",
"# Optimization scheme\n",
"#train_step = tf.train.GradientDescentOptimizer(0.02).minimize(total_loss)\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.15625 , loss= 1.78885\n",
"test accuracy= 0.21875\n",
"\n",
"Iteration i= 1000 , train accuracy= 0.828125 , loss= 0.616362\n",
"test accuracy= 0.234375\n",
"\n",
"Iteration i= 2000 , train accuracy= 0.84375 , loss= 0.38766\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 3000 , train accuracy= 0.90625 , loss= 0.343264\n",
"test accuracy= 0.328125\n",
"\n",
"Iteration i= 4000 , train accuracy= 0.953125 , loss= 0.17223\n",
"test accuracy= 0.21875\n",
"\n",
"Iteration i= 5000 , train accuracy= 0.96875 , loss= 0.073683\n",
"test accuracy= 0.234375\n",
"\n",
"Iteration i= 6000 , train accuracy= 0.953125 , loss= 0.0869235\n",
"test accuracy= 0.21875\n",
"\n",
"Iteration i= 7000 , train accuracy= 0.984375 , loss= 0.0683548\n",
"test accuracy= 0.296875\n",
"\n",
"Iteration i= 8000 , train accuracy= 1.0 , loss= 0.0803793\n",
"test accuracy= 0.28125\n",
"\n",
"Iteration i= 9000 , train accuracy= 0.96875 , loss= 0.0715012\n",
"test accuracy= 0.3125\n",
"\n",
"Iteration i= 10000 , train accuracy= 1.0 , loss= 0.00932471\n",
"test accuracy= 0.296875\n"
]
}
],
"source": [
"# Run Computational Graph\n",
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(10001):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) \n",
" idx = [indices.popleft() for i in range(batch_size)]\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" #print(batch_x.shape,batch_y.shape)\n",
" \n",
" # Run CG for vao to increase the test acriable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y})\n",
" \n",
" # Run CG for test set\n",
" if not i%1000:\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" acc_test = sess.run(accuracy, feed_dict={xin: test_data, y_label: test_labels})\n",
" print('test accuracy=',acc_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comments: as it can be seen, the performances improved a little bit but the test accuracy is still too low."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 3 \n",
"\n",
"For the reason aforementioned it has been decided to try with a different approach in order to have the trained neural network more efficient for the test data and thus for the new unknown data that it will receive. \n",
"To do so, a more complex model has been tought and implemented, composed of 10 layers.\n",
"And also, a drop out as well as pool techniques have been added to the network.\n",
"\n",
"The equation is approximately this (check):\n",
"$$\n",
"y=\\textrm{softmax}(ReLU( x_{in} \\ast W_1+b_1) \\ast W_2+b_2) \n",
"$$\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d = train_data.shape[1] \n",
"#Défining network\n",
"def weight_variable2(shape):\n",
" initial2 = tf.random_normal(shape, stddev=tf.sqrt(2./tf.to_float(ncl0)) )\n",
" return tf.Variable(initial2)\n",
"#def bias_variable(shape):\n",
"# initial=tf.random_normal(0.05,shape=shape)\n",
"# return tf.Variable(initial)\n",
"\n",
"def conv2dstride2(x,W):\n",
" return tf.nn.conv2d(x,W,strides=[1, 2, 2, 1], padding='SAME')\n",
"\n",
"def conv2d(x,W):\n",
" return tf.nn.conv2d(x,W,strides=[1, 1, 1, 1], padding='SAME')\n",
"def max_pool_2x2(x):\n",
" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
"def weight_variable(shape):\n",
" initial = tf.truncated_normal(shape, stddev=1/np.sqrt(d/2) )\n",
" return tf.Variable(initial)\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W_conv1= (5, 5, 1, 64)\n",
"b_conv1= (64,)\n",
"x_2d0= (64, 48, 48, 1)\n",
"h_conv1= (64, 48, 48, 64)\n",
"W_conv2= (3, 3, 64, 64)\n",
"b_conv2= (64,)\n",
"h_conv2= (64, 48, 48, 64)\n",
"x3= (64, 147456)\n",
"Wfc= (147456, 6)\n",
"bfc= (6,)\n",
"y1= (64, 6)\n",
"y2= (64, 6)\n",
"y3_{SOFTMAX}= (64, 6)\n"
]
}
],
"source": [
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# CG inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
"#d = tf.placeholder(tf.float32);\n",
"\n",
"# Convolutional layer\n",
"K0 = 5 # size of the patch\n",
"F0 = 64 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"F1=2*F0 #K=3,F=96, F1=192 = 2*F0\n",
"nfc = 48*48*F0\n",
"keep_prob_input=tf.placeholder(tf.float32)\n",
"keep_prob_pool=tf.placeholder(tf.float32)\n",
"\n",
"\n",
"#First set of conv followed by conv stride 2 operation and dropout 0.5\n",
"W_conv1=weight_variable([K0,K0,1,F0]); print('W_conv1=',W_conv1.get_shape())\n",
"b_conv1=bias_variable([F0]); print('b_conv1=',b_conv1.get_shape())\n",
"x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d0=',x_2d0.get_shape())\n",
"\n",
"#x_dropped=tf.nn.dropout(x_2d0,keep_prob_input); print('x_dropped=',x_dropped.get_shape())\n",
"h_conv1=tf.nn.relu(conv2d(x_2d0,W_conv1)+b_conv1); print('h_conv1=',h_conv1.get_shape())\n",
"#h_conv1=tf.reshape(h_conv1, [-1,48,48,1]);\n",
"# 2nd convolutional layer layer \n",
"W_conv2=weight_variable([3,3,64,64]); print('W_conv2=',W_conv2.get_shape())\n",
"b_conv2=bias_variable([64]); print('b_conv2=',b_conv2.get_shape())\n",
"\n",
"h_conv2=tf.nn.relu(conv2d(h_conv1,W_conv2)+b_conv2); print('h_conv2=',h_conv2.get_shape())\n",
"\n",
"#h_conv3=tf.nn.avg_pool(h_conv2,ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID'); print('h_conv3=',h_conv3.get_shape());\n",
"\n",
"x = tf.nn.dropout(h_conv2, 0.2)\n",
"\n",
"x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
"Wfc = tf.Variable(tf.truncated_normal([ 147456,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"y += bfc; print('y2=',y.get_shape())\n",
"\n",
"## Softmax\n",
"y = tf.nn.softmax(y); print('y3_{SOFTMAX}=',y.get_shape())\n",
"\n",
"\n",
"#regularization \n",
"#reg_par=1*1e-6\n",
"#reg_loss=0\n",
"#reg_loss+=tf.nn.l2_loss(W_conv1)\n",
"#reg_loss+=tf.nn.l2_loss(b_conv1)\n",
"#reg_loss+=tf.nn.l2_loss(W_conv2)\n",
"#reg_loss+=tf.nn.l2_loss(b_conv2)\n",
"#reg_loss+=tf.nn.l2_loss(Wfc)\n",
"#reg_loss+=tf.nn.l2_loss(bfc)\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy # + reg_par*reg_loss\n",
"\n",
"# Optimization scheme\n",
"#train_step = tf.train.GradientDescentOptimizer(0.2).minimize(total_loss)\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.203125 , loss= 1.7895\n",
"test accuracy= 0.203125\n"
]
}
],
"source": [
"# Run Computational Graph\n",
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(1001):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) \n",
" idx = [indices.popleft() for i in range(batch_size)]\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" #print(batch_x.shape,batch_y.shape)\n",
" \n",
" # Run CG for vao to increase the test acriable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={xin: batch_x, y_label: batch_y})\n",
" \n",
" # Run CG for test set\n",
" if not i%100:\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" acc_test = sess.run(accuracy, feed_dict={xin: test_data, y_label: test_labels})\n",
" print('test accuracy=',acc_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W_conv1= (10, 10, 1, 64)\n",
"b_conv1= (64,)\n",
"x_2d0= (64, 48, 48, 1)\n",
"x_dropped= (64, 48, 48, 1)\n",
"h_conv1= (64, 48, 48, 64)\n",
"W_conv2= (10, 10, 64, 64)\n",
"b_conv2= (64,)\n",
"h_conv2= (64, 48, 48, 64)\n",
"W_conv3= (10, 10, 64, 64)\n",
"b_conv3= (64,)\n",
"h_conv3= (64, 48, 48, 64)\n",
"h_conv3_dropped= (64, 48, 48, 64)\n",
"W_conv4= (10, 10, 64, 64)\n",
"b_conv4= (64,)\n",
"h_conv4= (64, 48, 48, 64)\n",
"W_conv5= (10, 10, 64, 64)\n",
"b_conv5= (64,)\n",
"h_conv5= (64, 48, 48, 64)\n",
"W_conv7= (10, 10, 64, 64)\n",
"b_conv7= (64,)\n",
"h_conv7= (64, 48, 48, 64)\n",
"h_conv7_dropped= (64, 48, 48, 64)\n",
"W_conv8= (10, 10, 64, 64)\n",
"b_conv8= (64,)\n",
"h_conv8= (64, 48, 48, 64)\n",
"W_conv9= (10, 10, 64, 64)\n",
"b_conv9= (64,)\n",
"h_conv9= (64, 48, 48, 64)\n",
"W_conv10= (10, 10, 64, 64)\n",
"b_conv10= (64,)\n",
"h_conv10= (64, 48, 48, 64)\n",
"h_conv10_pooled= (64, 24, 24, 64)\n",
"x3= (64, 147456)\n",
"Wfc= (147456, 6)\n",
"bfc= (6,)\n",
"y1= (64, 6)\n",
"y2= (64, 6)\n",
"y3(SOFTMAX)= (64, 6)\n"
]
}
],
"source": [
"# Define computational graph (CG)\n",
"batch_size = n_train # batch size\n",
"d = train_data.shape[1] # data dimensionality\n",
"nc = 6 # number of classes\n",
"\n",
"# CG inputs\n",
"xin = tf.placeholder(tf.float32,[batch_size,d]); #print('xin=',xin,xin.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); #print('y_label=',y_label,y_label.get_shape())\n",
"#d = tf.placeholder(tf.float32);\n",
"\n",
"# Convolutional layer\n",
"K0 = 10 # size of the patch\n",
"F0 = 64 # number of filters\n",
"ncl0 = K0*K0*F0\n",
"F1=2*F0 #K=3,F=96, F1=192 = 2*F0\n",
"nfc = 48*48*F0\n",
"keep_prob_input=tf.placeholder(tf.float32)\n",
"keep_prob_pool=tf.placeholder(tf.float32)\n",
"\n",
"\n",
"#First set of conv followed by conv stride 2 operation and dropout 0.5\n",
"W_conv1=weight_variable([K0,K0,1,F0]); print('W_conv1=',W_conv1.get_shape())\n",
"b_conv1=bias_variable([F0]); print('b_conv1=',b_conv1.get_shape())\n",
"x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d0=',x_2d0.get_shape())\n",
"\n",
"x_dropped=tf.nn.dropout(x_2d0,keep_prob_input); print('x_dropped=',x_dropped.get_shape())\n",
"h_conv1=tf.nn.relu(conv2d(x_dropped,W_conv1)+b_conv1); print('h_conv1=',h_conv1.get_shape())\n",
"\n",
"# 2nd convolutional layer layer \n",
"W_conv2=weight_variable([K0,K0,F0,F0]); print('W_conv2=',W_conv2.get_shape())\n",
"b_conv2=bias_variable([F0]); print('b_conv2=',b_conv2.get_shape())\n",
"\n",
"h_conv2=tf.nn.relu(conv2d(h_conv1,W_conv2)+b_conv2); print('h_conv2=',h_conv2.get_shape())\n",
"\n",
"W_conv3=weight_variable([K0,K0,F0,F0]); print('W_conv3=',W_conv3.get_shape())\n",
"b_conv3=bias_variable([F0]); print('b_conv3=',b_conv3.get_shape())\n",
"\n",
"h_conv3=tf.nn.relu(conv2d(h_conv2,W_conv3)+b_conv3); print('h_conv3=',h_conv3.get_shape())\n",
"\n",
"h_conv3_dropped=tf.nn.dropout(h_conv3,keep_prob_pool); print('h_conv3_dropped=',h_conv3_dropped.get_shape())\n",
"\n",
"#Second set of conv followed by conv stride 2 operation\n",
"\n",
"W_conv4=weight_variable([K0,K0,F0,F0]); print('W_conv4=',W_conv4.get_shape())\n",
"b_conv4=bias_variable([F0]); print('b_conv4=',b_conv4.get_shape())\n",
"\n",
"h_conv4=tf.nn.relu(conv2d(h_conv3_dropped,W_conv4)+b_conv4); print('h_conv4=',h_conv4.get_shape())\n",
"\n",
"\n",
"W_conv5=weight_variable([K0,K0,F0,F0]); print('W_conv5=',W_conv5.get_shape())\n",
"b_conv5=bias_variable([F0]); print('b_conv5=',b_conv5.get_shape())\n",
"\n",
"h_conv5=tf.nn.relu(conv2d(h_conv4,W_conv5)+b_conv5); print('h_conv5=',h_conv5.get_shape())\n",
"\n",
"\n",
"W_conv7=weight_variable([K0,K0,F0,F0]); print('W_conv7=',W_conv7.get_shape())\n",
"b_conv7=bias_variable([F0]); print('b_conv7=',b_conv7.get_shape())\n",
"\n",
"h_conv7=tf.nn.relu(conv2d(h_conv5,W_conv7)+b_conv7); print('h_conv7=',h_conv7.get_shape())\n",
"\n",
"h_conv7_dropped=tf.nn.dropout(h_conv7,keep_prob_pool); print('h_conv7_dropped=',h_conv7_dropped.get_shape())\n",
"\n",
"#Third set of conv followed by conv stride 2 operation\n",
"\n",
"W_conv8=weight_variable([K0,K0,F0,F0]); print('W_conv8=',W_conv8.get_shape())\n",
"b_conv8=bias_variable([F0]); print('b_conv8=',b_conv8.get_shape())\n",
"\n",
"h_conv8=tf.nn.relu(conv2d(h_conv7_dropped,W_conv8)+b_conv8); print('h_conv8=',h_conv8.get_shape())\n",
"\n",
"W_conv9=weight_variable([K0,K0,F0,F0]); print('W_conv9=',W_conv9.get_shape())\n",
"b_conv9=bias_variable([F0]); print('b_conv9=',b_conv9.get_shape())\n",
"\n",
"h_conv9=tf.nn.relu(conv2d(h_conv8,W_conv9)+b_conv9); print('h_conv9=',h_conv9.get_shape())\n",
"\n",
"W_conv10=weight_variable([K0,K0,F0,F0]); print('W_conv10=',W_conv10.get_shape())\n",
"b_conv10=bias_variable([F0]); print('b_conv10=',b_conv10.get_shape())\n",
"\n",
"h_conv10=tf.nn.relu(conv2d(h_conv9,W_conv10)+b_conv10); print('h_conv10=',h_conv10.get_shape())\n",
"\n",
"h_conv10_pooled=tf.nn.avg_pool(h_conv10,ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID'); print('h_conv10_pooled=',h_conv10_pooled.get_shape());\n",
"\n",
"#x = tf.nn.dropout(x, 0.25)\n",
"x = tf.reshape(h_conv9, [batch_size,-1]); print('x3=',x.get_shape())\n",
"Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"y += bfc; print('y2=',y.get_shape())\n",
"\n",
"#y_conv10_reshaped=tf.reshape(h_conv10_pooled, [batch_size,nc]); print('y_conv10=',y_conv10.get_shape()); print('y_conv10_reshaped=',y_conv10_reshaped.get_shape())\n",
"\n",
"##y_conv=y_conv4_reshaped\n",
"#y=tf.nn.softmax(y_conv10_reshaped); print('y=',y.get_shape())\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#Wcl0 = tf.Variable(tf.truncated_normal([K0,K0,1,F0], stddev=tf.sqrt(2./tf.to_float(ncl0)) )); print('Wcl=',Wcl0.get_shape())\n",
"#bcl0 = tf.Variable(tf.zeros([F0])); print('bcl=',bcl0.get_shape())\n",
"#bcl0 = bias_variable([F0]); print('bcl0=',bcl0.get_shape()) #in ReLu case, small positive bias added to prevent killing of gradient when input is negative.\n",
"\n",
"#x_2d0 = tf.reshape(xin, [-1,48,48,1]); print('x_2d=',x_2d0.get_shape())\n",
"#x = tf.nn.conv2d(x_2d0, Wcl0, strides=[1, 1, 1, 1], padding='SAME')\n",
"#x += bcl0; print('x2=',x.get_shape())\n",
"\n",
"## ReLU activation\n",
"#x = tf.nn.relu(x)\n",
"\n",
"## Dropout\n",
"#x = tf.nn.dropout(x, 0.25)\n",
"\n",
"## Fully Connected layer\n",
"\n",
"#x = tf.reshape(x, [batch_size,-1]); print('x3=',x.get_shape())\n",
"#Wfc = tf.Variable(tf.truncated_normal([nfc,nc], stddev=tf.sqrt(2./tf.to_float(nfc+nc)) )); print('Wfc=',Wfc.get_shape())\n",
"#bfc = tf.Variable(tf.zeros([nc])); print('bfc=',bfc.get_shape())\n",
"#y = tf.matmul(x, Wfc); print('y1=',y.get_shape())\n",
"#y += bfc; print('y2=',y.get_shape())\n",
"\n",
"## Softmax\n",
"y = tf.nn.softmax(y); print('y3(SOFTMAX)=',y.get_shape())\n",
"\n",
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))\n",
"total_loss = cross_entropy\n",
"\n",
"# Optimization scheme\n",
"#train_step = tf.train.GradientDescentOptimizer(0.02).minimize(total_loss)\n",
"train_step = tf.train.AdamOptimizer(0.001).minimize(total_loss)\n",
"\n",
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feeding the CNN with some data (camera/file)\n",
"\n",
"Finally to test if the model really works it's needed to feed some new row and unlabeled data into the neural network.\n",
"To do so \n",
"\n",
"The images are taken from "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'PIL'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-57-9452f781618f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mPIL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclear_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mclear_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'figure.figsize'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# set default size of plots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: No module named 'PIL'"
]
}
],
"source": [
"import PIL\n",
"from IPython.display import clear_output\n",
"\n",
"clear_output()\n",
"plt.rcParams['figure.figsize'] = (1.0, 1.0) # set default size of plots\n",
"#plot = plt.imshow(strange_im[0])\n",
"cntr = 0;\n",
"\n",
"faces, marked_img = ut.get_faces_from_img('diff_emotions.jpg');\n",
"# if some face was found in the camera image\n",
"if(len(faces)): \n",
" #creating the blank test vector\n",
" data_orig = np.zeros([n_train, 48,48])\n",
"\n",
" #putting face data into the vector (only first few)\n",
" for i in range(0, len(faces)):\n",
" data_orig[i,:,:] = ut.contrast_stretch(faces[i,:,:]);\n",
"\n",
" #preparing image and putting it into the batch\n",
" n = data_orig.shape[0];\n",
" data = np.zeros([n,48**2])\n",
" for i in range(n):\n",
" xx = data_orig[i,:,:]\n",
" xx -= np.mean(xx)\n",
" xx /= np.linalg.norm(xx)\n",
" data[i,:] = xx.reshape(2304); #np.reshape(xx,[-1])\n",
"\n",
" result = sess.run([y], feed_dict={xin: data, d: 1.0})\n",
" \n",
" for i in range(0, len(faces)):\n",
" emotion_nr = np.argmax(result[0][i]);\n",
" print(str_emotions[emotion_nr])\n",
" plt.subplots()\n",
" plt.imshow(np.reshape(data[i,:], (48,48)))\n",
" plt.axis('off')\n",
" fig, ax = plt.subplots()\n",
" ax.bar(np.arange(nc) , result[0][i])\n",
" ax.set_xticklabels(('0', '1', '2', '3','4','5' ))\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Conclusions and comments"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": 1
}
| gpl-3.0 |
Danghor/Formal-Languages | ANTLR4-Python/SLR-Parser-Generator/SLR-Table-Generator.ipynb | 1 | 35588 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.display import HTML\n",
"with open('../../style.css', 'r') as file:\n",
" css = file.read()\n",
"HTML(css)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing an SLR-Table-Generator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Grammar for Grammars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the goal is to generate an *SLR-table-generator* we first need to implement a parser for context free grammars.\n",
"The file `simple.g` contains an example grammar that describes arithmetic expressions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!cat Examples/c-grammar.g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use <span style=\"font-variant:small-caps;\">Antlr</span> to develop a parser for context free grammars. The pure grammar used to parse context free grammars is stored in the file `Pure.g4`. It is similar to the grammar that we have already used to implement *Earley's algorithm*, but allows additionally the use of the operator `|`, so that all grammar rules that define a variable can be combined in one rule."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!cat Pure.g4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The annotated grammar is stored in the file `Grammar.g4`.\n",
"The parser will return a list of grammar rules, where each rule of the form\n",
"$$ a \\rightarrow \\beta $$\n",
"is stored as the tuple `(a,) + 𝛽`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!cat -n Grammar.g4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by generating both scanner and parser. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!antlr4 -Dlanguage=Python3 Grammar.g4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from GrammarLexer import GrammarLexer\n",
"from GrammarParser import GrammarParser\n",
"import antlr4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Class `GrammarRule`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class `GrammarRule` is used to store a single grammar rule. As we have to use objects of type `GrammarRule` as keys in a dictionary later, we have to provide the methods `__eq__`, `__ne__`, and `__hash__`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class GrammarRule:\n",
" def __init__(self, variable, body):\n",
" self.mVariable = variable\n",
" self.mBody = body\n",
" \n",
" def __eq__(self, other):\n",
" return isinstance(other, GrammarRule) and \\\n",
" self.mVariable == other.mVariable and \\\n",
" self.mBody == other.mBody\n",
" \n",
" def __ne__(self, other):\n",
" return not self.__eq__(other)\n",
" \n",
" def __hash__(self):\n",
" return hash(self.__repr__())\n",
" \n",
" def __repr__(self):\n",
" return f'{self.mVariable} → {\" \".join(self.mBody)}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `parse_grammar` takes a string `filename` as its argument and returns the grammar that is stored in the specified file. The grammar is represented as list of rules. Each rule is represented as a tuple. The example below will clarify this structure."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def parse_grammar(filename):\n",
" input_stream = antlr4.FileStream(filename, encoding=\"utf-8\")\n",
" lexer = GrammarLexer(input_stream)\n",
" token_stream = antlr4.CommonTokenStream(lexer)\n",
" parser = GrammarParser(token_stream)\n",
" grammar = parser.start()\n",
" return [GrammarRule(head, tuple(body)) for head, *body in grammar.g]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grammar = parse_grammar('Examples/c-grammar.g')\n",
"grammar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a string `name`, which is either a *variable*, a *token*, or a *literal*, the function `is_var` checks whether `name` is a variable. The function can distinguish variable names from tokens and literals because variable names consist only of lower case letters, while tokens are all uppercase and literals start with the character \"`'`\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def is_var(name):\n",
" return name[0] != \"'\" and name.islower()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Fun Fact:** The invocation of `\"'return'\".islower()` returns `True`. This is the reason that we have to test that\n",
"`name` does not start with a `\"'\"` character because otherwise keywords like `'return'` or `'while'` appearing in a grammar would be mistaken for variables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"'return'\".islower()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a list `Rules` of `GrammarRules`, the function `collect_variables(Rules)` returns the set of all *variables* occuring in `Rules`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def collect_variables(Rules):\n",
" Variables = set()\n",
" for rule in Rules:\n",
" Variables.add(rule.mVariable)\n",
" for item in rule.mBody:\n",
" if is_var(item):\n",
" Variables.add(item)\n",
" return Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a set `Rules` of `GrammarRules`, the function `collect_tokens(Rules)` returns the set of all *tokens* and *literals* occuring in `Rules`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def collect_tokens(Rules):\n",
" Tokens = set()\n",
" for rule in Rules:\n",
" for item in rule.mBody:\n",
" if not is_var(item):\n",
" Tokens.add(item)\n",
" return Tokens"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Marked Rules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class `MarkedRule` stores a single *marked rule* of the form\n",
"$$ v \\rightarrow \\alpha \\bullet \\beta $$\n",
"where the *variable* $v$ is stored in the member variable `mVariable`, while $\\alpha$ and $\\beta$ are stored in the variables `mAlpha`and `mBeta` respectively. These variables are assumed to contain tuples of *grammar symbols*. A *grammar symbol* is either\n",
"- a *variable*,\n",
"- a *token*, or\n",
"- a *literal*, i.e. a string enclosed in single quotes.\n",
"\n",
"\n",
"Later, we need to maintain sets of *marked rules* to represent *states*. Therefore, we have to define the methods `__eq__`, `__ne__`, and `__hash__`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class MarkedRule():\n",
" def __init__(self, variable, alpha, beta):\n",
" self.mVariable = variable\n",
" self.mAlpha = alpha\n",
" self.mBeta = beta\n",
" \n",
" def __eq__(self, other):\n",
" return isinstance(other, MarkedRule) and \\\n",
" self.mVariable == other.mVariable and \\\n",
" self.mAlpha == other.mAlpha and \\\n",
" self.mBeta == other.mBeta\n",
" \n",
" def __ne__(self, other):\n",
" return not self.__eq__(other)\n",
" \n",
" def __hash__(self):\n",
" return hash(self.__repr__())\n",
" \n",
" def __repr__(self):\n",
" alphaStr = ' '.join(self.mAlpha)\n",
" betaStr = ' '.join(self.mBeta)\n",
" return f'{self.mVariable} → {alphaStr} • {betaStr}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a *marked rule* `self`, the function `is_complete` checks, whether the *marked rule* `self` has the form\n",
"$$ c \\rightarrow \\alpha\\; \\bullet,$$\n",
"i.e. it checks, whether the $\\bullet$ is at the end of the grammar rule."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def is_complete(self):\n",
" return len(self.mBeta) == 0\n",
"\n",
"MarkedRule.is_complete = is_complete\n",
"del is_complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a *marked rule* `self` of the form\n",
"$$ c \\rightarrow \\alpha \\bullet X\\, \\delta, $$\n",
"the function `symbol_after_dot` returns the *symbol* $X$. If there is no symbol after the $\\bullet$, the method returns `None`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def symbol_after_dot(self):\n",
" if len(self.mBeta) > 0:\n",
" return self.mBeta[0]\n",
" return None\n",
"\n",
"MarkedRule.symbol_after_dot = symbol_after_dot\n",
"del symbol_after_dot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a marked rule, this function returns the variable following the dot. If there is no variable following the dot, the function returns `None`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def next_var(self):\n",
" if len(self.mBeta) > 0:\n",
" var = self.mBeta[0]\n",
" if is_var(var):\n",
" return var\n",
" return None\n",
"\n",
"MarkedRule.next_var = next_var\n",
"del next_var"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `move_dot(self)` transforms a *marked rule* of the form \n",
"$$ c \\rightarrow \\alpha \\bullet X\\, \\beta $$\n",
"into a *marked rule* of the form\n",
"$$ c \\rightarrow \\alpha\\, X \\bullet \\beta, $$\n",
"i.e. the $\\bullet$ is moved over the next symbol. Invocation of this method assumes that there is a symbol\n",
"following the $\\bullet$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def move_dot(self):\n",
" return MarkedRule(self.mVariable, \n",
" self.mAlpha + (self.mBeta[0],), \n",
" self.mBeta[1:])\n",
"\n",
"MarkedRule.move_dot = move_dot\n",
"del move_dot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `to_rule(self)` turns the *marked rule* `self` into a `GrammarRule`, i.e. the *marked rule*\n",
"$$ c \\rightarrow \\alpha \\bullet \\beta $$\n",
"is turned into the grammar rule\n",
"$$ c \\rightarrow \\alpha\\, \\beta. $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def to_rule(self):\n",
" return GrammarRule(self.mVariable, self.mAlpha + self.mBeta)\n",
"\n",
"MarkedRule.to_rule = to_rule\n",
"del to_rule"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SLR-Table-Generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The class `Grammar` represents a context free grammar. It stores a list of the `GrammarRules` of the given grammar.\n",
"Each grammar rule is of the form\n",
"$$ a \\rightarrow \\beta $$\n",
"where $\\beta$ is a tuple of variables, tokens, and literals.\n",
"The start symbol is assumed to be the variable on the left hand side of the first rule. The grammar is *augmented* with the rule\n",
"$$ \\widehat{s} \\rightarrow s\\, \\$. $$\n",
"Here $s$ is the start variable of the given grammar and $\\widehat{s}$ is a new variable that is the start variable of the *augmented grammar*. The symbol `$` denotes the end of input. The non-obvious member variables of the class `Grammar` have the following interpretation\n",
"- `mStates` is the set of all states of the *SLR-parser*. These states are sets of *marked rules*.\n",
"- `mStateNames`is a dictionary assigning names of the form `s0`, `s1`, $\\cdots$, `sn` to the states stored in \n",
" `mStates`. The functions `action` and `goto` will be defined for *state names*, not for *states*, because \n",
" otherwise the table representing these functions would become both huge and unreadable.\n",
"- `mConflicts` is a Boolean variable that will be set to true if the table generation discovers \n",
" *shift/reduce conflicts* or *reduce/reduce conflicts*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Grammar():\n",
" def __init__(self, Rules):\n",
" self.mRules = Rules\n",
" self.mStart = Rules[0].mVariable\n",
" self.mVariables = collect_variables(Rules)\n",
" self.mTokens = collect_tokens(Rules)\n",
" self.mStates = set()\n",
" self.mStateNames = {}\n",
" self.mConflicts = False\n",
" self.mVariables.add('ŝ')\n",
" self.mTokens.add('$')\n",
" self.mRules.append(GrammarRule('ŝ', (self.mStart, '$'))) # augmenting\n",
" self.compute_tables()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a set of `Variables`, the function `initialize_dictionary` returns a dictionary that assigns the empty set to all variables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def initialize_dictionary(Variables):\n",
" return { a: set() for a in Variables }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a `Grammar`, the function `compute_tables` computes\n",
"- the sets `First(v)` and `Follow(v)` for every variable `v`,\n",
"- the set of all *states* of the *SLR-Parser*,\n",
"- the *action table*, and\n",
"- the *goto table*. \n",
"\n",
"Given a grammar `g`,\n",
"- the set `g.mFirst` is a dictionary such that `g.mFirst[a] = First[a]` and\n",
"- the set `g.mFollow` is a dictionary such that `g.mFollow[a] = Follow[a]` for all variables `a`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_tables(self):\n",
" self.mFirst = initialize_dictionary(self.mVariables)\n",
" self.mFollow = initialize_dictionary(self.mVariables)\n",
" self.compute_first()\n",
" self.compute_follow()\n",
" self.compute_rule_names()\n",
" self.all_states()\n",
" self.compute_action_table()\n",
" self.compute_goto_table()\n",
" \n",
"Grammar.compute_tables = compute_tables\n",
"del compute_tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `compute_rule_names` assigns a unique name to each *rule* of the grammar. These names are used later\n",
"to represent *reduce actions* in the *action table*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_rule_names(self):\n",
" self.mRuleNames = {}\n",
" counter = 0\n",
" for rule in self.mRules:\n",
" self.mRuleNames[rule] = 'r' + str(counter)\n",
" counter += 1\n",
" \n",
"Grammar.compute_rule_names = compute_rule_names\n",
"del compute_rule_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `compute_first(self)` computes the sets $\\texttt{First}(c)$ for all variables $c$ and stores them in the dictionary `mFirst`. Abstractly, given a variable $c$ the function $\\texttt{First}(c)$ is the set of all tokens that can start a string that is derived from $c$:\n",
"$$\\texttt{First}(\\texttt{c}) := \n",
" \\Bigl\\{ t \\in T \\Bigm| \\exists \\gamma \\in (V \\cup T)^*: \\texttt{c} \\Rightarrow^* t\\,\\gamma \\Bigr\\}.\n",
"$$\n",
"The definition of the function $\\texttt{First}()$ is extended to strings from $(V \\cup T)^*$ as follows:\n",
"- $\\texttt{FirstList}(\\varepsilon) = \\{\\}$.\n",
"- $\\texttt{FirstList}(t \\beta) = \\{ t \\}$ if $t \\in T$.\n",
"- $\\texttt{FirstList}(\\texttt{a} \\beta) = \\left\\{\n",
" \\begin{array}[c]{ll}\n",
" \\texttt{First}(\\texttt{a}) \\cup \\texttt{FirstList}(\\beta) & \\mbox{if $\\texttt{a} \\Rightarrow^* \\varepsilon$;} \\\\\n",
" \\texttt{First}(\\texttt{a}) & \\mbox{otherwise.}\n",
" \\end{array}\n",
" \\right.\n",
" $ \n",
"\n",
"If $\\texttt{a}$ is a variable of $G$ and the rules defining $\\texttt{a}$ are given as \n",
"$$\\texttt{a} \\rightarrow \\alpha_1 \\mid \\cdots \\mid \\alpha_n, $$\n",
"then we have\n",
"$$\\texttt{First}(\\texttt{a}) = \\bigcup\\limits_{i=1}^n \\texttt{FirstList}(\\alpha_i). $$\n",
"The dictionary `mFirst` that stores this function is computed via a *fixed point iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_first(self):\n",
" change = True\n",
" while change:\n",
" change = False\n",
" for rule in self.mRules:\n",
" a, body = rule.mVariable, rule.mBody\n",
" first_body = self.first_list(body)\n",
" if not (first_body <= self.mFirst[a]):\n",
" change = True\n",
" self.mFirst[a] |= first_body \n",
" print('First sets:')\n",
" for v in self.mVariables:\n",
" print(f'First({v}) = {self.mFirst[v]}')\n",
" \n",
"Grammar.compute_first = compute_first\n",
"del compute_first"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a tuple of variables and tokens `alpha`, the function `first_list(alpha)` computes the function $\\texttt{FirstList}(\\alpha)$ that has been defined above. If `alpha` is *nullable*, then the result will contain the empty string $\\varepsilon = \\texttt{''}$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def first_list(self, alpha):\n",
" if len(alpha) == 0:\n",
" return { '' }\n",
" elif is_var(alpha[0]): \n",
" v, *r = alpha\n",
" return eps_union(self.mFirst[v], self.first_list(r))\n",
" else:\n",
" t = alpha[0]\n",
" return { t }\n",
" \n",
"Grammar.first_list = first_list\n",
"del first_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The arguments `S` and `T` of `eps_union` are sets that contain tokens and, additionally, they might contain the empty string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def eps_union(S, T):\n",
" if '' in S: \n",
" if '' in T: \n",
" return S | T\n",
" return (S - { '' }) | T\n",
" return S"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given an augmented grammar $G = \\langle V,T,R\\cup\\{\\widehat{s} \\rightarrow s\\,\\$\\}, \\widehat{s}\\rangle$ \n",
"and a variable $a$, the set of tokens that might follow $a$ is defined as:\n",
"$$\\texttt{Follow}(a) := \n",
" \\bigl\\{ t \\in \\widehat{T} \\,\\bigm|\\, \\exists \\beta,\\gamma \\in (V \\cup \\widehat{T})^*: \n",
" \\widehat{s} \\Rightarrow^* \\beta \\,a\\, t\\, \\gamma \n",
" \\bigr\\}.\n",
"$$\n",
"The function `compute_follow` computes the sets $\\texttt{Follow}(a)$ for all variables $a$ via a *fixed-point iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_follow(self):\n",
" self.mFollow[self.mStart] = { '$' }\n",
" change = True\n",
" while change:\n",
" change = False\n",
" for rule in self.mRules:\n",
" a, body = rule.mVariable, rule.mBody\n",
" for i in range(len(body)):\n",
" if is_var(body[i]):\n",
" yi = body[i]\n",
" Tail = self.first_list(body[i+1:])\n",
" firstTail = eps_union(Tail, self.mFollow[a])\n",
" if not (firstTail <= self.mFollow[yi]): \n",
" change = True\n",
" self.mFollow[yi] |= firstTail \n",
" print('Follow sets (note that \"$\" denotes the end of file):');\n",
" for v in self.mVariables:\n",
" print(f'Follow({v}) = {self.mFollow[v]}')\n",
" \n",
"Grammar.compute_follow = compute_follow\n",
"del compute_follow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If $\\mathcal{M}$ is a set of *marked rules*, then the *closure* of $\\mathcal{M}$ is the smallest set $\\mathcal{K}$ such that\n",
"we have the following:\n",
"- $\\mathcal{M} \\subseteq \\mathcal{K}$,\n",
"- If $a \\rightarrow \\beta \\bullet c\\, \\delta$ is a *marked rule* from \n",
" $\\mathcal{K}$, and $c$ is a variable and if, furthermore,\n",
" $c \\rightarrow \\gamma$ is a grammar rule,\n",
" then the marked rule $c \\rightarrow \\bullet \\gamma$\n",
" is an element of $\\mathcal{K}$:\n",
" $$(a \\rightarrow \\beta \\bullet c\\, \\delta) \\in \\mathcal{K} \n",
" \\;\\wedge\\; \n",
" (c \\rightarrow \\gamma) \\in R\n",
" \\;\\Rightarrow\\; (c \\rightarrow \\bullet \\gamma) \\in \\mathcal{K}\n",
" $$\n",
"\n",
"We define $\\texttt{closure}(\\mathcal{M}) := \\mathcal{K}$. The function `cmp_closure` computes this closure for a given set of *marked rules* via a *fixed-point iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def cmp_closure(self, Marked_Rules):\n",
" All_Rules = Marked_Rules\n",
" New_Rules = Marked_Rules\n",
" while True:\n",
" More_Rules = set()\n",
" for rule in New_Rules:\n",
" c = rule.next_var()\n",
" if c == None:\n",
" continue\n",
" for rule in self.mRules:\n",
" head, alpha = rule.mVariable, rule.mBody\n",
" if c == head:\n",
" More_Rules |= { MarkedRule(head, (), alpha) }\n",
" if More_Rules <= All_Rules:\n",
" return frozenset(All_Rules)\n",
" New_Rules = More_Rules - All_Rules\n",
" All_Rules |= New_Rules\n",
"\n",
"Grammar.cmp_closure = cmp_closure\n",
"del cmp_closure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given a set of *marked rules* $\\mathcal{M}$ and a *grammar symbol* $X$, the function $\\texttt{goto}(\\mathcal{M}, X)$ \n",
"is defined as follows:\n",
"$$\\texttt{goto}(\\mathcal{M}, X) := \\texttt{closure}\\Bigl( \\bigl\\{ \n",
" a \\rightarrow \\beta\\, X \\bullet \\delta \\bigm| (a \\rightarrow \\beta \\bullet X\\, \\delta) \\in \\mathcal{M} \n",
" \\bigr\\} \\Bigr).\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def goto(self, Marked_Rules, x):\n",
" Result = set()\n",
" for mr in Marked_Rules:\n",
" if mr.symbol_after_dot() == x:\n",
" Result.add(mr.move_dot())\n",
" return self.cmp_closure(Result)\n",
"\n",
"Grammar.goto = goto\n",
"del goto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `all_states` computes the set of all states of an *SLR-parser*. The function starts with the state\n",
"$$ \\texttt{closure}\\bigl(\\{ \\widehat{s} \\rightarrow \\bullet s \\, $\\}\\bigr) $$\n",
"and then tries to compute new states by using the function `goto`. This computation proceeds via a \n",
"*fixed-point iteration*. Once all states have been computed, the function assigns names to these states.\n",
"This association is stored in the dictionary *mStateNames*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def all_states(self): \n",
" start_state = self.cmp_closure({ MarkedRule('ŝ', (), (self.mStart, '$')) })\n",
" self.mStates = { start_state }\n",
" New_States = self.mStates\n",
" while True:\n",
" More_States = set()\n",
" for Rule_Set in New_States:\n",
" for mr in Rule_Set: \n",
" if not mr.is_complete():\n",
" x = mr.symbol_after_dot()\n",
" if x != '$':\n",
" More_States |= { self.goto(Rule_Set, x) }\n",
" if More_States <= self.mStates:\n",
" break\n",
" New_States = More_States - self.mStates;\n",
" self.mStates |= New_States\n",
" print(\"All SLR-states:\")\n",
" counter = 1\n",
" self.mStateNames[start_state] = 's0'\n",
" print(f's0 = {set(start_state)}')\n",
" for state in self.mStates - { start_state }:\n",
" self.mStateNames[state] = f's{counter}'\n",
" print(f's{counter} = {set(state)}')\n",
" counter += 1\n",
"\n",
"Grammar.all_states = all_states\n",
"del all_states"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function computes the *action table* and is defined as follows:\n",
"- If $\\mathcal{M}$ contains a *marked rule* of the form $a \\rightarrow \\beta \\bullet t\\, \\delta$\n",
" then we have\n",
" $$\\texttt{action}(\\mathcal{M},t) := \\langle \\texttt{shift}, \\texttt{goto}(\\mathcal{M},t) \\rangle.$$\n",
"- If $\\mathcal{M}$ contains a marked rule of the form $a \\rightarrow \\beta\\, \\bullet$ and we have\n",
" $t \\in \\texttt{Follow}(a)$, then we define\n",
" $$\\texttt{action}(\\mathcal{M},t) := \\langle \\texttt{reduce}, a \\rightarrow \\beta \\rangle$$\n",
"- If $\\mathcal{M}$ contains the marked rule $\\widehat{s} \\rightarrow s \\bullet \\$ $, then we define \n",
" $$\\texttt{action}(\\mathcal{M},\\$) := \\texttt{accept}. $$\n",
"- Otherwise, we have\n",
" $$\\texttt{action}(\\mathcal{M},t) := \\texttt{error}. $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_action_table(self):\n",
" self.mActionTable = {}\n",
" print('\\nAction Table:')\n",
" for state in self.mStates:\n",
" stateName = self.mStateNames[state]\n",
" actionTable = {}\n",
" # compute shift actions\n",
" for token in self.mTokens:\n",
" if token != '$':\n",
" newState = self.goto(state, token)\n",
" if newState != set():\n",
" newName = self.mStateNames[newState]\n",
" actionTable[token] = ('shift', newName)\n",
" self.mActionTable[stateName, token] = ('shift', newName)\n",
" print(f'action(\"{stateName}\", {token}) = (\"shift\", {newName})')\n",
" # compute reduce actions\n",
" for mr in state:\n",
" if mr.is_complete():\n",
" for token in self.mFollow[mr.mVariable]:\n",
" action1 = actionTable.get(token)\n",
" action2 = ('reduce', mr.to_rule())\n",
" if action1 == None:\n",
" actionTable[token] = action2 \n",
" r = self.mRuleNames[mr.to_rule()]\n",
" self.mActionTable[stateName, token] = ('reduce', r)\n",
" print(f'action(\"{stateName}\", {token}) = {action2}')\n",
" elif action1 != action2: \n",
" self.mConflicts = True\n",
" print('')\n",
" print(f'conflict in state {stateName}:')\n",
" print(f'{stateName} = {state}')\n",
" print(f'action(\"{stateName}\", {token}) = {action1}') \n",
" print(f'action(\"{stateName}\", {token}) = {action2}')\n",
" print('')\n",
" for mr in state:\n",
" if mr == MarkedRule('ŝ', (self.mStart,), ('$',)):\n",
" actionTable['$'] = 'accept'\n",
" self.mActionTable[stateName, '$'] = 'accept'\n",
" print(f'action(\"{stateName}\", $) = accept')\n",
"\n",
"Grammar.compute_action_table = compute_action_table\n",
"del compute_action_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function `compute_goto_table` computes the *goto table*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def compute_goto_table(self):\n",
" self.mGotoTable = {}\n",
" print('\\nGoto Table:')\n",
" for state in self.mStates:\n",
" for var in self.mVariables:\n",
" newState = self.goto(state, var)\n",
" if newState != set():\n",
" stateName = self.mStateNames[state]\n",
" newName = self.mStateNames[newState]\n",
" self.mGotoTable[stateName, var] = newName\n",
" print(f'goto({stateName}, {var}) = {newName}')\n",
"\n",
"Grammar.compute_goto_table = compute_goto_table\n",
"del compute_goto_table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%%time\n",
"g = Grammar(grammar)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def strip_quotes(t):\n",
" if t[0] == \"'\" and t[-1] == \"'\":\n",
" return t[1:-1]\n",
" return t"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def dump_parse_table(self, file):\n",
" with open(file, 'w') as handle:\n",
" handle.write('# Grammar rules:\\n')\n",
" for rule in self.mRules:\n",
" rule_name = self.mRuleNames[rule] \n",
" handle.write(f'{rule_name} = (\"{rule.mVariable}\", {rule.mBody})\\n')\n",
" handle.write('\\n# Action table:\\n')\n",
" handle.write('actionTable = {}\\n')\n",
" for s, t in self.mActionTable:\n",
" action = self.mActionTable[s, t]\n",
" t = strip_quotes(t)\n",
" if action[0] == 'reduce':\n",
" rule_name = action[1]\n",
" handle.write(f\"actionTable['{s}', '{t}'] = ('reduce', {rule_name})\\n\")\n",
" elif action == 'accept':\n",
" handle.write(f\"actionTable['{s}', '{t}'] = 'accept'\\n\")\n",
" else:\n",
" handle.write(f\"actionTable['{s}', '{t}'] = {action}\\n\")\n",
" handle.write('\\n# Goto table:\\n')\n",
" handle.write('gotoTable = {}\\n')\n",
" for s, v in self.mGotoTable:\n",
" state = self.mGotoTable[s, v]\n",
" handle.write(f\"gotoTable['{s}', '{v}'] = '{state}'\\n\")\n",
" \n",
"Grammar.dump_parse_table = dump_parse_table\n",
"del dump_parse_table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g.dump_parse_table('parse-table.py')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"!cat parse-table.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!rm GrammarLexer.* GrammarParser.* Grammar.tokens GrammarListener.py Grammar.interp \n",
"!rm -r __pycache__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!ls"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
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"types_to_exclude": [
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| gpl-2.0 |
btel/2015_eitn_swc_pandas | 01 - Introduction.ipynb | 1 | 65714 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"<h1>Pandas Tutorial</h1>\n",
"<h3>Software Carpentry, EITN, Paris, November 20th, 2015</h3>\n",
"<h2>Bartosz Teleńczuk</h2>\n",
"\n",
"forked from the tutorial at EuroScipy 2015 by Joris Van den Bossche (Ghent University, Belgium)\n",
"\n",
"\n",
"Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"# Content of this talk\n",
"\n",
"- Why do you need pandas?\n",
"- Basic introduction to the data structures\n",
"- Guided tour through some of the pandas features with two case studies: **movie database** and a **case study about air quality**\n",
"\n",
"If you want to follow along, this is a notebook that you can view or run yourself:\n",
"\n",
"- All materials (notebook, data): https://github.com/btel/2015_eitn_swc_pandas\n",
"- You need `pandas` >= 0.15.2 (easy solution is using Anaconda)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Some imports:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.options.display.max_rows = 8"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Let's start with a showcase"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Case study: air quality in Europe\n",
"\n",
"AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe\n",
"\n",
"Starting from these hourly data for different stations:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"data = pd.read_csv('data/airbase_data.csv', index_col=0, parse_dates=True, na_values='-9999')"
]
},
{
"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>BETR801</th>\n",
" <th>BETN029</th>\n",
" <th>FR04037</th>\n",
" <th>FR04012</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1998-01-01 00:00:00</th>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-01-01 01:00:00</th>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-01-01 02:00:00</th>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-01-01 03:00:00</th>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 20:00:00</th>\n",
" <td>16.5</td>\n",
" <td>2.0</td>\n",
" <td>16</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 21:00:00</th>\n",
" <td>14.5</td>\n",
" <td>2.5</td>\n",
" <td>13</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 22:00:00</th>\n",
" <td>16.5</td>\n",
" <td>3.5</td>\n",
" <td>14</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31 23:00:00</th>\n",
" <td>15.0</td>\n",
" <td>3.0</td>\n",
" <td>13</td>\n",
" <td>49</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>131265 rows × 4 columns</p>\n",
"</div>"
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" BETR801 BETN029 FR04037 FR04012\n",
"1998-01-01 00:00:00 NaN 16.0 NaN NaN\n",
"1998-01-01 01:00:00 NaN 13.0 NaN NaN\n",
"1998-01-01 02:00:00 NaN 12.0 NaN NaN\n",
"1998-01-01 03:00:00 NaN 12.0 NaN NaN\n",
"... ... ... ... ...\n",
"2012-12-31 20:00:00 16.5 2.0 16 47\n",
"2012-12-31 21:00:00 14.5 2.5 13 43\n",
"2012-12-31 22:00:00 16.5 3.5 14 42\n",
"2012-12-31 23:00:00 15.0 3.0 13 49\n",
"\n",
"[131265 rows x 4 columns]"
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"execution_count": 3,
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"data"
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},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"to answering questions about this data in a few lines of code:\n",
"\n",
"**Does the air pollution show a decreasing trend over the years?**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f458c4c4f28>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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KcWYz48xmnkhOpqi2lo3WDua1BQVNKmz7x1g/PwL9/Vt9rbVHf5PJI49WEi013F5yypQp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\n8xJFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f458c4c48d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['1999':].resample('A').plot(ylim=[0,100])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"**How many exceedances of the limit values?**\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x7f458c1d15c0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qdz8meem+UfV6mtnhwI+AS4A2wGOEyn1jUgOGVb1m4f/CPnT3Q2rOS2K8qhxr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\nAuJ/AKkm7+D6MbiFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f458c1cdc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exceedances = data > 200\n",
"exceedances = exceedances.groupby(exceedances.index.year).sum()\n",
"ax = exceedances.loc[2005:].plot(kind='bar')\n",
"ax.axhline(18, color='k', linestyle='--')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"**What is the difference in diurnal profile between weekdays and weekend?**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f458c0ca0f0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JiSaMaTwmTS8b50601iw8upCBawfSpEQTRgWM4tGsjyZ5THCwqdS4di1UqeLc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\nU5IsbxsTE0P69M5boczjpv4n5n//M33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"text/plain": [
"<matplotlib.figure.Figure at 0x7f458c099048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['weekday'] = data.index.weekday\n",
"data['weekend'] = data['weekday'].isin([5, 6])\n",
"data_weekend = data.groupby(['weekend', data.index.hour])['FR04012'].mean().unstack(level=0)\n",
"data_weekend.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will come back to these example, and build them up step by step."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Why do you need pandas?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Why do you need pandas?\n",
"\n",
"When working with *tabular or structured data* (like R dataframe, SQL table, Excel spreadsheet, ...):\n",
"\n",
"- Import data\n",
"- Clean up messy data\n",
"- Explore data, gain insight into data\n",
"- Process and prepare your data for analysis\n",
"- Analyse your data (together with scikit-learn, statsmodels, ...)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Pandas: data analysis in python\n",
"\n",
"For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.\n",
"\n",
"What is ``pandas``?\n",
"\n",
"* Pandas can be thought of as NumPy arrays with labels for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.\n",
"* Pandas can also be thought of as `R`'s `data.frame` in Python.\n",
"\n",
"\n",
"It's documentation: http://pandas.pydata.org/pandas-docs/stable/"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Key features\n",
"\n",
"* Fast, easy and flexible input/output for a lot of different data formats\n",
"* Working with missing data (`.dropna()`, `pd.isnull()`)\n",
"* Merging and joining (`concat`, `join`)\n",
"* Grouping: `groupby` functionality\n",
"* Reshaping (`stack`, `pivot`)\n",
"* Powerful time series manipulation (resampling, timezones, ..)\n",
"* Easy plotting"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Further reading\n",
"\n",
"- the documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
"- Wes McKinney's book \"Python for Data Analysis\"\n",
"- lots of tutorials on the internet, eg http://github.com/jvns/pandas-cookbook\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# How can you help?\n",
"\n",
"**We need you!**\n",
"\n",
"Contributions are very welcome and can be in different domains:\n",
"\n",
"- reporting issues\n",
"- improving the documentation\n",
"- testing release candidates and provide feedback\n",
"- triaging and fixing bugs\n",
"- implementing new features\n",
"- spreading the word\n",
"\n",
"-> https://github.com/pydata/pandas"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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
} | bsd-2-clause |
birdsarah/bokeh-miscellany | vstack.ipynb | 1 | 45979 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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": [
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"(function(root) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
"\n",
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"\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",
" }\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",
" 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",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" };var element = document.getElementById(\"1001\");\n",
" if (element == null) {\n",
" console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
" return false;\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.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.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));"
],
"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(\"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 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 console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\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.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.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"
}
],
"source": [
"from bokeh.io import output_notebook, show\n",
"from bokeh.plotting import figure\n",
"from bokeh.palettes import Blues4\n",
"from bokeh.transform import factor_cmap\n",
"import bokeh.models as bm\n",
"import pandas as pd\n",
"import chartify\n",
"output_notebook()"
]
},
{
"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>similarity</th>\n",
" <th>group</th>\n",
" <th>absolute</th>\n",
" <th>percentual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100%</td>\n",
" <td>script_url</td>\n",
" <td>287460</td>\n",
" <td>0.348066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100%</td>\n",
" <td>script_url_clean</td>\n",
" <td>363694</td>\n",
" <td>0.440373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100%</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>656672</td>\n",
" <td>0.795120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80%+</td>\n",
" <td>script_url</td>\n",
" <td>90798</td>\n",
" <td>0.109941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80%+</td>\n",
" <td>script_url_clean</td>\n",
" <td>140444</td>\n",
" <td>0.170054</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" similarity group absolute percentual\n",
"0 100% script_url 287460 0.348066\n",
"1 100% script_url_clean 363694 0.440373\n",
"2 100% script_url_plus_1 656672 0.795120\n",
"3 80%+ script_url 90798 0.109941\n",
"4 80%+ script_url_clean 140444 0.170054"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('tmp_df2_percentual_and_absolute.csv', index_col=0)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>similarity</th>\n",
" <th>group</th>\n",
" <th>absolute</th>\n",
" <th>percentual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100%</td>\n",
" <td>script_url</td>\n",
" <td>287460</td>\n",
" <td>0.348066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100%</td>\n",
" <td>script_url_clean</td>\n",
" <td>363694</td>\n",
" <td>0.440373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100%</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>656672</td>\n",
" <td>0.795120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80%+</td>\n",
" <td>script_url</td>\n",
" <td>90798</td>\n",
" <td>0.109941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80%+</td>\n",
" <td>script_url_clean</td>\n",
" <td>140444</td>\n",
" <td>0.170054</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" similarity group absolute percentual\n",
"0 100% script_url 287460 0.348066\n",
"1 100% script_url_clean 363694 0.440373\n",
"2 100% script_url_plus_1 656672 0.795120\n",
"3 80%+ script_url 90798 0.109941\n",
"4 80%+ script_url_clean 140444 0.170054"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>similarity</th>\n",
" <th>group</th>\n",
" <th>absolute</th>\n",
" <th>percentual</th>\n",
" <th>similarity_index</th>\n",
" <th>top</th>\n",
" <th>bottom</th>\n",
" <th>absolute_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100%</td>\n",
" <td>script_url</td>\n",
" <td>287460</td>\n",
" <td>0.348066</td>\n",
" <td>0</td>\n",
" <td>0.348066</td>\n",
" <td>0.000000</td>\n",
" <td>n = 287,460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>80%+</td>\n",
" <td>script_url</td>\n",
" <td>90798</td>\n",
" <td>0.109941</td>\n",
" <td>1</td>\n",
" <td>0.458007</td>\n",
" <td>0.348066</td>\n",
" <td>n = 90,798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>50%+</td>\n",
" <td>script_url</td>\n",
" <td>311461</td>\n",
" <td>0.377127</td>\n",
" <td>2</td>\n",
" <td>0.835134</td>\n",
" <td>0.458007</td>\n",
" <td>n = 311,461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>50%-</td>\n",
" <td>script_url</td>\n",
" <td>136159</td>\n",
" <td>0.164866</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>0.835134</td>\n",
" <td>n = 136,159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100%</td>\n",
" <td>script_url_clean</td>\n",
" <td>363694</td>\n",
" <td>0.440373</td>\n",
" <td>0</td>\n",
" <td>0.440373</td>\n",
" <td>0.000000</td>\n",
" <td>n = 363,694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80%+</td>\n",
" <td>script_url_clean</td>\n",
" <td>140444</td>\n",
" <td>0.170054</td>\n",
" <td>1</td>\n",
" <td>0.610427</td>\n",
" <td>0.440373</td>\n",
" <td>n = 140,444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>50%+</td>\n",
" <td>script_url_clean</td>\n",
" <td>241902</td>\n",
" <td>0.292903</td>\n",
" <td>2</td>\n",
" <td>0.903330</td>\n",
" <td>0.610427</td>\n",
" <td>n = 241,902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>50%-</td>\n",
" <td>script_url_clean</td>\n",
" <td>79838</td>\n",
" <td>0.096670</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>0.903330</td>\n",
" <td>n = 79,838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100%</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>656672</td>\n",
" <td>0.795120</td>\n",
" <td>0</td>\n",
" <td>0.795120</td>\n",
" <td>0.000000</td>\n",
" <td>n = 656,672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>80%+</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>81609</td>\n",
" <td>0.098815</td>\n",
" <td>1</td>\n",
" <td>0.893935</td>\n",
" <td>0.795120</td>\n",
" <td>n = 81,609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>50%+</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>64849</td>\n",
" <td>0.078521</td>\n",
" <td>2</td>\n",
" <td>0.972456</td>\n",
" <td>0.893935</td>\n",
" <td>n = 64,849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>50%-</td>\n",
" <td>script_url_plus_1</td>\n",
" <td>22748</td>\n",
" <td>0.027544</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>0.972456</td>\n",
" <td>n = 22,748</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" similarity group absolute percentual similarity_index \\\n",
"0 100% script_url 287460 0.348066 0 \n",
"3 80%+ script_url 90798 0.109941 1 \n",
"6 50%+ script_url 311461 0.377127 2 \n",
"9 50%- script_url 136159 0.164866 3 \n",
"1 100% script_url_clean 363694 0.440373 0 \n",
"4 80%+ script_url_clean 140444 0.170054 1 \n",
"7 50%+ script_url_clean 241902 0.292903 2 \n",
"10 50%- script_url_clean 79838 0.096670 3 \n",
"2 100% script_url_plus_1 656672 0.795120 0 \n",
"5 80%+ script_url_plus_1 81609 0.098815 1 \n",
"8 50%+ script_url_plus_1 64849 0.078521 2 \n",
"11 50%- script_url_plus_1 22748 0.027544 3 \n",
"\n",
" top bottom absolute_text \n",
"0 0.348066 0.000000 n = 287,460 \n",
"3 0.458007 0.348066 n = 90,798 \n",
"6 0.835134 0.458007 n = 311,461 \n",
"9 1.000000 0.835134 n = 136,159 \n",
"1 0.440373 0.000000 n = 363,694 \n",
"4 0.610427 0.440373 n = 140,444 \n",
"7 0.903330 0.610427 n = 241,902 \n",
"10 1.000000 0.903330 n = 79,838 \n",
"2 0.795120 0.000000 n = 656,672 \n",
"5 0.893935 0.795120 n = 81,609 \n",
"8 0.972456 0.893935 n = 64,849 \n",
"11 1.000000 0.972456 n = 22,748 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookup = {'100%': 0, '80%+': 1, '50%+': 2, '50%-': 3}\n",
"df['similarity_index'] = df.similarity.apply(lambda x: lookup[x])\n",
"df = df.sort_values(by=['group', 'similarity_index'])\n",
"df['top'] = df.groupby('group').cumsum()['percentual']\n",
"df['bottom'] = df.top - df.percentual\n",
"df['absolute_text'] = df.absolute.apply(lambda x: f'n = {x:,}')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"e9b16b70-2a07-4703-9a2e-7f7dd11f47db\" data-root-id=\"1003\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
" \n",
" var docs_json = {\"7c696555-9aeb-4e78-94dd-2f0077c43a6c\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1012\",\"type\":\"CategoricalAxis\"}],\"center\":[{\"id\":\"1015\",\"type\":\"Grid\"},{\"id\":\"1020\",\"type\":\"Grid\"},{\"id\":\"1034\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1016\",\"type\":\"LinearAxis\"}],\"renderers\":[{\"id\":\"1026\",\"type\":\"GlyphRenderer\"},{\"id\":\"1040\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1029\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1021\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1004\",\"type\":\"FactorRange\"},\"x_scale\":{\"id\":\"1008\",\"type\":\"CategoricalScale\"},\"y_range\":{\"id\":\"1006\",\"type\":\"Range1d\"},\"y_scale\":{\"id\":\"1010\",\"type\":\"LinearScale\"}},\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"}},\"id\":\"1027\",\"type\":\"CDSView\"},{\"attributes\":{\"format\":\"1%\"},\"id\":\"1042\",\"type\":\"NumeralTickFormatter\"},{\"attributes\":{},\"id\":\"1049\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1020\",\"type\":\"Grid\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1029\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"factors\":[\"script_url\",\"script_url_clean\",\"script_url_plus_1\"]},\"id\":\"1004\",\"type\":\"FactorRange\"},{\"attributes\":{\"items\":[{\"id\":\"1035\",\"type\":\"LegendItem\"}],\"location\":[450,10]},\"id\":\"1034\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1031\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"label\":{\"field\":\"similarity\"},\"renderers\":[{\"id\":\"1026\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1035\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1024\",\"type\":\"VBar\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1025\",\"type\":\"VBar\"},\"selection_glyph\":null,\"view\":{\"id\":\"1027\",\"type\":\"CDSView\"}},\"id\":\"1026\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"text\":{\"field\":\"absolute_text\"},\"text_align\":\"center\",\"text_alpha\":{\"value\":0.1},\"text_color\":{\"value\":\"black\"},\"text_font_size\":{\"value\":\"8pt\"},\"x\":{\"field\":\"group\"},\"y\":{\"field\":\"top\"},\"y_offset\":{\"value\":15}},\"id\":\"1039\",\"type\":\"Text\"},{\"attributes\":{\"bottom\":{\"field\":\"bottom\"},\"fill_color\":{\"field\":\"similarity\",\"transform\":{\"id\":\"1022\",\"type\":\"CategoricalColorMapper\"}},\"line_color\":{\"field\":\"similarity\",\"transform\":{\"id\":\"1022\",\"type\":\"CategoricalColorMapper\"}},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"group\"}},\"id\":\"1024\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"text\":{\"field\":\"absolute_text\"},\"text_align\":\"center\",\"text_color\":{\"field\":\"similarity\",\"transform\":{\"id\":\"1036\",\"type\":\"CategoricalColorMapper\"}},\"text_font_size\":{\"value\":\"8pt\"},\"x\":{\"field\":\"group\"},\"y\":{\"field\":\"top\"},\"y_offset\":{\"value\":15}},\"id\":\"1038\",\"type\":\"Text\"},{\"attributes\":{\"callback\":null},\"id\":\"1006\",\"type\":\"Range1d\"},{\"attributes\":{\"factors\":[\"100%\",\"80%+\",\"50%+\",\"50%-\"],\"palette\":[\"#eff3ff\",\"#eff3ff\",\"#2171b5\",\"#2171b5\"]},\"id\":\"1036\",\"type\":\"CategoricalColorMapper\"},{\"attributes\":{},\"id\":\"1050\",\"type\":\"Selection\"},{\"attributes\":{\"bottom\":{\"field\":\"bottom\"},\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"top\":{\"field\":\"top\"},\"width\":{\"value\":0.9},\"x\":{\"field\":\"group\"}},\"id\":\"1025\",\"type\":\"VBar\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"BasicTicker\"},{\"attributes\":{\"callback\":null,\"data\":{\"absolute\":[287460,90798,311461,136159,363694,140444,241902,79838,656672,81609,64849,22748],\"absolute_text\":[\"n = 287,460\",\"n = 90,798\",\"n = 311,461\",\"n = 136,159\",\"n = 363,694\",\"n = 140,444\",\"n = 241,902\",\"n = 79,838\",\"n = 656,672\",\"n = 81,609\",\"n = 64,849\",\"n = 22,748\"],\"bottom\":{\"__ndarray__\":\"AAAAAAAAAAB2dSxatkbWP4MQ6BL9T90/673Cc2u56j8AAAAAAAAAADgN+VcQL9w/ph2/np2I4z9vtftdE+jsPwAAAAAAAAAAB/WONJ9x6T8VwD/wHJvsP2Kt9QBcHu8/\",\"dtype\":\"float64\",\"shape\":[12]},\"group\":[\"script_url\",\"script_url\",\"script_url\",\"script_url\",\"script_url_clean\",\"script_url_clean\",\"script_url_clean\",\"script_url_clean\",\"script_url_plus_1\",\"script_url_plus_1\",\"script_url_plus_1\",\"script_url_plus_1\"],\"index\":[0,3,6,9,1,4,7,10,2,5,8,11],\"percentual\":{\"__ndarray__\":\"dnUsWrZG1j80bO7iGiW8P1NrndTZItg/VAj1MFIaxT84DflXEC/cPyRcCstVxMU/lS95fuu+0j+FVCIQZb+4Pwf1jjSfcek/cliG3e1LuT9paq+F+Bm0P8ZTSuF/NJw/\",\"dtype\":\"float64\",\"shape\":[12]},\"similarity\":[\"100%\",\"80%+\",\"50%+\",\"50%-\",\"100%\",\"80%+\",\"50%+\",\"50%-\",\"100%\",\"80%+\",\"50%+\",\"50%-\"],\"similarity_index\":[0,1,2,3,0,1,2,3,0,1,2,3],\"top\":{\"__ndarray__\":\"dnUsWrZG1j+DEOgS/U/dP+u9wnNrueo/AAAAAAAA8D84DflXEC/cP6Udv56diOM/cLX7XRPo7D8AAAAAAADwPwf1jjSfcek/FcA/8Byb7D9irfUAXB7vPwAAAAAAAPA/\",\"dtype\":\"float64\",\"shape\":[12]}},\"selected\":{\"id\":\"1050\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1049\",\"type\":\"UnionRenderers\"}},\"id\":\"1002\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"ticker\":{\"id\":\"1013\",\"type\":\"CategoricalTicker\"}},\"id\":\"1015\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1038\",\"type\":\"Text\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1039\",\"type\":\"Text\"},\"selection_glyph\":null,\"view\":{\"id\":\"1041\",\"type\":\"CDSView\"}},\"id\":\"1040\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\"},\"id\":\"1021\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"formatter\":{\"id\":\"1042\",\"type\":\"NumeralTickFormatter\"},\"minor_tick_out\":null,\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1016\",\"type\":\"LinearAxis\"},{\"attributes\":{\"source\":{\"id\":\"1002\",\"type\":\"ColumnDataSource\"}},\"id\":\"1041\",\"type\":\"CDSView\"},{\"attributes\":{\"factors\":[\"100%\",\"80%+\",\"50%+\",\"50%-\"],\"palette\":[\"#2171b5\",\"#6baed6\",\"#bdd7e7\",\"#eff3ff\"]},\"id\":\"1022\",\"type\":\"CategoricalColorMapper\"},{\"attributes\":{\"formatter\":{\"id\":\"1031\",\"type\":\"CategoricalTickFormatter\"},\"ticker\":{\"id\":\"1013\",\"type\":\"CategoricalTicker\"}},\"id\":\"1012\",\"type\":\"CategoricalAxis\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
" var render_items = [{\"docid\":\"7c696555-9aeb-4e78-94dd-2f0077c43a6c\",\"roots\":{\"1003\":\"e9b16b70-2a07-4703-9a2e-7f7dd11f47db\"}}];\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": "1003"
}
},
"output_type": "display_data"
}
],
"source": [
"source = bm.ColumnDataSource(df)\n",
"similarities = list(df.similarity.unique())\n",
"groups = list(df.group.unique())\n",
"p = figure(x_range=groups, y_range=(0, 1), tools='')\n",
"p.vbar(\n",
" x='group', \n",
" bottom='bottom',\n",
" top='top', \n",
" width=0.9,\n",
" color=factor_cmap('similarity', palette=Blues4, factors=similarities),\n",
" source=source,\n",
" legend='similarity',\n",
")\n",
"text_palette = [Blues4[-1]] * 2 + [Blues4[0]] * 2\n",
"p.text(\n",
" x='group',\n",
" y='top',\n",
" text='absolute_text',\n",
" source=source,\n",
" text_color=factor_cmap('similarity', palette=text_palette, factors=similarities),\n",
" text_align='center',\n",
" text_font_size='8pt',\n",
" y_offset=15\n",
" \n",
")\n",
"p.yaxis.minor_tick_out = None\n",
"p.legend.location = (p.plot_width - 150, 10)\n",
"#p.yaxis.axis_label = \"%\"\n",
"p.yaxis.formatter = bm.NumeralTickFormatter(format='1%')\n",
"#p.add_tools(bm.HoverTool(tooltips=\"<p>Absolute: @absolute{,}</p><p>Similarity: @similarity</p>\"))\n",
"p.toolbar_location = None\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'chartify' has no attribute 'Chart'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-b336124d0124>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchartify\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_axis_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblank_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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 2\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sequential'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Chartified\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_subtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Some additional stuff\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m p.plot.bar_stacked(\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'chartify' has no attribute 'Chart'"
]
}
],
"source": [
"p = chartify.Chart(x_axis_type='categorical', blank_labels=True)\n",
"p.style.set_color_palette('sequential')\n",
"p.set_title(\"Chartified\")\n",
"p.set_subtitle(\"Some additional stuff\")\n",
"p.plot.bar_stacked(\n",
" data_frame=df,\n",
" categorical_columns=['group'],\n",
" numeric_column='percentual',\n",
" stack_column='similarity',\n",
" stack_order=similarities[::-1],\n",
")\n",
"p.figure.tools = []\n",
"p.figure.y_range.end = 1.2\n",
"p.figure.yaxis.minor_tick_out = None\n",
"p.axes.set_yaxis_tick_format('1%')\n",
"p.show()"
]
},
{
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-2.0 |
liboyin/horc | libo/error_analysis.ipynb | 1 | 24137 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"num_sift_kp=30\n",
"name object\n",
"class category\n",
"sift_kp_descriptors object\n",
"red_histogram object\n",
"green_histogram object\n",
"blue_histogram object\n",
"hue_histogram object\n",
"saturation_histogram object\n",
"value_histogram object\n",
"dtype: object\n"
]
}
],
"source": [
"from data import num_sift_kp, lazy_df\n",
"df = lazy_df()\n",
"print('num_sift_kp=' + str(num_sift_kp))\n",
"print df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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" 12: {'image066.JPG', 'image067.JPG', 'image068.JPG', 'image069.JPG'},\n",
" 13: {'image071.JPG', 'image072.JPG', 'image073.JPG', 'image074.JPG'},\n",
" 14: {'image076.JPG', 'image077.JPG', 'image078.JPG', 'image079.JPG'},\n",
" 15: {'image081.JPG', 'image082.JPG', 'image083.JPG', 'image084.JPG'},\n",
" 16: {'image086.JPG', 'image087.JPG', 'image088.JPG', 'image089.JPG'},\n",
" 17: {'image091.JPG', 'image092.JPG', 'image093.JPG', 'image094.JPG'},\n",
" 18: {'image096.JPG', 'image097.JPG', 'image098.JPG', 'image099.JPG'},\n",
" 19: {'image101.JPG', 'image102.JPG', 'image103.JPG', 'image104.JPG'},\n",
" 20: {'image106.JPG', 'image107.JPG', 'image108.JPG', 'image109.JPG'},\n",
" 21: {'image111.JPG', 'image112.JPG', 'image113.JPG', 'image114.JPG'},\n",
" 22: {'image116.JPG', 'image117.JPG', 'image118.JPG', 'image119.JPG'},\n",
" 23: {'image121.JPG', 'image122.JPG', 'image123.JPG', 'image124.JPG'},\n",
" 24: {'image126.JPG', 'image127.JPG', 'image128.JPG', 'image129.JPG'},\n",
" 25: {'image131.JPG', 'image132.JPG', 'image133.JPG', 'image134.JPG'},\n",
" 26: {'image136.JPG', 'image137.JPG', 'image138.JPG', 'image139.JPG'},\n",
" 27: {'image141.JPG', 'image142.JPG', 'image143.JPG', 'image144.JPG'},\n",
" 28: {'image151.JPG', 'image152.JPG', 'image153.JPG', 'image154.JPG'},\n",
" 29: {'image156.JPG', 'image157.JPG', 'image158.JPG', 'image159.JPG'},\n",
" 30: {'image161.JPG', 'image162.JPG', 'image163.JPG', 'image164.JPG'},\n",
" 31: {'image166.JPG', 'image167.JPG', 'image168.JPG', 'image169.JPG'},\n",
" 32: {'image171.JPG', 'image172.JPG', 'image173.JPG', 'image174.JPG'},\n",
" 33: {'image176.JPG', 'image177.JPG', 'image178.JPG', 'image179.JPG'},\n",
" 34: {'image181.JPG', 'image182.JPG', 'image183.JPG', 'image184.JPG'},\n",
" 35: {'image186.JPG', 'image187.JPG', 'image188.JPG', 'image189.JPG'},\n",
" 36: {'image191.JPG', 'image192.JPG', 'image193.JPG', 'image194.JPG'},\n",
" 37: {'image196.JPG', 'image197.JPG', 'image198.JPG', 'image199.JPG'},\n",
" 38: {'image201.JPG', 'image202.JPG', 'image203.JPG', 'image204.JPG'},\n",
" 39: {'image206.JPG', 'image207.JPG', 'image208.JPG', 'image209.JPG'},\n",
" 40: {'image211.JPG', 'image212.JPG', 'image213.JPG', 'image214.JPG'},\n",
" 41: {'image217.JPG', 'image218.JPG', 'image219.JPG', 'image220.JPG'},\n",
" 42: {'image222.JPG', 'image223.JPG', 'image224.JPG', 'image225.JPG'},\n",
" 43: {'image227.JPG', 'image228.JPG', 'image229.JPG', 'image230.JPG'},\n",
" 44: {'image232.JPG', 'image233.JPG', 'image234.JPG', 'image235.JPG'},\n",
" 45: {'image237.JPG', 'image238.JPG', 'image239.JPG', 'image240.JPG'},\n",
" 46: {'image242.JPG', 'image243.JPG', 'image244.JPG', 'image245.JPG'},\n",
" 47: {'image247.JPG', 'image248.JPG', 'image249.JPG', 'image250.JPG'},\n",
" 48: {'image257.JPG', 'image258.JPG', 'image259.JPG', 'image260.JPG'},\n",
" 49: {'image262.JPG', 'image263.JPG', 'image264.JPG', 'image265.JPG'}}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cid_filename = dict()\n",
"for k, v in zip(df['class'].astype('int8'), df['name']):\n",
" if k not in cid_filename:\n",
" cid_filename[k] = set()\n",
" cid_filename[k].add(v)\n",
"cid_filename"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[((16, 37), 326),\n",
" ((2, 39), 235),\n",
" ((11, 46), 230),\n",
" ((28, 31), 209),\n",
" ((23, 8), 202),\n",
" ((39, 37), 202),\n",
" ((23, 36), 201),\n",
" ((37, 16), 197),\n",
" ((33, 46), 191),\n",
" ((22, 38), 184),\n",
" ((40, 16), 166),\n",
" ((41, 16), 122),\n",
" ((37, 39), 121),\n",
" ((2, 4), 121),\n",
" ((23, 25), 118),\n",
" ((2, 21), 111),\n",
" ((39, 31), 111),\n",
" ((16, 24), 95),\n",
" ((2, 28), 87),\n",
" ((40, 24), 84)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from collections import Counter\n",
"pair_counts = Counter()\n",
"with open('sift_incorrect_log.txt', mode='r') as h:\n",
" for line in h:\n",
" if line == '[] -> []':\n",
" continue\n",
" [correct, incorrect] = line.split(' -> ')\n",
" correct = np.fromstring(correct[1:-1], dtype=np.uint8, sep=' ')\n",
" incorrect = np.fromstring(incorrect[1:-1], dtype=np.uint8, sep=' ')\n",
" pair_counts.update(zip(correct, incorrect))\n",
"pair_counts.most_common(20) # 20 most common errors and their counts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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MNgAAwOxddLCpqjdV1SNV9cdV9dWq+vnV5W+sqoer6mtV9VBVXbfjNh+uqq9X1dmqemf3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\nDQAAMHv/PyANAKCWdSPoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10df3c550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"pairs, counts = zip(*pair_counts.most_common())\n",
"fig = plt.figure(figsize=(14, 7))\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(np.arange(len(counts)), counts, color=\"green\", alpha=0.75)\n",
"ax.grid()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(2, 821),\n",
" (23, 571),\n",
" (16, 562),\n",
" (39, 487),\n",
" (37, 318),\n",
" (33, 277),\n",
" (11, 272),\n",
" (40, 250),\n",
" (28, 209),\n",
" (22, 196),\n",
" (41, 152),\n",
" (0, 3)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from_counts = dict()\n",
"for k, v in zip(zip(*pairs)[0], counts):\n",
" if k not in from_counts:\n",
" from_counts[k] = 0\n",
" from_counts[k] += v\n",
"sorted(from_counts.items(), key=lambda x: x[1], reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
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| gpl-2.0 |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module01/01-05-Polynomials.ipynb | 1 | 12785 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Polynomials\n",
"Some of the equations we've looked at so far include expressions that are actually *polynomials*; but what *is* a polynomial, and why should you care?\n",
"\n",
"A polynomial is an algebraic expression containing one or more *terms* that each meet some specific criteria. Specifically:\n",
"- Each term can contain:\n",
" - Numeric values that are coefficients or constants (for example 2, -5, <sup>1</sup>/<sub>7</sub>)\n",
" - Variables (for example, x, y)\n",
" - Non-negative integer exponents (for example <sup>2</sup>, <sup>64</sup>)\n",
"- The terms can be combined using arithmetic operations - but **not** division by a variable.\n",
"\n",
"For example, the following expression is a polynomial:\n",
"\n",
"\\begin{equation}12x^{3} + 2x - 16 \\end{equation}\n",
"\n",
"When identifying the terms in a polynomial, it's important to correctly interpret the arithmetic addition and subtraction operators as the sign for the term that follows. For example, the polynomial above contains the following three terms:\n",
"- 12x<sup>3</sup>\n",
"- 2x\n",
"- -16\n",
"\n",
"The terms themselves include:\n",
"- Two coefficients(12 and 2) and a constant (-16)\n",
"- A variable (x)\n",
"- An exponent (<sup>3</sup>)\n",
"\n",
"A polynomial that contains three terms is also known as a *trinomial*. Similarly, a polynomial with two terms is known as a *binomial* and a polynomial with only one term is known as a *monomial*.\n",
"\n",
"So why do we care? Well, polynomials have some useful properties that make them easy to work with. for example, if you multiply, add, or subtract a polynomial, the result is always another polynomial.\n",
"\n",
"## Standard Form for Polynomials\n",
"Techbnically, you can write the terms of a polynomial in any order; but the *standard form* for a polynomial is to start with the highest *degree* first and constants last. The degree of a term is the highest order (exponent) in the term, and the highest order in a polynomial determines the degree of the polynomial itself.\n",
"\n",
"For example, consider the following expression:\n",
"\\begin{equation}3x + 4xy^{2} - 3 + x^{3} \\end{equation}\n",
"\n",
"To express this as a polynomial in the standard form, we need to re-order the terms like this:\n",
"\n",
"\\begin{equation}x^{3} + 4xy^{2} + 3x - 3 \\end{equation}\n",
"\n",
"## Simplifying Polynomials\n",
"We saw previously how you can simplify an equation by combining *like terms*. You can simplify polynomials in the same way.\n",
"\n",
"For example, look at the following polynomial:\n",
"\n",
"\\begin{equation}x^{3} + 2x^{3} - 3x - x + 8 - 3 \\end{equation}\n",
"\n",
"In this case, we can combine x<sup>3</sup> and 2x<sup>3</sup> by adding them to make 3x<sup>3</sup>. Then we can add -3x and -x (which is really just a shorthand way to say -1x) to get -4x, and then add 8 and -3 to get 5. Our simplified polynomial then looks like this:\n",
"\n",
"\\begin{equation}3x^{3} - 4x + 5 \\end{equation}\n",
"\n",
"We can use Python to compare the original and simplified polynomials to check them - using an arbitrary random value for ***x***:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(1,100)\n",
"\n",
"(x**3 + 2*x**3 - 3*x - x + 8 - 3) == (3*x**3 - 4*x + 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding Polynomials\n",
"When you add two polynomials, the result is a polynomial. Here's an example:\n",
"\n",
"\\begin{equation}(3x^{3} - 4x + 5) + (2x^{3} + 3x^{2} - 2x + 2) \\end{equation}\n",
"\n",
"because this is an addition operation, you can simply add all of the like terms from both polynomials. To make this clear, let's first put the like terms together:\n",
"\n",
"\\begin{equation}3x^{3} + 2x^{3} + 3x^{2} - 4x -2x + 5 + 2 \\end{equation}\n",
"\n",
"This simplifies to:\n",
"\n",
"\\begin{equation}5x^{3} + 3x^{2} - 6x + 7 \\end{equation}\n",
"\n",
"We can verify this with Python:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(1,100)\n",
"\n",
"\n",
"(3*x**3 - 4*x + 5) + (2*x**3 + 3*x**2 - 2*x + 2) == 5*x**3 + 3*x**2 - 6*x + 7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Subtracting Polynomials\n",
"Subtracting polynomials is similar to adding them but you need to take into account that one of the polynomials is a negative.\n",
"\n",
"Consider this expression:\n",
"\n",
"\\begin{equation}(2x^{2} - 4x + 5) - (x^{2} - 2x + 2) \\end{equation}\n",
"\n",
"The key to performing this calculation is to realize that the subtraction of the second polynomial is really an expression that adds -1(x<sup>2</sup> - 2x + 2); so you can use the distributive property to multiply each of the terms in the polynomial by -1 (which in effect simply reverses the sign for each term). So our expression becomes:\n",
"\n",
"\\begin{equation}(2x^{2} - 4x + 5) + (-x^{2} + 2x - 2) \\end{equation}\n",
"\n",
"Which we can solve as an addition problem. First place the like terms together:\n",
"\n",
"\\begin{equation}2x^{2} + -x^{2} + -4x + 2x + 5 + -2 \\end{equation}\n",
"\n",
"Which simplifies to:\n",
"\n",
"\\begin{equation}x^{2} - 2x + 3 \\end{equation}\n",
"\n",
"Let's check that with Python:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(1,100)\n",
"\n",
"(2*x**2 - 4*x + 5) - (x**2 - 2*x + 2) == x**2 - 2*x + 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiplying Polynomials\n",
"To multiply two polynomials, you need to perform the following two steps:\n",
"1. Multiply each term in the first polynomial by each term in the second polynomial.\n",
"2. Add the results of the multiplication operations, combining like terms where possible.\n",
"\n",
"For example, consider this expression:\n",
"\n",
"\\begin{equation}(x^{4} + 2)(2x^{2} + 3x - 3) \\end{equation}\n",
"\n",
"Let's do the first step and multiply each term in the first polynomial by each term in the second polynomial. The first term in the first polynomial is x<sup>4</sup>, and the first term in the second polynomial is 2x<sup>2</sup>, so multiplying these gives us 2x<sup>6</sup>. Then we can multiply the first term in the first polynomial (x<sup>4</sup>) by the second term in the second polynomial (3x), which gives us 3x<sup>5</sup>, and so on until we've multipled all of the terms in the first polynomial by all of the terms in the second polynomial, which results in this:\n",
"\n",
"\\begin{equation}2x^{6} + 3x^{5} - 3x^{4} + 4x^{2} + 6x - 6 \\end{equation}\n",
"\n",
"We can verify a match between this result and the original expression this with the following Python code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(1,100)\n",
"\n",
"(x**4 + 2)*(2*x**2 + 3*x - 3) == 2*x**6 + 3*x**5 - 3*x**4 + 4*x**2 + 6*x - 6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dividing Polynomials\n",
"When you need to divide one polynomial by another, there are two approaches you can take depending on the number of terms in the divisor (the expression you're dividing by).\n",
"\n",
"### Dividing Polynomials Using Simplification\n",
"In the simplest case, division of a polynomial by a monomial, the operation is really just simplification of a fraction.\n",
"\n",
"For example, consider the following expression:\n",
"\n",
"\\begin{equation}(4x + 6x^{2}) \\div 2x \\end{equation}\n",
"\n",
"This can also be written as:\n",
"\n",
"\\begin{equation}\\frac{4x + 6x^{2}}{2x} \\end{equation}\n",
"\n",
"One approach to simplifying this fraction is to split it it into a separate fraction for each term in the dividend (the expression we're dividing), like this:\n",
"\n",
"\\begin{equation}\\frac{4x}{2x} + \\frac{6x^{2}}{2x}\\end{equation}\n",
"\n",
"Then we can simplify each fraction and add the results. For the first fraction, 2x goes into 4x twice, so the fraction simplifies to 2; and for the second, 6x<sup>2</sup> is 2x mutliplied by 3x. So our answer is 2 + 3x:\n",
"\n",
"\\begin{equation}2 + 3x\\end{equation}\n",
"\n",
"Let's use Python to compare the original fraction with the simplified result for an arbitrary value of ***x***:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(1,100)\n",
"\n",
"(4*x + 6*x**2) / (2*x) == 2 + 3*x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dividing Polynomials Using Long Division\n",
"Things get a little more complicated for divisors with more than one term.\n",
"\n",
"Suppose we have the following expression:\n",
"\\begin{equation}(x^{2} + 2x - 3) \\div (x - 2) \\end{equation}\n",
"\n",
"Another way of writing this is to use the long-division format, like this:\n",
"\\begin{equation} x - 2 |\\overline{x^{2} + 2x - 3} \\end{equation}\n",
"\n",
"We begin long-division by dividing the highest order divisor into the highest order dividend - so in this case we divide x into x<sup>2</sup>. X goes into x<sup>2</sup> x times, so we put an x on top and then multiply it through the divisor:\n",
"\\begin{equation} \\;\\;\\;\\;x \\end{equation}\n",
"\\begin{equation}x - 2 |\\overline{x^{2} + 2x - 3} \\end{equation}\n",
"\\begin{equation} \\;x^{2} -2x \\end{equation}\n",
"\n",
"Now we'll subtract the remaining dividend, and then carry down the -3 that we haven't used to see what's left:\n",
"\\begin{equation} \\;\\;\\;\\;x \\end{equation}\n",
"\\begin{equation}x - 2 |\\overline{x^{2} + 2x - 3} \\end{equation}\n",
"\\begin{equation}- (x^{2} -2x) \\end{equation}\n",
"\\begin{equation}\\;\\;\\;\\;\\;\\overline{\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;4x -3} \\end{equation}\n",
"\n",
"OK, now we'll divide our highest order divisor into the highest order of the remaining dividend. In this case, x goes into 4x four times, so we'll add a 4 to the top line, multiply it through the divisor, and subtract the remaining dividend:\n",
"\\begin{equation} \\;\\;\\;\\;\\;\\;\\;\\;x + 4 \\end{equation}\n",
"\\begin{equation}x - 2 |\\overline{x^{2} + 2x - 3} \\end{equation}\n",
"\\begin{equation}- (x^{2} -2x) \\end{equation}\n",
"\\begin{equation}\\;\\;\\;\\;\\;\\overline{\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;4x -3} \\end{equation}\n",
"\\begin{equation}- (\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;4x -8) \\end{equation}\n",
"\\begin{equation}\\;\\;\\;\\;\\;\\overline{\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;5} \\end{equation}\n",
"\n",
"We're now left with just 5, which we can't divide further by x - 2; so that's our remainder, which we'll add as a fraction.\n",
"\n",
"The solution to our division problem is:\n",
"\n",
"\\begin{equation}x + 4 + \\frac{5}{x-2} \\end{equation}\n",
"\n",
"Once again, we can use Python to check our answer:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"x = randint(3,100)\n",
"\n",
"(x**2 + 2*x -3)/(x-2) == x + 4 + (5/(x-2))\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
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"nbformat_minor": 2
}
| mit |
Lstyle1/Deep_learning_projects | transfer-learning/Transfer_Learning.ipynb | 1 | 577904 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Transfer Learning\n",
"\n",
"Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) trained on the [ImageNet dataset](http://www.image-net.org/) as a feature extractor. Below is a diagram of the VGGNet architecture.\n",
"\n",
"<img src=\"assets/cnnarchitecture.jpg\" width=700px>\n",
"\n",
"VGGNet is great because it's simple and has great performance, coming in second in the ImageNet competition. The idea here is that we keep all the convolutional layers, but replace the final fully connected layers with our own classifier. This way we can use VGGNet as a feature extractor for our images then easily train a simple classifier on top of that. What we'll do is take the first fully connected layer with 4096 units, including thresholding with ReLUs. We can use those values as a code for each image, then build a classifier on top of those codes.\n",
"\n",
"You can read more about transfer learning from [the CS231n course notes](http://cs231n.github.io/transfer-learning/#tf).\n",
"\n",
"## Pretrained VGGNet\n",
"\n",
"We'll be using a pretrained network from https://github.com/machrisaa/tensorflow-vgg. Make sure to clone this repository to the directory you're working from. You'll also want to rename it so it has an underscore instead of a dash.\n",
"\n",
"```\n",
"git clone https://github.com/machrisaa/tensorflow-vgg.git tensorflow_vgg\n",
"```\n",
"\n",
"This is a really nice implementation of VGGNet, quite easy to work with. The network has already been trained and the parameters are available from this link. **You'll need to clone the repo into the folder containing this notebook.** Then download the parameter file using the next cell."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting tqdm\n",
" Downloading tqdm-4.14.0-py2.py3-none-any.whl (46kB)\n",
"\u001b[K 100% |████████████████████████████████| 51kB 4.0MB/s ta 0:00:011\n",
"\u001b[?25hInstalling collected packages: tqdm\n",
"Successfully installed tqdm-4.14.0\n"
]
}
],
"source": [
"!pip install tqdm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"VGG16 Parameters: 553MB [00:33, 16.4MB/s] \n"
]
}
],
"source": [
"from urllib.request import urlretrieve\n",
"from os.path import isfile, isdir\n",
"from tqdm import tqdm\n",
"\n",
"vgg_dir = 'tensorflow_vgg/'\n",
"# Make sure vgg exists\n",
"if not isdir(vgg_dir):\n",
" raise Exception(\"VGG directory doesn't exist!\")\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(vgg_dir + \"vgg16.npy\"):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:\n",
" urlretrieve(\n",
" 'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',\n",
" vgg_dir + 'vgg16.npy',\n",
" pbar.hook)\n",
"else:\n",
" print(\"Parameter file already exists!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Flower power\n",
"\n",
"Here we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Flowers Dataset: 229MB [00:05, 41.4MB/s] \n"
]
}
],
"source": [
"import tarfile\n",
"\n",
"dataset_folder_path = 'flower_photos'\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('flower_photos.tar.gz'):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:\n",
" urlretrieve(\n",
" 'http://download.tensorflow.org/example_images/flower_photos.tgz',\n",
" 'flower_photos.tar.gz',\n",
" pbar.hook)\n",
"\n",
"if not isdir(dataset_folder_path):\n",
" with tarfile.open('flower_photos.tar.gz') as tar:\n",
" tar.extractall()\n",
" tar.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## ConvNet Codes\n",
"\n",
"Below, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.\n",
"\n",
"Here we're using the `vgg16` module from `tensorflow_vgg`. The network takes images of size $224 \\times 224 \\times 3$ as input. Then it has 5 sets of convolutional layers. The network implemented here has this structure (copied from [the source code](https://github.com/machrisaa/tensorflow-vgg/blob/master/vgg16.py)):\n",
"\n",
"```\n",
"self.conv1_1 = self.conv_layer(bgr, \"conv1_1\")\n",
"self.conv1_2 = self.conv_layer(self.conv1_1, \"conv1_2\")\n",
"self.pool1 = self.max_pool(self.conv1_2, 'pool1')\n",
"\n",
"self.conv2_1 = self.conv_layer(self.pool1, \"conv2_1\")\n",
"self.conv2_2 = self.conv_layer(self.conv2_1, \"conv2_2\")\n",
"self.pool2 = self.max_pool(self.conv2_2, 'pool2')\n",
"\n",
"self.conv3_1 = self.conv_layer(self.pool2, \"conv3_1\")\n",
"self.conv3_2 = self.conv_layer(self.conv3_1, \"conv3_2\")\n",
"self.conv3_3 = self.conv_layer(self.conv3_2, \"conv3_3\")\n",
"self.pool3 = self.max_pool(self.conv3_3, 'pool3')\n",
"\n",
"self.conv4_1 = self.conv_layer(self.pool3, \"conv4_1\")\n",
"self.conv4_2 = self.conv_layer(self.conv4_1, \"conv4_2\")\n",
"self.conv4_3 = self.conv_layer(self.conv4_2, \"conv4_3\")\n",
"self.pool4 = self.max_pool(self.conv4_3, 'pool4')\n",
"\n",
"self.conv5_1 = self.conv_layer(self.pool4, \"conv5_1\")\n",
"self.conv5_2 = self.conv_layer(self.conv5_1, \"conv5_2\")\n",
"self.conv5_3 = self.conv_layer(self.conv5_2, \"conv5_3\")\n",
"self.pool5 = self.max_pool(self.conv5_3, 'pool5')\n",
"\n",
"self.fc6 = self.fc_layer(self.pool5, \"fc6\")\n",
"self.relu6 = tf.nn.relu(self.fc6)\n",
"```\n",
"\n",
"So what we want are the values of the first fully connected layer, after being ReLUd (`self.relu6`). To build the network, we use\n",
"\n",
"```\n",
"with tf.Session() as sess:\n",
" vgg = vgg16.Vgg16()\n",
" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
" with tf.name_scope(\"content_vgg\"):\n",
" vgg.build(input_)\n",
"```\n",
"\n",
"This creates the `vgg` object, then builds the graph with `vgg.build(input_)`. Then to get the values from the layer,\n",
"\n",
"```\n",
"feed_dict = {input_: images}\n",
"codes = sess.run(vgg.relu6, feed_dict=feed_dict)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting scikit-image\n",
" Downloading scikit_image-0.13.0-cp35-cp35m-manylinux1_x86_64.whl (33.8MB)\n",
"\u001b[K 100% |████████████████████████████████| 33.8MB 46kB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.5/site-packages (from scikit-image)\n",
"Requirement already satisfied: matplotlib>=1.3.1 in /usr/local/lib/python3.5/site-packages (from scikit-image)\n",
"Requirement already satisfied: pillow>=2.1.0 in /usr/local/lib/python3.5/site-packages (from scikit-image)\n",
"Requirement already satisfied: six>=1.7.3 in /usr/local/lib/python3.5/site-packages (from scikit-image)\n",
"Collecting networkx>=1.8 (from scikit-image)\n",
" Downloading networkx-1.11-py2.py3-none-any.whl (1.3MB)\n",
"\u001b[K 100% |████████████████████████████████| 1.3MB 1.2MB/s eta 0:00:01\n",
"\u001b[?25hCollecting PyWavelets>=0.4.0 (from scikit-image)\n",
" Downloading PyWavelets-0.5.2-cp35-cp35m-manylinux1_x86_64.whl (5.7MB)\n",
"\u001b[K 100% |████████████████████████████████| 5.7MB 253kB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.5/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
"Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /usr/local/lib/python3.5/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
"Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.5/site-packages (from matplotlib>=1.3.1->scikit-image)\n",
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"Installing collected packages: networkx, PyWavelets, scikit-image\n",
"Successfully installed PyWavelets-0.5.2 networkx-1.11 scikit-image-0.13.0\n"
]
}
],
"source": [
"!pip install scikit-image"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow_vgg import vgg16\n",
"from tensorflow_vgg import utils"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"data_dir = 'flower_photos/'\n",
"contents = os.listdir(data_dir)\n",
"classes = [each for each in contents if os.path.isdir(data_dir + each)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Below I'm running images through the VGG network in batches.\n",
"\n",
"> **Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/output/tensorflow_vgg/vgg16.npy\n",
"npy file loaded\n",
"build model started\n",
"build model finished: 0s\n",
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]
}
],
"source": [
"# Set the batch size higher if you can fit in in your GPU memory\n",
"batch_size = 10\n",
"codes_list = []\n",
"labels = []\n",
"batch = []\n",
"\n",
"codes = None\n",
"\n",
"with tf.Session() as sess:\n",
" \n",
" # TODO: Build the vgg network here\n",
" vgg = vgg16.Vgg16()\n",
" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
" with tf.name_scope(\"content_vgg\"):\n",
" vgg.build(input_)\n",
"\n",
" for each in classes:\n",
" print(\"Starting {} images\".format(each))\n",
" class_path = data_dir + each\n",
" files = os.listdir(class_path)\n",
" for ii, file in enumerate(files, 1):\n",
" # Add images to the current batch\n",
" # utils.load_image crops the input images for us, from the center\n",
" img = utils.load_image(os.path.join(class_path, file))\n",
" batch.append(img.reshape((1, 224, 224, 3)))\n",
" labels.append(each)\n",
" \n",
" # Running the batch through the network to get the codes\n",
" if ii % batch_size == 0 or ii == len(files):\n",
" \n",
" # Image batch to pass to VGG network\n",
" images = np.concatenate(batch)\n",
" \n",
" # TODO: Get the values from the relu6 layer of the VGG network\n",
" feed_dict = {input_: images}\n",
" codes_batch = sess.run(vgg.relu6, feed_dict=feed_dict)\n",
" \n",
" # Here I'm building an array of the codes\n",
" if codes is None:\n",
" codes = codes_batch\n",
" else:\n",
" codes = np.concatenate((codes, codes_batch))\n",
" \n",
" # Reset to start building the next batch\n",
" batch = []\n",
" print('{} images processed'.format(ii))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# write codes to file\n",
"with open('codes', 'w') as f:\n",
" codes.tofile(f)\n",
" \n",
"# write labels to file\n",
"import csv\n",
"with open('labels', 'w') as f:\n",
" writer = csv.writer(f, delimiter='\\n')\n",
" writer.writerow(labels)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers', 'sunflowers']\n",
"[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 1.65599287e-02]\n",
" [ 6.81342840e+00 1.87480316e+01 0.00000000e+00 ..., 3.14530420e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" ..., \n",
" [ 0.00000000e+00 0.00000000e+00 4.99170780e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 3.35281163e-01]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 4.14870834e+00 1.34225979e+01 2.73851252e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]]\n",
"(3670, 4096)\n"
]
}
],
"source": [
"#Test\n",
"print(labels[:10])\n",
"print(codes[:10])\n",
"print(codes.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Building the Classifier\n",
"\n",
"Now that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# read codes and labels from file\n",
"import csv\n",
"\n",
"with open('labels') as f:\n",
" reader = csv.reader(f, delimiter='\\n')\n",
" labels = np.array([each for each in reader if len(each) > 0]).squeeze()\n",
"with open('codes') as f:\n",
" codes = np.fromfile(f, dtype=np.float32)\n",
" codes = codes.reshape((len(labels), -1))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sunflowers' 'sunflowers' 'sunflowers' 'sunflowers' 'sunflowers'\n",
" 'sunflowers' 'sunflowers' 'sunflowers' 'sunflowers' 'sunflowers']\n",
"[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 1.65599287e-02]\n",
" [ 6.81342840e+00 1.87480316e+01 0.00000000e+00 ..., 3.14530420e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" ..., \n",
" [ 0.00000000e+00 0.00000000e+00 4.99170780e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 3.35281163e-01]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 4.14870834e+00 1.34225979e+01 2.73851252e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]]\n",
"(3670, 4096)\n"
]
}
],
"source": [
"#Test\n",
"print(labels[:10])\n",
"print(codes[:10])\n",
"print(codes.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Data prep\n",
"\n",
"As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!\n",
"\n",
"> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the labels. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn import preprocessing\n",
"label_binarizer = preprocessing.LabelBinarizer()\n",
"label_binarizer.fit(classes)\n",
"labels_vecs = label_binarizer.transform(labels) # Your one-hot encoded labels array here"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 0 0 1 0]\n",
" [0 0 0 1 0]\n",
" [0 0 0 1 0]\n",
" [0 0 0 1 0]\n",
" [0 0 0 1 0]]\n"
]
}
],
"source": [
"#Test\n",
"label_binarizer.classes_\n",
"print(labels_vecs[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use [`StratifiedShuffleSplit`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) from scikit-learn.\n",
"\n",
"You can create the splitter like so:\n",
"```\n",
"ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\n",
"```\n",
"Then split the data with \n",
"```\n",
"splitter = ss.split(x, y)\n",
"```\n",
"\n",
"`ss.split` returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use `next(splitter)` to get the indices. Be sure to read the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) and the [user guide](http://scikit-learn.org/stable/modules/cross_validation.html#random-permutations-cross-validation-a-k-a-shuffle-split).\n",
"\n",
"> **Exercise:** Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"\n",
"#shufflesplitter for train and test(valid)\n",
"shuffle_train_test = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\n",
"shuffle_test_valid = StratifiedShuffleSplit(n_splits=1, test_size=0.5)\n",
"\n",
"train_index, test_valid_index = next(shuffle_train_test.split(codes, labels_vecs))\n",
"test_index, valid_index = next(shuffle_test_valid.split(codes[test_valid_index], labels_vecs[test_valid_index]))\n",
"\n",
"train_x, train_y = codes[train_index], labels_vecs[train_index]\n",
"val_x, val_y = codes[valid_index], labels_vecs[valid_index]\n",
"test_x, test_y = codes[test_index], labels_vecs[test_index]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train shapes (x, y): (2936, 4096) (2936, 5)\n",
"Validation shapes (x, y): (367, 4096) (367, 5)\n",
"Test shapes (x, y): (367, 4096) (367, 5)\n"
]
}
],
"source": [
"print(\"Train shapes (x, y):\", train_x.shape, train_y.shape)\n",
"print(\"Validation shapes (x, y):\", val_x.shape, val_y.shape)\n",
"print(\"Test shapes (x, y):\", test_x.shape, test_y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If you did it right, you should see these sizes for the training sets:\n",
"\n",
"```\n",
"Train shapes (x, y): (2936, 4096) (2936, 5)\n",
"Validation shapes (x, y): (367, 4096) (367, 5)\n",
"Test shapes (x, y): (367, 4096) (367, 5)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Classifier layers\n",
"\n",
"Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.\n",
"\n",
"> **Exercise:** With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])\n",
"labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])\n",
"\n",
"# TODO: Classifier layers and operations\n",
"fully_layer = tf.contrib.layers.fully_connected(inputs=inputs_,\\\n",
" num_outputs=256,\\\n",
" weights_initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
"logits = tf.contrib.layers.fully_connected(inputs=fully_layer,\\\n",
" num_outputs=len(classes),\\\n",
" activation_fn=None,\\\n",
" weights_initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
" # output layer logits\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels_, logits=logits)) # cross entropy loss\n",
"optimizer = tf.train.AdamOptimizer().minimize(cost) # training optimizer\n",
"\n",
"# Operations for validation/test accuracy\n",
"predicted = tf.nn.softmax(logits)\n",
"correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Batches!\n",
"\n",
"Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_batches(x, y, n_batches=10):\n",
" \"\"\" Return a generator that yields batches from arrays x and y. \"\"\"\n",
" batch_size = len(x)//n_batches\n",
" \n",
" for ii in range(0, n_batches*batch_size, batch_size):\n",
" # If we're not on the last batch, grab data with size batch_size\n",
" if ii != (n_batches-1)*batch_size:\n",
" X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] \n",
" # On the last batch, grab the rest of the data\n",
" else:\n",
" X, Y = x[ii:], y[ii:]\n",
" # I love generators\n",
" yield X, Y"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Training\n",
"\n",
"Here, we'll train the network.\n",
"\n",
"> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the `get_batches` function I wrote before to get your batches like `for x, y in get_batches(train_x, train_y)`. Or write your own!"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1, Training Loss: 30.40692, Validation Accuracy: 0.2343\n",
"Epoch: 1, Training Loss: 18.81519, Validation Accuracy: 0.4441\n",
"Epoch: 1, Training Loss: 12.79409, Validation Accuracy: 0.4959\n",
"Epoch: 1, Training Loss: 17.91831, Validation Accuracy: 0.6921\n",
"Epoch: 1, Training Loss: 8.33557, Validation Accuracy: 0.7684\n",
"Epoch: 1, Training Loss: 9.45283, Validation Accuracy: 0.7602\n",
"Epoch: 1, Training Loss: 5.96716, Validation Accuracy: 0.7520\n",
"Epoch: 1, Training Loss: 4.60262, Validation Accuracy: 0.7302\n",
"Epoch: 1, Training Loss: 4.35579, Validation Accuracy: 0.7248\n",
"Epoch: 1, Training Loss: 3.80106, Validation Accuracy: 0.7084\n",
"Epoch: 2, Training Loss: 3.43047, Validation Accuracy: 0.6948\n",
"Epoch: 2, Training Loss: 4.37863, Validation Accuracy: 0.7193\n",
"Epoch: 2, Training Loss: 1.90031, Validation Accuracy: 0.7166\n",
"Epoch: 2, Training Loss: 2.80273, Validation Accuracy: 0.7520\n",
"Epoch: 2, Training Loss: 2.61766, Validation Accuracy: 0.7847\n",
"Epoch: 2, Training Loss: 3.43213, Validation Accuracy: 0.8338\n",
"Epoch: 2, Training Loss: 1.78251, Validation Accuracy: 0.8801\n",
"Epoch: 2, Training Loss: 1.84152, Validation Accuracy: 0.9210\n",
"Epoch: 2, Training Loss: 1.51766, Validation Accuracy: 0.9074\n",
"Epoch: 2, Training Loss: 1.55161, Validation Accuracy: 0.8965\n",
"Epoch: 3, Training Loss: 1.22917, Validation Accuracy: 0.8747\n",
"Epoch: 3, Training Loss: 1.24837, Validation Accuracy: 0.8338\n",
"Epoch: 3, Training Loss: 1.23901, Validation Accuracy: 0.8256\n",
"Epoch: 3, Training Loss: 1.17615, Validation Accuracy: 0.8174\n",
"Epoch: 3, Training Loss: 1.09862, Validation Accuracy: 0.8120\n",
"Epoch: 3, Training Loss: 1.16637, Validation Accuracy: 0.8147\n",
"Epoch: 3, Training Loss: 1.02946, Validation Accuracy: 0.8501\n",
"Epoch: 3, Training Loss: 0.81573, Validation Accuracy: 0.8638\n",
"Epoch: 3, Training Loss: 0.43645, Validation Accuracy: 0.8719\n",
"Epoch: 3, Training Loss: 0.73736, Validation Accuracy: 0.8883\n",
"Epoch: 4, Training Loss: 0.60491, Validation Accuracy: 0.9155\n",
"Epoch: 4, Training Loss: 0.40664, Validation Accuracy: 0.9373\n",
"Epoch: 4, Training Loss: 0.14832, Validation Accuracy: 0.9455\n",
"Epoch: 4, Training Loss: 0.32877, Validation Accuracy: 0.9455\n",
"Epoch: 4, Training Loss: 0.59972, Validation Accuracy: 0.9455\n",
"Epoch: 4, Training Loss: 0.42868, Validation Accuracy: 0.9510\n",
"Epoch: 4, Training Loss: 0.34062, Validation Accuracy: 0.9428\n",
"Epoch: 4, Training Loss: 0.24177, Validation Accuracy: 0.9264\n",
"Epoch: 4, Training Loss: 0.04945, Validation Accuracy: 0.9074\n",
"Epoch: 4, Training Loss: 0.29299, Validation Accuracy: 0.9074\n",
"Epoch: 5, Training Loss: 0.20575, Validation Accuracy: 0.8937\n",
"Epoch: 5, Training Loss: 0.13432, Validation Accuracy: 0.8910\n",
"Epoch: 5, Training Loss: 0.04590, Validation Accuracy: 0.8828\n",
"Epoch: 5, Training Loss: 0.15074, Validation Accuracy: 0.8937\n",
"Epoch: 5, Training Loss: 0.24305, Validation Accuracy: 0.8910\n",
"Epoch: 5, Training Loss: 0.15383, Validation Accuracy: 0.8937\n",
"Epoch: 5, Training Loss: 0.08918, Validation Accuracy: 0.8965\n",
"Epoch: 5, Training Loss: 0.05598, Validation Accuracy: 0.9101\n",
"Epoch: 5, Training Loss: 0.02435, Validation Accuracy: 0.9128\n",
"Epoch: 5, Training Loss: 0.14490, Validation Accuracy: 0.9237\n"
]
}
],
"source": [
"epochs = 5\n",
"\n",
"saver = tf.train.Saver()\n",
"with tf.Session() as sess: \n",
" sess.run(tf.global_variables_initializer())\n",
" # TODO: Your training code here\n",
" for epoch in range(epochs):\n",
" for x, y in get_batches(train_x, train_y):\n",
" loss, _ = sess.run([cost,optimizer], feed_dict={inputs_: x, labels_: y})\n",
" #if epoch % 5 == 0:\n",
" val_accuracy = sess.run(accuracy, feed_dict={inputs_: val_x, labels_: val_y})\n",
" print(\"Epoch: {:>3}, Training Loss: {:.5f}, Validation Accuracy: {:.4f}\".format(epoch+1, loss, val_accuracy))\n",
" \n",
" saver.save(sess, \"checkpoints/flowers.ckpt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Testing\n",
"\n",
"Below you see the test accuracy. You can also see the predictions returned for images."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 0.9319\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
" \n",
" feed = {inputs_: test_x,\n",
" labels_: test_y}\n",
" test_acc = sess.run(accuracy, feed_dict=feed)\n",
" print(\"Test accuracy: {:.4f}\".format(test_acc))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from scipy.ndimage import imread"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Below, feel free to choose images and see how the trained classifier predicts the flowers in them."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fafaea8d9e8>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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6fpKo2MG7Lvsws1OTCMEkob4ghXKWNhbotwVeH1FJTxxkeWqaG9/5G1x+7buY\nHZ9BEUIMb90F5SZ2XkNMqkxVFU5MSAxvvIpM+hzFzDHmpybQU2sYXv8hTp18kIkTb3Dg5SNotCC4\nDplambpq0dpYZKKWp2VwFz1RiWJlhYmlDKlEO7fefA0RI0vQ6uGam/+EtZgceeUHnLbCdIda2CmJ\nXLp2jqVsk+fPhXlz/wDLwhIbd26hstDJ8mKDUFLDNnxUW8J2Wsg2ztI+MEi9WkGxE0yPFOjv1tEC\nRUS/j2ajSiJZY+xYmdERm6VihpIpQcGg7ufojyfoG+ig4mqkR8epTjTBbbJhbQQLCTfpU6mlkJ06\nITPB3humEIoas1mJ9MpphoKL3HzLy0yeUrGbFqHAEo89d4xA1wTdHSpf/WmSJWEvy0sGpflxPv77\nX/n/Tsjee+89dzuWQzAWR5FUDMvEsi3iPQmSfa0EoiGMisP0yQlqxSbdQ70IMQ0hGWJltki0NYYY\n9NFUhXy6iGBLOPUmzZUijm3i+g6xRIxapYYsqpSLZTRZRWwTkXWF9vYO8oUcoUSISr2CZbm4dR9R\nkwjGosihALKqYjaahGMRDNNAVyEUFonHW8jO5DBXmpiFOsFQGD0VwPYaeLZLPByjkM/TqNURXQnP\nOP+nkSSKSFKTgBrC9V1M22dpucD7fm8HF+zMk16SyNdPsbcvxI+/Pkc+NETTP0r6pMpEbhJDNMnW\ndJhp8onbQ8zmDMYXqlxx+Xq2r1vDc7+I8rt3P0gssgalUaZto4y4EOfq7U2qukq/Vyefb0AoyLvj\nO2k99gzjYZGJlZNsGr6dL97zPE5cxq7MEAoESacXsEyRgJ5AbvURHIH+zYOUKhPcuedy/vT33sVf\nf/PvKEoxsAW2basxuHOR+swGFhdb6F0/z59+7XYe/fkBHn3sdZSwRb0iMzlapWNdjdYOk8UVn+7+\nTqYbGRbG2vnDT97MZz/3TW4e3MbanWvYveHDfPK9Ozjx+hM88MgcRw4pbLh4keXyAtOTOms3Bwlo\nNuXqq9x2xVPEY3D3109x62U3MVK4hy0X3caDLyY4uqyg93Qyt/wE128cJsHTNJpBQkMyJirxZAM9\n08m8ZaOZCWZqGfK5Ee688QtUS2mem/wVs0c6eO7lV1i/fhPp5QKnT53m5PFjLGey7Hv1AAvLywx0\ntHP5ldfRqidpWpM8+sx9xGNtbBrYQLwrwevHDnF07DiSpdEeUjkzcpb9r76MpnSwmNnHju27eceV\nv0Gh2OBGfoTAAAAgAElEQVSjH/4se3ffwOPP/Ywv3/0dbvuN97OwMkeqS8N3mqysZIkFw/TUZvnY\nb13Gt/+PDzPY34MbEMiJAotOjTcPjLBn+AL2XHwzutBPozbP4ZMvUHR8qoKCV1pmoGcXN19zEyFE\nMg2R0rJPQo/y+v5X2X9ynE989k/5t4cfJHP2DHsvuoZgx1ZwdWRNYcReRnXr7NkwTKTX46W5Ze57\ncoFjY0GWawXERphC2mG2PEJ7v0tzNkdtYYF1Ow1CEYf5UZ2I1E04fI5KzgHbQLQNXH0IXbYoZ9OM\nHtcxxCZrN4r84ecENgZEfjUh04iVuPsDNr+9dZof/thEEsKcXcnR2daC5Wgoik45WyeogOv7eJqH\nHtzCm8/ZqF6ZFilOvCXMAz/OYHoKW9eG6NCifPL212n6lzJ2xuOVh8+xePYAuhFifjrDF/7o7rcU\nsv/b6mT/M0XVaO3rI5FoQfYFCvllMtk0QTWMYzjUK3WMfA0saFvXgRjUaJTy4ICGQmW5RNANIPky\nmqjg+y6iKmO6DsF4BNuzKdTKhFNRRFEglWhBDAhYgoEoni+cd5s+QkJgcLCfpbksgijQbNSRVAVJ\nV5E0lUQigYKIqGiYdQNd05hdmKBeKKGp50uY9EgIG49gOEI2vwSGj1U10DSVQCBA03JQVBlFU3EF\nF1E6X6ImahLIEGwJkkwqVNOzbO33uG33O1jasp9IoIEQ6OWi/3YdT7zwMoF28OtBXMPi1aMTVKQu\nGrrD2lQ/lq+htc+jK53kl06zbouEORflH+8RKFSDHH59hWUzS8UNsS7cyfyywnbNJZN+lmvefQV/\n8XdpTFlFU5fRiCIhMDC0jvn0ErLkoUkOnldjabqKYHdg6xP89XeP8sgvf5uLb72fvVdcgVkrc/Cp\nUWZPlrnzG+eIiSL//kObimGgxMCrtNO1xgbPppxWyMyKKJEq5XKZAy9CZ2uGF19+gzMnmow1Funt\nXebJ529h3ab38PEvW7y80MrUSCtJXaFqdSAlzxJLOezo3kpF+CfQj1C329nquGwTxij2BfEWc1Qn\njxLuvJSIOYrajLJryxky+d0I6nb2T/2QpqCjKkkqlkBAU6lKJmPz43SpTbzaPHr7dmbnnkYX45w4\nNkazKTC7uEQsmqBaqZDPlakUS8R7ZIxagHx2mQu27KVojtI70M4NN/wWBw+9xpFDz+Ah4HouzXSJ\nI+Y5To+MsmfXbjYNX8Po5D8zNvMmc+k8FaPB6/s0cnMlLrrqt2lUFhgbn2TtYDdb1u6ls2s9C1M1\nRGWK/nA/K9GNPP7aOQ4f+2fSGR0y0E0UI5JkaHgPNaOBGtFYnp1BqqlYfo32wc1UTy2Sy8zz/FPT\nbNl+CVa9zNAlrbiex6vP72fvtuvJHjvF1z/zBZbnjrN3+07UM3kG1rRSyCyS2Pl+XnzsW/zkwVHW\nbXonQelqzOJ+QKNhJsjWa8TbLGyrhtvYQdM7yKe/Osy//MM0544tcNlNe0kks5TGRcyKQrTdoTSr\nkc0X2LSjguA0iLYV6R8OcHp/g9GJFGcPmFiFZUKSwIMPr+HRwBTKoEJLtZ13/4bDG8cjpNNpjLpB\nIKoSiPoUFuq888oPEI/3MLv27+lOVnjHRRFqRpBTZyN4oQRGoINlqZe/evh1rr/mY+zcUWIu92kE\nMUpmykYLv/V8+7WYyf75PffcPbRhE+VKBdM2GRwYQBRFZiYnMW0Ht2ERcCRsz6ZjTS/ZaoGQFkR1\noVkyaTQMGo0mru3hmy6iICPKgOxh+B6SIhEOBrBNC89zsH2LQFgnqAVJRpLMjs9jVx2iqQi+6BGM\nBGgaDUJqCFcAw3UQJYmtGzYyMz5FrVzBdExkX2B5Lo3my/iOD6KIKfro8RCBUJjcUpbaShkFCcdy\nkCQZx7FRNRXP9xEEF8cW8HBxRRcTi8tv3ch1OzYR032626MsHp1iaKjG7NQ63vc7QYZ2tjFzUmR0\ntI5rLvPn/+My3ESOyXyEtOGwZ22CgXWbOXmqylOP7GfkxJ9x4AWBj/+RR1/iHK9MuAiVOmlTR2+z\n2dWxC6sCStjl4o8N82+PqHzju8+RGgjjuw6eKVIqVHEdl0RHBFmtghGjuiKRX/GxjCodAyFOn03z\nne++wm23bsTWduHJLrlzE0hhmUSyyS/+TqClZ5lc3iC/qOM7eQo5AwcRWSxRs+q09cik2jXKBRVB\nLXD46BRGM8BnPnYXJyYO8N2Hz1C0Z9E9jb/+ms6jL82yeZdDqS4Q0AMkA2mu2noZC4e38Y3v/gFK\n1yI3XdbHH9//GLf8pspi7hmkvhJBr590dYF1oQ08/G/PM1W8gP0nn6eQl1ASnQQDcywVg7iNKqGU\njhxcIFgPs32Lix3cwpvHDtMh9nF6fJ6ZiTlaO5PU6yU2b97AhRdewMjIOS6/ai1HXlqkp09jYKib\nai1MZ9cmSiWbl998iEymiGn7tIRi1HJFioUSLjJ7dl/G5e+8mkz+NOnFLFdf8j7susTj//5zetri\nXLLrIk6Nv0q+PM3KSonHn3iBhfQUTz39OP1DEYaSa4h1uuzZ1Ma5k6MsVnqo2YsU0hl6h66iI6GB\nlObQwX/nyP43uPX6O+jo2MymzZeyZf0m1q7v5M03X6SndwPJ5CCmI3Lg6AgnR8ZomEvsO3aMkekK\n9z/+BKPTR2htaWM+P8ITh5/DrcTwTY2M7fPG6BNMzR7HbPRSkxp4DZ/WDh2jqRAOJ0jPF/AshZnZ\nebKLARLtQWLhOCoWhXQGw5TxRJf2Pp2hbSVkOYzpmuRXQmSzHqWVCFPn8vRvrLNhTTubtvbgRKIs\n2UV8L0k+N8EV10vUzSvYufMSEvEkWsAjW5xmeHM3Bw6+QiTkIcphYoEgG4fbyOVsRCWC48VZyM7z\nVz95mYw3yA17byUqh1n/ziCSMIK4LLJcq/OVP/vqW5rJ/lpsdSiKEk3LJhgJE4nGqJRr4Pmoqo7T\ntAjIOpIg41g+xVyRZCxBUA+Sy+TxZAglIsiShmf7uAjosSC+LuMr0vnZpywi4NCo1vEcC6PWQJcU\nSnMlRl4/g1NxwINESxwpINIU6oQ7A5i2hSzLiKJIrVZjYWGBcqmGqqo4hofTcNAlhaB+/u0zX5Jp\nNpvguKiyRlAPnp9tSzIC0Gw2ERUZ0zExLBMBEc/2UGWZSq1KoidOe18rgrlIKp4nqd+KNlDk+GSC\nxJBFtVnmR4/8nG/edyeGVWDDVh1fS9AQ4/ihAKZkYAudBPQVKtVJPn7nJWzu+xSf/1IZ35jjzNEw\nPkkaiGxen2JTdwueqFBmBS69CpSNvPi0QjDUZGV+AbMepW2dAlIUmjWaBQ/BdVA8nUDMY/tujR8+\n28po6SCL1iAVIcSP/3WGdO1JBodjDG/dSCFX4On7ddrDQzz76At8//v/nZ07esCRkDwZs1Gnu7+b\n9TtAlJOUKzqRFoVKKYYvgRRWCbcVGdQuQjfbuKx7M4dOiHzqXof2uEuuGETR5vDMEYbXtFErDbDl\nhjF626+jMt3Pt1/0sHu2c/RAlXMT/RRmO3HaEiyVy3QPrEFKXM76zdsY7ItTRyafb9Ieg5KxQjCk\nUs0s0hH20VWFheU0c7NHMCsi5WqJWtUgHA+Tz2cYHGzjzg/eikeZ4a0tTE+k0UMhytVlDh3bTyze\njmkqyLKGKNnUTYdmpkBtKU8oEiSuaQQEl5f3v8y37v1Tjrw2hWCFePnFZ5mdnGf71n5CYYNS0cd1\nY0xOrDCwZohte3qZWnqJSKJBMryFlvXXsqHvcgS7j+tu/hDHDh9C1gJ0DSUpzfyIWuE0QnWaHWtS\nfOYTH8NX2zl8YpogPkv1Geq+x8CmLTSFMq8c/hVPvvAr3jzyJmrMwQ4ZVHyVc0smmy/ZxuDOnRw+\nk+EPv/QAM/MOWwZlbBfGJ0/i1rP092WQXBUtGKanN0K+YOB5KsVsFc9tIqpJlpc6uOiSi/noJ+5h\nevw4R1+bZse1cb50z150LQKJANlimPmsS8MJgCZgOk2iMRWPBNFUG6LcytyKyOzZMyyPFJmbkhHd\nCJ/7isoLLx3muWcPowhdbNlwA1dd8QG6Oy/gsksvJ5dbIr24wGxa4qkXBBZzHldeOkQzN0etusiO\nze2saVtHofZOCoUHOf3Ca9z75a28+OJugprylvPt12K5wPdBkxV8UaBeKaPJMvl8HqthkGxLIbgg\naSrReITcQgZFkcgXK8ioSMnA+fXVmoHg+TTsBoZnISgCkWj8/KO4KOLaNq2pGJ4HjmCzOLeI1FSQ\nHZGgHiDYEsS1PUzboNooI3g+IsH/eFdZIBQK41g2ogSNZhMkkWKuBJ5LsV4lFI0jC+A4LiuLaaKB\nGLIvIAhg2zaSLGN5DgFNwUXGNSwsy8G3BSLhEDRqdPd1IWg+DWGUaHw9o+kMjz5RZdeGvSzklglG\ndlCwHmMs9yqf+fhXePjRb7JUKiO2mDiKiFE1cDUdq6HRkezi+o/UOHPoPUyeOo0tqHT3aHQVI8zo\nkPDHUUpDmK0OfqRG97YrKOdX+OFDX+Yv/ynBP/3jjygvZ2nGFdp64jRXfMxak1rFZO1gk0IpiK7m\nmR1rsLTUxVL9JF1tKcrJBdpoYzDVzmPjL9Ea7cAJ5xG8JXzmaQtcBP63CSUcykUfXQuwlF1ECQuo\nmgBSmWZFJdWlUTVBC5nse/IBvvaz17njym0cPnCMWFsYX9G5ctdejGgMuzmFHi7RGluHpO7gjcMP\n4MVPMT6So72nk9sGw/zglQiXr9/C7140w33zjxH1yjx88Bnkhs11O2DyDYG6U8BvxAiL/YSrdULx\nCFZBRWnEkVrS7N9fpWfrFNFADLcZQJSgWqmRbJdobUswOTnG4OAA586dpSV2AZtu0WgY02zatJtc\n5Qzjc1NMjzk4gCoKpBKtWJZL1qzRqgdZN9hPGY/lExOsX7+dmdPnOGmc5JZb7qRvTT+LK/Ms12wW\nF0qcPJ4hGiuTSKXobOmnuDTO/lffJF8weCy9jy98+tN88c/vo2kpLOY8zGCd977jYsKRHJvXXsSm\njZeSrQR5/MWjhBMa+ZWTTMzO8/qrJXy/ySNPvkZnXyelcgDbFGlpjXDV1qtY072dnlSYcHQCE5Nv\n/ctT7F27maf+8QdMn3mFXZ++GVGf46UXmqzrsFmKLFL3w9SLNmpYZ6UyRkwZIBZqxZBmEXWZRt1G\nI8nVl93MSvVXTC3GGT97DFe0KJQjuM0IwfgSbqMHWc2jqgHKaYMNu4KcOFbC8nSqK9PUsh6xrrV4\nqkxT1NizpRstIrA0vcDTj56lf2CYS66+GsNYYeL0HDIC2y7YRK6SY6UaYWBjjMWpII3FCPVsgc27\nYrRWy1y9qZOR0au48eIc62IfRtZupLv7v9yK5f/m12Im63seRqNJuVxElARsx0RVVXQ9gGkY5AsF\nMrkVkCU0WYWGTS1fRRQU1JBCo1E/v4mG5eI4Fo16FVEUkWWZpmkgqhKO57CSL2OaJqKgIokqoiKC\n6J+vAW0YTI9NE9bC6EKAmB5HCwaQZRlNkqkWSsxOzSKpMkJARRZkFFEmFIkhhwIIuoIS0GhrbSWm\nahQyWRqlMr4Pju8hSCDIAoIPejCArut4toeIhCzK4EOqNUmhtMKrIw0Oz5zlyGiGshuk7FVAlSlU\nG6jaeg6OHeB979+CGgxQtUsoThRRDBPXssiqT31RZ3jTIGnjCeZLrxIPp9DFKlIwiWEYqG6DoJ5C\ni/WxUJgn3i4Rjfbg2SpPvPATDh0/xx9+5i52r2ln+nCDZFsErT2MpoOPytj4PIZTZdv2IDESuKbF\nuz+Qo7wso5sy7/3NM5i1n9C0ahh+A6NqM58Wuf/H9/Htb36S08eLZBfDaOFWFEEkFAwjOnEEGoi+\ngFEv/p/UvWe0XWd57f9bfa3dy+m9Ske9WZLliguWO6YlvpSEklxKQkKAkITcJDYkIYEEUm+SSwI4\ngIGYbmyDq6xmWZZsqx/p9H7O7n2vvv4fjs0/uWPcMfhI3k97lbH32h/23M87n/nMydpiFqsYwWsK\n/Mnnz1F3W2mPeYRbdRRNxjNrZINJRNuiWXaoln3Cxk6irRrnr8xz281/TLZSJa0qRNqqjI4pvOl9\nH2YqJ7BBM5mpRyjYfVSWdU4+f4L0YBbZ1fBrSS4taSiSge/4vOGmuxgbfBexlIojKEwsFKhYRaJh\nHde36e5ro7W1je72XioFC1VOcOvN93HzLQfZtGUz5y8u8eyhQwRyg0vjZ1nNXSEWi3Fg22YE0adW\nXx+CufHgHezZfy1u2SIWdTDtEnUrIBLtpVjwyGU9xEDkuWMPcf7CWa7ZfzOZ5Synjp9lbdrmqs1X\n0dth8syFQ7w0F3D/736ei2urRKIWjVmX+pUiLZZJOGZgua2sFGIcPnEW1zGJhTWm52c5/8oER547\nz8TFOhGjm2rFJRWNM9jRS1esk1Sim0y9yJXV04yNbmB5Lk/PWAdX33aAf//3L/D5h7/JK+On2bV1\nN9fcpXC5UqUoydi+h9uUKNfyqEorupjCdpap2w0sO4YcahD3Da66dYaeLQKXLlUpuTG08AipiEo8\n7KMLaULhBrIo4zUVunuhlFMol2UmKlN4hRBCoNIIimi1VcRonHqjwspCGadh05qOYKgBLxw/wvLK\nArrRJJGoMjMzhWmvsmEsRGdfKy9dOE7VnyORaJDLC1z9hgApZPIHf/+vVDwDOXqQINqK7do/N779\nQlSyruug6zpe00XXdaauXMap1bBME9mVMCJhmpZNpd5Acm0qa0UMUcM0bUJqiAAPXdOxaiYCoKgK\nsViMWq2GoImUqxUiqoYeCnBcn8D1CBQJhwauGFCulkASkWSJ/HKBaDhKIZPH81RUXaOtpZXAcnAj\nAq4QYHkusiDj+z7Veg3J0GnYFrqqIQcCousjiQGiHyAEkG5LYXkuihigiAK+D3ooguA5qL6xPnzh\nQTgcxjSbTFZaiJJmam2SaJtNMtlCvTnOuYsFotHrqdjThKLz3HrbHTT9H9OsFxGkCDFBRyCP3wgT\njnm8eqmDlDjH4uIC/X1hLrzQQzW9yM5RnflSPyW/TGs6TFdvGF8IgXCJpaVOfK3JR3/r3Rzceh0/\nfO4of/21Jxi6JkTWXCOkaoSDVipuwMTZJe66Q6c13kuv5uEj8KcP2Nz95ijnxk0e+pLGsivSIURZ\nwWMtX6RW1CjmTcQWqAcllIaOXDZI9hXRDI2JCw4feSDgmW+24AsFuocF4kaTt94V4g//cYpEKc3d\nB7PISORNk/rEKpEWF0mVSUbfSKlRZGBwiFR6jGzJZ2P3CgOddxB++SE+95HfRetMsHVrL30tAqXs\nHEN7W1koiCzpy3jNDiKRBc7lJGrOMvsHb2Lbrk2cOKHS3t5PLvcKTUaRIg2kRhU/cBFFaG/r5Pix\n00QjKS5cWKavd4hp5SnaYlv44K9/kisLR3j4Gz+io2sj++7sYnFxnLZwO0sxHcOXCHJ16r7E+NkL\nZGfWICLh2SWIQ662ystnn2duOkW1vILrVuns24mmaPjkWcssEFVD3PnGtzE7+Rx9QyOceOEKZy5f\noW/jVoY2tDJ1tkp5+TyXy+30NxRyusalUy8zO3+OpJ7CEMIkuroRT10gFlZpVGvIhk9gl7DqZYa7\nB2mJxbl44QjncmW6+nVeOXmWK6cnSY31kd8YobqU4+iRHHsGn2Jz/yjbN0k8faIb11QYaZGYLTew\n6iGiHWm8Rg3BT6MoBk2zRm61nYHh7/DvT8zy5FMKgWxS8lbo6wtjuAI1XyLwk7iiSdmeJJ2K0WxU\nWMnUiKR82oM+7HATRLACn3wtz5ZymEpEwm0GJGMhZM/EdWukWkdxHRdFjiAENcqVBjEvhV/xKS6n\n2HbNEJMXlhhs7eXohSmOXFQJUcVI+7iRJp4VRlIswqGffxjhFwNkHZvzpw4jSypBEOA4Dp63Prbm\n2h6i7KJI61Z/khqh0TAJHAsBsPJxVCmEaVo07DqqqhBWw5TWiliuQ1APUCMqVouN2iUhuKB4CrV8\nEyOUwG6W8BwfVQTZASxoiiaBIiF6Em7dJm+u0LRN7MDBkHV0BFzVw6nZpBNJRE0hk80QbW9FDmSC\npk6tUaGjo5OZmQUqlcq6daIkUPU8OnyZxXaIiDEUp4Dth9a/q1JB8uvgiBTUFhamj/Op9+3g+csO\n8VaVYlZGimQwzEGKykWuv34nFycfIb6hi0TGoiWhk9ArzLsx3Kk5lJLFdBQ29e7EDjl88V9e4Ld/\nI0TF8Dk/tcjVG95HQTvBUPcmGlaJmlXDCKr8cV+DYP5JdtzyDnbdeicvX5yjY89TTKk9TJ/Okb2S\nx4nqLNQG+NtvzLJhc5Z777b44d8ucv+bJQquyhc+U6Ox0oremqdc8NHkBn/64BmCYp5Ut8SH/hy+\n9MA2bjx4iaVchJQtc/2eOPs+fhPK1f/KP3ymwjvvD/HhDwhUcOiItvCRjxU4+ZMms6sGIxtUopU4\nLSMGJybyjHVtIrMcsLRc4M2//F0C4VGu2h5w/54tlK0yMSHChlt8fnLoJQr1Puq5JbxCGzvvnWOo\nZZDzZ97NIb5NmRrDlevI1SoMdRboTG3k7NxfsbFfISXEaZgNYi1d1HMethxwzXU3c+rEaWwlxMTq\nMs1chTPHT+FJ8NZfAsXezA+//RxCREcSi1w6I6GpEufsRaSQwjvvvIufPvEkjz3yVYx4EivhkTYM\n4kaYS+dWiLboVOxlVleW0A0JsxmQas9Tr3rEpQg7hoYYGhnF8nS09rvZMrqNJ57/PRTFYHP3Zu64\n7nYupE9QrQzRNbKLXWO7ePb4d8kVa5w/fx7XqdPV2UZH3x50YyOVwgm6+6PccO12itUM0xOTNOo5\n5BabwIzy7nvfw0Nf+y6Xl0u0pkNE4iYD7RkGOzNcGV/g7x8vYgonaUt0sDpbJt3eRiZwcUJFulpT\nNJoBmcoCYUXDL4skEz7LK1N89uEzLL/YRbM6zY5te+gdm2Mx77BUSiKWyrR2yFi+jyRJNC2XRlVG\ncEw0K43X4aM7GroQYXl5FUOPsdgoE3E1jFAL6cgcYbNGcbmCaV3G9gfp6WlBU2oklYBtA2kM8TRR\nWaZkbqQ9PI51UuGj94f5l6+W8It7ec/1axx+6iwHBs+zcdN+ZCX8c+PbL4S64NOffvABXvdufS2p\n4fXABkmSkBQZWVGQZBlZEPFcj8DzkEUJZIFquYisykiiQLolTblSxmxYBJaPokpIhkKo1UBSwHNd\nfCfA98DQDRRZwfc8HMsFMUCURZBEZFVD18P4votpmSCAFgqt+9W6Prbn0dnWQa1co1Qss3HnGJqm\nMj0xTWC5BGKAoijUag1kWQRBQBAFNFWlgY9kRfCsEiVJI6yJBBWFllGd5KCOZvQweeZVNrdU6fa3\n8vJihr62FIITpovz/MrmNN1dI8iFVaq185SmXUQlSiIyy+6B64g4Y1wsL1CdO0RhWeE3P3gTX/xG\nFy8+N8nV+3Zim3OIpTDpAY9uK0/fjjEWF1b41uG/4uNveAvp5ROEFxsstF0hHr+aR77yjxx5okp9\nySFhtJFsS5LenMZQ+5h7tcae7XmiySj/9Jdhfu2DvXz8g3m+9x8Rop1LNJ0WlKBAw4Otex0+8mGB\na29ROPyEw2zeoVqX2b2pl4jbgdYuo2q7Odj/PtbmTjOVz1Jt+jx7zOEnTzSYXDE4ebiAKsRIGAJt\n0jDfnT5BsjfN3tF9/OPffYyunlN4tRX+/C8/w4YtVyhUurD1i3zn6w7DI29jcGuSTGEKs5BGcOs4\n8hJHTyyyXFkhm/GR/AiRiEGoo8H1u9qpNJdZmE0TiqyRWZ3CUyTa2tuw1lRMTeeafQd49PFHUSMq\nwxsGGB7oY25ykQc/+yUee+zbHDt6kqsOXE+itZP2AYWVlUVkQrxh/ybiUZGF+cuIiNz35g9RLJq0\ntXXytrfdTrGawZfq9A10IisSV+3bhe8LCKJEb2cvoiBy7TUHSKTjvHz2FV48dYr2zm6+8tVvU6tm\nSaVDJGIhbKeCoUNPzwByEOGJF79LV+8gA30Rps5OM352HFSTQOvjysRRmnadWDTE9l3bcB04cuQc\nuhphbOM2dlx9M+WixcKVl1CUeaRIDCEawbKPk788T+eWJPOrGi3pTuyGAp5GuWJRq/rEEjHsbA89\niTqjrSaNqQJf+ps/xKmtcO5EHid6kM7NFnJ3K3hpnEaEwHFIkqdZEshlV5mbnaVScVAUBcdx8YG2\nzja0SBin6bA8u0DgB4T0MNnVPI1Kk10bTTSqTFyAnAlBoo26VMRuaGh+lF9913v46Mc/Qzzh8jef\n+zJ1N0qtAbe+dYW9Ax187P3vJiTPUpRlfu/jJ1hZOEJYHuYrX/83PvCBD/33URcEgOcHeH6AKEso\nmoqkyCCAF/hIgoAsSqjSfy28gyCgWaqAC/FIhFA0hKRKiBLoqowsge05SBLrlaQgoIUMREWm6Zg/\nex/bcggC0DWDUDhKEEg4poOiKyiaApKIaqivfSboeggxEMiuZtcdxARw3XVD33RrClfwMC2XcqWC\ntE63YjaddVtF10MNS5j1EqalIxsuUlPCo06l0UVSU6msZkg0TG7bNcDDX32adEcPjmkRDuUoGb0E\nOIS8TvbeeCN37t7O9bvfgeSbZGyZyWKZgptlrZJHCYt0jjiw6POm3giBUCeVMFiwLEb3xkmJSdwW\nl770EKcXLnHDxiFWzuaZb+hUsifpmxX5zG/fxvePX6avr4tkIsbgQJy9e3oY6vbILM0jy22o8Th/\n9JEGrrTGdTsu8sh/CGhJk1rTwLaXaVYjXH8A/s+3FG58S0DnVpOnH02xcUeRvVtuZ+euN7FjRy8d\nUg92/hCnjswwszBNblEmYiQIbBWjs43JCZFyKU332HbEUIS1yiXU8GY8z0MJDN75wW20D2T55O9H\neetNv05/yy9x4myD505lGNm3l0MvTTK7tohpNohoYeYuL3Ht3j/l1QVYqzrE5DhxPJLJOAXRJFOE\nfF2wi3MAACAASURBVOUn2PVZAsXAkhx0T2CwI0S2uohjB4RiMTZt2cDQaB+5wgqZwhId/REgwxsP\n3sCW/d2cnXyeS1OvIIgW8ZTAysoaE2cnGOjq4d577qTWrBGLttAST7N14yjTsxP4VGnv0ti8rYd4\nIoyqhHBsl1QqxujQRkJGnFRbF69cGCdXrnDjTW/gJ0/8AEk06exsI/B8ypUC7e0tKIpCJKTRN9BK\nYXWS1bVLrBVr9O/qIjYYJ9G+id07tzPQPUB/9zCVqsMzz7zAzOwSBw/eyr5rDmDEDVw7xJUrJxl/\ndRnPjhEoa1hWmemLSfbu/RBL83swS0OMn7HIZWwkOYYsqXT3ivQnDBR/mlvuabJxX5OebaP8xZcf\n4lRujt7b2gh1rHDh5QqNtVkyF8+wMLHM7HKOi6s5zHoVVRBoTaXp7+2kp7ufnoF+oqkoDa+K3/TJ\nLizTrPu0JVIEloPiKkTEGGtTJU48LbJcSJPeEUHsDKi7Itt3hLn/3RZd4RpeLcE1uz7GL995H0/9\nx/O0DhTIZGKcWJzjx6eOs39virjaS3YVWtVNvHrpcUrF5Z8b334hKtkHH3zwAUEESVpvVq2vAEEA\nUVxPOhBEEYIAz3VxXfe168I67wmIqgyyhOv5SKyDpGv5CCJ4vocW1gjpBkIggCPQqJrYDRPf9xEE\nYd0TQRTwg3W9q6YZEPg0qjV810ZTVZq1Jq7tIQsifgC+42OoOrZroxgKptXAcz082wFJQNN1JEUm\nEECURUKGjgCU8xZ33t3LSEs7E+MZjHiTagMifQJv2LedxtJ5NnTled//+DS/8fEnGbg1wpZwg21d\ncV6sKbjLdZK1k2RKV1E6+hDb3jBGzW4nJxgMeCW0vq0szo5DYZItoxKncwqileFcLs/YVRvw3Tl+\n+b53cHSpzpa+OMm2HSQy7YgSTE+n6KseJbF9gD/62tN84bsrpBMbmZ5aJBGxue9NtxNS+8iXG8zP\n5UEqcv50hWo2Rb3qU2vEiKQlPLmEKLvoYgyzKvFX/2JgmlUaVsDCXJQXjoq87c6b2NSxHcuvsbaY\nITWyCTE2ymLlizSEBHbLEOG+Ai1BhEefXUERkrSl25lYnEHVZIxQHEHJ4Fey3HXrJj7/uQLf++4U\nd9xygN/+ne9wcfkkckSjVoliq5c5eWKObTsT1Eo1cCxq5SIf+fB3ePbM5xDtQfTAp1bO09LdwZWF\nGq+8cJ6WLo+TpxZx5RB6rIRcT7BhOEbY2EYsOYChaNhOnVItR61aopTNs2XzZtpbbZ588hkUw2Ng\nYwpRlBADjdxigexSHavso4fTTMysULdZ7z8IFumkgSCF6WhPUa1lcCyXV09P4JgixWIFWYa9uw6w\nuLxEOBFhx649mI6P54IqGAxv7GJhfoHlpQLv/h8foLNjgCuXJzl75iVSqRC67OJ5HjOzGapuBpcA\nQ+tl80gvldIlRjdsZvee/Zx6+WXuuuNeBvo3cfnyDOFInGphgWOnXkLuqNLRnyCudmCXm2wc3cPZ\nmUkefWKSRGcGs+m+lgAi4Lgu3b2ttPS6VIUml5aKTK7orNVdbCAc6qdaErEvZ7j/zgyjA/28+12/\nz1Xba8ycK1PKlFF0HT0WRotEQFYQVQ3dCGG7TZrNMrnFHJ7j0pIKU6tWqFdN4kYC1/TIlzRcOULr\nmIUUieC6Yaw1mc09Ku6aiV0uYPkCjVqWSDTJdx9/hk1jNu99R4GvP5HiBy/pvOPgGt36MM+8cJGa\nrfPvTx9ibS7Db3z4I/99KlkATdNQFAXXdbEsiyBY325rmobjOFjNJqZp4jgOQrAuy0IUEJAJfBHH\nWo+WqdcaVCsNLNNFEhUMTcVteCiOihEY1LN17KpDWI4gSTJ4AbquEY6GsQOPutlEEAQUaZ2acJsW\nguuvG3j7AYHtYtUbeJ6Hpqh4nof4GkYHQUC9XscPAkKhEJFYdD29QVVIplOIsowsywQYdEZU/u3P\n97FtNEa2qROOQ6TRxAxMouIim7ZGyNdiOH6C7FSDq7oj7NvQx8qUST1qUVqZ5/zMZS6vCHzroR9T\nkFzaWw4ysn07XUaYntZuKkKAag5x9GydRsymXYgRiOO8Zf9uxqd30zOzQi3rMnvi38mbDzOxFiFd\nO0HP/p189sIwn/2ejGgpBLU5/KjJ/o0dlLMZuoeGePHMMpLqYBYyrF3SqNbXiMUsRDFKuVxA9MGt\nGQT1BqpkY7UWEXWRVFLh9I+qPPBnRWLO9RTKNbR6Er28QmXtJSTDYfTqCmfOxVjMXuDc49BoFolE\ndCLRAFHyqeagYeYREmXiEYtULEotn8a2be6+9xaGdwmcnrjIak0nomUxq3mwda65ejflwgqSm8D3\nBHpHYkTCce667m4U5tFCNRKpQYyIh9BwmS3KHHoBbEJkqyaOoqBoq/QmOoloo3gND7Nm4rkuZrlK\no1ClXLQpZCtUMk0un83TEu9m59j1BE2ZK68s8Wvv+g2u3n0r1UDi8EsTiPoAW3beQDY/R7NZZmlq\nlQ1De4gZnaTCPbRGukiH0jRKJvVSjVIuT71ZZf81B2g0HWbmlpmbXWVuNoMoGCzNL+J7Cm0t/eRy\nJZ595jCHnn6Bnz5+jKeeeIbkQD+xyAgtYZnh6CBvvvFeJDfD1776MMlQjGa1yfDIRu65515ESSAW\nNUgkYhSqZUqVCpLgY2gGi0smjbpMd0c/mUKJ01cukOpOoEUKOA0P1wrRaJYJhUJMX7JokaN0hFJU\nJrZi1Pcy3NlJTFPJTK8g1sK0jma4500KOmscefEbXLs1gpxfpb+1Gzkdo2g3yNTK1Gybpu1gOw6G\nYZBIJGhJx+gf6CDZEsf0fAYGOmk261iOiaNW8ZUqYbUVZ2WIlQs2iZZF+neuYoWL5OoGXX1x/vDB\nP+Pdn/hfqF0BG4c9dg6FuXn7EiuTLtVyjYpd5O+f/AJfOfYJzoy/Sr6y9nNj2y9E44sABEFCkiQ8\nz/svl3zfR0QgeC3D6/XrgrCelSULOl7gErgetXIF27HBF1F0HUnSEDBRVJnADqhkqzRLJoqo4Xsi\nkWgIy7KQNRVVVfGldV7Y810a9TqarCAEAbKoEHg+wWtSLM8P8DyPpu0SUjU0TaNer6MYCmHDwAtc\nTHO9SlbDBoEnkC8ViYXDlMtlEtE4jz41wcd+ZZD//Xe3cNO7nqFeNlHEFuLaPOm+LSwur9HdOYGq\nLqEU2kkBCb2PSyeOcs+Nezh35QojO1ROZ27BWPwJ85fn+eMP3Idpt+AIKrbpIMkBM3Yv5bkTtOzY\nRWfKo1vto98porz8EwZTEi/MTlCMuGS8JBWzyu75K3zHcPmTz3qgNBBiHoWaw7WpbQxs7aBmgxFx\nmBu/iCyDIURJd4g0XQ2nYeEIc4SUGEKjiR6SCQpx3GCVvv44UyfK/OUXfH75HSMkhybpvOpT/J8/\nuA55c4TIYBe6uYayGuH9fx/gUIBXJTxFZ2GpRNfOYc6/2KCnN8dtNwnE2UxmeY7omIPsJ1heKXP1\nlgQfem+KLmOI0Q0CRWcc2YsRUhtItf1E4vMYBGjhKOemp/nwB/+IRnmNpfEVRje4LM0pZEtlVhbD\npFtWyWS6yFV12tIxLH8OUegFeRZDHODs2fN885tfp7W9hZmZGXqGk2iKxt7dm7nxhlv5zr99A12H\nbGaZn/6owobBAarZOWStwG13XMP4v51iJbvKT596jk2bhylllqnlXEJSKzMrj3D7wesx1Hbya3li\nkRRtbT0MDQ1y5vzLfP/HP+D222/nyJEjFAolbMtZH3QRBGRVRJINotEw3/yPf0KRZEprTVTBILNc\n56ffP0E1W+LWmw+gaDLdAyPcnWzjX/7m87x0ZJl4x2bMZkBbWyvVyipTU2tsHOvAFpuUigFzDz9J\ne0svgWYxX68xMNyL1biAaMfYeCDF2WcTeJaGqFlokTCS7BON+2B2k5k/jJ1fxUh1sXg2R7x1mESX\nz8xqkexUhK/9s4kVhDk2foTFV/swUwNkVZ+QGOAIAeFoGFlW1i1IXytkPEcgJCpUG02UcIj23jai\niRTVyytoYZWoLGNXk0y/XKF9wxJdXV34Yivf+r6E74lEvWWmhE+zSoZE/wgrc+dZvbyPZ39ygt19\nsK37MovVEC+cG6L/qj/jQEznxsQ8n3z+1M8Nb78wdIH4WnChJK4HIAqA53o4josorBfc4mt2gbAO\nsgQBkiLjeTau56LI67xuPBHFFwUs30FWobW9DcexyeVzaIqK8FrCQSCISJKIqmsIEvi4+N56U00Q\nAkxz/RgCHD8ATV7XyYrrFowhTUcURGzHQtZkXM/FrFsYSohmo4HjOqiGDoKAWa2v+yk0bboSBotN\nFzGscNMtI9xy/RD/8fAqQ/uivPPeYRL+NurmAvHQNr72r4e49s7NbIiLzE7I/M3/Psztbx/EqVdJ\naDW2X3cnKy/8iP7+AGO+Tny6xExc4sjhr3P3/mv55y9dYeygT6UWRYxOEYqmafviywxtHiJjdXMy\ncBivqbQ0+4jlDnHnJ97Cnz0jceXIq8iOgaLLJIIQF5aKlGwP1Wjnb//q3/DqNslIklBkhKXKDI7j\noftpnEgT2bbA0bECh66WGA1fJX+ll3/+lMjEvMbSis93v6Vy991RRjck0YPf4pWSxRe+cpxziz+i\n7oiMDbTgihbi/jx9WgeK4pCMGtxwzzK//5uddMfewr88/Cp9g3m8QKJXuYWNQzme/fEPaO1UyVVv\nYWpqig39aRp2lWrOoK1zkogY58q5CTp7Ynzkg1+mVLF48M//kOFNfZTyPjYVRKkTR5sjP+dhCyax\nmEB/n8rCYpG2SD/tke1864eP07TqbNg0TLRNxzBUrjlwLbcdvItUSyfff+Sb3HLXPto6Uxw7dIHr\nrh3hwDU7eeibX2cpu0xmKY8oCNjNOjMXxwlHQ8xNLpMvlVnNTuMEDs8+8zyXLs6ytlYgX17DFQJM\nxyOajHPs2FFya3kCx0WVBCJhjXR7DFVVqJSrhCIykXhAoVAlnUyhKwZLC7MszZSo1tdYXivTu3kD\nNV9gZXGNPRsSuF6Bzr4BKmadhaVJitlVfBPqNSAIMTO+yJ8+eA+Hn36SWKqdji6F0vIao8MjXHdw\nlBevPEWLOEp7m8jOqzYyPTeHokqkUlHOZapI0W7iXe0EkkSlaOI5kIq1ghXmvffnOXBriEefHiHa\nM0QxsKmpLnZFICSA79gk4lFsy8T1/HUZlqJQLtewmlUCTWVw4xZMO8B2BMKJCIEqorsuvtAk0SHS\nNSpgeRKBHMZyIzgCOLaBqwwT0XTqK8sYToi3vvsKb3u7yNceh4FdKmfmmjx2SGDD1nH2jF3kjW9c\n4AePNPnwb37yv48L16c//ekH4PUqdZ1r9X0fz/cJAgj89ZRaURTXJ7BkGUVRCAQB17GRVYF0OsKm\nsRFKpRyu72A2LFJtKbSQRDKdxnUdKpUa8XgE3/exbAdZkgnwKderNMwahmGsp3IGHoEPmh5CkhUc\n18EXob2nEyMSAQFM08JtOkgCBEKAbCiAgBSIeJZLKKQTjURwPQ89ZJBMp6jWq7S3tLBUyiGIaWy5\nwWqtynvvuw67fIFvfy/L+z+4Ga1YQ5RlvvqlS5y9tMrG628kaolE9DUefmwNqU1g+5jC8nyDTXtv\nZc19lWJVZnJpgFD1ONUukXQsz2OvJjnQ1sv8rExvXwPfbBAaCFAKMl2t/by0eAYj2k2p3qSQfYkH\nPvv7vO1jp2nOT1Ka8/FDZaopD6Fu4/khsvOrLK5OYAUJ1HgN0zSRxSp+UwRdQPJkpIiIYqaR5CKO\nAJVCHamlxvSMRGt6FVSJlYk8zRqMXVvmb//OZPjGJe7a8yhdGzV+9K0wf/H2Ft5w+zIZQ8Q53seu\nfQs4mTA33mYxccni/GNFDr90lBdmVd62NUXTFdk80sXklWOszUXo3ubihfby0vFD9A4lKDVmMXSV\ndEQjv1CgNdbJ1Tds4/iLZ1FDLfzJg1+jv38bklSgXKpScfNYUpx9IxFsQaNerLJpaJSV4nn2brmD\n535ygmOn59myY5BoMkrTrbJz1zbyuSJGJMH84iqDgy1EkilKZZOZy2voeo2RoWvIFU1eunAROxOg\nKTJRQ2Hbjo1Mz84zsnmY7oFWEp06i0tLGHqUHbu2o4dEitUSmbUCRiyGZqjEIwnCqk7g2OiaxPBo\nL9nyIr5XYcOGTQwMDbKwOI3neuzdtY/hgS5Ws9PUCbNx8wZaUxJ+Lcyrpy4yPn6UrtYuBnt6EdQU\ngh7mx499B8+q0taSZmp6ikPHnuPqsQS/cv9H2HHtGIGs8xef/CKtssV3vvsoVWmAuYUKV28bYNvY\nXhYXLEJxkUx2meVpHblRRvRieJKDHDKo+EvEWgsUCjlaO1zKjXb+9asCObtB2nQp1AuIekCPHkfx\nBBq1KuGwhuc7GKEQICCKMrl8AateQjIitHb1Mzu/QiySQjE0HMFDcGLUbIlYt4Eeb8cOyvhWmMA1\n8U3Q1AJFs0BXtMozD/8Jsv9jqHfieQ3+8pspTAuOvTjErqEh5p86zVe+Mc7nH1rECKL81m9+4r8P\nyD744IMPvB5oKAgimqajaTogvNbkWl+vA60oiPie/9oWft3su16zqdWbBIICSAiqjO3YWEqN4ZEB\nZibnESQFPRTBcV0kQUAPKxjRCK7nI3oy9WwN0faR3XUeRQjrIK/HdoOH37BRnIBGuYbX9AmFdERZ\nwvN8PNMD2yNw19MXXc9DUXUESUZWVWzbwTA0yqUciVQnVmON3FKC0X0JvnN8gje8axcXn7fYuzPP\nddNx3P3387/++MsEjkL/FhmaOZJdGo++uoQ1ucLdb97EpexZtvVtIUILU1OXMO0aXbvGGBh6E5ZY\n4St/c5GPvW0nZmI77tJhyqE2hjSF0CmPrFLizJKONKxQcS5z834Y3flPnHopxw8e+jGhQZFMyaNb\njZL3dHSpRHtPO7Ji0NJiUM7XcW1o1B1UzcD3A/Sohi4ZKOEAK4DA9TCiOtgigmhSLDuYYhzNbdDa\ns5HjLzbo3lri7XcsMLC5xtU72hnszJIa2IfaNUtEqDM/aXJ5sh1Jz7Fvq8yf/n6TyZUk49UqcqHG\nL98eI1Mv0NG1FauS4dLMGqO9cRr2buKWw4EbBrj80isYRhdJTWTySgG5S8YS5jk1c4YXji2hpsu0\nd4VAkLGEMhfOKPSOWfSFNCxRZWV2mY7O/RT9i9yxYRfPzHssjZ9lsLuNcCJOPGghoXby0yePMDc9\nx/z5cS6tZTBViRYjjG/UyRQqnD31MjPZPF2RdizRYnB0A+95/wfwfInsyhQ3HNhKX6vOYmkGXRXQ\ndZlyuUIi2YrrgKLLBL5FqVIgFFJxXRPTaqJoIW67/R5sXKq2TU9HN4VciWi8HV+VkJQK1+7sZGNK\nRGkbYfPWHrbs3MEzh48wPn6Oulmjf2SQ6TWRrbv28+KJF8GViUXbWFjKMj05S1gzaNu4i3PjK2DF\n2LN1iOdPfIXDZ58l2TPM0lwZM7PG4KarOH3hKIXiHOVMQKFoIscbeLaF1TTxTYm56Qlaky2IfpKQ\nEcZzy8ysSHQmA0Jh8NwMbtNGCbooLTiInk1LaxotGsIOfOqmRzgao5BfxTKLCL5LdqlKX2sHIdWk\n0cjQbIp4fhU5YRAJSURbI+himdVVuP+dNfYl4JXpIo2YRbicpCwGHDk0zsWJOiuxm7kw3Ymq9qBb\n1/LQP/wexw4/zguXFuka7uWet0i8cniNT/zOz2d1+AvR+AqCANd18f110HydEpCkdZ72P9/n+/46\nb+p5P7vv9WWaNrZtE03EkSSJLVu20N/WiewGKIJIxDBwLRtFlGjWLRq2hWXbSKpMKGKghVUCIaBi\nNinUGtSyBTRfJKKEMLQQDduiZDWoe/b/X22/ziELAj4BXuDj+ut632q1TK1WI5fL4TgWtVqNRCJF\nqZgjGolgN7OMn6sSTpo89uhF3vXhvThCkkPT51HXslx7zR5cqYq1ElD1XRKdOrZqESib+dGpMsOt\ng0wsTxPWrqZgDJGru4iyRLL/Zr7zDxLdY2uk2gbZc1UXa0KZqK6g6w3ElE44FrB1fxQElcEeuHX7\n+7ly8Q/43V//FbRhkbzWQbxNJhqSaA3JeLZOs2nR1taGphoAWJa/vqMIPBRRQgygVq7g2Q6Oab2m\nZ1yP+WlWRKTAQJIsXM/Al+fpG5RobwlzatLmvW9P86nfy3DsxYBv/+gI//DXPk//1OCVMzabDxxk\naW2AL/6lycABn2ifT6JN4eDbQyzVsoSVAM/dSEx1sYsN2rpHMCtFziyc49TJc7SmOnGdGs2aR7lc\np1Er0ywZJGMtPPbj40SUbWg6JBI6a7MOTs1CckI06jq+a6BJnSBYSEEMXxRIJOPcdHAL4VSZSELk\n+cPHePrQjynbq0wtX6EWOEiex4WXzzI9PYsRitO0AzxBxrF9QGSwc4CxoRE826GtJU00meTc+CQn\nz16ip32YiJFi88bt9HT0klnOIQgCiXicaDTKQG8/tm1jOTZayCAQAwqlPAszMxgICL7H0MAgHZ29\nCLJBruzw08OncfQkm0aGSccTvHj0BF7DIh2P0ZFOoMgy5cIKp148zOmTx+jv7WZs82YUzaCjp49K\n0yGXya//5iSRxZUc584vkss77Nyxh2hEperaPPn9x5i7VCGTgYrloCugljrx3CaC6JHJTxKOBrie\nQKnoAiFKZQXHrFL3WpCFFmw5jhbroNlYQOnIIERkHMmnWCuhygpRXSO7uEw9XyGEjluP4lTDnD42\nj11VcBoNKsUCHW0R1EAgFutA1isIzTrvuquD+25KsPWGCmu+T7c/hlwXwbA5Mt5kslYj1GJgmz28\n/xaJj773RerLH8UpP4Mo+cjdC4yfr2DI+s+Nb78wlezrr33fX1cTWBaO4/xsOOE/r//7XBAAogCS\ngCiLqLpOOB5jam4GrV4lJCusrmSplpqYdhNVUelsa6PmNPECH1kUsJtNAt9FkAS0eJhIawK7VKde\nqWI2miQTKcKJOIIiIuoy1D38YD3/S9FUjGgYWVVAlhAVGTFYH2wQZYlkKoGu6yiSjGu6RIyARslA\nCjXJ5tblZ1v29HHy/JOM7tjCQlVg8sVvsO+aLXzvR+Nsv2qQrk06phPw5ENTPPKP93L2+DzZTIF7\nr1ol0XU1j75wknAwxcDWbeQuP8bbB0/zVtfAzz1C2xuu5vz0Mjpr+KbO0pLO5rZelp0VhC6Re3d9\ngMVj42y449O8enQn+/f8LcdPPsu1V7dw6vwKUd2l0hDWm4vVBpqm09nZiSxBrVZFkkWa9SaOZf9M\nYSEKYFkOsiwhSxIioGoRNC1MOKHQ1bEbRbxCOtTHk990iUtj/NJ7PV46XODvvvBlfuvXH2b87FmO\nPDaOadSozru4lSzhlgTveVfA/W9yuXGvSUPQsIpdhNr7sMurRDttLpxfoFhs5eirx8nNm2wYEmg2\nAiQbKvUqgSGR0NrI58u0DrZTd6fo6FSZPjvL1pEUBw4YSPUk+/Z1ki83qGUtULJ0Rnq447r/yYWL\nBVazrzIxJ3FuYpLcXI5SsUr/yE7++IHPsrwwR3bqHJ3d7XSk2nj58jiDoxsJ62FcISCsKLREWnnl\n5GkW5maIRsM889Pj5Eo19HArrYke7nzjfZQLFi++eJqVxRXMZpX21gTVcpZt2/YQi0VpOjY1y0SQ\nFeZmp7nr4I1ct+dqhvuGOfXSWQ799DlQdHwPCvkSDTNg7/a9OKbL7u276evtYmFhjj27d1Gp1hBF\nl7W1JUZHRtB0jSOHj5IvFOnp6SXwfeZnZpicvMQzh58hUEJctf82WltHmJ+dYXHhPKISorwMvpeg\na6TJ6izccUsn73z3JE89Z1LLaYSMEBs2d1MqmGghj6a/gCCG8Ip1ook0QTBF3TIoFCpEoi4L2Qqh\nVgNfcqhXK8iBj4bEwuQMrhUgizqB4zO2PSASdTnzyizRWIx0qpvAUfD9ErnVKcrzITLZDOfGLX7w\neJ4jz4VJqi005CxeusC7rtN4/oUlbrvW4ffurfDkhV4alcO0x1aozMRp73W4506J+/b30dM9xnce\nWeFTn/zUfx/T7teXIKzH7L4OooIgrDto/SdFwevr/wZaEQEJAUXW1kdzTQu/YnLtfbeSzxfxhFnS\nPWks20VRNCrNOlEjTKVWxQ9AURQQREzTpFl2iIoCia5WfC+gXm0wP7OApItEE1Fcq4EoSvgE/+U5\n/vPzC6wPP4iSSBD4+N66vrbZNAmUAM83UeUQkihw8skJ9h4YJh6PM7+SJRTxWSLMpvgCu3Z2Mlds\n0lFzqEeb/M4HBrh61OfaB8Z4+lSZxeqzdEWmiPkC4dBGLo5/jVYnRuSYR39blJAuUGy+A9F9hJDQ\nhuy42G0GBc2gISsMjm1lavIUpUtPsLXxefYP/x27blIx2+ETv3oF24xSaRiEYhkEK4JpmszPz1Ov\nWQwO9OD7Ps2m+bNhD1VR8HwHVVUJAosAkBSZeMoiFDWZvJTnlrtvRCXO7e8RicRnOXCgm+X5lwma\nm7jt4CiHj36PI955PveZRxCQKc/WyC6WEU2R667q4bqdJo47T0KGbWOtPDqd5fzEl4hLe3j6ZI4f\n/fNHed+HTnLNxqu5MrVMPG2xmgkQdYvdV23j8JlXGe0uoEkyotxk004RPAjsMLnVJdKpKBNnFulK\nbSEmdqFRRLB9bt43RmtLJ6I7z8YNXSQHHJ58foIde7sorwT86i+9naM//QFuaZHOjlaWcmvYtSqa\nIbKcWSAp6ehhmeLaKgvza0iSxM49u5menmbzto3kcjkyCxlieojvf//7hEIRevu62LBxkPa2JJou\nMTutkUykefqZZ0i3pohFE681Wde1qZWqycSVS1y5cgUCiEgQ0VV6+0YJ3AZNp8GPH3+chmkS4NPe\n2cbY1h08/fSTXJy4ROALxLJlBvsGMDQV33VYmLqE67o4lk17R4LueDvjE+OsZQsM9nVTKpWQk0VF\ncQAAIABJREFURfCrFtH2Tjbt9hgebqe/4xK/9bkFnvgm3HCbwslnTeLxXlxTojXVTbWZwW4qWHWJ\nREhh8uRREn1QyqySahsgU6ox2N1GvVql3GjS09FJOpGkkMuDHCApElosRjSRpG4tIIo+Axva8D1o\n2hXUSBOrqYMsgZSnMB8jNVKkJRXDdJuo9RBhyeAzf51n4VCTLfE23n+XzpnD00RFj/u2/TatV/0B\nnakKsmCyeEWiu09h7YdFYtrPbxDzC0EX/L9WEPxXEHv9+D+fkyTlZ1t323JwbBtcj9zyKkYiihFL\nMTE9i2KE8ASRQBRoNptYloVnuvhugKwqhGIRfHEdJBPRONs3b0eNh4i2JGjv7CCaTCIHMoovongi\nXrBObQiCAH6AGIAsiMiihCLJ62DvQzQaA0BRZHgNlAs5i2RKwqx7KL4ETfjhl8+waXSEQrZBNNFO\nIeyRKcyxZ2sb+XqJRDxKMiryr99yWHtuianjHeweacc1Y1Ryl8jGCnjuOG5tmIFgE6UNG1goSdjX\n7iFfS1Otq8TieWzVJYhZVCUHJeSQiG2gor9ITS3x/Jd+DVcTuHg+xqd/I09MHyIqOyQiZWSrbV2S\npqqoisbQcB+FQoF4PE40GkZRFCRJWqdf1kfsCIXD65RKELB/3y9hhDtJ6t08/ZPnuHj2LGLY4fRJ\nhx89Nk8kejv10vWo/rtQlB2gRvjG9z7Dzj0jLE0uobdoiFGF626Ks3TJ4IVDEbKlNl441KRaaqdW\ncShLU+y4IUk02eTcK8fpaFmiVLIRRcAXiEQlwrE4oXCU/uEQrl1FU1LUqhZm06Il3UYlF8JvROnv\niaNIKvm1ZQw1oFkrkY4YNC2brlSCy+crLC0sEQunCYdHOHDNdSytnCO39jL9nQHxdAvhWBjE9T9U\nP2hSKK0Qjins2reDutlANiRmFubZ9v9R955RkqX1mefv+rhxw5uMSF9Z3le199A00DR2EMggYARI\nGmmkBc2MxIqRG82uzGh10EiakR1pW1oZQEJAQ2Nb3bTvquou7ysrKyt9ZGZ4c73bD1HVgCTO9tlP\n6P2SeSIj4kack+eJN/7v8/yeQwf5yIc+zIF9+9ESCpY14OrVq/R6HZaWFlhdXWbr9m2MjY1RLpd5\n4oknAIiCmNZ6A7NjoYoJVlfWub64TL3ZwrQtdu3bjibE7Jgc557bb0FXFD712U/T6vVptnts1Ju0\ne22+9OWvcP7iZRRFYWJigunpLZw+fRZd19m7azd79uxh3549HDq8H9/32blzO6Fvc+zoc5w+fZx+\n36SQHwVPxYovEMcxx58eoOkev/3zVT7/2TK9XoyeMiBQOHv8MhdPnWFpbp68kSNnSDQ3anzqb3+S\nhJCgmi0S+Q6lwn5E3yN0AtKJDObAZfbaLIImcPD2vWRKGUxvQKRZxPIIfVfAiRu4gc/83CqL8yv0\nrRqRqiPIEe95f4ov/v04t+/u0exW2YhWMbQVwgsZnnvhzTz9xAaFQhlhRub1Ew0OPvJT/JsPpvjG\nc3dw8oiN1ff4o//nAlfnA3TltYvs99y4QBS/U/f/pXHBd/4dQGAYXh2mxhRFBSFmamqK8fFxnn/u\nZYrVEdrdDpqmISFgd/vIooSgyRiFAtlyHkmWkSSFdr1Nq96AREwYhASORxyC53gosoQoy7iWhyR9\n67VKoogoghAP23Qdbxiq0FNJwijE8zzCIIAgIvQ0XL+HpmoMOn00OaK7qTE+FbPvwA78gU5fOIto\naxTTCZ5/qc7r3riVklzn7z/X523v6DLhzlDuPEn60C3Ysci5EyaFXIzsBnT6Md2kT27MYy43xsH8\ne/mxX/ht3nCfQCLIsmSGBGqGKN0lM3aYtdozZLomt8zcQu72H6fbqPHlR58kOypjmz2IEsSRgpaS\nGZgDHNdhZstWPNe7YaPpIQjSq/XpURyh6zqyphIRk0gm6NsmA9MiNtfwpAy6LCFzH2dOatQ7DoRV\nNLlIIqGTT88wWknwtoce4vUPVDjz8hmCvEWvHTNWiJkpDliox/hBhlPnZigWRWw5Qg50du2okvGW\neds7bmVxfY4XXvQ5uFej3w8xEvDKiRX0ksL2bdDrxLRNgzCSCH2PfDLB2pzFtu1Zeh2LvmXjxTG+\nBclSyFS+QqBWeP6p43z2S2eoLTaYmjzAtuntRKKHkVPZvXOG0DI5fqlG3exRKRUIpWEcNnAspIRC\nYLv4fghxTBDEHHnxKK1ehwuz5xg4fULfJ4oiGu02sqLgeB7JRBLbslm8vsj80hqqqpCQFQLHx2xZ\nQ+ujBPlsms1Wi4Rh8Na3vZ0tU1Pce89duJ4HksTF2Vne9Ma3c8/d9yGIDqIQ09zsoioKu7aOs3v7\nDrbObCcMIrr9ARNTM1QnJymPjtNuNdmzay+hF5HNZsjn0iwtzKNKCRbm10kV0lQqbey2TN92ub6Y\nYG2limmqhEEGu6/RqjcpFdNYZg9FkDAHXYKggeDFnH4lYHUpJJBA0jQUrUNAA4kCnhcgyDLIEAoe\nRkobtkjHAnFigG1FpFIiqgpm36dQCOk1QBM10lmLH/mRiHd/f5cdU33MATz9ks6knuA/f7CKtdQn\nSt2HqHj8xh/+Pl96foLqZIsXv3GBS7MDzi8H/O0XVJ650iTKlBnYbWbPOfzH//Cv6ODr25cgCEiS\n9M/E9ubX0e/yoFfvH3g+7UaLZEKn12zz2b//PMlUEtu2Ga+OIsXQa7ZJKCqOH6Amk4i6Ste1aZt9\nJE1FkARM02bQ66JJIkldJ5kwECQFL4px/eFu7WZrQhiGeK6L5wxPUB3LxnMDojCm1+sRBAGtVpMo\n9Mlk0oCI60IQDNAkCE2R2HV54vMXqdeWh4ccisH8ZsCuPRbmQsh6vcX0mA+Sw9nr41TlI3jnBpRX\nk2hpgZQXIFlJYtliZSDRDDtkbvsw0akcXjLkv/1v97Dt1v8dJzOK7GfxOhKjagUh3uCpo33sYhIh\nuQM/EWPYx/m5Hxrntz/5Bt7+A3eTHa1y+C4D2zZJp9OoqsrKygqO47C+vo4oikRRRBAEqAmNMGDI\niBBFjHSKTCbD4soVmpsqEhpB2GVjfYWTZ16mYV5EV7dyaP/r8cIltDiN071GNSXDIGIqlefARJWg\nm+dND97HQ3fuJmr2kf0CWqKHJ++HMCCQfDKJDnJ8ghefXUEXO+TLd1IolVldGTBSyXJ9YQXDqCBr\n0O40kaQQ3bAw9ArdTp++tYwsS6zWZtHUFLZdxHGHmL1ICjEHAdc3ThHiImk648UxjNQAWzuDr7W4\nNL/GSg1K5dvIlycYm9iCkcqgKAqddpPA81haqtFud6mUcoxWygSeg6KovPTyUTxC1IyGIqu0exZ6\nwqBYGkEQVRBVzp65xKXL82TzOWzbxtCTFNJ59uzaxevuvZ+r1+Z55ZVjQEQmlyZXyFOuVGn3bNbq\nXfKVSV73uteR0JJsmZrm7rvu4P5772P79DZ6rTb7tm+hlDU4/fJRcukM99x9P6+cOsPXnnyO2cU1\niuVRGvUOpWKV6bEJquU8O3bMcH1+lU4nxhJN0kGZqJcgQEZMlVDSOaZmqqT0HYgiFMoqnV6NbCqN\nFBbJKTPkkiUE32Ch3mekkqPnSnjhgEJSo90dxwsj1GQaSU2gp1PoKQ2EECH0SKkyUaCRzRoEoUno\nCVTLad7wNoFCRsWqqeyfVnjnG7KsXczR7aS4dA7as2t87F1FtuzaSn/M4Oip/5sP/+IPsNBMkR9Z\npR6DtP8rTG7zqa37rEQhK/Mpxs11PvDmg3R66mvWtO8pkRUE4VUHwT8/3Ir/5V2tMNxFCjecBhGQ\nHquglgv4SQ0lpxPJAmbTYv3aGrEZUSyWiVQJISGSzaYwNJnYtUgZGlE0NDknFBVvw0N0ZSIvwvJN\nRALkMAYzwPfd4ZzK94nC8EZaLUbTdGRZRU/IyIqEgow7cJFjBS1hYAY2YtRFFAVc08AURbx0RKoo\nYS3D1/42QBiBpFRBxmbdbvHwBw1Wz9URrR7T6PzWnxylFam0/Q7HnnuKsrsfreCTEEXEQGUtXsKK\nfKLMDmL5HO3+HLdyjT36k+QEC03Xcd0LZCu7uXx8ls//rowolsh+8xmUP/4E5eOf48M/kaNgXeXo\nPxxhZCIHXYXpbTsQZQVBkujbPUy7TzKVICJCUEVkVSH0IzRVw+m7ZFNpCvkMttMln6ngO2vYahoj\nlSGzLYUl+KTSVTKlAtVciqqxj645h+02GWxuEHp1wuwYXzwxy669WW65dxdudBerYcCIluTCGZFk\nL2KQTFGKdJRQZSRRpRe2Kc38LDPpDEpRx0jvQBaWaA9GiPOL+I2Y5qZMIEfYtoAouhSTabwwQXJP\niuXBNB1/nTjbw64F9PUBkSfhhBEJNY9JiVypiDKlUSlnOPlNk/XWJsZYnRXzGk+dWWL2ygVKiRSZ\nVI50nGFrcSc7tt7O/Qcf5KHXv4dYNRh4FrFs8tDDB0glYtzWgIJSQhbzTE+WSaQsxsezTIxOcWD/\nrRy6exczBxXC0AcRAini8N0H2L57HMvtIGoGthuzutal1wm5cO4iZ06+yAvPfol6bYnAjsCyyOox\nttlHS4whqCWurS1SKqdJVnbRi1Ik8lUyxSLrtRW6jRUMySGv+UxtqYBiYjo1uoMmK0sbLM1t0m8O\nSMQuRiDipnKkZzLMTI9RMfIoSov1zgad7itoCZFmz0XVC3hRSLLQYu/dOqliGnVURZQCrtcuk44M\nYifJIHTI52RSyTKdehNNDFDDAMGRwNVIaGlCOQJXIq83sLsym40uV45u0rccPviTOlsfhIbp8duf\nbPDrvybz47804Mh1hTc9PMqPf+yzbK6aHEgHfOzHINnS+es//GEee1TkNz4Wcms1ZNdkg0/8p/N8\n6L5l3vdvEsRbpvnUk2sIUfjPtei7rO+pg6//X0uIIfrWwZhkaCRTOmEYYtk2QhARBRGSEOM6Hl3f\nR9MUYjFGSsqIgGs7WAMTTUmgKwkcWcO1bWRZptPpkEqnifwAwzAYdHsEnk8kgON6JHQNWZbp900A\nbM8lEiChyKiqTDKZpLc+zDlLgki/M8AXNITYRZdiPCtCS0C/38XIylw+c5zBtVsR9TJTIwLSxmU+\n+jF47Kk0Z+u3UbjjMksX4Jc+4/O7b5vhhV6HYrPGSHEnVuNpBn0FKzKYnqrQCU+TyfVZ2GiRnt9k\n5opGHORpGhLCQCDSYn7jzxvs3+Jx7/xWXMEh3LTwJqeQRn+TQ4fG+Ms/+Qwf/MVP4o5vY7uq4toO\n3W4HVZOJgxDHcUilMkQIEEZEkYhrOyRTBt1Bn6SQIJ8vst6ok85mMHs2maJIQjfJZlWsXpONxhK9\nQR/Xi5AlncO334PTaeNJBX7+479AYKU5fvQc+cwke964m9jZzfraKbLJKkePnGNKiZjeUcUPTpAr\njOFGO9DDPFcWTrFxdZnKW6ZYvBgyNuERCBMIxQYzewMunOwRmgZGMY0lSpiOT6ac5UptnompERJC\nFrJt2okQTTXwXAk1uYQoWjQbl1D1GS54TcpjBtdP1ND359mxo8CXX5xlamoKx7LpddsMBj1uu+0O\njFSOx774ZW45fBdSbOA7Jg+/+Y1MTY5w5dIcScMgjgVkocDAEljb7HH61EVymQk2N1u0mw7dlkCp\nUEQQhh7ybqeHjMDqSg1F1gj9Ab7Tw2/6fO5zF6mWcnzkQz9CsVjij//sUdRIR0sUaLTXQNZ46dg5\nCukSj7z5PTz97NOcPn2WpJFl7559nDl9ih0793DHbYdptZu0Nvvs23WYdqdBr9cmjCGIIpIpGSew\nEWOVyPMxG+OkCy0G/WUidx9tc4WRnIogCBRzKfqtDoOWw7bbt7CxsUGj08FzNZKGgKonsXsDojCg\n1zAwnQ5WZx01GYHgE4s+WjJJf9Amoeq0aw1avoAmZBi0Nkl6E/zHny3x3z9zlg/+TIvf+7jCz/yU\nxJE12HNHnv/wox/G63+FydTttJvHSRV2YqQrHJT28gcfP0XrcpL+7DsRlCcZr+r8u59o4rQM1G1p\nBv0P8MVvPIXnLjE29dpF9ntuJntz/X/NYm8uQRSJowgBQIRkNk2uVMR2LILQJ5cwCHwfKQQQhhSv\nMCQOI7SEMjz9lkQ0RUNCot/uYXct0sk0kiISRhGiIOC4LkQxgecThxGRACOjowThkC27fedOHN8j\nVy4xPjmFLAt0ez0EUcB1PERRwDbtYVxYkpH8AD/howYGH7xvO7//W+/jLz5zHDkRkVV3M32HRL92\nlne+NUs6/25OXozYteOdFLQ+nrPAhcsRDx3Msa61iBIR27e8m7OLX0XUK0SbAbc+cA+ZTpfk3lv4\n5tfnSAZL7Nw1Q0uNudwakIpjRifG+Man5vjv/+7d9GpXiZUynY1jTLQHxPOP8/u/+2v8+//6PA2h\ngbu6hKqmbqTuIlqNNomEiqJoSJJEEAYgCLi+hyCIVEZHmZgcug+WVtZA9vFciHyBdFqmVFXJGFkG\nTQWvabN9+07yhRE0TWfLjt28cmWOrz91hK986RlqtU3Gt0UcOHQHcqxTTJVBPs3ydYcg36ZR6zJx\nC8hBnT1j7+Efj7yALr/MnkOjHNr6AP3+KrJYQFMMBs4yly7m8GmwZ6bE5rqAEIeookSj3SGQFZL5\nEn13g0M7baYmOnTdCXRNIiEqVCf38NjfnWNxbplUTkdNq3hmlxE1ya6JAr1mDduMefDNb2bu6lVW\n11ZQFA1FUbm+sESvY3Hlyixmv0+5VKLXblPfqPPWtz6CIisEfsCBQ/s5f/483ZaFJCXptDtcuHCB\n93zf93Ng7x0Efsi5k+dxPI9yscjE6CSXL1+ha9lIRBgZA8cfsG3bVuJQRtEMvv7k08xdn0NRDPbv\n34Ft93jphRdZvr6JIqucPnkC2zYJ/JB8sczA8RAVlX379lMul3HMAe26ieN41NbXcf2QTqdPuVpF\nUmXUhMQ9t7+OF54/Sb8pYqhF7r/3LvYfKrNR24AojdmxWV5axFBDDDlLfaOOoIqg6OiaPuQ5qxGi\nmCUKHbqNCNfZJJtKk8urJLIRlt9GVMB1QnxbYn2piRwLMFBotTze+cFRPvT+H+axrz5FOVElv8Wj\nvMdhc1nk0T94hKqT4Fd/4ji/++hRfvNvHmPakHjnm3bz/KVP8+D3/S+2bPtR1txfI5FpkOwdJLQ/\nyPryT/Nzn3iJI5fOc61+HrBpLMl8/OO/8q/PwgWvXVxfvf+3CSxATEgUDYMNhpoAL0aKRILIRxJB\nlgSCcDh6MPsWqVQKDYXuoIdjuURuhCxKxPHQfiWLIv1+nziOsSyHYj5PFIZEscDmWo3R8THCG8Sw\niekpFEXBHpgEsY9u6MNZsjxspY1upNcUwBeAWMWR+txzqMiMtcJ7b0/zqVcGHDv5Ig/+6CMs9nxs\nvcQLX93BYHOOoqTwn+6+m//jR+7j8eeO8/Wnn+e+B0Y4u3KCO7S7SRffgNyep52yKE7dyu7MBEeO\niRxZ/1neeut9bCx6JKc6ZFsS1ZFxFme7/OS7x5CVKg0twKBDKT2Oc0uFD/5Fnc8ds6iWDG6fvJPV\ncJ1mszkEkYjDNFJ8syjSCwhij5FyFVGUcF2fjfo6YxOjjKcn8MKQXDmJ1Q2or3bot1vUWxYT1TSh\nU2Bz6TJ//ud/yc//519AVQ1+708fpWZ2uH55nihSUBMCmXwaRVFQkhn6Voa2mSWf8wm1LJYpslnr\n8JY7tvEXf3wGKa8zN2+hGV0evL/CX32xTaZ4C5Y5j+R4tLoKR54uct+hDJpmkc8k6LZdBDTwI0rl\nIq1BnQcfbjH34gianCaIVnBdH9+cYn52ldJIklhQsfsm0qDA9IE8o2MVvvylY6yuefzdp/8eUQJJ\nVMln8lyfX8I0bdqdAaqWZGw0yUjZ4MyZc9x7173U1waoQo6De6aQjYBe12Tnjv3Mzl7F6g+Y2jrJ\n6uoKS4urLC/OUx0fRdMTNBtdFLFGba3JgbtvQZUkpERAs7uJ65mgJDjyynE2WjUqW0skNI8vP/UE\ne6cPEPsFtmwtMrmlQrNZZ+vkDCfPnicIQ8LIxbQdnnr6GXRJRBVjQk+i1W+RLWQoVkbYu/8gFy5c\nYnmlRhT7fPkrjyHLGfSkhygGXJtdQjY2kWOFQO2SLbvs3pcnm9R56strTG8tYFQ0WpaNZwUM+iGR\nNEAJ8yiqCLFLPiOQ0DuoqoEs5RGFPL2OgCbpDLqb5DISipTCaoYUyknOXVzlhz7wS2TLEAci+WqA\ndyGFbaX4mV//O068EKAnVcb2Rbj1Uc7XPIKoyC273s/K9ROcuvQW9t7ZQtaL2NbzfPJPVghHniGa\nuMCOiRyzp6ZZWWjRqpuvWaO+p2ay/9L6roddr97hW79GEbiui2fZeJZN5Pls1DaxBza27eA4HmE4\n5M/KkogmK3i2T6feprXRxO6ZKKIyZMkSvYpd9B13yIi94adNpdOk9STJhE672SKVTDI3e5XN1Rqe\naXPtyiytVmtI9oqiV2HjoigO2bfYQzOEr0MCXrq6yIlnT/DhN0zy1kduY2l+gXOv9NASBYTaTn7s\nffczMraLnmly4euPs/mHf0wu73M6FFlsg6HCpdm/Ykvp+0lWZO6pjlOWVKTiIZpWmcn1ZXY1DKJB\nlmzeRBB9auY1uv0aqX0yi4MVMhevMpEZYLx9mtr9P0g/NU3aEBGTIrvummZzQcQ0TQRBIKGq6Lox\nPPBzPKIwpFgssnXrVg4cOMDUlklM0+TixYtsbjQYLY+ye8ftbJ3egue28T0Xvw/dhs/y0jKKJLNZ\na/GXf/E3/Nqv/wb/+PwzdHwTM7Cw3AF9O0YVpnAcmaVaEzU3TTJ1L1rOY6SsI+dtdN9Ck0f57F+v\nkNFcWg4sL3YRFYvCGHiSRd2axfFFBM3GNLNYvkEyLSNKIU4QkTQyhK6H17eolIZFgi99tUpvsIqA\nRLU6QkwH01ojoctkR2IIWlRKAjtumSEu6hS2jLLZWsdxHFzHJ6klURUN3/FJaEkkScL3XVRJRpIk\nyuUyk5PTHD36MtbAxrU9zp29gCjKPPLIW3nve9+DpMLS/DKf/8Knub4wR3Wkwnvf+14+8MPvJ5XN\nUqutA3DLocOURyqsbzZot9u4gYvr9bHcDsWKgZaKsEKPh97yCAduv52+a7PZrjPwLFKFApVKhdFq\nBdcZ4LkWnmsyMTGKqqqsbTSpNzZJGiqWaxEjsrC0giiojI5Okk7lKIxU0ZJ53EDBDRtcX5rjytkM\nmWyJSqnISHEUI1HC6iqktRQbqyELV9sMugKOKRHFGoahE8YDwsglnRYxtCJyuB27n6Gx3sOxB8iK\njZF0KJV0dm0fxzHaJLJgGH1q5xx+9RcfYbOv8PSpLv/XHxX4m88MMKQ6Ky+J6CqUZwTsBDz8033K\nk+f4+y8tUUzPYMf/haOnWnzhT4oMrjb5089McWG1xvryC/z8R8p88ud28qEP1KhM+IhK9E+V6Luu\n77md7D9dN72o322HK4oScRQOT7yA0A0xuybOYIAXQ+RFSJr8rTob8SbBS0SWFKy+SRSBFEmoqjrk\nwkYhsqLhOS62baOqKn4YoKka3W6fdDqNTMzoWAU/CGh1WyR0lSgOmL18CV3T8AMf13bxvIBUMoXn\ntIkihpU5WkhayONYbXwb/vqJNV7/0YeJ4gXe965p+vVNXnzyAv/tV95Ofm6WZ17+VXLbvo9LjRXa\n7Rb33THD+/7HNS5tRGh6i3fvT+JmZUJzgJZKcLhRRnj5OfyzMgc0hWKiSP3KPO4bd5MsV1lwzpEL\nNfzYJC1NkVlqUiiWyL11Hy/MnaS4tYk9sOlbEXIpy2OPPUUma+C7EoV8AduxkGWZhJqg3zMJg5jQ\nC/FdF0dx6PV65PIZNjY2aLVaVCsVFpYbRF4X37OQFVBigYBNFC1g4IKuJZm7eBkncJmsbiUOfALX\nwXUG3HL7TmprLQ4c9EkmE7StNi+f8MgXPIwRCzduoaGwdewAcbjO9LjBpZaNfVlkYuI6UVBkZbnJ\n1v0jLD8p4HYkeuYGTzwXMVPN4zgWPTtiMAgp5gxix6ffcfB738fhu0Uu+n2spk620KfRWMdxA6p6\nlaWFNTKCwaEHdmKk9mN5Llq6gSAppFIZaiurjO/fw65de9i9W6DRbtF48QiOaeGYKtn0KDvfcIjx\niQoHB13CwONr3/g89VYXxwk4ffoUi0vXuOOO/bQ7TWw35MMfeT+SL7K0VqNeb5DL5Zi7dBVEuHr1\nKvVOEz8MEcUErVaHico4XmChGUN+Rja5D8PI4oYNtuzOs7xiM7+4jiRpqJFELMCW6Ql6vR74Jo7V\nwbIHSKKIpkkoioIggihBNptl/uopcukUP/ahD3H+wkmeffEsgjSg1a4SeBIPvb1AZUeL3lqGxoaD\nrhm8/OxRQj+PqERk0hqu6OKRRVMsFEkgMyKxvuQjCzGDvoGaWieOXVzTwRBEZLVAbb5NIV2mP1BI\ndLeCu0E6NY5W6RB6u/CcYxQLaeqn+6i+TMsKkbUchtSifk7ByLmsXfR5+fgYnxt8mitzj7Hjji7P\nP2OwfXqUy3/lc8leYuuteXaMODz0jojr11/GLyfpW3lU+V8bT/Y1rO8mtN8RTBCGBCyr1wcvJAxi\nVM1AkgR8wUNVhtDsIIhufP33SCaTqLKCZVnDYIIm40URfbP3nde7wZB1XXdYN65AJpFDlROIKZVS\nqcS12asYRgLXcQiFISAlpQ/tY8MPCwijiMADzTCRhARB5BAICR49ssnHP3EfdAe88V1lvvTkKnOz\nMfILJ2iLU/SnVtCdDvKOMk+0cjz98iyFdIqLVyxev80hCjSKMw2q5l6sYB3ni0/TtxeBkML+24ma\nHrI4QJRvYVspx5VjZ9l7YAf2bJuE61N6030sKdeZn7tKaW+HzcXzGDF4iytEnkq7arK9MInjOJim\nSSaVpucFN3rZBGqrdQI/RFRker0eqUwaRRYJAw+rP8ANOwzaPTRFxAsj9LSCogsoCrTTlMFhAAAg\nAElEQVSdBGHfhShESAioqorZaGNudsincxTKMrqWo7OxhhjLnDz9IiePnOfh188Qpy3GyyGm6TI+\ndoW//mKKy0tLOK0q3fgiZ+Y0KsUSQtRAVcforstMlyOE8S0gr2K31xgZneHMYJ3NlsvMzDj9vk+3\na/Lcc/C2t1/h2jGXfi3J9MQMjz66ThhLSEqGcqbLysIGG50FUpsjnL9whcnJMqPbM9imS6FQZGlx\nhe3bt/LII49guRZPP/0UDz50P+uLPVrNDqMTVUKCYQWNkaFaGafeMtE0gSNHnqY6Nso73v5DrK2t\nsbS0wukTJzFkA5+Irz/5FKIokUgkCAIP13OIxWGoYn5+gX17D9NrNUgaCTLJEm9861vZO30Hl84f\n5/LsNZaXl6k3LNSkC6HCvAiqIvLIWx5kceEazfXrSKHGSClP6EUMBiZeZLJlxwyHDx+mUW9RHcmy\nZWKUHTMjNHse2UJMYUTn0olNEBz23VnlwrULnPjmCLt23MaJ85cR1RxEEr5vYfVklEQOUbFI55rU\n5n2mRkUiN4klDFBkmdALUJUsOBk8V6XfdVBlmz0HLMYnVbZtXaOzqvGVr8qs92N+9bd+H6uXYizn\nYlltut00YjJAFC0qRQPZ9Qi6WZ742y7v/+gae0pJmqT4s6/r9FMyS6KFWdNpbqRYO9Oj8NDt/Pov\n17m6GvLyJZOxEQMxfu0Wrn81Igv/stDGIYgMKVmSJBEGEb7tId9Ib3mui6opAARRSORFCIKEpmnI\niobjOMQhqPLQvjXo9XE8F0kSbliytFeFNQiCYT2OKFCqlpF1FTvwEBUJQRJI5zLULWvYC+YF2LZL\nKpWj3e4C3ACmeKiCQq+XgHQPaSCSjH2ePnuc+1vvpCTVCFMRB/ZGXJq/yOu2HOSVo/NInomx3mb3\nPSXe84MvU53oUxAVVo7oNB42GSEm1ASuXElx2/IyKSeDLTmo0zOkbQOtdpn+dZ/sm3awL3kr8+Iz\nWJ2LqLku2/7tfhpf+iaJgxNMVySSio5RnMLcrGGoFbKhScse0IpbJJNJtm3bxsXzF3AcF13XMU0b\nPaHSarYII0jnUkOQRzqN2etjWQMk3SObTRL7CZzQwbZsBCWFZytEmk9CkzCEJJtYxLHA2rUVnLaJ\nV8iwuTJAcOFy8xLpkU0a3jKjkwXEOI/je4xkx+kEXY49903EAJbWcqRVibap0AsWCdeXeOBtAr/3\nf1bJpcrIro9Hg60z9xIvf4Fep4vtBIiqxtzCIuXyOJEk0XdNSPTxohp9y2K69Mt8/rMfQ5BCOuYS\nkeuQL2VJGdto1i5hbSwy2xzQbLVw+iJjoxWa9SaaomKafWrra3zkIx/mlttu5R8+/Q9UxtK0u2uk\nMgJHj5xACBNosoEoyGTyBmHkUq9v8NWvPsH+vbewtLCBrAjossDF2atEgojrenimTTKZZHV1lUGw\nSYzOrYfv4f0//IM8/tjfcezYEfYf3MGObbeRoMGWiQn2bDuEKpeota+x/+AOxEBFUQ2+8fXHuTZ3\nhayhMj1RYXxiO3NzK0TESFKKMPbp9kzWa5s4roUkBrzz7Q9RLafw3DYPP/Rezp+7SHXsKlpS5g9+\nYx7THiGXVDh25CxOtMrktI7gh2iywuJSn4SnUtkvsWd/htqVHstzbeJgikDpkcpZ2I6O6/WRJI90\nWkeVAkqlMq3NAKfnEDkZJjPbUMJT/NT73sPh3dv4na8+xqmzVxEMmclxmVxFoLbUY3VZILAFMmmH\n0qTKR9/5UcpZBVf4X5z/6Q6RlicQ6siDcdylJpHg89LjTe46MMqIK3PL+CSWusJ6YL1m3fqedRfc\nXKIooqggCPFNpxaSpAyrY4iHbQWiAIJAcINxMAwzSAiSRBQFxHE03H1KMooybEEACIJwyCxg6FJw\nPZeY+DvaGaIoQpBEYiIUVUZQRBzPoW/bbNQ2ESOBwHRJSBob65tksnkUPUl1vEy+WKDdaeE5Hgg3\nWAxIhJFPTIgkqET4kBgyc69fO8bh+yeZH2xiiDoLmQwPDBI4l+bpTWzlWrTCttFx/uwvZ1l+4W5u\nm0zxR5+7zO5xhZn9Bv3GDur+OlsbArm4z8ZMj+TiOqUVm03fQ9+bI971CMdOv0Qvs8Ctuw3ecnAb\nW06fpNQdZS22cUdCtu37SbqbOV5+8SxiskcHm6yYoR90sJwk682rJBwRJZZxwgBBT+G7IUpBIYwC\n8hJEpQpe3USUY4REQELIEgUxIhE+NtmRDIHoI0oCRkJCzmjEKQU/CAmaJr2NLkYqie0OaDYsNlpd\njIqNEFc5uOsApcwoF891OLB7mTCborW6gaJKVDNbWVlJcslc5Q1bU2wplHjLg9+PoU+ipmdZvzZC\ntjyCpFgkYwXFd2mbLm97g4HPOlZSIzRNEmoKK4gQEiP0131KZYVefyef/szn2L2/jC6m8IKQeq3N\nD7z7rchCnlSqjKJE5NMaB/ce5MSxWYysRnEsz9WlV1haP80tB+6nvt7h7Ll5rs6uAiKqHHPm9Bwb\n9Ray7uN2OgShRKylCBSN2UuXubZ0mYKRZKo0xpVr19nYbEAckEyqhEJIYaSE43pU1RTvevjdzEzN\nsF5fwfQ71NtrpIw03UaHfqvDRnOTUiWDaXZxTIuvfvnr3H333XTbTY6eOEm6NMnd9z+EIibYqK1y\n3wN3UhrL4vY8HNMitCLWltdobmySziTZunMLZy+f5ZVjc4yUC1ycP0qoBaAWkFQVz6tjFBOEgwGj\nWoqH3+hRyessr/XJlkbZcUcbs9PnxDdVLNvCyCQZ2H2kWEaKJfqJHFUG7LtFZzybYNHxuHatS7cN\nC40snf421houarbN154e8OmjR5jevkrvvEqoa8RynuqYwtbJBLFiouZEXv8Ghd/55XdxT3UH//bX\nP8lv/q1LJGRYPtvC9fJ03VX2jOVodGzWbZeaF2GKHoh9NtdM7J7Af/nl1+Yu+J4R2X/pgOvV9NcN\n2uFNkY3jiOgGTvAmh/bmDlcUxVdrbMJwKKKCMPwKKsvyqwdpkiShqtoNVF9MHA+jr9+6rkgUDZ8/\nHl4UWZaGteSyjOe4pNNpAt/Hc4bBBNOyECWJseooyaROKpkCwDJNwiiCeFjqCCBIICsyAgFiLBN4\nAkHooyYkdh8aZ9Cz6YgxO4oZBnNXmctUIOqwOy3wM/cdYGbtGKOKyVVhhkXH4P6ZTZaCDHmzizbo\nILvrCPdOEMymCc15smM6iYRNZ3qC+lqLUnycAzsOM3PsBHNmjhOzdfbd98NcrB9j++E76dQrfP5L\nn0JKCHheRFIpI7l9JC2B76dxfItATpNMy2j0SWfz+O0BydQIcegyGNi4vS6uqJGM06BEiAyHea7n\noKYTGLkcjuejaTKyoiIoMoqsMugNMJJDnKKkKoSRiSiqw1FFX2Du+kXW1tZYnK/x/e8vsbzaZHJs\nhpnKFtKCzjePXMV2s7zu1mmWr5usrpns3vsQF5a+Qml8BIQEYdADJ8N6fIWG1WVscpJQ9agPbIS+\nRjaTw48dSpki3dYiB/bv4fHPdFhcu8DenQeRJRnDMCiV0hw6eBhB0Ni+Yx+W43Lr7bfTWG1RW2tS\nLlVYmNtkpJxGElvk9TFwshQnDRy/zsriNXKpMer1FTRDRxSLuKJNGIMkCAieTTGfxh4MQJbZaLdp\ndhvEQkR1tEK716VcLlMo5NGTCpokoyYMdu3Zz9rGcG5oWw7Li2scP3oKx1lHFET27jrMVx9/mqXl\ndfbv24cf9Ok0TeJIZHV1FUNVMTttsukUSSNFEIAUq8xfu4aqKciSPPxAzaXxApvN+gZqMkOz0WOz\nvkY2n8VIGDRrKxhazO49MhtrXYqjBqv1BMdPhCCNUCxEBH2fa7M+ujRGHEmAQKyajI4XcaOY+9++\nSTXMc3Ghz9XZAb6iUyprpFMybjfJwsYFaksWsqwieOB1RTbXkghJFV1IEIURSzWLelPnHd/n8rrb\nAlYu+kzfscL//IMVnj3ZIFtSKRQySEUNdVQkiFKI/TSmH5CvFvCCGKsvEgcpMtkC7XqLX/ml1yay\n3/Pugu+kXN38+a0Y7auQlhu330QlAsiyjKqqr0Zgbzbh3nzMtxi24T9j1fr+UAxFUUQSxG/tRGNQ\nxOHzWaaJ4ziIoshgMCAIAjzXZXVlhY1aneXl1VdHBaIoIIoMYSUCyDIg3ACSRxJCrGI2Y158ao7a\nvIme1JB6SyyXU0TFBNl8ipzb4860wtbwWdwFnea6yCd+4k3cOZXn9ckkXtinG7bxJZtBQWFk33Z4\n4PWQE1CnRJR2h6jnsd46ydbJbRyQ9iKvwrzYw6806DcqlHL7mF96kZmZGfIFg6nprZQK4wzMNqJk\nYJsCI9U2oQ9kReKkRELJ0xo0GDF07MDDQcLruGgaiEpI7LQJggDbdYYfZIoEooSS0tFzaYxsFj2T\nIpPNks5k8Bx/6H8GFEVFUSGTSSJJOVw/oLFpsbHewAk89LSDYntcW7hKMJCweyqCFJGKZV4+eo5Y\nsemZcPz8V8jk9hBpNgu1V1hfyHPX/RKyFrJ9ZxrPc0gqOyCMMAoWZn9AMinS3ojp9QbMTO7m/Ml1\nHnxoD3GQxhqYSILM/fc+wGa9iZ7McPLUWZ5+/iUWVtY4c/Ey7/vwu9myu4ic0Fiv92n12phBnYG/\nwUptBUUxePc7P0guVWF6ZoROr4Yft9BGinhijNltkdcUZiol7rz1EKEADctC1kXGt4xR29zA7Q0T\niuurK2ysLjO/dJ2QkEa7RSqZRRGSeFaMO/AIXIdOq8vmep1LFy5y6fIFhChmy+QUd952J//41W+y\nulQDL+DrX36c5559FtfxWZxf4/zJy5w6/QqKIlLIZ4EIazCgXq+jKQmefeY4xcoY9c02sqCiItOt\n18noClOVPC+/1CGTPsDiQpozZzvEKvhBxNqiyNWzATgimVRIQu4hSCZJrUB74PHej0XoCIylNAQh\ng5KroqtlgkCj53rk8jV+6Wd0fu0PBzhizIZnI2dsVCySYh6j0EMI2+RTAbYtcO2SzwN3J3j9vWP8\nw1/Z/MOzZ/GCPIGb4cp8g4Ej0O/0cNwBjiKRHiniRhZuXEdKtRESHUynjSC8dun8nhDZ72bT+nbq\n1j/dqYqi+GodDXwL8C2KIoIgIMvDtNW3g7+/XZBvimsY+t8BAP/213JTzCVJQrnxHDejtJqsELrD\nf/AgjrBch8D1yRgpcukMoiDTanbotNsIQoyqyiDGSLJwozhSIAh8whDC0EcGRDSayxZHnlyjnB9H\n6DXpkkad2cLi9VlUp8nP/+4FNuMEa7kempxkunaaX9mtoHbHWA9TyJk8G3abYHqMflqnn2izFiTI\nj5YwdZ1s16Sc8Th44CFmn3qU+a2jGLpJUh/lyMtfo5C8jUHfRU9k0dURsukxtu2cYXJrCksRSZZC\n7r3ldgpyHllq48dp3KiMkR6lObAIjDYj1RRS1scJFOTAATHEc+yhDS6KEWLwfR9V1l5FU4qCjKQq\nJIwEiayOrCnoqeTQzzwAy7VRFI0gMlESKlEsoKgxoqAwklRRFAk/7NBtryOrHpq0TrYwhmFIyNom\nJ1+5RKcJtfUO6xt99h4MqYx0uHtPmju2ZMgK4LVVooFMoWJQrCRwBj2SCYNSfoTmusr1xSv0ui4v\nHznN/PwCZtfBMl0QZWavX+Xq4lWWVq/xzWefYNna5PTqMXLbY4o7NfqxjyOmeebUcxyb+wKXrx2j\n2RhQW+3x5FNfo9d1qFRHsYMGkihTrVYxMjq79m1nfKrC8uoi1WqVUqmEmtCwbBvfibjtnjso5yt0\nWn1mJqbIFnUuzZ7jH//xG5w4foqjL72M1XMgikkmFHrNiNmL13j0L/4UhC7N1jxHjzzLSy++gqwq\n6IpMLmUgBhEzWyZIZwwuXbzI/Nx1du7cxrbtMzSbDWRZJJnUkSSFUyfPcMdtB+k2G8xfmUdCYWOt\nie/AzNQONjY6FMd3MvA3QVjiXe/KMFXMktSbjE8ZpCsCQpCg1/S4680+t96ZQtdK9K0+q4sJnnk8\nz67p/UTEKEkV19xAldM0rRQTe0Le8Q6ba0dUBhsjjE+PYos2rqfTaTWpd3KoWgbFd0nEDZ59PMkX\nPpdBL3rMjOa4/dYJUpkNur0mgmBRFG32Fyrs3ybgJyxqrWWUpEK5UkAzRKywhZ6LCSP/Nevb98S4\n4L9+24u4CYL5doGDiODGe7p5WxyHN4Tyxtz2Rn34zbHATWjJt3eD3RTfm7vYIPBwHBvXHeLwhiOI\nb11HRCAMwhu72KHpPibG9wK4WbAYBujJJLqu4zg2/U6fQa8LsYTnOsQ3yFR+ECGKAkEQkxB1ImEY\nzUUQEYiQlIBEIoZYYmO5y/FT57n7Tbfh6DFdxSW9mkUbhfGEwvigR0bTSffBGqyQ22zwe1ERTa+j\nbKwgenUe2KIj1lbYNfsCX5g+gL8yhzwQuLz5EvvYSf3Ulxm96zbs9z7K6vFnmBxpc/biBe6+5xfR\nywa27/KpTz3OZrPDwtIVIkfGUDUSikUqvZ0P/HuQlip89Ad2srWySX11nXYrZM8d++ldjDj0tttZ\nmO9QDEJ6goGhiaiSSuQPqWaSAEIkUF9ao73RJpUyMAcWmVQS27QQiEkbKbSEjhgm8b0+ihqgahKe\nKxD6oOkRp17SePM7DZaX8giSRiJzjZUlCcOo4tgWuC4Pvy3N4gWX+csaXjyEh+8+sEFo2VxvZ5Ed\nGTkTM7plkuaawKlzG+jIGLJPJmnxsR//BH/yO0e5dO0kZs+gZ7UIAp/WZh/LNnnyuSdYWFqk2d5k\ndLyAH3QpJD0WLtQ59cI83dU2fq+H4AiszZr06zabDYeRisDlaycIIpFu30RPpPEGOm67gSDEeEJA\nNxhwdW2B/YcOU1vZYHl2gYHpIQoKhw4f5qGHHmJyepr/l7r3jrLsMKt8fyefc3OunDpUV+dWK7Ql\ny5Yly7YcZDnhBF5eA5jhMfBgmMfAzDJhwKzxDI/FMMwimBknQDhg4zG2wLaSZUmWWq0O6lZXh+rK\n+eZwcnp/3Kq2YL2gN57H4p1/6oa6p+qudde+++xv7/294Z57SabzhLHJxsYazUYN17HwHJfJ8XEC\n1yYMHdKpBKOjowwNj/OaU3eTz5VZ39hg7voVRF3Bdx1C26ZSLDC1bw/XlxZxHJ+3PvB2bM9mbW0D\nSZZotNuIkoSiq2SzeQqFMo3NNUbLA+RzSUbHx9H0IjcWtnFcgajqkstZRJLG6fMW5fESt996G2uX\nr3DvLZOce9nioY9q+EKXR/8ainmbe1+fZv/4Ns2ewKnjeea6DcbGkwRBiOetY9sya6si578Xcfyu\ngJGRJrgBtY0kklYgXV4jbOgsWyGkBIrdMj/zkRbv/ckublLgqedNrmyYNLYV1CDEiHJcu9Kim45B\nGsK0IZEwiH2TTFpCNcC0LaJApLcd8Ksf/7X/f8kFu/rrLkDustd+fV7f/vRKttrXVPtDK1VV0TTt\nZiNW+PcGYP23KMvyTa3W8/pramJCJFkgmTQoFovk83m0HSfCzf+BGEWSd0qo+zYxYoiC/jBtYGCA\nfKmIomuIkoSuqwhRTLPZ3HEjiMiKiCzv1DjG7HxBxIiCgCjoiJKKKIMfRgShgKqJ/OqHXsMbrJC0\nY9PVMjTdddK6SK/l0y1pREkJRVomL0jM1QWWPZdebNFVW0zlCgzEPZLLXeraOG7SY15Pkc5NYsvj\ndJobOEUoDbyBoptg3YpQ9Bopw0DResiSRG37Onavh2u3cbsBoSuyvdmkkCqwXl1l6aUMP/6Qj7y6\nSEkP+cTP3s1nf/kBtG6VjN7i2rNzKIZLrIik8iGoMk7g43guqtH/Mmxu15BjieFiiayRRBdlHMum\nkMuQTaVJJpMUi0UUxe3vUfMEpFhHiH2SSZnQl1nb8Lmy3CZwO2z3TJa3fHRBo9ZpIMkxldEClt3j\nlrtbeGaI7xbZqtv8+WeaNK0Ezzx5mb1De0hoEVsbVcaHRwiJcdo5KgWJXrNGyshy8NBesukCB48c\nYHLvGIVSieGREuVSjr179xMEcMetd/POt/8It99yD//sgx9GJ2D/xCBJOUdSzuCaJmktj9NOo/tD\n3LhSY3pmP72gTSKtEYVdMikfI2X0PyN+iOcE6GqS06dfoN5qUh4dBD/Gtx1ajSrf/s43qdbWmb12\nmeLAAKduex23nriDj/3ET/L6u19Lr9Ml8ALaLZNuz6Zab3Jp9gqappFMJjh34Rxb1W0sx+XwiSMg\nSAioLK/UuHh5gaXVKm4INxYWMXs2A8MjbNeaZHI5xicn2X/gIJXBYWzPZ7Va5ejJwxw4OM74eIls\nSiGhiqQNnWy5y2Blkkjy2Xsky0a9xezLHQQ5xS/+0i+iGvDsY2m+8qciWkJHk2zumj7FYDzM7RMT\nlIsZKoNDnL84T8PTmDmYZTLjcN+JI7zlvvt44pEiixt5urSZOdjjt/7VG5guJ+m6dabGu9x3T4/x\nUY+f/heHufpomvcdD/jywz7ttRDfz9JqS5h1kUxxhMLIHqxIQJQs5FjCbnpsLG/wpjdPs3e/TMJQ\nd7TjV3f8UBYuQRAWgS4QAkEcx7cJglAAvghMAovA++M4bv4/nKff8/qKS/JXbkeI4n6/gCCIO/vA\ndgpzhYhEIoEgCDfBVRAEFEW5yWRlWcb3/R32G+P7Po7j9N/8js4qiPHNv72r5+7+7TiO+92oqoRt\n2/2d71FELpVGTyURVYW1tTUqlUp/nbgkIssyUSwSRj6eFyErP9CLJUklCgPCCCQgRkISZRQlxrJ9\nolCkUBngbWMKM3WBjZZArzKKMHidZBTglHWqxSID0giukMOUHIRTLtm1FsWtFEE+QejkcNxVLsRJ\nJsLjGPYcGCO8EOj0Nn3s/DzH770PXJWE3OKewbsZGB7loz82zkpnluKeCS5dOoPZCVELCrquIYkh\n+oCOKBUJ/ZDabJfKlImg6nTFUdbPvMiJgRw/8rZRQqfDuSsDPPbyIn5QwPdahKKOpCmogoSmKQhS\n/0pF1wySmk5jq0rHtUCCE4eP4js+vh9i90wU2SUOIfAlXNuHOCCZTNF2IgStTbZ4krTeoB67lHLH\n0d0uo+Ma3aUaPUti+ZpGfkBi8lCVS4s+xYFRDN3nT/7LCh/7yAhUt5HVBKHZQRUzZNJjNJsdOq11\nhkszbK6vc/vth9jacikMLVAo9L+QB/M5elaL+95wH9vbPcqlIVxT4sCeEyyun2F8Jo/jdDBXA/Kp\nAdK5gI11k3RpkMD2ieUkC8sL3HLnHq6edhH8gFIhwhJcUkaSqBUSWz6e56EqOq4IYzN7mBnfz7Ub\nVxkoF5jcO07PbBJEMd957FGGCllGRydYmF/kq1/9G0IXFheXCeMYTU0iRAaDQ1mMhMSlq89huQ1K\n5RzHjtxBaWKQ7maP6xevoWlJ1tcbhIKPIid44cXz6IZEt2NiJBPkcjlS2QzFYpFqfZsw9Jk5doyJ\nfZNsbLzMiy++QDo9TiGts73VoKUUGdbzFFIzbK1fwO6Os2SfQ/RCrm/Z3POWab77/dNkxyUeeGuC\ncDPmzLe3+cWf/wDDqoYar/Odb76AkhxjYMpgaKLKb//Uv2NxqcvvfuGTzC2luXXS4qG74WgpQ6/m\no6Y0wqzKoK7z3nu6fPH8NosLJa6cD5EJyZUqyCsOcdpBTCpoYZWcCpqv0DJXiG2VgprCaUsoaorP\n//HzRHKIHHYJQ/dV4+T/DJ/svXEc115x/1eAx+I4/qQgCL+yc/+X/+9PERNF/cv3RCLR7w0QFWzb\nvQmUfWbbv/xXFGWHWfbZbhiGNzVaUeyDXPiK+kFV1fE852ZMFkCWRSSpr7+KokijWSOOY2RFJIxC\nYgLiHXnWCRwUQSFXLpLJZvtpJy1Do15lc20DAp/2ZhUpFhBlCcv1IYyQZBFJUlAUCce2+uvNQ49Q\nABSQZY2xUoJ6y8RWfYJWhsnhDj/xlhL/+5fX+ZW3jJLcXGdy7xtZTS3RSjbY0q5zi51kYzBk/MiH\nCZdepqK/xOB2DTteI2rYNMfzPLY+xZzSQ/G6eFKNeAxa10SqQcj0rSPkrlTwv/ffsO6a5YDfIHaH\naDRvcPr8WT5w9yM8fv4r+JqMTI9EahCsFmpsMLlniGZvk61Oi5wwxUp4nYoC55JlDlS7GJFJpyBy\n4OQwb6jcxrJZ4KvXLlHfWqe2oqCKSVIDBrYcobVinJRNL3CIFJWB8hj1tknLk6htVol6JqFrEUo+\nCTWFJBt4Yh1NhIwaUPcSFKUuewZSDAgGLy11qOQjDiWOc6G+wqbbJvTamK5Kc9FjuDLE6nKXWHFw\nkgWC0RDbNlnsrZLMZogcgSgrU9R7rFQHEA/2CIFri2eoZB5ClWXq1W02VqvcevI2CgNJ7K0qxYLB\nxuoW+BazVy+zsX6DI7cd5eTtD9DteCBcY2F+iQQVFGmVZmsJQ1LREyWiIKTZXSZQYghDVlbTTB0c\nZmlhEUGQaLctBgbL6KrOcCHD0f2TVEqDpMtplpZWKeRGsVYWWb52lYsXr7CSznNWvUAoxkwdmCaZ\nTGK1u2zPbTJQqJDN5zh09BAxAasv14lRQNDoOTZlD7zYJzuWodVqUSzmMbsWvu8QCAGB6xP5kE2k\nqG52iDyVxmaTdq/BbbfdhqAUKCx9m7wZMb1nDAouf1yroYzqRA2PzWtzqKUEe49kmX+6huJZKJkk\nX//iV/iz3/og/+Zrp8GJ+Mbn1+kEearXX+JDHxrg3fe9jUdnQ+69t8dEAYRUl0c/L9JY+y5zF89h\nTCRIFSxatRzFcpvNqsen/vt5zCqYvsTaZpXzczovXR/m937vKo3AIygWkMImoVZC9DcQVYj1IQJE\nwkablJBC0hM06tuEgo3gqvhbKpIuYobO/ysN4P8LueAh4HM7tz8HvOvVvvAfDp0kSUBRpJ1UUR9c\nE4kEqqr2qwwt6++x112t1fO8Hda4y1778dg+S+0z5CCI8L0QYhHX8TF7LgISuf+ksMsAACAASURB\nVGwBY6d4WlVVdL0fr+23/wskk0kGBwcxex3a7SZB0O9DEEURw+hXLBKGyIoIRBiaiiSICEIf2AUJ\nCskUCFAeKBJ5kC+OcuzOuxkaSPH7/+uHec9olmzY5OPfWKGU3cLyevgDFqJ3mLy6hyW/zaK4RLLY\npLBXJ3vwARLhJF01jZEpsbjY49krq9i+QcsWSEVltrZB0AvU83kODd+G/dlvEvgSqYvXca9sELbX\n2a5W2a42aFe3mL+2gu/ZRKGAH1rEcv/9Ca5FyxFZ2Wjw9GqAr2boRCpqssj6VpHXdmwCW2J1fZbL\nCy1OGVvcPVPhD378ft770MfQB3VaCxvEcx2McorBwf3AIKpUQCDi0MwIS/Mvkk+ATIjdtgkQkJUs\nbrBF5IuUs0PsHUsjIWL5Sc4864GWZm65zuws2KHIwtplYsOi29bptiMEe5pub4PKsE/kp8jkQ7JD\nLq2uzktzXbZ6Fj2v09fHI4V6+xqyKiFKBs2mi+20cYM2oqDR6/isrs6zsrxBY7vFpZcuMXftOsvL\nywSex4kTJ0jqafZN7WPPxCT5TAZFEtAVFU0z2Ltnin37RgkcG7vt09qM8W0F1wmZmdnH+OgYQRBh\nqDrpZJa15Q1UUeet97+V8eEJ6tUqQ0ND7N27l2eeeYbrN+ZIZXNohkG73cb2XO655x7e85738M53\nvpPSQKUvw8kSpYES165dJQxDarUGTs/n5MmT+J5Lp9MDQNcTPPTQQ9x+6x0MDg4iIe0kKTUGKkW2\ntraJg4iN6hUGJrvc9poxXnj+EqK8wcuNBDcQmA17nK13WF4s4Pkx2dEkehEO3uZRyY0yONVDTc6w\n7cV88alLPPP0Gr/y2l/i9VMy7307aLpIWlX4i8c71L2QbMnibe/1+I+/YzNabOJKAS9emuVqdYs9\nGZXf/tc+3fNVGhs+zzyVxeps0NwuU5FbpDWDz/0HgXanhRuXeeYpKJVV/NCgpVWR5CRarNNob1KP\nmwjlQYIoxfamiW31pUfFEJFUiThQcFsKhK8eOn9YJhsD3xYEIQb+JI7jTwEDcRxv7Dy/CQy8qhPt\nyAO7w6pdD+zukGvXNbC7wVYUxZs67O7ak93BliRJN10A8c5G2V22KwjsDMv6hTK7Nq90WsL3fer1\n+g6g9+UD23ZJJg0i16Hb7aLrOi9fukRC07FMG12Xcd2AKPT7FrEoRlVURCkiCCIkWcB1PeKdchlB\nDLF7FiSh3qojthw8I6aVDgib6wzj8uK1Ze7Zm+YPH4HnjqYZGzvDlp5CWuyS1AeI7CtUWocJvvA8\nhrKCIzrcumpSH9IJky6NjkU99jiakImFiGNj4zxv1ghkl9RmxObm61nRv8udcgWjZOJvN5Dv/BiZ\npz+H64dcvHSWzaUGKUMk8iEQXWRRoZjLEgU+1V6AbKT57DPLfOj+MWpbm6QqMtL+MiOiSSaW0Q2R\njW2b3GiK0aQBA+NMPnCUH3vr6wguXeSZb/01N6wGb7j1KGsXLhELcOq1d6FpGmGksjS/gFpKIgoW\nvaZNccRhfCBP15TZNzDAWKGNxgr5CYkXzixy+6lDNC3IZCpsW9uk8zL1boScSKHIIr1uBzWhUMjK\nWLZA2wrw4wZiYoKOYrLcSKHoMdbmZQ7MDGJ1dCKxSxDLxFGC64v9foBYkIkigWp1k+WlVQ7M7GFw\nYJRm3eO5Z5/hQx98Dxubi9RrPW7MLiGrBr1uG7PbxcvlEAWF93/wI2wvzbK5+RilwRz17gK+K6Or\nAoIc9ZcgAqIgo2kgCBKXL84yvW8/qiZTHsmTTiX58pe/gmma/QJ5K0RURBIJjenpadLpNKvLK+gJ\nox/cUSSyxQInbj3Gs888x6OPPYGAxIMPvoNDh2eo16tcubHCrbfeTqfTYv/eabY3Nqnla5SyRQwj\nweLVRXrOFgODSUyrjarCxN4ks1fPc+DEXlz5As+tFMlmTRI5D7Oxjx/9QBvT7fDwp2tUCmk8L0dh\nYpV3fXQPTz7u0lssMH3Y5LNz/4U/H/hDbps+xS98/mkSjow7GPPdCxfJhL9Bc+VLfPFrCYZci8e+\nDfKETtDdYEQukR+scWACvvIYfO1xgXNnRO6+f4uq3qBrJugFSZyUw2vfPsMzz9+gY0JaalCcUlHM\nDLKVJCF4NJa3EX2b+YuzxHaeXE7HiZ1+UtPzEeKYlKEyVhnk5cuLrxokf1gme3ccxyeBtwL/QhCE\n1/8D4IzZpY7/4BAE4acEQTgjCMKZOO4DXhwLO70Cws37UfQDx8Gupur7/k0ddxd8d90IrxyceZ6H\n4/RlAuAVbgR2hmwinufR6XRuyhJh2LdyKYqCoijkchlM02Z4eJhiLk+tViOdTmOZNqIImqaiq311\n1fM8BPrDMVWWUSQJ33XxPHfHqtVn3RERCT2D3XbwRfC9Do0bawQhfOFvv89MZT8J1efuAzV+9wuL\nlIKI4TjDsrzIzPgBcskkGyuDPNdU2WqnkJweajqgmtKIHQc11omzSUIxxJSbZEQPUVWouiuk4xbP\nXTzH0ImjRGEX64UO8jtmYB/UejcYHs+RSPULW2QpQJJUEimDZrOLqohstWwKuTxqIo+ll3jy0gbE\nMRXHY8GY49HQR++ZZPQE+ybTfH1uk+kDU8jdEXRLpNWSCPcf4+2/8Qne+pq3Iq9ssn+yzNhggdrK\nOk/83ZPUtzqEgYjlBZheCF6WOJCorhZoVhVW1jbYd0zkLx85xX/69EMUh5pcuHyVYnGI1c0FemEb\nXVUxOzl6bgdJ7xHENXxXxnMFshkFUVSxujKCXEbIDrHQqlHvmjhd0MUEQ3tNum0fy+6QKwzy6OPf\nAwGCwEcUYzwnwDZDVle2+MqXvsrK4hKZTIq5uTkuX5qlWety5NAxcqk0zVoVXZWZm52j0+7xV1/+\nGvOLG7iBSzoXsme6wMyRMpmSzOr2AmtbK4hyjJyQ8WIfSZbZs38PBw4cYH5+ntWlRWyzx/3330ci\nlUTVNaSkxNT+fUiqTLPdZHNzk7m5OXzXp9frMTU1xfGTtyDKMpl8jhMnbmHf3gPYtsO1K1dp1mts\nbGwQhiG9bpcnH3uC2ZdnSSfTHD9+gtHRUbzQRlOKtJoxupGnkB3l8b+rs74CgxMitVqOTctGUBMk\njJja5ir3377M//JjEb/ziXFO3dElkwspjcQ89phJOqlx5yGJH39/SDkv8aNf+Cty4o9gbMB2YRtX\niXnfZIGXWldIGG9CFuHwkMbtMwkgQRTEGJUWpTIUKiJnnlL45E8fIdZ81BA++9kEeyoOTz26wfGT\nB3n2+edY3Oxw7OQIumdg1i2KioXVdFlb3uIT/+YjLDz9J9wzBeNpF99vks5o2K6Dqus4gc2HfvSN\n/OqvvY9cTnnVIPlDMdk4jtd2fm4LgvDXwB3AliAIQ3EcbwiCMARs/1+89lPApwBEUdwJVfXdBP/Q\nXbB7e9f7CtxktbuuBFVV8X3/plSw2zPwA+vWDzRb6LcIiULfztXtmjQaLRRFQlH6IG1ZDiOjA5TL\nZWq12k1PbTphUKs2keU+wEZRhCgLiKKMZzrIsoLrOeiahqCqN7sRBIH+3i92msB6MYQyYSIgn5KQ\nwiRhHPLV6xavuW2eMDvIxz85xYs/c4ZrCxKD401uSBLd7Dwb3QayorBVus7YdRfTbrGEx/6SRkEe\nJWEoNGiiyClM2yY0O6RSgzipJvnjJSR7nWq7g64K7L+3h/faMu2H/xOhsE0qmyaVlvEDmyCIEWRI\nJjI04x7lcpbtjkJeFZj3fEq5JLZWYXxII1GroZfKvNz2OJwMkNsmY9M6f3PBYmBjkdFxA6xbyEsd\nojasVQX2P/Ag2+15xCvrdLs3ePnSS2xvLzM6PIjs2HQaJsOJIr2sg+dvEJGg3rGQRBD1I1yYncWT\nz/KOt4/w0sU2rY6JpARsbTkYCYX6lkU+GdPzPcqDU7TbG+DJhL5CNucQ+Tpu4JMspYhUgfpGg/3j\nY7R6ddJ5j3ZPx0iGFPJFXnj+Cqlsn6GbaoiATi6rYFk2XsdFkQ0mJyc5e/YsvW6HViekUCiQSCQ4\nMD3Nd596mnwpx3ve+wFiUeKRR76BEwrMXlni9jtnSKdSFAfyGHoZzxZ56cIlem4XxZDR0ho9t4cd\nWmTzGeauX+XQoWPk8hHpdBLHM1EUiXarhaSrVKtVHMdhcHCIZ7/3NJubm+jJBKtry6TSGrlcDlPs\ncfr5M8SXAsbGhyHySOSHOHfuHN1Gi067jSIKzMzMYA5UuHHjBqG0jWkKaEkNWQ+IhAhZTpMtJrFs\nhzhMkExuYbfyVB2BeneNP//TQU494DF1zGWssZfu9RZby2kss4zobTI6uM4jf6YReDlWh1Q+/5Xv\n8Ov3PciPf/pZ/u3PH+SYFPNHnznPg28qsbYqEGkupeEMW9/YZmY0zb3vcDk8AZFX5K/+okbbvM7y\nUpKLEyW2ejUiv8T+YzU68nNkh2UGxAR3nZqmVrxG29vPhedXEKMa//xn3kvPSvPEkzf42Z9/L8+9\ncIXPf20Jz/coFDJomoZlNfjq179MNv1AfyPLqzz+h0FWEIQkIMZx3N25/WbgN4GvAx8FPrnz87+/\nyvP9PT/rriSwy1J3ZYDd53dlgl1nwa4ta9eZsKvj7kZmd88nycLNx4OoP2hLpjQcxwUhQpQkBCFG\nEKFarWKaZr+j1usHB0QRdF0mCAIEob8aXBAlwjgiiECTJBQlxrQ9VFUlCGJEsZ8SC8N+JWOsQ9bI\nIQgSgdIhYQi42yGBKrAQJpmvq1RSEn/6yFn+5Iu/yezDn6Hz+CyOnqFw6g0sWpeQgjyNbpvI0ZnP\nqVxwfPZ5eziYcpgYSFGr11BEF9PZwHY7yIkKoRezen2dB/a9m5b+AqODDrz5Pvxvfp/yShNvJsNa\n26XT82hbHRRFJYo9BEFBVQ1UOUJPJwnNKvlsEiXySZf2cHHuMmP6Cpnu7Shii56zgdzx6Sh5Tp06\nxPOPz5EqdjC1fbS7OfZLRabyKtduVAn9NMmpoxyaPsTY7ac4/f3HCe0mVucakhYQiy2SepLIzyOp\nAoV8ifrWJo4loGUqJESFJD1SqQyrSy4jY2mIMqhRhG5sgGgwP2cwsx8m9h/nxbOLhIKN07MR4g7Q\nIPRlCvkxzGrE/MocB0/kMS2JlJZkbDRLs2HRqDkY2b6trC21EAIBURXwHZ8903vZs2cP2VSSTqtN\nvdZmu1bjme8/S6VUxvd9eh2Pw8f3USjkiEWJQ0ePcP78JXxXYM/QmzGdOrHSZG1jjbHKNG++/z7m\nF1d4/vkXUKV+2GJ1dRkjpXNw+gAXzp3n6o0FHMchnUzh+Q6eZaOldBwnoNFosLm+iRiL+F5AJpVl\nfWWVjdVVRkZG8X2f4aEBTNPEtlxkEQYqJV48/QKdVhtVUulZHtevX6fRbpItZBiZOMriwiquExFG\nORRRpjyQRxRFNuZDpFQNzTfoWgpKVmXwkMymP8ij1zw6Tws0rTaltAC9JNCDeISFxZBYK5BLr6IH\nm5ytH+cP3jXEH8oVbrurxfE3/zX/9udO0m5ILCx5yHvS+Fc7lNUib31bh0zOJ1BTfPWxKrWgRH7Y\nxGz1sJqDCNYkP/u/vYkvv+vfU1vby5kz8wwUYsKuw8jwHr738Dyx4nN4ZpyXL19Fy9h86ak1UobF\nRx86hW2uoBoKltUlmZYZnxzE61mcebGJbb96rPxhmOwA8Nc7wyoZeDiO478TBOEF4EuCIPwEsAS8\n/9WecNfnustgdy/Zd4deYRgSBMHf89Puste+tSu4CcCv9MvGcb9RKww9RKk/gJJlmSiKaLc7SJKI\nYegYhrFTgWih6wq+79Nu94jjPrDquvSDSG8s9wMGirhTTNP/ZgvjAFVV6fYsRPEHzggBCUkUiaMA\non6lIpaH6MV02jY9S8UwXOjW+NbVNL96R5mN2gCf+P3f5cNTHhVjlFq3xlY7Qo+msJR1jIRES42Y\nz+iMb/R4m1lCySyjjuh4hTwXXBtECze20WOV7QasSjGOELFan8U5muQX/rnMByauctcDRcyaS6U4\ngG3JFAcqyIFEx1yFSCDwbPKlArqlcqXm0bPalMpDLC2usmWJZA+m2Jhd5tiBFF3LZ6RYwSlOcXii\nhOiPstl8gcPTMMyt7PcVfHedO4u30Ax95nObbLebuKS4+w3vJMZhffEap5/6DuvLy1TEPJ4VUCkU\n2GxsEgcesS1jpGWEQKdSKtFoPk8cD1BvtFCHBFRFoVTRiWKFrbUkovYyR0++hRdOv8SpezXMNR8l\nVBksDdCZN1ETNoeOD/LyS220ZJreWpvBRMRgucJLZ64hxQqhb+M6OVQlSRQ7BHGMqqusr6+jKBKp\ngweZmNrD+MQ+rixdJvIjXDegulVDkuDlC5c5dedr6NkW69urhL7HnrE9RKbOxlKbqZkcU8MGnZrF\nLSdOcmj6KK1ql9nZWToRdFtdZmam2Vpf4nvfe4zB0XGGhwfRdAXXtuk2ujTtHnrCoFfvoEkykiBj\nqBq1jS3sXoJWu0sqkUFRJOI44vDhQzQaDS6ef5lcaQPXtlElGV1T0DUFz3dZWV8mVznK2N4CPbeJ\nZXpoWoiiRvhRFcezSKZ01GQTpyFTGNQQdI+oqeGkWgjmOD17jpa9jtAdppKOKAy6bK5fRo+m8PQe\nA+I4gbHK5fowy/MNfvzn3s63v3KOVJxl5o4DXH12mdee0jj9/S5P/HmKd79RYrMm8cW/TZMWNY4e\nEWmbSWK1i+KHtBYXeexrA5w+9xR6LFDtzHHvu2PitR4vnVlkaUlhdH+Cyel9SMZVGptdqtt9T7Rp\nmph0cJ2IntVB0aHXMSmXyywubNHqXMF2/xEWKcZxPA8c/z95vA688X/knLsMdjfmupvk8v0fRF93\npQEA27ZxHOdmAQxw8/d22W8Y+qTTaTzPw7L79WSappHJZDDNHpZl7SSxfByHHdeCjyj2L/EFATRN\nvgnaoiiSSCTI5nI0W3XCMMbzfHRdQzcEPNcnFPpAK4gSmqpimxZhFCKLIpIgoMgxnV4LWQwgkogx\nGDt0Akk5TW+hx1k2OF8+TmowhXJ2gU9fsPi5VJ64M8q83CXbzpHKXMfzaojTd/C1p7Z4sJjDE7t4\n89sIvZBiUaBdNTFcnXqrg5KsYIQOWeUa/uI17vzJO8hvjvO+H2szZHVwtxZJJopEcolHzy4wNDqG\nHmW4urBOHIRIQghETJUNrqylGTAkFlZrqLrAtlBgcSMmoYnc6LqMKgkwIiLVZbtaJyxtIs5nuE94\nMxk3oNPr0RYH2OOJCPkk9+y5HblkYCdjbnQ3uLK6yHyywHhhBkVMkEw/zR/9/l+yuBiQyCgY5Lly\nusU7PzhAYbxIe75Dq2cShBaiFOEIy0RxCVlVKRUmESMXNWHgBxm++2SNU/ftQ9ebyN4Ugd9A8iX8\nLoyOlpk6XKPRWUKXZtC1LQqZEvNzy6QzSazIxrVFioUhfG8dScvQ7HQxbYv1zQ1M0+SuO+9h9tJV\nVF1haGKY73/3ORKJFAemDyJIcO7cWXp2j1qrQTJpsL46y1evb6AbBqtrMHPoIAtXbzB/ZZFUJker\n2mCgUKbbbnP2zDmSqo5mCCQTKU4eP8no5BhPPP4oeyYneal6oV+o47tUKhWq61V8z+qTFVnGtixU\nzeDChQvk8xlkRSSKg/5mhRiuXZ0lnUhzy/ET2KbNdq1Ko9PCJ6DZatGdW8F2DVK5ApLkYJvQbMSM\n70uTG1ymun4UiU2kRAPfCRnLxTz1rMJI9jR5ZYieqxLF6+SK+7i62mC4kiJqLINZxik12fxGA3Hv\nFl+b30vqL7/K4f3381PHm7zrTWme8if45B+KtDtHmZg8QVp7kufOKIzuiflnH9tg+4UkT3y5QyqX\nQhMDZg7q/M1zczzzSJ0P/nSGq/U889c36b1YwQlWGDsxxvSxDF4nzaVzbbLyGPsGG/zCT7+Lf/db\nn2Nh8RqOYyEqMkKkIUYJ1teqFEo5TLeNqPwjyAX/M49d69YuiO2CpGmaN4ddu4+n00l8373ZmCWK\n/crCMNwthRFxXR9ZFndqDqHT6fTtR/RZqGN7dDtbO4kwmSDsM2fHdvpDMXEH7KM+g/X9gDCEQiGD\n4zg4jkMUgipLuK5LJpmm0+miqQkkqR+EcN0elWwR3/eRFZEgiJBVGcfykSIVwwDHkYjiGDyX5SuP\n844HH+Bs4yyN77f5+sQLPFicZrSc59EefK/bYN/YCNudmIXQZtLNUionmSmPc581T3oUxBsyrYGj\nODWHg1s+a8Emj/ckOpk8J/emuBEL6KHKiTcnSH+vTm/sNPdNSUgnHuTSX36OjJNhdDLHNx6+wNkn\nzjM6NkwUJKiZWyQUg7VVi8yBDFJ7lbpjkEjoIK5hBFkuraQ4ftcilxYyDO4roSltMutzuENDXLh6\nlY8c+zhDiTQ1pw0aZIQmsVTA8zvIT9jEqky6nOb4UIE7Th6BN0PP6rGydINa727eee+7+Ne/8SnO\nfesxeoLMk2sZPhJ3cF7yWfO36Xmw1onwjSrKdpm6pJFJb1CWTLY8iaQcsXd8Ck2HKPCJe3lMbYOF\nG3mymRQNr0W7EzGdez2Xan/MXE/kxFiCxup+vvXod2hZ25RKKoFVp+WpDE4MYDYiMrqG1VnDtl1a\n3TVWvvYFAidi+vAQzz43h+n5pDIZ9k3vxfdsVmsrVMYGiS/YrCyvIQgygd9AkHJsXamxurCBqitE\nEYiyQrFUodVrURoqM31gD0JSZGrmKGp6gNGRMXrdFhfPXmT5+hyu7ePHKslkknqjRhD6yJqEH/nI\nkoIoysh6Ak3UCKQIx3P43tPfJ5nVEXUBIdYRZZmF9SUmxvcwmS9Tv/A8+ZxAItPkct3iYGUfjett\nHH+R47feTjftMTCyglUrY7fbBGGT+kqOoYkU69sQ9LaQhwrYRAwlE0wd8nDrAfffNcjFy9eJkkmk\nUERzYq5UHYYaC6TfdoKHGynet/Qn/O4zfwZPnSNRejv7JgcoDpSY9K7y6yc3Kd7rMz6U4+Kj8PFf\nM3ndfcNkA5UPPRTzt5eWcbweR9+colOd5sIz11DcBENTHlOHDnGglEXQJS5b81SrSaSkyUM/+m4+\n8+lH+M7ZTfSFOqGokcw6HD0xyupyEzU0sJ02ihTd3MTyao5/EiC7O9x6pea6Kw+8MiLbDypEOxsK\nvJ1mKxFNU24yX9f1UZQf1B/uWm9Ns7/4LI5jdF1HELwdB8MPCmh2XQe76ax4Z4Am7oCu54c4rt9f\nlrgjyvh+SHLHGibJAsOVIaIIqturWLaNZTkYhkG+kOu7C0QRx7bpdjpkcmmiqD+ljMKQp7/3DK1m\nFzENT3xhhbtPTdHJS8R2ggvX2+xR1ulKQ4SSTRDNM7w3x+WVBg9OJ9C0bRbjNtkVC1N26WQkNEK8\nZpOSkCHudgnbDbxBEf72b5Cu9EjcKdE7kOVvP2Nw8I0mTnMZMTHDoZOn+NZ3zmLa2/S6IVraQDN8\nZmevcOzoEKl0Aj2TZru+TTY9QGmgwEJ1i63rGU6NwLWGzbSWx4xsir1t7pw4xEM//VNU//PzdM2Q\nRCVFIhbAiRA0HSsVExATdkyCjU3UuTWUwSSp6WEOlg4SjKrIxhhPfuP9tOrzPPKlR/jcZz6HnJrg\n4vVvsh057BvaT2zN84GHjnLn3k+wGnyKv3g45viD+5C8l6i2HAYSpxgqiOwbVZh9ok0mpdJrJimW\nPCLH5KWza7z1/jRj+jBPPLVF+YFpvvnYszTtLplyBtMCpxuQzYm0GwKENisr6yRTKcIoQAzhyKFj\n3HrydpaWL+HaYKg+uVyOzc1NrlydZWR8iNpGk63NKpVKBcty6PVMOp0WqqIThyHtlkmhkMcLA1JJ\no9/X4DscP34LmUyKC+dfxvdDWokmnm3zpjfdj6ZIXL08x9WFRXxPQtE1DMOg2+2i6hryzhA2CB0I\nYwLXJ5vNosoGza0GqUyO0G8RBiGeI1Kv1/ECEd8TsC0Vy4o5XpnAbC3RcbdIp3IsLNwgU1bw3GGa\n0RrJpMf8SoyR8On02thuSGnAIJX1UDUX3zJYmHW59bWbdE2H9WvHULImiWyE6cfI2RTbHYtnLm8j\nSzrjyijGp5+jnOzxzRu/Q2JS5N6RTX5paJb/cDVLlIm5fKbFE4/P8PbXBbxuIsNSu8O20sV0Xsfl\nF88wmhvkmQvzVKQklf3DeFLE3HITN87imx3qax32jOYoVTR++0//nJ6jMXagTLmYZ+F6G8+Kaa25\nZNQEUqyx3Qo4cuAI55pzrxrf/kmALHBz8r87uNp1AezqrP2kVnQzEts398t4ng/0i11UtY+or2zt\n2o3LyrKMruuYponneTt2Kummx7b/u+IrvLkxnu9QqVQwezaIAu2uSS5XoNvtIu+4GxSl76+NI27q\nxYoik8qk6XZNiqUKoijSarXIZDJ0eyapRD9C3Gq00LS+zOH7IYLloqkKkRSik+IrX7zOh3/9bla3\nlllKwHbLQqkrFAIX2eyQ02dodwImrU08dYCFtMWpmosoQVxQGI0KiGsN9FDCDyxUPSBbnMD/zhLV\nj52gnDqMdarI7PfrFNaLjI0mcJUR7rzzfn4v/ASGkSSVyrLdXMfQRFrNDsQyA0MlbizVSGdUBPJ4\nfps9+SSX53vcNV1hMJPlLR/4Cf7uc/8e56UzvOOXv4ClqBgx5FIJuoGLKkmYvS6xqKGLMmYc4osy\nmpFADRTCJZva1iK2DmOiSOdAAi2lY+SSvO0jH+TDH/5ZMK/gRRMYG5fZrl/hxBuL7E+4TKWarC2d\nZzQVc3R0gHNnJBwfAnWRyWMlVrYMPFmmG9ikMzV6rogYZ8gmJK5cbJIuz1CoPIogvYfnn/suqWwW\nUa1itzwKA2U6vSq9GiBsgQJGyqDTapJL51i+sURoQ7u1Tts2mT5yhMOHD7O9tsngwBCXL8xi2T65\nfAojmcJybEQZXHsnSh7HRJFAoVBiYmIMI6HTaDdQlBLz1+exbZuhyjDrhawJSAAAIABJREFU6xt8\n99EniaKAu+86xZkzpxkcHkFNpUgnU2xublKt1pA1Fcfz0CQJUZaxuyaK0rdMtoMG2VSOdCaD2TZJ\nJDQiT6PXCnGdTRrdGqISk1JK6HoWe8XDT9iUp4p4oUdttYdhpMmlbKpbGk7Lw3c0ysVh9FyVgigi\nRmnq1XVkFVQxRxikePxrEpHSwiiv4YQWycwEKVugomtIuQRD6giiGPGIlSPcvMBDr1FZOh3y/ozL\nh8R15LFD3Hhkhu9896uM7b2LmcEcGWmDj777PRjDJWYbH6dxoYVQnqFmdlir2Rhdi/W2g6+7nDp+\nhJoXcvX0FYKeyEBZIZsfpBEING2RoYxKq71FIFqERKyur1EoSgxXxpjQhpicGOLcmauvGtv+SYDs\nK32wP2CgwiueixFF4SbD3QVh1/VIJhP0ehaq2g8VJJNJut3uzf4CxH4KTDeM/qoU28LzAyRZRIwF\nBEFEEPv+WXY+5MQh0o4eDDAyNkqv12PvgUP9zZ1rm6zMXyOOIZUy+sMurd8Ctra+QuBHaPpOAi0G\nWRDJ5AsoioKRSJBOarRaLRRJwLI8FEUgl8nSbLYxEjqJIIMr15l9Ieba3y1w16FxHutYzKETWF3E\npErgi+QyBQLPp7PWYmvwFGJ3ll5JR5INeomYop/ESIEly2xGbYR8zKg0h1eEcdUjfP+vMmjGfPzX\nL2NefIrssd/AMSMGSDCRy6BpBdqOh6BB2wHbsnnyu2d56J33srLxHAE2bsdlarJM1LUpDFX4m7Nt\nfvtfvo7SwZMM5DK01oscEN8FiHQKOuFig8F0HjN2yZVS9BwX3+niCGCpErKsYgkhEjFiAJIns2UE\niHMtWu06KU2hMKmC0COOSrzl1G8SCjJNcxZDFlm6cZ7Hz3+J+bbN9a0eYnsfJ8ZyvOz/EWZLweqk\nsaIujuzQtQdJF3xajZBI6KJJSVqNkNe+/nZeXniSibEMD7zpVv7gjz6HH9lk86MMDg2j9Syi0KHd\nSXD4wBTr61sY6RSiLBL4PleuXiJyA/S03ne9+D6iInHHHXdw/cp1NEnAcly2azVkUUBVZWRRwvNC\nJFEldEOWl1fRVBlRjNna2mJ0fIxrs9coDwzimz5PP/0sICKK8I1GHc9zGBvdg7bzOZ+YmOBb3/o2\npmmiGTq25VIqlfjwQ2/j+88/y+DIMK7r0m53aVTrpIo629tdInwy2Szbm1sUh2Um90+yurbN8lqD\npFOkVM7T9NpoKZ/8cAEhhLjdIV6v0+5GpJMH6DmbOE0H1xQIwx5Te0aotWZxgERKJ1kJsByJEANJ\nTNDsruBJCUrDMi3LwqouMm0UudCeYN8YpDnGz969ypHoSRKyiP/hhxn5+r9i1JggZzcQ8kNMj7yW\nbGoKq5okG/wC73u7x2hynfO1r+IrCWbPe7RqPRLZgIXL59HTBeiESOUEnZbL7Ok5xKxK5Hbp1Hqg\neTiSSHGoiECAj4sVxmQzGrZvYzv/uN0FP/Sx6wzYBdlX7vLquwj6LNE0bXRdvVna4jgeRkLD8x0k\nqV/0ghCBENFPQcQIiCiKRBB4WFaIokg7IQKNwOszYCGWbzJaP/SJBQlZFHAcH9u2cb2tnQWMAaVS\nidnLV4ljKBRy6IZKu9MBIiyrRxj0bWC9nkMykek7IsKIfD5PMmHQrG/syCAxsiyiqgKJRIJEIkEQ\nBHS6JhERWgoE0eYrn7rI4T/IM5RLsp7SmKhdo27oaFqPSKizdc1kWIRnBjxs3yejRaQiFbXbphzl\nGCmXWA90fL9LOhmhbdhUNRCvNxh4+D7C8QcRb/uXZPU0zcU1opJOO2yy0exQSibZt/cgl16qo+Vz\npJMyVy9voL5PIw4EdEOlOJJhbGiYi/4qudCnGXjsPXoEZ3OFs5t1bjn0fpyvt9BLRZIzw3jzK2TD\nBH4SQscmGQZ0iclLCnIsIMYxruCAIiBHHmogshUplFyHZFojbLqYQUAnbxNeaaBdb6Om96CmS6iV\nNkeGH2TUeD237V1BavwOf/nIfyNd2U8QKnz2v34Kp7OJbh/CCJoIHQ3Xs7F8l6FKmm7TodpeJpl8\nEwPGFOMFnV/8mft42/0n+Y+//6d8+4nTWFaB6X2vIwws/DhAVRMsr22g6DKu5yDIEel8ArvuMD09\ng6LqLC4u0m40uX7xKgISoigQhj6OaaNqEoIAmVwa1/WpVTvIqoYowuLiIrqu0e20/g/q3ivKsfs6\n8/2dnJBRQOXc1ZGd2AzNHCSSIkXKlGRFypZzGF9r7liS6RnbY1q+117Llsf2aK7TeJatcVa2JCqR\nYhApMXezu9m5KyeggEIGTj7nPqC7Rc+aBz3Nks8LsGrh4Am1z/7v/X2/D8tKoqk6lm7x/He+iyBI\nWFaSZrNOxamSy6VZXV1lYnoSz/MQRZHx8XFct28pj6OIaqXCrTdey5mTr/Arv/SzfPbzn8EwJpme\nnMLzPM4vzhNGKpulKh27TrWxycWz8yhKinxilK3GMlavQCYTE8dZjBEBt9bhO08K/OknP8LEwRpv\nvecJRic8xDBP04GavUgoagwVp/tows0yuaKJpIWIokOnqRF5g4QJGM9DtbtNx6izGKsMWsMcEbo0\n69/h5hubKIFIuxVh+El89wz37p/hZ95zJ5/7l8+ytrzEysiPEDc3SchFOiNP8HMf/HmWN5I8+v/+\nA5eSAoVECru6ztG37SGna5wdkNnwfKxultLaCq26i6YrZJIGWiIDTLFRmqfd7GCoadxqyKJ7kTDw\n6La9H7i+/VDwZK9kfF3pWEVRoA+NiRBFSKYsoqifVmBejiXJ5XL9H2scEsNl62pMr+egaf1CHF0u\n2pqmXdXRapp2FSATR98HgV+hcV1RJfQ7YYF220YWRWq1Bo1mg2pli0Z9m6SVYHJyAsftKxz6Ol65\nnxEWhBCDaRkYZoLo8vLONAxK6+uEUYCiqrhegGHoSJJEp9dhaGgY13WQ9QAilfyQSqXlMnN4hqK+\nxXqcYf96QDUqkbdaBL7FwrdX2J2I+c7MJFljmLC6gCRkUDSXQT9BO2dyetsmbaUYNwKkUgM3J1IQ\nEoglCasTsJ2fJ9B28ewX/4DpG3fw6uIAX/nHL9J02qRlhfGBCWb3TVMcHGDhQhnLFGg0G8zNzmHp\nMDs+gW9HnFla44YjAj/5Ux9Bq1dwtQJ7lbcxUEtQXj9HaraAtVnFa7ZwjAi720R1I9KChBVGyK6D\nJHrIYkRWk0i7PkbbJu9rJCKHKO6SViXcWYmB6wdJvXgCp6bRqa2wuXCB9pJK+dRLSBsBuWqRa8X3\nUsjneensH7D7yBB/+F+20Cd8EgkZ0x6mvnUWJ1Yw0xlMPcRSTRrtKm+993bicJET33AxFY2RYYuP\nfOQRDlw3ycuvvsK5kyusLVTYt38aRZK5eLEfyZLJpGk0aqiKQrdtMzBcQFRkwiCkWtpi8dISiYQJ\nkoCu909Jnheg6AZ+7JItZBBkn4gY0+p3wa7jYhpWP/ZIktiubVPbbhAB7VYHSZFIJk2iKKRea1Cp\n9o0IFy5cYHFxEbvjYOoanuNTHMjy4D03c+aNUxSKOV586bvk81lq9QrHXn0ZX1CpVLa4++7buf2O\nm3n/+36UifFhluYvkUvrvOUtu3DrDcJOh2TKotrewgtUPNviK199gt/82CP81We/iGYmiIUtdCPE\ncSIGxwawOx6NRpdI6KLISVRZI458Dt8gcuNbGpx6UiMxksSXEtycT3PGlpA7bW6WX6RoLlBNNUjM\nxKjtBJ1TB1i4cB5fsFHyozz8oZ+AXkTesFDdHgt1m28/9WXGAoFhYTeD5iAvn3uFvC7z0L1388Jr\nJxFjCT8loUsSpdIaTS9kZHaWZHoQVRFYuVCivNwkcm20WKFdjeg0bGLPxrMDglDgtx77rR+IJyv8\n72K2/09fgiDEoti3sl6Za1qWdbWz7T/t+6yCOI5ZXytf1bqaif5w//L3XI5H7n9Hf2aq4DjO1Xt7\nPQfT1ImiCN1KXO1g+46u8KpOVxCAy9HhqVTqsgusd1XVoComghAzOFRgZXUVTdO47rrreOGFl3Bd\nj9GhAo1mB8+PSeUG+t1FFNBq1DBMHQDbdhgdG0JWFLYq5e9rf+OIbrdHUR7j4etH+dD+JE86Zczb\ncnSWYezpLXa/N8loRmH5n95AmdzB4wdNfvSba0hJkUvSAF5mkyknS37PBH/8/AXunJwmar6G1hW4\nbS6JXO/QzKZYqWyi+gn2/8c/5yvf+jYP3Vhl4YuHCd8Y4S9qX+Z/HvsCQV0kacio6SISdVqVmErj\nu/zqR/6ImYkCt9y0m6++8ATdtS6f/K1PQusUC26d1WfXuTZ4kGTVJzKHEOUyYbBFIPXhP7IUEogp\nbKGLqPbdd6ZuQixAOomX0PHSBqZcY1tIUsjl8Y0qrXSBlJpAiSLcjSqaAnZVQZQ3IUigpRXabQ/Z\nEFDaElHCQ3KG+NuvXkf+yBn+/D8fxhpqstKVmBrVWK4uMZmdJJWoU2lIvP3Bfbzv1j9kbvIm0nqS\n93/oBn750Z/hL/7662xWV8ikI24/8jCC7jAwOMEtdz+MJGt9UBAKEgodscbo2ChxKBJ5MW63RxwF\nCLqIlU5gb7WxbQ9Vs2h2O+zYP4Lt1VHUGEnQsfQ0Sxc26NT6jkFZlVA0kVAI0FSTqakZdM1ku1Zh\ncf4C2Wwax/axPZ9CoQBEdLtdHnrg7XSaTRYvXuKu2+9gZFrh2LETaHoCxwsZGZ1k8dIy29tNPvCh\nd+O4dV566TlMzeDIwVuoV5sMDqbxgxY9x2V4aoxnn3uGlbMut91isf9gmkplBkepI66t87cvnqAb\n2DgtF6eRwI8kWsEqIwM5Dt04hDZwhpeeGMEyEiiIVMoXGR+a5v9++GGqZY9XXv4WN40m+af6MH8i\nfYuMb+OEAtPXxawO7+XYxsMkzn6WTz13kZ964L0M5EXyQYrNlQ3s6V10ujZRU6KYOUM8tZ8L52xy\nhkh6u8bkDXvZ0GP+6z/8M/OdDruGZrjpQ79BsxbTXHmFSy/9M5YxxAvnN+gENRrNKmOjBzCNPD23\nQbPTZGhoBkO3uPDaN7A7rf99pMv/cv3QdLKSJBNF8WW2QD9MLQwjPM9HUXSIRdqtLu1W93J4YtyX\nVgUhhqERi+CHAclsmjiOsR0HWVKwUgn8IIDL3aQs94uvpqoQhHi2Tcqy6LV7eI6HIilIgoKIhJ7K\nYCVTaKZFvdVEVBRUwyKVGUAUIhBENssVojAiDCO6rSa+3cVUBWQtx+joBI1GE0PTUEQIQgcv8NB0\nmV7sEwsxUc9HlRS2Gy0sM0NQ9wh8mbQV8Ve/dze/dLdGYcWlICi4ArRNm04t5NoJlz3v/zQz+2We\nKL3Okihx/YaPocY4Kw3WEwZ2cZiRZp0vLwvcPZJl9Y2LpDE5fETm4vAuPvSHr/KTew02e9sY+/eR\n0PZiJcp84+9yvOPorXzm9NP4qzay5dCWBYrEeJrOgKExnNH5yQ88wJCcJHFohpeeeYH7772d6V0q\n3nabVgTJ7hTDF0dwEi6ypiO4DmJeI8gIKIZKvGsUKS3DdAFxNEc0ksHJ6WiTBXqSR6xGWEkdT7NI\nOCFCu4doJAg8F5GIyFBxVjbRQwlFDhEjCVeJ8QUBVRJRvBjS4wRhHXWXTmQf5qvf+DQblTlkq4I1\n3cBUemws5NDNGqk8ZIU8CqeYmHs3f/On/8je647y4rmzFEeK/Nj77qCyCOmixaVyh2devkA23WX3\n6B6eff4FXCHEMC0U1SG2I4giVFNBksXL8kAB17OxvTYEKo5nM1BM02k32LvrAN2Gh9uLCGxobHdo\nVJsQxwiXYUiqovcXrKLCyPAItXqVtY1VdN0gCCMihMvBnx61boeR8VHw2qyUttmqVvjQfYeZP/Ei\nGTWNGJg02hFbtRb1To2ZqQGmCyrder2PmQyhWqsxOjFKvjjIVrVFaWOF6nqJ9779IbJJF1kQaDUF\nGvUShmDyxOsnePudN5M309SbPZYqm7Qih0gxuPu2Hu95T4NiOAgnZ3nXoSnedaDJbbsK3L7/KNtC\nCnXhFEqlAT2JueAch8UG800RKQFukOXMiQzr517g6VLE0Q/ejR4KbB1r001bwDpCfgSnWaJcOk96\npcJ6epQXP/95rk8UKCsbrC1vcN3wNQilHmeSBv+XNsHc9gtI7U3uV7rcsXcC7dD9nN0WUdMJCsWj\nZBIp1rfKdNdOk80O8K5f/E+88eoL1DcX+M3f+PV/O2m1n/jEJx57c8jhFWrWldcrfwOumhEURUGW\nZRzHxbR0wiBE1VQ810MUJeyO03eKXZZ3EX1/3ntViyvHKJqKF/j9sYMEiAKqoRLEAaaVuKwW6KsQ\nXNcnaSWYnpohP1Akk81RHBwhkczQ7HQQJBkEkQiBnhuSy+cpbWySTCbxg35hTyZNaPRw3H4kuC5J\nNNsN9LxJq9lEMHXGwzZ/+/Mf4L6JGsqcyOvnFfLrG5iZSV41bVLVTdLuJKlDIqZQJ9iSeWHlDLvC\nLBk/ZHXZYWssRZBX8b0Sm3aGfbLAufPnue7ONNkPfITRax9i7fFLjGzP08gZDFoB09deT9Pby7nf\n3cS822Vh2ufkmTdYbrrIXhslmeKGg9cgaDbPPn2OD//i2yjMHEBL7cKvb3HwmhlSssjKxRUCWcdy\n8yS3DSJDxDF0YguE6RTCbAFxKIOX1kAARVCQFRVZkpGFfpx7jEAoioiqjNrycDWBIKkQVxrIho46\nlEXu2ijbPVzPxXEdIgEMRUWTVSRZIpYEHK+CmR8g6vkMj+1heXGbV154CnMgw55DGywvhITeAK5T\nJZ/P43dEBvIRe655O//fn/0PRKok3GGe/OJJHv7QHqb3TfPaSysUrALl9ibdWo8HH7gVNxI5fvIi\nCcui02phmhnqrTa262EaKaJQoNN20TULIhnfDZicHGffvj0gSpTLFZrtBgC17RpRCCICiqr1o3jS\nSbq9Dj23h6aaiKJAo1Gn02pdRnLqdFsdZEVBVyQCQcLSDTq1bdZLNbrtNrJd4+6Znbz/Z3+W55bm\nObW6QjaV4Oje3Yym0oyOF7Asi6mZaaIopt3u0G03qW5tYXc71FsNZmdmWFhaxDAUut0WgqzQ6fgU\nBse4cWSY/TunkAgZHRrizqM3sWdklPKp0xzOZXjn0AFuGb+NG0eXKVVv5rbpBlJli/ylJdKvP0mr\nuY5r5Witdhgfy1KXJ9lqWoiJAi+eWuWcp7Fr327m9txAq7rM3l1D6EMZ2CiRtTzEyKTT3iZhWUia\nSDNMcK5aZ6VRQhxKU9geoNMSSI9oTM4NkVld51Jpm/nFN4iVHJ9ZUPhvJ8sUJyaJugGllfNUt7do\nLp8mkhRGZ/ewXS5Tq9bpbC3xm7/5gxXZH5r4mTeHGb7Z9XXl2O+67lXn1/eP9AKCAK7rXv3cFUvt\nlZnXFckXfN9RdhWFSIRqafR8G0mX0RI6giog6QqRBO1Wg0Z9m82NNRq1GpLQ1w+ePHmSU6dOc+Hi\nAl3HIZMrcMNNtzM4PoVkpOkFEm6vx+raGqqhYyYTJJNJFEWh1+miqQaZdK7f/doOlqWR0kWkENR2\nm9zYIGZmhe3lAP8Vh+sHyhh6BXN9HtyQ17Y3EIRdKN/5LnzhSVrnKiSSaSJFxVNU6k5IqeviSz7n\nvBqZKKa8soSQ1sk1mki5XyZ6/S/5nalzjMiDOGshlbaHEaVJJW/lnPRtbM/jg+PXU215/OpPvB9L\n0wiqKTrPZ3jrzg9y/sxpXn/+FO2gTCbSufPonWTTOoErEAoKTjdAlWUoODiSgBb2I8GDWCI2DByz\nD/QhCPEcl8j1iF0fMYyJnD73QU+YoCnghwSAIMqovojqhPiBT9d3EOs2RiSTzOQwUxnEWAIvxAXa\nqkjCiLADASmUEHWBB+77dTKajizZHNyh0G6H9BwHUTQJoiQxIu2WyOyOIkPDWbrlNBo+eT3DI/f9\nJXO7buXt79jL2sZZhgoptksea6UzTE4NYegW7WYT3xMxU2kOHroWCYFur00QOrh+332lqxa6adJ1\nbOYXl6lsV6lub9Pt9mi1WiBIJBIJQvrabVEW6Lk9krkkyXQS1+5RKW/hex6mpmPpBknTwnN9FFHC\n0ExUUUWWVGRZx3ZDkoksbs9lY2GJ5qVVbpnayS//2I8RRg5f/vbXuLB4nteOH6PnOliJFDOzs2ia\nQqm0wcULZygWsnR7HqqRZLvR5NTZc6SLRUJk6i2X7373OHvfegfHjx+noMhcW8xzbdrg/Uf28ve/\n8yj/6cO38qm/vsi9v/AlPvm1V/nPX/s6nzx3gKfa+ynOtEiNDpNL7Wa71WUkM89A12fV1rgkajy7\nKNBQMripPNVmTL1VZ/mls4wUhklOGMSDCl7RwEhIpIoW7VYNZXSIaKFMtb1GIRmwcyhJZEi4QxmG\nVJnCaysEUpL5lkk1Mc4fPfcK55bO89adATePbPErt81h6BGKJjI3O8zorgMIZo7GygUI+xr6H/T6\noVEXvNl0cAX6At/nGVyJhbki97ryPp1OXWUXRH4IUkzg+30nl+8TCf2llCyIV7/ryv2BGxKFEPgx\npqFcDlmUrjISNElCVhSCoB8F3mfDRriuixj3gxU7rTZxDFY6zeT0FNfsP8jG5hqrC4u02200TcNx\nHLqtNp12HcKQrmmSz6cZzliIfn/pVl1d4r//yvu4cyTN5vFjWMsCA9XTlEYOkLIXkGMF2ZEwfY9q\nusi5oEb6C6vs9tfZSA/SzEhEokRNDnDyCp4psGdomIWgi78MC7VL5K4ZYGBwP95/v5HEoUNUfu2P\nqfz5f8F/vkxbfR53TMRtyfiDM1j7J/DPNnn8o79BZ6NH7pFP8LYP30OuscLHH/0WHQl+/1Of5nPv\nvBevUyKXTtPqLiB5FvniCIodozoBpNvI9jCKHUMY4dZt1KyOo/noXoSMiKBr/SVlEKHKYj/mJ4qJ\nIpkwiggVCdGL0TwPDJ3A9/EbHXp5FT1rESsScRTguz4EPoZhoIkKQhSCWkDwIgLJRg4yjM1k+MD7\nPs63T36cIztvQ9VO4dsyejZDFKkg9xCEBN1uhQduf4gvfe7rdH2DQiqFla7w7z94B3/yd48jJSw+\n/0/fAxwmJmZQLJ/QbUCkkDEHGB0dZmSoyObaIrIKgR6TlhVEySVppgkjk2qtQqfXIRZFVFXHcQMk\nSSSMod5somkarts3s2imiuM56JaOFIVX8+MCL6Req+FZFqIIzWabTrOBH0nEXkAubbJv3zXMjo4Q\nXPwe11x7hNeeeo6nn3uVBz/4CI++/8d59dIJvvXcU6CKrGy8QKlSxTATzM3tYjKRIooiXj95ip4b\ncfr8JVaWFpidHSUWNLzApTA0yZe++HU+/JO/z+ROnV27YwS3DfUt9g4McMOuOS517uLIB+dYefwV\nPvW0TVa7yMk3dO6bm+VT3zM43+nhCxoLXpPPDcP2K+fIDkN88AjPfX2d/cUmdmOLuqexuLpEThzg\nH//sq0xcM4w2PkU2o7C17iAYOvnhASqSj9Pa4qGde5gbTNB49SW0RpHtaBsyAlN338mFco19ukjc\nbHP/XYfZu2cPzVgnJ2XQVzb5/fQgeRzCjkBhxwF27NrLkNRhY6PEv6y88gPXtx+accEV88G/cly9\nyVTwZvrWlc8Cl+UwYV/OpWvYdl9dEIYRsqYQhv0gRk3REASRIAj7EOO4P/MVY5HAC5AEqR97E4t4\njkfoh0SXUxZiIkzTwA98fLevUBCjgHwuh+e5fT2s67C1scbm5gYTkxPsmtuJoihUq1V0w0BW5D5r\nlghXlBFwSesKMRqSkcOpb/IfHriB6OWvIIk6G9oG5ZZNuykiCh6fKAn0jJDWSArXTLNda6EwRq3b\nY6ugct7pMK1lSGgu1SCgOa4xN1CkWhfYXqpjJStMThkMrdqkwxzdmZD82XkcJ89K5SxyfprzT/wD\ncjrk/vfcyezOcdbdlxjYfz3RXIt3f/DHSakZvvaFr/PK03Wa+Q3Ov77O9YcOs3ffAZztdSLJIXIl\nBE3FEhNYHYHAaaCnxwj9AEcMiAhQkwaxJfcf8bKEKMl4cf+hKssKoeshxiBA/2EmCqh+jGQHOFKE\noMroloFkqYSxRizLCFGEEAkIsoQoCYhBhORFBJKGJoQEZoCECtQZn7oRr/ME1x25iS9/6wJuI4+u\nN9ESIvXaPHfddDOCLPOWWx/ie8/MI6mnyBUvIQtpvKjH6bOv86Pv+1luufFW6s2LtJoRhw7u5TvP\nPkGr6rJn5378IKJWbjBWHEPRFILIY2R0BE1RGBsaQ9UttqqbtHsdwjBksDCILIkkLJMgEhksFtiu\n9ZUKCBGSLGFeXgZ3ai1cx0ZAuEyI65/sVFXGCyKSmobrhXiuy+BgHjOdw5RE8lGHnGFyYXmVPdfs\n59Vnvsv6sVPctP8Qb3ngPmp2wP4D15LKFlA0i2e+8zylrW1ERaXreCRTeSRJYaBYpGfb/ZOFlqTT\n8XCcGKUgMzGQZiCRY3Wlhpmb4cRKk+culmn1Gih2lw/fcj3vvWeOB6dMzr/2Hc5ttVmY2MmzC5c4\n61W4eSjD3iUXTwuJZQg7Ms16jZwUUZydZH5pnRtufSvrp9cZTSkoQQJHyqLZKhtbLZxmvf/AzSfI\nWSZhbohATtFrdbHmJsmPF9DrLbaDCLbW0WpvoBTn0A9fz1ZdxY+m2M5rfNNf5sR6hOS0cJUs47M7\n2dxc58zZNyiYIZcW5vm1Rz/+b2cme0XCBVwtoG9WPVwZI1yhcl1xcUFfugV9q6wkif0I7iBGURVU\nzSAI/b5C4bKJ4UqHLCAQRxJR2H8fBhFxGPfxhUGILMqX9bshcRwhqyqZXIYwDnE8m1TCpNtpIxAT\nBQEjQ4M4To8o8tlcXaZWbzI4OMjU7Aw926FaqeD0en3AjGTgNpuosYcgmbRDCcfvMGpF3LhjhF4Q\nUus1IB6goeWYX1pnIpb402+us/Oh/bT8dYSmzp7xgyzOl6mLNWx5E7PgAAAgAElEQVRDIKUnGE8I\n1LsdWgM6gRewuu7Qa25yYLdIoiuSa0jkD+2neWyVi8UMf/AnX2fXuEhFsXntWIu9BzPsvfEDdPyT\nPP34Rb7+B1/hbz7/RX721x7lb/7wr6gv7cdStzlbfZGhRB61F/OWe++FcAtJS0Ag48QRbsUn2RHw\nw4A4NIgUAU8IEIMIRRYILBFBiAjiiMjx8X0PpD49S4xjJEFARECKY1RRJowDHE1CjUDWNaIoRG30\n8FZclLqH2gVZ1pEtnR4hXhSiagqeGiGHPlJKx236yHoRIRGwZ7zAanmdl48vsnS6R36oSiJvUtuq\ncN9dtxDQ49CND9Msr3Lx+NMIsU/L3Us7HOapb32PiXSCmb1DTEyZrFzqUSwmGR0cwO1ErC6uMD46\njeIrxI6IribwgQsLi2hSglbNZb20ggAMjhRJJJL0uh0C30MR4d3v/yCJRIrNjXXCIMA0LW684Sjt\nVhvP8bDbvcu7Ag1ZVpAUEUVVgRgzmcDptFE1E0WVcZ0OlVqdYtLkmgGDwDJxIgG755E2LQxD46Xv\nfY+R/DBRNk293iYIBSJEcoVBPD+i3XMRFZUwiNhY36TTcbh0aYGB4jC5XJGJiVkq1QbdZRlZV1CS\naWp2l61KiQO7d3LTwQOsnF9CS0mcqi5zZnGLycN3cWDfHahujvWnF9ETGZKJJL9zKKZ9vgF+zNNl\nOB5bXGvFmNY4idlhfF3hma+8QC6XYarYwvVzSMlhOjUX13UxAo+BbJbyRp3NC5eQY1B8E1GxEO+5\nnWBgCK/i4OUHkcSI4v5bSDz4TjZmamTG56k1zvD1E9/h8RdfZe/+t2G3t4kSwyhiiG+3KdW2GUwp\nrKws8+jHP/Zvp8g+9thjj7053QC+z5e9giS8omW98nplKaYoCpZlks1k6HW7EMe4XghCjON6SHJ/\nRCBeHkFcufdKXkMcR32YDBFRHPX/eVUJJFCkyyMESewf2ywdQZZB6I8KHKcvEYvCiOnpaTY319FU\nBcvSadVblMplkokkVjJJNpelUiojEhO6NrIiEMZ9NYViyJj5FCfPzbPaETioe4xFGltyBkWq01pR\n0YpJknvHYdqi55Uxey6D3gCRvc2G0yDI9hkOuhkiNFxczUBMJ/BaPmmtxOSggrtSYE84hNG5hD48\nhbkWcKq+TpCRmZ2JibsSx9smd84+yIVLf883P/N3VF6R+Gr1HOMjeaJCm9Xmq0TJZZpeSGy1WFkr\ns++OGWaGM4jKJFIMvTCis9Am54iEooXvhBi6hiSAjoIg9gMLVS9AdCOUqP9wVS7rm2PhcmJxFON6\nXl/ap0n4qoAhaAhxRCdw0QQRnq0j1UN61Q5220ZSVCRVJ5AkMAxEOUaQQ2JXQBA0gkhDVHrouTnO\nnDiBF3ZZPl/j0I0RgahgN22u3T+DT4mpHXcxUpBorQ7QdbKcXy6x1doidhSWzlxg9rDD2fPzzM0e\nJqFprC2ukEtk2LNjjnxOp7y0Cn6E7Th0/A6iptBqdIl8MAwRTdcQpZgY8G0PRQCn12FsZg5Jlllb\n6RfiXqdDp92mXq3hdF0G8gOkUinCMMIP+1zjKAqJhIgQgbRhACKua2OYIkYyyY6hQXZlRTwjQ61c\nJ+x4JFMJ2qHLyI4pvv3E0wS6wlBhiKSV4Oy585w/f5GR0bG+zjuMsLs2QQieEyKKKrKoYFoJVpZX\nmZ2ZwdcUvOoGO4pDZFWL7Y0aoQeb5Srm4ASC0+bG/bs5t1xBSwUYpsT0vgxjR/O07TTipVXe6S+w\nlE0y3w4oSRneqHUYT/rk986xUq7xxmqD6UKe+ajGgNHDSo1S7nYRCnkM38UPOiTzeZolGB8aQ51I\nUrcEEjkTe9PHX6phjiSpKRqZzAjKzn3kOj47nQ4tSee12iT//PkSC+c3GR5OMTazC8e1qW0ukU1q\nGKrO+lYNp1nh4x/9lX87Rfa3f/u3H7vSob7Z7SWK4mW51vddYG+GekdRRBiFJBMpwqDfDTlO0FcD\nxAJR3IdvR5eNB2EYXi3axDFBFBITIaoisRATCxFBEKKaGmEUIsQSPbtHFIdIyveLrR/6JDN5JEXG\n7rlEUUiz2USWFQxD7xsKJI1YFNmqVPCCgNHRUSzLolatQhwQWSYuMWIcIMYOZiaFreQ5XRb5juOy\nw1CZrFfpeToaMeVkF2c2Q1N1iAOJVHID2EcpfJ3trohRUBAVgSAZk96O2N52scYKRNtNckYZwh6i\nczNZQyOXWkNMp5EXTrPTqLBtZ0nNqGSP/nue+sIXeOu77+fVpb/hhZMipx2X2cExerUKdS+D3BMJ\nHIWEaNDpSBTzA/ziL30YJe5iZA+BZ9NzfdSGRMIRsT2NRDJB4LZxBAFF1ggNCa+gofZcQsdH9HwE\nAURFwg7cvitPEpE1FUGAIPRR0gmEbn+M4PdspKyJvGOY1lfWMUdHUXM54iBC82IkN0J2A+RIIjJM\nXKmDFmoEgoQnRmiCRKwk2FpqoloV8olRDt0U8NqJFWolj3vuOgDaFlO5n2Gr/QLXHLqZhY1T7Ns/\nwlBBod1qIukhWu4M1x1+B4NDgxhygm6jx8Vzp5AFGctqcf2hg2iKDErEK6dfZXCiwJHDhzn2ymt4\nnk0U+XTsLqIs0m600VWVIPBIDgyzublBeXODdquFLEl021067R6KrOK4XWRZoWvbV7nLQdxHUYaC\ngBhGyLKGYanYTod6u0PBMkh2y0RdnSgCXxBYXV9HEUCWFLY8m7MXznHi+HEKxWF27d5Nz3VpddqE\nUYjTszENg2Qiw8ZGlYSVZGV1DVWRWVpapNGoM6jCzHgW5IhKs4kjSgSyRssJOLRjjPNxRGkjIEpl\nSG8VON5dYePiOZKRxsCgzk88chuLJ7d4eV3gtO1SVAvsGFbJH55CLLh4WwZnl0KO3lLk2U4Jpeqh\nBBHJhEkrpZHvOcQZmS3PxddGEdMJhsZ2ELTqaHELSzCx2yWqnVM0q1WE4WGiuEvHnwdP5n984Qxf\n+sbz+N4qN7/9NsrnT7P/jrchuQ3GBvOsLy2gSwKulqdXnudjP2CR/aFRF0A/HXZqauqqK+uKS+vK\nourNxKyrna6QJoo6dLst7F6IKEIcKYS+DoQEno8cgIhFnlkQPELVQDMlzHwWRZRQYgFBUREUAymW\nCBwX0RBR5Mvz4AjiMEKTNcRQQI1lAruNLAQoKqimgqoraIZOrd4gDGVEsYfg5jDlkGa5xPzZU0xP\n7SGQQhQL4m4PyVYQSWHbId1KhawKxQGdbqDx06dMttNJRlJtAqFNV93NJSGL5e0mcPZybXUQudcl\n6QwjqtuEWwUcQcUKDE7Im2ypUwStaQp6h7FoBLdxAGsoQyk6R+/Ww4TfeBpl02cyXWQoEaByDS89\ncQo7dnn65FcJYp3XTjfpVmO27E3afoQSdJHMBF21h+tXcVG59ugOpofTxKn9tJ0OYiaLktZRlJhA\nF0nGNrGjIJfAj2ycziay4yI1berSNr7sEFoqoq4hSGAlNXwxwDcFMPuFVrVy9NQITVEIMxrKiE6U\n9OjGImZSJahViSsNDMXCC2Wi0CCwTZwyuKs6VnuAWHXwlSYJXyLY0hF92LnvEHGvSHZ4i7GBe7h2\n/B1MFJPUm6eYK8ygpQJWl20KUzlywxpbq9vceeR+PvyhR/jdv7yHg5Pv4Fv/8hznLhzHShVIDqrI\nyTxNJ0lppUmjKyEWFd727nv4009+nF/90C/it0ugRQS6jKSmGbCmaG24ZIppipNj3PvwjzJTHKK0\nskY2m0VXNGInQBMkDMukJwV4vs12YxMnaBNFXp9X7EQMje9kZmoWK5Gn2trGD11U38IILQxNYGbX\nLC+vLjFhmnQbPWqqxsb2Ns8ee4N5V8WTTFpNm0//xV/wz5/+G9750IMc3LWTvO/zgSPXM6VOcH5p\nncX2JlbGYsf+vSy/sUgnoaGFIvFmm4HIZP7UCSobVcROkm5X5L57bmGuuIeFJ19m9+4hfvfjP005\nuIRRMUiaR9hqGlTKLQJ9jMJP/QLnZYHjyzbJuSRHds8yaBTwtTzdcZfZYdh7zwIFIUM8NUq9VaZT\nbTPcdjldvoighpTnVxGCEsvb32NV3ODALXdgC6M89eoTnK+8ipex2Png9ey5foxG/SUGhgfZLE7R\nC1W0CI7cdAuP/vh/IK2aCOV5xkcHqber5Ac0ZsbTDAkVNEX9gevaD0WRvZLXFQQBpVLpaierquq/\nyun6X5dioigSiS06XQfbFgEFUQZBdlCVGFUyUJNpksUMftSmE86DD3ogUtDHGBwcR5J14ljox18T\noqoykiDh2h6245KwDGRFIogiatUa3a6NZSVwPBfHc6+mNDQbLWRZJJVO43sOYaigWmUc20BSIur1\ngG986ykmdkzS60IQxqi6TM9t0e3aOLZPdWsL4gDcPHPDKzzwTMDrUwPkD1mYUxKP9mTKeofDzjBe\nU2RFKlENe2hSDre0hD6YxnW2kYQC9lqPUcVj7vYDLMz7bEzMIrhVZjwN6cwGLVnF1WLY2ELQQd7q\n8uXHv8i+Xffy+FN/RlI7gqzkmNiZYP6Sg5HWkASRyG+hygrmwCAZ2ebBO27BrVdIOzFJxQdZJ5Gf\nxdAt7EodQp2t2jzsn0bzBAzFohMFyIGMGmgYgYkkdqiGJdpyD8d1kX2RqCNQa0Z0UGm0asiBwJYa\nItUCsAU0M4v7T68TJRQQBKJOF1arBAslvOUtomqbuOOgtVtEqzGNsxpWMwNam+ZYmS2xgWJr1DYb\npC2TRmsD2Yj5sZ/6GM3eDINj7wKy6FaGJ5/+DB/72D+wUr3Ia2fP0vCOs7kQozdEDo/u5PYbbqJW\nr5LIZ3Fklwvls1ijcwzuKCBKPqtrG3zvtSXOb6zz8Dvfx//zm7/Gj95/G4Jbxe6sYpogRyp2x+Hp\nbz3FhcV51lZXKde3SWTTjI+NoUsKWSNBSlBJJ7IkrSwJPY0sKlimjqGpHD60jxuPXksYO0zPTCII\nAo4fYLsutUaH0fEput02ZibDanmLSqOLOVCk1rPp9hw21rcxzCzjozsobdT5+Z/5dwS2x7/7hV/k\n3e/8EW68e5D22inuOXCEvKFz86E5ilMpauUG602J4q77OdOdQkoeRZYnqbZD0msx6e0kpx9/kncd\nvhG5IvFHv/slvndqnpvfdYDb37UPV/HZsXc/pflNCtYAn/jjP+Yf/vZTRCFcalcRUlMM5wuMZDIc\nuH6I+VcFPvb+H+HuozfxRkOhhseF+VXMOMf6mRKmJGBmTZxQpnVijcWFKt/82jcZHjHZc+9B0mOj\nTKSG2G5vImoC335tnX/+9NdoOht0I4lP/van+MBPvhUjLbO8tsD6RomdOw9QHJpBUi0Wlxfxgh8c\nEPNDMS547LHHHlMU5SrYwrZtDMPAtu2rdtgro4MrM1m4DPuWIYpiFElHVWQE0SP0gag/JlDQkGSd\nRErDGrFoNh0mJ9I88N6HcEiwdnEBSQwJxAjd0BDDiDCICQWQJYlYAM/1kZR+fIwQxcRhP3ZGFGQ8\n3yfwA2IhplgoEgQhvW6PSIiIiVCVLKHQRpUH8H2474EH2FyrEMQ+nt9BFCMUWaHX9UhYCXzfJp/Q\nqJEmIwX83bGQQ+9Ok29FSN2AzaBNrO4g21olITmsBlD2QuaEDkIgEQxKbAQSqXiIpNIkqq1xpiww\nuH8/N5S2sLQeq989y8QNe3EXF1GGoaoVyO3ayVJviEAsc8NbjhC2xvjHzz5DSsnTcGvIgonvBuiK\nTywqtIOIw4Mmv/XRX0a2RKiKOBkBezvEP93AuLiMZFp0ex75YZ1GPUC6fpLmyiaJWowbhFiSDKIJ\nKQNTUwltBSM5iOh3UMIIw0wgal3MfBpZUMEykQOBeDCB2Okh/stJNC2FEIXEYUAYh7g9GzmIUHs+\nWscnzjlEGhCKqG4KJ7RILwdYa1t85nN/hT8gkhow8EKBVh1q1U0O7x9lfCiPyAyW4vHs116n0TzO\nrz/613z2K4/xtntGsNdBaU+yNL/Frmv3kMoMYiZkIkLuv+9uZuaGEOQtECLq9QbjIzOceP1JTp84\nwU3X3sJ4RmT/nilst8XGZgnf11lfXicOAy4tzWNqBrofEQUhcUKjMDVOq94kKShYyQFqtQZhGKOr\nGoHr0uv1yA/muLR0hs2VTR546CGazRaVUo0YgbHhHLft28Fnv/E8dx29gePza2zYDhDjxAKbjRZC\nLNHtdkkmU0SRQCqV4uXvfhdDjDi8bw833fdW2gurSI5Nrdnk1LEz1AWNyBaYve4Q6u69XHfwPszc\nEOO79pAqjKBMTFGXTKaH82yfXsbqdDl6537GpsY498JZmo0eNbWHUgFrcIBXTp5g/sx5xvfN8ODD\n9/LMl7+EJXUwc9dhx3WyUoTs5BASGi8dX6XX8pCUiDjQGC5MUW3XsVWfs4tL1DabNFa2Of3604zu\nG2DkwF7UVB5fAqwky+fr1Dp5FrcUzp4+zj3veYRH3vcRfu7HfwRtREQRYHxmB1/44tcYyI+wML/A\n2uoiUzOjbKwu87GP/mCLrx8KneyV7lSSJBzHIZfL9bWSl1kGb3Z8XQlbvNLNprOD9OwtRLq4PSAC\nSTSQFZEYB833aDcbFGdHUKczzBbfyn13HkUduoZC+5vEgoIsxQihQxB4iGGMJEioaMRx3/ygW/38\nr3azRSKRoNftImsGQhxjGCaB188Zq1arff2iKiFKIZ6toBo9xFBG0FdR4yQvP/s6R66/g1deeRJZ\nUWk3GkgoGGqfdauoMeUwYKRYQMiP0S6X+cs/Dfipn9cIL9QZHEgxT42JVo987GG5Oei02bSaZKsT\nOLvy5Nw11lhntzZFbclGnkgy0tmk0q5g3T/NfLnDnrU28nia+oRMfTOmYXq8/OIbvP19g8yMH2Hh\nWIjThVJpAyuhEjhgJjOYUpty3aft9HjPI+9BlWSaXY8XTh1DPy0RzncZShTZNzxMt17FSqTwJAgD\nB3NHEUEI6bywRqYT8fLKKVJqnsYbHsa6Sk4TWNfnsR2TtOjheuvI2VEioweCSmKgiJe0CIsK2VYH\nvZQg0LtImgKyiCyp6JKCFgrg+RB6CE8JyHMi8ZBOWfEZ/GqdXmeDResNSrktShcvML3jEJculqlt\nu+QHZAbyh1hZDBjde55KuYMqZnnquce56/6f5qP/8b9x4sX/SnPZYsE5z8BgHtf3aJVXGBsrkEdm\nyjIpHDpArznFpXybC8lTdBoRj7zzHoIwhe04dEtbtDs19uzdwfDkHH//90+Tz+awnSYDep+VEQsR\ngtZX1JimiZZLUS1XkKplEAVy+QytRhM/jEmkEmyUttiqrjE5MYUoqFQqNVRZxpNExifH2DE3RwyU\ntmt4MTR7Hpe8Etl0tr8UVkRCL6S8XSWXztDr9RgqDvPsU8/zlptuYfy2t/ETR9/D7/3PP6Ob1Zk0\nsxyLe8iDKS5dOEuvEaBOd3HFELvdZEjR8IMOY5ZCa2UdOWVRzCUpf+NZxmf3UCvs4dJmid52jXc8\n8g4uvHGCk68dZ3R2gpPPv8DgjXtRsxpGUKG++TrFkcP03FO4RoSSOMDsDQ0WXgupC12KmQxntuZp\nKR6YMqWlbWZSo7iKSCrVZeamEdLjY1RWG2zU6rx49gRF+Toy09djVs9BSuPxp4/xjc+9iJXV+LGf\n+z3++Hd+hdlZm26rwshgGjEaorzmsLm2ThT+GzMjQL/QXgF0J5NJXNe9Ss+6Mia4qgyAq3DtZreO\nLEropkoU9hDiFL4X0XNtIMSVe4imxdDMu5m57RqEeo6BGZ3zvQ56IgmiiAhEPkSCjy5rSMiEUYwg\n9rPDBEn8Vy6zKIpQJYVGq4Gma1iWhd1xcMX+ESIIQ+TQQlH7xCFdGOeXPrqDJ598lReefZ3pnTvY\nsXM3Gxvn0HUXpxviez5yJCIKMY7oUW3ZqJSYmU5xixyy+3SeHakK6692uHh4AG9AIFrrsFpzaIR1\nfuSOOZ55LmDPQszRToIv5sBttznZrTE9nMAPlmhcY3Ds2BlWLZ3NfB73wjrrqZiaBInEEIZWRNbr\n7Jq4m+f+5dNIiOSzKbbsCrFk0nMdQlxkJYEo+Dxw793Q84g1k+bJMhvpFKbT5a5rD1I5sYC5bxJJ\nUBGXbSwxwF5dx5gbRbMyeOfW6J3ewlc91IxM09nmxKuryGESWVORRANdNAkXXme2Z7Gd0NCiCgnB\nR9R80od2Ig1k8WOHyI6QYhCUEC0IwBAIxRhfjBGTItJyCXUxJNM9Bkf306opXHgx5uD/T917BlmW\nl3eaz/HnXG8yb3pfJst1+a62tEPqbqBpBBJOWhBuBgRIDItYxGi1CNTManZGoVFIMxpp5CGERijU\nQhKmae9d+cqsqsxKb25e7483+yGbnv3IfoMbcb/fiBP3jf95/7/f8yg/QyIcJLqUp+DvY7t1gWM/\nW6B/JsnOtkkgrZEddBmfSiFUTlPpPcehw+/lxe8/xvzVy0wfz+EITSzTwxdNmk2FXLKPhBijtLlM\naa2I2VKo11aJjH42Oj20SCKp7rJf7R2VbqvF8ZOn+ebf/IB00qDerKKlUjRKNWRA0RXUdILtaplu\nu7ObxIgLiMjU2lUURSWppWi3uyTiOfbP7qFXtrk+v0SvayGKEYFvgyRSqjToTydodk0OzN7EVCrP\n0tIFrl6axwoEhJyMoSm0ew0ifBJGHEOJYbku/+WP/5w9Z05QtEwe+pn3U455LCxeRi8vMCjEsbMF\nXC3Jpa1XmIwXqC5vst6rMBZ0MY9MIFyvk+7LsbnYxjHTuE6LykCXocEUsnaIF195hlRM59Rtxzm8\n/wD7Clle+sG3iUvgJUcJG5doNjr4+RzK+CyRLqCFCtn9g2wVV8n3QbPVID0xhh036N5Yp+S2CTN1\nzIEhip0U669uMb9Zwuy4SGKMobeKXFmYI6drFMaO8uILPyCXHeFrv/MHXH/hWQ7uP0G9WGUkl+Q7\n//AXHDhwgEpxnfXlZVKp2I89235i1gWapuH7/pvUrF3FjMOPmAY/SgX8aNjJsowsyzhhD11Nk4jl\n6PZM/CAkCEyURIoDh95Ceu/b2Td7gjDVhyv49CcERNGm3HaIY3L+uWeRXAsjpaOnkiAI+M4uy8D3\nA+LxBGGwyy2QRAmzZxKEICsKURTimh6qoSIpEgODAwRhgGZobzBqHSwLFNnFsqu89wNv4dknVijV\n6xw6NMv1a1eJ/IgwCPEDn3jsjVxvTMETUtiew1cfOsndnXmCtesY2wFb2yXWSyVOfep+ah2f7KbH\ntlfmXV96H2efWOTzqSHmFq+zHAZMD6eQUhGOKqNrNnuzSYTvLrGDyPW+GHvaChtiDyvlcuatn+af\nH10gP7nKmVt+lVeff5FYqp/azhZtu0PoS6SySVzHJ5ctcMeRMT76nndhhyKtUGGn6LOysEMhpnPo\nzCFqN4qolo1vtRFn9qBdKyLeMU2ki0i6hjWawl1axdloIAkC/piCOjLKyuVlRjI6XVHAE+P0pzQa\nmQBHgjAl4TklxvpjKAWDltIl5cmIbkDoehCFeJ6HJEHo+4SuS+ir+L6EsN5CzWfovH0cRfHJOyph\nXz+59gAJZwatfZjh5GEmC4OsXtlm67LJ0nczbL9Wx1s5AvUYlbkY2+frZMz9TKdPcOTgnaTEGaoN\nGNmXp2U7+KHPxsYmuYNZpkcncGp1ljZv8NL8KmJiEj1M4lWqzC9eo+lEaKkCYajy4ff+PL/9W19m\ne3OV189f5oO/9EFOnzpBcXubnZUtrFKNhKKjygq90NwFhAcB6WSSVr2B63hMTk1j9brUSy3KtRrt\nZpnI94kkiQN793Bmeh8vvHKNE0dO8qlPfRFRSbB3zwwxJcHtZ+7huVeeR9VU4nEdTZMw9Biu5xFP\nptkqVVi68jR3vfNeasuLjOoGF65dRvIE+sUkr61eZ2dhk1hri+n0MBk5y96JCdL5BAeOHWXbT7F5\ndQtDT5E+Ok1iNMPedJ59gwXiY3GG+7LUvDaHTt6EUGlTb1Z46rF/4vDoEDWhn6QaIgdLJBMGsjGL\nbTQQhElSfW3EzADoPkg+A8OjuxFL1UXJJ+jfN8Liap2XX1vD7GkEto/vd5k9fZBOaNMqVRGzPmKo\nM6C7mPEh0rkR5r//daodg+LmGglDIB3XmL9ygbhu0KhUiAj50m/8xk9XhAtA1/U3NDO7xYMfJQve\nBGq/Ua19Uw/juoyMZ2k3PJq1KiEBswdPocQThLJMYWiWwSPTbF55kZnjd9HcdDl5uECt4RJKafSo\nwtxLL4JrYaQSCJpCEET43m4sK3RDFFlC1TQQBDzXJfAD9LiB2emRTqaQVJkwCEimknhhgBt4jI6P\noccEmk0PQQgQVZPKtoGiCVydL2K6Jqqqk072UdwqIgoRurErZVS1GElLopbtIm4LnKLHe09O05YD\nnGw/6ZkxjGpAb+46txp7me05jJkey2cv87kPfhD+/jtcHU5SCwVOnZyk57QphxE3ZbIYry5SMH1e\nxyDZp5LbO8ziXB13n8x9b/sUTzy5gp6xuPkt91PdlHjp/ItUdxapN1NkciLddplm3SG0evzWR9/O\n4MAotiDR7IWsFLvkR4YoLW+Tl5JkPYHM5DCRLkFeQ1ozcaYzOH0GQiihBzBw52Hsq5ssl65i+oNo\nCZe33nucJ743h6ZIxBMdnFaMZhLEJkiegBdZHJjei9hT0FwZ2XEQPB8vDIjEaFcaGIJsOcg9B1vq\nEK9UCetlxPffgna5grzjkVm1GQ4UhsY0DMdn++oNehtNXnl0nfWnMnQvzGKt24SVHG49YCw5jdTU\nEVsWauCRS06gC/tIRBMsbtRIj7tEcogYBkRuSH8iwYtPznF5boXqTocH7j3E+x64mYxSZzCdpBnY\nLG9WGRo/wO/+zn/kb//7f6ZRqfOJj3+aD9//Lj72yX/LQ+95F8f2zBIW6/z+bz/C3/zD31MzIK3H\nkCWZVCLF1uoW3/7bbzJ/8TzX5i+zsrqO1e5Rq1cR5YDJsYQ6yXoAACAASURBVDEsV2AgNcjP3fkg\nBw/eyvTYHrqNEKsnUGvWmRyYJpcc46Yzt/Daa+d26769LrIsEk+kQFbQM2lee/0CD952C+2NNVpz\nK0wFGdztHud72yh+DzmUGd4/xsOf/wL9Z06Qnxwlf/okWnKK1MwMM/fczom3n2b2QA5DiKjVPRrd\nFr3WMkWri+p6hK7L6J4ZXvnuD6hurLLv5kNY3RBPrhE4GpmESCYzhhlkkLQm46kcudFxRAmMWIps\ntp/Ab+OrLZxIYqzvNvrVPuSUTssvEQu2uO3Oo1y8UUdoT9E2i8QTMWLAxYuLfOwzX+bYkWEOzaa4\nulghcHtEgUdpp0wY7d4HJQwRRdP4/I+5k/2J4cmqqoqk7Oo00uk0pmnTarVQZY1sdoyd6grxGISe\nzr5DMywXl5ncc4bbTp7mFz9wL9WqT/HaUzj0c2k7xj//3X9iZiCLNHUfcaeGMXyG5JjHg8dn+H/+\ncYPjh/KwfJm5Vx7nRn0Zd6GGqYhk/JCO4RNzFbri7g2aEY/tam1aLVRVJQoCfFHGtWx0bdfX9aP1\nhqbr9GyLZEqnslMlpsSxuu4uJclqcfLOY1x89jJ9E4eZuGOW9eefx15r4hsBtm0T6WnSlkkjE2c0\n7uM7fVR6Vf72k3dyJBDo2kkUtYEyNkonzDEtBeTZIBJdLp6r4F0pMb/f4bFli6n9HvLAfmYHPeyr\n6zxcj5P93M/ykQ//OQ995DZOzqjsSMfYsP6St/z8I3zrTwP+6InP8eT/OM/nf+Wz/OOzL9OXHsEq\ntnnnyRQfeeAUSs/hyRdW+aUvf5a5Wp0TQ5Ocr0uslUym9CQT2SHOXlpElXQKRsSJmycJkx6xsoh2\n/wlo2iBqNNWIjKjRC1zkhAZzc7TOXWK97HHu9RUODu6hdGmZne0W+SBHLATXs3hwbD+GIrOtumRD\nBU3c5UrsJk0EAkkgeOOCVJIkukqH9Ogw5LN4HQe70iHq+jh2QNd1qHe7dH2fru/gBG9Q2hCRIpAD\nESkVI8omIZ9EzacQdBVZAFWRMJDwMzpL669y7CN9xFouN6IOwrU6nXCV/uwEjcYy8dpr3LZxnbZS\nJhEI2EczmLd9lr69n+DD7/slzpz6RX7lEw9hlz10NIKL1+ls18mk+uk0ayTec5rf+8Z/5cu/+zVk\nQ+PffeAT7BsfJ0jIfOR9HwRNg7ZNzQ7IDxRYvHiJO97+s0ztneKTH/0oPUGmslblobHTJLWIKhHF\nZo9ew0FRDOK5DJVqkep2mRu1LZ6fP4vp1GmXVymk4ghChBkE7Bkb4NIr5/jS//4RxjYbNHc8PEXl\n9n/zPp44O0cppnDHidtQDo6hNixE28IVfdR2lx1thHtHS7z9vndzbi1kYirG3//DX6Fr/SSEDtd2\nzsFWlZ2ewowc57XzzzG4bxj7xnm8UMPaapCZ7KPvjofYru6Q6m0geynMhEFCy+Jo4/hRlblXvsdy\ndZOD+wQmOos4yf3YapZiHcxanGZL4cX5i6zsFPmFhz6OnfaIKQmacxaDB6b45je/xs9/7P8mEZrM\nnX+W+SsXiaka6VSOAAFZDHngrbfxrb/7Nts75R+LJ/sTsZNVVY1CoR/L6u0WDcIQs9chlUjSanVI\n5jXK1d2miReZJLIp7tn3Vu677yFOjhZ48lKd2T6BeqVLpVliev8DDB65m/Xnv0VeHaIwOo3pidwx\nPciVrQaG4BPaIkrWIJQC9qf6mBOqQEgzLoIdosoKUugjKv+L5qUoKqIo4rouRjyJ8IZZ4UeXdoIg\n4Lruru4Dl1wmS6PaQpY1HMdBEEVGR8a5mL+BvbnMmHIXm5k83UobT/TIDSQQGiLyoEK8atGWE+QE\nlYm+OH2tHlJc5OJKlnwgMRlaiLEbvPBanlM3xckPRFgbfRSU63i9EdzeOm1XYyK0CGWbgapBM+Zj\nRBYjgwI7bpvVqxbTD99Lp30LBQ8i12QoM4DZq7NTbjEzMoBmN3n7J84wPajxw7kqlC2KPZmdskzG\nE+lYEkEbkokczaZJXHVREmlSsRStygabK2WmDw4RNCx6DoSCSFJViUc+RAGyoaB2fYRD+8mO9NP+\nix+glCsI2T1kD+5FMTZony2xETicHJwiSsYp75TJKhq24RJGIQoisrB7yohE3tAQCYBAwg/oNYt0\n1RKmHxC4QCjiRBFWGGJZFnYU4hHs+t4EAUXYTZK46i7lTTRdxKi76xxLx5GzMRRDJ2x1sHotjHwS\nr7VIW9PJlWQudbcZ7r8VvXGdnzkxgfX4U/jPbpHaq9JJuBjXAmKjRbx//jX+9FOn+ML/fI7i9TNU\nMgGF1wXCSCWT7KfhtMkOxDG7G3zk5+7nyb/7G/7LH/xXxu9+K+2X59EMg/nnr5EOYyixBNuVFtez\nawymc/zn3/o9RqbGOXrgAKW1bRbaS9ieiJtWMTyJMV2lnbBwuhbiRomEa9G0LQ5PTjCxd4jJfSP8\nzZ//MS+/8BSpdBojlaJeqjA1McnlC5e5/a6fQSxdY1OXePJPvsHQ6VsZP3ULqhvSvb6B50fojk+Y\nN7BUA91p4zkOH/nUhzj3pT/kyJ7jDCb7WK82iRIGGysCDxy+hc6Fc2ytnsXyTUwpQZDbR+v6HLLk\nocdz9KpbyIFNJGq4iokkJejZRdrdJL7cwhF04oMOG8UpxrIFEpJMXDWIJUSccBMzu40V+hRLGo5j\nEQQS28U2UwemyCUV7n/7x4kJbdKDk1yZewxDz5FMDuAJu3LUmKIghGkkSfux59tPxLrgkUce+cqe\nvbP4voeua+iqSiadpt1ukcvlqJklZF/g6JET3Hv/HSwuLzKYH6A/oVNZWmX/wTNERoLa+lXmLs9z\n+M77cHo+O8V1/M4WspgkM32SY3sVvnexSEZJIocKUW+Fza0Fxgb7sIOIXtMkkYnh+A5WEO7WQFUd\nRZEJo/CNam64S/mSxF0QicDuaiEMCAJ/Vw0tgGM6uyLGaFdnI0oCqqbQ6ba5/d7bWL50CXlwP6Hj\nI64tkVFTNLstkqkMRjKLJ3RoKF3omVQVhVNCkmOqD4PjnH92DYU7MJI1gpUOzlrEC09vcOv9BeyF\n1yhro5yvFOkrxJjdO0RrdYtDVYOJw/3onR2ee6GOOKTSEVrMnriVfPou5IJPeUVmfvM13v/wPaxv\nLrN/Xx8P3nySta1LXLraparH2GpCYc8o2f497BlOsxwlKPYgdCWalS5tK8STFHw/JIbMQDyO3+6S\nzwwgHRrEdn30AEQRBF1GcD1EVaKNhCFp5I/OUL1wnasL2+SUOPERDcb7Ma+vcrp/EtNx8WI6ruki\n2i6OEO7WSaMQPwgIfQ8pjMAPCDwX2Y/R6dk0ezauExKGEq4X0vMC7MDHch2cwCcUBQRRQkREFkVU\nScaXQZVVFEAOBaQg2rUbBCFiEKDrBeg3aNptBsfLIIpsrloImkRuX5zJ5fMYPIf/Qx/LHsLL+YRL\nBjEjSTRi0VxZInnxIjc//IuUX63iXLmGovWw8h7q7RNIp8aITg4juD6pyXFa59e5/5P/FgSfBCre\nxQ3ktkS5blLeaCIlcqDp1HY6FOL9KJ7IzrV1OnPbZII4+sgIXT9AFVW6tkPd6eBFLr1GFavRIBI8\nEDxeeOVpHn/iu+xsrZGMx0mkktRbLaxWAzWdZe3GPKduPk4u3ocp+KQUja2FbUYn9qMYKmZoEUoR\nciBRU0SqMowCVnsHLS0TS6dplzp88uOfwAxc2iZUvIhWrYQtuFQWLzM8MEq95DKq5VCkkORAjNzQ\nPoLIptNtIBspSu0dllebVIIeC1tXmTu/ihCsUKmFJPtP0+3tEI+1KZYNLN+mVJ5nu6Ty8oUGtiIg\nGRGer9OXmiI+mmT58lnsIM/8uX9h+vhdtDYbJOKThGEKzTBIZCW6zg6yErK6vMYXvvBTxC746le/\n9hU/2DXFxnQD0zKxul2mZqap12u87d33c2hylvGhMU7cspcwdMjoCe678yhPPX4RQ4gYmJlhYmqc\nS5eWcIIaB8cPcnGjTH39MpKoM37sKIbi89yCBabLcF8aPahy7crrJJIqWigjiwqlrS0iRQDNAMvG\n8z2CKMJ1PFRV2QXMhBG25yKLu6CaHzERBEFAFAQ0XUeRZKJIIPACnDfMlrKsUN4pcejwHgKvSdfv\nQw1gq7iIm4ijyAGdRgslITAS6RjtDFoiTk1qI6UiHrr1IM889jqqn6Xry0h6QM6NQbeIpKgMTTtE\nlTLxQoZrZp1cWmJkapjGDze5ZTJDQvBwzleJUhodSUdL9FPIi1SkYbLjeXbmoWjaPHDbSV55/PtI\nmsXyVoXYeIxqLU57q0jkitx7aJTxVJapviGemqvQPzgIVkA6M8RGsUoylSCbyaB6ArLl0q/q2EkJ\nYWoAzfORFJlIF/FEAcWGhhSSaXgICQ3HMRl98G4KvsbitXn6M3E6sYCZqXG05QZtQkwEND+kk1Px\nLRtfEPCiCDcICCLwiHDeeMUtiyJ1IaAjgidJeFGI43m4nk8YhViBix+FCNKu+odot6EjiSK6rKCr\nKqKqgCwjShJiKCDZHnLPw+76ENh0x3vkxzwUXyKVH2UspuIP3Elq6bs4N/4V6+FfIP+2jyBn+4je\ncSdKTifwAhK1DN6rcRLXXiA4eIj+6QK5972Dwf2jxFL9CEYC3dRR8jnarRo3Dffx9d/5ApoUZ3xi\ngsq5JexQwElopLUEZa9De7tBKKuovoC5ViYr6aQDGdt2aSdUjFiCLauBo4SIcoTpOwR9CQan99Bu\nlrmxfYPvPPZPeKJLu9VAEATqjQbDI8O8+50P8ez1ecYGCyxvLDF+8wkGIoV3/NpHqZ/fwNpq03zl\nPMH8MsLZG8SKHXIzk/h2QK/XYKu4BZLLlcuX+bM/+zavnHsVHxerIzKohghmh303neQP//uf0D82\nxcDoMIbk4hMRZZI4KMQNnbHJKf71+Vd5/OXzpFPjLK7NsbKicOi2LkGQZmVZJ5baoC/hEJoSzaiH\nENvDc8/B8nqCjpclrvej+D6j44dQYxqZoRyZWJqZkQl++K9/i5EbIZPfT6dn4zptjFhILC4TS2XI\nFSa4evkyX/riTxGF65Gv/4evjIyM0W138Txn9zSiqiwsLDE8MsITj32P/RN7+cZf/QV79uTp70tT\n3S5x550nCC2J//bNp3nbz84Q6YfZcyjHjbPnueXBn2NxYZGlxRvYvS633HeSa6ttQmGIRqeDHNax\nnDKdpUUE12JcTLMleKQNlZZrknElQnG3kKAoCo7loOoqYRjiu96bxQjf94HdSztFUd4ctEEQoYgS\nvZ6JLO3qdFRVRVI1ljav87Z3PcDFs4sYokSPOlazjRZEmCMKUrmLlTWoVlrIKY1fy50mqjRIDhts\nXxWYTE8yv/IoTbtHPGVg2Rahq9JvbBGoFoWxPnZMk0boU9ATDFVdDh8ZofH8dZJ6jsqBEZyFeSxd\n46YDN5EcPgz2Eksbl2gAd+0/TLBWpdZV6b/p/YQDY+wbuoPQg1RfgQcOjXFidoRrWz5rDREl4eA2\nbTxPwQ1E4oaMFHoIrkBKUsmpMpkDw0R5g9C1kTUJXwU3EFF9CUOScEQX2YuwFR3NlcmemUS0uty4\ntEDrxgLHpvYhn9xLa3ETo+1Q1XzSYYBv7VLW/DDAf0NH5IcCvTCkEwQUQ4de5GGGHrZjY7n2bvZa\nCBGEiJ7v7sKxFQVRlBAFAUkQkCUFQRIQ3qCxRUGELCmooojMrk4+1W2iF6vs//w++kMNa6XKzupV\nxHiXWHOb5twWo+0FEuk6PPcSvYUfYBRfQ8yVEeML9OZLqF2bzYNfJxG+SP22X0aP1/HaIaur29iv\nbhEGCv7CNmE+Qbrpc+reuzk3v0XYBRJZSgmVVrGBXW5C4JISNFYbZZxOh6wVgN2j4/QIEgo9HepW\nF8NQ8JptnEoDI5bA1nXsZIKYIfP4S09zfWuBntMjmc3gegGB5/HHf/yHfODhh/n9P/lzMrqMHVh4\nuspEvp/NVpV4JaKv6tKJWlS8JtXVVTKTw9T7dSLbx0vF+etv/B1yYHJgz16qpkR2fIjr8+dJSylC\nqYMT2NhRjBura6ysrnP5/Flss4iQzuEnhpg6tIeNtRLf/s73efSFlxmcOsX6xg4r8z30wjaHTtzO\n0kZAbMAi3tG4eTbF8kYZL7iVC+dN0skeg0MyVqMf3R9hMBVhhQqxIYVm2WK92OPxv/8DtFgOQQg4\ncscv8trrTyNEZQbzOieO38zRE/cgxnKcffFpfuOLP95J9ieiVisK4ht2WHHXA99sI8kqf/Jn/4OL\nc/N874dPcuT0cU6cOsLcxauk9TRGPAl6nsvz16maK3z2C3+Ba19mIHWIm+69lQvnrvP7j3yRmRP3\nMTIzS6e3jqJm6G2uIaoGiWyawewghfwAyYEChmHgujY37z/I+GABx+7hOrtphnQ6jahIOI63W4QQ\nePOy5f9b9fW83VJC4HpYXYtOp0c8HkdWFaQ3DLgxLYZZcbk6t8Z9Dxxmq36dXrPHJ4/dxcdih5mu\n+LRECXulxsn9w3w2t5fLrQa/ted9WKughxqvXPhX0qMxkoUz3FizQBfwNA8xeQQ74SFmC2REgzYS\nmVrA1F6oFTuoro40GnH7H50ln0oS80o0t2xGBo5TrgSYPYVc3zTtbY+bx6a4de8pUNKQGCGeT7Hv\n/g9x7MH3sOnI+CmRS23wkNAUAV2G5eVVkkYKVRSwbRMhHkMvDFJxPPBkVFNAjydxpJDActAQ8aMA\nzwxQJZGe6JLoSMgxgaDd5PCHH+C+z3yIg/v34Ho9ijeukTw1QWtUQnI7NOpFlMgjDGyCcJf7G4a7\nBljL9un4ATgOkuOi2D643u4eXRHwxRDTt3E8jx/9DX70PGVFQ5AkLFXAkwUkJDRBQY9kZFHFlyRM\nSaAcFwmVFKT66c7cytj9H2Jo9k5iZorrL7zOOuCuJ2FLpPLMSzi9d7I1s0Op+5dYtX/CyP8mK2/7\nE3r+ICU/h/jMqzRebLHz0gKtjTJFvUeltcU5c5vNl5a4VhK4MScwPXgc0RhhzQVTTzF29CShZCB2\nJbq1JsOZLLqiUm1U2d7com638GISoecidnvkmj75DgihhB/tPheh6tA/dZD7HnoXgSiTLfRRaTQZ\nGRvj9rtuZ/7yBQ7tn2VIjCE4UOgfIt71SR/dw/qz51hdXWch1iW1b4TBwT5kIaCpeqyW16i0dxAF\nhT2Hj/N7/+mvOLjnMEMTY1Rsk9vvuptzl17h6pPniDsKiy9fZL1cpZtJsJbS+aMrF3n64kVeu7rB\nr/7m/8WX/+MfcXFllf3HZlFiKXpBj8m7p3j4Fz7Ltdda7B2yGIyyCFKSxeIUVfEE1+p1eto1zHAN\nq9vF0+bYdH/ItXKVTDaGKEk0qz7HT9+NILnYgURpdZFWu4Yg+xw7eZBsf5Z2x2Z1pcjGxi5t78f9\n/EScZH/7q1/9SiaTQxJFYjGDkbExvvzvf5P3vP99PPHMM8weOIie1lhbW0SwZOauLDN54DhRYoKZ\n2X2MFYbxYjLtLYd9R5NYwk2EjRdpVUwW2iKlYoO+iQTl1RozQ+O4soxtOhwb7efc5VdwJYdauUkm\nnSYWhoyMDdKX72OzWMF1d+WLZs9ClCVUVUOSRHwvQJEVREkmQsDzdwsFoRe8UfPV8FwX3TAwTRNR\nFJAlDcfy0GWJwfQod947xusXXscMZDaWF/k/jz3MJ1Jj3J3qo64mGA4k7spM8pbhGa6Eq7iuwMED\nIj0LlusKQsJiRJ0mFqQRsiJ+Nk86XaccpXl6qYSWDdkvp5C9Eu3FLhMDKYKcwtdqe7kzqPDQgf0U\nbpnEO3QzzYUKW/U2iWGNfNsm1aoyVDhJsSnRDEWiUEJwQpTSNkqzzqFD+/jGv65gCDEOj4wRSQHV\ntkCt1iOpQz6Xodhw2K42EJWQ0o0SrhOSHSwgJRRcd5ctG4QhohggSQpiL6SDje5FSJpMYNrE9w4w\nevwwpWvLNBtNLNdC609AUsJqVHElj45gY4ouoSQQEWH5Pq3IpSeEuJ5HGIREESAKRLKEL4hYnodl\nO4iyhqyrb3KKRUl5EwwfSBKqoiFKMgECnihiRSGm62G6NqGnsFRs0bdg4f31Ojy6Qv/wYZLvvo08\nFn17Z3C21lEuzyML70U89u9RrQ3srkmllGUjdopSw+L0Dz7O3ECEc+gA9R2bjJunEQr092QaTg9F\nElFaJs1yHW+9jt2wMBttVFFC8gN6yu6KqlPv0fNMnJaFqurEEgkEP6Dn2KT6Bwj9CHOnxZCeodfo\nsVGugaIRF1WcapPASJFLp7i+cJFet4Nl2uyd2cueiVHuvPU01VaFofwoV5auMTqQZWp4nIZgka15\nlFebGBJEKY10INHeLLNdKZFdaSCeXWJDDXGxKW1c46l/+T5D+2bZd+gwttlhZGqEWHYAuS+JiU7H\ns1m5scJE/ziR1WNgZJKhiT3YHmT6FDpek2xhAN0Q0GIW6jhErQmE4CWCWg5VsAjEvax3G2xveVxa\ne5pAsMAzMH2bnZ5N/8Q0YqqBkciSyuyl3lIAj0w2Qb26w6kTJzl34YfocodbzpzACVVcX8fpeSRk\nuH7pNX79p4kn+9WvfvUriWSKkeFh3vmudzIxMcGNlWWGRseRNY0wiCg2NrHNFlfPraDIadqhQivq\nZ2i8QCrl8+Cpe6grAecWmpzZl2J8bBS726GqDlIqdhmczrJx/iJW1yLdP0z/4BSi0EQVXdbOX8By\nXbSYQSPq4TZbHBydpjAxRa1Wp9szCYKAZDr5hhrHR0IkfAOXGP6IrSCKREGI7/pEkYBhxLBs803R\nXRiAY7ukxodJDE2SHSkxG2bwazHWogq5/DBicwciiff60xj5LlFaJ2bFGYo0qr6Po49z613rtEoq\npVKZfGqIptWjMHWAa0ur7J9O8/T8Zb6z0eTMFOi2AUWLgqqTHQ6Rax3mVgS+clQmltPR7jmCkC+w\n9cK3eOm6yfD+DEcTe3BXavSlbkZURFa6i4SZKv2yRH+nTDKhEGYyvLgEkh7DaTkk8yKJ9DRbG2XG\nBtKIikDLkxH1GKLkYQQp2ptVBiQDYSCJEFcIOz0MSYbQQvQMRE1DVAPCBAi+Ckj43RZiLkXqnmMM\nxFLYC2v0dkokpgq0Z9KUl5aoRV06QoAvgheFdF2XZuTSFEIsgd30gACeCL4o4oYRYRghIKPpOrqm\nIcrKG1yM3b1sGEX4bRvXD6g7FkWrS9ls0+z1MNtt3K7FdqtEWo6T2ASzYVLHx3q1SN+De4hNHEId\nnyYYux+78g7C8TupGyGtcpfI9zC1JhkhTfbbX4TjJa6EEoWb3k1Y8fGUCjuqA1YAyy1aixXSZytY\nUyM0dBltu4h4dRlPMPGVACOm0JFDWls1BEMn68r4fkgzdMnGUng9Bx+RwIkQsinkZBopkOitlqiu\nb6Nlk8i6jBEfRFehkItx7PgRPN9nsL+fSnGLz33uU3zwUx/nF97/YV689BqvPf0007ce4zc+/Vlq\ntTqJboykJtM0BFo7deoZDeWem5B0lb67jnFgfIJib4frc5cYSGV47pXXuLG+yembT+HqHkk1xbbZ\nxtUzjOzdz+bCGue+/xQz+Qx9s/sxYjE67ZBKZwkjJSAKGpJcIQjbRDWRauUJHBIU7TYrtTYptYfs\nB6y9/jq+qZHPTlItNuh2JCYn92FICmLTwElZxKSb2Og4jBfipIb3cuGlRxHlNCdnZsmnR1FjQ7Qt\nEVd0SMa6fOz9t/HEY0/w6c987qdnXSAIEYmkTBA6nH3pFfxIpNTrsri0BOUOlZZBp2yQTRr05DhK\nTiNwWzz5/GO4UZtaUGC5E3D3oRHevsfArpVxhC6FgzN86J7DDPUlUKKQWLIPwatR+uE3qW+skNb6\nGR6bQU+kyPYNUbd7zBb6GFMFrl55jawY8NV3niZZGAMN/HYbMe2hJ9I4MpimhSGrOB0Hq+0iiRqe\n7xMJ7CIVQwtZCIl8bxf0EUUoySQTe2cIOwrf/e4FMgez/KpU4NjQLHc6OqtWwEKnwXyqhbemIgcX\n2cx+m1JY5UhmkJ3VDQb1UR5+KEu/sU3b62IoU5SiFAdGD7PzfIIrCYMg06VRznH1/DJnprOMZAwk\n0aGcUvhv75ewJl3sZBLUg0iLOywFObY9jawxQbxfRO7rxz+Wxhw2kGyBQBpB6nSJ5C5jxiSvnV8h\nqAdEzR7depP1Cxb+5iZppUOx2yQMFcZwGY7aqHGZplMmlctTvF6n9WoZrSsgGjKICmEggLirbRcC\nicB28aUeSD6iYOD1Wri9FtE9M8x+6ReYuGWW+o1rpCKXtu5j1W1GgywJ26DripQsl0hRcP0OkQCy\nEkMWkxhiAtHxUYUAz3cwQ49i2GW1V2elWWa5VuZGeYfFSpnFSpnlMGDFdKjaAV4gQ6Du/j5Bxkeg\nT4nR69Qw8gJBukdbjSPoEghNur0aYWijPN7F7+iYVhd1vYoXs1AiDdUbQzn31zQWn6SUTOKKA/R6\nNTRLRsoOk6o6mKqCUC3Tf/4VOvviBKpGPOjQnRzAP7yHmBXDc1RsJY5pBWgyuIFPW412PXRdj7Yf\nEA3k0NMx1MAk3pLpNXuItS75IzMMHJrCbHdQJkfwGnVELYfvicihzvvf9hnGT93L/ttP89FPf57f\n/MznuXjpdf7D538dD3jXHQ/y/PmLjOzP4A22uVG+RvXlS6y4XaYfvI/cqROk330P2UN7kfceZGJQ\nRhZcfDdkaHgfa9sm//jX30IRR+hoPgO5KfqlNKIdoiXzeHGdlu/SanfZ2eiSt2z6fB3VjTExrOMl\nAwIlR06R0XuD9FY9msUmvtIl2Zdj4eI1arEeShIq21WEIM3QwCTddgfb76EMBghmg3jCYnx0gJWu\nT0YJefD0CT71gZNkZgYYODhCkBARjARjA0dQgj6KG/X/X/PtJ+Ik+8gjj3zFCSPiRoKYFiOezmLk\ncly9doOjh47TNh1yA3kuXn6eu25+kFQiRalt8+rCV2ag8AAAIABJREFUNWKJm5id1Gg1XDq6gav3\ncdPsLDftT/OdR1e5+e5BdlaXqFsCjdVNZsYGCHWXs1ee4/jMBJFnsnjlCn19GeKGzGg6RVKL4+gq\n25V17HKC/uEYppXAianEOzXaSgFVctANmW6nSzIex3FcXNtBFARURUaUVDRFx/dDJEnG9QIcL+CD\nH/plvHKJpcoO7bOLjJ6eINxpcLcwiBOYqJlRHLvCdqlD/0iS5maMfL9ENN5g4YbMarpHZTXJ6Nj3\niGtHWXrxCtq+FPLOIA/96S0s/fACawNX2WoI9DshsZ029xamiCobeFaLot3PcCCgzNwGh29DOe8h\nWGlqmyHPVL7LvbdMMVF4CyoJorTCeiliYblJQpsg1aswndfJxHv0NInVlUHGRhOkNIl2s4unauSG\nh9EQycXjBHg4kY+q6vSLaUqdFk5GJ1ZsoZoe/mAMbBs1mcK1PEJf2I29hQFCGCIhQBShWAFa3MDv\n2qjpJOl7jpLdO07txhpHz5xiKJ7l6rk5DCOB5fj4qRgVs0sylJDRiekRllMhFAOabkTNdmkLFlWv\nhO/K+P6uUFMQJCRZRVYUFEUlEKU3s7NCJCAQIUQBhD5h4CNE/psKbikWI9+N0I5lMB6YwPAjxB/I\ntOdKmPEOFhYdt4caSyN0RTrlc9SDvyB+eBbr1VfofewrmFWZjLlCY3QUqRSg1EPkgxOUjozRaoJR\nDuj4PtpYHqfZw2jYdNZLSIfGUcpdKu02SU8kkgQIQ7SOi2jaGP0ZbEPGFiO8rW1kPSLCJrm/j8xY\nllhfHEfxMLoui9cvEva7pCUfTxbJbJb4/qN/yaWz53jm5QvEohrDe28hl4Zf/NynGTWmubD+EvNL\ndQpbSbrTIq6kMHjqLUzq06hORKj69IQANQh58uU51potfKvIvr3DmFbAhSde5q33v41qpYssiVx9\n/Umunz9HtbqNEINEwkBv9fDpkRRCDuyf4fW5OSa0KWLyCJVajcJogeWtedL9Ov35Ac4+t4zVAT0Z\nI/IF7J6FJEW02lUmpscRELHtiLlLG2RSBd7x8AO0OxUmB+O858GTXL9+gWJZRBYkUvEMrgWlzTIb\nq+tUdrbY3trk05/57E/PuuBrv/PIV/R0ljCIIIjo2TZyMsXVhWVUOY7XaDEwNsbyygLLF7eYmj7M\nTk8hP5zFdNrEYsdJ5XSWF19FDl2ee/Y53K6C2FnjybM3ENseS8VtRnI629dvYBRGSQsGOnVK1Tq+\nE6IqITIWSV8gbqRZ6jaYnjqJ52xjNU1+5VffyevrGlmhg6Va4Efkcnkc26bbNZElgSgSIBKJAgnP\nD3BcnyCIEBQVzTA4c/ttbBd36FQ2qCzNowYJqqHJvjtvofHUDbJigOSLSKFCV22zhU4yqxJtS9Tq\ncYYOCIxKWcSVWaTiDKd+5WGkWw7w4nOXSccGGD2doTA0yGPfPUtP17GK2/wfs2cwe02e1A2KjkfZ\nqnJgpoCkRyjveghTi9FxFrm2usZqZpzjRxMUrD24xThGo0ta1ZjvrHCpVGG/7TA5JDI8AJe3fS5d\nUhDoYXXq9A2MkB4ZprjTYG/fEJJvEWgBniDgtD1MN6LhmIyEGi4Ca6tF9tyzH5eAwA2QFQNBlBDC\nXWW7GIUEnr9Lo4prUGojSgpmTMJ0bdLTw4zcdoqzv/stGhMBg6f2sXyxRLPjEipN0kkNN0xgZH2K\nlRKBKNO2LTp+i0Dz2W6UkRMpNDG2W1yQJEIkQsAPwA9DBEnbHbDsSh2FKECMAgh3Y3wRLpGiMpbp\nwwlUosYOqQ8cILMnR+/vtlh9agmyXeyei29D1Bch+XE0T2dbeozHxef4xqDOLz9wP1scRLUU4u1t\nnHKX4MhB6q5F+8YKibZKJkogGBKRohJYJr7l0V7epj+dw7l1htVHnyGZSiM6Hm4UEdW7sFkjmU3j\nFhJ01Ij+oUFISIiFBJ0jBUyzjVjv0A4dREPhxvcfpxC4ZNIhi51N2K6heF1uvv0wP/uOu1lYXOUz\nv/Ip/unR7zB84gQIIT3LYvbIHVTTMusLFxgc6yd0LMLIxp8y6EQ9jLZDiEQhOcgz33uMEJ+YIqAo\nKYLkJOVamb/84z/jf/ulX6bbaxBU13j+8SdoW13ihRRO16SQ0gmI2L9/D9dWLhHPDdDdCShvt7C1\nMqX2Nqn+NCljkIw0wSvPnifVp9Np2FhdC8vs4rg9CoUsju0gijoLV9ew2m0iAk6fvInBvnE2bmzw\ntgdP8ewzjyJJUCxexbGLbG/Ns3j9LLX6EotrF+k0Onzx17/00zNkv/71r38lN1hA01Q0Ud71xyeS\nSFqMSqnK7Ydy+J7JLbcdpV1ukhgYouELHJpKMWgtMjFkIDsWQXkVq9QiNTjBfHWTpj/AkaP7KGSH\n+edH/4Wh4RGiRhM37DCaiDEzO0S5GSIqKUqlNQbSOgcHp4kn0yx2yuiqTSZhkE4NMz6R58rCEvVI\nYihdIJMfRZR1ktk+bM9GFEKEKCKdyhFGAlpCI5fPMTA0wJkzNzOzdwbHdbl6fY6iV0UMLG4amuKz\n5gFSV11yQ3vwzQ5rjRvYLmgZlbVmi24kM5AaZEAYIamMcbxZwvHr/KO6ze/+zz/FPOfxUJihO6iS\nj0aZvF9h7qVzPBZb4fRCyGy8H6l7lYTTZFhPMOXEiU/tg7VrdG2NeOMoRmqSdtOiMPkZRqIqw8UK\nKlksL0UiPcTNQ2kenowT+AKpeJd4coDvvdymYyZQFIN83iBd6KNWa2PXuxhhSCohEEuqeF6I50JF\nUpjoiWhElD0XJZEj3wvQ9+YRQhdREkHcZVSI4RuNLUHEJ6Tr+xiDeURkNNNFM/TdUojvsuc9t1J9\nZRuzFZI6XcC2SogNgY1aFXnIoeF1sISIRtfFEyK8yMG2uySMFKqQ2C0gSDKRICFIEoKoIIgygiiB\nIP0voWcUIoTB7jcCkRBBAFfRyXgyihCHMYF97ztB9Xs3cJ828QbbBGmbuJNBElRwRAxPpyZtc+mZ\nC1xaWmSPvsrsFQvnpl8iDEEJAvKL17F9Bd2yEZwmvidRcv9f6t4zzJLsrPP8hY8b15u8edNnZWVW\nZnnb1VXtqrraqKvVaqklkEEDQhIwLF6IQTAgRixGMCNmZtmGHZwEAiGBkNRSq7331V3eV2VlVfq8\nN29eb8PHfMhil53ZBQ07H5bzJeJ573meOB/u84sT73nf/7/BLAUSiopfNQk8D6tcR0pGKIdAOrNI\nEDOwVYmg3SXRcimv5GEsizzcQyaRpl4o4fVlCRyBwPQJlz3CjkqAgu7qxA24dvUc4W6XrugiuD5q\nJkKjtMq+Qwf4rX//KK8fP8ZyYYkjj/wAKUVnOr9IqxtHGYqwUFjG+dLrhI0IsYkhfEPH6KrYYgjX\nrtB2bca8GagtI+gDNCoCRqafVWuGqC3wxONPcs89dzMxPszcQpGzFy+iqgqGEcUzoC/eR10WqYp1\nEAKasoyitImmZJSQhusqjGY3ceGNy+t16poHJjQbLWRJQVN1IpEIqhamXm1xy76DTM+c4Y7bbmd2\nepqJ8Ul6eoboWg1275rkledm0KQY505epl7pENFTiIGB74VoN2r88i995l8OZH/zN3/zc/F0glar\njcJ6TWksneHAgYOIAXzk3RvZkMsxPjVAOKpydvYqS4sFQt1ZtGQcWdIYGBvHlOLookL+wnM8eOQW\nCk2J82fOoqUGWb3xFEObN2BXFkiEbCJCHDUVxdXSmJ6CYch062W2DW+k07Uoih1atTIhKUe7u0jv\nQD87+1u8MxNhaGwvlhOQSA0iaAYDQ4OMDg0g4hKLhxkcGSEWNxgaHiAcDmHZJmtrqxRXVxDxEYsN\niIUZSEVIxQYwtCj1Sh66KoQ12pEuvhUmIjncWFzDT8pE9ApKJcaV7gJtKc6NmTfoDA8ROmygj9oM\nLM/xctflzontnJl/mgw2hw5P0L+2gLKmk1ShGwpoxDNkQym8yQx+b4juiQlc+14CwWMtU8TvDDCa\nl0ALsOQWTquCVophdbo0KGK6DqWqwRvnu4hqYh1OArhKgNoNUC0XWQVfsAiJIp4T0HBcFDmG5vmY\npokqhVBiKWavXqRXixIajOH5JoHoIYg+jushCTKiqmEFPlFJp9ao4Xvrn+eO6yIHAgoiHUxG33Ur\nw8NjWHMz5EZ6sA0RW2gyZ89SL0gomohDa32X6uh4bphYPEWAiS8o61bwN6EqiCKCICEIEr4gIOD/\nn4ANgnXArmcxhHWLcVkhmsyg1C1yh0YQ3Rb5p2eJDBp01QZhJ0nHaxF4AT3NPlZLN7B6K1xdOcnq\nhgaaUuNC2WDbkU/idRZZc9dwuiXajkY8EGhLNkXbI6aFSOoKwmqdFgKSLKG1bLThLMFcEfPGCq3J\nHGLgY9eaVBeWseMaI3fvw2y18VdriIqM7IhIPng3VlEkibVmnW65SSQUR5/opxbyQFWIrLmkdk+S\nFSPs2b6P2bUiv/Bzn+XKwhk+8rO/hlJuU2yvIXQjrAUdDHT8TJqov4ozkqDpa+RC/TRUF1lqEygu\nnVqLH7x7EFWp87ePnUARNarVabYPhnADEbNj89QTzzN1+514SoyxkQlOvvoaG6b6yPTFwZIpd22E\nyPrLNwiJ+M4qXltBU2NMjU9SnJ/DqqxSWutQbqxb8xCAooTQ9QQhI0oxX6TdapHrzeAJErqWIV+4\nQf+wwtZdO5hbrOJYAs8+9wyNdo3C2hKDg73Isodl1lFVqFfK/Nt/SSpcv/Wbv/m5ZC6LpBvklwrI\nosb7P/whbrv9dvbv3oXg1qEbIZXOkMwNUShVmTk3y9Pf+AqbxmU0fR9dw2e50ULQw4ghmVdfnefo\n0TsoVS5y9nqBuVPzXJ/z2Tw5TlyrkOuNg55lvriGL0pEomF81ycqKCSTMV47f5xUbCvNdgWv02Rw\nY4od/WGkIMrJcg+juQQt28cNbjYZeF3uOLiHdqdJ22wT+B5ra0WazSaNRhOz1UYSBGpraxDWGd90\ngGZliZfaC3g5keyYQqsp0HRiuNUuXtxH0X3SMVDVJJ1GAdM6RcmOMpBIkR3czpZLS+wsK+QbIc7s\nT1KrlvnG3BMcOjTGnnwfQ69mKP7WLsQ3zpANjSJvGCFuQr23jH/nBiKrbfROFLWd4bRcxrddYrlR\nBq1FVl0HsZNALpnQUVmJV2l2l4gmJmiaOjUzhCglEWWXRsslFFHZ3DeE125ixELUGk1kX0OQVMrt\nFnQE/IiK2vEQQyHqrokQUtAWG1iSTbwvhxSSMc0OuqojyAqmZYOiEMgiYQuUkAGKgGx5BKKAJfiI\nfhTZa9CI2iS37aMnnqVlzXCleImY3IvShlq1AoKKF8iokThKJELbcfCVEKogIArSep8vIgHrBpye\nHyCJAsHN9EDguQQ3heIDIBBEZElHsBzoTZARXPrHk6y+M42uZSmnChi2SmfJJaRKSC2Jhl3H7M2j\n1FSudL/M2OASzbksQ9EBStkBVM0kjUZ6to6V7KXacDFsibBoINQDFEeiJa87/kqAVa1hT2TxZgsU\naxWmDt9O2AlAlZHDKpnNI3i6hL7aJOVJmJkQIdOieuYipcYancUlYskYTT0ggkA9EqY3mcDTw2RG\nxylfuI7iqnSjKZZrVTzgh3/2F7CcDvVig2Q0yUqjQCySQbW79GRCnM1fYeyOfSg9cSp0ifbFsbDI\nFKOIs13+5u/+mqqscuHSGr4uYgotlDUHMyzQbXpEjCRyTw7JSJJIpOjLZrm2eByh1iY12E9POMHQ\nwABLpSJxO6Cvv4fxwRGGevu5bfetfP0vv4YqyajRKJZvc9utU2zbvo3L0zO0W23K5TKm2UGWYPrK\nRYKgSyo9yq5bdvPudx+muNrC8cJcuXKJxaVF/EAmkcySSvcRjyVpNttMTkwxPzfLZz7zi/9yIPuF\nL/yHz91y6C7e/YEPMTGxg6mp7Tz8yAdYWllE8F1qnYBMdiuGoSHIGt1alRcff5Ziq8DsapXbjt5H\nq22hOCK1akDf+CZ+5kcf4pU/+1N233GUy9NzPPzIe/FbAR3RZebaLCExzXB/H0g2rtWkp6efi5eu\nMZLNkkhEWG01WCovM7VhilZbYfvULfzl89/k85+9l9/701lSsXVn0HA0gS757N06zsc/+gFefel5\nPN9FFlVERDRFp9VsY+gGciBgSAp94SQdK6A3nSamq5w+fpLeW/uJDWukqiGSWj/5xgyubDKQnsKr\nOiQTKQI5yoTeTzHpEwr6SB86TP+pJeL3ljjeWaF/coi3qioj/atED1U4faZJMrcRZb7K0itXaA/E\ncWWd+NYYie1TCBeguS+Odm6RxcEMXSOLVJlm9MQcIWUnhpym6eWpCW1qco1YMoGk9tK2BUpVF0VW\nCYV1AjR0SSNwTOJRlVKphKwk8dUErigjitD2XWKehhXSaK5VSKei6B0PWwpRni+RiiTRYio+LvL6\nVhjXdVA0hcADWxdRfBGhbWHqAoIoIHccFNemJUpogYahBvgbYiT37iFk6wx4EmJYpeuK1FoeriCj\nR1Ucz0SU1j/7JN8Cgpu7VxFBlJAkGVGUCAKHwPfwPRv/7yGLAKIMooSoa0ieRbPRYP+tG8A3CVoK\npi2C6qI2BbRIGKUq4HVdar1lGt081prPydAJ+oc1zIrJT0Yk/uKUyYF7HqBQ6VK5OkMilcXTolgB\naKqA1wnwV01Eq02XgFalSkxTsQbjiEHAwPZJquUqcV+mo/hAQDIawTK7rJ2/Sm2liD7eT7tcRjy/\niL9aJpgpYAxkWBMdsq6G09OLUChit33WUgZ9bZt62wIhQjgaITqUAGGIuFimJsiEhDBNvUS/0o/n\nNWiHY3QWPZYvFekIMvpCg+hT1ykut2lfXsYzl5nu2Lx0/CrhSIh8vUaAQbR3cr0rzVGwHI+zZ05i\nOwJKOErFMxnaMIpnqmzYNIJv13j6yacJRzTuuWU3yWiSkC4zmE0T1STGRjdy9uoCh47exw/84MPc\nftvt3H7n7dx97yECycaIiORX1vBsm1yqhx/5oQ9y9L0fQQsbVFdL1Na6XLl2lQtX3mL/nj0ce+c4\n2dwQS/kSlhswObWFWqPB9enL/Oqv/Nt/OZD9vd/7j5+748GjmCh83/v/FRtGxsmXimSzKcTAZ6lZ\nRounyGUDdNXj4olnMfQAcUAAqcaYH6F+4Rnuuu295MbvJDeY4dhLb/HIhw/x3e88xXvec5httx/k\no/ffyYVKgXMXrpBUNLJhjZZVxPdMjr1zgVh6AEPwEQOHpUoZ9ID9W7ZQUUTCQZWXrQL7hmH75s1Y\nTgjnpiW5HLjENDAkj8e//U0isTiTk9uYnp5BFGVUWUMTNSRfxLccmrJFu1LGrXQoRQMaFYmBtsO7\nf+UO/uDrf85QMsK26O1ILRXT7TLYv4MWGoVqlZ5FlZ7yHKcGl5HDCVrZDs989xgrvQbXzArxWImq\nMcnEOZ2VCY+4bXDgp7/Eht/9BPb+KfLz7yAILdKH76DzzFXcPtBP+pweDGFoCQLnErFOiCuTm7nW\nqVKLxMhumyA74JCIbWApX6DVkZi9VkMzTFpNkUargdMOUCIC1XqBqBLGDyKUbRFPEpEcGxGfFbOL\n5IvEQzo6FjUBXEshaUWYmb+BGBHJjGSx2x1820FLhAk8H8WXCDpdfF3CCYkIjoMsyrgCdMMBkS7I\nDti+hW/7qG2R0f076d+5ncpKnZbfIZIxmJmbo90MyCWGsBpdwkqAJNzM/wbrKQA/AM/z8TyXIPDx\nPeempbwHgrAOYkkFSaQrecQiCgcyI6S3GCwWV2gHEcLtANEXiLZCNGlhV5p4CYuW10WZEzkrnaKU\neoFjKxH2ZSZ4TSjyZ8cW2ZxNsnfqVlatPF6tgZdOI5abKIUl7MlBOptHCW+M4CkaQtekM58n0ZOi\n1KxhGzKeINBcXqNgNcgoBobp4Engu+sNMEEshLzYIbJtI/lLs2g7J2jHw0jzDTqRMOJIP7XSIqIZ\nEBF1tP2DLD19HOHiGr4SUM8ZBAWT0clRLLfNrN2hVzMIR0EOVNS8z4I/T/7KNJuCXsSRQby7xrgt\nl6LZXiLYNM/ZepXlq4v09cnU1tr0iD20+pIYaPQPbmTz1kkmetN4DtihEGMH9+LZCXondnL6tWdZ\nqU6TzmXZkI1hqS1iGAwMbsdprzEypHPu8glOXp3hB3/k5wnrYQxdR1BAVBzuvHsfd961H0V0qRTL\nfODhD5FUcqzWO8wVr9Gq5JF8n5XSDUIRiRNvvsTePbvJr5bZsmsHRjRCy2zieDYrc7Pfc7rgn6yT\nFQThi4IgFAVBuPAPYilBEJ4TBOHazWvyZlwQBOH3BUGYEQThnCAIe76XRUSTKQ7uP8T8+RuceOME\nXsch6bkMax69RgOx2iZsutgtGdBwVIGaleA3fvon+PXP/BJf/KvfZ9Pee5heukCz8DLB4lVSqQgv\nX6/y4x//NPnCAktnL/HwL/w7vvM359gxsp/hHYdpJDPEh/aQf+cGA1KFdvFtxnN9aLEskiFhlUxM\nYYzDd91K3TfpkXfw69+5xmA8QPVlYkqCiOLhm0V+6CP3kkr00UChJEmkYxFGegcpL9axmgGaapDo\ni9PVO9g1DdUII8Uk1IZPZryXt+aWMU8pZDYZPFFb4ivdL9MdV1FjU0zXLzPUrzO2YYr2xoAbm36R\nXPHjnG4O8R/sBvOf2MctXZEh9pNoDtG9ssq5veN43QEe+vBBTj3655y7+9OMaiMMf+I/8beR8/DN\nAoZno339NNRFRkoiklPBje3hxYO3Mm2tcb6xzPnFJd585jKvn+jl6uUF7FY/bbWDkYnSlhziUYm+\nwQFSQwrIAnK8n5ocxtY1QrKA6kvU22B5EdRApd6p46keLhIxUaVLkxv+Mq5ocPm7s5TeKKKkI1Rj\nXdpdCyGQ8DyQDQMJE8FrEGDjeR6qoBMyRTxFwFOD9RdaICAqLl6rjhDyOfIb388nv/AJpm5LcdfR\nKTKTYS435miHXZq0qdsBtuLRFtoIgo2nSjQ1n07Qot1u4ws1WlYYSzKRfBBRwLcxzBiKIqDVY0x+\napLF5TWUxiDqqoOlmQhoVAybSKlN3Y1Tr9Zwj1loUo4bueNUGknshQKOXeD3Xmmxtdfk//jif+av\nHv05PGGFllXHa1aR3C6NrePEtk8h92mYyQixAQP9rnFSP/w+ppfLePPLJI7fwGnVEVMGqek8tfwy\nyb4cfqGFbKSITeykassIvkIwlEbqi+EGHt2ZMrEFD7ti4lybIeKECE9NUDe6tMs2mb3bsEajzFRH\nee5FhS0TPkm5RD5fp3DiLbSGRcLoRY3bRIcEMrlJbvn+97O2W8Mc7OeVV67zvz7+RYL3bsNLlhmO\nWVR9h3JbRtZiOBEX5/jbzJ98nUsvf523vv1nvP38V1g++Rhv/sGv4z/xJO8ZrHPPaJh9d26hWG5x\n267daI5CtDVGtdnixuVvEAgO11cUplfK3HHHJkoL36CxehldVhCBWHSYcjVBvZPgoQ9+GDkRI9wz\nhDaZQpBM9m7sZWJwO9dmL7Nn93Z6++K0iHDu8jW27T/AlflTjGYMFCuKkckRCke+F7St8/KfEu0W\nBOEuoAV8OQiCbTdj/x6oBEHwO4Ig/BKQDILgM4IgPAj8NPAgcCvwvwVBcOs/tYiJkdHgP/7h37Ii\nu/zdX/wnbg33s1EcYXz3JiLbFU6ePUuPGkdwFPYcfR9d/zrXzs9RU3x8WcZcWmH1ygxbt21BD0Wo\n1zqkB0boyirpsc3MvPUSGwZS3H7ofj7xqV9h0+ZNZMMexcINUmEDKWGzevkUQmBgJcbI6SkSmFwq\nrjGzssSmnj4EX+HUqkxYWaMWXKEvPkWrE0eVwmweNfj5TzzAi08+w3eefpm51Qb775jCtyXOnLqM\nrkZQJRXHdMFTMcUSrYZDVE3QFcDSHQKrTjaqMlNaYHxoBw8d+n6s3/4K6SBJIrabstakoTRY7n0I\nIRfl2onHUESTmJ1lQfwmK+kmG9MKnSWb/VqK1v4oc8lxSnKW93z7NfZqLvt+7ieov76KtTlN/fgC\ndnyUIBYmfm0JK61yQ5pE2KgTW5hD6tqsaAIRI0o4qrLcWEJWWkQGDvDmyl/zrty/5uw751lRmtit\nMJKikEjG0GQN3xHRpAh210ORZcDHZ70Zo1lpEJMVcpEEOF1UXcGPxNA6NgE2kZhEWOmy98EdKGMG\nnlWFThhPFkHVCGSFIADB8ZF8E9e96fnGujCPGIAQsF4V4Af4gYoXuKiqCrIMpQ7HX3uesxdf4tLM\nMbrXttCJVpC2lnGvJ8it7sBsdNCiAlLToScaoaNY2G4/gVakEa/TSTTpDW9gWSjwyOotbN0xysql\nVUYiQ8wpFTZLBrOiS1ZIsLZ0nry3zHWxgWt2mQ+3uXX8VirRGV44/yYb3RBhAd6UA9ylRfqTYQ5v\nGeNn/ugpPnrLUT75s/+aQj1Cy7NJLczhietW14JTRo4lCG8cxwvalN+4jjDQj1gokD83zb6H7qfQ\nmSUqBVhmP4aUIZqtMX9jFqtsE909heiYXP3Kt9CyMcYevo+wE6WTirA8V8MfiyC/dRlNczFGwjx3\nvIY2ZHLrrgPs2KIwe6NL1Wzj29epzrQ4+v7NrBS7pGJpLtVrPPPWs6wtXeO+ex6hZ2A3Z849T7vw\nOL1mim//9Rwb9w6wsHKVpWWbitYgJ4+iKyqOY6EqAp5vkulNcN/9dxNoGb78J48SD3n82I99DiGc\nITfeh2VGMZQ0CUngW9/6UVr1c7z4YpmhrUl+6qOfxKtfQ8weJBLO4OPRNWvImoymZVhaavJrn/0N\n7rnjDnZs205Pchuu2GGpMovpyKyuWHScAk69QbJ3lOv5UzTyZQaH9iBF4Zm/+nN81/6fI9odBMGr\ngiCM/jfh9wKHb97/BfAy8Jmb8S8H6+Q+JghCQhCEviAI8v/YMxpul0ItTyoxxO4dd+K2OlQr8OpX\nH2P3yQzOVIgrrz9PTNRJDMfp2dSDH7jU2r1u5niPAAAgAElEQVSoUoOhgU0snjqPVSoSyUGuN0Wl\nWUWPpmnk58jqGV4/9gajfUM8fGQLFVunVZjD9zrIUh9OsUwpaIK9TL+U5Npak/hclWUhQS4WYufW\nYa4sFtHyGs2qTtUAZa1GJrUZSQgo5tscO/UqN1ausfvABjpvT7M636E3N0Ak3EOlVsNxGwiCQKdh\noYV8JjZsoifVR6CFGN+1jbXKCq88/yQ7th3kxNnLbHzhy8QEh6ih0Gp6BFqSiJCkNHuFpBtnePAQ\npncDZbTNUGuMZmuBawurxDbEiYY0Pr3tIL999gJS22RpImBH4V288J1h9IZE0p2kXB4haogUVIOF\nIRG1cZKWnCJtJejERBqLy4RHhiGboF63sMNDZPtiXGuVWbhxkSulOaJDfXDaphWXiN00mXR9H0EQ\ncQIf03MwbQdJAEUPwBcxjAiBK7CyVqM/HsK3HEKRPNJgAq8p067LtCWDp/7qKjtvG2Tk3iE6ShfB\n98BtQQtEV0aVDATJQFDWLYrEAG4e+4Pn4Tsuvu/TkZrEZQO3tF4Xa0Vltj10hJHtw4y9NoTbv8Cl\nwirPnplhbHgPFmWWahYJN0UyanNDVdE9kVDUpMt2Bm5UGYlEODv2Ehv0HIGlcuHiKgPJfppOgEiI\npXpAf8vg7c5LXI7cwLJl9umbccIJpozjdFMuGzP9PPVMgboqoZUl5rw25bZNvQiSN8MD73+A9939\nw5RKJoguSUsi5GpcOfcq9tnL1DWTvTsfoFrTkQ5Nsdo4T29vm+lTr5GOh2nYiyjqLgQngxt+DWXz\nNCYx3Kfq9PTGqFw+h6jrZH7ug4iGQftbb1LqWNjZDNre3TRWr5O4epH67l7OXl0kX+tj91QbSwzz\n3CvnGBsYxQ+nWKsvMbF5gEisl+aVN/nq3/wlTg+kB7ZzYPcRSstNDCnE3OU3Ufwwu7ck2XdHmxPX\n86ikyBop4tk0cUXF7DaROxX6e7okE70cvf9h3nXPQ2y57xDhiEhybDNizwCJ2BiVQh2NClKkwaqn\n8uDDP8+pU3/H9OLX8K0kFftFRnMHWeyexvM24XYMFNUnaoTwPI9yJU8sI/LGK2+xZWoDq6VlCtVV\nHnrko1y+8Q6dxjIxYwAhkSFf6RAIEuFYlIN3HOTyzFn+Rxxl/rnOCL3/AJwFoPfm/QCw+A/mLd2M\n/aOQlQOR2YUZdvRuYs+B96P0xbDFFtXXXuPS06/TvD7LvkMHmL+xSrHb5q6N9xJtd6hPV6muNYjE\n4sRDBo3yGlt2baEjaawtzbOrf4yLp15D89N83w98mD/+vc/TtNbYf+8PM5uv0wliNOYXOLBnB6dW\nVtAkm5X5Olo8xUK+DMMqU70b6Elu5m9eusCeXYe4Xojit6tUFhbRlCUi2iSpEZFzVxqIEZXTF1fp\n2zjOaGSCnr4hduy8k/mlJc5fvkRPrpeNG7eS0GQO7N1Oo1mhY4qslDo0GyKOa/BvfvRn+ctf+22W\nZi8yGc0R8gNazTnS5SxadphGX5ap+DjTlTK1KyqCm8eUJKxmmKz7MWrSayypIt9+9kVOJxdxle0c\n4Q66hXGWFl+jX+tjUZjG0AYpXz6NXhhD9pP41T7k0CwNdxR17yaSUhJvbZV6YFE2HZRZl7aqsrx0\nDj2ynUtnTNShEAyEyTUT1M0CjumA6KEqMrIgoMgaXuAgShJ6CMy2Q6vVBEfAkFUUTcbttqmfCcOg\nhxwyGertpVm1iQgJLj01S2XJY/cHB0DS1zsC1j3fQYFAsvAcHzGAvzdoFhEQggBBEhFFgbivYDYa\niHEF1ACv2UKXDNTEOEc+tIvHPvNptuQ20TIDvvXcC2w9chv994xy49Uz6K2dVBWJDV0dbbKIc/QA\njfODlK5exegPs3l2BJww6ZaJuLqAKYnkEjHKosob8Qu8ZKxwt7WXcbUH0ZI51nqcLfcfJJMSeXPu\nHS53i/SkDJ4vyYQFGyHUz+xamb66zq9/6lMU5utE15pIkoS91uLymZOYy9fZcO/Pc+bS13jn2Asc\nHBiglY/Rn4xTaBeYr50mcD9MZukunE2v0dr2KFpUorb2MPr5flyjxZpnISsygQ/hpy8jx6OIW4bx\nzxXoSRjMVq7RfeZlqj1ZxLU6yBV0IcA2O+Tzi+gk0RMJKqUGixWRiWGdt68s0LthH0+98Mvcuv0w\nhzft5dq1Fv1jW6jU86idNr1jJoVmla9+5xI77j6A4TQJdXtoez7VRgct1GViey9D2WEePPoIR44c\n5oGj90C1hqwPsHvnu9AkGdVuElYSGHGVml0hIamUqgHb9/04rhyhXbvA1Ytd9Ft8+tLbyS/lGR4J\nsL0ii/kyrp/G8lv8+M88wLNfvcwbbx7n0F05XNbodiwa1RbvPnovn3/0PzOYShPv6eda/jS3b93F\n3s07ufe+O/nun//Z9wzL/8/2M0EQBIIg/A8bhQmC8GPAjwEYYYPBwWHEUJhSLaD4p8+RWVlEy6n0\nfOh9rHznUSQtQ9kqEBHbrBRLSJLNxrDF69MLVKIBG8YGsNtNkFSW10qISCSFEKvXzhGN9zOSPcpn\nf+2X6Nptri60uXTmAh2xysffP87Jq8fwW1EWyiobNmoULxxHiiVQ5RY//smP8+iXH6fSkrl8cYH+\nbUMsXKwhamsEYhlJq4IU4fi5aSJhhezQHrZs3c6wIVGqdhBVg337D7PvtgdAClGvOohOg+Mnp5mY\nGsZyLbQgQn/PRj7/K5/nqT/6Q2afOk5hi8LV8jzZ1FaEpoWVDTHbaVKPR3nzpTc43v0W7xl9BNUc\nYjmziirYpOs58q9c55ub47wUHUA9baG8V2Lm3Cma5TWuyDcQKrcgtaLYmktV8MgJGp7kEt92H9Kl\n10iZw0SOySz29yDdP8YtvsTj3/gagR1nWaqQCinY3WXCWzQa50/gbklhljw6fgtZkpAVBSIKDiK+\nLSK4QCDSqLdJRJIIMYFu20TWVFqdLtlIlJF7VeYX5ylLHnNdEKtgyTH0dIqZM9PMnl5h64FBJg/l\nYMADWrQ7HnhhQoJCQID/9+VVf9+hJd7s0+rICGKEQBHwBA8jrBM0TEQ1jO14vO8XP8uTf/2/c8vt\nexncvI+/evlxNkxupRCNcb3bQ0a8QnzLVkqyijP/HPLQNhbtdxg/PYHSNClG6tijMr4pENuQ5Vqx\nQDGWwGrG2Jk9hBbycI5pnPMuMHloEubD2FRZmy+huDqDeYe/DXxSSpt+KWDWqTAwdYCMMMSl9jxq\nYQHPSrCaX2J5+SrjuU1EQyuk7BLzlo2cGqGXNM+89U2ikRDZ3q3oB57AO/yHSEEE4/wHUMr30IpD\noXQO1y2SDnJ0IgnCtTa2ZhLkl9CeLaB97H0sXn2L1TevkUtlqKpxotUOG26J0Btp0Y4OYDcaJHr6\nMWURX5QortVouGkkBV566Sk+9xtf4Au/+ods3nGS0V2HKVUWeOqJTzMxHKNTGWR061380KffzfUb\nTXLaKr2TLS7MXmX7aJTD995HpqeH3qGtLK2U+dSv/DxvHH+D4fEskpRgYngT3tpZypUZMtER5vwA\nY2AjpmWT7dlMfm2BIwc+xoW3n+CNhed48sVpdgxV6ckmuXDhHI5fRo9EMIwEuhzmy3/ybeJhA6fe\nx9LSNOmBLo36RTrVgHfeOsWh2+/i5PF3ePcPvItLV99k39g2KtPLVKqr9PT2/iN0+7+Pfy5kV/8+\nDSAIQh9QvBlfBob+wbzBm7H/bgRB8MfAHwMk4/Egmu7HKrYpf/MlhKU87uQIsakpKqUWet8WCktt\nNo2Oc/3a26xuvoWNfTmW3/oOuR6NamuZLePjXD57mXrHQQ0lyMZ9Tj33GmIEjHidZ7/4Bzz0Ez+F\n6OTY37fePnly8QXMWJF3PbKDkNbi+eMN6raIrqVpt+u87847mJ6f4fL1qxiyimZkqKyUGe4bpRJd\nprSygKxIpHvezWtvLLMteQhZ1DA7Li2pQziepNkSyK+UMKI9uK6J78jIYQ1JS3H+ah5VlNFdBcdu\ncvrlOS498Sa7s1soF1f5/WyXSNGnHI7TGE5xebYOxas03fPk5DhzxTV6nQNYFZ3o6DNIg0+iLW3n\nyvnjWLvg3l0T2CtdtO0bEE6dwem2ecl8hQPqEfr29qOkN2DN1VFXr1Ep38AKFOSWxPy3X2Lo4EZW\nhASvrMwxkumnUG5jOFHCe1V6jllo17/Eyc0TLF5Ms2XURXXTJCJRFH39UK9Rt7BtF8lfdxkQApV2\no4tj2fgEOIFPtd4kjMqZ2RY9iT56wmCLNtGoTKW8hh/SUXp9gm6TixfPM3djmt7BXgY2DZLckEDK\nuAiODARI/9cfC/emFU0QBIgxEC0RBYVOo46iSWCI+GYTyRcgnObBn/lVXnjqS5SvnOX7Dxzh+vwV\ntu0a4NyrJW67/zY2b9c4/WqM1194nuH9VaLSINHeXSxml7AjBmWljq4mKZbXiOsek6koF0wBOlWM\nVg9FIc+B+0I0Tzbw4jW8xhhd72l2dAyWC2tMJXPUA5816uhAf3aAYq2CL8NKcYlsIk7Pri2szl3D\nzQ4xc77DwnyezXftxTIkTEsgdWSEYOxP2dzv4+RzVL76MEr3bqrU0PqPEwulkZbzhMIaLaeFVoKO\nJKA1Tfzrqwj7N1FemqGJhUGIhU6THi/JC8VpehZ0hsIbaYtpgmKL3kyDLhl64lHu3DuJ75q89Po3\nefyPvs5nf/2zqOkq33jycX737vv4yz//XbZv3UJP/yPsGN+Oow7Ttz3Ghotn6SwVcc0ldu3M8t53\n7WdxvsBjf/cMQ2NnaVlt/vpLXyGbTrGwWOTwnYP4zYtE5EX0cIGo1EIQarQXzyLEkqzOvIQe2kLH\nTTHZt4uvfetbhPqW6FgdFDXFcOIgXVOg0V5jZWmZ6WtLtMsQiQTML0+zY2cPg7khVpbeYDh3hHbX\nI5WNcD0xQ6XRZNfkNoJ8nUqpw/jRPetqbd/j+OdC9jvAx4DfuXn99j+I/5QgCF9j/eCr/k/lYwFM\nMcAQk1TXGhiHBnCMfjwhx4olkx3oZWb+OAUMdvTnKDzzGCeefZP0+3ei9kn0aCmcQCA7lMVzRVaq\ndTJD41w9cw5ttYk7IFOuT9M8cZpvxTPc/6GfRvTLDI9lmF8Z5UbLIbES5f57NzLvHuPSmSJ9GyfY\n5FU5uH2E//KNZ/EwkLw6LW+FHj9LvTWCEjpDMquDr2E6KzimTrO5xOT4QQypw9xylUx6vZTJ90W6\nzQauY6GLMhVfJirqeL6P4znQqYHQRXFsnHyb49Iyu/r7yF26zvKdGmu9GeKNClu2DvLiK+9gI3JH\n5iBmxKHUfoVNUo6rl3bwduxPGNy/nd5TOtrFJqc3LiOMj5OMJliLVrg2vUZu4wjLlQppsUZ9vkhy\nJYnZFil3q0SzWa4/9gLqUJr5dh31pRaO0KXgOziKSMm1mXyrTuI1jdWlN7n39jt4LJnk2tIi8XAS\nTdMIRAlJ9mg2m1htD11SEYBIKEa9Wiakr+vxFsslBmUDRTMwOzZ52yUlxOhUHYpqh9GxQSqFRaSk\nTjxq0LFa1PwOpYUZzsxeQlYE+vvSbLp1AFlRCIVC6GEDVdeQZQlRFgiCADcoo0SSODULIxLD9x2C\nwAVtXfAFr4lrwT0f+AQ7cic4+djjJHr2Qn8vqhRipRnQed3hlh2jtN37MYhgCHGqqSWU9BD2XJ5+\nPYup9SDoNtVMk1qtSDLZixy1WFhqEsraLL/l0IiFiEd0Vp0TzHvLrDXXyEfDeL6J0ezFToEtrSGt\nWbTqC6TkEO0gQmn5GlpuHxPKFGUtjKO3GH/PA+jDcWrxt+mmT+PZbyFdfZBLf2NSEVT8doVNE6fR\nKxJpYwMdVabaqpF20thKFX2lQEgxkMNpxA8+QHN3L9UvPMpqs0Ouf5KBtMJMu4IRUxiP7OCr9Rib\nQzb70xuIxU1OX7hAJhRnclDhmWdfplS8zK2H3kt9+jrumsqdRw6xNnOR9x39KAOb78EUErTqRc5e\nvcTMWpFhOcaOsRzv/uARZi9W+e4bS5TWimT33snj3/wahcU8nqPRqjW5Z9MO9vaL6M0L+LpIV8rQ\nbJh4fp6IGMasFukxpnDNs6y0/oRk6gF6YsPEQhkm+h/B6s7jx8rEcwZnXj7FuXPnWM136e3ZgGmb\nPPT+29i1bYBW1cH1y1SrS/T1bcLrmmzeNEW5XOHNl19nx44j1BccaK9rFH+v45+ErCAIX2X9kCsj\nCMIS8O9uwvVvBUH4JDAPfPDm9CdZryyYATrAx7+XRQSey8zi2+hKFNfr4Fg61W6RbCpDOX+V/InH\nkO7ZS3N5jGqQ5ZsXX2Xzobvoi6cY7pug1m6iRQw279tD+/XLWIUG9W6Z/e/Zw4vfXeRdO/4VwvAq\nx946Rdf/Ch/42AfoTxpsGokw786TF0P4pRUe3DdBfrGLK7Z48MhuSms+I4MSZ2auo6iDpBUdMVRA\ndxM0LZmwkmNhfpVr13Q+9r/8JO3VgJ6cxuYpn8JimJalUKl6OC2bhCQQ8m0aioRaFemmehADA1n3\naFgVshW4YGeZ3PMTuNYV6r7H3rTH+Ec+zhOf/Tx7b9vDWKiD2cjQP9xHbHeYwchlPnhLjUr1MsZM\nH3NPbcRSG/RuExgxRd7rjvBfjq/ymvwEuz9yJ6N/dJIN6UNEB8pcv3GawZkOK24TJ5vBtOapVjUy\nrTChoV1cufAWIx1w0jpyT4A8Mo4Us+menqdYP0FaNQk3ZtFuO0x2XqRHjzCLx6jXg9P1yKSiNII8\nTa9DjiyVUhU5HsJXRexGm2Q0jRh4eI4JgKqAazi0u23UQKKyXEMQYlCVMXWQtSQxNUOtVSceCQE+\n+cU2y1cvEOASjmjkemL09UUZzCVQdR+33aBrhGkJZQRtfYetyjpCN8CtO/hdh5ViBa/j4lsCnY4C\nmbuxSmtIyypZ0aUrdLFchzffmSMRMXCsDkQC4skcvtBFH0nQMLsY4RqCLGLUfRBDmN052kKC0W6N\nRqFIzdLJJmIsl67QysVp1XyMLth+hHZPBJQWrqcRcuHMwgr7JzsI8RRybIja3Elk4Qarm8+Sth28\n2yoYRgoptwHXTXHs0VX6kg8RuP10mMFIQCPq88rZF9nZO0GWEJ2ZJkWzgyqp+AUTadMU4U0b8SIK\nVa+C9eo0oW272NeXoWQVkPqm2DzXZO7G18mOReg+EUf0C/QdVui0O3htl7rQplKGsNPEXQ5hqTWk\nDbdx1yMZ7rn/CEuFVQQ5geCatNrLtFom89NnyF+bJkhqFKZtnn3GYt/B/bzy8hn8domya7Fy5iqB\n1AIrxENHd3J4h4GoRSmsLqBKdYZHe7DEgKi2AcNTaGf7kMMGcj3EaORBOprOzkg/vaqA9c6rSDvS\nTM9fwHDb+NUWkmGy9/5htE4EY8sW9usb6UrXaTQFfEGG5jFmV8+xYfB97EgM8+l/8zH2HTyKefoi\nsjHMatjG9bz/eZANguAj/y8/3fP/MDcAfvJ7fvrNISBw5sRF7rrlASQvSme1QlwVOP7at5Epoyf7\nsG5U6fS22JQZ5cqFiywtTLMzF0fRfORuQKVYYPPuTfQPJinkq+SG+ujYLiGrjdgnsHryBomBHl59\n+jkKnsWPPPwwW7fsYYQ7uHTteRqVKmFjkr54CqkrMDW1j5dOfZOLMyrVToqtOzdTuXqRTcYUiazF\nn7xe4ZEj78VlieLKHL7SZijXT7FaJj+nMZzLcPzyEpLRy8paBSkawWo2CKQQgt4gbip4SgK71caz\nm3TCAaUvP822ww9Rl3MMxdOMFvfQKNTZPH4bvrKBq/PTfN8nPLYfXCKkn0FxByDwyaQa7BtqM7RF\n5tFHT7BlT4pWfwZ72eJ9Z4Y5d2yBY5teYN8H38/yt1/kemGBklXhUxt/gDebBbaaArValXD8ALl7\n3odVP01EEyjH0pjRDmokgn5phsjWcUJdk7zhollx6IuzbSRGye2QHhhAKJsUi8tEkmnk1Qp64KL0\nJzC6Pr4uUDHrIBiogorT8fANkUq7jhLRcLpdHMdBEkRCqo7vOHiCuG7J7UNUNda1YUUR33bWqxiQ\nkOQxHMehXneo1xxmrzeJRCxihooiBTiNBZzAx5cEJE1DkTXwRDzLx7M9qs4auhomsKHdMul2LVzf\nw4joNLsdZFkmEtIBCEUMLLsL+JiuhSJLSIqMjr7upOC6OLYHgo/vxTEckdVWFb3dQhrpYXm5Sn0s\nSt0rEdjLSCmotRqsrVlYVhdBijCcHWUtOIGw6Q7Klddo37OGt2eRVukywSN34qpbCc5LRGMZLFfg\nu9/6BorZQyBq6H0ufrOPwuoC4YROpV7j9c5ZxGyUmatXGO0fJGUkSd6+BVcLmD/7GjPn3ua2Q0eo\nLpj0DSgInX4iA5sYlHS+MnsSNXsLX7xSwxOzxCYGiLQc2pLDYCJC1+2Q0ENEWyZ+T5j9gxvxWhYP\n3HOU8U0babcsfF9l7toCSsgml+yhsnIRr1vi8nKegd40kiIye/0q9x19gCe++yUePqTznDhGo61z\n/36JW5M2q3YT1W4yMLSRSHyYoLzK8ktfZ3Ksj2a0h25lFjewGUoNEBgZ6q29FJcXiRY8lGSW8989\nwz0fP0pyeBv3Hu3n8vQzvH7yMXLbhomIk4RknajxLuTcFcqUqa1O02jIvDh3gbeuvc7uI3eQOnmF\nJy9e5v0//svoQghdVL9nvv3/ouPrd373tz8XT4aQA3jzrdcxXZv5pQWWV5e4ePUco1NbECplBCWM\nrDUJF0qshSW2Te7EMZdRFY1WrUMu20tE12iaHaotk7YZMDdfYmjjCLG8xTsXLzA62ctwj8iGiTTh\nSAhDGMRQXRbzl1B1mYnhOMOZMEHgsloM8diTLzJTmMXzZX7wQ5u5cvwCmj2EMRphYtsdbEz08/jx\n73Lqme9ijIyQkXopNXwGeiRWSk1qtocajoEso0gyMjqWo+G3bdSwgqoodBbqND2fzvFzLDhzqP2b\n6XotQlaDpdMr/Ffq3vPbssMs8/ztHE4+5+acKmelKkXLtmw5yAHbBGOgwXgWDEx3MzTB4PZq0Zi2\nFz3AwAJ6EZoGg40RDhjJlmTJklWSqqRSValKFW7VrXBzOjnsHOfD9Xzu/jRrtP+CvfaHZ737fZ/n\n92TlOq9fP0kStfjkvxWRVYsw9hEzNo7oEYYVlDRHOetQ3DdDvblJqnbYPqBwcEPk0nLMY36Pv1Ua\nHNq3l9X6Go1UYbHY4cGhB3nj6veY7pvDczf5p4u/T7V+Hju1abtNCkIWySygUiEON1EX/glzps7h\n6Bhyaxv5m08y8eYZhNlptIEi44MTrF9YYOX6DejaaLZPUZQhL+FHAXKqo8YaqZ8SEJAkIdl8liCK\nSOMEU9XIqgae5yGqEmpGBTEkm8niOg5xEBJHCUmcIEgSXtwiVQJSOSAVY2IpJkpigiRBkDUqZh+i\naBBHMp4dYTkBnh+SiDKSbiAIEopkEPoQRSmCrIAokYgCoiIhawqIAkEaIUggygKyKaPoKn4Y7VTa\nsLOaCMP/lzMrEAQxkhzjbC0zMzJAPfFZq27QHXZZcs+z8EqNXpDQTGIGZvJ8+pf3cOQ4zB1rkKQS\n//D8S/xY4YPks/exfSPHxpM1Sgs5uksx0cYi69eu8LVvfIVQCpF1CQo6YUbFtnx0UUEIA+SiwSu3\nLnLy2lkst8fRvXuo9We4cv015v/5CdoXrnLo7kfYaqYc2nuQ5RsXmNz7DuzmGoePjvB3/3CG71wp\nIRsz7BvLUTabzBYzWKlF0miQFSPq1iarNxfoSCm6GLL7jnFGpkZZWbfoOTLD4yPYQY3e2gKd5ibj\nQyU2124wOVphZmqE7dom585d4MOP/gSmmcd0RFz7BmHcpb9sM+NmyFNjYs8ebE3ASbpkShrDhw6T\nTE3SLMhk9UEG0l00pS0cTabcvcrQ4VE2t7bQ8jGZsSl+4de+REUcYWZihsNHH2J5o8Uz332C2cMH\naWkpm80t9vQPc9vqcnH1HIWBn8Rtd7l7rcvSc69xuXqTR3/r15gsl/GfPsfJS6/wK597G7ELvvil\nLz5e6dd5/fXneejhQxy/e473v/de9s6NcnD/LrbrNm64Riv1WKktsDtX5o3qOrsPvQuDZeyOS+oL\nSFFKYagfQZa5tbhBqxOyubbJ4PQIyZkOWSPLoffu5mOfuJdg/S0acZew16Jv4AhBZLNdu8qusQwq\nPZ77/iLlIvzqb36SX/uV32B0MMcjj8zx2MePM7lrghsb62ystbl77iAtWty+dBZLk5ke2YeTGNyc\nv0DHhcGRWerNHhk9Qy5bxAlCRDtCyurEfkinm+AvirSrOSYOv4+iPYmg+KxfrSJaRVaXTxPGIXJG\n4qd/JYteaBP4Mpo+SySMIGXHKVbG0ZIMZpRno76J0yrTMBs0DYHWQXjrxYB7B3Xee+E2fzkcEL1y\ng28ceoCo2uPjbpc7KwWudi9wubfKlH43cmBSDc9ian10tjUWV04jZgSoL1LWzpDtl9lb+DGuO9ex\ngi3qqsJ4ZYInL73B099/BcGV2LZ7yKbBkJ7BstvEsoqomESxjBCAoqq0QxtVzaBIKUkKSZSgCDJi\nCrbTQzEVZFND1UUMQ6XVapHGO/7vJAVJlAhiF0FISdMUQRQRRQXbcWm0urS6PSQhIhISNENG1UVE\nISZIXPzYwYsspNTA9yIczyNOEwRRJExivMBDUZQdx0IaI4ggySmplAIJSRKzQwiAOIoJw5AwDEmS\nlDQFN0xwQpeBMKJBiNO2MffI9Mwldt+1j7lCyNH3zzJ3Yhw9G/PE31/k7EsBp1+G2pWQrfWACxeu\ncjitEE+PkM1XEMKQpeZt1JqNIKQkFZPtsEe708bQdPSGh1TJ0u02SYWUrugzv3INSAi8gMZqlZvn\nXyDTSbjrzndz8MM/gT43ixZGZFSBeN8hep06/uot6nFKu2vxoR/ZzbvHQu4ZKFDOxChGysbidcLY\nptKXp2f1iLNZMnoBJX+MvtEH2WgJCH4u61YAACAASURBVKaBF/dw/Tbdboezr56k07WYmpulXtsk\njnyuXr7MwsJtmo02n/i5f8s999/FS/MnmTRzZEodbm2VePO1BexNmYZfYWBiL5qQUKtuYJYG2Nyq\nUZYlVnt53tz4PnpY4Ma1N5m66yMk9ou891f+jGP3f5rRfR/kif9+FqnmMNGnImg6Fy6t881vfZkm\nXZq3TlJUyvhixP/4zlcZF/p5+qtP8rDfx9mzzxGUTT71mY+Rf2WbxSefJJxf5LRU5z987m1E4frd\nL/yXx088+E7cTovqzQU0q87CK9/l5ulnmMmJdJoeba1Oxxa4PH+NqeERVtY36Zua48T+Eltr20RO\nhKYZFMsDmIU+VjeadNsu5nREcv4CUw8+wu777mb7uy9gKTHp0AhB7KAJXTLZvfT3HWB5aZ7OxiJW\nJ2JhOUDL6Nx5/x34rYDdE1NcOHcOJ+gRJQrfe/kF7OUmp67Nk++5LIot5ooDrIUhc9N7qbc9VKNC\nu+kSdFxyWnYHRl4wSbvbdNwQAoFcNmK1+Qo5YZV1zaZfyGF1d1gAxd0BcnmI/HCRiblvM7yvgyT1\nkYoyjW5KqhwgjCZRFQnbvo3r1rnQCjl08H5GIp/mehvzwAhL3Zh9r7rc0++TvdrmsR/7APGrDS6V\nl+h16sSqw/VgA234Rzkx+kEGKgcwKndjCBq5fgWxf5r1lXO8o1ynZyyRujqOO8jC2jw9AnQpj7ex\nSb+iUxgZ5lZzG8NQSbpNJitZCHrEUY5Y0nBjAdIYQU7wERAEHSFxEESRJIE0SpBEmURI0LMmqQj5\nvIYiyfS6FpKs7nBmk5gojol9CSKJOBKQRQ1V1klikTRMEZFpbNUInAghllBR0QUVDQUpFhHChI7t\nEQYhYRwRRiFhEiNIKbIiICYCcRIikKAbyo5LAkiTmDhJdvrn0pQwCgmiiCSKSRKIophIs9FUn8lK\nDwbbxOo2W8orDBxSmH+zygtfOclLry/yvRc3uX5ZJKPoFMtZJLNNIiggxawHXWxS9rUN9NlxOqsb\ntOpVMtoggShzq1Xj9YWrKJpGrV0nsnpcXZ2n43W5sb7E61fOg5BiGFkGMwO8912P8Z5Dj3Li2H10\nBww6QzKy65A2e2TGRoidFsVl0FKDTF7Dmy5wYt/djMztQsqq9Gs2jRuL1Fo1Bo7OsNZr0FxrUcoP\nM7B7L7n+OQKhShjFuG2JyYE9SFFK3oDjDz3CVstiq95hfv46p0++giDKjI9O0elYiOLrSOlvMjHz\nt5x79g8ZHzzOyIjBpz+5n97Ybg7fMY6ERWhmSLJZmpsL9KmQ+gITSouJA0cpjA4zNfEorWSbjPI7\nnJzPc+HGBmtXL5FJEoZmpujYXVQ/ZHbXGP/3V/6YUbEPNa9zvr7K2Rcv8amPPMLp568yNbqHs69e\npJrv8ekPv4+Fr3+ZjbUVmFJJU5XXwzqf/e3PvX1E9gu/94XHJ2aHyZp5lhe2WF5Yx3McFF1l/uZt\nRmWTYEglbhS5trBCIZujIkMkW5w4uodiLo/V6SJrJlquD6MwiO942J0a8dYKW29cZq2+wcyuMXpC\ngjMyQqMpYsoFhCRBlkM0Y5hcRqHTusjIxBhb/lvM3+zQrIcMD+WQUpnJXXuJuyWe+e4zHL7/CFIk\ncf7SPOdfPcXYgRna9Tq5wQGIRSRjhDSSqK1toQoKrUaDer2OmjfJKBliJ8b3bfzmFk3Np3V9E8Ey\nsKUii5dOIalNdt1zkF133WLXkQs8sG+TNFmmPHKIWi3L0raHlzSoba8SWj6GLvLCq09zZtllOudT\nERSqq3Xi23Aw5/LPly1mFYF75DzPGhoHFh3OhhFp2eZEby+vRzVuNF+lUjyBZmhc77yIkMR0U4Xx\nd4xxT84kXv0mo2Pj5OJdvCQvcyA7RxwIJIJL3uogt9qMFjPoFZV2VGfcdZjr9GiJDcrSMFYU4UsS\nkqLihA6KbuJ3fRQpJkkFEnYmVN3Q0TQNURWJkoC+Up4oTQnCGEFRiUnxg4goClEBUUiRhZ3W2yTy\nCd0eUeAgJAGJpuHGER3Lpud6hEmCoMiIioIoy3Q6FnEakyQxURQSRB6iAGkSoYoKSRIhIqAoMlEc\nIQoioiihyiqRKBBGOwCZJElIBZHkh+/WTlpkQo2lFZubNwLWU5lWp448Nc6l79/G2+qwe3YSnxSx\nIKKlIVGkgaDTS2NkBwJTZGNzhSvRFU6/9TzfuPkm606LVrCBkIs5d+s8ncCm22tSs5ssWk3sbo3t\nboNat4WWMclqKnHP4x0n3s3IyC78wjStqI2Y9eiXRfzlHnJlmLYqYOqDNL0GjWib3FCRXCGLmY9B\naLO1fAW/voXb9LAn+vFCjzBJ0camyAYmtza3CZIqTi2itb7MYKmLEN1g8/YF8AK6ocyho3eyvlGF\nFG5cmad/YIjAD/E8jwuXb3N8z4eZe/hDjEzdy9jee0BYQKhts6fsE69fxF05j+qvMViUkZBYv7bF\ngFJB10OiyKeXGcSpbyNeOcu88aPYy7fpXPPorVcp5Aus1X3emu8iqaMEsc2Xv/JHNPUq9dtdMgWB\nQxPvouC5lNyY7Mg0URqzN2tw9fRLjI8Pc9mMEBKJ1NC43KzxW//x828fkf3Sl3738cpoSt9QiWq1\ni6jkGNm9m70n3o2tjSK2NvEHBNK6wVp1lSiNGM4WqHfXGB0pMTk6SL22iRcL9I/uQTOLGHLK+u2L\nLC+sUU6LZPozqCM50nKeXsdFGSySWmC7CbHikaQhuYxMSg/BiFjvXWVqfJv77vwQ89fXCI0u5y/e\n5MnvPMXP/cId/N03X+GJJ5/kzl37ubxxiz5FxVFg8cICxcogUWTQajaYnBjBsrvYroeiarjtNp2e\ngUyAKFvYnRoby1vcefAeFk69yt7jw8jRNvNvKpRm1rnjzm/i+ReQ7ZQwGsNji0bXIFIMao3LdLbf\nQBO3CNKUrz91FdmJOCqHrGCz0u2yUVSZHUvRzjqsrnbYP+Dx3I0VHho8yKJwC6Xh0zecYznwqCdd\ngtaL5I2AW7V5zmy+hjkD7ztikL74Gp3xMjOVA7grObShLNm1DKaWx8jn8ew6jhLjW22mKyX2PXgM\npdVmeLvLqZvnGdZ17DTClTMIWg4/SFDSBCWKUCUBLwxJRZk4FdA0FQSI4wDElMG+Eu1uDzdMCJKU\nMEmJ4hhJECAQIBGQRAlJEImDmDgEMVUglQjlhDCJ8OIAJ/ax4gA79Ok6PZq9LjISO0UMO+KpSDKa\nopAmKWEUIMsymqlhZAzCOCKVROJkpwI+SRN8zyNKYsIoIklSkgSCMELLTCI4TS5dP09QaHE7PE+i\nDTJ26Kc49e3ziNEid00MU+26bEUJRiwRpioKKa5XJxJjhl0NW5NpeAm+XibsxNhijtWgxfzGBmRN\nRE0mI4qouoKcRqCBKGnomQKuZUEUYiomBw/eRa4wRMlv0ZQDyjO7CLspieWSzWlUclnEYA1jaBeE\neaRSTLi1RDsxGRu4A1806dWXqUYyzE1TFBWGKkOs+z6pk1DMZZFUjbKewapvc3T/PuRU4qUfnKbR\ntFhaus7k2AjPPv1d7rvnOOfPvUG7UaVQzBOGPo++6ygjE+/k6sVrmOpbtGqn0OMzzBhjRLHGthVj\n+Q6q4NCo1ckN7kZQMmytXKdeGEOISijXbtG5+QxW5gEct8KbT22ycvN1RAz8yCeMO0xMDrDZvEC+\nfBODBRYvZnjonkl+6WPv5GcOq3juEicvX2bLrzGalKlfX+Lg3CzLS22KgyNsbDt0tJB62+Y3Pv82\nEtnf++IXHp870Mf9D9zFpbPncZoNZkaGGR2cIYmKbDdWaMhb4MhsOqtYvo+ETtfuMDFSIA4sOp0q\nSAa79p9AlDNoGYmLp5/GS3xW/JiV5XUud7bY07cLKwrIZAUU0SUWDVxfBmGbrdUqvVae50/+ADOz\nj1xrm4H+UbTMXv7hO0/x1996glxJp1nL8Dt/8mcUCwUevP8EgWvjOBZbm5tIsc6B2aOY2RLt1jbP\nPv80U7umUDQdEuitbCMWHWrddbqNOo4Xo97s4ooqhfwAy2euMTB3nMkT55i6Z5VBtYqFhCOkBKVJ\n5Og27d4QzZ6BKonkSRE9j2899RILqzL7BrOUpsZYaSxjr7RIh20WMvC+vmHKb/RoqiE3mmVC32Ev\nBapySjmjMyeNEglViq5C8eEKdiJgaqP8n5/6KNZXXyBnluibPshi/QapWkRrW1zsruJLLQa7AoGQ\noik6iiCRq3q4q3UkZCbkAnNGH9eWT+PrBo5aJlVKCOjgdxjKZpB1Az9OkXUdSVFQFJkkCoCIfDZD\npZBjq1bH9gMs3ydBQBLBVFVUGRBiBCkmSSN83ydNEiRBJIkTNE9ET1VUdNJQJHITAicksCNCJ8LM\n7By/QNjxPqagyApJnJBKAnrWxDANJEWi5zggSCSCSJwKiGJKEIT4gY/v+0RhTJQkeF5AmG5ye+Uk\n9FJWzYCQTdSxh/C8SU6/+t944KjKHrPA1eUt1gWBbJrgCw7EDaKwjJZAU3FIEekXi4gtHymboNPC\nMHXKuoKopuQrGQxDQc9pjBZLkM1gdwLERCYMfGRFRDF0JibmyGUrmLaAOqAhJh2EjkVffpTGcpvR\n/n6slkCvt8xWcIOxXA7fj5A7Fkkuohtvkh8ew1fKxIGMJyfUW3WUboqq6jiCTZxm0IU++vp1ri1+\nn1Nv/St1d4tu7HNkYpyL58/wwnPP41k2breDSMLRo4dYXV3mRz7+kyyZCZdee5IRdZPRXB89O8As\nznH71itIcYNCLgPaGJZtUl1apDysYs70EdgO/qVzdG9dY/iO/8DuD/8neusxb619BzcfYIQeo8Uc\nB+68m5mZQUaMeW5ceIp85QiTkwmZoEmwuEgxs483lA8Sjx1gqnAQ/5nrbOUkgs4qYSFLrDgouXEM\nBzYsm1///NuIJ/ulL33xcSVncHQ2pt3JEadlMlof8ugMsV8gVGvs74u4tOlTbia8f2SGUtJluNyP\nMCCyf+4wy9e3CUMwSwaiEePbDqEnUbt9C1GFLz/5JMcPHUcr5ukEAYX+MeRcjCAIeL2IVgNqbYur\nqwustLrI+QJVaS8vvXaG3XODWHWP77z8A44cPkKSSHz/1Cscm+3nxuYC11sBraaHIvrYQUh/dg9R\nLs9wuZ83X3+SzfUesZLHlmz6BhTcxSrZQCW0VbbbG1hxldqVUyxeeYPx2fcwsHuZBx5rkNECWraC\nkTHJFfYgpxGd9gKp06Nh5Ml4Ib0bb/B3JzdZbRQwhB5HxqcJ8wm1dsyp6ZCxXodoWGXgisBgs42d\nqCwpMqrjcbgyiWCrbBkxo+ooeSHDqrfOP9a3ePnmBqnmcbilYEqDxCd247+1REaXobMJvQzN1CIn\nalSGxqFnIQ2meJtdpvomqVrL6FGPtpzw96uvc+fUbial/Vy11mkOGGi2jxSUKGUTZCHFiVNcQSZJ\nRQgjZGISElJdRctotLodPNtHSQSKmk7i+iRBRKLIdK0upmYSuR4SKWKS7pC44gg/TvECn0ajhiSL\nuK5NkiS0Ox3iNGVru0Gr0cH3fCRRIpMx8QKPYimPIgqEfkgQhKSJiCypeKFPELmIUopfT5HTiG23\nDtFOWi2vtDEmHGSjw8SowZXFy0jDOvsnjiPkRqnd+h7C9mWmS1nOnVuGnsG66+KLPkVZwzVN0tAj\niHcqeKIgIFF8fBwgJYlkQj8GWUbXdGR+2AMpiCSSSLGgsb1Vw3UDkCUEBQxNo69QZqRvCK+5Ram/\ngFfRcZaq5EdHaLs2Spxib90kiVRaV6/RLbiMmtNEQRPLv02gSTRvrXLpwg+4NX+GsNsiq8rkCiaZ\nQoVcZoaclqHuX+H0D/6FMydP09x2UJsBchLw+tJNgjZo5UmGRjVqyhZT2UnevLDEBx87QmVsmqDW\n4tTJ7/LJX/5TZGWSqPoWTuQwbKSE7jb2Olw/eR0jqJHTugSBSpxqHL/jCGeevsKW1uThj5/kv/7V\nx8mo91K9dgaz3YPiFPLMQaRsG82LuPPQe6Avwyd+4TcJV1w60hD7PvB7bJsl1s4uo654qJ7L6def\nJSP4dMwAvdiHKhd4z89/hm+uvozVbvNbn/2Pbx+R/cJ//uLjct7EMAKMikYSGTx8/GHmb9QoFTy2\nN26SUxyWezFDSoHDA2W0JEXOaiiSRX9fhY/99I8Tk3Lr5i0sx+H0pQtsJQ7qsfejj4wwJLcQnA3m\nr79BYbzAteoiupqn2/VJUoFWp07HqdGyN1nevIaftPBsierKNpXyGNrQKE984ymMVGd5YZlaUmdk\ndAC720Z2u1j1FQQ9pRu4OHaHPaMajpXhofd9iPzgIEIisr1WIzbL5GaP4mZVBmZ1ums1egvzpK02\nTiQSjrzGx39URDO62E4XWR/CLO9lvVPDjzcwbZ9eNaUbqdxYMXj2DZlukiHuhATBDFl9FaU8y/LV\nczy04VK/a5j7rvjcOpKndc5mfsvlveOjNIyUgSRD/9AQsaCQBiZyeZjhyjS9ep07fJfPZEfZXZkm\nPDqBc+1N8h2VSjmPFsW4VkigBGSjENX10JWQbqvBcMkEYqwkz62uzY3uImohYo9+ECPNsxa2sSQN\noZuSIJBRI0RJx/JDAsSd2pc4QiAhAQRZRiTC9UJABEFElhWieOfi3+z1qJQrBH6AquhUN6uQCHS7\nFrblYbsevV4PQRBodzoAtNtt0jTFdh0sx0GUJSRVpd5qslHdIFssoJkGYeDhh9FOTbmQ4vk2maxB\nsZjDD10iCRrNbWRS5LKOF9Z4+KF9HHvXPvaMp9xc3eDAI5N4GxEXXn+TuvsWq9ubPDBXwZg1iWcS\n7r1vlKERlZs3EvZMH2dq7hgL81fIGiV8DyRZJY4TRFEgimJkUSSKIQx94jhClERkeSda7LouqRDj\nezG6lqFcKpIvZiBNECMRU81QHOgj9l2yZga94ZPUbPrec4Las6/hVGtsOz0G332A/sFxykaGq50O\nQ56GsJmwnayzuH6bVLeIxG0G+it4tsqbb5zhwMEC/ZUK//ztv8Lv1RkfGSYIRPA07EhlY2ODXCJg\nWauMFEoUc3s59+KrfOixe8jkunzlL77Mj//4byBV5jgwVMLdeJXqW1+hf3gWbJXlxgq1sEkqldD6\nFMzBUeavXaZPTNlMu9z9oZ+gePhBfubffZS9o8d58uvf5vDuuxl79yc58cA72b56EWN7iHxWoats\nUB46zLf+5Q+44zOf493v/E1ufP8G1gsW0fwlbp17iW7YYv9nfo6HP/o4jUILbWg/NXuDe9/xsxw7\ndj/nXn2OX/3VX3/7iOyf/smfPR4mEsvrDTKDGpViP2K6hSDkcXo2YXuDqF1jIw3IOAp39pkM5wep\nhy2kuZRdc+PoSUiztU2UkTAn+rFSj/PnzmBvX6Bc0Rg59hD53XthoEA93OL64utYlghSgu1a2F6H\nRqfG4upN3MCm0a7TzHaxazWcWpd8pcyzzzxNr95kY2uD3ZOjbLZddCmHu9mgMjrKuz70o2iiRmg3\n2F65hkeN0piHHwsEXh7FDInVAkrUY2hihG43Yc/MXrYbHredRe74sMm97x1E7J9BiEIkq5+8mWBX\nL6G11hGCBt7hL7J+Oea//+kPqK3rpKGAmbPQjYBbt1ZZSTzqdo/ZrZClh/qRe3UOr/m42RyHln1W\nO1BWs7wwkKLbXQqSS58yRFfMEMURpjnFA8W9FPMK7pEBolodb7WBsy3CQBHyMmHXQrQ8IsFGTRx0\nNSSYtVDKFTBlWk6L9UYbu5yilhRmxWkKwjhC6LHaWyFQcyS+TGKIZMSUFI1eGBMkAqQCKTss2DBJ\niEQB17YII0CQdqLIUUwQBURRTCpIhGGMbTl02z1UWWd9vYquZ3FcHz/esWfppolt2yiKjOXYGKaO\n73vk+yqEP2w9iNOdFYET+qysr0MMrusRxCFJkiDIIrquEEY+rWYdO+gSJgliEtEOqxze3cfc/n4i\nweHo8GFaUZ1/fr3IweMHKR412FP4Gc5//xRTyW1aCwab10Ia131GpCLFR3UKuRpnbrzCWEWn12vh\nBz65vI7vu8RJgq6ZxImArutEUUQQBsAP68lIMQyDIPQQkXFdH9u1aHdb9LoOkiAyO72LXrVOYbBM\nmiboWzatOEIv5hibmsJdX8GOO9Tqiwzc9xA1+xplv4JuCyQoXNy8QmnIYN/hg4yPHiT0U2K/gSHK\nVG/7nDr9BMMjo8S2xOuvvkWQCAixi5wHNQ3oNTxEQ2d1scPFs1c4PJjnxKNTbNg2J6ZKVO79CMnt\nZzBTGd++xcDYIcTaWRTWSOwQ2yoxeNhgdm6E+uVL6PUsnt0lo8I3X1jin752lYFMm3t2/QRBt0u7\nDqU4w+mXn2B8cBd6pkA6bOIUpvjzv/4bFi68TNy9mzfObJHbU+CiX2a1fIDpj3yUzJ5dVHIarfWI\nvfv30z81zvTUe2k1fPLmHp568i/47c/+r1m4/qfQ7v8vnsnBkfSzP/8bfO/KV7m+tI4ijTE9k8Wx\n36Qv+wGkG/N0Npe5NSkT3wr4+TvnMI1pntk+S/HIAaanZ0j8kL7yILO7D4CWpZOISJkcr55/lW98\n7a/45AcOoJsaLS9ltRFx6s1rDOtdJsdHueuOu7FaIZ1mSOAICElKoVCgf/IBIs2l63m4azY/O+xR\ni2qIc3OoYp6V9jJRHLDU6HK9Uedf/vx/cGRkjDseuo+kIlNSxtm6sUTWjLl4ZZlMeYZHPvhZXKNH\nGmcpKxputUXTu8qZK09hOCt85ITHQ7sKrLpLdNs+ShDgxLvITP8bZNXl7D/9AX//UoqhFrDWlqir\n0HfvOGNSD+2FNna/RLc/i6cp6EnEibEiG3sFHtwIWd+T4eln2mSv62wkXe5WKuSMHIqrkTPWSa1x\n+o2YW/enLL5xk8O9fayXPFbGW3xy6G4K4zOk7Q75C7foW90kMAICpU1a8ilUfGqTDsPiXpa+LtB0\nDQIjpCT3oWqjmEaBmyunuNLaIBk4TGLsIyoNMCT1I2dUUlEDxUQxdVQZRCFCkQQEJDLKDqtWJCFN\nY+IoIP1hrNG3XDzHwVRN0ijG6faQxR2WQaPRQNHEnUlW3pn4dgQrRFYV4jhEUzOI4g7ndnVjldHx\nMXRTw8hkWF/aQFElIMHMaGQyGt1em0ajjiRJhIFHnEaQ71FRYyIVNLPEb028h2SyiJSGCHmF3z3z\nr5xcbBGxzgf31ZmtZHj15WuYN2JWNgKWczpJpY/Wlsd4/xz5Po84UUFQuXp9ActpI0gBnt9DFEFI\nJXT9hw4MkR8SyBJkBVRNQhQkxFQmFkR6tkXgh9hdl4KW5UPHH2F6ZBxxrJ9etcFuW0XuL7Bxe41e\nQad99RxiXuSdP/WrnD3zRQ7v+gThos2FtMbi7Zc5eMcxUkXC6oXk5Qkkv0ASxKhagiQP8Gff/M+o\niotjddBKJbRAwpdl2hEUOilhZ53+kRn+t88+yj9+7Xs01lb56Z/+FLayB639LQYmj3LuG/+NYkWn\nLPcj3L+LvsYB0t4rWO0ValEbmOGue2f56r9c5HJNQxRTUj1GCyyK+gBy1mWsXSHIm+yfvY/Zu34E\nJ2iR2n+C0Khz10M/yc8//lsE6U8xMLKbdrWOLku8efUpdk99Ak0eJiM3UEoefXmVzdoCY9Of5uzF\nawxlJcL4GpcufJte+9b/ErT7/xeT7P/1+//l8d2FWSRFILQc3CggVYsQrNGtJfibdXKKRE1JmakM\nMzc8yM3tNueqK5iqjJ6ktLY3GRofJjfWR1LU8VXohRYz5jBzE7N86Y/+mJYX4fsx1vYGuypFdk3d\ngSLoNOsN1la3cbo+UZjQaNRZXlmg1mzhdlZ46+Xn+cCDJ0DqYW+vM+4lRLOHwW8jBzXmdg9RNEWO\nTo7z73/5l5g7eIiDE31M7xe47513MlguMJ7VWH7zHK1AYKr/HrSCxHJ3k4GxIazWVS6d+kuGcgnN\ndZ1Xry0TrxZQ3E066Qz5fT+Lr9ZQrU3+/O9P43RdSlrEXsPgTktH1EUGHjqANb9MbTSlclBCDnTK\nSwn1AzIfHTqBmT/EhuYz5ddZWupj797D9MoyC06bNVNhXg1JZoaYl5q0pkBfWmS8a6FaIQ4OS9k1\npiyTpes3EatNSiUdN3AJUwuzAjJd9IkMwXIF62KZrFEilwRk7BRBUWjYVa5sX8HWDHxRQs9XiNQ8\nJAqxkJIKIlGakgqQEBGnKZIoISIhCRAnCX4YkKYQhDEgEsYpkRdgdS3SVEBWVCzLplgqYHs93MhD\nUkVc3wVZIE5DkAT8yEOQxZ0kV+TveF3jiEwuSyokNLsdbN/7IToxIZM3ESSoNqq0ux2iJKZr9YgN\nCSVwQbYRMzqWZqL2VI4Fg1xZaHKulPJHb34Tafc4wXybZKVK99o5Tjy2j8P37Ofk02eYDSY42dri\n47v3c2rpMs1alfXqBreWb5OkIvlijs3tFcLQRVUlBEEgl82iKDIgEMcJIKAoCrKkoCgKcRDtfKcg\nwvMDRFFG+GEx5fmlBR4+fBdDuRKuZxPZXUzPxdBUdv/4J7hy9gyjwzPI+/czv3CS9ltVZvITbDgN\nxufGidKIJDCQglFSO4dpKGi6Rq8nEtPh3M1X8Z0eQioTJTJqpOOHAq3GGj3fI86aGNNT6NoyNxfW\nsT2feKjMOw+MURz7EJHRj3f7LGGlQm+lQfutZQp33San9OGtraE2Y8oz/fzVc4tc2WyT8bOUoyw5\nrYssSwimhe4V+cVHf52Dh36K/twR3nXXXi5d/GnKustI+W4Wl9f4x39cJjd4BGvlBqIboscBTm+B\nTu8cUroPKTfKxNTdrGwWaHe61Le6HDvyAPniPN3GEotLb/CfPv828sn+4R//8eNzw/2s3mpQzmis\nd1fo4pBPRbpbLr4T0Gtu4et5DkwM09xscXWtSksRGCiMIdoiw9lh3rx0ndLMLtJcARUT1RFpaCGl\n/grYPlPDUxTkLD/yrsd434l3R+0uAAAAIABJREFUki9N0Vcps7h0i06nSypI6IZJ/1AfohxRvbZE\n/+w0MTb7MhIlt5/iyAgdaQutf4g8KYLgEdrb9MsyDxw/Tph2aKyeIWOvYjnLIPWDmHL7ylUatzQ0\npcmF175Ds7HI+NQxYiXm+pU3GCpZ1L3r1IIKYqNE1krodB2C8WMM3/UAmtngheefxX5WYY9kkQMQ\nZIboEqYWa31lMqMGhuCwZuZ516bP3pLC6V6PG+tbeLUao7vfQb3gs/71ecpzs/zOA5/gwyO7WDWb\nBI2QTXebX/3Uz7G/by99xQHOL14j3bARtmJqlxvEmZAghRGjgOi5lM0KmgKt3iahLtPeGODmSY9s\nUkRSfHy7tbPOyPbTrS1xy6rhSn0kUgKSiq/k8KIAJAlJVEnSnV/fREhJkhBVViCFKAqJk5Q4+qG4\nhiFJCp4fkZEUWq02qqJQq1YxswatTpOe16PdbZOKMUEc7OxUQ59USAnCEEkWQRYxTZMgCuk6NplC\nliCNCZIYI2eiKSJh5OFFLs1unc3qOn7sIesyYRpi+Q2EqEsc+6x2LRqbHe6b3E3bcPiX0hv84OoK\ng7pEzrJ5//sf4amv/z4fe2Cap/7idQzf4d3/+yf41rnnSXM5/p30Ufbtnua5zTOkYsjAcB4/tKk2\n1ojiEMNUMY0s+VwFUQxJk5Q4jhFFCVGQSRMB23HwvADbcoiCAMvxCKIUP0wQSFEViUCOGS0MMDc0\nRq1b57a/RdJpspqLaFeb3PHJdzHUP8zC4jXC5gqxLGIrKhU35Ka3iCHtobWZpWCU0BSBbtsnVQwW\n1q5y8tRXEbMmUpohDVUSRMTAoevVGHZFhgdUTtwhMD4qc3ehjrxd44Fdo3zswz/Pasti/cW/4u//\n8G84cW8fq2/VcYaHaXx/m+yDH0FXTIam5jD23clTZxJWtrcQSYhFCStzGy0YZdL8CMfHP8W9k5+k\nPWRQlbZ5x8fu4dKZv6Sz/DpZRaW7XUX2NoiCdS6+dQNRW2W5ukQ31HG9cUx9kH3T70GQRGQ/YGD4\nABcv/S7uxlu8de1vWbh2nfXFVVQ15bd/+9fePiL7+3/wZ49/4keOUV3bZnPlFqGWoS32kJsRRV3h\nsY9+koGSxkY3YCgnk9f6WGm30foHGc6VKA32M3loD4kqYPc6DOfLGKmEKWvoIoi9kHvvfICW49IK\nu2SHs2x721hVj1xBxcjKyKqM4yX4PjTabdrtFmEoEHUFfvED76WfRTi8Bz3fxpn/MrEhEFs+RiWD\nKHXQQwdBl7k0/yTZ3osEzhLZQhVNmiWM+8gPidy8cZ3kZh39YZWgJbN57TLNjfOo4iYjlb3o5b0s\na2d45GydTSfBHyzSXA6IV6pcffElNp5bYSpt4WcEKp4BMtTNgIkE4q5FddcgrEToscb49RqvDjtU\nylnGFntci1xqt+pY0lGOrJzmW/PXeHGxzp39e4gqa2yW6txj9KHco5HEAeGqgnLkMPNayHUzopxT\nWDN6rLQ7DAgG01oBobdTl60WBBrNUdrnVYb0Iq64Qiv0aIsm20JAzbc4WCjTRKSd5FA0sH0XITdA\nmMYIaCiKhiiIKKpKKqakaYIqyRCnhEGAKO6cwkR2ElUCAp7r065uk81mCYOdvakf+mRzGSy3i6CA\nHzhIskhESkJKlMRIsgSSiKwo2J5N17bQMgaJIBAJIGgSUZoQuB1c38Z2WviRg6RCKiT0nB6OZ9OX\nTRkcK1FvdQgEk335Ycqhw1+3X8S92mNEvs2JO2ZpLLd49CPvpffdF9BrFkfuPsob31ngyuoSj/7U\nY2RUHS/IszS/wOvCOrGlUCyOYtsRlu2i6wqeGwApcZgwNT0CAkRRQhwnRFGM74eIooiqqiTRTmgi\nAbK5EgkCJAmKouIHHlP948zu3cPV5iLPfuebzBs9rt5eII6bXL9yBsv1qK5c5cTh3WxKbWQ7wpFc\nYj1Pmrr0VUwST4JYRc3GvHbluzzz8j9zeP8xlhtryEJM4Nu4oYUUeJg5FXNggDsOjHL3hMgLL2xj\nqoe565Efw2q8xsb168itTb7zrxeZngkwCzGHDoxSXYs4X/UYzRQxB6a43GnytW+/Sn25yXAmQ2o1\n6FcnqMgf4r6Zf8PwaAZZ6sdNW4wWBtEv/DVPfPXT3HrlOdA1moUMrtSH1LPZN5lla7NDnKpEaR49\ne4g77p7lyvzf4ATPo6X7yE9oTN49wvef/AuGCm0838ULqwwO9Oi0PT7/+beRu+AP/+ufPH5k30eR\n1YjbtXXWW21I85Qmp1HKJareKgwepT87w5YoQmmQXmaCXGmKkV27wChQ6BtA0VV6dpM08qmUszhW\nD0FIiYWQVhCQyRdoVWtoosBIfz+pF+NYHpKQJ451vAQGhvoZGSjh9CyQYzy7xezcPrLFFoKRRwkz\npLfPEbRPki8oxJGH42xj5AZIvIRMukRs1YmdHolXIzf2EO3NTcrpTbaudDhlbpAPTeqCTd/ugL0T\nFtOFlGK+RV+mwz3FI2h9Wbp2QLLqIsxXWXr+Cu4Vj6Iks11S0SIZO43oqQm9BORIpioGxIGHNj6K\nfXkNkgw9tUS7GTEu5mnNmhxeq2EToR6u4FZTes1tXmuuUJOvUFuHXlegefsKZm2Z27Vb3NTrvO/4\nXpauX0VVJ+gKHUyjwP3pELP9Q4gtl15XpNHrJ9yuYmgJa6qLIhYh1ukZAqltUTIgSiXecht0EhdT\nKtEzi8SiiSpKCGoJQZZBEolTdnaKokiYJIREBElKTEQYB4RRuNMhFsQ7YYVYpmfbOL6DasokYkSt\nWSWMA+IkQRQlojjBdh0ERSYWIEhj3MBHNnVcx0YxVGzPJkgiFFXGcV0s2yZ2fVq+Q5IKCEFEFCU0\nEgc/CZkY6GdoQGCl49BKsozHXe4ohtRCl/uDElV9EREw0iL3Tefx53t89N//H6xd/2se+9kfxd27\ni797+iS3z1zhM7/+Y3ztX5/jQucGzTBCTCM63RYZ08B3LCQxRVMVXDdAEmW6PYsoShFFiSRNSUmQ\n5B1iWRTHyKpKnEKSCIQ/rGNXFBlFVQjshFBIufO+43TbLW5urJJRFaYnB4lch8GhHKKQMjU+TMda\nQQ0E1qptpIJJV6jz4g9eYbh/F6aZp9nb5M3rr3Dm0gsUBmWUSCe1odexCAIbXVXw0pRy3wgZPcUL\n4HzNxSz28fHP/CLe4jla4TJGKaTRu0KfGOO6sOuuY4SqQqFj8eHfeIJd+3+J9qbBqYvnCGrr6IrE\nll9ndHiW4uz9jPe/E9jEcmtsba6giRkG91QwDj7Myzc1/unkGR7a/wijpTE0O2Gl1kQIAw48dD9S\nYQRtO8XqnEfRx+i1zlEypnCFJZqNyzRv3KLZPoUTCYSRhKHnsdw2Ymryuc999u0jsr//x3/4+B3H\nHqQnmGR37WZzu0HfoUOMP/hh8pn/h7o3DZbsPss8f/+zn5P7cm/evW7d2jepVKXVkrxI2HiBZhkD\ntqE93U0DPdMx0DQwRHSHQQENAw10zPQyvdABxDSeoQdwI9tYthGWZUnWrtJSqr3ufm/mzX05+zof\nssx81ZeOMCciIyM/ZEbkiYwn3//zPss9zCwt44kyQmikBZkkUUmsMuValSNlCyVLkEXGZNJFVSUk\nXaHdH2LlSwRhgBACRVHIkojxqI/v2lRrFbI44aDdQTUMNCtHvljj5KmzJKFgrraEEDElJM6eWCFf\nrBBHLcq6x2j7JnK8jbq4QqxLzOXvZ7A3ZlhqEogWpWwBOxtjJtBOTETBIPMFTcekceYs589cIGdq\nmLKgapXJ4hDb3SZnTahUYrKFBeqzKbmgShJYSMUOUcGjH4cYikC3A+Q0ozpOWcbEKRpUPIEeSiiz\nReqpihXJGJFEUMqhpCrmGJRQJh+E1A6vcqJ8nPJwRHOyR/G4wlx2CMIdvF2bK9/aoxUojNoDHDUl\n52Vs99r0cj6f1U5w0c3hKRGjjU3kkonX7hBbOqM4oKzp6G5EPl+EtsuZ2hGyYhXbS/l80iIo5hGR\nzFCRycU5JFknky1keXrslSSBLCsICbJsyjeSwR3T7VR9kEAUhsRhhOfYaIaKkDKCyMcLHNIsQVKk\nqc42TYmyFFlRiJMEK58jjuOpNTcISOJoysmmU8WCF/pTOoKUJJ4QC0hklbEfEsugyQlLVY2FgkR/\nK2VneJuPGDmqlQJpW8e4fUD1kRlMb0Lw6FnS7ZvUzhwhutzmkV/8AW69+m0K+106pHhXPOxqhT/4\n2gtsXOvQN2SKQYzQdZIowbJy06+fTtt2c7kcAkGSRMiKgmEYGIYxLbCMYyRZJk3//zBpRZFRVeVO\nt+S0KaJaK0zfLzIG4wGtdouF+Qbd9gEPP/gwc41ZVNUjDIfoosJoEBLjEUQu125vY6h5bt64iaLB\n5t51Lr3zKvlSgTBIKBolOv0hY2eEokoUCnly+Tz5fJHdns9YsxF9D0fJWJ2X2du5wZxcQBQCRgc+\nSukI9dMPkZtZZ04uMJj7hywVH6Y93qUgmaRyj9u3nqZcKVIsHUHumZTlCrjPEiSXuPL6k9zYv8Qr\ne6/x6b/7M1SWT3Hv3Q/wxpO/x4UHMoQSk6awMLtGL1XJaW3OHXk/+pzLyzdeYfN2i0NLj3IQtRg4\nO7jjPW7depFyNSF0QZEjsiQmDAPiIONXfvW9Ob7ee4fCf88rlZGzMqmvslBa4ejiCRaq85w+dZhY\nGrJ3+U12Lj/L9ptPs33pm+y8+izrzz5F/53n2b5+mfbmOgYZOc0gvEP6D8Yu7eEQ07TIkhRn2CNy\nJ5QLeWzbYTj08ZMI1TRA0alU56lU5olDHcucpd+PiBzBD3/kITSxT5Dlma3k2F5/FrOiEBU+QcdR\niOMRHWIwWkgjj1nvCFHmYVWWkaMESx4ihINtjzFmFap5i1TSWJ3PmDF6jEcbdN1NFCUkJ4r0d13U\n9lWa1m3yD/nMm0PSjRjfmGNoFinZCW0lYWwqbC6Z2KcXWcs3mMyWGScZ2maH4WIOJYxQ42nP1lA3\n8Zsh49gingSEW02WGhUqRZ2TZw5Tnq2zce0KolSlEtTQqTLq27gTm4M0w5UNJF3wo8oqZ2yFKIpw\n2kMSMgbCwRETHDdlVi4TuBHbmo8fudQLZdylBoFV448GGzhRhqfIyPUinppRFwYDSyZJQ+I4IE58\n4jgizaZNs9OjcEySTqmA7zzCOMELIsI4wfXGxLFPGHm43pgoCojTeBoek6YEKUiqilAUnCCgPxoy\n8VySNCXJYlIpw4t94jQiiAPG9gg/mhBGNr00IpVBIsJPBlhmxNpsjuVajvZgl/XEZXG2jB/s8dQb\nT7PUWCJaVblRtqk+dJzK7Q2CSsQwHrMr7fH0f/lDTp+9n0DIFAOfcjvkw4FJ5daIH/vRD7FYqlCZ\nm8G1PWRZxnEcfD9EyBKQIUQ2tfkqKmkY4ToOYRAAoKoqhmGQL1houoKQMmRlOlyoqoymKZimzn77\ngENrS6xv3+Ldq5cpFAqMxzZ3nbmHclnDHnuEUUC1lqfbtRmOxiiGoDMYM+57AAg94vLtl3jrxrfR\nCzKuE5BFOTr9AVEUMTNTJwwjhPIduVlIQZPpbHW42Rpz98nTNHI59gd7xPYIJw1ZOfYD3DvzM+Se\nXmb5D1aYhJ9AabyP3c5V7GGPiWjxrbe+hS3l2esNuLX+MrZ8DX2ux7FzZynUjhCai2xkNvViGzcM\nEAcW25t/TiWDvFIi7Mbc6m4wsK9gFWokgwp+cIvHHjnBxz7+fvxkzNhpo1sl7rrrRwBBta7TaWbo\nytT+G4UemqKSpOF7hrfvikn2N377d56477G/QzoZM9i4wTuvP8vW1Zd4/snPc+nZL0PaodXeYPfm\nLfwkIvMkdlsdFClkfmWehAyhqTiBRyJAsXIcdAcgFI4cPozveLiTMYE/Qdc1bNfFzBUxcwVMs4Rp\nVdCMAkIx8YOISqHGG69dIuoOOLLgk2ltIjlHXlFwxutIwQ65xklsV0YOLXJzZzEDl4JpEvbWcbIZ\nUjfF8bbI148zGGpIYZNIjjHKyzjRCMl9F1yHTDRIFRkRegg/IggmpMNbHD7yOOrSXRz7+EUubb5F\n97ZLVZtlN5vw7mqeYHkW1cpzYAhmN21eqAesJCZibHN5WWPBlhnrCrGQGJUM9EmEUzRxtDzWeJug\nVMQtFnnmyhXsrE+tVEMf51lyIDebp7/TpD+IOVeskpNjlhTBGS+PEmRYvowTg1kooUcQOj4TSaMj\nIuaUMiWRR8pkpESwZ6R8+cYbvCX1+b65E3xWWwVZMFBTJlqRYqiCqiPJEiCQhIwsqwCk6XT6EkmE\nLAuQIElS0ni69EnjiDgak2YJjjciI0XRVcLIxw98MlJsP0DSNBIEiq7ih8HU+RV4hFFEQsLYsYmz\nGAQIKcMwNSDGN00ye4QS9FiekVkqy8iTIcPJmNvDHsaCyyFDpn3dQVMbjJfW+Yr2LW4+d5Wf+tmf\n5qn/5z/wyAOPce3bz9F47BT6NzZY/sgZDrYPaD75KmOvSGv/Kh/SZ/no//wxdt/YZU8KuefMWVwv\nYDQaY1kWqqIQxgGKrBAEAYaq3QkIn07zcZKQZhmqriHdOQFEUUwURcRJRJJMBw9JksjlDQ4O2hRy\neeIsIUkjVg4tUCtbBP4WpXKdMEiZDD3syYQ0i9ludrl+7YBcIUeagpnPsb61QSYgDBNU2WIy8omj\nEITEYDxgtlHjfQ8/xHA8xsrlyMYjqnoJJ0v51Pf+fS5vTOj0diiumJzwD3Hs399Hf7dJ7ZRPct2m\n/FzGweEZ4oKOJYMkZ/hjg3uOf5L5mQdYPrxCYUYwCPbpF1MmDcE/uvdDVD2F7WDM2v0Wce9NlmZj\nxp02i+WMar7Ol17aJa/mcfY3cCYRlVPzDC7f5n0Pf4ivv3RAp/06Bd2g3dHRqWIoyyzOnmNn/yq5\nQoLvyQhJkKUZv/Irv/q3hy74lX/+vz7htvaIhz5eOCIpTGhKbf7pv/o9HvyhH2dc2cE8dZj7P/4J\nrLuOIubrHP87j7L4yApFpc52v4svJPRiiZHnkQqJxcXD3H3+Ap47Jg4zcobBxB4iywLTKlAp18gX\n5hlPAqRMo9sbISSFLE3Y396itb/D+cNVErbAUKhWS3e4QA8x2eT6cy+QGUOWZ9+P62yR+i8TqRdx\nRBlzNsesvgyLC0TKCpO+SpS06LT3yS0/QjBJUaIeJaMCaoWQCVnQI3Y8NM1i4b73k09voLopmnUX\nZ9cOc/nJF4iIaV84yvG+QeClFF3BQEopexnjyKdZych7gr7jUp6fw0ljkCCLIuqpyrYVIkQBLRqh\nNQ5xbTQmEBm6SBk6Psuv7lJTAwI5Q28cZxyG3H1+EenBMnuMiK9MeFeP+FLnFruayrGFYyzEOW71\n+0SxQ6di8la3Sz9v0MJHrphsttc5nDN4SKnwsFnj5sEWi0aBc7U1rjYnuPUyltCQJAkhppSBLMt3\nclyn2a0S2VSyJJgGY6cgsoww8iBzQEqI4gBJBqFI+GFAkqbT1oJ8kSgOmdhjgiggiiMUVUGSQZIF\nyBmIDNXQsQomxaJBuWyhquC6fRYLBhUjJmdmRJMBWpoSCeinHtVTCXftvZ/7Hr5GzzjgpfUrrCiz\nRFs2p04fom4o3JubZ+niccSROcq3hzRyOdbNhBdfv8IJZ5HaPQ3e2myy+uFz9L9xHbt1wF4w4dCh\nw6Qp9AcDJEWgaxpZmiHLClkYIKsyujZt6g2igDgKkWRBmiZI0vQPK/lOOliWEYXTXAchmxiqThhF\n5HIm1ZkiaeYhi5BqzQA5QCJPv+PjBw6XL99id9fHKJQplnU8P2Fvp0OlMsegP0FRDOJYoMgqKQmr\na2uMJyN+9FOfpFDI89xzz3Pxwn0MQ5mf/+VfYrm6yl+9tst+IBMmPTDzlD4fYOTHGFGP6LbKyHI4\nNLmGM19kv/wgSRgy6TgYno2u9Vk+MsPJtQ9wz6kfQrNrTL75p5T+9DLdb2zzBX9MaXmGRyslVorz\nBEmXwXAdZXLARHd445Udur0JF4+arG82OXbxMVQh6Gy/yeQgYGP/gCi2WV4uMFt/lPnG/SwvnObq\n7ScJEsjlGpQqRXzP43N/m1K4fu23f+MJ1RJsjlqkeZm1ygKF4hJH7n6YU9UTyHaB00sf40j+fiyj\nwb3H3sc95x5BCRUaq2vc+8jjWOU6dpBw9MRJzl+8l1q1jiSp+PaINJVQVIPBqEMY+lSKJSwth1Wc\nJ40lQj9GCAnT0Bj2+7x96VVmKgVOH7HQ1ZDd/ZBMRFTKKu3mkGQyoFypY5ky1aVlDkZfIldcIOtZ\nhOomoVrFMmbp6kdwQo1Rf8zEnuAGPtbi4+jiVbI0YX7+IY6ufZh66RBZ7GKaMWfOrKK0WvgTQSx8\nEvbQc2XanmBr64Dx/Az5QKKrZRRDyPU9XCVmaZCQz+cZBiFm20HTDORKjt3U4f4D6GkJIxGRczKk\nuQWK+UVymk6/vU3eyFMoLVFarRMvWrS3W8R3rWKsyDTuKzIaDDh4y+etXo9bwx75U6u8Mmzxtc5t\nrEKBaqYSZEN02SKWNdq9Po2cSTvs0gy71JKMhlbkWnuH440VbmQhf75zHWv+KJlQsWRjKkWSFCRZ\nRpZlICMjRpIFqiwhK9Mw7TCKyNKUNAkJQpfA7SPLEnE2pQiiJJwGemsqsqrjeDae56IoMplI0Q2N\nMApIJYEfhSBAUVUykRHHAUniEwUTQt9hTnGRUo+J74CsYo8dMkJkI2J+IU978xLP7baovHuVXf0w\notDinCz4xMm78VoHnDr7IYalAYesBW4/fxXzgRnsoSA/dlh89FEGJyvc8yNnKBZzJHttmnmdmlFm\nJxrRbB7QOmhjWTmsnHGHm4UwiJFlcQdIIUtTBAL5zuvpsk8iTVOiKIJMQhJ/0+VLGMTkcnnmGjNE\nSUQQu5iWTLFgUC7P4ocjXDeiuW9z49oGw6GCplcozqj4oc/mxg7F/Ay+HZHPl0jjDFXTkDUZz3e5\n+/xdaJrM1vYmzYMmuVwRx4k48/ADEG3xlUsvUDpxjLyzzsjZZOatlJVwhbJp0h/HhHoOJXZI9Bnc\nW7cJA4Ou00KrhlQXKjTWTjJTXYQJBKR0hzu89szT6B9cQDsaE0dNTuZKqM0dqmdq+OmEoL0NkYV8\nyOQjFxb54Peeo3mwwcaNhGqtiLp2EXnYIu++wsZ4jV6csVY+x+KJ07zw3MvMzCzh8RJmLkUkFZIs\nwR4PeeJXn/jbA7K//mv/2xP1YhU5n2AHPnFPkKkzLJ05Q07NmF08TqlxFAlB41gFVUrYah5QKVUo\nNJZR9Dy1+hKNuSXyxQJhGBGm0wDlLHCIInD8iLE9xLEH6LKKlAomoUCVJPrdLsV8nsDzuXn9KsNe\nh1qxSGXOJB2NGY1cnEzh+FyJSinPcDIhm6liSNtE2jmq5eN0fYPk9l8SqSOOnP1+huMRw4lKNOqy\nt30dt9skV6kCM6TOHrXFe6guHkFRAhTGFEoSxXIFhSXimQI57SGiIGTn4ApWfQVzQSG/Cv2NECNJ\nCNWEA9lHSBmKkDD9lKGU4GcJUiYoDAKS1TqBoXG8CRt5iZwnUaxU6XoeC2snaV3fRgoDaguLzDbm\n6PV9fNdB8l20BYnaiSIuIbe/eovJuoRi5ZH8gELZpLI7Is4JtrwDwjmVD6Q5tBgaGBwz8jT9JvWZ\nAveUlxmHCZfzMmMh85I35lXfJpqdJzfU8Yo6eQxkWUGSFBRFRZLEVHCVJUgSUw4siQmikCiJSbOU\nKAxIYhvXGSIkiOOp+iCMU9IsQ1LUO1mvAUkaoygSURwRxNPP0E0DRVURIkMzdBDpFGQjlyR0kUSM\nlflMsoS+Z+MMR6SBg5YHLxkQRj3GgcTc0SPoosfb4wF3yRkPnH2E8uFZVo8eI0oayHToNvvs2QmD\nm++yeOw4xxZXGO7uYZ7NweAWD37qY3z9Pz1Frlyi//oe6dEKAoHjBWQCoiQljiKiMME0LBQZwjAk\nDEOEEOi6jqaqkGVTg0aU4PsBYRBN6RYhI0nynXucUa7kCXwPTVfQdIX5+VmcyRBVL7K11eb1N66S\nRAr2KENRc7jRBNSUjc0dpEy9016RIBGTZgmZUPBDSDOPQrFArV5GVmRs20GRDWRZQ6XF4tCns9tj\nMNjGbr/NUcni0b0qQ1WmFgf4+RKOOkJTPYRnEVdjzo9GKF6NhWLAvFlGKawxShVyOQM/8jHcgODg\nqwy3WwwnPq4nmLNUsqUxodOjvTvm2Sdfob48Q3UuT2QPuHb1JarLhzDKx5ldkujZISeXGnzgg2f4\nV3/8IlhlFPckb127xPd8+G4ip4jrbtFs7pDFkCURWZbwufcYdfhdsfgSJIyFgyRrhEGAV5X5e7/w\n08zOzNOR8vh4dEb7DJ0Wb115gede+CqRH1E250kxcFwQkkWcaUzcGC9MQFZJJYHruvhRjBMkpELC\ncRyGgx7uaEIU2dj2kCSKGA+HtJr7dNsdLM2k1+lxMJZRM5PxYBPFqqNGeWJ/Qpo3kewJpvoAFMpk\n7utYRoXG4gdpnPo07d6YOB5TTVsEW+/Q3bxC3LuNpcvMpjucXP4gC/OP4VFi7DbxvB2C9ACMEDcI\ncZIVxpMheSkl7EQoXZn5bAFZWyaZVygdrbF2YoUjioUrInaiCSNd4pBvkUhgqxlRFNHpDzhdWeLb\nYoLAQJrAnhBY+AzUgMSokMYWfiKQzICaZmEUSugfPY9Vz+HECje/vkXSA7+R4IQZcpix3dlHXJxl\nZt7i7lNLTFYE/9ncpKU69N0DyEYcz5vYzogX97Z4ptfl6609nh31uIkglEvk+oK0lOOUrf0NNSCE\nQIjpc5YlU3WBSBFAFEVTS2ySkKQxURRMtbSaQpIkxGl0h3IQxGmC7/uMRxPSNEaRBWkWk8ubyLKg\nUMojhCCIIsIwJIoikiT5m8WSIgsKOROtvIAjyQhDomJkzBckfK/DRmud7dY+0VjlrCNxczLgrBFx\n15HzlJfPIWbmiMslbo5yLK6iAAAgAElEQVSauK0MdfOAru8ycGD4hb/kdSnAdHsk775DXp/h2c/9\nBUdmP8RCZZEr2oh+v8v2zjZB6E/zCMIQkNB1fTqhxjFxHP8NwCrK9B4IQNO0v5lyFUWbnmjkqVNM\nkiRUPSUMbExLQ1IE+VKR8cSmUKzzwkvP8fwLtxgOVMJIMBzZJNmYlbUKzb0RYSCQZRWJEFUJCcMh\nkkgYjoboRo6F5QXiOKbX62Ga5tToEYYYhkkuGpHbrPH31o9zoasxzM+xsjtD7EDJgU1JIupdxbNv\nYLg1unM6ZfccQ2/A3O3/yuLXn0T63T+icfUqjZUCvZzF6bVTLG/dZKU4R76RxytJGJqE60ecnb1A\nRSyzf/Uyu3s6zX6APshhzN7P0cUVDFNm7vxZbo8GrOXewChv8cv/5xv4YUoSbSPNdlhpnKPduQ2h\ngRSsIUdlBBGOMyQKo/eMb98VICtlGX5B42aryeHGKj/zT36Ba8MWnV4HOQzY6r1M+Pa3iO19KmGJ\n3PwxijM1ekqGIEFRMyaZQxyHaDaEkcowkbFHMYFawU1SRDLBHY5JMx07EnQjj6jfw+t3kJIxvb3r\nHNx+EzWyieKYSaIQ9DtMjBy52WOY/pg++2RqhOyAv/EyjdwRMvvrREGLopihPdskjO7G6ezitK9y\n7bk/xrbzHD1SoHz2JKL+Aay5VaSZEyRSF6ndJk18FG5Q1x5k683nUdM62q1vs29/gaR+nmrpIn77\nBW75ZSqBILN75I4fQas3MI7VIJcwimK6ikKWV5HHElJk0NNzqO2EfpiSrs4TjGyUssWssFg+dpKy\nnzG0t3FERjSJcEZ9ds0eZBq3373Jmxt9bnzxFQgCemYBo6dQ8QSx0JFHGjXyZHFA0Chw6fouxxc+\nztqHP42xuErTc9kYS9xulfmyVGRLsTAzC0XSscjQIp9EyjDlAtuAkAwyFGRFYJgqugGaLtBUgSxg\nEnjTYkQUkjgmSwJUPQMtwyxayLqGpBhkQkPXLUwjj6wqqJZOpuWQLB2sjFRLKddnkCUT33dJI5tM\nUxg7DmbqU4ra6O42s/UCanWOvuRjOi61oc2huTLvdm5xrdci1qvcOoiJm9vMMeSutfOcP/sYeesw\nXtBDN+Cgm7Ca+Vzf32ViK4xHIwJXx09KbPzpF9h4c4OVQcDOpQ7eusOabtJb7nPJHeKkOTJFJ0lh\nPOphaBqhH2FpMlIyJJJ9JEkwU7DIST5ENmkiEUQOSZIw8iJWl8tYSoAd2URKgZxmIXQZo2SSahGu\nJ4h8wfBgRGtni8uvjSgFDrO5IoVaDd8YkOVtLt7/CPfd970cP7vK2dPHsCSdJA0JZQ07KeKELkcW\nD/OT3/97fOjhH6ImaQR9FyFKVGeP0B9us7oYUOwGjA7ewjzdwIthuH+DWuBjKxKq2iMJ18kqBopU\nxjZ6VDvb2M63cGSHvHyGQD9KT9lm949/h4qt8YHjJ9j489/nz0ZfQf9hlcKhOqvlJY4eNcjUgGsv\nvcPW8IDinEU7F/D6RsIgC+nZr3Mtfy9PrS/yJ1/639HmUk4dnuNzv/Q1Xvjaq+jphNlEZrF/wL79\nJW69+yo3b/8znNFrzB1W+MvnnucL33gVM19+z/j2XUEXPPEbv/5EQchcvHgfH/vpzzK2QA4iqprO\nRAkY/vUzvPWv/yOnPvkITRGzNHuYimyRUwS77QFLUh7fdvEiFzWMmC0W8QYT5jAQuoQ3tgkdm3ar\nze7OPq4XUixVIZouBYbDEZPRELIURdUxc0VyxRIVI0VXM0p5i1pthlxJx/NSZKlMUr+LQJ/BHT5H\nqfIZBu4zSNUfIknGjJMtohuXKNQFgQaNpZPMz6/QPHiX+VOfIAq6OP0muXAPUp/Uy2jFX+Hi8o+z\nfuuL3HrmGdaOH0UyHiZ0dQ4mQ4y5o0hxG8MqMujZaLpOMacTRTG2E5GXdPr7OwhLRcgZfhISqxID\nd8KJteNEtocEqHoVMgVTr7Df3CLLfITiU67FeG8G7ORiKkaIcaVLmuRI1Bz71/Yor1isvq9KGieE\nE4fK6SIjz8YcjfnJR89z992P8PxXvk7VjvCX8ty24JloTCXJocUyqSxIkwRN1ZElDVnSUZQCVs4i\nS6c8rKrpKMqdAO10Wq5IJkiTjDiKiWKfLAmJY58o8oniEMd3po2zmo6qaSBkYlJSkYEiY5kSYQCq\nUkKSNIajDn40xNBMJGHiOn0Mq8PB4CYegtnF0ziTiGDYodd8l/lCQn0m5d32NUZZgK5qxO6YWlnh\neGWBdLdNdW6GIEyIwoR07KI0h3S0Hrm+z+V4gn1tm/JyhZ1rl8kdO8xbAoZRgVeubbPx1PN07j+F\n/8xrzL3/fVzbbzJuO0yGLnOVOYgUfD+kUMzT6w8wdA0tMlmYyaNkAZ4jQDFxIxtZqWARsLq4QHvr\ngNliHcuAhYZDHI/xPJ9iplIpzCPJFnLOI1dIMWSFSnGPg3iWSs6glNocq9cYdiMCzSUzfCaew/d9\nz99FM3K8feUtJoFNHLvcffZHefz9P4kTNokUjbwZUqxUcP0Y3w/QUgMj1Wjc6KIdO8HLp4q80r6G\nkmZ8ODtE1YeW5TKcuESSjEgEiQp9EaNlCkeyMnqlSrezwZY+4UOTCu/svM33/e6neHnnr1A1m0r+\nNMXS3WSKSxLIhGeWuP7SFYK6TKO+wlMv7iAVJS585AxXJ3Blp40W3uR3fulzfPyh3+CVr3ye/Oyj\nfOYTj/MLHz5L89Y6N7d7VAoq+fwhxkqLftShtzdBRDl+6h98lt//97/PL/3iz/3t4WT/xa/9+hMr\nx4/xqU//OPuTHvmuw5u//ydsX3+XpSjl7T/8M1x7QLPgMV8ycA8OyDcWgQJz5Vm8OMAo6WhZzLVv\nfxt8H4eY3dil9c41RJxy7e132Li9SRSlyLJKmgpIEmzXY39vH8eeoCsycRQRI5AUjXFri2o5h+tO\nWFlZRcup1GuLkOXJr6ZI0oiSOMd+FlCYrxOnK2jDGLf3OlbSpyfVOXT2Aoq5yrg1wG6tI6+cRgrG\n6GyQc1sEsQ1eg9mZR9m4/BRf+sPnsZs6R+8+R+PkvVx/49s4WUYhXyTx+4TDAZqmki9YTPwJlVoD\nx46QE8iikCgVJDE4kqAwN09eGLgjn9rMPL4bolsJsqJRzJVpH2yhGjp+kOE5MX6Q51itzN7oGq2C\ngR3maG4cUFmZ48zxEle3D5ivm8xU8yiLy9RrCsfzVcxtmY2/eI5huYCk59HHAfcXVzgnzfLysIUa\np2AoiEyQISELdXrslCw0TYdMR9O0aaqUkMlSmG56pmAriWlTQhy7pJlPEruEoQdJQirLCEVBSIIo\nyQjigDCJySSBoqokkY9pFvC8BM/zyBcMFAUC30MIQRS0GI865GtLJPoCrdYAiwH5ZJeyCn7BYSfY\nIVewaA8dHFVFpAHHLJ20H3J29TBGrYSu55AVg0nPRh2lJK19CrttzJMVzndSdss6u3s9ylcGqHev\nsjXRsRhy/5nv4xtP/jlnFu5Cmk84aG5i9ww+dN9jLM8uo0iCJHUZDPs8eN/DyJSwXAc5dZFEhqTp\n+Ek63WXEMqXU4Pw9Y2QtZtCZ8PEP5BgfOJjGMWqNkEL5CP3OBrXCHpXIxIomqCikUkgRiVI15dx9\nK+wfDJlfTLjvzA8wHibMrZSQRMIjj/4g7954kyQK+fQP/haN6gmu3HgGVUpYXr2APbhGziijqSar\nSwvcf+YjFDSTxvF5Xolc/vobX8McOyhqjbPMkighbuxihRp2mjAixkoFSZgQyyqxlUdK6iC1WZUk\ntOICuXbMs/YWngSuquHv3OLcTz9O+f/d4eNvVWnd6vK1oMN9uRWeeu01bKvGL//m/0izeY2t7XUO\nV0b84j/8X5g//Di3Xv5ntBMJtSrTWB5x6uOP8+g//h9YXarz5rdbbOzsM4lsojRB0zN+8Rd+iXPn\njvPv/s2/45/+/M++J5BV/nuC53u9TFRqvsnl6zeZ0Sze+aMn8fe22cHhxlNfoyip5A2L/dcvs7O/\nAdU5tN1NHjv1vcwsrTFxB2x/8VluffErMHZwTp+mm6QkC3VMLSNOQvb391A0FVVXiP0Id+JiyoKx\n7TAYDChYJpKkEMcRUeLhxxlaJOEHKTO1GpqmMh7s4PZ2KVk1wrZBOdvCNQyMIMP1z5AbXGI8uYLi\n+jD7vZjFeWJN5db6Ot5OiwVthWzQngKMkAlcnTg6xsR7GjvxGAxuEoQZ1/YVcq81+eS9E/bW36Bx\n5Ahp+22STOXYfJmru3uM+mMkvUCpVkPR9nBjBzdOyKlFQtcjzmlMvJS8FzO7WCVIY7wgIHVdKlaB\nwO2TCZdKYwXX0eju7aFGIeudA0Q3ZE0vsh6nkMToowlhDPNpgYMdl3E84oMXD7P9jsfLe2OKmU6h\nYSGGCS8OtvjBwhyVEXQzKFdNbNsmThysfJHIT0BMK7VlRSNNJYRI7vCwGVGSIDLu8LN3QDaLSbOQ\nJPVJYo8kDUizBDIFRcuRxglOEJB9J/dVANJ0AhaJgeuNyKSYTMT0BxmaKhBKguPtMAmGFPNHSP0c\naeJRUCbISRtFsUmMCtebB/giYKGQQ5UNvMBnsVDGymSW1xa5tL/B2aN1op4LqqB47Bh+kFDwavT1\nXQbru+zOWHQ6HeYKdd7cu8FCc5Z8kiAqRW62rnJUruPdVcLYiXnowgy9TZ+gM0TXdS4eO4qqDBkN\ne6zMrnFycZGn/uK3qOsFFE1m4AxJVYFh5NHklKUlH0uv01g2scPrNCoRg5rMbpgjSOZZmb/MvH4f\nY/9tHLvJwmyZUW9ANEqRszaRJLHfdJlbzNDjHLff/gZycQVTalApLbHXv0mQJNSMFUb7N3Ach8Mz\nNdzumO6tG2Sujx1khGnGOOtSIqDZbNIK93ltf5uyDrlYJdHnSeUG+1v7mMUivcU8yu6I3NilbYbI\nwKyY9rQNgohFOYcWquyoCqau8LnqR3na2ePP9i+TX1N55p/8Sxa2alyOY87KFXojiT9+7jr3fPQI\nn/zoWbaffRoR2+iZRaN8jv/0f/xr9nv/Bp8ay3MpB5N9PFTU//oW2wXB6dNr/P0nHmfY8rjx9gFf\n+8Y3MedmSI0yk1RhNBy/Z3z7rgBZkoxDTXjny89xcX6ZyiQjMUscNUp8a7SJLFRqQiU8CNmPYXZ3\nwCD+Jv/XN/6aMyc/hvbSZbT9HYrCJ5vJ4VsRkygm5zvTI+VkgiGBYWr4UUiUhRhKmSAIcMYTkigh\nixPCYJqCL8kqkyAmCQXN9oAstWg0XHTNJg2HKEmArAmiYQkvbmJJKXtX/xv2ZANm11Ab34erHMOa\ndbj2xjfpTfaQZYn84j2kcREZGzeNUIwenqijekvE0W3mZo/yo//TEuNRkUuXb3Hr7X0aM2fpdA8o\nLrqUZu/C3btBtSiTITESKv3uBHfk4QYhdiYhSRGFWoHCbAmjmKfkpxRrKnFRwlAs+h0bTSngDEIC\nPyGQAooNk2F3SC4/S+hH2AH4sYclUo6VFeLIxZfLyAR4yYSFI/PsvHMN1bfYHo/ZTAQLJ5c5Uaxx\noeXzrdqEjiIxOLDxFIkwyFBzEVZOYRzGyMrU6qmpJoZWwY98kiQhimLIpssrTb0j48pSHG9CkgQE\nkUMUu4gsRbpTGx66HiLNIEtQRIqQp/nISRwSJQmSpOAGQ1JpakVFypi4HlE0xg8H5IrLCE2Q2hsU\nsyEzBUHg+WwceHSsNgsUqRTm2WztUS0KHm8skE58tEqdIA2xSjm2t3cpySUK9TnsgkmWFzipgjDq\nmJHH7v46YnMTtZanXDW45PS5f2WWXSegVM8h0oTgUpPeXaexzHkevBjyV19/gZI1h5StcqRxhK1b\nLQa7XVbOnkItQXM0oWbkyRQIw+ni8Mzp4/jBDq1BgZXT91OZP8b19ZfIVVe5/+R9pMArX77J4uGX\ncJoVZueqLC9C4g2xW3B9CPfVUg6lUDHG2OGYufcNuXU7B+4hfHcbM7/CT3z6M9x+7m3G6xsUSnlG\n61s4vR1yqUu+mBGMw6ksLvRJcUg1h83dHeZVhV18sHXOHDvKgatSn11k00qp2yGTwKeLT5ZBCY1A\nSRipCXl/i4FQyZQaaeBgKB0WPZXPGLP8lQwFqY6+E7KjJuhxyuvVCXmKfPaHz7G6lNFux+QPLTFs\nR3SvXCGsjtndmOfJFy/xyNkc1qrKonEPcxd/gL1v/g73rLn0R7u8MXwGIz5Ld6Tzj37+p/nBz3wO\nh5D15hXyxfx7hrfvitDuimxmJ61FHqJCzx6SVE1G7pC2FHImsXhDC3FjnVKpwC2lQzBs4hmCVanK\nkmdyOtIQeZmeHuONXU489n7ChQXo+4iyzLA7ZDweo1smXuxj+za6pVMolvHCiDjKGA8GyBKUanXQ\nDbR8kfbmFsW8zNrKIpamc88FlZIV4I/HeO0b5OYuEAUxzXEb4V8nKC3hew0ub24iTJeKSGiUTnDQ\nusyFB7+fwsz7cdxXqBn3kaQuJlfZ3a+jxxt4OGTtCf3JK3ibEcaqRXd7h/pdj9CoLKGKCrYUYGUm\nG+tvEqsmBxOT11+8DoMeqexhNKrkGhFpEGMEAnvkYsspx04cxR0OSbMYzZlQn1/B3ojpBwktwyRv\nVdD72wy6I5RYp6N1KNsRS80x7pJM//QpMsdhEvTo7SXYew6lQonU01mZWyCNJtzu7PPo+fM85qgM\nuh3+szpB2AUGmcPKjEqtVqPbmhB4AlU20DWTnHEMy1pAVXVkWUaWFHRFR9VkhEgJI5cg8HCdNlk2\nFdfD1Ln0HcMCQiBIkdKINA7uSLYiYPq7zpQUzw8RikyQjOj091AUhYXZNfLWLKODyyRxm7ySIe7Y\neFVLZuz3eXjuEIkkk9kTFqSMRkFh+2CXcqmOFpkUzxxi4/l3CM6eor6wQLlUI5AVojjD9mLM1ibd\nrXdJSzpu06N9bIGVKy3e1jtox48xf+IDVMNr2F9cZzIRjOfPsni6wF98+U/42OMPYVKg27KR5QKz\nc4tcu3aDMydP8eK1V1hfv8KZtfM8+MAnmF05zItvPYNVVEjoE6cK3qTL6uIyqaRgKCHNzRbXr8bc\nf++Iz//5G0QCjBIcOQQNQybdT7h4AtqjOisPdVlaFjgHGd11MGdhc/0+2iOZYN5mfn6eza/extka\nUj88j5BkzCBHTQNP13BSmURTcdIJteUFbu5cQfYiAsPGjjUalXPUEot6Xse0A6ydAbvDq+xqCbac\n8rhn0kg11ESQxpAWdKI0wSRiPivjmEsYw5BvX7CRLpTp3WgxHrmccPPI5T1a+YRxVWfGz3PVucHG\ntsehi4/xuZ/9t1xcPMW73X3Gekrr7S/x3B/9C4I3FkmdTf7B06/TvvQxtNJVvvDHs3zmRxTqpxf5\nsc9u82//4IscObFKa9OBROPnfuqjXL381nsK7f6uUBdEWcJRNG5FHUYVideTFi/7TUqh4Bm1x0Ji\n8RGjyk+kc/xjzvKpmYc5UzrEA4Uabcvj89ome+WMo0oJxbK4HdtYskrQGhKEMYquYVk54jghCSNE\nBqokE/gukR9AkkA2rXMejUa0212QZGRZEIYhw4FPkpZ4+0oXP62j5g5TWDvCcHRAs3Wd4u4VNq5t\nogeLuFqR3b0+tzcLSEqBVrjNoaU1SorAm7xB0bufg2Cd0bjN7q0bRPFterbL4rH38fW//iqSm0da\nPg7VFYrqDGqoQ+cAXJs07SKbh0ilGW7e6vDM068ybNuUrQLueIRhKJjFMl3bh1AjL1U4fPQizU4K\nfoV+W6LIGbJoSOBeQ5GbZMmQYaeN2w+IfQlHkjDdEDHxCY422K9btA/62AObRmJyZFbnnvsWqVQV\nKvWA/miTsR2BWGTcSXCDkFFV8Pjxk2TCZsXwmV2N6bTH2OOIcmkOMgXf90GE6MZ3aIFpLkHCVLoV\nxQGuazOxhyBikiS4A6oSaQZBGBFGEWEc4IceTugRJB6piEFKSbKQKPFxbRlEymC8wUF7k1KxyqGl\nMySpzPrGdaJhi7pu4AYuW5MOO9GIq7ttyGSkrAQJJFnAIHFZ74yIswJDV4JiCf+gi2VZuDMF1HKR\noDcgHg4Z2APqaYu93RuYkUp5Pya0J9SkjNv7TUbza1idPorTRVs+xMw//zHi7znLN279Jdt7b/P4\nxz/Ff/vai7yzfom7Hlrmw99/P25s02gcYm/fRolkHrvwST76vp9ATUy6e2POn/rotK7HDoiTHmYq\ncXB7i93OVZrNJprf4cK9l3A5ilaEs/daNKoVehslrr6acP/3GOyMdU4/2KU8rrD1TEaBPMcuQBBo\nHD71KifPdWhUTjE4KLC7t8Hq2iGcWGGcZfQjG3sSMrG9KTWUk0mTqZTQMuoEtSpBd8R8eZGZax4f\niCxsu0379iaS67CfjrkQWPyE3aAVTHgx2uNSesCGPkENoejpOEYeD5NyJvOF8stsn9rj1We/zGD3\nGrGf8IL3NnvlOvsjk57X45v9y0h7M3zwwiK/+bu/jbm6yvXBVWwjw9JMvvovf5Pjcw2uy+uUHzjC\n7PkqW/unad8w+LmfGpNW5vm//8vLjG+1+Jkf/iFe/OYLqAWJwqyYUlLv8fquWHz91q/99hNSySTI\nRrQJiIKMw9UZBvkC2rjCmWqNvJRxW2rTk3vUZZ9jQsJPPDRZwo4lfHefqp4niBysokSuuERSjjAl\nkyT18OMIESv4rk+tmicVDnJWRFIi/DTE8wUZ4v+j7s2CLbvO+77fnsczD3e+t2/fHtAjgAZAEiAJ\nEKA4i5QsiZZjS5FsSqqKKTuxHVUixYlUsV1WEsUul1IlxynTNlV2NIumaJISJZEACWIeekTP3Xc8\n98xnnz3PeWg+uPIgI5VylbQev73WXk/7v1d96/v+PyQpRBJTEjcnz1y8WYCuyIiE+KFNKqvQEAjm\nUKtWEMUJNyZT7KUnMRYX8OZ93rm4T3mYcmcy4cxDdY61LKRiC6cwMaMZJO+geTdx5neR1DPoZgd1\ndp3rL1zj2HGbIHWx5C6hKaLqLoMDl4VuGz/1yaWTKNEtVqMrfOyJBvaSxN52CpmBsuJRBgnRHCKh\nhGZCmrmUkYYfKEiKge8OWast0b87wRIWSCs1xv6cugeJkKEcqaJcP6RiWNxt6LhzEcFNCdwEdZAi\nZTpRCK7nEsc5KAZGo87YG7CQm5y3DfzcwlvxqJyP6WlVVNnB29VQxBZlpiCIKYJYYqmrqDLooUGq\nepjtNlEBZTkhCsckbk6cBeSRRMocSTHJM5UsC1C1BEmBNCqhdCgLD6kUkQSNLE1I4oS8EHDlXfzZ\nkNzJWW6fpNpYpz+bEnn71JU5rbZJLxwTpzGqZCCUMqYg0sDk+LKOkJdUDmKmaca0iBCFkkYooDRb\n+FGEnxWsHlkliqYYozFC6CAFc4bvvEknl3DEjFitM5VjGs0ad+/PWT/aYqmrME/a2BWDl377eX78\n8z+MG5V8509f5tH3ncL1Eu7v7fHiy7fZvd3j5FaDk0fOcbB9F1Ppsrp8HGSLaq1OUXhs33uT7tIq\nsSpjWscJhiN6820WE5tITvGLgmNP/A3U4jrbvQFVKgwHIX4S8OkfLFnazDAsCUHNSYM15uGExa2E\n+QT6d3RiL6VdD7n22h2uXBxw/MJ7mMYe4SyhCFJi4RBMG19NqUgqgp9SqhaJaDGbpVRZZZIKLIzn\nPCZp3KypZLGOFCT43gjDqtP3RtwvJtQrmxyxz7BYe4hS7VImAr6R4s9SMmnCd90e81UT+bbJx2cr\nXCwkUlLMqkUcuyB4nJ7Uedrp8Je9h3nfdsyt//13UJfbFMfPkQstvvtrv4w7vs7pn/wc+toSn/2b\n/xuVdIFIrVM9+hMoy7+ItfjjNJf+Gm5h8W9/78vUW60HNphuxNd+/7f4W5//r/7iVBf8o3/wD39p\nUakizTMkTBbqi2iZyP3BHnpN4iFLpVPTya2UTPaRxZhSSYj1GFXvE5oJgrzFJJ5jnqiSWk2qnTqp\nKDJxIRF0MgJy0UM0c9AMCqlG6E4fsJ5K6XsVBwkCAp4Tk5cipik++FjzlCyPuPDYezl+ah03GiDF\nKqEbEnoemtwkT6v0hwfcvXuVimGh6AnOSOGZD51gqbtBEt5BiabMKjJdf87BwfPY+hms+joTv4es\nxVQWp/T7cwLfwBY7mHWN0jzC8tLHKaJDQu8mZvc5ov6fEPpX8QoPuyZy7vxx6p0W434PJSsYDX0y\nUQVVQTcbpEFJPJ2T+xPMZgXkBqOdPWpWAZKPokoMpw7rQoWdcIxaluxVMiJVYrnUmOQRC60Gdr1G\nKAkEZYGQFSR+QuKlZEFKLEg8sl5na/0Ut66/ysF0hN7dxMSh6FeJogf4FEl+0M0lomFqy8iihZfF\n2I1FtHkfJ7WRphq64PDejZChOyPPFUS5AClBUUsU2UARGhQlZOxTJBZCUSXPPfxoRJYXFMT4yR7R\nJEJWWqwcfYRUkZjMdzHKGWrq09RMDuNDFApkWSKQYZD4hH7K6cVFLFNEnMf4AhykLkf0CguLCyRp\njt5sUNc0UgTCMiecTuhfucHNm3chS6l3W8ilwigNiUWdaTIlKELyVMNYM4m8jO3hXTYe67Jx9DSn\nRwonOg3evHeZ4e6I04+d5u1LM5ZWBaRwSj5qcn37ZRaOHyW6fUhroYOkCPjDAf6d+8jOnMrqCpov\nUbfqLNYbTOI5qedjtCs88tDDuPOIO70+H3r6s0zEgo++/6Osrp7l9u23eFSCMsxhqKBmJQf7AbVW\nm0QJkA2DLDFI1YzRuM6psx9n7EwZz/ZpNFtkpYCqGhAExKpIVtFJNOUBWTeMMA0Vfb3FmUnEqVRl\n3DAoFY36NGVpXqKWIi29QSkIWIZBRzRZVi3U6ZxlVSMqXYrYZ7laoS+FZMcX0Z2QJyIbk4Jpuc1S\nlmBO5uh7Lk+MDP5H/wRPjQ2GecJ0lFH94Cc4fPohQl/AS4d87Qu/SPepR/nBv/SP2dg8iSgbjGcj\nVtaXaXdW8YOSIIZF244AACAASURBVAnpNht89CPPsb1zh6IsyPIU8oI/+oPfedci++ciXVCS01Ea\nrJubNKjTkuq0pRYyKbNij8IQCCXICg2RBqLQoUzbFF6NZFhnoWIjmLsIayHq6gqiXuXg8DaSVaPW\nOkQTHJryEgYqVV1GKVOkrKBatwjDEk2toqgiaRwiljoVu0kU+MRxiqYaBFGIpEqYVRNBECkSyNIp\n29tXmU3H5HHK1bdfZ7h7H0sVaVVVcrnkw8+e4vzpE0wPNok8F1XKUEqDm7f/A6E8o7C6xElGxbaZ\n+zaKtsDW4iOceeoZlEYHweyi1tpMJzeZ3P8GcnSbKN7GmVyl1V0mL5eoySYlOxw9rfPU+Q1mU58s\nzKhIVQRXx++FDHcP8eZjLE1iPPBJcYj1lMjUUQuNRpSjij6JpaFkIntSgGxrtJKC3XiCvVRj6caQ\n8cGQ0f4If+ajGDZqtUZh6kyLFKXMqQY+0/4Qs6JQSlVuXD9EGs+Y7cgUhYAo5ZRElIWELOvI6gOc\njGlLxHMXRTcYhTGf/9F9fuIj17jw0Gu4Y4FCOiCJZMqsQpyEzL1DgmhClhUkgU1aeoT5NnEeICka\nKTHzaEQYR7Qbx1g/cpbRfM5kvI9VBBjpnLqeE8TjB9UKkU88c4hmc3QKOhWVTqNGqWpYqcReGdBL\nEhbVBmalTmGbCKqM4ziUooA4niMGIZW6zWKnyoqsY5o2sqpRazVpri8iV1ViKcMhYOxM0EWV5pLN\nrTtDnnvmOQ77l3lyQeQfPPwsV3d3mXsTnvvICgd7DpO4xrXxPTKpzWw6Rl0wiCWX7d0r+M4ekTsg\nCwJiJ6Qxq+He6qOWVYqiyjSPqDabvP7GRW5ceZXJKGZ0+CaikBMtr6DXVI4f/RQH1WfwfPBLGBcj\nbNtAyxxqoY0WzSnDGWPfZnntGJPhDmE4prO+wiAMcdKMeRAzUjMquo1mWCiKCmmCPx9yf3SXIOvT\nijKyqKC/USWOXGp5Sun5qEadIjcplCpVqtxz9hmWE/qWw29zlVtLAZkYk8cT8qCg25/xaFinkiUc\nTg55j6tzbCxy1qnxEeEEj4qbXPJnHFQlzqsZwzMNDv/778fJc1abLbb/8AVKyeb7n/hh9gcztvcn\nxHlBc6GFGwaMnRmaoZLmE3Z2b3F4OEAWazjTlDITEUSPoojftb79uaguEAWRJa3DQrXOfjYiyWJq\nms359iY3hZvcye7TEm3iokARQJZ0dBXELEW0csZThcVOjVbnKP6ohj/eY2m1hSppOLMlpNxnMnoH\nq7pAFmtUqhZuMGHmRySZyXwQYFgZsiyT5QJxHCFrBVkmE0UZWSbgeDFvvX2J9oHF/uFVTi4domtT\nxDIiy2xqFY/64jKlYtEbpAT4yIaG54gYCijVE4TSGuKdr6K6A4zG0yhSDVWv4xUyRaWLpncxu9eZ\nzncRrS65XCea7DDeeQElHGI3zlAb9hCLkuEwotrcJCn2EE2LJBBo1o+QG1NExaN0MkgiCjKkKCQo\nE7zCQgWM6ZiTi0vccHwSr8BKQChkhr6HWNXI2y0W3RJz4mOYKsb9OZICYlpiKwaFqrG320eQRMxK\nk3rNpEhi2lEN2d9mKEgcNnUm4T5deZ1QEBGTNoVckucl5BqyZlHggqgTlxb1zGbqpgjeLi3jCs9+\nf8wX/hW8/4JC2TzH7/72DY6fWcYNIixLJolnZKlBXnoPCAlIKCoE6QEjZxdVXuXI8mfIlYDBeB8z\nd6jgIngRmiHipTPccsaSVCFTBCzFJEtK3CRB1xRMQyEVU1RZYzyaIRdQs6sMBAHVMBHzkjCOsA0D\nKUoIsgiraVKVa5S7U1JHQs9kbE0iUKHb7XDojqGiE6YZB70hn/7sxzmMNXQEyr0er89uMV/oUopt\nXviTy3zmk89iKPeZpT5R6jDb3cU60FlefgQraSKFc1w3pCxLrHqTliizvXuJQJsyjHfY2jpKsdkA\nWcds1Xjz0nc4s3WUIu5zduUYg8EeRjZHzCUOowrBdagtpShKi7OPjHF3BYZuSvUkTOJVDg4Ewult\nJqMpzeUlwkhhGpRYkoSupUSCjKxbhE6MLIjUTZvrd2+RWTLWrW3u9T2EpSVG8znnFYnZdA9V19FV\nmSwqaJQabuSyIuvcdvd5MdilaNUYCDF+26JfRMhJA9EZIzYW2Q9GJBSM4jaVWgMkiX4c0tdT1qoP\nfuqXHIUjSoU3tQwhChgEl/nK136d0xfWuT55jbcvfpMnLnyYZtzF2xsgSD4IGfPEpcgbSCJk5Ygw\nnFEiIedVhLREkbV3r2//2ZTz/8MQS5F8GjL2JhRqjF/Mmbh9FvQKy3qDYSCx5+RMIoEYnViRCIQU\nP/eIswadjTaS3cJqNHDce+iZyuy2D84A0dSJkFBbaxxGAh/5L36MD37mLyE1l2i0t/irP/Z5fuCH\nfpwbt/YRVZ172zsIqoxsKAymPktrJ+kNQ3Z7cxr1LuPRnOWFIyxWG7TrOqaWU6uVrK7JaEaOZjfp\nTSICT0TIp4RpTlhN8bIVkmLC5P43YP0C1ePfx1QR8fOC2Euwy5B1u8rO5a9QTK/juruYQoEVC3QW\nL7D+gV9g87G/z/6l30CzTJr2FkIokGgVgryGLFYJxIwsrCKXFoE3J3TnOOMhWewRuTHBDBYQ2aye\nQ/YtKpLA8mIFtWKi19egptOsVVk9CBDf6UGR0+qHdHo+t6wcXxaYpjETN8AUVJatFkfrXTbtFo3a\nMiNZJTUVtuOSUT7HEHJu7WTkQowk2pSFQpHLiIKOJJrEUU5ZChSKiWrPOHv2Wzxp73NjusAtr4e8\noPP4uW2+8bXvcu7Mebb33yRKQ5zJAz/ZPHfIGCCKBaKUczC8zvbeDgutx3lo6xmCcMpgdhchHoC7\njxANkasZjuAxCV0a1TrVXGC91sZGxSwVVjoLaJKIoSgUmUCuiNQkha4gkSoysaZiiSp5GCP7MZIA\nqqagUxA7DmGRMlNLxNCnIUFbUyijkJVmBzEScMKc3e0Jul7Hm7sMJnsc5tv83h/eQvG6/PQHnuHZ\nJ2okSchrF2/y1IcfI08c6orM4sJ5honGqfUTVCSVpqRilSVmKVDVNC6+9G0G5UWu73+TyzefZ9Lf\nRbeatJoPcemuR145hq53iYSzuPOE8o3XuXX5Phsn2iwP73BHhcs7NYIw5PZViEoVe1XkP3x5g++8\nJNMbZkRZTllEjAa7HGzfIokmlETsHfYQJzGZrqNbNoPJlFevXUKVZN7X3eRkqKPWmoSWSa0XMnNG\nTKMho3xMPh9SiV0ars9MjVHSjKle0P6BDzEYO1y6scdXxve47E1xRg4oCmmeIfkJISVifZ1Va4V1\ndNQ0oIgddiYjvtq7y80k4OqWgfrmDWqaz29+6Z9hTe5ydLnBFX/Ac089iY7C3t379HZ3cUYugePj\njAcIJeRZxGjcYzabEAQBw+GQudsnTcN3rW9/LnKyv/IP/5dfulBdZ5RPSaQEWRHJ0pimriDqCm46\nIud7zqKSTKnIlIaOYJl0WhWGmkJsd9g52CbzNlhZdHnuU69xcvMhKktTuq2MGzcdHnvqo6wdP8d4\nMmF3+x7nHn2GRmeJpz74NPe3t7l18zbPffg5xlOHm3fv8dd/6vNoVoejR0/zE5/7Gxzs3EcS4KET\nR5C8HMcZEUQximKS5iGFrLK6dYH+LEZKahzrXKS9fJqi+iFSZ0Tef4EsVbC1RW7fu4yi6miKiaGX\nzGc73N3+Lla7QOAZKu0LjJ0Z5DL1lfcRqArXX/5X6N7bCLbNsJ/SarVwNZnG4mlCL8HNBtx9I6NS\nNRB1gVJTmIZzRBFqaoXji6f5uQ/9JL/2wr/n5mgXQYU8TqBIiMMppWJCb8D6vku7YeN7Pka1zju1\nksk8wJQN0jinLCWWOouIoshsPudwPMQZpewyYVyAYxbYVo44U5lHFuuNFmESgZBR5KDINppao8hM\nTL2JRZ27hz0+/ZNv8nd/4i2uH/40/Vtv8fQnC77zYhXFWuHixRH1jkKalaiKRpTukGUJAgpessdu\n7xK62uLciR9AEZbpHV5H1O8jxgcoSUAWhyh1FUdxcAuHpXoHxdMIvSk1o0KzucBUgtuzIYHncaTZ\nQld0SrHEUCQIIlTNQF5ooLkRceCRHg6prS4SCSkWIqobEeky8mKH3Bkhxjl2zWaMjC3LrK5u8K2r\n27SqNX7owkluXHmbYhzg7d3n31y8x0ujGV966QUuXj6kbZasdBJqFkRhgG5ZzGY9KH0+9ugnmE1H\nWI0Kh4MBcZBxcDhmqGe8cfdtvCChs75JRbf55ksvUeYFldU6z33iSb7ypS+zdf4Crzz/J8iiTOf4\nKW7tXeWPvn2RPfEMRbJLV5URU5v6WZ8vfV0hKQpWWxCrKrs7hyiKTZpk2FWVPPfxvIDNzfO06i2K\nNCXujcn9ALNiEgcBWgK+65FWLKZxiB3nvBP1qbUf0AZ0AW6kh8jm9y6zdQHDS3jfY0/xfT/zOVbW\nj/Hhj32caq3K3Zu3qOUSdVnDTQMqK110qWA/2OFOcBdf8jAFWIlrHG8c5ZhZcO/9D/GHw3u88tXf\nwJ32eHpjA6eZ8ImTP0T/wMZS2tQqGiUxSQim0UCRRcJ4jzSaMpvMMfVVPFfHsnTu7X6bb339j/jv\nfu4vEEjxl//nf/RLx8wqjhCQ+ikyMrEQoJQxfhIRAAUqaSYQeAFR7CGpJZWaTjDbw20GNFsf4j0n\n6nzkwzs89p4Dmg2HirqOKFSpGC7NlSWWjjxJXi6RpxlrCzZyfQWkku29PVqtJb7vuY/z5FNPcu7C\n45w79xRrJzeo1Vd44n1P4zhTvvOtb7C+1sEySoQUagsGpahCrlKxVeK0IMFm7M1J/QOOqymqXEVQ\nNyjdl1iwuvSmIbmZYNZAETsossL04B0mt26w3jQZHs4Y5zGxlpBIPp3uAsPenHsXX8G5/AJHj1bZ\nPoDCFpCbcxTdZhaZ5EWV+QHcudVHUFKcwKNARxBk8jxCFSR0yWT/+pg3hetolsxKbYH+2CEmYdGU\nsH3twWWgKXFY+DQzjcszh6P/9Wcx+wGjnUMM3aJSqePHIf3ZhFkckOsShqhhtNuMshJZHVH4PpHT\nxFArCKVKWqRIgg484Hgpkk2R62g6THu3yAqPl765yPs+cRG74rKUT5m5Xf7g+QqrWwlvvxpT71pM\n3UOyzCUvh8iSjRsOmUwmLHbOcGTtSaLQx40uI8p9/LmPVEyQVYn6YpOIiFLKkBGZ9maYSoX2xhJO\nELE/mXGzd8A0TmlbKpW0pGJWydQcRXnAC5PjEr1ehyxhNh1SSgWddocoiYiTEClMmMcJpaKycvwI\nQgayZdHPCsrQZ3PzKL//0kVWmlVkZ8Scgnnb5FAX6d3YxXUl7rkuP/rcOT73Vxc4s2By/vQyqr3A\nO9fnmOqMjtBmc2ULWRG4cvUqlVqD8Tzmsac/zOqF05xaOUPdXuU7l1/n7NZx9kYzGrrI9uHbWEoE\nasbOvVu0qw1e2rvN9WuXeOfKfcqtNu+tjnhoPUWSMtSWwe98JUKsnsBeXGVeJEw9H6vaxqg0UBWV\nyJ2gySLrR04y80X80qPc7cN0ipBGSJSYpkl/PKBs2ISGiKJI9N0phaE+8LsNYubBjLGdkIoZy5mM\nkEU8U3TwLt6jePQk3c2zyLqMfnKDiepw88oVDDFHaFY5PNylF/YZSyGCrrIg1qmLddTGErpSIcpF\nTryZMfvsM8SJi3/Y57464Z35VaQbeyxuXUAWVPxgjqRKIFQQZQFBSjHlCjvbt5HkhPl8hqTKCGLK\nzsEl7l+7x9/52z/3F6ettiCnHxxAtYKd1JBiEU/J8fOAkIJhMCKjYHXR5PSxJkc2FFrNFNsY0WyV\n/N7bZ6nVIh5deZ6Gusd8WoIFGSNkq4s72eT46R9jKAh4cg/VLFHTFeLCIk5nVBoNZMl8cKveGyIr\nOkvLZ5nH9zArK2RIJIVAvdkgTUOEUiLRAuLcQbJ0NNUmiQIkBIQiRRYjROmAJeMT3Ln4m7ScnG5T\nZQ+HE0eOkhYug+2AUjbYS+6RhDdpVzQiP6S53mbF65LKGr6yzOFgjLP9Cne/+xpVW6PU6uRxQa29\nSCBFlH2ZsmWQCtC7OSI1U1RVRE9MbK2LpDfpDzzCLGZ3dsBlc5+6YPCe9gpWEnC39PG1ChWhBckc\nrWLg9g9R2haXxID2e88R7UwoEpgrBaKtIqgSiZuhIiJSImYgWhHs5WAGoLsEsYpgVlEihbRSQS4k\nVLlKnqeUZUSae2RJQZhMmNgDjgUVZpLPr37hAv/DT95h/xCqG7dY6Oisrj1Fmu8yD6Y4wV0a5jHI\nG4SRgOu5bK69D8tcwHEPCONdsmyGREqlIqBoixRiwCg8QCkFZF8icUGVdHrJIb1bB0QFWKjUtApL\nhooYzHEDH2M4pmgrSKRUVxewDyLwIlJdYuLNkGs61f0elarFsEyo6ypCGGPOEuJzddRQxM9TSkWm\n06wyHQ5oNCuMBz2+VjSoPqKyuJKzIlepd2v09gI+8sRDPHNuwuCmQKVT0D+4wzOPnkPLG/z+b41o\nn+gQzEaoYUJtFtEwdZYfPc3d3pg1ucNkcJ9WpcHjjz3Cq6+9xNmnPkm4u8dHn36Ol7/5MkN1l+O1\nD5KbEXXBQ0BHa9d4dqWG4gaMUriypyHtrdCpddCKIc6ey2GyR1I0EZQpWeLTVHUWjDad9gJ3h1MK\nq4VZiBD5+EpOVuRUo5K6rCKYFYoowhMS1FIglks2EgtZFqnICkqt4GQY4Zc5k8zHljK+W/RYePYZ\nJkJMZ89nW+ihRzEb90acbZzCSkcMeyMSNUcSTRZii1pWQ9AsXNVkTagixim+quPEt3hk8EHWnvsh\nbqQyWZmxL93i2uQmvZf+V97/xOeQtGU0oY6saRSCh6gWpE6GJJRQZBhGhTQriLOCNPPxA/dd69uf\nC5EVSoFUayAJEl5lTu4HqAkkqoVIgcSAZ56BT36sQqs+YO6oRGIVbTGn35fZnbc4vvUijfohY8XA\nyAwsf4zT/RTD+CTWSgdPbUEQY+clYhpQZgGFfoAsq5RFhmErRFGMqDQQJIkonqPJKhVTw50foBrw\nzId+iNvvfJeLV69yfKWgVu0g5B5JckgqpISZh+Lu0xYVTHOdPXkb0TzBrcvfpTzxGCgS98q3YZhx\nd2fC6lGXZreDuXUexx0jaCbRPGbgeUhBRP/gIvv37+KP+wgCaPUaY/kCxtE5CSqmsshhESNMSnQ1\n4YZ3DUleIClDWs0FNEFj5E1Q6ktMewM0c47tKPi6wlPhB7gWHrLVKSjqHlYvwjdr1NWYyWiKtx/T\nOt5EPW1w+8bbrOp1LEOnqpt4bsjUH3/Px1TEFmwSTwTbxa6GxJFBGbVByNGbClEUoGggiiVpnqKZ\nBaQKonifg5s15KWA9YdUGoHPuZWcl690WDlyHW9Y5S9/RGKcOIy0HvUop5218UUJWwrpD17m+LG/\nQpZ5ON4t0nRKVjiIQooki4iKTFQ65NMQWVJJJIkw9iCO6FTrjIKIsFCxNRlTkJCyhJai01hdJQl8\nwjymf6+PLAmcXdkgrWuotoSIjKxVaWVwODjksOyQFAWqaZCpGfeTER+IC5xugui2afcS8mWZm6MJ\nDWfG0Q2THWLOnT5JZ7XNt79yCyO3OGJEPLZS4PgR88KiPo9IeyH73j7PPLvJC29eo5ZKCNGEyLOI\nS4FQnFNEDookkEUDntp8hq+/+sdc2DrJF/O7BDu38NUCQpMf+Ot/i+df+C3mzpjSHdDprCIqdzi3\nBJYl8Pq+gTOWUSpLHD3zMIu1Lq+/9jyOe496KDHXBEzBxvMTmpUuVqPJ/YmD1mgyj0IOevscr9hM\nxiPW9C6LlQ7jyYSWLDGwNZqIZL5PQ5FJU4dKpYbdbRG7IXpZJ81SVosMrzwkaai8tphhXrtNdx2q\nFY1QSXH2ZyzkMtupjKBI1GQ41ED2A+ywQDNUQnzcSMQWFIJ8jODD3O0x8TPW146ztLaK1PwRYi/m\n+vZ3uXLtVd7z+NOQFCCEWGaT0ElIpreoajJRXkIc0NQFXvrjF/it3/tdus3uu9a3Pxcim1OSCBli\nkiCnCTJQaDKjPOLQm/H3f7HOma0Z/tRhHrQIJAFyUCYWV8cqx46W1P0PQHqDmroIUkhkPI7b+lms\n4CKKXkHWdCRBJktCslhAkFRIY4QSSqlAVsAyNWT5QcumligkWcZwOKTeaKAIOnmasnX8OF/60p/w\nxLGHmA93KPIJllUwG4fIRhvNWkRIQyRln4NeyNGVTapVmb1+j3a3zeW3Rhx/6DiPfPADlFKLTFDw\n/Ijt+zOuXrlE/3BGZ6PNya2j9AZDnOmEo5tr1LttPARKbZU0cajWFygLiaaRIQoCt29eJQtkbCOi\nkmr4O3dgcx1r9RjqvSFHmnXEukVQ9bmxu8Ml/RJ+5mGjcLAbEmglZl5StqrsTCSGssCJrWPkTspa\ns0XsP8hX9Q8nLHRXGI2G2FaVMEzIi5RSVBHkKUEAWaKhqDkSCmlkIEo+eWxi2DKStkw4T1CNAAqd\nH/mpOv/nFw55R5/yM7+ww6OGweXwo4j7OuLaa1SPqfzf/9xBCUMaa09wefwyuh/Rm06o1U8RRgOK\nMiaOp0TJGFlK0UwVWXpQJeJHc7RMBDFjGrmUWU7LstErFkqZUMQlWZYRSyWtRp2AHNeZULUr+JMp\ntXYdIc2oNZqkYUDmhVSrNkmWEogCRrdBEgasdFoIRc7heILdrqFrNSyzzfbkNsqiil5b4dWvfouk\nzDjopTzyoTU2F9u88cYVcsdDKQ7ZWFvA2e+jlTpV1SUqNtDEO5TpkDA5wc/8tUf59V+9ydHuJnvz\nV4hkiXm0yIquUE1T8p0Ru6KAXpV5+9U3ebh9HGmUodZUrl25SuP4Jp/+vs/x1otf5u07A451VyCP\naUqHmGHEKzcitIUajz+8xebWEpPrezjb+2jNFgeyzlohstvb58jaOrV2k71eH9OqEAwdyjRFjQqs\nroXtBeQ1g7njUtNN5hURQ5YRi5JSUR/ghUrIs4w8yRGKEicsiWo5ZuawlLZoiV2+8/XvcNWa8Mqr\nNqfPP8vSqVNohYYsZbiFSysV0VWd1VilUCX2LJGt2ZxqUTBZKBEnKYlZUMlKoixGB8ylJgd6QmUw\nQO12eOThT5IEIWkUU+aQxS6BED7ArwcVqg2Lw8kIQVLQTItpEOJ7IftR713r239SZAVB+ALw/cCg\nLMuz34v9EvDTwPB7036hLMuvfu/ZzwOfA3Lgb5dl+Yf/qT0icq55faQ0pSUrtHUTKUzJkwg7Fzl1\nIiXKICnbJGmOZLikxRKv36qzcxjyvvMv086ryKJEnt9FkEA0fh5Xd6jSBklGLMFQNeKyIFcyNK1C\nGqQIgvbAYCTPUDUZURGgLJEUkZrdBtEnij2i2MGSTBoLS6xsnOaLv/FNPvXJJ+i0LSaHt7EkG3IN\n5+AQu1knkyPIK+zuupSlQJgF9G/cIvY3GOeLzPsCui5jW1V2tkdcvXLA3Jco5CZJYaIYTc48/l7y\nh0+SFyGZpCHkKoFgYtWapKlCLhaoDYH+vTtkQ4faMOfk4gKPVE/wSvMyb84HSMOUk0vLnHjfEttv\n77ClrKGe6PKVey/QzEsutJ5gIypRugW7t3eZzEsKzcYPQu7f6yHcdlhbaOH4Ps3GEppuMhiMkBQF\nx51R5CW2rSApMYoBefKgMF2RDYRcIY5jjIpE4DvY5gJhNCDJExRkdL3N7v4en/3IWTbtHg+1+7SO\nqJhv/QtuenUufeUon/r8Dr//1XW+8E8W+NzffQd16QjZ5FU2lj+OXX2U/dHvUJQJaeZBGSLJDwxm\n4iQjCALyrEQXRUAkjDIEoDBEnDDEixMsxSRKMqIiZ56lzGYT4hyqUcCmXcfQK2RFwE6vz8mtTbzB\nkHDmsdN3WGzobKwucVrVGB72yHSZbrOBhMLN67s0qhGZKHKQS3z1X34RXWsRJiXnz2/w+IVzvHZ9\nmzvXfIa3ZBZaLUY9lwUtwpkITL2ApTWX+cjDVlTS8AAijx/84dN88f96g1atQMxUTEnDP+yR+g4t\nKSEYZOzdvoEgicReiICKQQ1mAentHbzWlEc/8yxHr5/j0jf/MevdKcHU4Pq04G/+8GfI8xxrzyG+\n+TzfHt4kqomIe9voooBo19norLC8vMq93R0Mw0JIS/zBjJpZIVU0wjxFaldxk4SqoFICUyFH9XOy\nLHmQXipyDFFBzAUUUcLQDUbjfTxk0jxltUy4MbvP4oaFSYm3JTNz+iQ3Kqy7Ek7iEVdERE8lVTXq\nc4XUUpHCmGC1SvLwOupXXyeyNWquyFt2SffxLaqlSBOYEVLttkFViUIdRVIJkz7kISkRklLB83xU\nrcVg7NBZXmZtbQ1bMfmdX/93qJJBEr376oJ3c5L918D/AXzx/xX/p2VZ/sp/HBAE4TTwV4AzwDLw\nx4IgnCjLMv+zNpCKkqak4hUZ/cRjLiYYsoogPlgmJBkjD6IYRoM6eyODfmRx4Ggc72jU4k8ztX+N\n0ngveOfww22sVpOWXiXMckoEijBE13VkWUbSNFTboqI+IKTmef6AJ6VJiLJAVgiIpUySRlSrVUaj\nPoqikRYJ4TzgobOPs7x0Dr0t8uobf0DpBlSYU7fnyIrK8HaPkhQhzxCKgjDOCQqB/UOPWxenzF66\niKgYPPb4U5RpSr93hzCaceqRU4y9OaJf8Oq332BpfYGVY0vEgkGelzSbCzjehLraQMoFNFNm6O4i\naD5Bf5cTqs2ip3NreJsDbUa3W6fhqhCM6U/Bi4fUFZt1wSJf3OBMdRF/LmEqVabxCMmosTuYkQQZ\npp8RjmasrS9zuDdGUk1q9Sbj8Rg/cJGUHLtiUm1UEQQBZ36IQAvTFJDQiFybPIesnCGwgFVJvmfe\nEqCaEUWp44UeL3yjR6UzpfaeLn/89Rqf+2WFo3cr3E/3eezhA57/1bNsnR5ht7dJhQUqesJ690fx\niwo3e/8W7EcXrAAAIABJREFUiQeIGUXK0U0FWRZJkoQwDB8wsVSTIiu/h8QWyMoSN4yYuQmJBGIe\nIcgikizj+D5x/mBeUorcGQ+pV5t0Wwv0Rj1WyxLNtBgMDlg9toCRKdw/7HNmbRULyCwDy7bJ04LS\nFIjcMUV1kRcv7+BSwxnD6uIGakPhn/+Lfa4dXOfoyWVcpkhhgRQmnG00iOYxrm9j7dxB0JsUpYF/\n7zq1IxeQsj0Wz6X030lYs6vgXiUKISdnYJfkOxm6ppCpAUt1MEgpEMkilRsvfIv6Z57l6NU5mThH\nkkRu7MHeJOTJU6conJjy7phXBkPSVot6tsSt+SEstqgMPDBUtra22N7do2pUmI1nFFlJq9HCmUzJ\nbJG5kDA3ZUwvBEFmLuWUWYkhaGSqgCCLlGmCqihIuYCEhCAJ1BZqVOYBhWxiqzZVveRP/dv0azFP\nyJsolTr7QYS2tYZTtsgrAocv3+CIm4JeoSHpJLZIOQ+YnFlAe0nlwJ+w2lxi/Wd/lKLdwvILbF3i\nqJxTb3cY3NomrynkcUS1pjMZHtBoN5hOPBS9glgXWWuvUa8YfOlf/muWGh3Orq5T//Rn+INvfP1d\nCSy8C5Ety/IFQRCOvMv3/QDwG2VZxsA9QRBuA+8BXvqzFkkC6BIUgoKkyxSiwCyJkLICSzf41is5\nzaYOepO3diWu72tIhoqp50zEdezzP0IgPMHBbI1u10DTNIbilHQ8wDJ1BETixEOSpAfwPEkkLQtE\n3UQsocwS8iIjFzMQVQRJRRA08jwlTEI0TcMw6oyG2/hhgGbUifIJ/+43v8ljZ89w7eI7nDti4oQB\nqlwhxSSaCTTrVYbjIXEmMPELfN8myEc0hCpIOnXNpjfYRs9koiDHGUxRKxrhZM6gP2E+CkgimePn\nT5IIEZmfIwshQVggxg7hros36xGPhpiyjr2wyPXDu/hrIk2thpUZiEc0em/fZymzMTQF0RRgOOah\n2jE2pEVenF1ja7ND4rsEYckwD3AyH72MaHU6dI+f4O7zb3Bi7SSDuzfxwhmLy01mzojNzTXSLCGO\nfQxNQyxkdMWAXMYJI0SpQJQK8jxDkWSm7k2q9lHyMiDL58RJj4XOEaauS6B2Ofrw32Nw7Xf55s51\n9i+J/E+/YvBl+S021AXeuJcgSidZFGWm7j6ePUcSPMoiRZJBVRUkSSJNM/wgIU1KRFElSjLktECQ\noRAlKArSrCADFF0jDmI04UFheZakGIqCaZqUZUmuaYzDgLEzJxdSru3v8cSRLfbfucQ0CzjRWCVN\nMsaTGZVWA7+iMs4TNM2k07WIneP00ylHTyxw92WNghjFKvnmi7eYeCZGUyLI+yTClELTySKZ2/fm\nPLa8RrXm404sRGIMwWLvRk53U+Hy/X1+8LNLfPnfXKcqVhCTEMOSycuConIUUbNoZQbj8V3aeURF\nl3HTnL6Q0GmYvPLvfx/1hz/GwR//KXuXxxhdePzZj6G2z/Hir/8zlM0V+osZZTIgCGacWuqyPdpH\nOtHCMNq8ffMGLbOGN5xRxhlxlpIKEMklSZbQNXTSMsasWni9AEk2qYg6oesi6SqyqpCkKVEOFDLj\n3KEsoZsYSGkCcsRdVeWVvM9+U+axlonoVQitB9es/aZJhsXG4iLuMGI+6IGYIYkp6sgl01VOrG3h\n/vwqi1d6CI9u4oY55wKLQeLRs0UqgkJLsrk5DxAVjaalE84n1GoVTMPGjwFU4iRh5/ZtfvcbX+dr\nv/2bNDFQNZ1ZHpCr777F4P9PTvZnBUH4L4HXgb9XluUUWAFe/o/m7H0v9meOlIJJ6iHnIrqiUJQC\nRS6QZhleGPBrX+xw+qF1NMPAiyQkMUPRNQxzg0ef+TCBVSX2n0Q3Ewo5J8hkUlmkWpVI4gRFVhBk\niRIeNBoIOVmRIYkCaZE9OHUKyQNzPEFEkFTKEkzbwp1P0TSdIhMRyhRBzEnSEv/wHu9/z3t45tln\nmYz2ubrzIucfPsWNnodaP0pFnXPx+iUMOWLuxPiRjRepqKaIPAqpNutIZU4UJBDqLFjHyGYSWSgR\n4mJrNsIoYef5a6Q7DlJTobXcIski8lLGGziYsxTDjZCinM6p0xSdBu2jy9jDO5S7MZ4m0Ty1QHpH\nQKmIuEEfZapyZLGLknVxRiUbq0eIiyHZ/oxOaCIrKpmikCYBcegzuNenI7fRcwPN1uhWGiwvLzE4\n1On1+g94T0nA1vp58tRFU9q4Tk4p7iMpdcAmyaaEYUAQ+6iaSSm6xIGCqReMBwW1isAr37rHlZeu\nc/79Z/ip/2bMNy4nvPRiytYjCieqTf7Of1vHNtqI6T6JFDKP36IuLyCICoqiAAWuHxAEAVlWIAoy\nQgFJGlGRNBRBQNNNkjRCygSyPCNJEmr2A9PvOI6RBIEiywkcF1EUiYWc3eBB3l5WJXoXrxL7HoWs\nIOQSk7mLphlEloFsGISUZIJMSk4j0RG7FuLQY0GSyZhi1lrsbbtM0xoVfYofFsQTk7pcI5g6rLe6\neOMhN8sp3/fIBvuihyLPSaKMu3sJ9de36W6c4OCtbT79qad452rB7l2XWiVEFkOyWGU6v8Sb90dU\nG3XUiYO42ibLwbIj1hYg+n+oe9NgydK7zO/3vu/ZT+6Zd791q27tS1dXt3pRI3VrQVKDhBAWCCQh\nFoEJmBkh0MiMATO220jAmAEcjvkwY4dnwniIAdQYMQKBkBharbWlbqm7S9XVXXvdqrvfm3vm2c/7\n+sNtxv5io5lPVmScyIx4I0+cyHPOk//z/J//88y12fiTTyCCHWYOtWgfeR1+w+Prf/K/oisn2Lve\nx1YSz7exjc9gkHH65OvZ7fVYG+1Qq/is3blLy61i2zaFhH40RIYeTgGTyZhGxaeoS8rMxexPMcIg\nKLAdF8+xUZ6Dqy0sYZMYQy4leKBpECzNctHssXOtT2Wk2akobl29STgnWHEXwLGYO7RCXiqu+YZR\nQ/PgOGMlslgPLHbtgloU0059yu9+mL1kwNnGMqM0RaSanJTGmeNMujHf2ljjlFcnFpAlIH2baZIS\nVkOSrGRmZoY//fSn+PMn/4iHF1eJd/vUaw1iq8EXtm5+20D5nzvx9S+BY8B9wBbwu/+pOxBC/KwQ\n4jkhxHPGGGrKwRWC0WTEzqjH1DLEvs1ullBfDClrOTc2rjDtRbz2njfygQ/+I972wz/KUvt1iETQ\nthOq1R2SooeY7tBKKkx7A0w2pswjlK0wlkRaEuUILKtEWRlCxNhWgueXeHaB7xh8JaEsmSYxyraw\nlIctQnSRY0wBwOxMneXVBbppxJt+4MdYvfAOPveVfRaPP879b/hRfuC//Kf06SD9JbLEZvfOPk5m\nERiFkYIki4lNxsrRVTrtGUIVIiOJiDSWrFLzmsx4VVpGsnX5JTYvXuHWs5fZubjB3vPXyG9tUc0F\n+VRw74XHiBND3a+S9AxLu3O444C9Scqd6/ukNZ9r3g5R3CNt1g/GO0dD9GDAvLa4cusGPVVg1wSm\nSJHTAlVAlhVMdgacml1m0N2k1q7Snm+z199hZ7uL0TbxJCf0azTdWVqVRSpOgyzJUUIjRUGpE9Iy\nxsgUzw2JylvkZoui0OhcUG1MuNUvOP2W5/jl3xlycSthenmdBx5b4JVX+tx5ZZaXX865fmmW0LnO\n9nSNUdHDTQ6SFLQRpFnGZBozmcYkaUlRGgp9ICMyAowlydFYroNlWRhKFKBzg+RVm0UMjudheS6l\nNBhLMOfXUa6LW6+RZCWRgReur5GgsXEoPMlwPGXkeaT1KlZQpVFvkwGHjxzldrLLTndMPQlQVkxs\nYmzZJmgsUCbnODz7Nuaa58lzQasFo3EXZQlub/a5cm2bo2dXaC4s0h9NOXr0KJcvdqm0Glz85oAX\nL21Qug5up8OVzQGRrBH7Y86fOUGj0WBrMoawwvpowtb+EFCk+z0e9DzaC1WG/TZro4JLty/y5L/+\nIlf7NSgzIt9QWiXd7h53zRhTtxj1NoisiHQ8ZdQd4ocBkyJF22AsCAMPq8yp2i4mz6hJSaEz3NUZ\n0lCRD0cIWYA4MFrSxUHycFJk9PKUqav4QrWH/dbH2G8epd/boVyAytI8o01Jy65w4dAZDq+ewPMt\ntOnxzUtPc/2lF5DVOn5meNmPeCXaYfWH34y73MR1LeLhgPa4YG+wzzfuXqEXDXnbAw/y53/4R7z3\nJ99HPOzzXQ89TKfZojO7AJaHHdYZTmM0hrg7xhE2Cskki7ADBxuJryWu5Xz7WPftmHa/Shf8xd81\nvv7f1l5temGM+a1X1/4aeMIY8/9JF9QrnkmTlLe+/d1UnSqf/tM/oKIUFx56gLA1w2T7Nv2NPm94\n54/ypp/4MLcHGVXbIShydBNMWZAnJY6qYHJJVqZMkl2qdflqemgb21/GsiWGBF0mCGmws32wU6bx\nHkJLHBYpilmwKgjfMEr2cK0QUSgcDDdvPItnO6ysHCY2gqKMMUKTlQWeWyXPBJZlYwmJ79ns7m3y\nT37hH/OL/+CjnD92ns988hN89tO/w3Ts8uC5C/hzDfq7PeT2lE6rzVBmlEVC4ijqc3P0X7nNydYs\na6MdhlEPO42plgETX2KKkhUcdDpFpVOOS5dSR/TsOnJunslcmw1Z0Bv1Ge3tYReGTnuW5kydfNpl\ntl6jGEfEoxRpV/AqdWja7I5iMmMjLYkpYhxX0Jyb4/rddRzjsXzmXkbJgJc/91kis0etajPnvYVQ\n75GWJZE2bOzuYYchZVniOAei89A5hOOWpPmA0WQXy7LxrEX2e1N++VdPcOGec7Tcz/AHT77EOx/3\nqN8zYP1LZ5hdbPLPf3edLz43wW7nNPJlEtGj8HaQOxYjilcvxFffpEQikAiUEChlMFKQFjllAY6l\nqPgBaEOSZFDkSAmWkhhjcJSFUtaBD6rjYDkOaTTixNwsZjwiKwq6ec5OqjnXsfFMwFP9IR969E2s\nLDQR0Yho0MW75wjPX7rKzfUR37o5QFlzKNuiZIARU9K8xcoSZMOQSSz4wR95Gy986d+QbsUs1wxt\n18crbbY3+1RnbcJWlUnc4/w9p6ndN8Pdr1zF9XYoafInV5Y5f9ShPn+OwSu36fTu8szaLZYrcLpR\n57JVw+4sYW29hAwOUxtfYhzW+ezNiLRewc9z7OEEN6jTmJlnMEk4tnKM0X6XvJiy1b/DuMxYkBU2\nyxinWqVtLPR4iKoaWst1HNswWk9ZWjnEaDSiSDNs5XD+3Hm+9PQX0KJDaCmqSiJTTW4EshJiaj6q\n5lFLA+zQx21U6fb3WV5Y4ItffIqizKhXF/H9Bb7vB36Iixe/zKc+8wdYliYuEx545CHe8OjbWV5a\nQJkDaurE8eM8+af/J3/2qT9nOt2jGswxzfYgczBlhu1o0sxBuCV2UZKWLljw8CP383u/87/w/PPP\ns3DkEDevXuU3fvFDHKu3MbqA3DDRGuG73B31iHTxbbnK/mfRBUKIBWPM32kY3g1cevXzp4B/J4T4\nPQ4aXyeAr/+9OzSG7/+eNzN/5BCf+cu/pt5osDo3h45Lpt0JTm2Z4zPnOXz2foKKT1MIRKEpM43W\nCtcJMDpFoHEcC9soplnGeBJRZBpjCiw/p0SASSlJsIRkVOzjyipSLqNMHZ2G5LlBijE6n+JakEZD\nAruCxrCwsMBwPCLOC+ygjiglRZkgsoQ8G+A4HrajDubxpYVSIf/j7/0rdFyyPezTXFrifR/8Jfby\nnPHWNgsr83SOTJne2kELybi3jWsc0vGA1o2C5Uww3LxDVWTMCxudJdS8DBVrHNcnyyIUmnqlRlpM\nGDiSWm64s3+H/XSHoZKkZBiZI2sB2snYvXuXeuAwSLvYucBVHq12Czescbn7PINJQa6qtOcWyJG4\nlTob20N292J++K1vxmscplvucb32deL9iKON47SznNL1SeKILJliO4IkHh+YbJsc1/ZI8y6FVkil\nCP0Z8jxHqIyVQw3++W+9ggj/hp/98aPUTmXsjdu8+BmFO4k5fL9gY6OPbni0wybZxoBU7hGngApR\neowR/3cmmBAHAGtJiTCAMNglWKVFgaYUgqIo8LWkYixS60BCaLShNH9nGn4Qe5MUBXU/QEmXUZxT\nDevsrm8SASdWD3O25VHJAr64/zx3+vscO7JA0bSp1X0+f/Emt+50uXJnB+U0UXaObUtMYmPbTWY6\nh4mndylTgyHnzz75adqeoBo0mdDDYcJo32J2IcQNPQajhNlDgsn0LqqokA33abdCdLPJg7FLtdqg\nYjks//i7mLzwDY6vtRhduszGNMZbWKbqhdwdj2m2XAruYV6t85ojszx7e4ooJ5QVhV09QiYklZrH\nnbWrBMaQjcccqc0yTRM8yyYgZJwmTMWEajOgU/dp+hXCpofV22fc7eP6Hq5lk5WaYTxl7shhbt3d\nJ8szMi0JXJ84LaiFPuOsT002GWUWjuXgWy3OnDgFOmf18AWGwx5NZ5XOQosXvnaR06ffyK98+BGe\nefFTrO1c4+ixC1SqLju768x2WiwvL3D5lRd55mtfYjzcw5KQpmOKTONYgFCkmaZS9ZlGEShNGGqU\nHfDcsxdZX79OteJRdXx+82NPUPOq7A37GErqYZ2ozCiSjP8U1+5vR8L1h8CbgI4QYh3474E3CSHu\n4yDj4zbwcwdYaV4SQnwCuAwUwIf+PmUBgECydvMWf/3UV2m3Z1hYWKAS1piMc0Ll8L0f+Flst0J9\nYZ5xMsGxDbgWohLQ78c4no1RGUkeYVQBJqMoE5JkROjPYgsPREmpCwo9RWiNsixc/zC25YHjYxU+\nuclwOKAWChNSmj0ocqZZjGd5+NUqWBaFMGRZBNqgtMZRmjjZRwofZRUYKqAswloFXSru3LxBoxaw\neHqB44dez1YyIl9fZ+fuDfLxmLARID2Po02PuraoXnyF7Ts32WFCSswxq4W2oE9GLSqZFDlWqRlS\nsJNNqWYWtoBEw6zjkOUJ4/GQQhvq7Rqm4mBXAjrtOdYnt9gbDpG5phVWCZVma7DHcGONwp3iuhXi\nNGGnt0ej0URLmzgacGr1GHkWMN16ic+//B/4wLt/ht/9Fx+jO+myGDZILZv5lUNEa7cY7q1Tq9XQ\npUBnKcKySfMeNj6OqCLxUcImyweUpk+1IelGDf7Fv7rDD33wNP/FW3O+9j9PCLVmdyOmuRAgLu6Q\nGI+o3EFWO8hUsVATbA/GSMR/fFni1UpWg0RQGoFVgmWgUIpYlBhdEBibjnLo65JUCzJTglRoo9FC\n4wSSohAURY5E0B2O0J0m+0BFwqF2h74eMB72cIBxmSGSksQkLAV1rm0PuLbeA6uGtGxyEZOnMY6s\noBOXve4dWrU6uZYU5RgpqyTxiDTdo96ch3hMq1JgBJSFphp66LwgMVOs7oDWYghpgpAlK60hfT2H\n43hMtzQLR15Le/E8Tz63Tu3oMkcuPMzdS1dI3ZBjRw5z6ZvP4XoVGjJi1pqAU2UrHXPv6x5hPNpm\nZ+0Scw2H6d11VqozmEiw6M5xJ+8y63jUHcGuJcjskslkwnTQpVSCw+EskS7QZUmBQVkOu/0ucZmj\n/RLHsUmLAi1StCUwgUBkJd3BFqGVYvKQvZtrvO7891FxKqweOsGm2uTQbIXeYMr+/h5PbV8mjjQf\n+MCHUBWXRPTIzXUsG6AgTqb87VN/w7UrV/F9lyxOCesOjcYs2zt3OHpslf2dPrYFj77lnXzpy18m\nzvf5wI+8n9nWUT75x5/m1o015hcWmAz6+CjqnTrTLGFzNMAJq6SmJE+Lbxtk/15O1hjzfmPMgjHG\nNsYsG2P+tTHmx40x540x9xpj3vX/qGoxxvyGMeaYMeaUMeavvp2DSMuS/WHE0sIiyzPz1MIGm3tj\npqXPvY99D4fOPMDCuYeQ9RkyXSJFBqqgUALHlTiejeXbCNvCCE2a5zh2hVZjidCt4TgellRIKZHC\nwbGrBN4MrncIZAvwEJaNkBlCTdFlSjaBaNjHIica7WNISfOCaqNJqTUwgTJC6BiXlDLeJ4/2KbIp\nloBe/xbGDNDlkLCuqc5ovJbh1s422vHZ2+0x3NnnxOEjLCwtgK2oegErzRmyw21m3vQa7MPzHDp5\nig074YaYEjuKW7bL7bDCixR0/ZDF6gqZrHLREuwbh55W7OeQGBuTQdpP6XUn7PbH7PcmpIFi4lik\nvstIKfZNzt1owFY5olY/wXTqUJQ+Ao9atcNgr0/Ncrhv9Sg5mucu/iW1xohF7fL6s/fTm3ZZK9YQ\nlQYyrLPV6+P4AWmWMY0jUBZRkiGVATRFmVCUKYiSJJ2y393GzgtmKm3mlmb5s/+9z+COxYVjY2Zq\n64w3b3PlquY999XwyhEazXRQgp7wsQ/pg3MqJdarmzAc6J61OUiuzcxBcq11oFqxNIRaECBwgABB\ngKSiPELbxRYSJQy+76HsjCQeEPgGt2KTqJKgZlNvBEy620zjnA2d0anV2er3GGUpVgzPXrnCqfPH\nMEIT+HWKUpJmGUmeoCwLZQe4QcpkHOMGIdVaiKV8CuOjQtiZtOmOl7EamnEaobUmqJT0N31EUUP0\nJjjzFZKpppzkWEGfLz93iW/c3EQkOwyTHmMz5a3vfwfTJvzt3/wJ0QtfZLWYsvPys0xHd7iUCuqt\nDm96cBFpK2p5yGKa4WxvE0wiRBwjPEVX5mwQcy0fMKUgyTOqrsfhWp3QGGzbpl5rU3MbTKKENE2x\nbYVSijRNGQ7G2OrVAQRpkQFRqcHz6fZH2MqFFOLYx5IVLKl54ZufZu32l/nKl56k2YwJ/FPUZ0K2\n+t9gr/sCg8FlPvGJ3+aZL/wp+SDiT/74kySTkq3NPh/96K/xlS99k5WloxSpxdLiaYbDhPf84AdR\nboUf++mf5cjx+4jinO9987v4gXf9NJ69ROjOc2zlAoFY4Ud/4B8iMokrHeIi5c5+l16SsLx6jEwo\nBkmM/rYh9v8nBjG//vGPP7F84gRVt4KPx3SYcPT0/fzIz/48C2fOMypLZCUkISXwJZBjhCQXAs8+\nMPAoCo1UEqks0rjAs0OatXmKIsHzQrwgREgFWkHpoEQIaLIiwpQgtUuRxiTxHnk2Jk4y8qSLUopC\nayqVOkZaKMciTiJ8L8MUU9Jxl3i6x3jYxXUCfL+FkiGurbGkA1qTpVOGw12yIsV1Z0gcSbq/hyUK\nutGAoFlj7shhkjQl2etj7Q6wM0O8N4KKj3dkhbVb67i5w1ApKsYlSzXPpV0iqenrjE2d8tP+KS4x\n4EY2RHsejpYUZUHqSrQSyLREhjZJnKOETZGXWJ5FJjR24FHkPt3BmNmFRaphkyIBkSrOrJxATkv2\n9u9wY/cKU5HyuvYFTh99mBduXGFt+wZvfsv7OLR6hKefeoqwWsP1fKSwsWyPvNS4lkNZFAdmxyKj\nKCbosqQSVrBESqkSxnoDE5WY8ZD3vDdDa8N49yh/+XSN97y9y9FVGzWU+LUJr30o5b0/OuH3/8jB\nehVoJQL1KjlrOKBphTbgKFJHUOgCtyipa4kjFJnJccTBNzzbQUpJnCYUWuP4Dq2GizIpvqexPUVO\nSeh7ND0Hu0hpSJ9X+vsoK+DW7j5hq8Jivcln777Mo689x95Wj63tAa4KycsCIQRZUWJZNnkxxOQV\nlJOx23uFRmWBySSlFGP6kYOvBL61R7NSxfFtxnEfTIgfakJXEVsZ+RjwazTnKnzqqXUOn3kIvxhR\nry/iez5f+uJTrJw7y/UXL3LCsfFqFuW0S0+3uVWmFKOYthWhrAQrPcSl/W+wtbeG40AUxShsdFYQ\n+h4yzxjbmtIqUabEywui4ZCp0WSFRuWCpMjw6gHKVmR5QVmUVJwAW9jYXoXJaILAwndq1MIZZOEQ\nWiELrQVMOcd85zCBHTIXNgmdFgtzZ7Blh0Gec/HaV7i69i0ME86dPkk8GqK04OorLzO3tMLLl28g\n8HHdCt29KT/3wQ/jGJ/H3/VDjMdj3vG2H8HzHe698ACt2hJveN0jZEOJ5br4TpO5uRnajRYiqZBE\nEX/zH/6YwXjAB37w/dTbba7eXudXf+nXOH72Hr7yta/h2Bb/9L/9DnLh+s3f/u0nOoeWSUcxUT/C\nrbR45/t/goXT5+gVBa4vcSoORRnjOII8j1BC4FgCIWLKsiBNS4RysJRFmqQ4ysPC5e76dbQReH4N\nSzooFLb0sIVDkUGWd0mzHqbISacDxqNNtNY4so7tSbLC4LgewnbxK02SNMayBeQj8mxKFPeZTCdY\ndpWZuVWCcIY013gqIJlk9Pd7CA1aS1ynhtEOZTrl4tNPMxn22O7ucufWGoPBiPtOn2XODrE6Pjvz\nIVuhYOQ6HLrnHjbvbDGaRgyY8EbabJgJr3EaZCrnMhE/Zs1xWfR5edpj6ggSbUiLlGpQxat79Po9\nDCmeDMjGESLLMWmGJxVFnh+kvroOST4mT2PSOEWKgLMn7uPw7Cr7mz1kFrNrtolzePDoW5lMUmba\ncxTkvPXtP0in3ebkqTPcvn6LZBozP7fAJJ5iOzZpFGHIMeTk5YRSp7iuj215TErDaDKgSD0ePnsW\nvyFoVXM6xwTLZ/b5wh9KksUhP/P2e/GTHcpuhY/+cotf/mcxm5vyPwKq0QaNwQClMGgBrlSUvk3h\nCtAFlUJQlTZCCCIKdFGAMdjyQFGS6ozcHIwrtusergsFGdKVYASyBFkYgsClO054eTJmPIkogWaz\nwtFqh+vZgNfdf44nn/xb6kGVSZLxUz/5U8TRlN3dLZqdGg+/5jF2d0aMk5u4YUI8KQn8BbQyKHuP\nuj+gaoX4niDJRmghaM4UlGWC5fosLMzRLyWp7dPyAm5uDXHsNnMLR5gJV9lb7/OmR9/A557+Glev\n3sBDctuk1LWhGx7DU02i8SYyirhw9DBdq8dkMqTmWbgoGtUmwzgiV4KoiCmtkmmeYAUucTzBL6Di\nh6jAJ0ozqo5HaVu059pkeUaaZtjSol5vk0QJrl9nb3cPtEIal6rXZnFmheHeBJ1pKhWfNB4RDXrM\nVBYaL18WAAAgAElEQVTYWk9xnEPce/9beeb6v2W336VSrdOsNyC3ScYhi/PHuOfCSSynwbvf9cOM\nRgnnTt/HidVzzNYWcWSF/WjC6x58FEdbnFg9TjSOWJhZoB00uP3ybY6eWODIygk67QBdTLGw2d6+\nweUrz/D9b38/Z46dZfXEKba29jl16AyVsEGpFJKCD//Cz3/ngOzHPv7xJ4JKgDKKwTQitzyGmebw\nyRMEoY/l2bhOiREx0jKUukToKQ4ThNQoaSGki1QOUpiD5osUjMYDAtemWm9ijHMgG4nGxNMh8WSI\n1D5xtk4cbSFKickhmQ7QxuC5TbywRmlAOA5COtiOR5JMqVRcdMpB9LQyeGGdmYXTVOrLSCsgLROU\nVnT7e9g2JEmEMQrb9slNzNVvPMts4LN09BBLR4/gKY87N++STSKG6xsEJiUajbn1ynVWZ1aIi5LO\nyVVG8ZC3jGz+Nl+nFvh085ihyfhue5Z+POWimmKUzez8UWY7RwndWcKgQaPRpj8akmuNySRSCiwB\njpTEcYyWEr9SY5RNwWSIMkNIiRYK5TkMJ0PqnZC1u1e5OvgWy/4iZ04+ilPTzFXavPa134MMNVkc\nc/+5e/m+x7+Po6tHeeaZr2DbkqxISJPhq5RBSVmUSHnwSBnFE3LgnpMhvb0hr7uv4MnP/Dx/8eRn\nuHnNYbiX8+gDU/786/ArvzJhuN/h9d8/ZPNmwf/0f1QRIsZoA8YcwOuBrQVGCrQU2K4FlkRpcAqN\nbySWUEgpQEiiMsVF4BiDNAYjJAZJnJeUaYYV2JROiR+EWKVH3M9Js5LUSLZFRr3dpj+KqFV9Dtfb\ndDod1ra2+IN/9zmQEGUxhpLpNGJzcxPHthkNumxubFJqQWq2EYBrNXCcFoWYUPP6BLKgruaIoz45\nJfVmB82YPHLZ7Y1wKz530gRZGExp41UnbN7a4ZY1xhcBmRnyiSf/Jcqk1IqIYxVJs+ExGqbYnQ4y\nr+C1IE5SikzSXoIruylFYSiyDNuxCEL3IK/M86hZDp5jUfE9pnFELkDZDjW/gihKbNelFIbSFFRq\nFRw/IElzHC/A8QMKaSh0AUJQZIZGbYb5+UNoDaPxhCwp8SyXLCmIJgMKGSErEX/2N/+Wk2fO89LL\nL+J5IWVcIUk1biVhd7yOUVXOnryPNM1YPXqcZ776LMk0Zbg9oIxLFk6uoEeayfYGepoRui6dRsDW\nzR2qyibOUowpcSyLVqNBlo4Iqy7zC0d4zYXHuHLxFY6dOMe50/eiInCdCqsnj/P5L3yWX/onH/3O\nAdnf+me/+cTRY6sYNGfuf4DH3/VuciRra2s8eO+9CFdh9JQ8iyi0xlIGqae4ZkphwHZClBVQaEOa\nTIijEUrkdPd3aTU6eG7INC4oihQlCzzb4NmgywmZ3kKXmrp3moa/gtYJhiG2l1OIOtK20YAfVkmT\nHG0yXNsgTZU4j8Au8asdwsZhJpFkkk4pSejubqPFFC8ocVxFq9VBKEFn3ube+17Dg488yNL54zRW\nD3Hi9FkefPS7SHXBeDwgun6ZjecvI8KQx975DtxejLPZ5VinzedvXYR2jXiaYEpY9mbYS1KeVxl1\nu4mymvhWmwv3vpGVw/fQ3Z2yNHuIar1Nb5pSbdbY2t0Eoak16wyiMbGBXCkoJXmS4tqKOItRFYfW\nQovr65f45ktf4s7kZVIX3jjzWlqLVTJbYCce/WIAIqHTbHH3xm3uXL/Nwvwc7XaDb730TaJoiG3n\nFEUGWLhugFI2WR5hO5IkKvn1X7ufx9/4ENboMk89dZd3vuUsO9sJPkMeOlnh339ace4RyRcv9Wke\nh8/8UcyV2xZaZq9yA+I/btJSGCURlqKUQJnjZCVOYZCvKhalAAdJYqDiOLhSIDHYjk+hbMZFji1c\nZGiRyow810TdjGRY4oUNUilRnqQ2hXBpkX5/n3wwRKzOcOPWLSaTECMFqBzHk2zv7KALjS4NtdBD\nuBNG4wTblYiyRpkpjJAMkhtUyhJPh5SRRpuC9mwV5UgGeyUmaXL27DzXp33y0KaVQg9oBDtkseCV\nlzdoTEKCisX67nVOHjuGq3PKvV18y8EOQ3obdxlXEpS/gGx47Gd9TspVbt8e0rYrWEUJOqWwCmJT\noh2HQRLT1hKRF0S6IA9c0iyn44SoQjPKI+I4wfZt/OCgMMo1oCziLGeUjZG2IIlToiilEtTJ8oKF\n5cUDuWA2ZjyeMDuzAFrgVm1ie8x67xpXXvkaQRiwu7MDaszcYpW7G33e/UP/kLjwWbvyMoPBgCOr\nq1SqNerVGt3NPcq0wGk61KwG6f5d9CTHkoYs6ZMNSqoWdAeasoio1RfY3x3SbNu0WrPocgYlbT73\nF3/N/Q88QGCHDDe7eH6IW6/ymc99kl/5b375OwdkP/bx33zCqrs0wjlcx+H62hfYvPgtHj73Fuxj\nhxFSE5VjXFHgpdsUw6sIKcnlMkbnaA2UFayiQ5EKHCdhb+cmeZxQDRtMkhiv4pPlExxLEoQBGsUk\nmqB1hSBYxK94RGWX4WSfLCsQuiTzXAoShCjJixzf8cji7GD6y9KUGGwnwGiF0hpbpqTTXUQ+wPIy\ngqBJtXaYamsOnBLhZNSDRUpd0ptMyJICkRuQEi0tmovLlJ0Ztq7cpNcbEY8HHD61xJ49ZT3Z58bO\nNsqusLW5BY5Ho1LjTtpDq4LvKnyWfBdlMpZlSX3tCuKVb+BN7lJu3qTZH3DCdjn10E8hqjbTXpft\n3S0mssBIhYwME5XhVTxSDBOTMhyvc/PKJfqDLaZ6SqEk840jnA8O0y6qyMTQ1DZj1yXMK8h2G+2O\nMWsp/lhw8oFZXrpWcHfja/x3/6DF019t4NYM6WiIU4aMTZe60nTCZZ66lPKRn/4Ajz6wwXs+8izv\nfcBmOL5Jp5zn95/VHD3uYDsxyspQYclXvtli7U6BsDWO52K7DqWAXJcYo7GUwpKKzBQEwsYvD+iA\nAzqhJEDRUg5+LpkLA7aTARUjaZWKO3aOyEuEAtt18L2ANC+IdcpIl5S+zVRL8EKCmXkGG1s0qxXG\nRcHNG5vsdyOaOEitqTgVFAJtMrBKsjLCrwcMBzG2U6KLAoFGIMCUuKIJKkBWLMbxKUzFYXcYMswF\nA2PRT89zfOE5sszHKaoYkaEG2zQHmrWg5O56HdWS3Jl0Wb90Dbu6ijezQmXa5YXuiFrVYbRvseAO\n2bNGKKfCubRNZ+46V3qarTJGEtD2WuwMugTKpuaEDCxFYUt00QcRMFETPAtaiYcnYrqxIW45BNI6\ncLVKS0ptyPISgUBmhsBqoagQ+jUaNYe6J2GSoCLBMBviWi71agfbkvR6Ux46/zbcYpEsjYiTMUEV\nsiyj4i3ypkfeQTSJmJ+pU0Y2d9bXee6bX+eRh+9HKc29r38I0woQ8YThToyOS3R6h4YtGdwGWbhM\n0y7ZYIdShJgwwPgGUbpkA0mWjFHTITuDLvPNOcpUM6272FUPMYl45uIX+K++kyrZj3/s40902g26\nRc7u1gZqcwffqfHd7/9JXOkhvIyiBEVOkqwzGN9BuQ0a7TNIaVNqiygpSfMpUiUYnTIZJtTDWYQQ\nGGXhVjwMBVIUGDRJkVEJQ0pjKI3hgM3TCAOO7eC6IfnwJiIZYxmNoxyUU6VUBi1KbBlgUNiWh6VC\njPHIU0iSlDgq8LyAWnUOKRySKDoYK7QVeS7IixylFEK+emO5HsPxAMtWLC8tUpupc2d9g36vy/6w\nz3g85tDCCq12GyMVO2t36PhVstGYeeHxyNwq+STiejbggqnjjyeM4ojI0tQzUHnB0MQUSUJ1f5PK\n6z5ENGnRWuxgFgTLnQ5OX7Moq2z2tonKGL0fc9o5wsnOKZJexExQ4Z218yBreLU5Tlw4R5xFpNmE\nXSaoSkEx2KIZzCOWcqaqSdJfp1m5w2j/9fzXv/40v/+/GXJVcGhBM4n7uLWTNMwMJhhBcIzn/nyT\nhx+/wN6upoxfgLHLn349R7Y1gTtPqQ2lKbm7bvPpT04pVIrjeAhxMLVltEYASkikkAgNNgq/lHgI\nBAZhKYqyBKPRBnzHYzDt49gOjrC45eV0MkUsNcM8JysS4iwHKV+NCgrp9UfYrktYrRCENRYXDrG5\ntU2RZ9TrdaaTMa4yGKkpTUFe5JRw0PRybGzLIs8yyvIgVEkIgeN4gMAAhX2EvW6C8AriNKbII2xn\nSOjazC0kbGy+kYnVoVHdZLi2jSUcKitN9uJVbt1YI+r32N8Z4tdDstEA2wzYzPfR6QKL9oDLJCzq\nGp1co9oNZitjqnrCNHGY7VRZargsz3ssHa4QuIZoPKE520FUbKbdKZ4KaDQPnPKq2sVvNtgTCZaG\n5UYbkecUWYElbSzpoEuD7XoYYXBdhyTK8d0G0TgnK1KifMgkHTOeTDBSsLS8QL3d4tbaXY6fPM1Y\nbrM92CCXEzJdsr2zRRrFLCwc4cbVdc6eXWF1doYwMTz7+a+gpKIY9emUJTo3KD1m4+ZFotHwwPFr\nuMsg3ufIydPsj0cYx1BS0qx2GA0m+J5E6IK99U3WNu7i2zZhtYLXCAhDHyHhqy98gY985Be+c0D2\n13/jN54oRElHBswOS5ppQV5r8Naf/Cm64zGVmgvYKGFQymC7IVawQClmAYHjtsjyAsuJKYoR8aTA\nt9q4duPAvtBx0Mog5IH3AJbGchTaSIQlsR0LYUs0YDQoK8DzaozGdzFGomwP26kinICg7iMsjacO\nfmwhBI7rI5BIy8K2HUpTEgQOtmWRRiOyZIBlp+R5hBYuYa3BxsYWJSVB6FMUKYNBjyBwKYqEsupz\n9OgqWVHQi8bsDQdMhyN0XhLUK6RZzrjXp1GpMS890v6QvgtuJaDMcr5lTRmoAl9KUtcgbUHHOPhA\nf3oD//Z15GDAYz/4Qc68+f209BJpZJG5MdvRgPZU8+Hvei8/9N4Pc+o97+GeucP8hF7lNe94B0vB\nAkoEbJZT9JEKL958nnkBsQdubFNv+cTBKplf0hItjp/6NC98seDE+YClxi4iadA4P8PuuEI722co\nh2hxgk7zOGt9zbtfX7Dce5YXNzP+9rahenQWt6rZvuOz291ge6vKX316wiT2sMMCJdxXQxVLpJTY\nysKSCgVIA6G2sXODLSTaGFCCNM8RQlIYQz0vuUZKRTlMy4IwK6lIi1MiYNTyifKUtCyp1pokqWY0\nitBArdWk3ZkjTku0trBsmyIriEZDXKkoSFCWJCsKCiOQ0qI0JY16jTSNSNPsoOK2LLQuQShKc1Bn\nZ/kmnWYH5exgh2OMXkaLo/T0dfYnNpETsLd7L6Zb0vJvY80pXtrVNJXDmcOKqxsJde8otujjNDTT\nYUKrVuPyzg732g1e8iJsLXFMTDrXwqlnyH5Ip+bRrNkcWarTrGtWjtY5ubrATKNGFk/RnmKwnoOE\nM/edJR6NcLXFODGYekEDj3IwQeQaZdnY9sFfW54UZEBYcfEdC51ronFJpdpglA7oR5sIqZhEU7b2\nttFKHAyGlIbQ87m0/lW2t/bQqs3y8lne8qbvZWlpkbt3rmG7JWu3XsZrV0kdi/7+gPuWjjG+ewMl\nEl6+egNXafJkSq1aY35xiSu3b7A7HLJy7CTrvW38UCOKjHZ1nm63T63pgM6JxlOu3r3JnZvXOXli\nlc7iDFId6LGf+upf89F//IvfOSD7P3zsY080qw0ODQ2drCRqh3zfz/0MYX0G5QXY0kOLHKVcwnCR\nMDyKoUWU5hhRQuljWQ6T6T6TYR/f6RC4bfKiRFhgpCQpIyQFWmdoNMpRFHmGsEC5EqEMpT6obpT0\nEJaDrrSwqh38RguvEjAejZAUjAcD4mxEXiTE8YiyzBhNemiTUqkF2J6FEEOMicnzPrroo/WUUmco\nt069sUDxarNGG00YBgz6PZI0AqPJshTKErsW8Jq3vJ6VU8doV+t87emnWd/e5tTD99Pd26fX3aPm\nhey5JdfNiLmx5opMsXKNb9kkaEbRmFRrjKWw0oLZIOBats4JnRJ98XMUl68wI11e3Fyju32d5bzJ\nL/34hzn16GMM6h26kxEr822Wzp9jb3ePWMLshXM8HtX495//Cy66+9gq5VB4jEpwlv3+LarWLLbs\nEY3naXYuIodf4FDnGDMn1+kPlsmyiH53g2ML300lr/Bv5iocWamzO/cY0bUN/vjFp7k0Oc7du4aw\nusVgLFi7tYuULs9/XTONc4JaG60dyjxB6wPVomVZKCmRBpQ+kG+5xkYYjWMduD+V5qAB49gOYKhp\nmMHjlltCljKLf7BuGa6WI+rtJsMopjCCWr3B3l4fx7MIKi7NZocTJ86QaQfXcZgM+2TTCZ6SJJQo\ny0cbCyldpLQRShCGHuPJGEsduIYtLs7j+8HBsZUlQoJlhtS8Ofb2NxmPM2ba9zCN28T2t7AqDvEw\nYzIZEqotWnMlO0mVwlRpmNvU7IgwmOXFtS5znsPUGGrtGR7s1HhlvEXFdSjbCabq47Tr1Nv34neW\nCZurmOFtdva79EZDOgtt4miEpw4c8uZaVa6/co29riZo1tlNB7ihSxalyCJEqC526VBzfDzXJUkL\npmlKYUBIhXBsRr0e1cBhrlXnzu2bjKZ9htMB0nWJxhOkpTh5+jSj0ZSyMGSTlGg0xa3WOLzyXbz9\n8Z/hwrk3cGhxmac//1e8ePGrFOUYbQeMe0MOzyxw8vhx3EbAdneLWr2OXfHY2RwSVppcvvYi12/f\n5vHveR83bu/gViwSM2Jr7SaP3Hs/jrJIdIpbc/jsZz/H9u4ejz3+JrY37nJidYU4j5iMR/huwJee\ne4qPfOTD3zkg+5sf+/gTx50WHd8j7fg88jMf4Fs3bmFnkoX5g+661gfVhzEeSWqTFwWOq5AiZjLM\nEaVia/02lrRZmlslig4s8PKyQNo2tmtwHENZJhR5ilAS2xUYk5PlMXleIAQoy0EoCyMkQVClzCZE\noy32927R29+n4tewLZ9CpziOc3Ax5Bk7O1tkWYzj/p3z+whLaBxlKIqEJM7w/CaW20EpH8excR2b\nssgp8ozhYID7qs3eqLdLEUUMhgMOnzrG7v4uC50WYejzjWe/yX465eSRo+ytbyHSksgU7BOzYIc8\n4yZ4hcCxHQaeYFhmCCOY2oLbdkZMwmvTI8Qqo7S6ZN1XCPp3WRtdYxBP+Ec/8av4M0v0XdAaWsSs\n3b2MffQEM5duENzdJ9Ipwcosr7WadF++yivjDfZvXeT62hVOn2xwfvUOOuqytrnB+sZXed/7ukjr\nBt3eYapzCdO+oRK4bF5v0zELhFubLDt9vrx9mfOzVd62knDp9j7WnCCelGyuJ7Q7MN4JGQwMnRkX\nYapk2QhTliDEAf0ixIHSoCgRpUEZsJWDZTtYrk3+f1H3ZrGyZfd532+tvfa8azzzcO+5c997u5vN\nbnaTTcqkqZCUTStyFMGWkjgBgsRwggSIQyvKhAAhHMlBbEfQS97iByGI7RhykESSNZKSLHHqZrPZ\nA+88n3PumWquXXtea+WhOs6jCGQAtV7qrVAFVP3rX9/6vt+n648abgWBcjHakDmarg0o6hrlB2y5\nEV0EZ6Ikix1U4JGXJVpb7EeXriurbU4Hpzx/fkpZNSi/xXw+YTw4IXAkjm4obYQQPlgPHBdtIGmF\nNLqkKEq0NhhjSJIWQjhURYXRGldJkuQzvHDj4zw7eos3P/VJdnYXTE6O6PJJVA2m3KSV7LPWm1Jb\nhfQtHXuKsj7VomGrK8hWfe49GrIe7SLUhPVoglfE3BYZW1GwTDe6IUwbjodzjrOUYnLEYK7ZHxQU\nxuXcxjmODvbJFhPW+wlJu8ODuym1YylFTbfTZj6YUFcOazsxk9ECaaGoa2ohUHGCloLxdIYxmkB5\n6KJkOj1h70KfhozxNGd39ybFfIrvBwyGZ2ys7RI6Pjvr2zhGc+3ca0Ruj7KoOT59zFvf+V16vZDj\ng2OkiLjUOs/s+Iz3v/s2s8WYtMm49NobDIuGuw/eZTgdYRzNNJvy/PSUWZbx2T//WZ48echssc+T\n20/YbK9zePIIGUryyiCEw6WrV/mj736LV195hXQ8ZjAckA9nrHV6fOO9P+ZvfuXP0JD9b3/x73z1\nUrhJ2XdYbId8/+3bOI3iCz/zlymFBzRgFFWdUpMiqNFNhrApTT7DVy6L8QRpNRvrq9R1RUODF7gI\nKVCexHUrHFlg6gxMjee5QIO2Sx1PCAVIpHBxpINUDunD71COn2KLU6RJcf0OcbKCF7YJoza+m6C1\nRFqJbio67S6+v8wS+ULT1BrHcVGOT9W4uN4KQbBBXswo8hRjGsBQVzmep2glMWWeczo9xbWQTue8\ndOMlHty+w6MHD/nYp98gSVp8/KWX+cM/+EMcIdlyYtati6kbZrGkn3nMRI2vBZd1woob4QhFXRum\n1rJh25wPDUfZCSbsUxKyb3MamTJ2OvzEz/0Nnh+d0k5TttsdKneGq3O2Tn3E0/uk2Yz+MKda6fB8\nOuLK1g77H9zhID9lPjjl6Q8e43uPWd25T+X8PjUHvPNuzLkXoJl6xO0J0zLh3r0+i6PnFPqUZ5tv\n8Hu6or+9ijce4o7mfNsu6DlDzqqQxHqERJw8ndIPfRazlDhwEbpGuC6e7yGVwgLWLnV1B4GQAsfx\nUJ6D47lIR1AWOQqQUqJ1Q4Fl4BsuND5uGCDLipW4RVPXpC0fpVzSLKOxGt/3iAKf0XhElIR4yufw\n6VPcICL0HYanz8nTGdZqjGqhjf3oNS1ZCGEckRf5EpzjRhhtMRrKoqTMciSCwPNpxzn9juCnvvx5\nYrXJwZMp3c5Fnh9UNFqx0vsEnjvF0XPKfEZVTfHninmrIira7JdzLm6t4vlwlmac6/W5eXGHllzj\n4fwJ624PaXtsbVyg1dmivxKyrhzO6pKktcUwh6f7A7Z66zR1QVWX6Kyiu7XOcFwyGM4R2pKenRH7\nMQtdkzk11gqaRlNqQymglgKUs3QaVDUCiadcgsAhait6G2v4fp80FUSRT60rgsBjfDLGFJqL53Z5\n/PA+k8lDjEx5+PRD7t5/jzhysVXFC5cvMzg+puV0aVxob/aZTYZUZxPKQcHFCy9wf/9D2is19598\niHLbBEHCD+58g+nsOYcHI4zOuPnCJ/CCmEUxptYNd249JY5iHCtY3dlgrbeCagRRmOAawdn+c97b\nf5+/9Z/8x392hux/93f/7levv/Y6C7/iaHDEaH/Oz/zcv04VKYpGkfgRQkiKaoYjBaEfU2dTFrN9\nTN5g6pSqGLPSSwh8GEz2iVoxlgAh82U9iR5S5QOqbAy2IfAU9UfcUc8JcWUE1kVrC1ikEmSjB5RN\nRqMbjFEIv09lLVaBacQSqVdUSKHxfMvm1gpKCVzlIs0S52ZokL4HysUYgetKssWEJPaYzQc0TUGv\n18L3HdL5FIFhls4QDQRhi8JYZvOMjfUt4l6Xj732Mrt7e1x46Qa3PrxNMZuzIgI2vJD78wGrKkZq\nQ6UgFIKkkRz6NQ/cEp1XNGGLxMBAGTJdsa4SjvI5tnFwO9t84Y2/RDE6QOopTQZJ4HB0+JDe2mX0\noyfk1Lixx6QV0k06qFyzSsjXHjzGc2ryheb2++vc/iBlMUpQvRkTt838ZMzGFcOzRzFPHg0o5teZ\nzA2UGXP2SYTLLh79+YzXVuHrzUMcq+inm0iv5OhuRdtr8IVE1D6OTAlsQu0KlFIglxQtRykCz8eV\nDkZrPJZarEHjuh55tlg2JKDR0uJbhwhFHbv4ac6xqvCFYmgrzhYZZ6MFylO0ux3quqapG6qqwfVC\nJBptIQwjsDWL2XiJwpQCLUCbCmsbDDV+6ONIl6oEY91lDY6xCCtphQnrK+tcOHeOq5evcK67wXpn\njU9/6nXe+u4fcPe2Zp7PcboLjOyBPWOSz8jmXZp8m5kuUZ2UbJZQmYxk9wrju2dshhpve4s6S2gb\n8Dd7PDp+SIiHE1yjv7KFEZ+mt9Imzl2yqCaQCaM0J88XjJ7u0++0mCwyYtXFdysqaxmMNUYbWq2E\nRVHitxVCJvQ2VpgOpwjXJWwlSM+jsWb5fZIBfhjghiFhu8N4XjMcV4zmC5AWL/SpmpwsnWJLg9GG\n119/lW++9cesbQeczo8Z5UOUr6gWFZHjstVvEbkNd7IxW+srqLxhOpjRXlnFN5KHb71Nf2eDfDHg\nxvWbRMEWcdJhY6uNI12kbdNf2cCNOwwXM7LSoAvL9QuX+OSrr5EPZ7zw4sscPTvm9PCEza1dpmdD\nBqcnPJk/4Ss//2doyP7yL//yV1deeolxeoItCy6/+Tl6Vy5xdXOHlo6pRIA12dIdQBdbtFjMhghz\njGtb3Prw22yshghhmE5PCFoGpA90sGKGtgUwxVTzJVvAAT9wUX4HoyWmcRDGwxE+Qqr/Kz6E7noQ\n91DxOmFrhyjuUzUZrqfRWALfw3dByJKqHuL5NeligpQNUlc4rkHLgsYUSAXKhbwYghUoZYEGzxMM\nhycs0hnoBulA1+8ShS2ClRVMt0XQ6XDt0jU8xyV3ChbS0r+yx/W9y7z/6D5Hw1PWS+jHMd8WYxKp\nWGkEhdCMfUOnctgqFKHnsSUrTpuC0nExlURXhmEi6ZeGH2SaG6+9ia7P8LMJLXeLcZXR6vp4C5/a\n1UT9NvrqGi4BItXITkw4yjhunTKdZ1SqReNMWBQnjMc+b78vaGIwzg6rJkVpxR/dCijVNbpFxcxv\nEa9eo5c8ZbNlOXpYcLxVcShO8esYK2dkI4FrDF65iqlnKEDUIXUzJ5MSBEtrllg6QwLfxxpDVZT4\nOFRVRV1XhJHPYjEHIaiFQfgeZ6aibR0qU/HELfmEWsEtLJk1pFYQJyHSc8mKAokkzwvCIF46HeoF\n/f4KYBmcHVFXSzZBUTZYsRywiBoEtNsdGi3Q2sFohaAh9ENarTbra+u4gO/5JFHIad6QVZoPHnyN\nOFzn+LFCqmeous/OWszo+WOOmwnTfEbU2uGn/q2f59d+69e50bFMN0LSD44wwqdlQnQZUfZanOdY\nUr8AACAASURBVB3dobt7ju88ygllSLQeEfp3ObmzybjRHD7bYmXzOdVkwfrWKmdnz7i+ucr6ep/C\nKmYnGUF2xo1XX+L+85LjxZySBm0Fjsgwc4/H40M2eis4jlqGQTDggOe7hEkf4UkaAc9PZlQ6Ic0F\nURJQNEOOT8dMZyOErWj5CdYazkYnNDYnjK5zPB7y9OgBe1fW6SYhO/01RGlo+yGzk0PKbIH0I7qr\n2wwnc4rE4F7qYp6ndKJ1ArVCVYHF4aUX3+Rwf8yX/sKX6fbOczA85HA84HOf/TJdv0PbgUAphk+O\n2LlyiWxe4kqfdJHhIfjE66/w9e//IV/5+a/8UEP2h+LJ/n99ds5ftv/Of/RVirpgZaNHlESYRuMr\nl3yR01ntM59MKMoBTXWG4wj8sIvn9vEDB1PPMM0U02iiZIvOykWM6lEaiW5SIMOXI87OzmglfWor\nabc6OJ6iaUIc6VFUGWGYgInRzQLNkDz/AGwPIVaoa5c0nZC0FXEYgTDU5QJd5uhqQVEu0FoTBBGt\nVgsZ9wBwXYWUkqYqqHRFUWR4ysHzJJaG4XBE6Ee0opjnzx8xm56StNdo96+B28VQoqygFa1RGwGu\noBfF5NkML3CJ45iju484fPsWv/Jf/20+T8So7fNIVqxVgtcyRRh5fJAdE7khp4Fhkc1Zkz5VLDiY\nzOgpkAqeelus713iy1c/zVopKadTgq2Yajemmc1Zi7awLZ/ME2yGbcazGcFqn/3vfsh3f+P/wFyN\nOCFD4LG6tsF0OOfenfcJ+5Zr13dhMqe1ohlPK6ZPtjmVMwZHB6zUktULCfOyoMgqPn5zHWE0Z2cw\nGGfEc4czSkIcrDVIY/GFw0JpslpgXYHxl3Q0acFVS1BMXhbEH9WIlLqh1W6hlCJLF9BotNEYISgV\nvO6vs5+PaHTNZrtP4PmkdYaeLpgmDgNX4xea1XaXBQ220QjXo8gXhJ5LXWusFqTzAikVQeTjEFJk\ny221042QGPrdFXQD1qRIFWGNwlhJbQvqJqMsFzieJkm6eE5Ar9PB933G0zknp2OUE+E5fRph6USf\no6wKqkVBk97Gtb9PfzWgH7c5mQ1YvfoxRsMpMhOo0GFoCq5c3OTZN2+z0n6dYHeX2XzA/ftnSP86\nH79xg0oe4svvs3//KauZoR+MsN0uw5Mpm+sRQmzws3/jHP/jr77NOx8WBKGgFynKGbi+4Sw1rJ6/\nRtCJabc9utSMD56TdSJcv0VZChZ5RlktGA5P8X2fIIhIkojByYB20OYT119lcHLKyeAM11Us5Ji8\nSKl1heMG5JmlqS1B6DKdnJEkDotUcv38azRNhR83pPO7mKLCzd7g2iu7DLMzDo9OUa5kPD9hkk1B\nhNzYvcLf/A//XW4/KPjg/ad8+fWfxIsfsf/kmAvXLrGoNfefPsF3XKqTIcxyeu0u/+Dbv8pZevpD\n8Q5/JDbZ//5XfuWrL332UyyabOlZtQJXhbTjFZQTMhw8R5ghup7iqZC98y9RViHSbdOKfRpySlsS\ntdaJ2pdA9pa/pGaZl3eUoSwLgqiD68WkRYkfR5iqpKo0jnSoqwJdlzT1gqYekmeHJJGFso3VJYZT\ntD0hCEp8tYIwBVKAQFA1lnRRkqY5VVVTNRq/1cVYg6McpICqybG6pkyn1LUmnafLpJIFJSTPD5+w\nv/+Aixe28Lw+OOHywoQGz3dpmmXBnxYFk/EZ7U7CLJ0zr0v8Vo/zV24QrW7zm3/yv9KqKtqm4Qkj\n7nlTlKnZ8TqMm5KajJlu6GmfJ2HIRm+Pnc9+gVc+/5dxG5/acbGhR7bdxrmywdbVPSpREwFG+uiq\nWYJ4rKGHYvr0gPf+8JuYdIDXhJgkYihLJpM5q/0WbqI4eD5hoc5Y22wxU6e0+mt84fW/Rqu3wWxy\nj9Vul7jj0NiCq3s7XN49x/PHJbcenlKhMbXBsdDHQ0tLg8WXCoOgFAItBMaRS46vbpbJKmPR2pCa\nCida/lUVQtAUDdSGwAsIvAArJS3hkecLZi7cWNul8RV3T57T0wrf8SiEZZ5X9OMWbgPpYoEXxxRV\njqsUs2lGkiQI61AU9VKDrzPKqsZVHmura4ReSBDEFFlNkTdMFmfM0yXXwXFdpIIwDJbWpaYh8EOq\nsqKpa9bX17BYZpOUsixp7IzazBkehazvKqw4oSyHbK1u0b3QJW7vsX86RIgek8lTdOET9wIyrTi/\ntoVYPMEWr2DVJSazU2T3e4z1h+xtdplRsNF8irW1n+OCSvgv/4NN/uk7bTr+AY5nyZ02tvmAK5tb\n7I8qZmmDawS2VaNcF+PlrF7YJbKGQXrIYJ4ybW3Q8kPKqibNMrR1CMIerh+hfAcrCo5PTqnKkmyx\n4OjwiPF8hFAGqwzD6ZxJOl0yEeoKkLRaferSEkZtuq02l3auMZukrK73UL5lOjtie2OTf/Wn/326\n6wlHw30eHzylv9Li6gu7SCUAh8lixOlxgsThMy/cYLczJG+6eCuK9fY6B3cf8RNf+iLbl3doKNjs\nt2mE5cOTO3zlh9Rk/590fP2/dmbjU+5++5+xvXuZMLwMpc80W1DlHqAw5hRrh5yenJC091hU0Nlc\nQ3kCW9Z4zhrWa9PqbqJUn2xRI02FcCqsXl5ALTLY2t4iy0vavQQvcJkNhggsMnZQbghWo6RGuQIv\nCJD1Gk2TUtkTpF8ThhGNhWl2QF37OCpAqhgRt4i9Pm6dYXWFEBpdlUvwszVICU1jUY6LH3UI46W+\nZ5qGOFYUeYqjfM6dv4jGpc5TqCVOaHG8BFdF1LrC1AWz6TP67TWqdIZoXNywjQpCsqbktZ/6MY7v\nfonv/frvc6GJWTeWReLyYZnxoTZEl6+y5Xr8+KtvsL53iTfPn2evc57VVz5Gc7JArP8DotUegfSo\nohBHOFjtUVYl4UoHrTXOSpuGmvG7D3BX+hSiYR5q4o2E7c4l3KTm6eQufiXJpxNU6HLj5c9wsv8U\nf2UFEWUMRs+4q94hvvwJuLXLdDDEmU/4zKVLlJnge2/d496zCZaEokhpIsVuBkiBNIIayxxNUltS\nT2HQyzCCs6z+NsYuq2kM4CsQAqsNurZLhoEXIqX8SGOt2HBizHoLYXLk8ZQZGaLlwbwhSFpQlngC\nXKlYDKd4oYtxJFpr1lb65GmG1hpjJK7yl7q+UGDB931836fOa/KsJM+yZTFn3CLLCsqmIisLeit9\n+v0+1lqkv0oYhBjPkOUp9x8c0jQGQYgrHIQc0u20qZyUw8N7FIshid/h+YlP1VujvfMKxmQoExPL\nPrUQpPMabSzDw4aViy/y8Du3qPUDHtcHxHIDMX3Ig1sFnb0e81JyNLnNX/nJinb/HYT3OY73u8Rr\nJ7RXhzh2k53NNS6cr9mfTJh4mmxe8lJ3HW9tjXy6IHE91t0W40qjtUOWW4TrkXRjur1trA2Yz0bs\n73/AcHoElUOgIvq9LokbMZtPQBqk0/Di1c9SmxzHN5wNDpnP51RVxbXLV6lrjawse5tX2O1Lzoan\n1I1DN3yBzZULDOb3ufPofY6On7K3tYVnJGICf+7a5ygzn852RHlaU52NOClOmQOrL13khdc/x+3v\nPuLKq69yNp9xMjrg9OgQfTbl6YN9bPOnYrL/xfmRGLI6r7n3R+9y0H7Cys5dkv4qbhwTJAlCKuKw\n4uDZIVeu32Tj/HWOZ1POr25QlBZpNVGyg2ddrOOQmwajMlxHUBY1Svoo11/2UeV2ORQdlh9aJ6Zp\naurGoI0AFIgGa0Ebl7zJqdQArIvDGrWJwGqsO0DnFqUUnhfh+RHKccAk6HqBqTKqbIF0JMIRGBTS\nCVBBhBs6+MoljCX5IsORYEuNH62glOT45AiPGVEsWSwatndXqVJNu+0zn+7TCQJmZ6cosaDd3kCn\nY0yVUeiSdrdD95Uvkrw1RjkBn3vhErlsGLcDdq6/zAY9/L1NOq7LSFVMR2O+/t4f8YWdhPffeof5\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F7UXN6ahkrsCpBW7QwwqPOPEZjfbJFxOUG2BlRDnNkMKgPEEU9ZAywpqIfvscsb/OZDLE\nd2oGpwfMphMQMC9nTMshdTMiqwypHjCf1jy994jdi29y4ZUf41z3HOe2XyXphByf3mE2niCES7K6\nyed+8udoC5/ReEo3SMjz7zObn/DTX/73WF+/SFkZXHymZcNrb3ySajLn4OQ5Z/mYeTXlF/6L//TP\n0CarDY6UBJ4EKcEu462uBlFX5HlOu9Pl6XDMl//lv8qVC+dJqznFrOLOvfdZX91jY92QLaY8uHOb\nOtthtbfOlcsvLjdDJdFItBG4yiPprNLqhFRVheM4uK5C2wbd1BizdAM4joNhgSMhz2dMJkM8X5Ik\nHa5feZH5rEDLmqox2FIQOZowgO3z5wjDNZABTbXMuidJzGR2jJCK4WyGlyR4wTqRdVBuQJoPccIY\nqyxHowc01RDM8n27nlpe4gQ1i+yQte6Po7ya6eI5s+wM15EshYg+cbiJrSsca9lY7VEZiREBd24/\nopOsEHsRrvGo8hZWR1y4uMXR4V1abUW7Z3hyNKbX36HTXmVSLpiPx+xc2uIv/hs/w/m91xDSIVrz\nmd0qaIYZvc/u8aoWPDh5ir/aJjwqOTs+YnV3hb5wOJgMmZUDFo+esvfSOtkBNPMByfouSlsYw43N\nDcrLHdzb+1RPclQjkXVJuNXjrJ4QCEFaV2htCD/qCluRinlZoGSDZFkLU2KRxmKFXUIXhMBKQ+WA\naQyNqaFZauFCOXjSQbiCpiqJHY+ZqcmlwFOKPM8JXZdjp2S93YMGBvMJraTHfjrgXKvH8/0DHKGQ\nykE0llAE5LbGFR7KDclryWrsEVlNNm8wTYSyXRLj4MiUIR2O02P8FUudzdltb0Jeocqc0WBG0u/i\n1w2uI0iCmO12n0leUmJRWgKGIPLYPLfNcHKE5zQUZc6iPCWnorIua+E6RaFw64JAaVSZknoPmU6n\nbPptNvvnuNsYvCrD1R+SYFiMatyVBOO4RK2aihmz8YCgCUi8y8TzKQ87FxgsGi6IMRv9Nq2uRa3A\nSnOek/F92ni8efUGybUV3v6jY+48sDxpTpnOJrjRKn4QkRclvW4fRwSIRuB4zpIJbGqKoqTbW8Ep\nCtDQYIijhNPBM9789GuEccSffPPbIEC5luHiCVk5IUn7vPb511GEeJ7Pyy+8zLee3gENl164zsHj\n+xhH4YYRFy9e4N69W7SKgnObOzwffEj6LOVTL36Jb//z32ZeRHQ2tkg8H1yXyXTGtesv8O33v8Pj\ng2c0fv1Dz7cfjU32F3/xq72kTVlV5FmBqBsCa/CNJRZLyEtalDQo+pubpEXGdHLK00e3effDW3z6\nU59hfW2NZ08ekC0mnD93jtFkjufGOG6DFQbrREgVIKXCGg1i6WEVUmLRaN1gTIMUBiktjgSpCqp6\nTllOcWgYjyc0taDT7iGUQrogHYfAC/B9yWC4j3AcwrhL4K3jyYCqni7lAmWJYg9DjOdtonVE2TQ4\nvmSezwmimMl8jvTHSB3S765ibM1sWiJEl9lshuNp1tdfYDYf0jQVoR+jlIcULlI5lFWFNCUYjVIC\n5UhwJE1jGZ2OCN2Y2fAp+Iqo7WNEQbvr4LgFZT1DxVsEHihZYJqGugBPtUjWV8nDBm1i0qePCNM5\nra0Nrn7pdd77jd/DKJ9KStTEMqtLlDJQldCK2X/6jGp8xsbrr3F//x4v3nyVbuzzG//7P+FTX3yT\nOnSxfovzq+d49MFtAsdhupgwNxnaE/hhQLKyAkqwWGRY3RCjcM2SB+A5wXLQySVD1lEOjiNxlcL3\nPLSuoWkQjcHV4CLxrIO0FqEtVcunLCsiren6CQObM6srdj963jU3Jp3OqfsJP5gOuHb1CmK0wJ8V\nlJ67bMVtGmgg9EKU4+MFIY0jaCPp1y7rbpe230XUBr8p6UrBVHUY1if4XVALS7vy0EWK0g3d7gqP\n9+9T5TPWuiuUaUmTaULPxZOGnaCzrD6KwG0JIGdrtc1oNmOymKIrQ2xDumWAORqzSghVRRRHeO0K\n12/TDOf4cQ+RfAzTGCKh2Oo6tDsRaT6h1Q9IAsl2v8vxg8dc3txl5DjMwjbzqIstR6w4M1+a8wAA\nIABJREFUhoux4Eq04HQ8JR05bOz4VOMpSr/E08PH3FwNubbSZev6BoGnGI7nLCm6ltFwiJIORVmS\nhMFHTGdoakESd8EK6qqhsoJSZxgyxtMTzsZnCGcJBdLGskhLPvXaG7x+8zP82j/5R5xM93n27Hu8\n/9Y/J7AwHudUpmH/YB9BwGw84O6d95FGcfPKFZ4d3+X2g3f5cz/2U9y7u8+5i5vs7F5hURvSwYzV\n9Q0aDE1Z8HxwxOb5bab5iL/1Cz//Q22yf2ol+P8fxxrDIk2p6xrPkbQCn0Q6qKrC0Q1hHCEdD+X5\n/MHv/S5f+83/jfsffpcHt77LztYmqytdZqMz5tMJO5s7hGFEp90jiiLqsmI0GJPNC6RxEFbiKIEx\nDZYGhEYIgxQ1UtYICrA5Ri8oihlVNsPqiiT28ZRDnqVk2YLQVbgCXAVRGCCBLMs4OTlhls4JXEVZ\nZSBqhANCujzdHxNG20j3GePFB0hvynD6lOm0pt9+g1ZwA1OuEUTnMfIC/bVPsn3+M1y7+VkuvHCB\nrB6TT+cEStBUQ4aDe2TpIUUxoS40vc4OJ8+fYUzG6ekz8nyI7xrOn1tnno74k298neODu3zvva/T\nWYOwXWGoaCoP16zjacM8HTFZHOO4DcqNEeE6qtunchZIaSm/fwuR51z8q1/mt3/rN0mLFOfJBFUb\nLrx4lY7v4xeauSORQcLLu1e5fPEKz58/Zzh5ynvvfp+3v/cNti60KeqGunTpHApu7L3EwoJO51yz\nHmuDOQynzKuC9aRDf2UNWiEZBoSk7UfEbkChlwb1qvrosamptaYxZglk13rJkZUC6zpoz6F0BZkj\nSB0YLFJcIdkVbdpZg1dbpCs5FjXZZsKgzhDK4Xh0Rui7BFIxHY6WkpKusNS0Wi0aXeMIiRv4tHpd\n6nIC0/+Tujd7tuw6zPt+a+157zPfc+exb0/oRgNoAARAEARHUaIohqLKUkpSHFlxRXpyypVSElFS\nZNGSK0+x4jiJk7ITWbRLVjQ4lihKFiVCAmfMQAPobvR0e7jzuWc+Z897r5WHA/mZj9R/cKrOOd/a\n+1vf9/sU9tCnltepIjGJyaIEQ1XxlYQy5eT4CF956DzB8WxULjkZjbBdH8tz6I2HKNMkRzIYjhn1\nupRHPTwNrm+zd7zLxz/xHFXPASXIQ5NwWoAoKIshW4t1nnrkIVwCTg4y5ANJZkguPP0kRTrGMCbM\nLbVYXtmgtbyEtFJqtQzyKXV7jltXDlhf3sL2CsxlOKnWmR5ep2lLojzGN0YcnIypZgGGV9Dp1jna\nTTm9vcnP/sIv0MtHCNNjo2bx+U89z7mtFU6OjllaXAWlcX0HjIxSKtI8pVAFruu+nztWFIUiySNK\nnTOJp3T6EybTmH5vSuC3+fn/+h/yY3/nZ7CAjzzzDE89uU1neJvjw3tstddQ5JimZGFuiXp1njRR\n1OtNtjc3ObO2xItXvs4f/NW/4QMf/yRnnriICHy++Z2bKCunUWvykQ9/lKtXr9LrdbEdk+eff46t\nrS2Gw9H3rG/fF3bB31R7fcdFKI1VlJh6tiCQFjBMFakyiHTOhQvnWN5aJdMpSpmM+iccHTxgvr3M\n2bNnCWoB3W4Xz/O4/t47zNeblAracz46zxmFfYSMqFQgz0os+/1RPZ2iywylcjQlShWkGbi2BaIk\nSTKazTqWk5LnBck0RckUkef0Jxk6DTGlxWA4xD7osbZWkBcxwsyIY4FluqyunuP113fYXhV4lstk\nkJAWMWsrWwx6fa68+Q2k2cMQCywtbnJ8vM/CwgqRaZEzhTKl272K0imIlDJPiZKMRt1CGDF3d94g\nL1KKIuP27ZsYtsfFR2yarTXOntuge3TI3u4xP/BT/wVFZjEaJEgFK4sbUBjk6auYtofrL8wu6q0c\n2/Zwszk80+fW9T2sKORgcELy+puonR6trXXU7RCmEb3ePlmWYNVdvFoNYVRxlWbQiZjceIsf+tjT\n3AzvkdfaxLLHYrTK5K3vsj1qcJhdJ7YMrIZDfzhm2amzYjgMeyGHwzuoqkuaZWSAWcT4erYjkGHM\nml+UFFoj9KyFpzRIXYJpzKbIS4USColAGBLDMNDSoDlImHN8dtMxDcPlUX+RB5NjopUaH72v8FRA\nj4IDpmw9+gi3r75H03VwtaTMQzIFG4ttHMtFChNtWPTGfaqFT92s4hGQxQWJLjAsD3yTwvWw4oi2\nU2EQSgK3QndwG19WcKhQDRxcx8K0TZI4JfDrDAcjkjDClhpha9IspncyZHVzkX63R1GUWNJk3qhQ\ntGuIusNAKqrz8+zrnNjQqLCgFi1wx9tnNwmp2CU19rhzuIfpNpmz6vQmR5zfXKBheTSMKkfDPq2l\ngNvHd5m4c4jwLmdX13jvzg7PnXdorFYYyyW8okfpN6gsTFk5N8+ff+df8Pkn/09+7pd+mRf/w4v0\njwdceeV1nn/mGQ5OQnYPjkBajMYDKoFDnMaYpiQrUjzXwHYtsiJ9v7EJg2H/P7X28tTEMWtU/VUq\n7jKf+8HP8+EnN/nSv/i/+Km/+xPc/T9ucHK/j+sW/PFfv8jP/f0vEOc5ooTtzW3On1qkc/cqyWjM\ng71r/NzP/z1WV7b56l98mQe7O6wub3Ow28GwKlw5eoNnnnycmw/usFCzGedTbt68iWVZ37O+fV/Y\nBb/+j//xF00hsW0blWSoaYKDnE12CEWsQLkeq6c2WV5bYjjq0h30QZjkRclJ94T1rW3eu7PDYDBh\nGsa8/PK3mY4HbCyvk2YphmWCUNiWQKuUMk/wXUGWxEzGPaJoiqTEMgFKsjTFsSsz2AUzKpA0TKRh\nIQ2Lql3Fdixcz6fqNQk8H8s0GA1jPH+e1vwqpZJokTI/t4KQAUUpqNVa1NwQy1SkiUFWaAa9FN9q\n0G4H7N3fIxrcYvf+K+zuvsrR/i10UnD84ICabZKqAbpMiMMUkyrtxgqqKOj1H1CWPebmtxDCIopn\nh0SJSas9z72dOyTRmKefeYTq3DZaCXxX4PsZUXyPcXgPnwxlmhRlwXhwSBJNKZRBGhe4QM23Obhz\nFyeo4PZLjCjnreM9Vs6cYXhwSHing7nZJq+ZhEcjRuOU6XDE9N4dcqGIjSHDIkc2PGLdYdN5nCo+\nk8Ua3sOrvHXlVaqDCfOGzTCPOchjYgOMcka/SlSB6Viz9Yli5seWrguWgZICbYiZpy8EQs4sAy9O\nsbISuxR4pcDDwFYCWwmcUrBk1egmU3LPJskSzEKxvXma/ZMOr+shd62cuypibnOL484J09EYA0FS\npGALMADTRioIh1NcJyAKQ85WH8IqCqQsUIbAqcyq0iUKZWSzWXGnSmDWsO0KuVlSFBrbdHHLDFkY\nBGaN8TBE6Zx2s8pys40vHFKvoHANvIaPYRnM1er0Oyfc2XlAUBQstVZwRRVynzDKuPLW26gkIzAt\nznsbvJO9i+MvU05TBrsDXKNCN0moNyS+42IrE+JZw9CwBIe9IV5jmYPdiPZSg2sHBzx7usXz59cJ\n0wSS+7S3NyhbI9KoiuVWEBwz7IHjtbjw0GXuv3ef5z72Q+we9ZhfWuGb3/oGtZpPqVLyIiWJSzzf\nI0pCwKDWrDGZjsjKiDAd4/gmZakI/AY/+tmf5LM/8hNI7fDgzh63d17mm9/4Ex5/6gIvv/VtXnr1\nTWrWGSr2Frc673Dx/GNUg4C7d27TqLV48/WXWVtsQS5Q9Lny2ru8+PUv41CnHsxTZEP6nYjMzCnD\nkI3TG4yjEXk2pbHQ5PrOLU4GR/zS//hLf3soXL/x67/+xTTPMKUkkA6eljiWQyYUiQHKsHDqdc4/\ncoGsTIiiECeoIawAKTOyvGDvqINXaSNtnxs3b3HwYAeDhKvXr6NlwdxSi3E4QVomaZoSJymmjomm\nY6LJhLLIcWwLz3URYpZ7rFQbpGmMRuG6HnGqKZWBaXroQs7EFwvTqGBZHuNJiBYeq+uXqM+tUqm0\niPOQIrOQwkEIhWkkmFmFQmnKskWjuUqhYTzpsX/4OsPBEF0WpGXCc89/jG9++xVs1+dTn/w72E4T\nx5+n1GCaJUImDEfHxHFCu73O9tZj1OZWqVQXOHfuMbbPXKJSb9FoNrn2zrvkaczWQw3iHBxHIeWA\nPN1nPN3Fr2oscwFsnzQJUeMulhYIs4owLSyzROcR9w6PWTz3ELsvv8Pac8/RPneO4f4uy3OLEJfc\nS0ekZAS5oEBQrRocHt1k7tRlvvP2n7O8+hjd0QGFOGGhcRnPW6RyeZ24ZvDii1+jJSV24HBilhSu\ng1XOmA2F/pvLq9miRKlL+Bt2gVZkZUGpSv4GdySZbX05WTEDymhBYNg4WuKVAjufiWxmCHTFIZ5O\nsG0bOVdlt3PInHAYFxn9skDON/GqdfZ3d2kFVUylscqcrISLj1xmfm2TQPpk4xzXcHAMj2WaDKZ9\nEiMjNUos2yWNEtI4IlMJ1eoiVirQeYGoWayfexzSAhjT8CykdkkTibQlftsmF1MODw6wpIfeskl0\nhmVYWAIOD/eot+qkYYyd+zRVjaWsRjO3sQtNGecs1NrUzRphOsWuCuKRgRU4VLJl0mNJApTjDq2g\ngmsLOv0OzaVlDvsDbu8fYVfnaLUaXN3b4aGtBufkhMPdE+rVOnNqRBwZmNqgknhkmUt00KclF6it\nPcW4OCKgirAdStNgOB4Bipu33sN9/xLMkBaKgixPKZQCFKNwQKETEAo0TCch585eIvBa3L5xF9ey\nKYuEmm8xmSr++M//lM//+N/l29/8LvVqQHe6S5ZElCH0D4/o905QWjKNh2hR0O1P0bpkv3sXr2mw\nuPQoFy89xf2jGzi1gLgMSaYTSlEyzSaMhj3euvEOoyxlGo34pV/53nKy3xci+0//6f/8Rc91oFRU\nTBezgLLImRQZuS2pN9pc/MCThFnMcWcfz/fR0ibJwZQxaVFy7tJlPvapzzK/ssFCu03n4C46D8nK\nDGWW3L5/l+Nun/bcAqoUDEZTwpMHUCo838f3PBzHQUiBVmJGO5QzsIzWGkNaGGYF0/QolYElHdxK\nhbwUDAYxSVwwjSNWN87z0IWn0MIly2ZIwzyXaCUoixDFmDJN0WYCRgthVrGdCkEQcHSwy8PnPoEM\n2px+6INcuPQZTG+RVORsbj9GVtZJ8h6VIEAaJkUO1coci0vreEGNoizICdDaIk0kQrjUai3SNOPc\nudOsriyzf3yX9uIKhpHjGDnT8YhWc51G7RSdpENQ38aMfXS/gyh6KF+BU2EyKbHEhMCdI5urI5Wg\n/tzTHB11sK/dZZrmdMMQWXew0xgEhFHI4PAeWT2mMV4gGo2wfYuFtU3G6QGb4sO0hSBo++x29rl1\n4ypt0yIejSm0Jo9CtnSFfSJsy8LBwNSCUhdIYWI5FmGWUyr1/h9zBuM2EFhyVl9OAkluSTJjBpEu\nJbOIl2EgLBOpYTrt0wxquI0qB4Pu7EdZKmrSA8+hWWsy2TlgHgfXNImKhBJNu7bAQ5ce5eBkSDyI\nsQsTnWpsw8GJYxIBZmOOUTShiHuUyRDHdDBkldzw8UuJFBlyMSAu2kS9Y4Q6IiVjHJVoGaArFr3i\nkNZaHWFYDEcpt8sdtNa40sR3LEpdsraxykKryXjXQY9K6rbHfvc+d0cPkK5LVdWpJ016fo4YT3Gl\nzYl4QHIiWXV95tdt6ClaDZNJfkgsc46HOcfdglJKai2XSfeQc8sBC5M94kGC70qG+ZRj5uH+fcq9\nBG8KsUxYCQqO71/hyc/9D7x09Rotx6Az6vHUB5/hxp1bzLfbHB106PWHVKoNbMtiPBkipUAYgtF4\nRFZEaFmgytlKretVmW+vUKs2ybOcyegErWLu3X6HR594jsvPPInnLXHm1FleefWvSeQxjvKZqyxg\nlIqTk2NqrTZKltzb2+GZZz/K/MoZ8mCXYH6V2txp7ux1efv228ROn4997JPcfPcqi2tLNBdb3L9/\nm6nKOP/oY+zt7vCLX/jeIlzfFyL7z/63//2Lz3ziQxzfvMa8mgXDU89hmsd4ecnpp54jmyT09nep\nr23zxI/9LNQW6HX2ZvXUQmEtbjK/dBZbldj1Koa22dl5Fy+oUyQJllZEoz6GoRFWzl7nLrffu0sp\nBfWGi+OVCCNGmgZKe0jZIDAjwiihMCXSlVBG5ElBu7GMs3ARjDrSqOL4AbVaDdOyWZqfI5x20CoF\nZWAZPiVj0nxMkUtUOWUYO/hNh27nCEdVqbgGo/4ucZyzuFKhubBGtbVJkpsstRpcffkbuGJId7jH\n+vwjHB0fYpg28/MbCGmRi5jSKMEJ0EVMnk+x7Zw865NEPdJ4jO04NNrzOF4bqWukWY7rOyhREvgV\nKA1G49vMz12kH3mkVoamT9UyKFOJDBqkkz5qro53nLH13LPcfuctvLfvUC0N9NGE4yCjudAgPQkZ\nZxn3D+7S3btBcXKMubpOzWpz/omPMmFMdrjL+c0Pc9TfxVhZJjw+4P6VV0mnI6Q0sIQBwFBOMaSN\nfH8TLdOa/H1rwCgVSqtZXVMAcubvz/zXEulayLKErMDKwCsFbikwNGgESmuSPATPwalXGPV6VISF\noSArNZGC6uI8YhxiFyWFDUahqGOSAkHN4PqtA7phH1tbqHiMTicsBMscpQPy0sKUGVESc/bhH2BU\nwCg/wbAMTMMmlQZG1SMeHRAIQTgZo02DNE4wDYe8mJBEBaZa4nDvHhtnm+SlRX/aJ1AtNrYW+eX/\n/h+we3UfXMGaXeP60XusTXwGSrDcXuUJ7xy2WMSSJjkdEnOKm3tYpU2cSu7nOxRzGZY2qBQT9u2E\nfFIS2zWm/ZyyyAl8WHQNbBvqVkkxSShzi3FakOmUqm+Bv0JYTEmPMrxhTDIImVYXOFI+5z7w0xzF\n1zGFDeOcZy8/w0uvXMNtrTOKwfOqJKqcRSq1fn99WGMaNnk6Gz/N8xLbclheWmUwGDMY9ClFgbZK\nzCqMph0mwzG2EMzNLfHVF79CrdWgmNh4lYDG3BKj/IikLNESJsldqv46yaTP8uop3r25Q6u9wtap\nLWrVGXjq4bUf4dW3vsI/+G9/kXudhFtHbzHpHFJONYfDPX7ll3/lb4/I/uZv/uYXG1j07+3hVxqk\nGAymU4RlIqo2TqtGXKZgKUZKUVs7xflHHqfIcrqHu+gsI3GqnN5+CMcQ5AIC2+Xo+D7JqIvn2MTT\nIXNzVb7+za9hGAZraxs88/TTnD17nsXFVQQmaapRSqDUbLqkKDUYNkIJRJaTRJJaa5vq3BmUNP9T\nxtbzfBzbmk19myZRFNM7uYWkREiB61RRJagipci7oBJITQa9fbTqs/ugy73d2/h1CdSY9gfYpkVQ\ns0iSI25cf4kkm5DmJpvbTe7dusp83UEx4WQ8ZWHxYTyrQtx7QDHep4hG5OEEA0E4jQnDlDTJsSwb\nrRVZls1gKqJA6RBDmBjSJ56M8L02qkyROsZxHcJQMk001WaNKBojC5M4Lhgc9bnxwnfo3dll89Q2\n/mPnmewecv/2Ld7r7nH92jXCvSPCYspDn3yas+ZDbH/wLO89eIeD2+9RiJILlz+BV3MIheCVb/4l\n4eFt2rbGKQqyPMe2q0T5bK5ZaBB65rlqwSwxoMGRJsI0wJTYjoPvetiWObvg0mC9P6poKoGBwJhN\nDKM0KKUImhUqns+008cRBkVRkgtJTMni5gbT6QgdpdQMC1dLHCnIDUE/D1FRhqZC1a/jld7MRjBM\ngqDNGivUXIdEh7Q22hSWoD8coEJNXTawrTplqcnLiPG0yzRmdqGlxiilKXKJZxlUDZsLG+eIwhHH\n40PcuQbjcoxC0PBK7h3vMKm59DrHiMV1JvsniMmQO8V9rhQ3uJ8+wFcpa0Gda4NjlNvElFXM2KBu\nuARzVcqmw2QaMulGTBybRxunuT2d4mQGeZnzyKktiqM+DTEl8BpkhoM2BVXLRo8zsjDCDySVlU2m\n6gRbmeRGlf5JzM7OOxxkE3zRRmiTSs2ivebz+LMXeOX11zk6nJJFAbafMx72QBUYpoFtOyRxjO97\n5FmOlAbr61s06m0M4VF7Pz3k+wGSCoVKOT7Z4fXXXuLiQ49RrbV4+aXvYDoJcdGl0+0QJjnLq+e5\nfvsdTDOjWTuPawoq9RaJinjzzWt89od/mg88eYHV1To7+/e48NBlWsstXrn2Tb77nSu4eoVpGDHN\n+vzqr/7q3x5od8319EJaUg1qjIqSUJV4jkVreR6jMQsrY0i0UKTConTb/PCP/Qynti/yJ//xt7n7\n+ssE2+f4/H/2k9Rsi15Zsthe5YUX/j13Xvoa7blFCjXh2ecvc/7hi+RlhXptA2WVCC2QShFOe2Tp\nEEOWqPdfRwtjhHqfZG9h0Vw4g79wjlFsYeoUlCKOpzi2pMjeb6wYmqIo6Nx6gVyWNNunEMYaQVAl\njPZIBl2ywmPYPyTPIix7yq07r7Gx8WHmWo9hVxXh8Q1effMmG6cu8dgjj/HNv/oKR7s3gIhasMX2\nqXkuXHqYSe5g16oMBvcJjIQKJk7exKs1GCYFbq1OqjJcTzAaHuA6Es0GcT7BsRdQIiNXR7iOQ8Vd\nJ07GROMRoixRUhLUW6RxwuTkLp6YYtfWAYdb791n9NINaoOYhlcj82xWz11kd/8mneyEfjzk7vXb\nWJ7N2vIcfhTjxY/z9t7/S205Rwen2b70Kc6tbnIiC/p3byK0Ynl5Gcet0mou8If/7rd55ztfpWkp\nJsKbMS20QJompZDEKiXXClcpCmkwpSBDg5hNgUsxswy8ctYCmz0dzQbyLGN2QBpCMJz2CYRFy68S\nhyExGtmsIyou8e4xnrSIXBimEQvawbctwixhXrhEnoOjF1kImpSpIBchsYzQss5c7KLVmMBbQjo1\nDsJdEmFhenXGyQGVos7aRpOd3XdxPJNqfYskH5OXA+xiEdcOIB6y7lcwwgqTwuLA3cc4FRFYm6zO\nNbh//wotHfHU6TNEnmR3OOGhMw+ze7LH7/7OlzltnmOrscLt6U1OVA+sCuuFoJdnjOoeci5ATCcs\nKBsyCUsTWpMWVw9HtAIX0xtwcXuOvfu7mMpnba3F4KjHQrOKV8kxLYUsLcwww9c5bSX4a2eR+xGM\npgXLluSJU4rN5dPcMXNkZZ5q+yz7OwMunV7jI89eYBSO+Jdf+gP+4msvk6dT8jRmZX0N03bZf7CL\nZWjSrEApWJhfwjIrVCttTMOhKAq0LvGbBoaqUvVbWDJmEu6x3n6cDz31Y9hLBn/2ld9la22JTueI\nN668xcHhA5549Gnq9mmkOcDyWoTFCa12wGr7LLdv3kAxYfXcRZYaDh//yI/x6rUdvvrib/HyN75M\nxa5x2O1TFuXfHmj3P/m1X/tiy3OItMIWBqrIMGs+c8uLnOwd4dRWqC0s4tUbDAZDLFNw5twFgvYS\nnc4D7t+4welHHmVz/SyW0CTShFLSHRwy2H0AwmTz3AaPPnGZwSQmqMyhhIu0TfI8JcvGxMkxRTFC\n65Q815SFROsQLXwa86exKivYQRutbVQJvD+CWBYFtjnruVumRVkqiqLAszwGk1ne0fWqlBRIUVBx\n1zDrbTx3m/7gkHt7r+OaTcajnNNnt+kdJ1SqAY3WAq7tMt9s8PUXXuBw9y6tms/Vt7/LY88+Q27P\n4VeXKaMp8eCIZmWJ0bRKL91FVBxkpYJwXZQ0KIq/+Zw2eS4xLAOlDSzbJMtiBBLTdChtiW3Nyg2G\nYYFOiCb3iU52mQ+qTMaCkyKnldt0du6RFykyzhgO+8RFwWDaxSwTbr77LgQOH7h4mZNrNzEdi3Hm\nItQDdBBj1DZYdM6Q6jHjfkgjmEOUFnlhUoqA+wcnXLp0nvvvXUFmEwrDI1MlmVYoPXuSLTWUQiE1\nKEOSv++1IkBoKBWUWmGbJllZkBY5JWI2qGiblFoR5xm6zBGWSY4i0SVeUMFxXaJJRJZFmNogNmcL\nrL5l4boeiSoxS8D3yTMTRwqSsiDSIaEKKbRFJZFIx2CQSZrbpzmYHCFMAxcDO58yt7GJ4cPO/R0M\nUeHM+gVGvZCqOYcvG+RRhir0bA05k3iVeZSpOOjcwqlJjjsPSJOSTz/3GT730R9i+9IF9tlHZwaX\ntx/iorlN+4bN484Wb07usDufIoYjdomRlkfNbWILjzRJcFyFXQtZ00vs7kxo1k6TjQsuza0ShIpO\nUKWgjsrnicMBp5YD6gEIK8auFciqzVQ57MZT/NIm0CbVuQYPBh2G2RLHDElin4N+HzOoUK3VOT64\nz6lT81QqdZ5//rN889vfIk9j4jjC8ly2trcZ9rukcYyQswMyjCakaUy1WsW2bTzfQUpFtzfGrwQ0\nmxVOTro4Rp08ytm58zbd6AGn1x5n5+qQ+doZVhdOc+bUk0z6Ka25nAd790mLjIXFDUaTI66/900e\nPv8hAn+JfniP73z9S5x03qQ53+TTn/4p2tUtvvbCV0EW/No/+uLfnlqt1prcFIRpgUxjNjY2MRfn\n6E5i5pfP8NSP/jjza4uEcUjnj3+PZDIgVhGhrWc3yJbF2sYmQpqzp1BDkmYxvmvjBRW63SM+vP4k\nJRYLy/MI6ZAVU4rERBURQoUgUqSZI6WFFCam4WKIBZRZxw9WUdLG1AZlWuAIk1xlAFiGZuYFaECh\nVUFZljjeIo1mgRQ5Udzh+PiY7c1tMEwKbRMXE6q1FnPhOmUxJktTdBHRqLrEucW1d15ha7PO21eu\n0e9d50PPfpDlxRW++9J3WFx5mubaI+RFlzQ7QqSSoLKE8qEq1pGmgxYWeWGALimyKZYl8D2HcXSM\nZ7dIixDXqGMZNXSZU5QheS6RQiAtGxXHdI93KKI9dJHS6/lIr0m10uTWCy+CpbkbHhMnFl5Q5bV3\nX4I5i+mdHcrOiI2nnmR5eYVXxmNKq2Rn5+tYfh/LM3nYnaeS2SRuyHxaZxxW8IWHk2UcXL8JtmSS\n+pw+dYk3XjpAu7MuUIpCU2CqWYdGaInlWpSqQOczS8E0JYYUZHqWNEilxpAGluEIAkRJAAAgAElE\nQVRiSgOpmQHTFSAkQVCjF47xbQvb8qjNtTk+7DBJJ0g/oB/FmKVNw/RQWUlIgrIsjnXGomFjWDaZ\nmRHqkjCfUOqMmmcQ2hZ5mrKw0CAJu9iGwvEMxr0Brh1wMHoHKxTYgSQrE9678W1UCqfOP4NrzzMa\n7bO9fprpKCTLUgbjlDjMcUsf1Y+59Mx5Tq9vYpYmX3vjW7i7Gd2dCMuPyPI+NWeJY7uEYkCruooc\nRqyWPvd0D3PJJBQ97CSjJgXS8MlTF7Xfpr3YQkVjzi+ewht4PDG/zL/t/XvOOymLpsHqhQZeO0dF\nEaeXtzgcTnhrPyLzz9CtDXhWK6q1IwZRzHtpg4kvcROPcQit9Tqdo10unl9n+/IHefG7bzIajOmf\nKKbjEefPn2UynqMzmNLv94EZPMoPPIoyI0kKDFPjehLTKukPjjk6OmB9+9xsfVibuG6DauBzamWJ\nF1/4K/78td/lf/rl3yaeDtHNJr2TQyrBMpvLT7J3/CKm41KpWpw7e5bW/Hke7L6Kb1ZJM58X/uJ/\noewU/OXeXxLM1XC9FVaWzrKwOEd3cPw969v3hcgiJAIbyxZoV1PfWCU3XaTOefhjn2bj0mPkqsTx\nmjTm1uiOR5iGQNgW40GXiu/RarXINUilSNMcF4M8izk8eEC9abJ9ehkMk3AckjOgUrOIwlk43bUk\nhnQoypyiUGiVIQ0TL1gAwyWLeghVIgyJIiDHY1YMLBFAUSq0ytFKI7XCQJOUCY7boMwHhP0Dhod3\nCWsNpukJpr3Iu++8wEJtjlYwz5Vrr7Kyus5J94BKvc0br77JnZsvs9w+y93bV/nMZz6MpsHJOOYL\n/+RfcfrcM/TGOaIQJEXJ/NYa/WgAto20x4hSInDw7Ors0LAEcaI5HkQUZURezJHlE5R28Ow640kX\naQwQWYPCzCiKgorhsNle4db1e0yiDLPtYUiTndfe5rvf+BajsAOyJGksUqZdpumU6f0p866L32yQ\nTzNeeu1NNh++wMn1a0TlA3xnngunPsBScxPTdFlsOvRGkKcpcT77roJGhfrcErlIMEwPhxrjLKEQ\nMy9UoVFaYWkBAnKtUEpharCFxMWiZEbi0qYEy555s8LAEQa6KGekNzSmbRGqkkqrAaXCthz2Dg8o\ncoVl+hRSkDoGUmlsaRGhibMcxwoofBNL+Jh2hVD0CCmIdYalBb7h4y87ZH2P+focx8eH1ESdcdhn\nonoYwSLD3iEUkqq3hMAmV/ssLJ6iUp1H+FUm+32ikzaNyhJDq4tixOnts2ysPYG/Cf3BIQ9e2UXc\nT9kwLRaMJQJdpVfuk+YW+/uHJPWU6933MFwLh5w7dFmzt4gizSgPWXBqrNe2qDtrRAPB1nKLbsti\nZWpwb3+fv3fqKf6o/wp1o45lKQJrH6ICW9YxApf+tODOgeAkXCEq6oTZIXtV8MIKr3W6RMJmNVok\nZczC1jk8f0w4zfjGX76F7c7x0qt/hG/HrLZXOT46wJQFzz//HL//x3+K63sAOI7JdJogxCw9UpY5\nh4cHlCWMRhMcxyLLFMtnNxl0xyRZzMJChYWlNmfOnONG9y3+5M9+h2Gny3j6HkHTZu/eNT79iZ/n\nzWtd1je2ePSxcyyvtIjiEVsbj7C7O6U0BZNxFy9dxDVr3Hj3Dpef9PjO1Vc47ByA/t4pXN8XIltq\nTRGXKNNi8/IFCt+lezjkgz/4ebae+TB5kpIUJc3WIou1ZcL8BhXhkQ5ijg53sQwxmxMpS0RREmYZ\nrueTTCek0YAf+InPkWUj/EoFaZjk2ZDDo30q/jK29BAECDxUkaPJkFJjOYpE5ASmJJ0eYWQjEjRm\ndYNc1CF3kFLiejZKF5gShCop1CzrZzgFKimxhUPdtxHL8xTxlFF4QBnfpx3YRMO7FIZmc/k0k/SA\n1975Mo8/9uOc2fZYa5/Ds2POb6+jlMJv1FC+JLE8vvS7/5znnvkIzdoiprNErEdIw8LW8ySjE1zL\nxZASXeTE5QTbtZBWSppGYGjKQsxC4FmMby+QRDmFHtKmzUl4gHQko5Fg2B8QjwsW1k8xIcAxJX/4\nL/8NLdPB8m3ycMLEzDicTCiLkM1Y0F5bpPbcRURiYYc5Kp/SaM9xYUMTxU0WvHXy0uTQCFk+mDCw\nlnHiAcIULJ4+RWw4TFyXCxcuce3KdymIKFSJkiZYFgpFWcz2DwwEURpjSZOq4+PbDpYwSLKUSBfg\nWOB7xKMJURRhGA6u7YA00WVOHqdMjRwXxVy1Sr/TwzRMME0KQ2LEJW6jSjwYz4YSKy5WrrCRCNdG\nJwZCSMbphASB1hpPm3jC4fj2q6w0P8jBQZ9TFy8TlZqd1/6YUnQJTyI22k+gCotW7RRpUhLmkpXV\nxzCsLWJnQLXhMe50CHSb1Iq59IFzbG6dZ//uHb79r1/kcHCbhcXTnDEWMMYR9/wpXctgeNLl3ljh\nRib1RZ+s4SMTQRErdGDTaY6Znzqckadp184SpibxMIN0zLW8S2Vsctczeao1R02M2OcBTvkAJzAo\npordu2NEL6W6YlM0DA5Lm9SEJHwAXsleeIag6VFxdrEWYsZml7XqCrfu7GHJfQwNk+EaXrNGc/EU\nFXtMr9PHcSxM0+Tw8BDXdVEljMdjLKl56Pwj9PodRqMeRaEYDAbvv3FKLl68yPLSx2k2NX/y5X/N\nBz/0HFfevkoymHL9+qsUZcGb73wDRxj0jUUWcegMDnn71leoLU04Ohzg3rjBlatvUvPmMQyDxz5w\nkVGScPH8JXbffhfPFjjmPJ1exOLyGX7rt17gC1/4r75nffu+EFlQSK1YPn0Kx68y2jvk0rPP89gP\n/RDHBylHB8ecXV0jFx1yv6SwKiSuSZDG5McDJlJjUNDOU05SKExBMR3jNOb4h//oF6nWKxSiQNuC\nIh0iypClWh2cVaSeAWMUIcIOKVWMED5JLnGdgijsUxV1Ut3HD9qk8RhHVshQmLaiLGOyvMSQPkUx\nA1rYliBRkBsppqGYdE0MFSDoo/IqfnWOq1deJJ6+xOrqMuPMI9cLfOIj/yWOGXD35DaRHlNal0gN\nSVA9xmSIndb5s//7N1jcXKVMlnnhu1/FtSR+BRbWtokcTWCDNkq0lOQChHTJS4FQHo60MYNNpscv\n45guZVxDugmOlZKMCk7ULrV5k+H4kPHeEeFBh9XVVZKySn3jSf7df/ffUEy6TGoeo26fRq3OJInI\nR0NWg3ny+YjO/hHLK+dZ+9TjTK/1eedLf8XZJ7a5SUyrGuAHNmkZ4qiQIvVZqkzpNW3MxMMRAZN4\nyMIc/NE//w0Or77FvAwIdUSqclAlspgVC2wMpOtQSTQV5VIVFmkW0s8S7EYdV/qMhyNGozG+7aPs\nKgNVUqQRPiValTz9oUf51hs3sAOHo04HyxaMVAplgZNpYlNgDGf8Ws/ycPAoLE1Mgcw0oexj2Aq7\ncElVSFFqWs0NKCyGRkSDCN8U7OzeZWXlFE+fewoVfxy/cppJ7zqZHlIMdkizmPX1D1A1moy779C7\n/jpBtUX1bJvaSo4ztei/dpUbf/ANRGrz8Nppnn7qg+x2donzCVesmCkPWDGXcc49h3NyQBFGVCOb\nwFvhjWyXU0vruOMOp/Y2GDtgehaj7gP2i4S6dPDjhGWjjRIl6dEJXzj/k/xa9y9Zn5pU/A8x4g5O\nQ2M7mkEeUuwo3IZkZa5NmkdUWw7dUNBYhjA5wKk5DI/7NNtz1ObPUkt3yLMlJuMuO3dfRN41aNQq\nzJ05hfIlvTTGsn2ySDAenWB5ig8+9zSPnHuM1999nSh1CUMbrXN0WVKWGe35eaZhj9u3/4w//Y+3\n2FjZZqO9yfGd15hGE5ZWTqEMh0aljikNosmUweGEj33sR/iDr/weC0tLpNk9bn9LcOGhy5iGy3A8\n4Ey0ReBWeeaTv8DZR4c4XoZhlHSPh8ThmH/1F79Hnkbfs7p9X4isQuAvLrLYbnMyGnH6Ax/i1NOf\nIB4nXP3Wn3IySjm9vEKW5GTTKdV6A6fSZNLrEp7s0rp4CaexyOA4pR+Nia2EoGLx/NPnaHgNxtMJ\ntWqFolBQTjDdKlrHFMUUXcx8OsuwEMKnjDRaerhuC10aaBWjTdDGjDOr1BTyE7zmNkUZU+ry/Zvr\nAiHeX6bNEixSkqRkRMzCqYcZdHbJ04hJv8vYFUjRoF67zGRokxYuH/roJ/A8j37/AUGzTTqYYEXX\n8colzmz/IKbls797jaee/Rwf/eSPkhSa1dWUN17+U85sb1JMlzGLmDDNsSwL0/YwpDWjdJnGrK8v\nBek0QmdqFjdzIc0z7KCCMFOKQcbRwZgimqLGIbnWhH6bM2ce5g+/9Dsc7+/hWjaGhrpfwTUtRFqw\nMr9MJaihYsGCFbDd2iJTmtTK2f7AWd7bv8bu4D4/8DMfYzIqKXcH1FpN2J4jTDSLygMknWmHpYeW\n+NL/87/Sv3uVJavKSVEQGDZaSPplSqFnkG7TcYjiEL/ikWUlKw+dZvfgLlWqHHdj1tfqPHF5ia+/\nco9+muMaFm0tSVRGYkGuIFAVFtsO+3tjJPOk+Qm2hL//sz/NK2+8xZUrNzFME99y8W0fy7LIywJR\nzjK2cQk1z0WKAkukVIIKDb+C6hUs1R7CKhSWqJOXI06O7tNyVynkPifdG5RlhBRQAGGSYbs2aRYx\nGB2ysLBE4DdIJiGvfeclTjp7tKs1arUmda/JSbTP4c4B0jGZhl2UmSEdg73xLYYdk6ojyPt9xq6D\ncipsVxbQnZjIX+dOZcy0nOLmEYOwjxFUaJsmjmkxNiT2MObM3Bl2RE6kDNJIszDf5ji/y8rWZeaz\nAVHWQYUxdaNF52jKKIqxLYWoGvR6PWwPet0uShV4tkVQcXjv+tsYoqBW9fBcE5A05hpMp2PiNKKY\nZJxa26J/3OWzn/lh3MDmeO+I3/vdf8s4jmm2Wlx4+CJ3bt1mMpkwv7BAvV7n5q2bWIbHQ+fPUGQl\nt+6+R3/UoV616XQ6bGyd5eL5i+giZ9QfsHv4AMMCQYEWMYXOmJ/f4KFzj/Hu1SvcuPUW337pa3zq\nB3+CC489RZkZOJUFxqMOjarJSeeIT3/qM1y/+sb3rG/fFyKLNFm+cB4hNJVqi4VHnqG18TAv/cnv\nsfv2X7Fy9jkM2yJRFmHvBLcaUJ1b5Oj2y6h4wMrGOok2CcsMr+7gMsGXEcVkQj8G07LI01l+VUgX\n2zYR2iGOj/HtGihmU8t5gZQelgwwcVDaRskxSgqwbQQ2WiWURUyWLiAMMEyJLkri9wExpikRSmMX\nFmVxSKX+GIN4SmJoNMuc394gWF4jP/co195+Ec9qsrKxzYP9HRYXBIIuSTpgbu4CR+/8PlXHJKVJ\nqFMWF9aYprMmUK/fQesJ9XpKYJWYWU4WjzAaAlSJLhXKdGYLs8qeHQRaI0oTKS0QBlJK4jzD8XyQ\nMWpUkCbQsBoM0gPc+iLVrUe5cuUGb/zR79OoVRCmYBxPKPICyzXxXJ/VlXUO7t5HFyVHx0Pu3dtj\nYTug4jrULp/hSrrD49sfxT5KEKUgq/uYpomlbAgCsmGIMBV+y+T/+w9fYrB/iw2/RhLl4NiINMEy\nBELBXLNGGqaM45CK4zFIEz78oYdp1Ju8u9thr9dhdcXm4sU2t968RT/N8bIMyChdB9+u0MwUeBIq\nku5BgmVIchVh6Bo/+rFT/Oefe4I3rz+YNf9yA+kYGBiQa3RRIFSJUQpKTJKsxK8E5GEKaU4WTikj\nhel7PHzqI+ztvYeUClOaDLuHSJGTiftouw5FgdIGnlvHNAR5NqHiSU6Oj+jqAaaaCdRCe46g5hFP\nYrL4BGlWycOEmuFTEjAKY8pwSk9P8ZcEjeUtnLl54m7IpJwwqQrKLCEhJhtlNBf+f+reO2iS/Lzv\n+/w6d0+eeXPafXffzbd7u3e3F3G8gDsEIhJFCgKKICmCwRYp0zYdZImmLZcoUxRBiqZsUiAJCZRI\ngQBMpMMhX8Llu92727z7vvvum8Pk2Ln75z/mBQ1V2eaxSn/QXTU18z7T83bVVM+3n9/T31Ai9VqM\nZ7L01YTl/iYmNpayjZJVuBxvsLS+w4YzQKm0OZyFuw6OIr1tiALitQ6jCwe4TsjKYEAxI3H1Jl5r\nzwRfaFQqFba21hi4fdx+h36vhmko5PMacRKiqTrZfIatrS3idMgqKuYLFDM5drtbnH/9NerVBlnL\nwU1h/uAhBCm+75OmKfl8lsWlRcqVEqSSZmuXarVOqVAmjFyqzW2a3SYL5ilu3bqFN3ARxOimhusO\nGBkfIZvNUGvUeeDeE7z88sss3rrE+ITJzP5ZdF3FsSM69QBTqTDoekTeDpoa8S9/75+T2ZsZv53t\nbwXIGqZJppihXa0zf/phinOHqa8scuXpb1AZVchrgp7nYhgW/WabsbkJwghWrlzE0DLMHz6O1/XY\nWb8JssZdt0/iNauYegY10yaTL9Hq7uLYOXRDJwg9IKSQLRAFMUEYoWly6N6UJiRJnyTVUOQYqqog\nNSAxSKWJpihIXNxBm1whD6kgDkLSJAJVQAphFNBvuhglHSEk/V6TQlalM0gJ0ElqHQxdQbMDlm+9\nzJXFpzmwsA8lNYmDNnnDptlJ0TWD0akZopEMjeYummcg9Co3lr+HoZtsbV9HSYaO/AopuiHQFImQ\nMaQKSSyQUpBIEEIMM68MA1UzEKqOECoySVBSFSXRMe0KaatDICNSs0h+fAFdy/Lb/+s/Y9LrETsG\ngR+QEBP4EaEZoqCyvbmJ2g1xpoqU5kYQUxncWsCEWeDi1XMcu/8M9mqf6rNvsf/MUXpTFmHQRVF7\nNKvbzCUag9Dlu68/w5srFzHShM0wZMYoEgYuqaqhkDJuODRbw8ylbhySqJKxmQJS1/jGMy/R6MZM\nTjtMzqqce+08NPJUUDDzDr2+iyEliatQlzFHD0zRWr+BxEFoAaoccHJ6gU/+9E/xpS99hddefJ2i\nXUYBTFVHFerQpxcVQ1NRVRU/1YYXtDQl8j3SMMLTe0iZELopY+VZmrUVtvs1coUsfuihKg5aLsWN\nh7PkfK6CqVv0GjU6rU2IBggtomDaZPUcMpVUuz1iBbqDACWN0C2VcaeITHViTNAdHE3FK+tUDlr0\nLm/heQbqSJ5j++dp17dxxwyiQcjCTZsIlUJ5lrTf41p7hU5eZ8NrMarnKXclGUXDdVLG3AwyiXGq\nPp10wNUw5R3T86QLed5Q2zgjoyxerRMrOqNGEUKPQhGiKEDXTHzfZ2dnizO3m4xUCgwGXWzLIAwh\nX8nQ7biEUUKUxhTzBeIkxHVdXnr5PKqmUSpNEwURDz54ikajxrXLV8jnslQqJarVKo5t0e/3URSF\nwGuj6zppGiI0Sd/tYdkGa+vLBJ7Pu975LjqtLrlClr7f49GHP8Daxi22d3Z468I5atUWuZyKFBHN\nZp3Jd4zw/WefwDFnyDg+12+8yokjp9jcWgbRYWt78Lbx7a/1kxVCzAohnhZCXBFCXBZC/MpevSyE\n+I4QYnHvubRXF0KI/00IsSSEuCCEuOOvO4am6TSau2THp5m57T50TePSdz6PHTYwC5NYloWRdUii\nGGKFhdtO4PV6dHbW0Uv70OwCaq/DU1/8LHZQRfSboCooShZFTQjDPuADPr7Xxvd6SJni9yNkItAU\nAUi6gy5u2CFIGvT8NeLEJ2XoaymFQip1FNVB1xwsM0UQ4vZ7RHGIY+kIGeIOurj9HmpOI+cc4ubS\nt9GjiKTn0qi/jlXMMF7Is7F8nY21m7x54UVW1y5SygtIA3RFxdEcMlpMv+bS2wQtENjSJu6mHJyd\np926iuetUs6OkXcOEcUmPb+DlR8qnZASmaSQpEiZIGSKsudKhS4J4xQQREGIEqckbojwBYqWRdXg\nxtotXGuE8cmj/Nvf+T2ixjaeqVHv1Ki16wRRSCxTojjFDwJqjQa5TJZSqcDk0RGSkkToNtffWibp\nueR8ycraKtv9Ov0rl8g88yrxd54m/exfID77GS596bOc//yfkC4vUjItAtPAVU3WwjaqpuBpQ6ml\nEoaMYRL2B4yPj9NyPfI5lWe+e4FWN+a2O8aZP1bizbeahF6RjKMxlRGEXRddNWkGMYGWIsyEEyeP\nkLEyhLJNGCaUdYVf/Pm72W22+Hd/epGsmpJzCliGja6bCFXZS8QVmLqFpVnkNRVLSWl3agzCDrql\nojgKnuqimBFr1UuMTx7Hi7osrp8jSFJivY0f5xgp5RgtlslbWdREJRr0cTSVnGGQpD7ddgO/PyBN\nJaqRB7OA4VTIlqYoTGVI9YCeX6fW3SQSA3Z7a0wfLrL9Qo1D82c489C9CCdhq7XGWrOK29dwLwWo\nhx00M6W626ThJ6TSIBMLTh/Yx8HcDLn5/dizM4gwwAkEF2TA8ohCpOZ577EprvZXUCenmc0dI1mN\n+am5B3jUz7O7tEy/76KoEtsx2NnZIU1TOp0WmYzF5NgMg94wmSLjGORzZWrVFpaZZdCPKGRzjI6P\n8PQL32dy5iDlyn42t3rcff/jmKbJlYuXkGmMbRlUd3cRgKYZWIaN7wVIwDAMOr0uvu/T8wY4mQz1\n9i4f+8mPcvbee9i/cBgnW2Fm+hhJmOH22x5BERm2tlYwzAHlEZ1jR49Sr3X5sz//N1y9dJ1Oe4uv\nfvXfcGhhAkXRWF+t02r3UdW3zy54O6bdMfCrUsrjwL3ALwkhjgP/EPielPIQ8L29vwHeCxzae/wC\n8Ad/3QFkEhPHGvO330t2bJzFV15g9cJrTMzM0vTAzldQbI2lS5fQrQLTBw+ycvktqmur5KYOUR6d\n4sWvfw3aO9iKRBUaIQYDF7otl2a9iaEk+IM2xBE520FNVXJWlkG3R6PRIoolUazg+ZJaq8vq5hZx\nGpKmkMQqaaIQyxih6KDmMUwBaUSaRJi6RpoENBvb9LpNkAn5SgnX65KxHXKmidtusbp4k32zc1x8\n4zl+8x/+D9xaaXDnnR/grjMfxDJnGfQVDKvAZmOZnKWwcVGy9GwVa71Kur5Co3YLGYySBDbBQJA1\nJigXDhD6glSVuFEHfY/nqzAcB6hCQVMUFCSqAIlPEkUkYUQc+sgkJvIj0lDiBQkjE1MMQjh89Ha+\n/qWvcOPVFyjkTPoMKTSWZQGgiWEEigAqpQLVsMu1Ny7RurVGbWOJ9Y0l/LTDA2dup/PKErEXEKox\n/u4Ove1Nrg+22G7f4kaxx1KwiW4nzGdtKp7LXGyTS3VApaX4eEnEIPbIaw4FJ4uhGWzs7nBgYR9X\nL9QQIuaB+44QRn2ee3YFy97Pthux6TZZH3iMZMrIyCHVHQKnzwd+9G5a1W3euL5NxjbI6CH/4Gc/\nyMRogd/8nT8mSDPktQKkQzFAmqakQJQmJKkkTeTQv1VIPK9DGHuYqommafihRz8eEGkD3rj6OtdX\nziNVQbFcoDShUqoUEZpBo1ajWasTDiJMRSP0fULPx3V9UBI8v4emaZiWQ6Jo1DpdvCghlyvRrg+o\ndgf0whTDzBCEEV4quXplkbvefZTwHodnVp7mzkPTqJbC/NnTNFp1pveNsZ332NrdIFVSLg2qbMoB\nShrR2d6hbldJrCpGuYU+W6eWXGZG73BPJeGOqRKtqovT8jn/8lNcvfQ9FpQ+cX0NLWtyUC3C3moq\nTWOiKIJUYFsWK7duMjI6iWmYdNp97rzzbnq9wTDiKQWShH2z+zl//jzdXptGvUvgJ/yDX/mvUU2L\n7337WwhgpFKiXC4Pm4c4IfBCRsrj7Nu3gKLoDAYeqqqj6ga5bAHDdlB1wWc++xn+6T/7DS5euoJl\nFbixuE6nFaIpOebnjqBrJrqh0qjXuX7tJqVChdAfcPjgSTJ2jmpthxs3bpBEBjNTRykWRkn3DIne\nzvbXjguklNvA9t7rnhDiKjANfAh4eG+3zwLPAP/9Xv1P5VCv+7IQoiiEmNz7P/+PWypT5g6fpbTv\nGM3qLZZeeo7yxAIzZ+6gdvUGZnkML4nYXr3FqTvvA8Pk1qXXCD2XI6fupNFtsXjxTaZnJtFyZdZr\nLmP796H6KYlIUGRC6FWRqUoaaNS2+2xtbXH9ylXevHiBkYlJfvITP8PE1AlUVSNJYizbwB0EKDiI\n2EbTUqTwifYoRErQJ5UGqmqiKrBTrdJq1iiVKqiKxrU33iRW+8zN3EksUzw/Yf2mz7/7/X/Fude/\nzZF7znLoyFk++IFP4HVdXLdKrpQlUVxG9geoQZb3ffyXWLuyxmsvfZmZY+MUJsfZrjcYHTuEZWss\nXryIHPTx3R4j0xMEbkypYiATCVJFUXRURUdK9rrbBK9XRwYBUQKGlh3S+xOJkqqYGZWtWsQ9dz3O\n0mtv8dKTX6ZUcFhd3yFnOyipRNUU2MvHIkmJIw9Mk37U5Z75s5RDi5s7S/hml4OjY3z1839CRZ9H\nzyaM6Rl8M+ByOqAWSqxIshgGaBmdxPcoRyEHnRxdL0aaDg1FZUN2MMOUUatAHMT0gohGEqDlTTL2\n8Obe7Wcm6Q42qW4r/Na/+ByWY/Olz3+a7u4aZmLy8pULOJbKbZMlbr/jNNe3N7l8aYPQB0nEf/Wz\nH+TYkQmee2GdtfUuhcwolhghlBGpjEHREaoKugIJoAxDGPvxgI7XQ7VNLM1ESVRCP8FQDVAUSuUy\nirFNySrgBilr6xcw9RmschnDMBGhikAhjmO8wEXXBbZdoNbdQJMGiYB6t02opthZh7AfcHPxEpXy\nDNmxIr1OFyPqE0QDTpy9h7q/y41Cg97Vaxw7UEKfsRkpz3Lj5Ssk6i6tIzb1SzXKCDpRB2kHSOni\nZS1cL+ZAFawcqGHIEStD/VCRNVPwdHuJKVUniSW72TI3lzaQUYxa0pm99wwvPvEkcUknl3UQImVt\n/RZRlMV1fSani6ysLFMsjDM+OUWruYPjWLRaDcYmJum7PXRDYXZsji9/5wky2SxTE6OcveMenn/+\nO7zx1mvkHYNet0vOybCxcmvYxSo6mqVTKZZZXF0mTVN0Uxte6FwXJZNhc8qRf/EAACAASURBVHsD\nmQqKxQqKqlEol/CjEN/3OXhogTRNOX7sNtbXbjE/N4OQEUITXLx4kTSJWVtfZGu7imnCWxfe4I7b\n30WmYBCnEvk3CJX5G81khRD7gTPAK8D4DwHnDjC+93oaWP+hj23s1f5fQVZRVPYdvpvYyrP0+lPY\nscdtD32Q6Ttup9XtkR0Zp97r0ms12P/Qe7mxfJOd1WXKhSKHjh3hm69+B8/tM7dwBiU/hmpbpJqO\nKQcEqYtlpDSbq3Q7fa5fW+balUX6/QGG7jA6VuShhx/m1Km7GbgaYCHQSGWCpm+iKSYiNVAIiYVP\nnIZ4oYYqXdLYxDA0/NSn3WqSyphMJoPb89CTHjMz++i6PoZeQc9MMzu7nyc/96d89Od+gjP3vodu\nErC2vYOt2bS6PjNzC/jSRQ3z6MU2HWWNTFlhTF/AtkZJlBQndYmiDK7vkyl4YHYhlTS220wfnECm\nAhB736uCogxPhnQPaFubGzikxFqCVVCIkcSKipBgKSF+qGEECk99/gvkRcBu3wXDwE5ibNPGD6Ph\nTaC9+aSqQOR74PYJ2pLNepso6qPLba5efQ1Ty2FXRgk3F3FMjWfjDZZVl/nY5qauUYgMCn6BOG3j\nZRMaoguWwFJUkiiikMui+dHQ4tDQ6IZ9shMlhKVz5eI1Hn74OK1GnbXVHh/+iV9hZa1JfjRPeXYf\nH/jxxymNPob1h/+I9s5zHJzPcPPmIjfe2iWSBpGh8Es//m5O7R9nZaXOXz75LKrikLPyxFFCmkTE\ncYyiDe33kqFMjIRhN9sL3WGkjUyRsYKtO9i6gXASUt0hjat0OgM6zQ66OcZo+QD11jJBW6LGCpXM\nBKqh0+t2QFcxHAMhNJxMDqWvYjk23X6Xnt8D0SermBSLFkoyoNn3SYgRSY/xqSy17jb5ok6yM45e\n9bjz/bfz+q2L7Czt0Oy6jOyfIm12UOoRnmbR9ltk0gBbeCzceQdbG31+5ORjfOUvPktL9tk2+rSs\nEpnQYSqbUvMGBJaBKBXoZdbIxjGXXnqO8ujDvPMDj/G1v3gCX7NBpLiuSxLqhGEMUmEwGNBoLmNb\nQ5bL9evX0A2BEAmKSDBMgZYauK7L2OwEI6U8X/vyX1DvVtEzClGQMj0xTqNeJY5jLF0bKrxiSRQO\nU0x0Y5g2HYUhTsak3R4qxuYPHMHUHPq9gEazRqfTo9fvcHBhjsuXL7NvboasM0rQMyhVCtiZlDAc\n/o6bnVVKpRKJDImiiK9/+z8wOzdJlHRR1bcPnW97TyFEFvg/gf9SStkV4v+eSUgppRDib+Q0I4T4\nBYbjBJxMHmu+QtpYZ3tpheKhUxw9cReNboOaW+fuMYNz3/wWpelZivNTbP0f/wF3fZd9j72XXVXn\nxktfY3bUYmzuQd58/SKT+0Z5a8nnbKVNaHq0ZY/G7jWuXL1Fo5Oy79AZjhw7zb65u5iaWiBTKNIN\n20QyQkUg0j5xUkX6a0RqBqEWUJU8hmYj/RDS+pAMn0R4u02iWGLKhNQooOkj+MmAtcbTfP/1V8hZ\nYzz0wCN0NldobG1y8MgJxg4dx/UDnvrat+j2djl6epZqq8rJ7lmmx49gq4LY7aGEHpbqoKl5okSj\nXm1QKk4hoz6GBmmoYagljGyIaggGXhedCN2w8KIUizxiz23KVAX9bpdao8ZstoiqGUjVxI8URKqQ\n0exhl2xKXvvWX3J55TxjlTHCdouMUIg1lY6mULEdIrcPVg4lVihmLLpxByNb4M32axzJj0A0oKl6\nRCnMSYN25xJrbLAjPDYSBU3m6Gg9snqROBG01RZZTWD4kqxqUQ9DdCNmRIFVt48voShsqlFI7sAo\n0u2wtdJhZnqUXhRx8VqVM6ceZGxsDDIhy8uvIr0Qv6Gzq17m4Jk5XntOcPX8Lul2l2mp0lIrrMbb\nnL73EJc3b/C9py+yWx0wkimRyeXYrTYIZRepCbzIQzHzDOIAx8ySz5UIvBhN+viBR8GwyDkOpmET\nh5J+z8eP3sLRs0SupGiPYAqN7qBFKBwsIxh2XPTYaDco5kt4gxQv2mJkbIywVeH+Bx7isY98mH//\nuT8mfuk62dhCjozSi1P0JMXqdIiNAZghGxHU04ATxSnczHkKMwe4vt5l0E4Jux4zo7MkA4fdKzcR\nZhnR1rGKOl23jqNMMVY7xCd+9ic5rM8wO34nv/9H/w0mMd1ejan9cxzKzfOt+jZxv8BDRon67DTL\nu6sU4iwvPP06f/9nfoHzs1O0mjV2N2v0WymVikm/O8C0MjTb20RRwuTEftyOgalZxInADwOkTMna\nDnrWIjQUyvkMN66eJxMnDAoZ3G6XI6dPsrm+xnRoc0UfULRy+F5MJV8kGgQM3N4epqhouiAIhupM\nKSWNRvWvmo3tGqyvrnPowDG+8eU/Z2xkHGXuAAcXDvHyq8+w4MzRjyFbyJOmIYbpoBkK0tfIZTLU\nqytsb17HcmwS6b9trHtbICuE0BkC7J9JKf9yr7z7gzGAEGISqO7VN4HZH/r4zF7tP9qklJ8GPg0w\nOrVf2naGneULZJKUAycO0rMEO9U+InbYvrFBbXuXD338Z7j2xg1WFq+Ak3Do1D2svfw81TcWufvH\nf440p3D9pavMzU6j1ba4qWk8cPdZ2u0dNjdbnDh5iAOHz7B/4SSZXJF2O0TVQvqdFYKwD3KAn7Tx\nvS5xEGJZExSKFTL5CggNb1BF6AbZXIlBVxAHOzgFl9rWMo1OyvFT7yFjjzCSH2UjKuNWV7m6+gzb\n69dod3bJZBw+/slfpOdG7NTeorbzAjs7VU6f+gSHp2+jVd3BED1SqVIo5HAyWQJPQxdFVNUhn5Wo\nakAc+vR7bTKWjtfoEDRdVJHi7nhoR/aB51IcGUc3dVy3TxAEDDptGs0akecSGDnsjEYqFaLAR1M0\nUpnitVu4vRZf+sJfMFkZJYgCDMPA0g3iKIB2hwiFSqWIMT1La7dGJ/KxKnmsTo9uHLLU3mJcMzhs\njdP2u6wmMdvdHfqWhxFZ5KMCiABdgOX7mAIstUQvrVPLSBRPMibyxHKA5hgc6uRYVPvs+m0qBybo\nC43qtsfsrM7sqSzeU9scK0xy9z13EZsqaSKolEfJzxpU5gp888k/5IUXrzNTGadbu8aYbhFIhSCp\n8nff/05qjVXeunidV89tozNJZarIytp5gsBkbLREz+2hCAU/cEmiAD+BnUFAmgoKRQfd1DFshdag\nhV/dRBMapm4hk+GFrpKtELgBhXyF+u42cg8ELFEkTfsoikPfD8mOCba2Ajbqb/L+kz/Bhz78UVob\nVe6aPsVK5TqGCd3NPqEusWnT8OrEakLRyeDVQ975rneRzedY2d1PxDne2LiGURolsccJDY2lxRcY\nNQ6RbTVoVxLyjTw2EWv2Lkqjykioslq/yZk7j/KJwj/m1/+nX+bxyVkKA4X6WImJqSwXVi9QSid5\np+/Q6scEo1nCtSrf+MrX+OBHP8zv/+6nCKM+TsbB8zwMQ8MwdYQQzM4OIeH206f53veepjI+gVBh\n0O+StWwUQ0eGMTdXbjE2NkattYPrBtx9z1mu3LyF22hS1HLk7QymGI7yTDFMaE6S5D9ate1hFoqi\nEAYBvu8zMjrK5uYmUiiMjY1x4+oS/b5LZmSaXN5BEtHrdZAkOI5DGCoEUQAyJAoikmjo+KWqQ2aJ\n/E8pqxXDlvVPgKtSyt/5obe+Cvw08Jt7z1/5ofovCyE+B9wDdP6/5rEAiq6zu1Olsb5BOV9gZHqS\nASGhonHqzh/h/IvPs3/+GBmnxPXXn2T15iInH7qP4vgoz37mX2NYZRYeeJwXn3uC1A/QjSLbS9/n\n0AMfpzB6EM0q8ejEfgwri6Y7uH7MdmMD27YQfgciDyVNUOigUCVrWNjZfZjFY+iGCZo69DVAIcUg\nlQmFkRydRodmt07GnmPgCrpNDUP32KmtEXcDDASa4qIbLuMzBaZm5knNLMdmTvOVr36ahx/+EIeP\n3IZhzhBEOorSp++u4/t1uu0eMgHbKpLNGLiej6Xr+H6LXr+JTFxSf0C32aK6vMuVN29yaP4kvh7h\nhhEHDkaEqaRWq6HrOp7bR8YRZdtCt7NIzaA3CFA1g5GszbW33qRSGeH3/uD30WWMhqQ9cFEsC93S\nSZOIk3ffxVypjOu6fP+182SlytTkKKtrGySRi6VpdHWNdpqSs0fohyrXm2v4hgUBTCYF7p69n+XB\nRez9JpvnO+iKQI9TNEUnIzVSRSVUU/oywfJDyqlKbOtkK1maQZfGjstkpUTuoMKVW7d4JHMYc2Ef\nApWNtXUMzeTI/CxXbr3Jqxeforv8Ou9738/xgXf/GP/tTz1KrePjC40H33+K205N8pdf+RavvFkj\nwWFydoSLi6uoIuQXf+EjnH91lSvXrxAEAbquY5jDeZ+KzuzMPjRNwfVd1rZvIYjIWg66SPG9Jho5\ndNVEETqOYxLFAcVSlna/g0AMjcGjLtlCmViF67fO82OP/TQZXeex0++nsdrACARHxg4zWZrn4tIF\n7j12lsrkPs4eOEmnX6eqDvj+N5/igw89wslTd0GU4tnbXGrMstX7AvVmj4nUJmiuM5o5imImDGyB\n14gJjD7jcYWHTt3Om6+8wPEvnOXEnadov7nI+04+woV3fJxrz32G+z75MVbL45z73c/w4FyefV7I\n1YIg42UZbDXRKkXeWFnkzl6fU7efxnYMzp9/E8s00U2NNI2ZGBtHonD+3GXuv8dGt0wMw8ALXJIo\nZmJiAo8EE0GSgpnNcfzBeYJGi8sXLuIi0NOUnpJgaznUCJIkIaeZ7Hjd4f2GH7JslXvG3z8AXSEE\njVqTUqVCuVih1ekgNQXNMtnaucXs3Bi5nEOr3UBRFDKZDL4Pnhth2xqWZYCUKKpOHIFtZPGV/7Sd\n7APAJ4CLQog392r/aA9cPy+E+CSwCvydvfeeBH4UWAJc4K8V+WqqQuT3aLg95o8sIDUH6SfMTk+i\nJSGFlQxn7nmczeubrL71LQqVEY7d8X4WL77EzuoNfvy/+3XWm9ssn3uZe97xbm6sbWNOjHD66FGi\nJCBKI+yMQxCGNNptFF2lkLcZ9KvYhoKlaaRRQppooIxhOfspFI6RWgZRHBOEPnGcopl5olQlDXWa\nnSpBoGBqh9CtEcbGJhGmydLyG1y49CKrF1/i4MJ+ilN3kK8UmJ2bZ2rmOK5bwlU7PPy+D2EaGr7v\n0wwGpIrAsWxCr0LBKkK0Sei3iKMOSdxF0wsYpoNpFrBMnW6vxvbuFsI0OXryBJGnkM/k2NraIowS\nTNMCoQ7NXiwTPeNgmQZmDH4iKGfLNHoek+M51pdv8Narz3HprWv0q1UmS2XcXg/HMgjSBIDHH38n\nXa/P0y+/QBRF6ELjyMwcikzJJAqapjFwYxIhaMqA140WA5lgZ0qYKUyVbN45ei/t1R6VySJ1xWNk\n4QHIe3Q3d4kGKvmBR0ZPacsh0Z4kYbsi0UcqBN0O7W2X/IhB+aRFpxMgVwyuK1XydRut0aOYy9Ct\nb9C2XTZXLrNa3+b+A49ilUaohyrH77+fV599jv1nD1Ozanz9+S/z8oU+KToHj55hbLxIvhTzvsc/\nztc+9wSrm12yWYdsMUu9WaeQKzC1fwYFlcHApbrbIE4jCoUCg14H13fRFYfp0Sl264O/ApKclaXX\n6zA+M0Wr1yWOJM2gTSbnUG1vkB0t8fOf/C+47/CjRDWPeqNHVpr4EhIt5acf+wnS930UkXHwm0PR\nSDbNkJkc59FfexCChDdeOcdYpNBsdThZOsaxo/+UT7/wv9CS6wzCAmLQ5YhT5JqZoW+0UfuSX/zd\nX2d28jQbT1zmXP0Fjl2aIJ20uLb1Ep/61V/nU9kOX/rq1+loGsW8gilM/oV/hcOTRxndt5+OH2O0\nBiiazqf/4F/zz3/7t3jiia9SKJVpt9uQJpimied2WVy6ydTUGK7rDzm0YYDneQihcv+99xNpNh9+\n9/t549YVgiimMDlBtzcg9HzUbJaMYhMaGhYCGcfYqk7G0Bk0XRShIBB7Nx6GNLsf1OIwGHafik6r\n1cJzA3p5n3w2z/T+GZ78xte4594zaDpIZch26Hb62I6JlQikTIZdq0wwNB2RSjRNHx7vbW5/K0y7\np+cPy4f/zk+y8/1znHnPB3FuO0rSabN04RxhvcOxxz/C+s0rvPXEZ9ClyekP/z1WdnZonH+Rs5/4\nCFu3erzw2d/g7CPvxiqeYnd9iff83R/l8OECqStI0ogoiUAKdE1DiHRosq1FGEqeKPYJ4iqancfK\nHgK7RKon6IGK2FuBpGlM0OsQBR5JHBH0VvH7A9qtGl7YYW3nJo1Ojx//yE/Ta8U0ey6FXIbx8UkU\nqZEkAlWAH9UxFZPED3H7HmEc7UlDAwbdFoYqiajR7TQIQ5+J8f0kiUGcaCTE5MwC7V4bxRBYFtR3\nNrl+9QblygSHjt6ORhbSlEajwaDXH4opkgRNUclmHRKhU5k7QJgoVLIlXnnmezz/rS9R3VhCxho5\nRaFoG+i6jlMq0+j3SZKIbruFY+goaCRoFE2FKaHTb3cYn9+PM30bomLhpNDdbVJZmOfmzVu8/Mrz\nuAWV2yoZZsYmWN5tsNjeRYstfukd/wSZa6KmGl9/4d+y7q6SjXJMoyOzbQamIOoYzPZ9VmzB+O2T\n9Lwey0se+dRiTAlJ3AGJanP4zruICirnV15HiIRTc8c4MnaUTrXPheoNfvRDP09r6yqXlr5ALUyZ\nGX2EKfN2sAxkUGNt53mWVm7Q3jIo6SMUNElfl+ScDJ7nDXnHQK83pFalaYoqPHwvIG8XEFIhDlMU\noWGaJr5oMxj0yBk5VBQMPYsfSoRiks0V8JSQarXOoUKF9c4Wn/6N79JZvUVZlNGzMQ1Lks1mSXZa\nKB2PsNZmEHvoR6cp7Jti0pWsXriKu9EkMzlHOjZCs9aGWpN+2uPYu49gW/u59NT3ubLxFE9ee5Xi\nvizs9jioKjzy8X/MxMHDqI5LvNtBnS3i7J/Futkibi0jX7jA7Ud+hD92L9KakQjp8JF4lOMffRf5\nWy1w+4RzOYysyYtXzvHFP/tz/vcvfo5P/uIv88UvfRG0mMpIkU6zRavRIY5SDh86xO7uLr2+i2bo\nZLNZCGPOHD9JnNNZvniJA/MLVDeaTB8+wNrNRVa3Voj8iPmxGfoZA92PaTfbLGRHiDS4XF1HKAlJ\nkvxVB6tp2t6SXhJFEUkKlcooqYDAH7ILZBIj0xTLtPCDPrm8DUKwf99hVlbWKBQKpIpCt9NGRWIa\nGvlsASFUojBla3eNIPDeFtL+rVB8SaFQr9dR8di4fo6osYK3U2V1Y4PTdz3E7soSm288S6GQJX/w\nPsozs7x56TUOn7kfXXNYffkJiqVpyjMH2dyuUR7NMDOWQU0dNC2gO/BJRUQqVeIgQlcVVN1AYBMR\nkWoBpjmGbowhRAaZRmikkAjiMCGVydCcRIaI1EXGLooS4nkNOu1doiTmtuP3Um93uLW8QqU0wpED\nx0hlhExiwihECA0pBLoq6XZaKNJHtxJkGOP7HqoQ2HofVfYJYoVSeYzAC3H7KZo2nAMneGyuXx/S\nVXSd9ZtVLEOjkK/gBlAam2Z318MyDQpjNtlCgO96eIMepJJUGkgrP1T8FIqsLd6iU6vRa3eQEnRN\nx7FM4iikXC7QHQxo1upIEmxdJ1IUDEWn1e9xIDtK3o0oqhbjwqLa9ymMZujSZ83fxV+XOIaB7tik\nsYelaaxX16g2fRJPIIVPLd1A8XzaTY+ZwjFuP3EXG61FVtaXkWaJKTVDqbuLUoTi0SlW3C7e5SaH\nKscwTJ+os43QRrDKgq3+DRKpYSYGM5U5yto4vUaLSNNYmD3C88/8ezJFje0aVCZOcfb2H2Nno8bq\nze/Sr25x48YyvjQ5euwkQXdAt9HAi7poSkwcJQSeT5IkaJpGEA5wYw8NcDQLQ9WI4xhVF4QypO12\ncRwdoRj4MiZrO4RxgFRVFF2ALml3Wjx44l7uO3QXsQeh28Up5Ei8cOhIpSbI0EeVKa1Bj5xjooYx\nB/bP0PRrdLsu+46MEWYsbq1t0vfrjMxOIaenmMOnkEzQ624yf3SO3OxPcfCuu1m7do3B7IDuzQ3M\nQRdFzxO22ozVa9wQGqf6uyRFh+D0ce5+8EE6r17lxNhp6hWLhQ3JqchEb/k0RiBfmiTQIpJilrOH\nf5T7PvwBbvv9e7l+awfTMEBXGQwGtNtdUikwTZtiocyRw8cIopBnX3ieTqdD0c7T7fa5ceM6qAoX\n3zjHo3c9RtgLmZicZHV7lUomj5XNEJAQRDFhmjBq57jY2UKKFPbA9YdHBD8AXcMySaSgPFpmeXmF\nJPTJFfP02i66rhJGPvl8FkUFRdVZWlqiUBiGj9ZaVUzDJpsvYCg6gR9jqApxEMLfoDn9WwGypAK7\nkGcnbeOvX8G9HhPtdjjxyGMcfOABnvyTfwkbiyilKU488hA3l5Y5ffQkuZFRVi9cY/m17/Ouv/f3\nQVh4zSUWHjyDFD2y6hz1wTXidIA0JEgNRRkavUgBmprHi9eRROTM/WTtBcIkJQz6qFIQxxGKUFEU\nQSpTWu0a/X4V3+/Q3Owikx4yCdm/f4HpuZMUChGGYaAqEUriIaWLUGIs2yZNVZAGCWUyRR1NlQS+\ni2kN42pEEmAYNl5nl2JmFs0IcNUGnU6bJHXp9mJyxSyN5i2CICIOYnJ2jqmJg5RzCrutgMZ2B6cy\nhecOl6qaYpDRTPLFIkKCiiCy8yiGzu7uLpapc/XSZbrd7pAWIxWiKKKSzdJoNOh6AbZtk8QhlqGh\negmappDLT1Kvb1JQbbQUWm6XnrfL3EyBi9WbBCWN9dYOB2YO876PfIhvfvEvWW7sMPBDSDIooQIM\nuLL8An6rzzve9TjGyCky6oCN9utsxm3es/BRzhpj1Iwn+W5xi1ptBa9qc/TEO5gcNag/+zoTSY7M\nAw8RZrZY3H2ZzmrKiZHbiNdVuu0mfilh/NSj+F6MkOe4truBlr2bU6c+hl2epLH7IvX1ReJ2jCkV\nfuwjP0Nx/AR/9Me/Ta7QQvUE3V5r+N0pGqoA09LwwgGOYSJVnThKiOOQMAwJ04hYB2ErhIkkFQJF\nSjr93lDymQgsXWWn3qUXuRycm+Tk/rtpVwc4uYTQNbGzkCga4xLc0Ce1TdxOD7IZDtxzkkGjid60\n0PQMdZGiODC+L8OoFIgoRp1MyQSj9AdNBn7ITKVI5K9jpwc4eugA69oq3txxri1e5PGF4+w6Y3QY\nMCs9euUZJhWFUtfmihFx6qOneDRNWF/pUdlZIj1eplrbYjpj0yopFGenEW+swNgE3miWn/3P/zMe\n+ZH3sm9unlZ3By9ySeKUOAbH0tna2abRaHDkxDE+9rGP8e1vfJvG1g5Cgj/oMXpghjtvO0x7o8a+\nkSnWug2K5RLOIB2mDPvR3sVMo+Lk6OwMSOIYdc/q9AdA+4MOVkpJlEo0XWfx5hKWZaHrNr1OG8vW\nyTg2/V6E67oUS3kazRamkaNerw/3NTSyWQfbdiCG0AvRTIMkSRBvf1rwtyN+5nd+71/9z8dPP8xm\ntU693qA8Msapd76XYw++l7XtPpe+8kc48zPc8d6fZ/n6EvP5acysyk4v5JVP/xalu05z26k72Lq2\njFE2ueeBs5SyFbA1hNKj1W5jkEVNDUzdAJmiK4Ig2UYVWbL2ASxzhESGyNRFETFpHBEJHUiQ4QC/\nW6PT2MZxMgz6IUIVoFRRRI/AhSRysLIOpbFhrpbqBShaDGqEqqWoUkFFw09j9NgnjgKEkMNxRBoh\nZTzsmFUDzY4QSgEpbYQM6HXbyNDGdRUmFg4TxEPO5vjkKJGSIm0Tq1JmQIhIIgzVYqQ4i8rwpEqF\njpEZIUgc1HyG/vY2Xn2XP/jUb9DYXkETIbZhYCcqqqLR9wd4cYCmC3zfx7YtgijBVVMSRSdSFSbz\nGnYiQc1QlyYzuUm2oxp9OvjNGvOT+yjZRVYuXMMKYuppiGnl8T2NQn4/SBM/XWe7VaNd3Wat/h2u\nBs9zbnmLew+c5c78AjfqlzlvX2dzV/JjHCffE1QOTbL94nnGAo14rMyqs8ZFbwnRH2GfN0bQDrhW\nXSISbSZKR2lmVtmqvcbuxi4FMc+PnH43GUPn6Wc+x5vnv8fj7/k5qm6f+x59L7pd4JVXvsvu1gUM\nXDo+xKlEVXWSMMZQLNJYYOgZ4khBwUNISQqEmiDSBYEMiSIPkVj4aTI8lxQfTbGRkYOpCSS7GHKE\nu4yDmJ6C7QuC2XEyjo0epBSFIIgFUddFNhoEnT4TzhiKDxQU1NaAhBC5vYu9vAObuygKyFIBP1aI\nNJtOMFSq5csFHNuhWa8hHIcwjIl2l8kC+dkT5Ow8yWAbfeDSOrdIddChGEiMjmDtYhdtMUYfRFin\n5uhJl6RfJaiMMNItkrzwJiLSYdUlnDUwtAz50OWbT32Ty9eWSFIFUzE4eGg/iRLQ7NSo1Wusr63Q\n6ra4+777UBSVTrXFQI0JBn2mZ8d49cobLCg5Dsg8m/4AVZgM9IRYeLTbDR6YPEpq2Vys3UKkMaqh\nEkUxKQnIIchqmoGCMsx1g6HBkJR7HiMKCIVYQj5bIAgiFFUFJHHsoe7ZheZElnKpPDRXEhJNV/GD\nAEUI+m6PX/sff+3/P/EzURKzcPQIuhIAMDm3gGnn6NW3efWrX8AameDRx36S5a1LEBWRZoDb8Vk+\n9yyhbnPv3ffT7LRJSThy5BjlQhHHHhp0h6lKximgqQZhGDIY7HVnEoQ1gSCDtAqkZoYk2ZNNppIE\niZA9fH+AmoakMiCXy1Eol/DcCEtJ6aY6zU5IqWiimQalYgGVGFMk6Lgo0iCRGURskMQqqlAxcZCi\njxAJUkKSpqRoaLoyNBRHRcYemqkSKQqq6RBFCY32DmAwJnMcGNtPN1Oh7/XQTYc0TRB+QEbTqLW2\nuH71ZQwlx+zsHKoKqRLjBxFOtojYTDDTgD/81KdQEp+cZRN5CVEQ6/HqsgAAIABJREFUkWoqihIP\nyfYCUk1F1VQ8mdAPAw4WythWnkanS0hMK0lwLAPNsEkLGhvNFp4pee8jP8HFly+x3t6h70Rs2nXS\nfoCplcGxIVEwtAr1eotcVqFn3kIxyngrPu+ff5jWVshXa19F5lu012P2i3lmTx+jtv5tdp5+Bsws\ndVtlYiaPGd0g1yuzLztGIWfgRW3u33eIWzea3Fi7jFM0aLdqKMk0D9z1MyzdOo9TeYUo7POrP/+n\nXFk8x6nbHoAk5dbqBm9cuEomM06zX8NQQVdVkjBCV1QQQ7mo0FSEIlEigWcIRnWLsUGKkP8XdW8W\nZNt5nuc9/7DmPfbuufucPvOAAxwABAGCMEVQIERSIymrojiyZJUiX+YiV7lIlXMXqUpVqdilqOJI\ndsTYsiuyIoWSyIiUbIoECVIEQAI4GM4BztRn6Hnv3tPaa17/n4t9rGuzKk5Rq6pv9kVfdO/1rfV/\n3/s9T8W2lDyQlsLJUNqhlTocApNixFZL82Ay5Xz3Ej//iV/ludUL1M0GQeBxmB1hnDZBO6D0FcFe\nRvzwkOTogOVmG9UKyFTJ7qvX2Fo8j80hnRVUSuI0A7SCg/t3aZ5YJ08rgqCBcFwGu/ssLnY5d/EC\n7924SVg3uX9vgNNS1EcDslojtlYJ9geYr/4Ge6Zi5QtfoBGusNbZxFtZQ2YW52CGs1BRFgWNDZfj\nvR0CIchaLkG7RfTN21Q/9yQvf+Gn+Iv/8495/84dsiKlIRVHu7usb5wmlG0WznfY29+nf9Dnzdff\nYGNlg+OdfRbCFr2lLvsP9qklvHHvQz7/0k9TfPc6jV6T4f49dMNj1WuzsbnJK++8CZVBufPhrlQC\nrV3gUboAQWUNSoLrzEWalZlHsALXpagr0lmGsBov9OafBxGTUYEykiopCZcb85OsVoj6PyYY5Jyh\nwt+xdoHSillZsnnhMhUepY5IJmPe/PqX2f/+V3nuC7/OeFJy/4P3+czP/gr72Yi9vQfce/NVrr74\nOZygyfVr73NudY2Ll84SRRHadUlLS1kptBPOaUl2vkKqnTmmzHUisA4KiyljyjqlqjKstZiqBpGS\nziZoLHWRg5BYI0jzAs/UWCNQTkizs0xvcYUo8MiTI0w8osr2cOQySi2jZEQpSoyZ93ZrwKIw2Pm/\nSliUUgglkHVNkU3xWg6TZIaRmpX1kxztvs+3v/FXDHYeUCvFwsoavfV1mu0G2IKzpzZY6XVYTCTf\n+Nb3GBzFvCkEaTZFKIvrO/R6SyydOMPbr7+Oa0uUkBTJDN9zAUNe5SAMjlRo7WAqg5QaW9V0wtY8\n3D0+RjFv/ndXVxgdzFgEGiKg1g6PX32Sez/4gHqcsvKxx/j+q++QHceEvksWxxhbUlYzIm+DZrRA\nd/UQE2n6/UPW9OM8c+pT/NnR7zGI9nDkEkFdsCQXuPX1a6wtrJAbh8PBEd31dbbLffK4ZKW5SRLH\n+JGH11ljNDrm5JOXechd+sM7ePVVXnrp73N/+/vs7r3Lmn2Mn/30P2J/9xp//n/8Jq6xKFEzq0s6\nrsbmAm1KjPWpjcV9hIWM0ykS8N2QMpsR9NYJy4wT0qUXzo+qChcnU0xtglPlnKxczocreF5Gq8gA\nyS/+vV/m8uZTPBz2OZtDev89eh87Sfk3H7B9ZYvL59cYuhlhGDLuD+mXBa1WRLF7wMLBkLE9ohFF\n+MrFWWlhioLZZEIUCMoiw/ObiHo+Dc8qy2QS01lb4cT5M+x8b5vlsIdxZrx37XUef/olKs+j7zps\nPPczLO31cZw2u5M91LJDeSJgSXt40wK9a9CPn2R4d4/gMCM42eJYQUMrkp1D7PYeLS/kJz76Am+8\n+x53mSF8jXEFRw8GTLOYOC3o9BbY2DhB5Hg8uHef49mUfH/EdDrFl9BZWsZzmox9w5Wz53lr7wbN\nMCDJSk53TiDCgDvjfVwqCkArhVLO/P6uqvm9HbhIpUiT+eDWcZy5Dh4BSBzpYFxDXmY4RuM4HrPx\nhCBoUuYVvfYynhuS5yUmqwCJ1hIpNEaZH6Yl+6NRZK0x+EHEdDrF9SXJ8SFvffs/cOO1b9IOHE6f\nvMiX/uQP+Omf+UVm1YAi0/zgB6+wtXmWlXOXSdKUxx9/jKefuMDK0hIIQZqX1EKCCMAm5GWKFXPA\niZIaY0CmU6yQUCbUwlKVCdZUSKnnxgRH4SmNsAahNUVREM8mczNtmWMrQ6e7TBgt0Gi3SNMJdTLB\nZhPy/AicCKUNRhgKk4DJqesIoSSVrUEolFTUpqY0JcrWWErKNGV4fARKk6WWyAkQGB7euYkwgjCK\nGOzuMewPWFldp6hy7n94n3arhas8rBNx7sopBDA42ufw4AGNwKeYjbh3/R1EHs+9WJ6DlQ5lVWGt\nxRPz3qOrnDmXoIBuu4XyBI1Gi6N6yO50yImFZTYXV5mmGc2ww4pqcfDeXaLTLjfeep0nmj2MGfLn\n3/wydVLyk+sf5bXJffqjQ4SGshIIJJ3FmsqkjPugBx1Eo+DP7/5rHga7qFIidqY8sfExTmQSt9fl\nlRvfxZEZS6st7nRGpMpnIz9D7gzw/YDSzXAaq5w+/SxvvfclRvoInS1z5twmX/vLf8XiYoPnn32J\nhnuB/ft9Nk57uJFDPk7wXUkYhkS9Za5+5JNs3zvi9jvfftTbVhRpgeM3qMuKqobl5XWKRgc9GFLG\nMTt1gVKKSHo8VodkuMShS9fvorIKNZzRba7y2cefZbVzEbWzR3e1xeE4xns4pHrxCkFbUf/he7yt\nvwIfeYreVND2Q+LLayyePcWD+w+YhBbMlKiUWKNI5bxsKBQqCsi1h+/7ZHlNicQPmhzHY9L+kBMX\nzmJnDl9/89/x+GcvE126zOxejH7zAPf8IsVjlynHd2hPNli//Dw7zhh/r0B6BdYTeMMxJm0RHI7x\nGy2sNLR3R3DKJTy/Bm/ucXdT8Adf/jMOxwOWz56mP9inkJKyIQhrl3R3j/HREdc9C5Xl0soJvFlN\noRwGxxNcKSiGE3baPku9ZS4trfP166/SaLYJC5de0OGDB/cBg+sHFDYFOx92WWtx3Tk7OSvmotOF\nbpssz8myAqTA0R6mskgJvushq5LiUWE2QhI2O+Qqp7e0SpmVhF4DgcTxA4oiY5bO0I7mh0hw/WgU\nWYmdb1i58+WAvbdeYXzjNZqex9Laab75tT8jFAXd1Q2G9ZTpsM+JzXUe23qCSVUQSMFj50/jhIqy\nLoj8JrM8R0qN6/pgKtLMPJo2upRVgRQOeT4BqdGOP2+Ymwpp5aPdfEWZK7QIgRKBJK9S0nJCGGgc\n4aIzH99v4rgBZVGTJlNEkeArSOQCVjSpK0Vd5xRVhpAlQjVw5KNMn6gRSiGsxVYFNRVKGnxXcuv2\ndU499iSe26AqCjqdDlHgg9+k24ywRUrgetRFSpFB2Fli6dRVTj15iaWFJSSCwPEpZ1N+97f/J6Zx\nnyqPqadHAESNiHE8I/RchONQVxUC87fRlzwvAInj+VRFyWg6ISans9jjaDTg8dNb7B8d0tMdYpty\n1ImZHs9wHINeW2P7wSHT2Yyr65dYcLvMdrZRMkAIg3A0OhigXIck7lDGhpbUiM6QPXVMrQT+Xdik\njU4SpqLk7Tv3iAKH2jdsb3gUwmHxXslZvcqt6iZZUdCoOpy9coo3H1zHiAzTL/CjJrc+uMbSUpcL\nZ58gcEIePHyTU2cukYwVSWxpBsukaczpM5dYOXmebvcKrWaEKPa49s67gAI0odU0Gz18xyVNc1SR\nUBQVdcPDywSqLBHSYhsuF6ZLxKrD7XpCMy14+blf4OQzLxMfCHSiOAxL1pVP7iqmjQ7hTgwHI4o6\nwY9CTpw5z61//xrZwQELjVMEiw2WT2zw4btHyKAAH+w0JR5MkdKh5QbYsInrhWRVjVWKvKqpjMR1\nIsqy5vj4mLhX0fNcWo+fIH9sHd1xEM2Y4t17ZE8/QblS49wdUR33aawZ1k4vc1PM6Dz/BJ84eZHB\nX36P7OQiSZTRsQ7Ct1T7Oww8w0rmcF9A1m3yyf/6V/mLP/lTznTWORgf4kQwKke43YDaUYjhlB4+\nxTChub6CsjFuVuFph82TPUpfc+v+TT7x0fNsLK9zbzzisr+IdD3e+/B9QBLXFbIG9DxNUJXlfHnG\nmb/Vep7HNJ4ihMJxfYR2kBZqM498zQeaFo2gqgy9xRWk0CwstfGDCFHN2FzbxHMjgiBid3+f6egW\nPLpH/lOvH4kiWxc5/Z27SMcn3r8D0322NhYoT5/loy/+NP/it3+Dq098nMrLmB67dFqKhSc+TiUU\n9fiIndu3COWUE4+fJwo8yrJEuRpTlJRVhhAWLR3KMgcMVVXiOJZEahwdoPwWQmhElVMbQ13Pm+Ki\nLJFSUNUWU837UWWZ4XseSA8pHQSaRtgkyxOEtPPfLRRSrSKJMCYDUaFMjpIupfUQsvhbiDa2nv88\n8pxZa6iKGWl6DLbEdSKUnIvlZlnKysYZTDUvgHVdkxU1V64+xzMv/hTrZy+TZgnW1tR5QZ2WrPdO\n8Kv/xa/xz37rf8AlRziKqq4pTE0YBSRJipaaIAio6pysyufamiikri33RkcURYHWmk2/TW0tRVXz\n9q0bOEHIQTJiKlNuyV1+bO0Ci16PV2/e5e4w5uypswSO5E9vfYtpqWmFyxSVwHEtveWc8XhCOjmB\nUjGtszGlLKgOApaMi64qZnXM/Xvf5xiNEBliucFRzyOZlFzYUSwkkmp5QFr6hEsOS80L3Lh/nbvj\n76HzAJM08VslK0sXiaKI/sFdnPWKU6cukKaW4fgmoTZU+RGuVHiiJpsOsdmQ/sE2H3xwk153keef\n/wTTScbN6zehsnjaQ9WCStTUUpCkBQ3l4DiKxJTERU5rs8NGu0leGmaTQ67v7dPe3ECZCXV9TLtc\n5LCs0ZllVXdgHJOPD1HnQvQzVzgcxCy0l5h0hxxde59Od5ELz34UPcx41z+mX8boNMMxEqsMcWVx\nqg6yMFSmxPFcyqICa/A8D2yBGo7ATBgLy+SL3+bhCw9YevpZOksh0eefZvQ//3P802scRxmt1Q4y\nqqkTl4tPPs27DwruvABe1GTB8bB1zXAaEyhN2NAsPH2WLHF4MbM8962/5n/8zd/CnVaUzYrZZEpb\nQFgJjpMJ7nKXphfQKDQSMIFLM+qwZAPcIKT/8Ab78TEyFTzYeQhlTT6e0Dpzntc+fIswUugwZDAd\n4RpNUTMXmhozZ3VUNVmSUmQpxkgQFsqSObXVoLUGW1NXxaMYV41wfPzaEIYeC1GHk0ur2EXLcm8V\nU0oc6VE1SyaNIYVMf6j69iNRZIUA16Rk8ZhW04PeEibscO7y08yckGde/hyr62cp6wyT1ZRYXMdh\nPO5zsH2dhz94nU675umXn0N78y0qHXmk02Om8YRWqwVIhFCPpvqQ5xm1cdBSgnUAjTUldV3OTaZV\nia/ma4GIeaNbCIEWeo4MLEryrKbR9PEdl6SoqU2FAfJKYJHkVZ+yGFBVMRJNEGxgvR61ACEkAh5h\nCSWOcrGlIZml7OzcJYgkpkgRtkldWu49eIj0fTbXTjE67pNal4XVdVY3TmK9kPc+vME7t28zG8e0\nuy2uXL5Er7vAaDrif/2936Emo5Ll/LsmBUmaoZVGy7l1IIunNKOAyhqEUARhA2sFB/0DpJS4vmZ9\nYZV7+9v0ej32+wMuRE1Ex2daF6zpBtZIvn/rNg/rjJVTW5zb2OT73/0ugzpD0aIWBdIF4VTkhcNk\nVII54vzjTcZFgEkL6MdMsw6rQYDUQ8qpS6YTFi6sc6wz3EnNyoOadbfDOMgo7JDTF55mP97lT7/z\nFU48tUGSzTDHll5zieVwi9mwz8HDDzl/4XGmoxLHGZJmiieeeJZ/+4f/Et93yLKMzTDAj0IeDu/y\n9Vf+irCSfPLjL2KsZHj4EF8rsAWzwT5rq8v4Cys4lWX3vWscaEPbcwhw2UBwfLxLY/eICJeLGx/B\nW7tEfO1DJu/c4OKlDWIpORm0GducYssh29/BDmMWz1xhMhU4BxktfEbSUOc5uwf7ZHXIuU9/itH9\ndxjt7tCfHdACgijEuA0mu/doNBdxw/kJR1YSlxpGE6oqphzAKM140jsHfo39k7dx04D3NxusH7Vp\n+RXpzT1OnHic7KiC8RT33Awxu85P/+TzvHU/ofzEZVavHzKTFbXrEo4M8RkHXzlUmx34f94iONHj\nxUuP88U6YfYgox26HBzP+PGFs3zuk5/iN/70iwwaktorWUAzuHmDxDE4peD0+ctMBwOWe20eTsaM\nbMHpUyc5/9hjTCczNhrrHO494KmPPMHbN+/gjQy606LdbrOz+4D93T2wlvXl+WnuF37pVylr+8jA\nMN9eTCZDbt+8we2b15GORuclruOhjOTU0gYb7R6PnTxF4s11TZNhRhpXNJwGG8trHI4P5qaM/8Tr\nR6LIGmOgmtIKfcbTErprLJ5ewV1cJk1zls8+C2HJaBDT8nKycpHcxFDOONq5i51NUaagrHLiMqYV\nLpAVKYcH+2TF7JHTa17UyrJEO5bZdIzCpy6hriVGO5g6QVMiKFGipKwdimLea7OmnhO6hCKdJZSF\nYZbmrDoOVVlizRzx5ikHYyXGTkjTB4yG75EnQxzRodfLidY2KAs1f5oqSVUXKDSuVKRVymySUtXZ\nvAeUJywttEknKXlWsHFyi07QQXVdwq0ubqOFakSsbW6wvNrjzt1bzGqLloZpf5diMuRf/G//nGx0\niB9KqhI8HWGFochzbG1wnblVUCmNnRWEQlNWFVkxxY9CumED3/eZZTPu7x2ig4CjaZ9O06MnHNK8\nYCJSzh10OYhi9myGkTltt+Y7r30TW0q6hCSuIin36C6D0IrJeIFOe4XW0hHTfEI+UUz2ITQeUTBl\nkMb0/AW6XcFkK+DWdMzZfkT7cAKuZq/Yo+F1yBND/50fMK0Ea5sOk8ke8rDJRhAAgv37uxiR4IcR\nx4cpYcdyfPcHrK9f5d69MS88/gJvvfsKnhC0vACRzfjBd79DWI955vEfhxI+uHEDz9WEjsLzXCZ5\nxcGDD3Efdjhz7iy202aazvDykp512HQ8Kr2Oa6a0zl7m6X/439JPA9JrH9KZBczeTWl+LuSWmtK4\ns0fxzjb+z3+K/G92qTcv0tvfoT+b601WLp1lOr5Do9LoxPB2ss/F9QtMmgvcKytGt28yzIa02ycJ\nCof0qI/ohqAVvgogL6gnMcXkkNsPb7CyeIXo0hMkwwNOXRS885X/QGd9kdaPfxL7sz/H8pffYTI4\nJDgVkdcxe2ON3F7hxpcPkUFKFfbYfXyd7N5tzpchRo1xfA95nNJwfdLnTsC1D3j5v/o53r7wFt//\n4h/yF9f/hn/wuf+Gj73w9xhMdvnvv/QvWRYNGlXNqJySdF0adUmkfDyl51jLNCUINa+8/QZuRyLi\nkrskBFWFTmH6/us89+QnePvbb5GlCRcuXGBpcZHhxglcrVldXmJ8POTb33kNYwVWCiwCbIWpMqSp\nOX3mDNmjGcxkELOxuMHplXWe2jrLqh/xYTmirAyB4zJOphRFhbaKLMl/qPr2I1FkLRZXO+SFQ+qs\nsLbcZslJGO7fROlF/DInnzh4YZfJeEioxzA9oh7tcnFjhTuy5mCSMn3/Pq3lFpmbYIyhG4Jc65HF\nMb4MSauUZrsx/2M5TWTVpypGxIWH57YoS0UYhhg5N84qDY4U2EpiKkmcTLEyJzcpZTohlCGTw4Re\nr6CgQPoahGE6PsRxFI3GKRqNk8ziQ5LZCKe1QIGLG865l7by0NrH1mOOR/dIhg8psjGtZg8VLqCd\nLrUwCMelrFuE4SbWVXgyICnGxAdDVsUa0wMHU1guX3qWw+iI/Tvvs/Pmm7z6ra9RFTOkI8iSEq1d\n8mqGoySBI6lrS17kIDRaeRR1Rm5LPMclJ4cyY0tGLPshr8cHKG1xrMQRknYGlZMhqoqoqviAI+5L\nQyAkyyPLwWyX2FFoLApL5mesdH1qBZOBpjEDcXZMZj3SiWSwP0LjUBmXfloQ+pr+0oxBQ1EmIZsz\ng54kVNolLwoyagqVseQ2uHNwC7kRUAiHOm8SOALRj1k3MbFy8fAoOhLbDdDuIsq2KeqKcvdbbN18\nC1f6HLgusra8/eF1RjPDhZNX6J09w3D7Js7BHVSR05JzmeaiG2KljzEp/Qcf4jY0rqqwViCVi8Sl\nVSYcOH10aMiOLenxjGg1pHYXqY81B9Mxy9OK4Y37qKN9giqme3GV4o13iaRgYZpyHAoaa1usS0ky\nnNCKMjqHGXf6KYsrHS585JMcbJynKDLKKmM6PUDvpKTHNQ2/QW2nmCrncG+b8XiHzc0uza01RisN\nFk6d5IE44OR0ROhWjLKMxmjK9AWP4mbM1rRJozqiauzgty6QZzDTAjMYcbysOPmpyxx/5wEuhjow\nuFVKmmuqxTbNh2C+dgM+ucJnfvuf8GN/fQdXd2BnwB/84e+TSEMtUxb0XH0zLUp0rTCeJi+mxE5E\nlVeUlWFWDYkGgBR0Ax/phRwXE37ps7/G9fc+4MSFJwhxme5NsdYS6ohkkvBwuktRZMi8pHI0XhgQ\nSYfj0Qyn3cILHGaHu+xlI5r4nIhWudI9weXeGhuNNrq2mImkpUOm+QhXVKQ6ZzAeEfrR3703WaEU\ntfYI3Catdg9flQwePmQ8nBL2PITboEpShLG0A4f+3j2GB/foNHysH/Jjn/wUo0nMQf+I5koLgOPj\nY1rSp0xzTFmzPz7AaihMRVHVLC4uw7hLWc1wPANyhrE1ZV5QlBYhFEWVI4SmLCqCsImbaVxH4FIx\nKCuuvfUOt25v89kvfJ6Pv/Qih/2UsqqJoh5ZkpPOBFJqAm8ZVzXAhijRoKwGWCsQIsdUBdPxDuPB\nQ6gTGo0GnupRyRDX9fA8j3G/T13XRFFEmRaUGMJGhyBsILTL2+9fp7c2pLWyyGKk+cu/eZXDu9cp\n85iG8lHKAd+QZFMqPY+vBLh4SqCERCJxrGLsaIYU5GWO5/uUVEyrkmxwgPJ9KgQKSylrtHBo5Ya4\nKEhlTtL2kNUMUQvQknYt8HNLhkX6cCZoMLQZVb+kPQG1YHEQpGNLcVQSFh6O1BSmoPYl4VqL2hNk\n6YSnxg0Gw4Rmq03pQJnCoutBbthVU5woQvsh02yGawxhqfAqixNorsZ9vKDLu+MhpJr8OMFmFc5B\nxOTNm2yUgtAL8E3BwXtvQjxg2ffQ9YDta29jJ8dQVPjKRWmP2ggqaqyqMcrBqSrGowluDZ4KqJXl\nISlr+Cjtcnj/LscPrtPsPUY2EzTDBbLZjOA4Zbq1RDd8jvzt27TjgMTRhEXJh7u32OgsY2Ko45xI\ndVBjy/TeAc5Kl04J06N9BrJE+i5La+soKSnGHcbBLmIE+WHKpJqw+NQGCx/7OD2nQydYYSLHqPGI\n2c4PuPHaX3J+knGrCzfffsg/fOKXSdUi7bMZ8fsz7AQ6p1yO7w+RrRpdOATC5/jmIY3VZRbPncaW\nE9q9BfK3PiC46XBwcYR6apnwLYv/1RsMPv80vckRDPr8+euv8Jtf+yJBFNAZlQwDzSB08XONDQ1p\nmZMe7GJmBQ6WAIGrNeZRPC6ZpeRFyhd+5idZDCPee/11FjpdhiZ+hDvU+F6DqqpR0ptLRIsZHgI9\nMjzIEhw3ROczVORz9flnaY0H5LsjfvzSczxz8jwmSZkMR7S6C8wmA3prJ7g/Oebg6IBgqUu310VK\nyfv3/zOZEf5zXUq7tFc2MbVEKUWRDKkqQ7PZxAtcpnlJs+GTTyfs798nHh7hiJp4PKAu4PRCD6/R\nQhrDZDxDNL15Zs7WzKYzWlGPvVkfHTiIRBI0mozGU3peRG4K8iKlrjNsDcqpqfIZVVFitKbdXaDW\ngiSfoUTNnRs3+N6rr3Dz+k2KwpCME8ajGWVa4+kmQrqYIkPIhKLKqU2J70m0tNSmwnUKilwQeC62\nShj2HzIZ7oAtCIIApXyU10IqD4P+2/XALMvmb78lrJw8SW99k2a3x+Coz+JqgbWGhXbIv//Xv8+9\nm2/TADZbK1grKIqCyPeIC0MhNSEKT7rzAmsl5lEcxdEeR9MDkIrNxTX2BweUjiSexWAkzWaEzXPy\nGio9t/JO64y+LknKmmZqCbVECItvmOeLI0XZ8kjyMVnlQBlQhgrTKqhGMfEooqh9LAlGGPwFH3fB\nx3cUeprRnDnUaU7TiVBGMpiNiW3FRNWgJd5ii3GV46Y5a1nA1skzzERNcesWSzNDs7dFejxmQ7mk\nt3bJsztEnscojlnFpSUW0XnOiphv9rmeR6JhvLuPVAWBNURS4TrBHA5SF/N0pqpx/SaNQjOtSyQW\nR0jiKmdUpSz2TmJmCpXNqEd3IFqirjwKFMJJKdOUhg2I44Jo9Qyz/YzwiVWG5ywL9SLp3hEtz6e/\nu4s5s4HNI9YKQXrrgNnJHmU+wxYVbdNGmRirXawXsbj5NOq8pZIJC9mITqtNfzSmUIZjPaZlLG98\n6Q+ZfvAaxYpg3FtlfzblaPeYwbk+TrNN5jVZfmyF8e2S+zvv01xZBxlRZBN6zS5pXHP7u/cZN10i\nVXJRB3hvHjErJCv9AbPXvwv0EGaF5jcfwM4h+cklbnYnDOM+C40W+UJEp3L5eLjEUbPm7vGHSClJ\n8gKn4VGVFQUGfI+kSjE1LK6s8flPfgZTGf6X3/3fAUl/MqGztIalJs3nJ1gvdGm2I9J0RnvxElGS\nQZlR+xqkxrWCTEr+5r33uNJb4fLmeba6K7i5RTnzCFypJOfXVugstGh95CpXnrmKiBps7+0QxzP8\nt/6OKcEtAuG3oKjnIA0jcFsraCdgkhnAQJkyPrzPaO8eWpQEUcT+4JjA6xDPEoQfImzN9r0H9ExO\nb2kBpT08CTfu3KLd7FJmKX6o0bVEaJeSHOHMFcW+18CWJdNkSpwckSZDGtEJjvtHtBaWEAJ+/3e/\nyFuvfgtPa6qqpK4F/sISlx67TJL8x4njo+UHVeErxSybUFTC9WOrAAAgAElEQVQ5BoHj+hhp0SrE\n2ooiiSmTEVpUhFETx2tQVRorAyrhIFEYBMYYDg8PcRzJ1WeusnX+IicvXGR3/4iPnrnAg1s3+NY3\nvsZffen/4juvfBVTZxgdkBWayG1gAVGD5ziEBATWQVmFZJ5wqLBYYXGyen48MhnDnX2sTQlWVhBa\noeOcjomIS4MoM8ai5o6tGTs1QwXN1LBYKBzrMhUFhbSUDY/Us2hT0Cstm5XHURQyWBBoCqb7kJca\nbAnKILoOwYKPpwR1f4LtZyx4XYRy6Odjjqsx0nEwNXieJljqkRYpubDY3FIex1Smj/UNXlnjey7O\npIlnXUSVEVhBYgxxnBAGDTpBB0du4k7HNKsxpZFIU5LFCRKJMjlIQa0UhTCYGrK6RIgarSEZjdBC\nYj2BEJDZGlfOoc676ZiotgR1xu72Gzxz6nHGU4nXWSSrJ3SLJsnBCHvlBP2dPguHQ5I33iM4dwJ5\n8SyjyRhndxevnNK5uMWu7zCKM4KwTfvWgFQUxMoQKwcpFc1mhBI1h+IBXS0RNiE3Gfd3Y/LDEn+a\nMX1wn0Mbc/XUFtduv8PhLOZGtYNTKp6mwW/91W/wyy/8EmutC9QrgvCxdcStPbLsLpXexA+b6Nyy\n3Fpkf6di4h1RByE74wEbrBHJjOTjp4i+ts/MHqOUxjnOGF09Q+fqOl/YcPlnv/PbTCWYrGDJ7zA6\nOKLvGpaiBaRWPJjuk8/KuQRUa0ojWI3aWCM4v3We3Tv32b57n4Woie+5pOmU0d423W4XXVYMx1MM\nUC1tkucl2/e+z4IfMJ2mWCei3W5SZ8cMx32s0PzEU5/lqRMnaZaKrErRnsaRGhmXbDY73Nm+x/rp\ns3TaXQ6mU2xaMxvEPxTq8EeiyEqgKCryosKVgtppkBkPqXwSMcPTGXu3b7N//xYr7YhklnG4+wDX\ndZB2HhTOEWDB1pLj/og0Tek1O0yKhFoo6spSxTl/+pU/4uatba4+/VFe/NmX6HRaSEeg/YCkNqR1\nTaPTprUQMD4o+dpX/4JPf+azlLXl+jtvoaTAUwpPWyZFzfnHLtLqthgOj9GOxBWCZitEiEW05+Gr\nLrbOUVICHnUdIERGGqeUaYHvh/iuwAiJoYnf6KLdkCLN0W5AZ6HHte9fI85yrl54nI9/5ieohORg\nPMb1PKo8gyxl+9rbvD89xooU6QlG+Yy0qpBuQG1LsrwGWWOVQSLwa4Gu5/BoX82V6Cpo0qoqZklC\noBxOLCyxazOyrODlS0/xwfY9RqYmt/Oc76FrMUISGEUraCDLgryuyaWk9DSZr6DM6U0tT7sdphhi\nxqjSIFPIMwdkTctOCXodnEWfLJvAIMOfWKyVjIxgmB3R8yK6uSUJfcyJHqmpUP2Y4fiYtVaHbrdL\nWkyo05RTqaK2Lts6pWokBGlJUWXM4owmPm21TFP3WMwjilZI7RRYSoIiY2pqJJpmo0tezqhMRWpr\nytKgrIOU8934GvCEYiQrKmOoK4NQ0FUOSzLkKIsxwseagu3779C59RrnrvwCh4cjAl9SlhDNSuy7\nt6F/QPCPX0YeZCQ39olUl9aTF0nNhGL7Fv2vztj42CcwCw3GuaVcaiPyDDed4R4lFAczqnFOvuig\n6pK87+CIJg2vSf/eNfLjXSgK5GTEZJKy9fxPs37+4ywfz0ljpU5Z9CIG9j2++dof83PP/mMyEZNH\nEWsnrrB373uE/gjJJuPxmEoXtJshUjsUSnF9OCHbXOaUdhAf5PDpnyK7e52etvDhgMZ94NQaJ86c\no7OxivnwASvtRY5GBxSRx7FXc6pu0AgaFEFFN2qy0GzjCrBFRVD38KImh28fIxzNR5Y+juc7hM0Q\n39HUS02UK0jzCZNkwO7hNq9f+w5laejoLv3xiMWtxwn1BmU2pqxjHGv5wgsv8tLWM0QyJJ+k8wdp\noPFkC1nUNMIAmdbE/RHx0YRJlhNYTcMN/7+11f7/cUlrcGyJ0QItJKKWCB1hBLiOZef6e9g8ZaER\nsr+3OwdFN5uMRiNUVM7VwvXcYOl7EWVdcXw0YbI/xutE1EIynh3z1T/6Y7bfvwFuyMP2Ntd+cJur\nT18ijATTZECWzZBaUVmHSb/gu9/4Bnc+uMnzzz/PeDzmwpmz7Ny9iyMkloL1syf5zE99BuFKjDCU\neUFWZoynfVyvTSVqhIJmFOFYl6rMcNyUIh6jhCGIQmxtyQsXU1mU00F7HazSaNfBcX08v8H2gz0u\nP3GVF158iVlRklbVHOtma0xW8vWvfIn4eJdIQem6IAVKgzCacTpGWYnjO9RW4Nq5YNEaQS3mUGgk\nWGFIJkPc0KXhh5RFSZrOSJmnECaHA3aqmExkIMFFU4kKhaSTKTJPkLoghMYoRa0MTlaxmFvOWZ+H\n5Nzo5GQiZ2EMs9wDHAKnotNqYlqatMopRxnt1GHZbZJQkWLxo5Bd19A8vw55ibc7YtEohrMpgbK4\ngyn+2FDlMyZIpOfRUBKZwcnVVcriAOFLHo6OUQikHzC0M6KwQTUeYssK3/dQJbja4nmK1Gb4UpPU\nJZUEIwyBAEfMBYFVVbMQRExtgq0KPKkRSs5XpcsK6wpiYx5pVRLeeOuv6a0+TtRYpTQGtdKibzKC\nwRila/Z/9/9m6/MvIy4uU7+f8PB4iHYkSytzyeQs28FvnsJPJVZKjOsQtBYJPZ9smlJXFc7uFLXo\nEsfH5HFGW0aIOwVt2QW/IF0UvPzsj3H35iE9u0y9tYnKp0zKIaaqeLF7BpF5vH7rSzx5+hMsyic4\nKHJ6K1dId98nCVsIx2V3fMRi6uPJGNFwiYzP/Tyl42l6wQL5iSbe0jL1176CurHP7MJjyKN1pL9I\nK9MoG7A7OaZwDCGCwBqK4QzlNljvrVJrwayuGMxislnCyLkPM4EThuRliRkISlPjui7WWoKqSaMZ\nohxJZ3GBi088zcqFy3zrW69SPzzi7JUniZYvUgwD+qMJvXaLyw3Np88/iakFWZoSap94MkIIj36y\nh/YjdFWx0OwyOJ5wOJ4ifA9cF1+rH6rI/khAu9dXV+0XfuXXmQlFPZsga0khArL4iIbtU9JkMplS\nljWTyQTHUdjaYGyNp3yeePklxlYSWgeVF9S2pBI1DhIZJ5SO5vBoj+133+Xam9/nqY//PbxuF1uW\nlHXN2QsXOHXuIllWcH/7LocHd7l69QIPbt/lL778FS5eOMf23dsoBEopGlFrjmdcWuLzv/KPWDux\nRTqeIssCYwumyQTPKCbpFO0KlhdXUMKlrsC4At/NMSgQHkLNddMWjbEOWAffa81lbXWNo1xu37hJ\n1OhQC8mf/Ls/YmNjjUuXzvD7v/NPGd79AN8V1HmM5ylkHpKTYXSNIwUeHo7QxHmKUXMqUpuIEAdX\nPIKe1AVplZMiiUKP3XyI1RDUltJz0NplLVYc6Jipyag0tHAwZYVxNJkxNEoLvgNKkpqMrXaHF91l\nyuGAV9I+017E+sznfGI5ZeBYOXxxMeeEStkd5sgEIusT4eEJD9nUpI0KmgqzN6bnhrizErcwBAgc\nK2gol0hIdlTJyBbYbEakNSNbUmiHQIfoDM75ATqbK4Yi3WJh6wmClXOsdc7gLLYRRjM52ObWje+w\n/fAaI5NQuS4N4zGoxuRuSVXXaBngK580L0FDVweERpJTYquaQGhyDYc2xVcwSguk6xAaS7uCBh0a\nnct87ud/HUKX7CjF9du0MkF/cpdummKyhFGg2dq6SuoGlAe34dqrVIzJtk4QrV6gcfEppkd9dJLD\nKEckIGuQUlA+vEZcDJjlfWpV4gYhkyQjyTLOnDvLiSt/n6PVGbu/969YbHTxRI9jXJKmoLAPeEO8\nwgd1l/5wmycbG7y8+Yuc2QjIq4LR4TXWlz6Bc/Y8kXEoJwkq3eZ531KlimptGRkntJYU5UfPoF94\nAfXde5QvbVF+/QdUtw64PRnw7H/3a9S+QlYGXdWc2jzBVnuN2/e3ScoKIn/O+ahKXO0wFSFKWooy\nRkuLqWuafpOFxhK97hKuWxIEAY7nMksSdvYOaXU3UdJlr3Q5f6LFB9dvkaSStYbhn/zqZ1gyLvdv\nlpgqR4Q+utMgcw2LqwtMDgfQT5FZSlwULK6uMZ2lPNzdYXc8YHs64C/f+muSfPZ3B9pdIsiyhCKJ\nyeMZRTHfiHIcl9zpcDzuz6HTGIwpSdMc3/MwtWHmaigMTQGlmVOJhHGxVU0tBLGqCH2XRqfDyoXL\nnFE+nbVTCCSqmXN4OODam9fmCwVZTP/4gHNXrvDCpz/LgzN3+Ma3v8v2nbs0/IDJdEa718L3JdNJ\nhiwrnCCiKC1auSgEx9MEGbQoiwpXzj3wtfSpARxwtCI18pEz6tEwpZIo5eGqAGMVUlm0nEeBRqMh\nrcVF8izh7Tfe4aPP/QTTg5v8m3/6mwwfXsdxNZXSVAqqskapGFVbWpWLFJpSC0rHomqgyhmLnKZK\nqaymNBqUh1GWrC4YknIgBO1asWwi4kBClZPZhLtORV6DKwPawsHIHL/RwPMChFXsVxmhqWkUdr4v\n77t8Mx9g5JTtLjRDQVzF3Gn7PHB9AhnQNCmF9FgMPHwVkGeGmVXEjoOqa5qJRd0b0coKKjmHgZyX\nCxSi4K6c0sJwVq4SWcsHZYIiYEKB8GsO8oS12rIYaHaqCVsGVvw2q/4yK06P8dIqu46lFxsaK5rp\nzjEFlo/+2C9w2L/Hjff+mtTNkOS0C4uHoLCWQzcjdSo6ONhaI7SPX9cM6xG5a2j4TVojgArPteyL\nnAXTxnUt950jwuGA178hufLszxMEEZKEOJT4RYBULvE4ppn1eX/0bTY/+eOEH/kIVdRG33mPyeQB\nduse8a7H+sUnOYwN0kj8JEZOpmQjcFtP0dAGX5Q4QkIuWXZDGltruCtdjkY3cAKP6vQWBx/s4i84\niKBBWIW4rUsc7r5F7uzTbXTZZsKf7P9bruQXaEc+2/kD/kG6w/L0AntdQbiUoqpN3poYnnYGqKN3\nCU49RfJfPoHX2qL64D3+3/bONdbSq7zvv2et9d729ex9LjPjuXk8M8Y42JgBDAWXJKQhgVLRS6Ki\nViKNKlXKRW0q9QNVpCj92Erph0pNoraJmtKW0OZCAAEJF9sYMAbbYDAYey4exjPnzLnvs2/vda3V\nD+82tVxsDGVyzqD9k7b22ut9dfQ/z9rnOe+6/Zd+212kf/kwnU9+nmmm0W86w1d/54MMVcGHH3mQ\nhy5eIg66hFHO5vmnkFARVROqypGWBa5QmHyTqNGn2ewQJpqs2mN3b5Xt0RrPbjSIjaa90Oaec6/l\ndefu5SfbyywtHqlP9NCKLz30KNqEdMwe/+Tt7+KMnGT18nVClXDtWMDywiLDcc5wmnL98hZUlk7c\noJFPwGhGWcFWlrNaFTxXTbg4WKN05SvObwciyTpb1TuxfO2BKSJEUYwXzTSrt18G2sxczwUQnPX1\n2eveU5QlEoSIEoT6Z1hrQTyd7jJ5mmKdotNZ4Pjxk3gH48mEXhcWez3Wiw0+97kHOXXmBJ1Wwu76\nKp/52Ed58utfpRwPWel1SCdTeit9Tpw+xWS6R6vZI1w8hEbjXa1nOpliwoRRnpKgcNbilWB0vZ/a\n+Yooiuptt0phTIj1glagJERUiHIKbMH169dZ6C1TVRVFUbF67TrHjh7i8c99ki984s/QDDmxssS1\njS0sFVEQUpUFFk+sQxa6fbwXdicjyizHW0+iQzpBTNsL2jpyCw5AaZSEKJcCIXEYMAF2bIbNM7yG\nUAlGBGdLKiU4ZxltjzDRlDCM600MZcFmnqFKTaiFSoUcixe42wRkWY5ptLloh3xnssthLYzWhhy6\n61VsXL6MbkX1WunxGJsXdHREpxR0Tn18iyhuDTpktl5idooQg+BjjxEhigJc6UEVNCuhawWMY5TH\nKCdsNcBUBfnuc6yNB7SnqxT9LtfMMsUzI649/Rj9qEluYl596ggLwRv59je+wbVYKGJDozSsZJ52\n6tkQTxrWsfAmAlObdCsj5NaRY1GRo1F5FlTEps2h2SId5fRUg8cuPc72oMW9976ZhcVDqCiEWLG+\ndx2JPTuJ4LYmVE9+k/HyInQbtN5wD91Li2RPbFH4x6lSobd4klwl+CogWlhGLzmicgXTjHFKY4IE\n7YSyyqiYsDdYZWtzg+VbVlg+fJR0PSdpL+BMhJSa26IeR5Mu29kWdpoTJ5ppmfK4+g7fvPQdmoMx\nrzl3gsNH38RCtkilIyYawsUW26OKQ9Mx1bGY1lcu4/7BrQRfWWU6sYRvOs3Ox79E/8wt3H3fOfy3\n15E9eOtv/R387oRfef9v8MGHPoV3jkALFBZflsTKECUx/ZUVkIC94YTV3V3KKiNsKNrNNmIDWskS\no+1dvnD/l3nogS+RZRl33H4bK4cWOX33G3nfP3o35S++ky987COcXIyohutYP8Ysr3BkaZFetw/l\nJtc3t5jmOXEQ0IwbaK2Z5Dl5OiH3FXvZmCtrV7m+uXrzjcmK1Os1C+cpKk9pHU7XRrl5aYmiDsYY\nlHhEhZR5gfP1McBxq41D0Ci8KASFxYISRBn2hjmj0ahO4s4TRwm7u3ukkymT4TZnzr6axm1naHWa\nrBzqcf36FdavrvLAX34Sa0sWOg2KPEVpOHHmNu5921v5xpNPsHV+HVAYFaBQlF7wOiQIDYEF8HgH\nThkwISqs93uLSYiC2cC56NqWUjxQDxV4B1UltNttNjfX0DqiKiwLrTaT4Tpf+KsP0GwapPKsbmwR\nBoYgaTEaDglmJ6OKdYyznDzPSW2GRtE0Ia04ISo0bdH1si2xWGXwQYDWEXleYnztnblJSqkKAlGI\nQOk8WissDoyjETVIsyEOj45CdFq7PlU6oKEDYqcZSsGg1JzNIo7ETVa3dsiOtFjN9tjC8lP3/ixB\nM2Hz2W12hyVR4nFpTjjNWQg1Ta8JVEzhc46phFuc4Vk/QSvHCdckIMQEGl8UuKCiDD04z0ppECL2\nxDG1Jc6mDKzUk1MmxJRbdNZK4qzDqrpMubdHhxSTZWxc2mXn2gX6pxe569jtJHsbDMoUX1QUSvCJ\nIfBCWeQ4UzEsBpQ6YGBzYgxJELDUv4XNvfO0dEhQOLzErA4HhLEhiNtUI81r734NrUbCzvYmKg5p\ntAMmQUWrGZGoBs1WQCQRw801dnccnZVb6ESH6YZdiumI3WcvElNCbxkfdiiSBbLYMx0KjarCpiO0\nGRK3EpwUTCbbTPa2UUWBmxTEjTZZ0iGIuzilETzP5jvspmOwHlt6prEnzTLszh6vPrHCFVtx/5VH\nuev0WzlUNVg4fpg1tcd6NiXotjh25nVw5Rk27jyGfPgTNJwivXyFpYWE6Nf+IeuffJDF8xcxmWbv\n+pj0f14lPtnlmUtPgK2P3HapJVIBkUlYWlzBC+yVe0zGA/LS0+l0ULpFWebEQUzUaKD8hN5CSJg0\n6Pf7tFodNq6t8dRXn+GB+x/iv/7u7/H6197NW25/FWYwwKgFlpYP89hgBFXKeHPEcDjGjjPEVZRV\nSaEVUZSgC8vWZMhmPubq9lWubl5hnA5x/iZLst4LeSlUEhF3YxIdYMIIE4SIDtDe4ZzDz9xz0smI\nLMtqI14JmKYW8hyH4nnjFCPgjMKhMGGjNu51njAMCKOAstdlNA5pdZdIreXk7XcwHm6SpSXZNGel\nt8wtp27h4tPfqq0AtfDspWusb36M3cE2Mq1oTgrKvMCYirywiA6YZBXaRGgglPrMeecNSIg2ACGi\nYvAW52aOad7hEWzla+Pw3FFNpzhryaZjqrRkurPNA5/5CCosmKR5bQwVCN4r0uEYQRGG4WynTEEl\nFQohVCGxCVDWU2QlidcoHWO0Au3weubp4Dy7oklKRejrDQcoRWIMhfJY5whUgA4dhXK0vKKpA/Ki\nIk8nBEmXjjRJjNAOQ7pac92NuMwEW3h+Rrc4feQYxc46t7ZaPJVnbO9usZS3OX3nOZ669C2mfkor\nCekihF5RZDk2iIi8cFwSxsWARCriuEFURtyml9kxFeV0j8JOybTHYVmRJpFWpDonjgtGowznFWMx\n+GabILfkBchgQlpcJ8JhQ824sCwny5hWAij2bEabkF6UsGdyvpPvMbYZ2lZ0c0eaZOSFxfR7OBeQ\n2pLhdErUaHAoXGCvGhMpcFVBjqMKYy4Oxvz6z/9zussnKdKMKIkJkpi8mtLqL9AMAooNwXabTIwi\nRNObjnDDAZWusK2KILyVjcllgp1nSNR1ksYtBO4YatBANSJke4PhM0+QjTcJui1Upw1hE6MDjnRa\nlMOMPAUTdxHTosRTkZH2YzauZaznYzrtZbYmG7zl7tfxpkNnefKRL5D1uwxDy7enz7DAItWGp3kk\nwGLZDho8ujnmDdqwspZT3dHFREska9+h+uoF/Lm76P3iT6MfPs+VwxFHB47mUpcPP/4Aa+Nd2kmM\nKyyNIKLRblFZzzibsj7YYEqJ0iFBENUOY6VHJIJMsbc7JIgy4rjFzjDl+voOK0vL3Hn21Zw8dpZ2\nt0E1GDNcXaU/1fSXFlG2ybUrIwoTMN5eRdCU1s5suC1pMWF9MqK3dIixLXhue42nrz/H01cvsJcN\nMQbIbjIXLi8K016kmTSJkgYqjFCz9X8A3jqKosBWJRohTVOm0zFKBOsErWMq7/AORCuUOHSgEaUQ\nX6ADi3IWJY44DvE0mY4nJN1TVKZ+gjt/8Sp5NiQIWgz2ppw+8yr+1t//23zgD/4L3/ziFzl1xx10\n+0eYjsZUuiRPRrVV32hM0uzXxwl7Wz9NewNaiKMGzkHlFJU1BEGAaEPlPNbNzoafeWG6yuJ97byP\nrb0xs7Qgn0xZbnX51lNPcunxr2Hy2nQ8KyuUaBraEGghMRrjhbVqVK/xDAOMMUjlUF5Q9UHJ+EBT\neIMRQQlogcAKtvQUrqTp6yfejnFMy5zQKaxSJEGI5A4beabViEBaHO8sszneZVIVTKoJXRdjbcCQ\ngqaHnrOkWjgfWxiscU/bku0M6FcNllqaS+vnqRYP0Vo8i01LvMsJtRCLIFVF5ktCQrpoSipySkIP\nLRUQBAGYkJKC0jtwJeApxdMSg1GKq6bEVBkmEnrEOOcZlRWRD9gIPAmerk7wBna8pXv4EKWHTr/F\npfVV9mwdAxNFVL5CZxWHASOGHMeeLXFSt5XStRevcYYATcM1KFRBbBwDXxDiqabw7ne8j+On3sKF\nzSvEJmJ5uY8RxTQ1RMqTjkaUoUGcRzJoBR3CbsxIxqT5HqRD4mqBxEQwGjDc2WScXyE3hzFpwLQf\nEKdDsmuXcDbF9xeYJi3CZo+o0cVlFWUF5dTS6i8RmBhJDGma0muk9FYWuLRxncF0yi//41/mPT/1\n0zz40U/yxMYlDp88QXUo4RPf+gSv+ck7cAPLUmOBQ82QTe8Z5gnbp/ssPryKWdRQtVDeUC0s4L/4\nBP6u08hr7mT5th7F2W3CLz7Nk1uXGU5KAgOL/WWU8+xkGSQhe3lGELdIdG2Co4xBqRDEIAS1uZYf\nU7kWUbKCMpY0HbOxOyR/6lFCE3Cyd5R3/MTr6faOcrzfZzqeMJym7O7mFC4gbKj6OBrvGE0m5Lag\npGBgUy4j7BUTLq5d4fzVi2wMN4mSkEYjZDyevuL8diCSrAlCDp96FUEcU1YOS31SQFmWpGmKRpM5\ncE4RagMBSH3SBKqCrLKIaKytTVy8F6xTlLYiL6aAQyuHLTKyIqcqc9I85Y7bb2c8tliXkU5TnAtI\nIsOxk2fotBsMRjmNVo973vI20vGU246d4eL5CywdWcR3CnTY5vr16/SWjhIEAZNpSbPZochytImJ\nkwZlWc9MZ0VZbyzwCnSJtbXblfLURtBVha9s7ewujqqEyTDjxOFDPPbQZ7n/o39KXBScaZxgVFqm\nytFoN9FliikqlpMutrRY0QzLFIsiLQtUaVEqItCGKIqZDVvinEdTEuFJPAQYVuIuNs/rXU2+JCw9\noQ7wOuSWsFMf30NJ6iYshW0Wm33AodSEUixhAZVSpFJhreXW0tATw3ljGXQMD092ONNqEKQVt8QR\nrmN4evsSixsparRLWylayhNVllAMEQGhqrearsuQFYkZklOUFYHSXHUj8spiA6EhGlVZMucoFWgP\nkVbYcYjShsRFZL6oz2hqhIx0hS8dXRqoOOJY/whLx4+RZztcuXyeKAoopkMm+YQsn+A99JWhHyQU\nPmfs6zaMg3pNc+CETpLQVQ2aYsito2NCyjJlMxbKrOJw1eDN5+7l8vqY/sljSGYpC0eeZQRimA7H\n5NOSqAdRAKrSZDkUhasfJpxGxS2ydITSEfg+ibTxyuHKgn5XaE1zjNZUh09D0MAGIeM8x6clvpgw\nKSBJ2uggJIk7uAp0t40stHl68hjPbV+l2h5yqnearWdX+dW/+JcMtrZpr3TpNJo8Nxij/JArxTMc\nS36CckcR+5igDVG8yHpkaN8TEj7yDNnrQ9Jrq3RMgu43mT52Cdop4ck++XIT/XN3cejjHQqEbqv2\nTTYYtscjICJuJSz2+oyHU3SgKEqLDmPCaKH+R1GWRC2DLSZMqoqigKixgBCyMxzQjBu0eyUL5ZTD\nWoirCUGry+begEg7Oh7GZUiW5lg83lpcWVFSsldMmaxfZWNvi8vrV9lNR3hxKAXO2h/Es/tgJFml\nDWGjhUNR+gxra99H8RXKVd/95VDgpF7nGQRRPUlmBJflhGFUn/Wj6l8/jAxVBSJNRDzNZsBotFsb\ndjvodBts7WxTZApX1guMp5OcwfaAMIw5/+yzfPrzD/HGe+7mzff9TR747IM8+NDnibSmcfQoK8cP\nk01hde0qx0+dJWx2qHztKCZOMDoiMDHega0cZVHVHgiJR4cBVeXQgPMeW1iqyiLegxNKKdjcGhGp\nBuefeoYP/eHvciQKeNXhs5wohUHk2KlKcleAUyQ6ICwV2JCmmjJxGeVsLEIFBi2GyjpcntEwAaDq\n8S9X4H1FpDSNIOB0o8flbI1BPmGqSiLAaUVDAk77LglW+5YAAAaiSURBVDYxXHEb5CqmoRKKSVYf\nzBeWiHLkNkdbg3KWoZ3iCk2vjIkkJY0iJr0GT2/sccIGyLTCG6m7gOWQfjOmh6KZW4xYQiVEaHRh\ncaoCKxitER9QWo/1loKCTAXYUBNVgqocOxpyqT0WxHrCuEGjyCm0IxBNURRUkaAnBWVZMlHQsAaz\naxm6Ae2TCVe3VjnWO8TWYIs2EYtJi1TXGzDGOLKioBCH8oZYR3SChByHLz02nzIqc5oxZK6itMKg\nyOgmLdo5/PnH/ztn73s3i41Xk6VDqizFpwVZXjAZD0miGJ85nLNUIbgYGjpmoQyYpCnj0JJ0oMg8\n+KBes+1TpsMdymaLttYUWkidB2KEgG5DiGIBUzIVTSdpU4YGJYZ8UpCWGb6Z8OnzD7OZbvPas2fJ\n1jz3P/AQE3JWOgucWFpga1pgSsV995zj2s5TJJ1ljBzDZIKJDU4XbNmIWzslLDSw0RC7M0IfMUyk\npNmMoRygP/ww0XvfwrRpuWBylkxI7jzDvSErnT69Xo8rO+tMswnD3R1uP3aW3kqPzcEO19Z2CCJP\ns7NCEAbkZQFBiicgDFv0en3ywpBPLUl8mLffeZajSQsznuBchcHRMGBVwfZoAxonyLMJBAFqdgRt\nVhaMi4y9POXK6hV2RtvkVYr1Vd2LHrsf4ISvA7JOVkQ2gQmwtd9afkiWmGvfD+ba94e5djjpvV9+\nJTceiCQLICKPeu/fsN86fhjm2veHufb9Ya79B+OV+3XNmTNnzpwfmHmSnTNnzpwbyEFKsv9pvwX8\nfzDXvj/Mte8Pc+0/AAdmTHbOnDlzfhw5SE+yc+bMmfNjx74nWRH5eRF5WkQuiMj791vP90NELovI\nN0TkayLy6KyuLyKfEpHzs/fefusEEJE/FJENEXnyBXXfU6vU/IdZO3xdRM7tn/KX1P7bInJtFvuv\nici7XnDtX8+0Py0iP7c/qr+r5biI3C8i3xKRb4rIv5jVH/jYv4z2Ax97EYlF5Msi8sRM+7+Z1Z8S\nkUdmGj8kUm9lEpFo9vnC7PqtN0SY937fXtQ78C8CtwEh8ARw535qegWaLwNLL6r7d8D7Z+X3A/92\nv3XOtLwNOAc8+f20Au8CPkFtp/Bm4JEDqP23gX/1Pe69c/bdiYBTs++U3kftR4Bzs3IbeGam8cDH\n/mW0H/jYz+LXmpUD4JFZPP8X8N5Z/e8DvzIr/yrw+7Pye4EP3Qhd+/0key9wwXt/yXtfAH8MvGef\nNf0wvAf4o1n5j4C/u49avov3/nPAzouqX0rre4D/5mu+BCyIyJG/HqX/Ly+h/aV4D/DH3vvce/8s\ncIH6u7UveO/XvPePz8oj4CngKDdB7F9G+0txYGI/i9949jGYvTzwduBPZvUvjvvz7fEnwM+IyA+y\nY/YVsd9J9ijw3As+X+XlG/Qg4IG/EpHHROSfzeoOee/XZuXrwKH9kfaKeCmtN0tb/PqsS/2HLxiW\nObDaZ13Q11E/Vd1UsX+RdrgJYi8iWkS+BmwAn6J+sh5476vvoe+72mfX94DFH7Wm/U6yNyP3ee/P\nAe8Efk1E3vbCi77ue9wUSzZuJq0zfg84DdwDrAG/s79yXh4RaQF/CvyG9374wmsHPfbfQ/tNEXvv\nvfXe3wMco36ivmOfJe17kr0GHH/B52OzugOL9/7a7H0D+HPqhlx/vns3e9/YP4Xfl5fSeuDbwnu/\nPvsjcsB/5v92Sw+cdhEJqJPU//De/9ms+qaI/ffSfjPFHsB7PwDuB/4G9fDL82ZYL9T3Xe2z611g\n+0etZb+T7FeAs7PZv5B68Pkj+6zpJRGRpoi0ny8D7wCepNb8S7Pbfgn4i/1R+Ip4Ka0fAd43m+l+\nM7D3gq7tgeBF45R/jzr2UGt/72y2+BRwFvjyX7e+55mN6/0B8JT3/t+/4NKBj/1Lab8ZYi8iyyKy\nMCsnwM9SjynfD/zC7LYXx/359vgF4LOzHsaPlv2YBXzRjOC7qGcwLwK/ud96vo/W26hnUp8Avvm8\nXupxnM8A54FPA/391jrT9UHqrl1JPRb1T19KK/XM7H+ctcM3gDccQO0fmGn7OvUfyJEX3P+bM+1P\nA+/cZ+33UQ8FfB342uz1rpsh9i+j/cDHHrgb+OpM45PAb83qb6NO/BeA/w1Es/p49vnC7PptN0LX\nfMfXnDlz5txA9nu4YM6cOXN+rJkn2Tlz5sy5gcyT7Jw5c+bcQOZJds6cOXNuIPMkO2fOnDk3kHmS\nnTNnzpwbyDzJzpkzZ84NZJ5k58yZM+cG8n8A2VPpZrGBDAQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7faf54e5b0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'\n",
"test_img = imread(test_img_path)\n",
"plt.imshow(test_img)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"vgg\" object already exists. Will not create again.\n"
]
}
],
"source": [
"# Run this cell if you don't have a vgg graph built\n",
"if 'vgg' in globals():\n",
" print('\"vgg\" object already exists. Will not create again.')\n",
"else:\n",
" #create vgg\n",
" with tf.Session() as sess:\n",
" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
" vgg = vgg16.Vgg16()\n",
" vgg.build(input_)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" img = utils.load_image(test_img_path)\n",
" img = img.reshape((1, 224, 224, 3))\n",
"\n",
" feed_dict = {input_: img}\n",
" code = sess.run(vgg.relu6, feed_dict=feed_dict)\n",
" \n",
"saver = tf.train.Saver()\n",
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
" \n",
" feed = {inputs_: code}\n",
" prediction = sess.run(predicted, feed_dict=feed).squeeze()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fafae27e4a8>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6fpKo2MG7Lvsws1OTCMEkob4ghXKWNhbotwVeH1FJTxxkeWqaG9/5G1x+7buY\nHZ9BEUIMb90F5SZ2XkNMqkxVFU5MSAxvvIpM+hzFzDHmpybQU2sYXv8hTp18kIkTb3Dg5SNotCC4\nDplambpq0dpYZKKWp2VwFz1RiWJlhYmlDKlEO7fefA0RI0vQ6uGam/+EtZgceeUHnLbCdIda2CmJ\nXLp2jqVsk+fPhXlz/wDLwhIbd26hstDJ8mKDUFLDNnxUW8J2Wsg2ztI+MEi9WkGxE0yPFOjv1tEC\nRUS/j2ajSiJZY+xYmdERm6VihpIpQcGg7ufojyfoG+ig4mqkR8epTjTBbbJhbQQLCTfpU6mlkJ06\nITPB3humEIoas1mJ9MpphoKL3HzLy0yeUrGbFqHAEo89d4xA1wTdHSpf/WmSJWEvy0sGpflxPv77\nX/n/Tsjee+89dzuWQzAWR5FUDMvEsi3iPQmSfa0EoiGMisP0yQlqxSbdQ70IMQ0hGWJltki0NYYY\n9NFUhXy6iGBLOPUmzZUijm3i+g6xRIxapYYsqpSLZTRZRWwTkXWF9vYO8oUcoUSISr2CZbm4dR9R\nkwjGosihALKqYjaahGMRDNNAVyEUFonHW8jO5DBXmpiFOsFQGD0VwPYaeLZLPByjkM/TqNURXQnP\nOP+nkSSKSFKTgBrC9V1M22dpucD7fm8HF+zMk16SyNdPsbcvxI+/Pkc+NETTP0r6pMpEbhJDNMnW\ndJhp8onbQ8zmDMYXqlxx+Xq2r1vDc7+I8rt3P0gssgalUaZto4y4EOfq7U2qukq/Vyefb0AoyLvj\nO2k99gzjYZGJlZNsGr6dL97zPE5cxq7MEAoESacXsEyRgJ5AbvURHIH+zYOUKhPcuedy/vT33sVf\nf/PvKEoxsAW2basxuHOR+swGFhdb6F0/z59+7XYe/fkBHn3sdZSwRb0iMzlapWNdjdYOk8UVn+7+\nTqYbGRbG2vnDT97MZz/3TW4e3MbanWvYveHDfPK9Ozjx+hM88MgcRw4pbLh4keXyAtOTOms3Bwlo\nNuXqq9x2xVPEY3D3109x62U3MVK4hy0X3caDLyY4uqyg93Qyt/wE128cJsHTNJpBQkMyJirxZAM9\n08m8ZaOZCWZqGfK5Ee688QtUS2mem/wVs0c6eO7lV1i/fhPp5QKnT53m5PFjLGey7Hv1AAvLywx0\ntHP5ldfRqidpWpM8+sx9xGNtbBrYQLwrwevHDnF07DiSpdEeUjkzcpb9r76MpnSwmNnHju27eceV\nv0Gh2OBGfoTAAAAgAElEQVSjH/4se3ffwOPP/Ywv3/0dbvuN97OwMkeqS8N3mqysZIkFw/TUZvnY\nb13Gt/+PDzPY34MbEMiJAotOjTcPjLBn+AL2XHwzutBPozbP4ZMvUHR8qoKCV1pmoGcXN19zEyFE\nMg2R0rJPQo/y+v5X2X9ynE989k/5t4cfJHP2DHsvuoZgx1ZwdWRNYcReRnXr7NkwTKTX46W5Ze57\ncoFjY0GWawXERphC2mG2PEJ7v0tzNkdtYYF1Ow1CEYf5UZ2I1E04fI5KzgHbQLQNXH0IXbYoZ9OM\nHtcxxCZrN4r84ecENgZEfjUh04iVuPsDNr+9dZof/thEEsKcXcnR2daC5Wgoik45WyeogOv7eJqH\nHtzCm8/ZqF6ZFilOvCXMAz/OYHoKW9eG6NCifPL212n6lzJ2xuOVh8+xePYAuhFifjrDF/7o7rcU\nsv/b6mT/M0XVaO3rI5FoQfYFCvllMtk0QTWMYzjUK3WMfA0saFvXgRjUaJTy4ICGQmW5RNANIPky\nmqjg+y6iKmO6DsF4BNuzKdTKhFNRRFEglWhBDAhYgoEoni+cd5s+QkJgcLCfpbksgijQbNSRVAVJ\nV5E0lUQigYKIqGiYdQNd05hdmKBeKKGp50uY9EgIG49gOEI2vwSGj1U10DSVQCBA03JQVBlFU3EF\nF1E6X6ImahLIEGwJkkwqVNOzbO33uG33O1jasp9IoIEQ6OWi/3YdT7zwMoF28OtBXMPi1aMTVKQu\nGrrD2lQ/lq+htc+jK53kl06zbouEORflH+8RKFSDHH59hWUzS8UNsS7cyfyywnbNJZN+lmvefQV/\n8XdpTFlFU5fRiCIhMDC0jvn0ErLkoUkOnldjabqKYHdg6xP89XeP8sgvf5uLb72fvVdcgVkrc/Cp\nUWZPlrnzG+eIiSL//kObimGgxMCrtNO1xgbPppxWyMyKKJEq5XKZAy9CZ2uGF19+gzMnmow1Funt\nXebJ529h3ab38PEvW7y80MrUSCtJXaFqdSAlzxJLOezo3kpF+CfQj1C329nquGwTxij2BfEWc1Qn\njxLuvJSIOYrajLJryxky+d0I6nb2T/2QpqCjKkkqlkBAU6lKJmPz43SpTbzaPHr7dmbnnkYX45w4\nNkazKTC7uEQsmqBaqZDPlakUS8R7ZIxagHx2mQu27KVojtI70M4NN/wWBw+9xpFDz+Ah4HouzXSJ\nI+Y5To+MsmfXbjYNX8Po5D8zNvMmc+k8FaPB6/s0cnMlLrrqt2lUFhgbn2TtYDdb1u6ls2s9C1M1\nRGWK/nA/K9GNPP7aOQ4f+2fSGR0y0E0UI5JkaHgPNaOBGtFYnp1BqqlYfo32wc1UTy2Sy8zz/FPT\nbNl+CVa9zNAlrbiex6vP72fvtuvJHjvF1z/zBZbnjrN3+07UM3kG1rRSyCyS2Pl+XnzsW/zkwVHW\nbXonQelqzOJ+QKNhJsjWa8TbLGyrhtvYQdM7yKe/Osy//MM0544tcNlNe0kks5TGRcyKQrTdoTSr\nkc0X2LSjguA0iLYV6R8OcHp/g9GJFGcPmFiFZUKSwIMPr+HRwBTKoEJLtZ13/4bDG8cjpNNpjLpB\nIKoSiPoUFuq888oPEI/3MLv27+lOVnjHRRFqRpBTZyN4oQRGoINlqZe/evh1rr/mY+zcUWIu92kE\nMUpmykYLv/V8+7WYyf75PffcPbRhE+VKBdM2GRwYQBRFZiYnMW0Ht2ERcCRsz6ZjTS/ZaoGQFkR1\noVkyaTQMGo0mru3hmy6iICPKgOxh+B6SIhEOBrBNC89zsH2LQFgnqAVJRpLMjs9jVx2iqQi+6BGM\nBGgaDUJqCFcAw3UQJYmtGzYyMz5FrVzBdExkX2B5Lo3my/iOD6KIKfro8RCBUJjcUpbaShkFCcdy\nkCQZx7FRNRXP9xEEF8cW8HBxRRcTi8tv3ch1OzYR032626MsHp1iaKjG7NQ63vc7QYZ2tjFzUmR0\ntI5rLvPn/+My3ESOyXyEtOGwZ22CgXWbOXmqylOP7GfkxJ9x4AWBj/+RR1/iHK9MuAiVOmlTR2+z\n2dWxC6sCStjl4o8N82+PqHzju8+RGgjjuw6eKVIqVHEdl0RHBFmtghGjuiKRX/GxjCodAyFOn03z\nne++wm23bsTWduHJLrlzE0hhmUSyyS/+TqClZ5lc3iC/qOM7eQo5AwcRWSxRs+q09cik2jXKBRVB\nLXD46BRGM8BnPnYXJyYO8N2Hz1C0Z9E9jb/+ms6jL82yeZdDqS4Q0AMkA2mu2noZC4e38Y3v/gFK\n1yI3XdbHH9//GLf8pspi7hmkvhJBr590dYF1oQ08/G/PM1W8gP0nn6eQl1ASnQQDcywVg7iNKqGU\njhxcIFgPs32Lix3cwpvHDtMh9nF6fJ6ZiTlaO5PU6yU2b97AhRdewMjIOS6/ai1HXlqkp09jYKib\nai1MZ9cmSiWbl998iEymiGn7tIRi1HJFioUSLjJ7dl/G5e+8mkz+NOnFLFdf8j7susTj//5zetri\nXLLrIk6Nv0q+PM3KSonHn3iBhfQUTz39OP1DEYaSa4h1uuzZ1Ma5k6MsVnqo2YsU0hl6h66iI6GB\nlObQwX/nyP43uPX6O+jo2MymzZeyZf0m1q7v5M03X6SndwPJ5CCmI3Lg6AgnR8ZomEvsO3aMkekK\n9z/+BKPTR2htaWM+P8ITh5/DrcTwTY2M7fPG6BNMzR7HbPRSkxp4DZ/WDh2jqRAOJ0jPF/AshZnZ\nebKLARLtQWLhOCoWhXQGw5TxRJf2Pp2hbSVkOYzpmuRXQmSzHqWVCFPn8vRvrLNhTTubtvbgRKIs\n2UV8L0k+N8EV10vUzSvYufMSEvEkWsAjW5xmeHM3Bw6+QiTkIcphYoEgG4fbyOVsRCWC48VZyM7z\nVz95mYw3yA17byUqh1n/ziCSMIK4LLJcq/OVP/vqW5rJ/lpsdSiKEk3LJhgJE4nGqJRr4Pmoqo7T\ntAjIOpIg41g+xVyRZCxBUA+Sy+TxZAglIsiShmf7uAjosSC+LuMr0vnZpywi4NCo1vEcC6PWQJcU\nSnMlRl4/g1NxwINESxwpINIU6oQ7A5i2hSzLiKJIrVZjYWGBcqmGqqo4hofTcNAlhaB+/u0zX5Jp\nNpvguKiyRlAPnp9tSzIC0Gw2ERUZ0zExLBMBEc/2UGWZSq1KoidOe18rgrlIKp4nqd+KNlDk+GSC\nxJBFtVnmR4/8nG/edyeGVWDDVh1fS9AQ4/ihAKZkYAudBPQVKtVJPn7nJWzu+xSf/1IZ35jjzNEw\nPkkaiGxen2JTdwueqFBmBS69CpSNvPi0QjDUZGV+AbMepW2dAlIUmjWaBQ/BdVA8nUDMY/tujR8+\n28po6SCL1iAVIcSP/3WGdO1JBodjDG/dSCFX4On7ddrDQzz76At8//v/nZ07esCRkDwZs1Gnu7+b\n9TtAlJOUKzqRFoVKKYYvgRRWCbcVGdQuQjfbuKx7M4dOiHzqXof2uEuuGETR5vDMEYbXtFErDbDl\nhjF626+jMt3Pt1/0sHu2c/RAlXMT/RRmO3HaEiyVy3QPrEFKXM76zdsY7ItTRyafb9Ieg5KxQjCk\nUs0s0hH20VWFheU0c7NHMCsi5WqJWtUgHA+Tz2cYHGzjzg/eikeZ4a0tTE+k0UMhytVlDh3bTyze\njmkqyLKGKNnUTYdmpkBtKU8oEiSuaQQEl5f3v8y37v1Tjrw2hWCFePnFZ5mdnGf71n5CYYNS0cd1\nY0xOrDCwZohte3qZWnqJSKJBMryFlvXXsqHvcgS7j+tu/hDHDh9C1gJ0DSUpzfyIWuE0QnWaHWtS\nfOYTH8NX2zl8YpogPkv1Geq+x8CmLTSFMq8c/hVPvvAr3jzyJmrMwQ4ZVHyVc0smmy/ZxuDOnRw+\nk+EPv/QAM/MOWwZlbBfGJ0/i1rP092WQXBUtGKanN0K+YOB5KsVsFc9tIqpJlpc6uOiSi/noJ+5h\nevw4R1+bZse1cb50z150LQKJANlimPmsS8MJgCZgOk2iMRWPBNFUG6LcytyKyOzZMyyPFJmbkhHd\nCJ/7isoLLx3muWcPowhdbNlwA1dd8QG6Oy/gsksvJ5dbIr24wGxa4qkXBBZzHldeOkQzN0etusiO\nze2saVtHofZOCoUHOf3Ca9z75a28+OJugprylvPt12K5wPdBkxV8UaBeKaPJMvl8HqthkGxLIbgg\naSrReITcQgZFkcgXK8ioSMnA+fXVmoHg+TTsBoZnISgCkWj8/KO4KOLaNq2pGJ4HjmCzOLeI1FSQ\nHZGgHiDYEsS1PUzboNooI3g+IsH/eFdZIBQK41g2ogSNZhMkkWKuBJ5LsV4lFI0jC+A4LiuLaaKB\nGLIvIAhg2zaSLGN5DgFNwUXGNSwsy8G3BSLhEDRqdPd1IWg+DWGUaHw9o+kMjz5RZdeGvSzklglG\ndlCwHmMs9yqf+fhXePjRb7JUKiO2mDiKiFE1cDUdq6HRkezi+o/UOHPoPUyeOo0tqHT3aHQVI8zo\nkPDHUUpDmK0OfqRG97YrKOdX+OFDX+Yv/ynBP/3jjygvZ2nGFdp64jRXfMxak1rFZO1gk0IpiK7m\nmR1rsLTUxVL9JF1tKcrJBdpoYzDVzmPjL9Ea7cAJ5xG8JXzmaQtcBP63CSUcykUfXQuwlF1ECQuo\nmgBSmWZFJdWlUTVBC5nse/IBvvaz17njym0cPnCMWFsYX9G5ctdejGgMuzmFHi7RGluHpO7gjcMP\n4MVPMT6So72nk9sGw/zglQiXr9/C7140w33zjxH1yjx88Bnkhs11O2DyDYG6U8BvxAiL/YSrdULx\nCFZBRWnEkVrS7N9fpWfrFNFADLcZQJSgWqmRbJdobUswOTnG4OAA586dpSV2AZtu0WgY02zatJtc\n5Qzjc1NMjzk4gCoKpBKtWJZL1qzRqgdZN9hPGY/lExOsX7+dmdPnOGmc5JZb7qRvTT+LK/Ms12wW\nF0qcPJ4hGiuTSKXobOmnuDTO/lffJF8weCy9jy98+tN88c/vo2kpLOY8zGCd977jYsKRHJvXXsSm\njZeSrQR5/MWjhBMa+ZWTTMzO8/qrJXy/ySNPvkZnXyelcgDbFGlpjXDV1qtY072dnlSYcHQCE5Nv\n/ctT7F27maf+8QdMn3mFXZ++GVGf46UXmqzrsFmKLFL3w9SLNmpYZ6UyRkwZIBZqxZBmEXWZRt1G\nI8nVl93MSvVXTC3GGT97DFe0KJQjuM0IwfgSbqMHWc2jqgHKaYMNu4KcOFbC8nSqK9PUsh6xrrV4\nqkxT1NizpRstIrA0vcDTj56lf2CYS66+GsNYYeL0HDIC2y7YRK6SY6UaYWBjjMWpII3FCPVsgc27\nYrRWy1y9qZOR0au48eIc62IfRtZupLv7v9yK5f/m12Im63seRqNJuVxElARsx0RVVXQ9gGkY5AsF\nMrkVkCU0WYWGTS1fRRQU1JBCo1E/v4mG5eI4Fo16FVEUkWWZpmkgqhKO57CSL2OaJqKgIokqoiKC\n6J+vAW0YTI9NE9bC6EKAmB5HCwaQZRlNkqkWSsxOzSKpMkJARRZkFFEmFIkhhwIIuoIS0GhrbSWm\nahQyWRqlMr4Pju8hSCDIAoIPejCArut4toeIhCzK4EOqNUmhtMKrIw0Oz5zlyGiGshuk7FVAlSlU\nG6jaeg6OHeB979+CGgxQtUsoThRRDBPXssiqT31RZ3jTIGnjCeZLrxIPp9DFKlIwiWEYqG6DoJ5C\ni/WxUJgn3i4Rjfbg2SpPvPATDh0/xx9+5i52r2ln+nCDZFsErT2MpoOPytj4PIZTZdv2IDESuKbF\nuz+Qo7wso5sy7/3NM5i1n9C0ahh+A6NqM58Wuf/H9/Htb36S08eLZBfDaOFWFEEkFAwjOnEEGoi+\ngFEv/p/UvWe0XWd57f9bfa3dy+m9Ske9WZLliguWO6YlvpSEklxKQkKAkITcJDYkIYEEUm+SSwI4\ngIGYbmyDq6xmWZZsqx/p9H7O7n2vvv4fjs0/uWPcMfhI3k97lbH32h/23M87n/nMydpiFqsYwWsK\n/Mnnz1F3W2mPeYRbdRRNxjNrZINJRNuiWXaoln3Cxk6irRrnr8xz281/TLZSJa0qRNqqjI4pvOl9\nH2YqJ7BBM5mpRyjYfVSWdU4+f4L0YBbZ1fBrSS4taSiSge/4vOGmuxgbfBexlIojKEwsFKhYRaJh\nHde36e5ro7W1je72XioFC1VOcOvN93HzLQfZtGUz5y8u8eyhQwRyg0vjZ1nNXSEWi3Fg22YE0adW\nXx+CufHgHezZfy1u2SIWdTDtEnUrIBLtpVjwyGU9xEDkuWMPcf7CWa7ZfzOZ5Synjp9lbdrmqs1X\n0dth8syFQ7w0F3D/736ei2urRKIWjVmX+pUiLZZJOGZgua2sFGIcPnEW1zGJhTWm52c5/8oER547\nz8TFOhGjm2rFJRWNM9jRS1esk1Sim0y9yJXV04yNbmB5Lk/PWAdX33aAf//3L/D5h7/JK+On2bV1\nN9fcpXC5UqUoydi+h9uUKNfyqEorupjCdpap2w0sO4YcahD3Da66dYaeLQKXLlUpuTG08AipiEo8\n7KMLaULhBrIo4zUVunuhlFMol2UmKlN4hRBCoNIIimi1VcRonHqjwspCGadh05qOYKgBLxw/wvLK\nArrRJJGoMjMzhWmvsmEsRGdfKy9dOE7VnyORaJDLC1z9hgApZPIHf/+vVDwDOXqQINqK7do/N779\nQlSyruug6zpe00XXdaauXMap1bBME9mVMCJhmpZNpd5Acm0qa0UMUcM0bUJqiAAPXdOxaiYCoKgK\nsViMWq2GoImUqxUiqoYeCnBcn8D1CBQJhwauGFCulkASkWSJ/HKBaDhKIZPH81RUXaOtpZXAcnAj\nAq4QYHkusiDj+z7Veg3J0GnYFrqqIQcCousjiQGiHyAEkG5LYXkuihigiAK+D3ooguA5qL6xPnzh\nQTgcxjSbTFZaiJJmam2SaJtNMtlCvTnOuYsFotHrqdjThKLz3HrbHTT9H9OsFxGkCDFBRyCP3wgT\njnm8eqmDlDjH4uIC/X1hLrzQQzW9yM5RnflSPyW/TGs6TFdvGF8IgXCJpaVOfK3JR3/r3Rzceh0/\nfO4of/21Jxi6JkTWXCOkaoSDVipuwMTZJe66Q6c13kuv5uEj8KcP2Nz95ijnxk0e+pLGsivSIURZ\nwWMtX6RW1CjmTcQWqAcllIaOXDZI9hXRDI2JCw4feSDgmW+24AsFuocF4kaTt94V4g//cYpEKc3d\nB7PISORNk/rEKpEWF0mVSUbfSKlRZGBwiFR6jGzJZ2P3CgOddxB++SE+95HfRetMsHVrL30tAqXs\nHEN7W1koiCzpy3jNDiKRBc7lJGrOMvsHb2Lbrk2cOKHS3t5PLvcKTUaRIg2kRhU/cBFFaG/r5Pix\n00QjKS5cWKavd4hp5SnaYlv44K9/kisLR3j4Gz+io2sj++7sYnFxnLZwO0sxHcOXCHJ16r7E+NkL\nZGfWICLh2SWIQ662ystnn2duOkW1vILrVuns24mmaPjkWcssEFVD3PnGtzE7+Rx9QyOceOEKZy5f\noW/jVoY2tDJ1tkp5+TyXy+30NxRyusalUy8zO3+OpJ7CEMIkuroRT10gFlZpVGvIhk9gl7DqZYa7\nB2mJxbl44QjncmW6+nVeOXmWK6cnSY31kd8YobqU4+iRHHsGn2Jz/yjbN0k8faIb11QYaZGYLTew\n6iGiHWm8Rg3BT6MoBk2zRm61nYHh7/DvT8zy5FMKgWxS8lbo6wtjuAI1XyLwk7iiSdmeJJ2K0WxU\nWMnUiKR82oM+7HATRLACn3wtz5ZymEpEwm0GJGMhZM/EdWukWkdxHRdFjiAENcqVBjEvhV/xKS6n\n2HbNEJMXlhhs7eXohSmOXFQJUcVI+7iRJp4VRlIswqGffxjhFwNkHZvzpw4jSypBEOA4Dp63Prbm\n2h6i7KJI61Z/khqh0TAJHAsBsPJxVCmEaVo07DqqqhBWw5TWiliuQ1APUCMqVouN2iUhuKB4CrV8\nEyOUwG6W8BwfVQTZASxoiiaBIiF6Em7dJm+u0LRN7MDBkHV0BFzVw6nZpBNJRE0hk80QbW9FDmSC\npk6tUaGjo5OZmQUqlcq6daIkUPU8OnyZxXaIiDEUp4Dth9a/q1JB8uvgiBTUFhamj/Op9+3g+csO\n8VaVYlZGimQwzEGKykWuv34nFycfIb6hi0TGoiWhk9ArzLsx3Kk5lJLFdBQ29e7EDjl88V9e4Ld/\nI0TF8Dk/tcjVG95HQTvBUPcmGlaJmlXDCKr8cV+DYP5JdtzyDnbdeicvX5yjY89TTKk9TJ/Okb2S\nx4nqLNQG+NtvzLJhc5Z777b44d8ucv+bJQquyhc+U6Ox0oremqdc8NHkBn/64BmCYp5Ut8SH/hy+\n9MA2bjx4iaVchJQtc/2eOPs+fhPK1f/KP3ymwjvvD/HhDwhUcOiItvCRjxU4+ZMms6sGIxtUopU4\nLSMGJybyjHVtIrMcsLRc4M2//F0C4VGu2h5w/54tlK0yMSHChlt8fnLoJQr1Puq5JbxCGzvvnWOo\nZZDzZ97NIb5NmRrDlevI1SoMdRboTG3k7NxfsbFfISXEaZgNYi1d1HMethxwzXU3c+rEaWwlxMTq\nMs1chTPHT+FJ8NZfAsXezA+//RxCREcSi1w6I6GpEufsRaSQwjvvvIufPvEkjz3yVYx4EivhkTYM\n4kaYS+dWiLboVOxlVleW0A0JsxmQas9Tr3rEpQg7hoYYGhnF8nS09rvZMrqNJ57/PRTFYHP3Zu64\n7nYupE9QrQzRNbKLXWO7ePb4d8kVa5w/fx7XqdPV2UZH3x50YyOVwgm6+6PccO12itUM0xOTNOo5\n5BabwIzy7nvfw0Nf+y6Xl0u0pkNE4iYD7RkGOzNcGV/g7x8vYgonaUt0sDpbJt3eRiZwcUJFulpT\nNJoBmcoCYUXDL4skEz7LK1N89uEzLL/YRbM6zY5te+gdm2Mx77BUSiKWyrR2yFi+jyRJNC2XRlVG\ncEw0K43X4aM7GroQYXl5FUOPsdgoE3E1jFAL6cgcYbNGcbmCaV3G9gfp6WlBU2oklYBtA2kM8TRR\nWaZkbqQ9PI51UuGj94f5l6+W8It7ec/1axx+6iwHBs+zcdN+ZCX8c+PbL4S64NOffvABXvdufS2p\n4fXABkmSkBQZWVGQZBlZEPFcj8DzkEUJZIFquYisykiiQLolTblSxmxYBJaPokpIhkKo1UBSwHNd\nfCfA98DQDRRZwfc8HMsFMUCURZBEZFVD18P4votpmSCAFgqt+9W6Prbn0dnWQa1co1Qss3HnGJqm\nMj0xTWC5BGKAoijUag1kWQRBQBAFNFWlgY9kRfCsEiVJI6yJBBWFllGd5KCOZvQweeZVNrdU6fa3\n8vJihr62FIITpovz/MrmNN1dI8iFVaq185SmXUQlSiIyy+6B64g4Y1wsL1CdO0RhWeE3P3gTX/xG\nFy8+N8nV+3Zim3OIpTDpAY9uK0/fjjEWF1b41uG/4uNveAvp5ROEFxsstF0hHr+aR77yjxx5okp9\nySFhtJFsS5LenMZQ+5h7tcae7XmiySj/9Jdhfu2DvXz8g3m+9x8Rop1LNJ0WlKBAw4Otex0+8mGB\na29ROPyEw2zeoVqX2b2pl4jbgdYuo2q7Odj/PtbmTjOVz1Jt+jx7zOEnTzSYXDE4ebiAKsRIGAJt\n0jDfnT5BsjfN3tF9/OPffYyunlN4tRX+/C8/w4YtVyhUurD1i3zn6w7DI29jcGuSTGEKs5BGcOs4\n8hJHTyyyXFkhm/GR/AiRiEGoo8H1u9qpNJdZmE0TiqyRWZ3CUyTa2tuw1lRMTeeafQd49PFHUSMq\nwxsGGB7oY25ykQc/+yUee+zbHDt6kqsOXE+itZP2AYWVlUVkQrxh/ybiUZGF+cuIiNz35g9RLJq0\ntXXytrfdTrGawZfq9A10IisSV+3bhe8LCKJEb2cvoiBy7TUHSKTjvHz2FV48dYr2zm6+8tVvU6tm\nSaVDJGIhbKeCoUNPzwByEOGJF79LV+8gA30Rps5OM352HFSTQOvjysRRmnadWDTE9l3bcB04cuQc\nuhphbOM2dlx9M+WixcKVl1CUeaRIDCEawbKPk788T+eWJPOrGi3pTuyGAp5GuWJRq/rEEjHsbA89\niTqjrSaNqQJf+ps/xKmtcO5EHid6kM7NFnJ3K3hpnEaEwHFIkqdZEshlV5mbnaVScVAUBcdx8YG2\nzja0SBin6bA8u0DgB4T0MNnVPI1Kk10bTTSqTFyAnAlBoo26VMRuaGh+lF9913v46Mc/Qzzh8jef\n+zJ1N0qtAbe+dYW9Ax187P3vJiTPUpRlfu/jJ1hZOEJYHuYrX/83PvCBD/33URcEgOcHeH6AKEso\nmoqkyCCAF/hIgoAsSqjSfy28gyCgWaqAC/FIhFA0hKRKiBLoqowsge05SBLrlaQgoIUMREWm6Zg/\nex/bcggC0DWDUDhKEEg4poOiKyiaApKIaqivfSboeggxEMiuZtcdxARw3XVD33RrClfwMC2XcqWC\ntE63YjaddVtF10MNS5j1EqalIxsuUlPCo06l0UVSU6msZkg0TG7bNcDDX32adEcPjmkRDuUoGb0E\nOIS8TvbeeCN37t7O9bvfgeSbZGyZyWKZgptlrZJHCYt0jjiw6POm3giBUCeVMFiwLEb3xkmJSdwW\nl770EKcXLnHDxiFWzuaZb+hUsifpmxX5zG/fxvePX6avr4tkIsbgQJy9e3oY6vbILM0jy22o8Th/\n9JEGrrTGdTsu8sh/CGhJk1rTwLaXaVYjXH8A/s+3FG58S0DnVpOnH02xcUeRvVtuZ+euN7FjRy8d\nUg92/hCnjswwszBNblEmYiQIbBWjs43JCZFyKU332HbEUIS1yiXU8GY8z0MJDN75wW20D2T55O9H\neetNv05/yy9x4myD505lGNm3l0MvTTK7tohpNohoYeYuL3Ht3j/l1QVYqzrE5DhxPJLJOAXRJFOE\nfF2wi3MAACAASURBVOUn2PVZAsXAkhx0T2CwI0S2uohjB4RiMTZt2cDQaB+5wgqZwhId/REgwxsP\n3sCW/d2cnXyeS1OvIIgW8ZTAysoaE2cnGOjq4d577qTWrBGLttAST7N14yjTsxP4VGnv0ti8rYd4\nIoyqhHBsl1QqxujQRkJGnFRbF69cGCdXrnDjTW/gJ0/8AEk06exsI/B8ypUC7e0tKIpCJKTRN9BK\nYXWS1bVLrBVr9O/qIjYYJ9G+id07tzPQPUB/9zCVqsMzz7zAzOwSBw/eyr5rDmDEDVw7xJUrJxl/\ndRnPjhEoa1hWmemLSfbu/RBL83swS0OMn7HIZWwkOYYsqXT3ivQnDBR/mlvuabJxX5OebaP8xZcf\n4lRujt7b2gh1rHDh5QqNtVkyF8+wMLHM7HKOi6s5zHoVVRBoTaXp7+2kp7ufnoF+oqkoDa+K3/TJ\nLizTrPu0JVIEloPiKkTEGGtTJU48LbJcSJPeEUHsDKi7Itt3hLn/3RZd4RpeLcE1uz7GL995H0/9\nx/O0DhTIZGKcWJzjx6eOs39virjaS3YVWtVNvHrpcUrF5Z8b334hKtkHH3zwAUEESVpvVq2vAEEA\nUVxPOhBEEYIAz3VxXfe168I67wmIqgyyhOv5SKyDpGv5CCJ4vocW1gjpBkIggCPQqJrYDRPf9xEE\nYd0TQRTwg3W9q6YZEPg0qjV810ZTVZq1Jq7tIQsifgC+42OoOrZroxgKptXAcz082wFJQNN1JEUm\nEECURUKGjgCU8xZ33t3LSEs7E+MZjHiTagMifQJv2LedxtJ5NnTled//+DS/8fEnGbg1wpZwg21d\ncV6sKbjLdZK1k2RKV1E6+hDb3jBGzW4nJxgMeCW0vq0szo5DYZItoxKncwqileFcLs/YVRvw3Tl+\n+b53cHSpzpa+OMm2HSQy7YgSTE+n6KseJbF9gD/62tN84bsrpBMbmZ5aJBGxue9NtxNS+8iXG8zP\n5UEqcv50hWo2Rb3qU2vEiKQlPLmEKLvoYgyzKvFX/2JgmlUaVsDCXJQXjoq87c6b2NSxHcuvsbaY\nITWyCTE2ymLlizSEBHbLEOG+Ai1BhEefXUERkrSl25lYnEHVZIxQHEHJ4Fey3HXrJj7/uQLf++4U\nd9xygN/+ne9wcfkkckSjVoliq5c5eWKObTsT1Eo1cCxq5SIf+fB3ePbM5xDtQfTAp1bO09LdwZWF\nGq+8cJ6WLo+TpxZx5RB6rIRcT7BhOEbY2EYsOYChaNhOnVItR61aopTNs2XzZtpbbZ588hkUw2Ng\nYwpRlBADjdxigexSHavso4fTTMysULdZ7z8IFumkgSCF6WhPUa1lcCyXV09P4JgixWIFWYa9uw6w\nuLxEOBFhx649mI6P54IqGAxv7GJhfoHlpQLv/h8foLNjgCuXJzl75iVSqRC67OJ5HjOzGapuBpcA\nQ+tl80gvldIlRjdsZvee/Zx6+WXuuuNeBvo3cfnyDOFInGphgWOnXkLuqNLRnyCudmCXm2wc3cPZ\nmUkefWKSRGcGs+m+lgAi4Lgu3b2ttPS6VIUml5aKTK7orNVdbCAc6qdaErEvZ7j/zgyjA/28+12/\nz1Xba8ycK1PKlFF0HT0WRotEQFYQVQ3dCGG7TZrNMrnFHJ7j0pIKU6tWqFdN4kYC1/TIlzRcOULr\nmIUUieC6Yaw1mc09Ku6aiV0uYPkCjVqWSDTJdx9/hk1jNu99R4GvP5HiBy/pvOPgGt36MM+8cJGa\nrfPvTx9ibS7Db3z4I/99KlkATdNQFAXXdbEsiyBY325rmobjOFjNJqZp4jgOQrAuy0IUEJAJfBHH\nWo+WqdcaVCsNLNNFEhUMTcVteCiOihEY1LN17KpDWI4gSTJ4AbquEY6GsQOPutlEEAQUaZ2acJsW\nguuvG3j7AYHtYtUbeJ6Hpqh4nof4GkYHQUC9XscPAkKhEJFYdD29QVVIplOIsowsywQYdEZU/u3P\n97FtNEa2qROOQ6TRxAxMouIim7ZGyNdiOH6C7FSDq7oj7NvQx8qUST1qUVqZ5/zMZS6vCHzroR9T\nkFzaWw4ysn07XUaYntZuKkKAag5x9GydRsymXYgRiOO8Zf9uxqd30zOzQi3rMnvi38mbDzOxFiFd\nO0HP/p189sIwn/2ejGgpBLU5/KjJ/o0dlLMZuoeGePHMMpLqYBYyrF3SqNbXiMUsRDFKuVxA9MGt\nGQT1BqpkY7UWEXWRVFLh9I+qPPBnRWLO9RTKNbR6Er28QmXtJSTDYfTqCmfOxVjMXuDc49BoFolE\ndCLRAFHyqeagYeYREmXiEYtULEotn8a2be6+9xaGdwmcnrjIak0nomUxq3mwda65ejflwgqSm8D3\nBHpHYkTCce667m4U5tFCNRKpQYyIh9BwmS3KHHoBbEJkqyaOoqBoq/QmOoloo3gND7Nm4rkuZrlK\no1ClXLQpZCtUMk0un83TEu9m59j1BE2ZK68s8Wvv+g2u3n0r1UDi8EsTiPoAW3beQDY/R7NZZmlq\nlQ1De4gZnaTCPbRGukiH0jRKJvVSjVIuT71ZZf81B2g0HWbmlpmbXWVuNoMoGCzNL+J7Cm0t/eRy\nJZ595jCHnn6Bnz5+jKeeeIbkQD+xyAgtYZnh6CBvvvFeJDfD1776MMlQjGa1yfDIRu65515ESSAW\nNUgkYhSqZUqVCpLgY2gGi0smjbpMd0c/mUKJ01cukOpOoEUKOA0P1wrRaJYJhUJMX7JokaN0hFJU\nJrZi1Pcy3NlJTFPJTK8g1sK0jma4500KOmscefEbXLs1gpxfpb+1Gzkdo2g3yNTK1Gybpu1gOw6G\nYZBIJGhJx+gf6CDZEsf0fAYGOmk261iOiaNW8ZUqYbUVZ2WIlQs2iZZF+neuYoWL5OoGXX1x/vDB\nP+Pdn/hfqF0BG4c9dg6FuXn7EiuTLtVyjYpd5O+f/AJfOfYJzoy/Sr6y9nNj2y9E44sABEFCkiQ8\nz/svl3zfR0QgeC3D6/XrgrCelSULOl7gErgetXIF27HBF1F0HUnSEDBRVJnADqhkqzRLJoqo4Xsi\nkWgIy7KQNRVVVfGldV7Y810a9TqarCAEAbKoEHg+wWtSLM8P8DyPpu0SUjU0TaNer6MYCmHDwAtc\nTHO9SlbDBoEnkC8ViYXDlMtlEtE4jz41wcd+ZZD//Xe3cNO7nqFeNlHEFuLaPOm+LSwur9HdOYGq\nLqEU2kkBCb2PSyeOcs+Nezh35QojO1ROZ27BWPwJ85fn+eMP3Idpt+AIKrbpIMkBM3Yv5bkTtOzY\nRWfKo1vto98porz8EwZTEi/MTlCMuGS8JBWzyu75K3zHcPmTz3qgNBBiHoWaw7WpbQxs7aBmgxFx\nmBu/iCyDIURJd4g0XQ2nYeEIc4SUGEKjiR6SCQpx3GCVvv44UyfK/OUXfH75HSMkhybpvOpT/J8/\nuA55c4TIYBe6uYayGuH9fx/gUIBXJTxFZ2GpRNfOYc6/2KCnN8dtNwnE2UxmeY7omIPsJ1heKXP1\nlgQfem+KLmOI0Q0CRWcc2YsRUhtItf1E4vMYBGjhKOemp/nwB/+IRnmNpfEVRje4LM0pZEtlVhbD\npFtWyWS6yFV12tIxLH8OUegFeRZDHODs2fN885tfp7W9hZmZGXqGk2iKxt7dm7nxhlv5zr99A12H\nbGaZn/6owobBAarZOWStwG13XMP4v51iJbvKT596jk2bhylllqnlXEJSKzMrj3D7wesx1Hbya3li\nkRRtbT0MDQ1y5vzLfP/HP+D222/nyJEjFAolbMtZH3QRBGRVRJINotEw3/yPf0KRZEprTVTBILNc\n56ffP0E1W+LWmw+gaDLdAyPcnWzjX/7m87x0ZJl4x2bMZkBbWyvVyipTU2tsHOvAFpuUigFzDz9J\ne0svgWYxX68xMNyL1biAaMfYeCDF2WcTeJaGqFlokTCS7BON+2B2k5k/jJ1fxUh1sXg2R7x1mESX\nz8xqkexUhK/9s4kVhDk2foTFV/swUwNkVZ+QGOAIAeFoGFlW1i1IXytkPEcgJCpUG02UcIj23jai\niRTVyytoYZWoLGNXk0y/XKF9wxJdXV34Yivf+r6E74lEvWWmhE+zSoZE/wgrc+dZvbyPZ39ygt19\nsK37MovVEC+cG6L/qj/jQEznxsQ8n3z+1M8Nb78wdIH4WnChJK4HIAqA53o4josorBfc4mt2gbAO\nsgQBkiLjeTau56LI67xuPBHFFwUs30FWobW9DcexyeVzaIqK8FrCQSCISJKIqmsIEvi4+N56U00Q\nAkxz/RgCHD8ATV7XyYrrFowhTUcURGzHQtZkXM/FrFsYSohmo4HjOqiGDoKAWa2v+yk0bboSBotN\nFzGscNMtI9xy/RD/8fAqQ/uivPPeYRL+NurmAvHQNr72r4e49s7NbIiLzE7I/M3/Psztbx/EqVdJ\naDW2X3cnKy/8iP7+AGO+Tny6xExc4sjhr3P3/mv55y9dYeygT6UWRYxOEYqmafviywxtHiJjdXMy\ncBivqbQ0+4jlDnHnJ97Cnz0jceXIq8iOgaLLJIIQF5aKlGwP1Wjnb//q3/DqNslIklBkhKXKDI7j\noftpnEgT2bbA0bECh66WGA1fJX+ll3/+lMjEvMbSis93v6Vy991RRjck0YPf4pWSxRe+cpxziz+i\n7oiMDbTgihbi/jx9WgeK4pCMGtxwzzK//5uddMfewr88/Cp9g3m8QKJXuYWNQzme/fEPaO1UyVVv\nYWpqig39aRp2lWrOoK1zkogY58q5CTp7Ynzkg1+mVLF48M//kOFNfZTyPjYVRKkTR5sjP+dhCyax\nmEB/n8rCYpG2SD/tke1864eP07TqbNg0TLRNxzBUrjlwLbcdvItUSyfff+Sb3HLXPto6Uxw7dIHr\nrh3hwDU7eeibX2cpu0xmKY8oCNjNOjMXxwlHQ8xNLpMvlVnNTuMEDs8+8zyXLs6ytlYgX17DFQJM\nxyOajHPs2FFya3kCx0WVBCJhjXR7DFVVqJSrhCIykXhAoVAlnUyhKwZLC7MszZSo1tdYXivTu3kD\nNV9gZXGNPRsSuF6Bzr4BKmadhaVJitlVfBPqNSAIMTO+yJ8+eA+Hn36SWKqdji6F0vIao8MjXHdw\nlBevPEWLOEp7m8jOqzYyPTeHokqkUlHOZapI0W7iXe0EkkSlaOI5kIq1ghXmvffnOXBriEefHiHa\nM0QxsKmpLnZFICSA79gk4lFsy8T1/HUZlqJQLtewmlUCTWVw4xZMO8B2BMKJCIEqorsuvtAk0SHS\nNSpgeRKBHMZyIzgCOLaBqwwT0XTqK8sYToi3vvsKb3u7yNceh4FdKmfmmjx2SGDD1nH2jF3kjW9c\n4AePNPnwb37yv48L16c//ekH4PUqdZ1r9X0fz/cJAgj89ZRaURTXJ7BkGUVRCAQB17GRVYF0OsKm\nsRFKpRyu72A2LFJtKbSQRDKdxnUdKpUa8XgE3/exbAdZkgnwKderNMwahmGsp3IGHoEPmh5CkhUc\n18EXob2nEyMSAQFM08JtOkgCBEKAbCiAgBSIeJZLKKQTjURwPQ89ZJBMp6jWq7S3tLBUyiGIaWy5\nwWqtynvvuw67fIFvfy/L+z+4Ga1YQ5RlvvqlS5y9tMrG628kaolE9DUefmwNqU1g+5jC8nyDTXtv\nZc19lWJVZnJpgFD1ONUukXQsz2OvJjnQ1sv8rExvXwPfbBAaCFAKMl2t/by0eAYj2k2p3qSQfYkH\nPvv7vO1jp2nOT1Ka8/FDZaopD6Fu4/khsvOrLK5OYAUJ1HgN0zSRxSp+UwRdQPJkpIiIYqaR5CKO\nAJVCHamlxvSMRGt6FVSJlYk8zRqMXVvmb//OZPjGJe7a8yhdGzV+9K0wf/H2Ft5w+zIZQ8Q53seu\nfQs4mTA33mYxccni/GNFDr90lBdmVd62NUXTFdk80sXklWOszUXo3ubihfby0vFD9A4lKDVmMXSV\ndEQjv1CgNdbJ1Tds4/iLZ1FDLfzJg1+jv38bklSgXKpScfNYUpx9IxFsQaNerLJpaJSV4nn2brmD\n535ygmOn59myY5BoMkrTrbJz1zbyuSJGJMH84iqDgy1EkilKZZOZy2voeo2RoWvIFU1eunAROxOg\nKTJRQ2Hbjo1Mz84zsnmY7oFWEp06i0tLGHqUHbu2o4dEitUSmbUCRiyGZqjEIwnCqk7g2OiaxPBo\nL9nyIr5XYcOGTQwMDbKwOI3neuzdtY/hgS5Ws9PUCbNx8wZaUxJ+Lcyrpy4yPn6UrtYuBnt6EdQU\ngh7mx499B8+q0taSZmp6ikPHnuPqsQS/cv9H2HHtGIGs8xef/CKtssV3vvsoVWmAuYUKV28bYNvY\nXhYXLEJxkUx2meVpHblRRvRieJKDHDKo+EvEWgsUCjlaO1zKjXb+9asCObtB2nQp1AuIekCPHkfx\nBBq1KuGwhuc7GKEQICCKMrl8AateQjIitHb1Mzu/QiySQjE0HMFDcGLUbIlYt4Eeb8cOyvhWmMA1\n8U3Q1AJFs0BXtMozD/8Jsv9jqHfieQ3+8pspTAuOvTjErqEh5p86zVe+Mc7nH1rECKL81m9+4r8P\nyD744IMPvB5oKAgimqajaTogvNbkWl+vA60oiPie/9oWft3su16zqdWbBIICSAiqjO3YWEqN4ZEB\nZibnESQFPRTBcV0kQUAPKxjRCK7nI3oy9WwN0faR3XUeRQjrIK/HdoOH37BRnIBGuYbX9AmFdERZ\nwvN8PNMD2yNw19MXXc9DUXUESUZWVWzbwTA0yqUciVQnVmON3FKC0X0JvnN8gje8axcXn7fYuzPP\nddNx3P3387/++MsEjkL/FhmaOZJdGo++uoQ1ucLdb97EpexZtvVtIUILU1OXMO0aXbvGGBh6E5ZY\n4St/c5GPvW0nZmI77tJhyqE2hjSF0CmPrFLizJKONKxQcS5z834Y3flPnHopxw8e+jGhQZFMyaNb\njZL3dHSpRHtPO7Ji0NJiUM7XcW1o1B1UzcD3A/Sohi4ZKOEAK4DA9TCiOtgigmhSLDuYYhzNbdDa\ns5HjLzbo3lri7XcsMLC5xtU72hnszJIa2IfaNUtEqDM/aXJ5sh1Jz7Fvq8yf/n6TyZUk49UqcqHG\nL98eI1Mv0NG1FauS4dLMGqO9cRr2buKWw4EbBrj80isYRhdJTWTySgG5S8YS5jk1c4YXji2hpsu0\nd4VAkLGEMhfOKPSOWfSFNCxRZWV2mY7O/RT9i9yxYRfPzHssjZ9lsLuNcCJOPGghoXby0yePMDc9\nx/z5cS6tZTBViRYjjG/UyRQqnD31MjPZPF2RdizRYnB0A+95/wfwfInsyhQ3HNhKX6vOYmkGXRXQ\ndZlyuUIi2YrrgKLLBL5FqVIgFFJxXRPTaqJoIW67/R5sXKq2TU9HN4VciWi8HV+VkJQK1+7sZGNK\nRGkbYfPWHrbs3MEzh48wPn6Oulmjf2SQ6TWRrbv28+KJF8GViUXbWFjKMj05S1gzaNu4i3PjK2DF\n2LN1iOdPfIXDZ58l2TPM0lwZM7PG4KarOH3hKIXiHOVMQKFoIscbeLaF1TTxTYm56Qlaky2IfpKQ\nEcZzy8ysSHQmA0Jh8NwMbtNGCbooLTiInk1LaxotGsIOfOqmRzgao5BfxTKLCL5LdqlKX2sHIdWk\n0cjQbIp4fhU5YRAJSURbI+himdVVuP+dNfYl4JXpIo2YRbicpCwGHDk0zsWJOiuxm7kw3Ymq9qBb\n1/LQP/wexw4/zguXFuka7uWet0i8cniNT/zOz2d1+AvR+AqCANd18f110HydEpCkdZ72P9/n+/46\nb+p5P7vv9WWaNrZtE03EkSSJLVu20N/WiewGKIJIxDBwLRtFlGjWLRq2hWXbSKpMKGKghVUCIaBi\nNinUGtSyBTRfJKKEMLQQDduiZDWoe/b/X22/ziELAj4BXuDj+ut632q1TK1WI5fL4TgWtVqNRCJF\nqZgjGolgN7OMn6sSTpo89uhF3vXhvThCkkPT51HXslx7zR5cqYq1ElD1XRKdOrZqESib+dGpMsOt\ng0wsTxPWrqZgDJGru4iyRLL/Zr7zDxLdY2uk2gbZc1UXa0KZqK6g6w3ElE44FrB1fxQElcEeuHX7\n+7ly8Q/43V//FbRhkbzWQbxNJhqSaA3JeLZOs2nR1taGphoAWJa/vqMIPBRRQgygVq7g2Q6Oab2m\nZ1yP+WlWRKTAQJIsXM/Al+fpG5RobwlzatLmvW9P86nfy3DsxYBv/+gI//DXPk//1OCVMzabDxxk\naW2AL/6lycABn2ifT6JN4eDbQyzVsoSVAM/dSEx1sYsN2rpHMCtFziyc49TJc7SmOnGdGs2aR7lc\np1Er0ywZJGMtPPbj40SUbWg6JBI6a7MOTs1CckI06jq+a6BJnSBYSEEMXxRIJOPcdHAL4VSZSELk\n+cPHePrQjynbq0wtX6EWOEiex4WXzzI9PYsRitO0AzxBxrF9QGSwc4CxoRE826GtJU00meTc+CQn\nz16ip32YiJFi88bt9HT0klnOIQgCiXicaDTKQG8/tm1jOTZayCAQAwqlPAszMxgICL7H0MAgHZ29\nCLJBruzw08OncfQkm0aGSccTvHj0BF7DIh2P0ZFOoMgy5cIKp148zOmTx+jv7WZs82YUzaCjp49K\n0yGXya//5iSRxZUc584vkss77Nyxh2hEperaPPn9x5i7VCGTgYrloCugljrx3CaC6JHJTxKOBrie\nQKnoAiFKZQXHrFL3WpCFFmw5jhbroNlYQOnIIERkHMmnWCuhygpRXSO7uEw9XyGEjluP4lTDnD42\nj11VcBoNKsUCHW0R1EAgFutA1isIzTrvuquD+25KsPWGCmu+T7c/hlwXwbA5Mt5kslYj1GJgmz28\n/xaJj773RerLH8UpP4Mo+cjdC4yfr2DI+s+Nb78wlezrr33fX1cTWBaO4/xsOOE/r//7XBAAogCS\ngCiLqLpOOB5jam4GrV4lJCusrmSplpqYdhNVUelsa6PmNPECH1kUsJtNAt9FkAS0eJhIawK7VKde\nqWI2miQTKcKJOIIiIuoy1D38YD3/S9FUjGgYWVVAlhAVGTFYH2wQZYlkKoGu6yiSjGu6RIyARslA\nCjXJ5tblZ1v29HHy/JOM7tjCQlVg8sVvsO+aLXzvR+Nsv2qQrk06phPw5ENTPPKP93L2+DzZTIF7\nr1ol0XU1j75wknAwxcDWbeQuP8bbB0/zVtfAzz1C2xuu5vz0Mjpr+KbO0pLO5rZelp0VhC6Re3d9\ngMVj42y449O8enQn+/f8LcdPPsu1V7dw6vwKUd2l0hDWm4vVBpqm09nZiSxBrVZFkkWa9SaOZf9M\nYSEKYFkOsiwhSxIioGoRNC1MOKHQ1bEbRbxCOtTHk990iUtj/NJ7PV46XODvvvBlfuvXH2b87FmO\nPDaOadSozru4lSzhlgTveVfA/W9yuXGvSUPQsIpdhNr7sMurRDttLpxfoFhs5eirx8nNm2wYEmg2\nAiQbKvUqgSGR0NrI58u0DrZTd6fo6FSZPjvL1pEUBw4YSPUk+/Z1ki83qGUtULJ0Rnq447r/yYWL\nBVazrzIxJ3FuYpLcXI5SsUr/yE7++IHPsrwwR3bqHJ3d7XSk2nj58jiDoxsJ62FcISCsKLREWnnl\n5GkW5maIRsM889Pj5Eo19HArrYke7nzjfZQLFi++eJqVxRXMZpX21gTVcpZt2/YQi0VpOjY1y0SQ\nFeZmp7nr4I1ct+dqhvuGOfXSWQ799DlQdHwPCvkSDTNg7/a9OKbL7u276evtYmFhjj27d1Gp1hBF\nl7W1JUZHRtB0jSOHj5IvFOnp6SXwfeZnZpicvMQzh58hUEJctf82WltHmJ+dYXHhPKISorwMvpeg\na6TJ6izccUsn73z3JE89Z1LLaYSMEBs2d1MqmGghj6a/gCCG8Ip1ook0QTBF3TIoFCpEoi4L2Qqh\nVgNfcqhXK8iBj4bEwuQMrhUgizqB4zO2PSASdTnzyizRWIx0qpvAUfD9ErnVKcrzITLZDOfGLX7w\neJ4jz4VJqi005CxeusC7rtN4/oUlbrvW4ffurfDkhV4alcO0x1aozMRp73W4506J+/b30dM9xnce\nWeFTn/zUfx/T7teXIKzH7L4OooIgrDto/SdFwevr/wZaEQEJAUXW1kdzTQu/YnLtfbeSzxfxhFnS\nPWks20VRNCrNOlEjTKVWxQ9AURQQREzTpFl2iIoCia5WfC+gXm0wP7OApItEE1Fcq4EoSvgE/+U5\n/vPzC6wPP4iSSBD4+N66vrbZNAmUAM83UeUQkihw8skJ9h4YJh6PM7+SJRTxWSLMpvgCu3Z2Mlds\n0lFzqEeb/M4HBrh61OfaB8Z4+lSZxeqzdEWmiPkC4dBGLo5/jVYnRuSYR39blJAuUGy+A9F9hJDQ\nhuy42G0GBc2gISsMjm1lavIUpUtPsLXxefYP/x27blIx2+ETv3oF24xSaRiEYhkEK4JpmszPz1Ov\nWQwO9OD7Ps2m+bNhD1VR8HwHVVUJAosAkBSZeMoiFDWZvJTnlrtvRCXO7e8RicRnOXCgm+X5lwma\nm7jt4CiHj36PI955PveZRxCQKc/WyC6WEU2R667q4bqdJo47T0KGbWOtPDqd5fzEl4hLe3j6ZI4f\n/fNHed+HTnLNxqu5MrVMPG2xmgkQdYvdV23j8JlXGe0uoEkyotxk004RPAjsMLnVJdKpKBNnFulK\nbSEmdqFRRLB9bt43RmtLJ6I7z8YNXSQHHJ58foIde7sorwT86i+9naM//QFuaZHOjlaWcmvYtSqa\nIbKcWSAp6ehhmeLaKgvza0iSxM49u5menmbzto3kcjkyCxlieojvf//7hEIRevu62LBxkPa2JJou\nMTutkUykefqZZ0i3pohFE681Wde1qZWqycSVS1y5cgUCiEgQ0VV6+0YJ3AZNp8GPH3+chmkS4NPe\n2cbY1h08/fSTXJy4ROALxLJlBvsGMDQV33VYmLqE67o4lk17R4LueDvjE+OsZQsM9nVTKpWQk0VF\ncQAAIABJREFURfCrFtH2Tjbt9hgebqe/4xK/9bkFnvgm3HCbwslnTeLxXlxTojXVTbWZwW4qWHWJ\nREhh8uRREn1QyqySahsgU6ox2N1GvVql3GjS09FJOpGkkMuDHCApElosRjSRpG4tIIo+Axva8D1o\n2hXUSBOrqYMsgZSnMB8jNVKkJRXDdJuo9RBhyeAzf51n4VCTLfE23n+XzpnD00RFj/u2/TatV/0B\nnakKsmCyeEWiu09h7YdFYtrPbxDzC0EX/L9WEPxXEHv9+D+fkyTlZ1t323JwbBtcj9zyKkYiihFL\nMTE9i2KE8ASRQBRoNptYloVnuvhugKwqhGIRfHEdJBPRONs3b0eNh4i2JGjv7CCaTCIHMoovongi\nXrBObQiCAH6AGIAsiMiihCLJ62DvQzQaA0BRZHgNlAs5i2RKwqx7KL4ETfjhl8+waXSEQrZBNNFO\nIeyRKcyxZ2sb+XqJRDxKMiryr99yWHtuianjHeweacc1Y1Ryl8jGCnjuOG5tmIFgE6UNG1goSdjX\n7iFfS1Otq8TieWzVJYhZVCUHJeSQiG2gor9ITS3x/Jd+DVcTuHg+xqd/I09MHyIqOyQiZWSrbV2S\npqqoisbQcB+FQoF4PE40GkZRFCRJWqdf1kfsCIXD65RKELB/3y9hhDtJ6t08/ZPnuHj2LGLY4fRJ\nhx89Nk8kejv10vWo/rtQlB2gRvjG9z7Dzj0jLE0uobdoiFGF626Ks3TJ4IVDEbKlNl441KRaaqdW\ncShLU+y4IUk02eTcK8fpaFmiVLIRRcAXiEQlwrE4oXCU/uEQrl1FU1LUqhZm06Il3UYlF8JvROnv\niaNIKvm1ZQw1oFkrkY4YNC2brlSCy+crLC0sEQunCYdHOHDNdSytnCO39jL9nQHxdAvhWBjE9T9U\nP2hSKK0Qjins2reDutlANiRmFubZ9v9R955RkqX1mefv+rhxw5uMSF9Z3le199A00DR2EMggYARI\nGmmkBc2MxIqRG82uzGh10EiakR1pW1oZQEJAQ2Nb3bTvquou7ysrKyt9ZGZ4c73bD1HVgCTO9tlP\n6P2SeSIj4kack+eJN/7v8/yeQwf5yIc+zIF9+9ESCpY14OrVq/R6HZaWFlhdXWbr9m2MjY1RLpd5\n4oknAIiCmNZ6A7NjoYoJVlfWub64TL3ZwrQtdu3bjibE7Jgc557bb0FXFD712U/T6vVptnts1Ju0\ne22+9OWvcP7iZRRFYWJigunpLZw+fRZd19m7azd79uxh3549HDq8H9/32blzO6Fvc+zoc5w+fZx+\n36SQHwVPxYovEMcxx58eoOkev/3zVT7/2TK9XoyeMiBQOHv8MhdPnWFpbp68kSNnSDQ3anzqb3+S\nhJCgmi0S+Q6lwn5E3yN0AtKJDObAZfbaLIImcPD2vWRKGUxvQKRZxPIIfVfAiRu4gc/83CqL8yv0\nrRqRqiPIEe95f4ov/v04t+/u0exW2YhWMbQVwgsZnnvhzTz9xAaFQhlhRub1Ew0OPvJT/JsPpvjG\nc3dw8oiN1ff4o//nAlfnA3TltYvs99y4QBS/U/f/pXHBd/4dQGAYXh2mxhRFBSFmamqK8fFxnn/u\nZYrVEdrdDpqmISFgd/vIooSgyRiFAtlyHkmWkSSFdr1Nq96AREwYhASORxyC53gosoQoy7iWhyR9\n67VKoogoghAP23Qdbxiq0FNJwijE8zzCIIAgIvQ0XL+HpmoMOn00OaK7qTE+FbPvwA78gU5fOIto\naxTTCZ5/qc7r3riVklzn7z/X523v6DLhzlDuPEn60C3Ysci5EyaFXIzsBnT6Md2kT27MYy43xsH8\ne/mxX/ht3nCfQCLIsmSGBGqGKN0lM3aYtdozZLomt8zcQu72H6fbqPHlR58kOypjmz2IEsSRgpaS\nGZgDHNdhZstWPNe7YaPpIQjSq/XpURyh6zqyphIRk0gm6NsmA9MiNtfwpAy6LCFzH2dOatQ7DoRV\nNLlIIqGTT88wWknwtoce4vUPVDjz8hmCvEWvHTNWiJkpDliox/hBhlPnZigWRWw5Qg50du2okvGW\neds7bmVxfY4XXvQ5uFej3w8xEvDKiRX0ksL2bdDrxLRNgzCSCH2PfDLB2pzFtu1Zeh2LvmXjxTG+\nBclSyFS+QqBWeP6p43z2S2eoLTaYmjzAtuntRKKHkVPZvXOG0DI5fqlG3exRKRUIpWEcNnAspIRC\nYLv4fghxTBDEHHnxKK1ehwuz5xg4fULfJ4oiGu02sqLgeB7JRBLbslm8vsj80hqqqpCQFQLHx2xZ\nQ+ujBPlsms1Wi4Rh8Na3vZ0tU1Pce89duJ4HksTF2Vne9Ma3c8/d9yGIDqIQ09zsoioKu7aOs3v7\nDrbObCcMIrr9ARNTM1QnJymPjtNuNdmzay+hF5HNZsjn0iwtzKNKCRbm10kV0lQqbey2TN92ub6Y\nYG2limmqhEEGu6/RqjcpFdNYZg9FkDAHXYKggeDFnH4lYHUpJJBA0jQUrUNAA4kCnhcgyDLIEAoe\nRkobtkjHAnFigG1FpFIiqgpm36dQCOk1QBM10lmLH/mRiHd/f5cdU33MATz9ks6knuA/f7CKtdQn\nSt2HqHj8xh/+Pl96foLqZIsXv3GBS7MDzi8H/O0XVJ650iTKlBnYbWbPOfzH//Cv6ODr25cgCEiS\n9M/E9ubX0e/yoFfvH3g+7UaLZEKn12zz2b//PMlUEtu2Ga+OIsXQa7ZJKCqOH6Amk4i6Ste1aZt9\nJE1FkARM02bQ66JJIkldJ5kwECQFL4px/eFu7WZrQhiGeK6L5wxPUB3LxnMDojCm1+sRBAGtVpMo\n9Mlk0oCI60IQDNAkCE2R2HV54vMXqdeWh4ccisH8ZsCuPRbmQsh6vcX0mA+Sw9nr41TlI3jnBpRX\nk2hpgZQXIFlJYtliZSDRDDtkbvsw0akcXjLkv/1v97Dt1v8dJzOK7GfxOhKjagUh3uCpo33sYhIh\nuQM/EWPYx/m5Hxrntz/5Bt7+A3eTHa1y+C4D2zZJp9OoqsrKygqO47C+vo4oikRRRBAEqAmNMGDI\niBBFjHSKTCbD4soVmpsqEhpB2GVjfYWTZ16mYV5EV7dyaP/r8cIltDiN071GNSXDIGIqlefARJWg\nm+dND97HQ3fuJmr2kf0CWqKHJ++HMCCQfDKJDnJ8ghefXUEXO+TLd1IolVldGTBSyXJ9YQXDqCBr\n0O40kaQQ3bAw9ArdTp++tYwsS6zWZtHUFLZdxHGHmL1ICjEHAdc3ThHiImk648UxjNQAWzuDr7W4\nNL/GSg1K5dvIlycYm9iCkcqgKAqddpPA81haqtFud6mUcoxWygSeg6KovPTyUTxC1IyGIqu0exZ6\nwqBYGkEQVRBVzp65xKXL82TzOWzbxtCTFNJ59uzaxevuvZ+r1+Z55ZVjQEQmlyZXyFOuVGn3bNbq\nXfKVSV73uteR0JJsmZrm7rvu4P5772P79DZ6rTb7tm+hlDU4/fJRcukM99x9P6+cOsPXnnyO2cU1\niuVRGvUOpWKV6bEJquU8O3bMcH1+lU4nxhJN0kGZqJcgQEZMlVDSOaZmqqT0HYgiFMoqnV6NbCqN\nFBbJKTPkkiUE32Ch3mekkqPnSnjhgEJSo90dxwsj1GQaSU2gp1PoKQ2EECH0SKkyUaCRzRoEoUno\nCVTLad7wNoFCRsWqqeyfVnjnG7KsXczR7aS4dA7as2t87F1FtuzaSn/M4Oip/5sP/+IPsNBMkR9Z\npR6DtP8rTG7zqa37rEQhK/Mpxs11PvDmg3R66mvWtO8pkRUE4VUHwT8/3Ir/5V2tMNxFCjecBhGQ\nHquglgv4SQ0lpxPJAmbTYv3aGrEZUSyWiVQJISGSzaYwNJnYtUgZGlE0NDknFBVvw0N0ZSIvwvJN\nRALkMAYzwPfd4ZzK94nC8EZaLUbTdGRZRU/IyIqEgow7cJFjBS1hYAY2YtRFFAVc08AURbx0RKoo\nYS3D1/42QBiBpFRBxmbdbvHwBw1Wz9URrR7T6PzWnxylFam0/Q7HnnuKsrsfreCTEEXEQGUtXsKK\nfKLMDmL5HO3+HLdyjT36k+QEC03Xcd0LZCu7uXx8ls//rowolsh+8xmUP/4E5eOf48M/kaNgXeXo\nPxxhZCIHXYXpbTsQZQVBkujbPUy7TzKVICJCUEVkVSH0IzRVw+m7ZFNpCvkMttMln6ngO2vYahoj\nlSGzLYUl+KTSVTKlAtVciqqxj645h+02GWxuEHp1wuwYXzwxy669WW65dxdudBerYcCIluTCGZFk\nL2KQTFGKdJRQZSRRpRe2Kc38LDPpDEpRx0jvQBaWaA9GiPOL+I2Y5qZMIEfYtoAouhSTabwwQXJP\niuXBNB1/nTjbw64F9PUBkSfhhBEJNY9JiVypiDKlUSlnOPlNk/XWJsZYnRXzGk+dWWL2ygVKiRSZ\nVI50nGFrcSc7tt7O/Qcf5KHXv4dYNRh4FrFs8tDDB0glYtzWgIJSQhbzTE+WSaQsxsezTIxOcWD/\nrRy6exczBxXC0AcRAini8N0H2L57HMvtIGoGthuzutal1wm5cO4iZ06+yAvPfol6bYnAjsCyyOox\nttlHS4whqCWurS1SKqdJVnbRi1Ik8lUyxSLrtRW6jRUMySGv+UxtqYBiYjo1uoMmK0sbLM1t0m8O\nSMQuRiDipnKkZzLMTI9RMfIoSov1zgad7itoCZFmz0XVC3hRSLLQYu/dOqliGnVURZQCrtcuk44M\nYifJIHTI52RSyTKdehNNDFDDAMGRwNVIaGlCOQJXIq83sLsym40uV45u0rccPviTOlsfhIbp8duf\nbPDrvybz47804Mh1hTc9PMqPf+yzbK6aHEgHfOzHINnS+es//GEee1TkNz4Wcms1ZNdkg0/8p/N8\n6L5l3vdvEsRbpvnUk2sIUfjPtei7rO+pg6//X0uIIfrWwZhkaCRTOmEYYtk2QhARBRGSEOM6Hl3f\nR9MUYjFGSsqIgGs7WAMTTUmgKwkcWcO1bWRZptPpkEqnifwAwzAYdHsEnk8kgON6JHQNWZbp900A\nbM8lEiChyKiqTDKZpLc+zDlLgki/M8AXNITYRZdiPCtCS0C/38XIylw+c5zBtVsR9TJTIwLSxmU+\n+jF47Kk0Z+u3UbjjMksX4Jc+4/O7b5vhhV6HYrPGSHEnVuNpBn0FKzKYnqrQCU+TyfVZ2GiRnt9k\n5opGHORpGhLCQCDSYn7jzxvs3+Jx7/xWXMEh3LTwJqeQRn+TQ4fG+Ms/+Qwf/MVP4o5vY7uq4toO\n3W4HVZOJgxDHcUilMkQIEEZEkYhrOyRTBt1Bn6SQIJ8vst6ok85mMHs2maJIQjfJZlWsXpONxhK9\nQR/Xi5AlncO334PTaeNJBX7+479AYKU5fvQc+cwke964m9jZzfraKbLJKkePnGNKiZjeUcUPTpAr\njOFGO9DDPFcWTrFxdZnKW6ZYvBgyNuERCBMIxQYzewMunOwRmgZGMY0lSpiOT6ac5UptnompERJC\nFrJt2okQTTXwXAk1uYQoWjQbl1D1GS54TcpjBtdP1ND359mxo8CXX5xlamoKx7LpddsMBj1uu+0O\njFSOx774ZW45fBdSbOA7Jg+/+Y1MTY5w5dIcScMgjgVkocDAEljb7HH61EVymQk2N1u0mw7dlkCp\nUEQQhh7ybqeHjMDqSg1F1gj9Ab7Tw2/6fO5zF6mWcnzkQz9CsVjij//sUdRIR0sUaLTXQNZ46dg5\nCukSj7z5PTz97NOcPn2WpJFl7559nDl9ih0793DHbYdptZu0Nvvs23WYdqdBr9cmjCGIIpIpGSew\nEWOVyPMxG+OkCy0G/WUidx9tc4WRnIogCBRzKfqtDoOWw7bbt7CxsUGj08FzNZKGgKonsXsDojCg\n1zAwnQ5WZx01GYHgE4s+WjJJf9Amoeq0aw1avoAmZBi0Nkl6E/zHny3x3z9zlg/+TIvf+7jCz/yU\nxJE12HNHnv/wox/G63+FydTttJvHSRV2YqQrHJT28gcfP0XrcpL+7DsRlCcZr+r8u59o4rQM1G1p\nBv0P8MVvPIXnLjE29dpF9ntuJntz/X/NYm8uQRSJowgBQIRkNk2uVMR2LILQJ5cwCHwfKQQQhhSv\nMCQOI7SEMjz9lkQ0RUNCot/uYXct0sk0kiISRhGiIOC4LkQxgecThxGRACOjowThkC27fedOHN8j\nVy4xPjmFLAt0ez0EUcB1PERRwDbtYVxYkpH8AD/howYGH7xvO7//W+/jLz5zHDkRkVV3M32HRL92\nlne+NUs6/25OXozYteOdFLQ+nrPAhcsRDx3Msa61iBIR27e8m7OLX0XUK0SbAbc+cA+ZTpfk3lv4\n5tfnSAZL7Nw1Q0uNudwakIpjRifG+Man5vjv/+7d9GpXiZUynY1jTLQHxPOP8/u/+2v8+//6PA2h\ngbu6hKqmbqTuIlqNNomEiqJoSJJEEAYgCLi+hyCIVEZHmZgcug+WVtZA9vFciHyBdFqmVFXJGFkG\nTQWvabN9+07yhRE0TWfLjt28cmWOrz91hK986RlqtU3Gt0UcOHQHcqxTTJVBPs3ydYcg36ZR6zJx\nC8hBnT1j7+Efj7yALr/MnkOjHNr6AP3+KrJYQFMMBs4yly7m8GmwZ6bE5rqAEIeookSj3SGQFZL5\nEn13g0M7baYmOnTdCXRNIiEqVCf38NjfnWNxbplUTkdNq3hmlxE1ya6JAr1mDduMefDNb2bu6lVW\n11ZQFA1FUbm+sESvY3Hlyixmv0+5VKLXblPfqPPWtz6CIisEfsCBQ/s5f/483ZaFJCXptDtcuHCB\n93zf93Ng7x0Efsi5k+dxPI9yscjE6CSXL1+ha9lIRBgZA8cfsG3bVuJQRtEMvv7k08xdn0NRDPbv\n34Ft93jphRdZvr6JIqucPnkC2zYJ/JB8sczA8RAVlX379lMul3HMAe26ieN41NbXcf2QTqdPuVpF\nUmXUhMQ9t7+OF54/Sb8pYqhF7r/3LvYfKrNR24AojdmxWV5axFBDDDlLfaOOoIqg6OiaPuQ5qxGi\nmCUKHbqNCNfZJJtKk8urJLIRlt9GVMB1QnxbYn2piRwLMFBotTze+cFRPvT+H+axrz5FOVElv8Wj\nvMdhc1nk0T94hKqT4Fd/4ji/++hRfvNvHmPakHjnm3bz/KVP8+D3/S+2bPtR1txfI5FpkOwdJLQ/\nyPryT/Nzn3iJI5fOc61+HrBpLMl8/OO/8q/PwgWvXVxfvf+3CSxATEgUDYMNhpoAL0aKRILIRxJB\nlgSCcDh6MPsWqVQKDYXuoIdjuURuhCxKxPHQfiWLIv1+nziOsSyHYj5PFIZEscDmWo3R8THCG8Sw\niekpFEXBHpgEsY9u6MNZsjxspY1upNcUwBeAWMWR+txzqMiMtcJ7b0/zqVcGHDv5Ig/+6CMs9nxs\nvcQLX93BYHOOoqTwn+6+m//jR+7j8eeO8/Wnn+e+B0Y4u3KCO7S7SRffgNyep52yKE7dyu7MBEeO\niRxZ/1neeut9bCx6JKc6ZFsS1ZFxFme7/OS7x5CVKg0twKBDKT2Oc0uFD/5Fnc8ds6iWDG6fvJPV\ncJ1mszkEkYjDNFJ8syjSCwhij5FyFVGUcF2fjfo6YxOjjKcn8MKQXDmJ1Q2or3bot1vUWxYT1TSh\nU2Bz6TJ//ud/yc//519AVQ1+708fpWZ2uH55nihSUBMCmXwaRVFQkhn6Voa2mSWf8wm1LJYpslnr\n8JY7tvEXf3wGKa8zN2+hGV0evL/CX32xTaZ4C5Y5j+R4tLoKR54uct+hDJpmkc8k6LZdBDTwI0rl\nIq1BnQcfbjH34gianCaIVnBdH9+cYn52ldJIklhQsfsm0qDA9IE8o2MVvvylY6yuefzdp/8eUQJJ\nVMln8lyfX8I0bdqdAaqWZGw0yUjZ4MyZc9x7173U1waoQo6De6aQjYBe12Tnjv3Mzl7F6g+Y2jrJ\n6uoKS4urLC/OUx0fRdMTNBtdFLFGba3JgbtvQZUkpERAs7uJ65mgJDjyynE2WjUqW0skNI8vP/UE\ne6cPEPsFtmwtMrmlQrNZZ+vkDCfPnicIQ8LIxbQdnnr6GXRJRBVjQk+i1W+RLWQoVkbYu/8gFy5c\nYnmlRhT7fPkrjyHLGfSkhygGXJtdQjY2kWOFQO2SLbvs3pcnm9R56strTG8tYFQ0WpaNZwUM+iGR\nNEAJ8yiqCLFLPiOQ0DuoqoEs5RGFPL2OgCbpDLqb5DISipTCaoYUyknOXVzlhz7wS2TLEAci+WqA\ndyGFbaX4mV//O068EKAnVcb2Rbj1Uc7XPIKoyC273s/K9ROcuvQW9t7ZQtaL2NbzfPJPVghHniGa\nuMCOiRyzp6ZZWWjRqpuvWaO+p2ay/9L6roddr97hW79GEbiui2fZeJZN5Pls1DaxBza27eA4HmE4\n5M/KkogmK3i2T6feprXRxO6ZKKIyZMkSvYpd9B13yIi94adNpdOk9STJhE672SKVTDI3e5XN1Rqe\naXPtyiytVmtI9oqiV2HjoigO2bfYQzOEr0MCXrq6yIlnT/DhN0zy1kduY2l+gXOv9NASBYTaTn7s\nffczMraLnmly4euPs/mHf0wu73M6FFlsg6HCpdm/Ykvp+0lWZO6pjlOWVKTiIZpWmcn1ZXY1DKJB\nlmzeRBB9auY1uv0aqX0yi4MVMhevMpEZYLx9mtr9P0g/NU3aEBGTIrvummZzQcQ0TQRBIKGq6Lox\nPPBzPKIwpFgssnXrVg4cOMDUlklM0+TixYtsbjQYLY+ye8ftbJ3egue28T0Xvw/dhs/y0jKKJLNZ\na/GXf/E3/Nqv/wb/+PwzdHwTM7Cw3AF9O0YVpnAcmaVaEzU3TTJ1L1rOY6SsI+dtdN9Ck0f57F+v\nkNFcWg4sL3YRFYvCGHiSRd2axfFFBM3GNLNYvkEyLSNKIU4QkTQyhK6H17eolIZFgi99tUpvsIqA\nRLU6QkwH01ojoctkR2IIWlRKAjtumSEu6hS2jLLZWsdxHFzHJ6klURUN3/FJaEkkScL3XVRJRpIk\nyuUyk5PTHD36MtbAxrU9zp29gCjKPPLIW3nve9+DpMLS/DKf/8Knub4wR3Wkwnvf+14+8MPvJ5XN\nUqutA3DLocOURyqsbzZot9u4gYvr9bHcDsWKgZaKsEKPh97yCAduv52+a7PZrjPwLFKFApVKhdFq\nBdcZ4LkWnmsyMTGKqqqsbTSpNzZJGiqWaxEjsrC0giiojI5Okk7lKIxU0ZJ53EDBDRtcX5rjytkM\nmWyJSqnISHEUI1HC6iqktRQbqyELV9sMugKOKRHFGoahE8YDwsglnRYxtCJyuB27n6Gx3sOxB8iK\njZF0KJV0dm0fxzHaJLJgGH1q5xx+9RcfYbOv8PSpLv/XHxX4m88MMKQ6Ky+J6CqUZwTsBDz8033K\nk+f4+y8tUUzPYMf/haOnWnzhT4oMrjb5089McWG1xvryC/z8R8p88ud28qEP1KhM+IhK9E+V6Luu\n77md7D9dN72o322HK4oScRQOT7yA0A0xuybOYIAXQ+RFSJr8rTob8SbBS0SWFKy+SRSBFEmoqjrk\nwkYhsqLhOS62baOqKn4YoKka3W6fdDqNTMzoWAU/CGh1WyR0lSgOmL18CV3T8AMf13bxvIBUMoXn\ntIkihpU5WkhayONYbXwb/vqJNV7/0YeJ4gXe965p+vVNXnzyAv/tV95Ofm6WZ17+VXLbvo9LjRXa\n7Rb33THD+/7HNS5tRGh6i3fvT+JmZUJzgJZKcLhRRnj5OfyzMgc0hWKiSP3KPO4bd5MsV1lwzpEL\nNfzYJC1NkVlqUiiWyL11Hy/MnaS4tYk9sOlbEXIpy2OPPUUma+C7EoV8AduxkGWZhJqg3zMJg5jQ\nC/FdF0dx6PV65PIZNjY2aLVaVCsVFpYbRF4X37OQFVBigYBNFC1g4IKuJZm7eBkncJmsbiUOfALX\nwXUG3HL7TmprLQ4c9EkmE7StNi+f8MgXPIwRCzduoaGwdewAcbjO9LjBpZaNfVlkYuI6UVBkZbnJ\n1v0jLD8p4HYkeuYGTzwXMVPN4zgWPTtiMAgp5gxix6ffcfB738fhu0Uu+n2spk620KfRWMdxA6p6\nlaWFNTKCwaEHdmKk9mN5Llq6gSAppFIZaiurjO/fw65de9i9W6DRbtF48QiOaeGYKtn0KDvfcIjx\niQoHB13CwONr3/g89VYXxwk4ffoUi0vXuOOO/bQ7TWw35MMfeT+SL7K0VqNeb5DL5Zi7dBVEuHr1\nKvVOEz8MEcUErVaHico4XmChGUN+Rja5D8PI4oYNtuzOs7xiM7+4jiRpqJFELMCW6Ql6vR74Jo7V\nwbIHSKKIpkkoioIggihBNptl/uopcukUP/ahD3H+wkmeffEsgjSg1a4SeBIPvb1AZUeL3lqGxoaD\nrhm8/OxRQj+PqERk0hqu6OKRRVMsFEkgMyKxvuQjCzGDvoGaWieOXVzTwRBEZLVAbb5NIV2mP1BI\ndLeCu0E6NY5W6RB6u/CcYxQLaeqn+6i+TMsKkbUchtSifk7ByLmsXfR5+fgYnxt8mitzj7Hjji7P\nP2OwfXqUy3/lc8leYuuteXaMODz0jojr11/GLyfpW3lU+V8bT/Y1rO8mtN8RTBCGBCyr1wcvJAxi\nVM1AkgR8wUNVhtDsIIhufP33SCaTqLKCZVnDYIIm40URfbP3nde7wZB1XXdYN65AJpFDlROIKZVS\nqcS12asYRgLXcQiFISAlpQ/tY8MPCwijiMADzTCRhARB5BAICR49ssnHP3EfdAe88V1lvvTkKnOz\nMfILJ2iLU/SnVtCdDvKOMk+0cjz98iyFdIqLVyxev80hCjSKMw2q5l6sYB3ni0/TtxeBkML+24ma\nHrI4QJRvYVspx5VjZ9l7YAf2bJuE61N6030sKdeZn7tKaW+HzcXzGDF4iytEnkq7arK9MInjOJim\nSSaVpucFN3rZBGqrdQI/RFRker0eqUwaRRYJAw+rP8ANOwzaPTRFxAsj9LSCogsoCrTTlMFhAAAg\nAElEQVSdBGHfhShESAioqorZaGNudsincxTKMrqWo7OxhhjLnDz9IiePnOfh188Qpy3GyyGm6TI+\ndoW//mKKy0tLOK0q3fgiZ+Y0KsUSQtRAVcforstMlyOE8S0gr2K31xgZneHMYJ3NlsvMzDj9vk+3\na/Lcc/C2t1/h2jGXfi3J9MQMjz66ThhLSEqGcqbLysIGG50FUpsjnL9whcnJMqPbM9imS6FQZGlx\nhe3bt/LII49guRZPP/0UDz50P+uLPVrNDqMTVUKCYQWNkaFaGafeMtE0gSNHnqY6Nso73v5DrK2t\nsbS0wukTJzFkA5+Irz/5FKIokUgkCAIP13OIxWGoYn5+gX17D9NrNUgaCTLJEm9861vZO30Hl84f\n5/LsNZaXl6k3LNSkC6HCvAiqIvLIWx5kceEazfXrSKHGSClP6EUMBiZeZLJlxwyHDx+mUW9RHcmy\nZWKUHTMjNHse2UJMYUTn0olNEBz23VnlwrULnPjmCLt23MaJ85cR1RxEEr5vYfVklEQOUbFI55rU\n5n2mRkUiN4klDFBkmdALUJUsOBk8V6XfdVBlmz0HLMYnVbZtXaOzqvGVr8qs92N+9bd+H6uXYizn\nYlltut00YjJAFC0qRQPZ9Qi6WZ742y7v/+gae0pJmqT4s6/r9FMyS6KFWdNpbqRYO9Oj8NDt/Pov\n17m6GvLyJZOxEQMxfu0Wrn81Igv/stDGIYgMKVmSJBEGEb7tId9Ib3mui6opAARRSORFCIKEpmnI\niobjOMQhqPLQvjXo9XE8F0kSbliytFeFNQiCYT2OKFCqlpF1FTvwEBUJQRJI5zLULWvYC+YF2LZL\nKpWj3e4C3ACmeKiCQq+XgHQPaSCSjH2ePnuc+1vvpCTVCFMRB/ZGXJq/yOu2HOSVo/NInomx3mb3\nPSXe84MvU53oUxAVVo7oNB42GSEm1ASuXElx2/IyKSeDLTmo0zOkbQOtdpn+dZ/sm3awL3kr8+Iz\nWJ2LqLku2/7tfhpf+iaJgxNMVySSio5RnMLcrGGoFbKhScse0IpbJJNJtm3bxsXzF3AcF13XMU0b\nPaHSarYII0jnUkOQRzqN2etjWQMk3SObTRL7CZzQwbZsBCWFZytEmk9CkzCEJJtYxLHA2rUVnLaJ\nV8iwuTJAcOFy8xLpkU0a3jKjkwXEOI/je4xkx+kEXY49903EAJbWcqRVibap0AsWCdeXeOBtAr/3\nf1bJpcrIro9Hg60z9xIvf4Fep4vtBIiqxtzCIuXyOJEk0XdNSPTxohp9y2K69Mt8/rMfQ5BCOuYS\nkeuQL2VJGdto1i5hbSwy2xzQbLVw+iJjoxWa9SaaomKafWrra3zkIx/mlttu5R8+/Q9UxtK0u2uk\nMgJHj5xACBNosoEoyGTyBmHkUq9v8NWvPsH+vbewtLCBrAjossDF2atEgojrenimTTKZZHV1lUGw\nSYzOrYfv4f0//IM8/tjfcezYEfYf3MGObbeRoMGWiQn2bDuEKpeota+x/+AOxEBFUQ2+8fXHuTZ3\nhayhMj1RYXxiO3NzK0TESFKKMPbp9kzWa5s4roUkBrzz7Q9RLafw3DYPP/Rezp+7SHXsKlpS5g9+\nYx7THiGXVDh25CxOtMrktI7gh2iywuJSn4SnUtkvsWd/htqVHstzbeJgikDpkcpZ2I6O6/WRJI90\nWkeVAkqlMq3NAKfnEDkZJjPbUMJT/NT73sPh3dv4na8+xqmzVxEMmclxmVxFoLbUY3VZILAFMmmH\n0qTKR9/5UcpZBVf4X5z/6Q6RlicQ6siDcdylJpHg89LjTe46MMqIK3PL+CSWusJ6YL1m3fqedRfc\nXKIooqggCPFNpxaSpAyrY4iHbQWiAIJAcINxMAwzSAiSRBQFxHE03H1KMooybEEACIJwyCxg6FJw\nPZeY+DvaGaIoQpBEYiIUVUZQRBzPoW/bbNQ2ESOBwHRJSBob65tksnkUPUl1vEy+WKDdaeE5Hgg3\nWAxIhJFPTIgkqET4kBgyc69fO8bh+yeZH2xiiDoLmQwPDBI4l+bpTWzlWrTCttFx/uwvZ1l+4W5u\nm0zxR5+7zO5xhZn9Bv3GDur+OlsbArm4z8ZMj+TiOqUVm03fQ9+bI971CMdOv0Qvs8Ctuw3ecnAb\nW06fpNQdZS22cUdCtu37SbqbOV5+8SxiskcHm6yYoR90sJwk682rJBwRJZZxwgBBT+G7IUpBIYwC\n8hJEpQpe3USUY4REQELIEgUxIhE+NtmRDIHoI0oCRkJCzmjEKQU/CAmaJr2NLkYqie0OaDYsNlpd\njIqNEFc5uOsApcwoF891OLB7mTCborW6gaJKVDNbWVlJcslc5Q1bU2wplHjLg9+PoU+ipmdZvzZC\ntjyCpFgkYwXFd2mbLm97g4HPOlZSIzRNEmoKK4gQEiP0131KZYVefyef/szn2L2/jC6m8IKQeq3N\nD7z7rchCnlSqjKJE5NMaB/ce5MSxWYysRnEsz9WlV1haP80tB+6nvt7h7Ll5rs6uAiKqHHPm9Bwb\n9Ray7uN2OgShRKylCBSN2UuXubZ0mYKRZKo0xpVr19nYbEAckEyqhEJIYaSE43pU1RTvevjdzEzN\nsF5fwfQ71NtrpIw03UaHfqvDRnOTUiWDaXZxTIuvfvnr3H333XTbTY6eOEm6NMnd9z+EIibYqK1y\n3wN3UhrL4vY8HNMitCLWltdobmySziTZunMLZy+f5ZVjc4yUC1ycP0qoBaAWkFQVz6tjFBOEgwGj\nWoqH3+hRyessr/XJlkbZcUcbs9PnxDdVLNvCyCQZ2H2kWEaKJfqJHFUG7LtFZzybYNHxuHatS7cN\nC40snf421houarbN154e8OmjR5jevkrvvEqoa8RynuqYwtbJBLFiouZEXv8Ghd/55XdxT3UH//bX\nP8lv/q1LJGRYPtvC9fJ03VX2jOVodGzWbZeaF2GKHoh9NtdM7J7Af/nl1+Yu+J4R2X/pgOvV9NcN\n2uFNkY3jiOgGTvAmh/bmDlcUxVdrbMJwKKKCMPwKKsvyqwdpkiShqtoNVF9MHA+jr9+6rkgUDZ8/\nHl4UWZaGteSyjOe4pNNpAt/Hc4bBBNOyECWJseooyaROKpkCwDJNwiiCeFjqCCBIICsyAgFiLBN4\nAkHooyYkdh8aZ9Cz6YgxO4oZBnNXmctUIOqwOy3wM/cdYGbtGKOKyVVhhkXH4P6ZTZaCDHmzizbo\nILvrCPdOEMymCc15smM6iYRNZ3qC+lqLUnycAzsOM3PsBHNmjhOzdfbd98NcrB9j++E76dQrfP5L\nn0JKCHheRFIpI7l9JC2B76dxfItATpNMy2j0SWfz+O0BydQIcegyGNi4vS6uqJGM06BEiAyHea7n\noKYTGLkcjuejaTKyoiIoMoqsMugNMJJDnKKkKoSRiSiqw1FFX2Du+kXW1tZYnK/x/e8vsbzaZHJs\nhpnKFtKCzjePXMV2s7zu1mmWr5usrpns3vsQF5a+Qml8BIQEYdADJ8N6fIWG1WVscpJQ9agPbIS+\nRjaTw48dSpki3dYiB/bv4fHPdFhcu8DenQeRJRnDMCiV0hw6eBhB0Ni+Yx+W43Lr7bfTWG1RW2tS\nLlVYmNtkpJxGElvk9TFwshQnDRy/zsriNXKpMer1FTRDRxSLuKJNGIMkCAieTTGfxh4MQJbZaLdp\ndhvEQkR1tEK716VcLlMo5NGTCpokoyYMdu3Zz9rGcG5oWw7Li2scP3oKx1lHFET27jrMVx9/mqXl\ndfbv24cf9Ok0TeJIZHV1FUNVMTttsukUSSNFEIAUq8xfu4aqKciSPPxAzaXxApvN+gZqMkOz0WOz\nvkY2n8VIGDRrKxhazO49MhtrXYqjBqv1BMdPhCCNUCxEBH2fa7M+ujRGHEmAQKyajI4XcaOY+9++\nSTXMc3Ghz9XZAb6iUyprpFMybjfJwsYFaksWsqwieOB1RTbXkghJFV1IEIURSzWLelPnHd/n8rrb\nAlYu+kzfscL//IMVnj3ZIFtSKRQySEUNdVQkiFKI/TSmH5CvFvCCGKsvEgcpMtkC7XqLX/ml1yay\n3/Pugu+kXN38+a0Y7auQlhu330QlAsiyjKqqr0Zgbzbh3nzMtxi24T9j1fr+UAxFUUQSxG/tRGNQ\nxOHzWaaJ4ziIoshgMCAIAjzXZXVlhY1aneXl1VdHBaIoIIoMYSUCyDIg3ACSRxJCrGI2Y158ao7a\nvIme1JB6SyyXU0TFBNl8ipzb4860wtbwWdwFnea6yCd+4k3cOZXn9ckkXtinG7bxJZtBQWFk33Z4\n4PWQE1CnRJR2h6jnsd46ydbJbRyQ9iKvwrzYw6806DcqlHL7mF96kZmZGfIFg6nprZQK4wzMNqJk\nYJsCI9U2oQ9kReKkRELJ0xo0GDF07MDDQcLruGgaiEpI7LQJggDbdYYfZIoEooSS0tFzaYxsFj2T\nIpPNks5k8Bx/6H8GFEVFUSGTSSJJOVw/oLFpsbHewAk89LSDYntcW7hKMJCweyqCFJGKZV4+eo5Y\nsemZcPz8V8jk9hBpNgu1V1hfyHPX/RKyFrJ9ZxrPc0gqOyCMMAoWZn9AMinS3ojp9QbMTO7m/Ml1\nHnxoD3GQxhqYSILM/fc+wGa9iZ7McPLUWZ5+/iUWVtY4c/Ey7/vwu9myu4ic0Fiv92n12phBnYG/\nwUptBUUxePc7P0guVWF6ZoROr4Yft9BGinhijNltkdcUZiol7rz1EKEADctC1kXGt4xR29zA7Q0T\niuurK2ysLjO/dJ2QkEa7RSqZRRGSeFaMO/AIXIdOq8vmep1LFy5y6fIFhChmy+QUd952J//41W+y\nulQDL+DrX36c5559FtfxWZxf4/zJy5w6/QqKIlLIZ4EIazCgXq+jKQmefeY4xcoY9c02sqCiItOt\n18noClOVPC+/1CGTPsDiQpozZzvEKvhBxNqiyNWzATgimVRIQu4hSCZJrUB74PHej0XoCIylNAQh\ng5KroqtlgkCj53rk8jV+6Wd0fu0PBzhizIZnI2dsVCySYh6j0EMI2+RTAbYtcO2SzwN3J3j9vWP8\nw1/Z/MOzZ/GCPIGb4cp8g4Ej0O/0cNwBjiKRHiniRhZuXEdKtRESHUynjSC8dun8nhDZ72bT+nbq\n1j/dqYqi+GodDXwL8C2KIoIgIMvDtNW3g7+/XZBvimsY+t8BAP/213JTzCVJQrnxHDejtJqsELrD\nf/AgjrBch8D1yRgpcukMoiDTanbotNsIQoyqyiDGSLJwozhSIAh8whDC0EcGRDSayxZHnlyjnB9H\n6DXpkkad2cLi9VlUp8nP/+4FNuMEa7kempxkunaaX9mtoHbHWA9TyJk8G3abYHqMflqnn2izFiTI\nj5YwdZ1s16Sc8Th44CFmn3qU+a2jGLpJUh/lyMtfo5C8jUHfRU9k0dURsukxtu2cYXJrCksRSZZC\n7r3ldgpyHllq48dp3KiMkR6lObAIjDYj1RRS1scJFOTAATHEc+yhDS6KEWLwfR9V1l5FU4qCjKQq\nJIwEiayOrCnoqeTQzzwAy7VRFI0gMlESKlEsoKgxoqAwklRRFAk/7NBtryOrHpq0TrYwhmFIyNom\nJ1+5RKcJtfUO6xt99h4MqYx0uHtPmju2ZMgK4LVVooFMoWJQrCRwBj2SCYNSfoTmusr1xSv0ui4v\nHznN/PwCZtfBMl0QZWavX+Xq4lWWVq/xzWefYNna5PTqMXLbY4o7NfqxjyOmeebUcxyb+wKXrx2j\n2RhQW+3x5FNfo9d1qFRHsYMGkihTrVYxMjq79m1nfKrC8uoi1WqVUqmEmtCwbBvfibjtnjso5yt0\nWn1mJqbIFnUuzZ7jH//xG5w4foqjL72M1XMgikkmFHrNiNmL13j0L/4UhC7N1jxHjzzLSy++gqwq\n6IpMLmUgBhEzWyZIZwwuXbzI/Nx1du7cxrbtMzSbDWRZJJnUkSSFUyfPcMdtB+k2G8xfmUdCYWOt\nie/AzNQONjY6FMd3MvA3QVjiXe/KMFXMktSbjE8ZpCsCQpCg1/S4680+t96ZQtdK9K0+q4sJnnk8\nz67p/UTEKEkV19xAldM0rRQTe0Le8Q6ba0dUBhsjjE+PYos2rqfTaTWpd3KoWgbFd0nEDZ59PMkX\nPpdBL3rMjOa4/dYJUpkNur0mgmBRFG32Fyrs3ybgJyxqrWWUpEK5UkAzRKywhZ6LCSP/Nevb98S4\n4L9+24u4CYL5doGDiODGe7p5WxyHN4Tyxtz2Rn34zbHATWjJt3eD3RTfm7vYIPBwHBvXHeLwhiOI\nb11HRCAMwhu72KHpPibG9wK4WbAYBujJJLqu4zg2/U6fQa8LsYTnOsQ3yFR+ECGKAkEQkxB1ImEY\nzUUQEYiQlIBEIoZYYmO5y/FT57n7Tbfh6DFdxSW9mkUbhfGEwvigR0bTSffBGqyQ22zwe1ERTa+j\nbKwgenUe2KIj1lbYNfsCX5g+gL8yhzwQuLz5EvvYSf3Ulxm96zbs9z7K6vFnmBxpc/biBe6+5xfR\nywa27/KpTz3OZrPDwtIVIkfGUDUSikUqvZ0P/HuQlip89Ad2srWySX11nXYrZM8d++ldjDj0tttZ\nmO9QDEJ6goGhiaiSSuQPqWaSAEIkUF9ao73RJpUyMAcWmVQS27QQiEkbKbSEjhgm8b0+ihqgahKe\nKxD6oOkRp17SePM7DZaX8giSRiJzjZUlCcOo4tgWuC4Pvy3N4gWX+csaXjyEh+8+sEFo2VxvZ5Ed\nGTkTM7plkuaawKlzG+jIGLJPJmnxsR//BH/yO0e5dO0kZs+gZ7UIAp/WZh/LNnnyuSdYWFqk2d5k\ndLyAH3QpJD0WLtQ59cI83dU2fq+H4AiszZr06zabDYeRisDlaycIIpFu30RPpPEGOm67gSDEeEJA\nNxhwdW2B/YcOU1vZYHl2gYHpIQoKhw4f5qGHHmJyepr/l7r3jrLsMKt8fyefc3OunDpUV+dWK7Ql\ny5Yly7YcZDnhBF5eA5jhMfBgmMfAzDJhwKzxDI/FMMwimBknQDhg4zG2wLaSZUmWWq0O6lZXh+rK\n+eZwcnp/3Kq2YL2gN57H4p1/6oa6p+qudde+++xv7/294Z57SabzhLHJxsYazUYN17HwHJfJ8XEC\n1yYMHdKpBKOjowwNj/OaU3eTz5VZ39hg7voVRF3Bdx1C26ZSLDC1bw/XlxZxHJ+3PvB2bM9mbW0D\nSZZotNuIkoSiq2SzeQqFMo3NNUbLA+RzSUbHx9H0IjcWtnFcgajqkstZRJLG6fMW5fESt996G2uX\nr3DvLZOce9nioY9q+EKXR/8ainmbe1+fZv/4Ns2ewKnjeea6DcbGkwRBiOetY9sya6si578Xcfyu\ngJGRJrgBtY0kklYgXV4jbOgsWyGkBIrdMj/zkRbv/ckublLgqedNrmyYNLYV1CDEiHJcu9Kim45B\nGsK0IZEwiH2TTFpCNcC0LaJApLcd8Ksf/7X/f8kFu/rrLkDustd+fV7f/vRKttrXVPtDK1VV0TTt\nZiNW+PcGYP23KMvyTa3W8/pramJCJFkgmTQoFovk83m0HSfCzf+BGEWSd0qo+zYxYoiC/jBtYGCA\nfKmIomuIkoSuqwhRTLPZ3HEjiMiKiCzv1DjG7HxBxIiCgCjoiJKKKIMfRgShgKqJ/OqHXsMbrJC0\nY9PVMjTdddK6SK/l0y1pREkJRVomL0jM1QWWPZdebNFVW0zlCgzEPZLLXeraOG7SY15Pkc5NYsvj\ndJobOEUoDbyBoptg3YpQ9Bopw0DResiSRG37Onavh2u3cbsBoSuyvdmkkCqwXl1l6aUMP/6Qj7y6\nSEkP+cTP3s1nf/kBtG6VjN7i2rNzKIZLrIik8iGoMk7g43guqtH/Mmxu15BjieFiiayRRBdlHMum\nkMuQTaVJJpMUi0UUxe3vUfMEpFhHiH2SSZnQl1nb8Lmy3CZwO2z3TJa3fHRBo9ZpIMkxldEClt3j\nlrtbeGaI7xbZqtv8+WeaNK0Ezzx5mb1De0hoEVsbVcaHRwiJcdo5KgWJXrNGyshy8NBesukCB48c\nYHLvGIVSieGREuVSjr179xMEcMetd/POt/8It99yD//sgx9GJ2D/xCBJOUdSzuCaJmktj9NOo/tD\n3LhSY3pmP72gTSKtEYVdMikfI2X0PyN+iOcE6GqS06dfoN5qUh4dBD/Gtx1ajSrf/s43qdbWmb12\nmeLAAKduex23nriDj/3ET/L6u19Lr9Ml8ALaLZNuz6Zab3Jp9gqappFMJjh34Rxb1W0sx+XwiSMg\nSAioLK/UuHh5gaXVKm4INxYWMXs2A8MjbNeaZHI5xicn2X/gIJXBYWzPZ7Va5ejJwxw4OM74eIls\nSiGhiqQNnWy5y2Blkkjy2Xsky0a9xezLHQQ5xS/+0i+iGvDsY2m+8qciWkJHk2zumj7FYDzM7RMT\nlIsZKoNDnL84T8PTmDmYZTLjcN+JI7zlvvt44pEiixt5urSZOdjjt/7VG5guJ+m6dabGu9x3T4/x\nUY+f/heHufpomvcdD/jywz7ttRDfz9JqS5h1kUxxhMLIHqxIQJQs5FjCbnpsLG/wpjdPs3e/TMJQ\nd7TjV3f8UBYuQRAWgS4QAkEcx7cJglAAvghMAovA++M4bv4/nKff8/qKS/JXbkeI4n6/gCCIO/vA\ndgpzhYhEIoEgCDfBVRAEFEW5yWRlWcb3/R32G+P7Po7j9N/8js4qiPHNv72r5+7+7TiO+92oqoRt\n2/2d71FELpVGTyURVYW1tTUqlUp/nbgkIssyUSwSRj6eFyErP9CLJUklCgPCCCQgRkISZRQlxrJ9\nolCkUBngbWMKM3WBjZZArzKKMHidZBTglHWqxSID0giukMOUHIRTLtm1FsWtFEE+QejkcNxVLsRJ\nJsLjGPYcGCO8EOj0Nn3s/DzH770PXJWE3OKewbsZGB7loz82zkpnluKeCS5dOoPZCVELCrquIYkh\n+oCOKBUJ/ZDabJfKlImg6nTFUdbPvMiJgRw/8rZRQqfDuSsDPPbyIn5QwPdahKKOpCmogoSmKQhS\n/0pF1wySmk5jq0rHtUCCE4eP4js+vh9i90wU2SUOIfAlXNuHOCCZTNF2IgStTbZ4krTeoB67lHLH\n0d0uo+Ma3aUaPUti+ZpGfkBi8lCVS4s+xYFRDN3nT/7LCh/7yAhUt5HVBKHZQRUzZNJjNJsdOq11\nhkszbK6vc/vth9jacikMLVAo9L+QB/M5elaL+95wH9vbPcqlIVxT4sCeEyyun2F8Jo/jdDBXA/Kp\nAdK5gI11k3RpkMD2ieUkC8sL3HLnHq6edhH8gFIhwhJcUkaSqBUSWz6e56EqOq4IYzN7mBnfz7Ub\nVxkoF5jcO07PbBJEMd957FGGCllGRydYmF/kq1/9G0IXFheXCeMYTU0iRAaDQ1mMhMSlq89huQ1K\n5RzHjtxBaWKQ7maP6xevoWlJ1tcbhIKPIid44cXz6IZEt2NiJBPkcjlS2QzFYpFqfZsw9Jk5doyJ\nfZNsbLzMiy++QDo9TiGts73VoKUUGdbzFFIzbK1fwO6Os2SfQ/RCrm/Z3POWab77/dNkxyUeeGuC\ncDPmzLe3+cWf/wDDqoYar/Odb76AkhxjYMpgaKLKb//Uv2NxqcvvfuGTzC2luXXS4qG74WgpQ6/m\no6Y0wqzKoK7z3nu6fPH8NosLJa6cD5EJyZUqyCsOcdpBTCpoYZWcCpqv0DJXiG2VgprCaUsoaorP\n//HzRHKIHHYJQ/dV4+T/DJ/svXEc115x/1eAx+I4/qQgCL+yc/+X/+9PERNF/cv3RCLR7w0QFWzb\nvQmUfWbbv/xXFGWHWfbZbhiGNzVaUeyDXPiK+kFV1fE852ZMFkCWRSSpr7+KokijWSOOY2RFJIxC\nYgLiHXnWCRwUQSFXLpLJZvtpJy1Do15lc20DAp/2ZhUpFhBlCcv1IYyQZBFJUlAUCce2+uvNQ49Q\nABSQZY2xUoJ6y8RWfYJWhsnhDj/xlhL/+5fX+ZW3jJLcXGdy7xtZTS3RSjbY0q5zi51kYzBk/MiH\nCZdepqK/xOB2DTteI2rYNMfzPLY+xZzSQ/G6eFKNeAxa10SqQcj0rSPkrlTwv/ffsO6a5YDfIHaH\naDRvcPr8WT5w9yM8fv4r+JqMTI9EahCsFmpsMLlniGZvk61Oi5wwxUp4nYoC55JlDlS7GJFJpyBy\n4OQwb6jcxrJZ4KvXLlHfWqe2oqCKSVIDBrYcobVinJRNL3CIFJWB8hj1tknLk6htVol6JqFrEUo+\nCTWFJBt4Yh1NhIwaUPcSFKUuewZSDAgGLy11qOQjDiWOc6G+wqbbJvTamK5Kc9FjuDLE6nKXWHFw\nkgWC0RDbNlnsrZLMZogcgSgrU9R7rFQHEA/2CIFri2eoZB5ClWXq1W02VqvcevI2CgNJ7K0qxYLB\nxuoW+BazVy+zsX6DI7cd5eTtD9DteCBcY2F+iQQVFGmVZmsJQ1LREyWiIKTZXSZQYghDVlbTTB0c\nZmlhEUGQaLctBgbL6KrOcCHD0f2TVEqDpMtplpZWKeRGsVYWWb52lYsXr7CSznNWvUAoxkwdmCaZ\nTGK1u2zPbTJQqJDN5zh09BAxAasv14lRQNDoOTZlD7zYJzuWodVqUSzmMbsWvu8QCAGB6xP5kE2k\nqG52iDyVxmaTdq/BbbfdhqAUKCx9m7wZMb1nDAouf1yroYzqRA2PzWtzqKUEe49kmX+6huJZKJkk\nX//iV/iz3/og/+Zrp8GJ+Mbn1+kEearXX+JDHxrg3fe9jUdnQ+69t8dEAYRUl0c/L9JY+y5zF89h\nTCRIFSxatRzFcpvNqsen/vt5zCqYvsTaZpXzczovXR/m937vKo3AIygWkMImoVZC9DcQVYj1IQJE\nwkablJBC0hM06tuEgo3gqvhbKpIuYobO/ysN4P8LueAh4HM7tz8HvOvVvvAfDp0kSUBRpJ1UUR9c\nE4kEqqr2qwwt6++x112t1fO8Hda4y1778dg+S+0z5CCI8L0QYhHX8TF7LgISuf+ksMsAACAASURB\nVGwBY6d4WlVVdL0fr+23/wskk0kGBwcxex3a7SZB0O9DEEURw+hXLBKGyIoIRBiaiiSICEIf2AUJ\nCskUCFAeKBJ5kC+OcuzOuxkaSPH7/+uHec9olmzY5OPfWKGU3cLyevgDFqJ3mLy6hyW/zaK4RLLY\npLBXJ3vwARLhJF01jZEpsbjY49krq9i+QcsWSEVltrZB0AvU83kODd+G/dlvEvgSqYvXca9sELbX\n2a5W2a42aFe3mL+2gu/ZRKGAH1rEcv/9Ca5FyxFZ2Wjw9GqAr2boRCpqssj6VpHXdmwCW2J1fZbL\nCy1OGVvcPVPhD378ft770MfQB3VaCxvEcx2McorBwf3AIKpUQCDi0MwIS/Mvkk+ATIjdtgkQkJUs\nbrBF5IuUs0PsHUsjIWL5Sc4864GWZm65zuws2KHIwtplYsOi29bptiMEe5pub4PKsE/kp8jkQ7JD\nLq2uzktzXbZ6Fj2v09fHI4V6+xqyKiFKBs2mi+20cYM2oqDR6/isrs6zsrxBY7vFpZcuMXftOsvL\nywSex4kTJ0jqafZN7WPPxCT5TAZFEtAVFU0z2Ltnin37RgkcG7vt09qM8W0F1wmZmdnH+OgYQRBh\nqDrpZJa15Q1UUeet97+V8eEJ6tUqQ0ND7N27l2eeeYbrN+ZIZXNohkG73cb2XO655x7e85738M53\nvpPSQKUvw8kSpYES165dJQxDarUGTs/n5MmT+J5Lp9MDQNcTPPTQQ9x+6x0MDg4iIe0kKTUGKkW2\ntraJg4iN6hUGJrvc9poxXnj+EqK8wcuNBDcQmA17nK13WF4s4Pkx2dEkehEO3uZRyY0yONVDTc6w\n7cV88alLPPP0Gr/y2l/i9VMy7307aLpIWlX4i8c71L2QbMnibe/1+I+/YzNabOJKAS9emuVqdYs9\nGZXf/tc+3fNVGhs+zzyVxeps0NwuU5FbpDWDz/0HgXanhRuXeeYpKJVV/NCgpVWR5CRarNNob1KP\nmwjlQYIoxfamiW31pUfFEJFUiThQcFsKhK8eOn9YJhsD3xYEIQb+JI7jTwEDcRxv7Dy/CQy8qhPt\nyAO7w6pdD+zukGvXNbC7wVYUxZs67O7ak93BliRJN10A8c5G2V22KwjsDMv6hTK7Nq90WsL3fer1\n+g6g9+UD23ZJJg0i16Hb7aLrOi9fukRC07FMG12Xcd2AKPT7FrEoRlVURCkiCCIkWcB1PeKdchlB\nDLF7FiSh3qojthw8I6aVDgib6wzj8uK1Ze7Zm+YPH4HnjqYZGzvDlp5CWuyS1AeI7CtUWocJvvA8\nhrKCIzrcumpSH9IJky6NjkU99jiakImFiGNj4zxv1ghkl9RmxObm61nRv8udcgWjZOJvN5Dv/BiZ\npz+H64dcvHSWzaUGKUMk8iEQXWRRoZjLEgU+1V6AbKT57DPLfOj+MWpbm6QqMtL+MiOiSSaW0Q2R\njW2b3GiK0aQBA+NMPnCUH3vr6wguXeSZb/01N6wGb7j1KGsXLhELcOq1d6FpGmGksjS/gFpKIgoW\nvaZNccRhfCBP15TZNzDAWKGNxgr5CYkXzixy+6lDNC3IZCpsW9uk8zL1boScSKHIIr1uBzWhUMjK\nWLZA2wrw4wZiYoKOYrLcSKHoMdbmZQ7MDGJ1dCKxSxDLxFGC64v9foBYkIkigWp1k+WlVQ7M7GFw\nYJRm3eO5Z5/hQx98Dxubi9RrPW7MLiGrBr1uG7PbxcvlEAWF93/wI2wvzbK5+RilwRz17gK+K6Or\nAoIc9ZcgAqIgo2kgCBKXL84yvW8/qiZTHsmTTiX58pe/gmma/QJ5K0RURBIJjenpadLpNKvLK+gJ\nox/cUSSyxQInbj3Gs888x6OPPYGAxIMPvoNDh2eo16tcubHCrbfeTqfTYv/eabY3Nqnla5SyRQwj\nweLVRXrOFgODSUyrjarCxN4ks1fPc+DEXlz5As+tFMlmTRI5D7Oxjx/9QBvT7fDwp2tUCmk8L0dh\nYpV3fXQPTz7u0lssMH3Y5LNz/4U/H/hDbps+xS98/mkSjow7GPPdCxfJhL9Bc+VLfPFrCYZci8e+\nDfKETtDdYEQukR+scWACvvIYfO1xgXNnRO6+f4uq3qBrJugFSZyUw2vfPsMzz9+gY0JaalCcUlHM\nDLKVJCF4NJa3EX2b+YuzxHaeXE7HiZ1+UtPzEeKYlKEyVhnk5cuLrxokf1gme3ccxyeBtwL/QhCE\n1/8D4IzZpY7/4BAE4acEQTgjCMKZOO4DXhwLO70Cws37UfQDx8Gupur7/k0ddxd8d90IrxyceZ6H\n4/RlAuAVbgR2hmwinufR6XRuyhJh2LdyKYqCoijkchlM02Z4eJhiLk+tViOdTmOZNqIImqaiq311\n1fM8BPrDMVWWUSQJ33XxPHfHqtVn3RERCT2D3XbwRfC9Do0bawQhfOFvv89MZT8J1efuAzV+9wuL\nlIKI4TjDsrzIzPgBcskkGyuDPNdU2WqnkJweajqgmtKIHQc11omzSUIxxJSbZEQPUVWouiuk4xbP\nXTzH0ImjRGEX64UO8jtmYB/UejcYHs+RSPULW2QpQJJUEimDZrOLqohstWwKuTxqIo+ll3jy0gbE\nMRXHY8GY49HQR++ZZPQE+ybTfH1uk+kDU8jdEXRLpNWSCPcf4+2/8Qne+pq3Iq9ssn+yzNhggdrK\nOk/83ZPUtzqEgYjlBZheCF6WOJCorhZoVhVW1jbYd0zkLx85xX/69EMUh5pcuHyVYnGI1c0FemEb\nXVUxOzl6bgdJ7xHENXxXxnMFshkFUVSxujKCXEbIDrHQqlHvmjhd0MUEQ3tNum0fy+6QKwzy6OPf\nAwGCwEcUYzwnwDZDVle2+MqXvsrK4hKZTIq5uTkuX5qlWety5NAxcqk0zVoVXZWZm52j0+7xV1/+\nGvOLG7iBSzoXsme6wMyRMpmSzOr2AmtbK4hyjJyQ8WIfSZbZs38PBw4cYH5+ntWlRWyzx/3330ci\nlUTVNaSkxNT+fUiqTLPdZHNzk7m5OXzXp9frMTU1xfGTtyDKMpl8jhMnbmHf3gPYtsO1K1dp1mts\nbGwQhiG9bpcnH3uC2ZdnSSfTHD9+gtHRUbzQRlOKtJoxupGnkB3l8b+rs74CgxMitVqOTctGUBMk\njJja5ir3377M//JjEb/ziXFO3dElkwspjcQ89phJOqlx5yGJH39/SDkv8aNf+Cty4o9gbMB2YRtX\niXnfZIGXWldIGG9CFuHwkMbtMwkgQRTEGJUWpTIUKiJnnlL45E8fIdZ81BA++9kEeyoOTz26wfGT\nB3n2+edY3Oxw7OQIumdg1i2KioXVdFlb3uIT/+YjLDz9J9wzBeNpF99vks5o2K6Dqus4gc2HfvSN\n/OqvvY9cTnnVIPlDMdk4jtd2fm4LgvDXwB3AliAIQ3EcbwiCMARs/1+89lPApwBEUdwJVfXdBP/Q\nXbB7e9f7CtxktbuuBFVV8X3/plSw2zPwA+vWDzRb6LcIiULfztXtmjQaLRRFQlH6IG1ZDiOjA5TL\nZWq12k1PbTphUKs2keU+wEZRhCgLiKKMZzrIsoLrOeiahqCqN7sRBIH+3i92msB6MYQyYSIgn5KQ\nwiRhHPLV6xavuW2eMDvIxz85xYs/c4ZrCxKD401uSBLd7Dwb3QayorBVus7YdRfTbrGEx/6SRkEe\nJWEoNGiiyClM2yY0O6RSgzipJvnjJSR7nWq7g64K7L+3h/faMu2H/xOhsE0qmyaVlvEDmyCIEWRI\nJjI04x7lcpbtjkJeFZj3fEq5JLZWYXxII1GroZfKvNz2OJwMkNsmY9M6f3PBYmBjkdFxA6xbyEsd\nojasVQX2P/Ag2+15xCvrdLs3ePnSS2xvLzM6PIjs2HQaJsOJIr2sg+dvEJGg3rGQRBD1I1yYncWT\nz/KOt4/w0sU2rY6JpARsbTkYCYX6lkU+GdPzPcqDU7TbG+DJhL5CNucQ+Tpu4JMspYhUgfpGg/3j\nY7R6ddJ5j3ZPx0iGFPJFXnj+Cqlsn6GbaoiATi6rYFk2XsdFkQ0mJyc5e/YsvW6HViekUCiQSCQ4\nMD3Nd596mnwpx3ve+wFiUeKRR76BEwrMXlni9jtnSKdSFAfyGHoZzxZ56cIlem4XxZDR0ho9t4cd\nWmTzGeauX+XQoWPk8hHpdBLHM1EUiXarhaSrVKtVHMdhcHCIZ7/3NJubm+jJBKtry6TSGrlcDlPs\ncfr5M8SXAsbGhyHySOSHOHfuHN1Gi067jSIKzMzMYA5UuHHjBqG0jWkKaEkNWQ+IhAhZTpMtJrFs\nhzhMkExuYbfyVB2BeneNP//TQU494DF1zGWssZfu9RZby2kss4zobTI6uM4jf6YReDlWh1Q+/5Xv\n8Ov3PciPf/pZ/u3PH+SYFPNHnznPg28qsbYqEGkupeEMW9/YZmY0zb3vcDk8AZFX5K/+okbbvM7y\nUpKLEyW2ejUiv8T+YzU68nNkh2UGxAR3nZqmVrxG29vPhedXEKMa//xn3kvPSvPEkzf42Z9/L8+9\ncIXPf20Jz/coFDJomoZlNfjq179MNv1AfyPLqzz+h0FWEIQkIMZx3N25/WbgN4GvAx8FPrnz87+/\nyvP9PT/rriSwy1J3ZYDd53dlgl1nwa4ta9eZsKvj7kZmd88nycLNx4OoP2hLpjQcxwUhQpQkBCFG\nEKFarWKaZr+j1usHB0QRdF0mCAIEob8aXBAlwjgiiECTJBQlxrQ9VFUlCGJEsZ8SC8N+JWOsQ9bI\nIQgSgdIhYQi42yGBKrAQJpmvq1RSEn/6yFn+5Iu/yezDn6Hz+CyOnqFw6g0sWpeQgjyNbpvI0ZnP\nqVxwfPZ5eziYcpgYSFGr11BEF9PZwHY7yIkKoRezen2dB/a9m5b+AqODDrz5Pvxvfp/yShNvJsNa\n26XT82hbHRRFJYo9BEFBVQ1UOUJPJwnNKvlsEiXySZf2cHHuMmP6Cpnu7Shii56zgdzx6Sh5Tp06\nxPOPz5EqdjC1fbS7OfZLRabyKtduVAn9NMmpoxyaPsTY7ac4/f3HCe0mVucakhYQiy2SepLIzyOp\nAoV8ifrWJo4loGUqJESFJD1SqQyrSy4jY2mIMqhRhG5sgGgwP2cwsx8m9h/nxbOLhIKN07MR4g7Q\nIPRlCvkxzGrE/MocB0/kMS2JlJZkbDRLs2HRqDkY2b6trC21EAIBURXwHZ8903vZs2cP2VSSTqtN\nvdZmu1bjme8/S6VUxvd9eh2Pw8f3USjkiEWJQ0ePcP78JXxXYM/QmzGdOrHSZG1jjbHKNG++/z7m\nF1d4/vkXUKV+2GJ1dRkjpXNw+gAXzp3n6o0FHMchnUzh+Q6eZaOldBwnoNFosLm+iRiL+F5AJpVl\nfWWVjdVVRkZG8X2f4aEBTNPEtlxkEQYqJV48/QKdVhtVUulZHtevX6fRbpItZBiZOMriwiquExFG\nORRRpjyQRxRFNuZDpFQNzTfoWgpKVmXwkMymP8ij1zw6Tws0rTaltAC9JNCDeISFxZBYK5BLr6IH\nm5ytH+cP3jXEH8oVbrurxfE3/zX/9udO0m5ILCx5yHvS+Fc7lNUib31bh0zOJ1BTfPWxKrWgRH7Y\nxGz1sJqDCNYkP/u/vYkvv+vfU1vby5kz8wwUYsKuw8jwHr738Dyx4nN4ZpyXL19Fy9h86ak1UobF\nRx86hW2uoBoKltUlmZYZnxzE61mcebGJbb96rPxhmOwA8Nc7wyoZeDiO478TBOEF4EuCIPwEsAS8\n/9WecNfnustgdy/Zd4deYRgSBMHf89Puste+tSu4CcCv9MvGcb9RKww9RKk/gJJlmSiKaLc7SJKI\nYegYhrFTgWih6wq+79Nu94jjPrDquvSDSG8s9wMGirhTTNP/ZgvjAFVV6fYsRPEHzggBCUkUiaMA\non6lIpaH6MV02jY9S8UwXOjW+NbVNL96R5mN2gCf+P3f5cNTHhVjlFq3xlY7Qo+msJR1jIRES42Y\nz+iMb/R4m1lCySyjjuh4hTwXXBtECze20WOV7QasSjGOELFan8U5muQX/rnMByauctcDRcyaS6U4\ngG3JFAcqyIFEx1yFSCDwbPKlArqlcqXm0bPalMpDLC2usmWJZA+m2Jhd5tiBFF3LZ6RYwSlOcXii\nhOiPstl8gcPTMMyt7PcVfHedO4u30Ax95nObbLebuKS4+w3vJMZhffEap5/6DuvLy1TEPJ4VUCkU\n2GxsEgcesS1jpGWEQKdSKtFoPk8cD1BvtFCHBFRFoVTRiWKFrbUkovYyR0++hRdOv8SpezXMNR8l\nVBksDdCZN1ETNoeOD/LyS220ZJreWpvBRMRgucJLZ64hxQqhb+M6OVQlSRQ7BHGMqqusr6+jKBKp\ngweZmNrD+MQ+rixdJvIjXDegulVDkuDlC5c5dedr6NkW69urhL7HnrE9RKbOxlKbqZkcU8MGnZrF\nLSdOcmj6KK1ql9nZWToRdFtdZmam2Vpf4nvfe4zB0XGGhwfRdAXXtuk2ujTtHnrCoFfvoEkykiBj\nqBq1jS3sXoJWu0sqkUFRJOI44vDhQzQaDS6ef5lcaQPXtlElGV1T0DUFz3dZWV8mVznK2N4CPbeJ\nZXpoWoiiRvhRFcezSKZ01GQTpyFTGNQQdI+oqeGkWgjmOD17jpa9jtAdppKOKAy6bK5fRo+m8PQe\nA+I4gbHK5fowy/MNfvzn3s63v3KOVJxl5o4DXH12mdee0jj9/S5P/HmKd79RYrMm8cW/TZMWNY4e\nEWmbSWK1i+KHtBYXeexrA5w+9xR6LFDtzHHvu2PitR4vnVlkaUlhdH+Cyel9SMZVGptdqtt9T7Rp\nmph0cJ2IntVB0aHXMSmXyywubNHqXMF2/xEWKcZxPA8c/z95vA688X/knLsMdjfmupvk8v0fRF93\npQEA27ZxHOdmAQxw8/d22W8Y+qTTaTzPw7L79WSappHJZDDNHpZl7SSxfByHHdeCjyj2L/EFATRN\nvgnaoiiSSCTI5nI0W3XCMMbzfHRdQzcEPNcnFPpAK4gSmqpimxZhFCKLIpIgoMgxnV4LWQwgkogx\nGDt0Akk5TW+hx1k2OF8+TmowhXJ2gU9fsPi5VJ64M8q83CXbzpHKXMfzaojTd/C1p7Z4sJjDE7t4\n89sIvZBiUaBdNTFcnXqrg5KsYIQOWeUa/uI17vzJO8hvjvO+H2szZHVwtxZJJopEcolHzy4wNDqG\nHmW4urBOHIRIQghETJUNrqylGTAkFlZrqLrAtlBgcSMmoYnc6LqMKgkwIiLVZbtaJyxtIs5nuE94\nMxk3oNPr0RYH2OOJCPkk9+y5HblkYCdjbnQ3uLK6yHyywHhhBkVMkEw/zR/9/l+yuBiQyCgY5Lly\nusU7PzhAYbxIe75Dq2cShBaiFOEIy0RxCVlVKRUmESMXNWHgBxm++2SNU/ftQ9ebyN4Ugd9A8iX8\nLoyOlpk6XKPRWUKXZtC1LQqZEvNzy6QzSazIxrVFioUhfG8dScvQ7HQxbYv1zQ1M0+SuO+9h9tJV\nVF1haGKY73/3ORKJFAemDyJIcO7cWXp2j1qrQTJpsL46y1evb6AbBqtrMHPoIAtXbzB/ZZFUJker\n2mCgUKbbbnP2zDmSqo5mCCQTKU4eP8no5BhPPP4oeyYneal6oV+o47tUKhWq61V8z+qTFVnGtixU\nzeDChQvk8xlkRSSKg/5mhRiuXZ0lnUhzy/ET2KbNdq1Ko9PCJ6DZatGdW8F2DVK5ApLkYJvQbMSM\n70uTG1ymun4UiU2kRAPfCRnLxTz1rMJI9jR5ZYieqxLF6+SK+7i62mC4kiJqLINZxik12fxGA3Hv\nFl+b30vqL7/K4f3381PHm7zrTWme8if45B+KtDtHmZg8QVp7kufOKIzuiflnH9tg+4UkT3y5QyqX\nQhMDZg7q/M1zczzzSJ0P/nSGq/U889c36b1YwQlWGDsxxvSxDF4nzaVzbbLyGPsGG/zCT7+Lf/db\nn2Nh8RqOYyEqMkKkIUYJ1teqFEo5TLeNqPwjyAX/M49d69YuiO2CpGmaN4ddu4+n00l8373ZmCWK\n/crCMNwthRFxXR9ZFndqDqHT6fTtR/RZqGN7dDtbO4kwmSDsM2fHdvpDMXEH7KM+g/X9gDCEQiGD\n4zg4jkMUgipLuK5LJpmm0+miqQkkqR+EcN0elWwR3/eRFZEgiJBVGcfykSIVwwDHkYjiGDyX5SuP\n844HH+Bs4yyN77f5+sQLPFicZrSc59EefK/bYN/YCNudmIXQZtLNUionmSmPc581T3oUxBsyrYGj\nODWHg1s+a8Emj/ckOpk8J/emuBEL6KHKiTcnSH+vTm/sNPdNSUgnHuTSX36OjJNhdDLHNx6+wNkn\nzjM6NkwUJKiZWyQUg7VVi8yBDFJ7lbpjkEjoIK5hBFkuraQ4ftcilxYyDO4roSltMutzuENDXLh6\nlY8c+zhDiTQ1pw0aZIQmsVTA8zvIT9jEqky6nOb4UIE7Th6BN0PP6rGydINa727eee+7+Ne/8SnO\nfesxeoLMk2sZPhJ3cF7yWfO36Xmw1onwjSrKdpm6pJFJb1CWTLY8iaQcsXd8Ck2HKPCJe3lMbYOF\nG3mymRQNr0W7EzGdez2Xan/MXE/kxFiCxup+vvXod2hZ25RKKoFVp+WpDE4MYDYiMrqG1VnDtl1a\n3TVWvvYFAidi+vAQzz43h+n5pDIZ9k3vxfdsVmsrVMYGiS/YrCyvIQgygd9AkHJsXamxurCBqitE\nEYiyQrFUodVrURoqM31gD0JSZGrmKGp6gNGRMXrdFhfPXmT5+hyu7ePHKslkknqjRhD6yJqEH/nI\nkoIoysh6Ak3UCKQIx3P43tPfJ5nVEXUBIdYRZZmF9SUmxvcwmS9Tv/A8+ZxAItPkct3iYGUfjett\nHH+R47feTjftMTCyglUrY7fbBGGT+kqOoYkU69sQ9LaQhwrYRAwlE0wd8nDrAfffNcjFy9eJkkmk\nUERzYq5UHYYaC6TfdoKHGynet/Qn/O4zfwZPnSNRejv7JgcoDpSY9K7y6yc3Kd7rMz6U4+Kj8PFf\nM3ndfcNkA5UPPRTzt5eWcbweR9+colOd5sIz11DcBENTHlOHDnGglEXQJS5b81SrSaSkyUM/+m4+\n8+lH+M7ZTfSFOqGokcw6HD0xyupyEzU0sJ02ihTd3MTyao5/EiC7O9x6pea6Kw+8MiLbDypEOxsK\nvJ1mKxFNU24yX9f1UZQf1B/uWm9Ns7/4LI5jdF1HELwdB8MPCmh2XQe76ax4Z4Am7oCu54c4rt9f\nlrgjyvh+SHLHGibJAsOVIaIIqturWLaNZTkYhkG+kOu7C0QRx7bpdjpkcmmiqD+ljMKQp7/3DK1m\nFzENT3xhhbtPTdHJS8R2ggvX2+xR1ulKQ4SSTRDNM7w3x+WVBg9OJ9C0bRbjNtkVC1N26WQkNEK8\nZpOSkCHudgnbDbxBEf72b5Cu9EjcKdE7kOVvP2Nw8I0mTnMZMTHDoZOn+NZ3zmLa2/S6IVraQDN8\nZmevcOzoEKl0Aj2TZru+TTY9QGmgwEJ1i63rGU6NwLWGzbSWx4xsir1t7pw4xEM//VNU//PzdM2Q\nRCVFIhbAiRA0HSsVExATdkyCjU3UuTWUwSSp6WEOlg4SjKrIxhhPfuP9tOrzPPKlR/jcZz6HnJrg\n4vVvsh057BvaT2zN84GHjnLn3k+wGnyKv3g45viD+5C8l6i2HAYSpxgqiOwbVZh9ok0mpdJrJimW\nPCLH5KWza7z1/jRj+jBPPLVF+YFpvvnYszTtLplyBtMCpxuQzYm0GwKENisr6yRTKcIoQAzhyKFj\n3HrydpaWL+HaYKg+uVyOzc1NrlydZWR8iNpGk63NKpVKBcty6PVMOp0WqqIThyHtlkmhkMcLA1JJ\no9/X4DscP34LmUyKC+dfxvdDWokmnm3zpjfdj6ZIXL08x9WFRXxPQtE1DMOg2+2i6hryzhA2CB0I\nYwLXJ5vNosoGza0GqUyO0G8RBiGeI1Kv1/ECEd8TsC0Vy4o5XpnAbC3RcbdIp3IsLNwgU1bw3GGa\n0RrJpMf8SoyR8On02thuSGnAIJX1UDUX3zJYmHW59bWbdE2H9WvHULImiWyE6cfI2RTbHYtnLm8j\nSzrjyijGp5+jnOzxzRu/Q2JS5N6RTX5paJb/cDVLlIm5fKbFE4/P8PbXBbxuIsNSu8O20sV0Xsfl\nF88wmhvkmQvzVKQklf3DeFLE3HITN87imx3qax32jOYoVTR++0//nJ6jMXagTLmYZ+F6G8+Kaa25\nZNQEUqyx3Qo4cuAI55pzrxrf/kmALHBz8r87uNp1AezqrP2kVnQzEts398t4ng/0i11UtY+or2zt\n2o3LyrKMruuYponneTt2Kummx7b/u+IrvLkxnu9QqVQwezaIAu2uSS5XoNvtIu+4GxSl76+NI27q\nxYoik8qk6XZNiqUKoijSarXIZDJ0eyapRD9C3Gq00LS+zOH7IYLloqkKkRSik+IrX7zOh3/9bla3\nlllKwHbLQqkrFAIX2eyQ02dodwImrU08dYCFtMWpmosoQVxQGI0KiGsN9FDCDyxUPSBbnMD/zhLV\nj52gnDqMdarI7PfrFNaLjI0mcJUR7rzzfn4v/ASGkSSVyrLdXMfQRFrNDsQyA0MlbizVSGdUBPJ4\nfps9+SSX53vcNV1hMJPlLR/4Cf7uc/8e56UzvOOXv4ClqBgx5FIJuoGLKkmYvS6xqKGLMmYc4osy\nmpFADRTCJZva1iK2DmOiSOdAAi2lY+SSvO0jH+TDH/5ZMK/gRRMYG5fZrl/hxBuL7E+4TKWarC2d\nZzQVc3R0gHNnJBwfAnWRyWMlVrYMPFmmG9ikMzV6rogYZ8gmJK5cbJIuz1CoPIogvYfnn/suqWwW\nUa1itzwKA2U6vSq9GiBsgQJGyqDTapJL51i+sURoQ7u1Tts2mT5yhMOHD7O9tsngwBCXL8xi2T65\nfAojmcJybEQZXHsnSh7HRJFAoVBiYmIMI6HTaDdQlBLz1+exbZuhyjDrhawJSAAAIABJREFU6xt8\n99EniaKAu+86xZkzpxkcHkFNpUgnU2xublKt1pA1Fcfz0CQJUZaxuyaK0rdMtoMG2VSOdCaD2TZJ\nJDQiT6PXCnGdTRrdGqISk1JK6HoWe8XDT9iUp4p4oUdttYdhpMmlbKpbGk7Lw3c0ysVh9FyVgigi\nRmnq1XVkFVQxRxikePxrEpHSwiiv4YQWycwEKVugomtIuQRD6giiGPGIlSPcvMBDr1FZOh3y/ozL\nh8R15LFD3Hhkhu9896uM7b2LmcEcGWmDj777PRjDJWYbH6dxoYVQnqFmdlir2Rhdi/W2g6+7nDp+\nhJoXcvX0FYKeyEBZIZsfpBEING2RoYxKq71FIFqERKyur1EoSgxXxpjQhpicGOLcmauvGtv+SYDs\nK32wP2CgwiueixFF4SbD3QVh1/VIJhP0ehaq2g8VJJNJut3uzf4CxH4KTDeM/qoU28LzAyRZRIwF\nBEFEEPv+WXY+5MQh0o4eDDAyNkqv12PvgUP9zZ1rm6zMXyOOIZUy+sMurd8Ctra+QuBHaPpOAi0G\nWRDJ5AsoioKRSJBOarRaLRRJwLI8FEUgl8nSbLYxEjqJIIMr15l9Ieba3y1w16FxHutYzKETWF3E\npErgi+QyBQLPp7PWYmvwFGJ3ll5JR5INeomYop/ESIEly2xGbYR8zKg0h1eEcdUjfP+vMmjGfPzX\nL2NefIrssd/AMSMGSDCRy6BpBdqOh6BB2wHbsnnyu2d56J33srLxHAE2bsdlarJM1LUpDFX4m7Nt\nfvtfvo7SwZMM5DK01oscEN8FiHQKOuFig8F0HjN2yZVS9BwX3+niCGCpErKsYgkhEjFiAJIns2UE\niHMtWu06KU2hMKmC0COOSrzl1G8SCjJNcxZDFlm6cZ7Hz3+J+bbN9a0eYnsfJ8ZyvOz/EWZLweqk\nsaIujuzQtQdJF3xajZBI6KJJSVqNkNe+/nZeXniSibEMD7zpVv7gjz6HH9lk86MMDg2j9Syi0KHd\nSXD4wBTr61sY6RSiLBL4PleuXiJyA/S03ne9+D6iInHHHXdw/cp1NEnAcly2azVkUUBVZWRRwvNC\nJFEldEOWl1fRVBlRjNna2mJ0fIxrs9coDwzimz5PP/0sICKK8I1GHc9zGBvdg7bzOZ+YmOBb3/o2\npmmiGTq25VIqlfjwQ2/j+88/y+DIMK7r0m53aVTrpIo629tdInwy2Szbm1sUh2Um90+yurbN8lqD\npFOkVM7T9NpoKZ/8cAEhhLjdIV6v0+5GpJMH6DmbOE0H1xQIwx5Te0aotWZxgERKJ1kJsByJEANJ\nTNDsruBJCUrDMi3LwqouMm0UudCeYN8YpDnGz969ypHoSRKyiP/hhxn5+r9i1JggZzcQ8kNMj7yW\nbGoKq5okG/wC73u7x2hynfO1r+IrCWbPe7RqPRLZgIXL59HTBeiESOUEnZbL7Ok5xKxK5Hbp1Hqg\neTiSSHGoiECAj4sVxmQzGrZvYzv/uN0FP/Sx6wzYBdlX7vLquwj6LNE0bXRdvVna4jgeRkLD8x0k\nqV/0ghCBENFPQcQIiCiKRBB4WFaIokg7IQKNwOszYCGWbzJaP/SJBQlZFHAcH9u2cb2tnQWMAaVS\nidnLV4ljKBRy6IZKu9MBIiyrRxj0bWC9nkMykek7IsKIfD5PMmHQrG/syCAxsiyiqgKJRIJEIkEQ\nBHS6JhERWgoE0eYrn7rI4T/IM5RLsp7SmKhdo27oaFqPSKizdc1kWIRnBjxs3yejRaQiFbXbphzl\nGCmXWA90fL9LOhmhbdhUNRCvNxh4+D7C8QcRb/uXZPU0zcU1opJOO2yy0exQSibZt/cgl16qo+Vz\npJMyVy9voL5PIw4EdEOlOJJhbGiYi/4qudCnGXjsPXoEZ3OFs5t1bjn0fpyvt9BLRZIzw3jzK2TD\nBH4SQscmGQZ0iclLCnIsIMYxruCAIiBHHmogshUplFyHZFojbLqYQUAnbxNeaaBdb6Om96CmS6iV\nNkeGH2TUeD237V1BavwOf/nIfyNd2U8QKnz2v34Kp7OJbh/CCJoIHQ3Xs7F8l6FKmm7TodpeJpl8\nEwPGFOMFnV/8mft42/0n+Y+//6d8+4nTWFaB6X2vIwws/DhAVRMsr22g6DKu5yDIEel8ArvuMD09\ng6LqLC4u0m40uX7xKgISoigQhj6OaaNqEoIAmVwa1/WpVTvIqoYowuLiIrqu0e20/g/q3ivKsfs6\n8/2dnJBRQOXc1ZGd2AzNHCSSIkXKlGRFypZzGF9r7liS6RnbY1q+117Llsf2aK7TeJatcVa2JCqR\nYhApMXezu9m5KyeggEIGTj7nPqC7Rc+aBz3Nks8LsGrh4Am1z/7v/X2/D8tKoqk6lm7x/He+iyBI\nWFaSZrNOxamSy6VZXV1lYnoSz/MQRZHx8XFct28pj6OIaqXCrTdey5mTr/Arv/SzfPbzn8EwJpme\nnMLzPM4vzhNGKpulKh27TrWxycWz8yhKinxilK3GMlavQCYTE8dZjBEBt9bhO08K/OknP8LEwRpv\nvecJRic8xDBP04GavUgoagwVp/tows0yuaKJpIWIokOnqRF5g4QJGM9DtbtNx6izGKsMWsMcEbo0\n69/h5hubKIFIuxVh+El89wz37p/hZ95zJ5/7l8+ytrzEysiPEDc3SchFOiNP8HMf/HmWN5I8+v/+\nA5eSAoVECru6ztG37SGna5wdkNnwfKxultLaCq26i6YrZJIGWiIDTLFRmqfd7GCoadxqyKJ7kTDw\n6La9H7i+/VDwZK9kfF3pWEVRoA+NiRBFSKYsoqifVmBejiXJ5XL9H2scEsNl62pMr+egaf1CHF0u\n2pqmXdXRapp2FSATR98HgV+hcV1RJfQ7YYF220YWRWq1Bo1mg2pli0Z9m6SVYHJyAsftKxz6Ol65\nnxEWhBCDaRkYZoLo8vLONAxK6+uEUYCiqrhegGHoSJJEp9dhaGgY13WQ9QAilfyQSqXlMnN4hqK+\nxXqcYf96QDUqkbdaBL7FwrdX2J2I+c7MJFljmLC6gCRkUDSXQT9BO2dyetsmbaUYNwKkUgM3J1IQ\nEoglCasTsJ2fJ9B28ewX/4DpG3fw6uIAX/nHL9J02qRlhfGBCWb3TVMcHGDhQhnLFGg0G8zNzmHp\nMDs+gW9HnFla44YjAj/5Ux9Bq1dwtQJ7lbcxUEtQXj9HaraAtVnFa7ZwjAi720R1I9KChBVGyK6D\nJHrIYkRWk0i7PkbbJu9rJCKHKO6SViXcWYmB6wdJvXgCp6bRqa2wuXCB9pJK+dRLSBsBuWqRa8X3\nUsjneensH7D7yBB/+F+20Cd8EgkZ0x6mvnUWJ1Yw0xlMPcRSTRrtKm+993bicJET33AxFY2RYYuP\nfOQRDlw3ycuvvsK5kyusLVTYt38aRZK5eLEfyZLJpGk0aqiKQrdtMzBcQFRkwiCkWtpi8dISiYQJ\nkoCu909Jnheg6AZ+7JItZBBkn4gY0+p3wa7jYhpWP/ZIktiubVPbbhAB7VYHSZFIJk2iKKRea1Cp\n9o0IFy5cYHFxEbvjYOoanuNTHMjy4D03c+aNUxSKOV586bvk81lq9QrHXn0ZX1CpVLa4++7buf2O\nm3n/+36UifFhluYvkUvrvOUtu3DrDcJOh2TKotrewgtUPNviK199gt/82CP81We/iGYmiIUtdCPE\ncSIGxwawOx6NRpdI6KLISVRZI458Dt8gcuNbGpx6UiMxksSXEtycT3PGlpA7bW6WX6RoLlBNNUjM\nxKjtBJ1TB1i4cB5fsFHyozz8oZ+AXkTesFDdHgt1m28/9WXGAoFhYTeD5iAvn3uFvC7z0L1388Jr\nJxFjCT8loUsSpdIaTS9kZHaWZHoQVRFYuVCivNwkcm20WKFdjeg0bGLPxrMDglDgtx77rR+IJyv8\n72K2/09fgiDEoti3sl6Za1qWdbWz7T/t+6yCOI5ZXytf1bqaif5w//L3XI5H7n9Hf2aq4DjO1Xt7\nPQfT1ImiCN1KXO1g+46u8KpOVxCAy9HhqVTqsgusd1XVoComghAzOFRgZXUVTdO47rrreOGFl3Bd\nj9GhAo1mB8+PSeUG+t1FFNBq1DBMHQDbdhgdG0JWFLYq5e9rf+OIbrdHUR7j4etH+dD+JE86Zczb\ncnSWYezpLXa/N8loRmH5n95AmdzB4wdNfvSba0hJkUvSAF5mkyknS37PBH/8/AXunJwmar6G1hW4\nbS6JXO/QzKZYqWyi+gn2/8c/5yvf+jYP3Vhl4YuHCd8Y4S9qX+Z/HvsCQV0kacio6SISdVqVmErj\nu/zqR/6ImYkCt9y0m6++8ATdtS6f/K1PQusUC26d1WfXuTZ4kGTVJzKHEOUyYbBFIPXhP7IUEogp\nbKGLqPbdd6ZuQixAOomX0PHSBqZcY1tIUsjl8Y0qrXSBlJpAiSLcjSqaAnZVQZQ3IUigpRXabQ/Z\nEFDaElHCQ3KG+NuvXkf+yBn+/D8fxhpqstKVmBrVWK4uMZmdJJWoU2lIvP3Bfbzv1j9kbvIm0nqS\n93/oBn750Z/hL/7662xWV8ikI24/8jCC7jAwOMEtdz+MJGt9UBAKEgodscbo2ChxKBJ5MW63RxwF\nCLqIlU5gb7WxbQ9Vs2h2O+zYP4Lt1VHUGEnQsfQ0Sxc26NT6jkFZlVA0kVAI0FSTqakZdM1ku1Zh\ncf4C2Wwax/axPZ9CoQBEdLtdHnrg7XSaTRYvXuKu2+9gZFrh2LETaHoCxwsZGZ1k8dIy29tNPvCh\nd+O4dV566TlMzeDIwVuoV5sMDqbxgxY9x2V4aoxnn3uGlbMut91isf9gmkplBkepI66t87cvnqAb\n2DgtF6eRwI8kWsEqIwM5Dt04hDZwhpeeGMEyEiiIVMoXGR+a5v9++GGqZY9XXv4WN40m+af6MH8i\nfYuMb+OEAtPXxawO7+XYxsMkzn6WTz13kZ964L0M5EXyQYrNlQ3s6V10ujZRU6KYOUM8tZ8L52xy\nhkh6u8bkDXvZ0GP+6z/8M/OdDruGZrjpQ79BsxbTXHmFSy/9M5YxxAvnN+gENRrNKmOjBzCNPD23\nQbPTZGhoBkO3uPDaN7A7rf99pMv/cv3QdLKSJBNF8WW2QD9MLQwjPM9HUXSIRdqtLu1W93J4YtyX\nVgUhhqERi+CHAclsmjiOsR0HWVKwUgn8IIDL3aQs94uvpqoQhHi2Tcqy6LV7eI6HIilIgoKIhJ7K\nYCVTaKZFvdVEVBRUwyKVGUAUIhBENssVojAiDCO6rSa+3cVUBWQtx+joBI1GE0PTUEQIQgcv8NB0\nmV7sEwsxUc9HlRS2Gy0sM0NQ9wh8mbQV8Ve/dze/dLdGYcWlICi4ArRNm04t5NoJlz3v/zQz+2We\nKL3Okihx/YaPocY4Kw3WEwZ2cZiRZp0vLwvcPZJl9Y2LpDE5fETm4vAuPvSHr/KTew02e9sY+/eR\n0PZiJcp84+9yvOPorXzm9NP4qzay5dCWBYrEeJrOgKExnNH5yQ88wJCcJHFohpeeeYH7772d6V0q\n3nabVgTJ7hTDF0dwEi6ypiO4DmJeI8gIKIZKvGsUKS3DdAFxNEc0ksHJ6WiTBXqSR6xGWEkdT7NI\nOCFCu4doJAg8F5GIyFBxVjbRQwlFDhEjCVeJ8QUBVRJRvBjS4wRhHXWXTmQf5qvf+DQblTlkq4I1\n3cBUemws5NDNGqk8ZIU8CqeYmHs3f/On/8je647y4rmzFEeK/Nj77qCyCOmixaVyh2devkA23WX3\n6B6eff4FXCHEMC0U1SG2I4giVFNBksXL8kAB17OxvTYEKo5nM1BM02k32LvrAN2Gh9uLCGxobHdo\nVJsQxwiXYUiqovcXrKLCyPAItXqVtY1VdN0gCCMihMvBnx61boeR8VHw2qyUttmqVvjQfYeZP/Ei\nGTWNGJg02hFbtRb1To2ZqQGmCyrder2PmQyhWqsxOjFKvjjIVrVFaWOF6nqJ9779IbJJF1kQaDUF\nGvUShmDyxOsnePudN5M309SbPZYqm7Qih0gxuPu2Hu95T4NiOAgnZ3nXoSnedaDJbbsK3L7/KNtC\nCnXhFEqlAT2JueAch8UG800RKQFukOXMiQzr517g6VLE0Q/ejR4KbB1r001bwDpCfgSnWaJcOk96\npcJ6epQXP/95rk8UKCsbrC1vcN3wNQilHmeSBv+XNsHc9gtI7U3uV7rcsXcC7dD9nN0WUdMJCsWj\nZBIp1rfKdNdOk80O8K5f/E+88eoL1DcX+M3f+PV/O2m1n/jEJx57c8jhFWrWldcrfwOumhEURUGW\nZRzHxbR0wiBE1VQ810MUJeyO03eKXZZ3EX1/3ntViyvHKJqKF/j9sYMEiAKqoRLEAaaVuKwW6KsQ\nXNcnaSWYnpohP1Akk81RHBwhkczQ7HQQJBkEkQiBnhuSy+cpbWySTCbxg35hTyZNaPRw3H4kuC5J\nNNsN9LxJq9lEMHXGwzZ/+/Mf4L6JGsqcyOvnFfLrG5iZSV41bVLVTdLuJKlDIqZQJ9iSeWHlDLvC\nLBk/ZHXZYWssRZBX8b0Sm3aGfbLAufPnue7ONNkPfITRax9i7fFLjGzP08gZDFoB09deT9Pby7nf\n3cS822Vh2ufkmTdYbrrIXhslmeKGg9cgaDbPPn2OD//i2yjMHEBL7cKvb3HwmhlSssjKxRUCWcdy\n8yS3DSJDxDF0YguE6RTCbAFxKIOX1kAARVCQFRVZkpGFfpx7jEAoioiqjNrycDWBIKkQVxrIho46\nlEXu2ijbPVzPxXEdIgEMRUWTVSRZIpYEHK+CmR8g6vkMj+1heXGbV154CnMgw55DGywvhITeAK5T\nJZ/P43dEBvIRe655O//fn/0PRKok3GGe/OJJHv7QHqb3TfPaSysUrALl9ibdWo8HH7gVNxI5fvIi\nCcui02phmhnqrTa262EaKaJQoNN20TULIhnfDZicHGffvj0gSpTLFZrtBgC17RpRCCICiqr1o3jS\nSbq9Dj23h6aaiKJAo1Gn02pdRnLqdFsdZEVBVyQCQcLSDTq1bdZLNbrtNrJd4+6Znbz/Z3+W55bm\nObW6QjaV4Oje3Yym0oyOF7Asi6mZaaIopt3u0G03qW5tYXc71FsNZmdmWFhaxDAUut0WgqzQ6fgU\nBse4cWSY/TunkAgZHRrizqM3sWdklPKp0xzOZXjn0AFuGb+NG0eXKVVv5rbpBlJli/ylJdKvP0mr\nuY5r5Witdhgfy1KXJ9lqWoiJAi+eWuWcp7Fr327m9txAq7rM3l1D6EMZ2CiRtTzEyKTT3iZhWUia\nSDNMcK5aZ6VRQhxKU9geoNMSSI9oTM4NkVld51Jpm/nFN4iVHJ9ZUPhvJ8sUJyaJugGllfNUt7do\nLp8mkhRGZ/ewXS5Tq9bpbC3xm7/5gxXZH5r4mTeHGb7Z9XXl2O+67lXn1/eP9AKCAK7rXv3cFUvt\nlZnXFckXfN9RdhWFSIRqafR8G0mX0RI6giog6QqRBO1Wg0Z9m82NNRq1GpLQ1w+ePHmSU6dOc+Hi\nAl3HIZMrcMNNtzM4PoVkpOkFEm6vx+raGqqhYyYTJJNJFEWh1+miqQaZdK7f/doOlqWR0kWkENR2\nm9zYIGZmhe3lAP8Vh+sHyhh6BXN9HtyQ17Y3EIRdKN/5LnzhSVrnKiSSaSJFxVNU6k5IqeviSz7n\nvBqZKKa8soSQ1sk1mki5XyZ6/S/5nalzjMiDOGshlbaHEaVJJW/lnPRtbM/jg+PXU215/OpPvB9L\n0wiqKTrPZ3jrzg9y/sxpXn/+FO2gTCbSufPonWTTOoErEAoKTjdAlWUoODiSgBb2I8GDWCI2DByz\nD/QhCPEcl8j1iF0fMYyJnD73QU+YoCnghwSAIMqovojqhPiBT9d3EOs2RiSTzOQwUxnEWAIvxAXa\nqkjCiLADASmUEHWBB+77dTKajizZHNyh0G6H9BwHUTQJoiQxIu2WyOyOIkPDWbrlNBo+eT3DI/f9\nJXO7buXt79jL2sZZhgoptksea6UzTE4NYegW7WYT3xMxU2kOHroWCYFur00QOrh+332lqxa6adJ1\nbOYXl6lsV6lub9Pt9mi1WiBIJBIJQvrabVEW6Lk9krkkyXQS1+5RKW/hex6mpmPpBknTwnN9FFHC\n0ExUUUWWVGRZx3ZDkoksbs9lY2GJ5qVVbpnayS//2I8RRg5f/vbXuLB4nteOH6PnOliJFDOzs2ia\nQqm0wcULZygWsnR7HqqRZLvR5NTZc6SLRUJk6i2X7373OHvfegfHjx+noMhcW8xzbdrg/Uf28ve/\n8yj/6cO38qm/vsi9v/AlPvm1V/nPX/s6nzx3gKfa+ynOtEiNDpNL7Wa71WUkM89A12fV1rgkajy7\nKNBQMripPNVmTL1VZ/mls4wUhklOGMSDCl7RwEhIpIoW7VYNZXSIaKFMtb1GIRmwcyhJZEi4QxmG\nVJnCaysEUpL5lkk1Mc4fPfcK55bO89adATePbPErt81h6BGKJjI3O8zorgMIZo7GygUI+xr6H/T6\noVEXvNl0cAX6At/nGVyJhbki97ryPp1OXWUXRH4IUkzg+30nl+8TCf2llCyIV7/ryv2BGxKFEPgx\npqFcDlmUrjISNElCVhSCoB8F3mfDRriuixj3gxU7rTZxDFY6zeT0FNfsP8jG5hqrC4u02200TcNx\nHLqtNp12HcKQrmmSz6cZzliIfn/pVl1d4r//yvu4cyTN5vFjWMsCA9XTlEYOkLIXkGMF2ZEwfY9q\nusi5oEb6C6vs9tfZSA/SzEhEokRNDnDyCp4psGdomIWgi78MC7VL5K4ZYGBwP95/v5HEoUNUfu2P\nqfz5f8F/vkxbfR53TMRtyfiDM1j7J/DPNnn8o79BZ6NH7pFP8LYP30OuscLHH/0WHQl+/1Of5nPv\nvBevUyKXTtPqLiB5FvniCIodozoBpNvI9jCKHUMY4dZt1KyOo/noXoSMiKBr/SVlEKHKYj/mJ4qJ\nIpkwiggVCdGL0TwPDJ3A9/EbHXp5FT1rESsScRTguz4EPoZhoIkKQhSCWkDwIgLJRg4yjM1k+MD7\nPs63T36cIztvQ9VO4dsyejZDFKkg9xCEBN1uhQduf4gvfe7rdH2DQiqFla7w7z94B3/yd48jJSw+\n/0/fAxwmJmZQLJ/QbUCkkDEHGB0dZmSoyObaIrIKgR6TlhVEySVppgkjk2qtQqfXIRZFVFXHcQMk\nSSSMod5somkarts3s2imiuM56JaOFIVX8+MCL6Req+FZFqIIzWabTrOBH0nEXkAubbJv3zXMjo4Q\nXPwe11x7hNeeeo6nn3uVBz/4CI++/8d59dIJvvXcU6CKrGy8QKlSxTATzM3tYjKRIooiXj95ip4b\ncfr8JVaWFpidHSUWNLzApTA0yZe++HU+/JO/z+ROnV27YwS3DfUt9g4McMOuOS517uLIB+dYefwV\nPvW0TVa7yMk3dO6bm+VT3zM43+nhCxoLXpPPDcP2K+fIDkN88AjPfX2d/cUmdmOLuqexuLpEThzg\nH//sq0xcM4w2PkU2o7C17iAYOvnhASqSj9Pa4qGde5gbTNB49SW0RpHtaBsyAlN338mFco19ukjc\nbHP/XYfZu2cPzVgnJ2XQVzb5/fQgeRzCjkBhxwF27NrLkNRhY6PEv6y88gPXtx+accEV88G/cly9\nyVTwZvrWlc8Cl+UwYV/OpWvYdl9dEIYRsqYQhv0gRk3REASRIAj7EOO4P/MVY5HAC5AEqR97E4t4\njkfoh0SXUxZiIkzTwA98fLevUBCjgHwuh+e5fT2s67C1scbm5gYTkxPsmtuJoihUq1V0w0BW5D5r\nlghXlBFwSesKMRqSkcOpb/IfHriB6OWvIIk6G9oG5ZZNuykiCh6fKAn0jJDWSArXTLNda6EwRq3b\nY6ugct7pMK1lSGgu1SCgOa4xN1CkWhfYXqpjJStMThkMrdqkwxzdmZD82XkcJ89K5SxyfprzT/wD\ncjrk/vfcyezOcdbdlxjYfz3RXIt3f/DHSakZvvaFr/PK03Wa+Q3Ov77O9YcOs3ffAZztdSLJIXIl\nBE3FEhNYHYHAaaCnxwj9AEcMiAhQkwaxJfcf8bKEKMl4cf+hKssKoeshxiBA/2EmCqh+jGQHOFKE\noMroloFkqYSxRizLCFGEEAkIsoQoCYhBhORFBJKGJoQEZoCECtQZn7oRr/ME1x25iS9/6wJuI4+u\nN9ESIvXaPHfddDOCLPOWWx/ie8/MI6mnyBUvIQtpvKjH6bOv86Pv+1luufFW6s2LtJoRhw7u5TvP\nPkGr6rJn5378IKJWbjBWHEPRFILIY2R0BE1RGBsaQ9UttqqbtHsdwjBksDCILIkkLJMgEhksFtiu\n9ZUKCBGSLGFeXgZ3ai1cx0ZAuEyI65/sVFXGCyKSmobrhXiuy+BgHjOdw5RE8lGHnGFyYXmVPdfs\n59Vnvsv6sVPctP8Qb3ngPmp2wP4D15LKFlA0i2e+8zylrW1ERaXreCRTeSRJYaBYpGfb/ZOFlqTT\n8XCcGKUgMzGQZiCRY3Wlhpmb4cRKk+culmn1Gih2lw/fcj3vvWeOB6dMzr/2Hc5ttVmY2MmzC5c4\n61W4eSjD3iUXTwuJZQg7Ms16jZwUUZydZH5pnRtufSvrp9cZTSkoQQJHyqLZKhtbLZxmvf/AzSfI\nWSZhbohATtFrdbHmJsmPF9DrLbaDCLbW0WpvoBTn0A9fz1ZdxY+m2M5rfNNf5sR6hOS0cJUs47M7\n2dxc58zZNyiYIZcW5vm1Rz/+b2cme0XCBVwtoG9WPVwZI1yhcl1xcUFfugV9q6wkif0I7iBGURVU\nzSAI/b5C4bKJ4UqHLCAQRxJR2H8fBhFxGPfxhUGILMqX9bshcRwhqyqZXIYwDnE8m1TCpNtpIxAT\nBQEjQ4M4To8o8tlcXaZWbzI4OMjU7Aw926FaqeD0en3AjGTgNpuosYcgmbRDCcfvMGpF3LhjhF4Q\nUus1IB6goeWYX1pnIpb402+us/Oh/bT8dYSmzp7xgyzOl6mLNWx5E7PgAAAgAElEQVRDIKUnGE8I\n1LsdWgM6gRewuu7Qa25yYLdIoiuSa0jkD+2neWyVi8UMf/AnX2fXuEhFsXntWIu9BzPsvfEDdPyT\nPP34Rb7+B1/hbz7/RX721x7lb/7wr6gv7cdStzlbfZGhRB61F/OWe++FcAtJS0Ag48QRbsUn2RHw\nw4A4NIgUAU8IEIMIRRYILBFBiAjiiMjx8X0PpD49S4xjJEFARECKY1RRJowDHE1CjUDWNaIoRG30\n8FZclLqH2gVZ1pEtnR4hXhSiagqeGiGHPlJKx236yHoRIRGwZ7zAanmdl48vsnS6R36oSiJvUtuq\ncN9dtxDQ49CND9Msr3Lx+NMIsU/L3Us7HOapb32PiXSCmb1DTEyZrFzqUSwmGR0cwO1ErC6uMD46\njeIrxI6IribwgQsLi2hSglbNZb20ggAMjhRJJJL0uh0C30MR4d3v/yCJRIrNjXXCIMA0LW684Sjt\nVhvP8bDbvcu7Ag1ZVpAUEUVVgRgzmcDptFE1E0WVcZ0OlVqdYtLkmgGDwDJxIgG755E2LQxD46Xv\nfY+R/DBRNk293iYIBSJEcoVBPD+i3XMRFZUwiNhY36TTcbh0aYGB4jC5XJGJiVkq1QbdZRlZV1CS\naWp2l61KiQO7d3LTwQOsnF9CS0mcqi5zZnGLycN3cWDfHahujvWnF9ETGZKJJL9zKKZ9vgF+zNNl\nOB5bXGvFmNY4idlhfF3hma+8QC6XYarYwvVzSMlhOjUX13UxAo+BbJbyRp3NC5eQY1B8E1GxEO+5\nnWBgCK/i4OUHkcSI4v5bSDz4TjZmamTG56k1zvD1E9/h8RdfZe/+t2G3t4kSwyhiiG+3KdW2GUwp\nrKws8+jHP/Zvp8g+9thjj7053QC+z5e9giS8omW98nplKaYoCpZlks1k6HW7EMe4XghCjON6SHJ/\nRCBeHkFcufdKXkMcR32YDBFRHPX/eVUJJFCkyyMESewf2ywdQZZB6I8KHKcvEYvCiOnpaTY319FU\nBcvSadVblMplkokkVjJJNpelUiojEhO6NrIiEMZ9NYViyJj5FCfPzbPaETioe4xFGltyBkWq01pR\n0YpJknvHYdqi55Uxey6D3gCRvc2G0yDI9hkOuhkiNFxczUBMJ/BaPmmtxOSggrtSYE84hNG5hD48\nhbkWcKq+TpCRmZ2JibsSx9smd84+yIVLf883P/N3VF6R+Gr1HOMjeaJCm9Xmq0TJZZpeSGy1WFkr\ns++OGWaGM4jKJFIMvTCis9Am54iEooXvhBi6hiSAjoIg9gMLVS9AdCOUqP9wVS7rm2PhcmJxFON6\nXl/ap0n4qoAhaAhxRCdw0QQRnq0j1UN61Q5220ZSVCRVJ5AkMAxEOUaQQ2JXQBA0gkhDVHrouTnO\nnDiBF3ZZPl/j0I0RgahgN22u3T+DT4mpHXcxUpBorQ7QdbKcXy6x1doidhSWzlxg9rDD2fPzzM0e\nJqFprC2ukEtk2LNjjnxOp7y0Cn6E7Th0/A6iptBqdIl8MAwRTdcQpZgY8G0PRQCn12FsZg5Jlllb\n6RfiXqdDp92mXq3hdF0G8gOkUinCMMIP+1zjKAqJhIgQgbRhACKua2OYIkYyyY6hQXZlRTwjQ61c\nJ+x4JFMJ2qHLyI4pvv3E0wS6wlBhiKSV4Oy585w/f5GR0bG+zjuMsLs2QQieEyKKKrKoYFoJVpZX\nmZ2ZwdcUvOoGO4pDZFWL7Y0aoQeb5Srm4ASC0+bG/bs5t1xBSwUYpsT0vgxjR/O07TTipVXe6S+w\nlE0y3w4oSRneqHUYT/rk986xUq7xxmqD6UKe+ajGgNHDSo1S7nYRCnkM38UPOiTzeZolGB8aQ51I\nUrcEEjkTe9PHX6phjiSpKRqZzAjKzn3kOj47nQ4tSee12iT//PkSC+c3GR5OMTazC8e1qW0ukU1q\nGKrO+lYNp1nh4x/9lX87Rfa3f/u3H7vSob7Z7SWK4mW51vddYG+GekdRRBiFJBMpwqDfDTlO0FcD\nxAJR3IdvR5eNB2EYXi3axDFBFBITIaoisRATCxFBEKKaGmEUIsQSPbtHFIdIyveLrR/6JDN5JEXG\n7rlEUUiz2USWFQxD7xsKJI1YFNmqVPCCgNHRUSzLolatQhwQWSYuMWIcIMYOZiaFreQ5XRb5juOy\nw1CZrFfpeToaMeVkF2c2Q1N1iAOJVHID2EcpfJ3trohRUBAVgSAZk96O2N52scYKRNtNckYZwh6i\nczNZQyOXWkNMp5EXTrPTqLBtZ0nNqGSP/nue+sIXeOu77+fVpb/hhZMipx2X2cExerUKdS+D3BMJ\nHIWEaNDpSBTzA/ziL30YJe5iZA+BZ9NzfdSGRMIRsT2NRDJB4LZxBAFF1ggNCa+gofZcQsdH9HwE\nAURFwg7cvitPEpE1FUGAIPRR0gmEbn+M4PdspKyJvGOY1lfWMUdHUXM54iBC82IkN0J2A+RIIjJM\nXKmDFmoEgoQnRmiCRKwk2FpqoloV8olRDt0U8NqJFWolj3vuOgDaFlO5n2Gr/QLXHLqZhY1T7Ns/\nwlBBod1qIukhWu4M1x1+B4NDgxhygm6jx8Vzp5AFGctqcf2hg2iKDErEK6dfZXCiwJHDhzn2ymt4\nnk0U+XTsLqIs0m600VWVIPBIDgyzublBeXODdquFLEl021067R6KrOK4XWRZoWvbV7nLQdxHUYaC\ngBhGyLKGYanYTod6u0PBMkh2y0RdnSgCXxBYXV9HEUCWFLY8m7MXznHi+HEKxWF27d5Nz3VpddqE\nUYjTszENg2Qiw8ZGlYSVZGV1DVWRWVpapNGoM6jCzHgW5IhKs4kjSgSyRssJOLRjjPNxRGkjIEpl\nSG8VON5dYePiOZKRxsCgzk88chuLJ7d4eV3gtO1SVAvsGFbJH55CLLh4WwZnl0KO3lLk2U4Jpeqh\nBBHJhEkrpZHvOcQZmS3PxddGEdMJhsZ2ELTqaHELSzCx2yWqnVM0q1WE4WGiuEvHnwdP5n984Qxf\n+sbz+N4qN7/9NsrnT7P/jrchuQ3GBvOsLy2gSwKulqdXnudjP2CR/aFRF0A/HXZqauqqK+uKS+vK\nourNxKyrna6QJoo6dLst7F6IKEIcKYS+DoQEno8cgIhFnlkQPELVQDMlzHwWRZRQYgFBUREUAymW\nCBwX0RBR5Mvz4AjiMEKTNcRQQI1lAruNLAQoKqimgqoraIZOrd4gDGVEsYfg5jDlkGa5xPzZU0xP\n7SGQQhQL4m4PyVYQSWHbId1KhawKxQGdbqDx06dMttNJRlJtAqFNV93NJSGL5e0mcPZybXUQudcl\n6QwjqtuEWwUcQcUKDE7Im2ypUwStaQp6h7FoBLdxAGsoQyk6R+/Ww4TfeBpl02cyXWQoEaByDS89\ncQo7dnn65FcJYp3XTjfpVmO27E3afoQSdJHMBF21h+tXcVG59ugOpofTxKn9tJ0OYiaLktZRlJhA\nF0nGNrGjIJfAj2ycziay4yI1berSNr7sEFoqoq4hSGAlNXwxwDcFMPuFVrVy9NQITVEIMxrKiE6U\n9OjGImZSJahViSsNDMXCC2Wi0CCwTZwyuKs6VnuAWHXwlSYJXyLY0hF92LnvEHGvSHZ4i7GBe7h2\n/B1MFJPUm6eYK8ygpQJWl20KUzlywxpbq9vceeR+PvyhR/jdv7yHg5Pv4Fv/8hznLhzHShVIDqrI\nyTxNJ0lppUmjKyEWFd727nv4009+nF/90C/it0ugRQS6jKSmGbCmaG24ZIppipNj3PvwjzJTHKK0\nskY2m0VXNGInQBMkDMukJwV4vs12YxMnaBNFXp9X7EQMje9kZmoWK5Gn2trGD11U38IILQxNYGbX\nLC+vLjFhmnQbPWqqxsb2Ns8ee4N5V8WTTFpNm0//xV/wz5/+G9750IMc3LWTvO/zgSPXM6VOcH5p\nncX2JlbGYsf+vSy/sUgnoaGFIvFmm4HIZP7UCSobVcROkm5X5L57bmGuuIeFJ19m9+4hfvfjP005\nuIRRMUiaR9hqGlTKLQJ9jMJP/QLnZYHjyzbJuSRHds8yaBTwtTzdcZfZYdh7zwIFIUM8NUq9VaZT\nbTPcdjldvoighpTnVxGCEsvb32NV3ODALXdgC6M89eoTnK+8ipex2Png9ey5foxG/SUGhgfZLE7R\nC1W0CI7cdAuP/vh/IK2aCOV5xkcHqber5Ac0ZsbTDAkVNEX9gevaD0WRvZLXFQQBpVLpaierquq/\nyun6X5dioigSiS06XQfbFgEFUQZBdlCVGFUyUJNpksUMftSmE86DD3ogUtDHGBwcR5J14ljox18T\noqoykiDh2h6245KwDGRFIogiatUa3a6NZSVwPBfHc6+mNDQbLWRZJJVO43sOYaigWmUc20BSIur1\ngG986ykmdkzS60IQxqi6TM9t0e3aOLZPdWsL4gDcPHPDKzzwTMDrUwPkD1mYUxKP9mTKeofDzjBe\nU2RFKlENe2hSDre0hD6YxnW2kYQC9lqPUcVj7vYDLMz7bEzMIrhVZjwN6cwGLVnF1WLY2ELQQd7q\n8uXHv8i+Xffy+FN/RlI7gqzkmNiZYP6Sg5HWkASRyG+hygrmwCAZ2ebBO27BrVdIOzFJxQdZJ5Gf\nxdAt7EodQp2t2jzsn0bzBAzFohMFyIGMGmgYgYkkdqiGJdpyD8d1kX2RqCNQa0Z0UGm0asiBwJYa\nItUCsAU0M4v7T68TJRQQBKJOF1arBAslvOUtomqbuOOgtVtEqzGNsxpWMwNam+ZYmS2xgWJr1DYb\npC2TRmsD2Yj5sZ/6GM3eDINj7wKy6FaGJ5/+DB/72D+wUr3Ia2fP0vCOs7kQozdEDo/u5PYbbqJW\nr5LIZ3Fklwvls1ijcwzuKCBKPqtrG3zvtSXOb6zz8Dvfx//zm7/Gj95/G4Jbxe6sYpogRyp2x+Hp\nbz3FhcV51lZXKde3SWTTjI+NoUsKWSNBSlBJJ7IkrSwJPY0sKlimjqGpHD60jxuPXksYO0zPTCII\nAo4fYLsutUaH0fEput02ZibDanmLSqOLOVCk1rPp9hw21rcxzCzjozsobdT5+Z/5dwS2x7/7hV/k\n3e/8EW68e5D22inuOXCEvKFz86E5ilMpauUG602J4q77OdOdQkoeRZYnqbZD0msx6e0kpx9/kncd\nvhG5IvFHv/slvndqnpvfdYDb37UPV/HZsXc/pflNCtYAn/jjP+Yf/vZTRCFcalcRUlMM5wuMZDIc\nuH6I+VcFPvb+H+HuozfxRkOhhseF+VXMOMf6mRKmJGBmTZxQpnVijcWFKt/82jcZHjHZc+9B0mOj\nTKSG2G5vImoC335tnX/+9NdoOht0I4lP/van+MBPvhUjLbO8tsD6RomdOw9QHJpBUi0Wlxfxgh8c\nEPNDMS547LHHHlMU5SrYwrZtDMPAtu2rdtgro4MrM1m4DPuWIYpiFElHVWQE0SP0gag/JlDQkGSd\nRErDGrFoNh0mJ9I88N6HcEiwdnEBSQwJxAjd0BDDiDCICQWQJYlYAM/1kZR+fIwQxcRhP3ZGFGQ8\n3yfwA2IhplgoEgQhvW6PSIiIiVCVLKHQRpUH8H2474EH2FyrEMQ+nt9BFCMUWaHX9UhYCXzfJp/Q\nqJEmIwX83bGQQ+9Ok29FSN2AzaBNrO4g21olITmsBlD2QuaEDkIgEQxKbAQSqXiIpNIkqq1xpiww\nuH8/N5S2sLQeq989y8QNe3EXF1GGoaoVyO3ayVJviEAsc8NbjhC2xvjHzz5DSsnTcGvIgonvBuiK\nTywqtIOIw4Mmv/XRX0a2RKiKOBkBezvEP93AuLiMZFp0ex75YZ1GPUC6fpLmyiaJWowbhFiSDKIJ\nKQNTUwltBSM5iOh3UMIIw0wgal3MfBpZUMEykQOBeDCB2Okh/stJNC2FEIXEYUAYh7g9GzmIUHs+\nWscnzjlEGhCKqG4KJ7RILwdYa1t85nN/hT8gkhow8EKBVh1q1U0O7x9lfCiPyAyW4vHs116n0TzO\nrz/613z2K4/xtntGsNdBaU+yNL/Frmv3kMoMYiZkIkLuv+9uZuaGEOQtECLq9QbjIzOceP1JTp84\nwU3X3sJ4RmT/nilst8XGZgnf11lfXicOAy4tzWNqBrofEQUhcUKjMDVOq94kKShYyQFqtQZhGKOr\nGoHr0uv1yA/muLR0hs2VTR546CGazRaVUo0YgbHhHLft28Fnv/E8dx29gePza2zYDhDjxAKbjRZC\nLNHtdkkmU0SRQCqV4uXvfhdDjDi8bw833fdW2gurSI5Nrdnk1LEz1AWNyBaYve4Q6u69XHfwPszc\nEOO79pAqjKBMTFGXTKaH82yfXsbqdDl6537GpsY498JZmo0eNbWHUgFrcIBXTp5g/sx5xvfN8ODD\n9/LMl7+EJXUwc9dhx3WyUoTs5BASGi8dX6XX8pCUiDjQGC5MUW3XsVWfs4tL1DabNFa2Of3604zu\nG2DkwF7UVB5fAqwky+fr1Dp5FrcUzp4+zj3veYRH3vcRfu7HfwRtREQRYHxmB1/44tcYyI+wML/A\n2uoiUzOjbKwu87GP/mCLrx8KneyV7lSSJBzHIZfL9bWSl1kGb3Z8XQlbvNLNprOD9OwtRLq4PSAC\nSTSQFZEYB833aDcbFGdHUKczzBbfyn13HkUduoZC+5vEgoIsxQihQxB4iGGMJEioaMRx3/ygW/38\nr3azRSKRoNftImsGQhxjGCaB188Zq1arff2iKiFKIZ6toBo9xFBG0FdR4yQvP/s6R66/g1deeRJZ\nUWk3GkgoGGqfdauoMeUwYKRYQMiP0S6X+cs/Dfipn9cIL9QZHEgxT42JVo987GG5Oei02bSaZKsT\nOLvy5Nw11lhntzZFbclGnkgy0tmk0q5g3T/NfLnDnrU28nia+oRMfTOmYXq8/OIbvP19g8yMH2Hh\nWIjThVJpAyuhEjhgJjOYUpty3aft9HjPI+9BlWSaXY8XTh1DPy0RzncZShTZNzxMt17FSqTwJAgD\nB3NHEUEI6bywRqYT8fLKKVJqnsYbHsa6Sk4TWNfnsR2TtOjheuvI2VEioweCSmKgiJe0CIsK2VYH\nvZQg0LtImgKyiCyp6JKCFgrg+RB6CE8JyHMi8ZBOWfEZ/GqdXmeDResNSrktShcvML3jEJculqlt\nu+QHZAbyh1hZDBjde55KuYMqZnnquce56/6f5qP/8b9x4sX/SnPZYsE5z8BgHtf3aJVXGBsrkEdm\nyjIpHDpArznFpXybC8lTdBoRj7zzHoIwhe04dEtbtDs19uzdwfDkHH//90+Tz+awnSYDep+VEQsR\ngtZX1JimiZZLUS1XkKplEAVy+QytRhM/jEmkEmyUttiqrjE5MYUoqFQqNVRZxpNExifH2DE3RwyU\ntmt4MTR7Hpe8Etl0tr8UVkRCL6S8XSWXztDr9RgqDvPsU8/zlptuYfy2t/ETR9/D7/3PP6Ob1Zk0\nsxyLe8iDKS5dOEuvEaBOd3HFELvdZEjR8IMOY5ZCa2UdOWVRzCUpf+NZxmf3UCvs4dJmid52jXc8\n8g4uvHGCk68dZ3R2gpPPv8DgjXtRsxpGUKG++TrFkcP03FO4RoSSOMDsDQ0WXgupC12KmQxntuZp\nKR6YMqWlbWZSo7iKSCrVZeamEdLjY1RWG2zU6rx49gRF+Toy09djVs9BSuPxp4/xjc+9iJXV+LGf\n+z3++Hd+hdlZm26rwshgGjEaorzmsLm2ThT+GzMjQL/QXgF0J5NJXNe9Ss+6Mia4qgyAq3DtZreO\nLEropkoU9hDiFL4X0XNtIMSVe4imxdDMu5m57RqEeo6BGZ3zvQ56IgmiiAhEPkSCjy5rSMiEUYwg\n9rPDBEn8Vy6zKIpQJYVGq4Gma1iWhd1xcMX+ESIIQ+TQQlH7xCFdGOeXPrqDJ598lReefZ3pnTvY\nsXM3Gxvn0HUXpxviez5yJCIKMY7oUW3ZqJSYmU5xixyy+3SeHakK6692uHh4AG9AIFrrsFpzaIR1\nfuSOOZ55LmDPQszRToIv5sBttznZrTE9nMAPlmhcY3Ds2BlWLZ3NfB73wjrrqZiaBInEEIZWRNbr\n7Jq4m+f+5dNIiOSzKbbsCrFk0nMdQlxkJYEo+Dxw793Q84g1k+bJMhvpFKbT5a5rD1I5sYC5bxJJ\nUBGXbSwxwF5dx5gbRbMyeOfW6J3ewlc91IxM09nmxKuryGESWVORRANdNAkXXme2Z7Gd0NCiCgnB\nR9R80od2Ig1k8WOHyI6QYhCUEC0IwBAIxRhfjBGTItJyCXUxJNM9Bkf306opXHgx5uD/T917BlmW\nl3eaz/HnXG8yb3pfJst1+a62tEPqbqBpBBJOWhBuBgRIDItYxGi1CNTManZGoVFIMxpp5CGERijU\nQhKmae9d+cqsqsxKb25e7483+yGbnv3IfoMbcb/fiBP3jf95/7/f8yg/QyIcJLqUp+DvY7t1gWM/\nW6B/JsnOtkkgrZEddBmfSiFUTlPpPcehw+/lxe8/xvzVy0wfz+EITSzTwxdNmk2FXLKPhBijtLlM\naa2I2VKo11aJjH42Oj20SCKp7rJf7R2VbqvF8ZOn+ebf/IB00qDerKKlUjRKNWRA0RXUdILtaplu\nu7ObxIgLiMjU2lUURSWppWi3uyTiOfbP7qFXtrk+v0SvayGKEYFvgyRSqjToTydodk0OzN7EVCrP\n0tIFrl6axwoEhJyMoSm0ew0ifBJGHEOJYbku/+WP/5w9Z05QtEwe+pn3U455LCxeRi8vMCjEsbMF\nXC3Jpa1XmIwXqC5vst6rMBZ0MY9MIFyvk+7LsbnYxjHTuE6LykCXocEUsnaIF195hlRM59Rtxzm8\n/wD7Clle+sG3iUvgJUcJG5doNjr4+RzK+CyRLqCFCtn9g2wVV8n3QbPVID0xhh036N5Yp+S2CTN1\nzIEhip0U669uMb9Zwuy4SGKMobeKXFmYI6drFMaO8uILPyCXHeFrv/MHXH/hWQ7uP0G9WGUkl+Q7\n//AXHDhwgEpxnfXlZVKp2I89235i1gWapuH7/pvUrF3FjMOPmAY/SgX8aNjJsowsyzhhD11Nk4jl\n6PZM/CAkCEyURIoDh95Ceu/b2Td7gjDVhyv49CcERNGm3HaIY3L+uWeRXAsjpaOnkiAI+M4uy8D3\nA+LxBGGwyy2QRAmzZxKEICsKURTimh6qoSIpEgODAwRhgGZobzBqHSwLFNnFsqu89wNv4dknVijV\n6xw6NMv1a1eJ/IgwCPEDn3jsjVxvTMETUtiew1cfOsndnXmCtesY2wFb2yXWSyVOfep+ah2f7KbH\ntlfmXV96H2efWOTzqSHmFq+zHAZMD6eQUhGOKqNrNnuzSYTvLrGDyPW+GHvaChtiDyvlcuatn+af\nH10gP7nKmVt+lVeff5FYqp/azhZtu0PoS6SySVzHJ5ctcMeRMT76nndhhyKtUGGn6LOysEMhpnPo\nzCFqN4qolo1vtRFn9qBdKyLeMU2ki0i6hjWawl1axdloIAkC/piCOjLKyuVlRjI6XVHAE+P0pzQa\nmQBHgjAl4TklxvpjKAWDltIl5cmIbkDoehCFeJ6HJEHo+4SuS+ir+L6EsN5CzWfovH0cRfHJOyph\nXz+59gAJZwatfZjh5GEmC4OsXtlm67LJ0nczbL9Wx1s5AvUYlbkY2+frZMz9TKdPcOTgnaTEGaoN\nGNmXp2U7+KHPxsYmuYNZpkcncGp1ljZv8NL8KmJiEj1M4lWqzC9eo+lEaKkCYajy4ff+PL/9W19m\ne3OV189f5oO/9EFOnzpBcXubnZUtrFKNhKKjygq90NwFhAcB6WSSVr2B63hMTk1j9brUSy3KtRrt\nZpnI94kkiQN793Bmeh8vvHKNE0dO8qlPfRFRSbB3zwwxJcHtZ+7huVeeR9VU4nEdTZMw9Biu5xFP\nptkqVVi68jR3vfNeasuLjOoGF65dRvIE+sUkr61eZ2dhk1hri+n0MBk5y96JCdL5BAeOHWXbT7F5\ndQtDT5E+Ok1iNMPedJ59gwXiY3GG+7LUvDaHTt6EUGlTb1Z46rF/4vDoEDWhn6QaIgdLJBMGsjGL\nbTQQhElSfW3EzADoPkg+A8OjuxFL1UXJJ+jfN8Liap2XX1vD7GkEto/vd5k9fZBOaNMqVRGzPmKo\nM6C7mPEh0rkR5r//daodg+LmGglDIB3XmL9ygbhu0KhUiAj50m/8xk9XhAtA1/U3NDO7xYMfJQve\nBGq/Ua19Uw/juoyMZ2k3PJq1KiEBswdPocQThLJMYWiWwSPTbF55kZnjd9HcdDl5uECt4RJKafSo\nwtxLL4JrYaQSCJpCEET43m4sK3RDFFlC1TQQBDzXJfAD9LiB2emRTqaQVJkwCEimknhhgBt4jI6P\noccEmk0PQQgQVZPKtoGiCVydL2K6Jqqqk072UdwqIgoRurErZVS1GElLopbtIm4LnKLHe09O05YD\nnGw/6ZkxjGpAb+46txp7me05jJkey2cv87kPfhD+/jtcHU5SCwVOnZyk57QphxE3ZbIYry5SMH1e\nxyDZp5LbO8ziXB13n8x9b/sUTzy5gp6xuPkt91PdlHjp/ItUdxapN1NkciLddplm3SG0evzWR9/O\n4MAotiDR7IWsFLvkR4YoLW+Tl5JkPYHM5DCRLkFeQ1ozcaYzOH0GQiihBzBw52Hsq5ssl65i+oNo\nCZe33nucJ743h6ZIxBMdnFaMZhLEJkiegBdZHJjei9hT0FwZ2XEQPB8vDIjEaFcaGIJsOcg9B1vq\nEK9UCetlxPffgna5grzjkVm1GQ4UhsY0DMdn++oNehtNXnl0nfWnMnQvzGKt24SVHG49YCw5jdTU\nEVsWauCRS06gC/tIRBMsbtRIj7tEcogYBkRuSH8iwYtPznF5boXqTocH7j3E+x64mYxSZzCdpBnY\nLG9WGRo/wO/+zn/kb//7f6ZRqfOJj3+aD9//Lj72yX/LQ+95F8f2zBIW6/z+bz/C3/zD31MzIK3H\nkCWZVCLF1uoW3/7bbzJ/8TzX5i+zsrqO1e5Rq1cR5YDJsYQ6yXoAACAASURBVDEsV2AgNcjP3fkg\nBw/eyvTYHrqNEKsnUGvWmRyYJpcc46Yzt/Daa+d26769LrIsEk+kQFbQM2lee/0CD952C+2NNVpz\nK0wFGdztHud72yh+DzmUGd4/xsOf/wL9Z06Qnxwlf/okWnKK1MwMM/fczom3n2b2QA5DiKjVPRrd\nFr3WMkWri+p6hK7L6J4ZXvnuD6hurLLv5kNY3RBPrhE4GpmESCYzhhlkkLQm46kcudFxRAmMWIps\ntp/Ab+OrLZxIYqzvNvrVPuSUTssvEQu2uO3Oo1y8UUdoT9E2i8QTMWLAxYuLfOwzX+bYkWEOzaa4\nulghcHtEgUdpp0wY7d4HJQwRRdP4/I+5k/2J4cmqqoqk7Oo00uk0pmnTarVQZY1sdoyd6grxGISe\nzr5DMywXl5ncc4bbTp7mFz9wL9WqT/HaUzj0c2k7xj//3X9iZiCLNHUfcaeGMXyG5JjHg8dn+H/+\ncYPjh/KwfJm5Vx7nRn0Zd6GGqYhk/JCO4RNzFbri7g2aEY/tam1aLVRVJQoCfFHGtWx0bdfX9aP1\nhqbr9GyLZEqnslMlpsSxuu4uJclqcfLOY1x89jJ9E4eZuGOW9eefx15r4hsBtm0T6WnSlkkjE2c0\n7uM7fVR6Vf72k3dyJBDo2kkUtYEyNkonzDEtBeTZIBJdLp6r4F0pMb/f4bFli6n9HvLAfmYHPeyr\n6zxcj5P93M/ykQ//OQ995DZOzqjsSMfYsP6St/z8I3zrTwP+6InP8eT/OM/nf+Wz/OOzL9OXHsEq\ntnnnyRQfeeAUSs/hyRdW+aUvf5a5Wp0TQ5Ocr0uslUym9CQT2SHOXlpElXQKRsSJmycJkx6xsoh2\n/wlo2iBqNNWIjKjRC1zkhAZzc7TOXWK97HHu9RUODu6hdGmZne0W+SBHLATXs3hwbD+GIrOtumRD\nBU3c5UrsJk0EAkkgeOOCVJIkukqH9Ogw5LN4HQe70iHq+jh2QNd1qHe7dH2fru/gBG9Q2hCRIpAD\nESkVI8omIZ9EzacQdBVZAFWRMJDwMzpL669y7CN9xFouN6IOwrU6nXCV/uwEjcYy8dpr3LZxnbZS\nJhEI2EczmLd9lr69n+DD7/slzpz6RX7lEw9hlz10NIKL1+ls18mk+uk0ayTec5rf+8Z/5cu/+zVk\nQ+PffeAT7BsfJ0jIfOR9HwRNg7ZNzQ7IDxRYvHiJO97+s0ztneKTH/0oPUGmslblobHTJLWIKhHF\nZo9ew0FRDOK5DJVqkep2mRu1LZ6fP4vp1GmXVymk4ghChBkE7Bkb4NIr5/jS//4RxjYbNHc8PEXl\n9n/zPp44O0cppnDHidtQDo6hNixE28IVfdR2lx1thHtHS7z9vndzbi1kYirG3//DX6Fr/SSEDtd2\nzsFWlZ2ewowc57XzzzG4bxj7xnm8UMPaapCZ7KPvjofYru6Q6m0geynMhEFCy+Jo4/hRlblXvsdy\ndZOD+wQmOos4yf3YapZiHcxanGZL4cX5i6zsFPmFhz6OnfaIKQmacxaDB6b45je/xs9/7P8mEZrM\nnX+W+SsXiaka6VSOAAFZDHngrbfxrb/7Nts75R+LJ/sTsZNVVY1CoR/L6u0WDcIQs9chlUjSanVI\n5jXK1d2miReZJLIp7tn3Vu677yFOjhZ48lKd2T6BeqVLpVliev8DDB65m/Xnv0VeHaIwOo3pidwx\nPciVrQaG4BPaIkrWIJQC9qf6mBOqQEgzLoIdosoKUugjKv+L5qUoKqIo4rouRjyJ8IZZ4UeXdoIg\n4Lruru4Dl1wmS6PaQpY1HMdBEEVGR8a5mL+BvbnMmHIXm5k83UobT/TIDSQQGiLyoEK8atGWE+QE\nlYm+OH2tHlJc5OJKlnwgMRlaiLEbvPBanlM3xckPRFgbfRSU63i9EdzeOm1XYyK0CGWbgapBM+Zj\nRBYjgwI7bpvVqxbTD99Lp30LBQ8i12QoM4DZq7NTbjEzMoBmN3n7J84wPajxw7kqlC2KPZmdskzG\nE+lYEkEbkokczaZJXHVREmlSsRStygabK2WmDw4RNCx6DoSCSFJViUc+RAGyoaB2fYRD+8mO9NP+\nix+glCsI2T1kD+5FMTZony2xETicHJwiSsYp75TJKhq24RJGIQoisrB7yohE3tAQCYBAwg/oNYt0\n1RKmHxC4QCjiRBFWGGJZFnYU4hHs+t4EAUXYTZK46i7lTTRdxKi76xxLx5GzMRRDJ2x1sHotjHwS\nr7VIW9PJlWQudbcZ7r8VvXGdnzkxgfX4U/jPbpHaq9JJuBjXAmKjRbx//jX+9FOn+ML/fI7i9TNU\nMgGF1wXCSCWT7KfhtMkOxDG7G3zk5+7nyb/7G/7LH/xXxu9+K+2X59EMg/nnr5EOYyixBNuVFtez\nawymc/zn3/o9RqbGOXrgAKW1bRbaS9ieiJtWMTyJMV2lnbBwuhbiRomEa9G0LQ5PTjCxd4jJfSP8\nzZ//MS+/8BSpdBojlaJeqjA1McnlC5e5/a6fQSxdY1OXePJPvsHQ6VsZP3ULqhvSvb6B50fojk+Y\nN7BUA91p4zkOH/nUhzj3pT/kyJ7jDCb7WK82iRIGGysCDxy+hc6Fc2ytnsXyTUwpQZDbR+v6HLLk\nocdz9KpbyIFNJGq4iokkJejZRdrdJL7cwhF04oMOG8UpxrIFEpJMXDWIJUSccBMzu40V+hRLGo5j\nEQQS28U2UwemyCUV7n/7x4kJbdKDk1yZewxDz5FMDuAJu3LUmKIghGkkSfux59tPxLrgkUce+cqe\nvbP4voeua+iqSiadpt1ukcvlqJklZF/g6JET3Hv/HSwuLzKYH6A/oVNZWmX/wTNERoLa+lXmLs9z\n+M77cHo+O8V1/M4WspgkM32SY3sVvnexSEZJIocKUW+Fza0Fxgb7sIOIXtMkkYnh+A5WEO7WQFUd\nRZEJo/CNam64S/mSxF0QicDuaiEMCAJ/Vw0tgGM6uyLGaFdnI0oCqqbQ6ba5/d7bWL50CXlwP6Hj\nI64tkVFTNLstkqkMRjKLJ3RoKF3omVQVhVNCkmOqD4PjnH92DYU7MJI1gpUOzlrEC09vcOv9BeyF\n1yhro5yvFOkrxJjdO0RrdYtDVYOJw/3onR2ee6GOOKTSEVrMnriVfPou5IJPeUVmfvM13v/wPaxv\nLrN/Xx8P3nySta1LXLraparH2GpCYc8o2f497BlOsxwlKPYgdCWalS5tK8STFHw/JIbMQDyO3+6S\nzwwgHRrEdn30AEQRBF1GcD1EVaKNhCFp5I/OUL1wnasL2+SUOPERDcb7Ma+vcrp/EtNx8WI6ruki\n2i6OEO7WSaMQPwgIfQ8pjMAPCDwX2Y/R6dk0ezauExKGEq4X0vMC7MDHch2cwCcUBQRRQkREFkVU\nScaXQZVVFEAOBaQg2rUbBCFiEKDrBeg3aNptBsfLIIpsrloImkRuX5zJ5fMYPIf/Qx/LHsLL+YRL\nBjEjSTRi0VxZInnxIjc//IuUX63iXLmGovWw8h7q7RNIp8aITg4juD6pyXFa59e5/5P/FgSfBCre\nxQ3ktkS5blLeaCIlcqDp1HY6FOL9KJ7IzrV1OnPbZII4+sgIXT9AFVW6tkPd6eBFLr1GFavRIBI8\nEDxeeOVpHn/iu+xsrZGMx0mkktRbLaxWAzWdZe3GPKduPk4u3ocp+KQUja2FbUYn9qMYKmZoEUoR\nciBRU0SqMowCVnsHLS0TS6dplzp88uOfwAxc2iZUvIhWrYQtuFQWLzM8MEq95DKq5VCkkORAjNzQ\nPoLIptNtIBspSu0dllebVIIeC1tXmTu/ihCsUKmFJPtP0+3tEI+1KZYNLN+mVJ5nu6Ty8oUGtiIg\nGRGer9OXmiI+mmT58lnsIM/8uX9h+vhdtDYbJOKThGEKzTBIZCW6zg6yErK6vMYXvvBTxC746le/\n9hU/2DXFxnQD0zKxul2mZqap12u87d33c2hylvGhMU7cspcwdMjoCe678yhPPX4RQ4gYmJlhYmqc\nS5eWcIIaB8cPcnGjTH39MpKoM37sKIbi89yCBabLcF8aPahy7crrJJIqWigjiwqlrS0iRQDNAMvG\n8z2CKMJ1PFRV2QXMhBG25yKLu6CaHzERBEFAFAQ0XUeRZKJIIPACnDfMlrKsUN4pcejwHgKvSdfv\nQw1gq7iIm4ijyAGdRgslITAS6RjtDFoiTk1qI6UiHrr1IM889jqqn6Xry0h6QM6NQbeIpKgMTTtE\nlTLxQoZrZp1cWmJkapjGDze5ZTJDQvBwzleJUhodSUdL9FPIi1SkYbLjeXbmoWjaPHDbSV55/PtI\nmsXyVoXYeIxqLU57q0jkitx7aJTxVJapviGemqvQPzgIVkA6M8RGsUoylSCbyaB6ArLl0q/q2EkJ\nYWoAzfORFJlIF/FEAcWGhhSSaXgICQ3HMRl98G4KvsbitXn6M3E6sYCZqXG05QZtQkwEND+kk1Px\nLRtfEPCiCDcICCLwiHDeeMUtiyJ1IaAjgidJeFGI43m4nk8YhViBix+FCNKu+odot6EjiSK6rKCr\nKqKqgCwjShJiKCDZHnLPw+76ENh0x3vkxzwUXyKVH2UspuIP3Elq6bs4N/4V6+FfIP+2jyBn+4je\ncSdKTifwAhK1DN6rcRLXXiA4eIj+6QK5972Dwf2jxFL9CEYC3dRR8jnarRo3Dffx9d/5ApoUZ3xi\ngsq5JexQwElopLUEZa9De7tBKKuovoC5ViYr6aQDGdt2aSdUjFiCLauBo4SIcoTpOwR9CQan99Bu\nlrmxfYPvPPZPeKJLu9VAEATqjQbDI8O8+50P8ez1ecYGCyxvLDF+8wkGIoV3/NpHqZ/fwNpq03zl\nPMH8MsLZG8SKHXIzk/h2QK/XYKu4BZLLlcuX+bM/+zavnHsVHxerIzKohghmh303neQP//uf0D82\nxcDoMIbk4hMRZZI4KMQNnbHJKf71+Vd5/OXzpFPjLK7NsbKicOi2LkGQZmVZJ5baoC/hEJoSzaiH\nENvDc8/B8nqCjpclrvej+D6j44dQYxqZoRyZWJqZkQl++K9/i5EbIZPfT6dn4zptjFhILC4TS2XI\nFSa4evkyX/riTxGF65Gv/4evjIyM0W138Txn9zSiqiwsLDE8MsITj32P/RN7+cZf/QV79uTp70tT\n3S5x550nCC2J//bNp3nbz84Q6YfZcyjHjbPnueXBn2NxYZGlxRvYvS633HeSa6ttQmGIRqeDHNax\nnDKdpUUE12JcTLMleKQNlZZrknElQnG3kKAoCo7loOoqYRjiu96bxQjf94HdSztFUd4ctEEQoYgS\nvZ6JLO3qdFRVRVI1ljav87Z3PcDFs4sYokSPOlazjRZEmCMKUrmLlTWoVlrIKY1fy50mqjRIDhts\nXxWYTE8yv/IoTbtHPGVg2Rahq9JvbBGoFoWxPnZMk0boU9ATDFVdDh8ZofH8dZJ6jsqBEZyFeSxd\n46YDN5EcPgz2Eksbl2gAd+0/TLBWpdZV6b/p/YQDY+wbuoPQg1RfgQcOjXFidoRrWz5rDREl4eA2\nbTxPwQ1E4oaMFHoIrkBKUsmpMpkDw0R5g9C1kTUJXwU3EFF9CUOScEQX2YuwFR3NlcmemUS0uty4\ntEDrxgLHpvYhn9xLa3ETo+1Q1XzSYYBv7VLW/DDAf0NH5IcCvTCkEwQUQ4de5GGGHrZjY7n2bvZa\nCBGEiJ7v7sKxFQVRlBAFAUkQkCUFQRIQ3qCxRUGELCmooojMrk4+1W2iF6vs//w++kMNa6XKzupV\nxHiXWHOb5twWo+0FEuk6PPcSvYUfYBRfQ8yVEeML9OZLqF2bzYNfJxG+SP22X0aP1/HaIaur29iv\nbhEGCv7CNmE+Qbrpc+reuzk3v0XYBRJZSgmVVrGBXW5C4JISNFYbZZxOh6wVgN2j4/QIEgo9HepW\nF8NQ8JptnEoDI5bA1nXsZIKYIfP4S09zfWuBntMjmc3gegGB5/HHf/yHfODhh/n9P/lzMrqMHVh4\nuspEvp/NVpV4JaKv6tKJWlS8JtXVVTKTw9T7dSLbx0vF+etv/B1yYHJgz16qpkR2fIjr8+dJSylC\nqYMT2NhRjBura6ysrnP5/Flss4iQzuEnhpg6tIeNtRLf/s73efSFlxmcOsX6xg4r8z30wjaHTtzO\n0kZAbMAi3tG4eTbF8kYZL7iVC+dN0skeg0MyVqMf3R9hMBVhhQqxIYVm2WK92OPxv/8DtFgOQQg4\ncscv8trrTyNEZQbzOieO38zRE/cgxnKcffFpfuOLP95J9ieiVisK4ht2WHHXA99sI8kqf/Jn/4OL\nc/N874dPcuT0cU6cOsLcxauk9TRGPAl6nsvz16maK3z2C3+Ba19mIHWIm+69lQvnrvP7j3yRmRP3\nMTIzS6e3jqJm6G2uIaoGiWyawewghfwAyYEChmHgujY37z/I+GABx+7hOrtphnQ6jahIOI63W4QQ\nePOy5f9b9fW83VJC4HpYXYtOp0c8HkdWFaQ3DLgxLYZZcbk6t8Z9Dxxmq36dXrPHJ4/dxcdih5mu\n+LRECXulxsn9w3w2t5fLrQa/ted9WKughxqvXPhX0qMxkoUz3FizQBfwNA8xeQQ74SFmC2REgzYS\nmVrA1F6oFTuoro40GnH7H50ln0oS80o0t2xGBo5TrgSYPYVc3zTtbY+bx6a4de8pUNKQGCGeT7Hv\n/g9x7MH3sOnI+CmRS23wkNAUAV2G5eVVkkYKVRSwbRMhHkMvDFJxPPBkVFNAjydxpJDActAQ8aMA\nzwxQJZGe6JLoSMgxgaDd5PCHH+C+z3yIg/v34Ho9ijeukTw1QWtUQnI7NOpFlMgjDGyCcJf7G4a7\nBljL9un4ATgOkuOi2D643u4eXRHwxRDTt3E8jx/9DX70PGVFQ5AkLFXAkwUkJDRBQY9kZFHFlyRM\nSaAcFwmVFKT66c7cytj9H2Jo9k5iZorrL7zOOuCuJ2FLpPLMSzi9d7I1s0Op+5dYtX/CyP8mK2/7\nE3r+ICU/h/jMqzRebLHz0gKtjTJFvUeltcU5c5vNl5a4VhK4MScwPXgc0RhhzQVTTzF29CShZCB2\nJbq1JsOZLLqiUm1U2d7com638GISoecidnvkmj75DgihhB/tPheh6tA/dZD7HnoXgSiTLfRRaTQZ\nGRvj9rtuZ/7yBQ7tn2VIjCE4UOgfIt71SR/dw/qz51hdXWch1iW1b4TBwT5kIaCpeqyW16i0dxAF\nhT2Hj/N7/+mvOLjnMEMTY1Rsk9vvuptzl17h6pPniDsKiy9fZL1cpZtJsJbS+aMrF3n64kVeu7rB\nr/7m/8WX/+MfcXFllf3HZlFiKXpBj8m7p3j4Fz7Ltdda7B2yGIyyCFKSxeIUVfEE1+p1eto1zHAN\nq9vF0+bYdH/ItXKVTDaGKEk0qz7HT9+NILnYgURpdZFWu4Yg+xw7eZBsf5Z2x2Z1pcjGxi5t78f9\n/EScZH/7q1/9SiaTQxJFYjGDkbExvvzvf5P3vP99PPHMM8weOIie1lhbW0SwZOauLDN54DhRYoKZ\n2X2MFYbxYjLtLYd9R5NYwk2EjRdpVUwW2iKlYoO+iQTl1RozQ+O4soxtOhwb7efc5VdwJYdauUkm\nnSYWhoyMDdKX72OzWMF1d+WLZs9ClCVUVUOSRHwvQJEVREkmQsDzdwsFoRe8UfPV8FwX3TAwTRNR\nFJAlDcfy0GWJwfQod947xusXXscMZDaWF/k/jz3MJ1Jj3J3qo64mGA4k7spM8pbhGa6Eq7iuwMED\nIj0LlusKQsJiRJ0mFqQRsiJ+Nk86XaccpXl6qYSWDdkvp5C9Eu3FLhMDKYKcwtdqe7kzqPDQgf0U\nbpnEO3QzzYUKW/U2iWGNfNsm1aoyVDhJsSnRDEWiUEJwQpTSNkqzzqFD+/jGv65gCDEOj4wRSQHV\ntkCt1iOpQz6Xodhw2K42EJWQ0o0SrhOSHSwgJRRcd5ctG4QhohggSQpiL6SDje5FSJpMYNrE9w4w\nevwwpWvLNBtNLNdC609AUsJqVHElj45gY4ouoSQQEWH5Pq3IpSeEuJ5HGIREESAKRLKEL4hYnodl\nO4iyhqyrb3KKRUl5EwwfSBKqoiFKMgECnihiRSGm62G6NqGnsFRs0bdg4f31Ojy6Qv/wYZLvvo08\nFn17Z3C21lEuzyML70U89u9RrQ3srkmllGUjdopSw+L0Dz7O3ECEc+gA9R2bjJunEQr092QaTg9F\nElFaJs1yHW+9jt2wMBttVFFC8gN6yu6KqlPv0fNMnJaFqurEEgkEP6Dn2KT6Bwj9CHOnxZCeodfo\nsVGugaIRF1WcapPASJFLp7i+cJFet4Nl2uyd2cueiVHuvPU01VaFofwoV5auMTqQZWp4nIZgka15\nlFebGBJEKY10INHeLLNdKZFdaSCeXWJDDXGxKW1c46l/+T5D+2bZd+gwttlhZGqEWHYAuS+JiU7H\ns1m5scJE/ziR1WNgZJKhiT3YHmT6FDpek2xhAN0Q0GIW6jhErQmE4CWCWg5VsAjEvax3G2xveVxa\ne5pAsMAzMH2bnZ5N/8Q0YqqBkciSyuyl3lIAj0w2Qb26w6kTJzl34YfocodbzpzACVVcX8fpeSRk\nuH7pNX79p4kn+9WvfvUriWSKkeFh3vmudzIxMcGNlWWGRseRNY0wiCg2NrHNFlfPraDIadqhQivq\nZ2i8QCrl8+Cpe6grAecWmpzZl2J8bBS726GqDlIqdhmczrJx/iJW1yLdP0z/4BSi0EQVXdbOX8By\nXbSYQSPq4TZbHBydpjAxRa1Wp9szCYKAZDr5hhrHR0IkfAOXGP6IrSCKREGI7/pEkYBhxLBs803R\nXRiAY7ukxodJDE2SHSkxG2bwazHWogq5/DBicwciiff60xj5LlFaJ2bFGYo0qr6Po49z613rtEoq\npVKZfGqIptWjMHWAa0ur7J9O8/T8Zb6z0eTMFOi2AUWLgqqTHQ6Rax3mVgS+clQmltPR7jmCkC+w\n9cK3eOm6yfD+DEcTe3BXavSlbkZURFa6i4SZKv2yRH+nTDKhEGYyvLgEkh7DaTkk8yKJ9DRbG2XG\nBtKIikDLkxH1GKLkYQQp2ptVBiQDYSCJEFcIOz0MSYbQQvQMRE1DVAPCBAi+Ckj43RZiLkXqnmMM\nxFLYC2v0dkokpgq0Z9KUl5aoRV06QoAvgheFdF2XZuTSFEIsgd30gACeCL4o4oYRYRghIKPpOrqm\nIcrKG1yM3b1sGEX4bRvXD6g7FkWrS9ls0+z1MNtt3K7FdqtEWo6T2ASzYVLHx3q1SN+De4hNHEId\nnyYYux+78g7C8TupGyGtcpfI9zC1JhkhTfbbX4TjJa6EEoWb3k1Y8fGUCjuqA1YAyy1aixXSZytY\nUyM0dBltu4h4dRlPMPGVACOm0JFDWls1BEMn68r4fkgzdMnGUng9Bx+RwIkQsinkZBopkOitlqiu\nb6Nlk8i6jBEfRFehkItx7PgRPN9nsL+fSnGLz33uU3zwUx/nF97/YV689BqvPf0007ce4zc+/Vlq\ntTqJboykJtM0BFo7deoZDeWem5B0lb67jnFgfIJib4frc5cYSGV47pXXuLG+yembT+HqHkk1xbbZ\nxtUzjOzdz+bCGue+/xQz+Qx9s/sxYjE67ZBKZwkjJSAKGpJcIQjbRDWRauUJHBIU7TYrtTYptYfs\nB6y9/jq+qZHPTlItNuh2JCYn92FICmLTwElZxKSb2Og4jBfipIb3cuGlRxHlNCdnZsmnR1FjQ7Qt\nEVd0SMa6fOz9t/HEY0/w6c987qdnXSAIEYmkTBA6nH3pFfxIpNTrsri0BOUOlZZBp2yQTRr05DhK\nTiNwWzz5/GO4UZtaUGC5E3D3oRHevsfArpVxhC6FgzN86J7DDPUlUKKQWLIPwatR+uE3qW+skNb6\nGR6bQU+kyPYNUbd7zBb6GFMFrl55jawY8NV3niZZGAMN/HYbMe2hJ9I4MpimhSGrOB0Hq+0iiRqe\n7xMJ7CIVQwtZCIl8bxf0EUUoySQTe2cIOwrf/e4FMgez/KpU4NjQLHc6OqtWwEKnwXyqhbemIgcX\n2cx+m1JY5UhmkJ3VDQb1UR5+KEu/sU3b62IoU5SiFAdGD7PzfIIrCYMg06VRznH1/DJnprOMZAwk\n0aGcUvhv75ewJl3sZBLUg0iLOywFObY9jawxQbxfRO7rxz+Wxhw2kGyBQBpB6nSJ5C5jxiSvnV8h\nqAdEzR7depP1Cxb+5iZppUOx2yQMFcZwGY7aqHGZplMmlctTvF6n9WoZrSsgGjKICmEggLirbRcC\nicB28aUeSD6iYOD1Wri9FtE9M8x+6ReYuGWW+o1rpCKXtu5j1W1GgywJ26DripQsl0hRcP0OkQCy\nEkMWkxhiAtHxUYUAz3cwQ49i2GW1V2elWWa5VuZGeYfFSpnFSpnlMGDFdKjaAV4gQ6Du/j5Bxkeg\nT4nR69Qw8gJBukdbjSPoEghNur0aYWijPN7F7+iYVhd1vYoXs1AiDdUbQzn31zQWn6SUTOKKA/R6\nNTRLRsoOk6o6mKqCUC3Tf/4VOvviBKpGPOjQnRzAP7yHmBXDc1RsJY5pBWgyuIFPW412PXRdj7Yf\nEA3k0NMx1MAk3pLpNXuItS75IzMMHJrCbHdQJkfwGnVELYfvicihzvvf9hnGT93L/ttP89FPf57f\n/MznuXjpdf7D538dD3jXHQ/y/PmLjOzP4A22uVG+RvXlS6y4XaYfvI/cqROk330P2UN7kfceZGJQ\nRhZcfDdkaHgfa9sm//jX30IRR+hoPgO5KfqlNKIdoiXzeHGdlu/SanfZ2eiSt2z6fB3VjTExrOMl\nAwIlR06R0XuD9FY9msUmvtIl2Zdj4eI1arEeShIq21WEIM3QwCTddgfb76EMBghmg3jCYnx0gJWu\nT0YJefD0CT71gZNkZgYYODhCkBARjARjA0dQgj6KG/X/X/PtJ+Ik+8gjj3zFCSPiRoKYFiOezmLk\ncly9doOjh47TNh1yA3kuXn6eu25+kFQiRalt8+rCV2ag8AAAIABJREFUNWKJm5id1Gg1XDq6gav3\ncdPsLDftT/OdR1e5+e5BdlaXqFsCjdVNZsYGCHWXs1ee4/jMBJFnsnjlCn19GeKGzGg6RVKL4+gq\n25V17HKC/uEYppXAianEOzXaSgFVctANmW6nSzIex3FcXNtBFARURUaUVDRFx/dDJEnG9QIcL+CD\nH/plvHKJpcoO7bOLjJ6eINxpcLcwiBOYqJlRHLvCdqlD/0iS5maMfL9ENN5g4YbMarpHZTXJ6Nj3\niGtHWXrxCtq+FPLOIA/96S0s/fACawNX2WoI9DshsZ029xamiCobeFaLot3PcCCgzNwGh29DOe8h\nWGlqmyHPVL7LvbdMMVF4CyoJorTCeiliYblJQpsg1aswndfJxHv0NInVlUHGRhOkNIl2s4unauSG\nh9EQycXjBHg4kY+q6vSLaUqdFk5GJ1ZsoZoe/mAMbBs1mcK1PEJf2I29hQFCGCIhQBShWAFa3MDv\n2qjpJOl7jpLdO07txhpHz5xiKJ7l6rk5DCOB5fj4qRgVs0sylJDRiekRllMhFAOabkTNdmkLFlWv\nhO/K+P6uUFMQJCRZRVYUFEUlEKU3s7NCJCAQIUQBhD5h4CNE/psKbikWI9+N0I5lMB6YwPAjxB/I\ntOdKmPEOFhYdt4caSyN0RTrlc9SDvyB+eBbr1VfofewrmFWZjLlCY3QUqRSg1EPkgxOUjozRaoJR\nDuj4PtpYHqfZw2jYdNZLSIfGUcpdKu02SU8kkgQIQ7SOi2jaGP0ZbEPGFiO8rW1kPSLCJrm/j8xY\nllhfHEfxMLoui9cvEva7pCUfTxbJbJb4/qN/yaWz53jm5QvEohrDe28hl4Zf/NynGTWmubD+EvNL\ndQpbSbrTIq6kMHjqLUzq06hORKj69IQANQh58uU51potfKvIvr3DmFbAhSde5q33v41qpYssiVx9\n/Umunz9HtbqNEINEwkBv9fDpkRRCDuyf4fW5OSa0KWLyCJVajcJogeWtedL9Ov35Ac4+t4zVAT0Z\nI/IF7J6FJEW02lUmpscRELHtiLlLG2RSBd7x8AO0OxUmB+O858GTXL9+gWJZRBYkUvEMrgWlzTIb\nq+tUdrbY3trk05/57E/PuuBrv/PIV/R0ljCIIIjo2TZyMsXVhWVUOY7XaDEwNsbyygLLF7eYmj7M\nTk8hP5zFdNrEYsdJ5XSWF19FDl2ee/Y53K6C2FnjybM3ENseS8VtRnI629dvYBRGSQsGOnVK1Tq+\nE6IqITIWSV8gbqRZ6jaYnjqJ52xjNU1+5VffyevrGlmhg6Va4Efkcnkc26bbNZElgSgSIBKJAgnP\nD3BcnyCIEBQVzTA4c/ttbBd36FQ2qCzNowYJqqHJvjtvofHUDbJigOSLSKFCV22zhU4yqxJtS9Tq\ncYYOCIxKWcSVWaTiDKd+5WGkWw7w4nOXSccGGD2doTA0yGPfPUtP17GK2/wfs2cwe02e1A2KjkfZ\nqnJgpoCkRyjveghTi9FxFrm2usZqZpzjRxMUrD24xThGo0ta1ZjvrHCpVGG/7TA5JDI8AJe3fS5d\nUhDoYXXq9A2MkB4ZprjTYG/fEJJvEWgBniDgtD1MN6LhmIyEGi4Ca6tF9tyzH5eAwA2QFQNBlBDC\nXWW7GIUEnr9Lo4prUGojSgpmTMJ0bdLTw4zcdoqzv/stGhMBg6f2sXyxRLPjEipN0kkNN0xgZH2K\nlRKBKNO2LTp+i0Dz2W6UkRMpNDG2W1yQJEIkQsAPwA9DBEnbHbDsSh2FKECMAgh3Y3wRLpGiMpbp\nwwlUosYOqQ8cILMnR+/vtlh9agmyXeyei29D1Bch+XE0T2dbeozHxef4xqDOLz9wP1scRLUU4u1t\nnHKX4MhB6q5F+8YKibZKJkogGBKRohJYJr7l0V7epj+dw7l1htVHnyGZSiM6Hm4UEdW7sFkjmU3j\nFhJ01Ij+oUFISIiFBJ0jBUyzjVjv0A4dREPhxvcfpxC4ZNIhi51N2K6heF1uvv0wP/uOu1lYXOUz\nv/Ip/unR7zB84gQIIT3LYvbIHVTTMusLFxgc6yd0LMLIxp8y6EQ9jLZDiEQhOcgz33uMEJ+YIqAo\nKYLkJOVamb/84z/jf/ulX6bbaxBU13j+8SdoW13ihRRO16SQ0gmI2L9/D9dWLhHPDdDdCShvt7C1\nMqX2Nqn+NCljkIw0wSvPnifVp9Np2FhdC8vs4rg9CoUsju0gijoLV9ew2m0iAk6fvInBvnE2bmzw\ntgdP8ewzjyJJUCxexbGLbG/Ns3j9LLX6EotrF+k0Onzx17/00zNkv/71r38lN1hA01Q0Ud71xyeS\nSFqMSqnK7Ydy+J7JLbcdpV1ukhgYouELHJpKMWgtMjFkIDsWQXkVq9QiNTjBfHWTpj/AkaP7KGSH\n+edH/4Wh4RGiRhM37DCaiDEzO0S5GSIqKUqlNQbSOgcHp4kn0yx2yuiqTSZhkE4NMz6R58rCEvVI\nYihdIJMfRZR1ktk+bM9GFEKEKCKdyhFGAlpCI5fPMTA0wJkzNzOzdwbHdbl6fY6iV0UMLG4amuKz\n5gFSV11yQ3vwzQ5rjRvYLmgZlbVmi24kM5AaZEAYIamMcbxZwvHr/KO6ze/+zz/FPOfxUJihO6iS\nj0aZvF9h7qVzPBZb4fRCyGy8H6l7lYTTZFhPMOXEiU/tg7VrdG2NeOMoRmqSdtOiMPkZRqIqw8UK\nKlksL0UiPcTNQ2kenowT+AKpeJd4coDvvdymYyZQFIN83iBd6KNWa2PXuxhhSCohEEuqeF6I50JF\nUpjoiWhElD0XJZEj3wvQ9+YRQhdREkHcZVSI4RuNLUHEJ6Tr+xiDeURkNNNFM/TdUojvsuc9t1J9\nZRuzFZI6XcC2SogNgY1aFXnIoeF1sISIRtfFEyK8yMG2uySMFKqQ2C0gSDKRICFIEoKoIIgygiiB\nIP0voWcUIoTB7jcCkRBBAFfRyXgyihCHMYF97ztB9Xs3cJ828QbbBGmbuJNBElRwRAxPpyZtc+mZ\nC1xaWmSPvsrsFQvnpl8iDEEJAvKL17F9Bd2yEZwmvidRcv9f6t4zzJLsrPP8hY8b15u8edNnZWVW\nZnnb1VXtqrraqKvVaqklkEEDQhIwLF6IQTAgRixGMCNmZtmGHZwEAiGBkNRSq7331V3eV2VlVfq8\nN29eb8PHfMhil53ZBQ07H5bzJeJ573meOB/u84sT73nf/7/BLAUSiopfNQk8D6tcR0pGKIdAOrNI\nEDOwVYmg3SXRcimv5GEsizzcQyaRpl4o4fVlCRyBwPQJlz3CjkqAgu7qxA24dvUc4W6XrugiuD5q\nJkKjtMq+Qwf4rX//KK8fP8ZyYYkjj/wAKUVnOr9IqxtHGYqwUFjG+dLrhI0IsYkhfEPH6KrYYgjX\nrtB2bca8GagtI+gDNCoCRqafVWuGqC3wxONPcs89dzMxPszcQpGzFy+iqgqGEcUzoC/eR10WqYp1\nEAKasoyitImmZJSQhusqjGY3ceGNy+t16poHJjQbLWRJQVN1IpEIqhamXm1xy76DTM+c4Y7bbmd2\nepqJ8Ul6eoboWg1275rkledm0KQY505epl7pENFTiIGB74VoN2r88i995l8OZH/zN3/zc/F0glar\njcJ6TWksneHAgYOIAXzk3RvZkMsxPjVAOKpydvYqS4sFQt1ZtGQcWdIYGBvHlOLookL+wnM8eOQW\nCk2J82fOoqUGWb3xFEObN2BXFkiEbCJCHDUVxdXSmJ6CYch062W2DW+k07Uoih1atTIhKUe7u0jv\nQD87+1u8MxNhaGwvlhOQSA0iaAYDQ4OMDg0g4hKLhxkcGSEWNxgaHiAcDmHZJmtrqxRXVxDxEYsN\niIUZSEVIxQYwtCj1Sh66KoQ12pEuvhUmIjncWFzDT8pE9ApKJcaV7gJtKc6NmTfoDA8ROmygj9oM\nLM/xctflzontnJl/mgw2hw5P0L+2gLKmk1ShGwpoxDNkQym8yQx+b4juiQlc+14CwWMtU8TvDDCa\nl0ALsOQWTquCVophdbo0KGK6DqWqwRvnu4hqYh1OArhKgNoNUC0XWQVfsAiJIp4T0HBcFDmG5vmY\npokqhVBiKWavXqRXixIajOH5JoHoIYg+jushCTKiqmEFPlFJp9ao4Xvrn+eO6yIHAgoiHUxG33Ur\nw8NjWHMz5EZ6sA0RW2gyZ89SL0gomohDa32X6uh4bphYPEWAiS8o61bwN6EqiCKCICEIEr4gIOD/\nn4ANgnXArmcxhHWLcVkhmsyg1C1yh0YQ3Rb5p2eJDBp01QZhJ0nHaxF4AT3NPlZLN7B6K1xdOcnq\nhgaaUuNC2WDbkU/idRZZc9dwuiXajkY8EGhLNkXbI6aFSOoKwmqdFgKSLKG1bLThLMFcEfPGCq3J\nHGLgY9eaVBeWseMaI3fvw2y18VdriIqM7IhIPng3VlEkibVmnW65SSQUR5/opxbyQFWIrLmkdk+S\nFSPs2b6P2bUiv/Bzn+XKwhk+8rO/hlJuU2yvIXQjrAUdDHT8TJqov4ozkqDpa+RC/TRUF1lqEygu\nnVqLH7x7EFWp87ePnUARNarVabYPhnADEbNj89QTzzN1+514SoyxkQlOvvoaG6b6yPTFwZIpd22E\nyPrLNwiJ+M4qXltBU2NMjU9SnJ/DqqxSWutQbqxb8xCAooTQ9QQhI0oxX6TdapHrzeAJErqWIV+4\nQf+wwtZdO5hbrOJYAs8+9wyNdo3C2hKDg73Isodl1lFVqFfK/Nt/SSpcv/Wbv/m5ZC6LpBvklwrI\nosb7P/whbrv9dvbv3oXg1qEbIZXOkMwNUShVmTk3y9Pf+AqbxmU0fR9dw2e50ULQw4ghmVdfnefo\n0TsoVS5y9nqBuVPzXJ/z2Tw5TlyrkOuNg55lvriGL0pEomF81ycqKCSTMV47f5xUbCvNdgWv02Rw\nY4od/WGkIMrJcg+juQQt28cNbjYZeF3uOLiHdqdJ22wT+B5ra0WazSaNRhOz1UYSBGpraxDWGd90\ngGZliZfaC3g5keyYQqsp0HRiuNUuXtxH0X3SMVDVJJ1GAdM6RcmOMpBIkR3czpZLS+wsK+QbIc7s\nT1KrlvnG3BMcOjTGnnwfQ69mKP7WLsQ3zpANjSJvGCFuQr23jH/nBiKrbfROFLWd4bRcxrddYrlR\nBq1FVl0HsZNALpnQUVmJV2l2l4gmJmiaOjUzhCglEWWXRsslFFHZ3DeE125ixELUGk1kX0OQVMrt\nFnQE/IiK2vEQQyHqrokQUtAWG1iSTbwvhxSSMc0OuqojyAqmZYOiEMgiYQuUkAGKgGx5BKKAJfiI\nfhTZa9CI2iS37aMnnqVlzXCleImY3IvShlq1AoKKF8iokThKJELbcfCVEKogIArSep8vIgHrBpye\nHyCJAsHN9EDguQQ3heIDIBBEZElHsBzoTZARXPrHk6y+M42uZSmnChi2SmfJJaRKSC2Jhl3H7M2j\n1FSudL/M2OASzbksQ9EBStkBVM0kjUZ6to6V7KXacDFsibBoINQDFEeiJa87/kqAVa1hT2TxZgsU\naxWmDt9O2AlAlZHDKpnNI3i6hL7aJOVJmJkQIdOieuYipcYancUlYskYTT0ggkA9EqY3mcDTw2RG\nxylfuI7iqnSjKZZrVTzgh3/2F7CcDvVig2Q0yUqjQCySQbW79GRCnM1fYeyOfSg9cSp0ifbFsbDI\nFKOIs13+5u/+mqqscuHSGr4uYgotlDUHMyzQbXpEjCRyTw7JSJJIpOjLZrm2eByh1iY12E9POMHQ\nwABLpSJxO6Cvv4fxwRGGevu5bfetfP0vv4YqyajRKJZvc9utU2zbvo3L0zO0W23K5TKm2UGWYPrK\nRYKgSyo9yq5bdvPudx+muNrC8cJcuXKJxaVF/EAmkcySSvcRjyVpNttMTkwxPzfLZz7zi/9yIPuF\nL/yHz91y6C7e/YEPMTGxg6mp7Tz8yAdYWllE8F1qnYBMdiuGoSHIGt1alRcff5Ziq8DsapXbjt5H\nq22hOCK1akDf+CZ+5kcf4pU/+1N233GUy9NzPPzIe/FbAR3RZebaLCExzXB/H0g2rtWkp6efi5eu\nMZLNkkhEWG01WCovM7VhilZbYfvULfzl89/k85+9l9/701lSsXVn0HA0gS757N06zsc/+gFefel5\nPN9FFlVERDRFp9VsY+gGciBgSAp94SQdK6A3nSamq5w+fpLeW/uJDWukqiGSWj/5xgyubDKQnsKr\nOiQTKQI5yoTeTzHpEwr6SB86TP+pJeL3ljjeWaF/coi3qioj/atED1U4faZJMrcRZb7K0itXaA/E\ncWWd+NYYie1TCBeguS+Odm6RxcEMXSOLVJlm9MQcIWUnhpym6eWpCW1qco1YMoGk9tK2BUpVF0VW\nCYV1AjR0SSNwTOJRlVKphKwk8dUErigjitD2XWKehhXSaK5VSKei6B0PWwpRni+RiiTRYio+LvL6\nVhjXdVA0hcADWxdRfBGhbWHqAoIoIHccFNemJUpogYahBvgbYiT37iFk6wx4EmJYpeuK1FoeriCj\nR1Ucz0SU1j/7JN8Cgpu7VxFBlJAkGVGUCAKHwPfwPRv/7yGLAKIMooSoa0ieRbPRYP+tG8A3CVoK\npi2C6qI2BbRIGKUq4HVdar1lGt081prPydAJ+oc1zIrJT0Yk/uKUyYF7HqBQ6VK5OkMilcXTolgB\naKqA1wnwV01Eq02XgFalSkxTsQbjiEHAwPZJquUqcV+mo/hAQDIawTK7rJ2/Sm2liD7eT7tcRjy/\niL9aJpgpYAxkWBMdsq6G09OLUChit33WUgZ9bZt62wIhQjgaITqUAGGIuFimJsiEhDBNvUS/0o/n\nNWiHY3QWPZYvFekIMvpCg+hT1ykut2lfXsYzl5nu2Lx0/CrhSIh8vUaAQbR3cr0rzVGwHI+zZ05i\nOwJKOErFMxnaMIpnqmzYNIJv13j6yacJRzTuuWU3yWiSkC4zmE0T1STGRjdy9uoCh47exw/84MPc\nftvt3H7n7dx97yECycaIiORX1vBsm1yqhx/5oQ9y9L0fQQsbVFdL1Na6XLl2lQtX3mL/nj0ce+c4\n2dwQS/kSlhswObWFWqPB9enL/Oqv/Nt/OZD9vd/7j5+748GjmCh83/v/FRtGxsmXimSzKcTAZ6lZ\nRounyGUDdNXj4olnMfQAcUAAqcaYH6F+4Rnuuu295MbvJDeY4dhLb/HIhw/x3e88xXvec5httx/k\no/ffyYVKgXMXrpBUNLJhjZZVxPdMjr1zgVh6AEPwEQOHpUoZ9ID9W7ZQUUTCQZWXrQL7hmH75s1Y\nTgjnpiW5HLjENDAkj8e//U0isTiTk9uYnp5BFGVUWUMTNSRfxLccmrJFu1LGrXQoRQMaFYmBtsO7\nf+UO/uDrf85QMsK26O1ILRXT7TLYv4MWGoVqlZ5FlZ7yHKcGl5HDCVrZDs989xgrvQbXzArxWImq\nMcnEOZ2VCY+4bXDgp7/Eht/9BPb+KfLz7yAILdKH76DzzFXcPtBP+pweDGFoCQLnErFOiCuTm7nW\nqVKLxMhumyA74JCIbWApX6DVkZi9VkMzTFpNkUargdMOUCIC1XqBqBLGDyKUbRFPEpEcGxGfFbOL\n5IvEQzo6FjUBXEshaUWYmb+BGBHJjGSx2x1820FLhAk8H8WXCDpdfF3CCYkIjoMsyrgCdMMBkS7I\nDti+hW/7qG2R0f076d+5ncpKnZbfIZIxmJmbo90MyCWGsBpdwkqAJNzM/wbrKQA/AM/z8TyXIPDx\nPeempbwHgrAOYkkFSaQrecQiCgcyI6S3GCwWV2gHEcLtANEXiLZCNGlhV5p4CYuW10WZEzkrnaKU\neoFjKxH2ZSZ4TSjyZ8cW2ZxNsnfqVlatPF6tgZdOI5abKIUl7MlBOptHCW+M4CkaQtekM58n0ZOi\n1KxhGzKeINBcXqNgNcgoBobp4Engu+sNMEEshLzYIbJtI/lLs2g7J2jHw0jzDTqRMOJIP7XSIqIZ\nEBF1tP2DLD19HOHiGr4SUM8ZBAWT0clRLLfNrN2hVzMIR0EOVNS8z4I/T/7KNJuCXsSRQby7xrgt\nl6LZXiLYNM/ZepXlq4v09cnU1tr0iD20+pIYaPQPbmTz1kkmetN4DtihEGMH9+LZCXondnL6tWdZ\nqU6TzmXZkI1hqS1iGAwMbsdprzEypHPu8glOXp3hB3/k5wnrYQxdR1BAVBzuvHsfd961H0V0qRTL\nfODhD5FUcqzWO8wVr9Gq5JF8n5XSDUIRiRNvvsTePbvJr5bZsmsHRjRCy2zieDYrc7Pfc7rgn6yT\nFQThi4IgFAVBuPAPYilBEJ4TBOHazWvyZlwQBOH3BUGYEQThnCAIe76XRUSTKQ7uP8T8+RuceOME\nXsch6bkMax69RgOx2iZsutgtGdBwVIGaleA3fvon+PXP/BJf/KvfZ9Pee5heukCz8DLB4lVSqQgv\nX6/y4x//NPnCAktnL/HwL/w7vvM359gxsp/hHYdpJDPEh/aQf+cGA1KFdvFtxnN9aLEskiFhlUxM\nYYzDd91K3TfpkXfw69+5xmA8QPVlYkqCiOLhm0V+6CP3kkr00UChJEmkYxFGegcpL9axmgGaapDo\ni9PVO9g1DdUII8Uk1IZPZryXt+aWMU8pZDYZPFFb4ivdL9MdV1FjU0zXLzPUrzO2YYr2xoAbm36R\nXPHjnG4O8R/sBvOf2MctXZEh9pNoDtG9ssq5veN43QEe+vBBTj3655y7+9OMaiMMf+I/8beR8/DN\nAoZno339NNRFRkoiklPBje3hxYO3Mm2tcb6xzPnFJd585jKvn+jl6uUF7FY/bbWDkYnSlhziUYm+\nwQFSQwrIAnK8n5ocxtY1QrKA6kvU22B5EdRApd6p46keLhIxUaVLkxv+Mq5ocPm7s5TeKKKkI1Rj\nXdpdCyGQ8DyQDQMJE8FrEGDjeR6qoBMyRTxFwFOD9RdaICAqLl6rjhDyOfIb388nv/AJpm5LcdfR\nKTKTYS435miHXZq0qdsBtuLRFtoIgo2nSjQ1n07Qot1u4ws1WlYYSzKRfBBRwLcxzBiKIqDVY0x+\napLF5TWUxiDqqoOlmQhoVAybSKlN3Y1Tr9Zwj1loUo4bueNUGknshQKOXeD3Xmmxtdfk//jif+av\nHv05PGGFllXHa1aR3C6NrePEtk8h92mYyQixAQP9rnFSP/w+ppfLePPLJI7fwGnVEVMGqek8tfwy\nyb4cfqGFbKSITeykassIvkIwlEbqi+EGHt2ZMrEFD7ti4lybIeKECE9NUDe6tMs2mb3bsEajzFRH\nee5FhS0TPkm5RD5fp3DiLbSGRcLoRY3bRIcEMrlJbvn+97O2W8Mc7OeVV67zvz7+RYL3bsNLlhmO\nWVR9h3JbRtZiOBEX5/jbzJ98nUsvf523vv1nvP38V1g++Rhv/sGv4z/xJO8ZrHPPaJh9d26hWG5x\n267daI5CtDVGtdnixuVvEAgO11cUplfK3HHHJkoL36CxehldVhCBWHSYcjVBvZPgoQ9+GDkRI9wz\nhDaZQpBM9m7sZWJwO9dmL7Nn93Z6++K0iHDu8jW27T/AlflTjGYMFCuKkckRCke+F7St8/KfEu0W\nBOEuoAV8OQiCbTdj/x6oBEHwO4Ig/BKQDILgM4IgPAj8NPAgcCvwvwVBcOs/tYiJkdHgP/7h37Ii\nu/zdX/wnbg33s1EcYXz3JiLbFU6ePUuPGkdwFPYcfR9d/zrXzs9RU3x8WcZcWmH1ygxbt21BD0Wo\n1zqkB0boyirpsc3MvPUSGwZS3H7ofj7xqV9h0+ZNZMMexcINUmEDKWGzevkUQmBgJcbI6SkSmFwq\nrjGzssSmnj4EX+HUqkxYWaMWXKEvPkWrE0eVwmweNfj5TzzAi08+w3eefpm51Qb775jCtyXOnLqM\nrkZQJRXHdMFTMcUSrYZDVE3QFcDSHQKrTjaqMlNaYHxoBw8d+n6s3/4K6SBJIrabstakoTRY7n0I\nIRfl2onHUESTmJ1lQfwmK+kmG9MKnSWb/VqK1v4oc8lxSnKW93z7NfZqLvt+7ieov76KtTlN/fgC\ndnyUIBYmfm0JK61yQ5pE2KgTW5hD6tqsaAIRI0o4qrLcWEJWWkQGDvDmyl/zrty/5uw751lRmtit\nMJKikEjG0GQN3xHRpAh210ORZcDHZ70Zo1lpEJMVcpEEOF1UXcGPxNA6NgE2kZhEWOmy98EdKGMG\nnlWFThhPFkHVCGSFIADB8ZF8E9e96fnGujCPGIAQsF4V4Af4gYoXuKiqCrIMpQ7HX3uesxdf4tLM\nMbrXttCJVpC2lnGvJ8it7sBsdNCiAlLToScaoaNY2G4/gVakEa/TSTTpDW9gWSjwyOotbN0xysql\nVUYiQ8wpFTZLBrOiS1ZIsLZ0nry3zHWxgWt2mQ+3uXX8VirRGV44/yYb3RBhAd6UA9ylRfqTYQ5v\nGeNn/ugpPnrLUT75s/+aQj1Cy7NJLczhietW14JTRo4lCG8cxwvalN+4jjDQj1gokD83zb6H7qfQ\nmSUqBVhmP4aUIZqtMX9jFqtsE909heiYXP3Kt9CyMcYevo+wE6WTirA8V8MfiyC/dRlNczFGwjx3\nvIY2ZHLrrgPs2KIwe6NL1Wzj29epzrQ4+v7NrBS7pGJpLtVrPPPWs6wtXeO+ex6hZ2A3Z849T7vw\nOL1mim//9Rwb9w6wsHKVpWWbitYgJ4+iKyqOY6EqAp5vkulNcN/9dxNoGb78J48SD3n82I99DiGc\nITfeh2VGMZQ0CUngW9/6UVr1c7z4YpmhrUl+6qOfxKtfQ8weJBLO4OPRNWvImoymZVhaavJrn/0N\n7rnjDnZs205Pchuu2GGpMovpyKyuWHScAk69QbJ3lOv5UzTyZQaH9iBF4Zm/+nN81/6fI9odBMGr\ngiCM/jfh9wKHb97/BfAy8Jmb8S8H6+Q+JghCQhCEviAI8v/YMxpul0ItTyoxxO4dd+K2OlQr8OpX\nH2P3yQzOVIgrrz9PTNRJDMfp2dSDH7jU2r1u5niPAAAgAElEQVSoUoOhgU0snjqPVSoSyUGuN0Wl\nWUWPpmnk58jqGV4/9gajfUM8fGQLFVunVZjD9zrIUh9OsUwpaIK9TL+U5Npak/hclWUhQS4WYufW\nYa4sFtHyGs2qTtUAZa1GJrUZSQgo5tscO/UqN1ausfvABjpvT7M636E3N0Ak3EOlVsNxGwiCQKdh\noYV8JjZsoifVR6CFGN+1jbXKCq88/yQ7th3kxNnLbHzhy8QEh6ih0Gp6BFqSiJCkNHuFpBtnePAQ\npncDZbTNUGuMZmuBawurxDbEiYY0Pr3tIL999gJS22RpImBH4V288J1h9IZE0p2kXB4haogUVIOF\nIRG1cZKWnCJtJejERBqLy4RHhiGboF63sMNDZPtiXGuVWbhxkSulOaJDfXDaphWXiN00mXR9H0EQ\ncQIf03MwbQdJAEUPwBcxjAiBK7CyVqM/HsK3HEKRPNJgAq8p067LtCWDp/7qKjtvG2Tk3iE6ShfB\n98BtQQtEV0aVDATJQFDWLYrEAG4e+4Pn4Tsuvu/TkZrEZQO3tF4Xa0Vltj10hJHtw4y9NoTbv8Cl\nwirPnplhbHgPFmWWahYJN0UyanNDVdE9kVDUpMt2Bm5UGYlEODv2Ehv0HIGlcuHiKgPJfppOgEiI\npXpAf8vg7c5LXI7cwLJl9umbccIJpozjdFMuGzP9PPVMgboqoZUl5rw25bZNvQiSN8MD73+A9939\nw5RKJoguSUsi5GpcOfcq9tnL1DWTvTsfoFrTkQ5Nsdo4T29vm+lTr5GOh2nYiyjqLgQngxt+DWXz\nNCYx3Kfq9PTGqFw+h6jrZH7ug4iGQftbb1LqWNjZDNre3TRWr5O4epH67l7OXl0kX+tj91QbSwzz\n3CvnGBsYxQ+nWKsvMbF5gEisl+aVN/nq3/wlTg+kB7ZzYPcRSstNDCnE3OU3Ufwwu7ck2XdHmxPX\n86ikyBop4tk0cUXF7DaROxX6e7okE70cvf9h3nXPQ2y57xDhiEhybDNizwCJ2BiVQh2NClKkwaqn\n8uDDP8+pU3/H9OLX8K0kFftFRnMHWeyexvM24XYMFNUnaoTwPI9yJU8sI/LGK2+xZWoDq6VlCtVV\nHnrko1y+8Q6dxjIxYwAhkSFf6RAIEuFYlIN3HOTyzFn+Rxxl/rnOCL3/AJwFoPfm/QCw+A/mLd2M\n/aOQlQOR2YUZdvRuYs+B96P0xbDFFtXXXuPS06/TvD7LvkMHmL+xSrHb5q6N9xJtd6hPV6muNYjE\n4sRDBo3yGlt2baEjaawtzbOrf4yLp15D89N83w98mD/+vc/TtNbYf+8PM5uv0wliNOYXOLBnB6dW\nVtAkm5X5Olo8xUK+DMMqU70b6Elu5m9eusCeXYe4Xojit6tUFhbRlCUi2iSpEZFzVxqIEZXTF1fp\n2zjOaGSCnr4hduy8k/mlJc5fvkRPrpeNG7eS0GQO7N1Oo1mhY4qslDo0GyKOa/BvfvRn+ctf+22W\nZi8yGc0R8gNazTnS5SxadphGX5ap+DjTlTK1KyqCm8eUJKxmmKz7MWrSayypIt9+9kVOJxdxle0c\n4Q66hXGWFl+jX+tjUZjG0AYpXz6NXhhD9pP41T7k0CwNdxR17yaSUhJvbZV6YFE2HZRZl7aqsrx0\nDj2ynUtnTNShEAyEyTUT1M0CjumA6KEqMrIgoMgaXuAgShJ6CMy2Q6vVBEfAkFUUTcbttqmfCcOg\nhxwyGertpVm1iQgJLj01S2XJY/cHB0DS1zsC1j3fQYFAsvAcHzGAvzdoFhEQggBBEhFFgbivYDYa\niHEF1ACv2UKXDNTEOEc+tIvHPvNptuQ20TIDvvXcC2w9chv994xy49Uz6K2dVBWJDV0dbbKIc/QA\njfODlK5exegPs3l2BJww6ZaJuLqAKYnkEjHKosob8Qu8ZKxwt7WXcbUH0ZI51nqcLfcfJJMSeXPu\nHS53i/SkDJ4vyYQFGyHUz+xamb66zq9/6lMU5utE15pIkoS91uLymZOYy9fZcO/Pc+bS13jn2Asc\nHBiglY/Rn4xTaBeYr50mcD9MZukunE2v0dr2KFpUorb2MPr5flyjxZpnISsygQ/hpy8jx6OIW4bx\nzxXoSRjMVq7RfeZlqj1ZxLU6yBV0IcA2O+Tzi+gk0RMJKqUGixWRiWGdt68s0LthH0+98Mvcuv0w\nhzft5dq1Fv1jW6jU86idNr1jJoVmla9+5xI77j6A4TQJdXtoez7VRgct1GViey9D2WEePPoIR44c\n5oGj90C1hqwPsHvnu9AkGdVuElYSGHGVml0hIamUqgHb9/04rhyhXbvA1Ytd9Ft8+tLbyS/lGR4J\nsL0ii/kyrp/G8lv8+M88wLNfvcwbbx7n0F05XNbodiwa1RbvPnovn3/0PzOYShPv6eda/jS3b93F\n3s07ufe+O/nun//Z9wzL/8/2M0EQBIIg/A8bhQmC8GPAjwEYYYPBwWHEUJhSLaD4p8+RWVlEy6n0\nfOh9rHznUSQtQ9kqEBHbrBRLSJLNxrDF69MLVKIBG8YGsNtNkFSW10qISCSFEKvXzhGN9zOSPcpn\nf+2X6Nptri60uXTmAh2xysffP87Jq8fwW1EWyiobNmoULxxHiiVQ5RY//smP8+iXH6fSkrl8cYH+\nbUMsXKwhamsEYhlJq4IU4fi5aSJhhezQHrZs3c6wIVGqdhBVg337D7PvtgdAClGvOohOg+Mnp5mY\nGsZyLbQgQn/PRj7/K5/nqT/6Q2afOk5hi8LV8jzZ1FaEpoWVDTHbaVKPR3nzpTc43v0W7xl9BNUc\nYjmziirYpOs58q9c55ub47wUHUA9baG8V2Lm3Cma5TWuyDcQKrcgtaLYmktV8MgJGp7kEt92H9Kl\n10iZw0SOySz29yDdP8YtvsTj3/gagR1nWaqQCinY3WXCWzQa50/gbklhljw6fgtZkpAVBSIKDiK+\nLSK4QCDSqLdJRJIIMYFu20TWVFqdLtlIlJF7VeYX5ylLHnNdEKtgyTH0dIqZM9PMnl5h64FBJg/l\nYMADWrQ7HnhhQoJCQID/9+VVf9+hJd7s0+rICGKEQBHwBA8jrBM0TEQ1jO14vO8XP8uTf/2/c8vt\nexncvI+/evlxNkxupRCNcb3bQ0a8QnzLVkqyijP/HPLQNhbtdxg/PYHSNClG6tijMr4pENuQ5Vqx\nQDGWwGrG2Jk9hBbycI5pnPMuMHloEubD2FRZmy+huDqDeYe/DXxSSpt+KWDWqTAwdYCMMMSl9jxq\nYQHPSrCaX2J5+SrjuU1EQyuk7BLzlo2cGqGXNM+89U2ikRDZ3q3oB57AO/yHSEEE4/wHUMr30IpD\noXQO1y2SDnJ0IgnCtTa2ZhLkl9CeLaB97H0sXn2L1TevkUtlqKpxotUOG26J0Btp0Y4OYDcaJHr6\nMWURX5QortVouGkkBV566Sk+9xtf4Au/+ods3nGS0V2HKVUWeOqJTzMxHKNTGWR061380KffzfUb\nTXLaKr2TLS7MXmX7aJTD995HpqeH3qGtLK2U+dSv/DxvHH+D4fEskpRgYngT3tpZypUZMtER5vwA\nY2AjpmWT7dlMfm2BIwc+xoW3n+CNhed48sVpdgxV6ckmuXDhHI5fRo9EMIwEuhzmy3/ybeJhA6fe\nx9LSNOmBLo36RTrVgHfeOsWh2+/i5PF3ePcPvItLV99k39g2KtPLVKqr9PT2/iN0+7+Pfy5kV/8+\nDSAIQh9QvBlfBob+wbzBm7H/bgRB8MfAHwMk4/Egmu7HKrYpf/MlhKU87uQIsakpKqUWet8WCktt\nNo2Oc/3a26xuvoWNfTmW3/oOuR6NamuZLePjXD57mXrHQQ0lyMZ9Tj33GmIEjHidZ7/4Bzz0Ez+F\n6OTY37fePnly8QXMWJF3PbKDkNbi+eMN6raIrqVpt+u87847mJ6f4fL1qxiyimZkqKyUGe4bpRJd\nprSygKxIpHvezWtvLLMteQhZ1DA7Li2pQziepNkSyK+UMKI9uK6J78jIYQ1JS3H+ah5VlNFdBcdu\ncvrlOS498Sa7s1soF1f5/WyXSNGnHI7TGE5xebYOxas03fPk5DhzxTV6nQNYFZ3o6DNIg0+iLW3n\nyvnjWLvg3l0T2CtdtO0bEE6dwem2ecl8hQPqEfr29qOkN2DN1VFXr1Ep38AKFOSWxPy3X2Lo4EZW\nhASvrMwxkumnUG5jOFHCe1V6jllo17/Eyc0TLF5Ms2XURXXTJCJRFH39UK9Rt7BtF8lfdxkQApV2\no4tj2fgEOIFPtd4kjMqZ2RY9iT56wmCLNtGoTKW8hh/SUXp9gm6TixfPM3djmt7BXgY2DZLckEDK\nuAiODARI/9cfC/emFU0QBIgxEC0RBYVOo46iSWCI+GYTyRcgnObBn/lVXnjqS5SvnOX7Dxzh+vwV\ntu0a4NyrJW67/zY2b9c4/WqM1194nuH9VaLSINHeXSxml7AjBmWljq4mKZbXiOsek6koF0wBOlWM\nVg9FIc+B+0I0Tzbw4jW8xhhd72l2dAyWC2tMJXPUA5816uhAf3aAYq2CL8NKcYlsIk7Pri2szl3D\nzQ4xc77DwnyezXftxTIkTEsgdWSEYOxP2dzv4+RzVL76MEr3bqrU0PqPEwulkZbzhMIaLaeFVoKO\nJKA1Tfzrqwj7N1FemqGJhUGIhU6THi/JC8VpehZ0hsIbaYtpgmKL3kyDLhl64lHu3DuJ75q89Po3\nefyPvs5nf/2zqOkq33jycX737vv4yz//XbZv3UJP/yPsGN+Oow7Ttz3Ghotn6SwVcc0ldu3M8t53\n7WdxvsBjf/cMQ2NnaVlt/vpLXyGbTrGwWOTwnYP4zYtE5EX0cIGo1EIQarQXzyLEkqzOvIQe2kLH\nTTHZt4uvfetbhPqW6FgdFDXFcOIgXVOg0V5jZWmZ6WtLtMsQiQTML0+zY2cPg7khVpbeYDh3hHbX\nI5WNcD0xQ6XRZNfkNoJ8nUqpw/jRPetqbd/j+OdC9jvAx4DfuXn99j+I/5QgCF9j/eCr/k/lYwFM\nMcAQk1TXGhiHBnCMfjwhx4olkx3oZWb+OAUMdvTnKDzzGCeefZP0+3ei9kn0aCmcQCA7lMVzRVaq\ndTJD41w9cw5ttYk7IFOuT9M8cZpvxTPc/6GfRvTLDI9lmF8Z5UbLIbES5f57NzLvHuPSmSJ9GyfY\n5FU5uH2E//KNZ/EwkLw6LW+FHj9LvTWCEjpDMquDr2E6KzimTrO5xOT4QQypw9xylUx6vZTJ90W6\nzQauY6GLMhVfJirqeL6P4znQqYHQRXFsnHyb49Iyu/r7yF26zvKdGmu9GeKNClu2DvLiK+9gI3JH\n5iBmxKHUfoVNUo6rl3bwduxPGNy/nd5TOtrFJqc3LiOMj5OMJliLVrg2vUZu4wjLlQppsUZ9vkhy\nJYnZFil3q0SzWa4/9gLqUJr5dh31pRaO0KXgOziKSMm1mXyrTuI1jdWlN7n39jt4LJnk2tIi8XAS\nTdMIRAlJ9mg2m1htD11SEYBIKEa9Wiakr+vxFsslBmUDRTMwOzZ52yUlxOhUHYpqh9GxQSqFRaSk\nTjxq0LFa1PwOpYUZzsxeQlYE+vvSbLp1AFlRCIVC6GEDVdeQZQlRFgiCADcoo0SSODULIxLD9x2C\nwAVtXfAFr4lrwT0f+AQ7cic4+djjJHr2Qn8vqhRipRnQed3hlh2jtN37MYhgCHGqqSWU9BD2XJ5+\nPYup9SDoNtVMk1qtSDLZixy1WFhqEsraLL/l0IiFiEd0Vp0TzHvLrDXXyEfDeL6J0ezFToEtrSGt\nWbTqC6TkEO0gQmn5GlpuHxPKFGUtjKO3GH/PA+jDcWrxt+mmT+PZbyFdfZBLf2NSEVT8doVNE6fR\nKxJpYwMdVabaqpF20thKFX2lQEgxkMNpxA8+QHN3L9UvPMpqs0Ouf5KBtMJMu4IRUxiP7OCr9Rib\nQzb70xuIxU1OX7hAJhRnclDhmWdfplS8zK2H3kt9+jrumsqdRw6xNnOR9x39KAOb78EUErTqRc5e\nvcTMWpFhOcaOsRzv/uARZi9W+e4bS5TWimT33snj3/wahcU8nqPRqjW5Z9MO9vaL6M0L+LpIV8rQ\nbJh4fp6IGMasFukxpnDNs6y0/oRk6gF6YsPEQhkm+h/B6s7jx8rEcwZnXj7FuXPnWM136e3ZgGmb\nPPT+29i1bYBW1cH1y1SrS/T1bcLrmmzeNEW5XOHNl19nx44j1BccaK9rFH+v45+ErCAIX2X9kCsj\nCMIS8O9uwvVvBUH4JDAPfPDm9CdZryyYATrAx7+XRQSey8zi2+hKFNfr4Fg61W6RbCpDOX+V/InH\nkO7ZS3N5jGqQ5ZsXX2Xzobvoi6cY7pug1m6iRQw279tD+/XLWIUG9W6Z/e/Zw4vfXeRdO/4VwvAq\nx946Rdf/Ch/42AfoTxpsGokw786TF0P4pRUe3DdBfrGLK7Z48MhuSms+I4MSZ2auo6iDpBUdMVRA\ndxM0LZmwkmNhfpVr13Q+9r/8JO3VgJ6cxuYpn8JimJalUKl6OC2bhCQQ8m0aioRaFemmehADA1n3\naFgVshW4YGeZ3PMTuNYV6r7H3rTH+Ec+zhOf/Tx7b9vDWKiD2cjQP9xHbHeYwchlPnhLjUr1MsZM\nH3NPbcRSG/RuExgxRd7rjvBfjq/ymvwEuz9yJ6N/dJIN6UNEB8pcv3GawZkOK24TJ5vBtOapVjUy\nrTChoV1cufAWIx1w0jpyT4A8Mo4Us+menqdYP0FaNQk3ZtFuO0x2XqRHjzCLx6jXg9P1yKSiNII8\nTa9DjiyVUhU5HsJXRexGm2Q0jRh4eI4JgKqAazi0u23UQKKyXEMQYlCVMXWQtSQxNUOtVSceCQE+\n+cU2y1cvEOASjmjkemL09UUZzCVQdR+33aBrhGkJZQRtfYetyjpCN8CtO/hdh5ViBa/j4lsCnY4C\nmbuxSmtIyypZ0aUrdLFchzffmSMRMXCsDkQC4skcvtBFH0nQMLsY4RqCLGLUfRBDmN052kKC0W6N\nRqFIzdLJJmIsl67QysVp1XyMLth+hHZPBJQWrqcRcuHMwgr7JzsI8RRybIja3Elk4Qarm8+Sth28\n2yoYRgoptwHXTXHs0VX6kg8RuP10mMFIQCPq88rZF9nZO0GWEJ2ZJkWzgyqp+AUTadMU4U0b8SIK\nVa+C9eo0oW272NeXoWQVkPqm2DzXZO7G18mOReg+EUf0C/QdVui0O3htl7rQplKGsNPEXQ5hqTWk\nDbdx1yMZ7rn/CEuFVQQ5geCatNrLtFom89NnyF+bJkhqFKZtnn3GYt/B/bzy8hn8domya7Fy5iqB\n1AIrxENHd3J4h4GoRSmsLqBKdYZHe7DEgKi2AcNTaGf7kMMGcj3EaORBOprOzkg/vaqA9c6rSDvS\nTM9fwHDb+NUWkmGy9/5htE4EY8sW9usb6UrXaTQFfEGG5jFmV8+xYfB97EgM8+l/8zH2HTyKefoi\nsjHMatjG9bz/eZANguAj/y8/3fP/MDcAfvJ7fvrNISBw5sRF7rrlASQvSme1QlwVOP7at5Epoyf7\nsG5U6fS22JQZ5cqFiywtTLMzF0fRfORuQKVYYPPuTfQPJinkq+SG+ujYLiGrjdgnsHryBomBHl59\n+jkKnsWPPPwwW7fsYYQ7uHTteRqVKmFjkr54CqkrMDW1j5dOfZOLMyrVToqtOzdTuXqRTcYUiazF\nn7xe4ZEj78VlieLKHL7SZijXT7FaJj+nMZzLcPzyEpLRy8paBSkawWo2CKQQgt4gbip4SgK71caz\nm3TCAaUvP822ww9Rl3MMxdOMFvfQKNTZPH4bvrKBq/PTfN8nPLYfXCKkn0FxByDwyaQa7BtqM7RF\n5tFHT7BlT4pWfwZ72eJ9Z4Y5d2yBY5teYN8H38/yt1/kemGBklXhUxt/gDebBbaaArValXD8ALl7\n3odVP01EEyjH0pjRDmokgn5phsjWcUJdk7zhollx6IuzbSRGye2QHhhAKJsUi8tEkmnk1Qp64KL0\nJzC6Pr4uUDHrIBiogorT8fANkUq7jhLRcLpdHMdBEkRCqo7vOHiCuG7J7UNUNda1YUUR33bWqxiQ\nkOQxHMehXneo1xxmrzeJRCxihooiBTiNBZzAx5cEJE1DkTXwRDzLx7M9qs4auhomsKHdMul2LVzf\nw4joNLsdZFkmEtIBCEUMLLsL+JiuhSJLSIqMjr7upOC6OLYHgo/vxTEckdVWFb3dQhrpYXm5Sn0s\nSt0rEdjLSCmotRqsrVlYVhdBijCcHWUtOIGw6Q7Klddo37OGt2eRVukywSN34qpbCc5LRGMZLFfg\nu9/6BorZQyBq6H0ufrOPwuoC4YROpV7j9c5ZxGyUmatXGO0fJGUkSd6+BVcLmD/7GjPn3ua2Q0eo\nLpj0DSgInX4iA5sYlHS+MnsSNXsLX7xSwxOzxCYGiLQc2pLDYCJC1+2Q0ENEWyZ+T5j9gxvxWhYP\n3HOU8U0babcsfF9l7toCSsgml+yhsnIRr1vi8nKegd40kiIye/0q9x19gCe++yUePqTznDhGo61z\n/36JW5M2q3YT1W4yMLSRSHyYoLzK8ktfZ3Ksj2a0h25lFjewGUoNEBgZ6q29FJcXiRY8lGSW8989\nwz0fP0pyeBv3Hu3n8vQzvH7yMXLbhomIk4RknajxLuTcFcqUqa1O02jIvDh3gbeuvc7uI3eQOnmF\nJy9e5v0//svoQghdVL9nvv3/ouPrd373tz8XT4aQA3jzrdcxXZv5pQWWV5e4ePUco1NbECplBCWM\nrDUJF0qshSW2Te7EMZdRFY1WrUMu20tE12iaHaotk7YZMDdfYmjjCLG8xTsXLzA62ctwj8iGiTTh\nSAhDGMRQXRbzl1B1mYnhOMOZMEHgsloM8diTLzJTmMXzZX7wQ5u5cvwCmj2EMRphYtsdbEz08/jx\n73Lqme9ijIyQkXopNXwGeiRWSk1qtocajoEso0gyMjqWo+G3bdSwgqoodBbqND2fzvFzLDhzqP2b\n6XotQlaDpdMr/Ffq3vPbssMs8/ztHE4+5+acKmelKkXLtmw5yAHbBGOgwXgWDEx3MzTB4PZq0Zi2\nFz3AwAJ6EZoGg40RDhjJlmTJklWSqqRSValKFW7VrXBzOjnsHOfD9Xzu/jRrtP+CvfaHZ737fZ/n\n92TlOq9fP0kStfjkvxWRVYsw9hEzNo7oEYYVlDRHOetQ3DdDvblJqnbYPqBwcEPk0nLMY36Pv1Ua\nHNq3l9X6Go1UYbHY4cGhB3nj6veY7pvDczf5p4u/T7V+Hju1abtNCkIWySygUiEON1EX/glzps7h\n6Bhyaxv5m08y8eYZhNlptIEi44MTrF9YYOX6DejaaLZPUZQhL+FHAXKqo8YaqZ8SEJAkIdl8liCK\nSOMEU9XIqgae5yGqEmpGBTEkm8niOg5xEBJHCUmcIEgSXtwiVQJSOSAVY2IpJkpigiRBkDUqZh+i\naBBHMp4dYTkBnh+SiDKSbiAIEopkEPoQRSmCrIAokYgCoiIhawqIAkEaIUggygKyKaPoKn4Y7VTa\nsLOaCMP/lzMrEAQxkhzjbC0zMzJAPfFZq27QHXZZcs+z8EqNXpDQTGIGZvJ8+pf3cOQ4zB1rkKQS\n//D8S/xY4YPks/exfSPHxpM1Sgs5uksx0cYi69eu8LVvfIVQCpF1CQo6YUbFtnx0UUEIA+SiwSu3\nLnLy2lkst8fRvXuo9We4cv015v/5CdoXrnLo7kfYaqYc2nuQ5RsXmNz7DuzmGoePjvB3/3CG71wp\nIRsz7BvLUTabzBYzWKlF0miQFSPq1iarNxfoSCm6GLL7jnFGpkZZWbfoOTLD4yPYQY3e2gKd5ibj\nQyU2124wOVphZmqE7dom585d4MOP/gSmmcd0RFz7BmHcpb9sM+NmyFNjYs8ebE3ASbpkShrDhw6T\nTE3SLMhk9UEG0l00pS0cTabcvcrQ4VE2t7bQ8jGZsSl+4de+REUcYWZihsNHH2J5o8Uz332C2cMH\naWkpm80t9vQPc9vqcnH1HIWBn8Rtd7l7rcvSc69xuXqTR3/r15gsl/GfPsfJS6/wK597G7ELvvil\nLz5e6dd5/fXneejhQxy/e473v/de9s6NcnD/LrbrNm64Riv1WKktsDtX5o3qOrsPvQuDZeyOS+oL\nSFFKYagfQZa5tbhBqxOyubbJ4PQIyZkOWSPLoffu5mOfuJdg/S0acZew16Jv4AhBZLNdu8qusQwq\nPZ77/iLlIvzqb36SX/uV32B0MMcjj8zx2MePM7lrghsb62ystbl77iAtWty+dBZLk5ke2YeTGNyc\nv0DHhcGRWerNHhk9Qy5bxAlCRDtCyurEfkinm+AvirSrOSYOv4+iPYmg+KxfrSJaRVaXTxPGIXJG\n4qd/JYteaBP4Mpo+SySMIGXHKVbG0ZIMZpRno76J0yrTMBs0DYHWQXjrxYB7B3Xee+E2fzkcEL1y\ng28ceoCo2uPjbpc7KwWudi9wubfKlH43cmBSDc9ian10tjUWV04jZgSoL1LWzpDtl9lb+DGuO9ex\ngi3qqsJ4ZYInL73B099/BcGV2LZ7yKbBkJ7BstvEsoqomESxjBCAoqq0QxtVzaBIKUkKSZSgCDJi\nCrbTQzEVZFND1UUMQ6XVapHGO/7vJAVJlAhiF0FISdMUQRQRRQXbcWm0urS6PSQhIhISNENG1UVE\nISZIXPzYwYsspNTA9yIczyNOEwRRJExivMBDUZQdx0IaI4ggySmplAIJSRKzQwiAOIoJw5AwDEmS\nlDQFN0xwQpeBMKJBiNO2MffI9Mwldt+1j7lCyNH3zzJ3Yhw9G/PE31/k7EsBp1+G2pWQrfWACxeu\ncjitEE+PkM1XEMKQpeZt1JqNIKQkFZPtsEe708bQdPSGh1TJ0u02SYWUrugzv3INSAi8gMZqlZvn\nXyDTSbjrzndz8MM/gT43ixZGZFSBeN8hep06/uot6nFKu2vxoR/ZzbvHQu4ZKFDOxChGysbidcLY\nptKXp2f1iLNZMnoBJX+MvtEH2WgJCH4u61YAACAASURBVKaBF/dw/Tbdboezr56k07WYmpulXtsk\njnyuXr7MwsJtmo02n/i5f8s999/FS/MnmTRzZEodbm2VePO1BexNmYZfYWBiL5qQUKtuYJYG2Nyq\nUZYlVnt53tz4PnpY4Ma1N5m66yMk9ou891f+jGP3f5rRfR/kif9+FqnmMNGnImg6Fy6t881vfZkm\nXZq3TlJUyvhixP/4zlcZF/p5+qtP8rDfx9mzzxGUTT71mY+Rf2WbxSefJJxf5LRU5z987m1E4frd\nL/yXx088+E7cTovqzQU0q87CK9/l5ulnmMmJdJoeba1Oxxa4PH+NqeERVtY36Zua48T+Eltr20RO\nhKYZFMsDmIU+VjeadNsu5nREcv4CUw8+wu777mb7uy9gKTHp0AhB7KAJXTLZvfT3HWB5aZ7OxiJW\nJ2JhOUDL6Nx5/x34rYDdE1NcOHcOJ+gRJQrfe/kF7OUmp67Nk++5LIot5ooDrIUhc9N7qbc9VKNC\nu+kSdFxyWnYHRl4wSbvbdNwQAoFcNmK1+Qo5YZV1zaZfyGF1d1gAxd0BcnmI/HCRiblvM7yvgyT1\nkYoyjW5KqhwgjCZRFQnbvo3r1rnQCjl08H5GIp/mehvzwAhL3Zh9r7rc0++TvdrmsR/7APGrDS6V\nl+h16sSqw/VgA234Rzkx+kEGKgcwKndjCBq5fgWxf5r1lXO8o1ynZyyRujqOO8jC2jw9AnQpj7ex\nSb+iUxgZ5lZzG8NQSbpNJitZCHrEUY5Y0nBjAdIYQU7wERAEHSFxEESRJIE0SpBEmURI0LMmqQj5\nvIYiyfS6FpKs7nBmk5gojol9CSKJOBKQRQ1V1klikTRMEZFpbNUInAghllBR0QUVDQUpFhHChI7t\nEQYhYRwRRiFhEiNIKbIiICYCcRIikKAbyo5LAkiTmDhJdvrn0pQwCgmiiCSKSRKIophIs9FUn8lK\nDwbbxOo2W8orDBxSmH+zygtfOclLry/yvRc3uX5ZJKPoFMtZJLNNIiggxawHXWxS9rUN9NlxOqsb\ntOpVMtoggShzq1Xj9YWrKJpGrV0nsnpcXZ2n43W5sb7E61fOg5BiGFkGMwO8912P8Z5Dj3Li2H10\nBww6QzKy65A2e2TGRoidFsVl0FKDTF7Dmy5wYt/djMztQsqq9Gs2jRuL1Fo1Bo7OsNZr0FxrUcoP\nM7B7L7n+OQKhShjFuG2JyYE9SFFK3oDjDz3CVstiq95hfv46p0++giDKjI9O0elYiOLrSOlvMjHz\nt5x79g8ZHzzOyIjBpz+5n97Ybg7fMY6ERWhmSLJZmpsL9KmQ+gITSouJA0cpjA4zNfEorWSbjPI7\nnJzPc+HGBmtXL5FJEoZmpujYXVQ/ZHbXGP/3V/6YUbEPNa9zvr7K2Rcv8amPPMLp568yNbqHs69e\npJrv8ekPv4+Fr3+ZjbUVmFJJU5XXwzqf/e3PvX1E9gu/94XHJ2aHyZp5lhe2WF5Yx3McFF1l/uZt\nRmWTYEglbhS5trBCIZujIkMkW5w4uodiLo/V6SJrJlquD6MwiO942J0a8dYKW29cZq2+wcyuMXpC\ngjMyQqMpYsoFhCRBlkM0Y5hcRqHTusjIxBhb/lvM3+zQrIcMD+WQUpnJXXuJuyWe+e4zHL7/CFIk\ncf7SPOdfPcXYgRna9Tq5wQGIRSRjhDSSqK1toQoKrUaDer2OmjfJKBliJ8b3bfzmFk3Np3V9E8Ey\nsKUii5dOIalNdt1zkF133WLXkQs8sG+TNFmmPHKIWi3L0raHlzSoba8SWj6GLvLCq09zZtllOudT\nERSqq3Xi23Aw5/LPly1mFYF75DzPGhoHFh3OhhFp2eZEby+vRzVuNF+lUjyBZmhc77yIkMR0U4Xx\nd4xxT84kXv0mo2Pj5OJdvCQvcyA7RxwIJIJL3uogt9qMFjPoFZV2VGfcdZjr9GiJDcrSMFYU4UsS\nkqLihA6KbuJ3fRQpJkkFEnYmVN3Q0TQNURWJkoC+Up4oTQnCGEFRiUnxg4goClEBUUiRhZ3W2yTy\nCd0eUeAgJAGJpuHGER3Lpud6hEmCoMiIioIoy3Q6FnEakyQxURQSRB6iAGkSoYoKSRIhIqAoMlEc\nIQoioiihyiqRKBBGOwCZJElIBZHkh+/WTlpkQo2lFZubNwLWU5lWp448Nc6l79/G2+qwe3YSnxSx\nIKKlIVGkgaDTS2NkBwJTZGNzhSvRFU6/9TzfuPkm606LVrCBkIs5d+s8ncCm22tSs5ssWk3sbo3t\nboNat4WWMclqKnHP4x0n3s3IyC78wjStqI2Y9eiXRfzlHnJlmLYqYOqDNL0GjWib3FCRXCGLmY9B\naLO1fAW/voXb9LAn+vFCjzBJ0camyAYmtza3CZIqTi2itb7MYKmLEN1g8/YF8AK6ocyho3eyvlGF\nFG5cmad/YIjAD/E8jwuXb3N8z4eZe/hDjEzdy9jee0BYQKhts6fsE69fxF05j+qvMViUkZBYv7bF\ngFJB10OiyKeXGcSpbyNeOcu88aPYy7fpXPPorVcp5Aus1X3emu8iqaMEsc2Xv/JHNPUq9dtdMgWB\nQxPvouC5lNyY7Mg0URqzN2tw9fRLjI8Pc9mMEBKJ1NC43KzxW//x828fkf3Sl3738cpoSt9QiWq1\ni6jkGNm9m70n3o2tjSK2NvEHBNK6wVp1lSiNGM4WqHfXGB0pMTk6SL22iRcL9I/uQTOLGHLK+u2L\nLC+sUU6LZPozqCM50nKeXsdFGSySWmC7CbHikaQhuYxMSg/BiFjvXWVqfJv77vwQ89fXCI0u5y/e\n5MnvPMXP/cId/N03X+GJJ5/kzl37ubxxiz5FxVFg8cICxcogUWTQajaYnBjBsrvYroeiarjtNp2e\ngUyAKFvYnRoby1vcefAeFk69yt7jw8jRNvNvKpRm1rnjzm/i+ReQ7ZQwGsNji0bXIFIMao3LdLbf\nQBO3CNKUrz91FdmJOCqHrGCz0u2yUVSZHUvRzjqsrnbYP+Dx3I0VHho8yKJwC6Xh0zecYznwqCdd\ngtaL5I2AW7V5zmy+hjkD7ztikL74Gp3xMjOVA7grObShLNm1DKaWx8jn8ew6jhLjW22mKyX2PXgM\npdVmeLvLqZvnGdZ17DTClTMIWg4/SFDSBCWKUCUBLwxJRZk4FdA0FQSI4wDElMG+Eu1uDzdMCJKU\nMEmJ4hhJECAQIBGQRAlJEImDmDgEMVUglQjlhDCJ8OIAJ/ax4gA79Ok6PZq9LjISO0UMO+KpSDKa\nopAmKWEUIMsymqlhZAzCOCKVROJkpwI+SRN8zyNKYsIoIklSkgSCMELLTCI4TS5dP09QaHE7PE+i\nDTJ26Kc49e3ziNEid00MU+26bEUJRiwRpioKKa5XJxJjhl0NW5NpeAm+XibsxNhijtWgxfzGBmRN\nRE0mI4qouoKcRqCBKGnomQKuZUEUYiomBw/eRa4wRMlv0ZQDyjO7CLspieWSzWlUclnEYA1jaBeE\neaRSTLi1RDsxGRu4A1806dWXqUYyzE1TFBWGKkOs+z6pk1DMZZFUjbKewapvc3T/PuRU4qUfnKbR\ntFhaus7k2AjPPv1d7rvnOOfPvUG7UaVQzBOGPo++6ygjE+/k6sVrmOpbtGqn0OMzzBhjRLHGthVj\n+Q6q4NCo1ckN7kZQMmytXKdeGEOISijXbtG5+QxW5gEct8KbT22ycvN1RAz8yCeMO0xMDrDZvEC+\nfBODBRYvZnjonkl+6WPv5GcOq3juEicvX2bLrzGalKlfX+Lg3CzLS22KgyNsbDt0tJB62+Y3Pv82\nEtnf++IXHp870Mf9D9zFpbPncZoNZkaGGR2cIYmKbDdWaMhb4MhsOqtYvo+ETtfuMDFSIA4sOp0q\nSAa79p9AlDNoGYmLp5/GS3xW/JiV5XUud7bY07cLKwrIZAUU0SUWDVxfBmGbrdUqvVae50/+ADOz\nj1xrm4H+UbTMXv7hO0/x1996glxJp1nL8Dt/8mcUCwUevP8EgWvjOBZbm5tIsc6B2aOY2RLt1jbP\nPv80U7umUDQdEuitbCMWHWrddbqNOo4Xo97s4ooqhfwAy2euMTB3nMkT55i6Z5VBtYqFhCOkBKVJ\n5Og27d4QzZ6BKonkSRE9j2899RILqzL7BrOUpsZYaSxjr7RIh20WMvC+vmHKb/RoqiE3mmVC32Ev\nBapySjmjMyeNEglViq5C8eEKdiJgaqP8n5/6KNZXXyBnluibPshi/QapWkRrW1zsruJLLQa7AoGQ\noik6iiCRq3q4q3UkZCbkAnNGH9eWT+PrBo5aJlVKCOjgdxjKZpB1Az9OkXUdSVFQFJkkCoCIfDZD\npZBjq1bH9gMs3ydBQBLBVFVUGRBiBCkmSSN83ydNEiRBJIkTNE9ET1VUdNJQJHITAicksCNCJ8LM\n7By/QNjxPqagyApJnJBKAnrWxDANJEWi5zggSCSCSJwKiGJKEIT4gY/v+0RhTJQkeF5AmG5ye+Uk\n9FJWzYCQTdSxh/C8SU6/+t944KjKHrPA1eUt1gWBbJrgCw7EDaKwjJZAU3FIEekXi4gtHymboNPC\nMHXKuoKopuQrGQxDQc9pjBZLkM1gdwLERCYMfGRFRDF0JibmyGUrmLaAOqAhJh2EjkVffpTGcpvR\n/n6slkCvt8xWcIOxXA7fj5A7Fkkuohtvkh8ew1fKxIGMJyfUW3WUboqq6jiCTZxm0IU++vp1ri1+\nn1Nv/St1d4tu7HNkYpyL58/wwnPP41k2breDSMLRo4dYXV3mRz7+kyyZCZdee5IRdZPRXB89O8As\nznH71itIcYNCLgPaGJZtUl1apDysYs70EdgO/qVzdG9dY/iO/8DuD/8neusxb619BzcfYIQeo8Uc\nB+68m5mZQUaMeW5ceIp85QiTkwmZoEmwuEgxs483lA8Sjx1gqnAQ/5nrbOUkgs4qYSFLrDgouXEM\nBzYsm1///NuIJ/ulL33xcSVncHQ2pt3JEadlMlof8ugMsV8gVGvs74u4tOlTbia8f2SGUtJluNyP\nMCCyf+4wy9e3CUMwSwaiEePbDqEnUbt9C1GFLz/5JMcPHUcr5ukEAYX+MeRcjCAIeL2IVgNqbYur\nqwustLrI+QJVaS8vvXaG3XODWHWP77z8A44cPkKSSHz/1Cscm+3nxuYC11sBraaHIvrYQUh/dg9R\nLs9wuZ83X3+SzfUesZLHlmz6BhTcxSrZQCW0VbbbG1hxldqVUyxeeYPx2fcwsHuZBx5rkNECWraC\nkTHJFfYgpxGd9gKp06Nh5Ml4Ib0bb/B3JzdZbRQwhB5HxqcJ8wm1dsyp6ZCxXodoWGXgisBgs42d\nqCwpMqrjcbgyiWCrbBkxo+ooeSHDqrfOP9a3ePnmBqnmcbilYEqDxCd247+1REaXobMJvQzN1CIn\nalSGxqFnIQ2meJtdpvomqVrL6FGPtpzw96uvc+fUbial/Vy11mkOGGi2jxSUKGUTZCHFiVNcQSZJ\nRQgjZGISElJdRctotLodPNtHSQSKmk7i+iRBRKLIdK0upmYSuR4SKWKS7pC44gg/TvECn0ajhiSL\nuK5NkiS0Ox3iNGVru0Gr0cH3fCRRIpMx8QKPYimPIgqEfkgQhKSJiCypeKFPELmIUopfT5HTiG23\nDtFOWi2vtDEmHGSjw8SowZXFy0jDOvsnjiPkRqnd+h7C9mWmS1nOnVuGnsG66+KLPkVZwzVN0tAj\niHcqeKIgIFF8fBwgJYlkQj8GWUbXdGR+2AMpiCSSSLGgsb1Vw3UDkCUEBQxNo69QZqRvCK+5Ram/\ngFfRcZaq5EdHaLs2Spxib90kiVRaV6/RLbiMmtNEQRPLv02gSTRvrXLpwg+4NX+GsNsiq8rkCiaZ\nQoVcZoaclqHuX+H0D/6FMydP09x2UJsBchLw+tJNgjZo5UmGRjVqyhZT2UnevLDEBx87QmVsmqDW\n4tTJ7/LJX/5TZGWSqPoWTuQwbKSE7jb2Olw/eR0jqJHTugSBSpxqHL/jCGeevsKW1uThj5/kv/7V\nx8mo91K9dgaz3YPiFPLMQaRsG82LuPPQe6Avwyd+4TcJV1w60hD7PvB7bJsl1s4uo654qJ7L6def\nJSP4dMwAvdiHKhd4z89/hm+uvozVbvNbn/2Pbx+R/cJ//uLjct7EMAKMikYSGTx8/GHmb9QoFTy2\nN26SUxyWezFDSoHDA2W0JEXOaiiSRX9fhY/99I8Tk3Lr5i0sx+H0pQtsJQ7qsfejj4wwJLcQnA3m\nr79BYbzAteoiupqn2/VJUoFWp07HqdGyN1nevIaftPBsierKNpXyGNrQKE984ymMVGd5YZlaUmdk\ndAC720Z2u1j1FQQ9pRu4OHaHPaMajpXhofd9iPzgIEIisr1WIzbL5GaP4mZVBmZ1ums1egvzpK02\nTiQSjrzGx39URDO62E4XWR/CLO9lvVPDjzcwbZ9eNaUbqdxYMXj2DZlukiHuhATBDFl9FaU8y/LV\nczy04VK/a5j7rvjcOpKndc5mfsvlveOjNIyUgSRD/9AQsaCQBiZyeZjhyjS9ep07fJfPZEfZXZkm\nPDqBc+1N8h2VSjmPFsW4VkigBGSjENX10JWQbqvBcMkEYqwkz62uzY3uImohYo9+ECPNsxa2sSQN\noZuSIJBRI0RJx/JDAsSd2pc4QiAhAQRZRiTC9UJABEFElhWieOfi3+z1qJQrBH6AquhUN6uQCHS7\nFrblYbsevV4PQRBodzoAtNtt0jTFdh0sx0GUJSRVpd5qslHdIFssoJkGYeDhh9FOTbmQ4vk2maxB\nsZjDD10iCRrNbWRS5LKOF9Z4+KF9HHvXPvaMp9xc3eDAI5N4GxEXXn+TuvsWq9ubPDBXwZg1iWcS\n7r1vlKERlZs3EvZMH2dq7hgL81fIGiV8DyRZJY4TRFEgimJkUSSKIQx94jhClERkeSda7LouqRDj\nezG6lqFcKpIvZiBNECMRU81QHOgj9l2yZga94ZPUbPrec4Las6/hVGtsOz0G332A/sFxykaGq50O\nQ56GsJmwnayzuH6bVLeIxG0G+it4tsqbb5zhwMEC/ZUK//ztv8Lv1RkfGSYIRPA07EhlY2ODXCJg\nWauMFEoUc3s59+KrfOixe8jkunzlL77Mj//4byBV5jgwVMLdeJXqW1+hf3gWbJXlxgq1sEkqldD6\nFMzBUeavXaZPTNlMu9z9oZ+gePhBfubffZS9o8d58uvf5vDuuxl79yc58cA72b56EWN7iHxWoats\nUB46zLf+5Q+44zOf493v/E1ufP8G1gsW0fwlbp17iW7YYv9nfo6HP/o4jUILbWg/NXuDe9/xsxw7\ndj/nXn2OX/3VX3/7iOyf/smfPR4mEsvrDTKDGpViP2K6hSDkcXo2YXuDqF1jIw3IOAp39pkM5wep\nhy2kuZRdc+PoSUiztU2UkTAn+rFSj/PnzmBvX6Bc0Rg59hD53XthoEA93OL64utYlghSgu1a2F6H\nRqfG4upN3MCm0a7TzHaxazWcWpd8pcyzzzxNr95kY2uD3ZOjbLZddCmHu9mgMjrKuz70o2iiRmg3\n2F65hkeN0piHHwsEXh7FDInVAkrUY2hihG43Yc/MXrYbHredRe74sMm97x1E7J9BiEIkq5+8mWBX\nL6G11hGCBt7hL7J+Oea//+kPqK3rpKGAmbPQjYBbt1ZZSTzqdo/ZrZClh/qRe3UOr/m42RyHln1W\nO1BWs7wwkKLbXQqSS58yRFfMEMURpjnFA8W9FPMK7pEBolodb7WBsy3CQBHyMmHXQrQ8IsFGTRx0\nNSSYtVDKFTBlWk6L9UYbu5yilhRmxWkKwjhC6LHaWyFQcyS+TGKIZMSUFI1eGBMkAqQCKTss2DBJ\niEQB17YII0CQdqLIUUwQBURRTCpIhGGMbTl02z1UWWd9vYquZ3FcHz/esWfppolt2yiKjOXYGKaO\n73vk+yqEP2w9iNOdFYET+qysr0MMrusRxCFJkiDIIrquEEY+rWYdO+gSJgliEtEOqxze3cfc/n4i\nweHo8GFaUZ1/fr3IweMHKR412FP4Gc5//xRTyW1aCwab10Ia131GpCLFR3UKuRpnbrzCWEWn12vh\nBz65vI7vu8RJgq6ZxImArutEUUQQBsAP68lIMQyDIPQQkXFdH9u1aHdb9LoOkiAyO72LXrVOYbBM\nmiboWzatOEIv5hibmsJdX8GOO9Tqiwzc9xA1+xplv4JuCyQoXNy8QmnIYN/hg4yPHiT0U2K/gSHK\nVG/7nDr9BMMjo8S2xOuvvkWQCAixi5wHNQ3oNTxEQ2d1scPFs1c4PJjnxKNTbNg2J6ZKVO79CMnt\nZzBTGd++xcDYIcTaWRTWSOwQ2yoxeNhgdm6E+uVL6PUsnt0lo8I3X1jin752lYFMm3t2/QRBt0u7\nDqU4w+mXn2B8cBd6pkA6bOIUpvjzv/4bFi68TNy9mzfObJHbU+CiX2a1fIDpj3yUzJ5dVHIarfWI\nvfv30z81zvTUe2k1fPLmHp568i/47c/+r1m4/qfQ7v8vnsnBkfSzP/8bfO/KV7m+tI4ijTE9k8Wx\n36Qv+wGkG/N0Npe5NSkT3wr4+TvnMI1pntk+S/HIAaanZ0j8kL7yILO7D4CWpZOISJkcr55/lW98\n7a/45AcOoJsaLS9ltRFx6s1rDOtdJsdHueuOu7FaIZ1mSOAICElKoVCgf/IBIs2l63m4azY/O+xR\ni2qIc3OoYp6V9jJRHLDU6HK9Uedf/vx/cGRkjDseuo+kIlNSxtm6sUTWjLl4ZZlMeYZHPvhZXKNH\nGmcpKxputUXTu8qZK09hOCt85ITHQ7sKrLpLdNs+ShDgxLvITP8bZNXl7D/9AX//UoqhFrDWlqir\n0HfvOGNSD+2FNna/RLc/i6cp6EnEibEiG3sFHtwIWd+T4eln2mSv62wkXe5WKuSMHIqrkTPWSa1x\n+o2YW/enLL5xk8O9fayXPFbGW3xy6G4K4zOk7Q75C7foW90kMAICpU1a8ilUfGqTDsPiXpa+LtB0\nDQIjpCT3oWqjmEaBmyunuNLaIBk4TGLsIyoNMCT1I2dUUlEDxUQxdVQZRCFCkQQEJDLKDqtWJCFN\nY+IoIP1hrNG3XDzHwVRN0ijG6faQxR2WQaPRQNHEnUlW3pn4dgQrRFYV4jhEUzOI4g7ndnVjldHx\nMXRTw8hkWF/aQFElIMHMaGQyGt1em0ajjiRJhIFHnEaQ71FRYyIVNLPEb028h2SyiJSGCHmF3z3z\nr5xcbBGxzgf31ZmtZHj15WuYN2JWNgKWczpJpY/Wlsd4/xz5Po84UUFQuXp9ActpI0gBnt9DFEFI\nJXT9hw4MkR8SyBJkBVRNQhQkxFQmFkR6tkXgh9hdl4KW5UPHH2F6ZBxxrJ9etcFuW0XuL7Bxe41e\nQad99RxiXuSdP/WrnD3zRQ7v+gThos2FtMbi7Zc5eMcxUkXC6oXk5Qkkv0ASxKhagiQP8Gff/M+o\niotjddBKJbRAwpdl2hEUOilhZ53+kRn+t88+yj9+7Xs01lb56Z/+FLayB639LQYmj3LuG/+NYkWn\nLPcj3L+LvsYB0t4rWO0ValEbmOGue2f56r9c5HJNQxRTUj1GCyyK+gBy1mWsXSHIm+yfvY/Zu34E\nJ2iR2n+C0Khz10M/yc8//lsE6U8xMLKbdrWOLku8efUpdk99Ak0eJiM3UEoefXmVzdoCY9Of5uzF\nawxlJcL4GpcufJte+9b/ErT7/xeT7P/1+//l8d2FWSRFILQc3CggVYsQrNGtJfibdXKKRE1JmakM\nMzc8yM3tNueqK5iqjJ6ktLY3GRofJjfWR1LU8VXohRYz5jBzE7N86Y/+mJYX4fsx1vYGuypFdk3d\ngSLoNOsN1la3cbo+UZjQaNRZXlmg1mzhdlZ46+Xn+cCDJ0DqYW+vM+4lRLOHwW8jBzXmdg9RNEWO\nTo7z73/5l5g7eIiDE31M7xe47513MlguMJ7VWH7zHK1AYKr/HrSCxHJ3k4GxIazWVS6d+kuGcgnN\ndZ1Xry0TrxZQ3E066Qz5fT+Lr9ZQrU3+/O9P43RdSlrEXsPgTktH1EUGHjqANb9MbTSlclBCDnTK\nSwn1AzIfHTqBmT/EhuYz5ddZWupj797D9MoyC06bNVNhXg1JZoaYl5q0pkBfWmS8a6FaIQ4OS9k1\npiyTpes3EatNSiUdN3AJUwuzAjJd9IkMwXIF62KZrFEilwRk7BRBUWjYVa5sX8HWDHxRQs9XiNQ8\nJAqxkJIKIlGakgqQEBGnKZIoISIhCRAnCX4YkKYQhDEgEsYpkRdgdS3SVEBWVCzLplgqYHs93MhD\nUkVc3wVZIE5DkAT8yEOQxZ0kV+TveF3jiEwuSyokNLsdbN/7IToxIZM3ESSoNqq0ux2iJKZr9YgN\nCSVwQbYRMzqWZqL2VI4Fg1xZaHKulPJHb34Tafc4wXybZKVK99o5Tjy2j8P37Ofk02eYDSY42dri\n47v3c2rpMs1alfXqBreWb5OkIvlijs3tFcLQRVUlBEEgl82iKDIgEMcJIKAoCrKkoCgKcRDtfKcg\nwvMDRFFG+GEx5fmlBR4+fBdDuRKuZxPZXUzPxdBUdv/4J7hy9gyjwzPI+/czv3CS9ltVZvITbDgN\nxufGidKIJDCQglFSO4dpKGi6Rq8nEtPh3M1X8Z0eQioTJTJqpOOHAq3GGj3fI86aGNNT6NoyNxfW\nsT2feKjMOw+MURz7EJHRj3f7LGGlQm+lQfutZQp33San9OGtraE2Y8oz/fzVc4tc2WyT8bOUoyw5\nrYssSwimhe4V+cVHf52Dh36K/twR3nXXXi5d/GnKustI+W4Wl9f4x39cJjd4BGvlBqIboscBTm+B\nTu8cUroPKTfKxNTdrGwWaHe61Le6HDvyAPniPN3GEotLb/CfPv828sn+4R//8eNzw/2s3mpQzmis\nd1fo4pBPRbpbLr4T0Gtu4et5DkwM09xscXWtSksRGCiMIdoiw9lh3rx0ndLMLtJcARUT1RFpaCGl\n/grYPlPDUxTkLD/yrsd434l3R+0uAAAAIABJREFUki9N0Vcps7h0i06nSypI6IZJ/1AfohxRvbZE\n/+w0MTb7MhIlt5/iyAgdaQutf4g8KYLgEdrb9MsyDxw/Tph2aKyeIWOvYjnLIPWDmHL7ylUatzQ0\npcmF175Ds7HI+NQxYiXm+pU3GCpZ1L3r1IIKYqNE1krodB2C8WMM3/UAmtngheefxX5WYY9kkQMQ\nZIboEqYWa31lMqMGhuCwZuZ516bP3pLC6V6PG+tbeLUao7vfQb3gs/71ecpzs/zOA5/gwyO7WDWb\nBI2QTXebX/3Uz7G/by99xQHOL14j3bARtmJqlxvEmZAghRGjgOi5lM0KmgKt3iahLtPeGODmSY9s\nUkRSfHy7tbPOyPbTrS1xy6rhSn0kUgKSiq/k8KIAJAlJVEnSnV/fREhJkhBVViCFKAqJk5Q4+qG4\nhiFJCp4fkZEUWq02qqJQq1YxswatTpOe16PdbZOKMUEc7OxUQ59USAnCEEkWQRYxTZMgCuk6NplC\nliCNCZIYI2eiKSJh5OFFLs1unc3qOn7sIesyYRpi+Q2EqEsc+6x2LRqbHe6b3E3bcPiX0hv84OoK\ng7pEzrJ5//sf4amv/z4fe2Cap/7idQzf4d3/+yf41rnnSXM5/p30Ufbtnua5zTOkYsjAcB4/tKk2\n1ojiEMNUMY0s+VwFUQxJk5Q4jhFFCVGQSRMB23HwvADbcoiCAMvxCKIUP0wQSFEViUCOGS0MMDc0\nRq1b57a/RdJpspqLaFeb3PHJdzHUP8zC4jXC5gqxLGIrKhU35Ka3iCHtobWZpWCU0BSBbtsnVQwW\n1q5y8tRXEbMmUpohDVUSRMTAoevVGHZFhgdUTtwhMD4qc3ehjrxd44Fdo3zswz/Pasti/cW/4u//\n8G84cW8fq2/VcYaHaXx/m+yDH0FXTIam5jD23clTZxJWtrcQSYhFCStzGy0YZdL8CMfHP8W9k5+k\nPWRQlbZ5x8fu4dKZv6Sz/DpZRaW7XUX2NoiCdS6+dQNRW2W5ukQ31HG9cUx9kH3T70GQRGQ/YGD4\nABcv/S7uxlu8de1vWbh2nfXFVVQ15bd/+9fePiL7+3/wZ49/4keOUV3bZnPlFqGWoS32kJsRRV3h\nsY9+koGSxkY3YCgnk9f6WGm30foHGc6VKA32M3loD4kqYPc6DOfLGKmEKWvoIoi9kHvvfICW49IK\nu2SHs2x721hVj1xBxcjKyKqM4yX4PjTabdrtFmEoEHUFfvED76WfRTi8Bz3fxpn/MrEhEFs+RiWD\nKHXQQwdBl7k0/yTZ3osEzhLZQhVNmiWM+8gPidy8cZ3kZh39YZWgJbN57TLNjfOo4iYjlb3o5b0s\na2d45GydTSfBHyzSXA6IV6pcffElNp5bYSpt4WcEKp4BMtTNgIkE4q5FddcgrEToscb49RqvDjtU\nylnGFntci1xqt+pY0lGOrJzmW/PXeHGxzp39e4gqa2yW6txj9KHco5HEAeGqgnLkMPNayHUzopxT\nWDN6rLQ7DAgG01oBobdTl60WBBrNUdrnVYb0Iq64Qiv0aIsm20JAzbc4WCjTRKSd5FA0sH0XITdA\nmMYIaCiKhiiIKKpKKqakaYIqyRCnhEGAKO6cwkR2ElUCAp7r065uk81mCYOdvakf+mRzGSy3i6CA\nHzhIskhESkJKlMRIsgSSiKwo2J5N17bQMgaJIBAJIGgSUZoQuB1c38Z2WviRg6RCKiT0nB6OZ9OX\nTRkcK1FvdQgEk335Ycqhw1+3X8S92mNEvs2JO2ZpLLd49CPvpffdF9BrFkfuPsob31ngyuoSj/7U\nY2RUHS/IszS/wOvCOrGlUCyOYtsRlu2i6wqeGwApcZgwNT0CAkRRQhwnRFGM74eIooiqqiTRTmgi\nAbK5EgkCJAmKouIHHlP948zu3cPV5iLPfuebzBs9rt5eII6bXL9yBsv1qK5c5cTh3WxKbWQ7wpFc\nYj1Pmrr0VUwST4JYRc3GvHbluzzz8j9zeP8xlhtryEJM4Nu4oYUUeJg5FXNggDsOjHL3hMgLL2xj\nqoe565Efw2q8xsb168itTb7zrxeZngkwCzGHDoxSXYs4X/UYzRQxB6a43GnytW+/Sn25yXAmQ2o1\n6FcnqMgf4r6Zf8PwaAZZ6sdNW4wWBtEv/DVPfPXT3HrlOdA1moUMrtSH1LPZN5lla7NDnKpEaR49\ne4g77p7lyvzf4ATPo6X7yE9oTN49wvef/AuGCm0838ULqwwO9Oi0PT7/+beRu+AP/+ufPH5k30eR\n1YjbtXXWW21I85Qmp1HKJareKgwepT87w5YoQmmQXmaCXGmKkV27wChQ6BtA0VV6dpM08qmUszhW\nD0FIiYWQVhCQyRdoVWtoosBIfz+pF+NYHpKQJ451vAQGhvoZGSjh9CyQYzy7xezcPrLFFoKRRwkz\npLfPEbRPki8oxJGH42xj5AZIvIRMukRs1YmdHolXIzf2EO3NTcrpTbaudDhlbpAPTeqCTd/ugL0T\nFtOFlGK+RV+mwz3FI2h9Wbp2QLLqIsxXWXr+Cu4Vj6Iks11S0SIZO43oqQm9BORIpioGxIGHNj6K\nfXkNkgw9tUS7GTEu5mnNmhxeq2EToR6u4FZTes1tXmuuUJOvUFuHXlegefsKZm2Z27Vb3NTrvO/4\nXpauX0VVJ+gKHUyjwP3pELP9Q4gtl15XpNHrJ9yuYmgJa6qLIhYh1ukZAqltUTIgSiXecht0EhdT\nKtEzi8SiiSpKCGoJQZZBEolTdnaKokiYJIREBElKTEQYB4RRuNMhFsQ7YYVYpmfbOL6DasokYkSt\nWSWMA+IkQRQlojjBdh0ERSYWIEhj3MBHNnVcx0YxVGzPJkgiFFXGcV0s2yZ2fVq+Q5IKCEFEFCU0\nEgc/CZkY6GdoQGCl49BKsozHXe4ohtRCl/uDElV9EREw0iL3Tefx53t89N//H6xd/2se+9kfxd27\ni797+iS3z1zhM7/+Y3ztX5/jQucGzTBCTCM63RYZ08B3LCQxRVMVXDdAEmW6PYsoShFFiSRNSUmQ\n5B1iWRTHyKpKnEKSCIQ/rGNXFBlFVQjshFBIufO+43TbLW5urJJRFaYnB4lch8GhHKKQMjU+TMda\nQQ0E1qptpIJJV6jz4g9eYbh/F6aZp9nb5M3rr3Dm0gsUBmWUSCe1odexCAIbXVXw0pRy3wgZPcUL\n4HzNxSz28fHP/CLe4jla4TJGKaTRu0KfGOO6sOuuY4SqQqFj8eHfeIJd+3+J9qbBqYvnCGrr6IrE\nll9ndHiW4uz9jPe/E9jEcmtsba6giRkG91QwDj7Myzc1/unkGR7a/wijpTE0O2Gl1kQIAw48dD9S\nYQRtO8XqnEfRx+i1zlEypnCFJZqNyzRv3KLZPoUTCYSRhKHnsdw2Ymryuc999u0jsr//x3/4+B3H\nHqQnmGR37WZzu0HfoUOMP/hh8pn/h7o3DZbsPss8f/+zn5P7cm/evW7d2jepVKXVkrxI2HiBZhkD\ntqE93U0DPdMx0DQwRHSHQQENAw10zPQyvdABxDSeoQdwI9tYthGWZUnWrtJSqr3ufm/mzX05+zof\nssx81ZeOMCciIyM/ZEbkiYwn3//zPss9zCwt44kyQmikBZkkUUmsMuValSNlCyVLkEXGZNJFVSUk\nXaHdH2LlSwRhgBACRVHIkojxqI/v2lRrFbI44aDdQTUMNCtHvljj5KmzJKFgrraEEDElJM6eWCFf\nrBBHLcq6x2j7JnK8jbq4QqxLzOXvZ7A3ZlhqEogWpWwBOxtjJtBOTETBIPMFTcekceYs589cIGdq\nmLKgapXJ4hDb3SZnTahUYrKFBeqzKbmgShJYSMUOUcGjH4cYikC3A+Q0ozpOWcbEKRpUPIEeSiiz\nReqpihXJGJFEUMqhpCrmGJRQJh+E1A6vcqJ8nPJwRHOyR/G4wlx2CMIdvF2bK9/aoxUojNoDHDUl\n52Vs99r0cj6f1U5w0c3hKRGjjU3kkonX7hBbOqM4oKzp6G5EPl+EtsuZ2hGyYhXbS/l80iIo5hGR\nzFCRycU5JFknky1keXrslSSBLCsICbJsyjeSwR3T7VR9kEAUhsRhhOfYaIaKkDKCyMcLHNIsQVKk\nqc42TYmyFFlRiJMEK58jjuOpNTcISOJoysmmU8WCF/pTOoKUJJ4QC0hklbEfEsugyQlLVY2FgkR/\nK2VneJuPGDmqlQJpW8e4fUD1kRlMb0Lw6FnS7ZvUzhwhutzmkV/8AW69+m0K+106pHhXPOxqhT/4\n2gtsXOvQN2SKQYzQdZIowbJy06+fTtt2c7kcAkGSRMiKgmEYGIYxLbCMYyRZJk3//zBpRZFRVeVO\nt+S0KaJaK0zfLzIG4wGtdouF+Qbd9gEPP/gwc41ZVNUjDIfoosJoEBLjEUQu125vY6h5bt64iaLB\n5t51Lr3zKvlSgTBIKBolOv0hY2eEokoUCnly+Tz5fJHdns9YsxF9D0fJWJ2X2du5wZxcQBQCRgc+\nSukI9dMPkZtZZ04uMJj7hywVH6Y93qUgmaRyj9u3nqZcKVIsHUHumZTlCrjPEiSXuPL6k9zYv8Qr\ne6/x6b/7M1SWT3Hv3Q/wxpO/x4UHMoQSk6awMLtGL1XJaW3OHXk/+pzLyzdeYfN2i0NLj3IQtRg4\nO7jjPW7depFyNSF0QZEjsiQmDAPiIONXfvW9Ob7ee4fCf88rlZGzMqmvslBa4ejiCRaq85w+dZhY\nGrJ3+U12Lj/L9ptPs33pm+y8+izrzz5F/53n2b5+mfbmOgYZOc0gvEP6D8Yu7eEQ07TIkhRn2CNy\nJ5QLeWzbYTj08ZMI1TRA0alU56lU5olDHcucpd+PiBzBD3/kITSxT5Dlma3k2F5/FrOiEBU+QcdR\niOMRHWIwWkgjj1nvCFHmYVWWkaMESx4ihINtjzFmFap5i1TSWJ3PmDF6jEcbdN1NFCUkJ4r0d13U\n9lWa1m3yD/nMm0PSjRjfmGNoFinZCW0lYWwqbC6Z2KcXWcs3mMyWGScZ2maH4WIOJYxQ42nP1lA3\n8Zsh49gingSEW02WGhUqRZ2TZw5Tnq2zce0KolSlEtTQqTLq27gTm4M0w5UNJF3wo8oqZ2yFKIpw\n2kMSMgbCwRETHDdlVi4TuBHbmo8fudQLZdylBoFV448GGzhRhqfIyPUinppRFwYDSyZJQ+I4IE58\n4jgizaZNs9OjcEySTqmA7zzCOMELIsI4wfXGxLFPGHm43pgoCojTeBoek6YEKUiqilAUnCCgPxoy\n8VySNCXJYlIpw4t94jQiiAPG9gg/mhBGNr00IpVBIsJPBlhmxNpsjuVajvZgl/XEZXG2jB/s8dQb\nT7PUWCJaVblRtqk+dJzK7Q2CSsQwHrMr7fH0f/lDTp+9n0DIFAOfcjvkw4FJ5daIH/vRD7FYqlCZ\nm8G1PWRZxnEcfD9EyBKQIUQ2tfkqKmkY4ToOYRAAoKoqhmGQL1houoKQMmRlOlyoqoymKZimzn77\ngENrS6xv3+Ldq5cpFAqMxzZ3nbmHclnDHnuEUUC1lqfbtRmOxiiGoDMYM+57AAg94vLtl3jrxrfR\nCzKuE5BFOTr9AVEUMTNTJwwjhPIduVlIQZPpbHW42Rpz98nTNHI59gd7xPYIJw1ZOfYD3DvzM+Se\nXmb5D1aYhJ9AabyP3c5V7GGPiWjxrbe+hS3l2esNuLX+MrZ8DX2ux7FzZynUjhCai2xkNvViGzcM\nEAcW25t/TiWDvFIi7Mbc6m4wsK9gFWokgwp+cIvHHjnBxz7+fvxkzNhpo1sl7rrrRwBBta7TaWbo\nytT+G4UemqKSpOF7hrfvikn2N377d56477G/QzoZM9i4wTuvP8vW1Zd4/snPc+nZL0PaodXeYPfm\nLfwkIvMkdlsdFClkfmWehAyhqTiBRyJAsXIcdAcgFI4cPozveLiTMYE/Qdc1bNfFzBUxcwVMs4Rp\nVdCMAkIx8YOISqHGG69dIuoOOLLgk2ltIjlHXlFwxutIwQ65xklsV0YOLXJzZzEDl4JpEvbWcbIZ\nUjfF8bbI148zGGpIYZNIjjHKyzjRCMl9F1yHTDRIFRkRegg/IggmpMNbHD7yOOrSXRz7+EUubb5F\n97ZLVZtlN5vw7mqeYHkW1cpzYAhmN21eqAesJCZibHN5WWPBlhnrCrGQGJUM9EmEUzRxtDzWeJug\nVMQtFnnmyhXsrE+tVEMf51lyIDebp7/TpD+IOVeskpNjlhTBGS+PEmRYvowTg1kooUcQOj4TSaMj\nIuaUMiWRR8pkpESwZ6R8+cYbvCX1+b65E3xWWwVZMFBTJlqRYqiCqiPJEiCQhIwsqwCk6XT6EkmE\nLAuQIElS0ni69EnjiDgak2YJjjciI0XRVcLIxw98MlJsP0DSNBIEiq7ih8HU+RV4hFFEQsLYsYmz\nGAQIKcMwNSDGN00ye4QS9FiekVkqy8iTIcPJmNvDHsaCyyFDpn3dQVMbjJfW+Yr2LW4+d5Wf+tmf\n5qn/5z/wyAOPce3bz9F47BT6NzZY/sgZDrYPaD75KmOvSGv/Kh/SZ/no//wxdt/YZU8KuefMWVwv\nYDQaY1kWqqIQxgGKrBAEAYaq3QkIn07zcZKQZhmqriHdOQFEUUwURcRJRJJMBw9JksjlDQ4O2hRy\neeIsIUkjVg4tUCtbBP4WpXKdMEiZDD3syYQ0i9ludrl+7YBcIUeagpnPsb61QSYgDBNU2WIy8omj\nEITEYDxgtlHjfQ8/xHA8xsrlyMYjqnoJJ0v51Pf+fS5vTOj0diiumJzwD3Hs399Hf7dJ7ZRPct2m\n/FzGweEZ4oKOJYMkZ/hjg3uOf5L5mQdYPrxCYUYwCPbpF1MmDcE/uvdDVD2F7WDM2v0Wce9NlmZj\nxp02i+WMar7Ol17aJa/mcfY3cCYRlVPzDC7f5n0Pf4ivv3RAp/06Bd2g3dHRqWIoyyzOnmNn/yq5\nQoLvyQhJkKUZv/Irv/q3hy74lX/+vz7htvaIhz5eOCIpTGhKbf7pv/o9HvyhH2dc2cE8dZj7P/4J\nrLuOIubrHP87j7L4yApFpc52v4svJPRiiZHnkQqJxcXD3H3+Ap47Jg4zcobBxB4iywLTKlAp18gX\n5hlPAqRMo9sbISSFLE3Y396itb/D+cNVErbAUKhWS3e4QA8x2eT6cy+QGUOWZ9+P62yR+i8TqRdx\nRBlzNsesvgyLC0TKCpO+SpS06LT3yS0/QjBJUaIeJaMCaoWQCVnQI3Y8NM1i4b73k09voLopmnUX\nZ9cOc/nJF4iIaV84yvG+QeClFF3BQEopexnjyKdZych7gr7jUp6fw0ljkCCLIuqpyrYVIkQBLRqh\nNQ5xbTQmEBm6SBk6Psuv7lJTAwI5Q28cZxyG3H1+EenBMnuMiK9MeFeP+FLnFruayrGFYyzEOW71\n+0SxQ6di8la3Sz9v0MJHrphsttc5nDN4SKnwsFnj5sEWi0aBc7U1rjYnuPUyltCQJAkhppSBLMt3\nclyn2a0S2VSyJJgGY6cgsoww8iBzQEqI4gBJBqFI+GFAkqbT1oJ8kSgOmdhjgiggiiMUVUGSQZIF\nyBmIDNXQsQomxaJBuWyhquC6fRYLBhUjJmdmRJMBWpoSCeinHtVTCXftvZ/7Hr5GzzjgpfUrrCiz\nRFs2p04fom4o3JubZ+niccSROcq3hzRyOdbNhBdfv8IJZ5HaPQ3e2myy+uFz9L9xHbt1wF4w4dCh\nw6Qp9AcDJEWgaxpZmiHLClkYIKsyujZt6g2igDgKkWRBmiZI0vQPK/lOOliWEYXTXAchmxiqThhF\n5HIm1ZkiaeYhi5BqzQA5QCJPv+PjBw6XL99id9fHKJQplnU8P2Fvp0OlMsegP0FRDOJYoMgqKQmr\na2uMJyN+9FOfpFDI89xzz3Pxwn0MQ5mf/+VfYrm6yl+9tst+IBMmPTDzlD4fYOTHGFGP6LbKyHI4\nNLmGM19kv/wgSRgy6TgYno2u9Vk+MsPJtQ9wz6kfQrNrTL75p5T+9DLdb2zzBX9MaXmGRyslVorz\nBEmXwXAdZXLARHd445Udur0JF4+arG82OXbxMVQh6Gy/yeQgYGP/gCi2WV4uMFt/lPnG/SwvnObq\n7ScJEsjlGpQqRXzP43N/m1K4fu23f+MJ1RJsjlqkeZm1ygKF4hJH7n6YU9UTyHaB00sf40j+fiyj\nwb3H3sc95x5BCRUaq2vc+8jjWOU6dpBw9MRJzl+8l1q1jiSp+PaINJVQVIPBqEMY+lSKJSwth1Wc\nJ40lQj9GCAnT0Bj2+7x96VVmKgVOH7HQ1ZDd/ZBMRFTKKu3mkGQyoFypY5ky1aVlDkZfIldcIOtZ\nhOomoVrFMmbp6kdwQo1Rf8zEnuAGPtbi4+jiVbI0YX7+IY6ufZh66RBZ7GKaMWfOrKK0WvgTQSx8\nEvbQc2XanmBr64Dx/Az5QKKrZRRDyPU9XCVmaZCQz+cZBiFm20HTDORKjt3U4f4D6GkJIxGRczKk\nuQWK+UVymk6/vU3eyFMoLVFarRMvWrS3W8R3rWKsyDTuKzIaDDh4y+etXo9bwx75U6u8Mmzxtc5t\nrEKBaqYSZEN02SKWNdq9Po2cSTvs0gy71JKMhlbkWnuH440VbmQhf75zHWv+KJlQsWRjKkWSFCRZ\nRpZlICMjRpIFqiwhK9Mw7TCKyNKUNAkJQpfA7SPLEnE2pQiiJJwGemsqsqrjeDae56IoMplI0Q2N\nMApIJYEfhSBAUVUykRHHAUniEwUTQt9hTnGRUo+J74CsYo8dMkJkI2J+IU978xLP7baovHuVXf0w\notDinCz4xMm78VoHnDr7IYalAYesBW4/fxXzgRnsoSA/dlh89FEGJyvc8yNnKBZzJHttmnmdmlFm\nJxrRbB7QOmhjWTmsnHGHm4UwiJFlcQdIIUtTBAL5zuvpsk8iTVOiKIJMQhJ/0+VLGMTkcnnmGjNE\nSUQQu5iWTLFgUC7P4ocjXDeiuW9z49oGw6GCplcozqj4oc/mxg7F/Ay+HZHPl0jjDFXTkDUZz3e5\n+/xdaJrM1vYmzYMmuVwRx4k48/ADEG3xlUsvUDpxjLyzzsjZZOatlJVwhbJp0h/HhHoOJXZI9Bnc\nW7cJA4Ou00KrhlQXKjTWTjJTXYQJBKR0hzu89szT6B9cQDsaE0dNTuZKqM0dqmdq+OmEoL0NkYV8\nyOQjFxb54Peeo3mwwcaNhGqtiLp2EXnYIu++wsZ4jV6csVY+x+KJ07zw3MvMzCzh8RJmLkUkFZIs\nwR4PeeJXn/jbA7K//mv/2xP1YhU5n2AHPnFPkKkzLJ05Q07NmF08TqlxFAlB41gFVUrYah5QKVUo\nNJZR9Dy1+hKNuSXyxQJhGBGm0wDlLHCIInD8iLE9xLEH6LKKlAomoUCVJPrdLsV8nsDzuXn9KsNe\nh1qxSGXOJB2NGY1cnEzh+FyJSinPcDIhm6liSNtE2jmq5eN0fYPk9l8SqSOOnP1+huMRw4lKNOqy\nt30dt9skV6kCM6TOHrXFe6guHkFRAhTGFEoSxXIFhSXimQI57SGiIGTn4ApWfQVzQSG/Cv2NECNJ\nCNWEA9lHSBmKkDD9lKGU4GcJUiYoDAKS1TqBoXG8CRt5iZwnUaxU6XoeC2snaV3fRgoDaguLzDbm\n6PV9fNdB8l20BYnaiSIuIbe/eovJuoRi5ZH8gELZpLI7Is4JtrwDwjmVD6Q5tBgaGBwz8jT9JvWZ\nAveUlxmHCZfzMmMh85I35lXfJpqdJzfU8Yo6eQxkWUGSFBRFRZLEVHCVJUgSUw4siQmikCiJSbOU\nKAxIYhvXGSIkiOOp+iCMU9IsQ1LUO1mvAUkaoygSURwRxNPP0E0DRVURIkMzdBDpFGQjlyR0kUSM\nlflMsoS+Z+MMR6SBg5YHLxkQRj3GgcTc0SPoosfb4wF3yRkPnH2E8uFZVo8eI0oayHToNvvs2QmD\nm++yeOw4xxZXGO7uYZ7NweAWD37qY3z9Pz1Frlyi//oe6dEKAoHjBWQCoiQljiKiMME0LBQZwjAk\nDEOEEOi6jqaqkGVTg0aU4PsBYRBN6RYhI0nynXucUa7kCXwPTVfQdIX5+VmcyRBVL7K11eb1N66S\nRAr2KENRc7jRBNSUjc0dpEy9016RIBGTZgmZUPBDSDOPQrFArV5GVmRs20GRDWRZQ6XF4tCns9tj\nMNjGbr/NUcni0b0qQ1WmFgf4+RKOOkJTPYRnEVdjzo9GKF6NhWLAvFlGKawxShVyOQM/8jHcgODg\nqwy3WwwnPq4nmLNUsqUxodOjvTvm2Sdfob48Q3UuT2QPuHb1JarLhzDKx5ldkujZISeXGnzgg2f4\nV3/8IlhlFPckb127xPd8+G4ip4jrbtFs7pDFkCURWZbwufcYdfhdsfgSJIyFgyRrhEGAV5X5e7/w\n08zOzNOR8vh4dEb7DJ0Wb115gede+CqRH1E250kxcFwQkkWcaUzcGC9MQFZJJYHruvhRjBMkpELC\ncRyGgx7uaEIU2dj2kCSKGA+HtJr7dNsdLM2k1+lxMJZRM5PxYBPFqqNGeWJ/Qpo3kewJpvoAFMpk\n7utYRoXG4gdpnPo07d6YOB5TTVsEW+/Q3bxC3LuNpcvMpjucXP4gC/OP4VFi7DbxvB2C9ACMEDcI\ncZIVxpMheSkl7EQoXZn5bAFZWyaZVygdrbF2YoUjioUrInaiCSNd4pBvkUhgqxlRFNHpDzhdWeLb\nYoLAQJrAnhBY+AzUgMSokMYWfiKQzICaZmEUSugfPY9Vz+HECje/vkXSA7+R4IQZcpix3dlHXJxl\nZt7i7lNLTFYE/9ncpKU69N0DyEYcz5vYzogX97Z4ptfl6609nh31uIkglEvk+oK0lOOUrf0NNSCE\nQIjpc5YlU3WBSBFAFEVTS2ySkKQxURRMtbSaQpIkxGl0h3IQxGmC7/uMRxPSNEaRBWkWk8ubyLKg\nUMojhCCIIsIwJIoikiT5m8WSIgsKOROtvIAjyQhDomJkzBckfK/DRmud7dY+0VjlrCNxczLgrBFx\n15HzlJfPIWbmiMslbo5yLK6iAAAgAElEQVSauK0MdfOAru8ycGD4hb/kdSnAdHsk775DXp/h2c/9\nBUdmP8RCZZEr2oh+v8v2zjZB6E/zCMIQkNB1fTqhxjFxHP8NwCrK9B4IQNO0v5lyFUWbnmjkqVNM\nkiRUPSUMbExLQ1IE+VKR8cSmUKzzwkvP8fwLtxgOVMJIMBzZJNmYlbUKzb0RYSCQZRWJEFUJCcMh\nkkgYjoboRo6F5QXiOKbX62Ga5tToEYYYhkkuGpHbrPH31o9zoasxzM+xsjtD7EDJgU1JIupdxbNv\nYLg1unM6ZfccQ2/A3O3/yuLXn0T63T+icfUqjZUCvZzF6bVTLG/dZKU4R76RxytJGJqE60ecnb1A\nRSyzf/Uyu3s6zX6APshhzN7P0cUVDFNm7vxZbo8GrOXewChv8cv/5xv4YUoSbSPNdlhpnKPduQ2h\ngRSsIUdlBBGOMyQKo/eMb98VICtlGX5B42aryeHGKj/zT36Ba8MWnV4HOQzY6r1M+Pa3iO19KmGJ\n3PwxijM1ekqGIEFRMyaZQxyHaDaEkcowkbFHMYFawU1SRDLBHY5JMx07EnQjj6jfw+t3kJIxvb3r\nHNx+EzWyieKYSaIQ9DtMjBy52WOY/pg++2RqhOyAv/EyjdwRMvvrREGLopihPdskjO7G6ezitK9y\n7bk/xrbzHD1SoHz2JKL+Aay5VaSZEyRSF6ndJk18FG5Q1x5k683nUdM62q1vs29/gaR+nmrpIn77\nBW75ZSqBILN75I4fQas3MI7VIJcwimK6ikKWV5HHElJk0NNzqO2EfpiSrs4TjGyUssWssFg+dpKy\nnzG0t3FERjSJcEZ9ds0eZBq3373Jmxt9bnzxFQgCemYBo6dQ8QSx0JFHGjXyZHFA0Chw6fouxxc+\nztqHP42xuErTc9kYS9xulfmyVGRLsTAzC0XSscjQIp9EyjDlAtuAkAwyFGRFYJgqugGaLtBUgSxg\nEnjTYkQUkjgmSwJUPQMtwyxayLqGpBhkQkPXLUwjj6wqqJZOpuWQLB2sjFRLKddnkCUT33dJI5tM\nUxg7DmbqU4ra6O42s/UCanWOvuRjOi61oc2huTLvdm5xrdci1qvcOoiJm9vMMeSutfOcP/sYeesw\nXtBDN+Cgm7Ca+Vzf32ViK4xHIwJXx09KbPzpF9h4c4OVQcDOpQ7eusOabtJb7nPJHeKkOTJFJ0lh\nPOphaBqhH2FpMlIyJJJ9JEkwU7DIST5ENmkiEUQOSZIw8iJWl8tYSoAd2URKgZxmIXQZo2SSahGu\nJ4h8wfBgRGtni8uvjSgFDrO5IoVaDd8YkOVtLt7/CPfd970cP7vK2dPHsCSdJA0JZQ07KeKELkcW\nD/OT3/97fOjhH6ImaQR9FyFKVGeP0B9us7oYUOwGjA7ewjzdwIthuH+DWuBjKxKq2iMJ18kqBopU\nxjZ6VDvb2M63cGSHvHyGQD9KT9lm949/h4qt8YHjJ9j489/nz0ZfQf9hlcKhOqvlJY4eNcjUgGsv\nvcPW8IDinEU7F/D6RsIgC+nZr3Mtfy9PrS/yJ1/639HmUk4dnuNzv/Q1Xvjaq+jphNlEZrF/wL79\nJW69+yo3b/8znNFrzB1W+MvnnucL33gVM19+z/j2XUEXPPEbv/5EQchcvHgfH/vpzzK2QA4iqprO\nRAkY/vUzvPWv/yOnPvkITRGzNHuYimyRUwS77QFLUh7fdvEiFzWMmC0W8QYT5jAQuoQ3tgkdm3ar\nze7OPq4XUixVIZouBYbDEZPRELIURdUxc0VyxRIVI0VXM0p5i1pthlxJx/NSZKlMUr+LQJ/BHT5H\nqfIZBu4zSNUfIknGjJMtohuXKNQFgQaNpZPMz6/QPHiX+VOfIAq6OP0muXAPUp/Uy2jFX+Hi8o+z\nfuuL3HrmGdaOH0UyHiZ0dQ4mQ4y5o0hxG8MqMujZaLpOMacTRTG2E5GXdPr7OwhLRcgZfhISqxID\nd8KJteNEtocEqHoVMgVTr7Df3CLLfITiU67FeG8G7ORiKkaIcaVLmuRI1Bz71/Yor1isvq9KGieE\nE4fK6SIjz8YcjfnJR89z992P8PxXvk7VjvCX8ty24JloTCXJocUyqSxIkwRN1ZElDVnSUZQCVs4i\nS6c8rKrpKMqdAO10Wq5IJkiTjDiKiWKfLAmJY58o8oniEMd3po2zmo6qaSBkYlJSkYEiY5kSYQCq\nUkKSNIajDn40xNBMJGHiOn0Mq8PB4CYegtnF0ziTiGDYodd8l/lCQn0m5d32NUZZgK5qxO6YWlnh\neGWBdLdNdW6GIEyIwoR07KI0h3S0Hrm+z+V4gn1tm/JyhZ1rl8kdO8xbAoZRgVeubbPx1PN07j+F\n/8xrzL3/fVzbbzJuO0yGLnOVOYgUfD+kUMzT6w8wdA0tMlmYyaNkAZ4jQDFxIxtZqWARsLq4QHvr\ngNliHcuAhYZDHI/xPJ9iplIpzCPJFnLOI1dIMWSFSnGPg3iWSs6glNocq9cYdiMCzSUzfCaew/d9\nz99FM3K8feUtJoFNHLvcffZHefz9P4kTNokUjbwZUqxUcP0Y3w/QUgMj1Wjc6KIdO8HLp4q80r6G\nkmZ8ODtE1YeW5TKcuESSjEgEiQp9EaNlCkeyMnqlSrezwZY+4UOTCu/svM33/e6neHnnr1A1m0r+\nNMXS3WSKSxLIhGeWuP7SFYK6TKO+wlMv7iAVJS585AxXJ3Blp40W3uR3fulzfPyh3+CVr3ye/Oyj\nfOYTj/MLHz5L89Y6N7d7VAoq+fwhxkqLftShtzdBRDl+6h98lt//97/PL/3iz/3t4WT/xa/9+hMr\nx4/xqU//OPuTHvmuw5u//ydsX3+XpSjl7T/8M1x7QLPgMV8ycA8OyDcWgQJz5Vm8OMAo6WhZzLVv\nfxt8H4eY3dil9c41RJxy7e132Li9SRSlyLJKmgpIEmzXY39vH8eeoCsycRQRI5AUjXFri2o5h+tO\nWFlZRcup1GuLkOXJr6ZI0oiSOMd+FlCYrxOnK2jDGLf3OlbSpyfVOXT2Aoq5yrg1wG6tI6+cRgrG\n6GyQc1sEsQ1eg9mZR9m4/BRf+sPnsZs6R+8+R+PkvVx/49s4WUYhXyTx+4TDAZqmki9YTPwJlVoD\nx46QE8iikCgVJDE4kqAwN09eGLgjn9rMPL4bolsJsqJRzJVpH2yhGjp+kOE5MX6Q51itzN7oGq2C\ngR3maG4cUFmZ48zxEle3D5ivm8xU8yiLy9RrCsfzVcxtmY2/eI5huYCk59HHAfcXVzgnzfLysIUa\np2AoiEyQISELdXrslCw0TYdMR9O0aaqUkMlSmG56pmAriWlTQhy7pJlPEruEoQdJQirLCEVBSIIo\nyQjigDCJySSBoqokkY9pFvC8BM/zyBcMFAUC30MIQRS0GI865GtLJPoCrdYAiwH5ZJeyCn7BYSfY\nIVewaA8dHFVFpAHHLJ20H3J29TBGrYSu55AVg0nPRh2lJK19CrttzJMVzndSdss6u3s9ylcGqHev\nsjXRsRhy/5nv4xtP/jlnFu5Cmk84aG5i9ww+dN9jLM8uo0iCJHUZDPs8eN/DyJSwXAc5dZFEhqTp\n+Ek63WXEMqXU4Pw9Y2QtZtCZ8PEP5BgfOJjGMWqNkEL5CP3OBrXCHpXIxIomqCikUkgRiVI15dx9\nK+wfDJlfTLjvzA8wHibMrZSQRMIjj/4g7954kyQK+fQP/haN6gmu3HgGVUpYXr2APbhGziijqSar\nSwvcf+YjFDSTxvF5Xolc/vobX8McOyhqjbPMkighbuxihRp2mjAixkoFSZgQyyqxlUdK6iC1WZUk\ntOICuXbMs/YWngSuquHv3OLcTz9O+f/d4eNvVWnd6vK1oMN9uRWeeu01bKvGL//m/0izeY2t7XUO\nV0b84j/8X5g//Di3Xv5ntBMJtSrTWB5x6uOP8+g//h9YXarz5rdbbOzsM4lsojRB0zN+8Rd+iXPn\njvPv/s2/45/+/M++J5BV/nuC53u9TFRqvsnl6zeZ0Sze+aMn8fe22cHhxlNfoyip5A2L/dcvs7O/\nAdU5tN1NHjv1vcwsrTFxB2x/8VluffErMHZwTp+mm6QkC3VMLSNOQvb391A0FVVXiP0Id+JiyoKx\n7TAYDChYJpKkEMcRUeLhxxlaJOEHKTO1GpqmMh7s4PZ2KVk1wrZBOdvCNQyMIMP1z5AbXGI8uYLi\n+jD7vZjFeWJN5db6Ot5OiwVthWzQngKMkAlcnTg6xsR7GjvxGAxuEoQZ1/YVcq81+eS9E/bW36Bx\n5Ahp+22STOXYfJmru3uM+mMkvUCpVkPR9nBjBzdOyKlFQtcjzmlMvJS8FzO7WCVIY7wgIHVdKlaB\nwO2TCZdKYwXX0eju7aFGIeudA0Q3ZE0vsh6nkMToowlhDPNpgYMdl3E84oMXD7P9jsfLe2OKmU6h\nYSGGCS8OtvjBwhyVEXQzKFdNbNsmThysfJHIT0BMK7VlRSNNJYRI7vCwGVGSIDLu8LN3QDaLSbOQ\nJPVJYo8kDUizBDIFRcuRxglOEJB9J/dVANJ0AhaJgeuNyKSYTMT0BxmaKhBKguPtMAmGFPNHSP0c\naeJRUCbISRtFsUmMCtebB/giYKGQQ5UNvMBnsVDGymSW1xa5tL/B2aN1op4LqqB47Bh+kFDwavT1\nXQbru+zOWHQ6HeYKdd7cu8FCc5Z8kiAqRW62rnJUruPdVcLYiXnowgy9TZ+gM0TXdS4eO4qqDBkN\ne6zMrnFycZGn/uK3qOsFFE1m4AxJVYFh5NHklKUlH0uv01g2scPrNCoRg5rMbpgjSOZZmb/MvH4f\nY/9tHLvJwmyZUW9ANEqRszaRJLHfdJlbzNDjHLff/gZycQVTalApLbHXv0mQJNSMFUb7N3Ach8Mz\nNdzumO6tG2Sujx1khGnGOOtSIqDZbNIK93ltf5uyDrlYJdHnSeUG+1v7mMUivcU8yu6I3NilbYbI\nwKyY9rQNgohFOYcWquyoCqau8LnqR3na2ePP9i+TX1N55p/8Sxa2alyOY87KFXojiT9+7jr3fPQI\nn/zoWbaffRoR2+iZRaN8jv/0f/xr9nv/Bp8ay3MpB5N9PFTU//oW2wXB6dNr/P0nHmfY8rjx9gFf\n+8Y3MedmSI0yk1RhNBy/Z3z7rgBZkoxDTXjny89xcX6ZyiQjMUscNUp8a7SJLFRqQiU8CNmPYXZ3\nwCD+Jv/XN/6aMyc/hvbSZbT9HYrCJ5vJ4VsRkygm5zvTI+VkgiGBYWr4UUiUhRhKmSAIcMYTkigh\nixPCYJqCL8kqkyAmCQXN9oAstWg0XHTNJg2HKEmArAmiYQkvbmJJKXtX/xv2ZANm11Ab34erHMOa\ndbj2xjfpTfaQZYn84j2kcREZGzeNUIwenqijekvE0W3mZo/yo//TEuNRkUuXb3Hr7X0aM2fpdA8o\nLrqUZu/C3btBtSiTITESKv3uBHfk4QYhdiYhSRGFWoHCbAmjmKfkpxRrKnFRwlAs+h0bTSngDEIC\nPyGQAooNk2F3SC4/S+hH2AH4sYclUo6VFeLIxZfLyAR4yYSFI/PsvHMN1bfYHo/ZTAQLJ5c5Uaxx\noeXzrdqEjiIxOLDxFIkwyFBzEVZOYRzGyMrU6qmpJoZWwY98kiQhimLIpssrTb0j48pSHG9CkgQE\nkUMUu4gsRbpTGx66HiLNIEtQRIqQp/nISRwSJQmSpOAGQ1JpakVFypi4HlE0xg8H5IrLCE2Q2hsU\nsyEzBUHg+WwceHSsNgsUqRTm2WztUS0KHm8skE58tEqdIA2xSjm2t3cpySUK9TnsgkmWFzipgjDq\nmJHH7v46YnMTtZanXDW45PS5f2WWXSegVM8h0oTgUpPeXaexzHkevBjyV19/gZI1h5StcqRxhK1b\nLQa7XVbOnkItQXM0oWbkyRQIw+ni8Mzp4/jBDq1BgZXT91OZP8b19ZfIVVe5/+R9pMArX77J4uGX\ncJoVZueqLC9C4g2xW3B9CPfVUg6lUDHG2OGYufcNuXU7B+4hfHcbM7/CT3z6M9x+7m3G6xsUSnlG\n61s4vR1yqUu+mBGMw6ksLvRJcUg1h83dHeZVhV18sHXOHDvKgatSn11k00qp2yGTwKeLT5ZBCY1A\nSRipCXl/i4FQyZQaaeBgKB0WPZXPGLP8lQwFqY6+E7KjJuhxyuvVCXmKfPaHz7G6lNFux+QPLTFs\nR3SvXCGsjtndmOfJFy/xyNkc1qrKonEPcxd/gL1v/g73rLn0R7u8MXwGIz5Ld6Tzj37+p/nBz3wO\nh5D15hXyxfx7hrfvitDuimxmJ61FHqJCzx6SVE1G7pC2FHImsXhDC3FjnVKpwC2lQzBs4hmCVanK\nkmdyOtIQeZmeHuONXU489n7ChQXo+4iyzLA7ZDweo1smXuxj+za6pVMolvHCiDjKGA8GyBKUanXQ\nDbR8kfbmFsW8zNrKIpamc88FlZIV4I/HeO0b5OYuEAUxzXEb4V8nKC3hew0ub24iTJeKSGiUTnDQ\nusyFB7+fwsz7cdxXqBn3kaQuJlfZ3a+jxxt4OGTtCf3JK3ibEcaqRXd7h/pdj9CoLKGKCrYUYGUm\nG+tvEqsmBxOT11+8DoMeqexhNKrkGhFpEGMEAnvkYsspx04cxR0OSbMYzZlQn1/B3ojpBwktwyRv\nVdD72wy6I5RYp6N1KNsRS80x7pJM//QpMsdhEvTo7SXYew6lQonU01mZWyCNJtzu7PPo+fM85qgM\nuh3+szpB2AUGmcPKjEqtVqPbmhB4AlU20DWTnHEMy1pAVXVkWUaWFHRFR9VkhEgJI5cg8HCdNlk2\nFdfD1Ln0HcMCQiBIkdKINA7uSLYiYPq7zpQUzw8RikyQjOj091AUhYXZNfLWLKODyyRxm7ySIe7Y\neFVLZuz3eXjuEIkkk9kTFqSMRkFh+2CXcqmOFpkUzxxi4/l3CM6eor6wQLlUI5AVojjD9mLM1ibd\nrXdJSzpu06N9bIGVKy3e1jtox48xf+IDVMNr2F9cZzIRjOfPsni6wF98+U/42OMPYVKg27KR5QKz\nc4tcu3aDMydP8eK1V1hfv8KZtfM8+MAnmF05zItvPYNVVEjoE6cK3qTL6uIyqaRgKCHNzRbXr8bc\nf++Iz//5G0QCjBIcOQQNQybdT7h4AtqjOisPdVlaFjgHGd11MGdhc/0+2iOZYN5mfn6eza/extka\nUj88j5BkzCBHTQNP13BSmURTcdIJteUFbu5cQfYiAsPGjjUalXPUEot6Xse0A6ydAbvDq+xqCbac\n8rhn0kg11ESQxpAWdKI0wSRiPivjmEsYw5BvX7CRLpTp3WgxHrmccPPI5T1a+YRxVWfGz3PVucHG\ntsehi4/xuZ/9t1xcPMW73X3Gekrr7S/x3B/9C4I3FkmdTf7B06/TvvQxtNJVvvDHs3zmRxTqpxf5\nsc9u82//4IscObFKa9OBROPnfuqjXL381nsK7f6uUBdEWcJRNG5FHUYVideTFi/7TUqh4Bm1x0Ji\n8RGjyk+kc/xjzvKpmYc5UzrEA4Uabcvj89ome+WMo0oJxbK4HdtYskrQGhKEMYquYVk54jghCSNE\nBqokE/gukR9AkkA2rXMejUa0212QZGRZEIYhw4FPkpZ4+0oXP62j5g5TWDvCcHRAs3Wd4u4VNq5t\nogeLuFqR3b0+tzcLSEqBVrjNoaU1SorAm7xB0bufg2Cd0bjN7q0bRPFterbL4rH38fW//iqSm0da\nPg7VFYrqDGqoQ+cAXJs07SKbh0ilGW7e6vDM068ybNuUrQLueIRhKJjFMl3bh1AjL1U4fPQizU4K\nfoV+W6LIGbJoSOBeQ5GbZMmQYaeN2w+IfQlHkjDdEDHxCY422K9btA/62AObRmJyZFbnnvsWqVQV\nKvWA/miTsR2BWGTcSXCDkFFV8Pjxk2TCZsXwmV2N6bTH2OOIcmkOMgXf90GE6MZ3aIFpLkHCVLoV\nxQGuazOxhyBikiS4A6oSaQZBGBFGEWEc4IceTugRJB6piEFKSbKQKPFxbRlEymC8wUF7k1KxyqGl\nMySpzPrGdaJhi7pu4AYuW5MOO9GIq7ttyGSkrAQJJFnAIHFZ74yIswJDV4JiCf+gi2VZuDMF1HKR\noDcgHg4Z2APqaYu93RuYkUp5Pya0J9SkjNv7TUbza1idPorTRVs+xMw//zHi7znLN279Jdt7b/P4\nxz/Ff/vai7yzfom7Hlrmw99/P25s02gcYm/fRolkHrvwST76vp9ATUy6e2POn/rotK7HDoiTHmYq\ncXB7i93OVZrNJprf4cK9l3A5ilaEs/daNKoVehslrr6acP/3GOyMdU4/2KU8rrD1TEaBPMcuQBBo\nHD71KifPdWhUTjE4KLC7t8Hq2iGcWGGcZfQjG3sSMrG9KTWUk0mTqZTQMuoEtSpBd8R8eZGZax4f\niCxsu0379iaS67CfjrkQWPyE3aAVTHgx2uNSesCGPkENoejpOEYeD5NyJvOF8stsn9rj1We/zGD3\nGrGf8IL3NnvlOvsjk57X45v9y0h7M3zwwiK/+bu/jbm6yvXBVWwjw9JMvvovf5Pjcw2uy+uUHzjC\n7PkqW/unad8w+LmfGpNW5vm//8vLjG+1+Jkf/iFe/OYLqAWJwqyYUlLv8fquWHz91q/99hNSySTI\nRrQJiIKMw9UZBvkC2rjCmWqNvJRxW2rTk3vUZZ9jQsJPPDRZwo4lfHefqp4niBysokSuuERSjjAl\nkyT18OMIESv4rk+tmicVDnJWRFIi/DTE8wUZ4v+j7s2CLbvO+77fnsczD3e+t2/fHtAjgAZAEiAJ\nEKA4i5QsiZZjS5FsSqqKKTuxHVUixYlUsV1WEsUul1IlxynTNlV2NIumaJISJZEACWIeekTP3Xc8\n98xnnz3PeWg+uPIgI5VylbQev73WXk/7v1d96/v+PyQpRBJTEjcnz1y8WYCuyIiE+KFNKqvQEAjm\nUKtWEMUJNyZT7KUnMRYX8OZ93rm4T3mYcmcy4cxDdY61LKRiC6cwMaMZJO+geTdx5neR1DPoZgd1\ndp3rL1zj2HGbIHWx5C6hKaLqLoMDl4VuGz/1yaWTKNEtVqMrfOyJBvaSxN52CpmBsuJRBgnRHCKh\nhGZCmrmUkYYfKEiKge8OWast0b87wRIWSCs1xv6cugeJkKEcqaJcP6RiWNxt6LhzEcFNCdwEdZAi\nZTpRCK7nEsc5KAZGo87YG7CQm5y3DfzcwlvxqJyP6WlVVNnB29VQxBZlpiCIKYJYYqmrqDLooUGq\nepjtNlEBZTkhCsckbk6cBeSRRMocSTHJM5UsC1C1BEmBNCqhdCgLD6kUkQSNLE1I4oS8EHDlXfzZ\nkNzJWW6fpNpYpz+bEnn71JU5rbZJLxwTpzGqZCCUMqYg0sDk+LKOkJdUDmKmaca0iBCFkkYooDRb\n+FGEnxWsHlkliqYYozFC6CAFc4bvvEknl3DEjFitM5VjGs0ad+/PWT/aYqmrME/a2BWDl377eX78\n8z+MG5V8509f5tH3ncL1Eu7v7fHiy7fZvd3j5FaDk0fOcbB9F1Ppsrp8HGSLaq1OUXhs33uT7tIq\nsSpjWscJhiN6820WE5tITvGLgmNP/A3U4jrbvQFVKgwHIX4S8OkfLFnazDAsCUHNSYM15uGExa2E\n+QT6d3RiL6VdD7n22h2uXBxw/MJ7mMYe4SyhCFJi4RBMG19NqUgqgp9SqhaJaDGbpVRZZZIKLIzn\nPCZp3KypZLGOFCT43gjDqtP3RtwvJtQrmxyxz7BYe4hS7VImAr6R4s9SMmnCd90e81UT+bbJx2cr\nXCwkUlLMqkUcuyB4nJ7Uedrp8Je9h3nfdsyt//13UJfbFMfPkQstvvtrv4w7vs7pn/wc+toSn/2b\n/xuVdIFIrVM9+hMoy7+ItfjjNJf+Gm5h8W9/78vUW60HNphuxNd+/7f4W5//r/7iVBf8o3/wD39p\nUakizTMkTBbqi2iZyP3BHnpN4iFLpVPTya2UTPaRxZhSSYj1GFXvE5oJgrzFJJ5jnqiSWk2qnTqp\nKDJxIRF0MgJy0UM0c9AMCqlG6E4fsJ5K6XsVBwkCAp4Tk5cipik++FjzlCyPuPDYezl+ah03GiDF\nKqEbEnoemtwkT6v0hwfcvXuVimGh6AnOSOGZD51gqbtBEt5BiabMKjJdf87BwfPY+hms+joTv4es\nxVQWp/T7cwLfwBY7mHWN0jzC8tLHKaJDQu8mZvc5ov6fEPpX8QoPuyZy7vxx6p0W434PJSsYDX0y\nUQVVQTcbpEFJPJ2T+xPMZgXkBqOdPWpWAZKPokoMpw7rQoWdcIxaluxVMiJVYrnUmOQRC60Gdr1G\nKAkEZYGQFSR+QuKlZEFKLEg8sl5na/0Ut66/ysF0hN7dxMSh6FeJogf4FEl+0M0lomFqy8iihZfF\n2I1FtHkfJ7WRphq64PDejZChOyPPFUS5AClBUUsU2UARGhQlZOxTJBZCUSXPPfxoRJYXFMT4yR7R\nJEJWWqwcfYRUkZjMdzHKGWrq09RMDuNDFApkWSKQYZD4hH7K6cVFLFNEnMf4AhykLkf0CguLCyRp\njt5sUNc0UgTCMiecTuhfucHNm3chS6l3W8ilwigNiUWdaTIlKELyVMNYM4m8jO3hXTYe67Jx9DSn\nRwonOg3evHeZ4e6I04+d5u1LM5ZWBaRwSj5qcn37ZRaOHyW6fUhroYOkCPjDAf6d+8jOnMrqCpov\nUbfqLNYbTOI5qedjtCs88tDDuPOIO70+H3r6s0zEgo++/6Osrp7l9u23eFSCMsxhqKBmJQf7AbVW\nm0QJkA2DLDFI1YzRuM6psx9n7EwZz/ZpNFtkpYCqGhAExKpIVtFJNOUBWTeMMA0Vfb3FmUnEqVRl\n3DAoFY36NGVpXqKWIi29QSkIWIZBRzRZVi3U6ZxlVSMqXYrYZ7laoS+FZMcX0Z2QJyIbk4Jpuc1S\nlmBO5uh7Lk+MDP5H/wRPjQ2GecJ0lFH94Cc4fPohQl/AS4d87Qu/SPepR/nBv/SP2dg8iSgbjGcj\nVtaXaXdW8YOSIIZF244AACAASURBVAnpNht89CPPsb1zh6IsyPIU8oI/+oPfedci++ciXVCS01Ea\nrJubNKjTkuq0pRYyKbNij8IQCCXICg2RBqLQoUzbFF6NZFhnoWIjmLsIayHq6gqiXuXg8DaSVaPW\nOkQTHJryEgYqVV1GKVOkrKBatwjDEk2toqgiaRwiljoVu0kU+MRxiqYaBFGIpEqYVRNBECkSyNIp\n29tXmU3H5HHK1bdfZ7h7H0sVaVVVcrnkw8+e4vzpE0wPNok8F1XKUEqDm7f/A6E8o7C6xElGxbaZ\n+zaKtsDW4iOceeoZlEYHweyi1tpMJzeZ3P8GcnSbKN7GmVyl1V0mL5eoySYlOxw9rfPU+Q1mU58s\nzKhIVQRXx++FDHcP8eZjLE1iPPBJcYj1lMjUUQuNRpSjij6JpaFkIntSgGxrtJKC3XiCvVRj6caQ\n8cGQ0f4If+ajGDZqtUZh6kyLFKXMqQY+0/4Qs6JQSlVuXD9EGs+Y7cgUhYAo5ZRElIWELOvI6gOc\njGlLxHMXRTcYhTGf/9F9fuIj17jw0Gu4Y4FCOiCJZMqsQpyEzL1DgmhClhUkgU1aeoT5NnEeICka\nKTHzaEQYR7Qbx1g/cpbRfM5kvI9VBBjpnLqeE8TjB9UKkU88c4hmc3QKOhWVTqNGqWpYqcReGdBL\nEhbVBmalTmGbCKqM4ziUooA4niMGIZW6zWKnyoqsY5o2sqpRazVpri8iV1ViKcMhYOxM0EWV5pLN\nrTtDnnvmOQ77l3lyQeQfPPwsV3d3mXsTnvvICgd7DpO4xrXxPTKpzWw6Rl0wiCWX7d0r+M4ekTsg\nCwJiJ6Qxq+He6qOWVYqiyjSPqDabvP7GRW5ceZXJKGZ0+CaikBMtr6DXVI4f/RQH1WfwfPBLGBcj\nbNtAyxxqoY0WzSnDGWPfZnntGJPhDmE4prO+wiAMcdKMeRAzUjMquo1mWCiKCmmCPx9yf3SXIOvT\nijKyqKC/USWOXGp5Sun5qEadIjcplCpVqtxz9hmWE/qWw29zlVtLAZkYk8cT8qCg25/xaFinkiUc\nTg55j6tzbCxy1qnxEeEEj4qbXPJnHFQlzqsZwzMNDv/778fJc1abLbb/8AVKyeb7n/hh9gcztvcn\nxHlBc6GFGwaMnRmaoZLmE3Z2b3F4OEAWazjTlDITEUSPoojftb79uaguEAWRJa3DQrXOfjYiyWJq\nms359iY3hZvcye7TEm3iokARQJZ0dBXELEW0csZThcVOjVbnKP6ohj/eY2m1hSppOLMlpNxnMnoH\nq7pAFmtUqhZuMGHmRySZyXwQYFgZsiyT5QJxHCFrBVkmE0UZWSbgeDFvvX2J9oHF/uFVTi4domtT\nxDIiy2xqFY/64jKlYtEbpAT4yIaG54gYCijVE4TSGuKdr6K6A4zG0yhSDVWv4xUyRaWLpncxu9eZ\nzncRrS65XCea7DDeeQElHGI3zlAb9hCLkuEwotrcJCn2EE2LJBBo1o+QG1NExaN0MkgiCjKkKCQo\nE7zCQgWM6ZiTi0vccHwSr8BKQChkhr6HWNXI2y0W3RJz4mOYKsb9OZICYlpiKwaFqrG320eQRMxK\nk3rNpEhi2lEN2d9mKEgcNnUm4T5deZ1QEBGTNoVckucl5BqyZlHggqgTlxb1zGbqpgjeLi3jCs9+\nf8wX/hW8/4JC2TzH7/72DY6fWcYNIixLJolnZKlBXnoPCAlIKCoE6QEjZxdVXuXI8mfIlYDBeB8z\nd6jgIngRmiHipTPccsaSVCFTBCzFJEtK3CRB1xRMQyEVU1RZYzyaIRdQs6sMBAHVMBHzkjCOsA0D\nKUoIsgiraVKVa5S7U1JHQs9kbE0iUKHb7XDojqGiE6YZB70hn/7sxzmMNXQEyr0er89uMV/oUopt\nXviTy3zmk89iKPeZpT5R6jDb3cU60FlefgQraSKFc1w3pCxLrHqTliizvXuJQJsyjHfY2jpKsdkA\nWcds1Xjz0nc4s3WUIu5zduUYg8EeRjZHzCUOowrBdagtpShKi7OPjHF3BYZuSvUkTOJVDg4Ewult\nJqMpzeUlwkhhGpRYkoSupUSCjKxbhE6MLIjUTZvrd2+RWTLWrW3u9T2EpSVG8znnFYnZdA9V19FV\nmSwqaJQabuSyIuvcdvd5MdilaNUYCDF+26JfRMhJA9EZIzYW2Q9GJBSM4jaVWgMkiX4c0tdT1qoP\nfuqXHIUjSoU3tQwhChgEl/nK136d0xfWuT55jbcvfpMnLnyYZtzF2xsgSD4IGfPEpcgbSCJk5Ygw\nnFEiIedVhLREkbV3r2//2ZTz/8MQS5F8GjL2JhRqjF/Mmbh9FvQKy3qDYSCx5+RMIoEYnViRCIQU\nP/eIswadjTaS3cJqNHDce+iZyuy2D84A0dSJkFBbaxxGAh/5L36MD37mLyE1l2i0t/irP/Z5fuCH\nfpwbt/YRVZ172zsIqoxsKAymPktrJ+kNQ3Z7cxr1LuPRnOWFIyxWG7TrOqaWU6uVrK7JaEaOZjfp\nTSICT0TIp4RpTlhN8bIVkmLC5P43YP0C1ePfx1QR8fOC2Euwy5B1u8rO5a9QTK/juruYQoEVC3QW\nL7D+gV9g87G/z/6l30CzTJr2FkIokGgVgryGLFYJxIwsrCKXFoE3J3TnOOMhWewRuTHBDBYQ2aye\nQ/YtKpLA8mIFtWKi19egptOsVVk9CBDf6UGR0+qHdHo+t6wcXxaYpjETN8AUVJatFkfrXTbtFo3a\nMiNZJTUVtuOSUT7HEHJu7WTkQowk2pSFQpHLiIKOJJrEUU5ZChSKiWrPOHv2Wzxp73NjusAtr4e8\noPP4uW2+8bXvcu7Mebb33yRKQ5zJAz/ZPHfIGCCKBaKUczC8zvbeDgutx3lo6xmCcMpgdhchHoC7\njxANkasZjuAxCV0a1TrVXGC91sZGxSwVVjoLaJKIoSgUmUCuiNQkha4gkSoysaZiiSp5GCP7MZIA\nqqagUxA7DmGRMlNLxNCnIUFbUyijkJVmBzEScMKc3e0Jul7Hm7sMJnsc5tv83h/eQvG6/PQHnuHZ\nJ2okSchrF2/y1IcfI08c6orM4sJ5honGqfUTVCSVpqRilSVmKVDVNC6+9G0G5UWu73+TyzefZ9Lf\nRbeatJoPcemuR145hq53iYSzuPOE8o3XuXX5Phsn2iwP73BHhcs7NYIw5PZViEoVe1XkP3x5g++8\nJNMbZkRZTllEjAa7HGzfIokmlETsHfYQJzGZrqNbNoPJlFevXUKVZN7X3eRkqKPWmoSWSa0XMnNG\nTKMho3xMPh9SiV0ars9MjVHSjKle0P6BDzEYO1y6scdXxve47E1xRg4oCmmeIfkJISVifZ1Va4V1\ndNQ0oIgddiYjvtq7y80k4OqWgfrmDWqaz29+6Z9hTe5ydLnBFX/Ac089iY7C3t379HZ3cUYugePj\njAcIJeRZxGjcYzabEAQBw+GQudsnTcN3rW9/LnKyv/IP/5dfulBdZ5RPSaQEWRHJ0pimriDqCm46\nIud7zqKSTKnIlIaOYJl0WhWGmkJsd9g52CbzNlhZdHnuU69xcvMhKktTuq2MGzcdHnvqo6wdP8d4\nMmF3+x7nHn2GRmeJpz74NPe3t7l18zbPffg5xlOHm3fv8dd/6vNoVoejR0/zE5/7Gxzs3EcS4KET\nR5C8HMcZEUQximKS5iGFrLK6dYH+LEZKahzrXKS9fJqi+iFSZ0Tef4EsVbC1RW7fu4yi6miKiaGX\nzGc73N3+Lla7QOAZKu0LjJ0Z5DL1lfcRqArXX/5X6N7bCLbNsJ/SarVwNZnG4mlCL8HNBtx9I6NS\nNRB1gVJTmIZzRBFqaoXji6f5uQ/9JL/2wr/n5mgXQYU8TqBIiMMppWJCb8D6vku7YeN7Pka1zju1\nksk8wJQN0jinLCWWOouIoshsPudwPMQZpewyYVyAYxbYVo44U5lHFuuNFmESgZBR5KDINppao8hM\nTL2JRZ27hz0+/ZNv8nd/4i2uH/40/Vtv8fQnC77zYhXFWuHixRH1jkKalaiKRpTukGUJAgpessdu\n7xK62uLciR9AEZbpHV5H1O8jxgcoSUAWhyh1FUdxcAuHpXoHxdMIvSk1o0KzucBUgtuzIYHncaTZ\nQld0SrHEUCQIIlTNQF5ooLkRceCRHg6prS4SCSkWIqobEeky8mKH3Bkhxjl2zWaMjC3LrK5u8K2r\n27SqNX7owkluXHmbYhzg7d3n31y8x0ujGV966QUuXj6kbZasdBJqFkRhgG5ZzGY9KH0+9ugnmE1H\nWI0Kh4MBcZBxcDhmqGe8cfdtvCChs75JRbf55ksvUeYFldU6z33iSb7ypS+zdf4Crzz/J8iiTOf4\nKW7tXeWPvn2RPfEMRbJLV5URU5v6WZ8vfV0hKQpWWxCrKrs7hyiKTZpk2FWVPPfxvIDNzfO06i2K\nNCXujcn9ALNiEgcBWgK+65FWLKZxiB3nvBP1qbUf0AZ0AW6kh8jm9y6zdQHDS3jfY0/xfT/zOVbW\nj/Hhj32caq3K3Zu3qOUSdVnDTQMqK110qWA/2OFOcBdf8jAFWIlrHG8c5ZhZcO/9D/GHw3u88tXf\nwJ32eHpjA6eZ8ImTP0T/wMZS2tQqGiUxSQim0UCRRcJ4jzSaMpvMMfVVPFfHsnTu7X6bb339j/jv\nfu4vEEjxl//nf/RLx8wqjhCQ+ikyMrEQoJQxfhIRAAUqaSYQeAFR7CGpJZWaTjDbw20GNFsf4j0n\n6nzkwzs89p4Dmg2HirqOKFSpGC7NlSWWjjxJXi6RpxlrCzZyfQWkku29PVqtJb7vuY/z5FNPcu7C\n45w79xRrJzeo1Vd44n1P4zhTvvOtb7C+1sEySoQUagsGpahCrlKxVeK0IMFm7M1J/QOOqymqXEVQ\nNyjdl1iwuvSmIbmZYNZAETsossL04B0mt26w3jQZHs4Y5zGxlpBIPp3uAsPenHsXX8G5/AJHj1bZ\nPoDCFpCbcxTdZhaZ5EWV+QHcudVHUFKcwKNARxBk8jxCFSR0yWT/+pg3hetolsxKbYH+2CEmYdGU\nsH3twWWgKXFY+DQzjcszh6P/9Wcx+wGjnUMM3aJSqePHIf3ZhFkckOsShqhhtNuMshJZHVH4PpHT\nxFArCKVKWqRIgg484Hgpkk2R62g6THu3yAqPl765yPs+cRG74rKUT5m5Xf7g+QqrWwlvvxpT71pM\n3UOyzCUvh8iSjRsOmUwmLHbOcGTtSaLQx40uI8p9/LmPVEyQVYn6YpOIiFLKkBGZ9maYSoX2xhJO\nELE/mXGzd8A0TmlbKpW0pGJWydQcRXnAC5PjEr1ehyxhNh1SSgWddocoiYiTEClMmMcJpaKycvwI\nQgayZdHPCsrQZ3PzKL//0kVWmlVkZ8Scgnnb5FAX6d3YxXUl7rkuP/rcOT73Vxc4s2By/vQyqr3A\nO9fnmOqMjtBmc2ULWRG4cvUqlVqD8Tzmsac/zOqF05xaOUPdXuU7l1/n7NZx9kYzGrrI9uHbWEoE\nasbOvVu0qw1e2rvN9WuXeOfKfcqtNu+tjnhoPUWSMtSWwe98JUKsnsBeXGVeJEw9H6vaxqg0UBWV\nyJ2gySLrR04y80X80qPc7cN0ipBGSJSYpkl/PKBs2ISGiKJI9N0phaE+8LsNYubBjLGdkIoZy5mM\nkEU8U3TwLt6jePQk3c2zyLqMfnKDiepw88oVDDFHaFY5PNylF/YZSyGCrrIg1qmLddTGErpSIcpF\nTryZMfvsM8SJi3/Y57464Z35VaQbeyxuXUAWVPxgjqRKIFQQZQFBSjHlCjvbt5HkhPl8hqTKCGLK\nzsEl7l+7x9/52z/3F6ettiCnHxxAtYKd1JBiEU/J8fOAkIJhMCKjYHXR5PSxJkc2FFrNFNsY0WyV\n/N7bZ6nVIh5deZ6Gusd8WoIFGSNkq4s72eT46R9jKAh4cg/VLFHTFeLCIk5nVBoNZMl8cKveGyIr\nOkvLZ5nH9zArK2RIJIVAvdkgTUOEUiLRAuLcQbJ0NNUmiQIkBIQiRRYjROmAJeMT3Ln4m7ScnG5T\nZQ+HE0eOkhYug+2AUjbYS+6RhDdpVzQiP6S53mbF65LKGr6yzOFgjLP9Cne/+xpVW6PU6uRxQa29\nSCBFlH2ZsmWQCtC7OSI1U1RVRE9MbK2LpDfpDzzCLGZ3dsBlc5+6YPCe9gpWEnC39PG1ChWhBckc\nrWLg9g9R2haXxID2e88R7UwoEpgrBaKtIqgSiZuhIiJSImYgWhHs5WAGoLsEsYpgVlEihbRSQS4k\nVLlKnqeUZUSae2RJQZhMmNgDjgUVZpLPr37hAv/DT95h/xCqG7dY6Oisrj1Fmu8yD6Y4wV0a5jHI\nG4SRgOu5bK69D8tcwHEPCONdsmyGREqlIqBoixRiwCg8QCkFZF8icUGVdHrJIb1bB0QFWKjUtApL\nhooYzHEDH2M4pmgrSKRUVxewDyLwIlJdYuLNkGs61f0elarFsEyo6ypCGGPOEuJzddRQxM9TSkWm\n06wyHQ5oNCuMBz2+VjSoPqKyuJKzIlepd2v09gI+8sRDPHNuwuCmQKVT0D+4wzOPnkPLG/z+b41o\nn+gQzEaoYUJtFtEwdZYfPc3d3pg1ucNkcJ9WpcHjjz3Cq6+9xNmnPkm4u8dHn36Ol7/5MkN1l+O1\nD5KbEXXBQ0BHa9d4dqWG4gaMUriypyHtrdCpddCKIc6ey2GyR1I0EZQpWeLTVHUWjDad9gJ3h1MK\nq4VZiBD5+EpOVuRUo5K6rCKYFYoowhMS1FIglks2EgtZFqnICkqt4GQY4Zc5k8zHljK+W/RYePYZ\nJkJMZ89nW+ihRzEb90acbZzCSkcMeyMSNUcSTRZii1pWQ9AsXNVkTagixim+quPEt3hk8EHWnvsh\nbqQyWZmxL93i2uQmvZf+V97/xOeQtGU0oY6saRSCh6gWpE6GJJRQZBhGhTQriLOCNPPxA/dd69uf\nC5EVSoFUayAJEl5lTu4HqAkkqoVIgcSAZ56BT36sQqs+YO6oRGIVbTGn35fZnbc4vvUijfohY8XA\nyAwsf4zT/RTD+CTWSgdPbUEQY+clYhpQZgGFfoAsq5RFhmErRFGMqDQQJIkonqPJKhVTw50foBrw\nzId+iNvvfJeLV69yfKWgVu0g5B5JckgqpISZh+Lu0xYVTHOdPXkb0TzBrcvfpTzxGCgS98q3YZhx\nd2fC6lGXZreDuXUexx0jaCbRPGbgeUhBRP/gIvv37+KP+wgCaPUaY/kCxtE5CSqmsshhESNMSnQ1\n4YZ3DUleIClDWs0FNEFj5E1Q6ktMewM0c47tKPi6wlPhB7gWHrLVKSjqHlYvwjdr1NWYyWiKtx/T\nOt5EPW1w+8bbrOp1LEOnqpt4bsjUH3/Px1TEFmwSTwTbxa6GxJFBGbVByNGbClEUoGggiiVpnqKZ\nBaQKonifg5s15KWA9YdUGoHPuZWcl690WDlyHW9Y5S9/RGKcOIy0HvUop5218UUJWwrpD17m+LG/\nQpZ5ON4t0nRKVjiIQooki4iKTFQ65NMQWVJJJIkw9iCO6FTrjIKIsFCxNRlTkJCyhJai01hdJQl8\nwjymf6+PLAmcXdkgrWuotoSIjKxVaWVwODjksOyQFAWqaZCpGfeTER+IC5xugui2afcS8mWZm6MJ\nDWfG0Q2THWLOnT5JZ7XNt79yCyO3OGJEPLZS4PgR88KiPo9IeyH73j7PPLvJC29eo5ZKCNGEyLOI\nS4FQnFNEDookkEUDntp8hq+/+sdc2DrJF/O7BDu38NUCQpMf+Ot/i+df+C3mzpjSHdDprCIqdzi3\nBJYl8Pq+gTOWUSpLHD3zMIu1Lq+/9jyOe496KDHXBEzBxvMTmpUuVqPJ/YmD1mgyj0IOevscr9hM\nxiPW9C6LlQ7jyYSWLDGwNZqIZL5PQ5FJU4dKpYbdbRG7IXpZJ81SVosMrzwkaai8tphhXrtNdx2q\nFY1QSXH2ZyzkMtupjKBI1GQ41ED2A+ywQDNUQnzcSMQWFIJ8jODD3O0x8TPW146ztLaK1PwRYi/m\n+vZ3uXLtVd7z+NOQFCCEWGaT0ElIpreoajJRXkIc0NQFXvrjF/it3/tdus3uu9a3Pxcim1OSCBli\nkiCnCTJQaDKjPOLQm/H3f7HOma0Z/tRhHrQIJAFyUCYWV8cqx46W1P0PQHqDmroIUkhkPI7b+lms\n4CKKXkHWdCRBJktCslhAkFRIY4QSSqlAVsAyNWT5QcumligkWcZwOKTeaKAIOnmasnX8OF/60p/w\nxLGHmA93KPIJllUwG4fIRhvNWkRIQyRln4NeyNGVTapVmb1+j3a3zeW3Rhx/6DiPfPADlFKLTFDw\n/Ijt+zOuXrlE/3BGZ6PNya2j9AZDnOmEo5tr1LttPARKbZU0cajWFygLiaaRIQoCt29eJQtkbCOi\nkmr4O3dgcx1r9RjqvSFHmnXEukVQ9bmxu8Ml/RJ+5mGjcLAbEmglZl5StqrsTCSGssCJrWPkTspa\ns0XsP8hX9Q8nLHRXGI2G2FaVMEzIi5RSVBHkKUEAWaKhqDkSCmlkIEo+eWxi2DKStkw4T1CNAAqd\nH/mpOv/nFw55R5/yM7+ww6OGweXwo4j7OuLaa1SPqfzf/9xBCUMaa09wefwyuh/Rm06o1U8RRgOK\nMiaOp0TJGFlK0UwVWXpQJeJHc7RMBDFjGrmUWU7LstErFkqZUMQlWZYRSyWtRp2AHNeZULUr+JMp\ntXYdIc2oNZqkYUDmhVSrNkmWEogCRrdBEgasdFoIRc7heILdrqFrNSyzzfbkNsqiil5b4dWvfouk\nzDjopTzyoTU2F9u88cYVcsdDKQ7ZWFvA2e+jlTpV1SUqNtDEO5TpkDA5wc/8tUf59V+9ydHuJnvz\nV4hkiXm0yIquUE1T8p0Ru6KAXpV5+9U3ebh9HGmUodZUrl25SuP4Jp/+vs/x1otf5u07A451VyCP\naUqHmGHEKzcitIUajz+8xebWEpPrezjb+2jNFgeyzlohstvb58jaOrV2k71eH9OqEAwdyjRFjQqs\nroXtBeQ1g7njUtNN5hURQ5YRi5JSUR/ghUrIs4w8yRGKEicsiWo5ZuawlLZoiV2+8/XvcNWa8Mqr\nNqfPP8vSqVNohYYsZbiFSysV0VWd1VilUCX2LJGt2ZxqUTBZKBEnKYlZUMlKoixGB8ylJgd6QmUw\nQO12eOThT5IEIWkUU+aQxS6BED7ArwcVqg2Lw8kIQVLQTItpEOJ7IftR713r239SZAVB+ALw/cCg\nLMuz34v9EvDTwPB7036hLMuvfu/ZzwOfA3Lgb5dl+Yf/qT0icq55faQ0pSUrtHUTKUzJkwg7Fzl1\nIiXKICnbJGmOZLikxRKv36qzcxjyvvMv086ryKJEnt9FkEA0fh5Xd6jSBklGLMFQNeKyIFcyNK1C\nGqQIgvbAYCTPUDUZURGgLJEUkZrdBtEnij2i2MGSTBoLS6xsnOaLv/FNPvXJJ+i0LSaHt7EkG3IN\n5+AQu1knkyPIK+zuupSlQJgF9G/cIvY3GOeLzPsCui5jW1V2tkdcvXLA3Jco5CZJYaIYTc48/l7y\nh0+SFyGZpCHkKoFgYtWapKlCLhaoDYH+vTtkQ4faMOfk4gKPVE/wSvMyb84HSMOUk0vLnHjfEttv\n77ClrKGe6PKVey/QzEsutJ5gIypRugW7t3eZzEsKzcYPQu7f6yHcdlhbaOH4Ps3GEppuMhiMkBQF\nx51R5CW2rSApMYoBefKgMF2RDYRcIY5jjIpE4DvY5gJhNCDJExRkdL3N7v4en/3IWTbtHg+1+7SO\nqJhv/QtuenUufeUon/r8Dr//1XW+8E8W+NzffQd16QjZ5FU2lj+OXX2U/dHvUJQJaeZBGSLJDwxm\n4iQjCALyrEQXRUAkjDIEoDBEnDDEixMsxSRKMqIiZ56lzGYT4hyqUcCmXcfQK2RFwE6vz8mtTbzB\nkHDmsdN3WGzobKwucVrVGB72yHSZbrOBhMLN67s0qhGZKHKQS3z1X34RXWsRJiXnz2/w+IVzvHZ9\nmzvXfIa3ZBZaLUY9lwUtwpkITL2ApTWX+cjDVlTS8AAijx/84dN88f96g1atQMxUTEnDP+yR+g4t\nKSEYZOzdvoEgicReiICKQQ1mAentHbzWlEc/8yxHr5/j0jf/MevdKcHU4Pq04G/+8GfI8xxrzyG+\n+TzfHt4kqomIe9voooBo19norLC8vMq93R0Mw0JIS/zBjJpZIVU0wjxFaldxk4SqoFICUyFH9XOy\nLHmQXipyDFFBzAUUUcLQDUbjfTxk0jxltUy4MbvP4oaFSYm3JTNz+iQ3Kqy7Ek7iEVdERE8lVTXq\nc4XUUpHCmGC1SvLwOupXXyeyNWquyFt2SffxLaqlSBOYEVLttkFViUIdRVIJkz7kISkRklLB83xU\nrcVg7NBZXmZtbQ1bMfmdX/93qJJBEr376oJ3c5L918D/AXzx/xX/p2VZ/sp/HBAE4TTwV4AzwDLw\nx4IgnCjLMv+zNpCKkqak4hUZ/cRjLiYYsoogPlgmJBkjD6IYRoM6eyODfmRx4Ggc72jU4k8ztX+N\n0ngveOfww22sVpOWXiXMckoEijBE13VkWUbSNFTboqI+IKTmef6AJ6VJiLJAVgiIpUySRlSrVUaj\nPoqikRYJ4TzgobOPs7x0Dr0t8uobf0DpBlSYU7fnyIrK8HaPkhQhzxCKgjDOCQqB/UOPWxenzF66\niKgYPPb4U5RpSr93hzCaceqRU4y9OaJf8Oq332BpfYGVY0vEgkGelzSbCzjehLraQMoFNFNm6O4i\naD5Bf5cTqs2ip3NreJsDbUa3W6fhqhCM6U/Bi4fUFZt1wSJf3OBMdRF/LmEqVabxCMmosTuYkQQZ\npp8RjmasrS9zuDdGUk1q9Sbj8Rg/cJGUHLtiUm1UEQQBZ36IQAvTFJDQiFybPIesnCGwgFVJvmfe\nEqCaEUWp44UeL3yjR6UzpfaeLn/89Rqf+2WFo3cr3E/3eezhA57/1bNsnR5ht7dJhQUqesJ690fx\niwo3e/8W7EcXrAAAIABJREFUiQeIGUXK0U0FWRZJkoQwDB8wsVSTIiu/h8QWyMoSN4yYuQmJBGIe\nIcgikizj+D5x/mBeUorcGQ+pV5t0Wwv0Rj1WyxLNtBgMDlg9toCRKdw/7HNmbRULyCwDy7bJ04LS\nFIjcMUV1kRcv7+BSwxnD6uIGakPhn/+Lfa4dXOfoyWVcpkhhgRQmnG00iOYxrm9j7dxB0JsUpYF/\n7zq1IxeQsj0Wz6X030lYs6vgXiUKISdnYJfkOxm6ppCpAUt1MEgpEMkilRsvfIv6Z57l6NU5mThH\nkkRu7MHeJOTJU6conJjy7phXBkPSVot6tsSt+SEstqgMPDBUtra22N7do2pUmI1nFFlJq9HCmUzJ\nbJG5kDA3ZUwvBEFmLuWUWYkhaGSqgCCLlGmCqihIuYCEhCAJ1BZqVOYBhWxiqzZVveRP/dv0azFP\nyJsolTr7QYS2tYZTtsgrAocv3+CIm4JeoSHpJLZIOQ+YnFlAe0nlwJ+w2lxi/Wd/lKLdwvILbF3i\nqJxTb3cY3NomrynkcUS1pjMZHtBoN5hOPBS9glgXWWuvUa8YfOlf/muWGh3Orq5T//Rn+INvfP1d\nCSy8C5Ety/IFQRCOvMv3/QDwG2VZxsA9QRBuA+8BXvqzFkkC6BIUgoKkyxSiwCyJkLICSzf41is5\nzaYOepO3diWu72tIhoqp50zEdezzP0IgPMHBbI1u10DTNIbilHQ8wDJ1BETixEOSpAfwPEkkLQtE\n3UQsocwS8iIjFzMQVQRJRRA08jwlTEI0TcMw6oyG2/hhgGbUifIJ/+43v8ljZ89w7eI7nDti4oQB\nqlwhxSSaCTTrVYbjIXEmMPELfN8myEc0hCpIOnXNpjfYRs9koiDHGUxRKxrhZM6gP2E+CkgimePn\nT5IIEZmfIwshQVggxg7hros36xGPhpiyjr2wyPXDu/hrIk2thpUZiEc0em/fZymzMTQF0RRgOOah\n2jE2pEVenF1ja7ND4rsEYckwD3AyH72MaHU6dI+f4O7zb3Bi7SSDuzfxwhmLy01mzojNzTXSLCGO\nfQxNQyxkdMWAXMYJI0SpQJQK8jxDkWSm7k2q9lHyMiDL58RJj4XOEaauS6B2Ofrw32Nw7Xf55s51\n9i+J/E+/YvBl+S021AXeuJcgSidZFGWm7j6ePUcSPMoiRZJBVRUkSSJNM/wgIU1KRFElSjLktECQ\noRAlKArSrCADFF0jDmI04UFheZakGIqCaZqUZUmuaYzDgLEzJxdSru3v8cSRLfbfucQ0CzjRWCVN\nMsaTGZVWA7+iMs4TNM2k07WIneP00ylHTyxw92WNghjFKvnmi7eYeCZGUyLI+yTClELTySKZ2/fm\nPLa8RrXm404sRGIMwWLvRk53U+Hy/X1+8LNLfPnfXKcqVhCTEMOSycuConIUUbNoZQbj8V3aeURF\nl3HTnL6Q0GmYvPLvfx/1hz/GwR//KXuXxxhdePzZj6G2z/Hir/8zlM0V+osZZTIgCGacWuqyPdpH\nOtHCMNq8ffMGLbOGN5xRxhlxlpIKEMklSZbQNXTSMsasWni9AEk2qYg6oesi6SqyqpCkKVEOFDLj\n3KEsoZsYSGkCcsRdVeWVvM9+U+axlonoVQitB9es/aZJhsXG4iLuMGI+6IGYIYkp6sgl01VOrG3h\n/vwqi1d6CI9u4oY55wKLQeLRs0UqgkJLsrk5DxAVjaalE84n1GoVTMPGjwFU4iRh5/ZtfvcbX+dr\nv/2bNDFQNZ1ZHpCr777F4P9PTvZnBUH4L4HXgb9XluUUWAFe/o/m7H0v9meOlIJJ6iHnIrqiUJQC\nRS6QZhleGPBrX+xw+qF1NMPAiyQkMUPRNQxzg0ef+TCBVSX2n0Q3Ewo5J8hkUlmkWpVI4gRFVhBk\niRIeNBoIOVmRIYkCaZE9OHUKyQNzPEFEkFTKEkzbwp1P0TSdIhMRyhRBzEnSEv/wHu9/z3t45tln\nmYz2ubrzIucfPsWNnodaP0pFnXPx+iUMOWLuxPiRjRepqKaIPAqpNutIZU4UJBDqLFjHyGYSWSgR\n4mJrNsIoYef5a6Q7DlJTobXcIski8lLGGziYsxTDjZCinM6p0xSdBu2jy9jDO5S7MZ4m0Ty1QHpH\nQKmIuEEfZapyZLGLknVxRiUbq0eIiyHZ/oxOaCIrKpmikCYBcegzuNenI7fRcwPN1uhWGiwvLzE4\n1On1+g94T0nA1vp58tRFU9q4Tk4p7iMpdcAmyaaEYUAQ+6iaSSm6xIGCqReMBwW1isAr37rHlZeu\nc/79Z/ip/2bMNy4nvPRiytYjCieqTf7Of1vHNtqI6T6JFDKP36IuLyCICoqiAAWuHxAEAVlWIAoy\nQgFJGlGRNBRBQNNNkjRCygSyPCNJEmr2A9PvOI6RBIEiywkcF1EUiYWc3eBB3l5WJXoXrxL7HoWs\nIOQSk7mLphlEloFsGISUZIJMSk4j0RG7FuLQY0GSyZhi1lrsbbtM0xoVfYofFsQTk7pcI5g6rLe6\neOMhN8sp3/fIBvuihyLPSaKMu3sJ9de36W6c4OCtbT79qad452rB7l2XWiVEFkOyWGU6v8Sb90dU\nG3XUiYO42ibLwbIj1hYg+n+oe9NgydK7zO/3vu/ZT+6Zd791q27tS1dXt3pRI3VrQVKDhBAWCCQh\nFoEJmBkh0MiMATO220jAmAEcjvkwY4dnwniIAdQYMQKBkBharbWlbqm7S9XVXXvdqrvfm3vm2c/7\n+sNtxv5io5lPVmScyIx4I0+cyHPOk//z/J//88y12fiTTyCCHWYOtWgfeR1+w+Prf/K/oisn2Lve\nx1YSz7exjc9gkHH65OvZ7fVYG+1Qq/is3blLy61i2zaFhH40RIYeTgGTyZhGxaeoS8rMxexPMcIg\nKLAdF8+xUZ6Dqy0sYZMYQy4leKBpECzNctHssXOtT2Wk2akobl29STgnWHEXwLGYO7RCXiqu+YZR\nQ/PgOGMlslgPLHbtgloU0059yu9+mL1kwNnGMqM0RaSanJTGmeNMujHf2ljjlFcnFpAlIH2baZIS\nVkOSrGRmZoY//fSn+PMn/4iHF1eJd/vUaw1iq8EXtm5+20D5nzvx9S+BY8B9wBbwu/+pOxBC/KwQ\n4jkhxHPGGGrKwRWC0WTEzqjH1DLEvs1ullBfDClrOTc2rjDtRbz2njfygQ/+I972wz/KUvt1iETQ\nthOq1R2SooeY7tBKKkx7A0w2pswjlK0wlkRaEuUILKtEWRlCxNhWgueXeHaB7xh8JaEsmSYxyraw\nlIctQnSRY0wBwOxMneXVBbppxJt+4MdYvfAOPveVfRaPP879b/hRfuC//Kf06SD9JbLEZvfOPk5m\nERiFkYIki4lNxsrRVTrtGUIVIiOJiDSWrFLzmsx4VVpGsnX5JTYvXuHWs5fZubjB3vPXyG9tUc0F\n+VRw74XHiBND3a+S9AxLu3O444C9Scqd6/ukNZ9r3g5R3CNt1g/GO0dD9GDAvLa4cusGPVVg1wSm\nSJHTAlVAlhVMdgacml1m0N2k1q7Snm+z199hZ7uL0TbxJCf0azTdWVqVRSpOgyzJUUIjRUGpE9Iy\nxsgUzw2JylvkZoui0OhcUG1MuNUvOP2W5/jl3xlycSthenmdBx5b4JVX+tx5ZZaXX865fmmW0LnO\n9nSNUdHDTQ6SFLQRpFnGZBozmcYkaUlRGgp9ICMyAowlydFYroNlWRhKFKBzg+RVm0UMjudheS6l\nNBhLMOfXUa6LW6+RZCWRgReur5GgsXEoPMlwPGXkeaT1KlZQpVFvkwGHjxzldrLLTndMPQlQVkxs\nYmzZJmgsUCbnODz7Nuaa58lzQasFo3EXZQlub/a5cm2bo2dXaC4s0h9NOXr0KJcvdqm0Glz85oAX\nL21Qug5up8OVzQGRrBH7Y86fOUGj0WBrMoawwvpowtb+EFCk+z0e9DzaC1WG/TZro4JLty/y5L/+\nIlf7NSgzIt9QWiXd7h53zRhTtxj1NoisiHQ8ZdQd4ocBkyJF22AsCAMPq8yp2i4mz6hJSaEz3NUZ\n0lCRD0cIWYA4MFrSxUHycFJk9PKUqav4QrWH/dbH2G8epd/boVyAytI8o01Jy65w4dAZDq+ewPMt\ntOnxzUtPc/2lF5DVOn5meNmPeCXaYfWH34y73MR1LeLhgPa4YG+wzzfuXqEXDXnbAw/y53/4R7z3\nJ99HPOzzXQ89TKfZojO7AJaHHdYZTmM0hrg7xhE2Cskki7ADBxuJryWu5Xz7WPftmHa/Shf8xd81\nvv7f1l5temGM+a1X1/4aeMIY8/9JF9QrnkmTlLe+/d1UnSqf/tM/oKIUFx56gLA1w2T7Nv2NPm94\n54/ypp/4MLcHGVXbIShydBNMWZAnJY6qYHJJVqZMkl2qdflqemgb21/GsiWGBF0mCGmws32wU6bx\nHkJLHBYpilmwKgjfMEr2cK0QUSgcDDdvPItnO6ysHCY2gqKMMUKTlQWeWyXPBJZlYwmJ79ns7m3y\nT37hH/OL/+CjnD92ns988hN89tO/w3Ts8uC5C/hzDfq7PeT2lE6rzVBmlEVC4ijqc3P0X7nNydYs\na6MdhlEPO42plgETX2KKkhUcdDpFpVOOS5dSR/TsOnJunslcmw1Z0Bv1Ge3tYReGTnuW5kydfNpl\ntl6jGEfEoxRpV/AqdWja7I5iMmMjLYkpYhxX0Jyb4/rddRzjsXzmXkbJgJc/91kis0etajPnvYVQ\n75GWJZE2bOzuYYchZVniOAei89A5hOOWpPmA0WQXy7LxrEX2e1N++VdPcOGec7Tcz/AHT77EOx/3\nqN8zYP1LZ5hdbPLPf3edLz43wW7nNPJlEtGj8HaQOxYjilcvxFffpEQikAiUEChlMFKQFjllAY6l\nqPgBaEOSZFDkSAmWkhhjcJSFUtaBD6rjYDkOaTTixNwsZjwiKwq6ec5OqjnXsfFMwFP9IR969E2s\nLDQR0Yho0MW75wjPX7rKzfUR37o5QFlzKNuiZIARU9K8xcoSZMOQSSz4wR95Gy986d+QbsUs1wxt\n18crbbY3+1RnbcJWlUnc4/w9p6ndN8Pdr1zF9XYoafInV5Y5f9ShPn+OwSu36fTu8szaLZYrcLpR\n57JVw+4sYW29hAwOUxtfYhzW+ezNiLRewc9z7OEEN6jTmJlnMEk4tnKM0X6XvJiy1b/DuMxYkBU2\nyxinWqVtLPR4iKoaWst1HNswWk9ZWjnEaDSiSDNs5XD+3Hm+9PQX0KJDaCmqSiJTTW4EshJiaj6q\n5lFLA+zQx21U6fb3WV5Y4ItffIqizKhXF/H9Bb7vB36Iixe/zKc+8wdYliYuEx545CHe8OjbWV5a\nQJkDaurE8eM8+af/J3/2qT9nOt2jGswxzfYgczBlhu1o0sxBuCV2UZKWLljw8CP383u/87/w/PPP\ns3DkEDevXuU3fvFDHKu3MbqA3DDRGuG73B31iHTxbbnK/mfRBUKIBWPM32kY3g1cevXzp4B/J4T4\nPQ4aXyeAr/+9OzSG7/+eNzN/5BCf+cu/pt5osDo3h45Lpt0JTm2Z4zPnOXz2foKKT1MIRKEpM43W\nCtcJMDpFoHEcC9soplnGeBJRZBpjCiw/p0SASSlJsIRkVOzjyipSLqNMHZ2G5LlBijE6n+JakEZD\nAruCxrCwsMBwPCLOC+ygjiglRZkgsoQ8G+A4HrajDubxpYVSIf/j7/0rdFyyPezTXFrifR/8Jfby\nnPHWNgsr83SOTJne2kELybi3jWsc0vGA1o2C5Uww3LxDVWTMCxudJdS8DBVrHNcnyyIUmnqlRlpM\nGDiSWm64s3+H/XSHoZKkZBiZI2sB2snYvXuXeuAwSLvYucBVHq12Czescbn7PINJQa6qtOcWyJG4\nlTob20N292J++K1vxmscplvucb32deL9iKON47SznNL1SeKILJliO4IkHh+YbJsc1/ZI8y6FVkil\nCP0Z8jxHqIyVQw3++W+9ggj/hp/98aPUTmXsjdu8+BmFO4k5fL9gY6OPbni0wybZxoBU7hGngApR\neowR/3cmmBAHAGtJiTCAMNglWKVFgaYUgqIo8LWkYixS60BCaLShNH9nGn4Qe5MUBXU/QEmXUZxT\nDevsrm8SASdWD3O25VHJAr64/zx3+vscO7JA0bSp1X0+f/Emt+50uXJnB+U0UXaObUtMYmPbTWY6\nh4mndylTgyHnzz75adqeoBo0mdDDYcJo32J2IcQNPQajhNlDgsn0LqqokA33abdCdLPJg7FLtdqg\nYjks//i7mLzwDY6vtRhduszGNMZbWKbqhdwdj2m2XAruYV6t85ojszx7e4ooJ5QVhV09QiYklZrH\nnbWrBMaQjcccqc0yTRM8yyYgZJwmTMWEajOgU/dp+hXCpofV22fc7eP6Hq5lk5WaYTxl7shhbt3d\nJ8szMi0JXJ84LaiFPuOsT002GWUWjuXgWy3OnDgFOmf18AWGwx5NZ5XOQosXvnaR06ffyK98+BGe\nefFTrO1c4+ixC1SqLju768x2WiwvL3D5lRd55mtfYjzcw5KQpmOKTONYgFCkmaZS9ZlGEShNGGqU\nHfDcsxdZX79OteJRdXx+82NPUPOq7A37GErqYZ2ozCiSjP8U1+5vR8L1h8CbgI4QYh3474E3CSHu\n4yDj4zbwcwdYaV4SQnwCuAwUwIf+PmUBgECydvMWf/3UV2m3Z1hYWKAS1piMc0Ll8L0f+Flst0J9\nYZ5xMsGxDbgWohLQ78c4no1RGUkeYVQBJqMoE5JkROjPYgsPREmpCwo9RWiNsixc/zC25YHjYxU+\nuclwOKAWChNSmj0ocqZZjGd5+NUqWBaFMGRZBNqgtMZRmjjZRwofZRUYKqAswloFXSru3LxBoxaw\neHqB44dez1YyIl9fZ+fuDfLxmLARID2Po02PuraoXnyF7Ts32WFCSswxq4W2oE9GLSqZFDlWqRlS\nsJNNqWYWtoBEw6zjkOUJ4/GQQhvq7Rqm4mBXAjrtOdYnt9gbDpG5phVWCZVma7DHcGONwp3iuhXi\nNGGnt0ej0URLmzgacGr1GHkWMN16ic+//B/4wLt/ht/9Fx+jO+myGDZILZv5lUNEa7cY7q1Tq9XQ\npUBnKcKySfMeNj6OqCLxUcImyweUpk+1IelGDf7Fv7rDD33wNP/FW3O+9j9PCLVmdyOmuRAgLu6Q\nGI+o3EFWO8hUsVATbA/GSMR/fFni1UpWg0RQGoFVgmWgUIpYlBhdEBibjnLo65JUCzJTglRoo9FC\n4wSSohAURY5E0B2O0J0m+0BFwqF2h74eMB72cIBxmSGSksQkLAV1rm0PuLbeA6uGtGxyEZOnMY6s\noBOXve4dWrU6uZYU5RgpqyTxiDTdo96ch3hMq1JgBJSFphp66LwgMVOs7oDWYghpgpAlK60hfT2H\n43hMtzQLR15Le/E8Tz63Tu3oMkcuPMzdS1dI3ZBjRw5z6ZvP4XoVGjJi1pqAU2UrHXPv6x5hPNpm\nZ+0Scw2H6d11VqozmEiw6M5xJ+8y63jUHcGuJcjskslkwnTQpVSCw+EskS7QZUmBQVkOu/0ucZmj\n/RLHsUmLAi1StCUwgUBkJd3BFqGVYvKQvZtrvO7891FxKqweOsGm2uTQbIXeYMr+/h5PbV8mjjQf\n+MCHUBWXRPTIzXUsG6AgTqb87VN/w7UrV/F9lyxOCesOjcYs2zt3OHpslf2dPrYFj77lnXzpy18m\nzvf5wI+8n9nWUT75x5/m1o015hcWmAz6+CjqnTrTLGFzNMAJq6SmJE+Lbxtk/15O1hjzfmPMgjHG\nNsYsG2P+tTHmx40x540x9xpj3vX/qGoxxvyGMeaYMeaUMeavvp2DSMuS/WHE0sIiyzPz1MIGm3tj\npqXPvY99D4fOPMDCuYeQ9RkyXSJFBqqgUALHlTiejeXbCNvCCE2a5zh2hVZjidCt4TgellRIKZHC\nwbGrBN4MrncIZAvwEJaNkBlCTdFlSjaBaNjHIica7WNISfOCaqNJqTUwgTJC6BiXlDLeJ4/2KbIp\nloBe/xbGDNDlkLCuqc5ovJbh1s422vHZ2+0x3NnnxOEjLCwtgK2oegErzRmyw21m3vQa7MPzHDp5\nig074YaYEjuKW7bL7bDCixR0/ZDF6gqZrHLREuwbh55W7OeQGBuTQdpP6XUn7PbH7PcmpIFi4lik\nvstIKfZNzt1owFY5olY/wXTqUJQ+Ao9atcNgr0/Ncrhv9Sg5mucu/iW1xohF7fL6s/fTm3ZZK9YQ\nlQYyrLPV6+P4AWmWMY0jUBZRkiGVATRFmVCUKYiSJJ2y393GzgtmKm3mlmb5s/+9z+COxYVjY2Zq\n64w3b3PlquY999XwyhEazXRQgp7wsQ/pg3MqJdarmzAc6J61OUiuzcxBcq11oFqxNIRaECBwgABB\ngKSiPELbxRYSJQy+76HsjCQeEPgGt2KTqJKgZlNvBEy620zjnA2d0anV2er3GGUpVgzPXrnCqfPH\nMEIT+HWKUpJmGUmeoCwLZQe4QcpkHOMGIdVaiKV8CuOjQtiZtOmOl7EamnEaobUmqJT0N31EUUP0\nJjjzFZKpppzkWEGfLz93iW/c3EQkOwyTHmMz5a3vfwfTJvzt3/wJ0QtfZLWYsvPys0xHd7iUCuqt\nDm96cBFpK2p5yGKa4WxvE0wiRBwjPEVX5mwQcy0fMKUgyTOqrsfhWp3QGGzbpl5rU3MbTKKENE2x\nbYVSijRNGQ7G2OrVAQRpkQFRqcHz6fZH2MqFFOLYx5IVLKl54ZufZu32l/nKl56k2YwJ/FPUZ0K2\n+t9gr/sCg8FlPvGJ3+aZL/wp+SDiT/74kySTkq3NPh/96K/xlS99k5WloxSpxdLiaYbDhPf84AdR\nboUf++mf5cjx+4jinO9987v4gXf9NJ69ROjOc2zlAoFY4Ud/4B8iMokrHeIi5c5+l16SsLx6jEwo\nBkmM/rYh9v8nBjG//vGPP7F84gRVt4KPx3SYcPT0/fzIz/48C2fOMypLZCUkISXwJZBjhCQXAs8+\nMPAoCo1UEqks0rjAs0OatXmKIsHzQrwgREgFWkHpoEQIaLIiwpQgtUuRxiTxHnk2Jk4y8qSLUopC\nayqVOkZaKMciTiJ8L8MUU9Jxl3i6x3jYxXUCfL+FkiGurbGkA1qTpVOGw12yIsV1Z0gcSbq/hyUK\nutGAoFlj7shhkjQl2etj7Q6wM0O8N4KKj3dkhbVb67i5w1ApKsYlSzXPpV0iqenrjE2d8tP+KS4x\n4EY2RHsejpYUZUHqSrQSyLREhjZJnKOETZGXWJ5FJjR24FHkPt3BmNmFRaphkyIBkSrOrJxATkv2\n9u9wY/cKU5HyuvYFTh99mBduXGFt+wZvfsv7OLR6hKefeoqwWsP1fKSwsWyPvNS4lkNZFAdmxyKj\nKCbosqQSVrBESqkSxnoDE5WY8ZD3vDdDa8N49yh/+XSN97y9y9FVGzWU+LUJr30o5b0/OuH3/8jB\nehVoJQL1KjlrOKBphTbgKFJHUOgCtyipa4kjFJnJccTBNzzbQUpJnCYUWuP4Dq2GizIpvqexPUVO\nSeh7ND0Hu0hpSJ9X+vsoK+DW7j5hq8Jivcln777Mo689x95Wj63tAa4KycsCIQRZUWJZNnkxxOQV\nlJOx23uFRmWBySSlFGP6kYOvBL61R7NSxfFtxnEfTIgfakJXEVsZ+RjwazTnKnzqqXUOn3kIvxhR\nry/iez5f+uJTrJw7y/UXL3LCsfFqFuW0S0+3uVWmFKOYthWhrAQrPcSl/W+wtbeG40AUxShsdFYQ\n+h4yzxjbmtIqUabEywui4ZCp0WSFRuWCpMjw6gHKVmR5QVmUVJwAW9jYXoXJaILAwndq1MIZZOEQ\nWiELrQVMOcd85zCBHTIXNgmdFgtzZ7Blh0Gec/HaV7i69i0ME86dPkk8GqK04OorLzO3tMLLl28g\n8HHdCt29KT/3wQ/jGJ/H3/VDjMdj3vG2H8HzHe698ACt2hJveN0jZEOJ5br4TpO5uRnajRYiqZBE\nEX/zH/6YwXjAB37w/dTbba7eXudXf+nXOH72Hr7yta/h2Bb/9L/9DnLh+s3f/u0nOoeWSUcxUT/C\nrbR45/t/goXT5+gVBa4vcSoORRnjOII8j1BC4FgCIWLKsiBNS4RysJRFmqQ4ysPC5e76dbQReH4N\nSzooFLb0sIVDkUGWd0mzHqbISacDxqNNtNY4so7tSbLC4LgewnbxK02SNMayBeQj8mxKFPeZTCdY\ndpWZuVWCcIY013gqIJlk9Pd7CA1aS1ynhtEOZTrl4tNPMxn22O7ucufWGoPBiPtOn2XODrE6Pjvz\nIVuhYOQ6HLrnHjbvbDGaRgyY8EbabJgJr3EaZCrnMhE/Zs1xWfR5edpj6ggSbUiLlGpQxat79Po9\nDCmeDMjGESLLMWmGJxVFnh+kvroOST4mT2PSOEWKgLMn7uPw7Cr7mz1kFrNrtolzePDoW5lMUmba\ncxTkvPXtP0in3ebkqTPcvn6LZBozP7fAJJ5iOzZpFGHIMeTk5YRSp7iuj215TErDaDKgSD0ePnsW\nvyFoVXM6xwTLZ/b5wh9KksUhP/P2e/GTHcpuhY/+cotf/mcxm5vyPwKq0QaNwQClMGgBrlSUvk3h\nCtAFlUJQlTZCCCIKdFGAMdjyQFGS6ozcHIwrtusergsFGdKVYASyBFkYgsClO054eTJmPIkogWaz\nwtFqh+vZgNfdf44nn/xb6kGVSZLxUz/5U8TRlN3dLZqdGg+/5jF2d0aMk5u4YUI8KQn8BbQyKHuP\nuj+gaoX4niDJRmghaM4UlGWC5fosLMzRLyWp7dPyAm5uDXHsNnMLR5gJV9lb7/OmR9/A557+Glev\n3sBDctuk1LWhGx7DU02i8SYyirhw9DBdq8dkMqTmWbgoGtUmwzgiV4KoiCmtkmmeYAUucTzBL6Di\nh6jAJ0ozqo5HaVu059pkeUaaZtjSol5vk0QJrl9nb3cPtEIal6rXZnFmheHeBJ1pKhWfNB4RDXrM\nVBYaL18WAAAgAElEQVTYWk9xnEPce/9beeb6v2W336VSrdOsNyC3ScYhi/PHuOfCSSynwbvf9cOM\nRgnnTt/HidVzzNYWcWSF/WjC6x58FEdbnFg9TjSOWJhZoB00uP3ybY6eWODIygk67QBdTLGw2d6+\nweUrz/D9b38/Z46dZfXEKba29jl16AyVsEGpFJKCD//Cz3/ngOzHPv7xJ4JKgDKKwTQitzyGmebw\nyRMEoY/l2bhOiREx0jKUukToKQ4ThNQoaSGki1QOUpiD5osUjMYDAtemWm9ijHMgG4nGxNMh8WSI\n1D5xtk4cbSFKickhmQ7QxuC5TbywRmlAOA5COtiOR5JMqVRcdMpB9LQyeGGdmYXTVOrLSCsgLROU\nVnT7e9g2JEmEMQrb9slNzNVvPMts4LN09BBLR4/gKY87N++STSKG6xsEJiUajbn1ynVWZ1aIi5LO\nyVVG8ZC3jGz+Nl+nFvh085ihyfhue5Z+POWimmKUzez8UWY7RwndWcKgQaPRpj8akmuNySRSCiwB\njpTEcYyWEr9SY5RNwWSIMkNIiRYK5TkMJ0PqnZC1u1e5OvgWy/4iZ04+ilPTzFXavPa134MMNVkc\nc/+5e/m+x7+Po6tHeeaZr2DbkqxISJPhq5RBSVmUSHnwSBnFE3LgnpMhvb0hr7uv4MnP/Dx/8eRn\nuHnNYbiX8+gDU/786/ArvzJhuN/h9d8/ZPNmwf/0f1QRIsZoA8YcwOuBrQVGCrQU2K4FlkRpcAqN\nbySWUEgpQEiiMsVF4BiDNAYjJAZJnJeUaYYV2JROiR+EWKVH3M9Js5LUSLZFRr3dpj+KqFV9Dtfb\ndDod1ra2+IN/9zmQEGUxhpLpNGJzcxPHthkNumxubFJqQWq2EYBrNXCcFoWYUPP6BLKgruaIoz45\nJfVmB82YPHLZ7Y1wKz530gRZGExp41UnbN7a4ZY1xhcBmRnyiSf/Jcqk1IqIYxVJs+ExGqbYnQ4y\nr+C1IE5SikzSXoIruylFYSiyDNuxCEL3IK/M86hZDp5jUfE9pnFELkDZDjW/gihKbNelFIbSFFRq\nFRw/IElzHC/A8QMKaSh0AUJQZIZGbYb5+UNoDaPxhCwp8SyXLCmIJgMKGSErEX/2N/+Wk2fO89LL\nL+J5IWVcIUk1biVhd7yOUVXOnryPNM1YPXqcZ776LMk0Zbg9oIxLFk6uoEeayfYGepoRui6dRsDW\nzR2qyibOUowpcSyLVqNBlo4Iqy7zC0d4zYXHuHLxFY6dOMe50/eiInCdCqsnj/P5L3yWX/onH/3O\nAdnf+me/+cTRY6sYNGfuf4DH3/VuciRra2s8eO+9CFdh9JQ8iyi0xlIGqae4ZkphwHZClBVQaEOa\nTIijEUrkdPd3aTU6eG7INC4oihQlCzzb4NmgywmZ3kKXmrp3moa/gtYJhiG2l1OIOtK20YAfVkmT\nHG0yXNsgTZU4j8Au8asdwsZhJpFkkk4pSejubqPFFC8ocVxFq9VBKEFn3ube+17Dg488yNL54zRW\nD3Hi9FkefPS7SHXBeDwgun6ZjecvI8KQx975DtxejLPZ5VinzedvXYR2jXiaYEpY9mbYS1KeVxl1\nu4mymvhWmwv3vpGVw/fQ3Z2yNHuIar1Nb5pSbdbY2t0Eoak16wyiMbGBXCkoJXmS4tqKOItRFYfW\nQovr65f45ktf4s7kZVIX3jjzWlqLVTJbYCce/WIAIqHTbHH3xm3uXL/Nwvwc7XaDb730TaJoiG3n\nFEUGWLhugFI2WR5hO5IkKvn1X7ufx9/4ENboMk89dZd3vuUsO9sJPkMeOlnh339ace4RyRcv9Wke\nh8/8UcyV2xZaZq9yA+I/btJSGCURlqKUQJnjZCVOYZCvKhalAAdJYqDiOLhSIDHYjk+hbMZFji1c\nZGiRyow810TdjGRY4oUNUilRnqQ2hXBpkX5/n3wwRKzOcOPWLSaTECMFqBzHk2zv7KALjS4NtdBD\nuBNG4wTblYiyRpkpjJAMkhtUyhJPh5SRRpuC9mwV5UgGeyUmaXL27DzXp33y0KaVQg9oBDtkseCV\nlzdoTEKCisX67nVOHjuGq3PKvV18y8EOQ3obdxlXEpS/gGx47Gd9TspVbt8e0rYrWEUJOqWwCmJT\noh2HQRLT1hKRF0S6IA9c0iyn44SoQjPKI+I4wfZt/OCgMMo1oCziLGeUjZG2IIlToiilEtTJ8oKF\n5cUDuWA2ZjyeMDuzAFrgVm1ie8x67xpXXvkaQRiwu7MDaszcYpW7G33e/UP/kLjwWbvyMoPBgCOr\nq1SqNerVGt3NPcq0wGk61KwG6f5d9CTHkoYs6ZMNSqoWdAeasoio1RfY3x3SbNu0WrPocgYlbT73\nF3/N/Q88QGCHDDe7eH6IW6/ymc99kl/5b375OwdkP/bx33zCqrs0wjlcx+H62hfYvPgtHj73Fuxj\nhxFSE5VjXFHgpdsUw6sIKcnlMkbnaA2UFayiQ5EKHCdhb+cmeZxQDRtMkhiv4pPlExxLEoQBGsUk\nmqB1hSBYxK94RGWX4WSfLCsQuiTzXAoShCjJixzf8cji7GD6y9KUGGwnwGiF0hpbpqTTXUQ+wPIy\ngqBJtXaYamsOnBLhZNSDRUpd0ptMyJICkRuQEi0tmovLlJ0Ztq7cpNcbEY8HHD61xJ49ZT3Z58bO\nNsqusLW5BY5Ho1LjTtpDq4LvKnyWfBdlMpZlSX3tCuKVb+BN7lJu3qTZH3DCdjn10E8hqjbTXpft\n3S0mssBIhYwME5XhVTxSDBOTMhyvc/PKJfqDLaZ6SqEk840jnA8O0y6qyMTQ1DZj1yXMK8h2G+2O\nMWsp/lhw8oFZXrpWcHfja/x3/6DF019t4NYM6WiIU4aMTZe60nTCZZ66lPKRn/4Ajz6wwXs+8izv\nfcBmOL5Jp5zn95/VHD3uYDsxyspQYclXvtli7U6BsDWO52K7DqWAXJcYo7GUwpKKzBQEwsYvD+iA\nAzqhJEDRUg5+LpkLA7aTARUjaZWKO3aOyEuEAtt18L2ANC+IdcpIl5S+zVRL8EKCmXkGG1s0qxXG\nRcHNG5vsdyOaOEitqTgVFAJtMrBKsjLCrwcMBzG2U6KLAoFGIMCUuKIJKkBWLMbxKUzFYXcYMswF\nA2PRT89zfOE5sszHKaoYkaEG2zQHmrWg5O56HdWS3Jl0Wb90Dbu6ijezQmXa5YXuiFrVYbRvseAO\n2bNGKKfCubRNZ+46V3qarTJGEtD2WuwMugTKpuaEDCxFYUt00QcRMFETPAtaiYcnYrqxIW45BNI6\ncLVKS0ptyPISgUBmhsBqoagQ+jUaNYe6J2GSoCLBMBviWi71agfbkvR6Ux46/zbcYpEsjYiTMUEV\nsiyj4i3ypkfeQTSJmJ+pU0Y2d9bXee6bX+eRh+9HKc29r38I0woQ8YThToyOS3R6h4YtGdwGWbhM\n0y7ZYIdShJgwwPgGUbpkA0mWjFHTITuDLvPNOcpUM6272FUPMYl45uIX+K++kyrZj3/s40902g26\nRc7u1gZqcwffqfHd7/9JXOkhvIyiBEVOkqwzGN9BuQ0a7TNIaVNqiygpSfMpUiUYnTIZJtTDWYQQ\nGGXhVjwMBVIUGDRJkVEJQ0pjKI3hgM3TCAOO7eC6IfnwJiIZYxmNoxyUU6VUBi1KbBlgUNiWh6VC\njPHIU0iSlDgq8LyAWnUOKRySKDoYK7QVeS7IixylFEK+emO5HsPxAMtWLC8tUpupc2d9g36vy/6w\nz3g85tDCCq12GyMVO2t36PhVstGYeeHxyNwq+STiejbggqnjjyeM4ojI0tQzUHnB0MQUSUJ1f5PK\n6z5ENGnRWuxgFgTLnQ5OX7Moq2z2tonKGL0fc9o5wsnOKZJexExQ4Z218yBreLU5Tlw4R5xFpNmE\nXSaoSkEx2KIZzCOWcqaqSdJfp1m5w2j/9fzXv/40v/+/GXJVcGhBM4n7uLWTNMwMJhhBcIzn/nyT\nhx+/wN6upoxfgLHLn349R7Y1gTtPqQ2lKbm7bvPpT04pVIrjeAhxMLVltEYASkikkAgNNgq/lHgI\nBAZhKYqyBKPRBnzHYzDt49gOjrC45eV0MkUsNcM8JysS4iwHKV+NCgrp9UfYrktYrRCENRYXDrG5\ntU2RZ9TrdaaTMa4yGKkpTUFe5JRw0PRybGzLIs8yyvIgVEkIgeN4gMAAhX2EvW6C8AriNKbII2xn\nSOjazC0kbGy+kYnVoVHdZLi2jSUcKitN9uJVbt1YI+r32N8Z4tdDstEA2wzYzPfR6QKL9oDLJCzq\nGp1co9oNZitjqnrCNHGY7VRZargsz3ssHa4QuIZoPKE520FUbKbdKZ4KaDQPnPKq2sVvNtgTCZaG\n5UYbkecUWYElbSzpoEuD7XoYYXBdhyTK8d0G0TgnK1KifMgkHTOeTDBSsLS8QL3d4tbaXY6fPM1Y\nbrM92CCXEzJdsr2zRRrFLCwc4cbVdc6eXWF1doYwMTz7+a+gpKIY9emUJTo3KD1m4+ZFotHwwPFr\nuMsg3ufIydPsj0cYx1BS0qx2GA0m+J5E6IK99U3WNu7i2zZhtYLXCAhDHyHhqy98gY985Be+c0D2\n13/jN54oRElHBswOS5ppQV5r8Naf/Cm64zGVmgvYKGFQymC7IVawQClmAYHjtsjyAsuJKYoR8aTA\nt9q4duPAvtBx0Mog5IH3AJbGchTaSIQlsR0LYUs0YDQoK8DzaozGdzFGomwP26kinICg7iMsjacO\nfmwhBI7rI5BIy8K2HUpTEgQOtmWRRiOyZIBlp+R5hBYuYa3BxsYWJSVB6FMUKYNBjyBwKYqEsupz\n9OgqWVHQi8bsDQdMhyN0XhLUK6RZzrjXp1GpMS890v6QvgtuJaDMcr5lTRmoAl9KUtcgbUHHOPhA\nf3oD//Z15GDAYz/4Qc68+f209BJpZJG5MdvRgPZU8+Hvei8/9N4Pc+o97+GeucP8hF7lNe94B0vB\nAkoEbJZT9JEKL958nnkBsQdubFNv+cTBKplf0hItjp/6NC98seDE+YClxi4iadA4P8PuuEI722co\nh2hxgk7zOGt9zbtfX7Dce5YXNzP+9rahenQWt6rZvuOz291ge6vKX316wiT2sMMCJdxXQxVLpJTY\nysKSCgVIA6G2sXODLSTaGFCCNM8RQlIYQz0vuUZKRTlMy4IwK6lIi1MiYNTyifKUtCyp1pokqWY0\nitBArdWk3ZkjTku0trBsmyIriEZDXKkoSFCWJCsKCiOQ0qI0JY16jTSNSNPsoOK2LLQuQShKc1Bn\nZ/kmnWYH5exgh2OMXkaLo/T0dfYnNpETsLd7L6Zb0vJvY80pXtrVNJXDmcOKqxsJde8otujjNDTT\nYUKrVuPyzg732g1e8iJsLXFMTDrXwqlnyH5Ip+bRrNkcWarTrGtWjtY5ubrATKNGFk/RnmKwnoOE\nM/edJR6NcLXFODGYekEDj3IwQeQaZdnY9sFfW54UZEBYcfEdC51ronFJpdpglA7oR5sIqZhEU7b2\nttFKHAyGlIbQ87m0/lW2t/bQqs3y8lne8qbvZWlpkbt3rmG7JWu3XsZrV0kdi/7+gPuWjjG+ewMl\nEl6+egNXafJkSq1aY35xiSu3b7A7HLJy7CTrvW38UCOKjHZ1nm63T63pgM6JxlOu3r3JnZvXOXli\nlc7iDFId6LGf+upf89F//IvfOSD7P3zsY080qw0ODQ2drCRqh3zfz/0MYX0G5QXY0kOLHKVcwnCR\nMDyKoUWU5hhRQuljWQ6T6T6TYR/f6RC4bfKiRFhgpCQpIyQFWmdoNMpRFHmGsEC5EqEMpT6obpT0\nEJaDrrSwqh38RguvEjAejZAUjAcD4mxEXiTE8YiyzBhNemiTUqkF2J6FEEOMicnzPrroo/WUUmco\nt069sUDxarNGG00YBgz6PZI0AqPJshTKErsW8Jq3vJ6VU8doV+t87emnWd/e5tTD99Pd26fX3aPm\nhey5JdfNiLmx5opMsXKNb9kkaEbRmFRrjKWw0oLZIOBats4JnRJ98XMUl68wI11e3Fyju32d5bzJ\nL/34hzn16GMM6h26kxEr822Wzp9jb3ePWMLshXM8HtX495//Cy66+9gq5VB4jEpwlv3+LarWLLbs\nEY3naXYuIodf4FDnGDMn1+kPlsmyiH53g2ML300lr/Bv5iocWamzO/cY0bUN/vjFp7k0Oc7du4aw\nusVgLFi7tYuULs9/XTONc4JaG60dyjxB6wPVomVZKCmRBpQ+kG+5xkYYjWMduD+V5qAB49gOYKhp\nmMHjlltCljKLf7BuGa6WI+rtJsMopjCCWr3B3l4fx7MIKi7NZocTJ86QaQfXcZgM+2TTCZ6SJJQo\ny0cbCyldpLQRShCGHuPJGEsduIYtLs7j+8HBsZUlQoJlhtS8Ofb2NxmPM2ba9zCN28T2t7AqDvEw\nYzIZEqotWnMlO0mVwlRpmNvU7IgwmOXFtS5znsPUGGrtGR7s1HhlvEXFdSjbCabq47Tr1Nv34neW\nCZurmOFtdva79EZDOgtt4miEpw4c8uZaVa6/co29riZo1tlNB7ihSxalyCJEqC526VBzfDzXJUkL\npmlKYUBIhXBsRr0e1cBhrlXnzu2bjKZ9htMB0nWJxhOkpTh5+jSj0ZSyMGSTlGg0xa3WOLzyXbz9\n8Z/hwrk3cGhxmac//1e8ePGrFOUYbQeMe0MOzyxw8vhx3EbAdneLWr2OXfHY2RwSVppcvvYi12/f\n5vHveR83bu/gViwSM2Jr7SaP3Hs/jrJIdIpbc/jsZz/H9u4ejz3+JrY37nJidYU4j5iMR/huwJee\ne4qPfOTD3zkg+5sf+/gTx50WHd8j7fg88jMf4Fs3bmFnkoX5g+661gfVhzEeSWqTFwWOq5AiZjLM\nEaVia/02lrRZmlslig4s8PKyQNo2tmtwHENZJhR5ilAS2xUYk5PlMXleIAQoy0EoCyMkQVClzCZE\noy32927R29+n4tewLZ9CpziOc3Ax5Bk7O1tkWYzj/p3z+whLaBxlKIqEJM7w/CaW20EpH8excR2b\nssgp8ozhYID7qs3eqLdLEUUMhgMOnzrG7v4uC50WYejzjWe/yX465eSRo+ytbyHSksgU7BOzYIc8\n4yZ4hcCxHQaeYFhmCCOY2oLbdkZMwmvTI8Qqo7S6ZN1XCPp3WRtdYxBP+Ec/8av4M0v0XdAaWsSs\n3b2MffQEM5duENzdJ9Ipwcosr7WadF++yivjDfZvXeT62hVOn2xwfvUOOuqytrnB+sZXed/7ukjr\nBt3eYapzCdO+oRK4bF5v0zELhFubLDt9vrx9mfOzVd62knDp9j7WnCCelGyuJ7Q7MN4JGQwMnRkX\nYapk2QhTliDEAf0ixIHSoCgRpUEZsJWDZTtYrk3+f1H3ZrGyZfd532+tvfa8azzzcO+5c997u5vN\nbnaTTcqkqZCUTStyFMGWkjgBgsRwggSIQyvKhAAhHMlBbEfQS97iByGI7RhykESSNZKSLHHqZrPZ\nA+88n3PumWquXXtea+WhOs6jCGQAtV7qrVAFVP3rX9/6vt+n648abgWBcjHakDmarg0o6hrlB2y5\nEV0EZ6Ikix1U4JGXJVpb7EeXriurbU4Hpzx/fkpZNSi/xXw+YTw4IXAkjm4obYQQPlgPHBdtIGmF\nNLqkKEq0NhhjSJIWQjhURYXRGldJkuQzvHDj4zw7eos3P/VJdnYXTE6O6PJJVA2m3KSV7LPWm1Jb\nhfQtHXuKsj7VomGrK8hWfe49GrIe7SLUhPVoglfE3BYZW1GwTDe6IUwbjodzjrOUYnLEYK7ZHxQU\nxuXcxjmODvbJFhPW+wlJu8ODuym1YylFTbfTZj6YUFcOazsxk9ECaaGoa2ohUHGCloLxdIYxmkB5\n6KJkOj1h70KfhozxNGd39ybFfIrvBwyGZ2ys7RI6Pjvr2zhGc+3ca0Ruj7KoOT59zFvf+V16vZDj\ng2OkiLjUOs/s+Iz3v/s2s8WYtMm49NobDIuGuw/eZTgdYRzNNJvy/PSUWZbx2T//WZ48echssc+T\n20/YbK9zePIIGUryyiCEw6WrV/mj736LV195hXQ8ZjAckA9nrHV6fOO9P+ZvfuXP0JD9b3/x73z1\nUrhJ2XdYbId8/+3bOI3iCz/zlymFBzRgFFWdUpMiqNFNhrApTT7DVy6L8QRpNRvrq9R1RUODF7gI\nKVCexHUrHFlg6gxMjee5QIO2Sx1PCAVIpHBxpINUDunD71COn2KLU6RJcf0OcbKCF7YJoza+m6C1\nRFqJbio67S6+v8wS+ULT1BrHcVGOT9W4uN4KQbBBXswo8hRjGsBQVzmep2glMWWeczo9xbWQTue8\ndOMlHty+w6MHD/nYp98gSVp8/KWX+cM/+EMcIdlyYtati6kbZrGkn3nMRI2vBZd1woob4QhFXRum\n1rJh25wPDUfZCSbsUxKyb3MamTJ2OvzEz/0Nnh+d0k5TttsdKneGq3O2Tn3E0/uk2Yz+MKda6fB8\nOuLK1g77H9zhID9lPjjl6Q8e43uPWd25T+X8PjUHvPNuzLkXoJl6xO0J0zLh3r0+i6PnFPqUZ5tv\n8Hu6or+9ijce4o7mfNsu6DlDzqqQxHqERJw8ndIPfRazlDhwEbpGuC6e7yGVwgLWLnV1B4GQAsfx\nUJ6D47lIR1AWOQqQUqJ1Q4Fl4BsuND5uGCDLipW4RVPXpC0fpVzSLKOxGt/3iAKf0XhElIR4yufw\n6VPcICL0HYanz8nTGdZqjGqhjf3oNS1ZCGEckRf5EpzjRhhtMRrKoqTMciSCwPNpxzn9juCnvvx5\nYrXJwZMp3c5Fnh9UNFqx0vsEnjvF0XPKfEZVTfHninmrIira7JdzLm6t4vlwlmac6/W5eXGHllzj\n4fwJ624PaXtsbVyg1dmivxKyrhzO6pKktcUwh6f7A7Z66zR1QVWX6Kyiu7XOcFwyGM4R2pKenRH7\nMQtdkzk11gqaRlNqQymglgKUs3QaVDUCiadcgsAhait6G2v4fp80FUSRT60rgsBjfDLGFJqL53Z5\n/PA+k8lDjEx5+PRD7t5/jzhysVXFC5cvMzg+puV0aVxob/aZTYZUZxPKQcHFCy9wf/9D2is19598\niHLbBEHCD+58g+nsOYcHI4zOuPnCJ/CCmEUxptYNd249JY5iHCtY3dlgrbeCagRRmOAawdn+c97b\nf5+/9Z/8x392hux/93f/7levv/Y6C7/iaHDEaH/Oz/zcv04VKYpGkfgRQkiKaoYjBaEfU2dTFrN9\nTN5g6pSqGLPSSwh8GEz2iVoxlgAh82U9iR5S5QOqbAy2IfAU9UfcUc8JcWUE1kVrC1ikEmSjB5RN\nRqMbjFEIv09lLVaBacQSqVdUSKHxfMvm1gpKCVzlIs0S52ZokL4HysUYgetKssWEJPaYzQc0TUGv\n18L3HdL5FIFhls4QDQRhi8JYZvOMjfUt4l6Xj732Mrt7e1x46Qa3PrxNMZuzIgI2vJD78wGrKkZq\nQ6UgFIKkkRz6NQ/cEp1XNGGLxMBAGTJdsa4SjvI5tnFwO9t84Y2/RDE6QOopTQZJ4HB0+JDe2mX0\noyfk1Lixx6QV0k06qFyzSsjXHjzGc2ryheb2++vc/iBlMUpQvRkTt838ZMzGFcOzRzFPHg0o5teZ\nzA2UGXP2SYTLLh79+YzXVuHrzUMcq+inm0iv5OhuRdtr8IVE1D6OTAlsQu0KlFIglxQtRykCz8eV\nDkZrPJZarEHjuh55tlg2JKDR0uJbhwhFHbv4ac6xqvCFYmgrzhYZZ6MFylO0ux3quqapG6qqwfVC\nJBptIQwjsDWL2XiJwpQCLUCbCmsbDDV+6ONIl6oEY91lDY6xCCtphQnrK+tcOHeOq5evcK67wXpn\njU9/6nXe+u4fcPe2Zp7PcboLjOyBPWOSz8jmXZp8m5kuUZ2UbJZQmYxk9wrju2dshhpve4s6S2gb\n8Dd7PDp+SIiHE1yjv7KFEZ+mt9Imzl2yqCaQCaM0J88XjJ7u0++0mCwyYtXFdysqaxmMNUYbWq2E\nRVHitxVCJvQ2VpgOpwjXJWwlSM+jsWb5fZIBfhjghiFhu8N4XjMcV4zmC5AWL/SpmpwsnWJLg9GG\n119/lW++9cesbQeczo8Z5UOUr6gWFZHjstVvEbkNd7IxW+srqLxhOpjRXlnFN5KHb71Nf2eDfDHg\nxvWbRMEWcdJhY6uNI12kbdNf2cCNOwwXM7LSoAvL9QuX+OSrr5EPZ7zw4sscPTvm9PCEza1dpmdD\nBqcnPJk/4Ss//2doyP7yL//yV1deeolxeoItCy6/+Tl6Vy5xdXOHlo6pRIA12dIdQBdbtFjMhghz\njGtb3Prw22yshghhmE5PCFoGpA90sGKGtgUwxVTzJVvAAT9wUX4HoyWmcRDGwxE+Qqr/Kz6E7noQ\n91DxOmFrhyjuUzUZrqfRWALfw3dByJKqHuL5NeligpQNUlc4rkHLgsYUSAXKhbwYghUoZYEGzxMM\nhycs0hnoBulA1+8ShS2ClRVMt0XQ6XDt0jU8xyV3ChbS0r+yx/W9y7z/6D5Hw1PWS+jHMd8WYxKp\nWGkEhdCMfUOnctgqFKHnsSUrTpuC0nExlURXhmEi6ZeGH2SaG6+9ia7P8LMJLXeLcZXR6vp4C5/a\n1UT9NvrqGi4BItXITkw4yjhunTKdZ1SqReNMWBQnjMc+b78vaGIwzg6rJkVpxR/dCijVNbpFxcxv\nEa9eo5c8ZbNlOXpYcLxVcShO8esYK2dkI4FrDF65iqlnKEDUIXUzJ5MSBEtrllg6QwLfxxpDVZT4\nOFRVRV1XhJHPYjEHIaiFQfgeZ6aibR0qU/HELfmEWsEtLJk1pFYQJyHSc8mKAokkzwvCIF46HeoF\n/f4KYBmcHVFXSzZBUTZYsRywiBoEtNsdGi3Q2sFohaAh9ENarTbra+u4gO/5JFHIad6QVZoPHnyN\nOFzn+LFCqmeous/OWszo+WOOmwnTfEbU2uGn/q2f59d+69e50bFMN0LSD44wwqdlQnQZUfZanOdY\nUr8AACAASURBVB3dobt7ju88ygllSLQeEfp3ObmzybjRHD7bYmXzOdVkwfrWKmdnz7i+ucr6ep/C\nKmYnGUF2xo1XX+L+85LjxZySBm0Fjsgwc4/H40M2eis4jlqGQTDggOe7hEkf4UkaAc9PZlQ6Ic0F\nURJQNEOOT8dMZyOErWj5CdYazkYnNDYnjK5zPB7y9OgBe1fW6SYhO/01RGlo+yGzk0PKbIH0I7qr\n2wwnc4rE4F7qYp6ndKJ1ArVCVYHF4aUX3+Rwf8yX/sKX6fbOczA85HA84HOf/TJdv0PbgUAphk+O\n2LlyiWxe4kqfdJHhIfjE66/w9e//IV/5+a/8UEP2h+LJ/n99ds5ftv/Of/RVirpgZaNHlESYRuMr\nl3yR01ntM59MKMoBTXWG4wj8sIvn9vEDB1PPMM0U02iiZIvOykWM6lEaiW5SIMOXI87OzmglfWor\nabc6OJ6iaUIc6VFUGWGYgInRzQLNkDz/AGwPIVaoa5c0nZC0FXEYgTDU5QJd5uhqQVEu0FoTBBGt\nVgsZ9wBwXYWUkqYqqHRFUWR4ysHzJJaG4XBE6Ee0opjnzx8xm56StNdo96+B28VQoqygFa1RGwGu\noBfF5NkML3CJ45iju484fPsWv/Jf/20+T8So7fNIVqxVgtcyRRh5fJAdE7khp4Fhkc1Zkz5VLDiY\nzOgpkAqeelus713iy1c/zVopKadTgq2Yajemmc1Zi7awLZ/ME2yGbcazGcFqn/3vfsh3f+P/wFyN\nOCFD4LG6tsF0OOfenfcJ+5Zr13dhMqe1ohlPK6ZPtjmVMwZHB6zUktULCfOyoMgqPn5zHWE0Z2cw\nGGfEc4czSkIcrDVIY/GFw0JpslpgXYHxl3Q0acFVS1BMXhbEH9WIlLqh1W6hlCJLF9BotNEYISgV\nvO6vs5+PaHTNZrtP4PmkdYaeLpgmDgNX4xea1XaXBQ220QjXo8gXhJ5LXWusFqTzAikVQeTjEFJk\ny221042QGPrdFXQD1qRIFWGNwlhJbQvqJqMsFzieJkm6eE5Ar9PB933G0zknp2OUE+E5fRph6USf\no6wKqkVBk97Gtb9PfzWgH7c5mQ1YvfoxRsMpMhOo0GFoCq5c3OTZN2+z0n6dYHeX2XzA/ftnSP86\nH79xg0oe4svvs3//KauZoR+MsN0uw5Mpm+sRQmzws3/jHP/jr77NOx8WBKGgFynKGbi+4Sw1rJ6/\nRtCJabc9utSMD56TdSJcv0VZChZ5RlktGA5P8X2fIIhIkojByYB20OYT119lcHLKyeAM11Us5Ji8\nSKl1heMG5JmlqS1B6DKdnJEkDotUcv38azRNhR83pPO7mKLCzd7g2iu7DLMzDo9OUa5kPD9hkk1B\nhNzYvcLf/A//XW4/KPjg/ad8+fWfxIsfsf/kmAvXLrGoNfefPsF3XKqTIcxyeu0u/+Dbv8pZevpD\n8Q5/JDbZ//5XfuWrL332UyyabOlZtQJXhbTjFZQTMhw8R5ghup7iqZC98y9RViHSbdOKfRpySlsS\ntdaJ2pdA9pa/pGaZl3eUoSwLgqiD68WkRYkfR5iqpKo0jnSoqwJdlzT1gqYekmeHJJGFso3VJYZT\ntD0hCEp8tYIwBVKAQFA1lnRRkqY5VVVTNRq/1cVYg6McpICqybG6pkyn1LUmnafLpJIFJSTPD5+w\nv/+Aixe28Lw+OOHywoQGz3dpmmXBnxYFk/EZ7U7CLJ0zr0v8Vo/zV24QrW7zm3/yv9KqKtqm4Qkj\n7nlTlKnZ8TqMm5KajJlu6GmfJ2HIRm+Pnc9+gVc+/5dxG5/acbGhR7bdxrmywdbVPSpREwFG+uiq\nWYJ4rKGHYvr0gPf+8JuYdIDXhJgkYihLJpM5q/0WbqI4eD5hoc5Y22wxU6e0+mt84fW/Rqu3wWxy\nj9Vul7jj0NiCq3s7XN49x/PHJbcenlKhMbXBsdDHQ0tLg8WXCoOgFAItBMaRS46vbpbJKmPR2pCa\nCida/lUVQtAUDdSGwAsIvAArJS3hkecLZi7cWNul8RV3T57T0wrf8SiEZZ5X9OMWbgPpYoEXxxRV\njqsUs2lGkiQI61AU9VKDrzPKqsZVHmura4ReSBDEFFlNkTdMFmfM0yXXwXFdpIIwDJbWpaYh8EOq\nsqKpa9bX17BYZpOUsixp7IzazBkehazvKqw4oSyHbK1u0b3QJW7vsX86RIgek8lTdOET9wIyrTi/\ntoVYPMEWr2DVJSazU2T3e4z1h+xtdplRsNF8irW1n+OCSvgv/4NN/uk7bTr+AY5nyZ02tvmAK5tb\n7I8qZmmDawS2VaNcF+PlrF7YJbKGQXrIYJ4ybW3Q8kPKqibNMrR1CMIerh+hfAcrCo5PTqnKkmyx\n4OjwiPF8hFAGqwzD6ZxJOl0yEeoKkLRaferSEkZtuq02l3auMZukrK73UL5lOjtie2OTf/Wn/326\n6wlHw30eHzylv9Li6gu7SCUAh8lixOlxgsThMy/cYLczJG+6eCuK9fY6B3cf8RNf+iLbl3doKNjs\nt2mE5cOTO3zlh9Rk/590fP2/dmbjU+5++5+xvXuZMLwMpc80W1DlHqAw5hRrh5yenJC091hU0Nlc\nQ3kCW9Z4zhrWa9PqbqJUn2xRI02FcCqsXl5ALTLY2t4iy0vavQQvcJkNhggsMnZQbghWo6RGuQIv\nCJD1Gk2TUtkTpF8ThhGNhWl2QF37OCpAqhgRt4i9Pm6dYXWFEBpdlUvwszVICU1jUY6LH3UI46W+\nZ5qGOFYUeYqjfM6dv4jGpc5TqCVOaHG8BFdF1LrC1AWz6TP67TWqdIZoXNywjQpCsqbktZ/6MY7v\nfonv/frvc6GJWTeWReLyYZnxoTZEl6+y5Xr8+KtvsL53iTfPn2evc57VVz5Gc7JArP8DotUegfSo\nohBHOFjtUVYl4UoHrTXOSpuGmvG7D3BX+hSiYR5q4o2E7c4l3KTm6eQufiXJpxNU6HLj5c9wsv8U\nf2UFEWUMRs+4q94hvvwJuLXLdDDEmU/4zKVLlJnge2/d496zCZaEokhpIsVuBkiBNIIayxxNUltS\nT2HQyzCCs6z+NsYuq2kM4CsQAqsNurZLhoEXIqX8SGOt2HBizHoLYXLk8ZQZGaLlwbwhSFpQlngC\nXKlYDKd4oYtxJFpr1lb65GmG1hpjJK7yl7q+UGDB931836fOa/KsJM+yZTFn3CLLCsqmIisLeit9\n+v0+1lqkv0oYhBjPkOUp9x8c0jQGQYgrHIQc0u20qZyUw8N7FIshid/h+YlP1VujvfMKxmQoExPL\nPrUQpPMabSzDw4aViy/y8Du3qPUDHtcHxHIDMX3Ig1sFnb0e81JyNLnNX/nJinb/HYT3OY73u8Rr\nJ7RXhzh2k53NNS6cr9mfTJh4mmxe8lJ3HW9tjXy6IHE91t0W40qjtUOWW4TrkXRjur1trA2Yz0bs\n73/AcHoElUOgIvq9LokbMZtPQBqk0/Di1c9SmxzHN5wNDpnP51RVxbXLV6lrjawse5tX2O1Lzoan\n1I1DN3yBzZULDOb3ufPofY6On7K3tYVnJGICf+7a5ygzn852RHlaU52NOClOmQOrL13khdc/x+3v\nPuLKq69yNp9xMjrg9OgQfTbl6YN9bPOnYrL/xfmRGLI6r7n3R+9y0H7Cys5dkv4qbhwTJAlCKuKw\n4uDZIVeu32Tj/HWOZ1POr25QlBZpNVGyg2ddrOOQmwajMlxHUBY1Svoo11/2UeV2ORQdlh9aJ6Zp\naurGoI0AFIgGa0Ebl7zJqdQArIvDGrWJwGqsO0DnFqUUnhfh+RHKccAk6HqBqTKqbIF0JMIRGBTS\nCVBBhBs6+MoljCX5IsORYEuNH62glOT45AiPGVEsWSwatndXqVJNu+0zn+7TCQJmZ6cosaDd3kCn\nY0yVUeiSdrdD95Uvkrw1RjkBn3vhErlsGLcDdq6/zAY9/L1NOq7LSFVMR2O+/t4f8YWdhPffeof5\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F7UXN6ahkrsCpBW7QwwqPOPEZjfbJFxOUG2BlRDnNkMKgPEEU9ZAywpqIfvscsb/OZDLE\nd2oGpwfMphMQMC9nTMshdTMiqwypHjCf1jy994jdi29y4ZUf41z3HOe2XyXphByf3mE2niCES7K6\nyed+8udoC5/ReEo3SMjz7zObn/DTX/73WF+/SFkZXHymZcNrb3ySajLn4OQ5Z/mYeTXlF/6L//TP\n0CarDY6UBJ4EKcEu462uBlFX5HlOu9Pl6XDMl//lv8qVC+dJqznFrOLOvfdZX91jY92QLaY8uHOb\nOtthtbfOlcsvLjdDJdFItBG4yiPprNLqhFRVheM4uK5C2wbd1BizdAM4joNhgSMhz2dMJkM8X5Ik\nHa5feZH5rEDLmqox2FIQOZowgO3z5wjDNZABTbXMuidJzGR2jJCK4WyGlyR4wTqRdVBuQJoPccIY\nqyxHowc01RDM8n27nlpe4gQ1i+yQte6Po7ya6eI5s+wM15EshYg+cbiJrSsca9lY7VEZiREBd24/\nopOsEHsRrvGo8hZWR1y4uMXR4V1abUW7Z3hyNKbX36HTXmVSLpiPx+xc2uIv/hs/w/m91xDSIVrz\nmd0qaIYZvc/u8aoWPDh5ir/aJjwqOTs+YnV3hb5wOJgMmZUDFo+esvfSOtkBNPMByfouSlsYw43N\nDcrLHdzb+1RPclQjkXVJuNXjrJ4QCEFaV2htCD/qCluRinlZoGSDZFkLU2KRxmKFXUIXhMBKQ+WA\naQyNqaFZauFCOXjSQbiCpiqJHY+ZqcmlwFOKPM8JXZdjp2S93YMGBvMJraTHfjrgXKvH8/0DHKGQ\nykE0llAE5LbGFR7KDclryWrsEVlNNm8wTYSyXRLj4MiUIR2O02P8FUudzdltb0Jeocqc0WBG0u/i\n1w2uI0iCmO12n0leUmJRWgKGIPLYPLfNcHKE5zQUZc6iPCWnorIua+E6RaFw64JAaVSZknoPmU6n\nbPptNvvnuNsYvCrD1R+SYFiMatyVBOO4RK2aihmz8YCgCUi8y8TzKQ87FxgsGi6IMRv9Nq2uRa3A\nSnOek/F92ni8efUGybUV3v6jY+48sDxpTpnOJrjRKn4QkRclvW4fRwSIRuB4zpIJbGqKoqTbW8Ep\nCtDQYIijhNPBM9789GuEccSffPPbIEC5luHiCVk5IUn7vPb511GEeJ7Pyy+8zLee3gENl164zsHj\n+xhH4YYRFy9e4N69W7SKgnObOzwffEj6LOVTL36Jb//z32ZeRHQ2tkg8H1yXyXTGtesv8O33v8Pj\ng2c0fv1Dz7cfjU32F3/xq72kTVlV5FmBqBsCa/CNJRZLyEtalDQo+pubpEXGdHLK00e3effDW3z6\nU59hfW2NZ08ekC0mnD93jtFkjufGOG6DFQbrREgVIKXCGg1i6WEVUmLRaN1gTIMUBiktjgSpCqp6\nTllOcWgYjyc0taDT7iGUQrogHYfAC/B9yWC4j3AcwrhL4K3jyYCqni7lAmWJYg9DjOdtonVE2TQ4\nvmSezwmimMl8jvTHSB3S765ibM1sWiJEl9lshuNp1tdfYDYf0jQVoR+jlIcULlI5lFWFNCUYjVIC\n5UhwJE1jGZ2OCN2Y2fAp+Iqo7WNEQbvr4LgFZT1DxVsEHihZYJqGugBPtUjWV8nDBm1i0qePCNM5\nra0Nrn7pdd77jd/DKJ9KStTEMqtLlDJQldCK2X/6jGp8xsbrr3F//x4v3nyVbuzzG//7P+FTX3yT\nOnSxfovzq+d49MFtAsdhupgwNxnaE/hhQLKyAkqwWGRY3RCjcM2SB+A5wXLQySVD1lEOjiNxlcL3\nPLSuoWkQjcHV4CLxrIO0FqEtVcunLCsiren6CQObM6srdj963jU3Jp3OqfsJP5gOuHb1CmK0wJ8V\nlJ67bMVtGmgg9EKU4+MFIY0jaCPp1y7rbpe230XUBr8p6UrBVHUY1if4XVALS7vy0EWK0g3d7gqP\n9+9T5TPWuiuUaUmTaULPxZOGnaCzrD6KwG0JIGdrtc1oNmOymKIrQ2xDumWAORqzSghVRRRHeO0K\n12/TDOf4cQ+RfAzTGCKh2Oo6tDsRaT6h1Q9IAsl2v8vxg8dc3txl5DjMwjbzqIstR6w4M1+a8wAA\nIABJREFUhoux4Eq04HQ8JR05bOz4VOMpSr/E08PH3FwNubbSZev6BoGnGI7nLCm6ltFwiJIORVmS\nhMFHTGdoakESd8EK6qqhsoJSZxgyxtMTzsZnCGcJBdLGskhLPvXaG7x+8zP82j/5R5xM93n27Hu8\n/9Y/J7AwHudUpmH/YB9BwGw84O6d95FGcfPKFZ4d3+X2g3f5cz/2U9y7u8+5i5vs7F5hURvSwYzV\n9Q0aDE1Z8HxwxOb5bab5iL/1Cz//Q22yf2ol+P8fxxrDIk2p6xrPkbQCn0Q6qKrC0Q1hHCEdD+X5\n/MHv/S5f+83/jfsffpcHt77LztYmqytdZqMz5tMJO5s7hGFEp90jiiLqsmI0GJPNC6RxEFbiKIEx\nDZYGhEYIgxQ1UtYICrA5Ri8oihlVNsPqiiT28ZRDnqVk2YLQVbgCXAVRGCCBLMs4OTlhls4JXEVZ\nZSBqhANCujzdHxNG20j3GePFB0hvynD6lOm0pt9+g1ZwA1OuEUTnMfIC/bVPsn3+M1y7+VkuvHCB\nrB6TT+cEStBUQ4aDe2TpIUUxoS40vc4OJ8+fYUzG6ekz8nyI7xrOn1tnno74k298neODu3zvva/T\nWYOwXWGoaCoP16zjacM8HTFZHOO4DcqNEeE6qtunchZIaSm/fwuR51z8q1/mt3/rN0mLFOfJBFUb\nLrx4lY7v4xeauSORQcLLu1e5fPEKz58/Zzh5ynvvfp+3v/cNti60KeqGunTpHApu7L3EwoJO51yz\nHmuDOQynzKuC9aRDf2UNWiEZBoSk7UfEbkChlwb1qvrosamptaYxZglk13rJkZUC6zpoz6F0BZkj\nSB0YLFJcIdkVbdpZg1dbpCs5FjXZZsKgzhDK4Xh0Rui7BFIxHY6WkpKusNS0Wi0aXeMIiRv4tHpd\n6nIC0/+Tujd7tuw6zPt+a+157zPfc+exb0/oRgNoAARAEARHUaIohqLKUkpSHFlxRXpyypVSElFS\nZNGSK0+x4jiJk7ITWbRLVjQ4lihKFiVCAmfMQAPobvR0e7jzuWc+Z897r5WHA/mZj9R/cKrOOd/a\n+1vf9/sU9tCnltepIjGJyaIEQ1XxlYQy5eT4CF956DzB8WxULjkZjbBdH8tz6I2HKNMkRzIYjhn1\nupRHPTwNrm+zd7zLxz/xHFXPASXIQ5NwWoAoKIshW4t1nnrkIVwCTg4y5ANJZkguPP0kRTrGMCbM\nLbVYXtmgtbyEtFJqtQzyKXV7jltXDlhf3sL2CsxlOKnWmR5ep2lLojzGN0YcnIypZgGGV9Dp1jna\nTTm9vcnP/sIv0MtHCNNjo2bx+U89z7mtFU6OjllaXAWlcX0HjIxSKtI8pVAFruu+nztWFIUiySNK\nnTOJp3T6EybTmH5vSuC3+fn/+h/yY3/nZ7CAjzzzDE89uU1neJvjw3tstddQ5JimZGFuiXp1njRR\n1OtNtjc3ObO2xItXvs4f/NW/4QMf/yRnnriICHy++Z2bKCunUWvykQ9/lKtXr9LrdbEdk+eff46t\nrS2Gw9H3rG/fF3bB31R7fcdFKI1VlJh6tiCQFjBMFakyiHTOhQvnWN5aJdMpSpmM+iccHTxgvr3M\n2bNnCWoB3W4Xz/O4/t47zNeblAracz46zxmFfYSMqFQgz0os+/1RPZ2iywylcjQlShWkGbi2BaIk\nSTKazTqWk5LnBck0RckUkef0Jxk6DTGlxWA4xD7osbZWkBcxwsyIY4FluqyunuP113fYXhV4lstk\nkJAWMWsrWwx6fa68+Q2k2cMQCywtbnJ8vM/CwgqRaZEzhTKl272K0imIlDJPiZKMRt1CGDF3d94g\nL1KKIuP27ZsYtsfFR2yarTXOntuge3TI3u4xP/BT/wVFZjEaJEgFK4sbUBjk6auYtofrL8wu6q0c\n2/Zwszk80+fW9T2sKORgcELy+puonR6trXXU7RCmEb3ePlmWYNVdvFoNYVRxlWbQiZjceIsf+tjT\n3AzvkdfaxLLHYrTK5K3vsj1qcJhdJ7YMrIZDfzhm2amzYjgMeyGHwzuoqkuaZWSAWcT4erYjkGHM\nml+UFFoj9KyFpzRIXYJpzKbIS4USColAGBLDMNDSoDlImHN8dtMxDcPlUX+RB5NjopUaH72v8FRA\nj4IDpmw9+gi3r75H03VwtaTMQzIFG4ttHMtFChNtWPTGfaqFT92s4hGQxQWJLjAsD3yTwvWw4oi2\nU2EQSgK3QndwG19WcKhQDRxcx8K0TZI4JfDrDAcjkjDClhpha9IspncyZHVzkX63R1GUWNJk3qhQ\ntGuIusNAKqrz8+zrnNjQqLCgFi1wx9tnNwmp2CU19rhzuIfpNpmz6vQmR5zfXKBheTSMKkfDPq2l\ngNvHd5m4c4jwLmdX13jvzg7PnXdorFYYyyW8okfpN6gsTFk5N8+ff+df8Pkn/09+7pd+mRf/w4v0\njwdceeV1nn/mGQ5OQnYPjkBajMYDKoFDnMaYpiQrUjzXwHYtsiJ9v7EJg2H/P7X28tTEMWtU/VUq\n7jKf+8HP8+EnN/nSv/i/+Km/+xPc/T9ucHK/j+sW/PFfv8jP/f0vEOc5ooTtzW3On1qkc/cqyWjM\ng71r/NzP/z1WV7b56l98mQe7O6wub3Ow28GwKlw5eoNnnnycmw/usFCzGedTbt68iWVZ37O+fV/Y\nBb/+j//xF00hsW0blWSoaYKDnE12CEWsQLkeq6c2WV5bYjjq0h30QZjkRclJ94T1rW3eu7PDYDBh\nGsa8/PK3mY4HbCyvk2YphmWCUNiWQKuUMk/wXUGWxEzGPaJoiqTEMgFKsjTFsSsz2AUzKpA0TKRh\nIQ2Lql3Fdixcz6fqNQk8H8s0GA1jPH+e1vwqpZJokTI/t4KQAUUpqNVa1NwQy1SkiUFWaAa9FN9q\n0G4H7N3fIxrcYvf+K+zuvsrR/i10UnD84ICabZKqAbpMiMMUkyrtxgqqKOj1H1CWPebmtxDCIopn\nh0SJSas9z72dOyTRmKefeYTq3DZaCXxX4PsZUXyPcXgPnwxlmhRlwXhwSBJNKZRBGhe4QM23Obhz\nFyeo4PZLjCjnreM9Vs6cYXhwSHing7nZJq+ZhEcjRuOU6XDE9N4dcqGIjSHDIkc2PGLdYdN5nCo+\nk8Ua3sOrvHXlVaqDCfOGzTCPOchjYgOMcka/SlSB6Viz9Yli5seWrguWgZICbYiZpy8EQs4sAy9O\nsbISuxR4pcDDwFYCWwmcUrBk1egmU3LPJskSzEKxvXma/ZMOr+shd62cuypibnOL484J09EYA0FS\npGALMADTRioIh1NcJyAKQ85WH8IqCqQsUIbAqcyq0iUKZWSzWXGnSmDWsO0KuVlSFBrbdHHLDFkY\nBGaN8TBE6Zx2s8pys40vHFKvoHANvIaPYRnM1er0Oyfc2XlAUBQstVZwRRVynzDKuPLW26gkIzAt\nznsbvJO9i+MvU05TBrsDXKNCN0moNyS+42IrE+JZw9CwBIe9IV5jmYPdiPZSg2sHBzx7usXz59cJ\n0wSS+7S3NyhbI9KoiuVWEBwz7IHjtbjw0GXuv3ef5z72Q+we9ZhfWuGb3/oGtZpPqVLyIiWJSzzf\nI0pCwKDWrDGZjsjKiDAd4/gmZakI/AY/+tmf5LM/8hNI7fDgzh63d17mm9/4Ex5/6gIvv/VtXnr1\nTWrWGSr2Frc673Dx/GNUg4C7d27TqLV48/WXWVtsQS5Q9Lny2ru8+PUv41CnHsxTZEP6nYjMzCnD\nkI3TG4yjEXk2pbHQ5PrOLU4GR/zS//hLf3soXL/x67/+xTTPMKUkkA6eljiWQyYUiQHKsHDqdc4/\ncoGsTIiiECeoIawAKTOyvGDvqINXaSNtnxs3b3HwYAeDhKvXr6NlwdxSi3E4QVomaZoSJymmjomm\nY6LJhLLIcWwLz3URYpZ7rFQbpGmMRuG6HnGqKZWBaXroQs7EFwvTqGBZHuNJiBYeq+uXqM+tUqm0\niPOQIrOQwkEIhWkkmFmFQmnKskWjuUqhYTzpsX/4OsPBEF0WpGXCc89/jG9++xVs1+dTn/w72E4T\nx5+n1GCaJUImDEfHxHFCu73O9tZj1OZWqVQXOHfuMbbPXKJSb9FoNrn2zrvkaczWQw3iHBxHIeWA\nPN1nPN3Fr2oscwFsnzQJUeMulhYIs4owLSyzROcR9w6PWTz3ELsvv8Pac8/RPneO4f4uy3OLEJfc\nS0ekZAS5oEBQrRocHt1k7tRlvvP2n7O8+hjd0QGFOGGhcRnPW6RyeZ24ZvDii1+jJSV24HBilhSu\ng1XOmA2F/pvLq9miRKlL+Bt2gVZkZUGpSv4GdySZbX05WTEDymhBYNg4WuKVAjufiWxmCHTFIZ5O\nsG0bOVdlt3PInHAYFxn9skDON/GqdfZ3d2kFVUylscqcrISLj1xmfm2TQPpk4xzXcHAMj2WaDKZ9\nEiMjNUos2yWNEtI4IlMJ1eoiVirQeYGoWayfexzSAhjT8CykdkkTibQlftsmF1MODw6wpIfeskl0\nhmVYWAIOD/eot+qkYYyd+zRVjaWsRjO3sQtNGecs1NrUzRphOsWuCuKRgRU4VLJl0mNJApTjDq2g\ngmsLOv0OzaVlDvsDbu8fYVfnaLUaXN3b4aGtBufkhMPdE+rVOnNqRBwZmNqgknhkmUt00KclF6it\nPcW4OCKgirAdStNgOB4Bipu33sN9/xLMkBaKgixPKZQCFKNwQKETEAo0TCch585eIvBa3L5xF9ey\nKYuEmm8xmSr++M//lM//+N/l29/8LvVqQHe6S5ZElCH0D4/o905QWjKNh2hR0O1P0bpkv3sXr2mw\nuPQoFy89xf2jGzi1gLgMSaYTSlEyzSaMhj3euvEOoyxlGo34pV/53nKy3xci+0//6f/8Rc91oFRU\nTBezgLLImRQZuS2pN9pc/MCThFnMcWcfz/fR0ibJwZQxaVFy7tJlPvapzzK/ssFCu03n4C46D8nK\nDGWW3L5/l+Nun/bcAqoUDEZTwpMHUCo838f3PBzHQUiBVmJGO5QzsIzWGkNaGGYF0/QolYElHdxK\nhbwUDAYxSVwwjSNWN87z0IWn0MIly2ZIwzyXaCUoixDFmDJN0WYCRgthVrGdCkEQcHSwy8PnPoEM\n2px+6INcuPQZTG+RVORsbj9GVtZJ8h6VIEAaJkUO1coci0vreEGNoizICdDaIk0kQrjUai3SNOPc\nudOsriyzf3yX9uIKhpHjGDnT8YhWc51G7RSdpENQ38aMfXS/gyh6KF+BU2EyKbHEhMCdI5urI5Wg\n/tzTHB11sK/dZZrmdMMQWXew0xgEhFHI4PAeWT2mMV4gGo2wfYuFtU3G6QGb4sO0hSBo++x29rl1\n4ypt0yIejSm0Jo9CtnSFfSJsy8LBwNSCUhdIYWI5FmGWUyr1/h9zBuM2EFhyVl9OAkluSTJjBpEu\nJbOIl2EgLBOpYTrt0wxquI0qB4Pu7EdZKmrSA8+hWWsy2TlgHgfXNImKhBJNu7bAQ5ce5eBkSDyI\nsQsTnWpsw8GJYxIBZmOOUTShiHuUyRDHdDBkldzw8UuJFBlyMSAu2kS9Y4Q6IiVjHJVoGaArFr3i\nkNZaHWFYDEcpt8sdtNa40sR3LEpdsraxykKryXjXQY9K6rbHfvc+d0cPkK5LVdWpJ016fo4YT3Gl\nzYl4QHIiWXV95tdt6ClaDZNJfkgsc46HOcfdglJKai2XSfeQc8sBC5M94kGC70qG+ZRj5uH+fcq9\nBG8KsUxYCQqO71/hyc/9D7x09Rotx6Az6vHUB5/hxp1bzLfbHB106PWHVKoNbMtiPBkipUAYgtF4\nRFZEaFmgytlKretVmW+vUKs2ybOcyegErWLu3X6HR594jsvPPInnLXHm1FleefWvSeQxjvKZqyxg\nlIqTk2NqrTZKltzb2+GZZz/K/MoZ8mCXYH6V2txp7ux1efv228ROn4997JPcfPcqi2tLNBdb3L9/\nm6nKOP/oY+zt7vCLX/jeIlzfFyL7z/63//2Lz3ziQxzfvMa8mgXDU89hmsd4ecnpp54jmyT09nep\nr23zxI/9LNQW6HX2ZvXUQmEtbjK/dBZbldj1Koa22dl5Fy+oUyQJllZEoz6GoRFWzl7nLrffu0sp\nBfWGi+OVCCNGmgZKe0jZIDAjwiihMCXSlVBG5ElBu7GMs3ARjDrSqOL4AbVaDdOyWZqfI5x20CoF\nZWAZPiVj0nxMkUtUOWUYO/hNh27nCEdVqbgGo/4ucZyzuFKhubBGtbVJkpsstRpcffkbuGJId7jH\n+vwjHB0fYpg28/MbCGmRi5jSKMEJ0EVMnk+x7Zw865NEPdJ4jO04NNrzOF4bqWukWY7rOyhREvgV\nKA1G49vMz12kH3mkVoamT9UyKFOJDBqkkz5qro53nLH13LPcfuctvLfvUC0N9NGE4yCjudAgPQkZ\nZxn3D+7S3btBcXKMubpOzWpz/omPMmFMdrjL+c0Pc9TfxVhZJjw+4P6VV0mnI6Q0sIQBwFBOMaSN\nfH8TLdOa/H1rwCgVSqtZXVMAcubvz/zXEulayLKErMDKwCsFbikwNGgESmuSPATPwalXGPV6VISF\noSArNZGC6uI8YhxiFyWFDUahqGOSAkHN4PqtA7phH1tbqHiMTicsBMscpQPy0sKUGVESc/bhH2BU\nwCg/wbAMTMMmlQZG1SMeHRAIQTgZo02DNE4wDYe8mJBEBaZa4nDvHhtnm+SlRX/aJ1AtNrYW+eX/\n/h+we3UfXMGaXeP60XusTXwGSrDcXuUJ7xy2WMSSJjkdEnOKm3tYpU2cSu7nOxRzGZY2qBQT9u2E\nfFIS2zWm/ZyyyAl8WHQNbBvqVkkxSShzi3FakOmUqm+Bv0JYTEmPMrxhTDIImVYXOFI+5z7w0xzF\n1zGFDeOcZy8/w0uvXMNtrTOKwfOqJKqcRSq1fn99WGMaNnk6Gz/N8xLbclheWmUwGDMY9ClFgbZK\nzCqMph0mwzG2EMzNLfHVF79CrdWgmNh4lYDG3BKj/IikLNESJsldqv46yaTP8uop3r25Q6u9wtap\nLWrVGXjq4bUf4dW3vsI/+G9/kXudhFtHbzHpHFJONYfDPX7ll3/lb4/I/uZv/uYXG1j07+3hVxqk\nGAymU4RlIqo2TqtGXKZgKUZKUVs7xflHHqfIcrqHu+gsI3GqnN5+CMcQ5AIC2+Xo+D7JqIvn2MTT\nIXNzVb7+za9hGAZraxs88/TTnD17nsXFVQQmaapRSqDUbLqkKDUYNkIJRJaTRJJaa5vq3BmUNP9T\nxtbzfBzbmk19myZRFNM7uYWkREiB61RRJagipci7oBJITQa9fbTqs/ugy73d2/h1CdSY9gfYpkVQ\ns0iSI25cf4kkm5DmJpvbTe7dusp83UEx4WQ8ZWHxYTyrQtx7QDHep4hG5OEEA0E4jQnDlDTJsSwb\nrRVZls1gKqJA6RBDmBjSJ56M8L02qkyROsZxHcJQMk001WaNKBojC5M4Lhgc9bnxwnfo3dll89Q2\n/mPnmewecv/2Ld7r7nH92jXCvSPCYspDn3yas+ZDbH/wLO89eIeD2+9RiJILlz+BV3MIheCVb/4l\n4eFt2rbGKQqyPMe2q0T5bK5ZaBB65rlqwSwxoMGRJsI0wJTYjoPvetiWObvg0mC9P6poKoGBwJhN\nDKM0KKUImhUqns+008cRBkVRkgtJTMni5gbT6QgdpdQMC1dLHCnIDUE/D1FRhqZC1a/jld7MRjBM\ngqDNGivUXIdEh7Q22hSWoD8coEJNXTawrTplqcnLiPG0yzRmdqGlxiilKXKJZxlUDZsLG+eIwhHH\n40PcuQbjcoxC0PBK7h3vMKm59DrHiMV1JvsniMmQO8V9rhQ3uJ8+wFcpa0Gda4NjlNvElFXM2KBu\nuARzVcqmw2QaMulGTBybRxunuT2d4mQGeZnzyKktiqM+DTEl8BpkhoM2BVXLRo8zsjDCDySVlU2m\n6gRbmeRGlf5JzM7OOxxkE3zRRmiTSs2ivebz+LMXeOX11zk6nJJFAbafMx72QBUYpoFtOyRxjO97\n5FmOlAbr61s06m0M4VF7Pz3k+wGSCoVKOT7Z4fXXXuLiQ49RrbV4+aXvYDoJcdGl0+0QJjnLq+e5\nfvsdTDOjWTuPawoq9RaJinjzzWt89od/mg88eYHV1To7+/e48NBlWsstXrn2Tb77nSu4eoVpGDHN\n+vzqr/7q3x5od8319EJaUg1qjIqSUJV4jkVreR6jMQsrY0i0UKTConTb/PCP/Qynti/yJ//xt7n7\n+ssE2+f4/H/2k9Rsi15Zsthe5YUX/j13Xvoa7blFCjXh2ecvc/7hi+RlhXptA2WVCC2QShFOe2Tp\nEEOWqPdfRwtjhHqfZG9h0Vw4g79wjlFsYeoUlCKOpzi2pMjeb6wYmqIo6Nx6gVyWNNunEMYaQVAl\njPZIBl2ywmPYPyTPIix7yq07r7Gx8WHmWo9hVxXh8Q1effMmG6cu8dgjj/HNv/oKR7s3gIhasMX2\nqXkuXHqYSe5g16oMBvcJjIQKJk7exKs1GCYFbq1OqjJcTzAaHuA6Es0GcT7BsRdQIiNXR7iOQ8Vd\nJ07GROMRoixRUhLUW6RxwuTkLp6YYtfWAYdb791n9NINaoOYhlcj82xWz11kd/8mneyEfjzk7vXb\nWJ7N2vIcfhTjxY/z9t7/S205Rwen2b70Kc6tbnIiC/p3byK0Ynl5Gcet0mou8If/7rd55ztfpWkp\nJsKbMS20QJompZDEKiXXClcpCmkwpSBDg5hNgUsxswy8ctYCmz0dzQbyLGN2QBpCMJz2CYRFy68S\nhyExGtmsIyou8e4xnrSIXBimEQvawbctwixhXrhEnoOjF1kImpSpIBchsYzQss5c7KLVmMBbQjo1\nDsJdEmFhenXGyQGVos7aRpOd3XdxPJNqfYskH5OXA+xiEdcOIB6y7lcwwgqTwuLA3cc4FRFYm6zO\nNbh//wotHfHU6TNEnmR3OOGhMw+ze7LH7/7OlzltnmOrscLt6U1OVA+sCuuFoJdnjOoeci5ATCcs\nKBsyCUsTWpMWVw9HtAIX0xtwcXuOvfu7mMpnba3F4KjHQrOKV8kxLYUsLcwww9c5bSX4a2eR+xGM\npgXLluSJU4rN5dPcMXNkZZ5q+yz7OwMunV7jI89eYBSO+Jdf+gP+4msvk6dT8jRmZX0N03bZf7CL\nZWjSrEApWJhfwjIrVCttTMOhKAq0LvGbBoaqUvVbWDJmEu6x3n6cDz31Y9hLBn/2ld9la22JTueI\nN668xcHhA5549Gnq9mmkOcDyWoTFCa12wGr7LLdv3kAxYfXcRZYaDh//yI/x6rUdvvrib/HyN75M\nxa5x2O1TFuXfHmj3P/m1X/tiy3OItMIWBqrIMGs+c8uLnOwd4dRWqC0s4tUbDAZDLFNw5twFgvYS\nnc4D7t+4welHHmVz/SyW0CTShFLSHRwy2H0AwmTz3AaPPnGZwSQmqMyhhIu0TfI8JcvGxMkxRTFC\n65Q815SFROsQLXwa86exKivYQRutbVQJvD+CWBYFtjnruVumRVkqiqLAszwGk1ne0fWqlBRIUVBx\n1zDrbTx3m/7gkHt7r+OaTcajnNNnt+kdJ1SqAY3WAq7tMt9s8PUXXuBw9y6tms/Vt7/LY88+Q27P\n4VeXKaMp8eCIZmWJ0bRKL91FVBxkpYJwXZQ0KIq/+Zw2eS4xLAOlDSzbJMtiBBLTdChtiW3Nyg2G\nYYFOiCb3iU52mQ+qTMaCkyKnldt0du6RFykyzhgO+8RFwWDaxSwTbr77LgQOH7h4mZNrNzEdi3Hm\nItQDdBBj1DZYdM6Q6jHjfkgjmEOUFnlhUoqA+wcnXLp0nvvvXUFmEwrDI1MlmVYoPXuSLTWUQiE1\nKEOSv++1IkBoKBWUWmGbJllZkBY5JWI2qGiblFoR5xm6zBGWSY4i0SVeUMFxXaJJRJZFmNogNmcL\nrL5l4boeiSoxS8D3yTMTRwqSsiDSIaEKKbRFJZFIx2CQSZrbpzmYHCFMAxcDO58yt7GJ4cPO/R0M\nUeHM+gVGvZCqOYcvG+RRhir0bA05k3iVeZSpOOjcwqlJjjsPSJOSTz/3GT730R9i+9IF9tlHZwaX\ntx/iorlN+4bN484Wb07usDufIoYjdomRlkfNbWILjzRJcFyFXQtZ00vs7kxo1k6TjQsuza0ShIpO\nUKWgjsrnicMBp5YD6gEIK8auFciqzVQ57MZT/NIm0CbVuQYPBh2G2RLHDElin4N+HzOoUK3VOT64\nz6lT81QqdZ5//rN889vfIk9j4jjC8ly2trcZ9rukcYyQswMyjCakaUy1WsW2bTzfQUpFtzfGrwQ0\nmxVOTro4Rp08ytm58zbd6AGn1x5n5+qQ+doZVhdOc+bUk0z6Ka25nAd790mLjIXFDUaTI66/900e\nPv8hAn+JfniP73z9S5x03qQ53+TTn/4p2tUtvvbCV0EW/No/+uLfnlqt1prcFIRpgUxjNjY2MRfn\n6E5i5pfP8NSP/jjza4uEcUjnj3+PZDIgVhGhrWc3yJbF2sYmQpqzp1BDkmYxvmvjBRW63SM+vP4k\nJRYLy/MI6ZAVU4rERBURQoUgUqSZI6WFFCam4WKIBZRZxw9WUdLG1AZlWuAIk1xlAFiGZuYFaECh\nVUFZljjeIo1mgRQ5Udzh+PiY7c1tMEwKbRMXE6q1FnPhOmUxJktTdBHRqLrEucW1d15ha7PO21eu\n0e9d50PPfpDlxRW++9J3WFx5mubaI+RFlzQ7QqSSoLKE8qEq1pGmgxYWeWGALimyKZYl8D2HcXSM\nZ7dIixDXqGMZNXSZU5QheS6RQiAtGxXHdI93KKI9dJHS6/lIr0m10uTWCy+CpbkbHhMnFl5Q5bV3\nX4I5i+mdHcrOiI2nnmR5eYVXxmNKq2Rn5+tYfh/LM3nYnaeS2SRuyHxaZxxW8IWHk2UcXL8JtmSS\n+pw+dYk3XjpAu7MuUIpCU2CqWYdGaInlWpSqQOczS8E0JYYUZHqWNEilxpAGluEIAkRJAAAgAElE\nQVRiSgOpmQHTFSAkQVCjF47xbQvb8qjNtTk+7DBJJ0g/oB/FmKVNw/RQWUlIgrIsjnXGomFjWDaZ\nmRHqkjCfUOqMmmcQ2hZ5mrKw0CAJu9iGwvEMxr0Brh1wMHoHKxTYgSQrE9678W1UCqfOP4NrzzMa\n7bO9fprpKCTLUgbjlDjMcUsf1Y+59Mx5Tq9vYpYmX3vjW7i7Gd2dCMuPyPI+NWeJY7uEYkCruooc\nRqyWPvd0D3PJJBQ97CSjJgXS8MlTF7Xfpr3YQkVjzi+ewht4PDG/zL/t/XvOOymLpsHqhQZeO0dF\nEaeXtzgcTnhrPyLzz9CtDXhWK6q1IwZRzHtpg4kvcROPcQit9Tqdo10unl9n+/IHefG7bzIajOmf\nKKbjEefPn2UynqMzmNLv94EZPMoPPIoyI0kKDFPjehLTKukPjjk6OmB9+9xsfVibuG6DauBzamWJ\nF1/4K/78td/lf/rl3yaeDtHNJr2TQyrBMpvLT7J3/CKm41KpWpw7e5bW/Hke7L6Kb1ZJM58X/uJ/\noewU/OXeXxLM1XC9FVaWzrKwOEd3cPw969v3hcgiJAIbyxZoV1PfWCU3XaTOefhjn2bj0mPkqsTx\nmjTm1uiOR5iGQNgW40GXiu/RarXINUilSNMcF4M8izk8eEC9abJ9ehkMk3AckjOgUrOIwlk43bUk\nhnQoypyiUGiVIQ0TL1gAwyWLeghVIgyJIiDHY1YMLBFAUSq0ytFKI7XCQJOUCY7boMwHhP0Dhod3\nCWsNpukJpr3Iu++8wEJtjlYwz5Vrr7Kyus5J94BKvc0br77JnZsvs9w+y93bV/nMZz6MpsHJOOYL\n/+RfcfrcM/TGOaIQJEXJ/NYa/WgAto20x4hSInDw7Ors0LAEcaI5HkQUZURezJHlE5R28Ow640kX\naQwQWYPCzCiKgorhsNle4db1e0yiDLPtYUiTndfe5rvf+BajsAOyJGksUqZdpumU6f0p866L32yQ\nTzNeeu1NNh++wMn1a0TlA3xnngunPsBScxPTdFlsOvRGkKcpcT77roJGhfrcErlIMEwPhxrjLKEQ\nMy9UoVFaYWkBAnKtUEpharCFxMWiZEbi0qYEy555s8LAEQa6KGekNzSmbRGqkkqrAaXCthz2Dg8o\ncoVl+hRSkDoGUmlsaRGhibMcxwoofBNL+Jh2hVD0CCmIdYalBb7h4y87ZH2P+focx8eH1ESdcdhn\nonoYwSLD3iEUkqq3hMAmV/ssLJ6iUp1H+FUm+32ikzaNyhJDq4tixOnts2ysPYG/Cf3BIQ9e2UXc\nT9kwLRaMJQJdpVfuk+YW+/uHJPWU6933MFwLh5w7dFmzt4gizSgPWXBqrNe2qDtrRAPB1nKLbsti\nZWpwb3+fv3fqKf6o/wp1o45lKQJrH6ICW9YxApf+tODOgeAkXCEq6oTZIXtV8MIKr3W6RMJmNVok\nZczC1jk8f0w4zfjGX76F7c7x0qt/hG/HrLZXOT46wJQFzz//HL//x3+K63sAOI7JdJogxCw9UpY5\nh4cHlCWMRhMcxyLLFMtnNxl0xyRZzMJChYWlNmfOnONG9y3+5M9+h2Gny3j6HkHTZu/eNT79iZ/n\nzWtd1je2ePSxcyyvtIjiEVsbj7C7O6U0BZNxFy9dxDVr3Hj3Dpef9PjO1Vc47ByA/t4pXN8XIltq\nTRGXKNNi8/IFCt+lezjkgz/4ebae+TB5kpIUJc3WIou1ZcL8BhXhkQ5ijg53sQwxmxMpS0RREmYZ\nrueTTCek0YAf+InPkWUj/EoFaZjk2ZDDo30q/jK29BAECDxUkaPJkFJjOYpE5ASmJJ0eYWQjEjRm\ndYNc1CF3kFLiejZKF5gShCop1CzrZzgFKimxhUPdtxHL8xTxlFF4QBnfpx3YRMO7FIZmc/k0k/SA\n1975Mo8/9uOc2fZYa5/Ds2POb6+jlMJv1FC+JLE8vvS7/5znnvkIzdoiprNErEdIw8LW8ySjE1zL\nxZASXeTE5QTbtZBWSppGYGjKQsxC4FmMby+QRDmFHtKmzUl4gHQko5Fg2B8QjwsW1k8xIcAxJX/4\nL/8NLdPB8m3ycMLEzDicTCiLkM1Y0F5bpPbcRURiYYc5Kp/SaM9xYUMTxU0WvHXy0uTQCFk+mDCw\nlnHiAcIULJ4+RWw4TFyXCxcuce3KdymIKFSJkiZYFgpFWcz2DwwEURpjSZOq4+PbDpYwSLKUSBfg\nWOB7xKMJURRhGA6u7YA00WVOHqdMjRwXxVy1Sr/TwzRMME0KQ2LEJW6jSjwYz4YSKy5WrrCRCNdG\nJwZCSMbphASB1hpPm3jC4fj2q6w0P8jBQZ9TFy8TlZqd1/6YUnQJTyI22k+gCotW7RRpUhLmkpXV\nxzCsLWJnQLXhMe50CHSb1Iq59IFzbG6dZ//uHb79r1/kcHCbhcXTnDEWMMYR9/wpXctgeNLl3ljh\nRib1RZ+s4SMTQRErdGDTaY6Znzqckadp184SpibxMIN0zLW8S2Vsctczeao1R02M2OcBTvkAJzAo\npordu2NEL6W6YlM0DA5Lm9SEJHwAXsleeIag6VFxdrEWYsZml7XqCrfu7GHJfQwNk+EaXrNGc/EU\nFXtMr9PHcSxM0+Tw8BDXdVEljMdjLKl56Pwj9PodRqMeRaEYDAbvv3FKLl68yPLSx2k2NX/y5X/N\nBz/0HFfevkoymHL9+qsUZcGb73wDRxj0jUUWcegMDnn71leoLU04Ohzg3rjBlatvUvPmMQyDxz5w\nkVGScPH8JXbffhfPFjjmPJ1exOLyGX7rt17gC1/4r75nffu+EFlQSK1YPn0Kx68y2jvk0rPP89gP\n/RDHBylHB8ecXV0jFx1yv6SwKiSuSZDG5McDJlJjUNDOU05SKExBMR3jNOb4h//oF6nWKxSiQNuC\nIh0iypClWh2cVaSeAWMUIcIOKVWMED5JLnGdgijsUxV1Ut3HD9qk8RhHVshQmLaiLGOyvMSQPkUx\nA1rYliBRkBsppqGYdE0MFSDoo/IqfnWOq1deJJ6+xOrqMuPMI9cLfOIj/yWOGXD35DaRHlNal0gN\nSVA9xmSIndb5s//7N1jcXKVMlnnhu1/FtSR+BRbWtokcTWCDNkq0lOQChHTJS4FQHo60MYNNpscv\n45guZVxDugmOlZKMCk7ULrV5k+H4kPHeEeFBh9XVVZKySn3jSf7df/ffUEy6TGoeo26fRq3OJInI\nR0NWg3ny+YjO/hHLK+dZ+9TjTK/1eedLf8XZJ7a5SUyrGuAHNmkZ4qiQIvVZqkzpNW3MxMMRAZN4\nyMIc/NE//w0Or77FvAwIdUSqclAlspgVC2wMpOtQSTQV5VIVFmkW0s8S7EYdV/qMhyNGozG+7aPs\nKgNVUqQRPiValTz9oUf51hs3sAOHo04HyxaMVAplgZNpYlNgDGf8Ws/ycPAoLE1Mgcw0oexj2Aq7\ncElVSFFqWs0NKCyGRkSDCN8U7OzeZWXlFE+fewoVfxy/cppJ7zqZHlIMdkizmPX1D1A1moy779C7\n/jpBtUX1bJvaSo4ztei/dpUbf/ANRGrz8Nppnn7qg+x2donzCVesmCkPWDGXcc49h3NyQBFGVCOb\nwFvhjWyXU0vruOMOp/Y2GDtgehaj7gP2i4S6dPDjhGWjjRIl6dEJXzj/k/xa9y9Zn5pU/A8x4g5O\nQ2M7mkEeUuwo3IZkZa5NmkdUWw7dUNBYhjA5wKk5DI/7NNtz1ObPUkt3yLMlJuMuO3dfRN41aNQq\nzJ05hfIlvTTGsn2ySDAenWB5ig8+9zSPnHuM1999nSh1CUMbrXN0WVKWGe35eaZhj9u3/4w//Y+3\n2FjZZqO9yfGd15hGE5ZWTqEMh0aljikNosmUweGEj33sR/iDr/weC0tLpNk9bn9LcOGhy5iGy3A8\n4Ey0ReBWeeaTv8DZR4c4XoZhlHSPh8ThmH/1F79Hnkbfs7p9X4isQuAvLrLYbnMyGnH6Ax/i1NOf\nIB4nXP3Wn3IySjm9vEKW5GTTKdV6A6fSZNLrEp7s0rp4CaexyOA4pR+Nia2EoGLx/NPnaHgNxtMJ\ntWqFolBQTjDdKlrHFMUUXcx8OsuwEMKnjDRaerhuC10aaBWjTdDGjDOr1BTyE7zmNkUZU+ry/Zvr\nAiHeX6bNEixSkqRkRMzCqYcZdHbJ04hJv8vYFUjRoF67zGRokxYuH/roJ/A8j37/AUGzTTqYYEXX\n8colzmz/IKbls797jaee/Rwf/eSPkhSa1dWUN17+U85sb1JMlzGLmDDNsSwL0/YwpDWjdJnGrK8v\nBek0QmdqFjdzIc0z7KCCMFOKQcbRwZgimqLGIbnWhH6bM2ce5g+/9Dsc7+/hWjaGhrpfwTUtRFqw\nMr9MJaihYsGCFbDd2iJTmtTK2f7AWd7bv8bu4D4/8DMfYzIqKXcH1FpN2J4jTDSLygMknWmHpYeW\n+NL/87/Sv3uVJavKSVEQGDZaSPplSqFnkG7TcYjiEL/ikWUlKw+dZvfgLlWqHHdj1tfqPHF5ia+/\nco9+muMaFm0tSVRGYkGuIFAVFtsO+3tjJPOk+Qm2hL//sz/NK2+8xZUrNzFME99y8W0fy7LIywJR\nzjK2cQk1z0WKAkukVIIKDb+C6hUs1R7CKhSWqJOXI06O7tNyVynkPifdG5RlhBRQAGGSYbs2aRYx\nGB2ysLBE4DdIJiGvfeclTjp7tKs1arUmda/JSbTP4c4B0jGZhl2UmSEdg73xLYYdk6ojyPt9xq6D\ncipsVxbQnZjIX+dOZcy0nOLmEYOwjxFUaJsmjmkxNiT2MObM3Bl2RE6kDNJIszDf5ji/y8rWZeaz\nAVHWQYUxdaNF52jKKIqxLYWoGvR6PWwPet0uShV4tkVQcXjv+tsYoqBW9fBcE5A05hpMp2PiNKKY\nZJxa26J/3OWzn/lh3MDmeO+I3/vdf8s4jmm2Wlx4+CJ3bt1mMpkwv7BAvV7n5q2bWIbHQ+fPUGQl\nt+6+R3/UoV616XQ6bGyd5eL5i+giZ9QfsHv4AMMCQYEWMYXOmJ/f4KFzj/Hu1SvcuPUW337pa3zq\nB3+CC489RZkZOJUFxqMOjarJSeeIT3/qM1y/+sb3rG/fFyKLNFm+cB4hNJVqi4VHnqG18TAv/cnv\nsfv2X7Fy9jkM2yJRFmHvBLcaUJ1b5Oj2y6h4wMrGOok2CcsMr+7gMsGXEcVkQj8G07LI01l+VUgX\n2zYR2iGOj/HtGihmU8t5gZQelgwwcVDaRskxSgqwbQQ2WiWURUyWLiAMMEyJLkri9wExpikRSmMX\nFmVxSKX+GIN4SmJoNMuc394gWF4jP/co195+Ec9qsrKxzYP9HRYXBIIuSTpgbu4CR+/8PlXHJKVJ\nqFMWF9aYprMmUK/fQesJ9XpKYJWYWU4WjzAaAlSJLhXKdGYLs8qeHQRaI0oTKS0QBlJK4jzD8XyQ\nMWpUkCbQsBoM0gPc+iLVrUe5cuUGb/zR79OoVRCmYBxPKPICyzXxXJ/VlXUO7t5HFyVHx0Pu3dtj\nYTug4jrULp/hSrrD49sfxT5KEKUgq/uYpomlbAgCsmGIMBV+y+T/+w9fYrB/iw2/RhLl4NiINMEy\nBELBXLNGGqaM45CK4zFIEz78oYdp1Ju8u9thr9dhdcXm4sU2t968RT/N8bIMyChdB9+u0MwUeBIq\nku5BgmVIchVh6Bo/+rFT/Oefe4I3rz+YNf9yA+kYGBiQa3RRIFSJUQpKTJKsxK8E5GEKaU4WTikj\nhel7PHzqI+ztvYeUClOaDLuHSJGTiftouw5FgdIGnlvHNAR5NqHiSU6Oj+jqAaaaCdRCe46g5hFP\nYrL4BGlWycOEmuFTEjAKY8pwSk9P8ZcEjeUtnLl54m7IpJwwqQrKLCEhJhtlNBf+f+reO2iS/Lzv\n+/w6d0+eeXPafXffzbd7u3e3F3G8gDsEIhJFCgKKICmCwRYp0zYdZImmLZcoUxRBiqZsUiAJCZRI\ngQBMpMMhX8Llu92727z7vvvum8Pk2Ln75z/mBQ1V2eaxSn/QXTU18z7T83bVVM+3n9/T31Ai9VqM\nZ7L01YTl/iYmNpayjZJVuBxvsLS+w4YzQKm0OZyFuw6OIr1tiALitQ6jCwe4TsjKYEAxI3H1Jl5r\nzwRfaFQqFba21hi4fdx+h36vhmko5PMacRKiqTrZfIatrS3idMgqKuYLFDM5drtbnH/9NerVBlnL\nwU1h/uAhBCm+75OmKfl8lsWlRcqVEqSSZmuXarVOqVAmjFyqzW2a3SYL5ilu3bqFN3ARxOimhusO\nGBkfIZvNUGvUeeDeE7z88sss3rrE+ITJzP5ZdF3FsSM69QBTqTDoekTeDpoa8S9/75+T2ZsZv53t\nbwXIGqZJppihXa0zf/phinOHqa8scuXpb1AZVchrgp7nYhgW/WabsbkJwghWrlzE0DLMHz6O1/XY\nWb8JssZdt0/iNauYegY10yaTL9Hq7uLYOXRDJwg9IKSQLRAFMUEYoWly6N6UJiRJnyTVUOQYqqog\nNSAxSKWJpihIXNxBm1whD6kgDkLSJAJVQAphFNBvuhglHSEk/V6TQlalM0gJ0ElqHQxdQbMDlm+9\nzJXFpzmwsA8lNYmDNnnDptlJ0TWD0akZopEMjeYummcg9Co3lr+HoZtsbV9HSYaO/AopuiHQFImQ\nMaQKSSyQUpBIEEIMM68MA1UzEKqOECoySVBSFSXRMe0KaatDICNSs0h+fAFdy/Lb/+s/Y9LrETsG\ngR+QEBP4EaEZoqCyvbmJ2g1xpoqU5kYQUxncWsCEWeDi1XMcu/8M9mqf6rNvsf/MUXpTFmHQRVF7\nNKvbzCUag9Dlu68/w5srFzHShM0wZMYoEgYuqaqhkDJuODRbw8ylbhySqJKxmQJS1/jGMy/R6MZM\nTjtMzqqce+08NPJUUDDzDr2+iyEliatQlzFHD0zRWr+BxEFoAaoccHJ6gU/+9E/xpS99hddefJ2i\nXUYBTFVHFerQpxcVQ1NRVRU/1YYXtDQl8j3SMMLTe0iZELopY+VZmrUVtvs1coUsfuihKg5aLsWN\nh7PkfK6CqVv0GjU6rU2IBggtomDaZPUcMpVUuz1iBbqDACWN0C2VcaeITHViTNAdHE3FK+tUDlr0\nLm/heQbqSJ5j++dp17dxxwyiQcjCTZsIlUJ5lrTf41p7hU5eZ8NrMarnKXclGUXDdVLG3AwyiXGq\nPp10wNUw5R3T86QLed5Q2zgjoyxerRMrOqNGEUKPQhGiKEDXTHzfZ2dnizO3m4xUCgwGXWzLIAwh\nX8nQ7biEUUKUxhTzBeIkxHVdXnr5PKqmUSpNEwURDz54ikajxrXLV8jnslQqJarVKo5t0e/3URSF\nwGuj6zppGiI0Sd/tYdkGa+vLBJ7Pu975LjqtLrlClr7f49GHP8Daxi22d3Z468I5atUWuZyKFBHN\nZp3Jd4zw/WefwDFnyDg+12+8yokjp9jcWgbRYWt78Lbx7a/1kxVCzAohnhZCXBFCXBZC/MpevSyE\n+I4QYnHvubRXF0KI/00IsSSEuCCEuOOvO4am6TSau2THp5m57T50TePSdz6PHTYwC5NYloWRdUii\nGGKFhdtO4PV6dHbW0Uv70OwCaq/DU1/8LHZQRfSboCooShZFTQjDPuADPr7Xxvd6SJni9yNkItAU\nAUi6gy5u2CFIGvT8NeLEJ2XoaymFQip1FNVB1xwsM0UQ4vZ7RHGIY+kIGeIOurj9HmpOI+cc4ubS\nt9GjiKTn0qi/jlXMMF7Is7F8nY21m7x54UVW1y5SygtIA3RFxdEcMlpMv+bS2wQtENjSJu6mHJyd\np926iuetUs6OkXcOEcUmPb+DlR8qnZASmaSQpEiZIGSKsudKhS4J4xQQREGIEqckbojwBYqWRdXg\nxtotXGuE8cmj/Nvf+T2ixjaeqVHv1Ki16wRRSCxTojjFDwJqjQa5TJZSqcDk0RGSkkToNtffWibp\nueR8ycraKtv9Ov0rl8g88yrxd54m/exfID77GS596bOc//yfkC4vUjItAtPAVU3WwjaqpuBpQ6ml\nEoaMYRL2B4yPj9NyPfI5lWe+e4FWN+a2O8aZP1bizbeahF6RjKMxlRGEXRddNWkGMYGWIsyEEyeP\nkLEyhLJNGCaUdYVf/Pm72W22+Hd/epGsmpJzCliGja6bCFXZS8QVmLqFpVnkNRVLSWl3agzCDrql\nojgKnuqimBFr1UuMTx7Hi7osrp8jSFJivY0f5xgp5RgtlslbWdREJRr0cTSVnGGQpD7ddgO/PyBN\nJaqRB7OA4VTIlqYoTGVI9YCeX6fW3SQSA3Z7a0wfLrL9Qo1D82c489C9CCdhq7XGWrOK29dwLwWo\nhx00M6W626ThJ6TSIBMLTh/Yx8HcDLn5/dizM4gwwAkEF2TA8ohCpOZ577EprvZXUCenmc0dI1mN\n+am5B3jUz7O7tEy/76KoEtsx2NnZIU1TOp0WmYzF5NgMg94wmSLjGORzZWrVFpaZZdCPKGRzjI6P\n8PQL32dy5iDlyn42t3rcff/jmKbJlYuXkGmMbRlUd3cRgKYZWIaN7wVIwDAMOr0uvu/T8wY4mQz1\n9i4f+8mPcvbee9i/cBgnW2Fm+hhJmOH22x5BERm2tlYwzAHlEZ1jR49Sr3X5sz//N1y9dJ1Oe4uv\nfvXfcGhhAkXRWF+t02r3UdW3zy54O6bdMfCrUsrjwL3ALwkhjgP/EPielPIQ8L29vwHeCxzae/wC\n8Ad/3QFkEhPHGvO330t2bJzFV15g9cJrTMzM0vTAzldQbI2lS5fQrQLTBw+ycvktqmur5KYOUR6d\n4sWvfw3aO9iKRBUaIQYDF7otl2a9iaEk+IM2xBE520FNVXJWlkG3R6PRIoolUazg+ZJaq8vq5hZx\nGpKmkMQqaaIQyxih6KDmMUwBaUSaRJi6RpoENBvb9LpNkAn5SgnX65KxHXKmidtusbp4k32zc1x8\n4zl+8x/+D9xaaXDnnR/grjMfxDJnGfQVDKvAZmOZnKWwcVGy9GwVa71Kur5Co3YLGYySBDbBQJA1\nJigXDhD6glSVuFEHfY/nqzAcB6hCQVMUFCSqAIlPEkUkYUQc+sgkJvIj0lDiBQkjE1MMQjh89Ha+\n/qWvcOPVFyjkTPoMKTSWZQGgiWEEigAqpQLVsMu1Ny7RurVGbWOJ9Y0l/LTDA2dup/PKErEXEKox\n/u4Ove1Nrg+22G7f4kaxx1KwiW4nzGdtKp7LXGyTS3VApaX4eEnEIPbIaw4FJ4uhGWzs7nBgYR9X\nL9QQIuaB+44QRn2ee3YFy97Pthux6TZZH3iMZMrIyCHVHQKnzwd+9G5a1W3euL5NxjbI6CH/4Gc/\nyMRogd/8nT8mSDPktQKkQzFAmqakQJQmJKkkTeTQv1VIPK9DGHuYqommafihRz8eEGkD3rj6OtdX\nziNVQbFcoDShUqoUEZpBo1ajWasTDiJMRSP0fULPx3V9UBI8v4emaZiWQ6Jo1DpdvCghlyvRrg+o\ndgf0whTDzBCEEV4quXplkbvefZTwHodnVp7mzkPTqJbC/NnTNFp1pveNsZ332NrdIFVSLg2qbMoB\nShrR2d6hbldJrCpGuYU+W6eWXGZG73BPJeGOqRKtqovT8jn/8lNcvfQ9FpQ+cX0NLWtyUC3C3moq\nTWOiKIJUYFsWK7duMjI6iWmYdNp97rzzbnq9wTDiKQWShH2z+zl//jzdXptGvUvgJ/yDX/mvUU2L\n7337WwhgpFKiXC4Pm4c4IfBCRsrj7Nu3gKLoDAYeqqqj6ga5bAHDdlB1wWc++xn+6T/7DS5euoJl\nFbixuE6nFaIpOebnjqBrJrqh0qjXuX7tJqVChdAfcPjgSTJ2jmpthxs3bpBEBjNTRykWRkn3DIne\nzvbXjguklNvA9t7rnhDiKjANfAh4eG+3zwLPAP/9Xv1P5VCv+7IQoiiEmNz7P/+PWypT5g6fpbTv\nGM3qLZZeeo7yxAIzZ+6gdvUGZnkML4nYXr3FqTvvA8Pk1qXXCD2XI6fupNFtsXjxTaZnJtFyZdZr\nLmP796H6KYlIUGRC6FWRqUoaaNS2+2xtbXH9ylXevHiBkYlJfvITP8PE1AlUVSNJYizbwB0EKDiI\n2EbTUqTwifYoRErQJ5UGqmqiKrBTrdJq1iiVKqiKxrU33iRW+8zN3EksUzw/Yf2mz7/7/X/Fude/\nzZF7znLoyFk++IFP4HVdXLdKrpQlUVxG9geoQZb3ffyXWLuyxmsvfZmZY+MUJsfZrjcYHTuEZWss\nXryIHPTx3R4j0xMEbkypYiATCVJFUXRURUdK9rrbBK9XRwYBUQKGlh3S+xOJkqqYGZWtWsQ9dz3O\n0mtv8dKTX6ZUcFhd3yFnOyipRNUU2MvHIkmJIw9Mk37U5Z75s5RDi5s7S/hml4OjY3z1839CRZ9H\nzyaM6Rl8M+ByOqAWSqxIshgGaBmdxPcoRyEHnRxdL0aaDg1FZUN2MMOUUatAHMT0gohGEqDlTTL2\n8Obe7Wcm6Q42qW4r/Na/+ByWY/Olz3+a7u4aZmLy8pULOJbKbZMlbr/jNNe3N7l8aYPQB0nEf/Wz\nH+TYkQmee2GdtfUuhcwolhghlBGpjEHREaoKugIJoAxDGPvxgI7XQ7VNLM1ESVRCP8FQDVAUSuUy\nirFNySrgBilr6xcw9RmschnDMBGhikAhjmO8wEXXBbZdoNbdQJMGiYB6t02opthZh7AfcHPxEpXy\nDNmxIr1OFyPqE0QDTpy9h7q/y41Cg97Vaxw7UEKfsRkpz3Lj5Ssk6i6tIzb1SzXKCDpRB2kHSOni\nZS1cL+ZAFawcqGHIEStD/VCRNVPwdHuJKVUniSW72TI3lzaQUYxa0pm99wwvPvEkcUknl3UQImVt\n/RZRlMV1fSani6ysLFMsjDM+OUWruYPjWLRaDcYmJum7PXRDYXZsji9/5wky2SxTE6OcveMenn/+\nO7zx1mvkHYNet0vOybCxcmvYxSo6mqVTKZZZXF0mTVN0Uxte6FwXJZNhc8qRf/EAACAASURBVHsD\nmQqKxQqKqlEol/CjEN/3OXhogTRNOX7sNtbXbjE/N4OQEUITXLx4kTSJWVtfZGu7imnCWxfe4I7b\n30WmYBCnEvk3CJX5G81khRD7gTPAK8D4DwHnDjC+93oaWP+hj23s1f5fQVZRVPYdvpvYyrP0+lPY\nscdtD32Q6Ttup9XtkR0Zp97r0ms12P/Qe7mxfJOd1WXKhSKHjh3hm69+B8/tM7dwBiU/hmpbpJqO\nKQcEqYtlpDSbq3Q7fa5fW+balUX6/QGG7jA6VuShhx/m1Km7GbgaYCHQSGWCpm+iKSYiNVAIiYVP\nnIZ4oYYqXdLYxDA0/NSn3WqSyphMJoPb89CTHjMz++i6PoZeQc9MMzu7nyc/96d89Od+gjP3vodu\nErC2vYOt2bS6PjNzC/jSRQ3z6MU2HWWNTFlhTF/AtkZJlBQndYmiDK7vkyl4YHYhlTS220wfnECm\nAhB736uCogxPhnQPaFubGzikxFqCVVCIkcSKipBgKSF+qGEECk99/gvkRcBu3wXDwE5ibNPGD6Ph\nTaC9+aSqQOR74PYJ2pLNepso6qPLba5efQ1Ty2FXRgk3F3FMjWfjDZZVl/nY5qauUYgMCn6BOG3j\nZRMaoguWwFJUkiiikMui+dHQ4tDQ6IZ9shMlhKVz5eI1Hn74OK1GnbXVHh/+iV9hZa1JfjRPeXYf\nH/jxxymNPob1h/+I9s5zHJzPcPPmIjfe2iWSBpGh8Es//m5O7R9nZaXOXz75LKrikLPyxFFCmkTE\ncYyiDe33kqFMjIRhN9sL3WGkjUyRsYKtO9i6gXASUt0hjat0OgM6zQ66OcZo+QD11jJBW6LGCpXM\nBKqh0+t2QFcxHAMhNJxMDqWvYjk23X6Xnt8D0SermBSLFkoyoNn3SYgRSY/xqSy17jb5ok6yM45e\n9bjz/bfz+q2L7Czt0Oy6jOyfIm12UOoRnmbR9ltk0gBbeCzceQdbG31+5ORjfOUvPktL9tk2+rSs\nEpnQYSqbUvMGBJaBKBXoZdbIxjGXXnqO8ujDvPMDj/G1v3gCX7NBpLiuSxLqhGEMUmEwGNBoLmNb\nQ5bL9evX0A2BEAmKSDBMgZYauK7L2OwEI6U8X/vyX1DvVtEzClGQMj0xTqNeJY5jLF0bKrxiSRQO\nU0x0Y5g2HYUhTsak3R4qxuYPHMHUHPq9gEazRqfTo9fvcHBhjsuXL7NvboasM0rQMyhVCtiZlDAc\n/o6bnVVKpRKJDImiiK9/+z8wOzdJlHRR1bcPnW97TyFEFvg/gf9SStkV4v+eSUgppRDib+Q0I4T4\nBYbjBJxMHmu+QtpYZ3tpheKhUxw9cReNboOaW+fuMYNz3/wWpelZivNTbP0f/wF3fZd9j72XXVXn\nxktfY3bUYmzuQd58/SKT+0Z5a8nnbKVNaHq0ZY/G7jWuXL1Fo5Oy79AZjhw7zb65u5iaWiBTKNIN\n20QyQkUg0j5xUkX6a0RqBqEWUJU8hmYj/RDS+pAMn0R4u02iWGLKhNQooOkj+MmAtcbTfP/1V8hZ\nYzz0wCN0NldobG1y8MgJxg4dx/UDnvrat+j2djl6epZqq8rJ7lmmx49gq4LY7aGEHpbqoKl5okSj\nXm1QKk4hoz6GBmmoYagljGyIaggGXhedCN2w8KIUizxiz23KVAX9bpdao8ZstoiqGUjVxI8URKqQ\n0exhl2xKXvvWX3J55TxjlTHCdouMUIg1lY6mULEdIrcPVg4lVihmLLpxByNb4M32axzJj0A0oKl6\nRCnMSYN25xJrbLAjPDYSBU3m6Gg9snqROBG01RZZTWD4kqxqUQ9DdCNmRIFVt48voShsqlFI7sAo\n0u2wtdJhZnqUXhRx8VqVM6ceZGxsDDIhy8uvIr0Qv6Gzq17m4Jk5XntOcPX8Lul2l2mp0lIrrMbb\nnL73EJc3b/C9py+yWx0wkimRyeXYrTYIZRepCbzIQzHzDOIAx8ySz5UIvBhN+viBR8GwyDkOpmET\nh5J+z8eP3sLRs0SupGiPYAqN7qBFKBwsIxh2XPTYaDco5kt4gxQv2mJkbIywVeH+Bx7isY98mH//\nuT8mfuk62dhCjozSi1P0JMXqdIiNAZghGxHU04ATxSnczHkKMwe4vt5l0E4Jux4zo7MkA4fdKzcR\nZhnR1rGKOl23jqNMMVY7xCd+9ic5rM8wO34nv/9H/w0mMd1ejan9cxzKzfOt+jZxv8BDRon67DTL\nu6sU4iwvPP06f/9nfoHzs1O0mjV2N2v0WymVikm/O8C0MjTb20RRwuTEftyOgalZxInADwOkTMna\nDnrWIjQUyvkMN66eJxMnDAoZ3G6XI6dPsrm+xnRoc0UfULRy+F5MJV8kGgQM3N4epqhouiAIhupM\nKSWNRvWvmo3tGqyvrnPowDG+8eU/Z2xkHGXuAAcXDvHyq8+w4MzRjyFbyJOmIYbpoBkK0tfIZTLU\nqytsb17HcmwS6b9trHtbICuE0BkC7J9JKf9yr7z7gzGAEGISqO7VN4HZH/r4zF7tP9qklJ8GPg0w\nOrVf2naGneULZJKUAycO0rMEO9U+InbYvrFBbXuXD338Z7j2xg1WFq+Ak3Do1D2svfw81TcWufvH\nf440p3D9pavMzU6j1ba4qWk8cPdZ2u0dNjdbnDh5iAOHz7B/4SSZXJF2O0TVQvqdFYKwD3KAn7Tx\nvS5xEGJZExSKFTL5CggNb1BF6AbZXIlBVxAHOzgFl9rWMo1OyvFT7yFjjzCSH2UjKuNWV7m6+gzb\n69dod3bJZBw+/slfpOdG7NTeorbzAjs7VU6f+gSHp2+jVd3BED1SqVIo5HAyWQJPQxdFVNUhn5Wo\nakAc+vR7bTKWjtfoEDRdVJHi7nhoR/aB51IcGUc3dVy3TxAEDDptGs0akecSGDnsjEYqFaLAR1M0\nUpnitVu4vRZf+sJfMFkZJYgCDMPA0g3iKIB2hwiFSqWIMT1La7dGJ/KxKnmsTo9uHLLU3mJcMzhs\njdP2u6wmMdvdHfqWhxFZ5KMCiABdgOX7mAIstUQvrVPLSBRPMibyxHKA5hgc6uRYVPvs+m0qBybo\nC43qtsfsrM7sqSzeU9scK0xy9z13EZsqaSKolEfJzxpU5gp888k/5IUXrzNTGadbu8aYbhFIhSCp\n8nff/05qjVXeunidV89tozNJZarIytp5gsBkbLREz+2hCAU/cEmiAD+BnUFAmgoKRQfd1DFshdag\nhV/dRBMapm4hk+GFrpKtELgBhXyF+u42cg8ELFEkTfsoikPfD8mOCba2Ajbqb/L+kz/Bhz78UVob\nVe6aPsVK5TqGCd3NPqEusWnT8OrEakLRyeDVQ975rneRzedY2d1PxDne2LiGURolsccJDY2lxRcY\nNQ6RbTVoVxLyjTw2EWv2Lkqjykioslq/yZk7j/KJwj/m1/+nX+bxyVkKA4X6WImJqSwXVi9QSid5\np+/Q6scEo1nCtSrf+MrX+OBHP8zv/+6nCKM+TsbB8zwMQ8MwdYQQzM4OIeH206f53veepjI+gVBh\n0O+StWwUQ0eGMTdXbjE2NkattYPrBtx9z1mu3LyF22hS1HLk7QymGI7yTDFMaE6S5D9ate1hFoqi\nEAYBvu8zMjrK5uYmUiiMjY1x4+oS/b5LZmSaXN5BEtHrdZAkOI5DGCoEUQAyJAoikmjo+KWqQ2aJ\n/E8pqxXDlvVPgKtSyt/5obe+Cvw08Jt7z1/5ofovCyE+B9wDdP6/5rEAiq6zu1Olsb5BOV9gZHqS\nASGhonHqzh/h/IvPs3/+GBmnxPXXn2T15iInH7qP4vgoz37mX2NYZRYeeJwXn3uC1A/QjSLbS9/n\n0AMfpzB6EM0q8ejEfgwri6Y7uH7MdmMD27YQfgciDyVNUOigUCVrWNjZfZjFY+iGCZo69DVAIcUg\nlQmFkRydRodmt07GnmPgCrpNDUP32KmtEXcDDASa4qIbLuMzBaZm5knNLMdmTvOVr36ahx/+EIeP\n3IZhzhBEOorSp++u4/t1uu0eMgHbKpLNGLiej6Xr+H6LXr+JTFxSf0C32aK6vMuVN29yaP4kvh7h\nhhEHDkaEqaRWq6HrOp7bR8YRZdtCt7NIzaA3CFA1g5GszbW33qRSGeH3/uD30WWMhqQ9cFEsC93S\nSZOIk3ffxVypjOu6fP+182SlytTkKKtrGySRi6VpdHWNdpqSs0fohyrXm2v4hgUBTCYF7p69n+XB\nRez9JpvnO+iKQI9TNEUnIzVSRSVUU/oywfJDyqlKbOtkK1maQZfGjstkpUTuoMKVW7d4JHMYc2Ef\nApWNtXUMzeTI/CxXbr3Jqxeforv8Ou9738/xgXf/GP/tTz1KrePjC40H33+K205N8pdf+RavvFkj\nwWFydoSLi6uoIuQXf+EjnH91lSvXrxAEAbquY5jDeZ+KzuzMPjRNwfVd1rZvIYjIWg66SPG9Jho5\ndNVEETqOYxLFAcVSlna/g0AMjcGjLtlCmViF67fO82OP/TQZXeex0++nsdrACARHxg4zWZrn4tIF\n7j12lsrkPs4eOEmnX6eqDvj+N5/igw89wslTd0GU4tnbXGrMstX7AvVmj4nUJmiuM5o5imImDGyB\n14gJjD7jcYWHTt3Om6+8wPEvnOXEnadov7nI+04+woV3fJxrz32G+z75MVbL45z73c/w4FyefV7I\n1YIg42UZbDXRKkXeWFnkzl6fU7efxnYMzp9/E8s00U2NNI2ZGBtHonD+3GXuv8dGt0wMw8ALXJIo\nZmJiAo8EE0GSgpnNcfzBeYJGi8sXLuIi0NOUnpJgaznUCJIkIaeZ7Hjd4f2GH7JslXvG3z8AXSEE\njVqTUqVCuVih1ekgNQXNMtnaucXs3Bi5nEOr3UBRFDKZDL4Pnhth2xqWZYCUKKpOHIFtZPGV/7Sd\n7APAJ4CLQog392r/aA9cPy+E+CSwCvydvfeeBH4UWAJc4K8V+WqqQuT3aLg95o8sIDUH6SfMTk+i\nJSGFlQxn7nmczeubrL71LQqVEY7d8X4WL77EzuoNfvy/+3XWm9ssn3uZe97xbm6sbWNOjHD66FGi\nJCBKI+yMQxCGNNptFF2lkLcZ9KvYhoKlaaRRQppooIxhOfspFI6RWgZRHBOEPnGcopl5olQlDXWa\nnSpBoGBqh9CtEcbGJhGmydLyG1y49CKrF1/i4MJ+ilN3kK8UmJ2bZ2rmOK5bwlU7PPy+D2EaGr7v\n0wwGpIrAsWxCr0LBKkK0Sei3iKMOSdxF0wsYpoNpFrBMnW6vxvbuFsI0OXryBJGnkM/k2NraIowS\nTNMCoQ7NXiwTPeNgmQZmDH4iKGfLNHoek+M51pdv8Narz3HprWv0q1UmS2XcXg/HMgjSBIDHH38n\nXa/P0y+/QBRF6ELjyMwcikzJJAqapjFwYxIhaMqA140WA5lgZ0qYKUyVbN45ei/t1R6VySJ1xWNk\n4QHIe3Q3d4kGKvmBR0ZPacsh0Z4kYbsi0UcqBN0O7W2X/IhB+aRFpxMgVwyuK1XydRut0aOYy9Ct\nb9C2XTZXLrNa3+b+A49ilUaohyrH77+fV599jv1nD1Ozanz9+S/z8oU+KToHj55hbLxIvhTzvsc/\nztc+9wSrm12yWYdsMUu9WaeQKzC1fwYFlcHApbrbIE4jCoUCg14H13fRFYfp0Sl264O/ApKclaXX\n6zA+M0Wr1yWOJM2gTSbnUG1vkB0t8fOf/C+47/CjRDWPeqNHVpr4EhIt5acf+wnS930UkXHwm0PR\nSDbNkJkc59FfexCChDdeOcdYpNBsdThZOsaxo/+UT7/wv9CS6wzCAmLQ5YhT5JqZoW+0UfuSX/zd\nX2d28jQbT1zmXP0Fjl2aIJ20uLb1Ep/61V/nU9kOX/rq1+loGsW8gilM/oV/hcOTRxndt5+OH2O0\nBiiazqf/4F/zz3/7t3jiia9SKJVpt9uQJpimied2WVy6ydTUGK7rDzm0YYDneQihcv+99xNpNh9+\n9/t549YVgiimMDlBtzcg9HzUbJaMYhMaGhYCGcfYqk7G0Bk0XRShIBB7Nx6GNLsf1OIwGHafik6r\n1cJzA3p5n3w2z/T+GZ78xte4594zaDpIZch26Hb62I6JlQikTIZdq0wwNB2RSjRNHx7vbW5/K0y7\np+cPy4f/zk+y8/1znHnPB3FuO0rSabN04RxhvcOxxz/C+s0rvPXEZ9ClyekP/z1WdnZonH+Rs5/4\nCFu3erzw2d/g7CPvxiqeYnd9iff83R/l8OECqStI0ogoiUAKdE1DiHRosq1FGEqeKPYJ4iqancfK\nHgK7RKon6IGK2FuBpGlM0OsQBR5JHBH0VvH7A9qtGl7YYW3nJo1Ojx//yE/Ta8U0ey6FXIbx8UkU\nqZEkAlWAH9UxFZPED3H7HmEc7UlDAwbdFoYqiajR7TQIQ5+J8f0kiUGcaCTE5MwC7V4bxRBYFtR3\nNrl+9QblygSHjt6ORhbSlEajwaDXH4opkgRNUclmHRKhU5k7QJgoVLIlXnnmezz/rS9R3VhCxho5\nRaFoG+i6jlMq0+j3SZKIbruFY+goaCRoFE2FKaHTb3cYn9+PM30bomLhpNDdbVJZmOfmzVu8/Mrz\nuAWV2yoZZsYmWN5tsNjeRYstfukd/wSZa6KmGl9/4d+y7q6SjXJMoyOzbQamIOoYzPZ9VmzB+O2T\n9Lwey0se+dRiTAlJ3AGJanP4zruICirnV15HiIRTc8c4MnaUTrXPheoNfvRDP09r6yqXlr5ALUyZ\nGX2EKfN2sAxkUGNt53mWVm7Q3jIo6SMUNElfl+ScDJ7nDXnHQK83pFalaYoqPHwvIG8XEFIhDlMU\noWGaJr5oMxj0yBk5VBQMPYsfSoRiks0V8JSQarXOoUKF9c4Wn/6N79JZvUVZlNGzMQ1Lks1mSXZa\nKB2PsNZmEHvoR6cp7Jti0pWsXriKu9EkMzlHOjZCs9aGWpN+2uPYu49gW/u59NT3ubLxFE9ee5Xi\nvizs9jioKjzy8X/MxMHDqI5LvNtBnS3i7J/Futkibi0jX7jA7Ud+hD92L9KakQjp8JF4lOMffRf5\nWy1w+4RzOYysyYtXzvHFP/tz/vcvfo5P/uIv88UvfRG0mMpIkU6zRavRIY5SDh86xO7uLr2+i2bo\nZLNZCGPOHD9JnNNZvniJA/MLVDeaTB8+wNrNRVa3Voj8iPmxGfoZA92PaTfbLGRHiDS4XF1HKAlJ\nkvxVB6tp2t6SXhJFEUkKlcooqYDAH7ILZBIj0xTLtPCDPrm8DUKwf99hVlbWKBQKpIpCt9NGRWIa\nGvlsASFUojBla3eNIPDeFtL+rVB8SaFQr9dR8di4fo6osYK3U2V1Y4PTdz3E7soSm288S6GQJX/w\nPsozs7x56TUOn7kfXXNYffkJiqVpyjMH2dyuUR7NMDOWQU0dNC2gO/BJRUQqVeIgQlcVVN1AYBMR\nkWoBpjmGbowhRAaZRmikkAjiMCGVydCcRIaI1EXGLooS4nkNOu1doiTmtuP3Um93uLW8QqU0wpED\nx0hlhExiwihECA0pBLoq6XZaKNJHtxJkGOP7HqoQ2HofVfYJYoVSeYzAC3H7KZo2nAMneGyuXx/S\nVXSd9ZtVLEOjkK/gBlAam2Z318MyDQpjNtlCgO96eIMepJJUGkgrP1T8FIqsLd6iU6vRa3eQEnRN\nx7FM4iikXC7QHQxo1upIEmxdJ1IUDEWn1e9xIDtK3o0oqhbjwqLa9ymMZujSZ83fxV+XOIaB7tik\nsYelaaxX16g2fRJPIIVPLd1A8XzaTY+ZwjFuP3EXG61FVtaXkWaJKTVDqbuLUoTi0SlW3C7e5SaH\nKscwTJ+os43QRrDKgq3+DRKpYSYGM5U5yto4vUaLSNNYmD3C88/8ezJFje0aVCZOcfb2H2Nno8bq\nze/Sr25x48YyvjQ5euwkQXdAt9HAi7poSkwcJQSeT5IkaJpGEA5wYw8NcDQLQ9WI4xhVF4QypO12\ncRwdoRj4MiZrO4RxgFRVFF2ALml3Wjx44l7uO3QXsQeh28Up5Ei8cOhIpSbI0EeVKa1Bj5xjooYx\nB/bP0PRrdLsu+46MEWYsbq1t0vfrjMxOIaenmMOnkEzQ624yf3SO3OxPcfCuu1m7do3B7IDuzQ3M\nQRdFzxO22ozVa9wQGqf6uyRFh+D0ce5+8EE6r17lxNhp6hWLhQ3JqchEb/k0RiBfmiTQIpJilrOH\nf5T7PvwBbvv9e7l+awfTMEBXGQwGtNtdUikwTZtiocyRw8cIopBnX3ieTqdD0c7T7fa5ceM6qAoX\n3zjHo3c9RtgLmZicZHV7lUomj5XNEJAQRDFhmjBq57jY2UKKFPbA9YdHBD8AXcMySaSgPFpmeXmF\nJPTJFfP02i66rhJGPvl8FkUFRdVZWlqiUBiGj9ZaVUzDJpsvYCg6gR9jqApxEMLfoDn9WwGypAK7\nkGcnbeOvX8G9HhPtdjjxyGMcfOABnvyTfwkbiyilKU488hA3l5Y5ffQkuZFRVi9cY/m17/Ouv/f3\nQVh4zSUWHjyDFD2y6hz1wTXidIA0JEgNRRkavUgBmprHi9eRROTM/WTtBcIkJQz6qFIQxxGKUFEU\nQSpTWu0a/X4V3+/Q3Owikx4yCdm/f4HpuZMUChGGYaAqEUriIaWLUGIs2yZNVZAGCWUyRR1NlQS+\ni2kN42pEEmAYNl5nl2JmFs0IcNUGnU6bJHXp9mJyxSyN5i2CICIOYnJ2jqmJg5RzCrutgMZ2B6cy\nhecOl6qaYpDRTPLFIkKCiiCy8yiGzu7uLpapc/XSZbrd7pAWIxWiKKKSzdJoNOh6AbZtk8QhlqGh\negmappDLT1Kvb1JQbbQUWm6XnrfL3EyBi9WbBCWN9dYOB2YO876PfIhvfvEvWW7sMPBDSDIooQIM\nuLL8An6rzzve9TjGyCky6oCN9utsxm3es/BRzhpj1Iwn+W5xi1ptBa9qc/TEO5gcNag/+zoTSY7M\nAw8RZrZY3H2ZzmrKiZHbiNdVuu0mfilh/NSj+F6MkOe4truBlr2bU6c+hl2epLH7IvX1ReJ2jCkV\nfuwjP0Nx/AR/9Me/Ta7QQvUE3V5r+N0pGqoA09LwwgGOYSJVnThKiOOQMAwJ04hYB2ErhIkkFQJF\nSjr93lDymQgsXWWn3qUXuRycm+Tk/rtpVwc4uYTQNbGzkCga4xLc0Ce1TdxOD7IZDtxzkkGjid60\n0PQMdZGiODC+L8OoFIgoRp1MyQSj9AdNBn7ITKVI5K9jpwc4eugA69oq3txxri1e5PGF4+w6Y3QY\nMCs9euUZJhWFUtfmihFx6qOneDRNWF/pUdlZIj1eplrbYjpj0yopFGenEW+swNgE3miWn/3P/zMe\n+ZH3sm9unlZ3By9ySeKUOAbH0tna2abRaHDkxDE+9rGP8e1vfJvG1g5Cgj/oMXpghjtvO0x7o8a+\nkSnWug2K5RLOIB2mDPvR3sVMo+Lk6OwMSOIYdc/q9AdA+4MOVkpJlEo0XWfx5hKWZaHrNr1OG8vW\nyTg2/V6E67oUS3kazRamkaNerw/3NTSyWQfbdiCG0AvRTIMkSRBvf1rwtyN+5nd+71/9z8dPP8xm\ntU693qA8Msapd76XYw++l7XtPpe+8kc48zPc8d6fZ/n6EvP5acysyk4v5JVP/xalu05z26k72Lq2\njFE2ueeBs5SyFbA1hNKj1W5jkEVNDUzdAJmiK4Ig2UYVWbL2ASxzhESGyNRFETFpHBEJHUiQ4QC/\nW6PT2MZxMgz6IUIVoFRRRI/AhSRysLIOpbFhrpbqBShaDGqEqqWoUkFFw09j9NgnjgKEkMNxRBoh\nZTzsmFUDzY4QSgEpbYQM6HXbyNDGdRUmFg4TxEPO5vjkKJGSIm0Tq1JmQIhIIgzVYqQ4i8rwpEqF\njpEZIUgc1HyG/vY2Xn2XP/jUb9DYXkETIbZhYCcqqqLR9wd4cYCmC3zfx7YtgijBVVMSRSdSFSbz\nGnYiQc1QlyYzuUm2oxp9OvjNGvOT+yjZRVYuXMMKYuppiGnl8T2NQn4/SBM/XWe7VaNd3Wat/h2u\nBs9zbnmLew+c5c78AjfqlzlvX2dzV/JjHCffE1QOTbL94nnGAo14rMyqs8ZFbwnRH2GfN0bQDrhW\nXSISbSZKR2lmVtmqvcbuxi4FMc+PnH43GUPn6Wc+x5vnv8fj7/k5qm6f+x59L7pd4JVXvsvu1gUM\nXDo+xKlEVXWSMMZQLNJYYOgZ4khBwUNISQqEmiDSBYEMiSIPkVj4aTI8lxQfTbGRkYOpCSS7GHKE\nu4yDmJ6C7QuC2XEyjo0epBSFIIgFUddFNhoEnT4TzhiKDxQU1NaAhBC5vYu9vAObuygKyFIBP1aI\nNJtOMFSq5csFHNuhWa8hHIcwjIl2l8kC+dkT5Ow8yWAbfeDSOrdIddChGEiMjmDtYhdtMUYfRFin\n5uhJl6RfJaiMMNItkrzwJiLSYdUlnDUwtAz50OWbT32Ty9eWSFIFUzE4eGg/iRLQ7NSo1Wusr63Q\n6ra4+777UBSVTrXFQI0JBn2mZ8d49cobLCg5Dsg8m/4AVZgM9IRYeLTbDR6YPEpq2Vys3UKkMaqh\nEkUxKQnIIchqmoGCMsx1g6HBkJR7HiMKCIVYQj5bIAgiFFUFJHHsoe7ZheZElnKpPDRXEhJNV/GD\nAEUI+m6PX/sff+3/P/EzURKzcPQIuhIAMDm3gGnn6NW3efWrX8AameDRx36S5a1LEBWRZoDb8Vk+\n9yyhbnPv3ffT7LRJSThy5BjlQhHHHhp0h6lKximgqQZhGDIY7HVnEoQ1gSCDtAqkZoYk2ZNNppIE\niZA9fH+AmoakMiCXy1Eol/DcCEtJ6aY6zU5IqWiimQalYgGVGFMk6Lgo0iCRGURskMQqqlAxcZCi\njxAJUkKSpqRoaLoyNBRHRcYemqkSKQqq6RBFCY32DmAwJnMcGNtPN1Oh7/XQTYc0TRB+QEbTqLW2\nuH71ZQwlx+zsHKoKqRLjBxFOtojYTDDTgD/81KdQEp+cZRN5CVEQ6/HqsgAAIABJREFUkWoqihIP\nyfYCUk1F1VQ8mdAPAw4WythWnkanS0hMK0lwLAPNsEkLGhvNFp4pee8jP8HFly+x3t6h70Rs2nXS\nfoCplcGxIVEwtAr1eotcVqFn3kIxyngrPu+ff5jWVshXa19F5lu012P2i3lmTx+jtv5tdp5+Bsws\ndVtlYiaPGd0g1yuzLztGIWfgRW3u33eIWzea3Fi7jFM0aLdqKMk0D9z1MyzdOo9TeYUo7POrP/+n\nXFk8x6nbHoAk5dbqBm9cuEomM06zX8NQQVdVkjBCV1QQQ7mo0FSEIlEigWcIRnWLsUGKkP8XdW8W\nZNt5nuc9/7DmPfbuufucPvOAAxwABAGCMEVQIERSIymrojiyZJUiX+YiV7lIlXMXqUpVqdilqOJI\ndsTYsiuyIoWSyIiUbIoECVIEQAI4GM4BztRn6Hnv3tPaa17/n4t9rGuzKk5Rq6pv9kVfdO/1rfV/\n3/s9T8W2lDyQlsLJUNqhlTocApNixFZL82Ay5Xz3Ej//iV/ludUL1M0GQeBxmB1hnDZBO6D0FcFe\nRvzwkOTogOVmG9UKyFTJ7qvX2Fo8j80hnRVUSuI0A7SCg/t3aZ5YJ08rgqCBcFwGu/ssLnY5d/EC\n7924SVg3uX9vgNNS1EcDslojtlYJ9geYr/4Ge6Zi5QtfoBGusNbZxFtZQ2YW52CGs1BRFgWNDZfj\nvR0CIchaLkG7RfTN21Q/9yQvf+Gn+Iv/8495/84dsiKlIRVHu7usb5wmlG0WznfY29+nf9Dnzdff\nYGNlg+OdfRbCFr2lLvsP9qklvHHvQz7/0k9TfPc6jV6T4f49dMNj1WuzsbnJK++8CZVBufPhrlQC\nrV3gUboAQWUNSoLrzEWalZlHsALXpagr0lmGsBov9OafBxGTUYEykiopCZcb85OsVoj6PyYY5Jyh\nwt+xdoHSillZsnnhMhUepY5IJmPe/PqX2f/+V3nuC7/OeFJy/4P3+czP/gr72Yi9vQfce/NVrr74\nOZygyfVr73NudY2Ll84SRRHadUlLS1kptBPOaUl2vkKqnTmmzHUisA4KiyljyjqlqjKstZiqBpGS\nziZoLHWRg5BYI0jzAs/UWCNQTkizs0xvcYUo8MiTI0w8osr2cOQySi2jZEQpSoyZ93ZrwKIw2Pm/\nSliUUgglkHVNkU3xWg6TZIaRmpX1kxztvs+3v/FXDHYeUCvFwsoavfV1mu0G2IKzpzZY6XVYTCTf\n+Nb3GBzFvCkEaTZFKIvrO/R6SyydOMPbr7+Oa0uUkBTJDN9zAUNe5SAMjlRo7WAqg5QaW9V0wtY8\n3D0+RjFv/ndXVxgdzFgEGiKg1g6PX32Sez/4gHqcsvKxx/j+q++QHceEvksWxxhbUlYzIm+DZrRA\nd/UQE2n6/UPW9OM8c+pT/NnR7zGI9nDkEkFdsCQXuPX1a6wtrJAbh8PBEd31dbbLffK4ZKW5SRLH\n+JGH11ljNDrm5JOXechd+sM7ePVVXnrp73N/+/vs7r3Lmn2Mn/30P2J/9xp//n/8Jq6xKFEzq0s6\nrsbmAm1KjPWpjcV9hIWM0ykS8N2QMpsR9NYJy4wT0qUXzo+qChcnU0xtglPlnKxczocreF5Gq8gA\nyS/+vV/m8uZTPBz2OZtDev89eh87Sfk3H7B9ZYvL59cYuhlhGDLuD+mXBa1WRLF7wMLBkLE9ohFF\n+MrFWWlhioLZZEIUCMoiw/ObiHo+Dc8qy2QS01lb4cT5M+x8b5vlsIdxZrx37XUef/olKs+j7zps\nPPczLO31cZw2u5M91LJDeSJgSXt40wK9a9CPn2R4d4/gMCM42eJYQUMrkp1D7PYeLS/kJz76Am+8\n+x53mSF8jXEFRw8GTLOYOC3o9BbY2DhB5Hg8uHef49mUfH/EdDrFl9BZWsZzmox9w5Wz53lr7wbN\nMCDJSk53TiDCgDvjfVwqCkArhVLO/P6uqvm9HbhIpUiT+eDWcZy5Dh4BSBzpYFxDXmY4RuM4HrPx\nhCBoUuYVvfYynhuS5yUmqwCJ1hIpNEaZH6Yl+6NRZK0x+EHEdDrF9SXJ8SFvffs/cOO1b9IOHE6f\nvMiX/uQP+Omf+UVm1YAi0/zgB6+wtXmWlXOXSdKUxx9/jKefuMDK0hIIQZqX1EKCCMAm5GWKFXPA\niZIaY0CmU6yQUCbUwlKVCdZUSKnnxgRH4SmNsAahNUVREM8mczNtmWMrQ6e7TBgt0Gi3SNMJdTLB\nZhPy/AicCKUNRhgKk4DJqesIoSSVrUEolFTUpqY0JcrWWErKNGV4fARKk6WWyAkQGB7euYkwgjCK\nGOzuMewPWFldp6hy7n94n3arhas8rBNx7sopBDA42ufw4AGNwKeYjbh3/R1EHs+9WJ6DlQ5lVWGt\nxRPz3qOrnDmXoIBuu4XyBI1Gi6N6yO50yImFZTYXV5mmGc2ww4pqcfDeXaLTLjfeep0nmj2MGfLn\n3/wydVLyk+sf5bXJffqjQ4SGshIIJJ3FmsqkjPugBx1Eo+DP7/5rHga7qFIidqY8sfExTmQSt9fl\nlRvfxZEZS6st7nRGpMpnIz9D7gzw/YDSzXAaq5w+/SxvvfclRvoInS1z5twmX/vLf8XiYoPnn32J\nhnuB/ft9Nk57uJFDPk7wXUkYhkS9Za5+5JNs3zvi9jvfftTbVhRpgeM3qMuKqobl5XWKRgc9GFLG\nMTt1gVKKSHo8VodkuMShS9fvorIKNZzRba7y2cefZbVzEbWzR3e1xeE4xns4pHrxCkFbUf/he7yt\nvwIfeYreVND2Q+LLayyePcWD+w+YhBbMlKiUWKNI5bxsKBQqCsi1h+/7ZHlNicQPmhzHY9L+kBMX\nzmJnDl9/89/x+GcvE126zOxejH7zAPf8IsVjlynHd2hPNli//Dw7zhh/r0B6BdYTeMMxJm0RHI7x\nGy2sNLR3R3DKJTy/Bm/ucXdT8Adf/jMOxwOWz56mP9inkJKyIQhrl3R3j/HREdc9C5Xl0soJvFlN\noRwGxxNcKSiGE3baPku9ZS4trfP166/SaLYJC5de0OGDB/cBg+sHFDYFOx92WWtx3Tk7OSvmotOF\nbpssz8myAqTA0R6mskgJvushq5LiUWE2QhI2O+Qqp7e0SpmVhF4DgcTxA4oiY5bO0I7mh0hw/WgU\nWYmdb1i58+WAvbdeYXzjNZqex9Laab75tT8jFAXd1Q2G9ZTpsM+JzXUe23qCSVUQSMFj50/jhIqy\nLoj8JrM8R0qN6/pgKtLMPJo2upRVgRQOeT4BqdGOP2+Ymwpp5aPdfEWZK7QIgRKBJK9S0nJCGGgc\n4aIzH99v4rgBZVGTJlNEkeArSOQCVjSpK0Vd5xRVhpAlQjVw5KNMn6gRSiGsxVYFNRVKGnxXcuv2\ndU499iSe26AqCjqdDlHgg9+k24ywRUrgetRFSpFB2Fli6dRVTj15iaWFJSSCwPEpZ1N+97f/J6Zx\nnyqPqadHAESNiHE8I/RchONQVxUC87fRlzwvAInj+VRFyWg6ISans9jjaDTg8dNb7B8d0tMdYpty\n1ImZHs9wHINeW2P7wSHT2Yyr65dYcLvMdrZRMkAIg3A0OhigXIck7lDGhpbUiM6QPXVMrQT+Xdik\njU4SpqLk7Tv3iAKH2jdsb3gUwmHxXslZvcqt6iZZUdCoOpy9coo3H1zHiAzTL/CjJrc+uMbSUpcL\nZ58gcEIePHyTU2cukYwVSWxpBsukaczpM5dYOXmebvcKrWaEKPa49s67gAI0odU0Gz18xyVNc1SR\nUBQVdcPDywSqLBHSYhsuF6ZLxKrD7XpCMy14+blf4OQzLxMfCHSiOAxL1pVP7iqmjQ7hTgwHI4o6\nwY9CTpw5z61//xrZwQELjVMEiw2WT2zw4btHyKAAH+w0JR5MkdKh5QbYsInrhWRVjVWKvKqpjMR1\nIsqy5vj4mLhX0fNcWo+fIH9sHd1xEM2Y4t17ZE8/QblS49wdUR33aawZ1k4vc1PM6Dz/BJ84eZHB\nX36P7OQiSZTRsQ7Ct1T7Oww8w0rmcF9A1m3yyf/6V/mLP/lTznTWORgf4kQwKke43YDaUYjhlB4+\nxTChub6CsjFuVuFph82TPUpfc+v+TT7x0fNsLK9zbzzisr+IdD3e+/B9QBLXFbIG9DxNUJXlfHnG\nmb/Vep7HNJ4ihMJxfYR2kBZqM498zQeaFo2gqgy9xRWk0CwstfGDCFHN2FzbxHMjgiBid3+f6egW\nPLpH/lOvH4kiWxc5/Z27SMcn3r8D0322NhYoT5/loy/+NP/it3+Dq098nMrLmB67dFqKhSc+TiUU\n9fiIndu3COWUE4+fJwo8yrJEuRpTlJRVhhAWLR3KMgcMVVXiOJZEahwdoPwWQmhElVMbQ13Pm+Ki\nLJFSUNUWU837UWWZ4XseSA8pHQSaRtgkyxOEtPPfLRRSrSKJMCYDUaFMjpIupfUQsvhbiDa2nv88\n8pxZa6iKGWl6DLbEdSKUnIvlZlnKysYZTDUvgHVdkxU1V64+xzMv/hTrZy+TZgnW1tR5QZ2WrPdO\n8Kv/xa/xz37rf8AlRziKqq4pTE0YBSRJipaaIAio6pysyufamiikri33RkcURYHWmk2/TW0tRVXz\n9q0bOEHIQTJiKlNuyV1+bO0Ci16PV2/e5e4w5uypswSO5E9vfYtpqWmFyxSVwHEtveWc8XhCOjmB\nUjGtszGlLKgOApaMi64qZnXM/Xvf5xiNEBliucFRzyOZlFzYUSwkkmp5QFr6hEsOS80L3Lh/nbvj\n76HzAJM08VslK0sXiaKI/sFdnPWKU6cukKaW4fgmoTZU+RGuVHiiJpsOsdmQ/sE2H3xwk153keef\n/wTTScbN6zehsnjaQ9WCStTUUpCkBQ3l4DiKxJTERU5rs8NGu0leGmaTQ67v7dPe3ECZCXV9TLtc\n5LCs0ZllVXdgHJOPD1HnQvQzVzgcxCy0l5h0hxxde59Od5ELz34UPcx41z+mX8boNMMxEqsMcWVx\nqg6yMFSmxPFcyqICa/A8D2yBGo7ATBgLy+SL3+bhCw9YevpZOksh0eefZvQ//3P802scRxmt1Q4y\nqqkTl4tPPs27DwruvABe1GTB8bB1zXAaEyhN2NAsPH2WLHF4MbM8962/5n/8zd/CnVaUzYrZZEpb\nQFgJjpMJ7nKXphfQKDQSMIFLM+qwZAPcIKT/8Ab78TEyFTzYeQhlTT6e0Dpzntc+fIswUugwZDAd\n4RpNUTMXmhozZ3VUNVmSUmQpxkgQFsqSObXVoLUGW1NXxaMYV41wfPzaEIYeC1GHk0ur2EXLcm8V\nU0oc6VE1SyaNIYVMf6j69iNRZIUA16Rk8ZhW04PeEibscO7y08yckGde/hyr62cp6wyT1ZRYXMdh\nPO5zsH2dhz94nU675umXn0N78y0qHXmk02Om8YRWqwVIhFCPpvqQ5xm1cdBSgnUAjTUldV3OTaZV\nia/ma4GIeaNbCIEWeo4MLEryrKbR9PEdl6SoqU2FAfJKYJHkVZ+yGFBVMRJNEGxgvR61ACEkAh5h\nCSWOcrGlIZml7OzcJYgkpkgRtkldWu49eIj0fTbXTjE67pNal4XVdVY3TmK9kPc+vME7t28zG8e0\nuy2uXL5Er7vAaDrif/2936Emo5Ll/LsmBUmaoZVGy7l1IIunNKOAyhqEUARhA2sFB/0DpJS4vmZ9\nYZV7+9v0ej32+wMuRE1Ex2daF6zpBtZIvn/rNg/rjJVTW5zb2OT73/0ugzpD0aIWBdIF4VTkhcNk\nVII54vzjTcZFgEkL6MdMsw6rQYDUQ8qpS6YTFi6sc6wz3EnNyoOadbfDOMgo7JDTF55mP97lT7/z\nFU48tUGSzTDHll5zieVwi9mwz8HDDzl/4XGmoxLHGZJmiieeeJZ/+4f/Et93yLKMzTDAj0IeDu/y\n9Vf+irCSfPLjL2KsZHj4EF8rsAWzwT5rq8v4Cys4lWX3vWscaEPbcwhw2UBwfLxLY/eICJeLGx/B\nW7tEfO1DJu/c4OKlDWIpORm0GducYssh29/BDmMWz1xhMhU4BxktfEbSUOc5uwf7ZHXIuU9/itH9\ndxjt7tCfHdACgijEuA0mu/doNBdxw/kJR1YSlxpGE6oqphzAKM140jsHfo39k7dx04D3NxusH7Vp\n+RXpzT1OnHic7KiC8RT33Awxu85P/+TzvHU/ofzEZVavHzKTFbXrEo4M8RkHXzlUmx34f94iONHj\nxUuP88U6YfYgox26HBzP+PGFs3zuk5/iN/70iwwaktorWUAzuHmDxDE4peD0+ctMBwOWe20eTsaM\nbMHpUyc5/9hjTCczNhrrHO494KmPPMHbN+/gjQy606LdbrOz+4D93T2wlvXl+WnuF37pVylr+8jA\nMN9eTCZDbt+8we2b15GORuclruOhjOTU0gYb7R6PnTxF4s11TZNhRhpXNJwGG8trHI4P5qaM/8Tr\nR6LIGmOgmtIKfcbTErprLJ5ewV1cJk1zls8+C2HJaBDT8nKycpHcxFDOONq5i51NUaagrHLiMqYV\nLpAVKYcH+2TF7JHTa17UyrJEO5bZdIzCpy6hriVGO5g6QVMiKFGipKwdimLea7OmnhO6hCKdJZSF\nYZbmrDoOVVlizRzx5ikHYyXGTkjTB4yG75EnQxzRodfLidY2KAs1f5oqSVUXKDSuVKRVymySUtXZ\nvAeUJywttEknKXlWsHFyi07QQXVdwq0ubqOFakSsbW6wvNrjzt1bzGqLloZpf5diMuRf/G//nGx0\niB9KqhI8HWGFochzbG1wnblVUCmNnRWEQlNWFVkxxY9CumED3/eZZTPu7x2ig4CjaZ9O06MnHNK8\nYCJSzh10OYhi9myGkTltt+Y7r30TW0q6hCSuIin36C6D0IrJeIFOe4XW0hHTfEI+UUz2ITQeUTBl\nkMb0/AW6XcFkK+DWdMzZfkT7cAKuZq/Yo+F1yBND/50fMK0Ea5sOk8ke8rDJRhAAgv37uxiR4IcR\nx4cpYcdyfPcHrK9f5d69MS88/gJvvfsKnhC0vACRzfjBd79DWI955vEfhxI+uHEDz9WEjsLzXCZ5\nxcGDD3Efdjhz7iy202aazvDykp512HQ8Kr2Oa6a0zl7m6X/439JPA9JrH9KZBczeTWl+LuSWmtK4\ns0fxzjb+z3+K/G92qTcv0tvfoT+b601WLp1lOr5Do9LoxPB2ss/F9QtMmgvcKytGt28yzIa02ycJ\nCof0qI/ohqAVvgogL6gnMcXkkNsPb7CyeIXo0hMkwwNOXRS885X/QGd9kdaPfxL7sz/H8pffYTI4\nJDgVkdcxe2ON3F7hxpcPkUFKFfbYfXyd7N5tzpchRo1xfA95nNJwfdLnTsC1D3j5v/o53r7wFt//\n4h/yF9f/hn/wuf+Gj73w9xhMdvnvv/QvWRYNGlXNqJySdF0adUmkfDyl51jLNCUINa+8/QZuRyLi\nkrskBFWFTmH6/us89+QnePvbb5GlCRcuXGBpcZHhxglcrVldXmJ8POTb33kNYwVWCiwCbIWpMqSp\nOX3mDNmjGcxkELOxuMHplXWe2jrLqh/xYTmirAyB4zJOphRFhbaKLMl/qPr2I1FkLRZXO+SFQ+qs\nsLbcZslJGO7fROlF/DInnzh4YZfJeEioxzA9oh7tcnFjhTuy5mCSMn3/Pq3lFpmbYIyhG4Jc65HF\nMb4MSauUZrsx/2M5TWTVpypGxIWH57YoS0UYhhg5N84qDY4U2EpiKkmcTLEyJzcpZTohlCGTw4Re\nr6CgQPoahGE6PsRxFI3GKRqNk8ziQ5LZCKe1QIGLG865l7by0NrH1mOOR/dIhg8psjGtZg8VLqCd\nLrUwCMelrFuE4SbWVXgyICnGxAdDVsUa0wMHU1guX3qWw+iI/Tvvs/Pmm7z6ra9RFTOkI8iSEq1d\n8mqGoySBI6lrS17kIDRaeRR1Rm5LPMclJ4cyY0tGLPshr8cHKG1xrMQRknYGlZMhqoqoqviAI+5L\nQyAkyyPLwWyX2FFoLApL5mesdH1qBZOBpjEDcXZMZj3SiWSwP0LjUBmXfloQ+pr+0oxBQ1EmIZsz\ng54kVNolLwoyagqVseQ2uHNwC7kRUAiHOm8SOALRj1k3MbFy8fAoOhLbDdDuIsq2KeqKcvdbbN18\nC1f6HLgusra8/eF1RjPDhZNX6J09w3D7Js7BHVSR05JzmeaiG2KljzEp/Qcf4jY0rqqwViCVi8Sl\nVSYcOH10aMiOLenxjGg1pHYXqY81B9Mxy9OK4Y37qKN9giqme3GV4o13iaRgYZpyHAoaa1usS0ky\nnNCKMjqHGXf6KYsrHS585JMcbJynKDLKKmM6PUDvpKTHNQ2/QW2nmCrncG+b8XiHzc0uza01RisN\nFk6d5IE44OR0ROhWjLKMxmjK9AWP4mbM1rRJozqiauzgty6QZzDTAjMYcbysOPmpyxx/5wEuhjow\nuFVKmmuqxTbNh2C+dgM+ucJnfvuf8GN/fQdXd2BnwB/84e+TSEMtUxb0XH0zLUp0rTCeJi+mxE5E\nlVeUlWFWDYkGgBR0Ax/phRwXE37ps7/G9fc+4MSFJwhxme5NsdYS6ohkkvBwuktRZMi8pHI0XhgQ\nSYfj0Qyn3cILHGaHu+xlI5r4nIhWudI9weXeGhuNNrq2mImkpUOm+QhXVKQ6ZzAeEfrR3703WaEU\ntfYI3Catdg9flQwePmQ8nBL2PITboEpShLG0A4f+3j2GB/foNHysH/Jjn/wUo0nMQf+I5koLgOPj\nY1rSp0xzTFmzPz7AaihMRVHVLC4uw7hLWc1wPANyhrE1ZV5QlBYhFEWVI4SmLCqCsImbaVxH4FIx\nKCuuvfUOt25v89kvfJ6Pv/Qih/2UsqqJoh5ZkpPOBFJqAm8ZVzXAhijRoKwGWCsQIsdUBdPxDuPB\nQ6gTGo0GnupRyRDX9fA8j3G/T13XRFFEmRaUGMJGhyBsILTL2+9fp7c2pLWyyGKk+cu/eZXDu9cp\n85iG8lHKAd+QZFMqPY+vBLh4SqCERCJxrGLsaIYU5GWO5/uUVEyrkmxwgPJ9KgQKSylrtHBo5Ya4\nKEhlTtL2kNUMUQvQknYt8HNLhkX6cCZoMLQZVb+kPQG1YHEQpGNLcVQSFh6O1BSmoPYl4VqL2hNk\n6YSnxg0Gw4Rmq03pQJnCoutBbthVU5woQvsh02yGawxhqfAqixNorsZ9vKDLu+MhpJr8OMFmFc5B\nxOTNm2yUgtAL8E3BwXtvQjxg2ffQ9YDta29jJ8dQVPjKRWmP2ggqaqyqMcrBqSrGowluDZ4KqJXl\nISlr+Cjtcnj/LscPrtPsPUY2EzTDBbLZjOA4Zbq1RDd8jvzt27TjgMTRhEXJh7u32OgsY2Ko45xI\ndVBjy/TeAc5Kl04J06N9BrJE+i5La+soKSnGHcbBLmIE+WHKpJqw+NQGCx/7OD2nQydYYSLHqPGI\n2c4PuPHaX3J+knGrCzfffsg/fOKXSdUi7bMZ8fsz7AQ6p1yO7w+RrRpdOATC5/jmIY3VZRbPncaW\nE9q9BfK3PiC46XBwcYR6apnwLYv/1RsMPv80vckRDPr8+euv8Jtf+yJBFNAZlQwDzSB08XONDQ1p\nmZMe7GJmBQ6WAIGrNeZRPC6ZpeRFyhd+5idZDCPee/11FjpdhiZ+hDvU+F6DqqpR0ptLRIsZHgI9\nMjzIEhw3ROczVORz9flnaY0H5LsjfvzSczxz8jwmSZkMR7S6C8wmA3prJ7g/Oebg6IBgqUu310VK\nyfv3/zOZEf5zXUq7tFc2MbVEKUWRDKkqQ7PZxAtcpnlJs+GTTyfs798nHh7hiJp4PKAu4PRCD6/R\nQhrDZDxDNL15Zs7WzKYzWlGPvVkfHTiIRBI0mozGU3peRG4K8iKlrjNsDcqpqfIZVVFitKbdXaDW\ngiSfoUTNnRs3+N6rr3Dz+k2KwpCME8ajGWVa4+kmQrqYIkPIhKLKqU2J70m0tNSmwnUKilwQeC62\nShj2HzIZ7oAtCIIApXyU10IqD4P+2/XALMvmb78lrJw8SW99k2a3x+Coz+JqgbWGhXbIv//Xv8+9\nm2/TADZbK1grKIqCyPeIC0MhNSEKT7rzAmsl5lEcxdEeR9MDkIrNxTX2BweUjiSexWAkzWaEzXPy\nGio9t/JO64y+LknKmmZqCbVECItvmOeLI0XZ8kjyMVnlQBlQhgrTKqhGMfEooqh9LAlGGPwFH3fB\nx3cUeprRnDnUaU7TiVBGMpiNiW3FRNWgJd5ii3GV46Y5a1nA1skzzERNcesWSzNDs7dFejxmQ7mk\nt3bJsztEnscojlnFpSUW0XnOiphv9rmeR6JhvLuPVAWBNURS4TrBHA5SF/N0pqpx/SaNQjOtSyQW\nR0jiKmdUpSz2TmJmCpXNqEd3IFqirjwKFMJJKdOUhg2I44Jo9Qyz/YzwiVWG5ywL9SLp3hEtz6e/\nu4s5s4HNI9YKQXrrgNnJHmU+wxYVbdNGmRirXawXsbj5NOq8pZIJC9mITqtNfzSmUIZjPaZlLG98\n6Q+ZfvAaxYpg3FtlfzblaPeYwbk+TrNN5jVZfmyF8e2S+zvv01xZBxlRZBN6zS5pXHP7u/cZN10i\nVXJRB3hvHjErJCv9AbPXvwv0EGaF5jcfwM4h+cklbnYnDOM+C40W+UJEp3L5eLjEUbPm7vGHSClJ\n8gKn4VGVFQUGfI+kSjE1LK6s8flPfgZTGf6X3/3fAUl/MqGztIalJs3nJ1gvdGm2I9J0RnvxElGS\nQZlR+xqkxrWCTEr+5r33uNJb4fLmeba6K7i5RTnzCFypJOfXVugstGh95CpXnrmKiBps7+0QxzP8\nt/6OKcEtAuG3oKjnIA0jcFsraCdgkhnAQJkyPrzPaO8eWpQEUcT+4JjA6xDPEoQfImzN9r0H9ExO\nb2kBpT08CTfu3KLd7FJmKX6o0bVEaJeSHOHMFcW+18CWJdNkSpwckSZDGtEJjvtHtBaWEAJ+/3e/\nyFuvfgtPa6qqpK4F/sISlx67TJL8x4njo+UHVeErxSybUFTC9WOrAAAgAElEQVQ5BoHj+hhp0SrE\n2ooiiSmTEVpUhFETx2tQVRorAyrhIFEYBMYYDg8PcRzJ1WeusnX+IicvXGR3/4iPnrnAg1s3+NY3\nvsZffen/4juvfBVTZxgdkBWayG1gAVGD5ziEBATWQVmFZJ5wqLBYYXGyen48MhnDnX2sTQlWVhBa\noeOcjomIS4MoM8ai5o6tGTs1QwXN1LBYKBzrMhUFhbSUDY/Us2hT0Cstm5XHURQyWBBoCqb7kJca\nbAnKILoOwYKPpwR1f4LtZyx4XYRy6Odjjqsx0nEwNXieJljqkRYpubDY3FIex1Smj/UNXlnjey7O\npIlnXUSVEVhBYgxxnBAGDTpBB0du4k7HNKsxpZFIU5LFCRKJMjlIQa0UhTCYGrK6RIgarSEZjdBC\nYj2BEJDZGlfOoc676ZiotgR1xu72Gzxz6nHGU4nXWSSrJ3SLJsnBCHvlBP2dPguHQ5I33iM4dwJ5\n8SyjyRhndxevnNK5uMWu7zCKM4KwTfvWgFQUxMoQKwcpFc1mhBI1h+IBXS0RNiE3Gfd3Y/LDEn+a\nMX1wn0Mbc/XUFtduv8PhLOZGtYNTKp6mwW/91W/wyy/8EmutC9QrgvCxdcStPbLsLpXexA+b6Nyy\n3Fpkf6di4h1RByE74wEbrBHJjOTjp4i+ts/MHqOUxjnOGF09Q+fqOl/YcPlnv/PbTCWYrGDJ7zA6\nOKLvGpaiBaRWPJjuk8/KuQRUa0ojWI3aWCM4v3We3Tv32b57n4Woie+5pOmU0d423W4XXVYMx1MM\nUC1tkucl2/e+z4IfMJ2mWCei3W5SZ8cMx32s0PzEU5/lqRMnaZaKrErRnsaRGhmXbDY73Nm+x/rp\ns3TaXQ6mU2xaMxvEPxTq8EeiyEqgKCryosKVgtppkBkPqXwSMcPTGXu3b7N//xYr7YhklnG4+wDX\ndZB2HhTOEWDB1pLj/og0Tek1O0yKhFoo6spSxTl/+pU/4uatba4+/VFe/NmX6HRaSEeg/YCkNqR1\nTaPTprUQMD4o+dpX/4JPf+azlLXl+jtvoaTAUwpPWyZFzfnHLtLqthgOj9GOxBWCZitEiEW05+Gr\nLrbOUVICHnUdIERGGqeUaYHvh/iuwAiJoYnf6KLdkCLN0W5AZ6HHte9fI85yrl54nI9/5ieohORg\nPMb1PKo8gyxl+9rbvD89xooU6QlG+Yy0qpBuQG1LsrwGWWOVQSLwa4Gu5/BoX82V6Cpo0qoqZklC\noBxOLCyxazOyrODlS0/xwfY9RqYmt/Oc76FrMUISGEUraCDLgryuyaWk9DSZr6DM6U0tT7sdphhi\nxqjSIFPIMwdkTctOCXodnEWfLJvAIMOfWKyVjIxgmB3R8yK6uSUJfcyJHqmpUP2Y4fiYtVaHbrdL\nWkyo05RTqaK2Lts6pWokBGlJUWXM4owmPm21TFP3WMwjilZI7RRYSoIiY2pqJJpmo0tezqhMRWpr\nytKgrIOU8934GvCEYiQrKmOoK4NQ0FUOSzLkKIsxwseagu3779C59RrnrvwCh4cjAl9SlhDNSuy7\nt6F/QPCPX0YeZCQ39olUl9aTF0nNhGL7Fv2vztj42CcwCw3GuaVcaiPyDDed4R4lFAczqnFOvuig\n6pK87+CIJg2vSf/eNfLjXSgK5GTEZJKy9fxPs37+4ywfz0ljpU5Z9CIG9j2++dof83PP/mMyEZNH\nEWsnrrB373uE/gjJJuPxmEoXtJshUjsUSnF9OCHbXOaUdhAf5PDpnyK7e52etvDhgMZ94NQaJ86c\no7OxivnwASvtRY5GBxSRx7FXc6pu0AgaFEFFN2qy0GzjCrBFRVD38KImh28fIxzNR5Y+juc7hM0Q\n39HUS02UK0jzCZNkwO7hNq9f+w5laejoLv3xiMWtxwn1BmU2pqxjHGv5wgsv8tLWM0QyJJ+k8wdp\noPFkC1nUNMIAmdbE/RHx0YRJlhNYTcMN/7+11f7/cUlrcGyJ0QItJKKWCB1hBLiOZef6e9g8ZaER\nsr+3OwdFN5uMRiNUVM7VwvXcYOl7EWVdcXw0YbI/xutE1EIynh3z1T/6Y7bfvwFuyMP2Ntd+cJur\nT18ijATTZECWzZBaUVmHSb/gu9/4Bnc+uMnzzz/PeDzmwpmz7Ny9iyMkloL1syf5zE99BuFKjDCU\neUFWZoynfVyvTSVqhIJmFOFYl6rMcNyUIh6jhCGIQmxtyQsXU1mU00F7HazSaNfBcX08v8H2gz0u\nP3GVF158iVlRklbVHOtma0xW8vWvfIn4eJdIQem6IAVKgzCacTpGWYnjO9RW4Nq5YNEaQS3mUGgk\nWGFIJkPc0KXhh5RFSZrOSJmnECaHA3aqmExkIMFFU4kKhaSTKTJPkLoghMYoRa0MTlaxmFvOWZ+H\n5Nzo5GQiZ2EMs9wDHAKnotNqYlqatMopRxnt1GHZbZJQkWLxo5Bd19A8vw55ibc7YtEohrMpgbK4\ngyn+2FDlMyZIpOfRUBKZwcnVVcriAOFLHo6OUQikHzC0M6KwQTUeYssK3/dQJbja4nmK1Gb4UpPU\nJZUEIwyBAEfMBYFVVbMQRExtgq0KPKkRSs5XpcsK6wpiYx5pVRLeeOuv6a0+TtRYpTQGtdKibzKC\nwRila/Z/9/9m6/MvIy4uU7+f8PB4iHYkSytzyeQs28FvnsJPJVZKjOsQtBYJPZ9smlJXFc7uFLXo\nEsfH5HFGW0aIOwVt2QW/IF0UvPzsj3H35iE9u0y9tYnKp0zKIaaqeLF7BpF5vH7rSzx5+hMsyic4\nKHJ6K1dId98nCVsIx2V3fMRi6uPJGNFwiYzP/Tyl42l6wQL5iSbe0jL1176CurHP7MJjyKN1pL9I\nK9MoG7A7OaZwDCGCwBqK4QzlNljvrVJrwayuGMxislnCyLkPM4EThuRliRkISlPjui7WWoKqSaMZ\nohxJZ3GBi088zcqFy3zrW69SPzzi7JUniZYvUgwD+qMJvXaLyw3Np88/iakFWZoSap94MkIIj36y\nh/YjdFWx0OwyOJ5wOJ4ifA9cF1+rH6rI/khAu9dXV+0XfuXXmQlFPZsga0khArL4iIbtU9JkMplS\nljWTyQTHUdjaYGyNp3yeePklxlYSWgeVF9S2pBI1DhIZJ5SO5vBoj+133+Xam9/nqY//PbxuF1uW\nlHXN2QsXOHXuIllWcH/7LocHd7l69QIPbt/lL778FS5eOMf23dsoBEopGlFrjmdcWuLzv/KPWDux\nRTqeIssCYwumyQTPKCbpFO0KlhdXUMKlrsC4At/NMSgQHkLNddMWjbEOWAffa81lbXWNo1xu37hJ\n1OhQC8mf/Ls/YmNjjUuXzvD7v/NPGd79AN8V1HmM5ylkHpKTYXSNIwUeHo7QxHmKUXMqUpuIEAdX\nPIKe1AVplZMiiUKP3XyI1RDUltJz0NplLVYc6Jipyag0tHAwZYVxNJkxNEoLvgNKkpqMrXaHF91l\nyuGAV9I+017E+sznfGI5ZeBYOXxxMeeEStkd5sgEIusT4eEJD9nUpI0KmgqzN6bnhrizErcwBAgc\nK2gol0hIdlTJyBbYbEakNSNbUmiHQIfoDM75ATqbK4Yi3WJh6wmClXOsdc7gLLYRRjM52ObWje+w\n/fAaI5NQuS4N4zGoxuRuSVXXaBngK580L0FDVweERpJTYquaQGhyDYc2xVcwSguk6xAaS7uCBh0a\nnct87ud/HUKX7CjF9du0MkF/cpdummKyhFGg2dq6SuoGlAe34dqrVIzJtk4QrV6gcfEppkd9dJLD\nKEckIGuQUlA+vEZcDJjlfWpV4gYhkyQjyTLOnDvLiSt/n6PVGbu/969YbHTxRI9jXJKmoLAPeEO8\nwgd1l/5wmycbG7y8+Yuc2QjIq4LR4TXWlz6Bc/Y8kXEoJwkq3eZ531KlimptGRkntJYU5UfPoF94\nAfXde5QvbVF+/QdUtw64PRnw7H/3a9S+QlYGXdWc2jzBVnuN2/e3ScoKIn/O+ahKXO0wFSFKWooy\nRkuLqWuafpOFxhK97hKuWxIEAY7nMksSdvYOaXU3UdJlr3Q5f6LFB9dvkaSStYbhn/zqZ1gyLvdv\nlpgqR4Q+utMgcw2LqwtMDgfQT5FZSlwULK6uMZ2lPNzdYXc8YHs64C/f+muSfPZ3B9pdIsiyhCKJ\nyeMZRTHfiHIcl9zpcDzuz6HTGIwpSdMc3/MwtWHmaigMTQGlmVOJhHGxVU0tBLGqCH2XRqfDyoXL\nnFE+nbVTCCSqmXN4OODam9fmCwVZTP/4gHNXrvDCpz/LgzN3+Ma3v8v2nbs0/IDJdEa718L3JdNJ\nhiwrnCCiKC1auSgEx9MEGbQoiwpXzj3wtfSpARxwtCI18pEz6tEwpZIo5eGqAGMVUlm0nEeBRqMh\nrcVF8izh7Tfe4aPP/QTTg5v8m3/6mwwfXsdxNZXSVAqqskapGFVbWpWLFJpSC0rHomqgyhmLnKZK\nqaymNBqUh1GWrC4YknIgBO1asWwi4kBClZPZhLtORV6DKwPawsHIHL/RwPMChFXsVxmhqWkUdr4v\n77t8Mx9g5JTtLjRDQVzF3Gn7PHB9AhnQNCmF9FgMPHwVkGeGmVXEjoOqa5qJRd0b0coKKjmHgZyX\nCxSi4K6c0sJwVq4SWcsHZYIiYEKB8GsO8oS12rIYaHaqCVsGVvw2q/4yK06P8dIqu46lFxsaK5rp\nzjEFlo/+2C9w2L/Hjff+mtTNkOS0C4uHoLCWQzcjdSo6ONhaI7SPX9cM6xG5a2j4TVojgArPteyL\nnAXTxnUt950jwuGA178hufLszxMEEZKEOJT4RYBULvE4ppn1eX/0bTY/+eOEH/kIVdRG33mPyeQB\nduse8a7H+sUnOYwN0kj8JEZOpmQjcFtP0dAGX5Q4QkIuWXZDGltruCtdjkY3cAKP6vQWBx/s4i84\niKBBWIW4rUsc7r5F7uzTbXTZZsKf7P9bruQXaEc+2/kD/kG6w/L0AntdQbiUoqpN3poYnnYGqKN3\nCU49RfJfPoHX2qL64D3+3/bONdbSq7zvv2et9d729ex9LjPjuXk8M8Y42JgBDAWXJKQhgVLRS6Ki\nViKNKlXKRW0q9QNVpCj92Erph0pNoraJmtKW0OZCAAEJF9sYMAbbYDAYey4exjPnzLnvs2/vda3V\nD+82tVxsDGVyzqD9k7b22ut9dfQ/z9rnOe+6/Zd+212kf/kwnU9+nmmm0W86w1d/54MMVcGHH3mQ\nhy5eIg66hFHO5vmnkFARVROqypGWBa5QmHyTqNGn2ewQJpqs2mN3b5Xt0RrPbjSIjaa90Oaec6/l\ndefu5SfbyywtHqlP9NCKLz30KNqEdMwe/+Tt7+KMnGT18nVClXDtWMDywiLDcc5wmnL98hZUlk7c\noJFPwGhGWcFWlrNaFTxXTbg4WKN05SvObwciyTpb1TuxfO2BKSJEUYwXzTSrt18G2sxczwUQnPX1\n2eveU5QlEoSIEoT6Z1hrQTyd7jJ5mmKdotNZ4Pjxk3gH48mEXhcWez3Wiw0+97kHOXXmBJ1Wwu76\nKp/52Ed58utfpRwPWel1SCdTeit9Tpw+xWS6R6vZI1w8hEbjXa1nOpliwoRRnpKgcNbilWB0vZ/a\n+Yooiuptt0phTIj1glagJERUiHIKbMH169dZ6C1TVRVFUbF67TrHjh7i8c99ki984s/QDDmxssS1\njS0sFVEQUpUFFk+sQxa6fbwXdicjyizHW0+iQzpBTNsL2jpyCw5AaZSEKJcCIXEYMAF2bIbNM7yG\nUAlGBGdLKiU4ZxltjzDRlDCM600MZcFmnqFKTaiFSoUcixe42wRkWY5ptLloh3xnssthLYzWhhy6\n61VsXL6MbkX1WunxGJsXdHREpxR0Tn18iyhuDTpktl5idooQg+BjjxEhigJc6UEVNCuhawWMY5TH\nKCdsNcBUBfnuc6yNB7SnqxT9LtfMMsUzI649/Rj9qEluYl596ggLwRv59je+wbVYKGJDozSsZJ52\n6tkQTxrWsfAmAlObdCsj5NaRY1GRo1F5FlTEps2h2SId5fRUg8cuPc72oMW9976ZhcVDqCiEWLG+\ndx2JPTuJ4LYmVE9+k/HyInQbtN5wD91Li2RPbFH4x6lSobd4klwl+CogWlhGLzmicgXTjHFKY4IE\n7YSyyqiYsDdYZWtzg+VbVlg+fJR0PSdpL+BMhJSa26IeR5Mu29kWdpoTJ5ppmfK4+g7fvPQdmoMx\nrzl3gsNH38RCtkilIyYawsUW26OKQ9Mx1bGY1lcu4/7BrQRfWWU6sYRvOs3Ox79E/8wt3H3fOfy3\n15E9eOtv/R387oRfef9v8MGHPoV3jkALFBZflsTKECUx/ZUVkIC94YTV3V3KKiNsKNrNNmIDWskS\no+1dvnD/l3nogS+RZRl33H4bK4cWOX33G3nfP3o35S++ky987COcXIyohutYP8Ysr3BkaZFetw/l\nJtc3t5jmOXEQ0IwbaK2Z5Dl5OiH3FXvZmCtrV7m+uXrzjcmK1Os1C+cpKk9pHU7XRrl5aYmiDsYY\nlHhEhZR5gfP1McBxq41D0Ci8KASFxYISRBn2hjmj0ahO4s4TRwm7u3ukkymT4TZnzr6axm1naHWa\nrBzqcf36FdavrvLAX34Sa0sWOg2KPEVpOHHmNu5921v5xpNPsHV+HVAYFaBQlF7wOiQIDYEF8HgH\nThkwISqs93uLSYiC2cC56NqWUjxQDxV4B1UltNttNjfX0DqiKiwLrTaT4Tpf+KsP0GwapPKsbmwR\nBoYgaTEaDglmJ6OKdYyznDzPSW2GRtE0Ia04ISo0bdH1si2xWGXwQYDWEXleYnztnblJSqkKAlGI\nQOk8WissDoyjETVIsyEOj45CdFq7PlU6oKEDYqcZSsGg1JzNIo7ETVa3dsiOtFjN9tjC8lP3/ixB\nM2Hz2W12hyVR4nFpTjjNWQg1Ta8JVEzhc46phFuc4Vk/QSvHCdckIMQEGl8UuKCiDD04z0ppECL2\nxDG1Jc6mDKzUk1MmxJRbdNZK4qzDqrpMubdHhxSTZWxc2mXn2gX6pxe569jtJHsbDMoUX1QUSvCJ\nIfBCWeQ4UzEsBpQ6YGBzYgxJELDUv4XNvfO0dEhQOLzErA4HhLEhiNtUI81r734NrUbCzvYmKg5p\ntAMmQUWrGZGoBs1WQCQRw801dnccnZVb6ESH6YZdiumI3WcvElNCbxkfdiiSBbLYMx0KjarCpiO0\nGRK3EpwUTCbbTPa2UUWBmxTEjTZZ0iGIuzilETzP5jvspmOwHlt6prEnzTLszh6vPrHCFVtx/5VH\nuev0WzlUNVg4fpg1tcd6NiXotjh25nVw5Rk27jyGfPgTNJwivXyFpYWE6Nf+IeuffJDF8xcxmWbv\n+pj0f14lPtnlmUtPgK2P3HapJVIBkUlYWlzBC+yVe0zGA/LS0+l0ULpFWebEQUzUaKD8hN5CSJg0\n6Pf7tFodNq6t8dRXn+GB+x/iv/7u7/H6197NW25/FWYwwKgFlpYP89hgBFXKeHPEcDjGjjPEVZRV\nSaEVUZSgC8vWZMhmPubq9lWubl5hnA5x/iZLst4LeSlUEhF3YxIdYMIIE4SIDtDe4ZzDz9xz0smI\nLMtqI14JmKYW8hyH4nnjFCPgjMKhMGGjNu51njAMCKOAstdlNA5pdZdIreXk7XcwHm6SpSXZNGel\nt8wtp27h4tPfqq0AtfDspWusb36M3cE2Mq1oTgrKvMCYirywiA6YZBXaRGgglPrMeecNSIg2ACGi\nYvAW52aOad7hEWzla+Pw3FFNpzhryaZjqrRkurPNA5/5CCosmKR5bQwVCN4r0uEYQRGG4WynTEEl\nFQohVCGxCVDWU2QlidcoHWO0Au3weubp4Dy7oklKRejrDQcoRWIMhfJY5whUgA4dhXK0vKKpA/Ki\nIk8nBEmXjjRJjNAOQ7pac92NuMwEW3h+Rrc4feQYxc46t7ZaPJVnbO9usZS3OX3nOZ669C2mfkor\nCekihF5RZDk2iIi8cFwSxsWARCriuEFURtyml9kxFeV0j8JOybTHYVmRJpFWpDonjgtGowznFWMx\n+GabILfkBchgQlpcJ8JhQ824sCwny5hWAij2bEabkF6UsGdyvpPvMbYZ2lZ0c0eaZOSFxfR7OBeQ\n2pLhdErUaHAoXGCvGhMpcFVBjqMKYy4Oxvz6z/9zussnKdKMKIkJkpi8mtLqL9AMAooNwXabTIwi\nRNObjnDDAZWusK2KILyVjcllgp1nSNR1ksYtBO4YatBANSJke4PhM0+QjTcJui1Upw1hE6MDjnRa\nlMOMPAUTdxHTosRTkZH2YzauZaznYzrtZbYmG7zl7tfxpkNnefKRL5D1uwxDy7enz7DAItWGp3kk\nwGLZDho8ujnmDdqwspZT3dHFREska9+h+uoF/Lm76P3iT6MfPs+VwxFHB47mUpcPP/4Aa+Nd2kmM\nKyyNIKLRblFZzzibsj7YYEqJ0iFBENUOY6VHJIJMsbc7JIgy4rjFzjDl+voOK0vL3Hn21Zw8dpZ2\nt0E1GDNcXaU/1fSXFlG2ybUrIwoTMN5eRdCU1s5suC1pMWF9MqK3dIixLXhue42nrz/H01cvsJcN\nMQbIbjIXLi8K016kmTSJkgYqjFCz9X8A3jqKosBWJRohTVOm0zFKBOsErWMq7/AORCuUOHSgEaUQ\nX6ADi3IWJY44DvE0mY4nJN1TVKZ+gjt/8Sp5NiQIWgz2ppw+8yr+1t//23zgD/4L3/ziFzl1xx10\n+0eYjsZUuiRPRrVV32hM0uzXxwl7Wz9NewNaiKMGzkHlFJU1BEGAaEPlPNbNzoafeWG6yuJ97byP\nrb0xs7Qgn0xZbnX51lNPcunxr2Hy2nQ8KyuUaBraEGghMRrjhbVqVK/xDAOMMUjlUF5Q9UHJ+EBT\neIMRQQlogcAKtvQUrqTp6yfejnFMy5zQKaxSJEGI5A4beabViEBaHO8sszneZVIVTKoJXRdjbcCQ\ngqaHnrOkWjgfWxiscU/bku0M6FcNllqaS+vnqRYP0Vo8i01LvMsJtRCLIFVF5ktCQrpoSipySkIP\nLRUQBAGYkJKC0jtwJeApxdMSg1GKq6bEVBkmEnrEOOcZlRWRD9gIPAmerk7wBna8pXv4EKWHTr/F\npfVV9mwdAxNFVL5CZxWHASOGHMeeLXFSt5XStRevcYYATcM1KFRBbBwDXxDiqabw7ne8j+On3sKF\nzSvEJmJ5uY8RxTQ1RMqTjkaUoUGcRzJoBR3CbsxIxqT5HqRD4mqBxEQwGjDc2WScXyE3hzFpwLQf\nEKdDsmuXcDbF9xeYJi3CZo+o0cVlFWUF5dTS6i8RmBhJDGma0muk9FYWuLRxncF0yi//41/mPT/1\n0zz40U/yxMYlDp88QXUo4RPf+gSv+ck7cAPLUmOBQ82QTe8Z5gnbp/ssPryKWdRQtVDeUC0s4L/4\nBP6u08hr7mT5th7F2W3CLz7Nk1uXGU5KAgOL/WWU8+xkGSQhe3lGELdIdG2Co4xBqRDEIAS1uZYf\nU7kWUbKCMpY0HbOxOyR/6lFCE3Cyd5R3/MTr6faOcrzfZzqeMJym7O7mFC4gbKj6OBrvGE0m5Lag\npGBgUy4j7BUTLq5d4fzVi2wMN4mSkEYjZDyevuL8diCSrAlCDp96FUEcU1YOS31SQFmWpGmKRpM5\ncE4RagMBSH3SBKqCrLKIaKytTVy8F6xTlLYiL6aAQyuHLTKyIqcqc9I85Y7bb2c8tliXkU5TnAtI\nIsOxk2fotBsMRjmNVo973vI20vGU246d4eL5CywdWcR3CnTY5vr16/SWjhIEAZNpSbPZochytImJ\nkwZlWc9MZ0VZbyzwCnSJtbXblfLURtBVha9s7ewujqqEyTDjxOFDPPbQZ7n/o39KXBScaZxgVFqm\nytFoN9FliikqlpMutrRY0QzLFIsiLQtUaVEqItCGKIqZDVvinEdTEuFJPAQYVuIuNs/rXU2+JCw9\noQ7wOuSWsFMf30NJ6iYshW0Wm33AodSEUixhAZVSpFJhreXW0tATw3ljGXQMD092ONNqEKQVt8QR\nrmN4evsSixsparRLWylayhNVllAMEQGhqrearsuQFYkZklOUFYHSXHUj8spiA6EhGlVZMucoFWgP\nkVbYcYjShsRFZL6oz2hqhIx0hS8dXRqoOOJY/whLx4+RZztcuXyeKAoopkMm+YQsn+A99JWhHyQU\nPmfs6zaMg3pNc+CETpLQVQ2aYsito2NCyjJlMxbKrOJw1eDN5+7l8vqY/sljSGYpC0eeZQRimA7H\n5NOSqAdRAKrSZDkUhasfJpxGxS2ydITSEfg+ibTxyuHKgn5XaE1zjNZUh09D0MAGIeM8x6clvpgw\nKSBJ2uggJIk7uAp0t40stHl68hjPbV+l2h5yqnearWdX+dW/+JcMtrZpr3TpNJo8Nxij/JArxTMc\nS36CckcR+5igDVG8yHpkaN8TEj7yDNnrQ9Jrq3RMgu43mT52Cdop4ck++XIT/XN3cejjHQqEbqv2\nTTYYtscjICJuJSz2+oyHU3SgKEqLDmPCaKH+R1GWRC2DLSZMqoqigKixgBCyMxzQjBu0eyUL5ZTD\nWoirCUGry+begEg7Oh7GZUiW5lg83lpcWVFSsldMmaxfZWNvi8vrV9lNR3hxKAXO2h/Es/tgJFml\nDWGjhUNR+gxra99H8RXKVd/95VDgpF7nGQRRPUlmBJflhGFUn/Wj6l8/jAxVBSJNRDzNZsBotFsb\ndjvodBts7WxTZApX1guMp5OcwfaAMIw5/+yzfPrzD/HGe+7mzff9TR747IM8+NDnibSmcfQoK8cP\nk01hde0qx0+dJWx2qHztKCZOMDoiMDHega0cZVHVHgiJR4cBVeXQgPMeW1iqyiLegxNKKdjcGhGp\nBuefeoYP/eHvciQKeNXhs5wohUHk2KlKcleAUyQ6ICwV2JCmmjJxGeVsLEIFBi2GyjpcntEwAaDq\n8S9X4H1FpDSNIOB0o8flbI1BPmGqSiLAaUVDAk77LglW+5YAAAaiSURBVDYxXHEb5CqmoRKKSVYf\nzBeWiHLkNkdbg3KWoZ3iCk2vjIkkJY0iJr0GT2/sccIGyLTCG6m7gOWQfjOmh6KZW4xYQiVEaHRh\ncaoCKxitER9QWo/1loKCTAXYUBNVgqocOxpyqT0WxHrCuEGjyCm0IxBNURRUkaAnBWVZMlHQsAaz\naxm6Ae2TCVe3VjnWO8TWYIs2EYtJi1TXGzDGOLKioBCH8oZYR3SChByHLz02nzIqc5oxZK6itMKg\nyOgmLdo5/PnH/ztn73s3i41Xk6VDqizFpwVZXjAZD0miGJ85nLNUIbgYGjpmoQyYpCnj0JJ0oMg8\n+KBes+1TpsMdymaLttYUWkidB2KEgG5DiGIBUzIVTSdpU4YGJYZ8UpCWGb6Z8OnzD7OZbvPas2fJ\n1jz3P/AQE3JWOgucWFpga1pgSsV995zj2s5TJJ1ljBzDZIKJDU4XbNmIWzslLDSw0RC7M0IfMUyk\npNmMoRygP/ww0XvfwrRpuWBylkxI7jzDvSErnT69Xo8rO+tMswnD3R1uP3aW3kqPzcEO19Z2CCJP\ns7NCEAbkZQFBiicgDFv0en3ywpBPLUl8mLffeZajSQsznuBchcHRMGBVwfZoAxonyLMJBAFqdgRt\nVhaMi4y9POXK6hV2RtvkVYr1Vd2LHrsf4ISvA7JOVkQ2gQmwtd9afkiWmGvfD+ba94e5djjpvV9+\nJTceiCQLICKPeu/fsN86fhjm2veHufb9Ya79B+OV+3XNmTNnzpwfmHmSnTNnzpwbyEFKsv9pvwX8\nfzDXvj/Mte8Pc+0/AAdmTHbOnDlzfhw5SE+yc+bMmfNjx74nWRH5eRF5WkQuiMj791vP90NELovI\nN0TkayLy6KyuLyKfEpHzs/fefusEEJE/FJENEXnyBXXfU6vU/IdZO3xdRM7tn/KX1P7bInJtFvuv\nici7XnDtX8+0Py0iP7c/qr+r5biI3C8i3xKRb4rIv5jVH/jYv4z2Ax97EYlF5Msi8sRM+7+Z1Z8S\nkUdmGj8kUm9lEpFo9vnC7PqtN0SY937fXtQ78C8CtwEh8ARw535qegWaLwNLL6r7d8D7Z+X3A/92\nv3XOtLwNOAc8+f20Au8CPkFtp/Bm4JEDqP23gX/1Pe69c/bdiYBTs++U3kftR4Bzs3IbeGam8cDH\n/mW0H/jYz+LXmpUD4JFZPP8X8N5Z/e8DvzIr/yrw+7Pye4EP3Qhd+/0key9wwXt/yXtfAH8MvGef\nNf0wvAf4o1n5j4C/u49avov3/nPAzouqX0rre4D/5mu+BCyIyJG/HqX/Ly+h/aV4D/DH3vvce/8s\ncIH6u7UveO/XvPePz8oj4CngKDdB7F9G+0txYGI/i9949jGYvTzwduBPZvUvjvvz7fEnwM+IyA+y\nY/YVsd9J9ijw3As+X+XlG/Qg4IG/EpHHROSfzeoOee/XZuXrwKH9kfaKeCmtN0tb/PqsS/2HLxiW\nObDaZ13Q11E/Vd1UsX+RdrgJYi8iWkS+BmwAn6J+sh5476vvoe+72mfX94DFH7Wm/U6yNyP3ee/P\nAe8Efk1E3vbCi77ue9wUSzZuJq0zfg84DdwDrAG/s79yXh4RaQF/CvyG9374wmsHPfbfQ/tNEXvv\nvfXe3wMco36ivmOfJe17kr0GHH/B52OzugOL9/7a7H0D+HPqhlx/vns3e9/YP4Xfl5fSeuDbwnu/\nPvsjcsB/5v92Sw+cdhEJqJPU//De/9ms+qaI/ffSfjPFHsB7PwDuB/4G9fDL82ZYL9T3Xe2z611g\n+0etZb+T7FeAs7PZv5B68Pkj+6zpJRGRpoi0ny8D7wCepNb8S7Pbfgn4i/1R+Ip4Ka0fAd43m+l+\nM7D3gq7tgeBF45R/jzr2UGt/72y2+BRwFvjyX7e+55mN6/0B8JT3/t+/4NKBj/1Lab8ZYi8iyyKy\nMCsnwM9SjynfD/zC7LYXx/359vgF4LOzHsaPlv2YBXzRjOC7qGcwLwK/ud96vo/W26hnUp8Avvm8\nXupxnM8A54FPA/391jrT9UHqrl1JPRb1T19KK/XM7H+ctcM3gDccQO0fmGn7OvUfyJEX3P+bM+1P\nA+/cZ+33UQ8FfB342uz1rpsh9i+j/cDHHrgb+OpM45PAb83qb6NO/BeA/w1Es/p49vnC7PptN0LX\nfMfXnDlz5txA9nu4YM6cOXN+rJkn2Tlz5sy5gcyT7Jw5c+bcQOZJds6cOXNuIPMkO2fOnDk3kHmS\nnTNnzpwbyDzJzpkzZ84NZJ5k58yZM+cG8n8A2VPpZrGBDAQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fafaebe0e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(test_img)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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pdotKNjGrqmq430W0kGRkpvQKM6tfe908bQ69enlNktSMoSNJasbQ+UOL+11AQzOpV5hZ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\nOpKkZgwdSVIz/weUv+erGPm5zgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fafae1fdeb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.barh(np.arange(5), prediction)\n",
"_ = plt.yticks(np.arange(5), label_binarizer.classes_)"
]
}
],
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
welcheb/pydcemri | example_in_vivo.ipynb | 1 | 19696 | {
"metadata": {
"name": "",
"signature": "sha256:365bd8ff0feb7a08113a12afd5d7e1b599335ff594c6cdd02172c3a7e3e5f101"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Scan Parameters and Initialization ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using matplotlib backend: MacOSX\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.io import loadmat, savemat\n",
"from util import mosaic, qimshow, status_check\n",
"import matplotlib.pyplot as plt\n",
"from numpy.ma import masked_array\n",
"from skimage.filter import threshold_otsu\n",
"import time\n",
"import dcemri\n",
"\n",
"# SCRIPT FLAGS\n",
"plotting = True\n",
"\n",
"# SCAN PARAMETERS\n",
"flip = pi * 20.0 / 180.0 # deg\n",
"TR = 7.939e-3 # s\n",
"TE = 4.6e-3 # s\n",
"Rel = 4.5 # Relaxivity of Gd-DTPA at 3T [ms^-1 [mmol Gd-DTPA]^{-1}] \n",
"scan_time = 16.42 # s\n",
"SNR = 15.0 # assumed for now, but can measure"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mpl.cm.register_cmap(name='cubehelix3', data=mpl._cm.cubehelix(gamma=1.0, s=0.4, r=-0.5, h=1.5))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load population-averaged arterial input function (AIF) ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat = loadmat('invivo/AIF.mat')\n",
"y_aif = mat['data'].flatten()\n",
"t_aif = mat['t'].flatten() / 60.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(1)\n",
" clf()\n",
" plot(t_aif, y_aif, 'ko-')\n",
" xlabel('time (min)')\n",
" ylabel('[Gd-DTPA] (mM)')\n",
" title('arterial input function')\n",
" savefig('invivo/aif.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load DCE and T1 multiflip data ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat = loadmat('invivo/data_t1.mat')\n",
"data_t1 = mat['data']\n",
"t1_flip_angles = mat['flip']\n",
"mat = loadmat('invivo/data_dce.mat')\n",
"data_dce = mat['data']\n",
"t_dce = mat['t']\n",
"nx, ny, nt = data_dce.shape"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat = loadmat('invivo/mask.mat')\n",
"mask = mat['mask']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(2)\n",
" clf()\n",
" imshow(mosaic(transpose(data_dce,(2,0,1))), interpolation='nearest', cmap='gray')\n",
" colorbar()\n",
" title('DCE data')\n",
" savefig('invivo/dcedata.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#snr_mask = data_dce[:,:,0] > 1e-2 * data_dce.max()\n",
"SNR, snr_mask = dcemri.signal_to_noise_ratio(data_dce[:,:,0], data_dce[:,:,1])\n",
"print 'DCE SNR: %.1f' % SNR\n",
"mask_dce = data_dce[:,:,0] > (1.0 / SNR)*data_dce.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"computing signal-to-noise ratio\n",
"DCE SNR: 6.2\n"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SER = reshape(dcemri.signal_enhancement_ratio(data_dce), (nx, ny))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"computing signal enhancement ratios\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(3)\n",
" clf()\n",
" imshow(SER, interpolation='nearest', cmap='spectral', vmax=10)\n",
" colorbar()\n",
" title('signal enhancement ratio')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" savefig('invivo/ser.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nt = data_dce.shape[-1]\n",
"data_dce = reshape(data_dce, (-1, nt))\n",
"nx, ny, nflip = data_t1.shape\n",
"data_t1 = reshape(data_t1, (nx*ny, nflip))\n",
"# Time vector for DCE data in minutes\n",
"t_dce = arange(nt)*scan_time / 60.0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(4)\n",
" imshow(mask, interpolation='nearest', cmap='gray')\n",
" title('mask')\n",
" savefig('invivo/mask.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make a T1 map ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcemri = reload(dcemri)\n",
"t1_flip_angles = pi*arange(20,0,-2)/180.0\n",
"R1map, S0map, covmap = dcemri.fit_R1(data_t1, t1_flip_angles, TR) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covmap = reshape(covmap, (nx, ny, 4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create a processing mask from the T1 map\n",
"r1mask = logical_and(R1map < 10, R1map > 0).flatten() \n",
"R1map[~r1mask] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(4)\n",
" clf()\n",
" title('$R_1 (s^{-1})$')\n",
" imshow(reshape(R1map, (nx, ny)), interpolation='nearest', cmap='spectral', vmax=10)\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" colorbar()\n",
" savefig('invivo/r1map.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(5)\n",
" clf()\n",
" imshow((sqrt(covmap[:,:,3])), interpolation='nearest', cmap='spectral', vmax=1)\n",
" title('$\\sigma_{R1}$')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" colorbar()\n",
" savefig('invivo/sigmar1.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if plotting:\n",
" figure(6)\n",
" clf()\n",
" imshow(reshape(S0map, (nx, ny)), interpolation='nearest', cmap='gray', vmax = mean(S0map) + 3*std(S0map))\n",
" title('$S_0$')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" colorbar()\n",
" savefig('invivo/s0map.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert DCE data to tissue concentration $C_t$ ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask.shape\n",
"idxs = find(mask.flatten())\n",
"idxs = range(nx*ny)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcemri = reload(dcemri)\n",
"S0 = data_dce[:,:5].mean()\n",
"R1_eff = dcemri.dce_to_r1eff(data_dce, S0, R1map.flatten(), TR, flip)\n",
"data_dce = reshape(data_dce, (-1, nt))\n",
"R1_eff_old = dcemri.dce_to_r1eff_old(data_dce, S0map.flatten(), idxs, TR, flip)\n",
"data_dce = reshape(data_dce, (nx, ny, nt))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"converting DCE signal to effective R1\n"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show map of R1_eff \n",
"R1_eff = reshape(R1_eff, (nx, ny, nt))\n",
"R1_eff_old = reshape(R1_eff_old, (nx, ny, nt))\n",
"if plotting:\n",
" figure(7)\n",
" clf()\n",
" subplot(211)\n",
" imshow(mosaic(R1_eff[:,:,-1]), interpolation='nearest', cmap='spectral', vmin=0, vmax=10)\n",
" title('$R_1(t)$')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" colorbar()\n",
" subplot(212)\n",
" imshow(mosaic(R1_eff_old[:,:,-1]), interpolation='nearest', cmap='spectral', vmin=0, vmax=10)\n",
" title('$R_1(t)$ old method')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" colorbar()\n",
" R1_eff = reshape(R1_eff, (-1, nt))\n",
" R1_eff_old = reshape(R1_eff_old, (-1, nt))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# convert effecitve R1 to tissue concentration Ct\n",
"#Ct = ((R1_eff.T - R1map.flatten()).T) / Rel\n",
"Ct = dcemri.r1eff_to_conc(R1_eff_old.T, R1map, Rel).T\n",
"Ct.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"converting effective R1 to tracer tissue concentration\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 73,
"text": [
"(36864, 25)"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show map of Ct\n",
"Ct = reshape(Ct, (nx, ny, nt))\n",
"if plotting:\n",
" figure(9)\n",
" clf()\n",
" imshow(Ct[:,:,-1], interpolation='nearest', cmap='spectral', vmin=0, vmax=2)\n",
" title('$C_t$ [mmol Gd-DTPA]')\n",
" colorbar()\n",
" savefig('invivo/Ct.pdf')\n",
"Ct = reshape(Ct, (-1, nt))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 74
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit voxel-wise $C_t$ to the model to get $K^{trans}$, $v_e$, and $v_p$ ##"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcemri = reload(dcemri)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask_dce = zeros((nx, ny))\n",
"mask = reshape(mask, (nx, ny))\n",
"mask_dce[90:120, 80:120] = True\n",
"mask_dce = logical_and(mask, mask_dce) #logical_and(Ct[:,-1] > 0.5, Ct[:,-1] < 0.8)\n",
"idxs = find(mask_dce)\n",
"print 'fitting %d voxels' % len(idxs)\n",
"params, covs = dcemri.fit_tofts_model(Ct, y_aif, t_dce, idxs, extended=False, plot_each_fit=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"fitting 733 voxels\n",
"fitting perfusion parameters\n",
"using Standard Tofts-Kety\n",
"fitting 733 voxels\n",
"10% complete, 73 of 81 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"20% complete, 65 of 82 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"30% complete, 51 of 73 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"40% complete, 42 of 71 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"50% complete, 35 of 71 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"60% complete, 27 of 68 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"70% complete, 19 of 64 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"80% complete, 12 of 63 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"90% complete, 6 of 61 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"100% complete, 0 of 61 s remain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"61 s elapsed"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Kt = zeros(nx*ny)\n",
"#Kt[idxs] = params[0]\n",
"Kt = params[0]\n",
"#ve = zeros(nx*ny)\n",
"#ve[idxs] = params[1]\n",
"ve = params[1]\n",
"#Kt_std = zeros(nx*ny)\n",
"#Kt_std[idxs] = covs[0]\n",
"Kt_std = covs[0]\n",
"#ve_std = zeros(nx*ny)\n",
"#ve_std[idxs] = covs[1]\n",
"ve_std = covs[1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"MAX_COV = 0.1\n",
"\n",
"Kt = reshape(Kt, (nx, ny))\n",
"ve = reshape(ve, (nx, ny))\n",
"#vp = reshape(vp, (nx, ny))\n",
"Kt_std = reshape(Kt_std, (nx, ny))\n",
"ve_std = reshape(ve_std, (nx, ny))\n",
"#vp_std = reshape(vp_std, (nx, ny))\n",
"mask_params = logical_or(logical_or(Kt <= 0.0, ve <= 0.0), ve > 1.0)\n",
"#mask = logical_or(logical_or(logical_or(logical_or(Kt <= 0.0, ve <= 0.0), vp <= 0.0), ve > 1.0), vp > 1.0)\n",
"#mask_params = ~logical_and(~mask_params, reshape(mask_dce, (nx, ny)))\n",
"mask_params = logical_or(logical_or(mask_params, ve_std > MAX_COV), Kt_std > MAX_COV)\n",
"\n",
"if plotting:\n",
" figure(12)\n",
" clf()\n",
" imshow(~mask_params, interpolation='nearest', cmap='gray')\n",
" xlabel('ro')\n",
" ylabel('pe')\n",
" title('Final ROI')\n",
" savefig('mask_dce.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_dce.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"(192, 192, 25)"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# PLOT DCE RESULTS\n",
"\n",
"x = masked_array(reshape(data_dce[:,:,0],(nx,ny)), ~mask_params)\n",
"\n",
"figure(13)\n",
"clf()\n",
"y = masked_array(Kt, mask_params)\n",
"imshow(x, interpolation='nearest', cmap='gray')\n",
"imshow(y, interpolation='nearest', cmap='jet', vmin=0, vmax=1)\n",
"colorbar()\n",
"title('$K^{trans}$')\n",
"savefig('invivo/ktrans.pdf')\n",
"\n",
"\n",
"figure(14)\n",
"clf()\n",
"y = masked_array(ve, mask_params)\n",
"imshow(x, interpolation='nearest', cmap='gray')\n",
"imshow(y, interpolation='nearest', cmap='jet', vmin=0, vmax=1)\n",
"title('$v_e$')\n",
"colorbar()\n",
"savefig('invivo/ve.pdf')\n",
"\n",
"figure(15)\n",
"clf()\n",
"y = masked_array(Kt_std, mask_params)\n",
"imshow(x, interpolation='nearest', cmap='gray')\n",
"imshow(y, interpolation='nearest', cmap='jet',vmax=MAX_COV)\n",
"title('$\\sigma(K^{trans})$')\n",
"colorbar()\n",
"savefig('invivo/ktrans_std.pdf')\n",
"\n",
"figure(16)\n",
"clf()\n",
"y = masked_array(ve_std, mask_params)\n",
"imshow(x, interpolation='nearest', cmap='gray')\n",
"imshow(y, interpolation='nearest', cmap='jet',vmax=MAX_COV)\n",
"title('$\\sigma(v_e)$')\n",
"colorbar()\n",
"savefig('invivo/ve_std.pdf')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"out_mat = {}\n",
"out_mat['ve'] = ve\n",
"out_mat['Kt'] = Kt\n",
"out_mat['Kt_std'] = Kt_std\n",
"out_mat['ve_std'] = ve\n",
"out_mat['mask'] = mask.astype('int')\n",
"out_mat['R1map'] = reshape(R1map, (nx, ny))\n",
"out_mat['S0map'] = reshape(S0map, (nx, ny))\n",
"savemat('invivo/out_trap.mat', out_mat)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_algo/td2a_cenonce_session_6A.ipynb | 1 | 11991 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2A.algo - Puzzles algorithmiques (1)\n",
"\n",
"Puzzles algorithmiques tir\u00e9s de [Google Code Jam](https://code.google.com/codejam/) et autres sites \u00e9quivalents, produits scalaires, probl\u00e8mes de recouvrements, soudoyer les prisonniers, d\u00e9coupage stratifi\u00e9."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-06T22:17:04.866129",
"start_time": "2016-11-06T22:17:04.763507"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n",
"<script>\n",
"function repeat_indent_string(n){\n",
" var a = \"\" ;\n",
" for ( ; n > 0 ; --n) {\n",
" a += \" \";\n",
" }\n",
" return a;\n",
"}\n",
"var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item) {\n",
" var anchors = document.getElementsByClassName(\"section\");\n",
" if (anchors.length == 0) {\n",
" anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n",
" }\n",
" var i,t;\n",
" var text_menu = begin;\n",
" var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n",
" var ind = \"\";\n",
" var memo_level = 1;\n",
" var href;\n",
" var tags = [];\n",
" var main_item = 0;\n",
" for (i = 0; i <= llast; i++) {\n",
" tags.push(\"h\" + i);\n",
" }\n",
"\n",
" for (i = 0; i < anchors.length; i++) {\n",
" text_memo += \"**\" + anchors[i].id + \"--\\n\";\n",
"\n",
" var child = null;\n",
" for(t = 0; t < tags.length; t++) {\n",
" var r = anchors[i].getElementsByTagName(tags[t]);\n",
" if (r.length > 0) {\n",
"child = r[0];\n",
"break;\n",
" }\n",
" }\n",
" if (child == null){\n",
" text_memo += \"null\\n\";\n",
" continue;\n",
" }\n",
" if (anchors[i].hasAttribute(\"id\")) {\n",
" // when converted in RST\n",
" href = anchors[i].id;\n",
" text_memo += \"#1-\" + href;\n",
" // passer \u00e0 child suivant (le chercher)\n",
" }\n",
" else if (child.hasAttribute(\"id\")) {\n",
" // in a notebook\n",
" href = child.id;\n",
" text_memo += \"#2-\" + href;\n",
" }\n",
" else {\n",
" text_memo += \"#3-\" + \"*\" + \"\\n\";\n",
" continue;\n",
" }\n",
" var title = child.textContent;\n",
" var level = parseInt(child.tagName.substring(1,2));\n",
"\n",
" text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n",
"\n",
" if ((level < lfirst) || (level > llast)) {\n",
" continue ;\n",
" }\n",
" if (title.endsWith('\u00b6')) {\n",
" title = title.substring(0,title.length-1).replace(\"<\", \"<\").replace(\">\", \">\").replace(\"&\", \"&\")\n",
" }\n",
"\n",
" if (title.length == 0) {\n",
" continue;\n",
" }\n",
"\n",
" while (level < memo_level) {\n",
" text_menu += \"</ul>\\n\";\n",
" memo_level -= 1;\n",
" }\n",
" if (level == lfirst) {\n",
" main_item += 1;\n",
" }\n",
" if (keep_item != -1 && main_item != keep_item + 1) {\n",
" // alert(main_item + \" - \" + level + \" - \" + keep_item);\n",
" continue;\n",
" }\n",
" while (level > memo_level) {\n",
" text_menu += \"<ul>\\n\";\n",
" memo_level += 1;\n",
" }\n",
" text_menu += repeat_indent_string(level-2) + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n",
" }\n",
" while (1 < memo_level) {\n",
" text_menu += \"</ul>\\n\";\n",
" memo_level -= 1;\n",
" }\n",
" text_menu += send;\n",
" //text_menu += \"\\n\" + text_memo;\n",
" return text_menu;\n",
"};\n",
"var update_menu = function() {\n",
" var sbegin = \"\";\n",
" var sformat = '<li><a href=\"#__HREF__\">__TITLE__</a></li>';\n",
" var send = \"\";\n",
" var keep_item = -1;\n",
" var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item);\n",
" var menu = document.getElementById(\"my_id_menu_nb\");\n",
" menu.innerHTML=text_menu;\n",
"};\n",
"window.setTimeout(update_menu,2000);\n",
" </script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from jyquickhelper import add_notebook_menu\n",
"add_notebook_menu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Produits scalaires\n",
"\n",
"Le probl\u00e8me est tir\u00e9 de [Google Jam 2008, round 1A](https://code.google.com/codejam/contest/32016/dashboard#s=p0).\n",
"\n",
"On consid\u00e8re deux tableaux $v=(v_1,..., v_n)$ et $w=(w_1,...,w_n)$. On souhaite le minimum : $$\\min_{\\sigma,\\sigma'} \\sum_{i=1}^{n} v_{\\sigma(i)} w_{\\sigma'(i)}$$ o\u00f9 $\\sigma,\\sigma'$ sont deux permutations de l'ensemble $[[1,...,n]]$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution na\u00efve"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution moins na\u00efve"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Probl\u00e8me de recouvrement\n",
"\n",
"[Google Jam 2008, round 1A](https://code.google.com/codejam/contest/32016/dashboard#s=p0)\n",
"\n",
"Couvrir le segment $[0, M]$ avec le nombre minimum d'intervalles de la forme $[a_i, b_i]$ ? Avec $M, a_i b_i \\in \\mathbb{N}$.\n",
"\n",
"Exemple, couvrir $[0, 1]$ avec les intervalles $[-1, 0]$, $[-5, -3]$, $[2, 5]$."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Soudoyer les prisonniers\n",
"\n",
"[Problem C. Bribe the Prisoners](https://code.google.com/codejam/contest/189252/dashboard#s=p2)\n",
"\n",
"Dans un royaume il y a des cellules de prison num\u00e9rot\u00e9es de 1 \u00e0 $P$ construites de telle sorte \u00e0 former un segment de ligne droite. Les cellules $i$ et $i + 1$ sont adjacentes et leur prisonniers sont appel\u00e9s \"voisins\". Un mur muni d'une fen\u00eatre les s\u00e9pare et ils peuvent communiquer via cette fen\u00eatre. Tous les prisonniers vivent en paix jusqu'a ce qu'un prisonnier soit rel\u00e2ch\u00e9. Quand cela se produit, le prisonnier lib\u00e9r\u00e9 fait part de la nouvelle \u00e0 ses voisins, qui en parlent \u00e0 leurs voisins, etc., jusqu'\u00e0 atteindre la cellule 1 ou $P$ ou une cellule dont la cellule voisine est vide. Quand un prisonnier d\u00e9couvre qu'un autre prisonnier a \u00e9t\u00e9 lib\u00e9r\u00e9, de col\u00e8re, il casse tout dans sa cellule, sauf s'il a \u00e9t\u00e9 pr\u00e9alablement soudoy\u00e9 par une pi\u00e8ce\n",
"d'or. Il faut donc veiller \u00e0 soudoyer tous les prisonniers susceptibles de tout casser dans leur cellule avant de lib\u00e9rer un prisonnier. En supposant que toutes les cellules sont initialement occup\u00e9es par un unique prisonnier et qu'un prisonnier par jour au plus puisse \u00eatre relach\u00e9, et en connaissant la liste des $Q$ prisonniers \u00e0 rel\u00e2cher, il faut trouver l'ordre de lib\u00e9ration des prisonniers de cette liste qui soit le moins co\u00fbteux en pi\u00e8ce d'or. Ordres de grandeur : $1 \\leqslant P \\leqslant 104$, $1 \\leqslant Q \\leqslant 102$. A noter que le soudoiement n'est actif qu'un seul jour.\n",
"\n",
"Exemple : 23 prisonniers, $P=20$, $Q=3$, les prisonniers \u00e0 lib\u00e9rer sont les num\u00e9ros $3, 6, 14$."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## D\u00e9coupage intelligent d'une base de donn\u00e9es\n",
"\n",
"On dispose d'une base de donn\u00e9es \u00e0 $N$ observations et $K$ variables $(X^1,...,X^K)$. On calcule la moyenne de chaque variable $\\bar{X_k} =\\frac{1}{N}\\sum_{i=1}^N X^k_i$ sur l'ensemble de la base.\n",
"\n",
"On souhaite maintenant diviser la base en deux bases apprentissage de taille \u00e9gale, test sous intersection. On mesure \u00e9galement la moyenne de chaque variable sur chacun des deux bases : $\\bar{X^k}_a$ et $\\bar{X^k}_t$. On souhaite effectuer un d\u00e9coupage de telle sorte que l'indicateur suivant soit minimum : \n",
"\n",
"$E = \\sum_{k=1}^K \\left( \\bar{X^k_a} - \\bar{X^k_t} \\right)^2$\n",
"\n",
"Imaginer un algorithme qui effectue un tel d\u00e9coupage."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
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ethen8181/machine-learning | model_selection/kl_divergence.ipynb | 1 | 196205 | {
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"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Kullback-Leibler-Divergence\" data-toc-modified-id=\"Kullback-Leibler-Divergence-1\"><span class=\"toc-item-num\">1 </span>Kullback-Leibler Divergence</a></span></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-2\"><span class=\"toc-item-num\">2 </span>Reference</a></span></li></ul></div>"
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"text": [
"Ethen 2018-10-13 21:20:42 \n",
"\n",
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"source": [
"os.chdir(path)\n",
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"\n",
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"# 2. magic to print version\n",
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"source": [
"# Kullback-Leibler Divergence"
]
},
{
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"In this post we're going to take a look at way of comparing two probability distributions called **Kullback-Leibler Divergence (a.k.a KL divergence)**. Very often in machine learning, we'll replace observed data or a complex distributions with a simpler, approximating distribution. KL Divergence helps us to measure just how much information we lose when we choose an approximation, thus we can even use it as our objective function to pick which approximation would work best for the problem at hand.\n",
"\n",
"Let's look at an example: (The example here is borrowed from the following link. [Blog: Kullback-Leibler Divergence Explained](https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained)).\n",
"\n",
"Suppose we're a group of scientists visiting space and we discovered some space worms. These space worms have varying number of teeth. After a decent amount of collecting, we have come to this empirical probability distribution of the number of teeth in each worm:"
]
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 392,
"width": 512
}
},
"output_type": "display_data"
}
],
"source": [
"# ensure the probability adds up to 1\n",
"true_data = np.array([0.02, 0.03, 0.05, 0.14, 0.16, 0.15, 0.12, 0.08, 0.1, 0.08, 0.07])\n",
"n = true_data.shape[0]\n",
"index = np.arange(n)\n",
"assert sum(true_data) == 1.0\n",
"\n",
"# change default style figure and font size\n",
"plt.rcParams['figure.figsize'] = 8, 6\n",
"plt.rcParams['font.size'] = 12\n",
"\n",
"plt.bar(index, true_data)\n",
"plt.xlabel('Teeth Number')\n",
"plt.title('Probability Distribution of Space Worm Teeth')\n",
"plt.ylabel('Probability')\n",
"plt.xticks(index)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to send this information back to earth. But the problem is that sending information from space to earth is expensive. So we wish to represent this information with a minimum amount of information, perhaps just one or two parameters. One option to represent the distribution of teeth in worms is a uniform distribution."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 392,
"width": 512
}
},
"output_type": "display_data"
}
],
"source": [
"uniform_data = np.full(n, 1.0 / n)\n",
"\n",
"# we can plot our approximated distribution against the original distribution\n",
"width = 0.3\n",
"plt.bar(index, true_data, width=width, label='True')\n",
"plt.bar(index + width, uniform_data, width=width, label='Uniform')\n",
"plt.xlabel('Teeth Number')\n",
"plt.title('Probability Distribution of Space Worm Teeth')\n",
"plt.ylabel('Probability')\n",
"plt.xticks(index)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another option is to use a binomial distribution."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p for binomial distribution: 0.49454545454545457\n"
]
},
{
"data": {
"text/plain": [
"array([0.00055018, 0.00592134, 0.0289677 , 0.08502751, 0.16638476,\n",
" 0.22791121, 0.22299226, 0.15584249, 0.07623949, 0.02486468,\n",
" 0.00486561])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we estimate the parameter of the binomial distribution\n",
"p = true_data.dot(index) / n\n",
"print('p for binomial distribution:', p)\n",
"binom_data = binom.pmf(index, n, p)\n",
"binom_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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7WtXjLv0me0Lk8TrOKJ5elhpfbWYqUGlJcWKHel5Fe2uUj6WUHqGG4rv/QPF0E3IXiZ5yTkppnStGpNzCpHI5xNE40LM6Yb3diZXUkv2qB5IhHzD8lXxgXRmR+jpyhrueOSmluxtMh/adrhfI2fCaUkr30t5U/YBoMIgZUPeyhMVO4bTi6XDqjKpcUQzOs3lEvDLyYIa7FjtGlR3aV0WDQccKV9abkFJaTG4+C/nMT7Odrv5WGRF8MPlAci2RB0esNHm8LaX0j45l+kD1zuPoTsz3PPmgEOA90WBArF5wTfMindJonfs7+YwSwGuikwNa9pPqLhd1R6UvDjCuKJ4OpfG11n+eUnqmTj2ryQeSAP/Uephr6Y2Yuy2lNJPcHeQz5G16R9uRm3f/PPLgcNvVKNNRj23jIg/Ct1VETOywzX2qKFLrYORQ2vc7L2x08F9HJamwELi1UcGULxNXSQDu28nlVPySfPYY1v2+Kwe393Z4H5VuAPXKQ+0BAA+gPcl+Rb3/xkJ1kqpjN6dqs4v/5Fb9rEHS9OHqxymlR2sVSrkbzrLi6Q61yvSR15LXY8jdeeoqvr9K8/yO68o7yS1kVpBbRzSq5wlyd41a9XRHo/+d6q4kXd0GqoRMAEgD01JyP83DU0pHNdnR+m2jiiJiI3J/M4D765wdqDajuB9F4x2AZtdA/n9Vj3erE9sREXEb+SzMXPK1tx+pulWy+YPJZ4Ia6XY864viLHWlz/DxRV/IakeTmxkDXN5nga1tVNXjRa3OVJwd/07x9PXA3yJfveLfIuLlPRlgB0tTSrUOxLrqRZr89mhf54J1+5Svj6pj/HXdUtmMqseNfk+PNamncnazM0mkar0Rc49IKS1MKX0hpbQT8AryVQa+UsRRfQm5NwL3Fn3IG+nWNi4iBkfECRFxNzmB9yT5+6ne5h5SFJ9Qo/49qx7XOghu5jXF/RhgdY2R1DteGWXTovyWXVhW5Qxr5cB39w5JuMoBfcfkeSUBsEdEjKpRvmb/f3pnPWy2femo0W+tesyAZr/JytgHXf1N9oTXVD2+p4V15X1F2c06nLio1LMR8GIL9VT+g7q0ztXQ1qTlTXXrjv78vLWBMQEgbdgeIO84VG6TyAP1jE4pHZRS+r8W6pjfZPo48gEItDe3baS6zKZ1S+VB+xqpnr5WPZEvH3gjcD1wEHmAtmaaXSqny/GspypnM3cA3tJhWiUx8jS5SXF/eFnV41abqFacQj4rUtnJP4l89nJ25EtXfTMiero5ZLPfSWe90EIybUNb5yoxrib3E26k1e1Es0EIK2dKu7o/0xsx97iU0l9SSt9OKZ2SUtqXPFDc2bQnArahfpeqiu5sc8eQD26vIA8Y2Gx7Wmt65Tdf6U/dWV3tdjGieZG6phf3g8jvm4jYgjwuDqybAJhBHtBtMEXLtciXM9yxmH5vnd999efd7LNpdT3s7DZrWYNp1S0SGpWrLtuoBWBv6+q6EuRWh92tpzvrXLVWt3/Qv5+3NjBeBUDasC0tmjp2R2ebYa4PPkV7v7zfkUcH/w35jNTSSouHiPgc7Vcu6HgWfKD7HnnU4NHk/v53wJo+z5WD46taOAjtccUZlsoo/onccqNlRR/pYyPiHPLI0/uTR1remHwgdBJwUkRcmFI6rYfC3hB/JxqgUkrzgc9FxHzywGsAR0TEyUWf/p52EbBP8fiX5KtnPEhOIi6rNFmPiG+TR6vvDZV91ifJV2VoVaeuZNHBdPLlUiGfxf8R7Wfzl9HeBQWAlNKSiHiIvD3aj9xVobr5//RuxNJZZd5mVR/fvIPa3WjqWV6jnvkUCaAu1CGtd0wASGpmHvkgLYAtWihfXabRIGybA40u21XdnLVjPZVLDf4VeF1Kqd6fbbNm/x2X16gvfKN41jsppaUR8R3y4F7viIgJxUCNlbP/ifY+zX3tTbSfIflDEVenFf1QpwJTizEe9iInhk4iJz5OjYhZKaX+GOSwmU0jYkiTBEyjdW7Nzn1EDGrQZ7gvx0ioxDiIfOas0RnnVrcTvW1DjLnaZeTLtw4mD5A5gfqXpezSNq5oyn508fRe8hVe6q1vjba5lbgq/yVPNShbb/6J5C4As3op0dFRZRyAwbT3668c0M9IKa2sMc895ARAx/JQPwFQvT5tQf3L9lam15pP7ap/A4u6caKkUs8o4I91vm9pg2MXAEkNFX2uK2do92oysB+0nyVaTONrX7+mwTTIg/hU/K7yICI2pb1/3S0NDv4hHxC2qkvxrOcq3QCGAf8eESNoH53457Wu5tBHqs/K/7AnKkwprUwp/Tql9F+s3eXhPR2L9sTyesAw2ltB1FNZ5yqX86q2uOpxo4OuSU2W0ZOfR/Xv4nVNyu5T9bizfZV70oYY8xrF1RGqE2iNvs+ubuNeSV5fAX5Q7+C/GGtkz1rTCg9UPd6/SSy1VM62jwQmd2H+TqszDsCU4nm9wXMr4wBMjoiRVeWXULv/P2zg6+F6qLplRmfO3NerZwhrf+6dtb7870iACQBJrbm9uJ9Ag0vqRcTraR/M6M4mgw++r97l9Iokw3HF0zbaR+iFtVsu1e1nVzR1b7YjVe34BnWNAt5dPH0K6G63i66oJDo2aliqSnHJosqAUe8nv4fKQFaX1Zypl0XEB8ijgUMe/O8rPb2MlNL9tPd/fVmHydUJo5Y/y17SaJ3bDjiweHpfcSBS7fGqx3s3WMZ7m8TQk5/H7VWPP1CvUPH7PqF4+iJ92yy6o/Uu5hqDdjYqux3t/ZQX0viMcFe3cS1tc8lNrRsNfvZj2luunNJCMrmj6lHYP9HJebtjenE/GHgX7Um1egmAX5IP+IaQr7byiuL1ev3/AX5BHjsA4P1NLjV7UtXj2+uWKrdf0p4Y+0BEdHWAvOpxlE7vRjzr0/+OZAJAUksuoX3n5KJao60XZ+arm5Rf1KTOV1PnWsfAZ4FdisfXFP1dK56jfUTiwyJinWvIF6Nhd/aSbW+LiONq1BXk918Z1frrTS7R1FsqzWVf2bDUuiqtACYB5xSPnwVu7omgWhURI4s++5VLWCXgAx2+21bq+aeIOKBJmdfQflb88Q6T55Ev6QSd/yx72gciouMAjUTEMOAq8uXmAC6uMe/0qscfr3XAUKzPb28SQ3Uz7G59Himlh2hP1r0tIt5fp+gXaf99fzelVK/Jeq9bT2M+OyL+t9ml/YoWPZfTPr7JjU2axXd1G/cX2gcbe09xZZiOdbySPC5AXSmlx8ljk0BunXVJowPdiNi2w/z3kA+UAY6MiLMaLa8YLPaEFq6O0Mz0qsefLu7bqHNVhWKbVkmgnFmnno7zPE0e1BZgd+r8N0bER8iXDITcimtWg7hLq2gZU/m/2wq4vlkSICL2i4i1WqYUV9S5pXh6aESc0yhBFxFDIuLYjr/dYtyaShK3v/93JMcAkNRcSulPEfFZ4PPkP9MHI+ICclPHVeSmpZ8kD8AGcGlKqd7ZkYr7yDu6u5EPdp4kX8HgBNoPWp4hD/hXHcvqYqCpjxax/DoivkTe4RpCHnn5VPLO7Axab7Z3H3BlRLwJuI589mBH4MO0NyGcBVzQYn097ZdFPJOLwQ1/TNXl81JK9S7N9ANyMmYc7WfnpvVCX8axka8DXrERMJZ8FYLXk8+cVVofvAicklL6fheWsx1wZ0T8lZzEuA/4O3mHfDNyf9sPFmUT+cBmjZTSqoj4NblZ7mERcQp5Pa6MbL2yhy/3V89z5MHJfhIRl5B3MheTr/1+Gu3dA36eUrq248wppd9FxD3kdfMtRT0Xkwdl25o8OOIx5D7bda9JnVKaExGzge2BEyJiFrnZayVJsiyl9PdOvK8Ti/k3AS6PiP3IB31zyZfIOhH4l6Lsk3TvrFpPWd9iHkleB06NiF8BPwceIm8PV5LX89eRt5WVg+TnaR/wtJ4ubeNSSi9ExI+BfyWvl7+KiAuBP5M/swOBj5C3vzNp3Dz/I0XsO5HPZO8bEZcW728pucXOXuTtxRxyq4Jq7yVfrnBb4PMR8Q5gGrmZ/hJyX+1Xkrf77yCPi/AKml8BoZHqcQAqo/n/v+Igs557yEnuHatem95kOaeSD+43J/83TgauJG/ftiC/90oXrkWs3RJA67qIvD/wTuCtwJ8i4jLgV+Qk+MbkbeXe5H2OieTvoOMlKo8nr3M7kvdHDomIK8nr7CLyOrcj7evcy8gD7Xbcbv6S3PrtLRHxX8CdtA9QuSql9JceeddSK1JK3rx524Bu5B31VNymd6OeSh3TOjHP58k7QqnB7ZvA4DrzT60qtzv5T7VePU8Br65Tzyjyzmy9eV8kD35Xvbzta9QzrWr6y8mDEtar8zFgmzrxTKkqd1yT72xqV74P8tnHpfXia/K9XVhVdjXwih5aF6fUi6fO7SXgNmDXTqzjU7u4zDbg5Dr1v5mcuKo13+wOZafXer1B7A3LV08n7yTObfAe7gXGNFjWjuQD0nrz30Zu+dFs3TuuQR3TO/t5kA/wGr2vyu9px85+/41+w91cl7sVcw/HclqD9bPW7SFgUrOY6N42bivyoKv15l1CPtBq+hmQD3DvaeF93dQglrta/GzagG27830Uy3ygQ72fbVL+3R3KLwaGtLCcncktLhq9pznAnnXmH1JV7ooWlrdTVfmzGpRrud4ivkROXnbrc6+q8/2txFkn7gs68Xs6vk49LyNvT1upYwUwsUYdryfvm9Sa5/kOZX9c6/Uade5VVceHe+rz9jbwb3YBkNSylNJnyActl5J3JJeS+7Y9AXwb2DeldHJq3Pe/YgE5O38aeWCkBUVdjwLnAruk3Ie9VhyLi3n/i7zzu6yY93HgW8BrU0qX1Jq3wXv7G/nP9GzygEyLi/f3MDnrv3tKaU5n6uxJKaU/FPF9i3zmrdm1mKtdVfV4ekrpzz0ZWw2VA4KnyINUfQf4GLBDSung1L1LV/6S/N1/ntz/9TFyf/9V5HXofuA88gHRN2tVkFK6k3xW/DrygXhbN+LpspSbn+9O3kF9jPydLgJ+DXwIeFNat+9/9fx/JZ9tvYh80LCC/FncS95h/hdauBxVSmka+bJqN5MTCi929T0V9f2GfNb1DHIrnBfIZ66fJZ/N/iCwW+qblhYtWZ9iTil9mXyQfDTwjSKeueTvdyX5O36Y/Ls+jHww+GgL9XZ5G5dSeoo8wN8XgT+QfzNLyAPEfrWY9/9qzVujrmdSSm8ityi4DvgbeT1dSd5m3E5uZXFyvVhSSvuTWx5cSf7tLCInGBeSB8y8FvgPYIuUUqMrH7Rqeofn99Qq1GB6o/7/a6TckutV5FYZvyC3FFpJXh9/RR77YGLKTdPVREppVUrpdPLZ/fPJJw6eJ/9fLCPvM/yE3ILxlSmlK+vU81xK6WByC7PLyb+BheR1bhG55cx15NZCW6WU1rm0bUrp1+RE4zXFcr1UoPpNpJT6OwZJUi+KiGNoHxPh6JTS9xqVlzQwRMQ04H0AKaWWBxeUJA1ctgCQpIHvxOL+edYeSVuSJEklYgJAkgaw4tKM+xVPr0iNB66SJEnSAOZVACRpgImIncgjVu9K+4jeS8iDAUqSJKmkTABI0sBTa5C/j6eUnu3zSCRJkrTesAuAJA1ci4DfAIenlC7r72AkSZLUv7wKgCRJkiRJJWALAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCXgZwAEgIp4ARgOz+zkUSZIkSVLP2x5YlFLaoTuVmAAYGEZvvPHG4ydNmjS+vwORJEmSJPWsRx99lOXLl3e7HhMAA8PsSZMmjX/wwQf7Ow5JkiRJUg/bc889mTlz5uzu1uMYAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVwJD+DkCSJElSubW1tbFo0SIWL17MypUrSSn1d0hSj4oIhg4dyqhRoxg9ejTDhw/vlzhMAEiSJEnqN0uWLGHOnDke9GtASynx4osv8sILLzBv3jy22WYbRo4c2edxmACQJEmS1C/a2trWHPyPHj2acePGMXz4cAYNsqeyBpbVq1fT1tbG/PnzWbRoEXPmzGGHHXZgo4026tNtNDuHAAAgAElEQVQ4TABIkiRJ6heLFi1ac/C/1VZbERH9HZLUKwYNGsSIESPYeOONgbzuL1y4kM0226xv4+jTpUmSJElSYfHixQCMGzfOg3+VQkQwbtw4oH3970smACRJkiT1i5UrVwL024BoUn+orO+V9b8vmQCQJEmS1C8qA//Z519lUmnt0h8DX/pLkyRJkiSpj/RndxcTAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJEkbkIjo0m3KlCn9Hbr62ZD+DkCSJEmSmtn+jJ/0dwjdMvu8Q3qsrs0337zm6/PmzWPlypUMHz6cMWPGrDN9/PjxPRaDNkwmACRJkiRpAzJ37tyar0+ZMoW7776bI488kmnTpvVtUNog2AVAkiRJkqQSMAEgSZIkSSXy+9//nohg5MiRANxzzz28/e1vZ4sttmDw4MGcddZZAHzta18jIjj00EPr1nX66acTEXz4wx+uW+bGG2/kkEMOYfPNN2fYsGFsscUWvPOd7+Suu+7q2TempkwASJIkSVJJXXXVVey///7ccsstrFixgkGDeu4Qsa2tjXe96128613v4tZbb+XZZ59l44035plnnuGmm27igAMO4HOf+1yPLU/NmQCQJEmSpBJqa2vjQx/6EEcffTT/+Mc/mD9/PsuWLePEE0/skfo/8pGPcOONNzJp0iRuuukmli5dysKFC1m4cCEXXnghI0aM4Oyzz+ZHP/pRjyxPzTkIoCRJkiSV0EsvvcSb3/xmvv3tbxMRAAwdOpSXv/zl3a774Ycf5oorrmDrrbfmrrvuWuvKBaNHj+aUU05h5MiRnHjiiXzxi1/ksMMO6/Yy1ZwJAEmStGGauu4lrnqu7oW9V7ckrUcqffh72tVXXw3AscceW/eyhUcddRQnnXQS999/PwsXLqx56UL1LBMAkiRJklRSr3/963ul3hkzZgDwjW98g6uuuqpuuZQSKSWefPJJEwB9wASAJEmSJJXQ8OHD11wJoKc9/fTTAGv6/DezbNmyXolDa3MQQEmSJEkqocGDB/da3atXrwbg8ssvX3OWv9Ftr7326rVY1M4EgCRJkiRpHUOG5AbjbW1tdcvUO7tf6ff/97//vecDU5eZAJAkSZIkrWPs2LEAzJkzp26Z+++/v+brlbEFfvrTn/Z8YOoyEwCSJEmSpHW8+tWvBuBPf/oTjz322DrTb7vtNn7729/WnPe4444D4IEHHuDaa69tuJz58+d3L1C1zASAJEmSJGkdr3rVq9hll11IKXHMMcesSQKsWLGCa665hiOPPJJx48bVnHevvfbiAx/4AJCTAVOnTl0zMCDkrgO33norRx555JpkgXqfCQBJkiRJUk2XXHIJw4YNY+bMmUyaNInRo0czatQojj32WA444ICGB+8XX3wx73vf+1i1ahWf/exn2WqrrRg7dixjxoxh7NixHHLIIfzgBz9YM2Cgep8JAEmSJElSTVOmTOHuu+/m4IMPZsyYMaxatYpJkybxla98hRtuuIFBg+ofUg4bNoxp06Zx5513ctRRR7HddtvR1tbGihUr2GGHHXjnO9/JN7/5Tb7zne/04Tsqt0gp9XcM6qaIeHDy5MmTH3zwwf4ORZKkvjN1TC/W3fya1ZK679FHHwVg0qRJ/RyJ1Lc6u+7vueeezJw5c2ZKac/uLNcWAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJTCkvwOQJElSH3DMBEkqPVsASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkldD06dOJCLbffvv+DqVPTJ06lYjguOOO67E6Z8+eTUQQET1WZ28yASBJkiRJG6jjjjtuzQFo9W3w4MGMHz+eN7zhDXz5y19m+fLl/R2q1gND+jsASZIkSWpq6pj+jqB7pi7s1eqHDh3K+PHj1zxva2tj/vz53Hvvvdx7771861vfYvr06bzsZS9bU2bEiBFMnDiRrbfeuldjW19MmDCBiRMnsuWWW/Z3KP3GFgCSJEmStIHbZ599mDt37prbggULWLBgARdccAGDBg3iD3/4A2ecccZa87zmNa/hscce48477+ynqPvWhz/8YR577DHOPffc/g6l35gAkCRJkqQBaMyYMXz84x/nhBNOAOBHP/pRP0ek/mYCQJIkSZIGsN122w2ApUuXrvV6o0EAp0yZQkQwbdo0li9fztSpU5k4cSIbb7wxm222Ge95z3v485//3HC5Dz30EO9973vZdttt2WijjZgwYQIHHXQQN9xwQ915tt9+eyKC6dOn8/TTT3PyySez7bbbsvHGGzNp0iQuvPBCVq9evab89ddfzxvf+EbGjh3L6NGjOeSQQ/j9739fs+5GgwDOmTOHCy64gIMPPphXvOIVjBgxgtGjR7PHHntw9tlns2DBgobvdUPhGACSJEmSNIA98sgjAOy0006dnnfRokXsu+++PPTQQ2y00UYMGjSI5557ju9///vccccd3Hfffey4447rzHfZZZfxwQ9+cM3B+tixY1mwYAG33347t99+O+9973uZNm0agwcPrrncJ554gqOOOoq5c+cyevRoVq5cyWOPPcZpp53G448/zsUXX8wZZ5zB+eefz+DBgxkxYgSLFy/m1ltvZcaMGdx333284hWvaPl9nnLKKWsSE8OGDWPkyJEsWLCAhx9+mIcffphrr72W6dOns80223T6M1yf2AJAkiRJkgagRYsWcdFFF3HFFVcAcOqpp3a6jrPPPpv58+dz2223sXTpUpYsWcI999zDNttsw7x58/jUpz61zjwzZsxYc/B/xBFH8I9//IP58+ezYMECvvCFLxARXHPNNQ374p966qnssMMO/Pa3v2XhwoUsWrSIz3/+8wBccsklnHPOOXz5y1/moosuWjP9kUceYeLEiSxYsIAzzzyzU+9z0qRJfPWrX+VPf/oTy5cv54UXXqCtrY3p06ez995789e//pWTTjqpcx/eesgWAJIkSZK0gZsxYwZbbLHFmudtbW0sXJivPLDHHntw6qmncuyxx3a63hUrVnDHHXes1XrgjW98IxdddBFHHHEEt9xyCy+++CLDhg1bM/0zn/kMq1evZt999+W6665bc5Z/5MiRnHnmmSxdupRzzz2X888/n49+9KOMHj16neUOGjSIW2+9lbFjxwL5igVnnXUWd911F7/4xS8488wz+exnP8vHPvaxNfPsuuuuXH755bzpTW+qGVcjleRCtaFDh7Lffvtx2223sfPOO/PTn/6U2bNn1+wysaGwBYAkSZIkbeBWrlzJM888s+ZWOfgHmDdvHs8++ywppU7Xe8QRR9TsOvCv//qvRAQrVqzgL3/5y1rLuuuuuwD41Kc+VbOJ/yc/+UmGDx/OkiVLuPXWW2su9+STT15z8F/tLW95C5Cb6Z922mnrTN93330ZPnz4OnF1x/jx49lnn31IKTFjxoweqbO/mACQJEmSpA3cfvvtR0ppzW3VqlU8/vjjfP3rX2fJkiWcfvrpvP/97+90vXvvvXfN14cOHcpmm20GwPz589e8/tBDD5FSIiLYb7/9as47ZswY9txzTwBmzpxZs8yrX/3qmq9Xlrn99tszcuTIdaYPGjSICRMmrBNXK+677z6OP/54dt55Z0aOHElErLndfPPNADz11FOdqnN9YwJAkiRJkgaYwYMHs8MOO/DBD36Qa6+9FoArr7ySX/3qV52qZ9SoUXWnDR8+HMitDyqee+45IB/k1zpAr6gMplcp39GWW25Z8/VKi4J606vLVMfVzAUXXMDrXvc6rrrqKv74xz/S1tbGuHHj2Hzzzdl8883XvNeOV1LY0JgAkCRJkqQB7KCDDlozPsAPfvCDPlnmihUr+mQ5PWHWrFl88pOfJKXEhz/8YWbNmsWKFSuYN28ec+fOZe7cuRxxxBEAXepGsT4xASBJkiRJA9x2220HwOOPP96ry3nZy14GwPLly+ue3QeYM2fOWuX70w033MDq1as56KCDuPjii9lll13WGbvgmWee6afoepYJAEmSJEka4J588kkg993vTXvssQcRAbBmMMCOFi5cyIMPPgjA5MmTezWeVlSSEXvssUfN6UuXLuU3v/lNX4bUa0wASJIkSdIAdu+9965JAPT2Aff48ePZf//9ATj//PNZvXr1OmXOP/982traGDlyJG9729t6NZ5WjBkzBoBHHnmk5vQvfvGLLF68uC9D6jUmACRJkiRpAFq+fDk33XQTRx11FAAjRozg+OOP7/Xlfv7zn2fQoEHMnDmT97znPWvOsC9ZsoRzzjmH8847D4AzzjiD0aNH93o8zRx44IEA/OQnP+Hcc89l2bJlQB6g8BOf+ATnnnsum266aX+G2GOG9HcAkiRJkqTumTFjxpqB/gBeeuklnn/++TXPN9lkE6677jq23nrrXo9ln3324etf/zof+tCHuP766/nhD3/I2LFjWbRoES+99BIAxxxzDGeccUavx9KKt771rRx++OHceOONfPrTn+bMM89k7NixLFiwgJQSJ5xwAqtWreLqq6/u71C7zRYAkiRJkrSBW7lyJc8888ya2/PPP8/IkSPZbbfd+PjHP86sWbM49NBD+yyek046ifvvv5+jjz6aLbfckiVLljBmzBgOPPBArr/+eq655pp1BtrrT9///vc577zzmDRpEkOHDiWlxL777svVV1/NFVdc0d/h9ZjY0C9jIIiIBydPnjy5MpCGJEmlMHVML9a9sPfq7i9+XloPPfroowBMmjSpnyOR+lZn1/0999yTmTNnzkwp7dmd5doCQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQ+klLqt2WbAJAkSZLULyICgJdeeqmfI5H6zurVq4H29b8vmQCQJEmS1C+GDx8OwKJFi/o5EqnvVNb3yvrfl4b0+RIlSZIkCRg3bhzLly/nmWeeYdWqVYwaNYphw4YREf1ydlTqDSklUkq8+OKLLF68mOeffx7I639fMwEgSZIkqV+MHj2atrY25s2bx/PPP7/mwEga6MaPH8/o0aP7fLkmACRJkiT1i4hg8803Z5NNNmHRokUsW7aMVatW9esgaVJviAiGDBnCiBEjGD16NCNHjuyXOEwASJIkSepXI0eO7LcDIqlMHARQkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJXAgEoARMQWEfGViPhrRLRFxDMR8aOIeHMX63tZRJwUEddX1bk0Ih6NiK9FxE4t1DEoIj4QEb+OiAURsTgiHoqIT0TEsK7EJUmSJElSZw3p7wB6SkTsBvwC2LR4aREwATgUOCQiPp1SOq+T1T7F2p/REmAYsHNxOyEijk8pfa9OTEOBm4C3FS+9CLwE7F7c/i0iDkgpLelkXJIkSZIkdcqAaAEQERsDt5AP/h8Cdk0pjQHGAf8LBHBORLy1k1UPAe4B3gdsmVIaBYwA3gA8DAwHvl0kH2r5Avngvw04rph3E+AwYB6wN3BpJ2OSJEmSJKnTBkQCADgJeDn5DP1hKaVZACmlRSml08ln4QM4t5P17pdS2i+l9O2U0tyizpdSSvcCbwWeJScJTu04Y0RsAXysePrJlNLVxbwppfRj4Phi2lENEgiSJEmSJPWIgZIAOKa4/25K6cka0/+nuJ8cERNbrTSldE+Dac8BtxZP96xR5F3ARsBC4LIa898M/ImcmDi61ZgkSZIkSeqKDT4BEBGjaD8A/1mdYr8hH4gDdGlAwDpeKO4H15i2f3F/T0qprc78txf3B/RgTJIkSZIkrWODTwAAk8hn0QFm1SqQUloN/LF4uksPLnu/4v73NaZVllMzpsIfivtJERENykmSJEmS1C0D4SoAW1Y9fqpBucq0LRuUaVlEvB3Yq3h6VYO4WolpZHFb3GSZD9aZtHOj+SRJkiRJGggtADapery8Qbllxf3I7i4wIramvV//LSml2xrE1UpMPRKXJEmSJEn1DIQWAH0qIkaSryqwGfA34IS+WnZKqdZgg5WWAZP7Kg5JkiRJ0oZnILQAWFr1eOMG5UYU90u6uqCIGA7cTG76/xxwUErp+SZxtRJTt+KSJEmSJKmZgZAAqO5jv1WDcpVpT3dlIRExDPghecT+BcBbU0p/bDBLJa5WYlqSUmrY/1+SJEmSpO4YCAmAx4BUPH5VrQIRMQiYWDz9Q60yjUTEEOB7wCHkM/VvSyk93GS2ynJqxlSoXCng0c7GJEmSJElSZ2zwCYDizPkDxdMD6xR7LTCmeHxnZ+ovkgdXA4eTB/T715TSr1uY9a7i/o1F14FaKvF2KiZJkiRJkjprg08AFL5b3B8TEbUu83d6cf9gk2b7a4mIII/2fzTwInB4SumuxnOtcSOwAhgLvL9G3YeRWyUkcusCSZIkSZJ6zUBJAFxKHpF/FPDjiNgFICJGRcSXyGfvAT7dccaISMVtao16LySP8r8KeHedy/3VlFKaC3ylePqliDg2IgYXy3wbcFUx7Xsppd+1Wq8kSZIkSV0xIC4DmFJaHhFvJzelnwzMiohFwEhykiMBn04p3d5qnRGxHfCxyiKASyPi0gYxbFHj5bOAXYG3Ad8GLo+Il2gf/f9+4ORWY5IkSZIkqasGRAIAIKX024jYFfgUcCiwNfACcB9wYUqps/3sq1tHDAU270JMK4um/icCx5EH/RsMPExu9n9RSunFztYrSZIkSVJnDZgEAKxpdv8x2s/ctzJP1Hl9NlBzWidjWk3uolC39YAkSZIkSb1toIwBIEmSJEmSGjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSVgAkCSJEmSpBIwASBJkiRJUgmYAJAkSZIkqQRMAEiSJEmSVAImACRJkiRJKgETAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSVgAkCSJEmSpBIwASBJkiRJUgmYAJAkSZIkqQRMAEiSJEmSVAImACRJkiRJKgETAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSVgAkCSJEmSpBIwASBJkiRJUgmYAJAkSZIkqQRMAEiSJEmSVAImACRJkiRJKgETAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSGNLfAUiSJGDqmF6se2Hv1S1JkjYYtgCQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSVgAkCSJEmSpBIwASBJkiRJUgmYAJAkSZIkqQRMAEiSJEmSVAImACRJkiRJKgETAJIkSZIklYAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCQzp7wAkSdpQbH/GT3qt7tnDe61qSZIkwBYAkiRJkiSVggkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJmACQJEmSJKkETABIkiRJklQCJgAkSZIkSSoBEwCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSUwpL8DkCRJktZLU8f0Yt0Le69uSarDFgCSJEmSJJWACQBJkiRJkkrABIAkSZIkSSVgAkCSJEmSpBIwASBJkiRJUgmYAJAkSZIkqQQGVAIgIraIiK9ExF8joi0inomIH0XEm7tY30YRcVBEnBURN0fEUxGRitvBLcw/u6p8vdvpXYlNkiRJkqTOGNLfAfSUiNgN+AWwafHSImACcChwSER8OqV0XiernQTc1gPhzQderDNtaQ/UL0mSJElSQwOiBUBEbAzcQj74fwjYNaU0BhgH/C8QwDkR8dYuVL8AuBM4D3hXF0M8PKW0RZ3bN7pYpyRJkiRJLRsoLQBOAl4OLAEOSyk9CZBSWgScHhE7Au8AzgVu70S9vwPGp5RS5YWI6LGgJUmSJEnqKwOiBQBwTHH/3crBfwf/U9xPjoiJrVaaUlpdffAvSZIkSdKGaoNPAETEKGDP4unP6hT7DbCweNylAQElSZIkSdqQbfAJAPJAfZV2+bNqFUgprQb+WDzdpS+C6uDCiHguIl6MiLkRcWtEHB0Rg/shFkmSJElSCQ2EBMCWVY+falCuMm3LBmV6y+7ACKAN2Bz4F+Ba4M6IGNsP8UiSJEmSSmYgDAK4SdXj5Q3KLSvuR/ZiLB3dBNwD3J1SegEgIrYDPgKcBuwH/ABo6eoEEfFgnUk7dz9USZIkSdJANhASAOutlNIpNV77O/CJiHgCuAQ4MCLemlLqzNUJJEnaIGx/xk96re7Zw3utakmSBqSBkABYWvV4Y2BxnXIjivslvRtOy74BfALYHjiMFi5PmFLas9brRcuAyT0ZnCRJkiRpYBkIYwBU9/vfqkG5yrSnezGWlhWXF7y/ePpP/RmLJEmSJGngGwgJgMeAVDx+Va0CETEImFg8/UNfBCVJkiRJ0vpkg08ApJQWAw8UTw+sU+y1wJji8Z29HlQLIiKAvYunT/RnLJIkSZKkgW+DTwAUvlvcHxMRtS7zd3px/2BK6Y99EVBxgN/ISeT+/wC9N0KSJEmSJEkMnATApcDfgFHAjyNiF4CIGBURXwIOL8p9uuOMEZGK29RaFUfEuIiYULlVTRpd/XpEDO0w61cj4isR8YaI2Liqvm0j4jzga8VLd6WUftqVNy1JkiRJUqsGwlUASCktj4i3k5v3TwZmRcQiYCQ5yZGAT3fxUnsPAS+v8fr3OzzfH5he9XwU8D7go8DqiFgIDAZGV5W5GziiCzFJkiRJktQpAyIBAJBS+m1E7Ap8CjgU2Bp4AbgPuDCl1Nd9/78JPAfsA2wHbEpORvyDPGbB94AbUkqr+zguSZIkSVIJDZgEAEBKaS7wseLW6jwN++qnlLbvYiy/AX7TlXklSZIkSeppA2UMAEmSJEmS1IAJAEmSJEmSSsAEgCRJkiRJJWACQJIkSZKkEjABIEmSJElSCZgAkCRJkiSpBEwASJIkSZJUAiYAJEmSJEkqARMAkiRJkiSVgAkASZIkSZJKwASAJEmSJEklYAJAkiRJkqQSMAEgSZIkSVIJ9EoCICJG9Ua9kiRJkiSpa3qrBcDTEXF1REzppfolSZIkSVIn9FYCYATwXuDOiPhLRJwZEdv00rIkSZIkSVITvZUAOAD4LrAc+Cfgc8ATEXFrRBwREUN7abmSJEmSJKmGIb1RaUppOjC9GAvgKOA/gNcCBwMHAfMi4lrgypTS73ojBklSc9uf8ZNeq3v2eYf0Wt2SJEnqvF69CkBKaXFK6bKU0uuBXYD/BZ4FNgU+AjwUEQ9ExAcjYkxvxiJJkiRJUpn12WUAU0qPpZQ+AWwDvAO4GVgF7AF8jTxw4LUOHChJkiRJUs/rswRARUrpJeCnwHXAQ8XLAQwndxe4MyJmRsT+fR2bJEmSJEkDVZ8mACJij4j4KvA08D3gNcBK4IfA0cC3gKXA7sAdEXFYX8YnSZIkSdJA1esJgIjYNCI+FhEPAw8A/wmMB/4InA5sk1J6d0rpupTSieQuAlcVsf13b8cnSZIkSVIZ9MpVACJiEPAv5NH/DwWGkpv5LwOuB65IKd1ba96U0qKIOBl4N/Cq3ohPkiRJkqSy6ZUEADAH2Jx80A8wE7gC+G5KaVGzmVNKKyPiBWDbXopPkiRJkqRS6a0EwBbAQuC7wOUppYe7UMfHgZE9GpUkSZIkSSXVWwmA9wHXp5TaulpBSumGHoxHkiRJkqRS661BAO8GNm21cERsFRHb9VIskiRJkiSVXm+1AJhNvtTf1i2Wv5fc37+34pEkSZIkqdR68zKA0bxIt8pLkiRJkqQW9WYCoDNGAKv6OwhJkiRJkgaqfk8ARMROwARgbn/HIkmSJEnSQNUjfe4j4u3A/2fvzuMsOct6gf8eEkgCmYQlYAIICXoJm2yRRVHDLqsRxQW5FxK9gOhlUVAhiCSAJLIIQWUTIuAFrqCArIIEAiiLyKYECEJIhAAhbNlDtuf+UdVM0+nu6Z7pM6dn6vv9fM6nzql66+2na7p7Tv3OW28dsWT1/lV14mq7Jblmkp8ZX79vI2oBAAAArmyjJt27bZIjl6zbZ5l1K/lSkqduUC0AAADAEhsVAJy85PXTkpyf5Hmr7HNFknOTnJLk5O42BwAAAADMyIYEAN39/iTvX3hdVU9Lcn53H7sR/QMAAAA7ZqNGACx1SJLLZ9Q3AAAAsE4zCQC6+4xZ9AsAAFH0ErsAACAASURBVABsn7nfBhAAAACYvR0eAVBVC0P9P9/dt1yybj26u2d1SQIAAABM2kaccNeS5dLn6+0HAAAA2GAbEQAcMi4vXWYdAAAAsAnscACw3IR/JgEEAACAzcUkgAAAADABAgAAAACYgI24C8DDNqKQJOnuV29UXwAAAMBWGzEJ4CuT9Ab0kyQCAAAAAJiBjQgAPpCNCwAAAACAGdiIuwDcdQPqAAAAAGbIJIAAAAAwAQIAAAAAmAABAAAAAEzARtwG8L3j0zO6+6gl69aju/seO1oPAAAAcGUbcReAu47Lzy+zbj3cSQAAAABmZCMCgKPG5TnLrAMAAAA2gY24DeCr1rIOAAAAmB+TAAIAAMAECAAAAABgAmYaAFTVHlX1G1X1xqo6vaouGB+nj+t+var2mGUNAAAAwMZMArisqjo0yRuS3DJJLdl8o/FxRJInV9Wvdveps6oFAAAApm4mAUBVHZjkA0mum+SSJH+f5P1JzhybXD/J4UkenOQnkpxcVbfr7m/Moh4AAACYulmNADg2w8n/aUnu191fWKbNy6vq6UnekeQmSZ6W5NEzqgcAAAAmbVZzANwvSSc5aoWT/yRJd/9Xkt/McInAA2ZUCwAAAEzerAKAA5Jc0N0f3FbDsc354z4AAADADMwqAPjaOvveY9wHAAAAmIFZBQBvSbJPVd13Ww3HNvskefOMagEAAIDJm1UAcGySLyc5sap+aqVGVXXnJCcm+WKSZ8yoFgAAAJi8Hb4LQFU9bIVNL0ry1CQfrKoPJjk5V74N4OFJzk3y7CS/kOTVO1oPAAAAcGUbcRvAV2aY8X85NS4PT/JzK2zbP8lzx+cCAAAAAJiBjQgAPpCVAwAAAABgE9jhAKC777oBdQAAAAAztBEjAAAA2AAHP+ntM+v79L1n1jUAu4hZ3QUAAAAA2EQEAAAAADABM70EoKrukOS3k9wlw63/rrFK8+5ulyQAAADADMzshLuq/ijJn2btowxq200AAACA7TGTSwCq6m5Jjstwe8A/SXL7cdPZSX48w4iApyX51vg4Iskhs6gFAAAAmN0cAI/JcPL/tO5+Znd/alx/eXef1t0f7u5nJLlNku8meUWSy2ZUCwAAAEzerAKAO43Ll6329br760l+J8kBSY6eUS0AAAAwebMKAA5IckF3f2vRusuSXH2Ztu9NclGS+86oFgAAAJi8WQUA301y+TLrrlFV+y9e2d2d5IokB82oFgAAAJi8WQUAX02yX1Xtu2jdZ8flXRc3rKrbZLg94AUzqgUAAAAmb1YBwMfH5Z0WrXtLhlv9Pbeq7lBVV62q2yd5VYYJA98/o1oAAABg8mYVALw5w8n+ry9a9+Ik/5Xkx5J8JMnFST6W5NYZ5gA4Zka1AAAAwOTNKgB4V5KfSPLshRXdfXGSw5O8IcklGQKCJPlwkrt393/OqBYAAACYvD1n0Wl3X5HklGXWfyPJr1XVVTPcKeC87j5/FjUAAAAAW80kANiW7r40ydfn8bUBAABgimZ1CQAAAACwicw0AKiqParqN6rqjVV1elVdMD5OH9f9elXtMcsaAAAAgBleAlBVh2aY8O+W2Trh34IbjY8jkjy5qn61u0+dVS0AAAAwdTMJAKrqwCQfSHLdDDP+/32S9yc5c2xy/Qx3BHhwhrsFnFxVtxsnCQQAAAA22KxGAByb4eT/tCT36+4vLNPm5VX19CTvSHKTJE9L8ugZ1QMAAACTNqs5AO6XpJMctcLJf5Kku/8ryW9muETgATOqBQAAACZvVgHAAUku6O4Pbqvh2Ob8cR8AAABgBmYVAHxtnX3vMe4DAAAAzMCsAoC3JNmnqu67rYZjm32SvHlGtQAAAMDkzSoAODbJl5OcWFU/tVKjqrpzkhOTfDHJM2ZUCwAAAEzeDt8FoKoetsKmFyV5apIPVtUHk5ycK98G8PAk5yZ5dpJfSPLqHa0HAAAAuLKNuA3gKzPM+L+cGpeHJ/m5Fbbtn+S543MBAAAAAMzARgQAH8jKAQAAAACwCexwANDdd92AOgAAAIAZmtUkgAAAAMAmIgAAAACACdiIOQBWVVU3SfLgJLdPct1x9dlJPpHkDd395VnXAAAAAFM3swCgqvZJckKS38ww438tafIrSZ5VVS9P8nvdfdGsagEAAICpm0kAUFVXSfKPSe6R4cT/zCQnJ/nq2OSGSe6a5AZJHpHkkKq6T3e7mwAAAADMwKxGAByV5J5JLk7yuCQvX3pyX1WV4eT/hLHtUUlOnFE9AAAAMGmzmgTwYUk6yWO7+6+X+2S/By9L8tgMowQePqNaAAAAYPJmFQD8RJJLk7xqDW1fNbb9iRnVAgAAAJM3qwBgnyQXdvel22rY3ZckuWDcBwAAAJiBWQUAX0uyf1X9+LYaVtVNk1xz3AcAAACYgVkFAO/JcF3/S6tq75UajdtekmG+gH+eUS0AAAAwebMKAP4swx0A7prkP6rqt6vqZlW1paquW1WHVdUTk/xXksPHts+eUS0AAAAweTO5DWB3n1ZVv5rkdUl+PMlfrdC0Mlz//5DuPm0WtQAAAACzGwGQ7n5bktsk+Zsk52Y42V/8OCfJiUluM7YFAAAAZmQmIwAWjJ/q/1aS36qqmyS57rjpbJ/4AwAAwM4zkwCgqn5hfPqh7v5W8oMwwEk/AAAAzMGsRgC8OcllSa49o/4BAACAdZjVHADfSXJud58/o/6XVVUHVtUJVfWlqrq4qs6qqrdW1T22s7+9qurnq+qPq+ofq+prVdXj4z7r6OdXquq9VfXtqrqwqj5XVc+sqi3bUxcAAACs16xGAJyS5Kerar/uPndGX+OHVNWtk7w3yXXGVecmOSDJA5Lcv6qO7u7j19ntzZP80w7W9bIkjxhfXpbhloc3S/KUJA+pqp/t7q/tyNcAAACAbZnVCICXJdkjyWNm1P8Pqap9krwlw8n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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 392,
"width": 512
}
},
"output_type": "display_data"
}
],
"source": [
"width = 0.3\n",
"plt.bar(index, true_data, width=width, label='True')\n",
"plt.bar(index + width, binom_data, width=width, label='Binomial')\n",
"plt.xlabel('Teeth Number')\n",
"plt.title('Probability Distribution of Space Worm Teeth')\n",
"plt.ylabel('Probability')\n",
"plt.xticks(np.arange(n))\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing each of our models with our original data we can see that neither one is the perfect match, but the question now becomes, which one is better?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 392,
"width": 512
}
},
"output_type": "display_data"
}
],
"source": [
"plt.bar(index - width, true_data, width=width, label='True')\n",
"plt.bar(index, uniform_data, width=width, label='Uniform')\n",
"plt.bar(index + width, binom_data, width=width, label='Binomial')\n",
"plt.xlabel('Teeth Number')\n",
"plt.title('Probability Distribution of Space Worm Teeth Number')\n",
"plt.ylabel('Probability')\n",
"plt.xticks(index)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given these two distributions that we are using to approximate the original distribution, we need a quantitative way to measure which one does the job better. This is where **Kullback-Leibler (KL) Divergence** comes in.\n",
"\n",
"KL Divergence has its origins in information theory. The primary goal of information theory is to quantify how much information is in our data. To recap, one of the most important metric in information theory is called Entropy, which we will denote as $H$. The entropy for a probability distribution is defined as:\n",
"\n",
"\\begin{align}\n",
"H = -\\sum_{i=1}^N p(x_i) \\cdot \\log p(x_i)\n",
"\\end{align}\n",
"\n",
"If we use $log_2$ for our calculation we can interpret entropy as, using a distribution $p$, the minimum number of bits it would take us to encode events drawn from distribution $p$. Knowing we have a way to quantify how much information is in our data, we now extend it to quantify how much information is lost when we substitute our observed distribution for a parameterized approximation.\n",
"\n",
"The formula for Kullback-Leibler Divergence is a slight modification of entropy. Rather than just having our probability distribution $p$ we add in our approximating distribution $q$, then we look at the difference of the log values for each:\n",
"\n",
"\\begin{align}\n",
"D_{KL}(p || q) = \\sum_{i=1}^{N} p(x_i)\\cdot (\\log p(x_i) - \\log q(x_i))\n",
"\\end{align}\n",
"\n",
"Essentially, what we're looking at with KL divergence is the expectation of the log difference between the probability of data in the original distribution with the approximating distribution. Because we're multiplying the difference between the two distribution with $p(x_i)$, this means that matching areas where the original distribution has a higher probability is more important than areas that has a lower probability. Again, if we think in terms of $\\log_2$, we can interpret this as, how many extra bits of information we need to encode events drawn from true distribution $p$, if using an optimal code from distribution $q$ rather than $p$.\n",
"\n",
"The more common way to see KL divergence written is as follows:\n",
"\n",
"\\begin{align}\n",
"D_{KL}(p || q) = \\sum_{i=1}^N p(x_i) \\cdot \\log \\frac{p(x_i)}{q(x_i)}\n",
"\\end{align}\n",
"\n",
"since $\\text{log}a - \\text{log}b = \\text{log}\\frac{a}{b}$.\n",
"\n",
"If two distributions, $p$ and $q$ perfectly match, $D_{KL}(p || q) = 0$, otherwise the lower the KL divergence value, the better we have matched the true distribution with our approximation.\n",
"\n",
"Side Note: If you're interested in having an understanding of the relationship between entropy, cross entropy and KL divergence, the following links are good places to start. Maybe they will clear up some of the hand-wavy explanation of these concepts ... [Youtube: A Short Introduction to Entropy, Cross-Entropy and KL-Divergence](https://www.youtube.com/watch?v=ErfnhcEV1O8) and [StackExchange: Why do we use Kullback-Leibler divergence rather than cross entropy in the t-SNE objective function?](https://stats.stackexchange.com/questions/265966/why-do-we-use-kullback-leibler-divergence-rather-than-cross-entropy-in-the-t-sne/265989)\n",
"\n",
"Given these information, we can go ahead and calculate the KL divergence for our two approximating distributions."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KL(True||Uniform): 0.13667971094966938\n",
"KL(True||Binomial): 0.32819435311402045\n"
]
}
],
"source": [
"# both function are equivalent ways of computing KL-divergence\n",
"# one uses for loop and the other uses vectorization\n",
"def compute_kl_divergence(p_probs, q_probs):\n",
" \"\"\"\"KL (p || q)\"\"\"\n",
" kl_div = 0.0\n",
" for p, q in zip(p_probs, q_probs):\n",
" kl_div += p * np.log(p / q)\n",
"\n",
" return kl_div\n",
"\n",
"\n",
"def compute_kl_divergence(p_probs, q_probs):\n",
" \"\"\"\"KL (p || q)\"\"\"\n",
" kl_div = p_probs * np.log(p_probs / q_probs)\n",
" return np.sum(kl_div)\n",
"\n",
"\n",
"print('KL(True||Uniform): ', compute_kl_divergence(true_data, uniform_data))\n",
"print('KL(True||Binomial): ', compute_kl_divergence(true_data, binom_data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see the information lost by using the Binomial approximation is greater than using the uniform approximation. If we have to choose one to represent our observations, we're better off sticking with the Uniform approximation.\n",
"\n",
"To close this discussion, we used KL-divergence to calculate which our approximate distribution more closely reflects our true distribution. One caveat to note is that it may be tempting to think of KL-divergence as a way of measuring distance, however, whenever we talk about KL-divergence, we do not categorized it as a distance metric due to the fact that it is asymmetric. In other words, $D_{KL}(p || q) \\neq D_{KL}(q || p)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Blog: Kullback-Leibler Divergence Explained](https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained)\n",
"- [Youtube: A Short Introduction to Entropy, Cross-Entropy and KL-Divergence](https://www.youtube.com/watch?v=ErfnhcEV1O8)\n",
"- [StackExchange: Why do we use Kullback-Leibler divergence rather than cross entropy in the t-SNE objective function?](https://stats.stackexchange.com/questions/265966/why-do-we-use-kullback-leibler-divergence-rather-than-cross-entropy-in-the-t-sne/265989)"
]
}
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| mit |
jaimefrio/pydatabcn2017 | taking_numpy_in_stride/Taking NumPy In Stride - Student Version.ipynb | 1 | 21956 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Array views and slicing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A NumPy array is an object of [`numpy.ndarray`](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.ndarray.html) type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = np.arange(3)\n",
"type(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All `ndarray`s have a `.base` attribute.\n",
"If this attribute is not `None`, then the array is a **view** of some other object's memory, typically another `ndarray`.\n",
"This is a very powerful tool, because allocating memory and copying memory contents are expensive operations, but updating metadata on how to interpret some already allocated memory is cheap!\n",
"\n",
"The simplest way of creating an array's view is by slicing it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = np.arange(3)\n",
"a.base is None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a[:].base is None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look more closely at what an array's metadata looks like. NumPy provides the [`np.info`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.info.html) function, which can list for us some low level attributes of an array:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.info(a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"By the end of the workshop you will understand what most of these mean.\n",
"But rather than listen through a lesson, you get to try and figure what they mean yourself.\n",
"To help you with that, here's a function that prints the information from two arrays side by side:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def info_for_two(one_array, another_array):\n",
" \"\"\"Prints side-by-side results of running np.info on its inputs.\"\"\"\n",
" def info_as_ordered_dict(array):\n",
" \"\"\"Converts return of np.infor into an ordered dict.\"\"\"\n",
" import collections\n",
" import io\n",
" buffer = io.StringIO()\n",
" np.info(array, output=buffer)\n",
" data = (\n",
" item.split(':') for item in buffer.getvalue().strip().split('\\n'))\n",
" return collections.OrderedDict(\n",
" ((key, value.strip()) for key, value in data))\n",
" one_dict = info_as_ordered_dict(one_array)\n",
" another_dict = info_as_ordered_dict(another_array)\n",
" name_w = max(len(name) for name in one_dict.keys())\n",
" one_w = max(len(name) for name in one_dict.values())\n",
" another_w = max(len(name) for name in another_dict.values())\n",
" output = (\n",
" f'{name:<{name_w}} : {one:>{one_w}} : {another:>{another_w}}'\n",
" for name, one, another in zip(\n",
" one_dict.keys(), one_dict.values(), another_dict.values()))\n",
" print('\\n'.join(output))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 1.\n",
" 1. Create a one dimensional NumPy array with a few items (consider using [`np.arange`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html)).\n",
" 2. Compare the printout of `np.info` on your array and on slices of it (use the `[start:stop:step]` indexing syntax, and make sure to try steps other than one).\n",
" 3. Do you see any patterns?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 1 debrief\n",
"Every array has an underlying block of memory assigned to it.\n",
"When we slice an array, rather than making a **copy** of it, NumPy makes a **view**, reusing the memory block, but interpreting it differently.\n",
"\n",
"Lets take a look at what NumPy did for us in the above examples, and make sense of some of the changes to info.\n",
"\n",
"![Exercise 1](img/exercise1.png \"Exercise 1\")\n",
"\n",
" * **shape**: for a one dimensional array *shape* is a single item tuple, equal to the total number of items in the array. You can get the shape of an array as its `.shape` attribute.\n",
" * **strides**: is also a single item tuple for one-dimensional arrays, its value being the number of bytes to skip in memory to get to the next item. And yes, strides can be negative. You can get this as the `.strides` attribute of any array.\n",
" * **data pointer**: this is the address in memory of the first byte of the first item of the array. Note that this doesn't have to be the same as the first byte of the underlying memory block! You rarely need to know the exact address of the data pointer, but it's part of the string representation of the arrays `.data` attribute. \n",
" * **itemsize**: this isn't properly an attribute of the array, but of it's data type. It is the number of bytes that an array item takes up in memory. You can get this value from an array as the `.itemsize` attribute of its `.dtype` attribute, i.e. `array.dtype.itemsize`.\n",
" * **type**: this lets us know how each array item should be interpreted e.g. for calculations. We'll talk more about this later, but you can get an array's type object through its `.dtype` attribute.\n",
" * **contiguous**: this is one of several boolean flags of an array. Its meaning is a little more specific, but for now lets say it tells us whether the array items use the memory block efficiently, without leaving unused spaces between items. It's value can be checked as the `.contiguous` attribute of the arrays `.flags` attribute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Take a couple or minutes to familiarize yourself with the NumPy array's attributes discussed above:\n",
"\n",
" 1. Create a small one dimensional array of your choosing.\n",
" 2. Look at its `.shape`, `.strides`, `.dtype`, `.flags` and `.data` attributes.\n",
" 3. For `.dtype` and `.flags`, store them into a separate variable, and use tab completion on those to explore their subattributes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A look at data types\n",
"\n",
"Similarly to how we can change the shape, strides and data pointer of an array through slicing, we can change how it's items are interpreted by changing it's data type.\n",
"This is done by calling the array's [`.view()`](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.ndarray.view.html) method, and passing it the new data type.\n",
"\n",
"But before we go there, lets look a little closer at dtypes. You are hopefully familiar with the basic NumPy numerical data types:\n",
"\n",
"| Type Family | NumPy Defined Types | Character Codes |\n",
"| :---: |\n",
"| boolean | `np.bool` | `'?'` |\n",
"| unsigned integers | `np.uint8` - `np.uint64` | `'u1'`, `'u2'`, `'u4'`, `'u8'` |\n",
"| signed integers | `np.int8` - `np.int64` | `'i1'`, `'i2'`, `'i4'`, `'i8'` |\n",
"| floating point | `np.float16` - `np.float128` | `'f2'`, `'f4'`, `'f8'`, `'f16'` |\n",
"| complex | `np.complex64`, `np.complex128` | `'c8'`, `'c16'` |\n",
"\n",
"You can create a new data type by calling its constructor, [`np.dtype()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.html), with either a NumPy defined type, or the character code.\n",
"\n",
"Character codes can have `'<'` or `'>'` prepended, to indicate whether the type is little or big endian. If unspecified, native encoding is used, which for all practical purposes is going to be little endian."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 3\n",
"\n",
"Let's play a little with dtype views:\n",
"\n",
" 1. Create a simple array of a type you feel comfortable you understand, e.g. `np.arange(4, dtype=np.uint16)`.\n",
" 2. Take a view of type `np.uint8` of your array. This will give you the raw byte contents of your array. Is this what you were expecting?\n",
" 3. Take a few views of your array, with dtypes of larger itemsize, or changing the endianess of the data type. Try to predict what the output will be before running the examples.\n",
" 4. Take a look at the wikipedia page on single precision floating point numbers, more specifically its [examples of encodings](https://en.wikipedia.org/wiki/Single-precision_floating-point_format#Single-precision_examples). Create arrays of four `np.uint8` values which, when viewed as a `np.float32` give the values 1, -2, and 1/3."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Constructor They Don't Want You To Know About.\n",
"\n",
"You typically construct your NumPy arrays using [one of the many factory fuctions](https://docs.scipy.org/doc/numpy-1.12.0/reference/routines.array-creation.html) provided, [`np.array()`](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.array.html) being the most popular.\n",
"But it is also possible to call the `np.ndarray` object constructor directly.\n",
"You will typically not want to do this, because there are probably simpler alternatives.\n",
"But it is a great way of putting your understanding of views of arrays to the test!\n",
"\n",
"You can check [the full documentation](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.ndarray.html), but the `np.ndarray` constructor takes the following arguments that we care about:\n",
"\n",
" * **shape**: the shape of the returned array,\n",
" * **dtype**: the data type of the returned array,\n",
" * **buffer**: an object to reuse the underlying memory from, e.g. an existing array or its `.data` attribute,\n",
" * **offset**: by how many bytes to move the starting data pointer of the returned array relative to the passed buffer,\n",
" * **strides**: the strides of the returned array."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 4\n",
"\n",
"Write a function, using the `np.ndarray` constructor, that takes a one dimensional array and returns a reversed view of it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reshaping Into Higher Dimensions\n",
"\n",
"So far we have sticked to one dimensional arrays. Things get substantially more interesting when we move into higher dimensions.\n",
"\n",
"One way of getting views with a different number of dimensions is by using the [`.reshape()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.reshape.html) method of NumPy arrays, or the equivalent [`np.reshape()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html) function.\n",
"\n",
"The first argument to any of the reshape functions is the new shape of the array. When providing it, keep in mind: \n",
"\n",
" * the total size of the array must stay unchanged, i.e. the product of the values of the new shape tuple must be equal to the product of the values of the old shape tuple.\n",
" * by entering `-1` for one of the new dimensions, you can have NumPy compute its value for you, but the other dimensions must be compatible with the calculated one being an integer.\n",
" \n",
"`.reshape()` can also take an `order=` kwarg, which can be set to `'C'` (as the programming language) or `'F'` (for the Fortran programming language). This correspond to [row and column major orders](https://en.wikipedia.org/wiki/Row-_and_column-major_order), respectively.\n",
"\n",
"### Exercise 5\n",
"\n",
"Let's look at how multidimensional arrays are represented in NumPy with an exercise.\n",
"\n",
" 1. Create a small linear array with a total length that is a multiple of two different small primes, e.g. `6 = 2 * 3`.\n",
" 2. Reshape the array into a two dimensional one, starting with the default `order='C'`. Try both possible combinations of rows and columns, e.g. `(2, 3)` and `(3, 2)`. Look at the resulting arrays, and compare their metadata. Do you understand what's going on?\n",
" 3. Try the same reshaping with `order='F'`. Can you see what the differences are?\n",
" 4. If you feel confident with these, give a higher dimensional array a try."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 5 debrief\n",
"\n",
"As the examples show, an n-dimensional array will have an n item tuple `.shape` and `.strides`. The number of dimensions can be directly queried from the `.ndim` attribute.\n",
"\n",
"The shape tells us how large the array is along each dimension, the strides tell us how many bytes to skip in memory to get to the next item along each dimension.\n",
"\n",
"When we reshape an array using C order, a.k.a. row major order, items along higher dimensions are closer in memory. When we use Fortran orser, a.k.a. column major order, it is items along smaller dimensions that are closer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Exercise 5](img/exercise5.png \"Exercise 5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reshaping with a purpose\n",
"\n",
"One typical use of reshaping is to apply some aggregation function to equal subdivision of an array.\n",
"\n",
"Say you have, e.g. a 12 item 1D array, and would like to compute the sum of every three items. This is how this is typically accomplished:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = np.arange(12, dtype=float)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.reshape(4, 3).sum(axis=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can apply fancier functions than `.sum()`, e.g. let's compute the variance of each group:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a.reshape(4, 3).var(axis=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 6\n",
"\n",
"Your turn to do a fancier reshaping: we will compute the average of a 2D array over non-overlapping rectangular patches:\n",
"\n",
" 1. Choose to small numbers `m` and `n`, e.g. `3` and `4`.\n",
" 2. Create a 2D array, with number of rows a multiple of one of those numbers, and number of columns a multiple of the other, e.g. `15 x 24`.\n",
" 3. Reshape and aggregate to create a 2D array holding the sums over non overlapping `m x n` tiles, e.g. a `5 x 6` array.\n",
" 4. **Hint**: `.sum()` can take a tuple of integers as `axis=`, so you can do the whole thing in a single reshape from 2D to 4D, then aggregate back to 2D. If tyou find this confusing, doing two aggregations will also work."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rearranging dimensions\n",
"\n",
"Once we have a multidimensional array, rearranging the order of its dimensions is as simple as rearranging its `.shape` and `.tuple` attributes. You could do this with `np.ndarray`, but it would be a pain. NumPy has [a bunch](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html#transpose-like-operations) of functions for doing that, but they are all watered down versions of [`np.transpose`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html), which takes a tuple with the desired permutation of the array dimensions.\n",
"\n",
"### Exercise 7\n",
"\n",
" 1. Write a function `roll_axis_to_end` that takes an array and an axis, and makes that axis the last dimension of the array.\n",
" 2. For extra credit, rewrite your function using `np.ndarray`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Playing with strides\n",
"\n",
"For the rest of the workshop we are going to dome some fancy tricks with strides, to create interesting views of an existing array."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 8\n",
"\n",
"Create a function to extract the diagonal of a 2-D array, using the `np.ndarray` constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 9\n",
"\n",
" 1. Something very interesting happens when we set a stride to zero. Give that idea some thought and then:\n",
" 2. Create two functions, `stacked_column_vector` and `stacked_row_vector`, that take a 1D array (the vector), and an integer `n`, and create a 2D view of the array that stack `n` copies of the vector, either as columns or rows of the view.\n",
" 3. Use this functions to create an `outer_product` function that takes two 1D vectors and computes their outer product."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 10\n",
"\n",
"In the last exercise we used zero strides to reuse an item more than once in the resulting view. Let's try to build on that idea:\n",
"\n",
" 1. Write a function that takes a 1D array and a `window` integer value, and creates a 2D view of the array, each row a view through a sliding window of size `window` into the original array.\n",
" 2. **Hint**: There are `len(array) - window + 1` such \"views through a window\".\n",
" 3. **Another hint**: Here's a small example expected run:\n",
" \n",
" `>>> sliding_window(np.arange(4), 2)\n",
" [[0, 1],\n",
" [1, 2],\n",
" [2, 3]]`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parting pro tip\n",
"\n",
"NumPy's worst kept secret is the existence of a mostly undocumented, mostly hidden, `as_strided` function, that makes creating views with funny strides much easier (and also much more dangerous!) than using `np.ndarray`. Here's the available documentation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy.lib.stride_tricks import as_strided\n",
"\n",
"np.info(as_strided)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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| unlicense |
mattssilva/UW-Machine-Learning-Specialization | Week 4/Document Retrieval.ipynb | 1 | 598440 | {
"cells": [
{
"cell_type": "code",
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"source": [
"import graphlab\n",
"import os\n",
"import pandas as pd"
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"cell_type": "markdown",
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"source": [
"# Load some text data from Wikipedia, pages on people"
]
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{
"cell_type": "code",
"execution_count": 3,
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"collapsed": false
},
"outputs": [
{
"name": "stdout",
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"text": [
"This non-commercial license of GraphLab Create for academic use is assigned to [email protected] and will expire on September 01, 2018.\n"
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"[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: C:\\Users\\Matheus\\AppData\\Local\\Temp\\graphlab_server_1506516765.log.0\n"
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"source": [
"people = graphlab.SFrame('M:/Google Drive/Data Science/Machine Learning/1-Machine Learning - Foundations/Week 4/Clustering and Similarity/Notebook-week4/people_wiki.csv')"
]
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{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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"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\">URI</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\">text</th>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Digby_Morrell> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Digby Morrell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">digby morrell born 10<br>october 1979 is a former ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Alfred_J._Lewy> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alfred J. Lewy</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">alfred j lewy aka sandy<br>lewy graduated from ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Harpdog_Brown> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Harpdog Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">harpdog brown is a singer<br>and harmonica player who ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Franz_Rottensteiner> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Franz Rottensteiner</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">franz rottensteiner born<br>in waidmannsfeld lower ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/G-Enka> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-Enka</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">henry krvits born 30<br>december 1974 in tallinn ...</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Sam_Henderson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sam Henderson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sam henderson born<br>october 18 1969 is an ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Aaron_LaCrate> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Aaron LaCrate</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">aaron lacrate is an<br>american music producer ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Trevor_Ferguson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Trevor Ferguson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">trevor ferguson aka john<br>farrow born 11 november ...</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Grant_Nelson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Grant Nelson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">grant nelson born 27<br>april 1971 in london ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Cathy_Caruth> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cathy Caruth</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cathy caruth born 1955 is<br>frank h t rhodes ...</td>\n",
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"+-------------------------------+---------------------+\n",
"| URI | name |\n",
"+-------------------------------+---------------------+\n",
"| <http://dbpedia.org/resour... | Digby Morrell |\n",
"| <http://dbpedia.org/resour... | Alfred J. Lewy |\n",
"| <http://dbpedia.org/resour... | Harpdog Brown |\n",
"| <http://dbpedia.org/resour... | Franz Rottensteiner |\n",
"| <http://dbpedia.org/resour... | G-Enka |\n",
"| <http://dbpedia.org/resour... | Sam Henderson |\n",
"| <http://dbpedia.org/resour... | Aaron LaCrate |\n",
"| <http://dbpedia.org/resour... | Trevor Ferguson |\n",
"| <http://dbpedia.org/resour... | Grant Nelson |\n",
"| <http://dbpedia.org/resour... | Cathy Caruth |\n",
"+-------------------------------+---------------------+\n",
"+-------------------------------+\n",
"| text |\n",
"+-------------------------------+\n",
"| digby morrell born 10 octo... |\n",
"| alfred j lewy aka sandy le... |\n",
"| harpdog brown is a singer ... |\n",
"| franz rottensteiner born i... |\n",
"| henry krvits born 30 decem... |\n",
"| sam henderson born october... |\n",
"| aaron lacrate is an americ... |\n",
"| trevor ferguson aka john f... |\n",
"| grant nelson born 27 april... |\n",
"| cathy caruth born 1955 is ... |\n",
"+-------------------------------+\n",
"[10 rows x 3 columns]"
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"people.head()"
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},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"59071"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(people)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the dataset and checkout the text it contains"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"obama = people[people['name']==\"Barack Obama\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Barack_Obama> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Barack Obama</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">barack hussein obama ii<br>brk husen bm born august ...</td>\n",
" </tr>\n",
"</table>\n",
"[? rows x 3 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"obama"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: str\n",
"Rows: ?\n",
"['barack hussein obama ii brk husen bm born august 4 1961 is the 44th and current president of the united states and the first african american to hold the office born in honolulu hawaii obama is a graduate of columbia university and harvard law school where he served as president of the harvard law review he was a community organizer in chicago before earning his law degree he worked as a civil rights attorney and taught constitutional law at the university of chicago law school from 1992 to 2004 he served three terms representing the 13th district in the illinois senate from 1997 to 2004 running unsuccessfully for the united states house of representatives in 2000in 2004 obama received national attention during his campaign to represent illinois in the united states senate with his victory in the march democratic party primary his keynote address at the democratic national convention in july and his election to the senate in november he began his presidential campaign in 2007 and after a close primary campaign against hillary rodham clinton in 2008 he won sufficient delegates in the democratic party primaries to receive the presidential nomination he then defeated republican nominee john mccain in the general election and was inaugurated as president on january 20 2009 nine months after his election obama was named the 2009 nobel peace prize laureateduring his first two years in office obama signed into law economic stimulus legislation in response to the great recession in the form of the american recovery and reinvestment act of 2009 and the tax relief unemployment insurance reauthorization and job creation act of 2010 other major domestic initiatives in his first term included the patient protection and affordable care act often referred to as obamacare the doddfrank wall street reform and consumer protection act and the dont ask dont tell repeal act of 2010 in foreign policy obama ended us military involvement in the iraq war increased us troop levels in afghanistan signed the new start arms control treaty with russia ordered us military involvement in libya and ordered the military operation that resulted in the death of osama bin laden in january 2011 the republicans regained control of the house of representatives as the democratic party lost a total of 63 seats and after a lengthy debate over federal spending and whether or not to raise the nations debt limit obama signed the budget control act of 2011 and the american taxpayer relief act of 2012obama was reelected president in november 2012 defeating republican nominee mitt romney and was sworn in for a second term on january 20 2013 during his second term obama has promoted domestic policies related to gun control in response to the sandy hook elementary school shooting and has called for full equality for lgbt americans while his administration has filed briefs which urged the supreme court to strike down the defense of marriage act of 1996 and californias proposition 8 as unconstitutional in foreign policy obama ordered us military involvement in iraq in response to gains made by the islamic state in iraq after the 2011 withdrawal from iraq continued the process of ending us combat operations in afghanistan and has sought to normalize us relations with cuba', ... ]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obama['text']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: str\n",
"Rows: ?\n",
"['george timothy clooney born may 6 1961 is an american actor writer producer director and activist he has received three golden globe awards for his work as an actor and two academy awards one for acting and the other for producingclooney made his acting debut on television in 1978 and later gained wide recognition in his role as dr doug ross on the longrunning medical drama er from 1994 to 1999 for which he received two emmy award nominations while working on er he began attracting a variety of leading roles in films including the superhero film batman robin 1997 and the crime comedy out of sight 1998 in which he first worked with a director who would become a longtime collaborator steven soderbergh in 1999 clooney took the lead role in three kings a wellreceived war satire set during the gulf warin 2001 clooneys fame widened with the release of his biggest commercial success the heist comedy oceans eleven the first of the film trilogy a remake of the 1960 film with frank sinatra as danny ocean he made his directorial debut a year later with the biographical thriller confessions of a dangerous mind and has since directed the drama good night and good luck 2005 the sports comedy leatherheads 2008 the political drama the ides of march 2011 and the comedydrama war film the monuments men 2014he won an academy award for best supporting actor for the middle east thriller syriana 2005 and subsequently earned best actor nominations for the legal thriller michael clayton 2007 the comedydrama up in the air 2009 and the drama the descendants 2011 in 2013 he received the academy award for best picture for producing the political thriller argo alongside ben affleck and grant heslov he is the only person ever to be nominated for academy awards in six categoriesclooney is sometimes described as one of the most handsome men in the world in 2005 tv guide ranked clooney no 1 on its 50 sexiest stars of all time list in 2009 he was included in times annual time 100 as one of the most influential people in the world clooney is also noted for his political activism and has served as one of the united nations messengers of peace since january 31 2008 his humanitarian work includes his advocacy of finding a resolution for the darfur conflict raising funds for the 2010 haiti earthquake 2004 tsunami and 911 victims and creating documentaries such as sand and sorrow to raise awareness about international crises he is also a member of the council on foreign relations', ... ]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clooney = people[people['name']=='George Clooney']\n",
"clooney['text']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get the word counts for Obama article"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'operations': 1L, 'represent': 1L, 'office': 2L, 'unemployment': 1L, 'doddfrank': 1L, 'over': 1L, 'unconstitutional': 1L, 'domestic': 2L, 'major': 1L, 'years': 1L, 'against': 1L, 'proposition': 1L, 'seats': 1L, 'graduate': 1L, 'debate': 1L, 'before': 1L, 'death': 1L, '20': 2L, 'taxpayer': 1L, 'representing': 1L, 'obamacare': 1L, 'barack': 1L, 'to': 14L, '4': 1L, 'policy': 2L, '8': 1L, 'he': 7L, '2011': 3L, '2010': 2L, '2013': 1L, '2012': 1L, 'bin': 1L, 'then': 1L, 'his': 11L, 'march': 1L, 'gains': 1L, 'cuba': 1L, 'school': 3L, '1992': 1L, 'new': 1L, 'not': 1L, 'during': 2L, 'ending': 1L, 'continued': 1L, 'presidential': 2L, 'states': 3L, 'husen': 1L, 'osama': 1L, 'californias': 1L, 'equality': 1L, 'prize': 1L, 'lost': 1L, 'made': 1L, 'inaugurated': 1L, 'january': 3L, 'university': 2L, 'rights': 1L, 'july': 1L, 'gun': 1L, 'stimulus': 1L, 'rodham': 1L, 'troop': 1L, 'withdrawal': 1L, 'brk': 1L, 'nine': 1L, 'where': 1L, 'referred': 1L, 'affordable': 1L, 'attorney': 1L, 'on': 2L, 'often': 1L, 'senate': 3L, 'regained': 1L, 'national': 2L, 'creation': 1L, 'related': 1L, 'hawaii': 1L, 'born': 2L, 'second': 2L, 'defense': 1L, 'election': 3L, 'close': 1L, 'operation': 1L, 'insurance': 1L, 'sandy': 1L, 'afghanistan': 2L, 'initiatives': 1L, 'for': 4L, 'reform': 1L, 'house': 2L, 'review': 1L, 'representatives': 2L, 'current': 1L, 'state': 1L, 'won': 1L, 'limit': 1L, 'victory': 1L, 'unsuccessfully': 1L, 'reauthorization': 1L, 'keynote': 1L, 'full': 1L, 'patient': 1L, 'august': 1L, 'degree': 1L, '44th': 1L, 'bm': 1L, 'mitt': 1L, 'attention': 1L, 'delegates': 1L, 'lgbt': 1L, 'job': 1L, 'harvard': 2L, 'term': 3L, 'served': 2L, 'ask': 1L, 'november': 2L, 'debt': 1L, 'by': 1L, 'wall': 1L, 'care': 1L, 'received': 1L, 'great': 1L, 'signed': 3L, 'libya': 1L, 'receive': 1L, 'of': 18L, 'months': 1L, 'urged': 1L, 'foreign': 2L, 'american': 3L, 'protection': 2L, 'economic': 1L, 'act': 8L, 'military': 4L, 'hussein': 1L, 'or': 1L, 'first': 3L, 'control': 4L, 'named': 1L, 'clinton': 1L, 'dont': 2L, 'campaign': 3L, 'russia': 1L, 'civil': 1L, 'reinvestment': 1L, 'into': 1L, 'address': 1L, 'primary': 2L, 'community': 1L, 'mccain': 1L, 'down': 1L, 'hook': 1L, '63': 1L, 'americans': 1L, 'elementary': 1L, 'total': 1L, 'earning': 1L, 'repeal': 1L, 'from': 3L, 'raise': 1L, 'district': 1L, 'spending': 1L, 'republican': 2L, 'legislation': 1L, 'three': 1L, 'relations': 1L, 'nobel': 1L, 'start': 1L, 'tell': 1L, 'iraq': 4L, 'convention': 1L, 'resulted': 1L, 'john': 1L, 'was': 5L, '2012obama': 1L, 'form': 1L, 'that': 1L, 'tax': 1L, 'sufficient': 1L, 'republicans': 1L, 'strike': 1L, 'hillary': 1L, 'ended': 1L, 'arms': 1L, 'honolulu': 1L, 'filed': 1L, 'worked': 1L, 'hold': 1L, 'with': 3L, 'obama': 9L, 'street': 1L, 'ii': 1L, 'has': 4L, '1997': 1L, '1996': 1L, 'whether': 1L, 'reelected': 1L, 'budget': 1L, 'us': 6L, 'nations': 1L, 'recession': 1L, 'while': 1L, 'taught': 1L, 'marriage': 1L, 'policies': 1L, 'promoted': 1L, 'called': 1L, 'and': 21L, 'supreme': 1L, 'ordered': 3L, 'nominee': 2L, 'process': 1L, '2000in': 1L, 'is': 2L, 'romney': 1L, 'briefs': 1L, 'defeated': 1L, 'general': 1L, '13th': 1L, 'as': 6L, 'at': 2L, 'in': 30L, 'sought': 1L, 'organizer': 1L, 'shooting': 1L, 'increased': 1L, 'normalize': 1L, 'lengthy': 1L, 'united': 3L, 'court': 1L, 'recovery': 1L, 'laden': 1L, 'laureateduring': 1L, 'peace': 1L, 'administration': 1L, '1961': 1L, 'illinois': 2L, 'other': 1L, 'which': 1L, 'party': 3L, 'primaries': 1L, 'sworn': 1L, 'relief': 2L, 'war': 1L, 'columbia': 1L, 'combat': 1L, 'after': 4L, 'islamic': 1L, 'running': 1L, 'levels': 1L, 'two': 1L, 'involvement': 3L, 'response': 3L, 'included': 1L, 'president': 4L, 'law': 6L, 'nomination': 1L, '2008': 1L, 'a': 7L, '2009': 3L, 'chicago': 2L, 'constitutional': 1L, 'defeating': 1L, 'treaty': 1L, 'federal': 1L, '2007': 1L, '2004': 3L, 'african': 1L, 'the': 40L, 'democratic': 4L, 'consumer': 1L, 'began': 1L, 'terms': 1L}]\n"
]
}
],
"source": [
"obama['word_count'] = graphlab.text_analytics.count_words(obama['text'])\n",
"print obama['word_count']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sort the word counts for the Obama article"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"obama_word_count_table = obama[['word_count']].stack('word_count',new_column_name = ['word','count'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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\">word</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">normalize</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sought</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">combat</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">continued</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">unconstitutional</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">californias</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1996</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">marriage</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">defense</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"[273 rows x 2 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",
"\tword\tstr\n",
"\tcount\tint\n",
"\n",
"Rows: 273\n",
"\n",
"Data:\n",
"+------------------+-------+\n",
"| word | count |\n",
"+------------------+-------+\n",
"| normalize | 1 |\n",
"| sought | 1 |\n",
"| combat | 1 |\n",
"| continued | 1 |\n",
"| unconstitutional | 1 |\n",
"| 8 | 1 |\n",
"| californias | 1 |\n",
"| 1996 | 1 |\n",
"| marriage | 1 |\n",
"| defense | 1 |\n",
"+------------------+-------+\n",
"[273 rows x 2 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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obama_word_count_table"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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\">word</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">the</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">40</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">in</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">and</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">21</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">of</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">to</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">his</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">obama</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">act</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">a</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">he</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" </tr>\n",
"</table>\n",
"[273 rows x 2 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",
"\tword\tstr\n",
"\tcount\tint\n",
"\n",
"Rows: 273\n",
"\n",
"Data:\n",
"+-------+-------+\n",
"| word | count |\n",
"+-------+-------+\n",
"| the | 40 |\n",
"| in | 30 |\n",
"| and | 21 |\n",
"| of | 18 |\n",
"| to | 14 |\n",
"| his | 11 |\n",
"| obama | 9 |\n",
"| act | 8 |\n",
"| a | 7 |\n",
"| he | 7 |\n",
"+-------+-------+\n",
"[273 rows x 2 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": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obama_word_count_table.sort('count',ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute TF-IDF for the corpus"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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\">URI</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\">text</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Digby_Morrell> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Digby Morrell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">digby morrell born 10<br>october 1979 is a former ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'since': 1L, 'carltons':<br>1L, 'being': 1L, '2005': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Alfred_J._Lewy> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alfred J. Lewy</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">alfred j lewy aka sandy<br>lewy graduated from ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'precise': 1L, 'thomas':<br>1L, 'closely': 1L, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Harpdog_Brown> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Harpdog Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">harpdog brown is a singer<br>and harmonica player who ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 1L, 'issued':<br>1L, 'mainly': 1L, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Franz_Rottensteiner> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Franz Rottensteiner</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">franz rottensteiner born<br>in waidmannsfeld lower ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 1L,<br>'bauforschung': 1L, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/G-Enka> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">G-Enka</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">henry krvits born 30<br>december 1974 in tallinn ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'legendary': 1L,<br>'gangstergenka': 1L, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Sam_Henderson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sam Henderson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sam henderson born<br>october 18 1969 is an ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'now': 1L, 'currently':<br>1L, 'less': 1L, 'being': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Aaron_LaCrate> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Aaron LaCrate</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">aaron lacrate is an<br>american music producer ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'exclusive': 2L,<br>'producer': 1L, 'tribe': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Trevor_Ferguson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Trevor Ferguson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">trevor ferguson aka john<br>farrow born 11 november ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'taxi': 1L, 'salon': 1L,<br>'gangs': 1L, 'being': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Grant_Nelson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Grant Nelson</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">grant nelson born 27<br>april 1971 in london ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'houston': 1L,<br>'frankie': 1L, 'labels': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Cathy_Caruth> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cathy Caruth</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cathy caruth born 1955 is<br>frank h t rhodes ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'phenomenon': 1L,<br>'deborash': 1L, ...</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tURI\tstr\n",
"\tname\tstr\n",
"\ttext\tstr\n",
"\tword_count\tdict\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+---------------------+\n",
"| URI | name |\n",
"+-------------------------------+---------------------+\n",
"| <http://dbpedia.org/resour... | Digby Morrell |\n",
"| <http://dbpedia.org/resour... | Alfred J. Lewy |\n",
"| <http://dbpedia.org/resour... | Harpdog Brown |\n",
"| <http://dbpedia.org/resour... | Franz Rottensteiner |\n",
"| <http://dbpedia.org/resour... | G-Enka |\n",
"| <http://dbpedia.org/resour... | Sam Henderson |\n",
"| <http://dbpedia.org/resour... | Aaron LaCrate |\n",
"| <http://dbpedia.org/resour... | Trevor Ferguson |\n",
"| <http://dbpedia.org/resour... | Grant Nelson |\n",
"| <http://dbpedia.org/resour... | Cathy Caruth |\n",
"+-------------------------------+---------------------+\n",
"+-------------------------------+-------------------------------+\n",
"| text | word_count |\n",
"+-------------------------------+-------------------------------+\n",
"| digby morrell born 10 octo... | {'since': 1L, 'carltons': ... |\n",
"| alfred j lewy aka sandy le... | {'precise': 1L, 'thomas': ... |\n",
"| harpdog brown is a singer ... | {'just': 1L, 'issued': 1L,... |\n",
"| franz rottensteiner born i... | {'all': 1L, 'bauforschung'... |\n",
"| henry krvits born 30 decem... | {'legendary': 1L, 'gangste... |\n",
"| sam henderson born october... | {'now': 1L, 'currently': 1... |\n",
"| aaron lacrate is an americ... | {'exclusive': 2L, 'produce... |\n",
"| trevor ferguson aka john f... | {'taxi': 1L, 'salon': 1L, ... |\n",
"| grant nelson born 27 april... | {'houston': 1L, 'frankie':... |\n",
"| cathy caruth born 1955 is ... | {'phenomenon': 1L, 'debora... |\n",
"+-------------------------------+-------------------------------+\n",
"[10 rows x 4 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"people['word_count'] = graphlab.text_analytics.count_words(people['text'])\n",
"people.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tfidf = graphlab.text_analytics.tf_idf(people['word_count'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: dict\n",
"Rows: 59071\n",
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0.02809592236291573, 'lower': 4.515695885442592, 'vfl': 10.506308224654898, 'onfield': 7.690658523220865, 'grieg': 7.585298007563039, 'nevertheless': 5.505856465883203, 'performances': 3.352641829543426, 'age': 2.138848033513307, 'st': 5.02274994846173, '2001': 1.9280249665871378, 'the': 0.0015236676386087002, 'playing': 2.0910027577735617}, ... ]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tfidf.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tf_idf = pd.DataFrame({'docs':tfidf},index=None)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"people['tfidf'] = tf_idf['docs']"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Examine the TF-IDF for the Obama article"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">henry krvits born 30<br>december 1974 in tallinn ...</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"><http://dbpedia.org/resou<br>rce/Grant_Nelson> ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Grant Nelson</td>\n",
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" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'precise':<br>6.44320060695519, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just':<br>2.7007299687108643, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'englishreading':<br>10.293348208665249, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'they':<br>1.8993401178193898, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'now': 1.96695239252401,<br>'since': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'exclusive':<br>10.455187230695827, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'taxi':<br>6.0520214560945025, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'houston':<br>3.935505942157149, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'phenomenon':<br>5.750053426395245, ...</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tURI\tstr\n",
"\tname\tstr\n",
"\ttext\tstr\n",
"\tword_count\tdict\n",
"\ttfidf\tdict\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+---------------------+\n",
"| URI | name |\n",
"+-------------------------------+---------------------+\n",
"| <http://dbpedia.org/resour... | Digby Morrell |\n",
"| <http://dbpedia.org/resour... | Alfred J. Lewy |\n",
"| <http://dbpedia.org/resour... | Harpdog Brown |\n",
"| <http://dbpedia.org/resour... | Franz Rottensteiner |\n",
"| <http://dbpedia.org/resour... | G-Enka |\n",
"| <http://dbpedia.org/resour... | Sam Henderson |\n",
"| <http://dbpedia.org/resour... | Aaron LaCrate |\n",
"| <http://dbpedia.org/resour... | Trevor Ferguson |\n",
"| <http://dbpedia.org/resour... | Grant Nelson |\n",
"| <http://dbpedia.org/resour... | Cathy Caruth |\n",
"+-------------------------------+---------------------+\n",
"+-------------------------------+-------------------------------+\n",
"| text | word_count |\n",
"+-------------------------------+-------------------------------+\n",
"| digby morrell born 10 octo... | {'since': 1L, 'carltons': ... |\n",
"| alfred j lewy aka sandy le... | {'precise': 1L, 'thomas': ... |\n",
"| harpdog brown is a singer ... | {'just': 1L, 'issued': 1L,... |\n",
"| franz rottensteiner born i... | {'all': 1L, 'bauforschung'... |\n",
"| henry krvits born 30 decem... | {'legendary': 1L, 'gangste... |\n",
"| sam henderson born october... | {'now': 1L, 'currently': 1... |\n",
"| aaron lacrate is an americ... | {'exclusive': 2L, 'produce... |\n",
"| trevor ferguson aka john f... | {'taxi': 1L, 'salon': 1L, ... |\n",
"| grant nelson born 27 april... | {'houston': 1L, 'frankie':... |\n",
"| cathy caruth born 1955 is ... | {'phenomenon': 1L, 'debora... |\n",
"+-------------------------------+-------------------------------+\n",
"+-------------------------------+\n",
"| tfidf |\n",
"+-------------------------------+\n",
"| {'selection': 3.8365785530... |\n",
"| {'precise': 6.443200606955... |\n",
"| {'just': 2.700729968710864... |\n",
"| {'englishreading': 10.2933... |\n",
"| {'they': 1.899340117819389... |\n",
"| {'now': 1.96695239252401, ... |\n",
"| {'exclusive': 10.455187230... |\n",
"| {'taxi': 6.052021456094502... |\n",
"| {'houston': 3.935505942157... |\n",
"| {'phenomenon': 5.750053426... |\n",
"+-------------------------------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"people.head()\n",
"#obama[['tfidf']].stack('tfidf',new_column_name=['word','tfidf']).sort('tfidf',ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"obama = people[people['name']==\"Barack Obama\"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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\">word</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">tfidf</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">obama</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">43.2956530721</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">act</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27.678222623</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">iraq</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17.747378588</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">control</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.8870608452</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">law</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.7229357618</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">ordered</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.5333739509</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">military</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.1159327785</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">involvement</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.7843852412</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">response</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.7843852412</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">democratic</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.4106886973</td>\n",
" </tr>\n",
"</table>\n",
"[273 rows x 2 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",
"\tword\tstr\n",
"\ttfidf\tfloat\n",
"\n",
"Rows: 273\n",
"\n",
"Data:\n",
"+-------------+---------------+\n",
"| word | tfidf |\n",
"+-------------+---------------+\n",
"| obama | 43.2956530721 |\n",
"| act | 27.678222623 |\n",
"| iraq | 17.747378588 |\n",
"| control | 14.8870608452 |\n",
"| law | 14.7229357618 |\n",
"| ordered | 14.5333739509 |\n",
"| military | 13.1159327785 |\n",
"| involvement | 12.7843852412 |\n",
"| response | 12.7843852412 |\n",
"| democratic | 12.4106886973 |\n",
"+-------------+---------------+\n",
"[273 rows x 2 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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obama[['tfidf']].stack('tfidf', new_column_name=['word', 'tfidf']).sort('tfidf',ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Manually compute distances between a few people"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clinton = people[people['name']=='Bill Clinton']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"beckham = people[people['name']=='David Beckham']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Is Obama closer to Clinton than to Beckham?"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.8339854936884276"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graphlab.distances.cosine(obama['tfidf'][0],clinton['tfidf'][0])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9791305844747478"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graphlab.distances.cosine(obama['tfidf'][0],beckham['tfidf'][0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a nearest neighbor model for document retrieval"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<pre>Starting brute force nearest neighbors model training.</pre>"
],
"text/plain": [
"Starting brute force nearest neighbors model training."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"knn_model = graphlab.nearest_neighbors.create(people, features=['tfidf'], label='name')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Applying the nearest-neighbors model for retrieval"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Who is closest to Obama?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<pre>Starting pairwise querying.</pre>"
],
"text/plain": [
"Starting pairwise querying."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
],
"text/plain": [
"| Query points | # Pairs | % Complete. | Elapsed Time |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 0 | 1 | 0.00169288 | 139.46ms |</pre>"
],
"text/plain": [
"| 0 | 1 | 0.00169288 | 139.46ms |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Done | | 100 | 505.433ms |</pre>"
],
"text/plain": [
"| Done | | 100 | 505.433ms |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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\">query_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Barack Obama</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Joe Biden</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.794117647059</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Joe Lieberman</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.794685990338</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Kelly Ayotte</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.811989100817</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bill Clinton</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.813852813853</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" </tr>\n",
"</table>\n",
"[5 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tquery_label\tint\n",
"\treference_label\tstr\n",
"\tdistance\tfloat\n",
"\trank\tint\n",
"\n",
"Rows: 5\n",
"\n",
"Data:\n",
"+-------------+-----------------+----------------+------+\n",
"| query_label | reference_label | distance | rank |\n",
"+-------------+-----------------+----------------+------+\n",
"| 0 | Barack Obama | 0.0 | 1 |\n",
"| 0 | Joe Biden | 0.794117647059 | 2 |\n",
"| 0 | Joe Lieberman | 0.794685990338 | 3 |\n",
"| 0 | Kelly Ayotte | 0.811989100817 | 4 |\n",
"| 0 | Bill Clinton | 0.813852813853 | 5 |\n",
"+-------------+-----------------+----------------+------+\n",
"[5 rows x 4 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_model.query(obama)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other examples of document retrieval"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"swift = people[people['name']=='Taylor Swift']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<pre>Starting pairwise querying.</pre>"
],
"text/plain": [
"Starting pairwise querying."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
],
"text/plain": [
"| Query points | # Pairs | % Complete. | Elapsed Time |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 0 | 1 | 0.00169288 | 8.523ms |</pre>"
],
"text/plain": [
"| 0 | 1 | 0.00169288 | 8.523ms |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Done | | 100 | 425.631ms |</pre>"
],
"text/plain": [
"| Done | | 100 | 425.631ms |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+--------------+---------+-------------+--------------+</pre>"
],
"text/plain": [
"+--------------+---------+-------------+--------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"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\">query_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reference_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Taylor Swift</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Carrie Underwood</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.76231884058</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alicia Keys</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.764705882353</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jordin Sparks</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.769633507853</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
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| mit |
HKUST-SING/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | 32 | 209796 | {
"cells": [
{
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"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from __future__ import print_function\n",
"\n",
"from IPython.display import Image\n",
"import base64\n",
"Image(data=base64.decodestring(\"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\".encode('utf-8')), embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J0QZYD_HuDJF"
},
"source": [
"We're going to be building a model that recognizes these digits as 5, 0, and 4.\n",
"\n",
"# Imports and input data\n",
"\n",
"We'll proceed in steps, beginning with importing and inspecting the MNIST data. This doesn't have anything to do with TensorFlow in particular -- we're just downloading the data archive."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:20.958307",
"start_time": "2016-09-16T14:49:20.864840"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 110,
"status": "ok",
"timestamp": 1446749124399,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "w5vKZqr6CDz9",
"outputId": "794eac6d-a918-4888-e8cf-a8628474d7f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Already downloaded train-images-idx3-ubyte.gz\n",
"Already downloaded train-labels-idx1-ubyte.gz\n",
"Already downloaded t10k-images-idx3-ubyte.gz\n",
"Already downloaded t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"import os\n",
"from six.moves.urllib.request import urlretrieve\n",
"\n",
"SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\n",
"WORK_DIRECTORY = \"/tmp/mnist-data\"\n",
"\n",
"def maybe_download(filename):\n",
" \"\"\"A helper to download the data files if not present.\"\"\"\n",
" if not os.path.exists(WORK_DIRECTORY):\n",
" os.mkdir(WORK_DIRECTORY)\n",
" filepath = os.path.join(WORK_DIRECTORY, filename)\n",
" if not os.path.exists(filepath):\n",
" filepath, _ = urlretrieve(SOURCE_URL + filename, filepath)\n",
" statinfo = os.stat(filepath)\n",
" print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')\n",
" else:\n",
" print('Already downloaded', filename)\n",
" return filepath\n",
"\n",
"train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n",
"train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n",
"test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n",
"test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gCtMhpIoC84F"
},
"source": [
"## Working with the images\n",
"\n",
"Now we have the files, but the format requires a bit of pre-processing before we can work with it. The data is gzipped, requiring us to decompress it. And, each of the images are grayscale-encoded with values from [0, 255]; we'll normalize these to [-0.5, 0.5].\n",
"\n",
"Let's try to unpack the data using the documented format:\n",
"\n",
" [offset] [type] [value] [description] \n",
" 0000 32 bit integer 0x00000803(2051) magic number \n",
" 0004 32 bit integer 60000 number of images \n",
" 0008 32 bit integer 28 number of rows \n",
" 0012 32 bit integer 28 number of columns \n",
" 0016 unsigned byte ?? pixel \n",
" 0017 unsigned byte ?? pixel \n",
" ........ \n",
" xxxx unsigned byte ?? pixel\n",
" \n",
"Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\n",
"\n",
"We'll start by reading the first image from the test data as a sanity check."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.112407",
"start_time": "2016-09-16T14:49:20.960204"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 57,
"status": "ok",
"timestamp": 1446749125010,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "P_3Fm5BpFMDF",
"outputId": "c8e777e0-d891-4eb1-a178-9809f293cc28"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"magic number 2051\n",
"image count 10000\n",
"rows 28\n",
"columns 28\n",
"First 10 pixels: [0 0 0 0 0 0 0 0 0 0]\n"
]
}
],
"source": [
"import gzip, binascii, struct, numpy\n",
"import matplotlib.pyplot as plt\n",
"\n",
"with gzip.open(test_data_filename) as f:\n",
" # Print the header fields.\n",
" for field in ['magic number', 'image count', 'rows', 'columns']:\n",
" # struct.unpack reads the binary data provided by f.read.\n",
" # The format string '>i' decodes a big-endian integer, which\n",
" # is the encoding of the data.\n",
" print(field, struct.unpack('>i', f.read(4))[0])\n",
" \n",
" # Read the first 28x28 set of pixel values. \n",
" # Each pixel is one byte, [0, 255], a uint8.\n",
" buf = f.read(28 * 28)\n",
" image = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
" \n",
" # Print the first few values of image.\n",
" print('First 10 pixels:', image[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7NXKCQENNRQT"
},
"source": [
"The first 10 pixels are all 0 values. Not very interesting, but also unsurprising. We'd expect most of the pixel values to be the background color, 0.\n",
"\n",
"We could print all 28 * 28 values, but what we really need to do to make sure we're reading our data properly is look at an image."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.525418",
"start_time": "2016-09-16T14:49:22.114324"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 887,
"status": "ok",
"timestamp": 1446749126640,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "F_5w-cOoNLaG",
"outputId": "77dabc81-e3ee-4fcf-ac72-88038494fb6c"
},
"outputs": [
{
"data": {
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sbIyRkRFOnTrFiRMnmJycZGZmhtnZWWZmZpLbfkxCue4GP2jRz27o6OgoGbio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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84680e3fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# We'll show the image and its pixel value histogram side-by-side.\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"\n",
"# To interpret the values as a 28x28 image, we need to reshape\n",
"# the numpy array, which is one dimensional.\n",
"ax1.imshow(image.reshape(28, 28), cmap=plt.cm.Greys);\n",
"\n",
"ax2.hist(image, bins=20, range=[0,255]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "weVoVR-nN0cN"
},
"source": [
"The large number of 0 values correspond to the background of the image, another large mass of value 255 is black, and a mix of grayscale transition values in between.\n",
"\n",
"Both the image and histogram look sensible. But, it's good practice when training image models to normalize values to be centered around 0.\n",
"\n",
"We'll do that next. The normalization code is fairly short, and it may be tempting to assume we haven't made mistakes, but we'll double-check by looking at the rendered input and histogram again. Malformed inputs are a surprisingly common source of errors when developing new models."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.895369",
"start_time": "2016-09-16T14:49:22.527595"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 531,
"status": "ok",
"timestamp": 1446749126656,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "jc1xCZXHNKVp",
"outputId": "bd45b3dd-438b-41db-ea8f-d202d4a09e63"
},
"outputs": [
{
"data": {
"image/png": 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55ufnmZubS+77MQeluhX8YEQ/W6GzszM1IFGhQERE4UAawEqVg2y3wvHjx5md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\nmpHQ0tKSO6ZAXQgiIluXwsEmEH7zb2t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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8444471358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's convert the uint8 image to 32 bit floats and rescale \n",
"# the values to be centered around 0, between [-0.5, 0.5]. \n",
"# \n",
"# We again plot the image and histogram to check that we \n",
"# haven't mangled the data.\n",
"scaled = image.astype(numpy.float32)\n",
"scaled = (scaled - (255 / 2.0)) / 255\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"ax1.imshow(scaled.reshape(28, 28), cmap=plt.cm.Greys);\n",
"ax2.hist(scaled, bins=20, range=[-0.5, 0.5]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PlqlwkX-O0Hd"
},
"source": [
"Great -- we've retained the correct image data while properly rescaling to the range [-0.5, 0.5].\n",
"\n",
"## Reading the labels\n",
"\n",
"Let's next unpack the test label data. The format here is similar: a magic number followed by a count followed by the labels as `uint8` values. In more detail:\n",
"\n",
" [offset] [type] [value] [description] \n",
" 0000 32 bit integer 0x00000801(2049) magic number (MSB first) \n",
" 0004 32 bit integer 10000 number of items \n",
" 0008 unsigned byte ?? label \n",
" 0009 unsigned byte ?? label \n",
" ........ \n",
" xxxx unsigned byte ?? label\n",
"\n",
"As with the image data, let's read the first test set value to sanity check our input path. We'll expect a 7."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.925176",
"start_time": "2016-09-16T14:49:22.897739"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 90,
"status": "ok",
"timestamp": 1446749126903,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "d8zv9yZzQOnV",
"outputId": "ad203b2c-f095-4035-e0cd-7869c078da3d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"magic number 2049\n",
"label count 10000\n",
"First label: 7\n"
]
}
],
"source": [
"with gzip.open(test_labels_filename) as f:\n",
" # Print the header fields.\n",
" for field in ['magic number', 'label count']:\n",
" print(field, struct.unpack('>i', f.read(4))[0])\n",
"\n",
" print('First label:', struct.unpack('B', f.read(1))[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zAGrQSXCQtIm"
},
"source": [
"Indeed, the first label of the test set is 7.\n",
"\n",
"## Forming the training, testing, and validation data sets\n",
"\n",
"Now that we understand how to read a single element, we can read a much larger set that we'll use for training, testing, and validation.\n",
"\n",
"### Image data\n",
"\n",
"The code below is a generalization of our prototyping above that reads the entire test and training data set."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.525119",
"start_time": "2016-09-16T14:49:22.928289"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 734,
"status": "ok",
"timestamp": 1446749128718,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "ofFZ5oJeRMDA",
"outputId": "ff2de90b-aed9-4ce5-db8c-9123496186b1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/mnist-data/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/mnist-data/t10k-images-idx3-ubyte.gz\n"
]
}
],
"source": [
"IMAGE_SIZE = 28\n",
"PIXEL_DEPTH = 255\n",
"\n",
"def extract_data(filename, num_images):\n",
" \"\"\"Extract the images into a 4D tensor [image index, y, x, channels].\n",
" \n",
" For MNIST data, the number of channels is always 1.\n",
"\n",
" Values are rescaled from [0, 255] down to [-0.5, 0.5].\n",
" \"\"\"\n",
" print('Extracting', filename)\n",
" with gzip.open(filename) as bytestream:\n",
" # Skip the magic number and dimensions; we know these values.\n",
" bytestream.read(16)\n",
"\n",
" buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)\n",
" data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)\n",
" data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\n",
" data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)\n",
" return data\n",
"\n",
"train_data = extract_data(train_data_filename, 60000)\n",
"test_data = extract_data(test_data_filename, 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0x4rwXxUR96O"
},
"source": [
"A crucial difference here is how we `reshape` the array of pixel values. Instead of one image that's 28x28, we now have a set of 60,000 images, each one being 28x28. We also include a number of channels, which for grayscale images as we have here is 1.\n",
"\n",
"Let's make sure we've got the reshaping parameters right by inspecting the dimensions and the first two images. (Again, mangled input is a very common source of errors.)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.829853",
"start_time": "2016-09-16T14:49:23.527283"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{},
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 400,
"status": "ok",
"timestamp": 1446749129657,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "0AwSo8mlSja_",
"outputId": "11490c39-7c67-4fe5-982c-ca8278294d96"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data shape (60000, 28, 28, 1)\n"
]
},
{
"data": {
"image/png": 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4Yn2gl5aWsLq6ivPz86Q5f3V1FZ1OJ8/tVwGj4MfIlg+1+upUyCngGxsbOfEy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8444461630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('Training data shape', train_data.shape)\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"ax1.imshow(train_data[0].reshape(28, 28), cmap=plt.cm.Greys);\n",
"ax2.imshow(train_data[1].reshape(28, 28), cmap=plt.cm.Greys);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cwBhQ3ouTQcW"
},
"source": [
"Looks good. Now we know how to index our full set of training and test images."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PBCB9aYxRvBi"
},
"source": [
"### Label data\n",
"\n",
"Let's move on to loading the full set of labels. As is typical in classification problems, we'll convert our input labels into a [1-hot](https://en.wikipedia.org/wiki/One-hot) encoding over a length 10 vector corresponding to 10 digits. The vector [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], for example, would correspond to the digit 1."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.854577",
"start_time": "2016-09-16T14:49:23.831545"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 191,
"status": "ok",
"timestamp": 1446749131421,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "9pK1j2WlRwY9",
"outputId": "1ca31655-e14f-405a-b266-6a6c78827af5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/mnist-data/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/mnist-data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"NUM_LABELS = 10\n",
"\n",
"def extract_labels(filename, num_images):\n",
" \"\"\"Extract the labels into a 1-hot matrix [image index, label index].\"\"\"\n",
" print('Extracting', filename)\n",
" with gzip.open(filename) as bytestream:\n",
" # Skip the magic number and count; we know these values.\n",
" bytestream.read(8)\n",
" buf = bytestream.read(1 * num_images)\n",
" labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
" # Convert to dense 1-hot representation.\n",
" return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)\n",
"\n",
"train_labels = extract_labels(train_labels_filename, 60000)\n",
"test_labels = extract_labels(test_labels_filename, 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hb3Vaq72UUxW"
},
"source": [
"As with our image data, we'll double-check that our 1-hot encoding of the first few values matches our expectations."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.864350",
"start_time": "2016-09-16T14:49:23.857177"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 127,
"status": "ok",
"timestamp": 1446749132853,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "uEBID71nUVj1",
"outputId": "3f318310-18dd-49ed-9943-47b4aae7ee69"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training labels shape (60000, 10)\n",
"First label vector [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
"Second label vector [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"print('Training labels shape', train_labels.shape)\n",
"print('First label vector', train_labels[0])\n",
"print('Second label vector', train_labels[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5EwtEhxRUneF"
},
"source": [
"The 1-hot encoding looks reasonable.\n",
"\n",
"### Segmenting data into training, test, and validation\n",
"\n",
"The final step in preparing our data is to split it into three sets: training, test, and validation. This isn't the format of the original data set, so we'll take a small slice of the training data and treat that as our validation set."
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Validation shape (5000, 28, 28, 1)\n",
"Train size 55000\n"
]
}
],
"source": [
"VALIDATION_SIZE = 5000\n",
"\n",
"validation_data = train_data[:VALIDATION_SIZE, :, :, :]\n",
"validation_labels = train_labels[:VALIDATION_SIZE]\n",
"train_data = train_data[VALIDATION_SIZE:, :, :, :]\n",
"train_labels = train_labels[VALIDATION_SIZE:]\n",
"\n",
"train_size = train_labels.shape[0]\n",
"\n",
"print('Validation shape', validation_data.shape)\n",
"print('Train size', train_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1JFhEH8EVj4O"
},
"source": [
"# Defining the model\n",
"\n",
"Now that we've prepared our data, we're ready to define our model.\n",
"\n",
"The comments describe the architecture, which fairly typical of models that process image data. The raw input passes through several [convolution](https://en.wikipedia.org/wiki/Convolutional_neural_network#Convolutional_layer) and [max pooling](https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer) layers with [rectified linear](https://en.wikipedia.org/wiki/Convolutional_neural_network#ReLU_layer) activations before several fully connected layers and a [softmax](https://en.wikipedia.org/wiki/Convolutional_neural_network#Loss_layer) loss for predicting the output class. During training, we use [dropout](https://en.wikipedia.org/wiki/Convolutional_neural_network#Dropout_method).\n",
"\n",
"We'll separate our model definition into three steps:\n",
"\n",
"1. Defining the variables that will hold the trainable weights.\n",
"1. Defining the basic model graph structure described above. And,\n",
"1. Stamping out several copies of the model graph for training, testing, and validation.\n",
"\n",
"We'll start with the variables."
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"# We'll bundle groups of examples during training for efficiency.\n",
"# This defines the size of the batch.\n",
"BATCH_SIZE = 60\n",
"# We have only one channel in our grayscale images.\n",
"NUM_CHANNELS = 1\n",
"# The random seed that defines initialization.\n",
"SEED = 42\n",
"\n",
"# This is where training samples and labels are fed to the graph.\n",
"# These placeholder nodes will be fed a batch of training data at each\n",
"# training step, which we'll write once we define the graph structure.\n",
"train_data_node = tf.placeholder(\n",
" tf.float32,\n",
" shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\n",
"train_labels_node = tf.placeholder(tf.float32,\n",
" shape=(BATCH_SIZE, NUM_LABELS))\n",
"\n",
"# For the validation and test data, we'll just hold the entire dataset in\n",
"# one constant node.\n",
"validation_data_node = tf.constant(validation_data)\n",
"test_data_node = tf.constant(test_data)\n",
"\n",
"# The variables below hold all the trainable weights. For each, the\n",
"# parameter defines how the variables will be initialized.\n",
"conv1_weights = tf.Variable(\n",
" tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32.\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"conv1_biases = tf.Variable(tf.zeros([32]))\n",
"conv2_weights = tf.Variable(\n",
" tf.truncated_normal([5, 5, 32, 64],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))\n",
"fc1_weights = tf.Variable( # fully connected, depth 512.\n",
" tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))\n",
"fc2_weights = tf.Variable(\n",
" tf.truncated_normal([512, NUM_LABELS],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "QHB_u04Z4HO6"
},
"source": [
"Now that we've defined the variables to be trained, we're ready to wire them together into a TensorFlow graph.\n",
"\n",
"We'll define a helper to do this, `model`, which will return copies of the graph suitable for training and testing. Note the `train` argument, which controls whether or not dropout is used in the hidden layer. (We want to use dropout only during training.)"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"def model(data, train=False):\n",
" \"\"\"The Model definition.\"\"\"\n",
" # 2D convolution, with 'SAME' padding (i.e. the output feature map has\n",
" # the same size as the input). Note that {strides} is a 4D array whose\n",
" # shape matches the data layout: [image index, y, x, depth].\n",
" conv = tf.nn.conv2d(data,\n",
" conv1_weights,\n",
" strides=[1, 1, 1, 1],\n",
" padding='SAME')\n",
"\n",
" # Bias and rectified linear non-linearity.\n",
" relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))\n",
"\n",
" # Max pooling. The kernel size spec ksize also follows the layout of\n",
" # the data. Here we have a pooling window of 2, and a stride of 2.\n",
" pool = tf.nn.max_pool(relu,\n",
" ksize=[1, 2, 2, 1],\n",
" strides=[1, 2, 2, 1],\n",
" padding='SAME')\n",
" conv = tf.nn.conv2d(pool,\n",
" conv2_weights,\n",
" strides=[1, 1, 1, 1],\n",
" padding='SAME')\n",
" relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))\n",
" pool = tf.nn.max_pool(relu,\n",
" ksize=[1, 2, 2, 1],\n",
" strides=[1, 2, 2, 1],\n",
" padding='SAME')\n",
"\n",
" # Reshape the feature map cuboid into a 2D matrix to feed it to the\n",
" # fully connected layers.\n",
" pool_shape = pool.get_shape().as_list()\n",
" reshape = tf.reshape(\n",
" pool,\n",
" [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\n",
" \n",
" # Fully connected layer. Note that the '+' operation automatically\n",
" # broadcasts the biases.\n",
" hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\n",
"\n",
" # Add a 50% dropout during training only. Dropout also scales\n",
" # activations such that no rescaling is needed at evaluation time.\n",
" if train:\n",
" hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)\n",
" return tf.matmul(hidden, fc2_weights) + fc2_biases\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7bvEtt8C4fLC"
},
"source": [
"Having defined the basic structure of the graph, we're ready to stamp out multiple copies for training, testing, and validation.\n",
"\n",
"Here, we'll do some customizations depending on which graph we're constructing. `train_prediction` holds the training graph, for which we use cross-entropy loss and weight regularization. We'll adjust the learning rate during training -- that's handled by the `exponential_decay` operation, which is itself an argument to the `MomentumOptimizer` that performs the actual training.\n",
"\n",
"The vaildation and prediction graphs are much simpler the generate -- we need only create copies of the model with the validation and test inputs and a softmax classifier as the output."
]
},
{
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"# Training computation: logits + cross-entropy loss.\n",
"logits = model(train_data_node, True)\n",
"loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
" labels=train_labels_node, logits=logits))\n",
"\n",
"# L2 regularization for the fully connected parameters.\n",
"regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +\n",
" tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))\n",
"# Add the regularization term to the loss.\n",
"loss += 5e-4 * regularizers\n",
"\n",
"# Optimizer: set up a variable that's incremented once per batch and\n",
"# controls the learning rate decay.\n",
"batch = tf.Variable(0)\n",
"# Decay once per epoch, using an exponential schedule starting at 0.01.\n",
"learning_rate = tf.train.exponential_decay(\n",
" 0.01, # Base learning rate.\n",
" batch * BATCH_SIZE, # Current index into the dataset.\n",
" train_size, # Decay step.\n",
" 0.95, # Decay rate.\n",
" staircase=True)\n",
"# Use simple momentum for the optimization.\n",
"optimizer = tf.train.MomentumOptimizer(learning_rate,\n",
" 0.9).minimize(loss,\n",
" global_step=batch)\n",
"\n",
"# Predictions for the minibatch, validation set and test set.\n",
"train_prediction = tf.nn.softmax(logits)\n",
"# We'll compute them only once in a while by calling their {eval()} method.\n",
"validation_prediction = tf.nn.softmax(model(validation_data_node))\n",
"test_prediction = tf.nn.softmax(model(test_data_node))\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4T21uZJq5UfH"
},
"source": [
"# Training and visualizing results\n",
"\n",
"Now that we have the training, test, and validation graphs, we're ready to actually go through the training loop and periodically evaluate loss and error.\n",
"\n",
"All of these operations take place in the context of a session. In Python, we'd write something like:\n",
"\n",
" with tf.Session() as s:\n",
" ...training / test / evaluation loop...\n",
" \n",
"But, here, we'll want to keep the session open so we can poke at values as we work out the details of training. The TensorFlow API includes a function for this, `InteractiveSession`.\n",
"\n",
"We'll start by creating a session and initializing the varibles we defined above."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.357483",
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"# Create a new interactive session that we'll use in\n",
"# subsequent code cells.\n",
"s = tf.InteractiveSession()\n",
"\n",
"# Use our newly created session as the default for \n",
"# subsequent operations.\n",
"s.as_default()\n",
"\n",
"# Initialize all the variables we defined above.\n",
"tf.global_variables_initializer().run()"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "hcG8H-Ka6_mw"
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"source": [
"Now we're ready to perform operations on the graph. Let's start with one round of training. We're going to organize our training steps into batches for efficiency; i.e., training using a small set of examples at each step rather than a single example."
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"BATCH_SIZE = 60\n",
"\n",
"# Grab the first BATCH_SIZE examples and labels.\n",
"batch_data = train_data[:BATCH_SIZE, :, :, :]\n",
"batch_labels = train_labels[:BATCH_SIZE]\n",
"\n",
"# This dictionary maps the batch data (as a numpy array) to the\n",
"# node in the graph it should be fed to.\n",
"feed_dict = {train_data_node: batch_data,\n",
" train_labels_node: batch_labels}\n",
"\n",
"# Run the graph and fetch some of the nodes.\n",
"_, l, lr, predictions = s.run(\n",
" [optimizer, loss, learning_rate, train_prediction],\n",
" feed_dict=feed_dict)\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7bL4-RNm_K-B"
},
"source": [
"Let's take a look at the predictions. How did we do? Recall that the output will be probabilities over the possible classes, so let's look at those probabilities."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.25393116e-04 4.76219611e-05 1.66867452e-03 5.67827519e-05\n",
" 6.03432178e-01 4.34969068e-02 2.19316553e-05 1.41286102e-04\n",
" 1.54903100e-05 3.50893795e-01]\n"
]
}
],
"source": [
"print(predictions[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "X5MgraJb_eQZ"
},
"source": [
"As expected without training, the predictions are all noise. Let's write a scoring function that picks the class with the maximum probability and compares with the example's label. We'll start by converting the probability vectors returned by the softmax into predictions we can match against the labels."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First prediction 4\n",
"(60, 10)\n",
"All predictions [4 4 2 7 7 7 7 7 7 7 7 7 0 8 9 0 7 7 0 7 4 0 5 0 9 9 7 0 7 4 7 7 7 0 7 7 9\n",
" 7 9 9 0 7 7 7 2 7 0 7 2 9 9 9 9 9 0 7 9 4 8 7]\n"
]
}
],
"source": [
"# The highest probability in the first entry.\n",
"print('First prediction', numpy.argmax(predictions[0]))\n",
"\n",
"# But, predictions is actually a list of BATCH_SIZE probability vectors.\n",
"print(predictions.shape)\n",
"\n",
"# So, we'll take the highest probability for each vector.\n",
"print('All predictions', numpy.argmax(predictions, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8pMCIZ3_C2ni"
},
"source": [
"Next, we can do the same thing for our labels -- using `argmax` to convert our 1-hot encoding into a digit class."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
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},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 232,
"status": "ok",
"timestamp": 1446750498351,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "kZWp4T0JDDUe",
"outputId": "47b588cd-bc82-45c3-a5d0-8d84dc27a3be"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch labels [7 3 4 6 1 8 1 0 9 8 0 3 1 2 7 0 2 9 6 0 1 6 7 1 9 7 6 5 5 8 8 3 4 4 8 7 3\n",
" 6 4 6 6 3 8 8 9 9 4 4 0 7 8 1 0 0 1 8 5 7 1 7]\n"
]
}
],
"source": [
"print('Batch labels', numpy.argmax(batch_labels, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bi5Z6whtDiht"
},
"source": [
"Now we can compare the predicted and label classes to compute the error rate and confusion matrix for this batch."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.841313",
"start_time": "2016-09-16T14:49:29.618274"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
},
{
"item_id": 2
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 330,
"status": "ok",
"timestamp": 1446751307304,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "U4hrLW4CDtQB",
"outputId": "720494a3-cbf9-4687-9d94-e64a33fdd78f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06666666666666667\n"
]
},
{
"data": {
"image/png": 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0RauALaWsA/5L7yZJ2gHnTiWpEgNWkioxYCWpEgNWkioxYCWpEgNWkioxYCWpEgNWkiox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\n9OY6UgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841ece8128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(batch_labels, 1))\n",
"total = predictions.shape[0]\n",
"\n",
"print(float(correct) / float(total))\n",
"\n",
"confusions = numpy.zeros([10, 10], numpy.float32)\n",
"bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(batch_labels, 1))\n",
"for predicted, actual in bundled:\n",
" confusions[predicted, actual] += 1\n",
"\n",
"plt.grid(False)\n",
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.yticks(numpy.arange(NUM_LABELS))\n",
"plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iZmx_9DiDXQ3"
},
"source": [
"Now let's wrap this up into our scoring function."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.857607",
"start_time": "2016-09-16T14:49:29.843904"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 178,
"status": "ok",
"timestamp": 1446751995007,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "DPJie7bPDaLa",
"outputId": "a06c64ed-f95f-416f-a621-44cccdaba0f8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"def error_rate(predictions, labels):\n",
" \"\"\"Return the error rate and confusions.\"\"\"\n",
" correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(labels, 1))\n",
" total = predictions.shape[0]\n",
"\n",
" error = 100.0 - (100 * float(correct) / float(total))\n",
"\n",
" confusions = numpy.zeros([10, 10], numpy.float32)\n",
" bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(labels, 1))\n",
" for predicted, actual in bundled:\n",
" confusions[predicted, actual] += 1\n",
" \n",
" return error, confusions\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sLv22cjeB5Rd"
},
"source": [
"We'll need to train for some time to actually see useful predicted values. Let's define a loop that will go through our data. We'll print the loss and error periodically.\n",
"\n",
"Here, we want to iterate over the entire data set rather than just the first batch, so we'll need to slice the data to that end.\n",
"\n",
"(One pass through our training set will take some time on a CPU, so be patient if you are executing this notebook.)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:53:26.998313",
"start_time": "2016-09-16T14:49:29.860079"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": false,
"id": "4cgKJrS1_vej"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 0 of 916\n",
"Mini-batch loss: 7.71249 Error: 91.66667 Learning rate: 0.01000\n",
"Validation error: 88.9%\n",
"Step 100 of 916\n",
"Mini-batch loss: 3.28715 Error: 8.33333 Learning rate: 0.01000\n",
"Validation error: 5.8%\n",
"Step 200 of 916\n",
"Mini-batch loss: 3.30949 Error: 8.33333 Learning rate: 0.01000\n",
"Validation error: 3.6%\n",
"Step 300 of 916\n",
"Mini-batch loss: 3.15385 Error: 3.33333 Learning rate: 0.01000\n",
"Validation error: 3.1%\n",
"Step 400 of 916\n",
"Mini-batch loss: 3.08212 Error: 1.66667 Learning rate: 0.01000\n",
"Validation error: 2.7%\n",
"Step 500 of 916\n",
"Mini-batch loss: 3.02827 Error: 1.66667 Learning rate: 0.01000\n",
"Validation error: 2.2%\n",
"Step 600 of 916\n",
"Mini-batch loss: 3.03260 Error: 5.00000 Learning rate: 0.01000\n",
"Validation error: 1.9%\n",
"Step 700 of 916\n",
"Mini-batch loss: 3.16032 Error: 6.66667 Learning rate: 0.01000\n",
"Validation error: 2.2%\n",
"Step 800 of 916\n",
"Mini-batch loss: 3.06246 Error: 3.33333 Learning rate: 0.01000\n",
"Validation error: 2.0%\n",
"Step 900 of 916\n",
"Mini-batch loss: 2.85098 Error: 0.00000 Learning rate: 0.01000\n",
"Validation error: 1.9%\n"
]
}
],
"source": [
"# Train over the first 1/4th of our training set.\n",
"steps = train_size // BATCH_SIZE\n",
"for step in range(steps):\n",
" # Compute the offset of the current minibatch in the data.\n",
" # Note that we could use better randomization across epochs.\n",
" offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)\n",
" batch_data = train_data[offset:(offset + BATCH_SIZE), :, :, :]\n",
" batch_labels = train_labels[offset:(offset + BATCH_SIZE)]\n",
" # This dictionary maps the batch data (as a numpy array) to the\n",
" # node in the graph it should be fed to.\n",
" feed_dict = {train_data_node: batch_data,\n",
" train_labels_node: batch_labels}\n",
" # Run the graph and fetch some of the nodes.\n",
" _, l, lr, predictions = s.run(\n",
" [optimizer, loss, learning_rate, train_prediction],\n",
" feed_dict=feed_dict)\n",
" \n",
" # Print out the loss periodically.\n",
" if step % 100 == 0:\n",
" error, _ = error_rate(predictions, batch_labels)\n",
" print('Step %d of %d' % (step, steps))\n",
" print('Mini-batch loss: %.5f Error: %.5f Learning rate: %.5f' % (l, error, lr))\n",
" print('Validation error: %.1f%%' % error_rate(\n",
" validation_prediction.eval(), validation_labels)[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J4LskgGXIDAm"
},
"source": [
"The error seems to have gone down. Let's evaluate the results using the test set.\n",
"\n",
"To help identify rare mispredictions, we'll include the raw count of each (prediction, label) pair in the confusion matrix."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:55:10.942063",
"start_time": "2016-09-16T14:53:26.999971"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
},
{
"item_id": 2
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 436,
"status": "ok",
"timestamp": 1446752934104,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "6Yh1jGFuIKc_",
"outputId": "4e411de4-0fe2-451b-e4ca-8a4854f0db89"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test error: 2.0%\n"
]
},
{
"data": {
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AvwLTrl2whQCO7IPjh2HgZJgXBTXqwuN9oXz+Htf74+DQHsjOhBYR8PoyOJdm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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841eb30f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_error, confusions = error_rate(test_prediction.eval(), test_labels)\n",
"print('Test error: %.1f%%' % test_error)\n",
"\n",
"plt.xlabel('Actual')\n",
"plt.ylabel('Predicted')\n",
"plt.grid(False)\n",
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.yticks(numpy.arange(NUM_LABELS))\n",
"plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');\n",
"\n",
"for i, cas in enumerate(confusions):\n",
" for j, count in enumerate(cas):\n",
" if count > 0:\n",
" xoff = .07 * len(str(count))\n",
" plt.text(j-xoff, i+.2, int(count), fontsize=9, color='white')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "yLnS4dGiMwI1"
},
"source": [
"We can see here that we're mostly accurate, with some errors you might expect, e.g., '9' is often confused as '4'.\n",
"\n",
"Let's do another sanity check to make sure this matches roughly the distribution of our test set, e.g., it seems like we have fewer '5' values."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:55:18.083458",
"start_time": "2016-09-16T14:55:17.830485"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 352,
"status": "ok",
"timestamp": 1446753006584,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "x5KOv1AJMgzV",
"outputId": "2acdf737-bab6-408f-8b3c-05fa66d04fe6"
},
"outputs": [
{
"data": {
"image/png": 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PNo4g8QNg7UjbWuC3+9+vpwsE+/PQWYn9gBuH+uw3fIAkuwL78PCZjBFnAcu23kWSpJ3W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\nG1beKa9dAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841c174f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.hist(numpy.argmax(test_labels, 1));"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "E6DzLSK5M1ju"
},
"source": [
"Indeed, we appear to have fewer 5 labels in the test set. So, on the whole, it seems like our model is learning and our early results are sensible.\n",
"\n",
"But, we've only done one round of training. We can greatly improve accuracy by training for longer. To try this out, just re-execute the training cell above."
]
}
],
"metadata": {
"anaconda-cloud": {},
"colab": {
"default_view": {},
"name": "Untitled",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"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": 0
}
| apache-2.0 |
KECB/learn | 学术画像/test.ipynb | 1 | 10802 | {
"cells": [
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import json\n",
"import pandas"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_file(file_name):\n",
" \"\"\"\n",
" 读取输入内容\n",
" :param file_name:\n",
" :return:\n",
" \"\"\"\n",
" train_dict = {}\n",
" with open(file_name) as f:\n",
" current_id = ''\n",
" for line in f:\n",
" if '#id' in line.split(':'):\n",
" if current_id != line.split(':')[1]:\n",
" current_id = line.split(':')[1].strip()\n",
" train_dict[current_id] = {'name':'', 'org':'', 'search_results_page':'',\n",
" 'homepage':'', 'pic':'', 'email':'', 'gender':'',\n",
" 'position':'', 'location':''}\n",
" if '#name' in line.split(':'):\n",
" train_dict[current_id]['name'] = line.split(':')[1].strip()\n",
" if '#org' in line.split(':'):\n",
" train_dict[current_id]['org'] = line.split(':')[1].strip()\n",
" if '#search_results_page' in line.split(':'):\n",
" train_dict[current_id]['search_results_page'] = line.split('#search_results_page:')[1].strip()\n",
"\n",
" return train_dict"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"result = read_file('small_training.txt')"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'5616d8a645cedb3397b889d7': {'email': '',\n",
" 'gender': '',\n",
" 'homepage': '',\n",
" 'location': '',\n",
" 'name': 'Chen Zhang',\n",
" 'org': 'Peking University(Peking University),Beijing,China',\n",
" 'pic': '',\n",
" 'position': '',\n",
" 'search_results_page': 'http://ifang.ml:8081/5616d8a645cedb3397b889d7.html'}}"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def scrap_from_google(words, file_name):\n",
" \"\"\"\n",
" 根据给定词语, 缓存 google 第一页搜索结果至指定文件\n",
" https://www.google.com.hk/search?q=[words]\n",
" :param words: 给定的关键词用空格分开\n",
" :param file_name: 要存储的文件名字\n",
" :return\n",
" \"\"\"\n",
" url = 'https://www.google.com.hk/search?q=' + words\n",
" response = requests.get(url)\n",
" with open('results/'+file_name, 'w') as f:\n",
" f.write(response.text)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for key in result:\n",
" words = result[key]['name'] + ' ' + result[key]['org']\n",
" file_name = result[key]['search_results_page'].split('/')[-1]\n",
" scrap_from_google(words, file_name)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extract_url(file_name):\n",
" \"\"\"\n",
" 将 google 搜索结果页的所有 url 解析出来\n",
" :param file_name:\n",
" :return:\n",
" \"\"\"\n",
" with open('results/'+file_name) as f:\n",
" result_page = BeautifulSoup(''.join(f.readlines()), 'lxml')\n",
" results = result_page.find('div',{'id':'ires'})\n",
" for url in results.find_all('a'):\n",
" print(url.get('href'))\n",
" print('-----')\n"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/url?q=http://mgv.pku.edu.cn/%3Fcatalog%3Denpiintro%26pname%3DChen_Zhang&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggTMAA&usg=AFQjCNE7gGwLNpzPUCQ7nlZu52z8Twzc1g\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:8y6bZdlEK_cJ:http://mgv.pku.edu.cn/%3Fcatalog%253Denpiintro%2526pname%253DChen_Zhang%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAgWMAA&usg=AFQjCNFER_skpHEsZNpOkMnGoGCqmeC8WQ\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:mgv.pku.edu.cn/%3Fcatalog%3Denpiintro%26pname%3DChen_Zhang+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwgXMAA\n",
"-----\n",
"/url?q=https://www.researchgate.net/profile/Chen_Zhang135&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggZMAE&usg=AFQjCNEke6qWGbWhIMvhNlgQNrmEEJa7Ww\n",
"-----\n",
"/url?q=https://www.researchgate.net/profile/Chen_Zhang82&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggbMAI&usg=AFQjCNFBtMmT4aDlq75VBc7jxlAxDdlBeQ\n",
"-----\n",
"/url?q=https://www.researchgate.net/profile/Qing_Chen9&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggdMAM&usg=AFQjCNFQxOvpQNmQBbl6vzf-mYofiY6meQ\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:https://www.researchgate.net/profile/Qing_Chen9+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwggMAM\n",
"-----\n",
"/url?q=https://www.facebook.com/public/Chen-Zhang/school/Peking-University-103771729661827/&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggiMAQ&usg=AFQjCNGb6PwZVjG5FTWtE8JyjNpE4n9WKA\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:qf-l6EjuT5UJ:https://www.facebook.com/public/Chen-Zhang/school/Peking-University-103771729661827/%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAglMAQ&usg=AFQjCNEiSdPpaFTMoqyS50ocEzLSJdI_wQ\n",
"-----\n",
"/url?q=https://www.linkedin.com/in/en00007128&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggnMAU&usg=AFQjCNH73Qu1UsQfn4eBznzkjGnJU17VLQ\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:https://www.linkedin.com/in/en00007128+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwgqMAU\n",
"-----\n",
"/url?q=http://www.cms.zju.edu.cn/conference/YCMC/PRIZES.html&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggsMAY&usg=AFQjCNGmJmQUBcW9P-qRJZHolpRNq3Ke0g\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:DlVK8vY75xwJ:http://www.cms.zju.edu.cn/conference/YCMC/PRIZES.html%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAgvMAY&usg=AFQjCNERHDytJq9iZDgZzS7kxlMP8ge-Ww\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:www.cms.zju.edu.cn/conference/YCMC/PRIZES.html+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwgwMAY\n",
"-----\n",
"/url?q=https://en.wikipedia.org/wiki/Peking_University&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFggyMAc&usg=AFQjCNFXS_FzK2CKtqYFJozJ8GdcqCQHFA\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:OKz-ybZdVSoJ:https://en.wikipedia.org/wiki/Peking_University%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAg1MAc&usg=AFQjCNFgoCo--IuvFNxbazkTJSCk5WWfhg\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:https://en.wikipedia.org/wiki/Peking_University+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwg2MAc\n",
"-----\n",
"/url?q=https://zfin.org/ZDB-LAB-110923-2&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFgg4MAg&usg=AFQjCNE82q4VOMIomw5KzybQZE_4w04VIg\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:iF1NBITQu3gJ:https://zfin.org/ZDB-LAB-110923-2%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAg7MAg&usg=AFQjCNGR52BFvP0yheSqZwRvx5W4Tf6S3A\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:https://zfin.org/ZDB-LAB-110923-2+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwg8MAg\n",
"-----\n",
"/url?q=http://pkuasc.fasic.org.au/scholars/&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQFgg-MAk&usg=AFQjCNHVXBDyciOBvfFpFFqxQ48IhOVJFA\n",
"-----\n",
"/url?q=http://webcache.googleusercontent.com/search%3Fq%3Dcache:dz5PP7qKTkkJ:http://pkuasc.fasic.org.au/scholars/%252BChen%2BZhang%2BPeking%2BUniversity(Peking%2BUniversity),Beijing,China%26newwindow%3D1%26hl%3Dzh-TW%26ct%3Dclnk&sa=U&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQIAhBMAk&usg=AFQjCNFadhFSFmcvJEzzBde32qwTSHyERw\n",
"-----\n",
"/search?newwindow=1&ie=UTF-8&q=related:pkuasc.fasic.org.au/scholars/+Chen+Zhang+Peking+University(Peking+University),Beijing,China&tbo=1&sa=X&ved=0ahUKEwi6sMyoxoPWAhUBy7wKHUwqC8cQHwhCMAk\n",
"-----\n"
]
}
],
"source": [
"for key in result:\n",
" file_name = result[key]['search_results_page'].split('/')[-1]\n",
" extract_url(file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:tflearn]",
"language": "python",
"name": "conda-env-tflearn-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": 1
}
| mit |
ivanslapnicar/GIAN-Applied-NLA-Course | src/Module C - Applications/T10 Examples in Principal Component Analysis.ipynb | 1 | 634 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial 10 - Examples in Principal Component Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assignment 1\n",
"\n",
"Analyse a data set of your choice with PCA and explain the results."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.2",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
planet-os/notebooks | aws/era5-s3-via-boto.ipynb | 1 | 323299 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Accessing ERA5 Data on S3\n",
"\n",
"This notebook explores how to access ERA5 data stored on a public S3 bucket as part of the [AWS Public Dataset program.](https://aws.amazon.com/opendata/public-datasets). We'll examine how the data is organized in S3, download sample files in NetCDF format, and perform some simple analysis on the data.\n",
"\n",
"ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 30km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km.\n",
"\n",
"A first segment of the ERA5 dataset is now available for public use (2008 to within 3 months of real time). Subsequent releases of ERA5 will cover the earlier decades. The entire ERA5 dataset from 1950 to present is expected to be available for use by early 2019.\n",
"\n",
"The ERA5 data available on S3 contains an initial subset of 15 near surface variables. If there are additional variables you would like to see on S3, please contact [[email protected]](mailto:[email protected]?subject=ERA5 data on S3) with your request. We'll be evaluating the feedback we receive and potentially adding more variables in the future."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Initialize notebook environment.\n",
"%matplotlib inline\n",
"import boto3\n",
"import botocore\n",
"import datetime\n",
"import matplotlib.pyplot as plt\n",
"import os.path\n",
"import xarray as xr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting Up S3 Access Using Boto\n",
"\n",
"We'll use `boto` to access the S3 bucket. Below, we'll set the bucket ID and create a resource to access it.\n",
"\n",
"Note that although the bucket is public, `boto` requires the presence of an AWS access key and secret key to use a s3 resource. To request data anonymously, we'll use a low-level client instead."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"era5_bucket = 'era5-pds'\n",
"\n",
"# AWS access / secret keys required\n",
"# s3 = boto3.resource('s3')\n",
"# bucket = s3.Bucket(era5_bucket)\n",
"\n",
"# No AWS keys required\n",
"client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ERA5 Data Structure on S3\n",
"\n",
"The ERA5 data is chunked into distinct NetCDF files per variable, each containing a month of hourly data. These files are organized in the S3 bucket by year, month, and variable name.\n",
"\n",
"The data is structured as follows:\n",
"\n",
" /{year}/{month}/main.nc\n",
" /data/{var1}.nc\n",
" /{var2}.nc\n",
" /{....}.nc\n",
" /{varN}.nc\n",
"\n",
"where year is expressed as four digits (e.g. YYYY) and month as two digits (e.g. MM). Individual data variables (var1 through varN) use names corresponding to CF standard names convention plus any applicable additional info, such as vertical coordinate.\n",
"\n",
"For example, the full file path for air temperature for January 2008 is:\n",
"\n",
" /2008/01/data/air_temperature_at_2_metres.nc\n",
"\n",
"Note that due to the nature of the ERA5 forecast timing, which is run twice daily at 06:00 and 18:00 UTC, the monthly data file begins with data from 07:00 on the first of the month and continues through 06:00 of the following month. We'll see this in the coordinate values of a data file we download later in the notebook.\n",
"\n",
"Granule variable structure and metadata attributes are stored in `main.nc`. This file contains coordinate and auxiliary variable data. This file is also annotated using NetCDF CF metadata conventions.\n",
"\n",
"We can use the paginate method to list the top level key prefixes in the bucket, which corresponds to the available years of ERA5 data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2008/\n",
"2009/\n",
"2010/\n",
"2011/\n",
"2012/\n",
"2013/\n",
"2014/\n",
"2015/\n",
"2016/\n",
"2017/\n",
"2018/\n"
]
}
],
"source": [
"paginator = client.get_paginator('list_objects')\n",
"result = paginator.paginate(Bucket=era5_bucket, Delimiter='/')\n",
"for prefix in result.search('CommonPrefixes'):\n",
" print(prefix.get('Prefix'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at the objects available for a specific month using boto's [list_objects_v2](http://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.Client.list_objects_v2) method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 19 objects available for January, 2018\n",
"--\n",
"2018/01/data/air_pressure_at_mean_sea_level.nc\n",
"2018/01/data/air_temperature_at_2_metres.nc\n",
"2018/01/data/air_temperature_at_2_metres_1hour_Maximum.nc\n",
"2018/01/data/air_temperature_at_2_metres_1hour_Minimum.nc\n",
"2018/01/data/dew_point_temperature_at_2_metres.nc\n",
"2018/01/data/eastward_wind_at_100_metres.nc\n",
"2018/01/data/eastward_wind_at_10_metres.nc\n",
"2018/01/data/integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation.nc\n",
"2018/01/data/lwe_thickness_of_surface_snow_amount.nc\n",
"2018/01/data/northward_wind_at_100_metres.nc\n",
"2018/01/data/northward_wind_at_10_metres.nc\n",
"2018/01/data/precipitation_amount_1hour_Accumulation.nc\n",
"2018/01/data/sea_surface_temperature.nc\n",
"2018/01/data/sea_surface_wave_mean_period.nc\n",
"2018/01/data/sea_surface_wind_wave_from_direction.nc\n",
"2018/01/data/significant_height_of_wind_and_swell_waves.nc\n",
"2018/01/data/snow_density.nc\n",
"2018/01/data/surface_air_pressure.nc\n",
"2018/01/main.nc\n"
]
}
],
"source": [
"keys = []\n",
"date = datetime.date(2018,1,1) # update to desired date\n",
"prefix = date.strftime('%Y/%m/')\n",
"\n",
"response = client.list_objects_v2(Bucket=era5_bucket, Prefix=prefix)\n",
"response_meta = response.get('ResponseMetadata')\n",
"\n",
"if response_meta.get('HTTPStatusCode') == 200:\n",
" contents = response.get('Contents')\n",
" if contents == None:\n",
" print(\"No objects are available for %s\" % date.strftime('%B, %Y'))\n",
" else:\n",
" for obj in contents:\n",
" keys.append(obj.get('Key'))\n",
" print(\"There are %s objects available for %s\\n--\" % (len(keys), date.strftime('%B, %Y')))\n",
" for k in keys:\n",
" print(k)\n",
"else:\n",
" print(\"There was an error with your request.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading Files \n",
"\n",
"Let's download `main.nc` file for that month and use **xarray** to inspect the metadata relating to the data files."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xarray.Dataset {\n",
"dimensions:\n",
"\tlat = 640 ;\n",
"\tlat_ocean = 501 ;\n",
"\tlon = 1280 ;\n",
"\tlon_ocean = 1002 ;\n",
"\tnv = 2 ;\n",
"\ttime0 = 744 ;\n",
"\ttime1 = 744 ;\n",
"\n",
"variables:\n",
"\tfloat32 lat(lat) ;\n",
"\t\tlat:standard_name = latitude ;\n",
"\t\tlat:long_name = latitude ;\n",
"\t\tlat:units = degrees_north ;\n",
"\tfloat64 time0(time0) ;\n",
"\t\ttime0:units = seconds since 1970-01-01 ;\n",
"\t\ttime0:standard_name = time ;\n",
"\tfloat64 time1(time1) ;\n",
"\t\ttime1:units = seconds since 1970-01-01 ;\n",
"\t\ttime1:standard_name = time ;\n",
"\t\ttime1:bounds = time1_bounds ;\n",
"\tfloat32 lat_ocean(lat_ocean) ;\n",
"\t\tlat_ocean:standard_name = latitude ;\n",
"\t\tlat_ocean:long_name = latitude ;\n",
"\t\tlat_ocean:units = degrees_north ;\n",
"\tfloat32 lon_ocean(lon_ocean) ;\n",
"\t\tlon_ocean:standard_name = longitude ;\n",
"\t\tlon_ocean:long_name = longitude ;\n",
"\t\tlon_ocean:units = degrees_east ;\n",
"\tfloat32 lon(lon) ;\n",
"\t\tlon:standard_name = longitude ;\n",
"\t\tlon:long_name = longitude ;\n",
"\t\tlon:units = degrees_east ;\n",
"\tfloat32 air_temperature_at_2_metres(time0, lat, lon) ;\n",
"\t\tair_temperature_at_2_metres:standard_name = air_temperature ;\n",
"\t\tair_temperature_at_2_metres:units = K ;\n",
"\t\tair_temperature_at_2_metres:long_name = 2 metre temperature ;\n",
"\t\tair_temperature_at_2_metres:nameECMWF = 2 metre temperature ;\n",
"\t\tair_temperature_at_2_metres:nameCDM = 2_metre_temperature_surface ;\n",
"\t\tair_temperature_at_2_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 air_temperature_at_2_metres_1hour_Maximum(time1, lat, lon) ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:standard_name = air_temperature ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:units = K ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:long_name = Maximum temperature at 2 metres since previous post-processing ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:nameECMWF = Maximum temperature at 2 metres since previous post-processing ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:nameCDM = Maximum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2 ;\n",
"\t\tair_temperature_at_2_metres_1hour_Maximum:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 air_pressure_at_mean_sea_level(time0, lat, lon) ;\n",
"\t\tair_pressure_at_mean_sea_level:standard_name = air_pressure_at_mean_sea_level ;\n",
"\t\tair_pressure_at_mean_sea_level:units = Pa ;\n",
"\t\tair_pressure_at_mean_sea_level:long_name = Mean sea level pressure ;\n",
"\t\tair_pressure_at_mean_sea_level:nameECMWF = Mean sea level pressure ;\n",
"\t\tair_pressure_at_mean_sea_level:nameCDM = Mean_sea_level_pressure_surface ;\n",
"\t\tair_pressure_at_mean_sea_level:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 dew_point_temperature_at_2_metres(time0, lat, lon) ;\n",
"\t\tdew_point_temperature_at_2_metres:standard_name = dew_point_temperature ;\n",
"\t\tdew_point_temperature_at_2_metres:units = K ;\n",
"\t\tdew_point_temperature_at_2_metres:long_name = 2 metre dewpoint temperature ;\n",
"\t\tdew_point_temperature_at_2_metres:nameECMWF = 2 metre dewpoint temperature ;\n",
"\t\tdew_point_temperature_at_2_metres:nameCDM = 2_metre_dewpoint_temperature_surface ;\n",
"\t\tdew_point_temperature_at_2_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 sea_surface_temperature(time0, lat, lon) ;\n",
"\t\tsea_surface_temperature:standard_name = sea_surface_temperature ;\n",
"\t\tsea_surface_temperature:units = K ;\n",
"\t\tsea_surface_temperature:long_name = Sea surface temperature ;\n",
"\t\tsea_surface_temperature:nameECMWF = Sea surface temperature ;\n",
"\t\tsea_surface_temperature:nameCDM = Sea_surface_temperature_surface ;\n",
"\t\tsea_surface_temperature:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 eastward_wind_at_10_metres(time0, lat, lon) ;\n",
"\t\teastward_wind_at_10_metres:standard_name = eastward_wind ;\n",
"\t\teastward_wind_at_10_metres:units = m s**-1 ;\n",
"\t\teastward_wind_at_10_metres:long_name = 10 metre U wind component ;\n",
"\t\teastward_wind_at_10_metres:nameECMWF = 10 metre U wind component ;\n",
"\t\teastward_wind_at_10_metres:nameCDM = 10_metre_U_wind_component_surface ;\n",
"\t\teastward_wind_at_10_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 northward_wind_at_100_metres(time0, lat, lon) ;\n",
"\t\tnorthward_wind_at_100_metres:standard_name = northward_wind ;\n",
"\t\tnorthward_wind_at_100_metres:units = m s**-1 ;\n",
"\t\tnorthward_wind_at_100_metres:long_name = 100 metre V wind component ;\n",
"\t\tnorthward_wind_at_100_metres:nameECMWF = 100 metre V wind component ;\n",
"\t\tnorthward_wind_at_100_metres:nameCDM = 100_metre_V_wind_component_surface ;\n",
"\t\tnorthward_wind_at_100_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 surface_air_pressure(time0, lat, lon) ;\n",
"\t\tsurface_air_pressure:standard_name = surface_air_pressure ;\n",
"\t\tsurface_air_pressure:units = Pa ;\n",
"\t\tsurface_air_pressure:long_name = Surface pressure ;\n",
"\t\tsurface_air_pressure:nameECMWF = Surface pressure ;\n",
"\t\tsurface_air_pressure:nameCDM = Surface_pressure_surface ;\n",
"\t\tsurface_air_pressure:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 lwe_thickness_of_surface_snow_amount(time0, lat, lon) ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:standard_name = lwe_thickness_of_surface_snow_amount ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:units = m of water equivalent ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:long_name = Snow depth ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:nameECMWF = Snow depth ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:nameCDM = Snow_depth_surface ;\n",
"\t\tlwe_thickness_of_surface_snow_amount:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 air_temperature_at_2_metres_1hour_Minimum(time1, lat, lon) ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:standard_name = air_temperature ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:units = K ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:long_name = Minimum temperature at 2 metres since previous post-processing ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:nameECMWF = Minimum temperature at 2 metres since previous post-processing ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:nameCDM = Minimum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2 ;\n",
"\t\tair_temperature_at_2_metres_1hour_Minimum:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 snow_density(time0, lat, lon) ;\n",
"\t\tsnow_density:standard_name = snow_density ;\n",
"\t\tsnow_density:units = kg m**-3 ;\n",
"\t\tsnow_density:long_name = Snow density ;\n",
"\t\tsnow_density:nameECMWF = Snow density ;\n",
"\t\tsnow_density:nameCDM = Snow_density_surface ;\n",
"\t\tsnow_density:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 significant_height_of_wind_and_swell_waves(time0, lat_ocean, lon_ocean) ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:standard_name = significant_height_of_wind_and_swell_waves ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:units = m ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:long_name = Significant height of combined wind waves and swell ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:nameECMWF = Significant height of combined wind waves and swell ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:nameCDM = Significant_height_of_combined_wind_waves_and_swell ;\n",
"\t\tsignificant_height_of_wind_and_swell_waves:_SuperchunkSizes = [ 744 501 1002] ;\n",
"\tfloat32 sea_surface_wave_mean_period(time0, lat_ocean, lon_ocean) ;\n",
"\t\tsea_surface_wave_mean_period:standard_name = sea_surface_wave_mean_period ;\n",
"\t\tsea_surface_wave_mean_period:units = s ;\n",
"\t\tsea_surface_wave_mean_period:long_name = Mean wave period ;\n",
"\t\tsea_surface_wave_mean_period:nameECMWF = Mean wave period ;\n",
"\t\tsea_surface_wave_mean_period:nameCDM = Mean_wave_period ;\n",
"\t\tsea_surface_wave_mean_period:_SuperchunkSizes = [ 744 501 1002] ;\n",
"\tfloat64 time1_bounds(time1, nv) ;\n",
"\t\ttime1_bounds:units = seconds since 1970-01-01 ;\n",
"\t\ttime1_bounds:_SuperchunkSizes = [744 2] ;\n",
"\tfloat32 precipitation_amount_1hour_Accumulation(time1, lat, lon) ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:standard_name = precipitation_amount ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:units = m ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:long_name = Total precipitation ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:nameECMWF = Total precipitation ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:nameCDM = Total_precipitation_1hour_Accumulation ;\n",
"\t\tprecipitation_amount_1hour_Accumulation:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 northward_wind_at_10_metres(time0, lat, lon) ;\n",
"\t\tnorthward_wind_at_10_metres:standard_name = northward_wind ;\n",
"\t\tnorthward_wind_at_10_metres:units = m s**-1 ;\n",
"\t\tnorthward_wind_at_10_metres:long_name = 10 metre V wind component ;\n",
"\t\tnorthward_wind_at_10_metres:nameECMWF = 10 metre V wind component ;\n",
"\t\tnorthward_wind_at_10_metres:nameCDM = 10_metre_V_wind_component_surface ;\n",
"\t\tnorthward_wind_at_10_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 eastward_wind_at_100_metres(time0, lat, lon) ;\n",
"\t\teastward_wind_at_100_metres:standard_name = eastward_wind ;\n",
"\t\teastward_wind_at_100_metres:units = m s**-1 ;\n",
"\t\teastward_wind_at_100_metres:long_name = 100 metre U wind component ;\n",
"\t\teastward_wind_at_100_metres:nameECMWF = 100 metre U wind component ;\n",
"\t\teastward_wind_at_100_metres:nameCDM = 100_metre_U_wind_component_surface ;\n",
"\t\teastward_wind_at_100_metres:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\tfloat32 sea_surface_wind_wave_from_direction(time0, lat_ocean, lon_ocean) ;\n",
"\t\tsea_surface_wind_wave_from_direction:standard_name = sea_surface_wind_wave_from_direction ;\n",
"\t\tsea_surface_wind_wave_from_direction:units = degrees ;\n",
"\t\tsea_surface_wind_wave_from_direction:long_name = Mean direction of wind waves ;\n",
"\t\tsea_surface_wind_wave_from_direction:nameECMWF = Mean direction of wind waves ;\n",
"\t\tsea_surface_wind_wave_from_direction:nameCDM = Mean_direction_of_wind_waves ;\n",
"\t\tsea_surface_wind_wave_from_direction:_SuperchunkSizes = [ 744 501 1002] ;\n",
"\tfloat32 integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation(time1, lat, lon) ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:standard_name = integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:units = J m**-2 ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:long_name = Surface solar radiation downwards ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:nameECMWF = Surface solar radiation downwards ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:nameCDM = Surface_solar_radiation_downwards_surface_1_Hour_Accumulation ;\n",
"\t\tintegral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation:_SuperchunkSizes = [ 744 640 1280] ;\n",
"\n",
"// global attributes:\n",
"\t:source = Reanalysis ;\n",
"\t:institution = ECMWF ;\n",
"\t:title = \"ERA5 forecasts\" ;\n",
"\t:history = Thu Jul 5 04:37:28 2018: ncatted /data.e1/wrk/s3_out_in/2018/01/air_pressure_at_mean_sea_level.nc -a tilte,global,d,, -a title,global,c,c,\"ERA5 forecasts\" ;\n",
"}"
]
}
],
"source": [
"metadata_file = 'main.nc'\n",
"metadata_key = prefix + metadata_file\n",
"client.download_file(era5_bucket, metadata_key, metadata_file)\n",
"ds_meta = xr.open_dataset('main.nc', decode_times=False)\n",
"ds_meta.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's acquire data for a single variable over the course of a month. Let's download air temperature for August of 2017 and open the NetCDF file using `xarray`.\n",
"\n",
"Note that the cell below may take some time to execute, depending on your connection speed. Most of the variable files are roughly 1 GB in size."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Dataset.info of <xarray.Dataset>\n",
"Dimensions: (lat: 640, lon: 1280, time0: 744)\n",
"Coordinates:\n",
" * lon (lon) float32 0.0 0.281249 0.562499 ...\n",
" * lat (lat) float32 89.7849 89.5062 89.2259 ...\n",
" * time0 (time0) datetime64[ns] 2017-08-01T07:00:00 ...\n",
"Data variables:\n",
" air_temperature_at_2_metres (time0, lat, lon) float32 ...\n",
"Attributes:\n",
" source: Reanalysis\n",
" institution: ECMWF\n",
" title: \"ERA5 forecasts\"\n",
" history: Thu Jul 5 04:23:06 2018: ncatted /data.e1/wrk/s3_out_in/20...>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select date and variable of interest\n",
"date = datetime.date(2017,8,1)\n",
"var = 'air_temperature_at_2_metres'\n",
"\n",
"# file path patterns for remote S3 objects and corresponding local file\n",
"s3_data_ptrn = '{year}/{month}/data/{var}.nc'\n",
"data_file_ptrn = '{year}{month}_{var}.nc'\n",
"\n",
"year = date.strftime('%Y')\n",
"month = date.strftime('%m')\n",
"s3_data_key = s3_data_ptrn.format(year=year, month=month, var=var)\n",
"data_file = data_file_ptrn.format(year=year, month=month, var=var)\n",
"\n",
"if not os.path.isfile(data_file): # check if file already exists\n",
" print(\"Downloading %s from S3...\" % s3_data_key)\n",
" client.download_file(era5_bucket, s3_data_key, data_file)\n",
"\n",
"ds = xr.open_dataset(data_file)\n",
"ds.info"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `ds.info` output above shows us that there are three dimensions to the data: lat, lon, and time0; and one data variable: air_temperature_at_2_metres. Let's inspect the coordinate values to see what they look like..."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<xarray.DataArray u'lon' (lon: 1280)>\n",
" array([ 0.000000e+00, 2.812494e-01, 5.624988e-01, ..., 3.591555e+02,\n",
" 3.594367e+02, 3.597180e+02], dtype=float32)\n",
" Coordinates:\n",
" * lon (lon) float32 0.0 0.281249 0.562499 0.843748 1.125 1.40625 ...\n",
" Attributes:\n",
" standard_name: longitude\n",
" long_name: longitude\n",
" units: degrees_east, <xarray.DataArray u'lat' (lat: 640)>\n",
" array([ 89.784874, 89.506203, 89.225883, ..., -89.225883, -89.506203,\n",
" -89.784874], dtype=float32)\n",
" Coordinates:\n",
" * lat (lat) float32 89.7849 89.5062 89.2259 88.9452 88.6644 88.3835 ...\n",
" Attributes:\n",
" standard_name: latitude\n",
" long_name: latitude\n",
" units: degrees_north, <xarray.DataArray u'time0' (time0: 744)>\n",
" array(['2017-08-01T07:00:00.000000000', '2017-08-01T08:00:00.000000000',\n",
" '2017-08-01T09:00:00.000000000', ..., '2017-09-01T04:00:00.000000000',\n",
" '2017-09-01T05:00:00.000000000', '2017-09-01T06:00:00.000000000'], dtype='datetime64[ns]')\n",
" Coordinates:\n",
" * time0 (time0) datetime64[ns] 2017-08-01T07:00:00 2017-08-01T08:00:00 ...\n",
" Attributes:\n",
" standard_name: time]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.coords.values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the coordinate values, we can see that longitude is expressed as degrees east, ranging from 0 to 359.718 degrees. Latitude is expressed as degrees north, ranging from -89.784874 to 89.784874. And finally the time0 coordinate, ranging from 2017-08-01T07:00:00Z to 2017-09-01T06:00:00Z.\n",
"\n",
"As mentioned above, due to the forecast run timing the first forecast run of the month results in data beginning at 07:00, while the last produces data through September 1 at 06:00.\n",
"\n",
"## Temperature at Specific Locations\n",
"\n",
"Let's create a list of various locations and plot their temperature values during the month. Note that the longitude values of the coordinates below are not given in degrees east, but rather as a mix of eastward and westward values. The data's longitude coordinate is degrees east, so we'll convert these location coordinates accordingly to match the data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'lat': 34.010341, 'lon': 241.503755, 'name': 'santa_monica'},\n",
" {'lat': 59.436962, 'lon': 24.753574, 'name': 'tallinn'},\n",
" {'lat': 21.290014, 'lon': 202.164062, 'name': 'honolulu'},\n",
" {'lat': -33.918861, 'lon': 18.4233, 'name': 'cape_town'},\n",
" {'lat': 25.266666, 'lon': 55.316666, 'name': 'dubai'}]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# location coordinates\n",
"locs = [\n",
" {'name': 'santa_monica', 'lon': -118.496245, 'lat': 34.010341},\n",
" {'name': 'tallinn', 'lon': 24.753574, 'lat': 59.436962},\n",
" {'name': 'honolulu', 'lon': -157.835938, 'lat': 21.290014},\n",
" {'name': 'cape_town', 'lon': 18.423300, 'lat': -33.918861},\n",
" {'name': 'dubai', 'lon': 55.316666, 'lat': 25.266666},\n",
"]\n",
"\n",
"# convert westward longitudes to degrees east\n",
"for l in locs:\n",
" if l['lon'] < 0:\n",
" l['lon'] = 360 + l['lon']\n",
"locs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Data variables:\n",
" santa_monica (time0) float32 ...\n",
" tallinn (time0) float32 ...\n",
" honolulu (time0) float32 ...\n",
" cape_town (time0) float32 ...\n",
" dubai (time0) float32 ..."
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_locs = xr.Dataset()\n",
"\n",
"# interate through the locations and create a dataset\n",
"# containing the temperature values for each location\n",
"for l in locs:\n",
" name = l['name']\n",
" lon = l['lon']\n",
" lat = l['lat']\n",
" var_name = name\n",
"\n",
" ds2 = ds.sel(lon=lon, lat=lat, method='nearest')\n",
"\n",
" lon_attr = '%s_lon' % name\n",
" lat_attr = '%s_lat' % name\n",
"\n",
" ds2.attrs[lon_attr] = ds2.lon.values.tolist()\n",
" ds2.attrs[lat_attr] = ds2.lat.values.tolist()\n",
" ds2 = ds2.rename({var : var_name}).drop(('lat', 'lon'))\n",
" \n",
" ds_locs = xr.merge([ds_locs, ds2])\n",
"\n",
"ds_locs.data_vars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert Units and Create a Dataframe\n",
"\n",
"Temperature data in the ERA5 dataset uses Kelvin. Let's convert it to something more meaningful. I've chosen to use Fahrenheit, because as a U.S. citizen (and stubborn metric holdout) Celcius still feels foreign to me ;-)\n",
"\n",
"While we're at it, let's also convert the dataset to a pandas dataframe and use the describe method to display some statistics about the data."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>santa_monica</th>\n",
" <th>tallinn</th>\n",
" <th>honolulu</th>\n",
" <th>cape_town</th>\n",
" <th>dubai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>744.000000</td>\n",
" <td>744.000000</td>\n",
" <td>744.000000</td>\n",
" <td>744.000000</td>\n",
" <td>744.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>77.158882</td>\n",
" <td>61.700226</td>\n",
" <td>78.779602</td>\n",
" <td>54.466312</td>\n",
" <td>97.611031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>10.729348</td>\n",
" <td>5.391929</td>\n",
" <td>0.590648</td>\n",
" <td>5.776023</td>\n",
" <td>9.913952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>60.370331</td>\n",
" <td>48.743866</td>\n",
" <td>76.477631</td>\n",
" <td>39.893616</td>\n",
" <td>78.019012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>68.041779</td>\n",
" <td>57.846649</td>\n",
" <td>78.422195</td>\n",
" <td>50.490135</td>\n",
" <td>89.455353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>76.008087</td>\n",
" <td>61.507019</td>\n",
" <td>78.841400</td>\n",
" <td>54.044983</td>\n",
" <td>96.465210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>84.933701</td>\n",
" <td>65.170883</td>\n",
" <td>79.186539</td>\n",
" <td>57.863327</td>\n",
" <td>106.155136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>106.625946</td>\n",
" <td>79.688385</td>\n",
" <td>80.241119</td>\n",
" <td>73.375641</td>\n",
" <td>117.020599</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" santa_monica tallinn honolulu cape_town dubai\n",
"count 744.000000 744.000000 744.000000 744.000000 744.000000\n",
"mean 77.158882 61.700226 78.779602 54.466312 97.611031\n",
"std 10.729348 5.391929 0.590648 5.776023 9.913952\n",
"min 60.370331 48.743866 76.477631 39.893616 78.019012\n",
"25% 68.041779 57.846649 78.422195 50.490135 89.455353\n",
"50% 76.008087 61.507019 78.841400 54.044983 96.465210\n",
"75% 84.933701 65.170883 79.186539 57.863327 106.155136\n",
"max 106.625946 79.688385 80.241119 73.375641 117.020599"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def kelvin_to_celcius(t):\n",
" return t - 273.15\n",
"\n",
"def kelvin_to_fahrenheit(t):\n",
" return t * 9/5 - 459.67\n",
"\n",
"ds_locs_f = ds_locs.apply(kelvin_to_fahrenheit)\n",
"\n",
"df_f = ds_locs_f.to_dataframe()\n",
"df_f.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show Me Some Charts!\n",
"\n",
"Finally, let's plot the temperature data for each of the locations over the period. The first plot displays the hourly temperature for each location over the month.\n",
"\n",
"The second plot is a [box plot](https://en.wikipedia.org/wiki/Box_plot). A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. The position of the whiskers is set by default to 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box. Outlier points are those past the end of the whiskers."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113f23750>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f058b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# readability please\n",
"plt.rcParams.update({'font.size': 16})\n",
"\n",
"ax = df_f.plot(figsize=(18, 10), title=\"ERA5 Air Temperature at 2 Meters\", grid=1)\n",
"ax.set(xlabel='Date', ylabel='Air Temperature (deg F)')\n",
"plt.show()\n",
"\n",
"ax = df_f.plot.box(figsize=(18, 10))\n",
"ax.set(xlabel='Location', ylabel='Air Temperature (deg F)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conclusions? Dubai was absolutely cooking in August, with a mean temperature of ~98° and a high of 117°! While Honolulu was a consistent 78° with a standard deviation of less than 1°!\n",
"\n",
"Questions? Feedback? Email us at [[email protected]](mailto:[email protected]). We also provide an API for accessing ERA5 data, for more details vis the [Planet OS Datahub](https://data.planetos.com/datasets/ecmwf_era5) or check out [another notebook example using ERA5 data.](https://github.com/planet-os/notebooks/blob/master/api-examples/ERA5_tutorial.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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 |
mdeff/ntds_2017 | projects/reports/course_suggester/Spectral_clustering.ipynb | 1 | 3035570 | null | mit |
Cesaaar/od_crimini_ita | data_visualization.ipynb | 2 | 1855033 | null | apache-2.0 |
mclaughlin6464/pearce | notebooks/Test Downsample.ipynb | 1 | 74580 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm gonna overwrite a lot of this notebook's old content. I changed the way I'm calculating wt, and wanna test that my training worked. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pearce.emulator import OriginalRecipe, ExtraCrispy\n",
"from pearce.mocks import cat_dict\n",
"import numpy as np\n",
"from os import path"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
"#matplotlib.use('Agg')\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"training_file = '/u/ki/swmclau2/des/PearceRedMagicWpCosmo.hdf5'\n",
"\n",
"em_method = 'gp'\n",
"split_method = 'random'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = 1.0\n",
"z = 1.0/a - 1.0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fixed_params = {'z':z}#, 'r':0.18477483}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/u/ki/swmclau2/.local/lib/python2.7/site-packages/pearce/emulator/emu.py:253: UserWarning: WARNING: NaN detected. Skipped 3941 points in training data.\n",
" warnings.warn('WARNING: NaN detected. Skipped %d points in training data.' % (num_skipped))\n"
]
}
],
"source": [
"n_leaves, n_overlap = 100, 2\n",
"emu = ExtraCrispy(training_file, n_leaves, n_overlap, split_method, method = em_method, fixed_params=fixed_params,\n",
" custom_mean_function = None, downsample_factor = 0.2)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100, 5781, 14)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emu.x.shape"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": [
"emu = OriginalRecipe(training_file, method = em_method, fixed_params=fixed_params, independent_variable=None,\\\n",
" custom_mean_function = None)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('ombh2', (0.02066455, 0.02371239)),\n",
" ('omch2', (0.1012181, 0.13177679999999997)),\n",
" ('w0', (-1.399921, -0.5658486)),\n",
" ('ns', (0.9278462, 0.9974495999999999)),\n",
" ('ln10As', (3.0009, 3.179424)),\n",
" ('H0', (61.69472, 74.76751999999999)),\n",
" ('Neff', (2.62125, 4.27875)),\n",
" ('logM1', (13.0, 15.0)),\n",
" ('logMmin', (11.5, 13.5)),\n",
" ('f_c', (0.01, 0.5)),\n",
" ('logM0', (12.0, 16.0)),\n",
" ('sigma_logM', (0.05, 0.6)),\n",
" ('alpha', (0.8, 1.2)),\n",
" ('r', (-1.1000000189853054, 1.6000000000507297))])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emu._ordered_params"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"params = {'ombh2': 0.021,\n",
" 'omch2': 0.11,\n",
" 'w0': -1.01,\n",
" 'ns': 0.9578462,\n",
" 'ln10As': 3.08,\n",
" 'H0': 68.1,\n",
" 'Neff': 3.04,\n",
" 'logM1': 14.0,\n",
" 'logMmin': 11.9,\n",
" 'f_c': 0.2,\n",
" 'logM0': 13.2,\n",
" 'sigma_logM': 0.12,\n",
" 'alpha':1.1}"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"params = {'ombh2': 0.021,\n",
" 'omch2': 0.12,\n",
" 'w0': -1,\n",
" 'ns': 0.9578462,\n",
" 'ln10As': 3.08,\n",
" 'H0': 68.1,\n",
" 'Neff': 3.04}"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"params = {'logM1': 14.0,\n",
" 'logMmin': 11.9,\n",
" 'f_c': 0.2,\n",
" 'logM0': 13.2,\n",
" 'sigma_logM': 0.12,\n",
" 'alpha':1.1}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wp = emu.emulate_wrt_r(params, emu.scale_bin_centers)[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 2.22473511e-02, 1.18123767e-01, -1.00485332e+00, 9.62216228e-01,\n",
" 3.08983029e+00, 6.83608759e+01, 3.44543083e+00, 1.39989072e+01,\n",
" 1.24850398e+01, 2.54063988e-01, 1.39632580e+01, 3.26371493e-01,\n",
" 1.00216109e+00, 2.56444085e-01]),\n",
" array([7.38594488e-04, 7.03286495e-03, 2.07402642e-01, 1.64803757e-02,\n",
" 4.99982658e-02, 3.85720859e+00, 4.90458762e-01, 5.77559450e-01,\n",
" 5.82243978e-01, 1.42270736e-01, 1.14254532e+00, 1.59030427e-01,\n",
" 1.16111216e-01, 7.78219120e-01]))"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emu._x_mean, emu._x_std"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100, 5781, 14)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emu.x.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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xf208sgu5OJCIbsYCQGSCLM01eHFsNwzv5/3b4sC1J5DE8wUQ0R+wABCZKJUkYVyED54e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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3d716fd7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(emu.scale_bin_centers, wp)\n",
"plt.xscale('log')\n",
"plt.xlabel(r'$r$ [Mpc]')\n",
"plt.ylabel(r'$w_p(r_p)$')\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"params = {'ombh2': 0.021,\n",
" 'omch2': 0.11,\n",
" 'w0': -1,\n",
" 'ns': 0.9578462,\n",
" 'ln10As': 3.08,\n",
" 'H0': 68.1,\n",
" 'Neff': 3.04,\n",
" 'logM1': 14.0,\n",
" 'logMmin': 11.9,\n",
" 'f_c': 0.2,\n",
" 'logM0': 13.2,\n",
" 'sigma_logM': 0.12,\n",
" 'alpha':1.1}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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P/xhVh0K8VdGJw2Ig+yQT9gxaA0v9paRa/NT0H+SdgTaGjAEKnVnERpoJ95UT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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3d716fd850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"param_name = 'logMmin'\n",
"param_bounds = emu.get_param_bounds(param_name)\n",
"pvals = np.linspace(param_bounds[0],param_bounds[1], 5)\n",
"\n",
"for val in pvals:\n",
" params[param_name] = val\n",
" #print params\n",
" wp = emu.emulate_wrt_r(params, emu.scale_bin_centers)[0]\n",
" #print(wp)\n",
" plt.plot(emu.scale_bin_centers, wp, label = '%s = %.2f'%(param_name, val))\n",
" \n",
"plt.plot(emu.scale_bin_centers, np.mean(emu._y_mean)*np.ones_like(emu.scale_bin_centers), color = 'k')\n",
"\n",
" \n",
"plt.xscale('log')\n",
"plt.xlabel(r'$r$ [Mpc]')\n",
"plt.ylabel(r'$w_p(r_p)$')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"432/18"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 450 is out of bounds for axis 0 with size 100",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-67a2fb639f99>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mbinlen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0memu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale_bin_centers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mpname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0memu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_param_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0memu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x_std\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0memu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbinlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0memu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x_mean\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\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[0m",
"\u001b[1;31mIndexError\u001b[0m: index 450 is out of bounds for axis 0 with size 100"
]
}
],
"source": [
"idx = 25\n",
"binlen = len(emu.scale_bin_centers)\n",
"\n",
"params = {pname: p for pname, p in zip(emu.get_param_names(), emu._x_std[:-1]*emu.x[idx*binlen, :-1] + emu._x_mean[:-1])}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wp = emu.emulate_wrt_r(params,emu.scale_bin_centers)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(emu.scale_bin_centers, wp, label = 'Emu')\n",
"plt.plot(emu.scale_bin_centers, emu._y_std*emu.y[idx*binlen:(idx+1)*binlen]+emu._y_mean, label = 'Truth')\n",
"#plt.plot(emu.x[idx*binlen:(idx+1)*binlen, -1], lm_pred)\n",
"plt.xscale('log')\n",
"plt.xlabel(r'$r$ [Mpc]')\n",
"plt.ylabel(r'$w_p(r_p)$')\n",
"plt.legend(loc = 'best')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"emu.y.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"emu._y_mean"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"params['f_c'] = 0.1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"params['r'] = emu.scale_bin_centers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t_list = [params[pname] for pname in emu._ordered_params if pname in params]\n",
"t_grid = np.meshgrid(*t_list)\n",
"t = np.stack(t_grid).T\n",
"t = t.reshape((-1, emu.emulator_ndim))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t-=emu._x_mean\n",
"t/=(emu._x_std + 1e-5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in xrange(emu.y.shape[0]):\n",
" print gp.predict(emu.y[i], t, return_cov= False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"emu.mean_function(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"emu._mean_func.named_steps['linearregression'].coef_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:hodemulator]",
"language": "python",
"name": "conda-env-hodemulator-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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
citxx/sis-python | crash-course/strings.ipynb | 1 | 20386 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Спецсимволы\" data-toc-modified-id=\"Спецсимволы-1\">Спецсимволы</a></span></li><li><span><a href=\"#Операции-со-строками\" data-toc-modified-id=\"Операции-со-строками-2\">Операции со строками</a></span><ul class=\"toc-item\"><li><span><a href=\"#Сложение\" data-toc-modified-id=\"Сложение-2.1\">Сложение</a></span></li><li><span><a href=\"#Повторение\" data-toc-modified-id=\"Повторение-2.2\">Повторение</a></span></li><li><span><a href=\"#Индексация\" data-toc-modified-id=\"Индексация-2.3\">Индексация</a></span></li><li><span><a href=\"#Длина-строки\" data-toc-modified-id=\"Длина-строки-2.4\">Длина строки</a></span></li><li><span><a href=\"#Проверка-наличия-подстроки\" data-toc-modified-id=\"Проверка-наличия-подстроки-2.5\">Проверка наличия подстроки</a></span></li></ul></li><li><span><a href=\"#Кодировка-символов\" data-toc-modified-id=\"Кодировка-символов-3\">Кодировка символов</a></span><ul class=\"toc-item\"><li><span><a href=\"#Код-по-символу\" data-toc-modified-id=\"Код-по-символу-3.1\">Код по символу</a></span></li><li><span><a href=\"#Символ-по-коду\" data-toc-modified-id=\"Символ-по-коду-3.2\">Символ по коду</a></span></li><li><span><a href=\"#ASCII\" data-toc-modified-id=\"ASCII-3.3\">ASCII</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Строки"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для представления строк в Python используется тип `str` (аналог `std::string` в С++ или `String` в Pascal)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Строки можно задавать в двойных кавычках <class 'str'>\n",
"А можно в одинарных <class 'str'>\n"
]
}
],
"source": [
"s1 = \"Строки можно задавать в двойных кавычках\"\n",
"s2 = 'А можно в одинарных'\n",
"print(s1, type(s1))\n",
"print(s2, type(s2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Спецсимволы"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для задания в строке особых символов (например переводов строк или табуляций) в Python используются специальный последовательности, вроде `\\n` для перевода строки или `\\t` для символа табуляции:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Эта строка\n",
"состоит из двух строк\n"
]
}
],
"source": [
"s = \"Эта строка\\nсостоит из двух строк\"\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"А в этой\tстроке\n",
"используются\tсимволы табуляции\n"
]
}
],
"source": [
"s = \"А в этой\\tстроке\\nиспользуются\\tсимволы табуляции\"\n",
"print(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Такой же синтаксис используетя для задания кавычек в строке:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Это \"строка\" с кавычками. И \"это\" тоже. С одинарными Кавычками '' всё работает также.\n"
]
}
],
"source": [
"s1 = \"Это \\\"строка\\\" с кавычками.\"\n",
"s2 = 'И \"это\" тоже.'\n",
"s3 = 'С одинарными Кавычками \\'\\' всё работает также.'\n",
"print(s1, s2, s3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Если надо задать обратный слэш \\, то его надо просто удвоить: '\\\\'\n"
]
}
],
"source": [
"print(\"Если надо задать обратный слэш \\\\, то его надо просто удвоить: '\\\\\\\\'\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Операции со строками"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Сложение"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Строки можно **складывать**. В этом случае они просто припишутся друг к другу. По-умному это называется *конкатенацией*."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Привет!!!\n"
]
}
],
"source": [
"greeting = \"Привет\"\n",
"exclamation = \"!!!\"\n",
"print(greeting + exclamation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Повторение"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Можно **умножать на целое число**, чтобы повторить строку нужное число раз."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"I will write in Python with style!\n",
"\n",
"Really\n",
"Really\n",
"Really\n",
"\n"
]
}
],
"source": [
"print(\"I will write in Python with style!\\n\" * 10)\n",
"print(3 * \"Really\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Индексация"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получить символ на заданной позиции можно также, как и в C++ или Pascal. Индекасация начинается с 0."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Э т о\n"
]
}
],
"source": [
"s = \"Это моя строка\"\n",
"print(s[0], s[1], s[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Но нельзя поменять отдельный символ. Это сделано для того, чтобы более логично и эффективно реализовать некоторые возможности Python."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'str' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-90c2df1ce1de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Вы не можете изменить символы этой строки\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Т\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
]
}
],
"source": [
"s = \"Вы не можете изменить символы этой строки\"\n",
"s[0] = \"Т\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Перевод: `ОшибкаТипа: объект 'str' не поддерживает присваивание элементов`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Можно указывать отрицательные индексы, тогда нумерация происходит с конца."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"а = а\n",
"к = к\n",
"о = о\n",
"р = р\n",
"т = т\n",
"С = С\n"
]
}
],
"source": [
"s = \"Строка\"\n",
"print(s[-1], \"=\", s[5])\n",
"print(s[-2], \"=\", s[4])\n",
"print(s[-3], \"=\", s[3])\n",
"print(s[-4], \"=\", s[2])\n",
"print(s[-5], \"=\", s[1])\n",
"print(s[-6], \"=\", s[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Длина строки"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"44\n"
]
}
],
"source": [
"s = \"Для получения длины используется функция len\"\n",
"print(len(s))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Проверка наличия подстроки"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Проверить наличие или отсутствие в строке подстроки или символа можно с помощью операций `in` и `not in`."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ы - гласная\n"
]
}
],
"source": [
"vowels = \"аеёиоуыэюя\"\n",
"c = \"ы\"\n",
"if c in vowels:\n",
" print(c, \"- гласная\")\n",
"else:\n",
" print(c, \"- согласная\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n"
]
}
],
"source": [
"s = \"Python - лучший из неторопливых языков :)\"\n",
"print(\"Python\" in s)\n",
"print(\"C++\" in s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Кодировка символов"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В памяти компьютера каждый символ хранится как число. Соответствие между символом и числом называется *кодировкой*.\n",
"\n",
"Самая простая кодировка для латинских букв, цифр и часто используемых символов — ASCII. Она задаёт коды (числа) для 128 символов и используется в Python для представления этих символов."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Код по символу"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"97\n"
]
}
],
"source": [
"# Код любого символа можно получить с помощью функции ord\n",
"print(ord(\"a\"))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Цифры: 48 49 50 51 ... 56 57\n",
"Маленькие буквы: 97 98 99 100 ... 121 122\n",
"Большие буквы: 65 66 67 68 ... 89 90\n"
]
}
],
"source": [
"# Можно пользоваться тем, что коды чисел, маленьких латинских букв и больших латинских букв идут подряд.\n",
"print(\"Цифры:\", ord(\"0\"), ord(\"1\"), ord(\"2\"), ord(\"3\"), \"...\", ord(\"8\"), ord(\"9\"))\n",
"print(\"Маленькие буквы:\", ord(\"a\"), ord(\"b\"), ord(\"c\"), ord(\"d\"), \"...\", ord(\"y\"), ord(\"z\"))\n",
"print(\"Большие буквы:\", ord(\"A\"), ord(\"B\"), ord(\"C\"), ord(\"D\"), \"...\", ord(\"Y\"), ord(\"Z\"))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"# Например, так можно получить номер буквы в алфавите\n",
"c = \"g\"\n",
"print(ord(c) - ord('a'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Символ по коду"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"d\n"
]
}
],
"source": [
"# Для получение символа по коду используется функция chr\n",
"print(chr(100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ASCII"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"chr(0) = '\\x00'\n",
"chr(1) = '\\x01'\n",
"chr(2) = '\\x02'\n",
"chr(3) = '\\x03'\n",
"chr(4) = '\\x04'\n",
"chr(5) = '\\x05'\n",
"chr(6) = '\\x06'\n",
"chr(7) = '\\x07'\n",
"chr(8) = '\\x08'\n",
"chr(9) = '\\t'\n",
"chr(10) = '\\n'\n",
"chr(11) = '\\x0b'\n",
"chr(12) = '\\x0c'\n",
"chr(13) = '\\r'\n",
"chr(14) = '\\x0e'\n",
"chr(15) = '\\x0f'\n",
"chr(16) = '\\x10'\n",
"chr(17) = '\\x11'\n",
"chr(18) = '\\x12'\n",
"chr(19) = '\\x13'\n",
"chr(20) = '\\x14'\n",
"chr(21) = '\\x15'\n",
"chr(22) = '\\x16'\n",
"chr(23) = '\\x17'\n",
"chr(24) = '\\x18'\n",
"chr(25) = '\\x19'\n",
"chr(26) = '\\x1a'\n",
"chr(27) = '\\x1b'\n",
"chr(28) = '\\x1c'\n",
"chr(29) = '\\x1d'\n",
"chr(30) = '\\x1e'\n",
"chr(31) = '\\x1f'\n",
"chr(32) = ' '\n",
"chr(33) = '!'\n",
"chr(34) = '\"'\n",
"chr(35) = '#'\n",
"chr(36) = '$'\n",
"chr(37) = '%'\n",
"chr(38) = '&'\n",
"chr(39) = \"'\"\n",
"chr(40) = '('\n",
"chr(41) = ')'\n",
"chr(42) = '*'\n",
"chr(43) = '+'\n",
"chr(44) = ','\n",
"chr(45) = '-'\n",
"chr(46) = '.'\n",
"chr(47) = '/'\n",
"chr(48) = '0'\n",
"chr(49) = '1'\n",
"chr(50) = '2'\n",
"chr(51) = '3'\n",
"chr(52) = '4'\n",
"chr(53) = '5'\n",
"chr(54) = '6'\n",
"chr(55) = '7'\n",
"chr(56) = '8'\n",
"chr(57) = '9'\n",
"chr(58) = ':'\n",
"chr(59) = ';'\n",
"chr(60) = '<'\n",
"chr(61) = '='\n",
"chr(62) = '>'\n",
"chr(63) = '?'\n",
"chr(64) = '@'\n",
"chr(65) = 'A'\n",
"chr(66) = 'B'\n",
"chr(67) = 'C'\n",
"chr(68) = 'D'\n",
"chr(69) = 'E'\n",
"chr(70) = 'F'\n",
"chr(71) = 'G'\n",
"chr(72) = 'H'\n",
"chr(73) = 'I'\n",
"chr(74) = 'J'\n",
"chr(75) = 'K'\n",
"chr(76) = 'L'\n",
"chr(77) = 'M'\n",
"chr(78) = 'N'\n",
"chr(79) = 'O'\n",
"chr(80) = 'P'\n",
"chr(81) = 'Q'\n",
"chr(82) = 'R'\n",
"chr(83) = 'S'\n",
"chr(84) = 'T'\n",
"chr(85) = 'U'\n",
"chr(86) = 'V'\n",
"chr(87) = 'W'\n",
"chr(88) = 'X'\n",
"chr(89) = 'Y'\n",
"chr(90) = 'Z'\n",
"chr(91) = '['\n",
"chr(92) = '\\\\'\n",
"chr(93) = ']'\n",
"chr(94) = '^'\n",
"chr(95) = '_'\n",
"chr(96) = '`'\n",
"chr(97) = 'a'\n",
"chr(98) = 'b'\n",
"chr(99) = 'c'\n",
"chr(100) = 'd'\n",
"chr(101) = 'e'\n",
"chr(102) = 'f'\n",
"chr(103) = 'g'\n",
"chr(104) = 'h'\n",
"chr(105) = 'i'\n",
"chr(106) = 'j'\n",
"chr(107) = 'k'\n",
"chr(108) = 'l'\n",
"chr(109) = 'm'\n",
"chr(110) = 'n'\n",
"chr(111) = 'o'\n",
"chr(112) = 'p'\n",
"chr(113) = 'q'\n",
"chr(114) = 'r'\n",
"chr(115) = 's'\n",
"chr(116) = 't'\n",
"chr(117) = 'u'\n",
"chr(118) = 'v'\n",
"chr(119) = 'w'\n",
"chr(120) = 'x'\n",
"chr(121) = 'y'\n",
"chr(122) = 'z'\n",
"chr(123) = '{'\n",
"chr(124) = '|'\n",
"chr(125) = '}'\n",
"chr(126) = '~'\n",
"chr(127) = '\\x7f'\n"
]
}
],
"source": [
"# Этот код выводит всю таблицу ASCII\n",
"for code in range(128):\n",
" print('chr(' + str(code) + ') =', repr(chr(code)))"
]
}
],
"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"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": false,
"skip_h1_title": true,
"title_cell": "Содержание",
"title_sidebar": "Содержание",
"toc_cell": true,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
seth2000/chinesepoem | PrepareData.ipynb | 1 | 17717 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the test file to the idea prove.\n",
"\n",
"Try to do the Json formatted corpus, but it is so hard, then I find the word2vec can avoid this hard work.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# -*- coding: utf-8 -*-\n",
"\n",
"import os\n",
"import re\n",
"import time\n",
"import codecs\n",
"import argparse\n",
"\n",
"TIME_FORMAT = '%Y-%m-%d %H:%M:%S'\n",
"BASE_FOLDER = os.getcwd() # os.path.abspath(os.path.dirname(__file__))\n",
"DATA_FOLDER = os.path.join(BASE_FOLDER, 'data')\n",
"DEFAULT_FIN = os.path.join(DATA_FOLDER, '唐诗语料库.txt')\n",
"DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n",
"reg_noisy = re.compile('[^\\u3000-\\uffee]')\n",
"reg_note = re.compile('(（.*）)') # Cannot deal with （） in seperate lines\n",
"# 中文及全角标点符号(字符)是\\u3000-\\u301e\\ufe10-\\ufe19\\ufe30-\\ufe44\\ufe50-\\ufe6b\\uff01-\\uffee\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 读取数据，去掉不用的数据"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-15 14:34:54 START\n",
"2017-10-15 14:34:56 STOP\n"
]
}
],
"source": [
"if __name__ == '__main__':\n",
" # parser = set_arguments()\n",
" # cmd_args = parser.parse_args()\n",
"\n",
" print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"\n",
" fd = codecs.open(DEFAULT_FIN, 'r', 'utf-8')\n",
" fw = codecs.open( DEFAULT_FOUT, 'w', 'utf-8')\n",
" reg = re.compile('〖(.*)〗')\n",
" start_flag = False\n",
" for line in fd:\n",
" line = line.strip()\n",
" if not line or '《全唐诗》' in line or '<http' in line or '□' in line:\n",
" continue\n",
" elif '〖' in line and '〗' in line:\n",
" if start_flag:\n",
" fw.write('\\n')\n",
" start_flag = True\n",
" g = reg.search(line)\n",
" if g:\n",
" fw.write(g.group(1))\n",
" fw.write('\\n')\n",
" else:a\n",
" # noisy data\n",
" print(line)\n",
" else:\n",
" line = reg_noisy.sub('', line)\n",
" line = reg_note.sub('', line)\n",
" line = line.replace(' .', '')\n",
" fw.write(line)\n",
"\n",
" fd.close()\n",
" fw.close()\n",
"\n",
" print('{} STOP'.format(time.strftime(TIME_FORMAT)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 分词实验\n",
"#DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n",
" \n",
"#thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n",
"#text = thu1.cut(\"我爱北京天安门\", text=True) #进行一句话分词\n",
"#print(text)\n",
"\n",
"thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n",
"thu1.cut_f(DEFAULT_FOUT, outp) #对input.txt文件内容进行分词，输出到output.txt"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-15 16:26:15 START\n",
"Model loaded succeed\n",
"2017-10-15 16:27:58 STOP\n"
]
}
],
"source": [
"print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"\n",
"import thulac \n",
"DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"\n",
"fd = codecs.open(DEFAULT_FOUT, 'r', 'utf-8')\n",
"fw = codecs.open(DEFAULT_Segment, 'w', 'utf-8')\n",
"\n",
"thu1 = thulac.thulac(seg_only=True) #只进行分词，不进行词性标注\n",
"\n",
"\n",
"for line in fd:\n",
" #print(line)\n",
" fw.write(thu1.cut(line, text=True))\n",
" fw.write('\\n')\n",
" \n",
"fd.close()\n",
"fw.close()\n",
"\n",
"print('{} STOP'.format(time.strftime(TIME_FORMAT)))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-15 16:30:20 START\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:862: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
" warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-15 16:30:31 STOP\n"
]
}
],
"source": [
"print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"from gensim.models import word2vec\n",
"\n",
"\n",
"#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"DEFAULT_Word2Vec = os.path.join(DATA_FOLDER, 'Word2Vec150.bin')\n",
"\n",
"sentences = word2vec.Text8Corpus(DEFAULT_Segment)\n",
"\n",
"model = word2vec.Word2Vec(sentences, size=150)\n",
"\n",
"#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"model.save(DEFAULT_Word2Vec)\n",
"\n",
"print('{} STOP'.format(time.strftime(TIME_FORMAT)))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.30962595, 0.16889741, -0.01463027, -0.15809815, 0.09206317,\n",
" -0.1456935 , 0.16657346, -0.16048834, 0.03577007, -0.13513733,\n",
" -0.09294472, -0.11723404, -0.12365381, -0.02067957, 0.1038581 ,\n",
" 0.00641506, -0.0062934 , 0.23415405, 0.37439978, -0.0564473 ,\n",
" -0.23397736, -0.19426669, 0.06946895, -0.3208392 , 0.19368722,\n",
" 0.02603251, -0.00743247, -0.22094592, 0.01184341, -0.12694272,\n",
" -0.32603887, -0.20273098, -0.07396571, 0.01315944, -0.10838111,\n",
" -0.0909251 , 0.00180263, -0.03625318, -0.2046182 , -0.09922028,\n",
" 0.34920788, 0.08904874, -0.25203493, -0.09772593, -0.03779411,\n",
" -0.17694817, 0.07821831, 0.08035509, 0.25622529, -0.08985876,\n",
" 0.03270766, -0.19293341, -0.30891556, 0.05773695, -0.03148178,\n",
" 0.33995509, -0.22352351, 0.09742409, 0.14914362, -0.07318434,\n",
" 0.03735919, -0.08370081, -0.16495866, 0.14458466, -0.04542416,\n",
" -0.24301586, 0.08908165, 0.06313832, 0.0586113 , -0.15221816,\n",
" 0.06224625, 0.08598434, -0.0115755 , -0.09099659, 0.06226088,\n",
" -0.07644724, 0.02220215, 0.07566795, 0.04833851, 0.00838657,\n",
" -0.05597517, -0.06397859, 0.03784521, 0.02023427, -0.12724152,\n",
" -0.01048566, 0.1487288 , 0.08827937, -0.17855296, 0.31425136,\n",
" 0.06090816, -0.16096003, -0.07982934, 0.10440107, -0.04465724,\n",
" 0.06235282, -0.1461063 , 0.22972585, -0.02483237, 0.1252525 ,\n",
" -0.17958631, 0.04755906, 0.26136953, 0.16259584, 0.11282863,\n",
" 0.10273369, -0.1521662 , -0.11136056, 0.44112033, -0.1723136 ,\n",
" 0.08373854, 0.16581547, -0.06470159, -0.14097695, 0.07161622,\n",
" 0.22370109, 0.26647383, 0.24355215, -0.11299301, 0.14951281,\n",
" -0.05022607, 0.196927 , -0.06548793, 0.50461113, 0.18641786,\n",
" -0.2149298 , -0.05788758, 0.28251058, 0.14605965, 0.4527784 ,\n",
" 0.00892602, 0.08880702, 0.16401401, -0.03404955, -0.3267473 ,\n",
" 0.14250852, 0.20599096, 0.13325472, -0.12572202, 0.02558975,\n",
" -0.06050026, -0.09717743, -0.20002677, 0.14861256, 0.22908178,\n",
" -0.05484885, 0.08654279, 0.07304503, 0.17076297, 0.38086078], dtype=float32)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model[u'男']\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"饮马长城 窟行\n",
"\n",
"塞外 悲风切 ， 交河 冰 已 结 。 瀚海 百重波 ， 阴山 千 里 雪 。 迥戍危 烽火 ， 层峦 引高节 。 悠悠 卷 旆旌 ， 饮马 出 长城 。 寒沙 连 骑迹 ， 朔吹断 边声 。 胡尘清玉塞 ， 羌 笛韵 金钲 。 绝漠 干戈戢 ， 车徒 振 原隰 。 都 尉反龙堆 ， 将 军旋 马邑 。 扬 麾氛 雾静 ， 纪石 功名 立 。 荒裔 一戎衣 ， 灵台 凯歌 入 。\n",
"\n",
"饮马长城窟行\n",
"\n",
"塞外悲风切，交河冰已结。瀚海百重波，阴山千里雪。迥戍危烽火，层峦引高节。悠悠卷旆旌，饮马出长城。寒沙连骑迹，朔吹断边声。胡尘清玉塞，羌笛韵金钲。绝漠干戈戢，车徒振原隰。都尉反龙堆，将军旋马邑。扬麾氛雾静，纪石功名立。荒裔一戎衣，灵台凯歌入。\n",
"\n"
]
}
],
"source": [
"DEFAULT_FIN = os.path.join(DATA_FOLDER, '唐诗语料库.txt')\n",
"DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n",
"DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"def GetFirstNline(filePath, linesNumber):\n",
" fd = codecs.open(filePath, 'r', 'utf-8')\n",
" for i in range(1,linesNumber):\n",
" print(fd.readline())\n",
" fd.close()\n",
"\n",
"GetFirstNline(DEFAULT_Segment, 3)\n",
"GetFirstNline(DEFAULT_FOUT, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 分词不是很成功，我们转向直接用汉字字符来代替分段，我们保留标点符号\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-15 17:22:55 START\n",
"2017-10-15 17:23:02 STOP\n"
]
}
],
"source": [
"print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"\n",
"DEFAULT_FOUT = os.path.join(DATA_FOLDER, 'poem.txt')\n",
"DEFAULT_charSegment = os.path.join(DATA_FOLDER, 'Charactersegment.txt')\n",
"\n",
"fd = codecs.open(DEFAULT_FOUT, 'r', 'utf-8')\n",
"fw = codecs.open(DEFAULT_charSegment, 'w', 'utf-8')\n",
"\n",
"start_flag = False\n",
"for line in fd:\n",
" if len(line) > 0:\n",
" for c in line:\n",
" if c != '\\n':\n",
" fw.write(c)\n",
" fw.write(' ')\n",
" fw.write('\\n')\n",
"\n",
"fd.close()\n",
"fw.close()\n",
"\n",
"print('{} STOP'.format(time.strftime(TIME_FORMAT)))\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"饮 马 长 城 窟 行 \n",
"\n",
"塞 外 悲 风 切 ， 交 河 冰 已 结 。 瀚 海 百 重 波 ， 阴 山 千 里 雪 。 迥 戍 危 烽 火 ， 层 峦 引 高 节 。 悠 悠 卷 旆 旌 ， 饮 马 出 长 城 。 寒 沙 连 骑 迹 ， 朔 吹 断 边 声 。 胡 尘 清 玉 塞 ， 羌 笛 韵 金 钲 。 绝 漠 干 戈 戢 ， 车 徒 振 原 隰 。 都 尉 反 龙 堆 ， 将 军 旋 马 邑 。 扬 麾 氛 雾 静 ， 纪 石 功 名 立 。 荒 裔 一 戎 衣 ， 灵 台 凯 歌 入 。 \n",
"\n"
]
}
],
"source": [
"GetFirstNline(DEFAULT_charSegment, 3)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-16 22:17:17 START\n",
"2017-10-16 22:17:32 STOP\n"
]
}
],
"source": [
"print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"from gensim.models import word2vec\n",
"\n",
"\n",
"#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"DEFAULT_Char2Vec = os.path.join(DATA_FOLDER, 'Char2Vec100.bin')\n",
"\n",
"fd = codecs.open(DEFAULT_charSegment, 'r', 'utf-8')\n",
"\n",
"sentences = fd.readlines()\n",
"\n",
"fd.close\n",
"\n",
"\n",
"model = word2vec.Word2Vec(sentences, size=100)\n",
"\n",
"#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"model.save(DEFAULT_Char2Vec)\n",
"\n",
"print('{} STOP'.format(time.strftime(TIME_FORMAT)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.2900829 , -0.04809159, -0.46607766, -0.60195959, -0.79692709,\n",
" 1.45317233, -0.73875636, -0.23516993, 0.52468306, -0.4141095 ,\n",
" 0.31254441, 0.06157973, 0.52587473, 0.98117661, 0.76936024,\n",
" 0.17090531, 0.54503411, 0.89224559, 0.63628626, -0.65704244,\n",
" 0.19324228, -2.19337821, -0.0736718 , -1.12545574, 0.36714867,\n",
" -0.23592179, 0.65851527, 1.97759676, 0.0664974 , 0.34336987,\n",
" 0.16321452, -0.45230347, -1.16129088, -1.37885571, -0.70058161,\n",
" -2.71629333, -0.47714323, -1.35716736, -0.5040586 , 0.84255946,\n",
" 0.29387042, 0.96084136, 0.5980038 , 1.53590572, 0.78642726,\n",
" -0.70572197, 2.15199852, -0.09091973, 0.70999056, -1.26367903,\n",
" -0.23834354, 0.40385616, 0.76464611, -0.65731245, 0.3340157 ,\n",
" 0.97213268, 1.46448743, 1.32762229, 0.21536438, -0.69748122,\n",
" -1.24047554, 0.52763128, 0.48480916, -0.98241204, -0.71260804,\n",
" -0.54136884, -1.04192448, 1.04139686, 0.46493888, 0.94138777,\n",
" 0.21847701, -0.44784865, -1.06913686, -1.06480539, -0.28641865,\n",
" -0.57710785, -0.42219958, 0.06467494, 0.29220659, 0.56308562,\n",
" -0.69409251, -1.28817475, 0.24338399, -0.0228632 , 0.33695638,\n",
" 0.73314172, 0.78557426, 0.78446829, 0.42267925, -0.7360608 ,\n",
" -0.18527743, 0.4405438 , 1.22639728, 1.25485229, 1.98212445,\n",
" 0.5071575 , -0.30095363, -0.10453363, -0.94564468, 0.3795009 ], dtype=float32)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model[u'男']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-10-16 20:25:31 START\n",
"2017-10-16 20:25:41 STOP\n"
]
}
],
"source": [
"print('{} START'.format(time.strftime(TIME_FORMAT)))\n",
"from gensim.models import word2vec\n",
"\n",
"DEFAULT_charSegment = os.path.join(DATA_FOLDER, 'Charactersegment.txt')\n",
"DEFAULT_Char2Vec50 = os.path.join(DATA_FOLDER, 'Char2Vec50.bin')\n",
"\n",
"fd = codecs.open(DEFAULT_charSegment, 'r', 'utf-8')\n",
"\n",
"sentences = fd.readlines()\n",
"\n",
"fd.close\n",
"\n",
"model = word2vec.Word2Vec(sentences, size=50)\n",
"\n",
"#DEFAULT_Segment = os.path.join(DATA_FOLDER, 'wordsegment.txt')\n",
"model.save(DEFAULT_Char2Vec50)\n",
"\n",
"print('{} STOP'.format(time.strftime(TIME_FORMAT)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('最', 0.7617394924163818),\n",
" ('爱', 0.7001036405563354),\n",
" ('共', 0.6234053373336792),\n",
" ('赏', 0.5743197202682495),\n",
" (' ', 0.5637354850769043),\n",
" ('似', 0.560402512550354),\n",
" ('近', 0.5548217296600342),\n",
" ('谢', 0.5457607507705688),\n",
" ('伴', 0.5440549850463867),\n",
" ('待', 0.5435962677001953)]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.wv.most_similar([u'好'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 把汉字转成拼音"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pypinyin import pinyin"
]
},
{
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ajallow07/Cloud-Metric | .ipynb_checkpoints/OpenStack_start_vm-checkpoint.ipynb | 2 | 19584 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import swiftclient.client\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"config = {'username':os.environ['OS_USERNAME'],\n",
" 'api_key':os.environ['OS_PASSWORD'],\n",
" 'project_id':os.environ['OS_TENANT_NAME'],\n",
" 'auth_url':os.environ['OS_AUTH_URL'],\n",
" }\n",
" \n",
"from novaclient.client import Client\n",
"nc = Client('2',**config)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Client' object has no attribute 'image'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-4120a2f5248f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#conn = swiftclient.client.Connection(auth_version=2, **config)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Client' object has no attribute 'image'"
]
}
],
"source": [
"#conn = swiftclient.client.Connection(auth_version=2, **config)\n",
"nc.images.list()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "Unauthorized",
"evalue": "The request you have made requires authentication. (HTTP 401)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mUnauthorized\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-d005f3dd3ca5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#nc.images.list()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#nc.flavors.list()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mnc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeypairs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"AJ_Hadoop_Cluster\"\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;31m# with open(os.path.expanduser('~/.ssh/id_rsa.pub')) as fpubkey:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m#nc.keypairs.create(name=\"AJ_Hadoop_Cluster\", public_key=fpubkey.read())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/base.pyc\u001b[0m in \u001b[0;36mfindall\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0msearches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msearches\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'deleted'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m \u001b[0mlisting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mlist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 453\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend_request_ids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlisting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/api_versions.pyc\u001b[0m in \u001b[0;36msubstitution\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 368\u001b[0m obj.api_version.get_string(), name)\n\u001b[1;32m 369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0;31m# Let's share \"arguments\" with original method and substitution to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/v2/keypairs.pyc\u001b[0m in \u001b[0;36mlist\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0mGet\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mkeypairs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \"\"\"\n\u001b[0;32m--> 163\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/%s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeypair_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'keypairs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mapi_versions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"2.10\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/base.pyc\u001b[0m in \u001b[0;36m_list\u001b[0;34m(self, url, response_key, obj_class, body)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mobj_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cs_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'GET'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36m_cs_request\u001b[0;34m(self, url, method, **kwargs)\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_cs_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanagement_url\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauthenticate\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 409\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;31m# To get API version information, it is necessary to GET\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36mauthenticate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mauth_url\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth_system\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth_system\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'keystone'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 565\u001b[0;31m \u001b[0mauth_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_v2_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauth_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 566\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0mauth_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plugin_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauth_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36m_v2_auth\u001b[0;34m(self, url)\u001b[0m\n\u001b[1;32m 654\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tenantName'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprojectid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_authenticate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_authenticate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36m_authenticate\u001b[0;34m(self, url, body, **kwargs)\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_service_catalog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrespbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36m_time_request\u001b[0;34m(self, url, method, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_time_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 404\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/ajallow/Python/Cloud_Cost_Estimator/venv/lib/python2.7/site-packages/novaclient/client.pyc\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, url, method, **kwargs)\u001b[0m\n\u001b[1;32m 395\u001b[0m if resp.headers else None)\n\u001b[1;32m 396\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mUnauthorized\u001b[0m: The request you have made requires authentication. (HTTP 401)"
]
}
],
"source": [
"import time\n",
"#nc.keypairs.list()\n",
"#nc.images.list()\n",
"#nc.flavors.list()\n",
"if not nc.keypairs.findall(name=\"AJ_Hadoop_Cluster\"):\n",
" with open(os.path.expanduser('~/.ssh/id_rsa.pub')) as fpubkey:\n",
" nc.keypairs.create(name=\"AJ_Hadoop_Cluster\", public_key=fpubkey.read())\n",
"image = nc.images.find(name=\"AJ_Hadoop_Master\")\n",
"flavor = nc.flavors.find(name=\"m1.large\")\n",
"#user_data = open(os.path.expanduser('~/setup.sh'),\"r\")\n",
"#user-data to set-up the instance\n",
"instance =nc.servers.create(name='AJ_Hadoop_Slave_1', flavor=flavor, image=image, key_name=\"AJ_Hadoop_Cluster\") #,userdata=user_data)\n",
"status = instance.status\n",
"while status == 'BUILD':\n",
" time.sleep(5)\n",
"# Retrieve the instance again so the status field updates\n",
" instance = nc.servers.get(instance.id)\n",
" status = instance.status\n",
"print \"status: %s\" % status"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc.floating_ips.list()\n",
"ip = nc.floating_ips.create(pool=\"ext-net\")\n",
"nc.servers.find(name=\"AJ_Hadoop_Slave_1\").add_floating_ip(fip)\n",
"nc.servers.find(name=\"AJ_Hadoop_Slave_1\").addresses"
]
}
],
"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 |
linhbngo/cpsc-4770_6770 | 11-intro-to-hadoop-01.ipynb | 1 | 7534 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <center> Introduction to Hadoop MapReduce </center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python Jupyter notebook supports execution of Linux command inside the notebook cells. This is done by adding the **!** to the beginning of the command line. It should be noted that each command begins with a **!** will create a new bash shell and close this cell once the execution is done:\n",
"- Full path is required\n",
"- Temporary results and environmental variables will be lost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Upload data into Hadoop\n",
"\n",
"Upload the **text** directory into the newly created **intro-to-hadoop** directory. \n",
"\n",
"```\n",
"$ wget https://raw.githubusercontent.com/linhbngo/Distributed-and-Cluster-Computing/master/text/gutenberg-shakespeare.txt\n",
"$ hdfs dfs -put gutenberg-shakespeare.txt intro-to-hadoop/\n",
"$ hdfs dfs -ls intro-to-hadoop\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run a sample MapReduce program\n",
"\n",
"```\n",
"$ yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples-3.1.1.3.0.1.0-187.jar wordcount intro-to-hadoop/gutenberg-shakespeare.txt output-wordcount\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Output\n",
"\n",
"```\n",
"18/11/01 17:03:38 INFO client.RMProxy: Connecting to ResourceManager at clnode188.clemson.cloudlab.us/130.127.133.197:8050\n",
"18/11/01 17:03:39 INFO client.AHSProxy: Connecting to Application History server at clnode195.clemson.cloudlab.us/130.127.133.204:10200\n",
"18/11/01 17:03:39 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /user/lngo/.staging/job_1541104508981_0008\n",
"18/11/01 17:03:39 INFO input.FileInputFormat: Total input files to process : 1\n",
"18/11/01 17:03:39 INFO mapreduce.JobSubmitter: number of splits:1\n",
"18/11/01 17:03:39 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1541104508981_0008\n",
"18/11/01 17:03:39 INFO mapreduce.JobSubmitter: Executing with tokens: []\n",
"18/11/01 17:03:39 INFO conf.Configuration: found resource resource-types.xml at file:/etc/hadoop/3.0.1.0-187/0/resource-types.xml\n",
"18/11/01 17:03:40 INFO impl.YarnClientImpl: Submitted application application_1541104508981_0008\n",
"18/11/01 17:03:40 INFO mapreduce.Job: The url to track the job: http://clnode188.clemson.cloudlab.us:8088/proxy/application_1541104508981_0008/\n",
"18/11/01 17:03:40 INFO mapreduce.Job: Running job: job_1541104508981_0008\n",
"18/11/01 17:03:44 INFO mapreduce.Job: Job job_1541104508981_0008 running in uber mode : false\n",
"18/11/01 17:03:44 INFO mapreduce.Job: map 0% reduce 0%\n",
"18/11/01 17:03:50 INFO mapreduce.Job: map 100% reduce 0%\n",
"18/11/01 17:03:54 INFO mapreduce.Job: map 100% reduce 100%\n",
"18/11/01 17:03:54 INFO mapreduce.Job: Job job_1541104508981_0008 completed successfully\n",
"18/11/01 17:03:54 INFO mapreduce.Job: Counters: 53\n",
" File System Counters\n",
" FILE: Number of bytes read=973082\n",
" FILE: Number of bytes written=2409015\n",
" FILE: Number of read operations=0\n",
" FILE: Number of large read operations=0\n",
" FILE: Number of write operations=0\n",
" HDFS: Number of bytes read=5447902\n",
" HDFS: Number of bytes written=713504\n",
" HDFS: Number of read operations=8\n",
" HDFS: Number of large read operations=0\n",
" HDFS: Number of write operations=2\n",
" Job Counters\n",
" Launched map tasks=1\n",
" Launched reduce tasks=1\n",
" Data-local map tasks=1\n",
" Total time spent by all maps in occupied slots (ms)=282360\n",
" Total time spent by all reduces in occupied slots (ms)=266760\n",
" Total time spent by all map tasks (ms)=3620\n",
" Total time spent by all reduce tasks (ms)=1710\n",
" Total vcore-milliseconds taken by all map tasks=3620\n",
" Total vcore-milliseconds taken by all reduce tasks=1710\n",
" Total megabyte-milliseconds taken by all map tasks=289136640\n",
" Total megabyte-milliseconds taken by all reduce tasks=273162240\n",
" Map-Reduce Framework\n",
" Map input records=124213\n",
" Map output records=899681\n",
" Map output bytes=8529629\n",
" Map output materialized bytes=973082\n",
" Input split bytes=158\n",
" Combine input records=899681\n",
" Combine output records=67109\n",
" Reduce input groups=67109\n",
" Reduce shuffle bytes=973082\n",
" Reduce input records=67109\n",
" Reduce output records=67109\n",
" Spilled Records=134218\n",
" Shuffled Maps =1\n",
" Failed Shuffles=0\n",
" Merged Map outputs=1\n",
" GC time elapsed (ms)=705\n",
" CPU time spent (ms)=28770\n",
" Physical memory (bytes) snapshot=3245010944\n",
" Virtual memory (bytes) snapshot=210530725888\n",
" Total committed heap usage (bytes)=3762814976\n",
" Peak Map Physical memory (bytes)=2774241280\n",
" Peak Map Virtual memory (bytes)=70509170688\n",
" Peak Reduce Physical memory (bytes)=470769664\n",
" Peak Reduce Virtual memory (bytes)=140021555200\n",
" Shuffle Errors\n",
" BAD_ID=0\n",
" CONNECTION=0\n",
" IO_ERROR=0\n",
" WRONG_LENGTH=0\n",
" WRONG_MAP=0\n",
" WRONG_REDUCE=0\n",
" File Input Format Counters\n",
" Bytes Read=5447744\n",
" File Output Format Counters\n",
" Bytes Written=713504\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### View Output\n",
"\n",
"```\n",
"$ hdfs dfs -cat output-wordcount/part-r-00000\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The Hello World of Hadoop: Word Count\n",
"\n",
"[Example Source Code](https://github.com/apache/hadoop/blob/branch-2.7.3/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples/WordCount.java)"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
yabebalFantaye/learning | plotly_practise/.ipynb_checkpoints/reports-checkpoint.ipynb | 1 | 2502751 | null | mit |
teuben/astr288p | notebooks/04-plotting.ipynb | 1 | 5433 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to do inline plotting within a notebook, ipython needs a magic command, commands that start with the %"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importing some modules (libraries) and giving them short names such as **np** and **plt**. You will find that most users will use these common ones."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It might be tempting to import a module in a blank namespace, to make for \"more readable code\" like the following example:\n",
"```\n",
" from math import *\n",
" s2 = sqrt(2)\n",
"```\n",
"but the danger of this is that importing multiple modules in blank namespace can make some invisible, plus obfuscates the code where the function came from. So it is safer to stick to import where you get the module namespace (or a shorter alias):\n",
"```\n",
" import math\n",
" s2 = math.sqrt(2)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line plot\n",
"\n",
"The array $x$ will contain numbers from 0 to 9.5 in steps of 0.5. We then compute two arrays $y$ and $z$ as follows:\n",
"$$\n",
"y = {1\\over{10}}{x^2} \n",
"$$\n",
"and\n",
"$$\n",
"z = 3\\sqrt{x}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = 0.5*np.arange(20)\n",
"y = x*x*0.1\n",
"z = np.sqrt(x)*3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(x,y,'o-',label='y')\n",
"plt.plot(x,z,'*--',label='z')\n",
"plt.title(\"$x^2$ and $\\sqrt{x}$\")\n",
"#plt.legend(loc='best')\n",
"plt.legend()\n",
"plt.xlabel('X axis')\n",
"plt.ylabel('Y axis')\n",
"#plt.xscale('log')\n",
"#plt.yscale('log')\n",
"#plt.savefig('sample1.png')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.scatter(x,y,s=40.0,c='r',label='y')\n",
"plt.scatter(x,z,s=20.0,c='g',label='z')\n",
"plt.legend(loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi planel plots|\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"fig1 = fig.add_subplot(121)\n",
"fig1.scatter(x,z,s=20.0,c='g',label='z')\n",
"fig2 = fig.add_subplot(122)\n",
"fig2.scatter(x,y,s=40.0,c='r',label='y');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 100000\n",
"mean = 4.0\n",
"disp = 2.0\n",
"bins = 32\n",
"g = np.random.normal(mean,disp,n)\n",
"p = np.random.poisson(mean,n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gh=plt.hist(g,bins)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"ph=plt.hist(p,bins)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.hist([g,p],bins)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that in this example the output from the plt.hist() command was not captured in a variable, but instead send to the output. You can see it contains the values and edges of the bins it computed. Of course this is also documented!\n",
"http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lots examples of plots and corresponding code on matplotlib's gallery:\n",
"\n",
"http://matplotlib.org/gallery.html"
]
}
],
"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 |
EricChiquitoG/Simulacion2017 | Modulo1/.ipynb_checkpoints/Clase4_OsciladorArmonico-checkpoint.ipynb | 5 | 406949 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ¿Cómo se mueve un péndulo? \n",
"\n",
"> Se dice que un sistema cualquiera, mecánico, eléctrico, neumático, etc., es un oscilador armónico si, cuando se deja en libertad fuera de su posición de equilibrio, vuelve hacia ella describiendo oscilaciones sinusoidales, o sinusoidales amortiguadas en torno a dicha posición estable.\n",
"- https://es.wikipedia.org/wiki/Oscilador_armónico"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Referencias: \n",
" - http://matplotlib.org\n",
" - https://seaborn.pydata.org\n",
" - http://www.numpy.org\n",
" - http://ipywidgets.readthedocs.io/en/latest/index.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**En realidad esto es el estudio de oscilaciones. **\n",
"___\n",
" <div>\n",
"<img style=\"float: left; margin: 0px 0px 15px 15px;\" src=\"http://images.iop.org/objects/ccr/cern/51/3/17/CCast2_03_11.jpg\" width=\"300px\" height=\"100px\" />\n",
"\n",
"<img style=\"float: right; margin: 0px 0px 15px 15px;\" src=\"https://qph.ec.quoracdn.net/main-qimg-f7a6d0342e57b06d46506e136fb7d437-c\" width=\"225px\" height=\"50px\" />\n",
"\n",
" </div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/k5yTVHr6V14\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7fdfb8051fd0>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('k5yTVHr6V14')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Los sistemas mas sencillos a estudiar en oscilaciones son el sistema ` masa-resorte` y el `péndulo simple`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div>\n",
"<img style=\"float: left; margin: 0px 0px 15px 15px;\" src=\"https://upload.wikimedia.org/wikipedia/commons/7/76/Pendulum.jpg\" width=\"150px\" height=\"50px\" />\n",
"<img style=\"float: right; margin: 15px 15px 15px 15px;\" src=\"https://upload.wikimedia.org/wikipedia/ko/9/9f/Mass_spring.png\" width=\"200px\" height=\"100px\" />\n",
"</div>\n",
"\n",
"\\begin{align}\n",
"\\frac{d^2 x}{dt^2} + \\omega_{0}^2 x &= 0, \\quad \\omega_{0} = \\sqrt{\\frac{k}{m}}\\notag\\\\\n",
"\\frac{d^2 \\theta}{dt^2} + \\omega_{0}^{2}\\, \\theta &= 0, \\quad\\mbox{donde}\\quad \\omega_{0}^2 = \\frac{g}{l} \n",
"\\end{align} "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"## Sistema `masa-resorte`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La solución a este sistema `masa-resorte` se explica en términos de la segunda ley de Newton. Para este caso, si la masa permanece constante y solo consideramos la dirección en $x$. Entonces,\n",
"\\begin{equation}\n",
"F = m \\frac{d^2x}{dt^2}.\n",
"\\end{equation}\n",
"\n",
"¿Cuál es la fuerza? ** Ley de Hooke! **\n",
"\\begin{equation}\n",
"F = -k x, \\quad k > 0.\n",
"\\end{equation}\n",
"\n",
"Vemos que la fuerza se opone al desplazamiento y su intensidad es proporcional al mismo. Y $k$ es la constante elástica o recuperadora del resorte. \n",
"\n",
"Entonces, un modelo del sistema `masa-resorte` está descrito por la siguiente **ecuación diferencial**:\n",
"\n",
"\\begin{equation}\n",
"\\frac{d^2x}{dt^2} + \\frac{k}{m}x = 0,\n",
"\\end{equation}\n",
"\n",
"cuya solución se escribe como \n",
"\\begin{equation}\n",
"x(t) = A \\cos(\\omega_{o} t) + B \\sin(\\omega_{o} t)\n",
"\\end{equation}\n",
"Y su primera derivada (velocidad) sería \n",
"\\begin{equation}\n",
"\\frac{dx(t)}{dt} = \\omega_{0}[- A \\sin(\\omega_{0} t) + B\\cos(\\omega_{0}t)]\n",
"\\end{equation}\n",
"\n",
"<font color=red> Ver en el tablero que significa solución de la ecuación diferencial.</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **¿Cómo se ven las gráficas de $x$ vs $t$ y $\\frac{dx}{dt}$ vs $t$?** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Esta instrucción es para que las gráficas aparezcan dentro de este entorno._"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Esta es la librería con todas las instrucciones para realizar gráficos. _"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"label_size = 14\n",
"mpl.rcParams['xtick.labelsize'] = label_size \n",
"mpl.rcParams['ytick.labelsize'] = label_size "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" _Y esta es la librería con todas las funciones matemáticas necesarias._"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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EmRGzcJXTM8BiKWGenlsGVQkbaq67Dti7d1FbNZHYLXFISYxHj9FmZHKI3zkH\n3Chfao9LSoBt24DHHgt/fSFSmmUzXnKM2kIL02LrHGGDt5MSVBM6lMPG7bezWaU6eTWWQ1hiDKX0\nt5TSEkqphVK6jVL6uuq5mymlJX7fkyW+Spa6ttEpzzaYG0nt2hgdDVs4T856MDI5u/SQ18xMoKeH\ndZPXIetrTYYV/RMzmJ4zUCNtDaUnapoH/eIoClYr8MlPAn/4g4ZFhoYh3c7qZLgwBXTzoGvpPQaA\nf/onXfeYJccYbI/vvJNlcIaRPKewKObqz403Ao88Argjr/wNlx26Giifb51mmDo2Dq4NgFkoxRk2\nmExLxBNra4GZGXZ9HbK+4s0mrElPQutQ5P+ITlFRsfA4TOFMKUVLIHcowGKNP/2pbinmhswQzc5m\nrjTNAjrAHl95JXD06EK3H8GUOezGS4559VXgn/85rOQ5heYBV+Bhxfn5LMYdgW5I/kglKIAMWwLM\nJoJBl0GC3RxcG8AyrlCAmxUUCoYT0J/8JMuS1SCch9yzIIQgPdDstW9/mwkmnVLMy7IMZgk2NgJ9\nfWEVbqthMcEA97LFwiyVP/1J42KDo8xoTQl8PpbdrCF3AFCaEQTYYwD4zGd0zSMIhFSCgihzGMgl\n+te/MuWnQTgDQMuAG6WBbuoI1AwarlbwpZeAv/xFo3BmgoMEyt7VuZ6t3GE3VtLG/fcDH/84s4TD\nxOP1oWNkCiWZywjoiy8GfvYzXSzu0iyDJSC9+iqQlQVs2KDpMs7lLEGAHeCOHo1Iuzo1UgkKwlA1\nVkePApdfrkk4A8skbACLawaTknTJ+irPMpCA7ugAjh0DLrlE02VaBl2BrW1A93o2QzXSppRZKJ/6\nlKbLdIxMITvZgsR4c+AXfetbulncNkscUpPi0T02JewzQuK++zRbgQDgDBTbVujqYge58vKIdo+R\nSlAQ5UayBB98kJ2eNRIwYQNYyPqanGTupMREzZ+3EuXZBnLVPfggcPXV7HfXwKJmBEuhrmfLyxN+\n2ChIS8KAyyAJSPv2MW/Gtm2aLhOwBEWNzhZ3WZZB4oJuN/DEE6wnqwbGp+fgnvEgN2UZOVBbyxL1\nKI1o9xipBAVhGHfo8DDwxhts2oMGlISN0qwVhIfFwm7mRx7R9HnBoNQKGiIB6YEHNAsOgJ2el7UE\nlcPGn/4EnH668BTzOLMJRRlWYwhoxQoMs9GDwrIeDQWdLe5SI3SOcTrZ7zk2Blx0kSbLbEW3PsAt\nYU8rUglfTY0bAAAgAElEQVQKwjDu0MceY/ENjd1chldK2FDzsY8Bf/ubps8LhnRbAuLMBAORbqRd\nV8eGE3/4w5ovtWzChporrwRefpnNgxRMxBM3lCYEv/41c9VpdJs1D7hRvtIeKxY3AKxZI9zijvge\nA+zw2tXFxTJr7vfrGboUEew9rEYqQUEUZVjROz6NGU+E3UicXKFKZuiyJzuFXbvY+JWuLs2fuxJl\nkZzaoTQhWL+elYe0ta38nmUIKmFDIT0dOOcc4OmnNX1mMJTNl/xEDHUTAqdTs9ssKHeoYnHfcQeL\n8wq2uA3RNYajC9i5XB2mgjqPIC4OePLJsD9PC1IJCiLObEJBWlJkp0b39QH797OkGI2sGORWk5DA\nLJWHH9b8uStR7ohgoTGn/ooKnSNTcNhXSNhQc911uuxxxJs8cy6/CelevuYaNq1CcCck5tqPcPgk\nVzXpTqMLeMXYNrBw0PB4WHx7KjKJQVIJCqQk0xo5F4fTCWzZwvz727drdiEtmr0WDDq5RCNaK6iO\naVDKQTgH6QpVuPJKVpbhEis8yx22yE7sWLt24bFG4Twxn7CRkxxk4lZ5OSvsfuONsD8zGArTkyLf\nAWnTJqaMNJZSAUxeLBvbVkMISyqLUOG8VIICKZmv/4kItbUsTsUp84ql7q9wslNTWsq6/AuusyqN\nZDE35474IR80MjJY1w3BLtHSrAh3QPr3f2cJV5yEc0lmgK5Hgbj2WuGJXnFmEwrTk9A2FCHP0fQ0\n8PbbwPvvay6l8vko2oZDUIIAs7ilElx9sMncEbqpOad4L9stZimuvpp9ruA6K1ZoHKE93rOHCWeT\nSbNwBhQ3XQgHDQA4/3zg858XetiIeAek994DvvtdzcIZCMPaBhYEtGL1C4KVSUToQPfyy8wS5DCk\nvHd8GimJ8bBZQmhosGMHO7Tr1KpOjVSCAinNsqE1Um6kHNVMY41WijISpSTLGvybdKqzKsqwomtk\nCh6vWAG1JPHxgM3GkmI0CmdgviNPKAcNALj3XpYhKviwEbH2aZSymNxVV3G5XMtyPUMDUV3NZgzu\n3ctlDYGIaHLMY48BH/0ol0u1DrpREup9bDaz/+MIWINSCQqkJDOC7tDNm1ksg4MLqXt0Cpm2BFgT\nQjjZ6VRnlRhvhiPZgs6RCATV9+xhSUcaWnipCctKaWpaeCzwsFGaZY/MvVxXxw4ZW7dyuVzL0CRK\nQznMAcy67u9n2bgCXfsRK5PwellmJqeDhnPQjdJgMpz9iVBcUCpBgeSnJWHYPYupWZ2D3TMzzL9/\n6BAnF9IyPUMDoWNnk9IsG1oiIaCfeEJzEwIF94wHY1NzyE9dYkzVcug0qbs0yxoZ1/4TT7AEII0F\n8gqtg+7gSlDU1NayphOCO5uw8EkE7uN33mEH5tJSLpcLyxIE2GSQfft0m5CiIJWgQMwmgjUZVv1P\n0K+9xm6i7Gwul2OdYkK8qZX053vuAc4+W2idVUmWVX+38/g4Ex4ae4UqLDumajn27GEZjACXuGQg\nSiLl2n/8caYEOUApRWuosW1At84mEavHfOwxblYgALQOuUO3tgEWe9Uhj8AfqQQFU5IZAeGxZw/X\nG4glxYQYR1G44grghReAWXFJFRHZ4+eeY8pdYycehdYhd2gxV4WyMjZeaN06NpZG0GEjIq797m72\nu3HoxAOwMVVmM0GaNSG0N+rU2STLngCPl2J0UqcEJKXZw513suknnCyvlnAtQZ37tSpIJSiY0iyr\nvq46Spl/n5ObDgixuNif3FxW5yWwzoq5Q3V21T35JDcLBdDgQlLYvRt46ilu6/GnJMuGtqFJ/cb9\nOJ2svnV0lNW7chDQYblCgcWdTRIThVnbhBAUZ1n1y3ZWd+JpbeVycPb6KDpGplCcEcY+69yvVUEq\nQcGUZtn1tVKOHGFZizU13C7ZEm6gW6G2VmhMUHdX3dwc8MwznK3tSW17vHu30D22W+JgT4xD38S0\nsM9YRG0t0NPDNQ4XcpmPguLad7tZvCo1VfNaAqGrV0OAm1dJoktKCLLrkRrlsMEhmS8UpBIUDItX\n6WilPPkkExicEglmPF70T8ygMD3EhA01ihIUVGy9Jt2K3rFpzHp0KJNwOoHKStYm7eKLubmQmDtU\ngxLcsQNobwc6O7msZylKM3VM3BDgGmsdCtMSVEhMBC64gLnCBaFrcoza0uJkebWEa20Di9uocSg5\nChapBBW/uKCMpNIsnWp/lN/jhz9kgW5Ov0fH8CQK0pIQZ9Zwq2zaxKynujoua/InIc6E3NREdIzo\ncNiorWXKBuAavA8rYUNNXBxw2WVCu8foeqDLz194zElAtw6GWOu6FLt3i91jPWOvf/4z21uOllfr\nUBiZ5BFGKkHFLy4oIyknORHuGQ8mpue4XvcDqP37HR3cfo+WwUmUZGoUHISsHpco52bOABtAOjXn\nRXaytoG8ol2iurYB3LGDNXzgKKA1WSkKl1/OLEGPR/N6lkJX1359Pfu75Gh5aQ6dRACpBAWnP5tM\nBMWZVvE9AQX9Hq2DbhTzuKm3bwd+9CNxFnemToNf1bVU3CwUtsdBjalajksvBV5/HZgUc6+V6OUO\npRR48032u3AS0JRStGl1OQNAQQG7B95+W9t1AlAyfx/r0qf16ae5TJhRoznBKwJIJahDsbEuwkOA\nfx9Qan443NQ//zkblSLI4tbNSvn4x1kLLc4WSlh1Vf4MD7P9TU4WctDQLWnjyBEgKYnFXjkxMDED\nS7wZqUlBDIVeCYEu0QxbAiiAkUnBniOvF3j+ef5KMJyOPBFGKkH1BOnyciHuJF1cHI88wt2/D3BI\n2FBoaFh4LMDiZnusQ7xq3z7gT3/i6kJqG5rU7qYD2MFiaortr5CDhhXtwzqUSTz9NKsv5ZTcBSiu\nUE7C+YorhJWjEEL0SY7ZuxdYswYoLOR2SY/Xh67RKazJkEowuigrY1PQP/EJ4DvfEZKRVKbHTd3d\nDZx+OvfMqlatqfsKgi1uXTIX3W7mBrvoIq6X5eZCEhCvVGNNiEOaNR7dY4L7tD7zjAALhaObLiOD\n7a0g174uFrcAV2jnyBSyky2wxIVRHhFBpBJUuPxy9scngBI9elsKEBzTc14MuGaQnxbkANLl2LMH\nqKhgj6uquFvchelJGHAJHkr6j38AH/oQkJLC9bItWlP3FXQoNmYCWqDFPTzM3KGcusQoaK7DVHPl\nlcydGM2u/WeeYRYtR1p4hU50RipBhUsuYUJuZob7pXXpbSlACbYPT6IwXWN5hEJZGXOJbtwI/PGP\n3C3uOLMJBWlJ6BgWKKAF7DGgWIIcXEjqpuVZWUJc+6WiBfTzz7MZiYkcDl4quCTFKAhu78WalQvc\n485OlkF+5plcLxt2R54II0wJEkK+TAhpIYRME0IOEELOXeH1GwkhrxFCpgghXYSQHxLN6XIhkJXF\n+i+++Sb3SzvsFsx6fBgTFexuaQGGhoDTTuN7WRHpzgItbqGxFEqFKMGxyTnMenxw2DWWRwALxcZP\nPcWUoQDXvvD4tqCDRtjdYpZCsMVdkmkTl03udDJvxsgIq9/l6MqNxsxQQJASJIRcD+AuAD8BsBXA\n2wCeJYQUBXh9CoAXAfQBOB3ANwB8B8A3RawvIJddJkRAE0LEukSffZat3cT3v5NbeYSayy5j6xWA\n0ELjEycWJshzpGXeQuF63jv/fODgQdZ3kzNC99jrZTV4nJUgK4+Y5Ceg1cl0hYXcLW5lGLeQMgkB\n7egUwprVaABEWYLfBHAvpfQeSmkdpfRrAHoAfCnA6z8JwArgJkrpMUrp3wHcAeCbulqDl18uTkCL\nPEGLctOJuKnPOgtobgZ6e/leF4Jn3gnIWAQEnZ6tVuDcc4EXX+R7XQi0tpV2dIOD7KDE0ULpG5+B\nzRIHu4XP8ONTFvfttzMlwtniTrMmwGQiGHILmCYh0JUr3aHzEEISAGwD8ILfUy8AOCvA23YAeINS\nqk47ex5APoAS3msMyGmnMbdiSwv3S5dmCmqfNjXFiop37eJ+aSECOj4e2LlTSP9FIQcNpR3d977H\nTvycMwGFddgQZHEXZ1rROTIFL+8yidpaNskA4G+h8KrD9Edx7Quw2IQdmtUlERxdubMeH3rHp6Ou\nPAIQYwlmATCDuTbV9AHIDfCe3ACvV55bBCHkC4SQ/YSQ/QMDA1rWuhiTSZjwKBV1U7/2GrB5M5Ce\nzv3SmhsOB0JQXFCIq07djq6zk3smINfUfTXKfezj21Q8Md6MTFsCukc5l0kILO8Qdh9v2MB64qpr\nYDkhrAPShRcCDgf3euKOkUnkpSYinkcSnc5E34oBUErvppRup5RudzgcfC++bRs79XOuARKS9ux0\nAp/8JPDWW9zrlaZmvRh2zyI/TcP0iEBceilz1XHuv5ifloRh9yymZjmWSQhuqyesw0ZFBSvlOHyY\n+6VLRLhEy8sXHnNONhGWsEGIuAOdqCzct95a6H3KsZ64ZSA6XaGAGCU4CMALIMfv5zkAAgWCegO8\nXnlOP37zG8Dl4l4DpMRSuAa7a2tZXZWAIHfbsBtrMqwwmwSEZKemgOlpwGLhqrzNJoI1GVa0DXMU\nHoLa0SkIjaNEk4D+7GeZ0hYwS45rZqg/gpLpSkV0QGpqAsbH2ZBiznBrrxgBuCtBSuksgAMA/INU\nu8CyRJfiHQDnEkIS/V7fDaCV9xqXpalp4THHk3+6NR4EwDDPYHe0Brlra1k9poD2Xty7bTzxBBPM\nAoTziHsWPkqRYUvgds1FiHLti+jOs38/cNddQmbJCXOHAqx70N697ODMESH9hgVlkQOCDxqCEeUO\nvRPAzYSQzxFCagghd4ElufweAAghPyWEvKx6/V8BTAK4lxCygRByNYB/AXAn1aWdugpB7b2UnoBc\nT9BZWQuPOVspXEYoBUJg/KfMwTkBaXSUZS0KEM5Khw1hCdBr1jABLcK1z3OP5+aAl15iApozPh9F\n+zCHOYKBSE4GzjgDePnllV8bAiVZNrQNcfYcCcoiBwTGtnVAiBKklD4E4FYAtwE4DOAcAJdTStvm\nX5IHoFz1+jEwyy8fwH4AvwHwf8GUqb7s2bMg6Dif/EuybHAOcBQeZWVM0AmwUrh22PBHYLExd0tQ\nQHspBeEp5ddeyw4Z3F37VrTyLOZ+6y0Ww8zxj4hop2d8GqlJ8bAmcCqPWIoPfQj4zGe4HjZSk+Jh\niTdjYIJTB6vJSdYIREAWOcCxx3AEEJYYQyn9LaW0hFJqoZRuo5S+rnruZkppid/rj1JKz6OUJlJK\n8yilP9LdCgSYYmlsBIqKgIcf5nry55q9OD4OHD3KirhFWCki3Rvq9l45OZwPGpynnwtoNKwgvMOG\nIHf5mgwrukan4PFyyjwVaKHokrDxt7+xDiycDxvFPDNEX3mFJf2lpvK5ngquPYYjQFRmhwqHEHb6\n5zwzrMzBMdj94ous8Nxu53M9P4S6N5Ri47/+ldVmclTepVkc3aH9/UxxnHMOn+v5IbzDhiCL2xJn\nRk6KBZ0jnMokBFrbLUNulDkEK0GlvhHgetgo5XloFnjQaBuaxBpePYYjQHSuWg8EKEGuwW6lg4kA\nJmc9GJuaQ16K4JPdJZewQv8pfjVnOcmJcM94MDHNoU/rc8+xxIcEMYkrQtrSqVG39yoq4mtx87qX\n29rYYWP7du3XWgJdupgIyiNgpSgcDs2C+t4qRHNSDCCVYGAuuIB7/0VuwW6fT3CsahJFGVaYRJRH\nqElPB7ZuZa4aTphMBMWZVj4NiAUeNCilaB10o0yk8FAs7ttuA66/nrvFzUUJPvMMqxsVkLEI6CSg\n9+xhsXmAa2yeS4MNpR1dayu7Bzh3PAIEZ9/qgFSCgbBamRvsBf/ub+HDLdh98CCQlra4wJgjrUOC\nLRQ1AqZ0cxHQHg9zOQvIWASAQdcs4swEaVZB5RFqdu823h4r7ei+8hU2wkyAcAaYJShcCSpjwlJS\n2IGO02GDSzZ5be3C3gqYfQjMx12lJbhKEeIS5RDsFmihADq7N3bvZr8PxxwoLkrw7beB0lIgL4/P\novzQdY9PPx0YGODaE1dzwbzSjo5SNtVAgHD2eH3oHJ1CkahSHzUWC+uJy7EuU9ljn5Y+rQLLkRRa\nhgR7NAQjleByXHEF9/6LXFpOCXSFAkxA63ZT19QwV9jx49wuqbmOzekErr4aOHSIezs6BWahiElq\n+gAmE4sHcTzQlWkt9xHcjg4AOkemkJ1sgSXOzP3aS1JbyzXuarfEISUxHj3j0+FfpKJi4bGAjkdA\n9M4RVJBKcDl8PmBsjE0+4CQMSzM1zBV0OtlNvG8f8NWvCnMh6WqlKJm4HN11pVpnN9bWsmkiAtrR\nKThFTTYIBOc9LkhLwoBrBtNzYfZpFdyODohAwsbll7Oi/1l+XaFKs2xo0XLY+NznWEG/gFpiAHDP\neDA+PYdc0Ul0ApFKcDlqa1k3C47tvUodGqyU2lpWwwiwk7MA4QzMWymi08rVbNsG/Md/cCs21uwO\nFdiOTqFl0KWfJQgAF1/MitI5tfeKM5tQmJ6E9uEwE5AEtqNT0F0JZmczz8Zrr3G7ZJlD44Fu715h\n7egAtsclmTbxSXQCkUpwOQT401lHkzAFhw7+/bHJOcx4fHDYLdyvHZCf/5yVSXAqNs60JcDrpRid\nDPNELrAdnULr4KS+AjolhbX3euklbpfU1EN0cJDtqyDhDCwIaF3hnISkyRKcnmbJXbt3c1uPP9Ge\nGQpIJbg8AoqNS7JsaBsOM9hdWbnwWJQLSXQ/y6VQz2PjoNwJIdpir4WFrK5OkJXi89H5ZgQ6ukOd\nTuDYMRbr5OXa17LHTzwBXHWV5jUsR+uQzh4NYCEuyCnRi9Vjhmm9v/IKsHEjmx8oiGiPBwJSCS6P\nur2Xw8FFGNotcUhOjEdvOMHur38dsNkEu5Bc+he+Cig2DlsJ9vWxSSINDcKslO6xKaRbE8T2s/Sn\ntpZliHKMc2pKQHr8ceDKKzWvYTmcA279+1nabEBHB7c8gjKHhoPGk08CH/mIps9fiZZBwV2PdEAq\nweVQio2ffprV5PGq/wnXjfTOO8Add4h1IUWi5mfPHmDtWvZ47Vouyj3sQuM9e1jxtkWcO1h3Vygg\nJBuzLNwWdQ0NLOFMUJcYYKGfZWG6gKHQy/GRjzC3PifX/poMK7rHpjEXap9WSpkSFHzQ0D22LQCp\nBIPhwgtZo+pePvN9S7LCqBWcm2PKWLALyalneYRCWRkTGLt2AT/+MRflXpplRUs4XWMef1z4HrcM\nuvQ/aKhd+4Rws7bDOmg88QRTFoK6xABA+/AkCtMi0M+Sc9zeEmdGbkoiOkJNQDpwgPUVFhAyUdM6\nJHBMlU5IJRgMFgvrHPLEE1wuV5plD114vPYaiwkWFHBZQyAi2gfw6quBRx/lcqmwYikTE6yXqaAu\nMQoROWgorn0lI/PJJzVfMjclEePTc3DNeEJ74xNP6GChROg+FpRHEPShWenE86EPsTIfQWVUwHwS\n3ZxX3yQ6AUglGCwf/Sjw2GNcLsXmsYWoBB99lK1BIJRSllEXKSV41VWsEcCM9hlqzB06GVqf1uee\nA84+W8i4GTURSSZQXPtzc8y1PzSk+ZImEwl9fmN/P0vQufBCzZ+/HBG7jwXkEZSFogTVnXiGh4WV\nUQEsia5E7yQ6AUglGCyXXcZaaXFoqB1y0obPx9x0gpVg/8QMrAlmpCbFC/2cgOTmAhs2cJnSnWZN\nQLyZYNAVQpmEDq5QIMLWNiHANdcAjzzC5XIhZYg6ncCWLWwW5mmnCbVSdOkZuhTKYeOZZ1jbPS6u\n/RD2WB37pVRIGZVCxPaYM1IJBovdDpx/PpfWU8UZNnSMTMEbbJnEvn1s4oKSPCII54ABbuprruHn\nEg1WeDidwLp1bL7hnXcKFc5zXh+6x6ZRlBHBOMq11wJ//zuXNP6Q4oK1taxPqMBOPArOSAvoiy5i\nCUAdHZovFZISFDTWaSmifYSSglSCoXD22cCXvqS5s0lSghmZtgR0rTSUVPHvn3UWcyMJFM6AQW7q\nj36UxYw8IcaZlqA0WFed4kICWHmEQOHcMTyJ3JREJMRF8E9vyxb27+HDmi8VspWiIKjZg0LE69fi\n45lX4e9/13ypkPZ4zx42/9JkElZGpbAaCuUBqQRD43//lyVPcEh/LnPY0LxS4obavz80JFQ4AwZJ\ndy4uBkpKgDfe0HypoHuI6tCJR8EQBw1CmDXIwSUaUp/W7OyFxwKtFNd8P0vhQ6FX4rrrgIcf1nyZ\n/LQkDLtnMTUbRJ9Wt5vFIufmhJVRKUQ0f4AjUgmGQlPTwmONwrLcYV+5C7+O/n3AIALa6QTa25k7\nSWOxcdCuOh068SgYYo8B5nZ++GHNLtGQrJSCAjZ8VmCzB4BZgcUZBuhnedFF7G9Wo0vUbCIoyggy\nme6BB4CPf1xo+QmwkERniHtZI1IJhgJHf3u5w47mgRUsQR39+8B86r7ebab8qa1lfSU5xI1Kgx33\n88UvCu/Eo2AYwZGZyQ4YGjubKH1aR9wrJCC1tbF5hk1NQps9AAba4/h4VgrCy+Je6bBBKfDgg8An\nPqH581Zi0DWLOBNBhk2HodCCkUowFPbsWVBEFRWahGWZw4bm/hWU4COPMOWng3D2eH3oHJmKbMIG\nwLWzSZmDDSVdMQHphReA3/9euHAGDCSgOXU2IYSgNJhJBw88wKzPBPFCM+LxQDXnnAN8//ua8wiC\nUoLvvsv2V4n5CqR5wIVyR3R3ilGQSjAUysqAujrgppuAW27RJCzLHfaVW07V17OkGB2Ec9coG0Ca\nGK/TANJAqIuNle/DxJoQhyy7ZfkEpL4+NmJIcPmJgmGUIMc4aFC1gvffD3zyk2F/Rii06D2rcTn+\n67+4TEgJSgk+8ABwww0L3iOBSCUY69x0E3DvvZriKbkpiXDPeDA2NRf4RX/+M/ssHYh4SrmCurOJ\nycT2WQNlDtvybucHH2QuK5v4331q1oth9yzy03TuZ7kUHNuoreh2PnKE9Qo955ywPyMUDCWgOU1I\nWVEJejzAQw8xJagDzf1ulGcbQF5wQCrBcPjwh1nBr4YUc5OJzAuPAAJ6cBD4xz9YhpkOtBihRhBY\nKDb2eFhs4623NF1uxdjrX/4C3Hijps8IltYhN4oyrDBHOmEDWDhsmEysBlaDq70ie4U9vv9+9n8p\nOFkDYAkbzQNuVGQbRAlyaqNWutw0CaeThWf6+phHQ3ApFWCwg4ZGpBIMB5MJ+PSnmaWmgWUzRB94\ngA3DTEnR9BnBYhg3nZrPfhb40580WdzlywlopSn6BReEff1QcEZiQkcglMPGyAizuu3hC7Ryhx1N\nS8W3lTrXn/2M1cvpIJz7xmeQGG9CmtUgCRvKYQNg2bFhHjYcdgtmPT6MTS7hOaqtZYlHgPAmBApS\nCUqYEvzrX4HZMKeXYwUr5d57dXOFAgZVgh/+MOByAQcPhn2JcoftgwJaEc4bNrDp24oAEUxTv8s4\nFopCSgor6r7vvrAvUeawoX14Eh7/cT+1tSyGDrDMUB2Ec1O/C2VGEs7KYeNPfwI2bw47rk8I8xwt\nWVusYxMCgLn1ByYiMKZKEFIJhovJxAR0UlLYWV/l2UvEq5xO1uD44EHgG9/Q5fQMMCVYFulCeX9M\nJuAzn2ECJEwqHHY0+1vb6iYEIyO6CGcAaBpwodJoShAAPv954A9/CNviTow3IyclEW3+4350bEKg\n0DxgwIMGAFx/Pes93N4e9iUqsgNY3JmZC491KKVqGXSjONOq/5gqQXD/LQghFkLIrwghg4QQNyHk\nSUJI4Qrv+Twh5A1CyAghZJQQ8gohRJ8oerjU1jIrwucL2wVRlrWEO7S2lp2aASY0dBDQU7NeDLpm\nkJ8W4Q4bS3HBBax8IcwUc0eyBXMeH4bVdWwCBswGgyEtQQDYsYMJzzffDPsS5UuV/JSWLjzWQTgD\nbI8N6aazWllc9A9/CPsSFdn2D+6x4okqK9OllApYXa5QQIwl+AsA1wC4AcC5AFIAPEUIWS73/nwA\nDwG4EMAZAOoBPE8IqVzmPZGFwym3NMuGNn83UoROz6VZNmOe7G65he1DmCnmhBCUZ9sXJyCphbFO\nwtnro2gZNKjwIIQlVNTWhn3YqMi2o8nfq3HGGUBWlm7CGTCwJQiwe/mPf2QtzcKgItuORn8l+Pe/\ns/+v5mZdSqkAqQSXhRCSCuCfAHyHUvoipfQggBsBbAKwM9D7KKWfpJT+mlJ6iFJaD+BLACYAXMpz\nfVzhUM+WlGBGdrIFHeo6toyMhcc6CejG/gnjCg4O8Y4PxF6/+102KFlH4dw1MoUMawJsljjhnxUW\njzzCyhjCPGx8wFU3OMgmrhw9qptwBgxsbQPMGhwZARITwz9oqPeYUuAXv2BhEx1pHlg95REAf0tw\nG4B4AC8oP6CUdgCoA3BWCNdJAJAIYITr6njiX8/2m9+EdZkyh8pKGR9nAqOiQlcB3djnQmV2svDP\nCQsO9Wws9qpyO//5z8A99+grnAcmUG5U4Qxo7ovLyiRUe/y737EOMbm5nBa4MsqU+4g3zg6ExhBK\ncYYVfePTmJ6bb6S9dy9rrL97t4DFBqbZqC7nMOGtBHMBeAEM+v28b/65YPkxABeAJ5d6khDyBULI\nfkLI/oGBgbAWqhl1Pdv3v89qocKgXF3M/dvfApdeCjQ26iqgG/tdqMwx6E2trmeLj2eDb0NkUQr/\nu+8y19HHP855ocvT2GdgCwXQXM9W7rDD2e8CpZQJ+t/8BvjmNwUsNDDN/S6UOQzQODsQGkMdcWYT\nijOtaN9/jFmSZ5/NpkbolN0MAD4fa5wdc0qQEPJjQghd4et8HgsihHwDwC0ArqaUji/1Gkrp3ZTS\n7ZTS7Q6Hg8fHauPWW5k7KYzMr3KHHc39bnYz//d/Az/4gYAFLk9zv0GzFoHFh43t24H33gv5Eovc\noXfcAXz720yh6khTv4GtbWDhsEEIkJoasgcizZoAS7wJg0dOsv+z/n7W6EGn7GZg3k1nZOHsH0IJ\nY3gjIx8AAByqSURBVEh2RbYduZ+6biG7eWBAt+xmAOgem0KaNd64bv0wCNYS/AWAmhW+9gHoBWAG\nkOX3/pz555aFEHIrmBV4OaV0X5BrizyZmWw+25YtIScW1EwN4Ktfv5LVa01NsZILHZnxeNE5OoVi\now/HJAT4j/8AfvSjkAfuFmdaEdfaAl95BfDYY8zi1lE4A6w8wtCWoHLYGBxkgjqMocblDjus11yl\n2/R4f5r6XagwshJUh1CSksKqA65w2GFvdUYkuxmIgoNGGASlBCmlg5TSkyt8TQI4AGAOwC7lvfPl\nETUA3l7uMwgh3wTwfwBcQSkNP1c7Urz2Ggt6h5hYsPnLn0Z+bxu7md1uXYUGwGp+1qQnRXbSebBc\neCGzUoqLQzpsxJtN+OMj/wHibGY/aGjQdZ8ppcZO2FCTkcHcmP/6ryG/tSLbDquzceEHugtol7Hj\nrmqvxiuvAL/6FfubD4HybDtG0/StC1TD4oEGPzCHCFfJRykdA/BHAD8jhOwkhGwF8BcARwC8pLyO\nEPIyIeSnqu+/A+B2sMzSBkJI7vxXKs/1CaW5eeFxCH/85sYGmHUuiVBj6KQYfwhhbrbu7pAPG0X9\n7TgVKdJ5nwcmZhBvNkXP7LWPfAR49FFmsYTg1ThjoBk+Qrj0ygyH5mg5aACsfGTLFlZLGcKBrsri\nBWbngJISXZPnFAx/0AgDEcf/WwE8Blb39xZYgkstpdSrek05gDzV918Byyp9CECP6usuAesTQ5hZ\njCQ3F6f6dOgsNACDJ8UsRWfnwuNgldmJEwAhTEADuu+z4d10/lx/PTtkhJLFODWFnXd8F/dd+40F\nl5+OAnrW45t36xtkhFIwNDSwmF4IB7qKO3+M59fugKdJv7pANautRhAQoAQppTOU0q9RSjMppVZK\nae18mYT6NSWU0pv9vidLfN3sf33Dovb3m83Av/zLyu85fhyYnMRQdiF8Jv1PdUCUnZ6B0A8b09PA\nDTfg2Je+g7780oicnpui7fQcShaj0ofVZkPiQC+eKNy64PLTUUC3DblRkJYES1yE52GGgtryC2af\nS0oQ94d7cFbHUXQdPC5+fUsQszFBSRCo/f333st6XgZyczidQE0Na+CclIRn7/wz7thzVPdTHWDw\nQvml8K/PHBpaep8V4Wy1Aq2tMF//MXz+e/dG5PTMXM5RtMf+WYzJyYH7iipNsikFcbvxX/fdhvHp\n8DqiaCEqLRT/fa6oCPzaXbtOlUIUDnYi84ZrBS/ug4xNzWFyxoOcFIvuny0SqQRF8JOfLLT6qqsD\nLrmECWRFWF94IXN/AEBvL6764ZeWn8cmiDmvD61Dk9ElPNSHjeJiNkNNcSep97m6mrlBKQVcLlTf\n8ik4B9zw+cIfyxQuUZMUo6A+aFRXs4J3h4Pta2Ul+4qLYwe5eQUIAMTnQ+lw59JNngXT1O+Kvi4m\n6n1OS2M9gxUZ8eqrC/dyXt6iA56ZUlhbmgNfVxBKPJDoMLleT1ZPsYeRULuTKGXdOAhhj0+cWPxa\nnw/2tmY09OkvONqGJpGXmojE+ChyIalRFwn7fIv32etd9Jy5oQE2Sxx6x6d1n+xu+PIIf5SDhkJ1\nNbO4gcWdZU6eZJYMIWz/TSYM5Jegqd+F04rSdV1yU78LZ1f4V2YZHPU+r1+/cKCoqwN27mR7Simb\neRkffypO6yMm9OcXh9R9hAerrVOMgrQEReDffQP4oDvJL4Ouf2IarpnQa7O00NQ/EV1uOn/844PA\n0m67+T1e1J1HJxQXUl6qQVt5BYNa8S2Fymp88fZ7IuLVMNQ0+XDwPzh7vYvvZa/31D7PVFTits/+\ndOnrCKR5wI0yo80c5YBUgiLwdydVVCxWehUVi54ne/agMjsZ9b0Tui6TuemipDxiKdT7XFOzeJ8J\nARISFiXCVGTbdbe4mZsuyl1I/rErBZOJ7a0qEcaxueaD434E4/PR6E/d9z84JyQs/l61z3NHjuJt\nmqK7a7++dxxVuVEsLwIglaAI1HGr48eB559frBSff/4DGXTVuforwcZoi1X5s9w+K/GqRXucgvre\nJTvxCaM52sojlkJ92KioWLbBe8DBrwLpHZ+G3RKHlER9W+Fxxf/g7C8zVPuckhiP5MQ4dI9NLXNB\n/tT1TKAmL0XXz9QDGRPUA/8YyxJU5SbjpM4CurHPhc+eXbryC6OFFfa5Ji8FD73XEfB5ETT2T6Ai\nmuowlyKI+1ehONOG7jE26UCvWHNDX5RlOC/FUnu8zJ4rh43CdH3qIkfcs3DPeFCYrm88XQ+kJWgQ\navJScLJHP0vQ66NwDka5CylEqnOT0dg/sXiIsWCirlBeI/FmE0oyrbpag3U9E1i3Ci2U5ahw6Gtx\nn+ydQFVucnS79QMglaBBUCxBGqgeizOdI5PItFlgX0Xd4FfCZolDTkoiWodC69eohajLDOXAurwU\n1PXo59U40TO+Kt10y1GRk6zzQWP17rFUggYhy25BQpwZPWPTunxe1NWucaImNwUndLK4x6fnMOSa\nNf6EDs6sy0/BCR2VYF3PONblr04BHYjKbDsa+vTzHJ3sHUd13upLigGkEjQUNXn6JcdEfVJMmNTo\naKXUdbNsOrNRh7wKYl1eKk5067PH03NedI5EWcMHDtTkpaC+dwJenTJEV2tSDCCVoKGoyklGnU7J\nMfW9E6jKWZ0nu+WoyUvGSZ2U4PHu8ZiLVQFsj0/06OPar++dQFmWPTpGgXEkNSkeGfYEtAyKd+17\nvD409q9eeRFbd47BqdYxOeZY1xjWF8SigE5BnU57fKJnHOvzo2caGC8y7RZYE8zoHBGfwh+L8UCF\n9XmpON49JvxzWocmkZOSuKqmyauRStBA6FUrODnrQcfIZPTMEeRIYXoS3DMejLhnhX/W8e5xrI+x\nWJWCXskxJ7pjLx6osD4/RRe3c13POGpyV+8eSyVoICqy7WgdcmPG4135xRqo65lAZXZyzLmQAIAQ\nguo88W7nGY8XLYOuVdlhIxj0So5hWYuxucfrC1JwXAcluJqTYgCpBA1FYrwZazKsaO4X6+c/3j2G\nDTHoClWozhXvEm3sc6Eowxq9zck1okdyjM9HcbI39moEFTbkM3eo6Njrak6KAaQSNBzVucmo7xMr\nPI51jcVkrEpBjwzRE92xGQ9U0MMS7BiZREpiHNKsCUI/x6hkpyTCbDIJL6s6Kd2hEj2pzk0Wnhxz\nrGscGwpiV0DX5IlvUXe8eyxm44EAUJxhxYh7FmNT4gbsxnI8UGF9vliX6NjkHManV2e7NAWpBA1G\ndW4KTgpMjpnxeOEcdKE6RmNVAOvO09TvEto+LVbLIxRMJsK6IAm0BldzF5NgYUpQXIZo3fzkCNMq\nrnWVStBgVAu2Uhp6XSjJtMVsrAoArAlxyEtNglNQjdWpWFWMWymiXaInemL7oAEA6/NThVqCJ3vG\nV/2BWSpBg1GQloTJGa+wFP5j3bEdD1SoyUsWFhdsG55EalJ8zMaqFEQnx5zolpag6DKJ1Z4UA0gl\naDgIYW4kUSn8x7piOzNUoUZghmisxwMVRFqCo5OzGJ/2oChDn1FCRqUow4rxqTlhh+aTvau/BEUq\nQQOyoSAVRzrF+PmPdcd2UoyCyAxRmbDBqMpJRvOAC7Me/rHXEz2rP1YVDCYTQU2emOQYr4+ioc+F\nqlWcGQpIJWhItpek40DbCPfrznl9aOhd/e6NYKgW6A49HuPlEQpJCWYUpCWheYD/yJ9YnCEYiHWC\nkmNaBl1wJK/+cWtSCRqQbcXpONg2wr0Itqnfhby0xFV/UwdDQVoSPD6KnjH+/S1juV2aP+vyU4Uc\nNqS1vYCoMokDbSPYVpzO/bpGQypBA5KXmgRLnAltQ5Ncr3usawwbpIUCgMVetxen471WvhZ3//g0\nPD4f8lITuV43WlmXJyZx43j3mPRozLM+X0wj7fdaR7C9RCpBSYQ4rZi/S/R497hMilFxekkG9rcO\nc73m8R5mBRIS27EqhU2FqTjUMcr1mmNTc+gYnpTu0Hkqc+zoGp3C5KyH63X3tw5je3EG12saEe5K\nkBBiIYT8ihAySAhxE0KeJIQUhvD+GwghlBDyFO+1RRPbitNxoJ2vEpSW4GJOL83gbgke65QlKGq2\nFqWhrmccU7P8msIfaBvG5jVpMdkAfinizSZUZidzzXYemJjByOQcKmNg8LaIu+gXAK4BcAOAcwGk\nAHiKELJidTYhpAzAzwG8IWBdUcW24nQc4CigvT6KuhidbxeI9fkpaB9yc23t9W7LME4vWf2n52Cx\nJsShOjcZhzge6N5tGcaHSuUeq9m8JhUHOXqODrQNY1txekxk33JVgoSQVAD/BOA7lNIXKaUHAdwI\nYBOAnSu8Nx7AAwB+AMDJc13RSE1eCjpGJrkJ6OYBFzLsCUi1xnO53mog3mzCpsI0HOQkoGc8Xhxq\nH5EC2o8zyjKxt4Wf2/ld5zDOKM3kdr3VwI6yLLzjHOJ2vViJBwL8LcFtAOIBvKD8gFLaAaAOwFkr\nvPc/AbRSSv/MeU1RSbzZhI0FqTjMKZ7ydtMgdpRJweHP6aX84oJHOsdQ5rAjNUkeNNScUZqBdzkJ\naPeMB/W9E9halMblequFM8sy8F7LMLd+uLESDwT4K8FcAF4Ag34/75t/bkkIIRcD+BiAWzivJ6rZ\nxjE55q3mIZxdkcXlWquJ00vS8V4Lnz1+p3kIZ5bFhuAIhW3F6TjaNYbpOe1xwUPto1ifnxLTvW+X\nItNuQUF6Eo52ac8SnZz1oKHPhU2FsRE6CUoJEkJ+PJ+sstzX+eEsgBDiAHAvgJsopUGZPYSQLxBC\n9hNC9g8MDITzsVGBUi+oFY/Xh73OIZxVLpWgP1uL0nGsewwzHu0C+p3mIewol9a2P8mJ8ajItnPp\ngrSvZUi6mwNwZlkmF5fo4Y5RrIuhg0awluAvANSs8LUPQC8AMwB/aZsz/9xSrAeQB+BlQoiHEOIB\n8GkAl89/X+X/Bkrp3ZTS7ZTS7Q6HI8hfIfo4rSgdhztG4fVpK5o/0jWGgrQkOJItnFa2erBb4lDu\nsOOYxhP09JwX73eOyqSYAPByicqkmMCcVZ6Jd5q17/H+GIoHAkEqQUrpIKX05ApfkwAOAJgDsEt5\n73x5RA2AtwNc/j0AGwFsUX09CZYhugVAS7i/XLSTbktATooF9RrnC77dNChdocuwvSQd+zS6RA+1\nj6IyJxnJiTIeuBRnlGbiXY3JMTMeL452jcVEF5NwOKM0EwfbRjT3an0vhuKBAOeYIKV0DMAfAfyM\nELKTELIVwF8AHAHwkvI6QsjLhJCfzr/HTSk9pv4CMApgYv57Me3RowQe9YJvNg3iHKkEA8KjaH6v\nc0gmHi3D6SUZONQ+gjkNiRtHOsdQ7rDLg0YAUq3xKHPY8X5n+Ml0Xh/F4fbRmDpoiKgTvBXAYwAe\nAvAWABeAWkqpOuhSDuYClayA1rjg5KwHRzrHpAtpGbaXpGN/2wh8GtzO7zhlUsxypFrjUZRp0xQX\n3CddoSuyozwTbzeF7xI92TuOnNREZNhiZxYmdyVIKZ2hlH6NUppJKbVSSmvnyyTUrymhlN68zDVu\nppTu5r22aGRbcTr2tQyH3Uz7vdYRbMhPhU02zQ5IdnIi0q3xaOwPb9rB9JwXx7rGZDxwBc4ozcC7\nLeEL6HdbhnGGVILLsqM8E+84/ZPzg2d/6whOj6F4ICB7hxqecocdZhMJu0u8jAcGx/aSDLwXpkv0\nQNsIqnOT5UFjBc4sy8C+MOOCHq8PB9tG5EFjBU4vycCRzvDLUfa1DmNbDMUDAakEDQ8hBLWb87Dn\nSHdY73+zaRBnV8hY1UrsKMvEq/XhldvI0ojgOL0kAwdaR8Iq6D7ePY6CtCSkx5CbLhzsFtamLpwQ\nytSsF280DOC8ytg6NEslGAXs3pSPp97vCdklOuyeRfvQJDavkd01VuLi9Tl4t2UIw+7Q87D2Oodw\npkyKWZFMuwV5aYlhFXS/Wj8gDxpBsqM8E2+HUSrxUl0fNq9JQ3ZKbI0Bk0owCqjOTUZSgjnkkTTv\nNLPC4niz/G9eieTEeFxQlY2nQrS4J6bncKJnPKZSyrVw+cY8PHqwK6T3+HwUfz/YgatPKxC0qtXF\nWeVZeLs59Ljg44e68NGtsbfHUjpGAYQQ7N6Uhz3vhyag35TxwJC4+rQCPBKigH78UBc+vNaBpITY\n6K6hleu2r8GT73eHNFppr3MItoQ4bCyIjTZeWtlWnI6WQTc6hoMfyj3kmsG+1mFcsj5gd8tVi1SC\nUcLuTfl45mhP0N1jJmc9eP54L3bW5Ahe2erhnIosdI9OoXkguCxRSinue6cNN+4oFryy1UNBWhK2\nFqXhmaM9Qb/nof0d+Nj2NXJQcZAkxptx7bZC/GVvW9DvefpoDy6szo7J5C6pBKOEimw7MmyWoDMY\nH97fiQ+VZKAo0yp4ZauHOLMJV27Ox2NBWoN7ncOggCySD5GPn16EB99rD+q1Y5Nz+MfJ/ph002nh\nxjNL8PD+jqAt7scOdeGqGN1jqQSjiN2b8oKKWXl9FH9404nPn1emw6pWF1efVojHDnUFVTj/l72t\nuPHMYmmhhMhFNdloGZxEUxB1mU++34Xz1jpkVmiIFGX+//buPUaq8g7j+PcB1EVYrEIBcblo8BIE\nxGWt1oBF46XWUpWiUoxK0ooNhqht2qq1VRO1/FFtTbRt0BppU6vWGtRYjBqLVBFwF1GuInd05Q7C\ncod9+8cZYFh3WALLHM68zyeZDHPeyeY3D7P7m/Oed845nv7dT2T8jKY/0C3JTZ0OjPTQiZtghgzu\n24UJM1c0ucT8jVkr6FheFtWpj5pLry7tKC9rxbQm9rhXbtzGe5+t8WKNQ3BMyxYM7V/BCwexN/hC\n9XJuqOpahKpKzy0X9mDc5CVNrip/ZUYt3+/bhVaRLqCL81VnVLf2x1NxYusDXi4lhMDYSQsZ6b3A\nQzak8pQmp0Sfm7qMH/Tr4vNYHqJh53Xl5elfHPASVrNrv2Jd3Q4v7jpEA3p2YOfu+gOeuDyEwPgZ\nca4K3cNNMGNuPL87YybMK3hGiA+XrGfjtl1eEHMYru53Cm/MXsGWHbsaHd+5u55/TlvGTRf0KG5h\nJaRHhzac0amct+esKvicFz9cztCqrrRs4enmQyFp795gIVMWrUMQzQV0G+MmmDHXVVXQs2Nb7n15\nZqPTHGMnLeTHA071H47D0KldGVf27syof0xv9LI0b85eSY8ObTizc3kK1ZWOYd/qyl/fW9ToB7oF\nqzbxyse1XNe/IoXKSseQygomL1xL7YatXxtbunYzdzz/EXdfeVbUx7XdBDNGEmOG9GXuik1f+4Q3\necEaZizfwFD/4ThsD13Tm2NbtuDOFz7a7xjsO/NW8uBrsxk50NPNh+t7fU7m5BNac+PTU1lbt33v\n9pql6xk2dir3XdWLrid5dfPhaHtcK4b2r+BnL87Y73uDqzdt5+ZnpnHnpWdweYTfDcynQ706wdGi\nqqoqVFdXp11G0S1bu4Uhf36fJ4dXEoAn3lnA4jWb+e3gXlF+4fVI2L5rNz8ZV03H8jIevPpsHn59\nLpPmr+bR68/xadKaSX194LG35vPqx7U8M6KKpWu38IuXPuHR68/h4jM7pl1eSdi5u56xkxbx9P8W\nMfqS0/lhZQXDn57C5b06c8elp6dd3hEjqSaEUNXk89wEs+vd+asZ+bdqOp9Qxu2DenJt5Sk+RVoz\n27JjF7c8M415X27iit6duX9wLy+GOQJeqvmcR/4zlxYST93cn3O7eWVzc1u0uo67X57JnNqNXN2v\nCw9d07ukp0HdBCOxYFUdPdofH+3y5mLYtG0nc2o3cr73/o6omZ9/RbvWrejevk3apZSs+vrAlEVr\nOf+09iW/buBgm2B858gpMT07tk27hJJXXnaMG2AR9Il4hWKxtGghLvRXTvbj3QczM4uWm6CZmUXL\nTdDMzKLlJmhmZtFyEzQzs2i5CZqZWbTcBM3MLFpugmZmFq3MnzFG0mpgaTP8qA7Ammb4OaXI2RTm\nbApzNoU5m8KaK5vuIYRvNvWkzDfB5iKp+mBOsRMjZ1OYsynM2RTmbAordjaeDjUzs2i5CZqZWbTc\nBPcZm3YBRzFnU5izKczZFOZsCitqNj4maGZm0fKeoJmZRctN0MzMohV9E5Q0StJiSdsk1UgamHZN\naZB0kaRXJX0hKUga0WBckh6QVCtpq6SJks5OqdyikXSPpA8lbZS0WtJrkno3eE6s2dwu6ZNcNhsl\nfSDpqrzxKHNpTO59FCQ9kbctynxyrzk0uK3IGy9qLlE3QUk3AI8DjwDnApOBCZK6pVpYOtoCs4A7\ngK2NjP8S+DkwGjgPWAW8Jam8aBWmYxDwJ+BC4BJgF/C2pJPynhNrNp8DvwIqgSrgHWC8pL658Vhz\n2Y+kC4CRwCcNhmLO51Pg5Lxbn7yx4uYSQoj2BkwFnmqw7TPgd2nXlnIudcCIvMcCvgR+nbetNbAJ\nuC3teoucTVtgNzDY2TSazzrgNuey9zWfACwELgYmAk/E/r4BHgBmFRgrei7R7glKOhboD7zZYOhN\nkk/9ts+pQGfysgohbAUmEV9W5SQzKOtzj50NIKmlpGEkHxIm41z2GAu8FEL4b4PtsedzWm66c7Gk\n5yWdltte9FyibYIk56drCaxssH0lyX+C7bMnD2eVTJ/PAD7IPY46G0l9JNUB24G/ANeGEGYSeS4A\nkm4FegL3NTIccz5TgRHAd4FbSV7vZEntSSGXVkfih5qVIkmPAQOAASGE3WnXc5T4FOhHMu03FBgn\naVCqFR0FJJ1JstZgQAhhZ9r1HE1CCBPyH0v6AFgM3AJMKXY9Me8JriE5ttOpwfZOwIqvPz1qe/KI\nNitJfwB+BFwSQliUNxR1NiGEHSGEBSGEmhDCPSR7yXcReS7At0lmm2ZL2iVpF/AdYFTu32tzz4s1\nn71CCJuB2cDppPC+ibYJhhB2ADXAZQ2GLiM5pmH7LCZ5A+7NSlIZMJAIspL0OPsa4LwGw1Fn04gW\nwHE4l/EkKx775d2qgedz/55P3PnslXvdZ5EsiCn6+yb26dDHgL9Lmga8D/wU6EJybCMqktqSHL+A\n5A9ZN0n9gHUhhGWS/gjcK2keyS/wfSSrSJ9LpeAikfQkcBNwDbBe0p7jEnUhhLoQQog4mzHA68By\nkgVDw0m+UnJVzLkAhBA2ABvyt0naTPL7NCv3OMp8JP0eeA1YBnQEfgO0Acal8r5Je7ls2jdgFLCE\n5MB+DXBR2jWllMMgIDRyezY3LpKlzV8C24B3gd5p112EXBrLJAAP5D0n1myeJbmg9XaS73K9DVwR\ney4HyGsiua9IxJwPyd5wLbAD+AL4N9ArrVx8Am0zM4tWtMcEzczM3ATNzCxaboJmZhYtN0EzM4uW\nm6CZmUXLTdDMzKLlJmhmZtFyEzQrAZLuyl2he3jatZhliZugWWnon7uvSbUKs4zxGWPMSoCkuUAF\n0C74l9rsoHlP0CzDJI2RFEjOwt8WqM9NiwZJN6VcntlRL/arSJhl3XRgHMkFSScDb+WNTUyjILMs\ncRM0y7AQwouSvkHSBMeFEMamXZNZlng61Cz7KnP301OtwiyD3ATNsq8S2AnMTLsQs6xxEzTLMEmt\ngD7AnBDC9rTrMcsaN0GzbOsFlOGpULND4iZolm39cvcfpVqFWUa5CZplW/vc/cZUqzDLKH9Fwizb\n9pwm7WFJvYHNwOwQwr9SrMksM7wnaJZhIYRJwGiS5jcauJ99U6Rm1gSfO9TMzKLlPUEzM4uWm6CZ\nmUXLTdDMzKLlJmhmZtFyEzQzs2i5CZqZWbTcBM3MLFpugmZmFi03QTMzi5aboJmZRev/tVrWgjzJ\nQnoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf953a30f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Definición de funciones a graficar\n",
"A, B, w0 = .5, .1, .5 # Parámetros\n",
"t = np.linspace(0, 50, 100) # Creamos vector de tiempo de 0 a 50 con 100 puntos\n",
"x = A*np.cos(w0*t)+B*np.sin(w0*t) # Función de posición\n",
"dx = w0*(-A*np.sin(w0*t)+B*np.cos(w0*t)) # Función de velocidad\n",
"\n",
"# Gráfico\n",
"plt.figure(figsize = (7, 4)) # Ventana de gráfica con tamaño\n",
"plt.plot(t, x, '-', lw = 1, ms = 4, \n",
" label = '$x(t)$') # Explicación\n",
"plt.plot(t, dx, 'ro-', lw = 1, ms = 4,\n",
" label = r'$\\dot{x(t)}$') \n",
"plt.xlabel('$t$', fontsize = 20) # Etiqueta eje x\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Iy8CBRG+8UfzH1q3ssp0gqhF88GCiyy/XYQT/8Ueijh0Tvp8jnD7NTk/nzsU8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\nJUnNsglKkpplE5QkNcsmKElq1t/lE8z8cE2viAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf92ca78d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Colores, etiquetas y otros formatos\n",
"\n",
"plt.figure(figsize = (7, 4))\n",
"plt.scatter(t, x, lw = 0, c = 'red',\n",
" label = '$x(t)$') # Gráfica con puntos\n",
"plt.plot(t, x, 'r-', lw = 1) # Grafica normal\n",
"plt.scatter(t, dx, lw = 0, c = 'b',\n",
" label = r'$\\frac{dx}{dt}$') # Con la r, los backslash se tratan como un literal, no como un escape\n",
"plt.plot(t, dx, 'b-', lw = 1)\n",
"plt.xlabel('$t$', fontsize = 20)\n",
"plt.legend(loc = 'best') # Leyenda con las etiquetas de las gráficas\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y si consideramos un conjunto de frecuencias de oscilación, entonces "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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T97fuD95DsHkttJVDXAbMWz3iZo7aOgAyN6z3vTAEEL1wIc6jR6lcdWXg5f1U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IhzuT0zbXWrSeM73vhRzef75R8klMT9y1Nvs799mEk1whOfO5e73reOk3zeOYb8Otx1fqSqEu6e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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf92a2c8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"frecuencias = np.array([.1, .2 , .5, .6]) # Vector de diferentes frecuencias\n",
"plt.figure(figsize = (7, 4)) # Ventana de gráfica con tamaño\n",
"\n",
"# Graficamos para cada frecuencia\n",
"for w0 in frecuencias:\n",
" x = A*np.cos(w0*t)+B*np.sin(w0*t)\n",
" plt.plot(t, x, '*-')\n",
"plt.xlabel('$t$', fontsize = 16) # Etiqueta eje x\n",
"plt.ylabel('$x(t)$', fontsize = 16) # Etiqueta eje y\n",
"plt.title('Oscilaciones', fontsize = 16) # Título de la gráfica\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estos colores, son el default de `matplotlib`, sin embargo existe otra librería dedicada, entre otras cosas, a la presentación de gráficos."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set(style='ticks', palette='Set2')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ohatkdXmXi+iz2t57GL2B7FVjYoVhi8WGlW0zMa1PuAnoCzSCFElelYsRLEec\nU+xbAQSRNc5pwWP/exATmmur9oJbipdK2rbRimjPCLhQDHSBHkDOenNKelBEqTBaOLtF8lylP68E\n6oK54LM00c21WIgNBkFb9WCs8tNeapxjkWQvJHd4F/jtjwjFJkDKhIXOzkXo6fWi64QLFAVF3qlk\niv19xc4lDO7NygWWrtEK6GjEFLaGqARubxhxlseMKU6svKK90odDIEhUrcCSwz333INvf/vboGll\ngbiNGzfiwQcf1Oio8hPqGkL40CAAgIIwaw/7+hBqrlVsuE0xvW9/BDi8ayytonCUjlxEA/zd72zD\n2WB2kcWBh/10FPq3TyAUNICihIu9Tka37+TVeoCmEKaEistiDav1tUaYjDqcS0Q9qhE1xpToEwIr\nPhws2ENLLWEkCoVks/zSBRNkCwiuezf4PVuB4bPCE5Y6IBwAHC05FwdcKAbOH4FhsrJ+UlqNguH3\n/SX784liE44Tigdu/vSFsNcV5wkq9vc1ZnBXLrAomgJTZ0R8KIhzD7wFndNSXFFAGegfEvxpLQoi\nmgRCOahagTVu3DicO3dOejwwMIBx48albHPw4EF861vfAgCMjIzg73//O3Q6Ha66KtMPkcz69eux\nfv36lOd6e3uxfPlylY4+N/m8ICVdvAZPZX1ajarCbKxsm5mzwvDCYQs+eyxp1AsPeF86CoAq+DOm\nrNYpCiN6Gi1RDk0KTboiFEWhpdGKnl4vAqFYVXZ4Ns9oRGBvL+IDAYCmoHOac44SyoWuUfBhxQYD\nBQVWZ7sDwVBUqgCUO4Io17462x3Yf3gAf9t7BhaTvPObtXIw6JPaMeQilriZ6puU3UwzRsHQFGyr\nVBgFU8A/dm44AJORQX1t/rmS+RB/L//33lmM+CIwGhgsXzyp4O8rdm4UdI0BTI3yzw51DY0Z3JOa\n2AKoOpHVl1g8tTYqF5IEgpZUrQdr9uzZOHnyJM6cOYNoNIqtW7di2bJlKdu8/vrr0n8rVqzAT37y\nk4LiqtKo5QXJwJXD8KpiVWEy+SoMr+jLng6UYyhOX5W7dcK+F7YU16cJAFoahJtxfxVHsfgIK4wS\n+tZlso3byegSgiOeECCFsJgFwbp0/gRFI4hy0Zw4x+eG5X2+ksrBZMSfT4n/SkQaBTPRBnCFh0TL\nIo9/LBiOwTsaQXODteRWB53tDnzpUxdCr6NhNesL/r7Y0Si40WhR0StAvaKActA/FIBOR6s6D5NA\nUIOqFVj6AjZAAAAgAElEQVQ6nQ4//vGPccstt2DVqlVYuXIlpk2bhi1btmDLli2VPryiydUokasr\nfoULoGxVhcnkqjBsDGaPYsgRkZ3tDtQkhhbTFBBL+EyaqeL/VFsSK9t+meKj3PAxFqwnDF2Dpegb\nsc5hARhKivAUYiAhhMY1qJNWaXRYQFOUbIFVbJuR+GBCYCmMYCWjaxC+g7kWO0rIV2winotmlc4x\nTVMY57TA7Q0XnEsoGtyVNBhNRrOFoMpEonG4PCE0Oy2g6eru10X46FG1KUIAWLp0KZYuXZry3Oc/\n//ms2953333lOKSSyeUFGRxfgxwSSRbUwlWZKRdoW1WYTHLKcMgSQ3MwM6UXtRX+c4vFWQRCMYxv\nqsHnVnaC9YYxtGmfZNgtBvEG1y/35l9m4olUjN5Z/I2YoinJyM1zPKgCN5tzwwFQFDDOkV3wK0Wf\niCAMuoJgWQ4MU0AQ25uF4ox0CiwIYkMBgKEEQVkkusTfQ3w4KHUtL5aUYhNXnzBVwVwLatol6D8w\nAABoaVAvdTXOaUXvwCgGXEG05WgeGuoagi/RtDb0/gAYu1l5RFTFogAtEUVsC0kPEqqQqo1gfVgR\nexcxNiFiRVn02Fmnx9ESfxMp5eMJqPmfUNXgno/klOGO1uyVRztaC48oGXKHwPNAUyLSF+3zARQQ\nOerC8GP7lTeGBGA0MHDWmzEwHJBMx9WEeCMTIyvFom+yAnEObJaKs2Q4jseAOwiHzQy9Xn7z10K0\nNFrBcjyGCnw+AMAxLuvT+RYEPMcjPhwUIn0lRCvEKHI2AVEM4hgd5l8eAjV3ORDyg3v4uzi3bw8A\noGmkW5XPAcYWCwM5Fgti01o+0bSW9clrWptOsU1sy40YlSb+K0I1UtURrA8r5hmN0LfWYnjTPhgn\n1cMdDsM/MIoNm/cVXcoNjFUV8sNnwW3+Mfh3XwP7zl81admQDbHC8PbAFjyHYaw55gQFYMASw45W\nHw7WBvFWUguHbON1BlyJ1JXTmrhZHJVeK8Vo29JohcsTgssTQqNKURu1UEtgib6k2FAgZyoaAFze\nEOJxDs0lfl46zQ1W/OPIEM4NB/KmxfigHzh1GDCagRoHMHIub+WgSNwdBFge+hKrxdRMEWaQSNXz\nAQ/O2RthY70wvfI0OL061bxiSlf8nqSjVhGNuK3v1ePgw3EwDjNqLptYtQZ3UkFIqEaIwKoQTJ0R\n0NEY7ffDn8imJY/BAJTNc0tG6inECqtYrVo25KLFYsPpWj9oUHjfGcBz01zSa8lDogFkiKyBxE1v\nnNOCwP92Zd1/MRWXLY1WHPxgGP1DgeoTWImbZT5RJAdRYMUHA0Bn7vMzkBB0zSWkJLNRyOgutGXY\nlmjLwAMzLwPzia/K3v+Y/6q0aAVt0oG2GjQRWPx7bwAAPLQNEdqEyZHTwvMqVfPaagwwGRmcyxF9\nU9M7ZZ7RCNYXweibJ1G7dDJMF1RXM2ee59E/FICtxghLFVYHEwgkRVghKIqCzmEG5Q0LyiqNUibB\nF1uhpRYr22aiISRc8IbNsZzbpXd9B4SVuV5Hw15nUvVmEU7MM3z17VPY/OIhxZ3htSQ+HBTmxVlK\nu0mIkZ1ClYTnVDa4izhsJhj0dFaBJbVlGO4FkPh7P7xL6nouh3iRLRqyoWuwgPNFwEXjJe8rhYR5\n/5xOSIE2x4Wed2pV81IUhXFOK7yjEYTCmceeS6QX653SJQatV5u5vbvHjf/+00FEoizC0XhVfZ8J\nBBEisCqIzmkBwwPWLL6gkibBl2EQdD4WNE3GtbXThEMx576B9QVTPVmxGAu3N4wmh1ARpNbNorvH\njR3vjHUKVzIsV2u4KAvWGyk5PQhAEGk1hoKVhAOuABiaQqPKZe3izd/tDSOSJlzUEP2xweJbNKQj\npQmHVRYOiRThgK4JANAcF4zualbzipHHbGlCtb1T4neNdWuQTi0ScZzWiC8CAIhE2ar5PhMIyRCB\nVUHE1WFdPFNglTQJvgItG9JpjQo/262XXZVzSDQNCrfv2IK739mGvYMnMTgSBM+PRVbUulmoNdxY\nC8QblxoCCxDEB+ePggtljxzGWQ5DIyE02M2FK/2KQDJhp0cfSxT9oa4hRE8JkwPcWw4UVeyQjGR0\nz+FlKhZq4SocMVyAQ6ZOgOfxqvVjOGK4QNVq3nGJv5VsAss8o3GsvxclNKCVMwA8F4zNBNCUVOla\nDVTz95lASIYIrAoiXuTr2EyBVcok+EoMgk5H7AKts5tzDomO81yKJ2vvSSHKNC5xXsSKS7FLOWiq\nqJuFWsONtSAmGtxL9F+JiNV1g7/ZnbXqcsgdBMfxqvVmSodlOQDAc389mpqKLUH0i5VxYmZRLHYo\nRWSNRbDUjcwcNU7D9tqrEaf0AEXBpXNie+3VOGqcptpnjHOKXrccx574G2i8fWFRTWuToRgaTL0J\ncXcIfBYrQyWo5u8zgZAMEVgVhElEsC5srIHRIJTL2+uMJU+CT2nZIDbotDeXrWUDIFR8MTYjKD2T\n0fVdl6Np6JH+YQBjNxBgrPu2fkIdwPMwTVNutHXWZ0+FlRQlVAm1KggBQYhEjicEDZ8pRLp73Hjx\njeMAgONnPKqnVLp73NjfNSg9Tk7FUvOyT1iQI/q16Co+FsFSNzKTM7ry+l6wm3+iyHOWi1qrAVaz\nPmclITsSAmVgSvb0iegcZvDhOLhgbj9lOanm7zOBkAwRWBVEZzcDFFAT43DpRcIK/9KLx5c8tgRI\n7c2D9jnAyDnwZz8oeb9y4MJxcIEYmKRqveSu71yOlTAXoGHQ07Bn6Wqvc5gBHmC9ylepuaKBpUQJ\n1WJMYJUeUconRETfSiCROhwNxlT3reRN3UQS0RZLHUAzQMOEgnMHRbToKk6bdKBrDKpHsHJGV5j6\nsWpeFUSW1azDaDCGDZv3pUQKeY5HfCQkFNCUOJ5HRGzqWqi/Wrmo5u8zgZAMadNQQSgdDcZmAusO\nSXO0hkeCgAoCKxl64SpwPQfAPf8rIB7VvC9WXPQVObKvNFssNpwNelKeozgKhpgBIUsY+4ZOZbRv\n0CXOT9wdUpxOEwXrnvf7MTwSgo6h8PEl7aoI2VKJu4KgrQbQptK/ivmESD7xo9Z5yJ26CYHv/yug\nN4K++eegTMrEpFZdxXVOC6KnPOAicdBGdS6FKcPKk3CwI9K/S23Z0N3jxmAiBZ/e2uUChwVgeTAq\nFjAwzrFKQsOE4meCqkVnuwMDwwG8c3gAFABnCcPKCQQtIRGsCqNzmsEFY3AmbrCyumArhPcnohSx\nsDDKQ8WVdDZEQ2wuIZTuyaodrcXk3kmgQIEP03j2nfexd/BkyjbiDaPYVXRnuwNrV89KVM5R6Jhs\nL2o/asJF4uB86lQQAvlL9MvhW8mZumFHgIAH0OnBnzyoeL9adRXXouForijK/NC7Yw9KrObNJ5Yl\n72OOxU0xSK0aqqiS0GQULBWrl12gyrByAkELiMCqMGIaTR+Iw2rWZ139lkq5+2KxrvwX+WRPVu1o\nLVoHW2CIC91WdZwOrYMteOXg8ZT3SBf5Es9Pg92MOMvB44+UtJ9SCXUNwbX5PQBAbHC05Ko4IL8Q\nKYdvJae4COwT/hEaLUrYm2c0grLqAQoATZVcGSeihdG9Y7Ideh0NmqZAg0NDfBif8L+CjuixsY1K\nrObNJ5bF74eaESwpRVhFlYTidbJB5VYjBIKakBRhhRHTHHF3EA12M071+RCOxmEyqPirKXNfLHGl\ny+RZRYtjdf5ty86sr1MDqTd+xmYCKJRcLj6Wig3BXlcZU6xUFZeAD8WLHgGUTMZ4E7sJNUsmwTyj\nEQtNjJRGSkZN34oYRdj17ll4/BGYEMHH/G+migsoT5HxHA8+FIe+uRbOf75IteNl/VEAgO8vxxB8\npw/WxW0li7ZgOI5YnMPUtnpc2+oCv+2PGduUWs2bMw1Zb9IkgkWbdKAt+qpqNjo8EoJBT6POmjlU\nnkCoFkgEq8KIq8O4KyQ1flQ9ilXmvlhxdwhU4qJcCEM0+wXSEDWk9MiidDSYOlPJRtuGeuF8D+eI\nApQDLariRMwzGlGzSBj4Xbu0XRIMne0OTEn4ZyhKEJqlVqtmo7PdgS+ungUAcMZcGeIKgGJhz/oi\nAMeDsasniENdQwjsOi09VqP1AzAWXWqwm7NX806YXrL3MZ/JW4sIFiAsllhvGHycU3W/xRBnObh9\nYTjr1TPyEwhaQARWhRF9M6w7iAZ74uavssAqZ18snuXAesKyq5istdkjdRFDNLVH1uBJMA4zuEAM\nXKT48SZiBMtVwYooLarikinkV7v1hos09a3odTTq64xw6ZzIWi+qUNiLP4cuR5qzGLQSuVLqKnGs\nYjUv/c3fA/ZmoO84+FFPvl0UpLPdgVVXToEp0dqlPqm1CzsSAl1jAJ14TS2khWAVVBKOeMPgeZIe\nJFQ/RGBVmOTwu1YRrNSVdEL0XHiFJlWErCcMcLzsFMXH5k7K+ry7PrV9wPbew9AlIhjsSPHG7BqL\nHkY9U9EIltrz4tJhEr6qeJqB3eUJwWLSwWLSfjCu02ZGmDIiSGX+TEqFPZv4OdSMYGklcsW/q3TP\nG0XRoOZdDXAsuCf+P7Ab1pXUF6uz3YFLLx4PALj0olZ0tjvAxxJjlzQQHmMjcyovsIYkEVtdQ9sJ\nhHSIwKoCGKcQfq+36EFTFIZG1K/WkVbS634h9CHqP65JZ2bxxpXcAysfne0OzJ8lDMalAEQMEfQ1\n9cNf40/Zri/olSIzpayiKYpCg92MEV8YcbYy6Q6tquJEdPWiEB07T9EYC+9oNKfZXW0aaoQIisva\nJgh7hb2vktHEuK2RyB0eCYGmqay93KBLCNuQX5VqXmfi9ywuyOKSENVAYFVRJeFwUhqWQKhmiMm9\nGkhElVwP7sYn9TQORVnwPK+Jv4CqsQPN7UDfMXC/Wgc4x6vaE2usRYP8i58p0YPo2n+aiqeH/g/+\noD9jG47n8YzrfXwS1pJX0Q12M84OjsLtCaNJpRE1SjDPaAQXisH/mmA61zVaYF1UusFahNIzQhPN\npAiWO9GgtVwCy+E9DqAersmXof3jV5S0LzGCpVOx4tG6uC2l0EB6vgSRy/M8XB6heCLbnEf+nb9m\nf1+RfbHENKTo+5JSqSoa3EUYhzad74thOLEAJQKLUO2QCFaFCXUNIXbaKzzggdooh8UjUbjf06bC\nj+veDfQljMc8r2pPrFDXEAJ7hHmC/r+dlG0YTr7555pbCABHKMG7MtBfmhFZFBnDnsqtxsUWAdbF\nE0qeF5cNxm4C54tIpmTJfK2xwOK6d4N9/MdwHt4OAHDHSk9Hxj2JogmzeqlNcc4lnYg00TWGkls/\n+AJRxOJc7nOscjWvxayH2aiDKyFApUifBgIr1ucDAIS7hrLOuCwnwyMhWBM/O4FQzRCBVWFymW3D\ne89q8nla9cQSWw/wERaAsJqWW5Xl8oTA0BRsNcaUHlnp+AwsYhSP4PBoScfaoFW1pgIkX5FG89NE\nQ7g4WmjMG6Rdawquezf4bZsA11nUs17QPIvh/uGSxDvP8ULRhAbHbZ7RCPunZgAATNOdJYtcl9Sb\nKcexalDN66w3wTsaQSzOpgxYV5NQ1xC8W49Kj9WquCyGcCSO0WCMRK8I5wVEYFWYXGZb2qdRI0yN\nemIVW5XF8zxc3jAcNhNoWkiJinMLaaSmSHkKcJtiqAtSJfnHGqQIVgUFVkL4MDZtBI9oCBfThK4c\n5ms1SRbpDDjYWQ9cOge43cWLd84fUX30SzKiwC1mxmU6uQzuIlpU84qf5faGhfQ8BTC2LP6vEtCy\nrYhSXMR/RTiPIAKrwuQy23oYKmWIq2po1BOr2Kos32gU8TiX9abUYsmce+Yyx2Fkafzr63+QemQp\nxWTUocaiTdd8uYz5irS5UUgRrMTP6PKEYTXrJb+bJqSJdyfrRowywO/N9NTJJa5xpI826UCZdBkV\nl8UwXCANm1LNm4Ba/sWS/I/OJB9WfCQEpt4EKov/qxS0biuiBPE720gEFuE8gAisCpOrouywRScN\ncVVTZGnVE6vYqiyXN3fqKpsfy2WKAQAcYV1KjyylNNjNGA3GEC6hp1YpsJ4wwFCga7TpRC1FZjxh\nRKIs/IGo5v6rdPHuZIW/W5dtatG7lIzbGt5QGZsJrDdSclWtayQEHUPDVps7giRW81LL/1l4IlS8\n+ASSIlhDAfChuCYGd63biihhTMSSFg2E6ocIrAojmm0pvfCr8DAUdtbpcco01igw13DXYsjaXdrW\nUHIVYbGtB/KlrpL9WDRFQUfRGDYJgsgZHovEbO89rPh4K50mjHvCYGwmULQ2nailFOFIKK+IVRNq\n3lUpj53xhMBqW1D0PrWOYAGJ6sQ4B240WvQ+WI6D2xuGs94kq/qXmrEYMJjAH/g7eI4t+nMb6k2Y\nFGYxfrcQPYz2+VX3RmndVkQu3T1uHDrmAgC8/Ja6C08CQQtIGUYVYJ7RiOhpD0IHBvCWTQ+fLlX3\nulVIXyRDdy6SysLZFzYCx98Df+4kqObJRe/TPKMR4WMuRLqHAUqokpPTekCsgHLacg+GXtAkHNft\nO7bAFhWE5+oTDiw6V4sdrT4cpryKjzeWqK774/YjcNrNWDi7RbPO5ulw4Tj4cBy61lrNPoM2CA1s\nWU9YMl87tU6rxIXoIiw2IDwKZ50QnXPrG4reZVkiWEk+LCZP9CkfHl8ELMfLjhJSBjPQPBU4fQjc\nr28tvl1KjwdLfDHpoVpzLZMR9zO66zRYdwiUkUHd1ReoXvmaj+4ed8osTZcnLD0u1/eWQFAKiWBV\nCaLZuYbNTFM4NFy903M+BgDg//FG6ftKDKh23jxXdusBqYJQxo3tcr8Ty3qF6kIKFJqDBnz2WAMu\n9zkVHWd3jxv/OCKs8nlAk1RsPrQ2uIswdqGBrSvRHFJTgzvPg//H3wCaAf3Fn4D55ibYv/gd6Bha\nEtHFwHrCoAwMKLN2a0Hx91CKD0uKxMoUglz3buD0IeFBCe1SymVAN89oRMOX5wE0BZ3DUlZxBeSO\n4qsZ3ScQ1IYIrCohn8DKNdxVFSbPAsy14A/tLHl8hyQc6uQJB57n4faGYU+qIMzHFX2ZpncAmHMy\ndTB0ISp9sda6RYMIU28CeCAwFACQO0qoCn3HANdZUNPmgbIKvyeapmA16zDoDmLD5n2KizZ4nhdS\nqXZ5abdiSfarFUN3jxuv7xYGR7/XPSjrZ1SrXUo5DegUTYGpM6pScakUV45UvtrRfQJBTUiKsEoQ\nBdaFLXUYhuDnMOoZLL90kqYhcP7I3jGjbfJKGlCcrmC9YdAWvexBs2JjRrmRFYMnlvV5R5BJGQwN\nQEorZqPSF+ty+IoASLMbo+4Qaq0GGFUeAAwkel/t2QYMCw1mUT9Oeq27xw1vwtfE82ORQkBeWocb\njQJxTtP0IFCawEpPXflGo/J+RpXapeicFsSHM0WWVgZ0xmZC9JQHXJRVfaB0Ppz15qxVv1pG9wmE\nUiERrCpB7F1j44EvfWoWdDoadTUGzf0Faq2keY4H64soSntJaRWZ78lVzTRkThVehUzvuQRduS7W\nbOLn1lpgDcQE87Q+US2pdgpUaiwqiisA/O6XpAhoqZFCqTO5xtWPTK0RoKmiBFbRP6NK7VLKbUAX\nv9/ljmLliuJrGt0nEEqECKwqgbboQelpsN4IKIqCo86EEV/ppeMFUWklzfkjAMcrEg2SwV3mDTTX\nzWRHqy/lcV8wv+m90hdrqQeWhh6s7h43dhwVfGY1LI9YnFPdZ1ZInJcaKZRSqbk6o6tEKamvYn9G\ntdqlmGc0wju3GYDgJ/QbGQQWjdfMI8XUCwvBcgusznaHtBCjKaHNyqorpxCDO6GqqWqB9eabb2LF\nihW4+uqrsWnTpozXX3zxRVx77bW49tprceONN6K7u7sCR6kOFEUl+vGEwfM87HUmxFkO/kDxpeOy\nUGklHZeM2/KrsNwKu4tL8+MSvaP8Rg5/vGAYBxtSUyQcz+f1Y3W2O7DqyimosQiz7Wos+rJerFlv\nGLTVAEqvXYplz/v98DOCb6k2ydenqs+sgDgvJVIY6hqC/82TAIDArjOaj2Vh6k3ggjFwUWV90Yr9\nGbO2S3GOV5yW7+5xY8cpQTQfNzH4s02PF3pcmhVsjEWwNJo0kYdwlEWt1YBvrp2PtatnEXFFqHqq\nVmCxLIu7774bDz/8MLZu3YqXXnoJx44dS9lmwoQJePLJJ/HnP/8Zt99+O+66664KHa06MPUm8FEW\nfDgOR+JC5tZ4pajWSlq84MpNEXb3uHHkpHATeOlvx2XfEMwzGlH38QuEz5xVnyGuRAo1IRVFFgB0\nTnGW7WLNs5yQStU4PejyhNAa5cABaI1yWOmKYFKYVddnVkCcFxsplOZahgSxw3rDms++G/NhKRMO\npURDxaajzL88BIyfBrjOgvco+xn3vN8vFcaMMlTK81pQqRRhNMYiEIrBIbOAhkB46qmnsGzZMsye\nPRtr1qzBvn378m6/d+9e3HbbbbjiiivQ0dGB559/vuRjqFqBdeDAAUyaNAltbW0wGAz45Cc/idde\ney1lm3nz5sFmEyqWLr74Ypw7d64Sh6oa0sXLEx4TWD5tL2SpK+nEBXrW5UUZ3AF5Aks0BscTN4Zh\nj7I2CWJqrSVuzjkYWiSfH0u8WI+U8WbB+iIAr73/ahbNYIkvBhoABcDO8ljii2Emrd5XvpA4F0Us\nk6gQlZvWqcTsO50ksJRV33W2O9DRbgcgfH2KTV1Rs68EAPAHdyh6n8sTgjXxPQokCSytCjZ0FRJY\n4kLTrnFrE8KHg23btuHee+/Fbbfdhj/96U+YO3cu1q1bh76+HFF3AMFgENOnT8cPf/hDmEzq/J1V\nbRXhwMAAmpubpcfjxo3DgQMHcm7/3HPP4corr5S1740bN+LBBx8s+RjVRurH4w3D0WQFUJ6bv9h4\nlB8dAffQd4Bh5TcyyS8j4wKYzxgs58ZE14k+kAgWNE3DgqbJuH3HFnDI9Kvl82OZTToYDYzmUcJk\nxmYQaiywgtlTXbmeLwaqfTZ4OinN6WjJaJbZ2e7A0ZNuHDvtwZqrpqHGUng0UCVm35USmdExwjlY\nu3pW0b3GqGmXgH91M/i9L4Pd+zLgbJXVeNRZb4bVL3gQkyNYWhVsUGYdKD1TdEuLYhlJLDQdRGAV\nZO/gSbx85jD6g160WGxY2TYzb1W1mtx7773Yt28fnnvuOdBpi7k1a9bgkksuwQ9/+EPNj+Oxxx7D\nddddhxtuuAEAcNddd2HHjh3YsmUL7rzzzqzvWbp0KZYuXQoA+P73v6/KcVStwFLC22+/jeeeew5P\nP/20rO3Xr1+P9evXpzzX29uL5cuXa3F4shH9S6w3jPoLBKFRzps/VWMHpswROrsPngLVNEn2e1lv\nGKAApq6wB6tU8zNtYIQu5UnnpsViw9mgJ2Pb1iwDo0UoioLDZsLAcBAsx4FRMbqTCyVCtBQYX/ZU\nF+NXz9PHH9kDcCyoJWtAL/pkzu3Em+KILyxLYJW79QAwVqlYTLPREV8YFAVZzXJzwR9/b6wTPiC7\nXcrC2S3wnRQWEYGkXnJaFWwIXlGjNLtRy/5kyYjXQSKw8rN38KTUqgaA7NY1anDixAk8+eSTePTR\nRzPEFQBMnToVXV1divb5u9/9Dr///e/zbvPQQw9h/vz50uNoNIpDhw7hK1/5Ssp2S5Yswbvvvqvo\n80ulagXWuHHjUlJ+AwMDGDduXMZ23d3d+NGPfoSHHnoIdru9nIeoOsk+EL2OQZ3VUFaBBQD0hVeA\nO/4euOf+A4iEZK+kWW8ETJ1R1mw9NXraMDYjYgMB6SK/sm1myoVFxB8L4/YdW3Ku5Ox1JvQPBeDz\nR8uSfihbD6wyiBT+/R0ARYGaeVne7exSKjaCtua8mwIQqkXFcS8pz2s4+06qjitGYHnDqKsxQscU\nL9DzVmTm+e51tjvQazGAjYYRplGW3nlMvQnx4SD4UBxUolBEa8RIvv0j5sF67sS72D98Wvb2nmj2\n6O9jR9/G/578h6x9zGuYiM9MmSv7M0UeffRRdHZ2YvHixVlft9lsUhbqjTfewH333Qee57Fu3Tp8\n9rOfzfqeG2+8EStXrsz7uem6YGRkBCzLoqEhdUSX0+nErl2Z9wgtqVoP1uzZs3Hy5EmcOXMG0WgU\nW7duxbJly1K26evrw/r163H//fejvb29QkeqHmIHdDbJbxAIxRCJFj8MVil8NCF8wgGA52SN8OBj\nLLhAVHZURo02CYzNBHC8NKA3fTC0lREiJd5oOKUJabrpvVzFBCKSV01jgaVlfySuezfYR78PDJwE\n9EbwZ4/m3d6eFMGSg3lGIyyXJAz0FKBrtMB2TYem41mSZzcqIRSJIxSJl26+LqFdij4Uh8Fuhk7H\noK5W+955yVaGcuH2haHX0VLlLyE7bI62PizPafq5HMfhL3/5C1asWCE9d++99+KJJ56QHgcCAZjN\nZsTjcdx3333YvHkznn/+eTz00EMYGRnJut/6+npMmjQp739q+aW0oGojWDqdDj/+8Y9xyy23gGVZ\nXH/99Zg2bRq2bNkCAPj85z+P3/zmN/B4PPjpT38KAGAYRhXnf6VIT305bCac6vNhxBdGc4O1LMfA\n792e/fk8K2k2kY6SKxo62x3Yc6Afw54QaApw1CsftpwsRsUBvcmDoe9+ZxsCwcx02PbewylRLHFF\n7PaFMVX2pxcP6wmB0tOgNb5RmGc04viZEegPDsHG8qCMOtRdPbVkkSI1FxWJhgumspLPsVyYWkEg\n139qBkzTlM2aLBbGZkJsYBQ8x8uKxAJJkZVSo5/O1pSGrRIF2qVwEWFwONNSi3oDpN55mo4WSvar\ntWg3sFyE53mM+MJw2sxlS0lWC5+ZMldRNOnud7ZltUpMsNbjrnnKKsOV0NvbC5/Ph+nTp0vPvfzy\ny/jGN74hPT5y5AimTp2KAwcO4IILLpAiT1deeSV27tyJa665JmO/xaQI7XY7GIbB8PBwynYulwuN\njfBIFqoAACAASURBVOWdoVm1AgtINZ2JfP7zn5f+fc899+Cee+4p92FpSvJFXlwVu73lE1jFrKSL\nGV4cjbGwmvW49YaLFB2eyJhfLQJMyHy9P4e5Pd307lAYXSmFUNcg4kNCCN/13+/CurhN06hMr0WP\nbocBNw5FoGtUZ0BvMakss1EHs1GnqGBDavshw9OnGhQAjsfAL3dC57TI+v1I5usSI1jUwlWpwjXp\n+XyMtUcxwqGnMDwSwmgwhlprYa9bsSR7RcuBLxAFy/KkglAGuawSn5gwU9PP9XqF66rFIkzb2L17\nNwYHB6HXCwvJkydPoqurC1/72tcwODiYktZrbm7GwMBA1v0WkyI0GAyYNWsWdu3alfLeXbt24eMf\n/7jyH64EqlpgfRRh6k2I9fvB+SPSBcXt1a56KoMiVtJxhcbtWJyDLxBFW3Pxq99CVV9yTe+2WiMo\nSvtqTaG301gqLT4clHxGWomsEV8YoGnQNQYpylgyRaay7DYT+odGwbIcGBleJdZXnmIAkVDXEGJ9\n4kxO+b8fUWDZFTTYzQbduQgcEkJ1+CwAHtSln5LhfRybCmDXUdIxaSuwiusZViwjxOAuGzE6v733\nMPqCXrRabPjEBO2rCFtbW0HTNF566SXU1dXh5z//Of7pn/4Jf/vb39DZ2Ymf/vSn6OjowNVXX41X\nXnlF9n7r6+tRX5+7DU8uvvzlL+M73/kO5syZg3nz5mHLli0YHBzEjTfeKG3z5JNP4sknn8T27ULW\nJhAI4PRpwe/GcRz6+vrQ1dUFm82G1tYcPf8KQARWlZHSqqFBWA2MqHVzlEExK2mlESyPv3TDqrSK\nznFucq3kzgY8uPudbZLhXcfQqKsxau7BytfbSUuBZas1gNHxiPX5FKW+clJkKsteZ0Lf4Cg8/ois\nVgasNwLKwIAylmegcLG/H7eK5mupXcrJg+Ce3wCMZvelJJMsRCWB5Q1jYktdyceTi3I3G5UqCD9i\nBvdiSbZKlAun04lvfetb2LRpE1599VXccccduOyyy3Dbbbfhc5/7HC677DL8+te/BsMwaGpqSolY\nDQwMYM6cOaoez6pVqzAyMoLf/va3GBwcxPTp07Fp0yaMHz9e2mZkZAQ9PT3S44MHD2Lt2rXS440b\nN2Ljxo247rrrcN999xV1HERgVRnJ4Xdrmw0GPV3WCJa0kt69FXCdBWgG1Ce+knclnZymkIMoGO0l\npH/SCwLSSV7JnQ14pA5ZPDJLlx11JvSc9SIUicNs1OYrUe7eTqFwHOEIi9bGGjAGDrGzAOuPlDz/\nkJqzFPzrT2U+XyCV5Uj8bYz4wgUFFs+Lg8ONZfPcFPv7GfGFYdDTsJpV9NRNnAnU2MEf2QP+YzeC\n0uf+niR/9+zMWARLS7K1SdGSsSghEVjVzLp167Bu3bqU5/7yl79kbDdnzhx88MEHGBgYQE1NDd58\n8018/etfV/14brrpJtx00005X09v17Ro0SIcOZJZvVwKVVtF+FFFatWQGPpsrzPB44uA4zQe+pwE\n3bkIzJfuFsrvORaUNX/7C9YbVmTc9vhKX/VTOhq01ZD3Ir+gaTLumrcKrZbsIWaxy7vkw9LwhqFz\nWnI8r01vp5Gkc1xIjCoilogY1tQDNAM0TAC16msFU1n2JD9hIfhwHHyUlY67HBTz++E4Hh5fBPY6\nk6pCkKJp4bsXDYN77IdgN6wDu/knWSt5pehxnamoYoJiYWwmsL4I+DJcl8aihGX04xE0Q6fT4bvf\n/S7Wrl2LT3/60/jKV75y3rdYygWJYFUZyeNyAMBhM2PAFYRvNIL6MofIqVlLwB/eBf7wTlBtHVm3\n4XkerCcMxib/JqNW5RVjMyLW7y+Y+ipkeE9uI9DaVFPSMeWi3L2dkkeLMEahzUepPiye58Ef2gkw\nOtBr7wZlkl94MVZMUPgYpKrUEn1NSijm9+MLRMFyGpmvzYm/QzFNmKPxqLC4YUCZdTBRFMwmXVks\nBeJ3jxuNaC6E3V7BU6bXlSddTNCe5cuXV7yxdzkgAqvKiJ0Vxl6Eu4YwPBRAnU0wqz72p4NwFtHO\noCQmTAfMteAP7QJ7+P+yNh2Vog0KbjJuXxg0RaGupjQjLmMzIdYnFATk+/xchncaFG7fsQXj+QZY\nYNfUh2We0YjR3b1ghwIATUHnNMO6SLsqwpQIlkEQWJy3xBvvuR7A3Q9q+gJF4goAbDWJYgIZ0ZXk\nqEy5EH8P3m1HAY6HrtFS8PczoqE3iD+0M/vzSdWaPM8LDX6TUqmOOhP6FBQTFMuYD0tbgSUOeZ6k\noaeMQNAKkiKsIkJdQ/BuTa00m3rcg0lhFjwPDI8oG4pcKvyRvUDID4DP2nQ01DUE15NCd+DoWR9C\nXUOy9jviiwjm6xJH08hteLiyLXuJcpznwIFHHyuczxNDGp9XjgNlZNB85xI03DxP0xYNybPbxgoC\nihOQXPduIUW1JdESxdaQ/w1ZYBgathqjrDSsUk+fWphnNEI/XqhsdX7xYtktGjSJYMmo1sy2uKmv\nM4HnAY9f2yiWaGUoZrSQEsiQZ8L5DBFYVUSuSqaZaQN6cw1LVpt8PY+EtgNHpFQmH47D+9KRgiJL\nMF/HVam6KlRJKJLe5V1Hpf7ZswwLlmIx5MludFYDnufB+bRPp4iI5muLSSc1YmWLiGBJjUWTKgf5\nvS/n7eyfC7vNJHU+z0e5WzQkI/nVZKTZNK1uc+YoC0+q1swmRJOLCbREjDL6tn+A4cf2y15cKaG7\nx40/v3EMAPDBqZGyLSwJBLUgAquKyFXJZIunGknlDkUumTyr6Hxl7fkYUaFFg4iScnHR8P7byz8P\nLn2cBCWILCrCYMPmfdj84iHVL+Z8KA4+xpUlKpNuvqZ0Yi8s5X83eRuLKkQUIoWiWJIHqwKmZvEz\n5QgsUcTUa3Ccuaoyk5/P1h5FmvuoocAKdQ0h8PaY4BZ7hqkpsrp73Nj25gn4g8IA7EAoVtboPYGg\nBkRgVRG5Kpm8ulQDt5KhyCWRZxVddFm7iiF/6WaoMDLTktZstHa0Foa4ARQozVKx5RQNkvk6ScQy\ndUaw/qjyqq8SZuSlI3cmoWTcNpXfIqpEYGlpvqY7F4Fa9bWxdKzJmlGtmV9gaZciLHZxpYRcUfpy\nRe8JBDUgAquKyDWg97Al9UajZChyKeRbRRfbdmDMfF260GDqjEL0SaE5Pd2T5fRkLxpQ82Iupb3K\nkCLM5g0aG46t8MYrI1UlF7mtGtKN2+VEiormEYHdPW48/sJBBEIxhCNxzaIqdOci0F/+N8BaD/Ac\nqAvmpbyeTbSXYzJBOXq6uTzZ91W26D2BoAJEYFUR5hmNsF3TIRlIaYsegUXjMZCoJKyvNWLVlVPK\nVkUoraIbJkAY1AZpfEcuMVio7cBYk9HShQbF0KBrjIojWOmeLEM0ezVjrot8MZRztl626rZio31y\nUlVyERvm7j14LmcalpOM25XpeSSeJy5HBEhMXbkSN/pYnNM0dUXRNKgZi4FICDjxXspr2SJY4mQC\nLVOE5ejplqsZbdmi9wSCChCBVWWYZzTCccOFAADDpHpMvbIdi+cIUYQlc8eXr0VDArpzEZi1PwW9\n5pvCEwGvdJy2a8Ymp+saLbBd0yGr8kqvU6/zNWMzghuNgGc5Re9L8WQZs5uu48aYGocIINm4XQaB\nlaWRa7HjTejORUCtM/GAlt1YNJ3uHjdee/u09DhXGrYSLRqSKVQQUInUFTXzMgAAd2hs9FOoawiR\nU0LrEfeWAyn+J3udEcFwHOFo/mKCYil2caWEXFH6ckXvCQQ1IH2wqhC6NjX1JZpota4MysvEmYDV\nNja+Q6eHcbLQfdd4gQP26wpPa+d5wXztUNCUVBY8MLBhF3ROC6yLlfeWGrQNo2Uw88I9WDes1hEm\npXO0FQ7dPW4cPu4CALz81gksmtOKznaHIm9RMvxQL+B3AVMvBvOp9YXfkIN8wiR50VBJgzuQNCEg\nx3etEqkrqmG84MXqOQB2wzqErXPh947NbksfTO2oM+HkWR9GvGG0NKrfOFf8fvlfPwEuGANjM6Hm\nikmqth3pbHfA5Qli94FzoAA47WXuAUggqACJYFUhFE2BrjVKNxsxEqF1b5v8x0SD6lwMRILACaH3\nldKboT8QRZzlVEkPAsIqPtYrNGYFX3w1U20Tg76mfvCJ/4UNEfQ19aNunHrmZdYXAaWnQZm1W9OI\n6atYXIjmuTxhKUokt6VFOnzX/wEA6BmXlnRscoWJ0sHhWpCvIKASqSuuezfgTYh9nkPQOyHrdqLJ\nPBITGss+s61bk4pYQBBZNZdPAgBYL9OmYW6tVfib/fiSyVi7ehYRV4TzDiKwqhSmzghuNAqe5VBX\nYwBNUZWNYCEpVfHXx8BuWIfYC/8NQL7AkvxXKqXJ1KpmWtk2E/4aP0KmEChQON16Gv4aP84GPLj7\nnW3YO3iy5GNlvWHQddoat/NFicZSX/L/hniOA9/1NmC0AFMuKunY5AqTSozJSYexGYWCgEA047VK\npK7SW2KwyD5bM+4KobvHjUPHhAgmD22bE0t+tVInBOTAo2EbDMKHn6eeegrLli3D7NmzsWbNGuzb\nty/v9r///e9x/fXXY968eVi8eDFuu+02HD16NO97CkEEVpUipXT8UTA0jbpaQ1lmjOWDd50V/hEN\nAzwn3Qwpf2FB093jxva3egAAhz4YVuWCr1Y1k2h615sF8aOPC/4wHsDZoAcPH9lVksjiInHwEe2H\nF+eLElF6BrRVLzuCxXXvBvfo94GAB6Ao8Mf2l3RscoVJpT1YyZ+dzYfV2e7AqivaIerkBrtZ+8KT\ntFYZDDLHPgGCybycHrFSJwQUQlqQ1RJjO0EZ27Ztw7333ovbbrsNf/rTnzB37lysW7cOfX052s4A\n2LNnD77whS/gmWeeweOPPw6GYfDlL38ZHk/275sciMCqUsY6SicMy7UmhCNCF/RKkb6S5iDMo6N7\ndmXbXEJMXQVCgmncH1SnaaCa1UwLmiZj2ZRpAABDLLOqcHvvYcX7FCmXr6hQlIipM4H1RQr2wpK6\nt/sSaalwIGVEUjF0tjuw6sopqE9E0kxGXVZhUo5UaiHG/GrZhcOk1jrwPDBlgq08qau0VhkW6kDW\nzayL2srqEZOiohot/Dy+MAx6BuYK9EM7nxFHW7Eb1gkjrkr43irl3nvvxZo1a8BxmUVHa9aswT33\n3FOW43jsscdw3XXX4YYbbsDUqVNx1113obGxEVu2bMn5nkceeQTXX389pk+fjo6ODtx///1wu93Y\nv7/4xSURWFVKuim5KozuaStplhcMtIzvVN63abWqVruaSezNZYhlVjj2Bb1F7RMoX9qrUJQoX+or\nmf+fvXOPj6K+9/5nZva+yV6zgQQCBBASKCjITejxAuoRar1wiuKhUu0BXmjV03peR09tPT34KKU9\nz9Men15UbLUiyjmtz6vYagDFqqAoclGwkMQgCRIu2SR7zd53Zp4/Zmf2NnuZ3ZlNYvf9evlqdzKZ\nHTazM9/f9/v5fr5yuren0tJswz/e2AoAaKgzigYmtDcMyiRzE4RECunV5LQaKYZMSwwd2Q01Ek7q\nRHoHbyU1YoSaAmkoPisqBZZl4fFHYFW4rP5lI220lcj8WCU5ffo0tm/fjoceegikyJzZKVOmoL29\nXdIxn376acyZMyfvf5mlv2g0ihMnTmDJkiVp25csWYKPP/646PcOBAJgGAYmU+mDxqtLgxFKpr4h\nVeiuRGdQUdgb02bSMagBEAdhs+b9NaVW1byw1tv2GcCwUDkMMC4sXXBrSZQi1CIZrMYM93cpVKrs\n1dJsw/FOJ3r7hkAQXEYrtfMqWfoKC9kHUWR0b89Ep1FBr1UJI5NSYcKJUuq44dXckAU8wzz+ymqD\nyJaFYJAIcAfOAWBBWB2AG6j/zkKQKZYnC2Y1oG3f6axjKKURo0xaxPoDYFlW1kDIn5hGYPkbLw8y\n7/4ebFd+7VAaQ+LlLHb3b0G/9/+KOgRxyTyQV91W/HsmeO6559DS0oJFixaJ/txsNuP4cS77+vbb\nb2PLli1gWRbr16/HqlWrRH9n9erVWL58ed73HTNmTNprt9sNmqZRV5c+lN5ut+PAgfzVllSeeOIJ\ntLa2Ys6cOUX/TibVAGuEIqyi/XyANfwZLGLBCm51lIBGDSgEQC7Mbzppt+gx4M4OsuRYVetbHQge\nPY/YBT/sa+eAIEu/yVvyZLCGYhHcs38HGgxmLG+agfn1k4o+biWtB1gWIAjggTVzQVHpq8iirRoy\nAmmBEtzbxbCYtOgbCIJhWJCJv1eovR9D73OZ0Oh5P0Lt/Yp0phVDZnk+k0pnsICEH1nLQrC9nWB+\n/1MwIYiOE+KD6bc/+gKhcBzmGi2WzFXOP480aYGLQ2ACMVA14oa9peDOyNxXKRKGlrZdrrdlGOzZ\nswfr1q0Ttm3evBlNTU248847AXAZIb1ej3g8ji1btmDbtm0wGo1YuXIlrr32Wlit2Qt1i8UCi0W8\nqUNJfvzjH+PIkSPYsWMHKKr0bvJqgDVCyez6slRgxlghhJX0wdfBDvSBhQ5knb6g6aTSq2rKrEPs\nvB/MULSsIEZFkag1aqCJk2CNFpwPeqEhKYTpODxRLkDkRe8Aig6ymAoGWG5fGCajJiu4ApIBg/f1\nzxA42JvTM4yYfRXYv7yUvb0E93YxrCYdLvQH4B3iBlKH2vsFHycAYMPxNF+nSkNqKBB6Vc5AVOhu\ny5cFVIpxlwC1NtAeAqRNLZo1amm2IR5n8MaBHiyYPVZRjVhqMCpngOURMcstlkPOHuw6exIXgt6S\nFkQjCfKq2wAJ2SR624/EF0d140Gt3STjmaXT29sLn8+HadOS5tO7du3CAw88ILzu7OzElClTcPz4\ncUydOlXIPF155ZV4//33ceONN2Yd9+mnn8YzzzyT972fffZZzJs3T3httVpBURQGBtJ9DAcHB+Fw\nFL6fbN68GW1tbXjhhRfQ1FSeea6kAOuTTz7B/v378cknn8DpdCISicBqtaK5uRnz58/HtddeC7O5\n9FJKlSSZ+oZagwYUSQg3nuGCX0lHd74IdAGqIvyKWpptcA4GcPhEnyKmgami5HKDGKtJiy8uRPFv\ns/4eajWFTUdeF9Vf7e49WfRNm/aFAZIAKeMDSIxIlEYwHMfExmzNQKi9H4GPzgmvM80p04gnHOxr\nLEDQD9gaQCxYIdm9PRd8YOLxcQFWPruN4ctiaREfDImWvty+CCiKQK1R2b+nGARBgr1kMdhDGlCq\n3IstPvPjUXhBlpYVzTG2shTcGZn7Yjnk7BEWQEBpC6LRTGaVIXW7kni93D3SYOAajw4ePAin0wm1\nmqsG9PT0oL29HRs2bIDT6Uwr640dOxZ9fX2ixy2lRKjRaDBz5kwcOHAg7XcPHDiA66+/Pu+xHn/8\ncezatQvbtm3DlClT8u5bDEUFWH/84x/x3HPPoaurC0ajES0tLZg0aRK0Wi28Xi+OHTuGV199FY89\n9hiWL1+O73znO2VHflXS9Q0kScBcq4XHF5Fd71AKzNhZQNcQyOA5AJcX3J8fjfO1qyZj2iR5V9Ry\ndjNZanX44oIfHn8EDpsBF4M+0f2kiN5pbwRUBcS6gjZIJLMiJYhh2z8ASArknf8BQl8r+3nyWQm3\nP4xmmCsyPFgqlEmHeF+AcypPCaQ48XUYltrhE18z4+YAh3pBDn4G+ue/A+yNWQEwfw0oLSkodcZl\nIZIeWNIyWLvOinf7SlkQjWbS9HquC7IvjnLR2NgIkiTx2muvwWQy4fHHH8c111yDd955By0tLdi0\naROmT5+O6667Dm+++WbRxy21RHj33XfjoYcewuzZszF37lzs2LEDTqcTq1evFvbZvn07tm/fjt27\ndwMANm3ahFdffRW/+tWvYDKZ0N/PGVYbDAYYjUbJ5wAUEWB9/etfh9vtxs0334yf/OQnaG1tFb2x\n+P1+vP322/jzn/+Mr33ta9iyZQtWrFA2av6yk6lvsJp0cHnDCEXiMOjkmeVXKrS2HsAQyMHPwMZj\nIFT5z4d3oZd6wyyGUl3KxUjVujlsBjQYzDgXzBaOFit6Z2M0mGAMmjpxSwk58eTRBhUbxLADvUD/\nWWDKZYoEV0B2dkVlNyA+kH1+cg4Plkrq0OfUACsYjiMaY4ZVfE2f55oNKMYLkCmdYoDwIDXq1VCr\nSMWnPxQajl0qbl8EOi0FvVaaiuVCjoVPOV3Aow2+ylBJ7HY7HnzwQWzduhV79+7Ffffdh8WLF2Pj\nxo24/fbbsXjxYjz55JOgKAr19fVpGau+vj7Mnj07z9Gls2LFCrjdbjz11FNwOp2YNm0atm7dinHj\nxgn7uN1udHd3C69ffvllAMBdd92Vdqz77rsP999f2piwglfvN77xDaxevRpabf5UbW1tLW666Sbc\ndNNN6OjoEKK/KqWTqW9ILa0Md4DF+LlWfzLuAvOLewD7uLwrJf5hqoRuhSxWvF0EloyxRMubZqSV\nHHh4l/dC+g6+SaFS+itAXBhcbBDDtn8IACBbxTuB5IAPTvjzNS5qStNg8cg5PFgqqaUvdUMy0Exq\ng4ZPfE23/xVAK0gMpW1nP2oTHqwEQcBi0sLtVTbjLQwRlzFTxjAsvEMR1NukL0rKXRBVKZ3169dj\n/fr1adv27NmTtd/s2bPR1dWFvr4+1NTUYN++fbj33ntlP581a9ZgzZo1OX9+//33pwVOnZ3Z96By\nKRhgfetb3xL+/wMPPIDvf//7aGjIL05uaWlBS0tL+Wf3N06mvsFqTj6YGuuHyaohQbyX0/NQCHCt\nayKr6FTc/jCMejU0avnm+/GUOshYjMxuTT542t17EucCHvAWnaku76n7ZcKXTirhTM4HhWLO14WC\nGKbjINiPXudsAAiCy0oqdJ5aDQWDTiUE3YLdxuudAIuy7TbkIFfgkOxuG8YM1hBnNkwRgfQfZNho\nWGt16HeFMBSMKaYXI7QUCA0lqxeWLxAFw7CSglhe2C4WXAHADeMLD6OvUhlUKhUefvhhrF27FgzD\nYN26daIdhF8GJBmNvvHGGzkzUx6PpyzH0yrZZOobKqWrKAba6QLAgET6TV7MjDJOM/AHooq1XJMa\nFQidSpZVtLlGC4JIFwfPr5+ER+euQKNBXAuQz+W9krP1PL4wCAIw1WY/TPWtDphvnC44pFMWnWBO\nmTQnTIjgWRbs7t8qak5oMengC0RA05zjs26qDWABzUQL6u6aO6zBFZA7aOd1bsOZwWI03GeTmcHK\ntNGohDkxQRDccGwZAyyp+ite2J4ZXBEgMN5owbrpi/8m9FejiWXLlmHPnj148803cfvttw/36ShG\nwQDr9OnT6OrqErW+T+XMmTN503FVpJOpbxDMRod5JiEAMDE1SARBEBljV0TMKL3+CFhWWd8gyqQF\nk2gAKOs4FAmTUSv6UJKq7wi198P/HuftNHTgC4TalS2bu/0RmGq0oERclAEuyKq5gstY1V45SQhi\nlHJuz4e1VguWBbxDXKm5kl5hxZArwBIyWMOpwTI0gFvcpOvnMjvFrBnlbqWgTFqwURpMWJ4xXskZ\nhMVdC7mE7eOMZjw6d0U1uKoybBQMsNra2vD1r38dc+bMAUEQePrpp7Ft2zYcPnwYgUAye+H3+wvq\ntKpII7NMUWNQQ0WRoi7YlYSlGTAwZq+gAVEzSiX1VzyUSQs2xoCV4SZvMWkRDMcRiaab8zXk0HGI\n6Tt4byc2yFke0N4IvK91KhZkRaJxhMLxgpmVVDd3AQWd23MhaN0S1/ZIC7AiPW7uf0+5MPD8UeHv\n5vGFoVKRqDEMnwaSiapBGkgQdQlfBJICsWJDVmm+UuO1ChmzSiXplF9cEFsVtlcZqRTUYN11112Y\nN28eTp48iZ/+9Kfo6urC/v37EYvFQJIkmpqaMHXqVHR0dGD69Omynty+ffvwxBNPgGEYrFq1Chs2\nbEj7OcuyeOKJJ/Duu+9Cp9Nhy5YtmDlzpqznMJxk6hs6e9xgwaLfFcK2V09gwWz5vKSkQA9FARDZ\nGhCI+624/aWbBhZLasYhdXRIKVhNOpw574PHH8YYe7I9N5fg3RcNZ7m8V9rbqdjMimhmRmHndjH4\nQJDProykAIsLjj8TXic9w7j5eMNp0cDGGTCBKDRNZlCrHwOz6zdg2z8AYbJn7ctr8ZTOeKc2mahl\n0Ia6JTYS2HRGDISzF3tVYXuV4aZggFVTU4NFixZh0aJFeOWVV/DTn/4U06dPx6lTp3DixAm0t7fj\n1KlTmDFjRppra7nQNI3HHnsMzz//PMaMGYNvfOMbWLp0KaZOnSrss2/fPvT09OCNN97AsWPH8B//\n8R/4wx/+INs5DDep+oaO04No259sKR3whAR39EoHWYIz+ZRLAP9pYPAcJ3SfNl9U4F6ZDFYyM6Me\nU95NPtVGIDXAShW8nw96YaA0GIpH4ItxD4RU0XtThb2diu1uEwuwhsOcMDmZID2DRY6AACtXcOz/\n4CxiFFvRETmZ8F2p/OdEtC4C2/4BF2Q1Tk3bV69TQaOmFDcnlrPJBOAWC3qdClpN7sdTqls7C3FZ\nQFXYXmW4kWQy0taW1GS0traitbVV9hPiOX78OCZOnCgYln7ta1/DW2+9lRZgvfXWW7jllltAEAQu\nu+wy+Hw+OJ1O1NfXK3ZelYYyaREfCOLoMfEyzkefXqh4gMWXl1STJ4O6dBPYSBDM09/jOglFna+V\nH44raydhho1AKvPrJwmB1mNH2jAUz36/3b0ncY99bEW9ndxF+owROhUINZlWziEmzgBLEACpAlim\nIuaEqZYjQLK8VIluy0Lk8gyjXSHAoRueETn8OWRm+ia0AkYz2M5DYK++AwSVvKUTBAGrSYsBt7gj\nvVzIZTba0e3CwePn4fVHoKIIdHS7RO9tmW7tPDatAZ5oCI0GM24YP3rH41T58jBiZxH29fVh7Nix\nwusxY8YIk7hz7cNb7hcKsH7xi1/gl7/8pbwnrBD8AyfiCgGqbMmcy1N5PVbmTZ7QGoDJlwJdE9UI\nrQAAIABJREFURwDnF8CYiWn7e/wR1BjUUKvkt2jgkTPAcvm4LNOBT87jszPunGN98mk/jIvmV9Tb\nSchgFXj4c1lRXdrnxH52GGBZEEtuBTnv7xU5v0w0agpGvVooHwvXlEgHZKXJ5RlGJz7bYc1gZX73\nSApwTAB6PgXzfzdm+dFZarXoGwzCH4jCVKNMYJg0+i39XtTR7UqbVxqn2ZwZ+lyidoNKgx8vuKXk\nc6hSRW4k2TR8Wbj//vvR2dmZ9t9bb7013KclCl8KGJtDV2SzVP5mL6aXIVuvAJAYs5JC0qJB2fOU\ny2y0o9uFdw8l9UgDbq4U29Htyto3n+hd3+qAMdGxB4LzduJtEZTA7YuAJIiiHqKkSQs2QoOJcA0B\nnLkoAaJlgSLnlguLSQt/IIo4zXDauRoNCJEh1ZXGuEg8CO5v4gxHlczEFiIz08d0HAR6PuV+mOpH\nl7DYyDTOVQLSqAEoAkwZ7/HRp+INFWLbC4naQ+39GHj+KC7+7/fSGhSqVKk0Be9mGzduxMmTuX1+\nMolEInj++eexY8eOsk5szJgxuHjxovC6r68va6hj5j4XL17M2me0Qw9xN63LzgWwfDCCieH0zrYF\ns5QTIuc8J14vk5otaZ4FqLVgP34L9M/Xg972IzAdB5PjWxQuq5AGNaAiyw6wpNzolzeJazx4l/cv\nKE54a7puquLeTh5/GOZaDUiycBkoNdvHevqB86eACa0gaipr9mc16cCygMcbBuOPjAiBO5D0DKMS\nixdCr0Jg4Ti87+GyWns/OCMacFcCQf+YyBoVstjINM5VAoIgQNVqyyoRDnrEtYliGfqxhuxh5gC3\nsOG7d+MDQYBNNihUg6wqw0HBAGv8+PG47bbbsGrVKmzbtg0nTpxAPJ7eCt/X14e9e/fikUcewVe/\n+lW88sorZXfzzZo1Cz09PTh79iyi0Shef/11LF26NG2fpUuXYufOnWBZFp988glqa2u/VPqrUHs/\nQke5BzsBwEqzWOKLYWKYBkUSWHHl5Irrr0Lt/Yj2citF10vHhBsX23UEiEU4DQ+bnI/maufKukpn\nsISbfJkPEik3+vn1k7Bu+mKMN1rSXM95l/djPVxTgtKBQygSRzhCF/0Z8+cT2/kcmOe+z220Vv57\nE4txi4X/9+oJgAUC1PAOME9F3+qA7Q5uPlrEbsCr3YOIxjgvQJc3nDOrqTTJUmrimipgsWGpUCch\nZdKCCcbAxvP7JebCbhHXJqZm6A85e/DYkbac9gs3jJ+Rt3u3SpVKU1CD9cMf/hBr167FCy+8gF/+\n8pfw+/0gCAI1NTXQaDTw+XyIxWJgWRazZ8/GI488gptuugkUVZ7eRqVS4d///d+xbt060DSNf/iH\nf8All1wiZMbuuOMOXHXVVXj33Xdx3XXXQa/XY/PmzWW950gj181idoRBrxGYPqmyGQd+dciTbF8H\nNIfEV9Kerk4AMyrifE2ZtKDdITBRGqSmtOvPbtFjwJ0dZOUqxfKi98eOtGU5SZsjKuG8lKRY/RUP\nOcSVQBlvGCATHVjH3gEzbpqiwvZUOrpd6Ex4TRni3Dl0DQYQzCFsHg5IoxqgCIQGAoApu0Q/LA0m\nvghIgxoEr8csYLFRiQwWALAM9zfs+68DUNkNMC6SNupowayGNA1W6nYgt7CdAIFxxqSo/eKgyGcB\n5bp3q1TJR1Ei9wkTJuDRRx/Fww8/jE8++QTHjh2D0+lEJBKB1WpFc3Mz5s+fnzapWg6uuuoqXHXV\nVWnb7rjjDuH/EwSBH/3oR7K+50giVzdTTZQGTbOKClfFyLc61LjFV9KeCAFoK+N8zZdNGH8EpF36\noFig8I0+F2K6EEuUC/KUDLA6ul3Yf5j7u3T2uDHWUVPwoU/2fABgAWik21mkDgtWmtSSqzHxcA5Q\nxLAELbngs6JaXxhAdoBV6QYTlmVB+yNQ1yetQwpZbOi0Kmg1lKIarFB7P2K9vsRJpi+8ig2y+L/5\n7ve6wTAs6qz6tOaSQm7tPMUONa9SpRJI6iLs6urCggULsGBBZcWwf6vkullEE4NbPYnRKJUiV8AX\nHwwBDvGVtEfL3WDNFWhtT9UWqUoMsPgb+vtHe+EdisKgU+HqBRMKPvQbDGbRDFZIzYBQYMA1kN15\nFQjFivJGI31nACwAA2P6DxR0bs8ktRRrpLkAK0gSw9IVmw/KpIXOEwbFsqAzbA4q3WDCBKIAzaZZ\nWZAtC8EgERzzfnTTFwiZyM4eN2iagcsbVsycWC5T3akTLGAYFuPH1OC2G1rSflasW3uhoeZV/nZ4\n6aWX8Nvf/hb9/f245JJL8Mgjj2DevHk59xdzF6irq8P7779f8jlIatlZu3YtPvzww5LfrIo0cnUz\nxVrrAFR+6HOuoEVl1+c0pfSo7ag1aqAWsZiQm+TIjvJW6y3NNqz6e24qQVODqagHUqbgnWC5AMuj\npXHP/h147EgbDjl7yjqvTKQI8lMh7RYADGg2w5BVQef2TFI1N3yAFaCIYemKzQc/rspAZ5tZVrrB\nJJcZK9myENTaTSDveRKgVIIfHR+AxxPnzpsTy60dy7vwkoA3j5ebSSN+XWS6tfMNCkg0eyjdvVtl\nZNLW1obNmzdj48aN2LlzJ+bMmYP169fj/PkcmsUEzc3NeO+994T//vznP5d1HpKeejfeeCM2bNiA\nPXv2ZP3s8OHDaeW7KuXD3ywIXULLY9XDfON0GGZygmS3wsLVTHIFfMaFTSBbFoJYsQGoGw8A6NRM\nxfZxGzAUIxCOxCsiCE5msMoPPGuNGlAkUbQLdqrgnSQIjIUBapaASxMDA1ZweZczyJIiyE+FXLgC\nJAJgMkqESjq3Z5IanBj4EiFJDEtXbD74a2qKlVtcEARQZ9UPS4NJoXFChM7I+dENngf6z5YcgEsl\n38JLCpkjcnhR+8b9O+CJil/rYm7tuhZuAapuqFG8e3ck0tHtwrY/ncDPtx3Gtj+dqGgzxubNm7Fy\n5UowTHazw8qVK/HEE09U5Dyef/553HrrrbjtttswZcoUPProo3A4HAXdDVQqFRwOh/CfzVbed1xS\niXDTpk1wOBx48MEH8cMf/hB33HEHPvvsM/zsZz/DO++8gylTppR1MlWy0bc6QHtCGHrvC5iWTYa2\n2QokBhorPQJD7FxCn/YhesbDeTvVGWBcmBSzki0LgZaFaH97H3Z/YQASpxeLMxUZ6yOXFxbA6W8s\nJi3cvkjRLtipLu9P/YVbhHi16R23u3tPyuYwLVWQz0O2LAS15zXEorVgoQJRN1Zx5/ZM+OvgLx+e\ngZFmESUJXH/1lBGjv+LhgxldouNx422XQa+rvD9zqL0f/re571Dg0DmQerVo4EC2LgLTdQRs+wcY\n9Ijfj+Uuw8pVluMXjFaTriy3dmYoCjDsiJgKUGkyZQO8jx+g/Ei106dPY/v27XjuuedAktm5mylT\npqC9vV3SMZ9++mk888wzefd59tln00p/0WgUJ06cwLe//e20/ZYsWYKPP/4477HOnj2Lr371q9Bo\nNLj00kvx4IMPCtNkSkHyneK+++7DmDFjsGnTJrz++us4evQoxo4di82bN+OWW6ouukqQOmcPQGJO\nF1XxDBYAEIlW+vr7FoHM8aA55LUAiGZtV1rAzAttwyf7MeAMSO5kysRSq8OgJ4xQOA6DxAHScV8I\ngBEeTXqAlavFvBRKFeSzkRDI+CAAC9i7fg6VrTS9Wrm0NNsQCERg7P0chEU34oIrIBm0M/4odDWq\nYQuuUgMYxhfJLSKfNAtQacAe3QubyYJBVfYQaLnLsPw5eNs+AxgWKkf6wqtY+AWjpVaHl09/ILpP\nMW7tI2lweLm8e/gsuhLdtsUwFIyJbt/9XjfeOyLeYZnJJZOsuGqe9KDiueeeQ0tLCxYtWiT6c7PZ\nLExjefvtt7FlyxawLIv169dj1apVor+zevVqLF++PO/7Znpfut1u0DSNurq6tO12ux0HDmQH7Tyz\nZ8/Gj3/8Y0yePBkulwtPPfUUVq9ejddeew1Wa2kd+5LvFl6vFz09PSBJEocPH8acOXPw4osvQqUa\nsVN3Rj3JURTJgMpq0sHpCoJh2KLMJeWC9kVAaKmcwRUADPqygytA2a6rUHs/d4NPUEonUyZCi7s/\nLDnAmsBw5TePNt0YNlMzUg4tzTa4vSF8cOwCCAD2jM6rXLBdR0CxfgBc4IBhCrAAwKpVQwUgqBl+\nB3cx+MWNKhSHpaG8IeKlIkVEzp46CsS579/80FHsrr0u6/eUKMPqWx0IfnwBsfM+2O+8rCRH/tSZ\npcWK2sXgF6IjYXB4pWFY8cHXDCO+Xbb3ZRjs2bMH69atE7Zt3rwZTU1NuPPOOwEAgUAAer0e8Xgc\nW7ZswbZt22A0GrFy5Upce+21okGMxWKBxWJR9Nx5Mh0LLrvsMixbtgw7d+7E3XffXdIxJUVFv/jF\nL/DCCy+ApmncfffdmDhxIn70ox9hy5Yt+OEPf1jSCVQpjNicPYtJi4sDAfgC0YoNn2VZFrQ3LDhc\n56LU0lU5yNXJlAovtnV7IxhXXyvpd2eo7AACWSXCoVgE9+zfgQaDGcubyh9IW2vk/vbXXjERs6YV\n9+9k2z8ACS7wk6OcWg4mgkAcI8tkNBWqVgMQgIFmhm0GoRQReaqz+/ToKcAPHNbPwYDKDpIkccNX\nmxXLFFJmLWLnANofhaqE77rbF4HJqIGKIlGvr8XFkC9rn2IWKEIGyzz6S4RXzWuSlE3a9qcTovfe\nOqsea28qz/w7H729vfD5fJg2bZqwbdeuXXjggQeE152dnZgyZQqOHz+OqVOnCpmnK6+8Eu+//z5u\nvPHGrOOWUiK0Wq2gKAoDAwNp+w0ODsLhKP5ZYDAYMHXqVPT09BT9O5lICrCeeeYZfOMb38B3vvMd\n4UQbGhpw3333YWBgAP/5n/8JtVraSr9KYcgaLUAkV2ZAcuCsxxeuXIAVjoONMQVvXPPtAexyZ69g\n59sDSp2abJ1MqQifsV965q0upkEEARisRpAxLzSkCmE6Joh1edE7gLKCrKQwuPDDhOk4CPbDPwGu\ni6BUE4CYPA0B5WCIM/AB8Cq7wC4ZgiLB6NUwhmMVMcsVQ5K3U4az+/ToKUyPnsIfTLfggqYBl0xQ\nLhuQ7OINSw6wYjEagVAMcWMU9+zfkVPzKCZqz+TLVCKUSqmygXLxernMosHAZcMPHjwIp9MpxAM9\nPT1ob2/Hhg0b4HQ608p6Y8eORV9fn+hxSykRajQazJw5EwcOHEj73QMHDuD6668v+t8UiUTQ3d2N\nhQtL16ZKCrDa2towYcKEtG1XXHEFtm3bhg0bNmDdunV44YUXSj6ZKuIQJMG5lKeWCGuTDs2TxslX\ndsoHP2us0I1r2uevg/XrsKdmGVgQqKMHMS/0MaZ9HgaWKOOhpoTBYNIFW3qWh/ZFQGgoPLyQ68zb\ndOR10fJGuaJ3j9Danv9vwnQcTDOkJGnOs4s+2wtgYsnvXy7sEFfOGozTBfYcPmI6CvpgDNYKes6l\nIklEnsPZ3aKO4TwLeIcisJmVMd0UkzIUy3tnuLFSfioIBizn54XiRO2Z8AtR/nz+luCzkx99egEu\nTxg2i64o2UC5NDY2giRJvPbaazCZTHj88cdxzTXX4J133kFLSws2bdqE6dOn47rrrsObb75Z9HFL\nLRHefffdeOihhzB79mzMnTsXO3bsgNPpxOrVq4V9tm/fju3bt2P37t0AgJ/85Ce45ppr0NDQAJfL\nhV//+tcIBoO49dZbJb8/j6QAKzO44pk5cyZefvll/NM//VPJJ1IlP6RJi9hZH9g4A0JFJstXFSzx\n8NmOgt05g+cxCSqwBImJ0TO4xZ8oW7iUMdwElDEYNOrVUKtIyX5jLMuC9qUPL74YzC53AOWL3t2+\nMNQqEsYCGrHMocAUuGwifXGwrPcvF/5h7KYZhKNx6DQjT8sZUpHQAjBXwMtNDH2rA7Q3jKH9ZwAg\nr4g8l7O7tWk8cJYrdysWYPENASUMfT7wxRnoYUZMla7fLEbUngnti4DQqUCOwGupErQ02yreMGK3\n2/Hggw9i69at2Lt3L+677z4sXrwYGzduxO23347FixfjySefBEVRqK+vT8tY9fX1Yfbs2bKez4oV\nK+B2u/HUU0/B6XRi2rRp2Lp1a9q0Gbfbje7ubuH1xYsX8eCDD8Lj8cBqteKyyy7D73//+7Im1Mh2\nBU6cOBH//d//LdfhqmRAmXSIwQfaH4HKqheyK6WUr0ql6NS7vRFud6Llmk4JIBQ0suQfNkP7z4D2\nhkEa1KhdOrmsLsJSrBoAgI3QYKN02gpazOkdKE/0zrIs3L4w7GZ94XPLKB0RBA0CQdCx4S3p89mG\nAEXA44tgbN3Ieyj6CcACoLa0OcayoLJxQVHtNc0wzst9w09zdk9ksoila2CzXgKc/VxRc2JeOlBK\n2TkwFIMeQFST3gUndQHCL274z6tK5Vi/fj3Wr1+ftk3MM3P27Nno6upCX18fampqsG/fPtx7772y\nn8+aNWuwZs2anD+///77cf/99wuvf/7zn8t+DrIuyTLbIqvIR6bQXavhWsYrmsESUu/5M1jEghXw\nUFxa10J70rYrib7VAesqTsipnWyVxWDQWqtDPM7kbH8WQ/icUjJ9mU7vPOcCnpJd3v2BKGiaLU4b\nZG/M2kQhAAZGsDk6j5Qm1N6PSLcbLICl7iiGTojrMIYbN81FVqSEa0Buii3PA0lnd2LZN7kNsaig\n0XMruCCjaksvEZpZbmxTNCODJXUBwgRjQJz5m9RfjRZUKhUefvhhrF27Frfccgu+/e1vl2yDMNIZ\necvFKqIIq8MMofuF/iHQNAOqhLZoqRSbwSJbFsLTSwDnACuTKI3VNVXEyFK4yZdQphDDYkpq3WoT\nMyALIfY58dqR3b0ncS7gAR/SsChd8O7JM1okE2LeDWB3/0Z4HWaaQcMMgMTAc0dRs3hCRR2vU72d\nCABWmgU+Oo9Qfe2Ict6O04ygD0v97lUaoTwvoTOOmDYf7Ns7wJ48APNlnF2DkgsyQkWCNGokfU6H\nnD3YdfYkqFAtVGARU6cHscWI2lNhvkQdhF9mli1bhmXLlg33aSjOyDSfqZKFmFWDtVYLlgW8Q+K+\nU3JDeyMg1CQIfeG43KPmspn2u78PjG0GBnvBBuQz2cwFoSJB1mhk645LdhIW/2DKNTNufv0kPDp3\nBRoN4qLN3b0nJZ2b21t8ByGhS3hd6WoQxhT42avBggsYaVcI3tc6EWrvl/T+5ZDPVmMk4fVHEEj4\nzA2npYWQwZISYOlrEqNzzkHlPodao0a4ZpSCMmlB+6Ngi/Bd4t3azwU9UMfUiKliAAEQIDDeaMG6\n6YslN4D8LXcQVhl5VAOsUYK4F1bSqqES0L4wKJOuKC2S2xcGRRGoNWpAzFgMsCzYjsoMCqfMOtC+\nSFE3+UIIpRUJn3GhZoByTBRTyZzdlg/2JJchI1d+DyHrDaL7VDK4UcJWQwncvrDg0TW8AVYYhIYC\noZXWKEKYuYUOs/0xWALnEAjFEI0p17FJmbUAw3Ljagqw6yy3oCAZEipGhWgiezXOaMajc1eU1F2b\nLM9XA6wqw081wBolCAFWWokwWb5SGiYSBxuhi3JH5sXX1kQwRkyfD5AU2JPi4y/khjJpARZgJGSd\ncsGXCD0SHq6FVtENOXQlUvUmbn9ydls+2HAA7OefcE0GYyaOiOBGrgHBSuPxRUATBBgtNWyeYUJX\nqllbdKMFkLDmOPIGfxRYw5zGzXX8sAJnySFF6M4vNNSJRouYmgvKyums/TKZjFYZ/VQDrFECQfGl\nr+SDni9bvXu4V/Gp6ckSReEAKxCKIRZPOl8T+lqgrgnoPwv65+tAb/sRmI6Dip1r8iZffoCl1/Jz\nH6VksCIARYA0info5RK8e6Mh3LN/R9Gid7cvDL1WBZ02f8mW/ewwQMdBzFgMgiBGRHBjXCRun6Ge\nky3GH074vztZy/nQDUdDABuOJ7pSpQUNmdYcfMOJ+6/5B96WQ3IhWPi7Z9Fy15smxpWq+QxWOZ21\nyexxNYNVZfipitxHEZRZh9h5H1iGRecZN947ek74mdJT04v2wEJSSMtn2JiOgwAfMLAsMNALtm0r\nGEAR4btgeOgNA03lmbASBAFLrRYD7lDRcx+ZhAdWrmxDquD9fNALo0oDfywCf4z73IoRvdMMA68/\ngoY6Y+7z6DiY1q4PDfe3U8IzTCr6VgdCJ5yIdrsBAogY1DhMAYsajKiMbW5x8FlCjVWH6EAQTCAG\nqqa4Zge5KFlXlGHNYUlYpnhCygWJhTJYvKj9QtDLGYoC0CQyWNFEBkuqsD0VTidKgRiGodxVqmRS\nzWCNIlJLXx99ekF0n1zbyyWZei98k88c35K5kubJtb1c+CAwLpOg12rSgWZY+AOFdSVsjAYTjBUM\nRHnB+1NfvQMmtXjmKJ/o3TcUBcvm7iAUnNtTXL3Zv7wEpuMg9K0OmG+cDpUjkckiCZhvnF7x7j0i\nYdzpuGcBfNdOxhkdBbdM3Z9y4faGufl4MmZFpVKsPUoWGdYcVj6DpR8ry3mJIaYV5UkVtfPBVe1Q\nLWxebkE4zjUWN9VeXvJkg1JLqVWqKEU1wBpFpI6iGPSI62VcHmV0IlLEo1nz8TJW0gIuZYLBZAZL\nnochTXMPg+f++GneUmyovR8DL3Dll1jfUNFdeaWI3gvNICwU1OpbHai7ay7UDTUAAN30ynvY0d4w\nCDUJ0qBONmxU0Di3ENHEfDyLSQsmxA3tdr18DAPPH61ox6UUD6xUMn3nTIwfJEvDUzNetnPLJF+J\nkBe189QO1aLR2QCS4R5DZFSFzmP+kqUOgsFvtTxYZYRQDbBGEcIwVW8Ydot41sNWwhT7YkiWKQof\n35NRIhQzuQSgmLN70vCw/Id1R7cLXV+4ASSqm4lSbOZDgPd1ot3ce7LheNHWB6WI3pMWDTkeJkUG\ntZRZV3TXl9zQ3mRXajlzH5WC1zhODDMIn3ByG1kgPhCsqK1FKR5YAFd+J1ZsAOrGAwQJEizMCMIT\nU658RqgpkAa16HcvcyFh94hLGUrNwosZ/FapMpxUA6xRRGr6Pdd0dKWmptPe/MLtVNy+MLQaShBf\n53JwV8rZXfDCkiGDVWwpthxfp1Jc3pM6txwPkyKDWjED20rAhLmuVD7b2HvRDwDoOuNWvGGjGDq6\nXXj1L6cAAKbTbtF9KmVrUYoHFg/v6k5971lg0ldgiQ0iHKERCsflPk0BfjB9ZkOAQ1+T9loTFdey\nlZqFlyJjqFKlElQDrFFEaomwpdmGFVdOhjkhuDXoVFhx5WTFhnwW64HFMCw8/ohg0QBkrqQTvz9j\nsaLO7pRZB8ZfvhdWsaXYcqwP5tdPwrrpizHeaEHqp5vq8p4ZZPGlNEut+MOk2KB2uAKsVF1RR7cL\nbfuTQ1dzZQkrRUe3C237Tguau9qY+BDCStlalOqBlQkxc0myk1BBywnSpAVoFkyA6wo85OzBpiOv\noy/kT9svqhHPmpaaheezZsVYyVSpUgmqAdYoIjkGhruRtDTbsOrvpwMAmhpMigVXTJQGG4oXpW3w\nDUXAMNnz8fiVNLnh/wAECQyey3EEeeAbAugyvbCKLcWWa30g1eXd7YugxqCGWi3+0CWaZwEkBZAq\n7n/rxoNYsSErqOWDdrkaAoolNcCqdMNGITLf10uJLyoqYWshp3CbmDIHVgwBAFx/+LVidimpQTsv\nbM/UExIgwI4Rv+ZKzcIntWrVEmEV4KWXXsLSpUsxa9YsrFy5EocPF/Z/czqdePjhh7Fo0SLMmjUL\nK1aswEcffVTyOVQDrFFEUt+QDBpqjRqoKFLRERiMlA7CAuaXhNEMNM8C+s6A7e8V3UcO5MrMFFuK\nzeXrJNX6oBjB+4lTA/AHohgKxnKW09jPDgMMDWLRjaC+u5ULbkUyhsnPqbLap2SApa14w0YhMs/n\npFFcs1QJW4tSPbBEj3XqKPwsd5w3jNdge2wxOvb+RfYgK1XKkCls5xlnNOORpcswaRynMyQIoM6q\nLysLXy0RVuFpa2vD5s2bsXHjRuzcuRNz5szB+vXrcf58Dm0qAJ/PhzvuuAMsy2Lr1q1oa2vDo48+\nCrvdXvJ5VM1CRhGh9n4wURoIxjDw/FEYFzVB3+qA1aSFyxcGy7KKtCdL88AqPB+PnLkEzOljYF75\nTyAcBOyNIBaskLVkmFpOLQf+Zv+Xg2cQjtCw1GqxeM64rIcAb3Hgfb0TYAGVwwDjwibJ1gcNBjPO\nBT1Z20kQuGf/DjRF66HrTYrfc/mfsSfeB0CAmLkk7/uJTQioBKm6IrtFjwF3dpClVMNGITLP54yO\nyxIuGIpBzQCUXY+aKyozHFvO2XodBz/BIcM87gVBYFBlx+7a64CDRzBD1u9e0gvrQrTQgoEr4d+z\n+jLoNKU/jkLt/YgkFhru3/9VuDf+LRJq70fgw7OIDwahshsq+lls3rwZhw8fxiuvvAKSTM/frFy5\nEpdffjl+8IMfKH4ezz//PG699VbcdtttAIBHH30U+/fvx44dO/Av//Ivor/zm9/8Bg6HAz/96U+F\nbU1N5S2iqhmsUQLfpYY4pwdJ7WSymXWIxxkMBWMFjlLi++7hxL7BYxfydk51dLvw4SfcCuH9j8/l\n1NCwsYT2IjQEsEzSeFTGlbSc2qKWZhuuuHQcAOCKyxpzrrC1zVaABbSTrai7a25JN7Vcgvc4y4AB\nC8IpHnTwZS2m4yDo5x8Bzp8C1Bqw57ryvh9BkZxL+TCWCCvdsFEIsfc9o6NAX8KtZM3XT63YA6tk\nDywRDsUniW4/nGN7qXTEBwEA73ScEPyuMuE7ZN3eMIx6ddnBFXdv5N6r0l2eIwn+s4gPBCve8Xr6\n9Gls374dDz30UFZwBQBTpkxBe3u7pGM+/fTTmDNnTt7/Mkt/0WgUJ06cwJIl6YvLJUuW4OOPc08x\n2Lt3Ly699FJ897vfxRVXXIGbb74Z27dvL2t6QzWDNUrI16VmvWwMAMDlDaHWKJ/LtHC69Yw4AAAg\nAElEQVTjSsD4o8LrzAcMLwzm8fgjOZ3l2cO7Rd+P/agNkGklLWVkRzHYEg84V55ARI6HYabLOwkC\ncTYpss7XeSWYi/LEIkU55lNmLWK9PrBxRjD/VJq4NwxCS4HUqYTr4+Dx8xj0hKFWkbhu8STFNIWF\naGm2YdAdxMFPL4IAYLfqsWBWA+p9Ufg6B0F7wsD4yvjNl+qBJYZLZZW0vRQOOXtw+K8ncCvqcLnT\niPFDGuxv9OGvdelNIDeMn4FYnIF3KIrxY2vLes9898bRnsXyvdONcOdA0fvnslvxtn0G/76eoo6h\nm14H09XNRb8nz3PPPYeWlhYsWrRI9OdmsxnHjx8HALz99tvYsmULWJbF+vXrsWrVKtHfWb16NZYv\nX573fceMGZP22u12g6Zp1NWle/vZ7XYcOHAg53HOnj2Ll19+GXfddRc2bNiA9vZ2PP744wCAb37z\nm3nPIRfVAGuUkK9LzWZKPvwnNsp345dy48onVM56UFbAeDSpA5EnM8MHWPm0bnRCM0SVWdqaXz9J\nCLTu2b8j7WdRTRTaaPbD1mbRgf3o96LHKxS4UmYdYr0+0P4IVNYKCbe9EahsyfdqabahpdmGZ185\nDoZhhy244jEauED2hr9rRutkLnMV6eFKt5VsCCjVA0sMu57AgIjczWaQT1bw2eHPcOtp7sFGgMDY\noAarTtWBIgfxqT2IRoMZN4yfgfn1k9Dv4u5ptjJF6SNhePmIIVfXdJnd1AXflmGwZ88erFu3Tti2\nefNmNDU14c477wQABAIB6PV6xONxbNmyBdu2bYPRaMTKlStx7bXXwmrNDvQtFgssFvHGH7lhWRZf\n+cpXhBLijBkzcObMGbz00kvVAOvLjspu4NK+Wdv1sJm5B1W+7EopSLlxSRIq2xvTRrgIyGg8ypW+\n5PHCAsB17KlIuPIEbHKWc3gyNVmDFhcandmf04JZDcDO0gLX1NmNlQiwmGAMiDOin5PNrMOZ8z5E\nonFoyygblQv/XbKlnCMfONMVFN/LmcFaMH9KWpZZ2D5vStnH5vlKt/jfbHFvLe69+ea0bWKfcSnk\nuzeOdkxXN0vKJg08f1T8s3AYUHfXXDlPLY3e3l74fD5MmzZN2LZr1y488MADwuvOzk5MmTIFx48f\nx9SpU4XM05VXXon3338fN954Y9Zxn376aTzzzDN53/vZZ5/FvHnzhNdWqxUURWFgID3zNzg4CIcj\nd0bT4XBgypT078LkyZNx4ULpC/8RGWB5PB5873vfw7lz5zBu3Dj813/9F8zm9MzMhQsX8NBDD2Fw\ncBAEQeC2227Dt771rWE6Y+XJN6BXJbhgy3vjl3LjkiJUJhasSC9lpWyXE8qUHI5NFDGkOR8EQcBm\n1uUd+hyXKYOVyvKmGcLgZwDwG/1gMIazE2MJMLo4Zkznsj90iYGrqsKdhPkCUXsiwHJ5w2hw1GT9\nvFK4vNy1nJpdoWo1AFHZjkvBA0uG4cV8VnD/kV74A1EY6QCudHjQ0jyvwG8WjyMkbkRcL7JdrgBr\nJAwvHykM12fh9XJNCwYDZ1dz8OBBOJ1OqNXc372npwft7e3YsGEDnE5nWllv7Nix6OvrEz1uKSVC\njUaDmTNn4sCBA2m/e+DAAVx//fU5jzN37lx0d3enbevp6UFjYw7T5iIYkSL3rVu34oorrsAbb7yB\nK664Alu3Zj+MKYrCv/3bv6GtrQ3/8z//g5dffhmnTp0ahrOtDMKA3rqE35KKFAb0qtUUao0a2TNY\nUqwHpAiVBeNR3m2cVIl6NJULZZbHC4vHZuaGPvuGxI+nRAYr1YSUJAgYGC1IkPAbh/DZ5C6cauzG\nn/xHcMjZA+LSa0SPUShwrbTZaLKDUKzUqUw2ViqDnjBqjZo0nzGCIjmX8gplsAQPLJN8w4tbmm1Y\nee0lAICJ7EVM++LNZNNJGRxy9uCxI6/DqRdvtIlbsrWDcgVY+lYH1BMSC3CCy9YMx/DykUDaIHeS\nqNhn0djYCJIk8dprrwnapWuuuQbvvPMOOjo68P3vfx/Tp0/HddddJ+m4FosFEydOzPufTpd9/dx9\n99344x//iD/84Q/4/PPP8fjjj8PpdGL16tXCPtu3b8cNN9wgvP7Wt76FY8eO4amnnsKZM2ewa9cu\nvPjii1izZk3Jn8uIzGC99dZbePHFFwEAt9xyC+68807867/+a9o+9fX1qK+vBwDU1NRg8uTJ6Ovr\nw9SpUyt+vpVC3+qAvtWB/q2HwcbptC+NzaTDmQs+RGM0NDnMJ0t5P9oTxtB7ZwDktx5oabahs3sQ\nn5/1giC4jNaCWQ05tTRky0KgZSGY3b8Fe/IA548lM6mBg0qGoEcQuvvCwnDiVGhPGKRBDVIjz+fP\nk6rJevydNwEAEXX6Q3F370lczk8/qbEAQT9gayjK/qLyAVbuQJTPGA0OY4AViXJDnic2mrJ+Rpl1\niH7hBRujQcj0PctF6Hgf2CiN+EAwzZalXCy1WhAE4DaOA7whsF1HQMy4ouTj8WaiALC/MYZVp7IH\nhzuWTM7a5vKFoKJIWRpz+AC0/oErZP/+jTb450QlsdvtePDBB7F161bs3bsX9913HxYvXoyNGzfi\n9ttvx+LFi/Hkk0+CoijU19enZaz6+vowe/ZsWc9nxYoVcLvdeOqpp+B0OjFt2jRs3boV48aNE/Zx\nu91pGavZs2fjV7/6FX72s5/h17/+NRobG/HP//zP+Md//MeSz2NEBliDg4NC8ORwODA4OJh3/97e\nXrS3t+PSSy8t6vi/+MUv8Mtf/rLs8xwuKIsO0TMeMFFauJlYzVyA5faFMcZulO+9rNwDr3ZpM4yX\njyuwN3eT27DqUhj1hWcWAgDxlb8De/IA2E/3g2hqKedUs2ASo07cv/+rLH4w1hSt2+Tx6T9jGS7b\noB6jbFkr4I/DACCaEWCdD3jAdn0KUGqQa/8XCJ24s7wYZI0GIImKB1hiQS9fUnbl0PRVAqE8KHJ+\nlEUHfOHlRPp1xX/GUgm198P3RjIjz7fbA9kdvFKhKBJWkw6uIMGZKLy1DfSe50r2o0s1E+W6BQew\n7KwZtogapEGN2qWTs86ZZVm4vRFYZXCoBwDaE1JkcVOleNavX4/169enbduzZ0/WfrNnz0ZXVxf6\n+vpQU1ODffv24d5775X9fNasWZM3+3T//ffj/vvvT9t29dVX4+qrr5btHIYtwLrrrruyRGgA8N3v\nfjftNUEQeb+AgUAADzzwAB555BHU1BT3cBP7YHt7e7Fs2bKifn+4oSw64EwiY1LPBVOpNgJyBljJ\nzrjCglGXNwSthoJBil5k3CWA0Qy240PQnR/JZjoaau9H6HhilcTK84BK7dbMhPFHAIaVVX8lhoXh\nrvHMOW5TfG7A48Rg8yzUSwiuAIAgCa70VTENVu4SoUGnhk6rGtYSIa9ltJuzr3n+exD3hhUNsJS2\nHrCZdXB5wwgSehhjiWCW96NDfluPTDKnD/y1LoiztRE8+PE4aCZaRM/XH4giTjOwmcoXorM0wy1u\nGsqze6hSGVQqFR5++GGsXbsWDMNg3bp1oh2EXwaGLcD63e9+l/NndrsdTqcT9fX1cDqdsNnEy0yx\nWAwPPPAAvv71r+cVr33ZUAndTCGoRQIsOeEDLFWBwCFOM/D4I2hw1EhakbKdHwGBxA061XQU0m7y\nmSjxgLKYuNKK2GfMt+4rHWDZUAsfYkIG6/LBi7jhwhk0hrgZcx9E/Gh09gglxWKhzNlZUaWgvVwp\nNVeJzW7W4Xz/EOI0AxVVeZnoYOKat4pksFQV6iRU2npAuF9QNhjj6XNBi/GjO+Tswa6zJ3Eh6BU1\nE/VpaNAEC1qk8QWQT38FJNzuWeW/e1XkY9myZaMmoVEOI1LkvnTpUuzcuRMAsHPnTtE/BMuy+MEP\nfoDJkyfj7rvvrvQpDiti7eL8aBq5ZxLGEzfIQsJtjy8CluUejlJgP2qTtL1YlHhAqSgS5lqtaIAl\nZPpkFLhnwrIsokEWBqMK42ssuHzwIv7p9AmMCw2BD2lvPN+DL45kp+ULUSkdFl9Kzfc5Wc06sCx3\nTQ0H/N9X7Fqu1OdU7vDwQvBDzF2UiMdQAVsPXnN1LujJ6dTOEgBjUgudtVlvIWeAJSwCR78tQ5Uv\nFyMywNqwYQPef/99XH/99Thw4AA2bNgAgBPD8TXeI0eO4NVXX8WHH36Im2++GTfffDPefffd4Tzt\niiGUKVJuXsX4NJUC7QmDrNUWdPgeTOhW7FJvcgqZjir1gLKZdAhH4giF07ulUocXK0UwHEckSmOc\nvRaPzl2BGy6cEd1vXo/4gN18UJbKzCRkhgqXUu1CNnZ4dFgubxhaDQW9SKm7Ul5Ycg0Pz4WQwRJz\ncS9g65FrgLOaJEESBMYbLVg3fTFq7LVgw3Ew4XjWvkoEWNUMVpWRxogUuVutVrzwwgtZ28eMGYNn\nn30WADBv3jx0dmb7ffwtIHaTF3yaPLl9mqTCxmgwQ1FoJhTu8OONRiXfMBUyHVXKD8Zm1uF0rxcu\nbxjjdEkhf7Gl1HLI/IwbQgHR/RrC4tvzkczMKJs1KsY4kzfOHY5OQppm4PGH0VBnFC11kzoVCC2l\neIClb3XAt/cU2Cg3Jkll15c0PDwXgp6QypZfFLL1yNRc8dAsi6e+eofw2mc9DcDNCdAzxuHwC0Gx\nblypxBPfi2qAVWWkMSIzWFXyQ2ookEZ11k2eIgnQNIsntx/Btj+dyDlsuVik6Ip4x3apGaxcN/Ny\nTUd5PxhCz60hKItOFj+YXK75tDcMkATIGuUyWMlVP3cOEYv4v8Wpr8E9+3fgsSNtOOTsKerYlSh9\nhdr74XmdC3pDJ5w5B9AqpScsBo+fK3VbRQTuPJRFh7g3XNYQ2EKwMRpsmIZmvAlj/2VJycPDc6FW\nUzDVaOAyNAJ1yZZY4srbCmof6/XiYnJ+gDMPf9+Iu7P/jm5vGOYaDdQyzL6sZrCqjFSqAdYohbLo\nQfvCYGluhdvR7cL5fi5zwbLAgDuEtn2nywqypHQQDnq5Ib01huLsGXgE09G68QCfMWhdJIvpqL7V\ngZrFEwAANUsmyNZ9BWQ//OOeMCiztmzH+HzwGSx74kFiWHyL6H5tYyeAAYtzQQ9+03mgqCAr7uSu\nneCR8xh4/mjO4KdU+MHhjJ8T5zND3OBwsfcx1WigoshhKRHm01/xUGYdEGfABMRNNeVAmAqg4Ogi\nm1mHYIxA7PZHQXxtI7dxyC26L2ck2oZ79u9Af8gvus8N42ekvc7VEBCOxhEIxUSbCEqB9oRBqCmQ\nEu89VaooTTXAGqVQFh3nUp4QAucbtlwqxZa9GIaF2xeG3aIvydOGbFkIau0mkBv/C6BUwIVusCxT\n0jlnws/Wo0VW0aUwkAhyjpzsE7KETDQONhRXVOAOpGSwEmUVoqmFC0opFUBS6DOa8dvJM3HEPjbt\n93b35tdk5fJckjPIytfVmQlBELCatXB7I4pmicTI54HFU4lOQr77TsmSsz0lG0tMnQNo9GA/3gv6\n5+tBb/sRmI6DALJF7XRC2G7TGtI0V5mdq/zCjE7xNOvoduGl19oBABcHgmVn2bnB4WFQFp1sbvdV\nqsjFiNRgVSlM6k1eZdVLG7ZcJHxQUij17vFHwDCs5A7CTAh9DYjpC8CePAB80QFMnFH4lwrAZwDi\nMhhXdnS7sPeDpLCczxKSsxthgvIlCpc3DFPK+Bb2r/sBlgVx1e0gL1uK/7V/h2hX1/kcmhkepT2X\nAOldnTazHv2uEHxDUZhrlSu7ZlKM+DqtnDo+2+1dDviymqIZLAvvmh/CWNcJIMr/Ldg0u5RdAXGj\nZ4NKgx8vEM+iAomGDyKZjevodqUNmw5H4sLrXBMfCsEEYmBjTLU8WGVEUs1gjVIyhe65tE9iw5aL\npVjxqCC+lqFNmph9NQCA+fOvslbSpUCZtJxLuQwZrFzZwM9PcoamcozjyQVfVuH/nixDgz3+LqDW\ngmjlxpw0GMSbERiWzavHUtpzCZDe1TkcOqyObhe6znAlsj+9/XnO7EolOgn5rI9KwQBLyGB5wnnt\nUnKJ2gsF7sLsxsR3T5ksu/KZvipVSqUaYI1SBAFp4gYjZdhysQiz9bT5E518WaXcDBYAsL6Eu380\nnG48WmKQRZAEJ0rOYXgohVxZQiTKtEquovlMpM2sB9NxEMxvvw/4XQClAtt9HACwvCl3xi+fHktp\nzyVAuu1AOMK19v/xrS5ZGjYKwWdX4jSXARzw5NYw8hmsuILBn+A/p+A1xQexg95QTrsUevBcTq+r\nTFG7GJRFDyYQBROllcmyVwXuVUYw1QBrlKIS9A3cDaal2YYVV06Glp9NaNJhxZWTS069F2MIycO3\n08uRwVLCeFRl1XF+PKHyRMm5soR2FfeZK6nB4jM51tAFsG1bAX+ibBMOCAHo/PpJWDd9McYbRcwj\nE4jpsZT2XAK4hgNjouEABDc4PFdXZ0e3Cx+3O4XXcjRsFEJKdoUycaUvZTVYYZCmwv5z5aDTqmDU\nq7lry94ous8FXe6xW5midjGEbJ83rFCWvfhGnCpVKk01wBqlEHoVCE26H09Lsw0LExmrJXPHlRxc\nAQnxPMMKw57z4fKEoaJImIyakt9PQAHjUcGYtcwyoVg2cGKYRtMQF7h5Xv9M9u47gAs49h/hvMIO\nd4fQqZmatQ8fgM6vn4RH564ACXHBr1hZh7e04B+GhF4li6VFJrz3lem6qXltB5QoJRVCSnYl/Nkg\nQBCInfMp0nHJRDn/uUqUvXQaCr6hKP4veyO2m2/Lurb2NEzM+p1conYxUrWiSmXZgWoGq8rIpBpg\njVIIgit90Rl+PHUJzcaAW1xXUyx0kforhmEx6A3BZtbJYm6aayVdjvFospOwvDIhnyXkbRImxxgs\n8cVAxLmOR3pQ/u47vnQVSpTMvDBid+112UFWRgCaS49FghD1yNK3OmD/5qUAAE1DrezBFQDQroRe\nxpY/26BEKakQxWZXeLsJMNx3TomOS7oCFg0Ad23x2WcWBAZVdu7a0nLX1mFrfVZHKkkQeHTuiqJn\nXQoZLHcosQDkjkeAu1eVk2UHEp9VYlh5lSojjWqANYqhLDqwsXQ/Hj7A6neVF0zwwtR88706ul14\n4dUToGkW3qGILCUcJYxHhU5CGXRYLc02fOvmr8Bco0HrUPYIEEDceqBUcmVtDuvnpG/ICEBz6bHi\nLJPTI4vUq0Ea1IiXee3kIu7ign6qQIClRCmpEMVmV6TYTZSKYNFQRPa4HHJdW3+y/h1CJIWpQ16Q\nTLpdSjG6q1SSXbzc/USfmH6w/O+asfammWUFV9xxQ6BMyvrPValSKtUAaxSjEvGZMerV0GtVgl9T\nqcQLpN75zIo7MfIiEqVl0cmkGY8mIBZ9vSzjUb7MKadmps5qQG1M3KtLzu67nNkcKn2GXGYAmqrH\nIgkCKkL8q56pyaKsei4rGpfHhywV2h0GoS1sCKlEKakQLc02GPVqEABIInd2pRIdl5UwGQVyX1vq\nmBYfOBphiUUwx+1M+1kxuqtUBEuLxL+pPxE8OmzijRVSYCIJ/7lqebDKCKXqgzWKSXYShqEZz60s\nCYJAnVWPsxf9iMZoaBKeSVIpVCLMp5Mpd1VKtiwEWhaCPdcF5n+2gM2lyyoSqlYLUIQsGSweh00P\nL0XASmd3WMnZfWe36DEgct42YgggKcDWAGLBCtEAdH79JKGUc8/+HaLHz9RkqWx6xM75OH+1uvIf\ngjwswyLuCUFdLz7jLxX++nnvaC98Q1EYdCpcvWBC2ddVPmJxGsFwDI31Nbh9eUvO/VR2A+ID2UGW\nnH/zZAZL2QAr17UV0UTxbt14XN13Fnf2tOOu0ycxaKxFYM4yTC2yNMjDjfXSCPeTAXcQFEnAKsMM\nwqQRclXgXmVkUs1gjWLoRPbIt6srTWwr6LDKyGLRnjAITe5sQ0V0Mo1TAXMd0HWkLE8s3qqBdodk\ncwZ3WA04aRRfn8jZfZczm3PlHFDf3co54BeR3SvWI4vXR/HlPLmgvWGAZovOyrQ023DHilYAQIOj\nRtHgCuA6FVm2cGalEh2XgkWDwpMBcl1bLosLEwI+kAA0DAMKLOoDPjS/98eSvn+UVQfaFwEdozHg\nCcFu0Zet1wy198P9R84RPtw1oEhzSZUq5VINsEYpofZ+BD7sFV6nim0dVu4hIbY6Le7YTsQHgmCj\nNAZ/97HozasSOhm28yPAm/DFKtMTS2XVg43QYEPiuimpOKx6nNFR8NcmAtAC1gOl0tJsw7harlxH\nsAzq4gNYPn5IcsBRrEcWr4+SM9sHpGRlJJSGjHo1DDoV+mUO9sRIlq7yB4B8x6XKkfh3qEjZ/+aV\nsGgAuGtr+qW1YEnu+oqoIzhffwH+Gj9uuHBG9HdKsUtRJcZ6uc/7QNMsHGVm5pJzLTn/OSYQk73R\noEoVOagGWKOUfGLbZCeh9Ickd/P6THidq0uqEjoZOT2xklYN8gQO5lot1CoSdIwBSAJjHlyS13qg\nVJiOg4i4XVCxMdzn2oo13j9g2rEXJQeZxXpkCRksGTVFAAThfKEOwkwcNgN8gahgPKoUfBDHL07y\noW91oO6uuVA5DCAIQNdSJ9t5MFEaTCCqeHkQ4GYM/sl/BINmTjfZbx+Av4Yb5Dw2FBD/pRLsUpiE\nVjH2+xNYPhhBU5gu7YQTVKLRoEoVOagGWKOUfGJbu0UHggD6S7BqKPbm1dJswxWXJi0V5Gi5zkJG\nTyy+I6tcqwYegiBQZ9HBGKFBWXWKdTHFDu6Gi7KiLu4CmeKoXUqQWYxHFmXWcaOFZO4k5AMsqcLt\n+kTGq5RrWQr9riAIIndmVgyV3cB18SYyKXIgaB8V7iAEgF1nuQaHsIY7f20kaXUwaKwV/yWJdimh\n9n5EOrksNAHASrOwfXyxrGxTJRoNqlSRg2qANUrJN95EraJgqdUmdCXSNEdSbl56HadBuuGrk2Rp\nuc5CRk+spFWDfBqxBqMWahagFRxG7PKGwRAUHPRAxg9KN93M55F174H/gUcXR3hwSDa9GpDigSUx\ncHDIZDuSD5Zl0e/mvNzUEspy/HdQzge7YI9SgQwWP2MwouUCLF2Uu45JgsCYq1aL/o5UuxQlsk2V\nGO1UpYocVAOsUUohsW2d1YBIlMZQUNp4GCk3r75EMFZvyz1Ooxzk9MSSy2w0lTEU9/UJ6krr1CyG\nfvMUAIAjnrHiL8N4tZBH1kVtBFSUxdGzp0t+j6xju0KcrkhiVysvOldSh+XxRxCLM0WVB1MRAiyR\nrsJSKXbAeqkccvbgsSNtuGf/DrCJjGiciiNOxoUMVqPBnG6Xwnd9Tr1csl2KEtmmSjQaVKkiB9UA\na5TCi23JhIMxaVSniW2Flb/E0oqUm5fTFYCKIoWhsXKTfpNPXKoGM4hp8yUfK3qOW62HOwdkG29i\nTkhJ3AqaHPbbuWDIQQ+mbS/HeLWQR9aAjtM7HTl1quT3SIWJxDldkUT9FcDN1FRRJJwKlghL9WZK\nZrDkObdQez8CH50DAPjf6ZZdtH3I2YPfdB7AuaAHjBBeASC4LJYmrgFJk4LXFdmykOtSve9XgFoL\nfH5UcjevEtkmfasDxgXjhHNXormkShU5qPpgjWL0rQ6ox9Rg4LdHoJlkTbvBpArdJ4/PLWzORNdS\nB++eLiDh76Sy62Fc2JR184rTDAbdYYypM8gzIicHvCcWADBv/A7sX/cDp44AEoKsXMJ9AGXdlPWh\nGCIALsbKE+3mo3+I5roHdQwQzO97JYV8HlmD+kTW0y2PtijZQSj9oUqSnK+b0xUETTOgKPnXhEmB\nu7Tzo6ycXk2OAEsYwZOAdodluUZT4TVXmahJEhFtBMaQESvHzM0ag8N+/gkQ468FNtnNCxS8Do2L\nmtL+XcL2MrNNpJ7r3rXc1ALdNPmaDKpUkZNqgDXKoSw6QEUiPpDe9eMbigIA3jt6Dh3dLiyY1VCU\nRooJxIAYA+1UG6y35m7tH3CHwLCsIEKuBMT85WD/uh/Mrt8Cr28F7I1FBRv5dCDlPLwYTxgsgLOh\nKFiWLWigKfn4QT/6wyrYWB80334chCq/A3qpNBjMOBf0CK/5DFZdWI179u9Ag8GM5U0zip4/l0mp\nAnceh02PiwMBuLxhWRzAMxECLInHJigSlFWH+GCw7L+/UtdoKhdEBn0DAM2y+ObseWjbdxo2xpT1\n87zdvAW+e/y5u944BSpKgzaoYVs6uex/UyxRllXVKSNPqFJFDqoB1iiHIAmo7HrEB0NgGRYESaCj\n24V3DiVv2APuENr2cXqaQkFWvJ8L1FSO/DcuZ+KhVJ+jBKAEbF8P93/oRIalyJW0EjoQlmURHwgi\noqUQjNIIhGKoMWhKPl4qTMdBsB+1we0aQsx6Bxw6RrHgCuA0Wb/pPCC8HkhksGwhKm1uIYCSgqxS\nLRp46hPaKKcrqEyA5Q7BqFfDqJf+GavsBkQGQ2ACUVA1pTc7KNUZd8jZg11nT+JC0ItcLQuNBrOw\nUHKKNROU2c2rb3Wgt6sfEztdUM8aI0vAGB8IABRRHZNTZURT1WB9CVDVGYE4I4yOyDfGphB8Jkxd\nKMBKPBDGKCRwF6NUXywldCBMMAY2HEdIz61Rnn3lOLb96UTZsxiZjoNg27YCA73oV9kBAA5XR0nm\nqsWSqcmKqoEgRaMulB5wZM4tLJZySoSAskL3UCQOfyBa0GA0F0mhe3mBkBLXaLbmSjzEumH8DFhN\nnK+bU+wzLrObt6PbhZMu7r5ytt1Z9neEZVjEB0NQ2Q3VIc9VRjTVAOtLgDoxM47PPpUzxibWz6fe\n82cK+gYDoEgC9kquIEtcSSvRdcTbDpyPcuU0lk1mCst5gKQGi/0qTlviiA+U5Hv1/9u78+Ao7mtf\n4N/unhnNpm1Go9FqQGCQLmA7GBB4e/VwjB3iJJRj51aKFDH3JSS+jv0cL3lOXDcvSVVc9ap8bb+y\n6yYmZIFU4vjdKl+c6+06BhMwxGCMHRkjIcsIgfbRjEbSLJqlu98fPT3M0t2zSldlA90AACAASURB\nVDMS5/MPaDQaWj9muk//fud3Ti7kGlm/uOnrECDCbYqiNqQDm9DzObVvYbainiCgY8HmWc6iLr5h\no/ilGnIpMKokHmAVGPzNxXtUK+eKZRi0WGrwrVU3YEP9UjAMA0etCZ6pICIpjb4L2c0rN4X3AuAB\nGAORgj8jvHcWiAoZZ9kJKTUKsBYB+UQjbxcvpI1NfOpdI1+GFwRMTAZRV2uak6RjVXneSae1N+GY\ngncdyWM9pfD7ZzNTqCohiHRxsQCLnyio7lWuGs3ViDIiOJHBv5xsxX1dDVgzYU7rW5iNYPc4ouN+\nICqotl3KxKDnYDbpMDg2g2f2nyrKTCEgXfzfONof/3s+r1msUg2mDgf0TbHingXujJNLMSTm1SXi\nRRG/uOnr+Jd125KWfB02M0Qx/QZNcTev0QJmxbqMxyJ/FkSGwZSOQXVUBCOKBX1G4rPsRWxGTshc\noABrEZBnmyKxE0++bWyynXr3eGfBC/Ob4A4UdicttzfRN1YCImC82l7QscgzFtO69HEqqOF1LIgU\nAYzr6lDJT8Mohgqqe5Wru6JLsWxGCsZZMGgIGHBPXx3WTJiT+hZmkm3bpUx6+j0IBKMQxeLNFMoz\nK/6glG824w/n9Zo6mwlgilOqQQzxYAwcnI/k33YpcVlQTZNKoVk5n1JpmVAu2cB9/1dgrt8KzPql\nHb0ZJAZrXh0DHQArLxb0GYlOZDfLTkipUYC1CLBWAxijLn7iaV9mw7Zb2uJLKywDbLt5WcYEd94b\nzGrqPV5gdB4T3AGVO+lKG5hV2Zds0NVbAEEs+IIoj/U0lx5gFdLwmrn2v+OcYQV+X/2PCLJmhBgj\nzhlWFFT3KlcNZ5UvfjcPX95hlk0+VrGqeBeSUzjXr8noWHDVxoLfT2KER9QTgM5hKWg3otqyYCK5\nzlWqQCzYfPtvA5qzhMz6OwCWg/jOixnrYiXOpntjVfJromJBn5Eo7SAkCwTtIlwEGIaBrs6MyNA0\nxAgPRs+hfZkN7cts+M/Dn+HTgUk01lszvk40ln+Vaeo9voNwnmewgJS6WG/+GuLZ4xD2/i/A582q\nbIPeaUUQQGTMB70z85ioibqDEMx6RBVm+gppeH3u0gzerLwt/nWYNeDNytvAVrShPe9XzY1asOBI\nSHof9HszlnAo1s64QnIK5+M1dXVmhPo8EAIRsOb8dntGJgKACOjrCwsa1EoxAECLpQZ3tCj/X/X0\ne3Dsw8vL01o7j8VLPYCQUPtNYzfvxrWN8dfxxmZ7a6IC1hbwGYlM+MEYOLCVxdm1S8hcoRmsRUJf\nZwHEy1viZU2x2ajhcV/G14hkUaKhp9+DM59KSzxvHb9QlFyYvNUvkf6c8QCicPlEr7HjTr6ARcf9\nqs/JRJiVKpMbnVZsu6UNNbHkbWOFLu+G10LPCfC/fQInx5QvGgXldeVIbUeby5TcdimxhIPSkmGx\ndsYVklM4H69ZjIru8vtR58w9wFJqf5OqxVKTlnOVKJcZvVx287bEbmIMehbTOqlN0qoac959S8Wo\nAN4ThK7OXPS6c4QUW1kGWF6vF7t27cLWrVuxa9cuTE2p35XxPI/t27fjO9/5zjweYfnRpewklMkz\nV9kEWPGpd4fyhVHOW4nGqry7vbMF58IUQi0HRGvHnc5hARggkmeAFex2wf37jwAAkdEZLJnlsfMr\nq8GxDKoshryDK/H1PcDkKDxcreJzCsrrypHajrajTdOqP6O0ZFisnXH55hTO12sKIWknqeelj/Nu\nwxSJfT71Wcw0J1Jtf5NCbVlQltOMXg67eeXzzsa1jfjOvdeDNeth8ufWHzVR1CPN9NEOQrIQlGWA\ntWfPHmzevBlvvfUWNm/ejD179qg+d//+/Vi+fPk8Hl15koOiSMpuJqfNDI5lMOLKHFBEXX4wRh1Y\nS26zKPM5u5Ikj7INjI6Fzm5GdNwPUVS7HCmT25nI9cbEYBRTr55DpNeNhjoLXJMBhMK5t81JDAht\n/KTicwqZrclVvM9lbLkrauVwcHUAZx3qS3uDfm/aDkNDi5SzxRg4gGXy3hkn5xSaKqSMhmqrIe+Z\nwsTXrI7NPLKMVAoin9cMdrsQ/GhU+kLMP5E/OuaXxijHvMZcSjFoyWlGL4fdvEOxAKu53hpPZeCn\nZiHk8TkBKMGdLCxlGWAdPHgQ27dvBwBs374db7/9tuLzRkdHcfjwYdx9993zeXhlSU74TN0uznFs\n/OIf1uiZJ4R58N5Z6BzqU+9zkQtTkDzLNujqLRAjPPjJ3I5bK2m72WmFKAIjrswzhWkSAsUNwdOK\nTylktiYfpg4Hqm5fAQCoubYZO7ZtxS9u+jqazep9LVOXCyMjMwAAS2cLGgrYGQdIAdHWG5dKf2+z\nFxRcAUA4wmPaF0Kjw4KHdq7Hzi+vzus1i5HILwoiohN+6OwmMLrsTsn5lmJQk8uMXi67eYfHfeBY\nBs7Y+elySZn8ZpApwCILSVkGWG63G/X19QAAh8MBt9ut+Lwnn3wSjz32GFi2LH+NecUadWArDWlL\nhIC0TCiKwKjGSY13ywnu6lPvc5ELU4h8yzbIyzCRLJZNE2klbTfXSzWMhnJ8TQCA9XLQsirchxWh\nzwAADIS8Z1aKQd8g/U6R0Zn4Y19o1V5qAi4vF0ZGfUmvU6jmWD7P0NhMhmdmNjzugygCLc7Cjq0Y\nify8NwgxIkCX5fJgIaUY1KTuPAaAO25aqvi+S9rNy3KATprxFo/9R9KuwnCEx7gnAKfdDF2sXpw8\n0y5vqMnV5RpYtERIyl/JdhHee++9mJiYSHv8oYceSvqaYRjFGZV33nkHNpsNa9aswYkTubURee65\n5/D888/ndsALAGPUg3f5MfrUu9DZzbBsaoWpw5GU6H5VY3ozVyC7BPf1q514890LaY/P9+yKjG3v\nhIDYEpt7WEp053QQ39gL/uTrqjsK5UTi6LgfaM9+RkVnNysWlNTZTWiMJc/nevEXQ0EgnHwxDrJG\nQBTxnfUizGtW5/R6xcRZDWCrKhAZ8cWbGcuzIW8OnsWgX/kCL+8w/B+9jWiBDvqG/HdrJjIadHDY\nTBhx+RHlhfhFOx9DY7GlqwJ2kgLa74lsRcZiQUOWOwjfuPRJxudkyrlSIu88PvjeAP5+zoWaSvUb\np6TdvO/9J8TjB4Cp2LJobLPJ8I2AKHJoSghi9XX5z2AFu10I9UvvOc9LH8fPb4SUq5IFWL/73e9U\nv2e32zE+Po76+nqMj4/DZku/izp9+jQOHTqEI0eOIBQKwefz4dFHH8VTTz2V8d9+4IEH8MADDyQ9\nNjg4iFtvvTXn36NcBLtd4OXZq4RcEABoWirNkAyrLF8Fu12YOXIBAOA/OQjGwCmeuOQTrrGCQzgs\nwFZjxMa1jSWZXZHJJ/p4ojgvJRxrbR2Pz2CN5TbbZNnUGh/TpMc7W6WLf60JIxN+8LyQscK93NAZ\nE0MARKBhGRCNIOoZx6i+AQ4zYF6jXm5ivugbrAj1usFPh6Crlv7/N9QvxYb6pZpLVKIoom6ahcsY\nwaXpQWwwLi3K8bQ4K+HyBDHq8qOlgJmxwbEZMIyUG1QIrfdEtuI7CDUCrMSmzYJqKrt2KYZstTZW\n4e/nXLg0Oo2mLMZH7D2l+PhQTx+AVUljLM/4BU6PIHxxKusgSc5/jL9OwvmNgixSrspybW3Lli04\ncOAAAODAgQOKgc8jjzyCI0eO4NChQ3j66aexadOmrIKrxUorF8Rs0qOmsgIjCond8olLDEqBCe+d\nVU3SHRiRdpHdtnkpHtp5fd55K3Mhl63jrFEHtqoi50R3U4cDFatiFeAV2pk01VvB8yLGMvSlS2zo\nDPliOdoPZuM2jP3j/wEPDlctbcj6uOaSoTG2TDiSPjOntVxon9XByLMYsobzbhKtRF7SGyxgmTDK\nCxid8MNRa0aFobB7THlDABdrFs1UcDkn8mfaQZi6U1BNplIM2WqNzepdVPg/V6Sy2WQkIgWMTQ7p\n9YLdLky98Wn8+7lsCChW0VpC5lNZBli7d+/GsWPHsHXrVhw/fhy7d+8GAIyNjeHb3/52iY+uPGXK\nBbGY9AhFeDz7+w+SqjTncuK6OCwFWK1Fyqkpqhx3FOrrLRACEQg5bhkXQ9JGgfp/7kxL2m6OXfyH\nMsyMaQWDl2L5ToXMzhSTPh5gpf9OG+qX4lurbkCLpQZsyjJ+k0/KyxmyhuJLhrn2MVQi11UqJMAa\ndfnBC2L8tQpl6nCg7p/WgTXrweg5GNvrsv5ZURQRHfeDraoAa0wO9uRE9r3njmf1WvksCyoxGfVw\n1JowPO5DlBcy/4DCZhMBDEb0DbBVG2GK/V6FBEnFKlpLyHwqy0rutbW12LdvX9rjTqcTv/rVr9Ie\n7+zsRGdn6ZdTSkkrF6Sn3xNPvk7s5QYANVmeuMIRHiMuP5x2M4wVZfi2sTfFZoRSqO0ojFVgd/3y\nZFK+mhZREBEZmgFnMylW7J6N1UM6+sEgus+71ZdPNYLBwVFp6apYF/9C6ZxWqW6YymyGvFwIIGnJ\nsMUnlUAYsoQBJBcllX8uHyajHvYaE4bHs1uKVSIHZ80FJrgnYhgG+pYqaTl1KgRdFhs/gt0u+I5f\nhBCIgDFwCHa74u9BedYqE5Zh0GSuLnhZMFVrQyVck0GMuHxobVDO25QxG7dJM7Ix5wwr8DfzBkSg\ng39qGt3HTqLjxo0FBUnFyHUjZL6V5QwWyZ1WUUet+lXZVtseGvNBEEUsadI+2ZaK6s7BWX9av7Rg\ntwuh3tjO1BxqF0XH/RAjfLy+U6Kefg8OnbgY/1qzIXG1ciAXqW3ByIQf9bbCl66KhTVwUhumMR9E\nQXs5NXHJsNlnQJQRMRYLsBLtPXe8oNmsFqcVUV6I98TMVk+/B/v//AmOfyQFuMFQ/gUvlRhapZ17\nkUH1wsiyeE21WOcFMcwnvQez6SnYYqnJqRRDLlpjm2GyWSZM3FV4zrACb1behilOyvsMwYA3+lh0\nHztZUGV/83rlkiy5Fq0lZD5RgLVIxItDxvpzsRZ9PBdEq35VttW2L8byr9R2IZZa2tZxfWwGwTeZ\n1kYn36WKcOzCaWhJ3wKfTRFWoecE+H0/Brxjis8dab8dgiCW3RKsvrESiAoZd37JS4ZXmWrQEDBg\nzBxGVOUMo9ViJxM2Nvv4pzd6NJsSJ5K7EExMXv4svP23i0XtQmBolj4b4UH1ivcytffg+cNncd/R\nFzVLMMiKtSSopMVpBcMgvmSdCdveCW7nT/F+pfJKwvt9MwVV9mfNsfOaWV9Q0VpC5lN53CaTojB1\nOGC4qhquf5PuFuWTj73GlHRhkdlqjKhYXgswADgWEETo7CZYOtOXywZGpsFxTFa7ikolces4v+/H\ngHso7TniydcRdd+W9jiQealCvnDqFWawMhVhjSe2p2IYwN6M3uVfxOELBgBR9A5Mot5uKZsNBPqG\nSgS7xhAZ8WVs5bKhfimuE+xwi3+HtyZzr7g3B8/mNPvS0+/Bh93j8a+1mhIn0gqAizXOOocFTAWH\n8KXMM1hqy2U1flYzkR0ozk7BTCoMOlRZDBge9+GZ/adgrzFltWPYI0pLyumPV8bPKf4Tl+J1sKw3\nL8kqSAoPSAFnzZfb4zOFhJQ7CrAWGc5igK7egvDQNMQID0bPJXW0T7RxbSPCF6cAEbCsb0blzUvS\nntPT78F7fx+GZ2oWeh2Lvovesrnwa1Jrl+MZySufQxRFRIamwVYawFVVpH1fK4gFNPoj2pvx6c3/\nE28cOQ9AyuGa9oWzChrmizAbO663+hA4PayZrxbsdmHmcD8AYNWkCWtMZpypU1/KkxPgG83V+EJr\n5qAh30BpProQMCwDQ3MVQucnwfvC4KzKLacA9Zyi1IbaqbJpe1MMPf0eTPmk5d3UvE2tcbYxPriR\nfgNiY6SZMFOHA6YOR1rZhUzCA14weja+6YKQhYCWCBehiiU1AC/GZ1yUqjTffqNUpTkcK9xXsSy9\nBYq8rOKZki5CkahQ0ubOOdFoo5PPUgU/GYQQiMDQUq1Y+DZjqxGNxPay6/GYINjtgi9WIw3QzleT\nL5pC7MKsC/C4p68Ot8xo76pLTIDPtGSYb6A0X10I9LHl43CGPKzRf1D+d5UaaufSU7BY8n1Pblyh\nPMO5PnImKReyIlabL9Sv3HszEe8LIToRgL6lOutWQoSUA3q3LkIG+eR14XIeR/syG3Z+eTU2rpXq\nK+ljJ6rQhUkwBk7xzrCcL/yZqCa9u4dgeP95VK3jLvczYxlU37lSc6lCDlblPJtUiUGsHH8trRZw\n9dH/C/6Zb0t5YEpsjeXX4zFBLvlqas+9fbwO31p1Q1b/XqYE+HwDpVx67RVC3gARUcnDkksvPBf+\nCKMmKRDlIWLUHMa/r5hIm+2by0R2Lfm+J69a9zkAInSIghUF1PEe3DHzF6ya7krKhcTAaegbKxEZ\nnoEQ232rJjwgBasVS2hpkCwstES4CBmaqwAdi/DAJIBlSd9btdSGkx+P4tyFSbTVmMF7Z1GxwgZG\nYbt7OV/4M0luozMkrXMAsfWOQVRM7IVx225Mn6/H7Cfj4KrVlwcTl738HwyBMeoUgzG51QjPC3jh\nTx9gbHIWwuQQWI2cGmbjNtjPmTChMNal6vGYKJet9VrPTWyxMxyYgqBR4FWrnIPWcreW9mU2nD47\nhtEJv5T2lmVOUa70DVaAZRDoGkXgo5GkEiCJpReMUQaOWT2GLCHsWau86QGY20R2LZmWvNWc6/cA\nYHBLZxuua68Hv//HQFg5F9Kw/J8QGZlBeMAL40r1Wc5QLP/KsKQ2q2OPd0lwDwP2JtWWWYTMNQqw\nFiFGz8HQXIXwgDctF6Su1gRbtRHnB70IWKWTZcVS5RNXvifZciEnvfP7/7dijSzx5Osw3vAgZj8Z\nR6h3Aoam9Fm81FwRfnI2Y4sOjmOxMnoBXewyDOhbsSxyMeGbeulO3tYYP/Ev8w8qBlil6vGYKJd8\ntUzPVauZpWZf73v4zbm/JeVnyQHRyY9H4PYGIYpAW0t1xkApMBvBuCcAe40JO7/8D4rLvMUw2+sG\nEspZyEuqv+45hm7H5RuTlZMmcCKDHlvy/7ueZcGL4pzUtspFvoFs93k3WIbBSvmc4lbPhaz4fC38\nxy8h1K8eYImiiPAFL1izPt4oWkvaZhKNllmEzDVaIlykWLMUO7t+eRITvz0dz5lhGAb2ahN4XkTv\nsQEAwKBO+WKzfrVT8fFyuPDnRC3/aWIQuj//CAwTRfCTIcW2OfmWdOiY+hAA0FOxMvkbogDuoT3g\ndv40fsKXZwprKivAMgzqak3YdktbWSS455KvZl6Xfa0irTY7sogoJOVnPX7iAO47+iL+n+c9dGyy\n4IEd62Cs4DAa6/+opfszNwRBxNqr6+YsuALU3y83DVchmrBM3OGRgoWztuSA9JtXbyrJkmAqpbzN\nm9Y1Z9xIMOYOYGlzFczGWCFetVxIgQf7l38Fo5fSFJQ+e8FuFyZ+/QEEfxgiL2C2ZyLjcefSMouQ\nuUYzWItQsNuF2e7YySil8fOAkUO4dwLb/FFU8yJ4AB8fH4BgNaiePE0VOoTCfFk0d86LWpV3AAyi\nMOAiQoE2hE+eQEXnpqTv57JElrg04YQIi+BDr2EFPrUth42fxIbgaayqSl5enfKFcH5wCk67GTvu\nLM1ykJakrfUTAUAE9K3VirN3YlRqI8Ra9BCCUdWSHwCSlgwH/ZlrPgHAZFj6v5ADrm+tAjra7Piw\nexyfDU5hpcoSkiiKONM3AZZl0NE2t+9dtfeLI3i58r+eZ7BiygiXMYIJk5R/NB+lF3IlL3l/dtGL\nV97pw9RMSPW5Pf0evHNSmql1TQbR0+9B+zJbWpX3RIx7EBw/hmjEibF/PZa0nJo6cyyGeM2Z48vN\n05U/56q7igmZQxRgLUJasy4XdQxunL68FZwDcON0BF3HBpICJ1EUceqTMTAMsOPODlRZ00sTLBRa\nJ3kAqGAuICS2wXvUD/Hou0kneq7GCH4yPecsdYksdWmi17ACflbaUSWCgVtnx5uVt4FZLqAD0gXp\n5Mcj8SVYp0qV63Igb60XRRETvzqF6OgMhNloUu88URQR/PsYwDGou3edYiuhVPKSYbZtYVLtPXcc\nrawdZtjxX+/247W/fpaWW9XT78GxD4cwNROCQc9hYGSm6DcI749fwBuXzmIkMIV/NjfC4U8/rcrl\nF9ZMmHHbxRoYBBamqIg1E2ZsuvG6sgqsUi1rqUaV1YDu8x7cdH0LTCmtsuTdxrIZf0KZkcRcyJTg\nZ1ZYhihis+QpN4Ja57DUAEu1xlwitZZZhMwhCrAWIa1Zl6tUFoVbXZd/pqffg3dPD2LaF4ZBz2HY\n5V/QAVZSwrtnBBD4pO+Loj72p/Q7Jp7oGZW+i/Kyl9qd8/umdYo/977bAiblggQAXb0TaGmoKuvZ\nQYZhYLq2Ab4jAwieHYclYUkwMjSDqDsAY3tdVsFVotQEeA4MImq7LlN4p8MwQyohAiTXawKQ9Pdw\nhC9KfbHEgKraYIrPrAHA4UYv7ulLzyc62jSNNRPmpO9Zoxzu6atDdbsFqM/7cOYcyzJocljR0+/B\nL/70Eepqk4PYTLXJ4rmQKbtpA+I1ij/nP3IOUZUC8vLMcVIiO8tl/B1UdxUTMocowFqEtJKNq1zK\nwVd1VMqBSL0bLdZFqdSSqrynJL0HxNWKPzPz6vsQUAXWArBmM6LuIHRWESamC4Y394F/twaYUa4J\n5uGUl6s83tl5qSo+V0xrnPAdHcDM4X7MHDoPNraBQpiRSg5wtfltgEhMgM9lRsvuVR6v/3ivGzqG\nA6dwistmnBODqEZzNVZW16N3ahzDAW/SntDE4ApArMzCBG4eroIzoAcDBqccMzhTF8B9XQ2K/5bS\nrEw56en3JNW+Sy06mvVu45Sleh7ptfcAIDotgOOC4HlL2vd0dlP6jJVW/l1dC+0iJCVDAdYiZNnU\nqlglWQhGlbpYAABm9Cye2X8KrEoC8EK4+GcrdclQ7UQvQNpVWBH4EFb9RYCZBPwJl1eV4AoAbPwk\n3Dp7+r/NQnFnJrAwyl/Ilf/BS+MgB1Yy/98GobNbCgoYUme0agwmeELKNwaGsHK1dLXHAcA1GcDP\nPng9HjRlCqKGAt6segPKztQFcKYuAGuYxfc/bMLnXFasc1lVP3uZWjSVmtoNwX8d68cbR89DreJG\n6m7j1M8dBy94KJ1TGPC8cqBuCr8H8fUPsjpu1LWA2/nT7J5LyBygAGsRSkpMdgfB6FmIIR6CL6x6\nkj9j4iCKAK9ytlwIF/9spS4Zcrz6iR4AguLnoJuagpHNvoL9huBpvFmZ3vOQ59XrPy2E8hdquTFJ\nzynCjEzijBYgzSgpJcSHDWFUhNOXrxnVdzoQMoTTgqZcg6hsLJ02Qidm3qit1aKpHKjNUGm9l4H0\n3capnzszPsEMf7PCTzKQskMBFj4IMIODF2amC0Zff9bHTcuCpNQowFqk5MRkAJj47WlEVWYAZio4\ndFWwGDBq5zEshIt/LhKXDM17/g0zUxnqKInXwIjsT+6rqmbBLBfwvtsCj3cWDAPwQm4XpHKklt+X\n/Jziz8ioJcS7azxoGs9t3Dw1c9/qyVZhxi3DylX/U2m1aCoHavXwUnEcI5V409htnPi5M/WcAF79\nCwLiNeBRC6Uu0QzCcHD/nt2BKtSYI6SUKMC6AqheFFkGr9boVaf4Ey2Ei3++TLdcn/FEr7aMqITZ\nthtseyc6AHTEHntm/ynV56cmDZcztfy+5OfM3YxM6vKh3s5jGCOweW2oCBsUZ67E2IJfyBCGp8aD\nGatKBnUBbBVmeMPBpAKho0feVf8BltEsY1FO1IqOphIF4KGd12f9umx7J0wAjCdfh2vsVhT8ubt9\nFwVVpKxQgHUF0Ep6t9dUKN6dZnM3ulgknug9Y9crLhdy0Fg+qrQB/inNO2e1WYC6WhN2flk5yb4c\nqeX3JT1njmdk1JYPw+eqFZcLQ4YwBloGin4cDBg0W9Qrrqt+7hxm1N2rvMu0HCXuFvR4Z8GwysuD\n+cxyyzNa3C/fBa8Q92p+7mSUyE7KFAVYVwC1i6KlsxUbjZzi3entNy5b1EFVKvlEb00pcCgzM13J\nDzAMYG/O+sSeb+uRcpOa38da9AAYCP5wyWZk5ICrpza9/AUAwDkLBtDoCKlODqKurqrHp9PjGA5M\nZd3GRutzt9DIRUeB9J3GskLey9b/tiq7z10KebaYkHJEAdYVIPWimHghbI89R747vRJmrLQojZW5\nNYSKoQjg4fLO70idBVjI45yY31dOMo2xPNMlB0mpQVM+QZQWrc/dQjYX7+VsPndoWQkM9kq17CjP\niiwAjKjUBOoKNDg4iFtvvRUHDx5ES0tLqQ+HEEJIidF1gRSCmj0TQgghhBQZBViEEEIIIUVGARYh\nhBBCSJFRgEUIIYQQUmS0izCG53kAwOjoaImPhBBCSDmQrwfy9YGQXFCAFeNyuQAAO3bsKPGREEII\nKSculwtLliwp9WGQBYbKNMTMzs7izJkzcDgc4Djtvnxa5C29JB2NjToaG3U0NupobNQVY2x4nofL\n5cKaNWtgNC6ufqxk7tEMVozRaMT69euL8lpUL0UdjY06Ght1NDbqaGzUFWNsaOaK5IuS3AkhhBBC\niowCLEIIIYSQIqMAixBCCCGkyLif/OQnPyn1QSw2nZ3UgFQNjY06Ght1NDbqaGzU0diQUqJdhIQQ\nQgghRUZLhIQQQgghRUYBFiGEEEJIkVGARQghhBBSZBRgEUIIIYQUGQVYhBBCCCFFRgEWIYQQQkiR\nUS/CIjpy5Ah+/vOfQxAE3HPPPdi9e3epD6lkfvjDH+Lw4cOw2+149dVXAQBerxff//73MTQ0hObm\nZjz77LOorq4u8ZHOv5GREfzgBz+A2+0GwzD42te+hm9+85s0PgBCoRB27NiBcDgMnudx++2348EH\nH6SxieF5Hl/96lfhdDrxwgsv0Lgk2LJlCywWC1iWBcdxePnll2l8SEnReHaGjQAABUtJREFUDFaR\n8DyPn/3sZ9i7dy9ee+01vPrqq+jr6yv1YZXMXXfdhb179yY9tmfPHmzevBlvvfUWNm/ejD179pTo\n6EqL4zg8/vjjeP311/HSSy/hj3/8I/r6+mh8ABgMBuzbtw9//vOfceDAARw9ehQfffQRjU3M/v37\nsXz58vjXNC7J9u3bh1deeQUvv/wyABofUloUYBVJV1cXlixZgtbWVhgMBnzxi1/EwYMHS31YJbNh\nw4a0O8WDBw9i+/btAIDt27fj7bffLsWhlVx9fT1Wr14NALBarWhra8PY2BiNDwCGYWCxWAAA0WgU\n0WgUDMPQ2AAYHR3F4cOHcffdd8cfo3HRRuNDSokCrCIZGxtDQ0ND/Gun04mxsbESHlH5cbvdqK+v\nBwA4HA643e4SH1HpDQ4Ooru7G9deey2NTwzP8/jKV76CG264ATfccAONTcyTTz6Jxx57DCx7+bRN\n45Js165duOuuu/DSSy8BoPEhpUU5WKQkGIYBwzClPoyS8vv9ePDBB/GjH/0IVqs16XtX8vhwHIdX\nXnkF09PTuP/++9Hb25v0/StxbN555x3YbDasWbMGJ06cUHzOlTguiV588UU4nU643W7s2rULbW1t\nSd+/0seHzD8KsIrE6XRidHQ0/vXY2BicTmcJj6j82O12jI+Po76+HuPj47DZbKU+pJKJRCJ48MEH\n8aUvfQlbt24FQOOTqqqqCp2dnTh69OgVPzanT5/GoUOHcOTIEYRCIfh8Pjz66KNX/Lgkks+3drsd\nt912G7q6umh8SEnREmGRrF27FhcuXMClS5cQDofx2muvYcuWLaU+rLKyZcsWHDhwAABw4MAB3Hrr\nrSU+otIQRRFPPPEE2trasGvXrvjjND6Ax+PB9PQ0AGB2dhbHjx9HW1vbFT82jzzyCI4cOYJDhw7h\n6aefxqZNm/DUU09d8eMiCwQC8Pl88b8fO3YMV199NY0PKSlGFEWx1AexWPz1r3/Fk08+Gd9Kfd99\n95X6kErm4YcfxsmTJzE5OQm73Y4HHngAn//85/HQQw9hZGQETU1NePbZZ1FTU1PqQ513p06dwo4d\nO7By5cp4Ps3DDz+Ma6655oofn56eHjz++OPgeR6iKOKOO+7A9773PUxOTl7xYyM7ceIEfvOb3+CF\nF16gcYm5dOkS7r//fgBSDt+dd96J++67j8aHlBQFWIQQQgghRUZLhIQQQgghRUYBFiGEEEJIkVGA\nRQghhBBSZBRgEUIIIYQUGQVYhBBCCCFFRgEWIYQQQkiRUYBFCInz+Xx47rnn8Nlnn5X6UAghZEGj\nAIsQEvfxxx/j+eefRyQSKfWhEELIgkYBFiEkrru7GwaDAStWrCj1oRBCyIJGldwJIQCAL3zhCzh/\n/nzSY1u3bsVzzz1XoiMihJCFiwIsQggAoKurCw8//DBWrFiB7373uwAAh8OB5ubmEh8ZIYQsPLpS\nHwAhpDy0t7djdHQU3/jGN3DdddeV+nAIIWRBoxwsQggAoK+vD5FIBKtXry71oRBCyIJHARYhBADw\nySefgGEYdHR0lPpQCCFkwaMAixACQNpBeNVVV8FqtZb6UAghZMGjAIsQAkBaIly+fHmpD4MQQhYF\nSnInhAAAqqqqcPbsWRw9ehSVlZVYsmQJamtrS31YhBCyIFGZBkIIAKC3txdPPPEEzp07h1AohD/8\n4Q9Yv359qQ+LEEIWJAqwCCGEEEKKjHKwCCGEEEKKjAIsQgghhJAiowCLEEIIIaTIKMAihBBCCCky\nCrAIIYQQQoqMAixCCCGEkCKjAIsQQgghpMgowCKEEEIIKbL/D6EJ8WpndN9TAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf79500128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"frecuencias = np.array([.1, .2 , .5, .6])\n",
"plt.figure(figsize = (7, 4))\n",
"for w0 in frecuencias:\n",
" x = A*np.cos(w0*t)+B*np.sin(w0*t)\n",
" plt.plot(t, x, 'o-', \n",
" label = '$\\omega_0 = %s$'%w0) # Etiqueta cada gráfica con frecuencia correspondiente (conversion float a string)\n",
"plt.xlabel('$t$', fontsize = 16)\n",
"plt.ylabel('$x(t)$', fontsize = 16)\n",
"plt.title('Oscilaciones', fontsize = 16)\n",
"plt.legend(loc='center left', bbox_to_anchor=(1.05, 0.5), prop={'size': 14})\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si queremos tener manipular un poco mas las cosas, hacemos uso de lo siguiente:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import *"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "12c6b25cb42340ec934d5ef91ac151e0"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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1tbS0qKWlRT6fTy+99BLxAIBJwlVANm3apLNnz6q0tFSvvfaaNm3aJKn3Vcajjz4qScrJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf79238828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def masa_resorte(t = 0):\n",
" A, B, w0 = .5, .1, .5 # Parámetros\n",
" x = A*np.cos(w0*t)+B*np.sin(w0*t) # Función de posición\n",
" \n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(1, 1, 1)\n",
" ax.plot(x, [0], 'ko', ms = 10)\n",
" ax.set_xlim(xmin = -0.6, xmax = .6)\n",
" ax.axvline(x=0, color = 'r')\n",
" ax.axhline(y=0, color = 'grey', lw = 1)\n",
" fig.canvas.draw()\n",
"\n",
"interact(masa_resorte, t = (0, 50,.01));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La opción de arriba generalmente será lenta, así que lo recomendable es usar `interact_manual`. "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75e00ebdd008447db891a1b3faa04a6a"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def masa_resorte(t = 0):\n",
" A, B, w0 = .5, .1, .5 # Parámetros\n",
" x = A*np.cos(w0*t)+B*np.sin(w0*t) # Función de posición\n",
" \n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(1, 1, 1)\n",
" ax.plot(x, [0], 'ko', ms = 10)\n",
" ax.set_xlim(xmin = -0.6, xmax = .6)\n",
" ax.axvline(x=0, color = 'r')\n",
" ax.axhline(y=0, color = 'grey', lw = 1)\n",
" fig.canvas.draw()\n",
" \n",
"interact_manual(masa_resorte, t = (0, 50,.01));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"## Péndulo simple\n",
"Ahora, si fijamos nuestra atención al movimiento de un péndulo simple _(oscilaciones pequeñas)_, la ecuación diferencial a resolver tiene la misma forma:\n",
"\n",
"\\begin{equation}\n",
"\\frac{d^2 \\theta}{dt^2} + \\omega_{0}^{2}\\, \\theta = 0, \\quad\\mbox{donde}\\quad \\omega_{0}^2 = \\frac{g}{l}.\n",
"\\end{equation}\n",
"\n",
"La diferencia más evidente es como hemos definido a $\\omega_{0}$. Esto quiere decir que, \n",
"\n",
"\\begin{equation}\n",
"\\theta(t) = A\\cos(\\omega_{0} t) + B\\sin(\\omega_{0}t)\n",
"\\end{equation}\n",
"\n",
"Si graficamos la ecuación de arriba vamos a encontrar un comportamiento muy similar al ya discutido anteriormente. Es por ello que ahora veremos el movimiento en el plano $xy$. Es decir, \n",
"\n",
"\\begin{align}\n",
"x &= l \\sin(\\theta), \\quad\n",
"y = l \\cos(\\theta) \n",
"\\end{align}"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Podemos definir una función que nos entregue theta dados los parámetros y el tiempo\n",
"def theta_t(a, b, g, l, t):\n",
" omega_0 = np.sqrt(g/l)\n",
" return a * np.cos(omega_0 * t) + b * np.sin(omega_0 * t) "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e44b3612eced460f9be1c1fbd1ef8046"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Hacemos un gráfico interactivo del péndulo\n",
"\n",
"def pendulo_simple(t = 0):\n",
" fig = plt.figure(figsize = (5,5))\n",
" ax = fig.add_subplot(1, 1, 1)\n",
" x = 2 * np.sin(theta_t(.4, .6, 9.8, 2, t))\n",
" y = - 2 * np.cos(theta_t(.4, .6, 9.8, 2, t))\n",
" ax.plot(x, y, 'ko', ms = 10)\n",
" ax.plot([0], [0], 'rD')\n",
" ax.plot([0, x ], [0, y], 'k-', lw = 1)\n",
" ax.set_xlim(xmin = -2.2, xmax = 2.2)\n",
" ax.set_ylim(ymin = -2.2, ymax = .2)\n",
" fig.canvas.draw()\n",
" \n",
"interact_manual(pendulo_simple, t = (0, 10,.01));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Condiciones iniciales "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Realmente lo que se tiene que resolver es, \n",
"\n",
"\\begin{equation}\n",
"\\theta(t) = \\theta(0) \\cos(\\omega_{0} t) + \\frac{\\dot{\\theta}(0)}{\\omega_{0}} \\sin(\\omega_{0} t)\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Actividad.** Modificar el programa anterior para incorporar las condiciones iniciales. "
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Solución: \n",
"def theta_t():\n",
" \n",
" return "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def pendulo_simple(t = 0):\n",
" fig = plt.figure(figsize = (5,5))\n",
" ax = fig.add_subplot(1, 1, 1)\n",
" x = 2 * np.sin(theta_t( , t))\n",
" y = - 2 * np.cos(theta_t(, t))\n",
" ax.plot(x, y, 'ko', ms = 10)\n",
" ax.plot([0], [0], 'rD')\n",
" ax.plot([0, x ], [0, y], 'k-', lw = 1)\n",
" ax.set_xlim(xmin = -2.2, xmax = 2.2)\n",
" ax.set_ylim(ymin = -2.2, ymax = .2)\n",
" fig.canvas.draw()\n",
"interact_manual(pendulo_simple, t = (0, 10,.01));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plano fase $(x, \\frac{dx}{dt})$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La posición y velocidad para el sistema `masa-resorte` se escriben como: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{align}\n",
"x(t) &= x(0) \\cos(\\omega_{o} t) + \\frac{\\dot{x}(0)}{\\omega_{0}} \\sin(\\omega_{o} t)\\\\\n",
"\\dot{x}(t) &= -\\omega_{0}x(0) \\sin(\\omega_{0} t) + \\dot{x}(0)\\cos(\\omega_{0}t)]\n",
"\\end{align}"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k = 3 #constante elástica [N]/[m] \n",
"m = 1 # [kg]\n",
"omega_0 = np.sqrt(k/m)\n",
"x_0 = .5\n",
"dx_0 = .1 "
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t = np.linspace(0, 50, 300)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"x_t = x_0 *np.cos(omega_0 *t) + (dx_0/omega_0) * np.sin(omega_0 *t)\n",
"dx_t = -omega_0 * x_0 * np.sin(omega_0 * t) + dx_0 * np.cos(omega_0 * t)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lglKp1P7z008/jW3btg36kU4/aqXQC2LkhkCUBxdhR5lI1lJd7qu3FwJnoxK1Svie5YeV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AggP905AjyItAg000n1aalBFHQODLel+QDz+Ra9bvEkrLb4t3wAag/Nwu56hUUgdSpgvou55eY7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\nVCQkJCQkhOL/AlQ6bhozLIAcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf794352e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (7, 4))\n",
"plt.plot(t, x_t, label = '$x(t)$', lw = 1)\n",
"plt.plot(t, dx_t, label = '$\\dot{x}(t)$', lw = 1)\n",
"#plt.plot(t, dx_t/omega_0, label = '$\\dot{x}(t)$', lw = 1) # Mostrar que al escalar, la amplitud queda igual\n",
"plt.legend(loc='center left', bbox_to_anchor=(1.01, 0.5), prop={'size': 14})\n",
"plt.xlabel('$t$', fontsize = 18)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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YU6dOxblz5+zuT0tLs/lap9NBp9PZHbd3714EBgZi8ODB+Oqrr1x+3aysLKxd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\nDGwiIkEwsImIBPH/aj6RvY3ljXgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf792a1cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (5, 5))\n",
"plt.plot(x_t, dx_t/omega_0, 'ro', ms = 2)\n",
"plt.xlabel('$x(t)$', fontsize = 18)\n",
"plt.ylabel('$\\dot{x}(t)/\\omega_0$', fontsize = 18)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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YWsdvhXn8ee1ij67c1+iTq2azmSuuuIL9+/c39kv7jPV5mewsLiA2IFRG10I0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\nQgihEhLYQgihEv8L6/JnP7qOukUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf792d6ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (5, 5))\n",
"plt.scatter(x_t, dx_t/omega_0, cmap = 'viridis', c = dx_t, s = 8, lw = 0)\n",
"plt.xlabel('$x(t)$', fontsize = 18)\n",
"plt.ylabel('$\\dot{x}(t)/\\omega_0$', fontsize = 18)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Multiples condiciones iniciales"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k = 3 #constante elástica [N]/[m] \n",
"m = 1 # [kg]\n",
"omega_0 = np.sqrt(k/m)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t = np.linspace(0, 50, 50)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_0s = np.array([.7, .5, .25, .1])\n",
"dx_0s = np.array([.2, .1, .05, .01])\n",
"cmaps = np.array(['viridis', 'inferno', 'magma', 'plasma'])"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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NDx6UP4U14WXEnAjZVi/eq36DwwqPxuxmM+zSqhi1xc0338xVV12FYexeo++u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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf78f29390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (6, 6))\n",
"for indx, x_0 in enumerate(x_0s):\n",
" x_t = x_0 *np.cos(omega_0 *t) + (dx_0s[indx]/omega_0) * np.sin(omega_0 *t)\n",
" dx_t = -omega_0 * x_0 * np.sin(omega_0 * t) + dx_0s[indx] * np.cos(omega_0 * t)\n",
" plt.scatter(x_t, dx_t/omega_0, cmap = cmaps[indx], \n",
" c = dx_t, s = 10, \n",
" lw = 0)\n",
" plt.xlabel('$x(t)$', fontsize = 18)\n",
" plt.ylabel('$\\dot{x}(t)/\\omega_0$', fontsize = 18)\n",
" #plt.legend(loc='center left', bbox_to_anchor=(1.05, 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trayectorias del oscilador armónico simple en el espacio fase $(x,\\, \\dot{x}\\,/\\omega_0)$ para diferentes valores de la energía. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<script>\n",
" $(document).ready(function(){\n",
" $('div.prompt').hide();\n",
" $('div.back-to-top').hide();\n",
" $('nav#menubar').hide();\n",
" $('.breadcrumb').hide();\n",
" $('.hidden-print').hide();\n",
" });\n",
"</script>\n",
"\n",
"<footer id=\"attribution\" style=\"float:right; color:#808080; background:#fff;\">\n",
"Created with Jupyter by Lázaro Alonso. Modified by Esteban Jiménez Rodríguez\n",
"</footer>"
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"metadata": {
"anaconda-cloud": {},
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| mit |
diegocavalca/Studies | deep-learnining-specialization/2. improving deep neural networks/week1/Regularization.ipynb | 1 | 273528 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regularization\n",
"\n",
"Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!\n",
"\n",
"**You will learn to:** Use regularization in your deep learning models.\n",
"\n",
"Let's first import the packages you are going to use."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jovyan/work/week5/Regularization/reg_utils.py:85: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
" assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])\n",
"/home/jovyan/work/week5/Regularization/reg_utils.py:86: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
" assert(parameters['W' + str(l)].shape == layer_dims[l], 1)\n"
]
}
],
"source": [
"# import packages\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec\n",
"from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters\n",
"import sklearn\n",
"import sklearn.datasets\n",
"import scipy.io\n",
"from testCases import *\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Problem Statement**: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head. \n",
"\n",
"<img src=\"images/field_kiank.png\" style=\"width:600px;height:350px;\">\n",
"<caption><center> <u> **Figure 1** </u>: **Football field**<br> The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head </center></caption>\n",
"\n",
"\n",
"They give you the following 2D dataset from France's past 10 games."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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Ts3Pr3a9tva519c3D3RrlFYPMBTMbPpWhXYcwRo3rVk13UpLAHGDgiuuq927t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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc33c12b8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_X, train_Y, test_X, test_Y = load_2D_dataset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each dot corresponds to a position on the football field where a football player has hit the ball with his/her head after the French goal keeper has shot the ball from the left side of the football field.\n",
"- If the dot is blue, it means the French player managed to hit the ball with his/her head\n",
"- If the dot is red, it means the other team's player hit the ball with their head\n",
"\n",
"**Your goal**: Use a deep learning model to find the positions on the field where the goalkeeper should kick the ball."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analysis of the dataset**: This dataset is a little noisy, but it looks like a diagonal line separating the upper left half (blue) from the lower right half (red) would work well. \n",
"\n",
"You will first try a non-regularized model. Then you'll learn how to regularize it and decide which model you will choose to solve the French Football Corporation's problem. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Non-regularized model\n",
"\n",
"You will use the following neural network (already implemented for you below). This model can be used:\n",
"- in *regularization mode* -- by setting the `lambd` input to a non-zero value. We use \"`lambd`\" instead of \"`lambda`\" because \"`lambda`\" is a reserved keyword in Python. \n",
"- in *dropout mode* -- by setting the `keep_prob` to a value less than one\n",
"\n",
"You will first try the model without any regularization. Then, you will implement:\n",
"- *L2 regularization* -- functions: \"`compute_cost_with_regularization()`\" and \"`backward_propagation_with_regularization()`\"\n",
"- *Dropout* -- functions: \"`forward_propagation_with_dropout()`\" and \"`backward_propagation_with_dropout()`\"\n",
"\n",
"In each part, you will run this model with the correct inputs so that it calls the functions you've implemented. Take a look at the code below to familiarize yourself with the model."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1):\n",
" \"\"\"\n",
" Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
" \n",
" Arguments:\n",
" X -- input data, of shape (input size, number of examples)\n",
" Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (output size, number of examples)\n",
" learning_rate -- learning rate of the optimization\n",
" num_iterations -- number of iterations of the optimization loop\n",
" print_cost -- If True, print the cost every 10000 iterations\n",
" lambd -- regularization hyperparameter, scalar\n",
" keep_prob - probability of keeping a neuron active during drop-out, scalar.\n",
" \n",
" Returns:\n",
" parameters -- parameters learned by the model. They can then be used to predict.\n",
" \"\"\"\n",
" \n",
" grads = {}\n",
" costs = [] # to keep track of the cost\n",
" m = X.shape[1] # number of examples\n",
" layers_dims = [X.shape[0], 20, 3, 1]\n",
" \n",
" # Initialize parameters dictionary.\n",
" parameters = initialize_parameters(layers_dims)\n",
"\n",
" # Loop (gradient descent)\n",
"\n",
" for i in range(0, num_iterations):\n",
"\n",
" # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
" if keep_prob == 1:\n",
" a3, cache = forward_propagation(X, parameters)\n",
" elif keep_prob < 1:\n",
" a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob)\n",
" \n",
" # Cost function\n",
" if lambd == 0:\n",
" cost = compute_cost(a3, Y)\n",
" else:\n",
" cost = compute_cost_with_regularization(a3, Y, parameters, lambd)\n",
" \n",
" # Backward propagation.\n",
" assert(lambd==0 or keep_prob==1) # it is possible to use both L2 regularization and dropout, \n",
" # but this assignment will only explore one at a time\n",
" if lambd == 0 and keep_prob == 1:\n",
" grads = backward_propagation(X, Y, cache)\n",
" elif lambd != 0:\n",
" grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n",
" elif keep_prob < 1:\n",
" grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n",
" \n",
" # Update parameters.\n",
" parameters = update_parameters(parameters, grads, learning_rate)\n",
" \n",
" # Print the loss every 10000 iterations\n",
" if print_cost and i % 10000 == 0:\n",
" print(\"Cost after iteration {}: {}\".format(i, cost))\n",
" if print_cost and i % 1000 == 0:\n",
" costs.append(cost)\n",
" \n",
" # plot the cost\n",
" plt.plot(costs)\n",
" plt.ylabel('cost')\n",
" plt.xlabel('iterations (x1,000)')\n",
" plt.title(\"Learning rate =\" + str(learning_rate))\n",
" plt.show()\n",
" \n",
" return parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's train the model without any regularization, and observe the accuracy on the train/test sets."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.6557412523481002\n",
"Cost after iteration 10000: 0.16329987525724216\n",
"Cost after iteration 20000: 0.13851642423255986\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc308efae48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the training set:\n",
"Accuracy: 0.947867298578\n",
"On the test set:\n",
"Accuracy: 0.915\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y)\n",
"print (\"On the training set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The train accuracy is 94.8% while the test accuracy is 91.5%. This is the **baseline model** (you will observe the impact of regularization on this model). Run the following code to plot the decision boundary of your model."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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sImFIqLf/JSCWP6zgeGE3LbheSLI1lyZdD0CVZi4xUZH94+j8sR/2CqcWp2zm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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc308bec7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model without regularization\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-0.75,0.40])\n",
"axes.set_ylim([-0.75,0.65])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The non-regularized model is obviously overfitting the training set. It is fitting the noisy points! Lets now look at two techniques to reduce overfitting."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 - L2 Regularization\n",
"\n",
"The standard way to avoid overfitting is called **L2 regularization**. It consists of appropriately modifying your cost function, from:\n",
"$$J = -\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} \\tag{1}$$\n",
"To:\n",
"$$J_{regularized} = \\small \\underbrace{-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} }_\\text{cross-entropy cost} + \\underbrace{\\frac{1}{m} \\frac{\\lambda}{2} \\sum\\limits_l\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2} }_\\text{L2 regularization cost} \\tag{2}$$\n",
"\n",
"Let's modify your cost and observe the consequences.\n",
"\n",
"**Exercise**: Implement `compute_cost_with_regularization()` which computes the cost given by formula (2). To calculate $\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2}$ , use :\n",
"```python\n",
"np.sum(np.square(Wl))\n",
"```\n",
"Note that you have to do this for $W^{[1]}$, $W^{[2]}$ and $W^{[3]}$, then sum the three terms and multiply by $ \\frac{1}{m} \\frac{\\lambda}{2} $."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: compute_cost_with_regularization\n",
"\n",
"def compute_cost_with_regularization(A3, Y, parameters, lambd):\n",
" \"\"\"\n",
" Implement the cost function with L2 regularization. See formula (2) above.\n",
" \n",
" Arguments:\n",
" A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)\n",
" Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
" parameters -- python dictionary containing parameters of the model\n",
" \n",
" Returns:\n",
" cost - value of the regularized loss function (formula (2))\n",
" \"\"\"\n",
" m = Y.shape[1]\n",
" W1 = parameters[\"W1\"]\n",
" W2 = parameters[\"W2\"]\n",
" W3 = parameters[\"W3\"]\n",
" \n",
" cross_entropy_cost = compute_cost(A3, Y) # This gives you the cross-entropy part of the cost\n",
" \n",
" ### START CODE HERE ### (approx. 1 line)\n",
" L2_regularization_cost = (lambd/(2*m))*(np.sum(np.square(W1)) + np.sum(np.square(W2)) + np.sum(np.square(W3)))\n",
" ### END CODER HERE ###\n",
" \n",
" cost = cross_entropy_cost + L2_regularization_cost\n",
" \n",
" return cost"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cost = 1.78648594516\n"
]
}
],
"source": [
"A3, Y_assess, parameters = compute_cost_with_regularization_test_case()\n",
"\n",
"print(\"cost = \" + str(compute_cost_with_regularization(A3, Y_assess, parameters, lambd = 0.1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**: \n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" **cost**\n",
" </td>\n",
" <td>\n",
" 1.78648594516\n",
" </td>\n",
" \n",
" </tr>\n",
"\n",
"</table> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost. \n",
"\n",
"**Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\\frac{d}{dW} ( \\frac{1}{2}\\frac{\\lambda}{m} W^2) = \\frac{\\lambda}{m} W$)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# GRADED FUNCTION: backward_propagation_with_regularization\n",
"\n",
"def backward_propagation_with_regularization(X, Y, cache, lambd):\n",
" \"\"\"\n",
" Implements the backward propagation of our baseline model to which we added an L2 regularization.\n",
" \n",
" Arguments:\n",
" X -- input dataset, of shape (input size, number of examples)\n",
" Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
" cache -- cache output from forward_propagation()\n",
" lambd -- regularization hyperparameter, scalar\n",
" \n",
" Returns:\n",
" gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n",
" \"\"\"\n",
" \n",
" m = X.shape[1]\n",
" (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
" \n",
" dZ3 = A3 - Y\n",
" \n",
" ### START CODE HERE ### (approx. 1 line)\n",
" dW3 = 1./m * np.dot(dZ3, A2.T) + (lambd/m)*W3\n",
" ### END CODE HERE ###\n",
" db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
" \n",
" dA2 = np.dot(W3.T, dZ3)\n",
" dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
" ### START CODE HERE ### (approx. 1 line)\n",
" dW2 = 1./m * np.dot(dZ2, A1.T) + (lambd/m)*W2\n",
" ### END CODE HERE ###\n",
" db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
" \n",
" dA1 = np.dot(W2.T, dZ2)\n",
" dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
" ### START CODE HERE ### (approx. 1 line)\n",
" dW1 = 1./m * np.dot(dZ1, X.T) + (lambd/m)*W1\n",
" ### END CODE HERE ###\n",
" db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
" \n",
" gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
" \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
" \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
" \n",
" return gradients"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dW1 = [[-0.25604646 0.12298827 -0.28297129]\n",
" [-0.17706303 0.34536094 -0.4410571 ]]\n",
"dW2 = [[ 0.79276486 0.85133918]\n",
" [-0.0957219 -0.01720463]\n",
" [-0.13100772 -0.03750433]]\n",
"dW3 = [[-1.77691347 -0.11832879 -0.09397446]]\n"
]
}
],
"source": [
"X_assess, Y_assess, cache = backward_propagation_with_regularization_test_case()\n",
"\n",
"grads = backward_propagation_with_regularization(X_assess, Y_assess, cache, lambd = 0.7)\n",
"print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
"print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
"print (\"dW3 = \"+ str(grads[\"dW3\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" **dW1**\n",
" </td>\n",
" <td>\n",
" [[-0.25604646 0.12298827 -0.28297129]\n",
" [-0.17706303 0.34536094 -0.4410571 ]]\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **dW2**\n",
" </td>\n",
" <td>\n",
" [[ 0.79276486 0.85133918]\n",
" [-0.0957219 -0.01720463]\n",
" [-0.13100772 -0.03750433]]\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **dW3**\n",
" </td>\n",
" <td>\n",
" [[-1.77691347 -0.11832879 -0.09397446]]\n",
" </td>\n",
" </tr>\n",
"</table> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now run the model with L2 regularization $(\\lambda = 0.7)$. The `model()` function will call: \n",
"- `compute_cost_with_regularization` instead of `compute_cost`\n",
"- `backward_propagation_with_regularization` instead of `backward_propagation`"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.6974484493131264\n",
"Cost after iteration 10000: 0.2684918873282239\n",
"Cost after iteration 20000: 0.2680916337127301\n"
]
},
{
"data": {
"image/png": 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QhGFtuJCsmVm99drTe0Z5rM2IeBA4vpom9Y8LyZqZ1VuvoTeQBpoGpnp6vfYS9xlTNfX8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\nFBHjLdO7rXM3Re/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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc308bc0a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the train set:\n",
"Accuracy: 0.938388625592\n",
"On the test set:\n",
"Accuracy: 0.93\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y, lambd = 0.7)\n",
"print (\"On the train set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congrats, the test set accuracy increased to 93%. You have saved the French football team!\n",
"\n",
"You are not overfitting the training data anymore. Let's plot the decision boundary."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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7rHRzDVPDGErDI4HbCkjXvDgrtpCYqqSal3KGGwIhbj48Jhs3C8w/qJFqdATd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\naKHUaDQajaYLWig1Go1Go+nC/w93O6WMf7YPywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc308a8a390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model with L2-regularization\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-0.75,0.40])\n",
"axes.set_ylim([-0.75,0.65])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Observations**:\n",
"- The value of $\\lambda$ is a hyperparameter that you can tune using a dev set.\n",
"- L2 regularization makes your decision boundary smoother. If $\\lambda$ is too large, it is also possible to \"oversmooth\", resulting in a model with high bias.\n",
"\n",
"**What is L2-regularization actually doing?**:\n",
"\n",
"L2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. It becomes too costly for the cost to have large weights! This leads to a smoother model in which the output changes more slowly as the input changes. \n",
"\n",
"<font color='blue'>\n",
"**What you should remember** -- the implications of L2-regularization on:\n",
"- The cost computation:\n",
" - A regularization term is added to the cost\n",
"- The backpropagation function:\n",
" - There are extra terms in the gradients with respect to weight matrices\n",
"- Weights end up smaller (\"weight decay\"): \n",
" - Weights are pushed to smaller values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 - Dropout\n",
"\n",
"Finally, **dropout** is a widely used regularization technique that is specific to deep learning. \n",
"**It randomly shuts down some neurons in each iteration.** Watch these two videos to see what this means!\n",
"\n",
"<!--\n",
"To understand drop-out, consider this conversation with a friend:\n",
"- Friend: \"Why do you need all these neurons to train your network and classify images?\". \n",
"- You: \"Because each neuron contains a weight and can learn specific features/details/shape of an image. The more neurons I have, the more featurse my model learns!\"\n",
"- Friend: \"I see, but are you sure that your neurons are learning different features and not all the same features?\"\n",
"- You: \"Good point... Neurons in the same layer actually don't talk to each other. It should be definitly possible that they learn the same image features/shapes/forms/details... which would be redundant. There should be a solution.\"\n",
"!--> \n",
"\n",
"\n",
"<center>\n",
"<video width=\"620\" height=\"440\" src=\"images/dropout1_kiank.mp4\" type=\"video/mp4\" controls>\n",
"</video>\n",
"</center>\n",
"<br>\n",
"<caption><center> <u> Figure 2 </u>: Drop-out on the second hidden layer. <br> At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\\_prob$ or keep it with probability $keep\\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration. </center></caption>\n",
"\n",
"<center>\n",
"<video width=\"620\" height=\"440\" src=\"images/dropout2_kiank.mp4\" type=\"video/mp4\" controls>\n",
"</video>\n",
"</center>\n",
"\n",
"<caption><center> <u> Figure 3 </u>: Drop-out on the first and third hidden layers. <br> $1^{st}$ layer: we shut down on average 40% of the neurons. $3^{rd}$ layer: we shut down on average 20% of the neurons. </center></caption>\n",
"\n",
"\n",
"When you shut some neurons down, you actually modify your model. The idea behind drop-out is that at each iteration, you train a different model that uses only a subset of your neurons. With dropout, your neurons thus become less sensitive to the activation of one other specific neuron, because that other neuron might be shut down at any time. \n",
"\n",
"### 3.1 - Forward propagation with dropout\n",
"\n",
"**Exercise**: Implement the forward propagation with dropout. You are using a 3 layer neural network, and will add dropout to the first and second hidden layers. We will not apply dropout to the input layer or output layer. \n",
"\n",
"**Instructions**:\n",
"You would like to shut down some neurons in the first and second layers. To do that, you are going to carry out 4 Steps:\n",
"1. In lecture, we dicussed creating a variable $d^{[1]}$ with the same shape as $a^{[1]}$ using `np.random.rand()` to randomly get numbers between 0 and 1. Here, you will use a vectorized implementation, so create a random matrix $D^{[1]} = [d^{[1](1)} d^{[1](2)} ... d^{[1](m)}] $ of the same dimension as $A^{[1]}$.\n",
"2. Set each entry of $D^{[1]}$ to be 0 with probability (`1-keep_prob`) or 1 with probability (`keep_prob`), by thresholding values in $D^{[1]}$ appropriately. Hint: to set all the entries of a matrix X to 0 (if entry is less than 0.5) or 1 (if entry is more than 0.5) you would do: `X = (X < 0.5)`. Note that 0 and 1 are respectively equivalent to False and True.\n",
"3. Set $A^{[1]}$ to $A^{[1]} * D^{[1]}$. (You are shutting down some neurons). You can think of $D^{[1]}$ as a mask, so that when it is multiplied with another matrix, it shuts down some of the values.\n",
"4. Divide $A^{[1]}$ by `keep_prob`. By doing this you are assuring that the result of the cost will still have the same expected value as without drop-out. (This technique is also called inverted dropout.)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# GRADED FUNCTION: forward_propagation_with_dropout\n",
"\n",
"def forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):\n",
" \"\"\"\n",
" Implements the forward propagation: LINEAR -> RELU + DROPOUT -> LINEAR -> RELU + DROPOUT -> LINEAR -> SIGMOID.\n",
" \n",
" Arguments:\n",
" X -- input dataset, of shape (2, number of examples)\n",
" parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n",
" W1 -- weight matrix of shape (20, 2)\n",
" b1 -- bias vector of shape (20, 1)\n",
" W2 -- weight matrix of shape (3, 20)\n",
" b2 -- bias vector of shape (3, 1)\n",
" W3 -- weight matrix of shape (1, 3)\n",
" b3 -- bias vector of shape (1, 1)\n",
" keep_prob - probability of keeping a neuron active during drop-out, scalar\n",
" \n",
" Returns:\n",
" A3 -- last activation value, output of the forward propagation, of shape (1,1)\n",
" cache -- tuple, information stored for computing the backward propagation\n",
" \"\"\"\n",
" np.random.seed(1)\n",
" \n",
" # retrieve parameters\n",
" W1 = parameters[\"W1\"]\n",
" b1 = parameters[\"b1\"]\n",
" W2 = parameters[\"W2\"]\n",
" b2 = parameters[\"b2\"]\n",
" W3 = parameters[\"W3\"]\n",
" b3 = parameters[\"b3\"]\n",
" \n",
" # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
" Z1 = np.dot(W1, X) + b1\n",
" A1 = relu(Z1)\n",
" ### START CODE HERE ### (approx. 4 lines) # Steps 1-4 below correspond to the Steps 1-4 described above. \n",
" D1 = np.random.rand(A1.shape[0], A1.shape[1]) # Step 1: initialize matrix D1 = np.random.rand(..., ...)\n",
" D1 = D1 < keep_prob # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)\n",
" A1 = A1 * D1 # Step 3: shut down some neurons of A1\n",
" A1 = A1 / keep_prob # Step 4: scale the value of neurons that haven't been shut down\n",
" ### END CODE HERE ###\n",
" Z2 = np.dot(W2, A1) + b2\n",
" A2 = relu(Z2)\n",
" ### START CODE HERE ### (approx. 4 lines)\n",
" D2 = np.random.rand(A2.shape[0], A2.shape[1]) # Step 1: initialize matrix D2 = np.random.rand(..., ...)\n",
" D2 = D2 < keep_prob # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold)\n",
" A2 = A2 * D2 # Step 3: shut down some neurons of A2\n",
" A2 = A2 / keep_prob # Step 4: scale the value of neurons that haven't been shut down\n",
" ### END CODE HERE ###\n",
" Z3 = np.dot(W3, A2) + b3\n",
" A3 = sigmoid(Z3)\n",
" \n",
" cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n",
" \n",
" return A3, cache"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A3 = [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n"
]
}
],
"source": [
"X_assess, parameters = forward_propagation_with_dropout_test_case()\n",
"\n",
"A3, cache = forward_propagation_with_dropout(X_assess, parameters, keep_prob = 0.7)\n",
"print (\"A3 = \" + str(A3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**: \n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" **A3**\n",
" </td>\n",
" <td>\n",
" [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n",
" </td>\n",
" \n",
" </tr>\n",
"\n",
"</table> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 - Backward propagation with dropout\n",
"\n",
"**Exercise**: Implement the backward propagation with dropout. As before, you are training a 3 layer network. Add dropout to the first and second hidden layers, using the masks $D^{[1]}$ and $D^{[2]}$ stored in the cache. \n",
"\n",
"**Instruction**:\n",
"Backpropagation with dropout is actually quite easy. You will have to carry out 2 Steps:\n",
"1. You had previously shut down some neurons during forward propagation, by applying a mask $D^{[1]}$ to `A1`. In backpropagation, you will have to shut down the same neurons, by reapplying the same mask $D^{[1]}$ to `dA1`. \n",
"2. During forward propagation, you had divided `A1` by `keep_prob`. In backpropagation, you'll therefore have to divide `dA1` by `keep_prob` again (the calculus interpretation is that if $A^{[1]}$ is scaled by `keep_prob`, then its derivative $dA^{[1]}$ is also scaled by the same `keep_prob`).\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: backward_propagation_with_dropout\n",
"\n",
"def backward_propagation_with_dropout(X, Y, cache, keep_prob):\n",
" \"\"\"\n",
" Implements the backward propagation of our baseline model to which we added dropout.\n",
" \n",
" Arguments:\n",
" X -- input dataset, of shape (2, number of examples)\n",
" Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
" cache -- cache output from forward_propagation_with_dropout()\n",
" keep_prob - probability of keeping a neuron active during drop-out, scalar\n",
" \n",
" Returns:\n",
" gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n",
" \"\"\"\n",
" \n",
" m = X.shape[1]\n",
" (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
" \n",
" dZ3 = A3 - Y\n",
" dW3 = 1./m * np.dot(dZ3, A2.T)\n",
" db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
" dA2 = np.dot(W3.T, dZ3)\n",
" ### START CODE HERE ### (≈ 2 lines of code)\n",
" dA2 = dA2 * D2 # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation\n",
" dA2 = dA2 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n",
" ### END CODE HERE ###\n",
" dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
" dW2 = 1./m * np.dot(dZ2, A1.T)\n",
" db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
" \n",
" dA1 = np.dot(W2.T, dZ2)\n",
" ### START CODE HERE ### (≈ 2 lines of code)\n",
" dA1 = dA1 * D1 # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation\n",
" dA1 = dA1 / keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n",
" ### END CODE HERE ###\n",
" dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
" dW1 = 1./m * np.dot(dZ1, X.T)\n",
" db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
" \n",
" gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
" \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
" \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
" \n",
" return gradients"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dA1 = [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n",
" [ 0.65515713 0. -0.00337459 0. -0. ]]\n",
"dA2 = [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n",
" [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n",
" [ 0. 0. -0.00292733 0. -0. ]]\n"
]
}
],
"source": [
"X_assess, Y_assess, cache = backward_propagation_with_dropout_test_case()\n",
"\n",
"gradients = backward_propagation_with_dropout(X_assess, Y_assess, cache, keep_prob = 0.8)\n",
"\n",
"print (\"dA1 = \" + str(gradients[\"dA1\"]))\n",
"print (\"dA2 = \" + str(gradients[\"dA2\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Expected Output**: \n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" **dA1**\n",
" </td>\n",
" <td>\n",
" [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n",
" [ 0.65515713 0. -0.00337459 0. -0. ]]\n",
" </td>\n",
" \n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **dA2**\n",
" </td>\n",
" <td>\n",
" [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n",
" [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n",
" [ 0. 0. -0.00292733 0. -0. ]]\n",
" </td>\n",
" \n",
" </tr>\n",
"</table> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now run the model with dropout (`keep_prob = 0.86`). It means at every iteration you shut down each neurons of layer 1 and 2 with 24% probability. The function `model()` will now call:\n",
"- `forward_propagation_with_dropout` instead of `forward_propagation`.\n",
"- `backward_propagation_with_dropout` instead of `backward_propagation`."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 0: 0.6543912405149825\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jovyan/work/week5/Regularization/reg_utils.py:236: RuntimeWarning: divide by zero encountered in log\n",
" logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
"/home/jovyan/work/week5/Regularization/reg_utils.py:236: RuntimeWarning: invalid value encountered in multiply\n",
" logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost after iteration 10000: 0.06101698657490559\n",
"Cost after iteration 20000: 0.060582435798513114\n"
]
},
{
"data": {
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MrDQcemZmVhoOPTNA0u3pz8mSzurndf9rT6+VF0nvkPT5XpZ5V9q1frukaTWW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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc30b5ee630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"On the train set:\n",
"Accuracy: 0.928909952607\n",
"On the test set:\n",
"Accuracy: 0.95\n"
]
}
],
"source": [
"parameters = model(train_X, train_Y, keep_prob = 0.86, learning_rate = 0.3)\n",
"\n",
"print (\"On the train set:\")\n",
"predictions_train = predict(train_X, train_Y, parameters)\n",
"print (\"On the test set:\")\n",
"predictions_test = predict(test_X, test_Y, parameters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dropout works great! The test accuracy has increased again (to 95%)! Your model is not overfitting the training set and does a great job on the test set. The French football team will be forever grateful to you! \n",
"\n",
"Run the code below to plot the decision boundary."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GhCvVCMZed+0ldG3WbxRGls9INNri7yCi8UzzYEJRshF3DxHdf6eNK7lDk4Uc\nz+Obf+nDLN5fJrQt7CDkha9+Gc++4dvRThy0K5J//TMDx6sqtWpEtRx2k2yO65qTzdk8+VUpGvUI\n1diYwMwkH22MUBrONdmKNzQECZCueQ+dVWaqHoXtZiy0nZt8oh2ysFxl7Waxu1/kxGHSraVpjXwK\nWMLuYpbdxcmNH1IjEnYsjTvETCqUXUY8OISORWRbWEF/Msso8ZQDaaESKpfuV3rau8XbF5arPHii\nNDLU/qr/+Bss3ruPE4Y4nQ5Yt//4C5RnZ/nsN77q0I+iqjy451GvRV39r1ZCZuYcFo6ZbGNZQi5v\niiQvCiYWYDjXHLeBUX67NbD2KMSZnLYfHvPdDV1E2FrKEcn+7ywa5YvQMRroJVMb0T2EePY59JRh\nyBOf/wJO2P97dIKAr/n87/LRH03zTPRTtF7/oaGzyUY96hNJiJ+ldrYC/CN0qzFcXMyM0nCuaRST\n5HdbQ2eVzTHq9w7rPGFFykWVythIfDBtNhKoF0/GHrCVdVm5XSK/08LxQloZBwmV4s7+708FasXk\nQHeSUT0nRek2cj6IHYZINPy4TLnBJ17xExx2i6tVR9c71msRpVkzjzDEGKE0nGu8lENlNk1huxkn\n83RmKVuXs2OVGTTyiXhWeeB1FTlSfeOxUCVb8chvN7EipZl1Kc9nTqZcwhI2ruZYuF8F6F67eiHZ\nl6k6bYKEzc6BUHGzkCRbid2FGvkE7SEJUq2sCxuD76fCSO/aIJGgMjtDaWt7YFsy+fBrao+yAjSu\nOYYDGKE0DGB7IU4Q4SXtsb08T5LyQoZ6IUmm5nXCdsmxreUqs+k4dBdqtyWVCmxfzp662XZpvdHX\nQszZbZOpeqxMwwt2CK1sguWXzJCpeF1h9k+4Vdow/JTD7kPO6ycd6h1BPdje7bDM42f/q2/jW//N\nh3A1gCA+0LIYq6C/ULLZ3gqGzirN+qKhFyOUhi4SRiws12LnFhFQpTKbpjyfPvM27kHSppKcvPYu\ncixWbpfI7bRI130C16I6mz7x3poHsYKIwoEQshCHf4/qBTsOkW1Rm6BuUiKluNEgV2lDZwa4u3D4\nrNdtB9h+FNejHmN2vH05SzPnktttI6rUiynqhcSh3731a9d4/l++ib/wsa+w9ZHfJ2xZzM65OO4Y\ntoNJi8Ull7UVv5txJMDVGwksk6Vq6MEIpaHL/EqNZMOPw5Sdx+zCdpMgYVMvns+GwOMQ2RaV+QyV\n+bMbQ6KJgnrYAAAgAElEQVQVEIlgH5i+WBqblZ/l2Lqosni3jNsO+zqgpBo+D26X+izyIBb/S/cq\nHRehuPSmOpM6clkNIjTzSZr58b5rB8s+5o7Qmq0445DNWzQbikhcymGdw36ehrPFCKUBiGeT6SHN\ndS2NxfJUhFKVVN0n2fQJXZt6PnEuQr/TIHSsgZIIiEPBo5x73HZAuhq7ETXyiRM3EU82gz6RhI7n\nbRCRrXoD34H5B1USXSee+KD8Tgsv6dA44e/L02/cHWisPCnlnYDNdZ8gANuB+QUHyzIhV8MgZ3oX\nEpE3iMgXROR5EfmhIdu/V0Q+IyJ/KCKfEJGnz2KcjwNWqCNLMUZlJE6TvQ4eC8tVilstZtbqXPvy\nLm4rOPFzT4LthxQ3G8w9qJLbjRs0j4OfcvAT9sA1HmUpV9xocPnFMqXNJqWNBksv7JLbHnSdPep4\nhpEYca0tHdxmBaOdeAo747jjHo9RjZXHpbwbsLYSiyRAGMD6asDuzsMN46NQCXxFD2sRcgDV+Jjo\nGL+f7rmDyc5tOD5nNqMUERv4aeDbgPvAcyLyYVX9XM9uLwCvU9UdEfkO4H3Aq09/tBef0LXiBroH\nUvGV0VmH0yS/3eybzYjGN5eFB9U47HcOWhIlGz6X7lVA4yfMTNWjsNVk9VZxrGSc2Au2SqoZxElF\nlrB1OTeQYOO2g32ThA6iMLPRoJnf97k97ngOMmrGGgn4B+zX9h6shv1WRpVzTINpWdFtrQ8m8ajC\n5npAaWb49z0MldVlr9shxHGExSsu2dzhs9CdLZ/NnvOVZmNz9EnabIWhsnLf6zaCdhzh8lX32C5C\nhvE4y9Drq4DnVfUrACLyAeBNQFcoVfUTPft/Erh2qiN8nBBhazHL/EoN0f0KvMgSdk8o0aSXXLk9\n1BjA9iPsIJqqCfqRUGVupdY3RksBP6Kw2RzLPSdyLNZvFLGC2As2cId7wWYOcSPKVL24G8kxxnP9\nS8/zik/+Dul6jdXr1/mD1/5paqUSzawbh4h7PG/jLGGhXugPpQaJ0Q9W49S3TkKmWiXZaHLzLdax\nw617+P7wCxwG8QPaMBG7f6dNq7l/nO8ry3c9bj6ZHFmOUikHbKz1i/LudhyuXrg8/nUadu77dzxu\nPZkkMUYpjOF4nKVQXgXu9fx8n8Nni98P/OqojSLyNuBtAMnCwjTG99jRLCRZcy0K200cL6KVcanO\npscuxTgWZz9hPJREK8DxB0PQFpCtehPZzEWOxaHB7EOuhXZu4HYQP0BMOp6v/tTv8srf/jiuH8cc\nn/jc57nx/PN8+PveQr1YZO1mkbmVGql6HIJspx22LucG14olng3PP6h2H6wiiR+synPT6QySaDb5\n5l/+dywsPyCybVK/GPHHf/s621Noi+UmBN8bFEvHYahItlsR7daQNeaOk8/lK8NFb9TMdWcnZH5x\nuCBPeu7FEec2TI9HIplHRF5PLJR/ZtQ+qvo+4tAs+aWnTAD/iHhpl82rp99UtlZMUtzsDzcqELj2\nmc8m97I7R6FTfo6o55MUtpqHuhGpyEg9HTUe2/d55W//565IAliqOJ7P1zz7X3j2Dd9O6MQdUOg0\nstZDMkCb+QSrN4sUtlvYfkgr61KbSU2tJvT1v/RhFpYfYEcRhCGRB5981wtcu5k4dshxYdFl5b7X\nJ2IiMD+i/tL3da9ianDbEMHdIwiGb9MIogjszsdQVZqN2E4vnenPvD3s3N4h5zZMj7MUymXges/P\n1zqv9SEiXwP8DPAdqrp1SmMznDASRtihEjgWWEJlJk265pNoBfH6pAWKsHk1N/2Tq+L4EZEtY93U\n91p8DZOMSKB6hLKEwwiSNrvzGUqbjb7Xt3s6h0SORTsV95vsHddh48nvlrsz0l4sVRbv3Tvwoozl\ns+unHLauTP93lC1XmF9ZjUWyB1XY3gyOLZT5gg3XEmys+fie4rrC/CWHQmn4LTGZsoYKlUgsbKNI\npiyajcGZv+3su/806iHLd72+7UvXEl3Tg2RKRp47kzVh19PgLIXyOeApEblNLJBvBr6ndwcRuQF8\nCPirqvrF0x+iYeqoMrtaJ1dpd2/Eu/MZqnNp1m4USDYDks24b2Qjnzh0RnMUsrstZtYbiMYzpkbW\nZWspjx5SYN7bE7LvoxBb7E3aCHkcqnNpmvlE3KyauPD/4Mx680qOxbuV2M+2M75mNjFyPM1sBjsc\n7m5bLxSmN/gpkGo0iEb4yAUj1hd7abcjwkBJpUfXReYLdiyYY+C6QqFoUyn3+8NaFpRmR99GFxZd\n7r3YHpi57iXzhKFy/66HHtDSB/c8nngqheMKrmuRL9pUh5y7OPNIBAUfec7sKqtqICJvB34NsIGf\nVdXPisgPdLa/F/hhYA74p51YfqCqX39WYzYcn9m1etf3c+/2VdpsxMJYTNLOuEO9QKdBsu4zu1bv\nE7103Wd+pcrGtdFC4SdsEs1g0C8W+poXT5sgYceJOyMIXZsHT5RINQLsIMRLOQNNqXtpZzLcf+I2\nV7/yQl/HDd9x+MOHtKQ6TZ5+4y4/8aeu8Qv/2htqWp/JjZ5F+b6yfKeN5+2HKxcuO8zMHv87tXjF\nJZkSdrZDolDJ5mzmFx0cZ/TvP52xuH4ryea6T7sV4brC3CW3O1usVcKRXVMq5YDZ+Xjclzvn3u07\nt3vouQ3T40wfR1T1GeCZA6+9t+fvfwP4G6c9LsMJESnZIdmtlkJxq3niRerFAyUXe+dO132sIBpp\nv1aZTcV+sT3HRoCXdgiSRwgBqpJq+CRaIYFr0cglBlxvxkakU74znhB8/Lu+k9f+6r/n+vNfJrIs\nIsviude/jtWbN492/iPitgNSDZ/Qtmjm9iMHT79xl/e8ZonGO9/F3JzDxoFkGNumKx4HUVXu32nj\ntbXzc/z6xmpAMmkdO1wrIszMuczMTSa6e2I5jDDUoWFV1Xhb77ln51xmJzy3YTqYebvh1LA6CSLD\nGJbBOW1G9Z9Uic8/SiiDpMPGtQKzKzWcjvlCM+uytTT52pxE+zZxex09Zi1h9WbxxJ13AIKEy8fe\n9N0kWi2SzSa1QgG1p3deCSOyFQ/HD2mn3bhTSe+Mu1PWkqnur8mpCGs3CgP1pDPzLm7SYnsrIPSV\nbL7j4zpiFuW1dWhijSrsbB9/XfMkyORsZEhmrAgPrc80nB5GKA2nRmQLkSXYQ2rvvPQRvoody7t0\n3Sey4j6Lh4lNK+Pieu1BsdbRxfbdY7MuD54sxYX2lhx57bS42Rg0VgiV+Qc1Vm8Vj/SeR8FLpfBS\n011bdVsBi3criMadWiJp4Sds1m4Wu9crU/HIVL3+mb0ql+5XWX6yNPCeubw9diePMBydHRo+3HDn\nTEilBtcfY5G0Dk0SMpwuRigNp4cI25cyzK3urxPutb3aWZjQ1ECVheUqqbrfLaMobLfYupylMaIx\ncWUuTbbTcmpP5iKJk4nGEj4RomOuCfW2keq+LXGdphVGJ9JuaxiOF5LfbpJoh7TTcULScctw5h9U\n+66tpeB6IYWtJuXO7zdfbg1NjLLCiD/xjbu85zVXaLzzXUeypjssMzWbP7+ic/lKvGZZ3onLdgql\nOMloEucew8lihNJwqjSKKSLHprjVwPEi2mmH8nz60CSUYWSqHqm6P2DzNrdap5lPDhW+0LVZuVWk\ntNUkVfcJHYtyJ7v0cWLP+m4voSrZDMjvtlm5WTzamiuxg5LT4+izh6Xxw8GeUI5KXMmkhB/8T8/w\niR8uc9Tbkm3HJR69dnEisd3bYZmpk+D7ytaGT6Me4TjC7Lxz7N6VIjJRBq7h9DFCeU5JtAJK6w0S\nrYDQtdidT4/dfui808q6tLLHCzNmhszMABAh1fBH2qiFCftIa4vTol5Ikt9pDRgreCn71GaTBzN/\nBSBSZtbrbFw/WpmIynjmSvVCIp49H/jd2fhsf7Ry7C4Ns/MuyZTFzlZAGCq5vE1p1sGeQn9J31de\n/HKLqLPU7XvKg3seC4vOxAk+hkeL8xuPeIxJtAIW75RJNXzsSEm0Q+Yf1MidQleGRwUdWRCv6HmN\nWHX6NQYJm0g6Xrod27fNpfypDEEixW0PJjUJcb3oUYkcC29Id5RIYtelPWqlVNzgWfa3Ownlb/2F\nB1hjWRw8nGzO5trNJDefSDG34E5FJAG2N/yuSO6hChvrwbG7ghjON2ZGeQ4pbTQGnGAshdJGk1op\ndS46aZw19WKKTHXQPFwRWidUh3lkVClsNSluN5EIIgtqxQSRbcflIYXhoeITGYqw73h/gOiYY9i8\nmufynTISaTej10s5VHprQTsZrum6H4e/beEdP1ThT+7Uj9UN5DTY69xxECHOuE2lzb/Li4oRynNI\nojXY5w9AVLEDJXTNP8hW1qU6kyK/04pf6FySjWv5c/cgUdhqUtzar+G0I8iVvUMTj04MEWqF5EBS\nUdTTF1PCiEQ7JLStidYsg4TN/SdnyNQ8HD9ef26nncHfhwjNXKIbHs+VKrBz7E924jiuDPVWVcUU\n/l9wjFCeQwLHGmk1Fk4pjHQR2L2UpVZKkWr4RJb0Fa6fG1Qpbg9meloKpc3m9IWyY2aQrnpElsQl\nMwfEbmcxixNEJBs+KoKlSiOfoDKXprDZoLjV7M46/aTN+rXCyBrTASyhUbgYa+kHmZ13aDb6jdTp\neL06Bx5eVZVaNaJeC3EcoViycRNmpetRxQjlOaQ8n4lT7Q8+8ZeSR3dwGRdV0j0zAi81ZEZwAMcL\nSdV9VOKOEqeVlALxLKZ2CoX6R0U0XhccxtRNFlSZX66Rru+HpAs7LbYXs9R7jNLVEtavF3C8EMcP\n8RNxh5Z01duf+XaOT7RCFparrN2cfo2nRBGXlh9Q+U/r+IvDHwzPE9mczaXLTtxfEkAhnbW4cq0/\ncUwj5d6dNq2Wdj1ctzcDrlxPHDtD1nA2GKE8hzTzCbYXs8xsNLo32Wopxe6lk22g7Hghi3fK8Y0y\nihdJ22knbrs0QiyLGw0K2z1JRmt1Nq7maU25ee+jikocBXDCQbH0j1iKMYp0zSdd9wZKZmbX6rHB\nfM8DjERKsuHjeiFWqDTsuA/pqBpP2w+n2u5sdnWNb/3FD2EHAXc+otz1QxZm7XNv8l2adSmUHDxP\ncWwZmEkC7O4GtJr91nSqsHLf4yUvS5n6yEeQ8/2tfIypl1LUi0msUOMki1MIKc4/qGKHPTZzGtfY\nFbaaVOYHRTrR9IfeXBeWq9x/avb8hUHPAhF2DpgsQBwh2FkYv9nzOGSqo0pmYj/bvZCo44XdpJvY\nQQdKjoUeknVqhUo4pRwpKwz59g/+IslWvL4cddzs1lYiUmmLZCoW9I3EDH9cuI1nudyu3+dWfTqZ\nsY16SGU3dsIpFG0yOWsi8bIsIZUavX91NxpqfADQakakM/0PHKpKvRYRBEq65/Mbzg9GKM8zU3CC\nGRcriBM4hhWM58rtoUKZK7eHNhcGSNe8C7tWNSmNYgq1LEqbDRw/wkvY7F7KIJGycK+CFSn1fCLO\naD7Gw4VKXDIz7B16H1pmV2pYYb+DjvgRfsIiQgdrxkSmOvu98sKLSDQk7GxBeTfg0uUEf1h4it+Z\n+xpCsVCxeDF7laXmJm9Y/e1jieXGms/O1r4hQbUSkivYLF11pzbTkxE6pzBwDs+LuPdCmzCiG+7O\n5S2WriXMzPMcYYTSAMQZtaNusqPEcOT9yvz7HqCZT3QdgJx2wPxylYS372STaAXkKm1WbxaPnLVb\nLya7Lcz6EZp7JTORkmoOZlUL4ARR/GAWxjPNPXvB7UsZRCG/3SBb9qBTG1mdOVqpUqLdHmrIqiHw\nLTd4xd9a4md+4klCa1+cA8tlJT3Pi9krPFEf6O8+Fp4X9YkkxMOoVUKaM/bUTNNLs0OSfgDbipsw\n9/LgnkcQ9O9Xq0bsbgfGxOAcYeb4BiC2dwvdwa9DJFArDF9vbBQSw4v7lU7rJ8NB3FbA0gvlPpGE\nji9qOyRb8UYe+zDaGZfKbDo2MZC4XjOyYP1afn+meoiuKcLK7RKVuTStlE0j77J2o0C9mOTS3TLF\nzSYJLyTRDiltNFi4Xx3uQP4QVm9cxxoyo/Rdl997+gk+73wjrj2Y3BNYLl/JXp/4fHvUq8OTp1Sh\nVp1eMlEub1Eo2YjEzxGWBZYNV28k+2aJvq/dlmAHx7O7c/6Tmx4nzIzS0GXjSp7Ldyug+2tXQcKm\nMjc8iaiVcWnkE32F/yqwvZg91czXiVGluNmMreQixUvZbC9m8dLTEfdEM2B2rU6iFRBZQq2UZHch\nAyLMrDfYq/k/iKVxyLp+jL6c5YUMtVKy01FlSMlMp39lqu73jSGS2F4usi3K8xnKPaH2dM0j0dPx\nZG+scU/NYOLr1sjn+eyrvoGXf+p3cfx4HL7rsHV5kfJrb5BMDldz0YhEdHT3IOuQr6Q1xfV0EeHy\nlQSzcxGNeoTtCNmcNXAOPcTN5wjPH4YTxAiloYufclh+skS23Mb2I7y0QyOfGB1eE2FrKUetFJCu\neagl1AvJU+mrCHHzX8eL8JI24QTnnF2t9xXcJ1shi3crrNwqEkxozn4QxwtZvFvuMRdQ8jst7CBi\n60qeZMsfOalTIJzCA0bo2tRKo6/H1uUci3fL2GGERPHDjZ+wYzEfQrLhD00SEoVkY3KhBPj0N72W\n1RvXeekffAa37fHCy1/GCy/7Kr7GqfLyp1NYogOhfVsjXlZ9YeJz7ZEr2KytDAqtSNyxY9okkhaJ\n5Ojfp5sQbJuB0KsIxiD9nGGE0tBHZFtUey3HHoYI7YxL+xRt4ySMuHS/SqIVxGbcCo1cgq0ruYeu\nmVlBRG7IOp4oFLeabF05nudqYas58N6WQrbqsRNEhLY1NOwIsWDVZkbMJlVJ13yy5ThTtF5MDTZF\nHpPQtXjwRIl0zcfxQvyUHdv+jXivwI29aQ+KpQqE4xoRDGH15g1Wb94YeN11hR+8+pu858U/hxK7\nrUdYfP32H3KpvX3k89m2cPVGguW7XvejqsLikkviDMwARISlawnu3/G6dZlixZ9/dt7cms8T5rdh\neOSYW62TaAbxAnvn5p2pefgjylh6cfwQFUEOxLYESAwxC5+UUfaDkQiuF1KZTTGz3hjoHgKwdTk7\nst3Y3Eqtr+Fxuu7TyCeOLuwiY7cXaxQSzGzU+2Z4caLP+O/xMJ5+4y7vec0SjXf+FJ/4m/E1eAu/\nzP3MIr44XG2tkw7bxz5PNmfzkpelqNeijmGA0G4qlXJAJmMPrYs8STJZm9tPpSjvBAS+ks5a5Av2\nVEPBhuNjhPJxoMdtx0uN8N98VIiUTM0bWsaS3x1extJL4NoDIgmdVldTKIHwUs6IMhvFT9i00w52\noF2TBlFoZl02r+T6DAF6STSDPpGM3y/uyVltBbF70gkS2RZr1wvMP6h13YRC12Ljav7QWlmJIq68\n8CLF7W125+d5cOvm0O/du9+xyitfeJ5PvOIX6L0l2UTcbKxM/fNYVtz/sdWMePH5NroX5VWf2QWH\n+YXTTURzXWH+kkl+O88Yobzg2H5cXG6FPR0dkg7rNwqnbwigSrIZr2fGPqTJid1eRAfXrvawxmh1\nFDnWUFNwFSjPTRByHkFlLj1QohEJNPKJrl9qeSFDZS6N44eEjvXQxKdUfbBLCsQim6r7Jy6UAF7a\n5cETJRw/FsrAtQ592Eo2GnzHL3yATLWGFYZEtk29UOBXv/fNeKlTNoIfgqpy/06bg5bK2xsBmYw1\ntVIRw8XACOUFJ54F7BeXi0KiHVDcbLB7abrOMIeiyvyDGulafNNXOmuCS7mJjAnUtvATNgmv/w6n\nxDOzcdi+nCV0rIGs1+Mm8kAsINVSivxuqytutWKSncX+a62WjAyzHkRt6a7F9r0ux2+NNREiYydq\nvfo//ga53TJ2Zz3WjiLyOzt8w2/8Jv/5u74D6A23fpBnf3Xyax9FyvZmQHk3/i4UihZz8y7WGI0D\nmo2IYc9VqrC7HRqhNPRxpkIpIm8A3gPYwM+o6o8e2C6d7d8JNIDvU9XfO/WBPqJIGJEcUlxuKWQr\n7VMVynTNJ13bDx8KgMZrb5N2/dhayrJ4t9Lt2RlJvF62MyJrcwARygsZyuPuPwEH1xKVeP10dyGD\nHrHzSz2fpLTeGLqtMaU1wqmiyo0vPt8VyT3sKOLWF75I7Z/9ad7zmiX0uY8PhFvHP4Vy78U27da+\np+rOVki9FnHzieRDXW2iaGRbzhNpwuyJw+cKT/Ji9iqpqM0ryl/ianN96ucxnAxnJpQiYgM/DXwb\ncB94TkQ+rKqf69ntO4CnOv+9Gvh/On8ajslIt50TIlsZbDUVD0RINfxub8Jx8NIuK7dL5HZaJLyQ\nVsqhNpMavxXUCeF44cBaohD7pOZ221SPGNqNnHg9cOFBte/1jSv5iT+zhBGFrSbZalzOUy0lT6QZ\n+LB14MNen5RGPaLdHjQe97zYN/VhXTrSGWtoraII5IvTnU364vCha99GzckQWg6ospy+zNdv/yFP\nl7841XMZToaznFG+CnheVb8CICIfAN4E9Arlm4CfV1UFPikiJRFZUtXpr/BfQNS28FI2iVZ/cklE\nPEs51bGM9CE9zIp7NEHCZnfxFEPHY5BoBUOnKZZCqulT5ehroK1cgnsvmSXVjOsAW2l3Yl9YiZSl\nO2VsP+qK+cx6g2QzOHZZTP+JhOXbt7j6wotYPWoUiVB/5SL/7b/4XZ753j8gasXdQuwjzLRbzajb\nwqoXjaDZCB8qlLYtXFpyWF/Zt7QTC1IpoTBlofx84fa+SEIcwhaH52ZfwcuqL5A8homC4XQ4y0fw\nq8C9np/vd16bdB8ARORtIvIpEfmU3yhPdaCPMptLOSJLiDr3okggTFjsLhw/cWUS6sXkcLs7JK7h\nuwAErj00lqeAP40WVZbQyiZoZRNHMk/PVNp9Ign72bOON13LtE9++7fSymTw3fh3a2UdUjmh+LsP\n+NI//zRrd5TN9YAXv9wmDCZ/VHITMtR8XISxGySXZlxuPJGkNGOTL9hcvuJy/dbDw7aTcidzdV8k\ne7A1Yj05O9Z7eO3Y/7VaCU8kNGw4nAuTzKOq7wPeB5Bfesp8kzoEyY7bTsXD8UO81EPcdg6iiuNH\nhLaMLF8Yh1bGpVpKkt/tr4XbuJo/VseMqdL5rPDwrM5heCmbIGHjHigPiY0Ezj7TMzXCYQfidmrT\ndFRqFAp86G3fzz+4/RyF//ICm7+5ycZqNBAqDQNle8tnYXGytdZc3sYSn4PyLgKFCVxtUimL1JWT\nXedNh614qntA2SMRUuHh3r6qyvqqT3nP+zX2X+D6rSSp9Dm2ibxgnKVQLgO9DsfXOq9Nuo/hIaht\nHelGnd1txd6kGmfN1vMJti/njlZWIsLuYo5aKU267hHZQiOXOJb4TpNE02dhuYYVduoEHYuNa/mx\nM1MBEOnWG6aafmxJ51hsXc71i5Aq6Xrskxq4Fo188vilOpGS7DgVeanhdbKBY43sEHMch51R/Pg/\n2OKVL0Q8+8/KpNM2SjCwT9y9I2JhcbL3tizhxu0kD+57XWNxNyFcuZYYK+v1NPmT5S9xJ3uVoEco\nRSOyQZN5b+fQY2vViPJOuP+A0ckYv3+3zZMvNU2gT4uzFMrngKdE5Dax+L0Z+J4D+3wYeHtn/fLV\nQNmsT54OqbrP7Fp/s+HY/LzG5tWjr2cFSZtq8nTDvg/DCiMW71Wweta8xI9YvFNh+SUzE4lY5Fis\n3yhghRESaSxAPTcziZTFO2VcL+zWtc6sN1i9USQ4ouFBuuoxv1IFBFRRS1i/VsBL9//zrpWSFHZa\nfYlce2LeypzsrcCyGF3/esSJbCJpcevJFEEQq8dpu+qMy+X2Fn968/d5dv6ViEaoWOSCOt+58lsP\n7UhX3gmGJh1FEbSaSjpzPj/zRePMhFJVAxF5O/BrxOUhP6uqnxWRH+hsfy/wDHFpyPPE5SF/7azG\n+7hR2GoMhOksjUsdrCA68wzTaZKpeAM38bjDhx65AXVkW/G3+gDFzQaut9+JQxQ0VOZXqqzeKk18\nHtsLmX9Q7bxf501D5dK9fpG3vZDFu9XujGQPL2mzcTXX6QQS4iesOAP5GDOVPaedZ1//GZ7tvOYm\nLJIpodU8YB0oMDN3TCP6U2pufhxeXv0KT9XusJGcJRl5zHrlsdq2jrAFjnPGjphBHIZKrRIShkom\nax8awt3fF7I5i2Tq4vy7n4RDv6EiUgAWVPXLB17/GlX9zHFPrqrPEIth72vv7fm7Aj943PMYJmdv\nre4gKmCHF0soHT8c3h0jAnvEdTgqBx2BoOMz2wqxwmji9mTDDN4hLsPoFfmF5SpOEA1kP1eLSRaW\na30z3Mi2WL1ZmMg1ad884F18+vVOVyB7uXo9yb07bXxPkXjyy8ysPVGnjGolZGPNx/cV1xUWFt1H\nptOGqyFXWhsTHVMsxVZ7wzTxKGuUzUYYm7BrfP1FAnIFm6Wr7kAYt1GP94X44WpzPe6ysrg0uO9F\nZ6RQishfAn4SWBcRl7jY/7nO5p8Dvvbkh2c4K9ppB8cf9FRFp5TBeY5oZdzYpWeI8037hEOSx8U6\nIH5928L4A9l+GAvhwe1AaTOOHPTOcCWImFups36j8NDzD5qZj75ejivcejJJu6UEgZJKWxPNBivl\ngNVlvysavqes3PfQqy6F4vn+PR2VQsmmvBv2iaUIXL6amNg4XVVZvuv1zVLjNeKQat7qu4Yaxfv2\nJV8Bld249OZh5TcXjcMeSf4n4OtU9U8RhzzfLyJ/sbPt8XqceAwpz2dQS/rCdJHA7nzmZLNUVUk2\nfHI7LVJ171Q62LayLl7S6ZbQQPxZWxl36j6q9UKy7zzQMWRP2Udqdt3KJQber7utY+knh0yK7Wiw\nfZYQZ8jKGGUIb31pC33u1/n0mBZ0IkIqbZHL2xOHTDfXBtfrVOPXzxthqJR3A3Y7XUGOiohw/VaC\nK9cSFGds5hYcbr0keaRZdKs52ravm1XbodEYEVHSeN30ceOwb7e9lzijqr8jIq8HPiIi1xm5LG+4\nKEUdLCMAACAASURBVAQJm5VbRYobDVJNn9CxKM+laZ6gUYFEyqW7FRLt/X+IoWuxeqN4sqFeEdZu\nFMjvNMmVPZA4JFmbmb5jTXk+Q6rhxyUknVCnWsLmEQv+m1mXdtoh2Qy6ghdJ7C+7l2kbJCwiS7oz\nzD32BPaoLk1Pv3GXr52/TePHPshppDv4IwRn1OvHoVEP2doI8DwllRLmLrmkxlyf25v57rGOz8Ki\nw8zc0eqFRYRcwSZ3zBDzYVfpFJ5HH2kO+3ZXReTJvfVJVV0RkW8Gfgn4E6cxOMPZEiRsto6R4Top\nxY0GiXbQbwHnRcyt1ti49vAw4LGwhOpchurcFP1fe2Nley9ZwurNYpw80wwIXJtmvt/rNlNpU9xs\n4AQRXtJhdyEzujG2COvXC2TLbbKVNipCrdRp6tyzz9aVHAv3q33+uKFr0Uo55Cr9IXYlDr2PyvZ9\n+o27/OQ3Xqb5Qz//0HDrNHEcCIZMZpwpn75WDXlwbz/sWPOVeq3N9dtJ0gfWBVW1b70uDLQvPLzH\nxlpAJmeTTE73gW8voWecNcN02hrqbysCxZl+EU5nrKHCKgKF0sUMcx/GYZ/4bwGWiLx8z39VVasd\nI/M3n8roDI8VuRGJLumav5d50L9RFSvUuIPGeTEtIK7JnF2tk2iHqEB1JsXuQmZ//NLjsHOA3E6z\nr7Fzqhlw6V6FtRsFvPRosayXUtRLo2tlW9kEK7dLZHdbuH5EM+vSKCQRVVLNADuIHXuizgx3ayk3\n8B7vfscqxfd+kk+8/iv8nK/YDswvKKXZ8WZKrWbExrpPuxnhuvEsbZK1rvlLLmsr/SIkAnNT7OWo\nqqyvDAqdKmys+ty4nURV2Vz32d0OiSJIpoTFJZd0xqZWDYfaGKpCtRySvDQdoYzC2IigUo5rLNMZ\ni8Ur7qFCLCJcuZ5g+a7XHZMIZLLWgG2fZcU1qQ/u9e+bzVvk8hcnkW9cRgqlqv4BgIj8kYi8H/gx\nINX58+uB95/KCA2PDYeFAPfClHv0miEAVEvJuBvKGWfjOe2QxbuVvuSY/E4LO4ge7qeqSmmjObQs\nZ2a9wdrN4rHGFiRsygc6xijCgydKZKoeiVaAn7BpFPoNEPbKPX7ta3+Pz/TMlsIA1lfjKd7DxLLV\njLj7Qnv/2FB5cM9jccmlODPeDKU446DE1ndhQEeoHUpjHj8OqqNDua1mvG63+sCnWt43AWi3lHsv\netx8Ihm/NuJ7PE3ruXt327Sb+6bwzUbE3a+0uf1U6tC132zO5omXpqjshoRhRDZnk85YQ2ekubzN\nE0+lqJQDwlAP3feiM8437NXAu4BPAHng/wNee5KDMjyeNHIu2WFhwFR/GDBd9QbMEPK7bQTY+f/b\ne/Mgx/arzvNz7qJ9SeW+VFZlVb16z5g2xja4oXF3Y2OatmFsEzTETLM4GiLcBNMeOqYJMEMwEbPE\njImJdmCmZ5hxQ/S4B4hmacfYMRho22AY84xX7Ifxs997frXnvim16y6/+eNKmanUlVLKlFLKrN8n\noqJSV1e6P/10dc8953fO98y1e0KDwvB8chslEoXgLrucjrA3m2xZP83sVtoMflNPdd/1uyrgBM21\nwy+mdm2wWqwtiFDORLnzYxXe//emKf/8rwS1Iw2a5R7bmx2SaTbdUw3l1kYHL23DITNh9nzxncjZ\nTOTstpDnoBDhsHTlJKYluK5qMZJNlIKdbZeZ2fBLqgikM4Mx6NWK32Ikj48hv+ue6mFbljA53dtY\nLFuYnL4aWsznoZfZcoAKECfwKO8qFabbr9Gcj73ZJLGyi+EdCwNKexgwux0uhpDar7E3kxxOGFYp\n5u/lsZyjcozkQZ1oxWX11sShJxuptvf/hOBzWHWvq6H0u0ivufbwwl2HAgE/+RzPAp0uC0493Ih7\nXvta3Uma3thJfD94fb/rjMPyakSE3KTJ3q7XFuKdnDZbakBPUq/62BGDqRmLna1jXUkkKPOIJwbz\nHdbrfugYlIJqVV+ah0Evp+fngA8D3w5MA/+HiPyQUuqHhzoyzROHbxlBGPCgFuigRkyK2WibHqzl\ndr4YmJ7CG4KhjBcdTK+1ZlEA0/WJF+uH2cD1mEWkFlKzqBTOaaLjIhxMxsnstoZffQmyZQdNmIJO\nNyIRoR5iLE3rdMNl2XKoydr2+jFb8pqes/F9yO8frTdOTgch3uCmIPx1TdWaqRmbZNrkYN8FFfS3\njCcGV3cYiXbupamF0odDL4byp5RSn2/8vQa8XUR+fIhj0jzBKKORmNJln3rMIlZy2oyREsEbkpyZ\nXXND6xFFQaTmUWksPx5MxUmeUMvxBcrpaE8lLvnpQAe3GcL1TWFvNkElPbgOF/0ayCbTc3ZQ4H/C\n05ruIZlmesZm7XH7aycmTWSMErEgMPpzixGm5wJhBNuWw+J+y4JM1jxMojl6DUzOHF1OYzGD2Pxw\nupLEYgaxuNGm2CMGPa/3avrj1Fk9ZiSPb9OJPFeZhvzZYblBNkYtOT7rFHszCebLeVBHyhe+EPTY\nHFJIzo2YKKO9eF8JLZ6iGzHZuJElt1EiWnHxDaGQix0awG6I57HytRe48cILVGMxXnr1t7C9MD+Q\nz/St37fNu9fybP+bD7P5OxZ/mTZPVXYpmTG+kbpO3bC5Vl5nLrPDwrVIICFXV1i2MD1rke2hXCCd\nNXE9q0U0IJszmZkbzHm1Gpvh6+mbuIbJ7eJDVkqPCS9w6B3TlNCm0nOLNpYt7O+6eB7E4sLcQmTg\npR/duHYj+B4O9oPM20TKYG7evhS6t5cROauw7jiTXrijXvfO9496GJcTpZh5XCBWCnoXKgJjcDAZ\nJz8z+PDfWYlUXSY2g7pL1zLITw9XDAGlWPrGPmZDMi5aKREtFylM5Lj3TfPnXhcVz+P7fvf3mdzY\nxHYcfBF80+QL//Dv87XXnV0t8tVv2+d/XknxkVe8n2r5qC2iacKNm7GOHTfuJRb5+Nx3AuCJiaU8\nVkqPeNPmZ84ly6WUwnWD4/crwdaJz+W+mecmXhG0sRIDy3dYrGzyj9c/pSXENId811f+8AtKqW87\ny2u1n65pIVZ2Do0kNLpoqCAUWMxG8QbY3Pc81GNWVy3SaNlpdOrwqcdM9qcTOOeRo5NAKGD68T6v\n+/OPM7m1im8aiO8zs/4tfO5NbzyX57fytRcOjSQEa5qG6/K6P/8LXv7mV1KP9ddP9HiCzh8+qFEp\nHj2nfHB92Fyvs7jcfnPhiMkn5r4DzziaL1cs7iWXuJ9YZKW8erYPSRDWtAcYnCiacb488U14x3p1\nuYbNanyWh4l5rpfXB3cwzROLNpSaFuJFJ7yeUcHcwwPMRolDfjpOKdt/M+iLIF6oH2s9BWbRJ1bK\ndy/a7wHPNrj91c+S217F9D1MPyjZuPPlv6EwkeNrr3vNmd975etfPzSSx/ENk7mHD3l4507o6179\ntv2Wx+98utqy/qiUolgIT37qtH0tPhN6DriGzQvplXMZykHzKDGP4HOyp5lr2NxLLGlDqRkI2lBq\nWvA7LLMIYDdaThmOz+R6CfEUxcnxasKMUuRO1Fg2veLzFu0brsvN57+G5bXWNNquyys//4VzGcpa\nLIpPeJcCJ9Lu9R3v2nFckLwKfSXohBEYyfAlmU51nqMi4rcndQGI8on47Tcelw3fV/heb5nFmuGh\nc4k1LZSysRYFnE4YCnLblYGrKZuOw+TGBvFi8fSdQxDVuXwkUj1f1wPLcToaiki1eq73fuFbX40f\nUkzoWRYby9fatvfatUNESGfCf+adRLYXqpuhYWTLd3imeLfr8S6a5fJaqE03lM8zhfEaaz/4vmLt\ncZ2Xvlbl5RerfOPr1aDcRDMStEepacGNmOzOJ5lcLx2mlIrfoa+aUpiuwuuQENIvr/zs53jNp57F\nNwwMz2Ptxg3+4j/7ftxo72n2SoJ/YaHDbsX+vVCPxSin06Tz+ZbtPrAeYsz6YXtxkS/+/Tfw2r/4\n//DNwIB5lsnHfviHUEbruPvt2jG7EKFareG66jCZx7KE2fnwMLSlfL53/S/5T/NvABQ+BoLiTuE+\ny2cIZVYrPjtbDrWaIhYLCvKjPXbiOIlSCqeuMEzBsgRbebx1/S/4o/m/37CXgi/CG7a/QM4pnOkY\n48D6Y4di4agExfMC6TzLFhLJE2FmV1Gt+FiWEI2J9jyHgM561TTKQRwM36cat/EiJuL5xMouSoLm\nvtFqu4SaL/DwzuRAlHCuv/Aib/jDj2I7x1psmSaPbt3kkz/49r7ea2Kz1NaI2RfYm01QzJ0vVLxw\n7x5v+tCHMTwPQyk8w8CzLf7wx3+Ug8nJc703QLRSYe7hI+rRCBvLyy1G8ijc+is9939s0lyrrNd8\nolGDZPp0zc6qEeHl5DUcw+ZaZZ2per7r/mGUSx6P7rfXTy6vRPtWqikWPNYfHzUejicMFq5FsCzB\nw2A1PosnBgvVLaKXOOzquYpvvFANDdYkkgbLK0EoPhBnd9nbcQ+VeuyIsHwj2jGbeVAopaiUfZy6\nItqo6xx3dNar5szYVZe5hwdBSLHxwyxMxNg/VuS+D8w8LrQZnkIuNjC5uL/zmc+2GEkA0/O49vJd\nIpUK9XjvBm5/JoH4ilS+drgtPxWn2KW7Rq+srazw0R/7p3zzZz9LdmePzaVFvvr6b6OUGUwbsFo8\nzoOnWxN3epWYC8P3FbvbLvk9D6UUqYzJRK43YeuYX+eVhZf7+wAnONntA4IL+uZ6nRu3ev8+qlW/\npfUVQLnk8+h+jZXbMUx8litXI3HHdVVoBxJolREsFnz2doK61Oa81GuKxw9rfc1tv3iu4uG9WotK\nUyxucO1GZGAlP+OGNpRPMkox+6gQiHEf25zer1JL2IeGspqKsDOfJLdVxnTVUV1lD0X0vRIvhWvx\n+IZBtFLty1Aiwt58iv3Z5GGWbqfeiqEv9xXJgxrRkoNrGxRzMTz7KNy1NzvDp37g+3sfzxk5q4LO\ncR4/qFMpHym45Pc8ykWflaeiQ7+oKaU6ytZVK/1FsvZ32gXZITAMtap/5lDuOGJHpGMHkuNe+G6H\nOalVFU490J0dBuurdWonvtdqxWd702F2SGpEo0YbyieYSNXDOKFfCk2B8WqLbFo5Gwv6F/oqMDoD\nXgdZu3Gd21/5KsaJX75vGhSzZ/PWlCG4fdZ9iuezcC9/2J9RAZm9KpvXMudWJ0rkq0xsV7AcH9c2\n2J+OUz5RYtMSXm107TgrlYrfYiSbuK6icOD1pKhzHkQEw+AwVHocs89y3DCN2eAY4DqK6HhWKp0J\nw5A2YXUI1panjsnk+V4Hayrg+TAMLa1O5UZKwcG+x+z8EA46BmhD+QQjzW6sIbelRljvPBFUlw4X\n5+HLf+87uf7CS1iOg+n7KMC1LD77pjei+r2qnoPsTuXQSMJRacn0WpHHtyfOfIOQyFeZWj8qW7Ed\nn6n1wIsuZ2Mt5R7P/nOLQfw0ax06digV9C/MTpz7EKcyMWkdhgebiEBuqr/Pl0y1a5tC8FmiXdbH\nKmWf3Z1Aci+RMJictoe+fjcIpmZs7Iiws+XiuYp4wmB6ziZyTCYvlTbYq7e3/BIgGu3tM/Y7P91S\nWgbYbnPsGImhFJFJ4HeBFeAe8CNKqb0T+ywD/x6YI7ix/4BSSmfoDJBazCIsxuMLlDIXG0IpZbN8\n5J/9BK/6q88y//AhxUyGr/zd17NxfflCx5Eo1NtaeEHQi9Jy/L491CYT2+ENmWfLJf6vX88N1EA2\nsSMSeh8kEnQCuQimZy08T3Gw7x2OJZsze+6H2GQiZ7G3GzRsbtIUVe+kb3qQd1k/1mi6VvXI5z1W\nbkWHFpYcJJmsRSbbeZ4mp2wO8h6ee/QdiwRatL2sQZ9lfgxDiMUlNHSeSo2HatcwGJVH+R7gE0qp\n94rIexqPf+HEPi7wr5RSXxSRNPAFEfmYUuqrFz3YK4shbM+nmF4rIo38AV+gHrUojkB1p5zJ8Jl/\n9ObBvqmvSO9VSR0EiT3FbDRIQupwIem2ltl8znB9cpslEkUHBZSyUfZnEl1fazkdvLt9xbOv+tcM\n46eYSBqYpuCfuNUP+iNezE9fRJhfjDAzF5R12JFwofHTMC1h5XaMnS2HYsHHNAOvNJMNvzgrpdgM\nSSTyvaDR9MK1/m8EmxUC41J+0ZyT/T2XcjEoD8lNWT1loPYzP7Wqz+a6Q6UczHs6Y1JrZME3g1KG\nCTMdyo2uAqMylG8Hvrvx9weBT3LCUCql1gjaeqGUKojI88ASoA3lAKlkoqzFLFL7VUxXUUnZlNOR\noXXhuFCUYu7hAZGqe+jNTWyViRfrbC5nQj9jYSJKbrO1MbQCnKiJZxmIrw7XMJuvTu1XiVRcNm6E\nvycENZxhQggpt3zOD9kZEWH5ZpT1R3XKjTBsJCIsLEUuvMuEaQpmvLdjum4w+SfHaFlBl465hR7e\nw1Gha6MQlKz0w0HeZWvDxXUUhglT0xa5KevMBtP3FZ6nsKzz1zyapjA1bTM13d/rep2fet3nwd3a\n4b6uC/t7HumMSSQa9BiNxYXshIUxpGWZcWBUhnKuYQgB1gnCqx0RkRXgNcBnuuzzLuBdANHMzEAG\n+aTgRkz2Z5OjHsbAiZWdFiMJQbgzWnGJVlxqifY74OJEjGjFJVGoH27zLIOtpaDhZOKg1pYAZSiI\n1Dq/JwQtwCbXW6X1LN/l23f/5lyf8TRsOzCWnhdkJplj3IapXvNZfVQ/zJSNRIWFa2drX9Xtot3P\nHAS1m0eeV9PjUipYR+wH5Ss21hwO8oEhEgNm5iwmcoP3xFyn0Uuzgwff6/zsbrttBlUpKBx43Ho6\n9sS09RqaoRSRjwNhOVC/dPyBUkqJhMpwN98nBfxH4F8qpQ467aeU+gDwAQgEB840aM2VIlpxQxV6\npGEsQ42aCLvzKRy7QrxUx7VN9mfih+UhJw3vceya19FQlrIx/ulb8nzu9xV7BZukW+bbd57j6eL9\nvj+XUopqJfBK4nGjpwv/WcKdF4nvKx7crXFcRrdWDbbdfjrWdymLaQrJlEGp6LclEk32kUi0vRFe\nB7q77TI53Z9Xub7mUDjW8Fl5sLnmYlkGqfRg1vea0nelgn+4JpybspiebR1rr/NT7ZAQJhLc2FjW\n1V2XPM7QDKVSquNik4hsiMiCUmpNRBaAzQ772QRG8reVUh8a0lA1VxTPMkLl7JSA18FwGJ7P/PHy\nkKpHolg/LA9xIya+EGos3S4JIs2ayJtfeg5FB0nAHqjXfR7dq+M2al8Dz8bq27vpxp6dZiM2TcKr\ncq28fu4GyL1QOPBCsyaVD4W8RzbX/6VqfinC6sOgjrRpNCanLdId1jXDqDvhn91XQdlLrwnZvqda\njGQTpWBnyxmYodxYcygV/BYRgr0dF9uGicnWc6SX+YlGjcP1yJPjvgwJUYNiVKHXjwDvBN7b+P/D\nJ3eQ4PbnN4HnlVLvu9jhaa4CpXSE3Ga5Je1TAUqEcia8yXPmlPKQYjZKdruCUkciDYqgBVe1gzd5\nkrMaSaUUj+7XcRoX7+an2tlyicUNkufMOlTAJ2dezzdSywgKUQpbebxt9U/JOmcTqe8V1wl0aNvG\npDj8vP1imsLyShSn7uO6ikjU6NuzjkSEWjWkfMoI/vWK26nmkbN/vpP4fmdjvLvjtRnKXuZnctqi\ncOC1eZ3JlIF9CcpsBsWobgneC3yviLwIvLnxGBFZFJGPNvb5LuDHgTeJyJca/946muFqLiPKNNi4\nnsGxDXwJMnpdO9jWKUP1tPIQZRqs38hSi1uB0QUqKZv169nQRJ73/dw6f/ZDn6L6xg/x6Z987lyf\np1ZVuCEXVaVgf/f8nSVeSK/wcmoZz7BwDRvHjFA2o/zJ/BvO/d6nEYsbSMjVSIRz64jaEYN4wjxT\n+Hlmzm77WkVoC2WeOgZbOubHxQekk9opOQcI1qg70G1+orFAmq5ZThRkTJtnyhq+zIzEo1RK7QDf\nE7J9FXhr4+9Pcfabb40GgHrMYvXWxGF5hmsbXTN6eykPcaNm0NfSb9TUnHi/09R1mh0wRPoLX/m+\n6qQPgddfImcof5t5Ctc4cUkQgwMrSd5KkXWH51UmkgbRiFCrqZaawEg0WEsbFcmUydL1CFvrDvV6\nkKk6NWP1HQoWEabnLLbW28UXpmcHEzY3zeCfG3LPlOhTgL7ltUmTm3fMw/NvXMpjLhKtzKO5+kjv\nUnady0Os9jZdRicD2Vk8oFzyWHtUPzRskaiwuBwh0oPBjMWNUCMpEqi0nBdPwudIUHjGcJM2mqUs\nO1tukBWqIDNhMDXTW/H8MEmmTJJPnf/z5yZtLMtgZ8vBdRSxuMHMnD0wnVoRYW4x0iYebxgwPXd+\nY3xVBc97QRtKjeYYLeUhjeuCZxpsLaVOfe1pzZRdR7W1nKpVFQ/v1rj1dOxUg2AYwuy8xeYxryTw\nSoWJyfP/lG8XH5C3U3gnvErb98idocVWvxiGMDNnMzOAi7rrKLY3HUpFD8MUcpMm2dzZax8HRTpj\nku7QMHsQpNImyytRdrcDDzgeN5icsXq6EdN0RhtKjeY4IuwspsnXPaIVF9cyqCWsUwUYemmmnO/Q\nod73oVT0e8p8nJi0icZM9nddXFeRShtkc9ZA7vZflX+Bl1PL5O0UrmFj+B4GijdtfvpSrYG4ruLe\nN6pH4WhXsbnuUqsp5hYu79qa5ym21h0KB8EHS2dMZubstvKgeMJg6Xp4sprmbGhDqdGE4EbMnsK1\n/YiZO3UVGjpVitAknU7EEwbxxOAv+Lby+MFHH+du6hqP4nOk3DLPFO6SHqJ60DDY3w0vks/veUzN\nqEtZJK9UUFN6vG1Zft+jXPa5+VR05J7yVUcbSs2VQ/ygiEyZww03tSTtdAi3HieRNDgISd+H1j6D\no8TE56niA54qPhj1UM5MudTeZQSCoECt6mNdQvHuUtEPLSNx3aDt1TDDuRptKDVXCNPxmForEisH\nIc56zGRnIYUTHY/TPJ0x2dl2WzzLZiLOVWo83AmlFJWyT7USSKul0sZQPKFIRKiEOMFKtevHXhZq\nVT+8ztQPnhs3Q3lcPSoWNy7tvDcZjyuIRnNelGL+/kGLWHmk6jF3/4DHtycG6l02VXY+/ZPP8SzQ\n689IDOHGzSg72y6FAw9DgpZTg0jEaeJ5it1th8KBj2FAbtIiM2EOzCAFF0CfWlURiQrxRG/GzvcV\nD+/VqFWDmwQxwDTg+s3Bt7zKTVmhnns0Jpf2hsSOCGLQZiwvsmVar5SKLmuPHDz/qL4vTEbvMqEN\npeZKEC86GH6rWHmgqqNI5msUJ+MDOU7TSFZ+/4uc5edjmIPL7DyJ7yvuv1wLVG4aRmJjLWiPNL90\n/jVN31M8vF87UqoRiNiBustperM7mw7VqjqUE1I+uD6sPXa4fnOwiSfRmMHicoSN1aMynETSYGEA\nczAq0mmTLcPhZAMaw4TUmHiTSinWHgd6tofbGv/v7QTqUePm+faKNpSaK4HleBASmjIU2A2xgez2\nDre/8hUi9ToPnnqK1ZsrZ24n1sua5FlQvqJS8Q8Vafq5A8/vuy1GEoJw40HeY2rGP7fntrXhHHqE\nwZtDrRZ0xFhc7m6E8o3ayJNUyj6+pwbeoimVNkk+HWu0xjpbD8xxQgzh+q0Y66t1ysXgfE4kDeYX\n7dCM52aYu5D3oNF/dFAKQJ042PcoHoQrXygFe7uuNpQazSipx6zAhTzZiFagFrO48+XneP0n/gzD\n8zCU4tbfPs/qyg0++Y63hRrLhXv3uPPlv8FyXe6+4hX8i3+b5bUPX+bTb3yuTWlnUJQKHquPgvZe\nikDPYOl6tOdEn3IxPIkFgUrl/IayUyJSoAWquhv1Lkm9w5JcFxHsMQtLngfbFpZvRHtqIL257pDf\nO/q+8nsek9PWwFSAwtjbdcPPvwZ+Fxm9cUcbSs2VoBa3qEctIrWjNliKoIOIG/F5/Sf+FMs9utu1\nHYfFe/dZfukbPLzzVMt7vebP/4Jv+uJfYzkuAtxYe8DWOxI8W6wPbY3FdRSPTyiqeMCj+402Uz14\nRN2MwiCSKbpdBE8jnTHZ32/3KqOxy+/tVSo+u1uNAv+EweT0cAv8TzsHqxW/xUjCUWuwTNYkcob+\nnr1w2vlxWb1JGJ0oukYzWETYvJ6hkIvhmoJnCoWJKOsrWRYePMQPkWCzHYeVr329ZVsyf8ArP/9F\n7IaRBPDLLlt/U6RU7KI6fU46iREooFDoLuTqeYrN9ToH++H7WaYMpPykk+ZqooeEnuk5u0UYXCRY\nX7vs4trFgsfDuzWKBZ96TZHf87j/jRr12vDOldMoHHT27IZ5DmeyZseVDMtmoElrF83lHblGcwJl\nCPuzSfZnky3bvQ5NA33AtVp/AgsPHqAMo01l3K34FAvewPoGnsTzwsUIUOB3sZPNBB7HUW3emkjg\nsS0uRwbiCc8uRKiUq/h+4D2IBNmrc4unh/NMU7h5O0qh4FGt+EQiBuns2Tp6nAel1NHYzzknSik2\nVutt35vvB+u5o1LH6abSNMyk09xU0JKrXms9l7M5g9m5yMDXoS8SbSg1V57VlRuh233L4qVveVXL\ntno0iupwNRmmfkEyZbK/G74GmEh2PnDxwMN1240kwOJyZKCG3baFW3di5PfdoDwkJmQnrJ6NnRhC\nJmuRyQ5sSD2jlGJ/12Vny8Xzgi4bU7MWucmzr9l5XueuLeXy6DzKdNZkZyvcqxxmhqxhCDduRSkc\neJSLPpYtZHPWlehbqUOvmsuBUkQqDhObJbJbZax6732lfMviT3/oB6lHItQjNo5t45omz33H69la\nWmzZ99Gtm6GGUoS+Wyv1QyJpNGoSW4+Zzppda//K5fBCdJH+ZPF6xTCF3JTN/FKEySn70qwv7u+5\nbG24h4bN82Br3SW/d/Y+nt0aN49yXiIRg9kFq+E1B16/CMwv2UMv/BcJbobmlyJMz9pXwkiC9ig1\nlwGlmFwvkTyoIY1rf2a3wt5skmIu1tNbbCxf4/d/5qdZevllbMdhdeUG5XS6bT/fsrj/v34fV7fx\nLgAAIABJREFUr/pvPoZ3UMFzARWEF4eVBAHBBebajQgHee9wrXEiZ5HKdD9mJCLhPSoFrCtykRoE\nYR6WUrC95Z75BsgwhHTWpHAiG1gEJqe6e26+pyiXgzKgRMJABtzCaiJnk0pblIoeAiTTFx/mvkpo\nQ6kZe6Jll+RBraVHpCjIbZYopyP4J/tEdsCN2Nx/xTOn7vfD70jz6m9+FX/2o19C+RBPGhfSi08k\nCGVmJ3r/WWYmrFAjYBqdk2+eNJRSwQ1PCOf1uucWbHxPUSr6hzcszZZencjvu2ysOsH+BFVNS9cj\nJJKDDYtalvR1Lmk6o2dRM/YkCkee5EniJYdS9vxJEy0C52+0+AwM/MI1DCwrUMZZe1THcRQKiMWE\nxWuDSeC5CogIti2houLnrbM0DGHpehTXUTiuIhLpXu5Sr/lsrDoodRQFUMDjB3VuPxO7kBsy11UU\n8h6ep46F/PW50g1tKDUXiviK1F6VRKGObwqFyRjV5CklAl1+xGpAv+/Tmi6PM7G4wc07gQqNCKfK\nyQ0K31dB4kbJxx7zxI3pOYv1x05biHR2QFKCli09hboP9sMTtiAoNclkh3v+lUsej+43RC0U7G4H\nkYdBZUZfVS7fVUFzaRFfMX8vj+V4h2HUWNkhPxXnYDrR8XWlTJTUfjXUq6wkh6c00iv1uk+pEChA\npzPmyDolXOSapOed0JWVoKD92o3BhhA9T5Hfcyk1sihzkxaxM0ixZbKBIPf2poNTD7qXzMzZQyv3\n6YTXQZ1GnVIGNAiUahe1UCqorSzkPTI6TNsRPTOaCyO5X20xkhBosU7sVCjmYvgd6i/qcYuDqTiZ\nnUrL9u3F9NB7Tp7GzpbDztbRAtjWusPcon3l14Z2t51WXdlGhcraY4dbdwYTyvNcxb2Xq3juUZiy\nkPeYX7LP5HmlM+bI1WFSGZN8B68yMeQ15WrFDy0jUipoAq0NZWf0zGgujHjJaTGSTZQI0YpLJdU5\nBJufTlDMRImXHJRAJR3paFiPY7g+qXwV01VUEzaVlD2wqutq1Q9NpNlYdUimRudZXgSFfLiurOcq\nHEf11PqpXvM5yHv4viKVNtvWynZ3nBYjCcHfG6sO6czgWoddJImkQSJptDSXFgkSgIYpe6c5HyMx\nlCIyCfwusALcA35EKbXXYV8T+DzwWCn1Axc1Rs3g8U05zPJrQSm8HlLXvYhJMdK7RxAtO8w+PAAC\nzzW1X6Uetdi4ngkUx89JYb+LVFjBG2rd5aiRLtd0owcDtr/nsLl2NH/7ux7pjMn8kn1oAIuFcGOs\nCLqWxGKXz1CKCEvXIxQPfA7ybpDpnDNJpobv6QbdaMLGFPRF7QffV0EbuwtIPhoHRnUL8x7gE0qp\nO8AnGo878bPA8xcyKs1QKeTibck3TeHyemzARkUpZh4XMBSHXqyhIFJzSe9XgSDT9ZPvjfNR/9eo\nvvFDfPonn+vvEN0Pf2lwHBWaEdqNiVy4rmckenpSi+eqFiMJwXw1E4OadJQ8U8NVSRo2IkH95dL1\nKIvLkQsxks3jLi5HDgUIgm2QSvfeJ7Ja9bn3jSovPl/lheerPH5Qw3Mv0cl+RkZ1ur0d+GDj7w8C\n7wjbSUSuAd8P/MYFjUszROpxi925JL6Abwi+gBMx2FzODFyE0q55iN/+AzYUJPO11nKQM2a6prNW\nx2FfdJLIWahVfe6+VOXui41/L1WpVXuTXpuYtEiljUP1F8MIhK+XTulLCVAqeSFhhYaxPNb0d3Iy\nfH6jMTl3y7AwfC8omzjIux2Tbi47iaTJ7adjzM7bTM9aLK9EWVyO9hTGdl3Fw7vHGncTeP0P79UO\nW39dVUYVG5pTSq01/l4H5jrs96vAzwPtEionEJF3Ae8CiGZmBjFGzRAoTcQoZ6JEqi6+IThRcyhK\nzV3LRiSkceUZiMcNJiZbNVpFYGbeGkgGqu+rQw8rMWDRA99XPLhXa8m0rNcUD+721tYr8E6i1Ko+\n1UqQkZpI9pbE03WfY0+lMga5isnerndYzG/ZgWJRqej1fLxeKBY8Vh/WD0UAUA5zC/aVDJ+bppyp\nk0d+L3ypoe4oqhWfeGL8bw7PytDOAhH5ODAf8tQvHX+glFIi7Yn/IvIDwKZS6gsi8t2nHU8p9QHg\nAwDphTtX+/bmkqMMoZYYblmHGzHxLANx/BbnxRcoTESZozqQ48zOR8hkg84iwLn7/dXrPuWiT63m\ns7/rteiJDjJMFzRbbt/eDIH2aiCiMaOrFm0YyaQRep8iQku2sIgwMx8hNx1ciMslj/1dj81153D/\naytRYn0e/ySeq1htlE0cn5ONNYd40hhKko3jKEoFDzGC6MNlkJerVTt0uIFGL86LHc9FMjRDqZR6\nc6fnRGRDRBaUUmsisgBshuz2XcDbROStQAzIiMhvKaV+bEhD1lxmlMKueygRXDtYhNlaSjP34AA5\n9uuupCINJZ92Q6mAl5PLfHniGSpmjGvldV6397ekvErbvseJxY0z1fadZGu9zt6u1/w4QNCyqUlT\nvWUQF1XXUaFi6krR93plvximsLQc4fHDesv2yWkrtG+mZQVqN03P/fjF+tG9GrefiZ3Ls+zU77MZ\nCp6aGayh3N122N48KinawGHhmk06M97eaywhFAsh6++Kvm+WLhuj+mY+ArwTeG/j/w+f3EEp9YvA\nLwI0PMqf00ZSE0a07DC9WsBorCt5lsHWtTROzOLRUzkSxTqm51ON2zhdkoa+MPFKvpz7Jlwj2Ofr\nmZvcS13jhx/+MQlvMB5oJ0pFj70ObbaOUzjwmBhAODAWNxCDNmMpRhBSHjbJtMntZ2IUCx6+D6mU\n0XXdcb9D2E8pKJf8c3naYTcMTfyQde7zUKv6bG+2f5a1Rw6JZ8bbs8xOWOxuuS2txUQgnjDO7dWP\nO6P6dO8FvldEXgTe3HiMiCyKyEdHNCbNJcRwfWYfHmC56jDD1XJ85h4cgK/AEMqZKIVc/NBIvu/n\n1vlV+ys8+6p/fZjIUxeLLx0zkgBKDOpi8eXs6ULq56VTEfpxFINTb0kkDWJRaWvrFY3K0Avfm5hm\nINqdm7ROTc7pqGhDq9d9FjqJxwcZoYP1JfJdSoqKHTzbccE0hRu3Y6QzJoYR9PTMTZksXT89geuy\nMxKPUim1A3xPyPZV4K0h2z8JfHLoA9NcOpL5dk9PCOTyEsU65cyRYPr7fm6d107fpPzzv8enT2S6\n7kaymMrn5KXKN0xWE7Ow27q9XPbI73r4Sh0qvpwn/NdL1qAwuI4gIsK1lSh7Oy75veBTZydMctPW\nWBbypzMm5WJIXaXq3ti6FyJRg9yUxd6O25KUlcmaxOKDnYtuX/NlSBy17aDE5EljvIPiGs0pmA1P\nMvy53l2NpFfBC6uiVz5pp9SyqSlb17ywlQo++T2PazfOLiydyVqUCvWOF8tmUfgg14IMQ5iasZma\nGb1e7mlksib5XZfqsYQSEZiZtQYSrgx0Xw3y+x6ooGH2ILNqm6QzJvm98OhBasj1lLWqz9aGQ7Xi\nY1rC1Iw1dBH2q4KeJc34oRSpfI1kvgZAcSJGKRMJLSOpJWz8/WqosQzLrFWf+1joIdNumbnqDuux\naXzj6IJlKZ9X73/98LHrqDbZOqWgUvYpFvwza4mm0gaJlNHqNQlEoxCJmGRz5rk9p8uE7yv2d10K\nBx6GEZQzXFtpKNoceBgCngtbmy5bmy7pjMnsvH2uzinxhDn0Eod4wiCTNTnIt5YUTc8OpqSoE7Wa\nz/27tcP1WM9TrD8O9Honp8f/RmnUaEOpGS+UYvZhgWjlSBc2Ui0SL0bYXmovp62kbJyoiV07Elv3\nJegq0kntp5PAwD/a+Ev+dPbv8ig+j4HCVB5v2PoCc7Wdw33Kpc4ZksUD78yGUiTIBC2XglIT0xQy\nE0+m/qfvB/Wc9VrTe1RUynUmJk1m5yOkMiZ3X6riOkevOch7VKs+K7d7K54fFSLC3KJNJmdSzAf1\noZkJa+hZozubTlvSklKwveUyMWldSB/My4w2lJqxIlZ2W4wkBAk68WKdSMWlHj9xyoqwcT1Laq9C\n6qCOAooTUYoTMaC9IfOnuxw76ju8Zf1TVIwIdTNC2ilhnCj4Mww5LH4/iXlOZ0RESKb61/30fcXe\nbmOtsRE2nJoZv4uf5ypqtaB3ZbfkncKBd8xIBigV6MHmpnwqJb/FSDZxHEWp6I+9KpKIkEiYJC6w\nQL9S6bwA6jqKSHS8zpVxQxtKzVgRLddD+06KCspA2gwlgYBBYSpBYaq94vksDZnjfp24Xw99rlNG\naLCGePE/J6UUjx/UqZSPQrZ7Oy6loseNW+PhXSml2Npw2D+msBNPGCwtR0IVgDqJoQOUiz47WyFW\nkqDMo17zYcwN5SiwbcENq49VF9fo+zLz5MV1NGONbxqh8nNKwB+DH7RhCNduRDHMoOawKTA9Oz/8\n8FkYlYrfYiQhMET1uqJYOGfdxIDI77mHYgG+f7Smu74abvDsDktmIpDPezjhL0MMzqWKdJWZmmnX\nzRUJog/jXLs5LmiPUnN2jmcjDIhSJsrEVrn9CRHKqWj79hEQTxg89UyMcsnH94PyhFFdbKrlcFkx\n5UO1fPY1015QKgh1Vsoetm10vOju7rRneSoV1A36nmrzKrM5q0U/t4kYUCl1Nv6WJQMrn7lqJFMm\nc4s2W+vOYd1pZiJIgNKcjjaUmr4xXJ+p9SLxYnBrX03a7Mwn8ezzX5R9y2DzWoaZ1UIgPaeCPpZb\nS2nUGN35NtcTR41lE66wI2CdIRFIqUCI3XUUsXhnHdfDhJt6IIUn4rG14bC8Em2T8/O7dOLwfTBO\nTGM0ajC/ZLPR8DgDMXRhfsnm0b3OJTTpjIHngaWvaqFkJywyWRPPDeZ83Nawxxl9Smn6Qynm7+ex\njomNx0oO8/fzPL6VG0hD5FrS5tFTOSLVoB1TvVOHEV9hej6eZQzUq71MpNImhjhtQgnNgvl+cByf\nB3frgQpOwxglU0bQw/DE/O5uuy0JN00N1tVHdW4+1bo2mkiZLe2zmpgmmB2uQJmsRTptUq0qDIPD\nZBPT6rDWBuztBKLp129FiR4LwSqlOMh7HOwHa6QTOYtkevA1kv1Sr/vs7bjUqkET6tzU6QpF50VE\nsLQT2TfaUGr6Il50MN3WjhwCGJ4iWag3BMcHgEho4g4ASjGxWT5swIwIe9NxipNxoLdM16bnVK0E\nWZipjHkp77ANQ7h+M8rqozr1WmBALFtYvBbpOxy8+rDeZoRKxeBifrLW7ngd4HFcR+E6CjtydOzp\nWYtSQ9O1iQjMLXYXaBBDiCdan59ftHn8INyrbBrrjVWH6zejjW2KR/dbk53KpTrZnMncQneFGeUr\n9vZcDhrKRZkJk9ykhQzgPKlWfB7cO6prrJQDGcPrN6NXXmD8MqINpaYv7LoXmpVqKLDqLjD8dcSJ\nrcBIHpaQKEVuq4xvGdz5scqpDZl9v9GAtuERiYCxHlxcL2MySCRqsHI7FnT9UArLlr69JddVLQ15\nmygF+3tef0XpJ44diRisPBVjb8ehXPKJRII+noYhOI7C7qPQPpkyuX4ryu62G+qlAg2jqBCRxhpq\ne7JTfs8jN+l3/L6VUjw6kU28velSLPgsr5xdganJxlq9LVzu+0Frr6aR14wPl++qoBkpTtQMzUr1\nBZzoBdx3KUV6r12Jx1CQ3Q5JAgphZ8s9NJKNt8TzgrDhZaZZn3iWi7jq0iUjzHvLTpih0W47IqGG\nz7aF2fkIK7djxBPCo/t1Ht6rcffFKg/v1TqKnocRixksXou09Ops4djhS4XOYvPlLolBlXK4ga1W\n/a6v6wWlFNUOdY2V8nhkKmta0YZS0xeVpI1rmy1l+IqgtVU5PXyxZMNXoR4tgNWjtutBh04dtZrC\ndcdTmdrzFOurdV58vsKLz1dYX633ZVxOw7IFK6z8RoIkmZPkpixiCePQWIoEa46nCWaXih5bG25L\nqUi55LP6sP+blFBjLbQI1HesERS6hqZPGskmyj+/MRORjkvqHY2/ZqTor0XTHyJs3MhQykbxJfAk\nS5kI6zeyF5JQ4xuC1+ECVz+nRzuI0SuleuoE0u97Pni5Rn4vWOfz/SB0+OBubWDHEhEWrtmHdaHB\ntsATDBNNNwxh+UaEazcizMxZzC/Z3Ho61pJEE8budnibqUrZ77th9PScHfTVlKN61mhUmFs4Gm8n\nz1eAZLrzWC0r3JiJEH5D0SfZXPu4RGBicvSZ1Jp29Bqlpm9802BnIcXOQuriDy7C3myCqfXSYfhV\nEQgS7M0mmKfU9eUAmawR2iQ5Eu3gVfWA6yo2VuuHRf6JpMHcoj0QrdZiwccJ8XQHLdkWT5jceirG\n/p6LU1ckkkFtZKckJxEhkTRJJI+OH4QVA6MXixttn7+Txy4SSNz1s17ZTGSqVnxqtWDtMxZvXZ+1\nIwYLSzZrqw5CcK4YAks3ol2Tt9IZk811p72Ws1Gkf15m5mzcxvfXVCtKpg2mL0EnlycRbSg1l45y\nNoZvGkxsV7Acj3rUYn8mwXt/eZvX3H2JZ1/1O3Q7tadmbEol/1gNYOCRLFw7W+hYqaCm0KkfXVXL\nJZ8HL9e49XTs3Nm0tarflvgBQRiwVh2stqllC9OzZ7tYu47i4b3akVFXgcGZX7IPjVcyaVCvtSfh\nKDiz3mgsbrTVbh4nnbWIxAzyuy5iQG7SwrK738AYprC8Eg0ygRufx7QC4fpBiEsYhrB0PYpTD87D\nSKS7/q1mtGhDqbmUVFMR1lOBYXvfz63zmrvP8ek3PtdV9LyJYQo3bkUpFY/KQ7p5TqdRKvqhnpLv\nB2UUE+fUgI1EJVxUwKClDGPUrD6qU6+3zsNB3sOyYWYu+K4mp20O8h7eMVspAjNzwxNxb/YPbbK3\n4zG/ZJ/aizEWN7h5J3p4A2RH+s8mPg07YmA/eX2QLx3aUGqeSESEVNociDfW9ExPolRDpPucpNIm\nhuHgnXgr02BsOmW4bhByDWN328O2HSYmbSxbWLkdY3fHoVT0sSxhctoamspRteq39Q8FWH/skEyd\nrnMqIrqzhkYbSo3mvEQ7eXzCQIrHDUO4cTPK+qpzWJqQSAYyb+MiktCtvARgc90llbGwLMFqlIpc\nBAf74clDEGjNZif0JVBzOjoortGck0TSIGJLW9qsaTIQUXKlFMWih+cpbBsmp02WliPYp6yzXSSW\nLae2ayoVwgUChkoX+z3g5GTNFUbfTmkuJf00ZB42IsLyzShbGw6FhrRbKhN0ZhiEx7f22KF4cJSl\nu7fjUSz4rNyKDkRObRCICAtLNg/vdamHHMFQ01mT/b3wutnUGIjaay4H2lBqLh0tRrKPhszDxDSF\n+cUI84uDfd9a1W8xkhB4Qk5dUTjwyIxR6DCRNFlcjnQUDxiFYYrFDbITJvljIhPN5CGrj1IUzZPN\nSGI3IjIpIh8TkRcb/+c67DchIn8gIl8TkedF5Dsveqyaq49SCqfuD1TpZlBUOiTINBVthoHyFbVq\neCbvaaQzJlOzQZPg4//ml+xTQ7PDQESYW4ywvBJlcspkasZi5XaU3JSuV9T0zqhuR98DfEIp9V4R\neU/j8S+E7Pd+4I+VUv9ERCJA4iIHqbn6HORdNteOmtkm0wYLi5G2ZsKjoqkQ0xY6FIbiEe3vOWyt\nu8HSngrWXxf67EQyPWOTyZqUCkExfSpjDkTN5jzEEwbxhK7D0JyNUWUDvB34YOPvDwLvOLmDiGSB\nfwD8JoBSqq6U2r+wEWquPJWyz/pjB887atFUKvg8HiNx9GTKCNX/FCB7zvrMk5SKHptrbqDB2tBh\nLZX8M4nFRyIGuSmLiUlr5EZSozkvozKUc0qptcbf68BcyD43gS3g34nIX4vIb4hI8sJGqBkbxPNJ\n71aYXCuS2q2gaoMJke5ut0uUKQWVko/jjEcXh2aiUDQqh2FMy4JrNyJ9yb31QqgOa2M+OjVL1mie\nBIYWehWRjwPzIU/90vEHSiklEtoPwgJeC7xbKfUZEXk/QYj2lzsc713AuwCimZnzDF0zRlh1j/n7\necRXGCpQqfH+TYU/+a9/nbR7vtPXqYdf/EXAdcAek2WsZj9Hp+7jK4gMQSEG6GgMRQJBAZ38onlS\nGZqhVEq9udNzIrIhIgtKqTURWQA2Q3Z7BDxSSn2m8fgPCAxlp+N9APgAQHrhjr79vSJMbpQwPHVY\nWVCvKRwV4VPTr+Ut658613vHkwa1MN1RdXbd0WEybC3QeNKgXr8886HRXBSjCr1+BHhn4+93Ah8+\nuYNSah14KCLPNDZ9D/DVixmeZixQiljJaSu/U2LwKLFw7refnLbb1v9Egl6LgxC+vmxMzdgYJyo4\nRGBmdng6rBrNZWBUWa/vBX5PRH4KuA/8CICILAK/oZR6a2O/dwO/3ch4fRn4Z6MYrGaENHsjncAI\nE1ftE9sWbtyOsr3pUi55mKYwNW0NpI3SZcS2hZXbUXa2XMpFH8sOdFiHqSfreYrdbYfCgY9hBH0a\nJ3LWUELLZ6XW0IutVnzsiDA1Y7W0FtNcfUZiKJVSOwQe4sntq8Bbjz3+EvBtFzg0zTghQikdIVWq\nw7GIoOl7PFW4P5BDRCIGi2dsr3UVsW2D+cWLmQ/fV9x/uYbrqMMkoq11l2pZnbnl2XGUUuc2uNWK\n32iQHTx2HEWlXGfxWoTUAOQJNZeD8RGL1GhO8Oq37fN///o0y9YOlu9g+S6W75Cr5/nOnS+Nenia\nc1LIey1GEoL10MKBd+auK8pXbK7XeeH5Ci98tcr9l6sdu5r0wtZGeGb0xrqD0mKxTwzjo3+l0Zzg\nnU9XiX/1E7zlq8+xHptm386Qcw6Yq26PQjZUM2BKRT9cmFwCRaJItP/7+LXHdYqFo/etVhQP7tVY\nuR0lcoZkqE5G1nUUvh8I32uuPtpQasYeARaq2yxUt0c9FM0A6daw+CwiBY7jtxjJJsoPakTPElI2\nLcEPKSMSIVQIQnM10V+1RqMZCUHSTvt20xQSyf4vTfWaCn0/CBJyzsLklNn2niJB0tE4JRxphos2\nlBqNZiTYEYOl6xFM60g8PRoTrq9EzmSEIlGjY4/J2BkbaGdzFpPT1qEHKRK07pqdHxM1Cs2FoEOv\nGo1mZCRTJrefjuHUFWLIuWT5bFtIpU2Khda2ZGJAbvpslzoRYXrWZnLawnEUliVPZI3tk442lJqx\nYpwaMmsuBhEZmPLPwpLN9hbs73r4PsTjwuxC5EyJPMcxDCGq1YmeWLSh1IwN49iQWXO5EEOYmYsw\nE9ZmQaM5I3qNUqPRaDSaLmhDqdFoNBpNF7Sh1IwFOuyq0WjGFX1F0oyUIwP5azz7zy30KanRaMYN\n7VFqRso7n66iPvcx7UVqNJqxRRtKjUaj0Wi6oA2lRqPRaDRd0IZSo9FoNJouaEOpGRmvftv+qIeg\n0Wg0p6IzKDQXzvFM1y/9kZap02g0441cxS7dIrIF3B/1OAbENKAbMQbouWhFz0crej5a0fPRyjNK\nqfRZXnglPUql1MyoxzAoROTzSqlvG/U4xgE9F63o+WhFz0crej5aEZHPn/W1eo1So9FoNJouaEOp\n0Wg0Gk0XtKEcfz4w6gGMEXouWtHz0Yqej1b0fLRy5vm4ksk8Go1Go9EMCu1RajQajUbTBW0oNRqN\nRqPpgjaUY4SITIrIx0Tkxcb/uQ77TYjIH4jI10TkeRH5zose60XQ63w09jVF5K9F5P+9yDFeJL3M\nh4gsi8ifichXReRvReRnRzHWYSIi/1hEvi4iL4nIe0KeFxH5tcbzz4nIa0cxzouih/n40cY8/I2I\nPCsirx7FOC+K0+bj2H7fLiKuiPyT095TG8rx4j3AJ5RSd4BPNB6H8X7gj5VSrwBeDTx/QeO7aHqd\nD4Cf5erOQ5Ne5sMF/pVS6pXAdwD/pYi88gLHOFRExAT+N+AtwCuB/yLk870FuNP49y7g1y90kBdI\nj/NxF/iHSqlXAf8DVzjJp8f5aO73K8B/6uV9taEcL94OfLDx9weBd5zcQUSywD8AfhNAKVVXSl1V\n0dRT5wNARK4B3w/8xgWNa1ScOh9KqTWl1BcbfxcIbh6WLmyEw+f1wEtKqZeVUnXgPxDMy3HeDvx7\nFfBXwISILFz0QC+IU+dDKfWsUmqv8fCvgGsXPMaLpJfzA+DdwH8ENnt5U20ox4s5pdRa4+91YC5k\nn5vAFvDvGqHG3xCR5IWN8GLpZT4AfhX4ecC/kFGNjl7nAwARWQFeA3xmuMO6UJaAh8ceP6L9RqCX\nfa4K/X7WnwL+aKgjGi2nzoeILAE/SB+RhispYTfOiMjHgfmQp37p+AOllBKRsNodC3gt8G6l1GdE\n5P0EIbhfHvhgL4DzzoeI/ACwqZT6goh893BGeXEM4Pxovk+K4I75XyqlDgY7Ss1lRETeSGAo3zDq\nsYyYXwV+QSnli0hPL9CG8oJRSr2503MisiEiC0qptUaoKCws8Ah4pJRqegl/QPe1u7FmAPPxXcDb\nROStQAzIiMhvKaV+bEhDHioDmA9ExCYwkr+tlPrQkIY6Kh4Dy8ceX2ts63efq0JPn1VEvoVgaeIt\nSqmdCxrbKOhlPr4N+A8NIzkNvFVEXKXU/9PpTXXodbz4CPDOxt/vBD58cgel1DrwUESeaWz6HuCr\nFzO8C6eX+fhFpdQ1pdQK8J8Df3pZjWQPnDofEvz6fxN4Xin1vgsc20XxOeCOiNwUkQjBd/6RE/t8\nBPiJRvbrdwD5YyHrq8ap8yEi14EPAT+ulHphBGO8SE6dD6XUTaXUSuOa8QfAz3QzkqAN5bjxXuB7\nReRF4M2Nx4jIooh89Nh+7wZ+W0SeA74V+J8ufKQXQ6/z8aTQy3x8F/DjwJtE5EuNf28dzXAHj1LK\nBf4F8CcEiUq/p5T6WxH5aRH56cZuHwVeBl4C/i3wMyMZ7AXQ43z8t8AU8L83zoczd9EYd3qcj77R\nEnYajUaj0XRBe5QajUaj0XRBG0qNRqPRaLqgDaVGo9FoNF3QhlKj0Wg0mi5oQ6nRaDTJVkTbAAAB\nE0lEQVQaTRe0odRorjAi8scisn+Vu6poNMNGG0qN5mrzvxDUVWo0mjOiDaVGcwVo9NZ7TkRiIpJs\n9KL8O0qpTwCFUY9Po7nMaK1XjeYKoJT6nIh8BPgfgTjwW0qpr4x4WBrNlUAbSo3m6vDfE2hdVoH/\nasRj0WiuDDr0qtFcHaaAFJAm6KSi0WgGgDaUGs3V4f8k6Ev628CvjHgsGs2VQYdeNZorgIj8BOAo\npX5HREzgWRF5E/DfAa8AUiLyCPgppdSfjHKsGs1lQ3cP0Wg0Go2mCzr0qtFoNBpNF7Sh1Gg0Go2m\nC9pQajQajUbTBW0oNRqNRqPpgjaUGo1Go9F0QRtKjUaj0Wi6oA2lRqPRaDRd+P8B1fUYtFNGevcA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc308c56f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Model with dropout\")\n",
"axes = plt.gca()\n",
"axes.set_xlim([-0.75,0.40])\n",
"axes.set_ylim([-0.75,0.65])\n",
"plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Note**:\n",
"- A **common mistake** when using dropout is to use it both in training and testing. You should use dropout (randomly eliminate nodes) only in training. \n",
"- Deep learning frameworks like [tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/dropout), [PaddlePaddle](http://doc.paddlepaddle.org/release_doc/0.9.0/doc/ui/api/trainer_config_helpers/attrs.html), [keras](https://keras.io/layers/core/#dropout) or [caffe](http://caffe.berkeleyvision.org/tutorial/layers/dropout.html) come with a dropout layer implementation. Don't stress - you will soon learn some of these frameworks.\n",
"\n",
"<font color='blue'>\n",
"**What you should remember about dropout:**\n",
"- Dropout is a regularization technique.\n",
"- You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.\n",
"- Apply dropout both during forward and backward propagation.\n",
"- During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 - Conclusions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Here are the results of our three models**: \n",
"\n",
"<table> \n",
" <tr>\n",
" <td>\n",
" **model**\n",
" </td>\n",
" <td>\n",
" **train accuracy**\n",
" </td>\n",
" <td>\n",
" **test accuracy**\n",
" </td>\n",
"\n",
" </tr>\n",
" <td>\n",
" 3-layer NN without regularization\n",
" </td>\n",
" <td>\n",
" 95%\n",
" </td>\n",
" <td>\n",
" 91.5%\n",
" </td>\n",
" <tr>\n",
" <td>\n",
" 3-layer NN with L2-regularization\n",
" </td>\n",
" <td>\n",
" 94%\n",
" </td>\n",
" <td>\n",
" 93%\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" 3-layer NN with dropout\n",
" </td>\n",
" <td>\n",
" 93%\n",
" </td>\n",
" <td>\n",
" 95%\n",
" </td>\n",
" </tr>\n",
"</table> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that regularization hurts training set performance! This is because it limits the ability of the network to overfit to the training set. But since it ultimately gives better test accuracy, it is helping your system. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congratulations for finishing this assignment! And also for revolutionizing French football. :-) "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<font color='blue'>\n",
"**What we want you to remember from this notebook**:\n",
"- Regularization will help you reduce overfitting.\n",
"- Regularization will drive your weights to lower values.\n",
"- L2 regularization and Dropout are two very effective regularization techniques."
]
}
],
"metadata": {
"coursera": {
"course_slug": "deep-neural-network",
"graded_item_id": "SXQaI",
"launcher_item_id": "UAwhh"
},
"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
}
| cc0-1.0 |
setiQuest/ML4SETI | tutorials/General_move_data_to_from_Nimbix_Cloud.ipynb | 1 | 5021 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# How to move data to/from your Nimbix Cloud machine.\n",
"\n",
"This tutorial shows you how to use the `pysftp` client to move data to/from your Nimbix cloud machine. \n",
"\n",
"This will be especially useful for moving data between your IBM Apache Spark service and your IBM PowerAI system available during the Hackathon. \n",
"\n",
"https://pysftp.readthedocs.io/en/release_0.2.9/#\n",
"\n",
"**Important for hackathon participants using the PowerAI systems. When those machines are shut down, all data in your local user space will be lost. So, be sure to save your work!**\n",
"\n",
"**BUG: It was recently found that installing `pysftp` breaks the `python-swiftclient`, which is used to transfer data to Object Storage. If you install `pysftp` and then wish to resume using `python-swiftclient` you'll need to:**\n",
"\n",
" * !pip uninstall -y pysftp\n",
" * !pip uninstall -y paramiko\n",
" * !pip uninstall -y pyasn1\n",
" * !pip uninstall -y cryptography"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#!pip install --user pysftp\n",
"#restart your kernel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pysftp "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Create Local File Space"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"mydatafolder = os.environ['PWD'] + '/' + 'my_team_name_data_folder'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# THIS DISABLES HOST KEY CHECKING! Should be okay for our temporary running machines though.\n",
"cnopts = pysftp.CnOpts()\n",
"cnopts.hostkeys = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#Get this from your Nimbix machine (or other cloud service provider!)\n",
"hostname='NAE-xxxx.jarvice.com'\n",
"username='nimbix'\n",
"password='xx'"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## PUT a file\n",
"\n",
"If you follow the Step 3 tutorial, you will have created some zip files containing the PNGs. These will be located in your `my_team_name_data_folder/zipfiles/` directory. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with pysftp.Connection(hostname, username=username, password=password, cnopts=cnopts) as sftp:\n",
" sftp.put(mydatafolder + '/zipfiles/classification_6_noise.zip') # upload file to remote"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## GET a file \n",
"\n",
"First, I define a separate location to hold files I get from remote. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fromnimbixfolder = mydatafolder + '/fromnimbix'\n",
"if os.path.exists(fromnimbixfolder) is False:\n",
" os.makedirs(fromnimbixfolder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with pysftp.Connection(hostname, username=username, password=password, cnopts=cnopts) as sftp:\n",
" with pysftp.cd(fromnimbixfolder):\n",
" sftp.get('test.csv') #data in local HOME space\n",
" sftp.get('/data/my_team_name_data_folder/our_results.csv') #data in persistent Nimbix Cloud storage"
]
}
],
"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
}
| apache-2.0 |
surajpaib/IndiaHacks-ML-Challenge | scripts/predict_road_sign.ipynb | 1 | 141867 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lib.modules_road_sign import describe_data, preprocessing, Model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Directory Read from:./data/roadsign/*\n\nFiles Read:\n\n./data/roadsign/sample_submission.csv\n./data/roadsign/test.csv\n./data/roadsign/train.csv\n"
]
}
],
"source": [
"train, test, details = describe_data('./data/roadsign')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"train_X, train_Y, test_X, id = preprocessing(train, test)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iter Train Loss Remaining Time \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iter Train Loss Remaining Time \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iter Train Loss Remaining Time \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iter Train Loss Remaining Time \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 25653.0755 1.43m\n"
]
},
{
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"output_type": "stream",
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" 1 25511.5887 50.80s\n"
]
},
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" 1 25616.6105 50.13s\n"
]
},
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" 1 25653.0755 1.31m\n"
]
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" 2 21832.9022 55.35s\n"
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" 2 22097.4551 51.95s\n"
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" 2 22256.3435 1.05m\n"
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" 4 16080.2923 51.53s\n"
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" 4 16610.9652 55.11s\n"
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" 4 16610.9652 2.10m\n"
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"text": [
"[Parallel(n_jobs=-1)]: Done 36 out of 36 | elapsed: 22.2min finished\n"
]
},
{
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"text": [
" Iter Train Loss Remaining Time \n 1 37922.0673 1.22m\n"
]
},
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" 2 31854.0276 1.29m\n 3 27062.0410 1.32m\n"
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" 90 910.8957 1.23m\n"
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{
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"text": [
" 300 92.7350 33.54s\n"
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{
"name": "stdout",
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"text": [
" 400 54.4210 16.30s\n"
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},
{
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"text": [
" 500 44.2389 0.00s\n"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"File Saved\n"
]
}
],
"source": [
"model = Model(train_X, train_Y, test_X, id)\n",
"model.CVmethod()\n",
"model.predict()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | apache-2.0 |
ml6973/Course | assignment/Baddigam-Akhileshreddy/assign-03-baddigamakhilesh.ipynb | 2 | 222584 | {
"cells": [
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import string\n",
"import tensorflow as tf\n",
"import scipy\n",
"import math\n",
"import random\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"random.seed(123)\n",
"# Display plots inline \n",
"%matplotlib inline\n",
"# Define plot's default figure size\n",
"matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Feature1 Feature2 Target\n",
"0 2.067788 0.258133 1\n",
"1 0.993994 -0.609145 1\n",
"2 -0.690315 0.749921 0\n",
"3 1.023582 0.529003 0\n",
"4 0.700747 -0.496724 1\n",
"\n",
"[5 rows x 3 columns]\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fb5eff17438>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/matplotlib/collections.py:549: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == 'face':\n"
]
},
{
"data": {
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16+LI4cNITU3V2H7wwH4AQL169QqyrH8VFRWFffv2YfTYsZku7QOAJo6OcP3oo0K1Opup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Y1yuZwGBgY5vwuZTMZ5Cxdp/d7//EsdsTEmJuatNI4aNYourq4al0meizyfx863o7+jc8mSWnWk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ozqeBrVvEK4EH2hRJLYBSFJEiGhIdxQZylS59aISidY5cn1a9cfmB8Pkv3799O2bVuo6qoE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\nwson7/hPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb5f0653c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learningrate=0.1\n",
"iterations=1000\n",
"train = pd.read_csv(\"intro_to_ann.csv\")\n",
"X_data, Y_data = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])\n",
"print (train.head())\n",
"plt.scatter(X_data[:,0], X_data[:,1], s=40, c=Y_data, cmap=plt.cm.BuGn)\n",
"\n",
"#print(X_data)\n",
"#print(Y_data)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"(500, 2)\n"
]
}
],
"source": [
"hotvec = (np.arange(2) == Y_data[:, None]).astype(np.float32)\n",
"print(Y_data[:, None])\n",
"print(np.arange(2)==Y_data[:,None])\n",
"print(hotvec)\n",
"print(hotvec.shape)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = tf.placeholder(\"float\", [None, 2])\n",
"Y = tf.placeholder(\"float\", [None, 2])\n",
"W1 = tf.Variable(tf.zeros([2, 4]))\n",
"b1 = tf.Variable(tf.zeros([4]))\n",
"y1 = tf.nn.sigmoid(tf.matmul(X, W1) + b1)\n",
"W2 = tf.Variable(tf.zeros([4, 2]))\n",
"b2 = tf.Variable(tf.zeros([2]))\n",
"y2 = tf.nn.sigmoid(tf.matmul(y1, W2) + b2)\n",
"cost = tf.reduce_mean(tf.pow(Y - y2, 2))\n",
"optimizer = tf.train.GradientDescentOptimizer(learningrate).minimize(cost)\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 3.76251243e-08 -4.65656846e-08]\n",
" [ 3.76251243e-08 -4.65656846e-08]\n",
" [ 3.76251243e-08 -4.65656846e-08]\n",
" [ 3.76251243e-08 -4.65656846e-08]] \n",
" [ 1.42026693e-08 -2.91038305e-08]\n",
"0.5\n"
]
}
],
"source": [
"init = tf.initialize_all_variables()\n",
"errors=[]\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
" correctval=tf.equal(tf.argmax(y2,1), tf.argmax(Y,1))\n",
" accuracy = tf.reduce_mean(tf.cast(correctval, tf.float32))\n",
" for i in range(iterations):\n",
" _,loss,predictedvalue=sess.run([optimizer,cost,y2],feed_dict={X:X_data,Y:hotvec})\n",
" accuracyeval=accuracy.eval(feed_dict={X:X_data, Y:hotvec})\n",
" errors.append(1 - accuracyeval)\n",
" print(sess.run(W2), \"\\n \", sess.run(b2))\n",
" print(errors[-1])\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"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": 1
}
| apache-2.0 |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1a-GB.ipynb | 1 | 48284 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Plan of the lecture"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"1. Introduction: Information Extraction and Named Entity Recognition (NER)\n",
"2. NER: definitions and tasks (extraction, classification, disambiguation)\n",
"3. basic programming concepts in Python\n",
"4. Doing NER with existing libraries:\n",
" * NER from Latin texts with CLTK\n",
" * NER from journal articles with NLTK\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Python: basic concepts"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Python is a very flexible and very powerful **programming language** that can help you working with texts and corpora. Python's phylosophy emphasizes code readability and features a simple and very expressive syntax. It is actually easy to master the basic aspects of Python's syntax: it is amazing how much you can do even with just the most basic concepts... The aim of these two lectures is to introduce to you some of these basic operation, let you see some code in action and also give you some exercise where you can apply what you've seen."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"It is also amazing how many thing you can accomplish with some well written lines of Python! By the end of this class, we'd like to show you how you use Python to perform (some) Natural Language Processing. But of course, you can even just use Python do somethin as easy as..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"2 + 3"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Variables and data types"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here we go! we've written our first line of code... But I guess we want to do something a little more interesting, right? Well, for a start, we might want to use Python to execute some operation (say: sum two numbers like 2 and 3) and process the result to print it on the screen, process it, and reuse it as many time as we want..."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Variables** is what we use to store values. Think of it as a shoebox where you place your content; next time you need that content (i.e. the result of a previous operation, or for example some input you've read from a file) you simply call the shoebox name..."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"result = 2 + 3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"#now we print the result\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm line nr. 5 and you DO see me!\n"
]
}
],
"source": [
"# by the way, I'm a comment. I'm not executed\n",
"# every line of code following the sign # is ignored:\n",
"# print(\"I'm line n. 3: do you see me?\")\n",
"# see? You don't see me...\n",
"print(\"I'm line nr. 5 and you DO see me!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"That's it! As easy as that (yes, in some programming languages you have to create or declare the variable first and then use it to fill the shoebox; in Python, you go ahead and simply use it!)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, what do you think we will get when we execute the following code?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result + 5"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"What types of values can we put into a variable? What goes into the shoebox? We can start by the members of this list:\n",
"\n",
"* **Integers** (-1,0,1,2,3,4...)\n",
"* **Strings** (\"Hello\", \"s\", \"Wolfgang Amadeus Mozart\", \"I am the α and the ω!\"...)\n",
"* **floats** (3.14159; 2.71828...)\n",
"* **Booleans** (True, False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If you're not sure what type of value you're dealing with, you can use the function `type()`. Yes, it works with variables too...!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"type(\"I am the α and the ω!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"type(2.7182818284590452353602874713527)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"type(True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"result = \"hello\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You declare strings with single ('') or double (\"\") quote: it's totally indifferent! But now two questions:\n",
"1. what happens if you forget the quotes?\n",
"2. what happens if you put quotes around a number?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"goodbye\n"
]
}
],
"source": [
"hello = \"goodbye\"\n",
"print(hello)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"print(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(\"2\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"String, integer, float... Why is that so important? Well, try to sum two strings and see what happens..."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'23'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"2\" + \"3\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#probably you wanted this...\n",
"int(\"2\") + int(\"3\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"But if we are working with strings, then the \"+\" sign is used to concatenate the strings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = \"interesting!\"\n",
"print(\"not very \" + a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Lists and dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Lists and dictionaries are two very useful types to store whole collections of data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles = [\"John\", \"Paul\", \"George\", \"Ringo\"]\n",
"type(beatles)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"dict"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# dictionaries collections of key : value pairs\n",
"beatles_dictionary = { \"john\" : \"John Lennon\" ,\n",
" \"paul\" : \"Paul McCartney\",\n",
" \"george\" : \"George Harrison\",\n",
" \"ringo\" : \"Ringo Starr\"}\n",
"type(beatles_dictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"(there are also other types of collection, like [Tuples](https://docs.python.org/3/tutorial/datastructures.html#tuples-and-sequences) and [Sets](https://docs.python.org/3/tutorial/datastructures.html#sets), but we won't talk about them now; read the links if you're interested!)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Items in list are accessible using their index. **Do remember** that indexing starts from 0!"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"John\n"
]
}
],
"source": [
"print(beatles[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'Ringo'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#indexes can be negative!\n",
"beatles[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Dictionaries are collections of *key : value* pairs. You access the value using the key as index"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'John Lennon'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles_dictionary[\"john\"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-31e6fcd3d0e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbeatles_dictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m: 0"
]
}
],
"source": [
"beatles_dictionary[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"There are a bunch of methods that you can apply to list to work with them.\n",
"\n",
"You can append items at the end of a list"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['John', 'Paul', 'George', 'Ringo', 'Billy Preston']"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles.append(\"Billy Preston\")\n",
"beatles"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You can learn the index of an item"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles.index(\"George\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You can insert elements at a predefinite index:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
"data": {
"text/plain": [
"['Pete Best', 'John', 'Paul', 'George', 'Ringo', 'Billy Preston']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles.insert(0, \"Pete Best\")\n",
"print(beatles.index(\"George\"))\n",
"beatles"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"But most importantly, you can **slice** lists, producing sub-lists by specifying the range of indexes you want:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['John', 'Paul', 'George', 'Ringo']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles[1:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Do you notice something strange? Yes, the limit index is **not** inclusive (i.e. item beatles[5] is not included)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'Billy Preston'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles[5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"What happens if you specify an index that is too high?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-b9b16afb11e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbeatles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"beatles[7]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"How can you know how long a list is?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(beatles)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Do remember that indexing starts at 0, so don't make the mistake of thinking that `len(yourlist)` will give you the last item of your list!"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-00884e355894>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbeatles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeatles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"beatles[len(beatles)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"This will work!"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'Billy Preston'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beatles[len(beatles) -1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## If-statements"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Most of the times, what you want to do when you program is to check a value and execute some operation depending on whether the value matches some condition. That's where **if statements** help!"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"In its easiest form, an If statement is syntactic construction that checks whether a condition is met; if it is some part of code is executed"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Paul played bass with the Beatles!\n"
]
}
],
"source": [
"bassist = \"Paul McCartney\"\n",
"\n",
"if bassist == \"Paul McCartney\":\n",
" print(\"Paul played bass with the Beatles!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Mind the **indentation** very much! This is the essential element in the syntax of the statement"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bassist = \"Bill Wyman\"\n",
"\n",
"if bassist == \"Paul McCartney\":\n",
" print(\"I'm part of the if statement...\")\n",
" print(\"Paul played bass in the Beatles!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"What happens if the condition is not met? Nothing! The indented code is not executed, because the condition is not met, so lines 4 and 5 are simply skipped.\n",
"\n",
"**But what happens if we de-indent line 5**? Can you guess why this is what happes?"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Most of the time, we need to specify what happens if the conditions are not met"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bassist = \"\"\n",
"\n",
"if bassist == \"Paul McCartney\":\n",
" print(\"Paul played bass in the Beatles!\")\n",
"else:\n",
" print(\"This guy did not play for the Beatles...\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"This is the flow:\n",
"* the condition in line 3 is checked\n",
"* is it met?\n",
" * **yes**: then line 4 is executed\n",
" * **no**: then line 6 is executed"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Or we can specify many different conditions..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bassist = \"Bill\"\n",
"\n",
"if bassist == \"Paul McCartney\":\n",
" print(\"Paul played bass in the Beatles!\")\n",
"elif bassist == \"Bill Wyman\":\n",
" print(\"Bill Wyman played for the Rolling Stones!\")\n",
"else:\n",
" print(\"I don't know what band this guy played for...\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## For loops"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The greatest thing about lists is that thet are **iterable**, that is you can loop through them. What do we do if we want to apply some line of code to each element in a list? Try with a for loop!"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"A **for loop** can be paraphrased as: \"for each element named x in an iterable (e.g. a list): do some code (e.g. print the value of x)\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for b in beatles:\n",
" print(b + \" was one of the Beatles\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's break the code down to its parts:\n",
"* **b**: an arbitrary name that we give to the variable holding every value in the loop (it could have been any name; b is just very convenient in this case!)\n",
"* **beatles**: the list we're iterating through\n",
"* **:** as in the if-statements: don't forget the colon!\n",
"* **indent**: also, don't forget to indent this code! it's the only thing that is telling python that line 2 is part of the for loop!\n",
"* **line 2**: the function that we want to execute for each item in the iterables"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, let's join if statements and for loop to do something nice..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"beatles = [\"John\", \"Paul\", \"George\", \"Ringo\"]\n",
"for b in beatles:\n",
" if b == \"Paul\":\n",
" instrument = \"bass\"\n",
" elif b == \"John\":\n",
" instrument = \"rhythm guitar\"\n",
" elif b == \"George\":\n",
" instrument = \"lead guitar\"\n",
" elif b == \"Ringo\":\n",
" instrument = \"drum\"\n",
" print(b + \" played \" + instrument + \" with the Beatles\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Input and Output"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"One of the most frequent tasks that programmers do is reading data from files, and write some of the output of the programs to a file. "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"In Python (as in many language), we need first to open a file-handler with the appropriate mode in order to process it. Files can be opened in:\n",
"* read mode (\"r\")\n",
"* write mode (\"w\")\n",
"* append mode\n",
"\n",
"Let's try to read the content of one of the txt files of our Sunoikisis directory"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"First, we open the file handler in **read mode**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#see? we assign the file-handler to a variable, or we wouldn't be able\n",
"#to do anything with that!\n",
"f = open(\"NOTES.md\", \"r\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"note that **\"r\" is optional**: read is the default mode!"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now there are a bunch of things we can do:\n",
"* read the full content in one variable with this code:\n",
"\n",
"`content = f.read()`\n",
"\n",
"* read the lines in a list of lines:\n",
"\n",
"`lines = f.readlines()`\n",
"\n",
"* or, which is the easiest, simply read the content one line at the time with a for loop; the **f object is iterable**, so this is as easy as:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"for l in f:\n",
" print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Once you're done, don't forget to **close the handle**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#all together\n",
"f = open(\"NOTES.md\")\n",
"for l in f:\n",
" print(l)\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, there's a shortcut statement, which you'll often see and is very convenient, because it takes care of opening, closing and cleaning up the mess, in case there's some error:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"NOTES.md\") as f:\n",
" #mind the indent!\n",
" for l in f:\n",
" #double indent, of course!\n",
" print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, how about **writing** to a file? Let's try to write a simple message on a file; first, we open the handler in write mode"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"out = open(\"test.txt\", \"w\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#the file is now open; let's write something in it\n",
"out.write(\"This is a test!\\nThis is a second line (separated with a new-line feed)\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The file has been created! Let's check this out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#don't worry if you don't understand this code!\n",
"#We're simply listing the content of the current directory...\n",
"import os\n",
"os.listdir()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"But before we can do anything (e.g. open it with your favorite text editor) you have to **close the file-handler**!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"out.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's look at its content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"test.txt\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Again, also for writing we can use a **with statement**, which is very handy.\n",
"\n",
"But let's have a look at what happens here, so we understand a bit better why **\"write mode\" must be used carefully!**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"test.txt\", \"w\") as out:\n",
" out.write(\"Oooops! new content\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's have a look at the content of \"test.txt\" now"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"test.txt\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"See? After we opened the file in \"write mode\" for the second time, **all content of the file was erased** and replaced with the new content that we wrote!!!\n",
"\n",
"So keep in mind: when you open a file in \"w\" mode:\n",
"\n",
"* if it doesn't exist, a new file with that name is created\n",
"* if it does exist, it is completely overwritten and all previous content is lost"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If you want to write content to an existing file without losing its pervious content, you have to open the file with the \"a\" mode:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"test.txt\", \"a\") as out:\n",
" out.write('''\\nAnd this is some additional content.\n",
"The new content is appended at the bottom of the existing file''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open(\"test.txt\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Functions"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Above, we have opened a file several times to inspect its content. Each time, we had to type the same code over and over. This is the typical case where you would like to save some typing (and write code that is much easier to maintain!) by defining a **function**"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"A function is a block of reusable code that can be invoked to perform a definite task. Most often (but not necessarily), it accepts one or more arguments and return a certain value.\n",
"\n",
"We have already seen one of the built-in functions of Python: `print(\"some str\")`\n",
"\n",
"But it's actually very easy to define your own. Let's define the function to print out the file content, as we said before. Note that this function takes one argument (the file name) and prints out some text, but doesn't return back any value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def printFileContent(file_name):\n",
" #the function takes one argument: file_name\n",
" with open(file_name) as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"As usual, mind the indent!\n",
"\n",
"`file_name` (line 1) is the placeholder that we use in the function for any argument that we want to pass to the function in our real-life reuse of the code.\n",
"\n",
"Now, if we want to use our function we simply call it with the file name that we want to print out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"printFileContent(\"README.md\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, let's see an example of a function that returns some value to the users. Those functions typically take some argument, process them and yield back the result of this processing.\n",
"\n",
"Here's the easiest example possible: a function that takes two numbers as arguments, sum them and returns the result."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def sumTwoNumbers(first_int, second_int):\n",
" s = first_int + second_int\n",
" return s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#could be even shorter:\n",
"def sumTwoNumbers(first_int, second_int):\n",
" return first_int + second_int"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"sumTwoNumbers(5, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Most often, you want to assign the result returned to a variable, so that you can go on working with the results..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"s = sumTwoNumbers(5,6)\n",
"s * 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Error and exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Things can go wrong, especially when you're a beginner. But no panic! Errors and exceptions are actually a good thing! Python gives you detailed reports about what is wrong, so read them carefully and try to figure out what is not right.\n",
"\n",
"Once you're getting better, you'll actually learn that you can do something good with the exceptions: you'll learn how to handle them, and to anticipate some of the most common problems that dirty data can face you with..."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, what happens if you forget the **all-important syntactic constraint of the code indent**?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"if 1 > 0:\n",
" print(\"Well, we know that 1 is bigger than 0!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Pretty clear, isn't it? What you get is an **error** a construct that is not grammatical in Python's syntax. Note that you're also told where (at what line, and at what point of the code) your error is occurring. That is not always perfect (there are cases where the problem is actually occuring before what Python thinks), but in this case it's pretty OK."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"What if you forget to define a variable (or you misspell the name of a variable)?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"var = \"bla bla\"\n",
"if var1:\n",
" print(\"If you see me, then I was defined...\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You get an **exception**! The syntax of your code is right, but the execution met with a problem that caused the program to stop.\n",
"\n",
"Now, in your program, you can handle selected exception: this means that you can write your code in a way that the program would still be executed even if a certain exception is raised.\n",
"\n",
"Let's see what happens if we use our function to try to print the content of a file that doesn't exist:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"printFileContent(\"file_that_is_not_there.txt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We get a `FileNotFoundError`! Now, let's re-write the function so that this event (somebody uses the function with a wrong file name) is taken care of..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def printFileContent(file_name):\n",
" #the function takes one argument: file_name\n",
" try:\n",
" with open(file_name) as f:\n",
" print(f.read())\n",
" except FileNotFoundError:\n",
" print(\"The file does not exist.\\nNevertheless, I do like you, and I will print something to you anyway...\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"printFileContent(\"file_that_doesnt_exist.txt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Appendix: useful links"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Python: how to install"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If you're using Mac OSX or Linux, you already have (at least one version) of Python installed. Anyway, it's very easy to install Python or upgrade your version. See:"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"https://wiki.python.org/moin/BeginnersGuide/Download"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Jupyter: how to install"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"http://jupyter.org/install.html"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Python and Jupyter come also in a pre-packaged environment (which is designed especially for data science) called [Anaconda](https://www.continuum.io/downloads). You might be interested to look at that."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Python 2 or Python 3? "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Python 3 is the latest version of Python (currently, 3.6.1). It's a major upgrade from Python 2, but the code has been somewhat dramatically changed in the passage from 2 to 3 and there is some problem of backward compatibility. Some version of Linux or Mac OSX still come with Python 2.7 (the final version of Python 2).\n",
"\n",
"Anyway, Python 3 is currently in active development: it's where the cutting-edge improvements and new stuff are being developed (especially for NLP and the NLTK library). In this code, we assume Python 3!"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"https://wiki.python.org/moin/Python2orPython3"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## NLTK: Book"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"Would you like a book that is a great introduction to Python for absolute beginners, is a wonderfull resource to learn the basics of Natural Language processing and gives you a thorough introduction to the NLTK library to do NLP in Python? Oh, yeah, I was forgetting: that can be read for free on the internet? Yes, it's Christmass time!"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"http://www.nltk.org/book/"
]
}
],
"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"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "161px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
DamienIrving/ocean-analysis | visualisation/drift_paper/drift_plots.ipynb | 1 | 1803322 | null | mit |
peterwittek/qml-rg | Archiv_Session_Spring_2017/Exercises/05_aps_capcha.ipynb | 1 | 135336 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Finding the right capcha with Keras"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import tools as im\n",
"from matplotlib import pyplot as plt\n",
"from skimage.transform import resize\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path=os.getcwd()+'/' # finds the path of the folder in which the notebook is\n",
"path_train=path+'images/train/'\n",
"path_test=path+'images/test/'\n",
"path_real=path+'images/real_world/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first define a function to prepare the datas in the format of keras (theano). The function also reduces the size of the imagesfrom 100X100 to 32X32."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def prep_datas(xset,xlabels):\n",
" X=list(xset)\n",
" for i in range(len(X)):\n",
" X[i]=resize(X[i],(32,32,1)) #reduce the size of the image from 100X100 to 32X32. Also flattens the color levels\n",
" \n",
" X=np.reshape(X,(len(X),1,32,32)) # reshape the liste to have the form required by keras (theano), ie (1,32,32)\n",
" X=np.array(X) #transforms it into an array\n",
" Y = np.eye(2, dtype='uint8')[xlabels] # generates vectors, here of two elements as required by keras (number of classes) \n",
" return X,Y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then load the training set and the test set and prepare them with the function prep_datas."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"training_set, training_labels = im.load_images(path_train)\n",
"test_set, test_labels = im.load_images(path_test)\n",
"X_train,Y_train=prep_datas(training_set,training_labels)\n",
"X_test,Y_test=prep_datas(test_set,test_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Image before/after compression"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x114385c88>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dtNXlp9vQul56mftsnhpwqxXDZwggmo9oGwhKGyGIpAtg6/8REHZJSxSOPxPd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1142cef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"i=11\n",
"plt.subplot(1,2,1)\n",
"plt.imshow(training_set[i],cmap='gray')\n",
"plt.subplot(1,2,2)\n",
"plt.imshow(X_train[i][0],cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lenet neural network "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"# import the necessary packages\n",
"from keras.models import Sequential\n",
"from keras.layers.convolutional import Convolution2D\n",
"from keras.layers.convolutional import MaxPooling2D\n",
"from keras.layers.core import Activation\n",
"from keras.layers.core import Flatten\n",
"from keras.layers.core import Dense\n",
"from keras.optimizers import SGD\n",
"\n",
"# this code comes from http://www.pyimagesearch.com/2016/08/01/lenet-convolutional-neural-network-in-python/\n",
"\n",
"class LeNet:\n",
"\t@staticmethod\n",
"\tdef build(width, height, depth, classes, weightsPath=None):\n",
"\t\t# initialize the model\n",
"\t\tmodel = Sequential()\n",
"\n",
"\t\t# first set of CONV => RELU => POOL\n",
"\t\tmodel.add(Convolution2D(20, 5, 5, border_mode=\"same\",input_shape=(depth, height, width)))\n",
"\t\tmodel.add(Activation(\"relu\"))\n",
"\t\tmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
"\n",
"\t\t# second set of CONV => RELU => POOL\n",
"\t\tmodel.add(Convolution2D(50, 5, 5, border_mode=\"same\"))\n",
"\t\tmodel.add(Activation(\"relu\"))\n",
"\t\tmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
"\n",
"\t\t# set of FC => RELU layers\n",
"\t\tmodel.add(Flatten())\n",
"\t\tmodel.add(Dense(500))\n",
"\t\tmodel.add(Activation(\"relu\"))\n",
"\n",
"\t\t# softmax classifier\n",
"\t\tmodel.add(Dense(classes))\n",
"\t\tmodel.add(Activation(\"softmax\"))\n",
"\n",
"\t\t# return the constructed network architecture\n",
"\t\treturn model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We build the neural network and fit it on the training set"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/300\n",
"25/25 [==============================] - 0s - loss: 0.6774 - acc: 0.6400 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 2/300\n",
"25/25 [==============================] - 0s - loss: 0.5405 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 3/300\n",
"25/25 [==============================] - 0s - loss: 0.4367 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 4/300\n",
"25/25 [==============================] - 0s - loss: 0.3663 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 5/300\n",
"25/25 [==============================] - 0s - loss: 0.3214 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 6/300\n",
"25/25 [==============================] - 0s - loss: 0.3057 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 7/300\n",
"25/25 [==============================] - 0s - loss: 0.2955 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 8/300\n",
"25/25 [==============================] - 0s - loss: 0.2910 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 9/300\n",
"25/25 [==============================] - 0s - loss: 0.2796 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 10/300\n",
"25/25 [==============================] - 0s - loss: 0.2775 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 11/300\n",
"25/25 [==============================] - 0s - loss: 0.2801 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 12/300\n",
"25/25 [==============================] - 0s - loss: 0.2751 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 13/300\n",
"25/25 [==============================] - 0s - loss: 0.2806 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 14/300\n",
"25/25 [==============================] - 0s - loss: 0.2720 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 15/300\n",
"25/25 [==============================] - 0s - loss: 0.2740 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 16/300\n",
"25/25 [==============================] - 0s - loss: 0.2687 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 17/300\n",
"25/25 [==============================] - 0s - loss: 0.2752 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 18/300\n",
"25/25 [==============================] - 0s - loss: 0.2707 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 19/300\n",
"25/25 [==============================] - 0s - loss: 0.2701 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 20/300\n",
"25/25 [==============================] - 0s - loss: 0.2686 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 21/300\n",
"25/25 [==============================] - 0s - loss: 0.2645 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 22/300\n",
"25/25 [==============================] - 0s - loss: 0.2693 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 23/300\n",
"25/25 [==============================] - 0s - loss: 0.2634 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 24/300\n",
"25/25 [==============================] - 0s - loss: 0.2735 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 25/300\n",
"25/25 [==============================] - 0s - loss: 0.2695 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 26/300\n",
"25/25 [==============================] - 0s - loss: 0.2639 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 27/300\n",
"25/25 [==============================] - 0s - loss: 0.2560 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 28/300\n",
"25/25 [==============================] - 0s - loss: 0.2558 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 29/300\n",
"25/25 [==============================] - 0s - loss: 0.2551 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 30/300\n",
"25/25 [==============================] - 0s - loss: 0.2635 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 31/300\n",
"25/25 [==============================] - 0s - loss: 0.2536 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 32/300\n",
"25/25 [==============================] - 0s - loss: 0.2678 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 33/300\n",
"25/25 [==============================] - 0s - loss: 0.2572 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 34/300\n",
"25/25 [==============================] - 0s - loss: 0.2518 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 35/300\n",
"25/25 [==============================] - 0s - loss: 0.2563 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 36/300\n",
"25/25 [==============================] - 0s - loss: 0.2549 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 37/300\n",
"25/25 [==============================] - 0s - loss: 0.2496 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 38/300\n",
"25/25 [==============================] - 0s - loss: 0.2505 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 39/300\n",
"25/25 [==============================] - 0s - loss: 0.2438 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 40/300\n",
"25/25 [==============================] - 0s - loss: 0.2436 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 41/300\n",
"25/25 [==============================] - 0s - loss: 0.2450 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 42/300\n",
"25/25 [==============================] - 0s - loss: 0.2438 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 43/300\n",
"25/25 [==============================] - 0s - loss: 0.2397 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 44/300\n",
"25/25 [==============================] - 0s - loss: 0.2381 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 45/300\n",
"25/25 [==============================] - 0s - loss: 0.2371 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 46/300\n",
"25/25 [==============================] - 0s - loss: 0.2364 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 47/300\n",
"25/25 [==============================] - 0s - loss: 0.2443 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 48/300\n",
"25/25 [==============================] - 0s - loss: 0.2355 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 49/300\n",
"25/25 [==============================] - 0s - loss: 0.2345 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 50/300\n",
"25/25 [==============================] - 0s - loss: 0.2352 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 51/300\n",
"25/25 [==============================] - 0s - loss: 0.2366 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 52/300\n",
"25/25 [==============================] - 0s - loss: 0.2383 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 53/300\n",
"25/25 [==============================] - 0s - loss: 0.2359 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 54/300\n",
"25/25 [==============================] - 0s - loss: 0.2354 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 55/300\n",
"25/25 [==============================] - 0s - loss: 0.2248 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 56/300\n",
"25/25 [==============================] - 0s - loss: 0.2281 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 57/300\n",
"25/25 [==============================] - 0s - loss: 0.2261 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 58/300\n",
"25/25 [==============================] - 0s - loss: 0.2285 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 59/300\n",
"25/25 [==============================] - 0s - loss: 0.2187 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 60/300\n",
"25/25 [==============================] - 0s - loss: 0.2176 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 61/300\n",
"25/25 [==============================] - 0s - loss: 0.2174 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 62/300\n",
"25/25 [==============================] - 0s - loss: 0.2183 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 63/300\n",
"25/25 [==============================] - 0s - loss: 0.2126 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 64/300\n",
"25/25 [==============================] - 0s - loss: 0.2202 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 65/300\n",
"25/25 [==============================] - 0s - loss: 0.2156 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 66/300\n",
"25/25 [==============================] - 0s - loss: 0.2185 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 67/300\n",
"25/25 [==============================] - 0s - loss: 0.2112 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 68/300\n",
"25/25 [==============================] - 0s - loss: 0.2104 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 69/300\n",
"25/25 [==============================] - 0s - loss: 0.2123 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 70/300\n",
"25/25 [==============================] - 0s - loss: 0.2053 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 71/300\n",
"25/25 [==============================] - 0s - loss: 0.2116 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 72/300\n",
"25/25 [==============================] - 0s - loss: 0.2104 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 73/300\n",
"25/25 [==============================] - 0s - loss: 0.1968 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 74/300\n",
"25/25 [==============================] - 0s - loss: 0.2067 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 75/300\n",
"25/25 [==============================] - 0s - loss: 0.2120 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 76/300\n",
"25/25 [==============================] - 0s - loss: 0.1937 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 77/300\n",
"25/25 [==============================] - 0s - loss: 0.1935 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 78/300\n",
"25/25 [==============================] - 0s - loss: 0.2028 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 79/300\n",
"25/25 [==============================] - 0s - loss: 0.1920 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 80/300\n",
"25/25 [==============================] - 0s - loss: 0.2003 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 81/300\n",
"25/25 [==============================] - 0s - loss: 0.2059 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 82/300\n",
"25/25 [==============================] - 0s - loss: 0.2092 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 83/300\n",
"25/25 [==============================] - 0s - loss: 0.1807 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 84/300\n",
"25/25 [==============================] - 0s - loss: 0.1790 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 85/300\n",
"25/25 [==============================] - 0s - loss: 0.1997 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 86/300\n",
"25/25 [==============================] - 0s - loss: 0.1935 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 87/300\n",
"25/25 [==============================] - 0s - loss: 0.1755 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 88/300\n",
"25/25 [==============================] - 0s - loss: 0.1903 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 89/300\n",
"25/25 [==============================] - 0s - loss: 0.1976 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 90/300\n",
"25/25 [==============================] - 0s - loss: 0.1697 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 91/300\n",
"25/25 [==============================] - 0s - loss: 0.1711 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 92/300\n",
"25/25 [==============================] - 0s - loss: 0.1718 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 93/300\n",
"25/25 [==============================] - 0s - loss: 0.1810 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 94/300\n",
"25/25 [==============================] - 0s - loss: 0.1689 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 95/300\n",
"25/25 [==============================] - 0s - loss: 0.1834 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 96/300\n",
"25/25 [==============================] - 0s - loss: 0.1635 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 97/300\n",
"25/25 [==============================] - 0s - loss: 0.1568 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 98/300\n",
"25/25 [==============================] - 0s - loss: 0.1754 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 99/300\n",
"25/25 [==============================] - 0s - loss: 0.1578 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 100/300\n",
"25/25 [==============================] - 0s - loss: 0.1565 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 101/300\n",
"25/25 [==============================] - 0s - loss: 0.1567 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 102/300\n",
"25/25 [==============================] - 0s - loss: 0.1578 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 103/300\n",
"25/25 [==============================] - 0s - loss: 0.1480 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 104/300\n",
"25/25 [==============================] - 0s - loss: 0.1582 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 105/300\n",
"25/25 [==============================] - 0s - loss: 0.1497 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 106/300\n",
"25/25 [==============================] - 0s - loss: 0.1610 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 107/300\n",
"25/25 [==============================] - 0s - loss: 0.1537 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 108/300\n",
"25/25 [==============================] - 0s - loss: 0.1771 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 109/300\n",
"25/25 [==============================] - 0s - loss: 0.1912 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 110/300\n",
"25/25 [==============================] - 0s - loss: 0.1431 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 111/300\n",
"25/25 [==============================] - 0s - loss: 0.1517 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 112/300\n",
"25/25 [==============================] - 0s - loss: 0.1402 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 113/300\n",
"25/25 [==============================] - 0s - loss: 0.1395 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 114/300\n",
"25/25 [==============================] - 0s - loss: 0.1330 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 115/300\n",
"25/25 [==============================] - 0s - loss: 0.1495 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 116/300\n",
"25/25 [==============================] - 0s - loss: 0.1364 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 117/300\n",
"25/25 [==============================] - 0s - loss: 0.1370 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 118/300\n",
"25/25 [==============================] - 0s - loss: 0.1341 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 119/300\n",
"25/25 [==============================] - 0s - loss: 0.1240 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 120/300\n",
"25/25 [==============================] - 0s - loss: 0.1484 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 121/300\n",
"25/25 [==============================] - 0s - loss: 0.1373 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 122/300\n",
"25/25 [==============================] - 0s - loss: 0.1326 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 123/300\n",
"25/25 [==============================] - 0s - loss: 0.1411 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 124/300\n",
"25/25 [==============================] - 0s - loss: 0.1332 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 125/300\n",
"25/25 [==============================] - 0s - loss: 0.1326 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 126/300\n",
"25/25 [==============================] - 0s - loss: 0.1519 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 127/300\n",
"25/25 [==============================] - 0s - loss: 0.1311 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 128/300\n",
"25/25 [==============================] - 0s - loss: 0.1346 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 129/300\n",
"25/25 [==============================] - 0s - loss: 0.1172 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 130/300\n",
"25/25 [==============================] - 0s - loss: 0.1353 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 131/300\n",
"25/25 [==============================] - 0s - loss: 0.1682 - acc: 0.9200 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 132/300\n",
"25/25 [==============================] - 0s - loss: 0.1144 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 133/300\n",
"25/25 [==============================] - 0s - loss: 0.1239 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 134/300\n",
"25/25 [==============================] - 0s - loss: 0.1175 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 135/300\n",
"25/25 [==============================] - 0s - loss: 0.1323 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 136/300\n",
"25/25 [==============================] - 0s - loss: 0.1195 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 137/300\n",
"25/25 [==============================] - 0s - loss: 0.1192 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 138/300\n",
"25/25 [==============================] - 0s - loss: 0.1131 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 139/300\n",
"25/25 [==============================] - 0s - loss: 0.1203 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 140/300\n",
"25/25 [==============================] - 0s - loss: 0.1133 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 141/300\n",
"25/25 [==============================] - 0s - loss: 0.1303 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 142/300\n",
"25/25 [==============================] - 0s - loss: 0.1072 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 143/300\n",
"25/25 [==============================] - 0s - loss: 0.1101 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 144/300\n",
"25/25 [==============================] - 0s - loss: 0.1126 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 145/300\n",
"25/25 [==============================] - 0s - loss: 0.1124 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 146/300\n",
"25/25 [==============================] - 0s - loss: 0.1097 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 147/300\n",
"25/25 [==============================] - 0s - loss: 0.1019 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 148/300\n",
"25/25 [==============================] - 0s - loss: 0.1159 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 149/300\n",
"25/25 [==============================] - 0s - loss: 0.1038 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 150/300\n",
"25/25 [==============================] - 0s - loss: 0.1092 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 151/300\n",
"25/25 [==============================] - 0s - loss: 0.1151 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 152/300\n",
"25/25 [==============================] - 0s - loss: 0.0972 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 153/300\n",
"25/25 [==============================] - 0s - loss: 0.0998 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 154/300\n",
"25/25 [==============================] - 0s - loss: 0.1036 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 155/300\n",
"25/25 [==============================] - 0s - loss: 0.0990 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 156/300\n",
"25/25 [==============================] - 0s - loss: 0.0970 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 157/300\n",
"25/25 [==============================] - 0s - loss: 0.1242 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 158/300\n",
"25/25 [==============================] - 0s - loss: 0.0970 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 159/300\n",
"25/25 [==============================] - 0s - loss: 0.0926 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 160/300\n",
"25/25 [==============================] - 0s - loss: 0.0864 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 161/300\n",
"25/25 [==============================] - 0s - loss: 0.0972 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 162/300\n",
"25/25 [==============================] - 0s - loss: 0.0991 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 163/300\n",
"25/25 [==============================] - 0s - loss: 0.0905 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 164/300\n",
"25/25 [==============================] - 0s - loss: 0.0864 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 165/300\n",
"25/25 [==============================] - 0s - loss: 0.0941 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 166/300\n",
"25/25 [==============================] - 0s - loss: 0.1287 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 167/300\n",
"25/25 [==============================] - 0s - loss: 0.0856 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 168/300\n",
"25/25 [==============================] - 0s - loss: 0.0896 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 169/300\n",
"25/25 [==============================] - 0s - loss: 0.0866 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 170/300\n",
"25/25 [==============================] - 0s - loss: 0.0840 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 171/300\n",
"25/25 [==============================] - 0s - loss: 0.0857 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 172/300\n",
"25/25 [==============================] - 0s - loss: 0.0882 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 173/300\n",
"25/25 [==============================] - 0s - loss: 0.0820 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 174/300\n",
"25/25 [==============================] - 0s - loss: 0.0991 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 175/300\n",
"25/25 [==============================] - 0s - loss: 0.1039 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 176/300\n",
"25/25 [==============================] - 0s - loss: 0.0773 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 177/300\n",
"25/25 [==============================] - 0s - loss: 0.1006 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 178/300\n",
"25/25 [==============================] - 0s - loss: 0.0797 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 179/300\n",
"25/25 [==============================] - 0s - loss: 0.0764 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 180/300\n",
"25/25 [==============================] - 0s - loss: 0.0765 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 181/300\n",
"25/25 [==============================] - 0s - loss: 0.0720 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 182/300\n",
"25/25 [==============================] - 0s - loss: 0.0791 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 183/300\n",
"25/25 [==============================] - 0s - loss: 0.0856 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 184/300\n",
"25/25 [==============================] - 0s - loss: 0.0722 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 185/300\n",
"25/25 [==============================] - 0s - loss: 0.0657 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 186/300\n",
"25/25 [==============================] - 0s - loss: 0.0685 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 187/300\n",
"25/25 [==============================] - 0s - loss: 0.0657 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 188/300\n",
"25/25 [==============================] - 0s - loss: 0.0655 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 189/300\n",
"25/25 [==============================] - 0s - loss: 0.0698 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 190/300\n",
"25/25 [==============================] - 0s - loss: 0.0886 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 191/300\n",
"25/25 [==============================] - 0s - loss: 0.0617 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 192/300\n",
"25/25 [==============================] - 0s - loss: 0.0636 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 193/300\n",
"25/25 [==============================] - 0s - loss: 0.0675 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 194/300\n",
"25/25 [==============================] - 0s - loss: 0.0627 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 195/300\n",
"25/25 [==============================] - 0s - loss: 0.0577 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 196/300\n",
"25/25 [==============================] - 0s - loss: 0.0641 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 197/300\n",
"25/25 [==============================] - 0s - loss: 0.0603 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 198/300\n",
"25/25 [==============================] - 0s - loss: 0.0523 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 199/300\n",
"25/25 [==============================] - 0s - loss: 0.0532 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 200/300\n",
"25/25 [==============================] - 0s - loss: 0.0613 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 201/300\n",
"25/25 [==============================] - 0s - loss: 0.0625 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 202/300\n",
"25/25 [==============================] - 0s - loss: 0.0636 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 203/300\n",
"25/25 [==============================] - 0s - loss: 0.0507 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 204/300\n",
"25/25 [==============================] - 0s - loss: 0.0533 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 205/300\n",
"25/25 [==============================] - 0s - loss: 0.0728 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 206/300\n",
"25/25 [==============================] - 0s - loss: 0.0451 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 207/300\n",
"25/25 [==============================] - 0s - loss: 0.0441 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 208/300\n",
"25/25 [==============================] - 0s - loss: 0.0500 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 209/300\n",
"25/25 [==============================] - 0s - loss: 0.0707 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 210/300\n",
"25/25 [==============================] - 0s - loss: 0.0597 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 211/300\n",
"25/25 [==============================] - 0s - loss: 0.0436 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 212/300\n",
"25/25 [==============================] - 0s - loss: 0.0460 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 213/300\n",
"25/25 [==============================] - 0s - loss: 0.0426 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 214/300\n",
"25/25 [==============================] - 0s - loss: 0.0417 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 215/300\n",
"25/25 [==============================] - 0s - loss: 0.0425 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 216/300\n",
"25/25 [==============================] - 0s - loss: 0.0443 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 217/300\n",
"25/25 [==============================] - 0s - loss: 0.0418 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 218/300\n",
"25/25 [==============================] - 0s - loss: 0.0460 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 219/300\n",
"25/25 [==============================] - 0s - loss: 0.0414 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 220/300\n",
"25/25 [==============================] - 0s - loss: 0.0394 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 221/300\n",
"25/25 [==============================] - 0s - loss: 0.0405 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 222/300\n",
"25/25 [==============================] - 0s - loss: 0.0364 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 223/300\n",
"25/25 [==============================] - 0s - loss: 0.0388 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 224/300\n",
"25/25 [==============================] - 0s - loss: 0.0506 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 225/300\n",
"25/25 [==============================] - 0s - loss: 0.0487 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 226/300\n",
"25/25 [==============================] - 0s - loss: 0.0313 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 227/300\n",
"25/25 [==============================] - 0s - loss: 0.0377 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 228/300\n",
"25/25 [==============================] - 0s - loss: 0.0355 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 229/300\n",
"25/25 [==============================] - 0s - loss: 0.0311 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 230/300\n",
"25/25 [==============================] - 0s - loss: 0.0337 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 231/300\n",
"25/25 [==============================] - 0s - loss: 0.0306 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 232/300\n",
"25/25 [==============================] - 0s - loss: 0.0350 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 233/300\n",
"25/25 [==============================] - 0s - loss: 0.0397 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 234/300\n",
"25/25 [==============================] - 0s - loss: 0.0392 - acc: 0.9600 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 235/300\n",
"25/25 [==============================] - 0s - loss: 0.0340 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 236/300\n",
"25/25 [==============================] - 0s - loss: 0.0287 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 237/300\n",
"25/25 [==============================] - 0s - loss: 0.0316 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 238/300\n",
"25/25 [==============================] - 0s - loss: 0.0254 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 239/300\n",
"25/25 [==============================] - 0s - loss: 0.0268 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 240/300\n",
"25/25 [==============================] - 0s - loss: 0.0267 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 241/300\n",
"25/25 [==============================] - 0s - loss: 0.0281 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 242/300\n",
"25/25 [==============================] - 0s - loss: 0.0262 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 243/300\n",
"25/25 [==============================] - 0s - loss: 0.0246 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 244/300\n",
"25/25 [==============================] - 0s - loss: 0.0247 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 245/300\n",
"25/25 [==============================] - 0s - loss: 0.0268 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 246/300\n",
"25/25 [==============================] - 0s - loss: 0.0363 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 247/300\n",
"25/25 [==============================] - 0s - loss: 0.0242 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 248/300\n",
"25/25 [==============================] - 0s - loss: 0.0294 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 249/300\n",
"25/25 [==============================] - 0s - loss: 0.0374 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 250/300\n",
"25/25 [==============================] - 0s - loss: 0.0233 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 251/300\n",
"25/25 [==============================] - 0s - loss: 0.0227 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 252/300\n",
"25/25 [==============================] - 0s - loss: 0.0247 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 253/300\n",
"25/25 [==============================] - 0s - loss: 0.0204 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 254/300\n",
"25/25 [==============================] - 0s - loss: 0.0209 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 255/300\n",
"25/25 [==============================] - 0s - loss: 0.0205 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 256/300\n",
"25/25 [==============================] - 0s - loss: 0.0211 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 257/300\n",
"25/25 [==============================] - 0s - loss: 0.0219 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 258/300\n",
"25/25 [==============================] - 0s - loss: 0.0226 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 259/300\n",
"25/25 [==============================] - 0s - loss: 0.0212 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 260/300\n",
"25/25 [==============================] - 0s - loss: 0.0209 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 261/300\n",
"25/25 [==============================] - 0s - loss: 0.0189 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 262/300\n",
"25/25 [==============================] - 0s - loss: 0.0200 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 263/300\n",
"25/25 [==============================] - 0s - loss: 0.0197 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 264/300\n",
"25/25 [==============================] - 0s - loss: 0.0188 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 265/300\n",
"25/25 [==============================] - 0s - loss: 0.0205 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 266/300\n",
"25/25 [==============================] - 0s - loss: 0.0182 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 267/300\n",
"25/25 [==============================] - 0s - loss: 0.0191 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 268/300\n",
"25/25 [==============================] - 0s - loss: 0.0186 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 269/300\n",
"25/25 [==============================] - 0s - loss: 0.0189 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 270/300\n",
"25/25 [==============================] - 0s - loss: 0.0184 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 271/300\n",
"25/25 [==============================] - 0s - loss: 0.0182 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 272/300\n",
"25/25 [==============================] - 0s - loss: 0.0160 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 273/300\n",
"25/25 [==============================] - 0s - loss: 0.0169 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 274/300\n",
"25/25 [==============================] - 0s - loss: 0.0155 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 275/300\n",
"25/25 [==============================] - 0s - loss: 0.0174 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 276/300\n",
"25/25 [==============================] - 0s - loss: 0.0179 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 277/300\n",
"25/25 [==============================] - 0s - loss: 0.0179 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 278/300\n",
"25/25 [==============================] - 0s - loss: 0.0194 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 279/300\n",
"25/25 [==============================] - 0s - loss: 0.0161 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 280/300\n",
"25/25 [==============================] - 0s - loss: 0.0150 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 281/300\n",
"25/25 [==============================] - 0s - loss: 0.0153 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 282/300\n",
"25/25 [==============================] - 0s - loss: 0.0145 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 283/300\n",
"25/25 [==============================] - 0s - loss: 0.0158 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 284/300\n",
"25/25 [==============================] - 0s - loss: 0.0163 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 285/300\n",
"25/25 [==============================] - 0s - loss: 0.0148 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 286/300\n",
"25/25 [==============================] - 0s - loss: 0.0168 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 287/300\n",
"25/25 [==============================] - 0s - loss: 0.0143 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 288/300\n",
"25/25 [==============================] - 0s - loss: 0.0129 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 289/300\n",
"25/25 [==============================] - 0s - loss: 0.0134 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 290/300\n",
"25/25 [==============================] - 0s - loss: 0.0141 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 291/300\n",
"25/25 [==============================] - 0s - loss: 0.0133 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 292/300\n",
"25/25 [==============================] - 0s - loss: 0.0128 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 293/300\n",
"25/25 [==============================] - 0s - loss: 0.0124 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 294/300\n",
"25/25 [==============================] - 0s - loss: 0.0121 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 295/300\n",
"25/25 [==============================] - 0s - loss: 0.0130 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 296/300\n",
"25/25 [==============================] - 0s - loss: 0.0137 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 297/300\n",
"25/25 [==============================] - 0s - loss: 0.0123 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 298/300\n",
"25/25 [==============================] - 0s - loss: 0.0129 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 299/300\n",
"25/25 [==============================] - 0s - loss: 0.0125 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
"Epoch 300/300\n",
"25/25 [==============================] - 0s - loss: 0.0131 - acc: 1.0000 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11919d1d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LeNet.build(width=32, height=32, depth=1, classes=2)\n",
"opt = SGD(lr=0.01)#Sochastic gradient descent with learning rate 0.01\n",
"model.compile(loss=\"categorical_crossentropy\", optimizer=opt,metrics=[\"accuracy\"])\n",
"model.fit(X_train, Y_train, batch_size=10, nb_epoch=300,verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2/2 [==============================] - 0s\n",
"[1 0]\n",
"[1, 0, 0, 0, 0, 0]\n"
]
}
],
"source": [
"y_pred = model.predict_classes(X_test)\n",
"print(y_pred)\n",
"print(test_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now compare with the real world images (with the deshear method)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"32/72 [============>.................] - ETA: 0s"
]
}
],
"source": [
"real_world_set=[]\n",
"for i in np.arange(1,73):\n",
" filename=path+'images/real_world/'+str(i)+'.png'\n",
" real_world_set.append(im.deshear(filename))\n",
"fake_label=np.ones(len(real_world_set),dtype='int32')\n",
"X_real,Y_real=prep_datas(real_world_set,fake_label)\n",
"y_pred = model.predict_classes(X_real)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with the labels of Peter"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"f=open(path+'images/real_world/labels.txt',\"r\")\n",
"lines=f.readlines()\n",
"result=[]\n",
"for x in lines:\n",
" result.append((x.split('\t')[1]).replace('\\n',''))\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2L/ZxwLUx8/ZuiAeO4LBrnjHQGGPrj+LKD39sxmvRc91d5hj4Z5+NtZmF9Z60DjOZY7ox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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116f79898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result=np.array([int(x) for x in result])\n",
"result[result>1]=1\n",
"plt.plot(y_pred,'o')\n",
"plt.plot(2*result,'o')\n",
"plt.ylim(-0.5,2.5);"
]
}
],
"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
}
| gpl-3.0 |
myuuuuun/various | 応用統計/HW1/HW1.ipynb | 2 | 153145 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 応用統計HW1\n",
"\n",
"詳細: [http://www.stat.t.u-tokyo.ac.jp/~takemura/ouyoutoukei/](http://www.stat.t.u-tokyo.ac.jp/~takemura/ouyoutoukei/)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#-*- encoding: utf-8 -*-\n",
"'''\n",
"Ouyoutoukei HW1\n",
"'''\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import statsmodels.api as sm\n",
"np.set_printoptions(precision=3)\n",
"pd.set_option('display.precision', 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 下準備\n",
"\n",
"データのインポート, 基礎統計量の表示"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" time bus walk price area bal kosuu \\\n",
"count 185.000 185.000 185.000 185.000 185.000 185.000 178.000 \n",
"mean 27.292 2.465 8.470 2929.730 72.682 9.620 89.449 \n",
"std 14.076 5.277 5.426 2596.096 27.722 6.479 203.317 \n",
"min 3.000 0.000 1.000 630.000 19.120 0.000 1.000 \n",
"25% 18.000 0.000 4.000 1490.000 56.850 6.000 21.000 \n",
"50% 26.000 0.000 8.000 2180.000 69.020 8.800 35.000 \n",
"75% 33.000 0.000 13.000 3580.000 80.990 11.670 73.750 \n",
"max 65.000 26.000 19.000 24800.000 230.720 39.670 2080.000 \n",
"\n",
" floor tf year \n",
"count 185.000 185.000 185.000 \n",
"mean 3.681 6.454 80.924 \n",
"std 2.703 3.420 18.423 \n",
"min 1.000 2.000 0.000 \n",
"25% 2.000 4.000 74.000 \n",
"50% 3.000 5.000 85.000 \n",
"75% 5.000 8.000 92.000 \n",
"max 14.000 20.000 99.000 \n"
]
}
],
"source": [
"# csvをインポート\n",
"df = pd.read_csv( 'odakyu-mansion.csv' )\n",
"# 基礎統計量を表示\n",
"print(df.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"家の向きは東, 西, 南, 北のダミー変数（0または1。南東の場合、南と東の両方に1）に分解し変換して、"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" time bus walk price area bal kosuu floor tf muki year d_N \\\n",
"0 3 0 6 1680 44.60 3.50 19 4 5 S 68 0 \n",
"1 3 0 4 2280 48.87 4.05 12 2 4 S 74 0 \n",
"2 3 0 7 2880 57.00 7.22 26 4 7 S 70 0 \n",
"3 3 0 2 4340 55.25 7.35 44 3 6 SW 92 0 \n",
"4 3 0 6 4980 88.02 8.70 30 4 8 SE 74 0 \n",
"5 3 0 6 9800 121.56 6.71 30 2 3 S 83 0 \n",
"6 5 0 3 1150 19.12 0.00 35 8 8 NE 70 1 \n",
"7 5 0 1 3850 52.08 5.67 21 5 9 S 98 0 \n",
"8 5 0 9 7580 78.60 14.10 68 3 4 W 99 0 \n",
"9 5 0 6 11870 123.29 14.14 26 2 6 E 98 0 \n",
"\n",
" d_E d_W d_S \n",
"0 0 0 1 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 1 1 \n",
"4 1 0 1 \n",
"5 0 0 1 \n",
"6 1 0 0 \n",
"7 0 0 1 \n",
"8 0 1 0 \n",
"9 1 0 0 \n"
]
}
],
"source": [
"# サンプルサイズ\n",
"data_len = df.shape[0]\n",
"\n",
"# 家の向きはdummyに\n",
"df['d_N'] = np.zeros(data_len, dtype=float)\n",
"df['d_E'] = np.zeros(data_len, dtype=float)\n",
"df['d_W'] = np.zeros(data_len, dtype=float)\n",
"df['d_S'] = np.zeros(data_len, dtype=float)\n",
"for i, row in df.iterrows():\n",
" for direction in [\"N\", \"W\", \"S\", \"E\"]:\n",
" if direction in str(row.muki):\n",
" df.loc[i, 'd_{0}'.format(direction)] = 1\n",
"\n",
"# 先頭10件を表示\n",
"print(df.head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"欠損値は平均値で置き換える。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df.fillna(df.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 最小二乗法\n",
"\n",
"被説明変数に与える影響の小さい説明変数を順に取り除いていく。具体的にはp > 0.05であるような説明変数を除いていく。 \n",
"同時に、外れ値の考慮もする。\n",
"\n",
"\n",
"### 最小二乗法その1\n",
"\n",
"説明変数13個で最小二乗法を実行すると、"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.805\n",
"Model: OLS Adj. R-squared: 0.790\n",
"Method: Least Squares F-statistic: 54.26\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 1.16e-53\n",
"Time: 07:21:42 Log-Likelihood: -1565.3\n",
"No. Observations: 185 AIC: 3159.\n",
"Df Residuals: 171 BIC: 3204.\n",
"Df Model: 13 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 659.0401 570.899 1.154 0.250 -467.877 1785.957\n",
"time -61.1605 7.044 -8.682 0.000 -75.065 -47.256\n",
"bus -88.3823 21.727 -4.068 0.000 -131.269 -45.495\n",
"walk -55.4500 20.468 -2.709 0.007 -95.852 -15.048\n",
"area 70.0731 3.379 20.737 0.000 63.403 76.743\n",
"bal -17.0300 14.871 -1.145 0.254 -46.385 12.325\n",
"kosuu 0.0837 0.477 0.176 0.861 -0.858 1.025\n",
"floor -2.9003 43.868 -0.066 0.947 -89.493 83.692\n",
"tf -52.3960 37.057 -1.414 0.159 -125.545 20.753\n",
"d_N -867.1676 653.815 -1.326 0.187 -2157.756 423.420\n",
"d_E -341.6601 225.624 -1.514 0.132 -787.027 103.707\n",
"d_S -684.7974 280.782 -2.439 0.016 -1239.043 -130.552\n",
"d_W -247.0280 232.685 -1.062 0.290 -706.333 212.277\n",
"year 9.7516 5.187 1.880 0.062 -0.487 19.990\n",
"==============================================================================\n",
"Omnibus: 126.693 Durbin-Watson: 1.586\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2453.138\n",
"Skew: 2.165 Prob(JB): 0.00\n",
"Kurtosis: 20.306 Cond. No. 1.80e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.8e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"# 定数項も加える\n",
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area',\n",
" 'bal', 'kosuu', 'floor', 'tf', 'd_N', 'd_E', 'd_S', 'd_W', 'year']])\n",
"\n",
"# 普通の最小二乗法\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"\n",
"# 結果を表示\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"となる。 \n",
"p値を見ると、kosuu, floorがほとんど無関係であるように見える。 \n",
"kosuuには外れ値が1つある（kosuu=2080）ので、それを除いてみる。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最小二乗法その2\n",
"\n",
"外れ値を除き、"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time 57\n",
"bus 0\n",
"walk 15\n",
"price 800\n",
"area 57.2\n",
"bal 0\n",
"kosuu 2080\n",
"floor 1\n",
"tf 4\n",
"muki S\n",
"year 67\n",
"d_N 0\n",
"d_E 0\n",
"d_W 0\n",
"d_S 1\n",
"Name: 161, dtype: object\n"
]
}
],
"source": [
"print(df.loc[161])\n",
"df = df.drop(161)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"再び最小二乗法を実行すると、"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.805\n",
"Model: OLS Adj. R-squared: 0.790\n",
"Method: Least Squares F-statistic: 53.92\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 2.63e-53\n",
"Time: 07:21:42 Log-Likelihood: -1557.0\n",
"No. Observations: 184 AIC: 3142.\n",
"Df Residuals: 170 BIC: 3187.\n",
"Df Model: 13 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 648.1009 571.820 1.133 0.259 -480.681 1776.883\n",
"time -61.6799 7.087 -8.703 0.000 -75.670 -47.689\n",
"bus -87.3626 21.797 -4.008 0.000 -130.391 -44.334\n",
"walk -56.4869 20.541 -2.750 0.007 -97.035 -15.939\n",
"area 70.1205 3.384 20.720 0.000 63.440 76.801\n",
"bal -16.8531 14.892 -1.132 0.259 -46.250 12.544\n",
"kosuu -0.3897 0.792 -0.492 0.623 -1.954 1.174\n",
"floor -2.9617 43.925 -0.067 0.946 -89.670 83.746\n",
"tf -42.4715 39.401 -1.078 0.283 -120.249 35.306\n",
"d_N -890.6252 655.405 -1.359 0.176 -2184.406 403.156\n",
"d_E -336.1515 226.034 -1.487 0.139 -782.346 110.043\n",
"d_S -688.7604 281.193 -2.449 0.015 -1243.841 -133.680\n",
"d_W -222.7084 235.237 -0.947 0.345 -687.070 241.653\n",
"year 9.6658 5.195 1.861 0.065 -0.589 19.920\n",
"==============================================================================\n",
"Omnibus: 125.689 Durbin-Watson: 1.592\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2436.830\n",
"Skew: 2.153 Prob(JB): 0.00\n",
"Kurtosis: 20.301 Cond. No. 1.37e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.37e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area', 'bal',\n",
" 'kosuu', 'floor', 'tf', 'd_N', 'd_E', 'd_S', 'd_W', 'year']])\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"やはりkosuu, floorのp値が大きいので、説明変数から除くと、"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最小二乗法その3"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.805\n",
"Model: OLS Adj. R-squared: 0.792\n",
"Method: Least Squares F-statistic: 64.35\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 4.59e-55\n",
"Time: 07:21:42 Log-Likelihood: -1557.1\n",
"No. Observations: 184 AIC: 3138.\n",
"Df Residuals: 172 BIC: 3177.\n",
"Df Model: 11 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 647.4256 568.107 1.140 0.256 -473.933 1768.784\n",
"time -61.7145 7.051 -8.753 0.000 -75.631 -47.797\n",
"bus -88.0394 21.589 -4.078 0.000 -130.653 -45.426\n",
"walk -55.9212 20.403 -2.741 0.007 -96.194 -15.649\n",
"area 70.0917 3.349 20.932 0.000 63.482 76.701\n",
"bal -16.5189 14.746 -1.120 0.264 -45.626 12.588\n",
"tf -52.1050 27.938 -1.865 0.064 -107.251 3.041\n",
"d_N -869.5508 649.087 -1.340 0.182 -2150.753 411.652\n",
"d_E -336.5608 222.059 -1.516 0.131 -774.872 101.751\n",
"d_S -682.6687 278.482 -2.451 0.015 -1232.352 -132.985\n",
"d_W -238.6622 231.247 -1.032 0.303 -695.109 217.784\n",
"year 9.8681 5.142 1.919 0.057 -0.281 20.018\n",
"==============================================================================\n",
"Omnibus: 125.761 Durbin-Watson: 1.586\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2414.208\n",
"Skew: 2.159 Prob(JB): 0.00\n",
"Kurtosis: 20.212 Cond. No. 924.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area',\n",
" 'bal', 'tf', 'd_N', 'd_E', 'd_S', 'd_W', 'year']])\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"となる。さらに、balと南向き以外の方角のダミー変数を説明変数から除く。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最小二乗法その4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.799\n",
"Model: OLS Adj. R-squared: 0.791\n",
"Method: Least Squares F-statistic: 99.83\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 6.87e-58\n",
"Time: 07:21:42 Log-Likelihood: -1559.8\n",
"No. Observations: 184 AIC: 3136.\n",
"Df Residuals: 176 BIC: 3161.\n",
"Df Model: 7 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 351.2443 548.343 0.641 0.523 -730.929 1433.418\n",
"time -63.7175 7.012 -9.087 0.000 -77.556 -49.879\n",
"bus -84.9009 21.357 -3.975 0.000 -127.050 -42.752\n",
"walk -54.6035 20.293 -2.691 0.008 -94.653 -14.554\n",
"area 69.5406 3.241 21.459 0.000 63.145 75.936\n",
"tf -58.8849 27.183 -2.166 0.032 -112.531 -5.239\n",
"year 8.3053 5.081 1.634 0.104 -1.723 18.334\n",
"d_S -433.0197 250.589 -1.728 0.086 -927.566 61.527\n",
"==============================================================================\n",
"Omnibus: 134.268 Durbin-Watson: 1.605\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2841.632\n",
"Skew: 2.343 Prob(JB): 0.00\n",
"Kurtosis: 21.673 Cond. No. 730.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area', 'tf', 'year', 'd_S']])\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"p値が大きい南向きのダミー変数d_E、築年数yearも説明変数から除く。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最小二乗法その5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.791\n",
"Model: OLS Adj. R-squared: 0.785\n",
"Method: Least Squares F-statistic: 135.0\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 1.25e-58\n",
"Time: 07:21:42 Log-Likelihood: -1563.2\n",
"No. Observations: 184 AIC: 3138.\n",
"Df Residuals: 178 BIC: 3158.\n",
"Df Model: 5 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 659.1196 411.570 1.601 0.111 -153.066 1471.305\n",
"time -63.7977 6.742 -9.463 0.000 -77.102 -50.494\n",
"bus -92.8873 21.396 -4.341 0.000 -135.109 -50.666\n",
"walk -58.2817 20.499 -2.843 0.005 -98.734 -17.829\n",
"area 69.1817 3.222 21.470 0.000 62.823 75.541\n",
"tf -46.2762 26.810 -1.726 0.086 -99.183 6.631\n",
"==============================================================================\n",
"Omnibus: 131.166 Durbin-Watson: 1.607\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2595.213\n",
"Skew: 2.288 Prob(JB): 0.00\n",
"Kurtosis: 20.820 Cond. No. 383.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area', 'tf']])\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"p値が大きいtfを取り除く"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最小二乗法その6"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.788\n",
"Model: OLS Adj. R-squared: 0.783\n",
"Method: Least Squares F-statistic: 166.1\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 3.96e-59\n",
"Time: 07:21:42 Log-Likelihood: -1564.7\n",
"No. Observations: 184 AIC: 3139.\n",
"Df Residuals: 179 BIC: 3155.\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 326.0739 365.543 0.892 0.374 -395.254 1047.401\n",
"time -64.2877 6.773 -9.492 0.000 -77.653 -50.923\n",
"bus -95.0077 21.478 -4.423 0.000 -137.391 -52.625\n",
"walk -52.6312 20.348 -2.587 0.010 -92.783 -12.479\n",
"area 69.2456 3.240 21.373 0.000 62.852 75.639\n",
"==============================================================================\n",
"Omnibus: 133.686 Durbin-Watson: 1.557\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 2729.285\n",
"Skew: 2.342 Prob(JB): 0.00\n",
"Kurtosis: 21.277 Cond. No. 338.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"X = sm.add_constant(df[['time', 'bus', 'walk', 'area']])\n",
"model = sm.OLS(df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"修正済みR^2 = 0.783, F統計量のp値3.96e-59 を見ると、**「新宿駅からの乗車時間」, 「バスの乗車時間」, 「徒歩時間」, 「部屋の広さ」の4つで十分に住宅価格を説明できている**と考えられる。 \n",
"最小二乗法1〜6と比較しても、AIC・BICは殆ど変わらないか、改善している。 \n",
" \n",
"あとは残差を検討して、誤差項に関する諸仮定が満たされているかをチェックする。"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 残差の分析\n",
"\n",
"残差に関する仮定は: \n",
"\n",
"* 誤差項の平均が0\n",
"* 誤差項の分散が一定\n",
"* 誤差項は互いに独立\n",
"* 誤差項は（少なくとも近似的には）正規分布に従う\n",
"* 誤差項と各説明変数の相関係数は0\n",
"\n",
"であった。 \n",
"※全ての項目を厳密にチェックする方法を知らないので、出来る項目だけを確認します。\n",
"\n",
"### まずは、横軸に予測値（価格）を、縦軸に残差をとって点をプロットする。"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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2jGvLtq22n1bgR5JGkFr1H83pdwPnR0SHpEuB\nqRW2fY+uO8BDypZ9JyJWlW8gaQqpZf9uSbdFxOKqpTIzaxDuKmNmVlsnSZqcpy8Gnqywzhrgs5LG\nQGcf8lOBLUCLpI/n9S6qcoxHgKvytoMkHQe8SWpNL/kLMLvQd/6jkk4AngC+LGmIpGNJleVu3Voi\n4i1SS/hCoDUiSusMB3bm1vFLCtuKrouA7cBZebrUul7K09WSmnKeTpM0VNJJwO6IWAQsInXtMTNr\neK64m5nV1lbg25I2kbqT3JHTOyvHEbEbuAy4X1I7uZtMRLxD6hrz5/xw6q7CdsV+49cC0yQ9CzwD\njI2I14C/54c7F+RW7fuAp/J6S4HhEbGB1A2mHXgIeLqHsiwhXXwsKaR9n3Th8TdSH/fOYhXy91Pg\nKknrgQ8W0hcBm4D1kjpybJpIrfZtef2vA7/oIU9mZg1DXY0iZmbWn/KDlq0RcUaNs2JmZnXALe5m\nZrXl1hMzM+sVt7ibmZmZmdUBt7ibmZmZmdUBV9zNzMzMzOqAK+5mZmZmZnXAFXczMzMzszrgiruZ\nmZmZWR34P2DMrJaNGp2AAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c5fe588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"residuals mean: -4.152041029832933e-12\n"
]
}
],
"source": [
"# 回帰に使った変数だけを抜き出す\n",
"new_df = df.loc[:, ['price', 'time', 'bus', 'walk', 'area']]\n",
"# 説明変数行列\n",
"exp_matrix = new_df.loc[:, ['time', 'bus', 'walk', 'area']]\n",
"# 回帰係数ベクトル\n",
"coefs = results.params\n",
"# 理論価格ベクトル\n",
"predicted = exp_matrix.dot(coefs[1:]) + coefs[0]\n",
"# 残差ベクトル\n",
"residuals = new_df.price - predicted\n",
"\n",
"# 残差をplot\n",
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"plt.plot(predicted, residuals, 'o', color='b', linewidth=1, label=\"residuals distribution\")\n",
"plt.xlabel(\"predicted values\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.show()\n",
"\n",
"# 残差平均\n",
"print(\"residuals mean:\", residuals.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"平均はほぼ0であり、グラフでも0付近に点が集中していることがわかる: 仮定1は満たす \n",
"\n",
"しかしながら、右側にいくつか外れ値が見える。右上の1点を除いて、再度回帰分析を行う。\n",
"\n",
"### 最小二乗法その7"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"price 24800.00\n",
"time 4.00\n",
"bus 0.00\n",
"walk 8.00\n",
"area 230.72\n",
"Name: 12, dtype: float64\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.790\n",
"Model: OLS Adj. R-squared: 0.786\n",
"Method: Least Squares F-statistic: 167.7\n",
"Date: Thu, 22 Oct 2015 Prob (F-statistic): 3.01e-59\n",
"Time: 07:21:42 Log-Likelihood: -1510.6\n",
"No. Observations: 183 AIC: 3031.\n",
"Df Residuals: 178 BIC: 3047.\n",
"Df Model: 4 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const 1050.7368 292.682 3.590 0.000 473.164 1628.309\n",
"time -59.3635 5.298 -11.205 0.000 -69.819 -48.908\n",
"bus -94.7889 16.739 -5.663 0.000 -127.822 -61.756\n",
"walk -54.4831 15.859 -3.435 0.001 -85.779 -23.187\n",
"area 56.8131 2.775 20.474 0.000 51.337 62.289\n",
"==============================================================================\n",
"Omnibus: 30.767 Durbin-Watson: 1.428\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 70.111\n",
"Skew: 0.741 Prob(JB): 5.97e-16\n",
"Kurtosis: 5.645 Cond. No. 340.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"print(new_df.loc[12] )\n",
"new_df = new_df.drop(12)\n",
"\n",
"X = sm.add_constant(new_df[['time', 'bus', 'walk', 'area']])\n",
"model = sm.OLS(new_df.price, X)\n",
"results = model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
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qZmdLWiVpt7uPSzpkZquD57ys5DEAAABAQ0hsxt3M3iZpVNLDkqY6cbWk3ZLukvRqSXsl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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c5fe438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"residuals mean: 1.6127378728159302e-12\n"
]
}
],
"source": [
"# 説明変数行列\n",
"exp_matrix = new_df.loc[:, ['time', 'bus', 'walk', 'area']]\n",
"# 回帰係数ベクトル\n",
"coefs = results.params\n",
"# 理論価格ベクトル\n",
"predicted = exp_matrix.dot(coefs[1:]) + coefs[0]\n",
"# 残差ベクトル\n",
"residuals = new_df.price - predicted\n",
"\n",
"# 残差をplot\n",
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"plt.plot(predicted, residuals, 'o', color='b', linewidth=1, label=\"residuals distribution\")\n",
"plt.xlabel(\"predicted values\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.show()\n",
"\n",
"# 残差平均\n",
"print(\"residuals mean:\", residuals.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"最小二乗法6の結果に比べ、ばらつきが均等になった。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 次に、縦軸に残差、横軸に各説明変数の観測値をとって、残差のばらつきを見る。"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PkDRc0rvOubHFDAzhxBRNSPwehElYiwf8jgyO/j65K6V8JJF4SPfcc6mka5m1\nETL8bQIIWlhzu9Bzzg35n5LTP78g6YZc7jfI4/0/SVslPZc2dpikNZJekdQmaVzadddJelXSS5Lq\n08ZPlvRc6rqVWbblULj6+iVOcgf8a2hYGnRo8BG/B+Hw8MNPuNraxf3eg9raxe7hh58IOjR+Rw7i\n4YefcDU1V/V7bWpqvh7Ye5faRxa8X/frn5f5iJ+5SOp2fe95ZeWZ+b9pKIqw/W0CKC9hzu38UEg+\nMpTlXtOLIPuccw9I8qqz4k8yPNY3JK1xzh0v6bHUzzKzaZLmSpqWus/NZtZ7/s0tki5xzh0n6Tgz\no/NjkWzenLlPW2fnLp8jQZCYqhsOra1t/WZESNL69Su0atWagCLaj9+RoXhHySaSLan/dwYaTZR4\nnI8Elov09BziQfjwHn+bAIIR5twu7IZyKsq5aT8OU/KIROb1NnPknPuNmU0dMHyWpFmpy3dIalcy\noThb0j3Oub2SNpjZHyWdamYbJVU759am7nOnkkdxVnsRI/rbsmVLlvG4z5EgSEzVDYcwFw/4HRlc\na2ub4vEf9xuLx5Mr6TDdNLNi5SNB5iLOcVAgbPjbBBCkMOd2YTeU5V4/r/3ntCYkbVByx14sE5xz\nW1OXt0qakLo8SdJTabfbJGmypL2py706U+MogpqacerqWiIpvZK4WDU1hwYVEgLQ1FSvZ5+9RPH4\nRCU/RhKqqdmsxsaLA46svIS5eMBSyoMjccmLn/mID7nI3+nII/d5ESs8xN8mgCCFObcLu6GsinKx\nD3Fk27YzM9ZoDZHJk4/UunX1Sk7NrFCyOf1sTZnC9Kjyc6ik9CXxrg4qkLJ12mmT9PjjlyuRuLVv\nrLLyMs2Y8akAo0rqPbK5alVz2lLKsznimULikrug8pHi5CLNkt7XlClTvH1YFIy/TQBBCnNuF3ZZ\nCxtmtirtRyfJ0i7LOddUpJi2mlmNcy5uZhMlbUuNd0o6Ou12U5Q8OtKZupw+3pnpgVtaWvou19XV\nqa6uzruoy0TyKOyjHIUtc8mpujf2G4vHb2Sqrs+efHKzEokvKb3QmEh8WU89FZ5CY7IP1P7/C1FK\nXcKDntHS3t6u9vZ2X7ZVqIDykaLlIkn/LmmqtmxZr/b2dvKREAn6bxNAeYtCbuclL/ORwWZs/Hfq\n/79QsknWfUomE+dJWufJ1jN7UNJFkr6b+v+BtPG7zexGJad3HidpbepIyk4zO1XSWkkXSmrN9MDp\nhY2wC2uswqbvAAAgAElEQVQCz1FYSDSRDYvklOmZqX/7dXc/Hkg86bxe7rXUlo8N+rN0YHH/+uuv\n92W7eQoiHylaLpK0TtLl6ukxihohE/TfJvwR1jwbCHNuVwye5iMHWzZF0tOShqf9PFzS0/kuwzLg\nse+RtFnSHklvSPpbJZdY+7UyL7G2WNIflVxirSFtvHeJtT9Kas2yrRwXmwlOuS/zg/Crrj4n41Ke\n1dVfDDq0shLmJVW9ji3Mz7UUKALLvRYrH/EzF0ndLu13+G8Ket8A5I48G2FW7vlOIfnIUJZ7HSdp\nbNrP1amxgjnnLnDOTXLOjXDOHe2c+4lz7m3n3Oecc8c75+qdczvSbv8d59yxzrmPOeceTRv/b+fc\nn6WuK9YpMr5hmR+EXU9Pj6QlA0YXa9++zOcmoziamupVW9v/fUhOmT49oIj287oBHw39oCLlI8Hm\nIiz3CviNPBthFubcLuyGsirKDZL+YGbtqZ9nKbmwN4qEBB5+y3VK5siRo/T++w0a2ER2xIjXfIoY\nUrinTHvdgC/fx2O6cUkpoXxkqaR6Se8HHQhQdsizEWZhzu3CbiirovzEzFZLOlXJRl3XOufiRY+s\njEW5IzdfIqInn94FU6eO0fbtj2rgsr9Tp44+6Lb4/fDWnDkzQ/kaJpcEvrpfk9mamqvU2HhO3o+X\na0O/UuvLUe5KKx9ZLulSHXLI9qADQYlg/zp0Uc6zUR7CmtuF3WCronzcOfeimZ2sZALxRuqqSWY2\nyTn3B18iLENR7cjNl4hoyj4lM/sKJ8uWfUULFtyheHz/jI2amriWLbs463b4/ShH76j/rJ6deT9S\nPkcw8vndRviUbj7yIyUSc4IOAiWA/WtuoppnAxjcYDM2rpZ0qaR/VGpJtQH+uigRwfMpSH5V8f38\nEuHlcyr3oxz5TMmcM2emLrvsed100xNKJEapsvIDXXbZLL5kok9ySeAf9xuLx1XQ+53rEQymG5eM\nks1H9uypCjoElAD2r7lhqj9QmrIWNpxzl6b+r/MtGvTxagqSn1V8v75EePmcOMqR35TMWKxDd93V\nqa6u+/rG7rpriaZP78j6uvEls7yE4f1munFpKO18pDvoAFACwvB5GzVM9QdKz0FXRTGz88xsbOpy\ns5n9u5n9efFDgxf87Pzs15cIL58TnbHz676cz+vGl8ziiMU61NCwVHV1LWpoWKpYrCPokCQV5/3O\n9bnSWby0lF4+cqWk94IOAiWA/SsADG1VlG865+43s09L+qykH0i6VdIpRY0MnvCziu/XOYtePieO\ncuQ3JTOf1y3ZTPISxeMTlfzoSaimZrMaGy8uIPryFuYZR15/HuTzXJluXHJKKB9plrRVzNiAF+gZ\nkbtyPw0ZKEVDKWz0lnvPlPQj59zDZrasiDHBQ35W8efMmanf/e553XTT3L6+C/PnD953IR+DPafc\nly0Nx1GOoHewuU7J3LlzW8bxXbvePMg9D1VyNYBeVw95mzhQmM+r9rqokO9zZbpxSSmhfKQ37L8J\nNAqUBoq4uQnzQQEA+RtKYaPTzP5V0umSbjCzKg3hFBaEQzGq+Nm+hOfTdyEf2Z7TjBlTct5RheEo\nRyzWoQULHui3LOazz16t224L8w52j6RLJO2ffSFtlnPZPxqSzSRv7DcWj98Yii/hURX2GUdeFhU2\nb34343hn5y5PHh+RUIL5yCFBB4ASQRF36MJ8UABA/oZS2DhfUoOk7zvndpjZREl/X9yw4JVirLCS\nrXjg144i23PKZ/thOMrR3Hyv4vGb+43F4zfqm9+8MrQ72D17KiSN1sDZF3v2ZD9f/GBfwoOetRJF\nYZlx5IctW7ZkGY/7HAkCVIL5yPtBBwCUnbAfFACQn4MWNpxz75nZm5I+LelVJQ/N/rHYgYVVFL98\neVnFH6x44OeOItNz+v73H89r+0Ef5XjttczFgNdey3yEOgzi8R2Sfjhg9EbF4/Oy3udgpxAxLTR3\nYZhx5JeamnHq6loiKf3zZ7Fqag4NKiT4rPTykctE81DAf+V0UAAoJwctbJhZi6STJX1U0k8kjZD0\nU0l/WdTIQiiqX768LMYMVrzwc0fR0nKzbrrpib5eHgsXzorsjspsd5Zr9vgaRy7GjBmrrq5M49VZ\n7zPYl3CmheYnDDOO/DJ58pFat65eyaaLFUq2W5itKVPKZwWjclda+cg8STMlbQw6EKDsnHbaJD32\n2GXq6dl/gKai4quaMePEAKMCUKihnIpyjqSTJP23JDnnOs0s+7eXEhbFL19eF2MGKx40Nvpz9Lil\n5WatWPGsEon9vTxWrLhc5513qGprc9/+l750re6//wU5N1pm7+m886bp7ru/62nMg5k6dYy2bz/w\nSPTUqaN9iyFX27Zlnv6fbVwa/Et4vrNtEPyMI78kC2OPlsXsFGRVQvnIvZKukvRB0IEAZefBB59X\nT8+nJM2VNErSB+rpmaWHHlqnlpZgYwOQv6EUNnY75/aZmSTJzML7bavIonhOntfFmMGOuvu1Kkpy\npsZ9/cYSiVvV1jZPd9xxRU5Hr7/0pWt1zz1vS3qob+yeey6VdK1vxY1ly76iBQvuUDy+/0h0TU1c\ny5Zd7Mv28/HBB+9KOrAY88EHg0+rzvYlPKqzbcIgiqfH5aOcZqcgqxLKR5olvRN0EEBZevnlTknj\nJaXnkkv08subAooI6K9ccjuvDVrYsGT28LCZ/VDSODP7qqS/k3SbH8GFTRS/fHldjBnsy4Vfq6Ik\nEqOyjFflfPT6/vtfUHpRI+lHuv/+z+vuu/OPMRdz5szUZZc93+/Umssu874gNJjcP0APU7KHX//T\nAqSX89p+U1O9nn326n6rptTUXKXGxnPyerxyEdXT4/JVLrNTcKDSzEculvSDoIMAys6ePfvU/8CM\nJK3Q7t2fDyIcoJ9yy+28NNRVUa6StEvS8ZKanXNleVJzFBv1FaMYk+3LhV+n6lRWZp66W1nZnfNj\nOZf5gF+28WLwqyA02PZz/QAdNmyP9u2bqeQ54unj3y8gknfUv1Cys4DHKg9RPD0OKEAJ5SPLUv9/\nO9AogHI0cuQYJTKkxyNHjvE/GGAAcrv8DVrYcM45M/tvSe845/6vTzGFVhSnQvtZjPHrVJ2FC2dp\n+fIDmz4tXJj7+2CW+dSJbOPFkPwAa5C0VMk/yYTWr2/QqlVrfPndymf7U6ceoj/9ybu+IK2tbYrH\nf9xvLB4XH+IHEcXT4wrB1MzyVXr5yFJJm5Vc2AVeK8fPinJ8zvk6/vjxeuaZA8c/+tHD/A8GGKDc\ncjsvDWXGxgxJ881so/avS+acc58sXljhFbWp0H4WY/w6VWf69BNUXf1r7dgxT1KVpG5VV+/R9Okn\n5PxY5503LdVT40dpowt03nnTPIr24Do735T0qPoXCZZo06a3Qrv91tYrNX/+j7Rjx/4ZFuPGva7W\n1ivyioEP8fzs3Lkt4/iuXW/6HEnxMTUTKql8ZLmkq5Vc2AVeKsfPinJ8zoVYtmyeFiw48PTXb397\nboBRAUlRbH0QFkMpbDQUPYoSF3QV3a9iTLJPwiWKxyeq98h/Tc1mNTZe7Ol2WlvbtGPH1yW19W1n\nx476vGY4JBuEXqv77/98YKuixOM7JP1wwOgKxePzQrv9OXNmatGi5w9Ycjff3zM+xPO1R5mauDqX\nbQnh6CrG1MygP5uRsxLKR5ZK+oKkF4MOpOSU4zTucnzOhdjf22xuYL3NgGyi2PogLA5a2HDObfAh\njpJVflX0Q5U8EtXras+34PUMh7vv/q5vjUIzGTYs8xG7YcOG+7L9iRMnqqsr03hN1vt43ReED/H8\njB07RdJnNLCJ69ixmZfPjTKvZ/WU32dz9JVWPrJcyaIkp6J4rRxnAJbjcy5E0L3NgMFEsfVBWAxl\nxgYKUE5V9GSfhBv7jcXjN3r+XIOe4eC17dt35DTutUmTxuj55w8cnzy5Out9vP695kM8P8mZLgc2\nca2qimg/xUF4PaunnD6bEVYrJP1N0EGUnHKcAViOz7kQfP4j7KLW+iAsKGwUWTlV0f16rskZBh1K\nPxVFqh90hkGYjRhRoUTiwNMJRozw53ckn9kS+b7Xg03950M8d+U008Xr51pOn80IM3pseK2cPhd7\nleNzLgSf/0BporBRZGGvont5jrlfz3X48HeV6VSU5Hj0jBo1Uu+/36CBpxOMGvUnX7afz2yJfN5r\npv57r5xmunj9XMP+2Yxy8X7QAZSccvpc7FWOz7kQfP4DpalkChtmNlvSPyv5zfA255x/3R8HEeYq\nutdfNJPNQw/sMt3YeE7hwfYzQv2LGpK0QmZXerwdfyxcOEsrVtytROLWvrHKysvyWr42X7nOlsjn\n95qpn8XjnOv3f6nyclZPmD+bEW1Dy0d6l3vd62ts5aIcZwCW43POF5//QGkqicKGmVVIuknS5yR1\nSvqdmT3onAu83XgYqujZZmUU54vmO+o/82BnYcFnMHbsURnHq6uP9HxbfmhpuUKvvHLgyiwtLfkt\nneqHfH6vmfrpPWbB5C8Mn80oPUPPR3qXex3ld4hA2ePzHyhNJVHYkHSKpD/2dkw3s3slna2QrKMW\nZBV9sC8+Xn/RTDYP/XG/sXhcnh+RL7UphLFYh9aurVQi8VDf2Nq1SxSLhbs7d66/1wd731h6M3fM\ngikMRzhRBDnkIzeK5qFAMPj8B0pPqRQ2Jkt6I+3nTZJODSiWUBnsi8/IkZmnredbIPDriHxUpxD6\nO3MmfAZ732KxDs2f/8/asWO4kkcwP9DatS/orruYeTAYP2fBRLXwFNW4EVlDzEfmSpql0knD4AU+\nrwAgf6WyRx3SieUtLS19l+vq6lRXV1ekcMJjsC8+f//3n/G0QODXTIooTiH0c+ZMWA32vtXWnqsd\nO46StL/PyI4dl6upaWWo39eg+fU3F9VTXqIatx/a29vV3t4edBilaIiNbj4u6f9J2qL29vayyEcw\nOD6vAJQjL/ORUilsdEo6Ou3no5U8StJPemGjXAz2xcfrAoHfMymi1DDRz5kzfsnnyFK2qZ+vv75H\n6UWNpFv1+uuf9y7gEnTaaZP0+OOXH9B4dsaMT3m6najOKopq3H4YWNy//vrrgwumtAwpH5FaUv9m\nU9QIoSBmTvB5BaAceZmPlEph4/eSjjOzqUq2GZ8r6YIgAwqLg61U4uU5hn7NpIjFOrRgwR2Kxyf2\njT377B267bbwHtXYvDnzUrSdnbt0ww1fjNypNV4fWXLukJzGkfTkk5uVSHxJ6Q17E4kv66mn1ni6\nnajOKopq3Ii0HPORMX7EhBwENXNisDwBAHBwJVHYcM4lzGyhpEeVzO5/HIYVUcKj+CuV9PKjGVNz\n852Kx2uU7CqfFI8vUXPznaEtbGzZsiXLeFxz5szU7373vG66aa4SiVGqrPxA8+fPCsXKOdkkjyw1\nKLlkYaWkhNavb9CqVWvyivuQQ/ZqV4bc7ZBDMs84QlLyi/vM1L/9ursf93Q7UW3YG9W4EV255yPv\n+xQZhiqomROD5QnwHv1MgNJTEoUNSXLO/UrSr4KOI2z8WqnETxs2vCvptgGjK7RhQ3gn6dTUjFNX\n1xJJ6cnSYtXUHKpYrEN33dWprq77+q65664lmj7dn1VR8jk61dn5ppJ5e/rzWaJNm97KK4arr/6c\nli+/TD09P+wbq6j4qq6++rN5PV658OuLe1Qb9kY1bkTb0PORyyR1FTsc5CiomV6D5QnwFv1MgNJU\nMoUNZJbcQXdIalPvkXWpPtJTsZ0bmeWaEb7GkU2mowCTJx+pdesmKTkrObnqhzRLU6ZUBH5ebT7b\nj8d3SPrhgNEVisfn5RVDS8sVkm7WTTfNUyJRpcrKbi1cODM1jmz8+uIexYa9UnTjRjmYJ2mXpNFB\nB4IBCi0Y5zsTIJkn1Kv/DNvZmjLF21MLQT8ToFRR2ChxO3duUqYj6zt3bg0oosIddpjTjh2Zxv2P\nZaBsRwFOOSWhyspnlUjsn5VRWXm5Zsz4pNrbt2V8LL+KT/kcnZo4caK6MhxonDixJu84pk8/QSef\nvLkvGZw+/YS8H6tc+PnF3Y/TzIohqnGj1DlJc5Q86IAwKaRgXMhMgOR2H2WGmQ/ovwSUJgobJW+E\n+hc1JGmFzK4MIhhPHHroCElXS7oxbfQqjR07PKCI9st2FGDHjrn9ihqSlEjcqqeeCn5VlDfe+FPG\n8U2bXst6n0mTxuj55w8cnzy5Oq8YmBaaP764A1F0n6Ql4lSU8CmkYFzITABmmPmH/ktAaaKwUeLG\njj0q43h19ZF5P2bQDZfGjp0i6TPqP13zHI0d623DxHxkOwqQSIzKON7dXaG///vPBNoHYNu2t5RM\nsPuf17t1a/Z+GV6fAsG0UADlZ4WkhqCDQAb5FowLnQlAodof9F8CShOFjRLndVU6DEfW9z8n1+//\nMFTas73elZUfKFOvk6qqnsCP0lRUHKVkct3/vN6Kis1Z7+N1zEwLBVCexgYdADwUVH+OQu9bboLO\nuwAUB4WNEleKR9ZPO22SHn/8biUSt/aN9farCFq213vv3rfV1fVTST9Ku/WlfauIBHmUZt++ncq0\nZOi+fSsPel/nXL//88W0UBQLyT7CaamkeknvBh0IPBRUf44wHHSKIq9yGADhQGGjxJXikfUnn9zc\nr6gh7e9XkQ8vv/hke73PPPMZ9S9qSNKPtG7d3/gWWzZHHmnatetySemv6WU6cpCzlbxOopgWimIg\n2Ud4LZd0qaToNvIuZfnue4PqzxGGg05Rwr4BKE0UNsqAl7MBwnBk3cviSjF2bplf79Yst86+1J9f\nO96ensMkHSrp86l43pM0TT09+7Lex+skimmhKAaSfYTbj5RcGQVhUui+N9+ca/PmzLN3Ojt3HfS+\nYTjoFCXsG4DSNCzoAEpFLNahhoalqqtrUUPDUsViHUGHVBRNTfWqrV3Sbyx5ZP1032LwsriSfefm\n9brx72UZfz/rPfyK7Y031itZ43xI0r2p/ytT45kVkoBlM2fOTK1evUzt7S1avXoZyQUKRrKP8ON3\nMWz8ywv627jx9Szjbxz0vmE46BQl7BuA0sSMDQ8crLpfSud4h+HIerLHxuUDemxcphkzPpXzYxXj\nC3pmbyk57Tj9dJQFkt7Meg//dryjlGlJ4MGOJG7ZsiXLeNyzqIBCkewj/PYGHQAGCOpLb09PjzKt\nULZvX+bPsXRNTfV69tmrFY/f2DdWU3OVGhvP8TzOUsC+AShNFDZylKlIMdiUNkkldx5f0MuRJXts\nfFLSXCW/lH+gRGKWnnoq85ftwRTjC3qm3xGzw+TcSf1ilmbJLPuRGL92vMOGHaJ9Gc46GTYs+2ky\no0fvU1fXZZJ+mDb6VY0eTVKA8KB3C8Jtsfav7oWwCGplk5EjR+n99w9coWzEiNeGtN3u7g1KzzG6\nuymaZXPaaZP02GOXqadnfw5TUfFVzZhxYoBRASgUhY0cZJuZMWpU5tMMursrOI+vCDo731QyGbwv\nbXRJ3wojuaipGaeurgOPkNTUHJpXbLFYhxYseKDfUZNnn71azu2T9MKAmBemxjPz60vZMceM0p/+\ndOD4hz40Kut9tm/fK+nL6p+Azdf27TdmvQ/gtzDMMAMya5Y0W9Ifgg4EAxS6skmmHOC22w5+MGvq\n1DHavv1RDcxHpk7NfpChV3PzndqxY7ykiepdUn7Hji1qbr6Tz7sMHnzwefX09M9henrm66GH7lNL\nS7CxAcgfhY0cZCtSHH743Iy3r6rqUXc35/F5LR7fof4zBSRpheLxeTk/1uTJR2rdunoNPEIyZUp+\n59I2N9+rePzmAfHeKOlzkm4acOubJGXvTeLXl7LW1gW68MKF2r59f3zjx1+plSsvyXqfffsOUeYl\nYm/NeHsgKEHPMAMyW6bk6YnvBB0IBihk35stB/jmN6886P2XLfuKFiy4Q/H4/nykpiauZcsuPuh2\nX3lli6QTlVxtp9cSvfLK/xz0vuXotdfeU6Yc5rXXfhxIPAC8QWEjB9nOu6ypGadx4zJX91tb2zLe\nh/P48jdx4kR1dWUar8n5sZJHZh71bFZEcmeZydgs49UHfcxir7M+Z85M/fSnA5O4uYMmYSNG7NN7\nGZ7qiBFMqwaAg5unZFPpg+8D4L98C6LZcoDXXsvcz2vgNm+7TVq1ao26u6WqKqmx8eIhxZHMTw/s\nlbV799lDiLr8mO3Ocs0eX+MA4C0KGznIdt7llClHqbHx9KzVfc7x9takSWP0/PMHjk+eXJ3zua1e\nz4rIfWeZ/RxYP9dZzzWJS06ZPfAUnqFMmQUAHCdpg6TOgOOAlxKJzIWNvXuzHfToL9+CysiRo5XI\nkKKOHMk+ORNyGKA0UdjIwWDnXWbbGXGOt/eyvQ8zZkzJqxDg5VT1bDtL6V1l6nZulj3ZSZ761CBp\nqXrPmV2/vkGrVq0J/PenkCmzAAAn6VJVVn4n6EDgqfeVaV+fbBhePMcfP07PPHPg+Ec/Or6o240q\nchigNFmxpreHjZk5L55rLNaRmibYW6Q4PfAvmeUo0/vQ2tqmtrblB9y2oaFZq1cv8y2u5M5ykvbv\nLDvV07NNb76ZkHSo9q+K8o6OOaZaGzfen/GxTjjhMq1bd4T6J0hL9IlPvKXnnx/YY8R/LS0366ab\nnlAiMUqVlR9o4cJZamm5IuiwAOTBzOScs6DjKAdm5pJffrdo5Mit6u5+OOiQ4JHkfjshaX8OIHXq\nE58YXtT9dizWofnzf6QdOz6k3gMh48Zt0F13fZUcNQvyeSCcCslHmLGRI78a0eW7XFixHyssMr0P\n3//+4xlv62ej1mznyDY336k336xS/waiC3X44d1ZH8vLJqlei8U6dNddnerq2r/Ky113LdH06R2R\n/90CgOJbLmmJ9u4d2lKeiIb9DcnTG5BfnHdD8lxUVR2p9OahVVVXF32bUUZjaaD0UNgIIS97K/jZ\npyFoha4975VMO8vm5nuVaVUUsyuzPo6XTVK9xjLGAFCoFdq378ygg4CHvG5IPlStrW39lpiVkqux\nsE8GUE4obOTIj9kPXn5pLKcvoIWsPV9sY8celXG8uvrIrPcZrElq0JId2Dsktal32qtUX9DsmFKc\nWQQAmS2VVC9pRNCBIIN890dB9VXLtmqfnzNWASBoFDZy4NfsBy93UOW0swtLo9ZMCdHOndsy3nbX\nrjezPk6YCzVvvPGikiu69O//8cYb6/N6vHKaWQQAvaeiSNn3AQhGofujIE5xCMuMVQAIEoWNHPg1\n+8HLHVS57eyCPmcy2Tz0gX5TQp999modckiXMnVKdy7b8rDJ5/K73z2vm26a29egc/78WaH4ov/m\nm7vV/7lI0gq9+eYX8nq8cppZBABJKyQ1BB0EBoji/ijMB0IAwC/DgtiomZ1nZuvMrMfM/nzAddeZ\n2atm9pKZ1aeNn2xmz6WuW5k2PtLM7kuNP2VmHypW3H7Nfmhqqldt7ZJ+Y8kd1OmBPhYOrrn53ozn\nuW7dWiFpsqS5ki5O/T9FY8dOyfpY6Q0633nndnV13ae77upULNZRvCcwRMOGjctp/GDKaWZRVMVi\nHWpoWKq6uhY1NCwNxe8hUKjg8pGlSp7ON9b7J4WCFLo/CuKzcs6cmVq5skENDc2aNatFDQ3NWrnS\n/xmrABCkoGZsPCfpHA1Y8sHMpin5jW+akt8Cf21mx6XWab1F0iXOubVm9oiZzXbOrZZ0iaQu59xx\nZjZX0nclFWXZCL9mP3h5SkWYj/qXotdeey/j+AcfbJPUKem+tNEl2rlza9bHCvNRo8rKD7KMZ1/l\nZTDlNrMoajhVCCUsoHyEU1HCqpD9UZCflUHPWAWAoAUyY8M595Jz7pUMV50t6R7n3F7n3AZJf5R0\nqplNlFTtnFubut2dknrnvJ8l6Y7U5V9I+myx4vZ79kMyf9r/fz7CfNS/FJllO7VkuDKdumE2UlLm\nIzxhnsWwcOEsVVRc1m+souKrWrgwv6SKmUXhlr3IVvwlDIFiCjYfWaHkvgFhUsj+iM9KAAhO2Hps\nTJL0VNrPm5Q8UrI3dblXZ2pcqf/fkCTnXMLM3jGzw5xzb3sdnF/NKb2s+If5qH8pmjp1jLZvP7CX\nxvDho7U7Q81jz57hWd/vsWO3Z9xGGGYxTJ9+gqqrf60dO+ZJqpLUrerqPZo+/YS8Hi8sjV+R2ebN\n72Yc7+zc5XMkgG98ykdGeRgyvFDI/ojPSgAITtEKG2a2RlJNhqsWO+ceKtZ2i82PqX5eFiPCfNS/\nFC1b9hUtWHCH4vFmSRWSelRTE9d77+3LWNjYsiWe9f3+yEfOVWXl5Uokbu0br6y8TDNmfKq4T2II\nWlvbtGPHv/cb27FDBRXMmEabHz+Wyd2yZUuW8bin2wGKIdz5yN5gN4+M8t0fbdz4epbxNwoNCQBw\nEEUrbDjn8plD3inp6LSfpyh5ZKQzdXngeO99jpG02cwqJR2a7ehIS0tL3+W6ujrV1dXlEWLxeVmM\noHeBv+bMmanbbpNWrVqj7m6pqkpqbLxY1177M61bd+BMjpqaQ1Pvd4ekNiX/JBOS6rV9+3AlEl+S\ntL9Ikkh8WU89FfyU1mwxUzDzl1/nc9fUjFNXV+bfX0RTe3u72tvbgw7DF2HMR6QWSY9J2qz29vbQ\n5iPITU9PjzKtgLZvX+ZcDADKnZf5SBhORbG0yw9KutvMblRySudxktY655yZ7TSzUyWtlXShpNa0\n+1yk5JTR/0/JTCGj9MJGmHlZjGAJsGAM7I8yefKRWreuXulFCmm2pkxZo23bNkl6QFL6aipXa88e\nkzQz9W+/7u7Hix3+Qe3cmTnmnTt3BhRRefLrVLPBfn8RTQOL+9dff31wwYSHb/lI8m9ohaTvUNQo\nAj9msmUycuQovf9+gwZ+Vo4Y8VrRtw0AUeRlPhJIYcPMzlEyEThCUszMnnHO/Y1z7gUz+7mkF5Q8\nBHyF29858wpJtyt5QuojqQ7kkvRjST81s1cldalIK6L08mNn6WUxgt4F/sp2BH3+/Mlav/7RjO9p\nUxxb/IYAACAASURBVNNt6l8gkKQbtW/f2Rm3EYbZNu+8s0eZYt6586Igwilbfs2cSX4mZf79BaIs\nuHxkmaTFkvZ5/pzKXZArkyT7bD2qgTM2pk4dXdTtSsEVcwAgLKyQFTeixMxcoc81086ytnaJVq5s\nKEoD0eTpDL3FiNPZQUVAQ8NStbUtzzDerMbG0zO+p+PHX6QdO+444D6jR5+tmpoTDvgyGYa16bPF\nPH78RXr77QPHURx//ucL9MwzE9Q/iV6ik07aqj/84TZPt8VnUmkzMznn7OC3RKHMzElLJZ0u6fsK\nvM1HiRlsP7x69bKibjsW60j12Zqk/X22OnXbbRcX9fPSz/wUAIqpkHwkDKeiRIafK4zQSDGaBuuP\nku09zbZE7IgRh2jlyoZQzrbJvqztHl/jwAhlXkb4Ss+3xGcS4KXeGRuZV9FA/oJsmp6tz1aUms4D\nQFRR2MgBK4zgYPLpj5JtidipU0eH9svkYDHDP2PHHpVxvLr6SJ8jAZCbZkmvimKw94Jumh7Efpv8\nFACkYUEHECVB7yyxXyzWoYaGpaqra1FDw1LFYh1BhyQp2YugtnZJv7FkL4LsTfmXLfuKamriSia6\nLZKaVVMT17JlXylmqAWJYsyliM8kIKpelbRLH/lIplVoUYh89sNRx74AAJixkRNWGAmHIBuDHUw+\nzVqDmrpaiCjGXIr4TAKizHThhZ8NOoiSU45N09kXAADNQ3NGA73gBdkYDPvRgT0c+EyCF2ge6p9k\n89AlkurV0LCG/RY8wb4AQCmgeaiPwtrzoJxwLmnwwjxrptzwmQRE0XJJS7Rp01tBB4ISwb4AQLmj\nx0aOwtrboZxwLmnwsndgXxNQRAAQNSsUj78TdBAAAJQEZmzkgKPU4cC5pMFj1gwAFG7MmOqgQwAA\noCRQ2MgB64SHQzk1BgtrHwtmzQBA4d59d1fQIQAAUBIobOSAo9ThUQ7nkoZ5hhCzZgCgUJdp9GiK\nwQAAeIHCRg44Sg0/hXmGUL6zZsI6AwUA/DVX0iy99x59ugAA8AKFjRxwlBp+CvsMoVxnzYR5BgoA\n+Os+SUs0Zkw4Ps8BAIg6Chs5KKfeDgheqc0QCvMMFADw3wq9++68oIMAAKAkUNjIUTn0doiCcjil\nodRmCIV9BgoA+G3ixJqgQwAAoCRQ2EDklMspDaU2Q6jUZqAAQKFGjNgbdAgAAJQEc84FHYMvzMyV\ny3MtdQ0NS9XWtjzDeLNWr14WQEQYikwFqdraxVq5MrrFGqBUmJmccxZ0HOXAzJzkJC3WSSdt0x/+\ncFvQIQEAEAqF5CPM2EDkcEpDNJXaDBQAyF+zpNkaO/bxoAMBAKAkUNhA5HBKQ3TRowYAJCk5u7Cq\nak3AcQAAUBqGBR0AkKumpnrV1i7pN5Zsqnl6QBEBAJAb9lsAAHiHHhuIpFisQ6tWrUk7peF0ZgIA\nQJ7oseEfM3MNDUvZbwEAMEAh+QiFDQAAyhyFDf+QjwAAkFkh+QinogAAAAAAgMiisAEAAAAAACIr\nkMKGmX3fzF40s/81s383s0PTrrvOzF41s5fMrD5t/GQzey513cq08ZFmdl9q/Ckz+5DfzwcAAEQP\n+QgAAKUhqBkbbZI+4Zz7lKRXJF0nSWY2TdJcSdMkzZZ0s5n1nmNzi6RLnHPHSTrOzGanxi+R1JUa\n/ydJ3/XvaURDLNahhoalqqtrUUPDUsViHUGHBABAGASSj7AvBgDAW5VBbNQ5l75w+9OSzk1dPlvS\nPc65vZI2mNkfJZ1qZhslVTvn1qZud6ekL0haLeksSd9Kjf9C0k3Fjj9KYrEOLVr0qNavX9E3tn59\ncqlUurEDAMpZUPlIW9ty9sUAAHgoDD02/k7SI6nLkyRtSrtuk6TJGcY7U+NK/f+GJDnnEpLeMbPD\nihlwlLS2tvUrakjS+vUrtGrVmiz3AACgLPmaj7AvBgDAO0WbsWFmayTVZLhqsXPuodRtlkja45y7\nu1hxlLvduzO/xd3dFT5HAgCA/8Kcj7AvBgDAG0UrbDjnTh/sejO7WNIZkj6bNtwp6ei0n6coeWSk\nM3V54HjvfY6RtNnMKiUd6px7O9M2W1pa+i7X1dWprq7u4E8k4kaOTGQcr6rq8TkSAEBYtLe3q729\nPegwfBHGfERqkSRt2vS42tvLIx8BAGAgL/MRc8558kA5bTTZaOsfJc1yzr2VNj5N0t2STlFySuev\nJR3rnHNm9rSkJklrJcUktTrnVpvZFZL+zDn3NTObJ+kLzrl5GbbpgniuQcvUY6O2drFWrpzNeb0A\nAEmSmck5Zwe/ZWkJKh+RHPtiAAAGKCQfCaqw8aqkEZJ6j2Q86Zy7InXdYiXPc01IWuScezQ1frKk\n2yWNkvSIc64pNT5S0k8lnSSpS9I859yGDNssy8KGlCxurFq1Rt3dFaqq6lFj4+kkUgCAPmVc2Agk\nH2loWMq+GACAASJX2AhCORc2gGKIxTrU2tqm3bsrNXJkQk1N9STpQESVa2EjCGbm6uuX8JkJAMAA\nheQjgSz3CiDaWEYYAPLHcq8AAHgrDMu9AogYlhEGgMLwmQkAgHcobADIGcsIA0Dh+MwEAMAbFDYA\n5IxlhAGgcHxmAgDgDQobAHLW1FSv2tol/cZqaxersfH0gCICgGjhMxMAAO+wKgqAvLCMMFA6WBXF\nPyz3CgBAZiz3OgQUNgAAyIzChn/IRwAAyKyQfIRTUQAAAAAAQGRR2AAAAAAAAJFFYQMAAAAAAEQW\nhQ0AAAAAABBZFDYAAAAAAEBkUdgAAAAAAACRRWHj/2fv3uPjLOu8j39+bdqm0JZDOaRQoBiOXWRB\nFmR9MAmuJF0KCOsjLY8g7FJEoQfl2VVoG5u1VEUXlJaDsmUVRQ6yPiI2UFLFNOhLLAoulINIpEBD\np4VaesCmbdrf88dMmplkJplk5p577pnv+/XKqzPXzNxzzdWZ+/rdv/u6rltEREREREREIkuJDRER\nERERERGJLCU2RERERERERCSylNgQERERERERkchSYkNEREREREREIkuJDRERERERERGJLCU2RERE\nRERERCSylNgQERERERERkchSYkOkiDU3t9HQMJ+6uiYaGubT3NwWdpVERCRH2p+LiIjkV0XYFRCR\n9Jqb25gz53Ha2xftLWtvnwfA1Kk1YVVLRERy1NJyo/bnIiIieRTKiA0zW2hm/2NmfzCzX5jZEUmP\n3WBmfzKzl82sPqn8NDN7PvHYrUnlo8zswUT5U2Z2VKE/T1S0traGXYWiEJV2WLy4JSWpAdDevogl\nS1bkvO2otEGQ1AZqg25qh/IVZjySr/15qdPvc3DUXoOj9hoctdfgqc0KJ6ypKF93979191OAh4EF\nAGY2GZgGTAamAHeYmSVecydwpbsfCxxrZlMS5VcCGxPl3wRuKuDniBT9sOKi0g47dqQfUNXZOTzn\nbUelDYKkNlAbdFM7lLVQ45F87M9LnX6fg6P2Ghy11+CovQZPbVY4oSQ23H1r0t0xwDuJ2x8D7nf3\nXe6+BngV+KCZTQDGuvuqxPO+D1yYuH0BcE/i9o+Bfwiy7iKFMmpUV9ryysrdBa6JiEhpCjse0f5c\nREQkP0JbPNTMFpnZG8AVwFcTxYcBa5OethY4PE15R6KcxL9vArh7F7DZzA4MruYihTF7dj3V1fNS\nyqqr5zJr1jkh1UhEpPSEFY9ofy4iIpI/5u7BbNhsBVCV5qG57v6zpOddDxzv7v9sZkuAp9z9h4nH\nlgKPAWuAr7n7OYnyDwNfcPfzzex5oMHd30o89ipwhrv/pVd9gvmgIiIiJcDdbeBnRY/iERERkegY\najwS2FVRujv9LNwHPJq43QEckfTYROJnRjoSt3uXd7/mSOAtM6sA9usdRCTqU5IBm4iIiGSmeERE\nRKT0hXVVlGOT7n4MeDZx+xFgupmNNLOjgWOBVe4eA7aY2QcTi3ddBvw06TWXJ27/b+AXgX8AERER\niTzFIyIiIqUhsBEbA/iqmR0P7Abagc8CuPuLZvYj4EWgC7jGe+bKXAN8DxgNPOruyxPldwM/MLM/\nARuB6QX7FCIiIhJlikdERERKQGBrbIiIiIiIiIiIBC20q6IUkplNMbOXzexPZvbFsOtTCGb2X2a2\nPrGYWXfZgWa2wsxeMbMWM9s/zDoGzcyOMLNfmtkLZrbazGYnysumHcys0sx+a2Z/MLMXzeyrifKy\naYNuZjbczJ41s58l7pdjG6wxs+cS7bAqUVZW7WBm+5vZf5vZS4nfxAfLqQ3M7PjE/3/332Yzm11O\nbRCmcoxHcpFunyU9FOsNTob2ajKztUn7xClh1rGYKI4enH7aS9+xNII4Rin5xIaZDQduA6YAk4FL\nzOzEcGtVEN8l/pmTXQ+scPfjiM/9vb7gtSqsXcDn3f1vgDOBaxP/92XTDu7eCZzt7qcAJwNnm9lZ\nlFEbJJlDfFh59zC1cmwDB+rc/VR3PyNRVm7tcCvx6QMnEv9NvEwZtYG7/zHx/38qcBrwV+AnlFEb\nhKWM45FcpNtnSQ/FeoOTrr0cuKV7v5g0tUwURw9WpvbSdyyNII5RSj6xAZwBvOrua9x9F/AA8QXC\nSpq7Pwls6lV8AXBP4vY9wIUFrVSBuXvM3f+QuL0NeAk4nPJrh78mbo4EhhP/XpRVG5jZROBcYCnQ\nfUWCsmqDJL2vyFA27WBm+wEfdvf/AnD3LnffTBm1QS8fJd4/vkn5tkEhlWU8kge6ikwGivUGJ0N7\ngb5jaSmOHpx+2gv0HUsr38co5ZDYOBx4M+n+Wnq+ZOXmUHdfn7i9Hjg0zMoUkplNAk4FfkuZtYOZ\nDTOzPxD/rL909xcoszYAvgn8G7Anqazc2gDiZw1+bma/M7OrEmXl1A5HA2+b2XfN7Bkz+08z25fy\naoNk04H7E7fLtQ0KSfHI4KXbZ0n/9FsevFlm9j9mdremVaRXznH0UCS111OJIn3H0sj3MUo5JDa0\nOmoaidXdy6JtzGwM8GNgjrtvTX6sHNrB3fckhnlNBGrM7Oxej5d0G5jZecAGd3+WDBnzUm+DJP8r\nMQXhH4kPkfxw8oNl0A4VwAeAO9z9A8B79BriWAZtAICZjQTOBx7q/Vi5tEEI1KaD1+8+S/qn33JW\n7iSe9D4FWAfcHG51ik+5x9GDlWiv/ybeXtvQdyyjfB+jlENiowM4Iun+EcTPkpSj9WZWBWBmE4AN\nIdcncGY2gvjO+Afu/nCiuOzaASAx5L6Z+Lz6cmqDDwEXmNlrxM9Of8TMfkB5tQEA7r4u8e/bxNdV\nOIPyaoe1wFp3fzpx/7+JJzpiZdQG3f4R+H3iuwDl9T0Ii+KRQcqwz5L+6bc8CO6+wROIT1fVdyyJ\n4ujBSWqve7vbS9+xgeXrGKUcEhu/A441s0mJM1TTgEdCrlNYHgEuT9y+HHi4n+dGnpkZcDfwort/\nK+mhsmkHMzuoe8ibmY0GzgGepYzawN3nuvsR7n408aH3T7j7ZZRRGwCY2T5mNjZxe1+gHnieMmoH\nd48Bb5rZcYmijwIvAD+jTNogySX0TEOBMvoehEjxyCD0s8+S/um3PAiJA6duF6Hv2F6KowcnU3vp\nO5ZeEMcoFk8elTYz+0fgW8QXJbnb3b8acpUCZ2b3A7XAQcTnJ30J+CnwI+BIYA1wsbu/G1Ydg5ZY\nWbcNeI6eYUw3AKsok3Yws/cTX3hnWOLvB+7+DTM7kDJpg2RmVgv8X3e/oNzawMyOJn7GE+JTMn7o\n7l8tw3b4W+JnTEYC7cA/E+8byqkN9gVeB47uHlZcbt+DsJRjPDJUmfZZIVap6CjWG5w07bUAqCM+\nRcCB14Crk+b3lzXF0YOTob3mEj+RoO9YL0Eco5RFYkNERERERERESlM5TEURERERERERkRKlxIaI\niIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyKS\nN2a2n5l9NnF7gpk9FHadREREpDyZ2SQzez7seohI8JTYEJF8OgC4BsDd17n7J0Kuj4iIiIiIlDgl\nNkQkn74GVJvZs2b2o+6zJGZ2hZk9bGYtZvaamc00s381s2fM7DdmdkDiedVm9piZ/c7M2szs+FA/\njYiIiERdhZnda2YvmtlDZraPma0xswMBzOzvzOyXidu1iRjm2USMMibcqotItpTYEJF8+iLQ7u6n\nAv/W67G/AS4CTgcWAVvc/QPAb4BPJZ5zFzDL3f8u8fo7ClJrERERKVXHA7e7+2RgC/GRpZ7huf8X\nuCYRx5wFbC9MFUUkVxVhV0BESopluA3wS3d/D3jPzN4FfpYofx442cz2BT4EPGS296Ujg6ysiIiI\nlLw33f03idv3AnP6ee6vgW+a2Q+B/+fuHYHXTkTyQokNESmUHUm39yTd30N8XzQM2JQ4SyIiIiKS\nD8mjM4x43NFFz8j1yr1PdL/JzJYBU4Ffm1mDu/+xYDUVkSHTVBQRyaetwNhBvsYA3H0r8JqZ/W8A\nizs5z/UTERGR8nKkmZ2ZuP1/gF8Ba4C/S5R9vPuJZlbt7i+4+9eBp4lPYxGRCFBiQ0Tyxt03Ej/D\n8TzwdXrOkjipZ0x63+6+/0ngSjP7A7AauCDYGouIiEgJc+CPwLVm9iKwH/H1u/4duNXMniY+eqM7\nDpljZs+b2f8AO4HHQqiziAyBuWdaO0dEREREREREpLhpxIaIiIiIiIiIRJYSGyIiIiIiIiISWUps\niIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEh\nIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaI\niIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIi\nIiIiIiISWaEmNsxsjZk9Z2bPmtmqRNmBZrbCzF4xsxYz2z/p+TeY2Z/M7GUzq08qP83Mnk88dmsY\nn0VERESiSfGIiIhItIU9YsOBOnc/1d3PSJRdD6xw9+OAXyTuY2aTgWnAZGAKcIeZWeI1dwJXuvux\nwLFmNqWQH0JEREQiTfGIiIhIhIWd2ACwXvcvAO5J3L4HuDBx+2PA/e6+y93XAK8CHzSzCcBYd1+V\neN73k14jIiIikg3FIyIiIhEVdmLDgZ+b2e/M7KpE2aHuvj5xez1waOL2YcDapNeuBQ5PU96RKBcR\nERHJhuIRERGRCKsI+f3/l7uvM7ODgRVm9nLyg+7uZub5eKN8bUdERKQUuXvvEQvlRPGIiIhIERhq\nPBLqiA13X5f4923gJ8AZwHozqwJIDOvckHh6B3BE0ssnEj8z0pG4nVzekeH99BfxvwULFoReB/3p\n/1F/+r8stb9y54pHSvJP+ye1d6n+qa3V1qX6l4vQEhtmto+ZjU3c3heoB54HHgEuTzztcuDhxO1H\ngOlmNtLMjgaOBVa5ewzYYmYfTCzedVnSa0REREQyUjwiIiISfWFORTkU+EliIfEK4Ifu3mJmvwN+\nZGZXAmuAiwHc/UUz+xHwItAFXOM9aZ1rgO8Bo4FH3X15IT+IiIiIRJbiERERkYgLLbHh7q8Bp6Qp\n/wvw0Qyv+QrwlTTlvwfen+86SvGpq6sLuwqSB/p/LB36v5SoUzxSurR/Kiy1d+GorQtHbR0dlutc\nlqgwMy+XzyoiIjIYZoaX9+KhBaN4REREJL1c4pGwr4oiIiJ5kBhGLzIgHVSLiEjQFJfIQPIdjyix\nISJSInTAKgNRoCkiIoWiuEQyCSIeCfVyryIiIiIiIiIiuVBiQ0REREREREQiS4kNEREREREREYks\nJTZERCQyfvjDH9LQ0JDx8bq6Ou6+++6c36e1tZUjjjgi5+0EZdiwYfz5z38G4IorrqCxsTHkGomI\niJQXxSTFRYkNERGJjE9+8pM8/vjjGR83s7JbILMcP7OIiEjYFJMUFyU2RESkoLq6usKuQsnRyvMi\nIiKDp5gkO1FoJyU2RERKXHNzGw0N86mra6KhYT7NzW0F38akSZP4+te/zsknn8zYsWPZs2cPTz31\nFB/60Ic44IADOOWUU1i5cuXe53/ve9+jurqacePG8b73vY/77rtvb/mHP/zhvc9bsWIFJ5xwAvvv\nvz+zZs1KOcBvamrisssu23t/zZo1DBs2jD179gDw3e9+l8mTJzNu3Diqq6u56667Mtb/pptuYuLE\niYwbN44TTjiBJ554YlCfv9tRRx3FM888A8SHsA4bNoyXXnoJgLvvvpuLLroIgFWrVvH3f//3HHDA\nARx22GHMmjWLXbt2Dbj9rVu3cvbZZ/O5z31uSPUTEREJkmKSuGKISdrb2/nIRz7CQQcdxMEHH8yl\nl17K5s2bh9xOg/kMgXD3sviLf1SRvpYtW+n19fO8tnaB19fP82XLVoZdJZFBy7SPW7ZspVdXz3Xw\nvX/V1XMH9T3PxzaOOuooP/XUU33t2rXe2dnpa9eu9fHjx/tjjz3m7u4rVqzw8ePH+zvvvOPbtm3z\ncePG+SuvvOLu7rFYzF944QV3d//ud7/rZ511lru7v/322z527Fj/8Y9/7F1dXf7Nb37TKyoq/O67\n73Z396amJr/00kv31uG1115zM/Pdu3e7u3tzc7P/+c9/dnf3lStX+j777OPPPPOMu7v/8pe/9IkT\nJ7q7+8svv+xHHHGEr1u3zt3dX3/9dW9vb8/6syf71Kc+5TfffLO7u1911VV+zDHH+J133unu7pdd\ndpl/61vfcnf33//+9/7b3/7Wd+/e7WvWrPETTzxx72Pu7ma2tw5XXHGFNzY2+jvvvOOnn366NzY2\nZnz/TN+TRHnofXU5/CkeEQmH4r3CSrevU0xSXDHJq6++6j//+c99586d/vbbb3tNTY1/7nOfG1I7\nDfQZegsiHtGIDSlrzc1tzJnzOC0tN7JyZRMtLTcyZ87jQ8oeixSjxYtbaG9flFLW3r6IJUtWFHQb\nZsbs2bM5/PDDGTVqFPfeey/nnnsuU6ZMAeCjH/0of/d3f0dzczNmxrBhw3j++efZvn07hx56KJMn\nT+6zzUcffZSTTjqJf/qnf2L48OF87nOfo6qqau/j8f4xs3PPPZejjz4agJqaGurr63nyySf7PG/4\n8OHs2LGDF154gV27dnHkkUfyvve9L+vPnqy2tnbv2Y1f/epX3HDDDXvvt7W1UVtbC8AHPvABzjjj\nDIYNG8ZRRx3Fpz/96ZSzIr11dHRQV1fHtGnT+PKXvzykuomIlCrFe8VBMUlmYcQk1dXV/MM//AMj\nRozgoIMO4vOf/3xKrDGYdhrMZwiKEhtS1vKxcxQpZjt2VKQt7+wcXtBtACkrer/++us89NBDHHDA\nAXv/fv3rXxOLxdhnn3148MEH+fa3v81hhx3Geeedxx//+Mc+23vrrbeYOHFixvcYyGOPPcaZZ57J\n+PHjOeCAA3j00UfZuHFjn+cdc8wxfOtb36KpqYlDDz2USy65hHXr1vV53htvvMHYsWMZO3Ys48aN\nS/ueNTU1PPnkk8RiMXbv3s0nPvEJfv3rX/P666+zefNmTjnlFABeeeUVzjvvPCZMmMB+++3HvHnz\n0tYN4sFSc3MznZ2dXH311Vl/fhGRcqF4rzgoJsksjJhk/fr1TJ8+nYkTJ7Lffvtx2WWX9XnPbNtp\nMJ8hKEpsSFnL185RpFiNGpV+safKyt0F3QaQsjL4kUceyWWXXcamTZv2/m3dupUvfOELANTX19PS\n0kIsFuOEE07gqquu6rO9ww47jDfffHPvfXdPuT9mzBj++te/7r3f3fEC7Nixg49//ON84QtfYMOG\nDWzatIlzzz034xmVSy65hCeffJLXX38dM+OLX/xin+cceeSRbN26la1bt7Jly5a02znmmGPYZ599\nWLJkCbW1tYwdO5aqqiruuuuulHm6n/3sZ5k8eTKvvvoqmzdvZtGiRXvn4fZmZlx11VU0NDRw7rnn\npnxmERFRvFcsFJMUV0wyd+5chg8fzurVq9m8eTM/+MEP+sQa2bbTYD9DEJTYkLKWr52jSLGaPbue\n6up5KWXV1XOZNeucgm6jt0svvZSf/exntLS0sHv3bjo7O2ltbaWjo4MNGzbw05/+lPfee48RI0aw\n7777Mnx43+Dz3HPP5YUXXuAnP/kJXV1dLF68OCVQOOWUU2hra+PNN99k8+bNfPWrX9372M6dO9m5\ncycHHXQQw4YN47HHHqOlpSVtXV955RWeeOIJduzYwahRo6isrExbn2zV1tZy22237Z12UldXl3If\nYNu2bYwdO5Z99tmHl19+mTvvvDPj9rqDhttuu43jjz+e888/n87OziHXT0Sk1CjeKw6KSYorJtm2\nbRv77rsv48aNo6Ojg2984xtDbqfBfIagKLEhZS2InaNIMZk6tYZbb22goaGR2tomGhoaufXWKUyd\nWlPQbfQ2ceJEfvrTn/KVr3yFQw45hCOPPJKbb74Zd2fPnj1885vf5PDDD2f8+PE8+eSTew/sk68J\nf9BBB/HQQw9x/fXXc9BBB/Hqq69y1lln7X2Pj370o0ybNo2TTz6Z008/nfPPP3/va8eOHcvixYu5\n+OKLOfDAA7n//vv52Mc+llLH7ufu2LGDG264gYMPPpgJEybwzjvvpAQkg1VbW8u2bduoqalJex/g\nP/7jP7jvvvsYN24cn/70p5k+fXrKWZPet7vv33XXXUycOJELL7yQHTt2DLmOIiKlRPFecVBMUlwx\nyYIFC3jmmWfYb7/9OP/88/n4xz+eEl8Mpp2y+QxBs0IODwmTmXm5fFYZnObmNpYsWUFn53AqK3cz\na9Y5Oe0cRcJgZgUd7ifRlOl7kijPHM1I3igeEQmH4r3CUlwi/QkiHlFiQ0SkBCiAkGwosRE+xSMi\nUg4Ul0h/gohHNBVFRERERERERCJLiQ0RERERERERiSwlNkREREREREQkspTYEBEREREREZHIkXmn\nCAAAIABJREFUCj2xYWbDzexZM/tZ4v6BZrbCzF4xsxYz2z/puTeY2Z/M7GUzq08qP83Mnk88dmsY\nn0NERESiS/GIiIhIdIWe2ADmAC8C3cuiXg+scPfjgF8k7mNmk4FpwGRgCnCH9Vxo907gSnc/FjjW\nzKYUsP4iIiISfYpHREREIqoizDc3s4nAucAi4LpE8QVAbeL2PUAr8WDiY8D97r4LWGNmrwIfNLPX\ngbHuvirxmu8DFwLLC/IhRESKRM+xlYgMhuIREZH8U1wihRRqYgP4JvBvwLikskPdfX3i9nrg0MTt\nw4Cnkp63Fjgc2JW43a0jUS4iUjZ0rXiRnCgeERHJI8UlUmihTUUxs/OADe7+LJA2nefxX4R+FSIi\nIhIIxSMiIiLRF+aIjQ8BF5jZuUAlMM7MfgCsN7Mqd4+Z2QRgQ+L5HcARSa+fSPzMSEfidnJ5R7o3\nbGpq2nu7rq6Ourq6/HwSERGRCGltbaW1tTXsahQLxSMiIiIhyGc8YsUwTMjMaoF/dffzzezrwEZ3\nv8nMrgf2d/frE4t13QecQXxo58+BY9zdzey3wGxgFdAMLHb35b3ew4vhs4qIiBQbM8Pdy34ytOIR\nERGR8OQSj4S9xkay7l7+a8CPzOxKYA1wMYC7v2hmPyK+YnkXcE1SZHAN8D1gNPBo7yBCREREJEuK\nR0RERCKmKEZsFILOkIiIiKSnERuFo3hEREQkvVzikdAWDxURERERERERyZUSGyIiIiIiIiISWUps\niIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEh\nIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaI\niIiIiIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIi\nIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIiIiISWUpsiIiI\niIiIiEhkhZbYMLNKM/utmf3BzF40s68myg80sxVm9oqZtZjZ/kmvucHM/mRmL5tZfVL5aWb2fOKx\nW8P4PCIiIhI9ikdERESiL7TEhrt3Ame7+ynAycDZZnYWcD2wwt2PA36RuI+ZTQamAZOBKcAdZmaJ\nzd0JXOnuxwLHmtmUwn4aERERiSLFIyIiItEX6lQUd/9r4uZIYDiwCbgAuCdRfg9wYeL2x4D73X2X\nu68BXgU+aGYTgLHuvirxvO8nvUZERESkX4pHREREoi3UxIaZDTOzPwDrgV+6+wvAoe6+PvGU9cCh\niduHAWuTXr4WODxNeUeiXERERGRAikdERESirSLMN3f3PcApZrYf8LiZnd3rcTczD6d2IiIiUg4U\nj4iIiERbqImNbu6+2cyagdOA9WZW5e6xxLDODYmndQBHJL1sIvEzIx2J28nlHenep6mpae/turo6\n6urq8vURREREIqO1tZXW1tawq1F0FI+IiIgUTj7jEXMP5wSEmR0EdLn7u2Y2Gngc+HegAdjo7jeZ\n2fXA/u5+fWKxrvuAM4gP7fw5cEziLMpvgdnAKqAZWOzuy3u9n4f1WUWiprm5jcWLW9ixo4JRo7qY\nPbueqVNrwq6WiATEzHB3G/iZpUfxiESB+mURKQe5xCNhjtiYANxjZsOIr/XxA3f/hZk9C/zIzK4E\n1gAXA7j7i2b2I+BFoAu4JikyuAb4HjAaeLR3ECEi2WtubmPOnMdpb1+0t6y9fR6AgigRKUWKR6So\nqV8WERlYaCM2Ck1nSESy09Awn5aWG9OUN7J8+cIQaiQiQSvnERuFpnhEBkv9soiUi1zikVCviiIi\nxWfHjvQDuTo7hxe4JiIiIqJ+WURkYEpsiEiKUaO60pZXVu4ucE1ERERE/bKIyMCU2BCRFLNn11Nd\nPS+lrLp6LrNmnRNSjURERMqX+mURkYFpjQ0R6aO5uY0lS1bQ2TmcysrdzJp1jhYoEylhWmOjcBSP\nyFCoXxaRcpBLPKLEhoiISJlTYqNwFI+IiIikp8VDRURERERERKQsKbEhIiIiIiIiIpGlxIaIiIiI\niIiIRJYSGyIiIiIiIiISWUpsiIiIiIiIiEhkKbEhIiIiIiIiIpGlxIaIiIiIiIiIRJYSGyIiIiIi\nIiISWRVhV0AKr7m5jcWLW9ixo4JRo7qYPbueqVNrwq5WaNQeIiIiIqVnKDGe4kKRaFJio8w0N7cx\nZ87jtLcv2lvW3j4PoCx32moPERERkdIzlBhPcaFIdGkqSplZvLglZWcN0N6+iCVLVoRUo3CpPURE\nRERKz1BiPMWFItGlxEaZ2bEj/SCdzs7hBa5JcVB7iIiIiJSeocR4igtFokuJjTIzalRX2vLKyt0F\nrklxUHuIiIiIlJ6hxHiKC0WiS4mNMjN7dj3V1fNSyqqr5zJr1jkh1Shcao/0mpvbaGiYT11dEw0N\n82lubgu7SiIiIpGlfrXwhhLjKS4UiS4tHlpmuhc+WrKkkc7O4VRW7mbWrClluyCS2qMvLZwlIiKS\nP+pXwzGUGE9xoUh0mbuHXYeCMDMvl88qkouGhvm0tNyYpryR5csXhlAjEQmameHuFnY9yoHikfKj\nflVEJDu5xCOaiiIiKbRwloiISP6oXxURCZ4SGyKSQgtniYiI5I/6VRGR4IWW2DCzI8zsl2b2gpmt\nNrPZifIDzWyFmb1iZi1mtn/Sa24wsz+Z2ctmVp9UfpqZPZ947NYwPo9IqdDCWSJSThSPSNDUr4qI\nBC+0NTbMrAqocvc/mNkY4PfAhcA/A++4+9fN7IvAAe5+vZlNBu4DTgcOB34OHOvubmargJnuvsrM\nHgUWu/vyXu+nOa0iWWpubmPJkhVJC2edo4WzREpYOa+xoXhECkH9qojIwHKJR4pm8VAzexi4LfFX\n6+7rE8FGq7ufYGY3AHvc/abE85cDTcDrwBPufmKifDpQ5+6f6bV9BRIiIiJplHNiozfFIyIiIuGI\n/OKhZjYJOBX4LXCou69PPLQeODRx+zBgbdLL1hI/U9K7vCNRLiIiIpI1xSMiIiLRlH6Z5gJKDPv8\nMTDH3bea9SRoEsM683Zao6mpae/turo66urq8rVpERGRyGhtbaW1tTXsahQVxSMiIiKFlc94JNSp\nKGY2AlgGPObu30qUvUx86GbMzCYAv0wM/bwewN2/lnjecmAB8aGfv0wa+nkJ8aGjGvopIiKShXKf\niqJ4REREJHyRnIpi8VMhdwMvdgcRCY8AlyduXw48nFQ+3cxGmtnRwLHAKnePAVvM7IOJbV6W9BoR\nERGRjBSPiIiIRF+YV0U5C2gDngO6K3EDsAr4EXAksAa42N3fTbxmLvAvQBfxoaKPJ8pPA74HjAYe\ndffZad5PZ0hERETSKOcRG4pHREREikNJXBUlaAokRERE0ivnxEahKR4RERFJL5JTUURERERERERE\nchX6VVFERMpZc3Mbixe3sGNHBaNGdTF7dj1Tp9aEXS0REQmZ+gcRkewpsSEiEpLm5jbmzHmc9vZF\ne8va2+cBKHgVESlj6h9ERAZHa2yIBEhnW6Q/DQ3zaWm5MU15I8uXLwyhRlKutMZG4SgekWzk2j8o\n/hCRKMolHtGIDZGANDe3MWPGw8Rit+wte+6561i6VGdbJG7HjvS74M7O4QWuiYiIFJNc+geN9hg6\nJYREokuJDZGANDY+QCx2R0pZLHYLX/rSteokBYBRo7rSlldW7i5wTUREpJjk0j8sXtySktQAaG9f\nxJIljYo/+qGEkEi0DXhVFDM7y8zGJG5fZma3mNlRwVdNJNpee+29DOXbClwTKVazZ9dTXT0vpay6\nei6zZp0TUo1EipfiESknufQPGg04NJkTQitCqpGIDEY2IzbuBE42s78FrgOWAt8HaoOsmEjUme3I\n8MjOvGxfwyWjr/v/a8mSRjo7h1NZuZtZs6bo/1EkPcUjUtTy2S/n0j9oNODQKCEkEm3ZJDa63N3N\n7ELgdndfamZXBl0xkaibNGkMmzbNA5Kz/3OZNGnfnLet4ZKlY+rUGv2fiWRH8YgUrSD65aH2D7Nn\n19PePi+lLvHRHlOGVI9yoYSQSLQNeFUUM2sDlgP/DHwYeBv4g7u/P/jq5Y9WIe+hM/2F0dzcxqWX\n/ifvvjsJGA7sZv/9X+Peez+dc3vrahoikk9RuCqK4hEpZn375TaghQMOeJPTTz+i4LFWc3MbS5as\nSBrtcY5ivQGkS05VV8/l0ksn8pvfvKW4WaQAgr4qyjTg/wD/4u4xMzsS+I+hvJmET2f6C6uycmSv\n+6Pysl0NlxSRMqR4RIpWar/cBjwOLGLTJmhpKXyspdGAg5du+s+ZZ07k3ns7FDeLRMCAiQ13Xwfc\nnHT/DeCeICslwdFK2YWzeHELsdjdKWWxGHlpaw2XFJFyo3hEillqv9xC6jRUxVpR0Tsh1NAwX3Gz\nSERkvCqKmW0zs60Z/rYUspKSPzrTXzhBtrWupiEi5ULxiERBar+sWKtUKG4WiY6MIzbcfUwhKyKF\noTP9fQW15kiQba2raUg2mpru4LbbVtLVNZqKiu3MnFlLU9M1YVerX1oDSHpTPBKsYvvNFVt9st2P\nJvfLq1b9iU2b+m6rnGOtqFLcLBIh7p7VH3AIcGT3X7avK5a/+EeVZctWenX1XAff+1ddfYMvW7Yy\n7KqFIn17zM1Le6itJUwLFtzuFRVXp3z/Kiqu9gULbg+7ahkF+XuU/iX6yND76mz+FI/kT7H95oqt\nPkPdj6r/Lx36vxQprFzikWyuinIB8TmthwEbgKOAl9z9b/KfZgmOViHvoZWyewR9dRG1tYTloIOm\nsXHjg33Kx4+fzjvvPBBCjQamq/2EJyJXRVE8kmfF9psrtvrksh9V/1869H8pUjhBXxXlRuDvgRXu\nfqqZnQ1cNpQ3k+KglbJ7BD13Um0tYenqGp2hvLLANcme5jLLABSP5Fmx/eaKrT657EfV/5cO/V+K\nREM2iY1d7v6OmQ0zs+Hu/kszuzXwmpW5YptjWqo0d1JKVUXF9gzlnQWuSfb0e5QBKB7Js2L7zRVb\nfaKwH1W8KCISl01iY5OZjQWeBH5oZhuAbcFWq7w1N7cxZ87jumZ2AcyeXU97+7yUto5fXWRKiLUS\nyd3MmbUsWvQZurq+vbesouJqZs4s3n2Ifo8yAMUjeVZsv7liq0+x70cVL4qI9MhmjY0xwHbil4b9\nJDAO+KG7bwy+evlTTHNaB1Jsc0xLneZOSqmKr+bfRldXJRUVncycWROJq6Lo91h4EVljQ/FIAIrt\nN1ds9Snm/ajiRREpNbnEIwMmNkpFsQUS/amra2LlyqY+5bW1TbS29i0XkWBpqK8MJOrfkSgkNkpF\nlOKRqMvH77KYf9uKF0Wk1AS6eKiZbQO6e+CRwAhgm7uPG8obysCKbY6plJ9iDuQKTUN9ZSD6jhSG\n4hEZjHz8LrPZRiH7y97vtWXLX9I+T/GiiJSlwVwblvjwzwuBrw31+rK9tvdfwHrg+aSyA4EVwCtA\nC7B/0mM3AH8CXgbqk8pPA55PPHZrhvcaxBV0w7Vgwe0+fPiMlGtmDx9+5YDXTRfJh2XLVnpV1edT\nvn9VVZ8v22u219fPS2mL7r+GhvlhV02KRCl8R8jhuvFh/OUzHilkLOIRi0eibKDf5bJlK72+fp7X\n1i7w+vp5afu4bLZRXT035bHq6rmB9Jep77XSYZ6PGHG+jxx5Va/3v6Fs+2sRib5c4pFhg0yC7HH3\nh4F8reL03TTbup74pdyOA36RuI+ZTQamAZMTr7nDzLqHqdwJXOnuxwLHmlmkV5p75JFn2L17D9AI\nNAGN7N7tPPLIM+FWTMpCY+MDxGK3pJTFYrfwpS89GFKNwlVslx+U4qPvSOHlOR5RLFKC+vtddo/E\naGm5kZUrm2hpuZE5cx6nubkt620ALF7ckjKaA6C9fRFLlqzIwydI1fNebcDjwI3s2vUIO3deyujR\n0zjppM/R0NDIrbdO0UgxESlL2UxF+XjS3WHEz0ikv/7VILn7k2Y2qVfxBUBt4vY9QCvxgOJjwP3u\nvgtYY2avAh80s9eBse6+KvGa7xM/i7M8H3UMw5o124AH0pRfUvjKSNl57bX3MpSX58UHNDVMBqLv\nSGEEFY8oFilN/f0uMyckGlOSAgP9tguZ1Ox5rxYgue41bN9ew+GHa8FQESlv2YzYOB84L/FXD2wl\n3rEH5VB3X5+4vR44NHH7MGBt0vPWAoenKe9IlEeW+6gMj4wsaD2kuDU3t9HQMJ+6uiYaGub3OdM0\nVGY7MjyyMy/bj5rZs+uprp6XUha//OA5IdVIio2+IwVTyHik7GORqOvvd5ltQmKg33Yhk5o975W+\n7k899WZeYwERkagZcMSGu19RgHpkem83s7JbOvzAA513301XXvi6SHEKcrHCSZPGsGnTPFLPCM1l\n0qR9c9puVHW355IljUmXH9RQX+mh70hhhBWPlGssEnX9/S4XL25J+5reCYmBftuzZ9fT3j4vpS+O\nJz7yPwtp9ux6nnvuOmKxfdI+vnnzEbS0LNTCxSJStjImNsxsSdJdByzpNu4+O6A6rTezKnePmdkE\nYEOivAM4Iul5E4mfHelI3E4u70i34aampr236+rqqKury1+t82i//UYC1wHJ6xx8nnHjRuRl+1G8\n4kUU6xykbIfRDsXChZ9ixox7iMUageHAbqqqYixceEVO242yqVNryvr7JgOL2nektbWV1tbWsKuR\nlZDikcBiEYhOPBJ1mX6Xg0lIZNpGd1zS1bWBiorzGTlyOKNHj+LSS2sD2RdMnVrDhAnfJxbbAnyW\n+JIu3a4GPgnkLxYQESmEfMYj/Y3Y+H3i3w8RXyTrQeLBxCeAF/Ly7uk9AlwO3JT49+Gk8vvM7Bbi\nwzuPBVYlzqRsMbMPAquAy4DF6TacHEgUs3HjJgIfIb54aPzAEi5i3Lgnct52FC9LGMU6By0+jLaN\n+FzbCqALqM/LvN6pU2tYuhSWLFlBZydUVsKsWVfkpa2VoBIpDr0Ppv/93/89vMoMLIx4JLBYBKIT\nj0RJf/1L92NvvbWNdevWUVW1P+PG7eYDH7iWsWMPHvQoq3RxSVfXPP761wbuvfdxTj+9LZC+LR4f\nNhHv/5NjRICe99PCxSISFXmNRwa6bArwW2BE0v0RwG+HehmWXtu+H3iL+OT9N4F/Jn6JtZ+T/hJr\nc4FXiV9irSGpvPsSa68CizO816AvNxOWU0/9bNrLi33gA9fkvO0oXpYwinUO2qmnXukwt1ebzPVT\nT70y7KplVMjL4onI4BCBy70GFY8UMhbxiMUjUdFf/5LusXj/uXLIfVCmuATmBxqfDPS+io9EJOpy\niUcGXGMD2B8YB2xM3B+bKMuZu2e6zMdHMzz/K8BX0pT/Hnh/PupUHHYCfdc4cM+0qGP2onhZwijW\nOXgjSf1+ACzC7NowKpOVIKfPiEhZCCQeUSwSff31L+7e57F4/9k45D4oU1wSH0ERXHySbgpNRcXV\ndHV9cu/9oNb4EBEpdtkkNr4GPGNmrYn7tcTHwUlA0k9FmZKXqShRvCxh0HWO4vSIceMOSVs+duzB\nedl+EG2iBJWI5EjxiKQ1tP5l6EmI1LgkeVroS0BbYDFVusVMzzzzb3nqqRV0dj7RZ0pNFOMbEZGh\nyuaqKN81s+XAB4kv1PVFd48FXrMyFu8wa0ieLwlQWbki520XcgXvfAmyzlFdvyPIZE9QbRLFpJpI\ntnQAETzFI5JJf/1LfGRzOn8E5rNly+C/Qj1xSQPwOMkjKCsqPsOZZ5486G1mK9uFiqMa34iIDFV/\nV0U50d1fMrPTiAcQbyYeOszMDnP3ZwpSwzIUv6TXlcRiE+heGLKq6i1mzboi521H8bKEQdY5qtMj\ngkz2BNUmUUyqiWSjubmNGTMeJhbruZLVc89dx9KlOoDIB8UjMpCB+pfej8WXSfksUMO6ddfR3Jy6\n2OdAicru25dffjsbNz6YUpeurm/z1FONef+MgxXV+EZEZKj6G7FxHXAVcDOJS6r1cnYgNZKE/YAb\nk+5fl7ctR+2yhBBcnaM6PSLIZE9QbRLFpJpINhobHyAWuyOlLBa7hS996Vp9v/ND8Yj0K5v+ZcmS\nRn71q3bee68amEL3qNhY7JaUg/1sRzpMnVrDSSc9wcqVfeuT3F+GNZrrrbe2pS3v6Nga+HuLiIQh\nY2LD3a9K/FtXsNoIEM+yJ5/5g74dr+RHlKdHBJXs2bJlQ9ryrVvfznnbUUyqRVmQAbWmXvR47bX3\nMpSnP7CQwVE8El2F3E/01790P1ZX18TKlU19Hk9ORGQa6XDZZZdw+uktKZ9hoBgizOkg69aty1Cu\n2Vv5on5QpLgMuMaGmX0CeNzdt5hZI3AqcKOGfgYnqqMIokjTI9IJ7qo8QVKAkSrIgFpzt1N1daVP\nbOzalb5chkbxSLQU434im5MZmWKwTZuOp6Wlieeeu5IJEx5g3LhD2LIlRlXVdSkno5JjiDCng1RV\n7c/GjX378qqq/QJ933JRjN9vkXKXzVVRvuTuD5nZWcA/AP8BfBs4I9CalTFdBaRwgp4eEcW2DvKq\nPEG1hwKMvoIMqDV3u7e/ki4ZCNvDqU7pUjwSIcW4n8jmZEbPqMXkq510ATGgjVisilis5/VVVVfy\ngQ9cy9ixB/eJIcI4UdXdz3Z0dAKbgBnARLr78okTc1+IXorz+y1S7rJJbHQfTZ8H/Ke7LzOzhQHW\nqezpKiCFFdT0iKi2dVBX5QmyPRRg9BVkQB3fdu+gv75sR5UdddQRvPDCS8A0YDTxhMYujjpqYrgV\nKz2KRyKkGEefDnQyo7m5jXXrdgBXAlWkJiuvAx4Aeq+ncze7dk3jnnum9elvCj3dNV0/G0+6fgSo\nieSI1GI9QVSM32+RcpdNYqPDzO4CzgG+ZmaVwLBgq1XedBWQvoq1Y+tPVNs6qMRakO2hAKOvIAPq\nLVvW0vsShzCPLVvW57ztKBo5cjcwCUheG+k6Ro7cEk6FSpfikQgJcw2r/mKG/k5mxNc4uxu4htT9\nG8R/35enfd3GjScyZ87je7ffbTD9aT7inHT9LCzigAMu4YwzVhRswe58xWzFfIIoymu0iZSqbBIb\nFwMNwDfc/V0zmwD8W7DVEl0FpEfQHVtQSZOorkgeVGItyPZQgNFXkCO/Nm/eSd+gfxFbtqQP+kvf\nSFKTGgC3YHZtGJUpZYpHIiSsNaxyiRl6RqNlWh8n01pTu9Mm6rPtT/MV52SK8U4++XiWL29K+1i+\nY6B8xmzFfIJIa7SJFJ8BExvu/p6ZvQ2cBfyJ+JjjV4OuWLkL6mA7igeAQXZsQSZNXn99zaDKi0kQ\nibUgV2hXgNFXkCO//vIXy1Ce86Yjady4Q9KWjx17cIFrUtoUj0RLGJf4bm5u4/LLb2fjxhOB+UA9\nUJN1zNAzGu2ItI8PG7aBQw65rteV6+YSv3xs+pNE2fSn+YpzBhvjBRED5TNmK+aTcbqEvUjxyeaq\nKE3AacDxwHeJn5r6AfC/Aq1ZGWtubuOyy37Epk237S17+umZ/OAHuR9sR/EAMMiOLcikyZ49u0i3\noGC8vPwEuUL71Kk1PP30am67bRpdXaOpqNjOpZfWln2AEdTIL7NMZy135v29okALPheG4pHoKeQl\nvpub25gx42E2bnwwqfS6xL81aWOG3r+tntFobaTrv0888VhuuulCLr98WiJ5El+Qs3tNqqFeFj3T\niMbBxjmDjfGCiIHyGbMV+8k4XcJepLhkMxXlIuKXVPs9gLt3mNnYQGtV5mbPXsqmTTOIn22IL8y3\nadPFzJlzd8470ChmmIPs2IJMmowYcRDxUdOpVxcZObIj520HLYgDqcMPP5gXXqind3vkY4X25uY2\nvvOdV1MC2u985zpOP72tqL/bUTVp0hg2beob9E+atG9YVQpV0As+z5hxD7HYhL1lzz13D0uXhj/H\nPASKRySjxsYHiMXu6FV6C3AtUNMnZkj32xoxojs52/3b6u6v/gh8llGjHmTx4haqqvZn69YOdu78\nz6QtzmX16rdparqDpqZrsq53c3Mb7e3pRzQONs4ZbIwXRAyUz5gtiifjRCQ82SQ2drj7HrP40GMz\nK8/ItYDWrNlAuoX5XnttQ4ZXDE7UMsxBdmxBJk2OPnpfnn2279VFjj76wfQvGKSmpju47baVe0co\nzJxZO6hgKpOgpufE/x8fD+T/MV1AG4vdwpe+dG2kvutRsXDhpxIHBD1JqqqqGAsXXhFuxUISZMK4\nsfH7xGJVwI17y2KxeTQ2fr8cv9uKR4pYUH1Stl57LdO6GNvS9jXpflu7dl2Y9Izk/vtaqqp+wltv\ndfLMM93Pn0HvRP3OnTV8/evTOP30k7L+fS5e3ML27dfSe4TI6NFXM2vWJ7PaRrLBxHhBxED5jNmi\neDJORMLTb2LD4tHDMjP7DrC/mX0a+BdgaSEqV+yCGh68Z88w0i3Mt2fP1Jy3HUVBdmxBJk0WLpzO\njBmpc3Grqj7Pl788LedtNzXdwcKFrezZcxzdo3oWLmxNPJZbIBnU9Jwg/x8zBbSvvZZ+eK/kZurU\nGq6+enXKQczVV5f31J+gEsZr1myjb5e7iDVrLsn7exUzxSPFranpDhYteo6urp7E/aJFnwEGN3oh\nF5mmyFVUvMutt17Z5/fZ89tKvnT1TtJNQRkzZg0TJkzg2WfvTiqfCDT1eb/t209kyZIVWe8P4qMm\neo8Q2c373hf8qKz+YqDuGPett7axbt06qqr25/DDDx4w1s13Xx+1k3EiEp5sr4ryeWArcBzQ6O65\njx2PuCAXnRw2rJI9e9KVj8ppu1EWVMcW5MH21Kk1LF3ae9sX5WXbN9+8jD17Komvnzca2M6ePV3c\nfHNzzkFkkNNztOZDaWhubuPeeztSpv7ce+88Tf0JgHum/f7IgtajSCgeKVLxJGfqaMRSAfllAAAg\nAElEQVSurm9z223TC5bYyDRF7v3vPzjtfin+22ojdYRsE/AReo/EOO207t9bchLkpQw1eZtVqzZS\nV9eU1UmvnlETqSM8J05szPxh8yRTDAT0iXE3bpzHCy/ER14mvzbTdtUXiEih9ZvYcHc3s98Dm939\nXwtUp0gIctHJSZPG8Oc/9y0/+mhNJQ5CkB1wUNvetm0LcCTw7aTSz7Bt2+qct13si3WlozUfCiu+\n/2sgeR2g9vaGQZ2lLDVBjeCLT2lLVz4m521HieKR4tbVNTpDeWVB3r+5uQ2AESNeZdeu7KbIxX9b\nLaT2G130TjAAVFauYMOGdaQmQdqAq4DkdTY+D3SyadMDrFwZf86TT95OdfX/47DDxqTdL4S9jkS6\nOKWhYX6fGDf+uRuL5nKrIiK9ZTNi40zgUjN7nZ4Le7u7nxxctYpfsFfqmMH06Vezbdt39paNGfNp\nbr31ypy3HTSt3l8oY0lNapC4f27OWw47yBqKhQs/xaWXfot3351G9wiW/fffxcKFnwu7aiWpo+Nt\n0q0DtHbtOyHVKFxBjuALckpbBCkeKVIVFdszlHcG/t49v7/uaSUrqKz8M5Mnj+XLX74i429w4cLp\nXHTRXexKuVBZPb2nonT3f42ND5C6z+ve7keAQ4BRwGZ6rsQSHw2yffuDrF4Nq1en7heS46Vx4zZx\n6qkzGDduYlGsI5Epxo0njAp7uVXFlSKSrWwSGw2B1yKCgj6rPWZMF9u29Zx1GDOmeM+WdwsyuI9q\nxxZUvc3G4J6uPPcRClFdrKuychLxFfC771+X8bmDFdXvX1BisXeB7/QqXUQsNj2M6oQuyBF8QU5p\niyDFI0Vq5sxaFi36DF1dPQn3ioqrmTkz+O9p6u8vPtqisxMOPrjn95dpH37SSQ/0GhEVf/748dM5\n6aQTUvq/b3zjibTvP2LEMezadVdSybzEv71Hg/TsF6DvVI/q6nlccMEh/OY3b/GNbzzB4sUtofU1\nmWLc+NScwo3gDDKuFJHSM2Biw93XFKAekTN7dj3PPdf3LNqsWRflvO3Fi1uIxS4n3ikCOLHY5UU/\nzDuo4D6qHVuQ9R4zZjdbt6YrT7M4yxBEbX5s/DdzS0pZLHZLXg4so/r9C9KECRPYuDFdeVXhK1ME\n4mc3k+fedwH1eTurGbXfY1AUjxSv+Doad3DbbdPp6qqkoqKTmTNr+qyvEUSSeKARtPHLuj6c0kc8\n99x1LF0aH7UxZ07qCMWqqu8xYcKBAHjSGYT0B/stvZIa0D1lI1OI3dk5PEO81MDXv34f27d/O6ks\nnL4m3chNmAtMKegIziCTxiJh0gmzYGQzYkMy2kzqAlNb8rLVqA7zDmp6TlQ7tiDrfd55x3L//b3n\n9s7gvPOOyWm7URXs1LBofv+CdNhhY1idZjmXww8vz3WAtmxZS7p99pYt60OqkUjhNTVd0+9CoUEl\niQcaQdvf5cB///vbgZ4RUVu2rGXduv159tmeJEh3HdMd7FdWvkFn2tk2w4knONPXq7MzXZ/VkpLU\niL93OH1N8sjNjo6trFsXo6pqPyZOXFHQEZxB9u0iYdEJs+AosTFE8TPEd6eUxWLkpQOK6jDvoKbn\nRLVjC7LeGzeOAE4FpgOVQCdQw1/+si7nbUP0MslBTg2L6vcvSFFchyVYI4nPkuhZTBUaMHuw31eJ\nlJOgksQDXbL0+ec7iF/tJD6Sqnu6SfflwJNHRDU0zOfZZ29MbCU+Cqu9fQSXX34799xzLbfe2pAy\nLWzDhjFpF/eFl4H9MLsS955Ysbteixe3pHlNcfU1xTBSLIqLmYsMRCfMgqPExhAFebAT1WHeQR3s\nRLVjC/5g+5rEX4/Ozqactx3FTHKQB9pR/f4FKarrsARl587dpBuxsWNHpnnqIuUnqLhpoEuWdnX9\nNOnZ3etf1JDucuA9dUy9DOzGjTBnzjxuvbWB5csX7n1+vL9MN2XjGqCGU06ZwSGHpN9P9u6zRo9+\nie1p1mAt575GSXQpRTphFpySSWyY2RTgW8TH/y1195uCfL8gD3aCHuYd1Nn4oA52otqxRfVgO4qZ\n5CAPtIP+/kVtdEy3YjibVyyiOspOglHoeCQqguy3BnvJUlie9nLgPXXMvPBn8vt03/7Sl67lxRe3\n0tl5FDAFqKG6ei4LF34q5QooyYuC9h79ceaZtdx7b/RinSApiR4dUY1lwqATZsEpicSGmQ0HbgM+\nCnQAT5vZI+7+UlDvGeTBTpDbDvpsfBAHO1On1vD006u57bZpdHWNpqJiO5deWlv0O8wg6x3k4rVR\nzSQHdaAdZGAVxdEx0ldUR9lJ/oURj0RFoU9SZL5k6RtUVQ1n4cIr+qnjiLSvTO4Hkw/kDjroAL74\nxb/hqafW0dn5BJWVPWtRZNrP9x79AXD66W1Z9zX5PpAs1gNTJdGLn2KZwYnqCdsoKInEBnAG8Gr3\niulm9gDwMSCwQCLIg50gtx3Fs/HNzW3ce28HGzf2zFe/9955nH56W9HWGQpR72AWrw0yk1ysgdNA\nggqsovh7lL60mKokKXg8EhWFPvueqS8bP34HS5dek/Z9u8suv/z2tMnK7n6wv2RF7+02Nj5Ae3vq\n4qWZ9vPZ9jX5PpDUgankQrHM4GgkUnBKJbFxOPBm0v21wAeDftOgs8jdlxlLvtxYrqJ4Nj6qO8wg\n6x3k4rVBZZIVOPUV9GVCpTB09kWShBKPREUhz77HRzZeSSw2ge79a1XVWxmTGsl1vOce+qydkfyb\nzrZ/b25u46WXtqV9n3T7+WyT//mOL6IaZ0lxiOKxRdg0EikYpZLYyN+RfxEI8gAwivO6gt5hBjWK\nIMh6B7ntqVNr+OEPm3n99fNx3xez9zjjjMl5ScYocEqly4SWBp19kSQlFY9E337AjUn3r8vqVQP9\nprPtgxcvbslwOdi+cVd/sV/3trrjlI6Ot7N6/2zpwFRyEcVjCylNpZLY6ACOSLp/BPGzJCmampr2\n3q6rq6Ouri7oeg1JkAeAUTyzGPTUiCgmkYLcdlPTHTz00Ga6un62t+yhhz7DccfdQVPTNf28sn9R\nTVAFayS9F6iDRZhdG0ZlJAdRO/vS2tpKa2tr2NUoRSUVj0RZfGTjLSllsdgtWcdS/f2mU/vgnlF3\nq1e/RHNzz3TTl1/+I2DEr8bSs68fPvxfmDXrij71TRf7zZ49HbPqXldQ+UzifVPrN9QYQAemkoso\nHltI8chrPOLukf8jnqBpByYRP1L4A3Bir+d4VJx00hyHlQ7zHBYk/l3pJ500Jy/bX7ZspTc0zPfa\n2gXe0DDfly1bmZftBmXBgtu9ouJqB9/7V1HxaV+w4Pact11fPy9lu91/DQ3zc972smUrvbp6bsp2\nq6tvyEt7B7nt8eMvTtsm48dPy2m7hW/ruUX/3a6tXZC2TWprF4RdNSkziT4y9P486n+lFo9EWTyW\n6rt/zUcs1dPnrHTI3PdUVJyXKF/pMD8R0833YcPO7rPNTP0BTE1bPnr0xb3ed+gxQJAxhZSHqB1b\nSPHKJR4piREb7t5lZjOJj+keDtztEV6BfM2aV4B7gAlJpfewZk0sL9uP2pnF3/zmLbq6TgamAaOB\n7XR11fLUU+ty3nbQUzoguAVmg7riSlfX6AzllTltN8iMflSnuUT5LFk0R8iIBKvU4pGoSd4vvfxy\nO+lGNaxbl3sslbrI6IMpjyX3PSNH7kdXF4k69NSjsvLSPvVdvTrT12Rc2tJDDhnHtm35iQE0nU5y\nFbVjCylNJZHYAHD3x4DHwq5HPuzatQuoInVe6Dx27XojpBqFKz6X9K9AcvBwHWvXvpfztoM+sAxq\nRx/kFVcqKrZnKM8wUThLQQZOUZ0fHNXhm1oIViSzUopHoiTdfgk+k/i3e780l6qq/bLa1kCJ26lT\nazjppCdYubLv67v7ntGjd/HXv/Z9fPTorjT1bQOuAv4z6ZmfJx7/9LVhwxa2b89fDKADUxGJupJJ\nbIQhqDOWu3fvQ7p597t3fyznbUfRG2+sJ77g13x6rhxxIW+8cUu/r8tGVA8sgxyhMHNmLYsWfYau\nrm/vLauouJqZM/Mz0iSIwCmqIx+iepYsqiNkRKR0pdsvwbeB6cATxC+LPoWJE1f0u53BJG4H6nv6\n60/71rcGWA2cD4wHNgEHE49/UtfoGDbsX9i+PXUtpuR9sEbUiUg5UmJjiJqb25gx457EZcTinnvu\nHpYuzceik/smhi72LS9HO3d2ke7KEfHy3AR9YBnFK67EFwi9g9tum05XVyUVFZ3MnFmT08Kh3YJq\nj6gmqCCaZ8miOkJGRKIn234j034JTgCagOz6hcEkbgfqe/rrT+vqmnq9cxvxtWe7F+7uPpnT/Z6N\nxGc37cY9/VTczs7hGlEnImVLiY0hamz8PrFY6nSRWGwejY3fz7njOO64/Xn22b7lxx9/QE7bjard\nu430I1jOz8v2g5wuEsUrrkA8GMtHIiNZkO0R1ZEPURXVETIiEi35GD0xfvzLnHRSU9b9wmASt9n0\nPZn60771bSE11qkHFidup67R4d5I/IQPpK7dsVsj6kSkbA0LuwJRtWbNNtIdbK9Zk/u6DwsXTqeq\nKvVa61VVn+fLX56W87ajaNSoMYMqLxaZg4v+h8FmY/bs+rTfkVmzzsl520EJsj0gHmAuX76Q1tYm\nli9fqAAuQOPH7yI+FzzZDA48cGcY1RGREjWYfiPTfqm+/qhB9QuDTdwOte+ZPbue6up5SSW9Eyo1\ngNOzTki3ucA5xGPQnnaIjxQ5p6Aj6pqb22homE9dXRMNDfNpbm7L+3uIiGRLIzaGyH1UhkdG5rzt\nqVNrWLq09xmAi4p+ekRQjjvugAwjWA4sfGUGIfjgYjPJQ1NhS562GwxNXygdLS1rgGtJ/f59ipaW\nO8KsVqiC3K9GbZ8tki+D6Tcy7Zfuv/9rPProFVRUbGfmzNoBRyMWampj79Eeq1e/xMaNvZ/1fuAj\nwCXA8XSvE9I9SuOAA97g5JNTR6MsXtyS9v0yJWaGun/RlBcRKTZKbAzR0Ufvm/Zg++ij8zOK4Omn\nV/O7372y9zJeTz89IS8dRRQ7ooULpzNjxnXEYj2LhUZhBEuQw/UXL24hFrs7pSwWo6iHmmr6QunY\nvn0EvYdGx8vvCqU+YQtyvxrFfbZIvgym34hfqrzvfgn+i82bvwfAokWfAe7oN7kR5NTGdEmE/9/e\nvcdJVd55Hv/8oIEmAqLEpLl4SXrYia4TQwxRd3agJxm7ie542U2CGhMdddSoNBNnc5GmpR1kMzE7\nZARvMy/NRoO3zJi4hs5gEw20m5GoMx0RwRHxxq3BdFBAu4GCZ/84p7qruk91d/WpU6dO9ff9evGi\n+qmqU0+dOud5nvM7z2XVqsXdz/Vu68AmvPlBWkjPE5Lps589gVWrstOzAzOtQAuVlW+ze/c4mpuz\nV00JU75oyIuIlBzn3LD4533Vwlm5cq2rqvqGA9f9r6rqr9zKlWtDb3vRojvdyJFXZ2175Mir3aJF\nd4bedm1tQ9Z20//q6haG3naUVq5c6+rqFrrZsxe5urqFBdnPUfOOkSscNDhY5KDBVVX9RUHyPnv2\nosDfcfbsReEzHpEozxkprpEjzwk8/kaOPCfurMUiynK1WGW2X0fGXlcPh3+Fbo+Us5Ur17rq6gVZ\nx3519U2B9cb48RcGnitwjl8Pr3Xg3KRJcyPLa21tg5s9e5GrrW3ok8fg77Ig63UzZlzpYKHfZljo\n4E4HC/y8p9+71kGDq6z8qpsx4+uB+2LlyrVuxowrXWXltf1+Xq7yZdKkLw9YNyexHSIipS9Me0Q9\nNoYoyuEiS5f+ksOHf5qVdvjwP7B06f8IPaFjUocDJHHlCM/RZE4w6y3bFp53F8u7E9OzBG5tAno/\nJGv4jAQ7cuT3wDXAP2SkXs2RI3tiylG8oixXk1pmixRCPr0nRox4D28+insyUq/Bq3cA7gUeobOz\nq+D5HEzPh8bGB9iypQqv54VXZ/fu4TBhwjT69sxo5Zhj7mbKlAm89da5HDgwlUOH/pGuLmhrg/nz\n+/awSA9JaWu7NWtLvT8vV/nS0XEy8+c/2We7mdQLU0RKjQIbIUR1sf3++8E/y/vvh2/IqiIqHm+4\nyNKstPb2pQXppnnWWVN46qkHOXy458Jy5MhrOPPM00JtN0pJHD4jwZwbD7wDXARUAl3AQZwr7Ql9\noxJluaoyW4aD/uZ5GKitlX7ve+9NBD5Jdrl0It7cFD0X+B98cEWfIRlhDTQso7m5lU2bRpF9o8ML\nSGQGKYPP91l89rOrWbVqMXV1C2lp6T9YkTaYoGiu8gUODzisJMnLrItIeVJgI4SoJnQ7cmRfjvT9\nobddX1/L+vV956uYN+/C0NuWbFHeaX3iiQ1ZQQ3wevX8/OfX09QUevOR2LEj+Pjdvj34eJdSNhL4\naUD6OcXOSEnwytUraW+fTLoHVVXVDubNu7wg29bFgxRaKU1IG2aeh+z3ng9c5/9LWwjc1+tdP+Tm\nm68v6PcdqL5ftqyFrq67ez27BJjL+vWV1NUtpL6+lrPOmsLTT19LKtXT66SiouemxWDbFc3NrWzY\nsCnwtZlB0aDyxVt1ZU7gdjNpmXURKTUKbAyRN8nT41kBgvXrb+TeewsxodsHwBXADzPSrvDTC0HD\nAYohyjutb7wRvKzwG2+ED35FZefOnTnS24ucEwkvulWhkiuaYWe6eJBCK7UJacNMQpn93kN4vSAy\nt/V24PsKXVcG1/decKGmpokXX9ya450ns2dPEy0t8Mwz12K2k1RqKnAVMA04TCr1FdatW93P52S3\nK9K/b0fH9fTeH5k3stLBrcrK31FR8eekUqfTe9WVgdoryR0mLCLlSIGNIWpsfIT29uylDdvblxbk\nLsCoUXDoUO/gw3uMGuVCbRc0HCBIVHeuorzTanYgxzMHQ287KuPGVdDR0bvRuYBx4zRXQNKMGnWA\nQ4eC08NqarqLO+5Y270i1GCWZ4xblMPOQBcPUliltppFmN6N2e/9b8AasttOv8/xzr51ZZi2QN/6\nvpWKiofo6HiUtWvB6zkSJL3qyW46O1PAROBY4FVgJ3Ac4O2L5uZW3nmnncrKr2f1/ujdruj7+6b3\nxyt0dY2isfEBvv3tB3n9daOzM90zxMtvZk8R9QwTkaRRYGOINm7cHZj+8su7Qm/78OGxwGMB6eEr\nGE1Ely3KO1dR3mk96aRx7NnTN0hw0klHhd52VPbvTwF1ZDc657B//139vk9Kz6mnTqWtre/koaee\nOjXUdpua7mLJkvWkUo92pw1meca4qVyVJCm14zVM78bs96bLiFZGjnSMHt1FZ+fvgK8DmcNAvtGn\nrgzbFuhd32/YsImOjkczXlFL394k1wDX07M8bQNeHTnLf7wLrxdYA1u3vsL8+YfYsuVevInDG6ms\nfItTThnP3/zN3Kw8Zv++mcvfNvHuu020tTUCjuweZrNIpWDSpIs49dRPqGeYiCSSAhtDdOBA8LCQ\nXOn5GZ9n+uBpIrps3p2NOry7Kd7Y+C1b6li+fHVJ32ldvPhrXHXV/bS39wQJqqraWbz48oJ/VqFU\nVU2ko+NJegdjqqqOjitLMkTBx18q9PHn9dR4NCstlbqHO+64qKQDGypXJUlK7XgN07ux5711pFcJ\nGzvW8a1veT29mpru4rvfXcnBgxfjDZUbR1VVZ5+yqhC9WDLr+5qaJr+nRpqXfswxF/PJT/4hv/71\nv5FKfZOeoAN4dWOjn7YEuLg7/Z13/jv79i3J2NYsurrguOP65q+/CUE9uQJYszj11KdZs6ap/y8q\nIlKiFNgYIjNwru8dc7Pw2x4x4iBHjgSnh6WJ6LJt3/4O8DiQ2Y38RrZtC57DolT0LDe8mq4uqKyE\nefMuL+m7K1OnHsfLL9fSu8fGtGmr482Y5C2q4y+VGpsjvTLUdqOmclWSpNSO1zC9G889dxbPP7+B\n2257qHtYRWcnrFjRwMyZrTQ1XcfMmaf6ZVV622cPaQWRTAMNW+kbXEgvzz6aMWNSTJ1axVtvBX2/\nzM/rmbNoxIgJg87fQBOCenVv8NBmBWNFJMkU2BiiceMq2bevb7f6ceP+I/S2J01y7NrVN2jy4Q+H\n3rQmouvl7bd34QU2Mi3l7bcviCM7eUnauHuvsfVkyTSmJZwojr+Kis4c6V0F2X5U8+moXJUkKcXj\nNUx58uyzOzLmivBk9rYYzLbz6cUymGEr2cGFVsDrrbhnD7S0wOjRX8yRk8zP61k+O1fZGJS/dB5u\nvvl6Nm7cR1fXifRMCJoZ4MhuZ6o+FpGkU2BjiG688c+49dYHs5bcHDnyam688fOhtz1lykfYtesl\nYC4wFugEPmDy5I+G3jYk74I4SkeOfCivdBm6UmxMD1YpLY1Yzm64YTZLlvRd6vCGG8Lv66hXglC5\nKklSTsdrIeYMyacXy2CGrWTWd7/+9Sb27//nrNcfPFjP6NFXc/DgP2akZgYdvoHXBvTycemls1mx\nIjt/VVVXsHt3JTU1TX3qpfTv29zcys03P8rrr99HKrWMgwc/4ODBnlVPxo6dS3X1ZKZOHZ+Y+lhE\nJBcFNobIG+99F3fccRGpVCUVFV3ccMOsgowDP3hwJPAH9B4ecfBgaQ+PSKLRo4/wfsBuHT06/Ao0\n0lcSG9OltjRiPpIWkImyXC21lSBEhoNilEGFmDMkn8D7YAMp6frumGMuC3j1LEaN+jv+9E+9z9u3\n7x2cO8CECU+zb9+j3Y8rK1d352PmzNbu/O3du42dOyfS1tbTTsxVL7333kTefffO7r/Hjr2Wj3/8\nQaZN+wjz5oVfyU9EpFQosBGCNzFV4Se0a29/l+zVBgCW0t5+UcE/a7hL4uoiSZa0C21I7gVxUgMy\nUZWrpbYShEi5K1YZVKg5Q3IF3nvXW3v3Bi8hmyuQkmt59tGjP8SqVYuHlL+6uoW0td2a9XxQvRRU\nf3V23sO0aY15fbaISBIosBFCVBdpkydPpqMjKL0q9LYlWxJXFymGKI7tpF5oJ/WCOKkBmaiU2koQ\nIuWuWGVQlMMcg+qtqqorqaq6kfb2nt4S/QVSgm+gXMMxxwx9QvjB1ktDqb+SeANCRAQU2BiyKC/S\npkwZx4YNfdOnTg2/3Ktki3p1kSQ2EKI6tqNeWjcqSb0gTmpAJqpzptRWghApd8UsgwoxzDGo7AkK\nzrS338eMGVdx2mnZgRTwelL0LrvOO+/TvPRSM6nUl/BuoIwDvsYHHzxOc3PrkPI92Hop3/qr1G9A\nJLFNJSLFo8DGEEV5J0IN8OKKat6HUm8g5BLVse0trevNDN+jgW3bfjfkbRbDWWdN4emn+05oeeaZ\np8WYq4ElMSDT3NzKVVc9nnUndP36G7n33vDnTJInrxVJoiSVQbnq67Fjg+c2mzBhGqtWNXW/t7Hx\nETZt2k9X1wnA54BZbNnSwPPPb2DFiu2kUs0Z7/baAe3tSwPr1cFcvA+2nZhve7KUe/oltU0lIsWj\nwMYQeXci0uuSe3efobYgdyLUAC8PpdxA6M+OHfsD07dv3xdqu8Fzxywp+bljnn12B6nUJWQu7ZxK\nfYV161bHnLP+JTFA2tj4CO3td2Wltbcv5eabCzfBnXMu638RiUaSyqBc9fWkSXMDX58OzvRcbGeW\nWw3d77/jjrl0dDza691L8OqTWX3ajIO5eE8HPiorf8ekSXOZPDn3qib5ticH08smrl4TSW1TiUjx\nxBLYMLMvAU3AJ4CZzrl/z3juJuAKvMW8651zLX766cCPgErgF865+X76GOAB4NNABzDXOfdW1N9h\n795tBN193rt3V0G2n8TVIyRbUocC7Ny5M0d6e6jtJnXuGO93nEV6eby0rq6nY8nPYCUxQPrGG8F3\nR994IzjYlg/d7ZMg5dAeKVVJKoNy1ddVVROZODF3cCboYjszcJFKjc3xiV47oHfvlYEu3oPKsYkT\nG5g37+yc+zWf9uRAvWziLEeT2qYSkeKJq8fGS8CF9Lp9a2an4C3cfQowFfilmU133q21u4ErnXPP\nmdkvzGyOc24VcCXQ4ZybbmZzge8BRbgFPJrsoAbAEsyuj/6jJRGi7oYb1V2TqqqJdHT0XSmmquro\nUNtN6twxSepO3VvSAqS5Vg+AoU+yl6a7fZJDGbRHSlcplEGDqStzlfPekqhn5wzO5LrYTgcuKio6\nczx/mKqqb7B79z5qapq68zXQxXvU5dhAvWziLEeTXBeLSHHEEthwzr0CYGa9nzofeNg5dwh408xe\nA84ws7eA8c655/zXPQBcAKwCzgMW+emPAXdEnH0ADh0aHZh+8OCoYnx8yYrqYjuJE0ZF2Q03yrsm\nU6cex8sv15I59ALmMG1auKEXSeqWnCmp+Y5aFOdklMsvRzXESpKtHNojkttAdWW6HNuxYz9jx86l\ns/N60r3z0uV8f8GZXBfbcJjq6gVceulsVqzIrj8qK69lypR3+eCDo2hruy8rXxMmBPf6raw8THNz\nK88//xpeByNv+HM6r2F6LfQuyy+9dCrr1gUHcuIsR1UXi8hASm2OjSnAuoy/t+HdKTnkP07b7qfj\n/78VwDmXMrP3zOxY51zwQuMFElV3/STzJv67n/b2yd1p69ffH3riv6i7PkYVNImyG270k9c+WfDG\nQ5K6JWdKar6jFNU5GeXyyyqzJU+JaY9Ibv3VlUCfcmzs2Gv5+Mcf9HtqDFzOB11sV1Zey8knp1i8\n+Guce+4sZs5s7VV/XMKyZS20tNzaJ1/V1Zf5AZaT8Q6z0VRW7mfzZrjqqvvZs+eRjHc0+P/PGnKv\nhVxl+e231wV+9zjLUdXFIjKQyAIbZrYaCBo8v8A59/OoPrc/TU1N3Y9ramqoqakZ8rai6q6fZI2N\nD9DefjTQU1m3t99IY+MDBVgmNJqL+KiDJlF1w41yrGmUjYdS6JY8FEnNd1SiOiejXH456jI7ab3K\n1qxZw5o1a+LORlGUe3tEcuuvrgwqxzo772HatEZWrVo8qO0H15eXZJ37QfXH9/0OUBsAABVoSURB\nVL8fPEfT9u2Orq5H8San9+Zx6+qC118HL5DRitdLoxUw4D7Gjr2TM8+c3b2NfMqifMvyuNu+qotF\nyk8h2yORBTacc2cP4W3bgeMz/p6GF7Le7j/unZ5+zwnADjOrAI7OdXcksyERVlTd9ZPs1Vc7gHt7\npS7l1VcvDLXdKC/ikzruPuqxpmo8SH+iDqxFcexFWWYncWLS3hfTt9xyS3yZiVi5t0ckt/7qyq6u\nwpRjQymzcuWrq+tE/1ELQfO4eeUXZE5e39kJK1Y0MHNmK9C3F0p/ZVG+ZbnaviJSaIVsj4woQH7C\nyhzY+gRwkZmNNrOPAdOB55xz7cBeMzvDvIGwXwX+b8Z7LvMffxF4qhiZrq+vparqfiC9ZKCjqupH\nzJs3lPZTechVQR44EG7ekSgv4nuW7V2IN251IdBa8rNs19fXUl3dkJXmDRcZvsdflJqbW6mrW0hN\nTRN1dQtpbm6NO0uxSuIkbt458ySwGO9cX0x19aqCnDO5A6Rq7CdMItsj0qN3WX3WWVNy1pVDKcf6\nqwvyqSeC6vDKymuBdHnU36SkfYMe6fIm37Io330QVI6OHv0Amze/qvpRRGIX13KvFwLLgA8DzWbW\n5pz7gnNuo5n9BNiINzPSdf4M5ADX4S2vNhZvebVVfvp9wI/NbDPe8mpFnIE8e9gF3Fi8jy5BFRUp\nUgF1ZEXFoVDbjXLCqK1bN+ENmc5etnfr1i2htx0ljTUtniTejY9aEidxi/Kc0TKEyVU+7RHJVVb3\nNxlmPuVYf3UB5NdTIqg82r07RVtb+rW5JyXN1XTvr7zJ9Vy+Zfm5587i+ec3cNtt6XlA3uHgQcfr\nrz/qD5dR/Sgi8bGeerq8mZkr5Hetq1vYZ+InL33wYzPLzfjx57B//wx6j70cN+5F9u1rDrXt5uZW\nf9x9umGSe832fEyYcAH79j3eJ338+AvYu7dvugw/OteDRXVOJlE5HCNmhnOuz9IgUniFbo+IZyjn\nYT7lWH/bd86FLgOyAyc9c2ykVVV9g8mT9/Hmm/t7TSIaLh/5luXZ+2Eh2Tf4Bv48EZH+hGmPlNqq\nKImhO3R9nXji8bz8cjvZYy/bOfHEaf2/cRCiGnc/YsTEvNJl+NG5HkzzsPRIYg8WkXIzlLI6n3Js\nKNvPp57o3Ytj795dmF3P+PHH+QGHC7uXqJ0/P3d5k29ZlG9Znr0fVD+KSOlQYGOIkjjGPGo9k0pl\njuW8vKQnlaqo6MyR3lXknEip0rlePpK4tLOIDE7UZXV/28/VAyffz06XGcuWtTBhwjS/nPpcn1VW\noP/yJsqyKHs/qH4UkRLinBsW/7yvWjgrV6511dULHLjuf9XVN7mVK9cW9HOSJIn7ZNGiO11FxTVZ\nea6ouNotWnRn3FmTEpHE41r6Cv4dF+h39Pl1ZOx19XD4V+j2iHiiLqv7236hPjsJ5VR2Htc6UP0o\nIoUTpj2iOTZC0BjzvpK4Ty655Nv80z9txLmjMHufL33pFB566HtxZ0tKSBKPa8lWDvNgRElzbBSP\n5tiITtRldX/bH+pnZ/Yk27BhEx0d1wPZ7yu1cirzu+7duw2zMRlDZlQ/isjQhWmPKLAhw1rQLOfV\n1Q3cfnudKmaRMlJT08TatU190mfPbmLNmr7pw40CG8Wj9oikBbVBoAGoIzO4oXJKRIaLMO2REYXO\njEiS5Lvmu4gkk+ZKESkPzc2t1NUtpKamibq6hTQ3t8adpSELaoN4K6Fkt0FUTomIDEyTh8qwtmPH\n/sD07dv3FTknIhIlrVwiknxBPRy2bGkASGQvy1wrrXgry3lUTomIDI4CGzKs7dy5M0d6e5FzIiJR\n0solIsmXu5dlYyLP5Vw9ySoq2hg//iJOOmkcixd/LZHfTUSk2BTYkGHtqKOO0NFxLXBPRuo1HHWU\nun2KlJtzz52lCwSRBMvVw6Gra2RgeqkL6kkGC0il/po9e2Zx7LENseVNRCRpFNiQYe3990cAlwCN\neF0/DwNf4f3374o1XzJ8ZM6IP2ZMivr6Wl18i4gEKLe5cjJ7kv3mN2/z7rsnAHNITxxa7N4oqo9E\nJMkU2JBhbfLkyXR0zKL30mqTJ/80ngzJsFJu48VFRKJUjnPlpHuS5Vq5qVi9UVQfiUjSKbAhw9qU\nKePYsKFv+tSp44ufGRl2ym28uIhIlMp5rpy4e6OoPhKRpFNgQ4a1qO/+qFun9KfcxouLiESt1OfK\nGWq9H3dvFNVHIpJ0CmzIsBbl3R9165SBxH2HTkRECidMvR93bxTVRyKSdOacizsPRWFmbrh8VykN\ndXULaWm5NSC9kVWrFseQIyk1QY3g6uoF3H57eXStluQwM5xzFnc+hgO1R8pXkut91UciUgrCtEfU\nY0MkIurWKQOJ+w6diIgUTpLrfdVHIpJ0CmyIRETdOmUwSn28uIiIDE7S633VRyKSZCPizoBIuaqv\nr6W6uiErzZsI7OyYciQiIiJRUb0vIhIfzbEhEqHm5laWL1+d0a3zbN0NEZGSozk2ikftkfKmel9E\nZOjCtEcU2BARERnmFNgoHrVHREREgoVpj2goioiIiIiIiIgklgIbIiIiIiIiIpJYsQQ2zOz7ZrbJ\nzF40s5+a2dEZz91kZpvN7BUzq81IP93MXvKfuz0jfYyZPeqnrzOzE4v9fURERCR51B4REREpD3H1\n2GgB/rNz7jTgVeAmADM7BZgLnALMAe4ys/QYm7uBK51z04HpZjbHT78S6PDTfwB8r3hfQ0RERBJM\n7REREZEyEEtgwzm32jl3xP/zN8A0//H5wMPOuUPOuTeB14AzzGwyMN4595z/ugeAC/zH5wH3+48f\nAz4fdf6Lobm5lbq6hdTUNFFXt5Dm5ta4syQiIlJW1B6RcqA2o4gIVMSdAeAK4GH/8RRgXcZz24Cp\nwCH/cdp2Px3//60AzrmUmb1nZsc6534faa4j1Nzcyvz5T7Jly5LutC1bvHXRtWSYiIhIJNQekcRR\nm1FExBNZjw0zW+2PQe39788zXtMAHHTOPRRVPpJo2bKWrAoKYMuWJSxfvjqmHImIiCST2iNSztRm\nFBHxRNZjwzl3dn/Pm9nlwDlkd9XcDhyf8fc0vDsj2+npHpqZnn7PCcAOM6sAjs51d6Spqan7cU1N\nDTU1NQN/kRgcOBD8s3R1jSxyTkREpBytWbOGNWvWxJ2NolB7RMqZ2owikmSFbI/EMhTFn2jrm8Bs\n51xXxlNPAA+Z2VK8Lp3Tgeecc87M9prZGcBzwFeBZRnvuQyvy+gXgadyfW5mQ6KUjRmTCkyvrDxc\n5JyIiEg56n0xfcstt8SXmRipPSJJpzajiCRZIdsjca2KshwYB6w2szYzuwvAObcR+AmwEfgX4Drn\nnPPfcx1wL7AZeM05t8pPvw+YZGabgb8CvlO8rxGN+vpaqqsbstKqqxcwb16/N51EREQkP2qPSKKp\nzSgi4rGeerq8mZlL0ndtbm5l+fLVdHWNpLLyMPPmna1JoEREhqHm5laWLWvhwIEKxoxJUV9fW/D6\nwMxwztnAr5SwktYekdKX1DZjMco2EUmWMO0RBTZERERKVNCKB9XVDdx+e11BLwAU2CgetUdEile2\niUiyhGmPxDUURURERAagFQ9EpBypbBORQlNgQ0REpERpxQMRKUcq20Sk0BTYEBERKVFa8UBEypHK\nNhEpNAU2RERESpRWPBCRcqSyTUQKTZOHioiIlLBirHigyUOLR+0REU9SV3MRkehoVZRBUENCREQk\nmAIbxaP2iIiISDCtiiIiIiIiIiIiw5ICGyIiIiIiIiKSWApsiIiIiIiIiEhiKbAhIiIiIiIiIoml\nwIaIiIiIiIiIJJYCGyIiIiIiIiKSWApsiIiIiIiIiEhiKbAhIiIiIiIiIomlwIaIiIiIiIiIJJYC\nGyIiIiIiIiKSWApsiIiIiIiIiEhiKbAhIiIiIiIiIomlwIaIiIiIiIiIJJYCGyIiIiIiIiKSWBVx\nZ0BkMJqbW1m2rIUDByoYMyZFfX0t5547K+5siYiIiIhIRHQNIIOlwIaUvObmVubPf5ItW5Z0p23Z\n0gCggk1EREREpAzpGkDyEctQFDNbbGYvmtlvzewpMzs+47mbzGyzmb1iZrUZ6aeb2Uv+c7dnpI8x\ns0f99HVmdmKxv49Ea9mylqwCDWDLliUsX746phyJiEg5UHtERKR06RpA8hHXHBu3OedOc859Cngc\nWARgZqcAc4FTgDnAXWZm/nvuBq50zk0HppvZHD/9SqDDT/8B8L0ifg8pggMHMjsWrel+1NU1suh5\nkcJYs2ZN3FmQAtFvKQmn9kgZU/lUXNrfxTNc9nX2NUCPYl4DDJd9XQ5iCWw45/Zl/DkO+J3/+Hzg\nYefcIefcm8BrwBlmNhkY75x7zn/dA8AF/uPzgPv9x48Bn48y71J8Y8akMv5a0/2osvJw0fMihaFK\nonzot5QkU3ukvKl8Ki7t7+IZLvs6+xqgRzGvAYbLvi4Hsa2KYmZLzOxt4HLgu37yFGBbxsu2AVMD\n0rf76fj/bwVwzqWA98zs2OhyLsVWX19LdXVDVlp19QLmzTs7phyJiEi5UHtERKQ06RpA8hHZ5KFm\nthqoCnhqgXPu5865BqDBzL4D/D3wF1HlRZItPTnQ8uWNvPLKM3ziE43MmzdHkwaJiMiA1B4REUmm\nzGuArq6RVFYe1jWA5GTOuXgzYHYC8Avn3Kl+owLn3N/6z63CG+/6FvAr59zJfvrFwCzn3Nf91zQ5\n59aZWQWw0zl3XMDnxPtFRURESphzzgZ+VflSe0RERCR+Q22PxLLcq5lNd85t9v88H2jzHz8BPGRm\nS/G6dE4HnnPOOTPba2ZnAM8BXwWWZbznMmAd8EXgqaDPHO4NNhEREcmm9oiIiEh5iCWwAXzXzP4Q\nOAxsAb4O4JzbaGY/ATYCKeA619Ol5DrgR8BYvDsqq/z0+4Afm9lmoAO4qGjfQkRERJJM7REREZEy\nEPtQFBERERERERGRoYptVZRiMrM5ZvaKmW02s2/HnR8ZGjN708zWm1mbmT038DukFJjZD81sl5m9\nlJF2rJmtNrNXzazFzCbGmUcZWI7fscnMtvnnZJuZzYkzjzIwMzvezH5lZi+b2QYzq/fTdU4Wgdoj\n0QpqJ+jYLox863Izu8k/zl8xs9p4cp1Mg6xvv5DxnPb1EA2lTtT+Hrp+9ndBju+y77FhZiOB/wD+\nDG9ZtueBi51zm2LNmOTNzN4ATnfO/T7uvMjgmdmfAPuBB5xzf+Sn3Qb8zjl3m9+4P8Y595048yn9\ny/E7LgL2OeeWxpo5GTQzqwKqnHO/NbNxwL8BF+CtBKJzMkJqj0QvqJ2g+qYw8qnLzewU4CFgJt4c\nNb8E/pNz7khM2U+UfOpb7etw8q0Ttb/D6Wd/f5kCHN/DocfGZ4HXnHNvOucOAY/gTRAmyaRJ1xLG\nOfcMsKdX8nnA/f7j+/EKNSlhOX5H0DmZKM65dufcb/3H+4FNeI0FnZPRU3ukOHqXSTq2CyDPuvx8\n4GHn3CHn3JvAa3jHvwxCnvWt9nUIQ6gTtb9D6Gd/QwGO7+EQ2JgKbM34exs9O1CSxQG/NLMXzOwv\n486MhPJR59wu//Eu4KNxZkZCmWdmL5rZferinSxmdhIwA/gNOieLQe2R6AW1E3RsRyfXvp2Cd3yn\n6VgvjKD6Vvu6QAZZJ2p/F0jG/l7nJ4U+vodDYKO8x9oML3/snJsBfAG43u+qJwnnrzSg8zSZ7gY+\nBnwK2An8XbzZkcHyu4A+Bsx3zu3LfE7nZGS0T6PXbztBx3Z0BrFvtd/Dyae+1b7OU8g6Ufs7T/7+\n/me8/b2fAh3fwyGwsR04PuPv48mO/EhCOOd2+v+/A/wMdf1Ksl3+ODvMbDKwO+b8yBA453Y7H3Av\nOicTwcxG4TXgfuyce9xP1jkZPbVHIpajnaBjOzq59m3vY32anyZD1E99q30dUp51ovZ3SBn7e0V6\nfxfq+B4OgY0XgOlmdpKZjQbmAk/EnCfJk5l9yMzG+4+PAmqBl/p/l5SwJ4DL/MeXAY/381opUX5l\nn3YhOidLnpkZcB+w0Tn39xlP6ZyMntojEeqnnaBjOzq59u0TwEVmNtrMPgZMB7SaXQj91Lfa1yEM\noU7U/g4h1/4u1PFdUfgslxbnXMrMbgCeBEYC92kG8kT6KPAz73ygAnjQOdcSb5ZkMMzsYWA28GEz\n2wrcDPwt8BMzuxJ4E282ZClhAb/jIqDGzD6F1y3wDeCaGLMog/PHwKXAejNr89NuQudk5NQeiVxg\nO8HMXkDHdmj51OXOuY1m9hNgI5ACrnPlvgxjAeVT32pfh5ZXnaj9HVrQ/l4AXFyI47vsl3sVERER\nERERkfI1HIaiiIiIiIiIiEiZUmBDRERERERERBJLgQ0RERERERERSSwFNkREREREREQksRTYEBER\nEREREZHEUmBDRERERERERBJLgQ0RiZWZrTGzT/uP98edHxERERERSRYFNkQkbi7HYxEREZFImJmu\ng0TKiE5oESkIM/ummc3zH//AzJ7yH3/OzFaY2V1m9ryZbTCzpgG29WEz+1cz+0IRsi4iIiJlxsx+\nZmYv+O2Ov/TT9pvZ/zaz3wJnmdmlZvYbM2szs3vSwY582iwiUhoU2BCRQmkF/sR//BngKDOr8NPW\nAg3OuZnAacBsM/ujoI2Y2UeAlUCjc+5fos+2iIiIlKErnHOfAWYC9WZ2LPAhYJ1z7lPA74EvA//F\nOTcDOAJ8xX/voNosIlI6KuLOgIiUjX8HTjez8UAX8AJegOO/AvXAXP+OSQUwGTgZeKnXNkYDTwHX\nOeeeKVbGRUREpOzMN7ML/MfTgOnAYeAxP+3zwOnAC2YGMBZo95/r3WY5hb5tFhEpIQpsiEhBOOcO\nmdkbwOXAvwLrgc8BfwB0An8NfMY5956Z/R+gMmAzh/ACInMABTZEREQkb2ZWgxe4ONM512Vmv8Jr\nd3Q55zLn87rfObeg13s/xuDaLCJSQjQURUQK6Rngf+INPXkGuBavJ8cE4H1gr5l9FMg1d4YDrgA+\nYWbfij67IiIiUoYmAHv8oMbJwJkBr3kK+KKZHQdgZsea2QnAePq2WTS5uUiJU2BDRArpGaAKeNY5\ntxuvp8Yzzrn1QBvwCvAg8P9yvN/5d1IuBj5nZtcWIc8iIiJSXlYBFWa2EfhfwLN+eneAwjm3CVgI\ntJjZi0ALUJVHm0VESohl98YSEREREREREUkO9dgQERERERERkcRSYENEREREREREEkuBDRERERER\nERFJLAU2RERERERERCSxFNgQERERERERkcRSYENEREREREREEkuBDRERERERERFJLAU2RERERERE\nRCSx/j/JgUdvOwO6oAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c6e1470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 残差をplot\n",
"fig = plt.figure(figsize=(18, 10)) \n",
"ax1 = plt.subplot(2, 2, 1)\n",
"plt.plot(exp_matrix['time'], residuals, 'o', color='b', linewidth=1, label=\"residuals - time\")\n",
"plt.xlabel(\"time\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.legend()\n",
"\n",
"ax2 = plt.subplot(2, 2, 2, sharey=ax1)\n",
"plt.plot(exp_matrix['bus'], residuals, 'o', color='b', linewidth=1, label=\"residuals - bus\")\n",
"plt.xlabel(\"bus\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.legend()\n",
"\n",
"ax3 = plt.subplot(2, 2, 3, sharey=ax1)\n",
"plt.plot(exp_matrix['walk'], residuals, 'o', color='b', linewidth=1, label=\"residuals - walk\")\n",
"plt.xlabel(\"walk\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.legend()\n",
"\n",
"ax4 = plt.subplot(2, 2, 4, sharey=ax1)\n",
"plt.plot(exp_matrix['area'], residuals, 'o', color='b', linewidth=1, label=\"residuals - area\")\n",
"plt.xlabel(\"area\")\n",
"plt.ylabel(\"residuals\")\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"どの説明変数と残差の間にも特徴的な相関関係は見られない: 仮定5は満たす。\n",
"\n",
"area変数だけ残差のばらつき方が異なるので、何らかの対策をとったほうが良い可能性がある（すみません、わかりません）。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## まとめ"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"当てはまりの良い回帰モデルを作ることが出来たが、残差の性質、特に等分散性の仮定を置いて良いのかについては問題が残った。等分散性の仮定に問題がある場合は、重みを付けて最小二乗法を使う必要があるので、慎重に考える必要がある。"
]
},
{
"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 |
pcmoritz/ray-1 | python/ray/tune/TuneClient.ipynb | 6 | 1615 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ray.tune.web_server import TuneClient\n",
"\n",
"manager = TuneClient(tune_address=\"localhost\", port_forward=4321)\n",
"\n",
"x = manager.get_all_trials()\n",
"\n",
"[((y[\"id\"]), y[\"status\"]) for y in x[\"trials\"]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for y in x[\"trials\"][-10:]:\n",
" manager.stop_trial(y[\"id\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import yaml\n",
"\n",
"with open(\"../rllib/tuned_examples/hyperband-cartpole.yaml\") as f:\n",
" d = yaml.safe_load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"name, spec = [x for x in d.items()][0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"manager.add_trial(name, spec)"
]
}
],
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
mlev71/sascalc_geom | .ipynb_checkpoints/Gui development-checkpoint.ipynb | 2 | 13179 | {
"metadata": {
"name": "",
"signature": "sha256:96c52f3138fff8d6bc190bf361be6e0d19fa22f2d63365b56b43d0138d9dd074"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import Statements"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import tkinter as tk\n",
"from tkinter import ttk\n",
"from analytical import *\n",
"from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\n",
"from matplotlib.figure import Figure"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define Convinence Classes"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SCT = Scatterer()\n",
"ICONST = 1\n",
"\n",
"class SphereRow():\n",
" def __init__(self,frame):\n",
" #Store \n",
" self.EXISTS = False\n",
" i = ICONST\n",
" self.i= ICONST\n",
" self.frame = frame\n",
" #Declare String Variables\n",
" self.x_var = tk.DoubleVar()\n",
" self.y_var = tk.DoubleVar()\n",
" self.z_var = tk.DoubleVar()\n",
" self.rho_var = tk.DoubleVar()\n",
" self.radius_var = tk.DoubleVar()\n",
" #X,Y,Z\n",
" self.x = tk.Label(self.frame, text=\"X:\", font=LARGE_FONT)\n",
" self.x.grid(column=2, row=i)\n",
" self.x_val = tk.Entry(self.frame, textvariable=self.x_var)\n",
" self.x_val.grid(column=3, row=i)\n",
" self.y = tk.Label(self.frame, text=\"Y:\", font=LARGE_FONT)\n",
" self.y.grid(column=4, row=i)\n",
" self.y_val = tk.Entry(self.frame, textvariable=self.y_var)\n",
" self.y_val.grid(column=5, row=i)\n",
" self.z = tk.Label(self.frame, text=\"Z:\", font=LARGE_FONT)\n",
" self.z.grid(column=6, row=i)\n",
" self.z_val = tk.Entry(self.frame, textvariable=self.z_var)\n",
" self.z_val.grid(column=7, row =i)\n",
" #Rho\n",
" self.rho = tk.Label(self.frame, text=\"Rho:\", font=LARGE_FONT)\n",
" self.rho.grid(column=8, row=i)\n",
" self.rho_val = tk.Entry(self.frame, textvariable=self.rho_var)\n",
" self.rho_val.grid(column=9, row =i)\n",
" #Radius\n",
" self.radius= tk.Label(self.frame, text=\"Radius:\", font=LARGE_FONT)\n",
" self.radius.grid(column=10, row=i)\n",
" self.radius_val = tk.Entry(self.frame, textvariable=self.radius_var)\n",
" self.radius_val.grid(column=11, row =i)\n",
" \n",
" #Declare Buttons\n",
" self.Confirm = tk.Button(self.frame, text= \"Confirm\", command =lambda: AddSphere(self))\n",
" self.Confirm.grid(column=12, row=i)\n",
" \n",
" self.Delete = tk.Button(self.frame,text=\"Delete\", command =lambda: Delete(self))\n",
" self.Delete.grid(column=13, row=i)\n",
" \n",
" self.WidgetList = [self.x,self.x_val,self.y,self.y_val,self.z,self.z_val,self.rho,\n",
" self.rho_val,self.radius,self.radius_val,self.Confirm,self.Delete]\n",
" \n",
" def AddSphere(self):\n",
" #store every value so entry.get() is called only once per entry feild\n",
" Rho,Radius = float(self.rho_var.get()), float(self.radius_var.get())\n",
" X,Y,Z = float(self.x_var.get()),float(self.y_var.get()),float(self.z_var.get())\n",
" \n",
" #if the shape is contained in the scatterer\n",
" #update each value\n",
" if self.EXISTS == True:\n",
" SCT.shapes[self.i-1].x = X\n",
" SCT.shapes[self.i-1].y = Y\n",
" SCT.shapes[self.i-1].z = Z\n",
" SCT.shapes[self.i-1].contrast = Rho\n",
" SCT.shapes[self.i-1].r = Radius\n",
" #if the shape isn't contained, declare a new one\n",
" else:\n",
" mono_sphere(SCT, Rho,Radius,X,Y,Z)\n",
" self.EXISTS = True\n",
"\n",
" def Delete(self):\n",
" if self.EXISTS==True:\n",
" self.EXISTS = False\n",
" SCT.shapes[self.i].remove()\n",
" for Widget in self.WidgetList:\n",
" Widget.grid_remove()\n",
" global ICONST\n",
" ICONST -=1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open up a frame and define function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#BROKEN\n",
"LARGE_FONT=(\"Verdanna\", 12)\n",
"\n",
"class BasicApp(tk.Tk):\n",
" def __init__(self, *args, **kwargs):\n",
" tk.Tk.__init__(self,*args,**kwargs)\n",
" container = tk.Frame(self)\n",
" container.pack(side=\"top\",fill=\"both\", expand= True)\n",
" container.grid_rowconfigure(0,weight=1)\n",
" container.grid_columnconfigure(0,weight=1)\n",
" \n",
" self.frames = {}\n",
" for F in (HOME, SCATTERER):\n",
" frame = F(container, self)\n",
" self.frames[F]=frame\n",
" frame.grid(row=0,column=0,sticky=\"nsew\")\n",
" self.show_frame(HOME)\n",
" \n",
" def show_frame(self,cont):\n",
" frame = self.frames[cont]\n",
" frame.tkraise()\n",
"\n",
"class HOME(tk.Frame): \n",
" def __init__(self, parent, controller):\n",
" tk.Frame.__init__(self,parent)\n",
" \n",
" label = tk.Label(self,text=\"Welcome to Pysaxs\", font=LARGE_FONT)\n",
" label.grid(column=1,row=1)\n",
" \n",
" button1 = tk.Button(self, text=\"NEW\",\n",
" command=lambda: controller.show_frame(SCATTERER))\n",
" button1.grid(column=1, row=2)\n",
"\n",
"class SCATTERER(tk.Frame):\n",
" def __init__(self,parent, controller):\n",
" tk.Frame.__init__(self,parent)\n",
" label = tk.Label(self,text=\"Scatterer Instance\", font=LARGE_FONT)\n",
" label.grid(column=1,row=1)\n",
" self.rows=[]\n",
" \n",
" AddSphere = tk.Button(self, text=\"Add Sphere\",\n",
" command = lambda: self.AddSphereRow() )\n",
" AddSphere.grid(column=1,row=2)\n",
" \n",
" IQPlot = tk.Button(self, text=\"Plot\",\n",
" command = lambda: self.Plot())\n",
" IQPlot.grid(column=1,row=3)\n",
" \n",
" self.i = 0 # i is the maximimum row number\n",
" \n",
" def AddSphereRow(self):\n",
" global ICONST\n",
" ICONST+=1\n",
" temp=SphereRow(self)\n",
" self.rows.append(temp)\n",
" \n",
" def Plot(self):\n",
" gen = SCT.genIQ()\n",
" IQ = np.asarray(list(gen))\n",
" print(IQ)\n",
" TOPLEVEL = tk.Toplevel()\n",
" TOPLEVEL.title(\"Plot\")\n",
" #plotting\n",
" f = Figure(figsize=(5,5), dpi=100)\n",
" a = f.add_subplot(111)\n",
" a.plot(Q_RANGE,IQ)\n",
"\n",
" canvas = FigureCanvasTkAgg(f, TOPLEVEL)\n",
" canvas.show()\n",
" canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n",
"\n",
" toolbar = NavigationToolbar2TkAgg(canvas, TOPLEVEL)\n",
" toolbar.update()\n",
" canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n",
" \n",
" \n",
"\n",
"root = BasicApp()\n",
"root.title(\"Pysaxs\")\n",
"root.mainloop()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"Exception in Tkinter callback\n",
"Traceback (most recent call last):\n",
" File \"/usr/lib/python3.5/tkinter/__init__.py\", line 1553, in __call__\n",
" return self.func(*args)\n",
" File \"<ipython-input-2-b33ff43e70b5>\", line 42, in <lambda>\n",
" self.Confirm = tk.Button(self.frame, text= \"Confirm\", command =lambda: AddSphere(self))\n",
" File \"<ipython-input-2-b33ff43e70b5>\", line 59, in AddSphere\n",
" SCT.shapes[self.i-1].x = X\n",
"IndexError: list index out of range\n",
"Exception in Tkinter callback"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"\n",
"Traceback (most recent call last):\n",
" File \"/usr/lib/python3.5/tkinter/__init__.py\", line 1553, in __call__\n",
" return self.func(*args)\n",
" File \"<ipython-input-2-b33ff43e70b5>\", line 42, in <lambda>\n",
" self.Confirm = tk.Button(self.frame, text= \"Confirm\", command =lambda: AddSphere(self))\n",
" File \"<ipython-input-2-b33ff43e70b5>\", line 59, in AddSphere\n",
" SCT.shapes[self.i-1].x = X\n",
"IndexError: list index out of range\n",
"/usr/local/lib/python3.5/dist-packages/analytical.py:292: RuntimeWarning: invalid value encountered in double_scalars\n",
" top = 3*self.contrast*(np.sin(q*self.r)-(q*self.r*np.cos(self.r*q)))/(q*self.r)**3\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan]\n",
"[ nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
" nan nan nan nan nan nan nan nan nan nan]"
]
}
]
},
{
"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": [
" print(\"BYE BYE\")\n",
" self.x.destroy()\n",
" self.x_val.destory()\n",
"\n",
" self.y.destroy()\n",
" self.y_val.destroy()\n",
"\n",
" self.z.destroy()\n",
" self.z_val.destroy()\n",
"\n",
" self.rho.destroy()\n",
" self.rho_val.destroy()\n",
"\n",
" self.radius.destroy()\n",
" self.radius_val.destroy()\n",
"\n",
" self.Confirm.destroy()\n",
" self.Delete.destroy()\n",
" if self.Scatterer.shapes[self.i]:\n",
" self.Scatterer.shapes.remove(self.Scatterer.shapes[self.i])"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
yhunroh/SSHS-Waifu | Temp/Train_02.ipynb | 1 | 5004 | {
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.78077e+08\n",
"1.46634e+08\n",
"1.31642e+08\n",
"끝났띠\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from PIL import Image\n",
"import numpy as np\n",
"from datetime import datetime\n",
"\n",
"\n",
"#이미지, 상수들\n",
"learning_rate=1e-3\n",
"W1=640\n",
"H1=360\n",
"W2=1280\n",
"H2=720\n",
"path=\"\"\n",
"pref1=\"360p/\"\n",
"pref2=\"720p/\"\n",
"suff1=\"_360.jpg\"\n",
"suff2=\"_720.jpg\"\n",
"train_num=30\n",
"file_num=8#6#30\n",
"#batch_num=1000\n",
"\n",
"\n",
"#가중치 초기화 함수\n",
"def weight_variable(shape, name):\n",
" initial = tf.truncated_normal(shape, stddev=0.01)\n",
" return tf.Variable(initial, name=name)\n",
"#절편 초기화 함수\n",
"def bias_variable(shape, name):\n",
" initial = tf.constant(0.01, shape=shape)\n",
" return tf.Variable(initial, name=name)\n",
"#2D 컨벌루션 실행\n",
"def conv2d(x, W):\n",
" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
"def getimage(idx):\n",
" img_1=Image.open(path+pref1+str(idx)+suff1)\n",
" array_1=np.array(img_1)[:, :]\n",
" array_1=array_1.astype(np.float32)\n",
"\n",
" img_1_720 = img_1.resize((W2, H2))\n",
" array_1_720=np.array(img_1_720)[:, :]\n",
" array_1_720=array_1_720.astype(np.float32)\n",
"\n",
" img_2=Image.open(path+pref2+str(idx)+suff2)\n",
" array_2=np.array(img_2)[:, :, 0:3]\n",
" array_2=array_2.astype(np.float32)\n",
" return array_1, array_1_720, array_2\n",
"\n",
"def l_relu(x, alpha=0.):\n",
" return tf.nn.relu(x)-alpha*tf.nn.relu(-x)\n",
"\n",
"\n",
"\n",
"#학습때 사용하는 변수들\n",
"x_image = tf.placeholder(np.float32, shape=[None, H1, W1, 3])\n",
"x_image_720 = tf.placeholder(np.float32, shape=[None, H2, W2, 3])\n",
"y_image = tf.placeholder(np.float32, shape=[None, H2, W2, 3])\n",
"#가중치, 절편, 결과\n",
"W_conv = weight_variable([5, 5, 3, 3], name='weight')##\n",
"b_conv = bias_variable([3], name='bias')##\n",
"y_conv = l_relu(conv2d(x_image_720, W_conv)+b_conv, alpha=0.5)##\n",
"y_res = tf.reshape(y_conv, [-1, H2, W2, 3])+x_image_720# 고쳐야됨\n",
"\n",
"cost = tf.reduce_sum((y_image-y_res)*(y_image-y_res))\n",
"train_step = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n",
"saver = tf.train.Saver()\n",
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())\n",
"#saver.restore(sess, \"01/models.ckpt\")\n",
"\n",
"\n",
"for steps in range(train_num):\n",
" for index in range(460, 460+file_num):\n",
" array360, array360_720, array720 = getimage(index)\n",
" sess.run(train_step, feed_dict={x_image:[array360], x_image_720:[array360_720], y_image:[array720]})\n",
" #print(sess.run(cost, feed_dict={x_image:[array360], y_image:[array720]}))\n",
" if(steps%5==4):\n",
" showres(index, steps)\n",
" if(steps%10==0):\n",
" print(sess.run(cost, feed_dict={x_image:[array360], x_image_720:[array360_720],y_image:[array720]}))\n",
" #f=open(\"C:/image/logs.txt\", \"a\")\n",
" #f.write(str(datetime.now())+\" | \"+str(steps)+\" : \"+str(sess.run(cost, feed_dict={x_image:[array360], y_image:[array720]}))+'\\n')\n",
" #f.close()\n",
" #if(steps%10==):\n",
" #save_path=saver.save(sess, \"01/models.ckpt\")\n",
"print (\"끝났띠\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def showres(index, steps):\n",
" test360, test_res, test720 = getimage(index)\n",
" A=sess.run(y_res, feed_dict={x_image:[test360], x_image_720:[test_res], y_image:[test720]})\n",
" result = A[0].astype(np.uint8)\n",
" img360=test360.astype(np.uint8)\n",
" img720=test720.astype(np.uint8)\n",
" #Image.fromarray(img360, 'RGB').save('res/img360_'+str(steps)+'.jpg')\n",
" #Image.fromarray(img720, 'RGB').save('res/img720_'+str(steps)+'.jpg')\n",
" Image.fromarray(result, 'RGB').save('res/result_'+str(steps)+'.jpg')"
]
}
],
"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": 2
}
| mit |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/machineLearning_notebooks/01_Naive_Bayes/Distributions.ipynb | 2 | 19764 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Probability Distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we are going to learn about probability distributions. Central to the fields of probability and statistics are *random variables*. Random variables (RVs) are used to model random experiments, such as the roll of a die. \n",
"\n",
"For each random variable, there is a *probability distribution function* (or probability mass function for discrete RVs) that gives the probability of each outcome in the experiment. \n",
"\n",
"We will learn about three important distributions today:\n",
"- the binomial distribution\n",
"- the normal (or \"Gaussian\") distribution\n",
"- and the t distribution. \n",
"\n",
"As you may remember, random variables (and their outcomes) can be either *discrete* or *continuous*. The binomial distribution is a discrete distribution, while the normal and t distributions are continuous distributions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Binomial Distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Binomial experiment:\n",
"* The process consists of a sequence of $n$ trials.\n",
" * $n=1$ is a common case, and this is known as the **Bernoulli distribution**\n",
"* Only two exclusive outcomes are possible for each trial (a success and a failure)\n",
"* If the probability of a success is '$p$' then the probability of failure is $q=1-p$\n",
"* The trials are independent.\n",
"* The random variable $X$ is the number of successes (after these $n$ trials)\n",
"\n",
"The formula for a Binomial Distribution Probability Mass Function turns out to be:\n",
"\n",
"$$Pr(X=k)={n \\choose k} p^k (1-p)^{n-k}$$\n",
"\n",
"where n = number of trials, $k$ = number of successes, $p$ = probability of success, $1-p$ = probability of failure (often written as $q=1-p$).\n",
"This means that to get exactly '$k$' successes in '$n$' trials, we want exactly '$k$' successes: $$p^k$$ and we want '$n-k$' failures:$$(1-p)^{n-k}$$ Then finally, there are ${n \\choose k}$ ways of putting '$k$' successes in '$n$' trials. So we multiply all these together to get the probability of exactly that many success and failures in those $n$ trials!\n",
"Quick note, ${n \\choose k}$ refers to the number of possible combinations of $N$ things taken $k$ at a time.\n",
"This is also equal to: $${n \\choose k} = \\frac{n!}{k!(n-k)!}$$\n",
"\n",
"\n",
"Quick example to get you thinking. Let's say I'm a basketball player and I shoot 200 shots a day with 50% accuracy. On any given day, this is a random experiment with a binomial distribution and 200 trials."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy.stats import binom\n",
"\n",
"n = 200 #number of trials\n",
"p = 0.5 #probability of success\n",
"\n",
"# We can get stats: Mean('m'), variance('v'), skew('s'), and/or kurtosis('k')\n",
"mn,vr= binom.stats(n,p)\n",
"\n",
"print(mn)\n",
"print(vr**0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's investigate the mean and standard deviation for the binomial distribution further.\n",
"The mean of a binomial distribution is simply: $$\\mu=n*p$$\n",
"This intuitively makes sense, the average number of successes should be the total trials multiplied by your average success rate.\n",
"Similarly we can see that the standard deviation of a binomial is: $$\\sigma=\\sqrt{n*q*p}$$\n",
"\n",
"\n",
"\n",
"Let's try another example to see the full PMF (Probability Mass Function) plot.\n",
"Imagine you flip a fair coin. Your probability of getting a heads is p=0.5 (success in this example).\n",
"So what does your probability mass function look like for 10 coin flips?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Set up a new example, let's say n= 10 coin flips and p=0.5 for a fair coin.\n",
"n=10\n",
"p=0.5\n",
"\n",
"# Set up n success, remember indexing starts at 0, so use n+1\n",
"x = range(n+1)\n",
"\n",
"# Now create the probability mass function\n",
"Y = binom.pmf(x,n,p)\n",
"\n",
"#Show\n",
"Y\n",
"\n",
"# Next we'll visualize the pmf by plotting it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally we will plot the binomial distribution. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# For simple plots, matplotlib is fine, seaborn is unnecessary.\n",
"\n",
"# Now simply use plot\n",
"plt.plot(x,Y,'o')\n",
"\n",
"#Title (use y=1.08 to raise the long title a little more above the plot)\n",
"plt.title('Binomial Distribution PMF: 10 coin Flips, Odds of Success for Heads is p=0.5',y=1.08)\n",
"\n",
"#Axis Titles\n",
"plt.xlabel('Number of Heads')\n",
"plt.ylabel('Probability')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks awfully bell shaped..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Going further\n",
"Suppose you play a Blackjack, and have a 50\\% chance of winning. You start with a 1 dollar bet, and if you lose, you double the amount you bet on the next play. So if you play three rounds, and Lose, Lose, Win, then you lost 1 dollar on the first round, 2 dollars on the second round, but gained 4 dollars on the first round, for a net profit of 1 dollar.\n",
"\n",
"What is the [expected value](https://en.wikipedia.org/wiki/Expected_value) of your winnings (assuming you play as many rounds as it takes until you win)?\n",
"\n",
"In fact, are you guaranteed to make money?\n",
"\n",
"Why might this be a bad idea in practice?\n",
"\n",
"Note: this is a famous strategy known as the [Gambler's Ruin](https://en.wikipedia.org/wiki/Gambler%27s_ruin)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Normal Distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we will talk about the normal distribution. This is the most important continuous distribution. It is also called the Gaussian distribution, or the bell curve. While the binomial distribution is often considered the most basic discrete distribution, the normal is the most fundamental of all continuous random variables."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://static.squarespace.com/static/549dcda5e4b0a47d0ae1db1e/54a06d6ee4b0d158ed95f696/54a06d70e4b0d158ed960413/1412514819046/1000w/Gauss_banknote.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(url='https://static.squarespace.com/static/549dcda5e4b0a47d0ae1db1e/54a06d6ee4b0d158ed95f696/54a06d70e4b0d158ed960413/1412514819046/1000w/Gauss_banknote.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the normal pdf. The first equation below is the pdf for the normal distribution with mean $\\mu$ and variance $\\sigma^2$. The second equations is the standard normal $\\Phi$ with mean $0$ and variance $1$. We can always transform our random variable $X \\sim {\\mathcal {N}}(\\mu ,\\,\\sigma ^{2})$ to the standard normal $Z \\sim {\\mathcal {N}}(0 , \\, 1)$ by using the change of variables formula in the third equation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ f(x\\;|\\;\\mu ,\\sigma ^{2})={\\frac {1}{\\sqrt {2\\pi \\sigma ^{2}}}}\\;e^{-{\\frac {(x-\\mu )^{2}}{2\\sigma ^{2}}}} $$\n",
"\n",
"$$ f(x,\\mu,\\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}}e^{-\\frac{z^2}{2}} $$\n",
"\n",
"$$ z=\\frac{(X-\\mu)}{\\sigma} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot of the pdf may be familiar to some of you by now."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Import\n",
"import matplotlib as mpl\n",
"%matplotlib inline\n",
"\n",
"#Import the stats library\n",
"from scipy import stats\n",
"\n",
"# Set the mean\n",
"mn = 0\n",
"\n",
"#Set the standard deviation\n",
"std_dev = 1\n",
"\n",
"# Create a range\n",
"X = np.arange(-4,4,0.001)\n",
"\n",
"#Create the normal distribution for the range\n",
"Y = stats.norm.pdf(X,mn,std_dev)\n",
"\n",
"#\n",
"plt.plot(X,Y)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"http://upload.wikimedia.org/wikipedia/commons/thumb/2/25/The_Normal_Distribution.svg/725px-The_Normal_Distribution.svg.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(url='http://upload.wikimedia.org/wikipedia/commons/thumb/2/25/The_Normal_Distribution.svg/725px-The_Normal_Distribution.svg.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bell curve, centered at the mean, also shows the variance with how wide the bell is. The x-axis gives the realized values of the random variable, and the y values of the curve give the probability these values are realized by the random variable/experiment. This image does a great job of giving a good interpretation of what percent of the outcomes lie within $n$ standard deviations of the mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using python, we can draw samples from a normal distribution and plot these samples using a histogram. The idea is that if we take enough samples, this histogram should look like the bell curve. We first start with 30, then up it to 1000. The histogram gives you a good idea of what the pdf of these samples will look like, but just as a visual aid we also plot a pdf estimator in blue. This is called a kernel density estimator, but it is beyond the scope of this tutorial. We plot the normal distribution these samples came from in green."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the mean and the standard deviaiton\n",
"mu,sigma = 0,1\n",
"\n",
"# Now grab 30 random numbers from the normal distribution\n",
"norm_set = np.random.normal(mu,sigma,30)\n",
"#Now let's plot it using seaborn\n",
"\n",
"import seaborn as sns\n",
"import sklearn as sk\n",
"from scipy.stats import gaussian_kde\n",
"\n",
"results, edges = np.histogram(norm_set, normed=True)\n",
"binWidth = edges[1] - edges[0]\n",
"plt.bar(edges[:-1], results*binWidth, binWidth)\n",
"\n",
"density = gaussian_kde(norm_set)\n",
"density.covariance_factor = lambda : .4 #this is the bandwidth in the kernel density estimator\n",
"density._compute_covariance()\n",
"plt.plot(X,density(X))\n",
"\n",
"plt.plot(X,Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With enough samples, this should start to look normal."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"norm2 = np.random.normal(mu, sigma, 1000)\n",
"\n",
"results, edges = np.histogram(norm2, normed=True)\n",
"binWidth = edges[1] - edges[0]\n",
"plt.bar(edges[:-1], results*binWidth, binWidth)\n",
"\n",
"density = gaussian_kde(norm2)\n",
"density.covariance_factor = lambda : .4\n",
"density._compute_covariance()\n",
"plt.plot(X,density(X))\n",
"\n",
"plt.plot(X,Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Central Limit Theorem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Central Limit Theorem is one of the most important theorems in statistical theory. It states that when independent random variables are added, their sum tends toward a normal distribution even if the original variables themselves are not normally distributed. \n",
"\n",
"This may seem obscure so we will explain it with the previous example. We saw that as we took more samples from the normal distribution, the total distribution looks more and more normal. Here we will take more and more samples from a normal, and see how the mean of the samples behaves."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n10 = np.random.normal(mu, sigma, 10)\n",
"n100 = np.random.normal(mu, sigma, 100)\n",
"n1000 = np.random.normal(mu, sigma, 1000)\n",
"n10000 = np.random.normal(mu, sigma, 10000)\n",
"\n",
"print(n10.mean(), n100.mean(), n1000.mean(), n10000.mean() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that as we add more samples, the mean approaches 0, which is the true mean. This is the central limit theorem: the sum of a lot of random variables tends towards a Gaussian distribution (under some conditions). The next image shows how adding more samples to a binomial distribution makes it look more and more Gaussian."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Image(url='https://upload.wikimedia.org/wikipedia/commons/8/8c/Dice_sum_central_limit_theorem.svg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Student's t distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the normal distribution it is often assumed that the sample size is assumed large ($N>30$). The t distribution allows for use of small samples, but does so by sacrificing certainty with a margin-of-error trade-off (i.e. a larger variance). The t distribution takes into account the sample size using n-1 degrees of freedom, which means there is a different t distribution for every different sample size. If we see the t distribution against a normal distribution, you'll notice the tail ends increase as the peak get 'squished' down.\n",
"\n",
"To be precise, the t-distribution models the **sample mean** of $N$ observations that are taken from a normal distrubtion.\n",
"\n",
"It's important to note, that as $N$ gets larger, the t distribution converges into a normal distribution.\n",
"To further explain degrees of freedom and how it relates to the t distribution, you can think of degrees of freedom as an adjustment to the sample size, such as (n-1). This is connected to the idea that we are estimating something of a larger population, in practice it gives a slightly larger margin of error in the estimate.\n",
"Let's define a new variable called t, where : $$t=\\frac{\\overline{X}-\\mu}{s}\\sqrt{N-1}=\\frac{\\overline{X}-\\mu}{s/\\sqrt{N}}$$\n",
"which is analogous to the z statistic given by $$z=\\frac{\\overline{X}-\\mu}{\\sigma/\\sqrt{N}}$$\n",
"The sampling distribution for t can be obtained:\n",
"$$ f(t) = \\frac {\\varGamma(\\frac{v+1}{2})}{\\sqrt{v\\pi}\\varGamma(\\frac{v}{2})} (1+\\frac{t^2}{v})^{-\\frac{v+1}{2}}$$\n",
"Where the gamma function is: $$\\varGamma(n)=(n-1)!$$\n",
"And v is the number of degrees of freedom, typically equal to N-1.\n",
"\n",
"Please don't worry about these formulas. Literally no one memorizes this distribution. Just know the binomial and the normal, and the idea of what a t distribution is for (small sample sizes)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The t distribution is plotted in blue, and the normal distribution is in green as well to compare."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Import for plots\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"#Import the stats library\n",
"from scipy.stats import t\n",
"\n",
"#import numpy\n",
"import numpy as np\n",
"\n",
"# Create x range\n",
"x = np.linspace(-5,5,100)\n",
"\n",
"# Create the t distribution with scipy\n",
"rv = t(3)\n",
"\n",
"# Plot the PDF versus the x range\n",
"plt.plot(x, rv.pdf(x))\n",
"plt.plot(X, Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the t distribution in blue has fatter tails than the normal distribution. This means there is more probability of realizing an observation in that region. As a consequence, there is less area under the peak/it is less spikey. This is a reflection of the fact that we have less certainty in where the observations will land because we have a smaller sample size of evidence to support this estimation of the data's distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multivariate Normal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A multivariate normal with unequal variances in the x and y direction and principal axes also rotated from the origin. Notice how each shade or horizontal slice looks like an ellipse.\n",
"\n",
"What *is* multivariate data? It just means more than one dimension. \n",
"* An example of a **one-dimensional** random variable is the height of a person randomly chosen from this class\n",
"* An example of a **multi-variate** random variable is the (height, shoe-size) of a person randomly chosen from this class\n",
"\n",
"For an example like (height, shoe-size), where x=height and y=shoe-size, we expect the two variables to be correlated. Other examples, like (height, first-letter-of-your-name) are not correlated. If the variables are correlated, statistical tests should know about it!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import scipy.stats\n",
"x, y = np.mgrid[-1:1:.01, -1:1:.01]\n",
"pos = np.empty(x.shape + (2,))\n",
"pos[:, :, 0] = x; pos[:, :, 1] = y\n",
"rv = scipy.stats.multivariate_normal([0.5, -0.2], [[2.0, 0.3], [0.3, 0.5]])\n",
"plt.contourf(x, y, rv.pdf(pos))"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
kunalj101/scipy2015-blaze-bokeh | 1.7 Animate.ipynb | 6 | 2616 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <img src=images/continuum_analytics_b&w.png align=\"left\" width=\"15%\" style=\"margin-right:15%\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 align='center'>Bokeh Tutorial</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.7 Animate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise: Animate the climate map**\n",
"\n",
"Create a loop that updates the data source of the `climate_map` through time (for every month and year). You'll need to use the bokeh-server.\n",
"\n",
"*Note: when using the output_server make sure that your data source doesn't contain `nans`*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Output option"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create layout"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Select data source for climate_map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a loop that goes through month and year and updates the image data"
]
}
],
"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 |
yunfeiz/py_learnt | quant/candlestick/kline_V2.1.ipynb | 1 | 11510 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# -*- coding:utf-8 -*-\n",
"import tushare as ts\n",
"import matplotlib.pyplot as plt\n",
"#import matplotlib.finance as mpf\n",
"import mpl_finance as mpf\n",
"import matplotlib.ticker as ticker\n",
"\n",
"import datetime\n",
"import numpy as np\n",
"import pandas as pd\n",
"import time\n",
"\n",
"from pyecharts.charts import Kline\n",
"from pyecharts.charts import Line\n",
"from pyecharts.charts import Bar\n",
"from pyecharts.charts import Grid\n",
"\n",
"from pandas import DataFrame, Series\n",
"\n",
"from pyecharts import options as opts\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def n_days_ago(n):\n",
" today=datetime.date.today()\n",
" ndays_ago=today-datetime.timedelta(n)\n",
" return str(ndays_ago)\n",
"\n",
"def hhv(s, n):\n",
" return Series.rolling(s, n).max()\n",
"\n",
"def llv(s, n):\n",
" return Series.rolling(s, n).min()\n",
"\n",
" \n",
"#ichimoku\n",
"def ichimoku(s, n1=9, n2=26, n3=52):\n",
"\n",
" #average of N-day high and N-day low\n",
" conv = (hhv(s, n1) + llv(s, n1)) / 2\n",
"\n",
" #mid point of the latest 26 days\n",
" base = (hhv(s, n2) + llv(s, n2)) / 2\n",
" \n",
" #mid-point between the first 2 lines, and plot 26 periods ahead\n",
" spana = (conv + base) / 2\n",
" \n",
" #mid-point between the 52-period low and 52-period high, and plot 26 periods\n",
" spanb = (hhv(s, n3) + llv(s, n3)) / 2\n",
" \n",
" k = s\n",
"\n",
" #Lspan is closing price, and plot 26 periods in the past\n",
" return DataFrame(dict(k=k,conv=conv, base=base, spana=spana.shift(n2),\n",
" spanb=spanb.shift(n2), lspan=s.shift(-n2)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start_date = '2018-07-01'\n",
"now = int(time.time())\n",
"timeArray = time.localtime(now)\n",
"Time = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n",
"end_date = Time \n",
"ndays = 360\n",
"stock_selected = '600487'\n",
"MAX_SMA=100\n",
"start_date = n_days_ago(ndays + MAX_SMA)\n",
"ktype = '60'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = ts.get_k_data(stock_selected,start_date,end_date)\n",
"#v_kline = np.array(df[['open','close','low','high']][MAX_SMA:])\n",
"date_kline = []\n",
"for i,j in enumerate(np.array(df[['date']][MAX_SMA:])):\n",
" #print(j)\n",
" temp_str = str(j[0]).replace('-0','/')\n",
" date_kline.append(temp_str.replace('-','/'))\n",
"\n",
"v_kline= []\n",
"for i,j in enumerate(np.array(df[['open','close','low','high']][MAX_SMA:])):\n",
" #print(j)\n",
" v_kline.append(list(j))\n",
"\n",
"SMA5= []\n",
"for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],5).mean()[MAX_SMA:])):\n",
" #print(j)\n",
" SMA5.append(list(j))\n",
"SMA55= []\n",
"for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],55).mean()[MAX_SMA:])):\n",
" #print(j)\n",
" SMA55.append(list(j))\n",
"SMA100= []\n",
"for i,j in enumerate(np.array(pd.Series.rolling(df[['close']],100).mean()[MAX_SMA:])):\n",
" #print(j)\n",
" SMA100.append(list(j))\n",
" \n",
"df_ich = ichimoku(df['close'])\n",
"\n",
"base= list(np.array(df_ich['base'])[MAX_SMA:])\n",
"conv= list(np.array(df_ich['conv'])[MAX_SMA:])\n",
"spana= list(np.array(df_ich['spana'])[MAX_SMA:])\n",
"spanb= list(np.array(df_ich['spanb'])[MAX_SMA:])\n",
"\n",
"v_volume = np.array(df['volume'][MAX_SMA:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kline=Kline().add_xaxis(date_kline).add_yaxis('Kline',list(v_kline)).set_global_opts(\n",
" xaxis_opts=opts.AxisOpts(is_scale=True),\n",
" yaxis_opts=opts.AxisOpts(\n",
" is_scale=True,\n",
" splitarea_opts=opts.SplitAreaOpts(\n",
" is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)\n",
" ),\n",
" ),\n",
" datazoom_opts=[opts.DataZoomOpts(pos_bottom=\"-2%\")],\n",
" title_opts=opts.TitleOpts(title=\"Kline\"),\n",
" )\n",
"\n",
"#kline.render_notebook()\n",
"\n",
"volume_bar = Bar().add_xaxis(date_kline).add_yaxis(\"\", list(v_volume),label_opts=opts.LabelOpts(is_show=False)).set_global_opts(\n",
" title_opts=opts.TitleOpts(title=\"\", pos_top=\"48%\"),\n",
" legend_opts=opts.LegendOpts(pos_top=\"48%\"),\n",
" )\n",
"#volume_bar.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sma_line = Line().add_xaxis(date_kline).add_yaxis(\"SMA5\",list(SMA5),is_smooth=True,label_opts=opts.LabelOpts(is_show=False),\n",
" linestyle_opts=opts.LineStyleOpts(color=\"red\", width=1))\n",
"sma_line.add_yaxis(\"base\",list(base),is_smooth=True,label_opts=opts.LabelOpts(is_show=False))\n",
"sma_line.add_yaxis(\"conv\",list(conv),is_smooth=True,label_opts=opts.LabelOpts(is_show=False),\n",
" linestyle_opts=opts.LineStyleOpts(color=\"black\", width=3))\n",
"sma_line.add_yaxis(\"spana\",list(spana),is_smooth=True,\n",
" label_opts=opts.LabelOpts(is_show=False),\n",
" areastyle_opts=opts.AreaStyleOpts(opacity=0.3),\n",
" linestyle_opts=opts.LineStyleOpts(color=\"red\", width=3))\n",
"sma_line.add_yaxis(\"spanb\",list(spanb),is_smooth=True,\n",
" label_opts=opts.LabelOpts(is_show=False),\n",
" areastyle_opts=opts.AreaStyleOpts(opacity=0.3),\n",
" linestyle_opts=opts.LineStyleOpts(color=\"green\", width=3))\n",
"kline.overlap(sma_line).render_notebook()\n",
"grid = (\n",
" Grid()\n",
" .add(kline, grid_opts=opts.GridOpts(pos_bottom=\"40%\"))\n",
" .add(volume_bar, grid_opts=opts.GridOpts(pos_top=\"70%\"))\n",
")\n",
"grid.render_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r_hex = '#dc2624' # red, RGB = 220,38,36\n",
"dt_hex = '#2b4750' # dark teal, RGB = 43,71,80\n",
"tl_hex = '#45a0a2' # teal, RGB = 69,160,162\n",
"r1_hex = '#e87a59' # red, RGB = 232,122,89\n",
"tl1_hex = '#7dcaa9' # teal, RGB = 125,202,169\n",
"g_hex = '#649E7D' # green, RGB = 100,158,125\n",
"o_hex = '#dc8018' # orange, RGB = 220,128,24\n",
"tn_hex = '#C89F91' # tan, RGB = 200,159,145\n",
"g50_hex = '#6c6d6c' # grey-50, RGB = 108,109,108\n",
"bg_hex = '#4f6268' # blue grey, RGB = 79,98,104\n",
"g25_hex = '#c7cccf' # grey-25, RGB = 199,204,207"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pyecharts.commons.utils import JsCode\n",
"x_data = [\"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\"]\n",
"y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200]\n",
"\n",
"background_color_js = (\n",
" \"new echarts.graphic.LinearGradient(0, 0, 0, 1, \"\n",
" \"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)\"\n",
" )\n",
"area_color_js = (\n",
" \"new echarts.graphic.LinearGradient(0, 0, 0, 1, \"\n",
" \"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)\"\n",
" )\n",
"\n",
"c=Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))).add_xaxis(xaxis_data=x_data).add_yaxis(\n",
" series_name=\"注册总量\",\n",
" y_axis=y_data,\n",
" is_smooth=True,\n",
" is_symbol_show=True,\n",
" symbol=\"circle\",\n",
" symbol_size=6,\n",
" linestyle_opts=opts.LineStyleOpts(color=\"#fff\"),\n",
" label_opts=opts.LabelOpts(is_show=True, position=\"top\", color=\"white\"),\n",
" itemstyle_opts=opts.ItemStyleOpts(\n",
" color=\"red\", border_color=\"#fff\", border_width=3\n",
" ),\n",
" tooltip_opts=opts.TooltipOpts(is_show=False),\n",
" areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),\n",
" ).set_global_opts(\n",
" title_opts=opts.TitleOpts(\n",
" title=\"OCTOBER 2015\",\n",
" pos_bottom=\"5%\",\n",
" pos_left=\"center\",\n",
" title_textstyle_opts=opts.TextStyleOpts(color=\"#fff\", font_size=16),\n",
" ),\n",
" xaxis_opts=opts.AxisOpts(\n",
" type_=\"category\",\n",
" boundary_gap=False,\n",
" axislabel_opts=opts.LabelOpts(margin=30, color=\"#ffffff63\"),\n",
" axisline_opts=opts.AxisLineOpts(is_show=False),\n",
" axistick_opts=opts.AxisTickOpts(\n",
" is_show=True,\n",
" length=25,\n",
" linestyle_opts=opts.LineStyleOpts(color=\"#ffffff1f\"),\n",
" ),\n",
" splitline_opts=opts.SplitLineOpts(\n",
" is_show=True, linestyle_opts=opts.LineStyleOpts(color=\"#ffffff1f\")\n",
" ),\n",
" ),\n",
" yaxis_opts=opts.AxisOpts(\n",
" type_=\"value\",\n",
" position=\"right\",\n",
" axislabel_opts=opts.LabelOpts(margin=20, color=\"#ffffff63\"),\n",
" axisline_opts=opts.AxisLineOpts(\n",
" linestyle_opts=opts.LineStyleOpts(width=2, color=\"#fff\")\n",
" ),\n",
" axistick_opts=opts.AxisTickOpts(\n",
" is_show=True,\n",
" length=15,\n",
" linestyle_opts=opts.LineStyleOpts(color=\"#ffffff1f\"),\n",
" ),\n",
" splitline_opts=opts.SplitLineOpts(\n",
" is_show=True, linestyle_opts=opts.LineStyleOpts(color=\"#ffffff1f\")\n",
" ),\n",
" ),\n",
" legend_opts=opts.LegendOpts(is_show=False),\n",
" )\n",
"c.render_notebook()"
]
},
{
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
mari-linhares/tensorflow-workshop | code_samples/estimators-for-free/.ipynb_checkpoints/estimators_for_free-checkpoint.ipynb | 1 | 49739 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *Before start: make sure you deleted the output_dir folder from this path*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some things we get for free by using Estimators\n",
"\n",
"Estimators are a high level abstraction (Interface) that supports all the basic operations you need to support a ML model on top of TensorFlow.\n",
"\n",
"Estimators:\n",
" * provide a simple interface for users of canned model architectures: Training, evaluation, prediction, export for serving.\n",
" * provide a standard interface for model developers\n",
" * drastically reduces the amount of user code required. This avoids bugs and speeds up development significantly.\n",
" * enable building production services against a standard interface.\n",
" * using experiments abstraction give you free data-parallelism (more [here](https://github.com/mari-linhares/tensorflow-workshop/tree/master/code_samples/distributed_tensorflow))\n",
"\n",
"In the Estimator's interface includes: Training, evaluation, prediction, export for serving.\n",
"\n",
"Image from [Effective TensorFlow for Non-Experts (Google I/O '17)](https://www.youtube.com/watch?v=5DknTFbcGVM)\n",
"\n",
"\n",
"You can use a already implemented estimator (canned estimator) or implement your own (custom estimator).\n",
"\n",
"This tutorial is not focused on how to build your own estimator, we're using a custom estimator that implements a [CNN classifier for MNIST dataset](https://www.tensorflow.org/get_started/mnist/pros) defined in the model.py file, but we're not going into details about how that's implemented.\n",
"\n",
"Here we're going to show how Estimators make your life easier, once you have a estimator model is very simple to change your model and compare results.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Having a look at the code and running the experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2.0-rc1\n"
]
}
],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"# our model \n",
"import model as m\n",
"\n",
"# tensorflow\n",
"import tensorflow as tf \n",
"print(tf.__version__) #tested with tf v1.2\n",
"\n",
"from tensorflow.contrib import learn\n",
"from tensorflow.contrib.learn.python.learn import learn_runner\n",
"from tensorflow.python.estimator.inputs import numpy_io\n",
"\n",
"# MNIST data\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"# Numpy\n",
"import numpy as np\n",
"\n",
"# Enable TensorFlow logs\n",
"tf.logging.set_verbosity(tf.logging.INFO)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the data\n",
"\n",
"We're not going into details here"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/MNIST/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/MNIST/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/MNIST/t10k-images-idx3-ubyte.gz\n",
"Extracting /tmp/MNIST/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"# Import the MNIST dataset\n",
"mnist = input_data.read_data_sets(\"/tmp/MNIST/\", one_hot=True)\n",
"\n",
"x_train = np.reshape(mnist.train.images, (-1, 28, 28, 1))\n",
"y_train = mnist.train.labels\n",
"x_test = np.reshape(mnist.test.images, (-1, 28, 28, 1))\n",
"y_test = mnist.test.labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining the input function\n",
"\n",
"If we look at the image above we can see that there're two main parts in the diagram, a input function interacting with data files and the Estimator interacting with the input function and checkpoints.\n",
"\n",
"This means that the estimator doesn't know about data files, it knows about input functions. So if we want to interact with a data set we need to creat an input function that interacts with it, in this example we are creating a input function for the train and test data set.\n",
"\n",
"You can learn more about input functions [here](https://www.tensorflow.org/get_started/input_fn)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BATCH_SIZE = 128\n",
"\n",
"x_train_dict = {'x': x_train }\n",
"train_input_fn = numpy_io.numpy_input_fn(\n",
" x_train_dict, y_train, batch_size=BATCH_SIZE, \n",
" shuffle=True, num_epochs=None, \n",
" queue_capacity=1000, num_threads=4)\n",
"\n",
"x_test_dict = {'x': x_test }\n",
"test_input_fn = numpy_io.numpy_input_fn(\n",
" x_test_dict, y_test, batch_size=BATCH_SIZE, shuffle=False, num_epochs=1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating an experiment\n",
"\n",
"After an experiment is created (by passing an Estimator and inputs for training and evaluation), an Experiment instance knows how to invoke training and eval loops in a sensible fashion for distributed training. More about it [here](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Experiment)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# parameters\n",
"LEARNING_RATE = 0.01\n",
"STEPS = 1000\n",
"\n",
"# create experiment\n",
"def generate_experiment_fn():\n",
" def _experiment_fn(run_config, hparams):\n",
" del hparams # unused, required by signature.\n",
" # create estimator\n",
" model_params = {\"learning_rate\": LEARNING_RATE}\n",
" estimator = tf.estimator.Estimator(model_fn=m.get_model(), \n",
" params=model_params,\n",
" config=run_config)\n",
"\n",
" train_input = train_input_fn\n",
" test_input = test_input_fn\n",
" \n",
" return tf.contrib.learn.Experiment(\n",
" estimator,\n",
" train_input_fn=train_input,\n",
" eval_input_fn=test_input,\n",
" train_steps=STEPS\n",
" )\n",
" return _experiment_fn"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Run the experiment"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"INFO:tensorflow:Using config: {'_model_dir': 'output_dir/fist_run', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb71872fcf8>, '_master': '', '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_evaluation_master': '', '_save_summary_steps': 100, '_task_id': 0, '_task_type': None, '_session_config': None, '_keep_checkpoint_max': 5, '_environment': 'local', '_keep_checkpoint_every_n_hours': 10000, '_tf_random_seed': None, '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
"}\n",
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:268: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
"Instructions for updating:\n",
"Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Saving checkpoints for 1 into output_dir/fist_run/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:step = 1, loss = 2.30245\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-19:39:47\n",
"INFO:tensorflow:Restoring parameters from output_dir/fist_run/model.ckpt-1\n",
"INFO:tensorflow:Evaluation [1/100]\n",
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"INFO:tensorflow:Finished evaluation at 2017-06-19-19:39:49\n",
"INFO:tensorflow:Saving dict for global step 1: accuracy = 0.0983, global_step = 1, loss = 2.30234\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Validation (step 1): global_step = 1, accuracy = 0.0983, loss = 2.30234\n",
"INFO:tensorflow:global_step/sec: 11.5827\n",
"INFO:tensorflow:step = 101, loss = 2.30115 (8.634 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6429\n",
"INFO:tensorflow:step = 201, loss = 2.29974 (5.668 sec)\n",
"INFO:tensorflow:global_step/sec: 17.366\n",
"INFO:tensorflow:step = 301, loss = 2.29684 (5.758 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5902\n",
"INFO:tensorflow:step = 401, loss = 2.29668 (5.685 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6571\n",
"INFO:tensorflow:step = 501, loss = 2.29329 (5.664 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5856\n",
"INFO:tensorflow:step = 601, loss = 2.28958 (5.686 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6614\n",
"INFO:tensorflow:step = 701, loss = 2.2871 (5.662 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5854\n",
"INFO:tensorflow:step = 801, loss = 2.2762 (5.687 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6107\n",
"INFO:tensorflow:step = 901, loss = 2.23801 (5.678 sec)\n",
"INFO:tensorflow:Saving checkpoints for 1000 into output_dir/fist_run/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Loss for final step: 2.20353.\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-19:40:47\n",
"INFO:tensorflow:Restoring parameters from output_dir/fist_run/model.ckpt-1000\n",
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"INFO:tensorflow:Finished evaluation at 2017-06-19-19:40:49\n",
"INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.4056, global_step = 1000, loss = 2.19769\n"
]
},
{
"data": {
"text/plain": [
"({'accuracy': 0.40560001, 'global_step': 1000, 'loss': 2.1976945}, [])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OUTPUT_DIR = 'output_dir/model1'\n",
"learn_runner.run(generate_experiment_fn(), run_config=tf.contrib.learn.RunConfig(model_dir=OUTPUT_DIR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running a second time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, the model is definitely not good... But, check OUTPUT_DIR path, you'll see that a output_dir folder was created and that there are a lot of files there that were created automatically by TensorFlow! \n",
"\n",
"So, most of these files are actually checkpoints, this means that **if we run the experiment again with the same model_dir it will just load the checkpoint and start from there instead of starting all over again!**\n",
"\n",
"This means that:\n",
"\n",
"- If we have a problem while training you can just restore from where you stopped instead of start all over again \n",
"- If we didn't train enough we can just continue to train \n",
"- If you have a big file you can just break it into small files and train for a while with each small file and the model will continue from where it stopped at each time :) \n",
"\n",
"**This is all true as long as you use the same model_dir!**\n",
"\n",
"So, let's run again the experiment for more 1000 steps to see if we can improve the accuracy. So, notice that the first step in this run will actually be the step 1001. So, we need to change the number of steps to 2000 (otherwhise the experiment will find the checkpoint and will think it already finished training)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"INFO:tensorflow:Using config: {'_model_dir': 'output_dir/fist_run', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb7002c3eb8>, '_master': '', '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_evaluation_master': '', '_save_summary_steps': 100, '_task_id': 0, '_task_type': None, '_session_config': None, '_keep_checkpoint_max': 5, '_environment': 'local', '_keep_checkpoint_every_n_hours': 10000, '_tf_random_seed': None, '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
"}\n",
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:268: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
"Instructions for updating:\n",
"Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Restoring parameters from output_dir/fist_run/model.ckpt-1000\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Saving checkpoints for 1001 into output_dir/fist_run/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:step = 1001, loss = 2.19622\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-20:02:36\n",
"INFO:tensorflow:Restoring parameters from output_dir/fist_run/model.ckpt-1001\n",
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"INFO:tensorflow:Finished evaluation at 2017-06-19-20:02:38\n",
"INFO:tensorflow:Saving dict for global step 1001: accuracy = 0.4069, global_step = 1001, loss = 2.19683\n",
"INFO:tensorflow:Validation (step 1001): global_step = 1001, accuracy = 0.4069, loss = 2.19683\n",
"INFO:tensorflow:global_step/sec: 12.175\n",
"INFO:tensorflow:step = 1101, loss = 2.09536 (8.214 sec)\n",
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"INFO:tensorflow:step = 1901, loss = 1.70633 (5.628 sec)\n",
"INFO:tensorflow:Saving checkpoints for 2000 into output_dir/fist_run/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Loss for final step: 1.74119.\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-20:03:36\n",
"INFO:tensorflow:Restoring parameters from output_dir/fist_run/model.ckpt-2000\n",
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"INFO:tensorflow:Finished evaluation at 2017-06-19-20:03:38\n",
"INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.7532, global_step = 2000, loss = 1.72087\n"
]
},
{
"data": {
"text/plain": [
"({'accuracy': 0.75319999, 'global_step': 2000, 'loss': 1.7208685}, [])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"STEPS = STEPS + 1000\n",
"learn_runner.run(generate_experiment_fn(), run_config=tf.contrib.learn.RunConfig(model_dir=OUTPUT_DIR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tensorboard\n",
"\n",
"Another thing we get for free is tensorboard. \n",
"\n",
"If you run: *tensorboard --logdir=OUTPUT_DIR*\n",
"\n",
"You'll see that we get the graph and some scalars, also if you use an [embedding layer](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence) you'll get an [embedding visualization](https://www.tensorflow.org/get_started/embedding_viz) in tensorboard as well!\n",
"\n",
"So, we can make small changes and we'll have an easy (and totally for free) way to compare the models.\n",
"\n",
"Let's make these changes:\n",
"1. change the learning rate to 0.05 \n",
"2. change the OUTPUT_DIR to some path in output_dir/ \n",
"\n",
"The 2. is must be inside output_dir/ because we can run: *tensorboard --logdir=output_dir/* \n",
"And we'll get both models visualized at the same time in tensorboard.\n",
"\n",
"You'll notice that the model will start from step 1, because there's no existing checkpoint in this path.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"INFO:tensorflow:Using config: {'_model_dir': 'output_dir/model2', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb71878bb70>, '_master': '', '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_evaluation_master': '', '_save_summary_steps': 100, '_task_id': 0, '_task_type': None, '_session_config': None, '_keep_checkpoint_max': 5, '_environment': 'local', '_keep_checkpoint_every_n_hours': 10000, '_tf_random_seed': None, '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
"}\n",
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/monitors.py:268: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
"Instructions for updating:\n",
"Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Saving checkpoints for 1 into output_dir/model2/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:step = 1, loss = 2.30336\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-21:39:48\n",
"INFO:tensorflow:Restoring parameters from output_dir/model2/model.ckpt-1\n",
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"INFO:tensorflow:Evaluation [79/100]\n",
"INFO:tensorflow:Finished evaluation at 2017-06-19-21:39:50\n",
"INFO:tensorflow:Saving dict for global step 1: accuracy = 0.121, global_step = 1, loss = 2.30222\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Validation (step 1): global_step = 1, accuracy = 0.121, loss = 2.30222\n",
"INFO:tensorflow:global_step/sec: 11.5818\n",
"INFO:tensorflow:step = 101, loss = 2.29624 (8.635 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5384\n",
"INFO:tensorflow:step = 201, loss = 2.26113 (5.702 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6803\n",
"INFO:tensorflow:step = 301, loss = 1.82687 (5.656 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7442\n",
"INFO:tensorflow:step = 401, loss = 1.75301 (5.636 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7827\n",
"INFO:tensorflow:step = 501, loss = 1.68407 (5.623 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7452\n",
"INFO:tensorflow:step = 601, loss = 1.59284 (5.635 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6379\n",
"INFO:tensorflow:step = 701, loss = 1.67805 (5.670 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6553\n",
"INFO:tensorflow:step = 801, loss = 1.60265 (5.664 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7286\n",
"INFO:tensorflow:step = 901, loss = 1.61349 (5.641 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5202\n",
"INFO:tensorflow:step = 1001, loss = 1.5987 (5.708 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7912\n",
"INFO:tensorflow:step = 1101, loss = 1.60118 (5.621 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7579\n",
"INFO:tensorflow:step = 1201, loss = 1.5579 (5.631 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7673\n",
"INFO:tensorflow:step = 1301, loss = 1.50544 (5.628 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7015\n",
"INFO:tensorflow:step = 1401, loss = 1.52473 (5.649 sec)\n",
"INFO:tensorflow:global_step/sec: 17.5113\n",
"INFO:tensorflow:step = 1501, loss = 1.5336 (5.711 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7525\n",
"INFO:tensorflow:step = 1601, loss = 1.51717 (5.633 sec)\n",
"INFO:tensorflow:global_step/sec: 17.8197\n",
"INFO:tensorflow:step = 1701, loss = 1.5301 (5.612 sec)\n",
"INFO:tensorflow:global_step/sec: 17.6017\n",
"INFO:tensorflow:step = 1801, loss = 1.53274 (5.681 sec)\n",
"INFO:tensorflow:global_step/sec: 17.7066\n",
"INFO:tensorflow:step = 1901, loss = 1.48323 (5.648 sec)\n",
"INFO:tensorflow:Saving checkpoints for 2000 into output_dir/model2/model.ckpt.\n",
"WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n",
"Type is unsupported, or the types of the items don't match field type in CollectionDef.\n",
"'dict' object has no attribute 'name'\n",
"INFO:tensorflow:Loss for final step: 1.50588.\n",
"INFO:tensorflow:Starting evaluation at 2017-06-19-21:41:44\n",
"INFO:tensorflow:Restoring parameters from output_dir/model2/model.ckpt-2000\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"INFO:tensorflow:Finished evaluation at 2017-06-19-21:41:46\n",
"INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9611, global_step = 2000, loss = 1.50564\n"
]
},
{
"data": {
"text/plain": [
"({'accuracy': 0.96109998, 'global_step': 2000, 'loss': 1.5056374}, [])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LEARNING_RATE = 0.05\n",
"OUTPUT_DIR = 'output_dir/model2'\n",
"learn_runner.run(generate_experiment_fn(), run_config=tf.contrib.learn.RunConfig(model_dir=OUTPUT_DIR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you run tensorboard how it's described above, you'll have something similar to the images bellow:\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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
thewtex/SimpleITK-Notebooks | 33_Segmentation_Thresholding_Edge_Detection.ipynb | 1 | 11344 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 align=\"center\">Segmentation: Thresholding and Edge Detection</h1>\n",
"\n",
"In this notebook our goal is to estimate the radius of spherical markers from an image (Cone-Beam CT volume).\n",
"\n",
"We will use two approaches:\n",
"1. Segment the fiducial using a thresholding approach, derive the sphere's radius from the segmentation. This approach is solely based on SimpleITK.\n",
"2. Localize the fiducial's edges using the Canny edge detector and then fit a sphere to these edges using a least squares approach. This approach is a combination of SimpleITK and scipy/numpy.\n",
"\n",
"It should be noted that all of the operations, filtering and computations, are natively in 3D. This is the \"magic\" of ITK and SimpleITK at work."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import SimpleITK as sitk\n",
"\n",
"from downloaddata import fetch_data as fdata\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"from scipy import linalg\n",
"\n",
"from ipywidgets import interact, fixed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the volume and look at the image (visualization requires window-leveling)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"spherical_fiducials_image = sitk.ReadImage(fdata(\"spherical_fiducials.mha\"))\n",
"sitk.Show(spherical_fiducials_image, \"spheres\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After looking at the image you should have identified two spheres. Now select a Region Of Interest (ROI) around the sphere which you want to analyze. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rois = {\"ROI1\":[range(280,320), range(65,90), range(8, 30)], \n",
" \"ROI2\":[range(200,240), range(65,100), range(15, 40)]}\n",
"mask_value = 255\n",
"\n",
"def select_roi_dropdown_callback(roi_name, roi_dict):\n",
" global mask, mask_ranges \n",
"\n",
" mask_ranges = roi_dict.get(roi_name)\n",
" if mask_ranges:\n",
" mask = sitk.Image(spherical_fiducials_image.GetSize(), sitk.sitkUInt8)\n",
" mask.CopyInformation(spherical_fiducials_image)\n",
" for x in mask_ranges[0]:\n",
" for y in mask_ranges[1]: \n",
" for z in mask_ranges[2]:\n",
" mask[x,y,z] = mask_value\n",
" # Use nice magic numbers for windowing the image. We need to do this as we are alpha blending the mask\n",
" # with the original image.\n",
" sitk.Show(sitk.LabelOverlay(sitk.Cast(sitk.IntensityWindowing(spherical_fiducials_image, windowMinimum=-32767, \n",
" windowMaximum=-29611), sitk.sitkUInt8), \n",
" mask, opacity=0.5))\n",
"\n",
"roi_list = rois.keys()\n",
"roi_list.insert(0,'Select ROI')\n",
"interact(select_roi_dropdown_callback, roi_name=roi_list, roi_dict=fixed(rois)); "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Thresholding based approach\n",
"\n",
"To see whether this approach is appropriate we look at the histogram of intensity values inside the ROI. We know that the spheres have higher intensity values. Ideally we would have a bimodal distribution with clear separation between the sphere and background."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"intensity_values = sitk.GetArrayFromImage(spherical_fiducials_image)\n",
"roi_intensity_values = intensity_values[mask_ranges[2][0]:mask_ranges[2][-1],\n",
" mask_ranges[1][0]:mask_ranges[1][-1],\n",
" mask_ranges[0][0]:mask_ranges[0][-1]].flatten()\n",
"plt.hist(roi_intensity_values, bins=100)\n",
"plt.title(\"Intensity Values in ROI\")\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can you identify the region of the histogram associated with the sphere?\n",
"\n",
"In our case it looks like we can automatically select a threshold separating the sphere from the background. We will use Otsu's method for threshold selection to segment the sphere and estimate its radius. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Set pixels that are in [min_intensity,otsu_threshold] to inside_value, values above otsu_threshold are\n",
"# set to outside_value. The sphere's have higher intensity values than the background, so they are outside.\n",
"\n",
"inside_value = 0\n",
"outside_value = 255\n",
"number_of_histogram_bins = 100\n",
"mask_output = True\n",
"\n",
"labeled_result = sitk.OtsuThreshold(spherical_fiducials_image, mask, inside_value, outside_value, \n",
" number_of_histogram_bins, mask_output, mask_value)\n",
"\n",
"# Estimate the sphere radius from the segmented image using the LabelShapeStatisticsImageFilter.\n",
"label_shape_analysis = sitk.LabelShapeStatisticsImageFilter()\n",
"label_shape_analysis.SetBackgroundValue(inside_value)\n",
"label_shape_analysis.Execute(labeled_result)\n",
"print(\"The sphere's radius is: {0:.2f}mm\".format(label_shape_analysis.GetEquivalentSphericalRadius(outside_value)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Visually inspect the results of segmentation, just to make sure.\n",
"sitk.Show(sitk.LabelOverlay(sitk.Cast(sitk.IntensityWindowing(spherical_fiducials_image, windowMinimum=-32767, windowMaximum=-29611),\n",
" sitk.sitkUInt8), labeled_result, opacity=0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Based on your visual inspection, did the automatic threshold correctly segment the sphere or did it over/under segment it?\n",
"\n",
"If automatic thresholding did not provide the desired result, you can correct it by allowing the user to modify the threshold under visual inspection. Implement this approach below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code here:"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Edge detection based approach\n",
"\n",
"In this approach we will localize the sphere's edges in 3D using SimpleITK. We then compute the least squares sphere that optimally fits the 3D points using scipy/numpy. The mathematical formulation for this solution is described in this [Insight Journal paper](http://www.insight-journal.org/download/viewpdf/769/1/download). \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a cropped version of the original image.\n",
"sub_image = spherical_fiducials_image[mask_ranges[0][0]:mask_ranges[0][-1],\n",
" mask_ranges[1][0]:mask_ranges[1][-1],\n",
" mask_ranges[2][0]:mask_ranges[2][-1]]\n",
"\n",
"# Edge detection on the sub_image with appropriate thresholds and smoothing.\n",
"edges = sitk.CannyEdgeDetection(sitk.Cast(sub_image, sitk.sitkFloat32), lowerThreshold=0.0, \n",
" upperThreshold=200.0, variance = (5.0,5.0,5.0))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Get the 3D location of the edge points and fit a sphere to them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"edge_indexes = np.where(sitk.GetArrayFromImage(edges) == 1.0)\n",
"\n",
"# Note the reversed order of access between SimpleITK and numpy (z,y,x)\n",
"physical_points = [edges.TransformIndexToPhysicalPoint([int(x), int(y), int(z)]) \\\n",
" for z,y,x in zip(edge_indexes[0], edge_indexes[1], edge_indexes[2])]\n",
"\n",
"# Setup and solve linear equation system.\n",
"A = np.ones((len(physical_points),4))\n",
"b = np.zeros(len(physical_points))\n",
"\n",
"for row, point in enumerate(physical_points):\n",
" A[row,0:3] = -2*np.array(point)\n",
" b[row] = -linalg.norm(point)**2\n",
"\n",
"res,_,_,_ = linalg.lstsq(A,b)\n",
"\n",
"print(\"The sphere's radius is: {0:.2f}mm\".format(np.sqrt(linalg.norm(res[0:3])**2 - res[3])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Visually inspect the results of edge detection, just to make sure. Note that because SimpleITK is working in the\n",
"# physical world (not pixels, but mm) we can easily transfer the edges localized in the cropped image to the original.\n",
"\n",
"edge_label = sitk.Image(spherical_fiducials_image.GetSize(), sitk.sitkUInt16)\n",
"edge_label.CopyInformation(spherical_fiducials_image)\n",
"e_label = 255\n",
"for point in physical_points:\n",
" edge_label[edge_label.TransformPhysicalPointToIndex(point)] = e_label\n",
"\n",
"sitk.Show(sitk.LabelOverlay(sitk.Cast(sitk.IntensityWindowing(spherical_fiducials_image, windowMinimum=-32767, windowMaximum=-29611),\n",
" sitk.sitkUInt8), edge_label, opacity=0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## You've made it to the end of the notebook, you deserve to know the correct answer\n",
"\n",
"The sphere's radius is 3mm. "
]
},
{
"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
}
| apache-2.0 |
phoebe-project/phoebe2-docs | 2.3/examples/animation_binary_complete.ipynb | 1 | 59999 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Complete Binary Animation\n",
"============================\n",
"\n",
"**NOTE**: animating within Jupyter notebooks can be very resource intensive. This script will likely run much quicker as a Python script.\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!pip install -I \"phoebe>=2.3,<2.4\""
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, let's do imports and initialize a logger and a new bundle."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import phoebe\n",
"from phoebe import u # units\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"logger = phoebe.logger()\n",
"\n",
"b = phoebe.default_binary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding Datasets\n",
"--------------------"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"times = np.linspace(0,1,21)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 73 parameters | contexts: compute, dataset, constraint, figure>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('lc', times=times, dataset='lc01')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 76 parameters | contexts: compute, dataset, constraint, figure>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('rv', times=times, dataset='rv01')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Thu, 17 Sep 2020 18:27 BUNDLE WARNING mesh dataset uses 'compute_times' instead of 'times', applying value sent as 'times' to 'compute_times'.\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 83 parameters | contexts: compute, dataset, constraint, figure>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('mesh', times=times, columns=['visibilities', 'intensities@lc01', 'rvs@rv01'], dataset='mesh01')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running Compute\n",
"--------------------"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 21/21 [00:00<00:00, 57.28it/s]\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 303 parameters | kinds: lc, mesh, rv>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(irrad_method='none')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting\n",
"-----------\n",
"\n",
"See the [Animations Tutorial](../tutorials/animations.ipynb) for more examples and details.\n",
"\n",
"Here we'll create a figure with multiple subplots. The top row will be the light curve and RV curve. The bottom three subplots will be various representations of the mesh (intensities, rvs, and visibilities).\n",
"\n",
"We'll do this by making separate calls to plot, passing the matplotlib subplot location for each axes we want to create. We can then call `b.show(animate=True)` or `b.save('anim.gif', animate=True)`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING: pad_aspect not supported for animations, ignoring\n",
"WARNING: pad_aspect not supported for animations, ignoring\n",
"WARNING: pad_aspect not supported for animations, ignoring\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"b['lc01@model'].plot(axpos=221)\n",
"b['rv01@model'].plot(c={'primary': 'blue', 'secondary': 'red'}, linestyle='solid', axpos=222)\n",
"b['mesh@model'].plot(fc='intensities@lc01', ec='None', axpos=425)\n",
"b['mesh@model'].plot(fc='rvs@rv01', ec='None', axpos=427)\n",
"b['mesh@model'].plot(fc='visibilities', ec='None', y='ws', axpos=224)\n",
"\n",
"fig = plt.figure(figsize=(11,4))\n",
"afig, mplanim = b.savefig('animation_binary_complete.gif', fig=fig, tight_layouot=True, draw_sidebars=False, animate=True, save_kwargs={'writer': 'imagemagick'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
infilect/ml-course1 | week2/vgg_transfer_imagenet_to_flower/transfer_learning_python.ipynb | 77 | 23492 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Transfer Learning\n",
"\n",
"Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) trained on the [ImageNet dataset](http://www.image-net.org/) as a feature extractor. Below is a diagram of the VGGNet architecture.\n",
"\n",
"<img src=\"assets/cnnarchitecture.jpg\" width=700px>\n",
"\n",
"VGGNet is great because it's simple and has great performance, coming in second in the ImageNet competition. The idea here is that we keep all the convolutional layers, but replace the final fully connected layers with our own classifier. This way we can use VGGNet as a feature extractor for our images then easily train a simple classifier on top of that. What we'll do is take the first fully connected layer with 4096 units, including thresholding with ReLUs. We can use those values as a code for each image, then build a classifier on top of those codes.\n",
"\n",
"You can read more about transfer learning from [the CS231n course notes](http://cs231n.github.io/transfer-learning/#tf).\n",
"\n",
"## Pretrained VGGNet\n",
"\n",
"We'll be using a pretrained network from https://github.com/machrisaa/tensorflow-vgg. Make sure to clone this repository to the directory you're working from. You'll also want to rename it so it has an underscore instead of a dash.\n",
"\n",
"```\n",
"git clone https://github.com/machrisaa/tensorflow-vgg.git tensorflow_vgg\n",
"```\n",
"\n",
"This is a really nice implementation of VGGNet, quite easy to work with. The network has already been trained and the parameters are available from this link. **You'll need to clone the repo into the folder containing this notebook.** Then download the parameter file using the next cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"\n",
"vgg_dir = 'tensorflow_vgg/'\n",
"# Make sure vgg exists\n",
"if not isdir(vgg_dir):\n",
" raise Exception(\"VGG directory doesn't exist!\")\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(vgg_dir + \"vgg16.npy\"):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:\n",
" urlretrieve(\n",
" 'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',\n",
" vgg_dir + 'vgg16.npy',\n",
" pbar.hook)\n",
"else:\n",
" print(\"Parameter file already exists!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Flower power\n",
"\n",
"Here we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the [TensorFlow inception tutorial](https://www.tensorflow.org/tutorials/image_retraining)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tarfile\n",
"\n",
"dataset_folder_path = 'flower_photos'\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('flower_photos.tar.gz'):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:\n",
" urlretrieve(\n",
" 'http://download.tensorflow.org/example_images/flower_photos.tgz',\n",
" 'flower_photos.tar.gz',\n",
" pbar.hook)\n",
"\n",
"if not isdir(dataset_folder_path):\n",
" with tarfile.open('flower_photos.tar.gz') as tar:\n",
" tar.extractall()\n",
" tar.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## ConvNet Codes\n",
"\n",
"Below, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.\n",
"\n",
"Here we're using the `vgg16` module from `tensorflow_vgg`. The network takes images of size $224 \\times 224 \\times 3$ as input. Then it has 5 sets of convolutional layers. The network implemented here has this structure (copied from [the source code](https://github.com/machrisaa/tensorflow-vgg/blob/master/vgg16.py)):\n",
"\n",
"```\n",
"self.conv1_1 = self.conv_layer(bgr, \"conv1_1\")\n",
"self.conv1_2 = self.conv_layer(self.conv1_1, \"conv1_2\")\n",
"self.pool1 = self.max_pool(self.conv1_2, 'pool1')\n",
"\n",
"self.conv2_1 = self.conv_layer(self.pool1, \"conv2_1\")\n",
"self.conv2_2 = self.conv_layer(self.conv2_1, \"conv2_2\")\n",
"self.pool2 = self.max_pool(self.conv2_2, 'pool2')\n",
"\n",
"self.conv3_1 = self.conv_layer(self.pool2, \"conv3_1\")\n",
"self.conv3_2 = self.conv_layer(self.conv3_1, \"conv3_2\")\n",
"self.conv3_3 = self.conv_layer(self.conv3_2, \"conv3_3\")\n",
"self.pool3 = self.max_pool(self.conv3_3, 'pool3')\n",
"\n",
"self.conv4_1 = self.conv_layer(self.pool3, \"conv4_1\")\n",
"self.conv4_2 = self.conv_layer(self.conv4_1, \"conv4_2\")\n",
"self.conv4_3 = self.conv_layer(self.conv4_2, \"conv4_3\")\n",
"self.pool4 = self.max_pool(self.conv4_3, 'pool4')\n",
"\n",
"self.conv5_1 = self.conv_layer(self.pool4, \"conv5_1\")\n",
"self.conv5_2 = self.conv_layer(self.conv5_1, \"conv5_2\")\n",
"self.conv5_3 = self.conv_layer(self.conv5_2, \"conv5_3\")\n",
"self.pool5 = self.max_pool(self.conv5_3, 'pool5')\n",
"\n",
"self.fc6 = self.fc_layer(self.pool5, \"fc6\")\n",
"self.relu6 = tf.nn.relu(self.fc6)\n",
"```\n",
"\n",
"So what we want are the values of the first fully connected layer, after being ReLUd (`self.relu6`). To build the network, we use\n",
"\n",
"```\n",
"with tf.Session() as sess:\n",
" vgg = vgg16.Vgg16()\n",
" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
" with tf.name_scope(\"content_vgg\"):\n",
" vgg.build(input_)\n",
"```\n",
"\n",
"This creates the `vgg` object, then builds the graph with `vgg.build(input_)`. Then to get the values from the layer,\n",
"\n",
"```\n",
"feed_dict = {input_: images}\n",
"codes = sess.run(vgg.relu6, feed_dict=feed_dict)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow_vgg import vgg16\n",
"from tensorflow_vgg import utils"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"data_dir = 'flower_photos/'\n",
"contents = os.listdir(data_dir)\n",
"classes = [each for each in contents if os.path.isdir(data_dir + each)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Below I'm running images through the VGG network in batches.\n",
"\n",
"> **Exercise:** Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"# Set the batch size higher if you can fit in in your GPU memory\n",
"batch_size = 10\n",
"codes_list = []\n",
"labels = []\n",
"batch = []\n",
"\n",
"codes = None\n",
"\n",
"with tf.Session() as sess:\n",
" \n",
" # TODO: Build the vgg network here\n",
"\n",
" for each in classes:\n",
" print(\"Starting {} images\".format(each))\n",
" class_path = data_dir + each\n",
" files = os.listdir(class_path)\n",
" for ii, file in enumerate(files, 1):\n",
" # Add images to the current batch\n",
" # utils.load_image crops the input images for us, from the center\n",
" img = utils.load_image(os.path.join(class_path, file))\n",
" batch.append(img.reshape((1, 224, 224, 3)))\n",
" labels.append(each)\n",
" \n",
" # Running the batch through the network to get the codes\n",
" if ii % batch_size == 0 or ii == len(files):\n",
" \n",
" # Image batch to pass to VGG network\n",
" images = np.concatenate(batch)\n",
" \n",
" # TODO: Get the values from the relu6 layer of the VGG network\n",
" codes_batch = \n",
" \n",
" # Here I'm building an array of the codes\n",
" if codes is None:\n",
" codes = codes_batch\n",
" else:\n",
" codes = np.concatenate((codes, codes_batch))\n",
" \n",
" # Reset to start building the next batch\n",
" batch = []\n",
" print('{} images processed'.format(ii))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# write codes to file\n",
"with open('codes', 'w') as f:\n",
" codes.tofile(f)\n",
" \n",
"# write labels to file\n",
"import csv\n",
"with open('labels', 'w') as f:\n",
" writer = csv.writer(f, delimiter='\\n')\n",
" writer.writerow(labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Building the Classifier\n",
"\n",
"Now that we have codes for all the images, we can build a simple classifier on top of them. The codes behave just like normal input into a simple neural network. Below I'm going to have you do most of the work."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# read codes and labels from file\n",
"import csv\n",
"\n",
"with open('labels') as f:\n",
" reader = csv.reader(f, delimiter='\\n')\n",
" labels = np.array([each for each in reader if len(each) > 0]).squeeze()\n",
"with open('codes') as f:\n",
" codes = np.fromfile(f, dtype=np.float32)\n",
" codes = codes.reshape((len(labels), -1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Data prep\n",
"\n",
"As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!\n",
"\n",
"> **Exercise:** From scikit-learn, use [LabelBinarizer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html) to create one-hot encoded vectors from the labels. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"labels_vecs = # Your one-hot encoded labels array here"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use [`StratifiedShuffleSplit`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) from scikit-learn.\n",
"\n",
"You can create the splitter like so:\n",
"```\n",
"ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\n",
"```\n",
"Then split the data with \n",
"```\n",
"splitter = ss.split(x, y)\n",
"```\n",
"\n",
"`ss.split` returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use `next(splitter)` to get the indices. Be sure to read the [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html) and the [user guide](http://scikit-learn.org/stable/modules/cross_validation.html#random-permutations-cross-validation-a-k-a-shuffle-split).\n",
"\n",
"> **Exercise:** Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"train_x, train_y = \n",
"val_x, val_y = \n",
"test_x, test_y = "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"print(\"Train shapes (x, y):\", train_x.shape, train_y.shape)\n",
"print(\"Validation shapes (x, y):\", val_x.shape, val_y.shape)\n",
"print(\"Test shapes (x, y):\", test_x.shape, test_y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If you did it right, you should see these sizes for the training sets:\n",
"\n",
"```\n",
"Train shapes (x, y): (2936, 4096) (2936, 5)\n",
"Validation shapes (x, y): (367, 4096) (367, 5)\n",
"Test shapes (x, y): (367, 4096) (367, 5)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Classifier layers\n",
"\n",
"Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.\n",
"\n",
"> **Exercise:** With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])\n",
"labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])\n",
"\n",
"# TODO: Classifier layers and operations\n",
"\n",
"logits = # output layer logits\n",
"cost = # cross entropy loss\n",
"\n",
"optimizer = # training optimizer\n",
"\n",
"# Operations for validation/test accuracy\n",
"predicted = tf.nn.softmax(logits)\n",
"correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Batches!\n",
"\n",
"Here is just a simple way to do batches. I've written it so that it includes all the data. Sometimes you'll throw out some data at the end to make sure you have full batches. Here I just extend the last batch to include the remaining data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_batches(x, y, n_batches=10):\n",
" \"\"\" Return a generator that yields batches from arrays x and y. \"\"\"\n",
" batch_size = len(x)//n_batches\n",
" \n",
" for ii in range(0, n_batches*batch_size, batch_size):\n",
" # If we're not on the last batch, grab data with size batch_size\n",
" if ii != (n_batches-1)*batch_size:\n",
" X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size] \n",
" # On the last batch, grab the rest of the data\n",
" else:\n",
" X, Y = x[ii:], y[ii:]\n",
" # I love generators\n",
" yield X, Y"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Training\n",
"\n",
"Here, we'll train the network.\n",
"\n",
"> **Exercise:** So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the `get_batches` function I wrote before to get your batches like `for x, y in get_batches(train_x, train_y)`. Or write your own!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"saver = tf.train.Saver()\n",
"with tf.Session() as sess:\n",
" \n",
" # TODO: Your training code here\n",
" saver.save(sess, \"checkpoints/flowers.ckpt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Testing\n",
"\n",
"Below you see the test accuracy. You can also see the predictions returned for images."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
" \n",
" feed = {inputs_: test_x,\n",
" labels_: test_y}\n",
" test_acc = sess.run(accuracy, feed_dict=feed)\n",
" print(\"Test accuracy: {:.4f}\".format(test_acc))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from scipy.ndimage import imread"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Below, feel free to choose images and see how the trained classifier predicts the flowers in them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'\n",
"test_img = imread(test_img_path)\n",
"plt.imshow(test_img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Run this cell if you don't have a vgg graph built\n",
"if 'vgg' in globals():\n",
" print('\"vgg\" object already exists. Will not create again.')\n",
"else:\n",
" #create vgg\n",
" with tf.Session() as sess:\n",
" input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])\n",
" vgg = vgg16.Vgg16()\n",
" vgg.build(input_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with tf.Session() as sess:\n",
" img = utils.load_image(test_img_path)\n",
" img = img.reshape((1, 224, 224, 3))\n",
"\n",
" feed_dict = {input_: img}\n",
" code = sess.run(vgg.relu6, feed_dict=feed_dict)\n",
" \n",
"saver = tf.train.Saver()\n",
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
" \n",
" feed = {inputs_: code}\n",
" prediction = sess.run(predicted, feed_dict=feed).squeeze()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.imshow(test_img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.barh(np.arange(5), prediction)\n",
"_ = plt.yticks(np.arange(5), lb.classes_)"
]
}
],
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
deepmind/arnheim | arnheim_3_patch_maker.ipynb | 1 | 8665 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "9G-3dxY01xJd"
},
"source": [
"# Arnheim 3 - Segmented Patch Creator\n",
"DeepMind, 2021\n",
"\n",
"## Intructions\n",
"This Colab is to support the creation of segmented patches for collage creation using Arnheim 3.\n",
"\n",
"The Colab uses [PixelLib](https://github.com/ayoolaolafenwa/PixelLib) and is pretty basic but good enough to get one started creating patches from JPG images.\n",
"\n",
"The process is\n",
"\n",
"1) Provide source images\n",
"\n",
"* Upload images using this Colab to either Google Drive or the temp folder\n",
"* Alternatively use a Google Drive folder that already contains images\n",
"\n",
"2) Create segmented patches\n",
"* The patch file is save to Google Drive. Be sure to copy the location of the file in the Arnheim 3 Colab.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "-X9f5OnKJ1-O"
},
"outputs": [],
"source": [
"#@title Installations\n",
"!pip3 install pixellib\n",
"!pip3 install tensorflow==2.0.1\n",
"!pip3 install Keras==2.3.0\n",
"!pip3 install h5py==2.10.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K41eEXVbu1k6"
},
"outputs": [],
"source": [
"#@title Imports\n",
"import glob\n",
"from google.colab import drive\n",
"from google.colab import files\n",
"import io\n",
"import numpy as np\n",
"import os\n",
"import pathlib\n",
"import pixellib\n",
"from pixellib.instance import instance_segmentation\n",
"import requests\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A_k0GI-du3E1"
},
"outputs": [],
"source": [
"#@title Function definitions\n",
"\n",
"def mkdir(path):\n",
" pathlib.Path(path).mkdir(parents=True, exist_ok=True)\n",
"\n",
"def upload_files(target_path):\n",
" \"\"\"Upload files to target directory.\"\"\"\n",
" mkdir(target_path)\n",
" uploaded = files.upload()\n",
" for k, v in uploaded.items():\n",
" open(target_path + \"/\" + k, 'wb').write(v)\n",
" return list(uploaded.keys())\n",
"\n",
"\n",
"def download_from_url(url, force=False):\n",
" \"\"\"Download file from URL and cache it.\"\"\"\n",
"\n",
" cache_dir = \"/content/cache\"\n",
" mkdir(cache_dir)\n",
" cache_filename = f\"{cache_dir}/{os.path.basename(url)}\"\n",
" cache = pathlib.Path(cache_filename)\n",
" if not cache.is_file() or force:\n",
" print(\"Downloading \" + url)\n",
" r = requests.get(url)\n",
" bytesio_object = io.BytesIO(r.content)\n",
" with open(cache_filename, \"wb\") as f:\n",
" f.write(bytesio_object.getbuffer())\n",
" else:\n",
" print(\"Using cached version of \" + url)\n",
" return cache "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "hpsPBACa4gps"
},
"outputs": [],
"source": [
"#@title Authorise and mount Google Drive\n",
"ROOT = \"/content\"\n",
"MOUNT_DIR = f\"{ROOT}/drive\"\n",
"drive.mount(MOUNT_DIR)\n",
"# ROOT_PATH = f\"{MOUNT_DIR}/MyDrive/Arnheim3\"\n",
"# \n",
"# mkdir(ROOT)\n",
"# IMAGE_PATH = f\"{ROOT}/source_images\"\n",
"# SEGMENTED_PATH = f\"{ROOT}/segmented\"\n",
"# \n",
"# print(f\"\\nUsing base directory: {ROOT}\")\n",
"# print(f\"Source images directory: {IMAGE_PATH}\")\n",
"# print(f\"Segmented directory: {SEGMENTED_PATH}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"collapsed": true,
"id": "f2Dhp16ptHuF"
},
"outputs": [],
"source": [
"#@title Source images and target file locations\n",
"#@markdown Source images can be stored temporarily with the Colab, be already on Google Drive, or can be uploaded to Google Drive.\n",
"use_google_drive_for_source_images = True #@param {type:\"boolean\"}\n",
"#@markdown Source images (if stored on Google Drive)\n",
"GOOGLE_DRIVE_PATH_SOURCE_IMAGES = \"Art/Collage/Images\" #@param {type:\"string\"}\n",
"#@markdown Target segmentation file will be saved to Google Drive for use with the Arnheim 3 Colab.\n",
"SEGMENTED_DATA_FILENAME = \"fruit.npy\" #@param {type: \"string\"}\n",
"GOOGLE_DRIVE_PATH_SEGMENTED_DATA = \"Art/Collage/Patches\" #@param {type:\"string\"}\n",
"\n",
"data_path = MOUNT_DIR + \"/MyDrive/\" + GOOGLE_DRIVE_PATH_SEGMENTED_DATA\n",
"data_file = data_path + \"/\" + SEGMENTED_DATA_FILENAME\n",
"\n",
"if use_google_drive_for_source_images:\n",
" IMAGE_PATH = MOUNT_DIR + \"/MyDrive/\" + GOOGLE_DRIVE_PATH_SOURCE_IMAGES\n",
"else:\n",
" IMAGE_PATH = f\"{ROOT}/source_images\"\n",
"mkdir(IMAGE_PATH)\n",
"mkdir(data_path)\n",
"\n",
"print(f\"Source images directory: {IMAGE_PATH}\")\n",
"print(f\"Segmented data will be saved to: {data_file}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Ylun-hVm5iGq"
},
"outputs": [],
"source": [
"#@title Run this cell to upload a new set of images to segment\n",
"empty_target_dir_before_upload = False #@param {type:\"boolean\"}\n",
"\n",
"if empty_target_dir_before_upload:\n",
" !rm {IMAGE_PATH}/*\n",
"\n",
"upload_files(IMAGE_PATH)\n",
"print(f\"Images uploaded images to {IMAGE_PATH}\")\n",
"\n",
"!ls -l {IMAGE_PATH}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TEmqp9LOdv9P"
},
"outputs": [],
"source": [
"#@title Segment images and save patch file\n",
"\n",
"# https://pixellib.readthedocs.io/en/latest/Image_instance.html\n",
"segment_image = instance_segmentation()\n",
"segmentation_model_file = download_from_url(\n",
" \"https://github.com/ayoolaolafenwa/PixelLib/releases/download/1.2/mask_rcnn_coco.h5\")\n",
"segment_image.load_model(segmentation_model_file)\n",
"\n",
"imagefiles = []\n",
"for file in glob.glob(f\"{IMAGE_PATH}/*.jpg\"):\n",
" imagefiles.append(file)\n",
"\n",
"print(imagefiles)\n",
"print(\"num images to process = \", len(imagefiles))\n",
"\n",
"segmented_images = []\n",
"for imagefile in imagefiles:\n",
" print(imagefile)\n",
" try:\n",
" seg, _ = segment_image.segmentImage(\n",
" imagefile,\n",
" extract_segmented_objects=True,\n",
" save_extracted_objects =False,\n",
" show_bboxes=False,\n",
" output_image_name=str(imagefile) + \"______.tiff\")\n",
" except:\n",
" print(\"Error encounted - skipping\")\n",
" continue\n",
"\n",
" if not len(seg[\"extracted_objects\"]):\n",
" print(\"Failed to segment\", imagefile)\n",
" else:\n",
" for result in seg[\"extracted_objects\"]:\n",
" print(result.shape)\n",
" segmented_image = result[..., ::-1].copy()\n",
" segmented_images.append(segmented_image)\n",
"\n",
"with open(data_file, \"wb\") as f:\n",
" np.save(f, segmented_images)\n",
"print(\"Saved patch file to\", data_file)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "MakeSegmentedPatches.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
probml/pyprobml | deprecated/arhmm_example.ipynb | 1 | 832622 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/arhmm_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "5918355f-c759-41e8-9cc9-64baf78695b3"
},
"id": "WeQdPp8KfQJ7"
},
"source": [
"# Autoregressive (AR) HMM Demo\n",
"\n",
"Modified from\n",
"\n",
"https://github.com/lindermanlab/ssm-jax-refactor/blob/main/notebooks/arhmm-example.ipynb\n",
"\n",
"\n",
"This notebook illustrates the use of the _auto_regression_ observation model. \n",
"Let $x_t$ denote the observation at time $t$. Let $z_t$ denote the corresponding discrete latent state.\n",
"\n",
"The autoregressive hidden Markov model has the following likelihood,\n",
"$$\n",
"\\begin{align}\n",
"x_t \\mid x_{t-1}, z_t &\\sim\n",
"\\mathcal{N}\\left(A_{z_t} x_{t-1} + b_{z_t}, Q_{z_t} \\right).\n",
"\\end{align}\n",
"$$\n",
"(Technically, higher-order autoregressive processes with extra linear terms from inputs are also implemented.) "
]
},
{
"cell_type": "code",
"source": [
"!pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git"
],
"metadata": {
"id": "-ne60jsthTe6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b40d03b1-1f3f-4388-c622-84b8f75b1034"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting git+git://github.com/lindermanlab/ssm-jax-refactor.git\n",
" Cloning git://github.com/lindermanlab/ssm-jax-refactor.git to /tmp/pip-req-build-2x6xx8nv\n",
" Running command git clone -q git://github.com/lindermanlab/ssm-jax-refactor.git /tmp/pip-req-build-2x6xx8nv\n",
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"Collecting jax==0.2.21\n",
" Downloading jax-0.2.21.tar.gz (756 kB)\n",
"\u001b[K |████████████████████████████████| 756 kB 15.4 MB/s \n",
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"Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->ssm==0.1) (0.5.1)\n",
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"Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.1.6)\n",
"Building wheels for collected packages: ssm, jax\n",
" Building wheel for ssm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for ssm: filename=ssm-0.1-py3-none-any.whl size=75282 sha256=bde077b314fa6664e8403f9bed8b69a2ae93fde31ed7843c500b456c71f7a819\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-s91lg85w/wheels/78/93/24/866323c03bb6444c9ad2485bc0abe61ad5e6828d66c2c2fda3\n",
" Building wheel for jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for jax: filename=jax-0.2.21-py3-none-any.whl size=869303 sha256=dc33be4778a51583c3656c00dad98a420e13b2905773a4f7fd321de9ac478eb7\n",
" Stored in directory: /root/.cache/pip/wheels/5c/69/0d/3784dd6d281be0837d8cef1db0c8b37d108c8bff727b961178\n",
"Successfully built ssm jax\n",
"Installing collected packages: jax, ssm\n",
" Attempting uninstall: jax\n",
" Found existing installation: jax 0.2.25\n",
" Uninstalling jax-0.2.25:\n",
" Successfully uninstalled jax-0.2.25\n",
"Successfully installed jax-0.2.21 ssm-0.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import ssm"
],
"metadata": {
"id": "TR3R3yM0hftZ"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"nbpresent": {
"id": "346a61a3-9216-480d-b5b8-39a78782a8c3"
},
"id": "eWi_LAC1fQJ_"
},
"outputs": [],
"source": [
"import copy\n",
"\n",
"import jax.numpy as np\n",
"import jax.random as jr\n",
"\n",
"from tensorflow_probability.substrates import jax as tfp\n",
"\n",
"from ssm.distributions.linreg import GaussianLinearRegression\n",
"from ssm.arhmm import GaussianARHMM\n",
"from ssm.utils import find_permutation, random_rotation\n",
"from ssm.plots import gradient_cmap # , white_to_color_cmap\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"nbpresent": {
"id": "346a61a3-9216-480d-b5b8-39a78782a8c3"
},
"id": "gFuWYNd_fQKA"
},
"outputs": [],
"source": [
"sns.set_style(\"white\")\n",
"sns.set_context(\"talk\")\n",
"\n",
"color_names = [\"windows blue\", \"red\", \"amber\", \"faded green\", \"dusty purple\", \"orange\", \"brown\", \"pink\"]\n",
"\n",
"\n",
"colors = sns.xkcd_palette(color_names)\n",
"cmap = gradient_cmap(colors)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "--VbhuWHfQKA",
"outputId": "1771fbef-cb26-46a9-b8a9-376460008735",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {}
}
],
"source": [
"# Make a transition matrix\n",
"num_states = 5\n",
"transition_probs = (np.arange(num_states) ** 10).astype(float)\n",
"transition_probs /= transition_probs.sum()\n",
"transition_matrix = np.zeros((num_states, num_states))\n",
"for k, p in enumerate(transition_probs[::-1]):\n",
" transition_matrix += np.roll(p * np.eye(num_states), k, axis=1)\n",
"\n",
"plt.imshow(transition_matrix, vmin=0, vmax=1, cmap=\"Greys\")\n",
"plt.xlabel(\"next state\")\n",
"plt.ylabel(\"current state\")\n",
"plt.title(\"transition matrix\")\n",
"plt.colorbar()\n",
"\n",
"plt.savefig(\"arhmm-transmat.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"nbpresent": {
"id": "564edd16-a99d-4329-8e31-98fe1e1cef79"
},
"id": "nNiT5hX4fQKC"
},
"outputs": [],
"source": [
"# Make observation distributions\n",
"data_dim = 2\n",
"num_lags = 1\n",
"\n",
"keys = jr.split(jr.PRNGKey(0), num_states)\n",
"angles = np.linspace(0, 2 * np.pi, num_states, endpoint=False)\n",
"theta = np.pi / 25 # rotational frequency\n",
"weights = np.array([0.8 * random_rotation(key, data_dim, theta=theta) for key in keys])\n",
"biases = np.column_stack([np.cos(angles), np.sin(angles), np.zeros((num_states, data_dim - 2))])\n",
"covariances = np.tile(0.001 * np.eye(data_dim), (num_states, 1, 1))\n",
"\n",
"# Compute the stationary points\n",
"stationary_points = np.linalg.solve(np.eye(data_dim) - weights, biases)"
]
},
{
"cell_type": "code",
"source": [
"print(theta / (2 * np.pi) * 360)\n",
"print(360 / 5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fzFKiyptoevv",
"outputId": "68275575-c151-4605-f618-a2be195c61df"
},
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"7.2\n",
"72.0\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HManGRbPfQKC"
},
"source": [
"# Plot dynamics functions"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"id": "RbPFU_M4fQKD",
"outputId": "43a07dff-331f-49c3-9d28-1e502a0f1bc8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 234
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 5 Axes>"
]
},
"metadata": {}
}
],
"source": [
"if data_dim == 2:\n",
" lim = 5\n",
" x = np.linspace(-lim, lim, 10)\n",
" y = np.linspace(-lim, lim, 10)\n",
" X, Y = np.meshgrid(x, y)\n",
" xy = np.column_stack((X.ravel(), Y.ravel()))\n",
"\n",
" fig, axs = plt.subplots(1, num_states, figsize=(3 * num_states, 6))\n",
" for k in range(num_states):\n",
" A, b = weights[k], biases[k]\n",
" dxydt_m = xy.dot(A.T) + b - xy\n",
" axs[k].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[k % len(colors)])\n",
"\n",
" axs[k].set_xlabel(\"$y_1$\")\n",
" # axs[k].set_xticks([])\n",
" if k == 0:\n",
" axs[k].set_ylabel(\"$y_2$\")\n",
" # axs[k].set_yticks([])\n",
" axs[k].set_aspect(\"equal\")\n",
"\n",
" plt.tight_layout()\n",
"\n",
" plt.savefig(\"arhmm-flow-matrices.pdf\")"
]
},
{
"cell_type": "code",
"source": [
"colors"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "_GJxepDyzZwP",
"outputId": "eac5fbe3-2039-4601-f83c-8ff684ff8deb"
},
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<svg width=\"440\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3778bf;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e50000;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#feb308;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7bb274;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#825f87;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f97306;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#653700;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff81c0;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
],
"text/plain": [
"[(0.21568627450980393, 0.47058823529411764, 0.7490196078431373),\n",
" (0.8980392156862745, 0.0, 0.0),\n",
" (0.996078431372549, 0.7019607843137254, 0.03137254901960784),\n",
" (0.4823529411764706, 0.6980392156862745, 0.4549019607843137),\n",
" (0.5098039215686274, 0.37254901960784315, 0.5294117647058824),\n",
" (0.9764705882352941, 0.45098039215686275, 0.023529411764705882),\n",
" (0.396078431372549, 0.21568627450980393, 0.0),\n",
" (1.0, 0.5058823529411764, 0.7529411764705882)]"
]
},
"metadata": {},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"source": [
"print(stationary_points)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9lzdZ70NzVp8",
"outputId": "6b108b7f-55f3-49d5-9ab2-99833a634e62"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 3.9209812 -1.9056123 ]\n",
" [ 3.0239954 3.140209 ]\n",
" [-2.0520504 3.8463693 ]\n",
" [-4.2922325 -0.7630231 ]\n",
" [-0.60069436 -4.3179426 ]]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JCk26lnYfQKE"
},
"source": [
"# Sample data from the ARHMM"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": [],
"id": "XkWFWd_DfQKE"
},
"outputs": [],
"source": [
"# Make an Autoregressive (AR) HMM\n",
"true_initial_distribution = tfp.distributions.Categorical(logits=np.zeros(num_states))\n",
"true_transition_distribution = tfp.distributions.Categorical(probs=transition_matrix)\n",
"\n",
"true_arhmm = GaussianARHMM(\n",
" num_states,\n",
" transition_matrix=transition_matrix,\n",
" emission_weights=weights,\n",
" emission_biases=biases,\n",
" emission_covariances=covariances,\n",
")\n",
"time_bins = 10000\n",
"true_states, data = true_arhmm.sample(jr.PRNGKey(0), time_bins)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"nbpresent": {
"id": "0feabc13-812b-4d5e-ac24-f8327ecb4d27"
},
"id": "AWNcIC9rfQKF",
"outputId": "abb93ea8-dcad-40bf-b968-a79cb7fbfeb9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 517
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {}
}
],
"source": [
"fig = plt.figure(figsize=(8, 8))\n",
"for k in range(num_states):\n",
" plt.plot(*data[true_states == k].T, \"o\", color=colors[k], alpha=0.75, markersize=3)\n",
"\n",
"plt.plot(*data[:1000].T, \"-k\", lw=0.5, alpha=0.2)\n",
"plt.xlabel(\"$y_1$\")\n",
"plt.ylabel(\"$y_2$\")\n",
"# plt.gca().set_aspect(\"equal\")\n",
"\n",
"plt.savefig(\"arhmm-samples-2d.pdf\")"
]
},
{
"cell_type": "code",
"source": [
"fig = plt.figure(figsize=(8, 8))\n",
"for k in range(num_states):\n",
" ndx = true_states == k\n",
" data_k = data[ndx]\n",
" T = 12\n",
" data_k = data_k[:T, :]\n",
" plt.plot(data_k[:, 0], data_k[:, 1], \"o\", color=colors[k], alpha=0.75, markersize=3)\n",
" for t in range(T):\n",
" plt.text(data_k[t, 0], data_k[t, 1], t, color=colors[k], fontsize=12)\n",
"\n",
"# plt.plot(*data[:1000].T, '-k', lw=0.5, alpha=0.2)\n",
"plt.xlabel(\"$y_1$\")\n",
"plt.ylabel(\"$y_2$\")\n",
"# plt.gca().set_aspect(\"equal\")\n",
"\n",
"plt.savefig(\"arhmm-samples-2d-temporal.pdf\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 517
},
"id": "K2BUiXsrrW34",
"outputId": "790c4ec7-ee96-4733-d0b3-60785456dfaf"
},
"execution_count": 53,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"print(biases)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v74obcLSwF8V",
"outputId": "6394c27e-9d0d-404b-cfd4-24b0d3a33b17"
},
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 1. 0. ]\n",
" [ 0.30901697 0.95105654]\n",
" [-0.80901706 0.58778524]\n",
" [-0.80901694 -0.5877853 ]\n",
" [ 0.30901712 -0.9510565 ]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(stationary_points)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mIVS3eU3wESp",
"outputId": "6fafaacc-de1a-44fa-fb5f-d4f25d97fd4a"
},
"execution_count": 56,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 3.9209812 -1.9056123 ]\n",
" [ 3.0239954 3.140209 ]\n",
" [-2.0520504 3.8463693 ]\n",
" [-4.2922325 -0.7630231 ]\n",
" [-0.60069436 -4.3179426 ]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"colors"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "BfiRdzsDwUuf",
"outputId": "74d339ba-61f2-409d-e940-e6a8264e0706"
},
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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],
"text/plain": [
"[(0.21568627450980393, 0.47058823529411764, 0.7490196078431373),\n",
" (0.8980392156862745, 0.0, 0.0),\n",
" (0.996078431372549, 0.7019607843137254, 0.03137254901960784),\n",
" (0.4823529411764706, 0.6980392156862745, 0.4549019607843137),\n",
" (0.5098039215686274, 0.37254901960784315, 0.5294117647058824),\n",
" (0.9764705882352941, 0.45098039215686275, 0.023529411764705882),\n",
" (0.396078431372549, 0.21568627450980393, 0.0),\n",
" (1.0, 0.5058823529411764, 0.7529411764705882)]"
]
},
"metadata": {},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a58c7a02-2777-4af8-982f-e279bd3bbeb6"
},
"id": "22ZahXInfQKF"
},
"source": [
"Below, we visualize each component of of the observation variable as a time series. The colors correspond to the latent state. The dotted lines represent the stationary point of the the corresponding AR state while the solid lines are the actual observations sampled from the HMM."
]
},
{
"cell_type": "code",
"source": [
"lim"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ssmnDQ3_uZRb",
"outputId": "fcec599a-1fbe-4597-97ae-4099408df14b"
},
"execution_count": 52,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DeviceArray(4.7118726, dtype=float32)"
]
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"nbpresent": {
"id": "1ec5ac27-2d23-4660-8702-4156f8ffdf39"
},
"id": "eLu1Bi-5fQKF",
"outputId": "6deca2a4-c65d-4ea7-ef05-7eadf850a623",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {}
}
],
"source": [
"# Plot the data and the smoothed data\n",
"plot_slice = (0, 200)\n",
"lim = 1.05 * abs(data).max()\n",
"plt.figure(figsize=(8, 6))\n",
"plt.imshow(\n",
" true_states[None, :],\n",
" aspect=\"auto\",\n",
" cmap=cmap,\n",
" vmin=0,\n",
" vmax=len(colors) - 1,\n",
" extent=(0, time_bins, -lim, (data_dim) * lim),\n",
")\n",
"\n",
"\n",
"Ey = np.array(stationary_points)[true_states]\n",
"for d in range(data_dim):\n",
" plt.plot(data[:, d] + lim * d, \"-k\")\n",
" plt.plot(Ey[:, d] + lim * d, \":k\")\n",
"\n",
"plt.xlim(plot_slice)\n",
"plt.xlabel(\"time\")\n",
"# plt.yticks(lim * np.arange(data_dim), [\"$y_{{{}}}$\".format(d+1) for d in range(data_dim)])\n",
"plt.ylabel(\"observations\")\n",
"\n",
"plt.tight_layout()\n",
"\n",
"plt.savefig(\"arhmm-samples-1d.pdf\")"
]
},
{
"cell_type": "code",
"source": [
"data.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r1aHBVjbxZTj",
"outputId": "65ccb5f1-ffa5-40cc-8470-2ead0a3005ce"
},
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(10000, 2)"
]
},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"source": [
"data[:10, :]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hupky83Hvteg",
"outputId": "32706a81-56d2-4b64-9814-51197efc2211"
},
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DeviceArray([[-0.8169615 , 0.55239207],\n",
" [-1.423961 , 1.1161395 ],\n",
" [-1.8636721 , 1.6114323 ],\n",
" [-2.194045 , 2.0294168 ],\n",
" [-2.3697448 , 2.4288142 ],\n",
" [-2.4682324 , 2.7841942 ],\n",
" [-2.4781866 , 2.9988554 ],\n",
" [-2.4822226 , 3.1525304 ],\n",
" [-2.489001 , 3.346456 ],\n",
" [-2.4774575 , 3.4129188 ]], dtype=float32)"
]
},
"metadata": {},
"execution_count": 55
}
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "759699ce-fffa-4667-90af-267122e39f01"
},
"id": "2QEsdj8DfQKG"
},
"source": [
"# Fit an ARHMM"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "a9CK4IevfQKG",
"outputId": "f6fd2a62-5c16-4dc2-82dc-ec9e6d850002",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85,
"referenced_widgets": [
"e6f056311e3a47ebbe9bc981d8861de3",
"9ae7f4c7c9234082afc36c57cae36036",
"01d4fa61844e45758f177a2c28ed70c4",
"d0115936959a4d11af7afad22cbd1887",
"48c34ee72aa544f897d0260017f3b689",
"7202fcfcb71c497588d4ce4b69ec435a",
"6f2018c97c344ddd84b8048bc036271f",
"6929d58b80ba4b758429134af3a89f56",
"9587c289109e4a3996b5599937ea3e74",
"0902dddb283c4f6aba2d959cc12119a5",
"ec5013b082484a7ebb485bafab2e5846"
]
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Initializing...\n",
"Done.\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6f056311e3a47ebbe9bc981d8861de3",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/100 [00:00<?, ?it/s]"
]
},
"metadata": {}
}
],
"source": [
"# Now fit an HMM to the data\n",
"key1, key2 = jr.split(jr.PRNGKey(0), 2)\n",
"test_num_states = num_states\n",
"initial_distribution = tfp.distributions.Categorical(logits=np.zeros(test_num_states))\n",
"transition_distribution = tfp.distributions.Categorical(logits=np.zeros((test_num_states, test_num_states)))\n",
"emission_distribution = GaussianLinearRegression(\n",
" weights=np.tile(0.99 * np.eye(data_dim), (test_num_states, 1, 1)),\n",
" bias=0.01 * jr.normal(key2, (test_num_states, data_dim)),\n",
" scale_tril=np.tile(np.eye(data_dim), (test_num_states, 1, 1)),\n",
")\n",
"\n",
"arhmm = GaussianARHMM(test_num_states, data_dim, num_lags, seed=jr.PRNGKey(0))\n",
"\n",
"lps, arhmm, posterior = arhmm.fit(data, method=\"em\")"
]
},
{
"cell_type": "code",
"source": [
"# Plot the log likelihoods against the true likelihood, for comparison\n",
"true_lp = true_arhmm.marginal_likelihood(data)\n",
"plt.plot(lps, label=\"EM\")\n",
"plt.plot(true_lp * np.ones(len(lps)), \":k\", label=\"True\")\n",
"plt.xlabel(\"EM Iteration\")\n",
"plt.ylabel(\"Log Probability\")\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 304
},
"id": "W8SlkN0dmB85",
"outputId": "e6252212-64e3-4131-d27d-94627e3f6fce"
},
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qz-kwUfRfQKH"
},
"outputs": [],
"source": [
"# # Find a permutation of the states that best matches the true and inferred states\n",
"# most_likely_states = posterior.most_likely_states()\n",
"# arhmm.permute(find_permutation(true_states[num_lags:], most_likely_states))\n",
"# posterior.update()\n",
"# most_likely_states = posterior.most_likely_states()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "ENBLYZJnfQKH",
"outputId": "7f1ea2ad-1e21-4073-a93b-ececc978a2e1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 10 Axes>"
]
},
"metadata": {}
}
],
"source": [
"if data_dim == 2:\n",
" lim = abs(data).max()\n",
" x = np.linspace(-lim, lim, 10)\n",
" y = np.linspace(-lim, lim, 10)\n",
" X, Y = np.meshgrid(x, y)\n",
" xy = np.column_stack((X.ravel(), Y.ravel()))\n",
"\n",
" fig, axs = plt.subplots(2, max(num_states, test_num_states), figsize=(3 * num_states, 6))\n",
" for i, model in enumerate([true_arhmm, arhmm]):\n",
" for j in range(model.num_states):\n",
" dist = model._emissions._distribution[j]\n",
" A, b = dist.weights, dist.bias\n",
" dxydt_m = xy.dot(A.T) + b - xy\n",
" axs[i, j].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[j % len(colors)])\n",
"\n",
" axs[i, j].set_xlabel(\"$x_1$\")\n",
" axs[i, j].set_xticks([])\n",
" if j == 0:\n",
" axs[i, j].set_ylabel(\"$x_2$\")\n",
" axs[i, j].set_yticks([])\n",
" axs[i, j].set_aspect(\"equal\")\n",
"\n",
" plt.tight_layout()\n",
"\n",
" plt.savefig(\"argmm-flow-matrices-true-and-estimated.pdf\")"
]
},
{
"cell_type": "code",
"source": [
"if data_dim == 2:\n",
" lim = abs(data).max()\n",
" x = np.linspace(-lim, lim, 10)\n",
" y = np.linspace(-lim, lim, 10)\n",
" X, Y = np.meshgrid(x, y)\n",
" xy = np.column_stack((X.ravel(), Y.ravel()))\n",
"\n",
" fig, axs = plt.subplots(1, max(num_states, test_num_states), figsize=(3 * num_states, 6))\n",
" for i, model in enumerate([arhmm]):\n",
" for j in range(model.num_states):\n",
" dist = model._emissions._distribution[j]\n",
" A, b = dist.weights, dist.bias\n",
" dxydt_m = xy.dot(A.T) + b - xy\n",
" axs[j].quiver(xy[:, 0], xy[:, 1], dxydt_m[:, 0], dxydt_m[:, 1], color=colors[j % len(colors)])\n",
"\n",
" axs[j].set_xlabel(\"$y_1$\")\n",
" axs[j].set_xticks([])\n",
" if j == 0:\n",
" axs[j].set_ylabel(\"$y_2$\")\n",
" axs[j].set_yticks([])\n",
" axs[j].set_aspect(\"equal\")\n",
"\n",
" plt.tight_layout()\n",
"\n",
" plt.savefig(\"arhmm-flow-matrices-estimated.pdf\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 241
},
"id": "VT-JvKEZmU9k",
"outputId": "17b0e47c-30ab-4d7c-e0c7-aee78f24d54e"
},
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 5 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "4PEtU6ICfQKH",
"outputId": "ae8729d6-2634-4fb3-e7ba-9edd9de781e8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {}
}
],
"source": [
"# Plot the true and inferred discrete states\n",
"plot_slice = (0, 1000)\n",
"plt.figure(figsize=(8, 4))\n",
"plt.subplot(211)\n",
"plt.imshow(true_states[None, num_lags:], aspect=\"auto\", interpolation=\"none\", cmap=cmap, vmin=0, vmax=len(colors) - 1)\n",
"plt.xlim(plot_slice)\n",
"plt.ylabel(\"$z_{\\\\mathrm{true}}$\")\n",
"plt.yticks([])\n",
"\n",
"plt.subplot(212)\n",
"# plt.imshow(most_likely_states[None,: :], aspect=\"auto\", cmap=cmap, vmin=0, vmax=len(colors)-1)\n",
"plt.imshow(posterior.expected_states[0].T, aspect=\"auto\", interpolation=\"none\", cmap=\"Greys\", vmin=0, vmax=1)\n",
"plt.xlim(plot_slice)\n",
"plt.ylabel(\"$z_{\\\\mathrm{inferred}}$\")\n",
"plt.yticks([])\n",
"plt.xlabel(\"time\")\n",
"\n",
"plt.tight_layout()\n",
"\n",
"plt.savefig(\"arhmm-state-est.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "vFlh-AmVfQKH"
},
"outputs": [],
"source": [
"# Sample the fitted model\n",
"sampled_states, sampled_data = arhmm.sample(jr.PRNGKey(0), time_bins)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"id": "dujqSI13fQKI",
"outputId": "a8c85dac-3880-45d9-c0e8-1688925cc97b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 517
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {}
}
],
"source": [
"fig = plt.figure(figsize=(8, 8))\n",
"for k in range(num_states):\n",
" plt.plot(*sampled_data[sampled_states == k].T, \"o\", color=colors[k], alpha=0.75, markersize=3)\n",
"\n",
"# plt.plot(*sampled_data.T, '-k', lw=0.5, alpha=0.2)\n",
"plt.plot(*sampled_data[:1000].T, \"-k\", lw=0.5, alpha=0.2)\n",
"plt.xlabel(\"$x_1$\")\n",
"plt.ylabel(\"$x_2$\")\n",
"# plt.gca().set_aspect(\"equal\")\n",
"\n",
"plt.savefig(\"arhmm-samples-2d-estimated.pdf\")"
]
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"interpreter": {
"hash": "b6376f36d2a576cb3aa96bdc8604fd3c48ab4175c4f0714c2dd2fe4ec268050b"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.6"
},
"colab": {
"name": "arhmm-example.ipynb",
"provenance": [],
"include_colab_link": true
},
"accelerator": "GPU",
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"e6f056311e3a47ebbe9bc981d8861de3": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HBoxView",
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OSGeo-live/CesiumWidget | GSOC/notebooks/ipython/examples/Index.ipynb | 1 | 1621 | {
"cells": [
{
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"metadata": {},
"source": [
"<img src=\"images/ipython_logo.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IPython Documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This directory contains IPython's notebook-based documentation. This augments our [Sphinx-based documentation](http://ipython.org/ipython-doc/stable/index.html) with notebooks that contain interactive tutorials and examples. Over time, more of our documentation will be pulled into this format."
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},
{
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"## Topics"
]
},
{
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"source": [
"* [IPython Kernel](IPython Kernel/Index.ipynb): IPython's core syntax and command line features available in all frontends\n",
"* [Notebook](Notebook/Index.ipynb): The IPython Notebook frontend\n",
"* [Interactive Widgets](Interactive Widgets/Index.ipynb): Interactive JavaScript/HTML widgets and `interact`\n",
"* [Parallel Computing](Parallel Computing/Index.ipynb): IPython's library for interactive parallel computing\n",
"* [Customization](Customization/Index.ipynb): How to configure IPython and customize it with magics, extensions, etc.\n",
"* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\n",
"* [Builtin Extensions](Builtin Extensions/Index.ipynb): Extensions we ship with IPython"
]
}
],
"metadata": {},
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} | apache-2.0 |
zhangh87/AmazonPricePredictor | Exploratory Analysis/Baby Dataset Preliminary - New Image Features Count Vectorized.ipynb | 1 | 148088 | {
"cells": [
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"import pandas as pd\n",
"import numpy as np\n",
"import struct\n",
"import gzip\n",
"import matplotlib.pyplot as plt\n",
"import seaborn\n",
" \n",
"def parse(path):\n",
" g = gzip.open(path, 'rb')\n",
" for l in g:\n",
" yield eval(l)\n",
"\n",
"def getDF(path):\n",
" i = 0\n",
" df = {}\n",
" for d in parse(path):\n",
" df[i] = d\n",
" i += 1\n",
" return pd.DataFrame.from_dict(df, orient='index')"
]
},
{
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"execution_count": 14,
"metadata": {
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"outputs": [],
"source": [
"reviews = getDF('reviews_Baby.json.gz')\n",
"meta = getDF('meta_Baby.json.gz')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
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"outputs": [],
"source": [
"img_feat = pd.read_csv('rcnn_image_features_stephan_1.tar')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reviewsDF = reviews.set_index('asin').groupby(level = 0)['unixReviewTime','overall'].agg(np.average)\n",
"del reviews"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"meta = meta[['price','asin','title']].set_index('asin')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>title</th>\n",
" <th>unixReviewTime</th>\n",
" <th>overall</th>\n",
" </tr>\n",
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" <td>69.99</td>\n",
" <td>Lifefactory 4oz BPA Free Glass Baby Bottles - ...</td>\n",
" <td>1.369613e+09</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0188399518</th>\n",
" <td>15.95</td>\n",
" <td>Planetwise Flannel Wipes</td>\n",
" <td>1.382789e+09</td>\n",
" <td>3.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0188399399</th>\n",
" <td>10.95</td>\n",
" <td>Planetwise Wipe Pouch</td>\n",
" <td>1.365466e+09</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0316967297</th>\n",
" <td>109.95</td>\n",
" <td>Annas Dream Full Quilt with 2 Shams</td>\n",
" <td>1.371168e+09</td>\n",
" <td>4.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0615447279</th>\n",
" <td>16.95</td>\n",
" <td>Stop Pacifier Sucking without tears with Thumb...</td>\n",
" <td>1.348464e+09</td>\n",
" <td>4.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price title \\\n",
"asin \n",
"0188399313 69.99 Lifefactory 4oz BPA Free Glass Baby Bottles - ... \n",
"0188399518 15.95 Planetwise Flannel Wipes \n",
"0188399399 10.95 Planetwise Wipe Pouch \n",
"0316967297 109.95 Annas Dream Full Quilt with 2 Shams \n",
"0615447279 16.95 Stop Pacifier Sucking without tears with Thumb... \n",
"\n",
" unixReviewTime overall \n",
"asin \n",
"0188399313 1.369613e+09 5.000000 \n",
"0188399518 1.382789e+09 3.500000 \n",
"0188399399 1.365466e+09 5.000000 \n",
"0316967297 1.371168e+09 4.500000 \n",
"0615447279 1.348464e+09 4.333333 "
]
},
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}
],
"source": [
"df = meta.merge(reviewsDF, how = 'inner', left_index = True, right_index = True).dropna(how = 'any')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
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},
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"source": [
"df['time'] = pd.to_datetime(df['unixReviewTime'], unit = 's')\n",
"df = df.drop('unixReviewTime',axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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hkNPpJB/EvJ17J0+ePGuHXfONjDq832jjxo268MILvb+4nJwc8kHK27l38uTJ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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x219394bc208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"df.set_index('time')['price'].groupby(pd.TimeGrouper(freq='6M')).count().plot(kind='bar')\n",
"plt.ylabel('Frequency')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFXCAYAAABOYlxEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngf4f7vlv7ehni9kAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2196c43ab38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"np.log(df['price']).plot.hist(bins = 50,)\n",
"plt.xlabel('ln(price)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(33378, 4)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df[df['time'] > '2013-01-01']\n",
"df.shape\n",
"#33378"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2190ac7afd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"df[df.apply(lambda x: 'sock' in x['title'].lower(), axis = 1)]['price'].plot.hist(bins = 20)\n",
"plt.xlabel('Price')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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UpLi4OKWmpio0NFTp6elKS0uTZVnKyspSeHi4HSMBANAvOSwD3yAezMM6MTFRmrv6QNC2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\nIQcAwGCEHAAAg/0XMw8YJL+AejYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2190c14b9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"df[df.apply(lambda x: 'stroller' in x['title'].lower(), axis = 1)]['price'].plot.hist(bins = 20)\n",
"plt.xlabel('Price')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"360191\n",
"344911\n"
]
}
],
"source": [
"import nltk\n",
"from nltk.collocations import *\n",
"from nltk.corpus import stopwords\n",
"words = nltk.word_tokenize(' '.join(df['title']))\n",
"print(len(words))\n",
"stopset = set(stopwords.words('english'))\n",
"bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
"filtered_words = [w for w in words if not w in stopwords.words('english')]\n",
"print(len(filtered_words))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"293700\n"
]
}
],
"source": [
"import string\n",
"filtered_words = [x for x in filtered_words if x not in string.punctuation]\n",
"print(len(filtered_words))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('Bedding', 'Set'),\n",
" ('Car', 'Seat'),\n",
" ('Diaper', 'Bag'),\n",
" ('2', 'Pack'),\n",
" ('Carter', \"'s\"),\n",
" ('Crib', 'Bedding'),\n",
" ('Jojo', 'Designs'),\n",
" ('Sweet', 'Jojo'),\n",
" ('Cloth', 'Diaper'),\n",
" ('Summer', 'Infant'),\n",
" ('One', 'Size'),\n",
" ('BPA', 'Free'),\n",
" ('Safety', '1st'),\n",
" ('Changing', 'Pad'),\n",
" ('Crib', 'Sheet'),\n",
" ('Lambs', 'amp'),\n",
" ('amp', 'Ivy'),\n",
" ('3', 'Pack'),\n",
" ('Gift', 'Set'),\n",
" ('4', 'Piece')]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"finder = BigramCollocationFinder.from_words(filtered_words)\n",
"finder.nbest(bigram_measures.raw_freq, 20)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Q3VuKJyeobD+BiIjIgcaPH9/iYw5rwYeEhOCTTz7BrFmz8Pvvv6Ourg5jxoxBXl4eRo0a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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2197893c6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"word_fd = nltk.FreqDist(filtered_words)\n",
"word_fd.plot(35,cumulative=False)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13978"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words_only = [w for w in filtered_words if w.isalpha()]\n",
"unique = set([w.lower() for w in words_only])\n",
"len(unique)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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noJ4zDIloBpyFcAbqO8PgtDbgMIQzUM/5DHHRGXAYwhmA2AANcBbCGajnqu9Z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\nDOEMAIDD/H9DusvJdw2tEwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2190225d9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"word_fd = nltk.FreqDist(filtered_words)\n",
"plt.figure()\n",
"plt.plot(list(range(len(word_fd))),np.log(sorted(word_fd.values(),reverse = True)))\n",
"plt.xlabel('# Words')\n",
"plt.ylabel('Log Frequency')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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OnDnk5ubi9XpDP+P1enG5XDidztB2r9dLSkpKk23f3W61Wn/wPU6lqupoJA+VjAwXFRXu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\nEZH4dNJZ68iRI1x++eUnbB8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3hg0bGDNmzFn9rIiEpzAgImHt3Lkz9DTOzz777IQFnq68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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2190bdf3e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"word_fd = nltk.FreqDist(filtered_words)\n",
"plt.figure()\n",
"plt.plot(list(range(len(word_fd))),np.array(sorted(word_fd.values(),reverse = True)).cumsum())\n",
"plt.xlabel('# Words')\n",
"plt.ylabel('Cumulative Frequency')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = img_feat.set_index('rcnn_image_features.csv').merge(df, how = 'inner', left_index = True, right_index = True).dropna(how = 'any')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"price = df['price']\n",
"rating = df['overall']\n",
"df = df.drop(['price','overall','time'],axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del img_feat"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(33378, 4097)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"countvect = CountVectorizer(analyzer = 'word', tokenizer = nltk.word_tokenize\n",
" , stop_words = 'english', max_features = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.sparse import hstack\n",
"allFeatures = hstack((df.drop('title',axis = 1).values, countvect.fit_transform(df['title'])))"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import GradientBoostingRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(allFeatures,price, test_size = 0.2, random_state = 1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gb = GradientBoostingRegressor(random_state = 1, n_estimators = 100)\n",
"param_grid = {'learning_rate': [0.01, 0.1, 1, 10],\n",
" 'max_depth': [3,5, 7]}"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=3)]: Done 2 tasks | elapsed: 10.2min\n",
"[Parallel(n_jobs=3)]: Done 7 tasks | elapsed: 51.2min\n",
"[Parallel(n_jobs=3)]: Done 12 tasks | elapsed: 61.0min\n",
"[Parallel(n_jobs=3)]: Done 19 tasks | elapsed: 110.7min\n",
"[Parallel(n_jobs=3)]: Done 26 tasks | elapsed: 149.9min\n",
"[Parallel(n_jobs=3)]: Done 36 out of 36 | elapsed: 201.3min finished\n",
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:672: RuntimeWarning: overflow encountered in square\n",
" array_means[:, np.newaxis]) ** 2,\n"
]
}
],
"source": [
"gs = GridSearchCV(estimator = gb, param_grid = param_grid, scoring = 'r2', cv = 3, n_jobs = 3, verbose = 10).fit(X_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle\n",
"pickle.dump(gs, open('gsgd','wb'))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gs = pickle.load(open('gsgd','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"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>mean_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>mean_test_score</th>\n",
" <th>mean_train_score</th>\n",
" <th>param_learning_rate</th>\n",
" <th>param_max_depth</th>\n",
" <th>params</th>\n",
" <th>rank_test_score</th>\n",
" <th>split0_test_score</th>\n",
" <th>split0_train_score</th>\n",
" <th>split1_test_score</th>\n",
" <th>split1_train_score</th>\n",
" <th>split2_test_score</th>\n",
" <th>split2_train_score</th>\n",
" <th>std_fit_time</th>\n",
" <th>std_score_time</th>\n",
" <th>std_test_score</th>\n",
" <th>std_train_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>566.670623</td>\n",
" <td>0.114586</td>\n",
" <td>2.786318e-01</td>\n",
" <td>3.243149e-01</td>\n",
" <td>0.01</td>\n",
" <td>3</td>\n",
" <td>{'learning_rate': 0.01, 'max_depth': 3}</td>\n",
" <td>6</td>\n",
" <td>2.746436e-01</td>\n",
" <td>3.206852e-01</td>\n",
" <td>2.756077e-01</td>\n",
" <td>3.257986e-01</td>\n",
" <td>2.856449e-01</td>\n",
" <td>3.264610e-01</td>\n",
" <td>0.880525</td>\n",
" <td>7.364516e-03</td>\n",
" <td>0.004974</td>\n",
" <td>0.002581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>986.255134</td>\n",
" <td>0.119795</td>\n",
" <td>3.309793e-01</td>\n",
" <td>4.812772e-01</td>\n",
" <td>0.01</td>\n",
" <td>5</td>\n",
" <td>{'learning_rate': 0.01, 'max_depth': 5}</td>\n",
" <td>5</td>\n",
" <td>3.311018e-01</td>\n",
" <td>4.812723e-01</td>\n",
" <td>3.247332e-01</td>\n",
" <td>4.761615e-01</td>\n",
" <td>3.371035e-01</td>\n",
" <td>4.863977e-01</td>\n",
" <td>1.923422</td>\n",
" <td>7.367271e-03</td>\n",
" <td>0.005051</td>\n",
" <td>0.004179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1485.886665</td>\n",
" <td>0.145834</td>\n",
" <td>3.545946e-01</td>\n",
" <td>5.994504e-01</td>\n",
" <td>0.01</td>\n",
" <td>7</td>\n",
" <td>{'learning_rate': 0.01, 'max_depth': 7}</td>\n",
" <td>4</td>\n",
" <td>3.550735e-01</td>\n",
" <td>6.018290e-01</td>\n",
" <td>3.471884e-01</td>\n",
" <td>5.952919e-01</td>\n",
" <td>3.615226e-01</td>\n",
" <td>6.012302e-01</td>\n",
" <td>7.834503</td>\n",
" <td>7.365808e-03</td>\n",
" <td>0.005862</td>\n",
" <td>0.002951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>562.740034</td>\n",
" <td>0.114584</td>\n",
" <td>4.494290e-01</td>\n",
" <td>6.398482e-01</td>\n",
" <td>0.1</td>\n",
" <td>3</td>\n",
" <td>{'learning_rate': 0.1, 'max_depth': 3}</td>\n",
" <td>3</td>\n",
" <td>4.584442e-01</td>\n",
" <td>6.370217e-01</td>\n",
" <td>4.378416e-01</td>\n",
" <td>6.420067e-01</td>\n",
" <td>4.520016e-01</td>\n",
" <td>6.405162e-01</td>\n",
" <td>0.943872</td>\n",
" <td>7.366033e-03</td>\n",
" <td>0.008606</td>\n",
" <td>0.002089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>989.149054</td>\n",
" <td>0.139613</td>\n",
" <td>4.848307e-01</td>\n",
" <td>8.158985e-01</td>\n",
" <td>0.1</td>\n",
" <td>5</td>\n",
" <td>{'learning_rate': 0.1, 'max_depth': 5}</td>\n",
" <td>2</td>\n",
" <td>4.922831e-01</td>\n",
" <td>8.141078e-01</td>\n",
" <td>4.711908e-01</td>\n",
" <td>8.157892e-01</td>\n",
" <td>4.910190e-01</td>\n",
" <td>8.177984e-01</td>\n",
" <td>0.990014</td>\n",
" <td>1.433218e-03</td>\n",
" <td>0.009659</td>\n",
" <td>0.001509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1452.639853</td>\n",
" <td>0.145834</td>\n",
" <td>4.886593e-01</td>\n",
" <td>9.063788e-01</td>\n",
" <td>0.1</td>\n",
" <td>7</td>\n",
" <td>{'learning_rate': 0.1, 'max_depth': 7}</td>\n",
" <td>1</td>\n",
" <td>4.976960e-01</td>\n",
" <td>9.064331e-01</td>\n",
" <td>4.791010e-01</td>\n",
" <td>9.076069e-01</td>\n",
" <td>4.891809e-01</td>\n",
" <td>9.050965e-01</td>\n",
" <td>4.112546</td>\n",
" <td>7.365639e-03</td>\n",
" <td>0.007600</td>\n",
" <td>0.001026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>565.015646</td>\n",
" <td>0.120319</td>\n",
" <td>1.792066e-02</td>\n",
" <td>8.619998e-01</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>{'learning_rate': 1, 'max_depth': 3}</td>\n",
" <td>7</td>\n",
" <td>4.932588e-02</td>\n",
" <td>8.617218e-01</td>\n",
" <td>-3.529163e-02</td>\n",
" <td>8.642543e-01</td>\n",
" <td>3.973017e-02</td>\n",
" <td>8.600232e-01</td>\n",
" <td>0.675001</td>\n",
" <td>7.765183e-03</td>\n",
" <td>0.037831</td>\n",
" <td>0.001738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>970.525649</td>\n",
" <td>0.140625</td>\n",
" <td>-1.198566e-01</td>\n",
" <td>9.623524e-01</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>{'learning_rate': 1, 'max_depth': 5}</td>\n",
" <td>8</td>\n",
" <td>-1.098837e-01</td>\n",
" <td>9.611571e-01</td>\n",
" <td>-1.242398e-01</td>\n",
" <td>9.622157e-01</td>\n",
" <td>-1.254470e-01</td>\n",
" <td>9.636842e-01</td>\n",
" <td>0.890323</td>\n",
" <td>3.893359e-07</td>\n",
" <td>0.007069</td>\n",
" <td>0.001036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1368.443473</td>\n",
" <td>0.145834</td>\n",
" <td>-1.649937e-01</td>\n",
" <td>9.907511e-01</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>{'learning_rate': 1, 'max_depth': 7}</td>\n",
" <td>9</td>\n",
" <td>-1.382360e-01</td>\n",
" <td>9.904348e-01</td>\n",
" <td>-2.061176e-01</td>\n",
" <td>9.913164e-01</td>\n",
" <td>-1.506260e-01</td>\n",
" <td>9.905020e-01</td>\n",
" <td>5.178201</td>\n",
" <td>7.365527e-03</td>\n",
" <td>0.029516</td>\n",
" <td>0.000401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>563.856970</td>\n",
" <td>0.104178</td>\n",
" <td>-1.796564e+190</td>\n",
" <td>-1.918447e+190</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>{'learning_rate': 10, 'max_depth': 3}</td>\n",
" <td>10</td>\n",
" <td>-1.592175e+190</td>\n",
" <td>-1.868740e+190</td>\n",
" <td>-1.839157e+190</td>\n",
" <td>-1.942958e+190</td>\n",
" <td>-1.958380e+190</td>\n",
" <td>-1.943643e+190</td>\n",
" <td>1.106056</td>\n",
" <td>7.351815e-03</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>981.543549</td>\n",
" <td>0.119794</td>\n",
" <td>-2.579831e+190</td>\n",
" <td>-2.902438e+190</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>{'learning_rate': 10, 'max_depth': 5}</td>\n",
" <td>11</td>\n",
" <td>-2.521610e+190</td>\n",
" <td>-2.819105e+190</td>\n",
" <td>-2.814671e+190</td>\n",
" <td>-2.901231e+190</td>\n",
" <td>-2.403191e+190</td>\n",
" <td>-2.986977e+190</td>\n",
" <td>0.944962</td>\n",
" <td>7.363448e-03</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1519.095625</td>\n",
" <td>0.130208</td>\n",
" <td>-4.126538e+190</td>\n",
" <td>-3.936789e+190</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>{'learning_rate': 10, 'max_depth': 7}</td>\n",
" <td>12</td>\n",
" <td>-3.475897e+190</td>\n",
" <td>-3.940914e+190</td>\n",
" <td>-5.155596e+190</td>\n",
" <td>-3.937756e+190</td>\n",
" <td>-3.748079e+190</td>\n",
" <td>-3.931695e+190</td>\n",
" <td>18.480392</td>\n",
" <td>7.365415e-03</td>\n",
" <td>inf</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_fit_time mean_score_time mean_test_score mean_train_score \\\n",
"0 566.670623 0.114586 2.786318e-01 3.243149e-01 \n",
"1 986.255134 0.119795 3.309793e-01 4.812772e-01 \n",
"2 1485.886665 0.145834 3.545946e-01 5.994504e-01 \n",
"3 562.740034 0.114584 4.494290e-01 6.398482e-01 \n",
"4 989.149054 0.139613 4.848307e-01 8.158985e-01 \n",
"5 1452.639853 0.145834 4.886593e-01 9.063788e-01 \n",
"6 565.015646 0.120319 1.792066e-02 8.619998e-01 \n",
"7 970.525649 0.140625 -1.198566e-01 9.623524e-01 \n",
"8 1368.443473 0.145834 -1.649937e-01 9.907511e-01 \n",
"9 563.856970 0.104178 -1.796564e+190 -1.918447e+190 \n",
"10 981.543549 0.119794 -2.579831e+190 -2.902438e+190 \n",
"11 1519.095625 0.130208 -4.126538e+190 -3.936789e+190 \n",
"\n",
" param_learning_rate param_max_depth \\\n",
"0 0.01 3 \n",
"1 0.01 5 \n",
"2 0.01 7 \n",
"3 0.1 3 \n",
"4 0.1 5 \n",
"5 0.1 7 \n",
"6 1 3 \n",
"7 1 5 \n",
"8 1 7 \n",
"9 10 3 \n",
"10 10 5 \n",
"11 10 7 \n",
"\n",
" params rank_test_score \\\n",
"0 {'learning_rate': 0.01, 'max_depth': 3} 6 \n",
"1 {'learning_rate': 0.01, 'max_depth': 5} 5 \n",
"2 {'learning_rate': 0.01, 'max_depth': 7} 4 \n",
"3 {'learning_rate': 0.1, 'max_depth': 3} 3 \n",
"4 {'learning_rate': 0.1, 'max_depth': 5} 2 \n",
"5 {'learning_rate': 0.1, 'max_depth': 7} 1 \n",
"6 {'learning_rate': 1, 'max_depth': 3} 7 \n",
"7 {'learning_rate': 1, 'max_depth': 5} 8 \n",
"8 {'learning_rate': 1, 'max_depth': 7} 9 \n",
"9 {'learning_rate': 10, 'max_depth': 3} 10 \n",
"10 {'learning_rate': 10, 'max_depth': 5} 11 \n",
"11 {'learning_rate': 10, 'max_depth': 7} 12 \n",
"\n",
" split0_test_score split0_train_score split1_test_score \\\n",
"0 2.746436e-01 3.206852e-01 2.756077e-01 \n",
"1 3.311018e-01 4.812723e-01 3.247332e-01 \n",
"2 3.550735e-01 6.018290e-01 3.471884e-01 \n",
"3 4.584442e-01 6.370217e-01 4.378416e-01 \n",
"4 4.922831e-01 8.141078e-01 4.711908e-01 \n",
"5 4.976960e-01 9.064331e-01 4.791010e-01 \n",
"6 4.932588e-02 8.617218e-01 -3.529163e-02 \n",
"7 -1.098837e-01 9.611571e-01 -1.242398e-01 \n",
"8 -1.382360e-01 9.904348e-01 -2.061176e-01 \n",
"9 -1.592175e+190 -1.868740e+190 -1.839157e+190 \n",
"10 -2.521610e+190 -2.819105e+190 -2.814671e+190 \n",
"11 -3.475897e+190 -3.940914e+190 -5.155596e+190 \n",
"\n",
" split1_train_score split2_test_score split2_train_score std_fit_time \\\n",
"0 3.257986e-01 2.856449e-01 3.264610e-01 0.880525 \n",
"1 4.761615e-01 3.371035e-01 4.863977e-01 1.923422 \n",
"2 5.952919e-01 3.615226e-01 6.012302e-01 7.834503 \n",
"3 6.420067e-01 4.520016e-01 6.405162e-01 0.943872 \n",
"4 8.157892e-01 4.910190e-01 8.177984e-01 0.990014 \n",
"5 9.076069e-01 4.891809e-01 9.050965e-01 4.112546 \n",
"6 8.642543e-01 3.973017e-02 8.600232e-01 0.675001 \n",
"7 9.622157e-01 -1.254470e-01 9.636842e-01 0.890323 \n",
"8 9.913164e-01 -1.506260e-01 9.905020e-01 5.178201 \n",
"9 -1.942958e+190 -1.958380e+190 -1.943643e+190 1.106056 \n",
"10 -2.901231e+190 -2.403191e+190 -2.986977e+190 0.944962 \n",
"11 -3.937756e+190 -3.748079e+190 -3.931695e+190 18.480392 \n",
"\n",
" std_score_time std_test_score std_train_score \n",
"0 7.364516e-03 0.004974 0.002581 \n",
"1 7.367271e-03 0.005051 0.004179 \n",
"2 7.365808e-03 0.005862 0.002951 \n",
"3 7.366033e-03 0.008606 0.002089 \n",
"4 1.433218e-03 0.009659 0.001509 \n",
"5 7.365639e-03 0.007600 0.001026 \n",
"6 7.765183e-03 0.037831 0.001738 \n",
"7 3.893359e-07 0.007069 0.001036 \n",
"8 7.365527e-03 0.029516 0.000401 \n",
"9 7.351815e-03 inf inf \n",
"10 7.363448e-03 inf inf \n",
"11 7.365415e-03 inf inf "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(gs.cv_results_)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"gb = GradientBoostingRegressor(random_state = 1, n_estimators = 100)\n",
"param_grid = {'learning_rate': [0.01, 0.05, 0.1, 0.5],\n",
" 'max_depth': [8, 10, 13]}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
]
}
],
"source": [
"gs2 = GridSearchCV(estimator = gb, param_grid = param_grid, scoring = 'r2', cv = 3, n_jobs = 3, verbose = 10).fit(X_train, y_train)\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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
vencejo/conecta4 | Programacion Interactiva/conecta4_PasoDe_ipynb_A_py.ipynb | 2 | 6345 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing conecta4.py\n"
]
}
],
"source": [
"#%%writefile conecta4.py\n",
"\n",
"## Representacion de los datos\n",
"# La representacion interna del tablero sera la de una lista compuesta por seis listas de numeros, estos numeros\n",
"# seran 0, para representar el espacio vacio, 1 para representar ficha roja y -1 para representar ficha amarilla\n",
"# Las coordenadas del tablero seran del tipo (fila,columna) y tendran su origen en la esquina superior izquierda \n",
"#, creciendo las columnas hacia la derecha y las filas hacia abajo \n",
"\n",
"# !pip install ipythonblocks\n",
"# Vease http://ipythonblocks.org/ para mas informacion\n",
"\n",
"import time\n",
"from IPython.display import clear_output\n",
"from ipythonblocks import BlockGrid, colors\n",
"\n",
"FILMAX = 6\n",
"COLMAX = 7\n",
"\n",
"def obtenNuevoTablero():\n",
" \"\"\" Crea un nuevo tablero \"\"\"\n",
" tablero = []\n",
" for i in range(FILMAX):\n",
" tablero.append([0] * COLMAX)\n",
"\n",
" return tablero\n",
"\n",
"def reseteaTablero(tablero):\n",
" \"\"\" Limpia el tablero \"\"\"\n",
" for col in range(COLMAX):\n",
" for fil in range(FILMAX):\n",
" tablero[fil][col] = 0\n",
"\n",
"def dibujaTablero(tablero):\n",
" \"\"\" Dibuja el tablero que se le pasa en formato grafico\"\"\"\n",
" grid = BlockGrid(width=COLMAX, height=FILMAX, block_size=25,lines_on = True)\n",
" \n",
" for fil in range(grid.height):\n",
" for col in range(grid.width):\n",
" if tablero[fil][col] == 1:\n",
" grid[fil,col] = colors['Red']\n",
" elif tablero[fil][col] == -1:\n",
" grid[fil,col] = colors['Green']\n",
" else:\n",
" grid[fil,col] = colors['Black']\n",
" \n",
" grid.show()\n",
" \n",
"def estaEnTablero(fil, col):\n",
" \"\"\" Devuelve verdadero si fil,col estan en el tablero \"\"\"\n",
" return fil >= 0 and fil < FILMAX and col >= 0 and col < COLMAX\n",
"\n",
"def esUnMovimientoValido(tablero, colJugada):\n",
" \"\"\" Devuelve falso si el movimiento a la coord colJugada no es posible,\n",
" y en caso de ser posible devuelve las coordenadas [fil,col] \n",
" de donde queda la ficha tras el movimiento\"\"\"\n",
" \n",
" if not estaEnTablero(0, colJugada):\n",
" return False\n",
" \n",
" for fil in range(FILMAX-1, -1, -1):\n",
" if (fil == FILMAX-1 and tablero[fil][colJugada] == 0) or \\\n",
" (tablero[fil][colJugada] == 0 and tablero[fil+1][colJugada] != 0) : \n",
" return [ fil, colJugada]\n",
" \n",
" return False\n",
"\n",
"def daLosMovimientosValidos(tablero):\n",
" \"\"\" Devuelve una lista [fil,col] con los movimientos validos del tablero\"\"\"\n",
" movimientosValidos = []\n",
"\n",
" for col in range(COLMAX):\n",
" movimiento = esUnMovimientoValido(tablero, col)\n",
" if movimiento:\n",
" movimientosValidos.append(movimiento)\n",
" return movimientosValidos\n",
"\n",
"def obtenCopiaTablero(tablero):\n",
" \"\"\" Devuelve una copia del tablero \"\"\"\n",
" copiaTablero = obtenNuevoTablero()\n",
"\n",
" for fil in range(FILMAX):\n",
" for col in range(COLMAX):\n",
" copiaTablero[fil][col] = tablero[fil][col]\n",
"\n",
" return copiaTablero\n",
"\n",
"def hazMovimiento(tablero,ficha, colJugada):\n",
" \"\"\" Pone la ficha en la columna, y actualiza el tablero. \n",
" Si el movimiento no es posible devuelve falso\"\"\"\n",
" \n",
" if ficha not in (-1,1):\n",
" raise TypeError(\"El valor de la ficha ha de ser -1 o 1\")\n",
" \n",
" movimiento = esUnMovimientoValido(tablero, colJugada)\n",
"\n",
" if movimiento == False:\n",
" return False\n",
"\n",
" tablero[movimiento[0]][movimiento[1]] = ficha\n",
" \n",
" return True\n",
"\n",
"def daLaVictoria(tablero,tipoFicha, filJugada, colJugada):\n",
" \"\"\" Analiza si una ficha en las coords. fil y col da una \n",
" situacion de victoria, en cuyo caso devuelve una lista con las coordenadas de\n",
" las fichas que forman la linea victoriosa, en caso contrario devuelve false \"\"\"\n",
" \n",
" if not estaEnTablero(filJugada,colJugada):\n",
" return False\n",
" \n",
" for cdirection in [0,1,-1]:\n",
" for fdirection in [0,1,-1]:\n",
" if cdirection == 0 and fdirection == 0:\n",
" continue\n",
" listaFichasGanadoras = [[filJugada, colJugada]]\n",
" c, f = colJugada, filJugada\n",
" c += cdirection # Primer paso en la direccion\n",
" f += fdirection \n",
" while estaEnTablero(f,c) and tablero[f][c] == tipoFicha:\n",
" listaFichasGanadoras.append( [f,c])\n",
" c += cdirection\n",
" f += fdirection \n",
" if len(listaFichasGanadoras) >= 4:\n",
" return listaFichasGanadoras\n",
" \n",
" return 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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
hvillanua/deep-learning | language-translation/dlnd_language_translation.ipynb | 1 | 160359 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Language Translation\n",
"In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n",
"## Get the Data\n",
"Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"source_path = 'data/small_vocab_en'\n",
"target_path = 'data/small_vocab_fr'\n",
"source_text = helper.load_data(source_path)\n",
"target_text = helper.load_data(target_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data\n",
"Play around with view_sentence_range to view different parts of the data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset Stats\n",
"Roughly the number of unique words: 227\n",
"Number of sentences: 137861\n",
"Average number of words in a sentence: 13.225277634719028\n",
"\n",
"English sentences 0 to 10:\n",
"new jersey is sometimes quiet during autumn , and it is snowy in april .\n",
"the united states is usually chilly during july , and it is usually freezing in november .\n",
"california is usually quiet during march , and it is usually hot in june .\n",
"the united states is sometimes mild during june , and it is cold in september .\n",
"your least liked fruit is the grape , but my least liked is the apple .\n",
"his favorite fruit is the orange , but my favorite is the grape .\n",
"paris is relaxing during december , but it is usually chilly in july .\n",
"new jersey is busy during spring , and it is never hot in march .\n",
"our least liked fruit is the lemon , but my least liked is the grape .\n",
"the united states is sometimes busy during january , and it is sometimes warm in november .\n",
"\n",
"French sentences 0 to 10:\n",
"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n",
"les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n",
"california est généralement calme en mars , et il est généralement chaud en juin .\n",
"les états-unis est parfois légère en juin , et il fait froid en septembre .\n",
"votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n",
"son fruit préféré est l'orange , mais mon préféré est le raisin .\n",
"paris est relaxant en décembre , mais il est généralement froid en juillet .\n",
"new jersey est occupé au printemps , et il est jamais chaude en mars .\n",
"notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n",
"les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n"
]
}
],
"source": [
"view_sentence_range = (0, 10)\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"\n",
"print('Dataset Stats')\n",
"print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n",
"\n",
"sentences = source_text.split('\\n')\n",
"word_counts = [len(sentence.split()) for sentence in sentences]\n",
"print('Number of sentences: {}'.format(len(sentences)))\n",
"print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n",
"\n",
"print()\n",
"print('English sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n",
"print()\n",
"print('French sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement Preprocessing Function\n",
"### Text to Word Ids\n",
"As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\n",
"\n",
"You can get the `<EOS>` word id by doing:\n",
"```python\n",
"target_vocab_to_int['<EOS>']\n",
"```\n",
"You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n",
" \"\"\"\n",
" Convert source and target text to proper word ids\n",
" :param source_text: String that contains all the source text.\n",
" :param target_text: String that contains all the target text.\n",
" :param source_vocab_to_int: Dictionary to go from the source words to an id\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: A tuple of lists (source_id_text, target_id_text)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" source_split, target_split = source_text.split('\\n'), target_text.split('\\n')\n",
" source_to_int, target_to_int = [], []\n",
" for source, target in zip(source_split, target_split):\n",
" source_to_int.append([source_vocab_to_int[word] for word in source.split()])\n",
" targets = [target_vocab_to_int[word] for word in target.split()]\n",
" targets.append((target_vocab_to_int['<EOS>']))\n",
" target_to_int.append(targets)\n",
" \n",
" #print(source_to_int, target_to_int)\n",
" return source_to_int, target_to_int\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_text_to_ids(text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocess all the data and save it\n",
"Running the code cell below will preprocess all the data and save it to file."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"helper.preprocess_and_save_data(source_path, target_path, text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Point\n",
"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the Version of TensorFlow and Access to GPU\n",
"This will check to make sure you have the correct version of TensorFlow and access to a GPU"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorFlow Version: 1.1.0\n",
"Default GPU Device: /gpu:0\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"from distutils.version import LooseVersion\n",
"import warnings\n",
"import tensorflow as tf\n",
"from tensorflow.python.layers.core import Dense\n",
"\n",
"# Check TensorFlow Version\n",
"assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n",
"print('TensorFlow Version: {}'.format(tf.__version__))\n",
"\n",
"# Check for a GPU\n",
"if not tf.test.gpu_device_name():\n",
" warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
"else:\n",
" print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Neural Network\n",
"You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n",
"- `model_inputs`\n",
"- `process_decoder_input`\n",
"- `encoding_layer`\n",
"- `decoding_layer_train`\n",
"- `decoding_layer_infer`\n",
"- `decoding_layer`\n",
"- `seq2seq_model`\n",
"\n",
"### Input\n",
"Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
"\n",
"- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n",
"- Targets placeholder with rank 2.\n",
"- Learning rate placeholder with rank 0.\n",
"- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n",
"- Target sequence length placeholder named \"target_sequence_length\" with rank 1\n",
"- Max target sequence length tensor named \"max_target_len\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\n",
"- Source sequence length placeholder named \"source_sequence_length\" with rank 1\n",
"\n",
"Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def model_inputs():\n",
" \"\"\"\n",
" Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\n",
" :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\n",
" max target sequence length, source sequence length)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" #max_tar_seq_len = np.max([len(sentence) for sentence in target_int_text])\n",
" #max_sour_seq_len = np.max([len(sentence) for sentence in source_int_text])\n",
" #max_source_len = np.max([max_tar_seq_len, max_sour_seq_len])\n",
" inputs = tf.placeholder(tf.int32, [None, None], name='input')\n",
" targets = tf.placeholder(tf.int32, [None, None])\n",
" learning_rate = tf.placeholder(tf.float32)\n",
" keep_probability = tf.placeholder(tf.float32, name='keep_prob')\n",
" target_seq_len = tf.placeholder(tf.int32, [None], name='target_sequence_length')\n",
" max_target_seq_len = tf.reduce_max(target_seq_len, name='target_sequence_length')\n",
" source_seq_len = tf.placeholder(tf.int32, [None], name='source_sequence_length')\n",
" return inputs, targets, learning_rate, keep_probability, target_seq_len, max_target_seq_len, source_seq_len\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_model_inputs(model_inputs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process Decoder Input\n",
"Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n",
" \"\"\"\n",
" Preprocess target data for encoding\n",
" :param target_data: Target Placehoder\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param batch_size: Batch Size\n",
" :return: Preprocessed target data\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n",
" dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n",
" return dec_input\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_process_encoding_input(process_decoder_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Encoding\n",
"Implement `encoding_layer()` to create a Encoder RNN layer:\n",
" * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n",
" * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\n",
" * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"from imp import reload\n",
"reload(tests)\n",
"\n",
"def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \n",
" source_sequence_length, source_vocab_size, \n",
" encoding_embedding_size):\n",
" \"\"\"\n",
" Create encoding layer\n",
" :param rnn_inputs: Inputs for the RNN\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param keep_prob: Dropout keep probability\n",
" :param source_sequence_length: a list of the lengths of each sequence in the batch\n",
" :param source_vocab_size: vocabulary size of source data\n",
" :param encoding_embedding_size: embedding size of source data\n",
" :return: tuple (RNN output, RNN state)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" embed_seq = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)\n",
" def lstm_cell():\n",
" return tf.contrib.rnn.LSTMCell(rnn_size)\n",
" rnn = tf.contrib.rnn.MultiRNNCell([lstm_cell() for i in range(num_layers)])\n",
" rnn = tf.contrib.rnn.DropoutWrapper(rnn, output_keep_prob=keep_prob)\n",
" output, state = tf.nn.dynamic_rnn(rnn, embed_seq, dtype=tf.float32)\n",
" return output, state\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_encoding_layer(encoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Training\n",
"Create a training decoding layer:\n",
"* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"\n",
"def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n",
" target_sequence_length, max_summary_length, \n",
" output_layer, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for training\n",
" :param encoder_state: Encoder State\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embed_input: Decoder embedded input\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_summary_length: The length of the longest sequence in the batch\n",
" :param output_layer: Function to apply the output layer\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing training logits and sample_id\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" training_helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length)\n",
" train_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, training_helper, encoder_state, output_layer)\n",
" output, _ = tf.contrib.seq2seq.dynamic_decode(train_decoder, impute_finished=False, maximum_iterations=max_summary_length)\n",
" \n",
" return output\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_train(decoding_layer_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Inference\n",
"Create inference decoder:\n",
"* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\n",
" end_of_sequence_id, max_target_sequence_length,\n",
" vocab_size, output_layer, batch_size, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for inference\n",
" :param encoder_state: Encoder state\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embeddings: Decoder embeddings\n",
" :param start_of_sequence_id: GO ID\n",
" :param end_of_sequence_id: EOS Id\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param vocab_size: Size of decoder/target vocabulary\n",
" :param decoding_scope: TenorFlow Variable Scope for decoding\n",
" :param output_layer: Function to apply the output layer\n",
" :param batch_size: Batch size\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing inference logits and sample_id\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" start_tokens = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_tokens')\n",
" inference_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id)\n",
" inference_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, inference_helper, encoder_state, output_layer)\n",
" output, _ = tf.contrib.seq2seq.dynamic_decode(inference_decoder,\n",
" impute_finished=True, maximum_iterations=max_target_sequence_length)\n",
" return output\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_infer(decoding_layer_infer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Decoding Layer\n",
"Implement `decoding_layer()` to create a Decoder RNN layer.\n",
"\n",
"* Embed the target sequences\n",
"* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\n",
"* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\n",
"* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\n",
"* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\n",
"\n",
"Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def decoding_layer(dec_input, encoder_state,\n",
" target_sequence_length, max_target_sequence_length,\n",
" rnn_size,\n",
" num_layers, target_vocab_to_int, target_vocab_size,\n",
" batch_size, keep_prob, decoding_embedding_size):\n",
" \"\"\"\n",
" Create decoding layer\n",
" :param dec_input: Decoder input\n",
" :param encoder_state: Encoder state\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param target_vocab_size: Size of target vocabulary\n",
" :param batch_size: The size of the batch\n",
" :param keep_prob: Dropout keep probability\n",
" :param decoding_embedding_size: Decoding embedding size\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" #embed_seq = tf.contrib.layers.embed_sequence(dec_input, target_vocab_size, decoding_embedding_size)\n",
" \n",
" dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\n",
" dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n",
" \n",
" def lstm_cell():\n",
" return tf.contrib.rnn.LSTMCell(rnn_size)\n",
" rnn = tf.contrib.rnn.MultiRNNCell([lstm_cell() for i in range(num_layers)])\n",
" rnn = tf.contrib.rnn.DropoutWrapper(rnn, output_keep_prob=keep_prob)\n",
" output_layer = Dense(target_vocab_size,\n",
" kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))\n",
" \n",
" with tf.variable_scope(\"decode\"):\n",
" training_output = decoding_layer_train(encoder_state, rnn, dec_embed_input, \n",
" target_sequence_length, max_target_sequence_length, output_layer, keep_prob)\n",
" \n",
" with tf.variable_scope(\"decode\", reuse=True):\n",
" inference_output = decoding_layer_infer(encoder_state, rnn, dec_embeddings, target_vocab_to_int['<GO>'],\n",
" target_vocab_to_int['<EOS>'], max_target_sequence_length, target_vocab_size,\n",
" output_layer, batch_size, keep_prob)\n",
" return training_output, inference_output\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer(decoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Neural Network\n",
"Apply the functions you implemented above to:\n",
"\n",
"- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\n",
"- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\n",
"- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def seq2seq_model(input_data, target_data, keep_prob, batch_size,\n",
" source_sequence_length, target_sequence_length,\n",
" max_target_sentence_length,\n",
" source_vocab_size, target_vocab_size,\n",
" enc_embedding_size, dec_embedding_size,\n",
" rnn_size, num_layers, target_vocab_to_int):\n",
" \"\"\"\n",
" Build the Sequence-to-Sequence part of the neural network\n",
" :param input_data: Input placeholder\n",
" :param target_data: Target placeholder\n",
" :param keep_prob: Dropout keep probability placeholder\n",
" :param batch_size: Batch Size\n",
" :param source_sequence_length: Sequence Lengths of source sequences in the batch\n",
" :param target_sequence_length: Sequence Lengths of target sequences in the batch\n",
" :max_target_sentence_length: Maximum target sequence lenght\n",
" :param source_vocab_size: Source vocabulary size\n",
" :param target_vocab_size: Target vocabulary size\n",
" :param enc_embedding_size: Decoder embedding size\n",
" :param dec_embedding_size: Encoder embedding size\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" _, enc_state = encoding_layer(input_data, rnn_size, num_layers, keep_prob, \n",
" source_sequence_length, source_vocab_size, \n",
" enc_embedding_size)\n",
" dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)\n",
" training_output, inference_output = decoding_layer(dec_input, enc_state, target_sequence_length, \n",
" max_target_sentence_length, rnn_size, num_layers, \n",
" target_vocab_to_int, target_vocab_size, batch_size, \n",
" keep_prob, dec_embedding_size)\n",
" return training_output, inference_output\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_seq2seq_model(seq2seq_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neural Network Training\n",
"### Hyperparameters\n",
"Tune the following parameters:\n",
"\n",
"- Set `epochs` to the number of epochs.\n",
"- Set `batch_size` to the batch size.\n",
"- Set `rnn_size` to the size of the RNNs.\n",
"- Set `num_layers` to the number of layers.\n",
"- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n",
"- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n",
"- Set `learning_rate` to the learning rate.\n",
"- Set `keep_probability` to the Dropout keep probability\n",
"- Set `display_step` to state how many steps between each debug output statement"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Number of Epochs\n",
"epochs = 10\n",
"# Batch Size\n",
"batch_size = 128\n",
"# RNN Size\n",
"rnn_size = 254\n",
"# Number of Layers\n",
"num_layers = 2\n",
"# Embedding Size\n",
"encoding_embedding_size = 200\n",
"decoding_embedding_size = 200\n",
"# Learning Rate\n",
"learning_rate = 0.01\n",
"# Dropout Keep Probability\n",
"keep_probability = 0.5\n",
"display_step = 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Graph\n",
"Build the graph using the neural network you implemented."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"save_path = 'checkpoints/dev'\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n",
"max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\n",
"\n",
"train_graph = tf.Graph()\n",
"with train_graph.as_default():\n",
" input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\n",
"\n",
" #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\n",
" input_shape = tf.shape(input_data)\n",
"\n",
" train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\n",
" targets,\n",
" keep_prob,\n",
" batch_size,\n",
" source_sequence_length,\n",
" target_sequence_length,\n",
" max_target_sequence_length,\n",
" len(source_vocab_to_int),\n",
" len(target_vocab_to_int),\n",
" encoding_embedding_size,\n",
" decoding_embedding_size,\n",
" rnn_size,\n",
" num_layers,\n",
" target_vocab_to_int)\n",
"\n",
"\n",
" training_logits = tf.identity(train_logits.rnn_output, name='logits')\n",
" inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\n",
"\n",
" masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n",
"\n",
" with tf.name_scope(\"optimization\"):\n",
" # Loss function\n",
" cost = tf.contrib.seq2seq.sequence_loss(\n",
" training_logits,\n",
" targets,\n",
" masks)\n",
"\n",
" # Optimizer\n",
" optimizer = tf.train.AdamOptimizer(lr)\n",
"\n",
" # Gradient Clipping\n",
" gradients = optimizer.compute_gradients(cost)\n",
" capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
" train_op = optimizer.apply_gradients(capped_gradients)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Batch and pad the source and target sequences"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def pad_sentence_batch(sentence_batch, pad_int):\n",
" \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n",
" max_sentence = max([len(sentence) for sentence in sentence_batch])\n",
" return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\n",
"\n",
"\n",
"def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\n",
" \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n",
" for batch_i in range(0, len(sources)//batch_size):\n",
" start_i = batch_i * batch_size\n",
"\n",
" # Slice the right amount for the batch\n",
" sources_batch = sources[start_i:start_i + batch_size]\n",
" targets_batch = targets[start_i:start_i + batch_size]\n",
"\n",
" # Pad\n",
" pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n",
" pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n",
"\n",
" # Need the lengths for the _lengths parameters\n",
" pad_targets_lengths = []\n",
" for target in pad_targets_batch:\n",
" pad_targets_lengths.append(len(target))\n",
"\n",
" pad_source_lengths = []\n",
" for source in pad_sources_batch:\n",
" pad_source_lengths.append(len(source))\n",
"\n",
" yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train\n",
"Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forums to see if anyone is having the same problem."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0 Batch 10/1077 - Train Accuracy: 0.2447, Validation Accuracy: 0.3402, Loss: 3.6887\n",
"Epoch 0 Batch 20/1077 - Train Accuracy: 0.3355, Validation Accuracy: 0.3878, Loss: 3.0551\n",
"Epoch 0 Batch 30/1077 - Train Accuracy: 0.3789, Validation Accuracy: 0.4357, Loss: 2.7872\n",
"Epoch 0 Batch 40/1077 - Train Accuracy: 0.4238, Validation Accuracy: 0.4869, Loss: 2.5160\n",
"Epoch 0 Batch 50/1077 - Train Accuracy: 0.4480, Validation Accuracy: 0.5170, Loss: 2.3008\n",
"Epoch 0 Batch 60/1077 - Train Accuracy: 0.4963, Validation Accuracy: 0.5352, Loss: 1.9147\n",
"Epoch 0 Batch 70/1077 - Train Accuracy: 0.4556, Validation Accuracy: 0.5071, Loss: 1.6960\n",
"Epoch 0 Batch 80/1077 - Train Accuracy: 0.2562, Validation Accuracy: 0.2859, Loss: 1.3617\n",
"Epoch 0 Batch 90/1077 - Train Accuracy: 0.3539, Validation Accuracy: 0.3707, Loss: 1.2548\n",
"Epoch 0 Batch 100/1077 - Train Accuracy: 0.4859, Validation Accuracy: 0.5195, Loss: 1.0572\n",
"Epoch 0 Batch 110/1077 - Train Accuracy: 0.5828, Validation Accuracy: 0.5419, Loss: 0.9233\n",
"Epoch 0 Batch 120/1077 - Train Accuracy: 0.5113, Validation Accuracy: 0.5724, Loss: 0.9251\n",
"Epoch 0 Batch 130/1077 - Train Accuracy: 0.5573, Validation Accuracy: 0.5810, Loss: 0.8014\n",
"Epoch 0 Batch 140/1077 - Train Accuracy: 0.5119, Validation Accuracy: 0.5874, Loss: 0.8533\n",
"Epoch 0 Batch 150/1077 - Train Accuracy: 0.6101, Validation Accuracy: 0.5913, Loss: 0.7517\n",
"Epoch 0 Batch 160/1077 - Train Accuracy: 0.5906, Validation Accuracy: 0.6140, Loss: 0.7538\n",
"Epoch 0 Batch 170/1077 - Train Accuracy: 0.5648, Validation Accuracy: 0.6048, Loss: 0.7696\n",
"Epoch 0 Batch 180/1077 - Train Accuracy: 0.5934, Validation Accuracy: 0.6108, Loss: 0.7277\n",
"Epoch 0 Batch 190/1077 - Train Accuracy: 0.6254, Validation Accuracy: 0.6140, Loss: 0.6937\n",
"Epoch 0 Batch 200/1077 - Train Accuracy: 0.5789, Validation Accuracy: 0.6140, Loss: 0.7119\n",
"Epoch 0 Batch 210/1077 - Train Accuracy: 0.6250, Validation Accuracy: 0.6151, Loss: 0.6736\n",
"Epoch 0 Batch 220/1077 - Train Accuracy: 0.5983, Validation Accuracy: 0.6374, Loss: 0.6787\n",
"Epoch 0 Batch 230/1077 - Train Accuracy: 0.6168, Validation Accuracy: 0.6286, Loss: 0.6376\n",
"Epoch 0 Batch 240/1077 - Train Accuracy: 0.6395, Validation Accuracy: 0.6250, Loss: 0.6000\n",
"Epoch 0 Batch 250/1077 - Train Accuracy: 0.6286, Validation Accuracy: 0.6250, Loss: 0.5548\n",
"Epoch 0 Batch 260/1077 - Train Accuracy: 0.6347, Validation Accuracy: 0.6151, Loss: 0.5610\n",
"Epoch 0 Batch 270/1077 - Train Accuracy: 0.5961, Validation Accuracy: 0.6200, Loss: 0.6164\n",
"Epoch 0 Batch 280/1077 - Train Accuracy: 0.6336, Validation Accuracy: 0.6371, Loss: 0.6046\n",
"Epoch 0 Batch 290/1077 - Train Accuracy: 0.6254, Validation Accuracy: 0.6406, Loss: 0.5982\n",
"Epoch 0 Batch 300/1077 - Train Accuracy: 0.6164, Validation Accuracy: 0.6175, Loss: 0.5605\n",
"Epoch 0 Batch 310/1077 - Train Accuracy: 0.6348, Validation Accuracy: 0.6428, Loss: 0.5610\n",
"Epoch 0 Batch 320/1077 - Train Accuracy: 0.6770, Validation Accuracy: 0.6499, Loss: 0.5327\n",
"Epoch 0 Batch 330/1077 - Train Accuracy: 0.6508, Validation Accuracy: 0.6435, Loss: 0.5100\n",
"Epoch 0 Batch 340/1077 - Train Accuracy: 0.6451, Validation Accuracy: 0.6548, Loss: 0.5102\n",
"Epoch 0 Batch 350/1077 - Train Accuracy: 0.6434, Validation Accuracy: 0.6388, Loss: 0.5007\n",
"Epoch 0 Batch 360/1077 - Train Accuracy: 0.6844, Validation Accuracy: 0.6751, Loss: 0.4801\n",
"Epoch 0 Batch 370/1077 - Train Accuracy: 0.6778, Validation Accuracy: 0.6705, Loss: 0.4667\n",
"Epoch 0 Batch 380/1077 - Train Accuracy: 0.6863, Validation Accuracy: 0.6776, Loss: 0.4595\n",
"Epoch 0 Batch 390/1077 - Train Accuracy: 0.6648, Validation Accuracy: 0.6982, Loss: 0.4892\n",
"Epoch 0 Batch 400/1077 - Train Accuracy: 0.7043, Validation Accuracy: 0.6818, Loss: 0.4613\n",
"Epoch 0 Batch 410/1077 - Train Accuracy: 0.6780, Validation Accuracy: 0.6950, Loss: 0.4672\n",
"Epoch 0 Batch 420/1077 - Train Accuracy: 0.7242, Validation Accuracy: 0.7021, Loss: 0.4154\n",
"Epoch 0 Batch 430/1077 - Train Accuracy: 0.6770, Validation Accuracy: 0.6982, Loss: 0.4376\n",
"Epoch 0 Batch 440/1077 - Train Accuracy: 0.6844, Validation Accuracy: 0.6971, Loss: 0.4465\n",
"Epoch 0 Batch 450/1077 - Train Accuracy: 0.6988, Validation Accuracy: 0.7028, Loss: 0.3913\n",
"Epoch 0 Batch 460/1077 - Train Accuracy: 0.7363, Validation Accuracy: 0.6921, Loss: 0.4315\n",
"Epoch 0 Batch 470/1077 - Train Accuracy: 0.6998, Validation Accuracy: 0.7237, Loss: 0.4263\n",
"Epoch 0 Batch 480/1077 - Train Accuracy: 0.7533, Validation Accuracy: 0.7184, Loss: 0.3890\n",
"Epoch 0 Batch 490/1077 - Train Accuracy: 0.7230, Validation Accuracy: 0.7234, Loss: 0.3788\n",
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"Epoch 0 Batch 540/1077 - Train Accuracy: 0.7941, Validation Accuracy: 0.7273, Loss: 0.3168\n",
"Epoch 0 Batch 550/1077 - Train Accuracy: 0.7254, Validation Accuracy: 0.7692, Loss: 0.3366\n",
"Epoch 0 Batch 560/1077 - Train Accuracy: 0.7539, Validation Accuracy: 0.7468, Loss: 0.3176\n",
"Epoch 0 Batch 570/1077 - Train Accuracy: 0.7656, Validation Accuracy: 0.7447, Loss: 0.3313\n",
"Epoch 0 Batch 580/1077 - Train Accuracy: 0.8032, Validation Accuracy: 0.7610, Loss: 0.2912\n",
"Epoch 0 Batch 590/1077 - Train Accuracy: 0.7451, Validation Accuracy: 0.7724, Loss: 0.3086\n",
"Epoch 0 Batch 600/1077 - Train Accuracy: 0.8080, Validation Accuracy: 0.8136, Loss: 0.2810\n",
"Epoch 0 Batch 610/1077 - Train Accuracy: 0.8059, Validation Accuracy: 0.8050, Loss: 0.2945\n",
"Epoch 0 Batch 620/1077 - Train Accuracy: 0.8387, Validation Accuracy: 0.7965, Loss: 0.2629\n",
"Epoch 0 Batch 630/1077 - Train Accuracy: 0.8254, Validation Accuracy: 0.7947, Loss: 0.2463\n",
"Epoch 0 Batch 640/1077 - Train Accuracy: 0.8010, Validation Accuracy: 0.7649, Loss: 0.2506\n",
"Epoch 0 Batch 650/1077 - Train Accuracy: 0.8219, Validation Accuracy: 0.7859, Loss: 0.2525\n",
"Epoch 0 Batch 660/1077 - Train Accuracy: 0.8176, Validation Accuracy: 0.7983, Loss: 0.2428\n",
"Epoch 0 Batch 670/1077 - Train Accuracy: 0.8317, Validation Accuracy: 0.8082, Loss: 0.2429\n",
"Epoch 0 Batch 680/1077 - Train Accuracy: 0.8203, Validation Accuracy: 0.8061, Loss: 0.2372\n",
"Epoch 0 Batch 690/1077 - Train Accuracy: 0.8602, Validation Accuracy: 0.8171, Loss: 0.2182\n",
"Epoch 0 Batch 700/1077 - Train Accuracy: 0.8742, Validation Accuracy: 0.8189, Loss: 0.2064\n",
"Epoch 0 Batch 710/1077 - Train Accuracy: 0.8398, Validation Accuracy: 0.7898, Loss: 0.2257\n",
"Epoch 0 Batch 720/1077 - Train Accuracy: 0.7903, Validation Accuracy: 0.8018, Loss: 0.2420\n",
"Epoch 0 Batch 730/1077 - Train Accuracy: 0.8367, Validation Accuracy: 0.8026, Loss: 0.2275\n",
"Epoch 0 Batch 740/1077 - Train Accuracy: 0.8477, Validation Accuracy: 0.8164, Loss: 0.2046\n",
"Epoch 0 Batch 750/1077 - Train Accuracy: 0.8668, Validation Accuracy: 0.8121, Loss: 0.1949\n",
"Epoch 0 Batch 760/1077 - Train Accuracy: 0.8359, Validation Accuracy: 0.8292, Loss: 0.2232\n",
"Epoch 0 Batch 770/1077 - Train Accuracy: 0.8415, Validation Accuracy: 0.8388, Loss: 0.1953\n",
"Epoch 0 Batch 780/1077 - Train Accuracy: 0.8281, Validation Accuracy: 0.8452, Loss: 0.2241\n",
"Epoch 0 Batch 790/1077 - Train Accuracy: 0.8035, Validation Accuracy: 0.8505, Loss: 0.2069\n",
"Epoch 0 Batch 800/1077 - Train Accuracy: 0.8523, Validation Accuracy: 0.8391, Loss: 0.1868\n",
"Epoch 0 Batch 810/1077 - Train Accuracy: 0.8594, Validation Accuracy: 0.8459, Loss: 0.1513\n",
"Epoch 0 Batch 820/1077 - Train Accuracy: 0.8426, Validation Accuracy: 0.8487, Loss: 0.1789\n",
"Epoch 0 Batch 830/1077 - Train Accuracy: 0.8555, Validation Accuracy: 0.8246, Loss: 0.1810\n",
"Epoch 0 Batch 840/1077 - Train Accuracy: 0.8715, Validation Accuracy: 0.8370, Loss: 0.1579\n",
"Epoch 0 Batch 850/1077 - Train Accuracy: 0.8322, Validation Accuracy: 0.8278, Loss: 0.1944\n",
"Epoch 0 Batch 860/1077 - Train Accuracy: 0.8635, Validation Accuracy: 0.8459, Loss: 0.1627\n",
"Epoch 0 Batch 870/1077 - Train Accuracy: 0.8405, Validation Accuracy: 0.8530, Loss: 0.1782\n",
"Epoch 0 Batch 880/1077 - Train Accuracy: 0.8762, Validation Accuracy: 0.8413, Loss: 0.1555\n"
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"Epoch 0 Batch 890/1077 - Train Accuracy: 0.8992, Validation Accuracy: 0.8491, Loss: 0.1460\n",
"Epoch 0 Batch 900/1077 - Train Accuracy: 0.8879, Validation Accuracy: 0.8058, Loss: 0.1658\n",
"Epoch 0 Batch 910/1077 - Train Accuracy: 0.8854, Validation Accuracy: 0.8636, Loss: 0.1417\n",
"Epoch 0 Batch 920/1077 - Train Accuracy: 0.8637, Validation Accuracy: 0.8523, Loss: 0.1422\n",
"Epoch 0 Batch 930/1077 - Train Accuracy: 0.8695, Validation Accuracy: 0.8423, Loss: 0.1289\n",
"Epoch 0 Batch 940/1077 - Train Accuracy: 0.8742, Validation Accuracy: 0.8246, Loss: 0.1356\n",
"Epoch 0 Batch 950/1077 - Train Accuracy: 0.8940, Validation Accuracy: 0.8491, Loss: 0.1376\n",
"Epoch 0 Batch 960/1077 - Train Accuracy: 0.8936, Validation Accuracy: 0.8537, Loss: 0.1357\n",
"Epoch 0 Batch 970/1077 - Train Accuracy: 0.8914, Validation Accuracy: 0.8572, Loss: 0.1553\n",
"Epoch 0 Batch 980/1077 - Train Accuracy: 0.8828, Validation Accuracy: 0.8580, Loss: 0.1372\n",
"Epoch 0 Batch 990/1077 - Train Accuracy: 0.9042, Validation Accuracy: 0.8668, Loss: 0.1501\n",
"Epoch 0 Batch 1000/1077 - Train Accuracy: 0.8873, Validation Accuracy: 0.8619, Loss: 0.1221\n",
"Epoch 0 Batch 1010/1077 - Train Accuracy: 0.9121, Validation Accuracy: 0.8608, Loss: 0.1326\n",
"Epoch 0 Batch 1020/1077 - Train Accuracy: 0.9086, Validation Accuracy: 0.8796, Loss: 0.1150\n",
"Epoch 0 Batch 1030/1077 - Train Accuracy: 0.9031, Validation Accuracy: 0.8466, Loss: 0.1277\n",
"Epoch 0 Batch 1040/1077 - Train Accuracy: 0.8993, Validation Accuracy: 0.8828, Loss: 0.1417\n",
"Epoch 0 Batch 1050/1077 - Train Accuracy: 0.9125, Validation Accuracy: 0.8519, Loss: 0.1224\n",
"Epoch 0 Batch 1060/1077 - Train Accuracy: 0.8957, Validation Accuracy: 0.8732, Loss: 0.1175\n",
"Epoch 0 Batch 1070/1077 - Train Accuracy: 0.8820, Validation Accuracy: 0.8594, Loss: 0.1294\n",
"Epoch 1 Batch 10/1077 - Train Accuracy: 0.9137, Validation Accuracy: 0.8576, Loss: 0.1251\n",
"Epoch 1 Batch 20/1077 - Train Accuracy: 0.9027, Validation Accuracy: 0.8707, Loss: 0.0894\n",
"Epoch 1 Batch 30/1077 - Train Accuracy: 0.9148, Validation Accuracy: 0.8640, Loss: 0.1024\n",
"Epoch 1 Batch 40/1077 - Train Accuracy: 0.9133, Validation Accuracy: 0.8580, Loss: 0.1176\n",
"Epoch 1 Batch 50/1077 - Train Accuracy: 0.9070, Validation Accuracy: 0.8654, Loss: 0.1025\n",
"Epoch 1 Batch 60/1077 - Train Accuracy: 0.8981, Validation Accuracy: 0.8761, Loss: 0.1008\n",
"Epoch 1 Batch 70/1077 - Train Accuracy: 0.9042, Validation Accuracy: 0.8857, Loss: 0.1211\n",
"Epoch 1 Batch 80/1077 - Train Accuracy: 0.9125, Validation Accuracy: 0.8874, Loss: 0.0954\n",
"Epoch 1 Batch 90/1077 - Train Accuracy: 0.8723, Validation Accuracy: 0.8722, Loss: 0.1148\n",
"Epoch 1 Batch 100/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.8668, Loss: 0.1136\n",
"Epoch 1 Batch 110/1077 - Train Accuracy: 0.9328, Validation Accuracy: 0.8743, Loss: 0.0869\n",
"Epoch 1 Batch 120/1077 - Train Accuracy: 0.9187, Validation Accuracy: 0.8729, Loss: 0.1192\n",
"Epoch 1 Batch 130/1077 - Train Accuracy: 0.9007, Validation Accuracy: 0.8700, Loss: 0.1004\n",
"Epoch 1 Batch 140/1077 - Train Accuracy: 0.9095, Validation Accuracy: 0.8643, Loss: 0.0968\n",
"Epoch 1 Batch 150/1077 - Train Accuracy: 0.9025, Validation Accuracy: 0.8739, Loss: 0.0990\n",
"Epoch 1 Batch 160/1077 - Train Accuracy: 0.9184, Validation Accuracy: 0.8761, Loss: 0.0869\n",
"Epoch 1 Batch 170/1077 - Train Accuracy: 0.8738, Validation Accuracy: 0.8704, Loss: 0.1073\n",
"Epoch 1 Batch 180/1077 - Train Accuracy: 0.9238, Validation Accuracy: 0.8778, Loss: 0.0875\n",
"Epoch 1 Batch 190/1077 - Train Accuracy: 0.9156, Validation Accuracy: 0.8857, Loss: 0.1051\n",
"Epoch 1 Batch 200/1077 - Train Accuracy: 0.9020, Validation Accuracy: 0.8846, Loss: 0.1104\n",
"Epoch 1 Batch 210/1077 - Train Accuracy: 0.8936, Validation Accuracy: 0.8665, Loss: 0.1039\n",
"Epoch 1 Batch 220/1077 - Train Accuracy: 0.9350, Validation Accuracy: 0.8764, Loss: 0.0967\n",
"Epoch 1 Batch 230/1077 - Train Accuracy: 0.9152, Validation Accuracy: 0.8750, Loss: 0.1059\n",
"Epoch 1 Batch 240/1077 - Train Accuracy: 0.9246, Validation Accuracy: 0.8761, Loss: 0.0992\n",
"Epoch 1 Batch 250/1077 - Train Accuracy: 0.9006, Validation Accuracy: 0.8942, Loss: 0.0931\n",
"Epoch 1 Batch 260/1077 - Train Accuracy: 0.9152, Validation Accuracy: 0.9119, Loss: 0.0740\n",
"Epoch 1 Batch 270/1077 - Train Accuracy: 0.9281, Validation Accuracy: 0.9062, Loss: 0.1025\n",
"Epoch 1 Batch 280/1077 - Train Accuracy: 0.9023, Validation Accuracy: 0.9006, Loss: 0.1103\n",
"Epoch 1 Batch 290/1077 - Train Accuracy: 0.8816, Validation Accuracy: 0.8807, Loss: 0.1413\n",
"Epoch 1 Batch 300/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.8906, Loss: 0.0880\n",
"Epoch 1 Batch 310/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.8736, Loss: 0.0955\n",
"Epoch 1 Batch 320/1077 - Train Accuracy: 0.9238, Validation Accuracy: 0.8853, Loss: 0.1033\n",
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"Epoch 1 Batch 340/1077 - Train Accuracy: 0.9206, Validation Accuracy: 0.8857, Loss: 0.0978\n",
"Epoch 1 Batch 350/1077 - Train Accuracy: 0.9293, Validation Accuracy: 0.8977, Loss: 0.0957\n",
"Epoch 1 Batch 360/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.8888, Loss: 0.0846\n",
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"Epoch 1 Batch 470/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.8654, Loss: 0.0772\n",
"Epoch 1 Batch 480/1077 - Train Accuracy: 0.9038, Validation Accuracy: 0.8825, Loss: 0.0898\n",
"Epoch 1 Batch 490/1077 - Train Accuracy: 0.8910, Validation Accuracy: 0.8832, Loss: 0.0838\n",
"Epoch 1 Batch 500/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.8967, Loss: 0.0718\n",
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"Epoch 1 Batch 560/1077 - Train Accuracy: 0.9102, Validation Accuracy: 0.8938, Loss: 0.0617\n",
"Epoch 1 Batch 570/1077 - Train Accuracy: 0.9248, Validation Accuracy: 0.8903, Loss: 0.0812\n",
"Epoch 1 Batch 580/1077 - Train Accuracy: 0.9196, Validation Accuracy: 0.8849, Loss: 0.0708\n",
"Epoch 1 Batch 590/1077 - Train Accuracy: 0.8766, Validation Accuracy: 0.8995, Loss: 0.0896\n",
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]
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"Epoch 1 Batch 700/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9173, Loss: 0.0624\n",
"Epoch 1 Batch 710/1077 - Train Accuracy: 0.9121, Validation Accuracy: 0.9016, Loss: 0.0659\n",
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"Epoch 1 Batch 760/1077 - Train Accuracy: 0.9035, Validation Accuracy: 0.9254, Loss: 0.0826\n",
"Epoch 1 Batch 770/1077 - Train Accuracy: 0.9089, Validation Accuracy: 0.9361, Loss: 0.0713\n",
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"Epoch 1 Batch 790/1077 - Train Accuracy: 0.8781, Validation Accuracy: 0.9134, Loss: 0.0775\n",
"Epoch 1 Batch 800/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9240, Loss: 0.0682\n",
"Epoch 1 Batch 810/1077 - Train Accuracy: 0.9308, Validation Accuracy: 0.9073, Loss: 0.0641\n",
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"Epoch 1 Batch 830/1077 - Train Accuracy: 0.8859, Validation Accuracy: 0.8977, Loss: 0.0803\n",
"Epoch 1 Batch 840/1077 - Train Accuracy: 0.9207, Validation Accuracy: 0.9031, Loss: 0.0579\n",
"Epoch 1 Batch 850/1077 - Train Accuracy: 0.9103, Validation Accuracy: 0.9070, Loss: 0.1027\n",
"Epoch 1 Batch 860/1077 - Train Accuracy: 0.9304, Validation Accuracy: 0.9364, Loss: 0.0765\n",
"Epoch 1 Batch 870/1077 - Train Accuracy: 0.9120, Validation Accuracy: 0.9251, Loss: 0.0670\n",
"Epoch 1 Batch 880/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9173, Loss: 0.0741\n",
"Epoch 1 Batch 890/1077 - Train Accuracy: 0.9386, Validation Accuracy: 0.8995, Loss: 0.0705\n",
"Epoch 1 Batch 900/1077 - Train Accuracy: 0.9316, Validation Accuracy: 0.9205, Loss: 0.0793\n",
"Epoch 1 Batch 910/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9162, Loss: 0.0544\n",
"Epoch 1 Batch 920/1077 - Train Accuracy: 0.9383, Validation Accuracy: 0.9006, Loss: 0.0625\n",
"Epoch 1 Batch 930/1077 - Train Accuracy: 0.9219, Validation Accuracy: 0.8867, Loss: 0.0522\n",
"Epoch 1 Batch 940/1077 - Train Accuracy: 0.9469, Validation Accuracy: 0.9268, Loss: 0.0518\n",
"Epoch 1 Batch 950/1077 - Train Accuracy: 0.9423, Validation Accuracy: 0.9251, Loss: 0.0598\n",
"Epoch 1 Batch 960/1077 - Train Accuracy: 0.9256, Validation Accuracy: 0.9190, Loss: 0.0586\n",
"Epoch 1 Batch 970/1077 - Train Accuracy: 0.9418, Validation Accuracy: 0.9393, Loss: 0.0835\n",
"Epoch 1 Batch 980/1077 - Train Accuracy: 0.9469, Validation Accuracy: 0.9233, Loss: 0.0786\n",
"Epoch 1 Batch 990/1077 - Train Accuracy: 0.9486, Validation Accuracy: 0.9254, Loss: 0.0660\n",
"Epoch 1 Batch 1000/1077 - Train Accuracy: 0.9364, Validation Accuracy: 0.9237, Loss: 0.0603\n",
"Epoch 1 Batch 1010/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9208, Loss: 0.0534\n",
"Epoch 1 Batch 1020/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9347, Loss: 0.0523\n",
"Epoch 1 Batch 1030/1077 - Train Accuracy: 0.9281, Validation Accuracy: 0.9474, Loss: 0.0671\n",
"Epoch 1 Batch 1040/1077 - Train Accuracy: 0.9186, Validation Accuracy: 0.9144, Loss: 0.0745\n",
"Epoch 1 Batch 1050/1077 - Train Accuracy: 0.9473, Validation Accuracy: 0.9418, Loss: 0.0561\n",
"Epoch 1 Batch 1060/1077 - Train Accuracy: 0.9172, Validation Accuracy: 0.9279, Loss: 0.0592\n",
"Epoch 1 Batch 1070/1077 - Train Accuracy: 0.9199, Validation Accuracy: 0.9009, Loss: 0.0661\n",
"Epoch 2 Batch 10/1077 - Train Accuracy: 0.9330, Validation Accuracy: 0.9251, Loss: 0.0688\n",
"Epoch 2 Batch 20/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9418, Loss: 0.0450\n",
"Epoch 2 Batch 30/1077 - Train Accuracy: 0.9324, Validation Accuracy: 0.9439, Loss: 0.0482\n",
"Epoch 2 Batch 40/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9240, Loss: 0.0553\n",
"Epoch 2 Batch 50/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9197, Loss: 0.0600\n",
"Epoch 2 Batch 60/1077 - Train Accuracy: 0.9234, Validation Accuracy: 0.9091, Loss: 0.0542\n",
"Epoch 2 Batch 70/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9489, Loss: 0.0648\n",
"Epoch 2 Batch 80/1077 - Train Accuracy: 0.9012, Validation Accuracy: 0.9229, Loss: 0.0553\n",
"Epoch 2 Batch 90/1077 - Train Accuracy: 0.9227, Validation Accuracy: 0.9187, Loss: 0.0581\n",
"Epoch 2 Batch 100/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9371, Loss: 0.0561\n",
"Epoch 2 Batch 110/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9258, Loss: 0.0505\n",
"Epoch 2 Batch 120/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9297, Loss: 0.0667\n",
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"Epoch 2 Batch 210/1077 - Train Accuracy: 0.9241, Validation Accuracy: 0.9066, Loss: 0.0658\n",
"Epoch 2 Batch 220/1077 - Train Accuracy: 0.9581, Validation Accuracy: 0.9208, Loss: 0.0549\n",
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"Epoch 2 Batch 300/1077 - Train Accuracy: 0.9576, Validation Accuracy: 0.9201, Loss: 0.0552\n",
"Epoch 2 Batch 310/1077 - Train Accuracy: 0.9176, Validation Accuracy: 0.9141, Loss: 0.0579\n",
"Epoch 2 Batch 320/1077 - Train Accuracy: 0.9457, Validation Accuracy: 0.9151, Loss: 0.0758\n",
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"Epoch 2 Batch 340/1077 - Train Accuracy: 0.9581, Validation Accuracy: 0.9308, Loss: 0.0579\n",
"Epoch 2 Batch 350/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9197, Loss: 0.0733\n",
"Epoch 2 Batch 360/1077 - Train Accuracy: 0.9629, Validation Accuracy: 0.9023, Loss: 0.0444\n",
"Epoch 2 Batch 370/1077 - Train Accuracy: 0.9576, Validation Accuracy: 0.9240, Loss: 0.0532\n",
"Epoch 2 Batch 380/1077 - Train Accuracy: 0.9434, Validation Accuracy: 0.9389, Loss: 0.0495\n",
"Epoch 2 Batch 390/1077 - Train Accuracy: 0.9348, Validation Accuracy: 0.9357, Loss: 0.0730\n",
"Epoch 2 Batch 400/1077 - Train Accuracy: 0.9082, Validation Accuracy: 0.9279, Loss: 0.0688\n",
"Epoch 2 Batch 410/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9130, Loss: 0.0641\n",
"Epoch 2 Batch 420/1077 - Train Accuracy: 0.9625, Validation Accuracy: 0.9201, Loss: 0.0469\n",
"Epoch 2 Batch 430/1077 - Train Accuracy: 0.9355, Validation Accuracy: 0.9251, Loss: 0.0663\n",
"Epoch 2 Batch 440/1077 - Train Accuracy: 0.9219, Validation Accuracy: 0.9151, Loss: 0.0803\n",
"Epoch 2 Batch 450/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.8988, Loss: 0.0716\n",
"Epoch 2 Batch 460/1077 - Train Accuracy: 0.9445, Validation Accuracy: 0.9158, Loss: 0.0625\n",
"Epoch 2 Batch 470/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9194, Loss: 0.0573\n",
"Epoch 2 Batch 480/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9162, Loss: 0.0603\n",
"Epoch 2 Batch 490/1077 - Train Accuracy: 0.9168, Validation Accuracy: 0.9126, Loss: 0.0608\n",
"Epoch 2 Batch 500/1077 - Train Accuracy: 0.9281, Validation Accuracy: 0.9382, Loss: 0.0461\n"
]
},
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"Epoch 2 Batch 510/1077 - Train Accuracy: 0.9195, Validation Accuracy: 0.9066, Loss: 0.0700\n",
"Epoch 2 Batch 520/1077 - Train Accuracy: 0.9498, Validation Accuracy: 0.9173, Loss: 0.0564\n",
"Epoch 2 Batch 530/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9187, Loss: 0.0648\n",
"Epoch 2 Batch 540/1077 - Train Accuracy: 0.9641, Validation Accuracy: 0.9201, Loss: 0.0527\n",
"Epoch 2 Batch 550/1077 - Train Accuracy: 0.9262, Validation Accuracy: 0.9350, Loss: 0.0517\n",
"Epoch 2 Batch 560/1077 - Train Accuracy: 0.9277, Validation Accuracy: 0.9183, Loss: 0.0525\n",
"Epoch 2 Batch 570/1077 - Train Accuracy: 0.9309, Validation Accuracy: 0.9304, Loss: 0.0716\n",
"Epoch 2 Batch 580/1077 - Train Accuracy: 0.9408, Validation Accuracy: 0.9318, Loss: 0.0461\n",
"Epoch 2 Batch 590/1077 - Train Accuracy: 0.9342, Validation Accuracy: 0.9116, Loss: 0.0677\n",
"Epoch 2 Batch 600/1077 - Train Accuracy: 0.9390, Validation Accuracy: 0.9201, Loss: 0.0702\n",
"Epoch 2 Batch 610/1077 - Train Accuracy: 0.9326, Validation Accuracy: 0.9201, Loss: 0.0552\n",
"Epoch 2 Batch 620/1077 - Train Accuracy: 0.9484, Validation Accuracy: 0.9116, Loss: 0.0680\n",
"Epoch 2 Batch 630/1077 - Train Accuracy: 0.9555, Validation Accuracy: 0.9244, Loss: 0.0513\n",
"Epoch 2 Batch 640/1077 - Train Accuracy: 0.9167, Validation Accuracy: 0.9325, Loss: 0.0560\n",
"Epoch 2 Batch 650/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9343, Loss: 0.0567\n",
"Epoch 2 Batch 660/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9318, Loss: 0.0498\n",
"Epoch 2 Batch 670/1077 - Train Accuracy: 0.9396, Validation Accuracy: 0.9513, Loss: 0.0549\n",
"Epoch 2 Batch 680/1077 - Train Accuracy: 0.9338, Validation Accuracy: 0.9435, Loss: 0.0543\n",
"Epoch 2 Batch 690/1077 - Train Accuracy: 0.9238, Validation Accuracy: 0.9375, Loss: 0.0699\n",
"Epoch 2 Batch 700/1077 - Train Accuracy: 0.9691, Validation Accuracy: 0.9293, Loss: 0.0435\n",
"Epoch 2 Batch 710/1077 - Train Accuracy: 0.9355, Validation Accuracy: 0.9226, Loss: 0.0404\n",
"Epoch 2 Batch 720/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9268, Loss: 0.0588\n",
"Epoch 2 Batch 730/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9229, Loss: 0.0646\n",
"Epoch 2 Batch 740/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9332, Loss: 0.0402\n",
"Epoch 2 Batch 750/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9403, Loss: 0.0462\n",
"Epoch 2 Batch 760/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9503, Loss: 0.0673\n",
"Epoch 2 Batch 770/1077 - Train Accuracy: 0.9092, Validation Accuracy: 0.9261, Loss: 0.0554\n",
"Epoch 2 Batch 780/1077 - Train Accuracy: 0.9109, Validation Accuracy: 0.9453, Loss: 0.0812\n",
"Epoch 2 Batch 790/1077 - Train Accuracy: 0.9141, Validation Accuracy: 0.9364, Loss: 0.0644\n",
"Epoch 2 Batch 800/1077 - Train Accuracy: 0.9344, Validation Accuracy: 0.9474, Loss: 0.0592\n",
"Epoch 2 Batch 810/1077 - Train Accuracy: 0.9606, Validation Accuracy: 0.9350, Loss: 0.0433\n",
"Epoch 2 Batch 820/1077 - Train Accuracy: 0.9328, Validation Accuracy: 0.9318, Loss: 0.0559\n",
"Epoch 2 Batch 830/1077 - Train Accuracy: 0.9102, Validation Accuracy: 0.9165, Loss: 0.0611\n",
"Epoch 2 Batch 840/1077 - Train Accuracy: 0.9707, Validation Accuracy: 0.9400, Loss: 0.0557\n",
"Epoch 2 Batch 850/1077 - Train Accuracy: 0.9278, Validation Accuracy: 0.9290, Loss: 0.0885\n",
"Epoch 2 Batch 860/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9276, Loss: 0.0656\n",
"Epoch 2 Batch 870/1077 - Train Accuracy: 0.9141, Validation Accuracy: 0.9137, Loss: 0.0595\n",
"Epoch 2 Batch 880/1077 - Train Accuracy: 0.9547, Validation Accuracy: 0.9158, Loss: 0.0644\n",
"Epoch 2 Batch 890/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9141, Loss: 0.0660\n",
"Epoch 2 Batch 900/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9141, Loss: 0.0685\n",
"Epoch 2 Batch 910/1077 - Train Accuracy: 0.9483, Validation Accuracy: 0.8981, Loss: 0.0564\n",
"Epoch 2 Batch 920/1077 - Train Accuracy: 0.9430, Validation Accuracy: 0.9240, Loss: 0.0484\n",
"Epoch 2 Batch 930/1077 - Train Accuracy: 0.9285, Validation Accuracy: 0.9258, Loss: 0.0566\n",
"Epoch 2 Batch 940/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9134, Loss: 0.0452\n",
"Epoch 2 Batch 950/1077 - Train Accuracy: 0.9315, Validation Accuracy: 0.9268, Loss: 0.0586\n",
"Epoch 2 Batch 960/1077 - Train Accuracy: 0.9211, Validation Accuracy: 0.9222, Loss: 0.0600\n",
"Epoch 2 Batch 970/1077 - Train Accuracy: 0.9551, Validation Accuracy: 0.9311, Loss: 0.0737\n",
"Epoch 2 Batch 980/1077 - Train Accuracy: 0.9395, Validation Accuracy: 0.9197, Loss: 0.0668\n",
"Epoch 2 Batch 990/1077 - Train Accuracy: 0.9260, Validation Accuracy: 0.9442, Loss: 0.0625\n",
"Epoch 2 Batch 1000/1077 - Train Accuracy: 0.9382, Validation Accuracy: 0.9052, Loss: 0.0561\n",
"Epoch 2 Batch 1010/1077 - Train Accuracy: 0.9570, Validation Accuracy: 0.9318, Loss: 0.0451\n",
"Epoch 2 Batch 1020/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9233, Loss: 0.0458\n",
"Epoch 2 Batch 1030/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9421, Loss: 0.0604\n",
"Epoch 2 Batch 1040/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9258, Loss: 0.0684\n",
"Epoch 2 Batch 1050/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9428, Loss: 0.0477\n",
"Epoch 2 Batch 1060/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9464, Loss: 0.0511\n",
"Epoch 2 Batch 1070/1077 - Train Accuracy: 0.9336, Validation Accuracy: 0.9251, Loss: 0.0576\n",
"Epoch 3 Batch 10/1077 - Train Accuracy: 0.9498, Validation Accuracy: 0.9371, Loss: 0.0701\n",
"Epoch 3 Batch 20/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9190, Loss: 0.0410\n",
"Epoch 3 Batch 30/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9002, Loss: 0.0570\n",
"Epoch 3 Batch 40/1077 - Train Accuracy: 0.9715, Validation Accuracy: 0.9258, Loss: 0.0513\n",
"Epoch 3 Batch 50/1077 - Train Accuracy: 0.9414, Validation Accuracy: 0.9414, Loss: 0.0574\n",
"Epoch 3 Batch 60/1077 - Train Accuracy: 0.9628, Validation Accuracy: 0.9077, Loss: 0.0493\n",
"Epoch 3 Batch 70/1077 - Train Accuracy: 0.9309, Validation Accuracy: 0.9325, Loss: 0.0518\n",
"Epoch 3 Batch 80/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9318, Loss: 0.0478\n",
"Epoch 3 Batch 90/1077 - Train Accuracy: 0.9457, Validation Accuracy: 0.9254, Loss: 0.0609\n",
"Epoch 3 Batch 100/1077 - Train Accuracy: 0.9668, Validation Accuracy: 0.9279, Loss: 0.0500\n",
"Epoch 3 Batch 110/1077 - Train Accuracy: 0.9688, Validation Accuracy: 0.9478, Loss: 0.0505\n",
"Epoch 3 Batch 120/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9162, Loss: 0.0743\n",
"Epoch 3 Batch 130/1077 - Train Accuracy: 0.9368, Validation Accuracy: 0.9055, Loss: 0.0547\n",
"Epoch 3 Batch 140/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9304, Loss: 0.0544\n",
"Epoch 3 Batch 150/1077 - Train Accuracy: 0.9446, Validation Accuracy: 0.9244, Loss: 0.0567\n",
"Epoch 3 Batch 160/1077 - Train Accuracy: 0.9582, Validation Accuracy: 0.9339, Loss: 0.0431\n",
"Epoch 3 Batch 170/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9304, Loss: 0.0577\n",
"Epoch 3 Batch 180/1077 - Train Accuracy: 0.9457, Validation Accuracy: 0.9428, Loss: 0.0487\n",
"Epoch 3 Batch 190/1077 - Train Accuracy: 0.9645, Validation Accuracy: 0.9244, Loss: 0.0520\n",
"Epoch 3 Batch 200/1077 - Train Accuracy: 0.9238, Validation Accuracy: 0.9308, Loss: 0.0592\n",
"Epoch 3 Batch 210/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9354, Loss: 0.0536\n",
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"Epoch 3 Batch 250/1077 - Train Accuracy: 0.9403, Validation Accuracy: 0.9428, Loss: 0.0536\n",
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]
},
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"Epoch 3 Batch 320/1077 - Train Accuracy: 0.9492, Validation Accuracy: 0.9442, Loss: 0.0654\n",
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"Epoch 3 Batch 670/1077 - Train Accuracy: 0.9496, Validation Accuracy: 0.9272, Loss: 0.0653\n",
"Epoch 3 Batch 680/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9308, Loss: 0.0598\n",
"Epoch 3 Batch 690/1077 - Train Accuracy: 0.9266, Validation Accuracy: 0.9290, Loss: 0.0560\n",
"Epoch 3 Batch 700/1077 - Train Accuracy: 0.9629, Validation Accuracy: 0.9393, Loss: 0.0502\n",
"Epoch 3 Batch 710/1077 - Train Accuracy: 0.9348, Validation Accuracy: 0.9268, Loss: 0.0481\n",
"Epoch 3 Batch 720/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9414, Loss: 0.0590\n",
"Epoch 3 Batch 730/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9173, Loss: 0.0621\n",
"Epoch 3 Batch 740/1077 - Train Accuracy: 0.9418, Validation Accuracy: 0.9261, Loss: 0.0575\n",
"Epoch 3 Batch 750/1077 - Train Accuracy: 0.9418, Validation Accuracy: 0.9471, Loss: 0.0474\n",
"Epoch 3 Batch 760/1077 - Train Accuracy: 0.9227, Validation Accuracy: 0.9354, Loss: 0.0710\n",
"Epoch 3 Batch 770/1077 - Train Accuracy: 0.9245, Validation Accuracy: 0.9393, Loss: 0.0556\n",
"Epoch 3 Batch 780/1077 - Train Accuracy: 0.9336, Validation Accuracy: 0.9403, Loss: 0.0761\n",
"Epoch 3 Batch 790/1077 - Train Accuracy: 0.8926, Validation Accuracy: 0.9386, Loss: 0.0652\n",
"Epoch 3 Batch 800/1077 - Train Accuracy: 0.9434, Validation Accuracy: 0.9233, Loss: 0.0527\n",
"Epoch 3 Batch 810/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9123, Loss: 0.0429\n",
"Epoch 3 Batch 820/1077 - Train Accuracy: 0.9230, Validation Accuracy: 0.9453, Loss: 0.0578\n",
"Epoch 3 Batch 830/1077 - Train Accuracy: 0.8988, Validation Accuracy: 0.9208, Loss: 0.0645\n",
"Epoch 3 Batch 840/1077 - Train Accuracy: 0.9508, Validation Accuracy: 0.9442, Loss: 0.0587\n",
"Epoch 3 Batch 850/1077 - Train Accuracy: 0.9111, Validation Accuracy: 0.9535, Loss: 0.0811\n",
"Epoch 3 Batch 860/1077 - Train Accuracy: 0.9397, Validation Accuracy: 0.9347, Loss: 0.0657\n",
"Epoch 3 Batch 870/1077 - Train Accuracy: 0.9252, Validation Accuracy: 0.9283, Loss: 0.0565\n",
"Epoch 3 Batch 880/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9371, Loss: 0.0582\n",
"Epoch 3 Batch 890/1077 - Train Accuracy: 0.9628, Validation Accuracy: 0.9268, Loss: 0.0596\n",
"Epoch 3 Batch 900/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9432, Loss: 0.0612\n",
"Epoch 3 Batch 910/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9542, Loss: 0.0546\n",
"Epoch 3 Batch 920/1077 - Train Accuracy: 0.9332, Validation Accuracy: 0.9240, Loss: 0.0532\n",
"Epoch 3 Batch 930/1077 - Train Accuracy: 0.9473, Validation Accuracy: 0.9414, Loss: 0.0512\n",
"Epoch 3 Batch 940/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9240, Loss: 0.0542\n",
"Epoch 3 Batch 950/1077 - Train Accuracy: 0.9513, Validation Accuracy: 0.9435, Loss: 0.0477\n",
"Epoch 3 Batch 960/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9467, Loss: 0.0506\n",
"Epoch 3 Batch 970/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9521, Loss: 0.0527\n",
"Epoch 3 Batch 980/1077 - Train Accuracy: 0.9344, Validation Accuracy: 0.9407, Loss: 0.0572\n",
"Epoch 3 Batch 990/1077 - Train Accuracy: 0.9433, Validation Accuracy: 0.9435, Loss: 0.0654\n",
"Epoch 3 Batch 1000/1077 - Train Accuracy: 0.9423, Validation Accuracy: 0.9137, Loss: 0.0563\n",
"Epoch 3 Batch 1010/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9091, Loss: 0.0487\n",
"Epoch 3 Batch 1020/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9308, Loss: 0.0484\n",
"Epoch 3 Batch 1030/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9265, Loss: 0.0721\n",
"Epoch 3 Batch 1040/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9240, Loss: 0.0622\n",
"Epoch 3 Batch 1050/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9364, Loss: 0.0500\n",
"Epoch 3 Batch 1060/1077 - Train Accuracy: 0.9621, Validation Accuracy: 0.9354, Loss: 0.0420\n",
"Epoch 3 Batch 1070/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9506, Loss: 0.0580\n",
"Epoch 4 Batch 10/1077 - Train Accuracy: 0.9511, Validation Accuracy: 0.9180, Loss: 0.0554\n",
"Epoch 4 Batch 20/1077 - Train Accuracy: 0.9328, Validation Accuracy: 0.9350, Loss: 0.0404\n",
"Epoch 4 Batch 30/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9460, Loss: 0.0619\n",
"Epoch 4 Batch 40/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9336, Loss: 0.0538\n",
"Epoch 4 Batch 50/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9304, Loss: 0.0588\n",
"Epoch 4 Batch 60/1077 - Train Accuracy: 0.9490, Validation Accuracy: 0.9151, Loss: 0.0560\n",
"Epoch 4 Batch 70/1077 - Train Accuracy: 0.9198, Validation Accuracy: 0.9368, Loss: 0.0507\n",
"Epoch 4 Batch 80/1077 - Train Accuracy: 0.9352, Validation Accuracy: 0.9457, Loss: 0.0553\n",
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]
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"Epoch 4 Batch 130/1077 - Train Accuracy: 0.9472, Validation Accuracy: 0.9290, Loss: 0.0601\n",
"Epoch 4 Batch 140/1077 - Train Accuracy: 0.9445, Validation Accuracy: 0.9070, Loss: 0.0611\n",
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"Epoch 4 Batch 360/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9389, Loss: 0.0497\n",
"Epoch 4 Batch 370/1077 - Train Accuracy: 0.9542, Validation Accuracy: 0.9208, Loss: 0.0585\n",
"Epoch 4 Batch 380/1077 - Train Accuracy: 0.9488, Validation Accuracy: 0.9162, Loss: 0.0532\n",
"Epoch 4 Batch 390/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9134, Loss: 0.0625\n",
"Epoch 4 Batch 400/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9229, Loss: 0.0589\n",
"Epoch 4 Batch 410/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9364, Loss: 0.0725\n",
"Epoch 4 Batch 420/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9343, Loss: 0.0327\n",
"Epoch 4 Batch 430/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9315, Loss: 0.0462\n",
"Epoch 4 Batch 440/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9169, Loss: 0.0614\n",
"Epoch 4 Batch 450/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9233, Loss: 0.0587\n",
"Epoch 4 Batch 460/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9286, Loss: 0.0609\n",
"Epoch 4 Batch 470/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9283, Loss: 0.0479\n",
"Epoch 4 Batch 480/1077 - Train Accuracy: 0.9280, Validation Accuracy: 0.9293, Loss: 0.0579\n",
"Epoch 4 Batch 490/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9098, Loss: 0.0540\n",
"Epoch 4 Batch 500/1077 - Train Accuracy: 0.9668, Validation Accuracy: 0.9134, Loss: 0.0447\n",
"Epoch 4 Batch 510/1077 - Train Accuracy: 0.9184, Validation Accuracy: 0.9258, Loss: 0.0633\n",
"Epoch 4 Batch 520/1077 - Train Accuracy: 0.9669, Validation Accuracy: 0.9212, Loss: 0.0474\n",
"Epoch 4 Batch 530/1077 - Train Accuracy: 0.9133, Validation Accuracy: 0.9102, Loss: 0.0654\n",
"Epoch 4 Batch 540/1077 - Train Accuracy: 0.9598, Validation Accuracy: 0.9315, Loss: 0.0434\n",
"Epoch 4 Batch 550/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9304, Loss: 0.0588\n",
"Epoch 4 Batch 560/1077 - Train Accuracy: 0.9262, Validation Accuracy: 0.9286, Loss: 0.0501\n",
"Epoch 4 Batch 570/1077 - Train Accuracy: 0.9161, Validation Accuracy: 0.9244, Loss: 0.0724\n",
"Epoch 4 Batch 580/1077 - Train Accuracy: 0.9635, Validation Accuracy: 0.9194, Loss: 0.0420\n",
"Epoch 4 Batch 590/1077 - Train Accuracy: 0.9157, Validation Accuracy: 0.9176, Loss: 0.0712\n",
"Epoch 4 Batch 600/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9279, Loss: 0.0704\n",
"Epoch 4 Batch 610/1077 - Train Accuracy: 0.9141, Validation Accuracy: 0.9371, Loss: 0.0701\n",
"Epoch 4 Batch 620/1077 - Train Accuracy: 0.9488, Validation Accuracy: 0.9151, Loss: 0.0640\n",
"Epoch 4 Batch 630/1077 - Train Accuracy: 0.9309, Validation Accuracy: 0.9414, Loss: 0.0555\n",
"Epoch 4 Batch 640/1077 - Train Accuracy: 0.9661, Validation Accuracy: 0.9361, Loss: 0.0510\n",
"Epoch 4 Batch 650/1077 - Train Accuracy: 0.9465, Validation Accuracy: 0.9506, Loss: 0.0612\n",
"Epoch 4 Batch 660/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9201, Loss: 0.0563\n",
"Epoch 4 Batch 670/1077 - Train Accuracy: 0.9496, Validation Accuracy: 0.9311, Loss: 0.0566\n",
"Epoch 4 Batch 680/1077 - Train Accuracy: 0.9330, Validation Accuracy: 0.9371, Loss: 0.0539\n",
"Epoch 4 Batch 690/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9276, Loss: 0.0565\n",
"Epoch 4 Batch 700/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9233, Loss: 0.0463\n",
"Epoch 4 Batch 710/1077 - Train Accuracy: 0.9270, Validation Accuracy: 0.9347, Loss: 0.0450\n",
"Epoch 4 Batch 720/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9478, Loss: 0.0625\n",
"Epoch 4 Batch 730/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9471, Loss: 0.0594\n",
"Epoch 4 Batch 740/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9439, Loss: 0.0535\n",
"Epoch 4 Batch 750/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.9382, Loss: 0.0420\n",
"Epoch 4 Batch 760/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9137, Loss: 0.0686\n",
"Epoch 4 Batch 770/1077 - Train Accuracy: 0.9263, Validation Accuracy: 0.9389, Loss: 0.0499\n",
"Epoch 4 Batch 780/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9418, Loss: 0.0624\n",
"Epoch 4 Batch 790/1077 - Train Accuracy: 0.9223, Validation Accuracy: 0.9329, Loss: 0.0658\n",
"Epoch 4 Batch 800/1077 - Train Accuracy: 0.9227, Validation Accuracy: 0.9244, Loss: 0.0528\n",
"Epoch 4 Batch 810/1077 - Train Accuracy: 0.9528, Validation Accuracy: 0.9283, Loss: 0.0411\n",
"Epoch 4 Batch 820/1077 - Train Accuracy: 0.9176, Validation Accuracy: 0.9428, Loss: 0.0486\n",
"Epoch 4 Batch 830/1077 - Train Accuracy: 0.8898, Validation Accuracy: 0.9237, Loss: 0.0588\n",
"Epoch 4 Batch 840/1077 - Train Accuracy: 0.9703, Validation Accuracy: 0.9322, Loss: 0.0508\n",
"Epoch 4 Batch 850/1077 - Train Accuracy: 0.9364, Validation Accuracy: 0.9471, Loss: 0.0807\n",
"Epoch 4 Batch 860/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9336, Loss: 0.0492\n",
"Epoch 4 Batch 870/1077 - Train Accuracy: 0.9293, Validation Accuracy: 0.9158, Loss: 0.0532\n",
"Epoch 4 Batch 880/1077 - Train Accuracy: 0.9629, Validation Accuracy: 0.9137, Loss: 0.0528\n",
"Epoch 4 Batch 890/1077 - Train Accuracy: 0.9606, Validation Accuracy: 0.9144, Loss: 0.0538\n",
"Epoch 4 Batch 900/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9325, Loss: 0.0648\n",
"Epoch 4 Batch 910/1077 - Train Accuracy: 0.9353, Validation Accuracy: 0.9102, Loss: 0.0526\n",
"Epoch 4 Batch 920/1077 - Train Accuracy: 0.9254, Validation Accuracy: 0.9265, Loss: 0.0499\n",
"Epoch 4 Batch 930/1077 - Train Accuracy: 0.9484, Validation Accuracy: 0.9357, Loss: 0.0511\n",
"Epoch 4 Batch 940/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9364, Loss: 0.0510\n",
"Epoch 4 Batch 950/1077 - Train Accuracy: 0.9621, Validation Accuracy: 0.9322, Loss: 0.0452\n",
"Epoch 4 Batch 960/1077 - Train Accuracy: 0.9479, Validation Accuracy: 0.9219, Loss: 0.0441\n",
"Epoch 4 Batch 970/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9258, Loss: 0.0528\n",
"Epoch 4 Batch 980/1077 - Train Accuracy: 0.9414, Validation Accuracy: 0.9155, Loss: 0.0501\n",
"Epoch 4 Batch 990/1077 - Train Accuracy: 0.9515, Validation Accuracy: 0.9421, Loss: 0.0539\n",
"Epoch 4 Batch 1000/1077 - Train Accuracy: 0.9576, Validation Accuracy: 0.9343, Loss: 0.0483\n"
]
},
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"Epoch 4 Batch 1010/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9332, Loss: 0.0405\n",
"Epoch 4 Batch 1020/1077 - Train Accuracy: 0.9629, Validation Accuracy: 0.9361, Loss: 0.0401\n",
"Epoch 4 Batch 1030/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9290, Loss: 0.0432\n",
"Epoch 4 Batch 1040/1077 - Train Accuracy: 0.9576, Validation Accuracy: 0.9389, Loss: 0.0557\n",
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"Epoch 4 Batch 1060/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9411, Loss: 0.0504\n",
"Epoch 4 Batch 1070/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9162, Loss: 0.0494\n",
"Epoch 5 Batch 10/1077 - Train Accuracy: 0.9593, Validation Accuracy: 0.9350, Loss: 0.0612\n",
"Epoch 5 Batch 20/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9400, Loss: 0.0427\n",
"Epoch 5 Batch 30/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9435, Loss: 0.0468\n",
"Epoch 5 Batch 40/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9315, Loss: 0.0615\n",
"Epoch 5 Batch 50/1077 - Train Accuracy: 0.9484, Validation Accuracy: 0.9300, Loss: 0.0635\n",
"Epoch 5 Batch 60/1077 - Train Accuracy: 0.9338, Validation Accuracy: 0.9258, Loss: 0.0497\n",
"Epoch 5 Batch 70/1077 - Train Accuracy: 0.9346, Validation Accuracy: 0.9187, Loss: 0.0665\n",
"Epoch 5 Batch 80/1077 - Train Accuracy: 0.9566, Validation Accuracy: 0.9155, Loss: 0.0533\n",
"Epoch 5 Batch 90/1077 - Train Accuracy: 0.9215, Validation Accuracy: 0.9212, Loss: 0.0648\n",
"Epoch 5 Batch 100/1077 - Train Accuracy: 0.9340, Validation Accuracy: 0.9283, Loss: 0.0530\n",
"Epoch 5 Batch 110/1077 - Train Accuracy: 0.9352, Validation Accuracy: 0.9183, Loss: 0.0462\n",
"Epoch 5 Batch 120/1077 - Train Accuracy: 0.9516, Validation Accuracy: 0.9297, Loss: 0.0722\n",
"Epoch 5 Batch 130/1077 - Train Accuracy: 0.9554, Validation Accuracy: 0.9027, Loss: 0.0605\n",
"Epoch 5 Batch 140/1077 - Train Accuracy: 0.9490, Validation Accuracy: 0.9173, Loss: 0.0580\n",
"Epoch 5 Batch 150/1077 - Train Accuracy: 0.9416, Validation Accuracy: 0.9272, Loss: 0.0547\n",
"Epoch 5 Batch 160/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9155, Loss: 0.0548\n",
"Epoch 5 Batch 170/1077 - Train Accuracy: 0.9211, Validation Accuracy: 0.9176, Loss: 0.0631\n",
"Epoch 5 Batch 180/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9226, Loss: 0.0515\n",
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"Epoch 5 Batch 580/1077 - Train Accuracy: 0.9394, Validation Accuracy: 0.9315, Loss: 0.0431\n",
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"Epoch 5 Batch 670/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9119, Loss: 0.0630\n",
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"Epoch 5 Batch 690/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9148, Loss: 0.0478\n",
"Epoch 5 Batch 700/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9183, Loss: 0.0363\n",
"Epoch 5 Batch 710/1077 - Train Accuracy: 0.9219, Validation Accuracy: 0.9350, Loss: 0.0405\n",
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"Epoch 5 Batch 740/1077 - Train Accuracy: 0.9609, Validation Accuracy: 0.9403, Loss: 0.0427\n",
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"Epoch 5 Batch 800/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9482, Loss: 0.0472\n",
"Epoch 5 Batch 810/1077 - Train Accuracy: 0.9394, Validation Accuracy: 0.9379, Loss: 0.0434\n"
]
},
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"Epoch 5 Batch 820/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9400, Loss: 0.0548\n",
"Epoch 5 Batch 830/1077 - Train Accuracy: 0.8812, Validation Accuracy: 0.9293, Loss: 0.0593\n",
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"Epoch 5 Batch 890/1077 - Train Accuracy: 0.9565, Validation Accuracy: 0.9141, Loss: 0.0560\n",
"Epoch 5 Batch 900/1077 - Train Accuracy: 0.9609, Validation Accuracy: 0.9329, Loss: 0.0654\n",
"Epoch 5 Batch 910/1077 - Train Accuracy: 0.9494, Validation Accuracy: 0.9354, Loss: 0.0464\n",
"Epoch 5 Batch 920/1077 - Train Accuracy: 0.9641, Validation Accuracy: 0.9251, Loss: 0.0553\n",
"Epoch 5 Batch 930/1077 - Train Accuracy: 0.9551, Validation Accuracy: 0.9386, Loss: 0.0617\n",
"Epoch 5 Batch 940/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9265, Loss: 0.0598\n",
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"Epoch 5 Batch 960/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9141, Loss: 0.0593\n",
"Epoch 5 Batch 970/1077 - Train Accuracy: 0.9445, Validation Accuracy: 0.9414, Loss: 0.0622\n",
"Epoch 5 Batch 980/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9318, Loss: 0.0528\n",
"Epoch 5 Batch 990/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9503, Loss: 0.0577\n",
"Epoch 5 Batch 1000/1077 - Train Accuracy: 0.9650, Validation Accuracy: 0.9354, Loss: 0.0511\n",
"Epoch 5 Batch 1010/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9144, Loss: 0.0494\n",
"Epoch 5 Batch 1020/1077 - Train Accuracy: 0.9484, Validation Accuracy: 0.9332, Loss: 0.0420\n",
"Epoch 5 Batch 1030/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9489, Loss: 0.0449\n",
"Epoch 5 Batch 1040/1077 - Train Accuracy: 0.9486, Validation Accuracy: 0.9297, Loss: 0.0621\n",
"Epoch 5 Batch 1050/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9332, Loss: 0.0507\n",
"Epoch 5 Batch 1060/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9329, Loss: 0.0434\n",
"Epoch 5 Batch 1070/1077 - Train Accuracy: 0.9637, Validation Accuracy: 0.9268, Loss: 0.0457\n",
"Epoch 6 Batch 10/1077 - Train Accuracy: 0.9519, Validation Accuracy: 0.9237, Loss: 0.0587\n",
"Epoch 6 Batch 20/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9293, Loss: 0.0453\n",
"Epoch 6 Batch 30/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9311, Loss: 0.0628\n",
"Epoch 6 Batch 40/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9329, Loss: 0.0418\n",
"Epoch 6 Batch 50/1077 - Train Accuracy: 0.9773, Validation Accuracy: 0.9439, Loss: 0.0453\n",
"Epoch 6 Batch 60/1077 - Train Accuracy: 0.9442, Validation Accuracy: 0.9311, Loss: 0.0507\n",
"Epoch 6 Batch 70/1077 - Train Accuracy: 0.9408, Validation Accuracy: 0.9194, Loss: 0.0537\n",
"Epoch 6 Batch 80/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9183, Loss: 0.0560\n",
"Epoch 6 Batch 90/1077 - Train Accuracy: 0.9273, Validation Accuracy: 0.9268, Loss: 0.0624\n",
"Epoch 6 Batch 100/1077 - Train Accuracy: 0.9598, Validation Accuracy: 0.9329, Loss: 0.0430\n",
"Epoch 6 Batch 110/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9311, Loss: 0.0441\n",
"Epoch 6 Batch 120/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9226, Loss: 0.0735\n",
"Epoch 6 Batch 130/1077 - Train Accuracy: 0.9260, Validation Accuracy: 0.9222, Loss: 0.0444\n",
"Epoch 6 Batch 140/1077 - Train Accuracy: 0.9474, Validation Accuracy: 0.9077, Loss: 0.0565\n",
"Epoch 6 Batch 150/1077 - Train Accuracy: 0.9386, Validation Accuracy: 0.9126, Loss: 0.0637\n",
"Epoch 6 Batch 160/1077 - Train Accuracy: 0.9582, Validation Accuracy: 0.9325, Loss: 0.0364\n",
"Epoch 6 Batch 170/1077 - Train Accuracy: 0.9398, Validation Accuracy: 0.9197, Loss: 0.0611\n",
"Epoch 6 Batch 180/1077 - Train Accuracy: 0.9832, Validation Accuracy: 0.9222, Loss: 0.0487\n",
"Epoch 6 Batch 190/1077 - Train Accuracy: 0.9719, Validation Accuracy: 0.9247, Loss: 0.0566\n",
"Epoch 6 Batch 200/1077 - Train Accuracy: 0.9457, Validation Accuracy: 0.9212, Loss: 0.0528\n",
"Epoch 6 Batch 210/1077 - Train Accuracy: 0.9382, Validation Accuracy: 0.9155, Loss: 0.0659\n",
"Epoch 6 Batch 220/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9155, Loss: 0.0454\n",
"Epoch 6 Batch 230/1077 - Train Accuracy: 0.9639, Validation Accuracy: 0.9219, Loss: 0.0642\n",
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"Epoch 6 Batch 280/1077 - Train Accuracy: 0.9332, Validation Accuracy: 0.9105, Loss: 0.0768\n",
"Epoch 6 Batch 290/1077 - Train Accuracy: 0.9199, Validation Accuracy: 0.9080, Loss: 0.0706\n",
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"Epoch 6 Batch 320/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9308, Loss: 0.0662\n",
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"Epoch 6 Batch 340/1077 - Train Accuracy: 0.9630, Validation Accuracy: 0.9471, Loss: 0.0735\n",
"Epoch 6 Batch 350/1077 - Train Accuracy: 0.9340, Validation Accuracy: 0.9119, Loss: 0.0691\n",
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"Epoch 6 Batch 390/1077 - Train Accuracy: 0.9211, Validation Accuracy: 0.9215, Loss: 0.0588\n",
"Epoch 6 Batch 400/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9315, Loss: 0.0585\n",
"Epoch 6 Batch 410/1077 - Train Accuracy: 0.9231, Validation Accuracy: 0.9173, Loss: 0.0701\n",
"Epoch 6 Batch 420/1077 - Train Accuracy: 0.9793, Validation Accuracy: 0.9094, Loss: 0.0411\n",
"Epoch 6 Batch 430/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9268, Loss: 0.0481\n",
"Epoch 6 Batch 440/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9205, Loss: 0.0689\n",
"Epoch 6 Batch 450/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9173, Loss: 0.0657\n",
"Epoch 6 Batch 460/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9265, Loss: 0.0633\n",
"Epoch 6 Batch 470/1077 - Train Accuracy: 0.9465, Validation Accuracy: 0.9251, Loss: 0.0513\n",
"Epoch 6 Batch 480/1077 - Train Accuracy: 0.9404, Validation Accuracy: 0.9254, Loss: 0.0598\n",
"Epoch 6 Batch 490/1077 - Train Accuracy: 0.9305, Validation Accuracy: 0.9283, Loss: 0.0548\n",
"Epoch 6 Batch 500/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9393, Loss: 0.0461\n",
"Epoch 6 Batch 510/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9464, Loss: 0.0604\n",
"Epoch 6 Batch 520/1077 - Train Accuracy: 0.9807, Validation Accuracy: 0.9371, Loss: 0.0459\n",
"Epoch 6 Batch 530/1077 - Train Accuracy: 0.9273, Validation Accuracy: 0.9343, Loss: 0.0702\n",
"Epoch 6 Batch 540/1077 - Train Accuracy: 0.9594, Validation Accuracy: 0.9322, Loss: 0.0528\n",
"Epoch 6 Batch 550/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9375, Loss: 0.0443\n",
"Epoch 6 Batch 560/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9350, Loss: 0.0379\n",
"Epoch 6 Batch 570/1077 - Train Accuracy: 0.9252, Validation Accuracy: 0.9222, Loss: 0.0657\n",
"Epoch 6 Batch 580/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9205, Loss: 0.0430\n",
"Epoch 6 Batch 590/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9190, Loss: 0.0648\n",
"Epoch 6 Batch 600/1077 - Train Accuracy: 0.9263, Validation Accuracy: 0.9229, Loss: 0.0728\n",
"Epoch 6 Batch 610/1077 - Train Accuracy: 0.9235, Validation Accuracy: 0.9283, Loss: 0.0671\n",
"Epoch 6 Batch 620/1077 - Train Accuracy: 0.9605, Validation Accuracy: 0.9389, Loss: 0.0494\n"
]
},
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"Epoch 6 Batch 630/1077 - Train Accuracy: 0.9582, Validation Accuracy: 0.9513, Loss: 0.0522\n",
"Epoch 6 Batch 640/1077 - Train Accuracy: 0.9594, Validation Accuracy: 0.9421, Loss: 0.0521\n",
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"Epoch 6 Batch 670/1077 - Train Accuracy: 0.9442, Validation Accuracy: 0.9403, Loss: 0.0582\n",
"Epoch 6 Batch 680/1077 - Train Accuracy: 0.9342, Validation Accuracy: 0.9155, Loss: 0.0545\n",
"Epoch 6 Batch 690/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9329, Loss: 0.0560\n",
"Epoch 6 Batch 700/1077 - Train Accuracy: 0.9496, Validation Accuracy: 0.9386, Loss: 0.0456\n",
"Epoch 6 Batch 710/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.9489, Loss: 0.0476\n",
"Epoch 6 Batch 720/1077 - Train Accuracy: 0.9576, Validation Accuracy: 0.9350, Loss: 0.0586\n",
"Epoch 6 Batch 730/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9315, Loss: 0.0645\n",
"Epoch 6 Batch 740/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9279, Loss: 0.0483\n",
"Epoch 6 Batch 750/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9371, Loss: 0.0419\n",
"Epoch 6 Batch 760/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9308, Loss: 0.0790\n",
"Epoch 6 Batch 770/1077 - Train Accuracy: 0.9193, Validation Accuracy: 0.9247, Loss: 0.0478\n",
"Epoch 6 Batch 780/1077 - Train Accuracy: 0.9070, Validation Accuracy: 0.9290, Loss: 0.0620\n",
"Epoch 6 Batch 790/1077 - Train Accuracy: 0.9168, Validation Accuracy: 0.9329, Loss: 0.0571\n",
"Epoch 6 Batch 800/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9318, Loss: 0.0501\n",
"Epoch 6 Batch 810/1077 - Train Accuracy: 0.9624, Validation Accuracy: 0.9293, Loss: 0.0501\n",
"Epoch 6 Batch 820/1077 - Train Accuracy: 0.9570, Validation Accuracy: 0.9251, Loss: 0.0473\n",
"Epoch 6 Batch 830/1077 - Train Accuracy: 0.9113, Validation Accuracy: 0.9290, Loss: 0.0647\n",
"Epoch 6 Batch 840/1077 - Train Accuracy: 0.9766, Validation Accuracy: 0.9265, Loss: 0.0479\n",
"Epoch 6 Batch 850/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9268, Loss: 0.0734\n",
"Epoch 6 Batch 860/1077 - Train Accuracy: 0.9528, Validation Accuracy: 0.9396, Loss: 0.0576\n",
"Epoch 6 Batch 870/1077 - Train Accuracy: 0.9087, Validation Accuracy: 0.9162, Loss: 0.0561\n",
"Epoch 6 Batch 880/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9339, Loss: 0.0466\n",
"Epoch 6 Batch 890/1077 - Train Accuracy: 0.9632, Validation Accuracy: 0.9258, Loss: 0.0548\n",
"Epoch 6 Batch 900/1077 - Train Accuracy: 0.9422, Validation Accuracy: 0.9464, Loss: 0.0571\n",
"Epoch 6 Batch 910/1077 - Train Accuracy: 0.9416, Validation Accuracy: 0.9442, Loss: 0.0448\n",
"Epoch 6 Batch 920/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9318, Loss: 0.0397\n",
"Epoch 6 Batch 930/1077 - Train Accuracy: 0.9508, Validation Accuracy: 0.9244, Loss: 0.0513\n",
"Epoch 6 Batch 940/1077 - Train Accuracy: 0.9430, Validation Accuracy: 0.9350, Loss: 0.0444\n",
"Epoch 6 Batch 950/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9208, Loss: 0.0375\n",
"Epoch 6 Batch 960/1077 - Train Accuracy: 0.9647, Validation Accuracy: 0.9212, Loss: 0.0452\n",
"Epoch 6 Batch 970/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9293, Loss: 0.0534\n",
"Epoch 6 Batch 980/1077 - Train Accuracy: 0.9328, Validation Accuracy: 0.9357, Loss: 0.0460\n",
"Epoch 6 Batch 990/1077 - Train Accuracy: 0.9424, Validation Accuracy: 0.9308, Loss: 0.0424\n",
"Epoch 6 Batch 1000/1077 - Train Accuracy: 0.9472, Validation Accuracy: 0.9506, Loss: 0.0506\n",
"Epoch 6 Batch 1010/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9339, Loss: 0.0507\n",
"Epoch 6 Batch 1020/1077 - Train Accuracy: 0.9746, Validation Accuracy: 0.9300, Loss: 0.0421\n",
"Epoch 6 Batch 1030/1077 - Train Accuracy: 0.9641, Validation Accuracy: 0.9435, Loss: 0.0661\n",
"Epoch 6 Batch 1040/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9251, Loss: 0.0526\n",
"Epoch 6 Batch 1050/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9339, Loss: 0.0424\n",
"Epoch 6 Batch 1060/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9513, Loss: 0.0406\n",
"Epoch 6 Batch 1070/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9400, Loss: 0.0450\n",
"Epoch 7 Batch 10/1077 - Train Accuracy: 0.9445, Validation Accuracy: 0.9382, Loss: 0.0540\n",
"Epoch 7 Batch 20/1077 - Train Accuracy: 0.9492, Validation Accuracy: 0.9513, Loss: 0.0317\n",
"Epoch 7 Batch 30/1077 - Train Accuracy: 0.9652, Validation Accuracy: 0.9521, Loss: 0.0406\n",
"Epoch 7 Batch 40/1077 - Train Accuracy: 0.9563, Validation Accuracy: 0.9428, Loss: 0.0380\n",
"Epoch 7 Batch 50/1077 - Train Accuracy: 0.9547, Validation Accuracy: 0.9347, Loss: 0.0484\n",
"Epoch 7 Batch 60/1077 - Train Accuracy: 0.9554, Validation Accuracy: 0.9162, Loss: 0.0452\n",
"Epoch 7 Batch 70/1077 - Train Accuracy: 0.9256, Validation Accuracy: 0.9364, Loss: 0.0519\n",
"Epoch 7 Batch 80/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9489, Loss: 0.0394\n",
"Epoch 7 Batch 90/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9350, Loss: 0.0573\n",
"Epoch 7 Batch 100/1077 - Train Accuracy: 0.9605, Validation Accuracy: 0.9315, Loss: 0.0472\n",
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]
},
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"text": [
"Epoch 7 Batch 440/1077 - Train Accuracy: 0.9250, Validation Accuracy: 0.9368, Loss: 0.0749\n",
"Epoch 7 Batch 450/1077 - Train Accuracy: 0.9398, Validation Accuracy: 0.9151, Loss: 0.0449\n",
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"Epoch 7 Batch 610/1077 - Train Accuracy: 0.9285, Validation Accuracy: 0.9087, Loss: 0.0717\n",
"Epoch 7 Batch 620/1077 - Train Accuracy: 0.9492, Validation Accuracy: 0.9073, Loss: 0.0532\n",
"Epoch 7 Batch 630/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9194, Loss: 0.0526\n",
"Epoch 7 Batch 640/1077 - Train Accuracy: 0.9308, Validation Accuracy: 0.9354, Loss: 0.0519\n",
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"Epoch 7 Batch 670/1077 - Train Accuracy: 0.9464, Validation Accuracy: 0.9130, Loss: 0.0541\n",
"Epoch 7 Batch 680/1077 - Train Accuracy: 0.9364, Validation Accuracy: 0.9290, Loss: 0.0604\n",
"Epoch 7 Batch 690/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9371, Loss: 0.0431\n",
"Epoch 7 Batch 700/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9261, Loss: 0.0486\n",
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"Epoch 7 Batch 730/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9318, Loss: 0.0481\n",
"Epoch 7 Batch 740/1077 - Train Accuracy: 0.9605, Validation Accuracy: 0.9261, Loss: 0.0436\n",
"Epoch 7 Batch 750/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9258, Loss: 0.0486\n",
"Epoch 7 Batch 760/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9453, Loss: 0.0619\n",
"Epoch 7 Batch 770/1077 - Train Accuracy: 0.9263, Validation Accuracy: 0.9325, Loss: 0.0513\n",
"Epoch 7 Batch 780/1077 - Train Accuracy: 0.9172, Validation Accuracy: 0.9325, Loss: 0.0779\n",
"Epoch 7 Batch 790/1077 - Train Accuracy: 0.9180, Validation Accuracy: 0.9332, Loss: 0.0639\n",
"Epoch 7 Batch 800/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9496, Loss: 0.0552\n",
"Epoch 7 Batch 810/1077 - Train Accuracy: 0.9617, Validation Accuracy: 0.9418, Loss: 0.0359\n",
"Epoch 7 Batch 820/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9439, Loss: 0.0432\n",
"Epoch 7 Batch 830/1077 - Train Accuracy: 0.9199, Validation Accuracy: 0.9308, Loss: 0.0619\n",
"Epoch 7 Batch 840/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9428, Loss: 0.0485\n",
"Epoch 7 Batch 850/1077 - Train Accuracy: 0.9319, Validation Accuracy: 0.9389, Loss: 0.0837\n",
"Epoch 7 Batch 860/1077 - Train Accuracy: 0.9442, Validation Accuracy: 0.9190, Loss: 0.0655\n",
"Epoch 7 Batch 870/1077 - Train Accuracy: 0.9248, Validation Accuracy: 0.9393, Loss: 0.0613\n",
"Epoch 7 Batch 880/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9300, Loss: 0.0615\n",
"Epoch 7 Batch 890/1077 - Train Accuracy: 0.9572, Validation Accuracy: 0.9400, Loss: 0.0538\n",
"Epoch 7 Batch 900/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9258, Loss: 0.0592\n",
"Epoch 7 Batch 910/1077 - Train Accuracy: 0.9438, Validation Accuracy: 0.9279, Loss: 0.0475\n",
"Epoch 7 Batch 920/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9229, Loss: 0.0494\n",
"Epoch 7 Batch 930/1077 - Train Accuracy: 0.9633, Validation Accuracy: 0.9134, Loss: 0.0632\n",
"Epoch 7 Batch 940/1077 - Train Accuracy: 0.9473, Validation Accuracy: 0.9208, Loss: 0.0576\n",
"Epoch 7 Batch 950/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9208, Loss: 0.0463\n",
"Epoch 7 Batch 960/1077 - Train Accuracy: 0.9327, Validation Accuracy: 0.9226, Loss: 0.0529\n",
"Epoch 7 Batch 970/1077 - Train Accuracy: 0.9551, Validation Accuracy: 0.9283, Loss: 0.0572\n",
"Epoch 7 Batch 980/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9222, Loss: 0.0536\n",
"Epoch 7 Batch 990/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9336, Loss: 0.0519\n",
"Epoch 7 Batch 1000/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9304, Loss: 0.0531\n",
"Epoch 7 Batch 1010/1077 - Train Accuracy: 0.9637, Validation Accuracy: 0.9201, Loss: 0.0469\n",
"Epoch 7 Batch 1020/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9180, Loss: 0.0531\n",
"Epoch 7 Batch 1030/1077 - Train Accuracy: 0.9383, Validation Accuracy: 0.9180, Loss: 0.0585\n",
"Epoch 7 Batch 1040/1077 - Train Accuracy: 0.9420, Validation Accuracy: 0.9244, Loss: 0.0656\n",
"Epoch 7 Batch 1050/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9261, Loss: 0.0359\n",
"Epoch 7 Batch 1060/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9283, Loss: 0.0557\n",
"Epoch 7 Batch 1070/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9158, Loss: 0.0566\n",
"Epoch 8 Batch 10/1077 - Train Accuracy: 0.9396, Validation Accuracy: 0.9205, Loss: 0.0621\n",
"Epoch 8 Batch 20/1077 - Train Accuracy: 0.9590, Validation Accuracy: 0.9162, Loss: 0.0424\n",
"Epoch 8 Batch 30/1077 - Train Accuracy: 0.9336, Validation Accuracy: 0.9286, Loss: 0.0488\n",
"Epoch 8 Batch 40/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9244, Loss: 0.0421\n",
"Epoch 8 Batch 50/1077 - Train Accuracy: 0.9781, Validation Accuracy: 0.9339, Loss: 0.0382\n",
"Epoch 8 Batch 60/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9205, Loss: 0.0416\n",
"Epoch 8 Batch 70/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9169, Loss: 0.0531\n",
"Epoch 8 Batch 80/1077 - Train Accuracy: 0.9430, Validation Accuracy: 0.9066, Loss: 0.0460\n",
"Epoch 8 Batch 90/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9354, Loss: 0.0528\n",
"Epoch 8 Batch 100/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9290, Loss: 0.0400\n",
"Epoch 8 Batch 110/1077 - Train Accuracy: 0.9598, Validation Accuracy: 0.9091, Loss: 0.0387\n",
"Epoch 8 Batch 120/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9251, Loss: 0.0524\n",
"Epoch 8 Batch 130/1077 - Train Accuracy: 0.9587, Validation Accuracy: 0.9094, Loss: 0.0525\n",
"Epoch 8 Batch 140/1077 - Train Accuracy: 0.9400, Validation Accuracy: 0.8913, Loss: 0.0629\n",
"Epoch 8 Batch 150/1077 - Train Accuracy: 0.9528, Validation Accuracy: 0.9169, Loss: 0.0563\n",
"Epoch 8 Batch 160/1077 - Train Accuracy: 0.9609, Validation Accuracy: 0.9094, Loss: 0.0386\n",
"Epoch 8 Batch 170/1077 - Train Accuracy: 0.9270, Validation Accuracy: 0.9112, Loss: 0.0577\n",
"Epoch 8 Batch 180/1077 - Train Accuracy: 0.9352, Validation Accuracy: 0.9070, Loss: 0.0465\n",
"Epoch 8 Batch 190/1077 - Train Accuracy: 0.9684, Validation Accuracy: 0.9272, Loss: 0.0466\n",
"Epoch 8 Batch 200/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9329, Loss: 0.0620\n",
"Epoch 8 Batch 210/1077 - Train Accuracy: 0.9364, Validation Accuracy: 0.9389, Loss: 0.0600\n",
"Epoch 8 Batch 220/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9187, Loss: 0.0457\n",
"Epoch 8 Batch 230/1077 - Train Accuracy: 0.9688, Validation Accuracy: 0.9087, Loss: 0.0514\n",
"Epoch 8 Batch 240/1077 - Train Accuracy: 0.9551, Validation Accuracy: 0.9070, Loss: 0.0551\n"
]
},
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"Epoch 8 Batch 250/1077 - Train Accuracy: 0.9474, Validation Accuracy: 0.9450, Loss: 0.0468\n",
"Epoch 8 Batch 260/1077 - Train Accuracy: 0.9342, Validation Accuracy: 0.9386, Loss: 0.0435\n",
"Epoch 8 Batch 270/1077 - Train Accuracy: 0.9332, Validation Accuracy: 0.9237, Loss: 0.0665\n",
"Epoch 8 Batch 280/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9158, Loss: 0.0689\n",
"Epoch 8 Batch 290/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9332, Loss: 0.0857\n",
"Epoch 8 Batch 300/1077 - Train Accuracy: 0.9531, Validation Accuracy: 0.9251, Loss: 0.0495\n",
"Epoch 8 Batch 310/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9361, Loss: 0.0517\n",
"Epoch 8 Batch 320/1077 - Train Accuracy: 0.9344, Validation Accuracy: 0.9400, Loss: 0.0626\n",
"Epoch 8 Batch 330/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9180, Loss: 0.0580\n",
"Epoch 8 Batch 340/1077 - Train Accuracy: 0.9650, Validation Accuracy: 0.9009, Loss: 0.0488\n",
"Epoch 8 Batch 350/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9141, Loss: 0.0606\n",
"Epoch 8 Batch 360/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9237, Loss: 0.0478\n",
"Epoch 8 Batch 370/1077 - Train Accuracy: 0.9546, Validation Accuracy: 0.9229, Loss: 0.0553\n",
"Epoch 8 Batch 380/1077 - Train Accuracy: 0.9672, Validation Accuracy: 0.9347, Loss: 0.0452\n",
"Epoch 8 Batch 390/1077 - Train Accuracy: 0.9332, Validation Accuracy: 0.9233, Loss: 0.0791\n",
"Epoch 8 Batch 400/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9077, Loss: 0.0611\n",
"Epoch 8 Batch 410/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.8952, Loss: 0.0624\n",
"Epoch 8 Batch 420/1077 - Train Accuracy: 0.9547, Validation Accuracy: 0.9176, Loss: 0.0477\n",
"Epoch 8 Batch 430/1077 - Train Accuracy: 0.9324, Validation Accuracy: 0.9070, Loss: 0.0547\n",
"Epoch 8 Batch 440/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9055, Loss: 0.0616\n",
"Epoch 8 Batch 450/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9073, Loss: 0.0727\n",
"Epoch 8 Batch 460/1077 - Train Accuracy: 0.9637, Validation Accuracy: 0.9048, Loss: 0.0615\n",
"Epoch 8 Batch 470/1077 - Train Accuracy: 0.9396, Validation Accuracy: 0.9070, Loss: 0.0614\n",
"Epoch 8 Batch 480/1077 - Train Accuracy: 0.9416, Validation Accuracy: 0.9173, Loss: 0.0560\n",
"Epoch 8 Batch 490/1077 - Train Accuracy: 0.9254, Validation Accuracy: 0.9183, Loss: 0.0704\n",
"Epoch 8 Batch 500/1077 - Train Accuracy: 0.9398, Validation Accuracy: 0.9173, Loss: 0.0605\n",
"Epoch 8 Batch 510/1077 - Train Accuracy: 0.9348, Validation Accuracy: 0.9283, Loss: 0.0727\n",
"Epoch 8 Batch 520/1077 - Train Accuracy: 0.9829, Validation Accuracy: 0.9268, Loss: 0.0597\n",
"Epoch 8 Batch 530/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9229, Loss: 0.0639\n",
"Epoch 8 Batch 540/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.8984, Loss: 0.0604\n",
"Epoch 8 Batch 550/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9130, Loss: 0.0651\n",
"Epoch 8 Batch 560/1077 - Train Accuracy: 0.9148, Validation Accuracy: 0.9116, Loss: 0.0585\n",
"Epoch 8 Batch 570/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9197, Loss: 0.0785\n",
"Epoch 8 Batch 580/1077 - Train Accuracy: 0.9405, Validation Accuracy: 0.9087, Loss: 0.0665\n",
"Epoch 8 Batch 590/1077 - Train Accuracy: 0.9145, Validation Accuracy: 0.9158, Loss: 0.0886\n",
"Epoch 8 Batch 600/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9201, Loss: 0.0793\n",
"Epoch 8 Batch 610/1077 - Train Accuracy: 0.9038, Validation Accuracy: 0.9318, Loss: 0.0833\n",
"Epoch 8 Batch 620/1077 - Train Accuracy: 0.9590, Validation Accuracy: 0.9237, Loss: 0.0602\n",
"Epoch 8 Batch 630/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9219, Loss: 0.0553\n",
"Epoch 8 Batch 640/1077 - Train Accuracy: 0.9293, Validation Accuracy: 0.9311, Loss: 0.0652\n",
"Epoch 8 Batch 650/1077 - Train Accuracy: 0.9422, Validation Accuracy: 0.9247, Loss: 0.0598\n",
"Epoch 8 Batch 660/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9229, Loss: 0.0531\n",
"Epoch 8 Batch 670/1077 - Train Accuracy: 0.9457, Validation Accuracy: 0.9201, Loss: 0.0807\n",
"Epoch 8 Batch 680/1077 - Train Accuracy: 0.9330, Validation Accuracy: 0.9144, Loss: 0.0667\n",
"Epoch 8 Batch 690/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9205, Loss: 0.0547\n",
"Epoch 8 Batch 700/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9055, Loss: 0.0511\n",
"Epoch 8 Batch 710/1077 - Train Accuracy: 0.9285, Validation Accuracy: 0.9066, Loss: 0.0585\n",
"Epoch 8 Batch 720/1077 - Train Accuracy: 0.9461, Validation Accuracy: 0.9293, Loss: 0.0672\n",
"Epoch 8 Batch 730/1077 - Train Accuracy: 0.9477, Validation Accuracy: 0.9240, Loss: 0.0629\n",
"Epoch 8 Batch 740/1077 - Train Accuracy: 0.9523, Validation Accuracy: 0.9403, Loss: 0.0542\n",
"Epoch 8 Batch 750/1077 - Train Accuracy: 0.9543, Validation Accuracy: 0.9247, Loss: 0.0481\n",
"Epoch 8 Batch 760/1077 - Train Accuracy: 0.9285, Validation Accuracy: 0.9165, Loss: 0.0616\n",
"Epoch 8 Batch 770/1077 - Train Accuracy: 0.9338, Validation Accuracy: 0.9258, Loss: 0.0630\n",
"Epoch 8 Batch 780/1077 - Train Accuracy: 0.9156, Validation Accuracy: 0.9165, Loss: 0.0979\n",
"Epoch 8 Batch 790/1077 - Train Accuracy: 0.8961, Validation Accuracy: 0.9229, Loss: 0.0650\n",
"Epoch 8 Batch 800/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9357, Loss: 0.0648\n",
"Epoch 8 Batch 810/1077 - Train Accuracy: 0.9464, Validation Accuracy: 0.9279, Loss: 0.0447\n",
"Epoch 8 Batch 820/1077 - Train Accuracy: 0.9391, Validation Accuracy: 0.9162, Loss: 0.0551\n",
"Epoch 8 Batch 830/1077 - Train Accuracy: 0.9199, Validation Accuracy: 0.9062, Loss: 0.0761\n",
"Epoch 8 Batch 840/1077 - Train Accuracy: 0.9656, Validation Accuracy: 0.9254, Loss: 0.0398\n",
"Epoch 8 Batch 850/1077 - Train Accuracy: 0.9156, Validation Accuracy: 0.9134, Loss: 0.1000\n",
"Epoch 8 Batch 860/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9283, Loss: 0.0711\n",
"Epoch 8 Batch 870/1077 - Train Accuracy: 0.9174, Validation Accuracy: 0.9180, Loss: 0.0479\n",
"Epoch 8 Batch 880/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.9279, Loss: 0.0681\n",
"Epoch 8 Batch 890/1077 - Train Accuracy: 0.9513, Validation Accuracy: 0.9102, Loss: 0.0651\n",
"Epoch 8 Batch 900/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9222, Loss: 0.0675\n",
"Epoch 8 Batch 910/1077 - Train Accuracy: 0.9252, Validation Accuracy: 0.9293, Loss: 0.0659\n",
"Epoch 8 Batch 920/1077 - Train Accuracy: 0.9527, Validation Accuracy: 0.9286, Loss: 0.0684\n",
"Epoch 8 Batch 930/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9098, Loss: 0.0490\n",
"Epoch 8 Batch 940/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9233, Loss: 0.0605\n",
"Epoch 8 Batch 950/1077 - Train Accuracy: 0.9312, Validation Accuracy: 0.9460, Loss: 0.0504\n",
"Epoch 8 Batch 960/1077 - Train Accuracy: 0.9353, Validation Accuracy: 0.9251, Loss: 0.0587\n",
"Epoch 8 Batch 970/1077 - Train Accuracy: 0.9500, Validation Accuracy: 0.9212, Loss: 0.0705\n",
"Epoch 8 Batch 980/1077 - Train Accuracy: 0.9262, Validation Accuracy: 0.9386, Loss: 0.0515\n",
"Epoch 8 Batch 990/1077 - Train Accuracy: 0.9268, Validation Accuracy: 0.9315, Loss: 0.0590\n",
"Epoch 8 Batch 1000/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9375, Loss: 0.0550\n",
"Epoch 8 Batch 1010/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9190, Loss: 0.0537\n",
"Epoch 8 Batch 1020/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9038, Loss: 0.0550\n",
"Epoch 8 Batch 1030/1077 - Train Accuracy: 0.9535, Validation Accuracy: 0.9261, Loss: 0.0774\n",
"Epoch 8 Batch 1040/1077 - Train Accuracy: 0.9396, Validation Accuracy: 0.9386, Loss: 0.0691\n",
"Epoch 8 Batch 1050/1077 - Train Accuracy: 0.9430, Validation Accuracy: 0.9276, Loss: 0.0558\n",
"Epoch 8 Batch 1060/1077 - Train Accuracy: 0.9242, Validation Accuracy: 0.9339, Loss: 0.0528\n",
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"Epoch 9 Batch 10/1077 - Train Accuracy: 0.9363, Validation Accuracy: 0.9233, Loss: 0.0643\n",
"Epoch 9 Batch 20/1077 - Train Accuracy: 0.9418, Validation Accuracy: 0.9382, Loss: 0.0429\n",
"Epoch 9 Batch 30/1077 - Train Accuracy: 0.9352, Validation Accuracy: 0.9478, Loss: 0.0473\n",
"Epoch 9 Batch 40/1077 - Train Accuracy: 0.9574, Validation Accuracy: 0.9290, Loss: 0.0640\n",
"Epoch 9 Batch 50/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9315, Loss: 0.0598\n"
]
},
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"text": [
"Epoch 9 Batch 60/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9126, Loss: 0.0545\n",
"Epoch 9 Batch 70/1077 - Train Accuracy: 0.9235, Validation Accuracy: 0.9119, Loss: 0.0744\n",
"Epoch 9 Batch 80/1077 - Train Accuracy: 0.9602, Validation Accuracy: 0.9148, Loss: 0.0523\n",
"Epoch 9 Batch 90/1077 - Train Accuracy: 0.9387, Validation Accuracy: 0.9187, Loss: 0.0562\n",
"Epoch 9 Batch 100/1077 - Train Accuracy: 0.9512, Validation Accuracy: 0.9137, Loss: 0.0525\n",
"Epoch 9 Batch 110/1077 - Train Accuracy: 0.9566, Validation Accuracy: 0.9052, Loss: 0.0543\n",
"Epoch 9 Batch 120/1077 - Train Accuracy: 0.9621, Validation Accuracy: 0.9158, Loss: 0.0609\n",
"Epoch 9 Batch 130/1077 - Train Accuracy: 0.9647, Validation Accuracy: 0.9059, Loss: 0.0420\n",
"Epoch 9 Batch 140/1077 - Train Accuracy: 0.9613, Validation Accuracy: 0.9212, Loss: 0.0631\n",
"Epoch 9 Batch 150/1077 - Train Accuracy: 0.9524, Validation Accuracy: 0.9158, Loss: 0.0691\n",
"Epoch 9 Batch 160/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9272, Loss: 0.0366\n",
"Epoch 9 Batch 170/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9141, Loss: 0.0598\n",
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"Epoch 9 Batch 190/1077 - Train Accuracy: 0.9324, Validation Accuracy: 0.9261, Loss: 0.0489\n",
"Epoch 9 Batch 200/1077 - Train Accuracy: 0.9406, Validation Accuracy: 0.9233, Loss: 0.0605\n",
"Epoch 9 Batch 210/1077 - Train Accuracy: 0.9568, Validation Accuracy: 0.9137, Loss: 0.0624\n",
"Epoch 9 Batch 220/1077 - Train Accuracy: 0.9564, Validation Accuracy: 0.9066, Loss: 0.0492\n",
"Epoch 9 Batch 230/1077 - Train Accuracy: 0.9323, Validation Accuracy: 0.9421, Loss: 0.0700\n",
"Epoch 9 Batch 240/1077 - Train Accuracy: 0.9578, Validation Accuracy: 0.9332, Loss: 0.0599\n",
"Epoch 9 Batch 250/1077 - Train Accuracy: 0.9347, Validation Accuracy: 0.9368, Loss: 0.0501\n",
"Epoch 9 Batch 260/1077 - Train Accuracy: 0.9435, Validation Accuracy: 0.9212, Loss: 0.0426\n",
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"Epoch 9 Batch 290/1077 - Train Accuracy: 0.9187, Validation Accuracy: 0.9268, Loss: 0.0779\n",
"Epoch 9 Batch 300/1077 - Train Accuracy: 0.9478, Validation Accuracy: 0.9300, Loss: 0.0602\n",
"Epoch 9 Batch 310/1077 - Train Accuracy: 0.9398, Validation Accuracy: 0.9304, Loss: 0.0586\n",
"Epoch 9 Batch 320/1077 - Train Accuracy: 0.9480, Validation Accuracy: 0.9112, Loss: 0.0747\n",
"Epoch 9 Batch 330/1077 - Train Accuracy: 0.9246, Validation Accuracy: 0.9141, Loss: 0.0652\n",
"Epoch 9 Batch 340/1077 - Train Accuracy: 0.9371, Validation Accuracy: 0.9070, Loss: 0.0607\n",
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"Epoch 9 Batch 360/1077 - Train Accuracy: 0.9449, Validation Accuracy: 0.9215, Loss: 0.0408\n",
"Epoch 9 Batch 370/1077 - Train Accuracy: 0.9658, Validation Accuracy: 0.9350, Loss: 0.0449\n",
"Epoch 9 Batch 380/1077 - Train Accuracy: 0.9359, Validation Accuracy: 0.9425, Loss: 0.0458\n",
"Epoch 9 Batch 390/1077 - Train Accuracy: 0.9219, Validation Accuracy: 0.9279, Loss: 0.0696\n",
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"Epoch 9 Batch 420/1077 - Train Accuracy: 0.9586, Validation Accuracy: 0.9354, Loss: 0.0544\n",
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"Epoch 9 Batch 460/1077 - Train Accuracy: 0.9410, Validation Accuracy: 0.9258, Loss: 0.0616\n",
"Epoch 9 Batch 470/1077 - Train Accuracy: 0.9437, Validation Accuracy: 0.9180, Loss: 0.0553\n",
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"Epoch 9 Batch 490/1077 - Train Accuracy: 0.9375, Validation Accuracy: 0.9084, Loss: 0.0598\n",
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"Epoch 9 Batch 520/1077 - Train Accuracy: 0.9773, Validation Accuracy: 0.9379, Loss: 0.0534\n",
"Epoch 9 Batch 530/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9265, Loss: 0.0660\n",
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"Epoch 9 Batch 570/1077 - Train Accuracy: 0.9252, Validation Accuracy: 0.9222, Loss: 0.0680\n",
"Epoch 9 Batch 580/1077 - Train Accuracy: 0.9301, Validation Accuracy: 0.9201, Loss: 0.0371\n",
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"Epoch 9 Batch 670/1077 - Train Accuracy: 0.9361, Validation Accuracy: 0.9347, Loss: 0.0715\n",
"Epoch 9 Batch 680/1077 - Train Accuracy: 0.9308, Validation Accuracy: 0.9279, Loss: 0.0622\n",
"Epoch 9 Batch 690/1077 - Train Accuracy: 0.9320, Validation Accuracy: 0.9134, Loss: 0.0562\n",
"Epoch 9 Batch 700/1077 - Train Accuracy: 0.9453, Validation Accuracy: 0.9027, Loss: 0.0542\n",
"Epoch 9 Batch 710/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9467, Loss: 0.0396\n",
"Epoch 9 Batch 720/1077 - Train Accuracy: 0.9260, Validation Accuracy: 0.9531, Loss: 0.0593\n",
"Epoch 9 Batch 730/1077 - Train Accuracy: 0.9203, Validation Accuracy: 0.9432, Loss: 0.0708\n",
"Epoch 9 Batch 740/1077 - Train Accuracy: 0.9367, Validation Accuracy: 0.9293, Loss: 0.0544\n",
"Epoch 9 Batch 750/1077 - Train Accuracy: 0.9129, Validation Accuracy: 0.9347, Loss: 0.0561\n",
"Epoch 9 Batch 760/1077 - Train Accuracy: 0.9469, Validation Accuracy: 0.9389, Loss: 0.0656\n",
"Epoch 9 Batch 770/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9407, Loss: 0.0580\n",
"Epoch 9 Batch 780/1077 - Train Accuracy: 0.9273, Validation Accuracy: 0.9226, Loss: 0.0808\n",
"Epoch 9 Batch 790/1077 - Train Accuracy: 0.8980, Validation Accuracy: 0.9300, Loss: 0.0665\n",
"Epoch 9 Batch 800/1077 - Train Accuracy: 0.9641, Validation Accuracy: 0.9421, Loss: 0.0573\n",
"Epoch 9 Batch 810/1077 - Train Accuracy: 0.9501, Validation Accuracy: 0.9283, Loss: 0.0404\n",
"Epoch 9 Batch 820/1077 - Train Accuracy: 0.9313, Validation Accuracy: 0.9471, Loss: 0.0577\n",
"Epoch 9 Batch 830/1077 - Train Accuracy: 0.9195, Validation Accuracy: 0.9254, Loss: 0.0792\n",
"Epoch 9 Batch 840/1077 - Train Accuracy: 0.9566, Validation Accuracy: 0.9251, Loss: 0.0586\n",
"Epoch 9 Batch 850/1077 - Train Accuracy: 0.9286, Validation Accuracy: 0.9393, Loss: 0.0859\n",
"Epoch 9 Batch 860/1077 - Train Accuracy: 0.9315, Validation Accuracy: 0.9233, Loss: 0.0609\n",
"Epoch 9 Batch 870/1077 - Train Accuracy: 0.9280, Validation Accuracy: 0.9105, Loss: 0.0579\n",
"Epoch 9 Batch 880/1077 - Train Accuracy: 0.9695, Validation Accuracy: 0.9190, Loss: 0.0539\n",
"Epoch 9 Batch 890/1077 - Train Accuracy: 0.9609, Validation Accuracy: 0.9276, Loss: 0.0528\n",
"Epoch 9 Batch 900/1077 - Train Accuracy: 0.9559, Validation Accuracy: 0.9297, Loss: 0.0740\n",
"Epoch 9 Batch 910/1077 - Train Accuracy: 0.9289, Validation Accuracy: 0.9442, Loss: 0.0579\n",
"Epoch 9 Batch 920/1077 - Train Accuracy: 0.9297, Validation Accuracy: 0.9315, Loss: 0.0549\n",
"Epoch 9 Batch 930/1077 - Train Accuracy: 0.9496, Validation Accuracy: 0.9311, Loss: 0.0494\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9 Batch 940/1077 - Train Accuracy: 0.9402, Validation Accuracy: 0.9407, Loss: 0.0485\n",
"Epoch 9 Batch 950/1077 - Train Accuracy: 0.9487, Validation Accuracy: 0.9506, Loss: 0.0396\n",
"Epoch 9 Batch 960/1077 - Train Accuracy: 0.9539, Validation Accuracy: 0.9371, Loss: 0.0470\n",
"Epoch 9 Batch 970/1077 - Train Accuracy: 0.9504, Validation Accuracy: 0.9251, Loss: 0.0629\n",
"Epoch 9 Batch 980/1077 - Train Accuracy: 0.9379, Validation Accuracy: 0.9403, Loss: 0.0514\n",
"Epoch 9 Batch 990/1077 - Train Accuracy: 0.9441, Validation Accuracy: 0.9332, Loss: 0.0625\n",
"Epoch 9 Batch 1000/1077 - Train Accuracy: 0.9513, Validation Accuracy: 0.9336, Loss: 0.0561\n",
"Epoch 9 Batch 1010/1077 - Train Accuracy: 0.9727, Validation Accuracy: 0.9460, Loss: 0.0514\n",
"Epoch 9 Batch 1020/1077 - Train Accuracy: 0.9594, Validation Accuracy: 0.9471, Loss: 0.0489\n",
"Epoch 9 Batch 1030/1077 - Train Accuracy: 0.9508, Validation Accuracy: 0.9339, Loss: 0.0553\n",
"Epoch 9 Batch 1040/1077 - Train Accuracy: 0.9511, Validation Accuracy: 0.9339, Loss: 0.0593\n",
"Epoch 9 Batch 1050/1077 - Train Accuracy: 0.9680, Validation Accuracy: 0.9304, Loss: 0.0481\n",
"Epoch 9 Batch 1060/1077 - Train Accuracy: 0.9520, Validation Accuracy: 0.9290, Loss: 0.0606\n",
"Epoch 9 Batch 1070/1077 - Train Accuracy: 0.9637, Validation Accuracy: 0.9400, Loss: 0.0504\n",
"Model Trained and Saved\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def get_accuracy(target, logits):\n",
" \"\"\"\n",
" Calculate accuracy\n",
" \"\"\"\n",
" max_seq = max(target.shape[1], logits.shape[1])\n",
" if max_seq - target.shape[1]:\n",
" target = np.pad(\n",
" target,\n",
" [(0,0),(0,max_seq - target.shape[1])],\n",
" 'constant')\n",
" if max_seq - logits.shape[1]:\n",
" logits = np.pad(\n",
" logits,\n",
" [(0,0),(0,max_seq - logits.shape[1])],\n",
" 'constant')\n",
"\n",
" return np.mean(np.equal(target, logits))\n",
"\n",
"# Split data to training and validation sets\n",
"train_source = source_int_text[batch_size:]\n",
"train_target = target_int_text[batch_size:]\n",
"valid_source = source_int_text[:batch_size]\n",
"valid_target = target_int_text[:batch_size]\n",
"(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\n",
" valid_target,\n",
" batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])) \n",
"with tf.Session(graph=train_graph) as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
"\n",
" for epoch_i in range(epochs):\n",
" for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\n",
" get_batches(train_source, train_target, batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])):\n",
"\n",
" _, loss = sess.run(\n",
" [train_op, cost],\n",
" {input_data: source_batch,\n",
" targets: target_batch,\n",
" lr: learning_rate,\n",
" target_sequence_length: targets_lengths,\n",
" source_sequence_length: sources_lengths,\n",
" keep_prob: keep_probability})\n",
"\n",
"\n",
" if batch_i % display_step == 0 and batch_i > 0:\n",
"\n",
"\n",
" batch_train_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: source_batch,\n",
" source_sequence_length: sources_lengths,\n",
" target_sequence_length: targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
"\n",
" batch_valid_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: valid_sources_batch,\n",
" source_sequence_length: valid_sources_lengths,\n",
" target_sequence_length: valid_targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
" train_acc = get_accuracy(target_batch, batch_train_logits)\n",
"\n",
" valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\n",
"\n",
" print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\n",
" .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n",
"\n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" saver.save(sess, save_path)\n",
" print('Model Trained and Saved')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Parameters\n",
"Save the `batch_size` and `save_path` parameters for inference."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"# Save parameters for checkpoint\n",
"helper.save_params(save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n",
"load_path = helper.load_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sentence to Sequence\n",
"To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n",
"\n",
"- Convert the sentence to lowercase\n",
"- Convert words into ids using `vocab_to_int`\n",
" - Convert words not in the vocabulary, to the `<UNK>` word id."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def sentence_to_seq(sentence, vocab_to_int):\n",
" \"\"\"\n",
" Convert a sentence to a sequence of ids\n",
" :param sentence: String\n",
" :param vocab_to_int: Dictionary to go from the words to an id\n",
" :return: List of word ids\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" sentence = sentence.lower()\n",
" sentence_to_id = [vocab_to_int[word] if word in vocab_to_int.keys() else vocab_to_int['<UNK>'] for word in sentence.split(' ')]\n",
" return sentence_to_id\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_sentence_to_seq(sentence_to_seq)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Translate\n",
"This will translate `translate_sentence` from English to French."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from checkpoints/dev\n",
"Input\n",
" Word Ids: [52, 28, 109, 153, 105, 79, 230]\n",
" English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n",
"\n",
"Prediction\n",
" Word Ids: [294, 318, 149, 301, 338, 353, 22, 186, 1]\n",
" French Words: il a vu un vieux camion jaune . <EOS>\n"
]
}
],
"source": [
"translate_sentence = 'he saw a old yellow truck .'\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n",
"\n",
"loaded_graph = tf.Graph()\n",
"with tf.Session(graph=loaded_graph) as sess:\n",
" # Load saved model\n",
" loader = tf.train.import_meta_graph(load_path + '.meta')\n",
" loader.restore(sess, load_path)\n",
"\n",
" input_data = loaded_graph.get_tensor_by_name('input:0')\n",
" logits = loaded_graph.get_tensor_by_name('predictions:0')\n",
" target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n",
" source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n",
" keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
"\n",
" translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,\n",
" target_sequence_length: [len(translate_sentence)*2]*batch_size,\n",
" source_sequence_length: [len(translate_sentence)]*batch_size,\n",
" keep_prob: 1.0})[0]\n",
"\n",
"print('Input')\n",
"print(' Word Ids: {}'.format([i for i in translate_sentence]))\n",
"print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n",
"\n",
"print('\\nPrediction')\n",
"print(' Word Ids: {}'.format([i for i in translate_logits]))\n",
"print(' French Words: {}'.format(\" \".join([target_int_to_vocab[i] for i in translate_logits])))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imperfect Translation\n",
"You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n",
"\n",
"You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n",
"## Submitting This Project\n",
"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
]
}
],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
jakubholynet/lodash-fp_issues | mutating_with_seamless-immutable.ipynb | 1 | 3623 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiences with seamless-immutable\n",
"\n",
"On our data the \"dev\" version (that actually does freeze data etc.) was 2-3 slower than the (nearly \"noop\") prod version - f.ex. 2200 ms x 800 ms.\n",
"=> it is great there are both the dev and prod versions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modifying data with seamless-immutable"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"var Immutable = require('seamless-immutable');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imutable arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Push, unshift => concat"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[ 2, 3 ]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var original = Immutable([2]);\n",
"original.concat([3]);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[ 1, 2 ]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Immutable([1]).concat(original);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pop => slice"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Immutable([1]).slice(0,-1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Other: splice OK, sort :(, ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Impractical\n",
"Mutating nested structures is cumbersome. F.ex. here we want to add a new \"event\" to a log at the last path:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[ { path: '/', log: [] },\n",
" { path: '/last', log: [ 'new event' ] } ]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var report = Immutable([{path: \"/\", log: []},{path: \"/last\", log: []}]);\n",
"// Update to [{path: \"/\", log: [\"new event\"]}]; It would be easy if we had updateIn and a curried concat:\n",
"// var updatedReport = updateIn(report, [report.length - 1, log], concat([\"new event\"]))\n",
"var oldEntry = report[report.length-1];\n",
"/*var updatedReport =*/ report.slice(0,-1).concat([\n",
" oldEntry.merge({log: oldEntry.log.concat([\"new event\"])})\n",
"]);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Javascript (Node.js)",
"language": "javascript",
"name": "javascript"
},
"language_info": {
"file_extension": "js",
"mimetype": "application/javascript",
"name": "javascript",
"version": "0.10.38"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
Olsthoorn/IHE-python-course-2017 | exercises/Mar07/readingText.ipynb | 1 | 46666 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<figure>\n",
" <IMG SRC=\"../../logo/logo.png\" WIDTH=250 ALIGN=\"right\">\n",
"</figure>\n",
"\n",
"# IHE Python course, 2017\n",
"\n",
"## Reading text files\n",
"\n",
"T.N.Olsthoorn, Feb 27, 2017"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading and writing files is one of the essential functions of computers as this is the connection between volatile data in the computer's memory and permanent data on disk, in the cloud.\n",
"\n",
"The three forms of reading text files are shown, and we'll illustrate handling its contents. We'll take the README.md file as an example as it is readily available on this site.\n",
"\n",
"## Finding the file in the directory tree\n",
"First steps are finding the file in the directory structure. We'll start with an illutration how this can be done using some functions in the os module, which deal with directories."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.DS_Store',\n",
" '.ipynb_checkpoints',\n",
" 'dealingWithStrings.ipynb',\n",
" 'introspection.ipynb',\n",
" 'readingText.ipynb']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir() # make a list of the files in the current directory, so that we may handle them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turn the relative refference of the current directory, '.' into an absolute path.\n",
"\n",
"Then specify the file name that we're looking for.\n",
"\n",
"And in a while-loop look upward in an ever higher diectory in the directory tree until we found that with our file.\n",
"\n",
"Walking upward in the tree is done by cutting off the tail of the path in each step."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting in folder </Users/Theo/GRWMODELS/Python_projects/IHEcourse2017/exercises/Mar07>,\n",
" to look for file <README.md> ...\n",
"\n",
"I'm searching: ...\n",
"/Users/Theo/GRWMODELS/Python_projects/IHEcourse2017/exercises\n",
"/Users/Theo/GRWMODELS/Python_projects/IHEcourse2017\n",
"... Yep, got'm!\n",
"\n",
"Ok, file <README.md> is in folder: /Users/Theo/GRWMODELS/Python_projects/IHEcourse2017\n",
"\n",
"Here is the list of files in this folder:\n"
]
},
{
"data": {
"text/plain": [
"['.DS_Store',\n",
" '.git',\n",
" '.gitignore',\n",
" '.ipynb_checkpoints',\n",
" 'exercises',\n",
" 'IMG_20170221_155447496.jpg',\n",
" 'IntroPythonCourse.pptx',\n",
" 'LICENSE',\n",
" 'logo',\n",
" 'mbakker7-exploratory_computing_with_python-ca15088',\n",
" 'photos',\n",
" 'README.md',\n",
" 'sandbox']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apth = os.path.abspath('.')\n",
"\n",
"fname = 'README.md'\n",
"\n",
"print(\"Starting in folder <{}>,\\n to look for file <{}> ...\".format(apth, fname))\n",
"print()\n",
"\n",
"print(\"I'm searching: ...\")\n",
"while not fname in os.listdir(apth):\n",
" apth = apth.rpartition(os.sep)[0]\n",
" print(apth)\n",
" \n",
"if fname in os.listdir(apth):\n",
" print(\"... Yep, got'm!\")\n",
"else:\n",
" print(\"... H'm, missed him\")\n",
" \n",
"print(\"\\nOk, file <{}> is in folder: \".format(fname), apth)\n",
"print('\\nHere is the list of files in this folder:')\n",
"os.listdir(apth)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading the file (at once)\n",
"\n",
"When we know where the file is, we can open it for reading.\n",
"\n",
"We have to open it, which yields a reader object, by which we can read the file.\n",
"\n",
"`reader = open(path, 'r')`\n",
"\n",
"`s = reader.read()`\n",
"\n",
"`reader.close()`\n",
"\n",
"Problem with this, is that when we are exploring the reader, we may easily reach the end of file after which nothing more is read and s is an empty string. Furthermore, when we experiment, we may easily open the same file many times and forget to close it.\n",
"\n",
"The with statement is a solution to that, because it automatically closes the file when we finish its block.\n",
"\n",
"With the with statement we may read the entire file into a string like so."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(os.path.join(apth, fname), 'r') as reader:\n",
" s = reader.read()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's the read that swallows the entire file at once and dumps its contents in the string s.\n",
"\n",
"Check if the reader in, indeed, closed after we finished the `with` block:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reader.closed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then show the contenct of the sring s:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# IHE-python-course-2017\n",
"Material for the extracurricular python course given at UNESCO-IHE from 21-Feb-2017\n",
"\n",
"Upon request of the virtually entire student community studying at UNESCO-IHE in Delft in 2017, an extracurricular course on Python was given as it was realized that learning python is an essential asset for future engineers and scientists.\n",
"\n",
"Next to my own exercises, we'll borrow much of the exercises Prof. Mark Bakker's tutorials for all students at the faculty of CEGS at TUDelft, which are availalbe on github. Other material from the internet may also be used.\n",
"\n",
"The focus is on the needs of scientists and engineers.\n",
"\n",
"The students are supposed to install the Anaconda packages on their laptop and the lessons will be on Tuesdays 16:45-17:30 as from Feb 21, 2017 in the auditorium of IHE at the Westvest in Delft.\n",
"\n",
"For your own practicing I advice the exercises in the book (2015) **\"Automate the Boring Stuff with Python\"** by Al Sweigart. The book can be read free on the Internet. The excercises are as simple as possible, systematically aranged, in each step explaining one new aspect, everything in it is extremely clearly explained and the book is even relaxed reading. Each chapter ends with a set of questions and one or two practical projects that are interesting and useful by themselves. In one word, the book is a wealth. I assume that students will do such exercises themselves, so that we can taken Python a step further in class.\n",
"\n",
"Theo Olsthoorn, Prof. groundwater\n",
"Delft Feb. 20, 2017\n",
"\n",
"**Feb 21:**\n",
"Introduction, see ppt presentation on this site.\n",
"MB's first notebook was used as a start thereafter. Basic plotting of polynomicals using matplotlib.pyplot and numpy. The resulting notebook is on this site.\n",
"\n",
"**Feb 28**\n",
"This time we'll heavily focus on tuples, lists, dicts and sets. We will read data from an excel workbook, turn this into dicts. We'll make heavily use of the list, dict and set comprehensions to generate the lists and dict that we need to order, join, analyze and visualize the results from the data that we have read in from different sheets of the excel workbook. Hence there will be quite some advanced working with sequences, iterators and generators so that afterwards you should feel comfortable with them.\n",
"\n"
]
}
],
"source": [
"print(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counting words and phrases\n",
"\n",
"Now that we have the entire file read into a single string, `s`, we can just as well analyze it a bit, by counting the number of words, letters, and the frequency of each letter.\n",
"\n",
"just split the sting in workds based on whitespace and count their number."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 362 words in file README.md\n"
]
}
],
"source": [
"print(\"There are {} words in file {}\".format(len(s.split(sep=' ')), fname))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We might estimate the number of sentences by counting the number of periods '.'\n",
"\n",
"One way is to use the `.` as a separator:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We find 24 phrases in file README.md\n"
]
}
],
"source": [
"nPhrases = len(s.split(sep='.')) # also works without the keyword sep\n",
"\n",
"print(\"We find {} phrases in file {}\".format(nPhrases, fname))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could just as wel count the number of dots in s directly, using one of the string methods, in this case s.count()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 23 dots in file README.md\n"
]
}
],
"source": [
"print(\"There are {} dots in file {}\".format(s.count('.'), fname))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counting the non-whitespace characters\n",
"\n",
"Now let's see how many non-whitespace characters there are in s.\n",
"\n",
"A coarse way to remove whitespace would be splitting s and rejoining the obtained list of words without any whitespace like so:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#ihe-python-course-2017materialfortheextracurricularpythoncoursegivenatunesco-ihefrom21-feb-2017uponrequestofthevirtuallyentirestudentcommunitystudyingatunesco-iheindelftin2017,anextracurricularcourseonpythonwasgivenasitwasrealizedthatlearningpythonisanessentialassetforfutureengineersandscientists.nexttomyownexercises,we'llborrowmuchoftheexercisesprof.markbakker'stutorialsforallstudentsatthefacultyofcegsattudelft,whichareavailalbeongithub.othermaterialfromtheinternetmayalsobeused.thefocusisontheneedsofscientistsandengineers.thestudentsaresupposedtoinstalltheanacondapackagesontheirlaptopandthelessonswillbeontuesdays16:45-17:30asfromfeb21,2017intheauditoriumofiheatthewestvestindelft.foryourownpracticingiadvicetheexercisesinthebook(2015)**\"automatetheboringstuffwithpython\"**byalsweigart.thebookcanbereadfreeontheinternet.theexcercisesareassimpleaspossible,systematicallyaranged,ineachstepexplainingonenewaspect,everythinginitisextremelyclearlyexplainedandthebookisevenrelaxedreading.eachchapterendswithasetofquestionsandoneortwopracticalprojectsthatareinterestingandusefulbythemselves.inoneword,thebookisawealth.iassumethatstudentswilldosuchexercisesthemselves,sothatwecantakenpythonastepfurtherinclass.theoolsthoorn,prof.groundwaterdelftfeb.20,2017**feb21:**introduction,seepptpresentationonthissite.mb'sfirstnotebookwasusedasastartthereafter.basicplottingofpolynomicalsusingmatplotlib.pyplotandnumpy.theresultingnotebookisonthissite.**feb28**thistimewe'llheavilyfocusontuples,lists,dictsandsets.wewillreaddatafromanexcelworkbook,turnthisintodicts.we'llmakeheavilyuseofthelist,dictandsetcomprehensionstogeneratethelistsanddictthatweneedtoorder,join,analyzeandvisualizetheresultsfromthedatathatwehavereadinfromdifferentsheetsoftheexcelworkbook.hencetherewillbequitesomeadvancedworkingwithsequences,iteratorsandgeneratorssothatafterwardsyoushouldfeelcomfortablewiththem.\n"
]
}
],
"source": [
"s1 = \"\".join(s.split()).lower() # also make all letters in lowerface()\n",
"print(s1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## All characters in a list\n",
"\n",
"If we convert a string into a list, we get the list of its individual characters."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
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"outputs": [
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" 'i',\n",
" 'u',\n",
" 'm',\n",
" 'o',\n",
" 'f',\n",
" 'i',\n",
" 'h',\n",
" 'e',\n",
" 'a',\n",
" 't',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'w',\n",
" 'e',\n",
" 's',\n",
" 't',\n",
" 'v',\n",
" 'e',\n",
" 's',\n",
" 't',\n",
" 'i',\n",
" 'n',\n",
" 'd',\n",
" 'e',\n",
" 'l',\n",
" 'f',\n",
" 't',\n",
" '.',\n",
" 'f',\n",
" 'o',\n",
" 'r',\n",
" 'y',\n",
" 'o',\n",
" 'u',\n",
" 'r',\n",
" 'o',\n",
" 'w',\n",
" 'n',\n",
" 'p',\n",
" 'r',\n",
" 'a',\n",
" 'c',\n",
" 't',\n",
" 'i',\n",
" 'c',\n",
" 'i',\n",
" 'n',\n",
" 'g',\n",
" 'i',\n",
" 'a',\n",
" 'd',\n",
" 'v',\n",
" 'i',\n",
" 'c',\n",
" 'e',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'e',\n",
" 'x',\n",
" 'e',\n",
" 'r',\n",
" 'c',\n",
" 'i',\n",
" 's',\n",
" 'e',\n",
" 's',\n",
" 'i',\n",
" 'n',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'b',\n",
" 'o',\n",
" 'o',\n",
" 'k',\n",
" '(',\n",
" '2',\n",
" '0',\n",
" '1',\n",
" '5',\n",
" ')',\n",
" '*',\n",
" '*',\n",
" '\"',\n",
" 'a',\n",
" 'u',\n",
" 't',\n",
" 'o',\n",
" 'm',\n",
" 'a',\n",
" 't',\n",
" 'e',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'b',\n",
" 'o',\n",
" 'r',\n",
" 'i',\n",
" 'n',\n",
" 'g',\n",
" 's',\n",
" 't',\n",
" 'u',\n",
" 'f',\n",
" 'f',\n",
" 'w',\n",
" 'i',\n",
" 't',\n",
" 'h',\n",
" 'p',\n",
" 'y',\n",
" 't',\n",
" 'h',\n",
" 'o',\n",
" 'n',\n",
" '\"',\n",
" '*',\n",
" '*',\n",
" 'b',\n",
" 'y',\n",
" 'a',\n",
" 'l',\n",
" 's',\n",
" 'w',\n",
" 'e',\n",
" 'i',\n",
" 'g',\n",
" 'a',\n",
" 'r',\n",
" 't',\n",
" '.',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'b',\n",
" 'o',\n",
" 'o',\n",
" 'k',\n",
" 'c',\n",
" 'a',\n",
" 'n',\n",
" 'b',\n",
" 'e',\n",
" 'r',\n",
" 'e',\n",
" 'a',\n",
" 'd',\n",
" 'f',\n",
" 'r',\n",
" 'e',\n",
" 'e',\n",
" 'o',\n",
" 'n',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'i',\n",
" 'n',\n",
" 't',\n",
" 'e',\n",
" 'r',\n",
" 'n',\n",
" 'e',\n",
" 't',\n",
" '.',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'e',\n",
" 'x',\n",
" 'c',\n",
" 'e',\n",
" 'r',\n",
" 'c',\n",
" 'i',\n",
" 's',\n",
" 'e',\n",
" 's',\n",
" 'a',\n",
" 'r',\n",
" 'e',\n",
" 'a',\n",
" 's',\n",
" 's',\n",
" 'i',\n",
" 'm',\n",
" 'p',\n",
" 'l',\n",
" 'e',\n",
" 'a',\n",
" 's',\n",
" 'p',\n",
" 'o',\n",
" 's',\n",
" 's',\n",
" 'i',\n",
" 'b',\n",
" 'l',\n",
" 'e',\n",
" ',',\n",
" 's',\n",
" 'y',\n",
" 's',\n",
" 't',\n",
" 'e',\n",
" 'm',\n",
" 'a',\n",
" 't',\n",
" 'i',\n",
" 'c',\n",
" 'a',\n",
" 'l',\n",
" 'l',\n",
" 'y',\n",
" 'a',\n",
" 'r',\n",
" 'a',\n",
" 'n',\n",
" 'g',\n",
" 'e',\n",
" 'd',\n",
" ',',\n",
" 'i',\n",
" 'n',\n",
" 'e',\n",
" 'a',\n",
" 'c',\n",
" 'h',\n",
" 's',\n",
" 't',\n",
" 'e',\n",
" 'p',\n",
" 'e',\n",
" 'x',\n",
" 'p',\n",
" 'l',\n",
" 'a',\n",
" 'i',\n",
" 'n',\n",
" 'i',\n",
" 'n',\n",
" 'g',\n",
" 'o',\n",
" 'n',\n",
" 'e',\n",
" 'n',\n",
" 'e',\n",
" 'w',\n",
" 'a',\n",
" 's',\n",
" 'p',\n",
" 'e',\n",
" 'c',\n",
" 't',\n",
" ',',\n",
" 'e',\n",
" 'v',\n",
" 'e',\n",
" 'r',\n",
" 'y',\n",
" 't',\n",
" 'h',\n",
" 'i',\n",
" 'n',\n",
" 'g',\n",
" 'i',\n",
" 'n',\n",
" 'i',\n",
" 't',\n",
" 'i',\n",
" 's',\n",
" 'e',\n",
" 'x',\n",
" 't',\n",
" 'r',\n",
" 'e',\n",
" 'm',\n",
" 'e',\n",
" 'l',\n",
" 'y',\n",
" 'c',\n",
" 'l',\n",
" 'e',\n",
" 'a',\n",
" 'r',\n",
" 'l',\n",
" 'y',\n",
" 'e',\n",
" 'x',\n",
" 'p',\n",
" 'l',\n",
" 'a',\n",
" 'i',\n",
" 'n',\n",
" 'e',\n",
" 'd',\n",
" 'a',\n",
" 'n',\n",
" 'd',\n",
" 't',\n",
" 'h',\n",
" 'e',\n",
" 'b',\n",
" 'o',\n",
" 'o',\n",
" 'k',\n",
" 'i',\n",
" 's',\n",
" 'e',\n",
" 'v',\n",
" 'e',\n",
" 'n',\n",
" 'r',\n",
" 'e',\n",
" 'l',\n",
" 'a',\n",
" 'x',\n",
" 'e',\n",
" 'd',\n",
" 'r',\n",
" 'e',\n",
" 'a',\n",
" 'd',\n",
" 'i',\n",
" 'n',\n",
" 'g',\n",
" '.',\n",
" 'e',\n",
" 'a',\n",
" 'c',\n",
" 'h',\n",
" 'c',\n",
" 'h',\n",
" 'a',\n",
" 'p',\n",
" 't',\n",
" ...]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(s1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The unique characters in a set\n",
"\n",
"By turning the string into a set, we get the set of its unique characters:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'\"',\n",
" '#',\n",
" \"'\",\n",
" '(',\n",
" ')',\n",
" '*',\n",
" ',',\n",
" '-',\n",
" '.',\n",
" '0',\n",
" '1',\n",
" '2',\n",
" '3',\n",
" '4',\n",
" '5',\n",
" '6',\n",
" '7',\n",
" '8',\n",
" ':',\n",
" 'a',\n",
" 'b',\n",
" 'c',\n",
" 'd',\n",
" 'e',\n",
" 'f',\n",
" 'g',\n",
" 'h',\n",
" 'i',\n",
" 'j',\n",
" 'k',\n",
" 'l',\n",
" 'm',\n",
" 'n',\n",
" 'o',\n",
" 'p',\n",
" 'q',\n",
" 'r',\n",
" 's',\n",
" 't',\n",
" 'u',\n",
" 'v',\n",
" 'w',\n",
" 'x',\n",
" 'y',\n",
" 'z'}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(s1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The number of occurences of each non-white character in the file\n",
"\n",
"To count the frequency of each character we could use those from the set as keys in a dict. We can generate the dict with the frequency if each character in a dict comprehension that combines the unique letter as a key with the method count(key) applied on s1, the string without whitespace:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pprint' 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-17-0c35a1c774f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mccnt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0ms1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\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[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mccnt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'pprint' is not defined"
]
}
],
"source": [
"ccnt = {c : s1.count(c) for c in set(s1)}\n",
"pprint(ccnt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets order the letters after their frequency of occurrence in the file:\n",
"\n",
"We can do so in one line, but this needs some explanaion.\n",
"\n",
"First we generate a list from the dict in which each item is a list of 2 itmes namely [char, number]\n",
"\n",
"Second we apply sorted on that list to get a sorted list. But we don't want it to be sorted based on the character, but based on the number. Therfore, we use the `key` argument. It tels that each item has to be compared on the second value (lambda x: x[1]).\n",
"\n",
"Finally, this yields the list that we want, but with the largest frequency at the bottom. So we turn this list upside down by using the slice [::-1] at the end.\n",
"\n",
"Here it is:"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['e', 236],\n",
" ['t', 186],\n",
" ['s', 140],\n",
" ['a', 131],\n",
" ['o', 122],\n",
" ['n', 120],\n",
" ['i', 118],\n",
" ['r', 102],\n",
" ['h', 78],\n",
" ['l', 77],\n",
" ['u', 56],\n",
" ['c', 56],\n",
" ['d', 55],\n",
" ['f', 46],\n",
" ['p', 36],\n",
" ['w', 33],\n",
" ['m', 32],\n",
" ['b', 29],\n",
" ['y', 28],\n",
" ['g', 24],\n",
" ['.', 23],\n",
" [',', 19],\n",
" ['k', 17],\n",
" ['v', 15],\n",
" ['x', 14],\n",
" ['*', 12],\n",
" ['2', 11],\n",
" ['1', 11],\n",
" ['0', 8],\n",
" ['-', 8],\n",
" ['7', 6],\n",
" [\"'\", 5],\n",
" ['q', 4],\n",
" [':', 3],\n",
" ['z', 3],\n",
" ['5', 2],\n",
" ['j', 2],\n",
" ['\"', 2],\n",
" ['#', 1],\n",
" ['4', 1],\n",
" ['8', 1],\n",
" ['3', 1],\n",
" ['6', 1],\n",
" ['(', 1],\n",
" [')', 1]]"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted([[k, ccnt[k]] for k in ccnt.keys()], key=lambda x: x[1])[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading the file and returning a list of strings, one per line\n",
"\n",
"For this we would read reader.readlines() instead of reader.read:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(os.path.join(apth, fname), 'r') as reader:\n",
" s = reader.readlines()\n",
"\n",
"type(s)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pretty printing has been turned OFF\n"
]
}
],
"source": [
"pprint(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this point onward, you can analyse each line in sequence, pick out lines, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading a single line and lines one by one\n",
"\n",
"Often you don't want to read the entire file into memory (into a single character) at once. It might blow up the computer's memory if the file size were gigabits, as can easily the case with output of some models. And if it wouldn't crash the memory, your pc may still become very slow with large files. So a better and more generally applied way to read in a file is line by line, based on the newline characters that are embedded in them.\n",
"\n",
"In that case you can read the file in line by line, one at a time, not using reader.read() or reader.readlines() but reader.readline()"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(os.path.join(apth, fname), 'r') as reader:\n",
" s = reader.readline()\n",
"\n",
"type(s)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# IHE-python-course-2017\n",
"\n"
]
}
],
"source": [
"print(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which yields a string, the first string of the file in this case.\n",
"\n",
"The problem is now, that no more lines can be read from this file, because with the `with` statement, the file closes automatically as soon as the python reaches the end of its block:"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "I/O operation on closed file.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-193-8850e808884b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
]
}
],
"source": [
"s = reader.readline()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Therefore, we should not use the `with` statement and hand-close the file when we're done, or put anything that we do with the strings that we read inside the `with` block.\n",
"\n",
"We may be tempted to put the reader in a while-loop like so\n",
"\n",
"s=[]\n",
"while True:\n",
" s.append(reader.readline())\n",
" \n",
"But don't do that, becaus the while-loop will never end"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['# IHE-python-course-2017\\n',\n",
" 'Material for the extracurricular python course given at UNESCO-IHE from '\n",
" '21-Feb-2017\\n',\n",
" '\\n',\n",
" 'Upon request of the virtually entire student community studying at '\n",
" 'UNESCO-IHE in Delft in 2017, an extracurricular course on Python was given '\n",
" 'as it was realized that learning python is an essential asset for future '\n",
" 'engineers and scientists.\\n',\n",
" '\\n',\n",
" \"Next to my own exercises, we'll borrow much of the exercises Prof. Mark \"\n",
" \"Bakker's tutorials for all students at the faculty of CEGS at TUDelft, which \"\n",
" 'are availalbe on github. Other material from the internet may also be '\n",
" 'used.\\n',\n",
" '\\n',\n",
" 'The focus is on the needs of scientists and engineers.\\n',\n",
" '\\n',\n",
" 'The students are supposed to install the Anaconda packages on their laptop '\n",
" 'and the lessons will be on Tuesdays 16:45-17:30 as from Feb 21, 2017 in the '\n",
" 'auditorium of IHE at the Westvest in Delft.\\n',\n",
" '\\n',\n",
" 'For your own practicing I advice the exercises in the book (2015) '\n",
" '**\"Automate the Boring Stuff with Python\"** by Al Sweigart. The book can be '\n",
" 'read free on the Internet. The excercises are as simple as possible, '\n",
" 'systematically aranged, in each step explaining one new aspect, everything '\n",
" 'in it is extremely clearly explained and the book is even relaxed reading. '\n",
" 'Each chapter ends with a set of questions and one or two practical projects '\n",
" 'that are interesting and useful by themselves. In one word, the book is a '\n",
" 'wealth. I assume that students will do such exercises themselves, so that we '\n",
" 'can taken Python a step further in class.\\n',\n",
" '\\n',\n",
" 'Theo Olsthoorn, Prof. groundwater\\n',\n",
" 'Delft Feb. 20, 2017\\n',\n",
" '\\n',\n",
" '**Feb 21:**\\n',\n",
" 'Introduction, see ppt presentation on this site.\\n',\n",
" \"MB's first notebook was used as a start thereafter. Basic plotting of \"\n",
" 'polynomicals using matplotlib.pyplot and numpy. The resulting notebook is on '\n",
" 'this site.\\n',\n",
" '\\n',\n",
" '**Feb 28**\\n',\n",
" \"This time we'll heavily focus on tuples, lists, dicts and sets. We will read \"\n",
" \"data from an excel workbook, turn this into dicts. We'll make heavily use of \"\n",
" 'the list, dict and set comprehensions to generate the lists and dict that we '\n",
" 'need to order, join, analyze and visualize the results from the data that we '\n",
" 'have read in from different sheets of the excel workbook. Hence there will '\n",
" 'be quite some advanced working with sequences, iterators and generators so '\n",
" 'that afterwards you should feel comfortable with them.\\n']\n"
]
}
],
"source": [
"with open(os.path.join(apth, fname), 'r') as reader:\n",
" lines = []\n",
" while True:\n",
" s = reader.readline()\n",
" if s==\"\":\n",
" break\n",
" lines.append(s)\n",
"\n",
"pprint(lines)\n"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reader.readline?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, there is much, much more, but this is probably the most important base knowledge about file reading. File writing of textfile is straightforward. You open a file with open( fname, 'w') for writing or open(fname,'a') for appending and you can start writing lines to it. Don't forget to close it when done. Still better, use the `with` statement to make sure that the file is automatically closed when its block is done."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
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