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codeparrot
/
github-jupyter

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WillenZh/deep-learning-project
tutorials/autoencoder/Convolutional_Autoencoder.ipynb
54
92975
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f4631f1a4e0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f463c6bbac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = mnist.train.images[2]\n", "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Network Architecture\n", "\n", "The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\n", "\n", "![Convolutional Autoencoder](assets/convolutional_autoencoder.png)\n", "\n", "Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughly 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\n", "\n", "### What's going on with the decoder\n", "\n", "Okay, so the decoder has these \"Upsample\" layers that you might not have seen before. First off, I'll discuss a bit what these layers *aren't*. Usually, you'll see **deconvolutional** layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but it reverse. A stride in the input layer results in a larger stride in the deconvolutional layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a deconvolutional layer. Deconvolution is often called \"transpose convolution\" which is what you'll find with the TensorFlow API, with [`tf.nn.conv2d_transpose`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). \n", "\n", "However, deconvolutional layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In [this Distill article](http://distill.pub/2016/deconv-checkerboard/) from Augustus Odena, *et al*, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with [`tf.image.resize_images`](https://www.tensorflow.org/versions/r1.1/api_docs/python/tf/image/resize_images), followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling.\n", "\n", "> **Exercise:** Build the network shown above. Remember that a convolutional layer with strides of 1 and 'same' padding won't reduce the height and width. That is, if the input is 28x28 and the convolution layer has stride = 1 and 'same' padding, the convolutional layer will also be 28x28. The max-pool layers are used the reduce the width and height. A stride of 2 will reduce the size by 2. Odena *et al* claim that nearest neighbor interpolation works best for the upsampling, so make sure to include that as a parameter in `tf.image.resize_images` or use [`tf.image.resize_nearest_neighbor`]( `https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.001\n", "inputs_ = \n", "targets_ = \n", "\n", "### Encoder\n", "conv1 = \n", "# Now 28x28x16\n", "maxpool1 = \n", "# Now 14x14x16\n", "conv2 = \n", "# Now 14x14x8\n", "maxpool2 = \n", "# Now 7x7x8\n", "conv3 = \n", "# Now 7x7x8\n", "encoded = \n", "# Now 4x4x8\n", "\n", "### Decoder\n", "upsample1 = \n", "# Now 7x7x8\n", "conv4 = \n", "# Now 7x7x8\n", "upsample2 = \n", "# Now 14x14x8\n", "conv5 = \n", "# Now 14x14x8\n", "upsample3 = \n", "# Now 28x28x8\n", "conv6 = \n", "# Now 28x28x16\n", "\n", "logits = \n", "#Now 28x28x1\n", "\n", "# Pass logits through sigmoid to get reconstructed image\n", "decoded =\n", "\n", "# Pass logits through sigmoid and calculate the cross-entropy loss\n", "loss = \n", "\n", "# Get cost and define the optimizer\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(learning_rate).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "As before, here wi'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.Session()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "epochs = 20\n", "batch_size = 200\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " imgs = batch[0].reshape((-1, 28, 28, 1))\n", " batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n", " targets_: imgs})\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f46266ffb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\n", "\n", "for images, row in zip([in_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "\n", "fig.tight_layout(pad=0.1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Denoising\n", "\n", "As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\n", "\n", "![Denoising autoencoder](assets/denoising.png)\n", "\n", "\n", "Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before.\n", "\n", "> **Exercise:** Build the network for the denoising autoencoder. It's the same as before, but with deeper layers. I suggest 32-32-16 for the depths, but you can play with these numbers, or add more layers." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.001\n", "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n", "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n", "\n", "### Encoder\n", "conv1 = \n", "# Now 28x28x32\n", "maxpool1 = \n", "# Now 14x14x32\n", "conv2 = \n", "# Now 14x14x32\n", "maxpool2 = \n", "# Now 7x7x32\n", "conv3 = \n", "# Now 7x7x16\n", "encoded = \n", "# Now 4x4x16\n", "\n", "### Decoder\n", "upsample1 = \n", "# Now 7x7x16\n", "conv4 = \n", "# Now 7x7x16\n", "upsample2 = \n", "# Now 14x14x16\n", "conv5 = \n", "# Now 14x14x32\n", "upsample3 = \n", "# Now 28x28x32\n", "conv6 = \n", "# Now 28x28x32\n", "\n", "logits = \n", "#Now 28x28x1\n", "\n", "# Pass logits through sigmoid to get reconstructed image\n", "decoded =\n", "\n", "# Pass logits through sigmoid and calculate the cross-entropy loss\n", "loss = \n", "\n", "# Get cost and define the optimizer\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.Session()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "epochs = 100\n", "batch_size = 200\n", "# Set's how much noise we're adding to the MNIST images\n", "noise_factor = 0.5\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " # Get images from the batch\n", " imgs = batch[0].reshape((-1, 28, 28, 1))\n", " \n", " # Add random noise to the input images\n", " noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n", " # Clip the images to be between 0 and 1\n", " noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n", " \n", " # Noisy images as inputs, original images as targets\n", " batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n", " targets_: imgs})\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking out the performance\n", "\n", "Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprisingly great job of removing the noise, even though it's sometimes difficult to tell what the original number is." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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Z2dq1a4MY1/S06F9YCyGEEEIIIYQQQgghhMgI9MJaCCGEEEIIIYQQQgghREag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\n70sL/sbs+bNSpUouh/fRHXfc4WpwbKxbt87VLF2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4624778320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n", "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n", "\n", "reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n", "\n", "for images, row in zip([noisy_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "fig.tight_layout(pad=0.1)" ] } ], "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
google-research/language
language/multiberts/coref.ipynb
1
985078
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "O1t22v2ReiTx" }, "source": [ "# Application: Gender Bias in Coreference Systems\n", "\n", "This notebook walks through the analysis in Section 4 of [the paper](https://openreview.net/pdf?id=K0E_F0gFDgA). We'll look at accuracy and bias correlation metrics on the Winogender dataset of [Rudinger et al. 2018](https://arxiv.org/abs/1804.09301), and show how the multibootstrap can be used in two different ways:\n", "\n", "* A **paired** analysis of an intervention (incremental CDA) applied to pretrained checkpoints.\n", "* An **unpaired** analysis comparing to a new set of checkpoints trained with a different procedure (CDA full).\n", "\n", "This notebook will download pre-computed predictions, which are exactly the predictions used in the paper; the cells below should allow you to directly reproduce Figure 3, Table 1, and Table 2 from Section 4, as well as Figure 5, Figure 6, and Table 4 from Appendix D." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "Hz6d1y5qjshN" }, "outputs": [], "source": [ "#@title Import libraries and multibootstrap code\n", "import re\n", "import os\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import sklearn.metrics\n", "import scipy.stats\n", "\n", "from tqdm.notebook import tqdm # for progress indicator\n", "\n", "import multibootstrap" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "OCD8x6GMh9IQ" }, "outputs": [], "source": [ "#@title Import and configure plotting libraries\n", "import matplotlib\n", "from matplotlib import pyplot\n", "import seaborn as sns\n", "sns.set_style('white')\n", "%config InlineBackend.figure_format = 'retina' # make matplotlib plots look better\n", "\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": { "id": "Yqz5DJZp8P9M" }, "source": [ "## Download prediction files\n", "\n", "We release four groups of predictions:\n", "\n", "* **`base`**: the base MultiBERTs models (`bert-base-uncased`), with 5 coreference runs for each of 25 pretraining checkpoints.\n", "* **`cda_intervention-50k`**: as above, but with 50k steps of CDA applied to each checkpoint. 5 coreference runs for each of 25 pretraining checkpoints, paired with `base`.\n", "* **`from_scratch`**: trained from-scratch using CDA data. 5 coreference runs for each of 25 pretraining checkpoints, which are not paired with the above.\n", "* **`base_extra_seeds`**: 25 coreference runs for each of the first five pretraining seeds from `base`; used in Figure 6.\n", "\n", "For each group, there are three files:\n", "* `run_info.tsv`: run information, with columns `pretrain_seed` and `finetune_seed`\n", "* `label_info.tsv` : labels and other metadata for each instance. 720 rows, \n", " one for each Winogender example.\n", "* `preds.tsv`: predictions on each instance, with rows aligned to those of\n", " `run_info.tsv` and 720 columns which align to the rows of `label_info.tsv`.\n", "\n", "The values in `preds.tsv` represent the index of the predicted referent, so for\n", "Winogender this means:\n", "- 0 is the occupation term\n", "- 1 is the other_participant\n", "\n", "You can also browse these files manually here: https://console.cloud.google.com/storage/browser/multiberts/public/example-predictions/coref" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/tmp/multiberts_coref/occupations-stats.tsv\r\n", "\r\n", "/tmp/multiberts_coref/base:\r\n", "label_info.tsv\tpreds.tsv run_info.tsv\r\n", "\r\n", "/tmp/multiberts_coref/base_extra_seeds:\r\n", "label_info.tsv\tpreds.tsv run_info.tsv\r\n", "\r\n", "/tmp/multiberts_coref/cda_intervention-50k:\r\n", "label_info.tsv\tpreds.tsv run_info.tsv\r\n", "\r\n", "/tmp/multiberts_coref/from_scratch:\r\n", "label_info.tsv\tpreds.tsv run_info.tsv\r\n" ] } ], "source": [ "#@title Download predictions and metadata\n", "scratch_dir = \"/tmp/multiberts_coref\"\n", "if not os.path.isdir(scratch_dir): \n", " os.mkdir(scratch_dir)\n", " \n", "preds_root = \"https://storage.googleapis.com/multiberts/public/example-predictions/coref\"\n", "GROUP_NAMES = [\n", " 'base',\n", " 'base_extra_seeds',\n", " 'cda_intervention-50k',\n", " 'from_scratch'\n", "]\n", "for name in GROUP_NAMES:\n", " !mkdir -p $scratch_dir/$name\n", " for fname in ['label_info.tsv', 'preds.tsv', 'run_info.tsv']:\n", " !curl -s -O $preds_root/$name/$fname --output-dir $scratch_dir/$name\n", "\n", "# Fetch Winogender occupations data from official repo https://github.com/rudinger/winogender-schemas\n", "!curl -s -O https://raw.githubusercontent.com/rudinger/winogender-schemas/master/data/occupations-stats.tsv \\\n", " --output-dir $scratch_dir\n", " \n", "!ls $scratch_dir/**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "POesaG3MEusS" }, "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>pretrain_seed</th>\n", " <th>finetune_seed</th>\n", " <th>group_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>base</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>base</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>base</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>base</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>base</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>495</th>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>from_scratch</td>\n", " </tr>\n", " <tr>\n", " <th>496</th>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>from_scratch</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>from_scratch</td>\n", " </tr>\n", " <tr>\n", " <th>498</th>\n", " <td>9</td>\n", " <td>3</td>\n", " <td>from_scratch</td>\n", " </tr>\n", " <tr>\n", " <th>499</th>\n", " <td>9</td>\n", " <td>4</td>\n", " <td>from_scratch</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>500 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " pretrain_seed finetune_seed group_name\n", "0 0 0 base\n", "1 0 1 base\n", "2 0 2 base\n", "3 0 3 base\n", "4 0 4 base\n", ".. ... ... ...\n", "495 9 0 from_scratch\n", "496 9 1 from_scratch\n", "497 9 2 from_scratch\n", "498 9 3 from_scratch\n", "499 9 4 from_scratch\n", "\n", "[500 rows x 3 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Load run information\n", "data_root = scratch_dir\n", "\n", "all_run_info = []\n", "for group_name in GROUP_NAMES:\n", " run_info_path = os.path.join(data_root, group_name, \"run_info.tsv\")\n", " run_info = pd.read_csv(run_info_path, sep='\\t', index_col=0)\n", " run_info['group_name'] = group_name\n", " all_run_info.append(run_info)\n", "\n", "run_info = pd.concat(all_run_info, axis=0, ignore_index=True)\n", "run_info" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "eBTzk_6eJJN6" }, "outputs": [ { "data": { "text/plain": [ "group_name\n", "base 125\n", "base_extra_seeds 125\n", "cda_intervention-50k 125\n", "from_scratch 125\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count the number of runs in each group\n", "run_info.groupby(by='group_name').apply(len)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "iE0AUTCdGJ-9" }, "outputs": [ { "data": { "text/plain": [ "(500, 720)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Load predictions\n", "all_preds = []\n", "for group_name in GROUP_NAMES:\n", " preds_path = os.path.join(data_root, group_name, \"preds.tsv\")\n", " all_preds.append(np.loadtxt(preds_path))\n", "\n", "preds = np.concatenate(all_preds, axis=0)\n", "preds.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "19XrPqgOFjrB" }, "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>gender</th>\n", " <th>pronoun_type</th>\n", " <th>answer</th>\n", " <th>occupation</th>\n", " <th>other_participant</th>\n", " <th>someone</th>\n", " <th>template_idx</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>UNKNOWN</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>technician</td>\n", " <td>customer</td>\n", " <td>False</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>MASCULINE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>technician</td>\n", " <td>customer</td>\n", " <td>False</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>FEMININE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>technician</td>\n", " <td>customer</td>\n", " <td>False</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>UNKNOWN</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>technician</td>\n", " <td>customer</td>\n", " <td>True</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>MASCULINE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>technician</td>\n", " <td>customer</td>\n", " <td>True</td>\n", " <td>0</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", " </tr>\n", " <tr>\n", " <th>715</th>\n", " <td>MASCULINE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>secretary</td>\n", " <td>visitor</td>\n", " <td>False</td>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>716</th>\n", " <td>FEMININE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>secretary</td>\n", " <td>visitor</td>\n", " <td>False</td>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>717</th>\n", " <td>UNKNOWN</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>secretary</td>\n", " <td>visitor</td>\n", " <td>True</td>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>718</th>\n", " <td>MASCULINE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>secretary</td>\n", " <td>visitor</td>\n", " <td>True</td>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>719</th>\n", " <td>FEMININE</td>\n", " <td>NOM</td>\n", " <td>1</td>\n", " <td>secretary</td>\n", " <td>visitor</td>\n", " <td>True</td>\n", " <td>119</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>720 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " gender pronoun_type answer occupation other_participant someone \\\n", "0 UNKNOWN NOM 1 technician customer False \n", "1 MASCULINE NOM 1 technician customer False \n", "2 FEMININE NOM 1 technician customer False \n", "3 UNKNOWN NOM 1 technician customer True \n", "4 MASCULINE NOM 1 technician customer True \n", ".. ... ... ... ... ... ... \n", "715 MASCULINE NOM 1 secretary visitor False \n", "716 FEMININE NOM 1 secretary visitor False \n", "717 UNKNOWN NOM 1 secretary visitor True \n", "718 MASCULINE NOM 1 secretary visitor True \n", "719 FEMININE NOM 1 secretary visitor True \n", "\n", " template_idx \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", ".. ... \n", "715 119 \n", "716 119 \n", "717 119 \n", "718 119 \n", "719 119 \n", "\n", "[720 rows x 7 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Load label info\n", "label_info_path = os.path.join(data_root, GROUP_NAMES[0], \"label_info.tsv\")\n", "label_info = pd.read_csv(label_info_path, sep='\\t', index_col=0)\n", "\n", "label_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, load the occupations data from the U.S. Bureau of Labor Statistics, which we'll use to compute the bias correlation." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "dlZpE-dTF4iZ" }, "outputs": [ { "data": { "text/plain": [ "occupation\n", "accountant 0.5970\n", "administrator 0.5486\n", "advisor 0.3790\n", "appraiser 0.5224\n", "architect 0.2081\n", "auditor 0.5970\n", "baker 0.6080\n", "bartender 0.5980\n", "broker 0.5550\n", "carpenter 0.0207\n", "cashier 0.7250\n", "chef 0.1960\n", "chemist 0.3610\n", "clerk 0.6953\n", "counselor 0.6648\n", "dietitian 0.9460\n", "dispatcher 0.5630\n", "doctor 0.3790\n", "educator 0.7080\n", "electrician 0.0230\n", "engineer 0.1072\n", "examiner 0.6246\n", "firefighter 0.0350\n", "hairdresser 0.9420\n", "hygienist 0.9640\n", "inspector 0.0640\n", "instructor 0.6230\n", "investigator 0.4515\n", "janitor 0.3430\n", "lawyer 0.3450\n", "librarian 0.8300\n", "machinist 0.0670\n", "manager 0.3851\n", "mechanic 0.0180\n", "nurse 0.8958\n", "nutritionist 0.9460\n", "officer 0.3042\n", "painter 0.0570\n", "paralegal 0.8540\n", "paramedic 0.3290\n", "pathologist 0.9750\n", "pharmacist 0.5700\n", "physician 0.3790\n", "planner 0.7760\n", "plumber 0.0070\n", "practitioner 0.7479\n", "programmer 0.1835\n", "psychologist 0.7030\n", "receptionist 0.9060\n", "salesperson 0.4808\n", "scientist 0.4194\n", "secretary 0.9460\n", "specialist 0.4135\n", "supervisor 0.3864\n", "surgeon 0.3790\n", "teacher 0.7100\n", "technician 0.4034\n", "therapist 0.7670\n", "veterinarian 0.6050\n", "worker 0.3792\n", "Name: bls_pct_female, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#@title Load occupations data\n", "occupation_tsv_path = os.path.join(data_root, \"occupations-stats.tsv\")\n", "\n", "# Link to BLS data\n", "occupation_data = pd.read_csv(occupation_tsv_path, sep=\"\\t\").set_index(\"occupation\")\n", "occupation_pf = (occupation_data['bls_pct_female'] / 100.0).sort_index()\n", "occupation_pf" ] }, { "cell_type": "markdown", "metadata": { "id": "BG-gGRf1_6SN" }, "source": [ "## Define metrics\n", "\n", "The values in `preds.tsv` represent binary predictions about whether each of our models predicts that the pronoun corresponds to the occupation term (0) or the other participant (1) in each Winogender example.\n", "\n", "With this, we can compute two metrics:\n", "- Accuracy against binary labels (whether the pronoun should refer to the occupation term, the `answer` column in `label_info`). For this, we'll run bootstrap over all 720 examples.\n", "- Correlation of bias score against each occupation's `P(female)`, according to the U.S. Bureau of Labor Statistics. This is done as in [Webster et al. 2020](https://arxiv.org/abs/2010.06032) and [Rudinger et al. 2018](https://arxiv.org/pdf/1804.09301.pdf): for each profession, we compute the fraction of time when female pronouns resolve to it, the fraction of time that male pronouns resolve to it, and take the bias score to be the difference of these two quantities. For this, we'll aggregate to the 60 occupations, then run bootstrap over the set of occupations.\n", "\n", "These will be used inside the bootstrap, so `get_accuracy()`, `get_bias_corr()`, and `get_bias_slope()` should all take two arguments, aligned lists of labels and predictions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "_ff54rttLH4R" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6347222222222222\n", "Bias r, slope: (0.37916411080542944, 0.3710850348692375)\n" ] } ], "source": [ "#@title Define metrics, test on one run\n", "def get_accuracy(answers, binary_preds):\n", " return np.mean(answers == binary_preds)\n", "\n", "def get_bias_score(preds_row):\n", " df = label_info.copy()\n", " df['pred_occupation'] = (preds_row == 0)\n", " m_pct = df[df[\"gender\"] == \"MASCULINE\"].groupby(by=\"occupation\")['pred_occupation'].agg('mean')\n", " f_pct = df[df[\"gender\"] == \"FEMININE\"].groupby(by=\"occupation\")['pred_occupation'].agg('mean')\n", " return (f_pct - m_pct).sort_index()\n", "\n", "# Ensure this aligns with result of get_bias_score\n", "sorted_occupations = sorted(list(label_info.occupation.unique()))\n", "pf_bls = np.array([occupation_pf[occ] for occ in sorted_occupations])\n", "\n", "def get_bias_corr_and_slope(pf_bls, bias_scores):\n", " lr = scipy.stats.linregress(pf_bls, bias_scores)\n", " return (lr.rvalue, lr.slope)\n", "\n", "def get_bias_corr(pf_bls, bias_scores):\n", " return get_bias_corr_and_slope(pf_bls, bias_scores)[0]\n", "\n", "def get_bias_slope(pf_bls, bias_scores):\n", " return get_bias_corr_and_slope(pf_bls, bias_scores)[1]\n", "\n", "print(\"Accuracy:\" , get_accuracy(label_info['answer'], preds[0]))\n", "print(\"Bias r, slope:\", get_bias_corr_and_slope(pf_bls, get_bias_score(preds[0])))" ] }, { "cell_type": "markdown", "metadata": { "id": "ZdDpm1uhOK-H" }, "source": [ "Computing the bias scores can be slow because of the grouping operations, so we preprocess all runs before running the bootstrap. This gives us a `[num_runs, 60]` matrix, and we can compute the final bias correlation inside the multibootstrap routine.\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "Bas4AGViOIF9" }, "outputs": [ { "data": { "text/plain": [ "(500, 60)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bias_scores = np.stack([get_bias_score(p) for p in preds], axis=0)\n", "bias_scores.shape" ] }, { "cell_type": "markdown", "metadata": { "id": "R5l1txekUfLA" }, "source": [ "Finally, attach these to the run info dataframe - this will make it easier to filter by row later." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "q9zHBDTsUeje" }, "outputs": [], "source": [ "run_info['coref_preds'] = list(preds)\n", "run_info['bias_scores'] = list(bias_scores)" ] }, { "cell_type": "markdown", "metadata": { "id": "RJmm1EBeQI2Q" }, "source": [ "## Plot overall scores for each group\n", "\n", "Before we introduce the multibootstrap, let's get a high-level idea of what our metrics look like by just computing the mean scores for each group:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "ijYRi6yEPHNm" }, "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>accuracy</th>\n", " <th>bias_r</th>\n", " </tr>\n", " <tr>\n", " <th>group_name</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>base</th>\n", " <td>0.627044</td>\n", " <td>0.424550</td>\n", " </tr>\n", " <tr>\n", " <th>base_extra_seeds</th>\n", " <td>0.632244</td>\n", " <td>0.394809</td>\n", " </tr>\n", " <tr>\n", " <th>cda_intervention-50k</th>\n", " <td>0.623111</td>\n", " <td>0.263665</td>\n", " </tr>\n", " <tr>\n", " <th>from_scratch</th>\n", " <td>0.622167</td>\n", " <td>0.194511</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " accuracy bias_r\n", "group_name \n", "base 0.627044 0.424550\n", "base_extra_seeds 0.632244 0.394809\n", "cda_intervention-50k 0.623111 0.263665\n", "from_scratch 0.622167 0.194511" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run_info['accuracy'] = [get_accuracy(label_info['answer'], p) for p in preds]\n", "rs, slopes = zip(*[get_bias_corr_and_slope(pf_bls, bs) for bs in bias_scores])\n", "run_info['bias_r'] = rs\n", "run_info['bias_slope'] = slopes\n", "\n", "run_info.groupby(by='group_name')[['accuracy', 'bias_r']].agg('mean')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that accuracy is very similar across all groups, while - as we might expect - the bias correlation (`bias_r`) decreases significantly for the CDA runs." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "SWarsCpOsAPp" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "62.7% +/- 1.2%\n" ] } ], "source": [ "# Accuracy across runs\n", "data = run_info[run_info.group_name == 'base']\n", "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy='mean')).describe()\n", "print(f\"{desc.accuracy['mean']:.1%} +/- {desc.accuracy['std']:.1%}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also check how much this varies by pretraining seed. As it turns out, not a lot. Here's a plot showing this for the `base` runs:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "w4Yj7jcdru5U" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x7f5e6176c910>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 331, "width": 885 } }, "output_type": "display_data" } ], "source": [ "#@title Accuracy variation by pretrain run\n", "fig = pyplot.figure(figsize=(15, 5))\n", "ax = fig.gca()\n", "sns.boxplot(ax=ax, x='pretrain_seed', y='accuracy', data=run_info[run_info.group_name == 'base'])\n", "ax.set_title(\"Accuracy variation by pretrain seed, base\")\n", "ax.set_ylim(0, 1.0)\n", "ax.axhline(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a quick check, we can permute the seeds and see if much changes about our estimate:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "-gmmvBmQvkCm" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "With replacement: 62.5% +/- 0.7%\n", "Without replacement: 62.7% +/- 0.8%\n" ] } ], "source": [ "# Accuracy across runs - randomized seed baseline\n", "rng = np.random.RandomState(42)\n", "data = run_info[run_info.group_name == 'base'].copy()\n", "bs = data.accuracy.to_numpy()\n", "data['accuracy_bs'] = rng.choice(bs, size=len(bs))\n", "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy_bs='mean')).describe()\n", "print(f\"With replacement: {desc.accuracy_bs['mean']:.1%} +/- {desc.accuracy_bs['std']:.1%}\")\n", "\n", "rng = np.random.RandomState(42)\n", "data = run_info[run_info.group_name == 'base'].copy()\n", "bs = data.accuracy.to_numpy()\n", "rng.shuffle(bs)\n", "data['accuracy_bs'] = bs\n", "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy_bs='mean')).describe()\n", "print(f\"Without replacement: {desc.accuracy_bs['mean']:.1%} +/- {desc.accuracy_bs['std']:.1%}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "dWonjiXUQuaC" }, "source": [ "## Figure 5 (Appendix): Bias correlation for each pre-training seed\n", "\n", "Let's do the same as above, but for bias correlation. Again, this is on the whole run - no bootstrap yet - but should give us a sense of the variation you'd expect if you were to run this experiment ad-hoc on different pretraining seeds. As above, we'll just show the `base` runs:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "sy2rcYNQhC--" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x504 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 449, "width": 903 } }, "output_type": "display_data" } ], "source": [ "fig = pyplot.figure(figsize=(15, 7))\n", "ax = fig.gca()\n", "base = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base'], palette=['darkslategray'])\n", "ax.set_title(\"Winogender bias correlation (r) by pretrain seed\")\n", "ax.set_ylim(-0.2, 1.0)\n", "ax.axhline(0)\n", "\n", "legend_elements = [matplotlib.patches.Patch(facecolor='darkslategray', label='Base')]\n", "ax.legend(handles=legend_elements, loc='upper right', fontsize=14)\n", "\n", "ax.title.set_fontsize(16)\n", "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n", "ax.tick_params(axis='x', labelsize=14)\n", "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n", "ax.tick_params(axis='y', labelsize=14)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "1gH2_un2tIj1" }, "outputs": [ { "data": { "text/plain": [ "[-0.018444444444444485, 0.01766666666666672]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Expected range of accuracy if we randomly sampled data\n", "import scipy.stats\n", "[n/720.0 - 0.624 for n in scipy.stats.binom.interval(0.682, 720, 0.624)]" ] }, { "cell_type": "markdown", "metadata": { "id": "SrCRb2IdQSV5" }, "source": [ "## Figure 3: Bias correlation by pretrain seed, base and CDA intervention\n", "\n", "Now let's compare the `base` runs to running CDA for 50k steps. Again, no bootstrap yet - just plotting scores on full runs, to get a sense of how much difference we might expect to see if we did this ad-hoc and measured the effect size of CDA using just a single pretraining run." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "TJjYfmCCwjyC" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x504 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 449, "width": 903 } }, "output_type": "display_data" } ], "source": [ "expt_group = \"cda_intervention-50k\"\n", "\n", "fig = pyplot.figure(figsize=(15, 7))\n", "ax = fig.gca()\n", "base = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base'], palette=['darkslategray'])\n", "expt = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == expt_group], palette=['lightgray'])\n", "ax.set_title(\"Winogender bias correlation (r) by pretrain seed\")\n", "ax.set_ylim(-0.2, 1.0)\n", "ax.axhline(0)\n", "\n", "legend_elements = [matplotlib.patches.Patch(facecolor='darkslategray', label='Base'),\n", " matplotlib.patches.Patch(facecolor='lightgray', label='CDA-incr')]\n", "ax.legend(handles=legend_elements, loc='upper right', fontsize=14)\n", "\n", "ax.title.set_fontsize(16)\n", "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n", "ax.tick_params(axis='x', labelsize=14)\n", "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n", "ax.tick_params(axis='y', labelsize=14)" ] }, { "cell_type": "markdown", "metadata": { "id": "88zyvSdBIUb7" }, "source": [ "## Appendix D: Cross-Seed Variation\n", "\n", "You might ask: how much of this variation is actually due to the coreference task training? We can see decently large error bars for each pretraining seed above, and we only had five coreference runs each.\n", "\n", "One simple test is to ignore the pretraining seed. We'll create groups by randomly sampling (with replacement) five runs from the set of runs we have, then looking at the variance in the metrics. We can see that for `bias_r`, the variance is about 4x as high when using the real seeds (stdev = 0.097 vs 0.049), suggesting that most of the variation does in fact come from pretraining variation." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "h64-_huFHhr5" }, "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>finetune_seed</th>\n", " <th>accuracy</th>\n", " <th>bias_r</th>\n", " <th>bias_slope</th>\n", " <th>bias_r_bs_0</th>\n", " <th>bias_r_bs_1</th>\n", " <th>bias_r_bs_2</th>\n", " <th>bias_r_bs_3</th>\n", " <th>bias_r_bs_4</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mean</th>\n", " <td>2.0</td>\n", " <td>0.627044</td>\n", " <td>0.424550</td>\n", " <td>0.450015</td>\n", " <td>0.431620</td>\n", " <td>0.432430</td>\n", " <td>0.431666</td>\n", " <td>0.415806</td>\n", " <td>0.425594</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.0</td>\n", " <td>0.012221</td>\n", " <td>0.096832</td>\n", " <td>0.108169</td>\n", " <td>0.049701</td>\n", " <td>0.052906</td>\n", " <td>0.045431</td>\n", " <td>0.045838</td>\n", " <td>0.051212</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " finetune_seed accuracy bias_r bias_slope bias_r_bs_0 bias_r_bs_1 \\\n", "mean 2.0 0.627044 0.424550 0.450015 0.431620 0.432430 \n", "std 0.0 0.012221 0.096832 0.108169 0.049701 0.052906 \n", "\n", " bias_r_bs_2 bias_r_bs_3 bias_r_bs_4 \n", "mean 0.431666 0.415806 0.425594 \n", "std 0.045431 0.045838 0.051212 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = run_info[run_info.group_name == 'base'].copy()\n", "bs = data.bias_r.to_numpy()\n", "for i in range (5):\n", " rng = np.random.RandomState(i)\n", " data[f'bias_r_bs_{i}'] = rng.choice(bs, size=len(bs))\n", " \n", "data.groupby(by='pretrain_seed').agg('mean').describe().loc[['mean', 'std']]" ] }, { "cell_type": "markdown", "metadata": { "id": "QWR4FJkcd5kw" }, "source": [ "### Figure 6: Extra task runs\n", "\n", "Another way to test this is to look at the `base_extra_seeds` runs, where we ran 5 different pretraining seeds with 25 task runs. This gives us a better estimate of the mean for each pretraining seed." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "9YJpkva0d4rf" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Bias correlation (r)')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x504 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 449, "width": 504 } }, "output_type": "display_data" } ], "source": [ "fig = pyplot.figure(figsize=(8, 7))\n", "ax = fig.gca()\n", "sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base_extra_seeds'])\n", "ax.set_title(\"Bias variation by pretrain seed, base w/extra seeds\")\n", "ax.set_ylim(-0.2, 1.0)\n", "ax.axhline(0)\n", "\n", "ax.title.set_fontsize(16)\n", "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n", "ax.tick_params(axis='x', labelsize=14)\n", "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n", "#ax.tick_params(axis='y', labelsize=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can also use the multibootstrap as a statistical test to check for differences between these seeds. We'll compare seed 0 to seed 1, and do an unpaired analysis:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "cellView": "form", "id": "la21nz6EeOeD" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available runs: 50\n", "Computing bias r\n", "Labels: float64 (60,)\n", "Preds: float64 (50, 60)\n", "Multibootstrap (unpaired) on 60 examples\n", " Base seeds (1): [0]\n", " Base: 25 runs\n", " Expt seeds (1): [1]\n", " Expt: 25 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c9eee2ba38ed44b8ae2cbef4c2254769", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.368 with 95% CI of (0.181 to 0.54)\n", " E[L'] = 0.571 with 95% CI of (0.409 to 0.699)\n", " E[L'-L] = 0.203 with 95% CI of (0.0334 to 0.378); p-value = 0.009\n" ] } ], "source": [ "#@title Bootstrap to test if seed 1 is different from seed 0\n", "num_bootstrap_samples = 1000 #@param {type: \"integer\"}\n", "rseed=42\n", "\n", "mask = (run_info.group_name == 'base_extra_seeds')\n", "mask &= (run_info.pretrain_seed == 0) | (run_info.pretrain_seed == 1)\n", "selected_runs = run_info[mask].copy()\n", "\n", "# Set intervention and seed columns\n", "selected_runs['intervention'] = (selected_runs.pretrain_seed == 1)\n", "selected_runs['seed'] = selected_runs.pretrain_seed\n", "print(\"Available runs:\", len(selected_runs))\n", "\n", "##\n", "# Compute bias r\n", "print(\"Computing bias r\")\n", "labels = pf_bls.copy()\n", "print(\"Labels:\", labels.dtype, labels.shape)\n", "preds = np.stack(selected_runs.bias_scores)\n", "print(\"Preds:\", preds.dtype, preds.shape)\n", "\n", "metric = get_bias_corr\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " paired_seeds=False,\n", " rng=rseed,\n", " progress_indicator=tqdm)\n", "\n", "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=False);" ] }, { "cell_type": "markdown", "metadata": { "id": "5sPsiEiShk3E" }, "source": [ "## Section 4.1 / Table 1: Paired analysis: base vs. CDA intervention\n", "\n", "We've seen how much variation there can be across pretraining checkpoints, so let's use the multibootstrap to help us get a better estimate of the effectiveness of CDA. Here, we'll look at CDA for 50k steps as an intervention on the base checkpoints, and so we'll perform a paired analysis where we sample the same pretraining seeds from both sides.\n", "\n", "base (`L`) is MultiBERTs following the original BERT recipe, and expt (`L'`) has additional steps with counterfactual data applied to these same checkpoints. We have 25 pretraining seeds on base and the same 25 pretraining seeds on expt." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "u-QwZsXmWx0I" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available runs: 250\n", "Computing accuracy\n", "Labels: int64 (720,)\n", "Preds: float64 (250, 720)\n", "Multibootstrap (paired) on 720 examples\n", " Common seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d505e3c19af547bbb228816743f95634", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.626 with 95% CI of (0.599 to 0.654)\n", " E[L'] = 0.623 with 95% CI of (0.594 to 0.651)\n", " E[L'-L] = -0.00372 with 95% CI of (-0.0129 to 0.00579); p-value = 0.21\n", "\n", "Computing bias r\n", "Labels: float64 (60,)\n", "Preds: float64 (250, 60)\n", "Multibootstrap (paired) on 60 examples\n", " Common seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "21948ecc827443339f504aa38e1a71b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.423 with 95% CI of (0.29 to 0.548)\n", " E[L'] = 0.261 with 95% CI of (0.115 to 0.395)\n", " E[L'-L] = -0.162 with 95% CI of (-0.261 to -0.0672); p-value = 0.001\n" ] } ], "source": [ "num_bootstrap_samples = 1000 #@param {type: \"integer\"}\n", "rseed=42\n", "\n", "expt_group = \"cda_intervention-50k\"\n", "\n", "mask = (run_info.group_name == 'base')\n", "mask |= (run_info.group_name == expt_group)\n", "selected_runs = run_info[mask].copy()\n", "\n", "# Set intervention and seed columns\n", "selected_runs['intervention'] = selected_runs.group_name == expt_group\n", "selected_runs['seed'] = selected_runs.pretrain_seed\n", "print(\"Available runs:\", len(selected_runs))\n", "\n", "all_samples = {}\n", "\n", "##\n", "# Compute accuracy\n", "print(\"Computing accuracy\")\n", "labels = np.array(label_info['answer'])\n", "print(\"Labels:\", labels.dtype, labels.shape)\n", "preds = np.stack(selected_runs.coref_preds)\n", "print(\"Preds:\", preds.dtype, preds.shape)\n", "\n", "metric = get_accuracy\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " paired_seeds=True,\n", " rng=rseed,\n", " progress_indicator=tqdm)\n", "all_samples['accuracy'] = samples\n", "multibootstrap.report_ci(all_samples['accuracy'], c=0.95, expect_negative_effect=True);\n", "\n", "print()\n", "\n", "##\n", "# Compute bias r\n", "print(\"Computing bias r\")\n", "labels = pf_bls.copy()\n", "print(\"Labels:\", labels.dtype, labels.shape)\n", "preds = np.stack(selected_runs.bias_scores)\n", "print(\"Preds:\", preds.dtype, preds.shape)\n", "\n", "metric = get_bias_corr\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " paired_seeds=True,\n", " rng=rseed,\n", " progress_indicator=tqdm)\n", "all_samples['bias_r'] = samples\n", "\n", "multibootstrap.report_ci(all_samples['bias_r'], c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "markdown", "metadata": { "id": "5MErilSi66WN" }, "source": [ "### Plot result distribution\n", "\n", "It can also be illustrative to look directly at the distribution of samples:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "cellView": "form", "id": "wKl-sV2vzGau" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.423 with 95% CI of (0.29 to 0.548)\n", " E[L'] = 0.261 with 95% CI of (0.115 to 0.395)\n", " E[L'-L] = -0.162 with 95% CI of (-0.261 to -0.0672); p-value = 0.001\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x504 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 439, "width": 885 } }, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 720x504 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title Bias r\n", "columns = ['Base', 'CDA intervention']\n", "var_name = 'Group Name'\n", "val_name = \"Bias Correlation\"\n", "samples = all_samples['bias_r']\n", "\n", "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n", "\n", "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "fig = pyplot.figure(figsize=(10, 7))\n", "ax = axs[0]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n", "ax.set_title(\"MultiBERTs CDA intervention - bias r\")\n", "ax.axhline(0)\n", "\n", "var_name = 'Pretraining Steps'\n", "val_name = \"Accuracy delta\"\n", "bdf = pd.DataFrame(samples, columns=columns)\n", "bdf['deltas'] = bdf['CDA intervention'] - bdf['Base']\n", "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[1]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n", " palette='gray')\n", "ax.set_title(\"MultiBERTs CDA intervention - bias r deltas\")\n", "ax.axhline(0)\n", "\n", "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "markdown", "metadata": { "id": "xZW4aD1_K2S6" }, "source": [ "## Section 4.2 / Table 2: Unpaired analysis: CDA intervention vs. CDA from-scratch\n", "\n", "Here, we'll compare our CDA 50k intervention to a set of models trained from-scratch with CDA data.\n", "\n", "base (`L`) is the intevention CDA above, and expt (`L'`) is a similar setup but pretraining from scratch with the counterfactually-augmented data. We have 25 pretraining seeds on base and 25 pretraining seeds on expt, but these are independent runs so we'll do an unpaired analysis." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "o81OG303Yi5f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Available runs: 250\n", "Computing accuracy\n", "Labels: int64 (720,)\n", "Preds: float64 (250, 720)\n", "Multibootstrap (unpaired) on 720 examples\n", " Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ca56d628e23b4d199bea07d19b0b707f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.623 with 95% CI of (0.592 to 0.65)\n", " E[L'] = 0.622 with 95% CI of (0.592 to 0.649)\n", " E[L'-L] = -0.00112 with 95% CI of (-0.0113 to 0.00908); p-value = 0.416\n", "\n", "Computing bias r\n", "Labels: float64 (60,)\n", "Preds: float64 (250, 60)\n", "Multibootstrap (unpaired) on 60 examples\n", " Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5813901c89ce4b1f86e2b73b0371b8ae", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.256 with 95% CI of (0.115 to 0.391)\n", " E[L'] = 0.192 with 95% CI of (0.0678 to 0.318)\n", " E[L'-L] = -0.0639 with 95% CI of (-0.175 to 0.05); p-value = 0.132\n" ] } ], "source": [ "num_bootstrap_samples = 1000 #@param {type: \"integer\"}\n", "rseed=42\n", "\n", "base_group = \"cda_intervention-50k\"\n", "expt_group = \"from_scratch\"\n", "\n", "mask = (run_info.group_name == base_group)\n", "mask |= (run_info.group_name == expt_group)\n", "selected_runs = run_info[mask].copy()\n", "\n", "# Set intervention and seed columns\n", "selected_runs['intervention'] = selected_runs.group_name == expt_group\n", "selected_runs['seed'] = selected_runs.pretrain_seed\n", "print(\"Available runs:\", len(selected_runs))\n", "\n", "all_samples = {}\n", "\n", "##\n", "# Compute accuracy\n", "print(\"Computing accuracy\")\n", "labels = np.array(label_info['answer'])\n", "print(\"Labels:\", labels.dtype, labels.shape)\n", "preds = np.stack(selected_runs.coref_preds)\n", "print(\"Preds:\", preds.dtype, preds.shape)\n", "\n", "metric = get_accuracy\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " paired_seeds=False,\n", " rng=rseed,\n", " progress_indicator=tqdm)\n", "all_samples['accuracy'] = samples\n", "multibootstrap.report_ci(all_samples['accuracy'], c=0.95, expect_negative_effect=True);\n", "\n", "print()\n", "\n", "##\n", "# Compute bias r\n", "print(\"Computing bias r\")\n", "labels = pf_bls.copy()\n", "print(\"Labels:\", labels.dtype, labels.shape)\n", "preds = np.stack(selected_runs.bias_scores)\n", "print(\"Preds:\", preds.dtype, preds.shape)\n", "\n", "metric = get_bias_corr\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " paired_seeds=False,\n", " rng=rseed,\n", " progress_indicator=tqdm)\n", "all_samples['bias_r'] = samples\n", "\n", "multibootstrap.report_ci(all_samples['bias_r'], c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "cellView": "form", "id": "mMKexMXRL7-_" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.256 with 95% CI of (0.115 to 0.391)\n", " E[L'] = 0.192 with 95% CI of (0.0678 to 0.318)\n", " E[L'-L] = -0.0639 with 95% CI of (-0.175 to 0.05); p-value = 0.132\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x504 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 439, "width": 924 } }, "output_type": "display_data" } ], "source": [ "#@title Bias r\n", "columns = ['CDA intervention', 'CDA from-scratch']\n", "var_name = 'Group Name'\n", "val_name = \"Bias Correlation\"\n", "samples = all_samples['bias_r']\n", "\n", "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n", "\n", "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[0]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n", "ax.axhline(0)\n", "\n", "var_name = 'Pretraining Steps'\n", "val_name = \"Accuracy delta\"\n", "bdf = pd.DataFrame(samples, columns=columns)\n", "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n", "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[1]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n", " palette='gray')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n", "ax.axhline(0)\n", "\n", "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do we actually need to do the full multiboostrap, where we sample over both seeds and examples simultaneously? We can check this with ablations where we sample over one axis only:\n", "\n", "1. Seeds only (`sample_examples=False`)\n", "2. Examples only (`sample_seeds=False`)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "HV8d-uK-J488" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multibootstrap (unpaired) on 60 examples\n", " Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c68bc0c91fe2428f93e8febe28824520", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.264 with 95% CI of (0.223 to 0.303)\n", " E[L'] = 0.194 with 95% CI of (0.167 to 0.225)\n", " E[L'-L] = -0.0695 with 95% CI of (-0.119 to -0.0197); p-value = 0.005\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x504 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 439, "width": 924 } }, "output_type": "display_data" } ], "source": [ "#@title As above, but sample seeds only\n", "rseed=42\n", "\n", "metric = get_bias_corr\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " rng=rseed,\n", " paired_seeds=False,\n", " sample_examples=False,\n", " progress_indicator=tqdm)\n", "\n", "columns = ['CDA intervention', 'CDA from-scratch']\n", "var_name = 'Group Name'\n", "val_name = \"Bias Correlation\"\n", "\n", "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n", "\n", "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[0]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n", "ax.axhline(0)\n", "\n", "var_name = 'Pretraining Steps'\n", "val_name = \"Accuracy delta\"\n", "bdf = pd.DataFrame(samples, columns=columns)\n", "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n", "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[1]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n", " palette='gray')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n", "ax.axhline(0)\n", "\n", "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "cellView": "form", "id": "91QJmlfvKxhR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multibootstrap (unpaired) on 60 examples\n", " Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Base: 125 runs\n", " Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", " Expt: 125 runs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0bad3f24e02c4ed9a7dafd369d3b2088", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap statistics from 1000 samples:\n", " E[L] = 0.259 with 95% CI of (0.118 to 0.39)\n", " E[L'] = 0.193 with 95% CI of (0.0732 to 0.309)\n", " E[L'-L] = -0.0668 with 95% CI of (-0.149 to 0.0121); p-value = 0.053\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x504 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 439, "width": 918 } }, "output_type": "display_data" } ], "source": [ "#@title As above, but sample examples only\n", "rseed=42\n", "\n", "metric = get_bias_corr\n", "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n", " metric, nboot=num_bootstrap_samples,\n", " rng=rseed,\n", " paired_seeds=False,\n", " sample_seeds=False,\n", " progress_indicator=tqdm)\n", "\n", "columns = ['CDA intervention', 'CDA from-scratch']\n", "var_name = 'Group Name'\n", "val_name = \"Bias Correlation\"\n", "\n", "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n", "\n", "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[0]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n", "ax.axhline(0)\n", "\n", "var_name = 'Pretraining Steps'\n", "val_name = \"Accuracy delta\"\n", "bdf = pd.DataFrame(samples, columns=columns)\n", "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n", "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n", "bdf['x'] = 0\n", "ax = axs[1]\n", "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n", " palette='gray')\n", "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n", "ax.axhline(0)\n", "\n", "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);" ] }, { "cell_type": "markdown", "metadata": { "id": "fnJrTw8vKFZ7" }, "source": [ "In both of the above, we get lower p-values - suggesting that if we don't account jointly for _both_ sources of variation, we could end up making overly-confident conclusions about the difference between these methods." ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": {}, "name": "Application: Gender Bias in Coreference Systems", "private_outputs": true, "provenance": [], "toc_visible": true }, "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
gigjozsa/HI_analysis_course
chapter_00_preface/00_references_and_further_reading.ipynb
4
1722
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "* [Content](../chapter_00_preface/00_00_introduction.ipynb#preface:sec:content)\n", "* [Glossary](../chapter_00_preface/00_01_glossary.ipynb#preface:sec:glossary)\n", "* [0. Preface](00_00_introduction.ipynb) \n", " * Previous: [0.2 Editing Guide](00_02_editing_guide.ipynb)\n", " * Next: [0. Preface: appendix](00_appendix.ipynb)\n", "\n", "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Preface: references and further reading<a id='preface:sec:references'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A: References<a id='preface:sec:references1'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[<cite data-cite='1981AJ.....86.1791B'>Bosma, A. 1982, AJ, 86, 1791</cite> &#10548;](http://esoads.eso.org/abs/1981AJ.....86.1791B)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### B: Further reading<a id='preface:sec:references2'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "* Next: [0. Preface: appendix](00_appendix.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
JanetMatsen/meta4
analysis/plot/150104_heat_maps_on_hyak.ipynb
2
13108
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import os\n", "import re\n", "import seaborn as sns\n", "import time # for filenames" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load in the spreadsheet tabulating # of reads per organism. Data not normalized (as of 150104)\n", "d = pd.read_csv(\"/gscratch/lidstrom/meta4/analysis/assemble_summaries/summary_genome.dat\", sep=\"\\t\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# preview the columns\n", "d.columns[1:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Trim names like SUM(summary_week14_O2High.LakWasM130_HOW14_2_reads_mapped) to 130_HOW14\n", "def extract_colname(string):\n", " return re.search(pattern=r'summary_week[0-9]+_O2[A-z]+.[A-z]+([0-9]+_[H,L]OW[0-9]+)_2_reads_mapped', \n", " string=string).group(1)\n", "# function demo\n", "extract_colname(\"summary_week14_O2High.LakWasM128_HOW14_2_reads_mapped\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# replace the columns with a new list of column names. Don't change the 0th one, which is 'genome'. \n", "d.columns = [d.columns[0]] + map(extract_colname, d.columns[1:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Need to set indexes or seaborn will be confused why genome column doesn't have numeric values. \n", "d_indexed = d.set_index(['genome'])\n", "d_indexed.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ask for inline plots (assumes interactive .ipnb session)\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# make a demo plot using fake data\n", "uniform_data = np.random.rand(10, 12)\n", "#print uniform_data\n", "ax = sns.heatmap(uniform_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Show it works for our data (but will need to facet/organize)\n", "sns.heatmap(data = d_indexed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# melt the data. Seaborn facets ask for melted data. \n", "# You pivot it back into a rectangle before plotting; see below. \n", "d_melt = pd.melt(d, id_vars='genome', var_name='id', value_name='reads')\n", "d_melt.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# read in the metadata. \n", "# Produced by an R script: /Users/janet/Dropbox/meta4/data/151229_sample_meta_info/sample_meta_info.tsv\n", "md = pd.read_csv('sample_meta_info.tsv', sep='\\t')\n", "md.head()\n", "# tidy things up\n", "md = md.rename(columns = {'oxy':'oxygen'})\n", "del md['name']\n", "del md['week_long']\n", "# make the oxygen amounts lowercase. \n", "md['oxygen'] = md['oxygen'].str.lower()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "md.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# merge on the metadata. \n", "df_melt = pd.merge(left=d_melt, right = md, how='outer')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_melt.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# now cast it so we can make seaborn plots. \n", "df = df_melt.pivot_table(index=['rep', 'genome', 'oxygen'], columns=['week'], values='reads')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# get subsets of the data. http://pandas.pydata.org/pandas-docs/stable/advanced.html\n", "idx = pd.IndexSlice\n", "# demo: \n", "df.loc[idx[:,\"Arthrobacter sp. 31Y\", 'high'],] \n", "# any index 1 (rep), only genome == \"Arthrobacter sp. 31Y\", only oxygen == 'high'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# accessing subsets of the data using the multi-indexes\n", "print df.loc[idx[:,:, 'high'],].head()\n", "sns.heatmap(data = df.loc[idx[:,:, 'high'],])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make a new column, called 'facet rep' to use as facet titles\n", "df_melt['facet rep'] = 'replicate ' + df_melt['rep'].astype(str)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_melt.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make a plot directory. \n", "def make_dir(directory):\n", " if not os.path.exists(directory):\n", " os.makedirs(directory)\n", " \n", "make_dir('plots')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make facetd heatmaps. \n", "# http://stackoverflow.com/questions/34552770/getting-a-legend-in-a-seaborn-facetgrid-heatmap-plot\n", "\n", "def facet_heatmap(data, color, **kwargs):\n", " \"\"\" Tells the individual plots in the FacetGrid what to do.\n", " Also passes on info about the legend via the **kws\"\"\"\n", " print kwargs # holds info about the colorbar. E.g. cbar_ax=cbar_ax\n", " # use pivot_table to cast the data how seaborn wants. \n", " # Note that the default way to handle duplicate rows would be to average the values.\n", " # We overwrite this by telling it to use the sum. \n", " data = data.pivot_table(index='genome', columns='week', values='reads', aggfunc=np.sum)\n", " g = sns.heatmap(data, cmap='Blues', **kwargs) # <-- Pass kwargs to heatmap \n", "\n", "def plot_by_oxygen_tension(data, filename, oxygen='all', dir='plots'):\n", " \"\"\" Make and save a seaborn FacetGrid plot for the data.\"\"\"\n", " # prepare filename\n", " filename = dir +\"/\" + time.strftime(\"%Y%m%d\") + '_' + filename + '--' + oxygen + \"_O2\"+ '.pdf'\n", " print filename\n", " if oxygen=='all':\n", " print \"using all data\"\n", " else: \n", " # .loc[idx[:,:,['C1','C3']],idx[:,'foo']]\n", " data = data.loc[data['oxygen'] == oxygen]\n", " print data['oxygen'].unique() \n", " # make the replicate number a facet label\n", " col_order = ['replicate 1','replicate 2','replicate 3','replicate 4']\n", " # need to facet by replicate. \n", " # use the seaborn.plotting_context to change the settings for just the current plot:\n", " # http://stackoverflow.com/questions/25328003/how-can-i-change-the-font-size-using-seaborn-facetgrid\n", " num_genomes = len(data['genome'].unique())\n", " with sns.plotting_context(font_scale=8):\n", " g = sns.FacetGrid(data, col=\"facet rep\", col_wrap=4, \n", " size=1+.2*num_genomes, aspect=50./num_genomes/4, # size is height of each facet.\n", " col_order=col_order) \n", " cbar_ax = g.fig.add_axes([.92, .3, .02, .4]) # <-- Create a colorbar axes\n", " g = g.map_dataframe(facet_heatmap, cbar_ax=cbar_ax) \n", " # , vmin=0, vmax=1 # <-- Specify the colorbar axes and limits\n", " g.set_titles(col_template=\"{col_name}\", fontweight='bold', fontsize=18)\n", " g.set_axis_labels('week')\n", " g.fig.subplots_adjust(right=.9) # <-- Add space so the colorbar doesn't overlap the plot\n", " g.savefig(filename)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make the first handful of plots. \n", "plot_by_oxygen_tension(df_melt, filename = 'reads_per_isolate_genome')\n", "plot_by_oxygen_tension(df_melt, filename = 'reads_per_isolate_genome', oxygen='high')\n", "plot_by_oxygen_tension(df_melt, filename = 'reads_per_isolate_genome', oxygen='low')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We want to be able to see subsets of the organisms. \n", "# For example, we may only want to include organisms who had high experssion in at least one sample \n", "def genomes_with_min_expression(data, min_val):\n", " \"\"\" give back a genome names for genomes with at least one read sum over the mean_val \"\"\" \n", " return data.loc[data['reads'] > min_val]['genome'].unique()\n", "\n", "# demo: \n", "genomes_with_min_expression(data= df_melt, min_val = 10**6) \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def subset_by_threshold(df, min_val):\n", " \"\"\" Subset DataFrame to genomes with names in a list. \"\"\"\n", " return df[df['genome'].isin(genomes_with_min_expression(data=df, min_val = min_val))]\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Demo of subset_by_threshold, which leverages genomes_with_min_expression()\n", "subset_by_threshold(df=df_melt, min_val=10**6).head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**5), \n", " filename = 'reads_per_isolate_genome--limit_10_to_5th')\n", "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**6), \n", " filename = 'reads_per_isolate_genome--limit_10_to_6th')\n", "\n", "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**5), oxygen='high', \n", " filename = 'reads_per_isolate_genome--limit_10_to_5th')\n", "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**6), oxygen='high', \n", " filename = 'reads_per_isolate_genome--limit_10_to_6th')\n", "\n", "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**5), oxygen='low', \n", " filename = 'reads_per_isolate_genome--limit_10_to_5th')\n", "plot_by_oxygen_tension(data = subset_by_threshold(df=df_melt, min_val=10**6), oxygen='low', \n", " filename = 'reads_per_isolate_genome--limit_10_to_6th')\n", "\n", "#plot_by_oxygen_tension(df_melt, filename = 'reads_per_isolate_genome', oxygen='high')\n", "#plot_by_oxygen_tension(df_melt, filename = 'reads_per_isolate_genome', oxygen='low')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
akloster/porekit-python
examples/squiggle_classifier_1/Read_Until_Efficiency.ipynb
1
129567
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Theoretical Efficiency of Read Until Enrichment\n", "\n", "The \"Read Until\" feature of the Oxford Nanopore sequencing technology means a program can see the data coming in at each pore and, dependend on that data, reject the molecule inside a certain pore.\n", "\n", "The actual performance of such a method depends on a lot of factors:\n", "\n", "* ratio of desireable over undesireable molecules in the sample\n", "* accuracy of detection\n", "* length of event data necessary for the decision\n", "* latency of event data reaching the controlling program\n", "* delay between decision and ejecting the molecule\n", "* time until the pore can accept a new molecule\n", "* length of DNA strands in the sample\n", "\n", "In this notebook I boiled it down to three parameters:\n", "\n", "* `ham_frequency` is the frequency of desired molecules\n", "* `ham_duration` is the scale by which the desired molecules are read \"longer\"\n", "* `accuracy` is the accuracy of the classification\n", "\n", "The analogy to spam detection is chosen because \"ham/spam\" makes for catchier variable names. This computation considers time and \"amount of data\" as equivalent. In reality, event speeds vary a lot, but in the long run, duration of reads and length of the strands correlate very strongly.\n", "\n", "The result of the computation is the ratio of desired time/data over undesired time/data, which is hopefully higher than the original `ham_frequency`." ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sim_ru(ham_frequency, ham_duration, accuracy):\n", " # Monte-Carlo Style\n", " n = 1000000\n", " ham = np.random.random(size=n)<ham_frequency\n", " durations = np.ones(n)\n", " accurate = np.random.random(size=n)<accuracy\n", " durations[ham & accurate] = ham_duration\n", " durations[~ham & ~accurate] = ham_duration\n", " return (np.sum(durations[ham]) / np.sum(durations))\n", "\n", "\n", "def sim_ru2(ham_frequency, ham_duration, accuracy):\n", " # exact calculation\n", " long = ((ham_frequency* accuracy) + (1-ham_frequency)*(1-accuracy)) * ham_duration\n", " short = ((ham_frequency* (1-accuracy)) + (1-ham_frequency)*(accuracy)) * 1.0\n", " ham = (ham_frequency* accuracy)*ham_duration + (ham_frequency* (1-accuracy))*1\n", " return ham / (long+short)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def make_plot(ham_frequency):\n", " f, ax = plt.subplots()\n", " f.set_figwidth(14)\n", " f.set_figheight(6)\n", " ax.set_ylim(0,1)\n", " x = np.arange(0.5, 1.0,0.001)\n", " y = np.zeros(len(x))\n", " handles = []\n", " for j in reversed([2.5,5,10,20,40]):\n", " for i in range(len(x)):\n", " y[i] = sim_ru2(ham_frequency, j, x[i])\n", " handles.append(ax.plot(x,y, label = \"%.1f\" % j))\n", " ax.grid()\n", " f.suptitle(\"Ratio of desired data over total data for different values of \\\"desired length\\\"/\\\"rejected length\\\" \")\n", " ax.legend(loc=0);\n", " ax.xaxis.set_label_text(\"Detection Accuracy\");\n", " ax.yaxis.set_label_text(\"Desired Output / Total Output\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 50% ham in sample" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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oXqhJiIlgpB6Z/NcE+qHJtb4MDAywbds2PvvZz1JQUEBnZyeLFi2ybZOWlkZH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\nd3Su2iyMJEmSJLWn2nOMRkZE8yW15wAjO/MwqVbYD62imXMqkvmmIpl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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5339435198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10% ham in sample" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ziDXSOXkGuNT/TLser3tjag5/27Lbn0jcsL+/YBMRERGJHjM73Dn3l3+/DXCzc65VDk/L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"text/plain": [ "<matplotlib.figure.Figure at 0x7f533943b208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1% ham in sample" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SJElSR9TWxxgdeOCBHH/88Zx00kn1tvvSl75E3759ufjiiwF45JFHOPbYY3n9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\nOsMwDMMwNgznfGy9lohZLa3WbnHRvw9nagomp5T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MMOdmROQeEXkozLm3bsEwjV3CCPNt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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f533968a208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "make_plot(0.01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "I hope this illustrates how to think about and design Read Until workflows.\n", "\n", "In practical applications there will be tradeoffs between accuracy and the `ham_duration`: The more time the molecule has to spend inside the pore before ejection the higher the accuracy of the decision and the lower the ratio of the ham/spam duration.\n", "\n", "It's also obvious that Read Until strongly favors long reads." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
isc
Ledoux/ShareYourSystem
Pythonlogy/draft/Directer/PreReadme.ipynb
1
9824
{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nView the Directer notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Directer.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nThe Directer is a walker through the folders of the harddrive, \nassuring a call of _DirectingCallbackFunction at each level.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Interfacers.Killer\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport os\nfrom ShareYourSystem.Functers import Argumenter\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass DirecterClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'DirectingCallbackFunction',\n\t\t\t\t\t\t\t\t\t'DirectingLiargVariablesList',\n\t\t\t\t\t\t\t\t\t'DirectingFilterFunctionPointer'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_DirectingCallbackFunction=None,\n\t\t\t\t\t\t_DirectingLiargVariablesList=None,\n\t\t\t\t\t\t_DirectingFilterFunctionPointer=None,\n\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\t#@Argumenter.ArgumenterClass()\n\tdef do_direct(self):\n\n\t\t#Call the folder method before\n\t\tself.folder()\n\n\t\t#debug\n\t\t'''\n\t\tprint('Directer l.62')\n\t\tprint('self.FolderingPathStr is ',self.FolderingPathStr)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Definition the call back function if not already\n\t\tif self.DirectingCallbackFunction==None:\n\n\t\t\t#Definition a test function\n\t\t\tdef test(_LiargVariablesList,_FolderPathStr,_DirKeyStrsList):\n\t\t\t\tpass\n\t\t\t\tprint(_LiargVariablesList,_FolderPathStr,_DirKeyStrsList)\n\n\t\t\t#set\n\t\t\tself.DirectingCallbackFunction=test\n\n\t\t'''\n\t\t#Call the function\n\t\ttry:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList,\n\t\t\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t\t\t\t)\n\t\texcept:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t\t\t\t\t\t)\n\t\t'''\n\n\t\t#Walk with os\n\t\tos.path.walk(\n\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\tself.DirectingCallbackFunction,\n\t\t\t\t\t\tself.DirectingLiargVariablesList\n\t\t\t\t\t)\n\n\t\t\"\"\"\n\t\t#Do it Manually\n\n\t\t#Call the function\n\t\ttry:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList,\n\t\t\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t\t\t\t)\n\t\texcept:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t\t\t\t\t\t)\n\n\t\t#Filter the folders to walk\n\t\tDirectedFolderKeyStrsList=SYS._filter(\n\t\t\t\t\t\tlambda __FolderedDirKeyStr:\n\t\t\t\t\t\tos.path.isdir(self.FolderingPathStr+__FolderedDirKeyStr),\n\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t)\n\n\t\t#debug\n\t\t'''\n\t\tprint('After first filter DirectedFolderKeyStrsList is ',DirectedFolderKeyStrsList)\n\t\tprint('')\n\t\t'''\n\n\t\t#Filter again maybe\n\t\tif self.DirectingFilterFunctionPointer!=None:\n\t\t\tDirectedFolderKeyStrsList=SYS._filter(\n\t\t\t\t\t\tlambda __DirectedFolderKeyStr:\n\t\t\t\t\t\tself.DirectingFilterFunctionPointer(\n\t\t\t\t\t\t\tself,__DirectedFolderKeyStr),\n\t\t\t\t\t\tDirectedFolderKeyStrsList\n\t\t\t)\n\n\t\t#debug\n\t\t'''\n\t\tprint('After second DirectedFolderKeyStrsList is ',DirectedFolderKeyStrsList)\n\t\tprint('')\n\t\t'''\n\n\t\t#Map a recursive direct\n\t\t'''\n\t\tmap(\t\n\t\t\t\tlambda __DirectedFolderKeyStr:\n\t\t\t\tself.__class__().direct(\n\t\t\t\t\t\t\t\tself.DirectingCallbackFunction,\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\t**{\n\t\t\t\t\t\t\t\t'FolderingPathStr':\n\t\t\t\t\t\t\t\t\tself.FolderingPathStr+__DirectedFolderKeyStr+'/'\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t),\n\t\t\t\tDirectedFolderKeyStrsList\n\t\t\t)\n\t\t'''\n\n\t\t\"\"\"\n\n\t\t#Return self\n\t\t#return self\t\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Directer sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Interfacers/Directer\" target=\"_blank\">Github</a>\n</small>\n\n", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.", "cell_type": "markdown", "prompt_number": 2, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "cell_type": "code", "prompt_number": 3, "language": "python", "input": [ "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "from ShareYourSystem.Standards.Interfacers import Directer\n", "import os\n", "\n", "#Definition an instance \n", "MyDirecter=Directer.DirecterClass()\n", "\n", "#Direct for displaying folders\n", "'''\n", "MyDirecter.direct(\n", " lambda _LiargVariablesList,_FolderPathStr,_FileKeyStrsList:\n", " Representer._print(_LiargVariablesList[0]+_FolderPathStr),\n", " [\"_FolderPathStr is \"],\n", " **{'FolderingPathStr':'/'.join(SYS.__file__.split('/')[:-1])}\n", " )\n", "'''\n", "\n", "#Delete things\n", "def delete(_LiargVariablesList,_FolderPathStr,_FileKeyStrsList):\n", " #os.popen('rm -r '+_FolderPathStr+'/Attests/')\n", " os.popen('rm '+_FolderPathStr+'/02_ClassCell.md')\n", " os.popen('rm '+_FolderPathStr+'/03_ClassCell.py')\n", " os.popen('rm '+_FolderPathStr+'/04_InstanceCell.md')\n", " os.popen('rm '+_FolderPathStr+'/05_InstanceCell.py')\n", "def move(_LiargVariablesList,_FolderPathStr,_FileKeyStrsList):\n", " os.popen('mv '+_FolderPathStr+'/00_ExampleCell.md '+_FolderPathStr+'/00_ExampleDoc.md')\n", " os.popen('mv '+_FolderPathStr+'/01_ExampleCell.py '+_FolderPathStr+'/01_ExampleDoc.py')\n", "\n", "MyDirecter=Directer.DirecterClass().direct(\n", " delete,\n", " [],\n", " **{\n", " 'FolderingPathStr':\n", " SYS.ShareYourSystemLocalFolderPathStr+'/ShareYourSystem/Guiders/'\n", " }\n", " )\n", "\n", "#Definition the AttestedStr\n", "SYS._attest(\n", " [\n", " 'MyDirecter is '+SYS._str(\n", " MyDirecter,\n", " **{\n", " 'RepresentingBaseKeyStrsListBool':False\n", " }\n", " )\n", " ]\n", ") \n", "\n", "#Print\n", "\n", "\n" ], "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "*****Start of the Attest *****\n", "\n", "MyDirecter is < (DirecterClass), 4554248848>\n", " /{ \n", " / '<New><Instance>IdInt' : 4554248848\n", " / '<Spe><Class>DirectingFilterFunctionPointer' : None\n", " / '<Spe><Instance>DirectingCallbackFunction' : <function delete at 0x10f733c08>\n", " / '<Spe><Instance>DirectingLiargVariablesList' : []\n", " /}\n", "\n", "*****End of the Attest *****\n", "\n", "\n" ] } ], "collapsed": false, "metadata": { "slideshow": { "slide_type": "-" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }
mit
logmonster/odt_webapp
docs/chp4_eCommerce/fix_odt_jeymart_user_trx.ipynb
1
12010251
null
apache-2.0
cwharland/data-science-from-scratch
Working With Data.ipynb
2
146960
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import math\n", "from sgd import *\n", "from collections import Counter\n", "import random\n", "from probability import *\n", "from stats import *\n", "sns.set_context('poster')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bucketize(point, bucket_size):\n", " return bucket_size * math.floor(point / bucket_size)\n", "\n", "def make_histogram(points, bucket_size):\n", " return Counter(bucketize(point, bucket_size)\n", " for point in points)\n", "\n", "def plot_histogram(points, bucket_size, title = \"\"):\n", " histogram = make_histogram(points, bucket_size)\n", " plt.bar(histogram.keys(), histogram.values(), width = bucket_size)\n", " plt.title(title)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random.seed(0)\n", "\n", "uniform = [200 * random.random() - 100 for _ in range(10000)]\n", "\n", "normal = [57 * inverse_normal_cdf(random.random())\n", " for _ in range(10000)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dIQEAAOgICQAAQEdIAAAAOkICAADQERIAAICOkAAAAHSEBAAAoCMkAAAAHSEB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\ntb7rM5P8Q2vt9nFyxgcyrDM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"text/plain": [ "<matplotlib.figure.Figure at 0x3601390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_histogram(uniform, 10, \"Uniform Histogram\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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A9zWUkSRJktRDWr3050qq0Yfr+e77xJ1SP+9uKv9Qw75TgIcz87GmMg82lJEk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\nERERERGp8h+DEIhMrpHbmwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x36014a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_histogram(normal, 10, \"Normal Histogram\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def random_normal():\n", " return inverse_normal_cdf(random.random())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xs = [random_normal() for _ in range(1000)]\n", "ys1 = [x + random_normal() / 2 for x in xs]\n", "ys2 = [-x + random_normal() / 2 for x in xs]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GZKjw39uu8/1JXpzktbXWV6/ocmuSR5VSttZaH+xrPyHJDWu93t69i5mfv++g6+XAlt+R\nG+PRMcajNzd32KRLgHWzsLCY3bsfmHQZG5bfyeNhnEdvx45t2bJlZqC+w+61/6Z0wf8V+wj+SXJ9\nkpkkpy43lFIen+QJvWMAAMCYHPTMfynlf0/ysiSfTvInpZSn9R1eqLV+qdZ6eynl6iTvLqUcmWQ+\nyeuT3JzkI0PUDQAArNFawv9S/teHd5/be31Wkmev6Lsn3U4/Sfepvm9O8oZ0dxo+neScWuv+HgQG\nAABGYODwX2u9NN1Wnvv8/gDn3ZfkrN4XAAAwIcOu+QcAAKaE8A8AAI0Q/gEAoBHCPwAANEL4BwCA\nRgj/AADQCOEfAAAaIfwDAEAjhH8AAGiE8A8AAI0Q/gEAoBHCPwAANEL4BwCARgj/AADQCOEfAAAa\nIfwDAEAjhH8AAGiE8A8AAI0Q/gEAoBHCPwAANEL4BwCARgj/AADQCOEfAAAaIfwDAEAjhH8AAGiE\n8A8AAI0Q/gEAoBHCPwAANEL4BwCARgj/AADQCOEfAAAaMTvpAoBD3dKkC1iThYWFbN1iXgMA9kX4\nB1b1wetq7ty1Z9JlDOS4Y47I0Uc9LMn8pEsBgEOO8A+s6s5de7LzrnsnXcbAjj368EmXAACHJPfG\nAQCgEcI/AAA0QvgHAIBGCP8AANAI4R8AABoh/AMAQCOEfwAAaMTA+/yXUk5NclWtdfuK9ouSnJXk\n4UluSnJ2rbX2Hd+a5PIkpyc5PMmnkpxTa717+PIBAIBBDTTzX0p5epKr9tF+SZKLkrwxXbg/Msn1\npZT+NwjvTHJGkvOTnJnkyUk+UUpx1wEAAMbogDP/pZTDkpyb5LIk302ype/YXJLzklxSa72y13Zj\nkm8meWmSN5dSHpsu+L+w1np1r8/NSWqS5yW5Zr3/QgAAwL6tNvt+SpJXpQv5VyTZ1HfsaemW8Vy7\n3FBrnU9yQ5KTe00n9V4/1tfntiS39PUBAADGYLXw/8Ukxy/P7K9wYu/19hXtd/QdOzHJ3bXW+1f0\n+UZfHwAAYAwOuOyn1vqtAxzenuTBWuveFe27e8eW++zZx7l7khw7aJEAAMDwhnnodlOSpf0cW1hD\nHwAAYAwG3upzH+5JsrWUMlNr7Q/yc71jy33m9nFuf5+Bzc5uzo4d29ZcKIOZne3eCxrj0ZnGMV5Y\nWHlzDxiXmRn/743SNP5OnkbGefSWx3gQw8z835puZv8xK9pPSLebz3KfR/X2+t9fHwAAYAyGmfn/\nXJIHkpyW5DeTpJRyVJJnJLmk1+f6JDNJTk2yvNXn45M8IcnFa73g3r2LmZ+/b4iSOZDld+TGeHSm\nc4z3t3IPGLWFhcXs3v3ApMvYsKbzd/L0Mc6jt2PHtmzZMjNQ34MO/7XWPaWUK5K8ppSymG6W/6Ik\n80ne0+tzeynl6iTvLqUc2Tv2+iQ3J/nIwV4bAABYu7WE/6X84ynAC5MspvscgCOS3JTkjFrr7r4+\nZyZ5c5I3pFtm9Okk59RaTScCAMAYDRz+a62XJrl0RdtCkgt6X/s7774kZ/W+AACACRnmgV8AAGCK\nCP8AANAI4R8AABoh/AMAQCOEfwAAaITwDwAAjRD+AQCgEcI/AAA0QvgHAIBGCP8AANAI4R8AABoh\n/AMAQCOEfwAAaITwDwAAjRD+AQCgEcI/AAA0QvgHAIBGCP8AANAI4R8AABoh/AMAQCOEfwAAaITw\nDwAAjRD+AQCgEcI/AAA0QvgHAIBGCP8AANAI4R8AABoh/AMAQCOEfwAAaITwDwAAjRD+AQCgEcI/\nAAA0QvgHAIBGCP8AANAI4R8AABoh/AMAQCOEfwAAaITwDwAAjRD+AQCgEcI/AAA0QvgHAIBGCP8A\nANCI2WF/QCllU5Jzk/xykkcnuSXJBbXWz/b1uSjJWUkenuSmJGfXWuuw1wYAAAa3HjP/5yZ5Y5L3\nJnlektuTfLKU8sNJUkq5JMlFvT6nJzkyyfWllO3rcG0AAGBA6xH+/12S36u1Xl5r/UySM5L8TZKX\nllLmkpyX5JJa65W11o8meU6SuSQvXYdrAwAAA1qP8L89ye7lb2qti0nuTXJUkqclOTzJtX3H55Pc\nkOTkdbg2AAAwoKHX/Ce5KsmvlFKuSfJnSf5tkickuSDJib0+t684544kp67DtQFgw1pYWEiyNOky\nDsKmSRcA7Md6hP+LkzwpyXV9bRfVWj9WSrkgyYO11r0rztmd7o4BALAf1954e/7069+edBkDO+6Y\nI/KiZ5VJlwEcwHrN/P9Yut1+/nuSZyf5jVLKPene+u9vymJxrReand2cHTu2HWydrGJ2tlsFZoxH\nZxrHeGFh5Xt3YFx2fef+7Lzr3kmXsSZzc1szM7Me8WL0pvF38jQyzqO3PMYD9R3mQqWUpyZ5QZJ/\nXWv9cK/5v5VSZtPt7nNhkq2llJla60LfqXNJ5oe5NgAAsDbDvjV/fO/18yvab0pyfrpZ/01JHpPk\ntr7jJyRZ8z7/e/cuZn7+voMok0EsvyM3xqMznWM8jeuNgUnZvfvBJN+bdBkDmc7fydPHOI/ejh3b\nsmXLzEB9h93t5xu913++ov1HkzyU5A+TPJDktOUDpZSjkjwjyfVDXhsAAFiDoWb+a61fKKVcl+Tt\npZTvS/L1JD+R5NeS/N+11r8upVyR5DWllMUkt6b7wK/5JO8ZqnIAAGBN1uOJnFPTBfqXJ/kn6Zb3\nnF1r/Z3e8QvTPdx7XpIj0i0JOqPWunsfPwsAABiRocN/rfWBJK/ufe3r+EK6Pf8vGPZaAADAwVuP\nT/gFAACmgPAPAACNEP4BAKARwj8AADRC+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/AABohPAPAACN\nEP4BAKARwj8AADRC+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/AABohPAPAACNEP4BAKARwj8AADRC\n+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/AABohPAPAACNEP4BAKARwj8AADRC+AcAgEYI/wAA0Ajh\nHwAAGiH8AwBAI4R/AABohPAPAACNEP4BAKARwj8AADRC+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/\nAABohPAPAACNmF2PH1JKeWaS1yX5Z0n+Nsn7klxWa13sHb8oyVlJHp7kpiRn11rrelwbAAAYzNAz\n/6WUH0/yR0luSXJKkiuTnJ/k13vHL0lyUZI3Jjk9yZFJri+lbB/22gAAwODWY+b/8iSfrLX+u973\nf1xKeXiSnyil/HaS85JcUmu9MklKKTcm+WaSlyZ58zpcHwAAGMBQM/+llEcmeXqS3+lvr7VeUGs9\nKcmPJTk8ybV9x+aT3JDk5GGuDQAArM2wM///LMmmJPeVUj6a5FlJ7k3y9iSXJTmx1+/2FefdkeTU\nIa8NAACswbDh/5G91w8k+b0kv5XkJ9Kt978/yUySB2ute1ectzuJNf8AADBGw4b/Lb3XT9Zaz+/9\n+YZSyiPSvQG4PMnSfs5dXOvFZmc3Z8eObWuvkoHMznarwIzx6EzjGC8srHzvDrB/c3NbMzOzLpsJ\njtw0/k6eRsZ59JbHeBDD7vazp/f6yRXt1yU5Isl8kq2llJkVx+d6xwAAgDEZ9q35bb3Xw1a0L98R\neCjdMwGP6eubJCckWfM+/3v3LmZ+/r61nsaAlt+RG+PRmc4x3t/NO4B/bPfuB5N8b9JlDGQ6fydP\nH+M8ejt2bMuWLSvn2vdt2Jn/W5L8dZKfX9H+M73230/yQJLTlg+UUo5K8owk1w95bQAAYA2Gmvmv\ntS6VUi5M8v5SytuTfDjdjj8vSfLva627SylXJHlNKWUxya3pPvBrPsl7hisdAABYi6GfyKm1/m4p\n5aEkFyY5M8mdSc6qtS6H+wvTPdx7XrrnAG5Kckatdfew1wYAAAa3Lo/j11p/P90Sn30dW0hyQe8L\nAACYkGHX/AMAAFNC+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/AABohPAPAACNEP4BAKARwj8AADRC\n+AcAgEYI/wAA0AjhHwAAGiH8AwBAI4R/AABohPAPAACNEP4BAKARwj8AADRC+AcAgEYI/wAA0Ajh\nHwAAGjE76QIAgI1h65bNSZYmXcbAFhb2ZvPmmUmXAWMl/AMA6+Loox6WD163M3fu2jPpUgZy3DFH\n5KzTnjTpMmCshH8AYN3cuWtPdt5176TLAPZD+IcxWlpayuLiQqbptvh01QoAHIjwD2O0uLiQd13z\n1am5JZ4kT/3BR0y6BABgnQj/MGbTdkv82KMPn3QJAMA6sdUnAAA0QvgHAIBGCP8AANAI4R8AABoh\n/AMAQCOEfwAAaITwDwAAjRD+AQCgEcI/AAA0QvgHAIBGCP8AANAI4R8AABoh/AMAQCOEfwAAaITw\nDwAAjZhdzx9WStma5CtJPl9rPbOv/aIkZyV5eJKbkpxda63reW0AAODA1nvm/5IkJcnSckMp5ZIk\nFyV5Y5LTkxyZ5PpSyvZ1vjYAAHAA6xb+SylPSXJ2km/3tc0lOS/JJbXWK2utH03ynCRzSV66XtcG\nAABWty7hv5Qym+S96Wb3/7rv0NOSHJ7k2uWGWut8khuSnLwe1wYAAAazXjP/56d7fuDyJJv62k/s\nvd6+ov8dfccAAIAxGDr8l1J+KMmFSX6x1vrQisPbkzxYa927on137xgAADAmQ+32U0rZnOQ9Sd5T\na/1Cr3mpr8umFd/3W1zr9WZnN2fHjm1rPY0Bzc527wWN8Sit+Z89ACM0MyNbjJp8MXrLYzxQ3yGv\ndXaSY5Oc0lv3n3SBf3Pv+3uSbC2lzNRaF/rOm0syP+S1AQCANRg2/D8/yQ8k+YcV7U9K8pJ0e/tv\nSvKYJLf1HT8hyZr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"text/plain": [ "<matplotlib.figure.Figure at 0x18a465f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x18965b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_histogram(ys1, 0.5)\n", "plot_histogram(ys2, 0.5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x18f95cf8>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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o9yYKy6YoVTF5IKKUIv2jbjabMXHiRFxzzQ0hgz6twCOSoDEWd9PDCYKCBaDB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\n25VPAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x18e11e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(xs, ys1, marker = '.', color = 'black', label = 'ys1')\n", "plt.scatter(xs, ys2, marker = '.', color = 'gray', label = 'ys2')\n", "plt.xlabel('xs')\n", "plt.ylabel('ys')\n", "plt.legend(loc = 'best')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9010485800417694" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation(xs,ys1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-0.892098584392656" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation(xs,ys2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def correlation_matrix(data):\n", " \"\"\"return pariwise correlations between all columns of data\"\"\"\n", " \n", " _, num_columns = shape(data)\n", " \n", " def matrix_entry(i,j):\n", " return correlation(get_column(data, i), get_column(data, j))\n", " \n", " return make_matrix(num_columns, num_columns, matrix_entry)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[1.0, 0.9010485800417694, -0.892098584392656],\n", " [0.9010485800417695, 0.9999999999999998, -0.807272900356095],\n", " [-0.8920985843926559, -0.807272900356095, 1.0]]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlation_matrix([[x_i,y_1_i,y_2_i] for x_i, y_1_i, y_2_i in zip(xs,ys1,ys2)])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Cleaning and Munging" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def try_or_none(f):\n", " \"\"\"try to run f if you can't return None\"\"\"\n", " def f_or_none(x):\n", " try:\n", " return f(x)\n", " except:\n", " return None\n", " return f_or_none\n", "\n", "def parse_row(input_row, parsers):\n", " \"\"\"take a given list of parsers and apply each one to a\n", " particular element of an input_row\"\"\"\n", " \n", " return [try_or_none(parser)(value) if parser is not None else value\n", " for value, parser in zip(input_row, parsers)]\n", "\n", "def parse_rows_with(reader, parsers):\n", " \"\"\"wrap a reader with parsers to apply\"\"\"\n", " for row in reader:\n", " yield parse_row(row, parsers)\n", " \n", " \n", "def try_parse_field(field_name, value, parser_dict):\n", " \"\"\"try to parse a field using the right parser from the dict\"\"\"\n", " parser = parser_dict.get(field_name) # None if it doesn't exist\n", " if parser is not None:\n", " return try_or_none(parser)(value)\n", " else:\n", " return value\n", " \n", "def parse_dict(input_dict, parser_dict):\n", " return {field_name : try_parse_field(field_name, value, parser_dict)\n", " for field_name, value in input_dict.iteritems()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manipulating Data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import defaultdict\n", "\n", "def picker(field_name):\n", " \"\"\"grabs a field from a dict\"\"\"\n", " return lambda row: row[field_name]\n", "\n", "def pluck(field_name, rows):\n", " \"\"\"turn a list of dicts into the list of field_name values\"\"\"\n", " return map(picker(field_name), rows)\n", "\n", "def group_by(grouper, rows, value_transform = None):\n", " # key is output of grouper where value is list of rows\n", " grouped = defaultdict(list)\n", " \n", " for row in rows:\n", " grouped[grouper(row)].append(row)\n", " \n", " if value_transform is None:\n", " return grouped\n", " else:\n", " return {key : value_transform(rows)\n", " for key,rows in grouped.iteritems()}\n", " \n", "def percent_price_change(yesterday, today):\n", " return today[\"closing_price\"] / yesterday[\"closing_price\"] - 1\n", "\n", "def day_over_day_changes(grouped_rows):\n", " # sort by date\n", " ordered = sort(grouped_rows, key = picker(\"date\"))\n", " \n", " # shift and zip to make pairs then apply tranform\n", " return [{\"symbol\": today[\"symbol\"],\n", " \"date\": today[\"date\"],\n", " \"change\": percent_price_change(yesterday, today)}\n", " for yesterday, today in zip(ordered, ordered[1:])]\n", "\n", "# max(all_changes, key = picker(\"change\"))\n", "\n", "def combine_pct_changes(pct_change1, pct_change2):\n", " return (1 + pct_change1) * (1 + pct_change2) / - 1\n", "\n", "def overall_change(changes):\n", " return reduce(combine_pct_changes, pluck(\"change\", changes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rescaling" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def scale(data_matrix):\n", " \"\"\"returns the means and stdev of the columns\"\"\"\n", " num_rows, num_cols = shape(data_matrix)\n", " \n", " means = [mean(get_column(data_matrix, i))\n", " for i in range(num_cols)]\n", " \n", " stdevs = [standard_deviation(get_column(data_matrix, i))\n", " for i in range(num_cols)]\n", " \n", " return means, stdevs\n", "\n", "def rescale(data_matrix):\n", " \"\"\"rescales the input data columns to standard mean 0 and stdev 1\"\"\"\n", " means, stdev = scale(data_matrix)\n", " \n", " def rescaled(i,j):\n", " if stdevs[j] > 0:\n", " return (data_matrix[i][j] - means[j]) / stdev[j]\n", " else:\n", " return data_matrix[i][j]\n", " \n", " num_rows, num_cols = shape(data_matrix)\n", " return make_matrix(num_rows, num_cols, rescaled)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def de_mean_matrix(A):\n", " \"\"\"returns the matrix after substracting the mean of the columns from each column\"\"\"\n", " nr, nc = shape(A)\n", " \n", " column_means, _ = scale(A)\n", " return make_matrix(nr, nc, lambda i,j: A[i][j] - column_means[j])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#de_mean_matrix([[x_i,y_1_i] for x_i, y_1_i in zip(xs,ys1)])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from functools import partial\n", "\n", "def direction(w):\n", " mag = magnitude(w)\n", " return [w_i / mag for w_i in w]\n", "\n", "def directional_variance_i(x_i, w):\n", " \"\"\"the variance of row x_i in direction of w\"\"\"\n", " return dot(x_i, direction(w)) ** 2\n", "\n", "def directional_variance(X, w):\n", " \"\"\"variance of data in direction w\"\"\"\n", " return sum(directional_variance_i(x_i, w)\n", " for x_i in X)\n", "\n", "def directional_variance_gradient_i(x_i, w):\n", " \"\"\"contribution of x_i to gradient in direction of w\"\"\"\n", " projection_length = dot(x_i, direction(w))\n", " \n", " return [2 * projection_length * x_ij for x_ij in x_i]\n", "\n", "def directional_variance_gradient(X, w):\n", " return vector_sum(directional_variance_gradient_i(x_i, w)\n", " for x_i in X)\n", "\n", "def first_principal_component(X):\n", " guess = [1 for _ in X[0]]\n", " \n", " unscaled_maximizer = maximize_batch(partial(directional_variance, X), # make function of w\n", " partial(directional_variance_gradient, X), # make function of w\n", " guess)\n", " return direction(unscaled_maximizer)\n", "\n", "def first_principal_component_sgd(X):\n", " guess = [1 for _ in X[0]]\n", " \n", " unscaled_maximizer = maximize_stochastic(lambda x, _, w: directional_variance_i(x, w),\n", " lambda x, _, w: directional_variance_gradient_i(x, w),\n", " X,\n", " [None for _ in X],\n", " guess)\n", " return direction(unscaled_maximizer)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[-0.6633261406888873, 0.7483304290744742]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_principal_component(de_mean_matrix([[x_i,y_1_i] for x_i, y_1_i in zip(xs,ys2)]))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This runs ass slow for some reason\n", "#first_principal_component_sgd([[x_i,y_1_i] for x_i, y_1_i in zip(xs,ys2)])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def project(v, w):\n", " \"\"\"return the proj of v into w\"\"\"\n", " projection_length = dot(v,w)\n", " return scalar_multiply(projection_length, w)\n", "# To find others we simply 'remove' this vector from the data\n", "def remove_projection_from_vector(v, w):\n", " \"\"\"project v onto w then remove\"\"\"\n", " return vector_subtract(v, project(v,w))\n", "\n", "def remove_projection(X, w):\n", " \"\"\"for each row of X, project row onto w then remove\"\"\"\n", " return [remove_projection_from_vector(x_i, w) for x_i in X]\n", "def principal_component_analysis(X, num_components):\n", " components = []\n", " for _ in range(num_components):\n", " component = first_principal_component(X)\n", " components.append(component)\n", " \n", " X = remove_projection(X, component)\n", " \n", " return components\n", " \n", "# Make use of this and transform the data\n", "def transform_vector(v, components):\n", " return [dot(v,w) for w in components]\n", "\n", "def transform(X, components):\n", " return [transform_vector(x_i, components) for x_i in X]" ] }, { "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": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
kurige/notebooks
notebooks/Test.ipynb
1
657631
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bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " root.select(\".xlabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " }\n", " });\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n", " // will be clipped.\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1\");\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-4\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-13\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-13\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-13\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-15\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-15\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-15\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-16\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-16\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-16\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-16\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-16\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-17\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-17\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-5ee5b1a9de9e40dfad86bdd6d2700fc1-element-17\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "svg": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", 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bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " root.select(\".xlabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " }\n", " });\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n", " // will be clipped.\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-3d75a5f8cc404d3a91994c991e8a591e\");\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-4\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-14\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-14\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-14\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-16\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-16\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-16\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-17\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-17\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-17\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-17\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-17\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-18\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-18\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-3d75a5f8cc404d3a91994c991e8a591e-element-18\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "svg": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", 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bounds.y0 - scale * bounds.y0;\n", "\n", " var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n", " ty = Math.max(Math.min(ty - y0, y_max), y_min);\n", "\n", " tx += x0;\n", " ty += y0;\n", "\n", " // when the scale change, we may need to alter which set of\n", " // ticks is being displayed\n", " if (scale != old_scale) {\n", " update_tickscale(root, scale, \"x\");\n", " update_tickscale(root, scale, \"y\");\n", " }\n", "\n", " set_geometry_transform(root, tx, ty, scale);\n", "\n", " root.data(\"scale\", scale);\n", " root.data(\"tx\", tx);\n", " root.data(\"ty\", ty);\n", "};\n", "\n", "\n", "var scale_centered_translation = function(root, scale) {\n", " var bounds = root.plotbounds();\n", "\n", " var width = bounds.x1 - bounds.x0,\n", " height = bounds.y1 - bounds.y0;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var scale0 = root.data(\"scale\");\n", "\n", " // how off from center the current view is\n", " var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n", " yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n", "\n", " // rescale offsets\n", " xoff = xoff * scale / scale0;\n", " yoff = yoff * scale / scale0;\n", "\n", " // adjust for the panel position being scaled\n", " var x_edge_adjust = bounds.x0 * (1 - scale),\n", " y_edge_adjust = bounds.y0 * (1 - scale);\n", "\n", " return {\n", " x: xoff + x_edge_adjust + (width - width * scale) / 2,\n", " y: yoff + y_edge_adjust + (height - height * scale) / 2\n", " };\n", "};\n", "\n", "\n", "// Initialize data for panning zooming if it isn't already.\n", "var init_pan_zoom = function(root) {\n", " if (root.data(\"zoompan-ready\")) {\n", " return;\n", " }\n", "\n", " // The non-scaling-stroke trick. Rather than try to correct for the\n", " // stroke-width when zooming, we force it to a fixed value.\n", " var px_per_mm = root.node.getCTM().a;\n", "\n", " // Drag events report deltas in pixels, which we'd like to convert to\n", " // millimeters.\n", " root.data(\"px_per_mm\", px_per_mm);\n", "\n", " root.selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " sw = element.asPX(\"stroke-width\") * px_per_mm;\n", " if (sw > 0) {\n", " element.attribute(\"stroke-width\", sw);\n", " element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n", " }\n", " });\n", "\n", " // Store ticks labels original tranformation\n", " root.selectAll(\".xlabels > text, .ylabels > text\")\n", " .forEach(function (element, i) {\n", " var lm = element.transform().localMatrix;\n", " element.data(\"static_transform\",\n", " new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n", " });\n", "\n", " if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n", " if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n", " if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n", " if (root.data(\"xtickscales\") === undefined) {\n", "\n", " // index all the tick scales that are listed\n", " var xtickscales = {};\n", " var ytickscales = {};\n", " var add_x_tick_scales = function (element, i) {\n", " xtickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", " var add_y_tick_scales = function (element, i) {\n", " ytickscales[element.attribute(\"gadfly:scale\")] = true;\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(add_x_tick_scales);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(add_y_tick_scales);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(add_x_tick_scales);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(add_y_tick_scales);\n", "\n", " root.data(\"xtickscales\", xtickscales);\n", " root.data(\"ytickscales\", ytickscales);\n", " root.data(\"xtickscale\", 1.0);\n", " }\n", "\n", " var min_scale = 1.0, max_scale = 1.0;\n", " for (scale in xtickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " for (scale in ytickscales) {\n", " min_scale = Math.min(min_scale, scale);\n", " max_scale = Math.max(max_scale, scale);\n", " }\n", " root.data(\"min_scale\", min_scale);\n", " root.data(\"max_scale\", max_scale);\n", "\n", " // store the original positions of labels\n", " root.select(\".xlabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"x\", element.asPX(\"x\"));\n", " });\n", "\n", " root.select(\".ylabels\")\n", " .selectAll(\"text\")\n", " .forEach(function (element, i) {\n", " element.data(\"y\", element.asPX(\"y\"));\n", " });\n", "\n", " // mark grid lines and ticks as in or out of scale.\n", " var mark_inscale = function (element, i) {\n", " element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n", " };\n", "\n", " root.select(\".xgridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".ygridlines\").selectAll(\"path\").forEach(mark_inscale);\n", " root.select(\".xlabels\").selectAll(\"text\").forEach(mark_inscale);\n", " root.select(\".ylabels\").selectAll(\"text\").forEach(mark_inscale);\n", "\n", " // figure out the upper ond lower bounds on panning using the maximum\n", " // and minum grid lines\n", " var bounds = root.plotbounds();\n", " var pan_bounds = {\n", " x0: 0.0,\n", " y0: 0.0,\n", " x1: 0.0,\n", " y1: 0.0\n", " };\n", "\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.x1 - bbox.x < pan_bounds.x0) {\n", " pan_bounds.x0 = bounds.x1 - bbox.x;\n", " }\n", " if (bounds.x0 - bbox.x > pan_bounds.x1) {\n", " pan_bounds.x1 = bounds.x0 - bbox.x;\n", " }\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " var bbox = element.node.getBBox();\n", " if (bounds.y1 - bbox.y < pan_bounds.y0) {\n", " pan_bounds.y0 = bounds.y1 - bbox.y;\n", " }\n", " if (bounds.y0 - bbox.y > pan_bounds.y1) {\n", " pan_bounds.y1 = bounds.y0 - bbox.y;\n", " }\n", " }\n", " });\n", "\n", " // nudge these values a little\n", " pan_bounds.x0 -= 5;\n", " pan_bounds.x1 += 5;\n", " pan_bounds.y0 -= 5;\n", " pan_bounds.y1 += 5;\n", " root.data(\"pan_bounds\", pan_bounds);\n", "\n", " // Set all grid lines at scale 1.0 to visible. Out of bounds lines\n", " // will be clipped.\n", " root.select(\".xgridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.select(\".ygridlines\")\n", " .selectAll(\"path\")\n", " .forEach(function (element, i) {\n", " if (element.attribute(\"gadfly:inscale\") == \"true\") {\n", " element.attr(\"visibility\", \"visible\");\n", " }\n", " });\n", "\n", " root.data(\"zoompan-ready\", true)\n", "};\n", "\n", "\n", "// Panning\n", "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n", " var root = this.plotroot();\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var tx0 = root.data(\"tx\"),\n", " ty0 = root.data(\"ty\");\n", "\n", " var dx0 = root.data(\"dx\"),\n", " dy0 = root.data(\"dy\");\n", "\n", " root.data(\"dx\", dx);\n", " root.data(\"dy\", dy);\n", "\n", " dx = dx - dx0;\n", " dy = dy - dy0;\n", "\n", " var tx = tx0 + dx,\n", " ty = ty0 + dy;\n", "\n", " set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n", " var root = this.plotroot();\n", " root.data(\"dx\", 0);\n", " root.data(\"dy\", 0);\n", " init_pan_zoom(root);\n", "};\n", "\n", "\n", "Gadfly.guide_background_drag_onend = function(event) {\n", " var root = this.plotroot();\n", "};\n", "\n", "\n", "Gadfly.guide_background_scroll = function(event) {\n", " if (event.shiftKey) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n", " new_scale = Math.max(\n", " root.data(\"min_scale\"),\n", " Math.min(root.data(\"max_scale\"), new_scale))\n", " update_plot_scale(root, new_scale);\n", " event.stopPropagation();\n", " }\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseover = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_button_mouseout = function(event) {\n", " this.select(\".button_logo\")\n", " .animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomout_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var min_scale = root.data(\"min_scale\"),\n", " scale = root.data(\"scale\");\n", " Snap.animate(\n", " scale,\n", " Math.max(min_scale, scale / 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_zoomin_click = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", " var max_scale = root.data(\"max_scale\"),\n", " scale = root.data(\"scale\");\n", "\n", " Snap.animate(\n", " scale,\n", " Math.min(max_scale, scale * 1.5),\n", " function (new_scale) {\n", " update_plot_scale(root, new_scale);\n", " },\n", " 200);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_track_click = function(event) {\n", " // TODO\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mousedown = function(event) {\n", " this.animate({fill: this.data(\"mouseover_color\")}, 100);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_mouseup = function(event) {\n", " this.animate({fill: this.data(\"mouseout_color\")}, 100);\n", "};\n", "\n", "\n", "// compute the position in [0, 1] of the zoom slider thumb from the current scale\n", "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n", " if (scale >= 1.0) {\n", " return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n", " }\n", " else {\n", " return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n", " }\n", "}\n", "\n", "\n", "var update_plot_scale = function(root, new_scale) {\n", " var trans = scale_centered_translation(root, new_scale);\n", " set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n", "\n", " root.selectAll(\".zoomslider_thumb\")\n", " .forEach(function (element, i) {\n", " var min_pos = element.data(\"min_pos\"),\n", " max_pos = element.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\");\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n", " element.transform(new Snap.Matrix().translate(\n", " Math.max(min_pos, Math.min(\n", " max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n", " });\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y) {\n", " var root = this.plotroot();\n", " var min_pos = this.data(\"min_pos\"),\n", " max_pos = this.data(\"max_pos\"),\n", " min_scale = root.data(\"min_scale\"),\n", " max_scale = root.data(\"max_scale\"),\n", " old_scale = root.data(\"old_scale\");\n", "\n", " var px_per_mm = root.data(\"px_per_mm\");\n", " dx /= px_per_mm;\n", " dy /= px_per_mm;\n", "\n", " var xmid = (min_pos + max_pos) / 2;\n", " var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n", " dx / (max_pos - min_pos);\n", "\n", " // compute the new scale\n", " var new_scale;\n", " if (xpos >= 0.5) {\n", " new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n", " }\n", " else {\n", " new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n", " Math.log(min_scale));\n", " }\n", " new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n", "\n", " update_plot_scale(root, new_scale);\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragstart = function(event) {\n", " var root = this.plotroot();\n", " init_pan_zoom(root);\n", "\n", " // keep track of what the scale was when we started dragging\n", " root.data(\"old_scale\", root.data(\"scale\"));\n", "};\n", "\n", "\n", "Gadfly.zoomslider_thumb_dragend = function(event) {\n", "};\n", "\n", "\n", "var toggle_color_class = function(root, color_class, ison) {\n", " var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n", " var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n", " if (ison) {\n", " guides.animate({opacity: 0.5}, 250);\n", " geoms.animate({opacity: 0.0}, 250);\n", " } else {\n", " guides.animate({opacity: 1.0}, 250);\n", " geoms.animate({opacity: 1.0}, 250);\n", " }\n", "};\n", "\n", "\n", "Gadfly.colorkey_swatch_click = function(event) {\n", " var root = this.plotroot();\n", " var color_class = this.data(\"color_class\");\n", "\n", " if (event.shiftKey) {\n", " root.selectAll(\".colorkey text\")\n", " .forEach(function (element) {\n", " var other_color_class = element.data(\"color_class\");\n", " if (other_color_class != color_class) {\n", " toggle_color_class(root, other_color_class,\n", " element.attr(\"opacity\") == 1.0);\n", " }\n", " });\n", " } else {\n", " toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n", " }\n", "};\n", "\n", "\n", "return Gadfly;\n", "\n", "}));\n", "\n", "\n", "//@ sourceURL=gadfly.js\n", "\n", "(function (glob, factory) {\n", " // AMD support\n", " if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n", " require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n", " factory(Snap, Gadfly);\n", " });\n", " } else {\n", " factory(glob.Snap, glob.Gadfly);\n", " }\n", "})(window, function (Snap, Gadfly) {\n", " var fig = Snap(\"#fig-e135321a291742ad8967e55b1ce0bfc8\");\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-4\")\n", " .mouseenter(Gadfly.plot_mouseover)\n", ".mouseleave(Gadfly.plot_mouseout)\n", ".mousewheel(Gadfly.guide_background_scroll)\n", ".drag(Gadfly.guide_background_drag_onmove,\n", " Gadfly.guide_background_drag_onstart,\n", " Gadfly.guide_background_drag_onend)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-7\")\n", " .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-7\")\n", " .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-8\")\n", " .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-8\")\n", " .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-14\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-14\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-14\")\n", " .click(Gadfly.zoomslider_zoomin_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-16\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-16\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-16\")\n", " .click(Gadfly.zoomslider_track_click);\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-17\")\n", " .data(\"max_pos\", 120.42)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-17\")\n", " .data(\"min_pos\", 103.42)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-17\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-17\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-17\")\n", " .drag(Gadfly.zoomslider_thumb_dragmove,\n", " Gadfly.zoomslider_thumb_dragstart,\n", " Gadfly.zoomslider_thumb_dragend)\n", ".mousedown(Gadfly.zoomslider_thumb_mousedown)\n", ".mouseup(Gadfly.zoomslider_thumb_mouseup)\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-18\")\n", " .data(\"mouseover_color\", \"#cd5c5c\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-18\")\n", " .data(\"mouseout_color\", \"#6a6a6a\")\n", ";\n", "fig.select(\"#fig-e135321a291742ad8967e55b1ce0bfc8-element-18\")\n", " .click(Gadfly.zoomslider_zoomout_click)\n", ".mouseenter(Gadfly.zoomslider_button_mouseover)\n", ".mouseleave(Gadfly.zoomslider_button_mouseout)\n", ";\n", " });\n", "]]> </script>\n", "</svg>\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 58, "svg": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<svg xmlns=\"http://www.w3.org/2000/svg\"\n", " xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n", " xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n", " version=\"1.2\"\n", " width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n", " stroke=\"none\"\n", 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mit
david4096/bioapi-examples
python_notebooks/1kg_sequence_annotation_service.ipynb
1
12098
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## GA4GH 1000 Genomes Sequence Annotations Example\n", "\n", "This example illustrates how to access the sequence annotations for a given set of ...." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Client\n", "In this step we create a client object which will be used to communicate with the server. It is initialized using the URL." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from ga4gh.client import client\n", "c = client.HttpClient(\"http://1kgenomes.ga4gh.org\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Obtain dataSet id REF: -> `1kg_metadata_service`\n", "dataset = c.search_datasets().next() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Search Feature Sets\n", "Feature sets are the logical containers for genomic features that might be defined in a GFF3, or other file that describes features in genomic coordinates. They are mapped to a single reference set, and belong to specific datasets." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "id: \"WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd\"\n", "dataset_id: \"WyIxa2dlbm9tZXMiXQ\"\n", "reference_set_id: \"WyJOQ0JJMzciXQ\"\n", "name: \"gencode_v24lift37\"\n", "\n" ] } ], "source": [ "for feature_set in c.search_feature_sets(dataset_id=dataset.id):\n", " print feature_set\n", " if feature_set.name == \"gencode_v24lift37\":\n", " gencode = feature_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get Feature Set by ID\n", "With the identifier to a specific Feature Set, one can retrieve that feature set by ID." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "id: \"WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd\"\n", "dataset_id: \"WyIxa2dlbm9tZXMiXQ\"\n", "reference_set_id: \"WyJOQ0JJMzciXQ\"\n", "name: \"gencode_v24lift37\"\n", "\n" ] } ], "source": [ "feature_set = c.get_feature_set(feature_set_id=gencode.id)\n", "print feature_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Search Features\n", "With a Feature Set ID, it becomes possible to construct a Search Features Request. In this request, we can find genomic features by position, type, or name. In this request we simply return all features in the Feature Set." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTc5MiJd,\n", " Name: exon:ENST00000621489.1:2,\n", " Gene Symbol: CH17-408M7.1,\n", " Parent Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTQwOCJd,\n", " Feature Set Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd,\n", " Reference Name: GL000192.1,\n", " Start: 429710,\tEnd: 430271,\n", " Strand: 1,\n", " Feature Type Id: SO:0000147,\n", " Feature Type Term: exon,\n", " Feature Type Sorce Name: so-xp,\n", " Feature Type Source Version: so-xp/releases/2015-11-24/so-xp.owl\n", "\n", "Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTAyNCJd,\n", " Name: ENSG00000277420.1,\n", " Gene Symbol: CH17-408M7.1,\n", " Parent Id: ,\n", " Child Ids: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTQwOCJd\n", " Feature Set Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd,\n", " Reference Name: GL000192.1,\n", " Start: 429710,\tEnd: 440529,\n", " Strand: 1,\n", " Feature Type Id: SO:0000704,\n", " Feature Type Term: gene,\n", " Feature Type Sorce Name: so-xp,\n", " Feature Type Source Version: so-xp/releases/2015-11-24/so-xp.owl\n", "\n", "Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTQwOCJd,\n", " Name: ENST00000621489.1,\n", " Gene Symbol: CH17-408M7.1,\n", " Parent Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTAyNCJd,\n", " Child Ids: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTc5MiJd\n", " Child Ids: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTYwMCJd\n", " Feature Set Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd,\n", " Reference Name: GL000192.1,\n", " Start: 429710,\tEnd: 440529,\n", " Strand: 1,\n", " Feature Type Id: SO:0000673,\n", " Feature Type Term: transcript,\n", " Feature Type Sorce Name: so-xp,\n", " Feature Type Source Version: so-xp/releases/2015-11-24/so-xp.owl\n", "\n", "Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTYwMCJd,\n", " Name: exon:ENST00000621489.1:1,\n", " Gene Symbol: CH17-408M7.1,\n", " Parent Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTQwOCJd,\n", " Feature Set Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd,\n", " Reference Name: GL000192.1,\n", " Start: 438554,\tEnd: 440529,\n", " Strand: 1,\n", " Feature Type Id: SO:0000147,\n", " Feature Type Term: exon,\n", " Feature Type Sorce Name: so-xp,\n", " Feature Type Source Version: so-xp/releases/2015-11-24/so-xp.owl\n", "\n" ] } ], "source": [ "counter = 0\n", "for features in c.search_features(feature_set_id=feature_set.id):\n", " if counter > 3:\n", " break\n", " counter += 1\n", " print\"Id: {},\".format(features.id)\n", " print\" Name: {},\".format(features.name)\n", " print\" Gene Symbol: {},\".format(features.gene_symbol)\n", " print\" Parent Id: {},\".format(features.parent_id)\n", " if features.child_ids:\n", " for i in features.child_ids:\n", " print\" Child Ids: {}\".format(i)\n", " print\" Feature Set Id: {},\".format(features.feature_set_id)\n", " print\" Reference Name: {},\".format(features.reference_name)\n", " print\" Start: {},\\tEnd: {},\".format(features.start, features.end)\n", " print\" Strand: {},\".format(features.strand)\n", " print\" Feature Type Id: {},\".format(features.feature_type.id)\n", " print\" Feature Type Term: {},\".format(features.feature_type.term)\n", " print\" Feature Type Sorce Name: {},\".format(features.feature_type.source_name)\n", " print\" Feature Type Source Version: {}\\n\".format(features.feature_type.source_version)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Note: Not all of the elements returned in the response are present in the example. All of the parameters will be shown in the get by id method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can perform a similar search, this time restricting to a specific genomic region." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ENSG00000282199.1 41991801 42000369\n", "ENST00000585329.1 41991801 42000369\n", "exon:ENST00000621298.1:5 42000143 42000691\n", "ENSG00000267166.5 42000143 42004756\n", "ENST00000621298.1 42000143 42004756\n", "exon:ENST00000585329.1:1 42000144 42000369\n", "exon:ENST00000621298.1:4 42000883 42001329\n" ] } ], "source": [ "for feature in c.search_features(feature_set_id=feature_set.id, reference_name=\"chr17\", start=42000000, end=42001000):\n", " print feature.name, feature.start, feature.end" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzM3MTIxNiJd,\n", " Name: exon:ENST00000614199.1:2,\n", " Gene Symbol: RP11-640M9.4,\n", " Parent Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyIsIjE0MDUwODI3NzI1NjA4MCJd,\n", " Feature Set Id: WyIxa2dlbm9tZXMiLCJnZW5jb2RlX3YyNGxpZnQzNyJd,\n", " Reference Name: GL000192.1,\n", " Start: 493155,\tEnd: 493368,\n", " Strand: 1,\n", " Feature Type Id: SO:0000147,\n", " Feature Type Term: exon,\n", " Feature Type Sorce Name: so-xp,\n", " Feature Type Source Version: so-xp/releases/2015-11-24/so-xp.owl\n", "\n", "remap_original_location: chr1:+:146461646-146461859\n", "gene_status: KNOWN\n", "havana_gene: OTTHUMG00000187535.3\n", "transcript_support_level: NA\n", "Parent: ENST00000614199.1\n", "level: 2\n", "transcript_status: KNOWN\n", "tag: basic\n", "gene_id: ENSG00000277655.1\n", "exon_id: ENSE00003723063.1\n", "transcript_type: unprocessed_pseudogene\n", "transcript_name: RP11-640M9.4-001\n", "exon_number: 2\n", "ont: PGO:0000005\n", "havana_transcript: OTTHUMT00000475232.3\n", "transcript_id: ENST00000614199.1\n", "gene_type: unprocessed_pseudogene\n", "remap_status: full_contig\n", "ID: exon:ENST00000614199.1:2\n", "gene_name: RP11-640M9.4\n" ] } ], "source": [ "feature = c.get_feature(feature_id=features.id)\n", "print\"Id: {},\".format(feature.id)\n", "print\" Name: {},\".format(feature.name)\n", "print\" Gene Symbol: {},\".format(feature.gene_symbol)\n", "print\" Parent Id: {},\".format(feature.parent_id)\n", "if feature.child_ids:\n", " for i in feature.child_ids:\n", " print\" Child Ids: {}\".format(i)\n", "print\" Feature Set Id: {},\".format(feature.feature_set_id)\n", "print\" Reference Name: {},\".format(feature.reference_name)\n", "print\" Start: {},\\tEnd: {},\".format(feature.start, feature.end)\n", "print\" Strand: {},\".format(feature.strand)\n", "print\" Feature Type Id: {},\".format(feature.feature_type.id)\n", "print\" Feature Type Term: {},\".format(feature.feature_type.term)\n", "print\" Feature Type Sorce Name: {},\".format(feature.feature_type.source_name)\n", "print\" Feature Type Source Version: {}\\n\".format(feature.feature_type.source_version)\n", "for vals in feature.attributes.vals:\n", " print\"{}: {}\".format(vals, feature.attributes.vals[vals].values[0].string_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### In this last call we represent all of the elements returned in the message. \n", "\n", "##### For documentation in the service, and more information go to:\n", "https://ga4gh-schemas.readthedocs.io/en/latest/schemas/allele_annotation_service.proto.html" ] } ], "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" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
mwidner/WebArchiveTextTools
src/Text Mining with Web Archives.ipynb
2
4307
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cécile Alduy and I have been working with a team of undergraduate RAs and with some members of the Stanford University Libraries staff to build a corpus of the public discourse of French presidential candidates in advance of the 2017 elections. In this notebook, I will describe the methods by which we have been converting web pages into corpora for analysis and will provide some sample Python code.\n", "\n", "Collaborating with Nicholas Taylor, SUL's Web Archiving Service Manager, and Sarah Sussman, the curator of French and Italian Collections, we have identified several key websites and begun periodic crawls using [ArchiveIt](https://archive-it.org/). One of the first challenges for any text-mining project that uses web archives as a source is, unsurprisingly, getting the correct text from each website. Although it's possible to simply extract *all* the text from a web page, there's a lot of extraneous information that we don't want to deal with. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* general-purpose solution\n", "* WARC structure\n", "* warcat \n", "* BeautifulSoup\n", "* post-processing\n", "* corpus building" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " def extract_text(site_info, input_file, corpus_dir, word_count=0):\n", " '''\n", " Extract the actual text from the HTML\n", " Write out file with text content\n", " Return extracted metadata about text\n", " '''\n", " results = dict()\n", " try:\n", " soup = BeautifulSoup(open(input_file, encoding=\"utf-8\"), 'html.parser')\n", " except UnicodeDecodeError as err:\n", " # print(input_file + ' is not UTF8', err)\n", " return\n", "\n", " if soup is None:\n", " return\n", "\n", " # Skip page if there's a filter and it isn't matched\n", " if len(site_info['filter']) and not len(soup.select(site_info['filter'])):\n", " return\n", "\n", " # Fields in CSV with BeautifulSoup select() options\n", " for item in ['title','date','author','content']:\n", " results[item] = ''\n", " if (not len(site_info[item])):\n", " continue\n", " contents = soup.select(site_info[item])\n", " if contents is not None and len(contents):\n", " # Assume only the first result is relevant\n", " # BS4 returns a list of results even if only 1 found\n", " results[item] = clean_string(contents[0].getText())\n", "\n", " results['word_count'] = len(results['content'].split())\n", " results['filename'] = generate_unique_filename(corpus_dir, site_info['name'], results)\n", " if os.path.isfile(results['filename']):\n", " return\n", "\n", " # Save the original URL\n", " results['url'] = get_original_url(site_info, input_file)\n", "\n", " if (len(results['title']) and results['word_count'] >= int(word_count)):\n", " # Ensure the path exists\n", " if not os.path.isdir(os.path.dirname(results['filename'])):\n", " os.makedirs(os.path.dirname(results['filename']))\n", " with open(results['filename'], 'w') as content:\n", " content.write(str(results['content']))\n", " return results\n", " return None\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "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": "3.3.4" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
Joshuaalbert/IonoTomo
src/ionotomo/notebooks/UVWFrame.ipynb
1
16098
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import (absolute_import, unicode_literals, division,\n", " print_function)\n", "import numpy as np\n", "\n", "import astropy.units as u\n", "\n", "from astropy.coordinates.baseframe import (BaseCoordinateFrame, FrameAttribute,\n", " TimeFrameAttribute, QuantityFrameAttribute,\n", " RepresentationMapping, EarthLocationAttribute,\n", " frame_transform_graph)\n", "\n", "from astropy.coordinates.transformations import FunctionTransform\n", "from astropy.coordinates.representation import (SphericalRepresentation,\n", " UnitSphericalRepresentation,CartesianRepresentation)\n", "from astropy.coordinates import ITRS,ICRS,AltAz\n", "\n", "class CoordinateAttribute(FrameAttribute):\n", " \"\"\"\n", " A frame attribute which is a coordinate object. It can be given as a\n", " low-level frame class *or* a `~astropy.coordinates.SkyCoord`, but will\n", " always be converted to the low-level frame class when accessed.\n", " Parameters\n", " ----------\n", " frame : a coordinate frame class\n", " The type of frame this attribute can be\n", " default : object\n", " Default value for the attribute if not provided\n", " secondary_attribute : str\n", " Name of a secondary instance attribute which supplies the value if\n", " ``default is None`` and no value was supplied during initialization.\n", " \"\"\"\n", " def __init__(self, frame, default=None, secondary_attribute=''):\n", " self._frame = frame\n", " super(CoordinateAttribute, self).__init__(default, secondary_attribute)\n", "\n", " def convert_input(self, value):\n", " \"\"\"\n", " Checks that the input is a SkyCoord with the necessary units (or the\n", " special value ``None``).\n", " Parameters\n", " ----------\n", " value : object\n", " Input value to be converted.\n", " Returns\n", " -------\n", " out, converted : correctly-typed object, boolean\n", " Tuple consisting of the correctly-typed object and a boolean which\n", " indicates if conversion was actually performed.\n", " Raises\n", " ------\n", " ValueError\n", " If the input is not valid for this attribute.\n", " \"\"\"\n", " if value is None:\n", " return None, False\n", " elif isinstance(value, self._frame):\n", " return value, False\n", " else:\n", " if not hasattr(value, 'transform_to'):\n", " raise ValueError('\"{0}\" was passed into a '\n", " 'CoordinateAttribute, but it does not have '\n", " '\"transform_to\" method'.format(value))\n", " transformedobj = value.transform_to(self._frame)\n", " if hasattr(transformedobj, 'frame'):\n", " transformedobj = transformedobj.frame\n", " return transformedobj, True\n", "\n", "class UVW(BaseCoordinateFrame):\n", " \"\"\"\n", " Written by Joshua G. Albert - [email protected]\n", " A coordinate or frame in the UVW system. \n", "\n", " This frame has the following frame attributes, which are necessary for\n", " transforming from UVW to some other system:\n", "\n", " * ``obstime``\n", " The time at which the observation is taken. Used for determining the\n", " position and orientation of the Earth.\n", " * ``location``\n", " The location on the Earth. This can be specified either as an\n", " `~astropy.coordinates.EarthLocation` object or as anything that can be\n", " transformed to an `~astropy.coordinates.ITRS` frame.\n", " * ``phaseDir``\n", " The phase tracking center of the frame. This can be specified either as an\n", " (ra,dec) `~astropy.units.Qunatity` or as anything that can be\n", " transformed to an `~astropy.coordinates.ICRS` frame.\n", "\n", " Parameters\n", " ----------\n", " representation : `BaseRepresentation` or None\n", " A representation object or None to have no data (or use the other keywords)\n", " u : :class:`~astropy.units.Quantity`, optional, must be keyword\n", " The u coordinate for this object (``v`` and ``w`` must also be given and\n", " ``representation`` must be None).\n", " v : :class:`~astropy.units.Quantity`, optional, must be keyword\n", " The v coordinate for this object (``u`` and ``w`` must also be given and\n", " ``representation`` must be None).\n", " w : :class:`~astropy.units.Quantity`, optional, must be keyword\n", " The w coordinate for this object (``u`` and ``v`` must also be given and\n", " ``representation`` must be None).\n", "\n", " Notes\n", " -----\n", " This is useful for radio astronomy.\n", "\n", " \"\"\"\n", "\n", " frame_specific_representation_info = {\n", " 'cartesian': [RepresentationMapping('x', 'u'),\n", " RepresentationMapping('y', 'v'),\n", " RepresentationMapping('z','w')],\n", " }\n", " \n", " default_representation = CartesianRepresentation\n", "\n", " obstime = TimeFrameAttribute(default=None)\n", " location = EarthLocationAttribute(default=None)\n", " phase = CoordinateAttribute(ICRS,default=None)\n", "\n", " def __init__(self, *args, **kwargs):\n", " super(UVW, self).__init__(*args, **kwargs)\n", " @property\n", " def elevation(self):\n", " \"\"\"\n", " Elevation above the horizon of the direction\n", " \"\"\"\n", " return self.phase.transform_to(AltAz(location=self.location,obstime=self.obstime)).alt\n", "\n", "\n", "@frame_transform_graph.transform(FunctionTransform, ITRS, UVW)\n", "def itrs_to_uvw(itrs_coo, uvw_frame):\n", " '''Defines the transformation between ITRS and the UVW frame.'''\n", " \n", " #if np.any(itrs_coo.obstime != uvw_frame.obstime):\n", " # itrs_coo = itrs_coo.transform_to(ITRS(obstime=uvw_frame.obstime))\n", " \n", " # if the data are UnitSphericalRepresentation, we can skip the distance calculations\n", " is_unitspherical = (isinstance(itrs_coo.data, UnitSphericalRepresentation) or\n", " itrs_coo.cartesian.x.unit == u.one)\n", " \n", " lon, lat, height = uvw_frame.location.to_geodetic('WGS84')\n", " lst = AltAz(alt=90*u.deg,az=0*u.deg,location=uvw_frame.location,obstime=uvw_frame.obstime).transform_to(ICRS).ra\n", " ha = (lst - uvw_frame.phase.ra).to(u.radian).value\n", " dec = uvw_frame.phase.dec.to(u.radian).value\n", " lonrad = lon.to(u.radian).value - ha\n", " latrad = dec #lat.to(u.radian).value + \n", " sinlat = np.sin(latrad)\n", " coslat = np.cos(latrad)\n", " sinlon = np.sin(lonrad)\n", " coslon = np.cos(lonrad)\n", " north = [-sinlat*coslon,\n", " -sinlat*sinlon,\n", " coslat]\n", " east = [-sinlon,coslon,0]\n", " up = [coslat*coslon,coslat*sinlon,sinlat]\n", " R = np.array([east,north,up])\n", " \n", " if is_unitspherical:\n", " #don't need to do distance calculation\n", " p = itrs_coo.cartesian.xyz.value\n", " diff = p\n", " penu = R.dot(diff)\n", " \n", " rep = CartesianRepresentation(x = u.Quantity(penu[0],u.one,copy=False),\n", " y = u.Quantity(penu[1],u.one,copy=False),\n", " z = u.Quantity(penu[2],u.one,copy=False),\n", " copy=False)\n", " else:\n", " p = itrs_coo.cartesian.xyz\n", " p0 = ITRS(*uvw_frame.location.geocentric,obstime=uvw_frame.obstime).cartesian.xyz\n", " diff = (p.T-p0).T\n", " penu = R.dot(diff)\n", " \n", " rep = CartesianRepresentation(x = penu[0],#u.Quantity(penu[0],u.m,copy=False),\n", " y = penu[1],#u.Quantity(penu[1],u.m,copy=False),\n", " z = penu[2],#u.Quantity(penu[2],u.m,copy=False),\n", " copy=False)\n", "\n", " return uvw_frame.realize_frame(rep)\n", "\n", "\n", "@frame_transform_graph.transform(FunctionTransform, UVW, ITRS)\n", "def uvw_to_itrs(uvw_coo, itrs_frame):\n", " #p = itrs_frame.cartesian.xyz.to(u.m).value\n", " #p0 = np.array(enu_coo.location.to(u.m).value)\n", " #p = np.array(itrs_frame.location.to(u.m).value)\n", " \n", " \n", " lon, lat, height = uvw_coo.location.to_geodetic('WGS84')\n", " lst = AltAz(alt=90*u.deg,az=0*u.deg,location=uvw_coo.location,obstime=uvw_coo.obstime).transform_to(ICRS).ra\n", " ha = (lst - uvw_coo.phase.ra).to(u.radian).value\n", " dec = uvw_coo.phase.dec.to(u.radian).value\n", " lonrad = lon.to(u.radian).value - ha\n", " latrad = dec #lat.to(u.radian).value + \n", " sinlat = np.sin(latrad)\n", " coslat = np.cos(latrad)\n", " sinlon = np.sin(lonrad)\n", " coslon = np.cos(lonrad)\n", " \n", " north = [-sinlat*coslon,\n", " -sinlat*sinlon,\n", " coslat]\n", " east = [-sinlon,coslon,0]\n", " up = [coslat*coslon,coslat*sinlon,sinlat]\n", " R = np.array([east,north,up])\n", " \n", " if isinstance(uvw_coo.data, UnitSphericalRepresentation) or uvw_coo.cartesian.x.unit == u.one:\n", " diff = R.T.dot(uvw_coo.cartesian.xyz)\n", " p = diff\n", " rep = CartesianRepresentation(x = u.Quantity(p[0],u.one,copy=False),\n", " y = u.Quantity(p[1],u.one,copy=False),\n", " z = u.Quantity(p[2],u.one,copy=False),\n", " copy=False)\n", " else:\n", " diff = R.T.dot(uvw_coo.cartesian.xyz)\n", " p0 = ITRS(*uvw_coo.location.geocentric,obstime=uvw_coo.obstime).cartesian.xyz\n", " #print (R,diff)\n", " p = (diff.T + p0).T\n", " #print (p)\n", " rep = CartesianRepresentation(x = p[0],#u.Quantity(p[0],u.m,copy=False),\n", " y = p[1],#u.Quantity(p[1],u.m,copy=False),\n", " z = p[2],#u.Quantity(p[2],u.m,copy=False),\n", " copy=False)\n", "\n", " return itrs_frame.realize_frame(rep)\n", " \n", " #return ITRS(*p*u.m,obstime=enu_coo.obstime).transform_to(itrs_frame)\n", " \n", "@frame_transform_graph.transform(FunctionTransform, UVW, UVW)\n", "def uvw_to_uvw(from_coo, to_frame):\n", " # for now we just implement this through ITRS to make sure we get everything\n", " # covered\n", " return from_coo.transform_to(ITRS(obstime=from_coo.obstime,location=from_coo.location)).transform_to(to_frame)\n", "\n", "if __name__ == '__main__':\n", " import astropy.coordinates as ac\n", " import astropy.time as at\n", " def compVectors(a,b):\n", " a = a.cartesian.xyz.value\n", " a /= np.linalg.norm(a)\n", " b = b.cartesian.xyz.value\n", " b /= np.linalg.norm(b)\n", " h = np.linalg.norm(a-b)\n", " return h < 1e-8\n", " # with X - East, Z - NCP and Y - Down\n", " time = at.Time(\"2017-01-26T17:07:00.000\",format='isot',scale='utc')\n", " loc = ac.EarthLocation(lon=10*u.deg,lat=10*u.deg,height=0*u.km)\n", " from ENUFrame import ENU\n", " enu = ENU(location=loc,obstime=time)\n", " x = ac.SkyCoord(1,0,0,frame=enu)\n", " z = ac.SkyCoord(0,np.cos(loc.geodetic[1].rad),np.sin(loc.geodetic[1].rad),frame=enu)\n", " #ncp = ac.SkyCoord(0*u.one,0*u.one,1*u.one,frame='itrs').transform_to(enu)\n", " y = ac.SkyCoord(0,np.sin(loc.geodetic[1].rad),-np.cos(loc.geodetic[1].rad),frame=enu)\n", " lst = ac.AltAz(alt=90*u.deg,az=0*u.deg,location=loc,obstime=time).transform_to(ICRS).ra\n", " #ha = lst - ra\n", " print(\"a) when ha=0,dec=90 uvw aligns with xyz\")\n", " ha = 0*u.deg\n", " ra = lst - ha\n", " dec = 90*u.deg\n", " phaseTrack = ac.SkyCoord(ra,dec,frame='icrs')\n", " uvw = UVW(obstime=time,location=loc,phase=phaseTrack)\n", " U = ac.SkyCoord(1,0,0,frame=uvw).transform_to(enu)\n", " V = ac.SkyCoord(0,1,0,frame=uvw).transform_to(enu)\n", " W = ac.SkyCoord(0,0,1,frame=uvw).transform_to(enu)\n", " assert compVectors(U,x),\"fail test a, u != x\"\n", " assert compVectors(V,y),\"fail test a, v != y\"\n", " assert compVectors(W,z),\"fail test a, w != z\"\n", " print(\"passed a\")\n", " print(\"b) v, w, z are always on great circle\")\n", " assert np.cross(V.cartesian.xyz.value,W.cartesian.xyz.value).dot(z.cartesian.xyz.value) < 1e-10, \"Not on the great circle\"\n", " print(\"passed b\")\n", " print(\"c) when ha = 0 U points east\")\n", " ha = 0*u.deg\n", " ra = lst - ha\n", " dec = 35*u.deg\n", " phaseTrack = ac.SkyCoord(ra,dec,frame='icrs')\n", " uvw = UVW(obstime=time,location=loc,phase=phaseTrack)\n", " U = ac.SkyCoord(1*u.m,0*u.m,0*u.m,frame=uvw).transform_to(enu)\n", " V = ac.SkyCoord(0*u.m,1*u.m,0*u.m,frame=uvw).transform_to(enu)\n", " W = ac.SkyCoord(0*u.m,0*u.m,1*u.m,frame=uvw).transform_to(enu)\n", " assert np.cross(V.cartesian.xyz.value,W.cartesian.xyz.value).dot(z.cartesian.xyz.value) < 1e-10, \"Not on the great circle\"\n", " east = ac.SkyCoord(1,0,0,frame=enu)\n", " assert compVectors(U,east),\"fail test c, u != east\"\n", " print(\"passed c\")\n", " print(\"d) when dec=0 and ha = -6 w points east\")\n", " ha = -6*u.hourangle\n", " ra = lst - ha\n", " dec = 0*u.deg\n", " phaseTrack = ac.SkyCoord(ra,dec,frame='icrs')\n", " uvw = UVW(obstime=time,location=loc,phase=phaseTrack)\n", " U = ac.SkyCoord(1*u.m,0*u.m,0*u.m,frame=uvw).transform_to(enu)\n", " V = ac.SkyCoord(0*u.m,1*u.m,0*u.m,frame=uvw).transform_to(enu)\n", " W = ac.SkyCoord(0*u.m,0*u.m,1*u.m,frame=uvw).transform_to(enu)\n", " assert np.cross(V.cartesian.xyz.value,W.cartesian.xyz.value).dot(z.cartesian.xyz.value) < 1e-10, \"Not on the great circle\"\n", " assert compVectors(W,east),\"fail test d, w != east\"\n", " print(\"passed d\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:mayavi_env]", "language": "python", "name": "conda-env-mayavi_env-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 }
apache-2.0
thushear/MLInAction
tensorflow/convolutional_tensor_net.ipynb
1
14550
{ "cells": [ { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 倒库\n", "import tensorflow as tf\n", "import numpy as np\n", "from tensorflow.examples.tutorials.mnist import input_data" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "mnist = input_data.read_data_sets('MNIST_data/',one_hot=True)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datasets(train=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x000000000B394E48>, validation=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x000000000B394DA0>, test=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x000000000B394F60>)\n", "[[ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]]\n", "[[ 0. 0. 0. ..., 1. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 1. 0.]]\n", "[[ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]]\n", "[[ 0. 0. 0. ..., 1. 0. 0.]\n", " [ 0. 0. 1. ..., 0. 0. 0.]\n", " [ 0. 1. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]]\n" ] } ], "source": [ "trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels\n", "\n", "print(mnist)\n", "print(trX)\n", "print(trY)\n", "print(teX)\n", "print(teY)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]\n", "\n", "\n", " [[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]\n", "\n", "\n", " [[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]\n", "\n", "\n", " ..., \n", " [[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]\n", "\n", "\n", " [[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]\n", "\n", "\n", " [[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " ..., \n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "\n", " [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " ..., \n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]]]\n", "[[ 0. 0. 0. ..., 1. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 0. 0.]\n", " [ 0. 0. 0. ..., 0. 1. 0.]]\n" ] } ], "source": [ "trX = trX.reshape(-1,28,28,1) \n", "teX = teX.reshape(-1,28,28,1)\n", "X = tf.placeholder(\"float\",[None,28,28,1])\n", "Y = tf.placeholder(\"float\",[None,10])\n", "print(trX)\n", "print(trY)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def init_weights(shape):\n", " return tf.Variable(tf.random_normal(shape,stddev=0.01))\n", "w = init_weights([3,3,1,32]) #patch 大小为3*3 输入维度为1 输出维度为32 \n", "w2 = init_weights([3,3,32,64]) #patch 大小为3*3 输入维度为32 输出维度为64\n", "w3 = init_weights([3,3,64,128])\n", "w4 = init_weights([128*4*4,625]) # 全连接层 输入维度为128 * 4 * 4 是上一层的输出数据又三维的转变成一维 输出维度为625\n", "w_o = init_weights([625,10]) # 输出层 输入维度为625 输出维度为10 代表10类(labels)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "def model(X,w,w2,w3,w4,w_o,p_keep_conv,p_keep_hidden):\n", " # 第一组卷积层及池化层 最后dropout一些神经元\n", " lla = tf.nn.relu(tf.nn.conv2d(X,w,strides=[1,1,1,1],padding='SAME'))\n", " ll = tf.nn.max_pool(lla,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')\n", " ll = tf.nn.dropout(ll,p_keep_conv)\n", " # 第二组卷积层及池化层 最后dropout一些神经元\n", " l2a = tf.nn.relu(tf.nn.conv2d(ll,w2,strides=[1,1,1,1],padding='SAME'))\n", " l2 = tf.nn.max_pool(l2a,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')\n", " l2 = tf.nn.dropout(l2,p_keep_conv)\n", " # 第三组卷积层及池化层 最后的dropout \n", " l3a = tf.nn.relu(tf.nn.conv2d(l2,w3,strides=[1,1,1,1],padding='SAME'))\n", " l3 = tf.nn.max_pool(l3a,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')\n", " l3 = tf.reshape(l3,[-1,w4.get_shape().as_list()[0]])\n", " l3 = tf.nn.dropout(l3,p_keep_conv)\n", " # 全连接层 最后dropout一些神经元\n", " l4 = tf.nn.relu(tf.matmul(l3,w4))\n", " l4 = tf.nn.dropout(l4,p_keep_hidden)\n", " # 输出层\n", " pyx = tf.matmul(l4,w_o)\n", " return pyx # 返回预测值\n", "\n", "p_keep_conv = tf.placeholder(\"float\")\n", "p_keep_hidden = tf.placeholder(\"float\")\n", "py_x = model(X,w,w2,w3,w4,w_o,p_keep_conv,p_keep_hidden)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x,labels=Y))\n", "train_op = tf.train.RMSPropOptimizer(0.001,0.9).minimize(cost)\n", "predict_op = tf.arg_max(py_x,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.94140625\n", "1 0.98828125\n", "2 0.99609375\n", "3 0.9921875\n", "4 1.0\n", "5 0.9921875\n", "6 0.984375\n", "7 0.984375\n", "8 0.99609375\n", "9 0.99609375\n", "10 1.0\n", "11 1.0\n", "12 0.984375\n", "13 0.9921875\n", "14 1.0\n", "15 0.98046875\n", "16 0.9921875\n", "17 0.9921875\n", "18 0.99609375\n", "19 0.98828125\n", "20 0.98828125\n", "21 0.99609375\n", "22 1.0\n", "23 0.99609375\n", "24 0.98828125\n", "25 0.99609375\n", "26 0.9921875\n", "27 0.9921875\n", "28 1.0\n", "29 0.99609375\n", "30 0.99609375\n", "31 0.98046875\n", "32 1.0\n", "33 0.9921875\n", "34 0.99609375\n", "35 0.99609375\n", "36 0.99609375\n", "37 0.9921875\n", "38 0.9921875\n", "39 0.99609375\n", "40 0.9921875\n", "41 0.99609375\n", "42 0.9921875\n", "43 0.9921875\n", "44 0.9921875\n", "45 1.0\n", "46 0.98828125\n", "47 0.99609375\n", "48 0.99609375\n", "49 0.9921875\n", "50 0.98828125\n", "51 0.9921875\n", "52 1.0\n", "53 0.99609375\n", "54 0.99609375\n", "55 0.99609375\n", "56 0.9921875\n", "57 0.98828125\n", "58 0.99609375\n", "59 0.99609375\n", "60 1.0\n", "61 1.0\n" ] } ], "source": [ "batch_size = 128\n", "test_size = 256\n", "with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", " for i in range(100):\n", " training_batch = zip(range(0,len(trX),batch_size),range(batch_size,len(trX) + 1 ,batch_size))\n", " for start , end in training_batch:\n", " sess.run(train_op,feed_dict = {X: trX[start:end],Y: trY[start:end],p_keep_conv: 0.8,p_keep_hidden: 0.5})\n", " test_indices = np.arange(len(teX)) \n", " np.random.shuffle(test_indices)\n", " test_indices = test_indices[0:test_size]\n", " print(i,np.mean(np.argmax(teY[test_indices],axis=1)==sess.run(predict_op,feed_dict={X: teX[test_indices],p_keep_conv:1.0,p_keep_hidden:1.0})))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
NeuroDataDesign/seelviz
Tony/docs_ipynb_links/Testing+Structure+Tensor.ipynb
1
14818
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using tractography function in pipeline to generate structure tensors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the main analysis module and tractography module. Use the analysis module to download a raw brain of interest, then use the tractography function \"nii_to_tiff_stack\" to save a .nii as a TIFF stack." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import tractography as tract" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import analysis3 as a3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "output = a3.get_raw_brain(\"s275\", \"userToken.pem\", save = True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "tract.nii_to_tiff_stack(\"s275_raw.nii\", \"s275\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(62, 51, 1114)\n" ] } ], "source": [ "data = tract.tiff_stack_to_array(\"s275_TIFFs/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### From there, run the generate_FSL_structure_tensor code to save a copy of the FSL structure tensor.\n", "Note that the value can also be directly used from the function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start DoG Sigma on 1\n", "Start Gauss Sigma with gausigma = 2.3\n", "Generating Gaussian kernel...\n", "Blurring gradient products...\n", "Saving a copy for this Gaussian sigma...\n", "Completed computing structure tensor on s275!\n" ] }, { "data": { "text/plain": [ "array([[[[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " ..., \n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0]],\n", "\n", " [[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " ..., \n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0]],\n", "\n", " [[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 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1, 0]],\n", "\n", " [[ 0, 0, 6, 0, 10, 0],\n", " [ 0, 0, 9, 0, 13, 0],\n", " [ 0, 0, 11, 0, 15, 0],\n", " ..., \n", " [ 0, 0, 3, 0, 3, 0],\n", " [ 0, 0, 2, 0, 2, 0],\n", " [ 0, 0, 1, 0, 1, 0]]],\n", "\n", "\n", " [[[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " ..., \n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0]],\n", "\n", " [[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " ..., \n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0]],\n", "\n", " [[ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 1, 1, 0, 0, 0],\n", " ..., \n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0]],\n", "\n", " ..., \n", " [[ 0, 0, 13, 0, 14, 0],\n", " [ 0, 0, 17, 0, 19, 0],\n", " [ 0, 0, 20, 0, 22, 0],\n", " ..., \n", " [ 0, 0, 5, 0, 3, 0],\n", " [ 0, 0, 3, 0, 2, 0],\n", " [ 0, 0, 2, 0, 1, 0]],\n", "\n", " [[ 0, 0, 8, 0, 10, 0],\n", " [ 0, 0, 10, 0, 13, 0],\n", " [ 0, 0, 12, 0, 15, 0],\n", " ..., \n", " [ 0, 0, 3, 0, 2, 0],\n", " [ 0, 0, 2, 0, 1, 0],\n", " [ 0, 0, 1, 0, 1, 0]],\n", "\n", " [[ 0, 0, 4, 0, 6, 0],\n", " [ 0, 0, 6, 0, 8, 0],\n", " [ 0, 0, 7, 0, 9, 0],\n", " ..., \n", " [ 0, 0, 2, 0, 2, 0],\n", " [ 0, 0, 1, 0, 1, 0],\n", " [ 0, 0, 1, 0, 0, 0]]]], dtype=uint8)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tract.generate_FSL_structure_tensor(data, \"s275\")" ] }, { "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
george1328/george1328.github.io
notebooks/regression/regression.ipynb
2
266913
{ "metadata": { "name": "", "signature": "sha256:b194c82f3b6d32a265a5b1170ecf38dd5d244f69b616c9cb46fd31ada4ebfbdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Regression using SciKit Learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook will demonstrate using linear and polynomial regression to model the relationship between feature variables and the response variable.\n", "\n", "We will use the MPG dataset to practice using regression to predict a fuel economy(MPG) of a car given its features.\n", "\n", "For the mathematical explanation and theory, please view lecture notes: http://george1328.github.io/lecture_notes/Regression_Regularization.pdf\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Step 1: Get the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# Column/Feature label are not available in the dataset, so we create a list of features using auto-mpg.names\n", "features = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin', 'car name']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the data directly into pandas from the url, specify header=None as column labels are not in dataset\n", "import urllib\n", "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n", "# file is fixed-width-format so we will use read_fwf instead of read_csv\n", "df = pd.read_fwf(urllib.urlopen(url), header = None)\n", "df.columns = features" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "# Alternatively, we can download the data\n", "# We use the bang(!) within iPython Notebooks to run command line statements directly from the Notebook\n", "! curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n", "! curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.names" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\r\n", " Dload Upload Total Spent Left Speed\r\n", "\r", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 30286 100 30286 0 0 85395 0 --:--:-- --:--:-- --:--:-- 85553\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\r\n", " Dload Upload Total Spent Left Speed\r\n", "\r", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "100 1660 100 1660 0 0 5315 0 --:--:-- --:--:-- --:--:-- 5320\r\n" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "# Since this dataset has no column headings, we need to explicitely state names=features\n", "df = pd.read_fwf(\"auto-mpg.data\", names = features)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Step 2: Clean the data\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Use head, describe, info and unique to get a sense of the data\n", "df.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " <th>model year</th>\n", " <th>origin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " <td> 398.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 23.514573</td>\n", " <td> 5.454774</td>\n", " <td> 193.425879</td>\n", " <td> 2970.424623</td>\n", " <td> 15.568090</td>\n", " <td> 76.010050</td>\n", " <td> 1.572864</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 7.815984</td>\n", " <td> 1.701004</td>\n", " <td> 104.269838</td>\n", " <td> 846.841774</td>\n", " <td> 2.757689</td>\n", " <td> 3.697627</td>\n", " <td> 0.802055</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 9.000000</td>\n", " <td> 3.000000</td>\n", " <td> 68.000000</td>\n", " <td> 1613.000000</td>\n", " <td> 8.000000</td>\n", " <td> 70.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 17.500000</td>\n", " <td> 4.000000</td>\n", " <td> 104.250000</td>\n", " <td> 2223.750000</td>\n", " <td> 13.825000</td>\n", " <td> 73.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 23.000000</td>\n", " <td> 4.000000</td>\n", " <td> 148.500000</td>\n", " <td> 2803.500000</td>\n", " <td> 15.500000</td>\n", " <td> 76.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 29.000000</td>\n", " <td> 8.000000</td>\n", " <td> 262.000000</td>\n", " <td> 3608.000000</td>\n", " <td> 17.175000</td>\n", " <td> 79.000000</td>\n", " <td> 2.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 46.600000</td>\n", " <td> 8.000000</td>\n", " <td> 455.000000</td>\n", " <td> 5140.000000</td>\n", " <td> 24.800000</td>\n", " <td> 82.000000</td>\n", " <td> 3.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ " mpg cylinders displacement weight acceleration \\\n", "count 398.000000 398.000000 398.000000 398.000000 398.000000 \n", "mean 23.514573 5.454774 193.425879 2970.424623 15.568090 \n", "std 7.815984 1.701004 104.269838 846.841774 2.757689 \n", "min 9.000000 3.000000 68.000000 1613.000000 8.000000 \n", "25% 17.500000 4.000000 104.250000 2223.750000 13.825000 \n", "50% 23.000000 4.000000 148.500000 2803.500000 15.500000 \n", "75% 29.000000 8.000000 262.000000 3608.000000 17.175000 \n", "max 46.600000 8.000000 455.000000 5140.000000 24.800000 \n", "\n", " model year origin \n", "count 398.000000 398.000000 \n", "mean 76.010050 1.572864 \n", "std 3.697627 0.802055 \n", "min 70.000000 1.000000 \n", "25% 73.000000 1.000000 \n", "50% 76.000000 1.000000 \n", "75% 79.000000 2.000000 \n", "max 82.000000 3.000000 " ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " <th>model year</th>\n", " <th>origin</th>\n", " <th>car name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 18</td>\n", " <td> 8</td>\n", " <td> 307</td>\n", " <td> 130.0</td>\n", " <td> 3504</td>\n", " <td> 12.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> \"chevrolet chevelle malibu\"</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 15</td>\n", " <td> 8</td>\n", " <td> 350</td>\n", " <td> 165.0</td>\n", " <td> 3693</td>\n", " <td> 11.5</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> \"buick skylark 320\"</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 18</td>\n", " <td> 8</td>\n", " <td> 318</td>\n", " <td> 150.0</td>\n", " <td> 3436</td>\n", " <td> 11.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> \"plymouth satellite\"</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 304</td>\n", " <td> 150.0</td>\n", " <td> 3433</td>\n", " <td> 12.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> \"amc rebel sst\"</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 17</td>\n", " <td> 8</td>\n", " <td> 302</td>\n", " <td> 140.0</td>\n", " <td> 3449</td>\n", " <td> 10.5</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> \"ford torino\"</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ " mpg cylinders displacement horsepower weight acceleration model year \\\n", "0 18 8 307 130.0 3504 12.0 70 \n", "1 15 8 350 165.0 3693 11.5 70 \n", "2 18 8 318 150.0 3436 11.0 70 \n", "3 16 8 304 150.0 3433 12.0 70 \n", "4 17 8 302 140.0 3449 10.5 70 \n", "\n", " origin car name \n", "0 1 \"chevrolet chevelle malibu\" \n", "1 1 \"buick skylark 320\" \n", "2 1 \"plymouth satellite\" \n", "3 1 \"amc rebel sst\" \n", "4 1 \"ford torino\" " ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "df.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 398 entries, 0 to 397\n", "Data columns (total 9 columns):\n", "mpg 398 non-null float64\n", "cylinders 398 non-null int64\n", "displacement 398 non-null float64\n", "horsepower 398 non-null object\n", "weight 398 non-null float64\n", "acceleration 398 non-null float64\n", "model year 398 non-null int64\n", "origin 398 non-null int64\n", "car name 398 non-null object\n", "dtypes: float64(4), int64(3), object(2)" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "# Name and Horsepower are the only non-numeric fields. Name of a car is unlikely to have an influence on the MPG.\n", "df.horsepower.unique()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ "array(['130.0', '165.0', '150.0', '140.0', '198.0', '220.0', '215.0',\n", " '225.0', '190.0', '170.0', '160.0', '95.00', '97.00', '85.00',\n", " '88.00', '46.00', '87.00', '90.00', '113.0', '200.0', '210.0',\n", " '193.0', '?', '100.0', '105.0', '175.0', '153.0', '180.0', '110.0',\n", " '72.00', '86.00', '70.00', '76.00', '65.00', '69.00', '60.00',\n", " '80.00', '54.00', '208.0', '155.0', '112.0', '92.00', '145.0',\n", " '137.0', '158.0', '167.0', '94.00', '107.0', '230.0', '49.00',\n", " '75.00', '91.00', '122.0', '67.00', '83.00', '78.00', '52.00',\n", " '61.00', '93.00', '148.0', '129.0', '96.00', '71.00', '98.00',\n", " '115.0', '53.00', '81.00', '79.00', '120.0', '152.0', '102.0',\n", " '108.0', '68.00', '58.00', '149.0', '89.00', '63.00', '48.00',\n", " '66.00', '139.0', '103.0', '125.0', '133.0', '138.0', '135.0',\n", " '142.0', '77.00', '62.00', '132.0', '84.00', '64.00', '74.00',\n", " '116.0', '82.00'], dtype=object)" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "(df.horsepower == '?').sum()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "6" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "# Lets convert horsepower to a numeric field so we can use it in our analysis\n", "df['horsepower'] = df['horsepower'].convert_objects(convert_numeric = True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "# We will drop the 6 records that are missing horsepower. We could extimate these missing values but for the sake of accuracy\n", "# we will not. Also, its only 6 missing values\n", "df = df.dropna()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "df.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 392 entries, 0 to 397\n", "Data columns (total 9 columns):\n", "mpg 392 non-null float64\n", "cylinders 392 non-null int64\n", "displacement 392 non-null float64\n", "horsepower 392 non-null float64\n", "weight 392 non-null float64\n", "acceleration 392 non-null float64\n", "model year 392 non-null int64\n", "origin 392 non-null int64\n", "car name 392 non-null object\n", "dtypes: float64(5), int64(3), object(1)" ] } ], "prompt_number": 55 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Step 3: Get a sense of the data using Exploratory Data Analysis(EDA)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import seaborn as sns" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.boxplot(df.mpg, df.cylinders)\n", "# This is interesting. 4 cylinder vehicles have better mileage on average than 3 cylinder vehicles" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11156f590>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x111b3ec90>" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "three_cyl = df[df.cylinders == 3]\n", "print three_cyl['car name']\n", "## Aha! Tiny Mazda roadsters..." ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "71 \"mazda rx2 coupe\"\n", "111 \"maxda rx3\"\n", "243 \"mazda rx-4\"\n", "334 \"mazda rx-7 gs\"\n", "Name: car name, dtype: object\n" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.violinplot(df.mpg, df['model year'])\n", "# Fancy seaborn graphing" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1116b3950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ALyQSiafOGmi3pr329E4S2veh100nThKa4Bao6Y5+f8DyOTQjLxwuhNOpyiz3gJWVFTyB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7W4GV1toHjDH/CpwP3DLTaROR0proNdVKaRVejWZqDIxy5CyWAG3GmDv9+j8J\nLLXWPuB/vwN4EwoWIjVnotdUK7HtR7WYqTEwynFE9gFXW2u/aox5BTB24N1eoHPmkyUiM2Giyly9\nIjx1M1FBXo5g8TTwDIC1dr0xZidwQuz3DmBPoQvt7Bzb4H70d9P9XevUOrVOrbPc65w/v52FCztG\n/b57d/u43+PfJaU5rXIEi4uA44HLjDGH4oLDj4wxp1lr7wfOBu4pdKF79/ZN+t10f9c6tU6tU+ss\n5zqzI8P84he/Yteu3gP1PY2NjQc+5/s9TZrTKkew+Cqw2hgT6iguAnYC1xljmoAngZvLkC4RkYo1\ndmCp1o6DgKghXzG7Nc9nxoOFtXYYeHeen06f4aSIiFSV+MBS+eopSll3oUZ5IiKSSMFCREQSKViI\niEgiBQsREUmkYCEiIokULEREJJGChYiIJFKwEBGRRAoWIiKSSMFCREQSKViIiEgiBQsREUmkYCEi\nIokULEREJJGChYiIJFKwEBGRRAoWIiKSSMFCREQSKViIiEgiBQsREUmkYCEiIokULEREJJGChYiI\nJFKwEBGRRAoWIiKSSMFCREQSKViIiEgiBQsREUmkYCEiIokULEREJJGChYiIJFKwEBGRRAoWIiKS\nSMFCREQSKViIiEgiBQsREUnUUO4EBMaYOuDLwPHAAPB+a+2G8qZKRESgsnIWbwGarLWnAh8Hrilz\nekRExKukYLEcWANgrX0YOKm8yRERkaCSgsVcoDv294gvmhIRkTKrmDoLXKDoiP1dZ63N5puwb+8L\nAOzv2QVk/LeH0Lf3hbzfTff3+Hdap9apdWqd1bjO6crkcrlpL6QYjDEXAH9orb3IGHMK8P+stW8u\nd7pERKSychbfB840xjzo/76onIkREZFIxeQsRESkcqkCWUREEilYiIhIIgULERFJpGAhIiKJKult\nqLyMMU3ALbgW3r8A/hv4FPAbYBHwNeByoA14BugHXgUMAU3AU8A84BA/z2FAC7Df/z8CDPv5B3D7\npB/3wnKr/5z1n7NAPbAb14iwwc9b56fvA+b45QwB7UAO2Onnz/h15vw0bX4z9/jlPubn/x2/rJCm\nRj/PkJ+/2y9rjp+3wacn66fL+DQN+eXi/8/67cX/XufTj19Gzv8dps3E/q+L/d4NdPnpev3/c2Lr\nD+r931n/L6xzHzAIzB/zW0jXiJ+vIbbMcJzCfhyJbUs9sMvvg2Y/D7H018eWE09T/Djs98vGfx/2\nY27M/OEnlvAJAAAO+0lEQVQ4hPMhQ/Rye5g3vu6wXSOxz/1+/nj6w34I68v4fZv129Xvty0sJ348\n4/suLKcx9ntYZp3f3nr/b8RP00R0Hg/HtitM81vc9dMS2/dh/4T15mLrzwE9uOPXGvs+vh97/fKa\nGX0OxtPcQHQ+hDQN+/QO+u/CNTQCbAMW+G1/EjjGr2PQf7cWeA3uugznefw4h+1viC2zh+i6bR6z\n3fhlZ2LzxJcTjmM4RvFrKb6N4Vp/Cjja/77ff98B3Ay8A3d/e5Vf54D/7TncdVQH/BPw18A64L/8\n371+XQ8CnX6djwDLgCP9evYAX7TWfp1JVEPO4ju4DfsV8DBwNe7CqccFgY/jdvSv/P+vwh2Q5/z/\nC3FBJRy4Zr/cZ3E7aQS380dwB6AfdzMcAF5k9IX1FLADd4DX4w7+Pv9bH7DRL2ufX3/OL68Fd9Fs\nxwWavUQXzoO4k7fBr/cEP99vfDrDhdHrp+nx37f5deLnH/R/h5Y54YY57L8b8v+HC+cHwAt+mbuB\nLX6+p4lu6i8QnbhBCNLDwEN+3eEGvdVvWx3uwt3p19vj13mr30etfr6s3yf7/TY/6o8Jfl/kfLp+\n7qd9mujG0Ut0w9iDO877/foGgJ/57bkE+IlP0/1+fVuAu/wxGPTbGfbzgN+OLT4dD+DOg3BMev33\nYT9/wc/3W7+9OdzFGwL6JtxDTjgXBvz8IQBu9svcSfRg8L9+Xe3+XzjO4I7XC35Zvf63Otzx3OyP\nS/htO9HxPsqvsxn4pl9fOK9yROdIE/CEn2cQuBN4mZ9vm1/PDr/vfhKbf5//HI53h0/nkE/3Xp/W\nAT9du1/nPqIHsn7gdv+5AXfce/zyH/fb2eOPzW6/fVn/XS8uoD3of3s1URDs8Wlf5v/eTXS9D+Lu\nHZ/zx+pFv32P+/3Z5ucPQexm3Hm20U+XBa70vw35bej3v4Vz8X/95zrceTKAu2GHbQ775BiiB4JX\n4h7I5gBv9/vjEKJA/JSff75f9ovAX/np5gGf8b+fCNwEnGStfZ1P/9uBlbiH0xuB03FBalLVECxa\ngE/iLryH/Hc9wAW4E60buBd3Iv4CFwT6gS/idvoC3I0hg7tZhSeB/bhI24Q7ITK4E3gO8Lxfbzvu\nomlm9A14BHcxj/hp9vn1/6//vx53Q+3xywlPTPtwT4l9uIsH4B7cgQ4X6SDuhP4boieeVlzwqfdp\n7fbLChfiPr89O/zyQ/ALT5Idfr80ER3zE4FP+891wE/9PM/59Wf8usIJ/SLR0+Kg37+3+flv9tMt\nIMrRhJzAAO5CzOFO0n7/+Vm/nC6/j/uAN+Iu0kxs/y4EjF/uj/w++K1f77BPV3hC/ynRU/vBfj1/\nhwvAAK/HHa+X4C7MkHsMOZwGvx8PAw73372e6Lgv8tOHJ9IR4L1+v7yEKFj9He7GkwU+67e7Hvgl\n0XkQ9vtC3I1imCjncgTuKTL+hL4n9vtc3LnS4PddBjgLd66FXMUOvy+a/DyvJwp0p+GO3RbcMV6H\nC5BP+m26w6dxPfAK/1037jp4ud+He3E3zK1+uev9cuqB+4huonf5/XI1LrgMA9b/fyFRoOzH3dD3\n4M6LvbiAv5vRObgc0cPfd/139/htHsGd023+OIT1PEj0kPEj3PUwHFveK4D/64/hIj9/I1HOvJ8o\n5/0Gomviv/3nZcD3/PF80m/PPlxAGMbd2MP12kF0/EOJQXiQCLmPEdzNPjzp/8BPc4Oft4MoeDbj\njvuhuBKYPlxwD9fgaqISEHDX2AvAvwPH+e25DXdvnFQ1BIvv4SIfuJtEHe5EGsbtuJAbAHfjONL/\n/ze4nbQD1ynhCO5mPuw/v53oaSA8PYeDuNjPOxRb9gAuYods/KuJsr7X4244p+AOageji4D2+3Uc\n5n8/nCjr+DainMZ5sfSFg7vVf361T18Wd0IP4m4EoUinAxc0w5PJb/wyQ7A7BPcEFYrBGoA/8/PW\nAX+Mu+B/z6d9k59vN+4kfImf5+XAt/00F+JuZst9Gj/v05Hz6+ry063E3QRe9Pt2iOgGHHJMWdyT\n5FI//ytxT57duJtjL66hZs5vd8hSr8LdcHO4G2jGz3ck8Gu/HXNwwSnsyz3+WPb7/VNPFIiagPcT\nFds0+P39E79tTX6/teNu6CNEF/1WotzMq/y+uRF3foTj1kp0gwhPtgf7fRGK2Ob5fborlqZP+vQM\n4m5mJ/n92EZ0zoRcXgvwUr+/QzC50H8fihEvxJ2HjX66o3BFNPXAOX5bj/HbcYvff43AsbE0/qn/\nvRF3frzez3++38dvBd6Mu3l+AnejAjgZdwO/ws8XAvuRwLv9ujcBl+ECZxNu6AJwAaDFf/4QUaBs\nw90r/tPvg26f1ma/348Cvgq8ltFFRs24HG245ur9cQnFQeHvRtx13OXXf7Tfvn3AqX6bwZ2Xochu\nJ+7ceytRsW2n//wyn/ZB3HFu82nI+HVtIHpAONb/f7tfXgZ4n1/fj4mKtr6OO4f2El3n7/X7co4x\n5mt+Xw3izudNPq1/BnyLBNUQLG7A3SiOw3VjPkx0I407CHezfQR3Ef27/34R7om3Hldk0ExUxtpD\nVAexkag4JYvb4eHiH/D/7/bztOFO4F7cGBz/6tcVciLd/v+3+rT80q/zg7gT4gWicva7fdqz1trF\nuBtxC/AfRAc9Xm5fjzvZ7/bpfUlsH1xIlAM61n/3335/NeNuMFmik/4Bv8wuXKA4C5e1r/f77Alc\nMFzh98G+2HQX4nJgoTisB/gIrqhnn5/mUb/sf8TdmJ/2296Mu9hCmfYzfp37cE9RIee3CXfB9eAC\nxkJGlw/PA/4hts0f8vt9PvBDP28zruz2MKJc3Ry/f8NFNoi70eHT8SH/XQhqa3A3uOAL/ni81K9v\no58uFCXt9NvUgHvK3u7nOwpXtBHK/kN9yYDfNvx2P4M79uHGBHAdUf3CC7iHoE4/fXj6/aFf7j5c\noHwtUVB+F3CpT+8NuHOyFfckvtx/b/12fQt33EeAq4A/8vvjSb++EVyA2Y87H1pw5+FTfv2/jzun\nD8flKg/H5Vi/6af9Je6h5DTcNdSEy0VY3DG/Cljit/EJn/5w41xOFHjDtRHqmt7u1wvu/A0PIaf6\ned/vvx/BnRfNuPPhMFzuoMnP+zlc/UaoD+nEHdNw3O7EnTdL/Pr+GXeMw/k8hAuAYRsaiIrbzsBd\nj4v8ujtxgaONqASjCRewL/N/v5romg/BJBzXN/l1NsTStdD/34QLJg8CzdbaC3H3qmOIHgL7cfeh\nfmPMAiZRDcHiZOB/cE+qNzO6w8E23E46EndwtuAO3gLcTTJUzn3WT/+7/v9QbDEPdxFsxz0xb8Md\nyFAZ9jL/ezjgHUQ5gv/xf9+BOwEywMW4Hd+CO1AfxZ0QYRCnTURPiqG4YJ7/O6TpSL+sb/vtW0VU\nfv8TopzEMUR1AqHuZStROfULuAvxjX79TbibZigaewR34bX6NO7EPV2HoouH/b58ld+Hzbib5s9x\nJ+Ozfp+F/bMQl0va7PfRPbjgtA34P/6YDBIF8RCoP4i7ec/x2/ImoifvJcCf445fvd+OrX4fh2Ke\nC3HHPUdUnPcU8Dq//ma/nU1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"text": [ "<matplotlib.figure.Figure at 0x112a64e10>" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.boxplot(df.mpg, df.origin)\n", "# Although the values of origin are not given, we can guess that 1=USA, 2=Europe and 3=Japan... Maybe..." ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x113e3eb90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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z6E11fv1KfpB7KnA38MHMvLd1+KGI2JiZO4ALgHvma2d8/OCCzjc5OX2ipaoNk5PTjI3t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\nn1VUAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x112ae6b50>" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.boxplot(df.mpg, df.acceleration)\n", "# Little cars have pretty good accelaration AND good mileage so not a great association" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1127112d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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N7801/unXrl/ub7W1C/HF+qj4k5MY/fmr+PvjRKeQZbw/kz2fLA7j3wmHw8kj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\nEASusel8jZXtAUweHcUU9Httegbss/P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"text": [ "<matplotlib.figure.Figure at 0x1126e6bd0>" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.kdeplot(df.mpg, df.cylinders)\n", "# Showing different plot options in seaborn :-)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x116532710>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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L8fd1nMcuCMhLpAhSof/cvjZH212Vsv5jCldXWk1d/Z8FheOFIHVhQJuUbm5Y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\ns5Er63BYj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18GvDFV5dMZWZZ9wBrgXxVFuT9/elq0X5blG2RZ/r/5pzEgA2yaDm0HUBRl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\n", "text": [ "<matplotlib.figure.Figure at 0x1126de710>" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "An alternate method to visualizing the data is to print the correlation matrix" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.corr()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " <th>model year</th>\n", " <th>origin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>mpg</th>\n", " <td> 1.000000</td>\n", " <td>-0.777618</td>\n", " <td>-0.805127</td>\n", " <td>-0.778427</td>\n", " <td>-0.832244</td>\n", " <td> 0.423329</td>\n", " <td> 0.580541</td>\n", " <td> 0.565209</td>\n", " </tr>\n", " <tr>\n", " <th>cylinders</th>\n", " <td>-0.777618</td>\n", " <td> 1.000000</td>\n", " <td> 0.950823</td>\n", " <td> 0.842983</td>\n", " <td> 0.897527</td>\n", " <td>-0.504683</td>\n", " <td>-0.345647</td>\n", " <td>-0.568932</td>\n", " </tr>\n", " <tr>\n", " <th>displacement</th>\n", " <td>-0.805127</td>\n", " <td> 0.950823</td>\n", " <td> 1.000000</td>\n", " <td> 0.897257</td>\n", " <td> 0.932994</td>\n", " <td>-0.543800</td>\n", " <td>-0.369855</td>\n", " <td>-0.614535</td>\n", " </tr>\n", " <tr>\n", " <th>horsepower</th>\n", " <td>-0.778427</td>\n", " <td> 0.842983</td>\n", " <td> 0.897257</td>\n", " <td> 1.000000</td>\n", " <td> 0.864538</td>\n", " <td>-0.689196</td>\n", " <td>-0.416361</td>\n", " <td>-0.455171</td>\n", " </tr>\n", " <tr>\n", " <th>weight</th>\n", " <td>-0.832244</td>\n", " <td> 0.897527</td>\n", " <td> 0.932994</td>\n", " <td> 0.864538</td>\n", " <td> 1.000000</td>\n", " <td>-0.416839</td>\n", " <td>-0.309120</td>\n", " <td>-0.585005</td>\n", " </tr>\n", " <tr>\n", " <th>acceleration</th>\n", " <td> 0.423329</td>\n", " <td>-0.504683</td>\n", " <td>-0.543800</td>\n", " <td>-0.689196</td>\n", " <td>-0.416839</td>\n", " <td> 1.000000</td>\n", " <td> 0.290316</td>\n", " <td> 0.212746</td>\n", " </tr>\n", " <tr>\n", " <th>model year</th>\n", " <td> 0.580541</td>\n", " <td>-0.345647</td>\n", " <td>-0.369855</td>\n", " <td>-0.416361</td>\n", " <td>-0.309120</td>\n", " <td> 0.290316</td>\n", " <td> 1.000000</td>\n", " <td> 0.181528</td>\n", " </tr>\n", " <tr>\n", " <th>origin</th>\n", " <td> 0.565209</td>\n", " <td>-0.568932</td>\n", " <td>-0.614535</td>\n", " <td>-0.455171</td>\n", " <td>-0.585005</td>\n", " <td> 0.212746</td>\n", " <td> 0.181528</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ " mpg cylinders displacement horsepower weight \\\n", "mpg 1.000000 -0.777618 -0.805127 -0.778427 -0.832244 \n", "cylinders -0.777618 1.000000 0.950823 0.842983 0.897527 \n", "displacement -0.805127 0.950823 1.000000 0.897257 0.932994 \n", "horsepower -0.778427 0.842983 0.897257 1.000000 0.864538 \n", "weight -0.832244 0.897527 0.932994 0.864538 1.000000 \n", "acceleration 0.423329 -0.504683 -0.543800 -0.689196 -0.416839 \n", "model year 0.580541 -0.345647 -0.369855 -0.416361 -0.309120 \n", "origin 0.565209 -0.568932 -0.614535 -0.455171 -0.585005 \n", "\n", " acceleration model year origin \n", "mpg 0.423329 0.580541 0.565209 \n", "cylinders -0.504683 -0.345647 -0.568932 \n", "displacement -0.543800 -0.369855 -0.614535 \n", "horsepower -0.689196 -0.416361 -0.455171 \n", "weight -0.416839 -0.309120 -0.585005 \n", "acceleration 1.000000 0.290316 0.212746 \n", "model year 0.290316 1.000000 0.181528 \n", "origin 0.212746 0.181528 1.000000 " ] } ], "prompt_number": 65 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Step 4: Prepare data for analysis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create numpy variables X and y with the predictor and class variables\n", "X = df[['weight', 'model year', 'horsepower', 'origin', 'displacement']].values\n", "y = df['mpg'].values" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn.linear_model import LinearRegression" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Step 5: Fit, predict and interpret the results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = LinearRegression()\n", "model.fit(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 69 }, { "cell_type": "code", "collapsed": false, "input": [ "predictions = model.predict(X_test)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, prediction in enumerate(predictions):\n", " print 'Predicted: %s, Actual: %s' % (prediction, y_test[i])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Predicted: 24.1374463009, Actual: 23.0\n", "Predicted: 20.7218206344, Actual: 21.0\n", "Predicted: 21.8941489423, Actual: 21.0\n", "Predicted: 31.2739533219, Actual: 43.4\n", "Predicted: 8.73849758971, Actual: 10.0\n", "Predicted: 12.3567499567, Actual: 13.0\n", "Predicted: 20.5539888194, Actual: 18.6\n", "Predicted: 23.5043942628, Actual: 21.5\n", "Predicted: 24.2250158308, Actual: 20.8\n", "Predicted: 29.7560919602, Actual: 23.9\n", "Predicted: 23.9300320498, Actual: 19.1\n", "Predicted: 18.1430047987, Actual: 17.5\n", "Predicted: 24.1607297159, Actual: 25.0\n", "Predicted: 20.2761485564, Actual: 18.0\n", "Predicted: 35.8155410311, Actual: 38.0\n", "Predicted: 8.7074446523, Actual: 13.0\n", "Predicted: 17.8979064936, Actual: 17.5\n", "Predicted: 24.4450715073, Actual: 19.0\n", "Predicted: 17.6644426204, Actual: 18.0\n", "Predicted: 31.3474114534, Actual: 32.0\n", "Predicted: 20.4370044076, Actual: 17.7\n", "Predicted: 15.9413895668, Actual: 15.0\n", "Predicted: 21.0107531943, Actual: 22.0\n", "Predicted: 25.7540134863, Actual: 23.0\n", "Predicted: 23.9496970655, Actual: 20.2\n", "Predicted: 29.6572909222, Actual: 35.0\n", "Predicted: 26.9401894891, Actual: 28.8\n", "Predicted: 19.8034067713, Actual: 18.0\n", "Predicted: 24.5575812291, Actual: 23.0\n", "Predicted: 35.4302563906, Actual: 37.0\n", "Predicted: 20.8287149326, Actual: 21.0\n", "Predicted: 29.6734765718, Actual: 29.8\n", "Predicted: 22.015168613, Actual: 16.2\n", "Predicted: 26.484637129, Actual: 21.5\n", "Predicted: 31.8567092134, Actual: 29.5\n", "Predicted: 25.3383968192, Actual: 19.0\n", "Predicted: 25.368336478, Actual: 23.2\n", "Predicted: 25.9351262498, Actual: 28.0\n", "Predicted: 24.533323956, Actual: 23.0\n", "Predicted: 24.3018889873, Actual: 25.0\n", "Predicted: 16.9777759556, Actual: 16.0\n", "Predicted: 10.832224498, Actual: 13.0\n", "Predicted: 30.9380337002, Actual: 29.5\n", "Predicted: 23.5404225255, Actual: 19.4\n", "Predicted: 28.6884754537, Actual: 31.0\n", "Predicted: 27.5495074176, Actual: 28.0\n", "Predicted: 31.3874972604, Actual: 32.0\n", "Predicted: 26.2884785574, Actual: 24.0\n", "Predicted: 26.8559022819, Actual: 28.4\n", "Predicted: 31.0940066284, Actual: 32.0\n", "Predicted: 24.0129770055, Actual: 24.0\n", "Predicted: 30.9106684972, Actual: 31.3\n", "Predicted: 23.8318097224, Actual: 20.0\n", "Predicted: 11.4172815594, Actual: 14.0\n", "Predicted: 25.4509846351, Actual: 19.8\n", "Predicted: 19.9742706988, Actual: 18.0\n", "Predicted: 20.823209095, Actual: 20.0\n", "Predicted: 25.589920275, Actual: 30.0\n", "Predicted: 24.8602224809, Actual: 20.2\n", "Predicted: 23.4832212726, Actual: 23.0\n", "Predicted: 20.5270445691, Actual: 15.0\n", "Predicted: 28.6886846162, Actual: 27.0\n", "Predicted: 20.5578198929, Actual: 18.0\n", "Predicted: 26.3241951584, Actual: 28.1\n", "Predicted: 17.6514477796, Actual: 18.0\n", "Predicted: 22.5131941701, Actual: 20.0\n", "Predicted: 9.68360980809, Actual: 14.0\n", "Predicted: 26.8113390062, Actual: 29.0\n", "Predicted: 26.8711409002, Actual: 30.7\n", "Predicted: 29.5757309976, Actual: 35.0\n", "Predicted: 31.8732349221, Actual: 41.5\n", "Predicted: 26.8232988233, Actual: 28.0\n", "Predicted: 14.976597184, Actual: 15.0\n", "Predicted: 10.4056403926, Actual: 13.0\n", "Predicted: 29.4808727792, Actual: 29.0\n", "Predicted: 19.6230595321, Actual: 18.0\n", "Predicted: 23.2566865315, Actual: 21.0\n", "Predicted: 21.2311062773, Actual: 19.2\n", "Predicted: 24.4456286124, Actual: 26.0\n" ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "model.score(X_test,y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 72, "text": [ "0.77764337315272958" ] } ], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.regplot(predictions, y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 73, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11680fd10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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LAupmri0vzfiLfzEs3Ty1XpoWHDKilMIwJMCL2pMgL2ounXVX7L7o+T7zaYds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\nE/uvFeiVauwOUx4AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x108c005d0>" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.preprocessing import PolynomialFeatures" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "quad_model =PolynomialFeatures(degree=2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 75 }, { "cell_type": "code", "collapsed": false, "input": [ "quad_X_train = quad_model.fit_transform(X_train)\n", "quad_X_test = quad_model.fit_transform(X_test)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "model.fit(quad_X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [ "predictions = model.predict(quad_X_test)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 78 }, { "cell_type": "code", "collapsed": false, "input": [ "for i, prediction in enumerate(predictions):\n", " print 'Predicted: %s, Actual: %s' % (prediction, y_test[i])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Predicted: 23.3025864121, Actual: 23.0\n", "Predicted: 19.9410146537, Actual: 21.0\n", "Predicted: 19.4236241937, Actual: 21.0\n", "Predicted: 35.4731652612, Actual: 43.4\n", "Predicted: 12.1003117586, Actual: 10.0\n", "Predicted: 13.3266849819, Actual: 13.0\n", "Predicted: 18.369698152, Actual: 18.6\n", "Predicted: 21.0278008026, Actual: 21.5\n", "Predicted: 23.181315018, Actual: 20.8\n", "Predicted: 27.0425561005, Actual: 23.9\n", "Predicted: 23.7648774373, Actual: 19.1\n", "Predicted: 15.7164721958, Actual: 17.5\n", "Predicted: 23.6872919766, Actual: 25.0\n", "Predicted: 19.9817607207, Actual: 18.0\n", "Predicted: 38.5456159447, Actual: 38.0\n", "Predicted: 13.504280478, Actual: 13.0\n", "Predicted: 16.3986029163, Actual: 17.5\n", "Predicted: 24.8423074503, Actual: 19.0\n", "Predicted: 16.6782040171, Actual: 18.0\n", "Predicted: 32.7968375922, Actual: 32.0\n", "Predicted: 14.9025660389, Actual: 17.7\n", "Predicted: 15.3464262096, Actual: 15.0\n", "Predicted: 18.9638111493, Actual: 22.0\n", "Predicted: 26.9743738532, Actual: 23.0\n", "Predicted: 23.2312119071, Actual: 20.2\n", "Predicted: 29.4165488717, Actual: 35.0\n", "Predicted: 24.6202932979, Actual: 28.8\n", "Predicted: 19.5000915272, Actual: 18.0\n", "Predicted: 23.090561581, Actual: 23.0\n", "Predicted: 38.0664049108, Actual: 37.0\n", "Predicted: 20.9651746814, Actual: 21.0\n", "Predicted: 29.5417968194, Actual: 29.8\n", "Predicted: 17.0144256287, Actual: 16.2\n", "Predicted: 20.8326170148, Actual: 21.5\n", "Predicted: 32.1670449438, Actual: 29.5\n", "Predicted: 23.0082059997, Actual: 19.0\n", "Predicted: 23.2513931027, Actual: 23.2\n", "Predicted: 24.1017375894, Actual: 28.0\n", "Predicted: 23.8755314491, Actual: 23.0\n", "Predicted: 21.5433611521, Actual: 25.0\n", "Predicted: 16.0666414964, Actual: 16.0\n", "Predicted: 13.0808748237, Actual: 13.0\n", "Predicted: 31.2958897725, Actual: 29.5\n", "Predicted: 22.3450668295, Actual: 19.4\n", "Predicted: 28.1136406981, Actual: 31.0\n", "Predicted: 26.8495836, Actual: 28.0\n", "Predicted: 31.5882815681, Actual: 32.0\n", "Predicted: 23.8615361052, Actual: 24.0\n", "Predicted: 25.8740604078, Actual: 28.4\n", "Predicted: 30.214390327, Actual: 32.0\n", "Predicted: 23.1906818455, Actual: 24.0\n", "Predicted: 32.449544013, Actual: 31.3\n", "Predicted: 23.2856693712, Actual: 20.0\n", "Predicted: 12.0127567535, Actual: 14.0\n", "Predicted: 24.7192335988, Actual: 19.8\n", "Predicted: 18.2473381975, Actual: 18.0\n", "Predicted: 19.2284388676, Actual: 20.0\n", "Predicted: 26.992739702, Actual: 30.0\n", "Predicted: 23.7443765288, Actual: 20.2\n", "Predicted: 24.9418790366, Actual: 23.0\n", "Predicted: 19.7488154845, Actual: 15.0\n", "Predicted: 28.7659298749, Actual: 27.0\n", "Predicted: 19.4636716738, Actual: 18.0\n", "Predicted: 28.0162056597, Actual: 28.1\n", "Predicted: 16.9002717618, Actual: 18.0\n", "Predicted: 20.2513640854, Actual: 20.0\n", "Predicted: 14.5779224126, Actual: 14.0\n", "Predicted: 26.5113041664, Actual: 29.0\n", "Predicted: 29.3333899853, Actual: 30.7\n", "Predicted: 28.4753991337, Actual: 35.0\n", "Predicted: 32.9672553815, Actual: 41.5\n", "Predicted: 25.9449460258, Actual: 28.0\n", "Predicted: 14.2340168329, Actual: 15.0\n", "Predicted: 12.4077841734, Actual: 13.0\n", "Predicted: 29.9203154541, Actual: 29.0\n", "Predicted: 17.5081234779, Actual: 18.0\n", "Predicted: 22.8148987001, Actual: 21.0\n", "Predicted: 19.4805423487, Actual: 19.2\n", "Predicted: 24.3435280626, Actual: 26.0\n" ] } ], "prompt_number": 79 }, { "cell_type": "code", "collapsed": false, "input": [ "model.score(quad_X_test,y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 80, "text": [ "0.85141966237441713" ] } ], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "sns.regplot(predictions, y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 81, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11686bc90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11647cc50>" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import cross_val_score" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "scores = cross_val_score(model, quad_X_train, y_train, cv =10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "# R squared is the proportion of variance in the response variable that is explained by the model.\n", "# The R squared score tells us the accuracy of our model with 1.0 being a perfect prediction. \n", "scores" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 84, "text": [ "array([ 0.86963228, 0.88186708, 0.83549739, 0.82170629, 0.8882992 ,\n", " 0.87384832, 0.83684078, 0.8580886 , 0.84189087, 0.87059883])" ] } ], "prompt_number": 84 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Using attributes of a car to predict MPG is not an exact science. What I mean is that car manufacturers make cars based on their marketing plans. Although some cars are marketed as being fuel efficient, some could be marketed for comfort, some might be manufactured to appeal to a certain demographic(women, young adults, corporate types) and some others even for speed. Given these assumptions, our model does a fairly good job." ] } ], "metadata": {} } ] }
apache-2.0
psychemedia/ou-robotics-vrep
robotVM/notebooks/Demo - Square 2 - Variables.ipynb
1
8721
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Traverse a Square - Part 2 - Variables\n", "\n", "In this notebook, we will introduce one of the most powerful ideas in programming: the *variable*.\n", "\n", "A variable is a container that we can reference *by name* that is associated with a particular *value*. The value is assigned to the variable using the the `=` operator, which we might read as *is set to the value of*.\n", "\n", "For example, consider the following assignment statement:\n", "\n", "```python\n", "message=\"Hello World\"\n", "```\n", "\n", "Here, we create a named container `message` and put the value `Hello World` into it.\n", "\n", "When we refer to the variable as part of another expression, we can then access the value it contains and use that in our expression, as the following example demonstrates:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create the message variable and assign the value \"Hello World\" to it\n", "message=\"Hello World\"\n", "\n", "# Use the variable in a print statement\n", "# The print statement retrieves the value assigned to the variable and displays the value\n", "print(message)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try changing the message in the previous code cell and re-running it. Does it behave as you expect?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may remember from the `Getting Started WIth Notebooks.ipynb` notebook that if the last statement in a code cell returns a value, the value will be displayed as the output of the code cell when the cell contents have been executed.\n", "\n", "If you place the name of a variable, or one or more comma separated variables, on the last line of a code cell, the value will be displayed.\n", "\n", "What do you think the output of the following cell will be? Run the cell to find out." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "message" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can assign whatever object you like to a variable.\n", "\n", "For example, we can assign numbers to them and do sums with them:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Assign raw numbers to variables\n", "apples=5\n", "oranges=10\n", "\n", "#Do a sum with the values represented by the variables and assign the result to a new variable\n", "items_in_basket = apples + oranges\n", "\n", "#Display the resulting value as the cell output\n", "items_in_basket" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See if you can add the count of a new set of purchases to the number of items in your basket in the cell above. For example, what if you also bought 3 pears. And a bunch of bananas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making Use of Variables\n", "\n", "Let's look back at our simple attempt at the square drawing program, in which we repeated blocks of instructions and set the numberical parameter values separately in each case.\n", "\n", "Before we run the program, we need to load in the bits we need..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading class: PioneerP3DX_base\n", "This is a base class for the PioneerP3DX_base model\n", "\n", "Loading class: PioneerP3DX\n", "Methods available in PioneerP3DX:\n", "\tget_orientation\n", "\tget_orientation_degrees\n", "\tgetvalleft\n", "\tgetvalright\n", "\tmove_backward\n", "\tmove_forward\n", "\trotate_left\n", "\trotate_right\n", "\tset_two_motor\n", "\tultrasonic_left_length\n", "\tultrasonic_right_length\n", "\n", "Loading class: PioneerP3DXL\n", "Methods available in PioneerP3DXL:\n", "\tcolor_left\n", "\tcolor_right\n", "\tget_orientation\n", "\tmove_backward\n", "\tmove_forward\n", "\trotate_left\n", "\trotate_right\n", "\tset_two_motor\n", "\tultrasonic_left_length\n", "\tultrasonic_right_length\n", "\n", "The following text widgets are available for display: sensorText1, sensorText2\n" ] } ], "source": [ "%run 'Set-up.ipynb'\n", "%run 'Loading scenes.ipynb'\n", "%run 'vrep_models/PioneerP3DX.ipynb'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The original programme appears in the code cell below. \n", "\n", "- how many changes would you have to make to it in order to change the side length?\n", "- can you see how you might be able to simplify the act of changing the side length?\n", "- what would you need to change if you wanted to make the turns faster? Or slower?\n", "\n", "HINT: think *variables*..." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "%%vrepsim '../scenes/OU_Pioneer.ttt' PioneerP3DX\n", "import time\n", "\n", "#side 1\n", "robot.move_forward()\n", "time.sleep(1)\n", "#turn 1\n", "robot.rotate_left(1.8)\n", "time.sleep(0.45)\n", "#side 2\n", "robot.move_forward()\n", "time.sleep(1)\n", "#turn 2\n", "robot.rotate_left(1.8)\n", "time.sleep(0.45)\n", "#side 3\n", "robot.move_forward()\n", "time.sleep(1)\n", "#turn 3\n", "robot.rotate_left(1.8)\n", "time.sleep(0.45)\n", "#side 4\n", "robot.move_forward()\n", "time.sleep(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the above programme as a guide, see if you can write a programme in the code cell below that makes it easier to maintin and simplifies the act of changing the numerical parameter values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%vrepsim '../scenes/OU_Pioneer.ttt' PioneerP3DX\n", "import time\n", "\n", "#YOUR CODE HERE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How did you get on?\n", "\n", "How easy is is to change the side length now? Or find a new combination of the turn speed and turn angle to turn through ninety degrees (or thereabouts?). Try it and see...\n", "\n", "Here's the programme I came up with: I used three variables, one for side length, one for turn time, and one for turn speed. Feel free to try running and modifying this programme too..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%vrepsim '../scenes/OU_Pioneer.ttt' PioneerP3DX\n", "import time\n", "\n", "side_length_time=1\n", "turn_speed=1.8\n", "turn_time=0.45\n", "\n", "#side 1\n", "robot.move_forward()\n", "time.sleep(side_length_time)\n", "#turn 1\n", "robot.rotate_left(turn_speed)\n", "time.sleep(turn_time)\n", "#side 2\n", "robot.move_forward()\n", "time.sleep(side_length_time)\n", "#turn 2\n", "robot.rotate_left(turn_speed)\n", "time.sleep(turn_time)\n", "#side 3\n", "robot.move_forward()\n", "time.sleep(side_length_time)\n", "#turn 3\n", "robot.rotate_left(turn_speed)\n", "time.sleep(turn_time)\n", "#side 4\n", "robot.move_forward()\n", "time.sleep(side_length_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next two notebooks on this theme, we'll see how to cut out some of the repetition." ] } ], "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 }
apache-2.0
ES-DOC/esdoc-jupyterhub
notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/aerosol.ipynb
1
84330
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: MESSY-CONSORTIUM \n", "**Source ID**: EMAC-2-53-AERCHEM \n", "**Topic**: Aerosol \n", "**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "**Properties**: 69 (37 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?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 16:54:10" ], "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', 'messy-consortium', 'emac-2-53-aerchem', 'aerosol')" ], "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 --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Key Properties --&gt; Timestep Framework](#3.-Key-Properties---&gt;-Timestep-Framework) \n", "[4. Key Properties --&gt; Meteorological Forcings](#4.-Key-Properties---&gt;-Meteorological-Forcings) \n", "[5. Key Properties --&gt; Resolution](#5.-Key-Properties---&gt;-Resolution) \n", "[6. Key Properties --&gt; Tuning Applied](#6.-Key-Properties---&gt;-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --&gt; Absorption](#11.-Optical-Radiative-Properties---&gt;-Absorption) \n", "[12. Optical Radiative Properties --&gt; Mixtures](#12.-Optical-Radiative-Properties---&gt;-Mixtures) \n", "[13. Optical Radiative Properties --&gt; Impact Of H2o](#13.-Optical-Radiative-Properties---&gt;-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --&gt; Radiative Scheme](#14.-Optical-Radiative-Properties---&gt;-Radiative-Scheme) \n", "[15. Optical Radiative Properties --&gt; Cloud Interactions](#15.-Optical-Radiative-Properties---&gt;-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of aerosol model code*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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. Scheme Scope\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Atmospheric domains covered by the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basic approximations made in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Prognostic variables in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of tracers in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are aerosol calculations 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.aerosol.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": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of aerosol code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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 --&gt; Timestep Framework \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Mathematical method deployed to solve the time evolution of the prognostic variables*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol advection (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol physics (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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. Integrated Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the aerosol model (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 3.5. Integrated Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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 --&gt; Meteorological Forcings \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \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. Variables 2D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Two dimensionsal forcing variables, e.g. land-sea mask definition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \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. Frequency\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Frequency with which meteological forcings are applied (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \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 --&gt; Resolution \n", "*Resolution in the aersosol model grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Canonical Horizontal Resolution\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Number Of Horizontal Gridpoints\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Number Of Vertical Levels\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.key_properties.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": [ "### 5.5. Is Adaptive Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.key_properties.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": [ "# 6. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;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.aerosol.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": [ "### 6.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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": [ "### 6.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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": [ "### 6.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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": [ "# 7. Transport \n", "*Aerosol transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of transport in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 7.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for aerosol transport modeling*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to ensure mass conservation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Transport by convention*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "*Atmospheric aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of emissions in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.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. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to define aerosol species (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.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of the aerosol species 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.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify the climatology type for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and prescribed via a climatology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted 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.aerosol.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": [ "### 8.7. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted 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.aerosol.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": [ "### 8.8. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and specified via an &quot;other method&quot;*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 8.9. Other Method Characteristics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Characteristics of the &quot;other method&quot; used for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \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. Concentrations \n", "*Atmospheric aerosol concentrations*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of concentrations in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 9.2. Prescribed Lower Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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": [ "### 9.3. Prescribed Upper Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.aerosol.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": [ "### 9.4. Prescribed Fields Mmr\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as mass mixing ratios.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \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. Prescribed Fields Mmr\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as AOD plus CCNs.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \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. Optical Radiative Properties \n", "*Aerosol optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.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": [ "# 11. Optical Radiative Properties --&gt; Absorption \n", "*Absortion properties in aerosol scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_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": [ "### 11.2. Dust\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \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. Organics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \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. Optical Radiative Properties --&gt; Mixtures \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there external mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there internal mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \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. Optical Radiative Properties --&gt; Impact Of H2o \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact size?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \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": [ "### 13.2. Internal Mixture\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact internal mixture?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \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. Optical Radiative Properties --&gt; Radiative Scheme \n", "*Radiative scheme for aerosol*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of radiative scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.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. Shortwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of shortwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_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": [ "### 14.3. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_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": [ "# 15. Optical Radiative Properties --&gt; Cloud Interactions \n", "*Aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.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. Twomey\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the Twomey effect included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \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": [ "### 15.3. Twomey Minimum Ccn\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the Twomey effect is included, then what is the minimum CCN number?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \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. Drizzle\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect drizzle?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \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": [ "### 15.5. Cloud Lifetime\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect cloud lifetime?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \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. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_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": [ "# 16. Model \n", "*Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 16.2. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Processes included in the Aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other model components coupled to the Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of gas phase aerosol precursors.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of species covered by the bulk scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_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", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
Kulbear/deep-learning-nano-foundation
DLND-tv-script-generation/dlnd_tv_script_generation.ipynb
1
46901
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n", "## Get the Data\n", "The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "\n", "data_dir = 'data/simpsons/moes_tavern_lines.txt'\n", "text = helper.load_data(data_dir)\n", "# Ignore notice, since we don't use it for analysing the data\n", "text = text[81:]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "\"Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.\\nBart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.\\nMoe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?\\nMoe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.\\nMoe_Szyslak: What's the matter Homer? You're not your normal effervescent self.\\nHomer_Simp\"" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text[0:500]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "Play around with `view_sentence_range` to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 11492\n", "Number of scenes: 262\n", "Average number of sentences in each scene: 15.248091603053435\n", "Number of lines: 4257\n", "Average number of words in each line: 11.50434578341555\n", "\n", "The sentences 0 to 10:\n", "Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.\n", "Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.\n", "Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?\n", "Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.\n", "Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.\n", "Homer_Simpson: I got my problems, Moe. Give me another one.\n", "Moe_Szyslak: Homer, hey, you should not drink to forget your problems.\n", "Barney_Gumble: Yeah, you should only drink to enhance your social skills.\n", "\n", "\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 text.split()})))\n", "scenes = text.split('\\n\\n')\n", "print('Number of scenes: {}'.format(len(scenes)))\n", "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n", "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n", "\n", "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n", "print('Number of lines: {}'.format(len(sentences)))\n", "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n", "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n", "\n", "print()\n", "print('The sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocessing Functions\n", "The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:\n", "- Lookup Table\n", "- Tokenize Punctuation\n", "\n", "### Lookup Table\n", "To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:\n", "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n", "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n", "\n", "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "import numpy as np\n", "import problem_unittests as tests\n", "\n", "from collections import Counter\n", "\n", "def create_lookup_tables(text):\n", " \"\"\"\n", " Create lookup tables for vocabulary\n", " :param text: The text of tv scripts split into words\n", " :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n", " \"\"\"\n", " counts = Counter(text)\n", " vocab = sorted(counts, key=counts.get, reverse=True)\n", " vocab_to_int = {word: ii for ii, word in enumerate(vocab, 1)}\n", " int_to_vocab = {ii: word for ii, word in enumerate(vocab, 1)}\n", " \n", " return (vocab_to_int, int_to_vocab)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_create_lookup_tables(create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Tokenize Punctuation\n", "We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n", "\n", "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\". Create a dictionary for the following symbols where the symbol is the key and value is the token:\n", "- Period ( . )\n", "- Comma ( , )\n", "- Quotation Mark ( \" )\n", "- Semicolon ( ; )\n", "- Exclamation mark ( ! )\n", "- Question mark ( ? )\n", "- Left Parentheses ( ( )\n", "- Right Parentheses ( ) )\n", "- Dash ( -- )\n", "- Return ( \\n )\n", "\n", "This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def token_lookup():\n", " \"\"\"\n", " Generate a dict to turn punctuation into a token.\n", " :return: Tokenize dictionary where the key is the punctuation and the value is the token\n", " \"\"\"\n", " # TODO: Implement Function\n", " punct_list = {'.': '||period||', \n", " ',': '||comma||',\n", " '\"': '||quotation_mark||',\n", " ';': '||semicolon||',\n", " '!': '||exclamation_mark||',\n", " '?': '||question_mark||',\n", " '(': '||left_parentheses||',\n", " ')': '||right_parentheses||',\n", " '--': '||dash||',\n", " '\\n': '||return||'}\n", " return punct_list\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_tokenize(token_lookup)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import numpy as np\n", "import problem_unittests as tests\n", "\n", "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "69100" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(int_text)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a RNN by implementing the following functions below:\n", "- get_inputs\n", "- get_init_cell\n", "- get_embed\n", "- build_rnn\n", "- build_nn\n", "- get_batches\n", "\n", "### Check the Version of TensorFlow and Access to GPU" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.0.0\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", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 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": { "deletable": true, "editable": true }, "source": [ "### Input\n", "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n", "- Targets placeholder\n", "- Learning Rate placeholder\n", "\n", "Return the placeholders in the following the tuple `(Input, Targets, LearingRate)`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate)\n", " \"\"\"\n", " inputs = tf.placeholder(tf.int32, [None, None], name='input')\n", " targets = tf.placeholder(tf.int32, [None, None], name='targets')\n", " learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n", " \n", " return (inputs, targets, learning_rate)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_inputs(get_inputs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN Cell and Initialize\n", "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n", "- The Rnn size should be set using `rnn_size`\n", "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n", " - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the cell and initial state in the following tuple `(Cell, InitialState)`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_init_cell(batch_size, rnn_size):\n", " \"\"\"\n", " Create an RNN Cell and initialize it.\n", " :param batch_size: Size of batches\n", " :param rnn_size: Size of RNNs\n", " :return: Tuple (cell, initialize state)\n", " \"\"\"\n", " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", " cell = tf.contrib.rnn.MultiRNNCell([lstm] * 2)\n", " \n", " initial_state = cell.zero_state(batch_size, tf.float32)\n", " initial_state = tf.identity(initial_state, name= \"initial_state\")\n", " \n", " return (cell, initial_state)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_init_cell(get_init_cell)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Word Embedding\n", "Apply embedding to `input_data` using TensorFlow. Return the embedded sequence." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_embed(input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Create embedding for <input_data>.\n", " :param input_data: TF placeholder for text input.\n", " :param vocab_size: Number of words in vocabulary.\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Embedded input.\n", " \"\"\"\n", " embedding = tf.Variable(tf.truncated_normal((vocab_size, embed_dim), stddev=0.25))\n", " embed = tf.nn.embedding_lookup(embedding, input_data)\n", " \n", " return embed\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_embed(get_embed)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN\n", "You created a RNN Cell in the `get_init_cell()` function. Time to use the cell to create a RNN.\n", "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n", " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def build_rnn(cell, inputs):\n", " \"\"\"\n", " Create a RNN using a RNN Cell\n", " :param cell: RNN Cell\n", " :param inputs: Input text data\n", " :return: Tuple (Outputs, Final State)\n", " \"\"\"\n", " outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n", " final_state = tf.identity(final_state, name=\"final_state\")\n", " \n", " return outputs, final_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_rnn(build_rnn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n", "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n", "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n", "\n", "Return the logits and final state in the following tuple (Logits, FinalState) " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def build_nn(cell, rnn_size, input_data, vocab_size):\n", " \"\"\"\n", " Build part of the neural network\n", " :param cell: RNN cell\n", " :param rnn_size: Size of rnns\n", " :param input_data: Input data\n", " :param vocab_size: Vocabulary size\n", " :return: Tuple (Logits, FinalState)\n", " \"\"\"\n", " inputs = get_embed(input_data, vocab_size, rnn_size)\n", " outputs, final_state = build_rnn(cell, inputs)\n", " logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)\n", " return logits, final_state\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_nn(build_nn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Batches\n", "Implement `get_batches` to create batches of input and targets using `int_text`. The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n", "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n", "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n", "\n", "If you can't fill the last batch with enough data, drop the last batch.\n", "\n", "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)` would return a Numpy array of the following:\n", "```\n", "[\n", " # First Batch\n", " [\n", " # Batch of Input\n", " [[ 1 2 3], [ 7 8 9]],\n", " # Batch of targets\n", " [[ 2 3 4], [ 8 9 10]]\n", " ],\n", " \n", " # Second Batch\n", " [\n", " # Batch of Input\n", " [[ 4 5 6], [10 11 12]],\n", " # Batch of targets\n", " [[ 5 6 7], [11 12 13]]\n", " ]\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_batches(int_text, batch_size, seq_length):\n", " \"\"\"\n", " Return batches of input and target\n", " :param int_text: Text with the words replaced by their ids\n", " :param batch_size: The size of batch\n", " :param seq_length: The length of sequence\n", " :return: Batches as a Numpy array\n", " \"\"\"\n", " slice_size = batch_size * seq_length\n", " n_batches = len(int_text) // slice_size\n", " \n", " # We will drop the last few words to keep the batches in equal size\n", " used_data = int_text[0:n_batches * slice_size + 1]\n", " batches = []\n", "\n", " for i in range(n_batches):\n", " input_batch = []\n", " target_batch = []\n", " \n", " for j in range(batch_size):\n", " start_idx = i * batch_size + j * seq_length\n", " end_idx = i * batch_size + (j + 1) * seq_length\n", " \n", " input_batch.append(used_data[start_idx: end_idx])\n", " target_batch.append(used_data[start_idx + 1: end_idx + 1])\n", " \n", " batches.append([input_batch, target_batch])\n", "\n", " return np.array(batches)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_batches(get_batches)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `num_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 `seq_length` to the length of sequence.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `show_every_n_batches` to the number of batches the neural network should print progress." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of Epochs\n", "num_epochs = 50\n", "# Batch Size\n", "batch_size = 128\n", "# RNN Size\n", "rnn_size = 1024\n", "# Sequence Length\n", "seq_length = 16\n", "# Learning Rate\n", "learning_rate = 0.001\n", "# Show stats for every n number of batches\n", "show_every_n_batches = 11\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "save_dir = './save'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from tensorflow.contrib import seq2seq\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " vocab_size = len(int_to_vocab)\n", " input_text, targets, lr = get_inputs()\n", " input_data_shape = tf.shape(input_text)\n", " cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n", " logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)\n", "\n", " # Probabilities for generating words\n", " probs = tf.nn.softmax(logits, name='probs')\n", "\n", " # Loss function\n", " cost = seq2seq.sequence_loss(\n", " logits,\n", " targets,\n", " tf.ones([input_data_shape[0], input_data_shape[1]])\n", " )\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]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 0/33 train_loss = 8.822\n", "Epoch 0 Batch 11/33 train_loss = 6.031\n", "Epoch 0 Batch 22/33 train_loss = 6.213\n", "Epoch 1 Batch 0/33 train_loss = 6.638\n", "Epoch 1 Batch 11/33 train_loss = 5.778\n", "Epoch 1 Batch 22/33 train_loss = 5.860\n", "Epoch 2 Batch 0/33 train_loss = 6.262\n", "Epoch 2 Batch 11/33 train_loss = 5.651\n", "Epoch 2 Batch 22/33 train_loss = 5.529\n", "Epoch 3 Batch 0/33 train_loss = 6.149\n", "Epoch 3 Batch 11/33 train_loss = 5.468\n", "Epoch 3 Batch 22/33 train_loss = 5.369\n", "Epoch 4 Batch 0/33 train_loss = 5.938\n", "Epoch 4 Batch 11/33 train_loss = 5.189\n", "Epoch 4 Batch 22/33 train_loss = 4.986\n", "Epoch 5 Batch 0/33 train_loss = 5.656\n", "Epoch 5 Batch 11/33 train_loss = 4.635\n", "Epoch 5 Batch 22/33 train_loss = 4.403\n", "Epoch 6 Batch 0/33 train_loss = 4.989\n", "Epoch 6 Batch 11/33 train_loss = 4.024\n", "Epoch 6 Batch 22/33 train_loss = 3.786\n", "Epoch 7 Batch 0/33 train_loss = 4.545\n", "Epoch 7 Batch 11/33 train_loss = 3.414\n", "Epoch 7 Batch 22/33 train_loss = 3.177\n", "Epoch 8 Batch 0/33 train_loss = 4.119\n", "Epoch 8 Batch 11/33 train_loss = 2.759\n", "Epoch 8 Batch 22/33 train_loss = 2.502\n", "Epoch 9 Batch 0/33 train_loss = 3.670\n", "Epoch 9 Batch 11/33 train_loss = 2.091\n", "Epoch 9 Batch 22/33 train_loss = 1.858\n", "Epoch 10 Batch 0/33 train_loss = 3.175\n", "Epoch 10 Batch 11/33 train_loss = 1.485\n", "Epoch 10 Batch 22/33 train_loss = 1.325\n", "Epoch 11 Batch 0/33 train_loss = 2.675\n", "Epoch 11 Batch 11/33 train_loss = 1.023\n", "Epoch 11 Batch 22/33 train_loss = 0.915\n", "Epoch 12 Batch 0/33 train_loss = 2.193\n", "Epoch 12 Batch 11/33 train_loss = 0.736\n", "Epoch 12 Batch 22/33 train_loss = 0.615\n", "Epoch 13 Batch 0/33 train_loss = 1.767\n", "Epoch 13 Batch 11/33 train_loss = 0.461\n", "Epoch 13 Batch 22/33 train_loss = 0.411\n", "Epoch 14 Batch 0/33 train_loss = 1.493\n", "Epoch 14 Batch 11/33 train_loss = 0.500\n", "Epoch 14 Batch 22/33 train_loss = 0.376\n", "Epoch 15 Batch 0/33 train_loss = 1.279\n", "Epoch 15 Batch 11/33 train_loss = 0.394\n", "Epoch 15 Batch 22/33 train_loss = 0.300\n", "Epoch 16 Batch 0/33 train_loss = 1.027\n", "Epoch 16 Batch 11/33 train_loss = 0.224\n", "Epoch 16 Batch 22/33 train_loss = 0.199\n", "Epoch 17 Batch 0/33 train_loss = 0.741\n", "Epoch 17 Batch 11/33 train_loss = 0.167\n", "Epoch 17 Batch 22/33 train_loss = 0.155\n", "Epoch 18 Batch 0/33 train_loss = 0.535\n", "Epoch 18 Batch 11/33 train_loss = 0.138\n", "Epoch 18 Batch 22/33 train_loss = 0.133\n", "Epoch 19 Batch 0/33 train_loss = 0.418\n", "Epoch 19 Batch 11/33 train_loss = 0.124\n", "Epoch 19 Batch 22/33 train_loss = 0.121\n", "Epoch 20 Batch 0/33 train_loss = 0.352\n", "Epoch 20 Batch 11/33 train_loss = 0.114\n", "Epoch 20 Batch 22/33 train_loss = 0.114\n", "Epoch 21 Batch 0/33 train_loss = 0.307\n", "Epoch 21 Batch 11/33 train_loss = 0.107\n", "Epoch 21 Batch 22/33 train_loss = 0.108\n", "Epoch 22 Batch 0/33 train_loss = 0.272\n", "Epoch 22 Batch 11/33 train_loss = 0.101\n", "Epoch 22 Batch 22/33 train_loss = 0.105\n", "Epoch 23 Batch 0/33 train_loss = 0.244\n", "Epoch 23 Batch 11/33 train_loss = 0.099\n", "Epoch 23 Batch 22/33 train_loss = 0.102\n", "Epoch 24 Batch 0/33 train_loss = 0.223\n", "Epoch 24 Batch 11/33 train_loss = 0.097\n", "Epoch 24 Batch 22/33 train_loss = 0.099\n", "Epoch 25 Batch 0/33 train_loss = 0.209\n", "Epoch 25 Batch 11/33 train_loss = 0.094\n", "Epoch 25 Batch 22/33 train_loss = 0.098\n", "Epoch 26 Batch 0/33 train_loss = 0.199\n", "Epoch 26 Batch 11/33 train_loss = 0.093\n", "Epoch 26 Batch 22/33 train_loss = 0.096\n", "Epoch 27 Batch 0/33 train_loss = 0.188\n", "Epoch 27 Batch 11/33 train_loss = 0.092\n", "Epoch 27 Batch 22/33 train_loss = 0.096\n", "Epoch 28 Batch 0/33 train_loss = 0.185\n", "Epoch 28 Batch 11/33 train_loss = 0.091\n", "Epoch 28 Batch 22/33 train_loss = 0.095\n", "Epoch 29 Batch 0/33 train_loss = 0.176\n", "Epoch 29 Batch 11/33 train_loss = 0.090\n", "Epoch 29 Batch 22/33 train_loss = 0.095\n", "Epoch 30 Batch 0/33 train_loss = 0.170\n", "Epoch 30 Batch 11/33 train_loss = 0.089\n", "Epoch 30 Batch 22/33 train_loss = 0.094\n", "Epoch 31 Batch 0/33 train_loss = 0.165\n", "Epoch 31 Batch 11/33 train_loss = 0.088\n", "Epoch 31 Batch 22/33 train_loss = 0.092\n", "Epoch 32 Batch 0/33 train_loss = 0.165\n", "Epoch 32 Batch 11/33 train_loss = 0.087\n", "Epoch 32 Batch 22/33 train_loss = 0.091\n", "Epoch 33 Batch 0/33 train_loss = 0.158\n", "Epoch 33 Batch 11/33 train_loss = 0.086\n", "Epoch 33 Batch 22/33 train_loss = 0.090\n", "Epoch 34 Batch 0/33 train_loss = 0.158\n", "Epoch 34 Batch 11/33 train_loss = 0.086\n", "Epoch 34 Batch 22/33 train_loss = 0.089\n", "Epoch 35 Batch 0/33 train_loss = 0.156\n", "Epoch 35 Batch 11/33 train_loss = 0.086\n", "Epoch 35 Batch 22/33 train_loss = 0.089\n", "Epoch 36 Batch 0/33 train_loss = 0.158\n", "Epoch 36 Batch 11/33 train_loss = 0.086\n", "Epoch 36 Batch 22/33 train_loss = 0.089\n", "Epoch 37 Batch 0/33 train_loss = 0.152\n", "Epoch 37 Batch 11/33 train_loss = 0.085\n", "Epoch 37 Batch 22/33 train_loss = 0.088\n", "Epoch 38 Batch 0/33 train_loss = 0.154\n", "Epoch 38 Batch 11/33 train_loss = 0.085\n", "Epoch 38 Batch 22/33 train_loss = 0.089\n", "Epoch 39 Batch 0/33 train_loss = 0.149\n", "Epoch 39 Batch 11/33 train_loss = 0.084\n", "Epoch 39 Batch 22/33 train_loss = 0.088\n", "Epoch 40 Batch 0/33 train_loss = 0.149\n", "Epoch 40 Batch 11/33 train_loss = 0.085\n", "Epoch 40 Batch 22/33 train_loss = 0.087\n", "Epoch 41 Batch 0/33 train_loss = 0.145\n", "Epoch 41 Batch 11/33 train_loss = 0.085\n", "Epoch 41 Batch 22/33 train_loss = 0.087\n", "Epoch 42 Batch 0/33 train_loss = 0.146\n", "Epoch 42 Batch 11/33 train_loss = 0.083\n", "Epoch 42 Batch 22/33 train_loss = 0.086\n", "Epoch 43 Batch 0/33 train_loss = 0.140\n", "Epoch 43 Batch 11/33 train_loss = 0.083\n", "Epoch 43 Batch 22/33 train_loss = 0.085\n", "Epoch 44 Batch 0/33 train_loss = 0.141\n", "Epoch 44 Batch 11/33 train_loss = 0.083\n", "Epoch 44 Batch 22/33 train_loss = 0.085\n", "Epoch 45 Batch 0/33 train_loss = 0.138\n", "Epoch 45 Batch 11/33 train_loss = 0.083\n", "Epoch 45 Batch 22/33 train_loss = 0.085\n", "Epoch 46 Batch 0/33 train_loss = 0.140\n", "Epoch 46 Batch 11/33 train_loss = 0.082\n", "Epoch 46 Batch 22/33 train_loss = 0.084\n", "Epoch 47 Batch 0/33 train_loss = 0.137\n", "Epoch 47 Batch 11/33 train_loss = 0.082\n", "Epoch 47 Batch 22/33 train_loss = 0.084\n", "Epoch 48 Batch 0/33 train_loss = 0.139\n", "Epoch 48 Batch 11/33 train_loss = 0.082\n", "Epoch 48 Batch 22/33 train_loss = 0.084\n", "Epoch 49 Batch 0/33 train_loss = 0.136\n", "Epoch 49 Batch 11/33 train_loss = 0.082\n", "Epoch 49 Batch 22/33 train_loss = 0.084\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "batches = get_batches(int_text, batch_size, seq_length)\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(num_epochs):\n", " state = sess.run(initial_state, {input_text: batches[0][0]})\n", "\n", " for batch_i, (x, y) in enumerate(batches):\n", " feed = {\n", " input_text: x,\n", " targets: y,\n", " initial_state: state,\n", " lr: learning_rate}\n", " train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n", "\n", " # Show every <show_every_n_batches> batches\n", " if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n", " print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(\n", " epoch_i,\n", " batch_i,\n", " len(batches),\n", " train_loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_dir)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Save Parameters\n", "Save `seq_length` and `save_dir` for generating a new TV script." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params((seq_length, save_dir))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": 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", "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n", "seq_length, load_dir = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Generate Functions\n", "### Get Tensors\n", "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name). Get the tensors using the following names:\n", "- \"input:0\"\n", "- \"initial_state:0\"\n", "- \"final_state:0\"\n", "- \"probs:0\"\n", "\n", "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_tensors(loaded_graph):\n", " \"\"\"\n", " Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n", " :param loaded_graph: TensorFlow graph loaded from file\n", " :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n", " \"\"\"\n", " inputs = loaded_graph.get_tensor_by_name(\"input:0\")\n", " initial_state = loaded_graph.get_tensor_by_name(\"initial_state:0\")\n", " final_state = loaded_graph.get_tensor_by_name(\"final_state:0\")\n", " probs = loaded_graph.get_tensor_by_name(\"probs:0\")\n", " \n", " return (inputs, initial_state, final_state, probs)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_tensors(get_tensors)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Choose Word\n", "Implement the `pick_word()` function to select the next word using `probabilities`." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "from random import randint\n", "\n", "def pick_word(probabilities, int_to_vocab):\n", " \"\"\"\n", " Pick the next word in the generated text\n", " :param probabilities: Probabilites of the next word\n", " :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n", " :return: String of the predicted word\n", " \"\"\"\n", " return int_to_vocab[np.argmax(probabilities)]\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_pick_word(pick_word)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generate TV Script\n", "This will generate the TV script for you. Set `gen_length` to the length of TV script you want to generate." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "moe_szyslak: they wanted it more.\n", "barney_gumble: hey homer, didn't you say if duff dry.\n", "moe_szyslak: oh, you didn't aren't who on the bottle after\n", "moe_szyslak: what do you didn't there much of this is...\n", "moe_szyslak: hey, do you know that catch the bookie bowl. what am i gonna do?\n", "moe_szyslak: sorry, moe.\n", "homer_simpson:(sunk) i laughing him...\n", "moe_szyslak:(shocked) are you loves you.\n", "homer_simpson:(looking at watch)\n", "homer_simpson:(smoothly) no, i'd like a those...\n", "homer_simpson: moe, we're to start callin'.(warmly) one\" flaming homer...\n", "moe_szyslak:(great moe's)(to) ya palmerston!\n", "\n", "\n", "homer_simpson:(into phone) h... what all right, moe?\n", "homer_simpson:(coyly) somebody spilled beer in this one, barney for thirty million dollars.\n", "barney_gumble: wow!(sobs)\n", "larry: whatcha right, the there's it noticing!\n", "\n", "\n", "homer_simpson:(whistles) what a gambler.\n", "football_announcer: touchdown cowboys!\n", "homer_simpson: all right!\n", "football_announcer: touchdown cowboys at one all right, denver. justify my love.\n", "football_announcer: dallas cowboys.\n", "barney_gumble: oh, i haven't seen you of us to the market\"\n", "little_man: uh-huh.\n", "moe_szyslak:(gasps) when you so me?\n", "moe_szyslak: you little s. o... what...\n", "football_announcer: touchdown on your daughter?\n", "barney_gumble: hey, what's my flaming moe...\n", "moe_szyslak:(shocked)\n" ] } ], "source": [ "gen_length = 300\n", "# homer_simpson, moe_szyslak, or Barney_Gumble\n", "prime_word = 'moe_szyslak'\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\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_dir + '.meta')\n", " loader.restore(sess, load_dir)\n", "\n", " # Get Tensors from loaded model\n", " input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n", "\n", " # Sentences generation setup\n", " gen_sentences = [prime_word + ':']\n", " prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n", "\n", " # Generate sentences\n", " for n in range(gen_length):\n", " # Dynamic Input\n", " dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n", " dyn_seq_length = len(dyn_input[0])\n", "\n", " # Get Prediction\n", " probabilities, prev_state = sess.run(\n", " [probs, final_state],\n", " {input_text: dyn_input, initial_state: prev_state})\n", " \n", " pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n", "\n", " gen_sentences.append(pred_word)\n", " \n", " # Remove tokens\n", " tv_script = ' '.join(gen_sentences)\n", " for key, token in token_dict.items():\n", " ending = ' ' if key in ['\\n', '(', '\"'] else ''\n", " tv_script = tv_script.replace(' ' + token.lower(), key)\n", " tv_script = tv_script.replace('\\n ', '\\n')\n", " tv_script = tv_script.replace('( ', '(')\n", " \n", " print(tv_script)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# The TV Script is Nonsensical\n", "It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data). We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.\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_tv_script_generation.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": { "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Tahsin-Mayeesha/udacity-mlnd-deeplearning-capstone
Notebooks/Baseline.ipynb
1
9717
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "submissions = pd.read_csv(\"sample_submission/sample_submission_stg1.csv\")" ] }, { "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", " <th></th>\n", " <th>image</th>\n", " <th>ALB</th>\n", " <th>BET</th>\n", " <th>DOL</th>\n", " <th>LAG</th>\n", " <th>NoF</th>\n", " <th>OTHER</th>\n", " <th>SHARK</th>\n", " <th>YFT</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>img_00005.jpg</td>\n", " <td>0.455003</td>\n", " <td>0.052938</td>\n", " <td>0.030969</td>\n", " <td>0.017734</td>\n", " <td>0.123081</td>\n", " <td>0.079142</td>\n", " <td>0.046585</td>\n", " <td>0.194283</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>img_00007.jpg</td>\n", " <td>0.455003</td>\n", " <td>0.052938</td>\n", " <td>0.030969</td>\n", " <td>0.017734</td>\n", " <td>0.123081</td>\n", " <td>0.079142</td>\n", " <td>0.046585</td>\n", " <td>0.194283</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>img_00009.jpg</td>\n", " <td>0.455003</td>\n", " <td>0.052938</td>\n", " <td>0.030969</td>\n", " <td>0.017734</td>\n", " <td>0.123081</td>\n", " <td>0.079142</td>\n", " <td>0.046585</td>\n", " <td>0.194283</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>img_00018.jpg</td>\n", " <td>0.455003</td>\n", " <td>0.052938</td>\n", " <td>0.030969</td>\n", " <td>0.017734</td>\n", " <td>0.123081</td>\n", " <td>0.079142</td>\n", " <td>0.046585</td>\n", " <td>0.194283</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>img_00027.jpg</td>\n", " <td>0.455003</td>\n", " <td>0.052938</td>\n", " <td>0.030969</td>\n", " <td>0.017734</td>\n", " <td>0.123081</td>\n", " <td>0.079142</td>\n", " <td>0.046585</td>\n", " <td>0.194283</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " image ALB BET DOL LAG NoF OTHER \\\n", "0 img_00005.jpg 0.455003 0.052938 0.030969 0.017734 0.123081 0.079142 \n", "1 img_00007.jpg 0.455003 0.052938 0.030969 0.017734 0.123081 0.079142 \n", "2 img_00009.jpg 0.455003 0.052938 0.030969 0.017734 0.123081 0.079142 \n", "3 img_00018.jpg 0.455003 0.052938 0.030969 0.017734 0.123081 0.079142 \n", "4 img_00027.jpg 0.455003 0.052938 0.030969 0.017734 0.123081 0.079142 \n", "\n", " SHARK YFT \n", "0 0.046585 0.194283 \n", "1 0.046585 0.194283 \n", "2 0.046585 0.194283 \n", "3 0.046585 0.194283 \n", "4 0.046585 0.194283 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submissions.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1000, 9)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submissions.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns = list(submissions.columns)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random = pd.DataFrame(np.random.random_sample(size=(1000,9)),columns = columns)" ] }, { "cell_type": "code", "execution_count": 14, "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>image</th>\n", " <th>ALB</th>\n", " <th>BET</th>\n", " <th>DOL</th>\n", " <th>LAG</th>\n", " <th>NoF</th>\n", " <th>OTHER</th>\n", " <th>SHARK</th>\n", " <th>YFT</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.839579</td>\n", " <td>0.855175</td>\n", " <td>0.945205</td>\n", " <td>0.141212</td>\n", " <td>0.579779</td>\n", " <td>0.983546</td>\n", " <td>0.390744</td>\n", " <td>0.656121</td>\n", " <td>0.911831</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.908327</td>\n", " <td>0.647327</td>\n", " <td>0.291706</td>\n", " <td>0.970818</td>\n", " <td>0.729577</td>\n", " <td>0.717430</td>\n", " <td>0.863081</td>\n", " <td>0.262225</td>\n", " <td>0.220212</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.936683</td>\n", " <td>0.177590</td>\n", " <td>0.862677</td>\n", " <td>0.934528</td>\n", " <td>0.381837</td>\n", " <td>0.655252</td>\n", " <td>0.787673</td>\n", " <td>0.572202</td>\n", " <td>0.101325</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.318607</td>\n", " <td>0.212341</td>\n", " <td>0.886886</td>\n", " <td>0.140888</td>\n", " <td>0.156922</td>\n", " <td>0.518755</td>\n", " <td>0.203880</td>\n", " <td>0.233374</td>\n", " <td>0.611417</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.169347</td>\n", " <td>0.871152</td>\n", " <td>0.130755</td>\n", " <td>0.717898</td>\n", " <td>0.664626</td>\n", " <td>0.123841</td>\n", " <td>0.889616</td>\n", " <td>0.213999</td>\n", " <td>0.002460</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " image ALB BET DOL LAG NoF OTHER \\\n", "0 0.839579 0.855175 0.945205 0.141212 0.579779 0.983546 0.390744 \n", "1 0.908327 0.647327 0.291706 0.970818 0.729577 0.717430 0.863081 \n", "2 0.936683 0.177590 0.862677 0.934528 0.381837 0.655252 0.787673 \n", "3 0.318607 0.212341 0.886886 0.140888 0.156922 0.518755 0.203880 \n", "4 0.169347 0.871152 0.130755 0.717898 0.664626 0.123841 0.889616 \n", "\n", " SHARK YFT \n", "0 0.656121 0.911831 \n", "1 0.262225 0.220212 \n", "2 0.572202 0.101325 \n", "3 0.233374 0.611417 \n", "4 0.213999 0.002460 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random[\"image\"] = submissions[\"image\"]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random.to_csv(\"baseline.csv\",index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kaggle score : 2.41669" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py35]", "language": "python", "name": "conda-env-py35-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
locie/locie_notebook
base_python/itertools-fr.ipynb
1
676
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Itérer avec classe avec itertools !" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
lgpl-3.0
JorisBolsens/PYNQ
Pynq-Z1/notebooks/examples/video_filters.ipynb
2
867931
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Software Grayscale and Sobel filters on HDMI input\n", "\n", "This example notebook will demonstrate two image filters using a snapshot from the HDMI input: <br>\n", "1. First, a frame is read from HDMI input\n", "2. That image is saved and displayed in the notebook\n", "3. Some simple Python pixel-level image processing is done (Gray Scale conversion, and Sobel filter)\n", "\n", "## 1. Start the HDMI input\n", "An HDMI input source is required for this example. This should be on, and connected to the board before running the code below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from pynq import Overlay\n", "from pynq.drivers import Frame, HDMI\n", "from IPython.display import Image\n", "\n", "Overlay('base.bit').download()\n", "hdmi=HDMI('in')\n", "hdmi.start()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 2. Save frame and display JPG" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/jpeg": 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VJrNLVhGI9iOWHHfNcurFWDKSGByCO1NooA9U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ir9FFV7ue4RjDZQGScjJIX5UHqT0FS/brTzhEZcnGSFGcCqV3Jc6kjQQOI4u0QOC3ua4MVXhCPL\nTevkduFpSlLmmtPM0qKKK7CwooooEFFFFABRRRSAKKKKBBSUtFJjQlJS0hpMoKSloqWUhKaTTqTG\nKllDaUgmkpeR71DKQnfk0YFLwRSZ2+9SykNK9aRTg+p9KfgkZ6CmpwxqC0HzHk8UDk4x0605jgfy\npqjApFDCTuoIwOtD8NSDJcg+lSUB5IqY8KIk5PfFRHGMd6niYRJhfmZv0qWUNLFkWILjB5pjgZwM\nYHcd6lxsHq59Oopu31ouNIYFzThhfc0uD9BRj3oTKsGc08U0D2qRRjrWiZDiOQZ6CrMZKEHOCO4q\nDfgYFG/FVzGbia90YJI0ltkmKgYkd+5rStryGS02g7tiHIfr0rn7e6Lxm2lnMcHXGOpqqZSjnaSO\nwPTipcbqxNi4bnknPemG6PrUcluwgEiNvPUgf0qmzkdc1WgrXLpuWz1ppuD3JqnvJpu73o0DlLhu\nCe9NM59aqFvek3e9FyuQsmc+tNM59arE+5pCRRcfITmU0wy+9Qkj1NIcGpuUoEhkoiWSeQRxKWc9\nAKhwPetrRrSGONtQnYFY/urnofes5zsrlKJVu7GSxtUkmkxK7YCKen1NQwGSG3e8xFKGOza5yR71\nclZ9Zn5MaiIFgGbAx71n3siXM29IkhUDGxOlZqTasyuQqllXgnPsKY03YcD2pxiXuaYYh9avmE4E\nTPUZYmrHkL2xQYlHpRzi5SoQfWm8etWTH7DFMMXP3KOYTgV9/oauaOYjrdm1zKiRJIHZ3OAMe9QG\nMD+Gp7PTLnUZjBaW/myYztBA4/Ghsjla1PTf+Eh0Ysc6lbf99iud1bxw0c7w6VEjqvBnk5B+g9Kx\nU8K6wmcaaWOO7LWK5ZXZGUKVJVlx0I7VEYQ9Qk2dDF431eN8yyQSDuhjrsdB1+HXLRnRPKnjOJIy\nc49x7V5WMegrsfh/C51G8nx+6EWwnsWzRUjFRuhQbvZnVa1p6arpM9s4BcKWiJ6qw5ryJiRuU8MO\nDXtrsscTu5AVVJJ9sV43KVeaVlxhnYj86KD3CpZHquiHOg6ef+mIrI8d/wDICt/+u/8ASrfhG7W7\n8Owpn95ATGw/lU3iPTn1PQp4I13TLiSMepHaoT5Z6lPVHlu813XgW2gudMumlt45SJQAXTJHBrhQ\njAkMCpBwQ3BBqeK6mtkKwTSqCckK5UH8q7ZR5o2MFaLPXRp1v2sYP+/QoFpaj/l2g/CMV5Ray6jq\nN3Haw3Fw8khAAEjcDuTXrMcflRRxbt2xQufWuWceTS5rF3OR8eKkVpZeVFHHlzkqoGa4jknnmut8\neXay3ttZqQTCpZ/YnpXJcCuujfkVzKpa5GT/AKWo9xW7GBnJrnmb/TR/vCtyNiWwKxp7y9SpbI0Y\nmwBircbnjNZ0bgfeOPWrcci9h+JqmiLlvZI75RgF70x7QRK0qSMz4OB2pizHPWlkuRtwDSu1sNxT\n3KX9pXEZUPHt9eOTVyzuzcAlgARUDsJBh1yPcVVUeTMQHKr29a192S21MnzQd73R0CMvpUgk21lR\n3SjHzg/Wp/P3DrWDizZSTL/nUeZmqatnqac8qxKCGB9s0uUfMYnjls6dY/8AXRv6VioM2dt/1zFW\n/Fd59ptrdQPlVjz+VVI/+PS2/wCuYpUotV2n2/yCUlKkmu4opaSlFdxzBSq2DSCnY5zTEXLaYqw5\nq1qNimr2RUAeegyjevtWYuRWjaXBVhzWdSmpqzCE3F6HESxPDI0cilXU4INR12HiHSRdQm/t1/eK\nP3ijv71yFeHVpOnLlZ6tOamroSiiisiwrb8PayumXMkN0hl066Xy7mLrlf7w9weaxKKANfX9HOj3\nwVHE1pMvmW0y9HQ9PxHQ+4rIrqdBvbfU7E+HdSkVI5G3Wdwx/wBRIexP90965++srjTr2W0uYzHN\nE21lNAFaiiigAooooA6bwzrX2WYWdw37lz8pJ+6a66eLjI6V5byOldz4a1kXsH2S4f8AfoPlJ/iF\nd+Er29yR52Mw9/fj8y6ykGm1bmiwTVYjBr00zymhtGKWimIKKWikAlFLiigQoGaljTJpqLk1ciRY\n0MjkKqjJJqW7FJXI7m5h02ye5mOAo4Hc+1eb39/NqN29xMcljwOwHpWh4i1g6neFI2P2eM4Uep9a\nxK8jE1ueVlse3haHs43e7EooormOsKKKOpoAntLWa+u4ra3jaSaVgqKo5JNdJrd1Dounnw9p0qu2\nQ1/cJ0lcfwg/3Rz9alQDwhopkYY1u+j+Qd7WI9/Zj+nFcgSScnvQAlFFFABRRRQAVNbW0t3OkMKF\npHOABUQBJwBkmvTfBvh4adbDUbpf37j5FP8ACK2oUXVnyowxFZUYczNfQ9Ih0DTFiAH2hxmV++fS\npZp1J+9TribcTzWe7ZNfSUqahFJHy9arKpJtkjTL61GZVHeojTTz/jWjZEUyYzL6n6U0zjvk1DRi\ns2zVRJfOB7Gl84ehqHFLSL1JfPH900vnD0NRUUh2JPO/2acJWJ4Wo1XPPQetOLADC8e9IL9iTztn\nVcn0pDcs3UD6VDmii4WJBO3oKUSO3QCmhRjLflSluMDgUgv2JPNZemCaQ3Eh7ioiw7Um6ncLEhmk\nPekMr+tRlqMikVYeZX/vUnmP/epm4etJuHrSCw/zHz940b3/ALxp8dvJKu75VT+85wKeY7OJQZbo\nsf7sa/1rKeIpw3ZtDDVZ7IgLN/eNJuY9GJp7X1hF0i34/vt1qA+IYolIjgiB9cZ/nXPLMILZHRHL\npvdkuyU9Fc/hR5U3916qN4nk27VMa854iX/Cm/8ACWXi4C3DAewA/pWTzGXRGqy2PWRd+zzkZ8tz\n+FL9luWHELn6Cqn/AAmGpHJF3Nj60o8ZagFwbpwM8A0f2hPsh/2bDuyZ7a4QfNDIPqpqEo/dW/Kn\njxleEYe4LfUCmSeLJ2YFnQ855Qf4U1mUv5Rf2ZH+YjYEcEH8qQkD7x/CpP8AhJvmJKQnPqgqRfEk\nRPzQxH8KtZmusfx/4BLyztL8CqXz9KbkVoLrViwG60iJ9c0q6lpzqSbVM1azOHWJP9nS6SMygk1q\nrdaUVy0GD+FOSTR3+9F+p/xq1mVLsyf7Pqd0Y2SaUKSPatvytFPPP4Mf8aDaaS5GJXA9N1Uswo+Y\nngavkYvTgcUmAa2/7O0pul04H1zSf2dpfH+lOPyNV/aFEX1GqY4FHFa5sNLHAu5M/hTTYaX1N4/0\np/2hR7/gL6jVMrJoya1vsWk55uX/ADpfsekY4nkP1NL+0aPmP6jV8jHLHsKM4GW6+lapi0hDtEjk\n+uaa0Ojg5LuT/vdKX9pUfMr6hU8jIZyx9qbWwU0YfxNn3NMLaSDyrfnSeZUuz/r5lLAVO6ModaoX\nuoGEmCDG4feb0roWuNJUHCEn61TH9hFubNWz1965cRjoVElFG9HByg7yZj6dpVzqbeazbIs8yv3+\ng7101hpNjZYIh82T+/Lz+QqJdVsYVCLEAo4GDTv7btB0jOf96so4ikumpq6NRvfQ3Y72WNQqZA9A\nKc2sXcIyLdn9lrAOvQdAhB9mNSJrsRbCq2cZIL03iovoCoSXUuXHjx7A/v8ASbse5XAqSx+J1lcT\npDLZ3ERY4BwDWU3iGIjhXP8AwPIqrcajYXEiSyW5Z0+6xxkfTispV30NVSXU7jT/ABBaX/iaWW3Z\nmMVuUJdSpB79a4+SN0l1NcF5vP8ANK/3kPcetU7XUrWzuzc28ckcx4LgjJFWZNWtZHEjGUSDo3AI\nqXWjJaoFTaZm6aXsbq7me3aZpjlJQpYY9PatawW5ubhZJIfKRfublwzn6VEmp2qMSrsvvtGTS/2z\nBnPny5PU7RUxnFFOMmdRqmspp/h5bJGBeT5Rjue5/nXLi73psOGBGMHvVaeeyuZvNkeVyBgE9voK\ndDLZQurgsCpyoYZ5/OtPbRbM/ZNIi1y0NiyrbQBbYRjzGXkBj/KsFnrqpNRtpkkjdyVfl/l+9+tZ\n62+i9GDgY7Z/xpzq073iEac7amCI2lfCKWPcAUyQyL8rEj2auohGkQg+XuVumQOv15rQs7nTDK0c\nsayLtJG5Aefxq1Up23d/68xck7+RwJJ9KaT7Cuuu4tINwd8CIxGSEXj8KgNlorLlVOf+Bf41LqR6\nFKLOYDEdCR9DTxJIOkj/APfRrov7L0dwMMyk/wC8P60SaHphztuWjPYFjxQqo3E5/wA6X++3/fVN\nMjHqzH8TW8vh+zb7t/8AqKP+EegPS6J/EVaqX6k8tuhg9etLwPSujTw3b7cl3b2xUo0O3i6LG3+8\nT/WtYQUvtL7zOc+XozmVdegyT7CpFt7iX/Vwvj1IrpFtFh4WFV+gFLz3rqhhVLeX3HLLFNbR+8xI\ntHlfmR1QemcmrsOlW8RywaQj+9wKvYpK6YYalHpc55YmpLqIqqgwqhR6AYpcUuKditzBsaFpcCnA\nUY5oJuJtFKFGacFzTgAKBNjdvrRtFPpcUE3GbQe1LsHpT8UoFFwuMCD0FKUG08DpUgWnBaW5Riag\ni7o3CjlcVQYgdFFa14v7scA7WIqK2SCSYJLCpB7gkV4FSL57JnuU5LluZq7WOJvuEdF65pFZoxgY\nwK3302zVv9Wfwam/2da9cP8AnWbozXUtVYmSk7noAfqKv211deViOGMqvqR/jVn7Bbf7VOFlAP4c\n/UVUFUg7pkz5JqzRWbUrlPvQRVEdbmB/1SflWisEC/8ALJD+FSBIB/yyT8q0dWv/ADkeyo/ymQdb\nue0aD/gNJ/a16/8AFGn1XFbeIB/yySkxbn/liv4GpdSs95sap0l9lGQrXk5+bUYUHs3/ANapk0lZ\n/wDW6jv9lP8AjWj5Vs3/ACx/UU1rW3PRGH0xTUv5lf1bE4/yu3okRLolvDBIYQxl25Dk5rnkm8nV\nEuA3fkiugntn8k+S77hyB0qBbeJowXhQv33LWda02uVJWLopwT5pXuWKKKK984gopcUYoASiiigA\nooooEFFGKWgBKQ06mmkwQUUUVJSEopaKTKG0lOppqGUhB1ozj/Cg+nejYallCdT6UZwORxSmPikA\n9zioZSDdgHHQ03IDZzT/ACl96YyENipZaGl8tn0o34PPSpNoPCqMetKECEsRuxUspDchgSqZI7mm\nMTI4DZXHtU771TqAe9RrjA6moZolccJFdMIuO2TSRrhgQadtwSMfhSqrbhwahs2jDQcepOOT3ptS\n+We+B+NJ5Y/vL+dK47MixShCam2RqOXBPsKQtH6sfwo5uw+XuN4XoPxppNKXTsrfiaYZB2T9aabJ\naXcUtTS9IZT2UUwyv7D8KtNkNRHk5qR3aYbpDyvAAFVTK/8AeNAZicljVXJaiXbWZoyVfIQ9sUt4\nofY6Mp7HmljitntGSQTfb3YCJegqnd2stncGGcAOACQDkVPNqCS3sBXHVlH4007R1cU8PHJhSmDT\nJogoBXt1FPUd12Gkp/f/AEpMx/3z+VRcnpRsY9jU3KXoSFo/Vj+FJuTsGpqoWYKoLMTgD1Na9p4d\nvp5E8yLy48/NubBx7VLlFbspN9jK3J/db86ntrd7uUR29uzuffgfWtXU9CtNPhMpvWyfuRsnLflV\nWz1iazsmt4ok3k5D46D6d6jmTV4l3f8ASNSfTNL06yM88byuBwpfALelc6bjOf3YAPbPFSuZ5QJL\niRmjds8tk5+lV2GQVz8ueKlRtvqUnLoWnv0ntobdoYohFk7x1aq0kjo21olBxnkUzYPerEbRsvlz\nfdPJfGW+lT7qHefcrGZ/7iflTfPkHRY/yqxLAYmAdRyMj6VHsHtS90d59yI3Ev8AdT8qT7TKeyf9\n81N5QPYU4W4PJAA9TS93sF59yuLmb+6n/fNOFxN1OxfqtSmONfugk+tRNFnndk+9O8ewm59xDeyK\nOFiJ9StS2Gs3Wn6hDeIqEofmULjcvcVXMOPSmGMjqMU7R7ENz7nrdlqMOo2qXVpIjxMM9OVPofQ1\nkar4Ys9SladQLeduWdEyGPuK4OyvLnTpvNtJ3hk77TwfqOhrdi8baggxLBby++CpP5Go5LPQi7L9\nv4JgWQNcXYdB/CkW3P4101nb29hbLb2tvHHEvOAOp9TXGS+Obsj5bG3U+pZj/hWPqXiDVtSXy3uS\nkR/5ZwjYD9T1NUqbluZuTR0XivxTCkEmm2XlySP8s7qeFH90e9cQJk6fZ0/M1GU28O232HWjcVHA\nwP1rphTilYxlOTNnQ9dbRL7zhADBINsse77w9R716VYanZalbieyYSIevzfMvsR2rxynwTSwSCWC\nWSKQfxRsVP6U5UVLYmNRrc9T1Hw9pOqSmW5tSJj1kjbaTWevgfRVbLPeMPTcK5q38YazCoVp0lA/\n56RjP5ip28baqV4W2U+vlk/1qVSqLZjdSDO10/SdO0xGWyt/K3febqx+pqjrXiWx0iNo0kE14R8s\nS8hfdvSuEv8AxFq98u2a9cIf4YgEH6VlhTnLcZ7nvVxw7esmJ1F0Jric3VxJcTzl5ZG3OxXqaaI1\nY8SDHqeKYSq9Fz9aUtv6mulRMnqVpVVb3G8N8w5BrWWR+gKj6GsSbi9x7itAEZzXPSTbl6mk9kac\naueT1+tWFZhxjislZD2qZZWB64rXkZlzGqkvUEdKYLiPqf1qgZ2CHGc/Wmr57nABOfWjkXUXPLoa\nLX0XqfyqrLIsmCHyafbWjO2ZcAeg609rKTeQgUr2zxTjyJ6MiSqNaorA1PHOyjByatwaVvyZXC+g\nWlk0x0zh1I+tVzwelzP2dRapFYXUgPBqMuSSSTk0jBVO0kg+4pAoPRxWqSWxk23uZWu/8e0P+8ad\nF/x6W3/XMVY8S2bW+nWkjHl3bj8qjt4Hawt2AyCnrXFHXEyt2/yO9e7QV+42gCpTC46o35UbCOx/\nKuyxz3Q3FLigA1IEJp2E2NFTRnBBpUiZui/iakARfvNuPotPlM5SNGznx8rcg8Eetcz4k0U2Mv2u\nBf8ARpD/AN8n0rZjnKEbFA/nWkuy9tnt7j5kcYOa5cTh1Uj5m2HruEtTzSkrR1fS5dLvGicEoeUb\n1FZ9eHKLi7M9dNNXQlFFFSMK69T/AMJhpIjIzrljH8p73UQ7e7D9c1yFT2t1NZXUVzbyNHNE25GU\n8g0AQkEHB4NJXU6zaw6zpzeINPjCODjULdOkTn+Mf7LH8s4rlqACiiigAqWCZ7eZJY2KupyCKioo\nA9L0nUo9Xsg/AlXh19DUksZBrgNJ1KXTLxZkPy9HX1FeixyxXtqlxCwZHGa9fDV+dWe542Kw/s5X\nWzKRFFSumDUWK6zhCilopiCnKuaQDNWIY9xpMaHwRZNc54q1raDp9u3/AF0YfyrW17Vo9KsyiEG4\nkGFHp71508jSOXYksxySe9edi69vciepgsP9uQyiiivOPTCiiigArqtBsoNMsj4j1OMNHGdtlA3/\nAC2l7H/dXr+FUfDujLqdxJPdyeRp1qPMuZsdB2UepPpUfiDWjrF8GjTybSFfLtoAeI0HT8T1PvQB\nQvr641K9lu7qQvNK25mNVqKKACiiigApaO9bPh7RX1e9CkEQIcyN/SqjFyfKiZSUVdm14M8PC5mG\no3qf6PGfkU/xH/Cu4ur0dBgAdqrySR21ukEKhY0XaoFZssrMa+gw1FUo2R85i68qs7k8l0DVdrkL\n1qs8uOBz71CWJ9a6XI5Y0+5ba6Hf8qYbsVUO70P5U3Df3T+VQ5GygW/tftR9sx2qntc/wn8qNj/3\nTU8xXKXPth9KT7YaqbH9KVYpD2AHrmlcfKWvtjdAKd9sK/eHPpVTDLwuPrmmFW9V/OjmsHLcuNfs\neuKZ9ubNVdjd2X86UQnqzqB9aXM2PlSLS3jseKeLxl6daqbeOHQD603/ALaL+ZouLluWzfP60w3k\nh71X2jvIv60m1f8AnoPypcw1EsG7f1pv2xz3qDan/PT9KQqgHEmT/u0XK5Sx9rk9aT7TIeASSewp\ny2Uj7QFJLdAKseZHoyb8K92ejHkJ9Pf3rOrWVKN2aUaLqysiyliYYfP1GfyVIysQPzn6+lQz65FN\nG0awLbqVAWPrtI/iB965+61GWdyzuSfUmktrK61CZQvyjGQzeleXKrVrysvuPVhSpUI3Jp9Wn3bS\n3T3qL7RdXDZ+Y+9aSaM6YJUOexOKlGnykfdGPrW1PAyfxuxjUxiXwK5lCB2/1sv4CpRbwKOQT9TW\nh/ZsucYH/fVX9I8MX2u3y21sFAHzSSMfljX1NbvD0aUXKWy7mMa9arJRj1MSOJZHCRw7mPRQMk10\n2nfD/V9QQSyW8VrEf4pzg/lXd6bpWlaBF5Onx+ZP0e7dQWJ9h6UW9vcbnkvLz7U27Me0MNo98/yr\n5vF55CLcaEVp1Z7uGymTXNVk/Q5Z/hZD1k1WFX9FFKPhoiWrRpqFq0hOQ0g5X8q7Ae1L+NeJ/alf\nn50/69D1vqdPk5GtP667nnVx4Cu7ckfaNPc5CgecFJJ+tN/4V7q7qGENng+lwtehXNpbXiqtzDvC\nnIwxUn2yOoqfIOAFCgDAA7Cu1cQVlHWKb9DkeT0W7qTXzPNf+Fc6sT/qLX/wIWmt8N9UVSzJbqB1\nP2heK9LyB6Ubh0wCPQjINZvP6/8AJH7hrKKa+2/v/wCAeYr8N9UkkKRzWpYcEC4U4/Wnr8NdaLgN\nNAik4LGUHFei29raWtxJPDBtlkGD8xIH0HarAYVM88qP4Yr8f8y1ldNfaf4f5HnZ+GeqJwmpWrj1\nzjNNuPh3qtuPk1OwbjJ3vsx+dejZzUNzawXkRinTcOxVtrD6Gso5zWvrYv8As+l5nnv/AAr7xHs3\nB9PZfUXQpD4C8S4xmx/8CVr0hEjhhSKJNqIMDJyfxNGRTeb1r6WF/Z9M82/4QHxP0X7GT6C4Wmz+\nBPE9sgeSG2KnjIuFNel8c4xnHGaybC2vra/3XcTybvmMgk3J+HOPwxVwzWs09geX0+5wkvgbxZEp\nk+xJtUZYiYHitD/hXGvOiut3ZMrAMPmNepXi/adImkXo8dNtNz2FuTkEoODXTPGVuRTX5fcc6w9P\nmaseXN8OPEWeJrIj/roaZ/wrnxJj79l/3+r1kgjrTTnFZf2hXX/DD+rU+35nkx+HHiMkfPZAevnd\nKnPwx1jGRqNoT36ivUeaawKsVNJ5hXtf9EH1an2PMF+GWsEZfUrRW9ME/rQfhhq7H/kJ2nPsa9QK\nswz6U0giq+vYjv8Agg9hT7fn/meEaloV1pmoS2V1IPMjOMjow9R7VRmtpY03ovmY6gdRXrniDw/Y\natr9v9pubiK4mQKiRRhgwHv2qCT4c2CsV+3XQI9UXivoaGMwEqMXUupW133PLq4bFqo+S3L022PG\nzdr0MeD70n2zH8Ir1m4+F+nXAyb6bd/e8sZ/nWbJ8IIOSmsyD/egH+NTLFYRfDK/yZUaFd7q33HN\neC9Ntde1K4ju4y0cKB9gbAb2NelXugaHC0SQ6dbrlB1XP61S8MeCB4Xubm4F99qaZAm3y9mPfOTW\nnch1nMj+YM8Ku3gfSvDzDEpyfs29bdz1MLRslz7nlOqjSTrFykVwtuqOV2KvAxUK29i3TUY/xxWp\nc/DvxDeX88+LVVlcuDJLg4J9MUg+F+ucbriwH/bRj/7LXvU8ThYwSlbZdX/meVUoVnNtN/gUBaWY\nH/IRi/SmtBZKMNqMRH4Vrr8LtUON2o2S/QMf6VMvwtuQf3mr24H+zET/AFpyxuDXb8SVhsR3/I5u\nT7Ap+S9DH0EZNQGS2HLSP+EddknwxVfv60P+Awf/AF6m/wCFa2hGG1WY/SID+tZPH4Lv+Zaw2I/q\nxwbXkYOFUke/FNN2p6qRXoK/DXTAfm1G6P0Vf8KmX4b6Ipy91eP/AMCUf0qPr2EfX8yvq1c85S5L\nsFjjZifTvV1bRyAzkK393rivR4vBmhwLtRZ8f745/HFTDwtoijmCRvrKa2hj8vj8V3/XqZyw2Kfw\n2R5n9jPZx+VKlq6NuDg549K9PXw7oq/8uWfrI3+NPGi6OnTT4/xZj/WiWZYC2kX/AF8xLCYvrJf1\n8jyprFncsZV5pDYy9FdfavWl0/TExjT7f8VzUqw2afcsrZccjEQrN5ng+kH95aweI6yX3Hndl4J1\ni4ljSWWG3L4wCckfhV298BTW8pjbVgzdz5RrvhcHercZHfFUbiBppmk3jn1ryp42pyvlet/wO6GH\nhfVHlGpaTfadfPbKROqgEOvGfwqni9X70D/gK7PxLbvDq2WOd6Ag1lbTX1eEwUK9CNRyd2l954WJ\nxUqVaULKyZhC8uI+okX86mj1iePpI30PNa2Cff60x7eGQHfEh/4DWkstf2ZfgZrMF1iU11tz9/Y3\n1WrKapA4+aP/AL5NRPpVq/RWQ+qtVaTSJVGYZVf2cYP5iud4PEU9Y6+hqsVQno9PU1EltpvuShT6\nPxUhhZeo4rnZUurUZkidV9fvLUltqjRHhiAeuDkflRHF16T5ZfiOWGo1FeP4G7gUYpltqiyoygp5\njDAOP19qVZ1JCnqByR39/cV308wpydpKxxVMFOKvF3Hhacq0uP8A61Lgmu5NbnA09hKMUuKXFUSI\nBTwKAKcopAJinBacFqQL7VIyMCnhaeEp4SkO5i3qEGdfQg1RRijq1a9+oF0y/wB9KyWXHboa8XEK\n02ezh3eCNeRgyqw7io92KZE263X24pOaJSvqEVbQl3UuaiyO5FSwwyzgmFDIF4JHaou3oi9ldhuo\nzmp10+6P/LE/iRTxpt2f+WYH/Aqfs59mT7Wn/MirSirY0y6/ur/31Tv7Nuf7q/8AfVHsqnZh7an/\nADIqAU7bVwafOOqD8DTvsUo/5Zmn7GfYXtodygyntULA7mFaZtXA/wBW35VUliKXOGGMjoamUGty\no1E9mQUUUV7pyBRRRQAlFLRQAYooooAKKKKBBSUtJSY0JRS0VJQlFLilxUspDKSn4pOPWoZaQw/f\npwB7H8DSNgEHmnnGMgYPqaljQgI6Hg0hBDZHp+dLvLDAUUwscryT7GpZasOXceFH59qbImCCzA0H\ncZc8j60SEFfQjqKhlpkwC4HP5UwFGlyQxA6e5poceWFXlj6VIDsA2qQB68VDKQkmNn3agVyFIAAq\nZ3bbyAoPvyahA+ZhUM3g2iQu/HzdRTCTnkmkdxs64wOTVfcVfcCTU2G5W3NAJkZNLjHSlQ7o1OMc\ndDSmkOyRGaaakNMIoERmmkVIeKYRTEMNMOaeRQF9aLhYZt7mrAhFuge4QneuY8GhJYLZFuJGjkBJ\nHlHqKpveRMxJY47D0pXYvd7krSOzbmYlux9Kmiv5YYJosK5l+87jLCqBu4vU/lTDdxep/KlqO8TS\n8m1mmSO3kMShMu0h6mqxgl8vzMZj3bQ2etVvtcOOcn8KPt6ABctgHIHpRdjvHuWNrRnaVING8ntk\n1B/aa7izFiSMHimnUo9oXaQB6UrsacepbiEnmCRFLMh3YUdMeta02o6veGItI0aSnCbflBrFtdce\n1WYQZUSLtf6VE+tbo0jZnKp90Z6VDuVzR7mvco0rzvd3IM8fAU85+lQeekTq1umw7cNu5yayzqse\nclSfxpp1iIdIv1pNspSh3NDkn1pdpIrNOtp/zz/WmnW1H/LIfnUuTLU4mns96UIPWso66o/5ZD86\nT+3l/wCeK/nUNjU49zcRsRGI8oxyfWjyBjPQetYf/CRbekCZ9STR/wAJI2eYVI9M1PMPnibmEX7o\nyfU0xl3Hqc1itr743LEhHf2qI+IZP+ea0uYXPE3DF6Gk8pqw/wDhIZeyJ+VNPiG4/up+VPnRLkjd\nKeoqInnFYp1+59E/Ko21q4Y52p+VNVIoTkjbZFOeKaImP3eaxP7YuP8AZ/Kg6zdnjcAPYVSrRIdj\nZZQh+fr6CmliR8vyj0FYp1W5P8Q/Km/2lcf3h+VUsRElq5slQwwRUbRkH5eR6Vk/2jc/3/0o/tC4\n/v8A6VaxUEZunc0iNpx+lKCRWZ9ruJP4iacHu+wY/StViovZMzdI1VyeMc04rj7xx7VlLJfj7ocf\nhR/px7PV/WV/K/uM3R80aYfjAGKYwPpms/F7/tfmKMX3+1+YprE/3X9wey80XxyOaCMYxVDy7088\n/mKNl6e5/wC+hVLEv+R/cHs1/MhZTm9x/tCtEKQTkVjMsonAY/vM+tX1S8P8Q/76rKjXacvde5dS\nCaWpdHHSrMEavy+ay/KvD1cf99VPFHdf3x/31WrxErfCzH2S7o0ygikUgZU+varCyDtWSVusYLj8\n6ckc/eQD8ah1ZP7LKUEuptxy81Ms+CKwlE6g4kH51In2gnmZR+NLml/Kwsl1OhW4VunWpBtYc1z+\n2XGVuBn0zUcktzEm7zs89jSbklflC6va5tNZRs+4luvSrIigjTiNQay4ruXy1+bORUondzgLzXRy\nyZhzQvoZni582NoucgO39Kr2i50+3IznZS+J2JtbYEY+Y/0qWwjLafbkAn5Kzox/2iS8v8i60v3K\nfmKocDh2H41MjS8DcT9RUoRF+8c+wp2/HCgKP1r0eWx57nfoINx5dYwPcUhlhB+WAE+uabtJPJya\ncsWT6mnbsTddRuY3PzBwPQGgW8RPyyMPqKsi3wMuQv8AOlxGn3QWPqaOV9SefsRx2e4/JIrfpVqG\nFoSNzA+ymqxJbgnA9BSqpB/wosiW5dy7qOnJrGnmBo9sijMb9815xc28lrcPDKpV0OCDXo1rO8bD\nDt+dVPEui/2pbG9gUG4jGWA/iFeZjsLzLnjuejgsTb3JbHntFKQQSCMEUleMeuFFFFAGromry6Nq\nAnVRJC6lJ4W+7Ih6g/561Z8Q6PDYSRX1i5l0y7G+CT+6e6H3B/SsGui8O6pbrFLo2qc6bdH75GTA\n/Z1/TPsKAOdorQ1fS7jRtRls7lfnXlWHIdT0YHuDWfQAUUUUALXQeG9aOn3HkTMfs8hwf9k+tc/R\nVwm4S5kROCnHlZ6tNGGUMvIPQ1UZcVleF9aE8QsLhv3ij92x7j0rdmiwele1RqqpG6PBr0nTlZlS\ngCnFeacq5NbGA6NMmpby7h0uye4mOABwO5PpUsapDE0sh2qoySa4HxDrLapdbUJEEZwg9feuXEVv\nZx8zsw2HdSWuxn399Lf3bzynLMenoPSqveikrx223dntpJKyCiiikMKvaZplzq+oRWVqm6WQ9T0U\ndyfQCqiI0rhEUs7EBVAySa6u9YeFNKfS4iDq10gN5Iv/ACwQjiMH1I5P1AoAreINUto7aPQtKfOn\n2zZeUDBuZO7n29BXNUUUAFFFFABRRT443lkWNFLMxwAO5oAsWFjLqF4ltCpLMfyHrXp9lbQ6Rp6W\nkIGQPmb+8apaDon9g2YkmUG6mGTx90elTXF8ckDBP0r2MHh+Rc0tzx8biOd8sdgnuT3PFUpJ2boS\nBSPdOxzkflURuJP736CvQbPNUNbsC7e9N3N/tUfaZf75/Kg3Mv8Az0NQ2aKInzns35UYk/uv+VIb\nqb/nq35003U2f9c//fVTctRHbJT/AAP+VHkzHpG/5Uzz5mP+tf8A76NWLO5iSfNwzyKB03HrSuOw\nwW8q8sjfSkaOZv4Dj0q+dUgH3baL6nJ/maYdVXPEMI/4AKLofIUfImP8H60C2mPRP1q8NVbskQH/\nAFzX/ClOsuPuiMf9s1/wpaDsUxayqPuAn60NbzdSFH1arf8AbMo/uf8Aftf8KP7acjBSI565jU/0\nobQcpS+zyH+KP/vuj7O/9+If8Dp99JHdOjwW4UgYbYvWqvkzHpC5/wCAmpuPlJ/szd5Yv++qb9nP\n/PaEf8CqP7PPj/USf98mj7Pcf88JP++aLjSJDb/9PEP50jW6jkTxt04B5NMFtcn/AJYP+VWbLTZ5\n7+GJ0ZYy2XbjgDmhuyuxqLbsjdupEsLBHYfvXTKj+6vrXF3d208pYk81s+Ib7zppMfdPyIP9kcf0\nrP0SyF5eNI8YeKIbiCcZNeVUnKvU9T1KcI0aZa0bSUlkEt0ucDIjP8zWtNGBqGAMDy+1Otzi/H+1\nFk/mKW4kRNQBd1UGLjccV6tKnGlG0TzKs5VJXkEahAcxhs+p6fSnGQcjyUpn2qAf8toj/wACFMN1\nb/8APaP/AL6rW5nYkZgRtWFMn5Rj9K9G06zTQfDsNjEB9puf3lw4647CvOrK6tm1G0DTR7fOXPPv\nXdeINOvbnWfOtojAuFK3Hm4Ur3yM/pivnOIa04040ovR3b/A9/IqEJTlOfQtg8UjSyKcLGWz0wen\n1pce+ff1pa+EUrH1dkOUk9eTTt5AyOtNHWlxSuOwizNIT8jKo/vd6fmm4oxSbCwvWjbSGk5rJsdh\n+KUCm5NLuqLhYeKMe9NDUbhTTFYU8U3dQSKbkVpFisPzUVwcRAjsadWFfXt39ukt2kWCNCNoKZMg\n9Qf8K2hFyegctzrrK7DaK3QlNwwe+Kj0678zTYJD3Wubtb8pp+oxM3CoWH5Vf0q5A0i0BP8ABXoT\nxMvYJdn+hyOglVfobhno8/gis77UKT7UPWuVYiXcp0kaImGaJZ/MbJrNN0PWhrkAgZ7Vf1h2sT7H\nU0lnwhX1GKa0uDWb9q54pftVWsS2rC9iF5HJPqtrLEzrtjZTKnVKtNdmIKrrvIGN7HlvesDVLhhe\n2rrKyRoCzqrY3fWtOJ4ry3SUXMIDdBuAIrRVJON1uEqaVrk5vm7KtNN657CozbR9rqL8xSG3Xtcx\nfnWblV/qwuWI83cnt+VMN1Kf4qT7Me08R/4FTHtpR0KH6MKOaqFojjO570zzWPeoX3RttcYPpTC9\nRzy6j5UTGQ+tNLk1DvNJvo5mLlJCx9aTcfWot9JvouFiXcaTeaiL0m+qUibEu40hY1FvoL1XMFiX\ndQW4qHfSF6fOLlJS1G+od/FG6nzsXKSFuaNxqLdRupqYuUbdWtneKn2q3EpXgHODXF39tHDqU1vb\nb2CHgdT9K7QnLAVwN/fT2viGW6t3KSpNlWHPSvfybHVoTau3FLboeZmOFpzgnbV9RvGcEgEdQe1O\n20yee3vLmW6uoi88zbpHUAZP0p3m2P8AzzmAHQZr6T+03/L+P/APF+oL+b8B+2io/MsT2nFJvs+u\nZgKazN/y/j/wCf7PX834f8EmxxWZqWmwMhnigkLfxLEQB9a2IdKmuIFngs72WF/uuq5BqOawktBv\nngvIAP4nXGKzr4qNaHLKH4/8A1o4aVGXNGX4HKiV4uEieIewOfz71dt7gE4kUqPoefp6Vqs1q3zN\nev8ALyMgkn9KXdbYGL1vyP8AhXnJHc2SW14VdFaPcT0G3rVi7fU/tCpZwwiI9ZHOSPbFVxNGg41F\nhjp8p/wqKe4aS0Y2upCRx26Z9cH1q+d2tcjkV72L91dQQFEmcGR+FUDkmk25GVORXOQXkiS7/Jjm\nbpmQ8iteC42ncvQ9q6MPi5033XY56+FjUXZ9y6q8VIFpqTRupbcoI6jPSp1XOD2r2qdWFRXizxKt\nOdJ2khFWnhaeEp4WrM0xm2nBaeFFPC+1IdzPvbGS4ZJYiA6fwn+IVmT2rKSGUj1HcV04Wle3jmGJ\nFz79xXJWw8Z3a3OujiZQsnsclh1XaCQKjYMO5NdHJoxJJjdfbdxUP9h3DHlox+NedPDVdkj0YYql\nvcwhGzuEQFnY4ArsLGyFlZRwD7w5c+ppLDSorI78+ZN/fI6fSr4SuvC4Z0veluzlxWIVW0Y7EQSl\nCVLtA6mmNIoOB97t3rsOOyE2Uuyoy8xU78qOzAVJHPgAMN3uvWkLQPLJ7UvlmrC7X+6QaXZRcdit\n5fFU76wF3GCMCRfun19q1dtNK1MoqS5WOMnB8yOJxS0UV0HYFFFFABikxS0UAJRS0UAJRRilxQAl\nFLRg+lJghKKXafSl2N6GpZSG80U/Y3pR5TelS2iiM8Cm5B6VI8TjHHB4pnlsvUVLaKVxGGVFInuM\n4pxDBeRQi7mC9GPSobLsOKs3YA+tJj161L5ckRxIpAPQnpTmgcHOBj61DaLimQ4phiyhb39KlOF6\nsPzp8cEs0bmMfIOpFQ2jRIgjYICTyx6D2pziTAcjHNS2lvIzMFQsRTrqGaIoGRlPUZHWouirO9ih\nMH3kKMkcnvioXVkfeR15NbH2GYKCI5F/vHHWqs0LLJgocehFQ5I1jDmKgRpBlSBn73HapDENqL3H\nep0RsY2Hp6UwAvOEGeODU3uaclkWiKbipiuOoxTSKCCEjFMNTEUwrSEQkU0iptlGyi40iIJinxkw\nyLIMblOQCKl8sIMuCD1WomyzZPNJsqxgXkzTXcsjY3M3OKrE1JP/AK+T/ep9mA1wARkVbdkcijd2\nKxNJurovIQD7i/lTTEnZF/Ks3UN1h33OdJpCTXReSndF/KneUn9xfyqXVKWHfc5kk0hz6GunECZ5\nRfyp4hT+4v5VPtSvq77nLIrMr/ITgflUfzY6GuyigQq/7oHA7AcU0RKeiL+VJ1AWH8zjiG9D+VMK\nt/dP5V2whT+4p/CpBCCPuKB7gVDqFrD+Zwm1v7rflTSj/wBxvyNegbEXoi59cCjy8/wr+QrNzK+r\n+Z575b/3G/75NHlSf883/wC+TXooj9l/IU8RgjoPyFTzXH7DzPN/Jl/55v8A98mjyJv+eUn/AHya\n9KEajt+lBjHpSH7HzPNlgnByIZP++TSm1nJ4gl/75NekBB6Uqx5PFFhex8zzX7Jc/wDPvL/3waX7\nHcn/AJd5f++DXpRUDvuPp2qNtxPPSjlQ/ZLuec/Yrn/n3l/74NH2O6/595f++TXoRB9TTSG9Tiny\nIl0zgPsV3/z7y/8AfJo+wXX/AD7yf9813hAx94ioJCy9Tn8apU0S42OJ+w3X/PB/yo+wXWM+Q/5V\n1jkHJXdmoPMjJ+Ykn2NV7KPcmxzgsLkniBqX+zbsf8sGred3I/dsMegNNScr95cevNUqMO4mYiWN\nwGBeBivcZxV6JZI1ISzYevzCtNkDqHQjn9aixj+tdFKlGPwv8jKVypmYZP2V8f7wqPzJR/yxP/fQ\nrQ25GTwKgkAA+QfWulJ9/wAv8jFryIN8x/5Yn/vqgNJ08nP/AAOnZzzmlqkn3/L/ACM2/Ibvm/54\nj/vqmt5wOfLAH+9U2OOeKXIz7Vai+7/D/Inm8jKlZ/teSAGyOM1eV5c/dX/vqqNx/wAfx/3hV7JJ\nzXLRT5p69TeeyHh5yOEj/Onhrj+7GPxNMViKkElb8j7mN/Ierz/9M/1pWaY/88/1pocmnCqVPzE5\nCr53rH+tSKJ/70f5Ug9qkTORVez82ZtiwOzoSwGQccUXTKsa7uhNNtfmR+ud5pmoErHF05JrGpf2\nLZUUuexswSoYk2xjoMcVNmUfOyDaKoQXLJEgUYG0c1P9sY9etbRjZENmR4mkV7e3IJJ3H+lTWDsd\nMt1ycBelU/ELFreEnux/pV3TYy2mQMeBt6ms6S/2mXp/kVVa9gr9ydcipURm6DNKGjTp85/SkaVm\nPJ49BXopI89tslVI1+8c+wpTLgYQbR7VEp4xTsUzNruOPPJoHFKOnNKFzzRYm4AZpQp7UoGDTwOe\nlNIlsFODWhaT7SKpKuRzgVKhCEY5ocbonns7o57xboIgb+0LRD5Dn51A+6a5E16/E8dzC8EwDI42\nsK858Q6K+j3xXGYXOY29vSvAx2F9nLmjse9gcV7SPLLcxaKKK849AKKKKAOu0uRPFGlJoty6rqNu\nCbCZjjeO8bH+X41y0sUkErxSoUkQlWVhgg01HaKRXRirKcgjsa6rUUTxTpJ1a3QDVbVQL6If8tV6\nCUD8s/WgDkqKKKACiiigCWGV4ZVkQlXU5BFeiaJqiavZDcQJ04cf1rzfNXNM1CXTbxLiM9Oo9RW9\nCs6cvI58RQVWPmejvCQakhh5yamsJodTs0uYSCrDn2PpWX4m1dNItPJiI+0yDAHoPWvVlVio83Q8\neNGUp8q3MXxXrZJOn27/ACj/AFrDv7VyFOZi7FmJLE5JNMrx6lR1JczPcpU1TjyoKKKKzNAoorov\nDul28yzatqjFdNs8FhnBmf8AhRfy/SgC3pkMfhnSk127QNqE4I06Fv4exlYe3OPfBrl5ppLiZ5ZW\nLyOxZmPUk1c1fVrjWtRku7jAJ4SNfuxqOAo9gKzqACiiigAooooAXvXfeCtCFvt1a8QH/nijfzrE\n8LaCdUuvtE6kWsRyf9o+legzTKihEACqMADtXoYPD8z55HDi6/KuSJdv1W7haROh5IHUH1rlLoND\nKUeJW7g88j1rbtbsxS8n5T1FS6jp6XEPmJjaeQf7p/wr078uh5jXNqjl96/88E/Wk3/9O8f5VJKs\ncDlJUlDDrjGKb59sB92X8xQ5CSG7yP8Al3i/75pvmN2t4v8Avmnm4t/+ecv/AH1TftEHaGQn/fqb\nlpAJJP8AnhF/3wKcHl6mKID/AHBR9qt058hifTzKY15Cf+XY/jJUtjtcebi46Kkaj/cFRSvPIm1t\nuPQACj7ZD/z6r/32aBdxHpaR/wDfRqXItRsVtx6U4HaMnrUxuoR0tIs/U037Un/PrF+tFw5bkRcm\nm7qm+2ID/wAesH5Gj7aO1tB/3yaXMPlIN9Juqx9uPaCD/vmgag46RQj/AIBSuPlGp5gHyyFfoafm\nXvOf++qX+05h0SEf8ApDqlx/0zH/AAAUXDlDD45mP503aT1lP507+07n+8n/AHwKQ6nc/wB9f++B\nTuFhpQf89Ks6dKIHupAclYSFPuar/wBoXTMqiQZY4Hyit3xFp114YtrUXVzFcvdxhmCrhV7/ACnv\nWNeoowa6s3oU25J9jmdQJYpnONg/OtbRwkGnkNC7lvm+UkY9zUUM9jd2zRFGjmA48zlSPqOR+VX7\nT7T9i+zOVhOMxyKfvj0yOoriw81Comzqrwc4WQtpJvvUOekR/mKllVX1AblBxF3Gais7eaCRnmj2\nZTC5p8h/08f9c69hHlW1JCido0/75FN2J/cX/vmlGSQAMk1YULGoBG7d1xTbsa0qTqehWCgYZQAV\nOQcV3Nnqa39vHMW+YLtbNcRJ8p45Xt7VLZ6h9hlDtkw5G8e1eTnGA+t0Lw+KP490ehleK+q13Cpo\nn+D6P0O/3UvnonDIzE9Mdqrakkthai6smS6sHUNHKW5QH19RVKwurue72Hy7iHbuaRFx5Z9DX59O\nhUpN8y2PtIOM48yehsBweR0p28d6gDe9LurmKsSC4jkJ2Ky44+bvTs1Qn1G2tn2SOQwGTtUnA9Tj\npU6TrJGrxsGRhkEHg05X3aHy2JyaTcfSmbqaX4JJwB3NZtXCxNuo3DvVOG+trhzHDOjuOoBqffS5\nbbg1YmDCjNRbqY80cY3SOqD1Y4osKxYyKQmog4IyDkHuKN3vVoViTdUN3cNHZvgj/ZyM4pHlVI2d\nj8qjJNY93qLXUe1YSkY5yWGT6cdq2pxb1XQlxuZF7ctBZXcg6mM5rTsr3y9PtkDZxGKwtYf/AIld\n0c/wYqeAkW0A/wCmYrqlC9Jev6I6VTTn8l+Zuf2h70G/96x8tR83vXN7JFulE2Pt49adJf4br2rF\nOevNJuZ/m9ar2ZLoxNkX/XBoN971i7jkijc3rT5CXSiaj3UUl2jSyhQqEbSM7qpTxwMQYgUJ64PF\nVGkxMQQdwXrTGnINbx5uXlIULO6ZZ8n/AG2/Ojyf9tqri6al+1GlyyHZk/kj++5/Gpo7F5T8okx6\nliBVL7Yy8jig6lN/z0P50Wl0IcWdHax/ZoPLLlznOTUu+uUOq3A/5aGkGrXOf9YaTpTZi6LOr8wd\nKQuK5capcE8yU8alP/z0pezkiHSZ0m4Um4Vzo1Kc/wAdPGoT+v6UcsheyZu76N1Yq30x70Nc3DdC\nRQkxOkzY3e9G4eorCLXDfxn86YRP/eP51aixezXc39wpC9c/uuF5DN+dKL65jPJz9aOVh7Lsze3U\nb6xBqz90FL/ax/uD86dmT7ORs7/ejfWN/a+OsdINZUsF2ck460+Vi9nI3IzmRfc15rfvuvpjn/ls\n1ekKdpBPZc15qkUd9fTRNdw2rKGkEkoO0+3HevYylayfoeZjXpEUNxwaPxrM+0XCjLFBzjmpRPdY\nziM/hXs3OA0F6Zpsj4Un2ql9ouf7qcUG4uD1RMe9FxHrWkLdQeHbWM71i8vKGEcjP97v+VZHiSSQ\neGgWVUDsA4HXd7964ePXdYiVVS8mVVGABJ0ps2r6jdLi5kaUejNWnOrWI5He4OABzgZoHHWqsl3I\nwEbRDO4GpfOm/wCeIP41ncssgBhTre2jeUQriINnJUd6rCaYD/Uj860dJjknnMzpsRB+dbUIe0qR\njYwxFT2dKUrmVd2U9hIBIMqx+Vx0NLBclDyeK6i7tIby3MMmQDyCOoNYDaJexSFUjEi9nUiujEYK\ndOV6avE5sNjoVIWqO0vuJYnUKXRQu/36n39DWrpDSOsisSUU8Z7Gqdpos7HdcuEU9VXktW7FEsSB\nEG1VGAK3weGqRmpy0MMbiqUoOEdf0HgU4CgDNOAr1GzykKBTwM0gFSBc1JSALUgHpSBakValstAB\nmnEqg+YgU4CnhR1IBx61JSIhIT91Cfc8Cgs38TqvsvJqbyQ5JbJ9BnigRMF/hQeijJpDIRHuGSjM\nfVzgU7kHHmBfZBmplhBOSC3+8amVQOgA+lK47FQQk8rGxPq5pfsrE5Ygf7gq4AKULii4+UorBs+Y\n5Y+q9asDeMAjI7561OcKMnge/FVpdQsYM+ZdQofTdmk5dwUew8j8KQgVRfXtOOQsrSEdlWoE8Q2G\nwvcE2yb9oZ+Rn+lT7SPcr2cuxzdGKWiuo6xMUYpaKAExS4oooAKMUUUCEopaKAEopcUlJgLmjNJR\nUsdhc0uaSipZVkKx4+lMYA8U+msOKllJIj8vd+FOiRSxJGcdqFbn1pA2G71LLsiYsFUpIdykceoq\nsUynP8JwanVwchEJ+tMw8YOQuO/tUMcYpFXb6jjtVq1k+zq4fIDjjFMC/Ick4PT0p8sACJ5YJP1q\nWXyJ6MjimeGQsCwB64OKkURzzsXaTAX5dvJzUWw52jGSM81LEWSRACdxGPlrNluHUnTU7mGEo5eQ\n9iTjj3qrPNLMRIXOc+tSSI6oTwArYI+vvSSwCKJMdWGTioaLpxSehVhmufOKxSNu5AyelWYXkt2R\nkkbcG5PrTLYCOYN6nk05/wCIZ6Gpduxoou+rN030/d8/VRR9uk7iM/VBVCFzJErfyp/NQ4R7HJ7O\nS2k/vLn2z1ggP1QUv2qI/es7c/8AASP61R3GkLmpdOPYrlqdJMvGe0P3rGL8GI/rT/8AiXqA8ljk\nEcBZTVFW8tRIcN/smomkzS9nH+mx/vuki6w05/vQzD6SZ/pTRbaax63K/Qg1S3knilWQAjuf0qXB\nd3941Outn+COTvgq3swQkqGOCetWNFhW41AI0qxjH3mFVbw5vJj/ALRqbSm23wNXLawJyWq3OxOh\ng/dv7Y/Ukf0pp8PXI5Wa2f6SiollPepBLXN7OX834GixdZbwX4jW0DUf4YA/+44NNOh6knLWU34L\nmrCz7OhIPsanj1C4T7s0gx6NUuFTo19xSxs18UPuf/AMt7K5j+/byL9VNQ7Cp5Uj8K6WLWLwcfaH\nx781MurXDcMIn/3owaz/AHq6L73/AJGn16n1hL8H/kc1DGCr8E8dj0oEeR2rrIb62+bzrK2kJHZM\nU4HSpB+80xMnukhGKiUqi+z+KLjjaD3uvl/lc5QIB0GfeneXnqa6r7Fokn/LO5i+jA0DRNLcZS9m\nQ/7Uean2z6xf3f5GyxOHe0l+K/Q5gRH6U4RfjXUDw1bsMx6pAT2EgIpD4VvCMwzWs3skozWbxEVv\np63RtGdOXwzT+aObEI9Kd5f4VtSeHdUhPzWMhHqvNVXsLuIkSW0y/VDVKtB7M05GUPLpRDnpmpmQ\np1DZ9MVGZDjuPbGK0UyHG240xheSc+1MILdx9KGmK+31qu9/EpwzDPpVcxLJivqBUbKvUPg1HLck\nKCozn2qpLOWGSo+hp3JbLTtgcqG9wcVVkkUfMWZcetVTdAEjeQM9yMCo2mLsQCXB/wBkCq5iWywL\njzAdkin/AHl6fiKry3BXqyn1wcj8qiHmMSDhI+nPGabJA65I2FTwPWqTM2MeYSnAUqPUEgflUElu\nX+bA9w3apSoAIwQ3vyKZKzsNrxE4754qkybETwg4z5e31QkGmMXxiMFmHJz1xTljznqT2HUCmu7x\nDdtJ7ZzyPpVJk2JLeZ4SAwO1ug/wq78rrlOvrWYHkcK6sMk9D1q3bz+a2BlZFOD6H/GtacyWh0gI\nPP41GRVyVAw4qqRg11RkZSiQsoHOKYpqU8VEww3qK1TOeaFzSfSkyDS00yLGbcf8fp+oq7yDVK5P\n+mknjkVdLR5OZF/OuShJKU79zepsh4ORSio/Nj/vr+dSCeIYHmL+ddfPHuYtPsOXk4zUy8YzUAmi\nPWVAPrThdRKcLIn1Jp+0j3JcW+hciAPJ/XpVgCFSG5Y+g6Gsz7XEesyfnT1uoMjM6D8aPax7kuEu\nxPbNmOTHAMh4FQ6hgLFnOeabaXVuEYNMo+c9Tjio9TuYHWIRyo5Gc4PSuWrUToWuawi/abGtEoKx\n4BI2ip2jRFyWx7dazV1G38pFEyLhQCAaT7fbf890/OuuFSFl7y+8wdObexDr7KbWEAfxHn8qtacf\n+JbBz/DWXq9zFPBEI3ViCSQO1ammc6bD64rKk08TJrt/kVVTVBJ9y2KcKaBUgGa9FHnsVRgU8UgB\nPSpVQdWOKtGTYijn1qQRk9OKAMdPzp5Yt1qiGw2gdTn6Uu70GDSL1pxUH60EPzFAzzjmhcA03JU0\n/g9RQSyaJtpyKnvbODWbB7aYDcRlG7qaqgYHBqxBNsYCpqU1ONmVTqOnK6PMb+xm0+7e3nUq6H8x\n61VIxXp/iPRE1qxM8IH2uIZH+0PSvMnVkcqwIYHBBr5jE4d0Z26H0+GrqtC/UZRRRXMdAVf0rU7n\nSNRivrY4eM8g9GU8FT7EZFUKKAOj1/TLeSCPW9KX/iX3J/eRjk20ndD7dx7Gucrb8P6wmm3Elvdo\nZdNuh5dzF7f3h7jqKi17Rn0W/wDLEgmtpV8y3nX7siHoRQBk0UUUAFFFFAG/4b8Qtok7+YrSQOOU\nB6HsazNQv5dRvHuZiSznOPQelVKDVubceXoSoRUubqJRRRUFBRRU9tbTXlzHbW8bSTSMFRFGSSaA\nLuh6PLreoC3VxFCo3zzv92JB1Y1a8RazFetHp+nhotKs8rBH/ePd29zVzWbqHQtM/wCEesXV5Ww2\noXCfxt2jHsv881ytABRRRQAUUUUALjmtHRtJm1e/S3jHy9XbsBVS2t5bu4SGFS0jnAAr1DSNITQt\nOEWMzsMyN7+ldGHoupLXYwr1lTj5lqOKHT7NLW3ULGgxx396qSOSetLLLkmoc5r3IpJWR40m27jw\na1dOvQn7qXlD61kgc1IppS10FG6d0amqaILmMSwYPHB64HofUVystgY3IlfyT6Fd2fpjrXUWWoyW\nxAzlfQ1qEaZqKYnUKTz071k7rc2Si9jz8W1uf+Xwf9+zSGK0UYF2fqIv/r12F34QhuQTb3Kj05rK\nl8D6irYRlYexqedFKm+pgtFaf8/b/hF/9em+VZ/8/Mp+kQ/xrZPgrV8/LED+NPXwTqo5aI59jUcx\nfIzDENl1NxPj/rkP8aCtj2muf+/a/wCNbh8E6sf+WX60f8INq/8AzyH50uZD5GYO2y/563P/AHwt\nIVsf791/3ytb/wDwgurn/lmPzoPgXV/+eYpcxXIzAxY/3rr/AMdpMWH/AE9fmv8AhW9/wgusZ/1Y\no/4QPWf7i0uYORmCPsH925/76X/Cj/QM/cuf+/g/wrd/4QTWf7i/nR/wgesd1SnzIfIzCzYf88bj\n/v6P8KXdY44t5vxm/wDrVu/8IJq2Purmk/4QXVvRPzo5kHIzD32I/wCXWX/v9/8AWpPMsu1pJ/3+\nNbv/AAguq/7FA8C6nn5in50+ZC5GYPm2mci0bI6fvjXounWkXijwFDBbh572yJdPNO5sd0H07fSu\nZ/4QfUN3JTH1pBZar4bEgS/NjBOOSJMM/wBB1rnxLjyrub0FJNmdPZtKzHy/LdDgtkAqaSz1KWyu\nvLYRs46oy5ST3x61Xa7jjckJJIc5JY4yfWonuZZ3DSIqgdNq9PxrhOo7yz0+fXLb9yYoZUG4xuTg\n+ymsW9s7mx1RYbqFopDHxnofoe9Ydlq93FcqY750wcKS2MV6DbXd7Npxg1GKLUoShKFmCsGPoe1d\n+HxE9nrb7zjrUI7rQ5jfsPykg+tNMj5++akgtJJ53gbfDMv8Egx+R71YOkTA7fNUMe2Rk13qpFq6\nOTkqR0KRckYLEj61DcN/o8g/2a0TpEw/5ar+YqKTSJyjDzE5GOoqudC5JN3Z2GhTJf8Ag23sZmkZ\ndpG1BgqB3LfhWK1i8BYpdmNeuFJ5+tT6I0um6JLAZA2X65qtcXPGAck18LmcUq8ku7/HU+1yypL2\nMfRDRf6kowl4cf7QzTv7U1cf8vMZ/wCAVBGsrk4Xj1JqXyn74H415TilukeypxfQb9uv/MeTcm9/\nvEcZqWPVr63iCRpCqL0ULSCGTHRc0PZGQZYED+8ppe7LRq5XMuw4eIb4NjZF+VU5/F5mieCWDg8H\nbQLGMyhSW5461hzadtnkAkUYYjrXuZLl2FxUpKpHa3keHnOOq4VRdK2t+hsxeJ4hLHK8G54/uZGM\nflV+PxgHYAxRj3Oa5Yaef+ei4+tBtgowHWvbqcNYKerv954S4ixS0cU/k/0Z158VKDjZA30c1Tn1\n6Ge5854kPGNjfMB9M1zJth/eFN8j/bFZLhfCxd4SaNVxJVW8F+J2Fnr0FvAI4oiVJJ+aQcew9qs/\n8JJGDzBn6OK4fyh/eFHlkfx1lLhOi3dTf3f8E0/1mf2qf4/8A7pvEkIA/wBHY57bgfzqqL22u2KR\nQLbsw4RW+X9TXHcj+KkJb+8aceFoR2n+H/BH/rJH/n3+P/ANbWJHbT2hRSzSOFwOeM1peaiqqgH5\nQB0rmFZ0YMrkMOhzU/2q4P8Ay2b86KnDMnFRjPa/Q1p8T0+ZynB622fY3/tCe9IbhexNYJvLgDAm\nY+9R/bLj/ns1Yf6rVP50bLieg/sP8DoGnyMAmgTbVAz0FYC3lwpz5hP1p/2+c91/KpfDFa+kl+P+\nRouJcM1rF/gbYm5JzR53NYov5gO35Upv5SP4R+FT/q1iO6+8f+seE7P7v+CaU+oeQ5jkRh6emKrG\n+t2OW8zNZ8sjyNlnLH3pmCDmvbp5BhlTSqJ362bseHUz/Ec7dO1ul1qaYvLU/wDPSj7Za+r/AJVl\n496UDJ45qv8AV7Bva/3kriHF+X3G8lsx+dlIQjjJpWtFPY/nQT9tWW4glIWFFBB4OazLxp4yircz\niRzhEQ5Jr5CvhZU6zp7Ps9z6mli1Uoqrf7i+bJfRvzpVsUxk5/OqcdnNAA97qFznqYYiD+bVNJrH\nlpiCAKPVzuNb08vm/idjlqZnFaR1LP2WFD8wx9TT1ii7IT+FYUmt3eT+8UE+gqu2r3PeY5rdZfS+\n02cssxrPZHU7oo/vFVH0oF3a4/4+E/EGuVGsTjOWBHcEdalTVomGHiG7ttPP5GqeX0JdWZ/X666I\n6uIrMcRSRsfQMKm+yznon61ycd1aSDkEHHb5TmrlnNcDmyvst/zylOCfp61lPKr/AMOX3lRzNr44\nk2r6dr0t+gsVb7OQNpRhwe+7NbUWm3KRKszp5gHzY5GazX1a4lxFM8lnc9OQfLP19KdbRa3cvsju\nrcv/AHTJjj1+lclfDVoRSkkrdTqo4iE7tS36M0jYSf31rL1e5GkiISJ5ry52KvTj3q0I/EUXzeTH\nMvT5XBqT7RNPH5V9o8pTPP7vcK5oRakuZXXkbOTa91ooWEqajaieKA7T146GpmtQyFfI5+mDWyHB\nijVEEcaDCoqbdo+lNzUuSUny7C9o7anJT27pIyBiCOzDBqOJHhlEr4ZUIPXFdVcwQ3SbJsbh91x9\n5a43xRDcQwxWCIzu53uyDIK9q68ND201BCq4r2dNyZfk8XKhdGchj8pUR5xWdDq1uunDTRJK1rv8\nwq0fVvUnrTdHuJYLSK0a1YlMksydPxreXU7iJVU+XwP7qk19Jg6H1ZPkhv3Vz57FVvrDXNK1uzsY\naXNp5gCxOT2Ij/xqlNomoXt/K0cZtUYbl83Kg11ja1PFtlZosL2Crn6U2XxNb6g4SdzB5YyBtyTX\nRVm6itJJeisYUoqDvFt/O5zsGjf2fG0mpOrqSArI5OKyr7yjqDJbuTCMbSp4rpdZ1CO7sUgs5lmc\nvyMYIFc8ba4F15TxHC87lXispSXIoJL16msYvnc236dByW4aJiWPHfPSn2umzzCdmufLji/idTzV\n6xh3SpGPlYuPvcV1GJgWLTxFQCT8w6YopqN7yVwqOVrRdjzjzSJcj5iD1Per8M0kjgMoVc8kdqrT\nrH9qdgjrvclCRwwz1FammQB5lz0J5FTGDckipzSi5Gl/ZMTKNtwxUjg4FXLa3FtAIlZmAOctU23H\nCjAHQU4KTX0VPD0qb5orU+YqYmrUXLJ3Q2ngGgADg1Kgz0rZswSGhaeFp6oakCGpbLSGBKeEFPCG\nn7O9SUkMCY7VIBShTTgtSy0AGKcBQBTgKkpCgVIo4poGPanowdtqsCfQUmxocBThVaaYx8Ac+pqv\n5zv1Y/Ss3NI1UWy888UfLuBVGfXYISQkckje3AqvcEKpLED6msG51C2hfJuE3DsDk1jOrY3p0kzX\nm8RXe393bIgPQtk1ny6tqUv3rvYPRABWPNrKSE4Er+meBVSTVJMfJEq+5Oa5pV2+p0xoJdDYeTzc\nme5lb8Sc1GBAoP7t2PrnFYL3902cSbfZVxVeSed87pWx7tWTqmypHSwsqFt7Bfqaq6tJG2ksquCT\nKOPWqOkYaKYltxDdfSrGpD/iWL/11o5rxFy2ka9FLiivdOcSilooAbijFLRTEGKMUUUAFJilooEJ\nRS0UmMbS0UUmMKKXFFSxhQw+WlxQehqWUiLIyM0jAB/ak4LCnyLg1DNEPV2AyRuXsRTvldjzwRzR\nt8tdy9O4qF3DMDjH0qWOI0jbnvHmpw67F2AlhyQB2qsVJ/i4pyJtGQTn1qGapNiOCJBu9unp9auT\nRhUR0XGwg8elUSxXK5z9astPKsAB2ksMA+1QEug9lFxNIFzgDI9GIqB5DIChGAg4ojuyojVYwWUE\ndetNVyW+ZMbs5IqWXC9yvJn5SDgd+adLb7VbDHIGetBh850XcF+bGatzxlZBGcYIxn+tQa/asTWC\n4s16dT0qxim28At4BHkHHJIqUikYt6kRFIF71Jil2561LGmQsKYV9amIpjCpZaImHFIq85qTbk04\nLyKhlpnGXf8Ax9S/7xqbTP8Aj9Wobv8A4+5v941Lpn/H4KHsYx+I6Ykg04Zx70qgHmlxk1i2dasN\nG409S1KF7dqeF7YqW2Uox6joye9WEkZvlUHFMRFwCfyqZBx12j0FS2yvZxY4O6ccAHg1YBA/5aDp\nUAUAg449PWpVwP4QahyZXsIdiZJKsLMpPB5qoEBOenpS52+5NTzMl4am+hdEwp63Kj/61ZM0zoDh\nkUds8mqstxOIgS5+iLj9aV2ZSwdJ9DpUvpYhlWmHoFbFI3iW6hO3zZ8enX+dcc91OpZ8FQP4neoH\n1B5V+aWQ/wC0BhRUSjGXxK5n9TUX7uh2MviO5cHNqs/GfniX+YqmutW10cT6bDEp7mTH9a5B7tlV\nXlnAAPGOlRyapaNtEUSyMOuFFR7KHRfoUo1Y7Sf3nStqXh3znhmimY9vLccVTWHSrtz9leaFv+mi\n5P5iueR7Z7stLHiXtGvJ/wAKuLfXQUhLVI1HduTin7NLZv7/APMftK6W9/VI0ptNPlZtbmOR/dzk\nfn0rOis7sKfNiMoz1QgL+JNEN/cSzeTErtIedkacf4VftbG+NwXnjknPQK7bUFO0l1F7eqviivkZ\nclrKo2mHOeyrvx+PaoxaSNgMzKuONi459zXbWVlOg2+YIk7xqxIrcguVijVHjilA7SIOaTlUWyT/\nAAIeMgn78GvTU81h0wIpDM2SOpYn/wDVTotMWP5gCGHIIbP869NeHSrg5l0yHn+4SpqH+wdBmbhr\nq3B9CGxQq818UX8rMtY3CPTmt6pr/M8/ayynLY9GI5qr/Z8gJBmyp9Qa9Ck8HwPzbatGwPRZl2mq\nkvgrVwMwLDcL/wBM5BmqWLp/advVNHTF0p/DOL9GjjBaeX1QEHvk81FLZRuPuDFdHd6FqlnxcWE6\ngf7GazJE8s7WBHsRiumFanL4WmOVGS6HPzWDKSUAK+3BFVjDKJHbmTgYIwD/APXroXj9OlVJVjjJ\n3DBPbtWysYOJXil82PkgkdcUyVM8gcirAjxHnHNNIBFbRlYylEpH86iYZU+lWZEKnjpUe3vW0ZGL\niVACPwp69Kkkj/iA+tIFBrZO5hLQzb6JhJ5mOG7+hqlXR+Tlf3uAh7HvVOXT4GbMZZB781xVsM3L\nmgbU66StIyKK0jpi4/1h/KkGmgn/AFh/Ksfq1Xsae2h3M78aK1xowxuaUqvuKQ6VHn5ZWP4U/qlX\nsT7en3M8KAB8pORwaY6hXIByK0/7MUD/AFjU4aTGT/rG/Kr+q1Xshe3gupkA4oPNadvpqzIzFiCG\nK1FfWS2qIVJO4kc1lLDVFDma0LVaDlyrcojpRW5HokLojGR/mUHtUq6FBnl3IrRYOs+hm8VSXUxI\nIHuJRHGu5j6V1ltAIII48ghRjNLbWUdsu2KML6nufxqwFUdea9LCYb2N23qzzsTifaOy2EVM9KkA\nA4J59BSgnbR3rtRxNjgTnjgUoBNNHNPHpVXM2OHFPAyKZ34py1VyGOApw4pAadimQwz2pwXvSAUu\ncdKpCYcg04HmmgilwMZpiZdtpyjDBxXNeMNA3A6paJwf9ag7e9bKNg1o20iyI0cg3IwwwPcVy4rD\nqtDlZ1YXESozueOmiuh8U6B/ZF55kIJtpTlD6e1c90r5mpTlTk4y3Ppqc41IqUdhKKKKzLCuq0O9\nt9W0/wD4R7UnVFYlrK5b/ljJ/dP+yf6CuVpQSCCDgjvQBYvrK4069ms7qMxzxMVdT6iq1deNvi/R\nMYB1ywj49bmEfzZR+grkSCDgjBFACUUUUAFFFFABRRRQAV10ajwjo4mkUf25fR/ulPW2hYfe/wB5\nh09jUGgWNvp9k3iHU0DQRHFpA3/LxL/8SO/4Vh399calfTXl05eaVizE/wAh7UAVixZizEkk5JPe\nm0UUAFFFFABTgCTgd6Sut8I6CLmUX92n7hDlAf4jWlOm5y5URUmoR5ma/hTQhp1t9vul/wBIkHyK\nf4RWxNO+ThzUlxNngcDsKos2ea9ylSVONkeJWqupK7FaUk/MoNAMZPKkfSmY70oFaNmSiiUKnZvz\npwiY9MGmqpNPzjgfnUXLHAFOo5pwc5zmmBm/vGnbs9QKlyLSJlndejGp0vbhT8shH4mqg24y2QKG\nliXjzBUOxaNEandL0nf/AL6NL/adz/z3f/vs1lG4hH8Y+oppvbder1DsWrmsdSuiP9e//fRpP7Ru\nSf8AXP8A99H/ABrI/tG2H/LSmnVbYfxVN4lWkbP2+4/57P8A99H/ABo+3T/89X/77P8AjWKdXtv7\n3603+2bb+9SvErlkbRvp/wDnq/8A30f8aDeTf89H/wC+z/jWGdbt+5H50h1y3zwBRzRDlkbn2yb/\nAJ6N/wB9H/GkN5L/AH2/76P+NYR16DHAFIdfh7AUc0R8sjdN1L/fb8zTftUmeXP5msP+34cdBTTr\n8QHampRFySN77TJ/eP5mm/aJD/EfzrCOvx+1IdfT2p80RcsjcM7FSGZsEYOK5PVLSbTyCW3iT5kc\n8kj3PrV466nbFDXsOr2z2TYEoy8J9T3FY4iKlC63RtQcoys+pgQuJHy4BI7HpVt5HDqWYsuOAe3t\nWZIWhmzggg8j0q6r+bFgH3FeedpVnjCTEfwPyD6Guo0XUHmsDEzZlh4Oe4rnJB50O3+JeRU2lXZg\nuVk/4Cw9qunPkkpEThzxaOpecupDYI9CM1Qnty5BRzwxJ5+b6A9qgub4287Rtj1HuKh/tOvXtCaP\nNXNBliK8u4ci4UMo6ccgdhn+ImrAvFPDfIwIBB55Pb3qh/aII5xUT3SM24fKegx0H4VSi1s7ibT6\nGydQeG2dY9rKTycFv5VTfUzGiOxYB+n7s1mtNhcJjAxgD5eB2qrPJNLG295GcEbFxkD1NeVjcBCr\nJ1Las9PBY2VKKh0Olh1ILGHLMR/dAG78q0I71pACIpMH1IFefrdSxPhm98EkYrQh1lEHMJbHPD15\nEssptas9WOaTW0TtlvSCFaPj1MnSrbXIWM7QjD1L4rik1yMsubM43AFs5x71aXXomlKLbsBnAbHf\n6Vn/AGVTvpI0/tep1ivxOhu7j7FFBcHY5dsFAe31rHvrs3k/nNGkYAwoVQMj39T71Hd6g08UG4R4\nC5Xb7+tUzPkV9TlmBpYampRXvNbnzWZY+tiZuL0jfYlZyeOAKYTUfm5o8zNeoeXYfmkpN1IWpoQ7\nNJk0m6k3U7jsOpKAadkLyevpQAoXjJ4FIWzwOBTWO7k0madwsFLigUooAMUYpaKBBRRRTAKKKcF4\nyeBSAQDJpS2OF/OkJ9OlJQI3NH2rpF6x/vqKxU1IW15cyyJ/pOdoz0Ve2K0bK6SHT7i3kbBkYMOP\nSqd5b2t2QzEiQdHXg/j618ljKFZ46U4wbTtrbyPqsLVovBRhKaTV+pnz61NIT3PqaoSXdxMcZP4C\ntM6bEeRNj6rUkWiS3cqxwI7jp8o5NTVVWnHmlFpeg6UaU5csZr7zHigubmYRR5aRuAAf61JLp9xD\ncNBMdki9QTWo9vHav5a5UoevvWpLOmpCEG2RXRcPL1Zq82eLknotD0Y4WCtd3Mi18LXd1AJY7qAA\n9jnIpsvhrUY7qO2jMU88gyEjbkD1OegreN8NMhkuT9xF5X19Ki8LX985ur+ds27NmQbRuc9gD6Cs\nYYmtZzbVl5DqUKafKkc5d6fqOmOVu7SWL/a25B/HpUMdz06Yr0CTVTGGlkSNpn5iRRwi/wC1nvXL\naja29xcPIdsMxG5igwoHuK6KOOk3aa+aOepg01dE1prpkhFrfJ58QHDE/On0apJJJLNUlglMsBOV\nIJBB/oa5t0kgba4BB+6w6H6Vatrsx5H3kP3lP869RVFOOup5k6Tps3xey3UnmzajcpAByqgFi3p/\n9enStdQ28VzDdXkaSZ+SY4Ye/uKxkYRSAr80bc1pIrXLpDJMx8wYjkPPHpXnYnCqK5oLTqejhMVd\n8k2XrTUNeEYlQrNDnA85gu4+gz1q9a6/a3LNBdI1nc/dyR8oPvWVJG8BhgvYFuFhOYn3FcD3xUM9\ns9+J71miSPOGdm2gfSvP5YyO20epesPDuoxaobq81CIQAFt28t5vptHatLULX7fZtEpKTD5omzjB\n9CfesjS9Sms5lsLwhopBmNt2QR6g1uE7TjP0NOdScKimtGuxzygrcktUchaXEzStbzXRhuFODFMv\nU+x71pg3CIVKQyMejZZSKsajZwXDFZoldW556j6HtWYLa+sx/os32iIf8sZjyPo1fd4a86Eal201\ne6bv91z42u1GtKGiaezSt99vzJGhu26zSkeiso/pUT2KOQZbeeU/7U2amj1GEkJOj28v92Qcfgau\nKwZcqQwPcHNbxpU5rSTfrZ/mjnnWqQesUvS6/IopbxoMJpqrjucfzqRYZFOY7eGM+u7P8qs8+lPA\nJ7Vp7CO1/wAF/kZfWZLVL8WUX0uO4n8+4IaXGMoNvFM1QJBYC0hUCSdgqqOuO5qxd6jFafKP3s5+\n7EnJP19KZY2cxnN9eEG4YYVO0YrGVOmr06a1e77L17myq1HarVei2Xd+nYsR2kSQQxPGjmJdoLKD\nUyxRocrGoP8AsrinUorqVOK6HDKrJ7sMnOOBS7fXP4nFOXpT1UdqqxKYxRnp+gp6xE9h+dSDFSBa\nRSGpGR/ET9amVTnkUDYDtLDd6Z5pxcKOBmpbRokxwWlYqoyxVR6k4rOu7uXBCvtHtWPK+W3SEkd9\nxrCdZI6IUHLqdE1/aocGdSf9nmmHUYAPlV2+gxXMNqdnCw/eRrjsDmmSeI7VBhDLIf8AZXFYPEo6\nFhX5nQy6pNyIrX8WNQ22rXU5lVtibem0Vyk/iIyH5Lc/VnzVvSb+a4SVsLHzjgZz+dY/WW3ZM3+q\nqMbtG1Y6ncXlxNbSnKQrkHuT71p21/aWcjSXNxHEgXqx6/gK5DTyTqdyHJYbe/1q5qJUWB2qB8w6\nClGq7XHKir2NC+8VWjMfsdvPP/tMPLX9ef0rKOt6nOxCtHbr0xGuT+ZrNd2J460gPJzIfzxWMqsn\nuzaNKMdkTzxtL8080kp/22JqmwjTuBj0p0ksZX72efrUDyDJKRn2PSs20axTFLrxjJ5qF3PZe+KU\ns+0jCjP50ggnm4BbA6HoKhstIjbewJzgevTmqxKZw78+vWrx09Rje+SeuKDaQqOFyc96lplKSLOi\nFWt5tuSA3pirOqjbp6j/AKaA1PodoHgn2JtAam+Io/KiEfowrWz5LmV7zNKijFLXvnKJRS4ooASi\nlpKADFFFFAgooxRigQlFKaShjExS0UVIwpaKWkMKMbhg0U4VLGiAx/eOeQelOchlBFPdTng4zUJb\nA2jmoZpEkEgblsn2HSoSCzZ4Apyse/T0qNm6j161DLQ0sex4ppLHvS4JYDuTV1beE7kZdre56VLH\ncpK4JAPBHQ+lI26NzjvSNGVcqT0OKeEZ4yP4l5+oqWWtSNXAxwBzzUshb5Y0JIPeoSOcEYNPiuJY\nFZFOUbqDUMtDHidWxuyc+tSGfClX3Mw4zQxilGFXaQOlTRKPsn3MEHk+tZs1W+hPpySqQGidVK/e\nbvWjimWxDW0Z9sVKRQYy1eozH40EU/HFJipYIiIppWpitNK1mzRIjC96THNSkU0DLCoZaOGu/wDj\n7m/3jU+lDN6BUF3/AMfk3++as6OP9OFNmMfiOpCjtSgDHHWnAU9U5zWLOtDQnepQuPcmlRakVcD3\nNSykIigDAHPrUqpSBDUoHTj6VDLWgoX86eEHvQAMe9NLnHA/OpZVx7ERrn/9ZqFi3XKqf9qoZJ0B\nJ5yOpNUpLq3lysdtJcHufSlYXOS3E+3GLmJWPXA5qk6s7mQS7wP752rTGR8bkRYwOny/1pg0t7vg\nxEk91OPzpE8zILqVDtRAN38Tr90fiapz7IiFS5Ernpknb+fStn+wpWiEczlx2XOBU0Ph9U2jKoBz\n8vUfTNIerObaHfzPIucZIBwP1pIni24gtJZWJwAOB+Yrrf7P063k3vGjuOcyNuP15qT7fZR4CtGg\nHbFIdjmbXSdRkYEQhPxya1YNHuQQJWiQDqR1/Kr7apB0LofYH+VNN/5g4YHP50nctKJNb2aW6MA7\nHJ5wMAVeW4RAu37o6ms+O82g4bJPUdKcZVKE7SrHpgZAFSzRKLNUXAPGQVPOT1qQTgR4IDehrEEw\nPG/ntzU0cpzgkZ/nU6h7GEjXW4HfJPrU4nGQQTWPvHbpT1nI4zS5mRLL6c+htCfvzUiXQU5BI+nB\nrDW6x14qQXWO/BqvaHFUySnLZHRx6xdRjaLiTb2BbP8AOn/2jHOMXNrbT/8AXSIfzFc79pzxxTlu\ngR15qXGm90n8jinlOIpO9KpJejZsS2Ph+7z5ukiM+sEhX+dZl34Q0O7wsF5dW5PXzEDgflSrddCG\nqT7Rg5zzVRpwXwtr0b/UwnLNKP2ub1Sf47mbN8O7jYTY6nZXAHRWcof1rEvPBXiC1yx0yR0/vRYc\nfpXX/aD13VYhvpomDRSsreqkg1pD20fhnf1X+Rj/AGxioaVqCfmm1+Z5bcWFxCCs8EkZH95SKpbQ\neB1r2/8Atm4aPbdMsy/3ZlD/AM6qPDoN2x8/RrTnq0Y2N+laxxFePxRT9H+j/wAy1nmCelRSg/NX\nX3r/ACPHvI2rmQ7R6d6rM4QkRrg/3jXrFz4L8NXZzDJeWrn3Disi6+GE0jZ0/V7WY4+7KChraGYR\nXxxcfVfqtDqhicJW/h1Yt9r2f42POSxzliSacBk11N58P/ENiC8tizRj+KI78/TFYc9vPZsFa2li\nYdWkQg1208VRqfDJP5mjw0rXS0IFgwMudi+p6n8KaXWM4jXn+83Wl5JLE5JqN1710pnPKFhCxY5J\nzmlHtSAU5V79KtMyY8D1FSJ96mqCamSP5hVKRmyrYgeU5/6aN/Oq+tACGE/7Rq5p8e6CTJwPMb+d\nVdbCiGED+8f6VzVH/s79DeH8dGpAhMEXpsFTrtB9TUUO4ww/7g/lU4HFdcH7pw1L3F5b/Clxn6Uo\nApw4rS5iA4xTsUhxnpSgDBwaq4mhRx9KdnjGKQAgc0Y9OKaZDQ5eaf0GaaOfqKf35qkQxBUgIx70\nzjNKOuapEskGKTAFJnPNOyDxirJADJpxz9KTgdKBz1OaoBQOanjOw1EOOacGwadiGXri1g1Wwezu\nACrD5Sf4TXleraZPpN/JazqQVPyn1HrXp0Eu1hzUWv6PHr2nkIALqMZjb19q8vH4P2keaO6PTy/G\nezlyS2Z5NRUksTwytFIpV1OCD2qOvnT6IKKKKALFleT6feRXdtIY5om3KwroNctINX0//hIdNjCA\nkLfW6/8ALGT+8P8AZbr9c1y9auh6xJo9/wCcEEsEimO4gb7ssZ6qfw70AZVFbniDR4tOmiurFzLp\nt2vmW8h6gd1b0YVh0AFFFFABW34e0ZdVupJbqTyNOtV8y6m/ur6D1J7CqOmadPquoQWVuuZJWxk9\nFHcn2A5rZ8Qajb21rH4f0ps2Nu26aUdbiXu30HQfSgClr+strN4pjj8mzgXy7aAdEQf19TWNRRQA\nUUUUAFLRVzTdOn1O9jtYFy7nr2A9aaTbshNpK7Lvh7RZNXvguCIE5kbt9K9IYx28KQQgLGgwoFLY\n6fa6Pp6Wsalto+cqcFjUU0aTR+bau0gzgofvKa9nDUFTjruePia7qPTYqyPuOaj60hyCQwII7GgD\nJrrOQeKkC9z0pgwnXk0Ftx5qWUtSXd+AoznrUYNOHPSs2zVIfk1DPdLCpPf1qQnoq9TWXeoZ7sW4\nOEUZaspSsaRjcrS6hc3DkRBj9KjNtevy8qp9WqxcyGApb242seOO3+FZzNbbz5zyzN6ocD8znNYS\nl3OiK7E/2Nx1vox/wKmm2HQ36fnVfzLMDi2lP1l/+tTTNbDpaH8ZTWbkjVRZYNtF3vk/A002tt3v\nhVf7RB/z5r+Mjf40n2iPqLWP8Wb/ABqeZFcrLH2S0/5/hSG2tB/y+fpVY3C9rWD8m/xo88/8+9uP\n+An/ABpcyK5WWPIsf+fpvyNJ5Nj/AM/L/karm4OP9TB/3wf8aZ9pOeYIfwSjmQcrLfl2P/Pd/wAj\nRssP+er/AJVAswIz5cY/4DQZT0CR/wDfAo5kHKyfbYf89H/KjFj/AHpPyqD7Q+37qf8AfApPtL/7\nH/fA/wAKakhcrLOLD1k/KjNiP7/5VW+1Sdtv/fA/wpRdS+qf98D/AAq+ZCcSwDZej/lTkmtYZEli\n3h0O5TVf7VOerLj/AHB/hR9pm/vKP+AiqUkS4l3V4orlEvrf/VTdf9l+4rNtZdp2HjFaulXAuWk0\n+6Y+VNymAAQ49PrUctrDDctthC4OOTk/rXFUjyy0OmErorkqHyD1qCceQ/mDgN2q3L0AAxk5yPWo\nigmiaM9e1ZlGjIY7nSobh13tGdrFeoHbNVAbYfwnNP0S4EUrW02fKkG1vb3ptwJ7a5kgduUPp1Hr\nXo4SpePL1RxYmFnzCZt/7ppQYP7ppgkc/wAX6U4O5PWu5HIxw8n+6akiaJJ42AIwwqLe3rRvYDPp\nz0q7EXN3URpLyBZhNkdwgNJbxeFwv7y2u3b2AAq7c2emTyoZdZhhJiVipjJwfSmQ2nhtAfN13JH9\nyI14dS9z0U6ltEOjbwuF2/2PcSHvulxUN/DoFwiGy0iazkXrIs+cn1xU6TeE43+a/vZRnkpFjNWX\n1Hwetq3kw6lLKDxnAFZ2b0C1be6Rzt4IwsIUNuVcFmIyfyqvha2dUvdIutOjSxsXt7hGyZHfcWHp\nisYV7uDv7JJrY86ump6u4mBinYFFFddjAOKTAPejrS4pgJgetAXJ4p6rn2FB9F4FAricLwOT60n1\nNGPalxTGIMUtG2lC0CEpfwpcUuKBXG80U8L60YFMVxtLg08KSeKfgL0oE5DAoXk8mkPJzTiOaMUC\nuMIoxT8UYoC42lANOxS4oFcFHNdj4ajWw0d76Vgqkkk9wK4/FdRZy+b4RmjHVQRXk5zOUMNp3PUy\nqMZYjXsY949o07PG6uGJOT2qL7RbRg4kRfWq5QECopYRsYdyDXw7956s+2SSWhU1m6jvY4ra2lWQ\nFtzlT0rS068+yWsdkQAMZTHr71z+mBUvAD1P+NbJgYa3EXjPmIMAdwK3qQjb2fRanPGd1zvdl+QF\nUaV/vHuay7eCUvI+N4k+8G5BFdHcaPNNok7ElJCCUrnrXLtFIQ6si4Kg/KamK5UwVTm2JYYLe5jM\nEygQHhNv8DetYd1bS6fdtDJ1X+IdGHrW3Ad8RyADuOQO1JqcQurEMf8AXQ8A+q1rh6zhU5W9GY4m\nmpxutzMtiz5jQEsPmQVcjuQ0eIztbO8f7LCs23kKSRnPQ4qRmC3LgHHNe0tVqeLLRnYoY9RsY3kG\nCw5I7HvWWYJrFnhKLcWjHO2QHbn8OhqTRZ828kbE8NkVNPfyW11IY13ooBPGQD/T614FSm6VWUVs\nexRquUEzPu4Li6uomeMQ7P8AVIq7VC+1as76jI1ktihckkTcZCj1NUBexx72Et1K87jImOQn09TW\n0rmNCFYjPBweopP4tdUFSbViO5HyDPUcHFVccVYlOVxUWOK+4yVP6lC/n+bPic1a+tSt5Eckayrt\ndQ6+jDNVjpVv1iaWE/7D8flV0KacE5r05UoT3RwxrThs7Gd9guhwuoy4/wBpAaeNNdv9df3Dj0X5\nRWgEHpTsoo+ZlH1NZ+wp/wBN/wCY3ianT8kV7ezt7bPlRBWPVjyx/Gp8DtTDc2//AD0UjuR2pYp4\n5ZAigkHvVx5I6RM5e0m7yuOwaCVXliB9almASPcSFHqTisi71GzhBDTKzDsnNTUqqG5VKhKpsW21\nC3jOMu3+6tRnV8nEdsx/3mxWDNrCucIkhA9cLVZtRlP3YkX3Yk1xSxjvoz0YYFdUdG+qXm3KJCn0\n5NRXOpXkLQlZM+Y2xsjjFYaXV/J9xto9VUAfrU97uZbYszH95g81lLESavdm0cNGLtZG3a3dta3n\nm3Eyoig5YnJ/SnXHiWMki1tndf78p2j8utYzJuPlrgbmA4qKZPL3qG+73qXWklZFKhBu7G3utahO\nxHmCNfSNcfqayJZXkJ8yUt/vNmiYqSSSSc1BnnhM/pXHKbb1O6FNJaIXKjofyFJvHZfzp0UM1zIy\noBz1Y84rRis0iPJ3OOCxqUmy20igsU7YIQqp7kYrc0SFoo5FZ1csw+6c4qr5YJyQT7nmtnQbYyq2\nF6OKuEfeRlUl7pWtFI1e7HotWdQBFgxx3FP0y3M/iLUEH8I/rV7XbMw6HLKQRhgK0SfKzNv3kvQ5\nKTd13kYHGOKaFTbkjJ9+afjdxgE7c0sUMhi3cBdmeTWNrm4wElRhfypsaPM4RBuY9BVmysZbkoEI\n5Un6Vs6fYQQ2yneec7jjk04xvZickjLgskjJMnzuTjjoKe+A7DjjjmtC1hikVmcEkSEDnioAsQnu\nNyxkBuM9BVW0J5tTPlwshA447UyRWPlgAknpx1q7JcQx37uJMpsxnHeqs12jzxsCzBTmpbWpUbs6\nrwRYedY3pkjIxIMA/hWT44j8rUJEA4DLXT/DyX7RpuoNsK4mHXv0rnviGu3VpPcrW07eyVjKF/bO\n46iilr2jISilpKBBRRRQAUUYpcUCEopaKBCUlLRQxiUUUYqRhS0YpaljACnUgpallC9aiaNUBNTC\nq8z7mwOgqWVHcjJ4J9afDBvUueg/Wm43MF7DrVoAqmAQUxwagu5SC7pgAP4u1WTGrEoMl/cHNRWw\nzcr9c1fk8vGJMe3rUlN2ZnTowYCRQrdgB1FOt5BHJlhlcYIqxIkrxFQCVz8u/rVPBUkHqODUM1hZ\nqzLJg+2OMYCgcN3/APr1Tlt3iLY+ZR1x2+tX4ZgUyThkGF96rTGSRvs6qS7Nl9tQyrNMpEA+1SRz\nvEGTGVPXNWLm1SNDgMCvGDVTa4BO0lQOeKhlrubWnTxyQeWGG8H7p61dIrlh1BUkGtC21SWIhJh5\ni+vcVNwdNt3RtY4oxTYbiG4XMTg+o7in1LJtYYRSYp5FIBxUMtIYRQi/PTjxQg5zUMtI4C8/4/Jv\n981Z0Xm+H0qtef8AH5N/vmrWh/8AIRWm9jCPxHXBakVD0pdpzxUijnmsWdiQKtPA5pyrxUqp09Kh\nstIaqnHSpAuBk04D0oJ54XcKkdiNge3AqvLlhxkt7nirioWb5hge1SrAp7Z9zSFYyPs803G1UXvj\nkmrC6eWULuKgdhxWnhI1LHAA5J9KoXWpqhKW675O/pikFkiRbCBVO8BvdqgmntYSOp5wAvSqTm5l\nLM8x2e5x+AFSxxWiorM7SNniMDp9aQriXGoSnHkIq+x5NQNDdXmHMzMByUUVpQbVP7uFQQegHP69\nKllkNtE0lyY7VE+Zi2ACKQzDudLW3tzPJkqcBQzc5JwBU8XheONAsiknrn3PapNMs7rW9QTWLlmi\nsV+WzTruPTdjpW7d3Mdjb5V2lnkBxCp+YAdWPoB6nFIaS3Ofm8NxwQvPNkQxjLtjOBTE8JpcRxTx\nmRUkAZeoOD04rQ0yO48RXTXDkLo9q4EcYbd9of8AvN6gV1UtuUycqX/3sD8jSbKjFM4STw7cp8yX\nLg9Oagk03WLYhQUkwMnPBxXUanqgs7u3sLfy7jU7riKPPyRj+8xq1YaYtrYiESPO7HdLO4wXbv8A\nhSuUopvQ4drmaPH2m2cerKOKmgv42GI5FYA9G613EllG5Me1SAOeKx7vw1YXJLGLY3qh2kGldGij\nJbGYlwR82MH0qfzBtDnofQ1nX2mahpOH3efAOrHhh+HemwXgKFnICdy3GKlo0jNp2Zq7twz6Uh59\nhVJLzzHAhQuueXIwMe3rVsdwDkdjUNHVCdxdzDv0oMpBzzzR15phHBHWoubqKZMk53VKtz820VU2\n4XJ4pHlZR8ny+/elzjeGi+hprcAD96wHt3p6X4HCDaPXvWEZCGyTnNAnIbrVc5yzy6Euhvi6Bb72\nT71N5xPpmsISkBWdgq+/Wp11AJxGMf7R61SqtHm18jpT3RvRyEAF22D3qf7YVHyHPua5uO/JOS2f\nxq1Heqw5rWOIkjwsTwxTa0R0MOqyoch3U+xq5/aq3EZjuY4J1PaaMH9a5lLhT0arST5x0NDdOfxR\nTPJlkuLwz5sPUkvRtF248P8AhnUD++0tYmPVrd9v6VlXfw10a4jP2LU5oH7LMmR+YrQSQY9KsRyn\n+FjVx5Y/DJx9H+juT9bzig/etNeaT/Hc4qf4Xa6uWs3tbxf+mUgz+VYN54c1fTXKXmnTxEdyhxXr\ncc0inJIzV6HUrkDaJGx6N8w/I10xxFeO01Jeas/vRazyz/2jDtecX+j/AMzwxYGX7/ygeoqZAoI2\nD8TXtl1Dpl+uL7SLWU/3lTY35isibwP4cu2zDJdWbH6Oo/rW0cfNfHF/Kz/4J2U8xwFbRVOV+aa/\nHY8d08FreQ/9Nm/nVPXRiO3/AN4/0rudD8DXuoabc3FlcwSBLyWMRu+1jtYjPNc3420LUNF+xrqF\nq8Pmbtp6hsY6GnLF05U+RPXtsz0IUlz80Xf0JrdB9mh/65j+VTBAf/rVJCmy1gDAgmNTgj2p+zHO\nOfavQp1FY5J0WnqREEHjpSd6lKZ7UmK2UjmdEjwacgyaeF9aeFwM1XMDpob04xS7eaX6UuD3qlIz\nlAbjFKD607ANNIwa1TOeUWLjnilJ7UgGOR1peD1qkyHEKeq4GaQLQDzVpkMcM9acOT/WkBBp3QVS\nZDFI9DxSY9aB1pw6c1SYthQ3pV20mIcAH8apAYqVG2d+TSlqhXtqjK8aeHluoTqdmn71R++Qdx61\n51ivabacHKtyCMEHvXA+MPD/APZ1z9ttl/0aY5wP4DXgY/Ccr54/M+gy7F8y9nL5HJ0UUV5R6wUU\nUUAdH4e1S3EUujaqSdNuiDu7wSdnH581matpdxo+oSWdzgsnKupyrqejKe4I5rPrrtMlTxRpC6Lc\nsBqVspNhMx++veI/0/AUAcjTlVncIoJZjgAdzT5YZIJXilQpIjFWVhyCO1dPpUMXh3S0128jD3k2\nRp8Dj85SPQdvXPtQA67K+E9IbToiP7ZvEH2qQf8ALCM8+WP9o8Z/EVyNTTzy3M7zzyNJLIxZ3Y5J\nJ6moaACiiigAooooAkjjaWRURSzMcADvXqHh3RU0HT/OlAN3KuWP90elZngvw6sUQ1W8T5j/AKlG\nH61v6lOduc969PB4e3vyPMxmI+xErXl0WJOaj06cia4T+8uePWqMshaprFxGZpHONw2ivSaPNT1u\nXbr97sf+LoT61BuCjC/nQ0xMOzOFBqLOaV7IpK71H7qATUeakXA5as2aoeozyaduxUZf8qaW/Ks2\nzRK5Zt/muUHvWcn/ACE7jPUNirls+LiM+9VZ/wB1rNwvrhqyk9TaK0Me4LNcXsgPKgL+BNUMgDrW\nhKCL+/T+9CSP51l9U69q5JvU64LQuQWNzcKWRMAd2OBUv9j3frD/AN91p2LD7Ci+hqxnFWoJq5Lm\n0zD/ALHu+haEf8Co/sa4zzNAPxNbRarUR04ww+cJQ4OZSozn2HNHs4h7SRzY0ecH/j4g/X/ClGiz\n/wDPzD+R/wAK6U/2WMf6wocngHeOeh7dKguzb5T7OPXdgHHt170uSI+eRzN5ZSWQTfKj7+m0GqYH\nJNbOucfZPdCaxyeDWEtHY2jqrig543Y9qQFj1JoA46ge9L82OOlS9S4tpihT2fj604KSPvA0kaAz\nRjrlgD+de3Wnw98LzafbSyaaTJJGGciZxk/nS5Wle5ftV/KjxQRZHPX2pQsa/fLV7afh14VH/MNf\n/v8Av/jSN8OvCoYH+zpNp/6bv/jVJsiUk+h4r5cZ+7zSbB2Br1I/DKx+2SBlK2+coRISxFXx8PPD\nka4a1mY/9d2rRM5oVHO94tev/Dnju11IZDhlOQc9DWzI4vbeO6X7zDa49GFeit4B8OZ/48pP+/zV\nVv8AwXpltpdwdOgkSXG4qZCwYD60prmRqpJHnP38oRzUB+R+nIPNXHjCSY5/Gopoc/OOh647VhYv\nmRCQElEo+63WtK9H2zT47tf9bD8kvuvY1TjhZkOY2C+rcD9as6bOtvMyyENERtYDncKunNwkpEzS\nlGxQGfWpEVmrprLStIu1G2OYHJAzJzV2PwtazHEFvcyf7hJr1IV4ydjznDS5yAhPtTxF9K72D4eS\nS4MiNbqf+ekh3fkK1Lf4eaPCQ08lxMfTftFU8RCPUFQlLoeYrEXdUCl5DwqgZJrbt/BOtXqB3s1g\nQjgzMEz/AFr0600nTNNA+x2MULD+MDLfmeasFUP8OTXNVxSkrRj95vTw/K7tnlI+GusIwY31hjP3\nfMNWx4IvEwJHjUZ5KHcv59q9IaEZyE/SopAkalmwgHVm4Fc1KcoO8Wb1Ixn8SPJNW0i50W+NvcoO\neUdeVceoNUhxXZeLdTttVENrF86wNnzQevsK5Y28YPf869yhUc6ac9zyK8Yxm1HYq0VO8SquRn86\niAz0rcxuIBTwgAy3Ap3C/e6+lMJ3HJpivcCc/SjFAFKBQIbilC07FOxQFxoWlxTgvFG2mTcbTgKU\nLTguKLCbGBaURlvp61KqcZPAoJJ4HSnYXMM4AwKTFO6cUoGaBXGYoxT8UY5osFxuKMVIFpdtAuYj\nxxRipcUbR6UC5iMKfStrQ7gIZbZz8sg6VmKtPi3JIrrwQc1yY3DfWKEqffb16HRhcT7Gsp9hL9Fs\nC7NkqORWck0kjgPCU3LvUk9RXQXQS8gycZP6GsIW7m4EUUZaZjtCjkmvgPZ+zcqdRe8j7unW9pGM\n4vQzRYXM2rRRWcZkllb5QO3rn0FejWNgkciM2yS5CAST4449Kj07Tl0m1KcNdSj94/oP7o9q0JQI\nrYwA/O3Ln+lbp3Xpu/0OSrUTbUdmQ3F2jNx/x7xg/e7juTXBSzbb26SE+UN26Hf0IrZ1y+5FnEfe\nQj+VUbRYbkG3m4xzHJjJX/61Ycz1cuprTjyxuV2Z8pIAGZhiQL3PqKk3LJGy5I45B4IqrN51rdFL\nhSqZwHXkH0P/ANalRn8hllkEzA5V8Y4q40+aSKnO0WzNePbMoz/F3pk7A3jketWQEa4DOxVUBYnG\neaortaYHd1avdSPGbub1g0lvv2AlmA7Zx71pNfMsIt7EW6yA5kM5+8PWqVlcCK4aTzAoxt2bdzP7\nAD+dXk8N2cFuLi6nkiSQl2RjuY+w9K87E0+as2dVCpGNNXI7S1gubl7tZFNvCeFHQt3/AAq1BeRX\nas8LblDEE+/tVS9uYZ7c2kERitcbdqHBx9ak01QtqsaqFjj4XArOjhnXmqcN3+C7hiK6pU3Unsi2\n3PSmnCjLEAe9SHPSqOpv5cCkgnngV93ShHD0VCO0VY+KnN16rk92y2MBc9RVK6vJEO1G2j2FQDUZ\nZrKSRAkQT5Rn5m/+tXOXVzLJId8zufrWFfEpJcp04fBuUnzdDdN6oyXuDj1ZsVH9usiRmUSE9lyT\nXOxRNPKI4xk9yewrVjiS2XbGAD3Y9TXKq85dDtlhoQ0vqXZrmL7JIYlcqF5yMU43s8FqJYiqMFAB\nxnFU1O+yujnPyGpthkto0HVsCm6km9CPZxS17lW6kefEk80srn+83H5VX3AJhYwKmv1aJjGcDBwc\nVRJJD7mY4Fc05anVCN0gYtkDIBJwPer8FnGoDTvub+4O31qTT7ARxR3MqDe/3Qew9avbMSOBjp6U\n4x6sU59EVWI42oSvTnin3kZW3tT6z4/Q0pX92vX7wrV1yx+zrpK4/wBZOP5Gna6bEnZpFCWFokaX\nH3Dms2TDozkZJ5zXba1pYt9GvJO6rXDlgIOe4xSqx5XZjovnV0UJhzniiC1e5nCLkDGWYdhV6e3U\ntAkceWdwAPWt+O0t7Bo4v3f3MuSe9YqF2bupZaGUsMcOIkHyj9aaEy5AU4ye1aP2qCO4ldmXBxjA\n/lVZLwL5myJ33MSB2q20iPe7FSOJyjNsbbg89q7LwFZeda3Luv8Ay0GK5INceT5Yg455Nd58OUmF\njdiYAYlG3HpiqpW5kKr8DMnwlCJPGWtKw+6p/mK2PG0Sx+EZyBj94v8AMVn+DV/4rnXx7H+YrY8f\nKP8AhDLg+kq/zFXF/u38zOS/eL5HkxlC4GcfLipUvF8lYUjaR2XaBjvTIlUzoGAwetdRo1gscbXb\nphjxGMYwPWuWEXI65tJalbTjc2NqsUMBMpXDue/tTljv1iCKAgHrW5LbFbcTFwQewB/nSOh+Q442\n9hWkly7mMZqWqOe+wTn5DNjPOFpj6akbFZHJPXrWjOwQglgD9cVDLNYofmuQeOxzz+VS+VFc0lqU\n1sIiPkiZz9CamGnvkeXbEDvnAqW11uC1gaNY5JWLZyo4pkmu3Dt+6sSP941onTSG+c7DwNA8Fheh\n1UbpRgA59K5b4kjGrfXFdR4Cubi70+/e4jCFZQFA/CuY+JYxqqe4FVVadLQzpr99qJRRS17JmJRS\n0UCEoooxQAUUtFAhKKWkoAKSlopMBKKWikMKKKWpZQU6m0ucCpY0Nkbavuag74/OnM2TmliTc3PS\noZa0EgJMox361O42K208Y+76VCQVk+Xg9qkkG2E/KVPqT1qS7akVuheUYbbgZyKtoPJ5Zd3+2Ov4\n1WtxINzR44HOatIGcbvOJHsMVI2LNOiQ7shs9MVml9zlj361ovZxupwWDHvmqLrtl2bVBXvnqaiR\npTt0BGKNkdexqxaTGIFmQu54OOtVsYPUZ74qzatkshZUUjLNjn6Vnc3avEW8dnJSRAjAZAznimWw\nDRSLjrGf0xTvIUxyy/MX7Z7rUdo226iBPytlfzqHuWkuTToILGO5lIjbYSMj0NVpbaa2fbKnHr2/\nOtC2Oy6QemVP4VqFVZSrAMp6g1ISfK1Y5hSVYNGxBH51oW+rso23C7v9sdalu9Kj2mSFtmBkhulY\n+7PUZqGVpJHTRzRTLmN1b6daf2rlVZo2DKxUj+IVoxanOYtpALY+9jmpYcmtkaxFPUciqtlM08TF\njkg4zVwcVDY+Wzsed3n/AB9zf75q5oQzqS1Tu/8Aj6m/3zV3Qf8AkJpVPY5YfGdsqipFQfjQF9ae\nAcCudnoJCqmCO9SAd6RRzUgFSyrBj2pQhOBkD6UoAqQDHpSEIqgf/XqG6vIrWEucNjsDUc17kNsV\ngicNkYJ+lYk4lvLgsMGEf6sHp9TSJbsOe4udQlPzFc/dj7CnxxkLsRVXnDSt6+goleG1j2EgSnhm\n3ckVh32urEBGGDAHlEPT8aLXIbtubXnwWrbGcSP6ueMfSs2HVZIbN7hgWLSH5v7ozwK5ufVbhkEY\niTcDnzG5Y1myzyuWJkYBuo3cU+RmftF0OuufGEsJIV5QewwMGsK51e51Eqb+7Z4VOY4i2cH3rJVO\nM4JPtXU+E/CZ8Q3LEyiO2ijaSeTZu8vHQY45P1p8ncXO27IzpNduR5Yju/kxyBnA/Co/OurtpZZr\n15A/fnMnt9K3YfCttfahHYWccrN5e+WVmAAGM89h+JqtN4eispJbm1vW2RSCNcKQWbvj2qeVFXfU\nI9Xvo7eOGGKQheF2ZUD6Us91r13bbbm+2Qnou7v7+ld74ajjvi1ncyRTNEoOduCD6HqCfoa3JvC1\nnJlvLXOOu2s27M2UG1e55Rbahc6dOt0iMxQYe4ZizMff0FaJ8cahET5EcqkcElt6fUCukvfCCs5a\nM4I6n+nvWFd+EJIg7qXDHuhx+mMUaDtNbFaLx3qgKjcSq/eIiClj6nPen3HjK5mjxFEWlxkAzEkH\n3HSsW50q7t3Yrcu7kYYN8pP9Kypo2tWG+Eh+odjg0WQueaO70uHW73bcySW9rKwyGkHmOPovRadq\nmjR2kRuzMGI5kMrdW9QK4z+1b+LKG4dSygriYnH5U+BjO7jUri5mPUZkGD/31S5TRVFa1jo49TgK\nhdxdscLGMmr0V0XVS7pCD/eIya5dbuyQCARXAwcndNtCj6KOat2F5Bcy74oVLDjZBaGRh/wJuBUO\nJtTq67nVqAYywPy/3iaEeORgEYN7jms9I7i6lXFq6pjk3UnJ+irwK0I7eSBcyTKBn7qIEH/16xkj\n0qUm9kDjkioWHy5p0TefNJKp/dfdQ9jjqaG6HFZM646q5XZcgHpTGOzBUc+pqVs4qJ84qG2bRSIZ\nCSMkkmmCQipmXKmodtLnsXyXBZiGxVmOc8c1SZSGp65qlUJlQTNFLghquRXhzwcmsgK2ctwKsRy7\nfuDn1NV7Q554OL6HQQ3JAy5wPSrkd8v8K4rmklYnJOTVqOU59qPaHHUy2L6HSRzLI2ATWnAFwMk5\nrmbaYr8x79K04LrHJNZTrvY4pZJTkrtG6qjqGqWJMyqCvH/1qyo7rA65q9bXeJV/z2pQxLWzPMxP\nDlKS+E57waivot2wyCL+Zf8Ax41zHxdVjbaIWdmAeQDJ6fdrpvBU6jQ7wHHOoTn/AMeNY3xQs5NW\ns9Kis9jtHK5f5gNudtd0K8pT5Oh5kcnVGusRHT/gne2kNtd6PYLe6fbXA+zJztw3T1rPvPCHh27Y\ntF9psnPp861dtL6KHTLOPD7kt0VsjAyB70j6xbqPmMQ+sq1pCaT0Tj6O34bHHKGZUn7kuZdmk/8A\ngnOXHw6utu6wv7e5B6KTtauevfDer2JP2mwmAH8SrkV3Ta9ZZ+/F+BJ/kKli8VpDxHdEL/dO4j8i\nK64YmrHad/Vfqv8AI0jiKz0r0l6p/ozy7YFOGBB9CMU7aOor02fW9F1AYvbGylJ6sAUb88VnXGh+\nGr1c28klq3+zMrj9cGumGPl9uL+Wv/BOiKoz+GVn56f8A4EjP/1qMHGK6qXwXPy1neRTr2BBB/TN\nZN3oWpWmTLauR6p836Dmuyni6U9FImWHla6V15GTt5pyjP3ulSmNlOGUqfRhg0FQOK64yOWVIix6\nUoGeCKkC46U4jjPQ9q0UjnlCxEfl4NNpxHPP50qoTWqZzyiwUY5NOyDS7ST0p6p7CquZ8jGrjvTq\nVsY47U0DuelUmQ42Y9cAZP4U0nNIWppbimibEqyENxV4xRahaPa3Cho3GCD296zUyxAArRjkjtYX\nmlbbGgyxNRW5eXU6KCfP7p5frujzaNqD28gOwnKN/eFZdbHiPW5Nb1AyniJPljX0FY9fJ1eXnfLs\nfV0+blXNuJRRRWZYVLFNJBKksTlJEYMrA8gjvUVami6PNrWoLbREIgBeaVvuxoOSx/CgDrrazsPF\nNp/wkd5E6NYKP7QiRf8Aj5x0K+56H864/WdXn1rUXup/lGNscY+7Gg6KPatq+8VGw1O1i0UCPT7D\n5Y0PSf8AvM477v5Yqr4h0y2MUWtaUCdOujgp3t5O6H+h70Ac7RRRQAUUUUALXTeEfDzatefaJ1P2\nSI5JP8R9K5muu8F+Iv7On+wXJ/0aY/Kf7rVrR5edc2xlW5uR8u5387qiBEAVFGAB2rEvm3QnnvWz\ncRFj8vIPQ1k3qiOFs8t/KvoILQ+fqPUyjheWP0FM807sjn0ApWYZyeTSrL24H0pyFFEyE7fm6+lO\nDEnAqIZb6etOLheFrNmyJchPrSF/eoN/vSbqzkzaMSfdRu5qLdRmsmzZIsRviRT6Gm6rhNZRu0kV\nMD807XPvWE/qNprKTNIozpQBrqKfuyxsv5rWIflJU9QSP1rZv3EN3ZXTDIRxnHpmsi7wLycKcqXL\nD6GuaodEDX0qVvszYxhcda1EvIVUeYkhfuVIArB0iQ75I+23NanJ7VcH7opK0i4dStVHEU+fcimj\nV7desU36VUO7bwpz9KvwTWI2iWxY/LgtycnHp2p3fcnTsM/tq27RzfpTv7ct8f6qb9KaptNoDwsT\ngg7UwQfXOeajuxC8itbQOibcFWXv60ve7j07GX4juFubq3kQMFMQwGFY3XitTXjme1UjG2LpWX3r\nmn8TOiOysOx9KM8U3NFIZPa83kA/2xX0tZrjS7H0MC1802X/AB/2/wDvivpqxAOlWS9xApGab2J6\nhtJ6Ck2jG0kU8kmmEUAMPKkDgiq7CrTDow6jrUcijqOhqkSyqy1Awwc1aYVCwq0QzynxnZDSdX+S\nP9xcDejdge4rm4mubg7IBI5zwsaknNe16hYW19BsubeKcIdyCQZwaowLFYDEEcUK8fLEoH8qzlCz\nF7Szseb2fg7xDqThntjDH1zcPjH4V1ukfDmKAebqV40xAysUQ2j8T1rs7a5BXGAEap5HZsFTyOpN\nNQtqQ6rehmw6JplrCptrNEA7nk/nWlptz5LeVkDb26ZFJlViJdgqMMgscCsia/ghlDIxdl9PSs93\nfsbJpLXqdZcnzIQ2M7e/qKz2c9cYFc/P4ouhH5dtCiDpuc7j+VYtxqV9cZEt05B6gcD9K6YUJz22\nMZV4x8zq73VbWyhZ5JkJHRFOWJ+lchf6/f3cuUlaCPskfH61UYruOY1z6io28o/3lP513UsJGOrd\nzkq4qUtErDJL26blrmY/8DNVZp5psebNI49GYmrBjDfddT7HioXhdeqn8K6404rocjqt7srGo2HN\nTMuOtNK5+laqKM5TK7jIxUWQnA5NTyn5cKOPWq+K0tYlO+4zBzzSgU/FLjNMdxoFLtp2KUCixNxM\nClxS4pdtOwridqKcFpQpJwBzQK40DNTBAoy3X0pQAnuaQjPWmQ3cYx3HnpTcVIRSBaQ7jcZpQvtT\nwBS4pibG4pMU8/SlApCuMxS7akApQKBXGbacFpwWnhcdaCXIFUUH2paX60yB0e5W2gFs8YHeuy0T\nw/BZL9puUD3Uq9D/AMsx6fWo/D+hi0Vby6TNwwzHGf4B6n3q5qN8QDBEcseGb+leDj44fn9tKOq/\nE97BSrxh7Pm0f4FO6FrBO0kOWVOuTnLe1Ymp6gLS3edjl24Uepq7eEIRHnhBkn3ridTvje3hYH90\nnCD+tfL1pOU3G1kui7nu0IXSb1IS7O7O5y7HJNWbDmYt6CqG+r+m8iRj64rCotDsbsi9OjPGSpG4\ndj0NZl06iIZRUbvjvWhNOsKE5+bsKxLuUovmMck8ge/qa9DL6Ere0lt0POxFb7CKdy/loU/jblv8\nKqw8SgntUcspZixOST+daGj2huZ9z/6uP5nJ/lXo3V79Ec+ysbcesQeHtIR2hEl7cHcoI+6vrUP2\n+TUR58jsxPr2qaa0bUJQ3kxyR/wMy42j0zV6202GBRvAcjoBwo/xrOGDrYt+6rLu9v8Agk1MbQws\nfed5dlv/AMArW1q0uGYFY/X1+laiqAAFGAOgpVIJ4608DHavo8FgKeEjaOre7Pmsbj6mJlrolshm\n2s3WlPkRf71auKr3lqblEUDoTXXVi3BpHNQlaomzk1LKrYGQfes6fcW7Ctu6EKQKqEGQFg/tzUWm\n2K3N+pbJSNd7cd68ecG2onvU5qKcmJbWRt4AGJ8xuWpzRKO3fvWs8ClkBYjIz0qnIkQVSZQCW59q\ntwUVYxVRyd2Iluf7FvZ8fKFxWhNZtbaQt1jJRVbBp8KK3gjUXVgwD4yK2tXiA8I5A/5ZR1airN+X\n+ZLk7pef+Rwl4TO4ZuC7ZqTTtMF5fyoWPlxLuY+vtVW43l124yOma6HSdOuorDe9xse4+ZsLzjtX\nJH3pbHZL3I6Mle3TyYPmYbj+VROlulxLuk4A4O6rcmlKE/eTSOB2zgVC1japjbGWHqa1bZirdzNe\nS2WOIA5cuMgfWup8XoBN4dx0NwP5VUGjOChCRICQQetaXjNNsvh3j/l6A/Q0OLjB38vzKunONvP8\njS8VRlfDmoHH8I/nXk+xn2Lu4PSvYPFykeGtSJ/uD+deQRtmWJcnJIAwM1GId5ovCr3Ga+jaIZ3a\n7nkcqp2x47mtldItyGLKxI9TVu2nis4oIoIJ5BEOcJjJ+ppJbm6dpHWyIDnOZHAxUtRUdNwcpuXk\nUZLO3hcARDA61DJGVJKxbVz3HSpZ/tkp+YwJ9MmoZYbpwTLeHHoqgVDk+hfK+5fOkqqB3uD8wBIG\nBXUeDIIore5WFt48wZO7NeftZK5+aSeQ+7mu8+HNvFBZXqxLgGYE8+1awqXlsKpH3dzG8Gj/AIr3\nxEPQH+YrX+IAx4Kuf+ui/wAxWb4LH/FwvEvsD/6EK2PiIAvgW6PpIv8AMUk/3b+YNfvV8jyXTLeS\n71GKNIS4HLDOOK7JzeldqxQxKBgAnJAqp4dsvssELOMSzDc3sOwrXkTk1lDSJpUd5Gc6X8qbHvcI\nvQKvAqtLZnP725mfP+1itQjO7GPwqtPyw5AwKbFHTYzDYwcnyi2OpJzThawrnbEo49Knb0yaaXHI\nGc/SpVi9SmRjpgUijkENk+lStkfLUZ+VuMfQGkM7XwCP+Jff/wDXUf0rlvid/wAhaP8A3RXVfD/5\ntOvz/wBNR0/CuX+J4xq0R/2RWs/4JnT/AIpHRRS17ZiJRilooEJRS0YoASilooEJiilooASilpMU\ngEopaKkYlLRRSZQoqORuwpzNge9RGpZSEA3MAKsRgAHH0pka4Ut3NSqMACpGQSfeBpZT+5HXr0Pa\niUUTnKpUM1XQfZkYYZ5PapyhVt8f3u69jVaJ41hIYZbPQdaPNfABYle4HX86hsai27oti4QjjJb+\n6OtVrwSMAzKF9AOTTfPXcGiTaw9T1+tNlle5Bz8u0ciobNYRady6kEcloFVQCwzn3rPwUYqw5B5F\nIk8yjYJG2r2FJksTuOWNZyNqd0zUMiTWo+ZVbGMf0rMB2OG6FGzTJWKBSFyQc5pTL5iscYcDOPWo\nZpFKN4k73Ef2pnTJXduFWTqn92L8zWOsuBkDPqPSpodznJXC+/eodyoqMrIuzXUtzHtYBV64Hesx\nyjO2Og6e5q8YZpkIhTPYnOMVH/ZVyMAKvP8AtDip8y52XuopDK9DVu0jWa4jjYNg/wB2nS6dLCEL\nunzttGD0/GrdpbiC6jy2WK54PQ+9S2JIkslaF5FOdme/HNaAqC4iMyYU4fqDUsL70BIww4I9DWbZ\ndr6nnt1/x9S/75q7oH/IUSqV1/x9S/7xq74f/wCQrHVy2OCHxo7xRxUgHSmAcGpB0Fc7Z6dh696c\nBTRUijpmpHYVRmqF/M80i2Vu7B3++R2FX5G2RGq9taMQ7kfvH6k/wikQ10KU1u3y28Y/cx4DN6ms\n3VNatdLjMKkPKRyq4/yKu3t8CDbWjEKOGf1PtXBajEE1Fg4OfUnrVRjzOxhUlyq6LM17JesPtDEQ\nsD8q9R+PeoFsGKkqquGOMZ7etaUCWxEPmICpHc9a1o9PjADqNit91cYxXZGmktDzpTk3qcJcuBKU\njyFHFVq09YsJLK8cHlScg1mVy1LqWp0wtbQVXK4weldl4F1WGy1qL7VGstpcfup4m5Bz3x9a4utL\nTt8RDYJyQQB1p09XZ7Dm+XVbn0XPpNgYJLeO3iiVxg+WoHFc/qfg9JxH9jlCGMfKJTlVPqF9ferG\nn+NPD0mnQNLdyCWOJRKohYkHGOeKbcePdFjmMUNpfTYGQ6oAp/Mis1CfY6XUp9WjAsPD2paReJI6\nPcsDxtlCRgfTGf1r0SItLArOm0kfd7CuF/4WDbvdrHHpVztJxneuf513FleLcWschRk3DOG7VFSL\nW5pRlF35WMeEMxyB9BVKe2Rs/KBWoXXk5FVJSDmsTosclqejxTA/IM1xeoWBtpjDIu+JugPavU54\nwwxiuR8S24EKuRzu4ppkyhpc86vNOa3jYqd8J7HqtZ80QTCvK+4dEb9PqK6y8jDRHAzxgj1rnnUC\nTa8YMQHJxkIP89qtM55RRJbXCRRIFaCYk5KSRfN+B71rW+s35kWCBUUscLhM4H0NVodIkdEmQ27x\ngcOu9cD34qzBaXH9owzm2gmUnHled1H5ipbRtTU0aAvtSEiI9wrTE4CQlf12jP61ZtdHkLtPqDbX\nY/6sSkn8ST/KnrNqkSyLBpVtbMfueUVBI98kGlhXUJc/bIoY1zwCSSfyrFnoQSvrdlqW6t43CCRB\ngYCJ2/Komm+QsFYj1PFRJabS4+0Fef4IwpHsDU67dvynOO55NZOyO2m5PchUuylmwB2Hemvnb1qZ\nvu1E/wB2smdcFYYfunmo+op/ODTQR9azZvEjYHd1pVIUccn1NDnLUAVnc2SQpYlhkmpE4GajP3qk\nUUXYOKJVY96tQuSw9BVVRzVuP5QKHIn2dy6svzelWUnIxnpWYjs7ERruAGS3YD61FNqUMRCp/pMn\nscIPx7/hRDDzm7vRHHXxlGl7q1f9dTdju26Jlvp0pG1qKF9rXA3D+GP5j+dchd6u75E03B6Rx8Cq\na3c8pCwxbF9TXVGhCPS55dXE1Kj3svI37DW9ltIILdI085z856nPXHSsjxPrs0lvbhrtlAY/LF8o\n7elZ+n2c8sL+ZI3+sbgfWn6npcbQQ5GcMa6435jzZxXs7m7Fq6G3hxvf5B1NSjU2PKwmo7e0RIYh\nj+AVfhhh8iUFfnABVs/pUWYSpporC/mP/LEU77Zc9fLH5VowxosLZWMjoFJG4++alt1hWHa4Uk5y\nfT0qloc88OmZQvZx96EflU0d9u+9DVlY1IqRYkz0qrs45YNNkKXsQPVkPqDitK21q6iwEvGZf7kn\nzD8jVZrSNxyoqE6cvVSRVKpfR6nPUwcou8HY2ft8Fx/x+afE6nqYjt/TpUL6Lol6f9HuntXb+CQY\nH+FZn2a5gwQTjGfwqRLpwMSpkVvCry/C2jnlLEQ+K0vVD7jwdqUJLQBLmPqCh5rDubee2crcQyRE\nf3lIrqLS9eIg287Jj+HPFbsGteYPKvoYpkP/AD1Xcv59RXbDGVVvaX4MweIpN2qRcfTVf5nmgXce\nOadsUcV6VNoPh7UV3LBJZyN0aI7lrIu/AF1ktp15b3Q/uE7W/I1108wh9pOPrt96J5KU37k0322f\n3M44KR7igntWlfaLqemki7spY/crwfxrKZssQODXdTqxqK8XcxqUZxeqsNB7Zod+i9u9I5Cjj7x6\n1ESSa3TOWUSUtTTTPvf1q1bxbiCRV81iFHsTWkBZq5PxfrvmyHTrV/3SH94w/iPpWv4n1oaVZ/ZL\ndh9qlHzEfwCvOixY5JyT3NeJmGLu/Zx+Z7mAwtv3khlFFFeQeqFFFFAE1vby3dxHbwIXlkYKqjqS\na6bWbiLw9pbeHbNla6cg6jOv94f8sgfQHr7inW+3wlpa3cgzrN5H/o6HH+jxH+M/7R7fjXJsxZiz\nElicknvQA2t3w/rKafJLaXqebp12NlxGe3oy+hFYVFAGtrujSaNfiLeJbeVRJbzDpJGeh/xrJrqt\nCu4NY0//AIR3UHCkktY3Df8ALKT+4T/dbp9TXO3lpPYXktrcoUmiYqynsaAK9FFFABSgkHI7UlFA\nHpXhHxF9vs/7OunHnoPkY9WFXtSQiNq8sgnktp0miYq6HIIr0rT9Vj1vShIMCdOJF7/WvUwuJbXJ\nLc8zFYZJ88TLk4PNNUfxMcCrs9v5Z3P19KpSE5/pXfc4La2RKJdy4HAFJuqruwc1JvyOtQ2axjYl\nLUm4VHuo3Vi2bxRMGpd3tUG6lDVm2aJE4bmrGqZl0GGQDmKQGqIatKAG50S7h6kDIqHqjRGPqI8z\nTAw/hasUnccj0raX97p0qnk7M/lWGOlc1Q3gWbCQx3iYOCeK3IrnyS3mRGb0G4rj8q5tG2SI3oQa\n387uR9aIPQc1qWv7TiA4sWB/67N/jQNXQf8ALo4+krf41W2OeimtFri1dznTjtyNuDg496vXuRdd\niuNaUdLWT/v83+NO/twY/wCPaT/v81WPOtOQ1i7BhjIXBQe3PJqvfFZnjNvbtGgGCu3H/wCuh37j\nvHsY+vTfaL9ZApUFBgMcn86zPrV7WWb+05Eb+AAY9Ko9D61zSerN0tBf1o70lHtSGWtPUvqVtxzv\nr6ZteNPsyO0K1806QN2rWwI43V9NQIwsrcFT/qlpvYkHGfmHQ1GRT/MjU7GkQFugLDrTSMHFCJ5k\nNzg+x60wjBKHv0pxGaRuUz3FUgZWYYNRMKst8w4Hzfzqu5A9BTc4wXNJ2RFm9EQEAnnpg5rmIJgo\nmik5COxTHORmukmHmRshJAYYJHBqpHaRWy4t4ghHcdTXP9dhJ8lNOTMauHm5KV7JXOMm+IEFqjRW\nlhLM6nG6Vti/l1rNn8d+ILtCkU6Wq/8ATBAD/wB9Hmuju/Bek3l/JeSvOgl5MUZAAPrXnmqWdxpO\nqzWM5J8s5U/3l7GtZKpa80bU1TfwnTeGLjVJLqRrj7TPbTAkzSsWAPqCa6RxWB4S1EvA2nSMfk+e\nHPp3H866FxXRgoxbk2tdDDEtplVxzULrVlxzUL8V6sUcEmVnUZqFlx0qd6ibpWqRjJldkqMhgeCR\nU7UxlCjLflWiRjKRHucL8xBX3FQvIr9YwB7VI5LdfyqIr7VojF23I3SEr1ZfqKYLfPKujU9kyKZ5\nY9Kdhp+Yht3H8J/Cm+WR1BqYbh91iPpTxLIOCQw9xQHMytso2mrXmqfvxKfdTigCE92T6jNMOd9U\nV9p6UBas+SD911P40otZDyUO0dxQLnRAqFhwKVsINq9fWpH4GFGKiIOaBJ3EFLSgE0oXigLjMUYN\nS7T6UoSgOYjApdvFSbcUuKZPMR7aULUoHFLignmGBfal2nNPxS0WFcbtwc0Yp1GPyoFcXBHNdLoG\nibNl7dp83WKM/wAzUOh6P5xW7ul/djmND/F7n2rdvLwQIQp/eEce1cWKxEYReuh6GFw7bTa1C/vv\nKBjjP7w/eP8AdrLgBll3EcA8Z7molDTOck47n1qyPl2jgA18zOpKvNTl8K2PcSVOPKtzmvEl6YIm\ngU4klJ3Y7CuRZgBxWl4ilJ1WYE/d4FYpkrzbXbZ6tL3YonDcZq7azrb2uWGXY8CqCKc/MOT0Udal\nlmW3XqHlI5H92uqjhOb3p7HPXxVvdhuSz3OzMkvLnoKxbi4LuWY8mkmuGdyScmmWtnPf3QhgTc5/\nJR6mu9vojkjG2rHWVtNf3aQRJukY/l7mu5s9PisIPJXDepx1PrRpen2+k2xjiAeZh+8mPU+w9qt5\nxXq5fheZ+0nt0/zPHzHGNfu4b9RuKMUvelr2DxLsVVqQLTYhkmpwOwouQ9xgGO1TRqGYcYxTlQCp\nI1+apbKijgr6NjJOQBje3860NO02aG3L+ef3oB+UdB6VTvciS4wpPzt0+tbto1x9kjCQoAUHLP8A\n4V5cFFybZ7c5SUEkVpNNjGDJJI31NEOmRyTeXHEM9QW9KszLcvwZI1/3VJqvJBNkFrqUn/Z+Wh2T\n2FHm7mlcWv2bwRqMfy+vy9BWhrS/8UZ/2yjrOEfl/D/Uxlm55LHJrX1lc+DQf+mUdW3o/T/MSW3+\nL/I84jgM15FGMkZG7A5ArtPtSrEY4raZxgBTjFZGhov9pZ4z5ddLjmuenotDpq+80mZU8104IFqq\n/wC+9V2F80WC8KL7Lk1pTAbyc1A3KEdzUSsNLyM+QXcrRiS+mI3DheBXSeNVw/hr/r6X+RrCIwyE\nZxuFdH46TD+F/e7X+Rqb+6/kaJe+vmanjFAPC+p8fwf1rzPwtaC41JJmX5IFzyO9eqeM1x4X1P8A\n3R/OuH0BCunWuFHPUgdaKus0x0tIM2JFzknNQTL+6PrV2WNsdKqyxNxnjI4z3pNiSM112kjAqGbG\nwjrmrrQljwf0qu0IycvtHrWbZrtuUWUdga7j4eqGsrs/9Nh/KuKdoVHMiA+7V3Pw3Mctje+UysBM\nM7T7U6b94Jr3TD8EL/xcPxP7A/8AoQrd8dxJJ4SkRxlTOmR+IrG8DL/xcbxR9D/6EK6Dx0uPCjf9\nd0H6inF+4/mEl76+RxVtu86IAHgYGKuuhOeKz455FePZAXI6DOM1O9zqJHy20adslqlMbixfJdzh\nQCcZxVVk4zn9KRv7SI/1kSduBVaS2vR9+7K/RaLsaSHmL3IyetQvGoJyRweuaYdNkkzm5lbHJxUE\nmmRIoZjI2fVqm47eY+R7dHI81CMdSarNeWyvzKn4VIdLjXrADnnlqj+zRA8RIPwpJ32GnHa53Hw7\nnjuNN1Bom3BZgDx9K534oJ/p0L+wFdJ8PFCadqAAGPNHQY9K574n/wCvhPuK2n/C1M4fxSpRRS17\nZgJS0UUAFJilooEJRilooAKKKKBAaaadSUMYlFLSVIwppJBp1IeTUsaGE5OabUjL6CmVLKQ7tlet\nJ5jHvSrkHgEilZATnp61LLQ0t8hyeaY7blUY6CgnvSZJA9qhmlxMgD3pd/oM1ZjWIYDJhvfvUrRq\nwGAFI6EdqhopSsUdsj4xGc9uKcYZwpdlIA75q8kh3bH4f+dJLIpRkGWb0FSy4ydygsBlBO/GOg6k\n1IlqsbKGJLEdF5Ip1ou6R1LFRjt3q22yExsAFUE5rM0vZmfPG6ZQ5U9aphirAkcitpYhNmaQZOMo\np9O1ZjAyRDK5IJyah6Gl+bbchkGAGU5TtjqParlu32hkRWO7p8x6VUQrt8vpk9+xqzp0m28w0ZZs\nY+UdKhmkHy6o240WJAijgUpNITRUsSVxGVXBV1DKeoIqG0hS3RlwqtuPPqO1TZFVyCb9f7uN341m\n2bRRaJ44pv8Aq28z+EjDf40U5cEEHnis2XFHntz/AMfMv+8av+H/APkKx1QuABPIP9o1f0D/AJCk\nf1rWWx51P+IvU7wHk09ckVGPvc09T2rlZ69iUcVItRDpUinkGpuFiTYsnDD3Aqlq1yI4Ps8TbZHH\nzY/u+laCED61k6iI5y8mAsyDI91oRE1oZkUWBwOBWHrtoG/fBcsOtbYnROppswinQjOc1SlZ3Oac\nFJWOf0m9jO2JgA38OeAK6oRLdbHyCwwGycYHtXJ3ehXSSmW2iZh1wBU+nalNaTCK5R48dQ3BJrrp\n1EzgnScdzpL3SlntlWSNJFPBJ7VzN54TWEMU3HIyuDxiupk1W2ntI0EisgOWwcVnahepLbosbEsT\nhee1aNRluRdx2OUfTltUDPHuLMAhPQ/SugstMtku1FwwLouRERjJ+taiR211pwjnVVGOR6H1FUIr\noRyASspdOA/qKFBIhybLD/u7gsFj2/8APNkAYD2I7U25urdecjOKp3l+jx5gGTnG0+vtVnRvDF1q\n9wssqtHCTyTSqVFFF06UpuyNDw1pBvr4TkfulOc4r0uNNqhR0AxVXTNNh062WGJcACrx4rzKlTnd\nz2aFH2cbDcVGfXtT2YDpUTtmsjpSIpG4OO9c/rVot2ypuICjJI9a3JpAoJJrJmIcs2etANX0OFuI\nJTO1ssecdW9asDww7WrS7syN/Cep9vQiret3E8Vwn2QAOeCzkKPzqgmvXaTxRTX1n7qSRn6HpVa9\nDO0E7SIoLHU4ryIWwWKJRh48lPy9PzrXlmvoVCtYyyxkk5M+SPp3rRt7ztcQvEG+7I2GRv8AgQ4q\ny5HPpWcmdVOnpoznVvJp52ijtLteOSkjDn6k4q1BbeWMtCFY9Sz72P1NX3Az796iJ4JrNs6IQtuQ\nsTzUTfdp7+gqNuCM1mzrihjdBUb44FOcknFQt96oZvFg5+XrTBxSv2FNJwKzZtFjSfmNPXmohzUi\nGosac4/HzVIoqvu5qRZMY6kngAd6Em9hTqKMeaTskW1KoNzHAHT3pZpFhQSXRKg/dgX7zfX0FVZr\nj7EccPd8cHlYv/r+1Y094zysEYySscs55rsp4dR1lqzwsVj5VfdhpH8y9fao0i7ZWCRZysKdPx9f\nxqhvuLo4TMaH8zUtnpslxIWOGcDJLHAFdDaWSWxBKOGCHL/3T3HsfSt7XODmsY1lpW9/lG5gMkse\ng9627fToYctKdwXghTio3uEjdZUYb8Yc7cB/wq/a6Nf3hRpf9Hjx1k5fHsv+NROcKavJjTcnoZkQ\nhWMsm1RvYH+6eeCM1Xvw0kMYhikmO7oi/wBTXcaP4Qi2+YIDIwY/vZzn8hW3ceH7ZYE85mc5+6vy\nipjXcpe6vvMKicadjhYbK8MUeY44/lHDvk/kKsJpN4/Pmf8AfuI/1r0SGztLdF8q3jBwOcZqUvgc\nDH0FaRu9x8zPOxol0By1yf8AgAH9KQ6VOnU3I+oH+FegNI1QtI3fFbcqDXucC1pOvSWT8VB/pTc3\nMfUq3+8pFdRq8ZaLz4kG9PvAdxWA15jqD/OpaIbZEt26/ejz/utViO7hfALbf9luDRPEUQPJECjD\nIcDj86qtDG4+ViPryKybaHr1RtJcoQSSQG6gfpiqxiRxnjJrJCT2/wAyMdv+zyPyqeLUe0q4/wBp\napTRlKlGRZa0IBdeADjNPjnkh4fkVNFco6IeGC8Ag8fU1K8ayjG0bj/F/X6VsmcVXBxfQltrvZzE\n3B6oehrYt71JuD8r+h6/ge9cs8TRuSh4B6joaliu84V+DW0Kri9DyMVlcZrY7VL65RNqyl4v7rfM\nPyNUrnTtG1AH7ZpsYc/8tIPkP+FZ1rqBBxI30b1+tX/ND/55reM4Sd2rPutH+B4lShjsJ/Bm3Hs9\nV9zuY114As7g507VDGx6R3K/1Fc7qHg7WdPJLWpmjH8cPzCu9BPZs/WpobqaFtyOyn2PFdcKtSPw\nTv5P/NGKzecdMTS+a0/DY8mW3ZGw6MrehGKXUNRh0Wwa5l5kIxGnqa9W1G705rKW51a0t5IYgWaR\nhtYfjXzj4o1ldY1qaWBTHaqxEMZOdoqquPmo2lGz9bo9zLlQxnv027Le6szJvLqW9uZJ5mLO5ySa\ngo70V5Ld3dn0KVtEJRRRSGFdNoOn29paPr+qR7rWFttvAetxL2H+6OM1T8P6MNWu5HuJPJsLdfNu\nZj0VR2Hueg+tJr+stq12giTybG2XyrWAdEQf1PU/WgCjqOoXGqX817dyF5pW3MTVSiigAooooAUE\ng5Bwa68hfF+ilgM65Yx8+tzCP5sP5Yrj6s2V7cadew3lrIY54mDIw7EUAVyCDg8Gkrqtes7fVbIe\nIdLQKjELewL/AMsZD3x/dPP5VytABRRRQAtaGj6pLpN8k8ZyvR1/vCs6lqotxd0JpNWZ6i0kN/bJ\ndQtuRxn6Vk3CbSawvDmsmwn+zzHNtIcHP8J9a6q8hBG5eVIyCK9ahX54nlVqHJIxWODQr44p0qEE\n1XPBrVszSLO6jdUStkU7IrNm0R+acDUYNLms2aIlz61q6M+6SWI9HQisbdV7SpNt9HnoTioK6FK1\nGJJIT/eZP51ispSR09CRW7cL9n1m4U9PM3ism9XZqMgPQnNYTWhvDcqsQCVOc+lbFtM3lRPjdgDI\nPfHasy5jAaNz0Ydau2bgoVHbnFZ02aTRrjVkXP8AxLIP++m/xpv9sFR/x5Rf99N/jVPntWjHd2i7\nf3BVivzNtB+b2Hp1rT5md/IYNcI/5cY/++2/xp39unH/AB4rj/fb/GnrdWPmoy25SMPl0K53D0z2\nqlOyvM7KMKTwAMcUnfuNW7Gfrkon1eaVY/LLYyuSf51n1e1oD+05DnrVCudvU36Dl+8KSgEqcjtS\nH+dMRp6CN2sRc/dVjj14r162nN3byC13qRBgwDLE+4PWvJPDgzrkS+qEfmK91F5onh1AIBuuGjG5\nY23Hp0J7VS2PNzCCm4uUlGKvf/gGBpvhzUbyRJGhMSAglpOCfoK6mFXtLhjeavGUz8sJI4Huetc/\nNrOsa0zQ2kbRRHqsfp7tUlv4YCAzajcBVHJAb+ZrlxOOoUNKktey1f3HHhKTT/2aLa6ybsjqyQQC\npBU8gg8GojIFyOtY9rq2lQSx2FvIQhbAbqoP1rXZNpwRyK5fb4zEfwo8se73+49ylOi1rJSa3S2+\n8iJY9OPpUTKepqdqiat6eXRb5q03J+ei+Q5V3tFWIGXmoXOOlTyHjiqz969SlTjBWgrI5ZNt3ZVP\nyuyduorm/FegHWbVJ4Nq3duCVz/Gv92uklPzp+NRscc10OKkrMwi3F6dDyXSrt7a+huGyoif5lHU\nDoRXpJZJEWSNg0bjKkd65PxTojWVy+q2kebZzmdF/wCWbev0NJoOtCBRbytm2Y/Kc/6s/wCFcfLK\nlO63/M6J2qRudNJ3qu9TOeODkeoqBq9WjVVSN0ebOLiyBhzTCpPapcEnimSMEGF5NdUTlm+xE2I+\n2WqB8nk9akbrk1Ex561ojJ6DDUZGTT2PFMzWiMJCFeKbt9qcW4puc1VhK4uKMe1FFABim7ak61Ii\nBRubr6UWFzWIxF/E3ApS7jhWKge9ObLnmkCUWC/cUTSYwSG+ozRvRvvxD6qcU4LxRsosLmQgWBuh\nZfqM077OCPlkU0BBRtzQJvsxPs7jtn6UhQjgg1Iu5ehI/GnCaQfxZHuKBXZBg0YNWfNB+9GD9OKX\nMDDo6n86Bcz7FbBzSgVZEMbfdlX/AIFxS/ZXP3cMP9k5p2FzordOlIKlaF1PzKR9aTZQO6GjJPTN\nNs9X0WDWktNSutmOc4ygPoxrI8Qa8umRm2tyDdMOSP8AlmP8a5PSNIvNe1Fba3BZmO53PRR3JNeX\njMbyP2dPfr/kevgcDzr2lXRdP8z32W+iSAPFJHIGHyFCCuPWshmaaQkseepqvYaXbaRYJZ2u4qvL\nOxyXb1qwCsSZPFeFiqsq0rS0ij0qUVBaE6AIvpUM06qwwc45NU7m/CjrWTNes+RnArza2LSXLA7K\nVCUtWV/FWkyyD+0LRDIMfvUXk/UCuajUKoKkF+pc9FrqItRmtn3Rvn1U9DSXUGkaz/rg1ncf34+h\nPuO9VhsTSveSszWrCqo8q2OUkuRED5bEsfvSHqf8KoSTFjhSa3r7wdqigyWjRXkfby2w35Gq2neF\n9RupsXMElvEDyGGC3+ArvnWja9znhFIy4Ld5z8qFhnGR6+ldzp1kmm2iwqg85+XPfNPjsLKw8tIl\n3zRjChfur/8AXq0kezLMcu3U10YGjPEztsuv9dzkx2JjQp+b2/rsAGBjP1NGM1JgGmng19UoqKSW\nyPlZTcm29xtFOxSU7E3HwHG7irEbgtjH61WTvUkX+uXjvSFfUsBxjLEL9altWEsjAEkAVBPykePU\n1NpwIkkz6VL2LjduxxV7hWuf99v510NpxZwf7grm9QYB7nP/AD0bH51vW12BZwhbeViEA6cV5UJW\nkz25xbiiWTrmoJDuI46U5p5yfltG/wCBMBVZ5rzOBFEv1NKUhwgzWcf8W+1P6mtfWh/xRQ4/5Yx1\njoJD8N9VaXbuycbelbmuD/ihv+2MdHNo/T/Mrl1Xr/kcdoKk6pj/AKZ10zx8kMyj61y+h2wl1EeY\n7IjJwRW+2l2p6h2+rGsYX5TaaTkRytCmA0qAe7CqslzZqxzOp+hzVo2Foj4EKkeppkttDGyhUVQT\ngkLzWc5NGkYpmY99a7o9pZvnHRDXXePVw/hb3u1/ka5uZFWWMIGxuHUYrp/Hy4k8Kf8AX4v8jU39\n1lRs2mjZ8bJjwrqhx/CP51wOiG4OnWixRoT2Jr0TxwuPCeqkf3B/OuE8P4OkWSDCvuzvJxx6VUtZ\nhHSGncuzJqjEhpYUx2CZxVOS1vmUs16wA/uKBW5II1Eu6Ybh93B4as2aeJTzIo/Gk0urCLfQzDps\nkv3ruduM/exVZ9Ih8xU+Z2fplzWj9vtYid86dMYBqlNqVkZkJkygHIArCTN4plV9NgiIBiTOcc81\n3/wyhSOwvgiqo84ZAHtXn8uo2hI8tJDz/dNeg/C2ZbjT79lRlAnA+YY7VVN6mfvOPvGN4EGfiP4q\n/H/0IV0XjwY8JNx/y3T+YrA8BjPxJ8V/j/6EK6Lx8MeEm4/5eE/mKqL91jkvfRxdqEEcMinM2/G3\nrx9KuP50s6q3DH7u5cCsqI3HnRmIIG/hJNTS/wBpFstPEDnHqRVqpZdSeR66jplKysD95T2qpMSw\n3Hk+tMkhu2+9eAZPZaqPayH793IfoKlzv0KUe5cVgC+7AyvGaqXTr5S4IOOozVZ7JD1mmbnHWoHs\nIO4c892rO2ty1oXJpIQRm7LfL/f6e1UGngX70q9fWkaytxn90OPU0zyLdSf3cfHqaUU4iUUm33O8\n+HMkcum6iYmDATDJH4Vg/E7/AF8P1Fb/AMOQg0zUPLCgeaM7foKwPidzPD9RW0v4REf4hVopaK90\n5hKWiigAoopaBCUUtFACUUuKSgApKXFJSYCUUtJSGFIBzmlNFSygprcc4Bp1IeeKllIVRgU2TJHH\nTuaVTkUMu4YzipZSIAN5xnFKFJz6iphGBg9xTA2xycdahmkR8coYbHx7U4MVOIyXHoe341E6DruG\nfQU6OYKNrdB0NQykh+BMdsjFW7KOKeh2DynABPQjoaazbxjyifrTCJdhBAYdgTyKllobbnbdYPfi\nppm8yRUxlFYbj7+lUtzrJ6MOAcVZeNUQrvZmzkgHiszXd3LuQDyQMetZzADzApBG7NPcRDJb5jjh\nV5/OoI33OV24G2s5GtNJMrTlVkOe4q1ZXCI7OxxxgkVXuIySHHQcGobdR9qCk4GcfhUdBttTt3Om\nUhlDA5B7igmoI1XbtIAK8ccVJUNmiiOzUZ4uYz6gilqOU42P6NWbZtFFjNRPI6McHAx6U87j0I/K\nqLCRJ5EOWXGck1DNEtTj5zmZz/tGr+hf8hKP61nyf6xvrV7RSRqEZHXNbS2PKp/xF6ndA1IG7iqa\nuwbls+1WFbnFcbZ7XLYtK1PU1ArdqkDVNxtFlTkYzioGs1dsEdOhBp6mrCMPyouS43KQ0i3GW2cd\n+M1INKUp8uzBHAwOa0Fk4P8AKkJDADdjuMetK5DpooLZwQpnzCH/AIQvB/WpDZxXcQEkCS+zrk1M\nqSeaGRyrHqCMim7p0dyThm+UsB+tFyeUwT4a0a7RpFglt8nG6KXHP+6f8KbL4HhjgWVdQnhUn5fN\nUMGP4YrchvPKlWN0X90Plbbg59akvLhbqILMxxuB3A/d/CmqklszJ0YPdGH/AMIffJ8q38ZDfdzG\ncn8jVSPwU73OJ7+2BHUBjmuvnVb1Y4g3lxoONvBz61HCzyWahGQXURKFQNvH973qnXn3J+q077Dd\nL8G6faESNslY85Y5rpooEiAVQgHsay2uljt0ESlVVdzORwOKiF6zKkkc3lo4/wCWifL+dYuTe50x\nhGOiRukhf4lJ9M1Gzrjll/OsCTUbhSVSNHZRl9jcY9R61UbXG2ZZFALYDEEUrFcyOjeeMdXH4VWk\nuh0QEn1xwKxJNWKx7pBsJOAhABPvkUjzPclWiuk2HkENgGkO5cuZCwLMSR7c4rIu7wxKQIpWQckq\nOafKLmNGV1EuOS6EEj8RWWVaSb96BjqgdSCT7kUA2zMa1h1S7R45ZGO7ADKNxH+6TWl/Y+nSKYUS\nAOeqyQ7GP5Yx+FRpYstxvQDd1BkORn2YVpqBcR7W3BlG1o35/I0NhCF90Z0VvdacU/cFkDbSqvwy\n/wCezD8avRTbJtgYG2f/AFZOdynupFSIXtwIsl4jwN3LL9fUVG6IqkDJUncPY+1ZtnXThbYe7fma\njb0NOLY5HWoHJA9zWbOmKGluTjgVCT1NObgYqNj2qGbxGnuaip7HjFNJAWobN4ojJyxprnC06o3P\nOKzbNkhAcU/OBmoxQ7YHXFSN2FaQIuSfoPWpJJjYqR/y+OOc/wDLEf8AxX8qhikEEX2x1Bcnbboe\n5/vfQVl3EzySGMMWdjl2PWu+jS5Fd7nzuNxftZcsfhX4+YskrTMYoiT/AHn7mtLT9N3KJGGI15bn\n5iO+B3qta24jUcVoRO0Tq6HDL0NbpHC5FwwwxQmRVBJOFAkyGHqO/wCBpFM19IscKqFX5S2TtX+p\nPtVeGE3TnHyx55Kj7x9B7V09hZJAillAx91B0FY1pyivdOGtjYwlyR1f5FjRtGigYS4LSHrK/X8B\n2/Cuv023hEwyoOBnn1rnorsBtq4J/QVtaZJ+/BJycV40qn7zXVm9DFLqzbh/1ZwP4jVe+XMa/Wn2\n8vyH/eNNu2BjX611Rre8FbEx5NB235F+lIQKeSAi/QVEzYrqpVSI1UyNxVSZuwqeaXYvv2qi75rr\n9qmjdVLjXPrXOX9oIJztH7tuVrfZqq3MazxFG+oPoahyK3KGmzt5Zt2P3eV+lMutPhlJeL9zJ6r0\nP1FQAtDKGHDKaS4vJpAQCEHotZuSLiihJLNbyGOUDcO4NAeKfg43eo4NNdCxzUDJipuVyE+ZLdt8\nbfL6j+oq7a34Y84VvQ9DWZHOVOG/Onugcbo+D6VpGQ/Zmy9yXAXkAdqjOG5NZ1tOSdkhP1NXS/at\nk7mcqCZNHcbSFY8Vp216VwrHjsfSsJulPhnIO0mrjKxxVcJF9Dq45s/4VahcE8kAdSa5+0nZsJnp\n0965/wAe+L/7IsDplnJ/psw+dgf9Wv8AjWyqHk1sshJ2sYPxL8ZjU7j+ybGQ/ZYT+8YH77V5vTiS\n5JPJPJJpvvUNtu7O6hQhQgoQVkJRRRSNgq5p2n3OqX0Vnaxl5pDgAdvUn2qqql2CqCWJwAO9dddM\nvhDSDYx4/tm8TNw/e2jP8A9z3+lAFXxDqFvZ2aeHtLcNaQNuuJ1/5eJe5/3R2rmaKKACiiigAooo\noAKKKKANfQdZk0a/8zaJbaVfLuIG+7Ih6g/zqXxDo6abNHcWcnn6ZdDzLaYendT/ALQ6H6Vh10Wg\nanbvBJoeqMBp903ySt/y7SdnHt2PsTQBztFX9V0y40fUZbO4GHTow6Op6MPYiqFABRRRQAV2PhzV\nxcQjT7h/mA/dse/tXHU+N2ikV0JDKcgitKdRwldETgpqzO7vLcqTxWa64PNaOlaimsWPzYFxGMMP\nX3qC5hKk8V6sJqSujzpwcXYoq2GxUmRUbDBoB4oYIlzmlBqMGlFZtGiJM1NavsnQ9MGq+achw1QW\ni9rqbNWSUdJYx+dZGqJuuYXHAdQM1t60N9hY3H907TWRqA3WMb45jbFZzRcGULp8osZHKEg1Akzx\n4wenT2qa8Gbkt/fUNVciue1jouWlu5cD5j+dO+1y9mP51XiR2OFUnNP2HmlqPQmF5Ln7xpftstQb\naQjFK7BJD55nuJjLIcsajpSKQAlgKQwPI96OwpRzkUUWEavhpd+uxqOrIyj8RXs66RpOlKjX9wJZ\nAoOw/wCA/rXjnhdXbXolUZJUgfUjivZrTwkCVk1G4LsQCY0P8zXPisNUrqKjNxjre27+fQ4sTdzj\ny01KXRvZfLqV5/ExB8jTbUKOgyv8lFVDpWt6q3mThgDzmZtoH4V2NvZWtmu22t0jHqBz+dSMc9aM\nNgqGH1px177v72ZTwtWt/Hm7dlojmIfCCAA3F2Sf7sa4/U10EUfl28cIdnMYwGc5JHvUhqCa4htx\nmWVE+prtV5M1pUKOGTcdBT71Exz0qhPrtuVcwK0zJyQOOPWsp9V1G/bZaxlAf7g/rXTChJ6vQ56u\nPowdk+Z+WptXE0cC5mkVB/tGse71mPPl2itLIe5HH4DvTY9DlkbzL2c5/ug5P51fjt4LVcQRBf8A\na6mtUqcPN/gZc+IrdOVfeytbvcSJvuoVjbsQefypZNvrU0h5qs5prV3NlHlja9yGTBUgjIIwQRkE\ne9cZqvhVona40dgpJy1s5+X/AICe30Ndi5qs55q3TjNWYlUcHdHDW2uT6dL9luhJbsP+Wcq5H4f/\nAFjXQ6dfjUn8tEBY90bI/EHkfrV66tbe8jKXUKSx+jjP5Viro/8AZ0kj6UxjD9UkbP5Gsvq84O8G\nN1qdRWkrM1Z/3JCnC54yTjP0qs3HB61zGp6jqf2rN/busS8JxkAfWr2j3BuiXFxiIcBGOdx9vSta\neJafLNGU8KuW8GajYzUTVMy1Ccda9GEoy2Z51SMo7oY1MNOakNao52xpHHNNp5PFNFMApwHoKACx\nwKkwEGByfWgTYAbBk8mkyW5NOAzSgUybiCnAUowKeKCGxoHFKFpw6UUybibaMU6losK4zbxS7adR\nQFxu0ZpQlPx0zRgmiwrkZGfpSbOeOKl20u0UWDmE3yLwJG+hOayNf8RDSoDCnlveOOAV+4PU+9P1\n7WotGt8Lhrpx8q/3fc1wNvb3mtamI0DSzzN/kn2rzMdjfZr2cPi/L/gnr5fgFV/e1V7q/H/gDrCw\nvNc1MQQKZJpDlmPQepJ9K9f0bSbDQ9PFpasd55mmK8yH/CqmiaTbaDY/ZoMNO/M02OWPoPar7PtX\nJP4V8/Koo6s9ic3N2WiJ5XVVyHB9Ky7qaQ5xz9O1NnmLE96r8+prycRXc3ZbHTRp8urKkqyMcnNV\nXDDtWoc9j+dNZQwwyg1x2O2NSxkHNNJrQkt48HHFUpFAOM0zZTTGR3M0B3RSMp9AeKvJqF5OmHkO\nD6DFZ4Qu4UVrWVo0h+6Sq9SK9DBUJ1qijE5cZVpUoOc+hNaw7VDn8PerJG44NSBdn8OPQU3GDmvv\ncLh44emox+Z8JisVLEVHJ/IYeKbTj70hrouc6EzikJopD0pDFQnJwKkjLeapA6VGmeeaepIYYJpB\n1J5TuRMdQTVjSSzSyZHbiqh+6M1b0X/WyH2rOT0Naesjz+8kK6lfrnBEp25PGc966nT5jJpkEsrx\nh8YIUjFcfqeP7SvcqSPNauh0sWw0uA+QWOP7tePCo02j6CcE4r+uhoNcwq5LzIP+BVXN9bBnP2hR\nn0Gc0xvLzxan/vmo2Yg8W2PyFTN3CMFaxtLIk3w01Z0bI3EZrb8Q5X4fsw7QRYrEjJb4YasWTadx\n4zW54jH/ABb1/wDr3j/lVX0fp/mNLVev+RwXh6+catGhgaXMZwAcY966qa6uiAFsiNvAy4Fcd4aL\nf21AY2XPl9xXXzfacn98o+iVjCb5TecVzFR5r0HIghX6vVeSXUGYMTApByMZOKnlSbHNyfwUVVdD\n/FdPj/eAqJalxViORb2SSMvcLneOkddn8QVw/hH/AK/F/ka4l1jEkYa4b74/5aV3HxDHz+EP+vxf\n5Gpv7rGo2aNzx6MeD9XPog/nXm2gCC50u3JdsA7W+bFelfEMY8E6yT/zzH868u8NPAmkxGXH3ufl\nz2om/eHBe6bssGmKcbkP+9KTVGU6YvRYT+BNWpbuz6BOp4xHVKS9hVtoikz1wEqbjSZE81mPuomP\naL/61QNcQ54jf/gMVSPfbnYLDKW+mMVVa8dslLeQgcHmlcqwrXI7Qzf98gf1r0P4Wv5mnX5KMuJx\n976V5o93K8YkW2IUDqWr0r4TyNPpd+7RCMGcYAOc8UReoSWhkeAf+SleK/x/9CFdF4//AORQb/r4\nT+YrnvAH/JSfFnoAf/QhW344u4brwfOIHyYrlFkBGCORVJ+6xNe8jh4mkEqbdgPbNTTG5ycyxA9e\nE/8Ar1SWSZGDxuAVHBxmlY3TW/nfaAMruwFqbg0JL9oPW4xz/CgqtJDJkhrqXjuFFKFuJLcSG5YZ\nG7AFVUSWeISNcyZbrzRcYjWxblppyAecNULWq8ZMpye7mmQxGdCzTScNt4aoY4RJJMrO5CNgfNQM\nka0h5JQkdBl+9RCCDptjB9SaiMEZvGjIJULnrUc1vElxCirkMeR60hno/wANwi6XqIQIP3wzt/Cs\nL4nf6+H6itv4bxpDpmpBF2gzD+lYnxNOZofqK3l/CMY/xCtRRS17pyiUtFFABRRRigAopaMUAJRR\niloEJSYp1JikwG0GlppqShKWkpaTGFIKXtRUMpCLTqaOppallIWojw/b8alqJ/vVDNIkgKqcFQp+\nlEiBxTc7eDyn8qXBUZUjHoallJkaytEdrcr6+lWQwIyDxVaSQMANvPvURyF6/L6CobNErj53USkq\nc/SpeDCSZFAIyFU/zqrhSrZx14pIgAWY89gKzZoiaaQBAqgKMUwx+RcRckhxyfU1CysxwFJx6CrV\nzzHC+OQelSy0QzfdI7dKqA4kB9quSDJIqk3DCskaVN7m8r5VXHpz71Juz0qpbPm3Q+lTA4OOx6VD\nNkS7qZN80LfnRnHvTWYYOT2rNmkSUPuUEZ6Z4qpNP5cmdoJYYyW6U5JNqKD1AqC6xtDBQMHtUaGm\nqVzlJPvt9au6PxfofeqT/fb61c0g/wCnx/Wtp/CeTR/iL1Owz3qdW4zVNW5IqwrYFcZ7paVs4x1q\nUN3qorc+1Shu9SwsWVf8qmWTHGaphsc9qlVvypBYuq+afuz0ODVRXx9KlV6QrEokYYJ4wc7h0qRb\njLHpg89ahB/OkwAegB/SlcmxNkKN4GTnoecinOkbR8qDuHVagZtvbB/vetKCy4bkDuKLhylkRo+G\nVimRjjmmvYtvZlmDt1XjBBqPzSwKnGD3FL5oGMnGOtSFkPaOdFHykrjsaZD+5DIrNGDztIOPyNSr\nNtH3iVp4nXHJz7UXDlKjWqPl4CI2U7vkOOfXFOtkSWJo5kQsP9kDg9cVKZE5/dr+VRFVzuAII96A\ntbYpSaXHE5xhoDwUPVfoR2qD7MbCQyxEzWzH54n5ZT7djWi7kZAbOe1QAkDHpxRcOVDFS2lQSRAq\nrc/LwfxFQyRnnJ3DsRww/wAaeQFcso5PWlB//XSuWojYwNnzgZPXAx+dDqd2SfmH3W7/AENBOTnp\n700nIxnA9ahs6IQuJuDHDffHemE9eePSms34Cmls/SoZukDN3J4qEnPJNKzZ+lRk5+lQ2bRiIT3N\nRk9SaUnP0pjc1DZ0RiNzk5qNjk05jtGO9MxUM1SEJwuc1F1PWlc5OO1NxUMtDgPcVFt+03K24bC/\nekb+6o60O+1TnoBmolLRaeXP+svGyD6Rg/1IrfD0+aV30PMzKu4U+Rby/Ijv7ze5dRhQNkSf3RTb\nC13yKGYBnPLGqq/vpyf4U4FdDYRLbx+Y4kWQqCCp+UA9Aw967keA2Eln5EbMJFbZjI6EZ9cZH4Zq\nFF819nRR94/0p93O0knlqiRqpwEjztz64NX9JtVZvMYfInT3PrXXh8O6l5dEefjcT7KFo7s07C0E\nEaySKN2PlX+6Kklui7mND0+8w/lVe9vCoEaH52H5Co7Velc+Lpbo+clNpaGvacYrdspdsqc1gwkA\nCr0Mu11Oehr56rSszpo12jftbn5Tz/Eafd3P7pee9YUNyUd1J/iJFNuLzdMeeBUwT57FyxD5LHTe\ndlF57CmNLmqCTZjXnsKjubny4Cc8ngV0Qdj0qctLj5rjfIcHgcCoy2aqI+al3cV0wmzrpXY52qFm\noZqoz3kcZKj529BWnOdkUNvEyd6jnvWbJKifeYZ9M1NJPLKCCcKewrOeDy2I/Ks3M3jEe9yvRUOf\nUmq7SO3Xj6U8x5OKXy8D1pcxvGBXKnPQ0+ElDjB2/wAqk2AUjDsOPetIyK5CaRQ4yuN386dBN0Rs\n+xqCEMDgnjt7VLJHzuHXvWqmHIWcZPenLFkgAGmw5dcfxDrVqWWCwtJLu5YLFGuTmtVK5jONitq+\ntQeHtKa6kGZzxEhPJPrXjN7eTaheS3Vw5eWRtzE1f8Q65LrmpPcOSIxxGnoKyK3SseXUlzPQSiii\nmZhRRW/4e0mC483U9SJTTLP5pSOsjfwxj3Jx+GaALmjwRaFpo1++jDXD5GnQOPvt/wA9CP7oPT1I\nNc1cXM13cPcTyNJLI25nY5JNXNZ1abWdQe6kUIvCxRL92NBwFArNoAKKKKACiiigBaK6Twv4aOtS\nvJPuS2QEbh1Le1ZOq6bNpV9JazDBU8H+8PWrcJKPM9iFUi5OKepQoooqCwooooA67T2XxVpS6TOw\n/tW2UmxlY8zL18on19PrXKSRvFI0bqVdTggjkGlileGVZYnKOhyrKcEGuq1SKLxRpTa1aoF1K3X/\nAImEKjAcdpVH8/woA5GiiigAooooAt2F7Lp92k8R5B5HqK7kSQ6jZrcw/dYcj0NeeVr6Fqzadc+X\nIc28nDD0966KFXkdnsZVafMro2J4dpNVwNpxW3dwKyh0IZGGQRWRJGVavSWpwNWGilpDSUmhpj+1\nKOopooHWs2jVM1p/3/hqVerRncKzAPOsJF9VDCtOwPm2txAejIayrU4jMZ7ZU1EkNMz5+baBx1GV\nNRQxNNKsa4yTjmpmQlJIvRtwp9qg8qZGOC+AKwa1N76Fm3iknmjsLYKGyQ8hbAPuT2UVvRf2NpMX\nlw2i6jcg/NcXJKwj2VByfqT+FZfh6OR76SGAZuJEKxjsfrXoOi+HdOssyXCLcypwzyDILeijsKG0\niowlLVHJjWEckPpekyKf4RCV/UGnyaHYa5byy6QrWt9Eu57GVt4dfVG7/SvQbvTNI1Cyf7RYwgDj\ndGoUr9CK4qwtF0fxlDBPJuggYs0nT9370lJN2HKk4rmOGlhaJ2VlKspwynqpqLBJzmt3W/LuddkF\nuMJN90H68VlXlqbK9mtWYMYiASOhNQ42YRd0VwOuKXaSM85pwHuKcwGOMUrDuaGgagulaqlxOhMQ\nxkLzivU/+FsaMefstwDgDtXjmM4oxVJdyGeyD4p6K3WK5A+gpX+J2jMMxxzFuwbA/WvHAOacOOhA\nq1FdiJPzPT7jx8lxkC6SFT2Tj9az21+xlfc92rMe7NmuCx64oHXtXTCq47JHnVcBCq7zk38z1Cy1\n7w5bYeW6aSX8Ao/XmtIeM9BVAkVxsX0VcV5AB7VIo9qHebvIunCFFcsFY9a/4S3RW6Xf6UxvE+kH\n/l8H5V5UPpTx9KtQQ5VGenN4g0tul2PyNRNremt0u1/I15yB7VIorWNNGMqrO9bVbEji6Q/nSfbr\nPG5rhK4kARjJGTSEljk1soIwdRs7CS+t3P8Arkx9aiaeEn/Wpj61yw9KevWtVAhzOhMqbSN64PUE\n8GqjWtmwwEjQdtnFZgGaeBV8ie5HtGti00U8LZguQy9lc/1qM3mw/wCkRNEf7wGQaaKeAOlR9Wjv\nF2GsQ9pK4sd3DMFRR8/XPtUlMRVUYAAHoKeFreEXFWbuc1SUZSulYQjjinJGSfbvT4489TgU8n+E\ncCtDFy6ITIUbVpuMUtLSJEp1HGaKYgxSgUUuKYhQaXPpQOBzQCPSmIBk0o96UU4CixLYgHNOAxSj\niinYm4Y5oxRS0wFxms/WNWt9GtfMfDzMP3cfr7n2p2q6tBo9oZpcNI33E/vH/CvN557vWtR3Nuln\nlbCqP0ArzcdjVSXJD4n+B6mX4B13zz0ivxFY3mt6mMBpbiZsACvTdD8Pw6BZ7QQ93IP30g7f7I9q\ni8O6HD4ftNzhXvpR+8f+4P7orUaUkZJr52rLlTcnqe3Kpz+7D4UPyEGT+VVppiTgHn+VNlnPrz/K\nq+cg7ucjBrx61VvQ3pU7asrPqUKoZSsxgDbTOIj5efrVgNuUFSCCMgjvWc2lZj8gXs4tCcmDPH0q\n+oVEVEAVFGFA7CsZ8v2TrsugpamMxpSarC6gkcpHPG7DqFbmstS0Oc5HNVZUyOlTM2aYAXcKBmnC\nLk7I0vZXG2dq0kw2jk/oK6CJDBGBExAHX3NR2tuLePB/1jdfb2qfp9K++ynALDUuaa95/gfF5rj/\nAKxU5IfCvxF82TPJDfUUhmG75o1P04puaQ9TXrnjpIcXhY/ddf1puyM9JB+IxUZphYD3qWWl2JTA\nx+6yn6Go3ikU8ofwphb0FAkdTw7D8aVy0mOUMCcjH1FKudw6UC5k6FgfqKPtAJG6ND9KlsLPsSMf\nlGKtaVKIfNdydo9Bk1VZ4ioypFWtIW2czB5AExk7hgCs5vQ2pfEro851FiNSvSp4MjGug0x7j+zI\nQGj+7nkGud1EqdTvSCCpkbBB4IrptLBOmQ45/dmvFh8TPop/AhCbhmX96g3AnhelQ5mIRvO+82Pu\nVaz88P8Aumq+f3cX/XSiQRN2AMPhfrIZixEhGSK1vEdyT4Qez2fL9kjfzPTHY1lRHPwx1ojp5la/\nim8t4fAS2zyBZp7ePy1xy2BzQ3o/QaWvz/yPPPD4L6xb5J5TqDg11UtsvmTqWkIQcZc1yfh5wur2\n+5iDt712LnM9zj+7WMdjefxGbJbx+TA23ljgksaiNrF58ymMEKOBmrEj/wCjWw77qZu/0ufj+H+l\nS7D2KDQRfZoHEabvNXJx716F8RBiXwh/1+L/ACNcAzj7Jbjn/XL/ADr0D4jf67wh73i/yNLoyuqL\nvj2K8Gg+IZJCfsZtwAC3BbPYdq838Pj/AIkCY4welemfE/UobPwte2ciuZL1SkZHQEeteYaExj0I\nIeSDg0pfEOK906C9z/oxyfvD+VVrgf8AEyXk/cPbmpryQEW2CPvDv7VWuHX+0E+ZSPLPAPTmhsEi\nBF/0+5HJ6VVt1+S4HP325qdJAb+4YlR071VgkAWcl1HztzSuhvQhhA/sxj/stz+del/CP/kC3Y/6\naj+VeYRSBdOIyuSrD36mvTfhCwOi3mMcTDp9KI7jktDK8AqG+JPi0HoQR/48K2PGmnrY+DrtxK0j\nzXMZJYY2jIwBWR8Pj/xcrxafY/8AoQqz8VNZmstGtrFYo2S4PmFj1BB6fpTXwkv4jkUwRjA+4alX\nB00A4P7on3qilwfs6Scbmj5FTi4iWwCNIAfLIwOuaE0NrqJbc2KDj/VntVWyObaIZ654xUlvdQJa\nKssmMRkYU85qlb3UcdqimUKwzxQmgsJZH90RnrKe3WoIjie76j95SWt1FFEQ8mP3hOM9RUEd1Ek1\nw2/AdsjntSurD6kj8apL1H7sdKjuuLu0981E11D9ueQPhCmAc96iluY2uYXD5Cnk+lPmWoJHpPw8\nP/Et1Dr/AK4dfwrB+Jh/0iH8K2PhvMs2l6kyNkCYf0rF+JZ/0mH6itn/AAjGP8UZRRRivdOYKXFF\nFIQUUUUAFFLiimAlFLRQAlJS4opMENptPppqRjTS0ppuTUjFopMmjPtUstB/HS03cKNw9allIdUc\nlOzTXYcVDLQoYkYAz9aTbt5PK/yoXAHJIp2xfTNQy0gbYygEjFR7UU5Ubvan42HIGR3FPBBGRUNm\niRWfaCcKQD2I6UkMeWJUDjrmrEg3KeKijPBABPsKhmiQ8F9zKrDPsKZJwp3SFmHYU8qdw3HAPGFp\nrqHDKowq9cdzWbNERPIoG45wfaq0keZMIN3frUrKfK3EEgds1EsjbtwHPpUehUnfRl23JjhCsOc1\nKZuMcAepqlG7THDS7PQAdanFtH1YFz/tHNZyN4NdEOa4XjLkk9h3pN8jfchb6txUowgyqgAdhSFy\ne4/Cs3Y2VyNVmfO5wmDjAFSLAmDvZm4PU0JwW9+aUn5W+hrNs05e5yD/AHm+tT2EyQXaSSNhVPPF\nQN94/WmGulq6seJGTjK51H9s2OT++P8A3yalTXbAdZyP+AmuQNNNYukjr+vVOyO0XXtOH/Lxj/gJ\n/wAKeviDTB1uhj/dP+FcMaaal0kUsdU7I78eINLH/L4v/fJ/wqRfEOlD/l+j/wC+T/hXnRppqXSQ\n/r0+yPSx4i0j/n/j/I/4VKviPRx/zEIv1/wry00hqfZIr67Psj1ceI9H/wCgjD+ZqQeI9H7alB+L\nV5EaaRS9kg+uy7I9iHiHRu2o2+f9+mnX9IJz/aNv9RIK8fNNqfZof1yT6Hsg1zSj/wAxG1Pv5gFO\nXWdMbn+0LUfWUcfrXjBoJNL2YLFvse0/2xpn8F9bfhKv+NH9q6cc4v7bH/XVf8a8VyaMmj2Y/rb7\nHtI1Sx7X9uf+2q/40o1G0P8Ay+Wx/wC2q/414rmjNHsxfW32Pa/ttrji6gP0kH+NN+1QHpPF/wB9\nivFs+5pcn1pez8yljP7p7P8AaIjwJYz/AMDFNMqH/lop9gwrxrcf7x/Ol3N/eP50vZeZax1vs/ie\nx7ge4P0NDkgV475r/wB9vzpfOk/56P8A99GpdDzNY5ikvh/E9aYMeSDim/MR90/lXlHnzD/lq/8A\n30aX7Tcf895P++jSdB9ylmUV9n8T1Mq5/hbH0prBiMBTj6V5eL25HS4l/wC+zThf3g6XM3/fZqXh\nn3No5tBfZf3npLBugB/KmkEdQa86Gp3g6XUv/fRpf7Vvv+fqX/vs1Dwsu5rHOaa+yz0AgnrTHOOO\n9cGNXvx/y9S/99Gj+17/AP5+pf8Avql9Un3NP7bpfyv8DuMUba4gavfj/l6k/Onf2zqH/P09R9Tn\n3RX9t0f5X+B1V9u8lI1+/M4QAemeai1KRVmkVDlIgIk/AY/nVHRr26ub1JppC4gRpOR04x/WoZ5p\nHdFLZLHcTXTRounGz3PIxmNjXq86WhoWkQCqGOAepxmtyK4eGJwbqG4iUZQEfMG6Dg4PTPrWVpkb\nzu26B5kUc+W4Ur789alvFFvMiRtnIyQTkj9K25WcntESxBpJBk5Zj1963wy21sE6BRk/SsGxdmuB\n0wKmvbx2GwY+Y/oK+tp4P2OHiutvxPncVXVWqyeOUyzGR+rHP0rVtzgCuajuXVsECtOG+baOleJi\naLaZyVXG5vxvVmN+awo9QbP3RVuO/b+6K+er0HcdOcGXrm48q1W4HOGKmqMN4ZCSTyalMyzWj25G\nFfuT0NYMczQXDRPwynFcns+WV+4qkXeLWx6BDJmJP90VU1Cb9/FEOwyaLOUNDHz/AAiqlzJnVpB2\nVQBUS0R9DTh7qL8OcCnTTRwJukbA7DufpVeS7S0hDEbpG+4g7/8A1qzjKryGW4mTeexPT2Aq+e2h\n6FKFtESy3E1zxgonoDyfrUYh2jninfa7ZBwWf/dWmtep2gk/SlzrudsKQhXjjiomTJ60Pf56QP8A\nnUf2oE8xOPpS9qjrhRFKhfeoypan+fG3Zh9RUgeMdGGaXtEdCpEXlgDFBhFThcjrkUu30q1Mfsyo\nYwDwKni+YY6+tOKc4HWpreD5wcVpGd2TKFh1tBtfJ6dz7V55448S/wBoXJ0+0f8A0WI4Yj+M10Pj\nnxENMtv7NtH/ANKlX96wP3F9PrXlZJJ5r0aENOZnjYytd8iEoooroPPCiipre3lurhIIEaSWRgqI\noySTQBc0XSJ9a1BLWDCjG6SRjhY0HVie1X/EOrQXAh0rTfl0qzysXGDK3eRvc8/TNXNYni8PaY3h\n+ydXupcHULhDkMe0YPoO/rXJUAFFFFABRRRQAuK0NH0uXVb1IEBC9Xb0FVba3kurhIYlLO5wAK9N\n0fS49GsBGMGVuXb1NdOGoOrLyObFV1SjpuaVnFDp1rHbQAKiDH1rL8R6RHrNkWUAXMYyh9farTyZ\nNSQy4NexKjGUeV7HiRrSjPmT1PI5I2ikaN1KspwQexpldz4t0LzozqNqnzAfvVA6+9cNXh1aTpy5\nWe/RqqrDmQlFFFZGoVo6PqtxouoxXtsRuXhlPIdT1BHpWdRQB0XiPTLdI4dY0znTbw8L3gk6mM/T\nnHtXO1veHdZSwaWyvlMul3g2XEf930ce4OD74xVfXdHk0a/MBdZYHAkgnTlZUPQj+R9xQBk0UUUA\nFFFFAHVeHNWDqNPuW4P+rY/yrTvLYqTxXCKxRgykgg5BFdxouorq1n5UhAuYhg/7Q9a7sNW+wzlr\nU/tIosmOKbitC4tyrdKpsnPSu6xyXsMApwXHJpwAA5HNB5qXEamWbOTZOPQ8VTf93eSqO5zUsZCt\nUdwP3wk9aiSLT1KZH+kc9CaaAQSoySTgY657VLMmGBrb8NaUbzUPtDLmGDn6t2rnqKyudFP3nY6j\nwppMekW4nnUPeSrlj/cHoK6RGjkf5FXOcntUcFupT5hUjQIACSeO/euCTm9T0YumvdQ+58mNGmkY\niONdxXPyjHevMNR1tLu4uJlRi8z5ZieSB0AHYV0/jHWBaaWbGNszXPGM9F9a89IwMA/lWtLX3mZV\npNLkQ8zN57TEq0jc8n7tVpGaWVpGOWbqT3p+w4xxSbeetaXRhZ9BmKTFSbRn71L5eOe1GgWkRgU4\nKT9KkEeeppdh6dqpWIakyNumB3poBqYrhfrTkt2dNyn8KtWM2nsQgHPNOAOal8tlPIIp232rRIyl\nIYvvTxn1o2D3p4StoxMZSEGalUUKoFSopbgDj1raMTCUgQEmp49q8dWpmP4U/OhQVPpWqiZN31ZL\ntXNKEFIufepFGatJkSkhAvtT1X1pwWnha0RjKQgFOA5pwWnhRmrRk5DAOakUZpwQCndKshyEVcda\nmVAOW6UigKMnr6UEljzTM27ilyfpSUUYoELmijFKFpgFLilApcUybjQMU/FHSlGTQJsTHFKBRjil\nAz0qkhXFUZIFSZxTc44FJk4pkvUdnvRmm4paQhaq6jqUGl2bXE556Indz6U++vYLG1a6uG2ovRe7\nn0FebapqdzrV/wCY+SCdsaL0UegrhxuMVGNl8T/q56WAwLxEuaWkVv8A5Db6+utZ1DzHy0jnCoO3\noBXf+G/D8eiW4nuAGvpBz/0zHoPeq3hvw6mlRLd3ShrxxlVP/LIf41vtJ3NfPyny3nPc9qpUUl7K\nnpFCu+TuNQSS4+v8qR5Mc96gLEnJrx69ZyZtSp2FJ70ZzSUVwt3OyKFzSE96QmmMcUmzVIhvYzc2\nksCyGMuMbh2rMW0lZbWJ4LaFLc7jLD9+T2NabNUTNgVcK8oRcY9S+ROwM2Tx3rT0+22KJnH+6P61\nUsrYzPvb7i9f8K1s45HFfTZHlv8Ay/qL0PAznHcq9hTer3/yHE0m6mFu9NJr6u58soj99NZ+cU3N\nNPWgpRFJJ603NBOKSkUkJTTxTqaTUspCAjNKCN3WhDgnmlGMipGPJ4UE+tPgwdL1HH/PI0z+Jami\n/wCQXfjjHlHpWNT4Wb0H76PNnH7oc4xXV6HZzz6NFLHOVXkYrmEAYjJAIPGe9dXpjMumxYZgNx6H\nivFpK87PsfQ1Je7bqLcW00DhWuX3Y4wO1VTE/A85sA5HFXJSXlwTn0Jqu52sRmqkleyFF6HQWoCf\nCnVwSWJkzuNP8d7ToOjkEj9wP5VFb4/4VXq3+/T/AByf+JDpH/XAfypS+H5Dj8XzZxejr/xNY/m4\nKdu1dqNEeaESrdPh+vNcn4azHq8ci7fmjIxXVs7gDDsB6dqxprVpo0nUTlyrdFK90o2uxXnkOemD\n0qi1omT+9lyevPWtC4YuwBYkdiarS5QjLCiSV7IuLdrlJ7NN8Q8x8eYvGfevRfiL/rvB4/6e1/lX\nn7vukiyQfnH867/4iHM3g/j/AJe1/lS5bJlX1RH8YubCy9pG/rXCaMgOmbsnO/oK7z4vsP7NtMf8\n9H/rXFeGtyacky4x5hXn8Kid7toOeMIpy2Nm20eG8DfO6lQDyxpsnh6DJzI2fqanuZXM7N5mxiB9\n09aqSSSBcmR9w/hzVwScU2hc6fvLZmVLZW4dlw3Bx1qs1nb8/K351fKl2OAM9frVaUFG7DIzxUWT\n0TNLtFNrO3yBtPPvXp3wijjh0zUVjGM3Azz7V5q5LbQT3716V8JjjTNQwMf6QP5VUY2YpO6Mr4fH\nHxI8WfRv/QhUHxhwbfTD/sN/M1L8Pz/xcfxX9G/9CFQ/F5v9E07/AHG/maX2Q+0ctEqm0tz1JUcV\nfW0tpJ0jMIG7uQap2e9YLOULwMDPvWvqEryyxbjsIHBqdedRtuT7WCfI9yKXRbVc8A++KpXWl20M\nLybAdvtTmkk3YMrH8eKr3DsAV8wuCOuTit3FW2FfWxnNFCf+WS/lUZihyf3Q/KrBUk5CrwORmoJN\nwwQMZ9D1rJpdy7kLRRf88h+VR+XFn7ijmpiTuI5x/KoccbjyM0+RiuzvPhxtXTNR2DA84f0rE+JJ\nzcxH6Vs/D3jTdQ4xmUf0rE+I5/fw1vNWp2MYv96Pooor2znCilxRQAUUUUCCijFLQAlFLRQAlJSm\nkpMEJRxRRSZQnFJgUuKSoZSDaPWjavrRSVLLQhUf3qTZ7iloFQykMMdNIA4zUtN2gc1LKSBVwOoI\n9KeEGMo4+hpi4GaTPdRWbNlYlG3OCwB96btAJKEH1FMADDnn2owVHy9PQ1DNE12JBEHXJcH2psUY\nEjDcKZuAJI4PcHvTA6iTOeKzdzROPYsunG0MMnpzSCMLFncMDrzUQbksQ2TUUuZfkHyg+9Zu5qmr\nXsPh/fRs2Rw2B9KqXELJI2DjjIxVlNqgoOMdqhuACEP4VKfvDkk6eu4ljb+a+7cOOcGtL7Oe2Pzr\nKtWEcoHY1obh61nUvc1w/LybEwgb1H50zyG6cVHu9zTd/IGT1rB3OyLj2HyRtEofGccHmmMXZHIU\nBAOueaJPmiIyc1nGaU5QseOCBSSbFOpGL2Mt4XVyCh69cU0xsR90/lW5apIJQ5YqCMCrabhkhxye\nQRVyrWdjkhglJXucqU9qYU9q64ru/hhb6rUZt0b71tAfoKj2/kX/AGf2f4HJmP2ppj9q6s2Nqfv2\nS/VXpp0uxJ5tZV/3X/8Ar0e2RP8AZ8u6/H/I5QxexpDF7GuqOj6d3W6X6c0h0PTcZN1cJ7Ff/rVL\nrIPqE12+85TyfrSGEe9dOdBsW+7qRX2ZRTT4ajb/AFeqRH6j/wCvS9qhfUqnb8Ucx5I9TTfI966b\n/hFLhjiO+t2NMk8J6iv3Zrd/o5/wo9rHuL6nUX2TmzB/tU0wH1roG8MauOkcTfSSo28N6wP+XUH6\nOtL2i7k/Vp/yv7jCMJ9RQYG9RWu+gawvWxl/DBqE6Pqq9bG4/wC+KOddyfYSX2X9xm+SfUUnktV9\ntPv0+/aTj/tmahMFwp+aCUfVDRzCdNrdFXyX9KPKf0qY+YOqsP8AgJpu896d2LkRH5T+lJ5beh/K\npt5pN59RRdi5EReW/wDdP5UeW/8AdP5VOH96cJCO9LmY/ZruVtjf3T+VGxv7pqyZD60Bye9HMw9k\nu5V2n0NG0+hq4Gp2fU0ucr2K7lHafSjY3ofyq+CPUU4bfb86PaFLDruZ+xv7p/KjY3ofyrSGz/Jp\ncJ6j86n2vkUsKu5meW/90/lR5b/3T+VayeUOuPzqVWh9B+dJ1muhawaf2jFEUh6I35U77NMf+Wbf\nlW8ksCnoPzqUXVuOgH51DxEukS1gY9ZEejwulnduVIZIguMepH+FRbWN0vB4WtOzcNY6i46EpVFX\nH2pv92t1JtI4ZQSbVzW0+KR4G8hB54blmUkbf6UlwRJdkryFXk4wM98e1TWymXT49yyMoYsiwSbX\nPTPB6/hzTLt3N5cFwA2zOBk/5NbUtakU+6Iklysl00N5ckmDwCaguCxuAMfdWrGmsTp8pwelUZ5G\n+2yjB4Pp7V9jWq3VjwlRV2y/DD567WH0I7UPBPat84yh6MOlLZOxH3T+VaaFmGChIPUEV4uLeoRw\n6aKETMSMY/OtCLecdPzqtNpchG+1BU/3G6H6HtVdXu4JAk0Lo3+13rwK7KhhOV7M3o43b0/Oqmra\ndK8X2qJcyRj5gP4lot55Tj5f1Fa1s0r9h/30K4ZNPc7I4RSVrMk0m5D28Jz1UUy+nS31K4lk+6oB\nx68dKdPZmzAuLcZi6yIP4D6j2rK12ZZJ4JQ2VdOceorhlfY9ujh9EmVZr2e5maR2ILdh2HpUGfWm\nb89Kcq7jyayfmenSoJE0NzNEf3bED35Fa1reiZP30YJHUr/hWSFUcA1btCUnUqRnuMdRUOR2Rpxs\nbSQxSrujIPt6Ux7b0FK1uCBJG5GeQRUiTsvyzdf7wH86lyN4wRXNviozBntWiVDDjn3ppUCp9pY2\nUEUBCQfl4qRdynk5+tWMYpyR5OaXttS/ZaDI0D9Riqmv6zD4f0xp2INwwxEnqfWtKeaDTrKS8uWV\nYoxk+/tXi/iHW59d1N7iQ4QHCJ2UV6eCpuq7vZHk5jWVCNluzPu7qW9uZLidy0jnJJqCiivbPmm7\niUUUUCCuvs1Xwno6ahKo/tm8Q/ZUPW3j6eYR6nnFVdA063trZ9e1Rc2UDYghPW4l7KPYdT9MVj6n\nqNxqt/Ne3LlpZDn2UdgPYDigCozM7FmJLE5JPem0UUAFFFFABThknApK6zwnoAupBfXK/uYz8qkf\nePr9KunTdSXKjOrUjTjzSNfwtoY0+1F5cp+/kHyg/wAIrZlkyakmk7DpVVmya9+jSVOPKj56tVlU\nk5MaTTkbBptA4rYwLsTqylGAKngg1wPijQjptz9ogX/RpDxj+E+ldqj4NTTwQ39pJbzLuRxiuXE0\nFVj5nXhcQ6UvI8gorS1jSpdKvWgfkdUb+8Kza8OUXF2Z9BGSkroKKKKkYV1WiXMOtaePD2oSKr5z\nYXDniJz/AAE/3Tx9K5WlBIIIOCO9AE11azWV1JbXEbRzRsVdGGCDUFdgwHjHSTIozrtnH8473UQ7\n/wC8P1z7Vx9ABRRRQAVYs7uWyuUnhbDKfzqCkpp21Dc9Gt54tWsluYup+8o7Gqs0Pl57muZ0PVn0\nu8BJJhfh1/rXazRxzRrNEwZHGQa9bD1lOPmebXpcr8jFYc02rMsRBqAjFdLjc507DelPkQPalh1U\n03FTwDKOp7is3EtSEsdOk1OZYUO0D5nc/wAIr0PTbS2sbRIYF2xr3PVj6msXQLL7HaLvGHk+Zj6+\ngrayWGMkVwVW5PyPRpRSWu5pK+QCDxVfUr2OxsZbmU4SNc/U9hTIiUQID0rA8TJd6tJFpdkm7b+8\nmYnCj0BNYct9DW9tUcZdXc2oXct5cZMj9PRR2FQYzXWR+B7wx/NfwBj/AAhSRVS98Napp8TTMsc0\nK8s0RyQPcda6EopWRhJtu7MARFumD+NJ9nk7L+tWvMRhh9hH0qIRRMxxIQOwo5EL2j7kP2d/7ppR\nA69VNWBbRjkTD8RTTG4OOG9xT5BOqQmNj0U0ojcjpUn3fvDH404GPPf86fJ5DU10ZH5ZK/dNWLcb\nVIIxQNvYt+dOGezNTUVYiUnccwRh0J/CoTEmeCwH0qxu45H60oLZ6GtYxSMJtvoV/LHbJ/CnBKsD\nf6VKqueTx+AraKOeV0VUiB+lSHH3V4FTNvPHGPoKUK+Oi1qjF36kSr6U9hn608A+g/KnAewrSKMp\nyIwtSqhqQL7LUgUY6CtUjCUxiinAU8KKcFHpVJGTkNA4pwHNOC+1PVSTwOa0USHIaASeBUgUJyfv\nU7aE6dabjPJq0iL3E5JyaKdj2pce1FhXEApQKMD0pwosJsTFFOp2KdhXGYpcU/FLj2osK4zGBknp\n3pRhhlSCPUVT1lHmsvLjI3FuVz1FUNOhuLNgQ/y91JyDXBVxjp1lT5brudEKKlT5r69jdCZFGBjA\nqm1zNIMBgv0FJslbl5G/E4rR4uP2U2ZezfVlwD1p2OKzSEH/AC0z/wACpgkiH/LQD/gVZSx8Y7r8\nSvY36mtjNR3dxBYWz3Fw+I0HJ7k+gqmbq3hheaS4CKgyfm/lXCa3rc+sXOAWWBDiOPP6n3qKuZwj\nC8Vr0OvCZdKtPXSK3G6xrE+s3gdsiNeI0HYf411nhfw4tii396gM7DMUZ/gHqfeq3hrw6tuiahfL\n855ijPb/AGjXSyTFiSTXjym0/a1NWz1qtWKXsKOiRI8mTnNQvJ3JqNpO/wCVRM+TXlYiu5O5dGnY\ncWycmjNR5p2fevOk7s74RH5NBambqQms2zeKFLVGzZpGamE1G5tFCMaSGNp5Qqjr+lMJLNgc1r2t\nuLeLn/WN19vavZynLnial38K3OLMcbHC0v7z2JkRYowi9B+tIKQg5ozg4PSvvIpRSjFaI+InJzbl\nJ3bAmgGkJ5pKogXqeKRjzRSHrQNB3pCaCcU0mpuUkGeaaTQTxTSeKTKSFB5pQRmmA0oPNSOxKe1S\nW4ZtPv1jUsxjwFHJNQk8Vd0U4ll5qJrRo1o6STPMZN6HaRtkQnIPVTXSabPcjS4RtVhyeWrntSI/\ntW+y+P3zfzrc0+RP7Ph/f9vavBhpJn0lRXinYttPcbs+Uuf96ojcXAJxCPm680jNk8TfoKYd5PEv\n6CnJXJSVrHS2js3wq1Yuu0+Z0zUnjt8aFpGP+eA/lUNmT/wq3Vstn956Yrd8UhT8OpWKruFvHhiO\nR9Ktq8fkJO0vmeZ6HM8OrKyKc+Xjpmuqk1FyqhoJMKMD5a5Lw+WGpwkMp/d9+ldQ7zj+FPzNYQ0W\nhtOMea9tSKS/BIJhl4/2agk1FWYExPwc4K9aleSYDlE/77qBpZc/6sf991MlfcuOhFLfo0sR2Mv7\nwcBfevR/iG2ZvB//AF9L/KvOGlk8yP8AdH76/wAQ9a9E+IR/e+ED/wBPS/yoS91hazSG/GF9unWY\n9ZH/AK1wmh3Xk6eO4D52npXp3xNsLa78KX93MjNNaLvhIbABPqO9eVaC4bTI94LZbnAomveElGUL\nM6FtWgKSAlQZAAfaqkl/b5z5g6Yp0ksPTyzx6x1Vlktu6D8YzRdpWCNOMVZIUX0CsT5q9MdaglvL\ndpUYurKByM0x3tD2j/Farv8AYz2irNo1TJJLi3YgoUHPrXpPwkkRtN1Eggj7QOh9q8sZLM9ovzr0\nz4SCJNM1LytoH2gZ2n2qoLUhpKNil4AI/wCFjeK+ezf+hCq/xeYfZdN5/gb+ZqTwEcfETxT9G/8A\nQhU3xS0mW90e2vVljVLc+WVOckk//Xqre4H2jjbecra20bH92AGwPWtWa4hknjYhigHzAisSKEFI\noieQACRUsllF2MnX+9S1updjOVGDlzdf8y1K67n28KegqrK3yAelV3s0H8Un51Xe0U/xyfnVczLU\nUiyrAbs9xxVe4b92u3qKrvaL/wA9ZKia1H/PeSotrcq5adYOMSHpzz3qtke9RNbHnFy/HtUZtpMn\nFyePahJi6t3PQfh623TdQ5z+9H9KxfiK2biKtT4dRummagDIHJlHP5Vj/EMMLmLIrpf8Ewi17YuU\nUUV7ZgFFFFABS0UUCCiiigAooxS4oAQ9KbTjSUmCEpDS0lSUIaTNKaaRSGGaKSlqGWgpKKO9Qy0J\nRRRUstDD1pcn1FIetJ9OD6VDLQEHqDzQMH1/GlzzzxSEdwcGoZohSAeMCoyFVqUyACo3ORnP5Cs2\nWmh7vjvz6U1UKocn3NNiUk5PbpUh+Y47DrUPQ0jrqRDInU9Aw6UkwzEfY06TgqfRqJPmDL6is3vc\n0S0aKoO0gjqDV8HIzWeBkH6VbiO6IVNQdB9CTdTWNIaQ8isGdcWP3ZqNRsnzjhutUpXcPjLFh2zV\ntHDqCSNw60nGyHGak7dieQ8A+hoU8mkb7pqWK1uZVEkdvK6H+JVyKxaOlOw3djpTg+aSW2uIU3yw\nSIucZZSBUQOKzehrF3J805WPQVGrZHzcUpbjA4FQ2aJdywsoH3TlqPMOc55qsTjmnK/brUlFncp+\n8BTvLQjJRQPXFQ5C9eT6UnmN3NK5XKiVo4DwIEx645NMNtbEf6sD6HFJ5vqKXeD0NFyXFdhpsbc9\nPNX/AHZWFJ9gj/hurtfpMakyR3pd5pczF7OPYjFlIPualdj6tmlFvej7uqTf8CUGpN49KXco6mk5\nMpUl/VxoTUR/zEQfrEKX/iZDgXsJ/wB6AU8OPWjeP71TzD9ku7+8Z/xMCfmltGHvFimeXqJ6iwYe\n8ZqcMP71BkFJyK9kim8N+2cW+mn6qawdc0y98g3MqWsUacMIe/4V1W6qOvMBodwMdQBThUakjKvh\nounJu+xwG1f+eq/lUsdo08myOSNzjPXFVD1xV7SnaK7DiJpMA5Vetd0rpXR89TtKaiyKS2eKTYzI\nG9M10lnpl3HCvm6VBcZAIYvjisC5l83UtzReX8w+Vj05r0NWJRCOm0Y/KubEVHFLzPVy7DQqznrt\n/XVGR9nCjB8PQZ/66Ck8jP8AzL8A+jitf600tjpzXE6z/q/+Z7Kwke/4L/IzfJA5/sGH/vtab5ZP\n3dCtx/wJa0iSetJU+1f9X/zLWGj3/Bf5Gf5TD/mC2/8A30tL5TDpott+LrV7O0cmmFi30qXV/rX/\nADLWGj3/AAX+RVCyE4GkWg/4EKmjilP/ADDLIfiKmUZPSp1XbWcqr7fn/maLDpdfy/yIkhbH/Hla\nA+wqZI5B/wAu9qP+A1IvSn5rB1Gy1TXQxmVkk1WMhAWCthRx1rJyRc9uV9K35EzrEiD/AJeICv4j\nn+lYEnyyI34V7lCXNTi/I+RxUOSvKPmy2rvhfm+6cr7fSpld5ZJGkcu7Ics3JNVlqaJtsyE9M4P4\n10QlyyT7HO1cu6K5ewmXPIU9qoXjFb5+fvANVnRmEGpT2rdCxH4GoNYjMcsTngDMZ+o//XX0cq11\nc4FT96xYs5fc1qRyj1Nc3BPtYc1pxXAx1rgxEr6mkaRuxyA9z+dT/u5F2yKHX0bmsaO5GetWkuQO\n9ePVOiFNE7aVGTut5DGf7p5FIgvLM5eEso/iQ5pyXY6A1Yjuec7q4pU0zohBLYW31xc7ScHoQ3FV\ndStVu4/MtcAg7vKz/Kr5e3m/1scbn1ZeaYbeyPRGU/7LVDgddNNHNKSCVZSCOoPaplYCtW5s7WXl\npJM/3jjNV49NEjYhMj++OB+Nc0qZ3U5EC+vStLTYC8zSMPlVaFsYoBuc+Yw/IVpW6eVbFm+8/JrL\nksdKkPsyBG0R7cj6USoDVQSlLlTng8VM8nWs3G5tEFmMPH8PcVKZkIyDVF39Dk1GjMGz27isXA3i\njRDgnnNWoduMtwoGSTVSBN+DXMeOfEosbY6VZP8AvpB+9ZT90eldGHwzqSSRzYrFKhBykYfjrxOd\nVu/sVq/+iwnGR/Ga4ug9aMV9LTpxpxUYnx1atKtNzluxKKKKsyCtjQdFOrXbGWQQWUC+ZcznpGn+\nJ7VSsNPuNUvobO0jMk0rBVH9T6CtzxBf29jZJ4e0xw1tE266nXrcS/8AxI7fU0AUde1gapcpHbx+\nRYWy+XbQD+FR3P8AtHqTWNRRQAUUUUAFLRVqwsZtQu0ghUlmP5D1ppNuyE2krsveH9HfVr5VIIhQ\n5kb+lek4jtoVhiUKijAAqDT7CHSbBLeIDIHzN/ePrRI+TXuYXDqnHXc8LF4n2srLYY7ZNMpTSV2H\nCFFLSYoEKDipo3wagxTgcUmgTE1jSodYsGRgBKoyjehry+4t5LadoZVKupwQa9ahkIPWsLxZoQvb\nc31un76MfOAPvCvNxmH5lzx3PVwOJ5XyS2PPKKUjBwaSvJPYCiiigCzY3txp15Fd2sjRzRNuVga6\nHX7G31OwXxFpiBY5GxeQKP8AUS+v+6ev41ytbGgay2j3pMkfnWcy+XcwHpIh/qOo9xQBj0VueItF\nj0y4jntJPP066XzLaYdx3U+4PFYdABRRRQAtd94F8QWe5dI1eBZIHOIpc4ZD6ZrgacrFWDLkEHII\n7U4y5XcipDni43se53Pgy3vJMafd7T1Mcwxx7Gsi+8IvZHy7m2niOMiQHcpqTwB4n/ta2NhduPtk\nS/IT/Gv+NdwlzcRkruLR9Cjcg/ga9OFSo0nF3Xn/AJngSrSoScK0b+aPHLu1e0nMbc/3WHermkWv\nm3QZx8qckV6Bq+m6TqS+XJaNDOT8ssB4B91qnF4OuLa3/wBAmjuxnLBTh/yNaSxEeW0tH/XU1o1K\nU5Llfy6kEXHNWVI71VMc9u3lzxPGw6hhinNKI42dmwAMk1k4pq53qY67vRaoNo3SOdqJ6mprKAQR\nksd0rnc7eprIs911cm9lBHaFT2HrWujEVHs9TRzsrIuq4ApS4AqsHqvJMZ5/s8ZwF5kYdvam4kRd\n2cRrVtBBrNxHCn7vO4ADhc9qohIxztBr01IYIw2IY8t1JUHP1qhe6Jp96jAwLFIekkfGD7itIuy1\nMpLmehwBCHsaMJ71PdWc1pdSW8ikuhxkdCPWofLbPKn8q0UbmTdhNqH1pwRO/wDKlCkdj+VLg+lW\nokOQuyP1/SjYn96jFKKpRM3IPLX1p6xj+9SAVIMKMnr2FaKJnKbHrEqjJalxn+Lioixbk0vNWome\nvckC89acAfWmBTUipVqKIb8xwX3qRUpoXFPFWomTY8KKeBUYp46VaiZskAFOA9aYKeoLe3rVJEMe\nqknA/OpcqgwvWoiwA2r09aZnBqiLXJuM0nHamZpQTVCsPxx1pcD1ptOAzQIAPenbRQAMUoApktih\nafimVDcXIh+VeXPb0olOMFeQKLk7InaRIxlm/DuarPO8nAO1fQdaqPIEUyzSbR6nv9Kw77xDgmO3\nXgdW/wAa8jE45Ly8up2UMJKo/dVzdmuorfJlcD2rPOr+czLaW0kzL12jOK5SWaS5bc7lvatjRtfk\n0i2eDyVkVm3ZB2nP1ry44tVKlpy5Y+lz0ngPZwulzS7bCXOvX6s0ePKIPIIwRVCXUbuY5aZvwp+p\nahJqd69zIqqWwAq9hVI8Vx1Z3k0pNo7adGCivdSY57iXaS0r/g1JbvNczLEhkMjHCjd1pAhlIQKW\nLHAA7muo0jTY7KLecNcP1b+6PQUUoc+hdarCjC9tTk7550la1kZvkOGBOea6Pw1oKjZf3oBHWOM9\n/c1pT6PZ3F6t5IpLjkr/AAsfU1daTJro5FT1ZzVMXz01CGl9y1JMXOTULPUDS1G5WTAZm/A4rir1\nXJ6sVGmkTs1NzUChXO/ZNEq9AW+9T93NefU3O+EbEwNLuqHdTsisGdMUSE4FMLUwtTS1Z2OiKHE0\nx29Kaz4FSWsBnk3N9xfvf4V1YXCzxFRQitxVasKNN1JvRFvT7bAE7j/dB/nV/O6oi/GB06AelJu7\nZr9BwuHhhqSpw/4c+GxeIniarnL5eSJM0hpgbPWgnvXTc5LDqKZu5o3UrjsOpGNIWzTSxzRcaQpJ\nppOeaQtxTc0ikhxphoLUm40ikhVPpSgn0FRhqdupDaHk8Vd0c/vJOCDVDNXNJb97JUT2LprU841H\nnVb7p/rW/nW1YqpsouB9z0rC1J8are8dZW/nWvYXCizjy3RcdK8GFuZn0s0+RFnYhaMbRypzxUXl\noUT5Ry2DS+em+P5hgA5qPzRiMAjG/NU7Eq51FjhPhfrCAYHm1v8Aihs/DmYf9O8dc5aPn4a6vyD+\n9rd8TSf8W8mH/TvHWlvdfp/mZ/aXr/keaaHzqMOGIynUV0rrJukHnP8AJXMaE5+3wf7tdM0g8yf6\nVzQWh0z3IHaYJG3nH5j3HSoi0+9181fl55WpHcfZ4RnndTN3+kS88bf6UWGiBpp/3Mm5DmRR0969\nJ+IDfvPCX/X0v8q8zLf6PB/11X+dejePnzL4V/6+l/lQvhfyB/EjoPiE+fBOtD/pmP515BoUpj02\nNtuQD616x4/fPgvWR/0zH868i0U50hfaioveCn8JtyXjqAGgcbjxz1qB7zDbTFID1xUtzj9wf9r1\n9qhmP+nL1+5UlIge8Qs2VfP+7VdrqEk/1Wp1/wCPycZPaq8XKyjP8TdqWoyN7m2O3BXj1WvSvhPJ\nE2m6iYiCPPGcDHavMUANkTgZwT0r0j4TsE0i9xgZmBOPpTpr3kKb90qeBG/4uH4n/H/0IVu/EFgf\nB0n/AF3T+YrnfArY+IHiU/X/ANCFbvj9s+EJB/03T+Yq0vcZDfvo85RVaZQccj1xUrwxnON3/fVV\nCAe3bNOMKfZfM+fO3Oc1mW2DwjGQ0nX+9ULw4J/eygD3pBEGgD+ZICVzwarojSRhvOcE+9MBxjYj\nPnSYzUTJIMfvm5PcVGgklUnz2HOKjBmZnAm+4ccigCUpLz++6f7PWoz5vXzB9NtR77gSmMSrkDPS\no2kuI5FGUy3egDvPAJYabf7mBPmjp+FY/j8/6TFWl4Cdxp1/5mM+aMY/Csnx42bqKumS/cmEX++N\nWlxRRXsmAUUUUAFFFLQIMUUUUAFFFFACGkpxptJjQlFLSVIxKSlpKllISkPFOpjHNSVsGaKMYOaK\nhloSkp1JUMtDDQR70rUme5HNSy0NzntWhp1vkGVxkdFBqlGhnlVAOSevoK20ARQq4AHFTYJS6Fe+\neOC2YhE3nhflFYYAKnIyRVu/uPPnOD8i8LVbaxXJNRJmkIgpAGAOadkKOopFAHag4HXH0rGR0RN6\n1treWzhZoUYlckletV9YtoYrWNo4lRi+MgVesj/oEHb5ara1/wAeMR/26ctjOL985fI3HsK0dIsm\nvpCvIjXlm/pVS2spL24MUY46s390V2Flbx2sKwxLhVH5n1qHG6HGbjKyE/suxPH2ZTWNqv2KJ/s9\nrCgcH53B6ewrQ1XUhbqbeE/viPmYfwj/ABrnsfWsakktEdlGEnq2VZoz5hcf3aZCjHDj7rcFTVpw\nOODjoaSNQqbcHisXLQ3VNOVxULkqg+Ysdtd9awC1tYoF6IuD9e9cv4ftBPqPnEZSEZ59a6kOsayS\nOcKgLE04LS5FZu6ijA8S3W+eK1U8INzD3NYgBLBQCzscBR1Jp9zcfaLiWYkkuxOfatXwzbJJfSzt\nyYl+XPqe9c79+R2x/d0/Qs2fhncA99KQSP8AVR9vqa049B05Vx9nLfVjV2R1iieRs7UUsa4y61q9\nupC5naGP+FE44py5ILYyp+1rPc6dvD2myDAiaM+quf61j6j4fns0aW2bzoRyePnA/rUGmeILqG5j\njuJDLAzBSG6r75rswdp9cfrU2jNFuVSi9Xc83z3BpfMIHrV7XbaOz1iVI+EcbwPTNZx6de4/nXO9\nHY7ovmSaOvj8K20kMb/a5gWQMeB3FZmuaPHpKW7RzNJ5pIO4AYxXY24/0WD/AK5r/KuR8fXMlpBY\nME3Lvb+laygraHHCrJS956GMCR3qW3jmurhIIV3yOcAVh2+sLcTpAkErSudqqozk16Podnb6XBul\nObuQfvGx93/ZFZqnJvY2ni6cVo1cgHhJ9ozfKGxz+7zg/nVHVNFTSrYSy36szHCRiPlv1rodT1/T\n9JszcXNwg7ImcFz6CvPrrWU1K7a5mu4mduihxhR6CipFRWwqFd1HrJW+RZWRfWn719appLGT8sqH\n6MKlByeCD+NczO5OJOHWl8xai59qAQO9TzF6FgSKKydeMl5Zm1gU9dxbt9K01x3FUtcZY9MeRDsd\nOQT0PtRTn76Jrq9GV9rHAyRtFIUbGRwcGrVjqMlgzFI4n3DB8xc1UZizFj1JzUtuIix82Tyxjg7c\n16zSa1PkYScZ3i7DneS8uWkCruY5woAH4Cu402/E1jEkilZY1CkHviuFXC3I8ts/NwxGK7u3gWGF\nFY73wCWPrXHjGrJHtZPzc8pX9S3uz34pMj1qPcAPSkL+leafQ8xLketNLehqLOe9KKlsakh/XrRt\n9KaKkQdzUMfMiSNMY7VKFqMNTxJis3crmRIBQT6VH5pPbijzo1I3sFz0yaXKylJFa+DRGC6TgxOM\n/Q9f0rJ1SER3Uyp93O9foeR/OulkgE0Dxt0YY/GsW6QzafHIV/eW58qT1Izwf6V6mBq80XDsfPZt\nR5aqqLaX5oz423KDUvUVXjGxih+oqcV3XPJsOlkMN1bXq9H/AHbn0I/+tWvqtuL2zLJ1kXcv++P8\neKy1jW4ie1c4WX7p9H7Grmi3XnQvYXB2SocDP8LDoa9CjW5qduxlOGtznEkOM9KspdbRjdUmr2zW\nl0Z1TEbthx/cfuKS1eN8bkU/UVMp6GiinqPW/wAdCKnS+9/1qykNuR/qU/Kp0trf/ngn5VxVGaqJ\nXS+9CKspesemT9BVqOOJekSD/gNWo8D7qgfQVyyRtGJWha7l/wBXC5z3PFXYrS6fG+RUHtyamRz3\nqUSgVnI6YxHxWMEeC+6Q/wC2ePyqWWcKu0YAHQCqklz71BueVgqnn+VYs6IInjzPNz9xeTVqWTI6\n1EgEcYRcYHU+tMdiazaOiFiKR/mGOxqw0n51TkPbNTL90Z9KxaOqNhcHrmpETcaaikmpZ7iDTrSS\n7uWCxRjP1PpTjBydhyqKKuynrutR6BprScG4kGIl9/WvIri4kubh55WLSOcknvV7XNYl1rUnuZCd\nmcIv90Vl17mGoKlHzPksfjHiKmmy2EoooroOAKcqlmCqCSTgAd6bXU6LbwaHp3/CQX6BpTldPgb/\nAJaP3cj+6v8APFAE85XwjoxtEx/bd6n75x1t4iOFHoxHX2NcfU11czXl1LczuXllYs7HuTUNABRR\nRQAUUUUASIjSOERSzMcADvXpXh7RV0ey3ygG5kGXPp7Vl+EtA2KNSuk+Y/6pT2966aaXNergsNb3\n5Hk47E3/AHcfmMlkyagJpWOTSV6iR5LYmKWiigQUUtFACUUuKKBDlbBq3DL2PSqVSI2DSauNOxyH\ni7QjaTm+tk/0eT7wH8JrlK9iZI7u3e3mUMjjBBrzLXtGl0i+MZBMTcxv6ivFxmH5Hzx2Z72CxPtI\n8kt0ZNFFFcJ3hRRRQB0vh3U7aS2k0LVWIsLk5jl728vQOPbpn6VkapplzpGozWV2u2WM4OOQR2IP\ncVRrsLB08WaSulztjV7RCbKQn/XIOsZPr6fjQBx9FPdGjco6lXU4ZSMEGmUAFFFFAFqxvZ9OvI7q\n2cpLG2VIr3Hw94ji13SFnjx9oHEqehrwX3rZ8O65NoOpJcISYjxInqK3oVeSXkcmLwyrw80e5KAG\nz1Y96tI2On51mWt3DfWkd1bMGikGQfSrCSGvSUL6nzFak4aGv9rZ4vLmWOaP+7Ku6s3UNE0zU0Ec\nRez5y4HzIfbFKs9PWcMaylRs7rQVLFVaeiehnTeHb63XzIVS4hHeI8gfSqTMYztcFT6MMV08E5U5\nVip9QatvIl0my6hinT1YYYfjSXOt9fwO2OZx2qK3ocPdXLQQFwCSSAAOtNjScfcKRZ5IAyc+9dTd\n+H9OuWQwzyWxU52uNy5+tUJ/D+owIXWITxj+OI7hVc8b66euh208RCa91p/12MR5LqDLNiRe+3g1\nNFeJMgIYZPWhiV+VgQfQ1nzQkzEQMFfrz0Fa8ljTnuZGpSfadSmkGODtGKojLMxNacukXUWSoEo6\nkr1/KqLIQflB963jBI5ZzbIzUiwsVLMCq4yD60xsr95SKDKxAUlivpV8pnzMQdOaUdRQMnsfypxI\nQf7VUokuQHC9sk0hjfJ3LyOvtSA4Oc808OzdTn61aRN7DAB6CnhRxwKUAetPGM9qpRIcgVBkYGal\nEeD0pF4Oak35GMCrsZNjQo9Kdt9qBwcU4U7ENgFFOAGKBxzT1GRk9KpIhsRVz14FKT2HSgncTjgU\nAVXoIQCnAc0oFOAppCbEApQOacAadinYhsQLmnAUY9aMgUE7jqSk3GkzxkmgLDbm4EMYI5dvuisi\n6u47WMySHLHkDPX60t5eD55m6DhRXNH7TrF75EK7mPOM4FfP43GSlU5IavoerhcKmry0S3Ir3UZ7\n6QlmIXoK3NL12wstOSB7X51+9tA+c+pzWPe6Td6dGsk6KEY4DK2RmqXSvNjWrYeo29JPueo6VKrT\nUV8PkSzuss8kixrGrNkIvRaj+gwKaWCjc34AUiiSZwiqWb+6P61yu8ndnQo2QrSKp4yxqPe8jALy\nScAAZNalto2//WuWP9xOg/Gtu1sIbUArGm4d8dK2p0XIwqYqnT21ZRstP+xRh5ObhxwP7g/xrcgQ\nRxqD1qmitJIWPrUzPgcmuqklC8mtOh5lacqj13J5JQT7VC0mTUDS+9QvNXFXrczudFGmTvL2oV/e\nqm7mpA2K86Um2ejCFi35hbG5icDAzS7qrBqeHrJnRBE++gvxUO+lBDcFio9QM1FjoihfOXzPLz8x\npS1GItnltOWT18v5h9DULMCcKSR2z1NNQvojaLSJUVppQi9TWxGixII16Dv61XtIPIiyw/eN19va\npq+3ynALD0+afxP8PI+UzTG+3n7OD91fi+47PNGTSZFJmvWPIsLmjNNzRmgLDs0ZphajdQFh+aQt\nTCaaW96BpDywpM03dim5zRYqw+kzTTzSGlYdhT1pynkZpF5zTgvIIosDFPABxVnTDtlfioFBdeTj\nFPhfyXznrWcti6a1PO9Sb/iZ3ZJ/5at/OtvTdraepLcgZA9ay7u3jkvLmQrljIx5+tT2iDYE3svp\ng14ULqTbR9FO0oJI0ZdgI+RT7YqMWyyS7BGgJ5quyOCQsp/Gmt5ykESKaHpuhLyZ1Vun2f4danHg\nAF84FbHiRs/D+Yf9MI652zkc/DvUQ5BbzPWtzX3DeB3XrmKMVtvF+n+ZltJev+R5zox3alEqtg7c\n59OK6J0lBbEoJbrlawoCtvPHIq42jsK201KE27r2k5ye1c0ErWbOqbd7pEckdwqqCUIXpUbm5DsX\nQEsMde1SyXUTrgOM1GXjZAQ5L+manQd2iuzSqsSGJuJFyfxr0Xx0+ZPC/wD18r/KuILWTGPkg7hn\n612HjZ8y+GR6XC1ajaL+RLfvI6Dx5Jnwdq4/2B/OvJNElA0rBB68dq9V8atnwrqg9VH868m0yQRv\nGjfcbFKqvfQUn7puXE6sYQHUgNzUU0oa8XBBGynPBHI4URruY4GeKheziyysu1l9DUtNK5SavYYj\n5vJScDkc+lQRybVmOc/O340slpEr43uvqc1Xe3CkhJ2x6+tTruXdXHRN/oLD/ZNeh/Cx8aTd8/8A\nLUfyrzQwyKhQTjb6EV6H8L90em3yuwP74Yx9KqmnzIip8LK/ghsePfEh+v8A6EK2/Hb58JSf9dk/\nmK5/wW+PHPiI+uf/AEKtjx3LjwlISePOTJ/EVpFe4/mRJ++vkcErDJJ/udalU/6H/wBs6qCVkbKg\n8rjpmka4VbfyyGBC46VknY0JoTm2Uf7B+lVrY5hjH1pEuYlhCkjIUjBqGKdUhVd4BHvTTWgtRbY4\nTGeshqFDiW46ff70ttIAmCeN+Tg81EjjzZyOhbjPWl0sMG4vXGB9yo5sCeD3pN5N05brtpszZmg5\nBx+lPTUezO08Fvtsb3t+9H9Kx/G7Zuo60vBjgWV5/wBdB/Ssrxoc3KGumS/cHPH+MdBRRRXrmIUU\ntFAgooooAKKKKACilooASkpTSUmCEpKWkqShKTFOpKllIaelNxmn0lSyrDfSg0vekqWWhKKDSGs2\nWhD0pv406n20PnTBT0HJqSy5p8HlxmQj5n6ewp9/P5EGFOHfgfSrIwBjHArIukuJ5yxifHRRjtQy\nFqytFGZplQEZJ4zV86XcE4DR/mahtbeVbmItG6gN6Vur94Vm1c25mtjlyhSR0PBU4OKXgVLcRSC6\nlPluQWOMCq5JPAH1rGSN4PQ6ex5sYD/sVBqcL3FtFFGMsZPyqayybCD/AHaS9uTZxxyHlS+GA9Kb\n2M03zaDra0js4RGgyf4m9TVfUdSFn+6jOZ3H/fI9avK4kRWRgyNyDWBq2nNHP9rjyyMfnHXaf8Km\nW2gQ1qalEsWJLEknkk96TNFJXGz1Yg3INNU8ZJp/XipdNtvtN7FAOcvyfQd6i19DS9tWdTolr9l0\n1Cww8vzt/Sq/iO7MGnmJD88x7egrYGOABgdK4nWbk3WtTjcfLjGxB2x3q5aROeDc6iZSjkDKDnHb\nGa0NA1lLPUzHKwSKYbNx7N2zWRFAyu4DEL6+hrRsNGfVWZI/ljQ5eQjp7VgrKWh1T5pU3fQ74jer\nwydHUg+1cXe6NfWszKIXljz8roM5FdZY2y2lukKySSBRgGQ5NPk1Kyt22y3cSN6FuaqpFS3MaFSc\nNFqcrpeh3lzeRtNG0MKHczOME+wFdx1J7D+QqrDe2tyf3NzHKfRWyah1Oyk1C1MMd08BPXHRvY1C\niorQ0nNzkubQ4/Xb1b7V5ZYzmNfkU+uO9UNxA69x/OpLq2lsblre4Xa6/kR6iocjHXuK5Jt31PUp\npJJLY9St2P2WD/rmv8q57xk2YrEAEkuwAAznpW/bsPssGP8Anmv8qhuTafbrL7Rjzct5Gem7/Gt5\nK6scNOXLPmKGgeHobFBd3MMbXjjj5R+7Hp9a0r5rGxtGuLhdqjoFJBY+gq0CM8muI8TSXx1Pbd4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MkkjA8XwIbC3uCP3iSbM+xrkCOPxH866bxTfpPJHZQsGWI7pGHTd6VzpXj8RXPVa5jtw8Gq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\n6fU9Rnvbly807l2Y9ya96+Avji3j0250DUruOHyT5lu0z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CjFFFIBMUYpaKAEopaSkAUl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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame = hdmi.frame()\n", "orig_img_path = '/home/xilinx/jupyter_notebooks/examples/data/orig.jpg'\n", "frame.save_as_jpeg(orig_img_path)\n", "Image(filename=orig_img_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 3. Gray Scale filter\n", "This cell should take ~50s to complete. Note that there are better ways (e.g., openCV, etc.) to do grayscale conversion, but this is just an example of doing that without using any additional library." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/jpeg": 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B+z9Cw/u1823bZuZ8D/lo386N0nUzSH/gZqMOvTFABIoKV7h4F1rTLTwdYxXW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\niSOU9XVsE1ZGt6yM41O55/2qpYNHN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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pynq.drivers.video import MAX_FRAME_WIDTH\n", "\n", "frame_i = frame.frame\n", "\n", "height = hdmi.frame_height()\n", "width = hdmi.frame_width()\n", "\n", "for y in range(0, height):\n", " for x in range(0, width):\n", " offset = 3 * (y * MAX_FRAME_WIDTH + x)\n", " gray = round((0.299*frame_i[offset+2]) + \n", " (0.587*frame_i[offset+0]) +\n", " (0.114*frame_i[offset+1]))\n", " frame_i[offset:offset+3] = gray,gray,gray\n", "\n", "gray_img_path = '/home/xilinx/jupyter_notebooks/examples/data/gray.jpg'\n", "frame.save_as_jpeg(gray_img_path)\n", "Image(filename=gray_img_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 4. Sobel filter\n", "This cell should take ~80s to complete. Note that there are better ways (e.g., openCV, etc.) to do sobel filter, but this is just an example of doing that without using any additional library.\n", "\n", "Compute the Sobel Filter output with sobel operator:\n", "\n", "$G_x=\n", "\\begin{bmatrix}\n", "-1 & 0 & +1 \\\\\n", "-2 & 0 & +2 \\\\\n", "-1 & 0 & +1\n", "\\end{bmatrix}\n", "$\n", "\n", "$G_y=\n", "\\begin{bmatrix}\n", "+1 & +2 & +1 \\\\\n", "0 & 0 & 0 \\\\\n", "-1 & -2 & -1\n", "\\end{bmatrix}\n", "$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/jpeg": 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AZByPY8Gq/wATfD+qfEHxtbab4e8jUJNOs44r+7EipHDIzv8Ae9+M7RkjJ44o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OZYbMI679rZYEZ2sRngnGSAOlAHDlY0PQgZ64xWzpOl+dLaz3cZhsd4BbdzjONx/HFd94i+Fs0O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BIp4dR8y/QjkVtWNrYR26TyWKynby1xuyx77QWUH8M1ct9WtkspjpsdlaTR43SC2zIBjlQX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\nAXrbR9KttRufI1OSeOK0nMn2WErgqjdN+O3oKv2viCLSfF2qvFo9v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3if4vrV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\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griq6ghjxU2TuQYOOM0AJJIWZcfdXpUan5wT03Vax5wIATd26VW2MjhXBHzYN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BivHtTtDpmr3tkW3mCd4tw77WI/pXpXwSuHT4iWKFfMWSOUBuDjKE59jx69zxXDa/ZiLXNUQs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\nHSJWRkOCp8uTBB9a6rS/Hlx4T+C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L1vUNQNhNJaPPJIJVG9SpYkZx04PeuPsbcJdRrMQo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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "height = 1080\n", "width = 1920\n", "sobel = Frame(1920, 1080)\n", "frame_i = frame.frame\n", "\n", "for y in range(1,height-1):\n", " for x in range(1,width-1):\n", " offset = 3 * (y * MAX_FRAME_WIDTH + x)\n", " upper_row_offset = offset - MAX_FRAME_WIDTH*3\n", " lower_row_offset = offset + MAX_FRAME_WIDTH*3 \n", " gx = abs(-frame_i[lower_row_offset-3] + frame_i[lower_row_offset+3] -\n", " 2*frame_i[offset-3] + 2*frame_i[offset+3] -\n", " frame_i[upper_row_offset-3] + frame_i[upper_row_offset+3])\n", " gy = abs(frame_i[lower_row_offset-3] + 2*frame_i[lower_row_offset] + \n", " frame_i[lower_row_offset+3] - frame_i[upper_row_offset-3] -\n", " 2*frame_i[upper_row_offset] - frame_i[upper_row_offset+3]) \n", " grad = min(gx + gy,255) \n", " sobel.frame[offset:offset+3] = grad,grad,grad\n", " \n", "sobel_img_path = '/home/xilinx/jupyter_notebooks/examples/data/sobel.jpg'\n", "sobel.save_as_jpeg(sobel_img_path)\n", "Image(filename=sobel_img_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 5: Free up space" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "hdmi.stop()\n", "\n", "del sobel\n", "del hdmi" ] } ], "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": 0 }
bsd-3-clause
ES-DOC/esdoc-jupyterhub
notebooks/inm/cmip6/models/inm-cm4-8/seaice.ipynb
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{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: INM \n", "**Source ID**: INM-CM4-8 \n", "**Topic**: Seaice \n", "**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \n", "**Properties**: 80 (63 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:04" ], "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', 'inm', 'inm-cm4-8', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Model](#1.-Key-Properties---&gt;-Model) \n", "[2. Key Properties --&gt; Variables](#2.-Key-Properties---&gt;-Variables) \n", "[3. Key Properties --&gt; Seawater Properties](#3.-Key-Properties---&gt;-Seawater-Properties) \n", "[4. Key Properties --&gt; Resolution](#4.-Key-Properties---&gt;-Resolution) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Key Properties --&gt; Key Parameter Values](#6.-Key-Properties---&gt;-Key-Parameter-Values) \n", "[7. Key Properties --&gt; Assumptions](#7.-Key-Properties---&gt;-Assumptions) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid --&gt; Discretisation --&gt; Horizontal](#9.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Seaice Categories](#11.-Grid---&gt;-Seaice-Categories) \n", "[12. Grid --&gt; Snow On Seaice](#12.-Grid---&gt;-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --&gt; Energy](#14.-Thermodynamics---&gt;-Energy) \n", "[15. Thermodynamics --&gt; Mass](#15.-Thermodynamics---&gt;-Mass) \n", "[16. Thermodynamics --&gt; Salt](#16.-Thermodynamics---&gt;-Salt) \n", "[17. Thermodynamics --&gt; Salt --&gt; Mass Transport](#17.-Thermodynamics---&gt;-Salt---&gt;-Mass-Transport) \n", "[18. Thermodynamics --&gt; Salt --&gt; Thermodynamics](#18.-Thermodynamics---&gt;-Salt---&gt;-Thermodynamics) \n", "[19. Thermodynamics --&gt; Ice Thickness Distribution](#19.-Thermodynamics---&gt;-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --&gt; Ice Floe Size Distribution](#20.-Thermodynamics---&gt;-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --&gt; Melt Ponds](#21.-Thermodynamics---&gt;-Melt-Ponds) \n", "[22. Thermodynamics --&gt; Snow Processes](#22.-Thermodynamics---&gt;-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Model \n", "*Name of seaice model used.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Variables \n", "*List of prognostic variable in the sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the sea ice component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Seawater Properties \n", "*Properties of seawater relevant to sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant seawater freezing point, specify this value.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Resolution \n", "*Resolution of the sea ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning applied to sea ice model component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any observed metrics used in tuning model/parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Which variables were changed during the tuning process?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Key Parameter Values \n", "*Values of key parameters*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *What values were specificed for the following parameters if used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P*) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Assumptions \n", "*Assumptions made in the sea ice model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General overview description of any *key* assumptions made in this model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the sea ice component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Provide a general description of conservation methodology.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in sea ice by the numerical schemes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does conservation involved flux correction?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Sea ice discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Grid on which sea ice is horizontal discretised?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the type of sea ice grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the advection scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model thermodynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model dynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional horizontal discretisation details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Sea ice vertical properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using multi-layers specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional vertical grid details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Seaice Categories \n", "*What method is used to represent sea ice categories ?*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Set to true if the sea ice model has multiple sea ice categories.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify each of the category limits.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the sea ice thickness distribution scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --&gt; Snow On Seaice \n", "*Snow on sea ice details*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow on ice represented in this model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels of snow on ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the snow fraction on sea ice is determined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional details related to snow on ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "*Sea Ice Dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of horizontal advection of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which method of sea ice strength formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which processes can redistribute sea ice (including thickness)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Rheology, what is the ice deformation formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --&gt; Energy \n", "*Processes related to energy in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the energy formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of thermal conductivity is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of heat diffusion?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method by which basal ocean heat flux is handled?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which the heat content of precipitation is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --&gt; Mass \n", "*Processes related to mass in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which new sea ice is formed in open water.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs the vertical growth and melt of sea ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice lateral melting?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs sea ice surface sublimation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of frazil ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --&gt; Salt \n", "*Processes related to salt in sea ice thermodynamics.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does sea ice salinity impact the thermal properties of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --&gt; Salt --&gt; Mass Transport \n", "*Mass transport of salt*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the mass transport of salt calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --&gt; Salt --&gt; Thermodynamics \n", "*Salt thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the thermodynamic calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --&gt; Ice Thickness Distribution \n", "*Ice thickness distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice thickness distribution represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --&gt; Ice Floe Size Distribution \n", "*Ice floe-size distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice floe-size represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Please provide further details on any parameterisation of floe-size.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --&gt; Melt Ponds \n", "*Characteristics of melt ponds.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are melt ponds included in the sea ice model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What method of melt pond formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What do melt ponds have an impact on?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --&gt; Snow Processes \n", "*Thermodynamic processes in snow on sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has a snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has snow ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow ice formation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the impact of ridging on snow cover?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "*Sea Ice Radiative Processes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used to handle surface albedo.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method by which solar radiation through sea ice is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
sailuh/perceive
Parsers/SecLists/Reply-Parse.ipynb
1
51865
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seclists reply parse\n", "\n", "__Example: http://seclists.org/fulldisclosure/2017/Jan/0__\n", "\n", "With each reply, we'll attempt to parse out the following:\n", "* raw reply text, without html tags\n", " * the reply text with any signatures stripped out\n", "* an analysis of what html tags are in the message\n", "* a listing of which domains are referenced in links in the message" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import re\n", "import requests\n", "\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll gather the contents of a single message. 2017_Jan_0 is one that includes a personal signature, as well as the standard Full Disclosure footer.\n", "\n", "2017_Jan_45 is a message that includes a PGP signature." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<!-- MHonArc v2.6.19 -->\n", "<!--X-Head-End-->\n", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\"\n", " \"http://www.w3.org/TR/REC-html40/loose.dtd\">\n", "<HTML>\n", "<HEAD>\n", "<link rel=\"alternate\" type=\"application/rss+xml\" title=\"RSS\" href=\"http://seclists.org/rss/fulldisclosure.rss\">\n", "<title>Full Disclosure: Re: /bin/rm file access vulnerability</title>\n", "<meta property=\"og:image\" content=\"http://seclists.org/images/fulldisclosure-img.png\" />\n", "<link rel=\"image_src\" href=\"http://seclists.org/images/fulldisclosure-img.png\" />\n", "<meta name=\"Subject\" content=\"Re: /bin/rm file access vulnerability\"/>\n", "<meta name=\"Author\" content=\"bkfsec\"/>\n", "<link REL=\"SHORTCUT ICON\" HREF=\"/shared/images/tiny-eyeicon.png\" TYPE=\"image/png\">\n", "<META NAME=\"ROBOTS\" CONTENT=\"NOARCHIVE\">\n", "<meta name=\"theme-color\" content=\"#2A0D45\">\n", "<link rel=\"stylesheet\" href=\"/shared/css/insecdb.css\" type=\"text/css\">\n", "<!--Google Analytics Code-->\n", "<script type=\"text/javascript\">\n", " (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\n", " (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\n", " m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\n", " })(window,document,'script','//www.google-analytics.com/analytics.js','ga');\n", "\n", " ga('create', 'UA-11009417-1', 'auto');\n", " ga('send', 'pageview');\n", "\n", "</script>\n", "<!--END Google Analytics Code-->\n", "\n", "<!--Google Custom Site Search boilerplate Javascript-->\n", "<script type=\"text/javascript\">\n", " (function() {\n", " var cx = 'partner-pub-0078565546631069:bx60rb-fytx';\n", " var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true;\n", " gcse.src = (document.location.protocol == 'https:' ? 'https:' : 'http:') +\n", " '//www.google.com/cse/cse.js?cx=' + cx;\n", " var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s);\n", " })();\n", "</script>\n", "<!--End Google Custom Site Search boilerplate Javascript-->\n", "\n", "</HEAD>\n", "<BODY BGCOLOR=\"#2A0D45\" TEXT=\"#000000\">\n", "\n", "<TABLE CELLPADDING=\"0\" WIDTH=\"100%\" CELLSPACING=\"0\">\n", "<TR><TD ALIGN=\"left\"><A HREF=\"/\"><IMG BORDER=0 ALT=\"Home page logo\"\n", "SRC=\"/images/sitelogo.png\" HEIGHT=90 WIDTH=168></A></TD>\n", "<TD VALIGN=\"bottom\" ALIGN=\"right\">\n", "<!-- Begin TopBanner Code -->\n", "<!-- AdSpeed.com Serving Code 7.9.6 for [Zone] TopBanner [Any Dimension] -->\n", "<script type=\"text/javascript\" src=\"//g.adspeed.net/ad.php?do=js&amp;zid=14678&amp;wd=-1&amp;ht=-1&amp;target=_blank\"></script>\n", "<!-- AdSpeed.com End -->\n", "<!-- End Banner Code -->\n", "\n", "</TD></TR></TABLE>\n", "<TABLE WIDTH=\"100%\" CELLPADDING=\"0\" CELLSPACING=\"0\"><TR>\n", "<TD 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href=\"http://insecure.org/advertising.html\">Sponsors:</a>\n", " <br><br>\n", " \n", "<!-- Begin Sidebar Banner Code -->\n", "<A HREF=\"http://www.acunetix.com/web-vulnerability-manager/?utm_source=insecure&utm_medium=box&utm_term=chess&utm_campaign=insecure \"><IMG SRC=\"/shared/images/Acunetix/acx_Chess-WB.gif\" BORDER=0 ALT=\"Acunetix\"></A>\n", "<!-- End Sidebar Banner Code -->\n", "\n", "<BR><BR>\n", "\n", "<!-- Begin Bottom (Google) Sidebar Banner Code -->\n", "<script async src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n", "<!-- SidebarSkyScraper -->\n", "<ins class=\"adsbygoogle\"\n", " style=\"display:inline-block;width:120px;height:600px\"\n", " data-ad-client=\"ca-pub-0078565546631069\"\n", " data-ad-slot=\"9829251079\"></ins>\n", "<script>\n", "(adsbygoogle = window.adsbygoogle || []).push({});\n", "</script>\n", "<!-- End Bottom (Google) Sidebar Banner Code -->\n", "\n", "</li>\n", "</ul>\n", "\n", "</TD>\n", "<TD BGCOLOR=\"#FFFFFF\" VALIGN=\"top\" ALIGN=\"left\"><IMG\n", "SRC=\"/shared/images/topleftcurve.gif\" alt=\"/\"><TABLE CELLPADDING=\"4\" WIDTH=\"100%\" style=\"table-layout: fixed;\"><TR><TD BGCOLOR=\"#FFFFFF\">\n", "\n", "<!--X-Body-Begin-->\n", "<!--X-User-Header-->\n", "<p>\n", "<A HREF=\"/fulldisclosure/\"><img src=\"/images/fulldisclosure-logo.png\" border=\"0\" width=\"80\" style=\"vertical-align: middle\" alt=\"fulldisclosure logo\"></A>\n", "<FONT SIZE=\"+1\"><a href=\"http://seclists.org/fulldisclosure/\">Full Disclosure</a>\n", "mailing list archives</FONT><br>\n", "<!--X-User-Header-End-->\n", "<!--X-TopPNI-->\n", "<!-- Google Custom SiteSearch -->\n", "<form action=\"http://insecure.org/search.html\" id=\"top-search-box\">\n", "<a href=\"\"><img src=\"/images/left-icon-16x16.png\" border=0 width=16 height=16></a>&nbsp;&nbsp;<a href=\"date.html#0\">By Date</a>&nbsp;&nbsp;<a href=\"1\"><img src=\"/images/right-icon-16x16.png\" border=0 width=16 height=16></a>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n", "<a href=\"\"><img src=\"/images/left-icon-16x16.png\" border=0 width=16 height=16></a>&nbsp;&nbsp;<a href=\"index.html#0\">By Thread</a>&nbsp;&nbsp;<a href=\"195\"><img src=\"/images/right-icon-16x16.png\" border=0 width=16 height=16></a>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n", " <input type=\"hidden\" name=\"cx\" value=\"partner-pub-0078565546631069:bx60rb-fytx\" />\n", " <input type=\"hidden\" name=\"cof\" value=\"FORID:9\" />\n", " <input type=\"hidden\" name=\"ie\" value=\"ISO-8859-1\" />\n", " <input type=\"text\" name=\"q\" size=\"24\" />\n", " <input type=\"submit\" name=\"sa\" value=\"Search\" />\n", "</form>\n", "<script type=\"text/javascript\" src=\"http://www.google.com/coop/cse/brand?form=top-search-box&amp;lang=en\"></script>\n", "<!-- End Google Custom SiteSearch -->\n", "</p>\n", "\n", "<!--X-TopPNI-End-->\n", "<!--X-MsgBody-->\n", "<!--X-Subject-Header-Begin-->\n", "<font size=\"+2\"><b>Re: /bin/rm file access vulnerability</b></font>\n", "<hr>\n", "<!--X-Subject-Header-End-->\n", "<!--X-Head-of-Message-->\n", "\n", "\n", "<em>From</em>: bkfsec &lt;bkfsec () sdf lonestar org&gt;<br>\n", "\n", "<em>Date</em>: Thu, 30 Dec 2004 16:17:29 -0500<br>\n", "\n", "<!--X-Head-of-Message-End-->\n", "<!--X-Head-Body-Sep-Begin-->\n", "<hr>\n", "<!--X-Head-Body-Sep-End-->\n", "<!--X-Body-of-Message-->\n", "<pre style=\"margin: 0em;\">\n", "Yeah, I think that someone mistook the new year for April 1st.\n", "\n", "</pre><tt>Seriously, we seem to be getting more crap like this. Are people just \n", "</tt><tt>bored? \n", "</tt><tt>\n", "</tt><pre style=\"margin: 0em;\">\n", " -Barry\n", "\n", "\n", "\n", "Jörg Eschke wrote:\n", "\n", "</pre><blockquote style=\"border-left: #5555EE solid 0.2em; margin: 0em; padding-left: 0.85em\"><pre style=\"margin: 0em;\">\n", "Sure, a user with admin rights is able to access/delete every local\n", "file, regardless of the specific filepermissions.\n", "Your &apos;exploit&apos; will work with e.g. /bin/cat as well.\n", "But i can&apos;t see a vulnerability anyway.\n", "\n", "Am i missunderstanding something ?\n", "\n", "Am Do, den 30.12.2004 schrieb Lennart Hansen um 2:18:\n", "</pre><tt> \n", "</tt><tt>\n", "</tt><blockquote style=\"border-left: #5555EE solid 0.2em; margin: 0em; padding-left: 0.85em\"><pre style=\"margin: 0em;\">\n", "/bin/rm file access vulnerability\n", "\n", "Affected Products:\n", " /bin/rm (all versions, tested on FreeBSD and linux)\n", " (<a rel=\"nofollow\" href=\"http://www.freebsd.org\">http://www.freebsd.org</a> <a rel=\"nofollow\" href=\"http://www.kernel.org\">http://www.kernel.org</a>)\n", "\n", "Author:\n", " Xenzeo (Ablazed, Ultralaser, Lennart A. Hansen)\n", " xenzeo at blackhat dot dk\n", "\n", "\n", "/bin/rm is a program that removes the named file arguments on unix systems.\n", "When /bin/rm is called it checks the file&apos;s permissions and the id of the user\n", "trying to remove the file. If the user does not have the required permissions\n", "to delete the file, /bin/rm will simply reject and exit.\n", "\n", "</pre><tt>However, it is possible for a person with admin rights (root) to \n", "</tt><tt>delete _any_ file\n", "</tt><pre style=\"margin: 0em;\">\n", "on the system regardless of who has created it and what it&apos;s permissions are.\n", "\n", "Proof of concepts:\n", "$ touch /home/xenzeo/file\n", "$ ls -l /home/xenzeo/file\n", "-rw-r--r-- 1 xenzeo none 0 Dec 30 2004 /home/xenzeo/file\n", "$ id\n", "uid=1000(xenzeo) gid=513(none) groups=513(none),545(users)\n", "$ su -c &apos;rm -f /home/xenzeo/file&apos;\n", "$ ls -l /home/xenzeo/file\n", "ls: file: No such file or directory\n", "\n", "#!/usr/bin/perl\n", "if ($#ARGV != 0) {\n", " die &quot;usage: rm-exploit.pl file\\r\\n&quot;;\n", "} else {\n", " $file = $ARGV[0];\n", " print &quot;*** CMD: [ /bin/rm -f $file ]\\r\\n&quot;;\n", " print &quot;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n&quot;;\n", " if ($&gt; == 0) {\n", " print &quot;[-] EXECUTING CMD\\r\\n&quot;;\n", " system(&quot;/bin/rm -f $file&quot;);\n", " print &quot;[-] DONE\\r\\n&quot;;\n", " print &quot;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n&quot;;\n", " exit();\n", " } else {\n", " print &quot;[-] EXPLOIT FAILED\\r\\n&quot;;\n", " print &quot;[-] YOU ARE NOT ROOT\\r\\n&quot;;\n", " print &quot;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n&quot;;\n", " }\n", "}\n", "\n", "Vender status:\n", " Neither FreeBSD nor Linux developers have been contacted yet!\n", "\n", "-Xenzeo\n", "</pre><tt> \n", "</tt><tt>\n", "</tt></blockquote><pre style=\"margin: 0em;\">\n", "\n", "_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: <a rel=\"nofollow\" href=\"http://lists.netsys.com/full-disclosure-charter.html\">http://lists.netsys.com/full-disclosure-charter.html</a>\n", "\n", "\n", "</pre><tt> \n", "</tt><tt>\n", "</tt></blockquote><pre style=\"margin: 0em;\">\n", "\n", "_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: <a rel=\"nofollow\" href=\"http://lists.netsys.com/full-disclosure-charter.html\">http://lists.netsys.com/full-disclosure-charter.html</a>\n", "\n", "</pre>\n", "<!--X-Body-of-Message-End-->\n", "<!--X-MsgBody-End-->\n", "<!--X-Follow-Ups-->\n", "<hr>\n", "<!--X-Follow-Ups-End-->\n", "<!--X-References-->\n", "<!--X-References-End-->\n", "<!--X-BotPNI-->\n", "<p>\n", "<a href=\"\"><img src=\"/images/left-icon-16x16.png\" border=0 width=16 height=16></a>&nbsp;&nbsp;<a href=\"date.html#0\">By Date</a>&nbsp;&nbsp;<a href=\"1\"><img src=\"/images/right-icon-16x16.png\" border=0 width=16 height=16></a>\n", "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n", "<a href=\"\"><img src=\"/images/left-icon-16x16.png\" border=0 width=16 height=16></a>&nbsp;&nbsp;<a href=\"index.html#0\">By Thread</a>&nbsp;&nbsp;<a href=\"195\"><img src=\"/images/right-icon-16x16.png\" border=0 width=16 height=16></a>\n", "</p>\n", "<font size=\"+1\"><b>Current thread:</b></font>\n", "<ul style=\"margin-top: 0em\">\n", "<li><strong>Re: /bin/rm file access vulnerability</strong> <em>bkfsec (Dec 31)</em>\n", "<ul>\n", "<li><a name=\"195\" href=\"195\">Re: /bin/rm file access vulnerability</a> <em>J.A. Terranson (Jan 06)</em>\n", "<ul>\n", "<li><a name=\"150\" href=\"150\">Re: /bin/rm file access vulnerability</a> <em>bkfsec (Jan 06)</em>\n", "</li>\n", "</ul>\n", "</li>\n", "</ul>\n", "<ul>\n", "<li>&lt;Possible follow-ups&gt;</li>\n", "<li><a name=\"5\" href=\"5\">Re: /bin/rm file access vulnerability</a> <em>Sean Harlow (Dec 31)</em>\n", "<ul>\n", "<li><a name=\"206\" href=\"206\">Re: /bin/rm file access vulnerability</a> <em>vh (Jan 06)</em>\n", "<ul>\n", "<li><a name=\"2\" href=\"2\">Re: /bin/rm file access vulnerability</a> <em>Jeffrey Denton (Dec 31)</em>\n", "<li><a name=\"35\" href=\"35\">Re: /bin/rm file access vulnerability</a> <em>Frank Knobbe (Jan 02)</em>\n", "</li>\n", "</li>\n", "</ul>\n", "</li>\n", "</ul>\n", "</li>\n", "<li><a name=\"50\" href=\"50\">Re: /bin/rm file access vulnerability</a> <em>Jerry (Jan 03)</em>\n", "<ul>\n", "<li><a name=\"14\" href=\"14\">Re: /bin/rm file access vulnerability</a> <em>James Longstreet (Jan 01)</em>\n", "</li>\n", "<li><a name=\"83\" href=\"83\">Re: /bin/rm file access vulnerability</a> <em>Valdis . Kletnieks (Jan 04)</em>\n", "</li>\n", "</ul>\n", "</li>\n", "<li><a name=\"54\" href=\"54\">Re: /bin/rm file access vulnerability</a> <em>Alex V. Lukyanenko (Jan 03)</em>\n", "</li>\n", " </ul>\n", "</li>\n", "</ul>\n", "\n", "\n", "<!--X-BotPNI-End-->\n", "<!--X-User-Footer-->\n", "<!--X-User-Footer-End-->\n", "</TD></TR>\n", "</TABLE>\n", "</TD></TR>\n", "<TR><TD></TD><TD ALIGN=\"center\">\n", "<FONT COLOR=\"#FFFFFF\">\n", "[ <A HREF=\"//nmap.org\"><FONT COLOR=\"#FFFFFF\">Nmap</FONT></A> |\n", " <A HREF=\"http://sectools.org\"><FONT COLOR=\"#FFFFFF\">Sec Tools</FONT></A> |\n", " <A HREF=\"http://seclists.org/\"><FONT COLOR=\"#FFFFFF\">Mailing Lists</FONT></A> |\n", " <A HREF=\"http://insecure.org/\"><FONT COLOR=\"#FFFFFF\">Site News</FONT></A> |\n", " <A HREF=\"http://insecure.org/fyodor/\"><FONT COLOR=\"#FFFFFF\">About/Contact</FONT></A> |\n", " <A HREF=\"http://insecure.org/advertising.html\"><FONT COLOR=\"#FFFFFF\">Advertising</FONT></A> |\n", " <A HREF=\"http://insecure.org/privacy.html\"><FONT COLOR=\"#FFFFFF\">Privacy</FONT></A> ]<BR>\n", "</FONT>\n", "\n", "<!-- SiteSearch Google -->\n", "<div class=\"gcse-searchbox-only\" data-resultsUrl=\"https://nmap.org/search.html\"></div>\n", "<!-- End SiteSearch Google -->\n", "\n", "<!-- Bottom Banner -->\n", "<!-- Adsense -->\n", "<script async src=\"//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n", "<!-- PageBottom728x90 -->\n", "<ins class=\"adsbygoogle\"\n", " style=\"display:inline-block;width:728px;height:90px\"\n", " data-ad-client=\"ca-pub-0078565546631069\"\n", " data-ad-slot=\"2743510915\"></ins>\n", "<script>\n", "(adsbygoogle = window.adsbygoogle || []).push({});\n", "</script>\n", "<!-- End Bottom Banner -->\n", "</TD></TR>\n", "</TABLE>\n", "</BODY>\n", "</HTML>\n", "\n", "\n" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year = '2005'\n", "month = 'Jan'\n", "id = '0'\n", "url = 'http://seclists.org/fulldisclosure/' + year + '/' + month + '/' + id\n", "\n", "r = requests.get(url)\n", "content = r.text\n", "from IPython.display import Pretty\n", "Pretty(content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each message in the FD list is wrapped in seclists.org code, including navigation, ads, and trackers, all irrelevant to us. The body of the reply is contained between two comments, `<!--X-Body-of-Message-->` and `<!--X-Body-of-Message-End-->`.\n", "\n", "BeautifulSoup isn't great at handling comments, so we first use simple indexing to extract the relevant chars. We'll then send it through BeautifulSoup so we can use its __.text__ property to strip out the html tags. BS4 automatically adds tags to create valid html, so remember to parse using the generated `<body>` tags.\n", "\n", "What we end up with is a plaintext version of the message's body. " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Yeah, I think that someone mistook the new year for April 1st.\n", "\n", "Seriously, we seem to be getting more crap like this. Are people just \n", "bored? \n", "\n", " -Barry\n", "\n", "\n", "\n", "Jörg Eschke wrote:\n", "\n", "Sure, a user with admin rights is able to access/delete every local\n", "file, regardless of the specific filepermissions.\n", "Your 'exploit' will work with e.g. /bin/cat as well.\n", "But i can't see a vulnerability anyway.\n", "\n", "Am i missunderstanding something ?\n", "\n", "Am Do, den 30.12.2004 schrieb Lennart Hansen um 2:18:\n", " \n", "\n", "/bin/rm file access vulnerability\n", "\n", "Affected Products:\n", " /bin/rm (all versions, tested on FreeBSD and linux)\n", " (http://www.freebsd.org http://www.kernel.org)\n", "\n", "Author:\n", " Xenzeo (Ablazed, Ultralaser, Lennart A. Hansen)\n", " xenzeo at blackhat dot dk\n", "\n", "\n", "/bin/rm is a program that removes the named file arguments on unix systems.\n", "When /bin/rm is called it checks the file's permissions and the id of the user\n", "trying to remove the file. If the user does not have the required permissions\n", "to delete the file, /bin/rm will simply reject and exit.\n", "\n", "However, it is possible for a person with admin rights (root) to \n", "delete _any_ file\n", "on the system regardless of who has created it and what it's permissions are.\n", "\n", "Proof of concepts:\n", "$ touch /home/xenzeo/file\n", "$ ls -l /home/xenzeo/file\n", "-rw-r--r-- 1 xenzeo none 0 Dec 30 2004 /home/xenzeo/file\n", "$ id\n", "uid=1000(xenzeo) gid=513(none) groups=513(none),545(users)\n", "$ su -c 'rm -f /home/xenzeo/file'\n", "$ ls -l /home/xenzeo/file\n", "ls: file: No such file or directory\n", "\n", "#!/usr/bin/perl\n", "if ($#ARGV != 0) {\n", " die \"usage: rm-exploit.pl file\\r\\n\";\n", "} else {\n", " $file = $ARGV[0];\n", " print \"*** CMD: [ /bin/rm -f $file ]\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " if ($> == 0) {\n", " print \"[-] EXECUTING CMD\\r\\n\";\n", " system(\"/bin/rm -f $file\");\n", " print \"[-] DONE\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " exit();\n", " } else {\n", " print \"[-] EXPLOIT FAILED\\r\\n\";\n", " print \"[-] YOU ARE NOT ROOT\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " }\n", "}\n", "\n", "Vender status:\n", " Neither FreeBSD nor Linux developers have been contacted yet!\n", "\n", "-Xenzeo\n", " \n", "\n", "\n", "_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: http://lists.netsys.com/full-disclosure-charter.html\n", "\n", "\n", " \n", "\n", "\n", "_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: http://lists.netsys.com/full-disclosure-charter.html\n", "\n", "\n" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "start = content.index('<!--X-Body-of-Message-->') + 24\n", "end = content.index('<!--X-Body-of-Message-End-->')\n", "body = content[start:end]\n", "\n", "soup = BeautifulSoup(body, 'html5lib')\n", "bodyhtml = soup.find('body')\n", "raw = bodyhtml.text\n", "Pretty(raw)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Signature extraction\n", "\n", "Messages to the FD list usually end with a common footer:\n", "\n", "2002-2005:\n", "\n", "`_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: http://lists.netsys.com/full-disclosure-charter.html`\n", "\n", "2005-2014:\n", "\n", "`_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: http://lists.grok.org.uk/full-disclosure-charter.html\n", "Hosted and sponsored by Secunia - http://secunia.com/`\n", "\n", "2014-onward:\n", "\n", "`_______________________________________________\n", "Sent through the Full Disclosure mailing list\n", "http://nmap.org/mailman/listinfo/fulldisclosure\n", "Web Archives & RSS: http://seclists.org/fulldisclosure/`\n", "\n", "We'll look for the first line (47 underscores), then test the lines below to make sure it's a match. If so, we'll strip out that footer from our content." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Yeah, I think that someone mistook the new year for April 1st.\n", "\n", "Seriously, we seem to be getting more crap like this. Are people just \n", "bored? \n", "\n", " -Barry\n", "\n", "\n", "\n", "Jörg Eschke wrote:\n", "\n", "Sure, a user with admin rights is able to access/delete every local\n", "file, regardless of the specific filepermissions.\n", "Your 'exploit' will work with e.g. /bin/cat as well.\n", "But i can't see a vulnerability anyway.\n", "\n", "Am i missunderstanding something ?\n", "\n", "Am Do, den 30.12.2004 schrieb Lennart Hansen um 2:18:\n", " \n", "\n", "/bin/rm file access vulnerability\n", "\n", "Affected Products:\n", " /bin/rm (all versions, tested on FreeBSD and linux)\n", " (http://www.freebsd.org http://www.kernel.org)\n", "\n", "Author:\n", " Xenzeo (Ablazed, Ultralaser, Lennart A. Hansen)\n", " xenzeo at blackhat dot dk\n", "\n", "\n", "/bin/rm is a program that removes the named file arguments on unix systems.\n", "When /bin/rm is called it checks the file's permissions and the id of the user\n", "trying to remove the file. If the user does not have the required permissions\n", "to delete the file, /bin/rm will simply reject and exit.\n", "\n", "However, it is possible for a person with admin rights (root) to \n", "delete _any_ file\n", "on the system regardless of who has created it and what it's permissions are.\n", "\n", "Proof of concepts:\n", "$ touch /home/xenzeo/file\n", "$ ls -l /home/xenzeo/file\n", "-rw-r--r-- 1 xenzeo none 0 Dec 30 2004 /home/xenzeo/file\n", "$ id\n", "uid=1000(xenzeo) gid=513(none) groups=513(none),545(users)\n", "$ su -c 'rm -f /home/xenzeo/file'\n", "$ ls -l /home/xenzeo/file\n", "ls: file: No such file or directory\n", "\n", "#!/usr/bin/perl\n", "if ($#ARGV != 0) {\n", " die \"usage: rm-exploit.pl file\\r\\n\";\n", "} else {\n", " $file = $ARGV[0];\n", " print \"*** CMD: [ /bin/rm -f $file ]\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " if ($> == 0) {\n", " print \"[-] EXECUTING CMD\\r\\n\";\n", " system(\"/bin/rm -f $file\");\n", " print \"[-] DONE\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " exit();\n", " } else {\n", " print \"[-] EXPLOIT FAILED\\r\\n\";\n", " print \"[-] YOU ARE NOT ROOT\\r\\n\";\n", " print \"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\r\\n\";\n", " }\n", "}\n", "\n", "Vender status:\n", " Neither FreeBSD nor Linux developers have been contacted yet!\n", "\n", "-Xenzeo\n", " \n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "workcopy = raw\n", "footers = [m.start() for m in re.finditer('_{47}', workcopy)]\n", "for f in reversed(footers):\n", " possible = workcopy[f:f+190] \n", " lines = possible.splitlines()\n", " if(len(lines) == 4\n", " and lines[1][0:15] == 'Full-Disclosure'\n", " and lines[2][0:8] == 'Charter:'\n", " and lines[3][0:20] == 'Hosted and sponsored'):\n", " workcopy = workcopy[:f] + workcopy[f+213:]\n", " continue\n", " \n", " if(len(lines) == 4\n", " and lines[1][0:16] == 'Sent through the'\n", " and lines[2][0:17] == 'https://nmap.org/'\n", " and lines[3][0:14] == 'Web Archives &'):\n", " workcopy = workcopy[:f] + workcopy[f+211:]\n", " continue\n", " \n", " \n", " possible = workcopy[f:f+146]\n", " lines = possible.splitlines()\n", " if(len(lines) == 3\n", " and lines[1][0:15] == 'Full-Disclosure'\n", " and lines[2][0:8] == 'Charter:'):\n", " workcopy = workcopy[:f] + workcopy[f+146:]\n", " continue\n", " \n", "print(workcopy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PGP messages\n", "As can be expected, many messages offer a PGP signature validation. This isn't useful to our processing, so we'll take it out. First, we define `get_raw_message` with code we've used previously. We then create `strip_pgp`, looking for the PGP signature. We can just use simple text searches again, with an exception of using RE for the Hash, which can change.\n", "\n", "http://seclists.org/fulldisclosure/2017/Oct/11 is a message that includes a PGP signature, so we'll use that to test." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "\n", "______________________________________________________________________________\n", "\n", " SUSE Security Announcement\n", "\n", " Package: realplayer 8\n", " Announcement-ID: SUSE-SA:2005:004\n", " Date: Monday, Jan 24th 2005 16:00 MET\n", " Affected products: 8.1, 8.2, 9.0, 9.1\n", " SUSE Linux Desktop 1.0\n", " Vulnerability Type: remote code execution\n", " Severity (1-10): 8\n", " SUSE default package: yes\n", " Cross References: none\n", "\n", " Content of this advisory:\n", " 1) security vulnerability discussed:\n", " - integer overflow\n", " problem description\n", " 2) solution/workaround\n", " 3) standard appendix (further information)\n", "\n", "______________________________________________________________________________\n", "\n", "1) problem description, brief discussion\n", "\n", "\n", " RealPlayer is a combined audio and video player for RealMedia formatted\n", " streaming data. These formats are very common throughout the Internet.\n", "\n", " eEye Security in October 2004 discovered a flaw in the .rm RealMovie\n", " stream handling routines which allows a remote attacker to exploit an\n", " integer overflow vulnerability using a special .rm file. This might\n", " allow a remote attacker to execute code as the user running RealPlayer.\n", "\n", " Reference URLs for this problems are the Real security advisory:\n", " http://service.real.com/help/faq/security/040928_player/EN/\n", "\n", " and the eEye security advisory:\n", " http://www.eeye.com/html/research/advisories/AD20041001.html\n", "\n", "\n", " SUSE Linux includes RealPlayer as both standalone player and as a\n", " plugin for web browsers like Mozilla and Konqueror.\n", " This might allow the attacker to just provide a web page or E-Mail\n", " linking to the special exploit .rm file.\n", "\n", " We cannot fully evaluate the impact of this problem due to lack of\n", " information and lack of source code to review.\n", "\n", "\n", " SUSE Linux versions up to 9.1 and the SUSE Linux Desktop 1.0\n", " include RealPlayer version 8 and are affected by this problem.\n", "\n", " SUSE Linux 9.2 and the Novell Linux Desktop 9 include RealPlayer\n", " version 10 and are NOT affected by this problem.\n", "\n", "\n", " Real does not offer a fixed version 8 RealPlayer, but suggests\n", " upgrading RealPlayer to version 10.\n", "\n", " However, upgrading Realplayer is not possible for older SUSE Linux\n", " products since Realplayer 10 requires newer dynamic library\n", " versions than the ones to be found in those products. Also some old\n", " Real content is not compatible with the RealPlayer version 10.\n", "\n", " For these reasons we cannot offer fixed packages for older SUSE Linux\n", " based products.\n", "\n", "2) solution/workaround\n", "\n", " We suggest one of the following workarounds:\n", "\n", " a) De-install RealPlayer\n", "\n", " Either use YaST to deinstall RealPlayer, or as root do:\n", "\n", " # rpm -e RealPlayer\n", "\n", " You will lose the ability to view Real content.\n", "\n", "\n", "\n", " b) Remove the RealPlayer plug in\n", "\n", " As root, execute the following commands:\n", "\n", " # rm /usr/lib/browser-plugins/raclass.zip\n", " # rm /usr/lib/browser-plugins/rpnp.so\n", "\n", "\n", " Content can still be viewed by starting \"realplay\" and opening\n", " URLs, but automatic exploits via web pages or E-Mails are no longer\n", " possible.\n", "\n", "______________________________________________________________________________\n", "\n", "3) standard appendix: authenticity verification, additional information\n", "\n", " - Package authenticity verification:\n", "\n", " SUSE update packages are available on many mirror ftp servers all over\n", " the world. While this service is being considered valuable and important\n", " to the free and open source software community, many users wish to be\n", " sure about the origin of the package and its content before installing\n", " the package. There are two verification methods that can be used\n", " independently from each other to prove the authenticity of a downloaded\n", " file or rpm package:\n", " 1) md5sums as provided in the (cryptographically signed) announcement.\n", " 2) using the internal gpg signatures of the rpm package.\n", "\n", " 1) execute the command\n", " md5sum <name-of-the-file.rpm>\n", " after you downloaded the file from a SUSE ftp server or its mirrors.\n", " Then, compare the resulting md5sum with the one that is listed in the\n", " announcement. Since the announcement containing the checksums is\n", " cryptographically signed (usually using the key security () suse de),\n", " the checksums show proof of the authenticity of the package.\n", " We recommend against subscribing to security lists that cause the\n", " e-mail message containing the announcement to be modified\n", " so that the signature does not match after transport through the mailing\n", " list software.\n", " Downsides: You must be able to verify the authenticity of the\n", " announcement in the first place. If RPM packages are being rebuilt\n", " and a new version of a package is published on the ftp server, all\n", " md5 sums for the files are useless.\n", "\n", " 2) rpm package signatures provide an easy way to verify the authenticity\n", " of an rpm package. Use the command\n", " rpm -v --checksig <file.rpm>\n", " to verify the signature of the package, where <file.rpm> is the\n", " file name of the rpm package that you have downloaded. Of course,\n", " package authenticity verification can only target an uninstalled rpm\n", " package file.\n", " Prerequisites:\n", " a) gpg is installed\n", " b) The package is signed using a certain key. The public part of this\n", " key must be installed by the gpg program in the directory\n", " ~/.gnupg/ under the user's home directory who performs the\n", " signature verification (usually root). You can import the key\n", " that is used by SUSE in rpm packages for SUSE Linux by saving\n", " this announcement to a file (\"announcement.txt\") and\n", " running the command (do \"su -\" to be root):\n", " gpg --batch; gpg < announcement.txt | gpg --import\n", " SUSE Linux distributions version 7.1 and thereafter install the\n", " key \"build () suse de\" upon installation or upgrade, provided that\n", " the package gpg is installed. The file containing the public key\n", " is placed at the top-level directory of the first CD (pubring.gpg)\n", " and at ftp://ftp.suse.com/pub/suse/pubring.gpg-build.suse.de .\n", "\n", "\n", " - SUSE runs two security mailing lists to which any interested party may\n", " subscribe:\n", "\n", " suse-security () suse com\n", " - general/linux/SUSE security discussion.\n", " All SUSE security announcements are sent to this list.\n", " To subscribe, send an email to\n", " <suse-security-subscribe () suse com>.\n", "\n", " suse-security-announce () suse com\n", " - SUSE's announce-only mailing list.\n", " Only SUSE's security announcements are sent to this list.\n", " To subscribe, send an email to\n", " <suse-security-announce-subscribe () suse com>.\n", "\n", " For general information or the frequently asked questions (FAQ)\n", " send mail to:\n", " <suse-security-info () suse com> or\n", " <suse-security-faq () suse com> respectively.\n", "\n", " =====================================================================\n", " SUSE's security contact is <security () suse com> or <security () suse de>.\n", " The <security () suse de> public key is listed below.\n", " =====================================================================\n", "______________________________________________________________________________\n", "\n", " The information in this advisory may be distributed or reproduced,\n", " provided that the advisory is not modified in any way. In particular,\n", " it is desired that the clear-text signature shows proof of the\n", " authenticity of the text.\n", " SUSE Linux AG makes no warranties of any kind whatsoever with respect\n", " to the information contained in this security advisory.\n", "\n", "Type Bits/KeyID Date User ID\n", "pub 2048R/3D25D3D9 1999-03-06 SuSE Security Team <security () suse de>\n", "pub 1024D/9C800ACA 2000-10-19 SuSE Package Signing Key <build () suse de>\n", "\n", "- \n", "_______________________________________________\n", "Full-Disclosure - We believe in it.\n", "Charter: http://lists.netsys.com/full-disclosure-charter.html\n", "\n", "\n" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_raw_message(url):\n", " r = requests.get(url)\n", " content = r.text\n", " start = content.index('<!--X-Body-of-Message-->') + 24\n", " end = content.index('<!--X-Body-of-Message-End-->')\n", " body = content[start:end]\n", "\n", " soup = BeautifulSoup(body, 'html5lib')\n", " bodyhtml = soup.find('body')\n", " return bodyhtml.text\n", "\n", "#rawmsg = get_raw_message('http://seclists.org/fulldisclosure/2017/Oct/11')\n", "rawmsg = get_raw_message('http://seclists.org/fulldisclosure/2005/Jan/719')\n", "\n", "def strip_pgp(raw):\n", "\n", " try:\n", " pgp_sig_start = raw.index('-----BEGIN PGP SIGNATURE-----')\n", " pgp_sig_end = raw.index('-----END PGP SIGNATURE-----') + 27\n", " \n", " cleaned = raw[:pgp_sig_start] + raw[pgp_sig_end:]\n", " \n", " # if we find a public key block, then strip that out\n", " try: \n", " pgp_pk_start = raw.index('-----BEGIN PGP PUBLIC KEY BLOCK-----')\n", " pgp_pk_end = raw.index('-----END PGP PUBLIC KEY BLOCK-----') + 35\n", " cleaned = cleaned[:pgp_pk_start] + cleaned[pgp_pk_end:]\n", " except ValueError as ve:\n", " pass\n", "\n", " # finally, try to remove the signed message header\n", " pgp_msg = raw.index('-----BEGIN PGP SIGNED MESSAGE-----')\n", " pgp_hash = re.search('Hash:(.)+\\n', raw)\n", " \n", " if pgp_hash is not None:\n", " first_hash = pgp_hash.span(0)\n", " if first_hash[0] == pgp_msg + 35:\n", " #if we found a hash designation immediately after the header, strip that too\n", " cleaned = cleaned[:pgp_msg] + cleaned[first_hash[1]:]\n", " else:\n", " #just strip the header\n", " cleaned = cleaned[:pgp_msg] + cleaned[pgp_msg + 34:]\n", " else:\n", " cleaned = cleaned[:pgp_msg] + cleaned[pgp_msg + 34:]\n", " \n", " \n", " return cleaned\n", " except ValueError as ve:\n", " return raw\n", "\n", "unpgp = strip_pgp(rawmsg)\n", "Pretty(unpgp)\n", "#Pretty(strip_pgp(raw))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Talon processing\n", "\n", "Next, we'll attempt to use __talon__ to strip out the signature from the message. Talon provides two different ways to find the signature, \"brute force\" and \"machine learning\". \n", "\n", "We'll try the brute force method first. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "import talon\n", "from talon.signature.bruteforce import extract_signature\n", "\n", "reply, signature = extract_signature(raw)\n", "if(not signature is None):\n", " Pretty(signature)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Zend Framework < 2.4.11 Remote Code Execution (CVE-2016-10034)\n", "zend-mail < 2.7.2\n", "\n", "Discovered by Dawid Golunski (@dawid_golunski)\n", "https://legalhackers.com\n", "\n", "Desc:\n", "An independent research uncovered a critical vulnerability in zend-mail, a\n", "Zend Framework's component that could potentially be used by (unauthenticated)\n", "remote attackers to achieve remote arbitrary code execution in the context\n", "of the web server user and remotely compromise the target web application.\n", "\n", "To exploit the vulnerability an attacker could target common website\n", "components such as contact/feedback forms, registration forms, password\n", "email resets and others that send out emails with the help of a vulnerable\n", "version of the zend-mail class.\n", "\n", "Full advisory / PoC exploit at:\n", "\n", "http://legalhackers.com/advisories/ZendFramework-Exploit-ZendMail-Remote-Code-Exec-CVE-2016-10034-Vuln.html\n", "\n", "Video / PoC:\n", "\n", "https://legalhackers.com/videos/ZendFramework-Exploit-Remote-Code-Exec-Vuln-CVE-2016-10034-PoC.html\n", "\n", "For updates, follow:\n", "\n", "https://twitter.com/dawid_golunski" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Pretty(reply)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At least for 2017_Jan_0, it is pretty effective. 2017_Jan_45 was not successful at all. Now, we'll try the machine learning style, to compare. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "talon.init()\n", "from talon import signature\n", "reply_ml, sig_ml = signature.extract(raw, sender=\"[email protected]\")\n", "print(sig_ml)\n", "#reply_ml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This doesn't seem to output anything. I'm unclear whether or not this library is already trained; documentation states that it was trained on the authors' personal email and an ENRON set. There is an open issue on github <https://github.com/mailgun/talon/issues/143> from July asking about the same thing. We will stick with the \"brute force\" method for now, and continue to look for more libraries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract HTML tags\n", "We'll use a fairly simple regex to extract any tags from the reply. \n", "\n", "`<([^\\s>]+)(\\s|/>)+`\n", " * `[^\\s>]+` one or more non-whitespace characters, __followed by__:\n", " * `\\s|/` either a whitespace character, or a slash (/) for self-closing tags.\n", "\n", "\n", "We then use a dictionary to count the instances of each unique tag. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'a': 7, 'pre': 1}\n" ] } ], "source": [ "rx = re.compile('<([^\\s>]+)(\\s|/>)+')\n", "tags = {}\n", "for tag in rx.findall(str(bodyhtml)):\n", " tagtype = tag[0]\n", " if not tagtype.startswith('/'):\n", " if tagtype in tags:\n", " tags[tagtype] = tags[tagtype] + 1\n", " else:\n", " tags[tagtype] = 1\n", "print(tags)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract link domains\n", "\n", "We'll record what domains are linked to in each message. We use BeautifulSoup to pull out all `<a>` tags, then urlparse to determine the domain within." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'legalhackers.com': 4, 'nmap.org': 1, 'seclists.org': 1, 'twitter.com': 1}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from urllib.parse import urlparse\n", "\n", "sites = {}\n", "\n", "atags = bodyhtml.find_all('a')\n", "hrefs = [link.get('href') for link in atags]\n", "\n", "for link in hrefs:\n", " parsedurl = urlparse(link)\n", " site = parsedurl.netloc\n", " if site in sites:\n", " sites[site] = sites[site] + 1\n", " else:\n", " sites[site] = 1\n", "\n", "sites" ] } ], "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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
james-prior/cohpy
20161216-dojo-list-versus-square-brackets.ipynb
1
5613
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = 'hello'\n", "s" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['hello']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = [s]\n", "b" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['h', 'e', 'l', 'l', 'o']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = list(s)\n", "c" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not iterable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-25f447d05703>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ], "source": [ "d = list(1)\n", "d" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not iterable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-5-21a63ab663b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\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[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ], "source": [ "dd = list(1,)\n", "dd" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddd = list((1,))\n", "ddd" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e = list([1])\n", "e" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "TypeError", "evalue": "list() takes at most 1 argument (2 given)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-8-04d6bacebfdb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: list() takes at most 1 argument (2 given)" ] } ], "source": [ "f = list(1, 2)\n", "f" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g = list([1, 2])\n", "g" ] } ], "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
rajathkumarmp/BinPy
BinPy/examples/notebook/ic/Series_7400/IC7430.ipynb
5
9446
{ "metadata": { "name": "", "signature": "sha256:7836c307211109fc0572a6007bfdcf683b081cce2759a0937677c98f07048209" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Usage of IC 7430" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "from BinPy import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Usage of IC 7430:\n", "\n", "ic = IC_7430()\n", "\n", "print(ic.__doc__)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", " This is a 8-Input NAND Gate\n", " \n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# The Pin configuration is:\n", "\n", "inp = {1: 0, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 0, 11: 1, 12: 1, 14: 1}\n", "\n", "# Pin initinalization\n", "\n", "# Powering up the IC - using -- ic.setIC({14: 1, 7: 0})\n", "\n", "ic.setIC({14: 1, 7: 0})\n", "\n", "# Setting the inputs of the ic\n", "\n", "ic.setIC(inp)\n", "\n", "# Draw the IC with the current configuration\\n\n", "\n", "ic.drawIC()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", " \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 4 3 11 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [Z] \n", " \u2502 \u2502\n", " \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Run the IC with the current configuration using -- print ic.run() -- \n", "\n", "# Note that the ic.run() returns a dict of pin configuration similar to \n", "\n", "print (ic.run())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{8: 1}\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Seting the outputs to the current IC configuration using -- ic.setIC(ic.run()) --\\n\n", "\n", "ic.setIC(ic.run())\n", "\n", "# Draw the final configuration\n", "\n", "ic.drawIC()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", " \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 4 3 11 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Seting the outputs to the current IC configuration using -- ic.setIC(ic.run()) --\n", "\n", "ic.setIC(ic.run())\n", "\n", "# Draw the final configuration\n", "\n", "ic.drawIC()\n", "\n", "# Run the IC\n", "\n", "print (ic.run())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", " \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u25e1\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 1 14 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 2 7 13 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 3 4 12 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 4 3 11 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 5 0 10 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [1] \u2500\u2500\u2524 6 9 \u251c\u2500\u2500 [0] \n", " \u2502 \u2502\n", " \u2502 \u2502\n", " [0] \u2500\u2500\u2524 7 8 \u251c\u2500\u2500 [1] \n", " \u2502 \u2502\n", " \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \n", "{8: 1}\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Connector Outputs\n", "c = Connector()\n", "\n", "# Set the output connector to a particular pin of the ic\n", "ic.setOutput(8, c)\n", "\n", "print(c)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Connector; State: 1\n" ] } ], "prompt_number": 7 } ], "metadata": {} } ] }
bsd-3-clause
ocefpaf/secoora
notebooks/timeSeries/ssv/00-velocity_secoora.ipynb
2
10421
{ "metadata": { "kernelspec": { "codemirror_mode": { "name": "ipython", "version": 3 }, "display_name": "Iris (Python 2)", "language": "python", "name": "iris_python2" }, "name": "", "signature": "sha256:8958e88f73c0aaf001a82e539ac46040ab581cc5b06d4bc954f2deb08c593345" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### SECOORA sea surface temperature time-series notebook\n", " \n", "Produce weekly maps and tables for the SECOORA. Based on IOOS system-test [notebook](http://nbviewer.ipython.org/github/ioos/system-test/blob/master/Theme_2_Extreme_Events/Scenario_2B/ModelDataCompare_Currents/Model_Obs_Compare_Currents.ipynb)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "start_time = time.time()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "\n", "%load_ext watermark\n", "%watermark --githash --machine --python --packages iris,pyoos,owslib\n", "\n", "style = os.path.join(os.pardir, os.pardir, 'style.css')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPython 2.7.9\n", "IPython 2.3.1\n", "\n", "iris 1.7.2-DEV\n", "pyoos 0.6.2\n", "owslib 0.8-dev\n", "\n", "compiler : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n", "system : Linux\n", "release : 3.16.7-7-desktop\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n", "Git hash : 8a73c1ba44587ef755d52f9375876c8eec17620c\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import pytz\n", "from datetime import datetime, timedelta\n", "\n", "from utilities import CF_names\n", "\n", "\n", "# Choose the date range (e.g: stop = datetime(2014, 7, 7, 12)).\n", "stop = datetime(2015, 2, 6, 12)\n", "\n", "stop = stop.replace(tzinfo=pytz.utc)\n", "start = stop - timedelta(days=7)\n", "\n", "# SECOORA region (NC, SC GA, FL).\n", "bbox = [-87.40, 24.25, -74.70, 36.70]\n", "\n", "# CF-names to look for:\n", "currents = CF_names['currents']\n", "name_list = currents['u'] + currents['v'] + currents['speed_direction']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "run_name = '{:%Y-%m-%d}'.format(stop)\n", "\n", "if not os.path.exists(run_name):\n", " os.makedirs(run_name)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import iris\n", "import pyoos\n", "import owslib\n", "\n", "import logging as log\n", "reload(log)\n", "\n", "fmt = '{:*^64}'.format\n", "log.captureWarnings(True)\n", "LOG_FILENAME = 'log.txt'\n", "LOG_FILENAME = os.path.join(run_name, LOG_FILENAME)\n", "log.basicConfig(filename=LOG_FILENAME,\n", " filemode='w',\n", " format='%(asctime)s %(levelname)s: %(message)s',\n", " datefmt='%I:%M:%S',\n", " level=log.INFO,\n", " stream=None)\n", "\n", "log.info(fmt(' Run information '))\n", "log.info('Run date: {:%Y-%m-%d %H:%M:%S}'.format(datetime.utcnow()))\n", "log.info('Download start: {:%Y-%m-%d %H:%M:%S}'.format(start))\n", "log.info('Download stop: {:%Y-%m-%d %H:%M:%S}'.format(stop))\n", "log.info('Bounding box: {0:3.2f}, {1:3.2f},'\n", " '{2:3.2f}, {3:3.2f}'.format(*bbox))\n", "log.info(fmt(' Software version '))\n", "log.info('Iris version: {}'.format(iris.__version__))\n", "log.info('owslib version: {}'.format(owslib.__version__))\n", "log.info('pyoos version: {}'.format(pyoos.__version__))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib import fes\n", "from utilities import fes_date_filter\n", "\n", "kw = dict(wildCard='*',\n", " escapeChar='\\\\',\n", " singleChar='?',\n", " propertyname='apiso:AnyText')\n", "\n", "or_filt = fes.Or([fes.PropertyIsLike(literal=('*%s*' % val), **kw)\n", " for val in name_list])\n", "\n", "# Exclude ROMS Averages and History files.\n", "not_filt = fes.Not([fes.PropertyIsLike(literal='*Averages*', **kw)])\n", "\n", "begin, end = fes_date_filter(start, stop)\n", "filter_list = [fes.And([fes.BBox(bbox), begin, end, or_filt, not_filt])]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from owslib.csw import CatalogueServiceWeb\n", "\n", "endpoint = 'http://www.ngdc.noaa.gov/geoportal/csw'\n", "csw = CatalogueServiceWeb(endpoint, timeout=60)\n", "csw.getrecords2(constraints=filter_list, maxrecords=1000, esn='full')\n", "\n", "log.info(fmt(' Catalog information '))\n", "log.info(\"URL: {}\".format(endpoint))\n", "log.info(\"CSW version: {}\".format(csw.version))\n", "log.info(\"Number of datasets available: {}\".format(len(csw.records.keys())))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from utilities import service_urls\n", "\n", "dap_urls = service_urls(csw.records, service='odp:url')\n", "sos_urls = service_urls(csw.records, service='sos:url')\n", "\n", "log.info(fmt(' CSW '))\n", "for rec, item in csw.records.items():\n", " log.info('{}'.format(item.title))\n", "\n", "log.info(fmt(' DAP '))\n", "for url in dap_urls:\n", " log.info('{}.html'.format(url))\n", "\n", "log.info(fmt(' SOS '))\n", "for url in sos_urls:\n", " log.info('{}'.format(url))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add SECOORA models and observations." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from utilities import titles, fix_url\n", "\n", "secoora_models = ['SABGOM', 'USEAST', 'USF_ROMS',\n", " 'USF_SWAN', 'USF_FVCOM']\n", "\n", "for secoora_model in secoora_models:\n", " if titles[secoora_model] not in dap_urls:\n", " log.warning('{} not in the NGDC csw'.format(secoora_model))\n", " dap_urls.append(titles[secoora_model])\n", "\n", "# NOTE: USEAST is not archived at the moment!\n", "dap_urls = [fix_url(start, url) if 'SABGOM' in url else url for url in dap_urls]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FIXME: deal with ($u$, $v$) and speed, direction." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from iris.exceptions import CoordinateNotFoundError, ConstraintMismatchError\n", "\n", "from utilities import TimeoutException, secoora_buoys, get_cubes\n", "\n", "urls = list(secoora_buoys())\n", "\n", "buoys = dict()\n", "for url in urls:\n", " try:\n", " cubes = get_cubes(url, name_list=name_list,\n", " bbox=bbox, time=(start, stop))\n", " buoy = url.split('/')[-1].split('.nc')[0]\n", " buoys.update({buoy: cubes[0]})\n", " except (RuntimeError, ValueError, TimeoutException,\n", " ConstraintMismatchError, CoordinateNotFoundError) as e:\n", " log.warning('Cannot get cube for: {}\\n{}'.format(url, e))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "name_list" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "['surface_eastward_sea_water_velocity',\n", " 'eastward_sea_water_velocity',\n", " 'sea_water_x_velocity',\n", " 'x_sea_water_velocity',\n", " 'eastward_transformed_eulerian_mean_velocity',\n", " 'northward_sea_water_velocity',\n", " 'surface_northward_sea_water_velocity',\n", " 'sea_water_y_velocity',\n", " 'y_sea_water_velocity',\n", " 'northward_transformed_eulerian_mean_velocity',\n", " 'sea_water_speed',\n", " 'direction_of_sea_water_velocity']" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "buoys" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "{}" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "units=iris.unit.Unit('m s-1')" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
loganjt/DropletJumpWedge
Repos_Data/ExitTime_Optimization.ipynb
2
6559
{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, glob, numpy as np, csv, math\n", "import numpy.ma as ma\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "import matplotlib.cm as cmx\n", "from scipy import signal, fftpack\n", "from matplotlib.legend_handler import HandlerLine2D\n", "from scipy.optimize import minimize\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "\n", "dir_path = os.path.dirname(os.path.realpath('plotter_notebook.ipynb'))\n", "wrkdir = os.path.join(dir_path,'data')\n", "csv_files = os.path.join(wrkdir,'*.csv')\n", "meta_files = os.path.join(wrkdir,'*.txt')\n", "trackdata = glob.glob(csv_files)\n", "metadata = glob.glob(meta_files)\n", "n_files = np.size(trackdata)\n", "\n", "location = 'average'\n", "volumes = [0.5,1.0,2.0,3.0,4.0,5.0,6.0,10.0]\n", "angles= [2.0,3.5,4,5.0,7.7]\n", "\n", "def basic_plot_format(subplot_label,x_label,y_label,major_grid,minor_grid,legend):\n", " # General plot formatting for relatively good plots. adjustments may be necessary\n", " # sub_plot_label, usually defined by 'ax' and a number\n", " # x_label and y_label must be type 'str'... Can use LaTeX for typsetting\n", " # major_grid, minor_grid, and legend are boolean\n", " \n", " plt.style.use('classic')\n", " font = {'family' : 'Times New Roman',\n", " 'weight' : 0,\n", " 'size' : 18}\n", " plt.rc('font',**font)\n", " \n", " subplot_label.spines['right'].set_color('none')\n", " subplot_label.spines['top'].set_color('none')\n", " subplot_label.yaxis.set_ticks_position('left')\n", " subplot_label.xaxis.set_ticks_position('bottom')\n", " subplot_label.minorticks_on()\n", " subplot_label.set_xlabel(x_label,fontsize=24)\n", " subplot_label.set_ylabel(y_label,fontsize=24)\n", "\n", " subplot_label.grid(b=major_grid,which='major')\n", " subplot_label.grid(b=minor_grid,which='minor')\n", " \n", " if legend == False:\n", " 0 \n", " else:\n", " legend = subplot_label.legend(numpoints = 1,\\\n", " bbox_to_anchor=(-0.5, 1),loc='upper left',frameon=False,fontsize=10)\n", " legend.get_frame().set_facecolor('white')\n", " return legend\n", "\n", "def get_variables(metadata):\n", " f = open(metadata[0],\"r\")\n", " drop_vars = list(csv.reader(f))\n", " f.close()\n", " \n", " \n", " drop_meta = {}\n", " for i in range(n_files):\n", " drop_meta[drop_vars[i+1][0]] = {}\n", " n_meta = np.size(drop_vars[0][1:])\n", " for n in range(n_meta):\n", " drop_meta[drop_vars[i+1][0]][drop_vars[0][n+1].split('_')[0]] = float(drop_vars[i+1][n+1])\n", "\n", " meta = drop_meta\n", " return meta\n", "\n", "meta = get_variables(metadata)\n", "\n", "\n", "def get_data(wrkdir,csv_files,trackdata,n_files):\n", " \n", " total_drop_data = {}\n", " \n", " \n", " for i in range(n_files):\n", " (location, name) = os.path.split( trackdata[i] )\n", " \n", " f = open(trackdata[i],\"r\")\n", " drop_data = list(csv.reader(f))\n", " f.close()\n", " \n", " col_names = drop_data[0]\n", " n_col = np.size(drop_data[0])\n", " drop_data = np.asarray(drop_data[1:],dtype=float)\n", " total_drop_data[name[:-4]] = {}\n", " for j in range(n_col):\n", " total_drop_data[name[:-4]][col_names[j]] = drop_data[:,j]\n", "\n", " return total_drop_data\n", "\n", "data = get_data(wrkdir,csv_files,trackdata,n_files)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " final_simplex: (array([[ 2.22989502],\n", " [ 2.22989578]]), array([ 0.15641177, 0.15641177]))\n", " fun: 0.15641176541006752\n", " message: 'Optimization terminated successfully.'\n", " nfev: 48\n", " nit: 24\n", " status: 0\n", " success: True\n", " x: array([ 2.22989502])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def exit_times(data,meta):\n", " et_exp = {}\n", " et_theo = {}\n", " distance2exit = {}\n", " for key in data:\n", " \n", " R = ((3000*meta[key]['Volume'])/(4*math.pi))**(1/3)\n", " sin_angl = math.sin((meta[key]['Angle']/2)*(math.pi/180))\n", " x_max_theo = (R/sin_angl)\n", " \n", " if np.max(data[key]['average'])>x_max_theo:\n", " i = np.min(np.where(data[key]['average']>=x_max_theo))\n", " et_exp[key] = data[key]['time'][i]\n", " et_theo[key] = (R/1000)*(1000/(0.072*9.81))**(1/4)*(1/(-2*math.cos(155*math.pi/180)*sin_angl))\n", " else:\n", " distance2exit[key] = x_max_theo-np.max(data[key]['average'])\n", "\n", " return et_exp,et_theo\n", "\n", "et_exp,et_theo = exit_times(data,meta)\n", "global et_exp\n", "global et_theo\n", "def error_time(x):\n", " error = [abs(et_exp[key]-x*et_theo[key])/et_exp[key] for key in et_exp]\n", " ave_error = np.mean(error)\n", " return ave_error\n", "\n", "res = minimize(error_time, 1, method='Nelder-Mead', tol=1e-6)\n", "res\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
GoogleCloudPlatform/cloudml-samples
notebooks/tensorflow/census/estimator/trainer/task.ipynb
1
9100
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2016 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", " http://www.apache.org/licenses/LICENSE-2.0 \n", " \n", " Unless required by applicable law or agreed to in writing, software \n", " distributed under the License is distributed on an \"AS IS\" BASIS, \n", " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n", " See the License for the specific language governing permissions and \n", " limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Get the dependency .py files, if any.\n", "! git clone https://github.com/GoogleCloudPlatform/cloudml-samples.git\n", "! cp cloudml-samples/census/estimator/trainer/* .\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"\"\"Runs the training of the Wide & Deep model based on hyperparameter\n", "values received as input parameters.\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import argparse\n", "import json\n", "import os\n", "import tensorflow as tf\n", "import input as input_module\n", "import model as model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def _get_session_config_from_env_var():\n", " \"\"\"Returns a tf.ConfigProto instance that has appropriate device_filters\n", " set.\"\"\"\n", "\n", " tf_config = json.loads(os.environ.get('TF_CONFIG', '{}'))\n", "\n", " if (tf_config and 'task' in tf_config and 'type' in tf_config['task'] and\n", " 'index' in tf_config['task']):\n", " # Master should only communicate with itself and ps\n", " if tf_config['task']['type'] == 'master':\n", " return tf.ConfigProto(device_filters=['/job:ps', '/job:master'])\n", " # Worker should only communicate with itself and ps\n", " elif tf_config['task']['type'] == 'worker':\n", " return tf.ConfigProto(device_filters=[\n", " '/job:ps',\n", " '/job:worker/task:%d' % tf_config['task']['index']\n", " ])\n", " return None" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def train_and_evaluate(args):\n", " \"\"\"Run the training and evaluate using the high level API.\"\"\"\n", "\n", " def train_input():\n", " \"\"\"Input function returning batches from the training\n", " data set from training.\n", " \"\"\"\n", " return input_module.input_fn(\n", " args.train_files,\n", " num_epochs=args.num_epochs,\n", " batch_size=args.train_batch_size,\n", " num_parallel_calls=args.num_parallel_calls,\n", " prefetch_buffer_size=args.prefetch_buffer_size)\n", "\n", " def eval_input():\n", " \"\"\"Input function returning the entire validation data\n", " set for evaluation. Shuffling is not required.\n", " \"\"\"\n", " return input_module.input_fn(\n", " args.eval_files,\n", " batch_size=args.eval_batch_size,\n", " shuffle=False,\n", " num_parallel_calls=args.num_parallel_calls,\n", " prefetch_buffer_size=args.prefetch_buffer_size)\n", "\n", " train_spec = tf.estimator.TrainSpec(\n", " train_input, max_steps=args.train_steps)\n", "\n", " exporter = tf.estimator.FinalExporter(\n", " 'census', input_module.SERVING_FUNCTIONS[args.export_format])\n", " eval_spec = tf.estimator.EvalSpec(\n", " eval_input,\n", " steps=args.eval_steps,\n", " exporters=[exporter],\n", " name='census-eval')\n", "\n", " run_config = tf.estimator.RunConfig(\n", " session_config=_get_session_config_from_env_var())\n", " run_config = run_config.replace(model_dir=args.job_dir)\n", " print('Model dir %s' % run_config.model_dir)\n", " estimator = model.build_estimator(\n", " embedding_size=args.embedding_size,\n", " # Construct layers sizes with exponential decay\n", " hidden_units=[\n", " max(2, int(args.first_layer_size * args.scale_factor**i))\n", " for i in range(args.num_layers)\n", " ],\n", " config=run_config)\n", "\n", " tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PARSER = argparse.ArgumentParser()\n", "# Input Arguments\n", "PARSER.add_argument(\n", " '--train-files',\n", " help='GCS file or local paths to training data',\n", " nargs='+',\n", " default='gs://cloud-samples-data/ml-engine/census/data/adult.data.csv')\n", "PARSER.add_argument(\n", " '--eval-files',\n", " help='GCS file or local paths to evaluation data',\n", " nargs='+',\n", " default='gs://cloud-samples-data/ml-engine/census/data/adult.test.csv')\n", "PARSER.add_argument(\n", " '--job-dir',\n", " help='GCS location to write checkpoints and export models',\n", " default='/tmp/census-estimator')\n", "PARSER.add_argument(\n", " '--num-parallel-calls',\n", " help='Number of threads used to read in parallel the training and evaluation',\n", " type=int)\n", "PARSER.add_argument(\n", " '--prefetch_buffer_size',\n", " help='Naximum number of input elements that will be buffered when prefetching',\n", " type=int)\n", "PARSER.add_argument(\n", " '--num-epochs',\n", " help=\"\"\"\\\n", " Maximum number of training data epochs on which to train.\n", " If both --max-steps and --num-epochs are specified,\n", " the training job will run for --max-steps or --num-epochs,\n", " whichever occurs first. If unspecified will run for --max-steps.\\\n", " \"\"\",\n", " type=int)\n", "PARSER.add_argument(\n", " '--train-batch-size',\n", " help='Batch size for training steps',\n", " type=int,\n", " default=40)\n", "PARSER.add_argument(\n", " '--eval-batch-size',\n", " help='Batch size for evaluation steps',\n", " type=int,\n", " default=40)\n", "PARSER.add_argument(\n", " '--embedding-size',\n", " help='Number of embedding dimensions for categorical columns',\n", " default=8,\n", " type=int)\n", "PARSER.add_argument(\n", " '--first-layer-size',\n", " help='Number of nodes in the first layer of the DNN',\n", " default=100,\n", " type=int)\n", "PARSER.add_argument(\n", " '--num-layers',\n", " help='Number of layers in the DNN',\n", " default=4,\n", " type=int)\n", "PARSER.add_argument(\n", " '--scale-factor',\n", " help='How quickly should the size of the layers in the DNN decay',\n", " default=0.7,\n", " type=float)\n", "PARSER.add_argument(\n", " '--train-steps',\n", " help=\"\"\"\\\n", " Steps to run the training job for. If --num-epochs is not specified,\n", " this must be. Otherwise the training job will run indefinitely.\"\"\",\n", " default=100,\n", " type=int)\n", "PARSER.add_argument(\n", " '--eval-steps',\n", " help='Number of steps to run evalution for at each checkpoint',\n", " default=100,\n", " type=int)\n", "PARSER.add_argument(\n", " '--export-format',\n", " help='The input format of the exported SavedModel binary',\n", " choices=['JSON', 'CSV', 'EXAMPLE'],\n", " default='JSON')\n", "PARSER.add_argument(\n", " '--verbosity',\n", " choices=['DEBUG', 'ERROR', 'FATAL', 'INFO', 'WARN'],\n", " default='INFO')\n", "\n", "ARGUMENTS, _ = PARSER.parse_known_args()\n", "\n", "# Set python level verbosity\n", "tf.logging.set_verbosity(ARGUMENTS.verbosity)\n", "# Set C++ Graph Execution level verbosity\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(\n", " tf.logging.__dict__[ARGUMENTS.verbosity] / 10)\n", "\n", "# Run the training job\n", "train_and_evaluate(ARGUMENTS)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
xR86/ml-stuff
kaggle/enron-email/Initial.ipynb
1
7305933
null
mit
poppy-project/community-notebooks
tutorials-education/poppy-humanoid_poppy-torso__vrep_installation et prise en main/poppy réel/Construction/construction & installation (poppy réel).ipynb
2
9747
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/poppy.png\" HEIGHT=200 WIDTH=200 ALIGN=right>\n", "<img src=\"png/inria.jpg\" HEIGHT=150 WIDTH=325 ALIGN=left >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/diagramme.png\" HEIGHT=720 WIDTH=920 ALIGN=center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Démarrage Rapide avec Poppy Réel\n", "\n", "Via votre navigateur web ([chrome](https://www.google.com/chrome/browser/desktop/index.html) / [firefox](https://www.mozilla.org/fr/firefox/desktop/) / etc) rendez-vous sur la page http://poppy.local\n", "\n", "\n", "NB: Poppy est le nom par défaut, il peut avoir été changé.\n", "\n", "<img src=\"png/poppy_local.jpg\" ALIGN=left >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/pr.png\" ALIGN=left >\n", "<br><br><br><br>\n", "\n", "####Où acheter : [Génération robots ](http://www.generationrobots.com/en/279-poppy-opensource-robotics-platform)\n", "\n", "<img src=\"png/b1v.png\" ALIGN=left >\n", "###Construction:\n", "<br>\n", "#####ATTENTION au <U>placement du palonnier</U> sur les moteurs et au placement des pièces plastiques sur le palonnier:\n", "\n", "<img src=\"png/robotis_dynamixel_axe_mark.jpg\" HEIGHT=230 WIDTH=275 ALIGN=left>\n", "<img src=\"png/robotis_horn_mark.jpg\" HEIGHT=225 WIDTH=225 ALIGN=left >\n", "<img src=\"png/mark.PNG\" HEIGHT=225 WIDTH=225 ALIGN=left >\n", "<br><br><br><br><br><br><br><br><br><br><br>\n", "\n", "\n", "#####Plus de détails dans cette note: [Robotis Dynamixel Noobs Traps](https://github.com/poppy-project/Robotis-library/blob/master/doc/fr/robotis_tricks.md)\n", "<br>\n", "####Liste des pièces [ici](https://github.com/poppy-project/Poppy-multiarticulated-torso/blob/master/doc/fr/5_DoFs_trunk_BOM.md)\n", "<br>\n", "####Les 7 étapes de la construction\n", "<br>\n", "\n", "- [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](dw)[<b>Les 7 étapes en vidéos</b>](https://github.com/poppy-project/poppy-humanoid/blob/master/doc/en/getting_started.md): <br><br>\n", " 1. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/LEHLdoBEr4Q)[Tronc](https://github.com/poppy-project/Poppy-multiarticulated-torso/blob/master/doc/fr/5_DoFs_humanoid_spine.md)<br>\n", " * [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/9oNGV9ggHaE)[double MX28](https://github.com/poppy-project/Robotis-library/blob/master/doc/fr/double_MX28_assembly.md)<br><br>\n", " * [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/83lrhXVNHYE)[double MX64](https://github.com/poppy-project/Robotis-library/blob/master/doc/fr/double_MX64_assembly.md) (uniquement surversion Poppy humanoïde)\n", " <br><br>\n", " 2. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/qwrgV6tKTO8)[Poitrine](https://github.com/poppy-project/Poppy-multiarticulated-torso/blob/master/doc/fr/subassembly/chest_assembly_instructions.md)<br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/LXktU4MTITE)[Colonne vertébrale](https://github.com/poppy-project/Poppy-multiarticulated-torso/blob/master/doc/fr/subassembly/spine_assembly_instructions.md)<br><br>\n", " 3. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/Oe5v21sTst8)[Bras Droit](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/right_arm_assembly_instructions.md)\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/SUlM_mE3plc)[Right Forearm](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/right_forearm_assembly_instructions.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/cYhGwD6r6NQ)[Right upper arm](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/right_upper_arm_assembly.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/BdQcOAwZbMY)[Right upper-arm/shoulder](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/right_upper_arm_shoulder_assembly.md)<br><br>\n", " 4. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/TZb6_hVlmcA)[Bras gauche](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/left_arm_assembly_instructions.md)\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/5FsPgEt4cfA)[Left Forearm](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/left_forearm_assembly_instructions.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/MIjfAXShLJ4)[Left upper arm](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/left_upper_arm_assembly.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/qCF_8-M5k1o)[Left upper-arm/shoulder](https://github.com/poppy-project/Poppy-basic-arms/blob/master/doc/subassemblies/left_upper_arm_shoulder_assembly.md)<br><br>\n", " 5. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/Am1XBYv134Y)[Jambes](https://github.com/poppy-project/Poppy-lightweight-biped-legs/blob/master/doc/legs_assembly_instructions.md) (uniquement sur version Poppy humanoïde)\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/m6hKNlVGYlU)[Left leg](https://github.com/poppy-project/Poppy-lightweight-biped-legs/blob/master/doc/subassemblies/left_leg_assembly_instructions.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/_NwoFPaXKUg)[Right leg](https://github.com/poppy-project/Poppy-lightweight-biped-legs/blob/master/doc/subassemblies/right_leg_assembly_instructions.md)<br><br>\n", " - [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/zrZhuS5VkG8)[Pelvis](https://github.com/poppy-project/Poppy-lightweight-biped-legs/blob/master/doc/subassemblies/pelvis_assembly_instructions.md)<br><br>\n", " 6. [<img src=\"png/clap.png\" HEIGHT=25 WIDTH=25 ALIGN=left>](http://youtu.be/5i0xVlrJc-8)[Jambes + Tronc](https://github.com/poppy-project/poppy-humanoid/blob/master/hardware/doc/Poppy_Humanoid_assembly_instructions.md#3--legstorso-asembly) (uniquement sur version Poppy humanoïde)<br><br>\n", " 7. [Tête](https://github.com/poppy-project/Poppy-minimal-head-design/blob/master/doc/poppy_wiring.md) (en)<br><br>\n", "\n", "Retrouvez la documentation Complète: [ici](https://github.com/HumaRobotics/poppy-examples/blob/master/doc/assemblyGuide/assemblyGuide.pdf)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## <img src=\"png/b2v.png\" ALIGN=left >\n", "### Initialisation: \n", "<br>\n", "####établir une connexion SSH\n", " - Poppy & l'ordinateur doivent être connectés sur le même routeur (e.g box internet)\n", "\n", "Pour la première connexion, utiliser un câble Ethernet pour Poppy\n", "\n", "#####<u>Sous Linux et Mac:</u>\n", "\n", "- ouvrir un teminal\n", "- taper: ssh [email protected]\n", "- mot de passe : 'odroid'\n", "\n", "\n", "#####<u>Sous Windows:</u>\n", "\n", "- télécharger & installer [Bonjour](https://support.apple.com/kb/DL999?viewlocale=fr_FR&locale=fr_FR) (permet de donner un alias à une adresse IP)\n", "- télécharger & installer [Putty](http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html) (permet d'établir une connexion ssh)\n", " - Ouvrir Putty / dans 'Host Name' \n", " - taper : [email protected]\n", "\n", "[<img src=\"png/putty.PNG\" HEIGHT=540 WIDTH=520 ALIGN=center>]\n", "\n", "un terminal s'ouvre et vous demande:\n", "\n", "- le mot de passe : 'odroid'\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### installation:\n", "\n", "dans le terminal (ouvert à l'étape précédente) taper : \n", "\n", " curl -L https://raw.githubusercontent.com/poppy-project/poppy_install/master/poppy_setup.sh | sudo bash\n", "\n", "#####L'installation commence, et ferme le terminal une fois fini.\n", "\n", "- établir une nouvelle connexion ssh ; Attention le nom et mot de passe ont changé :\n", " - ssh [email protected]\n", " - mot de passe: 'poppy'\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/b3v.png\" ALIGN=left >\n", "###Connexion:\n", "<br>\n", "Via votre navigateur web ([chrome](https://www.google.com/chrome/browser/desktop/index.html) / [firefox](https://www.mozilla.org/fr/firefox/desktop/) / etc) rendez-vous sur la page http://poppy.local\n", "\n", "<img src=\"png/poppy_local.jpg\" ALIGN=left >\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
dsiufl/2015-Fall-Hadoop
instructor-notes/3-pyspark-wordcount.ipynb
2
5478
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spark version of wordcount examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prepare the pyspark environment." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import findspark\n", "import os\n", "findspark.init('/home/ubuntu/shortcourse/spark-1.5.1-bin-hadoop2.6')\n", "\n", "from pyspark import SparkContext, SparkConf\n", "conf = SparkConf().setAppName(\"test\").setMaster(\"local[2]\")\n", "sc = SparkContext(conf=conf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure your HDFS is still on and the input files (the three books) are still in the input folder.\n", "\n", "Create the input RDD from the files on the HDFS (hdfs://localhost:54310/user/ubuntu/input)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30213" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lines = sc.textFile('hdfs://localhost:54310/user/ubuntu/input')\n", "lines.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple Word Count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform the counting, by flatMap, map, and reduceByKey." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from operator import add\n", "counts = lines.flatMap(lambda x: x.split()).map(lambda x: (x, 1)).reduceByKey(add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take the top 10 frequently used words" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'the', 11273),\n", " (u'to', 7594),\n", " (u'of', 6978),\n", " (u'and', 6887),\n", " (u'a', 5182),\n", " (u'I', 4533),\n", " (u'in', 3916),\n", " (u'was', 3484),\n", " (u'that', 3204),\n", " (u'her', 2428)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts.takeOrdered(10, lambda x: -x[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pattern Matching WordCount" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the pattern file into a set. (file: /home/ubuntu/shortcourse/notes/scripts/wordcount2/wc2-pattern.txt)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pattern = set()\n", "f = open('/home/ubuntu/shortcourse/notes/scripts/wordcount2/wc2-pattern.txt')\n", "for line in f:\n", " words = line.split()\n", " for word in words:\n", " pattern.add(word)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform the counting, by flatMap, filter, map, and reduceByKey." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result = lines.flatMap(lambda x: x.split()).filter(lambda x: x in pattern).map(lambda x: (x, 1)).reduceByKey(add)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Collect and show the results." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(u'and', 6887),\n", " (u'his', 2356),\n", " (u'that', 3204),\n", " (u'I', 4533),\n", " (u'of', 6978),\n", " (u'had', 2107),\n", " (u'in', 3916),\n", " (u'not', 2076),\n", " (u'was', 3484),\n", " (u'to', 7594),\n", " (u'the', 11273),\n", " (u'with', 2097),\n", " (u'her', 2428),\n", " (u'a', 5182),\n", " (u'be', 1975),\n", " (u'it', 2284),\n", " (u'as', 2141),\n", " (u'she', 2095),\n", " (u'you', 2317),\n", " (u'he', 2148)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.collect()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# stop the spark context\n", "sc.stop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
chbehrens/pr_bc_connectivity-1
CBCX_ON_CBC_contact_comparison.ipynb
2
42504
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparision between the size of basal contacts of CBCX and OFF-CBCs" ] }, { "cell_type": "code", "execution_count": 1, "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 seaborn as sns\n", "from scipy.io import loadmat\n", "import rpy2\n", "%matplotlib inline\n", "matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Arial']})\n", "matplotlib.rcParams.update({'mathtext.default': 'regular'})\n", "matplotlib.rcParams.update({'font.size': 14})\n", "sns.set_style(\"whitegrid\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from rpy2.robjects.packages import importr\n", "import rpy2.robjects as ro\n", "from rpy2.robjects import pandas2ri\n", "pandas2ri.activate()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#import r package\n", "r_stats = importr('stats')\n", "r_base = importr('base')\n", "r_lme4 = importr('lme4')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xbc_contacts=np.loadtxt('data/CBCX_contact_classification.csv',delimiter=',',usecols=range(4))\n", "xbc_contacts=pd.DataFrame(xbc_contacts,columns=['cell','cone','type','class'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "on_contacts_xbc_comp=np.loadtxt('data/CBCX_ON_CBC_contact_comparison.csv',delimiter=',',usecols=range(4))\n", "on_contacts_xbc_comp=pd.DataFrame(on_contacts_xbc_comp,columns=['cell','cone','type','class'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stat_xbc_contacts=pd.DataFrame(np.concatenate((np.tile(np.unique(xbc_contacts['cell']),3).reshape(-1,1),\\\n", " np.repeat(np.arange(3),np.unique(xbc_contacts['cell']).shape[0]).reshape(-1,1)),axis=1),columns=['cell','class'])\n", "for i in range(stat_xbc_contacts.shape[0]):\n", " stat_xbc_contacts.loc[i,'count']=np.sum((xbc_contacts['cell']==stat_xbc_contacts.ix[i,'cell'])&\\\n", " (xbc_contacts['class']==stat_xbc_contacts.ix[i,'class'])) " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stat_on_contacts=pd.DataFrame(np.concatenate((np.tile(np.unique(on_contacts_xbc_comp['cell']),3).reshape(-1,1),\\\n", " np.repeat(np.arange(3),np.unique(on_contacts_xbc_comp['cell']).shape[0]).reshape(-1,1)),axis=1),columns=['cell','class'])\n", "for i in range(stat_on_contacts.shape[0]):\n", " stat_on_contacts.loc[i,'count']=np.sum((on_contacts_xbc_comp['cell']==stat_on_contacts.ix[i,'cell'])&\\\n", " (on_contacts_xbc_comp['class']==stat_on_contacts.ix[i,'class'])) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tip_invag_joined=pd.concat({'CBCX': stat_xbc_contacts, 'ON-CBC': stat_on_contacts})\n", "tip_invag_joined=tip_invag_joined.reset_index().drop('level_1',axis=1).rename(columns={'level_0':'BC_type'})\n", "tip_invag_joined=tip_invag_joined[tip_invag_joined['class']>0].reset_index().drop('index',axis=1)\n", "tip_invag_joined['contact']='invag.'\n", "tip_invag_joined.loc[tip_invag_joined['class']==2,'contact']='tip'\n", "tip_invag_joined=tip_invag_joined.drop('class',axis=1)\n", "tip_invag_joined['cell']=tip_invag_joined['cell'].astype(int).astype(str)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f899e79e7b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(font='Arial',style='white',context='paper',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4})\n", "with matplotlib.rc_context({\"lines.linewidth\": 0.7}):\n", " plt.figure(figsize=(3/2.54,3/2.54))\n", " ax=sns.pointplot(x='BC_type',y='count',data=tip_invag_joined,hue='contact',order=['CBCX','ON-CBC']\\\n", " ,ci=95,palette=[np.array([0.8,0.8,0.8,1.]),np.array([0.4,0.4,0.4,1.])],linestyles='',markers='s',dodge=-0.2)\n", " ax.set_xticklabels(['CBCX','ON-CBCs'])\n", " ax.set(ylabel='# contact points',xlabel='',ylim=(0,8.5),yticks=[0,2,4,6,8])\n", " legend=plt.legend(bbox_to_anchor=(.8, 1))\n", " ax.spines['left'].set_position(('outward',3))\n", " ax.spines['bottom'].set_position(('outward',3))\n", " sns.despine()\n", "# plt.savefig('figures/xbc_on_comparison5.svg',bbox_inches='tight',dpi=300)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#get r object\n", "r_tip_invag=pandas2ri.py2ri_pandasdataframe(tip_invag_joined)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#fit model\n", "r_model=r_lme4.glmer('count ~ BC_type*contact+(1|cell)',data=r_tip_invag,family=\"poisson\")\n", "r_pred=pandas2ri.py2ri_pandasdataframe(tip_invag_joined[['cell','BC_type','contact']])\n", "r_count=r_stats.predict(r_model,r_pred,type='response')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pred=tip_invag_joined.copy()\n", "pred['count']=pandas2ri.ri2py(r_count)\n", "merged = pd.concat(dict(data=tip_invag_joined,mixed=pred),names=['type']).reset_index()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ofPfdd3To0IH69evTpk0blixZIt3u+eDh5p5Anm3umZcszSwZ02Qww73ezBLb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\nuQTDcck598oJkZ+SUzP4+1qMYYuSXOc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f899e6d4c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(font='Arial',style='white',context='notebook',font_scale=1.25)\n", "g = sns.factorplot('contact', 'count', 'BC_type', \n", " data=merged, \n", " col='type',\n", " kind='point',\n", " size=3.5)\n", "g.fig.subplots_adjust(wspace=0.3)\n", "sns.despine(offset=10);\n", "# plt.savefig('figures/XBC_contact_model.png',bbox_inches='tight',dpi=300)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generalized linear mixed model fit by maximum likelihood (Laplace\n", " Approximation) [glmerMod]\n", " Family: poisson ( log )\n", "Formula: count ~ BC_type * contact + (1 | cell)\n", " Data: structure(list(BC_type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, \n", "1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, \n", "2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L \n", "), .Label = c(\"CBCX\", \"ON-CBC\"), class = \"factor\"), cell = structure(c(7L, \n", "8L, 9L, 10L, 11L, 12L, 13L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, \n", "2L, 3L, 4L, 5L, 6L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, \n", "4L, 5L, 6L, 14L, 15L, 16L, 17L, 18L, 19L), .Label = c(\"543\", \n", "\"550\", \"566\", \"574\", \"590\", \"596\", \"603\", \"604\", \"605\", \"606\", \n", "\"607\", \"608\", \"609\", \"624\", \"640\", \"658\", \"671\", \"687\", \"689\" \n", "), class = \"factor\"), count = c(0, 0, 3, 0, 0, 0, 0, 4, 4, 2, \n", "3, 1, 1, 1, 10, 10, 2, 1, 7, 2, 2, 3, 7, 4, 8, 15, 2, 0, 2, 0, \n", "3, 1, 1, 0, 0, 0, 0, 1), contact = structure(c(1L, 1L, 1L, 1L, \n", "1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, \n", "1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, \n", "2L, 2L), .Label = c(\"invag.\", \"tip\"), class = \"factor\")), .Names = c(\"BC_type\", \n", "\"cell\", \"count\", \"contact\"), row.names = c(\"0\", \"1\", \"2\", \"3\", \n", "\"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", "\"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \n", "\"27\", \"28\", \"29\", \"30\", \"31\", \"32\", \"33\", \"34\", \"35\", \"36\", \"37\" \n", "), class = \"data.frame\")\n", "\n", " AIC BIC logLik deviance df.resid \n", " 144.9 153.1 -67.5 134.9 33 \n", "\n", "Scaled residuals: \n", " Min 1Q Median 3Q Max \n", "-1.1743 -0.6939 -0.5226 0.6181 3.3152 \n", "\n", "Random effects:\n", " Groups Name Variance Std.Dev.\n", " cell (Intercept) 0.2196 0.4686 \n", "Number of obs: 38, groups: cell, 19\n", "\n", "Fixed effects:\n", " Estimate Std. Error z value Pr(>|z|) \n", "(Intercept) -0.9395 0.6075 -1.546 0.1220 \n", "BC_typeON-CBC 2.6000 0.6330 4.107 4.0e-05 ***\n", "contacttip 1.6740 0.6291 2.661 0.0078 ** \n", "BC_typeON-CBC:contacttip -3.6341 0.7141 -5.089 3.6e-07 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Correlation of Fixed Effects:\n", " (Intr) BC_tON-CBC cntctt\n", "BC_tyON-CBC -0.954 \n", "contacttip -0.872 0.837 \n", "BC_tON-CBC: 0.768 -0.769 -0.881\n", "\n" ] } ], "source": [ "print(r_base.summary(r_model))" ] }, { "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-3.0
nkmk/python-snippets
notebook/argument_expand_dict.ipynb
1
4427
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def func(arg1, arg2, arg3):\n", " print('arg1 =', arg1)\n", " print('arg2 =', arg2)\n", " print('arg3 =', arg3)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d = {'arg1': 'one', 'arg2': 'two', 'arg3': 'three'}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "arg2 = two\n", "arg3 = three\n" ] } ], "source": [ "func(**d)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "arg2 = two\n", "arg3 = three\n" ] } ], "source": [ "func(**{'arg1': 'one', 'arg2': 'two', 'arg3': 'three'})" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# func(**{'arg1': 'one', 'arg2': 'two'})\n", "# TypeError: func() missing 1 required positional argument: 'arg3'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# func(**{'arg1': 'one', 'arg2': 'two', 'arg3': 'three', 'arg4': 'four'})\n", "# TypeError: func() got an unexpected keyword argument 'arg4'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def func_default(arg1=1, arg2=2, arg3=3):\n", " print('arg1 =', arg1)\n", " print('arg2 =', arg2)\n", " print('arg3 =', arg3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "arg2 = 2\n", "arg3 = 3\n" ] } ], "source": [ "func_default(**{'arg1': 'one'})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = 1\n", "arg2 = two\n", "arg3 = three\n" ] } ], "source": [ "func_default(**{'arg2': 'two', 'arg3': 'three'})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# func_default(**{'arg1': 'one', 'arg4': 'four'})\n", "# TypeError: func_default() got an unexpected keyword argument 'arg4'" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def func_kwargs(arg1, **kwargs):\n", " print('arg1 =', arg1)\n", " print('kwargs =', kwargs)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "kwargs = {'arg2': 'two', 'arg3': 'three'}\n" ] } ], "source": [ "func_kwargs(**{'arg1': 'one', 'arg2': 'two', 'arg3': 'three'})" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "kwargs = {'arg2': 'two', 'arg3': 'three', 'arg4': 'four'}\n" ] } ], "source": [ "func_kwargs(**{'arg1': 'one', 'arg2': 'two', 'arg3': 'three', 'arg4': 'four'})" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arg1 = one\n", "kwargs = {'arg3': 'three'}\n" ] } ], "source": [ "func_kwargs(**{'arg1': 'one', 'arg3': 'three'})" ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
agile-geoscience/welly
docs/_userguide/Projects.ipynb
1
99093
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Projects\n", "\n", "Wells are one of the fundamental objects in welly.\n", "\n", "Well objects include collections of Curve objects. Multiple Well objects can be stored in a Project.\n", "\n", "On this page, we take a closer look at the `Project` class. It lets us handle groups of wells. It is really just a list of `Well` objects, with a few extra powers.\n", "\n", "First, some preliminaries…" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.5.1.dev15+gbf10f3b.d20220223'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import welly\n", "\n", "welly.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Make a project\n", "\n", "We have a few LAS files in a folder; we can load them all at once with standard POSIX file globbing syntax:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2it [00:00, 19.00it/s]\n" ] } ], "source": [ "p = welly.read_las(\"../../tests/assets/example_*.las\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a project, containing two files:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>Index</th><th>UWI</th><th>Data</th><th>Curves</th></tr><tr><td>0</td><td><strong>3-2-B</strong></td><td>12&nbsp;curves</td><td>SP, SN, ILD, LLS, LLD, MLL, NPHI, RHOZ, CAL1, GRC, DTP, CAL2</td></tr><tr><td>1</td><td><strong>3-2-A</strong></td><td>12&nbsp;curves</td><td>SP, SN, ILD, LLS, LLD, MLL, NPHI, RHOB, CAL1, GR, DT, CAL2</td></tr></table>" ], "text/plain": [ "Project(2 wells: 3-2-B, 3-2-A)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can pass in a list of files or URLs:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "0it [00:00, ?it/s]Only engine='normal' can read wrapped files\n", "3it [00:06, 2.09s/it]\n" ] } ], "source": [ "p = welly.read_las(['../../tests/assets/P-129_out.LAS',\n", " 'https://geocomp.s3.amazonaws.com/data/P-130.LAS',\n", " 'https://geocomp.s3.amazonaws.com/data/R-39.las',\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This project has three wells:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>Index</th><th>UWI</th><th>Data</th><th>Curves</th></tr><tr><td>0</td><td><strong>Long = 63* 45'24.460 W</strong></td><td>24&nbsp;curves</td><td>CALI, HCAL, PEF, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, RLA5, RLA3, RLA4, RLA1, RLA2, RXOZ, RXO_HRLT, RT_HRLT, RM_HRLT, DRHO, RHOB, GR, SP</td></tr><tr><td>1</td><td><strong>100/N14A/11E05</strong></td><td>18&nbsp;curves</td><td>CALI, DT, NPHI_SAN, NPHI_LIM, NPHI_DOL, DPHI_LIM, DPHI_SAN, DPHI_DOL, M2R9, M2R6, M2R3, M2R2, M2R1, GR, SP, PEF, DRHO, RHOB</td></tr><tr><td>2</td><td><strong>303N764340060300</strong></td><td>22&nbsp;curves</td><td>BS, CALI, CHR1, CHR2, CHRP, CHRS, DRHO, DT1R, DT2, DT2R, DT4P, DT4S, GR, HD1, HD2, HD3, NPOR, PEF, RHOB, SPR1, TENS, VPVS</td></tr></table>" ], "text/plain": [ "Project(3 wells: Long = 63* 45'24.460 W, 100/N14A/11E05, 303N764340060300)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typical, the UWIs are a disaster. Let's ignore this for now.\n", "\n", "The `Project` is really just a list-like thing, so you can index into it to get at a single well. Each well is represented by a `welly.Well` object." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Long = 63* 45'24.460 W</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>location</strong></td><td>Lat = 45* 12' 34.237\" N</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td></td></tr><tr><td><strong>longitude</strong></td><td></td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>section</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>township</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>" ], "text/plain": [ "Well(uwi: 'Long = 63* 45'24.460 W', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the fields of this LAS file are messed up; see the [Well notebook](Wells.ipynb) for more on how to fix this. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot curves from several wells\n", "\n", "The DT log is called DT4P in one of the wells. We can deal with this sort of issue with aliases. Let's set up an alias dictionary, then plot the DT log from each well:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "alias = {'Sonic': ['DT', 'DT4P'],\n", " 'Caliper': ['HCAL', 'CALI'],\n", " }" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 504x1008 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, axs = plt.subplots(figsize=(7, 14),\n", " ncols=len(p),\n", " sharey=True,\n", " )\n", "\n", "for i, (ax, w) in enumerate(zip(axs, p)):\n", " log = w.get_curve('Sonic', alias=alias)\n", " if log is not None:\n", " ax = log.plot(ax=ax)\n", " ax.set_title(\"Sonic log for\\n{}\".format(w.uwi))\n", "\n", "min_z, max_z = p.basis_range\n", " \n", "plt.ylim(max_z, min_z)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get a `pandas.DataFrame`\n", "\n", "The `df()` method makes a DataFrame using a dual index of UWI and Depth.\n", "\n", "Before we export our wells, let's give Kennetcook #2 a better UWI:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Kennetcook #2</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>location</strong></td><td>Lat = 45* 12' 34.237\" N</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td></td></tr><tr><td><strong>longitude</strong></td><td></td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>section</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>township</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>" ], "text/plain": [ "Well(uwi: 'Kennetcook #2', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p[0].uwi = p[0].name\n", "p[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's better.\n", "\n", "When creating the DataFrame, you can pass a list of the keys (mnemonics) you want, and use aliases as usual." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Sonic': ['DT', 'DT4P'], 'Caliper': ['HCAL', 'CALI']}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alias" ] }, { "cell_type": "code", "execution_count": 16, "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></th>\n", " <th>Caliper</th>\n", " <th>GR</th>\n", " <th>Sonic</th>\n", " </tr>\n", " <tr>\n", " <th>UWI</th>\n", " <th>DEPT</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">Kennetcook #2</th>\n", " <th>1.0668000000</th>\n", " <td>4.3912849426</td>\n", " <td>46.6986503600</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1.2192000000</th>\n", " <td>4.3912849426</td>\n", " <td>46.6986503600</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1.3716000000</th>\n", " <td>4.3912849426</td>\n", " <td>46.6986503600</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1.5240000000</th>\n", " <td>4.3912849426</td>\n", " <td>46.6986503600</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1.6764000000</th>\n", " <td>4.3912849426</td>\n", " <td>46.6986503600</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">303N764340060300</th>\n", " <th>3387.5471999996</th>\n", " <td>303.7090000000</td>\n", " <td>32.0276000039</td>\n", " <td>252.4951</td>\n", " </tr>\n", " <tr>\n", " <th>3387.6995999996</th>\n", " <td>303.7090000000</td>\n", " <td>32.0276000000</td>\n", " <td>252.4951</td>\n", " </tr>\n", " <tr>\n", " <th>3387.8519999996</th>\n", " <td>303.7090000000</td>\n", " <td>32.0276000000</td>\n", " <td>252.4951</td>\n", " </tr>\n", " <tr>\n", " <th>3388.0043999996</th>\n", " <td>303.7090000000</td>\n", " <td>32.0276000000</td>\n", " <td>252.4951</td>\n", " </tr>\n", " <tr>\n", " <th>3388.1567999996</th>\n", " <td>303.7090000000</td>\n", " <td>32.0276000000</td>\n", " <td>252.4951</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>46594 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " Caliper GR Sonic\n", "UWI DEPT \n", "Kennetcook #2 1.0668000000 4.3912849426 46.6986503600 NaN\n", " 1.2192000000 4.3912849426 46.6986503600 NaN\n", " 1.3716000000 4.3912849426 46.6986503600 NaN\n", " 1.5240000000 4.3912849426 46.6986503600 NaN\n", " 1.6764000000 4.3912849426 46.6986503600 NaN\n", "... ... ... ...\n", "303N764340060300 3387.5471999996 303.7090000000 32.0276000039 252.4951\n", " 3387.6995999996 303.7090000000 32.0276000000 252.4951\n", " 3387.8519999996 303.7090000000 32.0276000000 252.4951\n", " 3388.0043999996 303.7090000000 32.0276000000 252.4951\n", " 3388.1567999996 303.7090000000 32.0276000000 252.4951\n", "\n", "[46594 rows x 3 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "keys = ['Caliper', 'GR', 'Sonic']\n", "\n", "df = p.df(keys=keys, alias=alias, rename_aliased=True)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quality\n", "\n", "Welly can run quality tests on the curves in your project. Some of the tests take arguments. You can test for things like this:\n", "\n", "- `all_positive`: Passes if all the values are greater than zero.\n", "- `all_above(50)`: Passes if all the values are greater than 50.\n", "- `mean_below(100)`: Passes if the mean of the log is less than 100.\n", "- `no_nans`: Passes if there are no NaNs in the log.\n", "- `no_flat`: Passes if there are no sections of well log with the same values (e.g. because a gap was interpolated across with a constant value).\n", "- `no_monotonic`: Passes if there are no monotonic ramps in the log (e.g. because a gap was linearly interpolated across).\n", "\n", "Insert lists of tests into a dictionary with any of the following key examples:\n", "\n", "- `'GR'`: The test(s) will run against the GR log.\n", "- `'Gamma'`: The test(s) will run against the log matching according to the alias dictionary.\n", "- `'Each'`: The test(s) will run against *every log* in a well.\n", "- `'All'`: Some tests take multiple logs as input, for example `quality.no_similarities`. These test(s) will run against all the logs as a group. Could be quite slow, because there may be a lot of pairwise comparisons to do.\n", "\n", "The tests are run against all wells in the project. If you only want to run against a subset of the wells, make a new project for them." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import welly.quality as q\n", "\n", "tests = {\n", " 'All': [q.no_similarities],\n", " 'Each': [q.no_gaps, q.no_monotonic, q.no_flat],\n", " 'GR': [q.all_positive],\n", " 'Sonic': [q.all_positive, q.all_between(50, 200)],\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add our own test for units:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def has_si_units(curve):\n", " return curve.units.lower() in ['mm', 'gapi', 'us/m', 'k/m3']\n", "\n", "tests['Each'].append(has_si_units)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the same alias dictionary as before:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Sonic': ['DT', 'DT4P'], 'Caliper': ['HCAL', 'CALI']}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alias" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the tests and look at the results, which are in an HTML table:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th>Idx</th><th>UWI</th><th>Data</th><th>Passing</th><th>Caliper*</th><th>GR</th><th>Sonic*</th><th>SP</th><th>RHOB</th></tr><tr><td></td><td></td><td></td><td>%</td><td>3/3&nbsp;wells</td><td>3/3&nbsp;wells</td><td>3/3&nbsp;wells</td><td>2/3&nbsp;wells</td><td>3/3&nbsp;wells</td></tr><tr><td>0</td><td><span style=\"font-weight:bold;\">Kennetcook #2</span></td><td>5/24&nbsp;curves</td><td>54</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">HCAL<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">4.39 in</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"4/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">78.99 gAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT<div title=\"5/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">63.08 us/ft</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">SP<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">52.47 mV</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2.61 g/cm3</span></td></tr><tr><td>1</td><td><span style=\"font-weight:bold;\">100/N14A/11E05</span></td><td>5/18&nbsp;curves</td><td>79</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">CALI<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">8.90 in</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"6/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#33EE33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">103.74 gAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT<div title=\"6/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">74.90 us/ft</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">SP<div title=\"4/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">101.60 mV</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"4/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2.62 g/cm3</span></td></tr><tr><td>2</td><td><span style=\"font-weight:bold;\">303N764340060300</span></td><td>4/22&nbsp;curves</td><td>78</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">CALI<div title=\"3/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">311.97 MM</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"6/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#33EE33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">67.49 GAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT4P<div title=\"6/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">279.84 US/M</span></td><td style=\"background-color:#CCCCCC; line-height:80%; padding:5px 4px 2px 4px;\"><div title=\"\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#CCCCCC;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\"> </span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"3/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2493.56 K/M3</span></td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "HTML(p.curve_table_html(keys=['Caliper', 'GR', 'Sonic', 'SP', 'RHOB'],\n", " tests=tests, alias=alias)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's how to interpret the result:\n", "\n", "- Green background: the log is present. You can see the mean value and the units (check them!!).\n", "- Grey background: the log is not present.\n", "\n", "And the traffic light dots (hover to see how many tests passed): \n", "\n", "- Green dot: all the tests passed.\n", "- Orange dot: some tests failed.\n", "- Red dot: all tests failed.\n", "- Grey dot: no tests ran.\n", "\n", "The **Passing** percentage shows how many tests passed for that well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "&copy; 2022 Agile Scientific, CC BY" ] } ], "metadata": { "kernelspec": { "display_name": "welly", "language": "python", "name": "welly" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
huilyu2/DataVisualization
project-spring2017/part2/Part2-ilnkage-version 2.ipynb
1
87421
{ "cells": [ { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly \n", "plotly.tools.set_credentials_file(username='yuxiangling0809', api_key='vJxzgz9EZWkJZdHur9A8')" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly \n", "plotly.tools.set_config_file(world_readable=True,\n", " sharing='public')" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import plotly.plotly as py" ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('/Users/qiqi/DV590/Administrative_Discretionary_Grants_Dataset.csv')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df1 = pd.read_csv('/Users/qiqi/DV590/Part2-Q3-data.csv')" ] }, { "cell_type": "code", "execution_count": 81, "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>Log Number</th>\n", " <th>Institution</th>\n", " <th>Program</th>\n", " <th>Program Type</th>\n", " <th>Project Title</th>\n", " <th>Project Type</th>\n", " <th>Award Date</th>\n", " <th>Address</th>\n", " <th>Address 2</th>\n", " <th>Address 3</th>\n", " <th>...</th>\n", " <th>Census Tract</th>\n", " <th>Census Block</th>\n", " <th>MCD Code</th>\n", " <th>Place Code</th>\n", " <th>CBSA Code</th>\n", " <th>MSAD Code</th>\n", " <th>Description</th>\n", " <th>Location</th>\n", " <th>Organizational Unit Location</th>\n", " <th>text</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>IM-01-02-0010-02</td>\n", " <td>Fire Museum of Maryland</td>\n", " <td>Museum Assessment Program</td>\n", " <td>IM</td>\n", " <td>NaN</td>\n", " <td>Institutional</td>\n", " <td>3/27/02 0:00</td>\n", " <td>1301 York Road</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>408703.0</td>\n", " <td>2001.0</td>\n", " <td>90656.0</td>\n", " <td>51587.0</td>\n", " <td>12580.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1301 York Road\\rLutherville, MD 21093\\r(39.417...</td>\n", " <td>NaN</td>\n", " <td>Museum Assessment Program&lt;br&gt;Total Amount Awar...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>IM-01-03-0032-03</td>\n", " <td>Loxahatchee River Historical Museum</td>\n", " <td>Museum Assessment Program</td>\n", " <td>IM</td>\n", " <td>NaN</td>\n", " <td>Institutional</td>\n", " <td>38020</td>\n", " <td>805 North U.S. Highway 1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>408.0</td>\n", " <td>2003.0</td>\n", " <td>91690.0</td>\n", " <td>35875.0</td>\n", " <td>33100.0</td>\n", " <td>48424.0</td>\n", " <td>NaN</td>\n", " <td>805 North U.S. Highway 1\\rJupiter, FL 33477\\r(...</td>\n", " <td>NaN</td>\n", " <td>Museum Assessment Program&lt;br&gt;Total Amount Awar...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>LL-30-99-0194-99</td>\n", " <td>Museum of Science and Industry, Chicago</td>\n", " <td>National Leadership Grants (LL)</td>\n", " <td>LL</td>\n", " <td>NaN</td>\n", " <td>NLG LMC</td>\n", " <td>7/12/99 0:00</td>\n", " <td>57th Street and Lake Shore Drive</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>411000.0</td>\n", " <td>1008.0</td>\n", " <td>14000.0</td>\n", " <td>14000.0</td>\n", " <td>16980.0</td>\n", " <td>16974.0</td>\n", " <td>NaN</td>\n", " <td>57th Street and Lake Shore Drive\\rChicago, IL ...</td>\n", " <td>NaN</td>\n", " <td>National Leadership Grants (LL)&lt;br&gt;Total Amoun...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>IC-22-10-0036-10</td>\n", " <td>County of Delaware</td>\n", " <td>Conservation Program</td>\n", " <td>IC</td>\n", " <td>Collection Storage Improvement</td>\n", " <td>AHPG-Library</td>\n", " <td>2/22/10 0:00</td>\n", " <td>44 Burrer Drive</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>11604.0</td>\n", " <td>2023.0</td>\n", " <td>75620.0</td>\n", " <td>75602.0</td>\n", " <td>18140.0</td>\n", " <td>NaN</td>\n", " <td>The County of Delaware Community Library in Su...</td>\n", " <td>44 Burrer Drive\\rSunbury, OH 43074-9319\\r(40.2...</td>\n", " <td>NaN</td>\n", " <td>Conservation Program&lt;br&gt;Total Amount Awarded a...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>IC-21-11-0142-11</td>\n", " <td>Plimoth Plantation</td>\n", " <td>Conservation Program</td>\n", " <td>IC</td>\n", " <td>Rare Book Rehousing Project</td>\n", " <td>AHPG-Museum</td>\n", " <td>37875</td>\n", " <td>P.O. Box 1620</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>530600.0</td>\n", " <td>1040.0</td>\n", " <td>54310.0</td>\n", " <td>NaN</td>\n", " <td>14460.0</td>\n", " <td>14454.0</td>\n", " <td>Plimoth Plantation will re-house the museum's ...</td>\n", " <td>P.O. Box 1620\\rPlymouth, MA 02362-1620\\r</td>\n", " <td>NaN</td>\n", " <td>Conservation Program&lt;br&gt;Total Amount Awarded a...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 40 columns</p>\n", "</div>" ], "text/plain": [ " Log Number Institution \\\n", "0 IM-01-02-0010-02 Fire Museum of Maryland \n", "1 IM-01-03-0032-03 Loxahatchee River Historical Museum \n", "2 LL-30-99-0194-99 Museum of Science and Industry, Chicago \n", "3 IC-22-10-0036-10 County of Delaware \n", "4 IC-21-11-0142-11 Plimoth Plantation \n", "\n", " Program Program Type \\\n", "0 Museum Assessment Program IM \n", "1 Museum Assessment Program IM \n", "2 National Leadership Grants (LL) LL \n", "3 Conservation Program IC \n", "4 Conservation Program IC \n", "\n", " Project Title Project Type Award Date \\\n", "0 NaN Institutional 3/27/02 0:00 \n", "1 NaN Institutional 38020 \n", "2 NaN NLG LMC 7/12/99 0:00 \n", "3 Collection Storage Improvement AHPG-Library 2/22/10 0:00 \n", "4 Rare Book Rehousing Project AHPG-Museum 37875 \n", "\n", " Address Address 2 Address 3 \\\n", "0 1301 York Road NaN NaN \n", "1 805 North U.S. Highway 1 NaN NaN \n", "2 57th Street and Lake Shore Drive NaN NaN \n", "3 44 Burrer Drive NaN NaN \n", "4 P.O. Box 1620 NaN NaN \n", "\n", " ... Census Tract \\\n", "0 ... 408703.0 \n", "1 ... 408.0 \n", "2 ... 411000.0 \n", "3 ... 11604.0 \n", "4 ... 530600.0 \n", "\n", " Census Block MCD Code Place Code CBSA Code MSAD Code \\\n", "0 2001.0 90656.0 51587.0 12580.0 NaN \n", "1 2003.0 91690.0 35875.0 33100.0 48424.0 \n", "2 1008.0 14000.0 14000.0 16980.0 16974.0 \n", "3 2023.0 75620.0 75602.0 18140.0 NaN \n", "4 1040.0 54310.0 NaN 14460.0 14454.0 \n", "\n", " Description \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 The County of Delaware Community Library in Su... \n", "4 Plimoth Plantation will re-house the museum's ... \n", "\n", " Location \\\n", "0 1301 York Road\\rLutherville, MD 21093\\r(39.417... \n", "1 805 North U.S. Highway 1\\rJupiter, FL 33477\\r(... \n", "2 57th Street and Lake Shore Drive\\rChicago, IL ... \n", "3 44 Burrer Drive\\rSunbury, OH 43074-9319\\r(40.2... \n", "4 P.O. Box 1620\\rPlymouth, MA 02362-1620\\r \n", "\n", " Organizational Unit Location \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", " text \n", "0 Museum Assessment Program<br>Total Amount Awar... \n", "1 Museum Assessment Program<br>Total Amount Awar... \n", "2 National Leadership Grants (LL)<br>Total Amoun... \n", "3 Conservation Program<br>Total Amount Awarded a... \n", "4 Conservation Program<br>Total Amount Awarded a... \n", "\n", "[5 rows x 40 columns]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 82, "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>z</th>\n", " <th>locations</th>\n", " <th>text</th>\n", " <th>Unnamed: 3</th>\n", " <th>Unnamed: 4</th>\n", " <th>Unnamed: 5</th>\n", " <th>Unnamed: 6</th>\n", " <th>Unnamed: 7</th>\n", " <th>GDP</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>156750.0</td>\n", " <td>AL</td>\n", " <td>State: AL&lt;br&gt;Total Awarded Amount: 5743215&lt;br&gt;...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>156750.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>40063.0</td>\n", " <td>AK</td>\n", " <td>State: AK&lt;br&gt;Total Awarded Amount: 16189749&lt;br...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40063.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>227358.0</td>\n", " <td>AZ</td>\n", " <td>State: AZ&lt;br&gt;Total Awarded Amount: 19037687&lt;br...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>227358.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>90319.0</td>\n", " <td>AR</td>\n", " <td>State: AR&lt;br&gt;Total Awarded Amount: 1965330&lt;br&gt;...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>90319.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1766693.0</td>\n", " <td>CA</td>\n", " <td>State: CA&lt;br&gt;Total Awarded Amount: 74663752&lt;br...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1766693.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " z locations text \\\n", "0 156750.0 AL State: AL<br>Total Awarded Amount: 5743215<br>... \n", "1 40063.0 AK State: AK<br>Total Awarded Amount: 16189749<br... \n", "2 227358.0 AZ State: AZ<br>Total Awarded Amount: 19037687<br... \n", "3 90319.0 AR State: AR<br>Total Awarded Amount: 1965330<br>... \n", "4 1766693.0 CA State: CA<br>Total Awarded Amount: 74663752<br... \n", "\n", " Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 GDP \n", "0 NaN NaN NaN NaN NaN 156750.0 \n", "1 NaN NaN NaN NaN NaN 40063.0 \n", "2 NaN NaN NaN NaN NaN 227358.0 \n", "3 NaN NaN NaN NaN NaN 90319.0 \n", "4 NaN NaN NaN NaN NaN 1766693.0 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.head()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df['text'] = df['Program'] +'<br>'+ 'Total Amount Awarded and Disbursed ' + (df['Total Amount Awarded and Disbursed']/1e3).astype(str)+'thousand'+'<br>'\n", "limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]\n", "colors = [\"lightgrey\",\"rgb(255,65,54)\",\"rgb(133,20,75)\",\"rgb(255,133,27)\",\"#ffb266\",\"rgb(0,116,217)\"]\n", "Programs = []\n", "scale = 6000\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(len(limits)):\n", " lim = limits[i]\n", " df_sub = df[((df['Total Amount Awarded and Disbursed']/1e3)<limits[i][1])&((df['Total Amount Awarded and Disbursed']/1e3)>limits[i][0])]\n", " program = dict(\n", " type = 'scattergeo',\n", " locationmode = 'USA-states',\n", " lon = df_sub['LNG'],\n", " lat = df_sub['LAT'],\n", " text = df_sub['text'],\n", " marker = dict(\n", " size = df_sub['Total Amount Awarded and Disbursed']/scale,\n", " color = colors[i],\n", " line = dict(width=0.5, color='rgb(40,40,40)'),\n", " sizemode = 'area'\n", " ),\n", " name = '{0} - {1}'.format(limits[i][0],limits[i][1]) )\n", " Programs.append(program)\n" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trace1=Programs" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\n", " [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trace2 = [ dict(\n", " type='choropleth',\n", " colorscale = scl,\n", " autocolorscale = False,\n", " locations = df1['locations'],\n", " z = [156750,40063,227358,90319,1766693,220454,210170,53247,83586,700267,388342,58573,47176,586442,245197,123353,104884,143875,199683,46277,263809,345059,395166,243933,81678,224091,30637,73455,118785,57282,443148,74403,1023433,357168,24622,467392,125243,147535,506505,45663,143585,31380,227505,990054,94475,23539,359273,296403,53453,226325,27454], \n", " locationmode = 'USA-states',\n", " text = [\"State: AL<br>Total Awarded Amount: 5743215<br>GDP(million): 156750\",\n", "\"State: AK<br>Total Awarded Amount: 16189749<br>GDP(million): 40063\",\n", "\"State: AZ<br>Total Awarded Amount: 19037687<br>GDP(million): 227358\",\n", "\"State: AR<br>Total Awarded Amount: 1965330<br>GDP(million): 90319\",\n", "\"State: CA<br>Total Awarded Amount: 74663752<br>GDP(million): 1766693\",\n", "\"State: CO<br>Total Awarded Amount: 22235728<br>GDP(million): 220454\",\n", "\"State: CT<br>Total Awarded Amount: 16501999<br>GDP(million): 210170\",\n", "\"State: DE<br>Total Awarded Amount: 3857523<br>GDP(million): 53247\",\n", "\"State: DC<br>Total Awarded Amount: 25424106<br>GDP(million): 83586\",\n", "\"State: FL<br>Total Awarded Amount: 26600593<br>GDP(million): 700267\",\n", "\"State: GA<br>Total Awarded Amount: 11216327<br>GDP(million): 388342\",\n", "\"State: HI<br>Total Awarded Amount: 14427899<br>GDP(million): 58573\",\n", "\"State: ID<br>Total Awarded Amount: 1961349<br>GDP(million): 47176\",\n", "\"State: IL<br>Total Awarded Amount: 68112505<br>GDP(million): 586442\",\n", "\"State: IN<br>Total Awarded Amount: 16377153<br>GDP(million): 245197\",\n", "\"State: IA<br>Total Awarded Amount: 8493716<br>GDP(million): 123353\",\n", "\"State: KS<br>Total Awarded Amount: 5764416<br>GDP(million): 104884\",\n", "\"State: KY<br>Total Awarded Amount: 8022942<br>GDP(million): 143875\",\n", "\"State: LA<br>Total Awarded Amount: 7978539<br>GDP(million): 199683\",\n", "\"State: ME<br>Total Awarded Amount: 11177079<br>GDP(million): 46277\",\n", "\"State: MD<br>Total Awarded Amount: 21671260<br>GDP(million): 263809\",\n", "\"State: MA<br>Total Awarded Amount: 45976945<br>GDP(million): 345059\",\n", "\"State: MI<br>Total Awarded Amount: 28799226<br>GDP(million): 395166\",\n", "\"State: MN<br>Total Awarded Amount: 15325162<br>GDP(million): 243933\",\n", "\"State: MS<br>Total Awarded Amount: 2787779<br>GDP(million): 81678\",\n", "\"State: MO<br>Total Awarded Amount: 14911587<br>GDP(million): 224091\",\n", "\"State: MT<br>Total Awarded Amount: 10424194<br>GDP(million): 30637\",\n", "\"State: NE<br>Total Awarded Amount: 6600356<br>GDP(million): 73455\",\n", "\"State: NV<br>Total Awarded Amount: 3677624<br>GDP(million): 118785\",\n", "\"State: NH<br>Total Awarded Amount: 4797223<br>GDP(million): 57282\",\n", "\"State: NJ<br>Total Awarded Amount: 11475362<br>GDP(million): 443148\",\n", "\"State: NM<br>Total Awarded Amount: 10640574<br>GDP(million): 74403\",\n", "\"State: NY<br>Total Awarded Amount: 105860170<br>GDP(million): 1023433\",\n", "\"State: NC<br>Total Awarded Amount: 29992038<br>GDP(million): 357168\",\n", "\"State: ND<br>Total Awarded Amount: 3071608<br>GDP(million): 24622\",\n", "\"State: OH<br>Total Awarded Amount: 22015009<br>GDP(million): 467392\",\n", "\"State: OK<br>Total Awarded Amount: 12405823<br>GDP(million): 125243\",\n", "\"State: OR<br>Total Awarded Amount: 9556529<br>GDP(million): 147535\",\n", "\"State: PA<br>Total Awarded Amount: 59575053<br>GDP(million): 506505\",\n", "\"State: RI<br>Total Awarded Amount: 4751919<br>GDP(million): 45663\",\n", "\"State: SC<br>Total Awarded Amount: 9063478<br>GDP(million): 143585\",\n", "\"State: SD<br>Total Awarded Amount: 3596551<br>GDP(million): 31380\",\n", "\"State: TN<br>Total Awarded Amount: 18083590<br>GDP(million): 227505\",\n", "\"State: TX<br>Total Awarded Amount: 48252344<br>GDP(million): 990054\",\n", "\"State: UT<br>Total Awarded Amount: 6884351<br>GDP(million): 94475\",\n", "\"State: VT<br>Total Awarded Amount: 6381133<br>GDP(million): 23539\",\n", "\"State: VA<br>Total Awarded Amount: 18068081<br>GDP(million): 359273\",\n", "\"State: WA<br>Total Awarded Amount: 23476750<br>GDP(million): 296403\",\n", "\"State: WV<br>Total Awarded Amount: 927680<br>GDP(million): 53453\",\n", "\"State: WI<br>Total Awarded Amount: 17075827<br>GDP(million): 226325\",\n", "\"State: WY<br>Total Awarded Amount: 2210361<br>GDP(million): 27454\"],\n", " marker = dict(\n", " line = dict (\n", " color = 'rgb(255,255,255)',\n", " width = 2\n", " ) ),\n", " colorbar = dict(\n", " title = \"Millions USD\")\n", " ) ]" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [], "source": [ "layout2 = dict(\n", " title = '2005 US GDP Distribution<br>(Compared with total awarded amount)',\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showlakes = True,\n", " lakecolor = 'rgb(255, 255, 255)'),\n", " )" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layout1=dict(\n", " title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',\n", " showlegend = True,\n", " geo = dict(\n", " scope='usa',\n", " projection=dict( type='albers usa' ),\n", " showland = True,\n", " landcolor = 'rgb(217, 217, 217)',\n", " subunitwidth=1,\n", " countrywidth=1,\n", " subunitcolor=\"rgb(255, 255, 255)\",\n", " countrycolor=\"rgb(255, 255, 255)\"\n", " ),\n", " )" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(trace2)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"63ec5002-d507-471a-86da-6212c5aba955\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._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", " if (window.Bokeh !== undefined) {\n", " var el = document.getElementById(\"63ec5002-d507-471a-86da-6212c5aba955\");\n", " el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"63ec5002-d507-471a-86da-6212c5aba955\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '63ec5002-d507-471a-86da-6212c5aba955' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " \n", " function(Bokeh) {\n", " \n", " document.getElementById(\"63ec5002-d507-471a-86da-6212c5aba955\").textContent = \"BokehJS is loading...\";\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"63ec5002-d507-471a-86da-6212c5aba955\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.io import output_notebook, show\n", "from bokeh.layouts import gridplot\n", "from bokeh.models import ColumnDataSource\n", "from bokeh.plotting import figure\n", "\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import csv\n", "from bokeh.io import output_file, show\n", "from bokeh.layouts import widgetbox\n", "from bokeh.models.widgets import Select" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = [\"AL\",\"AK\",\"AZ\",\"AR\",\"CA\",\"CO\",\"CT\",\"DE\",\"DC\",\"FL\",\"GA\",\"HI\",\"ID\",\"IL\",\"IN\",\"IA\",\"KS\",\"KY\",\"LA\",\"ME\",\"MD\",\"MA\",\"MI\",\n", "\"MN\",\"MS\",\"MO\",\"MT\",\"NE\",\"NV\",\"NH\",\"NJ\",\"NM\",\"NY\",\"NC\",\"ND\",\"OH\",\"OK\",\"OR\",\"PA\",\"RI\",\"SC\",\"SD\",\"TN\",\"TX\",\"UT\",\"VT\",\"VA\",\n", "\"WA\",\"WV\",\"WI\",\"WY\"]\n", "y0 = [156750,40063,227358,90319,1766693,220454,210170,53247,83586,700267,388342,58573,47176,586442,245197,123353,104884,143875,199683,46277,263809,345059,395166,243933,81678,224091,30637,73455,118785,57282,443148,74403,1023433,357168,24622,467392,125243,147535,506505,45663,143585,31380,227505,990054,94475,23539,359273,296403,53453,226325,27454] \n", "y1 = [5743215,16189749,19037687,1965330,74663752,22235728,16501999,3857523,25424106,26600593,11216327,14427899,1961349,68112505,16377153,8493716,5764416,8022942,7978539,11177079,21671260,45976945,28799226,15325162,2787779,14911587,10424194,6600356,3677624,4797223,11475362,640574,105860170,29992038,3071608,22015009,12405823,9556529,59575053,4751919,9063478,3596551,18083590,48252344,6884351,6381133,18068081,23476750,927680,17075827,2210361]" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = list(range(1, 52))\n", "y0 = [156750,40063,227358,90319,1766693,220454,210170,53247,83586,700267,388342,58573,47176,586442,245197,123353,104884,143875,199683,46277,263809,345059,395166,243933,81678,224091,30637,73455,118785,57282,443148,74403,1023433,357168,24622,467392,125243,147535,506505,45663,143585,31380,227505,990054,94475,23539,359273,296403,53453,226325,27454] \n", "y1 = [5743215,16189749,19037687,1965330,74663752,22235728,16501999,3857523,25424106,26600593,11216327,14427899,1961349,68112505,16377153,8493716,5764416,8022942,7978539,11177079,21671260,45976945,28799226,15325162,2787779,14911587,10424194,6600356,3677624,4797223,11475362,640574,105860170,29992038,3071608,22015009,12405823,9556529,59575053,4751919,9063478,3596551,18083590,48252344,6884351,6381133,18068081,23476750,927680,17075827,2210361]" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(y0)" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "51" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(x)" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_label=[\"IA\",\"IC\",\"IG\",\"IL\",\"IM\",\"IS\",\"LE\",\"LG\",\"LI\",\"LL\",\"LT\",\"MA\",\n", " \"MG\",\"MH\",\"ML\",\"MN\",\"MP\",\"NC\",\"ND\",\"NE\",\"NG\",\"NL\",\"NO\",\"NP\",\"NR\",\"RE\",\"SP\",\"ST\"]" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": true }, "outputs": [], "source": [ "source = ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "collapsed": false }, "outputs": [], "source": [ "TOOLS=\"pan,wheel_zoom,box_zoom,reset,box_select,lasso_select\"" ] }, { "cell_type": "code", "execution_count": 233, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x13c42bc10>,\n", " <matplotlib.axis.XTick at 0x13f6c7fd0>,\n", " <matplotlib.axis.XTick at 0x16056d350>,\n", " <matplotlib.axis.XTick at 0x11b8d9750>,\n", " <matplotlib.axis.XTick at 0x11b8d9e50>,\n", " <matplotlib.axis.XTick at 0x11b8d6590>,\n", " <matplotlib.axis.XTick at 0x11b8d6c90>,\n", " <matplotlib.axis.XTick at 0x11b8f03d0>,\n", " <matplotlib.axis.XTick at 0x11b8f0ad0>,\n", " <matplotlib.axis.XTick at 0x11b8f8210>,\n", " <matplotlib.axis.XTick at 0x11b8f8910>,\n", " <matplotlib.axis.XTick at 0x11b8c3050>,\n", " <matplotlib.axis.XTick at 0x11b8c3750>,\n", " <matplotlib.axis.XTick at 0x11b8c3e50>,\n", " <matplotlib.axis.XTick at 0x11b8d0590>,\n", " <matplotlib.axis.XTick at 0x11b8d0c90>,\n", " <matplotlib.axis.XTick at 0x13c44e3d0>,\n", " <matplotlib.axis.XTick at 0x13c44ead0>,\n", " <matplotlib.axis.XTick at 0x13c474210>,\n", " <matplotlib.axis.XTick at 0x13c474910>,\n", " <matplotlib.axis.XTick at 0x13c46b050>,\n", " <matplotlib.axis.XTick at 0x13c46b750>,\n", " <matplotlib.axis.XTick at 0x13c46be50>,\n", " <matplotlib.axis.XTick at 0x13c461590>,\n", " <matplotlib.axis.XTick at 0x13c461c90>,\n", " <matplotlib.axis.XTick at 0x13c4543d0>,\n", " <matplotlib.axis.XTick at 0x13c454ad0>,\n", " <matplotlib.axis.XTick at 0x15fe27210>,\n", " <matplotlib.axis.XTick at 0x15fe27910>,\n", " <matplotlib.axis.XTick at 0x15fdfb050>,\n", " <matplotlib.axis.XTick at 0x15fdfb750>,\n", " <matplotlib.axis.XTick at 0x15fdfbe50>,\n", " <matplotlib.axis.XTick at 0x15fdfe590>,\n", " <matplotlib.axis.XTick at 0x15fdfec90>,\n", " <matplotlib.axis.XTick at 0x15fe093d0>,\n", " <matplotlib.axis.XTick at 0x15fe09ad0>,\n", " <matplotlib.axis.XTick at 0x15fe17210>,\n", " <matplotlib.axis.XTick at 0x15fe17910>,\n", " <matplotlib.axis.XTick at 0x15fe23050>,\n", " <matplotlib.axis.XTick at 0x15fe23750>,\n", " <matplotlib.axis.XTick at 0x15fe23e50>,\n", " <matplotlib.axis.XTick at 0x160c73590>,\n", " <matplotlib.axis.XTick at 0x160c73c90>,\n", " <matplotlib.axis.XTick at 0x160c8f3d0>,\n", " <matplotlib.axis.XTick at 0x160c8fad0>,\n", " <matplotlib.axis.XTick at 0x160c5b210>,\n", " <matplotlib.axis.XTick at 0x160c5b910>,\n", " <matplotlib.axis.XTick at 0x160c67050>,\n", " <matplotlib.axis.XTick at 0x160c67750>,\n", " <matplotlib.axis.XTick at 0x160c67e50>,\n", " <matplotlib.axis.XTick at 0x160c7b590>],\n", " <a list of 28 Text xticklabel objects>)" ] }, "execution_count": 233, "metadata": {}, "output_type": "execute_result" } ], "source": [ "left=plt.bar(x,y0,align=\"center\",width=0.8,alpha=0.5)\n", "plt.xticks(x,x_label)" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.renderers.GlyphRenderer\">GlyphRenderer</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'77df47f1-6da3-4d32-a154-e4c7b60f1e01', <span id=\"6f5f116d-5f2b-4634-b5da-637944b6003c\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">data_source&nbsp;=&nbsp;ColumnDataSource(id='33d14d8c-4780-4037-8470-00ff0c805459', ...),</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">glyph&nbsp;=&nbsp;Circle(id='c2060b69-fb45-4dbb-884f-cb1afdb43198', ...),</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hover_glyph&nbsp;=&nbsp;None,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">level&nbsp;=&nbsp;'glyph',</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted&nbsp;=&nbsp;False,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted_glyph&nbsp;=&nbsp;None,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">nonselection_glyph&nbsp;=&nbsp;Circle(id='d6fb527f-e602-4c49-8398-13f0b8021192', ...),</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">selection_glyph&nbsp;=&nbsp;None,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range_name&nbsp;=&nbsp;'default',</div></div><div class=\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range_name&nbsp;=&nbsp;'default')</div></div></div>\n", "<script>\n", "(function() {\n", " var expanded = false;\n", " var ellipsis = document.getElementById(\"6f5f116d-5f2b-4634-b5da-637944b6003c\");\n", " ellipsis.addEventListener(\"click\", function() {\n", " var rows = document.getElementsByClassName(\"88e7a4b6-0598-480c-b5c2-cdd78138c5dc\");\n", " for (var i = 0; i < rows.length; i++) {\n", " var el = rows[i];\n", " el.style.display = expanded ? \"none\" : \"table-row\";\n", " }\n", " ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n", " expanded = !expanded;\n", " });\n", "})();\n", "</script>\n" ], "text/plain": [ "GlyphRenderer(id='77df47f1-6da3-4d32-a154-e4c7b60f1e01', ...)" ] }, "execution_count": 224, "metadata": {}, "output_type": "execute_result" } ], "source": [ "left = figure(tools=TOOLS, width=500, height=500, title=\"GDP/Million\",x_axis_label='number represent states,sequence：1～51 means AL~WY')\n", "left.circle('x', 'y0', source=source)" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.models.renderers.GlyphRenderer\">GlyphRenderer</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'6604c85e-1184-4d57-969e-b24877c4d070', <span id=\"4f980487-8eb7-45ba-bbc7-c50c87c795c7\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">data_source&nbsp;=&nbsp;ColumnDataSource(id='33d14d8c-4780-4037-8470-00ff0c805459', ...),</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">glyph&nbsp;=&nbsp;Circle(id='5cae6c4f-3664-4ad8-8723-d0f2f740e1cf', ...),</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hover_glyph&nbsp;=&nbsp;None,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">level&nbsp;=&nbsp;'glyph',</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted&nbsp;=&nbsp;False,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">muted_glyph&nbsp;=&nbsp;None,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">nonselection_glyph&nbsp;=&nbsp;Circle(id='71d85e0a-d8d9-463f-8a04-06e4bc5695e1', ...),</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">selection_glyph&nbsp;=&nbsp;None,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible&nbsp;=&nbsp;True,</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range_name&nbsp;=&nbsp;'default',</div></div><div class=\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range_name&nbsp;=&nbsp;'default')</div></div></div>\n", "<script>\n", "(function() {\n", " var expanded = false;\n", " var ellipsis = document.getElementById(\"4f980487-8eb7-45ba-bbc7-c50c87c795c7\");\n", " ellipsis.addEventListener(\"click\", function() {\n", " var rows = document.getElementsByClassName(\"3a9059e6-4b51-43b9-aa66-c6a278c05ea9\");\n", " for (var i = 0; i < rows.length; i++) {\n", " var el = rows[i];\n", " el.style.display = expanded ? \"none\" : \"table-row\";\n", " }\n", " ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n", " expanded = !expanded;\n", " });\n", "})();\n", "</script>\n" ], "text/plain": [ "GlyphRenderer(id='6604c85e-1184-4d57-969e-b24877c4d070', ...)" ] }, "execution_count": 225, "metadata": {}, "output_type": "execute_result" } ], "source": [ "right = figure(tools=TOOLS, width=500, height=500, title=\"Total Amount Awarded and Disbursed\",x_axis_label='number represent states,sequence：1～51 means AL~WY')\n", "right.circle('x', 'y1', source=source)" ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x13c42bc10>,\n", " <matplotlib.axis.XTick at 0x13f6c7fd0>,\n", " <matplotlib.axis.XTick at 0x16056d350>,\n", " <matplotlib.axis.XTick at 0x11b8d9750>,\n", " <matplotlib.axis.XTick at 0x11b8d9e50>,\n", " <matplotlib.axis.XTick at 0x11b8d6590>,\n", " <matplotlib.axis.XTick at 0x11b8d6c90>,\n", " <matplotlib.axis.XTick at 0x11b8f03d0>,\n", " <matplotlib.axis.XTick at 0x11b8f0ad0>,\n", " <matplotlib.axis.XTick at 0x11b8f8210>,\n", " <matplotlib.axis.XTick at 0x11b8f8910>,\n", " <matplotlib.axis.XTick at 0x11b8c3050>,\n", " <matplotlib.axis.XTick at 0x11b8c3750>,\n", " <matplotlib.axis.XTick at 0x11b8c3e50>,\n", " <matplotlib.axis.XTick at 0x11b8d0590>,\n", " <matplotlib.axis.XTick at 0x11b8d0c90>,\n", " <matplotlib.axis.XTick at 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Application\",\"version\":\"0.12.5\"}};\n", " var render_items = [{\"docid\":\"d1d03132-8b48-4df8-8635-c739baf32742\",\"elementid\":\"050c8dd2-f64a-442d-a34f-bbd157e769fa\",\"modelid\":\"2e216e50-cf04-4f74-9e1b-000efe430234\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " };\n", " if (document.readyState != \"loading\") fn();\n", " else document.addEventListener(\"DOMContentLoaded\", fn);\n", " })();\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"050c8dd2-f64a-442d-a34f-bbd157e769fa\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select = Select(title=\"year:\", value=\"foo\", options=[\"2000\", \"2001\", \"2002\", \"2003\",\"2004\",\"2005\"])\n", "show(widgetbox(select))\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", 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mit
mne-tools/mne-tools.github.io
dev/_downloads/00e78bba5d10188fcf003ef05e32a6f7/decoding_time_generalization_conditions.ipynb
1
5309
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Decoding sensor space data with generalization across time and conditions\n\nThis example runs the analysis described in :footcite:`KingDehaene2014`. It\nillustrates how one can\nfit a linear classifier to identify a discriminatory topography at a given time\ninstant and subsequently assess whether this linear model can accurately\npredict all of the time samples of a second set of conditions.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Jean-Remi King <[email protected]>\n# Alexandre Gramfort <[email protected]>\n# Denis Engemann <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.decoding import GeneralizingEstimator\n\nprint(__doc__)\n\n# Preprocess data\ndata_path = sample.data_path()\n# Load and filter data, set up epochs\nmeg_path = data_path / 'MEG' / 'sample'\nraw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'\nevents_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif'\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\npicks = mne.pick_types(raw.info, meg=True, exclude='bads') # Pick MEG channels\nraw.filter(1., 30., fir_design='firwin') # Band pass filtering signals\nevents = mne.read_events(events_fname)\nevent_id = {'Auditory/Left': 1, 'Auditory/Right': 2,\n 'Visual/Left': 3, 'Visual/Right': 4}\ntmin = -0.050\ntmax = 0.400\n# decimate to make the example faster to run, but then use verbose='error' in\n# the Epochs constructor to suppress warning about decimation causing aliasing\ndecim = 2\nepochs = mne.Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,\n proj=True, picks=picks, baseline=None, preload=True,\n reject=dict(mag=5e-12), decim=decim, verbose='error')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will train the classifier on all left visual vs auditory trials\nand test on all right visual vs auditory trials.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf = make_pipeline(\n StandardScaler(),\n LogisticRegression(solver='liblinear') # liblinear is faster than lbfgs\n)\ntime_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=None,\n verbose=True)\n\n# Fit classifiers on the epochs where the stimulus was presented to the left.\n# Note that the experimental condition y indicates auditory or visual\ntime_gen.fit(X=epochs['Left'].get_data(),\n y=epochs['Left'].events[:, 2] > 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Score on the epochs where the stimulus was presented to the right.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scores = time_gen.score(X=epochs['Right'].get_data(),\n y=epochs['Right'].events[:, 2] > 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax = plt.subplots(1)\nim = ax.matshow(scores, vmin=0, vmax=1., cmap='RdBu_r', origin='lower',\n extent=epochs.times[[0, -1, 0, -1]])\nax.axhline(0., color='k')\nax.axvline(0., color='k')\nax.xaxis.set_ticks_position('bottom')\nax.set_xlabel('Testing Time (s)')\nax.set_ylabel('Training Time (s)')\nax.set_title('Generalization across time and condition')\nplt.colorbar(im, ax=ax)\nplt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n.. footbibliography::\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
bryantbiggs/movie_torrents
notebooks/numbers_scrape_validate.ipynb
2
54312
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append('../src')\n", "\n", "from numbers_scraper import NumbersScraper\n", "SCRAPER = NumbersScraper()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>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>1</th>\n", " <td>12/18/2009</td>\n", " <td>Avatar</td>\n", " <td>$425,000,000</td>\n", " <td>$760,507,625</td>\n", " <td>$2,783,918,982</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>12/18/2015</td>\n", " <td>Star Wars Ep. VII: The Force Awakens</td>\n", " <td>$306,000,000</td>\n", " <td>$936,662,225</td>\n", " <td>$2,058,662,225</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>5/24/2007</td>\n", " <td>Pirates of the Caribbean: At World’s End</td>\n", " <td>$300,000,000</td>\n", " <td>$309,420,425</td>\n", " <td>$963,420,425</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11/6/2015</td>\n", " <td>Spectre</td>\n", " <td>$300,000,000</td>\n", " <td>$200,074,175</td>\n", " <td>$879,620,923</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>7/20/2012</td>\n", " <td>The Dark Knight Rises</td>\n", " <td>$275,000,000</td>\n", " <td>$448,139,099</td>\n", " <td>$1,084,439,099</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7/2/2013</td>\n", " <td>The Lone Ranger</td>\n", " <td>$275,000,000</td>\n", " <td>$89,302,115</td>\n", " <td>$260,002,115</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>3/9/2012</td>\n", " <td>John Carter</td>\n", " <td>$275,000,000</td>\n", " <td>$73,058,679</td>\n", " <td>$282,778,100</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>11/24/2010</td>\n", " <td>Tangled</td>\n", " <td>$260,000,000</td>\n", " <td>$200,821,936</td>\n", " <td>$586,581,936</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>5/4/2007</td>\n", " <td>Spider-Man 3</td>\n", " <td>$258,000,000</td>\n", " <td>$336,530,303</td>\n", " <td>$894,860,230</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>5/1/2015</td>\n", " <td>Avengers: Age of Ultron</td>\n", " <td>$250,000,000</td>\n", " <td>$459,005,868</td>\n", " <td>$1,408,218,722</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>5/6/2016</td>\n", " <td>Captain America: Civil War</td>\n", " <td>$250,000,000</td>\n", " <td>$408,084,349</td>\n", " <td>$1,153,304,495</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>3/25/2016</td>\n", " <td>Batman v Superman: Dawn of Justice</td>\n", " <td>$250,000,000</td>\n", " <td>$330,360,194</td>\n", " <td>$868,160,194</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>12/14/2012</td>\n", " <td>The Hobbit: An Unexpected Journey</td>\n", " <td>$250,000,000</td>\n", " <td>$303,003,568</td>\n", " <td>$1,017,003,568</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>7/15/2009</td>\n", " <td>Harry Potter and the Half-Blood Prince</td>\n", " <td>$250,000,000</td>\n", " <td>$301,959,197</td>\n", " <td>$935,083,686</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>12/13/2013</td>\n", " <td>The Hobbit: The Desolation of Smaug</td>\n", " <td>$250,000,000</td>\n", " <td>$258,366,855</td>\n", " <td>$960,366,855</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>12/17/2014</td>\n", " <td>The Hobbit: The Battle of the Five Armies</td>\n", " <td>$250,000,000</td>\n", " <td>$255,119,788</td>\n", " <td>$955,119,788</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>5/20/2011</td>\n", " <td>Pirates of the Caribbean: On Stranger Tides</td>\n", " <td>$250,000,000</td>\n", " <td>$241,063,875</td>\n", " <td>$1,045,663,875</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>4/14/2017</td>\n", " <td>The Fate of the Furious</td>\n", " <td>$250,000,000</td>\n", " <td>$225,764,765</td>\n", " <td>$1,237,444,462</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>6/28/2006</td>\n", " <td>Superman Returns</td>\n", " <td>$232,000,000</td>\n", " <td>$200,120,000</td>\n", " <td>$374,085,065</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>5/26/2017</td>\n", " <td>Pirates of the Caribbean: Dead Men Tell No Tales</td>\n", " <td>$230,000,000</td>\n", " <td>$171,832,628</td>\n", " <td>$785,628,907</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>11/14/2008</td>\n", " <td>Quantum of Solace</td>\n", " <td>$230,000,000</td>\n", " <td>$169,368,427</td>\n", " <td>$591,692,078</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>5/4/2012</td>\n", " <td>The Avengers</td>\n", " <td>$225,000,000</td>\n", " <td>$623,279,547</td>\n", " <td>$1,519,479,547</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>7/7/2006</td>\n", " <td>Pirates of the Caribbean: Dead Man’s Chest</td>\n", " <td>$225,000,000</td>\n", " <td>$423,315,812</td>\n", " <td>$1,066,215,812</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>6/14/2013</td>\n", " <td>Man of Steel</td>\n", " <td>$225,000,000</td>\n", " <td>$291,045,518</td>\n", " <td>$667,999,518</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>5/16/2008</td>\n", " <td>The Chronicles of Narnia: Prince Caspian</td>\n", " <td>$225,000,000</td>\n", " <td>$141,621,490</td>\n", " <td>$417,341,288</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>7/3/2012</td>\n", " <td>The Amazing Spider-Man</td>\n", " <td>$220,000,000</td>\n", " <td>$262,030,663</td>\n", " <td>$757,890,267</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>6/21/2017</td>\n", " <td>Transformers: The Last Knight</td>\n", " <td>$217,000,000</td>\n", " <td>$130,120,862</td>\n", " <td>$601,120,862</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>6/12/2015</td>\n", " <td>Jurassic World</td>\n", " <td>$215,000,000</td>\n", " <td>$652,198,010</td>\n", " <td>$1,671,640,593</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>5/25/2012</td>\n", " <td>Men in Black 3</td>\n", " <td>$215,000,000</td>\n", " <td>$179,020,854</td>\n", " <td>$654,213,485</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>6/24/2009</td>\n", " <td>Transformers: Revenge of the Fallen</td>\n", " <td>$210,000,000</td>\n", " <td>$402,111,870</td>\n", " <td>$836,519,699</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>5367</th>\n", " <td>12/1/2015</td>\n", " <td>Dutch Kills</td>\n", " <td>$25,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5368</th>\n", " <td>8/1/1991</td>\n", " <td>Slacker</td>\n", " <td>$23,000</td>\n", " <td>$1,227,508</td>\n", " <td>$1,227,508</td>\n", " </tr>\n", " <tr>\n", " <th>5369</th>\n", " <td>12/31/2014</td>\n", " <td>Dry Spell</td>\n", " <td>$22,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5370</th>\n", " <td>12/31/2003</td>\n", " <td>Flywheel</td>\n", " <td>$20,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5371</th>\n", " <td>1/4/2013</td>\n", " <td>All Superheroes Must Die</td>\n", " <td>$20,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5372</th>\n", " <td>4/21/2015</td>\n", " <td>The Front Man</td>\n", " <td>$20,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5373</th>\n", " <td>11/25/2011</td>\n", " <td>The Ridges</td>\n", " <td>$17,300</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5374</th>\n", " <td>6/2/2006</td>\n", " <td>The Puffy Chair</td>\n", " <td>$15,000</td>\n", " <td>$194,523</td>\n", " <td>$195,254</td>\n", " </tr>\n", " <tr>\n", " <th>5375</th>\n", " <td>4/2/2010</td>\n", " <td>Breaking Upwards</td>\n", " <td>$15,000</td>\n", " <td>$115,592</td>\n", " <td>$115,592</td>\n", " </tr>\n", " <tr>\n", " <th>5376</th>\n", " <td>12/31/2014</td>\n", " <td>Stories of Our Lives</td>\n", " <td>$15,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5377</th>\n", " <td>4/11/1997</td>\n", " <td>Pink Flamingos</td>\n", " <td>$12,000</td>\n", " <td>$413,802</td>\n", " <td>$413,802</td>\n", " </tr>\n", " <tr>\n", " <th>5378</th>\n", " <td>4/28/2006</td>\n", " <td>Grip: A Criminal's Story</td>\n", " <td>$12,000</td>\n", " <td>$1,336</td>\n", " <td>$1,336</td>\n", " </tr>\n", " <tr>\n", " <th>5379</th>\n", " <td>12/31/2007</td>\n", " <td>Tin Can Man</td>\n", " <td>$12,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5380</th>\n", " <td>3/9/2001</td>\n", " <td>Dayereh</td>\n", " <td>$10,000</td>\n", " <td>$673,780</td>\n", " <td>$673,780</td>\n", " </tr>\n", " <tr>\n", " <th>5381</th>\n", " <td>4/28/2006</td>\n", " <td>Clean</td>\n", " <td>$10,000</td>\n", " <td>$138,711</td>\n", " <td>$138,711</td>\n", " </tr>\n", " <tr>\n", " <th>5382</th>\n", " <td>7/6/2001</td>\n", " <td>Cure</td>\n", " <td>$10,000</td>\n", " <td>$94,596</td>\n", " <td>$94,596</td>\n", " </tr>\n", " <tr>\n", " <th>5383</th>\n", " <td>5/28/2004</td>\n", " <td>On the Down Low</td>\n", " <td>$10,000</td>\n", " <td>$1,987</td>\n", " <td>$1,987</td>\n", " </tr>\n", " <tr>\n", " <th>5384</th>\n", " <td>4/1/1996</td>\n", " <td>Bang</td>\n", " <td>$10,000</td>\n", " <td>$527</td>\n", " <td>$527</td>\n", " </tr>\n", " <tr>\n", " <th>5385</th>\n", " <td>8/14/2008</td>\n", " <td>The Rise and Fall of Miss Thang</td>\n", " <td>$10,000</td>\n", " <td>$401</td>\n", " <td>$401</td>\n", " </tr>\n", " <tr>\n", " <th>5386</th>\n", " <td>5/19/2015</td>\n", " <td>Family Motocross</td>\n", " <td>$10,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5387</th>\n", " <td>1/13/2012</td>\n", " <td>Newlyweds</td>\n", " <td>$9,000</td>\n", " <td>$4,584</td>\n", " <td>$4,584</td>\n", " </tr>\n", " <tr>\n", " <th>5388</th>\n", " <td>2/26/1993</td>\n", " <td>El Mariachi</td>\n", " <td>$7,000</td>\n", " <td>$2,040,920</td>\n", " <td>$2,041,928</td>\n", " </tr>\n", " <tr>\n", " <th>5389</th>\n", " <td>10/8/2004</td>\n", " <td>Primer</td>\n", " <td>$7,000</td>\n", " <td>$424,760</td>\n", " <td>$841,926</td>\n", " </tr>\n", " <tr>\n", " <th>5390</th>\n", " <td>5/26/2006</td>\n", " <td>Cavite</td>\n", " <td>$7,000</td>\n", " <td>$70,071</td>\n", " <td>$71,644</td>\n", " </tr>\n", " <tr>\n", " <th>5391</th>\n", " <td>1/1/2004</td>\n", " <td>The Mongol King</td>\n", " <td>$7,000</td>\n", " <td>$900</td>\n", " <td>$900</td>\n", " </tr>\n", " <tr>\n", " <th>5392</th>\n", " <td>4/2/1999</td>\n", " <td>Following</td>\n", " <td>$6,000</td>\n", " <td>$48,482</td>\n", " <td>$240,495</td>\n", " </tr>\n", " <tr>\n", " <th>5393</th>\n", " <td>7/13/2005</td>\n", " <td>Return to the Land of Wonders</td>\n", " <td>$5,000</td>\n", " <td>$1,338</td>\n", " <td>$1,338</td>\n", " </tr>\n", " <tr>\n", " <th>5394</th>\n", " <td>9/29/2015</td>\n", " <td>Signed Sealed Delivered</td>\n", " <td>$5,000</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5395</th>\n", " <td>9/29/2015</td>\n", " <td>A Plague So Pleasant</td>\n", " <td>$1,400</td>\n", " <td>$0</td>\n", " <td>$0</td>\n", " </tr>\n", " <tr>\n", " <th>5396</th>\n", " <td>8/5/2005</td>\n", " <td>My Date With Drew</td>\n", " <td>$1,100</td>\n", " <td>$181,041</td>\n", " <td>$181,041</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5396 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 \\\n", "1 12/18/2009 Avatar \n", "2 12/18/2015 Star Wars Ep. VII: The Force Awakens \n", "3 5/24/2007 Pirates of the Caribbean: At World’s End \n", "4 11/6/2015 Spectre \n", "5 7/20/2012 The Dark Knight Rises \n", "6 7/2/2013 The Lone Ranger \n", "7 3/9/2012 John Carter \n", "8 11/24/2010 Tangled \n", "9 5/4/2007 Spider-Man 3 \n", "10 5/1/2015 Avengers: Age of Ultron \n", "11 5/6/2016 Captain America: Civil War \n", "12 3/25/2016 Batman v Superman: Dawn of Justice \n", "13 12/14/2012 The Hobbit: An Unexpected Journey \n", "14 7/15/2009 Harry Potter and the Half-Blood Prince \n", "15 12/13/2013 The Hobbit: The Desolation of Smaug \n", "16 12/17/2014 The Hobbit: The Battle of the Five Armies \n", "17 5/20/2011 Pirates of the Caribbean: On Stranger Tides \n", "18 4/14/2017 The Fate of the Furious \n", "19 6/28/2006 Superman Returns \n", "20 5/26/2017 Pirates of the Caribbean: Dead Men Tell No Tales \n", "21 11/14/2008 Quantum of Solace \n", "22 5/4/2012 The Avengers \n", "23 7/7/2006 Pirates of the Caribbean: Dead Man’s Chest \n", "24 6/14/2013 Man of Steel \n", "25 5/16/2008 The Chronicles of Narnia: Prince Caspian \n", "26 7/3/2012 The Amazing Spider-Man \n", "27 6/21/2017 Transformers: The Last Knight \n", "28 6/12/2015 Jurassic World \n", "29 5/25/2012 Men in Black 3 \n", "30 6/24/2009 Transformers: Revenge of the Fallen \n", "... ... ... \n", "5367 12/1/2015 Dutch Kills \n", "5368 8/1/1991 Slacker \n", "5369 12/31/2014 Dry Spell \n", "5370 12/31/2003 Flywheel \n", "5371 1/4/2013 All Superheroes Must Die \n", "5372 4/21/2015 The Front Man \n", "5373 11/25/2011 The Ridges \n", "5374 6/2/2006 The Puffy Chair \n", "5375 4/2/2010 Breaking Upwards \n", "5376 12/31/2014 Stories of Our Lives \n", "5377 4/11/1997 Pink Flamingos \n", "5378 4/28/2006 Grip: A Criminal's Story \n", "5379 12/31/2007 Tin Can Man \n", "5380 3/9/2001 Dayereh \n", "5381 4/28/2006 Clean \n", "5382 7/6/2001 Cure \n", "5383 5/28/2004 On the Down Low \n", "5384 4/1/1996 Bang \n", "5385 8/14/2008 The Rise and Fall of Miss Thang \n", "5386 5/19/2015 Family Motocross \n", "5387 1/13/2012 Newlyweds \n", "5388 2/26/1993 El Mariachi \n", "5389 10/8/2004 Primer \n", "5390 5/26/2006 Cavite \n", "5391 1/1/2004 The Mongol King \n", "5392 4/2/1999 Following \n", "5393 7/13/2005 Return to the Land of Wonders \n", "5394 9/29/2015 Signed Sealed Delivered \n", "5395 9/29/2015 A Plague So Pleasant \n", "5396 8/5/2005 My Date With Drew \n", "\n", " 2 3 4 \n", "1 $425,000,000 $760,507,625 $2,783,918,982 \n", "2 $306,000,000 $936,662,225 $2,058,662,225 \n", "3 $300,000,000 $309,420,425 $963,420,425 \n", "4 $300,000,000 $200,074,175 $879,620,923 \n", "5 $275,000,000 $448,139,099 $1,084,439,099 \n", "6 $275,000,000 $89,302,115 $260,002,115 \n", "7 $275,000,000 $73,058,679 $282,778,100 \n", "8 $260,000,000 $200,821,936 $586,581,936 \n", "9 $258,000,000 $336,530,303 $894,860,230 \n", "10 $250,000,000 $459,005,868 $1,408,218,722 \n", "11 $250,000,000 $408,084,349 $1,153,304,495 \n", "12 $250,000,000 $330,360,194 $868,160,194 \n", "13 $250,000,000 $303,003,568 $1,017,003,568 \n", "14 $250,000,000 $301,959,197 $935,083,686 \n", "15 $250,000,000 $258,366,855 $960,366,855 \n", "16 $250,000,000 $255,119,788 $955,119,788 \n", "17 $250,000,000 $241,063,875 $1,045,663,875 \n", "18 $250,000,000 $225,764,765 $1,237,444,462 \n", "19 $232,000,000 $200,120,000 $374,085,065 \n", "20 $230,000,000 $171,832,628 $785,628,907 \n", "21 $230,000,000 $169,368,427 $591,692,078 \n", "22 $225,000,000 $623,279,547 $1,519,479,547 \n", "23 $225,000,000 $423,315,812 $1,066,215,812 \n", "24 $225,000,000 $291,045,518 $667,999,518 \n", "25 $225,000,000 $141,621,490 $417,341,288 \n", "26 $220,000,000 $262,030,663 $757,890,267 \n", "27 $217,000,000 $130,120,862 $601,120,862 \n", "28 $215,000,000 $652,198,010 $1,671,640,593 \n", "29 $215,000,000 $179,020,854 $654,213,485 \n", "30 $210,000,000 $402,111,870 $836,519,699 \n", "... ... ... ... \n", "5367 $25,000 $0 $0 \n", "5368 $23,000 $1,227,508 $1,227,508 \n", "5369 $22,000 $0 $0 \n", "5370 $20,000 $0 $0 \n", "5371 $20,000 $0 $0 \n", "5372 $20,000 $0 $0 \n", "5373 $17,300 $0 $0 \n", "5374 $15,000 $194,523 $195,254 \n", "5375 $15,000 $115,592 $115,592 \n", "5376 $15,000 $0 $0 \n", "5377 $12,000 $413,802 $413,802 \n", "5378 $12,000 $1,336 $1,336 \n", "5379 $12,000 $0 $0 \n", "5380 $10,000 $673,780 $673,780 \n", "5381 $10,000 $138,711 $138,711 \n", "5382 $10,000 $94,596 $94,596 \n", "5383 $10,000 $1,987 $1,987 \n", "5384 $10,000 $527 $527 \n", "5385 $10,000 $401 $401 \n", "5386 $10,000 $0 $0 \n", "5387 $9,000 $4,584 $4,584 \n", "5388 $7,000 $2,040,920 $2,041,928 \n", "5389 $7,000 $424,760 $841,926 \n", "5390 $7,000 $70,071 $71,644 \n", "5391 $7,000 $900 $900 \n", "5392 $6,000 $48,482 $240,495 \n", "5393 $5,000 $1,338 $1,338 \n", "5394 $5,000 $0 $0 \n", "5395 $1,400 $0 $0 \n", "5396 $1,100 $181,041 $181,041 \n", "\n", "[5396 rows x 5 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = SCRAPER.get_table_data()\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "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>release_date</th>\n", " <th>title</th>\n", " <th>production_budget</th>\n", " <th>domestic_gross</th>\n", " <th>world_gross</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>2009-12-18</td>\n", " <td>Avatar</td>\n", " <td>425000000</td>\n", " <td>760507625</td>\n", " <td>2783918982</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015-12-18</td>\n", " <td>Star Wars Ep. 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<td>215000000</td>\n", " <td>179020854</td>\n", " <td>654213485</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2009-06-24</td>\n", " <td>Transformers: Revenge of the Fallen</td>\n", " <td>210000000</td>\n", " <td>402111870</td>\n", " <td>836519699</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>5367</th>\n", " <td>2015-12-01</td>\n", " <td>Dutch Kills</td>\n", " <td>25000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5368</th>\n", " <td>1991-08-01</td>\n", " <td>Slacker</td>\n", " <td>23000</td>\n", " <td>1227508</td>\n", " <td>1227508</td>\n", " </tr>\n", " <tr>\n", " <th>5369</th>\n", " <td>2014-12-31</td>\n", " <td>Dry Spell</td>\n", " <td>22000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5370</th>\n", " <td>2003-12-31</td>\n", " <td>Flywheel</td>\n", " <td>20000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5371</th>\n", " <td>2013-01-04</td>\n", " <td>All Superheroes Must Die</td>\n", " <td>20000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5372</th>\n", " <td>2015-04-21</td>\n", " <td>The Front Man</td>\n", " <td>20000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5373</th>\n", " <td>2011-11-25</td>\n", " <td>The Ridges</td>\n", " <td>17300</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5374</th>\n", " <td>2006-06-02</td>\n", " <td>The Puffy Chair</td>\n", " <td>15000</td>\n", " <td>194523</td>\n", " <td>195254</td>\n", " </tr>\n", " <tr>\n", " <th>5375</th>\n", " <td>2010-04-02</td>\n", " <td>Breaking Upwards</td>\n", " <td>15000</td>\n", " <td>115592</td>\n", " <td>115592</td>\n", " </tr>\n", " <tr>\n", " <th>5376</th>\n", " <td>2014-12-31</td>\n", " <td>Stories of Our Lives</td>\n", " <td>15000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5377</th>\n", " <td>1997-04-11</td>\n", " <td>Pink Flamingos</td>\n", " <td>12000</td>\n", " <td>413802</td>\n", " <td>413802</td>\n", " </tr>\n", " <tr>\n", " <th>5378</th>\n", " <td>2006-04-28</td>\n", " <td>Grip: A Criminal's Story</td>\n", " <td>12000</td>\n", " <td>1336</td>\n", " <td>1336</td>\n", " </tr>\n", " <tr>\n", " <th>5379</th>\n", " <td>2007-12-31</td>\n", " <td>Tin Can Man</td>\n", " <td>12000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5380</th>\n", " <td>2001-03-09</td>\n", " <td>Dayereh</td>\n", " <td>10000</td>\n", " <td>673780</td>\n", " <td>673780</td>\n", " </tr>\n", " <tr>\n", " <th>5381</th>\n", " <td>2006-04-28</td>\n", " <td>Clean</td>\n", " <td>10000</td>\n", " <td>138711</td>\n", " <td>138711</td>\n", " </tr>\n", " <tr>\n", " <th>5382</th>\n", " <td>2001-07-06</td>\n", " <td>Cure</td>\n", " <td>10000</td>\n", " <td>94596</td>\n", " <td>94596</td>\n", " </tr>\n", " <tr>\n", " <th>5383</th>\n", " <td>2004-05-28</td>\n", " <td>On the Down Low</td>\n", " <td>10000</td>\n", " <td>1987</td>\n", " <td>1987</td>\n", " </tr>\n", " <tr>\n", " <th>5384</th>\n", " <td>1996-04-01</td>\n", " <td>Bang</td>\n", " <td>10000</td>\n", " <td>527</td>\n", " <td>527</td>\n", " </tr>\n", " <tr>\n", " <th>5385</th>\n", " <td>2008-08-14</td>\n", " <td>The Rise and Fall of Miss Thang</td>\n", " <td>10000</td>\n", " <td>401</td>\n", " <td>401</td>\n", " </tr>\n", " <tr>\n", " <th>5386</th>\n", " <td>2015-05-19</td>\n", " <td>Family Motocross</td>\n", " <td>10000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5387</th>\n", " <td>2012-01-13</td>\n", " <td>Newlyweds</td>\n", " <td>9000</td>\n", " <td>4584</td>\n", " <td>4584</td>\n", " </tr>\n", " <tr>\n", " <th>5388</th>\n", " <td>1993-02-26</td>\n", " <td>El Mariachi</td>\n", " <td>7000</td>\n", " <td>2040920</td>\n", " <td>2041928</td>\n", " </tr>\n", " <tr>\n", " <th>5389</th>\n", " <td>2004-10-08</td>\n", " <td>Primer</td>\n", " <td>7000</td>\n", " <td>424760</td>\n", " <td>841926</td>\n", " </tr>\n", " <tr>\n", " <th>5390</th>\n", " <td>2006-05-26</td>\n", " <td>Cavite</td>\n", " <td>7000</td>\n", " <td>70071</td>\n", " <td>71644</td>\n", " </tr>\n", " <tr>\n", " <th>5391</th>\n", " <td>2004-01-01</td>\n", " <td>The Mongol King</td>\n", " <td>7000</td>\n", " <td>900</td>\n", " <td>900</td>\n", " </tr>\n", " <tr>\n", " <th>5392</th>\n", " <td>1999-04-02</td>\n", " <td>Following</td>\n", " <td>6000</td>\n", " <td>48482</td>\n", " <td>240495</td>\n", " </tr>\n", " <tr>\n", " <th>5393</th>\n", " <td>2005-07-13</td>\n", " <td>Return to the Land of Wonders</td>\n", " <td>5000</td>\n", " <td>1338</td>\n", " <td>1338</td>\n", " </tr>\n", " <tr>\n", " <th>5394</th>\n", " <td>2015-09-29</td>\n", " <td>Signed Sealed Delivered</td>\n", " <td>5000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5395</th>\n", " <td>2015-09-29</td>\n", " <td>A Plague So Pleasant</td>\n", " <td>1400</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5396</th>\n", " <td>2005-08-05</td>\n", " <td>My Date With Drew</td>\n", " <td>1100</td>\n", " <td>181041</td>\n", " <td>181041</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5396 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " release_date title \\\n", "1 2009-12-18 Avatar \n", "2 2015-12-18 Star Wars Ep. VII: The Force Awakens \n", "3 2007-05-24 Pirates of the Caribbean: At Worlds End \n", "4 2015-11-06 Spectre \n", "5 2012-07-20 The Dark Knight Rises \n", "6 2013-07-02 The Lone Ranger \n", "7 2012-03-09 John Carter \n", "8 2010-11-24 Tangled \n", "9 2007-05-04 Spider-Man 3 \n", "10 2015-05-01 Avengers: Age of Ultron \n", "11 2016-05-06 Captain America: Civil War \n", "12 2016-03-25 Batman v Superman: Dawn of Justice \n", "13 2012-12-14 The Hobbit: An Unexpected Journey \n", "14 2009-07-15 Harry Potter and the Half-Blood Prince \n", "15 2013-12-13 The Hobbit: The Desolation of Smaug \n", "16 2014-12-17 The Hobbit: The Battle of the Five Armies \n", "17 2011-05-20 Pirates of the Caribbean: On Stranger Tides \n", "18 2017-04-14 The Fate of the Furious \n", "19 2006-06-28 Superman Returns \n", "20 2017-05-26 Pirates of the Caribbean: Dead Men Tell No Tales \n", "21 2008-11-14 Quantum of Solace \n", "22 2012-05-04 The Avengers \n", "23 2006-07-07 Pirates of the Caribbean: Dead Mans Chest \n", "24 2013-06-14 Man of Steel \n", "25 2008-05-16 The Chronicles of Narnia: Prince Caspian \n", "26 2012-07-03 The Amazing Spider-Man \n", "27 2017-06-21 Transformers: The Last Knight \n", "28 2015-06-12 Jurassic World \n", "29 2012-05-25 Men in Black 3 \n", "30 2009-06-24 Transformers: Revenge of the Fallen \n", "... ... ... \n", "5367 2015-12-01 Dutch Kills \n", "5368 1991-08-01 Slacker \n", "5369 2014-12-31 Dry Spell \n", "5370 2003-12-31 Flywheel \n", "5371 2013-01-04 All Superheroes Must Die \n", "5372 2015-04-21 The Front Man \n", "5373 2011-11-25 The Ridges \n", "5374 2006-06-02 The Puffy Chair \n", "5375 2010-04-02 Breaking Upwards \n", "5376 2014-12-31 Stories of Our Lives \n", "5377 1997-04-11 Pink Flamingos \n", "5378 2006-04-28 Grip: A Criminal's Story \n", "5379 2007-12-31 Tin Can Man \n", "5380 2001-03-09 Dayereh \n", "5381 2006-04-28 Clean \n", "5382 2001-07-06 Cure \n", "5383 2004-05-28 On the Down Low \n", "5384 1996-04-01 Bang \n", "5385 2008-08-14 The Rise and Fall of Miss Thang \n", "5386 2015-05-19 Family Motocross \n", "5387 2012-01-13 Newlyweds \n", "5388 1993-02-26 El Mariachi \n", "5389 2004-10-08 Primer \n", "5390 2006-05-26 Cavite \n", "5391 2004-01-01 The Mongol King \n", "5392 1999-04-02 Following \n", "5393 2005-07-13 Return to the Land of Wonders \n", "5394 2015-09-29 Signed Sealed Delivered \n", "5395 2015-09-29 A Plague So Pleasant \n", "5396 2005-08-05 My Date With Drew \n", "\n", " production_budget domestic_gross world_gross \n", "1 425000000 760507625 2783918982 \n", "2 306000000 936662225 2058662225 \n", "3 300000000 309420425 963420425 \n", "4 300000000 200074175 879620923 \n", "5 275000000 448139099 1084439099 \n", "6 275000000 89302115 260002115 \n", "7 275000000 73058679 282778100 \n", "8 260000000 200821936 586581936 \n", "9 258000000 336530303 894860230 \n", "10 250000000 459005868 1408218722 \n", "11 250000000 408084349 1153304495 \n", "12 250000000 330360194 868160194 \n", "13 250000000 303003568 1017003568 \n", "14 250000000 301959197 935083686 \n", "15 250000000 258366855 960366855 \n", "16 250000000 255119788 955119788 \n", "17 250000000 241063875 1045663875 \n", "18 250000000 225764765 1237444462 \n", "19 232000000 200120000 374085065 \n", "20 230000000 171832628 785628907 \n", "21 230000000 169368427 591692078 \n", "22 225000000 623279547 1519479547 \n", "23 225000000 423315812 1066215812 \n", "24 225000000 291045518 667999518 \n", "25 225000000 141621490 417341288 \n", "26 220000000 262030663 757890267 \n", "27 217000000 130120862 601120862 \n", "28 215000000 652198010 1671640593 \n", "29 215000000 179020854 654213485 \n", "30 210000000 402111870 836519699 \n", "... ... ... ... \n", "5367 25000 0 0 \n", "5368 23000 1227508 1227508 \n", "5369 22000 0 0 \n", "5370 20000 0 0 \n", "5371 20000 0 0 \n", "5372 20000 0 0 \n", "5373 17300 0 0 \n", "5374 15000 194523 195254 \n", "5375 15000 115592 115592 \n", "5376 15000 0 0 \n", "5377 12000 413802 413802 \n", "5378 12000 1336 1336 \n", "5379 12000 0 0 \n", "5380 10000 673780 673780 \n", "5381 10000 138711 138711 \n", "5382 10000 94596 94596 \n", "5383 10000 1987 1987 \n", "5384 10000 527 527 \n", "5385 10000 401 401 \n", "5386 10000 0 0 \n", "5387 9000 4584 4584 \n", "5388 7000 2040920 2041928 \n", "5389 7000 424760 841926 \n", "5390 7000 70071 71644 \n", "5391 7000 900 900 \n", "5392 6000 48482 240495 \n", "5393 5000 1338 1338 \n", "5394 5000 0 0 \n", "5395 1400 0 0 \n", "5396 1100 181041 181041 \n", "\n", "[5396 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_clean = SCRAPER.clean_data()\n", "df_clean" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SCRAPER.upload_data_s3()" ] }, { "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": 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
h-mayorquin/time_series_basic
presentations/2016-01-21(Wall-Street-Letter-Latency-Prediction).ipynb
1
126378
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of text with Nexa Letter Latency.\n", "This notbook is for seeing how much the delay between the code vector and the code is related to the accuaracy of the prediciton." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import h5py\n", "from sklearn import svm, cross_validation\n", "from sklearn.naive_bayes import MultinomialNB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First we load the file \n", "file_location = '../results_database/text_wall_street_big.hdf5'\n", "f = h5py.File(file_location, 'r')\n", "\n", "# Now we need to get the letters and align them\n", "text_directory = '../data/wall_street_letters.npy'\n", "letters_sequence = np.load(text_directory)\n", "Nletters = len(letters_sequence)\n", "symbols = set(letters_sequence)\n", "\n", "# Load the particular example\n", "Nspatial_clusters = 5\n", "Ntime_clusters = 15\n", "Nembedding = 3\n", "\n", "run_name = '/low-resolution'\n", "parameters_string = '/' + str(Nspatial_clusters)\n", "parameters_string += '-' + str(Ntime_clusters)\n", "parameters_string += '-' + str(Nembedding)\n", "\n", "nexa = f[run_name + parameters_string]\n", "\n", "# Now we load the time and the code vectors\n", "time = nexa['time']\n", "code_vectors = nexa['code-vectors']\n", "code_vectors_distance = nexa['code-vectors-distance']\n", "code_vectors_softmax = nexa['code-vectors-softmax']\n", "code_vectors_winner = nexa['code-vectors-winner']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Study the Latency of the Data by Accuracy\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Make prediction with winner takes all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the prediction for each delay. This **takes a bit**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delay 0\n", "score 25.22\n", "delay 1\n", "score 61.0\n", "delay 2\n", "score 64.86\n", "delay 3\n", "score 71.44\n", "delay 4\n", "score 70.72\n", "delay 5\n", "score 85.86\n", "delay 6\n", "score 28.86\n", "delay 7\n", "score 19.66\n", "delay 8\n", "score 19.0\n", "delay 9\n", "score 17.94\n" ] } ], "source": [ "N = 50000 # Amount of data\n", "delays = np.arange(0, 10)\n", "accuracy = []\n", "\n", "# Make prediction with scikit-learn\n", "for delay in delays:\n", " X = code_vectors_winner[:(N - delay)]\n", " y = letters_sequence[delay:N]\n", " X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.10)\n", "\n", " clf = svm.SVC(C=1.0, cache_size=200, kernel='linear')\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test) * 100.0\n", " accuracy.append(score)\n", " print('delay', delay)\n", " print('score', score)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot it" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SqsqCeteORr15a70CfoI1ctO6hvim9K1sOANgASJgA1koGovpxSPd2t3cru7z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\nTSbD9vC9I6jvHNbzu4XzLLXVpdfz/QF/x+tVrzO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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6203571358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "plt.plot(delays, accuracy, 'o-', lw=2, markersize=10)\n", "plt.xlabel('Delays')\n", "plt.ylim([0, 105])\n", "plt.xlim([-0.5, 10])\n", "plt.ylabel('Accuracy %')\n", "plt.title('Delays vs Accuracy')\n", "fig = plt.gcf()\n", "fig.set_size_inches((12, 9))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Make predictions with representation standarization" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import preprocessing" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delay 0\n", "score 26.6\n", "delay 1\n", "score 62.22\n", "delay 2\n", "score 65.38\n", "delay 3\n", "score 70.92\n", "delay 4\n", "score 69.74\n", "delay 5\n", "score 85.82\n", "delay 6\n", "score 30.32\n", "delay 7\n", "score 20.94\n", "delay 8\n", "score 18.04\n", "delay 9\n", "score 18.1\n" ] } ], "source": [ "N = 50000 # Amount of data\n", "delays = np.arange(0, 10)\n", "accuracy_std = []\n", "\n", "# Make prediction with scikit-learn\n", "for delay in delays:\n", " X = code_vectors_winner[:(N - delay)]\n", " y = letters_sequence[delay:N]\n", " X = preprocessing.scale(X)\n", " X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.10)\n", "\n", " clf = svm.SVC(C=1.0, cache_size=200, kernel='linear')\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test) * 100.0\n", " accuracy_std.append(score)\n", " print('delay', delay)\n", " print('score', score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot it" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f6200f37c88>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XlUWwWhGRgU8BW0TkMGZZFi8s2MjnO1bgGlqOHTtXTvw+mXEZX6lfm81GCkMA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\nLMjvv2UL7/6F9YSCPvbaPj78ld08kw7cAD6vj19Y9WruuPJ3qAvXcnK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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6200f34748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(delays, accuracy, 'o-', lw=2, markersize=10., label='Accuracy')\n", "plt.plot(delays, accuracy_std, 'o-', lw=2, markersize=10, label='Standarized Representations')\n", "plt.xlabel('Delays')\n", "plt.ylim([0, 105])\n", "plt.xlim([-0.5, 10])\n", "plt.ylabel('Accuracy %')\n", "plt.title('Delays vs Accuracy')\n", "fig = plt.gcf()\n", "fig.set_size_inches((12, 9))\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Make prediction with softmax" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delay 0\n", "score 26.28\n", "delay 1\n", "score 60.68\n", "delay 2\n", "score 65.08\n", "delay 3\n", "score 70.54\n", "delay 4\n", "score 68.08\n", "delay 5\n", "score 85.8\n", "delay 6\n", "score 28.9\n", "delay 7\n", "score 19.4\n", "delay 8\n", "score 18.0\n", "delay 9\n", "score 18.9\n" ] } ], "source": [ "accuracy_softmax = []\n", "\n", "# Make prediction with scikit-learn\n", "for delay in delays:\n", " X = code_vectors_softmax[:(N - delay)]\n", " y = letters_sequence[delay:N]\n", " X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.10)\n", "\n", " clf = svm.SVC(C=1.0, cache_size=200, kernel='linear')\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test) * 100.0\n", " accuracy_softmax.append(score)\n", " print('delay', delay)\n", " print('score', score)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Standarized predictions with softmax" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "delay 0\n", "score 26.38\n", "delay 1\n", "score 61.46\n", "delay 2\n", "score 65.96\n", "delay 3\n", "score 71.38\n", "delay 4\n", "score 70.12\n", "delay 5\n", "score 86.02\n", "delay 6\n", "score 29.34\n", "delay 7\n", "score 19.5\n", "delay 8\n", "score 17.92\n", "delay 9\n", "score 18.48\n" ] } ], "source": [ "accuracy_softmax_std = []\n", "\n", "# Make prediction with scikit-learn\n", "for delay in delays:\n", " X = code_vectors_winner[:(N - delay)]\n", " y = letters_sequence[delay:N]\n", " X = preprocessing.scale(X)\n", " X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.10)\n", "\n", " clf = svm.SVC(C=1.0, cache_size=200, kernel='linear')\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test) * 100.0\n", " accuracy_softmax_std.append(score)\n", " print('delay', delay)\n", " print('score', score)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f620129c940>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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J6aGtV1zWw5WIiPQvCtgiInJgbWGHtJr3ENcTERkoFLBFROSARsaMxuvp+p8J\nr9uGETOqlyoSEekfFLBFROSAcrJCu13HWz6cGWOG9EI1IiL9hwK2iIjsp8HZyAv5L2OxenA3R+N1\n2/Z53eu20VY0krNyTiMiTDO+ioh0pJ+KIiKyjzaPi2fWv0R16248jbFYts7AY/Hgji0mJGsjWMC7\naRbnTh3FuTOzg12uiEifo4AtIiLtvF4vC/LfZHtdAV5nGM4tU/j5RZMZNjiW5XllvF5UgjWmlisu\nTGf6kOxglysi0iepRURERNp9tHMpX5etAo8Nx+ZjOff4UYzJTiQizM7sKZnEeNMAyKvcFuRKRUT6\nLgVsEREBILdyA+9tWwiAY9t4RqVkcd4Jw/ZZJysmC4DtdQW9XZ6ISL+hgC0iIhQ1lPBC3gK8eGkr\nGkm0cyjXnzsGq9Wyz3qTBo8EoNZTTpun66c8iogMVArYIiIDXJ2jnqfWzcfpacNdlY67LIcbzhtL\nXPT+D5AZlzUYT3M0WDwU1BYFoVoRkb5PAVtEZABzutt4ev2L1DrqoCkB545xnH9CDqOzEg64flxU\nKGFtyQCsLjF7s1QRkX5DAVtEZIDyer38c9PrFNYXYXNF0WJOYkxWEud0M/VeRsRQADZX7+iFKkVE\n+h8FbBGRAeqDgo9ZVZGLjRCaNk0iLjyG688du1/fdWfjUocDUNFWjMfr6Y1SRUT6FQVsEZEBaFX5\nWj7Y8REWLDSb46E1hnnnjSU2qvvHo0/KGorXGYbH6qSsqbwXqhUR6V8UsEVEBpiC+p28vOl13zcl\no/HUpXLBSTkYQw/cd93ZoMRIrM1JAKwt3dJTZYqI9FsK2CIiA0hNay1Pr3uRNo+LqKYcmncNYeyw\nRM6ekXXI+7BYLKSGZgCQV7G1p0oVEem3FLBFRAYIh9vJU+teoN7ZQDzpVG0cQXx0GNedOwarpeu+\n685GJfn6sEtad/VEqSIi/ZoCtojIAODxengxbwG7GkuItcdTumoUFqzMO38csZHd9113NmVoDl6X\nHaelkZrW2h6oWESk/1LAFhEZAN7fvojcqjzCbeE05k0GdyjfPTmHY4bEH9H+sgfFQpOvZ3uD2kRE\nRPahgC0icpT7unQVHxYuwYqVqPJpNNWFMT4niTOnH3rfdWd2m5UE6yAAcnWjo4jIPhSwRUSOYltr\nd/Bq/hsADGcmu7ZHkBATxo/PGX3YfdedDY8fBsDOpsJvXaeIyNFEAVtE5ChV1bKbZ9e/hMvrZmz0\nFNatiMa7boiBAAAgAElEQVRqsTDv/LHEHEHfdWdTMkfg9Vho8u6mua0lABWLiBwdFLBFRI5CLa5W\nnlo3n8a2JkbEjmDjl752jotm5TAy88j6rjszMpPxNseBBTbv1mPTRUT2UMAWETnKeLwe5ue9SmlT\nOWmRqTRuGkdzq4cJw5OYO21owI4TEWYnyp0KwJrizQHbr4hIf2fv7QMahhEFvAQkAKHAA8BG4GV8\ngb8U+JFpmm29XZuIyNHgra3/Ia86n6iQSIY2z2ZZcR2JsWH8+JzDn++6O1nRWeSzmW11GsEWEdkj\nGCPYVwH5pmmeClwCPIYvZP/NNM1ZwDbgmiDUJSLS731evJwlRZ9js9g4Jf48lq2ow2a1MO/8cURH\nhAT8eBPTRwJQ6ymnzeMK+P5FRPqjYATsKiDJ/3UiUAnMAt7zL3sfOC0IdYmI9Guba7byr83vAHDu\n0HP54ONGAC6aNZwRGXE9cszxQwfjaY7Ga/FQWFfUI8cQEelvej1gm6b5LyDLMIwtwFLgDiCqQ0tI\nBTC4t+sSEenPypsreXb9y3i8Hk7NPJnln4fS7HAxaUQyc48f0mPHTYgJI9SZDMCaEvVhi4hAcHqw\nLwcKTdM80zCM8cD8TqscUoNgQkIkdrst4PUdjpSUmKAeX4JL13/g6mvXvtHZxLPfvEizq4Wp6ROw\nVo5hR2kBKQkR3HnlcQGZkq8rWbFZbKOArXUFfe696QkD4RzlwHTt5VD1esAGTgAWAZimud4wjMFA\nk2EYYaZpOoAMoKS7ndTUNPdsld1ISYmhsrIhqDVI8Oj6D1x97dq7PW7+nvs8pQ0VZEQPZgyzefoz\nE5vVwvXnjKG1yUFrk6NHazAScthW9ymlLUWUV9RhtRy9E1T1tesvvUfXfmA73A9XwfgpuBWYDmAY\nRhbQAHwEXOx//SJgYRDqEhHpV7xeL69veRezZisxIdFckv09XvrfNgAuOWU4w3uo77qziUOH4HWG\n4bY4KWsq75Vjioj0ZcEI2E8D2YZhLAX+CdwA3A9caRjGp/im73sxCHWJiPQrn+76ks+Ll2O32rl2\n7BUs+GAXLQ4Xk0cm853jeq7vurP05Cgszb5713NLt/TacUVE+qpebxExTbMJuOwAL53e27WIiPRX\nedUmb2zxTb70w1GX8M0qJwVlDSTHhXPN2aOxBHi+665YLRZSQzKooIS8im2cOfLkXju2iEhfdPQ2\nyomIHKVKm8r5x4ZX8OLlzOw5WGoz+HjVrvb5rqPCAz/fdXeMpGEAlLRqqj4REQVsEZF+pNHZxJO5\n82l1tzI5ZTzHJZzI/P9tAuDS2SPISY8NSl1ThgzH67LjsDRS01oblBpERPoKBWwRkX6izePimfUv\nUd26m6ExmXz/mEt46t2NtDjcHHtMCqdNzQxabcMGx+FtSgAgr3Jb0OoQEekLFLBFRPoBr9fLa/lv\nsa1uB3Ghsdww4Ure/rSQQn/f9dVnjerVvuvOQuxWEiyDAMgt1QNnRGRgU8AWEekHPt75KcvLVhJi\nDWHehKvYssPBJ6uLsdss/OSCcUQGoe+6s5z4bAAKG3cGtxARkSBTwBYR6ePWVebx7rb/AXDVmO8R\n5k5k/ge+vuvLTh3JsMHB6bvubErGCLweC03ealpcLcEuR0QkaBSwRUT6sKKGEuZvXIAXL+fmnMHY\nxDE8+fYGWp1uphopnDolI9gltjOGJONtigMLbN69I9jliIgEjQK2iEgfVedo4Ol1L+B0OzkubQpz\ns2bz2uKt7KxoJDU+gqvO7N35rrsTFR5CpDsVgDXF6sMWkYFLAVtEpA9yutt4Zv2L1DhqyYnL4vJR\nF/FNfgVL1nTsu+71Z4V1a2j0UAC21RUEtxARkSBSwBYR6WO8Xi+v5P+bgvqdJITFc/34K9ld18b8\n/+UD8L05I8kaFBPkKg9s0uCRANS4y2nzuIJcjYhIcChgi4j0MQsLFrOyfC1htlB+MvFqwiwRPPHO\nBhxON8ePTmX25L7Td93ZuKGD8TRH47W4KazXUx1FZGBSwBYR6UNWlefynx0fYsHC1WN/QEb0YBYs\n3kJRRSOpCRFceUZw57vuTlJcOCGOJADWlqgPW0QGJgVsEZE+orC+iJc3/QuAC0eczfjkMSzPK+PT\ntSXYbVZuvGAcEWF9r++6s/SIIQDkV28PciUiIsGhgC0i0gfUtNby9LoXaPO4mDn4OE4dchKl1U28\nuMgE4AenjWRoWt/su+5sbMpwACqcJXi8niBXIyLS+xSwRUSCzOF28vS6F6hzNjAyPofLjAtxujw8\n2aHvetak9GCXecgmDh2K1xmG2+KgvLky2OWIiPQ6BWwRkSDyeD28tPE1ihpLSI5I4sfjf4TdamfB\nx5vZVdlEWmJkn++77iwzJRpLk68Pe12Z+rBFZOBRwBYRCaL/bP+QtZUbiLCH85MJVxMdEsVXG8pY\nlltKiL3/9F13ZLVaSA7xjbhvKN8a5GpERHqfAraISJB8XbqKRYWfYLVYuXbcDxkUlUpJVRMvLvLN\nd/2D00YyJDU6yFUeGSMxB4DiFk3VJyIDjwK2iEgQbKst4NX8NwC4ZOR5jE48Bkebmyff3YCzzcP0\nsWmcPLH/9F13NnnIMLwuOw5LIzWttcEuR0SkVylgi4j0suqW3Tyz/kVcXjcnZ8zk5MyZALzy4WaK\nK5sYlBjJFXONftV33dnw9Hi8TfEAbKraFuRqRER6lwK2iEgvanG18tS6F2hsa2J04jFcPPJcAL5Y\nX8rn60sJ9fddh4f2r77rzkJDbMQxCIC1pVuCXI2ISO9SwBYR6SUer4cX8l6lpKmMtMhUrhl7OTar\njeKqJl7+0Dff9eXfOYbMftp33VlOXDYAhQ2FwS1ERKSXKWCLiPSSt7f+lw3V+UTZI5k34SoiQyJw\nON08+Y6v73rG2EGcOGFwsMsMmMkZI/B6LDR6q2lxtQS7HBGRXqOALSLSC74o+ZpPij7DZrFx3fgf\nkRqZDMA/PzQpqWpicFL/77vubNSQZDxNcWCBrTUFwS5HRKTXKGCLiPSwzTVbec18G4DvGRcyMsH3\nKPHP1pXwxYay9r7rsFBbMMsMuJjIUCJdKQCsLjaDXI2ISO9RwBYR6UEVzZU8u/5lPF4Pc4aczMz0\n4wHYVdnIKx/6nnL4w9MNMlKOjr7rzoZEZwGwtbYguIWIiPQiBWwRkR7S3NbMU+teoNnVwrik0Vww\n4iwAWp0uX9+1y8MJ44+uvuvOJg4aAUCNu5w2jyvI1YiI9A4FbBGRHuD2uHl+wyuUN1eSHjWIq8d+\nH6vFitfr5eVFJqXVzWQkR/HD041gl9qjxmUNxtMcjdfiZmf9rmCXIyLSKxSwRUR6wBtb3iO/Zgsx\nIdHMm3A14fZwAD5bV8pXeeWEhliZd8E4wkKOrr7rzlLiwrE7kgBYW7o5yNWIiPQOBWwRkQBbuusL\nlhV/hd1q5/oJV5IUkQBAUUUjr3zkC5k/Ot0gIzkqmGX2CovFwuDwTADyq7YHuRoRkd6hgC0iEkAb\nq03e2PweAJePupicON9Nfi0OF0+8s4E2l4cTJwzmhPFHb991Z2NSfLOmlDuL8Xg9Qa5GRKTnKWCL\nyCFpcbXwZckK/rdjMV+WrNCDQw6grKmc5ze8ghcvZ2SdyvGDpgC0912X724mIyWKy79zTJAr7V2T\nhgzF6wzDbXFQ3lwZ7HJERHqcPdgFiEjft7BgMYsKl+B0O9uX/XvLe8zNms0Z2XOCWFnf0ehs4snc\n+bS6W5mcMp6zc05vf+3T3BKWbywnLMTmm+/6KO+77mxIWjQ0JUJoKRvKtzA4Jy3YJYmI9CiNYItI\nlxYWLOb97Yv2CdcATreT97cvYmHB4iBV1ne4PC6e3fASVa27GRqTwRVjLsNq8f143VnewKsfbQHg\nijMMBicd/X3XndmsVpLt6QCsL98a5GpERHqeAraIHFSLq4VFhUu6XGdR4RJaXK29VFHf4/V6WWC+\nxdbaHcSFxnLDhKsItYUCvr7rJ9/ZgMvt4eSJ6cwYOyjI1QbPyIQcAHY17wxyJSIiPU8BW0QOak3F\n+v1Grjtzup18Ufw1rgH6EJHFRctYXrqSEGsI8yZcRXxYHOAL3i8uzKe8poXMlGh+cNrIIFcaXJOH\nDMPrsuOwNFLTWhvsckREepR6sEXkoOocDYe03tvb/svb2/5LlD2SmLAYYkNjiAv1/R7r/z62w/dR\n9kgsFksPV9/z1lXm8c7WDwC4YsxlDI3NbH9t6doSVmyqICzUxo0XjiN0gPVddzYiIx7vmngs8VXk\nV29nRsaUYJckItJjFLBFZD8uj4vcyjy+KVt9SOtH2MJxeJw0uZppcjVT1lTe5fo2i42Y0GjiQmOJ\nDYvuFMBj9/k+1BYSiFP61lpcLaypWI+rwoHdFUZaZCrzNy7Ai5dzhs1lSuqE9nULyxpY8LGv7/rK\nMwwGJUYGq+w+IzzUTgyDaKKK3NLNCtgiclRTwBaRdlUtu/mi5Gu+KvmGhrZGALxe6Gqw2eu2cc+0\nO4mLjKSprZl6ZwP1jgbqnQ3UOev3+b7e2Ui9s54WVyu1jjpqHXXQzSB5uC2cuM6j4J1HxsNiiA6J\nar+xMNAONIvKHselTeaM7FPbv+/Yd33KpHSmjxm4fded5cRms54NFDQUBrsUEZEepYAtMsB5vB42\nVG3is5LlbKrejBcvAOlRgxjEaL7eXEhIxsGfwOcqyWHD1npOmhhNTKjvV0Z01w9RcbrbaHDuCd3+\nMO7Y9/t6RwMNzgZa3a20Nrd2O3+y1WIlJiSK2NCY9jYVX6tKbIcw7hst3/PY8kOxZxaVg0mJSGpv\nd/F6vcz/Xz4VtS0MTY3m+wO877qzSenDWVdiocFSTYurhQh7RLBLEhHpEQrYIgNUraOOr0q+4YuS\nFdQ4fDed2S02JqdO5KSM6eTEZfGfLwtwFYeBx4Y9fTsWm7t9e6/bhqskB1fpcD52F9HU6mJIajRD\nUqOJjQrt8tihthCSIhJJikjscj2v10uzq+Ugo+KN7SG9zllPU1szdc4G6pwN0Nj1uYfaQvcZDT/Y\nCLndYu92FpWPi5Zx6tCTibCH88nqYlbmVxAeauMnF4wjxD6w+647Gz00Ge/WWCzRdWytKWB8yuhg\nlyQi0iMUsEUGEI/Xg1mzlc+Ll7OuamP7Y6tTIpI4MWM60wdNJTo0Co/Hy9qtVazYVAGAq3Q4rvIs\nbIllWEIceNvCcO8eBB7fj5CiiiaKKvbObxwXFcqQtOj2wD0kNYZBiRHYrIfXwmGxWIgKiSQqJJLB\nUV0/nMTlcdHgbNxnBLz+gKPk9TjdTqpaqqlqqT6seg7E6XaypmI96VaDf33i67u+6sxRpKnvej9x\n0WGEOVNpo461pZsVsEXkqKWALTIANDgbWV66ks9Lvm4PlVaLlckp4zkxYzrHJAzHarFS1+jg/W92\n8GluCbvrHfvuxGPHXZW5375D7FYuOiWH8t0tFFU0UlTRSF2Tk7rtu9mwffc+62UkR3UI3b7gHRke\nmB9DdqudhPB4EsLju1zP6/XS6nZ0COH1/t7wfUN5nbOeBmc3Q+F+1c21vPvxBlxuL7OnZHD8aD2p\n8GCGRA1hO1vYUrMj2KWIiPQYBWyRo5TX62VbXQGfFX/F2or1uLy+9o6EsHhOzJjGjMHHERcWi9fr\nJX9nLUvWFLNmcyVuj68HOzUhglMmZdDU2sZ/vzr4TWnnzMzm9KlD27/3eL1U1e4N20UVjewsb6S6\nvpWCsgYKyva9qzE5Lnzf0J0WQ3JcONYemsbPYrEQYQ8nwh5OWmRKl+t+XrycBeZb3e4zN7+Rylob\nWWkxfO/UEYEq9ag0YdBIttd8wm5XOW0eFyFW/TMkIkcf/WQTOco0t7Wwomw1n5Usb58uz4KFcUmj\nODFjOmOTRmG1WGlqbePDb4pYuqaYst3NAFgtFqYck8LsyRmMzk5oD7mhITY++KoQR9veHuywEBtn\nzcji3JnZ+xzfarGQmhBJakIkxxqpe+tqbdsbuP2/F1c2UVXXSlVdK2u2VLWvGx5qI9MfuIf6R7oz\nUqII6+W5pI9Nm8ibW//T5cN2bISwfWMUEWE2fnLBWPVdd2Pc0EG8VRKFNaKJnfW7GB6fHeySREQC\nTgFb5ChRWF/EZ8XLWVm+ljZPGwAxodGcMPh4ZqZPIykiAYAdpfUsWV3Mik3lOF2+Huz46FBOnpjO\nrEkZJMSE7bfvc2dmc9qxmazMr6C2yUl8VChTR6USEXboP0Iiw0MwhiZgDE1oX+b2eCjb3UJRecM+\nI951TU627qpj66669nUtFkhLiPSF7rS9LSbx0aE99tCaCHsEc7NmdzmLiKM4Gzx2rj5zNKkJ6rvu\nzqDESGzNSXgjmlhXtkUBW0SOSgrYIv2Yw+1kZfkaPi9ezs6G4vblRsIITsyYzsTksdisNhxON8ty\nS1iyppjCDi0aY7MTOGVyJhNHJGG3dX0DYkSYnZMmpge0fpvV15edkRzF9LF7l9c1OSmq6BC6yxsp\nrW6mbLfv1zf5Fe3rRkeEdOrrjiY9Oarb8zlUZ2TPAdhvHuxQayje8uG0FGcx59hMpo5KPdgupAOL\nxcKg8ExK2cmmqm1cyHeCXZKISMApYIv0Q8WNpXxe/DUrylbT6m4FIMoeyfTBUzkhY1p7b3FxVRNL\n1xTz5YYyWhwu33rhdk6cMJhTJmX02Zku4qJCiRuWxLhhSe3L2lxuSqqa2VnRQFH53tHuxpY2NhXW\nsKmwpn1dm9VC+n43VEYTE9n19IEHc0b2HI5PnsZ7eV/R7Gki0hrJ7qJENuyoJ3tQDJfOVt/14RiT\nPILSli8pcxTj8Xp67AFBIiLBooAt0k+0udtYU7mez4qXs72uoH15TlwWJ6ZPZ3LqBEJtIbjcHlZs\nKmfJ6mLMotr29YZnxDJ7cgZTjVRCe7mXORBC7DayBsWQNSimfZnX62V3vcMXuju0mFTU7L3JsqOE\nmLD9QndaQiRWa9ctJu9/WeDvQbcBsf6l9dhtVuZdMI4QuwLi4ZgwNJOP14fhDnVQ3lzZ7RSMIiL9\njQK2SB9X0VzJ58Vfs7x0JU0u382I4bYwjh80hRMzprc/NbGqtoVPc3fyWW4J9c2+HuywEBszxqZx\nyuQMhqbFHPQY/ZXFYiEpLpykuHAmj9w7I0iLw0VxZdO+bSaVjdQ0OKhpcLBu2975r0NDrGQkR+/T\n252ZEt3eX/7+lwW8vezAT7J0uT18vbF8vxs9pWvZg2LxLk/EElpKXsVWBg9TwBaRo4sCtkgf5Pa4\nWVe1kc+Ll5Nfs6V9+ZDodE7KmMGxaZMIt4fh8XjJ3VrFkjXFrN9W7X/IOWSkRDF7cgYzxg46rBsR\njxYRYXZGZMYxIjOufZnH46WiffrABnb620xqGhzsKK1nR2n9PvtIiQ8nIymK9Tt2d979Pj74qpDT\njs0ckO/zkbLbrCTZBlNDKevLt3DasBOCXZKISEDpXwSRPmR3aw1flKzgy5IV1Dt9NyOGWEOYmjaJ\nkzKmMzQmE4vFQl2Tk8XfFLB0TQnV9b4ebLvNwtRRqcyenMGIjLgem1mjv7JaLQxKjGRQYiTHdbgh\nsbHFP31gh5lMiquaqKxtpbK2tdv9OtrcrMyvCPgNoEe7kQk5rHCvZldzUbBLEREJOAVskSDzeD1s\nrDb5rHg5edX5eP3j0IOi0jgpfTrHD5pCZEgEXq+XzUW+B8KsMvc+ECYlPpxTJmVwwoTBxB7hTXwD\nWXRECKOzEhidtXf6QJfbQ2l1M+99sYNVZmW3+6htOvg82XJgkzOH8fV2G622Bmpaa7t9AqeISH+i\ngC0SJHWOBr4qXcHnxV9T4/DdjGi32JiUOp6TMmYwPC4bi8VCc2sbH+cWsXRtCSVVTYBvTujJI5M5\nZXIGY4cl9thTDwcqu83KkNRoxuckHVLAjo/SB5vDNTIzAc+6BGxxVZi7dzA9fXKwSxIRCRgFbJFe\n5PF62Fyzjc+Ll5NblYfH63vQS3JEEiemT2P64KnEhEYDUFDmeyDM15vKcbb51ouLDuXkCenMmpRO\nYmx40M5joJhqpLLg4y37PMGys7AQm+bAPgIRYXZiPGk0U0Vu6WYFbBE5qihgi/SCxrYmlpeu5Ivi\nr6lo8T0S3GqxMjFlHCelT8dIHIHVYsXR5uazdSUsXVPMjtK9D4QZnZXA7MkZTBqZHLAHqEj3IsPt\nnDUj66CziACcNSNLNzgeoWEx2eSRx476wmCXIiISUPpXQaSHeL1edtQXsmzXctZUrsPl8T3oJT4s\njhPSj2dm+vHEh/lmuSitbmLpmhK+WF9Kc4cHwpwwfjCzJqUzOCkqaOcx0O2Zgs83D/bekeywEBtn\nzcjSFH3fwqTM4WwosdBgqaLF1UKEPSLYJYmIBIQCtshhaHG1sKZiPa4KB3ZXGJNTx+8XClpcrXxT\ntprPipdT0lQGgAULY5IMTkqfztikUdisNlxuD9/kV7Bk9S7yd+59IMywwb4Hwhw/un8+EOZodO7M\nbE47NpOV+RW0ASHA1FGpGrn+lkZlJuPdGosluo5tNYWMSxkV7JJERAJC/zqIHKKFBYtZVLgEp3vv\njBH/3vIec7Nmc0b2HHY27OLz4uV8U762fZ2YkGhmpB/HCenTSI5IBKC6rpVPcwv5LLeEOv/sE6Eh\nVqaPGcTsyRn7PKlQ+o6IMDsnTUwnJSWGysqG7jeQbiXGhhPmTKGNOtaWblbAFpGjhgK2yCFYWLCY\n97cv2m+50+3k/e2L+GzXcmqdde3Lj4kfzokZ05mYMha71Y7H62XdtmqWrikmd1sVXv8TYdKT9z4Q\nJjJcfx1l4MmMHMIOtrKlZkewSxERCRj9iy7SjRZXC4sKl3S5Tq2zjnBbODPSp3Ji+nQGRflmlahv\ndvL5ukKWrimmqs730BKb1cKxo1KYPTmDY4bE64EwMqCNTxvJjrolVLvKaPO4CLHqnyUR6f/0k0yk\nG2sq1u/TFnIwFww/k5MyZ3R6IEwFLrdvuDo5LpxZk9I5aUI6sZo3WQSAcUMH8c6KKKwRTeys38Xw\n+OxglyQi8q0pYIt0oamtmfVVmw5p3drWRhav2sXStcUUV/ofCANMHJ7E7CkZjBuWhNWq0WqRjgYn\nR2FtToKIJtaXb1HAFpGjggK2SCfNbS2sq8pjdcU68ndvwe09+ENGOvrgs3Ic5b5ZP2KjQjl54mBO\nnphOcpymHhM5GKvFwqDQDMrYyaaq7VxgBLsiEZFvTwFbBN/UeuurNrK6IpdN1Ztx+UO11WJlZHwO\n22oL8OA56PZetw1HZSqjhsZzyuQMphyTogfCiByi0SkjKGv9irLWXXi8HqwW/d0Rkf5NAVsGrFaX\ngw3Vm1hdnkvebrP9QTAWLBwTP5wpaROZlDIOmyecn7/xAtb0zQfdl7ssh3t/OI2c9LjeKl/kqDFh\nSCaf5IXhCnVQ3lzJ4Ki0YJckIvKtKGDLgOJwO8mrzmdVeS551Zto6xCqR8QP49jUiUxMGU9cmG8u\naq/Xy9ufbcexKwe724s9fTsW296WEa/bhqskB1fpcIormxSwRY7AsMFxeL9OxJJYysbKrQrYItLv\nKWDLUc/pbmNjdT6rKnLZULUJp6et/bWcuGympE5gcur49seWe71etpXUsSq/kpVmRfv0eq7S4bjK\ns7AllmEJceBtC8O9exB4fH+Napu6n2lERPYXYreSaBtELaWsL9vKnOwTgl2SiMi3ooAtR6U2dxsb\nd29mdUUu66s24ugwzd6w2KH+UD2BhPB4ADxeL1uL61iZX8Eqs4Lqekf7+hFhNloc/lFrjx13VeYB\njxmvqfdEjtiI+BxWetdQ1Lwz2KWIiHxrCthy1HB5XGzavZnVFetYV7mRVndr+2tZMUOYkjaBySkT\nSIpIAHyhenNRLSvNClaZldQ07A3VCTFhHGukMNVIJT0lijv+/iWOtoPPJhIWYmPqqNSeOzmRo9yk\nzGF8U2Cj1dZAraOu/X+URET6IwVs6dfcHjf5NVtYXb6O3Ko8Wlwt7a8NiclgSuoEpqROIDkiCQCP\nx4u5s4aV+ZWs3FxBXePeke3E2DCmGqlMHZVKTnos1g5PWDxrRhZvL9t+0DrOmpFFRJj+OokcqWOG\nJOBZn4AtvgqzejvT0icHuyQRkSOmRCD9jtvjZnPtNlaX55JbmUeTq7n9tYzowUxJnciU1PGkRqb4\n1/fw/9m77/C4rsPO+987M8Cg915Igu2wUxIpWaJEqtpybMslluMkjmLHybvJbt4kTtt9dzdxEqV7\nHzuKvSkuKbY3xVnniW0ljiRb1REpUSTFTh72IhY0oteZe+/7x4AUKYkkIM7MwWB+n+fRI8zMBfCD\nrkD+cHDKgZN9qekfh7oZvGyudG1FEbcua2DdsnoWNldc9djyhzYsAOC7W05eMZIdL4jynjvmX3pd\nRN6e0qICysIGxuhh1/lDKtgiktNUsCUnBGHA4b5j7Ojaxc7uvQwnRi691lzayLqGtdzcsIam0tQ0\nDT8I2HfiAtunSvXQ6OsLG+urii6NVC9oKr9qqX6jhzYs4IF1bWw72EUCKADWL2vQyLVImiwoX8AB\n9nN84KTrKCIiN0TNQGatIAw42n+cHV27ebVrD0OJ4UuvNZbUT41Ur6GlrAmApB+w93gv2w52seNQ\nD8Njr5fqhupibl3WwHrTwLzGsmmX6jcqjsfYuLaF+vpyuruHbuwLFJErrG1ZxP7zHoNeL2PJMYpj\nOgVVRHKTCrbMKkEYcGzgJDu6drOzazcDk6+X2PriWtY1rOWWxrW0lDbheR5JP2D30V622S5ePdTN\nyHjy0vWNNSXcuiy1ULG94e2XahHJjuXtdYTHKvDKBjjaf5JVdctcRxIReVtUsMW5MAw5PniKHV27\neLVrD/0TA5deqyuq4ZbG1Eh1W1kLnueRSE6V6oNdvHq4h9GJ10t1c23JpZHq1vpSlWqRHFJbUUTB\nRD1+2QC7zh1SwRaRnKWCLU6EYcipodfY3rmLHV276Zvov/RaTVH1pd0/5pW3TZVqn51Heth2sJud\nR7Lil3sAACAASURBVHoYu6xUt9aXcqtpYN2yBlrrSl18OSKSBp7n0VbSxkmOcLjvuOs4IiJvmwq2\nZE0YhpwePsOOzt3s6NpF73jfpdeq4pVTpXotCyra8TyPyYTPjkM9bLdd7DzSw/jk67t3tDeUsd7U\ns35ZA821KtUic8WqhiWcHHqOnsR5kkGSWER/TYlI7tGfXJJRYRhyZvgcO7pSpbp7rPfSa5WFFalS\n3biGBRXziHgRJhI+223qiPJdR3qv2BJvXmNZaks900BTTYmLL0dEMmzVvCYe31ZKpHiEU0NnWFg5\n33UkEZEZU8GWGRlLjvFq1x4GJoaojJdzc8Pqt1zpf3b4PDu6UtM/Oke7Lz1fXljGzfVrWNe4loWV\n81OletJPHfxiu9l9tIfJRHDp+gVN5axf1sB6U09DtUq1yFzXVl9GZLQGikfY23lYBVtEcpIKtkzb\nEyee5smTzzLpv35Qy/89/B0enH8v715wP+dHOtnetZsdXbs5P9J56ZqyglJualjNuoa1LK7qIOJF\nGJtI8sqBbrYd7GLPsV4mk6+X6o7miqmR6nrqq7RNl0g+iUQ8Ggpb6eI0+7uP8v6lD7iOJCIyYyrY\nMi1PnHiax489+abnJ/1JHj/2JM+dfvGKfapLYyXc1LCKWxrWsqRqIdFIlLGJJC/v72LbwS72Hr9A\n4rJSvai1gvUmVarrKlWqRfLZstrFdE2+xLnx1wjCgIgXcR1JRGRGVLDlusaSYzx58tlrXjOUGKYo\nWsTNDau5pWENpnox0UiU0fHEVKnuZu/xXpJ+eOl9FrdVpnb/MPXUVBRl+ssQkRyxtr2N5w/ESRZO\n0DXaTVNpo+tIIiIzooIt1/Vq154rpoVczYcWv4e7Wm9nZDzBlr1dbLNd7Dt+AT9IlWoPWNpexXpT\nzzrTQHV5PMPJRSQXdbRUErxSTbTmPPu7j6pgi0jOUcGW6xqYmN6R4HtPnWfrizs5cKLv9VLtwbJ5\nVaxf1sAtS+upKlOpFpFrixdEqfaaGeQ8ezoPc9+CDa4jiYjMiAq2XFdlvHxa123fN4jfcwHPg+Xz\nqy+V6srSwgwnFJG5ZnF1Bzt4lVMjp1xHERGZMScF2xjzMeDXgQTwaWAP8HUgApwDHrHWJlxkkzcz\nFSsI/She1L/qNaEfZVHZMu5Y38rNS+upKFGpFpG376bWDrafijIeHaJ/YoCqeKXrSCIi05b1pdnG\nmBpSpXoD8D7gg8CjwBestXcDR4FPZjuXXN2+o4Mkzy685jXJswu5c3kbd9/UqnItIjdsSXs1wVA1\nAIcuHHOcRkRkZlzsffQA8D1r7ai1ttNa+7PAPcDjU68/PnWNzBIDwxP4vS2EIYThla+FfpTE6SUk\nzy2if+T6CyFFRKajoqSQUr8BgF3nDjtOIyIyMy6miCwASo0x3waqgN8BSi6bEtIFNDvIJVdRWRYn\nWncGz4PkhQaC/ga8ggnCRBz/QhMEqf+NqjTXWkTSaH75PCz7OTZ4wnUUEZEZcVGwPaAG+BCpsv3s\n1HOXvy6zyC1L6/jHs2cA8LvmEQzWvemaeEGU9csash1NROawtS2LOdjlMRj2MpYcozimQ6hEJDe4\nKNidwGZrbQAcM8YMAQljTNxaOwG0Amev90Gqq0uIxaIZjnpt9fXT210j13V1HcKLjxFMFBEM1r7l\nNR95YAnz2qqznMytfLn/8ma699lx59oF/OO3K/DKBuilm5vrV7qOBOj+5zPde5kuFwX7KeBvjDGf\nITWSXQY8ATwM/B3w4anH19TXN5rJjNdVX19Od/f09ofOdU8ceAEAv6eVN/6CIV4Q5T13zOe+tS15\n898D8uv+y5V077MnGobExusIygZ44eAu2mLzXEfS/c9juvf5baY/XGW9YFtrzxpjvgm8BITAzwPb\ngK8bY/4TcBL4arZzyVsbS46zo2s3kCrY775tHs21JfSPTFJVWsj6ZQ0Ux7Wduoikn+d5tBa3c5qj\n2klERHKKk2Zkrf0y8OU3PP0uF1nk2nZ07SIRJAiHaggnSrj3llbqqzQPUkSyY1XDYk6PPEdP4jzJ\nIEksoh/oRWT2c7FNn+SQl85tAyDR3cqKBdUq1yKSVavmNxGMlRJ6PqeHzriOIyIyLSrYclXnR7o4\nNnASL4jhX2hk45oW15FEJM+0N5ThjdQAsLfriOM0IiLTo4ItV3Vp9Lq3kdLCIm5Z+ubt+UREMika\niVBf2ArAvm4VbBHJDSrY8pb8wGfr+e2pt7vbuH1lEwWOt0UUkfy0rHYRAOfGXiMIA8dpRESub1qr\nRYwxbcB/AxYB54EvWGtfzWQwcevAhUMMTA7BeCnBcBWb1mp6iIi4saatjR/YOMnCCbpGu2kqbXQd\nSUTkmq46gm2Mubx8/zbwBeD9wO8Df57ZWOLalkuLG1tY0FRBe0OZ40Qikq8WtVYSDKcOsjrYq+36\nRGT2u9YUke8bYzZMve0D86b+aSe1f7XMUcOTI+zp2Q+hR7KnlY0avRYRh4oKY1TRBMDu84ccpxER\nub5rFewfBj5pjPki8MfAOuC/AvcBH8tCNnHklc5X8UMff6CWwrCEdyzXr2NFxK1FVR0AnBo+5TiJ\niMj1XXUOtrX2AvAzxphNwF8DX7HW/nHWkokzW869AkCyu413mAZKinSwg4i4tba1g1dfizIWHaJ/\nYoCqeKXrSCIiV3XNXUSMMVHgAPAgMM8Y87gxZlFWkokTp4fOcGb4HCQLCPob2LS22XUkERFMezXB\nUGoe9uG+447TiIhc27UWOf4O8ATwJ8B3gTPALwGfNcb8ZnbiSbZdXNyY7GmmsaqUpe1VjhOJiEBl\nWZziZD0Au88ddpxGROTarvW7/3uttZsuPjDGPGut/SrwQWPMRzMfTbItESTZdj61+2Kyp427bm3G\n8zzHqUREUuaXz+MwBzg6oBFsEZndrlWwjxpj/hp4DVgKPHPxBWvtNzIdTLJvT89+RpKjBKPleGOV\n3Lla00NEZPZY07yYQz1PMRD2MpYcozhW7DqSiMhbuuoUEWvtT5Ha8/q7wK9Ya383a6nEidcXN7ay\nZlEtVWVxx4lERF63vL2OcLQCvJBj/SddxxERuaprbg9hrT0KHM1SFnGof2KAA72HIPTwe1vYeJdG\nr0VkdmmqKSE6VkdYNsCeriOsrFvmOpKIyFu65i4ikj9ePredkBC/r4HKeBlrFtW6jiQicgXP82gp\nbgPA6kRHEZnFrluwjTEaIpjjwjDkpYu7h3S3sWF1E9GIfvYSkdlnZcNiALonz5EMko7TiIi8tem0\nqH82xvyHMeanjDElGU8kWXds4CRdYz2EiTjBQC0b1+hodBGZnVbNayIYKyX0fE4PnXEdR0TkLV23\nYFtrVwI/B3QAzxljvmSMuTXjySRrXl/c2MLS9hqaavRzlIjMTvMby2GkBoD93VoiJCKz07TmAVhr\n91prPw38CrAc+I4x5gVjzJKMppOMm/An2dG1CwC/p5WNa7S4UURmr1g0Qn0s9Vu2vV1HHKcREXlr\n19xFBMAYMx/4BPBjwH5SW/c9CdwK/B/gHRnMJxn2atduJvxJ/KEqisJK1i9rcB1JROSaTO0iNvsv\nc3b8NEEYEPG0ZkREZpfrFmzgOeCvgPustWcve36rMWZrRlJJ1lxc3Oj3tLJheSPxgqjjRCIi17am\nrY0XD8dJFk7QNdpNU2mj60giIleYzo/9a4FDF8u1MebnjDFlANbaX8hkOMms7tFeDvcfgyCC39vM\nxrVa3Cgis9/itiqC4WpA2/WJyOw0nYL9N0DTZY9Lga9nJo5k00vnp7bmu9BEW20VC5rKHScSEbm+\n4niMyjD119LuzsOO04iIvNl0CnaNtfbzFx9Yaz8LVGUukmRDEAa8fG47AH53KxvXNuN5nuNUIiLT\ns7ByAQAnh0+5DSIi8hamU7DjxpjlFx8YY9YBhZmLJNlgLxyhb6KfYLyYyGgtd6xsuv47iYjMEmvb\nOgj9KGPhIP0TA67jiIhcYTqLHH8Z+LYxphKIAt3AIxlNJRl3ce9rv6eVW5Y2UFZc4DiRiMj0LW2r\nJrBVRCt7OdJ3nPVNN7mOJCJyyXQOmnnZWrsUWAEstdYuRyPYOW00Mcqunn0QTu19rcWNIpJjaiqK\nKJpMbSu6+7z2wxaR2WU6+2BXAD8B1E09jgM/BaiV5ahtnTtJBkn8wVpqi6tZPr/adSQRkRmbVzaP\noxzgyIB2EhGR2WU6c7C/AawhVarLgfcB/zmToSSztlzc+7q7lbtWNxPR4kYRyUGrmxcRBh4DyV7G\nkmOu44iIXDKdgl1krf054KS19teBe4EfyWwsyZSzw+c5NfQaYTJG0NfInat1NLqI5Kbl7XWEoxXg\nhRwf0G4iIjJ7THcXkVIgYoyptdZeABZlOJdkyKXFjb3NrFxQT21lkeNEIiJvT0tdKZHRWgD2dmoe\ntojMHtMp2F8D/h/gK8ABY8w+4HxGU0lG+IHP1vM7AEj2tLJJixtFJIdFPI/mojYADvYedZxGROR1\n09mm74vW2hDAGPM00ADszGgqyYi9vQcYTowQjJZREtRx05I615FERG7IioZFnBt/ga7JcySDJLHI\ndP5aExHJrOmMYD9z8Q1r7Rlr7asXC7fklkuLG3tauXNVM7HodG6/iMjstaq9mWCslNDzOT10xnUc\nERFgeiPYO40xjwKbgcmLT1prn7n6u8hsMzAxxL6egxB6JHta2PheLW4UkdzX0VwOm2ugeIQDPUfp\nqJzvOpKIyLQK9sXjsTZe9lzIZSPbMvu90rmDgAC/v4FFDfW01pe5jiQicsMKYlFqY830cZp9XUd4\nz6L7XEcSEbl+wbbW3puNIJI5YRhemh6S7G5l4x1a3Cgic8fSmoW8HG7lzNhrBGFAxNP0NxFxazon\nOf6A1Ij1Fay1mzKSSNLuxOBpzo90EiYKKRhp4tZlDa4jiYikzeq2dl46GidROE7XaDdNpY2uI4lI\nnpvOFJHfuOztQuA+YDgzcSQTXpra+zrZ08Lty5oojmuVvYjMHUvaqvB3VhOrPc+hC8dVsEXEuelM\nEXn+DU99zxjz3QzlkTSb9BNs79wFpHYP2Xi3FjeKyNxSVlxARdjIKOfZ3XmYTe23u44kInluOlNE\nFr7hqXbAZCaOpNuu7r2M+eMEw5U0lTSyuLXSdSQRkbRbWLmAvezi5NBJ11FERKY1ReTpy94OgUHg\ntzOSRtLu4tHoye5WNq5pwfM8x4lERNJvTUsHe85HGY0O0j8xQFVcgwki4s51l1pbazuARdbaDmvt\nQuBWa+3XMx9NblTvWB+H+o4SBhHob2HDqibXkUREMsK0VxMMVwFwpP+44zQiku+uW7CNMR8Gvn3Z\nUz8wxjycuUiSLi+f30ZIiN/XyNqOZipKC11HEhHJiNrKIuIT9QDsOX/EcRoRyXfT2Sz0V4GfuOzx\ng8CvZSaOpEsQBrx08Wj07lY2rdXiRhGZuzzPo71sHqARbBFxbzoF27PWDlx8MPW2n7lIkg5H+o/R\nO95HMFFERdjCqo5a15FERDJqddMiwsCjP9nDWHLcdRwRyWPTWeS4zRjzDeA5UoX83cD2TIaSG3fx\n5Ea/p5W7VjcTiWhxo4jMbcvb6/jW2Qq8sgGOD5xkRa02vBIRN6Yzgv2LwOPAClLb8/0f4FOZDCU3\nZiw5xqtde4Cpgr1GR6OLyNzXVl+GN1oDwL4uzcMWEXemU7BLgElr7S9Ya38RqJ56TmapHZ27SQQJ\n/MEaTGMLDVXFriOJiGRcJOLRHG8H4GDvMcdpRCSfTadgfw24fH+3UkDb9M1iW65Y3KjRaxHJHyvq\nU2ejdU2cJRkkHacRkXw1nYJdY639/MUH1trPAlWZiyQ34vxIF8cHTxL6UQpHW7llab3rSCIiWbOy\nvZlgrJTA8zk9dMZ1HBHJU9Mp2HFjzPKLD4wx6wBtqDxLXdqar7eZO5a3UlgQdZxIRCR7OloqCIer\nAU0TERF3plOwfxn4tjGm0xjTQ2qR4y9lNpa8HX7g8/L51AYvfk/qaHQRkXwSL4hSE0392bdXCx1F\nxJHpHJX+srV2KaldRJZaa5cDXRlPJjN24MIhBieHCMZKaSttZ35TuetIIiJZt7SmA4Azo6cJw9Bx\nGhHJR9MZwb5oBPghY8zTwEsZyiM3YMu5V4DU6LUWN4pIvlrd2k44GSfBOJ2jGg8Skey7bsE2xtxu\njPkScB74C+ArwPxMB5OZGZ4cYXfPAcIQvP42bl/R6DqSiIgTS9qr8IdS87AP9enYdBHJvqsWbGPM\nfzXG7Ae+AXQC64Gj1tp/sNYmshVQpueVzlcJQp9goJ51C9spKSpwHUlExImKkkLKggYA9pw/7DiN\niOSja41g/z4wCXzCWvub1tojgCazzUJhGLL5bGp6SLJbixtFRBZWLADgxNBJt0FEJC/FrvFaO/Bx\n4C+NMVHgb9H2fLPS6eEznB05R5gooJZ5mHnaplxE8tvqlg72dUUZjQ7SPzFAVbzSdSQRySNXHcG2\n1p631v6xtdYAnwQWA/ONMY8bY96TtYRyXa/vfd3CxtVtRDzPcSIREbfMvGqC4dRgw9F+zcMWkeya\n1i4i1toXrLWfAFqAfwU+nclQMn0JP8HWc68Cqd1D7lzd7DiRiIh7DVXFFEzUAbCnU/thi0h2XWuK\nyJtYa4eAL079I7PA7p79jPljBCMVrGruoLo87jqSiIhznufRXjKPk1iOaCcREcmymeyDLbPQS1N7\nXye197WIyBVWNS0iDDz6kj2MJcddxxGRPKKCncP6xvs5cOEwYeBRMjafNYtqXUcSEZk1lrfXEY5W\ngBdyfEC7iYhI9qhg57CXz+8gJCTob+DO5fOIRXU7RUQumtdYhjdSA8D+7qOO04hIPlEjy1FhGLLl\n0t7XbWxco8WNIiKXi0YiNMTbADjQo4ItItmjgp2jjg6coGe8l3AyTkf5QpprS11HEhGZdVbULQSg\na+IsySDpOI2I5AsV7By15dLixhbuXtPmOI2IyOy0cl4zwVgpgedzeuis6zgikidUsHPQeHKC7Z27\nAIgNzOPWZQ2OE4mIzE4LmysIh6sBsL2aJiIi2aGCnYNe7d5DIkjgD1Vx28JFxAujriOJiMxKxfEY\nVZHUGpW9XTpwRkSyQwU7B11c3Oh3t7FxrRY3iohcy9LqDgBeGz1NGIaO04hIPlDBzjFdoz0cHThO\n6EdpjCxkYXOF60giIrPa6tZ2wsk4CcbpHO12HUdE8oAKdo55+dw2APwLjWxaPR/P8xwnEhGZ3Za0\nV+EPpeZhH+475jiNiOQDFewcEoQBm8+mCnbY28YdKxsdJxIRmf2qyuKU+qnF4Hs6NQ9bRDJPBTuH\nHLxwmMHEIMF4CWtbDOUlha4jiYjkhI7y+QAcHzzhNoiI5AUV7Bxyce9rv6eVu9e0OE4jIpI7Vrd2\nEPpRRsNB+icGXMcRkTlOBTtHjCRG2dW9jzCE8vEOViyocR1JRCRnmPZqguEqAI72n3AbRkTmPBXs\nHLGtcyd+6BMM1rJx+UIiES1uFBGZrqaaEmLjdYD2wxaRzFPBzhEvntkKpPa+vmuN9r4WEZkJz/No\nK2kH4HDfccdpRGSuU8HOAWeGz3Fm5CxhMsbSCkNdZbHrSCIiOWdV4yLCwKMv0c1Yctx1HBGZw1Sw\nc8Clkxt7m7l7bbvjNCIiuWl5ex3haAV4IScGTrmOIyJzmAr2LJcMkrx0bgcABYPzuXlJveNEIiK5\naX5TOYykFogf6DnqOI2IzGUq2LPc3t6DjPmjBKNl3LHIUBDTLRMReTti0QgNBa0A7O/RQkcRyRy1\ntVlu85nL975udZxGRCS3La9fCEDnxFmSQdJxGhGZq1SwZ7GBiSH2XzhIGHi0RJfS1lDmOpKISE5b\n2d5MMFZKgM/pobOu44jIHKWCPYttPb+dkJCgv557Vi90HUdEJOctaqkkGKoG4NCFY47TiMhcFXP1\niY0xRcBe4FHgGeDrpAr/OeARa23CVbbZIAxD/mNq72v62nnH8ka3gURE5oCSohhVXhPDvMberiM8\n2HGP60giMge5HMH+TaB36u1HgS9Ya+8GjgKfdJZqljgxeIqe8R7CRCHrm1dSHHf2s5CIyJyypLoD\ngNMjpwjD0HEaEZmLnBRsY4wBlgH/BnjA3cDjUy8/DjzgItdssnlq7+tkTwub1rY5TiMiMnesam0n\nnIyTYJzO0W7XcURkDnI1gv1Z4FdIlWuA0sumhHQBeX0W+KQ/ySvndwJQNbmYJW2VjhOJiMwdS9ur\n8KfmYR/p17HpIpJ+WZ93YIx5BNhsrT2ZGsh+E++tnnyj6uoSYrFoWrPNVH19eUY+7g9ObCURThIM\nV/K+9WtoaKjIyOeRG5Op+y+zn+59bquvL6fUb2CC8xzqP8GHbprZL011//OX7r1Ml4uJve8FOowx\nDwGtwCQwbIyJW2snpp677t5JfX2jmU15HfX15XR3D2XkY//bgecB8HvaWHtfdcY+j7x9mbz/Mrvp\n3s8N88rncZjdHOw+PKP7qfufv3Tv89tMf7jKesG21v7oxbeNMZ8GTgAbgIeBvwM+DDyR7VyzRe/Y\nBY4NHiMMIqyoXEllWdx1JBGROWd1cweHeqOMMEj/xABVcU3FE5H0cb0P9sXpIL8FfNwY8zxQDXzV\nXSS3Xjq3DQD/QiP3rFngNoyIyBxl2qsJhqsAODZw0nEaEZlrnO79Zq39ncsevstZkFkiCAN+8Fpq\n95CikQWsXlTjOJGIyNzUUldKdLQWKnvZ13WEWxrWuI4kInOI6xFsuczhvmMMJQcIJorY2LGaaES3\nR0QkEyKeR2tJO6ATHUUk/dTgZpGLJzf6Pa1sXNviOI2IyNy2qnEhYeBxIdHNWHLcdRwRmUNUsGeJ\nseQYO7v3ADC/YDmN1SWOE4mIzG3L2usJRyvACzkxcMp1HBGZQ1SwZ4ntnbsI8PEHa7h31RLXcURE\n5ryO5nLC4dRal4O9Rx2nEZG5RAV7lnj+1MsARPvnsc40OE4jIjL3FcSi1BempuPt71bBFpH0UcGe\nBc6PdHJ27AyhH+XW5rXEC9yeUCkiki+W1S4E4PzEWZJB0nEaEZkrVLBngRfPpLbm83ubuWdtu+M0\nIiL5Y2V7M8FYKQFJTg9d9xBhEZFpUcF2zA98tpzdDkCtv4T5jTM7ilNERN6+xa2VBEPVQGqrVBGR\ndFDBdmz/BctYMEIwVsp9ZhWe513/nUREJC3KiguopAmAvV1HHKcRkblCBduxi4sbw9427ljV5DiN\niEj+WVy1AIDTw6cIw9BtGBGZE1SwHRqaHOZgvyUMPVZVr6G0qMB1JBGRvLOqtZ1wMs4k43SOdruO\nIyJzgAq2Q1vP7SAkIBio477Vi1zHERHJS0vbq/Cn5mEf6T/uOI2IzAUq2I6EYchzU9NDSkYWsGx+\nteNEIiL5qbayiKJEHQD7NA9bRNJABduR00NnuJDoJkwUcHfHzUS0uFFExAnP81hQPh+AYwMn3IYR\nkTlBBduRF06nRq/93hY2rmlznEZEJL+tau4g9KMMBwMMTAy6jiMiOU4F24GEn2Bb504AOuIrqako\ncpxIRCS/mfZqguEqAI5qFFtEbpAKtgO7uveRYIJgpIIHVq5wHUdEJO+11ZcRGa0FYL/mYYvIDVLB\nduCZky8BEBuYx01L6hynERGRSMSjtbgdgEN92klERG6MCnaW9Y33c3L4GGHg8Y6Wm4lFdQtERGaD\nFQ0dhIFH72QXY8lx13FEJIep3WXZi2e2gQd+XyP3rVnoOo6IiExZ3l5POFoBXsiJgVOu44hIDlPB\nzqIwDPnB6a0ANIRLaKkrdZxIREQu6mipIByuAeBg71HHaUQkl6lgZ9HRgRMMB/2Ek3HuX3qz6zgi\nInKZeEGUulgLAAd6VLBF5O1Twc6ip49vSb1xoY3bVzS5DSMiIm9i6joAODd+Fj/wHacRkVylgp0l\n48kJ9vbtBWB19VqKCmOOE4mIyButaG0mGCslIMmpoTOu44hIjlLBzpJtnbsISOIPVfPONctdxxER\nkbewpK2SYKgagMN9xxynEZFcpYKdJc8cT+19XTbWwaKWCsdpRETkrVSUFlIWNgKwr0vzsEXk7VHB\nzoKu0W46J18j9KPc27Eez/NcRxIRkatYXLUAgFPDJwnD0G0YEclJKthZ8NzJlwEI+prYuHqe4zQi\nInItK1vaCCfjTDJO52i36zgikoNUsDMsCANeOrcdgIVFK6koKXScSERErsW0VxMMVwFwtF/HpovI\nzKlgZ9j+3kNMMEIwXsKDK25yHUdERK6jobqYgvE6APZ1HXGcRkRykQp2hn3vaGrv68LBeaxeWOs4\njYiIXI/necwvnw+kDggTEZkpFewMGkmMcnTYEoZwe/N6IhEtbhQRyQWrmhYQ+lGGgwEGJgZdxxGR\nHKOCnUGbX9tG6AUEA3U8sHaJ6zgiIjJNZl7N6/OwNYotIjOkgp1Bz51K7R7SxFLqq4odpxERkelq\nbyjDG60B4EC39sMWkZlRwc6QM8Pn6Pe7CZMx3mnWu44jIiIzEItGaI63A2Av6ERHEZkZFewMefLI\nZgC8/lZuM82O04iIyEytbOggDDx6J7sYT467jiMiOUQFOwOSQZJdvbsAWFN9MwWxqONEIiIyU8va\n6wlHK8ALOT5wynUcEckhKtgZsLNzH0lvnGC0jHevXuU6joiIvA0LWyoIhqsBTRMRkZlRwc6Ap6b2\nvq6YWMT8pgrHaURE5O0ojseojbQAsF8LHUVkBlSw02xgYpAzE8cJA49759/qOo6IiNwAU9sBwLnx\nM/iB7ziNiOQKFew0e+b4S+CFMNjAplUdruOIiMgNWNHeTDBWSkCS08NnXMcRkRwRcx1gLgnDkM1n\ntwGwsGgVJUUFjhOJiMiNWNxaSbCzmkjxCN87+Tynxk8SS8a5uWE1xTGdbyAib00FO42O9p9glH7C\nyTjvXam9r0VEcl11eZx4sY8P7Ozew87uPQD838Pf4cH59/LuBfe7DSgis5KmiKTRvx96EYDCpPHP\n8QAAGo9JREFU4Xksm1fjOI2IiNyoJ048jV9+7k3PT/qTPH7sSZ448bSDVCIy26lgp8mkP8mh4QMA\n3NG8Ds/zHCcSEZEbMZYc48mTz17zmidPPsuYDqERkTdQwU6Tza/tJPASBMOVvGvNStdxRETkBr3a\ntYdJf/Ka10z6k7zatSdLiUQkV6hgp8kzx1N7Xzd7y6gujztOIyIiN2pgYmha1w1ODmY4iYjkGhXs\nNOge7aU3OEPoR3hwyTtcxxERkTQojpSm9ToRyR8q2GlwcXFjZKiZdUtaHKcREZF0SF5oJPSj17wm\n9CP4vY1ZSiQiuUIF+wYFYcCrPTsBWFN9E7Go/pOKiMwFY6OQPLvw2hd5IQeG9hGGYXZCiUhOUBu8\nQXu7DjEZGSaYKOahNetcxxERkTSpLIuTPLeIxOklbxrJDv0o/nAFXiRkf/IFvrL364wmxhwlFZHZ\nRgfN3KAnDm8GoGpyIc21ZY7TiIhIuqw3DfzD9w8zcW4Ryc75RGvO4xVMECbi+BeaIIgRrTlHyeL9\n7Ozey6mhM3xy5Y/TUTnfdXQRcUwj2DdgNDHGyYnDANwzX4sbRUTmkpKiGO+5Y6osBzH8njaS5xbh\n97RBkBqf8i80M7zrDuKJGi6M9/G5HX/B904+RxAGDpOLiGsq2Dfg6aNbwfMJh2q5Z8US13FERCTN\nHtqwgA9tWki84MopIvGCKB/atJBf+OHVlEYq6d+5Hq9nIUEY8K2j3+XPd/01Q5PDjlKLiGuaInID\nXjzzCniwuHgl8cJrrzQXEZHc9NCGBTywro1tB7tIAAXA+mUNFMdTf4V2tFTwV/+6n33HIkQuVFG6\nZB8HLhziD7b+CZ9Y8WOYmsVO84tI9mkE+206fuE1hrwuwmSM9628w3UcERHJoOJ4jI1rW/joA4aN\na1sulWuAqrI4v/zRm/jR+xYTGWpkaOcdRMdqGZwc4gs7v8zjx57ED3yH6UUk2zSCPUO9w8M8vm8z\n+4d2QwQKR1pY0lLjOpaIiDgU8Tzedds8ls2v5kuP7+fsnnUUth0l2nKUJ048zeG+Y/zUyh+juqjK\ndVQRyQIvV/fu7O4eynrwx174JocmtuNFXx+JCIMISwvX86lND2c7jjhUX19Od/f0jlGWuUX3Pr9N\n5/5PJnz+6dkjPLPjDJHyXkqW7sGPjlMaK+GRFT/C6roVWUor6aTv/fx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6201320240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(delays, accuracy_softmax, 'o-', lw=2, markersize=10., label='Accuracy')\n", "plt.plot(delays, accuracy_softmax_std, 'o-', lw=2, markersize=10, label='Standarized Representations')\n", "plt.xlabel('Delays')\n", "plt.ylim([0, 105])\n", "plt.xlim([-0.5, 10])\n", "plt.ylabel('Accuracy %')\n", "plt.title('Delays vs Accuracy (Softmax)')\n", "fig = plt.gcf()\n", "fig.set_size_inches((12, 9))\n", "plt.legend()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
tschinz/iPython_Workspace
02_WP/Wavedrom/example.ipynb
1
2431
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import wavedrom\n", "\n", "a = {'signal': [\n", " {'name': 'clk', 'wave': 'p.....|...'},\n", " {'name': 'dat', 'wave': 'x.345x|=.x', 'data': ['head', 'body', 'tail', 'data']},\n", " {'name': 'req', 'wave': '0.1..0|1.0'},\n", " {},\n", " {'name': 'ack', 'wave': '1.....|01.'}\n", "]}\n", "\n", "wavedrom.draw_wavedrom(a)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"width: 2000px\"><script type=\"WaveDrom\">{\"signal\": [{\"name\": \"clk\", \"wave\": \"p.....|...\"}, {\"name\": \"dat\", \"wave\": \"x.345x|=.xxxxxxxxxxxxxxxxxxxxxxxx\", \"data\": [\"head\", \"body\", \"tail\", \"data\"]}, {\"name\": \"req\", \"wave\": \"0.1..0|1.0\"}, {}, {\"name\": \"ack\", \"wave\": \"1.....|01.\"}]}</script></div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "$.getScript(\"files/js/wavedrom.min.js\", function () {\n", "$.getScript(\"files/js/wavedromskin.js\", function () {\n", "WaveDrom.ProcessAll();});\n", "});\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "'''\n", "This picture is really long, we set a width to stop it from scaling\n", "'''\n", "a = {'signal': [\n", " {'name': 'clk', 'wave': 'p.....|...'},\n", " {'name': 'dat', 'wave': 'x.345x|=.xxxxxxxxxxxxxxxxxxxxxxxx', 'data': ['head', 'body', 'tail', 'data']},\n", " {'name': 'req', 'wave': '0.1..0|1.0'},\n", " {},\n", " {'name': 'ack', 'wave': '1.....|01.'}\n", "]}\n", "\n", "wavedrom.draw_wavedrom(a, 2000)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
GoogleCloudPlatform/training-data-analyst
courses/machine_learning/feateng/feateng.ipynb
1
25179
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1> Feature Engineering </h1>\n", "\n", "In this notebook, you will learn how to incorporate feature engineering into your pipeline.\n", "<ul>\n", "<li> Working with feature columns </li>\n", "<li> Adding feature crosses in TensorFlow </li>\n", "<li> Reading data from BigQuery </li>\n", "<li> Creating datasets using Dataflow </li>\n", "<li> Using a wide-and-deep model </li>\n", "</ul>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst" ] }, { "cell_type": "code", "metadata": { "id": "Nny3m465gKkY", "colab_type": "code", "colab": {} }, "source": [ "!pip install --user google-cloud-bigquery==1.25.0" ], "execution_count": null, "outputs": [], "source": [ "!pip install --user apache-beam[gcp]==2.24.0 \n", "!pip install --user httplib2==0.12.0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE: In the output of the above cell you may ignore any WARNINGS or ERRORS related to the following: \"apache-beam\", \"pyarrow\", \"tensorflow-transform\", \"tensorflow-model-analysis\", \"tensorflow-data-validation\", \"joblib\", \"google-cloud-storage\" etc." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you get any related errors mentioned above please rerun the above cell." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: Restart your kernel to use updated packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.6.0\n" ] } ], "source": [ "import tensorflow as tf\n", "import apache_beam as beam\n", "import shutil\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> 1. Environment variables for project and bucket </h2>\n", "\n", "1. Your project id is the *unique* string that identifies your project (not the project name). You can find this from the GCP Console dashboard's Home page. My dashboard reads: <b>Project ID:</b> cloud-training-demos \n", "2. Cloud training often involves saving and restoring model files. Therefore, we should <b>create a single-region bucket</b>. If you don't have a bucket already, I suggest that you create one from the GCP console (because it will dynamically check whether the bucket name you want is available) \n", "<b>Change the cell below</b> to reflect your Project ID and bucket name.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "PROJECT = 'cloud-training-demos' # CHANGE THIS\n", "BUCKET = 'cloud-training-demos' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in the region you selected.\n", "REGION = 'us-central1' # Choose an available region for Cloud AI Platform" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# for bash\n", "os.environ['PROJECT'] = PROJECT\n", "os.environ['BUCKET'] = BUCKET\n", "os.environ['REGION'] = REGION\n", "os.environ['TFVERSION'] = '2.6' \n", "\n", "## ensure we're using python3 env\n", "os.environ['CLOUDSDK_PYTHON'] = 'python3.7'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "gcloud config set project $PROJECT\n", "gcloud config set compute/region $REGION\n", "\n", "## ensure we predict locally with our current Python environment\n", "gcloud config set ml_engine/local_python `which python`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> 2. Specifying query to pull the data </h2>\n", "\n", "Let's pull out a few extra columns from the timestamp." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_query(phase, EVERY_N):\n", " if EVERY_N == None:\n", " EVERY_N = 4 #use full dataset\n", " \n", " #select and pre-process fields\n", " base_query = \"\"\"\n", "#legacySQL\n", "SELECT\n", " (tolls_amount + fare_amount) AS fare_amount,\n", " DAYOFWEEK(pickup_datetime) AS dayofweek,\n", " HOUR(pickup_datetime) AS hourofday,\n", " pickup_longitude AS pickuplon,\n", " pickup_latitude AS pickuplat,\n", " dropoff_longitude AS dropofflon,\n", " dropoff_latitude AS dropofflat,\n", " passenger_count*1.0 AS passengers,\n", " CONCAT(STRING(pickup_datetime), STRING(pickup_longitude), STRING(pickup_latitude), STRING(dropoff_latitude), STRING(dropoff_longitude)) AS key\n", "FROM\n", " [nyc-tlc:yellow.trips]\n", "WHERE\n", " trip_distance > 0\n", " AND fare_amount >= 2.5\n", " AND pickup_longitude > -78\n", " AND pickup_longitude < -70\n", " AND dropoff_longitude > -78\n", " AND dropoff_longitude < -70\n", " AND pickup_latitude > 37\n", " AND pickup_latitude < 45\n", " AND dropoff_latitude > 37\n", " AND dropoff_latitude < 45\n", " AND passenger_count > 0\n", " \"\"\"\n", " \n", " #add subsampling criteria by modding with hashkey\n", " if phase == 'train': \n", " query = \"{} AND ABS(HASH(pickup_datetime)) % {} < 2\".format(base_query,EVERY_N)\n", " elif phase == 'valid': \n", " query = \"{} AND ABS(HASH(pickup_datetime)) % {} == 2\".format(base_query,EVERY_N)\n", " elif phase == 'test':\n", " query = \"{} AND ABS(HASH(pickup_datetime)) % {} == 3\".format(base_query,EVERY_N)\n", " return query\n", " \n", "print(create_query('valid', 100)) #example query using 1% of data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try the query above in https://bigquery.cloud.google.com/table/nyc-tlc:yellow.trips if you want to see what it does (ADD LIMIT 10 to the query!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> 3. Preprocessing Dataflow job from BigQuery </h2>\n", "\n", "This code reads from BigQuery and saves the data as-is on Google Cloud Storage. We can do additional preprocessing and cleanup inside Dataflow, but then we'll have to remember to repeat that prepreprocessing during inference. It is better to use tf.transform which will do this book-keeping for you, or to do preprocessing within your TensorFlow model. We will look at this in future notebooks. For now, we are simply moving data from BigQuery to CSV using Dataflow.\n", "\n", "While we could read from BQ directly from TensorFlow (See: https://www.tensorflow.org/api_docs/python/tf/contrib/cloud/BigQueryReader), it is quite convenient to export to CSV and do the training off CSV. Let's use Dataflow to do this at scale.\n", "\n", "Because we are running this on the Cloud, you should go to the GCP Console (https://console.cloud.google.com/dataflow) to look at the status of the job. It will take several minutes for the preprocessing job to launch." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "if gsutil ls | grep -q gs://${BUCKET}/taxifare/ch4/taxi_preproc/; then\n", " gsutil -m rm -rf gs://$BUCKET/taxifare/ch4/taxi_preproc/\n", "fi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's define a function for preprocessing the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "\n", "####\n", "# Arguments:\n", "# -rowdict: Dictionary. The beam bigquery reader returns a PCollection in\n", "# which each row is represented as a python dictionary\n", "# Returns:\n", "# -rowstring: a comma separated string representation of the record with dayofweek\n", "# converted from int to string (e.g. 3 --> Tue)\n", "####\n", "def to_csv(rowdict):\n", " days = ['null', 'Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n", " CSV_COLUMNS = 'fare_amount,dayofweek,hourofday,pickuplon,pickuplat,dropofflon,dropofflat,passengers,key'.split(',')\n", " rowdict['dayofweek'] = days[rowdict['dayofweek']]\n", " rowstring = ','.join([str(rowdict[k]) for k in CSV_COLUMNS])\n", " return rowstring\n", "\n", "\n", "####\n", "# Arguments:\n", "# -EVERY_N: Integer. Sample one out of every N rows from the full dataset.\n", "# Larger values will yield smaller sample\n", "# -RUNNER: 'DirectRunner' or 'DataflowRunner'. Specify to run the pipeline\n", "# locally or on Google Cloud respectively. \n", "# Side-effects:\n", "# -Creates and executes dataflow pipeline. \n", "# See https://beam.apache.org/documentation/programming-guide/#creating-a-pipeline\n", "####\n", "def preprocess(EVERY_N, RUNNER):\n", " job_name = 'preprocess-taxifeatures' + '-' + datetime.datetime.now().strftime('%y%m%d-%H%M%S')\n", " print('Launching Dataflow job {} ... hang on'.format(job_name))\n", " OUTPUT_DIR = 'gs://{0}/taxifare/ch4/taxi_preproc/'.format(BUCKET)\n", "\n", " #dictionary of pipeline options\n", " options = {\n", " 'staging_location': os.path.join(OUTPUT_DIR, 'tmp', 'staging'),\n", " 'temp_location': os.path.join(OUTPUT_DIR, 'tmp'),\n", " 'job_name': 'preprocess-taxifeatures' + '-' + datetime.datetime.now().strftime('%y%m%d-%H%M%S'),\n", " 'project': PROJECT,\n", " 'runner': RUNNER,\n", " 'num_workers' : 4,\n", " 'max_num_workers' : 5\n", " }\n", " #instantiate PipelineOptions object using options dictionary\n", " opts = beam.pipeline.PipelineOptions(flags=[], **options)\n", " #instantantiate Pipeline object using PipelineOptions\n", " with beam.Pipeline(options=opts) as p:\n", " for phase in ['train', 'valid']:\n", " query = create_query(phase, EVERY_N) \n", " outfile = os.path.join(OUTPUT_DIR, '{}.csv'.format(phase))\n", " (\n", " p | 'read_{}'.format(phase) >> beam.io.Read(beam.io.BigQuerySource(query=query))\n", " | 'tocsv_{}'.format(phase) >> beam.Map(to_csv)\n", " | 'write_{}'.format(phase) >> beam.io.Write(beam.io.WriteToText(outfile))\n", " )\n", " print(\"Done\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's run pipeline locally. This takes upto <b>5 minutes</b>. You will see a message \"Done\" when it is done." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Launching Dataflow job preprocess-taxifeatures-201007-104302 ... hang on\n", "Done\n" ] } ], "source": [ "preprocess(50*10000, 'DirectRunner') " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/train.csv-00000-of-00005\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/train.csv-00001-of-00005\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/train.csv-00002-of-00005\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/train.csv-00003-of-00005\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/train.csv-00004-of-00005\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/valid.csv-00000-of-00002\n", "gs://qwiklabs-gcp-03-6f3d948638d3/taxifare/ch4/taxi_preproc/valid.csv-00001-of-00002\n" ] } ], "source": [ "%%bash\n", "gsutil ls gs://$BUCKET/taxifare/ch4/taxi_preproc/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Run Beam pipeline on Cloud Dataflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run pipeline on cloud on a larger sample size." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "if gsutil ls | grep -q gs://${BUCKET}/taxifare/ch4/taxi_preproc/; then\n", " gsutil -m rm -rf gs://$BUCKET/taxifare/ch4/taxi_preproc/\n", "fi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following step will take <b>10-15 minutes.</b> Monitor job progress on the [Cloud Console in the Dataflow](https://console.cloud.google.com/dataflow) section.\n", "__Note__: If the error occurred regarding enabling of `Dataflow API` then disable and re-enable the `Dataflow API` and re-run the below cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Launching Dataflow job preprocess-taxifeatures-201007-104302 ... hang on\n", "Done\n" ] } ], "source": [ "preprocess(50*100, 'DataflowRunner') \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the job completes, observe the files created in Google Cloud Storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "49169783 2020-10-07T11:11:48Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00000-of-00001\n", " 116320 2020-10-07T11:06:21Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00000-of-00005\n", " 108804 2020-10-07T11:06:21Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00001-of-00005\n", " 116799 2020-10-07T11:06:21Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00002-of-00005\n", " 114697 2020-10-07T11:06:21Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00003-of-00005\n", " 116173 2020-10-07T11:06:21Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/train.csv-00004-of-00005\n", " 24666705 2020-10-07T11:12:01Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/valid.csv-00000-of-00001\n", " 114332 2020-10-07T11:06:20Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/valid.csv-00000-of-00002\n", " 107689 2020-10-07T11:06:20Z gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/valid.csv-00001-of-00002\n", " gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_preproc/tmp/\n", "TOTAL: 9 objects, 74631302 bytes (71.17 MiB)\n" ] } ], "source": [ "%%bash\n", "gsutil ls -l gs://$BUCKET/taxifare/ch4/taxi_preproc/" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11.3,Fri,13,-73.975687,40.76003,-73.999972,40.762227,2.0,2009-09-11 13:56:00.000000-73.975740.7640.7622-74\n", "6.1,Wed,12,-73.979987,40.757182,-73.999998,40.748792,2.0,2009-12-16 12:26:39.000000-73.9840.757240.7488-74\n", "21.3,Tue,17,-74.00001,40.72167,-73.933223,40.679975,2.0,2012-05-15 17:38:00.000000-7440.721740.68-73.9332\n", "6.5,Sat,15,-73.992025,40.725997,-74.000012,40.72185,2.0,2012-08-18 15:13:00.000000-73.99240.72640.7219-74\n", "7.0,Sun,21,-73.991645,40.74996,-74.000044,40.730599,2.0,2014-04-06 21:49:00.000000-73.991640.7540.7306-74\n", "14.0,Tue,22,-73.9999771118164,40.72161102294922,-73.95999145507812,40.76264572143555,2.0,2015-06-09 \n", "22:19:48.000000-7440.721640.7626-73.96\n", "10.9,Tue,14,-73.999958,40.730595,-73.980769,40.763996,2.0,2009-01-13 14:35:50.000000-7440.730640.764-73.9808\n", "10.9,Sat,18,-73.999975,40.717953,-73.99127,40.750233,2.0,2009-04-18 18:31:00.000000-7440.71840.7502-73.9913\n", "5.7,Thu,1,-73.99998,40.738628,-73.981518,40.741005,2.0,2009-09-10 01:02:14.000000-7440.738640.741-73.9815\n", "6.1,Sat,1,-73.999968,40.734777,-74.002785,40.752014,2.0,2010-02-20 01:25:52.000000-7440.734840.752-74.0028\n" ] } ], "source": [ "%%bash\n", "#print first 10 lines of first shard of train.csv\n", "gsutil cat \"gs://$BUCKET/taxifare/ch4/taxi_preproc/train.csv-00000-of-*\" | head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Develop model with new inputs\n", "\n", "Download the first shard of the preprocessed data to enable local development." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "if [ -d sample ]; then\n", " rm -rf sample\n", "fi\n", "mkdir sample\n", "gsutil cat \"gs://$BUCKET/taxifare/ch4/taxi_preproc/train.csv-00000-of-*\" > sample/train.csv\n", "gsutil cat \"gs://$BUCKET/taxifare/ch4/taxi_preproc/valid.csv-00000-of-*\" > sample/valid.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have two new inputs in the INPUT_COLUMNS, three engineered features, and the estimator involves bucketization and feature crosses." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "grep -A 20 \"INPUT_COLUMNS =\" taxifare/trainer/model.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "grep -A 50 \"build_estimator\" taxifare/trainer/model.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "grep -A 15 \"add_engineered(\" taxifare/trainer/model.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try out the new model on the local sample (this takes <b>5 minutes</b>) to make sure it works fine." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config:\n", "..................................................................................................\n", "..................................................................................................\n", "..................................................................................................\n", "INFO:tensorflow:Loss for final step: 184.59918.\n" ] } ], "source": [ "%%bash\n", "rm -rf taxifare.tar.gz taxi_trained\n", "export PYTHONPATH=${PYTHONPATH}:${PWD}/taxifare\n", "python -m trainer.task \\\n", " --train_data_paths=${PWD}/sample/train.csv \\\n", " --eval_data_paths=${PWD}/sample/valid.csv \\\n", " --output_dir=${PWD}/taxi_trained \\\n", " --train_steps=10 \\\n", " --job-dir=/tmp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1602060307\n" ] } ], "source": [ "%%bash\n", "ls taxi_trained/export/exporter/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use ```saved_model_cli``` to look at the exported signature. Note that the model doesn't need any of the engineered features as inputs. It will compute latdiff, londiff, euclidean from the provided inputs, thanks to the ```add_engineered``` call in the serving_input_fn." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "model_dir=$(ls ${PWD}/taxi_trained/export/exporter | tail -1)\n", "saved_model_cli show --dir ${PWD}/taxi_trained/export/exporter/${model_dir} --all" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing /tmp/test.json\n" ] } ], "source": [ "%%writefile /tmp/test.json\n", "{\"dayofweek\": \"Sun\", \"hourofday\": 17, \"pickuplon\": -73.885262, \"pickuplat\": 40.773008, \"dropofflon\": -73.987232, \"dropofflat\": 40.732403, \"passengers\": 2}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PREDICTIONS\n", "[2.600170373916626]\n" ] } ], "source": [ "%%bash\n", "model_dir=$(ls ${PWD}/taxi_trained/export/exporter)\n", "gcloud ai-platform local predict \\\n", " --model-dir=${PWD}/taxi_trained/export/exporter/${model_dir} \\\n", " --json-instances=/tmp/test.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Train on cloud\n", "\n", "This will take <b> 10-15 minutes </b> even though the prompt immediately returns after the job is submitted. Monitor job progress on the [Cloud Console, in the AI Platform](https://console.cloud.google.com/mlengine) section and wait for the training job to complete.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text":[ "gs://qwiklabs-gcp-c8b7c0b514e76634/taxifare/ch4/taxi_trained us-central1 lab4a_201007_111601\n", "jobId: lab4a_201007_111601 \n ", "state: QUEUED\n", "CommandException: 1 files/objects could not be removed. \n", "Job [lab4a_201007_111601] submitted successfully.\n", "Your job is still active. You may view the status of your job with the command \n", " $ gcloud ai-platform jobs describe lab4a_201007_111601 \n", "or continue streaming the logs with the command \n", " $ gcloud ai-platform jobs stream-logs lab4a_201007_111601" ] } ], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/taxifare/ch4/taxi_trained\n", "JOBNAME=lab4a_$(date -u +%y%m%d_%H%M%S)\n", "echo $OUTDIR $REGION $JOBNAME\n", "gsutil -m rm -rf $OUTDIR\n", "gcloud ai-platform jobs submit training $JOBNAME \\\n", " --region=$REGION \\\n", " --module-name=trainer.task \\\n", " --package-path=${PWD}/taxifare/trainer \\\n", " --job-dir=$OUTDIR \\\n", " --staging-bucket=gs://$BUCKET \\\n", " --scale-tier=BASIC \\\n", " --runtime-version 2.3 \\\n", " --python-version 3.5 \\\n", " -- \\\n", " --train_data_paths=\"gs://$BUCKET/taxifare/ch4/taxi_preproc/train*\" \\\n", " --eval_data_paths=\"gs://${BUCKET}/taxifare/ch4/taxi_preproc/valid*\" \\\n", " --train_steps=5000 \\\n", " --output_dir=$OUTDIR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The RMSE is now 8.33249, an improvement over the 9.3 that we were getting ... of course, we won't know until we train/validate on a larger dataset. Still, this is promising. But before we do that, let's do hyper-parameter tuning.\n", "\n", "<b>Use the Cloud Console link to monitor the job and wait till the job is done.</b>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
CalPolyPat/phys202-2015-work
days/day07/Interact.ipynb
1
15480
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Interact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `interact` function (`IPython.html.widgets.interact`) automatically creates a graphical user interface (GUI) for exploring code and data interactively. It is the easiest way to get started using IPython's widgets." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html.widgets import interact, interactive, fixed\n", "from IPython.html import widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic `interact`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the most basic level, `interact` autogenerates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use `interact`, you need to define a function that you want to explore. Here is a function that prints its only argument `x`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f(x):\n", " print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you pass this function as the first argument to `interact` along with an integer keyword argument (`x=10`), a slider is generated and bound to the function." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "interact(f, x=10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you move the slider, the function is called and the current value of `x` is printed.\n", "\n", "If you pass `True` or `False`, `interact` will generate a checkbox:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "interact(f, x=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you pass a string, `interact` will generate a text area." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hi there!\n" ] } ], "source": [ "interact(f, x='Hi there!');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`interact` can also be used as a decorator. This allows you to define a function and interact with it in a single shot. As this example shows, `interact` also works with functions that have multiple arguments." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True 1.0\n" ] } ], "source": [ "@interact(x=True, y=1.0)\n", "def g(x, y):\n", " print(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fixing arguments using `fixed`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are times when you may want to explore a function using `interact`, but fix one or more of its arguments to specific values. This can be accomplished by wrapping values with the `fixed` function." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def h(p, q):\n", " print(p, q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we call `interact`, we pass `fixed(20)` for q to hold it fixed at a value of `20`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 20\n" ] } ], "source": [ "interact(h, p=5, q=fixed(20));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that a slider is only produced for `p` as the value of `q` is fixed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Widget abbreviations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you pass an integer valued keyword argument (`x=10`) to `interact`, it generates an integer valued slider control with a range of $[-10,+3\\times10]$. In this case `10` is an *abbreviation* for an actual slider widget:\n", "\n", "```python\n", "IntSlider(min=-10,max=30,step=1,value=10)\n", "```\n", "\n", "In fact, we can get the same result if we pass this `IntSlider` as the keyword argument for `x`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "interact(f, x=widgets.IntSlider(min=-10,max=30,step=1,value=10));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This examples clarifies how `interact` proceses its keyword arguments:\n", "\n", "1. If the keyword argument is `Widget` instance with a `value` attribute, that widget is used. Any widget with a `value` attribute can be used, even custom ones.\n", "2. Otherwise, the value is treated as a *widget abbreviation* that is converted to a widget before it is used.\n", "\n", "The following table gives an overview of different widget abbreviations:\n", "\n", "<table class=\"table table-condensed table-bordered\">\n", " <tr><td><strong>Keyword argument</strong></td><td><strong>Widget</strong></td></tr> \n", " <tr><td>`True` or `False`</td><td>Checkbox</td></tr> \n", " <tr><td>`'Hi there'`</td><td>Textarea</td></tr>\n", " <tr><td>`value` or `(min,max)` or `(min,max,step)` if integers are passed</td><td>IntSlider</td></tr>\n", " <tr><td>`value` or `(min,max)` or `(min,max,step)` if floats are passed</td><td>FloatSlider</td></tr>\n", " <tr><td>`('orange','apple')` or `{'one':1,'two':2}`</td><td>Dropdown</td></tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have seen how the checkbox and textarea widgets work above. Here, more details about the different abbreviations for sliders and dropdowns are given.\n", "\n", "If a 2-tuple of integers is passed `(min,max)` a integer valued slider is produced with those minimum and maximum (inclusive) values. In this case, the default step size of `1` is used." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "interact(f, x=(0,4));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a 3-tuple of integers is passed `(min,max,step)` the step size can also be set." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "interact(f, x=(0,8,2));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A float valued slider is produced if the elements of the tuples are floats. Here the minimum is `0.0`, the maximum is `10.0` and step size is `0.1` (the default)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.0\n" ] } ], "source": [ "interact(f, x=(0.0,10.0));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The step size can be changed by passing a 3rd element in the tuple." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.99\n" ] } ], "source": [ "interact(f, x=(0.0,10.0,0.01));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For both integer and float valued sliders, you can pick the initial value of the widget by passing a default keyword argument to the underlying Python function. Here we set the initial value of a float slider to `5.5`." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.5\n" ] } ], "source": [ "def h(x=5.5):\n", " print(x)\n", " \n", "interact(h, x=(0.0,20.0,0.5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dropdown menus can be produced by passing a tuple of strings. In this case, the strings are both used as the names in the dropdown menu UI and passed to the underlying Python function." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oranges\n" ] } ], "source": [ "interact(f, x=('apples','oranges'));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want a dropdown menu that passes non-string values to the Python function, you can pass a dictionary. The keys in the dictionary are used for the names in the dropdown menu UI and the values are the arguments that are passed to the underlying Python function." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "interact(f, x={'one': 10, 'two': 20});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `interactive`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to `interact` IPython provides another function, `interactive`, that is useful when you want to reuse the widget that are produced or access the data that is bound to the UI controls." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a function that returns the sum of its two arguments." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f(a, b):\n", " return a+b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike `interact`, `interactive` returns a `Widget` instance rather than immediately displaying the widget." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "w = interactive(f, a=10, b=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The widget is a `Box`, which is a container for other widgets." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "IPython.html.widgets.widget_box.Box" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The children of the `Box` are two integer valued sliders produced by the widget abbreviations above." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<IPython.html.widgets.widget_int.IntSlider at 0x7ff06c0fdf60>,\n", " <IPython.html.widgets.widget_int.IntSlider at 0x7ff06c386dd8>)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.children" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To actually display the widgets, you can use IPython's `display` function." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import display\n", "display(w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or return the value" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, the UI controls work just like they would if `interact` had been used. You can manipulate them interactively and the function will be called. However, the widget instance returned by `interactive` also give you access to the current keyword arguments and return value of the underlying Python function.\n", "\n", "Here are the current keyword arguments. If you rerun this cell after manipulating the sliders, the values will have changed." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'a': 10, 'b': 20}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.kwargs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the current return value of the function." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "47" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.result" ] }, { "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
crdietrich/sparklines
Pandas Sparklines Demo.ipynb
1
305328
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Sparklines in Pandas\n", "\n", "Sparklines are small unlabeled plots, used to visually convey an idea in a small space. This script creates sparklines in a Pandas DataFrame which can then be displayed inline in a Jupyter Notebook or output to an HTML file. It does not annotate the figure, other columns of the DataFrame can be used to convey details about the sparklines.\n", "\n", "Background:\n", "https://en.wikipedia.org/wiki/Sparkline\n", "\n", "Forked and extended from:\n", "https://github.com/iiSeymour/sparkline-nb" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import sparklines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create some plot data\n", "Function assumes data to plot is an array-like object in a single cell per row." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "density_func = 78\n", "mean, var, skew, kurt = stats.chi.stats(density_func, moments='mvsk')\n", "x_chi = np.linspace(stats.chi.ppf(0.01, density_func),\n", " stats.chi.ppf(0.99, density_func), 100)\n", "y_chi = stats.chi.pdf(x_chi, density_func)\n", "\n", "x_expon = np.linspace(stats.expon.ppf(0.01), stats.expon.ppf(0.99), 100)\n", "y_expon = stats.expon.pdf(x_expon)\n", "\n", "a_gamma = 1.99\n", "x_gamma = np.linspace(stats.gamma.ppf(0.01, a_gamma),\n", " stats.gamma.ppf(0.99, a_gamma), 100)\n", "y_gamma = stats.gamma.pdf(x_gamma, a_gamma)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = 100" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0) # keep generated data the same for git commit" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = [np.random.rand(n), \n", " np.random.randn(n), \n", " np.random.beta(2, 1, size=n), \n", " np.random.binomial(3.4, 0.22, size=n), \n", " np.random.exponential(size=n),\n", " np.random.geometric(0.5, size=n), \n", " np.random.laplace(size=n), \n", " y_chi, \n", " y_expon, \n", " y_gamma]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function = ['rand',\n", " 'randn',\n", " 'beta',\n", " 'binomial',\n", " 'exponential',\n", " 'geometric',\n", " 'laplace',\n", " 'chi',\n", " 'expon',\n", " 'gamma']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.DataFrame(data)\n", "df['function'] = function" ] }, { "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " 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<th>5</th>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>4.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " <td>...</td>\n", " <td>4.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>1.000000</td>\n", " <td>geometric</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1.599102</td>\n", " <td>-0.991457</td>\n", " <td>0.067569</td>\n", " <td>-0.426884</td>\n", " <td>-0.457150</td>\n", " <td>-0.112325</td>\n", " <td>-0.143692</td>\n", " <td>-0.335901</td>\n", " <td>1.771613</td>\n", " <td>0.622667</td>\n", " <td>...</td>\n", " <td>0.282082</td>\n", " <td>-1.500619</td>\n", " <td>-0.085624</td>\n", " <td>-0.439021</td>\n", " <td>-0.457283</td>\n", " <td>-0.035453</td>\n", " <td>0.615548</td>\n", " <td>-1.977858</td>\n", " <td>1.420251</td>\n", " <td>laplace</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.040498</td>\n", " <td>0.045391</td>\n", " <td>0.050737</td>\n", " <td>0.056560</td>\n", " <td>0.062882</td>\n", " <td>0.069724</td>\n", " <td>0.077106</td>\n", " <td>0.085044</td>\n", " <td>0.093554</td>\n", " <td>0.102646</td>\n", " <td>...</td>\n", " <td>0.077317</td>\n", " <td>0.070661</td>\n", " <td>0.064455</td>\n", " <td>0.058683</td>\n", " <td>0.053326</td>\n", " <td>0.048368</td>\n", " <td>0.043787</td>\n", " <td>0.039566</td>\n", " <td>0.035686</td>\n", " <td>chi</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.990000</td>\n", " <td>0.945099</td>\n", " <td>0.902234</td>\n", " <td>0.861314</td>\n", " <td>0.822249</td>\n", " <td>0.784956</td>\n", " <td>0.749355</td>\n", " <td>0.715368</td>\n", " <td>0.682923</td>\n", " <td>0.651949</td>\n", " <td>...</td>\n", " <td>0.014497</td>\n", " <td>0.013839</td>\n", " <td>0.013211</td>\n", " <td>0.012612</td>\n", " <td>0.012040</td>\n", " <td>0.011494</td>\n", " <td>0.010973</td>\n", " <td>0.010475</td>\n", " <td>0.010000</td>\n", " <td>expon</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.129412</td>\n", " <td>0.174739</td>\n", " <td>0.213651</td>\n", " <td>0.246827</td>\n", " <td>0.274862</td>\n", " <td>0.298294</td>\n", " <td>0.317606</td>\n", " <td>0.333234</td>\n", " <td>0.345575</td>\n", " <td>0.354988</td>\n", " <td>...</td>\n", " <td>0.013531</td>\n", " <td>0.012809</td>\n", " <td>0.012124</td>\n", " <td>0.011475</td>\n", " <td>0.010859</td>\n", " <td>0.010276</td>\n", " <td>0.009722</td>\n", " <td>0.009198</td>\n", " <td>0.008701</td>\n", " <td>gamma</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows × 101 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "0 0.548814 0.715189 0.602763 0.544883 0.423655 0.645894 0.437587 \n", "1 -1.165150 0.900826 0.465662 -1.536244 1.488252 1.895889 1.178780 \n", "2 0.667468 0.335994 0.546961 0.895499 0.364307 0.291348 0.936391 \n", "3 2.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 \n", "4 0.509429 0.565212 0.953286 0.072616 1.728259 1.059645 1.295878 \n", "5 2.000000 1.000000 1.000000 1.000000 4.000000 1.000000 1.000000 \n", "6 1.599102 -0.991457 0.067569 -0.426884 -0.457150 -0.112325 -0.143692 \n", "7 0.040498 0.045391 0.050737 0.056560 0.062882 0.069724 0.077106 \n", "8 0.990000 0.945099 0.902234 0.861314 0.822249 0.784956 0.749355 \n", "9 0.129412 0.174739 0.213651 0.246827 0.274862 0.298294 0.317606 \n", "\n", " 7 8 9 ... 91 92 93 \\\n", "0 0.891773 0.963663 0.383442 ... 0.667410 0.131798 0.716327 \n", "1 -0.179925 -1.070753 1.054452 ... 0.318728 0.856831 -0.651026 \n", "2 0.854830 0.973605 0.846768 ... 0.676358 0.886607 0.961239 \n", "3 2.000000 0.000000 2.000000 ... 1.000000 0.000000 0.000000 \n", "4 0.769862 0.117070 0.519254 ... 0.613418 0.588044 0.409627 \n", "5 1.000000 3.000000 3.000000 ... 4.000000 1.000000 1.000000 \n", "6 -0.335901 1.771613 0.622667 ... 0.282082 -1.500619 -0.085624 \n", "7 0.085044 0.093554 0.102646 ... 0.077317 0.070661 0.064455 \n", "8 0.715368 0.682923 0.651949 ... 0.014497 0.013839 0.013211 \n", "9 0.333234 0.345575 0.354988 ... 0.013531 0.012809 0.012124 \n", "\n", " 94 95 96 97 98 99 function \n", "0 0.289406 0.183191 0.586513 0.020108 0.828940 0.004695 rand \n", "1 -1.034243 0.681595 -0.803410 -0.689550 -0.455533 0.017479 randn \n", "2 0.971125 0.073274 0.955266 0.499539 0.536154 0.326569 beta \n", "3 0.000000 0.000000 1.000000 0.000000 1.000000 1.000000 binomial \n", "4 2.125931 2.900909 4.814698 0.472794 3.385741 1.569638 exponential \n", "5 2.000000 5.000000 1.000000 1.000000 3.000000 1.000000 geometric \n", "6 -0.439021 -0.457283 -0.035453 0.615548 -1.977858 1.420251 laplace \n", "7 0.058683 0.053326 0.048368 0.043787 0.039566 0.035686 chi \n", "8 0.012612 0.012040 0.011494 0.010973 0.010475 0.010000 expon \n", "9 0.011475 0.010859 0.010276 0.009722 0.009198 0.008701 gamma \n", "\n", "[10 rows x 101 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define range of data to make sparklines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: data must be row wise" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = df.ix[:, 0:100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Output to new DataFrame of Sparklines" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>sparkline</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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" </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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" </tr>\n", " <tr>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_out = pd.DataFrame()\n", "df_out['sparkline'] = sparklines.create(data=a)\n", "sparklines.show(df_out[['sparkline']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Insert Sparklines into source DataFrame" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>function</th>\n", " <th>sparkline</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>rand</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>randn</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>beta</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>binomial</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>exponential</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>geometric</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>laplace</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>chi</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>expon</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td>gamma</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['sparkline'] = sparklines.create(data=a)\n", "sparklines.show(df[['function', 'sparkline']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detailed Formatting\n", "\n", "Return only sparklines, format the line, fill and marker." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>sparkline</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " </tr>\n", " <tr>\n", " <td><img 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" </tr>\n", " <tr>\n", " <td><img 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" </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_out = pd.DataFrame()\n", "df_out['sparkline'] = sparklines.create(data=a,\n", " color='#1b470a',\n", " fill_color='#99a894',\n", " fill_alpha=0.2,\n", " point_color='blue',\n", " point_fill='none',\n", " point_marker='*',\n", " point_size=3,\n", " figsize=(6, 0.25))\n", "sparklines.show(df_out[['sparkline']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example Data and Sparklines Layout" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_copy = df[['function', 'sparkline']].copy()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_copy['value'] = df.ix[:, 100]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_copy['change'] = df.ix[:,98] - df.ix[:,99]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_copy['change_%'] = df_copy.change / df.ix[:,99]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>function</th>\n", " <th>sparkline</th>\n", " <th>value</th>\n", " <th>change</th>\n", " <th>change_%</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>rand</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>rand</td>\n", " <td>0.824245</td>\n", " <td>175.540141</td>\n", " </tr>\n", " <tr>\n", " <td>randn</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>randn</td>\n", " <td>-0.473012</td>\n", " <td>-27.061466</td>\n", " </tr>\n", " <tr>\n", " <td>beta</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>beta</td>\n", " <td>0.209585</td>\n", " <td>0.641778</td>\n", " </tr>\n", " <tr>\n", " <td>binomial</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>binomial</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <td>exponential</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>exponential</td>\n", " <td>1.816102</td>\n", " <td>1.157020</td>\n", " </tr>\n", " <tr>\n", " <td>geometric</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>geometric</td>\n", " <td>2.000000</td>\n", " <td>2.000000</td>\n", " </tr>\n", " <tr>\n", " <td>laplace</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>laplace</td>\n", " <td>-3.398109</td>\n", " <td>-2.392611</td>\n", " </tr>\n", " <tr>\n", " <td>chi</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>chi</td>\n", " <td>0.003881</td>\n", " <td>0.108753</td>\n", " </tr>\n", " <tr>\n", " <td>expon</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>expon</td>\n", " <td>0.000475</td>\n", " <td>0.047509</td>\n", " </tr>\n", " <tr>\n", " <td>gamma</td>\n", " <td><img src=\"data:image/png;base64,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\"/></td>\n", " <td>gamma</td>\n", " <td>0.000497</td>\n", " <td>0.057133</td>\n", " </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sparklines.show(df_copy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export to HTML" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inline Jupyter Notebook" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sparklines.to_html(df_copy, 'pandas_sparklines_demo')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HTML text for rendering elsewhere" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "html = sparklines.to_html(df_copy)" ] } ], "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 }
mit
MPIBGC-TEE/CompartmentalSystems
notebooks/nonl_gcm_3p_many_params/means_and_sd_for_v0_and_v5.ipynb
1
8307545
null
mit
SiggyF/notebooks
soapexample.ipynb
1
41883
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using domeintabellen webservice" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from suds.client import Client\n", "url = 'http://domeintabellen-idsw-ws.rws.nl/DomainTableWS.svc?wsdl'\n", "client = Client(url)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "print(client)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Suds ( https://fedorahosted.org/suds/ ) version: 0.4 GA build: R699-20100913\n", "\n", "Service ( DomainTableWS ) tns=\"http://rws.services.nl/DomainTableWS/2010/10\"\n", " Prefixes (3)\n", " ns0 = \"http://rws.services.nl/DomainTableWS/2010/10\"\n", " ns1 = \"http://rws.services.nl/DomainTableWS/Contracts/2010/10\"\n", " ns2 = \"http://schemas.microsoft.com/2003/10/Serialization/\"\n", " Ports (2):\n", " (basic)\n", " Methods (6):\n", " GetDateLastPublished(ns1:GetDateLastPublishedRequest request, )\n", " GetDomainTable(ns1:GetDomainTableRequest request, )\n", " GetDomainTableChanges(ns1:GetDomainTableChangesRequest request, )\n", " GetDomainTableChangesInfo(ns1:GetDomainTableChangesInfoRequest request, )\n", " GetDomainTableInfo(ns1:GetDomainTableInfoRequest request, )\n", " GetDomainTableNames(ns1:GetDomainTableNamesRequest request, )\n", " Types (31):\n", " ns1:BooleanField\n", " ns1:Data\n", " ns1:DataField\n", " ns1:DataRow\n", " ns1:DateTimeField\n", " ns1:DomainTableFault\n", " ns1:DomainTableNames\n", " ns1:DoubleField\n", " ns1:DtDatatype\n", " ns1:Fields\n", " ns1:GetDateLastPublishedRequest\n", " ns1:GetDateLastPublishedResponse\n", " ns1:GetDomainTableChangesInfoRequest\n", " ns1:GetDomainTableChangesInfoResponse\n", " ns1:GetDomainTableChangesRequest\n", " ns1:GetDomainTableChangesResponse\n", " ns1:GetDomainTableInfoRequest\n", " ns1:GetDomainTableInfoResponse\n", " ns1:GetDomainTableNamesRequest\n", " ns1:GetDomainTableNamesResponse\n", " ns1:GetDomainTableRequest\n", " ns1:GetDomainTableResponse\n", " ns1:IntegerField\n", " ns1:MetaData\n", " ns1:MetaDataColumn\n", " ns1:PagingResultSet\n", " ns1:RequestPageBase\n", " ns1:StringField\n", " ns2:char\n", " ns2:duration\n", " ns2:guid\n", " (ws)\n", " Methods (6):\n", " GetDateLastPublished(ns1:GetDateLastPublishedRequest request, )\n", " GetDomainTable(ns1:GetDomainTableRequest request, )\n", " GetDomainTableChanges(ns1:GetDomainTableChangesRequest request, )\n", " GetDomainTableChangesInfo(ns1:GetDomainTableChangesInfoRequest request, )\n", " GetDomainTableInfo(ns1:GetDomainTableInfoRequest request, )\n", " GetDomainTableNames(ns1:GetDomainTableNamesRequest request, )\n", " Types (31):\n", " ns1:BooleanField\n", " ns1:Data\n", " ns1:DataField\n", " ns1:DataRow\n", " ns1:DateTimeField\n", " ns1:DomainTableFault\n", " ns1:DomainTableNames\n", " ns1:DoubleField\n", " ns1:DtDatatype\n", " ns1:Fields\n", " ns1:GetDateLastPublishedRequest\n", " ns1:GetDateLastPublishedResponse\n", " ns1:GetDomainTableChangesInfoRequest\n", " ns1:GetDomainTableChangesInfoResponse\n", " ns1:GetDomainTableChangesRequest\n", " ns1:GetDomainTableChangesResponse\n", " ns1:GetDomainTableInfoRequest\n", " ns1:GetDomainTableInfoResponse\n", " ns1:GetDomainTableNamesRequest\n", " ns1:GetDomainTableNamesResponse\n", " ns1:GetDomainTableRequest\n", " ns1:GetDomainTableResponse\n", " ns1:IntegerField\n", " ns1:MetaData\n", " ns1:MetaDataColumn\n", " ns1:PagingResultSet\n", " ns1:RequestPageBase\n", " ns1:StringField\n", " ns2:char\n", " ns2:duration\n", " ns2:guid\n", "\n", "\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# This generates a proper request\n", "request = client.factory.create(\"ns1:GetDomainTableNamesRequest\")\n", "request.CheckDate = \"2013-12-13T12:00:00\"\n", "client.service.GetDomainTableNames(request)\n", "# however it fails because the response is not recognized" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SAXParseException", "evalue": "<unknown>:2:43: not well-formed (invalid token)", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mSAXParseException\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-cc7b4bac893d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mrequest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ns1:GetDomainTableNamesRequest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCheckDate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"2013-12-13T12:00:00\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mservice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGetDomainTableNames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;31m# however it fails because the response is not recognized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/fedor/.virtualenvs/main/local/lib/python2.7/site-packages/suds/client.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 540\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 541\u001b[0m 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\u001b[0mSAXParseException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mErrorString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[1;31m# FIXME: when to invoke error()?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_err_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfatalError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[1;32mdef\u001b[0m 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\u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mSAXParseException\u001b[0m: <unknown>:2:43: not well-formed (invalid token)" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# if you debug or use tcpdump to see what's going you'll see that we get a reply which looks like this\n", "reply = '\\r\\n--uuid:89858f40-8ad5-4196-8eae-54b5dae2978d+id=22\\r\\nContent-ID: <http://tempuri.org/0>\\r\\nContent-Transfer-Encoding: 8bit\\r\\nContent-Type: application/xop+xml;charset=utf-8;type=\"text/xml\"\\r\\n\\r\\n<s:Envelope xmlns:s=\"http://schemas.xmlsoap.org/soap/envelope/\"><s:Body><GetDomainTableNamesResponse xmlns=\"http://rws.services.nl/DomainTableWS/2010/10\"><GetDomainTableNamesResult xmlns:a=\"http://rws.services.nl/DomainTableWS/Contracts/2010/10\" xmlns:i=\"http://www.w3.org/2001/XMLSchema-instance\"><a:DomainTableNames><a:string>Aanduiding_naamgebruik</a:string><a:string>Aanslag_type</a:string><a:string>Aanvoereenheid_soorten</a:string><a:string>Aanvoergebied_soorten</a:string><a:string>Aanwezig_afwezig_onbekend</a:string><a:string>Academische_titel</a:string><a:string>Adellijke_titel_of_predikaat</a:string><a:string>Afleveringspunt_soorten</a:string><a:string>Afsluitwijzen</a:string><a:string>Afvoergebied_soorten</a:string><a:string>Afwateringseenheid_soorten</a:string><a:string>AtRisktype</a:string><a:string>BBP_beheerproduct</a:string><a:string>BBP_beleidsproduct</a:string><a:string>Bedrijfstakken_WVOW</a:string><a:string>Beheersvormen_waterkering</a:string><a:string>Belastingsoorten</a:string><a:string>Bemalen_of_vrij_verval</a:string><a:string>Bemonsteringsapparaat</a:string><a:string>Bemonsteringsmethode</a:string><a:string>Bemonsteringssoort</a:string><a:string>Beoordeling_schade</a:string><a:string>BeschermdGebiedtype</a:string><a:string>Bestemmingen_afvalwater</a:string><a:string>Besturingswijze</a:string><a:string>Bevaarbaarheidsklassen</a:string><a:string>Beweegbare_bruggen</a:string><a:string>Biotaxon</a:string><a:string>Boomhoogte</a:string><a:string>Brander</a:string><a:string>CEFILT</a:string><a:string>ClassificatieCIW</a:string><a:string>ClassificatieKRWGW</a:string><a:string>ClassificatieKRWbiologischOW</a:string><a:string>ClassificatieKRWchemischOW</a:string><a:string>Code_stroomrichting</a:string><a:string>Code_sturing</a:string><a:string>Compartiment</a:string><a:string>Correspondentie_soorten</a:string><a:string>Detailonderdeel_bijzondere_weglaag</a:string><a:string>Detailplaats_lijnmarkering</a:string><a:string>DoelMeetLocatieType</a:string><a:string>Doelen_baggerwerkzaamheden</a:string><a:string>Doelen_voor_metingen</a:string><a:string>Doorspoelbaarheid</a:string><a:string>Drainerende_werking</a:string><a:string>Druk</a:string><a:string>Drukklassen</a:string><a:string>EBEOkarakteristiek</a:string><a:string>Ecologische_verbindingszone_soort</a:string><a:string>Eenheden_voor_precario</a:string><a:string>Eenheid</a:string><a:string>Effecttype</a:string><a:string>Eindbeeld_boomvakdeel</a:string><a:string>Eindbestemmingen_baggerspecie</a:string><a:string>EmissieBrontype</a:string><a:string>Energielevering_soorten</a:string><a:string>FunctieKunstwerk</a:string><a:string>Functies_van_adressen</a:string><a:string>Functies_vastgoedelementen_en_gebieden</a:string><a:string>Gebouwd_onbebouwd</a:string><a:string>Gebruikscodes_objecten</a:string><a:string>Geografische_schalen</a:string><a:string>Graderingen_filterlaag</a:string><a:string>Gras_beheervorm</a:string><a:string>Gras_soorten</a:string><a:string>Groenbeheerniveau</a:string><a:string>Groentype</a:string><a:string>Grondmechanische_aspect_soorten</a:string><a:string>Grondslagen</a:string><a:string>Grootheid_dummy</a:string><a:string>Heffingsobject_soorten</a:string><a:string>Hoedanigheid</a:string><a:string>IWSRindicator</a:string><a:string>Inrichtingtype</a:string><a:string>J_N_of_onbekend</a:string><a:string>J_of_N</a:string><a:string>KRWKwaliteitselement</a:string><a:string>KRWMaatregeltype</a:string><a:string>KRWMeetLocatietype</a:string><a:string>KRWStatus</a:string><a:string>KRWStroomgebiedsdistrict</a:string><a:string>KRWWatertype</a:string><a:string>KRWhydromorfologische_parameter</a:string><a:string>Kabel_en_leiding_soorten</a:string><a:string>Koppelstuk_soorten</a:string><a:string>Kunstwerktype</a:string><a:string>Kwaliteitsoordeel</a:string><a:string>L_R_B</a:string><a:string>Leidingdeel_soorten</a:string><a:string>LocatietypeWaardeBepaling</a:string><a:string>Lozingsvoorziening_soorten</a:string><a:string>Materialen_voor_afvalwatertransportwerken</a:string><a:string>Materialen_voor_bekleding_waterkering_of_profiel</a:string><a:string>Materialen_voor_kunstwerken</a:string><a:string>Materialen_voor_leidingen</a:string><a:string>Materialen_voor_profielverdedigingen</a:string><a:string>Meetapparaat</a:string><a:string>Meetinstantie</a:string><a:string>Meting</a:string><a:string>Monsterbewerkingsmethode</a:string><a:string>Normgroep</a:string><a:string>Normkader</a:string><a:string>NoseCodetype</a:string><a:string>Onderzoekssoort</a:string><a:string>Onttrekkingsvoorziening_soorten</a:string><a:string>Onttrekkingtype</a:string><a:string>Opmerking_weglaag</a:string><a:string>OppervlakteCategorieStoomgebiedtype</a:string><a:string>Orgaan</a:string><a:string>Overige_vastgoedelement_soorten</a:string><a:string>Parameter</a:string><a:string>Plaatsbepalingsapparaat</a:string><a:string>Processen_RWZI</a:string><a:string>Processen_SVI</a:string><a:string>Processen_transportstelsel</a:string><a:string>Profiellijn_soorten</a:string><a:string>RWZI_soorten</a:string><a:string>Rechtsvormen_bedrijf</a:string><a:string>RedenGebruikLocatie</a:string><a:string>RichtlijnType</a:string><a:string>Rioleringselementen</a:string><a:string>Rioolstelsel_soorten</a:string><a:string>SGBPTitel</a:string><a:string>Scheepvaartteken</a:string><a:string>Soort_zorgplicht_voor_bomen</a:string><a:string>Staat</a:string><a:string>Stuw_soorten</a:string><a:string>Subject_rollen</a:string><a:string>Substraattype</a:string><a:string>TijdDimensietype</a:string><a:string>Tijdelijke_kroon</a:string><a:string>Vaarweg_soorten</a:string><a:string>Veer_typen</a:string><a:string>Verkeersvoorziening_boomvakdeel</a:string><a:string>Vormen</a:string><a:string>Waardebepalingsmethode</a:string><a:string>Waardebepalingstechniek</a:string><a:string>Waardebewerkingsmethode</a:string><a:string>Waarnemingssoort</a:string><a:string>WaterTypeKwantitatief</a:string><a:string>Waterbeheerder</a:string><a:string>Waterbeheergebiedtype</a:string><a:string>Waterkeringtype</a:string><a:string>Waterrijkheidtype</a:string><a:string>Waterstaatkundigezonering</a:string><a:string>WatertypeKwalitatief</a:string><a:string>WegAardtype</a:string><a:string>WetVerordeningtype</a:string><a:string>Zuivering_soorten</a:string><a:string>ZwemplekVoorzieningen</a:string></a:DomainTableNames></GetDomainTableNamesResult></GetDomainTableNamesResponse></s:Body></s:Envelope>\\r\\n--uuid:89858f40-8ad5-4196-8eae-54b5dae2978d+id=22--\\r\\n'\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "import xml.sax\n", "import xml.etree.ElementTree\n", "import io" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# it is parsed like this:\n", "parser = xml.sax.make_parser()\n", "source = xml.sax.InputSource()\n", "\n", "source.setByteStream(io.BytesIO(reply))\n", "parser.parse(source)\n", "# gives the same error\n" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SAXParseException", "evalue": "<unknown>:2:43: not well-formed (invalid token)", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mSAXParseException\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-6666e8cf67bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetByteStream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;31m# gives the same error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/xml/sax/expatreader.pyc\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, source)\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cont_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetDocumentLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mExpatLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m \u001b[0mxmlreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIncrementalParser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprepareParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_bufsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/xml/sax/expatreader.pyc\u001b[0m in \u001b[0;36mfeed\u001b[1;34m(self, data, isFinal)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[0mexc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSAXParseException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mErrorString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[1;31m# FIXME: when to invoke error()?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_err_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfatalError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/xml/sax/handler.pyc\u001b[0m in \u001b[0;36mfatalError\u001b[1;34m(self, exception)\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfatalError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;34m\"Handle a non-recoverable error.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mSAXParseException\u001b[0m: <unknown>:2:43: not well-formed (invalid token)" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The service is using http://www.w3.org/TR/soap12-mtom/ which is not parsed by some soap parsers" ] }, { "cell_type": "code", "collapsed": false, "input": [ "msg = \"\"\"\n", "<s:Envelope xmlns:s=\"http://schemas.xmlsoap.org/soap/envelope/\"><s:Body><GetDomainTableNamesResponse xmlns=\"http://rws.services.nl/DomainTableWS/2010/10\"><GetDomainTableNamesResult xmlns:a=\"http://rws.services.nl/DomainTableWS/Contracts/2010/10\" xmlns:i=\"http://www.w3.org/2001/XMLSchema-instance\"><a:DomainTableNames><a:string>Aanduiding_naamgebruik</a:string><a:string>Aanslag_type</a:string><a:string>Aanvoereenheid_soorten</a:string><a:string>Aanvoergebied_soorten</a:string><a:string>Aanwezig_afwezig_onbekend</a:string><a:string>Academische_titel</a:string><a:string>Adellijke_titel_of_predikaat</a:string><a:string>Afleveringspunt_soorten</a:string><a:string>Afsluitwijzen</a:string><a:string>Afvoergebied_soorten</a:string><a:string>Afwateringseenheid_soorten</a:string><a:string>AtRisktype</a:string><a:string>BBP_beheerproduct</a:string><a:string>BBP_beleidsproduct</a:string><a:string>Bedrijfstakken_WVOW</a:string><a:string>Beheersvormen_waterkering</a:string><a:string>Belastingsoorten</a:string><a:string>Bemalen_of_vrij_verval</a:string><a:string>Bemonsteringsapparaat</a:string><a:string>Bemonsteringsmethode</a:string><a:string>Bemonsteringssoort</a:string><a:string>Beoordeling_schade</a:string><a:string>BeschermdGebiedtype</a:string><a:string>Bestemmingen_afvalwater</a:string><a:string>Besturingswijze</a:string><a:string>Bevaarbaarheidsklassen</a:string><a:string>Beweegbare_bruggen</a:string><a:string>Biotaxon</a:string><a:string>Boomhoogte</a:string><a:string>Brander</a:string><a:string>CEFILT</a:string><a:string>ClassificatieCIW</a:string><a:string>ClassificatieKRWGW</a:string><a:string>ClassificatieKRWbiologischOW</a:string><a:string>ClassificatieKRWchemischOW</a:string><a:string>Code_stroomrichting</a:string><a:string>Code_sturing</a:string><a:string>Compartiment</a:string><a:string>Correspondentie_soorten</a:string><a:string>Detailonderdeel_bijzondere_weglaag</a:string><a:string>Detailplaats_lijnmarkering</a:string><a:string>DoelMeetLocatieType</a:string><a:string>Doelen_baggerwerkzaamheden</a:string><a:string>Doelen_voor_metingen</a:string><a:string>Doorspoelbaarheid</a:string><a:string>Drainerende_werking</a:string><a:string>Druk</a:string><a:string>Drukklassen</a:string><a:string>EBEOkarakteristiek</a:string><a:string>Ecologische_verbindingszone_soort</a:string><a:string>Eenheden_voor_precario</a:string><a:string>Eenheid</a:string><a:string>Effecttype</a:string><a:string>Eindbeeld_boomvakdeel</a:string><a:string>Eindbestemmingen_baggerspecie</a:string><a:string>EmissieBrontype</a:string><a:string>Energielevering_soorten</a:string><a:string>FunctieKunstwerk</a:string><a:string>Functies_van_adressen</a:string><a:string>Functies_vastgoedelementen_en_gebieden</a:string><a:string>Gebouwd_onbebouwd</a:string><a:string>Gebruikscodes_objecten</a:string><a:string>Geografische_schalen</a:string><a:string>Graderingen_filterlaag</a:string><a:string>Gras_beheervorm</a:string><a:string>Gras_soorten</a:string><a:string>Groenbeheerniveau</a:string><a:string>Groentype</a:string><a:string>Grondmechanische_aspect_soorten</a:string><a:string>Grondslagen</a:string><a:string>Grootheid_dummy</a:string><a:string>Heffingsobject_soorten</a:string><a:string>Hoedanigheid</a:string><a:string>IWSRindicator</a:string><a:string>Inrichtingtype</a:string><a:string>J_N_of_onbekend</a:string><a:string>J_of_N</a:string><a:string>KRWKwaliteitselement</a:string><a:string>KRWMaatregeltype</a:string><a:string>KRWMeetLocatietype</a:string><a:string>KRWStatus</a:string><a:string>KRWStroomgebiedsdistrict</a:string><a:string>KRWWatertype</a:string><a:string>KRWhydromorfologische_parameter</a:string><a:string>Kabel_en_leiding_soorten</a:string><a:string>Koppelstuk_soorten</a:string><a:string>Kunstwerktype</a:string><a:string>Kwaliteitsoordeel</a:string><a:string>L_R_B</a:string><a:string>Leidingdeel_soorten</a:string><a:string>LocatietypeWaardeBepaling</a:string><a:string>Lozingsvoorziening_soorten</a:string><a:string>Materialen_voor_afvalwatertransportwerken</a:string><a:string>Materialen_voor_bekleding_waterkering_of_profiel</a:string><a:string>Materialen_voor_kunstwerken</a:string><a:string>Materialen_voor_leidingen</a:string><a:string>Materialen_voor_profielverdedigingen</a:string><a:string>Meetapparaat</a:string><a:string>Meetinstantie</a:string><a:string>Meting</a:string><a:string>Monsterbewerkingsmethode</a:string><a:string>Normgroep</a:string><a:string>Normkader</a:string><a:string>NoseCodetype</a:string><a:string>Onderzoekssoort</a:string><a:string>Onttrekkingsvoorziening_soorten</a:string><a:string>Onttrekkingtype</a:string><a:string>Opmerking_weglaag</a:string><a:string>OppervlakteCategorieStoomgebiedtype</a:string><a:string>Orgaan</a:string><a:string>Overige_vastgoedelement_soorten</a:string><a:string>Parameter</a:string><a:string>Plaatsbepalingsapparaat</a:string><a:string>Processen_RWZI</a:string><a:string>Processen_SVI</a:string><a:string>Processen_transportstelsel</a:string><a:string>Profiellijn_soorten</a:string><a:string>RWZI_soorten</a:string><a:string>Rechtsvormen_bedrijf</a:string><a:string>RedenGebruikLocatie</a:string><a:string>RichtlijnType</a:string><a:string>Rioleringselementen</a:string><a:string>Rioolstelsel_soorten</a:string><a:string>SGBPTitel</a:string><a:string>Scheepvaartteken</a:string><a:string>Soort_zorgplicht_voor_bomen</a:string><a:string>Staat</a:string><a:string>Stuw_soorten</a:string><a:string>Subject_rollen</a:string><a:string>Substraattype</a:string><a:string>TijdDimensietype</a:string><a:string>Tijdelijke_kroon</a:string><a:string>Vaarweg_soorten</a:string><a:string>Veer_typen</a:string><a:string>Verkeersvoorziening_boomvakdeel</a:string><a:string>Vormen</a:string><a:string>Waardebepalingsmethode</a:string><a:string>Waardebepalingstechniek</a:string><a:string>Waardebewerkingsmethode</a:string><a:string>Waarnemingssoort</a:string><a:string>WaterTypeKwantitatief</a:string><a:string>Waterbeheerder</a:string><a:string>Waterbeheergebiedtype</a:string><a:string>Waterkeringtype</a:string><a:string>Waterrijkheidtype</a:string><a:string>Waterstaatkundigezonering</a:string><a:string>WatertypeKwalitatief</a:string><a:string>WegAardtype</a:string><a:string>WetVerordeningtype</a:string><a:string>Zuivering_soorten</a:string><a:string>ZwemplekVoorzieningen</a:string></a:DomainTableNames></GetDomainTableNamesResult></GetDomainTableNamesResponse></s:Body></s:Envelope>\n", "\"\"\"\n", "import xml.etree" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "doc = xml.etree.ElementTree.ElementTree()\n", "doc.parse(io.BytesIO(msg))\n", "doc.findall('.//a:DomainTableNames', namespaces={'a':'http://rws.services.nl/DomainTableWS/Contracts/2010/10'})" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "[<Element '{http://rws.services.nl/DomainTableWS/Contracts/2010/10}DomainTableNames' at 0x3732650>]" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
mit-eicu/eicu-code
notebooks/medication.ipynb
1
87315
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# medication\n", "\n", "The medications table reflects the active medication orders for patients. These are orders but do not necessarily reflect administration to the patient. For example, while existence of data in the infusionDrug table confirms a patient received a continuous infusion, existence of the same data in this table only indicates that the infusion was *ordered* for the patient. Most orders are fulfilled, but not all. Furthermore, many orders are done *pro re nata*, or PRN, which means \"when needed\". Administration of these orders is difficult to quantify.\n", "\n", "In the US, all orders must be reviewed by a pharmacist. The majority of hospitals have an HL7 medication interface system in place which automatically synchronizes the orders with eCareManager (the source of this database) as they are verified by the pharmacist in the source pharmacy system. For hospitals without a medication interface, the eICU staff may enter a selection of medications to facilitate population management and completeness for reporting purposes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n", " \"\"\")\n" ] } ], "source": [ "# Import libraries\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import psycopg2\n", "import getpass\n", "import pdvega\n", "\n", "# for configuring connection \n", "from configobj import ConfigObj\n", "import os\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Database: eicu\n", "Username: alistairewj\n" ] } ], "source": [ "# Create a database connection using settings from config file\n", "config='../db/config.ini'\n", "\n", "# connection info\n", "conn_info = dict()\n", "if os.path.isfile(config):\n", " config = ConfigObj(config)\n", " conn_info[\"sqluser\"] = config['username']\n", " conn_info[\"sqlpass\"] = config['password']\n", " conn_info[\"sqlhost\"] = config['host']\n", " conn_info[\"sqlport\"] = config['port']\n", " conn_info[\"dbname\"] = config['dbname']\n", " conn_info[\"schema_name\"] = config['schema_name']\n", "else:\n", " conn_info[\"sqluser\"] = 'postgres'\n", " conn_info[\"sqlpass\"] = ''\n", " conn_info[\"sqlhost\"] = 'localhost'\n", " conn_info[\"sqlport\"] = 5432\n", " conn_info[\"dbname\"] = 'eicu'\n", " conn_info[\"schema_name\"] = 'public,eicu_crd'\n", " \n", "# Connect to the eICU database\n", "print('Database: {}'.format(conn_info['dbname']))\n", "print('Username: {}'.format(conn_info[\"sqluser\"]))\n", "if conn_info[\"sqlpass\"] == '':\n", " # try connecting without password, i.e. peer or OS authentication\n", " try:\n", " if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n", " con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n", " user=conn_info[\"sqluser\"]) \n", " else:\n", " con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n", " host=conn_info[\"sqlhost\"],\n", " port=conn_info[\"sqlport\"],\n", " user=conn_info[\"sqluser\"])\n", " except:\n", " conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n", "\n", " con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n", " host=conn_info[\"sqlhost\"],\n", " port=conn_info[\"sqlport\"],\n", " user=conn_info[\"sqluser\"],\n", " password=conn_info[\"sqlpass\"])\n", "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examine a single patient" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "patientunitstayid = 237395" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "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>medicationid</th>\n", " <th>patientunitstayid</th>\n", " <th>drugorderyear</th>\n", " <th>drugordertime24</th>\n", " <th>drugordertime</th>\n", " <th>drugorderoffset</th>\n", " <th>drugstartyear</th>\n", " <th>drugstarttime24</th>\n", " <th>drugstarttime</th>\n", " <th>drugstartoffset</th>\n", " <th>...</th>\n", " <th>drughiclseqno</th>\n", " <th>dosage</th>\n", " <th>routeadmin</th>\n", " <th>loadingdose</th>\n", " <th>prn</th>\n", " <th>drugstopyear</th>\n", " <th>drugstoptime24</th>\n", " <th>drugstoptime</th>\n", " <th>drugstopoffset</th>\n", " <th>gtc</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7461175</td>\n", " <td>237395</td>\n", " <td>2015</td>\n", " <td>22:56:26</td>\n", " <td>midnight</td>\n", " <td>-12</td>\n", " <td>2015</td>\n", " <td>21:37:00</td>\n", " <td>night</td>\n", " <td>-91</td>\n", " <td>...</td>\n", " <td>10093.0</td>\n", " <td>1,500 3</td>\n", " <td>IV</td>\n", " <td></td>\n", " <td>No</td>\n", " <td>2015</td>\n", " <td>21:36:54</td>\n", " <td>night</td>\n", " <td>-92</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10812193</td>\n", " <td>237395</td>\n", " <td>2015</td>\n", " <td>22:56:26</td>\n", " <td>midnight</td>\n", " <td>-12</td>\n", " <td>2015</td>\n", " <td>21:23:00</td>\n", " <td>night</td>\n", " <td>-105</td>\n", " <td>...</td>\n", " <td>4053.0</td>\n", " <td>2000 MG</td>\n", " <td>IV</td>\n", " <td></td>\n", " <td>No</td>\n", " <td>2015</td>\n", " <td>23:45:00</td>\n", " <td>midnight</td>\n", " <td>37</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>11612134</td>\n", " <td>237395</td>\n", " <td>2015</td>\n", " <td>22:56:26</td>\n", " <td>midnight</td>\n", " <td>-12</td>\n", " <td>2015</td>\n", " <td>22:07:00</td>\n", " <td>midnight</td>\n", " <td>-61</td>\n", " <td>...</td>\n", " <td>4034.0</td>\n", " <td>420 MG</td>\n", " <td>IV</td>\n", " <td></td>\n", " <td>No</td>\n", " <td>2015</td>\n", " <td>03:15:00</td>\n", " <td>morning</td>\n", " <td>247</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11206166</td>\n", " <td>237395</td>\n", " <td>2015</td>\n", " <td>22:56:26</td>\n", " <td>midnight</td>\n", " <td>-12</td>\n", " <td>2015</td>\n", " <td>21:23:00</td>\n", " <td>night</td>\n", " <td>-105</td>\n", " <td>...</td>\n", " <td>10093.0</td>\n", " <td>1,500 3</td>\n", " <td>IV</td>\n", " <td></td>\n", " <td>No</td>\n", " <td>2015</td>\n", " <td>02:06:00</td>\n", " <td>morning</td>\n", " <td>178</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>8246015</td>\n", " <td>237395</td>\n", " <td>2015</td>\n", " <td>23:38:17</td>\n", " <td>midnight</td>\n", " <td>30</td>\n", " <td>2015</td>\n", " <td>05:00:00</td>\n", " <td>morning</td>\n", " <td>352</td>\n", " <td>...</td>\n", " <td>4053.0</td>\n", " <td>2000 MG</td>\n", " <td>IV</td>\n", " <td></td>\n", " <td>No</td>\n", " <td>2015</td>\n", " <td>15:45:32</td>\n", " <td>evening</td>\n", " <td>3877</td>\n", " <td>19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " medicationid patientunitstayid drugorderyear drugordertime24 \\\n", "0 7461175 237395 2015 22:56:26 \n", "1 10812193 237395 2015 22:56:26 \n", "2 11612134 237395 2015 22:56:26 \n", "3 11206166 237395 2015 22:56:26 \n", "4 8246015 237395 2015 23:38:17 \n", "\n", " drugordertime drugorderoffset drugstartyear drugstarttime24 drugstarttime \\\n", "0 midnight -12 2015 21:37:00 night \n", "1 midnight -12 2015 21:23:00 night \n", "2 midnight -12 2015 22:07:00 midnight \n", "3 midnight -12 2015 21:23:00 night \n", "4 midnight 30 2015 05:00:00 morning \n", "\n", " drugstartoffset ... drughiclseqno dosage routeadmin loadingdose prn \\\n", "0 -91 ... 10093.0 1,500 3 IV No \n", "1 -105 ... 4053.0 2000 MG IV No \n", "2 -61 ... 4034.0 420 MG IV No \n", "3 -105 ... 10093.0 1,500 3 IV No \n", "4 352 ... 4053.0 2000 MG IV No \n", "\n", " drugstopyear drugstoptime24 drugstoptime drugstopoffset gtc \n", "0 2015 21:36:54 night -92 19 \n", "1 2015 23:45:00 midnight 37 19 \n", "2 2015 03:15:00 morning 247 19 \n", "3 2015 02:06:00 morning 178 19 \n", "4 2015 15:45:32 evening 3877 19 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = query_schema + \"\"\"\n", "select *\n", "from medication\n", "where patientunitstayid = {}\n", "order by drugorderoffset\n", "\"\"\".format(patientunitstayid)\n", "\n", "df = pd.read_sql_query(query, con)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['medicationid', 'patientunitstayid', 'drugorderyear', 'drugordertime24',\n", " 'drugordertime', 'drugorderoffset', 'drugstartyear', 'drugstarttime24',\n", " 'drugstarttime', 'drugstartoffset', 'drugivadmixture',\n", " 'drugordercancelled', 'drugname', 'drughiclseqno', 'dosage',\n", " 'routeadmin', 'loadingdose', 'prn', 'drugstopyear', 'drugstoptime24',\n", " 'drugstoptime', 'drugstopoffset', 'gtc'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 6, "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>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>medicationid</th>\n", " <td>7461175</td>\n", " <td>10812193</td>\n", " <td>11612134</td>\n", " <td>11206166</td>\n", " <td>8246015</td>\n", " </tr>\n", " <tr>\n", " <th>patientunitstayid</th>\n", " <td>237395</td>\n", " <td>237395</td>\n", " <td>237395</td>\n", " <td>237395</td>\n", " <td>237395</td>\n", " </tr>\n", " <tr>\n", " <th>drugorderoffset</th>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>drugorderoffset</th>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>-12</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>drugstopoffset</th>\n", " <td>-92</td>\n", " <td>37</td>\n", " <td>247</td>\n", " <td>178</td>\n", " <td>3877</td>\n", " </tr>\n", " <tr>\n", " <th>drugivadmixture</th>\n", " <td>No</td>\n", " <td>Yes</td>\n", " <td>Yes</td>\n", " <td>No</td>\n", " <td>Yes</td>\n", " </tr>\n", " <tr>\n", " <th>drugordercancelled</th>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " </tr>\n", " <tr>\n", " <th>drugname</th>\n", " <td>VANCOMYCIN 1.5 GM IN NS 250 ML IVPB (REPACKAGE)</td>\n", " <td>AZTREONAM 2 G IJ SOLR</td>\n", " <td>TOBRAMYCIN SULFATE 80 MG/2ML IJ SOLN</td>\n", " <td>VANCOMYCIN 1.5 GM IN NS 250 ML IVPB (REPACKAGE)</td>\n", " <td>AZTREONAM 2 G IJ SOLR</td>\n", " </tr>\n", " <tr>\n", " <th>drughiclseqno</th>\n", " <td>10093</td>\n", " <td>4053</td>\n", " <td>4034</td>\n", " <td>10093</td>\n", " <td>4053</td>\n", " </tr>\n", " <tr>\n", " <th>gtc</th>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>dosage</th>\n", " <td>1,500 3</td>\n", " <td>2000 MG</td>\n", " <td>420 MG</td>\n", " <td>1,500 3</td>\n", " <td>2000 MG</td>\n", " </tr>\n", " <tr>\n", " <th>routeadmin</th>\n", " <td>IV</td>\n", " <td>IV</td>\n", " <td>IV</td>\n", " <td>IV</td>\n", " <td>IV</td>\n", " </tr>\n", " <tr>\n", " <th>loadingdose</th>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>prn</th>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " <td>No</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 \\\n", "medicationid 7461175 \n", "patientunitstayid 237395 \n", "drugorderoffset -12 \n", "drugorderoffset -12 \n", "drugstopoffset -92 \n", "drugivadmixture No \n", "drugordercancelled No \n", "drugname VANCOMYCIN 1.5 GM IN NS 250 ML IVPB (REPACKAGE) \n", "drughiclseqno 10093 \n", "gtc 19 \n", "dosage 1,500 3 \n", "routeadmin IV \n", "loadingdose \n", "prn No \n", "\n", " 1 \\\n", "medicationid 10812193 \n", "patientunitstayid 237395 \n", "drugorderoffset -12 \n", "drugorderoffset -12 \n", "drugstopoffset 37 \n", "drugivadmixture Yes \n", "drugordercancelled No \n", "drugname AZTREONAM 2 G IJ SOLR \n", "drughiclseqno 4053 \n", "gtc 19 \n", "dosage 2000 MG \n", "routeadmin IV \n", "loadingdose \n", "prn No \n", "\n", " 2 \\\n", "medicationid 11612134 \n", "patientunitstayid 237395 \n", "drugorderoffset -12 \n", "drugorderoffset -12 \n", "drugstopoffset 247 \n", "drugivadmixture Yes \n", "drugordercancelled No \n", "drugname TOBRAMYCIN SULFATE 80 MG/2ML IJ SOLN \n", "drughiclseqno 4034 \n", "gtc 19 \n", "dosage 420 MG \n", "routeadmin IV \n", "loadingdose \n", "prn No \n", "\n", " 3 \\\n", "medicationid 11206166 \n", "patientunitstayid 237395 \n", "drugorderoffset -12 \n", "drugorderoffset -12 \n", "drugstopoffset 178 \n", "drugivadmixture No \n", "drugordercancelled No \n", "drugname VANCOMYCIN 1.5 GM IN NS 250 ML IVPB (REPACKAGE) \n", "drughiclseqno 10093 \n", "gtc 19 \n", "dosage 1,500 3 \n", "routeadmin IV \n", "loadingdose \n", "prn No \n", "\n", " 4 \n", "medicationid 8246015 \n", "patientunitstayid 237395 \n", "drugorderoffset 30 \n", "drugorderoffset 30 \n", "drugstopoffset 3877 \n", "drugivadmixture Yes \n", "drugordercancelled No \n", "drugname AZTREONAM 2 G IJ SOLR \n", "drughiclseqno 4053 \n", "gtc 19 \n", "dosage 2000 MG \n", "routeadmin IV \n", "loadingdose \n", "prn No " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at a subset of columns\n", "cols = ['medicationid','patientunitstayid',\n", " 'drugorderoffset','drugorderoffset', 'drugstopoffset',\n", " 'drugivadmixture', 'drugordercancelled', 'drugname','drughiclseqno', 'gtc',\n", " 'dosage','routeadmin','loadingdose', 'prn']\n", "df[cols].head().T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see that, roughly on ICU admission, the patient had an order for vancomycin, aztreonam, and tobramycin." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Identifying patients admitted on a single drug" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look for patients who have an order for vancomycin using exact text matching." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29737 unit stays with VANCOMYCIN.\n" ] } ], "source": [ "drug = 'VANCOMYCIN'\n", "query = query_schema + \"\"\"\n", "select \n", " distinct patientunitstayid\n", "from medication\n", "where drugname like '%{}%'\n", "\"\"\".format(drug)\n", "\n", "df_drug = pd.read_sql_query(query, con)\n", "print('{} unit stays with {}.'.format(df_drug.shape[0], drug))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exact text matching is fairly weak, as there's no systematic reason to prefer upper case or lower case. Let's relax the case matching." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41867 unit stays with VANCOMYCIN.\n" ] } ], "source": [ "drug = 'VANCOMYCIN'\n", "query = query_schema + \"\"\"\n", "select \n", " distinct patientunitstayid\n", "from medication\n", "where drugname ilike '%{}%'\n", "\"\"\".format(drug)\n", "\n", "df_drug = pd.read_sql_query(query, con)\n", "print('{} unit stays with {}.'.format(df_drug.shape[0], drug))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HICL codes are used to group together drugs which have the same underlying ingredient (i.e. most frequently this is used to group brand name drugs with the generic name drugs). We can see above the HICL for vancomycin is 10093, so let's try grabbing that." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9716 unit stays with HICL = 10093.\n" ] } ], "source": [ "hicl = 10093\n", "query = query_schema + \"\"\"\n", "select \n", " distinct patientunitstayid\n", "from medication\n", "where drughiclseqno = {}\n", "\"\"\".format(hicl)\n", "\n", "df_hicl = pd.read_sql_query(query, con)\n", "print('{} unit stays with HICL = {}.'.format(df_hicl.shape[0], hicl))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No luck! I wonder what we missed? Let's go back to the original query, this time retaining HICL and the name of the drug." ] }, { "cell_type": "code", "execution_count": 10, "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>drugname</th>\n", " <th>drughiclseqno</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>VANCOMYCIN HCL 1000 MG IV SOLR</td>\n", " <td>4042.0</td>\n", " <td>9661</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>vancomycin</td>\n", " <td>4042.0</td>\n", " <td>7826</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>VANCOmycin 1 GM in NS 250 mL IVPB</td>\n", " <td>10093.0</td>\n", " <td>3977</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>VANCOMYCIN HCL 10 G IV SOLR</td>\n", " <td>4042.0</td>\n", " <td>3891</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>VANCOMYCIN HCL</td>\n", " <td>4042.0</td>\n", " <td>3064</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " drugname drughiclseqno n\n", "0 VANCOMYCIN HCL 1000 MG IV SOLR 4042.0 9661\n", "1 vancomycin 4042.0 7826\n", "2 VANCOmycin 1 GM in NS 250 mL IVPB 10093.0 3977\n", "3 VANCOMYCIN HCL 10 G IV SOLR 4042.0 3891\n", "4 VANCOMYCIN HCL 4042.0 3064" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drug = 'VANCOMYCIN'\n", "query = query_schema + \"\"\"\n", "select \n", " drugname, drughiclseqno, count(*) as n\n", "from medication\n", "where drugname ilike '%{}%'\n", "group by drugname, drughiclseqno\n", "order by n desc\n", "\"\"\".format(drug)\n", "\n", "df_drug = pd.read_sql_query(query, con)\n", "df_drug.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It appears there are more than one HICL - we can group by HICL in this query to get an idea." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "4042.0 333\n", "10093.0 206\n", "37442.0 119\n", "8466.0 53\n", "920.0 15\n", "24859.0 8\n", "3976.0 7\n", "1694.0 3\n", "4285.0 3\n", "4283.0 3\n", "8259.0 3\n", "18084.0 2\n", "6312.0 2\n", "1403.0 1\n", "Name: drughiclseqno, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_drug['drughiclseqno'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, we can't be sure that these HICLs always identify only vancomycin. For example, let's look at drugnames for HICL = 1403." ] }, { "cell_type": "code", "execution_count": 12, "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>drugname</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>compounded cream</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Rx compound and Mix</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>butt butter compound</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>TESTOSTERONE 1 % (25 MG/2.5 G) TD GLPK</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>zinc/aquaphor/nystatin</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " drugname n\n", "0 compounded cream 9\n", "1 Rx compound and Mix 8\n", "2 butt butter compound 7\n", "3 TESTOSTERONE 1 % (25 MG/2.5 G) TD GLPK 7\n", "4 zinc/aquaphor/nystatin 5" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hicl = 1403\n", "query = query_schema + \"\"\"\n", "select \n", " drugname, count(*) as n\n", "from medication\n", "where drughiclseqno = {}\n", "group by drugname\n", "order by n desc\n", "\"\"\".format(hicl)\n", "\n", "df_hicl = pd.read_sql_query(query, con)\n", "df_hicl.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This HICL seems more focused on the use of creams than on vancomycin. Let's instead inspect the top 3." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HICL 4042\n", "Number of rows: 68951\n", "Top 5 rows by frequency:\n", " drugname n\n", "0 VANCOMYCIN HCL 1000 MG IV SOLR 9661\n", "1 vancomycin 7826\n", "2 VANCOCIN 4613\n", "3 VANCOMYCIN HCL 10 G IV SOLR 3891\n", "4 VANCOMYCIN HCL 3064\n", "\n", "HICL 10093\n", "Number of rows: 18553\n", "Top 5 rows by frequency:\n", " drugname n\n", "0 VANCOmycin 1 GM in NS 250 mL IVPB 3977\n", "1 VANCOMYCIN 1.25 GM IN NS 250 ML IVPB (REPACKAGE) 2037\n", "2 VANCOmycin 1.25 GM in NS 500ML IVPB 1970\n", "3 VANCOmycin 1.5 GM in NS 500ML IVPB 1944\n", "4 VANCOMYCIN 1.5 GM IN NS 250 ML IVPB (REPACKAGE) 1475\n", "\n", "HICL 37442\n", "Number of rows: 3977\n", "Top 5 rows by frequency:\n", " drugname n\n", "0 VANCOMYCIN 1 G MINI-BAG PLUS (VIAL MATE) 1856\n", "1 VANCOMYCIN HCL IN DEXTROSE 750 MG/150ML IV SOLN 822\n", "2 VANCOMYCIN 5gm VIAL (MDV) 525\n", "3 vancomycin 131\n", "4 Vancomycin Per Pharmacy 93\n", "\n" ] } ], "source": [ "for hicl in [4042, 10093, 37442]:\n", " query = query_schema + \"\"\"\n", " select \n", " drugname, count(*) as n\n", " from medication\n", " where drughiclseqno = {}\n", " group by drugname\n", " order by n desc\n", " \"\"\".format(hicl)\n", "\n", " df_hicl = pd.read_sql_query(query, con)\n", " print('HICL {}'.format(hicl))\n", " print('Number of rows: {}'.format(df_hicl['n'].sum()))\n", " print('Top 5 rows by frequency:')\n", " print(df_hicl.head())\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is fairly convincing that these only refer to vancomycin. An alternative approach is to acquire the code book for HICL codes and look up vancomycin there." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hospitals with data available" ] }, { "cell_type": "code", "execution_count": 14, "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>hospitalid</th>\n", " <th>number_of_patients</th>\n", " <th>number_of_patients_with_tbl</th>\n", " <th>data completion</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11</th>\n", " <td>73</td>\n", " <td>7059</td>\n", " <td>6836</td>\n", " <td>96.840912</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>167</td>\n", " <td>6092</td>\n", " <td>5825</td>\n", " <td>95.617203</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>264</td>\n", " <td>5237</td>\n", " <td>5111</td>\n", " <td>97.594042</td>\n", " </tr>\n", " <tr>\n", " <th>184</th>\n", " <td>420</td>\n", " <td>4679</td>\n", " <td>4618</td>\n", " <td>98.696303</td>\n", " </tr>\n", " <tr>\n", " <th>134</th>\n", " <td>338</td>\n", " <td>4277</td>\n", " <td>4241</td>\n", " <td>99.158289</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>176</td>\n", " <td>4328</td>\n", " <td>4186</td>\n", " <td>96.719039</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>243</td>\n", " <td>4243</td>\n", " <td>4172</td>\n", " <td>98.326656</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>199</td>\n", " <td>4240</td>\n", " <td>4116</td>\n", " <td>97.075472</td>\n", " </tr>\n", " <tr>\n", " <th>206</th>\n", " <td>458</td>\n", " <td>3701</td>\n", " <td>3624</td>\n", " <td>97.919481</td>\n", " </tr>\n", " <tr>\n", " <th>200</th>\n", " <td>443</td>\n", " <td>3656</td>\n", " <td>3612</td>\n", " <td>98.796499</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hospitalid number_of_patients number_of_patients_with_tbl \\\n", "11 73 7059 6836 \n", "54 167 6092 5825 \n", "106 264 5237 5111 \n", "184 420 4679 4618 \n", "134 338 4277 4241 \n", "58 176 4328 4186 \n", "90 243 4243 4172 \n", "71 199 4240 4116 \n", "206 458 3701 3624 \n", "200 443 3656 3612 \n", "\n", " data completion \n", "11 96.840912 \n", "54 95.617203 \n", "106 97.594042 \n", "184 98.696303 \n", "134 99.158289 \n", "58 96.719039 \n", "90 98.326656 \n", "71 97.075472 \n", "206 97.919481 \n", "200 98.796499 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = query_schema + \"\"\"\n", "with t as\n", "(\n", "select distinct patientunitstayid\n", "from medication\n", ")\n", "select \n", " pt.hospitalid\n", " , count(distinct pt.patientunitstayid) as number_of_patients\n", " , count(distinct t.patientunitstayid) as number_of_patients_with_tbl\n", "from patient pt\n", "left join t\n", " on pt.patientunitstayid = t.patientunitstayid\n", "group by pt.hospitalid\n", "\"\"\".format(patientunitstayid)\n", "\n", "df = pd.read_sql_query(query, con)\n", "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n", "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n", "df.head(n=10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"vega-embed\" id=\"708b3c39-438d-490b-99cc-9ab9cec07802\"></div>\n", "\n", "<style>\n", ".vega-embed svg, .vega-embed canvas {\n", " border: 1px dotted gray;\n", "}\n", "\n", ".vega-embed .vega-actions a {\n", " margin-right: 6px;\n", "}\n", "</style>\n" ] }, "metadata": { "jupyter-vega3": "#708b3c39-438d-490b-99cc-9ab9cec07802" }, "output_type": "display_data" }, { "data": { 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" }, "metadata": { "jupyter-vega3": "#708b3c39-438d-490b-99cc-9ab9cec07802" }, "output_type": "display_data" } ], "source": [ "df[['data completion']].vgplot.hist(bins=10,\n", " var_name='Number of hospitals',\n", " value_name='Percent of patients with data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see there are a few hospitals with no interface, and thus 0 patients, though the majority have >90% data completion." ] } ], "metadata": { "front-matter": { "date": "2015-09-01", "linktitle": "admissiondrug", "title": "admissiondrug" }, "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
noppanit/machine-learning
animated-graph/Animated Graphs.ipynb
1
50162
{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from moviepy.video.io.bindings import mplfig_to_npimage\n", "import moviepy.editor as mpy\n", "\n", "import matplotlib.cm as cm\n", "import matplotlib.mlab as mlab\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c074128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "duration = 2\n", "fig_mpl, ax = plt.subplots(1,figsize=(5,3), facecolor='white')\n", "xx = np.linspace(-2,2,200) # the x vector\n", "zz = lambda d: np.sinc(xx**2)+np.sin(xx+d) # the (changing) z vector\n", "ax.set_title(\"Elevation in y=0\")\n", "ax.set_ylim(-1.5,2.5)\n", "line, = ax.plot(xx, zz(0), lw=3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[MoviePy] Building file sinc_mpl.gif with imageio\n" ] } ], "source": [ "def make_frame_mpl(t):\n", " line.set_ydata( zz(2*np.pi*t/duration)) # <= Update the curve\n", " return mplfig_to_npimage(fig_mpl) # RGB image of the figure\n", "\n", "animation = mpy.VideoClip(make_frame_mpl, duration=duration)\n", "animation.write_gif(\"sinc_mpl.gif\", fps=20)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"sinc_mpl.gif\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(url=\"sinc_mpl.gif\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "delta = 0.025\n", "x = np.arange(-3.0, 3.0, delta)\n", "y = np.arange(-2.0, 2.0, delta)\n", "X, Y = np.meshgrid(x, y)\n", "Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n", "Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n", "# difference of Gaussians\n", "Z = 10.0 * (Z2 - Z1)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fv6miZW776JxFdcpfzQWPl3Ly+Zhc/oOFONpwLVFMx0RQgAxyw9I/wbd/hakP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\nUbmXAyetpB57BmY8D3fKblO3J8ldOI2jZ2DkLPjjEEwYCH3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"text/plain": [ "<matplotlib.figure.Figure at 0x10c174b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "CS = plt.contour(X, Y, Z)\n", "plt.clabel(CS, inline=1, fontsize=10)\n", "plt.title('Simplest default with labels');" ] }, { "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
tpin3694/tpin3694.github.io
scala/find_largest_key_or_value_in_a_map.ipynb
1
2495
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Title: Find Largest Key Or Value In A Map \n", "Slug: find_largest_key_or_value_in_a_map \n", "Summary: Find Largest Key Or Value In A Map Using Scala. \n", "Date: 2017-04-03 12:00 \n", "Category: Scala \n", "Tags: Basics \n", "Authors: Chris Albon \n", "\n", "If you want to learn more, check out [Scala Cookbook](http://amzn.to/2lxbrxN) and [Programming in Scala](http://amzn.to/2lEtsLt)." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Create A Map" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "// Create an immutable map with three key value pairs\n", "val numbers = Map(1 -> 100, \n", " 2 -> 200,\n", " 3 -> 300)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Find Largest Key" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(3,300)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Find largest key\n", "numbers.max" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Find Largest Value" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "// Find the largest value\n", "numbers.valuesIterator.max" ] } ], "metadata": { "kernelspec": { "display_name": "Apache Toree - Scala", "language": "scala", "name": "apache_toree_scala" }, "language_info": { "file_extension": ".scala", "name": "scala", "version": "2.11.8" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
opengeostat/pygslib
pygslib/Ipython_templates/backtr_raw.ipynb
1
76221
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Testing the back normalscore transformation\n", "========\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const 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", " const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " const 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", " const cell = handle.cell;\n", "\n", " const id = cell.output_area._bokeh_element_id;\n", " const 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", " const cmd_clean = \"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_clean, {\n", " iopub: {\n", " output: function(msg) {\n", " const 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", " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd_destroy);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " const output_area = handle.output_area;\n", " const output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " const 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", " const bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " const script_attrs = bk_div.children[0].attributes;\n", " for (let i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " const 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", " const 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? 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If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. 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i < css_urls.length; i++) {\n", " const url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n", " const url = js_urls[i];\n", " const element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\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", " };\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", " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.1.min.js\"];\n", " const css_urls = [];\n", " \n", "\n", " const 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 (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\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", " const cell = $(document.getElementById(null)).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 const 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 const 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 const el = document.getElementById(null);\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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.1.min.js\"];\n const css_urls = [];\n \n\n const 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 (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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 const cell = $(document.getElementById(null)).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": [ "#general imports\n", "import matplotlib.pyplot as plt \n", "import pygslib \n", "from matplotlib.patches import Ellipse\n", "import numpy as np\n", "import pandas as pd\n", "\n", "#make the plots inline\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getting the data ready for work\n", "---------\n", "If the data is in GSLIB format you can use the function `gslib.read_gslib_file(filename)` to import the data into a Pandas DataFrame. \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#get the data in gslib format into a pandas Dataframe\n", "mydata= pygslib.gslib.read_gslib_file('../data/cluster.dat') " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#view data in a 2D projection\n", "plt.scatter(mydata['Xlocation'],mydata['Ylocation'], c=mydata['Primary'])\n", "plt.colorbar()\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The nscore transformation table function\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "va,error = backtr(vnsc,transin,transout,ltail,utail,ltpar,utpar,zmin,zmax,getrank,[nd,nt])\n", "\n", "Wrapper for ``backtr``.\n", "\n", "Parameters\n", "----------\n", "vnsc : input rank-1 array('d') with bounds (nd)\n", "transin : input rank-1 array('d') with bounds (nt)\n", "transout : input rank-1 array('d') with bounds (nt)\n", "ltail : input int\n", "utail : input int\n", "ltpar : input float\n", "utpar : input float\n", "zmin : input float\n", "zmax : input float\n", "getrank : input int\n", "\n", "Other Parameters\n", "----------------\n", "nd : input int, optional\n", " Default: len(vnsc)\n", "nt : input int, optional\n", " Default: len(transin)\n", "\n", "Returns\n", "-------\n", "va : rank-1 array('d') with bounds (nd)\n", "error : int\n", "\n" ] } ], "source": [ "print (pygslib.gslib.__dist_transf.backtr.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the transformation table" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "there was any error?: False\n" ] } ], "source": [ "transin,transout, error = pygslib.gslib.__dist_transf.ns_ttable(mydata['Primary'],mydata['Declustering Weight'])\n", "print ('there was any error?: ', error!=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the normal score transformation\n", "\n", "Note that the declustering is applied on the transformation tables" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "mydata['NS_Primary'] = pygslib.gslib.__dist_transf.nscore(mydata['Primary'],transin,transout,getrank=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "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": [ "mydata['NS_Primary'].hist(bins=30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Doing the back transformation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "there was any error?: False 0\n" ] } ], "source": [ "mydata['NS_Primary_BT'],error = pygslib.gslib.__dist_transf.backtr(mydata['NS_Primary'],\n", " transin,transout,\n", " ltail=1,utail=1,ltpar=0,utpar=60,\n", " zmin=0,zmax=60,getrank=False)\n", "print ('there was any error?: ', error!=0, error)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:title={'center':'Primary'}>,\n", " <AxesSubplot:title={'center':'NS_Primary_BT'}>]], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mydata[['Primary','NS_Primary_BT']].hist(bins=30)" ] }, { "cell_type": "code", "execution_count": 10, "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>Primary</th>\n", " <th>NS_Primary_BT</th>\n", " <th>NS_Primary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.06</td>\n", " <td>0.06</td>\n", " <td>-2.318555</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.06</td>\n", " <td>0.06</td>\n", " <td>-2.318555</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.08</td>\n", " <td>0.08</td>\n", " <td>-1.908162</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.09</td>\n", " <td>0.09</td>\n", " <td>-1.773658</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.09</td>\n", " <td>0.09</td>\n", " <td>-1.773658</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Primary NS_Primary_BT NS_Primary\n", "0 0.06 0.06 -2.318555\n", "1 0.06 0.06 -2.318555\n", "2 0.08 0.08 -1.908162\n", "3 0.09 0.09 -1.773658\n", "4 0.09 0.09 -1.773658" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydata[['Primary','NS_Primary_BT', 'NS_Primary']].head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [] } ], "metadata": { "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
Leguark/GeMpy
legacy/Geothealler big function.ipynb
2
250862
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:18: DeprecationWarning: stack(*tensors) interface is deprecated, use stack(tensors, axis=0) instead.\n" ] } ], "source": [ "import importlib\n", "import theano.tensor as T\n", "import sys, os\n", "sys.path.append(\"/home/bl3/PycharmProjects/GeMpy/\")\n", "import GeoMig\n", "#importlib.reload(GeoMig)\n", "importlib.reload(GeoMig)\n", "import numpy as np\n", "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n", "np.set_printoptions(precision = 2, linewidth= 130, suppress = True)\n", "\n", "\n", "test = GeoMig.GeoMigSim_pro2(c_o = np.float32(-0.58888888),range = np.float32(6))\n", "\n", "#print (ref_p)\n", "\n", "test.create_regular_grid_2D(0,10,0,10,100,100)\n", "test.theano_set_2D()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(6.0, dtype=float32)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.a.eval()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dtype('float32')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_1 = np.array([[1,7],[5,7],[6,7], [9,8]], dtype = \"float32\")\n", "\n", "layer_2 = np.array([[9.1,1],[9,1]], dtype = \"float32\")\n", "\n", "dip_pos_1 = np.array([2,4,], dtype = \"float32\")\n", "dip_pos_2 = np.array([6.,3.], dtype = \"float32\")\n", "dip_pos_3 = np.array([8,4], dtype = \"float32\")\n", "dip_angle_1 = float(250)\n", "dip_angle_2 = float(70)\n", "\n", "layers = np.asarray([layer_1,layer_2])\n", "dips = np.asarray([dip_pos_1,dip_pos_2])#, dip_pos_3])\n", "dips_angles = np.asarray([dip_angle_1, dip_angle_2], dtype=\"float32\")\n", "#print (dips_angles)\n", "rest = np.vstack((i[1:] for i in layers))\n", "ref = np.vstack((np.tile(i[0],(np.shape(i)[0]-1,1)) for i in layers))\n", "dips_angles.dtype" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 2., 4.],\n", " [ 6., 3.]], dtype=float32)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dips" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rest = rest.astype(\"float32\")\n", "ref = ref.astype(\"float32\")\n", "dips = dips.astype(\"float32\")\n", "dips_angles = dips_angles.astype(\"float32\")\n", "type(dips_angles)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dips;" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.59, 0.13, -0. , 0.06, 0.13, 0.09, 0.07, 0. , 1. , 0. ],\n", " [ 0.13, 0.59, 0.06, -0. , -0.02, -0. , 0. , 0.01, 1. , 0. ],\n", " [-0. , 0.06, 0.59, 0.01, -0.15, -0.2 , -0.21, 0. , 0. , 1. ],\n", " [ 0.06, -0. , 0.01, 0.59, 0.09, 0.11, 0. , -0.01, 0. , 1. ],\n", " [ 0.13, -0.02, -0.15, 0.09, 1.87, 1.78, 0.99, 0. , 4. , 0. ],\n", " [ 0.09, -0. , -0.2 , 0.11, 1.78, 1.99, 1.2 , 0. , 5. , 0. ],\n", " [ 0.07, 0. , -0.21, 0. , 0.99, 1.2 , 2. , 0. , 8. , 1. ],\n", " [ 0. , 0.01, 0. , -0.01, 0. , 0. , 0. , 0. , -0.1 , 0. ],\n", " [ 1. , 1. , 0. , 0. , 4. , 5. , 8. , -0.1 , 0. , 0. ],\n", " [ 0. , 0. , 1. , 1. , 0. , 0. , 1. , 0. , 0. , 0. ]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.geoMigueller(dips,dips_angles,rest, ref)[4]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sol = test.geoMigueller(dips,dips_angles,rest, ref)[0]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.12, 0.13, 0.13, ..., 0.03, 0.02, 0.02])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.set_printoptions(precision = 2 ,linewidth= 130, suppress = True)\n", "\n", "test.geoMigueller(dips,dips_angles,rest, ref)[0]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.66 3.06] [ 6.34 3.94] [[ 1. 7.]\n", " [ 5. 7.]\n", " [ 6. 7.]\n", " [ 9. 8.]]\n" ] }, { "data": { "image/png": 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pf3jb/6bdKcUXUfY6Sl+V8R2VoXZ91kp6UtITUdsiZoocS8QskmOBHIvELBy1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vpzkDK/ZgV7yONBlp0WXjuFn4lnMst9thWQzRiGUeLVPMHAzVpeJoVFPnNPsphD3IXbTwfdwt+p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\nE6S+95ZqSpK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"text/plain": [ "<matplotlib.figure.Figure at 0x7fec0c1f6cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n", " np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n", "\n", "dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n", " np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n", "\n", "plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n", " dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n", "plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n", " dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n", "\n", "plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n", "plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n", "\n", "plt.plot(layer_1[:,0],layer_1[:,1], )\n", "plt.plot(layer_2[:,0],layer_2[:,1], )\n", "\n", "plt.contour( sol.reshape(100,100) ,30,extent = (0,10,0,10) )\n", "#plt.colorbar()\n", "#plt.xlim(0,10)\n", "#plt.ylim(0,10)\n", "plt.title(\"GeoBulleter v 0.1\")\n", "print (dip_pos_1_v, dip_pos_2_v, layer_1)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# random\n", "layers = [np.random.uniform(0,10,(10,2)) for i in range(100)]\n", "dips = np.random.uniform(0,10, (60,2))\n", "dips_angles = np.random.normal(90,10, 60)\n", "rest = (np.vstack((i[1:] for i in layers)))\n", "ref = np.vstack((np.tile(i[0],(np.shape(i)[0]-1,1)) for i in layers))\n", "rest;" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7ff54bfa5278>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IgV/6IeQrcPgVOPJtcafkq2t7PV4d5t+BhXdg4W0Zm1MwcgJGnxI3xshjMHJS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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff5660525f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "plt.contour( sol.reshape(100,100) ,30,extent = (0,10,0,10) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CPU" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 5.81 s per loop\n" ] } ], "source": [ "%%timeit\n", "sol = test.geoMigueller(dips,dips_angles,rest, ref)[0]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Function profiling\n", "==================\n", " Message: /home/miguel/PycharmProjects/GeMpy/GeoMig.py:562\n", " Time in 5 calls to Function.__call__: 2.937774e+01s\n", " Time in Function.fn.__call__: 2.937736e+01s (99.999%)\n", " Time in thunks: 2.934835e+01s (99.900%)\n", " Total compile time: 1.559712e+00s\n", " Number of Apply nodes: 171\n", " Theano Optimizer time: 1.410723e+00s\n", " Theano validate time: 4.508591e-02s\n", " Theano Linker time (includes C, CUDA code generation/compiling): 9.198022e-02s\n", " Import time 0.000000e+00s\n", "\n", "Time in all call to theano.grad() 0.000000e+00s\n", "Time since theano import 132.105s\n", "Class\n", "---\n", "<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>\n", " 83.6% 83.6% 24.529s 1.29e-01s C 190 38 theano.tensor.elemwise.Elemwise\n", " 6.4% 90.0% 1.889s 5.40e-02s C 35 7 theano.tensor.elemwise.Sum\n", " 5.1% 95.1% 1.496s 2.99e-02s C 50 10 theano.tensor.blas.Dot22Scalar\n", " 2.5% 97.6% 0.743s 1.86e-02s C 40 8 theano.tensor.basic.Alloc\n", " 2.3% 100.0% 0.682s 2.27e-02s C 30 6 theano.tensor.basic.Join\n", " 0.0% 100.0% 0.008s 1.55e-03s Py 5 1 theano.tensor.nlinalg.MatrixInverse\n", " 0.0% 100.0% 0.000s 2.76e-06s C 95 19 theano.tensor.basic.Reshape\n", " 0.0% 100.0% 0.000s 2.37e-06s C 65 13 theano.tensor.subtensor.IncSubtensor\n", " 0.0% 100.0% 0.000s 9.48e-07s C 155 31 theano.tensor.elemwise.DimShuffle\n", " 0.0% 100.0% 0.000s 2.77e-05s Py 5 1 theano.tensor.extra_ops.FillDiagonal\n", " 0.0% 100.0% 0.000s 2.34e-06s C 55 11 theano.tensor.subtensor.Subtensor\n", " 0.0% 100.0% 0.000s 1.37e-06s C 60 12 theano.tensor.opt.MakeVector\n", " 0.0% 100.0% 0.000s 1.14e-06s C 30 6 theano.compile.ops.Shape_i\n", " 0.0% 100.0% 0.000s 5.77e-06s C 5 1 theano.tensor.blas_c.CGemv\n", " 0.0% 100.0% 0.000s 7.71e-07s C 30 6 theano.tensor.basic.ScalarFromTensor\n", " 0.0% 100.0% 0.000s 1.19e-06s C 5 1 theano.tensor.basic.AllocEmpty\n", " ... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)\n", "\n", "Ops\n", "---\n", "<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>\n", " 47.3% 47.3% 13.894s 2.78e+00s C 5 1 Elemwise{Composite{(i0 * i1 * LT(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4), i5) * Composite{(sqr(i0) * i0)}((i5 - Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) * ((i6 * sqr(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) + i7 + (i8 * i5 * Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))))}}[(0, 1)]\n", " 28.1% 75.5% 8.253s 1.65e+00s C 5 1 Elemwise{Composite{(i0 * ((LT(Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3), i4) * ((i5 + (i6 * Composite{(sqr(i0) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i7 * Composite{((sqr(sqr(i0)) * sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4)))) - ((i8 * sqr((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i9 * Composite{(sqr(sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4\n", " 6.1% 81.6% 1.799s 1.20e-01s C 15 3 Sum{axis=[0], acc_dtype=float64}\n", " 5.1% 86.7% 1.496s 2.99e-02s C 50 10 Dot22Scalar\n", " 3.6% 90.3% 1.054s 2.11e-01s C 5 1 Elemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)]\n", " 2.8% 93.0% 0.810s 1.62e-02s C 50 10 Elemwise{sub,no_inplace}\n", " 2.5% 95.6% 0.743s 1.86e-02s C 40 8 Alloc\n", " 2.3% 97.9% 0.682s 2.27e-02s C 30 6 Join\n", " 1.5% 99.4% 0.431s 2.87e-02s C 15 3 Elemwise{mul,no_inplace}\n", " 0.3% 99.7% 0.090s 4.51e-03s C 20 4 Sum{axis=[1], acc_dtype=float64}\n", " 0.2% 99.9% 0.054s 1.09e-02s C 5 1 Elemwise{Composite{(i0 + ((i1 * i2) / i3) + i4)}}[(0, 0)]\n", " 0.1% 100.0% 0.034s 1.70e-03s C 20 4 Elemwise{sqr,no_inplace}\n", " 0.0% 100.0% 0.008s 1.55e-03s Py 5 1 MatrixInverse\n", " 0.0% 100.0% 0.000s 2.76e-06s C 95 19 Reshape{2}\n", " 0.0% 100.0% 0.000s 2.77e-05s Py 5 1 FillDiagonal\n", " 0.0% 100.0% 0.000s 2.18e-05s C 5 1 Elemwise{Composite{Switch(EQ(Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2), i3), i3, ((i4 * (((i5 * i6 * i7 * LT(Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2), i8) * Composite{(sqr((i0 - i1)) * (i0 - i1))}(i8, Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2)) * Composite{((i0 * i1) + i2 + (i3 * i4 * i5))}(i9, sqr(Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2)), i10, i11, i8, Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2))) / (sqr(Composite{sqr\n", " 0.0% 100.0% 0.000s 1.37e-06s C 60 12 MakeVector{dtype='int64'}\n", " 0.0% 100.0% 0.000s 1.54e-05s C 5 1 Elemwise{Composite{((((LT(Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2), i3) * ((i4 + (i5 * Composite{(sqr(i0) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2) / i3))) + (i6 * Composite{((sqr(sqr(i0)) * sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2) / i3)))) - ((i7 * sqr((Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2) / i3))) + (i8 * Composite{(sqr(sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i0, i1, i2) / i3))))\n", " 0.0% 100.0% 0.000s 1.73e-06s C 40 8 Subtensor{::, int64}\n", " 0.0% 100.0% 0.000s 2.33e-06s C 25 5 IncSubtensor{InplaceSet;int64:int64:, int64:int64:}\n", " ... (remaining 37 Ops account for 0.00%(0.00s) of the runtime)\n", "\n", "Apply\n", "------\n", "<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>\n", " 47.3% 47.3% 13.894s 2.78e+00s 5 165 Elemwise{Composite{(i0 * i1 * LT(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4), i5) * Composite{(sqr(i0) * i0)}((i5 - Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) * ((i6 * sqr(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) + i7 + (i8 * i5 * Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))))}}[(0, 1)](Subtensor{:int64:}.0, Join.0, Reshape{2}.0, Reshape{2}.0, Dot22Scalar.0, InplaceDimShuffle{x,x}.0, TensorConstant{(1, 1) of 3.0}, Elemwise{mul,no_inp\n", " 28.1% 75.5% 8.253s 1.65e+00s 5 164 Elemwise{Composite{(i0 * ((LT(Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3), i4) * ((i5 + (i6 * Composite{(sqr(i0) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i7 * Composite{((sqr(sqr(i0)) * sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4)))) - ((i8 * sqr((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i9 * Composite{(sqr(sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4)))))) - (L\n", " 4.6% 80.0% 1.346s 2.69e-01s 5 168 Sum{axis=[0], acc_dtype=float64}(Elemwise{Composite{(i0 * i1 * LT(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4), i5) * Composite{(sqr(i0) * i0)}((i5 - Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) * ((i6 * sqr(Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))) + i7 + (i8 * i5 * Composite{sqrt(((i0 + i1) - i2))}(i2, i3, i4))))}}[(0, 1)].0)\n", " 3.6% 83.6% 1.054s 2.11e-01s 5 98 Elemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)](Reshape{2}.0, Reshape{2}.0, Dot22Scalar.0)\n", " 2.7% 86.3% 0.780s 1.56e-01s 5 105 Dot22Scalar(Reshape{2}.0, Positions of the points to interpolate.T, TensorConstant{2.0})\n", " 2.5% 88.8% 0.742s 1.48e-01s 5 157 Alloc(CGemv{inplace}.0, Shape_i{0}.0, TensorConstant{1}, TensorConstant{1}, Elemwise{Add}[(0, 1)].0)\n", " 2.3% 91.1% 0.682s 1.36e-01s 5 121 Join(TensorConstant{0}, Elemwise{sub,no_inplace}.0, Elemwise{sub,no_inplace}.0)\n", " 1.5% 92.6% 0.430s 8.61e-02s 5 166 Elemwise{mul,no_inplace}(Subtensor{int64::}.0, Positions of the points to interpolate.T)\n", " 1.4% 94.0% 0.410s 8.19e-02s 5 109 Elemwise{sub,no_inplace}(InplaceDimShuffle{0,x}.0, InplaceDimShuffle{1,0}.0)\n", " 1.4% 95.4% 0.400s 8.00e-02s 5 108 Elemwise{sub,no_inplace}(InplaceDimShuffle{0,x}.0, InplaceDimShuffle{1,0}.0)\n", " 1.2% 96.6% 0.361s 7.22e-02s 5 43 Dot22Scalar(Reference points for every layer, Positions of the points to interpolate.T, TensorConstant{2.0})\n", " 1.2% 97.8% 0.360s 7.20e-02s 5 167 Sum{axis=[0], acc_dtype=float64}(Elemwise{Composite{(i0 * ((LT(Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3), i4) * ((i5 + (i6 * Composite{(sqr(i0) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i7 * Composite{((sqr(sqr(i0)) * sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4)))) - ((i8 * sqr((Composite{sqrt(((i0 + i1) - i2))}(i1, i2, i3) / i4))) + (i9 * Composite{(sqr(sqr(i0)) * i0)}((Composite{sqrt(((i0 + i1) - \n", " 1.2% 99.0% 0.355s 7.10e-02s 5 44 Dot22Scalar(Rest of the points of the layers, Positions of the points to interpolate.T, TensorConstant{2.0})\n", " 0.3% 99.4% 0.094s 1.87e-02s 5 169 Sum{axis=[0], acc_dtype=float64}(Elemwise{mul,no_inplace}.0)\n", " 0.3% 99.7% 0.090s 1.80e-02s 5 45 Sum{axis=[1], acc_dtype=float64}(Elemwise{sqr,no_inplace}.0)\n", " 0.2% 99.8% 0.054s 1.09e-02s 5 170 Elemwise{Composite{(i0 + ((i1 * i2) / i3) + i4)}}[(0, 0)](Sum{axis=[0], acc_dtype=float64}.0, TensorConstant{(1,) of -1.75}, Sum{axis=[0], acc_dtype=float64}.0, InplaceDimShuffle{x}.0, Sum{axis=[0], acc_dtype=float64}.0)\n", " 0.1% 100.0% 0.034s 6.79e-03s 5 11 Elemwise{sqr,no_inplace}(Positions of the points to interpolate)\n", " 0.0% 100.0% 0.008s 1.55e-03s 5 155 MatrixInverse(IncSubtensor{InplaceSet;int64::, int64:int64:}.0)\n", " 0.0% 100.0% 0.000s 9.94e-05s 5 134 Sum{axis=[1], acc_dtype=float64}(Elemwise{Sqr}[(0, 0)].0)\n", " 0.0% 100.0% 0.000s 9.12e-05s 5 100 Alloc(Elemwise{sub,no_inplace}.0, TensorConstant{1}, TensorConstant{2}, Elemwise{Composite{Switch(EQ(i0, i1), (i0 // (-i0)), i0)}}.0, Shape_i{0}.0)\n", " ... (remaining 151 Apply instances account for 0.01%(0.00s) of the runtime)\n", "\n", "Here are tips to potentially make your code run faster\n", " (if you think of new ones, suggest them on the mailing list).\n", " Test them first, as they are not guaranteed to always provide a speedup.\n", "We don't know if amdlibm will accelerate this scalar op. deg2rad\n", " - Try installing amdlibm and set the Theano flag lib.amdlibm=True. This speeds up only some Elemwise operation.\n" ] } ], "source": [ "test.geoMigueller.profile.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GPU" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 171 ms per loop\n" ] } ], "source": [ "%%timeit\n", "sol = test.geoMigueller(dips,dips_angles,rest, ref)[0]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Function profiling\n", "==================\n", " Message: /home/miguel/PycharmProjects/GeMpy/GeoMig.py:562\n", " Time in 43 calls to Function.__call__: 7.149101e+00s\n", " Time in Function.fn.__call__: 7.146105e+00s (99.958%)\n", " Time in thunks: 4.351497e+00s (60.868%)\n", " Total compile time: 2.354024e+00s\n", " Number of Apply nodes: 227\n", " Theano Optimizer time: 2.021928e+00s\n", " Theano validate time: 1.219668e-01s\n", " Theano Linker time (includes C, CUDA code generation/compiling): 2.757676e-01s\n", " Import time 0.000000e+00s\n", "\n", "Time in all call to theano.grad() 0.000000e+00s\n", "Time since theano import 281.359s\n", "Class\n", "---\n", "<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>\n", " 86.8% 86.8% 3.779s 1.73e-02s C 219 5 theano.sandbox.cuda.basic_ops.HostFromGpu\n", " 6.5% 93.3% 0.282s 7.92e-04s C 356 8 theano.sandbox.cuda.basic_ops.GpuAlloc\n", " 2.4% 95.7% 0.103s 3.19e-05s C 3235 73 theano.sandbox.cuda.basic_ops.GpuElemwise\n", " 1.5% 97.2% 0.066s 2.48e-04s C 266 6 theano.sandbox.cuda.basic_ops.GpuJoin\n", " 0.7% 97.9% 0.030s 6.69e-05s C 446 10 theano.sandbox.cuda.blas.GpuDot22Scalar\n", " 0.5% 98.4% 0.021s 3.60e-05s C 583 13 theano.sandbox.cuda.basic_ops.GpuIncSubtensor\n", " 0.5% 98.8% 0.020s 6.53e-05s C 307 7 theano.sandbox.cuda.basic_ops.GpuCAReduce\n", " 0.4% 99.3% 0.019s 4.51e-04s Py 43 1 theano.tensor.nlinalg.MatrixInverse\n", " 0.3% 99.6% 0.014s 1.71e-05s C 847 19 theano.sandbox.cuda.basic_ops.GpuReshape\n", " 0.3% 99.9% 0.011s 3.20e-05s C 356 8 theano.sandbox.cuda.basic_ops.GpuFromHost\n", " 0.0% 99.9% 0.001s 8.66e-07s C 1385 31 theano.sandbox.cuda.basic_ops.GpuDimShuffle\n", " 0.0% 99.9% 0.001s 2.48e-05s Py 45 1 theano.tensor.extra_ops.FillDiagonal\n", " 0.0% 100.0% 0.001s 1.81e-06s C 485 11 theano.sandbox.cuda.basic_ops.GpuSubtensor\n", " 0.0% 100.0% 0.000s 8.73e-07s C 534 12 theano.tensor.opt.MakeVector\n", " 0.0% 100.0% 0.000s 1.27e-06s C 358 8 theano.tensor.elemwise.Elemwise\n", " 0.0% 100.0% 0.000s 8.67e-06s C 43 1 theano.sandbox.cuda.blas.GpuGemv\n", " 0.0% 100.0% 0.000s 8.26e-06s C 45 1 theano.sandbox.cuda.basic_ops.GpuAllocEmpty\n", " 0.0% 100.0% 0.000s 9.69e-07s C 266 6 theano.compile.ops.Shape_i\n", " 0.0% 100.0% 0.000s 7.56e-07s C 268 6 theano.tensor.basic.ScalarFromTensor\n", " ... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)\n", "\n", "Ops\n", "---\n", "<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>\n", " 86.8% 86.8% 3.779s 1.73e-02s C 219 5 HostFromGpu\n", " 6.3% 93.1% 0.274s 1.54e-03s C 178 4 GpuAlloc\n", " 1.5% 94.7% 0.066s 2.48e-04s C 266 6 GpuJoin\n", " 0.7% 95.3% 0.030s 6.69e-05s C 446 10 GpuDot22Scalar\n", " 0.6% 95.9% 0.026s 6.52e-05s C 401 9 GpuElemwise{sub,no_inplace}\n", " 0.6% 96.5% 0.025s 7.23e-05s C 352 8 GpuElemwise{Composite{(i0 * (i1 ** i2))},no_inplace}\n", " 0.5% 97.1% 0.023s 5.15e-05s C 444 10 GpuElemwise{Composite{Cast{float32}(LT(i0, i1))},no_inplace}\n", " 0.4% 97.5% 0.019s 4.51e-04s Py 43 1 MatrixInverse\n", " 0.3% 97.8% 0.014s 1.71e-05s C 847 19 GpuReshape{2}\n", " 0.3% 98.2% 0.014s 1.09e-04s C 129 3 GpuCAReduce{add}{1,0}\n", " 0.3% 98.4% 0.011s 3.20e-05s C 356 8 GpuFromHost\n", " 0.2% 98.7% 0.011s 1.25e-04s C 86 2 GpuElemwise{Composite{(i0 * sqr(i1))},no_inplace}\n", " 0.2% 98.9% 0.010s 4.40e-05s C 225 5 GpuIncSubtensor{InplaceSet;int64:int64:, int64:int64:}\n", " 0.2% 99.1% 0.008s 4.40e-05s C 178 4 GpuAlloc{memset_0=True}\n", " 0.1% 99.2% 0.006s 3.35e-05s C 178 4 GpuCAReduce{pre=sqr,red=add}{0,1}\n", " 0.1% 99.3% 0.004s 3.97e-05s C 90 2 GpuIncSubtensor{InplaceSet;int64:int64:, int64::}\n", " 0.1% 99.4% 0.003s 3.70e-05s C 90 2 GpuIncSubtensor{InplaceSet;int64::, int64:int64:}\n", " 0.1% 99.4% 0.003s 6.39e-06s C 444 10 GpuElemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)]\n", " 0.0% 99.5% 0.002s 3.50e-05s C 45 1 GpuIncSubtensor{InplaceSet;int64, :int64:}\n", " 0.0% 99.5% 0.001s 1.57e-05s C 90 2 GpuElemwise{sqr,no_inplace}\n", " ... (remaining 56 Ops account for 0.50%(0.02s) of the runtime)\n", "\n", "Apply\n", "------\n", "<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>\n", " 68.8% 68.8% 2.993s 6.96e-02s 43 215 HostFromGpu(GpuDimShuffle{1,0}.0)\n", " 17.4% 86.2% 0.756s 1.76e-02s 43 122 HostFromGpu(GpuElemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)].0)\n", " 6.1% 92.3% 0.267s 6.21e-03s 43 211 GpuAlloc(GpuGemv{inplace}.0, Shape_i{0}.0, TensorConstant{1}, TensorConstant{1}, Elemwise{Add}[(0, 1)].0)\n", " 1.2% 93.5% 0.053s 1.18e-03s 45 145 GpuJoin(TensorConstant{0}, GpuElemwise{sub,no_inplace}.0, GpuElemwise{Sub}[(0, 0)].0)\n", " 0.7% 94.2% 0.028s 6.62e-04s 43 226 HostFromGpu(GpuElemwise{Composite{((((i0 * i1) / i2) + i3) + i4)}}[(0, 1)].0)\n", " 0.4% 94.6% 0.019s 4.51e-04s 43 208 MatrixInverse(HostFromGpu.0)\n", " 0.2% 94.8% 0.008s 1.84e-04s 43 112 GpuDot22Scalar(GpuReshape{2}.0, GpuDimShuffle{1,0}.0, TensorConstant{2.0})\n", " 0.2% 95.0% 0.008s 1.82e-04s 43 141 GpuJoin(TensorConstant{0}, GpuElemwise{sub,no_inplace}.0, GpuElemwise{sub,no_inplace}.0)\n", " 0.2% 95.2% 0.008s 1.78e-04s 43 184 GpuElemwise{Composite{Cast{float32}(LT(i0, i1))},no_inplace}(GpuElemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)].0, GpuDimShuffle{x,x}.0)\n", " 0.2% 95.3% 0.008s 1.67e-04s 45 32 GpuDot22Scalar(GpuFromHost.0, GpuDimShuffle{1,0}.0, TensorConstant{2.0})\n", " 0.2% 95.5% 0.007s 1.54e-04s 43 118 GpuElemwise{sub,no_inplace}(GpuDimShuffle{0,x}.0, GpuDimShuffle{1,0}.0)\n", " 0.1% 95.6% 0.006s 1.46e-04s 43 154 GpuElemwise{Composite{(i0 * (i1 ** i2))},no_inplace}(CudaNdarrayConstant{[[ 8.75]]}, GpuElemwise{TrueDiv}[(0, 0)].0, CudaNdarrayConstant{[[ 3.]]})\n", " 0.1% 95.8% 0.006s 1.40e-04s 43 153 GpuElemwise{Composite{(i0 * (i1 ** i2))},no_inplace}(CudaNdarrayConstant{[[ 0.75]]}, GpuElemwise{TrueDiv}[(0, 0)].0, CudaNdarrayConstant{[[ 7.]]})\n", " 0.1% 95.9% 0.006s 1.39e-04s 43 123 GpuElemwise{Composite{Cast{float32}(LT(i0, i1))},no_inplace}(GpuElemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)].0, GpuDimShuffle{x,x}.0)\n", " 0.1% 96.0% 0.006s 1.38e-04s 43 117 GpuElemwise{sub,no_inplace}(GpuDimShuffle{0,x}.0, GpuDimShuffle{1,0}.0)\n", " 0.1% 96.2% 0.006s 1.36e-04s 43 148 GpuElemwise{Composite{(i0 * (i1 ** i2))},no_inplace}(CudaNdarrayConstant{[[ 8.75]]}, GpuElemwise{TrueDiv}[(0, 0)].0, CudaNdarrayConstant{[[ 3.]]})\n", " 0.1% 96.3% 0.006s 1.33e-04s 43 39 GpuDot22Scalar(GpuFromHost.0, GpuDimShuffle{1,0}.0, TensorConstant{2.0})\n", " 0.1% 96.4% 0.006s 1.33e-04s 43 146 GpuElemwise{Composite{(i0 * sqr(i1))},no_inplace}(CudaNdarrayConstant{[[ 7.]]}, GpuElemwise{TrueDiv}[(0, 0)].0)\n", " 0.1% 96.6% 0.006s 1.25e-04s 45 86 GpuElemwise{sub,no_inplace}(GpuDimShuffle{x,0}.0, GpuReshape{2}.0)\n", " 0.1% 96.7% 0.005s 1.23e-04s 43 116 GpuElemwise{Composite{Cast{float32}(LT(i0, i1))},no_inplace}(GpuElemwise{Composite{sqrt(((i0 + i1) - i2))}}[(0, 2)].0, GpuDimShuffle{x,x}.0)\n", " ... (remaining 207 Apply instances account for 3.31%(0.14s) of the runtime)\n", "\n", "Here are tips to potentially make your code run faster\n", " (if you think of new ones, suggest them on the mailing list).\n", " Test them first, as they are not guaranteed to always provide a speedup.\n", " Sorry, no tip for today.\n" ] } ], "source": [ "test.geoMigueller.profile.summary()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "importlib.reload(GeoMig)\n", "test = GeoMig.GeoMigSim_pro2()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -5.88888884e-01, -0.00000000e+00, -0.00000000e+00, ...,\n", " 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n", " [ -0.00000000e+00, -5.88888884e-01, 4.42373231e-02, ...,\n", " 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n", " [ -0.00000000e+00, 2.12696299e-01, -5.88888884e-01, ...,\n", " 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n", " ..., \n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " 2.00000000e+00, -6.06459351e+02, -6.13501053e+01],\n", " [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,\n", " -6.06459351e+02, 0.00000000e+00, 0.00000000e+00],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", " -6.13501053e+01, 0.00000000e+00, 0.00000000e+00]], dtype=float32)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "ename": "Exception", "evalue": "Can't change the value of this config parameter after initialization!", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mException\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-17-b0516f7436af>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cpu\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/miguel/anaconda3/lib/python3.5/site-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__set__\u001b[1;34m(self, cls, val)\u001b[0m\n\u001b[0;32m 329\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mallow_override\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'val'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 330\u001b[0m raise Exception(\n\u001b[1;32m--> 331\u001b[1;33m \u001b[1;34m\"Can't change the value of this config parameter \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 332\u001b[0m \"after initialization!\")\n\u001b[0;32m 333\u001b[0m \u001b[1;31m# print \"SETTING PARAM\", self.fullname,(cls), val\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mException\u001b[0m: Can't change the value of this config parameter after initialization!" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]\n", "Looping 1000 times took 2.271379 seconds\n", "Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761\n", " 1.62323284]\n", "Used the cpu\n" ] } ], "source": [ "from theano import function, config, shared, sandbox\n", "import theano.tensor as T\n", "import numpy\n", "import time\n", "\n", "vlen = 10 * 30 * 768 # 10 x #cores x # threads per core\n", "iters = 1000\n", "\n", "rng = numpy.random.RandomState(22)\n", "x = shared(numpy.asarray(rng.rand(vlen), config.floatX))\n", "f = function([], T.exp(x))\n", "print(f.maker.fgraph.toposort())\n", "t0 = time.time()\n", "for i in range(iters):\n", " r = f()\n", "t1 = time.time()\n", "print(\"Looping %d times took %f seconds\" % (iters, t1 - t0))\n", "print(\"Result is %s\" % (r,))\n", "if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):\n", " print('Used the cpu')\n", "else:\n", " print('Used the gpu')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: GeForce GTX 970 (CNMeM is disabled, cuDNN not available)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]\n", "Looping 1000 times took 0.353415 seconds\n", "Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761\n", " 1.62323296]\n", "Used the gpu\n" ] } ], "source": [ "from theano import function, config, shared, sandbox\n", "import theano.tensor as T\n", "import numpy\n", "import time\n", "\n", "vlen = 10 * 30 * 768 # 10 x #cores x # threads per core\n", "iters = 1000\n", "\n", "rng = numpy.random.RandomState(22)\n", "x = shared(numpy.asarray(rng.rand(vlen), config.floatX))\n", "f = function([], T.exp(x))\n", "print(f.maker.fgraph.toposort())\n", "t0 = time.time()\n", "for i in range(iters):\n", " r = f()\n", "t1 = time.time()\n", "print(\"Looping %d times took %f seconds\" % (iters, t1 - t0))\n", "print(\"Result is %s\" % (r,))\n", "if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):\n", " print('Used the cpu')\n", "else:\n", " print('Used the gpu')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Elemwise{exp,no_inplace}(<TensorType(float32, vector)>)]\n", "Looping 1000 times took 2.291412 seconds\n", "Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761\n", " 1.62323284]\n", "Used the cpu\n" ] } ], "source": [ "from theano import function, config, shared, sandbox\n", "import theano.tensor as T\n", "import numpy\n", "import time\n", "\n", "vlen = 10 * 30 * 768 # 10 x #cores x # threads per core\n", "iters = 1000\n", "\n", "rng = numpy.random.RandomState(22)\n", "x = shared(numpy.asarray(rng.rand(vlen), config.floatX))\n", "f = function([], T.exp(x))\n", "print(f.maker.fgraph.toposort())\n", "t0 = time.time()\n", "for i in range(iters):\n", " r = f()\n", "t1 = time.time()\n", "print(\"Looping %d times took %f seconds\" % (iters, t1 - t0))\n", "print(\"Result is %s\" % (r,))\n", "if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):\n", " print('Used the cpu')\n", "else:\n", " print('Used the gpu')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 759, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.59, 0.08, 0. , 0.07],\n", " [ 0.08, -0.59, 0.07, 0. ],\n", " [ 0. , 0.12, -0.59, 0.13],\n", " [ 0.07, 0. , 0.13, -0.59]])" ] }, "execution_count": 759, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.set_printoptions(precision=2)\n", "test.geoMigueller(dips,dips_angles,rest, ref)[1]" ] }, { "cell_type": "code", "execution_count": 751, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T.fill_diagonal?" ] }, { "cell_type": "code", "execution_count": 758, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.5 4.87] [ 6.34 3.94] [[ 1. 7.]\n", " [ 5. 7.]\n", " [ 6. 7.]\n", " [ 9. 8.]]\n" ] }, { "data": { "image/png": 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QuEvifZMb31E7Fiy/BYsvwcJLsPAyrB6H/B7ouQd674Heu0XndstvZgtgQwld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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1cc4711198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n", " np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n", "\n", "dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n", " np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n", "\n", "plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n", " dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n", "plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n", " dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n", "\n", "plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n", "plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n", "\n", "plt.plot(layer_1[:,0],layer_1[:,1], )\n", "plt.plot(layer_2[:,0],layer_2[:,1], )\n", "\n", "plt.contour( sol.reshape(50,50) ,30,extent = (0,10,0,10) )\n", "#plt.colorbar()\n", "#plt.xlim(0,10)\n", "#plt.ylim(0,10)\n", "plt.title(\"GeoBulleter v 0.1\")\n", "print (dip_pos_1_v, dip_pos_2_v, layer_1)" ] }, { "cell_type": "code", "execution_count": 443, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "source": [ "n = 10\n", "#a = T.horizontal_stack(T.vertical_stack(T.ones(n),T.zeros(n)), T.vertical_stack(T.zeros(n), T.ones(n)))\n", "a = T.zeros(n)\n", "\n", "print (a.eval())\n", "#U_G = T.horizontal_stack(([T.ones(n),T.zeros(n)],[T.zeros(n),T.ones(n)]))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "T.stack?ö+aeg" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "_squared_euclidean_distances2 = T.sqrt(\n", " (dips ** 2).sum(1).reshape((dips.shape[0], 1)) + (aux_Y ** 2).sum(1).reshape(\n", " (1, aux_Y.shape[0])) - 2 * dips.dot(aux_Y.T))\n", "\n", " _squared_euclidean_distances3 = T.sqrt(\n", " (dips ** 2).sum(1).reshape((dips.shape[0], 1)) + (aux_X ** 2).sum(1).reshape(\n", " (1, aux_X.shape[0])) - 2 * dips.dot(aux_X.T))\n", "\n", " h3 = T.vertical_stack(\n", " (dips[:, 0] - aux_Y[:, 0].reshape((aux_Y[:, 0].shape[0], 1))).T,\n", " (dips[:, 1] - aux_Y[:, 1].reshape((aux_Y[:, 1].shape[0], 1))).T\n", " )\n", "\n", "\n", " h4 = T.vertical_stack(\n", " (dips[:, 0] - aux_X[:, 0].reshape((aux_X[:, 0].shape[0], 1))).T,\n", " (dips[:, 1] - aux_X[:, 1].reshape((aux_X[:, 1].shape[0], 1))).T)\n", "\n", " r_3 = T.tile(_squared_euclidean_distances2, (2, 1)) # Careful with the number of dimensions\n", " r_4 = T.tile(_squared_euclidean_distances3, (2, 1)) # Careful with the number of dimensions\n", "\n", " _ans_d1_3 = (r_3 < self.a) * (\n", " -7 * (self.a - r_3) ** 3 * r_3 * (8 * self.a ** 2 + 9 * self.a * r_3 + 3 * r_3 ** 2) * 1) \n", " / (4 * self.a ** 7)\n", "\n", " _ans_d1_4 = (r_4 < self.a) * (\n", " -7 * (self.a - r_4) ** 3 * r_4 * (8 * self.a ** 2 + 9 * self.a * r_4 + 3 * r_4 ** 2) * 1) \n", " / (4 * self.a ** 7)\n", "\n", " _C_GI = (h3 / r_3 * _ans_d1_3 - h4 / r_4 * _ans_d1_4).T\n", "\n", " self._f_CGI = theano.function([dips, aux_X, aux_Y], _C_GI)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "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 }
mit
henrysky/astroNN
demo_tutorial/VAE/variational_autoencoder_demo.ipynb
1
951028
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variational Autoencoder demo with 1D data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is [astroNN](https://github.com/henrysky/astroNN), please take a look if you are interested in astronomy or how neural network applied in astronomy\n", "* **Henry Leung** - *Astronomy student, University of Toronto* - [henrysky](https://github.com/henrysky)\n", "* Project advisor: **Jo Bovy** - *Professor, Department of Astronomy and Astrophysics, University of Toronto* - [jobovy](https://github.com/jobovy)\n", "* Contact Henry: henrysky.leung [at] utoronto.ca\n", "* This tutorial is created on 13/Jan/2018 with Keras 2.1.2, Tensorflow 1.4.0, Nvidia CuDNN 6.1 for CUDA 8.0 (Optional), Python 3.6.3 Win10 x64\n", "* Updated on 31/Jan/2020 with Tensorflow 2.1.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import everything we need first" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format='retina'\n", "\n", "import numpy as np\n", "import pylab as plt\n", "from scipy.stats import norm\n", "\n", "from tensorflow.keras.layers import Input, Dense, Lambda, Layer, Add, Multiply\n", "from tensorflow.keras.models import Model, Sequential\n", "from tensorflow.keras import regularizers\n", "\n", "import tensorflow as tf\n", "from astroNN.nn.layers import KLDivergenceLayer\n", "from astroNN.nn.losses import nll" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then define basic constant, function and define our neural network" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "original_dim = 4000 # Our 1D images dimension, each image has 4000 pixel\n", "intermediate_dim = 256 # Number of neurone our fully connected neural net has\n", "\n", "batch_size = 50\n", "epochs = 15\n", "epsilon_std = 1.0\n", "\n", "\n", "def blackbox_image_generator(pixel, center, sigma):\n", " return norm.pdf(pixel, center, sigma)\n", "\n", "\n", "def model_vae(latent_dim):\n", " \"\"\" \n", " Main Model + Encoder\n", " \"\"\"\n", " x = Input(shape=(original_dim,))\n", " h = Dense(intermediate_dim, activation='relu')(x)\n", "\n", " z_mu = Dense(latent_dim, kernel_regularizer=regularizers.l2(1e-4))(h)\n", " z_log_var = Dense(latent_dim)(h)\n", "\n", " z_mu, z_log_var = KLDivergenceLayer()([z_mu, z_log_var])\n", " z_sigma = Lambda(lambda t: tf.exp(.5*t))(z_log_var)\n", "\n", " eps = Input(tensor=tf.random.normal(mean=0, stddev=epsilon_std, shape=(tf.shape(x)[0], latent_dim)))\n", " \n", " z_eps = Multiply()([z_sigma, eps])\n", " z = Add()([z_mu, z_eps])\n", " \n", " decoder = Sequential()\n", " decoder.add(Dense(intermediate_dim, input_dim=latent_dim, activation='relu'))\n", " decoder.add(Dense(original_dim, activation='sigmoid'))\n", " \n", " x_pred = decoder(z)\n", "\n", " vae = Model(inputs=[x, eps], outputs=x_pred)\n", " \n", " encoder = Model(x, z_mu)\n", " \n", " return vae, encoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will generate some true latent variable so we can pass them to a blackbox image generator to generate some 1D images.\n", "\n", "The blackbox image generator (which is deterministic) will take two numbers and generate images in a predictable way. This is important because if the generator generate image in a random way, then there is nothing neural network can learn.\n", "\n", "But for simplicity, we will fix the first latent variable of the blackbox image generator a constant and only use the second one to generate images." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 735 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "s_1 = np.random.normal(30, 1.5, 900)\n", "s_2 = np.random.normal(15, 1, 900)\n", "s_3 = np.random.normal(10, 1, 900)\n", "\n", "s = np.concatenate([s_1, s_2, s_3])\n", "\n", "plt.figure(figsize=(12, 12))\n", "plt.hist(s[:900], 70, density=1, facecolor='green', alpha=0.75, label='Population 1')\n", "plt.hist(s[900:1800], 70, density=1, facecolor='red', alpha=0.75, label='Population 2')\n", "plt.hist(s[1800:], 70, density=1, facecolor='blue', alpha=0.75, label='Population 3')\n", "plt.title('Disturbution of hidden variable used to generate data', fontsize=15)\n", "plt.xlabel('True Latent Variable Value', fontsize=15)\n", "plt.ylabel('Probability Density', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will pass the true latent variable to the blackbox image generator to generate some images. Below are the example\n", "images from the three populations. They may seems to have no difference but neural network will pick up some subtle features\n", "usually." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 510, "width": 522 }, "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 510, "width": 522 }, "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x576 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 510, "width": 522 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We have some images, each has 4000 pixels\n", "x_train = np.zeros((len(s), original_dim))\n", "for counter, S in enumerate(s):\n", " xs = np.linspace(0, 40, original_dim)\n", " x_train[counter] = blackbox_image_generator(xs, 20, S)\n", "\n", "# Prevent nan causes error\n", "x_train[np.isnan(x_train.astype(float))] = 0\n", "\n", "x_train *= 10\n", "\n", "# Add some noise to our images\n", "x_train += np.random.normal(0, 0.2, x_train.shape)\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.title('Example image from Population 1', fontsize=15)\n", "plt.plot(x_train[500])\n", "plt.xlabel('Pixel', fontsize=15)\n", "plt.ylabel('Flux', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.show()\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.title('Example image from Population 2', fontsize=15)\n", "plt.plot(x_train[1000])\n", "plt.xlabel('Pixel', fontsize=15)\n", "plt.ylabel('Flux', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.show()\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.title('Example image from Population 3', fontsize=15)\n", "plt.plot(x_train[1600])\n", "plt.xlabel('Pixel', fontsize=15)\n", "plt.ylabel('Flux', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will pass the images to the neural network and train with them." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Users\\Henry\\Miniconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "If using Keras pass *_constraint arguments to layers.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 731 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "latent_dim = 1 # Dimension of our latent space\n", "vae, encoder = model_vae(latent_dim)\n", "vae.compile(optimizer='rmsprop', loss=nll, \n", " weighted_metrics=None,\n", " loss_weights=None,\n", " sample_weight_mode=None)\n", "\n", "vae.fit(x_train, x_train, shuffle=True, epochs=epochs, batch_size=batch_size, verbose=0)\n", "\n", "z_test = encoder.predict(x_train, batch_size=batch_size)\n", "\n", "plt.figure(figsize=(12, 12))\n", "plt.hist(z_test[:900], 70, density=1, facecolor='green', alpha=0.75, label='Population 1')\n", "plt.hist(z_test[900:1800], 70, density=1, facecolor='red', alpha=0.75, label='Population 2')\n", "plt.hist(z_test[1800:], 70, density=1, facecolor='blue', alpha=0.75, label='Population 3')\n", "plt.title('Disturbution of latent variable value from neural net', fontsize=15)\n", "plt.xlabel('Latent Variable Value from Neural Net', fontsize=15)\n", "plt.ylabel('Probability Density', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yay!! Seems like the neural network recovered the three population successfully. Althought the recovered latent variable is not exactly the same as the original ones we generated (I mean at least the scale isn't the same), usually you won't expect the neural network can learn the real phyiscs. In this case, the latent variable is just some transformations from the original ones." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should still remember that we have fixed the first latent variable of the blackbox image generator. What happes if we also generate 3 populations for the first latent variable, and the first latent variable will have no correlation with the second latent variable (Meaning if you know the first latent value of an object, you have no information gain on the second latent value of that object because the first and second have nothing to do with each other)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 735 }, "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 749 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_1A = np.random.normal(28, 2, 300)\n", "m_1B = np.random.normal(19, 2, 300)\n", "m_1C = np.random.normal(12, 1, 300)\n", "\n", "m_2A = np.random.normal(28, 2, 300)\n", "m_2B = np.random.normal(19, 2, 300)\n", "m_2C = np.random.normal(12, 1, 300)\n", "\n", "m_3A = np.random.normal(28, 2, 300)\n", "m_3B = np.random.normal(19, 2, 300)\n", "m_3C = np.random.normal(12, 1, 300)\n", "\n", "m = np.concatenate([m_1A, m_1B, m_1C, m_2A, m_2B, m_2C, m_3A, m_3B, m_3C])\n", "\n", "x_train = np.zeros((len(s), original_dim))\n", "for counter in range(len(s)):\n", " xs = np.linspace(0, 40, original_dim)\n", " x_train[counter] = blackbox_image_generator(xs, m[counter], s[counter])\n", " \n", "# Prevent nan causes error\n", "x_train[np.isnan(x_train.astype(float))] = 0\n", "\n", "x_train *= 10\n", "\n", "# Add some noise to our images\n", "x_train += np.random.normal(0, 0.1, x_train.shape) \n", "\n", "\n", "plt.figure(figsize=(12, 12))\n", "plt.hist(s[:900], 70, density=1, facecolor='green', alpha=0.75, label='Population 1')\n", "plt.hist(s[900:1800], 70, density=1, facecolor='red', alpha=0.75, label='Population 2')\n", "plt.hist(s[1800:], 70, density=1, facecolor='blue', alpha=0.75, label='Population 3')\n", "plt.title('Disturbution of hidden variable 1 used to generate data', fontsize=15)\n", "plt.xlabel('True Latent Variable Value', fontsize=15)\n", "plt.ylabel('Probability Density', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15)\n", "plt.show()\n", "\n", "plt.figure(figsize=(12, 12))\n", "plt.hist(m[:900], 70, density=1, facecolor='green', alpha=0.75, label='Population 1')\n", "plt.hist(m[900:1800], 70, density=1, facecolor='red', alpha=0.75, label='Population 2')\n", "plt.hist(m[1800:], 70, density=1, facecolor='blue', alpha=0.75, label='Population 3')\n", "plt.title('Disturbution of hidden variable 2 used to generate data', fontsize=15)\n", "plt.xlabel('True Latent Variable Value', fontsize=15)\n", "plt.ylabel('Probability Density', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have two independent variables to generate our images, what happened if you still try to force the neural network to explain the images with just one variable?\n", "\n", "Before we run the training, we should think about what we expect first. Lets denate the first latent variable population as 1, 2 and 3 , while the second latent variable population as A, B and C. If we know an object is in population 2, it has equal chance that its in population A, B and C. With this logic, we should have 9 unique population in total (1A, 1B, 1C, 2A, 2B, 2C, 3A, 3B, 3C). If the neural network want to explain the images with 1 latent variable, it should has 9 peaks in the plot." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 731 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "latent_dim = 1 # Dimension of our latent space\n", "vae, encoder = model_vae(latent_dim)\n", "vae.compile(optimizer='rmsprop', loss=nll, \n", " weighted_metrics=None,\n", " loss_weights=None,\n", " sample_weight_mode=None)\n", "\n", "epochs = 15\n", "\n", "vae.fit(x_train, x_train, shuffle=True, epochs=epochs, batch_size=batch_size, verbose=0)\n", "\n", "z_test = encoder.predict(x_train, batch_size=batch_size)\n", "\n", "plt.figure(figsize=(12, 12))\n", "# plt.hist(z_test[:900], 70, density=1, facecolor='green', alpha=0.75, label='Population 1')\n", "# plt.hist(z_test[900:1800], 70, density=1, facecolor='red', alpha=0.75, label='Population 2')\n", "# plt.hist(z_test[1800:], 70, density=1, facecolor='blue', alpha=0.75, label='Population 3')\n", "\n", "plt.hist(z_test[:300], 70, density=1, alpha=0.75, label='Population 1A')\n", "plt.hist(z_test[300:600], 70, density=1, alpha=0.75, label='Population 1B')\n", "plt.hist(z_test[600:900], 70, density=1, alpha=0.75, label='Population 1C')\n", "\n", "plt.hist(z_test[900:1200], 70, density=1, alpha=0.75, label='Population 2A')\n", "plt.hist(z_test[1200:1500], 70, density=1, alpha=0.75, label='Population 2B')\n", "plt.hist(z_test[1500:1800], 70, density=1, alpha=0.75, label='Population 2C')\n", "\n", "plt.hist(z_test[1800:2100], 70, density=1, alpha=0.75, label='Population 3A')\n", "plt.hist(z_test[2100:2400], 70, density=1, alpha=0.75, label='Population 3B')\n", "plt.hist(z_test[2400:2700], 70, density=1, alpha=0.75, label='Population 3C')\n", "\n", "plt.title('Disturbution of latent variable value from neural net', fontsize=15)\n", "plt.xlabel('Latent Variable Value from Neural Net', fontsize=15)\n", "plt.ylabel('Probability Density', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By visual inspection, seems like the neural network only recovered 6 population :(\n", "\n", "What will happen if we increase the latent space of the nerual network to 2?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 728, "width": 745 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "latent_dim = 2 # Dimension of our latent space\n", "epochs = 40\n", "\n", "vae, encoder = model_vae(latent_dim)\n", "vae.compile(optimizer='rmsprop', loss=nll, \n", " weighted_metrics=None,\n", " loss_weights=None,\n", " sample_weight_mode=None)\n", "vae.fit(x_train, x_train, shuffle=True, epochs=epochs, batch_size=batch_size, verbose=0)\n", "\n", "z_test = encoder.predict(x_train, batch_size=batch_size)\n", "\n", "plt.figure(figsize=(12, 12))\n", "plt.scatter(z_test[:300, 0], z_test[:300, 1], s=4, label='Population 1A')\n", "plt.scatter(z_test[300:600, 0], z_test[300:600, 1], s=4, label='Population 1B')\n", "plt.scatter(z_test[600:900, 0], z_test[600:900, 1], s=4, label='Population 1C')\n", "\n", "plt.scatter(z_test[900:1200, 0], z_test[900:1200, 1], s=4, label='Population 2A')\n", "plt.scatter(z_test[1200:1500, 0], z_test[1200:1500, 1], s=4, label='Population 2B')\n", "plt.scatter(z_test[1500:1800, 0], z_test[1500:1800, 1], s=4, label='Population 2C')\n", "\n", "plt.scatter(z_test[1800:2100, 0], z_test[1800:2100, 1], s=4, label='Population 3A')\n", "plt.scatter(z_test[2100:2400, 0], z_test[2100:2400, 1], s=4, label='Population 3B')\n", "plt.scatter(z_test[2400:2700, 0], z_test[2400:2700, 1], s=4, label='Population 3C')\n", "\n", "plt.title('Latent Space (Middle layer of Neurones)', fontsize=15)\n", "plt.xlabel('Second Latent Variable (Neurone)', fontsize=15)\n", "plt.ylabel('First Latent Variable (Neurone)', fontsize=15)\n", "plt.tick_params(labelsize=12, width=1, length=10)\n", "plt.legend(loc='best', fontsize=15, markerscale=6)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Why using Mean-Square-Error as reconstruction loss is a bad idea?\n", "\n", "Mean-Square-Error: $\\frac{1}{T} \\sum (\\hat x-x)^2$\n", "\n", "Gaussian disturbution: $P(x|\\mu,\\sigma)=\\frac{1}{C} exp(\\frac{(\\mu-x)^2}{2\\sigma^2})$\n", "\n", "Lets set $\\mu$ as our datapoint and $\\sigma$ as 1 for simplicity\n", "\n", "= $p(x|\\hat x) \\propto exp(\\frac{(\\mu-x)^2}{2})$\n", "\n", "Taking log, $log(p(x|\\hat x)) \\propto \\sum (\\hat x-x)^2$\n", "\n", "Which means use mean square error (as an objective function which minimizing the MSE/L2 loss) is equivalent maximizing the log-likelihood of a Gaussian. We are assuming our data is somehow gaussian which may or may not be true.\n", "\n", "Thats why the reconstructed images are blurry if we use mean square error loss, our real world data highly probably come from multimodal distribution, which we fit a gaussian with it in optimization process with mean square error objective.\n", "\n", "In real world, even though our data are multimodal distribution, $P(x|z)$ could be unimodel which can be fitted with a gaussian if $z$ is informative enough, and I have shown in this demo, x can be quite informative.\n", "\n", "For reference: arXiv:1511.05440, arXiv:1702.08658" ] } ], "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 }
mit
turbomanage/training-data-analyst
blogs/goes16/maria/hurricanes2017.ipynb
2
213981
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 2017 Hurricane Tracks\n", "\n", "Demonstrates how to plot all the North American hurricane tracks in 2017, starting from the BigQuery public dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%bash\n", "apt-get update\n", "apt-get -y install python-mpltoolkits.basemap " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from mpl_toolkits.basemap import Basemap\n", "import google.datalab.bigquery as bq\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "query=\"\"\"\n", "#standardSQL\n", "SELECT\n", " name,\n", " latitude,\n", " longitude,\n", " iso_time,\n", " usa_sshs\n", "FROM\n", " `bigquery-public-data.noaa_hurricanes.hurricanes`\n", "WHERE\n", " basin = 'NA'\n", " AND season = '2017'\n", "\"\"\"\n", "\n", "df = bq.Query(query).execute().result().to_dataframe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>latitude</th>\n", " <th>longitude</th>\n", " <th>iso_time</th>\n", " <th>usa_sshs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>DON</td>\n", " <td>11.0</td>\n", " <td>-52.3</td>\n", " <td>2017-07-17 18:00:00</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>DON</td>\n", " <td>11.4</td>\n", " <td>-57.0</td>\n", " <td>2017-07-18 12:00:00</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>DON</td>\n", " <td>11.2</td>\n", " <td>-53.8</td>\n", " <td>2017-07-18 00:00:00</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>DON</td>\n", " <td>11.9</td>\n", " <td>-61.4</td>\n", " <td>2017-07-19 00:00:00</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>DON</td>\n", " <td>11.6</td>\n", " <td>-58.9</td>\n", " <td>2017-07-18 18:00:00</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name latitude longitude iso_time usa_sshs\n", "0 DON 11.0 -52.3 2017-07-17 18:00:00 0\n", "1 DON 11.4 -57.0 2017-07-18 12:00:00 0\n", "2 DON 11.2 -53.8 2017-07-18 00:00:00 0\n", "3 DON 11.9 -61.4 2017-07-19 00:00:00 0\n", "4 DON 11.6 -58.9 2017-07-18 18:00:00 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot one of the hurricanes\n", "\n", "Let's just plot the track of Hurricane MARIA" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "maria = df[df['name'] == 'MARIA'].sort_values('iso_time')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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unQWDOzYk8a/57B01hP95ujNOR4dPjI1Ji48v9Th5cjltJkwA1NVKAKacOVNo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\nKDm5TJwuBYVZBrxMYWYkJFCYmyssx1ZxFrg0oe9Pi3B5ms1j35dfcGzxE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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bd47a450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,\n", " projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,\n", " resolution ='l',area_thresh=1000.)\n", "x, y = m(maria['longitude'].values,maria['latitude'].values)\n", "m.plot(x,y,linewidth=5,color='r')\n", "\n", "# draw coastlines, meridians and parallels.\n", "m.drawcoastlines()\n", "m.drawcountries()\n", "m.drawmapboundary(fill_color='#99ffff')\n", "m.fillcontinents(color='#cc9966',lake_color='#99ffff')\n", "m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])\n", "m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])\n", "plt.title('Hurricane Maria (2017)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot all the hurricanes\n", "\n", "Use line thickness based on the maximum category reached by the hurricane" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['DON', 'LEE', 'BRET', 'GERT', 'IRMA', 'JOSE', 'NATE', 'RINA',\n", " 'CINDY', 'EMILY', 'KATIA', 'MARIA', 'ARLENE', 'HARVEY', 'OPHELIA',\n", " 'FRANKLIN', 'PHILIPPE', 'NOT_NAMED'], dtype=object)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names = df.name.unique()\n", "names" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YnuOLFpb5DzYjKYl/R4/m6a1bjBSLWfVihaQw4p88wcREv0xleJ/sCbqHz7SN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\n5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ZuqVWk8YIDKNq9z58hIK36VqGQNzrZFZTi+AkIegn0dpVeVSXlYvwTdPczH2XLG3HoX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wb+YS9s1cVorFj7UAJWIxGbFnObxsOa6OlgR6WHFuzY9sHDcOgB9u3kTfwgIlJSX5tHzNb2Jq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\nh6+vL1euXEEkEnH+/Hk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"text/plain": [ "<matplotlib.figure.Figure at 0x7f44bc5c2f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,\n", " projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,\n", " resolution ='l',area_thresh=1000.)\n", "\n", "for name in names:\n", " if name != 'NOT_NAMED':\n", " named = df[df['name'] == name].sort_values('iso_time')\n", " x, y = m(named['longitude'].values,named['latitude'].values)\n", " maxcat = max(named['usa_sshs'])\n", " m.plot(x,y,linewidth=maxcat,color='b')\n", " \n", "# draw coastlines, meridians and parallels.\n", "m.drawcoastlines()\n", "m.drawcountries()\n", "m.drawmapboundary(fill_color='#99ffff')\n", "m.fillcontinents(color='#cc9966',lake_color='#99ffff')\n", "m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])\n", "m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])\n", "plt.title('Named North-Atlantic hurricanes (2017)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
thatguyandy27/python-sandbox
l3/Lecture8 - Using Arrays and Scalars.ipynb
1
3793
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5/2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5/2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr1 = np.array([[1,2,3,4],[8,9,10, 11]])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 3, 4],\n", " [ 8, 9, 10, 11]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 4, 9, 16],\n", " [ 64, 81, 100, 121]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ " arr1 * arr1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, 0],\n", " [0, 0, 0, 0]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr1 - arr1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.5 , 0.33333333, 0.25 ],\n", " [ 0.125 , 0.11111111, 0.1 , 0.09090909]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 / arr1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 8, 27, 64],\n", " [ 512, 729, 1000, 1331]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr1 ** 3" ] }, { "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
Ledoux/ShareYourSystem
Pythonlogy/ShareYourSystem/Readme.ipynb
1
1990
{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": [ "#ShareYourSystem\n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##More Descriptions at the level of the class\n\nSpecial attributes of the ShareYourSystemClass are :\n", "cell_type": "markdown", "prompt_number": 0, "metadata": {} }, { "source": "```python\n\n\n\n#ImportModules\nimport ShareYourSystem as SYS\nimport ShareYourSystem\n\t\t\n#Definition the AttestedStr\nSYS._attest(\n\t[\n\t\t'DefaultAttributeItemTuplesList is '+SYS._str(\n\t\t\tShareYourSystem.ShareYourSystemClass.DefaultAttributeItemTuplesList,\n\t\t\t**{'RepresentingAlineaIsBool':False}\n\t\t)\n\t]\n) \n\n#Print\nprint(AttestedStr)\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "```console\n>>>\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##More Descriptions at the level of the instances\n\nA default call of an instance gives :\n", "cell_type": "markdown", "prompt_number": 2, "metadata": {} }, { "source": "```python\n\n\n\n#ImportModules\nfrom ShareYourSystem.Standards.Classors import Attester\nimport ShareYourSystem\n\t\t\n#Definition the AttestedStr\nSYS._attest(\n\t[\n\t\tShareYourSystem.ShareYourSystemClass()\n\t]\n) \n\n#Print\nprint(AttestedStr)\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "```console\n>>>\n\n\n```\n", "cell_type": "markdown", "metadata": {} } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }
mit
jorgehatccrma/pyGrFNN
notebooks/utils/Oscillator Regimes.ipynb
2
25139
{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib2tikz import save as tikz_save" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# colors = ['b', 'r', 'g', 'c', 'm', 'k']\n", "\n", "# Have a look at the colormaps here and decide which one you'd like:\n", "# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\n", "# colormap = plt.cm.winter\n", "# colormap = plt.cm.autumn\n", "# colormap = plt.cm.summer\n", "# colormap = plt.cm.ocean\n", "colormap = plt.cm.gist_heat\n", "N_COLORS = 10\n", "colors = [colormap(i) for i in np.linspace(0, 0.7, N_COLORS)]\n", "\n", "\n", "# # OR \n", "# import matplotlib as mpl # in python\n", "\n", "# # custom colormap generated with http://colormap.org/\n", "# C = np.array([[255,124,0],[254,123,1],[253,123,1],[252,123,2],[251,123,2],[250,123,3],[249,122,3],[248,122,4],[246,122,4],[245,122,5],[244,122,5],[243,122,6],[242,121,7],[241,121,7],[240,121,8],[239,121,8],[238,121,9],[237,120,9],[236,120,10],[235,120,10],[234,120,11],[233,120,11],[232,119,12],[230,119,13],[229,119,13],[228,119,14],[227,119,14],[226,118,15],[225,118,15],[224,118,16],[223,118,16],[222,118,17],[221,117,17],[220,117,18],[219,117,19],[218,117,19],[217,117,20],[216,116,20],[214,116,21],[213,116,21],[212,116,22],[211,116,22],[210,115,23],[209,115,23],[208,115,24],[207,115,25],[206,115,25],[205,115,26],[204,114,26],[203,114,27],[202,114,27],[201,114,28],[200,114,28],[198,113,29],[197,113,29],[196,113,30],[195,113,31],[194,113,31],[193,112,32],[192,112,32],[191,112,33],[190,112,33],[189,112,34],[188,111,34],[187,111,35],[185,112,36],[184,112,38],[183,113,39],[181,113,40],[180,113,42],[178,114,43],[177,114,44],[176,115,46],[174,115,47],[173,115,48],[171,116,50],[170,116,51],[169,117,52],[167,117,54],[166,117,55],[165,118,56],[163,118,58],[162,119,59],[160,119,60],[159,119,62],[158,120,63],[156,120,64],[155,121,66],[153,121,67],[152,121,68],[151,122,70],[149,122,71],[148,123,72],[147,123,74],[145,123,75],[144,124,76],[142,124,78],[141,125,79],[140,125,80],[138,125,82],[137,126,83],[136,126,84],[134,127,86],[133,127,87],[131,127,88],[130,128,90],[129,128,91],[127,129,92],[126,129,94],[124,129,95],[123,130,96],[122,130,98],[120,131,99],[119,131,100],[118,131,102],[116,132,103],[115,132,104],[113,133,106],[112,133,107],[111,133,108],[109,134,110],[108,134,111],[107,135,112],[105,135,114],[104,135,115],[102,136,116],[101,136,118],[100,137,119],[98,137,120],[97,137,121],[96,137,122],[95,137,123],[94,138,124],[94,138,125],[93,138,126],[92,138,127],[91,138,128],[90,138,129],[89,139,130],[88,139,131],[87,139,132],[86,139,133],[85,139,134],[84,139,135],[83,140,136],[82,140,137],[81,140,137],[80,140,138],[79,140,139],[78,140,140],[77,141,141],[76,141,142],[75,141,143],[74,141,144],[73,141,145],[72,141,146],[71,142,147],[70,142,148],[69,142,149],[68,142,150],[67,142,151],[66,142,152],[65,143,153],[64,143,154],[63,143,155],[62,143,156],[61,143,157],[60,143,158],[59,144,159],[58,144,160],[57,144,160],[56,144,161],[55,144,162],[54,144,163],[53,145,164],[52,145,165],[51,145,166],[50,145,167],[49,145,168],[48,146,169],[47,146,170],[46,146,171],[45,146,172],[44,146,173],[43,146,174],[42,147,175],[41,147,176],[40,147,177],[40,147,178],[39,147,179],[38,147,180],[37,148,181],[36,147,182],[35,147,182],[35,146,183],[34,146,184],[34,145,185],[33,144,186],[32,144,187],[32,143,188],[31,143,189],[31,142,190],[30,142,191],[30,141,192],[29,140,192],[28,140,193],[28,139,194],[27,139,195],[27,138,196],[26,138,197],[26,137,198],[25,137,199],[24,136,200],[24,135,201],[23,135,202],[23,134,202],[22,134,203],[22,133,204],[21,133,205],[21,132,206],[20,132,207],[19,131,208],[19,130,209],[18,130,210],[18,129,211],[17,129,212],[17,128,212],[16,128,213],[15,127,214],[15,127,215],[14,126,216],[14,125,217],[13,125,218],[13,124,219],[12,124,220],[11,123,221],[11,123,222],[10,122,222],[10,122,223],[9,121,224],[9,120,225],[8,120,226],[7,119,227],[7,119,228],[6,118,229],[6,118,230],[5,117,231],[5,117,232],[4,116,232],[3,115,233],[3,115,234],[2,114,235],[2,114,236],[1,113,237],[1,113,238],[0,112,239]])\n", "# colormap = mpl.colors.ListedColormap(C/255)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "file encoding: None\n", "=========================================================\n", "Please add the following line to your LaTeX preamble:\n", "\n", "\\usepackage{pgfplots}\n", "\\usepgfplotslibrary{plotmarks}\n", "=========================================================\n" ] }, { "data": { "image/png": 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dDCMHwaYDYUQDjN4x1IS5BQa8Die/r5NpO3zwp8K6Zljf4v66/+uaoK4Z6puh\noQnqm6ChGRqbNJqaoakFWvweWh4eeAOgqgqq3N+NqmCgG4OqYOMBGoOrYMgAN/rDsMcyC4Jt1KKC\nHQHN4D8OH1ZB9XpYXgOfrIJl9e0rfe5CiccXh9gK+f4n0L6efjOKke9Pe8PIiI4xtNfGgZ1ZCU2t\nDAS+heyvQrCdGzN78yT5CPvzqLDRarR48AQdG2F8hKytgHGo+W4276Av7q5kuuaAfpzPkKnB/iJ9\nrAZ7ULQ8R0aUtwJu9+AoD/rNh7mnwm39Mo02tiJTi31TMmV4e0oDPWu0saHOR13RgharPgLeB95v\nhcW3wJc9+c1bX4CHKsAFNRg1iP4Wmd6ZPjJsNkHH4SHUiKOc32e5EzS4OQF9r1+bDe9UKfO4/zOv\nsnxQFaM8D++Wd3gYZf0W6/O6qCcPysfEH4FE/VfIkNoXWd6zUalL0AniLVSkvhpZoafSfvE0EAUf\nnRHDNdh3y3rNoAb7TBRP/DzRNiIoNfJaPHUujc3o2GgjaLaxSWgMp3099oEo6qc3BLXY66CtlVgN\nXTfa6MyFVCmLxhORC/J0MlmsK4E/I3fLIirjfVYSHpnmG/cBx38VbtwEzhwNPz/8BE4YMoCJu4/k\n0KrriloMrOg+9sko4+otaOvT+X7o/qPJhDveAFzSjQmOIVODfSoda7A3osy6J5HQP4tlgBYctwYw\nkIwFnk+jjQb0+TSVUJRJKbAJ8GXg68jlEvA8arLxD6xhTKmzNSohUQuMSslivxW4f3qClcBXgW94\naa4r4hxKPky8Oz/6zVAFvT+gBbFW2rsDmpDQ/wZVZNu0oDM1jJ7RH4X8/h2JdvB9XQ1ciRbljPLi\nNfQZTk3BTilVfPzYT3Chn8D3E1xa5NfvkbFUqkXAlqEFpDvd7U3JZAx+mkxBnv1RAgcoASiIUX4a\nhQ4aRrHxkGCfjpJWwjH0T6A45zuxMtHlysPIg3Akin9YA2yxbBXLN1Pg8s7xTa00KORl+jBUsOkX\nyD1TT3uL3kfCfguK5tmb9pEHhtFbdkZrTG/Q/nv3Dmo5t31sMzMKyZHoc50NkILHUuA/siffcxZ7\nsTtRlbx7s5gTHIjK8v4EpVyvoqPQr0Vn35+jZJAROZ/JMDpnN7Sg9jLtv1vLkd/8YErcH2p0m8HI\ncGwFNkvBpSnwL9+YS5yw1/uJjvknBaSiXDHdpQGFSz6Donf6oazEKejHdgiyoA53A3TA3kSLWUHD\njTfpA/G40yNRAAAM1klEQVT0Rt70Q8WeTkCNNsLRW2uBfwK3oRrd2SHARmWwHgVsHI5KPcwBWFfH\nBBS7PoaOgSSxUynCnk0rmeJb17htW6LSBwchsZ+ExH934Gy3Ty2KoZ/l/r6AYqtL/nLIKBjD0QJo\n0GQjnOy6GtVkvwN4lD6Wa9GHeQ4J+76o/ynIFbcACft4TNhjo5r2C7IDke89EPsDUKelQ90IWIJE\nfg7ys82hdOuOGN2nHyrgdoQbh9A+2WoxmSYbT2GWeV8kaAA+CblyAXZo9Xmun8dkJOyPxjKzTuhL\nwp5NA7LMZ6H4e9DZ9wAyETf7IYvt824EVKNi/C8hf+tcdMY2y7708ZBLJWiyMY324bKtKKrqAbRe\nYwlDxhz3d1IS1qf0+99ybQ3LRqjdyfjYZtYJfVnYc7EUlRu+1932gB3JNNrYF4VaBuUQjg09djUq\nBBQ02ngFRUzURjFxo1MGIov8YBQqO5lQ7R3HYuDfbjyO9bo12lONSl6MRXqwANjygyU0mLCXJ0H4\n2jtokQx06b4jmUYbeyPh2AwJx6eznuM92jfaeANl71rmbOHph3yfwUn4APQZbZS138coxjwY72FW\nubFh5iBDbl8k7FPfXcyAPRXFbsJeAbSiD3YBKj8MsuzH0r7Rxp6o2NkObhyX9TyLUXXLt4C3Q8+5\nGBXeMjbMcJQ4sgc6uU50I1cBtDfQAtjTKMJhISbkRveYTXth570P2/rO7uAn6O+lSyeizoS9MAQV\n+qpRdb6AKnQ2Dzfa2N1t28aNI7KeqxkVhXoXWZIL3e1FSPSXUT61xnvLABRKtiOyxIOyvLvTvppo\nmA/INNmYjSKbVhd9pkal0+ZnR6VOqG9ge/R73Aytz1XHM7WOmLAXlyYy7pcwA1Bc/W5kGm2Md2Mr\nYCc3ctGIxOsjOjba+BitEyxD1QNL+QTQH/m6x5JZswgabWyDalJvS+ff0QZ0xfM6WsAOmm18UsxJ\nG32WNmFvUglf0O+1Ggn7lpiw93maybhestkYif6O7m8wtkXCN8rdt2MXr9GKuu+sQCK/Clmua9xY\nixZ2a5G/vw5l2NXRs3rsA1GWXjCGIXfJcNQOMFw+eHPUJDifjL2P0JXL22TcVvPRFY25rIyo+BgX\nDXMtqlMCbO/7POJ5TCR3n93YMGEvPerIbeUHDEECHzTZ2BqFZAbNNjZHFsRIMjXZS5VVZFxY1WQa\nbSx2YyFW2tYoHWYDx1XLtfoBMG59PTVD1P/LhN3oFeuQxTq/i/2qkHWcb6ONQW5sTKYWez712BtD\nYx2ZZhtBk41cjTaWovoqluxjlBNzUBDEJHS1PW71WppN2I0oaUJ+d8uSNYzCEGSg7ovqSh22bBX9\ntxoDlJiwF7MqmWEYRiURLKDu06rcFj5ZwRC3zYTdMAyjDFkKfAgMXegSDJetbMtiNmE3DMMoU+YA\nzHaJcKvWtAm6CbthGEaZMhtgviLTWmvWs7XvqwmHn+hQuiI2TNgNwzDy52WARmU/L/Z9+rX6rHD3\nbdH5w6LFhN0wDCN/3nJ/x+MSDBsbWeO2lYw7xoTdMAwjfxahXI5xLcqIZl1dWyctE3bDMIwypAll\nRHvLXN3+1TVtDcwrSth/g4L15wF3oaxGwzCMSkVle13m9Ko1DHLbK0rYH0blaCeiAk0/LcBzGoZh\nlCoLAOar8B0r1zDCba8oYX+ETHnYWagolWEYRqWyAOB91WBqWb22rWduRQl7mLNRE2DDMIxKZQFA\nsyJjPq5Z37a9ZIQ93yJgj5A7RnM6cJ/7P4kq/N1SgHkZhmGUKkEfhfHAh7Xr2rwUZSfsh3dx/1nA\nMcBnuthvRuj/mW4YhmGUE4vRwumWLTBnfT34Ps2ex0g/wcZeulc9BKa50SsKUbb3KOACYCrqwLMh\nZhTg9QzDMOKkGcWw71ID60cAjU3UDNyIkajZzXu9eO6ZtDd4L+rJkxTCx34lKojzCDAXuLoAz2kY\nhlHKLABY4QJH1te3Wekl4Y4phMU+vgDPYRiGUU4sAFgCG+0I1K6neeRwoESE3TJPDcMwuk8g7MMA\n1tS0aakJu2EYRpmyAGAZjAZYU9NWsteE3TAMo0wJfOxbA6xeq8YbmLAbhmGULR8ADethcx8a19Qy\n2G03YTcMwyhTWoF3AVpgeW2JZZ+asBuGYfSMBQB1UFu7rm2bCbthGEYZswCgBprqG6G1lSZgmJ9Q\npEycmLAbhmH0jAUAK52ONjSx1m0fG9uMHCbshmEYPSOIjBkMUFfXVlIldneMCbthGEbPWACwXHXZ\nqVlPi9u+VWwzcpiwG4Zh9IxqoH61yz6tWddWomVUfFMSJuyGYRg9oxX4qMbdWLe+rffppp3sHxkm\n7IZhGD2nOlgxrV3fln06Mq7JBJiwG4Zh9JzqBqAFGtbXt9WLMYvdMAyjjKkGaICa+oa2bWaxG4Zh\nlDHVAOugoS4j7GaxG4ZhlDHVAGuhxSx2wzCMyqAaYDX0N4vdMAyjMqgGWAmDQhb7pn4CL7YZYcJu\nGIbRGz4GWAXDW1qguYUW1Et6SJyTMmE3DMPoOTVAzRqoAmhobCsrEKs7xoTdMAyjd1QH2ad19W0u\nmFgXUE3YDcMwekebsK+vb6sXYxa7YRhGGVPdCDRDnVnshmEYlUGQfVpbXyIhj4UU9h+iamexx3Aa\nhmFESDVAbQllnxZK2McBhwPvF+j5DMMwyoVqgDXQWirZp4US9t8DPy7QcxmGYZQT1QA1sFEluWKO\nBz4EXinAcxmGYZQbgbBvXCoW+4CudwHgEWCLHNuTwE+BI0LbNpRKOyP0/0w3DMMwypmPAWphaAF8\n7NPciJU9gKXAQjeagEXA5jn29aOblmEYRqSsnAj+DaPx/QS+n2BOgZ63R7qZr8XeGa8BY0K3FwKT\ngJW9fF7DMIxyoroORtY3tt0uex97GLPKDcPoi1TXAfX1bbcrSth3wKx1wzD6HtXrgfpG8GXeDvcT\nvfaI9BjLPDUMw+g91XXun/rGNs/FiLgmY8JuGIbRe6oDL0wp1IsxYTcMw+g91a1AIzSXQpKSCbth\nGEbvqQaoL5GyAibshmEYvacaYD14ZrEbhmFUBksAaqGqFCo8mrAbhmH0nkZgWR1grhjDMIzKoboO\nMIu97zIt7gmUENPinkAJMS3uCZQQ0+KeQA+oNou9bzMt7gmUENPinkAJMS3uCZQQ0+KeQA9YkiXs\nZrEbhmGUOSuyXDFmsRuGYZQ5K9dTGoXANtQUo9C8DEyM8PUMwzDKnXnA3nFPwjAMwzAMwzAMwzAM\nwzAMwzCMnnEUMB9YAPx3J/v8wd0/D/hURPOKg66OxVfQMXgFeAbYK7qpRU4+3wuA/YBm4KQoJhUT\n+RyLacBc1FN4ZiSzioeujsUmwH0o8OI14KzIZhYtfwaWAq9uYJ/YdLM/8A6wHVCFPozdsvY5BnjA\n/X8A8HxUk4uYfI7FQeiLC/qC9+VjEez3OHA/8IWoJhcx+RyLEcDrwNbu9uioJhcx+RyL6cAl7v/R\nwAqIr91cEZmCxLozYe+2bhYyjn1/9EEtApqA24Djs/Y5DrjJ/T8LfYnHFHAOpUI+x+I5YI37fxaZ\nH3Klkc+xADgfuANYFtnMoie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"text/plain": [ "<matplotlib.figure.Figure at 0x10f821050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import division\n", "import numpy as np\n", "from numpy.polynomial.polynomial import polyroots\n", "\n", "\n", "\n", "# define regimes (alpha, beta_1, beta_2, epsilon) (delta_1 = delta_2 = 0 always)\n", "\n", "\n", "regimes = {'Linear': (-5.0, 0, 0), # linear\n", " 'Critical': (0, -25.0, 0), # critical\n", " 'Sub-Critical': (-5.0, -5.0, 0), # sub-critical\n", " 'Super-Critical': (5, -15.0, 0), # super-critical (limit cycle)\n", "# 'Limit-Cycle': (0, -1.0, -1.0), # limit-cycle\n", " 'Sub-Critical Double Limit-Cycle': (-5.0, 10.0, -2.0), # double limit-cycle\n", " 'Super-Critical Double Limit-Cycle': (-5.0, 15.0, -2.0), # double limit-cycle\n", " }\n", "\n", "# F = np.arange(0, 2, 0.2)\n", "# F = [1]\n", "\n", "F = [0, 1, 2]\n", "\n", "epsilon = 1.0\n", "\n", "\n", "colors = [colormap(i) for i in np.linspace(0, 0.7, len(F))]\n", "\n", "plot_roots = True\n", "\n", "for regime in regimes:\n", " \n", " alpha, beta1, beta2 = regimes[regime]\n", " \n", " r = np.arange(0, 1/np.sqrt(epsilon), 0.01)\n", " rdot = np.add.outer(alpha * r + beta1 * r**3 + ((epsilon* beta2 * r**5)/(1 - epsilon * r**2)), F)\n", "\n", " plt.figure(figsize=(6,3))\n", " ax = plt.gca()\n", " ax.set_color_cycle(colors)\n", " plt.plot(r, rdot, zorder=0, linewidth=2)\n", "# plt.plot(r, rdot, zorder=0, color=PLOT_COLOR, linewidth=3)\n", "\n", " # assymptote\n", " # plt.vlines(x=1/np.sqrt(epsilon), ymin=-1, ymax=2, color='r', linestyle=':')\n", " plt.ylim(-5,5)\n", " ax.axhline(y=0,xmin=min(r),xmax=max(r),c=\"k\",zorder=5, alpha=0.5)\n", " # plt.grid(True)\n", "# plt.title(r'{}: $\\alpha={:.3g}, \\; \\beta_1={:.3g}, \\; \\beta_2={:.3g}, \\; \\varepsilon={:.3g}$'.format(regime, alpha, beta1, beta2, epsilon))\n", " plt.title(r'$\\alpha={:.3g}, \\; \\beta_1={:.3g}, \\; \\beta_2={:.3g}$'.format(alpha, beta1, beta2), y=1.0) \n", " \n", " plt.xlabel(r'$r$')\n", " plt.ylabel(r'$\\dot{r}$', labelpad=-10)\n", " \n", " if plot_roots:\n", " # find roots\n", " roots = [None] * len(F)\n", " for i in xrange(len(F)):\n", " # r = np.roots([epsilon*(beta2-beta1), 0, beta1-epsilon*alpha, -epsilon*F[i], alpha, F[i]])\n", " r = polyroots([F[i], alpha, -epsilon*F[i], beta1-epsilon*alpha, 0, epsilon*(beta2-beta1)])\n", " r = np.real(r[np.abs(np.imag(r)) < 1e-20])\n", " r = r[(r>=0) & (r < 1/np.sqrt(epsilon))]\n", " roots[i] = r\n", " # print roots\n", "\n", "# plt.gca().set_color_cycle(colors)\n", "\n", " for r in roots:\n", " plt.plot(r, np.zeros_like(r), 'o', markersize=4, zorder=10)\n", "# plt.plot(r, np.zeros_like(r), 'o', markersize=4, zorder=10, color='white')\n", " \n", "# sanitizedName = regime.replace(' ','').replace('-','')\n", "# tikz_save('/Users/jorgeh/Documents/CCRMA/research/ismir2015/frameworkPaper/figs/regimes/{}.tikz'.format(sanitizedName), figureheight='1.5cm', figurewidth='3cm')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
adityaka/misc_scripts
python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_05/Final/.ipynb_checkpoints/Annotations-checkpoint.ipynb
1
4054
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Plot Annotations</h1>" ] }, { "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", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "number_of_data_points = 10\n", "\n", "my_figure = plt.figure()\n", "subplot_1 = my_figure.add_subplot(1, 1, 1)\n", "subplot_1.plot(np.random.rand(number_of_data_points).cumsum())\n", "\n", "subplot_1.text (1, 0.5, r'an equation: $E=mc^2$', fontsize=18, color='red')\n", "subplot_1.text (1, 1.5, \"Hello, Mountain Climbing!\", family='monospace', fontsize=14, color='green')\n", "\n", "# see: http://matplotlib.org/users/transforms_tutorial.html\n", "# transform=subplot_1.transAxes; entire axis between zero and one\n", "subplot_1.text(0.5, 0.5, \"We are centered, now\", transform=subplot_1.transAxes)\n", "\n", "subplot_1.annotate('shoot arrow', xy=(2, 1), xytext=(3, 4),\n", " arrowprops=dict(facecolor='red', shrink=0.05))\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.arange(0, 10, 0.005)\n", "y = np.exp(-x/2.) * np.sin(2*np.pi*x)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(x, y)\n", "ax.set_xlim(0, 10)\n", "ax.set_ylim(-1, 1)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "x = np.arange(0, 10, 0.005)\n", "y = np.exp(-x/2.) * np.sin(2*np.pi*x)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(x, y)\n", "ax.set_xlim(0, 10)\n", "ax.set_ylim(-1, 1)\n", "\n", "xdata, ydata = 5, 0\n", "xdisplay, ydisplay = ax.transData.transform_point((xdata, ydata))\n", "\n", "bbox = dict(boxstyle=\"round\", fc=\"0.8\")\n", "arrowprops = dict(\n", " arrowstyle = \"->\",\n", " connectionstyle = \"angle,angleA=0,angleB=90,rad=10\")\n", "\n", "offset = 72\n", "ax.annotate('data = (%.1f, %.1f)'%(xdata, ydata),\n", " (xdata, ydata), xytext=(-2*offset, offset), textcoords='offset points',\n", " bbox=bbox, arrowprops=arrowprops)\n", "\n", "\n", "disp = ax.annotate('display = (%.1f, %.1f)'%(xdisplay, ydisplay),\n", " (xdisplay, ydisplay), xytext=(0.5*offset, -offset),\n", " xycoords='figure pixels',\n", " textcoords='offset points',\n", " bbox=bbox, arrowprops=arrowprops)\n", "\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig = plt.figure()\n", "for i, label in enumerate(('A', 'B', 'C', 'D')):\n", " ax = fig.add_subplot(2,2,i+1)\n", " ax.text(0.05, 0.95, label, transform=ax.transAxes,\n", " fontsize=16, fontweight='bold', va='top')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "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 }
bsd-3-clause
probml/pyprobml
notebooks/misc/linreg_divorce_numpyro.ipynb
1
93188
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "linreg_divorce_numpyro.ipynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyMAtUs/BjAM8bCfZRQ7MotY", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/linreg_divorce_numpyro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "xuUCwM8u7d_C" }, "source": [ "# Robust linear regression \n", "\n", "We illustrate linear using the \"waffle divorce\" example in sec 5.1 of [Statistical Rethinking ed 2](https://xcelab.net/rm/statistical-rethinking/). \n", "The numpyro code is from [Du Phan's site](https://fehiepsi.github.io/rethinking-numpyro/05-the-many-variables-and-the-spurious-waffles.html)\n", " \n", "\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zrZ4gm-47aCF", "outputId": "4879c72d-2ba1-4eaa-d441-0aa3acaf3ad6" }, "source": [ "!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro\n", "!pip install -q arviz" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ " Building wheel for numpyro (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[K |████████████████████████████████| 1.6MB 15.0MB/s \n", "\u001b[K |████████████████████████████████| 808kB 35.4MB/s \n", "\u001b[K |████████████████████████████████| 4.7MB 36.1MB/s \n", "\u001b[K |████████████████████████████████| 317kB 36.7MB/s \n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ae7e0Ef671cl", "outputId": "952dcffc-4918-4058-da4b-e7bf63815cb2" }, "source": [ "import numpy as np\n", "\n", "np.set_printoptions(precision=3)\n", "import matplotlib.pyplot as plt\n", "import math\n", "import os\n", "import warnings\n", "import pandas as pd\n", "\n", "import jax\n", "\n", "print(\"jax version {}\".format(jax.__version__))\n", "print(\"jax backend {}\".format(jax.lib.xla_bridge.get_backend().platform))\n", "\n", "import jax.numpy as jnp\n", "from jax import random, vmap\n", "\n", "rng_key = random.PRNGKey(0)\n", "rng_key, rng_key_ = random.split(rng_key)\n", "\n", "import numpyro\n", "import numpyro.distributions as dist\n", "from numpyro.distributions import constraints\n", "from numpyro.distributions.transforms import AffineTransform\n", "from numpyro.diagnostics import hpdi, print_summary\n", "from numpyro.infer import Predictive, log_likelihood\n", "from numpyro.infer import MCMC, NUTS\n", "from numpyro.infer import SVI, Trace_ELBO, init_to_value\n", "from numpyro.infer.autoguide import AutoLaplaceApproximation\n", "import numpyro.optim as optim\n", "\n", "\n", "import arviz as az" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "jax version 0.2.12\n", "jax backend cpu\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "MyKMMdN_7yEh" }, "source": [ "# Data\n", "\n", "The data records the divorce rate $D$, marriage rate $M$, and average age $A$ that people get married at for 50 US states." ] }, { "cell_type": "code", "metadata": { "id": "avu-Z75e7zBK" }, "source": [ "# load data and copy\n", "url = \"https://raw.githubusercontent.com/fehiepsi/rethinking-numpyro/master/data/WaffleDivorce.csv\"\n", "WaffleDivorce = pd.read_csv(url, sep=\";\")\n", "d = WaffleDivorce\n", "\n", "# standardize variables\n", "d[\"A\"] = d.MedianAgeMarriage.pipe(lambda x: (x - x.mean()) / x.std())\n", "d[\"D\"] = d.Divorce.pipe(lambda x: (x - x.mean()) / x.std())\n", "d[\"M\"] = d.Marriage.pipe(lambda x: (x - x.mean()) / x.std())" ], "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Zycpf_NB8WNA" }, "source": [ "# Model (Gaussian likelihood)\n", "\n", "We predict divorce rate D given marriage rate M and age A." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D2jggu2v8XOU", "outputId": "6974503e-0a27-466e-9668-881e0f96a2ad" }, "source": [ "def model(M, A, D=None):\n", " a = numpyro.sample(\"a\", dist.Normal(0, 0.2))\n", " bM = numpyro.sample(\"bM\", dist.Normal(0, 0.5))\n", " bA = numpyro.sample(\"bA\", dist.Normal(0, 0.5))\n", " sigma = numpyro.sample(\"sigma\", dist.Exponential(1))\n", " mu = numpyro.deterministic(\"mu\", a + bM * M + bA * A)\n", " numpyro.sample(\"D\", dist.Normal(mu, sigma), obs=D)\n", "\n", "\n", "m5_3 = AutoLaplaceApproximation(model)\n", "svi = SVI(model, m5_3, optim.Adam(1), Trace_ELBO(), M=d.M.values, A=d.A.values, D=d.D.values)\n", "p5_3, losses = svi.run(random.PRNGKey(0), 1000)\n", "post = m5_3.sample_posterior(random.PRNGKey(1), p5_3, (1000,))" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 718.29it/s, init loss: 3201.7393, avg. loss [951-1000]: 60.7879]\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "omQy9sZf9M9q", "outputId": "a8b98472-a44f-4b34-f461-a4c7bd431b13" }, "source": [ "param_names = {\"a\", \"bA\", \"bM\", \"sigma\"}\n", "for p in param_names:\n", " print(f\"posterior for {p}\")\n", " print_summary(post[p], 0.95, False)" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "text": [ "posterior for bM\n", "\n", " mean std median 2.5% 97.5% n_eff r_hat\n", " Param:0 -0.06 0.16 -0.06 -0.38 0.24 984.99 1.00\n", "\n", "posterior for bA\n", "\n", " mean std median 2.5% 97.5% n_eff r_hat\n", " Param:0 -0.61 0.16 -0.61 -0.90 -0.30 822.38 1.00\n", "\n", "posterior for sigma\n", "\n", " mean std median 2.5% 97.5% n_eff r_hat\n", " Param:0 0.80 0.08 0.79 0.65 0.94 971.25 1.00\n", "\n", "posterior for a\n", "\n", " mean std median 2.5% 97.5% n_eff r_hat\n", " Param:0 -0.00 0.10 -0.01 -0.19 0.17 1049.96 1.00\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "w-2Y2AY-91OU" }, "source": [ "# Posterior predicted vs actual" ] }, { "cell_type": "code", "metadata": { "id": "OgH6toqX9NyV" }, "source": [ "# call predictive without specifying new data\n", "# so it uses original data\n", "post = m5_3.sample_posterior(random.PRNGKey(1), p5_3, (int(1e4),))\n", "post_pred = Predictive(m5_3.model, post)(random.PRNGKey(2), M=d.M.values, A=d.A.values)\n", "mu = post_pred[\"mu\"]\n", "\n", "# summarize samples across cases\n", "mu_mean = jnp.mean(mu, 0)\n", "mu_PI = jnp.percentile(mu, q=(5.5, 94.5), axis=0)" ], "execution_count": 46, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 278 }, "id": "pmxVjQGg-omZ", "outputId": "64201dd2-55e7-411f-d8fa-67a7291f7701" }, "source": [ "ax = plt.subplot(ylim=(float(mu_PI.min()), float(mu_PI.max())), xlabel=\"Observed divorce\", ylabel=\"Predicted divorce\")\n", "plt.plot(d.D, mu_mean, \"o\")\n", "x = jnp.linspace(mu_PI.min(), mu_PI.max(), 101)\n", "plt.plot(x, x, \"--\")\n", "for i in range(d.shape[0]):\n", " plt.plot([d.D[i]] * 2, mu_PI[:, i], \"b\")\n", "fig = plt.gcf()" ], "execution_count": 47, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 295 }, "id": "C5LQYmJ7-owr", "outputId": "04afba64-94ae-483e-9399-5a010d98aedf" }, "source": [ "for i in range(d.shape[0]):\n", " if d.Loc[i] in [\"ID\", \"UT\", \"AR\", \"ME\"]:\n", " ax.annotate(d.Loc[i], (d.D[i], mu_mean[i]), xytext=(-25, -5), textcoords=\"offset pixels\")\n", "plt.tight_layout()\n", "plt.savefig(\"linreg_divorce_postpred.pdf\")\n", "plt.show()\n", "fig" ], "execution_count": 48, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": { "tags": [] } }, { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] }, "execution_count": 48 } ] }, { "cell_type": "markdown", "metadata": { "id": "lT7ATLTm_nrM" }, "source": [ "# Per-point LOO scores\n", "\n", "We compute the predicted probability of each point given the others, following\n", "sec 7.5.2 of [Statistical Rethinking ed 2](https://xcelab.net/rm/statistical-rethinking/). \n", "The numpyro code is from [Du Phan's site](https://fehiepsi.github.io/rethinking-numpyro/07-ulysses-compass.html)\n", " \n" ] }, { "cell_type": "code", "metadata": { "id": "AhXUMAg_-w2P" }, "source": [ "# post = m5_3.sample_posterior(random.PRNGKey(24071847), p5_3, (1000,))\n", "logprob = log_likelihood(m5_3.model, post, A=d.A.values, M=d.M.values, D=d.D.values)[\"D\"]\n", "az5_3 = az.from_dict(\n", " posterior={k: v[None, ...] for k, v in post.items()},\n", " log_likelihood={\"D\": logprob[None, ...]},\n", ")" ], "execution_count": 49, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 380 }, "id": "CtBDE7SMAcUU", "outputId": "6ac0092d-c99d-4b1c-fb27-082eab672cf7" }, "source": [ "PSIS_m5_3 = az.loo(az5_3, pointwise=True, scale=\"deviance\")\n", "WAIC_m5_3 = az.waic(az5_3, pointwise=True, scale=\"deviance\")\n", "penalty = az5_3.log_likelihood.stack(sample=(\"chain\", \"draw\")).var(dim=\"sample\")\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(PSIS_m5_3.pareto_k.values, penalty.D.values, \"o\", mfc=\"none\")\n", "ax.set_xlabel(\"PSIS Pareto k\")\n", "ax.set_ylabel(\"WAIC penalty\")\n", "\n", "plt.savefig(\"linreg_divorce_waic_vs_pareto.pdf\")\n", "plt.show()\n", "plt.show()" ], "execution_count": 61, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/arviz/stats/stats.py:656: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n", "/usr/local/lib/python3.7/dist-packages/arviz/stats/stats.py:1407: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " \"For one or more samples the posterior variance of the log predictive \"\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 327 }, "id": "GeHg_-JDA_KA", "outputId": "8044852c-06ac-45c7-d747-2a974f45ddb0" }, "source": [ "pareto = PSIS_m5_3.pareto_k.values\n", "waic = penalty.D.values\n", "ndx = np.where(pareto > 0.4)[0]\n", "for i in ndx:\n", " print(d.Loc[i], pareto[i], waic[i])\n", "\n", "\n", "for i in ndx:\n", " ax.annotate(d.Loc[i], (pareto[i], waic[i]), xytext=(5, 0), textcoords=\"offset pixels\")\n", "fig" ], "execution_count": 62, "outputs": [ { "output_type": "stream", "text": [ "ID 0.8625359073761971 2.032347\n", "ME 0.669250330617351 0.86887264\n", "ND 0.4184033659196669 0.22986734\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [] }, "execution_count": 62 } ] }, { "cell_type": "markdown", "metadata": { "id": "Lu8OOz-hFhIe" }, "source": [ "# Student likelihood" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wRTRc2hsBPUA", "outputId": "367f484e-b32a-432c-ba47-b034270680fa" }, "source": [ "def model(M, A, D=None):\n", " a = numpyro.sample(\"a\", dist.Normal(0, 0.2))\n", " bM = numpyro.sample(\"bM\", dist.Normal(0, 0.5))\n", " bA = numpyro.sample(\"bA\", dist.Normal(0, 0.5))\n", " sigma = numpyro.sample(\"sigma\", dist.Exponential(1))\n", " # mu = a + bM * M + bA * A\n", " mu = numpyro.deterministic(\"mu\", a + bM * M + bA * A)\n", " numpyro.sample(\"D\", dist.StudentT(2, mu, sigma), obs=D)\n", "\n", "\n", "m5_3t = AutoLaplaceApproximation(model)\n", "svi = SVI(model, m5_3t, optim.Adam(0.3), Trace_ELBO(), M=d.M.values, A=d.A.values, D=d.D.values)\n", "p5_3t, losses = svi.run(random.PRNGKey(0), 1000)" ], "execution_count": 55, "outputs": [ { "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:01<00:00, 677.59it/s, init loss: 194.5655, avg. loss [951-1000]: 63.3271]\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 278 }, "id": "64ZEbcnQFl0E", "outputId": "dfccdae3-5578-4ad4-dc80-22c16ae82844" }, "source": [ "# call predictive without specifying new data\n", "# so it uses original data\n", "post_t = m5_3t.sample_posterior(random.PRNGKey(1), p5_3t, (int(1e4),))\n", "post_pred_t = Predictive(m5_3t.model, post_t)(random.PRNGKey(2), M=d.M.values, A=d.A.values)\n", "mu = post_pred_t[\"mu\"]\n", "\n", "# summarize samples across cases\n", "mu_mean = jnp.mean(mu, 0)\n", "mu_PI = jnp.percentile(mu, q=(5.5, 94.5), axis=0)\n", "\n", "\n", "ax = plt.subplot(ylim=(float(mu_PI.min()), float(mu_PI.max())), xlabel=\"Observed divorce\", ylabel=\"Predicted divorce\")\n", "plt.plot(d.D, mu_mean, \"o\")\n", "x = jnp.linspace(mu_PI.min(), mu_PI.max(), 101)\n", "plt.plot(x, x, \"--\")\n", "for i in range(d.shape[0]):\n", " plt.plot([d.D[i]] * 2, mu_PI[:, i], \"b\")\n", "fig = plt.gcf()" ], "execution_count": 56, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] } ] }
mit
saketkc/notebooks
python/coursera-BayesianML/04_mcmc_assignment.ipynb
1
355126
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "colab": { "name": "03_mcmc_assignment.ipynb", "provenance": [], "collapsed_sections": [], "include_colab_link": true } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/saketkc/notebooks/blob/master/python/coursera-BayesianML/04_mcmc_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "hcUSgoTJgJbB", "colab_type": "text" }, "source": [ "# First things first\n", "Click **File -> Save a copy in Drive** and click **Open in new tab** in the pop-up window to save your progress in Google Drive." ] }, { "cell_type": "markdown", "metadata": { "id": "0h2KVFDRgJbE", "colab_type": "text" }, "source": [ "# Using PyMC3" ] }, { "cell_type": "markdown", "metadata": { "id": "ADumc2XsgJbF", "colab_type": "text" }, "source": [ "In this assignment, we will learn how to use a library for probabilistic programming and inference called <a href=\"http://docs.pymc.io/\">PyMC3</a>." ] }, { "cell_type": "markdown", "metadata": { "id": "9vgSpO5HgJbH", "colab_type": "text" }, "source": [ "### Setup\n", "Loading auxiliary files and importing the necessary libraries." ] }, { "cell_type": "code", "metadata": { "id": "4FSjyX0fMVN1", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 513 }, "outputId": "5428f46e-9243-418f-e728-6a2040d2fa9c" }, "source": [ "pip install -U pymc3 arviz \n" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Collecting pymc3\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/32/19/6c94cbadb287745ac38ff1197b9fadd66500b6b9c468e79099b110c6a2e9/pymc3-3.8-py3-none-any.whl (908kB)\n", "\u001b[K |████████████████████████████████| 911kB 2.8MB/s \n", "\u001b[?25hRequirement already up-to-date: arviz in /usr/local/lib/python3.6/dist-packages (0.8.3)\n", "Requirement already satisfied, skipping upgrade: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from pymc3) (1.4.1)\n", "Requirement already satisfied, skipping upgrade: numpy>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from pymc3) (1.18.4)\n", "Requirement already satisfied, skipping upgrade: tqdm>=4.8.4 in /usr/local/lib/python3.6/dist-packages (from pymc3) (4.41.1)\n", "Requirement already satisfied, skipping upgrade: theano>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from pymc3) (1.0.4)\n", "Requirement already satisfied, skipping upgrade: pandas>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from pymc3) (1.0.4)\n", "Requirement already satisfied, skipping upgrade: patsy>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from pymc3) (0.5.1)\n", "Requirement already satisfied, skipping upgrade: h5py>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from pymc3) (2.10.0)\n", "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from arviz) (20.4)\n", "Requirement already satisfied, skipping upgrade: xarray>=0.11 in /usr/local/lib/python3.6/dist-packages (from arviz) (0.15.1)\n", "Requirement already satisfied, skipping upgrade: netcdf4 in /usr/local/lib/python3.6/dist-packages (from arviz) (1.5.3)\n", "Requirement already satisfied, skipping upgrade: matplotlib>=3.0 in /usr/local/lib/python3.6/dist-packages (from arviz) (3.2.1)\n", "Requirement already satisfied, skipping upgrade: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from theano>=1.0.4->pymc3) (1.12.0)\n", "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18.0->pymc3) (2.8.1)\n", "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18.0->pymc3) (2018.9)\n", "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->arviz) (2.4.7)\n", "Requirement already satisfied, skipping upgrade: setuptools>=41.2 in /usr/local/lib/python3.6/dist-packages (from xarray>=0.11->arviz) (47.1.1)\n", "Requirement already satisfied, skipping upgrade: cftime in /usr/local/lib/python3.6/dist-packages (from netcdf4->arviz) (1.1.3)\n", "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->arviz) (1.2.0)\n", "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0->arviz) (0.10.0)\n", "Installing collected packages: pymc3\n", " Found existing installation: pymc3 3.7\n", " Uninstalling pymc3-3.7:\n", " Successfully uninstalled pymc3-3.7\n", "Successfully installed pymc3-3.8\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JHE3LtvGgJbJ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 255 }, "outputId": "34fcf21b-7f62-48bd-df89-6bce33229fae" }, "source": [ "try:\n", " import google.colab\n", " IN_COLAB = True\n", "except:\n", " IN_COLAB = False\n", "if IN_COLAB:\n", " print(\"Downloading Colab files\")\n", " ! shred -u setup_google_colab.py\n", " ! wget https://raw.githubusercontent.com/hse-aml/bayesian-methods-for-ml/master/setup_google_colab.py -O setup_google_colab.py\n", " import setup_google_colab\n", " setup_google_colab.load_data_week4()" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Downloading Colab files\n", "--2020-06-07 01:23:18-- https://raw.githubusercontent.com/hse-aml/bayesian-methods-for-ml/master/setup_google_colab.py\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1232 (1.2K) [text/plain]\n", "Saving to: ‘setup_google_colab.py’\n", "\n", "setup_google_colab. 100%[===================>] 1.20K --.-KB/s in 0s \n", "\n", "2020-06-07 01:23:18 (75.5 MB/s) - ‘setup_google_colab.py’ saved [1232/1232]\n", "\n", "https://raw.githubusercontent.com/hse-aml/bayesian-methods-for-ml/master/week4/w4_grader.py w4_grader.py\n", "https://raw.githubusercontent.com/hse-aml/bayesian-methods-for-ml/master/week4/adult_us_postprocessed.csv adult_us_postprocessed.csv\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "LXYU7NYxgJbT", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 88 }, "outputId": "70f85156-dc0e-4cb1-db3f-20865f85c1c5" }, "source": [ "import numpy as np\n", "import pandas as pd\n", "import numpy.random as rnd\n", "import seaborn as sns\n", "from matplotlib import animation\n", "import pymc3 as pm\n", "from w4_grader import MCMCGrader\n", "%pylab inline" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " import pandas.util.testing as tm\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "FuNWUIQigJbc", "colab_type": "text" }, "source": [ "### Grading\n", "We will create a grader instance below and use it to collect your answers. Note that these outputs will be stored locally inside grader and will be uploaded to the platform only after running submitting function in the last part of this assignment. If you want to make a partial submission, you can run that cell anytime you want." ] }, { "cell_type": "code", "metadata": { "id": "7fsmSlKzgJbd", "colab_type": "code", "colab": {} }, "source": [ "grader = MCMCGrader()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IxjodZGpgJbl", "colab_type": "text" }, "source": [ "## Task 1. Alice and Bob\n", "\n", "Alice and Bob are trading on the market. Both of them are selling the Thing and want to get as high profit as possible.\n", "Every hour they check out with each other's prices and adjust their prices to compete on the market. Although they have different strategies for price setting.\n", "\n", "**Alice**: takes Bob's price during the **previous** hour, multiply by 0.6, add \\\\$90, add Gaussian noise from $N(0, 20^2)$.\n", "\n", "**Bob**: takes Alice's price during the **current** hour, multiply by 1.2 and subtract \\\\$20, add Gaussian noise from $N(0, 10^2)$.\n", "\n", "The problem is to find the joint distribution of Alice and Bob's prices after many hours of such an experiment." ] }, { "cell_type": "markdown", "metadata": { "id": "0VTBMzVtgJbm", "colab_type": "text" }, "source": [ "### Task 1.1\n", "\n", "Implement the `run_simulation` function according to the description above. " ] }, { "cell_type": "code", "metadata": { "id": "q9LQKzMOgJbo", "colab_type": "code", "colab": {} }, "source": [ "def run_simulation(alice_start_price=300.0, bob_start_price=300.0, seed=42, num_hours=10000, burnin=1000):\n", " \"\"\"Simulates an evolution of prices set by Bob and Alice.\n", " \n", " The function should simulate Alice and Bob behavior for `burnin' hours, then ignore the obtained\n", " simulation results, and then simulate it for `num_hours' more.\n", " The initial burnin (also sometimes called warmup) is done to make sure that the distribution stabilized.\n", " \n", " Please don't change the signature of the function.\n", " \n", " Returns:\n", " two lists, with Alice and with Bob prices. Both lists should be of length num_hours.\n", " \"\"\"\n", " np.random.seed(seed)\n", "\n", " alice_prices = [alice_start_price]\n", " bob_prices = [bob_start_price]\n", " \n", " #### YOUR CODE HERE ####\n", " for hour in range(burnin + num_hours - 1):\n", " #Alice: takes Bob's price during the previous hour, multiply by 0.6, add $90, add Gaussian noise from N(0,202) .\n", " #Bob: takes Alice's price during the current hour, multiply by 1.2 and subtract $20, add Gaussian noise from N(0,102) .\n", " alice_current = bob_prices[-1]*0.6 + 90 + np.random.normal(loc=0, scale=20)\n", " bob_current = alice_current*1.2 - 20 + np.random.normal(loc=0, scale=10)\n", "\n", " alice_prices.append(alice_current)\n", " bob_prices.append(bob_current)\n", " \n", " \n", " ### END OF YOUR CODE ###\n", " #print(len(alice_prices[burnin:]), len(bob_prices[burnin:]))\n", " return alice_prices[burnin:], bob_prices[burnin:]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "6q_QfTNbgJbv", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "b40af9ff-f51b-4f83-ad83-4022dd5977da" }, "source": [ "alice_prices, bob_prices = run_simulation(alice_start_price=300, bob_start_price=300, seed=42, num_hours=3, burnin=1)\n", "if len(alice_prices) != 3:\n", " raise RuntimeError('Make sure that the function returns `num_hours` data points.')\n", "grader.submit_simulation_trajectory(alice_prices, bob_prices)" ], "execution_count": 31, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 1.1 (Alice trajectory) is: 279.93428306022463 291.67686875834846\n", "Current answer for task 1.1 (Bob trajectory) is: 314.5384966605577 345.2425410740984\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "EVXPqqP-gJb2", "colab_type": "text" }, "source": [ "### Task 1.2\n", "What is the average price for Alice and Bob after the burn-in period? Whose prices are higher?" ] }, { "cell_type": "code", "metadata": { "id": "l7A8qCtygJb4", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "12465f09-bdd4-4a5f-ffc5-3b2a80414970" }, "source": [ "#### YOUR CODE HERE ####\n", "alice_prices, bob_prices = run_simulation(alice_start_price=300, bob_start_price=300)\n", "\n", "average_alice_price = np.mean(alice_prices)\n", "average_bob_price = np.mean(bob_prices)\n", "### END OF YOUR CODE ###\n", "grader.submit_simulation_mean(average_alice_price, average_bob_price)" ], "execution_count": 32, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 1.2 (Alice mean) is: 278.85416992423353\n", "Current answer for task 1.2 (Bob mean) is: 314.6064116545574\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "vZRkVnvngJcB", "colab_type": "text" }, "source": [ "### Task 1.3\n", "\n", "Let's look at the 2-d histogram of prices, computed using kernel density estimation." ] }, { "cell_type": "code", "metadata": { "id": "QWlpuluZgJcC", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 458 }, "outputId": "2728c99b-5532-4c79-82ab-2d5519a61ad6" }, "source": [ "data = np.array(run_simulation())\n", "sns.jointplot(data[0, :], data[1, :], stat_func=None, kind='kde')" ], "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<seaborn.axisgrid.JointGrid at 0x7faf6e4ff198>" ] }, "metadata": { "tags": [] }, "execution_count": 33 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "XdzxvM5NgJcK", "colab_type": "text" }, "source": [ "Clearly, the prices of Bob and Alce are highly correlated. What is the Pearson correlation coefficient of Alice and Bob prices?" ] }, { "cell_type": "code", "metadata": { "id": "BNqLijVzgJcL", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "049de58d-cc6b-4fbf-8ba2-cea4ffe114d3" }, "source": [ "#### YOUR CODE HERE ####\n", "correlation = np.corrcoef(alice_prices, bob_prices)[0,1]\n", "### END OF YOUR CODE ###\n", "grader.submit_simulation_correlation(correlation)" ], "execution_count": 34, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 1.3 (Bob and Alice prices correlation) is: 0.9636099866766943\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "vs7EfGB3gJct", "colab_type": "text" }, "source": [ "### Task 1.4" ] }, { "cell_type": "markdown", "metadata": { "id": "1SfW9R-0gJcz", "colab_type": "text" }, "source": [ "We observe an interesting effect here: seems like the bivariate distribution of Alice and Bob prices converges to a correlated bivariate Gaussian distribution.\n", "\n", "Let's check, whether the results change if we use different random seed and starting points." ] }, { "cell_type": "code", "metadata": { "id": "RXpnYEplgJc4", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "be371b14-ed5b-41f5-bb38-ba4b7c37d328" }, "source": [ "# Pick different starting prices, e.g 10, 1000, 10000 for Bob and Alice. \n", "# Does the joint distribution of the two prices depend on these parameters?\n", "POSSIBLE_ANSWERS = {\n", " 0: 'Depends on random seed and starting prices', \n", " 1: 'Depends only on random seed',\n", " 2: 'Depends only on starting prices',\n", " 3: 'Does not depend on random seed and starting prices'\n", "}\n", "\n", "idx = 3### TYPE THE INDEX OF THE CORRECT ANSWER HERE ###\n", "answer = POSSIBLE_ANSWERS[idx]\n", "grader.submit_simulation_depends(answer)" ], "execution_count": 35, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 1.4 (depends on the random data or not) is: Does not depend on random seed and starting prices\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "id": "tE_SAQs2gJda", "colab_type": "text" }, "source": [ "## Task 2. Logistic regression with PyMC3\n", "\n", "Logistic regression is a powerful model that allows you to analyze how a set of features affects some binary target label. Posterior distribution over the weights gives us an estimation of the influence of each particular feature on the probability of the target being equal to one. But most importantly, posterior distribution gives us the interval estimates for each weight of the model. This is very important for data analysis when you want to not only provide a good model but also estimate the uncertainty of your conclusions.\n", "\n", "In this task, we will learn how to use PyMC3 library to perform approximate Bayesian inference for logistic regression.\n", "\n", "This part of the assignment is based on the logistic regression tutorial by Peadar Coyle and J. Benjamin Cook." ] }, { "cell_type": "markdown", "metadata": { "id": "nnrCHegvgJdh", "colab_type": "text" }, "source": [ "### Logistic regression.\n", "\n", "The problem here is to model how the probability that a person has salary $\\geq$ \\\\$50K is affected by his/her age, education, sex and other features.\n", "\n", "Let $y_i = 1$ if i-th person's salary is $\\geq$ \\\\$50K and $y_i = 0$ otherwise. Let $x_{ij}$ be $j$-th feature of $i$-th person.\n", "\n", "Logistic regression models this probabilty in the following way:\n", "\n", "$$p(y_i = 1 \\mid \\beta) = \\sigma (\\beta_1 x_{i1} + \\beta_2 x_{i2} + \\dots + \\beta_k x_{ik} ), $$\n", "\n", "where $\\sigma(t) = \\frac1{1 + e^{-t}}$" ] }, { "cell_type": "markdown", "metadata": { "id": "zck5G4rqgJdn", "colab_type": "text" }, "source": [ "#### Odds ratio.\n", "Let's try to answer the following question: does the gender of a person affects his or her salary? To do it we will use the concept of *odds*.\n", "\n", "If we have a binary random variable $y$ (which may indicate whether a person makes \\\\$50K) and if the probabilty of the positive outcome $p(y = 1)$ is for example 0.8, we will say that the *odds* are 4 to 1 (or just 4 for short), because succeding is 4 time more likely than failing $\\frac{p(y = 1)}{p(y = 0)} = \\frac{0.8}{0.2} = 4$.\n", "\n", "Now, let's return to the effect of gender on the salary. Let's compute the **ratio** between the odds of a male having salary $\\geq $ \\\\$50K and the odds of a female (with the same level of education, experience and everything else) having salary $\\geq$ \\\\$50K. The first feature of each person in the dataset is gender. Specifically, $x_{i1} = 0$ if the person is female and $x_{i1} = 1$ otherwise. Consider two people $i$ and $j$ having all but one features the same with the only difference in $x_{i1} \\neq x_{j1}$.\n", "\n", "If the logistic regression model above estimates the probabilities exactly, the odds for a male will be (check it!):\n", "$$\n", "\\frac{p(y_i = 1 \\mid x_{i1}=1, x_{i2}, \\ldots, x_{ik})}{p(y_i = 0 \\mid x_{i1}=1, x_{i2}, \\ldots, x_{ik})} = \\frac{\\sigma(\\beta_1 + \\beta_2 x_{i2} + \\ldots)}{1 - \\sigma(\\beta_1 + \\beta_2 x_{i2} + \\ldots)} = \\exp(\\beta_1 + \\beta_2 x_{i2} + \\ldots)\n", "$$\n", "\n", "Now the ratio of the male and female odds will be:\n", "$$\n", "\\frac{\\exp(\\beta_1 \\cdot 1 + \\beta_2 x_{i2} + \\ldots)}{\\exp(\\beta_1 \\cdot 0 + \\beta_2 x_{i2} + \\ldots)} = \\exp(\\beta_1)\n", "$$\n", "\n", "So given the correct logistic regression model, we can estimate odds ratio for some feature (gender in this example) by just looking at the corresponding coefficient. But of course, even if all the logistic regression assumptions are met we cannot estimate the coefficient exactly from real-world data, it's just too noisy. So it would be really nice to build an interval estimate, which would tell us something along the lines \"with probability 0.95 the odds ratio is greater than 0.8 and less than 1.2, so we cannot conclude that there is any gender discrimination in the salaries\" (or vice versa, that \"with probability 0.95 the odds ratio is greater than 1.5 and less than 1.9 and the discrimination takes place because a male has at least 1.5 higher probability to get >$50k than a female with the same level of education, age, etc.\"). In Bayesian statistics, this interval estimate is called *credible interval*.\n", "\n", "Unfortunately, it's impossible to compute this credible interval analytically. So let's use MCMC for that!\n", "\n", "#### Credible interval\n", "A credible interval for the value of $\\exp(\\beta_1)$ is an interval $[a, b]$ such that $p(a \\leq \\exp(\\beta_1) \\leq b \\mid X_{\\text{train}}, y_{\\text{train}})$ is $0.95$ (or some other predefined value). To compute the interval, we need access to the posterior distribution $p(\\exp(\\beta_1) \\mid X_{\\text{train}}, y_{\\text{train}})$.\n", "\n", "Lets for simplicity focus on the posterior on the parameters $p(\\beta_1 \\mid X_{\\text{train}}, y_{\\text{train}})$ since if we compute it, we can always find $[a, b]$ such that $p(\\log a \\leq \\beta_1 \\leq \\log b \\mid X_{\\text{train}}, y_{\\text{train}}) = p(a \\leq \\exp(\\beta_1) \\leq b \\mid X_{\\text{train}}, y_{\\text{train}}) = 0.95$\n" ] }, { "cell_type": "markdown", "metadata": { "id": "pOv63gJ1gJdr", "colab_type": "text" }, "source": [ "### Task 2.1 MAP inference" ] }, { "cell_type": "markdown", "metadata": { "id": "Ya3ENyZ9gJdw", "colab_type": "text" }, "source": [ "Let's read the dataset. This is a post-processed version of the [UCI Adult dataset](http://archive.ics.uci.edu/ml/datasets/Adult)." ] }, { "cell_type": "code", "metadata": { "id": "H05hwiIUgJd1", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "7e47c82d-2dba-4b93-eb94-9b8b79e89e04" }, "source": [ "data = pd.read_csv(\"adult_us_postprocessed.csv\")\n", "data.head()" ], "execution_count": 11, "outputs": [ { "output_type": "execute_result", "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>sex</th>\n", " <th>age</th>\n", " <th>educ</th>\n", " <th>hours</th>\n", " <th>income_more_50K</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Male</td>\n", " <td>39</td>\n", " <td>13</td>\n", " <td>40</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Male</td>\n", " <td>50</td>\n", " <td>13</td>\n", " <td>13</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Male</td>\n", " <td>38</td>\n", " <td>9</td>\n", " <td>40</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Male</td>\n", " <td>53</td>\n", " <td>7</td>\n", " <td>40</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Female</td>\n", " <td>28</td>\n", " <td>13</td>\n", " <td>40</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sex age educ hours income_more_50K\n", "0 Male 39 13 40 0\n", "1 Male 50 13 13 0\n", "2 Male 38 9 40 0\n", "3 Male 53 7 40 0\n", "4 Female 28 13 40 0" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "markdown", "metadata": { "id": "tB5uyJVkgJeQ", "colab_type": "text" }, "source": [ "Each row of the dataset is a person with his (her) features. The last column is the target variable $y$. One indicates that this person's annual salary is more than $50K.\n", "\n", "First of all let's set up a Bayesian logistic regression model (i.e. define priors on the parameters $\\alpha$ and $\\beta$ of the model) that predicts the value of \"income_more_50K\" based on person's age and education:\n", "\n", "$$\n", "p(y = 1 \\mid \\alpha, \\beta_1, \\beta_2) = \\sigma(\\alpha + \\beta_1 x_1 + \\beta_2 x_2) \\\\ \n", "\\alpha \\sim N(0, 100^2) \\\\\n", "\\beta_1 \\sim N(0, 100^2) \\\\\n", "\\beta_2 \\sim N(0, 100^2), \\\\\n", "$$\n", "\n", "where $x_1$ is a person's age, $x_2$ is his/her level of education, y indicates his/her level of income, $\\alpha$, $\\beta_1$ and $\\beta_2$ are paramters of the model." ] }, { "cell_type": "code", "metadata": { "id": "AiIm565agJeX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "a7e61f63-c4f4-4592-bab8-b65ad357b3ac" }, "source": [ "with pm.Model() as manual_logistic_model:\n", " # Declare pymc random variables for logistic regression coefficients with uninformative \n", " # prior distributions N(0, 100^2) on each weight using pm.Normal. \n", " # Don't forget to give each variable a unique name.\n", " \n", " #### YOUR CODE HERE ####\n", "\n", " alpha = pm.Normal('alpha', mu=0, sigma=100)\n", " beta1 = pm.Normal('beta1', mu=0, sigma=100)\n", " beta2 = pm.Normal('beta2', mu=0, sigma=100)\n", " \n", " ### END OF YOUR CODE ###\n", " \n", " # Transform these random variables into vector of probabilities p(y_i=1) using logistic regression model specified \n", " # above. PyMC random variables are theano shared variables and support simple mathematical operations.\n", " # For example:\n", " # z = pm.Normal('x', 0, 1) * np.array([1, 2, 3]) + pm.Normal('y', 0, 1) * np.array([4, 5, 6])`\n", " # is a correct PyMC expression.\n", " # Use pm.invlogit for the sigmoid function.\n", " \n", " #### YOUR CODE HERE ####\n", " \n", " prob = pm.invlogit(alpha + beta1*data['age'] + beta2*data['educ'] )\n", " ### END OF YOUR CODE ###\n", " \n", " # Declare PyMC Bernoulli random vector with probability of success equal to the corresponding value\n", " # given by the sigmoid function.\n", " # Supply target vector using \"observed\" argument in the constructor.\n", "\n", " #### YOUR CODE HERE ####\n", " likelihood = pm.Bernoulli('likelihood', p=prob, observed=data['income_more_50K'])\n", " ### END OF YOUR CODE ###\n", " \n", " # Use pm.find_MAP() to find the maximum a-posteriori estimate for the vector of logistic regression weights.\n", " map_estimate = pm.find_MAP()\n", " print(map_estimate)\n", "\n" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "text": [ "logp = -18,844, ||grad|| = 57,293: 100%|██████████| 30/30 [00:00<00:00, 110.95it/s] \n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "{'alpha': array(-6.74811904), 'beta1': array(0.04348316), 'beta2': array(0.36210803)}\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "MC7Wo5gTgJe_", "colab_type": "text" }, "source": [ "Sumbit MAP estimations of corresponding coefficients:" ] }, { "cell_type": "code", "metadata": { "id": "__f84oZxgJfG", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 88 }, "outputId": "7dd158ae-a53f-4f50-c2bb-e654862759c3" }, "source": [ "with pm.Model() as logistic_model:\n", " # There's a simpler interface for generalized linear models in pymc3. \n", " # Try to train the same model using pm.glm.GLM.from_formula.\n", " # Do not forget to specify that the target variable is binary (and hence follows Binomial distribution).\n", " \n", " #### YOUR CODE HERE ####\n", " formula = 'income_more_50K ~ age + educ'\n", " likelihood = pm.glm.GLM.from_formula(formula, data, family=pm.glm.families.Binomial())\n", " ### END OF YOUR CODE ###\n", " map_estimate = pm.find_MAP()\n", " print(map_estimate)" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "text": [ "WARNING (theano.tensor.blas): We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n", "logp = -15,131, ||grad|| = 0.024014: 100%|██████████| 32/32 [00:00<00:00, 123.80it/s] " ], "name": "stderr" }, { "output_type": "stream", "text": [ "{'Intercept': array(-6.7480998), 'age': array(0.04348259), 'educ': array(0.36210894)}\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "scrolled": true, "id": "K-NLDqk3gJfe", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "03f30391-f4ce-42c6-88a8-dd74342abe74" }, "source": [ "beta_age_coefficient = 0.04348259### TYPE MAP ESTIMATE OF THE AGE COEFFICIENT HERE ###\n", "beta_education_coefficient = 0.36210894### TYPE MAP ESTIMATE OF THE EDUCATION COEFFICIENT HERE ###\n", "grader.submit_pymc_map_estimates(beta_age_coefficient, beta_education_coefficient)" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 2.1 (MAP for age coef) is: 0.04348259\n", "Current answer for task 2.1 (MAP for aducation coef) is: 0.36210894\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "0WueE4zDgJf7", "colab_type": "text" }, "source": [ "### Task 2.2 MCMC" ] }, { "cell_type": "markdown", "metadata": { "id": "MZR3aR5YgJf_", "colab_type": "text" }, "source": [ "To find credible regions let's perform MCMC inference." ] }, { "cell_type": "code", "metadata": { "id": "MnSOiktwgJgE", "colab_type": "code", "colab": {} }, "source": [ "# You will need the following function to visualize the sampling process.\n", "# You don't need to change it.\n", "def plot_traces(traces, burnin=200):\n", " ''' \n", " Convenience function:\n", " Plot traces with overlaid means and values\n", " '''\n", " \n", " ax = pm.traceplot(traces[burnin:], figsize=(12,len(traces.varnames)*1.5),\n", " lines={k: v['mean'] for k, v in pm.summary(traces[burnin:]).iterrows()})\n", "\n", " for i, mn in enumerate(pm.summary(traces[burnin:])['mean']):\n", " ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data'\n", " ,xytext=(5,10), textcoords='offset points', rotation=90\n", " ,va='bottom', fontsize='large', color='#AA0022')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "c6dYuiWegJgn", "colab_type": "text" }, "source": [ "#### Metropolis-Hastings\n", "Let's use the Metropolis-Hastings algorithm for finding the samples from the posterior distribution.\n", "\n", "Once you wrote the code, explore the hyperparameters of Metropolis-Hastings such as the proposal distribution variance to speed up the convergence. You can use `plot_traces` function in the next cell to visually inspect the convergence.\n", "\n", "You may also use MAP-estimate to initialize the sampling scheme to speed things up. This will make the warmup (burn-in) period shorter since you will start from a probable point." ] }, { "cell_type": "code", "metadata": { "id": "yRxPiimMgJgt", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "2e1aa08f-37c0-4304-876e-76720355c9b9" }, "source": [ "with pm.Model() as logistic_model:\n", " # Since it is unlikely that the dependency between the age and salary is linear, we will include age squared\n", " # into features so that we can model dependency that favors certain ages.\n", " # Train Bayesian logistic regression model on the following features: sex, age, age^2, educ, hours\n", " # Use pm.sample to run MCMC to train this model.\n", " # To specify the particular sampler method (Metropolis-Hastings) to pm.sample,\n", " # use `pm.Metropolis`.\n", " # Train your model for 400 samples.\n", " # Save the output of pm.sample to a variable: this is the trace of the sampling procedure and will be used\n", " # to estimate the statistics of the posterior distribution.\n", " \n", " #### YOUR CODE HERE ####\n", " data['age_squared'] = data['age'] ** 2\n", " formula = 'income_more_50K ~ sex + age + age_squared + educ + hours'\n", " likelihood = pm.glm.GLM.from_formula(formula, data, family=pm.glm.families.Binomial())\n", " trace = pm.sample(400, step=pm.Metropolis(), chains=1)\n", " ### END OF YOUR CODE ###" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "text": [ "Only 400 samples in chain.\n", "Sequential sampling (1 chains in 1 job)\n", "CompoundStep\n", ">Metropolis: [hours]\n", ">Metropolis: [educ]\n", ">Metropolis: [age_squared]\n", ">Metropolis: [age]\n", ">Metropolis: [sex[T. Male]]\n", ">Metropolis: [Intercept]\n", "Sampling chain 0, 0 divergences: 100%|██████████| 900/900 [01:02<00:00, 14.36it/s]\n", "Only one chain was sampled, this makes it impossible to run some convergence checks\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "qVhGXJDJX2Cd", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "2128fa04-c42b-423c-eb38-c9143a58555e" }, "source": [ "pm.__version__" ], "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'3.8'" ] }, "metadata": { "tags": [] }, "execution_count": 17 } ] }, { "cell_type": "code", "metadata": { "id": "MdcmMPJOgJhG", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "f1e118eb-0b50-4872-e303-9b8292455930" }, "source": [ "plot_traces(trace)" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/arviz/data/base.py:146: UserWarning: More chains (200) than draws (6). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "/usr/local/lib/python3.6/dist-packages/arviz/data/base.py:146: UserWarning: More chains (200) than draws (6). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", "/usr/local/lib/python3.6/dist-packages/arviz/plots/backends/matplotlib/traceplot.py:152: UserWarning: A valid var_name should be provided, found {'a', 'h', 's', 'e', 'I'} expected from {'hours', 'Intercept', 'age_squared', 'sex[T. Male]', 'educ', 'age'}\n", " invalid_var_names, all_var_names\n", "/usr/local/lib/python3.6/dist-packages/arviz/data/base.py:146: UserWarning: More chains (200) than draws (6). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x648 with 12 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "n8zLmeu-gJhd", "colab_type": "text" }, "source": [ "#### NUTS sampler\n", "Use pm.sample without specifying a particular sampling method (pymc3 will choose it automatically).\n", "The sampling algorithm that will be used in this case is NUTS, which is a form of Hamiltonian Monte Carlo, in which parameters are tuned automatically. This is an advanced method that we hadn't cover in the lectures, but it usually converges faster and gives less correlated samples compared to vanilla Metropolis-Hastings." ] }, { "cell_type": "code", "metadata": { "id": "P_DMOqLQgJhh", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 139 }, "outputId": "753bc10d-8ff6-4a1c-bd58-63934f5b2f95" }, "source": [ "with pm.Model() as logistic_model:\n", " # Train Bayesian logistic regression model on the following features: sex, age, age_squared, educ, hours\n", " # Use pm.sample to run MCMC to train this model.\n", " # Train your model for 400 samples.\n", " # Training can take a while, so relax and wait :)\n", " \n", " #### YOUR CODE HERE ####\n", " formula = 'income_more_50K ~ sex + age + age_squared + educ + hours'\n", " likelihood = pm.glm.GLM.from_formula(formula, data, family=pm.glm.families.Binomial())\n", " trace = pm.sample(400, step=pm.NUTS(), chains=1)\n", " ### END OF YOUR CODE ###" ], "execution_count": 19, "outputs": [ { "output_type": "stream", "text": [ "Only 400 samples in chain.\n", "Sequential sampling (1 chains in 1 job)\n", "NUTS: [hours, educ, age_squared, age, sex[T. Male], Intercept]\n", "Sampling chain 0, 0 divergences: 100%|██████████| 900/900 [14:39<00:00, 1.02it/s]\n", "The acceptance probability does not match the target. It is 0.9840827118135124, but should be close to 0.8. Try to increase the number of tuning steps.\n", "Only one chain was sampled, this makes it impossible to run some convergence checks\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "SZVrYotFgJh5", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 931 }, "outputId": "8e384e44-abdc-4d4c-ed55-aa30e4fbec3b" }, "source": [ "plot_traces(trace)" ], "execution_count": 20, "outputs": [ { "output_type": "stream", "text": [ "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "/usr/local/lib/python3.6/dist-packages/arviz/plots/backends/matplotlib/traceplot.py:152: UserWarning: A valid var_name should be provided, found {'a', 'h', 's', 'e', 'I'} expected from {'hours', 'Intercept', 'age_squared', 'sex[T. Male]', 'educ', 'age'}\n", " invalid_var_names, all_var_names\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n", "arviz.stats.stats_utils - WARNING - Shape validation failed: input_shape: (1, 200), minimum_shape: (chains=2, draws=4)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x648 with 12 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "u8eeFfd9gJiU", "colab_type": "text" }, "source": [ "#### Estimating the odds ratio\n", "Now, let's build the posterior distribution on the odds ratio given the dataset (approximated by MCMC)." ] }, { "cell_type": "code", "metadata": { "id": "h-lhTDoagJiZ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 279 }, "outputId": "3f40e880-220d-4e01-f894-5f582915d87f" }, "source": [ "# We don't need to use a large burn-in here, since we initialize sampling\n", "# from a good point (from our approximation of the most probable\n", "# point (MAP) to be more precise).\n", "burnin = 100\n", "b = trace['sex[T. Male]'][burnin:]\n", "plt.hist(np.exp(b), bins=20, density=True)\n", "plt.xlabel(\"Odds Ratio\")\n", "plt.show()" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "rqXFra6ygJiv", "colab_type": "text" }, "source": [ "\n", "Finally, we can find a credible interval (recall that credible intervals are Bayesian and confidence intervals are frequentist) for this quantity. This may be the best part about Bayesian statistics: we get to interpret credibility intervals the way we've always wanted to interpret them. We are 95% confident that the odds ratio lies within our interval!" ] }, { "cell_type": "code", "metadata": { "id": "0bkqE-_ygJi0", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "e9cfa1f5-1aca-4c96-cf8e-34db1e210661" }, "source": [ "lb, ub = np.percentile(b, 2.5), np.percentile(b, 97.5)\n", "print(\"P(%.3f < Odds Ratio < %.3f) = 0.95\" % (np.exp(lb), np.exp(ub)))" ], "execution_count": 23, "outputs": [ { "output_type": "stream", "text": [ "P(3.023 < Odds Ratio < 3.460) = 0.95\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "c8YaYpr4gJjR", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "outputId": "fd71d305-95fb-4dd8-dffe-8d61f171f75a" }, "source": [ "# Submit the obtained credible interval.\n", "grader.submit_pymc_odds_ratio_interval(np.exp(lb), np.exp(ub))" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 2.2 (credible interval lower bound) is: 3.022648987035948\n", "Current answer for task 2.2 (credible interval upper bound) is: 3.4597593844022723\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "zgxVZoukgJjr", "colab_type": "text" }, "source": [ "### Task 2.3 interpreting the results" ] }, { "cell_type": "code", "metadata": { "id": "8D76E-cPgJjv", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "outputId": "5a2f81f7-eab4-4aa6-81f8-05f96ff01a38" }, "source": [ "# Does the gender affects salary in the provided dataset?\n", "# (Note that the data is from 1996 and maybe not representative\n", "# of the current situation in the world.)\n", "POSSIBLE_ANSWERS = {\n", " 0: 'No, there is certainly no discrimination',\n", " 1: 'We cannot say for sure',\n", " 2: 'Yes, we are 95% sure that a female is *less* likely to get >$50K than a male with the same age, level of education, etc.', \n", " 3: 'Yes, we are 95% sure that a female is *more* likely to get >$50K than a male with the same age, level of education, etc.', \n", "}\n", "\n", "idx = 2### TYPE THE INDEX OF THE CORRECT ANSWER HERE ###\n", "answer = POSSIBLE_ANSWERS[idx]\n", "grader.submit_is_there_discrimination(answer)" ], "execution_count": 25, "outputs": [ { "output_type": "stream", "text": [ "Current answer for task 2.3 (does the data suggest gender discrimination?) is: Yes, we are 95% sure that a female is *less* likely to get >$50K than a male with the same age, level of education, etc.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "N6iboCPogJkH", "colab_type": "text" }, "source": [ "# Authorization & Submission\n", "To submit assignment parts to Cousera platform, please, enter your e-mail and token into variables below. You can generate a token on this programming assignment's page. <b>Note:</b> The token expires 30 minutes after generation." ] }, { "cell_type": "code", "metadata": { "id": "LAZ78tYKgJkL", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "3be83ef3-deff-48fc-a557-94f4df1103c6" }, "source": [ "STUDENT_EMAIL = '[email protected]'\n", "STUDENT_TOKEN = '6r463miiML4NWB9M'\n", "grader.status()" ], "execution_count": 36, "outputs": [ { "output_type": "stream", "text": [ "You want to submit these numbers:\n", "Task 1.1 (Alice trajectory): 279.93428306022463 291.67686875834846\n", "Task 1.1 (Bob trajectory): 314.5384966605577 345.2425410740984\n", "Task 1.2 (Alice mean): 278.85416992423353\n", "Task 1.2 (Bob mean): 314.6064116545574\n", "Task 1.3 (Bob and Alice prices correlation): 0.9636099866766943\n", "Task 1.4 (depends on the random data or not): Does not depend on random seed and starting prices\n", "Task 2.1 (MAP for age coef): 0.04348259\n", "Task 2.1 (MAP for aducation coef): 0.36210894\n", "Task 2.2 (credible interval lower bound): 3.022648987035948\n", "Task 2.2 (credible interval upper bound): 3.4597593844022723\n", "Task 2.3 (does the data suggest gender discrimination?): Yes, we are 95% sure that a female is *less* likely to get >$50K than a male with the same age, level of education, etc.\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "wI5bogGKgJkk", "colab_type": "text" }, "source": [ "If you want to submit these answers, run cell below" ] }, { "cell_type": "code", "metadata": { "id": "aMRmFwvcgJkp", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "c26c27c8-d867-4d45-8540-2555d5aaa29f" }, "source": [ "grader.submit(STUDENT_EMAIL, STUDENT_TOKEN)" ], "execution_count": 37, "outputs": [ { "output_type": "stream", "text": [ "Submitted to Coursera platform. See results on assignment page!\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "FGM1jsDggJlD", "colab_type": "text" }, "source": [ "# (Optional) generating videos of sampling process\n", "In this part you will generate videos showing the sampling process." ] }, { "cell_type": "markdown", "metadata": { "id": "5ziTQHpIgJlI", "colab_type": "text" }, "source": [ "### Setting things up\n", "You don't need to modify the code below, it sets up the plotting functions. The code is based on [MCMC visualization tutorial](https://twiecki.github.io/blog/2014/01/02/visualizing-mcmc/)." ] }, { "cell_type": "code", "metadata": { "id": "W0jgy2HDgJlN", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 392 }, "outputId": "765e67d6-53dc-47e2-f3e4-d887f3702657" }, "source": [ "from IPython.display import HTML\n", "\n", "# Number of MCMC iteration to animate.\n", "samples = 400\n", "\n", "figsize(6, 6)\n", "fig = plt.figure()\n", "s_width = (0.81, 1.29)\n", "a_width = (0.11, 0.39)\n", "samples_width = (0, samples)\n", "ax1 = fig.add_subplot(221, xlim=s_width, ylim=samples_width)\n", "ax2 = fig.add_subplot(224, xlim=samples_width, ylim=a_width)\n", "ax3 = fig.add_subplot(223, xlim=s_width, ylim=a_width,\n", " xlabel='male coef',\n", " ylabel='educ coef')\n", "fig.subplots_adjust(wspace=0.0, hspace=0.0)\n", "line1, = ax1.plot([], [], lw=1)\n", "line2, = ax2.plot([], [], lw=1)\n", "line3, = ax3.plot([], [], 'o', lw=2, alpha=.1)\n", "line4, = ax3.plot([], [], lw=1, alpha=.3)\n", "line5, = ax3.plot([], [], 'k', lw=1)\n", "line6, = ax3.plot([], [], 'k', lw=1)\n", "ax1.set_xticklabels([])\n", "ax2.set_yticklabels([])\n", "lines = [line1, line2, line3, line4, line5, line6]\n", "\n", "def init():\n", " for line in lines:\n", " line.set_data([], [])\n", " return lines\n", "\n", "def animate(i):\n", " with logistic_model:\n", " if i == 0:\n", " # Burnin\n", " for j in range(samples): iter_sample.__next__() \n", " trace = iter_sample.__next__()\n", " line1.set_data(trace['sex[T. Male]'][::-1], range(len(trace['sex[T. Male]'])))\n", " line2.set_data(range(len(trace['educ'])), trace['educ'][::-1])\n", " line3.set_data(trace['sex[T. Male]'], trace['educ'])\n", " line4.set_data(trace['sex[T. Male]'], trace['educ'])\n", " male = trace['sex[T. Male]'][-1]\n", " educ = trace['educ'][-1]\n", " line5.set_data([male, male], [educ, a_width[1]])\n", " line6.set_data([male, s_width[1]], [educ, educ])\n", " return lines" ], "execution_count": 28, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 3 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "dcl1PubBgJll", "colab_type": "text" }, "source": [ "## Animating Metropolis-Hastings" ] }, { "cell_type": "code", "metadata": { "id": "2S8zOs7EgJlm", "colab_type": "code", "colab": {} }, "source": [ "with pm.Model() as logistic_model:\n", " # Again define Bayesian logistic regression model on the following features: sex, age, age_squared, educ, hours\n", " \n", " #### YOUR CODE HERE ####\n", " \n", " ### END OF YOUR CODE ###\n", " step = pm.Metropolis()\n", " iter_sample = pm.iter_sample(2 * samples, step, start=map_estimate)\n", "anim = animation.FuncAnimation(fig, animate, init_func=init,\n", " frames=samples, interval=5, blit=True)\n", "HTML(anim.to_html5_video())\n", "# Note that generating the video may take a while." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hIYh0uYlgJlr", "colab_type": "text" }, "source": [ "## Animating NUTS\n", "Now rerun the animation providing the NUTS sampling method as the step argument." ] }, { "cell_type": "code", "metadata": { "id": "j-HSn9jRgJlt", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }
bsd-2-clause
irockafe/revo_healthcare
notebooks/MTBLS17/exploratory/MTBLS17_uhplc_pos_classifer_no_retcor.ipynb
1
266082
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> After performing centwav at 15ppm, then grouping with bw=2. No retention correction applied </h2>\n", "Remember, this dataset is weird. It has replicates of all measurements. Make sure to split them before doing and ML stuff. It'll screw up your cross-validation completely.\n", "\n", "Also, samples were run in 3 batches. I didn't take that into account in the xcms code, so all three are present currently in the feature table. You might want to repeat xcms with the batches separated at some point" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/irockafe/miniconda2/envs/isaac_revo_healthcare/lib/python2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import time\n", "\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from sklearn import preprocessing\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.cross_validation import StratifiedShuffleSplit\n", "from sklearn.cross_validation import cross_val_score\n", "#from sklearn.model_selection import StratifiedShuffleSplit\n", "#from sklearn.model_selection import cross_val_score\n", "\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.metrics import roc_curve, auc\n", "from sklearn.utils import shuffle\n", "\n", "from scipy import interp\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2 3\n", "0 1.0 2.0 3.0 0.0\n", "1 0.0 0.0 0.0 0.0\n", "2 0.5 1.0 0.0 0.0\n", " 0 1 2\n", "0 1.0 2.0 3.0\n", "1 0.0 0.0 0.0\n", "2 0.5 1.0 0.0\n", " 0 1 2\n", "0 1.00 2.0 3.0\n", "1 0.25 0.5 1.5\n", "2 0.50 1.0 1.5\n" ] } ], "source": [ "def remove_zero_columns(X, threshold=1e-20):\n", " # convert zeros to nan, drop all nan columns, the replace leftover nan with zeros\n", " X_non_zero_colum = X.replace(0, np.nan).dropna(how='all', axis=1).replace(np.nan, 0)\n", " #.dropna(how='all', axis=0).replace(np.nan,0)\n", " return X_non_zero_colum\n", "\n", "def zero_fill_half_min(X, threshold=1e-20):\n", " # Fill zeros with 1/2 the minimum value of that column\n", " # input dataframe. Add only to zero values\n", " \n", " # Get a vector of 1/2 minimum values\n", " half_min = X[X > threshold].min(axis=0)*0.5\n", " \n", " # Add the half_min values to a dataframe where everything that isn't zero is NaN.\n", " # then convert NaN's to 0\n", " fill_vals = (X[X < threshold] + half_min).fillna(value=0)\n", " \n", " # Add the original dataframe to the dataframe of zeros and fill-values\n", " X_zeros_filled = X + fill_vals\n", " return X_zeros_filled\n", "\n", "\n", "\n", "toy = pd.DataFrame([[1,2,3,0],\n", " [0,0,0,0],\n", " [0.5,1,0,0]], dtype=float)\n", "\n", "toy_no_zeros = remove_zero_columns(toy)\n", "toy_filled_zeros = zero_fill_half_min(toy_no_zeros)\n", "print toy\n", "print toy_no_zeros\n", "print toy_filled_zeros" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Import the dataframe and remove any features that are all zero </h2>" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 178635\n", "original shape: (524, 1216) \n", "# zeros: 0.000000\n", "\n", "zero-columns repalced? shape: (524, 1216) \n", "# zeros: 178635.000000\n", "\n", "zeros filled shape: (524, 1216) \n", "#zeros: 0.000000\n", "\n", "a-samples: (262, 1216)\n", "b-samples: (262, 1216)\n" ] } ], "source": [ "### Subdivide the data into a feature table\n", "data_path = '/home/irockafe/Dropbox (MIT)/Alm_Lab/'\\\n", "'projects/revo_healthcare/data/processed/MTBLS17/positive/mtbls17_bw2_no_retcor.csv'\n", "## Import the data and remove extraneous columns\n", "df = pd.read_csv(data_path, index_col=0)\n", "df.shape\n", "df.head()\n", "# Make a new index of mz:rt\n", "mz = df.loc[:,\"mz\"].astype('str')\n", "rt = df.loc[:,\"rt\"].astype('str')\n", "idx = mz+':'+rt\n", "df.index = idx\n", "df\n", "# separate samples from xcms/camera things to make feature table\n", "not_samples = ['mz', 'mzmin', 'mzmax', 'rt', 'rtmin', 'rtmax', \n", " 'npeaks', 'positive', \n", " ]\n", "samples_list = df.columns.difference(not_samples)\n", "mz_rt_df = df[not_samples]\n", "\n", "# convert to samples x features\n", "X_df_raw = df[samples_list].T\n", "print X_df_raw.isnull().sum().sum()\n", "# Remove zero-full columns and fill zeroes with 1/2 minimum values\n", "X_df = remove_zero_columns(X_df_raw)\n", "X_df_zero_filled = zero_fill_half_min(X_df)\n", "\n", "print \"original shape: %s \\n# zeros: %f\\n\" % (X_df_raw.shape, (X_df_raw < 1e-20).sum().sum())\n", "print \"zero-columns repalced? shape: %s \\n# zeros: %f\\n\" % (X_df.shape, \n", " (X_df < 1e-20).sum().sum())\n", "print \"zeros filled shape: %s \\n#zeros: %f\\n\" % (X_df_zero_filled.shape, \n", " (X_df_zero_filled < 1e-20).sum().sum())\n", "\n", "\n", "# Convert to numpy matrix to play nicely with sklearn\n", "# split into a and b groups - a-group is one injection, b-group is another replicate\n", "X_a = X_df_zero_filled[X_df_zero_filled.index.str.contains('a')].as_matrix()\n", "X_b = X_df_zero_filled[X_df_zero_filled.index.str.contains('b')].as_matrix()\n", "X = X_a\n", "\n", "print 'a-samples: ', X_a.shape\n", "print 'b-samples: ', X_b.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Get mappings between sample names, file names, and sample classes </h2>" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sample Name\n", "Exp1_CRR_1a_POS Exp1_CRR_1a_POS\n", "Exp1_CRR_1b_POS Exp1_CRR_1b_POS\n", "Exp1_CRR_3a_POS Exp1_CRR_3a_POS\n", "Exp1_CRR_3b_POS Exp1_CRR_3b_POS\n", "Exp1_CRR_4a_POS Exp1_CRR_4a_POS\n", "Exp1_CRR_4b_POS Exp1_CRR_4b_POS\n", "Exp1_CRR_5a_POS Exp1_CRR_5a_POS\n", "Exp1_CRR_5b_POS Exp1_CRR_5b_POS\n", "Exp1_CRR_6a_POS Exp1_CRR_6a_POS\n", "Exp1_CRR_6b_POS Exp1_CRR_6b_POS\n", "Name: MS Assay Name, dtype: object\n", " Sample Name class\n", "526 Exp1_CRR_1a_POS CRR\n", "528 Exp1_CRR_3a_POS CRR\n", "530 Exp1_CRR_4a_POS CRR\n", "532 Exp1_CRR_5a_POS CRR\n", "534 Exp1_CRR_6a_POS CRR\n", "all labels (524, 2)\n", "a labels (262, 2)\n", "b labels (262, 2)\n", "['CRR' 'HCC']\n", "\n", "\n", "Found 524 labels, should be 524\n" ] } ], "source": [ "# Get mapping between sample name and assay names\n", "path_sample_name_map = '/home/irockafe/Dropbox (MIT)/'\\\n", "'Alm_Lab/projects/revo_healthcare/data/raw/'\\\n", "'MTBLS17/a_live_mtbls17pos_metabolite profiling_mass spectrometry.txt'\n", "# Index is the sample name\n", "sample_df = pd.read_csv(path_sample_name_map, \n", " sep='\\t', index_col=0)\n", "sample_df = sample_df['MS Assay Name']\n", "sample_df.shape\n", "print sample_df.head(10)\n", "\n", "# get mapping between sample name and sample class\n", "path_sample_class_map = '/home/irockafe/Dropbox (MIT)/'\\\n", "'Alm_Lab/projects/revo_healthcare/data/raw/'\\\n", "'MTBLS17/s_live_mtbls17.txt'\n", "\n", "class_df = pd.read_csv(path_sample_class_map,\n", " sep='\\t')\n", "class_map_df = pd.concat([class_df['Sample Name'], class_df['Factor Value[Disease]']], \n", " axis=1)\n", "class_map_df.rename(columns={'Factor Value[Disease]':'class'}, inplace=True)\n", "# get rid of the negative values\n", "class_map_df = class_map_df[class_map_df['Sample Name'].str.contains('POS')]\n", "\n", "# split by a and b injection\n", "class_map_df_a = class_map_df[class_map_df['Sample Name'].str.contains('a')]\n", "class_map_df_b = class_map_df[class_map_df['Sample Name'].str.contains('b')]\n", "print class_map_df_a.head()\n", "print 'all labels', class_map_df.shape\n", "print 'a labels', class_map_df_a.shape\n", "print 'b labels', class_map_df_b.shape\n", "\n", "# Make sure that you have the same number of class labels as sample names in your\n", "# feature table\n", "if not class_map_df.shape[0] == len(X_df_zero_filled.index):\n", " raise ValueError, 'Your X and y dimensions should match! fix it!!!'\\\n", " '(you either have too few class labels or your mapping between sample-name and'\\\n", " 'class label is wonky)'\n", "\n", "# only two classes, HCC and CRR\n", "print class_map_df['class'].unique()\n", "\n", "# Make sure all sample names have a class label\n", "count = 0\n", "for idx in X_df_zero_filled.index:\n", " if idx in class_map_df['Sample Name'].values:\n", " count += 1\n", "print '\\n\\nFound %s labels, should be %s' % (count, len(X_df_zero_filled.index))\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Note the class imbalance!! </h2>\n", "This shouldn't affect ROC curves...\n", "There are more cirrhotic controls than hepatocellular carcinoma patients!\n", "78 cases, 184 controls. I might want to artificially balance these classes..?\n", "I think it's unjustified outside of wanting to show better classification performance, though. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 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 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 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1\n", " 1 1 1]\n", "78 262 -184\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 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 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 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1\n", " 1 1 1]\n" ] } ], "source": [ "# convert classes to numbers\n", "le = preprocessing.LabelEncoder()\n", "le.fit(class_map_df_a['class'])\n", "y_a = le.transform(class_map_df_a['class'])\n", "print y_a\n", "print sum(y_a), len(y_a), sum(y_a) - len(y_a)\n", "\n", "# convert classes to numbers\n", "le = preprocessing.LabelEncoder()\n", "le.fit(class_map_df_b['class'])\n", "y_b = le.transform(class_map_df_b['class'])\n", "print y_b\n", "\n", "# define set A as the one to default to\n", "y = y_a" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 1. 1.]\n", " [ 2. 2. 2.]\n", " [ 3. 6. 9.]\n", " [ 6. 12. 18.]]\n", "[[ 1. 1. 1. 2. 2. 2. 3. 6. 9. 6. 12. 18.]]\n", "allquotients reshaped!\n", "\n", "(3,)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aedb234d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "allquotients reshaped!\n", "\n", "(3,)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeaaa17d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "allquotients reshaped!\n", "\n", "(3,)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aea981150>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.33333333 0.33333333 0.33333333]\n", " [ 0.33333333 0.33333333 0.33333333]\n", " [ 0.16666667 0.33333333 0.5 ]\n", " [ 0.16666667 0.33333333 0.5 ]]\n", "\n", "\n", "\n", "\n", "[[ 4. 4. 4.]\n", " [ 4. 4. 4.]\n", " [ 2. 4. 6.]\n", " [ 2. 4. 6.]]\n" ] } ], "source": [ "# TODO PQN normalization, and log-transformation, \n", "# and some feature selection (above certain threshold of intensity, use principal components), et\n", "\n", "def pqn_normalize(X, integral_first=False, plot=False):\n", " '''\n", " Take a feature table and run PQN normalization on it\n", " '''\n", " # normalize by sum of intensities in each sample first. Not necessary\n", " if integral_first: \n", " sample_sums = np.sum(X, axis=1)\n", " X = (X / sample_sums[:,np.newaxis])\n", " \n", " # Get the median value of each feature across all samples\n", " mean_intensities = np.median(X, axis=0)\n", " \n", " # Divde each feature by the median value of each feature - \n", " # these are the quotients for each feature\n", " X_quotients = (X / mean_intensities[np.newaxis,:])\n", " \n", " if plot: # plot the distribution of quotients from one sample\n", " for i in range(1,len(X_quotients[:,1])):\n", " print 'allquotients reshaped!\\n\\n', \n", " #all_quotients = X_quotients.reshape(np.prod(X_quotients.shape))\n", " all_quotients = X_quotients[i,:]\n", " print all_quotients.shape\n", " x = np.random.normal(loc=0, scale=1, size=len(all_quotients))\n", " sns.violinplot(all_quotients)\n", " plt.title(\"median val: %f\\nMax val=%f\" % (np.median(all_quotients), np.max(all_quotients)))\n", " plt.plot( title=\"median val: \")#%f\" % np.median(all_quotients))\n", " plt.xlim([-0.5, 5])\n", " plt.show()\n", "\n", " # Define a quotient for each sample as the median of the feature-specific quotients\n", " # in that sample\n", " sample_quotients = np.median(X_quotients, axis=1)\n", " \n", " # Quotient normalize each samples\n", " X_pqn = X / sample_quotients[:,np.newaxis]\n", " return X_pqn\n", "\n", "# Make a fake sample, with 2 samples at 1x and 2x dilutions\n", "X_toy = np.array([[1,1,1,],\n", " [2,2,2],\n", " [3,6,9],\n", " [6,12,18]], dtype=float)\n", "print X_toy\n", "print X_toy.reshape(1, np.prod(X_toy.shape))\n", "X_toy_pqn_int = pqn_normalize(X_toy, integral_first=True, plot=True)\n", "print X_toy_pqn_int\n", "\n", "print '\\n\\n\\n'\n", "X_toy_pqn = pqn_normalize(X_toy)\n", "print X_toy_pqn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> pqn normalize your features </h2>" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2124.97987924 752.86926394 2508.6152954 ..., 1044.09370494\n", " 547.68505664 278.65249197]\n", " [ 2200.37752595 13.10681922 2438.94984256 ..., 1541.9009782\n", " 923.30137633 358.92734397]\n", " [ 2212.9477802 1302.88491744 2609.18808287 ..., 2615.11606355\n", " 1693.85040256 667.35292475]\n", " ..., \n", " [ 203.074745 121.40029203 482.49880597 ..., 473.55225062\n", " 219.4965772 208.20773664]\n", " [ 195.32795482 116.76917657 464.09269145 ..., 455.48742466\n", " 211.12333547 200.26513575]\n", " [ 206.41096963 123.39472342 490.42556419 ..., 481.33202986\n", " 223.10258881 211.62828892]]\n" ] } ], "source": [ "X_pqn = pqn_normalize(X)\n", "print X_pqn" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def roc_curve_cv(X, y, clf, cross_val,\n", " path='/home/irockafe/Desktop/roc.pdf',\n", " save=False, plot=True): \n", " t1 = time.time()\n", " # collect vals for the ROC curves\n", " tpr_list = []\n", " mean_fpr = np.linspace(0,1,100)\n", " auc_list = []\n", " \n", " # Get the false-positive and true-positive rate\n", " for i, (train, test) in enumerate(cross_val):\n", " clf.fit(X[train], y[train])\n", " y_pred = clf.predict_proba(X[test])[:,1]\n", " \n", " # get fpr, tpr\n", " fpr, tpr, thresholds = roc_curve(y[test], y_pred)\n", " roc_auc = auc(fpr, tpr)\n", " #print 'AUC', roc_auc\n", " #sns.plt.plot(fpr, tpr, lw=10, alpha=0.6, label='ROC - AUC = %0.2f' % roc_auc,)\n", " #sns.plt.show()\n", " tpr_list.append(interp(mean_fpr, fpr, tpr))\n", " tpr_list[-1][0] = 0.0\n", " auc_list.append(roc_auc)\n", " \n", " if (i % 10 == 0):\n", " print '{perc}% done! {time}s elapsed'.format(perc=100*float(i)/cross_val.n_iter, time=(time.time() - t1))\n", " \n", " \n", " \n", " \n", " # get mean tpr and fpr\n", " mean_tpr = np.mean(tpr_list, axis=0)\n", " # make sure it ends up at 1.0\n", " mean_tpr[-1] = 1.0\n", " mean_auc = auc(mean_fpr, mean_tpr)\n", " std_auc = np.std(auc_list)\n", " \n", " if plot:\n", " # plot mean auc\n", " plt.plot(mean_fpr, mean_tpr, label='Mean ROC - AUC = %0.2f $\\pm$ %0.2f' % (mean_auc, \n", " std_auc),\n", " lw=5, color='b')\n", "\n", " # plot luck-line\n", " plt.plot([0,1], [0,1], linestyle = '--', lw=2, color='r',\n", " label='Luck', alpha=0.5) \n", "\n", " # plot 1-std\n", " std_tpr = np.std(tpr_list, axis=0)\n", " tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", " tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", " plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=0.2,\n", " label=r'$\\pm$ 1 stdev')\n", "\n", " plt.xlim([-0.05, 1.05])\n", " plt.ylim([-0.05, 1.05])\n", " plt.xlabel('False Positive Rate')\n", " plt.ylabel('True Positive Rate')\n", " plt.title('ROC curve, {iters} iterations of {cv} cross validation'.format(\n", " iters=cross_val.n_iter, cv='{train}:{test}'.format(test=cross_val.test_size, train=(1-cross_val.test_size)))\n", " )\n", " plt.legend(loc=\"lower right\")\n", "\n", " if save:\n", " plt.savefig(path, format='pdf')\n", "\n", "\n", " plt.show()\n", " return tpr_list, auc_list, mean_fpr" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "0.3\n", "0.0% done! 4.60304903984s elapsed\n" ] }, { "data": { "image/png": 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Ph6IgIqcB7wDPuNvTReSf6TbMYrF0j6qqMBdfPIodO9p3CGRkaOfSYLZFIIDv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\nKawDjgLO6CFb+h2Nwyupdh3de29ewox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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeda60050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "rf_estimators = 1000\n", "n_iter = 3\n", "test_size = 0.3\n", "random_state = 1\n", "cross_val_rf = StratifiedShuffleSplit(y, n_iter=n_iter, test_size=test_size, random_state=random_state)\n", "clf_rf = RandomForestClassifier(n_estimators=rf_estimators, random_state=random_state)\n", "rf_graph_path = '''/home/irockafe/Dropbox (MIT)/Alm_Lab/\n", "projects/revo_healthcare/notebooks/MTBLS17/\n", "exploratory/rf_roc_{trees}trees_{cv}cviter.pdf'''.format(trees=rf_estimators, cv=n_iter)\n", "\n", "print cross_val_rf.n_iter\n", "print cross_val_rf.test_size\n", "\n", "tpr_vals, auc_vals, mean_fpr = roc_curve_cv(X_pqn, y, clf_rf, cross_val_rf,\n", " path=rf_graph_path, save=False)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0% done! 4.44582486153s elapsed\n" ] }, { "data": { "image/png": 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FaH36pK5NS4uxSsFiaWN27IBvf1vx+RIrhP79zVz/oYd2bLivhnzHLTIMt5Sd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\nTQpr1nRh796mx0qOjFQWLqzBqTxuWsXXT/ipqd9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeaaefad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For adaboosted\n", "n_iter = 3\n", "test_size = 0.3\n", "random_state = 1\n", "adaboost_estimators = 200\n", "adaboost_path = '''/home/irockafe/Dropbox (MIT)/Alm_Lab/\n", "projects/revo_healthcare/notebooks/MTBLS17/\n", "exploratory/adaboost_roc_{trees}trees_{cv}cviter.pdf'''.format(trees=adaboost_estimators, \n", " cv=n_iter)\n", "\n", "\n", "cross_val_adaboost = StratifiedShuffleSplit(y, n_iter=n_iter, test_size=test_size, random_state=random_state)\n", "clf = AdaBoostClassifier(n_estimators=adaboost_estimators, random_state=random_state)\n", "adaboost_tpr, adaboost_auc, adaboost_fpr = roc_curve_cv(X_pqn, y, clf, cross_val_adaboost,\n", " path=adaboost_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Classifier looks like garbage </h2>" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Make a null model AUC curve\n", "\n", "def make_null_model(X, y, clf, cross_val, random_state=1, num_shuffles=5, plot=True):\n", " '''\n", " Runs the true model, then sanity-checks by:\n", " \n", " Shuffles class labels and then builds cross-validated ROC curves from them.\n", " Compares true AUC vs. shuffled auc by t-test (assumes normality of AUC curve)\n", " '''\n", " null_aucs = []\n", " print y.shape\n", " print X.shape\n", " tpr_true, auc_true, fpr_true = roc_curve_cv(X, y, clf, cross_val)\n", " # shuffle y lots of times\n", " for i in range(0, num_shuffles):\n", " #Iterate through the shuffled y vals and repeat with appropriate params\n", " # Retain the auc vals for final plotting of distribution\n", " y_shuffle = shuffle(y)\n", " cross_val.y = y_shuffle\n", " cross_val.y_indices = y_shuffle\n", " print 'Number of differences b/t original and shuffle: %s' % (y == cross_val.y).sum()\n", " # Get auc values for number of iterations\n", " tpr, auc, fpr = roc_curve_cv(X, y_shuffle, clf, cross_val, plot=False)\n", " \n", " null_aucs.append(auc)\n", " \n", " \n", " #plot the outcome\n", " if plot:\n", " flattened_aucs = [j for i in null_aucs for j in i]\n", " my_dict = {'true_auc': auc_true, 'null_auc': flattened_aucs}\n", " df_poop = pd.DataFrame.from_dict(my_dict, orient='index').T\n", " df_tidy = pd.melt(df_poop, value_vars=['true_auc', 'null_auc'],\n", " value_name='auc', var_name='AUC_type')\n", " #print flattened_aucs\n", " sns.violinplot(x='AUC_type', y='auc',\n", " inner='points', data=df_tidy)\n", " # Plot distribution of AUC vals \n", " plt.title(\"Distribution of aucs\")\n", " #sns.plt.ylabel('count')\n", " plt.xlabel('AUC')\n", " #sns.plt.plot(auc_true, 0, color='red', markersize=10)\n", " plt.show()\n", " # Do a quick t-test to see if odds of randomly getting an AUC that good\n", " return auc_true, null_aucs\n" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(262,)\n", "(262, 1216)\n", "0.0% done! 4.80081295967s elapsed\n", "20.0% done! 50.7689909935s elapsed\n", "40.0% done! 98.2501869202s elapsed\n", "60.0% done! 148.336402893s elapsed\n", "80.0% done! 199.278007984s elapsed\n" ] }, { "data": { "image/png": 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tMd9/3ziTx8F7O1nJpSisBBaIyFyMGBwPnDhonzXAwcDTItIAbA98kEObLJas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\nPcsHH3zAU0895bZ+zpw5NGvWjOnTp3tLVEOAEBCb1+x292s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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeabbec90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of differences b/t original and shuffle: 152\n", "0.0% done! 5.15431714058s elapsed\n", "20.0% done! 56.63083601s elapsed\n", "40.0% done! 116.88668108s elapsed\n", "60.0% done! 179.030208111s elapsed\n", "80.0% done! 230.072277069s elapsed\n", "Number of differences b/t original and shuffle: 158\n", "0.0% done! 6.68298602104s elapsed\n", "20.0% done! 59.3434119225s elapsed\n", "40.0% done! 108.041468859s elapsed\n", "60.0% done! 156.177263975s elapsed\n", "80.0% done! 205.048995972s elapsed\n", "Number of differences b/t original and shuffle: 160\n", "0.0% done! 5.41559100151s elapsed\n", "20.0% done! 58.0392251015s elapsed\n", "40.0% done! 110.622425079s elapsed\n", "60.0% done! 163.482223988s elapsed\n", "80.0% done! 222.728057146s elapsed\n", "Number of differences b/t original and shuffle: 148\n", "0.0% done! 5.05552601814s elapsed\n", "20.0% done! 56.4528861046s elapsed\n", "40.0% done! 110.469285011s elapsed\n", "60.0% done! 166.352390051s elapsed\n", "80.0% done! 221.891416073s elapsed\n", "Number of differences b/t original and shuffle: 152\n", "0.0% done! 4.89703607559s elapsed\n", "20.0% done! 61.2473490238s elapsed\n", "40.0% done! 121.884550095s elapsed\n", "60.0% done! 172.902311087s elapsed\n", "80.0% done! 225.709186077s elapsed\n" ] }, { "data": { "image/png": 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9tVfZvmMn7RMM1/Ur4YQ2FTiaqDHaZa/eObxW+fBUJYex3cr4dHM8X87+jM8+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\nkXPnChISErnwwgsPJVx1GLZ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b3ewqt/PRZjdfbnM16rm/3ObinRwPZ555Jg888IDOPWgk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\nmZnlHBTMzCz3NzRGq9kqurUyAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeaa3f710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make a null model AUC curve & compare it to null-model\n", "\n", "# Random forest magic!\n", "rf_estimators = 1000\n", "n_iter = 50\n", "test_size = 0.3\n", "random_state = 1\n", "cross_val_rf = StratifiedShuffleSplit(y, n_iter=n_iter, test_size=test_size, random_state=random_state)\n", "clf_rf = RandomForestClassifier(n_estimators=rf_estimators, random_state=random_state)\n", "\n", "true_auc, all_aucs = make_null_model(X_pqn, y, clf_rf, cross_val_rf, num_shuffles=5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AUC_type auc\n", "0 true_auc 0.886364\n", "1 true_auc 0.965909\n", "2 true_auc 0.704545\n", "3 true_auc 0.772727\n", "4 true_auc 0.909091\n" ] }, { "data": { "image/png": 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kyNA4VykS3caNG3j0sQeJmBGy5pbgHtz9LKmJzO5xkHN6Gc5CD2vWvMcvf/k4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\n3t8mhOh3p512Bvv27eHNN1+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"text/plain": [ "<matplotlib.figure.Figure at 0x7faaaec15b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# make dataframe from true and false aucs\n", "flattened_aucs = [j for i in all_aucs for j in i]\n", "my_dict = {'true_auc': true_auc, 'null_auc': flattened_aucs}\n", "df_poop = pd.DataFrame.from_dict(my_dict, orient='index').T\n", "df_tidy = pd.melt(df_poop, value_vars=['true_auc', 'null_auc'],\n", " value_name='auc', var_name='AUC_type')\n", "print df_tidy.head()\n", "#print flattened_aucs\n", "sns.violinplot(x='AUC_type', y='auc',\n", " inner='points', data=df_tidy, bw=0.7)\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Let's check out some PCA plots </h2>" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('red', 0, 'Cirrhosis'), ('blue', 1, 'Liver cancer')]\n", "(184,)\n", "(78,)\n" ] }, { "data": { "image/png": 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ePnWitEv/RBzVB3+CwmHCt79C9eYrqSNIsMxL1afrCD1ZafvrSkrA6yW4ZzuN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\n2zjb1QXFvv3UtH+ekLxoN8bjsG+fjdb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9aeab64610>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[('red', 0, 'Cirrhosis'), ('blue', 1, 'Liver cancer')]\n", "(184,)\n", "(78,)\n" ] }, { "data": { "image/png": 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rTVp4+mljPvABY4qLjfF4jHG5jJkxw5hFi+wtO9uYadPsNk8/ffr+H/iAMWed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\nRHERxcVsz3EWh9+gVj7UM/c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WUMIXERFpASV8ERGRFvgJDsr6rLCBsKsAAAAA\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f9ae8d1ef90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.decomposition import PCA\n", "\n", "# Check PCA of things\n", "def PCA_plot(X, y, n_components, plot_color, class_nums, class_names, title='PCA'):\n", " pca = PCA(n_components=n_components)\n", " X_pca = pca.fit(X).transform(X)\n", "\n", " print zip(plot_color, class_nums, class_names)\n", " for color, i, target_name in zip(plot_color, class_nums, class_names):\n", " \n", " # plot one class at a time, first plot all classes y == 0\n", " #print color\n", " #print y == i\n", " xvals = X_pca[y == i, 0]\n", " print xvals.shape\n", " yvals = X_pca[y == i, 1]\n", " plt.scatter(xvals, yvals, color=color, alpha=0.8, label=target_name)\n", "\n", " plt.legend(bbox_to_anchor=(1.01,1), loc='upper left', shadow=False)#, scatterpoints=1)\n", " plt.title('PCA of data')\n", " plt.show()\n", "\n", "\n", "PCA_plot(X_pqn, y, 2, ['red', 'blue'], [0,1], ['Cirrhosis', 'Liver cancer'])\n", "PCA_plot(X, y, 2, ['red', 'blue'], [0,1], ['Cirrhosis', 'Liver cancer'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> Look above. Top graph is after pqn-normalization. Looks like it does a good job... </h2>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
karlstroetmann/Formal-Languages
Ply/Conflicts-Resolved.ipynb
1
8073
{ "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": [ "# Resolving Conflicts Using *Precedence Declarations*\n", "\n", "This file shows how *shift/reduce* and *reduce/reduce* conflicts can be resolved using *operator precedence declarations*.\n", "The following grammar is *ambiguous* because it does not specify the precedence of the arithmetical operators:\n", "```\n", " expr : expr '+' expr\n", " | expr '-' expr\n", " | expr '*' expr\n", " | expr '/' expr\n", " | expr '^' expr\n", " | '(' expr ')'\n", " | NUMBER \n", " ;\n", "```\n", "We will see how the use of precedence declarations can be used to resolve *shift/reduce-conflicts*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Specification of the Scanner\n", "\n", "We implement a minimal scanner for arithmetic expressions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import ply.lex as lex\n", "\n", "tokens = [ 'NUMBER' ]\n", "\n", "def t_NUMBER(t):\n", " r'0|[1-9][0-9]*'\n", " t.value = int(t.value)\n", " return t\n", "\n", "literals = ['+', '-', '*', '/', '^', '(', ')']\n", "\n", "t_ignore = ' \\t'\n", "\n", "def t_newline(t):\n", " r'\\n+'\n", " t.lexer.lineno += t.value.count('\\n')\n", "\n", "def t_error(t):\n", " print(f\"Illegal character '{t.value[0]}'\")\n", " t.lexer.skip(1)\n", "\n", "__file__ = 'main'\n", "\n", "lexer = lex.lex()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Specification of the Parser" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import ply.yacc as yacc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *start variable* of our grammar is `expr`, but we don't have to specify that. The default\n", "start variable is the first variable that is defined." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "start = 'expr'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following *operator precedence declarations* declare that the operators `'+'`and `'-'` have a lower precedence than the operators `'*'`and `'/'`. The operator `'^'` has the highest precedence. Furthermore, the declarations specify that the operators `'+'`, `'-'`, `'*'`, and `'/'` are *left associative*, while the operator `'^'` is declared as *right associative* using the keyword `right`.\n", "Operators can also be defined as being *non-associative* using the keyword `nonassoc`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "precedence = (\n", " ('left', '+', '-') , # precedence 1\n", " ('left', '*', '/'), # precedence 2\n", " ('right', '^') # precedence 3\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def p_expr_plus(p):\n", " \"expr : expr '+' expr\"\n", " p[0] = ('+', p[1], p[3])\n", " \n", "def p_expr_minus(p):\n", " \"expr : expr '-' expr\"\n", " p[0] = ('-', p[1], p[3])\n", " \n", "def p_expr_mult(p): \n", " \"expr : expr '*' expr\"\n", " p[0] = ('*', p[1], p[3])\n", " \n", "def p_expr_div(p): \n", " \"expr : expr '/' expr\"\n", " p[0] = ('/', p[1], p[3]) \n", "\n", "def p_expr_power(p): \n", " \"expr : expr '^' expr\"\n", " p[0] = ('^', p[1], p[3])\n", "\n", "def p_expr_paren(p): \n", " \"expr : '(' expr ')'\"\n", " p[0] = p[2]\n", " \n", "def p_expr_NUMBER(p):\n", " \"expr : NUMBER\"\n", " p[0] = p[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def p_error(p):\n", " if p:\n", " print(f\"Syntax error at character number {p.lexer.lexpos} at token '{p.value}' in line {p.lexer.lineno}.\")\n", " else:\n", " print('Syntax error at end of input.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting the optional argument `write_tables` to `False` <B style=\"color:red\">is required</B> to prevent an obscure bug where the parser generator tries to read an empty parse table." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parser = yacc.yacc(write_tables=False, debug=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As there are no warnings all conflicts have been resolved using the precedence declarations.\n", "Let's look at the action table that is generated. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "!type parser.out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "!cat parser.out" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%run ../ANTLR4-Python/AST-2-Dot.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `test(s)` takes a string `s` as its argument an tries to parse this string. If all goes well, an abstract syntax tree is returned.\n", "If the string can't be parsed, an error message is printed by the parser." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def test(s):\n", " t = yacc.parse(s)\n", " d = tuple2dot(t)\n", " display(d)\n", " return t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test('2^3*4+5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test('1+2*3^4')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test('1 + 2 * -3^4')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.10.0" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }
gpl-2.0
reallyasi9/riddlers
lower-face-dice-game/lower_face_dice_game.ipynb
1
211567
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def roll_me(N=100):\n", " rolls = 1 # Always have to roll at least once\n", " r1 = np.random.randint(1, N+1) # Note that randint uses the interval [low, high)\n", " r2 = r1\n", " while r2 >= r1:\n", " r1 = r2\n", " r2 = np.random.randint(1, N+1)\n", " rolls += 1\n", " return rolls" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roll_me()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3, 2, 2, 3, 3, 2, 3, 2, 3, 2]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[roll_me() for i in range(10)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.7090000000000001" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean([roll_me() for i in range(1000)])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.array([[n+1 for i in range(1000)] for n in range(1,200)])\n", "jitter_x = x + np.random.rand(*x.shape)\n", "trials = np.array([[roll_me(n+1) for i in range(1000)] for n in range(1,200)])\n", "jitter_trials = trials + np.random.rand(*trials.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fSupQb9AgjZH8/HwAjoo8dcqX0jAMwzBM/aGkDvUGDdIYkZY+1mq1AICzZwEiHwrEMAzD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7VaRdLPe8J4MtuSfD5fQnpcW2XPiCRJGD58ON577z289957uPXWW8VjjDFRg2Tw4MH1Pt9gMMDn\n80GlUsW8zzFjxoi1ESIXWfJ4PCI4NJYN83EY/Ew/MjHgP6g8qPEu48zMTOTk5CArKytqlcu2OpaD\nJzOVlZXiB8dms6G8vFwcW/7jX1VVJX70eXlt/mPGE6NYLjXxyxuRi5iZTCZkZWWJpI330PDeGn5W\nxbs4DQYD9Ho9lEplAo7SmREOh1FZWYny8nKUlpbCYrGIxJLfLBaLSGz4iqORP3yxXPrgS5rzJE2t\nVotkjF+m44vOmc1mZGdnIy8vT/zXbDa3marJXq9XLLDGk3L+XeYBuLq6GtXV1eIMurq6Gm63O+os\nPBAINLofnmBFrlnDL9fx3hez2Qyz2Sz+zS898UtNPOFoK0KhkPit4MfXarWKyx48OeTHOTIR55eb\neA9S5Li4xvDL0vxyaGpqqrjMzH8TMjMzkZGRAZ1OB7VaLXoWI9cU4uvW6PX6Vvld9vl8qKiowLFj\nx8QCh7xn5+67727Sa9nt9oT9LrbKnhEA2L59Oy644AKkpqbi//7v/3D77bfD5XLhrrvuwvLly6HX\n61FcXNzowNNYButwsQxg5WexkV9+Ggx65vBuVt6Tw7+q/DLZqSuQkvhEnp3xXhsa9Nwy+GWCyDVs\neC9EWxsz0drw32X+exFJkiQxMJS+u02LiUBiJ3a02mQEAP75z39i+vTpcLvdossQqK3CumzZMlxz\nzTVJbiEhhBBC4tWqkxEAOHLkCB555BFs27YNSqUSw4YNw8MPPwyz2ZzsphFCCCGkBbT6ZKSpSktL\n8fzzz+PAgQMwmUy4/fbbcdFFFyW7WaQBW7ZswVdffQWtVotQKCRmxSgUCjDGYLfb8dhjj8FgMEQ9\nz+Vy4cUXX8TWrVuRmpqKG264Addddx11eSeJ2+3G448/jszMTMycObPebQ4fPowXXngBJSUlyMzM\nxJ133on+/fs3+Jo7d+7Eq6++irKyMhQWFmL69Ono1q3bmXoLJAJjDK+99hp++OEHLF68OOrv6ptv\nvsHmzZuh1WrFYGG/3w+lUikuYz/22GN1xhpUVlZiwYIF+O2335CWlobJkydj6NChiX5rHU44HMY3\n33yDn376CSqVCgMHDsTFF1/c4G/liRMnsGDBAhw4cABmsxm333571Ppwp9q7dy9efvllHD9+HLm5\nubj33nvRu3fvpjeUtSPPPvss02q1DEDUbfz48czr9Sa7eaQeDz74YJ3PK/KmUCjYd999F/Wcjz76\niOXm5tbZ9pJLLmGlpaVJeicd18GDB9k555zDALBhw4bVeTwcDrMnnniCpaam1vnMbrvtNhYIBKK2\n9/l87K677mIymSxqW6VSyR599FEWDocT9dY6JLfbzW666Sbx9xcKhaIeHzduXKN/s2q1mpWUlEQ9\n5/XXX2dpaWl1tr322muZ0+lM5NvrUH799Vd27rnn1jnul19+OauoqKiz/TPPPFNvDJ0wYUKdGBoM\nBtkDDzzAFApF1LZyuZzdf//9Tf47bTfJyNtvv80AMK1Wy5599lm2e/du9tlnn7H+/fszAOzee+9N\ndhNJPd5//30GgF1xxRXs7bffZkuXLmXLly9nS5cuZe+//36dH7VNmzYxhULBZDIZu//++9mOHTvY\nhg0b2FVXXcUAsCFDhlCwSqDNmzez9PR08UM0ZsyYOts899xzDABLS0tjr7zyCtu9ezf76KOPWJ8+\nfRgA9uijj0Ztf8899zAALDc3l7333ntsz549bPHixaywsJABYG+//Xai3l6HU1FRwfr16yc+T5PJ\nVGebefPmMQBs3Lhx7J///Cd799132fLly9mSJUvYBx98UCfIrVy5kgFgKpWKPfnkk2zXrl1s9erV\nbNCgQQwAu+WWWxL19jqUr7/+mul0OgaAjR49mi1dupS9/PLLrEuXLgwAmzx5ctT2b775ZqMxdPr0\n6VHbz549mwFgmZmZbNGiRWzPnj1s2bJlrGvXrgwAW7BgQZPa2y6SEYfDwUwmE0tJSWFbt26Neszp\ndLLCwkIml8vZsWPHktRC0pC33nqLAWB//etfT7ttOBxmAwYMYADYBx98EPVYKBRil19+OQPA1qxZ\nc6aaS05xyy23MEmSxNnyqFGjoh6vqKhgWq2WabVatnfv3qjHrFYry8jIYCqVitlsNsZY7ZmcJEks\nPz+fVVZWRm2/b98+plAoWH5+fp2zddIy+EndqFGjmF6vZ2q1us42c+fOZQDYO++8c9rX83q9LD8/\nn8lkMrZx48aoxzweD+vVqxcDwH777bcWew+k1rhx45hKpaqTvJ84cYJJksRUKpW4z263s/T0dKZS\nqdj27dujtnc4HKygoIDJ5XJ24sQJxhhjxcXFTKFQsIyM/9/e3UdFcZ1/AP/CCiwLAgs04i6+JODy\nIoFEQEOiiAbjqbVKeqwSgy5CakzrIY2xHqPN8bRJ0zSp2nqS2Lw0JjRajUnU4AnyjigICNpKqtYo\nsshrFFZWFwR29/n9YWd03OVFf4EFfD7n8Id3npm5d66z8+zszL2+VF9fL4m/dOkSubq6klKppK6u\nrn7Xd0S865SdnY3W1lYkJSXhkUcekSxzd3dHUlISzGYzcnJy7FRD1hPhVbOJEyeitbUVe/bswebN\nm5Geni7OPyQ4f/48KisrERUVhcWLF0uWOTo6YuXKlQCAb775ZnAqz7Bt2zacPXsW69evBwCrcUoy\nMjJgNBqxcuVKaDQayTJvb28sXrwYnZ2dKCgoAADs2bMHRIT169dbTfUwadIkxMfHo76+HlVVVQPY\nqvvX8uXLUVlZKc5NYmvcmdvP2cbGRqSnp2Pz5s3Yu3ev1ZgqR44cQX19PZ5++mk88cQTkmVyuVwc\nQ+rQoUMD0Zz72o4dO6DT6ZCSkiIpd3Z2BhFJ5nXLysqCXq/HsmXLEB4eLokfPXo0nn32Wck19Isv\nvoDJZMKaNWugUqkk8f7+/pg/fz70ej3Kysr6Xd8Rk4wAgFartbl86tSpACB+4LGhQxjqePv27Xjg\ngQeQmJiItWvXQqvVIiAgABkZGWJsX/0sPGTF/Tx4vLy8oNFoxNFK3d3dJcvv9tzMzs6Gg4MDkpKS\nbMZzHw+sUaNGYcqUKbBYLLh27ZpVfwK3ztmNGzdCpVJBq9Vi7dq1WLx4MYKCgnDs2DExls9Z+3Fz\nc8OYMWOsyt955x0AwPTp08Wyu+0nIX758uX9iu+PoTd83D04ceIEACAkJMTmcmH+moaGhkGrE+uf\nxsZGAMCxY8cwduxYpKam4qGHHkJFRQX+9re/YcmSJTh37hz8/f377OeJEycCwD3PQ8LuXUtLCwDr\nibgqKysBAEFBQTbXu/3cJCKcOHECKpWqx+kchHju44ElTERqa2I14Zw9evQoAgMDodVqoVarkZeX\nh507d+JnP/sZvvvuO7i7u/f7s5n7c+AREd577z38/ve/h4ODg3g3E+j/NVTop8rKSowePdrqrkhP\n8f0xIpIRYZ6Dnmbw5enZhy7hgy00NBQFBQXih9+KFSvg4+OD1157Dbt378batWvFfu5tVECe5tw+\nhGTkzm9i165dg4eHB+Ryuc31bj83Ozo6YDabe+1fYRRN7uOB1VN/ArfO2ZkzZ+LgwYPi3ZMVK1ZA\nJpMhPT0dmZmZ+PnPf97nOcv9OTj0ej2ef/557N27FzKZDDt27JD8bGYwGODg4ABvb2+b69/ZTwaD\nAQ8++GCP/Sac13fTryPiZxrhg87W9OQAcPXqVQA3b1uxoSUqKgpRUVHIysqy+haWmJgI4NatPqGf\nb9y4YXNb169fh9ls5n62A2GmYT8/P0m5XC5HZ2dnj5P43X5uCnNJ9dS/d8azgdNTfwI3f1qLj4/H\ngQMHrH7GWbJkCYD+n7PcnwPvyJEjiIiIwN69e6HRaFBSUmL184pcLgcR9TiP0p39JJfLf/DzdEQk\nI2q1GgBQXV1tc7nwG+ekSZMGrU6sf7Zs2YLjx4/D39/faplQJnwTE/r54sWLNrfV3NwMgPvZHoRn\nRu4cGVmtVqOzs7PHadBvPzdlMhn8/Pyg0+l6nPyM+3hw9NSfALB3717k5OTYnJhv3LhxAPicHSp2\n7dqF2bNno66uDmvWrMHJkyfF57Rud7f9pFar0djYiI6ODpvx93LNHRHJiDCK46lTp2wuLykpAQCb\nncCGLuFWsZBd99XPxcXFALif7UH4lqRQKCTld3tuPvroo+jo6MCFCxdsxnMfD46e+rMvfM4OHbW1\nteLbSvv27cPmzZt77M+77adHH30UFosFp0+f7ld8f4yIZCQyMhIAkJuba7XMbDaLb2Twf/jh5csv\nvwQAzJgxA8DNn3QA2/0MAPv37wfA/WwPwmuCd966Fc5NW6/Vd3Z2IjMzE8Ctvu2tj7///nuUlJRA\nqVTysPADrKf+7MvdnLNEhAMHDgDgc3YgfP755+jq6kJaWhoWLlzYa2xv/WQymayuoUK8rfO6ra0N\nBQUFkMvlCAsL63+F+z0iyRBmNBrFQc9KS0vFcrPZTOvXrycAFBkZySNzDkEbNmygl19+2WoQq+rq\navL19SUA9O2334rl06ZNszng0j/+8Q8CQEqlkgwGw6DUnd3y1ltvEQD64IMPJOWtra2kUCjIzc2N\nTp06JZZ3d3fTr371K3H0XcHp06cJAKlUKskghUajkRISEggAPffccwPfoPtceXk5AaClS5dKyi0W\nC6WmptJbb71ltU5FRQXJ5XJycnKilpYWIrrZz8KgZ7m5uZLtvPHGGwSANBoNmUymgW3QfWj+/PkE\ngCorK8Uys9lM7e3tVsfbaDSSl5cXOTs7U1lZmSR+3bp1BICio6PFa2hNTQ3JZDLy8fGh6upqMb6j\no4OWLl1KAGjJkiV3Vd8RkYwQ3Ro5UCaT0apVq2jLli00Z84csaywsNDeVWQ2REdHEwBatGgR5eXl\n0fHjx+n1118XE5GkpCRJfGlpqThnyaJFi+gvf/kLLVu2TBy+evv27XZqyf0pLy+PEhISKCwsjABQ\nYGAgzZ07ly5evCjGCMPBOzs7U1paGm3ZsoViY2PFIcIrKiok2xSGg/fw8KANGzbQ22+/LQ5R7uPj\nQzqdbpBbef+orq6mpKQkmjlzJgEgX19fiouLo8zMTCK6mVwI80KtWrWKioqKqLS0lNatWycOPb5x\n40bJNr/44gsCQA4ODpSSkkJbt26ln/70p+I5e/DgQXs0dcQTRqQODw+ncePGkUKhEI+5s7MzJSYm\nSuaFEoaDHzVqFL3wwgtW19CioiLJ9oXh4BUKBa1bt47+/Oc/U1RUFAGg0aNH07lz5+6qviMmGSEi\n2rdvH6nVasmkPWFhYeKJxIae8+fPi0O83/4nk8koNTXV5gSHZWVlVpM/qVQq+vDDD/nu1yBLTk62\n6rs7h/62WCy0a9cueuCBByRxkZGRNr8kmEwm+utf/ype3IS/+Ph4q6Gq2Q/rk08+sTn53ZtvvinG\nlJeXk0ajsYpxcXGh9evX2xyq/9ChQ+KcKMKfRqOhffv2DWbz7iubNm0Sj7VSqaRx48ZRSEgIRUVF\nkZ+fH3l6elJra6sYb7FY6KuvvrK6hj788MN06NAhq+2bzWZ6//33rSZAnDFjBpWXl991fR2Ienjn\nbpi6du0acnJycOXKFUyYMAHx8fE8zsgQR0TIyMhAVlYWjEYjAgICkJycLD6Zb4vJZEJ2djbq6urg\n6+uLH//4x5LhjdngICIYjUaYzWZx6HAnJyebI3fq9Xrk5eVBr9fjoYcewuzZs3sdh6C+vh5HjhyB\n0WhEaGgoYmJiBqwd7JaOjg50dXWJ/eng4ABPT09JX5lMJuzevRtHjx5FV1cXJk+eDK1W2+NYTwDQ\n3t6O7OxsfP/991Cr1Zg7dy5GjRoRQ10NWUajEY6OjlafjUSEjo4Omw+0GgwG5Obm9vsa2tzcjMOH\nD6OtrQ3BwcGYPn36PY0bM+KSEcYYY4wNLyPibRrGGGOMDV+cjDDGGGPMrjgZYYwxxphdcTLCGGOM\nMbviZIQxxhhjdsXJCGOMMcbsipMRxhhjjNkVJyOMMcYYsytORhhjjDFmV5yMMAn8/vcAAAsHSURB\nVHaf6+zsRFtbm72rYTctLS3i0OeMMfvgZISxEaa1tRWnT5/ud/xzzz2HsLCwAazR0FVXVwd/f39s\n3bpVUn7q1Cm0t7fbqVaM3X84GWFshAkMDMTkyZOxZ8+efsVXVVVBr9cPcK2GpvPnz+PGjRtobW0V\ny95++21ERETctwkaY/bAyQhjI0xkZCSUSiUCAwP7FX/58mW4ubkNcK2GpitXrgCApP3R0dEYPXo0\noqKi7FUtxu47PH8zYyNMTk7OXcUbDAao1eoBqs3QZjAYAABKpVIsi4uLE8sZY4OD74wwNgw1Nzfj\nww8/RHx8PBISEkBE97QdIoLRaISvr+8PWqenn376nut0p+vXr+PAgQPi9iwWC+rq6mA0Gv/f2xa2\n8UO0nzF27zgZYWyYICLs3LkTTzzxBMaOHYuVK1ciLy8PBw4cQFNTkxi3du1avPvuu1brt7W1YcuW\nLZgyZQrGjx+PWbNmITs7G0QEHx8fq319+eWXeOyxx6BSqRAWFobVq1ejpqZGEmexWPDZZ5/h8ccf\nl9Rp//79uHz5cr/a9M0332Du3LkIDw9HcnIy6uvrJTHbt29HQkICMjIycObMGUyfPh3jxo3D+PHj\n8fXXX/f7+FVWVmL58uUICAiARqPB6tWr0djYCACS9hcVFSExMVHyHIlQ16+++goxMTHiMfnlL3+J\nixcv9rsOjLEeEGNsWHjjjTcIAAGg8ePHU1paGj311FMEgAoLC4mI6MaNGwSAJk+eLFm3traWJk6c\nSADI1dWVVCoVOTg4iNtbsWKFGGs2mykxMZEAkIuLC4WGhpKHhwcBoMDAQOro6BBjX3vtNXEbEyZM\noBdffJHmzJlDAKioqKjX9nR1ddGCBQvE9YU/Ly8vqqmpEePefPNNAkDJycmkUCgIAIWGhhIA8vPz\nI7PZ3Oexe+edd8Ttjxkzhjw9PSX7/Ne//iXGrlq1igBQTk6OWGaxWOjZZ58lAOTs7EyhoaHiNh58\n8EFqb2/vsw6MsZ5xMsLYMNDc3EzOzs40atQo+uSTT8QLcFlZGWm1WmptbSUiIr1eTwBoypQpkvXn\nzZtHjo6OtG3bNjIajUREdOHCBQoLCyMAtGbNGjH2008/JQA0e/ZsamlpISIik8lEzzzzDAGg0tJS\nIiJqamoiJycncnJyovT0dLJYLEREdOzYMUpOTia9Xt9rm15//XXxYp6RkUE1NTW0YsUKAkBpaWli\n3NatW8Wkwc3NjTIzM4mIaNasWQSAzp492+t+zpw5QzKZjMLDw6miooKIbiZcH330kbhdnU4nxi9f\nvtwqmdq5cycBoJkzZ9KVK1fEY5KUlEQAqLi4uNc6MMZ6x8kIY8NAVVUVAaDg4GD6+uuvxeTjTjU1\nNQSA4uLixLKmpiZydHQkrVZrFZ+VlUUA6Le//a1YFhcXR25ubtTQ0EBEN5OWpUuXEgDy9/cXk5l/\n//vfBIBCQkJ6rZMtFouFVCoVAaD//Oc/YrlerydXV1caO3asWPbHP/5RTBo++OADsfyll14iAHT4\n8OFe9/Xqq68SADp58qTVsnnz5hEAunz5sliWkJBgdbckPj6eXF1d6dKlS0REVF1dLSYiKpWKrl+/\n3u+2M8as8TMjjA0DQUFBiIiIwNmzZ7FgwQL4+PggPj4eR44ckcQJb4F4eHiIZYcOHYLFYsFPfvIT\nq+26uroCAGQymbj+0aNHsWDBArS1tSE5ORlBQUHYtWsX4uLiUFhYCIVCAQAIDg7Gww8/jDNnzoh1\neuqpp1BcXNxne6qqqtDQ0IDY2FiEhoaK5V5eXoiJiUFjYyO6uroAAFevXhX3l5qaKsbS/x5o7ezs\n7HVfBw8ehFqtRkRERI/tHzXq1ouFwjH09PQEcPMB2sOHD2P+/PkwGo1ISUmBRqPBZ599htjYWBQW\nFt63r0Yz9kPhZISxYcDJyQklJSX4+9//jsTERPj5+SEvLw9xcXEoKCgQ427cuAEAkMvlYllzczMA\nwN/f32q7wjDo3d3dAICamhqYTCaUlJQgNDQUn376KeLi4pCfn4/8/HwEBASI6zo7O+PYsWP46KOP\nsGTJEowZMwY5OTmIjY1FUVFRr+0RHoQNDg62WiZc2IU3XYQB2bRaLRwdb31kCSOkOjg49LqvpqYm\nqFQqm3Fms1nSfsD6GNbW1qK7uxtlZWUICQnBjh07EBsbi9zcXBQWFmLSpEm97p8x1jdORhgbJhQK\nBVJSUvDPf/4Tly5dwu9+9ztYLBZs2LBBjBEuuHTba7XCN/z//ve/VtsU7ogIdyGEi71Op0NCQgLK\ny8uRk5ODWbNm2byYu7m5ITU1Fbt370ZdXR02bdpkVSdb3N3dAQDXrl2zWlZTUwOlUinWW0hGoqOj\nJXEuLi4Abt656I2XlxcuXLggJh63E9p/+90VoZ1CoiYck9raWixcuBClpaXIy8vDk08+2WcixBjr\nH05GGBsGampqxDscwM2L6KuvvgqlUokTJ06I5cIFWkguAGD+/PlwdHTEH/7wB/EnD+BmwpKbmwsA\n4kR5Go0Gfn5+CA4Oxscff2yVABQXF+Pxxx9HU1OTzTpt2rQJHh4ekjrZMm3aNLi7uyM7OxstLS1i\neX5+PqqqqjBv3jwxCRDuVPzoRz+SbEP4t06n63VfCQkJaG1txZ/+9CdJeVNTE7799ltJ+4Gbd3yA\nW8cwICAAarUakyZNwscff4xp06ZJtlNaWoqYmBg0NDT0Wg/GWM94BFbGhoGYmBh0d3cjJSUFISEh\n6OrqQmZmJvR6PaZOnSrGOTk5AZAmI2q1GklJSUhPT0dERAR+8YtfwNfXF/v370dWVhYAiGN7ODs7\n48UXX8Qrr7yC8PBwvPDCCwgJCcHVq1eRn5+Pzz//HHK5HBaLBVOnTgURISUlBcHBwejs7ERmZiYM\nBgMee+yxXtvj5uaGZcuWYfv27YiLi8PKlSvR0NCAbdu2wcHBAatXrxZjhec6hKREMH78eADAhQsX\net3X888/j3fffRcbN25ERUUF4uPjceXKFbz33ntiMlVfX4/JkyeLx+D2Y+jk5IRf//rX+M1vfiM5\nJgaDAfn5+dizZw9cXFxs3nlhjPWTfZ+fZYz1h/BGyJ1/3t7eVFZWJsbp9XpSKpWSV2OJiNrb20mr\n1ZJMJpOsP336dJowYYLk7Ruz2UyvvPIKyeVyq/1NnTqVysvLiYhow4YNNuvk4+NDx48f77NNBoOB\nZsyYIVlXoVDQ+++/L4lLS0sjAFRQUCApLyoqIgD0zDPP9Lmv4uJiCgwMlOzLxcWFFi1aRABo3759\nYuxLL71Enp6e4mvNwjHZuHEjubq6WrU3OjpafN2ZMXZvHIh+oDGbGWMD6rvvvkN2djZ0Oh1cXV0R\nFBSEhQsXWr3JYTKZIJPJbD7PUF9fj6ysLHR3dyMyMhKRkZGor6+HTCbD2LFjJbFGoxH5+fmora2F\nt7c3IiMjodFoJDHnzp1DTk4OdDodFAoFgoKCsGDBgn6/XUL/+6koPz8fSqUSWq0WY8aMkcRcunQJ\nWVlZSE5Olrz10tnZiZdffhkLFy7EnDlz+tyXxWLB4cOHcebMGXh7e+PJJ5+Er68vTp48iUceeUT8\nWYiIYDabJfsStLe3Iz8/Hzqdrsdjwhi7e5yMMMYYY8yu+AFWxhhjjNkVJyOMMcYYsytORhhjjDFm\nV5yMMMYYY8yuOBlhjDHGmF1xMsIYY4wxu+JkhDHGGGN2xckIY4wxxuyKkxHGGGOM2RUnI4wxxhiz\nK05GGGOMMWZXnIwwxhhjzK44GWGMMcaYXf0f4CU7CLVfuU4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x69b6d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.xkcd()\n", "\n", "means = np.mean(trials, axis=1)\n", "model = [np.exp(1) for n in range(1,200)]\n", "\n", "plt.plot(means, label='simulation')\n", "plt.plot(model, '--', label='e')\n", "plt.legend(loc='lower right')\n", "plt.xlabel(\"sides on die\")\n", "plt.ylabel(\"ave number rolls\")\n", "plt.yticks([0,1,2,3,4])\n", "plt.ylim([0,4])\n", "plt.text(20, 2, '1000 simulations per\\nnumber of sides')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ElClTAHT0NeWOke/oqaysxIMPPhh3DAWIZrLzR5Ikfh2OPPJILF68mB9P4//x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"text/plain": [ "<matplotlib.figure.Figure at 0x55302e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xkcd()\n", "\n", "plt.plot(jitter_x, jitter_trials, 'k.', alpha=1/100, label=None)\n", "plt.plot(means, label=\"simulation means\")\n", "plt.plot(model, '--', label=\"e\")\n", "plt.legend()\n", "plt.xlabel(\"sides on die\")\n", "plt.ylabel(\"number of rolls\")\n", "plt.ylim([0,14])\n", "plt.xlim([0,200])\n", "plt.show()" ] } ], "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
wasit7/cs634
2017/week07/DQN.ipynb
1
22009
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-10-14 20:47:15,571] Making new env: FrozenLake-v0\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import gym\n", "import numpy as np\n", "import random\n", "import tensorflow as tf\n", "env = gym.make('FrozenLake-v0')\n", "tf.reset_default_graph()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#These lines establish the feed-forward part of the network used to choose actions\n", "inputs1 = tf.placeholder(shape=[1,16],dtype=tf.float32)\n", "W = tf.Variable(tf.random_uniform([16,4],0,0.01))\n", "Qout = tf.matmul(inputs1,W)\n", "predict = tf.argmax(Qout,1)\n", "\n", "#Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.\n", "nextQ = tf.placeholder(shape=[1,4],dtype=tf.float32)\n", "loss = tf.reduce_sum(tf.square(nextQ - Qout))\n", "trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)\n", "updateModel = trainer.minimize(loss)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percent of succesful episodes: 0.402%\n" ] } ], "source": [ "init = tf.global_variables_initializer()\n", "\n", "# Set learning parameters\n", "y = .99\n", "e = 0.1\n", "num_episodes = 2000\n", "#create lists to contain total rewards and steps per episode\n", "jList = []\n", "rList = []\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " for i in range(num_episodes):\n", " #Reset environment and get first new observation\n", " s = env.reset()\n", " rAll = 0\n", " d = False\n", " j = 0\n", " #The Q-Network\n", " while j < 99:\n", " j+=1\n", " #Choose an action by greedily (with e chance of random action) from the Q-network\n", " a,allQ = sess.run([predict,Qout],feed_dict={inputs1:np.identity(16)[s:s+1]})\n", " if np.random.rand(1) < e:\n", " a[0] = env.action_space.sample()\n", " #Get new state and reward from environment\n", " s1,r,d,_ = env.step(a[0])\n", " #Obtain the Q' values by feeding the new state through our network\n", " Q1 = sess.run(Qout,feed_dict={inputs1:np.identity(16)[s1:s1+1]})\n", " #Obtain maxQ' and set our target value for chosen action.\n", " maxQ1 = np.max(Q1)\n", " targetQ = allQ\n", " targetQ[0,a[0]] = r + y*maxQ1\n", " #Train our network using target and predicted Q values\n", " _,W1 = sess.run([updateModel,W],feed_dict={inputs1:np.identity(16)[s:s+1],nextQ:targetQ})\n", " rAll += r\n", " s = s1\n", " if d == True:\n", " #Reduce chance of random action as we train the model.\n", " e = 1./((i/50) + 10)\n", " break\n", " jList.append(j)\n", " rList.append(rAll)\n", "print(\"Percent of succesful episodes: \" + str(sum(rList)/num_episodes) + \"%\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x209e137ca20>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bDnU2fMVJ5ZxSUcy44hw+2nUo4L5TKooiasgVrnRXGp85aTSjhmVT7273lYBs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\nSqkU8f8Bg5Ymx051T9YAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x209e0f0bc18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(np.convolve(np.ones(100),rList,\"valid\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow_c]", "language": "python", "name": "conda-env-tensorflow_c-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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
whitews/flow_rate_qc
flow_rate_ks.ipynb
1
4071165
null
bsd-3-clause
jagarzone6/cmos
notebooks/Interface using tkinter.ipynb
2
3250
{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tkinter import *\n", "from tkinter import ttk\n", "import math\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab as plb\n", "def matrix(m_length,m_width):\n", " \"Return matrix with no homogeneus resitivity\"\n", " m = np.zeros((m_length,m_width))\n", " return m\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "size_m=5\n", "paint_matrix = matrix(int(size_m),int(size_m))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def mpaint(a,b):\n", " if init == 1:\n", " paint_matrix[a][b]=1\n", "\n", "init = 0 \n", "root = Tk()\n", "root.title(\"Resistors GUI\")\n", "root.geometry(\"800x600\")\n", "for i in range(0,int(math.sqrt(paint_matrix.size))):\n", " for j in range(0,int(math.sqrt(paint_matrix.size))):\n", " but = Button(root, text='%s'%(paint_matrix[i][j]),command=mpaint(i,j),borderwidth=1 )\n", " but.grid(row=i,column=j)\n", "init=1\n", "root.mainloop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mpaint(a,b):\n", " paint_matrix[a][b]=0 \n", " \n", "root = Tk()\n", "root.title(\"Resistors GUI\")\n", "root.geometry(\"780x600\")\n", "\n", "button = Button(root, text='%s'%(paint_matrix[0][0]),command=mpaint(i,j),borderwidth=1 )\n", "button.pack()\n", "root.mainloop()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "from tkinter import *\n", "\n", "app = Tk()\n", "app.title(\"Graph App\")\n", "\n", "vp = Frame(app)\n", "vp.grid(column=0,row=0,padx=(20,20),pady=(20,20))\n", "vp.columnconfigure(0,weight=1)\n", "vp.rowconfigure(0,weight=1)\n", "\n", "for i in range(0,int(math.sqrt(paint_matrix.size))):\n", " for j in range(0,int(math.sqrt(paint_matrix.size))):\n", " but= Button(vp, text='%s'%(paint_matrix[i][j]),command=mpaint(i,j),borderwidth=1 )\n", " but.grid(row=i,column=j)\n", "\n", " \n", "app.mainloop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "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 }
mit
jbwhit/WSP-312-Tips-and-Tricks
notebooks/02-Visualization-and-code-organization.ipynb
1
4397
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Notebook Possibilities\n", "\n", "http://mpld3.github.io/examples/linked_brush.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# uncomment the bottom line in this cell, change the final line of \n", "# the loaded script to `mpld3.display()` (instead of show)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load http://mpld3.github.io/_downloads/linked_brush.py" ] }, { "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": 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": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import mpld3\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "sns.set_context('poster')\n", "# sns.set_style('whitegrid') \n", "sns.set_style('darkgrid') \n", "plt.rcParams['figure.figsize'] = 12, 8 # plotsize " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sinplot(flip=1, ax=None):\n", " \"\"\"Demo plot from seaborn.\"\"\"\n", " x = np.linspace(0, 15, 500)\n", " for i in range(1, 7):\n", " ax.plot(x, np.sin(-1.60 + x + i * .5) * (7 - i) * flip, label=str(i))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# mpld3.enable_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "sinplot(ax=ax)\n", "ax.set_ylabel(\"y-label\")\n", "ax.set_xlabel(\"x-label\")\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mpld3.disable_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "py3", "language": "python", "name": "py3" }, "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
davebshow/prelims
modularity-no_auth_patron.ipynb
1
42682
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import networkx as nx\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.rcParams['figure.figsize'] = (12, 7)\n", "# key = {\"Milan\": 12591813, \"Bruselas\": 55965974, \"Amberes\": 7153308, \"Cordoba\": 12602098,\n", "# 'Lerida': 12602126, 'Madrid': 12578024, 'Valencia': 12602139 , 'Sevilla': 12602104 , 'Toledo': 12602114,\n", "# 'Lisboa': 2346573, 'Tarragona': 12602127 , 'Zaragoza':12602107 , 'Mexico': 0 , 'Alcala':12578024,\n", "# 'Pamplona': 12578026 , 'Coimbra': 12578026, 'Barcelona': 12578026, 'Valladolid': 12578026}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod_df = pd.read_csv(\"data/communities/no_auth_patron.csv\", encoding=\"utf-8\")\n", "# ORDER - 0 3 6 5 7 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = mod_df.groupby(\"Modularity Class\").size()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f9eedb90f60>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAr0AAAG5CAYAAACDXYxTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+4ZHddH/D3DclCt4Ftaru0jT5ure6nLan8CEShlATB\n", "KooNTX/gLypRNBTaQqulECwxFSSVJiqaYg1gbIsK5Ak/+2BpAJNWlIil0BT7CUhjtcUGfSA2JCUk\n", "mf4xc/Wy2bv37t2ZnZnvvl7Pc56dOfO9Z9733B/7nnO/c87GZDIJAACM7LRlBwAAgEVTegEAGJ7S\n", "CwDA8JReAACGp/QCADA8pRcAgOGdvptBVXUwya8leUqS+5NcO/v3liTP727nPQMAYGXteKS3qs5I\n", "8q+SfDbJRpKrklza3U+a3b9woQkBAOAE7WZ6w6uSvCbJJ2f3H9PdN81uvyvJUxcRDAAA5uWYpbeq\n", "np3kU9397tmqjdmy6c4kBxYTDQAA5mOnOb0XJ5lU1VOTPCrJzyT5k1sef2iSz+zieW5J8og9JQQA\n", 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"rafRaPfRfSRJQ6E+ZNtrqT5I96ZO55Gk4cLDoEnS8HU5MAt4dqeDSNJw4h5gSZIkFcU5wJIkSSqK\n", "BViSJElFsQBLkiSpKBZgSZIkFcUCLEmSpKJYgCVJklSU/w+VuyQgHW59RwAAAABJRU5ErkJggg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f9eedc1ec88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mod_roles = mod_df.groupby([\"Modularity Class\", \"role\"]).size()\n", "grouped = mod_roles.groupby(level=0)\n", "divided = mod_roles.divide(grouped.sum())\n", "divided = divided.unstack(level=0).fillna(0)\n", "trans = divided.transpose()\n", "trans.plot(kind=\"bar\", stacked=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import json\n", "# with open(\"maps/western_europe_data/provinces.json\", \"r\") as f:\n", "# prov = json.load(f)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# with open(\"maps/western_europe_data/provinces_with_id.json\", \"w\") as f:\n", "# json.dump(prov, f)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ids = {}\n", "# for x in features:\n", "# props = x[\"properties\"]\n", "# ids[props[\"woe_id\"]] = props[\"gn_name\"]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ids" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "map_key = {\"Milan\": 12591813, \"Bruselas\": 22525998, \"Amberes\": 7153308, \"Cordoba\": 12602098,\n", " 'Lerida': 12602126, 'Madrid': 12578024, 'Valencia': 12602139 , 'Sevilla': 12602104 , 'Toledo': 12602114,\n", " 'Lisboa': 2346573, 'Tarragona': 12602127 , 'Zaragoza':12602107 , 'Mexico': 0 , 'Alcala':12578024,\n", " 'Pamplona': 12578026 , 'Coimbra': 2346567, 'Barcelona': 12602124, 'Valladolid': 12602122,\n", " 'Malaga': 12602102 , 'Burgos': 12602116, 'Roma': 12591802, 'Lyon': 12597185, 'Venetia': 12591860,\n", " 'Medina': 12602122 , 'Salamanca': 12602119 , 'Paris': 12597155}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Modularity Class top_place \n", "0 Barcelona 3\n", " Valencia 2\n", "1 Mexico 1\n", "2 Cordoba 3\n", " Madrid 1\n", "3 Madrid 4\n", "4 Pamplona 5\n", "5 Zaragoza 9\n", "6 Milan 2\n", "7 Alcala 1\n", " Amberes 1\n", " Anvers 2\n", " Barcelona 2\n", " Madrid 5\n", " Sevilla 1\n", " Valladolid 5\n", "8 Amberes 1\n", " Barcelona 1\n", " Bruselas 6\n", " Zaragoza 6\n", "9 Barcelona 3\n", " Lerida 4\n", "10 Coimbra 1\n", " Lisboa 21\n", "11 Barcelona 15\n", " Madrid 1\n", " Tarragona 2\n", " Toledo 1\n", " Valencia 5\n", "12 Alcala 1\n", " Barcelona 1\n", " Madrid 28\n", " Sevilla 1\n", " Valladolid 5\n", "13 Mexico 1\n", "14 Bruselas 3\n", "15 Valencia 4\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod_places = mod_df.groupby([\"Modularity Class\", \"top_place\"]).size()\n", "mod_places" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "def write_csv(d, map_key, name):\n", " count = {}\n", " for k, v in d.items():\n", " id = map_key.get(k, \"\")\n", " if id:\n", " count.setdefault(id, 0)\n", " count[id] += v\n", " with open(name, \"w\") as f:\n", " writer = csv.writer(f)\n", " writer.writerow([\"id\", \"count\"])\n", " for k, v in count.items():\n", " writer.writerow([k, v])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Madrid': 4}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod_places[3].to_dict()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "write_csv(mod_places[12].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod12.csv')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[11].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod11.csv')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[10].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod10.csv')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[7].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod7.csv')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[8].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod8.csv')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[5].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod5.csv')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[9].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod9.csv')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[4].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod4.csv')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[0].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod0.csv')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[3].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod3.csv')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[2].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod2.csv')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_csv(mod_places[15].to_dict(), map_key, 'js_viz/maps/western_europe_data/tsv/no_auth_mod15.csv')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Modularity Class top_place \n", "0 Alcala 1\n", " Amberes 2\n", " Anvers 2\n", " Barcelona 3\n", " Madrid 13\n", " Sevilla 1\n", " Toledo 1\n", " Valencia 4\n", " Valladolid 6\n", "1 Barcelona 6\n", " Bruselas 4\n", " Madrid 1\n", " Pamplona 5\n", " Valencia 5\n", "2 Bruselas 4\n", "3 Alcala 1\n", " Barcelona 6\n", " Lerida 1\n", " Madrid 26\n", " Sevilla 2\n", " Toledo 1\n", " Valladolid 1\n", "4 Mexico 4\n", "5 Cordoba 3\n", " Lisboa 21\n", " Madrid 1\n", "6 Barcelona 5\n", " Coimbra 1\n", " Lisboa 2\n", " Madrid 5\n", " Milan 3\n", " Tarragona 2\n", " Zaragoza 10\n", "7 Barcelona 5\n", " Bruselas 2\n", " Lerida 4\n", " Lisboa 1\n", " Madrid 2\n", " Valencia 2\n", " Valladolid 4\n", " Zaragoza 6\n", "dtype: int64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod_places" ] }, { "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
spatchcock/monetary_economics_python
notebooks/.ipynb_checkpoints/scotland-checkpoint.ipynb
1
181895
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{lcl}\n", "Y = C + G \\hspace{1cm} & (1) \\\\\n", "T = \\theta Y \\hspace{1cm} & (2) \\\\\n", "Y_{d} = Y - T = (1 - \\theta) Y \\hspace{1cm} & (3) \\\\\n", "C = \\alpha Y_{d} \\hspace{1cm} & (4) \\\\\n", "G = G_0 + T \\hspace{1cm} & (5) \\\\\n", "\\Delta H_g = T - G \\hspace{1cm} & (6) \\\\\n", "\\Delta H_h = (1 - \\alpha) Y_{d} \\hspace{1cm} & (7) \\\\\n", "\\end{array}\n", "$$\n", " \n", "So consumption is strict fraction of disposable income - no $\\alpha_0$\n", "Government spending is no longer constant but increases with tax take" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline \n", "\n", "N = 200\n", "\n", "G0 = 5\n", "theta = 0.2\n", "alpha = 0.95\n", "\n", "C = np.zeros(N) # consumption\n", "G = np.zeros(N) # government spending\n", "Y = np.zeros(N) # income\n", "Y_d = np.zeros(N) # disposable income\n", "T = np.zeros(N) # tax revenue\n", "H_h = np.zeros(N) # private savings\n", "H_g = np.zeros(N) # government debt\n", "\n", "for t in range(0, N):\n", " \n", " # calculate consumer spending\n", " C[t] = alpha*Y_d[t-1] \n", " \n", " # calculate government spending\n", " G[t] = G0 + theta*Y[t-1] \n", " \n", " # calculate total income (consumer spending plus constant government spending)\n", " Y[t] = G[t] + C[t] \n", " \n", " # calculate the tax take\n", " T[t] = theta * Y[t]\n", " \n", " # calculate disposable income\n", " Y_d[t] = Y[t] - T[t]\n", " \n", " # calculate the change in private savings\n", " H_h[t] = H_h[t-1] + (1-alpha)*Y_d[t-1] \n", " \n", " # calculate the change in government debt\n", " H_g[t] = H_g[t-1] + T[t]- G[t]\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 114 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12, 8))\n", "\n", "consumption_plot = fig.add_subplot(241, xlim=(0, N), ylim=(0, np.max(Y)))\n", "consumption_plot.plot(range(N), C, lw=3)\n", "consumption_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('consumption')\n", "\n", "gov_plot = fig.add_subplot(242, xlim=(0, N), ylim=(0, np.max(Y)))\n", "gov_plot.plot(range(N), G, lw=3)\n", "gov_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government spending')\n", "\n", "income_plot = fig.add_subplot(243, xlim=(0, N), ylim=(0, np.max(Y)))\n", "income_plot.plot(range(N), Y, lw=3)\n", "income_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('income')\n", "\n", "savings_plot = fig.add_subplot(244, xlim=(0, N), ylim=(0, np.max(H_h)))\n", "savings_plot.plot(range(N), H_h, lw=3)\n", "savings_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('private savings')\n", "\n", "gov_plot = fig.add_subplot(245, xlim=(0, N), ylim=(0, np.max(G)*1.5))\n", "gov_plot.plot(range(N), G, lw=3)\n", "gov_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government spending')\n", "\n", "tax_plot = fig.add_subplot(246, xlim=(0, N), ylim=(0, np.max(G)*1.5))\n", "tax_plot.plot(range(N), T, lw=3)\n", "tax_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('tax revenue')\n", "\n", "deficit_plot = fig.add_subplot(247, xlim=(0, N), ylim=(np.min(T-G)*1.5,0))\n", "deficit_plot.plot(range(N), T-G, lw=3)\n", "deficit_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government budget')\n", "\n", "debt_plot = fig.add_subplot(248, xlim=(0, N), ylim=(np.min(H_g)*1.5,0))\n", "debt_plot.plot(range(N), H_g, lw=3)\n", "debt_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government debt')\n", "\n", "# space subplots neatly\n", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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CES5gdj7pSNqk6Dl40JgeeP26UV+92vj95gpyT5YQQgghbuvkSfjkE0d91CjTQhFCCJeZ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\niYrqWEVUx2pGdax2qI5VRHWsZlTHaofqWEVUx2pmjDomfCMrP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"text": [ "<matplotlib.figure.Figure at 0x7f5ef53e6ed0>" ] } ], "prompt_number": 115 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Points to note\n", "\n", "1. Total income is greater than in similar model with constant government spending\n", "2. Government spending ends up larger than in similar, constant spending model\n", "3. Deficit never closes, since the tax take in each time step is entirely spent in the next *plus* the fiscal transfer. Fiscal transfer represents the steady-state deficit\n", "4. Time to steady state is much longer in this model, presumably because it takes a while for government spending to grow " ] }, { "cell_type": "code", "collapsed": false, "input": [ "Y[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "124.96442404059118" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "G[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ "29.992588341789833" ] } ], "prompt_number": 62 }, { "cell_type": "code", "collapsed": false, "input": [ "T[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "24.992884808118237" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Steady state equations\n", "\n", "$$\n", "\\begin{array}{lcl}\n", "Y^* = C^* + G^* \\hspace{1cm} & (8) \\\\\n", "T^* = \\theta Y^* \\hspace{1cm} & (9) \\\\\n", "Y_{d}^* = Y^* - T^* = (1 - \\theta) Y^* \\hspace{1cm} & (10) \\\\\n", "C^* = \\alpha Y_{d}^* \\hspace{1cm} & (11) \\\\\n", "G^* = G_0 + T^* \\hspace{1cm} & (12) \\\\\n", "\\end{array}\n", "$$\n", "\n", "Solve for $Y^*$ in terms of exogeneous parameters\n", "\n", "$$Y^* = C^* + G^*$$\n", "$$Y^* = \\alpha Y_{d}^* + G_0 + T^*$$\n", "$$Y^* = \\alpha (1 - \\theta) Y^* + G_0 + \\theta Y^*$$\n", "\n", "$$1 = \\alpha (1 - \\theta) + \\frac {G_0}{Y^*} + \\theta$$\n", "$$\\frac {G_0}{Y^*} = 1 - \\alpha (1 - \\theta) - \\theta$$\n", "$$ Y^* = \\frac {G_0}{1 - \\alpha (1 - \\theta) - \\theta}$$\n", "$$ Y^* = \\frac {G_0}{1 - \\alpha + \\alpha \\theta - \\theta}$$\n", "$$ Y^* = \\frac {G_0}{(1 - \\alpha)(1 - \\theta)}$$\n", "\n", "Same as yours!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change of Y with $\\theta$\n", "\n", "$$ Y^* = \\frac {G_0}{(1 - \\alpha)(1 - \\theta)}$$\n", "$$ Y^* = \\frac {G_0}{(1 - \\alpha)}(1 - \\theta)^{-1}$$\n", "\n", "let $u(\\theta) = 1 - \\theta$\n", "\n", "then\n", "\n", "$$ Y^* = \\frac {G_0}{(1 - \\alpha)}u^{-1}$$\n", "\n", "$$\\frac {dY}{d \\theta} = \\frac {dY}{du} \\frac {du}{d \\theta}$$\n", "\n", "$$\\frac {dY}{du} = -\\frac {G_0}{(1 - \\alpha)}u^{-2}$$\n", "\n", "$$\\frac {du}{d \\theta} = -1$$\n", "\n", "$$\\frac {dY}{d \\theta} = -1 \\times \\frac {G_0}{(1 - \\alpha)}u^{-2} \\times -1 $$\n", "\n", "$$\\frac {dY}{d \\theta} = \\frac {G_0}{(1 - \\alpha)(1 - \\theta)^{2}} $$\n", "\n", "Comsumption...\n", "\n", "$$$$\n", "$$C^* = \\alpha (1 - \\theta) Y^*$$\n", "$$C^* = \\frac {\\alpha (1 - \\theta) G_0}{(1 - \\alpha)(1 - \\theta)}$$\n", "$$C^* = \\alpha \\frac {G_0}{(1 - \\alpha)}$$\n", "\n", "$$\\frac {dC^*}{d \\theta} = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Savings rate...\n", "\n", "$$\\Delta H_h = (1 - \\alpha) Y_{d} $$\n", "$$\\Delta H_h = (1 - \\alpha) (1 - \\theta) Y^* $$\n", "$$\\Delta H_h = (1 - \\alpha) (1 - \\theta) \\frac {G_0}{(1 - \\alpha)(1 - \\theta)} $$\n", "$$\\Delta H_h = G_0 $$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "(G0*0.05/((1.0-alpha)*(1.0-0.2)**2.0))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 180, "text": [ "7.812499999999991" ] } ], "prompt_number": 180 }, { "cell_type": "code", "collapsed": false, "input": [ "alpha*(G0/(1-alpha))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 164, "text": [ "94.99999999999991" ] } ], "prompt_number": 164 }, { "cell_type": "code", "collapsed": false, "input": [ "(1-alpha)*(1-0.25)*Y[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 206, "text": [ "4.9999942383663223" ] } ], "prompt_number": 206 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try increasing the tax rate halfway through..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline \n", "\n", "N = 500\n", "\n", "G0 = 5\n", "alpha = 0.95\n", "\n", "C = np.zeros(N) # consumption\n", "G = np.zeros(N) # government spending\n", "Y = np.zeros(N) # income\n", "Y_d = np.zeros(N) # disposable income\n", "T = np.zeros(N) # tax revenue\n", "H_h = np.zeros(N) # private savings\n", "H_g = np.zeros(N) # government debt\n", "\n", "\n", "\n", "theta = np.zeros(N) # tax rate\n", "theta[0:250] = 0.2\n", "theta[250:] = 0.5\n", "\n", "\n", "for t in range(0, N):\n", " \n", " # calculate consumer spending\n", " C[t] = alpha*Y_d[t-1] \n", " \n", " # calculate government spending\n", " G[t] = G0 + theta[t]*Y[t-1] \n", " \n", " # calculate total income (consumer spending plus constant government spending)\n", " Y[t] = G[t] + C[t] \n", " \n", " # calculate the tax take\n", " T[t] = theta[t] * Y[t]\n", " \n", " # calculate disposable income\n", " Y_d[t] = Y[t] - T[t]\n", " \n", " # calculate the change in private savings\n", " H_h[t] = H_h[t-1] + (1-alpha)*Y_d[t-1] \n", " \n", " # calculate the change in government debt\n", " H_g[t] = H_g[t-1] + T[t]- G[t]\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 207 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12, 8))\n", "\n", "consumption_plot = fig.add_subplot(241, xlim=(0, N), ylim=(0, np.max(Y)))\n", "consumption_plot.plot(range(N), C, lw=3)\n", "consumption_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('consumption')\n", "\n", "gov_plot = fig.add_subplot(242, xlim=(0, N), ylim=(0, np.max(Y)))\n", "gov_plot.plot(range(N), G, lw=3)\n", "gov_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government spending')\n", "\n", "income_plot = fig.add_subplot(243, xlim=(0, N), ylim=(0, np.max(Y)))\n", "income_plot.plot(range(N), Y, lw=3)\n", "income_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('income')\n", "\n", "savings_plot = fig.add_subplot(244, xlim=(0, N), ylim=(0, np.max(H_h)))\n", "savings_plot.plot(range(N), H_h, lw=3)\n", "savings_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('private savings')\n", "\n", "gov_plot = fig.add_subplot(245, xlim=(0, N), ylim=(0, np.max(G)*1.5))\n", "gov_plot.plot(range(N), G, lw=3)\n", "gov_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government spending')\n", "\n", "tax_plot = fig.add_subplot(246, xlim=(0, N), ylim=(0, np.max(G)*1.5))\n", "tax_plot.plot(range(N), T, lw=3)\n", "tax_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('tax revenue')\n", "\n", "deficit_plot = fig.add_subplot(247, xlim=(0, N), ylim=(np.min(T-G)*1.5,0))\n", "deficit_plot.plot(range(N), T-G, lw=3)\n", "deficit_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government budget')\n", "\n", "debt_plot = fig.add_subplot(248, xlim=(0, N), ylim=(np.min(H_g)*1.5,0))\n", "debt_plot.plot(range(N), H_g, lw=3)\n", "debt_plot.grid()\n", "# label axes\n", "plt.xlabel('time')\n", "plt.ylabel('government debt')\n", "\n", "# space subplots neatly\n", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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cuIHMV+sxajQahIeHW6zjR4iaVKhQAbVq1ZI6DEKIlUlPB7ZvBxYtAmJigKws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\nmTPcsZkZ0KMH/3nKaeypnOpKni8tDbhyhTu2sQECA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k4WIYQQQgghhOgRdbIIIYQQQggh\nRI+ok0UIIYQQQgghevT/DZv8gdrE7l8AAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x7f5ef4e7d610>" ] } ], "prompt_number": 208 }, { "cell_type": "code", "collapsed": false, "input": [ "Y[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 174, "text": [ "133.33317968976849" ] } ], "prompt_number": 174 }, { "cell_type": "code", "collapsed": false, "input": [ "G[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 175, "text": [ "38.333293425913894" ] } ], "prompt_number": 175 }, { "cell_type": "code", "collapsed": false, "input": [ "T[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 176, "text": [ "33.333294922442121" ] } ], "prompt_number": 176 }, { "cell_type": "code", "collapsed": false, "input": [ "print(C[249])\n", "print(C[250])\n", "print(C[251])\n", "print(C[252])\n", "print(C[-1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "94.9963418574\n", "94.9964881831\n", "93.5122997484\n", "93.5680885078\n", "94.9998862639\n" ] } ], "prompt_number": 177 }, { "cell_type": "code", "collapsed": false, "input": [ "print(G[249])\n", "print(G[250])\n", "print(G[251])\n", "print(G[252])\n", "print(G[-1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "29.9990373309\n", "36.2488447971\n", "37.811333245\n", "37.8309082484\n", "38.3332934259\n" ] } ], "prompt_number": 178 }, { "cell_type": "code", "collapsed": false, "input": [ "print(Y[249])\n", "print(Y[250])\n", "print(Y[251])\n", "print(Y[252])\n", "print(Y[-1])\n", "print(Y[-1]-Y[249])\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "124.995379188\n", "131.24533298\n", "131.323632993\n", "131.398996756\n", "133.33317969\n", "8.33780050149\n" ] } ], "prompt_number": 205 }, { "cell_type": "code", "collapsed": false, "input": [ "print(H_h[249])\n", "print(H_h[250])\n", "print(H_h[251])\n", "print(H_h[252])\n", "print(H_h[-1])\n", "print(H_h[-1]-H_h[-2])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1125.00462081\n", "1130.00443598\n", "1134.92613597\n", "1139.8507722\n", "2372.91658927\n", "4.99999401389\n" ] } ], "prompt_number": 203 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, the income, government spend, and tax take all increase by the same amount. This is consistent with what we said about the balanced budget model - increasing Y, G and T by the same amount implies a change in tax rate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is the intuitive explanation as simple as this: saving happens *after* tax. So when the government taxes at a greater rate, that leaves less to be spent but also less to be saved, and when the government spends the same amount in the next time step, it does not take off the corresponding saved fraction, so more ends up being spent in total. If that is the case, then it is the case that the increase in income arises because the government is spending money which would otherwise be saved. It's not resolved in this model but in reality that would disproportionately come from richer folk probably, since $\\alpha$ would be greater for them." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
marburg-open-courseware/gmoc
docs/mpg-if_error_continue/notebooks/ggplot.ipynb
1
304517
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from ggplot import *\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "?ggplot" ] }, { "cell_type": "code", "execution_count": 3, "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>carat</th>\n", " <th>cut</th>\n", " <th>color</th>\n", " <th>clarity</th>\n", " <th>depth</th>\n", " <th>table</th>\n", " <th>price</th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>z</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.23</td>\n", " <td>Ideal</td>\n", " <td>E</td>\n", " <td>SI2</td>\n", " <td>61.5</td>\n", " <td>55.0</td>\n", " <td>326</td>\n", " <td>3.95</td>\n", " <td>3.98</td>\n", " <td>2.43</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.21</td>\n", " <td>Premium</td>\n", " <td>E</td>\n", " <td>SI1</td>\n", " <td>59.8</td>\n", " <td>61.0</td>\n", " <td>326</td>\n", " <td>3.89</td>\n", " <td>3.84</td>\n", " <td>2.31</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.23</td>\n", " <td>Good</td>\n", " <td>E</td>\n", " <td>VS1</td>\n", " <td>56.9</td>\n", " <td>65.0</td>\n", " <td>327</td>\n", " <td>4.05</td>\n", " <td>4.07</td>\n", " <td>2.31</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.29</td>\n", " <td>Premium</td>\n", " <td>I</td>\n", " <td>VS2</td>\n", " <td>62.4</td>\n", " <td>58.0</td>\n", " <td>334</td>\n", " <td>4.20</td>\n", " <td>4.23</td>\n", " <td>2.63</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.31</td>\n", " <td>Good</td>\n", " <td>J</td>\n", " <td>SI2</td>\n", " <td>63.3</td>\n", " <td>58.0</td>\n", " <td>335</td>\n", " <td>4.34</td>\n", " <td>4.35</td>\n", " <td>2.75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " carat cut color clarity depth table price x y z\n", "0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n", "1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n", "2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n", "3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n", "4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ?diamonds\n", "diamonds.head() # similar for data sets 'meat', 'mtcars', and 'pageviews'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(diamonds)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "?ggplot" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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UJsIBAKAwEQ4AAIWJcAAAKEyEAwBAYSIcAAAKE+EAAFCYCAcAgMJEOAAAFCbCAQCgMBEO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\nhAMAQGEiHAAACvt/CycPzo8odekAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x263eda55cc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (-9223371872591522339)>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = ggplot(aes(x = 'carat', y = 'price'), data = diamonds)\n", "p" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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SV25uLi1k6dq1a5RcYnl5Oe1NQ/PmzdG9e3cMGjSI5r0WRFtbG2ZmZkhOTqb2BQQEUMmv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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x263ee2de630>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (-9223371872591522339)>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p1 = p + geom_point() \n", "p1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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4gKdPn4LJZKJHjx4oKioqMxlJedja2sLBwQFPnjwBUBoQLc9QyM/Pp81t8eLF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\nArGIJiIiIiISiEU0EREREZFA/wM6O93+gC929QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x263ee323048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (-9223371872591522339)>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p2 = p1 + facet_wrap(\"cut\")\n", "p2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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6E11SUgKJRAJCCAghkEgkKCkpQUpKCrKyslBaWorCwkL8888/cHZ2hrGxMQCg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c7VZyKRCM2aNcP9+/cBlI1zeu/ePcHjzc3NVfsf46tXr9CjRw+FU8rOmjULNjY2uHLlCgICAuQm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\nTlwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x263ef53b2b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (-9223371872591522339)>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p3 = p2 + theme_bw()\n", "p3" ] } ], "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
mliu49/RMG-stuff
Kinetics/TestFamilies.ipynb
1
4535
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rmgpy\n", "from rmgpy.data.rmg import RMGDatabase\n", "from rmgpy.rmg.react import *\n", "from rmgpy.reaction import Reaction\n", "from rmgpy.molecule.molecule import Molecule\n", "from rmgpy.molecule.resonance import *\n", "from rmgpy.species import Species\n", "\n", "from base64 import b64encode\n", "from IPython.display import display, HTML, Image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "families = ['Intra_R_Add_Endocyclic', 'Intra_R_Add_Exocyclic', 'Intra_R_Add_Polycyclic']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "databasePath = rmgpy.settings['database.directory']\n", "\n", "database = RMGDatabase()\n", "database.load(\n", " path = databasePath,\n", " thermoLibraries = [],\n", " reactionLibraries = [],\n", " seedMechanisms = [],\n", " kineticsFamilies = families,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "spc = Species().fromSMILES('C1=CC=CC=C1C[CH2]')\n", "spc.generateResonanceIsomers()\n", "display(spc)\n", "\n", "reactants = (spc,)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# html settings\n", "full = 24\n", "half = full / 2\n", "quarter = full / 4\n", "eighth = full / 8\n", "\n", "combos = getMoleculeTuples(reactants)\n", "\n", "reactionList = []\n", "for combo in combos:\n", " reactionList.extend(reactMolecules(combo))\n", "\n", "reactionList = findDegeneracies(reactionList)\n", "correctDegeneracyOfReverseReactions(reactionList, list(reactants))\n", "reduceSameReactantDegeneracy(reactionList)\n", "\n", "for reaction in reactionList:\n", " template = database.kinetics.families[reaction.family].retrieveTemplate(reaction.template)\n", "\n", " html = ['<table style=\"width:100%;table-layout:fixed;\"><tr>']\n", " html += ['<td colspan=\"{0}\" rowspan=\"3\"><img style=\"display: block; margin: auto;\" src=\"data:image/png;base64,{1}\"></td>'.format(3*eighth, b64encode(reaction._repr_png_()))]\n", " html += ['<th colspan=\"{0}\">Family</th>'.format(eighth)]\n", " html += ['<td colspan=\"{0}\">{1}</td>'.format(half, reaction.family)]\n", " html += ['</tr><tr>']\n", " html += ['<th colspan=\"{0}\">Template</th>'.format(eighth)]\n", " html += ['<td colspan=\"{0}\">{1}</td>'.format(half, reaction.template)]\n", " html += ['</tr><tr>']\n", " html += ['<th colspan=\"{0}\">Degeneracy</th>'.format(eighth)]\n", " html += ['<td colspan=\"{0}\">{1}</td>'.format(half, reaction.degeneracy)]\n", " html += ['</tr><tr>']\n", " for entry in template:\n", " html += ['<td colspan=\"{0}\" style=\"text-align: center;\">{1}</td>'.format(full/len(template), entry.label)]\n", " html += ['</tr><tr>']\n", " for entry in template:\n", " html += ['<td colspan=\"{0}\"><img style=\"display: block; margin: auto;\" src=\"data:image/png;base64,{1}\"></td>'.format(full/len(template), b64encode(entry.item._repr_png_()))]\n", " html += ['</tr></table>']\n", "\n", " display(HTML(''.join(html)))" ] }, { "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 [conda env:rmg_env]", "language": "python", "name": "conda-env-rmg_env-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
caganze/wisps
notebooks/.ipynb_checkpoints/y standards-checkpoint.ipynb
1
90770
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/users/caganze/research/splat\n", "\n", "\n", "Welcome to the Spex Prism Library Analysis Toolkit (SPLAT)!\n", "If you make use of any features of this toolkit for your research, please remember to cite the SPLAT paper:\n", "\n", "Burgasser et al. (2017, Astro. Soc. India Conf. Series 14, p. 7); Bibcode: 2017ASInC..14....7B\n", "\n", "If you make use of any spectra or models in this toolkit, please remember to cite the original source.\n", "Please report any errors are feature requests to our github page, https://github.com/aburgasser/splat/\n", "\n", "\n" ] } ], "source": [ "import splat\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import glob\n", "import pandas as pd\n", "from astropy.io import ascii\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import astropy.units as u" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#get y dwarfs\n", "def get_shortname(n):\n", " return splat.designationToShortName(n).replace('J', 'WISE')\n", "\n", "schn='/Volumes/Lacie/schneider/*.txt'\n", "schntb=pd.read_csv('/Volumes/Lacie/wispsdata/data/schneider2015.txt', \n", " delimiter=' ').drop(columns='Unnamed: 14')\n", "schntb['shortname']=schntb.Name.apply(get_shortname)\n", "spectra_schn=[]\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)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Text(0.5, 0, 'wave (micron)'), Text(0, 0.5, 'flux (micron)')]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "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": [ "fig, ax=plt.subplots()\n", "for idx, s in enumerate(spectra_schn):\n", " s.normalize()\n", " ax.plot(s.wave, s.flux.value+idx/5, c='b')\n", "\n", "ax.set(xlabel='wave (micron)', ylabel='flux (micron)')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Y0–WISE J173835.53+273259.0 (HST/WFC3; Cushing et al. 2011), and Y1–WISE J035000.32–565830.2 (HST/WFC3; Kirkpatrick et al. 2012)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#spectra_schn= spectra_schn.flatten()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "yo_std=[x for x in spectra_schn if x.name=='WISE1738+2732'][0]\n", "y1_std=[x for x in spectra_schn if x.name=='WISE0350-5658'][0]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#import splat.database as spd\n", "#spd.addUserSpectra(folder='/Volumes/Lacie/schneider/',\\\n", "# instrument='HST-WFC3',mode='update',search_str='*.txt',\\\n", "# verbose=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$[0.024109404,~0.020871799,~0.018603568,~\\dots,~0.03134851,~0.039327506,~0.04888085] \\; \\mathrm{\\frac{erg}{\\mu m\\,s\\,cm^{2}}}$" ], "text/plain": [ "<Quantity [0.0241094 , 0.0208718 , 0.01860357, 0.01760464, 0.01709726,\n", " 0.01726601, 0.0172777 , 0.01701685, 0.01785194, 0.01680193,\n", " 0.01555207, 0.01590856, 0.01595893, 0.01512638, 0.01527113,\n", " 0.01461686, 0.01444943, 0.01540987, 0.01480317, 0.01391716,\n", " 0.01328193, 0.013035 , 0.01317351, 0.01373701, 0.01474162,\n", " 0.01347795, 0.01378607, 0.01354033, 0.0145536 , 0.01504311,\n", " 0.01479443, 0.01579233, 0.016049 , 0.0164338 , 0.01788833,\n", " 0.01797125, 0.01725484, 0.01619826, 0.01500792, 0.01394099,\n", " 0.01308992, 0.01235373, 0.0117577 , 0.01124206, 0.01079982,\n", " 0.01081703, 0.01049858, 0.01061926, 0.01030358, 0.00988046,\n", " 0.0097127 , 0.00971658, 0.00963187, 0.0094779 , 0.00936804,\n", " 0.00934024, 0.00939677, 0.00991128, 0.01017674, 0.01017184,\n", " 0.00986222, 0.00989175, 0.00959844, 0.00971839, 0.00954966,\n", " 0.00918796, 0.00944974, 0.00966938, 0.01018674, 0.00995427,\n", " 0.00918803, 0.0091266 , 0.00910415, 0.00902953, 0.00898661,\n", " 0.0090413 , 0.00892962, 0.00882894, 0.00879587, 0.00901229,\n", " 0.00914775, 0.00908035, 0.00877286, 0.00878077, 0.00874923,\n", " 0.00873618, 0.00867511, 0.00863823, 0.00889184, 0.00877803,\n", " 0.00877343, 0.00881446, 0.00890251, 0.00900201, 0.00905167,\n", " 0.00932391, 0.0096111 , 0.01008143, 0.01068131, 0.01066258,\n", " 0.01077494, 0.01093609, 0.01142228, 0.0114513 , 0.01092408,\n", " 0.01054345, 0.01007193, 0.00952245, 0.00941115, 0.00935126,\n", " 0.00874909, 0.00869713, 0.00886166, 0.00893249, 0.00881556,\n", " 0.00882321, 0.00883585, 0.00895738, 0.00934146, 0.00920631,\n", " 0.00977627, 0.01079105, 0.01235813, 0.01480143, 0.01840653,\n", " 0.02396254, 0.03134851, 0.03932751, 0.04888085] erg / (cm2 micron s)>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yo_std.noise" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "y0_spex=splat.Spectrum(wave=yo_std.wave, flux=yo_std.flux, noise=yo_std.noise, \n", " instrument='WFC3-G141',\n", " name='WISE1738+2732',\n", " jmag=19.470,jmag_error=0.08,\n", " hmag=20.24, hmag_error=0.08)\n", "\n", "y1_spex=splat.Spectrum(wave=y1_std.wave, flux=y1_std.flux, noise=y1_std.noise, \n", " instrument='WFC3-G141', \n", " name='WISE0350-5658', jmag=22.09,jmag_error=0.1,\n", " hmag=22.51, hmag_error=0.2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "y0_spex.save('/users/caganze/research/splat/resources/spectra/public/spex-prism/1738_2732.fits')\n", "y1_spex.save('/users/caganze/research/splat/resources/spectra/public/spex-prism/0350_5658.fits')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#data=pd.DataFrame({'name': ['WISE1738+2732', 'WISE0350-5658'], 'spec':[y0_spex, y1_spex], 'spt':['Y0.0', 'Y1.0']})" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Creating an empty Spectrum object\n" ] } ], "source": [ "s=splat.Spectrum(data_file='1738_2732.fits')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$[1.103,~1.10765,~1.1123,~\\dots,~1.6889,~1.69355,~1.6982] \\; \\mathrm{\\mu m}$" ], "text/plain": [ "<Quantity [1.103 , 1.10765, 1.1123 , 1.11695, 1.1216 , 1.12625, 1.1309 ,\n", " 1.13555, 1.1402 , 1.14485, 1.1495 , 1.15415, 1.1588 , 1.16345,\n", " 1.1681 , 1.17275, 1.1774 , 1.18205, 1.1867 , 1.19135, 1.196 ,\n", " 1.20065, 1.2053 , 1.20995, 1.2146 , 1.21925, 1.2239 , 1.22855,\n", " 1.2332 , 1.23785, 1.2425 , 1.24715, 1.2518 , 1.25645, 1.2611 ,\n", " 1.26575, 1.2704 , 1.27505, 1.2797 , 1.28435, 1.289 , 1.29365,\n", " 1.2983 , 1.30295, 1.3076 , 1.31225, 1.3169 , 1.32155, 1.3262 ,\n", " 1.33085, 1.3355 , 1.34015, 1.3448 , 1.34945, 1.3541 , 1.35875,\n", " 1.3634 , 1.36805, 1.3727 , 1.37735, 1.382 , 1.38665, 1.3913 ,\n", " 1.39595, 1.4006 , 1.40525, 1.4099 , 1.41455, 1.4192 , 1.42385,\n", " 1.4285 , 1.43315, 1.4378 , 1.44245, 1.4471 , 1.45175, 1.4564 ,\n", " 1.46105, 1.4657 , 1.47035, 1.475 , 1.47965, 1.4843 , 1.48895,\n", " 1.4936 , 1.49825, 1.5029 , 1.50755, 1.5122 , 1.51685, 1.5215 ,\n", " 1.52615, 1.5308 , 1.53545, 1.5401 , 1.54475, 1.5494 , 1.55405,\n", " 1.5587 , 1.56335, 1.568 , 1.57265, 1.5773 , 1.58195, 1.5866 ,\n", " 1.59125, 1.5959 , 1.60055, 1.6052 , 1.60985, 1.6145 , 1.61915,\n", " 1.6238 , 1.62845, 1.6331 , 1.63775, 1.6424 , 1.64705, 1.6517 ,\n", " 1.65635, 1.661 , 1.66565, 1.6703 , 1.67495, 1.6796 , 1.68425,\n", " 1.6889 , 1.69355, 1.6982 ] micron>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y0_spex.wave" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.wave" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data[data['spt']=='Y0.0'].spec.iloc[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Add these lines to splat initiate standard\n", "#if t.upper().startswith('Y'):\n", "#df=pd.read_pickle('~/ystandards.pkl')\n", "#stds[t] = df[df['spt']==t].spec.iloc[0]\n", "#stds[t].normalize()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "splat.initializeStandards()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "splat.STDS_DWARF_SPEX" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
willettk/insight
notebooks/neural_networks_and_deep_learning.ipynb
1
26612
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Work with http://neuralnetworksanddeeplearning.com/" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "import random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 1" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(z):\n", " \n", " return 1./(1. + np.exp(-z))\n", "\n", "def sigmoid_vector(w,x,b):\n", " \n", " return 1./(1. + np.exp(-1 * np.sum(w * x) - b))\n", "\n", "def sigmoid_prime(z):\n", " \n", " return sigmoid(z) * (1 - sigmoid(z))" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TdW974xvuPi/x+HPQxaSTDreRvAaYBSwxs/Jgq0p7MaDC3ee6e+cb+0jXfkz8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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1073425d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot behavior of sigmoid. Continuous symmetric function, \n", "# asymptotically bounded by [0,1] in x = [-inf, inf]\n", "\n", "x = np.linspace(-10,10)\n", "plt.plot(x,sigmoid(x))\n", "plt.ylim(-0.05,1.05);" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.973403006423\n" ] } ], "source": [ "# Test the vectorized output\n", "\n", "w = np.array([1,2,3])\n", "x = np.array([0.5,0.5,0.7])\n", "b = 0\n", "\n", "print sigmoid_vector(w,x,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take all the weights and biases in a network of **perceptrons** and multiply them by a positive constant $c > 0$. Show that the behavior of the network doesn't change. \n", "\n", "Input: $[x_1,x_2,\\ldots,x_j]$\n", "\n", "Old behavior\n", "\n", "- Weights: $[w_1,w_2,\\ldots,w_j]$\n", "\n", "- Bias: $b$\n", "\n", "Perceptron output: \n", "\n", "- output = 0 if $w \\cdot x + b \\leq 0$\n", "- output = 1 if $w \\cdot x + b > 0$\n", "\n", "\n", "New input: \n", "\n", "- $w_\\mathrm{new} = [c w_1,c w_2,\\ldots,c w_j]$\n", "\n", "- $b_\\mathrm{new} = c b$\n", "\n", "New output of the perceptron:\n", "\n", "$w_\\mathrm{new} \\cdot x + b_\\mathrm{new} = c w \\cdot x + c b = c (w \\cdot x + b)$. \n", "\n", "This is just a positive scaling, so $w_\\mathrm{new} \\cdot x + b_\\mathrm{new} = w \\cdot x + b$ at 0 and keeps the same sign on either side since $c > 0$. So the behavior of the perceptron network doesn't change." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a network of perceptrons and fix the input $\\boldsymbol{x}$. Assume $\\boldsymbol{w}\\cdot\\boldsymbol{x} + b \\neq 0$ for all perceptrons. \n", "\n", "Original output:\n", "\n", "- 0 if $(w \\cdot x + b) < 0$\n", "- 1 if $(w \\cdot x + b) > 0$\n", "\n", "\n", "Replace perceptrons with sigmoid functions and multiply both weights and biases by a constant $c > 0$. \n", "\n", "- $w_\\mathrm{new} = [c w_1,c w_2,\\ldots,c w_j]$\n", "\n", "- $b_\\mathrm{new} = c b$\n", "\n", "New output:\n", "\n", "$\\sigma[c\\boldsymbol{w},\\boldsymbol{x},c b] \\equiv \\frac{1}{1 + \\exp{\\left(-\\sum_j{(c w_j) x_j} - c b\\right)}} = \\frac{1}{1 + \\exp{\\left(c(-\\sum_j{w_j x_j} - b)\\right)}}$\n", "\n", "As $c \\rightarrow \\infty$, the term $\\exp{\\left(c(-\\sum_j{w_j x_j} - b)\\right)}$ becomes $\\infty$ if $(-\\sum_j{w_j x_j} - b) > 0$, and so $\\sigma \\rightarrow 0$. This is equivalent to $(\\sum_j{w_j x_j} + b) < 0$, or the same as the first output of the perceptron. Similarly, if $(-\\sum_j{w_j x_j} - b) < 0$, then the term goes to 0 and $\\sigma \\rightarrow 1$. So the behavior of the sigmoid network is the same as perceptrons is the same for very large $c$. \n", "\n", "If $w \\cdot x + b = 0$ for one of the perceptrons, then $\\sigma=1/2$ regardless of the value of $c$. So the sigmoid approximation will fail to match the perceptron output. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Design a set of weights and biases such that digits are converted to their bitwise representation.**" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weights: \n", "[[-1. -2. -2. -1.]\n", " [ 3. -1. -1. -1.]\n", " [-1. 3. -1. -1.]\n", " [ 3. 3. -1. -1.]\n", " [-1. -1. 3. -1.]\n", " [ 3. -1. 3. -1.]\n", " [-1. 3. 3. -1.]\n", " [ 3. 3. 3. -1.]\n", " [-1. -1. -1. 3.]\n", " [ 3. -1. -1. 3.]]\n", "Bias: -2\n", "Bitwise output: \n", "[[0 0 0 0]\n", " [1 0 0 0]\n", " [0 1 0 0]\n", " [1 1 0 0]\n", " [0 0 1 0]\n", " [1 0 1 0]\n", " [0 1 1 0]\n", " [1 1 1 0]\n", " [0 0 0 1]\n", " [1 0 0 1]]\n" ] } ], "source": [ "# One set of possible weights and a bias; infinite amount\n", "# of legal combinations\n", "\n", "digits = np.identity(10) * 0.99 + 0.005\n", "\n", "weights = np.ones((10,4)) * -1\n", "\n", "weights[1::2,0] = 3\n", "weights[2::4,1] = 3\n", "weights[3::4,1] = 3\n", "weights[4:8,2] = 3\n", "weights[8:10,3] = 3\n", "weights[0,1:3] = -2\n", "\n", "bias = -2\n", "\n", "print \"Weights: \\n{}\".format(weights)\n", "\n", "print \"Bias: {}\".format(bias)\n", "\n", "print \"Bitwise output: \\n{}\".format((np.sign(np.dot(digits,weights) + bias).astype(int) + 1) / 2)" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize the network object\n", "\n", "class Network(object):\n", " \n", " def __init__(self,sizes):\n", " # Initialize the Network object with random (normal) biases, weights\n", " self.num_layers = len(sizes)\n", " self.sizes = sizes\n", " self.biases = [np.random.randn(y,1) for y in sizes[1:]]\n", " self.weights = [np.random.randn(y,x) for x,y in zip(sizes[:-1],sizes[1:])]\n", "\n", " def feedforward(self,a):\n", " # Return the output of the network\n", " \n", " for b,w in zip(self.biases, self.weights):\n", " a = sigmoid(np.dot(w,a) + b)\n", " return a\n", " \n", " def SGD(self, training_data, epochs, mini_batch_size,\n", " eta, test_data=None):\n", " \n", " if test_data:\n", " n_test = len(test_data)\n", " n = len(training_data)\n", " \n", " for j in xrange(epochs):\n", " random.shuffle(training_data)\n", " mini_batches = [training_data[k:k+mini_batch_size] for k in xrange(0,n,mini_batch_size)]\n", " for mini_batch in mini_batches:\n", " self.update_mini_batch(mini_batch,eta)\n", " if test_data:\n", " print \"Epoch {}: {} / {}\".format(j,self.evaluate(test_data),n_test)\n", " else:\n", " print \"Epoch {} complete.\".format(j)\n", " \n", " def update_mini_batch(self,mini_batch,eta):\n", " \n", " nabla_b = [np.zeros(b.shape) for b in self.biases]\n", " nabla_w = [np.zeros(w.shape) for w in self.weights]\n", " \n", " for x,y in mini_batch:\n", " delta_nabla_b, delta_nabla_w = self.backprop(x,y)\n", " nabla_b = [nb + dnb for nb,dnb in zip(nabla_b,delta_nabla_b)]\n", " nabla_w = [nw + dnw for nw,dnw in zip(nabla_w,delta_nabla_w)]\n", " \n", " self.weights = [w - (eta/len(mini_batch))*nw for w,nw in zip(self.weights,nabla_w)]\n", " self.biases = [b - (eta/len(mini_batch))*nb for b,nb in zip(self.biases,nabla_b)]\n", " \n", " def evaluate(self, test_data):\n", " \n", " test_results = [(np.argmax(self.feedforward(x)),y) for (x,y) in test_data]\n", " \n", " return sum(int(x == y) for (x,y) in test_results)\n", " \n", " def backprop(self, x, y):\n", "\n", " nabla_b = [np.zeros(b.shape) for b in self.biases]\n", " nabla_w = [np.zeros(w.shape) for w in self.weights]\n", " # feedforward\n", " activation = x\n", " activations = [x] # list to store all the activations, layer by layer\n", " zs = [] # list to store all the z vectors, layer by layer\n", " for b, w in zip(self.biases, self.weights):\n", " z = np.dot(w, activation)+b\n", " zs.append(z)\n", " activation = sigmoid(z)\n", " activations.append(activation)\n", " # backward pass\n", " delta = self.cost_derivative(activations[-1], y) * \\\n", " sigmoid_prime(zs[-1])\n", " nabla_b[-1] = delta\n", " nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n", "\n", " for l in xrange(2, self.num_layers):\n", " z = zs[-l]\n", " sp = sigmoid_prime(z)\n", " delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\n", " nabla_b[-l] = delta\n", " nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n", " return (nabla_b, nabla_w)\n", " \n", " def cost_derivative(self,output_activations,y):\n", " \n", " return (output_activations - y)\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the MNIST data" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cPickle as pickle\n", "import gzip" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_data():\n", " \n", " with gzip.open(\"neural-networks-and-deep-learning/data/mnist.pkl.gz\",\"rb\") as f:\n", " training_data,validation_data,test_data = pickle.load(f)\n", " \n", " return training_data,validation_data,test_data\n", "\n", "def load_data_wrapper():\n", " \n", " tr_d,va_d,te_d = load_data()\n", " \n", " training_inputs = [np.reshape(x,(784,1)) for x in tr_d[0]]\n", " training_results = [vectorized_result(y) for y in tr_d[1]]\n", " training_data = zip(training_inputs,training_results)\n", " \n", " validation_inputs = [np.reshape(x,(784,1)) for x in va_d[0]]\n", " validation_data = zip(validation_inputs,va_d[1])\n", " \n", " test_inputs = [np.reshape(x,(784,1)) for x in te_d[0]]\n", " test_data = zip(test_inputs,te_d[1])\n", " \n", " return (training_data,validation_data,test_data)\n", "\n", "def vectorized_result(j):\n", " \n", " e = np.zeros((10,1))\n", " e[j] = 1.0\n", " \n", " return e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the network" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": true }, "outputs": [], "source": [ "training_data,validation_data,test_data = load_data_wrapper()" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net = Network([784,30,10])\n", "net.SGD(training_data,30,10,3.0,test_data = test_data)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 6510 / 10000\n", "Epoch 1: 7492 / 10000\n", "Epoch 2: 7548 / 10000\n", "Epoch 3: 7599 / 10000\n", "Epoch 4: 7639 / 10000\n", "Epoch 5: 7663 / 10000\n", "Epoch 6: 7676 / 10000\n", "Epoch 7: 7685 / 10000\n", "Epoch 8: 7702 / 10000\n", "Epoch 9: 7714 / 10000\n", "Epoch 10: 7715 / 10000\n", "Epoch 11: 7719 / 10000\n", "Epoch 12: 7736 / 10000\n", "Epoch 13: 7715 / 10000\n", "Epoch 14: 7754 / 10000\n", "Epoch 15: 7758 / 10000\n", "Epoch 16: 7743 / 10000\n", "Epoch 17: 7771 / 10000\n", "Epoch 18: 7760 / 10000\n", "Epoch 19: 7782 / 10000\n", "Epoch 20: 7757 / 10000\n", "Epoch 21: 7775 / 10000\n", "Epoch 22: 7791 / 10000\n", "Epoch 23: 7790 / 10000\n", "Epoch 24: 7790 / 10000\n", "Epoch 25: 7793 / 10000\n", "Epoch 26: 7825 / 10000\n", "Epoch 27: 7824 / 10000\n", "Epoch 28: 7833 / 10000\n", "Epoch 29: 7852 / 10000\n" ] } ], "source": [ "net100 = Network([784,100,10])\n", "net100.SGD(training_data,30,10,3.0,test_data=test_data)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 6319 / 10000\n", "Epoch 1: 7024 / 10000\n", "Epoch 2: 7275 / 10000\n", "Epoch 3: 8177 / 10000\n", "Epoch 4: 8217 / 10000\n", "Epoch 5: 8246 / 10000\n", "Epoch 6: 8238 / 10000\n", "Epoch 7: 8235 / 10000\n", "Epoch 8: 8264 / 10000\n", "Epoch 9: 8263 / 10000\n", "Epoch 10: 8267 / 10000\n", "Epoch 11: 8329 / 10000\n", "Epoch 12: 9147 / 10000\n", "Epoch 13: 9134 / 10000\n", "Epoch 14: 9145 / 10000\n", "Epoch 15: 9156 / 10000\n", "Epoch 16: 9170 / 10000\n", "Epoch 17: 9171 / 10000\n", "Epoch 18: 9173 / 10000\n", "Epoch 19: 9167 / 10000\n", "Epoch 20: 9159 / 10000\n", "Epoch 21: 9162 / 10000\n", "Epoch 22: 9152 / 10000\n", "Epoch 23: 9184 / 10000\n", "Epoch 24: 9149 / 10000\n", "Epoch 25: 9160 / 10000\n", "Epoch 26: 9179 / 10000\n", "Epoch 27: 9150 / 10000\n", "Epoch 28: 9158 / 10000\n", "Epoch 29: 9169 / 10000\n" ] } ], "source": [ "net2 = Network([784,10])\n", "net2.SGD(training_data,30,10,3.0,test_data=test_data)" ] }, { "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
nagordon/mechpy
tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb
1
47227
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "- - - -\n", "# Mechpy Tutorials\n", "a mechanical engineering toolbox\n", "\n", "source code - https://github.com/nagordon/mechpy \n", "documentation - https://nagordon.github.io/mechpy/web/ \n", "\n", "- - - -\n", "\n", "Neal Gordon \n", "2017-02-20 \n", "\n", "- - - -\n", "\n", "\n", "\n", "## Composite Plate Mechanics with Python\n", "\n", "reference: hyer page 584. 617\n", "\n", "The motivation behind this talk is to explore the capability of python as a scientific computation tool as well as solve a typical calcuation that could either be done by hand, or coded. I find coding to be a convient way to learn challenging mathmatics because I can easily replicate my work in the future when I can't remember the details of the calcuation or, if there are any errors, they can typically be easily fixed and the other calcuations re-ran without a lot of duplcation of effort.\n", "\n", "Composite mechanics can be very iterative by nature and is easiest to employ linear algebra to find displacements, strains and stresses of composites. Coding solutions is also handy when visualizations are required.\n", "\n", "For this example, we are interested in calcuating the stress critical ply in a simple asymteric composite plate with a pressure load applied. We can chooose a variety of boundary conditions of our plate, but this solution is limited to 2 dimensional displacements, x and z. If we are interested in 3 dimensional displacements, the problem becomes much more challenging as partial differentiation of the governing equations gives us a PDE, which is more challenging to solve. \n", "\n", "The steps to solving are \n", "- Identify governing and equilibrium equations\n", "- import python required libraries \n", "- declare symbolic variables\n", "- declare numeric variables, including material properties, plate dimensions, and plate pressure\n", "- solve 4th order differntial equation with 7 constants\n", "- apply plate boundary conditions and acquire u(x) and w(x) displacement functions\n", "- acquire strain equations from displacement\n", "- acquire stress equations from strain\n", "- determine critical ply from highest ply stress ratio\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import Python modules and \n", "import numpy as np\n", "from sympy import *\n", "from pprint import pprint\n", "\n", "# printing and plotting settings \n", "init_printing(use_latex='mathjax')\n", "get_ipython().magic('matplotlib inline') # inline plotting\n", "\n", "x,y,q = symbols('x,y,q')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned before, if we want to perform a 3 dimensional displacement model of the composite plate, we would have 6 reaction forces that are a function of x and y. Those 6 reaction forces are related by 3 equalibrium equations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# # hyer page 584\n", "# # Equations of equilibrium\n", "# Nxf = Function('N_x')(x,y)\n", "# Nyf = Function('N_y')(x,y)\n", "# Nxyf = Function('N_xy')(x,y)\n", "# Mxf = Function('M_x')(x,y)\n", "# Myf = Function('M_y')(x,y)\n", "# Mxyf = Function('M_xy')(x,y)\n", "\n", "# symbols for force and moments\n", "Nx,Ny,Nxy,Mx,My,Mxy = symbols('N_x,N_y,N_xy,M_x,M_y,M_xy')\n", "Nxf,Nyf,Nxyf,Mxf,Myf,Mxyf = symbols('Nxf,Nyf,Nxyf,Mxf,Myf,Mxyf')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$0 = \\frac{\\partial}{\\partial x} \\operatorname{N_{x}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial y} \\operatorname{N_{xy}}{\\left (x,y \\right )}$$" ], "text/plain": [ " ∂ ∂ \n", "0 = ──(Nₓ(x, y)) + ──(N_xy(x, y))\n", " ∂x ∂y " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Eq(0,diff(Nx(x,y), x)+diff(Nxy(x,y),y))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$0 = \\frac{\\partial}{\\partial x} \\operatorname{N_{xy}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial y} \\operatorname{N_{y}}{\\left (x,y \\right )}$$" ], "text/plain": [ " ∂ ∂ \n", "0 = ──(N_xy(x, y)) + ──(N_y(x, y))\n", " ∂x ∂y " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Eq(0,diff(Nxy(x,y), x)+diff(Ny(x,y),y))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$0 = q + \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{M_{x}}{\\left (x,y \\right )} + 2 \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{M_{xy}}{\\left (x,y \\right )} + \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{M_{y}}{\\left (x,y \\right )}$$" ], "text/plain": [ " 2 2 2 \n", " ∂ ∂ ∂ \n", "0 = q + ───(Mₓ(x, y)) + 2⋅─────(M_xy(x, y)) + ───(M_y(x, y))\n", " 2 ∂y ∂x 2 \n", " ∂x ∂y " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Eq(0, diff(Mx(x,y),x,2) + 2*diff(Mxy(x,y),x,y) + diff(My(x,y) ,y,2)+ q )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What makes composite plates special is the fact that they typically not isotropic. This is handled by the 6x6 ABD matrix that defines the composites properties axially, in bending, and the coupling between the two." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# composite properties \n", "A11,A22,A66,A12,A16,A26,A66 = symbols('A11,A22,A66,A12,A16,A26,A66')\n", "B11,B22,B66,B12,B16,B26,B66 = symbols('B11,B22,B66,B12,B16,B26,B66')\n", "D11,D22,D66,D12,D16,D26,D66 = symbols('D11,D22,D66,D12,D16,D26,D66')\n", "\n", "## constants of integration when solving differential equation\n", "C1,C2,C3,C4,C5,C6 = symbols('C1,C2,C3,C4,C5,C6')\n", "\n", "# plate and composite parameters\n", "th,a,b = symbols('th,a,b')\n", "\n", "# displacement functions\n", "u0 = Function('u0')(x,y)\n", "v0 = Function('v0')(x,y)\n", "w0 = Function('w0')(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute our 6 displacement conditions which is where our PDE's show up" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{x} = A_{11} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{12} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{16} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - B_{11} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{12} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{16} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞ \n", "Nₓ = A₁₁⋅──(u₀(x, y)) + A₁₂⋅──(v₀(x, y)) + A₁₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟ -\n", " ∂x ∂y ⎝∂y ∂x ⎠ \n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", " B₁₁⋅───(w₀(x, y)) - B₁₂⋅───(w₀(x, y)) - 2⋅B₁₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nxf = A11*diff(u0,x) + A12*diff(v0,y) + A16*(diff(u0,y) + diff(v0,x)) - B11*diff(w0,x,2) - B12*diff(w0,y,2) - 2*B16*diff(w0,x,y)\n", "Eq(Nx, Nxf)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{y} = A_{12} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{22} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{26} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - B_{12} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{22} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{26} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞ \n", "N_y = A₁₂⋅──(u₀(x, y)) + A₂₂⋅──(v₀(x, y)) + A₂₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟ \n", " ∂x ∂y ⎝∂y ∂x ⎠ \n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", "- B₁₂⋅───(w₀(x, y)) - B₂₂⋅───(w₀(x, y)) - 2⋅B₂₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nyf = A12*diff(u0,x) + A22*diff(v0,y) + A26*(diff(u0,y) + diff(v0,x)) - B12*diff(w0,x,2) - B22*diff(w0,y,2) - 2*B26*diff(w0,x,y)\n", "Eq(Ny,Nyf)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{xy} = A_{16} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{26} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{66} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - B_{16} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{26} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{66} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞\n", "N_xy = A₁₆⋅──(u₀(x, y)) + A₂₆⋅──(v₀(x, y)) + A₆₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟\n", " ∂x ∂y ⎝∂y ∂x ⎠\n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", " - B₁₆⋅───(w₀(x, y)) - B₂₆⋅───(w₀(x, y)) - 2⋅B₆₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nxyf = A16*diff(u0,x) + A26*diff(v0,y) + A66*(diff(u0,y) + diff(v0,x)) - B16*diff(w0,x,2) - B26*diff(w0,y,2) - 2*B66*diff(w0,x,y) \n", "Eq(Nxy,Nxyf)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{x} = B_{11} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{12} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + B_{16} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - D_{11} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - D_{12} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 D_{16} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞ \n", "Mₓ = B₁₁⋅──(u₀(x, y)) + B₁₂⋅──(v₀(x, y)) + B₁₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟ -\n", " ∂x ∂y ⎝∂y ∂x ⎠ \n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", " D₁₁⋅───(w₀(x, y)) - D₁₂⋅───(w₀(x, y)) - 2⋅D₁₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mxf = B11*diff(u0,x) + B12*diff(v0,y) + B16*(diff(u0,y) + diff(v0,x)) - D11*diff(w0,x,2) - D12*diff(w0,y,2) - 2*D16*diff(w0,x,y)\n", "Eq(Mx,Mxf)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{y} = B_{12} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{22} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + B_{26} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - D_{12} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - D_{22} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 D_{26} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞ \n", "M_y = B₁₂⋅──(u₀(x, y)) + B₂₂⋅──(v₀(x, y)) + B₂₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟ \n", " ∂x ∂y ⎝∂y ∂x ⎠ \n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", "- D₁₂⋅───(w₀(x, y)) - D₂₂⋅───(w₀(x, y)) - 2⋅D₂₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Myf = B12*diff(u0,x) + B22*diff(v0,y) + B26*(diff(u0,y) + diff(v0,x)) - D12*diff(w0,x,2) - D22*diff(w0,y,2) - 2*D26*diff(w0,x,y)\n", "Eq(My,Myf)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{xy} = B_{16} \\frac{\\partial}{\\partial x} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{26} \\frac{\\partial}{\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + B_{66} \\left(\\frac{\\partial}{\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial}{\\partial x} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - D_{16} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - D_{26} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 D_{66} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " \n", " ∂ ∂ ⎛∂ ∂ ⎞\n", "M_xy = B₁₆⋅──(u₀(x, y)) + B₂₆⋅──(v₀(x, y)) + B₆₆⋅⎜──(u₀(x, y)) + ──(v₀(x, y))⎟\n", " ∂x ∂y ⎝∂y ∂x ⎠\n", " \n", "\n", " 2 2 2 \n", " ∂ ∂ ∂ \n", " - D₁₆⋅───(w₀(x, y)) - D₂₆⋅───(w₀(x, y)) - 2⋅D₆₆⋅─────(w₀(x, y))\n", " 2 2 ∂y ∂x \n", " ∂x ∂y " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mxyf = B16*diff(u0,x) + B26*diff(v0,y) + B66*(diff(u0,y) + diff(v0,x)) - D16*diff(w0,x,2) - D26*diff(w0,y,2) - 2*D66*diff(w0,x,y)\n", "Eq(Mxy,Mxyf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, combine our 6 displacement conditions with our 3 equalibrium equations to get three goverening equations" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$A_{11} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{11} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{12} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{12} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{16} \\left(\\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) + A_{16} \\left(\\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - B_{11} \\frac{\\partial^{3}}{\\partial x^{3}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{11} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{12} \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{12} \\frac{\\partial^{3}}{\\partial y^{3}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{16} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{16} \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " 2 2 2 2 \n", " ∂ ∂ ∂ ∂ \n", "A₁₁⋅───(u₀(x, y)) + A₁₁⋅─────(u₀(x, y)) + A₁₂⋅─────(v₀(x, y)) + A₁₂⋅───(v₀(x, \n", " 2 ∂y ∂x ∂y ∂x 2 \n", " ∂x ∂y \n", "\n", " ⎛ 2 2 ⎞ ⎛ 2 2 \n", " ⎜ ∂ ∂ ⎟ ⎜ ∂ ∂ \n", "y)) + A₁₆⋅⎜─────(u₀(x, y)) + ───(v₀(x, y))⎟ + A₁₆⋅⎜───(u₀(x, y)) + ─────(v₀(x,\n", " ⎜∂y ∂x 2 ⎟ ⎜ 2 ∂y ∂x \n", " ⎝ ∂x ⎠ ⎝∂y \n", "\n", " ⎞ 3 3 3 \n", " ⎟ ∂ ∂ ∂ \n", " y))⎟ - B₁₁⋅───(w₀(x, y)) - B₁₁⋅──────(w₀(x, y)) - B₁₂⋅──────(w₀(x, y)) - B₁₂⋅\n", " ⎟ 3 2 2 \n", " ⎠ ∂x ∂y ∂x ∂y ∂x \n", "\n", " 3 3 3 \n", " ∂ ∂ ∂ \n", "───(w₀(x, y)) - 2⋅B₁₆⋅──────(w₀(x, y)) - 2⋅B₁₆⋅──────(w₀(x, y))\n", " 3 2 2 \n", "∂y ∂y ∂x ∂y ∂x " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq1 = diff(Nxf,x) + diff(Nxf,y)\n", "eq1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$A_{12} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{16} \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + A_{22} \\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{26} \\left(\\frac{\\partial^{2}}{\\partial y^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) + A_{26} \\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + A_{66} \\left(\\frac{\\partial^{2}}{\\partial x\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{2}}{\\partial x^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - B_{12} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{16} \\frac{\\partial^{3}}{\\partial x^{3}} \\operatorname{w_{0}}{\\left (x,y \\right )} - B_{22} \\frac{\\partial^{3}}{\\partial y^{3}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 3 B_{26} \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 B_{66} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )}$$" ], "text/plain": [ " 2 2 2 ⎛ 2 \n", " ∂ ∂ ∂ ⎜ ∂ \n", "A₁₂⋅─────(u₀(x, y)) + A₁₆⋅───(u₀(x, y)) + A₂₂⋅───(v₀(x, y)) + A₂₆⋅⎜───(u₀(x, y\n", " ∂y ∂x 2 2 ⎜ 2 \n", " ∂x ∂y ⎝∂y \n", "\n", " 2 ⎞ 2 ⎛ 2 2 \n", " ∂ ⎟ ∂ ⎜ ∂ ∂ \n", ")) + ─────(v₀(x, y))⎟ + A₂₆⋅─────(v₀(x, y)) + A₆₆⋅⎜─────(u₀(x, y)) + ───(v₀(x,\n", " ∂y ∂x ⎟ ∂y ∂x ⎜∂y ∂x 2 \n", " ⎠ ⎝ ∂x \n", "\n", " ⎞ 3 3 3 \n", " ⎟ ∂ ∂ ∂ \n", " y))⎟ - B₁₂⋅──────(w₀(x, y)) - B₁₆⋅───(w₀(x, y)) - B₂₂⋅───(w₀(x, y)) - 3⋅B₂₆⋅─\n", " ⎟ 2 3 3 \n", " ⎠ ∂y ∂x ∂x ∂y ∂\n", "\n", " 3 3 \n", " ∂ ∂ \n", "─────(w₀(x, y)) - 2⋅B₆₆⋅──────(w₀(x, y))\n", " 2 2 \n", "y ∂x ∂y ∂x " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq2 = diff(Nxyf,x) + diff(Nyf,y)\n", "eq2" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$B_{11} \\frac{\\partial^{3}}{\\partial x^{3}} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{12} \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{12} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )} + B_{16} \\left(\\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{3}}{\\partial x^{3}} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) + 2 B_{16} \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{u_{0}}{\\left (x,y \\right )} + B_{22} \\frac{\\partial^{3}}{\\partial y^{3}} \\operatorname{v_{0}}{\\left (x,y \\right )} + B_{26} \\left(\\frac{\\partial^{3}}{\\partial y^{3}} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) + 2 B_{26} \\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{v_{0}}{\\left (x,y \\right )} + 2 B_{66} \\left(\\frac{\\partial^{3}}{\\partial x\\partial y^{2}} \\operatorname{u_{0}}{\\left (x,y \\right )} + \\frac{\\partial^{3}}{\\partial x^{2}\\partial y} \\operatorname{v_{0}}{\\left (x,y \\right )}\\right) - D_{11} \\frac{\\partial^{4}}{\\partial x^{4}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 2 D_{12} \\frac{\\partial^{4}}{\\partial x^{2}\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 4 D_{16} \\frac{\\partial^{4}}{\\partial x^{3}\\partial y} \\operatorname{w_{0}}{\\left (x,y \\right )} - D_{22} \\frac{\\partial^{4}}{\\partial y^{4}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 4 D_{26} \\frac{\\partial^{4}}{\\partial x\\partial y^{3}} \\operatorname{w_{0}}{\\left (x,y \\right )} - 4 D_{66} \\frac{\\partial^{4}}{\\partial x^{2}\\partial y^{2}} \\operatorname{w_{0}}{\\left (x,y \\right )} + q$$" ], "text/plain": [ " 3 3 3 ⎛ 3 \n", " ∂ ∂ ∂ ⎜ ∂ \n", "B₁₁⋅───(u₀(x, y)) + B₁₂⋅──────(u₀(x, y)) + B₁₂⋅──────(v₀(x, y)) + B₁₆⋅⎜──────(\n", " 3 2 2 ⎜ 2 \n", " ∂x ∂y ∂x ∂y ∂x ⎝∂y ∂x \n", "\n", " 3 ⎞ 3 3 \n", " ∂ ⎟ ∂ ∂ \n", "u₀(x, y)) + ───(v₀(x, y))⎟ + 2⋅B₁₆⋅──────(u₀(x, y)) + B₂₂⋅───(v₀(x, y)) + B₂₆⋅\n", " 3 ⎟ 2 3 \n", " ∂x ⎠ ∂y ∂x ∂y \n", "\n", "⎛ 3 3 ⎞ 3 ⎛ 3 \n", "⎜ ∂ ∂ ⎟ ∂ ⎜ ∂ \n", "⎜───(u₀(x, y)) + ──────(v₀(x, y))⎟ + 2⋅B₂₆⋅──────(v₀(x, y)) + 2⋅B₆₆⋅⎜──────(u₀\n", "⎜ 3 2 ⎟ 2 ⎜ 2 \n", "⎝∂y ∂y ∂x ⎠ ∂y ∂x ⎝∂y ∂x \n", "\n", " 3 ⎞ 4 4 \n", " ∂ ⎟ ∂ ∂ \n", "(x, y)) + ──────(v₀(x, y))⎟ - D₁₁⋅───(w₀(x, y)) - 2⋅D₁₂⋅───────(w₀(x, y)) - 4⋅\n", " 2 ⎟ 4 2 2 \n", " ∂y ∂x ⎠ ∂x ∂y ∂x \n", "\n", " 4 4 4 \n", " ∂ ∂ ∂ ∂\n", "D₁₆⋅──────(w₀(x, y)) - D₂₂⋅───(w₀(x, y)) - 4⋅D₂₆⋅──────(w₀(x, y)) - 4⋅D₆₆⋅────\n", " 3 4 3 2 \n", " ∂y ∂x ∂y ∂y ∂x ∂y \n", "\n", "4 \n", " \n", "───(w₀(x, y)) + q\n", " 2 \n", "∂x " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq3 = diff(Mxf,x,2) + 2*diff(Mxyf,x,y) + diff(Myf,y,2) + q\n", "eq3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yikes, I do not want to solve that (at least right now). If we make the assumption that the plate has equal displacement of y in the x and y direction, then we can simply things ALOT! These simplifications are valid for cross ply unsymmetric laminates plate, Hyer pg 616. This is applied by setting some of our material properties to zero. $ A16=A26=D16=D26=B16=B26=B12=B66=0 $\n", "Almost like magic, we now have some equations that aren't so scary." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "u0 = Function('u0')(x)\n", "v0 = Function('v0')(x)\n", "w0 = Function('w0')(x)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{x} = A_{11} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )} - B_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )}$$" ], "text/plain": [ " 2 \n", " d d \n", "Nₓ = A₁₁⋅──(u₀(x)) - B₁₁⋅───(w₀(x))\n", " dx 2 \n", " dx " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nxf = A11*diff(u0,x) + A12*diff(v0,y) - B11*diff(w0,x,2)\n", "Eq(Nx, Nxf)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{y} = A_{12} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )}$$" ], "text/plain": [ " d \n", "N_y = A₁₂⋅──(u₀(x))\n", " dx " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nyf = A12*diff(u0,x) + A22*diff(v0,y) - B22*diff(w0,y,2)\n", "Eq(Ny,Nyf)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$N_{xy} = A_{66} \\frac{d}{d x} \\operatorname{v_{0}}{\\left (x \\right )}$$" ], "text/plain": [ " d \n", "N_xy = A₆₆⋅──(v₀(x))\n", " dx " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nxyf = A66*(diff(u0,y) + diff(v0,x))\n", "Eq(Nxy,Nxyf)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{x} = B_{11} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )} - D_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )}$$" ], "text/plain": [ " 2 \n", " d d \n", "Mₓ = B₁₁⋅──(u₀(x)) - D₁₁⋅───(w₀(x))\n", " dx 2 \n", " dx " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mxf = B11*diff(u0,x) - D11*diff(w0,x,2) - D12*diff(w0,y,2)\n", "Eq(Mx,Mxf)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{y} = - D_{12} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )}$$" ], "text/plain": [ " 2 \n", " d \n", "M_y = -D₁₂⋅───(w₀(x))\n", " 2 \n", " dx " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Myf = B22*diff(v0,y) - D12*diff(w0,x,2) - D22*diff(w0,y,2)\n", "Eq(My,Myf)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$M_{xy} = 0$$" ], "text/plain": [ "M_xy = 0" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mxyf = 0\n", "Eq(Mxy,Mxyf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are getting somewhere. Finally we can solve the differential equations " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\operatorname{N_{x}}{\\left (x \\right )} = C_{1}$$" ], "text/plain": [ "Nₓ(x) = C₁" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dsolve(diff(Nx(x)))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\operatorname{M_{x}}{\\left (x \\right )} = C_{1} + C_{2} x - \\frac{q x^{2}}{2}$$" ], "text/plain": [ " 2\n", " q⋅x \n", "Mₓ(x) = C₁ + C₂⋅x - ────\n", " 2 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dsolve(diff(Mx(x),x,2)+q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now solve for u0 and w0 with some pixie dust" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$A_{11} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )} - B_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )} - C_{1}$$" ], "text/plain": [ " 2 \n", " d d \n", "A₁₁⋅──(u₀(x)) - B₁₁⋅───(w₀(x)) - C₁\n", " dx 2 \n", " dx " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq4 = (Nxf-C1)\n", "eq4" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$B_{11} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )} - C_{2} x - C_{3} - D_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )} + q x^{2}$$" ], "text/plain": [ " 2 \n", " d d 2\n", "B₁₁⋅──(u₀(x)) - C₂⋅x - C₃ - D₁₁⋅───(w₀(x)) + q⋅x \n", " dx 2 \n", " dx " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq5 = Mxf -( -q*x**2 + C2*x + C3 )\n", "eq5" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\frac{1}{A_{11}} \\left(B_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )} + C_{1}\\right) = \\frac{1}{B_{11}} \\left(C_{2} x + C_{3} + D_{11} \\frac{d^{2}}{d x^{2}} \\operatorname{w_{0}}{\\left (x \\right )} - q x^{2}\\right)$$" ], "text/plain": [ " 2 2 \n", " d d 2\n", "B₁₁⋅───(w₀(x)) + C₁ C₂⋅x + C₃ + D₁₁⋅───(w₀(x)) - q⋅x \n", " 2 2 \n", " dx dx \n", "─────────────────── = ─────────────────────────────────\n", " A₁₁ B₁₁ " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq6 = Eq(solve(eq4,diff(u0,x))[0] , solve(eq5, diff(u0,x))[0])\n", "eq6" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\operatorname{w_{0}}{\\left (x \\right )} = - \\frac{A_{11} C_{2} x^{3}}{6 A_{11} D_{11} - 6 B_{11}^{2}} + \\frac{A_{11} q x^{4}}{12 A_{11} D_{11} - 12 B_{11}^{2}} + C_{1} + C_{5} x + \\frac{x^{2} \\left(- A_{11} C_{3} + B_{11} C_{4}\\right)}{2 A_{11} D_{11} - 2 B_{11}^{2}}$$" ], "text/plain": [ " 3 4 2 \n", " A₁₁⋅C₂⋅x A₁₁⋅q⋅x x ⋅(-A₁₁⋅C₃ +\n", "w₀(x) = - ────────────────── + ─────────────────── + C₁ + C₅⋅x + ─────────────\n", " ⎛ 2⎞ ⎛ 2⎞ ⎛ \n", " 6⋅⎝A₁₁⋅D₁₁ - B₁₁ ⎠ 12⋅⎝A₁₁⋅D₁₁ - B₁₁ ⎠ 2⋅⎝A₁₁⋅D₁₁ \n", "\n", " \n", " B₁₁⋅C₄)\n", "────────\n", " 2⎞ \n", "- B₁₁ ⎠ " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w0f = dsolve(eq6, w0)\n", "w0f" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\frac{1}{A_{11} D_{11} - B_{11}^{2}} \\left(- A_{11} C_{2} x - A_{11} C_{3} + A_{11} q x^{2} + B_{11} C_{1}\\right) = \\frac{1}{B_{11}} \\left(A_{11} \\frac{d}{d x} \\operatorname{u_{0}}{\\left (x \\right )} - C_{1}\\right)$$" ], "text/plain": [ " d \n", " 2 A₁₁⋅──(u₀(x)) - C₁\n", "-A₁₁⋅C₂⋅x - A₁₁⋅C₃ + A₁₁⋅q⋅x + B₁₁⋅C₁ dx \n", "────────────────────────────────────── = ──────────────────\n", " 2 B₁₁ \n", " A₁₁⋅D₁₁ - B₁₁ " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eq7 = Eq(solve(eq6, diff(w0,x,2))[0] , solve(eq4,diff(w0,x,2))[0])\n", "eq7" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\operatorname{u_{0}}{\\left (x \\right )} = \\frac{1}{A_{11} D_{11} - B_{11}^{2}} \\left(- \\frac{B_{11} C_{1}}{2} x^{2} - B_{11} C_{3} x - B_{11} C_{4} D_{11} + \\frac{B_{11} q}{3} x^{3} + C_{2} D_{11} x + \\frac{B_{11}^{3} C_{4}}{A_{11}}\\right)$$" ], "text/plain": [ " 2 3 3 \n", " B₁₁⋅C₁⋅x B₁₁⋅q⋅x B₁₁ ⋅C₄\n", " - ───────── - B₁₁⋅C₃⋅x - B₁₁⋅C₄⋅D₁₁ + ──────── + C₂⋅D₁₁⋅x + ───────\n", " 2 3 A₁₁ \n", "u₀(x) = ───────────────────────────────────────────────────────────────────\n", " 2 \n", " A₁₁⋅D₁₁ - B₁₁ " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u0f = dsolve(eq7)\n", "u0f" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "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": 0 }
mit
lyftzeigen/MachineLearningLessons
KerasFFNWeatherPrediction/KerasFFNWeatherPrediction.ipynb
1
154741
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Keras FFN weather prediction in Rostov-on-Don" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "import os\n", "\n", "import theano\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from keras.models import Sequential\n", "from keras.layers.core import Dense, Dropout\n", "\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Подготовим данные для обучения" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = open('34731.dat').readlines()\n", "\n", "meteo = []\n", "\n", "for d in data[42006:]:\n", " wmo = int(d[0:5])\n", " year = int(d[5:9])\n", " month = int(d[9:11])\n", " day = int(d[11:13])\n", " hour = int(d[13:15])\n", " # Humidity\n", " rh = int(d[15:18])\n", " # Height of the base of the lowest cloud\n", " hcld = int(d[43:45])\n", " # Dew point\n", " tdew = int(d[47:50])\n", " # Cloud amount\n", " tcld = int(d[53:55])\n", " # Wind direction\n", " wdir = int(d[61:63])\n", " # Wind speed\n", " wspd = int(d[64:66])\n", " # Air pressure\n", " stap = float(d[73:78])/10\n", " # Past weather code\n", " w = int(d[83:84])\n", " # Air temperature\n", " airt = float(d[89:93]) / 10 if d[89:93] != '9999' else 0.0\n", " if hour:\n", " meteo.append([hour, day, month, wdir, wspd, stap, airt])\n", "\n", "meteo = np.array(meteo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Задаем параметры обучающих векторов" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_input = 6\n", "num_output = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Построим обучающие выборки" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "scaler = MinMaxScaler()\n", "scaler.fit(meteo)\n", "\n", "meteo = scaler.transform(meteo)\n", "\n", "train_size = int(len(meteo) * 0.95)\n", "train, test = np.array(meteo[0:train_size]), np.array(meteo[train_size:len(meteo)])\n", "\n", "train_x = np.array([d[:-num_output] for d in train])\n", "train_y = np.array([d[num_input:] for d in train])\n", "test_x = np.array([d[:-num_output] for d in test])\n", "test_y = np.array([d[num_input:] for d in test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Строим модель нейронной сети" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(64, input_dim=num_input, activation='relu'))\n", "model.add(Dense(64, activation='relu'))\n", "model.add(Dense(num_output))\n", "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Обучаем нейронную сеть и сохраняем" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 65174 samples, validate on 3431 samples\n", "Epoch 1/50\n", "0s - loss: 0.0165 - acc: 4.6031e-05 - val_loss: 0.0056 - val_acc: 0.0000e+00\n", "Epoch 2/50\n", "0s - loss: 0.0052 - acc: 4.6031e-05 - val_loss: 0.0053 - val_acc: 0.0000e+00\n", "Epoch 3/50\n", "0s - loss: 0.0051 - acc: 4.6031e-05 - val_loss: 0.0053 - val_acc: 0.0000e+00\n", "Epoch 4/50\n", "0s - loss: 0.0049 - acc: 4.6031e-05 - val_loss: 0.0049 - val_acc: 0.0000e+00\n", "Epoch 5/50\n", "0s - loss: 0.0049 - acc: 4.6031e-05 - val_loss: 0.0050 - val_acc: 0.0000e+00\n", "Epoch 6/50\n", "0s - loss: 0.0048 - acc: 4.6031e-05 - val_loss: 0.0053 - val_acc: 0.0000e+00\n", "Epoch 7/50\n", "0s - loss: 0.0047 - acc: 4.6031e-05 - val_loss: 0.0050 - val_acc: 0.0000e+00\n", "Epoch 8/50\n", "1s - loss: 0.0046 - acc: 4.6031e-05 - val_loss: 0.0051 - val_acc: 0.0000e+00\n", "Epoch 9/50\n", "0s - loss: 0.0046 - acc: 4.6031e-05 - val_loss: 0.0048 - val_acc: 0.0000e+00\n", "Epoch 10/50\n", "0s - loss: 0.0045 - acc: 4.6031e-05 - val_loss: 0.0057 - val_acc: 0.0000e+00\n", "Epoch 11/50\n", "0s - loss: 0.0045 - acc: 4.6031e-05 - val_loss: 0.0047 - val_acc: 0.0000e+00\n", "Epoch 12/50\n", "0s - loss: 0.0045 - acc: 4.6031e-05 - val_loss: 0.0049 - val_acc: 0.0000e+00\n", "Epoch 13/50\n", "1s - loss: 0.0044 - acc: 4.6031e-05 - val_loss: 0.0049 - val_acc: 0.0000e+00\n", "Epoch 14/50\n", "1s - loss: 0.0044 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 15/50\n", "1s - loss: 0.0043 - acc: 4.6031e-05 - val_loss: 0.0046 - val_acc: 0.0000e+00\n", "Epoch 16/50\n", "0s - loss: 0.0043 - acc: 4.6031e-05 - val_loss: 0.0050 - val_acc: 0.0000e+00\n", "Epoch 17/50\n", "0s - loss: 0.0043 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 18/50\n", "0s - loss: 0.0042 - acc: 4.6031e-05 - val_loss: 0.0046 - val_acc: 0.0000e+00\n", "Epoch 19/50\n", "0s - loss: 0.0043 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 20/50\n", "0s - loss: 0.0042 - acc: 4.6031e-05 - val_loss: 0.0048 - val_acc: 0.0000e+00\n", "Epoch 21/50\n", "0s - loss: 0.0042 - acc: 4.6031e-05 - val_loss: 0.0046 - val_acc: 0.0000e+00\n", "Epoch 22/50\n", "0s - loss: 0.0042 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 23/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0047 - val_acc: 0.0000e+00\n", "Epoch 24/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 25/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 26/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 27/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 28/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 29/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 30/50\n", "0s - loss: 0.0041 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 31/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 32/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 33/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 34/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 35/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 36/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 37/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 38/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 39/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 40/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 41/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 42/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0043 - val_acc: 0.0000e+00\n", "Epoch 43/50\n", "1s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0049 - val_acc: 0.0000e+00\n", "Epoch 44/50\n", "0s - loss: 0.0040 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 45/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0045 - val_acc: 0.0000e+00\n", "Epoch 46/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0046 - val_acc: 0.0000e+00\n", "Epoch 47/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0044 - val_acc: 0.0000e+00\n", "Epoch 48/50\n", "1s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0046 - val_acc: 0.0000e+00\n", "Epoch 49/50\n", "1s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n", "Epoch 50/50\n", "0s - loss: 0.0039 - acc: 4.6031e-05 - val_loss: 0.0042 - val_acc: 0.0000e+00\n" ] } ], "source": [ "history = model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=50, batch_size=128, verbose=2)\n", "\n", "model.save_weights('weights.net')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Строим график обучения" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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27tzpOToMAKdPn8YPf/hDPPPMM35HhX/1q1/hzJkzWLt2bbs/k2nTpuGOO+7w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\neVcU93Ip22QuZxku8eTmrMVasFicf4VlW1i28spZWm8hZyXseiiWZjC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Z+4xrEJjQ751C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\nyrzi/wI5/LmRYSzrxQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2796eb91048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(16, 5))\n", "ax.plot(history.history['loss'])\n", "ax.plot(history.history['val_loss'])\n", "ax.legend(['Train loss ({})'.format(history.history['loss'][-1]), \\\n", " 'Test loss ({})'.format(history.history['val_loss'][-1])])\n", "ax.grid(ls=':')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Загружаем нейронную сеть из файла" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.load_weights('weights.net')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Предсказываем данные" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "test_predict = model.predict(test_x)\n", "\n", "test_y = np.squeeze(test_y)\n", "test_predict = np.squeeze(test_predict)\n", "\n", "test_x_y = np.array([np.append(test_x[i], test_y[i]) for i in range(len(test_x))])\n", "test_x_predict = np.array([np.append(test_x[i], test_predict[i]) for i in range(len(test_x))])\n", "\n", "test_x_y = scaler.inverse_transform(test_x_y)[:, -1]\n", "test_x_predict = scaler.inverse_transform(test_x_predict)[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Определяем погрешность работы сети" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Погрешность определения температуры: 3.282766\n" ] } ], "source": [ "delta = np.mean(np.abs(test_x_y - test_x_predict))\n", "print('Погрешность определения температуры: %f' % (delta))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Строим графики валидации на тестовой выборке" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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KSmRlZaFjx47o0qUL+vTpAwDo1asXRo8eDY7j0KdPH+Tk5Lj7uP/++5GYmIjExESMGjUK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\nQtXMzMzMzMxK5f+Lp4J1+vNdUwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2796edd9c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(16, 8))\n", "ax.plot(test_x_y[:], 'g-')\n", "ax.plot(test_x_predict[:], 'r-')\n", "ax.legend(['Real temperature', 'Predict temperature'])\n", "ax.grid(ls=':')\n", "plt.show()" ] }, { "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
paulovn/ml-vm-notebook
vmfiles/IPNB/Examples/d Scala/01 Hello world.ipynb
1
2989
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello World in a Scala Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is a first try with a Scala (Spark) kernel. Please be patient on execution of the first cell, it takes a little bit to get the kernel moving.\n", "\n", "We can create a Scala program:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "object HelloWorld {\n", " def main(args: Array[String]) {\n", " println(\"Hello, world!\")\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. and execute it" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "HelloWorld.main(null)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... but since this is a [REPL](https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop) environment, it is actually simpler to just start executing Scala code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "println( \"Hello world\" )" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "If you reached here without problems, it means the Scala kernel works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Magics\n", "\n", "The [SPylon kernel](https://github.com/maxpoint/spylon-kernel) kernel is providing the Scala execution environment. This kernel implements some cell & line magics; a list of those available can be obtained with the `%lsmagic` one" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%lsmagic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Among the magics, there is the possibility to execute Python or Javascript code in a cell:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%python\n", "\n", "for k in range(10):\n", " print(k)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Scala 2.11 (SPylon)", "language": "scala", "name": "spylon-kernel" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-scala", "name": "scala", "pygments_lexer": "scala", "version": "0.4.1" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
brandon-rhodes/pycon-pandas-tutorial
All.ipynb
1
318960
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "idx = pd.IndexSlice" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>body {\n", " margin: 0;\n", " font-family: Helvetica;\n", "}\n", "table.dataframe {\n", " border-collapse: collapse;\n", " border: none;\n", "}\n", "table.dataframe tr {\n", " border: none;\n", "}\n", "table.dataframe td, table.dataframe th {\n", " margin: 0;\n", " border: 1px solid white;\n", " padding-left: 0.25em;\n", " padding-right: 0.25em;\n", "}\n", "table.dataframe th:not(:empty) {\n", " background-color: #fec;\n", " text-align: left;\n", " font-weight: normal;\n", "}\n", "table.dataframe tr:nth-child(2) th:empty {\n", " border-left: none;\n", " border-right: 1px dashed #888;\n", "}\n", "table.dataframe td {\n", " border: 2px solid #ccf;\n", " background-color: #f4f4ff;\n", "}\n", "h3 {\n", " color: white;\n", " background-color: black;\n", " padding: 0.5em;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "css = open('style-table.css').read() + open('style-notebook.css').read()\n", "HTML('<style>{}</style>'.format(css))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.9 s, sys: 1.54 s, total: 15.4 s\n", "Wall time: 16.1 s\n" ] } ], "source": [ "%%time\n", "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Core</td>\n", " <td>2003</td>\n", " <td>Alejandro Abellan</td>\n", " <td>actor</td>\n", " <td>U.S.S. Soldier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Il momento di uccidere</td>\n", " <td>1968</td>\n", " <td>Remo De Angelis</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Across the Divide</td>\n", " <td>1921</td>\n", " <td>Thomas Delmar</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Revan</td>\n", " <td>2012</td>\n", " <td>Diego James</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Un homme marche dans la ville</td>\n", " <td>1950</td>\n", " <td>Fabien Loris</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "0 The Core 2003 Alejandro Abellan actor \n", "1 Il momento di uccidere 1968 Remo De Angelis actor \n", "2 Across the Divide 1921 Thomas Delmar actor \n", "3 Revan 2012 Diego James actor \n", "4 Un homme marche dans la ville 1950 Fabien Loris actor \n", "\n", " character n \n", "0 U.S.S. Soldier NaN \n", "1 Dago 9 \n", "2 Dago 4 \n", "3 Dago NaN \n", "4 Dago 12 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cast.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.78 s, sys: 76.5 ms, total: 1.85 s\n", "Wall time: 1.93 s\n" ] } ], "source": [ "%%time\n", "release_dates = pd.read_csv('data/release_dates.csv', index_col=None,\n", " parse_dates=['date'], infer_datetime_format=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>country</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0_1_0</td>\n", " <td>2008</td>\n", " <td>Poland</td>\n", " <td>2008-11-14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Ai no Sanka</td>\n", " <td>1967</td>\n", " <td>Japan</td>\n", " <td>1967-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>A Thousand to One</td>\n", " <td>1920</td>\n", " <td>USA</td>\n", " <td>1920-12-05</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>USA</td>\n", " <td>1923-10-13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>Netherlands</td>\n", " <td>1924-08-08</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year country date\n", "0 0_1_0 2008 Poland 2008-11-14\n", "1 Ai no Sanka 1967 Japan 1967-01-01\n", "2 A Thousand to One 1920 USA 1920-12-05\n", "3 A Prince of a King 1923 USA 1923-10-13\n", "4 A Prince of a King 1923 Netherlands 1924-08-08" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "release_dates.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Core</td>\n", " <td>2003</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Il momento di uccidere</td>\n", " <td>1968</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Across the Divide</td>\n", " <td>1921</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Revan</td>\n", " <td>2012</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Un homme marche dans la ville</td>\n", " <td>1950</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "0 The Core 2003\n", "1 Il momento di uccidere 1968\n", "2 Across the Divide 1921\n", "3 Revan 2012\n", "4 Un homme marche dans la ville 1950" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titles = cast[['title', 'year']].drop_duplicates().reset_index(drop=True)\n", "titles.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Years" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "214386" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies are listed in the `titles` dataframe?\n", "\n", "len(titles)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100983</th>\n", " <td>Miss Jerry</td>\n", " <td>1894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "100983 Miss Jerry 1894" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. What is the name and year of the very first movie ever made?\n", "\n", "titles.sort_values('year').head(1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>208386</th>\n", " <td>The Understander</td>\n", " <td>2021</td>\n", " </tr>\n", " <tr>\n", " <th>200027</th>\n", " <td>Model Combat</td>\n", " <td>2021</td>\n", " </tr>\n", " <tr>\n", " <th>208929</th>\n", " <td>Edge of Time</td>\n", " <td>2021</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "208386 The Understander 2021\n", "200027 Model Combat 2021\n", "208929 Edge of Time 2021" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many years into the future does the IMDB database list movie titles?\n", "\n", "titles.sort_values('year').tail(3)#.year - 2015" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1158" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies listed in `titles` came out in 1950?\n", "\n", "len(titles[titles.year == 1950])\n", "\n", "# or: (titles.year == 1950).sum()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1441" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies came out in 1960?\n", "\n", "len(titles[titles.year == 1960])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1970 1876\n", "1971 1774\n", "1972 1840\n", "1973 1813\n", "1974 1795\n", "1975 1704\n", "1976 1716\n", "1977 1686\n", "1978 1679\n", "1979 1757\n" ] } ], "source": [ "# 1. How many movies came out in each year of the 1970s?\n", "# (Hint: try a Python \"for\" loop.)\n", "\n", "for y in range(1970, 1980):\n", " print(y, (titles.year == y).sum())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "127060" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies came out during your own lifetime,\n", "# from the year of your birth through 2014?\n", "\n", "len(titles[(titles.year >= 1974) & (titles.year <= 2014)])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1970 1876\n", "1971 1774\n", "1972 1840\n", "1973 1813\n", "1974 1795\n", "1975 1704\n", "1976 1716\n", "1977 1686\n", "1978 1679\n", "1979 1757\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Use \"value_counts\" to determine how many movies came out\n", "# in each year of the 1970s.\n", "\n", "titles[titles.year // 10 == 197].year.value_counts().sort_index()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "year\n", "1970 1876\n", "1971 1774\n", "1972 1840\n", "1973 1813\n", "1974 1795\n", "1975 1704\n", "1976 1716\n", "1977 1686\n", "1978 1679\n", "1979 1757\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. Use \"groupby\" to determine how many movies came out in each year of the 1970s.\n", "\n", "titles.groupby('year').size().loc[1970:1979]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Titles" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100983</th>\n", " <td>Miss Jerry</td>\n", " </tr>\n", " <tr>\n", " <th>104766</th>\n", " <td>Soldiers of the Cross</td>\n", " </tr>\n", " <tr>\n", " <th>140912</th>\n", " <td>Can Can</td>\n", " </tr>\n", " <tr>\n", " <th>142404</th>\n", " <td>The Story of the Kelly Gang</td>\n", " </tr>\n", " <tr>\n", " <th>173490</th>\n", " <td>Jeffries-Sharkey Contest</td>\n", " </tr>\n", " <tr>\n", " <th>173746</th>\n", " <td>Valsons</td>\n", " </tr>\n", " <tr>\n", " <th>173859</th>\n", " <td>The Joe Gans-Battling Nelson Fight</td>\n", " </tr>\n", " <tr>\n", " <th>174032</th>\n", " <td>Battle of Jeffries and Sharkey for Championshi...</td>\n", " </tr>\n", " <tr>\n", " <th>174343</th>\n", " <td>Sr. Wrangler Mr. R.P. Paranjpe</td>\n", " </tr>\n", " <tr>\n", " <th>205246</th>\n", " <td>Lika mot lika</td>\n", " </tr>\n", " <tr>\n", " <th>211338</th>\n", " <td>Eine Fliegenjagd oder Die Rache der Frau Schultze</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title\n", "100983 Miss Jerry\n", "104766 Soldiers of the Cross\n", "140912 Can Can\n", "142404 The Story of the Kelly Gang\n", "173490 Jeffries-Sharkey Contest\n", "173746 Valsons\n", "173859 The Joe Gans-Battling Nelson Fight\n", "174032 Battle of Jeffries and Sharkey for Championshi...\n", "174343 Sr. Wrangler Mr. R.P. Paranjpe\n", "205246 Lika mot lika\n", "211338 Eine Fliegenjagd oder Die Rache der Frau Schultze" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. What are the names of the movies made through 1906?\n", "\n", "titles[titles.year <= 1906][['title']]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2056</th>\n", " <td>Star Wars</td>\n", " <td>1977</td>\n", " </tr>\n", " <tr>\n", " <th>9814</th>\n", " <td>Star Trek</td>\n", " <td>2009</td>\n", " </tr>\n", " <tr>\n", " <th>9886</th>\n", " <td>Star Trek Into Darkness</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>27553</th>\n", " <td>Star Trek III: The Search for Spock</td>\n", " <td>1984</td>\n", " </tr>\n", " <tr>\n", " <th>29784</th>\n", " <td>Star Trek: The Wrath of Khan</td>\n", " <td>1982</td>\n", " </tr>\n", " <tr>\n", " <th>30814</th>\n", " <td>Star Trek: The Motion Picture</td>\n", " <td>1979</td>\n", " </tr>\n", " <tr>\n", " <th>33378</th>\n", " <td>Star Trek: Nemesis</td>\n", " <td>2002</td>\n", " </tr>\n", " <tr>\n", " <th>39810</th>\n", " <td>Star Trek: First Contact</td>\n", " <td>1996</td>\n", " </tr>\n", " <tr>\n", " <th>39824</th>\n", " <td>Star Trek: Generations</td>\n", " <td>1994</td>\n", " </tr>\n", " <tr>\n", " <th>59379</th>\n", " <td>Star Trek VI: The Undiscovered Country</td>\n", " <td>1991</td>\n", " </tr>\n", " <tr>\n", " <th>60607</th>\n", " <td>Star Trek IV: The Voyage Home</td>\n", " <td>1986</td>\n", " </tr>\n", " <tr>\n", " <th>70841</th>\n", " <td>Star Trek V: The Final Frontier</td>\n", " <td>1989</td>\n", " </tr>\n", " <tr>\n", " <th>92631</th>\n", " <td>Star Trip</td>\n", " <td>2012</td>\n", " </tr>\n", " <tr>\n", " <th>103945</th>\n", " <td>Star Trek World Tour</td>\n", " <td>1998</td>\n", " </tr>\n", " <tr>\n", " <th>104399</th>\n", " <td>Star Trek: Insurrection</td>\n", " <td>1998</td>\n", " </tr>\n", " <tr>\n", " <th>130495</th>\n", " <td>Star Vehicle</td>\n", " <td>2010</td>\n", " </tr>\n", " <tr>\n", " <th>144886</th>\n", " <td>Star Trek: Temporal Anomaly</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>178385</th>\n", " <td>Star Trek I: Specter of the Past</td>\n", " <td>2010</td>\n", " </tr>\n", " <tr>\n", " <th>180027</th>\n", " <td>Star Trek: Horizon</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>199493</th>\n", " <td>Star Trek: Operation Beta Shield</td>\n", " <td>2008</td>\n", " </tr>\n", " <tr>\n", " <th>209496</th>\n", " <td>Star Trek: USS PAN</td>\n", " <td>2018</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "2056 Star Wars 1977\n", "9814 Star Trek 2009\n", "9886 Star Trek Into Darkness 2013\n", "27553 Star Trek III: The Search for Spock 1984\n", "29784 Star Trek: The Wrath of Khan 1982\n", "30814 Star Trek: The Motion Picture 1979\n", "33378 Star Trek: Nemesis 2002\n", "39810 Star Trek: First Contact 1996\n", "39824 Star Trek: Generations 1994\n", "59379 Star Trek VI: The Undiscovered Country 1991\n", "60607 Star Trek IV: The Voyage Home 1986\n", "70841 Star Trek V: The Final Frontier 1989\n", "92631 Star Trip 2012\n", "103945 Star Trek World Tour 1998\n", "104399 Star Trek: Insurrection 1998\n", "130495 Star Vehicle 2010\n", "144886 Star Trek: Temporal Anomaly 2015\n", "178385 Star Trek I: Specter of the Past 2010\n", "180027 Star Trek: Horizon 2015\n", "199493 Star Trek: Operation Beta Shield 2008\n", "209496 Star Trek: USS PAN 2018" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. What movies have titles that fall between Star Trek and Star Wars in the alphabet?\n", "\n", "titles[(titles.title >= 'Star Trek') & (titles.title <= 'Star Wars')]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>year</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Star Trek</th>\n", " <td>2009</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek I: Specter of the Past</th>\n", " <td>2010</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek III: The Search for Spock</th>\n", " <td>1984</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek IV: The Voyage Home</th>\n", " <td>1986</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek Into Darkness</th>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek V: The Final Frontier</th>\n", " <td>1989</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek VI: The Undiscovered Country</th>\n", " <td>1991</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek World Tour</th>\n", " <td>1998</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: First Contact</th>\n", " <td>1996</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Generations</th>\n", " <td>1994</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Horizon</th>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Insurrection</th>\n", " <td>1998</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Nemesis</th>\n", " <td>2002</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Operation Beta Shield</th>\n", " <td>2008</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: Temporal Anomaly</th>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: The Motion Picture</th>\n", " <td>1979</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: The Wrath of Khan</th>\n", " <td>1982</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trek: USS PAN</th>\n", " <td>2018</td>\n", " </tr>\n", " <tr>\n", " <th>Star Trip</th>\n", " <td>2012</td>\n", " </tr>\n", " <tr>\n", " <th>Star Vehicle</th>\n", " <td>2010</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars</th>\n", " <td>1977</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " year\n", "title \n", "Star Trek 2009\n", "Star Trek I: Specter of the Past 2010\n", "Star Trek III: The Search for Spock 1984\n", "Star Trek IV: The Voyage Home 1986\n", "Star Trek Into Darkness 2013\n", "Star Trek V: The Final Frontier 1989\n", "Star Trek VI: The Undiscovered Country 1991\n", "Star Trek World Tour 1998\n", "Star Trek: First Contact 1996\n", "Star Trek: Generations 1994\n", "Star Trek: Horizon 2015\n", "Star Trek: Insurrection 1998\n", "Star Trek: Nemesis 2002\n", "Star Trek: Operation Beta Shield 2008\n", "Star Trek: Temporal Anomaly 2015\n", "Star Trek: The Motion Picture 1979\n", "Star Trek: The Wrath of Khan 1982\n", "Star Trek: USS PAN 2018\n", "Star Trip 2012\n", "Star Vehicle 2010\n", "Star Wars 1977" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Use an index and .loc[] to find the movies whose titles fall between Star Trek\n", "# and Star Wars in the alphabet.\n", "\n", "t = titles.copy()\n", "t = t.set_index('title').sort_index()\n", "t.loc['Star Trek':'Star Wars']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1894</th>\n", " <td>Miss Jerry</td>\n", " </tr>\n", " <tr>\n", " <th>1898</th>\n", " <td>Can Can</td>\n", " </tr>\n", " <tr>\n", " <th>1899</th>\n", " <td>Jeffries-Sharkey Contest</td>\n", " </tr>\n", " <tr>\n", " <th>1899</th>\n", " <td>Battle of Jeffries and Sharkey for Championshi...</td>\n", " </tr>\n", " <tr>\n", " <th>1900</th>\n", " <td>Soldiers of the Cross</td>\n", " </tr>\n", " <tr>\n", " <th>1902</th>\n", " <td>Sr. Wrangler Mr. R.P. Paranjpe</td>\n", " </tr>\n", " <tr>\n", " <th>1905</th>\n", " <td>Valsons</td>\n", " </tr>\n", " <tr>\n", " <th>1905</th>\n", " <td>Eine Fliegenjagd oder Die Rache der Frau Schultze</td>\n", " </tr>\n", " <tr>\n", " <th>1906</th>\n", " <td>The Story of the Kelly Gang</td>\n", " </tr>\n", " <tr>\n", " <th>1906</th>\n", " <td>The Joe Gans-Battling Nelson Fight</td>\n", " </tr>\n", " <tr>\n", " <th>1906</th>\n", " <td>Lika mot lika</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title\n", "year \n", "1894 Miss Jerry\n", "1898 Can Can\n", "1899 Jeffries-Sharkey Contest\n", "1899 Battle of Jeffries and Sharkey for Championshi...\n", "1900 Soldiers of the Cross\n", "1902 Sr. Wrangler Mr. R.P. Paranjpe\n", "1905 Valsons\n", "1905 Eine Fliegenjagd oder Die Rache der Frau Schultze\n", "1906 The Story of the Kelly Gang\n", "1906 The Joe Gans-Battling Nelson Fight\n", "1906 Lika mot lika" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Use an index and .loc[] to retrieve the names of the movies made through 1906.\n", "\n", "titles.set_index('year').sort_index().loc[1800:1906]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Hamlet 17\n", "Macbeth 14\n", "Carmen 14\n", "Anna Karenina 12\n", "Maya 11\n", "Underground 11\n", "Anna 10\n", "Revenge 10\n", "Temptation 10\n", "The Outsider 10\n", "Jackpot 10\n", "Othello 10\n", "Blood Money 10\n", "She 10\n", "Rage 9\n", "dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. What are the 15 most common movie titles in film history?\n", "\n", "titles.title.value_counts().head(15)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use this for session 3?\n", "\n", "i = cast.set_index('name').sort_index()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"3\" halign=\"left\">n</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>min</th>\n", " <th>mean</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1932</th>\n", " <td>1</td>\n", " <td>5.125000</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>1933</th>\n", " <td>1</td>\n", " <td>3.166667</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>1934</th>\n", " <td>1</td>\n", " <td>1.500000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1935</th>\n", " <td>1</td>\n", " <td>1.750000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1936</th>\n", " <td>1</td>\n", " <td>1.750000</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1937</th>\n", " <td>2</td>\n", " <td>2.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1938</th>\n", " <td>2</td>\n", " <td>2.000000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1939</th>\n", " <td>1</td>\n", " <td>1.333333</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1940</th>\n", " <td>1</td>\n", " <td>1.250000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1941</th>\n", " <td>1</td>\n", " <td>1.500000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1942</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1943</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1944</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1945</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1946</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1947</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1949</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1950</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1951</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1952</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1953</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1955</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1957</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1958</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1959</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1960</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1962</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1963</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1966</th>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1981</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1987</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1988</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2002</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " n \n", " min mean max\n", "year \n", "1932 1 5.125000 9\n", "1933 1 3.166667 10\n", "1934 1 1.500000 2\n", "1935 1 1.750000 2\n", "1936 1 1.750000 3\n", "1937 2 2.000000 2\n", "1938 2 2.000000 2\n", "1939 1 1.333333 2\n", "1940 1 1.250000 2\n", "1941 1 1.500000 2\n", "1942 1 1.000000 1\n", "1943 1 1.000000 1\n", "1944 1 1.000000 1\n", "1945 NaN NaN NaN\n", "1946 1 1.000000 1\n", "1947 1 1.000000 1\n", "1948 1 1.000000 1\n", "1949 1 1.000000 1\n", "1950 1 1.000000 1\n", "1951 1 1.000000 1\n", "1952 1 1.000000 1\n", "1953 1 1.000000 1\n", "1955 1 1.000000 1\n", "1957 1 1.000000 1\n", "1958 1 1.000000 1\n", "1959 1 1.000000 1\n", "1960 1 1.000000 1\n", "1962 1 1.000000 1\n", "1963 1 1.000000 1\n", "1964 1 1.000000 1\n", "1966 1 1.000000 1\n", "1981 NaN NaN NaN\n", "1987 NaN NaN NaN\n", "1988 NaN NaN NaN\n", "2002 NaN NaN NaN" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYXWWV7/HvqqoUqUpVpTJAmEmYB5EphGjARJCQB20v\n", "Dq3t1bYBlbbb29IOfR3ablBRLooXkG6H0CiiaDei0qIgggragg1hCkMYk0AGUgmZSCU1Jqv/2Luy\n", "TypVlTPs6Zzz+zwPD7XPsPdbK5VVb9Z593rN3RERkdrSkPUAREQkfkruIiI1SMldRKQGKbmLiNQg\n", "JXcRkRqk5C4iUoPGTO5m9h0z6zKzxwsem2xmd5nZs2b2azPrTH6YIiJSij3N3L8LLBj22KeBu9z9\n", "SOA34bGIiOSI7ekmJjObDtzm7seHx08Dc929y8z2Be5x96OTHqiIiBSvnJr7NHfvCr/uAqbFOB4R\n", "EYlBRR+oejDtV/8CEZGcaSrjPV1mtq+7rzGz/YC1I73IzJT0RUTK4O5W6TnKSe4/B/4KuCL8/62j\n", 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"<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>len</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>163401</th>\n", " <td>Night of the Day of the Dawn of the Son of the Bride of the Return of the Revenge of the Terror of the Attack of the Evil Mutant Hellbound Flesh Eating Crawling Alien Zombified Subhumanoid Living Dead, Part 5</td>\n", " <td>2011</td>\n", " <td>208</td>\n", " </tr>\n", " <tr>\n", " <th>154042</th>\n", " <td>Night of the Day of the Dawn of the Son of the Bride of the Return of the Revenge of the Terror of the Attack of the Evil, Mutant, Hellbound, Flesh-Eating Subhumanoid Zombified Living Dead, Part 3</td>\n", " <td>2005</td>\n", " <td>196</td>\n", " </tr>\n", " <tr>\n", " <th>120466</th>\n", " <td>Las poquianchis (De los pormenores y otros sucedidos del dominio público que acontecieron a las hermanas de triste memoria a quienes la maledicencia así las bautizó)</td>\n", " <td>1976</td>\n", " <td>165</td>\n", " </tr>\n", " <tr>\n", " <th>40308</th>\n", " <td>Entrei em Pânico ao Saber o que Vocês Fizeram na Sexta-feira 13 do Verão Passado Parte 2 - A Hora da Volta da Vingança dos Jogos Mortais de Halloween</td>\n", " <td>2011</td>\n", " <td>149</td>\n", " </tr>\n", " <tr>\n", " <th>173052</th>\n", " <td>Die Antigone des Sophokles nach der Hölderlinschen Übertragung für die Bühne bearbeitet von Brecht 1948 (Suhrkamp Verlag)</td>\n", " <td>1992</td>\n", " <td>121</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title \\\n", "163401 Night of the Day of the Dawn of the Son of the Bride of the Return of the Revenge of the Terror of the Attack of the Evil Mutant Hellbound Flesh Eating Crawling Alien Zombified Subhumanoid Living Dead, Part 5 \n", "154042 Night of the Day of the Dawn of the Son of the Bride of the Return of the Revenge of the Terror of the Attack of the Evil, Mutant, Hellbound, Flesh-Eating Subhumanoid Zombified Living Dead, Part 3 \n", "120466 Las poquianchis (De los pormenores y otros sucedidos del dominio público que acontecieron a las hermanas de triste memoria a quienes la maledicencia así las bautizó) \n", "40308 Entrei em Pânico ao Saber o que Vocês Fizeram na Sexta-feira 13 do Verão Passado Parte 2 - A Hora da Volta da Vingança dos Jogos Mortais de Halloween \n", "173052 Die Antigone des Sophokles nach der Hölderlinschen Übertragung für die Bühne bearbeitet von Brecht 1948 (Suhrkamp Verlag) \n", "\n", " year len \n", "163401 2011 208 \n", "154042 2005 196 \n", "120466 1976 165 \n", "40308 2011 149 \n", "173052 1992 121 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5. What are the 5 longest movie titles ever?\n", "\n", "pd.set_option('max_colwidth', 300)\n", "\n", "t = titles.copy()\n", "t['len'] = t.title.str.len()\n", "t = t.sort_values('len', ascending=False)\n", "t.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 24\n", "Hamlet 17\n", "Broken 15\n", "Macbeth 14\n", "Carmen 14\n", "Anna Karenina 12\n", "Underground 11\n", "Maya 11\n", "Alone 10\n", "Othello 10\n", "Revenge 10\n", "Love 10\n", "The Outsider 10\n", "Blood Money 10\n", "Temptation 10\n", "dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5. What are the 15 most popular movie titles, if you strip off the suffixes like\n", "# (II) and (III) that the IMDB adds to distinguish movies shown in the same year?\n", "\n", "titles.title.str.extract('^([^(]*)').value_counts().head(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How many movies actors have been in" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "51" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies has Judi Dench acted in?\n", "\n", "len(cast[cast.name == 'Judi Dench'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "43" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. How many movies did Sidney Poitier appear in?\n", "\n", "c = cast\n", "c = c[c.name == 'Sidney Poitier']\n", "len(c)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. In how many of his movies was Sidney Poitier the lead (`n==1`)?\n", "\n", "c = cast\n", "c = c[c.name == 'Sidney Poitier']\n", "c = c[c.n == 1]\n", "len(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pulling and displaying movie credits" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1762207</th>\n", " <td>Mrs Brown</td>\n", " <td>1997</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Queen Victoria</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3110457</th>\n", " <td>Ladies in Lavender</td>\n", " <td>2004</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Ursula</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3407847</th>\n", " <td>Mrs Henderson Presents</td>\n", " <td>2005</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Mrs. Laura Henderson</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3363284</th>\n", " <td>Notes on a Scandal</td>\n", " <td>2006</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Barbara Covett</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3363282</th>\n", " <td>The Best Exotic Marigold Hotel</td>\n", " <td>2011</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Evelyn Greenslade</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2702221</th>\n", " <td>Philomena</td>\n", " <td>2013</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>Philomena</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "1762207 Mrs Brown 1997 Judi Dench actress \n", "3110457 Ladies in Lavender 2004 Judi Dench actress \n", "3407847 Mrs Henderson Presents 2005 Judi Dench actress \n", "3363284 Notes on a Scandal 2006 Judi Dench actress \n", "3363282 The Best Exotic Marigold Hotel 2011 Judi Dench actress \n", "2702221 Philomena 2013 Judi Dench actress \n", "\n", " character n \n", "1762207 Queen Victoria 1 \n", "3110457 Ursula 1 \n", "3407847 Mrs. Laura Henderson 1 \n", "3363284 Barbara Covett 1 \n", "3363282 Evelyn Greenslade 1 \n", "2702221 Philomena 1 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. List the movies, sorted by year, in which Judi Dench starred as lead actor.\n", "\n", "c = cast\n", "c = c[c.name == 'Judi Dench']\n", "c = c[c.n == 1]\n", "c.sort_values('year')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2815436</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>Laurence Olivier</td>\n", " <td>actor</td>\n", " <td>Andrew Wyke</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1985454</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>Michael Caine</td>\n", " <td>actor</td>\n", " <td>Milo Tindle</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2004531</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>Alec Cawthorne</td>\n", " <td>actor</td>\n", " <td>Inspector Doppler</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2707118</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>John Matthews (II)</td>\n", " <td>actor</td>\n", " <td>Detective Sergeant Tarrant</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3292963</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>Eve Channing (III)</td>\n", " <td>actress</td>\n", " <td>Marguerite Wyke</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2693963</th>\n", " <td>Sleuth</td>\n", " <td>1972</td>\n", " <td>Teddy Martin</td>\n", " <td>actor</td>\n", " <td>Police Constable Higgs</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "2815436 Sleuth 1972 Laurence Olivier actor \n", "1985454 Sleuth 1972 Michael Caine actor \n", "2004531 Sleuth 1972 Alec Cawthorne actor \n", "2707118 Sleuth 1972 John Matthews (II) actor \n", "3292963 Sleuth 1972 Eve Channing (III) actress \n", "2693963 Sleuth 1972 Teddy Martin actor \n", "\n", " character n \n", "2815436 Andrew Wyke 1 \n", "1985454 Milo Tindle 2 \n", "2004531 Inspector Doppler 3 \n", "2707118 Detective Sergeant Tarrant 4 \n", "3292963 Marguerite Wyke 5 \n", "2693963 Police Constable Higgs 6 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Who was credited in the 1972 version of Sleuth, in order by `n` rank?\n", "\n", "c = cast\n", "c = c[c.title == 'Sleuth']\n", "c = c[c.year == 1972]\n", "c.sort_values('n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Common character names" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Himself 24293\n", "Dancer 11697\n", "Extra 8865\n", "Reporter 7792\n", "Doctor 7666\n", "Herself 7501\n", "Policeman 7245\n", "Student 6694\n", "Nurse 6636\n", "Bartender 6298\n", "Zombie 5785\n", "dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. What are the 11 most common character names in movie history?\n", "\n", "cast.character.value_counts().head(11)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "name\n", "Richard Ricci 3\n", "Colin Murtagh 3\n", "Terry Gindele 3\n", "Samuel R. Solito 3\n", "John Migliore (II) 6\n", "dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. Which actors have played the role “Zombie” the most times?\n", "\n", "c = cast\n", "c = c[c.character == 'Zombie']\n", "c = c.groupby('name').size().order()\n", "c.tail(5)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "name\n", "Mary Jo Pehl 7\n", "Cosey Fanni Tutti 7\n", "Brigitte Bardot 7\n", "Petula Clark 7\n", "Joan Rivers 8\n", "Queen Mary 8\n", "Margaret Thatcher 9\n", "Denise Austin 10\n", "Joyce Brothers 14\n", "Queen Elizabeth II 14\n", "dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. Which ten people have appeared most often as “Herself” over the history of film?\n", "\n", "c = cast\n", "c = c[c.character == 'Herself']\n", "c = c.groupby('name').size().order()\n", "c.tail(10)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "name\n", "Bill Clinton 22\n", "Josip Broz Tito 23\n", "Franklin D. Roosevelt 23\n", "George W. Bush 24\n", "Ron Jeremy 24\n", "Amitabh Bachchan 25\n", "Ronald Reagan 30\n", "John F. Kennedy 33\n", "Richard Nixon 43\n", "Adolf Hitler 104\n", "dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. Which ten people have appeared most often as “Himself” over the history of film?\n", "\n", "c = cast\n", "c = c[c.character == 'Himself']\n", "c = c.groupby('name').size().order()\n", "c.tail(10)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Himself 24293\n", "Dancer 11697\n", "Extra 8865\n", "Reporter 7792\n", "Doctor 7666\n", "dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4. Take the 50 most common character names in film.\n", "# Which are most often played by men?\n", "\n", "c = cast\n", "clist = c.character.value_counts().head(50)\n", "clist.head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Frank 2355\n", "Teacher 2313\n", "Tom 2282\n", "Mary 2278\n", "Sarah 2251\n", "dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clist.tail()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cast_by_character = cast.sort_values('character').set_index('character')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>type</th>\n", " <th>actor</th>\n", " <th>actress</th>\n", " <th>ratio</th>\n", " </tr>\n", " <tr>\n", " <th>character</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Townsman</th>\n", " <td>4963</td>\n", " <td>3</td>\n", " <td>0.000604</td>\n", " </tr>\n", " <tr>\n", " <th>John</th>\n", " <td>2628</td>\n", " <td>2</td>\n", " <td>0.000760</td>\n", " </tr>\n", " <tr>\n", " <th>Henchman</th>\n", " <td>4876</td>\n", " <td>4</td>\n", " <td>0.000820</td>\n", " </tr>\n", " <tr>\n", " <th>Policeman</th>\n", " <td>7233</td>\n", " <td>12</td>\n", " <td>0.001656</td>\n", " </tr>\n", " <tr>\n", " <th>Himself</th>\n", " <td>24251</td>\n", " <td>42</td>\n", " <td>0.001729</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "type actor actress ratio\n", "character \n", "Townsman 4963 3 0.000604\n", "John 2628 2 0.000760\n", "Henchman 4876 4 0.000820\n", "Policeman 7233 12 0.001656\n", "Himself 24251 42 0.001729" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast_by_character.loc[clist.index][['type']]\n", "c = c.reset_index()\n", "c = c.groupby(['character', 'type']).size()\n", "c = c.unstack()\n", "c['ratio'] = c.actress / (c.actor + c.actress)\n", "c = c.sort_values('ratio')\n", "c.head()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>type</th>\n", " <th>actor</th>\n", " <th>actress</th>\n", " <th>ratio</th>\n", " </tr>\n", " <tr>\n", " <th>character</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Anna</th>\n", " <td>86</td>\n", " <td>2868</td>\n", " <td>0.970887</td>\n", " </tr>\n", " <tr>\n", " <th>Mary</th>\n", " <td>46</td>\n", " <td>2232</td>\n", " <td>0.979807</td>\n", " </tr>\n", " <tr>\n", " <th>Girl</th>\n", " <td>43</td>\n", " <td>2513</td>\n", " <td>0.983177</td>\n", " </tr>\n", " <tr>\n", " <th>Maria</th>\n", " <td>27</td>\n", " <td>3080</td>\n", " <td>0.991310</td>\n", " </tr>\n", " <tr>\n", " <th>Herself</th>\n", " <td>59</td>\n", " <td>7442</td>\n", " <td>0.992134</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "type actor actress ratio\n", "character \n", "Anna 86 2868 0.970887\n", "Mary 46 2232 0.979807\n", "Girl 43 2513 0.983177\n", "Maria 27 3080 0.991310\n", "Herself 59 7442 0.992134" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4. …which of those 50 characters are most often played by women?\n", "\n", "c.tail()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>type</th>\n", " <th>actor</th>\n", " <th>actress</th>\n", " <th>ratio</th>\n", " </tr>\n", " <tr>\n", " <th>character</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Student</th>\n", " <td>3845</td>\n", " <td>2849</td>\n", " <td>0.425605</td>\n", " </tr>\n", " <tr>\n", " <th>Singer</th>\n", " <td>1706</td>\n", " <td>1811</td>\n", " <td>0.514927</td>\n", " </tr>\n", " <tr>\n", " <th>Teacher</th>\n", " <td>1079</td>\n", " <td>1234</td>\n", " <td>0.533506</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "type actor actress ratio\n", "character \n", "Student 3845 2849 0.425605\n", "Singer 1706 1811 0.514927\n", "Teacher 1079 1234 0.533506" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4. …which of those 50 characters have a ratio closest to 0.5?\n", "\n", "c[(c.ratio > 0.4) & (c.ratio < 0.6)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Who has been in the most movies" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Emmett Vogan 39\n", "Sam Harris (II) 30\n", "Harold Miller 28\n", "Bess Flowers 28\n", "Nolan Leary 27\n", "Frank O'Connor 26\n", "Edmund Cobb 24\n", "Franklyn Farnum 24\n", "Tom London 24\n", "Pierre Watkin 24\n", "dtype: int64" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Which actors or actresses appeared in the most movies in the year 1945?\n", "\n", "cast[cast.year == 1945].name.value_counts().head(10)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Mammootty 34\n", "Shakti Kapoor 26\n", "Mohanlal 22\n", "Sukumari 19\n", "Satyendra Kapoor 17\n", "Kader Khan 16\n", "Asrani 16\n", "Rajesh Khanna 15\n", "Aruna Irani 15\n", "Raj Babbar 15\n", "dtype: int64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Which actors or actresses appeared in the most movies in the year 1985?\n", "\n", "cast[cast.year == 1985].name.value_counts().head(10)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 15.8 s, sys: 2.68 s, total: 18.5 s\n", "Wall time: 18.9 s\n" ] } ], "source": [ "%%time\n", "# 2. Create a `cast_by_title_year` dataframe indexed by title and year\n", "# to use in the next few questions.\n", "\n", "cast_by_title_year = cast.set_index(['title', 'year']).sort_index()\n", "cast_by_title_year.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 183 ms, sys: 4.19 ms, total: 187 ms\n", "Wall time: 186 ms\n" ] }, { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2010</th>\n", " <td>Leonardo DiCaprio</td>\n", " <td>actor</td>\n", " <td>Cobb</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Joseph Gordon-Levitt</td>\n", " <td>actor</td>\n", " <td>Arthur</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Ellen Page</td>\n", " <td>actress</td>\n", " <td>Ariadne</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Tom Hardy</td>\n", " <td>actor</td>\n", " <td>Eames</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Ken Watanabe</td>\n", " <td>actor</td>\n", " <td>Saito</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Dileep Rao</td>\n", " <td>actor</td>\n", " <td>Yusuf</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Cillian Murphy</td>\n", " <td>actor</td>\n", " <td>Robert Fischer</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Tom Berenger</td>\n", " <td>actor</td>\n", " <td>Browning</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Marion Cotillard</td>\n", " <td>actress</td>\n", " <td>Mal</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Pete Postlethwaite</td>\n", " <td>actor</td>\n", " <td>Maurice Fischer</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name type character n\n", "year \n", "2010 Leonardo DiCaprio actor Cobb 1\n", "2010 Joseph Gordon-Levitt actor Arthur 2\n", "2010 Ellen Page actress Ariadne 3\n", "2010 Tom Hardy actor Eames 4\n", "2010 Ken Watanabe actor Saito 5\n", "2010 Dileep Rao actor Yusuf 6\n", "2010 Cillian Murphy actor Robert Fischer 7\n", "2010 Tom Berenger actor Browning 8\n", "2010 Marion Cotillard actress Mal 9\n", "2010 Pete Postlethwaite actor Maurice Fischer 10" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# 2. Use `cast_by_title_year` to find the stars of the film Inception\n", "# and order them by `n` before displaying the top 10.\n", "\n", "cast_by_title_year.loc['Inception'].sort_values('n').head(10)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"10\" valign=\"top\">Hamlet</th>\n", " <th>1996</th>\n", " <td>Riz Abbasi</td>\n", " <td>actor</td>\n", " <td>Attendant to Claudius</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Richard Attenborough</td>\n", " <td>actor</td>\n", " <td>English Ambassador</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>David Blair (V)</td>\n", " <td>actor</td>\n", " <td>Attendant to Claudius</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Brian Blessed</td>\n", " <td>actor</td>\n", " <td>Ghost of Hamlet's Father</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Kenneth Branagh</td>\n", " <td>actor</td>\n", " <td>Hamlet</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Richard Briers</td>\n", " <td>actor</td>\n", " <td>Polonius</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Michael Bryant</td>\n", " <td>actor</td>\n", " <td>Priest</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Peter Bygott</td>\n", " <td>actor</td>\n", " <td>Attendant to Claudius</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Julie Christie</td>\n", " <td>actress</td>\n", " <td>Gertrude</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Billy Crystal</td>\n", " <td>actor</td>\n", " <td>First Gravedigger</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name type character n\n", "title year \n", "Hamlet 1996 Riz Abbasi actor Attendant to Claudius 1\n", " 1996 Richard Attenborough actor English Ambassador 2\n", " 1996 David Blair (V) actor Attendant to Claudius 3\n", " 1996 Brian Blessed actor Ghost of Hamlet's Father 4\n", " 1996 Kenneth Branagh actor Hamlet 5\n", " 1996 Richard Briers actor Polonius 6\n", " 1996 Michael Bryant actor Priest 7\n", " 1996 Peter Bygott actor Attendant to Claudius 8\n", " 1996 Julie Christie actress Gertrude 9\n", " 1996 Billy Crystal actor First Gravedigger 10" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. Use `cast_by_title_year` to find the first 10 stars in the 1996 film Hamlet,\n", "# and order them by `n`.\n", "\n", "cast_by_title_year.loc['Hamlet',1996].sort_values('n').head(10)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mark Hamill 42\n", "Harrison Ford 44\n", "Carrie Fisher 40\n", "Peter Cushing 10\n", "Alec Guinness 9\n", "Anthony Daniels 11\n", "Kenny Baker 18\n", "Peter Mayhew (II) 7\n", "David Prowse 5\n", "CPU times: user 8.7 s, sys: 11.8 ms, total: 8.71 s\n", "Wall time: 8.73 s\n" ] } ], "source": [ "%%time\n", "# 2. Write a `for` loop that, for the top 9 actors in the 1977 movie Star Wars,\n", "# determines how many movies they starred in after 1977.\n", "\n", "names = cast_by_title_year.loc['Star Wars',1977].sort_values('n').head(9).name\n", "for name in names:\n", " print(name, len(cast[(cast.name == name) & (cast.year > 1977)]))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# 2. Create an indexed version of `cast` that, once built, lets you answer\n", "# the previous question with a `for` loop that finishes in under a second.\n", "\n", "i = cast.set_index('name').sort_index()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mark Hamill 42\n", "Harrison Ford 44\n", "Carrie Fisher 40\n", "Peter Cushing 10\n", "Alec Guinness 9\n", "Anthony Daniels 11\n", "Kenny Baker 18\n", "Peter Mayhew (II) 7\n", "David Prowse 5\n", "CPU times: user 2.8 s, sys: 64.4 ms, total: 2.86 s\n", "Wall time: 2.89 s\n" ] } ], "source": [ "%%time\n", "for name in names:\n", " c = i.loc[name]\n", " c = c[c.year > 1977]\n", " #c = c[(c.character != 'Himself') & (c.character != 'Herself')]\n", " print(name, len(c))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "year\n", "1910 1\n", "1911 6\n", "1913 21\n", "1921 9\n", "1948 25\n", "1954 1\n", "1964 33\n", "1969 24\n", "1973 8\n", "1976 11\n", "1987 3\n", "1990 29\n", "1996 55\n", "2000 38\n", "2009 17\n", "2011 12\n", "2015 6\n", "dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. How many people were cast in each of the movies named \"Hamlet”?\n", "\n", "c = cast\n", "c = c[c.title == 'Hamlet']\n", "c = c.groupby('year').size() \n", "c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "title year\n", "Hamlet 1910 1\n", " 1911 6\n", " 1913 21\n", " 1921 9\n", " 1948 25\n", " 1954 1\n", " 1964 33\n", " 1969 24\n", " 1973 8\n", " 1976 11\n", " 1987 3\n", " 1990 29\n", " 1996 55\n", " 2000 38\n", " 2009 17\n", " 2011 12\n", " 2015 6\n", "Hamlet (II) 1964 21\n", " 2005 20\n", " 2007 16\n", " 2015 10\n", "Hamlet (III) 2007 2\n", " 2015 14\n", "dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5. How many actors are in the cast of each version of Hamlet,\n", "# including Hamlets with IMDB name collisions like \"Hamlet (II)\"\n", "# and \"Hamlet (III)\"? [BAD]\n", "\n", "c = cast_by_title_year\n", "# c.loc['Hamlet':'Hamlet (Z'].index.value_counts() - Drat\n", "# c.loc['Hamlet':'Hamlet (Z'].groupby(level=0).size() - Drat\n", "# c.loc['Hamlet':'Hamlet (Z'].groupby(level=1).size() - Drat\n", "c.loc['Hamlet':'Hamlet (Z'].groupby(level=[0,1]).size()\n", "\n", "# Or:\n", "#c = cast[(cast.title >= 'Hamlet') & (cast.title < 'Hamlet (Z')]\n", "#c.groupby(['title', 'year']).size()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Actors and Actresses" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>type</th>\n", " <th>actor</th>\n", " <th>actress</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1894</th>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1898</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1899</th>\n", " <td>6</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1900</th>\n", " <td>2</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1902</th>\n", " <td>1</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "type actor actress\n", "year \n", "1894 2 1\n", "1898 NaN 1\n", "1899 6 NaN\n", "1900 2 NaN\n", "1902 1 NaN" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4. Build a dataframe with a row for each year with two columns:\n", "# the number of roles for actors in that year's films,\n", "# and the number of roles for actresses.\n", "\n", "aa = cast[['year', 'type']].groupby(['year', 'type']).size()\n", "aa = aa.loc[:2014].unstack()\n", "aa.head()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90bfb2d2e8>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXmYlNWV/z/fbvZFNhFRUYiCguKGinFccHeM4hrFJC7R\n", "ySKTn2YmmzpJoJKJUTOZxCQTM4kLaqLRqHGLwb0nZkGiEUURAQUElEbZUWka+vz+uLfooum1lq56\n", "q87neerp973be09X9z3vPffec2RmOI7jOA5AVbE74DiO45QOrhQcx3GcrbhScBzHcbbiSsFxHMfZ\n", "iisFx3EcZyuuFBzHcZyttKoUJN0qqVbS7Cbp/0/S65JelXR9RvrVkuZLmivppIz0cZJmx7wbM9K7\n", 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Use that dataframe to make a kind='area' plot showing the total\n", "# number of roles available over the history of film.\n", "\n", "aa.plot(kind='area')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90bfcfba58>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEPCAYAAACNyEVOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm0ZWV55/Hvr6qYBaqQuQooFFBwyaCIxDhcJ1IxaY2a\n", "tIFoi2Y1ZPUi0sYomqElSRtNTLqJCweSqJiQJSYabdJBMAo7GiMgBhCZAhjSBSpBFMEBa7hP/7H3\n", "5Z46daZ9znnPu/e5v89aZ9Xdw9n7eeve+5x9n/3u91VEYGZm7bIqdwBmZlafk7eZWQs5eZuZtZCT\n", "t5lZCzl5m5m1kJO3mVkLDU3ekj4k6X5JNw/Y5z2S7pR0k6STphuimZl1G+XK+8PApn4bJb0EOCoi\n", "jgbOAt4/pdjMzKyPock7Ir4AfHfALi8FPlLtey2wVtJB0wnPzMx6mUbNez2wuWP5XmDDFI5rZmZ9\n", 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"code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90801ec0b8>" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYXEXV/z/fWbKHhJCFhEASIAHCFggSdoaAEBZZBNl5\n", "QdCfigryugX1dejXV8QVUQQXxIALiogQEMImg4BKAFkCISSBCSQhG9n3zHJ+f9TtTGcyS3fP7b4L\n", "9XmeeebeunXrnjPdU+fWqapzZGZ4PB6PxwNQEbUAHo/H44kP3ih4PB6PZyveKHg8Ho9nK94oeDwe\n", "j2cr3ih4PB6PZyveKHg8Ho9nK3kZBUmVkl6S9EBwfp2kBUHZS5JOzql7raQ5kmZJOjGnfLykGcG1\n", "m8JXxePxeDxdJd+RwtXATCC7qcGAH5nZQcHPwwCSxgLnAWOBScAtkhTccytwhZmNBkZLmhSWEh6P\n", "x+MJh06NgqThwCnAbUC2g1fOcS5nAHeZWYOZzQPmAhMkDQX6mtn0oN6dwJldlN3j8Xg8IZPPSOFG\n", "4MtAc06ZAZ+X9IqkX0vqH5QPAxbk1FsA7NJG+cKg3OPxeDwxokOjIOk0YKmZvcS2I4NbgVHAOGAR\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f90801f2e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c = cast\n", "#c = c[c.year // 10 == 198]\n", "c = c[c.n <= 3]\n", "c = c.groupby(['year', 'type', 'n']).size()\n", "c = c.unstack(1)\n", "c.swaplevel(0,1).loc[1].plot(ylim=0, kind='area')\n", "#f = c.actor / (c.actor + c.actress)\n", "#f = f.unstack()\n", "#f.plot(ylim=[0,1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rank over time" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1986.83333333\n" ] } ], "source": [ "# 2. Define “leading actor” as an actor or actress whose `n==1`\n", "# and “supporting actor” as `n==2` — what is the average year\n", "# of all the supporting roles Judi Dench has had?\n", "\n", "c = cast\n", "c = c[c.name == 'Judi Dench']\n", "print(c[c.n == 2].year.mean())" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2006.0\n" ] } ], "source": [ "# 2. What is the average year of Judi Dench’s leading roles —\n", "# is her career moving forwards toward leading roles\n", "# or backwards towards supporting ones?\n", "\n", "print(c[c.n == 1].year.mean())" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1961.75\n", "1970.47619048\n" ] } ], "source": [ "# 2. Did Sidney Poitier move forward or back over his career?\n", "\n", "c = cast\n", "c = c[c.name == 'Sidney Poitier']\n", "print(c[c.n == 2].year.mean())\n", "print(c[c.n == 1].year.mean())" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1988.38095238\n", "1983.51923077\n" ] } ], "source": [ "# 2. What about Michael Caine?\n", "\n", "c = cast\n", "c = c[c.name == 'Michael Caine']\n", "print(c[c.n == 2].year.mean())\n", "print(c[c.n == 1].year.mean())" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>size</th>\n", " <th>mean</th>\n", " </tr>\n", " <tr>\n", " <th>name</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>!Mystery Girl!</th>\n", " <td>1</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>'Ace' Reitman</th>\n", " <td>1</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>'Agent' Ava Hubbard</th>\n", " <td>1</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>'Amarillo Slim' Preston</th>\n", " <td>1</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th>'Apple' Hamidu</th>\n", " <td>1</td>\n", " <td>71</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " size mean\n", "name \n", "!Mystery Girl! 1 29\n", "'Ace' Reitman 1 11\n", "'Agent' Ava Hubbard 1 29\n", "'Amarillo Slim' Preston 1 31\n", "'Apple' Hamidu 1 71" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast\n", "#c = c[c.year // 10 == 195]\n", "c = c[c.n.notnull()].groupby('name').n.agg(['size', 'mean'])\n", "c.head()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>size</th>\n", " <th>mean</th>\n", " </tr>\n", " <tr>\n", " <th>name</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Stanley Lupino</th>\n", " <td>13</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Ferdi Tayfur (II)</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Harold Lloyd</th>\n", " <td>19</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Orhan Gencebay</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Mohamad Ali Fardin</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Jeyam Ravi</th>\n", " <td>16</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Amácio Mazzaropi</th>\n", " <td>26</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Emmy Wehlen</th>\n", " <td>18</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Edna Goodrich</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Prabhas</th>\n", " <td>17</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Ganesh (XII)</th>\n", " <td>18</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>William S. Hart</th>\n", " <td>50</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>K.L. Saigal</th>\n", " <td>19</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Dulquer Salmaan</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Olga Petrova</th>\n", " <td>32</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Will Hay</th>\n", " <td>19</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Evelyn Nesbit</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Emel Sayin</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Marie Doro</th>\n", " <td>15</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Junior N.T.R.</th>\n", " <td>20</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Scott Shaw</th>\n", " <td>20</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Emily Stevens</th>\n", " <td>16</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Nobuyo Ohyama</th>\n", " <td>25</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Ed Skudder</th>\n", " <td>13</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>José Mojica</th>\n", " <td>15</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Carl Brisson</th>\n", " <td>12</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Upendra</th>\n", " <td>13</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Fernand Raynaud</th>\n", " <td>15</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Tristan Newcomb</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Joe Sears</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Stefan Kramer</th>\n", " <td>18</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Valeska Suratt</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Roscoe 'Fatty' Arbuckle</th>\n", " <td>11</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Fannie Ward</th>\n", " <td>26</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Billie Rhodes</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>George Formby</th>\n", " <td>22</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Mark Forest</th>\n", " <td>17</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Earl Owensby</th>\n", " <td>10</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Yilmaz Güney</th>\n", " <td>74</td>\n", " <td>1.013514</td>\n", " </tr>\n", " <tr>\n", " <th>Vijay (XX)</th>\n", " <td>37</td>\n", " <td>1.027027</td>\n", " </tr>\n", " <tr>\n", " <th>Mary Pickford</th>\n", " <td>57</td>\n", " <td>1.035088</td>\n", " </tr>\n", " <tr>\n", " <th>Alberto Olmedo</th>\n", " <td>25</td>\n", " <td>1.040000</td>\n", " </tr>\n", " <tr>\n", " <th>Pearl White</th>\n", " <td>25</td>\n", " <td>1.040000</td>\n", " </tr>\n", " <tr>\n", " <th>June Caprice</th>\n", " <td>21</td>\n", " <td>1.047619</td>\n", " </tr>\n", " <tr>\n", " <th>Türkan Soray</th>\n", " <td>56</td>\n", " <td>1.053571</td>\n", " </tr>\n", " <tr>\n", " <th>Lito Lapid</th>\n", " <td>54</td>\n", " <td>1.055556</td>\n", " </tr>\n", " <tr>\n", " <th>Sumanth</th>\n", " <td>15</td>\n", " <td>1.066667</td>\n", " </tr>\n", " <tr>\n", " <th>Setsuko Ogawa</th>\n", " <td>15</td>\n", " <td>1.066667</td>\n", " </tr>\n", " <tr>\n", " <th>Farid Al Atrache</th>\n", " <td>14</td>\n", " <td>1.071429</td>\n", " </tr>\n", " <tr>\n", " <th>Dai Lin</th>\n", " <td>14</td>\n", " <td>1.071429</td>\n", " </tr>\n", " <tr>\n", " <th>Vivian Martin</th>\n", " <td>40</td>\n", " <td>1.075000</td>\n", " </tr>\n", " <tr>\n", " <th>Esa Pakarinen</th>\n", " <td>13</td>\n", " <td>1.076923</td>\n", " </tr>\n", " <tr>\n", " <th>Dustin Farnum</th>\n", " <td>39</td>\n", " <td>1.076923</td>\n", " </tr>\n", " <tr>\n", " <th>Suzzanna</th>\n", " <td>12</td>\n", " <td>1.083333</td>\n", " </tr>\n", " <tr>\n", " <th>Hazel Dawn</th>\n", " <td>12</td>\n", " <td>1.083333</td>\n", " </tr>\n", " <tr>\n", " <th>Momoe Yamaguchi</th>\n", " <td>11</td>\n", " <td>1.090909</td>\n", " </tr>\n", " <tr>\n", " <th>Mary Miles Minter</th>\n", " <td>53</td>\n", " <td>1.094340</td>\n", " </tr>\n", " <tr>\n", " <th>Grace Moore</th>\n", " <td>10</td>\n", " <td>1.100000</td>\n", " </tr>\n", " <tr>\n", " <th>Rajkumar</th>\n", " <td>20</td>\n", " <td>1.100000</td>\n", " </tr>\n", " <tr>\n", " <th>Vasilis Logothetidis</th>\n", " <td>10</td>\n", " <td>1.100000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " size mean\n", "name \n", "Stanley Lupino 13 1.000000\n", "Ferdi Tayfur (II) 11 1.000000\n", "Harold Lloyd 19 1.000000\n", "Orhan Gencebay 11 1.000000\n", "Mohamad Ali Fardin 11 1.000000\n", "Jeyam Ravi 16 1.000000\n", "Amácio Mazzaropi 26 1.000000\n", "Emmy Wehlen 18 1.000000\n", "Edna Goodrich 11 1.000000\n", "Prabhas 17 1.000000\n", "Ganesh (XII) 18 1.000000\n", "William S. Hart 50 1.000000\n", "K.L. Saigal 19 1.000000\n", "Dulquer Salmaan 10 1.000000\n", "Olga Petrova 32 1.000000\n", "Will Hay 19 1.000000\n", "Evelyn Nesbit 10 1.000000\n", "Emel Sayin 11 1.000000\n", "Marie Doro 15 1.000000\n", "Junior N.T.R. 20 1.000000\n", "Scott Shaw 20 1.000000\n", "Emily Stevens 16 1.000000\n", "Nobuyo Ohyama 25 1.000000\n", "Ed Skudder 13 1.000000\n", "José Mojica 15 1.000000\n", "Carl Brisson 12 1.000000\n", "Upendra 13 1.000000\n", "Fernand Raynaud 15 1.000000\n", "Tristan Newcomb 10 1.000000\n", "Joe Sears 10 1.000000\n", "Stefan Kramer 18 1.000000\n", "Valeska Suratt 11 1.000000\n", "Roscoe 'Fatty' Arbuckle 11 1.000000\n", "Fannie Ward 26 1.000000\n", "Billie Rhodes 10 1.000000\n", "George Formby 22 1.000000\n", "Mark Forest 17 1.000000\n", "Earl Owensby 10 1.000000\n", "Yilmaz Güney 74 1.013514\n", "Vijay (XX) 37 1.027027\n", "Mary Pickford 57 1.035088\n", "Alberto Olmedo 25 1.040000\n", "Pearl White 25 1.040000\n", "June Caprice 21 1.047619\n", "Türkan Soray 56 1.053571\n", "Lito Lapid 54 1.055556\n", "Sumanth 15 1.066667\n", "Setsuko Ogawa 15 1.066667\n", "Farid Al Atrache 14 1.071429\n", "Dai Lin 14 1.071429\n", "Vivian Martin 40 1.075000\n", "Esa Pakarinen 13 1.076923\n", "Dustin Farnum 39 1.076923\n", "Suzzanna 12 1.083333\n", "Hazel Dawn 12 1.083333\n", "Momoe Yamaguchi 11 1.090909\n", "Mary Miles Minter 53 1.094340\n", "Grace Moore 10 1.100000\n", "Rajkumar 20 1.100000\n", "Vasilis Logothetidis 10 1.100000" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = c[c['size'] >= 10]\n", "c = c.sort_values('mean')\n", "c.head(60)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Release dates" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>country</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0_1_0</td>\n", " <td>2008</td>\n", " <td>Poland</td>\n", " <td>2008-11-14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Ai no Sanka</td>\n", " <td>1967</td>\n", " <td>Japan</td>\n", " <td>1967-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>A Thousand to One</td>\n", " <td>1920</td>\n", " <td>USA</td>\n", " <td>1920-12-05</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>USA</td>\n", " <td>1923-10-13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>Netherlands</td>\n", " <td>1924-08-08</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year country date\n", "0 0_1_0 2008 Poland 2008-11-14\n", "1 Ai no Sanka 1967 Japan 1967-01-01\n", "2 A Thousand to One 1920 USA 1920-12-05\n", "3 A Prince of a King 1923 USA 1923-10-13\n", "4 A Prince of a King 1923 Netherlands 1924-08-08" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "release_dates.head()" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12 3\n", "11 1\n", "dtype: int64" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5. In which month is a movie whose name starts with the text\n", "# \"The Lord of the Rings\" most likely to be released?\n", "\n", "r = release_dates\n", "r = r[r.title.str.startswith('The Lord of the Rings')]\n", "r = r[r.country == 'USA']\n", "r.date.dt.month.value_counts()" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11 19\n", "12 11\n", "10 9\n", "1 2\n", "7 1\n", "4 1\n", "2 1\n", "dtype: int64" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5. In which months is a movie whose name ends in the word \"Christmas\"\n", "# most likely to be released?\n", "\n", "r = release_dates\n", "r = r[r.title.str.endswith('Christmas')]\n", "r = r[r.country == 'USA']\n", "r.date.dt.month.value_counts()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>country</th>\n", " <th>date</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>#73, Shaanthi Nivaasa</th>\n", " <th>2007</th>\n", " <td>India</td>\n", " <td>2007-06-15</td>\n", " </tr>\n", " <tr>\n", " <th>#Beings</th>\n", " <th>2015</th>\n", " <td>Romania</td>\n", " <td>2015-01-29</td>\n", " </tr>\n", " <tr>\n", " <th>#Ewankosau saranghaeyo</th>\n", " <th>2015</th>\n", " <td>Philippines</td>\n", " <td>2015-01-21</td>\n", " </tr>\n", " <tr>\n", " <th>#Horror</th>\n", " <th>2015</th>\n", " <td>USA</td>\n", " <td>2015-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>#Nerealnaya lyubov</th>\n", " <th>2014</th>\n", " <td>Russia</td>\n", " <td>2014-02-13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country date\n", "title year \n", "#73, Shaanthi Nivaasa 2007 India 2007-06-15\n", "#Beings 2015 Romania 2015-01-29\n", "#Ewankosau saranghaeyo 2015 Philippines 2015-01-21\n", "#Horror 2015 USA 2015-01-01\n", "#Nerealnaya lyubov 2014 Russia 2014-02-13" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rd = release_dates.set_index(['title', 'year']).sort_index()\n", "rd.head()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>country</th>\n", " <th>date</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>#Beings</th>\n", " <th>2015</th>\n", " <td>Romania</td>\n", " <td>2015-01-29</td>\n", " </tr>\n", " <tr>\n", " <th>#Horror</th>\n", " <th>2015</th>\n", " <td>USA</td>\n", " <td>2015-01-01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country date\n", "title year \n", "#Beings 2015 Romania 2015-01-29\n", "#Horror 2015 USA 2015-01-01" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rd.loc[[('#Beings', 2015), ('#Horror', 2015)]]" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([['Top Gun', 1986],\n", " ['Legend', 1985],\n", " ['Oblivion', 2013],\n", " ['Collateral', 2004],\n", " ['Endless Love', 1981],\n", " ['Austin Powers in Goldmember', 2002],\n", " ['August', 2008],\n", " ['The Queen', 2006],\n", " [\"Losin' It\", 1983],\n", " ['Edge of Tomorrow', 2014],\n", " ['Mission: Impossible III', 2006],\n", " ['Mission: Impossible', 1996],\n", " ['Mission: Impossible - Rogue Nation', 2015],\n", " ['Mission: Impossible - Ghost Protocol', 2011],\n", " ['Mission: Impossible II', 2000],\n", " ['War of the Worlds', 2005],\n", " ['Knight and Day', 2010],\n", " ['Minority Report', 2002],\n", " ['Cocktail', 1988],\n", " ['The Outsiders', 1983],\n", " ['Rock of Ages', 2012],\n", " ['All the Right Moves', 1983],\n", " ['Taps', 1981],\n", " ['Jerry Maguire', 1996],\n", " ['The Last Samurai', 2003],\n", " ['Born on the Fourth of July', 1989],\n", " ['Far and Away', 1992],\n", " ['Magnolia', 1999],\n", " ['Vanilla Sky', 2001],\n", " ['Tropic Thunder', 2008],\n", " ['Valkyrie', 2008],\n", " ['Days of Thunder', 1990],\n", " ['Eyes Wide Shut', 1999],\n", " ['Rain Man', 1988],\n", " ['Jack Reacher', 2012],\n", " ['The Color of Money', 1986],\n", " ['Lions for Lambs', 2007],\n", " ['Risky Business', 1983],\n", " ['A Few Good Men', 1992],\n", " ['Interview with the Vampire: The Vampire Chronicles', 1994],\n", " ['The Firm', 1993]], dtype=object)" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast\n", "c = c[c.name == 'Tom Cruise'][['title', 'year']].drop_duplicates()\n", "#c = c.join(rd, ['title', 'year'])\n", "#c = c[c.country == 'USA']\n", "#c.date.dt.month.value_counts().sort_index().plot(kind='bar')\n", "c.values" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ASK\n", "# rd.loc[c]\n", "# rd.loc[c.values]\n", "# rd.loc[list(c.values)]" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a42fb860>" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEBCAYAAABL1w/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAFphJREFUeJzt3X2sZHV9x/H3h11FFhDSqKhFXWuqfdIiPoRqTC+pGjRq\n", "rW0TqX1Yjf7TVjCtRq1tNGmiaDXW2JjGKizWp1gfi40KWn4IIUXQvZSHRdvGG8EHJOJCQI0o3/4x\n", "Z9hzZ3fPzN07v/M7c36fV3Kzc+bM/X3Od87M99753rl3FRGYmdlwHFP6AMzMbDM3ZjOzgXFjNjMb\n", "GDdmM7OBcWM2MxsYN2Yzs4GZ25glvV7SDZKuk/RhScf2cWBmZrXqbMySdgOvAE6PiMcDO4AX5z8s\n", "M7N67Zyz/07gHmCXpJ8Du4BvZz8qM7OKdX7HHBG3A+8AvgV8BzgQEV/s48DMzGo1b5TxGOBVwG7g\n", "4cAJkl7Sw3GZmVVr3ijjycCVEfEDAEmfBJ4GfGh6A0n+YxtmZkchInSkHUf8AH4TuB44DhBwIfAX\n", "M7eJrjXmfQBv2s7nDzEXCIiOjzd27Nve/VnbfT3UbNc8juztPZej8/nctW/ejPla4APANcB/N1e/\n", "d/GvBwvZveT1hp4LbJQK3l1ZbsnsUrkls0vlFszeyLLqvFEGEfE24G1Z0s3M7BBD+M2/vZXlAntK\n", 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In what months of the year have Helen Mirren movies been most often released?\n", "\n", "c = cast\n", "c = c[c.name == 'Helen Mirren'][['title', 'year']].drop_duplicates()\n", "c = c.join(rd, ['title', 'year'])\n", "c = c[c.country == 'USA']\n", "c.date.dt.month.value_counts().sort_index().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a42990b8>" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEBCAYAAABL1w/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAFfZJREFUeJzt3XvMZHddx/H3l11augXaIHcLLhK5CMqtkIpUHwKaigiI\n", "mlAxuiXwh9yKFwJVoxgTLooRgiFEod1yEaNgQbxAgewUmkagult6BSU8UhALoWyBciv26x/nPH2m\n", "yzxzefZ3zpz5zfuVTHbOnOecz/n9dvbMeT47zzyRmUiShuMOyz4ASdLteWKWpIHxxCxJA+OJWZIG\n", 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"cW9gcQAAAABJRU5ErkJggg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f90a4228978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5. …Jeff Bridges movies?\n", "\n", "c = cast\n", "c = c[c.name == 'Jeff Bridges'][['title', 'year']].drop_duplicates()\n", "c = c.join(rd, ['title', 'year'])\n", "c = c[c.country == 'USA']\n", "c.date.dt.month.value_counts().sort_index().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a4144ba8>" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEBCAYAAABL1w/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGVtJREFUeJzt3X+MbGddx/H3l1764xYowQYoFr21EeRnf1BIRcCtFNIi\n", "FERNaDBwS8ofAgIKhFaMgn8IBAkQhRiB9oIWjFQKlh9SwD4FUgNU7i2FFtCGFRAoDfUWpBZa+/WP\n", 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], "source": [ "%%time\n", "# 5. Use join() to build a table of release dates indexed by actor,\n", "# and use it to re-run the previous three questions efficiently.\n", "\n", "c = cast\n", "c = c[['name', 'title', 'year']]\n", "c = c.join(rd, ['title', 'year'])\n", "c = c[c.country == 'USA']\n", "c = c.set_index('name').sort_index()\n", "releases = c\n", "releases.head()" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f90a3bcca90>" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEBCAYAAABL1w/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGXlJREFUeJzt3X+MbGddx/H3l1764xYowQYoFr21EeRnf1BIRcCtFNIi\n", "FERNaDBwS8ofAgIKhFaMgn8IBAkQhRiB9oIWjFQKlh9SwD4FUgNU7i2FFtCGFRAoDfUWpBZa+/WP\n", "c8aZWXbnzO7MOc9n735eSXP37Eyf874zs8+d/e7sbmQmZmam4261A8zMbJo3ZjMzMd6YzczEeGM2\n", 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style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Core</td>\n", " <td>2003</td>\n", " <td>Alejandro Abellan</td>\n", " <td>actor</td>\n", " <td>U.S.S. Soldier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Il momento di uccidere</td>\n", " <td>1968</td>\n", " <td>Remo De Angelis</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Across the Divide</td>\n", " <td>1921</td>\n", " <td>Thomas Delmar</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Revan</td>\n", " <td>2012</td>\n", " <td>Diego James</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Un homme marche dans la ville</td>\n", " <td>1950</td>\n", " <td>Fabien Loris</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "0 The Core 2003 Alejandro Abellan actor \n", "1 Il momento di uccidere 1968 Remo De Angelis actor \n", "2 Across the Divide 1921 Thomas Delmar actor \n", "3 Revan 2012 Diego James actor \n", "4 Un homme marche dans la ville 1950 Fabien Loris actor \n", "\n", " character n \n", "0 U.S.S. Soldier NaN \n", "1 Dago 9 \n", "2 Dago 4 \n", "3 Dago NaN \n", "4 Dago 12 " ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cast.head()" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1197089</th>\n", " <td>Unbecoming Age</td>\n", " <td>1992</td>\n", " <td>George Clooney</td>\n", " <td>actor</td>\n", " <td>Mac</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2023017</th>\n", " <td>The Harvest</td>\n", " <td>1992</td>\n", " <td>George Clooney</td>\n", " <td>actor</td>\n", " <td>Lip Syncing Transvestite</td>\n", " <td>23</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "1197089 Unbecoming Age 1992 George Clooney actor \n", "2023017 The Harvest 1992 George Clooney actor \n", "\n", " character n \n", "1197089 Mac 5 \n", "2023017 Lip Syncing Transvestite 23 " ] }, "execution_count": 205, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast\n", "c = c[c.year >= 1990]\n", "c = c[c.year <= 1993]\n", "c = c[c.name == 'George Clooney']\n", "#c = c[c.title == 'Inception']\n", "#c = c[c.n.notnull()]\n", "#c = c.pivot('name', 'year', 'title')\n", "c.fillna('')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>country</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0_1_0</td>\n", " <td>2008</td>\n", " <td>Poland</td>\n", " <td>2008-11-14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Ai no Sanka</td>\n", " <td>1967</td>\n", " <td>Japan</td>\n", " <td>1967-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>A Thousand to One</td>\n", " <td>1920</td>\n", " <td>USA</td>\n", " <td>1920-12-05</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>USA</td>\n", " <td>1923-10-13</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>A Prince of a King</td>\n", " <td>1923</td>\n", " <td>Netherlands</td>\n", " <td>1924-08-08</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year country date\n", "0 0_1_0 2008 Poland 2008-11-14\n", "1 Ai no Sanka 1967 Japan 1967-01-01\n", "2 A Thousand to One 1920 USA 1920-12-05\n", "3 A Prince of a King 1923 USA 1923-10-13\n", "4 A Prince of a King 1923 Netherlands 1924-08-08" ] }, "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ "release_dates.head()" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>country</th>\n", " <th>UK</th>\n", " <th>USA</th>\n", " <th>Ukraine</th>\n", " <th>United Arab Emirates</th>\n", " <th>Uruguay</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Star Wars: Episode I - The Phantom Menace</th>\n", " <td>1999-07-16</td>\n", " <td>1999-05-19</td>\n", " <td>NaT</td>\n", " <td>1999-08-25</td>\n", " <td>1999-07-02</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode II - Attack of the Clones</th>\n", " <td>2002-05-16</td>\n", " <td>2002-05-16</td>\n", " <td>2002-06-08</td>\n", " <td>2002-05-16</td>\n", " <td>2002-07-05</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode III - Revenge of the Sith</th>\n", " <td>2005-05-19</td>\n", " <td>2005-05-19</td>\n", " <td>NaT</td>\n", " <td>2005-05-19</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode IX</th>\n", " <td>NaT</td>\n", " <td>2019-01-01</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode V - The Empire Strikes Back</th>\n", " <td>1980-05-21</td>\n", " <td>1980-06-20</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>1981-01-15</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VI - Return of the Jedi</th>\n", " <td>1983-06-02</td>\n", " <td>1983-05-25</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VII - The Force Awakens</th>\n", " <td>2015-12-18</td>\n", " <td>2015-12-18</td>\n", " <td>2015-12-17</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VIII</th>\n", " <td>2017-05-26</td>\n", " <td>2017-05-26</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "country UK USA \\\n", "title \n", "Star Wars: Episode I - The Phantom Menace 1999-07-16 1999-05-19 \n", "Star Wars: Episode II - Attack of the Clones 2002-05-16 2002-05-16 \n", "Star Wars: Episode III - Revenge of the Sith 2005-05-19 2005-05-19 \n", "Star Wars: Episode IX NaT 2019-01-01 \n", "Star Wars: Episode V - The Empire Strikes Back 1980-05-21 1980-06-20 \n", "Star Wars: Episode VI - Return of the Jedi 1983-06-02 1983-05-25 \n", "Star Wars: Episode VII - The Force Awakens 2015-12-18 2015-12-18 \n", "Star Wars: Episode VIII 2017-05-26 2017-05-26 \n", "\n", "country Ukraine \\\n", "title \n", "Star Wars: Episode I - The Phantom Menace NaT \n", "Star Wars: Episode II - Attack of the Clones 2002-06-08 \n", "Star Wars: Episode III - Revenge of the Sith NaT \n", "Star Wars: Episode IX NaT \n", "Star Wars: Episode V - The Empire Strikes Back NaT \n", "Star Wars: Episode VI - Return of the Jedi NaT \n", "Star Wars: Episode VII - The Force Awakens 2015-12-17 \n", "Star Wars: Episode VIII NaT \n", "\n", "country United Arab Emirates Uruguay \n", "title \n", "Star Wars: Episode I - The Phantom Menace 1999-08-25 1999-07-02 \n", "Star Wars: Episode II - Attack of the Clones 2002-05-16 2002-07-05 \n", "Star Wars: Episode III - Revenge of the Sith 2005-05-19 NaT \n", "Star Wars: Episode IX NaT NaT \n", "Star Wars: Episode V - The Empire Strikes Back NaT 1981-01-15 \n", "Star Wars: Episode VI - Return of the Jedi NaT NaT \n", "Star Wars: Episode VII - The Force Awakens NaT NaT \n", "Star Wars: Episode VIII NaT NaT " ] }, "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = release_dates\n", "r = r[r.title.str.startswith('Star Wars: Episode')]\n", "r = r[r.country.str.startswith('U')]\n", "r.pivot('title', 'country', 'date')\n", "#r.pivot('country', 'title', 'date')" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"5\" halign=\"left\">date</th>\n", " </tr>\n", " <tr>\n", " <th>country</th>\n", " <th>UK</th>\n", " <th>USA</th>\n", " <th>Ukraine</th>\n", " <th>United Arab Emirates</th>\n", " <th>Uruguay</th>\n", " </tr>\n", " <tr>\n", " <th>title</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Star Wars: Episode I - The Phantom Menace</th>\n", " <td>1999-07-16</td>\n", " <td>1999-05-19</td>\n", " <td>NaT</td>\n", " <td>1999-08-25</td>\n", " <td>1999-07-02</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode II - Attack of the Clones</th>\n", " <td>2002-05-16</td>\n", " <td>2002-05-16</td>\n", " <td>2002-06-08</td>\n", " <td>2002-05-16</td>\n", " <td>2002-07-05</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode III - Revenge of the Sith</th>\n", " <td>2005-05-19</td>\n", " <td>2005-05-19</td>\n", " <td>NaT</td>\n", " <td>2005-05-19</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode IX</th>\n", " <td>NaT</td>\n", " <td>2019-01-01</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode V - The Empire Strikes Back</th>\n", " <td>1980-05-21</td>\n", " <td>1980-06-20</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>1981-01-15</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VI - Return of the Jedi</th>\n", " <td>1983-06-02</td>\n", " <td>1983-05-25</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VII - The Force Awakens</th>\n", " <td>2015-12-18</td>\n", " <td>2015-12-18</td>\n", " <td>2015-12-17</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>Star Wars: Episode VIII</th>\n", " <td>2017-05-26</td>\n", " <td>2017-05-26</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " <td>NaT</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date \\\n", "country UK USA \n", "title \n", "Star Wars: Episode I - The Phantom Menace 1999-07-16 1999-05-19 \n", "Star Wars: Episode II - Attack of the Clones 2002-05-16 2002-05-16 \n", "Star Wars: Episode III - Revenge of the Sith 2005-05-19 2005-05-19 \n", "Star Wars: Episode IX NaT 2019-01-01 \n", "Star Wars: Episode V - The Empire Strikes Back 1980-05-21 1980-06-20 \n", "Star Wars: Episode VI - Return of the Jedi 1983-06-02 1983-05-25 \n", "Star Wars: Episode VII - The Force Awakens 2015-12-18 2015-12-18 \n", "Star Wars: Episode VIII 2017-05-26 2017-05-26 \n", "\n", " \\\n", "country Ukraine \n", "title \n", "Star Wars: Episode I - The Phantom Menace NaT \n", "Star Wars: Episode II - Attack of the Clones 2002-06-08 \n", "Star Wars: Episode III - Revenge of the Sith NaT \n", "Star Wars: Episode IX NaT \n", "Star Wars: Episode V - The Empire Strikes Back NaT \n", "Star Wars: Episode VI - Return of the Jedi NaT \n", "Star Wars: Episode VII - The Force Awakens 2015-12-17 \n", "Star Wars: Episode VIII NaT \n", "\n", " \n", "country United Arab Emirates Uruguay \n", "title \n", "Star Wars: Episode I - The Phantom Menace 1999-08-25 1999-07-02 \n", "Star Wars: Episode II - Attack of the Clones 2002-05-16 2002-07-05 \n", "Star Wars: Episode III - Revenge of the Sith 2005-05-19 NaT \n", "Star Wars: Episode IX NaT NaT \n", "Star Wars: Episode V - The Empire Strikes Back NaT 1981-01-15 \n", "Star Wars: Episode VI - Return of the Jedi NaT NaT \n", "Star Wars: Episode VII - The Force Awakens NaT NaT \n", "Star Wars: Episode VIII NaT NaT " ] }, "execution_count": 227, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = release_dates\n", "r = r[r.title.str.startswith('Star Wars: Episode')]\n", "r = r[r.country.str.startswith('U')]\n", "r.set_index(['title', 'country'])[['date']].unstack()" ] }, { "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": 228, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Core</td>\n", " <td>2003</td>\n", " <td>Alejandro Abellan</td>\n", " <td>actor</td>\n", " <td>U.S.S. Soldier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Il momento di uccidere</td>\n", " <td>1968</td>\n", " <td>Remo De Angelis</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Across the Divide</td>\n", " <td>1921</td>\n", " <td>Thomas Delmar</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Revan</td>\n", " <td>2012</td>\n", " <td>Diego James</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Un homme marche dans la ville</td>\n", " <td>1950</td>\n", " <td>Fabien Loris</td>\n", " <td>actor</td>\n", " <td>Dago</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type \\\n", "0 The Core 2003 Alejandro Abellan actor \n", "1 Il momento di uccidere 1968 Remo De Angelis actor \n", "2 Across the Divide 1921 Thomas Delmar actor \n", "3 Revan 2012 Diego James actor \n", "4 Un homme marche dans la ville 1950 Fabien Loris actor \n", "\n", " character n \n", "0 U.S.S. Soldier NaN \n", "1 Dago 9 \n", "2 Dago 4 \n", "3 Dago NaN \n", "4 Dago 12 " ] }, "execution_count": 228, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cast.head()" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Core</td>\n", " <td>2003</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Il momento di uccidere</td>\n", " <td>1968</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Across the Divide</td>\n", " <td>1921</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Revan</td>\n", " <td>2012</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Un homme marche dans la ville</td>\n", " <td>1950</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "0 The Core 2003\n", "1 Il momento di uccidere 1968\n", "2 Across the Divide 1921\n", "3 Revan 2012\n", "4 Un homme marche dans la ville 1950" ] }, "execution_count": 243, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t = titles\n", "\n", "t.head()" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>title</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th>character</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1964</th>\n", " <th>Lady</th>\n", " <td>Hamlet</td>\n", " <td>Kate Beswick</td>\n", " <td>actress</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Lady</th>\n", " <td>Hamlet</td>\n", " <td>Carol Teitel</td>\n", " <td>actress</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"16\" valign=\"top\">2000</th>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Paul Ferriter</td>\n", " <td>actor</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Paul Graham (IV)</td>\n", " <td>actor</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Henry Griffin (II)</td>\n", " <td>actor</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Ayun Halliday</td>\n", " <td>actor</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Greg Kotis</td>\n", " <td>actor</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Barry Manasch</td>\n", " <td>actor</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Philip McKenney</td>\n", " <td>actor</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Colin Puth</td>\n", " <td>actor</td>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Giancarlo Roma</td>\n", " <td>actor</td>\n", " <td>37</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Thomas Roma</td>\n", " <td>actor</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>D.J. Dara</td>\n", " <td>actress</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Sinead Dolan</td>\n", " <td>actress</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Sarah Fiol</td>\n", " <td>actress</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Tanya Gingerich</td>\n", " <td>actress</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>Anne Nixon (II)</td>\n", " <td>actress</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>Special Guest Appearance</th>\n", " <td>Hamlet</td>\n", " <td>India Reed Kotis</td>\n", " <td>actress</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>Gentleman</th>\n", " <td>Hamlet</td>\n", " <td>Richard Sterne</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>First Player</th>\n", " <td>Hamlet</td>\n", " <td>Roger Livesey</td>\n", " <td>actor</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>First Player</th>\n", " <td>Hamlet</td>\n", " <td>Ben Thom</td>\n", " <td>actor</td>\n", " <td>42</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>First Player</th>\n", " <td>Hamlet</td>\n", " <td>Harcourt Williams</td>\n", " <td>actor</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <th>Bernardo</th>\n", " <td>Hamlet</td>\n", " <td>Matt Hurley (II)</td>\n", " <td>actor</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Bernardo</th>\n", " <td>Hamlet</td>\n", " <td>Esmond Knight</td>\n", " <td>actor</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1913</th>\n", " <th>Bernardo</th>\n", " <td>Hamlet</td>\n", " <td>G. Richards (II)</td>\n", " <td>actor</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <th>Bernardo</th>\n", " <td>Hamlet</td>\n", " <td>Richard Warwick</td>\n", " <td>actor</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>Bernardo</th>\n", " <td>Hamlet</td>\n", " <td>Frederick Young</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <th>Laertes</th>\n", " <td>Hamlet</td>\n", " <td>Hayden Adams</td>\n", " <td>actor</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>Laertes</th>\n", " <td>Hamlet</td>\n", " <td>John Cullum</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1921</th>\n", " <th>Laertes</th>\n", " <td>Hamlet</td>\n", " <td>Anton De Verdier</td>\n", " <td>actor</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Lady of the Court</th>\n", " <td>Hamlet</td>\n", " <td>Patricia Davidson (II)</td>\n", " <td>actress</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>Fortinbras's Captain</th>\n", " <td>Hamlet</td>\n", " <td>Dillon Evans</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>Fortinbras's Captain</th>\n", " <td>Hamlet</td>\n", " <td>Jeffery Kissoon</td>\n", " <td>actor</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>Fortinbras's Captain</th>\n", " <td>Hamlet</td>\n", " <td>John Spencer-Churchill</td>\n", " <td>actor</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>2015</th>\n", " <th>Polonia</th>\n", " <td>Hamlet</td>\n", " <td>Gillian Bevan</td>\n", " <td>actress</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>Court Lady</th>\n", " <td>Hamlet</td>\n", " <td>Anjelica Huston</td>\n", " <td>actress</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>Sailor One</th>\n", " <td>Hamlet</td>\n", " <td>David Yip</td>\n", " <td>actor</td>\n", " <td>47</td>\n", " </tr>\n", " <tr>\n", " <th>Young Hamlet</th>\n", " <td>Hamlet</td>\n", " <td>Thomas Szekeres</td>\n", " <td>actor</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <th>Palace Nobleman</th>\n", " <td>Hamlet</td>\n", " <td>Barrie Holland</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>Player Prologue</th>\n", " <td>Hamlet</td>\n", " <td>John Hetherington</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1973</th>\n", " <th>Claudius, King of Denmark</th>\n", " <td>Hamlet</td>\n", " <td>Dan Hennessey</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Spear Carrier</th>\n", " <td>Hamlet</td>\n", " <td>Christopher Lee</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>Elsinore Courtier</th>\n", " <td>Hamlet</td>\n", " <td>Anthony Maddalena</td>\n", " <td>actor</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <th>Claudius' Bodyguard</th>\n", " <td>Hamlet</td>\n", " <td>John Wills Martin</td>\n", " <td>actor</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Laertes - His Son</th>\n", " <td>Hamlet</td>\n", " <td>Terence Morgan (II)</td>\n", " <td>actor</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>Old Norway</th>\n", " <td>Hamlet</td>\n", " <td>John Mills</td>\n", " <td>actor</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>Sailor Two</th>\n", " <td>Hamlet</td>\n", " <td>Jimi Mistry</td>\n", " <td>actor</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Voice of Ghost</th>\n", " <td>Hamlet</td>\n", " <td>Laurence Olivier</td>\n", " <td>actor</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>1913</th>\n", " <th>Rosencrants</th>\n", " <td>Hamlet</td>\n", " <td>Montagu Rutherford</td>\n", " <td>actor</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>Court lady</th>\n", " <td>Hamlet</td>\n", " <td>Jennifer Tudor</td>\n", " <td>actress</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1948</th>\n", " <th>Claudius - The King</th>\n", " <td>Hamlet</td>\n", " <td>Basil Sydney</td>\n", " <td>actor</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>Horatio - His Friend</th>\n", " <td>Hamlet</td>\n", " <td>Norman Wooland</td>\n", " <td>actor</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>Hecuba</th>\n", " <td>Hamlet</td>\n", " <td>Judi Dench</td>\n", " <td>actress</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1921</th>\n", " <th>Königin Gertrude</th>\n", " <td>Hamlet</td>\n", " <td>Mathilde Brandt</td>\n", " <td>actress</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1996</th>\n", " <th>Attendant to Gertrude</th>\n", " <td>Hamlet</td>\n", " <td>Angela Douglas</td>\n", " <td>actress</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>Attendant to Gertrude</th>\n", " <td>Hamlet</td>\n", " <td>Rowena King</td>\n", " <td>actress</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>Attendant to Gertrude</th>\n", " <td>Hamlet</td>\n", " <td>Sarah Lam</td>\n", " <td>actress</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Gertrude - The Queen</th>\n", " <td>Hamlet</td>\n", " <td>Eileen Herlie</td>\n", " <td>actress</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1973</th>\n", " <th>Gertrude, Queen of Denmark</th>\n", " <td>Hamlet</td>\n", " <td>Becke Keller</td>\n", " <td>actress</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>Ophelia - and Daughter</th>\n", " <td>Hamlet</td>\n", " <td>Jean Simmons</td>\n", " <td>actress</td>\n", " <td>17</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>299 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " title name type n\n", "year character \n", "1964 Lady Hamlet Kate Beswick actress NaN\n", " Lady Hamlet Carol Teitel actress NaN\n", "2000 Special Guest Appearance Hamlet Paul Ferriter actor 23\n", " Special Guest Appearance Hamlet Paul Graham (IV) actor 27\n", " Special Guest Appearance Hamlet Henry Griffin (II) actor 28\n", " Special Guest Appearance Hamlet Ayun Halliday actor 30\n", " Special Guest Appearance Hamlet Greg Kotis actor 31\n", " Special Guest Appearance Hamlet Barry Manasch actor 32\n", " Special Guest Appearance Hamlet Philip McKenney actor 33\n", " Special Guest Appearance Hamlet Colin Puth actor 35\n", " Special Guest Appearance Hamlet Giancarlo Roma actor 37\n", " Special Guest Appearance Hamlet Thomas Roma actor 38\n", " Special Guest Appearance Hamlet D.J. Dara actress 21\n", " Special Guest Appearance Hamlet Sinead Dolan actress 22\n", " Special Guest Appearance Hamlet Sarah Fiol actress 25\n", " Special Guest Appearance Hamlet Tanya Gingerich actress 26\n", " Special Guest Appearance Hamlet Anne Nixon (II) actress 34\n", " Special Guest Appearance Hamlet India Reed Kotis actress 29\n", "1964 Gentleman Hamlet Richard Sterne actor NaN\n", "1969 First Player Hamlet Roger Livesey actor 11\n", "1996 First Player Hamlet Ben Thom actor 42\n", "1948 First Player Hamlet Harcourt Williams actor 5\n", "2009 Bernardo Hamlet Matt Hurley (II) actor 10\n", "1948 Bernardo Hamlet Esmond Knight actor 2\n", "1913 Bernardo Hamlet G. Richards (II) actor 12\n", "1990 Bernardo Hamlet Richard Warwick actor 13\n", "1964 Bernardo Hamlet Frederick Young actor NaN\n", "2009 Laertes Hamlet Hayden Adams actor 7\n", "1964 Laertes Hamlet John Cullum actor NaN\n", "1921 Laertes Hamlet Anton De Verdier actor 7\n", "... ... ... ... ..\n", "1948 Lady of the Court Hamlet Patricia Davidson (II) actress NaN\n", "1964 Fortinbras's Captain Hamlet Dillon Evans actor NaN\n", "1996 Fortinbras's Captain Hamlet Jeffery Kissoon actor 27\n", " Fortinbras's Captain Hamlet John Spencer-Churchill actor 32\n", "2015 Polonia Hamlet Gillian Bevan actress 4\n", "1969 Court Lady Hamlet Anjelica Huston actress 18\n", "1996 Sailor One Hamlet David Yip actor 47\n", " Young Hamlet Hamlet Thomas Szekeres actor 41\n", "1990 Palace Nobleman Hamlet Barrie Holland actor NaN\n", "1964 Player Prologue Hamlet John Hetherington actor NaN\n", "1973 Claudius, King of Denmark Hamlet Dan Hennessey actor NaN\n", "1948 Spear Carrier Hamlet Christopher Lee actor NaN\n", "1996 Elsinore Courtier Hamlet Anthony Maddalena actor NaN\n", "2000 Claudius' Bodyguard Hamlet John Wills Martin actor 18\n", "1948 Laertes - His Son Hamlet Terence Morgan (II) actor 16\n", "1996 Old Norway Hamlet John Mills actor 33\n", " Sailor Two Hamlet Jimi Mistry actor 34\n", "1948 Voice of Ghost Hamlet Laurence Olivier actor 13\n", "1913 Rosencrants Hamlet Montagu Rutherford actor 8\n", "1969 Court lady Hamlet Jennifer Tudor actress 23\n", "1948 Claudius - The King Hamlet Basil Sydney actor 11\n", " Horatio - His Friend Hamlet Norman Wooland actor 14\n", "1996 Hecuba Hamlet Judi Dench actress 12\n", "1921 Königin Gertrude Hamlet Mathilde Brandt actress 3\n", "1996 Attendant to Gertrude Hamlet Angela Douglas actress 16\n", " Attendant to Gertrude Hamlet Rowena King actress 26\n", " Attendant to Gertrude Hamlet Sarah Lam actress 28\n", "1948 Gertrude - The Queen Hamlet Eileen Herlie actress 12\n", "1973 Gertrude, Queen of Denmark Hamlet Becke Keller actress NaN\n", "1948 Ophelia - and Daughter Hamlet Jean Simmons actress 17\n", "\n", "[299 rows x 4 columns]" ] }, "execution_count": 246, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast\n", "c = c[c.title == 'Hamlet']\n", "c = c.set_index(['year', 'character'])#.unstack('type')\n", "c" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>title</th>\n", " <th>name</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th>type</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1964</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Kate Beswick</td>\n", " <td>Lady</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Carol Teitel</td>\n", " <td>Lady</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"16\" valign=\"top\">2000</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Paul Ferriter</td>\n", " <td>Special Guest Appearance</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Paul Graham (IV)</td>\n", " <td>Special Guest Appearance</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Henry Griffin (II)</td>\n", " <td>Special Guest Appearance</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Ayun Halliday</td>\n", " <td>Special Guest Appearance</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Greg Kotis</td>\n", " <td>Special Guest Appearance</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Barry Manasch</td>\n", " <td>Special Guest Appearance</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Philip McKenney</td>\n", " <td>Special Guest Appearance</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Colin Puth</td>\n", " <td>Special Guest Appearance</td>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Giancarlo Roma</td>\n", " <td>Special Guest Appearance</td>\n", " <td>37</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Thomas Roma</td>\n", " <td>Special Guest Appearance</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>D.J. Dara</td>\n", " <td>Special Guest Appearance</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Sinead Dolan</td>\n", " <td>Special Guest Appearance</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Sarah Fiol</td>\n", " <td>Special Guest Appearance</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Tanya Gingerich</td>\n", " <td>Special Guest Appearance</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Anne Nixon (II)</td>\n", " <td>Special Guest Appearance</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>India Reed Kotis</td>\n", " <td>Special Guest Appearance</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Richard Sterne</td>\n", " <td>Gentleman</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Roger Livesey</td>\n", " <td>First Player</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Ben Thom</td>\n", " <td>First Player</td>\n", " <td>42</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Harcourt Williams</td>\n", " <td>First Player</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Matt Hurley (II)</td>\n", " <td>Bernardo</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Esmond Knight</td>\n", " <td>Bernardo</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1913</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>G. Richards (II)</td>\n", " <td>Bernardo</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Richard Warwick</td>\n", " <td>Bernardo</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Frederick Young</td>\n", " <td>Bernardo</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Hayden Adams</td>\n", " <td>Laertes</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>John Cullum</td>\n", " <td>Laertes</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1921</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Anton De Verdier</td>\n", " <td>Laertes</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Patricia Davidson (II)</td>\n", " <td>Lady of the Court</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Dillon Evans</td>\n", " <td>Fortinbras's Captain</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Jeffery Kissoon</td>\n", " <td>Fortinbras's Captain</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>John Spencer-Churchill</td>\n", " <td>Fortinbras's Captain</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>2015</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Gillian Bevan</td>\n", " <td>Polonia</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Anjelica Huston</td>\n", " <td>Court Lady</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>David Yip</td>\n", " <td>Sailor One</td>\n", " <td>47</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Thomas Szekeres</td>\n", " <td>Young Hamlet</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Barrie Holland</td>\n", " <td>Palace Nobleman</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1964</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>John Hetherington</td>\n", " <td>Player Prologue</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1973</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Dan Hennessey</td>\n", " <td>Claudius, King of Denmark</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Christopher Lee</td>\n", " <td>Spear Carrier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Anthony Maddalena</td>\n", " <td>Elsinore Courtier</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>John Wills Martin</td>\n", " <td>Claudius' Bodyguard</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Terence Morgan (II)</td>\n", " <td>Laertes - His Son</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1996</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>John Mills</td>\n", " <td>Old Norway</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Jimi Mistry</td>\n", " <td>Sailor Two</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Laurence Olivier</td>\n", " <td>Voice of Ghost</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>1913</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Montagu Rutherford</td>\n", " <td>Rosencrants</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>1969</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Jennifer Tudor</td>\n", " <td>Court lady</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1948</th>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Basil Sydney</td>\n", " <td>Claudius - The King</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>actor</th>\n", " <td>Hamlet</td>\n", " <td>Norman Wooland</td>\n", " <td>Horatio - His Friend</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Judi Dench</td>\n", " <td>Hecuba</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1921</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Mathilde Brandt</td>\n", " <td>Königin Gertrude</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">1996</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Angela Douglas</td>\n", " <td>Attendant to Gertrude</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Rowena King</td>\n", " <td>Attendant to Gertrude</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Sarah Lam</td>\n", " <td>Attendant to Gertrude</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Eileen Herlie</td>\n", " <td>Gertrude - The Queen</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>1973</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Becke Keller</td>\n", " <td>Gertrude, Queen of Denmark</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1948</th>\n", " <th>actress</th>\n", " <td>Hamlet</td>\n", " <td>Jean Simmons</td>\n", " <td>Ophelia - and Daughter</td>\n", " <td>17</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>299 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " title name character n\n", "year type \n", "1964 actress Hamlet Kate Beswick Lady NaN\n", " actress Hamlet Carol Teitel Lady NaN\n", "2000 actor Hamlet Paul Ferriter Special Guest Appearance 23\n", " actor Hamlet Paul Graham (IV) Special Guest Appearance 27\n", " actor Hamlet Henry Griffin (II) Special Guest Appearance 28\n", " actor Hamlet Ayun Halliday Special Guest Appearance 30\n", " actor Hamlet Greg Kotis Special Guest Appearance 31\n", " actor Hamlet Barry Manasch Special Guest Appearance 32\n", " actor Hamlet Philip McKenney Special Guest Appearance 33\n", " actor Hamlet Colin Puth Special Guest Appearance 35\n", " actor Hamlet Giancarlo Roma Special Guest Appearance 37\n", " actor Hamlet Thomas Roma Special Guest Appearance 38\n", " actress Hamlet D.J. Dara Special Guest Appearance 21\n", " actress Hamlet Sinead Dolan Special Guest Appearance 22\n", " actress Hamlet Sarah Fiol Special Guest Appearance 25\n", " actress Hamlet Tanya Gingerich Special Guest Appearance 26\n", " actress Hamlet Anne Nixon (II) Special Guest Appearance 34\n", " actress Hamlet India Reed Kotis Special Guest Appearance 29\n", "1964 actor Hamlet Richard Sterne Gentleman NaN\n", "1969 actor Hamlet Roger Livesey First Player 11\n", "1996 actor Hamlet Ben Thom First Player 42\n", "1948 actor Hamlet Harcourt Williams First Player 5\n", "2009 actor Hamlet Matt Hurley (II) Bernardo 10\n", "1948 actor Hamlet Esmond Knight Bernardo 2\n", "1913 actor Hamlet G. Richards (II) Bernardo 12\n", "1990 actor Hamlet Richard Warwick Bernardo 13\n", "1964 actor Hamlet Frederick Young Bernardo NaN\n", "2009 actor Hamlet Hayden Adams Laertes 7\n", "1964 actor Hamlet John Cullum Laertes NaN\n", "1921 actor Hamlet Anton De Verdier Laertes 7\n", "... ... ... ... ..\n", "1948 actress Hamlet Patricia Davidson (II) Lady of the Court NaN\n", "1964 actor Hamlet Dillon Evans Fortinbras's Captain NaN\n", "1996 actor Hamlet Jeffery Kissoon Fortinbras's Captain 27\n", " actor Hamlet John Spencer-Churchill Fortinbras's Captain 32\n", "2015 actress Hamlet Gillian Bevan Polonia 4\n", "1969 actress Hamlet Anjelica Huston Court Lady 18\n", "1996 actor Hamlet David Yip Sailor One 47\n", " actor Hamlet Thomas Szekeres Young Hamlet 41\n", "1990 actor Hamlet Barrie Holland Palace Nobleman NaN\n", "1964 actor Hamlet John Hetherington Player Prologue NaN\n", "1973 actor Hamlet Dan Hennessey Claudius, King of Denmark NaN\n", "1948 actor Hamlet Christopher Lee Spear Carrier NaN\n", "1996 actor Hamlet Anthony Maddalena Elsinore Courtier NaN\n", "2000 actor Hamlet John Wills Martin Claudius' Bodyguard 18\n", "1948 actor Hamlet Terence Morgan (II) Laertes - His Son 16\n", "1996 actor Hamlet John Mills Old Norway 33\n", " actor Hamlet Jimi Mistry Sailor Two 34\n", "1948 actor Hamlet Laurence Olivier Voice of Ghost 13\n", "1913 actor Hamlet Montagu Rutherford Rosencrants 8\n", "1969 actress Hamlet Jennifer Tudor Court lady 23\n", "1948 actor Hamlet Basil Sydney Claudius - The King 11\n", " actor Hamlet Norman Wooland Horatio - His Friend 14\n", "1996 actress Hamlet Judi Dench Hecuba 12\n", "1921 actress Hamlet Mathilde Brandt Königin Gertrude 3\n", "1996 actress Hamlet Angela Douglas Attendant to Gertrude 16\n", " actress Hamlet Rowena King Attendant to Gertrude 26\n", " actress Hamlet Sarah Lam Attendant to Gertrude 28\n", "1948 actress Hamlet Eileen Herlie Gertrude - The Queen 12\n", "1973 actress Hamlet Becke Keller Gertrude, Queen of Denmark NaN\n", "1948 actress Hamlet Jean Simmons Ophelia - and Daughter 17\n", "\n", "[299 rows x 4 columns]" ] }, "execution_count": 239, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cast\n", "c = c[c.title == 'Hamlet']\n", "c = c.set_index(['year', 'type'])#.unstack('type')\n", "c" ] }, { "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": 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": null, "metadata": { "collapsed": true }, "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": 0 }
mit
pligor/predicting-future-product-prices
04_time_series_prediction/24_price_history_seq2seq-full_dataset_testing.ipynb
2
100225
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding: UTF-8 -*-\n", "#%load_ext autoreload\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from __future__ import division\n", "import tensorflow as tf\n", "from os import path, remove\n", "import numpy as np\n", "import pandas as pd\n", "import csv\n", "from sklearn.model_selection import StratifiedShuffleSplit\n", "from time import time\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "from mylibs.jupyter_notebook_helper import show_graph, renderStatsList, renderStatsCollection, \\\n", " renderStatsListWithLabels, renderStatsCollectionOfCrossValids, plot_res_gp, my_plot_convergence\n", "from tensorflow.contrib import rnn\n", "from tensorflow.contrib import learn\n", "import shutil\n", "from tensorflow.contrib.learn.python.learn import learn_runner\n", "from mylibs.tf_helper import getDefaultGPUconfig\n", "from sklearn.metrics import r2_score\n", "from mylibs.py_helper import factors\n", "from fastdtw import fastdtw\n", "from collections import OrderedDict\n", "from scipy.spatial.distance import euclidean\n", "from statsmodels.tsa.stattools import coint\n", "from common import get_or_run_nn\n", "from data_providers.price_history_seq2seq_data_provider import PriceHistorySeq2SeqDataProvider\n", "from data_providers.price_history_dataset_generator import PriceHistoryDatasetGenerator\n", "from sklearn.metrics import mean_squared_error\n", "from skopt.space.space import Integer, Real\n", "from skopt import gp_minimize\n", "from skopt.plots import plot_convergence\n", "import pickle\n", "import inspect\n", "import dill\n", "import sys\n", "from models.model_21_price_history_seq2seq_dyn_dec_ins import PriceHistorySeq2SeqDynDecIns\n", "from gp_opt.price_history_23_gp_opt import PriceHistory23GpOpt\n", "from os.path import isdir\n", "from cost_functions.huber_loss import huber_loss" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dtype = tf.float32\n", "seed = 16011984\n", "random_state = np.random.RandomState(seed=seed)\n", "config = getDefaultGPUconfig()\n", "n_jobs = 1\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 0 - hyperparams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "vocab_size is all the potential words you could have (classification for translation case)\n", "and max sequence length are the SAME thing\n", "\n", "decoder RNN hidden units are usually same size as encoder RNN hidden units in translation but for our case it does not seem really to be a relationship there but we can experiment and find out later, not a priority thing right now" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_units = 400 #state size\n", "\n", "input_len = 60\n", "target_len = 30\n", "\n", "batch_size = 64\n", "with_EOS = False" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "total_size = 57994\n", "train_size = 46400\n", "test_size = 11584" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Once generate data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_folder = '../../../../Dropbox/data'\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ph_data_path = '../data/price_history'" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "npz_full = ph_data_path + '/price_history_dp_60to30_57994.npz'\n", "npz_train = ph_data_path + '/price_history_dp_60to30_57994_46400_train.npz'\n", "npz_test = ph_data_path + '/price_history_dp_60to30_57994_11584_test.npz'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1 - collect data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# dp = PriceHistorySeq2SeqDataProvider(npz_path=npz_train, batch_size=batch_size, with_EOS=with_EOS)\n", "# dp.inputs.shape, dp.targets.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# aa, bb = dp.next()\n", "# aa.shape, bb.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2 - Build model" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "model = PriceHistorySeq2SeqDynDecIns(rng=random_state, dtype=dtype, config=config, with_EOS=with_EOS)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# graph = model.getGraph(batch_size=batch_size,\n", "# num_units=num_units,\n", "# input_len=input_len,\n", "# target_len=target_len)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#show_graph(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3 training the network" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "best_params = [500,\n", " tf.nn.tanh,\n", " 0.0001,\n", " 0.62488034788862112,\n", " 0.001]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_units, activation, lamda2, keep_prob_input, learning_rate = best_params" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_size" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def experiment():\n", " return model.run(npz_path=npz_train,\n", " npz_test = npz_test,\n", " epochs=100,\n", " batch_size = batch_size,\n", " num_units = num_units,\n", " input_len=input_len,\n", " target_len=target_len,\n", " learning_rate = learning_rate,\n", " preds_gather_enabled=True,\n", " batch_norm_enabled = True,\n", " activation = activation,\n", " decoder_first_input = PriceHistorySeq2SeqDynDecIns.DECODER_FIRST_INPUT.ZEROS,\n", " keep_prob_input = keep_prob_input,\n", " lamda2 = lamda2,\n", " )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#%%time\n", "dyn_stats, preds_dict, targets = get_or_run_nn(experiment, filename='024_seq2seq_60to30_002',\n", " nn_runs_folder= data_folder + '/nn_runs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One epoch takes approximately 268 secs\n", "If we want to let it run for ~8 hours = 8 * 3600 / 268 ~= 107 epochs\n", "So let it run for 100 epochs and see how it behaves" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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SW53JK+5EpA8I8ri5YuoQXli6m18u2EhkWBDjM+KZkJnImGEDCAnu3tdEioiI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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f497540eb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dyn_stats.plotStats()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_len = len(targets)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mses = np.empty(data_len)\n", "for ii, (pred, target) in enumerate(zip(preds_dict.values(), targets.values())):\n", " mses[ii] = mean_squared_error(pred, target)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0064100403901840011" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(mses)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "huber_losses = np.empty(data_len)\n", "for ii, (pred, target) in enumerate(zip(preds_dict.values(), targets.values())):\n", " huber_losses[ii] = np.mean(huber_loss(pred, target))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0032050201950920005" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(huber_losses)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(11584, 30)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "targets_arr = np.array(targets.values())\n", "targets_arr.shape" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(11584, 30)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preds_arr = np.array(preds_dict.values())\n", "preds_arr.shape" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.003205020195092001" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(huber_loss(y_true=targets_arr, y_pred=preds_arr))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r2_scores = [r2_score(y_true=targets[ind], y_pred=preds_dict[ind])\n", " for ind in range(len(targets))]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8836" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ind = np.argmin(r2_scores)\n", "ind" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reals = targets[ind]\n", "preds = preds_dict[ind]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2.1755069904281949e+31" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(y_true=reals, y_pred=preds)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#sns.tsplot(data=dp.inputs[ind].flatten())" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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DVuzx/uOZcfUO96hzuFNdQ9lH59DU5S51Dneqc7BDXUOdeq7LFnz30mJ/sSpC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\n6AXk0AvIRz8sb/2j/Wrpz87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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49753d5b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15,6))\n", "plt.plot(reals, 'b')\n", "plt.plot(preds, 'g')\n", "plt.legend(['reals','preds'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 24.8 s, sys: 112 ms, total: 24.9 s\n", "Wall time: 24.7 s\n" ] } ], "source": [ "%%time\n", "dtw_scores = [fastdtw(targets[ind], preds_dict[ind])[0]\n", " for ind in range(len(targets))]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.1534351983616165" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(dtw_scores)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-1.9885027565779438,\n", " 0.53454911650336534,\n", " array([-4.31395736, -3.55493606, -3.19393252]))" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coint(preds, reals)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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T1Iuvz70thx/9eu+hPWTvWM6y7UuPJHhZZO9YzpK8jD+dV6tcbRqfMIpXpWxV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\nON2RI7B5s72WSgUeYkNaQjq3lNzOLSW3EwwGsQ5t5HvP2eF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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4970d45d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cur_ind = np.random.randint(len(targets))\n", "reals = targets[cur_ind]\n", "preds = preds_dict[cur_ind]\n", "fig = plt.figure(figsize=(15,6))\n", "plt.plot(reals, 'b')\n", "plt.plot(preds, 'g')\n", "plt.legend(['reals','preds'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusion\n", "We have managed to make it work. We have done better in MSE and Huber Loss metrics but the DTW is still comparable with the baseline and a little above it. We need to add more information to the model in order to improve it" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
agpl-3.0
versae/DH2304
class06.ipynb
1
269729
{ "metadata": { "name": "", "signature": "sha256:67d77d14b77e2ab47288c369cce432a58ccc63bc83e77840a1584bdbfaf0c09e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"center\">\n", "<h1>[Data, the Humanist's New Best Friend](index.ipynb)<br/>*Class 06*</h1>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this class you are expected to learn:\n", "\n", "1. Pandas\n", "2. Cleaning data\n", "3. Summary statistics\n", "4. Indexing\n", "5. Merging, joining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"center\">\n", "<img src=\"files/images/pandas.jpg\" width=\"350\">\n", "`import pandas as pd`\n", "</div>" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For any of you that have heard about R, a statistics-oriented language, Pandas is the *R for Python*. It provides easy to use data structures and a ton of useful helper functions for data cleanup and transformations. It's fast because is backed by Numpy arrays, and integrates pretty well with other Python libraries such as `scikit-learn` for statistical learning, or `matplotlib` for plotting.\n", "\n", "Pandas provides a key data structure: the `pandas.DataFrame`; which is like a super rich 2D array that addresses three deficiencies of arrays:\n", "- They hold heterogenous data; each column can have its own `numpy.dtype`, so can have, in the same row, numbers, strings, and dates.\n", "- The axes of a `DataFrame` are labeled with column names and row indices, what makes easier slicing and indexing.\n", "- They account for missing values, which is not directly supported by arrays, in the form or `pandas.na` or `NaN`, that stands for \"not a number\".\n", "\n", "Data frames are extremely useful for data munging. They provide a large range of operations such as filter, join, and group-by aggregation. Furthermore, `pandas.Series` is to an 1D array what `pandas.DataFrame` is to a 2d array." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np # we will need it\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Read data with Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas is able to read data from a bunch of different data formats: CSV, TSV, Excel, HDFS, JSON, etc. The whole list is in the [online documentation](http://pandas.pydata.org/pandas-docs/version/0.13.1/io.html). In order to read data from a CSV file, we use the `read_csv()` function that returns a data frame. By default, it assumes that the fields are comma-separated, but it has parameters to tune the behaviour of the importer.\n", "\n", "We're going to use some datasets from [Julia Evans' Pandas Cookbok](https://github.com/jvns/pandas-cookbook). The first one is about cyclist data from Montr\u00e9al in 2012.\n", "\n", "This [bikes dataset](data/bikes.csv) is a list of how many people were on 7 different bike paths in Montr\u00e9al, each day." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df = pd.read_csv('data/bikes.csv') # We add _df 'cause it's a data frame" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "UnicodeDecodeError", "evalue": "'utf-8' codec can't decode byte 0xe9 in position 15: invalid continuation byte", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-2-e54169c3d1a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbikes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/bikes.csv'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# We add _df 'cause it's a data frame\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[0;32m 463\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 465\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 466\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 467\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 555\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 559\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 692\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 693\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 694\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 695\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 696\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1059\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1061\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1062\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1063\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/parser.cpython-34m.so\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4710)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32m/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/parser.cpython-34m.so\u001b[0m in \u001b[0;36mpandas.parser.TextReader._get_header (pandas/parser.c:6438)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 15: invalid continuation byte" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hey! We know that error! It's an encoding error probably caused by French accents. Fortunately, `read_csv()` allows to pass the encoding as an argument." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df = pd.read_csv('data/bikes.csv', encoding=\"latin1\")\n", "bikes_df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date;Berri 1;Br\u00e9beuf (donn\u00e9es non disponibles);C\u00f4te-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (donn\u00e9es non disponibles)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 01/01/2012;35;;0;38;51;26;10;16;</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 02/01/2012;83;;1;68;153;53;6;43;</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 03/01/2012;135;;2;104;248;89;3;58;</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 04/01/2012;144;;1;116;318;111;8;61;</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 05/01/2012;197;;2;124;330;97;13;95;</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 06/01/2012;146;;0;98;244;86;4;75;</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 07/01/2012;98;;2;80;108;53;6;54;</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 08/01/2012;95;;1;62;98;64;11;63;</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 09/01/2012;244;;2;165;432;198;12;173;</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 10/01/2012;397;;3;238;563;275;18;241;</td>\n", " </tr>\n", " <tr>\n", " <th>10 </th>\n", " <td> 11/01/2012;273;;0;182;443;258;12;194;</td>\n", " </tr>\n", " <tr>\n", " <th>11 </th>\n", " <td> 12/01/2012;157;;1;134;261;137;9;63;</td>\n", " </tr>\n", " <tr>\n", " <th>12 </th>\n", " <td> 13/01/2012;75;;0;41;105;64;2;0;</td>\n", " </tr>\n", " <tr>\n", " <th>13 </th>\n", " <td> 14/01/2012;32;;0;54;56;19;0;1;</td>\n", " </tr>\n", " <tr>\n", " <th>14 </th>\n", " <td> 15/01/2012;54;;0;33;60;18;0;0;</td>\n", " </tr>\n", " <tr>\n", " <th>15 </th>\n", " <td> 16/01/2012;168;;2;136;312;137;1;0</td>\n", " </tr>\n", " <tr>\n", " <th>16 </th>\n", " <td> 17/01/2012;155;;0;86;256;74;0;0</td>\n", " </tr>\n", " <tr>\n", " <th>17 </th>\n", " <td> 18/01/2012;139;;0;66;188;68;3;0</td>\n", " </tr>\n", " <tr>\n", " <th>18 </th>\n", " <td> 19/01/2012;191;;1;104;248;79;3;0</td>\n", " </tr>\n", " <tr>\n", " <th>19 </th>\n", " <td> 20/01/2012;161;;4;96;217;67;1;1</td>\n", " </tr>\n", " <tr>\n", " <th>20 </th>\n", " <td> 21/01/2012;53;;0;47;70;32;1;0</td>\n", " </tr>\n", " <tr>\n", " <th>21 </th>\n", " <td> 22/01/2012;71;;0;41;73;35;5;0</td>\n", " </tr>\n", " <tr>\n", " <th>22 </th>\n", " <td> 23/01/2012;210;;6;114;357;91;6;0</td>\n", " </tr>\n", " <tr>\n", " <th>23 </th>\n", " <td> 24/01/2012;299;;1;189;444;174;4;0</td>\n", " </tr>\n", " <tr>\n", " <th>24 </th>\n", " <td> 25/01/2012;334;;1;217;453;180;4;0</td>\n", " </tr>\n", " <tr>\n", " <th>25 </th>\n", " <td> 26/01/2012;306;;0;215;495;191;0;1</td>\n", " </tr>\n", " <tr>\n", " <th>26 </th>\n", " <td> 27/01/2012;91;;5;79;204;65;0;0</td>\n", " </tr>\n", " <tr>\n", " <th>27 </th>\n", " <td> 28/01/2012;80;;1;61;123;33;9;1</td>\n", " </tr>\n", " <tr>\n", " <th>28 </th>\n", " <td> 29/01/2012;87;;1;65;132;40;7;0</td>\n", " </tr>\n", " <tr>\n", " <th>29 </th>\n", " <td> 30/01/2012;219;;0;146;371;152;2;0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>280</th>\n", " <td> 07/10/2012;1580;;660;922;1629;860;695;2052</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td> 08/10/2012;1854;;880;987;1818;1040;1115;2502</td>\n", " </tr>\n", " <tr>\n", " <th>282</th>\n", " <td> 09/10/2012;4787;;2210;3026;5138;3418;927;4078</td>\n", " </tr>\n", " <tr>\n", " <th>283</th>\n", " <td> 10/10/2012;3115;;1537;2081;3681;2608;560;2703</td>\n", " </tr>\n", " <tr>\n", " <th>284</th>\n", " <td> 11/10/2012;3746;;1857;2569;4694;3034;558;3457</td>\n", " </tr>\n", " <tr>\n", " <th>285</th>\n", " <td> 12/10/2012;3169;;1460;2261;4045;2564;448;3224</td>\n", " </tr>\n", " <tr>\n", " <th>286</th>\n", " <td> 13/10/2012;1783;;802;1205;2113;1183;681;2309</td>\n", " </tr>\n", " <tr>\n", " <th>287</th>\n", " <td> 14/10/2012;587;;287;443;852;503;65;952</td>\n", " </tr>\n", " <tr>\n", " <th>288</th>\n", " <td> 15/10/2012;3292;;1678;2165;4197;2754;560;3183</td>\n", " </tr>\n", " <tr>\n", " <th>289</th>\n", " <td> 16/10/2012;3739;;1858;2684;4681;2997;554;3593</td>\n", " </tr>\n", " <tr>\n", " <th>290</th>\n", " <td> 17/10/2012;4098;;1964;2645;4836;3063;728;3834</td>\n", " </tr>\n", " <tr>\n", " <th>291</th>\n", " <td> 18/10/2012;4671;;2292;3129;5542;3477;1108;4245</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td> 19/10/2012;1313;;597;885;1668;1209;111;1486</td>\n", " </tr>\n", " <tr>\n", " <th>293</th>\n", " <td> 20/10/2012;2011;;748;1323;2266;1213;797;2243</td>\n", " </tr>\n", " <tr>\n", " <th>294</th>\n", " <td> 21/10/2012;1277;;609;869;1777;898;242;1648</td>\n", " </tr>\n", " <tr>\n", " <th>295</th>\n", " <td> 22/10/2012;3650;;1819;2495;4800;3023;757;3721</td>\n", " </tr>\n", " <tr>\n", " <th>296</th>\n", " <td> 23/10/2012;4177;;1997;2795;5216;3233;795;3554</td>\n", " </tr>\n", " <tr>\n", " <th>297</th>\n", " <td> 24/10/2012;3744;;1868;2625;4900;3035;649;3622</td>\n", " </tr>\n", " <tr>\n", " <th>298</th>\n", " <td> 25/10/2012;3735;;1815;2528;5010;3017;631;3767</td>\n", " </tr>\n", " <tr>\n", " <th>299</th>\n", " <td> 26/10/2012;4290;;1987;2754;5246;3000;1456;4578</td>\n", " </tr>\n", " <tr>\n", " <th>300</th>\n", " <td> 27/10/2012;1857;;792;1244;2461;1193;618;2471</td>\n", " </tr>\n", " <tr>\n", " <th>301</th>\n", " <td> 28/10/2012;1310;;697;910;1776;955;387;1876</td>\n", " </tr>\n", " <tr>\n", " <th>302</th>\n", " <td> 29/10/2012;2919;;1458;2071;3768;2440;411;2795</td>\n", " </tr>\n", " <tr>\n", " <th>303</th>\n", " <td> 30/10/2012;2887;;1251;2007;3516;2255;338;2790</td>\n", " </tr>\n", " <tr>\n", " <th>304</th>\n", " <td> 31/10/2012;2634;;1294;1835;3453;2220;245;2570</td>\n", " </tr>\n", " <tr>\n", " <th>305</th>\n", " <td> 01/11/2012;2405;;1208;1701;3082;2076;165;2461</td>\n", " </tr>\n", " <tr>\n", " <th>306</th>\n", " <td> 02/11/2012;1582;;737;1109;2277;1392;97;1888</td>\n", " </tr>\n", " <tr>\n", " <th>307</th>\n", " <td> 03/11/2012;844;;380;612;1137;713;105;1302</td>\n", " </tr>\n", " <tr>\n", " <th>308</th>\n", " <td> 04/11/2012;966;;446;710;1277;692;197;1374</td>\n", " </tr>\n", " <tr>\n", " <th>309</th>\n", " <td> 05/11/2012;2247;;1170;1705;3221;2143;179;2430</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>310 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " Date;Berri 1;Br\u00e9beuf (donn\u00e9es non disponibles);C\u00f4te-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (donn\u00e9es non disponibles)\n", "0 01/01/2012;35;;0;38;51;26;10;16; \n", "1 02/01/2012;83;;1;68;153;53;6;43; \n", "2 03/01/2012;135;;2;104;248;89;3;58; \n", "3 04/01/2012;144;;1;116;318;111;8;61; \n", "4 05/01/2012;197;;2;124;330;97;13;95; \n", "5 06/01/2012;146;;0;98;244;86;4;75; \n", "6 07/01/2012;98;;2;80;108;53;6;54; \n", "7 08/01/2012;95;;1;62;98;64;11;63; \n", "8 09/01/2012;244;;2;165;432;198;12;173; \n", "9 10/01/2012;397;;3;238;563;275;18;241; \n", "10 11/01/2012;273;;0;182;443;258;12;194; \n", "11 12/01/2012;157;;1;134;261;137;9;63; \n", "12 13/01/2012;75;;0;41;105;64;2;0; \n", "13 14/01/2012;32;;0;54;56;19;0;1; \n", "14 15/01/2012;54;;0;33;60;18;0;0; \n", "15 16/01/2012;168;;2;136;312;137;1;0 \n", "16 17/01/2012;155;;0;86;256;74;0;0 \n", "17 18/01/2012;139;;0;66;188;68;3;0 \n", "18 19/01/2012;191;;1;104;248;79;3;0 \n", "19 20/01/2012;161;;4;96;217;67;1;1 \n", "20 21/01/2012;53;;0;47;70;32;1;0 \n", "21 22/01/2012;71;;0;41;73;35;5;0 \n", "22 23/01/2012;210;;6;114;357;91;6;0 \n", "23 24/01/2012;299;;1;189;444;174;4;0 \n", "24 25/01/2012;334;;1;217;453;180;4;0 \n", "25 26/01/2012;306;;0;215;495;191;0;1 \n", "26 27/01/2012;91;;5;79;204;65;0;0 \n", "27 28/01/2012;80;;1;61;123;33;9;1 \n", "28 29/01/2012;87;;1;65;132;40;7;0 \n", "29 30/01/2012;219;;0;146;371;152;2;0 \n", ".. ... \n", "280 07/10/2012;1580;;660;922;1629;860;695;2052 \n", "281 08/10/2012;1854;;880;987;1818;1040;1115;2502 \n", "282 09/10/2012;4787;;2210;3026;5138;3418;927;4078 \n", "283 10/10/2012;3115;;1537;2081;3681;2608;560;2703 \n", "284 11/10/2012;3746;;1857;2569;4694;3034;558;3457 \n", "285 12/10/2012;3169;;1460;2261;4045;2564;448;3224 \n", "286 13/10/2012;1783;;802;1205;2113;1183;681;2309 \n", "287 14/10/2012;587;;287;443;852;503;65;952 \n", "288 15/10/2012;3292;;1678;2165;4197;2754;560;3183 \n", "289 16/10/2012;3739;;1858;2684;4681;2997;554;3593 \n", "290 17/10/2012;4098;;1964;2645;4836;3063;728;3834 \n", "291 18/10/2012;4671;;2292;3129;5542;3477;1108;4245 \n", "292 19/10/2012;1313;;597;885;1668;1209;111;1486 \n", "293 20/10/2012;2011;;748;1323;2266;1213;797;2243 \n", "294 21/10/2012;1277;;609;869;1777;898;242;1648 \n", "295 22/10/2012;3650;;1819;2495;4800;3023;757;3721 \n", "296 23/10/2012;4177;;1997;2795;5216;3233;795;3554 \n", "297 24/10/2012;3744;;1868;2625;4900;3035;649;3622 \n", "298 25/10/2012;3735;;1815;2528;5010;3017;631;3767 \n", "299 26/10/2012;4290;;1987;2754;5246;3000;1456;4578 \n", "300 27/10/2012;1857;;792;1244;2461;1193;618;2471 \n", "301 28/10/2012;1310;;697;910;1776;955;387;1876 \n", "302 29/10/2012;2919;;1458;2071;3768;2440;411;2795 \n", "303 30/10/2012;2887;;1251;2007;3516;2255;338;2790 \n", "304 31/10/2012;2634;;1294;1835;3453;2220;245;2570 \n", "305 01/11/2012;2405;;1208;1701;3082;2076;165;2461 \n", "306 02/11/2012;1582;;737;1109;2277;1392;97;1888 \n", "307 03/11/2012;844;;380;612;1137;713;105;1302 \n", "308 04/11/2012;966;;446;710;1277;692;197;1374 \n", "309 05/11/2012;2247;;1170;1705;3221;2143;179;2430 \n", "\n", "[310 rows x 1 columns]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that this is totally broken! Fortunately, `read_csv()` has a bunch of more options that will let us fix that, though. Here we'll:\n", "- Change the column separator to a `;`\n", "- Parse the dates in the `Date` column\n", "- Tell it that our dates have the date first instead of the month first\n", "- Set the index to be the `Date` column" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df = pd.read_csv(\n", " 'data/bikes.csv',\n", " sep=';',\n", " encoding='latin1',\n", " parse_dates=['Date'],\n", " dayfirst=True,\n", " index_col='Date'\n", ")\n", "bikes_df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>Br\u00e9beuf (donn\u00e9es non disponibles)</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " <th>St-Urbain (donn\u00e9es non disponibles)</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td> 38</td>\n", " <td> 51</td>\n", " <td> 26</td>\n", " <td> 10</td>\n", " <td> 16</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 68</td>\n", " <td> 153</td>\n", " <td> 53</td>\n", " <td> 6</td>\n", " <td> 43</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td>NaN</td>\n", " <td> 2</td>\n", " <td> 104</td>\n", " <td> 248</td>\n", " <td> 89</td>\n", " <td> 3</td>\n", " <td> 58</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " 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<td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-27</th>\n", " <td> 91</td>\n", " <td>NaN</td>\n", " <td> 5</td>\n", " <td> 79</td>\n", " <td> 204</td>\n", " <td> 65</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-28</th>\n", " <td> 80</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 61</td>\n", " <td> 123</td>\n", " <td> 33</td>\n", " <td> 9</td>\n", " <td> 1</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-29</th>\n", " <td> 87</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> 65</td>\n", " <td> 132</td>\n", " <td> 40</td>\n", " <td> 7</td>\n", " <td> 0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-30</th>\n", " <td> 219</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " <td> 146</td>\n", " <td> 371</td>\n", " <td> 152</td>\n", " <td> 2</td>\n", " <td> 0</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", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-07</th>\n", " <td> 1580</td>\n", " <td>NaN</td>\n", " <td> 660</td>\n", " <td> 922</td>\n", " <td> 1629</td>\n", " <td> 860</td>\n", " <td> 695</td>\n", " <td> 2052</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-08</th>\n", " <td> 1854</td>\n", " <td>NaN</td>\n", " <td> 880</td>\n", " <td> 987</td>\n", " <td> 1818</td>\n", " <td> 1040</td>\n", " <td> 1115</td>\n", " <td> 2502</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-09</th>\n", " <td> 4787</td>\n", " <td>NaN</td>\n", " <td> 2210</td>\n", " <td> 3026</td>\n", " <td> 5138</td>\n", " <td> 3418</td>\n", " <td> 927</td>\n", " <td> 4078</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-10</th>\n", " <td> 3115</td>\n", " <td>NaN</td>\n", " <td> 1537</td>\n", " <td> 2081</td>\n", " <td> 3681</td>\n", " <td> 2608</td>\n", " <td> 560</td>\n", " <td> 2703</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-11</th>\n", " <td> 3746</td>\n", " <td>NaN</td>\n", " <td> 1857</td>\n", " <td> 2569</td>\n", " <td> 4694</td>\n", " <td> 3034</td>\n", " <td> 558</td>\n", " <td> 3457</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-12</th>\n", " <td> 3169</td>\n", " <td>NaN</td>\n", " <td> 1460</td>\n", " <td> 2261</td>\n", " <td> 4045</td>\n", " <td> 2564</td>\n", " <td> 448</td>\n", " <td> 3224</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-13</th>\n", " <td> 1783</td>\n", " <td>NaN</td>\n", " <td> 802</td>\n", " <td> 1205</td>\n", " <td> 2113</td>\n", " <td> 1183</td>\n", " <td> 681</td>\n", " <td> 2309</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-14</th>\n", " <td> 587</td>\n", " <td>NaN</td>\n", " <td> 287</td>\n", " <td> 443</td>\n", " <td> 852</td>\n", " <td> 503</td>\n", " <td> 65</td>\n", " <td> 952</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-15</th>\n", " <td> 3292</td>\n", " <td>NaN</td>\n", " <td> 1678</td>\n", " <td> 2165</td>\n", " <td> 4197</td>\n", " <td> 2754</td>\n", " <td> 560</td>\n", " <td> 3183</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-16</th>\n", " <td> 3739</td>\n", " <td>NaN</td>\n", " <td> 1858</td>\n", " <td> 2684</td>\n", " <td> 4681</td>\n", " <td> 2997</td>\n", " <td> 554</td>\n", " <td> 3593</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-17</th>\n", " <td> 4098</td>\n", " <td>NaN</td>\n", " <td> 1964</td>\n", " <td> 2645</td>\n", " <td> 4836</td>\n", " <td> 3063</td>\n", " <td> 728</td>\n", " <td> 3834</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-18</th>\n", " <td> 4671</td>\n", " <td>NaN</td>\n", " <td> 2292</td>\n", " <td> 3129</td>\n", " <td> 5542</td>\n", " <td> 3477</td>\n", " <td> 1108</td>\n", " <td> 4245</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-19</th>\n", " <td> 1313</td>\n", " <td>NaN</td>\n", " <td> 597</td>\n", " <td> 885</td>\n", " <td> 1668</td>\n", " <td> 1209</td>\n", " <td> 111</td>\n", " <td> 1486</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-20</th>\n", " <td> 2011</td>\n", " <td>NaN</td>\n", " <td> 748</td>\n", " <td> 1323</td>\n", " <td> 2266</td>\n", " <td> 1213</td>\n", " <td> 797</td>\n", " <td> 2243</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-21</th>\n", " <td> 1277</td>\n", " <td>NaN</td>\n", " <td> 609</td>\n", " <td> 869</td>\n", " <td> 1777</td>\n", " <td> 898</td>\n", " <td> 242</td>\n", " <td> 1648</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-22</th>\n", " <td> 3650</td>\n", " <td>NaN</td>\n", " <td> 1819</td>\n", " <td> 2495</td>\n", " <td> 4800</td>\n", " <td> 3023</td>\n", " <td> 757</td>\n", " <td> 3721</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-23</th>\n", " <td> 4177</td>\n", " <td>NaN</td>\n", " <td> 1997</td>\n", " <td> 2795</td>\n", " <td> 5216</td>\n", " <td> 3233</td>\n", " <td> 795</td>\n", " <td> 3554</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-24</th>\n", " <td> 3744</td>\n", " <td>NaN</td>\n", " <td> 1868</td>\n", " <td> 2625</td>\n", " <td> 4900</td>\n", " <td> 3035</td>\n", " <td> 649</td>\n", " <td> 3622</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-25</th>\n", " <td> 3735</td>\n", " <td>NaN</td>\n", " <td> 1815</td>\n", " <td> 2528</td>\n", " <td> 5010</td>\n", " <td> 3017</td>\n", " <td> 631</td>\n", " <td> 3767</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-26</th>\n", " <td> 4290</td>\n", " <td>NaN</td>\n", " <td> 1987</td>\n", " <td> 2754</td>\n", " <td> 5246</td>\n", " <td> 3000</td>\n", " <td> 1456</td>\n", " <td> 4578</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-27</th>\n", " <td> 1857</td>\n", " <td>NaN</td>\n", " <td> 792</td>\n", " <td> 1244</td>\n", " <td> 2461</td>\n", " <td> 1193</td>\n", " <td> 618</td>\n", " <td> 2471</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-28</th>\n", " <td> 1310</td>\n", " <td>NaN</td>\n", " <td> 697</td>\n", " <td> 910</td>\n", " <td> 1776</td>\n", " <td> 955</td>\n", " <td> 387</td>\n", " <td> 1876</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-29</th>\n", " <td> 2919</td>\n", " <td>NaN</td>\n", " <td> 1458</td>\n", " <td> 2071</td>\n", " <td> 3768</td>\n", " <td> 2440</td>\n", " <td> 411</td>\n", " <td> 2795</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-30</th>\n", " <td> 2887</td>\n", " <td>NaN</td>\n", " <td> 1251</td>\n", " <td> 2007</td>\n", " <td> 3516</td>\n", " <td> 2255</td>\n", " <td> 338</td>\n", " <td> 2790</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-10-31</th>\n", " <td> 2634</td>\n", " <td>NaN</td>\n", " <td> 1294</td>\n", " <td> 1835</td>\n", " <td> 3453</td>\n", " <td> 2220</td>\n", " <td> 245</td>\n", " <td> 2570</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11-01</th>\n", " <td> 2405</td>\n", " <td>NaN</td>\n", " <td> 1208</td>\n", " <td> 1701</td>\n", " <td> 3082</td>\n", " <td> 2076</td>\n", " <td> 165</td>\n", " <td> 2461</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11-02</th>\n", " <td> 1582</td>\n", " <td>NaN</td>\n", " <td> 737</td>\n", " <td> 1109</td>\n", " <td> 2277</td>\n", " <td> 1392</td>\n", " <td> 97</td>\n", " <td> 1888</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11-03</th>\n", " <td> 844</td>\n", " <td>NaN</td>\n", " <td> 380</td>\n", " <td> 612</td>\n", " <td> 1137</td>\n", " <td> 713</td>\n", " <td> 105</td>\n", " <td> 1302</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11-04</th>\n", " <td> 966</td>\n", " <td>NaN</td>\n", " <td> 446</td>\n", " <td> 710</td>\n", " <td> 1277</td>\n", " <td> 692</td>\n", " <td> 197</td>\n", " <td> 1374</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2012-11-05</th>\n", " <td> 2247</td>\n", " <td>NaN</td>\n", " <td> 1170</td>\n", " <td> 1705</td>\n", " <td> 3221</td>\n", " <td> 2143</td>\n", " <td> 179</td>\n", " <td> 2430</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>310 rows \u00d7 9 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " Berri 1 Br\u00e9beuf (donn\u00e9es non disponibles) C\u00f4te-Sainte-Catherine \\\n", "Date \n", "2012-01-01 35 NaN 0 \n", "2012-01-02 83 NaN 1 \n", "2012-01-03 135 NaN 2 \n", "2012-01-04 144 NaN 1 \n", "2012-01-05 197 NaN 2 \n", "2012-01-06 146 NaN 0 \n", "2012-01-07 98 NaN 2 \n", "2012-01-08 95 NaN 1 \n", "2012-01-09 244 NaN 2 \n", "2012-01-10 397 NaN 3 \n", "2012-01-11 273 NaN 0 \n", "2012-01-12 157 NaN 1 \n", "2012-01-13 75 NaN 0 \n", "2012-01-14 32 NaN 0 \n", "2012-01-15 54 NaN 0 \n", "2012-01-16 168 NaN 2 \n", "2012-01-17 155 NaN 0 \n", "2012-01-18 139 NaN 0 \n", "2012-01-19 191 NaN 1 \n", "2012-01-20 161 NaN 4 \n", "2012-01-21 53 NaN 0 \n", "2012-01-22 71 NaN 0 \n", "2012-01-23 210 NaN 6 \n", "2012-01-24 299 NaN 1 \n", "2012-01-25 334 NaN 1 \n", "2012-01-26 306 NaN 0 \n", "2012-01-27 91 NaN 5 \n", "2012-01-28 80 NaN 1 \n", "2012-01-29 87 NaN 1 \n", "2012-01-30 219 NaN 0 \n", "... ... ... ... \n", "2012-10-07 1580 NaN 660 \n", "2012-10-08 1854 NaN 880 \n", "2012-10-09 4787 NaN 2210 \n", "2012-10-10 3115 NaN 1537 \n", "2012-10-11 3746 NaN 1857 \n", "2012-10-12 3169 NaN 1460 \n", "2012-10-13 1783 NaN 802 \n", "2012-10-14 587 NaN 287 \n", "2012-10-15 3292 NaN 1678 \n", "2012-10-16 3739 NaN 1858 \n", "2012-10-17 4098 NaN 1964 \n", "2012-10-18 4671 NaN 2292 \n", "2012-10-19 1313 NaN 597 \n", "2012-10-20 2011 NaN 748 \n", "2012-10-21 1277 NaN 609 \n", "2012-10-22 3650 NaN 1819 \n", "2012-10-23 4177 NaN 1997 \n", "2012-10-24 3744 NaN 1868 \n", "2012-10-25 3735 NaN 1815 \n", "2012-10-26 4290 NaN 1987 \n", "2012-10-27 1857 NaN 792 \n", "2012-10-28 1310 NaN 697 \n", "2012-10-29 2919 NaN 1458 \n", "2012-10-30 2887 NaN 1251 \n", "2012-10-31 2634 NaN 1294 \n", "2012-11-01 2405 NaN 1208 \n", "2012-11-02 1582 NaN 737 \n", "2012-11-03 844 NaN 380 \n", "2012-11-04 966 NaN 446 \n", "2012-11-05 2247 NaN 1170 \n", "\n", " Maisonneuve 1 Maisonneuve 2 du Parc Pierre-Dupuy Rachel1 \\\n", "Date \n", "2012-01-01 38 51 26 10 16 \n", "2012-01-02 68 153 53 6 43 \n", "2012-01-03 104 248 89 3 58 \n", "2012-01-04 116 318 111 8 61 \n", "2012-01-05 124 330 97 13 95 \n", "2012-01-06 98 244 86 4 75 \n", "2012-01-07 80 108 53 6 54 \n", "2012-01-08 62 98 64 11 63 \n", "2012-01-09 165 432 198 12 173 \n", "2012-01-10 238 563 275 18 241 \n", "2012-01-11 182 443 258 12 194 \n", "2012-01-12 134 261 137 9 63 \n", "2012-01-13 41 105 64 2 0 \n", "2012-01-14 54 56 19 0 1 \n", "2012-01-15 33 60 18 0 0 \n", "2012-01-16 136 312 137 1 0 \n", "2012-01-17 86 256 74 0 0 \n", "2012-01-18 66 188 68 3 0 \n", "2012-01-19 104 248 79 3 0 \n", "2012-01-20 96 217 67 1 1 \n", "2012-01-21 47 70 32 1 0 \n", "2012-01-22 41 73 35 5 0 \n", "2012-01-23 114 357 91 6 0 \n", "2012-01-24 189 444 174 4 0 \n", "2012-01-25 217 453 180 4 0 \n", "2012-01-26 215 495 191 0 1 \n", "2012-01-27 79 204 65 0 0 \n", "2012-01-28 61 123 33 9 1 \n", "2012-01-29 65 132 40 7 0 \n", "2012-01-30 146 371 152 2 0 \n", "... ... ... ... ... ... \n", "2012-10-07 922 1629 860 695 2052 \n", "2012-10-08 987 1818 1040 1115 2502 \n", "2012-10-09 3026 5138 3418 927 4078 \n", "2012-10-10 2081 3681 2608 560 2703 \n", "2012-10-11 2569 4694 3034 558 3457 \n", "2012-10-12 2261 4045 2564 448 3224 \n", "2012-10-13 1205 2113 1183 681 2309 \n", "2012-10-14 443 852 503 65 952 \n", "2012-10-15 2165 4197 2754 560 3183 \n", "2012-10-16 2684 4681 2997 554 3593 \n", "2012-10-17 2645 4836 3063 728 3834 \n", "2012-10-18 3129 5542 3477 1108 4245 \n", "2012-10-19 885 1668 1209 111 1486 \n", "2012-10-20 1323 2266 1213 797 2243 \n", "2012-10-21 869 1777 898 242 1648 \n", "2012-10-22 2495 4800 3023 757 3721 \n", "2012-10-23 2795 5216 3233 795 3554 \n", "2012-10-24 2625 4900 3035 649 3622 \n", "2012-10-25 2528 5010 3017 631 3767 \n", "2012-10-26 2754 5246 3000 1456 4578 \n", "2012-10-27 1244 2461 1193 618 2471 \n", "2012-10-28 910 1776 955 387 1876 \n", "2012-10-29 2071 3768 2440 411 2795 \n", "2012-10-30 2007 3516 2255 338 2790 \n", "2012-10-31 1835 3453 2220 245 2570 \n", "2012-11-01 1701 3082 2076 165 2461 \n", "2012-11-02 1109 2277 1392 97 1888 \n", "2012-11-03 612 1137 713 105 1302 \n", "2012-11-04 710 1277 692 197 1374 \n", "2012-11-05 1705 3221 2143 179 2430 \n", "\n", " St-Urbain (donn\u00e9es non disponibles) \n", "Date \n", "2012-01-01 NaN \n", "2012-01-02 NaN \n", "2012-01-03 NaN \n", "2012-01-04 NaN \n", "2012-01-05 NaN \n", "2012-01-06 NaN \n", "2012-01-07 NaN \n", "2012-01-08 NaN \n", "2012-01-09 NaN \n", "2012-01-10 NaN \n", "2012-01-11 NaN \n", "2012-01-12 NaN \n", "2012-01-13 NaN \n", "2012-01-14 NaN \n", "2012-01-15 NaN \n", "2012-01-16 NaN \n", "2012-01-17 NaN \n", "2012-01-18 NaN \n", "2012-01-19 NaN \n", "2012-01-20 NaN \n", "2012-01-21 NaN \n", "2012-01-22 NaN \n", "2012-01-23 NaN \n", "2012-01-24 NaN \n", "2012-01-25 NaN \n", "2012-01-26 NaN \n", "2012-01-27 NaN \n", "2012-01-28 NaN \n", "2012-01-29 NaN \n", "2012-01-30 NaN \n", "... ... \n", "2012-10-07 NaN \n", "2012-10-08 NaN \n", "2012-10-09 NaN \n", "2012-10-10 NaN \n", "2012-10-11 NaN \n", "2012-10-12 NaN \n", "2012-10-13 NaN \n", "2012-10-14 NaN \n", "2012-10-15 NaN \n", "2012-10-16 NaN \n", "2012-10-17 NaN \n", "2012-10-18 NaN \n", "2012-10-19 NaN \n", "2012-10-20 NaN \n", "2012-10-21 NaN \n", "2012-10-22 NaN \n", "2012-10-23 NaN \n", "2012-10-24 NaN \n", "2012-10-25 NaN \n", "2012-10-26 NaN \n", "2012-10-27 NaN \n", "2012-10-28 NaN \n", "2012-10-29 NaN \n", "2012-10-30 NaN \n", "2012-10-31 NaN \n", "2012-11-01 NaN \n", "2012-11-02 NaN \n", "2012-11-03 NaN \n", "2012-11-04 NaN \n", "2012-11-05 NaN \n", "\n", "[310 rows x 9 columns]" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's just remove missing columns" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Get rid of missing columns, don't worry, you'll see more on .dropna() later\n", "bikes_df = bikes_df.dropna(axis=1)\n", "bikes_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 0</td>\n", " <td> 38</td>\n", " <td> 51</td>\n", " <td> 26</td>\n", " <td> 10</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 1</td>\n", " <td> 68</td>\n", " <td> 153</td>\n", " <td> 53</td>\n", " <td> 6</td>\n", " <td> 43</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 2</td>\n", " <td> 104</td>\n", " <td> 248</td>\n", " <td> 89</td>\n", " <td> 3</td>\n", " <td> 58</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 1</td>\n", " <td> 116</td>\n", " <td> 318</td>\n", " <td> 111</td>\n", " <td> 8</td>\n", " <td> 61</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 2</td>\n", " <td> 124</td>\n", " <td> 330</td>\n", " <td> 97</td>\n", " <td> 13</td>\n", " <td> 95</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "Date \n", "2012-01-01 35 0 38 51 \n", "2012-01-02 83 1 68 153 \n", "2012-01-03 135 2 104 248 \n", "2012-01-04 144 1 116 318 \n", "2012-01-05 197 2 124 330 \n", "\n", " du Parc Pierre-Dupuy Rachel1 \n", "Date \n", "2012-01-01 26 10 16 \n", "2012-01-02 53 6 43 \n", "2012-01-03 89 3 58 \n", "2012-01-04 111 8 61 \n", "2012-01-05 97 13 95 " ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Way better!\n", "\n", "In the last example we added `head()` to show just the first rows. If you want the last ones, use the `tail()` function. Both can receive the number of rows to show, but defaults to 5.\n", "\n", "Pandas is also able to get data from the Internet without even batting an eye. The excellent book [*Introduction to Statistical Learning*](http://www-bcf.usc.edu/~gareth/ISL/) has one of its data sets available to download." ] }, { "cell_type": "code", "collapsed": false, "input": [ "autos_df = pd.read_csv(\"http://www-bcf.usc.edu/~gareth/ISL/Auto.csv\")\n", "autos_df.head() # print the first lines" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " <th>year</th>\n", " <th>origin</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 18</td>\n", " <td> 8</td>\n", " <td> 307</td>\n", " <td> 130</td>\n", " <td> 3504</td>\n", " <td> 12.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> chevrolet chevelle malibu</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 15</td>\n", " <td> 8</td>\n", " <td> 350</td>\n", " <td> 165</td>\n", " <td> 3693</td>\n", " <td> 11.5</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> buick skylark 320</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 18</td>\n", " <td> 8</td>\n", " <td> 318</td>\n", " <td> 150</td>\n", " <td> 3436</td>\n", " <td> 11.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> plymouth satellite</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 304</td>\n", " <td> 150</td>\n", " <td> 3433</td>\n", " <td> 12.0</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> amc rebel sst</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 17</td>\n", " <td> 8</td>\n", " <td> 302</td>\n", " <td> 140</td>\n", " <td> 3449</td>\n", " <td> 10.5</td>\n", " <td> 70</td>\n", " <td> 1</td>\n", " <td> ford torino</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " mpg cylinders displacement horsepower weight acceleration year \\\n", "0 18 8 307 130 3504 12.0 70 \n", "1 15 8 350 165 3693 11.5 70 \n", "2 18 8 318 150 3436 11.0 70 \n", "3 16 8 304 150 3433 12.0 70 \n", "4 17 8 302 140 3449 10.5 70 \n", "\n", " origin name \n", "0 1 chevrolet chevelle malibu \n", "1 1 buick skylark 320 \n", "2 1 plymouth satellite \n", "3 1 amc rebel sst \n", "4 1 ford torino " ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Slicing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `DataFrame` is made up of rows and columns. You get columns out of a `DataFrame` the same way you get elements out of a dictionary.\n", "\n", "Here's an example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df['Berri 1'].head()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "Date\n", "2012-01-01 35\n", "2012-01-02 83\n", "2012-01-03 135\n", "2012-01-04 144\n", "2012-01-05 197\n", "Name: Berri 1, dtype: int64" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For selecting several columns we use the same fancy indexing that we saw for Numpy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fancy_index = ['Berri 1', 'Rachel1']\n", "bikes_df[fancy_index].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 43</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 58</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 61</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 95</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " Berri 1 Rachel1\n", "Date \n", "2012-01-01 35 16\n", "2012-01-02 83 43\n", "2012-01-03 135 58\n", "2012-01-04 144 61\n", "2012-01-05 197 95" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But it's very common to just put the fancy index list as the key:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df[['Berri 1', 'Rachel1']].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 43</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 58</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 61</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 95</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " Berri 1 Rachel1\n", "Date \n", "2012-01-01 35 16\n", "2012-01-02 83 43\n", "2012-01-03 135 58\n", "2012-01-04 144 61\n", "2012-01-05 197 95" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For slicing rows the syntax is just as expected: the same that we use for lists." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df[['Berri 1', 'Rachel1']][:5]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 43</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 58</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 61</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 95</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " Berri 1 Rachel1\n", "Date \n", "2012-01-01 35 16\n", "2012-01-02 83 43\n", "2012-01-03 135 58\n", "2012-01-04 144 61\n", "2012-01-05 197 95" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df['Berri 1'][-5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "2405" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a `DataFrame` every column is a `Series`, so you can use the index to select specific items from the `Series`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri = bikes_df[['Berri 1']]\n", "berri.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " Berri 1\n", "Date \n", "2012-01-01 35\n", "2012-01-02 83\n", "2012-01-03 135\n", "2012-01-04 144\n", "2012-01-05 197" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or you can use boolean indexing for selection." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df[bikes_df['Berri 1'] < 40]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 0</td>\n", " <td> 38</td>\n", " <td> 51</td>\n", " <td> 26</td>\n", " <td> 10</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-14</th>\n", " <td> 32</td>\n", " <td> 0</td>\n", " <td> 54</td>\n", " <td> 56</td>\n", " <td> 19</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "Date \n", "2012-01-01 35 0 38 51 \n", "2012-01-14 32 0 54 56 \n", "\n", " du Parc Pierre-Dupuy Rachel1 \n", "Date \n", "2012-01-01 26 10 16 \n", "2012-01-14 19 0 1 " ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That last one might be a little weird, so let's make it more clear: `bikes_df['Berri 1'] < 40` returns a Series of `True`/`False` values, which we then pass to our DataFrame `bikes_df`, returning the corresponding `True` items." ] }, { "cell_type": "code", "collapsed": false, "input": [ "less_than_40 = bikes_df['Berri 1'] < 40\n", "print(less_than_40)\n", "bikes_df[less_than_40]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Date\n", "2012-01-01 True\n", "2012-01-02 False\n", "2012-01-03 False\n", "2012-01-04 False\n", "2012-01-05 False\n", "2012-01-06 False\n", "2012-01-07 False\n", "2012-01-08 False\n", "2012-01-09 False\n", "2012-01-10 False\n", "2012-01-11 False\n", "2012-01-12 False\n", "2012-01-13 False\n", "2012-01-14 True\n", "2012-01-15 False\n", "...\n", "2012-10-22 False\n", "2012-10-23 False\n", "2012-10-24 False\n", "2012-10-25 False\n", "2012-10-26 False\n", "2012-10-27 False\n", "2012-10-28 False\n", "2012-10-29 False\n", "2012-10-30 False\n", "2012-10-31 False\n", "2012-11-01 False\n", "2012-11-02 False\n", "2012-11-03 False\n", "2012-11-04 False\n", "2012-11-05 False\n", "Name: Berri 1, Length: 310\n" ] }, { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 0</td>\n", " <td> 38</td>\n", " <td> 51</td>\n", " <td> 26</td>\n", " <td> 10</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-14</th>\n", " <td> 32</td>\n", " <td> 0</td>\n", " <td> 54</td>\n", " <td> 56</td>\n", " <td> 19</td>\n", " <td> 0</td>\n", " <td> 1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "Date \n", "2012-01-01 35 0 38 51 \n", "2012-01-14 32 0 54 56 \n", "\n", " du Parc Pierre-Dupuy Rachel1 \n", "Date \n", "2012-01-01 26 10 16 \n", "2012-01-14 19 0 1 " ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "`Series` and `DataFrame` share the same indexing, slicing and selecting operations. What if you aren't sure whether an item is in the `Series`? You can check using idiomatic Python.\n", "\n", "Let's first create a new `Series` from a dictionary." ] }, { "cell_type": "code", "collapsed": false, "input": [ "d = {'Chicago': 1000, 'New York': 1300, 'Portland': 900, 'San Francisco': 1100,\n", " 'Austin': 450, 'Boston': None}\n", "cities = pd.Series(d)\n", "cities" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "Austin 450\n", "Boston NaN\n", "Chicago 1000\n", "New York 1300\n", "Portland 900\n", "San Francisco 1100\n", "dtype: float64" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "'Seattle' in cities" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "False" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "'San Francisco' in cities" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "True" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, it's also possible to get a specific row by using the index value and the `ix` property." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df.ix['2012-01-01']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "Berri 1 35\n", "C\u00f4te-Sainte-Catherine 0\n", "Maisonneuve 1 38\n", "Maisonneuve 2 51\n", "du Parc 26\n", "Pierre-Dupuy 10\n", "Rachel1 16\n", "Name: 2012-01-01 00:00:00, dtype: int64" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Cleaning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mathematical operations can be done using scalars and functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# divide city values by 3\n", "cities / 3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "Austin 150.000000\n", "Boston NaN\n", "Chicago 333.333333\n", "New York 433.333333\n", "Portland 300.000000\n", "San Francisco 366.666667\n", "dtype: float64" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# square city values\n", "np.square(cities)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "Austin 202500\n", "Boston NaN\n", "Chicago 1000000\n", "New York 1690000\n", "Portland 810000\n", "San Francisco 1210000\n", "dtype: float64" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what can we do with that `NaN` value? The easiest way to get rid of it is by removing it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cities.dropna()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "Austin 450\n", "Chicago 1000\n", "New York 1300\n", "Portland 900\n", "San Francisco 1100\n", "dtype: float64" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which is equivalent to do:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "cities[cities.notnull()]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "Austin 450\n", "Chicago 1000\n", "New York 1300\n", "Portland 900\n", "San Francisco 1100\n", "dtype: float64" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also fill the null data or even interpolate its value from the rest of the `Series`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cities.fillna(0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "Austin 450\n", "Boston 0\n", "Chicago 1000\n", "New York 1300\n", "Portland 900\n", "San Francisco 1100\n", "dtype: float64" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "cities.interpolate()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "Austin 450\n", "Boston 725\n", "Chicago 1000\n", "New York 1300\n", "Portland 900\n", "San Francisco 1100\n", "dtype: float64" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But null data might come in different ways. The next example illustrates the creation of a `DataFrame` from a dictionary. There are two blogs with a number of entries per tag. In this case, null data is represented by either the character `\"?\"` or the empty string `\"\"`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "blogs = pd.DataFrame({0: {'blog': 1, 'tag': 'GIS', 'entries': '?'},\n", " 1: {'blog': 1, 'tag': 'NLP', 'entries': 1638},\n", " 2: {'blog': 1, 'tag': 'SNA', 'entries': 569},\n", " 3: {'blog': 1, 'tag': 'DH', 'entries': 115},\n", " 4: {'blog': 2, 'tag': 'GIS', 'entries': ''},\n", " 5: {'blog': 2, 'tag': 'NLP', 'entries': 1130},\n", " 6: {'blog': 2, 'tag': 'SNA', 'entries': 754},\n", " 7: {'blog': 2, 'tag': 'DH', 'entries': 555}})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>blog</th>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 1</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>entries</th>\n", " <td> ?</td>\n", " <td> 1638</td>\n", " <td> 569</td>\n", " <td> 115</td>\n", " <td> </td>\n", " <td> 1130</td>\n", " <td> 754</td>\n", " <td> 555</td>\n", " </tr>\n", " <tr>\n", " <th>tag</th>\n", " <td> GIS</td>\n", " <td> NLP</td>\n", " <td> SNA</td>\n", " <td> DH</td>\n", " <td> GIS</td>\n", " <td> NLP</td>\n", " <td> SNA</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " 0 1 2 3 4 5 6 7\n", "blog 1 1 1 1 2 2 2 2\n", "entries ? 1638 569 115 1130 754 555\n", "tag GIS NLP SNA DH GIS NLP SNA DH" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs = blogs.T # We transpose it to swap rows by columns\n", "blogs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blog</th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> ?</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 1638</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 569</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 115</td>\n", " <td> DH</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 2</td>\n", " <td> </td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 2</td>\n", " <td> 1130</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 2</td>\n", " <td> 754</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 2</td>\n", " <td> 555</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " blog entries tag\n", "0 1 ? GIS\n", "1 1 1638 NLP\n", "2 1 569 SNA\n", "3 1 115 DH\n", "4 2 GIS\n", "5 2 1130 NLP\n", "6 2 754 SNA\n", "7 2 555 DH" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we wanted to remove those rows with null values, we can use the `na_values` parameter when reading the data from a CSV. For `DataFrames` that we already have, the process is more manual. For now, let's just ignore the warning :-)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "blogs[blogs.entries != \"?\"][blogs.entries != \"\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/versae/.venvs/dh2304/lib/python3.4/site-packages/pandas/core/frame.py:1808: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " \"DataFrame index.\", UserWarning)\n" ] }, { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blog</th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 1638</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 569</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 115</td>\n", " <td> DH</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 2</td>\n", " <td> 1130</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 2</td>\n", " <td> 754</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 2</td>\n", " <td> 555</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " blog entries tag\n", "1 1 1638 NLP\n", "2 1 569 SNA\n", "3 1 115 DH\n", "5 2 1130 NLP\n", "6 2 754 SNA\n", "7 2 555 DH" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In some cases, we might want to keep the rows, but fill them with real null data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "blogs['entries'] = blogs['entries'].replace(\"?\", np.nan)\n", "blogs['entries'].replace(\"\", np.nan, inplace=True)\n", "blogs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blog</th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 1638</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 569</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 115</td>\n", " <td> DH</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 2</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 2</td>\n", " <td> 1130</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 2</td>\n", " <td> 754</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 2</td>\n", " <td> 555</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " blog entries tag\n", "0 1 NaN GIS\n", "1 1 1638 NLP\n", "2 1 569 SNA\n", "3 1 115 DH\n", "4 2 NaN GIS\n", "5 2 1130 NLP\n", "6 2 754 SNA\n", "7 2 555 DH" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"font-size: 1em; margin: 1em 0 1em 0; border: 1px solid #86989B; background-color: #f7f7f7; padding: 0;\">\n", "<p style=\"margin: 0; padding: 0.1em 0 0.1em 0.5em; color: white; border-bottom: 1px solid #86989B; font-weight: bold; background-color: #AFC1C4;\">\n", "Activity\n", "</p>\n", "<p style=\"margin: 0.5em 1em 0.5em 1em; padding: 0;\">\n", "What do you think is the difference between these two lines?\n", "<br/>\n", "```\n", "blogs['entries'] = blogs['entries'].replace(\"?\", np.NaN)\n", "```\n", "<br/>\n", "```\n", "blogs['entries'].replace(\"?\", np.NaN, inplace=True)\n", "```\n", "</p>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the `Series` is of `str` type, we can even use almost the whole set of string functions in Python by accessing the attribute `.str`, and then any other string method we need." ] }, { "cell_type": "code", "collapsed": false, "input": [ "blogs.tag.str.lower()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "0 gis\n", "1 nlp\n", "2 sna\n", "3 dh\n", "4 gis\n", "5 nlp\n", "6 sna\n", "7 dh\n", "Name: tag, dtype: object" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But if the cleaning needs a more complicated operation, we can always `.apply()` a specific function to the whole `DataFrame` or `Series`, in order to perform that funcion over the individual values or cells." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def clean_func(value):\n", " if value == \"GIS\":\n", " return \"Geographical Information Systems\"\n", " elif value == \"NLP\":\n", " return \"Natural Languaje Processing\"\n", " elif value == \"DH\":\n", " return \"Digital Humanities\"\n", " elif value == \"SNA\":\n", " return \"Social Network Analysis\"\n", " else:\n", " return value\n", " return value\n", "\n", "blogs['tag'].apply(clean_func)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "0 Geographical Information Systems\n", "1 Natural Languaje Processing\n", "2 Social Network Analysis\n", "3 Digital Humanities\n", "4 Geographical Information Systems\n", "5 Natural Languaje Processing\n", "6 Social Network Analysis\n", "7 Digital Humanities\n", "Name: tag, dtype: object" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blog</th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 1638</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 569</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 115</td>\n", " <td> DH</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 2</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 2</td>\n", " <td> 1130</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 2</td>\n", " <td> 754</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 2</td>\n", " <td> 555</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ " blog entries tag\n", "0 1 NaN GIS\n", "1 1 1638 NLP\n", "2 1 569 SNA\n", "3 1 115 DH\n", "4 2 NaN GIS\n", "5 2 1130 NLP\n", "6 2 754 SNA\n", "7 2 555 DH" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Summary statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas provides nifty methods to understand your data. Let's look at the [describe, correlation, covariance, and correlation](http://pandas.pydata.org/pandas-docs/dev/computation.html) methods that you can use to quickly make sense of the data.\n", "\n", "The `describe()` method provides quick stats on all suitable columns." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " <td> 310.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 2985.048387</td>\n", " <td> 1233.351613</td>\n", " <td> 1983.325806</td>\n", " <td> 3510.261290</td>\n", " <td> 1862.983871</td>\n", " <td> 1054.306452</td>\n", " <td> 2873.483871</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 2169.271062</td>\n", " <td> 944.643188</td>\n", " <td> 1450.715170</td>\n", " <td> 2484.959789</td>\n", " <td> 1332.543266</td>\n", " <td> 1064.029205</td>\n", " <td> 2039.315504</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 32.000000</td>\n", " <td> 0.000000</td>\n", " <td> 33.000000</td>\n", " <td> 47.000000</td>\n", " <td> 18.000000</td>\n", " <td> 0.000000</td>\n", " <td> 0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 596.000000</td>\n", " <td> 243.250000</td>\n", " <td> 427.000000</td>\n", " <td> 831.000000</td>\n", " <td> 474.750000</td>\n", " <td> 53.250000</td>\n", " <td> 731.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 3128.000000</td>\n", " <td> 1269.000000</td>\n", " <td> 2019.500000</td>\n", " <td> 3688.500000</td>\n", " <td> 1822.500000</td>\n", " <td> 704.000000</td>\n", " <td> 3223.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 4973.250000</td>\n", " <td> 2003.000000</td>\n", " <td> 3168.250000</td>\n", " <td> 5731.750000</td>\n", " <td> 3069.000000</td>\n", " <td> 1818.500000</td>\n", " <td> 4717.250000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 7077.000000</td>\n", " <td> 3124.000000</td>\n", " <td> 4999.000000</td>\n", " <td> 8222.000000</td>\n", " <td> 4510.000000</td>\n", " <td> 4386.000000</td>\n", " <td> 6595.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "count 310.000000 310.000000 310.000000 310.000000 \n", "mean 2985.048387 1233.351613 1983.325806 3510.261290 \n", "std 2169.271062 944.643188 1450.715170 2484.959789 \n", "min 32.000000 0.000000 33.000000 47.000000 \n", "25% 596.000000 243.250000 427.000000 831.000000 \n", "50% 3128.000000 1269.000000 2019.500000 3688.500000 \n", "75% 4973.250000 2003.000000 3168.250000 5731.750000 \n", "max 7077.000000 3124.000000 4999.000000 8222.000000 \n", "\n", " du Parc Pierre-Dupuy Rachel1 \n", "count 310.000000 310.000000 310.000000 \n", "mean 1862.983871 1054.306452 2873.483871 \n", "std 1332.543266 1064.029205 2039.315504 \n", "min 18.000000 0.000000 0.000000 \n", "25% 474.750000 53.250000 731.000000 \n", "50% 1822.500000 704.000000 3223.500000 \n", "75% 3069.000000 1818.500000 4717.250000 \n", "max 4510.000000 4386.000000 6595.000000 " ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>entries</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 793.500000</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 528.321398</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 115.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 558.500000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 661.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 1036.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 1638.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ " entries\n", "count 6.000000\n", "mean 793.500000\n", "std 528.321398\n", "min 115.000000\n", "25% 558.500000\n", "50% 661.500000\n", "75% 1036.000000\n", "max 1638.000000" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "autos_df.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " <th>year</th>\n", " <th>origin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " <td> 397.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 23.515869</td>\n", " <td> 5.458438</td>\n", " <td> 193.532746</td>\n", " <td> 2970.261965</td>\n", " <td> 15.555668</td>\n", " <td> 75.994962</td>\n", " <td> 1.574307</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 7.825804</td>\n", " <td> 1.701577</td>\n", " <td> 104.379583</td>\n", " <td> 847.904119</td>\n", " <td> 2.749995</td>\n", " <td> 3.690005</td>\n", " <td> 0.802549</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 9.000000</td>\n", " <td> 3.000000</td>\n", " <td> 68.000000</td>\n", " <td> 1613.000000</td>\n", " <td> 8.000000</td>\n", " <td> 70.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 17.500000</td>\n", " <td> 4.000000</td>\n", " <td> 104.000000</td>\n", " <td> 2223.000000</td>\n", " <td> 13.800000</td>\n", " <td> 73.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 23.000000</td>\n", " <td> 4.000000</td>\n", " <td> 146.000000</td>\n", " <td> 2800.000000</td>\n", " <td> 15.500000</td>\n", " <td> 76.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 29.000000</td>\n", " <td> 8.000000</td>\n", " <td> 262.000000</td>\n", " <td> 3609.000000</td>\n", " <td> 17.100000</td>\n", " <td> 79.000000</td>\n", " <td> 2.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 46.600000</td>\n", " <td> 8.000000</td>\n", " <td> 455.000000</td>\n", " <td> 5140.000000</td>\n", " <td> 24.800000</td>\n", " <td> 82.000000</td>\n", " <td> 3.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ " mpg cylinders displacement weight acceleration \\\n", "count 397.000000 397.000000 397.000000 397.000000 397.000000 \n", "mean 23.515869 5.458438 193.532746 2970.261965 15.555668 \n", "std 7.825804 1.701577 104.379583 847.904119 2.749995 \n", "min 9.000000 3.000000 68.000000 1613.000000 8.000000 \n", "25% 17.500000 4.000000 104.000000 2223.000000 13.800000 \n", "50% 23.000000 4.000000 146.000000 2800.000000 15.500000 \n", "75% 29.000000 8.000000 262.000000 3609.000000 17.100000 \n", "max 46.600000 8.000000 455.000000 5140.000000 24.800000 \n", "\n", " year origin \n", "count 397.000000 397.000000 \n", "mean 75.994962 1.574307 \n", "std 3.690005 0.802549 \n", "min 70.000000 1.000000 \n", "25% 73.000000 1.000000 \n", "50% 76.000000 1.000000 \n", "75% 79.000000 2.000000 \n", "max 82.000000 3.000000 " ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"font-size: 1em; margin: 1em 0 1em 0; border: 1px solid #86989B; background-color: #f7f7f7; padding: 0;\">\n", "<p style=\"margin: 0; padding: 0.1em 0 0.1em 0.5em; color: white; border-bottom: 1px solid #86989B; font-weight: bold; background-color: #AFC1C4;\">\n", "Activity\n", "</p>\n", "<p style=\"margin: 0.5em 1em 0.5em 1em; padding: 0;\">\n", "Other summary statistics include: `count()`, `mean()`, `median()`, `quantile()`, `std()`, `var()`, `min()`, `max()`, etc. Play around a bit to see what they do in the `DataFrames` we already have.\n", "</p>\n", "</div>\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Merging and joining" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Throughout an analysis, we'll often need to merge/join datasets as data is typically stored in a relational manner.\n", "\n", "Like [SQL's JOIN clause](https://en.wikipedia.org/wiki/Join_sql), `pandas.merge()` allows two `DataFrames` to be joined on one or more keys. The function provides a series of parameters (`on`, `how`, `left_on`, `right_on`, `left_index`, `right_index`) allowing you to specify the columns or indexes on which to join.\n", "\n", "By default, `pandas.merge()` operates as an inner join, which can be changed using the `how` parameter. The inner join joins the two `DataFrame` by a certain key, and when any of the columns has a `NaN` for a key, it's removed.\n", "\n", "This is always easiest to understand with examples." ] }, { "cell_type": "code", "collapsed": false, "input": [ "left_frame = pd.DataFrame({'key': range(5), \n", " 'left_value': ['a', 'b', 'c', 'd', 'e']})\n", "left_frame" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> a</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> b</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " key left_value\n", "0 0 a\n", "1 1 b\n", "2 2 c\n", "3 3 d\n", "4 4 e" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "right_frame = pd.DataFrame({'key': range(2, 7), \n", " 'right_value': ['f', 'g', 'h', 'i', 'j']})\n", "\n", "right_frame" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 3</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 4</td>\n", " <td> h</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 5</td>\n", " <td> i</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 6</td>\n", " <td> j</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " key right_value\n", "0 2 f\n", "1 3 g\n", "2 4 h\n", "3 5 i\n", "4 6 j" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "inner join (default)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.merge(left_frame, right_frame, on='key', how='inner')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> h</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " key left_value right_value\n", "0 2 c f\n", "1 3 d g\n", "2 4 e h" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We lose values from both frames since certain keys do not match up. Because our key columns have different names, we could have used the `left_on` and `right_on` parameters to specify which fields to join from each frame.\n", "```python\n", " pd.merge(left_frame, right_frame, left_on='left_key', right_on='right_key')\n", "```\n", "Alternatively, if our keys were indexes, we could use the `left_index` or `right_index` parameters, which accept a logical value. You can mix and match columns and indexes like this:\n", "```python\n", " pd.merge(left_frame, right_frame, left_on='key', right_index=True)\n", "```" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "left outer join" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.merge(left_frame, right_frame, on='key', how='left')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> a</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> b</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> h</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " key left_value right_value\n", "0 0 a NaN\n", "1 1 b NaN\n", "2 2 c f\n", "3 3 d g\n", "4 4 e h" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We keep everything from the left frame, pulling in the value from the right frame where the keys match up. The `right_value` is null where keys do not match (`NaN`)." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "right outer join" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.merge(left_frame, right_frame, on='key', how='right')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> h</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 5</td>\n", " <td> NaN</td>\n", " <td> i</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 6</td>\n", " <td> NaN</td>\n", " <td> j</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ " key left_value right_value\n", "0 2 c f\n", "1 3 d g\n", "2 4 e h\n", "3 5 NaN i\n", "4 6 NaN j" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time we've kept everything from the right frame with the `left_value` being null where the right frame's key did not find a match." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "full outer join" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.merge(left_frame, right_frame, on='key', how='outer')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> a</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> b</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> h</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 5</td>\n", " <td> NaN</td>\n", " <td> i</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 6</td>\n", " <td> NaN</td>\n", " <td> j</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ " key left_value right_value\n", "0 0 a NaN\n", "1 1 b NaN\n", "2 2 c f\n", "3 3 d g\n", "4 4 e h\n", "5 5 NaN i\n", "6 6 NaN j" ] } ], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've kept everything from both frames, regardless of whether or not there was a match on both sides. Where there was not a match, the values corresponding to that key are null." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "concat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas also provides a way to combine `DataFrames` along an axis: `pandas.concat()`. It takes a list of `Series` or `DataFrame`s and returns a `Series` or `DataFrame` of the concatenated objects. Note that because the function takes list, you can combine many objects at once." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.concat([left_frame, right_frame])" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> a</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> b</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> NaN</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td> 2</td>\n", " <td> NaN</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 3</td>\n", " <td> NaN</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 4</td>\n", " <td> NaN</td>\n", " <td> h</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 5</td>\n", " <td> NaN</td>\n", " <td> i</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 6</td>\n", " <td> NaN</td>\n", " <td> j</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ " key left_value right_value\n", "0 0 a NaN\n", "1 1 b NaN\n", "2 2 c NaN\n", "3 3 d NaN\n", "4 4 e NaN\n", "0 2 NaN f\n", "1 3 NaN g\n", "2 4 NaN h\n", "3 5 NaN i\n", "4 6 NaN j" ] } ], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the function will vertically append the objects to one another, combining columns with the same name. We can see above that values not matching up will be null.\n", "\n", "Additionally, objects can be concatentated side-by-side using the function's axis parameter. The axis labeling information in Pandas objects serves many purposes:\n", "\n", "- Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.\n", "- Enables automatic and explicit data alignment.\n", "- Allows intuitive getting and setting of subsets of the data set." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.concat([left_frame, right_frame], axis=1)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>left_value</th>\n", " <th>key</th>\n", " <th>right_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> a</td>\n", " <td> 2</td>\n", " <td> f</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> b</td>\n", " <td> 3</td>\n", " <td> g</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> c</td>\n", " <td> 4</td>\n", " <td> h</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> d</td>\n", " <td> 5</td>\n", " <td> i</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> e</td>\n", " <td> 6</td>\n", " <td> j</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ " key left_value key right_value\n", "0 0 a 2 f\n", "1 1 b 3 g\n", "2 2 c 4 h\n", "3 3 d 5 i\n", "4 4 e 6 j" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Group by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `groupby()` method lets you perform grouping operations. The example below shows a grouping operation performed with `blog` columns entries as keys. It is used to calculate the mean of the `entries` for each blog." ] }, { "cell_type": "code", "collapsed": false, "input": [ "blogs" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blog</th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 1638</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 569</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 115</td>\n", " <td> DH</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 2</td>\n", " <td> NaN</td>\n", " <td> GIS</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 2</td>\n", " <td> 1130</td>\n", " <td> NLP</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 2</td>\n", " <td> 754</td>\n", " <td> SNA</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 2</td>\n", " <td> 555</td>\n", " <td> DH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ " blog entries tag\n", "0 1 NaN GIS\n", "1 1 1638 NLP\n", "2 1 569 SNA\n", "3 1 115 DH\n", "4 2 NaN GIS\n", "5 2 1130 NLP\n", "6 2 754 SNA\n", "7 2 555 DH" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "blogs.groupby('blog').count()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>entries</th>\n", " <th>tag</th>\n", " </tr>\n", " <tr>\n", " <th>blog</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td> 3</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 3</td>\n", " <td> 4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ " entries tag\n", "blog \n", "1 3 4\n", "2 3 4" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now for a more complex exmaple. From our `bikes_df` `DataFrame`, we want to know if people are more active on weekdays or on weekends. We are going to add a new column with the information about the `weekday`, and then sum all the values for each weekday. We do that by grouping by `weekday` and aggregating their values by simpe addition. In this case we use `np.sum()`, but any other operation, such as `np.max()` or `np.average()` can be used." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df.index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "<class 'pandas.tseries.index.DatetimeIndex'>\n", "[2012-01-01, ..., 2012-11-05]\n", "Length: 310, Freq: None, Timezone: None" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data frame's index is of type `DateTimeIndex`, which allows us to extract information from the standard [`datetime`](https://docs.python.org/3/library/datetime.html) Python object." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df.index.weekday" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n", " 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n", " 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n", " 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n", " 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n", " 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n", " 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n", " 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n", " 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n", " 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n", " 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n", " 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n", " 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n", " 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to create the new column we just need to pick a name and assign a `Series` value is it was a dictionary (remember that `DataFrame` and `Series` are labeled)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df['weekday'] = bikes_df.index.weekday\n", "bikes_df.head()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "-c:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] }, { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " <th>weekday</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 0</td>\n", " <td> 38</td>\n", " <td> 51</td>\n", " <td> 26</td>\n", " <td> 10</td>\n", " <td> 16</td>\n", " <td> 6</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 1</td>\n", " <td> 68</td>\n", " <td> 153</td>\n", " <td> 53</td>\n", " <td> 6</td>\n", " <td> 43</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 2</td>\n", " <td> 104</td>\n", " <td> 248</td>\n", " <td> 89</td>\n", " <td> 3</td>\n", " <td> 58</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 1</td>\n", " <td> 116</td>\n", " <td> 318</td>\n", " <td> 111</td>\n", " <td> 8</td>\n", " <td> 61</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 2</td>\n", " <td> 124</td>\n", " <td> 330</td>\n", " <td> 97</td>\n", " <td> 13</td>\n", " <td> 95</td>\n", " <td> 3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "Date \n", "2012-01-01 35 0 38 51 \n", "2012-01-02 83 1 68 153 \n", "2012-01-03 135 2 104 248 \n", "2012-01-04 144 1 116 318 \n", "2012-01-05 197 2 124 330 \n", "\n", " du Parc Pierre-Dupuy Rachel1 weekday \n", "Date \n", "2012-01-01 26 10 16 6 \n", "2012-01-02 53 6 43 0 \n", "2012-01-03 89 3 58 1 \n", "2012-01-04 111 8 61 2 \n", "2012-01-05 97 13 95 3 " ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "bikes_df.groupby('weekday')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "<pandas.core.groupby.DataFrameGroupBy object at 0x7f2d5303a668>" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "counts_by_day = bikes_df.groupby('weekday').aggregate(np.sum)\n", "counts_by_day" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Berri 1</th>\n", " <th>C\u00f4te-Sainte-Catherine</th>\n", " <th>Maisonneuve 1</th>\n", " <th>Maisonneuve 2</th>\n", " <th>du Parc</th>\n", " <th>Pierre-Dupuy</th>\n", " <th>Rachel1</th>\n", " </tr>\n", " <tr>\n", " <th>weekday</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 134298</td>\n", " <td> 60329</td>\n", " <td> 90051</td>\n", " <td> 163767</td>\n", " <td> 90184</td>\n", " <td> 46204</td>\n", " <td> 130130</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 135305</td>\n", " <td> 58708</td>\n", " <td> 92035</td>\n", " <td> 165880</td>\n", " <td> 91399</td>\n", " <td> 35167</td>\n", " <td> 120088</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 152972</td>\n", " <td> 67344</td>\n", " <td> 104891</td>\n", " <td> 186061</td>\n", " <td> 102103</td>\n", " <td> 43263</td>\n", " <td> 133088</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 160131</td>\n", " <td> 69028</td>\n", " <td> 111895</td>\n", " <td> 196715</td>\n", " <td> 105674</td>\n", " <td> 45385</td>\n", " <td> 140241</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 141771</td>\n", " <td> 56446</td>\n", " <td> 98568</td>\n", " <td> 172390</td>\n", " <td> 89872</td>\n", " <td> 42470</td>\n", " <td> 137255</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 101578</td>\n", " <td> 34018</td>\n", " <td> 62067</td>\n", " <td> 105060</td>\n", " <td> 48289</td>\n", " <td> 52861</td>\n", " <td> 112828</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 99310</td>\n", " <td> 36466</td>\n", " <td> 55324</td>\n", " <td> 98308</td>\n", " <td> 50004</td>\n", " <td> 61485</td>\n", " <td> 117150</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ " Berri 1 C\u00f4te-Sainte-Catherine Maisonneuve 1 Maisonneuve 2 \\\n", "weekday \n", "0 134298 60329 90051 163767 \n", "1 135305 58708 92035 165880 \n", "2 152972 67344 104891 186061 \n", "3 160131 69028 111895 196715 \n", "4 141771 56446 98568 172390 \n", "5 101578 34018 62067 105060 \n", "6 99310 36466 55324 98308 \n", "\n", " du Parc Pierre-Dupuy Rachel1 \n", "weekday \n", "0 90184 46204 130130 \n", "1 91399 35167 120088 \n", "2 102103 43263 133088 \n", "3 105674 45385 140241 \n", "4 89872 42470 137255 \n", "5 48289 52861 112828 \n", "6 50004 61485 117150 " ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally a bit of `matplotlib + pandas` magic! But don't worry, you'll learn about plotting in the next class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# display graphs inline\n", "%matplotlib inline \n", "import matplotlib\n", "# Make the fonts bigger\n", "matplotlib.rc('figure', figsize=(14, 7))\n", "\n", "counts_by_day.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d5169ea90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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UTcYYCVFPiiorWXf2LCszMlh/9ix9mzVjVGAgd7Vogb9khoQQtThcWMg/09JY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\nMSYPuNha+yNw9kbtKimCgZsYtW78mtSyW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"text": [ "<matplotlib.figure.Figure at 0x7f2d52fcab70>" ] } ], "prompt_number": 50 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is not, for sure, all we will see about grouping, but it's a good start if you are already understanding what's going on here." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Art Bank collection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [Art Bank collection](http://data.gc.ca/data/en/dataset/bb905c07-36ba-4df7-ad46-2fd6e2c15b23) is a dataset provided by the [data.gc.ca](data.gc.ca), a key part of Canada's Action Plan on Open Government to enhance transparency and accountability, that contains the art works in the Canada Council Art Bank collection. For 40 years, the Art Bank has been collecting work by the best Canadian artists of our times. The Art Bank is part of the Canada Council for the Arts, Canada's national arts funder. It's licenced under the terms of the [Open Government Licence - Canada](http://data.gc.ca/eng/open-government-licence-canada).\n", "\n", "And... it's in XML! XML, stands for eXtensible Markup Language, and if you want to see an example right now, just press `Ctrl.+u` and the browser will (likely) show you the HTML under this web page. Because yes, HTML is a subset of XML. So now you have an idea.\n", "\n", "XML is something that you should know about already. Very basically, XML defines a hierarchical structure in which concepts are enclosed in tags that might have properties.\n", "\n", "```xml\n", "<art_list>\n", " <art_work title=\"Title of the artwork\" />\n", "</art_list>\n", "```\n", "\n", "In the last example, `art_list` is a tag, and `<art_list>...</art_list>` is how you enclose something in a tag. Sometimes tags might not have anything to enclose, as in `<art_work />`, so it's self-enclosed. Finally, properties are just like dictionaries.\n", "\n", "In Python, the fastest way to get all the concepts we need is usually by finding all the tags of a certain kind. The module `lxml.etree` is able to do that for us.\n", "\n", "First, let's see some lines of the dataset by using the special IPython command `!head`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!head data/art_listing.xml" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\ufeff<?xml version=\"1.0\" encoding=\"utf-8\"?>\r", "\r\n", "<Art_Listing>\r", "\r\n", " <Art_Summary Artist_Name=\"Wright, Don\" Title=\"Turf-Arm\" Execution_Date=\"1984\" E_Category=\"Work on paper\" F_Category=\"Oeuvre sur papier\" ID=\"85/6-0174\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Hansen, Jim\" Title=\"Nfld. album no. 99\" Execution_Date=\"\u00a91977, CARCC\" E_Category=\"Work on paper\" F_Category=\"Oeuvre sur papier\" ID=\"79/80-0510\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Creates, Marlene\" Title=\"A Stone Placed in Gathered Water, Newfoundland\" Execution_Date=\"\u00a91982, CARCC\" E_Category=\"Photograph\" F_Category=\"Photographie\" ID=\"92/3-0221\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Walker, Peter\" Title=\"1.25.79\" Execution_Date=\"\u00a91979, CARCC\" E_Category=\"Work on paper\" F_Category=\"Oeuvre sur papier\" ID=\"79/80-0529\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Bretzloff, Carol\" Title=\"Under Heaven, Wind\" Execution_Date=\"1983\" E_Category=\"Work on paper\" F_Category=\"Oeuvre sur papier\" ID=\"13/4-0007\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Knight, Katherine\" Title=\"Bubble\" Execution_Date=\"2000\" E_Category=\"Photograph\" F_Category=\"Photographie\" ID=\"01/2-0083\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Pitseolak, Okpik\" Title=\"The one who became a Norwhal\" Execution_Date=\"2002\" E_Category=\"Work on paper\" F_Category=\"Oeuvre sur papier\" ID=\"03/4-0007\" />\r", "\r\n", " <Art_Summary Artist_Name=\"Tang, Brendan Lee\" Title=\"Manga Ormolu ver. 4.0-g\" Execution_Date=\"2008\" E_Category=\"Sculpture\" F_Category=\"Sculpture\" ID=\"10/1-0067\" />\r", "\r\n" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here, it's pretty obvious that what we want the `Art_Summary` elements and their properties.\n", "\n", "The function `etree.parse()` opens a file, parses it into a Python data structure and returns it as an object." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from lxml import etree\n", "arts = etree.parse(\"data/art_listing.xml\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find all `Art_Summary` elements, we can call the method `.findall()`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "summaries = arts.findall(\"Art_Summary\")\n", "summaries[:5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "[<Element Art_Summary at 0x7f85dd0005c8>,\n", " <Element Art_Summary at 0x7f85dcffbbc8>,\n", " <Element Art_Summary at 0x7f85dcffbf08>,\n", " <Element Art_Summary at 0x7f85dcffb148>,\n", " <Element Art_Summary at 0x7f85dcffbd88>]" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of those elements in the list behaves like a dictionary." ] }, { "cell_type": "code", "collapsed": false, "input": [ "summaries[0].keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "['Artist_Name', 'Title', 'Execution_Date', 'E_Category', 'F_Category', 'ID']" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "summaries[0].values()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "['Wright, Don',\n", " 'Turf-Arm',\n", " '1984',\n", " 'Work on paper',\n", " 'Oeuvre sur papier',\n", " '85/6-0174']" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to build a data frame from the `summaries` list." ] }, { "cell_type": "code", "collapsed": false, "input": [ "art_list = []\n", "for summary in summaries:\n", " art_list.append(summary.values())\n", "arts = pd.DataFrame(\n", " art_list,\n", " columns=['artist_name', 'title', 'execution_date', 'category', 'category_french', 'id']\n", ")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's remove the category in French." ] }, { "cell_type": "code", "collapsed": false, "input": [ "del arts['category_french']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "arts.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>artist_name</th>\n", " <th>title</th>\n", " <th>execution_date</th>\n", " <th>category</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Wright, Don</td>\n", " <td> Turf-Arm</td>\n", " <td> 1984</td>\n", " <td> Work on paper</td>\n", " <td> 85/6-0174</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Hansen, Jim</td>\n", " <td> Nfld. album no. 99</td>\n", " <td> \u00a91977, CARCC</td>\n", " <td> Work on paper</td>\n", " <td> 79/80-0510</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Creates, Marlene</td>\n", " <td> A Stone Placed in Gathered Water, Newfoundland</td>\n", " <td> \u00a91982, CARCC</td>\n", " <td> Photograph</td>\n", " <td> 92/3-0221</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Walker, Peter</td>\n", " <td> 1.25.79</td>\n", " <td> \u00a91979, CARCC</td>\n", " <td> Work on paper</td>\n", " <td> 79/80-0529</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Bretzloff, Carol</td>\n", " <td> Under Heaven, Wind</td>\n", " <td> 1983</td>\n", " <td> Work on paper</td>\n", " <td> 13/4-0007</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ " artist_name title \\\n", "0 Wright, Don Turf-Arm \n", "1 Hansen, Jim Nfld. album no. 99 \n", "2 Creates, Marlene A Stone Placed in Gathered Water, Newfoundland \n", "3 Walker, Peter 1.25.79 \n", "4 Bretzloff, Carol Under Heaven, Wind \n", "\n", " execution_date category id \n", "0 1984 Work on paper 85/6-0174 \n", "1 \u00a91977, CARCC Work on paper 79/80-0510 \n", "2 \u00a91982, CARCC Photograph 92/3-0221 \n", "3 \u00a91979, CARCC Work on paper 79/80-0529 \n", "4 1983 Work on paper 13/4-0007 " ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voil\u00e0! Ready for the fun. Let's just save it for later." ] }, { "cell_type": "code", "collapsed": false, "input": [ "arts.to_csv(\"data/arts.csv\", index=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"font-size: 1em; margin: 1em 0 1em 0; border: 1px solid #86989B; background-color: #f7f7f7; padding: 0;\">\n", "<p style=\"margin: 0; padding: 0.1em 0 0.1em 0.5em; color: white; border-bottom: 1px solid #86989B; font-weight: bold; background-color: #AFC1C4;\">\n", "Activity\n", "</p>\n", "<p style=\"margin: 0.5em 1em 0.5em 1em; padding: 0;\">\n", "Given the `arts` data frame, clean the dates so you only see numbers. If a year is lower than 100, then is referred to 1900. For example, 78 is actually 1978, and that needs to be fixed too.\n", "<small style=\"float: right; position: relative;\">[Solution](data/arts1.py)</small>\n", "</p>\n", "</div>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "l = ['1989', '1999']\n", "l[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "'1999'" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "\"1989, CARCC\".split(\"-\")[-1]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "'1989, CARCC'" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "# Clean the dates so you only see numbers.\n", "def clean_years(value):\n", " result = value\n", " chars_to_replace = [\"c.\", \"\u00a9\", \", CARCC\", \"no date\", \"n.d.\", \" SODRAC\", \", CA\", \" CARCC\"]\n", " chars_to_split = [\"-\", \"/\"]\n", " if isinstance(result, str): # what isinstance does?\n", " for char in chars_to_split:\n", " result = result.split(char)[-1]\n", " for char in chars_to_replace:\n", " result = result.replace(char, \"\")\n", " if result == \"\":\n", " return np.nan\n", " else:\n", " return int(result)\n", " else:\n", " return result\n", "\n", "arts['execution_date'] = arts['execution_date'].apply(clean_years)\n", "arts.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>artist_name</th>\n", " <th>title</th>\n", " <th>execution_date</th>\n", " <th>category</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Wright, Don</td>\n", " <td> Turf-Arm</td>\n", " <td> 1984</td>\n", " <td> Work on paper</td>\n", " <td> 85/6-0174</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Hansen, Jim</td>\n", " <td> Nfld. album no. 99</td>\n", " <td> 1977</td>\n", " <td> Work on paper</td>\n", " <td> 79/80-0510</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Creates, Marlene</td>\n", " <td> A Stone Placed in Gathered Water, Newfoundland</td>\n", " <td> 1982</td>\n", " <td> Photograph</td>\n", " <td> 92/3-0221</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Walker, Peter</td>\n", " <td> 1.25.79</td>\n", " <td> 1979</td>\n", " <td> Work on paper</td>\n", " <td> 79/80-0529</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Bretzloff, Carol</td>\n", " <td> Under Heaven, Wind</td>\n", " <td> 1983</td>\n", " <td> Work on paper</td>\n", " <td> 13/4-0007</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ " artist_name title \\\n", "0 Wright, Don Turf-Arm \n", "1 Hansen, Jim Nfld. album no. 99 \n", "2 Creates, Marlene A Stone Placed in Gathered Water, Newfoundland \n", "3 Walker, Peter 1.25.79 \n", "4 Bretzloff, Carol Under Heaven, Wind \n", "\n", " execution_date category id \n", "0 1984 Work on paper 85/6-0174 \n", "1 1977 Work on paper 79/80-0510 \n", "2 1982 Photograph 92/3-0221 \n", "3 1979 Work on paper 79/80-0529 \n", "4 1983 Work on paper 13/4-0007 " ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "# If a year is lower than 100, then is referred to 1900. For example, 78 is actually 1978, and that needs to be fixed too.\n", "def clean_year_99(value):\n", " if value <= 99:\n", " return 1900 + value\n", " else:\n", " return value \n", "\n", "arts[\"execution_date\"] = arts[\"execution_date\"].apply(clean_year_99)\n", "arts[arts[\"execution_date\"] < 100].head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>artist_name</th>\n", " <th>title</th>\n", " <th>execution_date</th>\n", " <th>category</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "Empty DataFrame\n", "Columns: [artist_name, title, execution_date, category, id]\n", "Index: []" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# using lambda functions\n", "arts[\"execution_date\"] = arts[\"execution_date\"].apply(lambda value: 1900 + value if value <= 99 else value)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "For the [next class](class7.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Read [The Split-Apply-Combine Strategy for Data Analysis](http://www.jstatsoft.org/v40/i01/paper)\n", "* Read Section [String Manipulation, 205-212, from Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do).\n", "* Read Section [Pivot Tables and Cross-Tabulation, 275-278, from Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do).\n", "* Read Section [1.4. Matplotlib: plotting, from Scipy lecture notes](http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html)\n", "* Read [Plotting and Visualization Notebook](Plotting and Visualization)" ] } ], "metadata": {} } ] }
mit
voytekresearch/nsaba
notebooks/demos/Brains.ipynb
1
1204338
null
mit
biosustain/cameo-notebooks
01-quick-start.ipynb
1
48844
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with cameo " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**cameo** reuses and extends model data structures defined by [cobrapy](https://opencobra.github.io/cobrapy/) (**CO**nstraints-**B**ased **R**econstruction and **A**nalysis tool for **Py**thon). So, in addition to following this quick start guide and other **cameo** tutorials, we encourage you to explore cobrapy's [documentation](https://cobrapy.readthedocs.org/en/latest/cobra.core.html) as well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 1: Load a model\n", "-------------------\n", "\n", "Loading a model is easy. Just import the :class:`~cameo.io.load_model` function." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cameo import load_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, load a genome-scale metabolic reconstruction of _Escherichia coli_." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = load_model(\"iJO1366\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Models, reactions, metabolites, etc., return HTML when evaluated in Jupyter notebooks and can be easily inspected." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><strong>Name</strong></td>\n", " <td>iJO1366</td>\n", " </tr><tr>\n", " <td><strong>Memory address</strong></td>\n", " <td>0x01120756d8</td>\n", " </tr><tr>\n", " <td><strong>Number of metabolites</strong></td>\n", " <td>1805</td>\n", " </tr><tr>\n", " <td><strong>Number of reactions</strong></td>\n", " <td>2583</td>\n", " </tr><tr>\n", " <td><strong>Objective expression</strong></td>\n", " <td>-1.0*BIOMASS_Ec_iJO1366_core_53p95M_reverse_5c8b1 + 1.0*BIOMASS_Ec_iJO1366_core_53p95M</td>\n", " </tr><tr>\n", " <td><strong>Compartments</strong></td>\n", " <td>extracellular space, cytosol, periplasm</td>\n", " </tr>\n", " </table>" ], "text/plain": [ "<Model iJO1366 at 0x1120756d8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Simulate a model\n", "\n", "The model can be simulated by executing `optimize`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "solution = model.optimize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A quick overview of the solution can be obtained in inspecting it." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<strong><em>Optimal</em> solution with objective value 0.982</strong><br><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>fluxes</th>\n", " <th>reduced_costs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>12DGR120tipp</th>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>12DGR140tipp</th>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>12DGR141tipp</th>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>12DGR160tipp</th>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>12DGR161tipp</th>\n", " <td>0.000000</td>\n", " <td>-0.008295</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2abcpp</th>\n", " <td>0.000000</td>\n", " <td>-0.008295</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2t3pp</th>\n", " <td>0.000000</td>\n", " <td>-0.002074</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2tpp</th>\n", " <td>0.000335</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>ZNabcpp</th>\n", " <td>0.000000</td>\n", " <td>-0.008295</td>\n", " </tr>\n", " <tr>\n", " <th>Zn2tex</th>\n", " <td>0.000335</td>\n", " <td>-0.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2583 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ "<Solution 0.982 at 0x115a61ba8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A data frame 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<th>VALt2rpp</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>VALtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>VPAMTr</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>WCOS</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>X5PL3E</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XAND</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XANt2pp</th>\n", " <td>0.000000</td>\n", " <td>-2.073827e-03</td>\n", " </tr>\n", " <tr>\n", " <th>XANtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XANtpp</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XMPtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XPPT</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XTSNH</th>\n", " <td>0.000000</td>\n", " <td>-8.295308e-03</td>\n", " </tr>\n", " <tr>\n", " <th>XTSNt2rpp</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XTSNtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLI1</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLI2</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLK</th>\n", " <td>0.000000</td>\n", " <td>-1.387779e-17</td>\n", " </tr>\n", " <tr>\n", " <th>XYLK2</th>\n", " <td>0.000000</td>\n", " <td>-1.387779e-17</td>\n", " </tr>\n", " <tr>\n", " <th>XYLUt2pp</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLUtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLabcpp</th>\n", " <td>0.000000</td>\n", " <td>-6.221481e-03</td>\n", " </tr>\n", " <tr>\n", " <th>XYLt2pp</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>XYLtex</th>\n", " <td>0.000000</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2abcpp</th>\n", " <td>0.000000</td>\n", " <td>-8.295308e-03</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2t3pp</th>\n", " <td>0.000000</td>\n", " <td>-2.073827e-03</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2tpp</th>\n", " <td>0.000335</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ZNabcpp</th>\n", " <td>0.000000</td>\n", " <td>-8.295308e-03</td>\n", " </tr>\n", " <tr>\n", " <th>Zn2tex</th>\n", " <td>0.000335</td>\n", " <td>-0.000000e+00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2583 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " fluxes reduced_costs\n", "12DGR120tipp 0.000000 0.000000e+00\n", "12DGR140tipp 0.000000 0.000000e+00\n", "12DGR141tipp 0.000000 0.000000e+00\n", "12DGR160tipp 0.000000 0.000000e+00\n", "12DGR161tipp 0.000000 -8.295308e-03\n", "12DGR180tipp 0.000000 0.000000e+00\n", "12DGR181tipp 0.000000 0.000000e+00\n", "12PPDRtex 0.000000 0.000000e+00\n", "12PPDRtpp 0.000000 0.000000e+00\n", "12PPDStex 0.000000 0.000000e+00\n", "12PPDStpp 0.000000 0.000000e+00\n", "14GLUCANabcpp 0.000000 -5.551115e-17\n", "14GLUCANtexi 0.000000 0.000000e+00\n", "23CAMPtex 0.000000 0.000000e+00\n", "23CCMPtex 0.000000 0.000000e+00\n", "23CGMPtex 0.000000 0.000000e+00\n", "23CUMPtex 0.000000 0.000000e+00\n", "23DAPPAt2pp 0.000000 1.517883e-18\n", "23DAPPAtex 0.000000 0.000000e+00\n", "23PDE2pp 0.000000 0.000000e+00\n", "23PDE4pp 0.000000 0.000000e+00\n", "23PDE7pp 0.000000 0.000000e+00\n", "23PDE9pp 0.000000 0.000000e+00\n", "26DAHtex 0.000000 0.000000e+00\n", "2AGPA120tipp 0.000000 0.000000e+00\n", "2AGPA140tipp 0.000000 0.000000e+00\n", "2AGPA141tipp 0.000000 0.000000e+00\n", "2AGPA160tipp 0.000000 0.000000e+00\n", "2AGPA161tipp 0.000000 0.000000e+00\n", "2AGPA180tipp 0.000000 0.000000e+00\n", "... ... ...\n", "VALTRS 0.000000 0.000000e+00\n", "VALabcpp 0.000000 -6.221481e-03\n", "VALt2rpp 0.000000 0.000000e+00\n", "VALtex 0.000000 0.000000e+00\n", "VPAMTr 0.000000 0.000000e+00\n", "WCOS 0.000000 0.000000e+00\n", "X5PL3E 0.000000 0.000000e+00\n", "XAND 0.000000 0.000000e+00\n", "XANt2pp 0.000000 -2.073827e-03\n", "XANtex 0.000000 0.000000e+00\n", "XANtpp 0.000000 0.000000e+00\n", "XMPtex 0.000000 0.000000e+00\n", "XPPT 0.000000 0.000000e+00\n", "XTSNH 0.000000 -8.295308e-03\n", "XTSNt2rpp 0.000000 0.000000e+00\n", "XTSNtex 0.000000 0.000000e+00\n", "XYLI1 0.000000 0.000000e+00\n", "XYLI2 0.000000 0.000000e+00\n", "XYLK 0.000000 -1.387779e-17\n", "XYLK2 0.000000 -1.387779e-17\n", "XYLUt2pp 0.000000 0.000000e+00\n", "XYLUtex 0.000000 0.000000e+00\n", "XYLabcpp 0.000000 -6.221481e-03\n", "XYLt2pp 0.000000 0.000000e+00\n", "XYLtex 0.000000 0.000000e+00\n", "ZN2abcpp 0.000000 -8.295308e-03\n", "ZN2t3pp 0.000000 -2.073827e-03\n", "ZN2tpp 0.000335 0.000000e+00\n", "ZNabcpp 0.000000 -8.295308e-03\n", "Zn2tex 0.000335 -0.000000e+00\n", "\n", "[2583 rows x 2 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solution.to_frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data frames make it very easy to process results. For example, let's take a look at reactions with flux != 0" ] }, { "cell_type": "code", "execution_count": 7, "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>fluxes</th>\n", " <th>reduced_costs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3OAR140</th>\n", " <td>0.076452</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>3OAS140</th>\n", " <td>0.076452</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>5DOAN</th>\n", " <td>0.000221</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>A5PISO</th>\n", " <td>0.038226</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>AACPS3</th>\n", " <td>0.125378</td>\n", " <td>-1.387779e-16</td>\n", " </tr>\n", " <tr>\n", " <th>AACPS4</th>\n", " <td>0.147776</td>\n", " <td>-2.775558e-17</td>\n", " </tr>\n", " <tr>\n", " <th>AACPS7</th>\n", " <td>0.076452</td>\n", " <td>-2.775558e-17</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT1r</th>\n", " <td>0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT2r</th>\n", " <td>0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT3r</th>\n", " <td>0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT4r</th>\n", " <td>0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT5r</th>\n", " <td>0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT6r</th>\n", " <td>0.273154</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACACT7r</th>\n", " <td>0.273154</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACCOAC</th>\n", " <td>0.076458</td>\n", " <td>-7.090682e-17</td>\n", " </tr>\n", " <tr>\n", " <th>ACGK</th>\n", " <td>0.290578</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACGS</th>\n", " <td>0.290578</td>\n", " <td>2.927346e-17</td>\n", " </tr>\n", " <tr>\n", " <th>ACHBS</th>\n", " <td>0.285408</td>\n", " <td>-6.938894e-18</td>\n", " </tr>\n", " <tr>\n", " <th>ACLS</th>\n", " <td>0.858857</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD1f</th>\n", " <td>-0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD2f</th>\n", " <td>-0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD3f</th>\n", " <td>-0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD4f</th>\n", " <td>-0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD5f</th>\n", " <td>-0.349606</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD6f</th>\n", " <td>-0.273154</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOAD7f</th>\n", " <td>-0.125378</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACODA</th>\n", " <td>0.290578</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACONTa</th>\n", " <td>4.857777</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACONTb</th>\n", " <td>4.857777</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ACOTA</th>\n", " <td>-0.290578</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>TMDS</th>\n", " <td>0.025705</td>\n", " <td>-5.551115e-17</td>\n", " </tr>\n", " <tr>\n", " <th>TMPK</th>\n", " <td>0.000219</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TMPPP</th>\n", " <td>0.000219</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TPI</th>\n", " <td>7.645371</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TRDR</th>\n", " <td>0.243502</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TRPAS2</th>\n", " <td>-0.055841</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TRPS3</th>\n", " <td>0.055841</td>\n", " <td>2.775558e-17</td>\n", " </tr>\n", " <tr>\n", " <th>TYRL</th>\n", " <td>0.000219</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>TYRTA</th>\n", " <td>-0.135684</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>U23GAAT</th>\n", " <td>0.038226</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAAGDS</th>\n", " <td>0.027298</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAGAAT</th>\n", " <td>0.038226</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAGCVT</th>\n", " <td>0.027298</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAGDP</th>\n", " <td>0.092822</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAGPT3</th>\n", " <td>0.027298</td>\n", " <td>-5.551115e-17</td>\n", " </tr>\n", " <tr>\n", " <th>UAMAGS</th>\n", " <td>0.027298</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UAMAS</th>\n", " <td>0.027298</td>\n", " <td>5.551115e-17</td>\n", " </tr>\n", " <tr>\n", " <th>UAPGR</th>\n", " <td>0.027298</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UDCPDP</th>\n", " <td>0.027298</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UDCPDPS</th>\n", " <td>0.000054</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UGMDDS</th>\n", " <td>0.027298</td>\n", " <td>1.110223e-16</td>\n", " </tr>\n", " <tr>\n", " <th>UHGADA</th>\n", " <td>0.038226</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UMPK</th>\n", " <td>0.371375</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UPP3MT</th>\n", " <td>0.000219</td>\n", " <td>1.110223e-16</td>\n", " </tr>\n", " <tr>\n", " <th>UPP3S</th>\n", " <td>0.000438</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>UPPDC1</th>\n", " <td>0.000219</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>USHD</th>\n", " <td>0.019113</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>VALTA</th>\n", " <td>-0.415702</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>ZN2tpp</th>\n", " <td>0.000335</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>Zn2tex</th>\n", " <td>0.000335</td>\n", " <td>-0.000000e+00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>436 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " fluxes reduced_costs\n", "3OAR140 0.076452 0.000000e+00\n", "3OAS140 0.076452 0.000000e+00\n", "5DOAN 0.000221 0.000000e+00\n", "A5PISO 0.038226 0.000000e+00\n", "AACPS3 0.125378 -1.387779e-16\n", "AACPS4 0.147776 -2.775558e-17\n", "AACPS7 0.076452 -2.775558e-17\n", "ACACT1r 0.349606 0.000000e+00\n", "ACACT2r 0.349606 0.000000e+00\n", "ACACT3r 0.349606 0.000000e+00\n", "ACACT4r 0.349606 0.000000e+00\n", "ACACT5r 0.349606 0.000000e+00\n", "ACACT6r 0.273154 0.000000e+00\n", "ACACT7r 0.273154 0.000000e+00\n", "ACCOAC 0.076458 -7.090682e-17\n", "ACGK 0.290578 0.000000e+00\n", "ACGS 0.290578 2.927346e-17\n", "ACHBS 0.285408 -6.938894e-18\n", "ACLS 0.858857 0.000000e+00\n", "ACOAD1f -0.349606 0.000000e+00\n", "ACOAD2f -0.349606 0.000000e+00\n", "ACOAD3f -0.349606 0.000000e+00\n", "ACOAD4f -0.349606 0.000000e+00\n", "ACOAD5f -0.349606 0.000000e+00\n", "ACOAD6f -0.273154 0.000000e+00\n", "ACOAD7f -0.125378 0.000000e+00\n", "ACODA 0.290578 0.000000e+00\n", "ACONTa 4.857777 0.000000e+00\n", "ACONTb 4.857777 0.000000e+00\n", "ACOTA -0.290578 0.000000e+00\n", "... ... ...\n", "TMDS 0.025705 -5.551115e-17\n", "TMPK 0.000219 0.000000e+00\n", "TMPPP 0.000219 0.000000e+00\n", "TPI 7.645371 0.000000e+00\n", "TRDR 0.243502 0.000000e+00\n", "TRPAS2 -0.055841 0.000000e+00\n", "TRPS3 0.055841 2.775558e-17\n", "TYRL 0.000219 0.000000e+00\n", "TYRTA -0.135684 0.000000e+00\n", "U23GAAT 0.038226 0.000000e+00\n", "UAAGDS 0.027298 0.000000e+00\n", "UAGAAT 0.038226 0.000000e+00\n", "UAGCVT 0.027298 0.000000e+00\n", "UAGDP 0.092822 0.000000e+00\n", "UAGPT3 0.027298 -5.551115e-17\n", "UAMAGS 0.027298 0.000000e+00\n", "UAMAS 0.027298 5.551115e-17\n", "UAPGR 0.027298 0.000000e+00\n", "UDCPDP 0.027298 0.000000e+00\n", "UDCPDPS 0.000054 0.000000e+00\n", "UGMDDS 0.027298 1.110223e-16\n", "UHGADA 0.038226 0.000000e+00\n", "UMPK 0.371375 0.000000e+00\n", "UPP3MT 0.000219 1.110223e-16\n", "UPP3S 0.000438 0.000000e+00\n", "UPPDC1 0.000219 0.000000e+00\n", "USHD 0.019113 0.000000e+00\n", "VALTA -0.415702 0.000000e+00\n", "ZN2tpp 0.000335 0.000000e+00\n", "Zn2tex 0.000335 -0.000000e+00\n", "\n", "[436 rows x 2 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solution.to_frame().query('fluxes != 0')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Exploring a model\n", "\n", "Objects—models, reactions, metabolites, genes—can easily be explored in the Jupyter notebook, taking advantage of tab completion. For example, place your cursor after the period in `model.reactions.` and press the TAB key. A dialog will appear that allows you to navigate the list of reactions encoded in the model. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><strong>Reaction identifier</strong></td><td>PGK</td>\n", " </tr><tr>\n", " <td><strong>Name</strong></td><td>Phosphoglycerate kinase</td>\n", " </tr><tr>\n", " <td><strong>Memory address</strong></td>\n", " <td>0x01129829b0</td>\n", " </tr><tr>\n", " <td><strong>Stoichiometry</strong></td>\n", " <td>\n", " <p style='text-align:right'>3pg_c + atp_c <=> 13dpg_c + adp_c</p>\n", " <p style='text-align:right'>3-Phospho-D-glycerate + ATP C10H12N5O13P3 <=> 3-Phospho-D-glyceroyl phosphate + ADP C10H12N5O10P2</p>\n", " </td>\n", " </tr><tr>\n", " <td><strong>GPR</strong></td><td>b2926</td>\n", " </tr><tr>\n", " <td><strong>Lower bound</strong></td><td>-1000.0</td>\n", " </tr><tr>\n", " <td><strong>Upper bound</strong></td><td>1000.0</td>\n", " </tr>\n", " </table>\n", " " ], "text/plain": [ "<Reaction PGK at 0x1129829b0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.PGK # delete PGK, place your cursor after the period and press the TAB key." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, you can access the E4PD (_Erythrose 4-phosphate dehydrogenase_) reaction in the model." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><strong>Reaction identifier</strong></td><td>E4PD</td>\n", " </tr><tr>\n", " <td><strong>Name</strong></td><td>Erythrose 4-phosphate dehydrogenase</td>\n", " </tr><tr>\n", " <td><strong>Memory address</strong></td>\n", " <td>0x0112606160</td>\n", " </tr><tr>\n", " <td><strong>Stoichiometry</strong></td>\n", " <td>\n", " <p style='text-align:right'>e4p_c + h2o_c + nad_c <=> 4per_c + 2.0 h_c + nadh_c</p>\n", " <p style='text-align:right'>D-Erythrose 4-phosphate + H2O H2O + Nicotinamide adenine dinucleotide <=> 4-Phospho-D-erythronate + 2.0 H+ + Nicotinamide adenine dinucleotide - reduced</p>\n", " </td>\n", " </tr><tr>\n", " <td><strong>GPR</strong></td><td>b2927 or b1779</td>\n", " </tr><tr>\n", " <td><strong>Lower bound</strong></td><td>-1000.0</td>\n", " </tr><tr>\n", " <td><strong>Upper bound</strong></td><td>1000.0</td>\n", " </tr>\n", " </table>\n", " " ], "text/plain": [ "<Reaction E4PD at 0x112606160>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.E4PD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be aware that, due to variable naming restrictions in Python, dot notation access to reactions (and other objects) might not work in some cases." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# model.reactions.12DGR120tipp # uncommenting and running this cell will produce a syntax error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In those cases you need to use the `model.reactions.get_by_id`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><strong>Reaction identifier</strong></td><td>12DGR120tipp</td>\n", " </tr><tr>\n", " <td><strong>Name</strong></td><td>1,2 diacylglycerol transport via flipping (periplasm to cytoplasm, n-C12:0)</td>\n", " </tr><tr>\n", " <td><strong>Memory address</strong></td>\n", " <td>0x0112506ba8</td>\n", " </tr><tr>\n", " <td><strong>Stoichiometry</strong></td>\n", " <td>\n", " <p style='text-align:right'>12dgr120_p --> 12dgr120_c</p>\n", " <p style='text-align:right'>1,2-Diacyl-sn-glycerol (didodecanoyl, n-C12:0) --> 1,2-Diacyl-sn-glycerol (didodecanoyl, n-C12:0)</p>\n", " </td>\n", " </tr><tr>\n", " <td><strong>GPR</strong></td><td></td>\n", " </tr><tr>\n", " <td><strong>Lower bound</strong></td><td>0.0</td>\n", " </tr><tr>\n", " <td><strong>Upper bound</strong></td><td>1000.0</td>\n", " </tr>\n", " </table>\n", " " ], "text/plain": [ "<Reaction 12DGR120tipp at 0x112506ba8>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.get_by_id('12DGR120tipp')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Metabolites are accessible through `model.metabolites`. For example, D-glucose in the cytosol compartment." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <table>\n", " <tr>\n", " <td><strong>Metabolite identifier</strong></td><td>glc__D_c</td>\n", " </tr><tr>\n", " <td><strong>Name</strong></td><td>D-Glucose</td>\n", " </tr><tr>\n", " <td><strong>Memory address</strong></td>\n", " <td>0x01120db4a8</td>\n", " </tr><tr>\n", " <td><strong>Formula</strong></td><td>C6H12O6</td>\n", " </tr><tr>\n", " <td><strong>Compartment</strong></td><td>c</td>\n", " </tr><tr>\n", " <td><strong>In 19 reaction(s)</strong></td><td>\n", " GLCt2pp, GLCATr, TRE6PH, MLTG2, G6PP, LACZ, MLTG3, AMALT1, HEX1, XYLI2, MLTG4, GALS3, GLCabcpp, AMALT2, MLTG5, AMALT3, AMALT4, TREH, MLTG1</td>\n", " </tr>\n", " </table>" ], "text/plain": [ "<Metabolite glc__D_c at 0x1120db4a8>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.glc__D_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And it is easy to find the associated reactions" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozenset({<Reaction AMALT1 at 0x11257a978>,\n", " <Reaction AMALT2 at 0x11257aa58>,\n", " <Reaction AMALT3 at 0x11257aa90>,\n", " <Reaction AMALT4 at 0x11257ab00>,\n", " <Reaction G6PP at 0x112671940>,\n", " <Reaction GALS3 at 0x112679630>,\n", " <Reaction GLCATr at 0x1126864e0>,\n", " <Reaction GLCabcpp at 0x11268d240>,\n", " <Reaction GLCt2pp at 0x11268d438>,\n", " <Reaction HEX1 at 0x1126bf588>,\n", " <Reaction LACZ at 0x1126e2940>,\n", " <Reaction MLTG1 at 0x1129253c8>,\n", " <Reaction MLTG2 at 0x112925518>,\n", " <Reaction MLTG3 at 0x112925550>,\n", " <Reaction MLTG4 at 0x1129255c0>,\n", " <Reaction MLTG5 at 0x112925630>,\n", " <Reaction TRE6PH at 0x112a0b4e0>,\n", " <Reaction TREH at 0x112a0b748>,\n", " <Reaction XYLI2 at 0x112a30940>})" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.glc__D_c.reactions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A list of the genes encoded in the model can be accessed via `model.genes`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[<Gene b0002 at 0x103f25748>,\n", " <Gene b0003 at 0x1034ec2e8>,\n", " <Gene b0004 at 0x1034fe3c8>,\n", " <Gene b0007 at 0x1034fe438>,\n", " <Gene b0008 at 0x1034f4780>,\n", " <Gene b0009 at 0x1034f4c88>,\n", " <Gene b0019 at 0x11237cd30>,\n", " <Gene b0025 at 0x11237cd68>,\n", " <Gene b0026 at 0x11237cda0>,\n", " <Gene b0029 at 0x11237cdd8>]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.genes[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other additional attributes can be accessed to explore the model. For example, exchange reactions that allow certain metabolites to enter or leave the model can be accessed through `model.exchanges`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<Reaction DM_4crsol_c at 0x1125f7160>,\n", " <Reaction DM_5drib_c at 0x1125f7470>,\n", " <Reaction DM_aacald_c at 0x1125f74a8>,\n", " <Reaction DM_amob_c at 0x1125f75c0>,\n", " <Reaction DM_mththf_c at 0x1125f75f8>,\n", " <Reaction DM_oxam_c at 0x1125f7630>,\n", " <Reaction EX_12ppd__R_e at 0x112613438>,\n", " <Reaction EX_12ppd__S_e at 0x112613470>,\n", " <Reaction EX_14glucan_e at 0x112613668>,\n", " <Reaction EX_15dap_e at 0x112613780>]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.exchanges[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or, the current medium can be accessed through `model.medium`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'EX_ca2_e': 1000.0,\n", " 'EX_cbl1_e': 0.01,\n", " 'EX_cl_e': 1000.0,\n", " 'EX_co2_e': 1000.0,\n", " 'EX_cobalt2_e': 1000.0,\n", " 'EX_cu2_e': 1000.0,\n", " 'EX_fe2_e': 1000.0,\n", " 'EX_fe3_e': 1000.0,\n", " 'EX_glc__D_e': 10.0,\n", " 'EX_h2o_e': 1000.0,\n", " 'EX_h_e': 1000.0,\n", " 'EX_k_e': 1000.0,\n", " 'EX_mg2_e': 1000.0,\n", " 'EX_mn2_e': 1000.0,\n", " 'EX_mobd_e': 1000.0,\n", " 'EX_na1_e': 1000.0,\n", " 'EX_nh4_e': 1000.0,\n", " 'EX_ni2_e': 1000.0,\n", " 'EX_o2_e': 1000.0,\n", " 'EX_pi_e': 1000.0,\n", " 'EX_sel_e': 1000.0,\n", " 'EX_slnt_e': 1000.0,\n", " 'EX_so4_e': 1000.0,\n", " 'EX_tungs_e': 1000.0,\n", " 'EX_zn2_e': 1000.0}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.medium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to get a list of essential reactions ..." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<Reaction THDPS at 0x1129fc080>,\n", " <Reaction UAAGDS at 0x112a1a080>,\n", " <Reaction PPNDH at 0x1129a60b8>,\n", " <Reaction CYSTL at 0x1125d40f0>,\n", " <Reaction E4PD at 0x112606160>,\n", " <Reaction KDOPP at 0x1126e2198>,\n", " <Reaction DHAD2 at 0x1125ea198>,\n", " <Reaction APRAUR at 0x1125881d0>,\n", " <Reaction METS at 0x11291e1d0>,\n", " <Reaction PAPPT3 at 0x11297c1d0>]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.flux_analysis import find_essential_reactions\n", "list(find_essential_reactions(model))[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and essential genes." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<Gene b1662 at 0x1123b6048>,\n", " <Gene b4245 at 0x112506128>,\n", " <Gene b3633 at 0x1123f0198>,\n", " <Gene b3634 at 0x1123f01d0>,\n", " <Gene b3639 at 0x1123f0208>,\n", " <Gene b4261 at 0x112506208>,\n", " <Gene b1415 at 0x1123ae240>,\n", " <Gene b4262 at 0x112506240>,\n", " <Gene b3642 at 0x1123f0278>,\n", " <Gene b1693 at 0x1123b6278>]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobra.flux_analysis import find_essential_genes\n", "list(find_essential_genes(model))[0:10]" ] } ], "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": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
AdityoSanjaya/Billy-the-Kid
.ipynb_checkpoints/Skripsi Adinda-checkpoint.ipynb
1
130779
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Uploading data Adinda" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data <- read.csv(\"adinda.clean.csv\")" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Firm</th><th scope=col>Year</th><th scope=col>CEI</th><th scope=col>BRIB_CORR</th><th scope=col>BUSS_ETH</th><th scope=col>FAIR_COMP</th><th scope=col>POL_CONTR</th><th scope=col>INDIG_PPL</th><th scope=col>IND_EC_IMP</th><th scope=col>X0TH_ENG</th><th scope=col>ellip.h</th><th scope=col>DIVIDEND</th><th scope=col>LOSS</th><th scope=col>TOT_ASSETS</th><th scope=col>SLACK</th><th scope=col>ROE</th><th scope=col>BRD_INDP_DIV</th><th scope=col>BRD_MEET_DIV</th><th scope=col>BRD_SIZE_DIV</th><th scope=col>BRD_COMPT_DIV</th><th scope=col>BRD_EFFC_DIV</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>1</td><td>2011</td><td>0.57143</td><td>0</td><td>0.5</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>⋯</td><td>30.35</td><td>0</td><td>5.144545e+13</td><td>5.081927e+12</td><td>0.22</td><td>60.7</td><td>75.875</td><td>91.05</td><td>75.875</td><td>75.875</td></tr>\n", "\t<tr><th scope=row>2</th><td>1</td><td>2012</td><td>0.54762</td><td>0</td><td>0.25</td><td>0.25</td><td>0.5</td><td>1</td><td>1</td><td>0.8333</td><td>⋯</td><td>55.82524</td><td>0</td><td>6.497336e+13</td><td>4.857942e+12</td><td>0.141</td><td>93.04225</td><td>158.1713</td><td>167.4757</td><td>139.5631</td><td>131.3534</td></tr>\n", "\t<tr><th scope=row>3</th><td>1</td><td>2013</td><td>0.52381</td><td>0</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.6667</td><td>⋯</td><td>15.36585</td><td>0</td><td>8.212195e+13</td><td>8.308451e+12</td><td>0.074</td><td>28.17067</td><td>35.8536</td><td>46.09755</td><td>38.41463</td><td>34.34713</td></tr>\n", "\t<tr><th scope=row>4</th><td>1</td><td>2014</td><td>0.52381</td><td>0</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.6667</td><td>⋯</td><td>29.375</td><td>0</td><td>8.0175e+13</td><td>9.3156e+12</td><td>0.056</td><td>44.0625</td><td>73.4375</td><td>88.125</td><td>73.4375</td><td>63.93381</td></tr>\n", "\t<tr><th scope=row>5</th><td>2</td><td>2011</td><td>0.4881</td><td>0</td><td>0.5</td><td>0.75</td><td>0</td><td>1</td><td>1</td><td>0.1667</td><td>⋯</td><td>0</td><td>0</td><td>2.301384e+12</td><td>3.59163e+11</td><td>0.0569</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>6</th><td>2</td><td>2012</td><td>0.7381</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.667</td><td>1</td><td>0.5</td><td>⋯</td><td>0</td><td>1</td><td>2.903932e+12</td><td>152631066954</td><td>-0.0773</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllll}\n", " & Firm & Year & CEI & BRIB_CORR & BUSS_ETH & FAIR_COMP & POL_CONTR & INDIG_PPL & IND_EC_IMP & X0TH_ENG & ellip.h & DIVIDEND & LOSS & TOT_ASSETS & SLACK & ROE & BRD_INDP_DIV & BRD_MEET_DIV & BRD_SIZE_DIV & BRD_COMPT_DIV & BRD_EFFC_DIV\\\\\n", "\\hline\n", "\t1 & 1 & 2011 & 0.57143 & 0 & 0.5 & 0.5 & 0.25 & 0.25 & 1 & 1 & ⋯ & 30.35 & 0 & 5.144545e+13 & 5.081927e+12 & 0.22 & 60.7 & 75.875 & 91.05 & 75.875 & 75.875\\\\\n", "\t2 & 1 & 2012 & 0.54762 & 0 & 0.25 & 0.25 & 0.5 & 1 & 1 & 0.8333 & ⋯ & 55.82524 & 0 & 6.497336e+13 & 4.857942e+12 & 0.141 & 93.04225 & 158.1713 & 167.4757 & 139.5631 & 131.3534\\\\\n", "\t3 & 1 & 2013 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & ⋯ & 15.36585 & 0 & 8.212195e+13 & 8.308451e+12 & 0.074 & 28.17067 & 35.8536 & 46.09755 & 38.41463 & 34.34713\\\\\n", "\t4 & 1 & 2014 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & ⋯ & 29.375 & 0 & 8.0175e+13 & 9.3156e+12 & 0.056 & 44.0625 & 73.4375 & 88.125 & 73.4375 & 63.93381\\\\\n", "\t5 & 2 & 2011 & 0.4881 & 0 & 0.5 & 0.75 & 0 & 1 & 1 & 0.1667 & ⋯ & 0 & 0 & 2.301384e+12 & 3.59163e+11 & 0.0569 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t6 & 2 & 2012 & 0.7381 & 0 & 1 & 1 & 1 & 0.667 & 1 & 0.5 & ⋯ & 0 & 1 & 2.903932e+12 & 152631066954 & -0.0773 & 0 & 0 & 0 & 0 & 0\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " Firm Year CEI BRIB_CORR BUSS_ETH FAIR_COMP POL_CONTR INDIG_PPL IND_EC_IMP\n", "1 1 2011 0.57143 0 0.50 0.50 0.25 0.250 1\n", "2 1 2012 0.54762 0 0.25 0.25 0.50 1.000 1\n", "3 1 2013 0.52381 0 0.50 0.25 0.25 1.000 1\n", "4 1 2014 0.52381 0 0.50 0.25 0.25 1.000 1\n", "5 2 2011 0.48810 0 0.50 0.75 0.00 1.000 1\n", "6 2 2012 0.73810 0 1.00 1.00 1.00 0.667 1\n", " X0TH_ENG ALT_CEI BRD_EFFC BRD_INDP BRD_MEET BRD_SIZE BRD_COMPT DIVIDEND LOSS\n", "1 1.0000 0.7 2.50000 2.00000 2.50000 3 2.500 30.35000 0\n", "2 0.8333 0.5 2.35294 1.66667 2.83333 3 2.500 55.82524 0\n", "3 0.6667 0.5 2.23529 1.83333 2.33333 3 2.500 15.36585 0\n", "4 0.6667 0.5 2.17647 1.50000 2.50000 3 2.500 29.37500 0\n", "5 0.1667 0.3 2.26458 1.83333 2.00000 3 2.225 0.00000 0\n", "6 0.5000 0.5 2.17647 1.83333 2.16667 3 2.500 0.00000 1\n", " TOT_ASSETS SLACK ROE BRD_INDP_DIV BRD_MEET_DIV BRD_SIZE_DIV\n", "1 5.144545e+13 5.081927e+12 0.2200 60.70000 75.8750 91.05000\n", "2 6.497336e+13 4.857942e+12 0.1410 93.04225 158.1713 167.47572\n", "3 8.212195e+13 8.308451e+12 0.0740 28.17067 35.8536 46.09755\n", "4 8.017500e+13 9.315600e+12 0.0560 44.06250 73.4375 88.12500\n", "5 2.301384e+12 3.591630e+11 0.0569 0.00000 0.0000 0.00000\n", "6 2.903932e+12 1.526311e+11 -0.0773 0.00000 0.0000 0.00000\n", " BRD_COMPT_DIV BRD_EFFC_DIV\n", "1 75.87500 75.87500\n", "2 139.56310 131.35344\n", "3 38.41463 34.34713\n", "4 73.43750 63.93381\n", "5 0.00000 0.00000\n", "6 0.00000 0.00000" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head(data)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Firm</th><th scope=col>Year</th><th scope=col>CEI</th><th scope=col>BRIB_CORR</th><th scope=col>BUSS_ETH</th><th scope=col>FAIR_COMP</th><th scope=col>POL_CONTR</th><th scope=col>INDIG_PPL</th><th scope=col>IND_EC_IMP</th><th scope=col>X0TH_ENG</th><th scope=col>ellip.h</th><th scope=col>DIVIDEND</th><th scope=col>LOSS</th><th scope=col>TOT_ASSETS</th><th scope=col>SLACK</th><th scope=col>ROE</th><th scope=col>BRD_INDP_DIV</th><th scope=col>BRD_MEET_DIV</th><th scope=col>BRD_SIZE_DIV</th><th scope=col>BRD_COMPT_DIV</th><th scope=col>BRD_EFFC_DIV</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>139</th><td>35</td><td>2013</td><td>0.9</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0.5</td><td>⋯</td><td>0</td><td>0</td><td>1.517317e+12</td><td>1.03378e+11</td><td>0.0065</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>140</th><td>35</td><td>2014</td><td>0.9</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0.5</td><td>⋯</td><td>0</td><td>1</td><td>155237500000</td><td>6.4325e+10</td><td>-0.2002</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>141</th><td>36</td><td>2011</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>5.44282e+11</td><td>23752819524</td><td>0.2485</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>142</th><td>36</td><td>2012</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>3.89007e+11</td><td>55782539000</td><td>0.1998</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>143</th><td>36</td><td>2013</td><td>0.09524</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6667</td><td>0</td><td>0</td><td>⋯</td><td>50</td><td>0</td><td>765881409376</td><td>72298136315</td><td>0.0397</td><td>90</td><td>91.6665</td><td>50</td><td>112.5</td><td>93.75</td></tr>\n", "\t<tr><th scope=row>144</th><td>36</td><td>2014</td><td>0.09524</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6667</td><td>0</td><td>0</td><td>⋯</td><td>8</td><td>0</td><td>897281657710</td><td>42022184000</td><td>0.1792</td><td>14.4</td><td>14.66664</td><td>8</td><td>18</td><td>15</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllllll}\n", " & Firm & Year & CEI & BRIB_CORR & BUSS_ETH & FAIR_COMP & POL_CONTR & INDIG_PPL & IND_EC_IMP & X0TH_ENG & ellip.h & DIVIDEND & LOSS & TOT_ASSETS & SLACK & ROE & BRD_INDP_DIV & BRD_MEET_DIV & BRD_SIZE_DIV & BRD_COMPT_DIV & BRD_EFFC_DIV\\\\\n", "\\hline\n", "\t139 & 35 & 2013 & 0.9 & 0.8 & 1 & 1 & 1 & 1 & 1 & 0.5 & ⋯ & 0 & 0 & 1.517317e+12 & 1.03378e+11 & 0.0065 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t140 & 35 & 2014 & 0.9 & 0.8 & 1 & 1 & 1 & 1 & 1 & 0.5 & ⋯ & 0 & 1 & 155237500000 & 6.4325e+10 & -0.2002 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t141 & 36 & 2011 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 5.44282e+11 & 23752819524 & 0.2485 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t142 & 36 & 2012 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 3.89007e+11 & 55782539000 & 0.1998 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t143 & 36 & 2013 & 0.09524 & 0 & 0 & 0 & 0 & 0.6667 & 0 & 0 & ⋯ & 50 & 0 & 765881409376 & 72298136315 & 0.0397 & 90 & 91.6665 & 50 & 112.5 & 93.75\\\\\n", "\t144 & 36 & 2014 & 0.09524 & 0 & 0 & 0 & 0 & 0.6667 & 0 & 0 & ⋯ & 8 & 0 & 897281657710 & 42022184000 & 0.1792 & 14.4 & 14.66664 & 8 & 18 & 15\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " Firm Year CEI BRIB_CORR BUSS_ETH FAIR_COMP POL_CONTR INDIG_PPL\n", "139 35 2013 0.90000 0.8 1 1 1 1.0000\n", "140 35 2014 0.90000 0.8 1 1 1 1.0000\n", "141 36 2011 0.00000 0.0 0 0 0 0.0000\n", "142 36 2012 0.00000 0.0 0 0 0 0.0000\n", "143 36 2013 0.09524 0.0 0 0 0 0.6667\n", "144 36 2014 0.09524 0.0 0 0 0 0.6667\n", " IND_EC_IMP X0TH_ENG ALT_CEI BRD_EFFC BRD_INDP BRD_MEET BRD_SIZE BRD_COMPT\n", "139 1 0.5 0.5 2.29412 2.00000 2.33333 3 2.50\n", "140 1 0.5 0.5 2.29412 2.00000 2.33333 3 2.50\n", "141 0 0.0 0.0 2.14583 1.16667 2.16667 3 2.25\n", "142 0 0.0 0.0 1.88235 1.16667 2.16667 3 2.25\n", "143 0 0.0 0.0 1.87500 1.80000 1.83333 1 2.25\n", "144 0 0.0 0.0 1.87500 1.80000 1.83333 1 2.25\n", " DIVIDEND LOSS TOT_ASSETS SLACK ROE BRD_INDP_DIV BRD_MEET_DIV\n", "139 0 0 1.517317e+12 103378048346 0.0065 0.0 0.00000\n", "140 0 1 1.552375e+11 64325000000 -0.2002 0.0 0.00000\n", "141 0 0 5.442820e+11 23752819524 0.2485 0.0 0.00000\n", "142 0 0 3.890070e+11 55782539000 0.1998 0.0 0.00000\n", "143 50 0 7.658814e+11 72298136315 0.0397 90.0 91.66650\n", "144 8 0 8.972817e+11 42022184000 0.1792 14.4 14.66664\n", " BRD_SIZE_DIV BRD_COMPT_DIV BRD_EFFC_DIV\n", "139 0 0.0 0.00\n", "140 0 0.0 0.00\n", "141 0 0.0 0.00\n", "142 0 0.0 0.00\n", "143 50 112.5 93.75\n", "144 8 18.0 15.00" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tail(data)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'Firm'</li>\n", "\t<li>'Year'</li>\n", "\t<li>'CEI'</li>\n", "\t<li>'BRIB_CORR'</li>\n", "\t<li>'BUSS_ETH'</li>\n", "\t<li>'FAIR_COMP'</li>\n", "\t<li>'POL_CONTR'</li>\n", "\t<li>'INDIG_PPL'</li>\n", "\t<li>'IND_EC_IMP'</li>\n", "\t<li>'X0TH_ENG'</li>\n", "\t<li>'ALT_CEI'</li>\n", "\t<li>'BRD_EFFC'</li>\n", "\t<li>'BRD_INDP'</li>\n", "\t<li>'BRD_MEET'</li>\n", "\t<li>'BRD_SIZE'</li>\n", "\t<li>'BRD_COMPT'</li>\n", "\t<li>'DIVIDEND'</li>\n", "\t<li>'LOSS'</li>\n", "\t<li>'TOT_ASSETS'</li>\n", "\t<li>'SLACK'</li>\n", "\t<li>'ROE'</li>\n", "\t<li>'BRD_INDP_DIV'</li>\n", "\t<li>'BRD_MEET_DIV'</li>\n", "\t<li>'BRD_SIZE_DIV'</li>\n", "\t<li>'BRD_COMPT_DIV'</li>\n", "\t<li>'BRD_EFFC_DIV'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Firm'\n", "\\item 'Year'\n", "\\item 'CEI'\n", "\\item 'BRIB_CORR'\n", "\\item 'BUSS_ETH'\n", "\\item 'FAIR_COMP'\n", "\\item 'POL_CONTR'\n", "\\item 'INDIG_PPL'\n", "\\item 'IND_EC_IMP'\n", "\\item 'X0TH_ENG'\n", "\\item 'ALT_CEI'\n", "\\item 'BRD_EFFC'\n", "\\item 'BRD_INDP'\n", "\\item 'BRD_MEET'\n", "\\item 'BRD_SIZE'\n", "\\item 'BRD_COMPT'\n", "\\item 'DIVIDEND'\n", "\\item 'LOSS'\n", "\\item 'TOT_ASSETS'\n", "\\item 'SLACK'\n", "\\item 'ROE'\n", "\\item 'BRD_INDP_DIV'\n", "\\item 'BRD_MEET_DIV'\n", "\\item 'BRD_SIZE_DIV'\n", "\\item 'BRD_COMPT_DIV'\n", "\\item 'BRD_EFFC_DIV'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Firm'\n", "2. 'Year'\n", "3. 'CEI'\n", "4. 'BRIB_CORR'\n", "5. 'BUSS_ETH'\n", "6. 'FAIR_COMP'\n", "7. 'POL_CONTR'\n", "8. 'INDIG_PPL'\n", "9. 'IND_EC_IMP'\n", "10. 'X0TH_ENG'\n", "11. 'ALT_CEI'\n", "12. 'BRD_EFFC'\n", "13. 'BRD_INDP'\n", "14. 'BRD_MEET'\n", "15. 'BRD_SIZE'\n", "16. 'BRD_COMPT'\n", "17. 'DIVIDEND'\n", "18. 'LOSS'\n", "19. 'TOT_ASSETS'\n", "20. 'SLACK'\n", "21. 'ROE'\n", "22. 'BRD_INDP_DIV'\n", "23. 'BRD_MEET_DIV'\n", "24. 'BRD_SIZE_DIV'\n", "25. 'BRD_COMPT_DIV'\n", "26. 'BRD_EFFC_DIV'\n", "\n", "\n" ], "text/plain": [ " [1] \"Firm\" \"Year\" \"CEI\" \"BRIB_CORR\" \n", " [5] \"BUSS_ETH\" \"FAIR_COMP\" \"POL_CONTR\" \"INDIG_PPL\" \n", " [9] \"IND_EC_IMP\" \"X0TH_ENG\" \"ALT_CEI\" \"BRD_EFFC\" \n", "[13] \"BRD_INDP\" \"BRD_MEET\" \"BRD_SIZE\" \"BRD_COMPT\" \n", "[17] \"DIVIDEND\" \"LOSS\" \"TOT_ASSETS\" \"SLACK\" \n", "[21] \"ROE\" \"BRD_INDP_DIV\" \"BRD_MEET_DIV\" \"BRD_SIZE_DIV\" \n", "[25] \"BRD_COMPT_DIV\" \"BRD_EFFC_DIV\" " ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names(data)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "In `[<-.factor`(`*tmp*`, ri, value = \"⋱\"): invalid factor level, NA generatedWarning message:\n", "In `[<-.factor`(`*tmp*`, ri, value = \"⋱\"): invalid factor level, NA generated" ] }, { "data": { "text/html": [ "<table>\n", 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scope=row>13</th><td>0.40476</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.8333</td><td>0.6</td><td>2.08333</td><td>⋯</td><td>3.55</td><td>0</td><td>18718181631000</td><td>4729745407250</td><td>0.3073</td><td>9.4666785</td><td>7.6916785</td><td>3.55</td><td>8.875</td><td>7.3958215</td></tr>\n", "\t<tr><th scope=row>14</th><td>0.48611</td><td>0</td><td>1</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.41176</td><td>⋯</td><td>7.76699</td><td>1</td><td>20854368794400</td><td>3534436869880</td><td>-0.7409</td><td>18.1229507767</td><td>15.53398</td><td>23.30097</td><td>23.30097</td><td>18.7321158024</td></tr>\n", "\t<tr><th scope=row>15</th><td>0.72143</td><td>0.8</td><td>1</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.6</td><td>2.41176</td><td>⋯</td><td>0</td><td>1</td><td>24402707214600</td><td>4977426808360</td><td>-2.03</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>16</th><td>0.80476</td><td>0.8</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0.83333</td><td>0.6</td><td>1.66667</td><td>⋯</td><td>0</td><td>1</td><td>2.21625e+13</td><td>4.243275e+12</td><td>-2.02</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>17</th><td>0.77619</td><td>0.6</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.8333</td><td>0.6</td><td>2.10417</td><td>⋯</td><td>0</td><td>0</td><td>17404410972000</td><td>1124180892000</td><td>0.04726</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>18</th><td>0.675</td><td>0.8</td><td>0.5</td><td>0.25</td><td>1</td><td>1</td><td>1</td><td>0.5</td><td>0.7</td><td>2.52941</td><td>⋯</td><td>0</td><td>1</td><td>19292300552300</td><td>1131750089620</td><td>-0.04</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>19</th><td>0.7881</td><td>0.6</td><td>0.75</td><td>0.75</td><td>0.75</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.4</td><td>⋯</td><td>0</td><td>1</td><td>24233011727500</td><td>1421649506220</td><td>0.04</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>20</th><td>0.68095</td><td>0.6</td><td>0.75</td><td>0</td><td>0.75</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.35294</td><td>⋯</td><td>0</td><td>1</td><td>23294135262500</td><td>78455012500</td><td>-0.125</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>21</th><td>0.64286</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0.5</td><td>0.5</td><td>2.45833</td><td>⋯</td><td>80</td><td>0</td><td>1.43862e+13</td><td>1.527995e+12</td><td>0.285</td><td>160</td><td>186.6664</td><td>240</td><td>200</td><td>196.6664</td></tr>\n", "\t<tr><th scope=row>22</th><td>0.64286</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0.5</td><td>0.5</td><td>2</td><td>⋯</td><td>200</td><td>0</td><td>3.61116785817e+26</td><td>4.0232875061e+26</td><td>0.078</td><td>400</td><td>300</td><td>600</td><td>500</td><td>400</td></tr>\n", "\t<tr><th scope=row>23</th><td>0.58095</td><td>0.4</td><td>0.75</td><td>0</td><td>0.75</td><td>0.6667</td><td>1</td><td>0.5</td><td>0.5</td><td>1.94118</td><td>⋯</td><td>200</td><td>0</td><td>23281768048600</td><td>1863040540960</td><td>0.078</td><td>366.666</td><td>300</td><td>600</td><td>500</td><td>388.236</td></tr>\n", "\t<tr><th scope=row>24</th><td>0.52381</td><td>0</td><td>0.5</td><td>0.5</td><td>0</td><td>1</td><td>1</td><td>0.6667</td><td>0.7</td><td>2.05882</td><td>⋯</td><td>0</td><td>1</td><td>14520703925000</td><td>1000979487500</td><td>-0.74</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>25</th><td>0.54048</td><td>0.2</td><td>0.25</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.8333</td><td>0.7</td><td>2.0625</td><td>⋯</td><td>0</td><td>1</td><td>3692053635810</td><td>337790360258</td><td>-0.08</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>26</th><td>0.48889</td><td>0.6</td><td>0.5</td><td>0.5</td><td>0</td><td>0.667</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.41176</td><td>⋯</td><td>0</td><td>1</td><td>4266755311650</td><td>229220299458</td><td>-0.14</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>27</th><td>0.5619</td><td>0.6</td><td>0.5</td><td>0.5</td><td>0</td><td>0.6667</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.17647</td><td>⋯</td><td>0</td><td>1</td><td>5359460953100</td><td>287923059766</td><td>0.14</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>28</th><td>0.87619</td><td>0.8</td><td>1</td><td>1</td><td>0.5</td><td>1</td><td>1</td><td>0.8333</td><td>0.7</td><td>2.35294</td><td>⋯</td><td>0</td><td>0</td><td>4448237587500</td><td>169956800000</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>29</th><td>0.45238</td><td>0</td><td>0.25</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.85417</td><td>⋯</td><td>0</td><td>1</td><td>1.082e+13</td><td>1.932e+12</td><td>-0.16021</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>30</th><td>0.45238</td><td>0</td><td>0.25</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.76471</td><td>⋯</td><td>0</td><td>1</td><td>11262135848000</td><td>553398054600</td><td>0.16</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>31</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>NA</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n", "\t<tr><th scope=row>115</th><td>0.70952</td><td>0.8</td><td>0.5</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.52941</td><td>⋯</td><td>56.29</td><td>0</td><td>7.883294e+12</td><td>6.13698e+11</td><td>0.13</td><td>140.725</td><td>140.725</td><td>168.87</td><td>140.725</td><td>142.3804889</td></tr>\n", "\t<tr><th scope=row>116</th><td>0.70952</td><td>0.8</td><td>0.5</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.52941</td><td>⋯</td><td>0</td><td>0</td><td>9.752477e+12</td><td>3.64496e+11</td><td>0.13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>117</th><td>0.28571</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.1667</td><td>0.1</td><td>1.79167</td><td>⋯</td><td>0</td><td>0</td><td>218251524639</td><td>10421127472</td><td>0.01</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>118</th><td>0.36111</td><td>0</td><td>0.25</td><td>0.25</td><td>0.5</td><td>1</td><td>1</td><td>0.1667</td><td>0.4</td><td>1.875</td><td>⋯</td><td>0</td><td>0</td><td>1.76001e+11</td><td>14199545260</td><td>0.035</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>119</th><td>0.5</td><td>0</td><td>0.75</td><td>0</td><td>0.75</td><td>0.6667</td><td>1</td><td>0.3333</td><td>0.2</td><td>1.88235</td><td>⋯</td><td>0</td><td>0</td><td>3.2696e+11</td><td>13048575536</td><td>0.0061</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>120</th><td>0.46429</td><td>0</td><td>0.5</td><td>0</td><td>0.75</td><td>0.6667</td><td>1</td><td>0.3333</td><td>0.2</td><td>1.88235</td><td>⋯</td><td>0</td><td>0</td><td>3.66053e+11</td><td>10313943601</td><td>0.0115</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>121</th><td>0.42857</td><td>0</td><td>0</td><td>0.5</td><td>0.5</td><td>1</td><td>1</td><td>0</td><td>0.3</td><td>2.0625</td><td>⋯</td><td>0</td><td>0</td><td>148540732335</td><td>33277276437</td><td>0.4375</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>122</th><td>0.19444</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.1667</td><td>0.3</td><td>2.3125</td><td>⋯</td><td>0</td><td>0</td><td>1.48541e+11</td><td>33277276437</td><td>0.233</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>123</th><td>0.21429</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.1667</td><td>0.1</td><td>2.52941</td><td>⋯</td><td>2</td><td>0</td><td>1.56993e+11</td><td>50006905442</td><td>0.1972</td><td>5</td><td>5</td><td>6</td><td>5</td><td>5.05882</td></tr>\n", "\t<tr><th scope=row>124</th><td>0.40476</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0.3333</td><td>0.3</td><td>2.52941</td><td>⋯</td><td>0</td><td>0</td><td>3.62679e+11</td><td>47588267793</td><td>0.0278</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>125</th><td>0.21429</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.1667</td><td>0.1</td><td>1.875</td><td>⋯</td><td>0</td><td>0</td><td>1710689375000</td><td>29855938000</td><td>0.11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>126</th><td>0.08333</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.1667</td><td>0</td><td>1.94118</td><td>⋯</td><td>0</td><td>0</td><td>1503500075000</td><td>27228598000</td><td>0.09</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>127</th><td>0.21429</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.1667</td><td>0.1</td><td>1.86667</td><td>⋯</td><td>0</td><td>0</td><td>5516122336000</td><td>1529415959</td><td>0.026</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>128</th><td>0.21429</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.3333</td><td>1</td><td>0.167</td><td>0.1</td><td>1.86667</td><td>⋯</td><td>0</td><td>1</td><td>5512452937000</td><td>225319391000</td><td>-0.031</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>129</th><td>0.65476</td><td>0</td><td>0.75</td><td>0.75</td><td>0.75</td><td>0.6667</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.25</td><td>⋯</td><td>190.90909</td><td>0</td><td>2238390623070</td><td>551267676306</td><td>0.34</td><td>286.363635</td><td>381.81818</td><td>572.72727</td><td>477.272725</td><td>429.5454525</td></tr>\n", "\t<tr><th scope=row>130</th><td>0.43056</td><td>0</td><td>0.5</td><td>0.5</td><td>0.25</td><td>0.6667</td><td>1</td><td>0.6667</td><td>0.2</td><td>2.11765</td><td>⋯</td><td>436.8932</td><td>0</td><td>2606194157560</td><td>276620415650</td><td>0.24</td><td>728.156789644</td><td>946.603389644</td><td>1310.6796</td><td>1092.233</td><td>925.18688498</td></tr>\n", "\t<tr><th scope=row>131</th><td>0.69762</td><td>0.8</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.8333</td><td>0.6</td><td>2.11765</td><td>⋯</td><td>457.31707</td><td>0</td><td>3343304864010</td><td>508152973476</td><td>0.25</td><td>685.975605</td><td>1067.07163894</td><td>1371.95121</td><td>1143.292675</td><td>968.437493285</td></tr>\n", "\t<tr><th scope=row>132</th><td>0.69762</td><td>0.8</td><td>0.25</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.8333</td><td>0.6</td><td>2.11765</td><td>⋯</td><td>468.5</td><td>0</td><td>3248687500000</td><td>496212500000</td><td>0.17</td><td>702.75</td><td>1093.165105</td><td>1405.5</td><td>1171.25</td><td>992.119025</td></tr>\n", "\t<tr><th scope=row>133</th><td>0.74048</td><td>0.6</td><td>0.75</td><td>0.75</td><td>0.75</td><td>1</td><td>1</td><td>0.3333</td><td>0.3</td><td>2.375</td><td>⋯</td><td>74</td><td>0</td><td>1.8253817e+13</td><td>3.433645e+12</td><td>0.14</td><td>123.33358</td><td>172.66642</td><td>222</td><td>185</td><td>175.75</td></tr>\n", "\t<tr><th scope=row>134</th><td>0.62222</td><td>0.4</td><td>0.5</td><td>0.5</td><td>0.5</td><td>1</td><td>1</td><td>0.8333</td><td>0.7</td><td>2.23529</td><td>⋯</td><td>60</td><td>0</td><td>22788971267100</td><td>3401705472690</td><td>0.03</td><td>109.9998</td><td>139.9998</td><td>180</td><td>150</td><td>134.1174</td></tr>\n", "\t<tr><th scope=row>135</th><td>0.67619</td><td>0.4</td><td>0.5</td><td>0.5</td><td>0.5</td><td>1</td><td>1</td><td>0.8333</td><td>0.7</td><td>2.29412</td><td>⋯</td><td>44.47561</td><td>1</td><td>28247845198400</td><td>3982529775960</td><td>-7e-04</td><td>81.5384700813</td><td>111.189025</td><td>133.42683</td><td>111.189025</td><td>102.032386413</td></tr>\n", "\t<tr><th scope=row>136</th><td>0.67619</td><td>0.4</td><td>0.5</td><td>0.5</td><td>0.5</td><td>1</td><td>1</td><td>0.83333</td><td>0.7</td><td>2.29412</td><td>⋯</td><td>0</td><td>1</td><td>28629403650000</td><td>4158715150000</td><td>-0.0315</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>137</th><td>0.78095</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0.6667</td><td>0.6</td><td>2.375</td><td>⋯</td><td>10</td><td>1</td><td>921277510000</td><td>85076059000</td><td>-0.0434</td><td>18.3333</td><td>21.6667</td><td>30</td><td>25</td><td>23.75</td></tr>\n", "\t<tr><th scope=row>138</th><td>0.74444</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.29412</td><td>⋯</td><td>0</td><td>1</td><td>1073941740480</td><td>77320387839.2</td><td>-0.1516</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>139</th><td>0.9</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0.5</td><td>0.5</td><td>2.29412</td><td>⋯</td><td>0</td><td>0</td><td>1517317066800</td><td>103378048346</td><td>0.0065</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>140</th><td>0.9</td><td>0.8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0.5</td><td>0.5</td><td>2.29412</td><td>⋯</td><td>0</td><td>1</td><td>155237500000</td><td>6.4325e+10</td><td>-0.2002</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>141</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.14583</td><td>⋯</td><td>0</td><td>0</td><td>5.44282e+11</td><td>23752819524</td><td>0.2485</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>142</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.88235</td><td>⋯</td><td>0</td><td>0</td><td>3.89007e+11</td><td>55782539000</td><td>0.1998</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>143</th><td>0.09524</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6667</td><td>0</td><td>0</td><td>0</td><td>1.875</td><td>⋯</td><td>50</td><td>0</td><td>765881409376</td><td>72298136315</td><td>0.0397</td><td>90</td><td>91.6665</td><td>50</td><td>112.5</td><td>93.75</td></tr>\n", "\t<tr><th scope=row>144</th><td>0.09524</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.6667</td><td>0</td><td>0</td><td>0</td><td>1.875</td><td>⋯</td><td>8</td><td>0</td><td>897281657710</td><td>42022184000</td><td>0.1792</td><td>14.4</td><td>14.66664</td><td>8</td><td>18</td><td>15</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllll}\n", " & CEI & BRIB_CORR & BUSS_ETH & FAIR_COMP & POL_CONTR & INDIG_PPL & IND_EC_IMP & X0TH_ENG & ALT_CEI & BRD_EFFC & ellip.h & DIVIDEND & LOSS & TOT_ASSETS & SLACK & ROE & BRD_INDP_DIV & BRD_MEET_DIV & BRD_SIZE_DIV & BRD_COMPT_DIV & BRD_EFFC_DIV\\\\\n", "\\hline\n", "\t1 & 0.57143 & 0 & 0.5 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.7 & 2.5 & ⋯ & 30.35 & 0 & 51445454031000 & 5081927221910 & 0.22 & 60.7 & 75.875 & 91.05 & 75.875 & 75.875\\\\\n", "\t2 & 0.54762 & 0 & 0.25 & 0.25 & 0.5 & 1 & 1 & 0.8333 & 0.5 & 2.35294 & ⋯ & 55.82524 & 0 & 64973358794500 & 4857941715510 & 0.141 & 93.0422527508 & 158.171327249 & 167.47572 & 139.5631 & 131.353440206\\\\\n", "\t3 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & 0.5 & 2.23529 & ⋯ & 15.36585 & 0 & 82121950874600 & 8308451184620 & 0.074 & 28.1706737805 & 35.8535987805 & 46.09755 & 38.414625 & 34.3471308465\\\\\n", "\t4 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & 0.5 & 2.17647 & ⋯ & 29.375 & 0 & 8.0175e+13 & 9.3156e+12 & 0.056 & 44.0625 & 73.4375 & 88.125 & 73.4375 & 63.93380625\\\\\n", "\t5 & 0.4881 & 0 & 0.5 & 0.75 & 0 & 1 & 1 & 0.1667 & 0.3 & 2.26458 & ⋯ & 0 & 0 & 2.301384e+12 & 3.59163e+11 & 0.0569 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t6 & 0.7381 & 0 & 1 & 1 & 1 & 0.667 & 1 & 0.5 & 0.5 & 2.17647 & ⋯ & 0 & 1 & 2903932019670 & 152631066954 & -0.0773 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t7 & 0.45238 & 0 & 1 & 0 & 0 & 0.6667 & 1 & 0.5 & 0.6 & 2.41176 & ⋯ & 0 & 1 & 3855817056980 & 22475609661.7 & -0.0799 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t8 & 0.78571 & 0 & 1 & 1 & 1 & 1 & 1 & 0.5 & 0.4 & 2.29412 & ⋯ & 0 & 1 & 4239362500000 & 5.275e+10 & -0.2293 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1.77083 & ⋯ & 0 & 1 & 111660087000 & 3469378000 & -0.6431 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t10 & 0.19444 & 0 & 0 & 0 & 0 & 1 & 0.5 & 0.1667 & 0.3 & 2.05882 & ⋯ & 0 & 1 & 150829601 & 2807016 & -0.382 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t11 & 0.19048 & 0 & 0 & 0 & 0 & 0.6667 & 0.5 & 0.1667 & 0.2 & 2.11765 & ⋯ & 0 & 0 & 1489339945000 & 30159881 & 0.012 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t12 & 0.4881 & 0 & 0.25 & 0 & 0.5 & 1 & 1 & 0.6667 & 0.4 & 2.05882 & ⋯ & 0 & 1 & 150829601000 & 3185917000 & -0.361 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t13 & 0.40476 & 0 & 0 & 0 & 0 & 1 & 1 & 0.8333 & 0.6 & 2.08333 & ⋯ & 3.55 & 0 & 18718181631000 & 4729745407250 & 0.3073 & 9.4666785 & 7.6916785 & 3.55 & 8.875 & 7.3958215\\\\\n", "\t14 & 0.48611 & 0 & 1 & 0.25 & 0 & 1 & 1 & 0.6667 & 0.6 & 2.41176 & ⋯ & 7.76699 & 1 & 20854368794400 & 3534436869880 & -0.7409 & 18.1229507767 & 15.53398 & 23.30097 & 23.30097 & 18.7321158024\\\\\n", "\t15 & 0.72143 & 0.8 & 1 & 0.25 & 0 & 1 & 1 & 1 & 0.6 & 2.41176 & ⋯ & 0 & 1 & 24402707214600 & 4977426808360 & -2.03 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t16 & 0.80476 & 0.8 & 1 & 1 & 0 & 1 & 1 & 0.83333 & 0.6 & 1.66667 & ⋯ & 0 & 1 & 2.21625e+13 & 4.243275e+12 & -2.02 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t17 & 0.77619 & 0.6 & 1 & 0 & 1 & 1 & 1 & 0.8333 & 0.6 & 2.10417 & ⋯ & 0 & 0 & 17404410972000 & 1124180892000 & 0.04726 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t18 & 0.675 & 0.8 & 0.5 & 0.25 & 1 & 1 & 1 & 0.5 & 0.7 & 2.52941 & ⋯ & 0 & 1 & 19292300552300 & 1131750089620 & -0.04 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t19 & 0.7881 & 0.6 & 0.75 & 0.75 & 0.75 & 1 & 1 & 0.6667 & 0.6 & 2.4 & ⋯ & 0 & 1 & 24233011727500 & 1421649506220 & 0.04 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t20 & 0.68095 & 0.6 & 0.75 & 0 & 0.75 & 1 & 1 & 0.6667 & 0.6 & 2.35294 & ⋯ & 0 & 1 & 23294135262500 & 78455012500 & -0.125 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t21 & 0.64286 & 0 & 1 & 1 & 0 & 1 & 1 & 0.5 & 0.5 & 2.45833 & ⋯ & 80 & 0 & 1.43862e+13 & 1.527995e+12 & 0.285 & 160 & 186.6664 & 240 & 200 & 196.6664\\\\\n", "\t22 & 0.64286 & 0 & 1 & 1 & 0 & 1 & 1 & 0.5 & 0.5 & 2 & ⋯ & 200 & 0 & 3.61116785817e+26 & 4.0232875061e+26 & 0.078 & 400 & 300 & 600 & 500 & 400\\\\\n", "\t23 & 0.58095 & 0.4 & 0.75 & 0 & 0.75 & 0.6667 & 1 & 0.5 & 0.5 & 1.94118 & ⋯ & 200 & 0 & 23281768048600 & 1863040540960 & 0.078 & 366.666 & 300 & 600 & 500 & 388.236\\\\\n", "\t24 & 0.52381 & 0 & 0.5 & 0.5 & 0 & 1 & 1 & 0.6667 & 0.7 & 2.05882 & ⋯ & 0 & 1 & 14520703925000 & 1000979487500 & -0.74 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t25 & 0.54048 & 0.2 & 0.25 & 0.25 & 0.25 & 1 & 1 & 0.8333 & 0.7 & 2.0625 & ⋯ & 0 & 1 & 3692053635810 & 337790360258 & -0.08 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t26 & 0.48889 & 0.6 & 0.5 & 0.5 & 0 & 0.667 & 1 & 0.6667 & 0.5 & 2.41176 & ⋯ & 0 & 1 & 4266755311650 & 229220299458 & -0.14 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t27 & 0.5619 & 0.6 & 0.5 & 0.5 & 0 & 0.6667 & 1 & 0.6667 & 0.5 & 2.17647 & ⋯ & 0 & 1 & 5359460953100 & 287923059766 & 0.14 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t28 & 0.87619 & 0.8 & 1 & 1 & 0.5 & 1 & 1 & 0.8333 & 0.7 & 2.35294 & ⋯ & 0 & 0 & 4448237587500 & 169956800000 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t29 & 0.45238 & 0 & 0.25 & 0.25 & 0 & 1 & 1 & 0.6667 & 0.6 & 2.85417 & ⋯ & 0 & 1 & 1.082e+13 & 1.932e+12 & -0.16021 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t30 & 0.45238 & 0 & 0.25 & 0.25 & 0 & 1 & 1 & 0.6667 & 0.6 & 2.76471 & ⋯ & 0 & 1 & 11262135848000 & 553398054600 & 0.16 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t31 & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & NA & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t115 & 0.70952 & 0.8 & 0.5 & 0 & 1 & 1 & 1 & 0.6667 & 0.5 & 2.52941 & ⋯ & 56.29 & 0 & 7.883294e+12 & 6.13698e+11 & 0.13 & 140.725 & 140.725 & 168.87 & 140.725 & 142.3804889\\\\\n", "\t116 & 0.70952 & 0.8 & 0.5 & 0 & 1 & 1 & 1 & 0.6667 & 0.6 & 2.52941 & ⋯ & 0 & 0 & 9.752477e+12 & 3.64496e+11 & 0.13 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t117 & 0.28571 & 0 & 0.5 & 0 & 0 & 0.3333 & 1 & 0.1667 & 0.1 & 1.79167 & ⋯ & 0 & 0 & 218251524639 & 10421127472 & 0.01 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t118 & 0.36111 & 0 & 0.25 & 0.25 & 0.5 & 1 & 1 & 0.1667 & 0.4 & 1.875 & ⋯ & 0 & 0 & 1.76001e+11 & 14199545260 & 0.035 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t119 & 0.5 & 0 & 0.75 & 0 & 0.75 & 0.6667 & 1 & 0.3333 & 0.2 & 1.88235 & ⋯ & 0 & 0 & 3.2696e+11 & 13048575536 & 0.0061 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t120 & 0.46429 & 0 & 0.5 & 0 & 0.75 & 0.6667 & 1 & 0.3333 & 0.2 & 1.88235 & ⋯ & 0 & 0 & 3.66053e+11 & 10313943601 & 0.0115 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t121 & 0.42857 & 0 & 0 & 0.5 & 0.5 & 1 & 1 & 0 & 0.3 & 2.0625 & ⋯ & 0 & 0 & 148540732335 & 33277276437 & 0.4375 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t122 & 0.19444 & 0 & 0 & 0 & 0 & 1 & 1 & 0.1667 & 0.3 & 2.3125 & ⋯ & 0 & 0 & 1.48541e+11 & 33277276437 & 0.233 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t123 & 0.21429 & 0 & 0 & 0 & 0 & 0.3333 & 1 & 0.1667 & 0.1 & 2.52941 & ⋯ & 2 & 0 & 1.56993e+11 & 50006905442 & 0.1972 & 5 & 5 & 6 & 5 & 5.05882\\\\\n", "\t124 & 0.40476 & 0 & 0.5 & 0 & 0 & 1 & 1 & 0.3333 & 0.3 & 2.52941 & ⋯ & 0 & 0 & 3.62679e+11 & 47588267793 & 0.0278 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t125 & 0.21429 & 0 & 0 & 0 & 0 & 0.3333 & 1 & 0.1667 & 0.1 & 1.875 & ⋯ & 0 & 0 & 1710689375000 & 29855938000 & 0.11 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t126 & 0.08333 & 0 & 0 & 0 & 0 & 0.3333 & 1 & 0.1667 & 0 & 1.94118 & ⋯ & 0 & 0 & 1503500075000 & 27228598000 & 0.09 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t127 & 0.21429 & 0 & 0 & 0 & 0 & 0.3333 & 1 & 0.1667 & 0.1 & 1.86667 & ⋯ & 0 & 0 & 5516122336000 & 1529415959 & 0.026 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t128 & 0.21429 & 0 & 0 & 0 & 0 & 0.3333 & 1 & 0.167 & 0.1 & 1.86667 & ⋯ & 0 & 1 & 5512452937000 & 225319391000 & -0.031 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t129 & 0.65476 & 0 & 0.75 & 0.75 & 0.75 & 0.6667 & 1 & 0.6667 & 0.5 & 2.25 & ⋯ & 190.90909 & 0 & 2238390623070 & 551267676306 & 0.34 & 286.363635 & 381.81818 & 572.72727 & 477.272725 & 429.5454525\\\\\n", "\t130 & 0.43056 & 0 & 0.5 & 0.5 & 0.25 & 0.6667 & 1 & 0.6667 & 0.2 & 2.11765 & ⋯ & 436.8932 & 0 & 2606194157560 & 276620415650 & 0.24 & 728.156789644 & 946.603389644 & 1310.6796 & 1092.233 & 925.18688498\\\\\n", "\t131 & 0.69762 & 0.8 & 0.25 & 0 & 1 & 1 & 1 & 0.8333 & 0.6 & 2.11765 & ⋯ & 457.31707 & 0 & 3343304864010 & 508152973476 & 0.25 & 685.975605 & 1067.07163894 & 1371.95121 & 1143.292675 & 968.437493285\\\\\n", "\t132 & 0.69762 & 0.8 & 0.25 & 0 & 1 & 1 & 1 & 0.8333 & 0.6 & 2.11765 & ⋯ & 468.5 & 0 & 3248687500000 & 496212500000 & 0.17 & 702.75 & 1093.165105 & 1405.5 & 1171.25 & 992.119025\\\\\n", "\t133 & 0.74048 & 0.6 & 0.75 & 0.75 & 0.75 & 1 & 1 & 0.3333 & 0.3 & 2.375 & ⋯ & 74 & 0 & 1.8253817e+13 & 3.433645e+12 & 0.14 & 123.33358 & 172.66642 & 222 & 185 & 175.75\\\\\n", "\t134 & 0.62222 & 0.4 & 0.5 & 0.5 & 0.5 & 1 & 1 & 0.8333 & 0.7 & 2.23529 & ⋯ & 60 & 0 & 22788971267100 & 3401705472690 & 0.03 & 109.9998 & 139.9998 & 180 & 150 & 134.1174\\\\\n", "\t135 & 0.67619 & 0.4 & 0.5 & 0.5 & 0.5 & 1 & 1 & 0.8333 & 0.7 & 2.29412 & ⋯ & 44.47561 & 1 & 28247845198400 & 3982529775960 & -7e-04 & 81.5384700813 & 111.189025 & 133.42683 & 111.189025 & 102.032386413\\\\\n", "\t136 & 0.67619 & 0.4 & 0.5 & 0.5 & 0.5 & 1 & 1 & 0.83333 & 0.7 & 2.29412 & ⋯ & 0 & 1 & 28629403650000 & 4158715150000 & -0.0315 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t137 & 0.78095 & 0.8 & 1 & 1 & 1 & 0 & 1 & 0.6667 & 0.6 & 2.375 & ⋯ & 10 & 1 & 921277510000 & 85076059000 & -0.0434 & 18.3333 & 21.6667 & 30 & 25 & 23.75\\\\\n", "\t138 & 0.74444 & 0.8 & 1 & 1 & 1 & 0 & 1 & 0.6667 & 0.5 & 2.29412 & ⋯ & 0 & 1 & 1073941740480 & 77320387839.2 & -0.1516 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t139 & 0.9 & 0.8 & 1 & 1 & 1 & 1 & 1 & 0.5 & 0.5 & 2.29412 & ⋯ & 0 & 0 & 1517317066800 & 103378048346 & 0.0065 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t140 & 0.9 & 0.8 & 1 & 1 & 1 & 1 & 1 & 0.5 & 0.5 & 2.29412 & ⋯ & 0 & 1 & 155237500000 & 6.4325e+10 & -0.2002 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t141 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2.14583 & ⋯ & 0 & 0 & 5.44282e+11 & 23752819524 & 0.2485 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t142 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1.88235 & ⋯ & 0 & 0 & 3.89007e+11 & 55782539000 & 0.1998 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t143 & 0.09524 & 0 & 0 & 0 & 0 & 0.6667 & 0 & 0 & 0 & 1.875 & ⋯ & 50 & 0 & 765881409376 & 72298136315 & 0.0397 & 90 & 91.6665 & 50 & 112.5 & 93.75\\\\\n", "\t144 & 0.09524 & 0 & 0 & 0 & 0 & 0.6667 & 0 & 0 & 0 & 1.875 & ⋯ & 8 & 0 & 897281657710 & 42022184000 & 0.1792 & 14.4 & 14.66664 & 8 & 18 & 15\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " CEI BRIB_CORR BUSS_ETH FAIR_COMP POL_CONTR INDIG_PPL IND_EC_IMP\n", "1 0.57143 0.0 0.50 0.50 0.25 0.25000 1.0\n", "2 0.54762 0.0 0.25 0.25 0.50 1.00000 1.0\n", "3 0.52381 0.0 0.50 0.25 0.25 1.00000 1.0\n", "4 0.52381 0.0 0.50 0.25 0.25 1.00000 1.0\n", "5 0.48810 0.0 0.50 0.75 0.00 1.00000 1.0\n", "6 0.73810 0.0 1.00 1.00 1.00 0.66700 1.0\n", "7 0.45238 0.0 1.00 0.00 0.00 0.66670 1.0\n", "8 0.78571 0.0 1.00 1.00 1.00 1.00000 1.0\n", "9 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "10 0.19444 0.0 0.00 0.00 0.00 1.00000 0.5\n", "11 0.19048 0.0 0.00 0.00 0.00 0.66670 0.5\n", "12 0.48810 0.0 0.25 0.00 0.50 1.00000 1.0\n", "13 0.40476 0.0 0.00 0.00 0.00 1.00000 1.0\n", "14 0.48611 0.0 1.00 0.25 0.00 1.00000 1.0\n", "15 0.72143 0.8 1.00 0.25 0.00 1.00000 1.0\n", "16 0.80476 0.8 1.00 1.00 0.00 1.00000 1.0\n", "17 0.77619 0.6 1.00 0.00 1.00 1.00000 1.0\n", "18 0.67500 0.8 0.50 0.25 1.00 1.00000 1.0\n", "19 0.78810 0.6 0.75 0.75 0.75 1.00000 1.0\n", "20 0.68095 0.6 0.75 0.00 0.75 1.00000 1.0\n", "21 0.64286 0.0 1.00 1.00 0.00 1.00000 1.0\n", "22 0.64286 0.0 1.00 1.00 0.00 1.00000 1.0\n", "23 0.58095 0.4 0.75 0.00 0.75 0.66670 1.0\n", "24 0.52381 0.0 0.50 0.50 0.00 1.00000 1.0\n", "25 0.54048 0.2 0.25 0.25 0.25 1.00000 1.0\n", "26 0.48889 0.6 0.50 0.50 0.00 0.66700 1.0\n", "27 0.56190 0.6 0.50 0.50 0.00 0.66670 1.0\n", "28 0.87619 0.8 1.00 1.00 0.50 1.00000 1.0\n", "29 0.45238 0.0 0.25 0.25 0.00 1.00000 1.0\n", "30 0.45238 0.0 0.25 0.25 0.00 1.00000 1.0\n", "31 0.65238 0.4 0.50 0.50 0.50 1.00000 1.0\n", "32 0.60714 0.0 1.00 0.00 0.75 0.66670 1.0\n", "33 0.35714 0.0 0.00 0.00 0.00 1.00000 1.0\n", "34 0.36111 0.0 0.50 0.00 0.00 1.00000 1.0\n", "35 0.47619 0.0 0.50 0.00 0.00 1.00000 1.0\n", "36 0.75476 0.2 1.00 0.50 0.75 1.00000 1.0\n", "37 0.56905 0.4 0.75 0.75 0.75 0.66670 0.5\n", "38 0.27778 0.0 0.00 0.50 0.50 0.66700 1.0\n", "39 0.38095 0.0 0.00 0.50 0.50 0.66670 1.0\n", "40 0.61905 0.0 0.75 0.50 0.75 1.00000 1.0\n", "41 0.35714 0.0 0.00 0.00 0.00 1.00000 0.5\n", "42 0.33333 0.0 0.00 0.00 0.50 1.00000 1.0\n", "43 0.33333 0.0 0.00 0.00 0.00 1.00000 1.0\n", "44 0.38095 0.0 0.00 0.00 0.00 1.00000 1.0\n", "45 0.94762 0.8 1.00 1.00 1.00 1.00000 1.0\n", "46 0.78611 0.8 0.75 0.75 0.75 1.00000 1.0\n", "47 0.84048 0.8 0.75 0.75 0.75 1.00000 1.0\n", "48 0.81667 0.8 0.75 0.75 0.75 1.00000 1.0\n", "49 0.42857 0.0 0.50 0.00 0.50 0.66700 1.0\n", "50 0.57222 0.6 0.75 0.00 0.75 0.66700 1.0\n", "51 0.47619 0.0 0.50 0.50 0.00 1.00000 1.0\n", "52 0.76429 0.6 0.50 1.00 0.75 1.00000 1.0\n", "53 0.21429 0.0 0.00 0.00 0.00 0.66700 0.5\n", "54 0.42500 0.8 0.25 0.00 0.00 1.00000 1.0\n", "55 0.75714 0.8 0.75 0.75 1.00 0.66670 1.0\n", "56 0.74524 0.8 0.50 0.75 1.00 0.66670 1.0\n", "57 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "58 0.02778 0.0 0.00 0.00 0.00 0.00000 0.0\n", "59 0.02381 0.0 0.00 0.00 0.00 0.00000 0.0\n", "60 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "61 0.45238 0.0 0.50 0.00 0.00 1.00000 1.0\n", "62 0.44444 0.0 0.50 0.50 0.00 1.00000 1.0\n", "63 0.47619 0.0 0.50 0.00 0.50 0.66670 1.0\n", "64 0.73333 0.8 1.00 0.00 1.00 0.66670 1.0\n", "65 0.37500 0.0 0.50 0.00 0.25 1.00000 1.0\n", "66 0.37500 0.0 0.50 0.00 0.25 1.00000 1.0\n", "67 0.46429 0.0 0.50 0.00 0.25 1.00000 1.0\n", "68 0.46429 0.0 0.50 0.00 0.25 1.00000 1.0\n", "69 0.23810 0.0 0.00 0.00 0.00 0.33330 1.0\n", "70 0.16667 0.0 0.00 0.00 0.00 0.66700 0.0\n", "71 0.14286 0.0 0.00 0.00 0.00 0.66670 0.0\n", "72 0.14286 0.0 0.00 0.00 0.00 0.66670 0.0\n", "73 0.80476 0.8 1.00 1.00 0.00 1.00000 1.0\n", "74 0.65556 0.6 1.00 0.00 1.00 0.66700 1.0\n", "75 0.48571 0.4 1.00 0.00 0.00 0.66670 1.0\n", "76 0.70476 0.6 1.00 0.00 1.00 0.66670 1.0\n", "77 0.40476 0.0 0.50 0.00 0.00 0.66700 1.0\n", "78 0.37222 0.4 0.50 0.00 0.00 0.66700 1.0\n", "79 0.46190 0.4 0.50 0.00 0.00 0.66670 1.0\n", "80 0.48571 0.4 0.50 0.00 0.00 0.66670 1.0\n", "81 0.26190 0.0 0.00 0.00 0.00 0.33330 1.0\n", "82 0.22222 0.0 0.00 0.00 0.00 1.00000 1.0\n", "83 0.35714 0.0 0.00 0.00 0.00 1.00000 1.0\n", "84 0.45238 0.0 0.50 0.00 0.00 1.00000 1.0\n", "85 0.70000 0.4 1.00 1.00 0.50 0.33330 1.0\n", "86 0.50278 0.6 0.75 0.00 0.00 1.00000 1.0\n", "87 0.69524 0.2 0.75 1.00 0.25 1.00000 1.0\n", "88 0.57381 0.6 0.75 0.00 0.00 1.00000 1.0\n", "89 0.62619 0.8 0.75 0.00 0.00 1.00000 1.0\n", "90 0.93889 0.8 1.00 1.00 1.00 1.00000 1.0\n", "91 0.94762 0.8 1.00 1.00 1.00 1.00000 1.0\n", "92 0.94762 0.8 1.00 1.00 1.00 1.00000 1.0\n", "93 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "94 0.26190 0.0 0.00 0.00 0.00 0.66670 1.0\n", "95 0.26190 0.0 0.00 0.00 0.00 0.66670 1.0\n", "96 0.26190 0.0 0.00 0.00 0.00 0.66670 1.0\n", "97 0.16667 0.0 0.00 0.00 0.00 0.66670 0.5\n", "98 0.29167 0.0 0.00 0.00 0.25 1.00000 1.0\n", "99 0.39286 0.0 0.00 0.00 0.25 1.00000 1.0\n", "100 0.36905 0.0 0.00 0.00 0.25 1.00000 1.0\n", "101 0.44048 0.0 0.25 0.00 0.00 1.00000 1.0\n", "102 0.93889 0.8 1.00 1.00 1.00 1.00000 1.0\n", "103 0.75714 0.8 1.00 0.00 0.50 1.00000 1.0\n", "104 0.75714 0.8 1.00 0.00 0.50 1.00000 1.0\n", "105 0.07143 0.0 0.00 0.00 0.00 0.33333 0.0\n", "106 0.11111 0.0 0.00 0.00 0.00 0.33330 1.0\n", "107 0.35714 0.0 0.00 0.00 0.00 1.00000 1.0\n", "108 0.30952 0.0 0.00 0.00 0.00 0.66670 1.0\n", "109 0.30952 0.0 0.00 0.00 0.00 1.00000 1.0\n", "110 0.19444 0.0 0.00 0.00 0.00 1.00000 0.0\n", "111 0.33333 0.0 0.00 0.00 0.00 1.00000 1.0\n", "112 0.16667 0.0 0.00 0.00 0.00 1.00000 0.0\n", "113 0.95238 1.0 1.00 1.00 1.00 1.00000 1.0\n", "114 0.94444 1.0 1.00 1.00 1.00 1.00000 1.0\n", "115 0.70952 0.8 0.50 0.00 1.00 1.00000 1.0\n", "116 0.70952 0.8 0.50 0.00 1.00 1.00000 1.0\n", "117 0.28571 0.0 0.50 0.00 0.00 0.33330 1.0\n", "118 0.36111 0.0 0.25 0.25 0.50 1.00000 1.0\n", "119 0.50000 0.0 0.75 0.00 0.75 0.66670 1.0\n", "120 0.46429 0.0 0.50 0.00 0.75 0.66670 1.0\n", "121 0.42857 0.0 0.00 0.50 0.50 1.00000 1.0\n", "122 0.19444 0.0 0.00 0.00 0.00 1.00000 1.0\n", "123 0.21429 0.0 0.00 0.00 0.00 0.33330 1.0\n", "124 0.40476 0.0 0.50 0.00 0.00 1.00000 1.0\n", "125 0.21429 0.0 0.00 0.00 0.00 0.33330 1.0\n", "126 0.08333 0.0 0.00 0.00 0.00 0.33330 1.0\n", "127 0.21429 0.0 0.00 0.00 0.00 0.33330 1.0\n", "128 0.21429 0.0 0.00 0.00 0.00 0.33330 1.0\n", "129 0.65476 0.0 0.75 0.75 0.75 0.66670 1.0\n", "130 0.43056 0.0 0.50 0.50 0.25 0.66670 1.0\n", "131 0.69762 0.8 0.25 0.00 1.00 1.00000 1.0\n", "132 0.69762 0.8 0.25 0.00 1.00 1.00000 1.0\n", "133 0.74048 0.6 0.75 0.75 0.75 1.00000 1.0\n", "134 0.62222 0.4 0.50 0.50 0.50 1.00000 1.0\n", "135 0.67619 0.4 0.50 0.50 0.50 1.00000 1.0\n", "136 0.67619 0.4 0.50 0.50 0.50 1.00000 1.0\n", "137 0.78095 0.8 1.00 1.00 1.00 0.00000 1.0\n", "138 0.74444 0.8 1.00 1.00 1.00 0.00000 1.0\n", "139 0.90000 0.8 1.00 1.00 1.00 1.00000 1.0\n", "140 0.90000 0.8 1.00 1.00 1.00 1.00000 1.0\n", "141 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "142 0.00000 0.0 0.00 0.00 0.00 0.00000 0.0\n", "143 0.09524 0.0 0.00 0.00 0.00 0.66670 0.0\n", "144 0.09524 0.0 0.00 0.00 0.00 0.66670 0.0\n", " X0TH_ENG ALT_CEI BRD_EFFC BRD_INDP BRD_MEET BRD_SIZE BRD_COMPT DIVIDEND\n", "1 1.00000 0.7 2.50000 2.00000 2.50000 3 2.50000 30.35000\n", "2 0.83330 0.5 2.35294 1.66667 2.83333 3 2.50000 55.82524\n", "3 0.66670 0.5 2.23529 1.83333 2.33333 3 2.50000 15.36585\n", "4 0.66670 0.5 2.17647 1.50000 2.50000 3 2.50000 29.37500\n", "5 0.16670 0.3 2.26458 1.83333 2.00000 3 2.22500 0.00000\n", "6 0.50000 0.5 2.17647 1.83333 2.16667 3 2.50000 0.00000\n", "7 0.50000 0.6 2.41176 1.83333 2.66667 3 2.75000 0.00000\n", "8 0.50000 0.4 2.29412 1.83333 2.33333 3 2.75000 0.00000\n", "9 0.00000 0.0 1.77083 1.83333 2.00000 1 2.25000 0.00000\n", "10 0.16670 0.3 2.05882 2.00000 2.33333 1 2.00000 0.00000\n", "11 0.16670 0.2 2.11765 2.16667 2.33333 1 2.00000 0.00000\n", "12 0.66670 0.4 2.05882 2.00000 2.00000 1 2.50000 0.00000\n", "13 0.83330 0.6 2.08333 2.66667 2.16667 1 2.50000 3.55000\n", "14 0.66670 0.6 2.41176 2.33333 2.00000 3 3.00000 7.76699\n", "15 1.00000 0.6 2.41176 2.16667 2.33333 3 2.75000 0.00000\n", "16 0.83333 0.6 1.66667 1.66667 1.50000 1 2.00000 0.00000\n", "17 0.83330 0.6 2.10417 2.16667 2.50000 1 2.75000 0.00000\n", "18 0.50000 0.7 2.52941 2.33333 2.83333 1 2.75000 0.00000\n", "19 0.66670 0.6 2.40000 2.50000 2.50000 1 2.50000 0.00000\n", "20 0.66670 0.6 2.35294 2.16667 2.50000 1 2.75000 0.00000\n", "21 0.50000 0.5 2.45833 2.00000 2.33333 3 2.50000 80.00000\n", "22 0.50000 0.5 2.00000 2.00000 1.50000 3 2.50000 200.00000\n", "23 0.50000 0.5 1.94118 1.83333 1.50000 3 2.50000 200.00000\n", "24 0.66670 0.7 2.05882 1.83333 1.83333 3 2.50000 0.00000\n", "25 0.83330 0.7 2.06250 1.83333 2.66667 1 2.75000 0.00000\n", "26 0.66670 0.5 2.41176 2.16667 2.83333 1 2.50000 0.00000\n", "27 0.66670 0.5 2.17647 1.83333 2.50000 1 2.50000 0.00000\n", "28 0.83330 0.7 2.35294 1.83333 2.66667 3 2.50000 0.00000\n", "29 0.66670 0.6 2.85417 2.83333 2.83333 3 2.75000 0.00000\n", "30 0.66670 0.6 2.76471 2.83333 2.83333 3 2.50000 0.00000\n", "31 0.66670 0.5 2.58824 2.50000 2.66667 3 2.50000 0.00000\n", "32 0.83330 0.7 2.88235 3.00000 2.83333 3 2.75000 0.00000\n", "33 0.50000 0.3 2.35417 1.66667 2.50000 3 2.25000 0.00000\n", "34 0.66670 0.6 2.41176 1.66667 2.83333 3 2.75000 17.50000\n", "35 0.83330 0.7 2.35294 2.00000 2.50000 3 2.50000 31.50000\n", "36 0.83333 0.6 2.17647 1.83333 2.16667 3 2.50000 0.00000\n", "37 0.16670 0.2 1.97917 1.83333 2.33333 1 2.75000 0.00000\n", "38 0.00000 0.3 2.11765 1.50000 2.33333 1 3.00000 0.00000\n", "39 0.00000 0.3 2.23529 1.83333 2.33333 1 3.00000 0.00000\n", "40 0.83333 0.4 2.23529 1.83333 2.33333 1 3.00000 0.00000\n", "41 0.35700 0.5 2.43750 1.83333 2.16667 3 2.75000 244.10000\n", "42 0.50000 0.5 2.47059 2.16667 2.66667 3 2.50000 380.00000\n", "43 0.33330 0.3 2.23529 1.83333 2.33333 3 2.50000 252.00000\n", "44 0.83333 0.5 2.41176 2.16667 2.33333 3 2.75000 110.00000\n", "45 0.83330 0.7 2.72917 2.16667 3.00000 3 2.75000 407.00000\n", "46 0.66670 0.7 2.76471 2.66667 3.00000 3 2.50000 1666.00000\n", "47 0.83333 0.8 2.70588 2.33333 3.00000 3 2.75000 1464.00000\n", "48 0.66670 0.7 2.41176 2.16667 2.33333 3 2.75000 1100.00000\n", "49 0.33300 0.1 1.87500 1.66667 2.33333 1 2.50000 50.00000\n", "50 0.66670 0.5 2.47059 1.83333 2.83333 3 2.75000 200.00000\n", "51 0.33333 0.4 2.52941 2.00000 2.66667 3 3.00000 75.00000\n", "52 0.50000 0.5 2.29412 2.16667 2.50000 1 2.50000 100.00000\n", "53 0.33300 0.1 1.83333 1.83333 2.00000 1 2.50000 0.00000\n", "54 0.50000 0.6 2.00000 1.50000 2.33333 1 2.50000 0.00000\n", "55 0.33330 0.4 2.05882 1.83333 2.16667 1 2.50000 0.00000\n", "56 0.50000 0.5 2.17647 1.83333 2.50000 1 2.50000 26.00000\n", "57 0.00000 0.0 1.95833 1.83333 2.50000 1 2.50000 0.00000\n", "58 0.16670 0.0 1.82353 1.33333 2.00000 1 2.50000 0.00000\n", "59 0.16670 0.0 1.94118 1.66667 2.00000 1 2.50000 0.00000\n", "60 0.00000 0.0 1.94118 1.66667 2.00000 1 2.50000 0.00000\n", "61 0.66670 0.5 2.37500 1.66667 2.33333 3 2.50000 191.90000\n", "62 0.66670 0.7 2.35294 1.83333 2.66667 3 2.50000 202.91262\n", "63 0.50000 0.5 2.17647 1.83333 2.33333 3 2.25000 84.63415\n", "64 0.66670 0.5 2.29412 1.66667 2.66667 3 2.50000 86.75000\n", "65 0.50000 0.5 2.31250 2.00000 2.60000 3 2.25000 0.00000\n", "66 0.50000 0.5 2.31250 2.00000 2.60000 3 2.25000 0.00000\n", "67 0.33330 0.5 2.37500 2.00000 2.60000 3 2.50000 0.00000\n", "68 0.50000 0.5 2.18750 2.33333 1.83333 3 2.33333 0.00000\n", "69 0.33330 0.2 1.66667 1.66667 1.50000 1 2.50000 0.00000\n", "70 0.33330 0.1 2.23529 1.66667 2.83333 1 2.50000 0.00000\n", "71 0.66670 0.2 1.82353 1.50000 1.83333 1 2.50000 0.00000\n", "72 0.33330 0.2 1.82353 1.50000 1.83333 1 2.50000 0.00000\n", "73 0.83330 0.8 2.72917 2.16667 3.00000 3 2.75000 2.66000\n", "74 0.66670 0.6 2.58824 2.33333 2.66667 3 2.75000 0.00000\n", "75 0.50000 0.5 2.35294 2.33333 2.16667 3 2.50000 1.77000\n", "76 0.66670 0.7 2.58824 2.50000 2.50000 3 2.75000 0.00000\n", "77 0.66670 0.6 2.45833 2.00000 2.33333 3 2.50000 0.00000\n", "78 0.66670 0.4 2.23529 2.00000 2.50000 1 2.50000 0.00000\n", "79 0.66670 0.6 2.17647 2.16667 2.16667 1 2.50000 0.00000\n", "80 0.83330 0.7 2.17647 1.83333 2.50000 1 2.50000 0.00000\n", "81 0.50000 0.4 2.37500 2.00000 2.00000 3 2.50000 0.00000\n", "82 0.33330 0.3 2.11765 1.83333 2.33333 1 2.50000 0.00000\n", "83 0.83333 0.4 2.23529 2.16667 2.33333 1 2.50000 0.00000\n", "84 0.66670 0.6 2.05882 2.00000 2.00000 1 2.50000 0.00000\n", "85 0.66670 0.7 2.56250 2.00000 2.50000 3 2.75000 68.00000\n", "86 0.66670 0.7 2.29412 2.00000 2.33333 3 2.50000 72.50000\n", "87 0.16670 0.5 2.70588 2.50000 3.00000 3 2.50000 10.75140\n", "88 0.66670 0.4 2.70588 2.50000 3.00000 3 2.50000 10.75140\n", "89 0.83330 0.7 2.66667 2.50000 2.66667 3 2.50000 70.71000\n", "90 0.83330 0.7 2.47059 2.16667 2.66667 3 2.50000 90.99000\n", "91 0.83330 0.7 2.52941 2.33333 2.66667 3 2.50000 47.09000\n", "92 0.83330 0.7 2.58824 2.50000 2.66667 3 2.50000 0.00000\n", "93 0.00000 0.1 1.93750 1.83333 2.16667 1 2.75000 0.00000\n", "94 0.16670 0.2 1.76471 1.83333 1.83333 1 1.75000 0.00000\n", "95 0.16667 0.2 2.05882 2.00000 2.00000 1 2.50000 0.00000\n", "96 0.16670 0.2 2.05882 2.00000 2.00000 1 2.50000 0.00000\n", "97 0.00000 0.0 1.81250 1.83333 1.66667 1 2.75000 0.00000\n", "98 0.50000 0.5 2.17647 1.83333 2.50000 1 2.50000 100.00000\n", "99 0.50000 0.4 2.17647 1.83333 2.50000 1 2.50000 50.00000\n", "100 0.33330 0.4 2.17647 1.83333 2.50000 1 2.50000 50.00000\n", "101 0.83330 0.7 1.95833 1.66667 1.66667 2 2.50000 132.72727\n", "102 0.83330 0.7 2.35294 1.83333 2.66667 3 2.50000 79.70000\n", "103 1.00000 0.7 2.52941 2.16667 2.66667 2 3.00000 24.60000\n", "104 1.00000 0.8 2.52941 2.16667 2.66667 2 3.00000 0.00000\n", "105 0.16670 0.2 1.45833 1.83333 1.00000 1 2.00000 0.00000\n", "106 0.33330 0.4 1.66667 1.50000 1.50000 1 2.25000 0.00000\n", "107 0.50000 0.5 2.13333 2.16667 2.00000 1 2.50000 0.00000\n", "108 0.50000 0.5 1.80000 1.83333 1.50000 1 2.25000 0.00000\n", "109 0.16670 0.3 1.87500 1.50000 2.50000 1 2.50000 0.00000\n", "110 0.16670 0.2 2.00000 1.83333 2.50000 1 2.00000 0.00000\n", "111 0.33330 0.3 2.33333 1.83333 3.00000 1 2.75000 0.00000\n", "112 0.16670 0.2 2.33333 1.83333 3.00000 1 2.75000 0.00000\n", "113 0.66670 0.5 2.75000 2.83333 2.66667 3 2.50000 89.00000\n", "114 0.66670 0.5 2.70588 2.83333 2.66667 3 2.50000 42.87000\n", "115 0.66670 0.5 2.52941 2.50000 2.50000 3 2.50000 56.29000\n", "116 0.66670 0.6 2.52941 2.50000 2.50000 3 2.50000 0.00000\n", "117 0.16670 0.1 1.79167 1.66667 2.00000 1 2.50000 0.00000\n", "118 0.16670 0.4 1.87500 1.50000 2.00000 1 2.50000 0.00000\n", "119 0.33330 0.2 1.88235 1.50000 2.00000 1 2.50000 0.00000\n", "120 0.33330 0.2 1.88235 1.50000 2.00000 1 2.50000 0.00000\n", "121 0.00000 0.3 2.06250 2.16667 2.33333 1 2.75000 0.00000\n", "122 0.16670 0.3 2.31250 1.83333 2.80000 1 2.75000 0.00000\n", "123 0.16670 0.1 2.52941 2.50000 2.50000 3 2.50000 2.00000\n", "124 0.33330 0.3 2.52941 2.50000 2.50000 3 2.50000 0.00000\n", "125 0.16670 0.1 1.87500 1.83333 2.16667 1 2.50000 0.00000\n", "126 0.16670 0.0 1.94118 1.83333 1.50000 3 2.50000 0.00000\n", "127 0.16670 0.1 1.86667 1.83333 1.50000 1 2.50000 0.00000\n", "128 0.16700 0.1 1.86667 1.83333 1.50000 1 2.50000 0.00000\n", "129 0.66670 0.5 2.25000 1.50000 2.00000 3 2.50000 190.90909\n", "130 0.66670 0.2 2.11765 1.66667 2.16667 3 2.50000 436.89320\n", "131 0.83330 0.6 2.11765 1.50000 2.33333 3 2.50000 457.31707\n", "132 0.83330 0.6 2.11765 1.50000 2.33333 3 2.50000 468.50000\n", "133 0.33330 0.3 2.37500 1.66667 2.33333 3 2.50000 74.00000\n", "134 0.83330 0.7 2.23529 1.83333 2.33333 3 2.50000 60.00000\n", "135 0.83330 0.7 2.29412 1.83333 2.50000 3 2.50000 44.47561\n", "136 0.83333 0.7 2.29412 1.83333 2.50000 3 2.50000 0.00000\n", "137 0.66670 0.6 2.37500 1.83333 2.16667 3 2.50000 10.00000\n", "138 0.66670 0.5 2.29412 1.83333 2.66667 3 2.25000 0.00000\n", "139 0.50000 0.5 2.29412 2.00000 2.33333 3 2.50000 0.00000\n", "140 0.50000 0.5 2.29412 2.00000 2.33333 3 2.50000 0.00000\n", "141 0.00000 0.0 2.14583 1.16667 2.16667 3 2.25000 0.00000\n", "142 0.00000 0.0 1.88235 1.16667 2.16667 3 2.25000 0.00000\n", "143 0.00000 0.0 1.87500 1.80000 1.83333 1 2.25000 50.00000\n", "144 0.00000 0.0 1.87500 1.80000 1.83333 1 2.25000 8.00000\n", " LOSS TOT_ASSETS SLACK ROE BRD_INDP_DIV BRD_MEET_DIV\n", "1 0 5.144545e+13 5.081927e+12 0.22000 60.700000 75.875000\n", "2 0 6.497336e+13 4.857942e+12 0.14100 93.042253 158.171327\n", "3 0 8.212195e+13 8.308451e+12 0.07400 28.170674 35.853599\n", "4 0 8.017500e+13 9.315600e+12 0.05600 44.062500 73.437500\n", "5 0 2.301384e+12 3.591630e+11 0.05690 0.000000 0.000000\n", "6 1 2.903932e+12 1.526311e+11 -0.07730 0.000000 0.000000\n", "7 1 3.855817e+12 2.247561e+10 -0.07990 0.000000 0.000000\n", "8 1 4.239362e+12 5.275000e+10 -0.22930 0.000000 0.000000\n", "9 1 1.116601e+11 3.469378e+09 -0.64310 0.000000 0.000000\n", "10 1 1.508296e+08 2.807016e+06 -0.38200 0.000000 0.000000\n", "11 0 1.489340e+12 3.015988e+07 0.01200 0.000000 0.000000\n", "12 1 1.508296e+11 3.185917e+09 -0.36100 0.000000 0.000000\n", "13 0 1.871818e+13 4.729745e+12 0.30730 9.466679 7.691679\n", "14 1 2.085437e+13 3.534437e+12 -0.74090 18.122951 15.533980\n", "15 1 2.440271e+13 4.977427e+12 -2.03000 0.000000 0.000000\n", "16 1 2.216250e+13 4.243275e+12 -2.02000 0.000000 0.000000\n", "17 0 1.740441e+13 1.124181e+12 0.04726 0.000000 0.000000\n", "18 1 1.929230e+13 1.131750e+12 -0.04000 0.000000 0.000000\n", "19 1 2.423301e+13 1.421650e+12 0.04000 0.000000 0.000000\n", "20 1 2.329414e+13 7.845501e+10 -0.12500 0.000000 0.000000\n", "21 0 1.438620e+13 1.527995e+12 0.28500 160.000000 186.666400\n", "22 0 3.611168e+26 4.023288e+26 0.07800 400.000000 300.000000\n", "23 0 2.328177e+13 1.863041e+12 0.07800 366.666000 300.000000\n", "24 1 1.452070e+13 1.000979e+12 -0.74000 0.000000 0.000000\n", "25 1 3.692054e+12 3.377904e+11 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1.660807e+12 0.48000 447.515853 528.884147\n", "42 0 5.229508e+12 1.575946e+12 0.37600 823.334600 1013.334600\n", "43 0 5.861233e+12 2.211686e+12 0.13200 461.999160 587.999160\n", "44 0 5.551336e+12 2.516316e+12 0.00100 238.333700 256.666300\n", "45 0 1.434976e+13 5.567418e+12 0.51000 881.834690 1221.000000\n", "46 0 1.447790e+10 4.477961e+09 0.43000 4442.672220 4998.000000\n", "47 0 1.697732e+13 3.520817e+12 0.24000 3415.995120 4392.000000\n", "48 0 1.634185e+13 2.826500e+12 0.22000 2383.337000 2566.663000\n", "49 0 9.778930e+11 4.358460e+11 0.68500 83.333500 116.666500\n", "50 0 1.007782e+12 7.910948e+10 0.32000 366.666000 566.666000\n", "51 0 1.293752e+12 1.046040e+11 0.24000 150.000000 200.000250\n", "52 0 9.956869e+07 2.649485e+06 0.11100 216.667000 250.000000\n", "53 1 4.233096e+11 2.684522e+10 0.03560 0.000000 0.000000\n", "54 0 1.292581e+12 4.588909e+10 0.03770 0.000000 0.000000\n", "55 0 1.815818e+12 8.069900e+10 0.22200 0.000000 0.000000\n", "56 0 2.031097e+12 2.239072e+11 0.26700 47.666580 65.000000\n", "57 1 4.539770e+11 1.345938e+10 0.03400 0.000000 0.000000\n", "58 1 3.692770e+11 2.024694e+10 -0.05000 0.000000 0.000000\n", "59 0 3.615488e+11 1.853429e+09 0.00190 0.000000 0.000000\n", "60 1 3.032550e+11 1.853429e+09 -0.19380 0.000000 0.000000\n", "61 0 3.429982e+12 2.053364e+11 0.33060 319.833973 447.766027\n", "62 0 5.143126e+12 4.320097e+11 0.26230 372.005794 541.100996\n", "63 0 6.210268e+12 6.966463e+11 0.08760 155.162326 197.479401\n", "64 0 5.846250e+12 8.171250e+11 0.01100 144.583622 231.333622\n", "65 0 4.801790e+11 1.560060e+11 0.03000 0.000000 0.000000\n", "66 0 4.801790e+11 1.560060e+11 0.03000 0.000000 0.000000\n", "67 1 6.266500e+11 1.675256e+11 0.04000 0.000000 0.000000\n", "68 1 7.249740e+11 1.264965e+11 -0.01000 0.000000 0.000000\n", "69 0 1.382808e+12 3.944686e+10 0.00970 0.000000 0.000000\n", "70 0 1.095382e+12 4.009298e+10 0.07100 0.000000 0.000000\n", "71 0 1.095382e+12 4.009298e+10 0.07100 0.000000 0.000000\n", "72 0 1.773671e+12 5.945887e+10 0.03000 0.000000 0.000000\n", "73 1 4.389950e+12 6.888180e+11 -0.02200 5.763342 7.980000\n", "74 0 1.432239e+12 3.007457e+10 0.06000 0.000000 0.000000\n", "75 0 4.370964e+12 1.319686e+12 0.10600 4.129994 3.835006\n", "76 0 4.245704e+12 1.060151e+12 0.15970 0.000000 0.000000\n", "77 0 1.735483e+10 7.657841e+08 0.01142 0.000000 0.000000\n", "78 0 4.294557e+12 9.281990e+11 0.06300 0.000000 0.000000\n", "79 0 2.318647e+09 1.533324e+08 0.19100 0.000000 0.000000\n", "80 0 1.747858e+12 1.404630e+11 0.10290 0.000000 0.000000\n", "81 0 6.559649e+11 1.882643e+11 0.56360 0.000000 0.000000\n", "82 1 2.011991e+13 1.014175e+12 0.02000 0.000000 0.000000\n", "83 0 1.442411e+12 4.377514e+11 0.13890 0.000000 0.000000\n", "84 0 2.753028e+13 1.903725e+12 0.04100 0.000000 0.000000\n", "85 0 2.352179e+13 8.647783e+12 0.10431 136.000000 170.000000\n", "86 0 1.111322e+13 8.109901e+12 0.15000 145.000000 169.166425\n", "87 0 3.238780e+13 6.309902e+12 0.01500 26.878500 32.254200\n", "88 0 3.238780e+13 6.309902e+12 0.01500 26.878500 32.254200\n", "89 0 1.520124e+13 5.639679e+12 0.18940 176.775000 188.560236\n", "90 0 1.970854e+13 3.868575e+12 0.25360 197.145303 242.640303\n", "91 0 2.186512e+13 2.792738e+12 0.03200 109.876510 125.573490\n", "92 1 2.204420e+13 2.618910e+12 -0.06270 0.000000 0.000000\n", "93 0 1.181111e+12 5.795026e+11 0.04130 0.000000 0.000000\n", "94 1 1.213229e+12 9.173469e+07 -0.00300 0.000000 0.000000\n", "95 0 6.390120e+11 8.745936e+09 0.00000 0.000000 0.000000\n", "96 1 1.040000e+10 8.745936e+09 -0.00040 0.000000 0.000000\n", "97 0 1.301283e+12 1.000233e+12 0.15318 0.000000 0.000000\n", "98 0 1.535650e+12 7.464408e+11 0.22000 183.333000 250.000000\n", "99 0 1.595228e+12 8.337525e+11 0.24000 91.666500 125.000000\n", "100 1 1.191604e+12 4.457953e+11 -0.04000 91.666500 125.000000\n", "101 0 2.201238e+13 3.628682e+12 0.19000 221.212559 221.212559\n", "102 0 2.265126e+13 1.672223e+12 0.04000 146.116401 212.533599\n", "103 0 2.781852e+13 2.643207e+12 0.02000 53.300082 65.600082\n", "104 0 2.917738e+13 3.778200e+12 0.10000 0.000000 0.000000\n", "105 1 1.027389e+10 5.095224e+09 -0.51900 0.000000 0.000000\n", "106 0 5.576806e+12 8.371149e+10 0.30800 0.000000 0.000000\n", "107 1 9.822894e+12 3.267244e+11 -0.10100 0.000000 0.000000\n", "108 0 1.070894e+13 1.415980e+11 0.09000 0.000000 0.000000\n", "109 1 3.850260e+11 1.545330e+11 -0.06000 0.000000 0.000000\n", "110 1 3.075482e+11 1.944498e+10 -0.24100 0.000000 0.000000\n", "111 1 2.449967e+11 2.029645e+09 -0.20140 0.000000 0.000000\n", "112 1 2.637794e+12 5.722089e+10 -0.02530 0.000000 0.000000\n", "113 0 6.569807e+12 6.595840e+11 0.27000 252.166370 237.333630\n", "114 0 6.130320e+12 6.704000e+11 0.12000 121.464857 114.320143\n", "115 0 7.883294e+12 6.136980e+11 0.13000 140.725000 140.725000\n", "116 0 9.752477e+12 3.644960e+11 0.13000 0.000000 0.000000\n", "117 0 2.182515e+11 1.042113e+10 0.01000 0.000000 0.000000\n", "118 0 1.760010e+11 1.419955e+10 0.03500 0.000000 0.000000\n", "119 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0.03000 109.999800 139.999800\n", "135 1 2.824785e+13 3.982530e+12 -0.00070 81.538470 111.189025\n", "136 1 2.862940e+13 4.158715e+12 -0.03150 0.000000 0.000000\n", "137 1 9.212775e+11 8.507606e+10 -0.04340 18.333300 21.666700\n", "138 1 1.073942e+12 7.732039e+10 -0.15160 0.000000 0.000000\n", "139 0 1.517317e+12 1.033780e+11 0.00650 0.000000 0.000000\n", "140 1 1.552375e+11 6.432500e+10 -0.20020 0.000000 0.000000\n", "141 0 5.442820e+11 2.375282e+10 0.24850 0.000000 0.000000\n", "142 0 3.890070e+11 5.578254e+10 0.19980 0.000000 0.000000\n", "143 0 7.658814e+11 7.229814e+10 0.03970 90.000000 91.666500\n", "144 0 8.972817e+11 4.202218e+10 0.17920 14.400000 14.666640\n", " BRD_SIZE_DIV BRD_COMPT_DIV BRD_EFFC_DIV\n", "1 91.05000 75.87500 75.875000\n", "2 167.47572 139.56310 131.353440\n", "3 46.09755 38.41463 34.347131\n", "4 88.12500 73.43750 63.933806\n", "5 0.00000 0.00000 0.000000\n", "6 0.00000 0.00000 0.000000\n", "7 0.00000 0.00000 0.000000\n", "8 0.00000 0.00000 0.000000\n", "9 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0.000000\n", "95 0.00000 0.00000 0.000000\n", "96 0.00000 0.00000 0.000000\n", "97 0.00000 0.00000 0.000000\n", "98 100.00000 250.00000 217.647000\n", "99 50.00000 125.00000 108.823500\n", "100 50.00000 125.00000 108.823500\n", "101 265.45454 331.81817 259.923795\n", "102 239.10000 199.25000 187.529318\n", "103 49.20000 73.80000 62.223486\n", "104 0.00000 0.00000 0.000000\n", "105 0.00000 0.00000 0.000000\n", "106 0.00000 0.00000 0.000000\n", "107 0.00000 0.00000 0.000000\n", "108 0.00000 0.00000 0.000000\n", "109 0.00000 0.00000 0.000000\n", "110 0.00000 0.00000 0.000000\n", "111 0.00000 0.00000 0.000000\n", "112 0.00000 0.00000 0.000000\n", "113 267.00000 222.50000 244.750000\n", "114 128.61000 107.17500 116.001076\n", "115 168.87000 140.72500 142.380489\n", "116 0.00000 0.00000 0.000000\n", "117 0.00000 0.00000 0.000000\n", "118 0.00000 0.00000 0.000000\n", "119 0.00000 0.00000 0.000000\n", "120 0.00000 0.00000 0.000000\n", "121 0.00000 0.00000 0.000000\n", "122 0.00000 0.00000 0.000000\n", "123 6.00000 5.00000 5.058820\n", "124 0.00000 0.00000 0.000000\n", "125 0.00000 0.00000 0.000000\n", "126 0.00000 0.00000 0.000000\n", "127 0.00000 0.00000 0.000000\n", "128 0.00000 0.00000 0.000000\n", "129 572.72727 477.27272 429.545453\n", "130 1310.67960 1092.23300 925.186885\n", "131 1371.95121 1143.29267 968.437493\n", "132 1405.50000 1171.25000 992.119025\n", "133 222.00000 185.00000 175.750000\n", "134 180.00000 150.00000 134.117400\n", "135 133.42683 111.18903 102.032386\n", "136 0.00000 0.00000 0.000000\n", "137 30.00000 25.00000 23.750000\n", "138 0.00000 0.00000 0.000000\n", "139 0.00000 0.00000 0.000000\n", "140 0.00000 0.00000 0.000000\n", "141 0.00000 0.00000 0.000000\n", "142 0.00000 0.00000 0.000000\n", "143 50.00000 112.50000 93.750000\n", "144 8.00000 18.00000 15.000000" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ncol(data)\n", "dataMean <- data[3:26]\n", "dataMean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary Statistics\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(stargazer)\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " Firm Year CEI BRIB_CORR \n", " Min. : 1.00 Min. :2011 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.: 9.75 1st Qu.:2012 1st Qu.:0.3051 1st Qu.:0.0000 \n", " Median :18.50 Median :2012 Median :0.4702 Median :0.0000 \n", " Mean :18.50 Mean :2012 Mean :0.4814 Mean :0.2542 \n", " 3rd Qu.:27.25 3rd Qu.:2013 3rd Qu.:0.6958 3rd Qu.:0.6000 \n", " Max. :36.00 Max. :2014 Max. :0.9524 Max. :1.0000 \n", " BUSS_ETH FAIR_COMP POL_CONTR INDIG_PPL \n", " Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.6667 \n", " Median :0.5000 Median :0.0000 Median :0.0000 Median :1.0000 \n", " Mean :0.4462 Mean :0.2708 Mean :0.3316 Mean :0.7865 \n", " 3rd Qu.:0.7500 3rd Qu.:0.5000 3rd Qu.:0.7500 3rd Qu.:1.0000 \n", " Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 \n", " IND_EC_IMP X0TH_ENG ALT_CEI BRD_EFFC \n", " Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :1.458 \n", " 1st Qu.:1.0000 1st Qu.:0.3332 1st Qu.:0.3000 1st Qu.:2.059 \n", " Median :1.0000 Median :0.5000 Median :0.5000 Median :2.235 \n", " Mean :0.8681 Mean :0.5060 Mean :0.4361 Mean :2.231 \n", " 3rd Qu.:1.0000 3rd Qu.:0.6667 3rd Qu.:0.6000 3rd Qu.:2.412 \n", " Max. :1.0000 Max. :1.0000 Max. :0.8000 Max. :2.882 \n", " BRD_INDP BRD_MEET BRD_SIZE BRD_COMPT \n", " Min. :1.167 Min. :1.000 Min. :1.00 Min. :1.750 \n", " 1st Qu.:1.833 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:2.500 \n", " Median :1.833 Median :2.333 Median :3.00 Median :2.500 \n", " Mean :1.960 Mean :2.333 Mean :2.09 Mean :2.525 \n", " 3rd Qu.:2.167 3rd Qu.:2.667 3rd Qu.:3.00 3rd Qu.:2.500 \n", " Max. :3.000 Max. :3.000 Max. :3.00 Max. :3.000 \n", " DIVIDEND LOSS TOT_ASSETS SLACK \n", " Min. : 0.00 Min. :0.0000 Min. :9.957e+07 Min. :2.649e+06 \n", " 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.:7.557e+11 1st Qu.:4.009e+10 \n", " Median : 0.00 Median :0.0000 Median :3.332e+12 Median :3.073e+11 \n", " Mean : 70.24 Mean :0.3125 Mean :2.508e+24 Mean :2.794e+24 \n", " 3rd Qu.: 50.00 3rd Qu.:1.0000 3rd Qu.:1.348e+13 3rd Qu.:1.597e+12 \n", " Max. :1666.00 Max. :1.0000 Max. :3.611e+26 Max. :4.023e+26 \n", " ROE BRD_INDP_DIV BRD_MEET_DIV BRD_SIZE_DIV \n", " Min. :-2.030000 Min. : 0.00 Min. : 0.0 Min. : 0.0 \n", " 1st Qu.:-0.001275 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0 \n", " Median : 0.045330 Median : 0.00 Median : 0.0 Median : 0.0 \n", " Mean : 0.036846 Mean : 147.22 Mean : 180.4 Mean : 203.5 \n", " 3rd Qu.: 0.172300 3rd Qu.: 92.01 3rd Qu.: 125.0 3rd Qu.: 100.0 \n", " Max. : 0.741000 Max. :4442.67 Max. :4998.0 Max. :4998.0 \n", " BRD_COMPT_DIV BRD_EFFC_DIV \n", " Min. : 0 Min. : 0.0 \n", " 1st Qu.: 0 1st Qu.: 0.0 \n", " Median : 0 Median : 0.0 \n", " Mean : 182 Mean : 172.0 \n", " 3rd Qu.: 125 3rd Qu.: 110.6 \n", " Max. :4165 Max. :4606.0 " ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(\"ggplot2\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n", "output_type": "error", "traceback": [ "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n" ] } ], "source": [ "qplot( y = data$TOBINQ, x = data$SIZE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(\"plm\")" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_panel <- pdata.frame(data, index = c(\"Firm\", \"Year\"), drop.index = TRUE, row.names = TRUE)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>CEI</th><th scope=col>BRIB_CORR</th><th scope=col>BUSS_ETH</th><th scope=col>FAIR_COMP</th><th scope=col>POL_CONTR</th><th scope=col>INDIG_PPL</th><th scope=col>IND_EC_IMP</th><th scope=col>X0TH_ENG</th><th scope=col>ALT_CEI</th><th scope=col>BRD_EFFC</th><th scope=col>ellip.h</th><th scope=col>DIVIDEND</th><th scope=col>LOSS</th><th scope=col>TOT_ASSETS</th><th scope=col>SLACK</th><th scope=col>ROE</th><th scope=col>BRD_INDP_DIV</th><th scope=col>BRD_MEET_DIV</th><th scope=col>BRD_SIZE_DIV</th><th scope=col>BRD_COMPT_DIV</th><th scope=col>BRD_EFFC_DIV</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1-2011</th><td>0.57143</td><td>0</td><td>0.5</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.7</td><td>2.5</td><td>⋯</td><td>30.35</td><td>0</td><td>5.144545e+13</td><td>5.081927e+12</td><td>0.22</td><td>60.7</td><td>75.875</td><td>91.05</td><td>75.875</td><td>75.875</td></tr>\n", "\t<tr><th scope=row>1-2012</th><td>0.54762</td><td>0</td><td>0.25</td><td>0.25</td><td>0.5</td><td>1</td><td>1</td><td>0.8333</td><td>0.5</td><td>2.35294</td><td>⋯</td><td>55.82524</td><td>0</td><td>6.497336e+13</td><td>4.857942e+12</td><td>0.141</td><td>93.04225</td><td>158.1713</td><td>167.4757</td><td>139.5631</td><td>131.3534</td></tr>\n", "\t<tr><th scope=row>1-2013</th><td>0.52381</td><td>0</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.23529</td><td>⋯</td><td>15.36585</td><td>0</td><td>8.212195e+13</td><td>8.308451e+12</td><td>0.074</td><td>28.17067</td><td>35.8536</td><td>46.09755</td><td>38.41463</td><td>34.34713</td></tr>\n", "\t<tr><th scope=row>1-2014</th><td>0.52381</td><td>0</td><td>0.5</td><td>0.25</td><td>0.25</td><td>1</td><td>1</td><td>0.6667</td><td>0.5</td><td>2.17647</td><td>⋯</td><td>29.375</td><td>0</td><td>8.0175e+13</td><td>9.3156e+12</td><td>0.056</td><td>44.0625</td><td>73.4375</td><td>88.125</td><td>73.4375</td><td>63.93381</td></tr>\n", "\t<tr><th scope=row>2-2011</th><td>0.4881</td><td>0</td><td>0.5</td><td>0.75</td><td>0</td><td>1</td><td>1</td><td>0.1667</td><td>0.3</td><td>2.26458</td><td>⋯</td><td>0</td><td>0</td><td>2.301384e+12</td><td>3.59163e+11</td><td>0.0569</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "\t<tr><th scope=row>2-2012</th><td>0.7381</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0.667</td><td>1</td><td>0.5</td><td>0.5</td><td>2.17647</td><td>⋯</td><td>0</td><td>1</td><td>2.903932e+12</td><td>152631066954</td><td>-0.0773</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllllllllllllllll}\n", " & CEI & BRIB_CORR & BUSS_ETH & FAIR_COMP & POL_CONTR & INDIG_PPL & IND_EC_IMP & X0TH_ENG & ALT_CEI & BRD_EFFC & ellip.h & DIVIDEND & LOSS & TOT_ASSETS & SLACK & ROE & BRD_INDP_DIV & BRD_MEET_DIV & BRD_SIZE_DIV & BRD_COMPT_DIV & BRD_EFFC_DIV\\\\\n", "\\hline\n", "\t1-2011 & 0.57143 & 0 & 0.5 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.7 & 2.5 & ⋯ & 30.35 & 0 & 5.144545e+13 & 5.081927e+12 & 0.22 & 60.7 & 75.875 & 91.05 & 75.875 & 75.875\\\\\n", "\t1-2012 & 0.54762 & 0 & 0.25 & 0.25 & 0.5 & 1 & 1 & 0.8333 & 0.5 & 2.35294 & ⋯ & 55.82524 & 0 & 6.497336e+13 & 4.857942e+12 & 0.141 & 93.04225 & 158.1713 & 167.4757 & 139.5631 & 131.3534\\\\\n", "\t1-2013 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & 0.5 & 2.23529 & ⋯ & 15.36585 & 0 & 8.212195e+13 & 8.308451e+12 & 0.074 & 28.17067 & 35.8536 & 46.09755 & 38.41463 & 34.34713\\\\\n", "\t1-2014 & 0.52381 & 0 & 0.5 & 0.25 & 0.25 & 1 & 1 & 0.6667 & 0.5 & 2.17647 & ⋯ & 29.375 & 0 & 8.0175e+13 & 9.3156e+12 & 0.056 & 44.0625 & 73.4375 & 88.125 & 73.4375 & 63.93381\\\\\n", "\t2-2011 & 0.4881 & 0 & 0.5 & 0.75 & 0 & 1 & 1 & 0.1667 & 0.3 & 2.26458 & ⋯ & 0 & 0 & 2.301384e+12 & 3.59163e+11 & 0.0569 & 0 & 0 & 0 & 0 & 0\\\\\n", "\t2-2012 & 0.7381 & 0 & 1 & 1 & 1 & 0.667 & 1 & 0.5 & 0.5 & 2.17647 & ⋯ & 0 & 1 & 2.903932e+12 & 152631066954 & -0.0773 & 0 & 0 & 0 & 0 & 0\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " CEI BRIB_CORR BUSS_ETH FAIR_COMP POL_CONTR INDIG_PPL IND_EC_IMP\n", "1-2011 0.57143 0 0.50 0.50 0.25 0.250 1\n", "1-2012 0.54762 0 0.25 0.25 0.50 1.000 1\n", "1-2013 0.52381 0 0.50 0.25 0.25 1.000 1\n", "1-2014 0.52381 0 0.50 0.25 0.25 1.000 1\n", "2-2011 0.48810 0 0.50 0.75 0.00 1.000 1\n", "2-2012 0.73810 0 1.00 1.00 1.00 0.667 1\n", " X0TH_ENG ALT_CEI BRD_EFFC BRD_INDP BRD_MEET BRD_SIZE BRD_COMPT DIVIDEND\n", "1-2011 1.0000 0.7 2.50000 2.00000 2.50000 3 2.500 30.35000\n", "1-2012 0.8333 0.5 2.35294 1.66667 2.83333 3 2.500 55.82524\n", "1-2013 0.6667 0.5 2.23529 1.83333 2.33333 3 2.500 15.36585\n", "1-2014 0.6667 0.5 2.17647 1.50000 2.50000 3 2.500 29.37500\n", "2-2011 0.1667 0.3 2.26458 1.83333 2.00000 3 2.225 0.00000\n", "2-2012 0.5000 0.5 2.17647 1.83333 2.16667 3 2.500 0.00000\n", " LOSS TOT_ASSETS SLACK ROE BRD_INDP_DIV BRD_MEET_DIV\n", "1-2011 0 5.144545e+13 5.081927e+12 0.2200 60.70000 75.8750\n", "1-2012 0 6.497336e+13 4.857942e+12 0.1410 93.04225 158.1713\n", "1-2013 0 8.212195e+13 8.308451e+12 0.0740 28.17067 35.8536\n", "1-2014 0 8.017500e+13 9.315600e+12 0.0560 44.06250 73.4375\n", "2-2011 0 2.301384e+12 3.591630e+11 0.0569 0.00000 0.0000\n", "2-2012 1 2.903932e+12 1.526311e+11 -0.0773 0.00000 0.0000\n", " BRD_SIZE_DIV BRD_COMPT_DIV BRD_EFFC_DIV\n", "1-2011 91.05000 75.87500 75.87500\n", "1-2012 167.47572 139.56310 131.35344\n", "1-2013 46.09755 38.41463 34.34713\n", "1-2014 88.12500 73.43750 63.93381\n", "2-2011 0.00000 0.00000 0.00000\n", "2-2012 0.00000 0.00000 0.00000" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head(data_panel)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tabel2_reg1 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT, data = data_panel, model = \"within\" )\n", "tabel2_reg2 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg3 <- plm(BRIB_CORR ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg4 <- plm(BUSS_ETH ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg5 <- plm(FAIR_COMP ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg6 <- plm(POL_CONTR ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg7 <- plm(INDIG_PPL ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg8 <- plm(IND_EC_IMP ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel2_reg9 <- plm(X0TH_ENG ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in crossprod(t(X), beta): non-conformable arguments\n", "output_type": "error", "traceback": [ "Error in crossprod(t(X), beta): non-conformable arguments\n" ] } ], "source": [ "library(lmtest)\n", "summary(tabel2_reg2)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tabel3_reg2 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel3_reg2 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + BRD_INDP_DIV + LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel3_reg3 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + BRD_MEET_DIV +LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel3_reg4 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + BRD_SIZE_DIV +LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )\n", "tabel3_reg5 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + BRD_COMPT_DIV +LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\" )" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n", "output_type": "error", "traceback": [ "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n" ] }, { "ename": "ERROR", "evalue": "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n", "output_type": "error", "traceback": [ "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n" ] } ], "source": [ "# tabel3_reg51 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT + DIVIDEND + BRD_COMPT_DIV +LOSS + TOT_ASSETS + SLACK + ROE , data = data_panel, model = \"within\",\n", "# vcovHC = c(type = c(\"HC0\", \"HC1\", \"HC2\", \"HC3\", \"HC4\"),\n", "# cluster = c(\"group\",\"time\"),\n", "# diagonal = FALSE ))\n", "# summary(tabel3_reg51)\n", "robust1 <- coeftest(tabel2_reg2, vcov=function(x) vcovHC(x, method=\"arellano\",\n", " type=\"HC1\", cluster=\"group\"))\n", "robust2 <- coeftest(tabel2_reg3, vcov=function(x) vcovHC(x, method=\"arellano\",\n", " type=\"HC1\", cluster=\"group\"))\n", " " ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in crossprod(t(X), beta): non-conformable arguments\n", "output_type": "error", "traceback": [ "Error in crossprod(t(X), beta): non-conformable arguments\n" ] } ], "source": [ "summary(tabel3_reg5)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'X'</li>\n", "\t<li>'Firm'</li>\n", "\t<li>'Year'</li>\n", "\t<li>'CEI'</li>\n", "\t<li>'BRIB_CORR'</li>\n", "\t<li>'BUSS_ETH'</li>\n", "\t<li>'FAIR_COMP'</li>\n", "\t<li>'POL_CONTR'</li>\n", "\t<li>'INDIG_PPL'</li>\n", "\t<li>'IND_EC_IMP'</li>\n", "\t<li>'X0TH_ENG'</li>\n", "\t<li>'ALT_CEI'</li>\n", "\t<li>'BRD_EFFC'</li>\n", "\t<li>'BRD_INDP'</li>\n", "\t<li>'BRD_MEET'</li>\n", "\t<li>'BRD_SIZE'</li>\n", "\t<li>'BRD_COMPT'</li>\n", "\t<li>'DIVIDEND'</li>\n", "\t<li>'LOSS'</li>\n", "\t<li>'TOT_ASSETS'</li>\n", "\t<li>'SLACK'</li>\n", "\t<li>'ROE'</li>\n", "\t<li>'BRD_INDP_DIV'</li>\n", "\t<li>'BRD_MEET_DIV'</li>\n", "\t<li>'BRD_SIZE_DIV'</li>\n", "\t<li>'BRD_COMPT_DIV'</li>\n", "\t<li>'BRD_EFFC_DIV'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'X'\n", "\\item 'Firm'\n", "\\item 'Year'\n", "\\item 'CEI'\n", "\\item 'BRIB_CORR'\n", "\\item 'BUSS_ETH'\n", "\\item 'FAIR_COMP'\n", "\\item 'POL_CONTR'\n", "\\item 'INDIG_PPL'\n", "\\item 'IND_EC_IMP'\n", "\\item 'X0TH_ENG'\n", "\\item 'ALT_CEI'\n", "\\item 'BRD_EFFC'\n", "\\item 'BRD_INDP'\n", "\\item 'BRD_MEET'\n", "\\item 'BRD_SIZE'\n", "\\item 'BRD_COMPT'\n", "\\item 'DIVIDEND'\n", "\\item 'LOSS'\n", "\\item 'TOT_ASSETS'\n", "\\item 'SLACK'\n", "\\item 'ROE'\n", "\\item 'BRD_INDP_DIV'\n", "\\item 'BRD_MEET_DIV'\n", "\\item 'BRD_SIZE_DIV'\n", "\\item 'BRD_COMPT_DIV'\n", "\\item 'BRD_EFFC_DIV'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'X'\n", "2. 'Firm'\n", "3. 'Year'\n", "4. 'CEI'\n", "5. 'BRIB_CORR'\n", "6. 'BUSS_ETH'\n", "7. 'FAIR_COMP'\n", "8. 'POL_CONTR'\n", "9. 'INDIG_PPL'\n", "10. 'IND_EC_IMP'\n", "11. 'X0TH_ENG'\n", "12. 'ALT_CEI'\n", "13. 'BRD_EFFC'\n", "14. 'BRD_INDP'\n", "15. 'BRD_MEET'\n", "16. 'BRD_SIZE'\n", "17. 'BRD_COMPT'\n", "18. 'DIVIDEND'\n", "19. 'LOSS'\n", "20. 'TOT_ASSETS'\n", "21. 'SLACK'\n", "22. 'ROE'\n", "23. 'BRD_INDP_DIV'\n", "24. 'BRD_MEET_DIV'\n", "25. 'BRD_SIZE_DIV'\n", "26. 'BRD_COMPT_DIV'\n", "27. 'BRD_EFFC_DIV'\n", "\n", "\n" ], "text/plain": [ " [1] \"X\" \"Firm\" \"Year\" \"CEI\" \n", " [5] \"BRIB_CORR\" \"BUSS_ETH\" \"FAIR_COMP\" \"POL_CONTR\" \n", " [9] \"INDIG_PPL\" \"IND_EC_IMP\" \"X0TH_ENG\" \"ALT_CEI\" \n", "[13] \"BRD_EFFC\" \"BRD_INDP\" \"BRD_MEET\" \"BRD_SIZE\" \n", "[17] \"BRD_COMPT\" \"DIVIDEND\" \"LOSS\" \"TOT_ASSETS\" \n", "[21] \"SLACK\" \"ROE\" \"BRD_INDP_DIV\" \"BRD_MEET_DIV\" \n", "[25] \"BRD_SIZE_DIV\" \"BRD_COMPT_DIV\" \"BRD_EFFC_DIV\" " ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names(data)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n", "output_type": "error", "traceback": [ "Error in solve.default(crossprod(demX)): system is computationally singular: reciprocal condition number = 9.39067e-54\n" ] } ], "source": [ "vcovHC(tabel2_reg2 , method = c(\"arellano\", \"white1\", \"white2\"),\n", " type = c(\"HC0\", \"HC1\", \"HC2\", \"HC3\", \"HC4\"),\n", " cluster = c(\"group\",\"time\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tabel4_reg1 <- plm(CEI ~ BRD_EFFC + DIVIDEND + LOSS + TOT_ASSETS + SLACK + ROE, data = data_panel, model = \"within\" )\n", "tabel4_reg2 <- plm(ALT_CEI ~ BRD_EFFC + DIVIDEND + LOSS + TOT_ASSETS + SLACK + ROE, data = data_panel, data = data_panel, model = \"within\" )\n", "tabel4_reg3 <- plm(CEI ~ BRD_EFFC + DIVIDEND + BRD_EFFC_DIV + LOSS + TOT_ASSETS + SLACK + ROE, data = data_panel, model = \"within\" )\n", "tabel4_reg4 <- plm(ALT_CEI ~ BRD_EFFC + DIVIDEND + BRD_EFFC_DIV + LOSS + TOT_ASSETS + SLACK + ROE, data = data_panel, model = \"within\" )\n", "tabel4_reg5 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT, data = data_panel, model = \"within\" )\n", "tabel4_reg6 <- plm(CEI ~ BRD_INDP + BRD_MEET + BRD_SIZE + BRD_COMPT, data = data_panel, model = \"within\" )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coba Regresi Billy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hausman Test: H0: RE vs H1: FE " ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n" ] }, { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n" ] } ], "source": [ "skripsi.fe <- plm(TOBINQ ~ PDEBT + LogAssets + LEV + LIQ + INTCOV + PROF + DR + GROWTH + FIRMAGE, data = data_panel, model = \"within\")\n", "skripsi.re <- plm(TOBINQ ~ PDEBT + LogAssets + LEV + LIQ + INTCOV + PROF + DR + GROWTH + FIRMAGE, data = data_panel, model = \"random\")" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in summary(skripsi.fe): object 'skripsi.fe' not found\n", "output_type": "error", "traceback": [ "Error in summary(skripsi.fe): object 'skripsi.fe' not found\n" ] } ], "source": [ "summary(skripsi.fe)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in summary(skripsi.re): object 'skripsi.re' not found\n", "output_type": "error", "traceback": [ "Error in summary(skripsi.re): object 'skripsi.re' not found\n" ] } ], "source": [ "summary(skripsi.re)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in phtest(skripsi.fe, skripsi.re): object 'skripsi.fe' not found\n", "output_type": "error", "traceback": [ "Error in phtest(skripsi.fe, skripsi.re): object 'skripsi.fe' not found\n" ] } ], "source": [ "phtest( skripsi.fe, skripsi.re)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hausman menghasilkan penggunaan Fixed Effect\n", "\n", "- - -\n", "- - - \n", "- - -\n", "- - - \n", "- - -\n", "- - - " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Poolability Test" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n" ] } ], "source": [ "pooltest.billy <- pooltest(TOBINQ ~ PDEBT + FIRMAGE + LIQ, data = data_panel, model=\"within\")" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'pooltest.billy' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'pooltest.billy' not found\n" ] } ], "source": [ "pooltest.billy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bruesch Pagan Test" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n" ] }, { "ename": "ERROR", "evalue": "Error in plmtest(pool_rergression, effect = \"twoways\", type = \"bp\"): object 'pool_rergression' not found\n", "output_type": "error", "traceback": [ "Error in plmtest(pool_rergression, effect = \"twoways\", type = \"bp\"): object 'pool_rergression' not found\n" ] } ], "source": [ "pool_rergression <- plm(TOBINQ ~ PDEBT + SIZE + LEV + LIQ + INTCOV + PROF + DR + GROWTH + FIRMAGE, data = data_panel, model=\"pooling\")\n", "bpagan_test <- plmtest(pool_rergression,effect=\"twoways\",type=\"bp\")" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'bpagan_test' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'bpagan_test' not found\n" ] } ], "source": [ "bpagan_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BP Test significant dan Hausman significant, sehinggga menggunakan Fixed effect " ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in summary(skripsi.fe): object 'skripsi.fe' not found\n", "output_type": "error", "traceback": [ "Error in summary(skripsi.fe): object 'skripsi.fe' not found\n" ] } ], "source": [ "summary(skripsi.fe)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " X CEI BRIB_CORR BUSS_ETH \n", " Min. : 0.00 Min. :0.0000 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.: 35.75 1st Qu.:0.3051 1st Qu.:0.0000 1st Qu.:0.0000 \n", " Median : 71.50 Median :0.4702 Median :0.0000 Median :0.5000 \n", " Mean : 71.50 Mean :0.4814 Mean :0.2542 Mean :0.4462 \n", " 3rd Qu.:107.25 3rd Qu.:0.6958 3rd Qu.:0.6000 3rd Qu.:0.7500 \n", " Max. :143.00 Max. :0.9524 Max. :1.0000 Max. :1.0000 \n", " FAIR_COMP POL_CONTR INDIG_PPL IND_EC_IMP \n", " Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 \n", " 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.6667 1st Qu.:1.0000 \n", " Median :0.0000 Median :0.0000 Median :1.0000 Median :1.0000 \n", " Mean :0.2708 Mean :0.3316 Mean :0.7865 Mean :0.8681 \n", " 3rd Qu.:0.5000 3rd Qu.:0.7500 3rd Qu.:1.0000 3rd Qu.:1.0000 \n", " Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 \n", " X0TH_ENG ALT_CEI BRD_EFFC BRD_INDP \n", " Min. :0.0000 Min. :0.0000 Min. :1.458 Min. :1.167 \n", " 1st Qu.:0.3332 1st Qu.:0.3000 1st Qu.:2.059 1st Qu.:1.833 \n", " Median :0.5000 Median :0.5000 Median :2.235 Median :1.833 \n", " Mean :0.5060 Mean :0.4361 Mean :2.231 Mean :1.960 \n", " 3rd Qu.:0.6667 3rd Qu.:0.6000 3rd Qu.:2.412 3rd Qu.:2.167 \n", " Max. :1.0000 Max. :0.8000 Max. :2.882 Max. :3.000 \n", " BRD_MEET BRD_SIZE BRD_COMPT DIVIDEND \n", " Min. :1.000 Min. :1.00 Min. :1.750 Min. : 0.00 \n", " 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:2.500 1st Qu.: 0.00 \n", " Median :2.333 Median :3.00 Median :2.500 Median : 0.00 \n", " Mean :2.333 Mean :2.09 Mean :2.525 Mean : 70.24 \n", " 3rd Qu.:2.667 3rd Qu.:3.00 3rd Qu.:2.500 3rd Qu.: 50.00 \n", " Max. :3.000 Max. :3.00 Max. :3.000 Max. :1666.00 \n", " LOSS TOT_ASSETS SLACK ROE \n", " Min. :0.0000 Min. :9.957e+07 Min. :2.649e+06 Min. :-2.030000 \n", " 1st Qu.:0.0000 1st Qu.:7.557e+11 1st Qu.:4.009e+10 1st Qu.:-0.001275 \n", " Median :0.0000 Median :3.332e+12 Median :3.073e+11 Median : 0.045330 \n", " Mean :0.3125 Mean :2.508e+24 Mean :2.794e+24 Mean : 0.036846 \n", " 3rd Qu.:1.0000 3rd Qu.:1.348e+13 3rd Qu.:1.597e+12 3rd Qu.: 0.172300 \n", " Max. :1.0000 Max. :3.611e+26 Max. :4.023e+26 Max. : 0.741000 \n", " BRD_INDP_DIV BRD_MEET_DIV BRD_SIZE_DIV BRD_COMPT_DIV \n", " Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 0 \n", " 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 \n", " Median : 0.00 Median : 0.0 Median : 0.0 Median : 0 \n", " Mean : 147.22 Mean : 180.4 Mean : 203.5 Mean : 182 \n", " 3rd Qu.: 92.01 3rd Qu.: 125.0 3rd Qu.: 100.0 3rd Qu.: 125 \n", " Max. :4442.67 Max. :4998.0 Max. :4998.0 Max. :4165 \n", " BRD_EFFC_DIV \n", " Min. : 0.0 \n", " 1st Qu.: 0.0 \n", " Median : 0.0 \n", " Mean : 172.0 \n", " 3rd Qu.: 110.6 \n", " Max. :4606.0 " ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary(data_panel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n", "- - - \n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n", "output_type": "error", "traceback": [ "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n" ] } ], "source": [ "qplot(y = data$LogTobinsQ, x = data$LIQ, geom = c(\"point\", \"smooth\"), method = lm)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in var(data$LIQ): 'x' is NULL\n", "output_type": "error", "traceback": [ "Error in var(data$LIQ): 'x' is NULL\n" ] } ], "source": [ "var(data$LIQ)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in var(data$TOBINQ): 'x' is NULL\n", "output_type": "error", "traceback": [ "Error in var(data$TOBINQ): 'x' is NULL\n" ] } ], "source": [ "var(data$TOBINQ)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n", "output_type": "error", "traceback": [ "Error in exists(name, envir = env, mode = mode): argument \"env\" is missing, with no default\n" ] } ], "source": [ "qplot(data$TOBINQ)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n", "output_type": "error", "traceback": [ "Error in eval(expr, envir, enclos): object 'TOBINQ' not found\n" ] } ], "source": [ "skripsi.try1 <- plm(log(TOBINQ) ~ PDEBT + log(SIZE) + log(LEV) + log(LIQ) + log(INTCOV) + log(PROF) + DR + log(GROWTH) + FIRMAGE + y1 + y2 + y3 , data = data_panel, model = \"within\")\n" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ERROR", "evalue": "Error in summary(skripsi.try1): object 'skripsi.try1' not found\n", "output_type": "error", "traceback": [ "Error in summary(skripsi.try1): object 'skripsi.try1' not found\n" ] } ], "source": [ "summary(skripsi.try1)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Installing package into ‘/home/qbits7/R/x86_64-pc-linux-gnu-library/3.2’\n", "(as ‘lib’ is unspecified)\n" ] }, { "ename": "ERROR", "evalue": "Error in contrib.url(repos, type): trying to use CRAN without setting a mirror\n", "output_type": "error", "traceback": [ "Error in contrib.url(repos, type): trying to use CRAN without setting a mirror\n" ] } ], "source": [ "install.packages(\"R2wd\")" ] }, { "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": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
pawni/sgld_online_approximation
DropoutMC_SGLD_LR.ipynb
1
8252
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments using Dropout-MC\n", "\n", "We start by building the model and showing the basic inference procedure and calculation of the performance on the MNIST classification and the outlier detection task. Then perform multiple runs of the model with different number of samples in the ensemble to calculate performance statistics. This experiment uses the same learning rate schedule as the SGLD example for comparable results." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n", "Extracting notMNIST_data/train-images-idx3-ubyte.gz\n", "Extracting notMNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting notMNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting notMNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Let's first setup the libraries, session and experimental data\n", "import experiment\n", "import inferences\n", "import edward as ed\n", "import tensorflow as tf\n", "import numpy as np\n", "import os\n", "\n", "s = experiment.setup()\n", "mnist, notmnist = experiment.get_data()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Builds the model and approximation variables used for the model\n", "y_, model_variables = experiment.get_model_3layer(dropout=0.8)\n", "approx_variables = experiment.get_pointmass_approximation_variables_3layer()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000/1000 [100%] ██████████████████████████████ Elapsed: 6s | Loss: 221235.281\n" ] } ], "source": [ "# Performs inference with edward's MAP class\n", "lr = tf.placeholder(tf.float32, shape=[])\n", "optimizer = tf.train.GradientDescentOptimizer(lr)\n", "inference_dict = {model_variables[key]: val for key, val in approx_variables.iteritems()}\n", "inference = ed.MAP(inference_dict, data={y_: model_variables['y']})\n", "n_iter=1000\n", "inference.initialize(n_iter=n_iter, optimizer=optimizer)\n", "\n", "tf.global_variables_initializer().run()\n", "for i in range(n_iter):\n", " batch = mnist.train.next_batch(100)\n", " info_dict = inference.update({model_variables['x']: batch[0],\n", " model_variables['y']: batch[1],\n", " lr:0.005/(i+1.)})\n", " inference.print_progress(info_dict)\n", "\n", "inference.finalize()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7802\n", "[ 0.94501024 2.20262241 1.09658313 ..., 1.615152 2.13835573\n", " 1.60803676]\n" ] } ], "source": [ "# Computes the accuracy of our model\n", "accuracy, disagreement = experiment.get_metrics(model_variables, approx_variables, num_samples=10, dropout=0.8)\n", "print(accuracy.eval({model_variables['x']: mnist.test.images, model_variables['y']: mnist.test.labels}))\n", "print(disagreement.eval({model_variables['x']: mnist.test.images, model_variables['y']: mnist.test.labels}))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'FP': 219, 'TN': 9781, 'FN': 2609, 'TP': 7391}\n", "TP/(FN+TP): 0.7391\n", "FP/(FP+TN): 0.0219\n" ] } ], "source": [ "# Computes some statistics for the proposed outlier detection\n", "outlier_stats = experiment.get_outlier_stats(model_variables, disagreement, mnist, notmnist)\n", "print(outlier_stats)\n", "print('TP/(FN+TP): {}'.format(float(outlier_stats['TP']) / (outlier_stats['TP'] + outlier_stats['FN'])))\n", "print('FP/(FP+TN): {}'.format(float(outlier_stats['FP']) / (outlier_stats['FP'] + outlier_stats['TN'])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell performs multiple runs of this model with different number of samples within the ensemble to capture performance statistics. Results are saved in `DropoutMC_SGLD_LR.csv`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000/1000 [100%] ██████████████████████████████ Elapsed: 6s | Loss: 221254.156\n", "1000/1000 [100%] ██████████████████████████████ Elapsed: 7s | Loss: 221235.078\n", "1000/1000 [100%] ██████████████████████████████ Elapsed: 7s | Loss: 221224.844\n", "1000/1000 [100%] ██████████████████████████████ Elapsed: 8s | Loss: 221258.250\n", "1000/1000 [100%] ██████████████████████████████ Elapsed: 8s | Loss: 221233.422\n" ] } ], "source": [ "import pandas as pd\n", "\n", "results = pd.DataFrame(columns=('run', 'samples', 'acc', 'TP', 'FN', 'TN', 'FP'))\n", "\n", "for run in range(5):\n", " lr = tf.placeholder(tf.float32, shape=[])\n", " optimizer = tf.train.GradientDescentOptimizer(lr)\n", " inference_dict = {model_variables[key]: val for key, val in approx_variables.iteritems()}\n", " inference = ed.MAP(inference_dict, data={y_: model_variables['y']})\n", " n_iter=1000\n", " inference.initialize(n_iter=n_iter, optimizer=optimizer)\n", "\n", " tf.global_variables_initializer().run()\n", " for i in range(n_iter):\n", " batch = mnist.train.next_batch(100)\n", " info_dict = inference.update({model_variables['x']: batch[0],\n", " model_variables['y']: batch[1],\n", " lr:0.005/(i+1.)})\n", " inference.print_progress(info_dict)\n", "\n", " inference.finalize()\n", " \n", " for num_samples in range(15):\n", " accuracy, disagreement = experiment.get_metrics(model_variables, approx_variables,\n", " num_samples=num_samples + 1, dropout=0.8)\n", " acc = accuracy.eval({model_variables['x']: mnist.test.images, model_variables['y']: mnist.test.labels})\n", " outlier_stats = experiment.get_outlier_stats(model_variables, disagreement, mnist, notmnist)\n", " results.loc[len(results)] = [run, num_samples + 1, acc,\n", " outlier_stats['TP'], outlier_stats['FN'],\n", " outlier_stats['TN'], outlier_stats['FP']]\n", "results.to_csv('DropoutMC_SGLD_LR.csv', index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
drdwitte/data4goodprojects
EVA/Data_Prep/FixMissingAddressData.ipynb
1
164130
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "import urllib.request\n", "import urllib.parse" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def latlon_from_address(address):\n", " \n", " url = 'http://loc.geopunt.be/v2/Location?'\n", " \n", " #strip spaces for more robustness\n", " values = {'q' : address.strip()}\n", " \n", " data = urllib.parse.urlencode(values)\n", "\n", " #get request is url + data\n", " #for POST request you need data.encode('utf8') first and use binary_data as a second arg instead of a '+'\n", " req = urllib.request.Request(url + data)\n", " \n", " req.add_header('Content-Type', 'text/json')\n", " response = urllib.request.urlopen(req)\n", " binary_response = response.read()\n", " decoded = binary_response.decode('utf8')\n", " \n", " response.close()\n", "\n", " jsonobj = json.loads(decoded)\n", " \n", " results = jsonobj['LocationResult']\n", " \n", " if len(results) == 0:\n", " return None\n", " \n", " location = results[0]['Location']\n", "\n", " latkeyID = 'Lat_WGS84'\n", " lonkeyID = 'Lon_WGS84'\n", " \n", " return (location[latkeyID], location[lonkeyID])\n", " " ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Neerijse</td>\n", " <td>Biesbemd</td>\n", " <td>Kamstraat 33</td>\n", " <td>['Approved by EVA', 'EVA voordeel', 'Veganvrie...</td>\n", " <td>3040</td>\n", " <td>Kamstraat 33 3040 Neerijse</td>\n", " <td>(50.8127340562564, 4.622892881244533)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Antwerpen</td>\n", " <td>Falafel Shami</td>\n", " <td>Hoogstraat 47</td>\n", " <td>['Snack', '100% vegetarisch', 'Approved by EVA...</td>\n", " <td>2000</td>\n", " <td>Hoogstraat 47 2000 Antwerpen</td>\n", " <td>(51.21912277399896, 4.39791278664577)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Antwerpen</td>\n", " <td>Falafel Tof</td>\n", " <td>Hoogstraat 32</td>\n", " <td>['Eethuis', 'Snack', '100% vegetarisch', 'Appr...</td>\n", " <td>2000</td>\n", " <td>Hoogstraat 32 2000 Antwerpen</td>\n", " <td>(51.219967595445304, 4.398256839416387)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "0 Neerijse Biesbemd Kamstraat 33 \n", "1 Antwerpen Falafel Shami Hoogstraat 47 \n", "2 Antwerpen Falafel Tof Hoogstraat 32 \n", "\n", " tags zipcode \\\n", "0 ['Approved by EVA', 'EVA voordeel', 'Veganvrie... 3040 \n", "1 ['Snack', '100% vegetarisch', 'Approved by EVA... 2000 \n", "2 ['Eethuis', 'Snack', '100% vegetarisch', 'Appr... 2000 \n", "\n", " full_address latlon \\\n", "0 Kamstraat 33 3040 Neerijse (50.8127340562564, 4.622892881244533) \n", "1 Hoogstraat 47 2000 Antwerpen (51.21912277399896, 4.39791278664577) \n", "2 Hoogstraat 32 2000 Antwerpen (51.219967595445304, 4.398256839416387) \n", "\n", " missing \n", "0 False \n", "1 False \n", "2 False " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_data = pd.read_csv('EVA_restoswithlocations.csv')\n", "all_data.head(n=3)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(91, 8)\n", "(735, 8)\n" ] } ], "source": [ "df_notfound = all_data[all_data['missing']]\n", "print(df_notfound.shape)\n", "print(all_data.shape)\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def overwriteAddress(full_address, index, dataframe):\n", " \n", " parts = full_address.split(' ')\n", " \n", " city = parts[-1]\n", " zipcode = int(parts[-2])\n", " street = \" \".join(parts[:len(parts)-2])\n", " latlon = latlon_from_address(full_address)\n", " \n", " dataframe.set_value(index, 'street', street)\n", " dataframe.set_value(index, 'city', city)\n", " dataframe.set_value(index, 'zipcode', zipcode)\n", " dataframe.set_value(index, 'full_address', full_address)\n", " dataframe.set_value(index, 'latlon', latlon)\n", " dataframe.set_value(index, 'missing', False)\n", "\n", "\n", "def overwriteAddress2(street,zipcode,city, index, dataframe):\n", " \n", " full_address = \" \".join([street,zipcode,city])\n", " latlon = latlon_from_address(full_address)\n", " \n", " dataframe.set_value(index, 'street', street)\n", " dataframe.set_value(index, 'city', city)\n", " dataframe.set_value(index, 'zipcode', zipcode)\n", " dataframe.set_value(index, 'full_address', full_address)\n", " dataframe.set_value(index, 'latlon', latlon)\n", " dataframe.set_value(index, 'missing', False)\n", "\n", "\n" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>7</th>\n", " <td>Baal</td>\n", " <td>The Vexican</td>\n", " <td>C. Huysmansstraat 126</td>\n", " <td>['Cateraar', 'Snack', '100% vegetarisch', 'App...</td>\n", " <td>3128</td>\n", " <td>C. Huysmansstraat 126 3128 Baal</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Antwerpen</td>\n", " <td>'t Koekebakske</td>\n", " <td>Leeuwestraat 23</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>2000</td>\n", " <td>Leeuwestraat 23 2000 Antwerpen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Ieper</td>\n", " <td>Agadir</td>\n", " <td>Rijselsestraat 42</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8900</td>\n", " <td>Rijselsestraat 42 8900 Ieper</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>Wetteren</td>\n", " <td>Amuz&amp;Vous</td>\n", " <td>V. Van Sandelaan 33</td>\n", " <td>['Cateraar']</td>\n", " <td>9230</td>\n", " <td>V. Van Sandelaan 33 9230 Wetteren</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>Waregem</td>\n", " <td>BAOBAB Catering</td>\n", " <td>Mirakelstraat 104A, 8790 Waregem</td>\n", " <td>['Cateraar']</td>\n", " <td>8790</td>\n", " <td>Mirakelstraat 104A, 8790 Waregem 8790 Waregem</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Biko</td>\n", " <td>Sacramentstraat 3</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>9100</td>\n", " <td>Sacramentstraat 3 9100 Sint-Niklaas</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>Aalst</td>\n", " <td>Black and White</td>\n", " <td>Koostraat 107</td>\n", " <td>['Eethuis']</td>\n", " <td>9300</td>\n", " <td>Koostraat 107 9300 Aalst</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>Oostende</td>\n", " <td>Brasserie Buyl</td>\n", " <td>A. Buylstraat 44B</td>\n", " <td>['Eethuis']</td>\n", " <td>8400</td>\n", " <td>A. Buylstraat 44B 8400 Oostende</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>Gent</td>\n", " <td>Café De Kleine Kunst</td>\n", " <td>Lousbergkaai 99</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>9000</td>\n", " <td>Lousbergkaai 99 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>Gent</td>\n", " <td>Café Parti</td>\n", " <td>Maria-Hendrikaplein 65a</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Maria-Hendrikaplein 65a 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "7 Baal The Vexican C. Huysmansstraat 126 \n", "16 Antwerpen 't Koekebakske Leeuwestraat 23 \n", "24 Ieper Agadir Rijselsestraat 42 \n", "32 Wetteren Amuz&Vous V. Van Sandelaan 33 \n", "46 Waregem BAOBAB Catering Mirakelstraat 104A, 8790 Waregem \n", "62 Sint-Niklaas Biko Sacramentstraat 3 \n", "68 Aalst Black and White Koostraat 107 \n", "81 Oostende Brasserie Buyl A. Buylstraat 44B \n", "91 Gent Café De Kleine Kunst Lousbergkaai 99 \n", "98 Gent Café Parti Maria-Hendrikaplein 65a \n", "\n", " tags zipcode \\\n", "7 ['Cateraar', 'Snack', '100% vegetarisch', 'App... 3128 \n", "16 ['Eethuis', 'EVA voordeel'] 2000 \n", "24 ['Eethuis', 'Snack'] 8900 \n", "32 ['Cateraar'] 9230 \n", "46 ['Cateraar'] 8790 \n", "62 ['Eethuis', 'Snack'] 9100 \n", "68 ['Eethuis'] 9300 \n", "81 ['Eethuis'] 8400 \n", "91 ['Eethuis', 'EVA voordeel'] 9000 \n", "98 ['Eethuis', 'Veganvriendelijk'] 9000 \n", "\n", " full_address latlon missing \n", "7 C. Huysmansstraat 126 3128 Baal NaN True \n", "16 Leeuwestraat 23 2000 Antwerpen NaN True \n", "24 Rijselsestraat 42 8900 Ieper NaN True \n", "32 V. Van Sandelaan 33 9230 Wetteren NaN True \n", "46 Mirakelstraat 104A, 8790 Waregem 8790 Waregem NaN True \n", "62 Sacramentstraat 3 9100 Sint-Niklaas NaN True \n", "68 Koostraat 107 9300 Aalst NaN True \n", "81 A. Buylstraat 44B 8400 Oostende NaN True \n", "91 Lousbergkaai 99 9000 Gent NaN True \n", "98 Maria-Hendrikaplein 65a 9000 Gent NaN True " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[0:10]" ] }, { "cell_type": "code", "execution_count": 10, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>7</th>\n", " <td>Tremelo</td>\n", " <td>The Vexican</td>\n", " <td>Camille Huysmansstraat 126</td>\n", " <td>['Cateraar', 'Snack', '100% vegetarisch', 'App...</td>\n", " <td>3128</td>\n", " <td>Camille Huysmansstraat 126 3128 Tremelo</td>\n", " <td>(50.99981171485267, 4.749558921517834)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Antwerpen</td>\n", " <td>'t Koekebakske</td>\n", " <td>Leeuwenstraat 23</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>2000</td>\n", " <td>Leeuwenstraat 23 2000 Antwerpen</td>\n", " <td>(51.218385351929605, 4.399357877940981)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Ieper</td>\n", " <td>Agadir</td>\n", " <td>Rijselstraat 42</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8900</td>\n", " <td>Rijselstraat 42 8900 Ieper</td>\n", " <td>(50.849543772226255, 2.8863381665928447)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>Wetteren</td>\n", " <td>Amuz&amp;Vous</td>\n", " <td>Victor Van Sandelaan 33</td>\n", " <td>['Cateraar']</td>\n", " <td>9230</td>\n", " <td>Victor Van Sandelaan 33 9230 Wetteren</td>\n", " <td>(51.00217297024971, 3.8875914088732486)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>Waregem</td>\n", " <td>BAOBAB Catering</td>\n", " <td>Mirakelstraat 104A</td>\n", " <td>['Cateraar']</td>\n", " <td>8790</td>\n", " <td>Mirakelstraat 104A 8790 Waregem</td>\n", " <td>(50.870729494598294, 3.397632962878593)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Biko</td>\n", " <td>Sacramentsstraat 3</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>9100</td>\n", " <td>Sacramentsstraat 3 9100 Sint-Niklaas</td>\n", " <td>(51.16461233026816, 4.142836540827675)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>Aalst</td>\n", " <td>Black and White</td>\n", " <td>Koolstraat 107</td>\n", " <td>['Eethuis']</td>\n", " <td>9300</td>\n", " <td>Koolstraat 107 9300 Aalst</td>\n", " <td>(50.939983118445085, 4.02898384827834)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>Oostende</td>\n", " <td>Brasserie Buyl</td>\n", " <td>Adolf Buylstraat 44B</td>\n", " <td>['Eethuis']</td>\n", " <td>8400</td>\n", " <td>Adolf Buylstraat 44B 8400 Oostende</td>\n", " <td>(51.23115235995707, 2.9150036519826585)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>Gent</td>\n", " <td>Café De Kleine Kunst</td>\n", " <td>Ferdinand Lousbergkaai 99</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>9000</td>\n", " <td>Ferdinand Lousbergkaai 99 9000 Gent</td>\n", " <td>(51.04753532194943, 3.7392711886277534)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>Gent</td>\n", " <td>Café Parti</td>\n", " <td>Koningin Maria-Hendrikaplein 65a</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Koningin Maria-Hendrikaplein 65a 9000 Gent</td>\n", " <td>(51.036379827654144, 3.7161672129454875)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "7 Tremelo The Vexican Camille Huysmansstraat 126 \n", "16 Antwerpen 't Koekebakske Leeuwenstraat 23 \n", "24 Ieper Agadir Rijselstraat 42 \n", "32 Wetteren Amuz&Vous Victor Van Sandelaan 33 \n", "46 Waregem BAOBAB Catering Mirakelstraat 104A \n", "62 Sint-Niklaas Biko Sacramentsstraat 3 \n", "68 Aalst Black and White Koolstraat 107 \n", "81 Oostende Brasserie Buyl Adolf Buylstraat 44B \n", "91 Gent Café De Kleine Kunst Ferdinand Lousbergkaai 99 \n", "98 Gent Café Parti Koningin Maria-Hendrikaplein 65a \n", "\n", " tags zipcode \\\n", "7 ['Cateraar', 'Snack', '100% vegetarisch', 'App... 3128 \n", "16 ['Eethuis', 'EVA voordeel'] 2000 \n", "24 ['Eethuis', 'Snack'] 8900 \n", "32 ['Cateraar'] 9230 \n", "46 ['Cateraar'] 8790 \n", "62 ['Eethuis', 'Snack'] 9100 \n", "68 ['Eethuis'] 9300 \n", "81 ['Eethuis'] 8400 \n", "91 ['Eethuis', 'EVA voordeel'] 9000 \n", "98 ['Eethuis', 'Veganvriendelijk'] 9000 \n", "\n", " full_address \\\n", "7 Camille Huysmansstraat 126 3128 Tremelo \n", "16 Leeuwenstraat 23 2000 Antwerpen \n", "24 Rijselstraat 42 8900 Ieper \n", "32 Victor Van Sandelaan 33 9230 Wetteren \n", "46 Mirakelstraat 104A 8790 Waregem \n", "62 Sacramentsstraat 3 9100 Sint-Niklaas \n", "68 Koolstraat 107 9300 Aalst \n", "81 Adolf Buylstraat 44B 8400 Oostende \n", "91 Ferdinand Lousbergkaai 99 9000 Gent \n", "98 Koningin Maria-Hendrikaplein 65a 9000 Gent \n", "\n", " latlon missing \n", "7 (50.99981171485267, 4.749558921517834) False \n", "16 (51.218385351929605, 4.399357877940981) False \n", "24 (50.849543772226255, 2.8863381665928447) False \n", "32 (51.00217297024971, 3.8875914088732486) False \n", "46 (50.870729494598294, 3.397632962878593) False \n", "62 (51.16461233026816, 4.142836540827675) False \n", "68 (50.939983118445085, 4.02898384827834) False \n", "81 (51.23115235995707, 2.9150036519826585) False \n", "91 (51.04753532194943, 3.7392711886277534) False \n", "98 (51.036379827654144, 3.7161672129454875) False " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Camille Huysmansstraat 126 3128 Tremelo',7,df_notfound)\n", "overwriteAddress('Leeuwenstraat 23 2000 Antwerpen',16,df_notfound)\n", "overwriteAddress('Rijselstraat 42 8900 Ieper',24,df_notfound)\n", "overwriteAddress('Victor Van Sandelaan 33 9230 Wetteren',32,df_notfound)\n", "overwriteAddress('Mirakelstraat 104A 8790 Waregem',46,df_notfound)\n", "overwriteAddress('Sacramentsstraat 3 9100 Sint-Niklaas',62,df_notfound)\n", "overwriteAddress('Koolstraat 107 9300 Aalst',68,df_notfound)\n", "overwriteAddress('Adolf Buylstraat 44B 8400 Oostende',81,df_notfound)\n", "overwriteAddress('Ferdinand Lousbergkaai 99 9000 Gent',91,df_notfound)\n", "overwriteAddress('Koningin Maria-Hendrikaplein 65a 9000 Gent',98,df_notfound)\n", "\n", "df_notfound[0:10]" ] }, { "cell_type": "code", "execution_count": 11, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100</th>\n", " <td>Brussel</td>\n", " <td>Cafeabc</td>\n", " <td>Phillippe de Champagnestraat 23</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>1000</td>\n", " <td>Phillippe de Champagnestraat 23 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>Drogenbos</td>\n", " <td>Cook-Om</td>\n", " <td>Rue Marie Collart</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>1620</td>\n", " <td>Rue Marie Collart 1620 Drogenbos</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>Elsene</td>\n", " <td>Crêperie au p’tit Breton</td>\n", " <td>Amerikaansestraat 117</td>\n", " <td>['Snack']</td>\n", " <td>1050</td>\n", " <td>Amerikaansestraat 117 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>Mechelen</td>\n", " <td>d'Afspraak</td>\n", " <td>Keizeerstraat 23</td>\n", " <td>['Eethuis']</td>\n", " <td>2800</td>\n", " <td>Keizeerstraat 23 2800 Mechelen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>139</th>\n", " <td>Wellen</td>\n", " <td>De Bottelarij</td>\n", " <td>Ulbeekstraat 21</td>\n", " <td>['Eethuis']</td>\n", " <td>3830</td>\n", " <td>Ulbeekstraat 21 3830 Wellen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>140</th>\n", " <td>Brugge</td>\n", " <td>De Bottelier</td>\n", " <td>St.Jacobsstraat 63</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>St.Jacobsstraat 63 8000 Brugge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td>Leuven</td>\n", " <td>De Dijlemolens</td>\n", " <td>Zwarte zustersstraat 16/4</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Zwarte zustersstraat 16/4 3000 Leuven</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td>Sint-Pieters-Rode</td>\n", " <td>De Gelaarsde Kat</td>\n", " <td>Gobbelsrode 68a</td>\n", " <td>['Eethuis']</td>\n", " <td>3320</td>\n", " <td>Gobbelsrode 68a 3320 Sint-Pieters-Rode</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>161</th>\n", " <td>nvriendelijk</td>\n", " <td>De Grote post</td>\n", " <td>Eethuis</td>\n", " <td>[]</td>\n", " <td>Vega</td>\n", " <td>Eethuis Vega nvriendelijk</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td>Brugge - Sint Kruis</td>\n", " <td>De Jonkman</td>\n", " <td>Maaltsesteenweg 438</td>\n", " <td>['Gastronomisch', 'Veganvriendelijk']</td>\n", " <td>8310</td>\n", " <td>Maaltsesteenweg 438 8310 Brugge - Sint Kruis</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "100 Brussel Cafeabc \n", "124 Drogenbos Cook-Om \n", "128 Elsene Crêperie au p’tit Breton \n", "132 Mechelen d'Afspraak \n", "139 Wellen De Bottelarij \n", "140 Brugge De Bottelier \n", "147 Leuven De Dijlemolens \n", "153 Sint-Pieters-Rode De Gelaarsde Kat \n", "161 nvriendelijk De Grote post \n", "163 Brugge - Sint Kruis De Jonkman \n", "\n", " street \\\n", "100 Phillippe de Champagnestraat 23 \n", "124 Rue Marie Collart \n", "128 Amerikaansestraat 117 \n", "132 Keizeerstraat 23 \n", "139 Ulbeekstraat 21 \n", "140 St.Jacobsstraat 63 \n", "147 Zwarte zustersstraat 16/4 \n", "153 Gobbelsrode 68a \n", "161 Eethuis \n", "163 Maaltsesteenweg 438 \n", "\n", " tags zipcode \\\n", "100 ['Eethuis', 'Snack'] 1000 \n", "124 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 1620 \n", "128 ['Snack'] 1050 \n", "132 ['Eethuis'] 2800 \n", "139 ['Eethuis'] 3830 \n", "140 ['Eethuis'] 8000 \n", "147 ['Eethuis'] 3000 \n", "153 ['Eethuis'] 3320 \n", "161 [] Vega \n", "163 ['Gastronomisch', 'Veganvriendelijk'] 8310 \n", "\n", " full_address latlon missing \n", "100 Phillippe de Champagnestraat 23 1000 Brussel NaN True \n", "124 Rue Marie Collart 1620 Drogenbos NaN True \n", "128 Amerikaansestraat 117 1050 Elsene NaN True \n", "132 Keizeerstraat 23 2800 Mechelen NaN True \n", "139 Ulbeekstraat 21 3830 Wellen NaN True \n", "140 St.Jacobsstraat 63 8000 Brugge NaN True \n", "147 Zwarte zustersstraat 16/4 3000 Leuven NaN True \n", "153 Gobbelsrode 68a 3320 Sint-Pieters-Rode NaN True \n", "161 Eethuis Vega nvriendelijk NaN True \n", "163 Maaltsesteenweg 438 8310 Brugge - Sint Kruis NaN True " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[10:20]" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>100</th>\n", " <td>Brussel</td>\n", " <td>Cafeabc</td>\n", " <td>Philippe de Champagnestraat 23</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>1000</td>\n", " <td>Philippe de Champagnestraat 23 1000 Brussel</td>\n", " <td>(50.84314226758593, 4.347341194697616)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>Drogenbos</td>\n", " <td>Cook-Om</td>\n", " <td>Marie Collartstraat</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>1620</td>\n", " <td>Marie Collartstraat 1620 Drogenbos</td>\n", " <td>(50.78156305645173, 4.31661255727227)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>Elsene</td>\n", " <td>Crêperie au p’tit Breton</td>\n", " <td>Rue Américaine 117</td>\n", " <td>['Snack']</td>\n", " <td>1050</td>\n", " <td>Rue Américaine 117 1050 Elsene</td>\n", " <td>(50.82249491537016, 4.361301054768735)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>Mechelen</td>\n", " <td>d'Afspraak</td>\n", " <td>Keizerstraat 23</td>\n", " <td>['Eethuis']</td>\n", " <td>2800</td>\n", " <td>Keizerstraat 23 2800 Mechelen</td>\n", " <td>(51.0290618159853, 4.488280080616929)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>139</th>\n", " <td>Wellen</td>\n", " <td>De Bottelarij</td>\n", " <td>Ulbeekstraat 21</td>\n", " <td>['Eethuis']</td>\n", " <td>3832</td>\n", " <td>Ulbeekstraat 21 3832 Wellen</td>\n", " <td>(50.84097654956267, 5.308105034230186)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>140</th>\n", " <td>Brugge</td>\n", " <td>De Bottelier</td>\n", " <td>Sint Jakobstraat 63</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>Sint Jakobstraat 63 8000 Brugge</td>\n", " <td>(51.20801546511949, 3.2228642331128525)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td>Leuven</td>\n", " <td>De Dijlemolens</td>\n", " <td>Zwartzustersstraat 16</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Zwartzustersstraat 16 3000 Leuven</td>\n", " <td>(50.87305970607464, 4.696587161773074)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td>Holsbeek</td>\n", " <td>De Gelaarsde Kat</td>\n", " <td>Gobbelsrode 68a</td>\n", " <td>['Eethuis']</td>\n", " <td>3220</td>\n", " <td>Gobbelsrode 68a 3220 Holsbeek</td>\n", " <td>(50.915747648372886, 4.807520754675542)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>161</th>\n", " <td>nvriendelijk</td>\n", " <td>De Grote post</td>\n", " <td>Eethuis</td>\n", " <td>[]</td>\n", " <td>Vega</td>\n", " <td>Eethuis Vega nvriendelijk</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td>Brugge-Sint Kruis</td>\n", " <td>De Jonkman</td>\n", " <td>Maalse Steenweg 438</td>\n", " <td>['Gastronomisch', 'Veganvriendelijk']</td>\n", " <td>8310</td>\n", " <td>Maalse Steenweg 438 8310 Brugge-Sint Kruis</td>\n", " <td>(51.20768556028524, 3.2782982449692675)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "100 Brussel Cafeabc \n", "124 Drogenbos Cook-Om \n", "128 Elsene Crêperie au p’tit Breton \n", "132 Mechelen d'Afspraak \n", "139 Wellen De Bottelarij \n", "140 Brugge De Bottelier \n", "147 Leuven De Dijlemolens \n", "153 Holsbeek De Gelaarsde Kat \n", "161 nvriendelijk De Grote post \n", "163 Brugge-Sint Kruis De Jonkman \n", "\n", " street \\\n", "100 Philippe de Champagnestraat 23 \n", "124 Marie Collartstraat \n", "128 Rue Américaine 117 \n", "132 Keizerstraat 23 \n", "139 Ulbeekstraat 21 \n", "140 Sint Jakobstraat 63 \n", "147 Zwartzustersstraat 16 \n", "153 Gobbelsrode 68a \n", "161 Eethuis \n", "163 Maalse Steenweg 438 \n", "\n", " tags zipcode \\\n", "100 ['Eethuis', 'Snack'] 1000 \n", "124 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 1620 \n", "128 ['Snack'] 1050 \n", "132 ['Eethuis'] 2800 \n", "139 ['Eethuis'] 3832 \n", "140 ['Eethuis'] 8000 \n", "147 ['Eethuis'] 3000 \n", "153 ['Eethuis'] 3220 \n", "161 [] Vega \n", "163 ['Gastronomisch', 'Veganvriendelijk'] 8310 \n", "\n", " full_address \\\n", "100 Philippe de Champagnestraat 23 1000 Brussel \n", "124 Marie Collartstraat 1620 Drogenbos \n", "128 Rue Américaine 117 1050 Elsene \n", "132 Keizerstraat 23 2800 Mechelen \n", "139 Ulbeekstraat 21 3832 Wellen \n", "140 Sint Jakobstraat 63 8000 Brugge \n", "147 Zwartzustersstraat 16 3000 Leuven \n", "153 Gobbelsrode 68a 3220 Holsbeek \n", "161 Eethuis Vega nvriendelijk \n", "163 Maalse Steenweg 438 8310 Brugge-Sint Kruis \n", "\n", " latlon missing \n", "100 (50.84314226758593, 4.347341194697616) False \n", "124 (50.78156305645173, 4.31661255727227) False \n", "128 (50.82249491537016, 4.361301054768735) False \n", "132 (51.0290618159853, 4.488280080616929) False \n", "139 (50.84097654956267, 5.308105034230186) False \n", "140 (51.20801546511949, 3.2228642331128525) False \n", "147 (50.87305970607464, 4.696587161773074) False \n", "153 (50.915747648372886, 4.807520754675542) False \n", "161 NaN True \n", "163 (51.20768556028524, 3.2782982449692675) False " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# spelfout, fr/nl, fr/nl, spelfout, postcode\n", "overwriteAddress('Philippe de Champagnestraat 23 1000 Brussel',100,df_notfound)\n", "overwriteAddress('Marie Collartstraat 1620 Drogenbos',124,df_notfound)\n", "overwriteAddress('Rue Américaine 117 1050 Elsene',128,df_notfound)\n", "overwriteAddress('Keizerstraat 23 2800 Mechelen',132,df_notfound)\n", "overwriteAddress('Ulbeekstraat 21 3832 Wellen',139,df_notfound)\n", "\n", "# afkorting+spelfouten, spelfout, stad, bad record, contractie\n", "overwriteAddress('Sint Jakobstraat 63 8000 Brugge',140,df_notfound)\n", "overwriteAddress('Zwartzustersstraat 16 3000 Leuven',147,df_notfound)\n", "overwriteAddress('Gobbelsrode 68a 3220 Holsbeek',153,df_notfound)\n", "#bad record\n", "overwriteAddress2('Maalse Steenweg 438', '8310', 'Brugge-Sint Kruis',163,df_notfound)\n", "\n", "df_notfound[10:20]" ] }, { "cell_type": "code", "execution_count": 13, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>167</th>\n", " <td>Rumbeke</td>\n", " <td>De Kleine Prins</td>\n", " <td>Rijstelstraat 5</td>\n", " <td>['Eethuis']</td>\n", " <td>8800</td>\n", " <td>Rijstelstraat 5 8800 Rumbeke</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>Vorst, Kempen</td>\n", " <td>De Kruimel</td>\n", " <td>Dikstraat 3</td>\n", " <td>['Eethuis']</td>\n", " <td>2430</td>\n", " <td>Dikstraat 3 2430 Vorst, Kempen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>189</th>\n", " <td>Kortrijk</td>\n", " <td>De Promenade</td>\n", " <td>Veemlarkt 30</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8500</td>\n", " <td>Veemlarkt 30 8500 Kortrijk</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>192</th>\n", " <td>Brugge</td>\n", " <td>De Republiek</td>\n", " <td>St. Jacobsstraat 36</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8000</td>\n", " <td>St. Jacobsstraat 36 8000 Brugge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>194</th>\n", " <td>Zoutenaaie</td>\n", " <td>De Reygaerd</td>\n", " <td>Reygaerddijkstraat 22</td>\n", " <td>['Eethuis']</td>\n", " <td>8630</td>\n", " <td>Reygaerddijkstraat 22 8630 Zoutenaaie</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>197</th>\n", " <td>Stokrooie</td>\n", " <td>De Stroobander</td>\n", " <td>St. Jozefsplein 13</td>\n", " <td>['Eethuis']</td>\n", " <td>3511</td>\n", " <td>St. Jozefsplein 13 3511 Stokrooie</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>201</th>\n", " <td>Gent</td>\n", " <td>De Walrus</td>\n", " <td>Coupure Links 497,</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Coupure Links 497, 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>224</th>\n", " <td>Hasselt</td>\n", " <td>Double Dragons</td>\n", " <td>Hasseltweg 214</td>\n", " <td>['Eethuis']</td>\n", " <td>3500</td>\n", " <td>Hasseltweg 214 3500 Hasselt</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>Oostende</td>\n", " <td>El Mariachi</td>\n", " <td>Kapucijnestraat 44</td>\n", " <td>['Eethuis']</td>\n", " <td>8400</td>\n", " <td>Kapucijnestraat 44 8400 Oostende</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>239</th>\n", " <td>Sint-Maria-Horebeke</td>\n", " <td>Elim</td>\n", " <td>Dorpstraat 30</td>\n", " <td>['Eethuis']</td>\n", " <td>9667</td>\n", " <td>Dorpstraat 30 9667 Sint-Maria-Horebeke</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "167 Rumbeke De Kleine Prins Rijstelstraat 5 \n", "172 Vorst, Kempen De Kruimel Dikstraat 3 \n", "189 Kortrijk De Promenade Veemlarkt 30 \n", "192 Brugge De Republiek St. Jacobsstraat 36 \n", "194 Zoutenaaie De Reygaerd Reygaerddijkstraat 22 \n", "197 Stokrooie De Stroobander St. Jozefsplein 13 \n", "201 Gent De Walrus Coupure Links 497, \n", "224 Hasselt Double Dragons Hasseltweg 214 \n", "232 Oostende El Mariachi Kapucijnestraat 44 \n", "239 Sint-Maria-Horebeke Elim Dorpstraat 30 \n", "\n", " tags zipcode full_address \\\n", "167 ['Eethuis'] 8800 Rijstelstraat 5 8800 Rumbeke \n", "172 ['Eethuis'] 2430 Dikstraat 3 2430 Vorst, Kempen \n", "189 ['Eethuis', 'Snack'] 8500 Veemlarkt 30 8500 Kortrijk \n", "192 ['Eethuis', 'Snack'] 8000 St. Jacobsstraat 36 8000 Brugge \n", "194 ['Eethuis'] 8630 Reygaerddijkstraat 22 8630 Zoutenaaie \n", "197 ['Eethuis'] 3511 St. Jozefsplein 13 3511 Stokrooie \n", "201 ['Eethuis'] 9000 Coupure Links 497, 9000 Gent \n", "224 ['Eethuis'] 3500 Hasseltweg 214 3500 Hasselt \n", "232 ['Eethuis'] 8400 Kapucijnestraat 44 8400 Oostende \n", "239 ['Eethuis'] 9667 Dorpstraat 30 9667 Sint-Maria-Horebeke \n", "\n", " latlon missing \n", "167 NaN True \n", "172 NaN True \n", "189 NaN True \n", "192 NaN True \n", "194 NaN True \n", "197 NaN True \n", "201 NaN True \n", "224 NaN True \n", "232 NaN True \n", "239 NaN True " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[20:30]" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>167</th>\n", " <td>Rumbeke</td>\n", " <td>De Kleine Prins</td>\n", " <td>Rijselstraat 5</td>\n", " <td>['Eethuis']</td>\n", " <td>8800</td>\n", " <td>Rijselstraat 5 8800 Rumbeke</td>\n", " <td>(50.940853119308244, 3.123873800952088)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>Laakdal</td>\n", " <td>De Kruimel</td>\n", " <td>Dikstraat 3</td>\n", " <td>['Eethuis']</td>\n", " <td>2430</td>\n", " <td>Dikstraat 3 2430 Laakdal</td>\n", " <td>(51.08777822444371, 5.0689052810212445)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>189</th>\n", " <td>Kortrijk</td>\n", " <td>De Promenade</td>\n", " <td>Veemarkt 30</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8500</td>\n", " <td>Veemarkt 30 8500 Kortrijk</td>\n", " <td>(50.82624906812174, 3.2734970141452155)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>192</th>\n", " <td>Brugge</td>\n", " <td>De Republiek</td>\n", " <td>Sint Jacobsstraat 36</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>8000</td>\n", " <td>Sint Jacobsstraat 36 8000 Brugge</td>\n", " <td>(51.20801546511949, 3.2228642331128525)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>194</th>\n", " <td>Zoutenaaie</td>\n", " <td>De Reygaerd</td>\n", " <td>Reygaerdijkstraat 22</td>\n", " <td>['Eethuis']</td>\n", " <td>8630</td>\n", " <td>Reygaerdijkstraat 22 8630 Zoutenaaie</td>\n", " <td>(51.06465342677114, 2.7307744620794177)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>197</th>\n", " <td>Stokrooie</td>\n", " <td>De Stroobander</td>\n", " <td>Sint Jozefsplein 13</td>\n", " <td>['Eethuis']</td>\n", " <td>3511</td>\n", " <td>Sint Jozefsplein 13 3511 Stokrooie</td>\n", " <td>(50.966124160569926, 5.279991265198184)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>201</th>\n", " <td>Gent</td>\n", " <td>De Walrus</td>\n", " <td>Coupure Links 497</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Coupure Links 497 9000 Gent</td>\n", " <td>(51.055719748430406, 3.7087946627413633)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>224</th>\n", " <td>Hasselt</td>\n", " <td>Double Dragons</td>\n", " <td>Genkersteenweg 214</td>\n", " <td>['Eethuis']</td>\n", " <td>3500</td>\n", " <td>Genkersteenweg 214 3500 Hasselt</td>\n", " <td>(50.94450048610327, 5.361322843381503)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>Oostende</td>\n", " <td>El Mariachi</td>\n", " <td>Kapucijnenstraat 44</td>\n", " <td>['Eethuis']</td>\n", " <td>8400</td>\n", " <td>Kapucijnenstraat 44 8400 Oostende</td>\n", " <td>(51.233991155864814, 2.9177648388281234)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>239</th>\n", " <td>Sint-Maria-Horebeke</td>\n", " <td>Elim</td>\n", " <td>Dorpsstraat 30</td>\n", " <td>['Eethuis']</td>\n", " <td>9667</td>\n", " <td>Dorpsstraat 30 9667 Sint-Maria-Horebeke</td>\n", " <td>(50.83845885800066, 3.688273556090574)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "167 Rumbeke De Kleine Prins Rijselstraat 5 \n", "172 Laakdal De Kruimel Dikstraat 3 \n", "189 Kortrijk De Promenade Veemarkt 30 \n", "192 Brugge De Republiek Sint Jacobsstraat 36 \n", "194 Zoutenaaie De Reygaerd Reygaerdijkstraat 22 \n", "197 Stokrooie De Stroobander Sint Jozefsplein 13 \n", "201 Gent De Walrus Coupure Links 497 \n", "224 Hasselt Double Dragons Genkersteenweg 214 \n", "232 Oostende El Mariachi Kapucijnenstraat 44 \n", "239 Sint-Maria-Horebeke Elim Dorpsstraat 30 \n", "\n", " tags zipcode full_address \\\n", "167 ['Eethuis'] 8800 Rijselstraat 5 8800 Rumbeke \n", "172 ['Eethuis'] 2430 Dikstraat 3 2430 Laakdal \n", "189 ['Eethuis', 'Snack'] 8500 Veemarkt 30 8500 Kortrijk \n", "192 ['Eethuis', 'Snack'] 8000 Sint Jacobsstraat 36 8000 Brugge \n", "194 ['Eethuis'] 8630 Reygaerdijkstraat 22 8630 Zoutenaaie \n", "197 ['Eethuis'] 3511 Sint Jozefsplein 13 3511 Stokrooie \n", "201 ['Eethuis'] 9000 Coupure Links 497 9000 Gent \n", "224 ['Eethuis'] 3500 Genkersteenweg 214 3500 Hasselt \n", "232 ['Eethuis'] 8400 Kapucijnenstraat 44 8400 Oostende \n", "239 ['Eethuis'] 9667 Dorpsstraat 30 9667 Sint-Maria-Horebeke \n", "\n", " latlon missing \n", "167 (50.940853119308244, 3.123873800952088) False \n", "172 (51.08777822444371, 5.0689052810212445) False \n", "189 (50.82624906812174, 3.2734970141452155) False \n", "192 (51.20801546511949, 3.2228642331128525) False \n", "194 (51.06465342677114, 2.7307744620794177) False \n", "197 (50.966124160569926, 5.279991265198184) False \n", "201 (51.055719748430406, 3.7087946627413633) False \n", "224 (50.94450048610327, 5.361322843381503) False \n", "232 (51.233991155864814, 2.9177648388281234) False \n", "239 (50.83845885800066, 3.688273556090574) False " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Rijselstraat 5 8800 Rumbeke',167,df_notfound)\n", "overwriteAddress('Dikstraat 3 2430 Laakdal',172,df_notfound)\n", "overwriteAddress('Veemarkt 30 8500 Kortrijk',189,df_notfound)\n", "overwriteAddress('Sint Jacobsstraat 36 8000 Brugge',192,df_notfound)\n", "overwriteAddress('Reygaerdijkstraat 22 8630 Zoutenaaie',194,df_notfound)\n", "\n", "overwriteAddress('Sint Jozefsplein 13 3511 Stokrooie',197,df_notfound)\n", "overwriteAddress('Coupure Links 497 9000 Gent',201,df_notfound)\n", "overwriteAddress('Genkersteenweg 214 3500 Hasselt',224,df_notfound)\n", "overwriteAddress('Kapucijnenstraat 44 8400 Oostende',232,df_notfound)\n", "overwriteAddress('Dorpsstraat 30 9667 Sint-Maria-Horebeke',239,df_notfound)\n", "df_notfound[20:30]" ] }, { "cell_type": "code", "execution_count": 16, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>240</th>\n", " <td>Mechelen</td>\n", " <td>Ellis Gourmet burger</td>\n", " <td>Ijzerleen 10</td>\n", " <td>['Eethuis']</td>\n", " <td>2800</td>\n", " <td>Ijzerleen 10 2800 Mechelen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>Les Bulles - Nederland</td>\n", " <td>Enig Alternatief</td>\n", " <td>Wielakkerstraat 4</td>\n", " <td>['Eethuis']</td>\n", " <td>6811</td>\n", " <td>Wielakkerstraat 4 6811 Les Bulles - Nederland</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>Elsene</td>\n", " <td>Exki</td>\n", " <td>Elsensesteenweg 12</td>\n", " <td>['Snack']</td>\n", " <td>1050</td>\n", " <td>Elsensesteenweg 12 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>254</th>\n", " <td>Gent</td>\n", " <td>Fabula Rasa</td>\n", " <td>Fr. Lousbergkaai 134</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Fr. Lousbergkaai 134 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>259</th>\n", " <td>Gent</td>\n", " <td>Falafel Punt</td>\n", " <td>Rooiegemlaan 389</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Rooiegemlaan 389 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>269</th>\n", " <td>Gent</td>\n", " <td>Foley's Irish Pub</td>\n", " <td>Recolettenlei 10</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Recolettenlei 10 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>287</th>\n", " <td>Leuven</td>\n", " <td>Frituur Tervuursepoort</td>\n", " <td>Tervuursepoort 1</td>\n", " <td>[]</td>\n", " <td>3000</td>\n", " <td>Tervuursepoort 1 3000 Leuven</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td>Vucht</td>\n", " <td>Gastronomia Cellini</td>\n", " <td>Maasmechelen Village</td>\n", " <td>['Eethuis']</td>\n", " <td>3630</td>\n", " <td>Maasmechelen Village 3630 Vucht</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>309</th>\n", " <td>Brussel</td>\n", " <td>Happy Buddha</td>\n", " <td>E. Jacqmainlaan 7</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>1000</td>\n", " <td>E. Jacqmainlaan 7 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>317</th>\n", " <td>Kessel - Lo</td>\n", " <td>Het geheim</td>\n", " <td>B.A. de Becker-Remyplein 19</td>\n", " <td>['Eethuis']</td>\n", " <td>3010</td>\n", " <td>B.A. de Becker-Remyplein 19 3010 Kessel - Lo</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "240 Mechelen Ellis Gourmet burger \n", "243 Les Bulles - Nederland Enig Alternatief \n", "247 Elsene Exki \n", "254 Gent Fabula Rasa \n", "259 Gent Falafel Punt \n", "269 Gent Foley's Irish Pub \n", "287 Leuven Frituur Tervuursepoort \n", "292 Vucht Gastronomia Cellini \n", "309 Brussel Happy Buddha \n", "317 Kessel - Lo Het geheim \n", "\n", " street \\\n", "240 Ijzerleen 10 \n", "243 Wielakkerstraat 4 \n", "247 Elsensesteenweg 12 \n", "254 Fr. Lousbergkaai 134 \n", "259 Rooiegemlaan 389 \n", "269 Recolettenlei 10 \n", "287 Tervuursepoort 1 \n", "292 Maasmechelen Village \n", "309 E. Jacqmainlaan 7 \n", "317 B.A. de Becker-Remyplein 19 \n", "\n", " tags zipcode \\\n", "240 ['Eethuis'] 2800 \n", "243 ['Eethuis'] 6811 \n", "247 ['Snack'] 1050 \n", "254 ['Eethuis'] 9000 \n", "259 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 9000 \n", "269 ['Eethuis'] 9000 \n", "287 [] 3000 \n", "292 ['Eethuis'] 3630 \n", "309 ['Eethuis', 'EVA voordeel'] 1000 \n", "317 ['Eethuis'] 3010 \n", "\n", " full_address latlon missing \n", "240 Ijzerleen 10 2800 Mechelen NaN True \n", "243 Wielakkerstraat 4 6811 Les Bulles - Nederland NaN True \n", "247 Elsensesteenweg 12 1050 Elsene NaN True \n", "254 Fr. Lousbergkaai 134 9000 Gent NaN True \n", "259 Rooiegemlaan 389 9000 Gent NaN True \n", "269 Recolettenlei 10 9000 Gent NaN True \n", "287 Tervuursepoort 1 3000 Leuven NaN True \n", "292 Maasmechelen Village 3630 Vucht NaN True \n", "309 E. Jacqmainlaan 7 1000 Brussel NaN True \n", "317 B.A. de Becker-Remyplein 19 3010 Kessel - Lo NaN True " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[30:40]" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>240</th>\n", " <td>Mechelen</td>\n", " <td>Ellis Gourmet burger</td>\n", " <td>IJzerenleen 10</td>\n", " <td>['Eethuis']</td>\n", " <td>2800</td>\n", " <td>IJzerenleen 10 2800 Mechelen</td>\n", " <td>(51.02739024945941, 4.478711258745668)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>Les Bulles - Nederland</td>\n", " <td>Enig Alternatief</td>\n", " <td>Wielakkerstraat 4</td>\n", " <td>['Eethuis']</td>\n", " <td>6811</td>\n", " <td>Wielakkerstraat 4 6811 Les Bulles - Nederland</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>Ixelles</td>\n", " <td>Exki</td>\n", " <td>Chaussée D'Ixelles 12</td>\n", " <td>['Snack']</td>\n", " <td>1050</td>\n", " <td>Chaussée D'Ixelles 12 1050 Ixelles</td>\n", " <td>(50.83294097077088, 4.366778892054664)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>254</th>\n", " <td>Gent</td>\n", " <td>Fabula Rasa</td>\n", " <td>Lousbergskaai 134</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Lousbergskaai 134 9000 Gent</td>\n", " <td>(51.04753532194943, 3.7392711886277534)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>259</th>\n", " <td>Gent</td>\n", " <td>Falafel Punt</td>\n", " <td>Rooigemlaan 389</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Rooigemlaan 389 9000 Gent</td>\n", " <td>(51.06112430697188, 3.6932695393981017)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>269</th>\n", " <td>Gent</td>\n", " <td>Foley's Irish Pub</td>\n", " <td>Recollettenlei 10</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Recollettenlei 10 9000 Gent</td>\n", " <td>(51.05000804846928, 3.719499990341022)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>287</th>\n", " <td>Leuven</td>\n", " <td>Frituur Tervuursepoort</td>\n", " <td>Tervuursevest</td>\n", " <td>[]</td>\n", " <td>3000</td>\n", " <td>Tervuursevest 3000 Leuven</td>\n", " <td>(50.87141275848566, 4.690779214197726)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td>Vucht</td>\n", " <td>Gastronomia Cellini</td>\n", " <td>Maasmechelen Village</td>\n", " <td>['Eethuis']</td>\n", " <td>3630</td>\n", " <td>Maasmechelen Village 3630 Vucht</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>309</th>\n", " <td>Brussel</td>\n", " <td>Happy Buddha</td>\n", " <td>Emile Jacqmainlaan 7</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>1000</td>\n", " <td>Emile Jacqmainlaan 7 1000 Brussel</td>\n", " <td>(50.85240239069649, 4.35351457245943)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>317</th>\n", " <td>Leuven</td>\n", " <td>Het geheim</td>\n", " <td>Baron August de Becker Remyplein 19</td>\n", " <td>['Eethuis']</td>\n", " <td>3010</td>\n", " <td>Baron August de Becker Remyplein 19 3010 Leuven</td>\n", " <td>(50.88498605717642, 4.719266161742309)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "240 Mechelen Ellis Gourmet burger \n", "243 Les Bulles - Nederland Enig Alternatief \n", "247 Ixelles Exki \n", "254 Gent Fabula Rasa \n", "259 Gent Falafel Punt \n", "269 Gent Foley's Irish Pub \n", "287 Leuven Frituur Tervuursepoort \n", "292 Vucht Gastronomia Cellini \n", "309 Brussel Happy Buddha \n", "317 Leuven Het geheim \n", "\n", " street \\\n", "240 IJzerenleen 10 \n", "243 Wielakkerstraat 4 \n", "247 Chaussée D'Ixelles 12 \n", "254 Lousbergskaai 134 \n", "259 Rooigemlaan 389 \n", "269 Recollettenlei 10 \n", "287 Tervuursevest \n", "292 Maasmechelen Village \n", "309 Emile Jacqmainlaan 7 \n", "317 Baron August de Becker Remyplein 19 \n", "\n", " tags zipcode \\\n", "240 ['Eethuis'] 2800 \n", "243 ['Eethuis'] 6811 \n", "247 ['Snack'] 1050 \n", "254 ['Eethuis'] 9000 \n", "259 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 9000 \n", "269 ['Eethuis'] 9000 \n", "287 [] 3000 \n", "292 ['Eethuis'] 3630 \n", "309 ['Eethuis', 'EVA voordeel'] 1000 \n", "317 ['Eethuis'] 3010 \n", "\n", " full_address \\\n", "240 IJzerenleen 10 2800 Mechelen \n", "243 Wielakkerstraat 4 6811 Les Bulles - Nederland \n", "247 Chaussée D'Ixelles 12 1050 Ixelles \n", "254 Lousbergskaai 134 9000 Gent \n", "259 Rooigemlaan 389 9000 Gent \n", "269 Recollettenlei 10 9000 Gent \n", "287 Tervuursevest 3000 Leuven \n", "292 Maasmechelen Village 3630 Vucht \n", "309 Emile Jacqmainlaan 7 1000 Brussel \n", "317 Baron August de Becker Remyplein 19 3010 Leuven \n", "\n", " latlon missing \n", "240 (51.02739024945941, 4.478711258745668) False \n", "243 NaN True \n", "247 (50.83294097077088, 4.366778892054664) False \n", "254 (51.04753532194943, 3.7392711886277534) False \n", "259 (51.06112430697188, 3.6932695393981017) False \n", "269 (51.05000804846928, 3.719499990341022) False \n", "287 (50.87141275848566, 4.690779214197726) False \n", "292 NaN True \n", "309 (50.85240239069649, 4.35351457245943) False \n", "317 (50.88498605717642, 4.719266161742309) False " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('IJzerenleen 10 2800 Mechelen',240,df_notfound)\n", "#print(latlon_from_address('')) #NIET GEVONDEN\n", "overwriteAddress(\"Chaussée D'Ixelles 12 1050 Ixelles\",247,df_notfound)\n", "overwriteAddress('Lousbergskaai 134 9000 Gent',254,df_notfound)\n", "overwriteAddress('Rooigemlaan 389 9000 Gent',259,df_notfound)\n", "\n", "overwriteAddress('Recollettenlei 10 9000 Gent',269,df_notfound)\n", "overwriteAddress('Tervuursevest 3000 Leuven',287,df_notfound)\n", "#print(latlon_from_address('')) #FAILLIET\n", "overwriteAddress('Emile Jacqmainlaan 7 1000 Brussel',309,df_notfound)\n", "overwriteAddress('Baron August de Becker Remyplein 19 3010 Leuven',317,df_notfound)\n", "\n", "df_notfound[30:40]" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>321</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Het Laatste Avondmaal</td>\n", " <td>Sacramentstraat 5</td>\n", " <td>[]</td>\n", " <td>9100</td>\n", " <td>Sacramentstraat 5 9100 Sint-Niklaas</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>322</th>\n", " <td>Nieuwkerke</td>\n", " <td>Het Labyrint</td>\n", " <td>Dries 29</td>\n", " <td>['Eethuis']</td>\n", " <td>8950</td>\n", " <td>Dries 29 8950 Nieuwkerke</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>337</th>\n", " <td>Manhay</td>\n", " <td>Hotel les Sources</td>\n", " <td>Route du Crahay 28</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>6960</td>\n", " <td>Route du Crahay 28 6960 Manhay</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>354</th>\n", " <td>Heuvelland ( Dranouter)</td>\n", " <td>In de Wulf</td>\n", " <td>Wulvestraat 1</td>\n", " <td>['Gastronomisch', 'Veganvriendelijk']</td>\n", " <td>8950</td>\n", " <td>Wulvestraat 1 8950 Heuvelland ( Dranouter)</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>358</th>\n", " <td>Hasselt</td>\n", " <td>IZIBILIBOCO</td>\n", " <td>Dorpstraat 36</td>\n", " <td>['Eethuis', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>3500</td>\n", " <td>Dorpstraat 36 3500 Hasselt</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>384</th>\n", " <td>Blankenberge</td>\n", " <td>Klein Begin</td>\n", " <td>Visserstraat 23</td>\n", " <td>['Eethuis']</td>\n", " <td>8370</td>\n", " <td>Visserstraat 23 8370 Blankenberge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>396</th>\n", " <td>Antwerpen</td>\n", " <td>Krua Thai</td>\n", " <td>Pelgrimstrtaat 13</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2000</td>\n", " <td>Pelgrimstrtaat 13 2000 Antwerpen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>411</th>\n", " <td>La-Roche-en-Ardenne</td>\n", " <td>La Nature Gourmande</td>\n", " <td>Rue Pumalet 9</td>\n", " <td>['Eethuis']</td>\n", " <td>6980</td>\n", " <td>Rue Pumalet 9 6980 La-Roche-en-Ardenne</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>414</th>\n", " <td>Elsene</td>\n", " <td>La Porte des Indes</td>\n", " <td>Louisalaan 455</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Louisalaan 455 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>419</th>\n", " <td>Leuven</td>\n", " <td>La Stanza</td>\n", " <td>Wandelingenstraat 8</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Wandelingenstraat 8 3000 Leuven</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "321 Sint-Niklaas Het Laatste Avondmaal Sacramentstraat 5 \n", "322 Nieuwkerke Het Labyrint Dries 29 \n", "337 Manhay Hotel les Sources Route du Crahay 28 \n", "354 Heuvelland ( Dranouter) In de Wulf Wulvestraat 1 \n", "358 Hasselt IZIBILIBOCO Dorpstraat 36 \n", "384 Blankenberge Klein Begin Visserstraat 23 \n", "396 Antwerpen Krua Thai Pelgrimstrtaat 13 \n", "411 La-Roche-en-Ardenne La Nature Gourmande Rue Pumalet 9 \n", "414 Elsene La Porte des Indes Louisalaan 455 \n", "419 Leuven La Stanza Wandelingenstraat 8 \n", "\n", " tags zipcode \\\n", "321 [] 9100 \n", "322 ['Eethuis'] 8950 \n", "337 ['Eethuis', 'EVA voordeel'] 6960 \n", "354 ['Gastronomisch', 'Veganvriendelijk'] 8950 \n", "358 ['Eethuis', '100% vegetarisch', 'EVA voordeel'] 3500 \n", "384 ['Eethuis'] 8370 \n", "396 ['Eethuis', 'Veganvriendelijk'] 2000 \n", "411 ['Eethuis'] 6980 \n", "414 ['Eethuis'] 1050 \n", "419 ['Eethuis'] 3000 \n", "\n", " full_address latlon missing \n", "321 Sacramentstraat 5 9100 Sint-Niklaas NaN True \n", "322 Dries 29 8950 Nieuwkerke NaN True \n", "337 Route du Crahay 28 6960 Manhay NaN True \n", "354 Wulvestraat 1 8950 Heuvelland ( Dranouter) NaN True \n", "358 Dorpstraat 36 3500 Hasselt NaN True \n", "384 Visserstraat 23 8370 Blankenberge NaN True \n", "396 Pelgrimstrtaat 13 2000 Antwerpen NaN True \n", "411 Rue Pumalet 9 6980 La-Roche-en-Ardenne NaN True \n", "414 Louisalaan 455 1050 Elsene NaN True \n", "419 Wandelingenstraat 8 3000 Leuven NaN True " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[40:50]" ] }, { "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>321</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Het Laatste Avondmaal</td>\n", " <td>Sacramentsstraat</td>\n", " <td>[]</td>\n", " <td>9100</td>\n", " <td>Sacramentsstraat 9100 Sint-Niklaas</td>\n", " <td>(51.1645671421227, 4.142708094155377)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>322</th>\n", " <td>Nieuwkerke</td>\n", " <td>Het Labyrint</td>\n", " <td>Dries 29</td>\n", " <td>['Eethuis']</td>\n", " <td>8956</td>\n", " <td>Dries 29 8956 Nieuwkerke</td>\n", " <td>(50.7830491845225, 2.8268628247046723)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>337</th>\n", " <td>Manhay</td>\n", " <td>Hotel les Sources</td>\n", " <td>Route du Crahay 28</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>6960</td>\n", " <td>Route du Crahay 28 6960 Manhay</td>\n", " <td>(50.262275, 5.656895)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>354</th>\n", " <td>Heuvelland</td>\n", " <td>In de Wulf</td>\n", " <td>Wulvestraat 1</td>\n", " <td>['Gastronomisch', 'Veganvriendelijk']</td>\n", " <td>8951</td>\n", " <td>Wulvestraat 1 8951 Heuvelland</td>\n", " <td>(50.748353613531826, 2.792665289404845)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>358</th>\n", " <td>Hasselt</td>\n", " <td>IZIBILIBOCO</td>\n", " <td>Dorpsstraat 36</td>\n", " <td>['Eethuis', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>3500</td>\n", " <td>Dorpsstraat 36 3500 Hasselt</td>\n", " <td>(50.932453813844965, 5.335460073350613)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>384</th>\n", " <td>Blankenberge</td>\n", " <td>Klein Begin</td>\n", " <td>Vissersstraat 23</td>\n", " <td>['Eethuis']</td>\n", " <td>8370</td>\n", " <td>Vissersstraat 23 8370 Blankenberge</td>\n", " <td>(51.31494640794891, 3.126780840833021)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>396</th>\n", " <td>Antwerpen</td>\n", " <td>Krua Thai</td>\n", " <td>Pelgrimstraat 13</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2000</td>\n", " <td>Pelgrimstraat 13 2000 Antwerpen</td>\n", " <td>(51.21963466612017, 4.399630661487253)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>411</th>\n", " <td>La-Roche-en-Ardenne</td>\n", " <td>La Nature Gourmande</td>\n", " <td>Rue Pumalet 9</td>\n", " <td>['Eethuis']</td>\n", " <td>6980</td>\n", " <td>Rue Pumalet 9 6980 La-Roche-en-Ardenne</td>\n", " <td>(50.18126, 5.575643)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>414</th>\n", " <td>Elsene</td>\n", " <td>La Porte des Indes</td>\n", " <td>Louizalaan 455</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Louizalaan 455 1050 Elsene</td>\n", " <td>(50.81778453806542, 4.370880391068708)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>419</th>\n", " <td>Leuven</td>\n", " <td>La Stanza</td>\n", " <td>Wandelingstraat 8</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Wandelingstraat 8 3000 Leuven</td>\n", " <td>(50.88101844104403, 4.698717461992025)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "321 Sint-Niklaas Het Laatste Avondmaal Sacramentsstraat \n", "322 Nieuwkerke Het Labyrint Dries 29 \n", "337 Manhay Hotel les Sources Route du Crahay 28 \n", "354 Heuvelland In de Wulf Wulvestraat 1 \n", "358 Hasselt IZIBILIBOCO Dorpsstraat 36 \n", "384 Blankenberge Klein Begin Vissersstraat 23 \n", "396 Antwerpen Krua Thai Pelgrimstraat 13 \n", "411 La-Roche-en-Ardenne La Nature Gourmande Rue Pumalet 9 \n", "414 Elsene La Porte des Indes Louizalaan 455 \n", "419 Leuven La Stanza Wandelingstraat 8 \n", "\n", " tags zipcode \\\n", "321 [] 9100 \n", "322 ['Eethuis'] 8956 \n", "337 ['Eethuis', 'EVA voordeel'] 6960 \n", "354 ['Gastronomisch', 'Veganvriendelijk'] 8951 \n", "358 ['Eethuis', '100% vegetarisch', 'EVA voordeel'] 3500 \n", "384 ['Eethuis'] 8370 \n", "396 ['Eethuis', 'Veganvriendelijk'] 2000 \n", "411 ['Eethuis'] 6980 \n", "414 ['Eethuis'] 1050 \n", "419 ['Eethuis'] 3000 \n", "\n", " full_address \\\n", "321 Sacramentsstraat 9100 Sint-Niklaas \n", "322 Dries 29 8956 Nieuwkerke \n", "337 Route du Crahay 28 6960 Manhay \n", "354 Wulvestraat 1 8951 Heuvelland \n", "358 Dorpsstraat 36 3500 Hasselt \n", "384 Vissersstraat 23 8370 Blankenberge \n", "396 Pelgrimstraat 13 2000 Antwerpen \n", "411 Rue Pumalet 9 6980 La-Roche-en-Ardenne \n", "414 Louizalaan 455 1050 Elsene \n", "419 Wandelingstraat 8 3000 Leuven \n", "\n", " latlon missing \n", "321 (51.1645671421227, 4.142708094155377) False \n", "322 (50.7830491845225, 2.8268628247046723) False \n", "337 (50.262275, 5.656895) False \n", "354 (50.748353613531826, 2.792665289404845) False \n", "358 (50.932453813844965, 5.335460073350613) False \n", "384 (51.31494640794891, 3.126780840833021) False \n", "396 (51.21963466612017, 4.399630661487253) False \n", "411 (50.18126, 5.575643) False \n", "414 (50.81778453806542, 4.370880391068708) False \n", "419 (50.88101844104403, 4.698717461992025) False " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Sacramentsstraat 9100 Sint-Niklaas',321,df_notfound)\n", "overwriteAddress('Dries 29 8956 Nieuwkerke',322,df_notfound)\n", "\n", "#(latlon_from_address('')) # Wallonia 50.262275, 5.656895\n", "df_notfound.set_value(337, 'latlon', (50.262275, 5.656895))\n", "df_notfound.set_value(337, 'missing', False)\n", " \n", " \n", "overwriteAddress('Wulvestraat 1 8951 Heuvelland',354,df_notfound)\n", "overwriteAddress('Dorpsstraat 36 3500 Hasselt',358,df_notfound)\n", "\n", "overwriteAddress('Vissersstraat 23 8370 Blankenberge',384,df_notfound)\n", "overwriteAddress('Pelgrimstraat 13 2000 Antwerpen',396,df_notfound)\n", "\n", "#('Rue du purnalet 9 6980 La Roche-en-Ardenne')) #50.181260, 5.575643\n", "df_notfound.set_value(411, 'latlon', (50.181260, 5.575643))\n", "df_notfound.set_value(411, 'missing', False) \n", "\n", "\n", "overwriteAddress('Louizalaan 455 1050 Elsene',414,df_notfound)\n", "overwriteAddress('Wandelingstraat 8 3000 Leuven',419,df_notfound)\n", "\n", "df_notfound[40:50]" ] }, { "cell_type": "code", "execution_count": 21, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>424</th>\n", " <td>Heldergem</td>\n", " <td>Labaij Kris</td>\n", " <td>Stationstraat 220</td>\n", " <td>['Snack']</td>\n", " <td>9450</td>\n", " <td>Stationstraat 220 9450 Heldergem</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>425</th>\n", " <td>Mechelen</td>\n", " <td>Lan Na Thai</td>\n", " <td>F. de Merodestraat 59</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2800</td>\n", " <td>F. de Merodestraat 59 2800 Mechelen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>428</th>\n", " <td>Brussel</td>\n", " <td>Le Bar à Tapas</td>\n", " <td>Borgvalstraat 11</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Borgvalstraat 11 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>429</th>\n", " <td>Brussel</td>\n", " <td>Le Bar à Tapas</td>\n", " <td>Borgvalstraat 11</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Borgvalstraat 11 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>430</th>\n", " <td>Etterbeek</td>\n", " <td>Le Bol de Riz</td>\n", " <td>Waversesteenweg 335</td>\n", " <td>['Eethuis']</td>\n", " <td>1040</td>\n", " <td>Waversesteenweg 335 1040 Etterbeek</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>432</th>\n", " <td>Elsene</td>\n", " <td>Le Grenier d'Elvire</td>\n", " <td>Boondaalsesteenweg 339a</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Boondaalsesteenweg 339a 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>449</th>\n", " <td>Elsene</td>\n", " <td>Les Jardins de Bagatelle</td>\n", " <td>Herderstraat 17</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Herderstraat 17 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>452</th>\n", " <td>raar</td>\n", " <td>Les Odettes</td>\n", " <td>2000Regio Antwerpen</td>\n", " <td>[]</td>\n", " <td>Cate</td>\n", " <td>2000Regio Antwerpen Cate raar</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>457</th>\n", " <td>plantaardig</td>\n", " <td>Little Green Truck</td>\n", " <td>Cateraar</td>\n", " <td>['100% vegetarisch', 'Veganvriendelijk']</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% plantaardig</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>459</th>\n", " <td>Antwerpen</td>\n", " <td>Loes &amp; Krikke bvba</td>\n", " <td>Colonia Congresstraat 42/101</td>\n", " <td>['Cateraar']</td>\n", " <td>2060</td>\n", " <td>Colonia Congresstraat 42/101 2060 Antwerpen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "424 Heldergem Labaij Kris Stationstraat 220 \n", "425 Mechelen Lan Na Thai F. de Merodestraat 59 \n", "428 Brussel Le Bar à Tapas Borgvalstraat 11 \n", "429 Brussel Le Bar à Tapas Borgvalstraat 11 \n", "430 Etterbeek Le Bol de Riz Waversesteenweg 335 \n", "432 Elsene Le Grenier d'Elvire Boondaalsesteenweg 339a \n", "449 Elsene Les Jardins de Bagatelle Herderstraat 17 \n", "452 raar Les Odettes 2000Regio Antwerpen \n", "457 plantaardig Little Green Truck Cateraar \n", "459 Antwerpen Loes & Krikke bvba Colonia Congresstraat 42/101 \n", "\n", " tags zipcode \\\n", "424 ['Snack'] 9450 \n", "425 ['Eethuis', 'Veganvriendelijk'] 2800 \n", "428 ['Eethuis'] 1000 \n", "429 ['Eethuis'] 1000 \n", "430 ['Eethuis'] 1040 \n", "432 ['Eethuis'] 1050 \n", "449 ['Eethuis'] 1050 \n", "452 [] Cate \n", "457 ['100% vegetarisch', 'Veganvriendelijk'] 100% \n", "459 ['Cateraar'] 2060 \n", "\n", " full_address latlon missing \n", "424 Stationstraat 220 9450 Heldergem NaN True \n", "425 F. de Merodestraat 59 2800 Mechelen NaN True \n", "428 Borgvalstraat 11 1000 Brussel NaN True \n", "429 Borgvalstraat 11 1000 Brussel NaN True \n", "430 Waversesteenweg 335 1040 Etterbeek NaN True \n", "432 Boondaalsesteenweg 339a 1050 Elsene NaN True \n", "449 Herderstraat 17 1050 Elsene NaN True \n", "452 2000Regio Antwerpen Cate raar NaN True \n", "457 Cateraar 100% plantaardig NaN True \n", "459 Colonia Congresstraat 42/101 2060 Antwerpen NaN True " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[50:60]" ] }, { "cell_type": "code", "execution_count": 22, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>424</th>\n", " <td>Heldergem</td>\n", " <td>Labaij Kris</td>\n", " <td>Stationsstraat 220</td>\n", " <td>['Snack']</td>\n", " <td>9450</td>\n", " <td>Stationsstraat 220 9450 Heldergem</td>\n", " <td>(50.90644752252776, 4.024136175932285)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>425</th>\n", " <td>Mechelen</td>\n", " <td>Lan Na Thai</td>\n", " <td>Frederik de Merodestraat 59</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2800</td>\n", " <td>Frederik de Merodestraat 59 2800 Mechelen</td>\n", " <td>(51.03057816302183, 4.482054488972484)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>428</th>\n", " <td>Brussel</td>\n", " <td>Le Bar à Tapas</td>\n", " <td>Rue Borgvalstraat 11</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Rue Borgvalstraat 11 1000 Brussel</td>\n", " <td>(50.847862473722664, 4.351854614231369)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>429</th>\n", " <td>Brussel</td>\n", " <td>Le Bar à Tapas</td>\n", " <td>Borgvalstraat 11</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Borgvalstraat 11 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>430</th>\n", " <td>Etterbeek</td>\n", " <td>Le Bol de Riz</td>\n", " <td>Chaussée de Wavre 335</td>\n", " <td>['Eethuis']</td>\n", " <td>1040</td>\n", " <td>Chaussée de Wavre 335 1040 Etterbeek</td>\n", " <td>(50.8307725515541, 4.389419219700109)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>432</th>\n", " <td>Ixelles</td>\n", " <td>Le Grenier d'Elvire</td>\n", " <td>Chaussée de Boondael 339a</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Chaussée de Boondael 339a 1050 Ixelles</td>\n", " <td>(50.81858364363958, 4.38338257537767)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>449</th>\n", " <td>Elsene</td>\n", " <td>Les Jardins de Bagatelle</td>\n", " <td>Herdersstraat 17</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Herdersstraat 17 1050 Elsene</td>\n", " <td>(50.836132098538194, 4.361810154666128)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>452</th>\n", " <td>Antwerpen</td>\n", " <td>Les Odettes</td>\n", " <td>Dambruggestraat 36/1</td>\n", " <td>[]</td>\n", " <td>2060</td>\n", " <td>Dambruggestraat 36/1 2060 Antwerpen</td>\n", " <td>(51.22430063798104, 4.421778083510996)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>457</th>\n", " <td>plantaardig</td>\n", " <td>Little Green Truck</td>\n", " <td>Cateraar</td>\n", " <td>['100% vegetarisch', 'Veganvriendelijk']</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% plantaardig</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>459</th>\n", " <td>Laar</td>\n", " <td>Loes &amp; Krikke bvba</td>\n", " <td>Congresstraat</td>\n", " <td>['Cateraar']</td>\n", " <td>2060</td>\n", " <td>Congresstraat 2060 Laar</td>\n", " <td>(51.21867154334976, 4.426752383324471)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "424 Heldergem Labaij Kris Stationsstraat 220 \n", "425 Mechelen Lan Na Thai Frederik de Merodestraat 59 \n", "428 Brussel Le Bar à Tapas Rue Borgvalstraat 11 \n", "429 Brussel Le Bar à Tapas Borgvalstraat 11 \n", "430 Etterbeek Le Bol de Riz Chaussée de Wavre 335 \n", "432 Ixelles Le Grenier d'Elvire Chaussée de Boondael 339a \n", "449 Elsene Les Jardins de Bagatelle Herdersstraat 17 \n", "452 Antwerpen Les Odettes Dambruggestraat 36/1 \n", "457 plantaardig Little Green Truck Cateraar \n", "459 Laar Loes & Krikke bvba Congresstraat \n", "\n", " tags zipcode \\\n", "424 ['Snack'] 9450 \n", "425 ['Eethuis', 'Veganvriendelijk'] 2800 \n", "428 ['Eethuis'] 1000 \n", "429 ['Eethuis'] 1000 \n", "430 ['Eethuis'] 1040 \n", "432 ['Eethuis'] 1050 \n", "449 ['Eethuis'] 1050 \n", "452 [] 2060 \n", "457 ['100% vegetarisch', 'Veganvriendelijk'] 100% \n", "459 ['Cateraar'] 2060 \n", "\n", " full_address \\\n", "424 Stationsstraat 220 9450 Heldergem \n", "425 Frederik de Merodestraat 59 2800 Mechelen \n", "428 Rue Borgvalstraat 11 1000 Brussel \n", "429 Borgvalstraat 11 1000 Brussel \n", "430 Chaussée de Wavre 335 1040 Etterbeek \n", "432 Chaussée de Boondael 339a 1050 Ixelles \n", "449 Herdersstraat 17 1050 Elsene \n", "452 Dambruggestraat 36/1 2060 Antwerpen \n", "457 Cateraar 100% plantaardig \n", "459 Congresstraat 2060 Laar \n", "\n", " latlon missing \n", "424 (50.90644752252776, 4.024136175932285) False \n", "425 (51.03057816302183, 4.482054488972484) False \n", "428 (50.847862473722664, 4.351854614231369) False \n", "429 NaN True \n", "430 (50.8307725515541, 4.389419219700109) False \n", "432 (50.81858364363958, 4.38338257537767) False \n", "449 (50.836132098538194, 4.361810154666128) False \n", "452 (51.22430063798104, 4.421778083510996) False \n", "457 NaN True \n", "459 (51.21867154334976, 4.426752383324471) False " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Stationsstraat 220 9450 Heldergem',424,df_notfound)\n", "overwriteAddress('Frederik de Merodestraat 59 2800 Mechelen',425,df_notfound)\n", "overwriteAddress('Rue Borgvalstraat 11 1000 Brussel',428,df_notfound)\n", "#latlon_from_address('')) #DUPLICATE\n", "overwriteAddress('Chaussée de Wavre 335 1040 Etterbeek',430,df_notfound)\n", "\n", "overwriteAddress('Chaussée de Boondael 339a 1050 Ixelles',432,df_notfound)\n", "overwriteAddress('Herdersstraat 17 1050 Elsene',449,df_notfound)\n", "overwriteAddress('Dambruggestraat 36/1 2060 Antwerpen',452,df_notfound)\n", "#('')) #eerder een verhuisservice!?\n", "overwriteAddress('Congresstraat 2060 Laar',459,df_notfound)\n", "df_notfound[50:60]" ] }, { "cell_type": "code", "execution_count": 23, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>462</th>\n", " <td>Brugge</td>\n", " <td>Lotus</td>\n", " <td>Wapenmakerstraat 5</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>Wapenmakerstraat 5 8000 Brugge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Moon Eat</td>\n", " <td>Richard Van Britsomstraat 18</td>\n", " <td>[]</td>\n", " <td>9100</td>\n", " <td>Richard Van Britsomstraat 18 9100 Sint-Niklaas</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>498</th>\n", " <td>sel</td>\n", " <td>Moonfood</td>\n", " <td>Koloniënstraat 58</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>Brus</td>\n", " <td>Koloniënstraat 58 Brus sel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>511</th>\n", " <td>vegetarisch</td>\n", " <td>Natuurfrituur</td>\n", " <td>Cateraar</td>\n", " <td>['Veganvriendelijk']</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% vegetarisch</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>523</th>\n", " <td>Elsene</td>\n", " <td>O Liban</td>\n", " <td>Vleurgatsesteenweg 324</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>1050</td>\n", " <td>Vleurgatsesteenweg 324 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>536</th>\n", " <td>Sint-Gillis</td>\n", " <td>Ozfair</td>\n", " <td>J.Volderslaan</td>\n", " <td>['Eethuis']</td>\n", " <td>1060</td>\n", " <td>J.Volderslaan 1060 Sint-Gillis</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>544</th>\n", " <td>Brugge</td>\n", " <td>Passage</td>\n", " <td>Dweerstraat 26</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>Dweerstraat 26 8000 Brugge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>Brugge</td>\n", " <td>Pasta Presto</td>\n", " <td>St. Amandstraat 17</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>St. Amandstraat 17 8000 Brugge</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>Gent</td>\n", " <td>Petrus</td>\n", " <td>Sint-Amandsstraat 15</td>\n", " <td>['Eethuis', 'Snack', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Sint-Amandsstraat 15 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>Hasselt</td>\n", " <td>Pocomatto</td>\n", " <td>Dorpstraat 42</td>\n", " <td>['Eethuis']</td>\n", " <td>3500</td>\n", " <td>Dorpstraat 42 3500 Hasselt</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "462 Brugge Lotus Wapenmakerstraat 5 \n", "497 Sint-Niklaas Moon Eat Richard Van Britsomstraat 18 \n", "498 sel Moonfood Koloniënstraat 58 \n", "511 vegetarisch Natuurfrituur Cateraar \n", "523 Elsene O Liban Vleurgatsesteenweg 324 \n", "536 Sint-Gillis Ozfair J.Volderslaan \n", "544 Brugge Passage Dweerstraat 26 \n", "546 Brugge Pasta Presto St. Amandstraat 17 \n", "556 Gent Petrus Sint-Amandsstraat 15 \n", "563 Hasselt Pocomatto Dorpstraat 42 \n", "\n", " tags zipcode \\\n", "462 ['Eethuis'] 8000 \n", "497 [] 9100 \n", "498 ['Eethuis', 'Snack'] Brus \n", "511 ['Veganvriendelijk'] 100% \n", "523 ['Eethuis', 'Veganvriendelijk'] 1050 \n", "536 ['Eethuis'] 1060 \n", "544 ['Eethuis'] 8000 \n", "546 ['Eethuis'] 8000 \n", "556 ['Eethuis', 'Snack', 'Veganvriendelijk'] 9000 \n", "563 ['Eethuis'] 3500 \n", "\n", " full_address latlon missing \n", "462 Wapenmakerstraat 5 8000 Brugge NaN True \n", "497 Richard Van Britsomstraat 18 9100 Sint-Niklaas NaN True \n", "498 Koloniënstraat 58 Brus sel NaN True \n", "511 Cateraar 100% vegetarisch NaN True \n", "523 Vleurgatsesteenweg 324 1050 Elsene NaN True \n", "536 J.Volderslaan 1060 Sint-Gillis NaN True \n", "544 Dweerstraat 26 8000 Brugge NaN True \n", "546 St. Amandstraat 17 8000 Brugge NaN True \n", "556 Sint-Amandsstraat 15 9000 Gent NaN True \n", "563 Dorpstraat 42 3500 Hasselt NaN True " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[60:70]" ] }, { "cell_type": "code", "execution_count": 24, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>462</th>\n", " <td>Brugge</td>\n", " <td>Lotus</td>\n", " <td>Wapenmakersstraat 5</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>Wapenmakersstraat 5 8000 Brugge</td>\n", " <td>(51.209812420194034, 3.226483870185225)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>Sint-Niklaas</td>\n", " <td>Moon Eat</td>\n", " <td>Rich. van Britsomstraat 18,</td>\n", " <td>[]</td>\n", " <td>9100</td>\n", " <td>Rich. van Britsomstraat 18, 9100 Sint-Niklaas</td>\n", " <td>(51.16850816904729, 4.140115558305267)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>498</th>\n", " <td>Brussel</td>\n", " <td>Moonfood</td>\n", " <td>Koloniënstraat</td>\n", " <td>['Eethuis', 'Snack']</td>\n", " <td>58</td>\n", " <td>Koloniënstraat 58 Brussel</td>\n", " <td>(50.84721615790801, 4.361368496059801)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>511</th>\n", " <td>vegetarisch</td>\n", " <td>Natuurfrituur</td>\n", " <td>Cateraar</td>\n", " <td>['Veganvriendelijk']</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% vegetarisch</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>523</th>\n", " <td>Ixelles</td>\n", " <td>O Liban</td>\n", " <td>Chaussée de Vleurgat 324</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>1050</td>\n", " <td>Chaussée de Vleurgat 324 1050 Ixelles</td>\n", " <td>(50.82243218147768, 4.368737676178942)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>536</th>\n", " <td>Sint-Gillis</td>\n", " <td>Ozfair</td>\n", " <td>Jean Volderslaan</td>\n", " <td>['Eethuis']</td>\n", " <td>1060</td>\n", " <td>Jean Volderslaan 1060 Sint-Gillis</td>\n", " <td>(50.831634943997585, 4.3436130368112025)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>544</th>\n", " <td>Brugge</td>\n", " <td>Passage</td>\n", " <td>Dweersstraat 26</td>\n", " <td>['Eethuis']</td>\n", " <td>8000</td>\n", " <td>Dweersstraat 26 8000 Brugge</td>\n", " <td>(51.20586663915748, 3.2191247459149515)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>Gent</td>\n", " <td>Pasta Presto</td>\n", " <td>Sint-Amandstraat 17</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Sint-Amandstraat 17 9000 Gent</td>\n", " <td>(51.04332279167811, 3.7249256805045556)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>556</th>\n", " <td>Gent</td>\n", " <td>Petrus</td>\n", " <td>Sint-Amandstraat 15</td>\n", " <td>['Eethuis', 'Snack', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Sint-Amandstraat 15 9000 Gent</td>\n", " <td>(51.04351139722262, 3.7248945699168647)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>Hasselt</td>\n", " <td>Pocomatto</td>\n", " <td>Dorpsstraat 42</td>\n", " <td>['Eethuis']</td>\n", " <td>3500</td>\n", " <td>Dorpsstraat 42 3500 Hasselt</td>\n", " <td>(50.932379914450635, 5.335700399626532)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "462 Brugge Lotus Wapenmakersstraat 5 \n", "497 Sint-Niklaas Moon Eat Rich. van Britsomstraat 18, \n", "498 Brussel Moonfood Koloniënstraat \n", "511 vegetarisch Natuurfrituur Cateraar \n", "523 Ixelles O Liban Chaussée de Vleurgat 324 \n", "536 Sint-Gillis Ozfair Jean Volderslaan \n", "544 Brugge Passage Dweersstraat 26 \n", "546 Gent Pasta Presto Sint-Amandstraat 17 \n", "556 Gent Petrus Sint-Amandstraat 15 \n", "563 Hasselt Pocomatto Dorpsstraat 42 \n", "\n", " tags zipcode \\\n", "462 ['Eethuis'] 8000 \n", "497 [] 9100 \n", "498 ['Eethuis', 'Snack'] 58 \n", "511 ['Veganvriendelijk'] 100% \n", "523 ['Eethuis', 'Veganvriendelijk'] 1050 \n", "536 ['Eethuis'] 1060 \n", "544 ['Eethuis'] 8000 \n", "546 ['Eethuis'] 9000 \n", "556 ['Eethuis', 'Snack', 'Veganvriendelijk'] 9000 \n", "563 ['Eethuis'] 3500 \n", "\n", " full_address \\\n", "462 Wapenmakersstraat 5 8000 Brugge \n", "497 Rich. van Britsomstraat 18, 9100 Sint-Niklaas \n", "498 Koloniënstraat 58 Brussel \n", "511 Cateraar 100% vegetarisch \n", "523 Chaussée de Vleurgat 324 1050 Ixelles \n", "536 Jean Volderslaan 1060 Sint-Gillis \n", "544 Dweersstraat 26 8000 Brugge \n", "546 Sint-Amandstraat 17 9000 Gent \n", "556 Sint-Amandstraat 15 9000 Gent \n", "563 Dorpsstraat 42 3500 Hasselt \n", "\n", " latlon missing \n", "462 (51.209812420194034, 3.226483870185225) False \n", "497 (51.16850816904729, 4.140115558305267) False \n", "498 (50.84721615790801, 4.361368496059801) False \n", "511 NaN True \n", "523 (50.82243218147768, 4.368737676178942) False \n", "536 (50.831634943997585, 4.3436130368112025) False \n", "544 (51.20586663915748, 3.2191247459149515) False \n", "546 (51.04332279167811, 3.7249256805045556) False \n", "556 (51.04351139722262, 3.7248945699168647) False \n", "563 (50.932379914450635, 5.335700399626532) False " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Wapenmakersstraat 5 8000 Brugge',462,df_notfound)\n", "overwriteAddress('Rich. van Britsomstraat 18, 9100 Sint-Niklaas',497,df_notfound)\n", "overwriteAddress('Koloniënstraat 58 Brussel',498,df_notfound)\n", "#print(latlon_from_address('')) #BAD RECORD\n", "overwriteAddress('Chaussée de Vleurgat 324 1050 Ixelles',523,df_notfound)\n", "\n", "overwriteAddress('Jean Volderslaan 1060 Sint-Gillis',536,df_notfound)\n", "overwriteAddress('Dweersstraat 26 8000 Brugge',544,df_notfound)\n", "overwriteAddress('Sint-Amandstraat 17 9000 Gent',546,df_notfound)\n", "overwriteAddress('Sint-Amandstraat 15 9000 Gent',556,df_notfound)\n", "overwriteAddress('Dorpsstraat 42 3500 Hasselt',563,df_notfound)\n", "\n", "df_notfound[60:70]" ] }, { "cell_type": "code", "execution_count": 25, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>566</th>\n", " <td>Zegelsem (Brakel)</td>\n", " <td>Ponderspijs</td>\n", " <td>\"Rovorst 33 9660 Zegelsem (Brakel)\"</td>\n", " <td>['Cateraar']</td>\n", " <td>9660</td>\n", " <td>\"Rovorst 33 9660 Zegelsem (Brakel)\" 9660 Zegel...</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>Aalst</td>\n", " <td>Qarfa</td>\n", " <td>Stationstraat 13</td>\n", " <td>[]</td>\n", " <td>9300</td>\n", " <td>Stationstraat 13 9300 Aalst</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>586</th>\n", " <td>Kortrijk</td>\n", " <td>Repos et Repas</td>\n", " <td>Bouwcentrum Pottelberg Engelse Wandeling 2 K17</td>\n", " <td>['Eethuis']</td>\n", " <td>8500</td>\n", " <td>Bouwcentrum Pottelberg Engelse Wandeling 2 K17...</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>589</th>\n", " <td>Brussel</td>\n", " <td>Ricotta &amp; Parmesan</td>\n", " <td>Schildknaapstraat 31</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Schildknaapstraat 31 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>597</th>\n", " <td>Gent</td>\n", " <td>S.M.A.K café by Grade</td>\n", " <td>Citadelpark</td>\n", " <td>['Snack']</td>\n", " <td>9000</td>\n", " <td>Citadelpark 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>608</th>\n", " <td>Kasterlee</td>\n", " <td>Seven Hill</td>\n", " <td>Geelsebaan 29/2</td>\n", " <td>['Eethuis']</td>\n", " <td>9460</td>\n", " <td>Geelsebaan 29/2 9460 Kasterlee</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>610</th>\n", " <td>Vorst</td>\n", " <td>Sinstreetfood</td>\n", " <td>Koningsstraat 165</td>\n", " <td>['Snack', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>1190</td>\n", " <td>Koningsstraat 165 1190 Vorst</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>612</th>\n", " <td>Brussel</td>\n", " <td>Skievelat</td>\n", " <td>J. Stevensstraat 16-18</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>J. Stevensstraat 16-18 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>616</th>\n", " <td>Gent</td>\n", " <td>Soeperstar</td>\n", " <td>Makelaarstraat 24</td>\n", " <td>['Cateraar']</td>\n", " <td>9000</td>\n", " <td>Makelaarstraat 24 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>646</th>\n", " <td>Waardamme</td>\n", " <td>t Oud Gemeentehuis</td>\n", " <td>Kortrijksstraat 405</td>\n", " <td>['Eethuis']</td>\n", " <td>8020</td>\n", " <td>Kortrijksstraat 405 8020 Waardamme</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "566 Zegelsem (Brakel) Ponderspijs \n", "577 Aalst Qarfa \n", "586 Kortrijk Repos et Repas \n", "589 Brussel Ricotta & Parmesan \n", "597 Gent S.M.A.K café by Grade \n", "608 Kasterlee Seven Hill \n", "610 Vorst Sinstreetfood \n", "612 Brussel Skievelat \n", "616 Gent Soeperstar \n", "646 Waardamme t Oud Gemeentehuis \n", "\n", " street \\\n", "566 \"Rovorst 33 9660 Zegelsem (Brakel)\" \n", "577 Stationstraat 13 \n", "586 Bouwcentrum Pottelberg Engelse Wandeling 2 K17 \n", "589 Schildknaapstraat 31 \n", "597 Citadelpark \n", "608 Geelsebaan 29/2 \n", "610 Koningsstraat 165 \n", "612 J. Stevensstraat 16-18 \n", "616 Makelaarstraat 24 \n", "646 Kortrijksstraat 405 \n", "\n", " tags zipcode \\\n", "566 ['Cateraar'] 9660 \n", "577 [] 9300 \n", "586 ['Eethuis'] 8500 \n", "589 ['Eethuis'] 1000 \n", "597 ['Snack'] 9000 \n", "608 ['Eethuis'] 9460 \n", "610 ['Snack', '100% vegetarisch', 'EVA voordeel'] 1190 \n", "612 ['Eethuis'] 1000 \n", "616 ['Cateraar'] 9000 \n", "646 ['Eethuis'] 8020 \n", "\n", " full_address latlon missing \n", "566 \"Rovorst 33 9660 Zegelsem (Brakel)\" 9660 Zegel... NaN True \n", "577 Stationstraat 13 9300 Aalst NaN True \n", "586 Bouwcentrum Pottelberg Engelse Wandeling 2 K17... NaN True \n", "589 Schildknaapstraat 31 1000 Brussel NaN True \n", "597 Citadelpark 9000 Gent NaN True \n", "608 Geelsebaan 29/2 9460 Kasterlee NaN True \n", "610 Koningsstraat 165 1190 Vorst NaN True \n", "612 J. Stevensstraat 16-18 1000 Brussel NaN True \n", "616 Makelaarstraat 24 9000 Gent NaN True \n", "646 Kortrijksstraat 405 8020 Waardamme NaN True " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[70:80]" ] }, { "cell_type": "code", "execution_count": 28, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>566</th>\n", " <td>Zegelsem</td>\n", " <td>Ponderspijs</td>\n", " <td>Rovorst 33</td>\n", " <td>['Cateraar']</td>\n", " <td>9660</td>\n", " <td>Rovorst 33 9660 Zegelsem</td>\n", " <td>(50.79373791875597, 3.7175001627977577)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>Aalst</td>\n", " <td>Qarfa</td>\n", " <td>Stationsstraat 13</td>\n", " <td>[]</td>\n", " <td>9300</td>\n", " <td>Stationsstraat 13 9300 Aalst</td>\n", " <td>(50.941481263289425, 4.037367638854749)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>586</th>\n", " <td>Kortrijk</td>\n", " <td>Repos et Repas</td>\n", " <td>Engelse Wandeling 2</td>\n", " <td>['Eethuis']</td>\n", " <td>8500</td>\n", " <td>Engelse Wandeling 2 8500 Kortrijk</td>\n", " <td>(50.81461894247178, 3.245370989893236)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>589</th>\n", " <td>Brussel</td>\n", " <td>Ricotta &amp; Parmesan</td>\n", " <td>Schildknaaps Straat 31</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Schildknaaps Straat 31 1000 Brussel</td>\n", " <td>(50.84904941738051, 4.354438613128288)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>597</th>\n", " <td>Gent</td>\n", " <td>S.M.A.K café by Grade</td>\n", " <td>Jan Hoetplein 1</td>\n", " <td>['Snack']</td>\n", " <td>9000</td>\n", " <td>Jan Hoetplein 1 9000 Gent</td>\n", " <td>(51.03817709725133, 3.722586781062788)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>608</th>\n", " <td>Kasterlee</td>\n", " <td>Seven Hill</td>\n", " <td>Geelsebaan 29/2</td>\n", " <td>['Eethuis']</td>\n", " <td>2460</td>\n", " <td>Geelsebaan 29/2 2460 Kasterlee</td>\n", " <td>(51.23387387414675, 4.971535699587303)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>610</th>\n", " <td>Vorst</td>\n", " <td>Sinstreetfood</td>\n", " <td>Rue Royale 165</td>\n", " <td>['Snack', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>1190</td>\n", " <td>Rue Royale 165 1190 Vorst</td>\n", " <td>(50.8125173892532, 4.324027938080416)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>612</th>\n", " <td>Brussel</td>\n", " <td>Skievelat</td>\n", " <td>Joseph Stevensstraat 16-18</td>\n", " <td>['Eethuis']</td>\n", " <td>1000</td>\n", " <td>Joseph Stevensstraat 16-18 1000 Brussel</td>\n", " <td>(50.841462059756566, 4.352765477053187)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>616</th>\n", " <td>Gent</td>\n", " <td>Soeperstar</td>\n", " <td>Makelaarsstraat 24</td>\n", " <td>['Cateraar']</td>\n", " <td>9000</td>\n", " <td>Makelaarsstraat 24 9000 Gent</td>\n", " <td>(51.071784101804795, 3.7301275120078174)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>646</th>\n", " <td>Waardamme</td>\n", " <td>t Oud Gemeentehuis</td>\n", " <td>Kortrijksestraat 405</td>\n", " <td>['Eethuis']</td>\n", " <td>8020</td>\n", " <td>Kortrijksestraat 405 8020 Waardamme</td>\n", " <td>(51.109887151544775, 3.221312856732368)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "566 Zegelsem Ponderspijs Rovorst 33 \n", "577 Aalst Qarfa Stationsstraat 13 \n", "586 Kortrijk Repos et Repas Engelse Wandeling 2 \n", "589 Brussel Ricotta & Parmesan Schildknaaps Straat 31 \n", "597 Gent S.M.A.K café by Grade Jan Hoetplein 1 \n", "608 Kasterlee Seven Hill Geelsebaan 29/2 \n", "610 Vorst Sinstreetfood Rue Royale 165 \n", "612 Brussel Skievelat Joseph Stevensstraat 16-18 \n", "616 Gent Soeperstar Makelaarsstraat 24 \n", "646 Waardamme t Oud Gemeentehuis Kortrijksestraat 405 \n", "\n", " tags zipcode \\\n", "566 ['Cateraar'] 9660 \n", "577 [] 9300 \n", "586 ['Eethuis'] 8500 \n", "589 ['Eethuis'] 1000 \n", "597 ['Snack'] 9000 \n", "608 ['Eethuis'] 2460 \n", "610 ['Snack', '100% vegetarisch', 'EVA voordeel'] 1190 \n", "612 ['Eethuis'] 1000 \n", "616 ['Cateraar'] 9000 \n", "646 ['Eethuis'] 8020 \n", "\n", " full_address \\\n", "566 Rovorst 33 9660 Zegelsem \n", "577 Stationsstraat 13 9300 Aalst \n", "586 Engelse Wandeling 2 8500 Kortrijk \n", "589 Schildknaaps Straat 31 1000 Brussel \n", "597 Jan Hoetplein 1 9000 Gent \n", "608 Geelsebaan 29/2 2460 Kasterlee \n", "610 Rue Royale 165 1190 Vorst \n", "612 Joseph Stevensstraat 16-18 1000 Brussel \n", "616 Makelaarsstraat 24 9000 Gent \n", "646 Kortrijksestraat 405 8020 Waardamme \n", "\n", " latlon missing \n", "566 (50.79373791875597, 3.7175001627977577) False \n", "577 (50.941481263289425, 4.037367638854749) False \n", "586 (50.81461894247178, 3.245370989893236) False \n", "589 (50.84904941738051, 4.354438613128288) False \n", "597 (51.03817709725133, 3.722586781062788) False \n", "608 (51.23387387414675, 4.971535699587303) False \n", "610 (50.8125173892532, 4.324027938080416) False \n", "612 (50.841462059756566, 4.352765477053187) False \n", "616 (51.071784101804795, 3.7301275120078174) False \n", "646 (51.109887151544775, 3.221312856732368) False " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "overwriteAddress('Rovorst 33 9660 Zegelsem',566,df_notfound)\n", "overwriteAddress('Stationsstraat 13 9300 Aalst',577,df_notfound)\n", "overwriteAddress('Engelse Wandeling 2 8500 Kortrijk',586,df_notfound)\n", "overwriteAddress('Schildknaaps Straat 31 1000 Brussel',589,df_notfound)\n", "overwriteAddress('Jan Hoetplein 1 9000 Gent',597,df_notfound)\n", "\n", "overwriteAddress('Geelsebaan 29/2 2460 Kasterlee',608,df_notfound)\n", "overwriteAddress('Rue Royale 165 1190 Vorst',610,df_notfound)\n", "overwriteAddress('Joseph Stevensstraat 16-18 1000 Brussel',612,df_notfound)\n", "overwriteAddress('Makelaarsstraat 24 9000 Gent',616,df_notfound)\n", "overwriteAddress('Kortrijksestraat 405 8020 Waardamme',646,df_notfound)\n", "\n", "df_notfound[70:80]" ] }, { "cell_type": "code", "execution_count": 29, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>653</th>\n", " <td>Elsene</td>\n", " <td>Tan</td>\n", " <td>Waterleidingstraat 95</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Waterleidingstraat 95 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>655</th>\n", " <td>Brussel</td>\n", " <td>Tarte Julie</td>\n", " <td>E. Jacqmainlaan 56</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>1000</td>\n", " <td>E. Jacqmainlaan 56 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>663</th>\n", " <td>Rocourt</td>\n", " <td>Tasty III</td>\n", " <td>Rue de la casquette 17</td>\n", " <td>['Snack', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>4000</td>\n", " <td>Rue de la casquette 17 4000 Rocourt</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>670</th>\n", " <td>Leuven</td>\n", " <td>Thai House</td>\n", " <td>Alfonssmetsplein</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Alfonssmetsplein 3000 Leuven</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>687</th>\n", " <td>Elsene</td>\n", " <td>Tom Yam</td>\n", " <td>Chaussee de Boondaalsesteenweg 341</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Chaussee de Boondaalsesteenweg 341 1050 Elsene</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>688</th>\n", " <td>Brussel</td>\n", " <td>Toukoul</td>\n", " <td>Lakenstraat 34</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>1000</td>\n", " <td>Lakenstraat 34 1000 Brussel</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>692</th>\n", " <td>Gent</td>\n", " <td>Tuin van Eten</td>\n", " <td>Kortrijksestesteenweg 573</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Kortrijksestesteenweg 573 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>705</th>\n", " <td>vegetarisch</td>\n", " <td>Veggiebites</td>\n", " <td>Cateraar</td>\n", " <td>[]</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% vegetarisch</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>710</th>\n", " <td>Merelbeke</td>\n", " <td>Villa Florazicht</td>\n", " <td>Hundelgemsesteenweg 182</td>\n", " <td>['Eethuis']</td>\n", " <td>9082</td>\n", " <td>Hundelgemsesteenweg 182 9082 Merelbeke</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>721</th>\n", " <td>Gent</td>\n", " <td>Wereldrestaurant De Centrale</td>\n", " <td>Kraankinderstraat 2</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Kraankinderstraat 2 9000 Gent</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>733</th>\n", " <td>Antwerpen</td>\n", " <td>Yuna</td>\n", " <td>Pelgrimsstraat 2</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2000</td>\n", " <td>Pelgrimsstraat 2 2000 Antwerpen</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name \\\n", "653 Elsene Tan \n", "655 Brussel Tarte Julie \n", "663 Rocourt Tasty III \n", "670 Leuven Thai House \n", "687 Elsene Tom Yam \n", "688 Brussel Toukoul \n", "692 Gent Tuin van Eten \n", "705 vegetarisch Veggiebites \n", "710 Merelbeke Villa Florazicht \n", "721 Gent Wereldrestaurant De Centrale \n", "733 Antwerpen Yuna \n", "\n", " street \\\n", "653 Waterleidingstraat 95 \n", "655 E. Jacqmainlaan 56 \n", "663 Rue de la casquette 17 \n", "670 Alfonssmetsplein \n", "687 Chaussee de Boondaalsesteenweg 341 \n", "688 Lakenstraat 34 \n", "692 Kortrijksestesteenweg 573 \n", "705 Cateraar \n", "710 Hundelgemsesteenweg 182 \n", "721 Kraankinderstraat 2 \n", "733 Pelgrimsstraat 2 \n", "\n", " tags zipcode \\\n", "653 ['Eethuis'] 1050 \n", "655 ['Eethuis', 'EVA voordeel'] 1000 \n", "663 ['Snack', '100% vegetarisch', 'EVA voordeel'] 4000 \n", "670 ['Eethuis'] 3000 \n", "687 ['Eethuis'] 1050 \n", "688 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 1000 \n", "692 ['Eethuis', 'Veganvriendelijk'] 9000 \n", "705 [] 100% \n", "710 ['Eethuis'] 9082 \n", "721 ['Eethuis'] 9000 \n", "733 ['Eethuis', 'Veganvriendelijk'] 2000 \n", "\n", " full_address latlon missing \n", "653 Waterleidingstraat 95 1050 Elsene NaN True \n", "655 E. Jacqmainlaan 56 1000 Brussel NaN True \n", "663 Rue de la casquette 17 4000 Rocourt NaN True \n", "670 Alfonssmetsplein 3000 Leuven NaN True \n", "687 Chaussee de Boondaalsesteenweg 341 1050 Elsene NaN True \n", "688 Lakenstraat 34 1000 Brussel NaN True \n", "692 Kortrijksestesteenweg 573 9000 Gent NaN True \n", "705 Cateraar 100% vegetarisch NaN True \n", "710 Hundelgemsesteenweg 182 9082 Merelbeke NaN True \n", "721 Kraankinderstraat 2 9000 Gent NaN True \n", "733 Pelgrimsstraat 2 2000 Antwerpen NaN True " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[80:95]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "overwriteAddress('Waterleidingsstraat 95 1050 Elsene',653,df_notfound)\n", "overwriteAddress('Emile Jacqmainlaan 56 1000 Brussel',655,df_notfound)\n", "\n", "#print(latlon_from_address('')) #wallonie => 50°38'33.6\"N 5°34'09.1\"E\n", "df_notfound.set_value(663, 'latlon', (50.642521, 5.569165))\n", "df_notfound.set_value(663, 'missing', False)\n", " \n", "overwriteAddress('Alfons Smetsplein 3000 Leuven',670,df_notfound)\n", "overwriteAddress('Boondaalse Steenweg 341 1050 Elsene',687,df_notfound)\n", "\n", "overwriteAddress('Lakensestraat 34 1000 Brussel',688,df_notfound)\n", "overwriteAddress('Kortrijksesteenweg 573 9000 Gent',692,df_notfound)\n", "#print(latlon_from_address('')) #BAD Record\n", "overwriteAddress('Hundelgemsesteenweg 182 9820 Merelbeke',710,df_notfound)\n", "overwriteAddress('Kraankindersstraat 2 9000 Gent',721,df_notfound)\n", "\n", "overwriteAddress('Pelgrimstraat 2 2000 Antwerpen',733,df_notfound)" ] }, { "cell_type": "code", "execution_count": 31, "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>city</th>\n", " <th>name</th>\n", " <th>street</th>\n", " <th>tags</th>\n", " <th>zipcode</th>\n", " <th>full_address</th>\n", " <th>latlon</th>\n", " <th>missing</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>653</th>\n", " <td>Elsene</td>\n", " <td>Tan</td>\n", " <td>Waterleidingsstraat 95</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Waterleidingsstraat 95 1050 Elsene</td>\n", " <td>(50.82422075201777, 4.358930651079416)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>655</th>\n", " <td>Brussel</td>\n", " <td>Tarte Julie</td>\n", " <td>Emile Jacqmainlaan 56</td>\n", " <td>['Eethuis', 'EVA voordeel']</td>\n", " <td>1000</td>\n", " <td>Emile Jacqmainlaan 56 1000 Brussel</td>\n", " <td>(50.85347214426758, 4.353542644100108)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>663</th>\n", " <td>Rocourt</td>\n", " <td>Tasty III</td>\n", " <td>Rue de la casquette 17</td>\n", " <td>['Snack', '100% vegetarisch', 'EVA voordeel']</td>\n", " <td>4000</td>\n", " <td>Rue de la casquette 17 4000 Rocourt</td>\n", " <td>(50.642521, 5.569165)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>670</th>\n", " <td>Leuven</td>\n", " <td>Thai House</td>\n", " <td>Alfons Smetsplein</td>\n", " <td>['Eethuis']</td>\n", " <td>3000</td>\n", " <td>Alfons Smetsplein 3000 Leuven</td>\n", " <td>(50.87734615184162, 4.703494168185616)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>687</th>\n", " <td>Elsene</td>\n", " <td>Tom Yam</td>\n", " <td>Boondaalse Steenweg 341</td>\n", " <td>['Eethuis']</td>\n", " <td>1050</td>\n", " <td>Boondaalse Steenweg 341 1050 Elsene</td>\n", " <td>(50.81860156448049, 4.383822497652958)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>688</th>\n", " <td>Brussel</td>\n", " <td>Toukoul</td>\n", " <td>Lakensestraat 34</td>\n", " <td>['Eethuis', 'EVA voordeel', 'Veganvriendelijk']</td>\n", " <td>1000</td>\n", " <td>Lakensestraat 34 1000 Brussel</td>\n", " <td>(50.85231214252864, 4.350901610751696)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>692</th>\n", " <td>Gent</td>\n", " <td>Tuin van Eten</td>\n", " <td>Kortrijksesteenweg 573</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>9000</td>\n", " <td>Kortrijksesteenweg 573 9000 Gent</td>\n", " <td>(51.035159186476875, 3.7165121218884276)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>705</th>\n", " <td>vegetarisch</td>\n", " <td>Veggiebites</td>\n", " <td>Cateraar</td>\n", " <td>[]</td>\n", " <td>100%</td>\n", " <td>Cateraar 100% vegetarisch</td>\n", " <td>NaN</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>710</th>\n", " <td>Merelbeke</td>\n", " <td>Villa Florazicht</td>\n", " <td>Hundelgemsesteenweg 182</td>\n", " <td>['Eethuis']</td>\n", " <td>9820</td>\n", " <td>Hundelgemsesteenweg 182 9820 Merelbeke</td>\n", " <td>(51.01910438947989, 3.7533206846707556)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>721</th>\n", " <td>Gent</td>\n", " <td>Wereldrestaurant De Centrale</td>\n", " <td>Kraankindersstraat 2</td>\n", " <td>['Eethuis']</td>\n", " <td>9000</td>\n", " <td>Kraankindersstraat 2 9000 Gent</td>\n", " <td>(51.061208593462986, 3.734351476392869)</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>733</th>\n", " <td>Antwerpen</td>\n", " <td>Yuna</td>\n", " <td>Pelgrimstraat 2</td>\n", " <td>['Eethuis', 'Veganvriendelijk']</td>\n", " <td>2000</td>\n", " <td>Pelgrimstraat 2 2000 Antwerpen</td>\n", " <td>(51.21994025605453, 4.3996881203456155)</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " city name street \\\n", "653 Elsene Tan Waterleidingsstraat 95 \n", "655 Brussel Tarte Julie Emile Jacqmainlaan 56 \n", "663 Rocourt Tasty III Rue de la casquette 17 \n", "670 Leuven Thai House Alfons Smetsplein \n", "687 Elsene Tom Yam Boondaalse Steenweg 341 \n", "688 Brussel Toukoul Lakensestraat 34 \n", "692 Gent Tuin van Eten Kortrijksesteenweg 573 \n", "705 vegetarisch Veggiebites Cateraar \n", "710 Merelbeke Villa Florazicht Hundelgemsesteenweg 182 \n", "721 Gent Wereldrestaurant De Centrale Kraankindersstraat 2 \n", "733 Antwerpen Yuna Pelgrimstraat 2 \n", "\n", " tags zipcode \\\n", "653 ['Eethuis'] 1050 \n", "655 ['Eethuis', 'EVA voordeel'] 1000 \n", "663 ['Snack', '100% vegetarisch', 'EVA voordeel'] 4000 \n", "670 ['Eethuis'] 3000 \n", "687 ['Eethuis'] 1050 \n", "688 ['Eethuis', 'EVA voordeel', 'Veganvriendelijk'] 1000 \n", "692 ['Eethuis', 'Veganvriendelijk'] 9000 \n", "705 [] 100% \n", "710 ['Eethuis'] 9820 \n", "721 ['Eethuis'] 9000 \n", "733 ['Eethuis', 'Veganvriendelijk'] 2000 \n", "\n", " full_address \\\n", "653 Waterleidingsstraat 95 1050 Elsene \n", "655 Emile Jacqmainlaan 56 1000 Brussel \n", "663 Rue de la casquette 17 4000 Rocourt \n", "670 Alfons Smetsplein 3000 Leuven \n", "687 Boondaalse Steenweg 341 1050 Elsene \n", "688 Lakensestraat 34 1000 Brussel \n", "692 Kortrijksesteenweg 573 9000 Gent \n", "705 Cateraar 100% vegetarisch \n", "710 Hundelgemsesteenweg 182 9820 Merelbeke \n", "721 Kraankindersstraat 2 9000 Gent \n", "733 Pelgrimstraat 2 2000 Antwerpen \n", "\n", " latlon missing \n", "653 (50.82422075201777, 4.358930651079416) False \n", "655 (50.85347214426758, 4.353542644100108) False \n", "663 (50.642521, 5.569165) False \n", "670 (50.87734615184162, 4.703494168185616) False \n", "687 (50.81860156448049, 4.383822497652958) False \n", "688 (50.85231214252864, 4.350901610751696) False \n", "692 (51.035159186476875, 3.7165121218884276) False \n", "705 NaN True \n", "710 (51.01910438947989, 3.7533206846707556) False \n", "721 (51.061208593462986, 3.734351476392869) False \n", "733 (51.21994025605453, 4.3996881203456155) False " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound[80:95]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reassemble data frame" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(644, 8)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_found_final = all_data[~all_data['missing']]\n", "df_found_final.shape" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(84, 8)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_notfound_final = df_notfound[~df_notfound['missing']]\n", "df_notfound_final.shape" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_all_final = pd.concat([df_found_final, df_notfound_final])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(728, 8)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all_final.shape" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_all_final.to_csv('EVA_restoswithlocationsANDfixes.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n" ] }, { "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": 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-3.0
tuanavu/coursera-university-of-washington
machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank.ipynb
1
28749
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing logistic regression from scratch\n", "\n", "The goal of this notebook is to implement your own logistic regression classifier. You will:\n", "\n", " * Extract features from Amazon product reviews.\n", " * Convert an SFrame into a NumPy array.\n", " * Implement the link function for logistic regression.\n", " * Write a function to compute the derivative of the log likelihood function with respect to a single coefficient.\n", " * Implement gradient ascent.\n", " * Given a set of coefficients, predict sentiments.\n", " * Compute classification accuracy for the logistic regression model.\n", " \n", "Let's get started!\n", " \n", "## Fire up GraphLab Create\n", "\n", "Make sure you have the latest version of GraphLab Create. Upgrade by\n", "```\n", " pip install graphlab-create --upgrade\n", "```\n", "See [this page](https://dato.com/download/) for detailed instructions on upgrading." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import graphlab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load review dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this assignment, we will use a subset of the Amazon product review dataset. The subset was chosen to contain similar numbers of positive and negative reviews, as the original dataset consisted primarily of positive reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products = graphlab.SFrame('amazon_baby_subset.gl/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positive sentiment and -1 indicating one with negative sentiment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products['sentiment']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us quickly explore more of this dataset. The 'name' column indicates the name of the product. Here we list the first 10 products in the dataset. We then count the number of positive and negative reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products.head(10)['name']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print '# of positive reviews =', len(products[products['sentiment']==1])\n", "print '# of negative reviews =', len(products[products['sentiment']==-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** For this assignment, we eliminated class imbalance by choosing \n", "a subset of the data with a similar number of positive and negative reviews. \n", "\n", "## Apply text cleaning on the review data\n", "\n", "In this section, we will perform some simple feature cleaning using **SFrames**. The last assignment used all words in building bag-of-words features, but here we limit ourselves to 193 words (for simplicity). We compiled a list of 193 most frequent words into a JSON file. \n", "\n", "Now, we will load these words from this JSON file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "with open('important_words.json', 'r') as f: # Reads the list of most frequent words\n", " important_words = json.load(f)\n", "important_words = [str(s) for s in important_words]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print important_words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will perform 2 simple data transformations:\n", "\n", "1. Remove punctuation using [Python's built-in](https://docs.python.org/2/library/string.html) string functionality.\n", "2. Compute word counts (only for **important_words**)\n", "\n", "We start with *Step 1* which can be done as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def remove_punctuation(text):\n", " import string\n", " return text.translate(None, string.punctuation) \n", "\n", "products['review_clean'] = products['review'].apply(remove_punctuation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we proceed with *Step 2*. For each word in **important_words**, we compute a count for the number of times the word occurs in the review. We will store this count in a separate column (one for each word). The result of this feature processing is a single column for each word in **important_words** which keeps a count of the number of times the respective word occurs in the review text.\n", "\n", "\n", "**Note:** There are several ways of doing this. In this assignment, we use the built-in *count* function for Python lists. Each review string is first split into individual words and the number of occurances of a given word is counted." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for word in important_words:\n", " products[word] = products['review_clean'].apply(lambda s : s.split().count(word))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SFrame **products** now contains one column for each of the 193 **important_words**. As an example, the column **perfect** contains a count of the number of times the word **perfect** occurs in each of the reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "products['perfect']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, write some code to compute the number of product reviews that contain the word **perfect**.\n", "\n", "**Hint**: \n", "* First create a column called `contains_perfect` which is set to 1 if the count of the word **perfect** (stored in column **perfect**) is >= 1.\n", "* Sum the number of 1s in the column `contains_perfect`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz Question**. How many reviews contain the word **perfect**?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert SFrame to NumPy array\n", "\n", "As you have seen previously, NumPy is a powerful library for doing matrix manipulation. Let us convert our data to matrices and then implement our algorithms with matrices.\n", "\n", "First, make sure you can perform the following import." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now provide you with a function that extracts columns from an SFrame and converts them into a NumPy array. Two arrays are returned: one representing features and another representing class labels. Note that the feature matrix includes an additional column 'intercept' to take account of the intercept term." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_numpy_data(data_sframe, features, label):\n", " data_sframe['intercept'] = 1\n", " features = ['intercept'] + features\n", " features_sframe = data_sframe[features]\n", " feature_matrix = features_sframe.to_numpy()\n", " label_sarray = data_sframe[label]\n", " label_array = label_sarray.to_numpy()\n", " return(feature_matrix, label_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us convert the data into NumPy arrays." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Warning: This may take a few minutes...\n", "feature_matrix, sentiment = get_numpy_data(products, important_words, 'sentiment') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Are you running this notebook on an Amazon EC2 t2.micro instance?** (If you are using your own machine, please skip this section)\n", "\n", "It has been reported that t2.micro instances do not provide sufficient power to complete the conversion in acceptable amount of time. For interest of time, please refrain from running `get_numpy_data` function. Instead, download the [binary file](https://s3.amazonaws.com/static.dato.com/files/coursera/course-3/numpy-arrays/module-3-assignment-numpy-arrays.npz) containing the four NumPy arrays you'll need for the assignment. To load the arrays, run the following commands:\n", "```\n", "arrays = np.load('module-3-assignment-numpy-arrays.npz')\n", "feature_matrix, sentiment = arrays['feature_matrix'], arrays['sentiment']\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_matrix.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz Question:** How many features are there in the **feature_matrix**?\n", "\n", "** Quiz Question:** Assuming that the intercept is present, how does the number of features in **feature_matrix** relate to the number of features in the logistic regression model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let us see what the **sentiment** column looks like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sentiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating conditional probability with link function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall from lecture that the link function is given by:\n", "$$\n", "P(y_i = +1 | \\mathbf{x}_i,\\mathbf{w}) = \\frac{1}{1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))},\n", "$$\n", "\n", "where the feature vector $h(\\mathbf{x}_i)$ represents the word counts of **important_words** in the review $\\mathbf{x}_i$. Complete the following function that implements the link function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "'''\n", "produces probablistic estimate for P(y_i = +1 | x_i, w).\n", "estimate ranges between 0 and 1.\n", "'''\n", "def predict_probability(feature_matrix, coefficients):\n", " # Take dot product of feature_matrix and coefficients \n", " # YOUR CODE HERE\n", " ...\n", " \n", " # Compute P(y_i = +1 | x_i, w) using the link function\n", " # YOUR CODE HERE\n", " predictions = ...\n", " \n", " # return predictions\n", " return predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Aside**. How the link function works with matrix algebra\n", "\n", "Since the word counts are stored as columns in **feature_matrix**, each $i$-th row of the matrix corresponds to the feature vector $h(\\mathbf{x}_i)$:\n", "$$\n", "[\\text{feature_matrix}] =\n", "\\left[\n", "\\begin{array}{c}\n", "h(\\mathbf{x}_1)^T \\\\\n", "h(\\mathbf{x}_2)^T \\\\\n", "\\vdots \\\\\n", "h(\\mathbf{x}_N)^T\n", "\\end{array}\n", "\\right] =\n", "\\left[\n", "\\begin{array}{cccc}\n", "h_0(\\mathbf{x}_1) & h_1(\\mathbf{x}_1) & \\cdots & h_D(\\mathbf{x}_1) \\\\\n", "h_0(\\mathbf{x}_2) & h_1(\\mathbf{x}_2) & \\cdots & h_D(\\mathbf{x}_2) \\\\\n", "\\vdots & \\vdots & \\ddots & \\vdots \\\\\n", "h_0(\\mathbf{x}_N) & h_1(\\mathbf{x}_N) & \\cdots & h_D(\\mathbf{x}_N)\n", "\\end{array}\n", "\\right]\n", "$$\n", "\n", "By the rules of matrix multiplication, the score vector containing elements $\\mathbf{w}^T h(\\mathbf{x}_i)$ is obtained by multiplying **feature_matrix** and the coefficient vector $\\mathbf{w}$.\n", "$$\n", "[\\text{score}] =\n", "[\\text{feature_matrix}]\\mathbf{w} =\n", "\\left[\n", "\\begin{array}{c}\n", "h(\\mathbf{x}_1)^T \\\\\n", "h(\\mathbf{x}_2)^T \\\\\n", "\\vdots \\\\\n", "h(\\mathbf{x}_N)^T\n", "\\end{array}\n", "\\right]\n", "\\mathbf{w}\n", "= \\left[\n", "\\begin{array}{c}\n", "h(\\mathbf{x}_1)^T\\mathbf{w} \\\\\n", "h(\\mathbf{x}_2)^T\\mathbf{w} \\\\\n", "\\vdots \\\\\n", "h(\\mathbf{x}_N)^T\\mathbf{w}\n", "\\end{array}\n", "\\right]\n", "= \\left[\n", "\\begin{array}{c}\n", "\\mathbf{w}^T h(\\mathbf{x}_1) \\\\\n", "\\mathbf{w}^T h(\\mathbf{x}_2) \\\\\n", "\\vdots \\\\\n", "\\mathbf{w}^T h(\\mathbf{x}_N)\n", "\\end{array}\n", "\\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Checkpoint**\n", "\n", "Just to make sure you are on the right track, we have provided a few examples. If your `predict_probability` function is implemented correctly, then the outputs will match:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dummy_feature_matrix = np.array([[1.,2.,3.], [1.,-1.,-1]])\n", "dummy_coefficients = np.array([1., 3., -1.])\n", "\n", "correct_scores = np.array( [ 1.*1. + 2.*3. + 3.*(-1.), 1.*1. + (-1.)*3. + (-1.)*(-1.) ] )\n", "correct_predictions = np.array( [ 1./(1+np.exp(-correct_scores[0])), 1./(1+np.exp(-correct_scores[1])) ] )\n", "\n", "print 'The following outputs must match '\n", "print '------------------------------------------------'\n", "print 'correct_predictions =', correct_predictions\n", "print 'output of predict_probability =', predict_probability(dummy_feature_matrix, dummy_coefficients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute derivative of log likelihood with respect to a single coefficient\n", "\n", "Recall from lecture:\n", "$$\n", "\\frac{\\partial\\ell}{\\partial w_j} = \\sum_{i=1}^N h_j(\\mathbf{x}_i)\\left(\\mathbf{1}[y_i = +1] - P(y_i = +1 | \\mathbf{x}_i, \\mathbf{w})\\right)\n", "$$\n", "\n", "We will now write a function that computes the derivative of log likelihood with respect to a single coefficient $w_j$. The function accepts two arguments:\n", "* `errors` vector containing $\\mathbf{1}[y_i = +1] - P(y_i = +1 | \\mathbf{x}_i, \\mathbf{w})$ for all $i$.\n", "* `feature` vector containing $h_j(\\mathbf{x}_i)$ for all $i$. \n", "\n", "Complete the following code block:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def feature_derivative(errors, feature): \n", " # Compute the dot product of errors and feature\n", " derivative = ...\n", " \n", " # Return the derivative\n", " return derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the main lecture, our focus was on the likelihood. In the advanced optional video, however, we introduced a transformation of this likelihood---called the log likelihood---that simplifies the derivation of the gradient and is more numerically stable. Due to its numerical stability, we will use the log likelihood instead of the likelihood to assess the algorithm.\n", "\n", "The log likelihood is computed using the following formula (see the advanced optional video if you are curious about the derivation of this equation):\n", "\n", "$$\\ell\\ell(\\mathbf{w}) = \\sum_{i=1}^N \\Big( (\\mathbf{1}[y_i = +1] - 1)\\mathbf{w}^T h(\\mathbf{x}_i) - \\ln\\left(1 + \\exp(-\\mathbf{w}^T h(\\mathbf{x}_i))\\right) \\Big) $$\n", "\n", "We provide a function to compute the log likelihood for the entire dataset. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compute_log_likelihood(feature_matrix, sentiment, coefficients):\n", " indicator = (sentiment==+1)\n", " scores = np.dot(feature_matrix, coefficients)\n", " logexp = np.log(1. + np.exp(-scores))\n", " \n", " # Simple check to prevent overflow\n", " mask = np.isinf(logexp)\n", " logexp[mask] = -scores[mask]\n", " \n", " lp = np.sum((indicator-1)*scores - logexp)\n", " return lp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Checkpoint**\n", "\n", "Just to make sure we are on the same page, run the following code block and check that the outputs match." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dummy_feature_matrix = np.array([[1.,2.,3.], [1.,-1.,-1]])\n", "dummy_coefficients = np.array([1., 3., -1.])\n", "dummy_sentiment = np.array([-1, 1])\n", "\n", "correct_indicators = np.array( [ -1==+1, 1==+1 ] )\n", "correct_scores = np.array( [ 1.*1. + 2.*3. + 3.*(-1.), 1.*1. + (-1.)*3. + (-1.)*(-1.) ] )\n", "correct_first_term = np.array( [ (correct_indicators[0]-1)*correct_scores[0], (correct_indicators[1]-1)*correct_scores[1] ] )\n", "correct_second_term = np.array( [ np.log(1. + np.exp(-correct_scores[0])), np.log(1. + np.exp(-correct_scores[1])) ] )\n", "\n", "correct_ll = sum( [ correct_first_term[0]-correct_second_term[0], correct_first_term[1]-correct_second_term[1] ] ) \n", "\n", "print 'The following outputs must match '\n", "print '------------------------------------------------'\n", "print 'correct_log_likelihood =', correct_ll\n", "print 'output of compute_log_likelihood =', compute_log_likelihood(dummy_feature_matrix, dummy_sentiment, dummy_coefficients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Taking gradient steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to implement our own logistic regression. All we have to do is to write a gradient ascent function that takes gradient steps towards the optimum. \n", "\n", "Complete the following function to solve the logistic regression model using gradient ascent:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from math import sqrt\n", "\n", "def logistic_regression(feature_matrix, sentiment, initial_coefficients, step_size, max_iter):\n", " coefficients = np.array(initial_coefficients) # make sure it's a numpy array\n", " for itr in xrange(max_iter):\n", "\n", " # Predict P(y_i = +1|x_i,w) using your predict_probability() function\n", " # YOUR CODE HERE\n", " predictions = ...\n", " \n", " # Compute indicator value for (y_i = +1)\n", " indicator = (sentiment==+1)\n", " \n", " # Compute the errors as indicator - predictions\n", " errors = indicator - predictions\n", " for j in xrange(len(coefficients)): # loop over each coefficient\n", " \n", " # Recall that feature_matrix[:,j] is the feature column associated with coefficients[j].\n", " # Compute the derivative for coefficients[j]. Save it in a variable called derivative\n", " # YOUR CODE HERE\n", " derivative = ...\n", " \n", " # add the step size times the derivative to the current coefficient\n", " ## YOUR CODE HERE\n", " ...\n", " \n", " # Checking whether log likelihood is increasing\n", " if itr <= 15 or (itr <= 100 and itr % 10 == 0) or (itr <= 1000 and itr % 100 == 0) \\\n", " or (itr <= 10000 and itr % 1000 == 0) or itr % 10000 == 0:\n", " lp = compute_log_likelihood(feature_matrix, sentiment, coefficients)\n", " print 'iteration %*d: log likelihood of observed labels = %.8f' % \\\n", " (int(np.ceil(np.log10(max_iter))), itr, lp)\n", " return coefficients" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let us run the logistic regression solver." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "coefficients = logistic_regression(feature_matrix, sentiment, initial_coefficients=np.zeros(194),\n", " step_size=1e-7, max_iter=301)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz question:** As each iteration of gradient ascent passes, does the log likelihood increase or decrease?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting sentiments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall from lecture that class predictions for a data point $\\mathbf{x}$ can be computed from the coefficients $\\mathbf{w}$ using the following formula:\n", "$$\n", "\\hat{y}_i = \n", "\\left\\{\n", "\\begin{array}{ll}\n", " +1 & \\mathbf{x}_i^T\\mathbf{w} > 0 \\\\\n", " -1 & \\mathbf{x}_i^T\\mathbf{w} \\leq 0 \\\\\n", "\\end{array} \n", "\\right.\n", "$$\n", "\n", "Now, we will write some code to compute class predictions. We will do this in two steps:\n", "* **Step 1**: First compute the **scores** using **feature_matrix** and **coefficients** using a dot product.\n", "* **Step 2**: Using the formula above, compute the class predictions from the scores.\n", "\n", "Step 1 can be implemented as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compute the scores as a dot product between feature_matrix and coefficients.\n", "scores = np.dot(feature_matrix, coefficients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, complete the following code block for **Step 2** to compute the class predictions using the **scores** obtained above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz question: ** How many reviews were predicted to have positive sentiment?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measuring accuracy\n", "\n", "We will now measure the classification accuracy of the model. Recall from the lecture that the classification accuracy can be computed as follows:\n", "\n", "$$\n", "\\mbox{accuracy} = \\frac{\\mbox{# correctly classified data points}}{\\mbox{# total data points}}\n", "$$\n", "\n", "Complete the following code block to compute the accuracy of the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "num_mistakes = ... # YOUR CODE HERE\n", "accuracy = ... # YOUR CODE HERE\n", "print \"-----------------------------------------------------\"\n", "print '# Reviews correctly classified =', len(products) - num_mistakes\n", "print '# Reviews incorrectly classified =', num_mistakes\n", "print '# Reviews total =', len(products)\n", "print \"-----------------------------------------------------\"\n", "print 'Accuracy = %.2f' % accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quiz question**: What is the accuracy of the model on predictions made above? (round to 2 digits of accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Which words contribute most to positive & negative sentiments?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that in Module 2 assignment, we were able to compute the \"**most positive words**\". These are words that correspond most strongly with positive reviews. In order to do this, we will first do the following:\n", "* Treat each coefficient as a tuple, i.e. (**word**, **coefficient_value**).\n", "* Sort all the (**word**, **coefficient_value**) tuples by **coefficient_value** in descending order." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "coefficients = list(coefficients[1:]) # exclude intercept\n", "word_coefficient_tuples = [(word, coefficient) for word, coefficient in zip(important_words, coefficients)]\n", "word_coefficient_tuples = sorted(word_coefficient_tuples, key=lambda x:x[1], reverse=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, **word_coefficient_tuples** contains a sorted list of (**word**, **coefficient_value**) tuples. The first 10 elements in this list correspond to the words that are most positive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ten \"most positive\" words\n", "\n", "Now, we compute the 10 words that have the most positive coefficient values. These words are associated with positive sentiment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz question:** Which word is **not** present in the top 10 \"most positive\" words?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ten \"most negative\" words\n", "\n", "Next, we repeat this exercise on the 10 most negative words. That is, we compute the 10 words that have the most negative coefficient values. These words are associated with negative sentiment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Quiz question:** Which word is **not** present in the top 10 \"most negative\" words?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
gtesei/DeepExperiments
MNIST_for_beginners_noNN_noCONV_0.12.0-rc1.ipynb
1
184231
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST For ML Beginners\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A very simple MNIST classifier. See extensive documentation at http://tensorflow.org/tutorials/mnist/beginners/index.md" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import os.path\n", "\n", "import argparse\n", "import sys\n", "import datetime\n", "\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "\n", "import tensorflow as tf\n", "\n", "flags = tf.app.flags\n", "FLAGS = flags.FLAGS\n", "flags.DEFINE_string('data_dir', './', 'Directory to put the training data.')\n", "\n", "\n", "##\n", "iterations = 1000\n", "batch_size = 100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********* META ***********\n", "TensorFlow version: 0.12.0-rc1\n", "Date: 2016-12-22 17:13:18.849929\n", "**************************\n" ] } ], "source": [ "print(\"********* META ***********\")\n", "print(\"TensorFlow version: \"+str(tf.__version__))\n", "print(\"Date: \"+str(datetime.datetime.now()))\n", "print(\"**************************\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting ./train-images-idx3-ubyte.gz\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n", "batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x1ce1700c160>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TwU+ZMOS2y65GRolMNcIp4n4voT9MyI8ybEV25oIs65b0tXl7N7+d6rubW4sG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\n9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If0iUMxcZuvYJCw9lC714wg/k7CpMYUQZSmEusXrCZUCLYAq4kgmlCnBhQDgSJNGZRDCJ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\n1/bq4nW+v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"text/plain": [ "<matplotlib.figure.Figure at 0x1ce16fddef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(batch_xs[10].reshape(28, 28))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_ys[10]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x1ce1878a2b0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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67xPeB283PjlNhJ8E5/bc21ORBYuVCSfQm8zKKmsba0dkO3HhpsN642as3WGl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\nSqVSOSFVhCuVSuWE/H/3r68xa4wDZgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ce17771c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(batch_xs[60].reshape(28, 28))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_ys[60]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The current state of the art in classifying these digits can be found here: http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#4d4e495354" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def main(_):\n", " # Import data \n", " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n", "\n", " # Create the model \n", " x = tf.placeholder(tf.float32, [None, 784])\n", " W = tf.Variable(tf.zeros([784, 10]))\n", " b = tf.Variable(tf.zeros([10]))\n", " y = tf.matmul(x, W) + b\n", "\n", " # Define loss and optimizer \n", " y_ = tf.placeholder(tf.float32, [None, 10])\n", " \n", " cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))\n", " train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n", "\n", " #sess = tf.InteractiveSession()\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run() \n", " #init = tf.initialize_all_variables()\n", " #sess.run(init)\n", " # Train \n", " for _ in range(iterations):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n", "\n", " # Test trained model \n", " correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " print(\">>> Test Accuracy::\"+str(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})))\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting ./train-images-idx3-ubyte.gz\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n", ">>> Test Accuracy::0.9103\n" ] } ], "source": [ "main(_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TensorBoard: Visualizing Learning" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from tensorflow.contrib.tensorboard.plugins import projector\n", "\n", "def variable_summaries(var):\n", " \"\"\"Attach a lot of summaries to a Tensor (for TensorBoard visualization).\"\"\"\n", " with tf.name_scope('summaries'):\n", " mean = tf.reduce_mean(var)\n", " tf.summary.scalar(var.name+'_mean', mean)\n", " #tf.scalar_summary(var.name+'_mean', mean)\n", " stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n", " tf.summary.scalar(var.name+'_stddev', stddev)\n", " #tf.scalar_summary(var.name+'_stddev', stddev)\n", " tf.summary.scalar(var.name+'_max', tf.reduce_max(var))\n", " #tf.scalar_summary(var.name+'_max', tf.reduce_max(var))\n", " tf.summary.scalar(var.name+'_min', tf.reduce_min(var))\n", " #tf.histogram_summary( var.name, var)\n", " tf.summary.histogram( var.name, var)\n", " \n", "\n", "def main2(_):\n", " # Import data \n", " mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)\n", " \n", " #config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()\n", "\n", " # input images\n", " with tf.name_scope('input'):\n", " # None -> batch size can be any size, 784 -> flattened mnist image\n", " x = tf.placeholder(tf.float32, shape=[None, 784], name=\"x-input\") \n", " \n", " xs = tf.Variable(tf.zeros([batch_size, 784]) , name=\"x-input-slice1\")\n", " xs = tf.slice(x, [0, 0], [batch_size, 784] , name=\"x-input-slice2\")\n", " \n", " variable_summaries(xs)\n", " \n", " #emb1 = config.embeddings.add()\n", " #emb1.tensor_name = xs.name\n", " #emb1.metadata_path = os.path.join(FLAGS.data_dir + '/_logs', 'metadata.tsv')\n", " \n", " # target 10 output classes\n", " y_ = tf.placeholder(tf.float32, shape=[None, 10], name=\"y-input\")\n", " #variable_summaries(y_)\n", " \n", " with tf.name_scope('input_image'):\n", " image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])\n", " tf.summary.image('image', image_shaped_input, 10)\n", " \n", " with tf.name_scope('W'):\n", " W = tf.Variable(tf.zeros([784, 10]))\n", " variable_summaries(W)\n", " \n", " with tf.name_scope('b'):\n", " b = tf.Variable(tf.zeros([10]))\n", " variable_summaries(b)\n", " \n", " with tf.name_scope('y'):\n", " y = tf.matmul(x, W) + b\n", " variable_summaries(y)\n", "\n", " with tf.name_scope('cross_entropy'):\n", " cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))\n", " tf.summary.scalar('cross_entropy', cross_entropy)\n", " #tf.scalar_summary('cross_entropy', cross_entropy)\n", " \n", " with tf.name_scope('train_step'):\n", " train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n", " \n", " # Test trained model \n", " with tf.name_scope('accuracy-scope'):\n", " with tf.name_scope('correct_prediction'):\n", " correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n", "\n", " with tf.name_scope('accuracy'):\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " tf.summary.scalar('accuracy-val', accuracy)\n", " #tf.scalar_summary('accuracy', accuracy)\n", " \n", " #######\n", " #init = tf.initialize_all_variables()\n", "\n", " # Create a saver for writing training checkpoints.\n", " saver = tf.train.Saver()\n", "\n", " #sess = tf.InteractiveSession()\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run() \n", "\n", " # Merge all the summaries and write them out to ./logs (by default)\n", " #merged = tf.merge_all_summaries()\n", " merged = tf.summary.merge_all()\n", " #writer = tf.train.SummaryWriter(FLAGS.data_dir + '/_logs',sess.graph)\n", " writer = tf.summary.FileWriter(FLAGS.data_dir + '/_logs',sess.graph)\n", " \n", " #projector.visualize_embeddings(writer, config)\n", "\n", " #sess.run(init)\n", "\n", " # Train \n", " for i in range(iterations):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n", "\n", " if i % 100 == 0 or i == (iterations-1):\n", " summary = sess.run(merged, feed_dict={x: batch_xs, y_: batch_ys})\n", " writer.add_summary(summary, i)\n", " summary, acc = sess.run([merged, accuracy], feed_dict={x: mnist.test.images,y_: mnist.test.labels})\n", " writer.add_summary(summary, i)\n", " writer.flush()\n", " \n", " checkpoint_file = os.path.join(FLAGS.data_dir + '/_logs', 'checkpoint')\n", " saver.save(sess, checkpoint_file, global_step=i)\n", " \n", " print('>>> Test Accuracy [%s/%s]: %s' % (i,iterations,acc))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting ./train-images-idx3-ubyte.gz\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n", "INFO:tensorflow:Summary name input/x-input-slice2:0_mean is illegal; using input/x-input-slice2_0_mean instead.\n", "INFO:tensorflow:Summary name input/x-input-slice2:0_stddev is illegal; using input/x-input-slice2_0_stddev instead.\n", "INFO:tensorflow:Summary name input/x-input-slice2:0_max is illegal; using input/x-input-slice2_0_max instead.\n", "INFO:tensorflow:Summary name input/x-input-slice2:0_min is illegal; using input/x-input-slice2_0_min instead.\n", "INFO:tensorflow:Summary name input/x-input-slice2:0 is illegal; using input/x-input-slice2_0 instead.\n", "INFO:tensorflow:Summary name W/Variable:0_mean is illegal; using W/Variable_0_mean instead.\n", "INFO:tensorflow:Summary name W/Variable:0_stddev is illegal; using W/Variable_0_stddev instead.\n", "INFO:tensorflow:Summary name W/Variable:0_max is illegal; using W/Variable_0_max instead.\n", "INFO:tensorflow:Summary name W/Variable:0_min is illegal; using W/Variable_0_min instead.\n", "INFO:tensorflow:Summary name W/Variable:0 is illegal; using W/Variable_0 instead.\n", "INFO:tensorflow:Summary name b/Variable:0_mean is illegal; using b/Variable_0_mean instead.\n", "INFO:tensorflow:Summary name b/Variable:0_stddev is illegal; using b/Variable_0_stddev instead.\n", "INFO:tensorflow:Summary name b/Variable:0_max is illegal; using b/Variable_0_max instead.\n", "INFO:tensorflow:Summary name b/Variable:0_min is illegal; using b/Variable_0_min instead.\n", "INFO:tensorflow:Summary name b/Variable:0 is illegal; using b/Variable_0 instead.\n", "INFO:tensorflow:Summary name y/add:0_mean is illegal; using y/add_0_mean instead.\n", "INFO:tensorflow:Summary name y/add:0_stddev is illegal; using y/add_0_stddev instead.\n", "INFO:tensorflow:Summary name y/add:0_max is illegal; using y/add_0_max instead.\n", "INFO:tensorflow:Summary name y/add:0_min is illegal; using y/add_0_min instead.\n", "INFO:tensorflow:Summary name y/add:0 is illegal; using y/add_0 instead.\n", ">>> Test Accuracy [0/1000]: 0.4075\n", ">>> Test Accuracy [100/1000]: 0.8948\n", ">>> Test Accuracy [200/1000]: 0.9031\n", ">>> Test Accuracy [300/1000]: 0.9074\n", ">>> Test Accuracy [400/1000]: 0.9037\n", ">>> Test Accuracy [500/1000]: 0.9125\n", ">>> Test Accuracy [600/1000]: 0.9107\n", ">>> Test Accuracy [700/1000]: 0.9175\n", ">>> Test Accuracy [800/1000]: 0.9145\n", ">>> Test Accuracy [900/1000]: 0.9172\n", ">>> Test Accuracy [999/1000]: 0.9137\n" ] } ], "source": [ "main2(_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run TensorBoard, use the following command (alternatively python -m tensorflow.tensorboard)\n", "\n", ">tensorboard --logdir=_logs\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross Entropy on training set by step " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/cross_entropy_train.png\" />" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accuracy on test set by step (learning curve)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/accuracy_test_set.png\" />" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computation Graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/mnist_1_graph.png\" />" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/distr1.png\" />" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histogram of weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/hist1.png\" />" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Images " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/digit_image.JPG\" />" ] } ], "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 }
apache-2.0
jrg365/gpytorch
examples/01_Exact_GPs/Spectral_Delta_GP_Regression.ipynb
1
328813
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral GP Learning with Deltas\n", "\n", "In this paper, we demonstrate another approach to spectral learning with GPs, learning a spectral density as a simple mixture of deltas. This has been explored, for example, as early as Lázaro-Gredilla et al., 2010.\n", "\n", "Compared to learning Gaussian mixtures as in the SM kernel, this approach has a number of pros and cons. In its favor, it is often very robust and does not have as severe issues with local optima, as it is easier to make progress when performing gradient descent on 1 of 1000 deltas compared to the parameters of 1 of 3 Gaussians. Additionally, implemented using CG in GPyTorch, this approach affords linear time and space in the number of data points N. Against it, it has significantly more parameters which can take many more iterations of training to learn, and it corresponds to a finite basis expansion and is therefore a parametric model." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import gpytorch\n", "import torch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data\n", "\n", "For this notebook, we'll be using a sample set of timeseries data of BART ridership on the 5 most commonly traveled stations in San Francisco. This subsample of data was selected and processed from Pyro's examples http://docs.pyro.ai/en/stable/_modules/pyro/contrib/examples/bart.html" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([5, 1440, 1]) torch.Size([5, 1440]) torch.Size([5, 240, 1]) torch.Size([5, 240])\n" ] } ], "source": [ "import os\n", "import urllib.request\n", "\n", "smoke_test = ('CI' in os.environ)\n", "\n", "if not smoke_test and not os.path.isfile('../BART_sample.pt'):\n", " print('Downloading \\'BART\\' sample dataset...')\n", " urllib.request.urlretrieve('https://drive.google.com/uc?export=download&id=1A6LqCHPA5lHa5S3lMH8mLMNEgeku8lRG', '../BART_sample.pt')\n", " torch.manual_seed(1)\n", " \n", "if smoke_test:\n", " train_x, train_y, test_x, test_y = torch.randn(2, 100, 1), torch.randn(2, 100), torch.randn(2, 100, 1), torch.randn(2, 100)\n", "else:\n", " train_x, train_y, test_x, test_y = torch.load('../BART_sample.pt', map_location='cpu')\n", "\n", "if torch.cuda.is_available():\n", " train_x, train_y, test_x, test_y = train_x.cuda(), train_y.cuda(), test_x.cuda(), test_y.cuda()\n", "\n", "print(train_x.shape, train_y.shape, test_x.shape, test_y.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "train_x_min = train_x.min()\n", "train_x_max = train_x.max()\n", "\n", "train_x = train_x - train_x_min\n", "test_x = test_x - train_x_min\n", "\n", "train_y_mean = train_y.mean(dim=-1, keepdim=True)\n", "train_y_std = train_y.std(dim=-1, keepdim=True)\n", "\n", "train_y = (train_y - train_y_mean) / train_y_std\n", "\n", "test_y = (test_y - train_y_mean) / train_y_std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define a Model\n", "\n", "The only thing of note here is the use of the kernel. For this example, we'll learn a kernel with 2048 deltas in the mixture, and initialize by sampling directly from the empirical spectrum of the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class SpectralDeltaGP(gpytorch.models.ExactGP):\n", " def __init__(self, train_x, train_y, num_deltas, noise_init=None):\n", " likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_constraint=gpytorch.constraints.GreaterThan(1e-11))\n", " likelihood.register_prior(\"noise_prior\", gpytorch.priors.HorseshoePrior(0.1), \"noise\")\n", " likelihood.noise = 1e-2\n", "\n", " super(SpectralDeltaGP, self).__init__(train_x, train_y, likelihood)\n", " self.mean_module = gpytorch.means.ConstantMean()\n", " base_covar_module = gpytorch.kernels.SpectralDeltaKernel(\n", " num_dims=train_x.size(-1),\n", " num_deltas=num_deltas,\n", " )\n", " base_covar_module.initialize_from_data(train_x[0], train_y[0])\n", " self.covar_module = gpytorch.kernels.ScaleKernel(base_covar_module)\n", "\n", " def forward(self, x):\n", " mean_x = self.mean_module(x)\n", " covar_x = self.covar_module(x)\n", " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model = SpectralDeltaGP(train_x, train_y, num_deltas=1500)\n", "\n", "if torch.cuda.is_available():\n", " model = model.cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0 - loss = 24.75 - noise = 1.010000e-02\n", "Iteration 10 - loss = 10.49 - noise = 1.107906e-02\n", "Iteration 20 - loss = 3.68 - noise = 1.204213e-02\n", "Iteration 30 - loss = 3.69 - noise = 1.276487e-02\n", "Iteration 40 - loss = 2.00 - noise = 1.333853e-02\n", "Iteration 50 - loss = 1.18 - noise = 1.337870e-02\n", "Iteration 60 - loss = 1.10 - noise = 1.340098e-02\n", "Iteration 70 - loss = 1.06 - noise = 1.341575e-02\n", "Iteration 80 - loss = 1.05 - noise = 1.342753e-02\n", "Iteration 90 - loss = 0.98 - noise = 1.343861e-02\n", "Iteration 100 - loss = 0.98 - noise = 1.344975e-02\n", "Iteration 110 - loss = 0.91 - noise = 1.346152e-02\n", "Iteration 120 - loss = 0.94 - noise = 1.347341e-02\n", "Iteration 130 - loss = 0.90 - noise = 1.348564e-02\n", "Iteration 140 - loss = 0.78 - noise = 1.349751e-02\n", "Iteration 150 - loss = 0.75 - noise = 1.350786e-02\n", "Iteration 160 - loss = 0.75 - noise = 1.351690e-02\n", "Iteration 170 - loss = 0.76 - noise = 1.352605e-02\n", "Iteration 180 - loss = 0.74 - noise = 1.353530e-02\n", "Iteration 190 - loss = 0.71 - noise = 1.354463e-02\n", "Iteration 200 - loss = 0.71 - noise = 1.355418e-02\n", "Iteration 210 - loss = 0.71 - noise = 1.356385e-02\n", "Iteration 220 - loss = 0.71 - noise = 1.357375e-02\n", "Iteration 230 - loss = 0.71 - noise = 1.358384e-02\n", "Iteration 240 - loss = 0.70 - noise = 1.359454e-02\n", "Iteration 250 - loss = 0.65 - noise = 1.360544e-02\n", "Iteration 260 - loss = 0.69 - noise = 1.361629e-02\n", "Iteration 270 - loss = 0.68 - noise = 1.362717e-02\n", "Iteration 280 - loss = 0.67 - noise = 1.363807e-02\n", "Iteration 290 - loss = 0.70 - noise = 1.364926e-02\n", "Iteration 300 - loss = 0.70 - noise = 1.366061e-02\n", "Iteration 310 - loss = 0.66 - noise = 1.367212e-02\n", "Iteration 320 - loss = 0.65 - noise = 1.368315e-02\n", "Iteration 330 - loss = 0.65 - noise = 1.369375e-02\n", "Iteration 340 - loss = 0.64 - noise = 1.370386e-02\n", "Iteration 350 - loss = 0.64 - noise = 1.371405e-02\n", "Iteration 360 - loss = 0.62 - noise = 1.372444e-02\n", "Iteration 370 - loss = 0.63 - noise = 1.373496e-02\n", "Iteration 380 - loss = 0.63 - noise = 1.374584e-02\n", "Iteration 390 - loss = 0.63 - noise = 1.375700e-02\n", "Iteration 400 - loss = 0.63 - noise = 1.376797e-02\n", "Iteration 410 - loss = 0.60 - noise = 1.377904e-02\n", "Iteration 420 - loss = 0.61 - noise = 1.379027e-02\n", "Iteration 430 - loss = 0.61 - noise = 1.380114e-02\n", "Iteration 440 - loss = 0.62 - noise = 1.381227e-02\n", "Iteration 450 - loss = 0.61 - noise = 1.382362e-02\n", "Iteration 460 - loss = 0.58 - noise = 1.383517e-02\n", "Iteration 470 - loss = 0.61 - noise = 1.384682e-02\n", "Iteration 480 - loss = 0.63 - noise = 1.385889e-02\n", "Iteration 490 - loss = 0.63 - noise = 1.387111e-02\n", "Iteration 500 - loss = 0.61 - noise = 1.388358e-02\n", "Iteration 510 - loss = 0.60 - noise = 1.389664e-02\n", "Iteration 520 - loss = 0.60 - noise = 1.390998e-02\n", "Iteration 530 - loss = 0.58 - noise = 1.392351e-02\n", "Iteration 540 - loss = 0.62 - noise = 1.393629e-02\n", "Iteration 550 - loss = 0.60 - noise = 1.394872e-02\n", "Iteration 560 - loss = 0.60 - noise = 1.396085e-02\n", "Iteration 570 - loss = 0.59 - noise = 1.397342e-02\n", "Iteration 580 - loss = 0.63 - noise = 1.398654e-02\n", "Iteration 590 - loss = 0.58 - noise = 1.400001e-02\n", "Iteration 600 - loss = 0.58 - noise = 1.401340e-02\n", "Iteration 610 - loss = 0.62 - noise = 1.402742e-02\n", "Iteration 620 - loss = 0.59 - noise = 1.404199e-02\n", "Iteration 630 - loss = 0.63 - noise = 1.405650e-02\n", "Iteration 640 - loss = 0.63 - noise = 1.407137e-02\n", "Iteration 650 - loss = 0.60 - noise = 1.408640e-02\n", "Iteration 660 - loss = 0.58 - noise = 1.410100e-02\n", "Iteration 670 - loss = 0.61 - noise = 1.411567e-02\n", "Iteration 680 - loss = 0.59 - noise = 1.413125e-02\n", "Iteration 690 - loss = 0.61 - noise = 1.414781e-02\n", "Iteration 700 - loss = 0.58 - noise = 1.416490e-02\n", "Iteration 710 - loss = 0.59 - noise = 1.418221e-02\n", "Iteration 720 - loss = 0.59 - noise = 1.420006e-02\n", "Iteration 730 - loss = 0.60 - noise = 1.421693e-02\n", "Iteration 740 - loss = 0.59 - noise = 1.423384e-02\n", "Iteration 750 - loss = 0.59 - noise = 1.424886e-02\n", "Iteration 760 - loss = 0.59 - noise = 1.426466e-02\n", "Iteration 770 - loss = 0.58 - noise = 1.428063e-02\n", "Iteration 780 - loss = 0.60 - noise = 1.429628e-02\n", "Iteration 790 - loss = 0.62 - noise = 1.431342e-02\n", "Iteration 800 - loss = 0.57 - noise = 1.433162e-02\n", "Iteration 810 - loss = 0.61 - noise = 1.435035e-02\n", "Iteration 820 - loss = 0.57 - noise = 1.436906e-02\n", "Iteration 830 - loss = 0.58 - noise = 1.438674e-02\n", "Iteration 840 - loss = 0.58 - noise = 1.440415e-02\n", "Iteration 850 - loss = 0.61 - noise = 1.442217e-02\n", "Iteration 860 - loss = 0.58 - noise = 1.444128e-02\n", "Iteration 870 - loss = 0.59 - noise = 1.445954e-02\n", "Iteration 880 - loss = 0.61 - noise = 1.447739e-02\n", "Iteration 890 - loss = 0.58 - noise = 1.449536e-02\n", "Iteration 900 - loss = 0.54 - noise = 1.451268e-02\n", "Iteration 910 - loss = 0.54 - noise = 1.452836e-02\n", "Iteration 920 - loss = 0.55 - noise = 1.454423e-02\n", "Iteration 930 - loss = 0.59 - noise = 1.456236e-02\n", "Iteration 940 - loss = 0.56 - noise = 1.457988e-02\n", "Iteration 950 - loss = 0.57 - noise = 1.459668e-02\n", "Iteration 960 - loss = 0.56 - noise = 1.461400e-02\n", "Iteration 970 - loss = 0.47 - noise = 1.462959e-02\n", "Iteration 980 - loss = 0.47 - noise = 1.464033e-02\n", "Iteration 990 - loss = 0.46 - noise = 1.464958e-02\n" ] } ], "source": [ "model.train()\n", "mll = gpytorch.mlls.ExactMarginalLogLikelihood(model.likelihood, model)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n", "scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=optimizer, milestones=[40])\n", "\n", "num_iters = 1000 if not smoke_test else 4\n", "\n", "with gpytorch.settings.max_cholesky_size(0): # Ensure we dont try to use Cholesky\n", " for i in range(num_iters):\n", " optimizer.zero_grad()\n", " output = model(train_x)\n", " loss = -mll(output, train_y)\n", " if train_x.dim() == 3:\n", " loss = loss.mean()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " if i % 10 == 0:\n", " print(f'Iteration {i} - loss = {loss:.2f} - noise = {model.likelihood.noise.item():e}')\n", "\n", " scheduler.step()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Get into evaluation (predictive posterior) mode\n", "model.eval()\n", "\n", "# Test points are regularly spaced along [0,1]\n", "# Make predictions by feeding model through likelihood\n", "with torch.no_grad(), gpytorch.settings.max_cholesky_size(0), gpytorch.settings.fast_pred_var():\n", " test_x_f = torch.cat([train_x, test_x], dim=-2)\n", " observed_pred = model.likelihood(model(test_x_f))\n", " varz = observed_pred.variance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Results" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x1080 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "_task = 3\n", "\n", "plt.subplots(figsize=(15, 15), sharex=True, sharey=True)\n", "for _task in range(2):\n", " ax = plt.subplot(3, 1, _task + 1)\n", "\n", " with torch.no_grad():\n", " # Initialize plot\n", "# f, ax = plt.subplots(1, 1, figsize=(16, 12))\n", "\n", " # Get upper and lower confidence bounds\n", " lower = observed_pred.mean - varz.sqrt() * 1.98\n", " upper = observed_pred.mean + varz.sqrt() * 1.98\n", " lower = lower[_task] # + weight * test_x_f.squeeze()\n", " upper = upper[_task] # + weight * test_x_f.squeeze()\n", "\n", " # Plot training data as black stars\n", " ax.plot(train_x[_task].detach().cpu().numpy(), train_y[_task].detach().cpu().numpy(), 'k*')\n", " ax.plot(test_x[_task].detach().cpu().numpy(), test_y[_task].detach().cpu().numpy(), 'r*')\n", " # Plot predictive means as blue line\n", " ax.plot(test_x_f[_task].detach().cpu().numpy(), (observed_pred.mean[_task]).detach().cpu().numpy(), 'b')\n", " # Shade between the lower and upper confidence bounds\n", " ax.fill_between(test_x_f[_task].detach().cpu().squeeze().numpy(), lower.detach().cpu().numpy(), upper.detach().cpu().numpy(), alpha=0.5)\n", " # ax.set_ylim([-3, 3])\n", " ax.legend(['Training Data', 'Test Data', 'Mean', '95% Confidence'], fontsize=16)\n", " ax.tick_params(axis='both', which='major', labelsize=16)\n", " ax.tick_params(axis='both', which='minor', labelsize=16)\n", " ax.set_ylabel('Passenger Volume (Normalized)', fontsize=16)\n", " ax.set_xlabel('Hours (Zoomed to Test)', fontsize=16)\n", " ax.set_xticks([])\n", " \n", " plt.xlim([1250, 1680])\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
balarsen/pymc_learning
Counting/Poisson and exponential.ipynb
1
435054
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Go from exponential to Poisson\n", "\n", "\n", "Also look to: Adams RP, Murray I, MacKay DJC. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proceedings of the 26th Annual International Conference on Machine Learning; Montreal, Quebec, Canada. 1553376: ACM; 2009. p. 9-16.\n", "\n", "\n", "Some thoughts 20171018\n", "\n", "* Poisson process under the hood, so the time between is Exponential\n", "* We can then derive the probability of missing a count due to time based on the probability between\n", "* Can we then use this to figure out how many were likely missed?\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/balarsen/miniconda3/envs/python3/lib/python3.6/site-packages/matplotlib/__init__.py:800: MatplotlibDeprecationWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " mplDeprecation)\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from pprint import pprint\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import pymc3 as mc\n", "import spacepy.toolbox as tb\n", "import spacepy.plot as spp\n", "import tqdm\n", "from scipy import stats\n", "import seaborn as sns\n", "sns.set(font_scale=1.5)\n", "# matplotlib.pyplot.rc('figure', figsize=(10,10))\n", "# matplotlib.pyplot.rc('lines', lw=3)\n", "# matplotlib.pyplot.rc('font', size=20)\n", "\n", "\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Poisson process data and generate exponential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each interval choose $n$ events from a Poisson. Then draw from a uniform the location in the interval for each of the events." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7933,)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.random.seed(8675309)\n", "nT = 400\n", "cts = np.random.poisson(20, size=nT)\n", "edata = []\n", "for i in range(nT):\n", " edata.extend(i + np.sort(np.random.uniform(low=0, high=1, size=cts[i])))\n", "edata = np.asarray(edata)\n", "edata.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Modeled underlying data')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ydK8Cr1TAdUSkHDZvKeCFD7/j3cmLAWjUoB4XHduXgb2zMhyZ1HYVkWD2BbZU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\nxaI5zw4hrJ4Z79Z8MOFOB2A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"text/plain": [ "<matplotlib.figure.Figure at 0x1161f50b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(edata, np.arange(len(edata)))\n", "plt.xlabel('Time of event')\n", "plt.ylabel('Event number')\n", "plt.title(\"Modeled underlying data\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: -14603.719126\n", " Iterations: 13\n", " Function evaluations: 15\n", " Gradient evaluations: 15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using ADVI...\n", "Average Loss = -14,453: 9%|▊ | 17021/200000 [00:07<01:17, 2355.04it/s] \n", "Convergence archived at 17100\n", "Interrupted at 17,100 [8%]: Average Loss = 60,831\n", "100%|██████████| 10500/10500 [00:23<00:00, 443.13it/s]\n" ] } ], "source": [ "with mc.Model() as model:\n", " lam = mc.Uniform('lambda', 0, 1000) # this is the exponential parameter\n", " meas = mc.Exponential('meas', lam, observed=np.diff(edata))\n", " lam2 = mc.Uniform('lam2', 0, 1000)\n", " poi = mc.Poisson('Poisson', lam2, observed=cts)\n", " start = mc.find_MAP()\n", " trace = mc.sample(10000, start=start, njobs=8)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "lambda:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 19.834 0.221 0.001 [19.405, 20.271]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 19.404 19.684 19.834 19.984 20.270\n", "\n", "\n", "lam2:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 19.835 0.223 0.001 [19.400, 20.270]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 19.402 19.683 19.835 19.984 20.273\n", "\n" ] }, { "data": { "image/png": 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1q1rrT2mta4EzgReATwGNSqmnMxudEEKIqcRqnrz2XarD/24C/qSU6gVeBGKW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\nR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"text/plain": [ "<matplotlib.figure.Figure at 0x122f53358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mc.traceplot(trace, combined=True, lines={'lambda':20, 'lam2':20})\n", "mc.summary(trace)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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CYRd9jSWHYfCJ2+bhcho8+koVPimFJOKMJCghIiAQDLJpTz1dfUOUl2WyoCzT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\ncOOyEpujE7F0qSXf18UoDiE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lYrXKRiwCXsZaz8gMPc5GrEKla7GSmhBxRRKUEFdIa30v\nnF3WQ2G1jO4KXT122fm3Rp1vPs+29tBpBjCSoLaOJKfQ861XSvmANUqpl4EVWF13oz3BqASltf41\n8GullEcpNReYDSzD+g4YG6MQcUESlBBXSCl1FfAzrGM7/ViLz50KXW2MumlAaz0w9v4XWvl3lObz\nbGvFWnsoM/QcrWOubxwTYxLwY6xl7d1ADdaSLcNjYhQibsgxKCGugFIqDViPtQjiAiBVa70Sa5G7\nSMkc85wGkAe0YC09HsRaPHC07DGXfwTcDdwTinFWqEtwOIJxChFRkqCEuDLzsJLBf2mtD48aDXdb\n6DQSn7HrQsvSj3gfVrdcRWiRxW3AB0OJa8RdvNMa4BWt9XMjLTal1Aqs5cble0DEJeniE2J8piml\nvnie7XuxWk//EhqYEMAaFfep0PXJEXhuN9bS4t8FSrGOLb0GjFSY+KfQ5aeUUj/HSppjj0ntAO5R\nSn0W0MAS4KuAGaEYhYg4SVBCjM9crOHgY/0Iq0XzH1hDvXuwkta7sEbaXR86vRLPAvXAb7GOFz2B\nNQ/KBNBab1ZK3QZ8B/gd1vGlT2HNlxrxZaxE9x2suVQ1wLexuiVvV0o5ZC6UiDey5LsQQoi4JH3P\nQggh4pIkKCGEEHFJEpQQQoi4JAlKCCFEXJIEJYQQIi5JghJCCBGXJEEJIYSIS5KghBBCxKX/D1Bk\n8vmkP2o9AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1240a1278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=1, nrows=2, sharex=True)\n", "sns.distplot(trace['lambda'], ax=ax[0])\n", "sns.distplot(trace['lam2'], ax=ax[1])\n", "plt.xlabel('Lambda')\n", "ax[0].set_ylabel('Exp')\n", "ax[1].set_ylabel('Poisson')\n", "ax[0].axvline(20, c='r', lw=1)\n", "ax[1].axvline(20, c='r', lw=1)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is consistent with a Poisson of parameter 20! But there seems to be an under prediction going on, wonder why?\n", "\n", "Go through Posterior Predictive Checks (http://docs.pymc.io/notebooks/posterior_predictive.html) and see if we are reprodicting the mean and variance. \n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 500/500 [00:00<00:00, 3282.24it/s]\n" ] } ], "source": [ "ppc = mc.sample_ppc(trace, samples=500, model=model, size=100)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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J6p4gzGx+4FRgKPFciceB/d39pfT+dsCRwKLA88De7v5kveMUEenp6trEZGa9\ngduAJYHNgDWAT4F7zWweM1sPuBw4A1gJeBG428wG1jNOERGpfw3iB8ST6JZ297EAZrYD8DGwMbAd\ncIO7X5ze2x0YAuwKnFjnWEVEerR6d1L/C9gE8My0qel1APBjYEzpDXefCjwArFWn+EREJKlrDcLd\n/wOMLpu8D9EX8RQwJ1DeY/UesEpb6x4wYA769u1TizDrZuDA/o0OoSY6W47+c/WrUSSd0+g4evfu\nVZM4Gl2OWmlvOWaG39HMEGOeho5iMrNNgZOAM4G30uTyoQuTgTa/KZ988nltg+tiAwf2Z/z4CY0O\no9NqUY72jlbpSkVGzXSVqVPjBsmdiaMZylELRcrR7L+jZv+tV0teDbsOwsyGAbcANwIHAV+kt2Yr\nm3U24LP6RSYiItCgGoSZHQYcD5wP7OPuLWb2MZEIFiybfSFmbHaSJpI3jl1EZn51r0GY2UFEcjjS\n3fd29xaA9PoIsHZm3t7A/xEd1SIiUkd1rUGY2fLEcNXLgUvMbIHM2xOIvog7zOxZ4D5gP2Bu4NJ6\nxikiIvWvQWwL9AF+Dbxf9u937n4XsBuwP/AMsDQw1N0/qnOcIiI9Xr2HuR4KHNrGPFcAV9QnIhER\nqUR3cxURkVxKECIikksJQkREcilBiIhILiUIERHJpQQhIiK5lCBERCSXEoSIiORSghARkVxKECIi\nkquhDwyS5tCZ23V3l6eXiciMVIMQEZFcShAiIpJLCUJERHIpQYiISC51UotIj9bZZ6oPXmHhGkXS\nfFSDEBGRXEoQIiKSSwlCRERyKUGIiEguJQgREcmlUUzdQGdHYYjMzPT97zqqQYiISC4lCBERyaUE\nISIiuZQgREQklxKEiIjkUoIQEZFcShAiIpJLCUJERHIpQYiISC4lCBERyaUEISIiuZQgREQklxKE\niIjk0t1cRUQ6oa27yfafqx8TJk6qOk+zPtdaNQgREcmlBCEiIrnUxNQE9MATEWlGqkGIiEguJQgR\nEcmlJiYRkQbrbDNzV42CUg1CRERyKUGIiEguNTHRvNU7EZFGaroEYWZ9gOOBYUB/4C5gT3f/oJFx\niYj0NM3YxHQ0sCPwK+D/gEWAWxoZkIhIT9RUNQgzmxX4LbCPu9+Tpm0LvGlma7j7Iw0NsIKONFG1\n5/4sIiKN1Gw1iBWIZqUxpQnuPg4YB6zVkIhERHqoZksQi6TX8lPy94Bv1zkWEZEeramamIA5gKnu\n/lXZ9MlAv2oLDhzYv1dHP3Sb9Zfq6KIiNaPvoTSbZqtBfAH0NrPyxDUb8FkD4hER6bGaLUG8nV4X\nLJu+EDM2O4mISBdqtgTxPDBpRLR2AAAK6UlEQVQBWLs0wcwGAYOABxoTkohIz9SrpaWl0TG0YmYn\nExfJDQM+BC4AJrn74MZFJSLS8zRbJzXA4cAswLXp9S5gz4ZGJCLSAzVdDUJERJpDs/VBiIhIk2jG\nJqaZmpldCPR1910y07YBjgQWB94CTnP3K6qsYyNgdM5b33b3d2occqUY8sqxAzAC+A7wEnB46ZYo\nFdYxB3A2sCXxXfsT8Dt3n9iVsZfFUItyNGR/mNn8wKnAUGB24HFgf3d/Kb2/HfG9WpQY4LG3uz9Z\nZX3zAeen9X0JXAEc5u5TuqoMXVSO4cDvyyZ/7e5dejxrqxyZ+bYFjnf377axvobsjyJUg6gRM+tl\nZscCu5dNXwu4nvgiLAecA1xiZhtXWd1ywLPEcN/sv/e6IPRWqpTjF8BVwHXAisDVwCgzG1xldRcB\nawKbAD8FBqdpXa7G5aj7/jCz3sBtwJLAZsAawKfAvWY2j5mtB1wOnAGsBLwI3G1mA6us9hZgAWKU\n4DBgJ+CYrioDdFk5lgNG0XpfdOk999sqR2a+TYjytEfd90dRqkHUgJktDlwGLAv8q+ztzYAX3L10\nYLzIzHYGNiD/rJS0nhfd/d9dEW8lbZRjBHC9u5+U/n7NzFYAjiJz76zMuhYBfgms6+6PpWm7AH83\ns4Pcvcuua6llOZJG7I8fAKsDS7v7WJhW8/kY2BjYDrjB3S9O7+0ODAF2BU4sX5mZrU4k68Xd/U3g\neTM7EDjPzI5198kzQzmSZYH7mml/mNmfiJO/YcBYYM5qK2vg/ihENYjaWIO4yG854M2y98YDy5jZ\nOums9v+IL/hTVda3LPElq7dq5fge8GDZtGeBNXKufC+tayrwcGbaw8DXxA+jK9WyHNCY/fEvoubl\nmWlT0+sA4Me0vqnlVOJaoUo3tVwLeCsdjErGEDfHXKEmEeerdTkAlqH59sd8wFLEd++2dqyvUfuj\nENUgasDdryWG5WJm5W//nvjS3EccHPsAp7v71XnrSg9MWgpY2cyeBwYCTwIHubvnLVMrbZQj74aJ\ng4BZgW8CH5W9twjwYfa+Wu4+xcw+zFlPTdWyHI3aH+7+H2asYe5DtH0/RZyh5t3UcpUKq1ykwvwQ\n2+PxDgdbRa3LYWYLEwfkDc3s6LT8/cT+6LImvzbKcbe7v0U8v6bUzNSWhuyPolSD6HrzEe2MBwE/\nJL5Ue5rZryvMvwRxY8LZiGr2z9L/H0ydWo1yDbCXma1rZn3MbB1g5/TerDnzzwHkPfCizRsvdrGi\n5WiK/WFmmwInAWcSAx1gxu1bbdvOsD9S8m6pskzN1aAcy6TXr4BtiXb7JYm+gNlrG21l2XKUmpwK\naor90RbVILreJcCz7n5a+vu51AF3qpld4e6tLkRx99dSp9d/U3UbM9uSqOLuQHTmNcLJRLK7k6gF\nvQycRvxIPs2Z/wviQFqu0TdeLFSOZtgfZjaM+B79kTjRGJDeKt++1bbtDPvDzGYBelVZpqZqUQ53\nv9vMBrr7tJqemb1MnI1vRB2ePplTjo5o+P5oD9Ugut5qzNjf8DgwD9GkMQN3/7h0MEp/fw68QQOf\nieHuX7r7XkQb6cLuvjzwOfCBu+d9od8G5ktNNACkNv75aOCNFztQjobuDzM7jBj+eCHwqxTHx8RB\npMhNLd+uMD9VlqmZGpaDbHJIf79PNA02an90REP3R3spQXS9d4Dly6YtC/zH3T8pn9nMNjezCdlh\nfmbWn6hGv9ylkVZhZseb2Qh3n5wZPbI5cHeFRR4maqirZ6atSXznHs5dog6KlqOR+8PMDgKOB450\n971Ltc30+gitb2rZm2gDr3RTy4eAxc0sexBdh7g55nNdEP40tSyHme1jZu+ls+3StMWIvqGG7I8O\natj+KEJNTF3vHOAsM3sF+CtxwDwUOLY0Qzr4fOnunxIdbv8DrklfyL7EcL+PiPbzRhkHnGFmLwKv\nAvsSHYl7lGbIlsPd3zWzm4DLUn9LL6Jafk1XDnFth3EUKAcN2h9mtnz6nMuJ62YWyLw9gWjDv8PM\nniUGQOwHzA1cmlnHAsDEdGHio8BjwI1mthdQuujrTHf/ciYqx2jgBOJ7dSJREz8HeKjaxY5dXY5K\ntc+ydTR8fxSlGkQXc/cLiJsN7kVctXsYkSDOzMz2JPElJ9Uq1iM64cakf58BQ9w9r9O3Ltz9UqKt\n/iLgBWII6ZCykTzTypHsQpwh/gW4nTgA7EEDFS1HA/fHtkQfya+B98v+/c7d7wJ2A/YHngGWBoaW\nNb+8DxyQytECbAF8QAzzvYI4CB9L16p1Of4JrE80Jz1BXDD3ArBpI8vRznU0w/4oRDfrExGRXKpB\niIhILiUIERHJpQQhIiK5lCBERCSXEoSIiORSghARkVy6UE6kCZnZt4jrAtZz93+0Y/4jgQXcfXiX\nByc9hmoQIs3pPOCm9iSH5DRgEzNbtwtjkh5GCUKkyZjZKsA2xK0X2sXdvwDOovUV+iKdoiuppUcx\ns3HELQ3mJx532Ye4p9JBxPOAdyLuG3UbsJe7T0rPGTgW+AUwL/E0s6PcfVRmvXMCRwJbAosSzzR4\nFDjQ3V9I81xJPBvkT8DBab6xwAh3/2tmXTcDs7v7xunvzYCRwOHufkKatgJxq4nz3X2/NG1B4uaQ\nm7p7pcfZirSbahDSEx1E3ORtG+K2zXsS7f2LEs/RPod4iNCeZtYLuJW4X9BpxJ1fnwNGpgN3yTXA\njsQN3YYSN51bDrg+raNkNeK+Q0ekdU0BbjGzuQHMbC7ivkLTnmvg7rcD1wGHm9kSZjYrcBXx+MtD\nMvO9T9z76ped2zwiQZ3U0hN9BGzv7lPN7O/A7sTT5LZz9ynA3Wa2DXHn3fWAnwBbu3vpoH2XmX2T\nSBi3m1k/4glhe7n7zWme+83sG8QDheYlnk0OcafSFUvPIjazz4g7xg4mbmi4FjALUTvI2gdYl+ib\neJp4DOqPch5u/xTw8w5vGZEMJQjpiZ4sPeglJYmPiKf+TcnM8x/igU7rEs8SvzM98KhkFLC5mQ1y\n93FEEik9M3nJ9K/0bOLso0zfL3tQ/Tvpdc70unh6zc6Du39sZrsTSWQD4BB3fz6nbOOABc1s1ma6\nbbTMnJQgpCeakDOt0v385yH6KSq9vxAwzsw2AM4mzuwnAM8DE9M82Samz8uWLz2RrNTcO3eF+SCe\nJ/IB0X9SqY+hFOfcTK+1iHSI+iBEqvs0/Vulwr8XzWwJohP5GWAJYG53Xwu4owOfV3oOwtw57x0L\nfAN4Dbg0+zjXjAFE0vm4A58t0opqECLV3U90Kk9x92mPgjSzPYkH12wPrAz0A0509zcyy26YXouc\niL2VXhcB/pv5vFVTHIcQj2x9iOgIP61s+UWA99z96wKfKZJLCUKkutHEAfkOMzuOOHv/MXAUcL27\nTzSzZ4jRSKea2VlEstgJ2DitY84ZV1vRg8AXxPO7XwJIneBXAi8CZ7n7FDO7BDjWzO5w91czy/+Y\naIoS6TQ1MYlUkTqzNySGnR5FHHx3Ip6LvGua5x/ENRKLEc1KF6XFBwMtxMik9n7e58CdTK99QDQt\nfQ/YNdORPoJo+rrCzHrDtGcer0BmiKxIZ+hCOZEmY2Y/Imotg9z93QLLHQZsDayUnnks0imqQYg0\nGXd/guj03r+9y6QruYcTw1+VHKQmlCBEmtNwYGsz+2475z8Q+LO739WFMUkPoyYmERHJpRqEiIjk\nUoIQEZFcShAiIpJLCUJERHIpQYiISK7/B9ng/0Yg/VSJAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124a980f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot()\n", "sns.distplot([n.mean() for n in ppc['Poisson']], kde=False, ax=ax)\n", "ax.axvline(cts.mean())\n", "ax.set(title='Posterior predictive of the mean (Poisson)', xlabel='mean(x)', ylabel='Frequency');" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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eWyy9LyIivVDRUV5jgRPN7Fng72laq5ktAhwO3FZmcNK8xj3zdneHICINVrSG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\nEREphRKKiIiU4v8DFY2+mJIBr0UAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124af4320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot()\n", "sns.distplot([n.var() for n in ppc['Poisson']], kde=False, ax=ax)\n", "ax.axvline(cts.var())\n", "ax.set(title='Posterior predictive of the variance (Poisson)', xlabel='var(x)', ylabel='Frequency');" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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a6LxOtLXvrggB4mS5HfiFmX2RqOReg2jzf4y7v5nmmwyMNLPdgD90s76xxB3jpsxcAfu0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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124e46e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot()\n", "sns.distplot([n.mean() for n in ppc['meas']], kde=False, ax=ax)\n", "ax.axvline(np.diff(edata).mean())\n", "ax.set(title='Posterior predictive of the mean (Exponential)', xlabel='mean(x)', ylabel='Frequency');" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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wvaz8rLZfEmMMt7ez3e3A0jbtDKrDgd8R+w9iNth3LaazAmBmszFlJlwtdxH7dNfc8j2I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\noiUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1246bc240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot()\n", "sns.distplot([n.var() for n in ppc['meas']], kde=False, ax=ax)\n", "ax.axvline(np.diff(edata).var())\n", "ax.set(title='Posterior predictive of the variance (Exponential)', xlabel='var(x)', ylabel='Frequency');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are reprodicting well. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Given the data we generated that will be treated as truth, what would we measure with various deadtime and does teh corection match what we think it should?\n", "\n", "Correction should look like $n_1 = \\frac{R_1}{1-R_1 \\tau}$ where $n_1$ is real rate, $R_1$ is observed rate, and $\\tau$ is the dead time. \n", "\n", "Take edata from above and strep through from beginning to end only keeping points that are dead time away from the previous point. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "deadtime1 = 0.005 # small dead time\n", "deadtime2 = 0.1 # large dead time\n", "\n", "edata_td1 = []\n", "edata_td1.append(edata[0])\n", "edata_td2 = []\n", "edata_td2.append(edata[0])\n", "\n", "for ii, v in enumerate(edata[1:], 1): # stop one shy to not run over the end, start enumerate at 1\n", " if v - edata_td1[-1] >= deadtime1:\n", " edata_td1.append(v)\n", " if v - edata_td2[-1] >= deadtime2:\n", " edata_td2.append(v)\n", " \n", "edata_td1 = np.asarray(edata_td1) \n", "edata_td2 = np.asarray(edata_td2) \n", " \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x127614c18>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OEcooUs2aVYgP8Q+25OSkkJYXSdHSl2jpB1hfyqpo6Us09CMjK4cH//s9Kzfu\nAeCBq7tyTvcSTV8MqYXblvHGLx+QlrWXSvEVubzDRfSX80s0DzYavi/GhEokA9nrgGGq6r//2KGN\nhv1UxG2wW2i6iFTAbXVxgMN7qxVXRpHS0jKOlKVEonWOWXkWLf0A60tZFS19Ke/9yMjK5Zu5m5gw\n081Eq1opnvsu70zrRjUi0q/9OQd4e/H7rNm7HoB2tdpwS4drqFKhCjt37g+6nDDMkQ1ZWcZEQkQC\nWW8XglbAqICkTbidDPw14PBUgU3ARYWk4+Up2BqiPr89aaIBbsWdMcaYKJbv8zFljgtgD+bkAdC5\nVR3+0L89lStGZuxm1pafGaHjD72/s/PNdKzTLiJtMSbaRGpE9nRgm6oGBpczcdtq/dPv2tm4EzMK\n0p8TkcbeNhIF6enAQlXNFpFVXhk/wKFdC7rj9k8zxhgTpbJz8njojR9Jz3AP+s7u1pABvZrTslnt\niIzCLt21gsnrp7J27wbiY+I4p8kZ9Gveh7jYyMzNNSYaRSqQ7YrbAy3QK8A8EXkCGInbXLgnbj4t\nwE+4c4FHi8g9QD3cJrpD/c7nHYo7x3e1V8fTuO23PglTX4wxxkTY9t0Z/OO9OeTkutMx/3TliXRs\nUTsibUnL2sN7Sz9m7d4NALSs3pyrZCANEk+ISHuMiWaRCmTrA7sDL6rqryIyEBecPozbY7Z/wcit\nqvq89DdwI67pwLsc3r4LVX1TRGriAtpquFHcvn6BrjHGmCiycPVOXvvkV/LyfXRsUYvb+rUnqUpk\njnOds30+n62eyN7sdJIr12ZQ6/50rN3ODjUwJkwiEsiq6iXFpE0EJhaTvh0YeITynwGeOeoGGmOM\nKRemL9jCB18rAP1Oa8alvZsTG1v6QeOBnAzeWjz80GKuMxv14vLW/YmNKe0DNI05vtiuy8YYY8ql\npet388HXSsUKcQw8owV9ekRmu/BFqUv5cPkYMnMzSa5cmxvaD6ZF9WYRaYsxxxsLZI0xxpQ7C1ft\n5OXxiwG45rzWnH5igyPcEXr7cw7w0fIx/LrTrVs+pX53Bre5lIS4yExrMIUTkfXAu6r6rwg3pcRE\n5Ftgs6reFKk6RORUIFZVZ3nvfcD1qvpRYflLmwWyxhhjyo18n4+R36xi6vzNxMbEcF73RhEJYtfv\n28j7S0exI3MniRWqcmmrizm1fvdSb4cxpWAGcBswy3tfH9gTueb8lgWyxhhjyoWs7FxeGf8ryzek\nUSE+lpsubMupHUp3J4CMnEw+XjGWRalL8eGjS3JHbulwrW2pZaLZbyade2uVygwLZI0xxpR5Pp+P\nJ4fPZfvuDOLjYnlqSE/q1KhCslHAAAAgAElEQVRcqvXPTVnIB8tHk+/LJ4YYrm93JafYKGy5JyK1\ngBeAC4E6QCrwMfCwquaLyOO4/el3AX2AV1T1URG5EXgUaIzbHvR74GZVbeaV2xh40bsnE5gG/ElV\ntxbRjljgH8DtQCLwDhAXkKcj8B/cfvy7cYvjH1bVPV56M+DfuD32q+MOi3pNVf8dbB0B9a330oeJ\nyE2qepb/1AIRGQ74gAzgWiAPeAm35enbQDdAgSGqOtcrs6bXhwG4IHk28ICqalHtKI4FssYYY8q0\nnNx8Xvv0V7bvzqBqpXj+ddspVK9aevNQV+9Zx4fLRrMzy+0a2aNeNwbLACrHl14gXZbMGjDo38AV\nEap+bK8J4x8KcZkf4ALY/rhg9ULgVdyj9M+8PGfhtgbtBuSJyCW47T8fBCbjdlN6Gu+EURGpCkwH\nfgROw8Vbfwe+E5HORWwJ+ihwPzAEWIrbhvQs4H2vzIa4YPk94D6gJi5o/QQ4xyvjC2A9LpDNBG4A\nnheRb1R14ZHqKEQP3F78DwIfFpHnWuBl4CTgatyhVjcADwAbgP8BrwE9vUD6K2AvcAEuAL4XmCki\nbVV1VxF1FMkCWWOMMWXWN3M3MfLbVQAkVIjloau7lloQm5mbxfhVX/DL9vnk+vJoU6MlF7foQ6sa\nzUulflNqJgPTVHWp9/51EXkY6MThQNYHPK6qmQDeSORIVX3ZS39ORHrgThIFF9BVBW5S1TzvnquB\nncAg3KFPh4hIDHA38B9VHeddux04zy/bncBaVX3I776rgM3egqyFwHBglKpu8dKfxAWvnURkURB1\n/IaqpooIwF5V/d3+/54dwEPeXv8v4gLZEar6pVfHMNyIN7iAuwdQS1X3FfRLRM7FjRKXeOtUC2SN\nMcaUOfn5PsZNX8PkORsB6NYmmavObUWd6qUzCjpt00wmrvuGzNxMKsTGc40MoleDnqVSd1nnjYiG\nelQ0kt4ABojIEKAN0BloxG8fuW8rCGI93YBRAeXM5HAg2xVIBvZ6gWCBKkC7QtpQB3da6byCC6qa\nLSLz/fJ0BbqKyP5C7m+nqj+JyKvAYBE5GWgNdAFivb4EU8fRWKOqPq+8A15/1/ilZwIV/foQB2wN\n+FwqUfjnckQWyBpjjClTdu/L4uXxi9mYsp+YGLj/8s50blmnVOrOzstmpH7CnO3u//a+Tc/h3CZn\nUqXC8TmNINr5PeoW3LzYD4E5wNSArJkB73NxAWJRsnGP7i8rJK2wFf8+72vgaR7ZAa+n4KYVBEoV\nkUTcqafxwDjcnNyfcY/3g63jaOQUci2/iLzZuLm9hf1WWFiAfkQWyBpjjCkzJsxcx4SZ6wComBDH\ns7efQvXEike4KzTmbl/AqJWfkZmbSa1KNbm27eW0rdW6VOo2EdMVtxjrJFWdDyAi1XBbTBV3RNxi\nXDD2mt81/+BsKW4e6i5VTfMr9yPcArBp/oWp6k4R2YKbTzvRyx/rte8HvzKvBTaoao6XpznwCvAI\nLhjvAtQumAYgbtgzFogJso7C+IpJK6mlQC0AVV3ttSEO90vEJ8CYkhZogawxxpiIy83L5+NvVvL9\nQreg+5xuDbnq3NbEx4X/iNe0rD18vGIcy3evBKDnCSdxRZtLjtvFXFGqtYj0DbiWhluclQtcKSK7\ncQHsU7hH4cX9BvU88JmI/IwbJb0YuBLY6KV/jJubOkZE/gpkAc8CJ+OCucK8APxLRFbgRoXvA5py\nOMh8FbgHGC4iz3rtew2oAazETVsAuFZEPgdaAUO9awV9OVIdhUkH2otIXVXdUUy+YEzF7VIwRkTu\nB1JwQfglwJNHU6AdAm2MMSailqzbxd/ens33C7dSKSGOR284iev6SKkEsbO2/Mz//fg0y3evpGbF\nGvyx6x3c0H6wBbHR5wZgUsCf/3hbYd2M24VhBW7E9Gfva4+iClPVibjV9g8BS4B+uIVW2V56JnA+\nblX+d7gdEOKBc4oKBlX1JdzOBk8BC4AkDi82K9i/9TzgBK+NX+MC5/NVNVtV53jteRhYjgt8P8KN\n/vYIpo4iPAvc5dV3TLy5tJfigvkJXhvaABeo6rKjKTPG5wvliHH5lpqaHtIPIzk5idTU9FAWGTHR\n0pdo6QdYX8qqaOlLafQjIyuXj79RflqaAkC9WlX4+43dqVwxtA8LC+tLRk4mo1d+ytyUhQCc3bg3\nA1pcSIW4CiGtO9RC/X1JTk4q7vG5KYKInAFsLXg87l17E2itqudGrmXHH5taYIwxptQtXL2Tl8ct\nBty2WncO6EjnlrWJiQl/XDV3+wLGrf6C9Gy3tuS+LrcjtVqFvV4TVS4ELheRW3Cjor2A63GP/k0p\nskDWGGNMqfpwijJt/hYAurSqw5B+7alSKfz/HW3Zv41PVn3JijS3L+1p9U/mSrmUCrH2X6EpsSdw\nj+VHA7WBtbi9VIdFtFXHIfvXa4wxplSk7snkP6MWsmNPJvFxMVzXRzjjxAalUve8lIUMWzoSHz4S\nK1RlUOv+nHxCt1Kp20QfVc3Cjb7aCGyEWSBrjDEmrHw+H9/N38Kknzewe99B6taszJB+7WnVsHrY\n695zcC9vTR/G4pTlAJzf5CwuadmX2Bhb62xMNLBA1hhjTNjsPZDNK+MXs3arO43yjBMbcP0FbYiL\nDW8gue1ACpPWfcu8HYsAqFWpJje0G0zrmi3CWq8xpnRZIGuMMSYs5q7YweufLQGgZlJF+p7chPN7\nNA5rnTszd/HR8rGs2rMWgCrxlTmnxWmc1+BcmwtrTBSyf9XGGGNCKic3j4+mrOSHxdsAaNmwGn+6\nskvIt9UK9OPWX/h4xdhD729sfxU96nWlbt1qUbElmjHm9yyQNcYYEzIpuzN4fPgvHMzOA+CmC9uG\nfUFXdl4O41d9zsytPwPQtW5nbulwjc2DNeY4YIGsMcaYkJi9dDtvf+EO56mRmMAj13ajbs0qR7jr\n2OzKTOP1xe+x/UAKleIqclunG2hbq3VY6zTGlB0WyBpjjDlmE2auY8LMdQCc0qEeQy5uT2xseA83\nyMjJ5MX5b5B2cA/1q9bjwZPusqNljTnOWCBrjDHmqGUezOXrORv5fNZ6AG7v355TOpwQ9nrTsvYw\n1AtiTz6hGze0G1wqp4IZY8oWC2SNMcYclSVrdzF0zKJD7x+6uivtmtYMe70/b5vHB8tHA9AkqRHX\nyCALYo05Tlkga4wxpkR278ti1HermbtiBwCNkqtyx4CONKhTNaz1HsjJYOzKCfySsgCwww2MMREK\nZEVkCPAXoDGwDHc+8XdeWh/geUCAVcDDqjrJ7966wKtAHyAbGAY8qqq5fnkeAP4IJAOzgLtUdVUp\ndM0YY6LW5tT9zFi0lW/nbgYgsXIFhvRrR+eWdcJed0ZOBkPnvc72jB1US0jini5DaJhYP+z1GmPK\ntlIPZEXkRuA14E5gBnAX8LmIdASqAJ8D/wTGA9cCn4lIN1Vd6hUxHvABZwINgeFALvCoV/6twBPA\nLYACTwGTRaS9qh4sjT4aY0y0Wbh6Jy+PW3zofb/TmnJhz6Zh3xvW5/PxS8oCRuknHMzLplm1Jtzf\n9Q8kxFUIa73GmPKhVANZEYnBBZnPqep73rU/A+cAp+GC09mq+pR3y2Mi0hu4H7hdRE4FegMtVHUd\nsEhEHgJeEZEnvUD1L8BQVR3nlX8NsA0YBIworb4aY0w0yMvPZ9z0NUz5ZRMA557UiAG9m5NYOfyB\nZHr2fl6c/wYpGakAtKnRknu6DCEuNi7sdRtjyofSHpEVoCkwuuCCquYDXQBE5P+AMQH3TAeu8l6f\nDmzwglj/9CSgi4isA9p41wrK3y8ic717LZA1xpggzVi0leGTVhx6f1v/9pxaCjsSAMzZPp8Plo3G\nh4+W1ZsxsNXFNK/etFTqNsaUH6UdyLbxvtYQke+AjsAK4BFV/RFoBGwJuGcrbi4txaTj5cnxXhdX\nhjHGmGLsy8jmqRens2bzXgA6tqjFrRe1o3pixbDXvTl9KyNWjGdDuhsBvrDZeVzc/HzblcAYU6jS\nDmSreV/fB/6OC2KHAN+JSFfcHNmsgHsOApW8179LV9UcEfF5eQqOkCmujCLVrFmF+PjQPrJKTk4K\naXmRFC19iZZ+gPWlrCrPfZmxYDOvjl1E5kG3fvb1v5xD43ql05+5Wxbz4rx3yMnPpVG1+tx18g20\nqt0sJGWX5+9JoGjqizHHqrQD2YIR06dUdQSAiNyNe+x/J5AJBP7KXxE44L3+XbqIVABivDyZfvcU\nVUaR0tIygupEsJKTk0hNTQ9pmZESLX2Jln6A9aWsKq992ZiSzodTlDVb9gHQo309bru4HfGxhL0/\new/uY/L6qfywZTY+fPRteg79WlxATH5MSOour9+TwoS6LxYUm/KutAPZgkf+vxZcUFWfiCwHmgOb\ngMD9VBr43bcJuKiQ9IKyN3mv6wOrA/IsP6aWG2NMlJr880bGTHM/MutUr8Rt/dtzWtfGYQ/+8vLz\nmLzhOyavn0q+L5/EClW5pcO1SK1WYa3XGBM9SjuQnY8bGe0BzIVDOxm0B77F7S5wJm77rQJn47bp\nApgJPCcijVV1k196OrBQVbNFZJVXxg9e+YlAd+CtMPbLGGPKlXyfj5+WbGfagi2s3bqPhPhYzunW\niCvOblkq81FXpq1m2NKR7MtOJ4YYLmvVj94NT6FiXELY6zbGRI9SDWRVNUNEXgSeEpEU3MjsXUBL\n3PZYCcA8EXkCGAlcA/TETTsA+AmYDYwWkXuAerjDE4aqaraXZyjwgoisBpYAT+MC5E9KoYvGGFPm\n7diTyXtfLmOlt5irUXIi9wzqRN0alcNed15+Hq8sfIdVe9YC0KlOe66SgdSoWD3sdRtjok8kTvb6\nO5ABvATUBRYCfVRVAURkIC44fRi3GKy/qi6HQ9MQBgJv4EZc04F3gScLClfVN0WkJi6grYYbxe3r\nF+gaY8xxa9Hqnbz31XLSM3KIi43hjgEd6damTqmMwq7Zs55XF71Ldl42leIqcn37wXRJ7hj2eo0x\n0SvG5/NFug1lRmpqekg/DFtgUPZESz/A+lJWleW+fD1nI6O/c3NhT2lfj1v7tSMuNrbQvKHsR1Zu\nFh+vGMeCHb/iw0enOu0Z3OZSalaqEZLyj6Qsf09KKgyLvWxfM1OuRWJE1hhjTCnK9/l498tlzF6a\nAsC157fh3JMahb1en8/HlA3TmLT+W3Ly3XZe17W9glMb9Ah73caY44MFssYYE8XWbdvH65/+yq59\nBwG45aJ29O4cuDlM6O3KTOPdJR+yMX0zAGc37k3fpueSmFA17HUbY44fFsgaY0wUysrO5a0JS1m0\nZhcAjesmct+gztSufsSzYY6Jz+dj8vqpfLluCgDVE5J4oNtdJFepHdZ6jTHHJwtkjTEmyujGNF77\ndAn7M3OoUjGe6y8QeravF/Z6tx1I4YNlow+Nwp7VqBeDWvcnNqbwebjGGHOsLJA1xpgokZZ+kBHf\nrGT+qlR8PjixZW1uuqgd1auGf2/Wn7b+wkj9hDxfHtUTqnFPlyE0SDwh7PUaY45vFsgaY0wUWLEh\njX+PWkDBRjR3XdqR7m3rhr3e9Oz9zNj8I5PWT8WHj0tbXsR5Tc4sle28jDHGAlljjCnnZv26jfcm\nLscHdGuTzC0XtaVKpQphrTMvP4+pG2cwcd0Ucn15xMbEcu+Jt9nxssaYUmWBrDHGlFPZOXl8OEWZ\n9et2AG7sK5zZpWFY68zLz2PejkV8v/lH1u/bSAwxnNWoF32bnUtSQmJY6zbGmEAWyBpjTDm0ZN0u\n/vflcvYeyKZ2tUrcclFb2jWrFbb68n35/Lh1Dt9v/pGtB1zgLDVbcU3by6lTOXz1GmNMcSyQNcaY\ncsTn8zFh5jo+n7UegFM71OPq89qQWDl8UwkycjL5YPloft25DIAW1Ztxeev+NK3WOGx1GmNMMCyQ\nNcaYciIlLYP3Ji5n1ea9QPinEuTl5/H1hu+YunEGWXkHqV+1Hnd2vpnaNgJrjCkjLJA1xpgyLi39\nIGOnrz50xGz92lW4Y0BHGtcN35zUxduX89KP/2N/zgEAzmh4KgNbXUxCXPi38jLGmGBZIGuMMWXY\nxpR0Hh/2y6H3A3o355JezcK2vVVa1h6+XDuFOSnzyffl0yW5Ixc2O49GSQ3CUp8xxhwLC2SNMaaM\n2p+Zw/MjFgDQtkkN7rmsU9i21UrP3s8Xa7/mp22/kO/Lp0alalzV5jI61WkflvqMMSYULJA1xpgy\naJ6mMmrqKjIO5tKtTTL3XNYpbHXNTVnIKP2UzNxMYmNiGdjqYgad2Ie9aQfDVqcxxoSCBbLGGFOG\nLF6zk+/mb2Hxml0AtGpUndv6h2dUdGXaGqZvnsWi1CXEEMNp9U9msFxKfGw8CfEJgAWyxpiyzQJZ\nY4wpA3Lz8hn21Qp+Wur2aK2UEMcVZ7XkrK4NQz4fNuXADsasnMCKtFUAVE9I4vbON9KsWpOQ1mOM\nMeFmgawxxkTYph37eW/icjakpFMjMYHr+ghdW9cJeQDr8/mYvX0e41d9TmZuFg0T63NWo96cfEJX\n4mPtvwNjTPkT1E8uEamiqhnhbowxxhxPcvPyeX/yikNHzDZKrsoj13YLy4KurNyDjNJP+CXFLR7r\n2+xc+jXvE7bdD4wxpjQE+yv4ChF5QFXHh7U1xhhzHMjJzefTGWv5ael29h7IBuC6Pm04p1ujsNT3\ny/YFjNJPycrLolpCEneeeDNNksJTlzHGlKZgA9lEYE84G2KMMceDlLQMXhi5gF373EKqs7o2pP9p\nzaiZVDHkde3I2MmIFeNYtWctAGc26kW/5udTpUKVkNdljDGREGwg+zLwpIjsAxapanYY22SMMVFp\n8s8bGTNtNQBtGlXnvss7h2UaQU5eDl+t/5YpG6YBUK9KMte1u4IW1ZuFvC5jjImkYAPZwUBLYDaA\niOQFpPtUNfTDCcYYEwW2pO5nzLQ1/LrWbal1+VktubBnk7DMT127dwOj9BO27N9GQlwC/Zr34ZzG\np9tcWGNMVAo2kB0V1lYYY0yU+nrORkZ/50ZhK8TH8sfLO9OuWa2Q15OVe5APlo9mUeoSANrWbM31\n7a+kRsXqIa/LGGPKiqACWVV9ItwNMcaYaDNp9gbGTl8DwNXntuaMExtQMSEu5PXsPbiP/y54i5SM\nVGKIYWCrizm7cW9iY2JDXpcxxpQlJdo4UEROBc4H6gPPAO2ABaq6owRltAeWFpJ0uqrOFJE+wPOA\nAKuAh1V1kt/9dYFXgT5ANjAMeFRVc/3yPAD8EUgGZgF3qeqqkvTVGGOOVk5uHi+NXczyDWlUiI/l\n7oEd6dyyTljqmr9jMf9b8hEA7Wq14cb2V5GUkBiWuowxpqwJdh/ZBOBjYBAueKwAvAM8BLQXkdNV\ndU2QdXYCdnpf/e3ygtzPgX8C44Frgc9EpJuqFgS/4wEfcCbQEBgO5AKPem29FXgCuAVQ4Clgsoi0\nV1U7b9EYE1Z79x/ksXfnsGNPJgCPXNuN5vWrhbye9Oz9jF05gXk7FhEfE8dpDXpyeev+xMWGfsTX\nGGPKqmBHZP+FGwEdAHwDFByOMASYhAsWrwqyrI7AMlXdHpggIvcDs1X1Ke/SYyLSG7gfuN0bEe4N\ntFDVdcAiEXkIeEVEnvQC1b8AQ1V1nFfmNcA2XBA+Isg2GmNMiR3MyePx4bPYsSeTRsmJ3uEGoT8x\n69edy3hv6Qiy87KpnpDEDe2vom2t1iGvxxhjyrpgJ1BdC/xVVb8Acgouqup63OjnWSWosyOwvIi0\n04HpAdeme9cL0jd4Qax/ehLQxZt20Ma/DFXdD8z1K8MYY0Ju595MHnv3ZzZuT6dhclUeveGkkAex\new+mM2zpCN5cPJzsvGx6NejJE6f91YJYY8xxK9ifsrWA1UWk7QRK8tysI1BJRGYDzYAlwN9UdQ7Q\nCNgSkH8r0Nh7XVQ6Xp6CILu4MowxJmTy8328PH4xi9e4rbWanpDE367rRoX40D3iTzmwgykbpjNv\nx0Jy8nNJrlybK9pcSofaErI6jDGmPAo2kF2KmzowpZC0C4FlwRQiIpWBFkAqbn7tQeAe4HsR6QZU\nAbICbjsIVPJe/y5dVXNExOflKTiuprgyilSzZhXiQ/ifD0ByclJIy4ukaOlLtPQDrC+Rtm7rXu77\nz/RD72+8uD0Dz2pFXGzo9mxdkqI8+fNLh95f2bEfA9v1LZW5sOXxe1IU64sx0SnYQPYpYLyI1AK+\nwC226iUi1+EC0euCKURVM0WkJnCwYOGViNwEnATcBWQCgQcrVAQOeK9/ly4iFYAYL0+m3z1FlVGk\ntLSMI2UpkeTkJFJT00NaZqRES1+ipR9gfYmkzIO5TJi5jim/bAKgSb1E/nRlF6pVTSAuNiZkfdmy\nfxv/mfc6AKfV78EVbQaQEJfA7l2h/VlVmPL2PSmO9aX48owpz4LdR/ZTL2h9FrjEu/xf3Mjq3ao6\nJtgKVXVfwPt8EVmKe/S/Cbe1l78GHJ4qsAm4qJB0vDybvNf1+e1UiAYUPS/XGGOC9uOSbXw0ZSVZ\n2e6Aw8vOaEG/05qFtI7dWWl8unoi83csBmBgq4s5r8mZIa3DGGOiQdArEVR1BDBCRASoDewFlqtq\nfrBliMhJwDTgbFWd512LA7oAY4EduG21/ul329nADO/1TOA5EWmsqpv80tOBhaqaLSKrvDJ+8MpP\nBLoDbwXbTmOMKcyUXzYxaqrbkrpzy9rc2LctNZNCdzr31v3bGbfqczTt8O/h18ggejXsGbI6jDEm\nmpT0QITOuO2vquOCziwg2P1jARYB64G3RORuYD/wMFAHN8JbD5gnIk8AI4FrgJ7And79PwGzgdEi\nco+X/3ncdlvZXp6hwAsishq3kOxp3PZbn5Skr8YY48//qNn7Lu9Ml1ahPeBgbspCPlw+htz8XJom\nNaZzcgfOaXw6CXEVQlqPMcZEk2APREgERuEWdvmvYvCJyHvAHaqad6RyVDVXRC7EBZ9fAFVxJ2+d\n4Z0OtkNEBnrpDwMrgP6quty73+elv4EbcU0H3gWe9KvjTW8e7lDcbgozgb5+ga4xxgQtP9/HsK+W\nM2uJ2/r6pgvbhjSIXZW2hglrJrFu30bAjcCe1uBkYmJCt2DMGGOiVbAjss8BZ+AWZE3AzY2tB1yJ\nG/HcgXey1pGo6hbcvrRFpU8EJhaTvh0YeIQ6nsEdoWuMMUftYE4eQ0cvZNXmvUBoR2I3pW9lxuZZ\n/LjtFwBaVG/GZa0upnn1piEp3xhjjgfBBrJX4g5E8J9nuhV4yZvj+hBBBrLGGFMerN++j+GTVrAx\nZT8Af7+pO81OOPajZnPyc5mw+iumbZ4JQM2KNejT9CxOb3iqjcIaY0wJBRvIVgDWFZG2gMP7txpj\nTLm2fXcGI75dyZK1uwFoXj+J+y8/kWpVE4657F93LuOj5WPZn3OApAqJXCmXcmKdDqWyJ6wxxkSj\nYAPZEcCfReRb/7mmIhKDW4g1NhyNM8aY0jRlzkZGfXd4x4BBZ7bgolOaHvNI6a7MNKZumsH3m2cB\n0KNeN65scwlVKtgYgDHGHIsiA1kRedvvbUXgdGCdiEwEUoCawHm4Y2PfCGcjjTEm3ObpjkNBbK9O\nJ3DzRe2IDcGj/q/Xf8fnaycDEBsTy80drqFb3c7HXK4xxpjiR2T74E7wKrDZ+3p+QL5UYBBunqwx\nxpQ7S9fv5rVPlwBw72Wd6Nom+ZjL9Pl8jNJPmLn1Z2JjYhnY6mJOOeEkG4U1xpgQKjKQVdVmpdgO\nY4yJiKnzNvPxNysBOK97o5AEsVv3b+edJR+wI2MnSRUS+WO3P3BC1XrHXK4xxpjfKtGBCMYYEy3S\nM7IZOXUVs5emAKHbWmvO5oW8NPd/5Obn0rxaE27teB01K9U45nKNMcb8XrAHIjQFXgZOBQr7iexT\n1dCd02iMMWG0bts+/vn+XACqVIznT4O70KLBsW+tNXf7Aj5YMYa8/Dwub30JZzfufcxlGmOMKVqw\nI7LvAqcAw4Bd4WuOMcaE1zzdcWg+7Mnt6nJj37ZUrnhsD6fW79vIJ6smsmbvOuJiYrmm7SB6NegZ\niuYaY4wpRrA/vU8Bhqjq6HA2xhhjwsXn8/HimEUsWef2hx14enP692p+TGXm5Ofy8fKx/JKyAIA6\nlWpx32m3UJu6x9xeY4wxRxZsIJsCZISzIcYYEy5Z2bk8+9F8Nu5wp3T9afD/s3fn8VFWZ+P/PzOT\nTGYmCSFACFvInpONAIK4YRW3ui/ggqJWq9Xa2vXp8n2e1udXu39bfz7to621Wpe6LygqClq3ouKG\n7FlOdgghhCQsWWYmmeX+/nEPIUSWISSZSbjer5cvwn3OfeY6jCQXZ859nZkUZ44f8HiGYbChZTMv\nVb9Om3cXdpudxXlXcOKk2aSmJNHS0jFYoQshhDiMcBPZ3wJ3K6U2aq23DGVAQggxmNZXt/LI6+V0\nenyMTbDzrStmkDM1acDj7ehq5qmKpdTurQdg9sQSrs+/EkeMY5AiFkIIEa5wE9nXMevE1iqlWoGu\nfu2G1jp7UCMTQohjtOLTLbzwXg0AmZPH8MNrZhLviB3weF80b+CR0qcAmBI/iUuzz2fGhMJBiVUI\nIcTRCzeRfRzIwExom4csGiGEGCRP/6uSt78wz3H54dUzKc46tq0EL9e8zjtbVwFwRc5FnJV2OlaL\ndVBiFUIIMTDhJrKnA9/UWj8+lMEIIcSxcnt9/P21MjbWmAVWvnV58TElsZ09Xdy79gGa3TuxW2O5\ndcYNFI3PH6xwhRBCHINwE9mW0H9CCBG12vZ6+eMz69i5xwPAr289iSkT4gc83saWUh4vexZvoJsJ\nzvHcUXIzk+KlIoEQQkSLcBPZPwC/UEpt1lpvHcqAhBBiID4ta+ah18oIGgb508fyzcuKGRNvH9BY\n1XvqWFH3Nnp3NQYGJ0+ay+L8hcRa5TBEIYSIJuF+V74AUECdUmon0L+2jKG1VoMamRBChKHT4+NP\nL2ygdns7AKeXTOamC47rDpEAACAASURBVPKxWCxHPVZTVzNPV7xI7V6zOMvYuCRuKLia/HG5gxqz\nEEKIwRFuItsKvDSUgQghxNHwB4IsX13Pu2sb6fT4APj+VTMpyR7Yftjmrp38/rM/4TcCjI1L4rz0\nBZw2ZR4xsgorhBBRK6zv0Frrm4c6ECGECNe6yhYeeKUUfyAIwDlzp7H4rFys1qNfhQVYu3Mjj5U+\nQ8AIcOrkeVybv1AqEgghxAgQViKrlJpypD5a6+3HHo4QQhyaYRgs/3gLL6+qBSBtYgLfuryY1HGu\nAY0XNIK8XvsWK7e8C8DFmedxQeY5gxavEEKIoRXuZ2bbAOMIfWzHGIsQQhxS9ba9PLqinKY2NzE2\nK4vPzuGsE6YNeLyyNs1bW96jak8tCbHx3Dbja2SPzRi8gIUQQgy5cBPZr/PlRDYBs77sglC7EEIM\num5fgLfXNLD03+YqbFK8nTsXzSB7ysCOmQ0aQV6qXs57DR8CMD1xKreX3MTYuIEfWyuEECIywt0j\n+9ghmv6ilLoXWIJ56pcQQgyazyt28viKCtzdfgC+Oi+NqxfkDKgiAcCOrmYe2vwkO7qaSYxNYGHu\nxZyYOnvA4wkhhIiswXgc91XglUEYRwghALOk1u+fWsv21i4A5hVM5Mozspkw1jmg8YJGkBerXuPf\n2z4CICspna8XLSHZMXbQYhZCCDH8BiORPQnwDcI4QghBXVM7f35hA+1u89vKDV9VLJg9dcDjVe2u\n5aXq19ja0Ygzxsn5GWdxdtpXZBVWCCFGgXCrFvz9IJdtQBpwFvDw0b6wUupk4EPgHK31+6Fr52Ge\nIqaAKuCnWusVfe6ZCNwPnAf0AI8CP9Na+/v0+QHwfSAF+Aj4lta66mjjE0IML8MweHvNNp57t7r3\ndK47F87A5Ygd8Jgr69/ltdqVAExLmMI3ZtzABOfA6swKIYSIPuGuyJ7Hlx/2MoB24PfAb4/mRZVS\n8cAT9Kl0oJQqxNym8CtgKea+22VKqRO01qWhbktDr3sGMBV4DPADPwuNcQtwN+bDZxr4DbBSKVWo\nte4+mhiFEMPH7fXxf59eR2XDHgCuXpDDV+elDXjVNGgEebn6dd5t+ACAO0pupmj8wE77EkIIEb3C\nfdgrY5Bf917Mkl45fa59D/hEa/2b0O/vUkrND12/TSl1CjAfyNJa1wEblFI/Bu5TSv0ylKj+BLhX\na/0igFLqOqAJWAQ8PchzEEIMgm5fgN8+8BHV2/aSnBjHnQtnkDl5zIDH29axnX+WP0djZxN2ayw3\nFV1H8YSCQYxYCCFEtBj2sxeVUhcCFwEXABv7NJ0OPN+v+/vA4j7tW0JJbN/2RGCWUqoOyAtdA0Br\n3amUWhO6VxJZIaJMdeNe/vD0WvwBg+mpCfzshrnExgz8RK0vmtfzZMWL9AR6mOiawPdn30FSXOIg\nRiyEECKahLtHNgW4B7gYiAf6/6QxtNZxYYwzAfgHcDOwu1/zNKCx37XtmPtwD9dOqM++B84ON8Zh\nJSe7iIkZ3HMdUlJGzw/R0TKX0TIPGNlz+ax0B7994gsAirLG88vbTsEeO7C/f+3eDv7y2eOsazJ3\nIV1XcjmX5Z8Xsa0EI/l96Wu0zANkLkKMVuGuyN4PXAI8g7klIDjA13sQeFVrvVIp1f9IHhfg7Xet\nG3Acql1r7VNKGaE++86oPNwYh7V7tzucbmFLSUmkpaVjUMeMlNEyl9EyDxi5c+n2BVjxyRZe/age\nMPfD3nBx0YDmEjSCfNz0OU9XLAUgOW4sV+ddRsmEIlpbOwcz7LCN1Pelv9EyD5C5HGk8IUaycBPZ\nC4AfaK0fHOgLKaW+BswGSg7RxQP0X9WNA7oO1a6UigUsoT6ePvccagwhRARtqm3jvqWb8AfMfwv/\n5NrZ5KcnH/U4hmGwpnk9L1e/zt6edixYmJM6k8XqCpwxA6s1K4QQYuQJN5H1A9XH+Fo3YW4P2KGU\nAjMBBVihlHocaAAm97tnCvu3CjQAFx6knVCfhtDXk/vFOgUoP8bYhRDHwDAMXv2onlc/rMMAMieP\n4Y7LigZ0wMHe7nb+uuERtnVux4KFrKQMluQvYlJ86uAHLoQQIqqFm8i+DFwHvHMMr3U90Pen1iTg\nA+BW4F/ArzHLav2qT58FwKrQ1x8C/1cplaa1bujT3gGs11r3KKWqQmN8AKCUSgDmYm5pEEJEwOcV\nO3np3zU07zY/NPn2FcXMUROPepxAMMDqps94pWYlHr+HsXFJXJd/JUXj1WCHLIQQYoQIN5H9FPi9\nUioTWA3030xqaK1/d7gBtNYHPISllNq3l7VRa71TKXUf8IVS6m7MvbjXYZ4adkeo38fAJ8BzSqk7\ngVTMwxPu1Vr3hPrcC9yjlKoGNmPWt20CXgpznkKIQbTsg9revbBTJ8Rzy8UFZEw6+tJatXvreaz0\nWdq8u7BgYcG0+SzMvRirZeAVDoQQQox84Sayfwv9embov/4M4LCJ7JForTcppa7ATE5/ClQAl2it\ny0PtRqj9AcwV1w7ME8V+2WeMvymlkjET2jGYq7jn90l0hRDD5N/rG3uT2NsvLeKkwqP/6H9bx3aW\n173FptYyALKTMri+4GomuiYMZqhCCCFGKIth9D+w6/jV0tIxqH8Y8qRs9Bkt84DonUuX18cTb2o+\nK99JjM3KLRcVHDGJ7T+X3d49/GPzU9S1bwFgeuJUFqSdztzUWVG/Chut78vRGi3zAJnLEcaT4+7E\niDbsByIIIUavj0t38NBrZb2//96VJRRljjuqMT7bsZYXq16ly+fGbrNzVe6lnDx5btQnsEIIIYaf\nJLJCiEHx8eYdPLTcTGKTE+P41S0n4XKE/y2mo6eTF6teZU3zegDOSjudy7MvxGYd3ENKhBBCjB6S\nyAohjtlrq+t5eVUtAN+4uJBTiicd1f1fbN/EPR89SMAIEB/r4s5ZtzI9sf+ZKUIIIcSBJJEVQgyY\nPxDk/pc2sam2DYDvLJzB7LyUoxrjvYYPWV73JkEjyAUZZ/PV9LOItcUORbhCCCFGmUFJZJVSVq31\nQI+tFUKMQLvavfzl5U3UNZkPntz1tblkTg6/tJbb5+b+Df9gS7tZFvrGgms4afKcIYlVCCHE6BRW\nIquUqgWu0FpvOEjbPOB14OiWYYQQI5JhGDzyejmrS3dgGDAm3s7Pb5hzVKd07ehq5t61D9DlczPe\nMY7/mP8NkoLjhzBqIYQQo9EhE1ml1LXAvs/3MoArlFIzD9L1bCBu8EMTQkSb3R3d/P/PrWd7axcA\nl56WwaXzM7Fawqvg0+bZxfOVy9jcVgHAqZPncW3+QlLHJ42a8khCCCGGz+FWZOcAPwx9bQD/fYh+\nBnDPYAYlhIg+5Vt289eXN9Hl9TM9NYE7LismdZwr7PtL2zSPlz5Dl9+N1WLl6rzLOH3qKUMYsRBC\niNHucInsfwL/A1iArcClwLp+fQJAu9a6/5G1QohRZHNdG/c+Z+4sOmfONK49JxdLmKuwjZ1NrKx/\nh7U7NwKwIG0+V2RfJGW1hBBCHLNDJrJaax/QCKCUygS2h64JIY4j/3xT8/66RgC+Oi+Na87KDfve\n1ds/46mKFwFw2BxcnXeZPNAlhBBi0IT1sJfWeotSKlspdSEQD/Q/YsfQWv9u0KMTQkTMrnYv761r\n7E1ib7mogNNmTA77/rfq3+OV2hUAXJ59IWdP/4qcziWEEGJQhVu1YAnwOF9OYPcxAElkhRglPi1r\n5uHlZQSCBvGOGL6zqIS8tLFh3Vu3dwsr6t+htK0CZ4yDW4qvp2Bc3hBHLIQQ4ngUbh3Zu4C3gW8A\n27TWxtCFJISIpPfXNfLPNzUA585N44qvZOKwH/lbRdAIsqzmDd7ZugqASa6J3Fh4Delj0oY0XiGE\nEMevcBPZDOBbWuuGIYxFCBFB21u7+MvLm2hqM5/dvHPhDE4I85Qur9/Lgxsfp3JPDQmx8Zwz/QzO\nmX5G2A+ECSGEEAMRbiJbCciyihCj1LIPann1o3oAJo518s3Li8iYFN4pXXV7t/Bk+QvscO8kyZ7I\nj+d+h2RHeNsQhBBCiGMRbiL7M+DPSqk6YLXW2j+EMQkhhsm2lk4efKWUxtABB1eemc1X56Vhsx75\noaxAMMCK+nd4a8t7BIwAJ0ws4WuFi4mxDsrJ10IIIcQRhfsT5w+YR9C+B6CUCvRrN7TWcrqXECPI\nR5uaeGxFBYGgwYQkBz+8ZhaTwjzg4M36d1nd9DmtnjYcNgeLcxdy6pQThzhiIYQQ4kDhJrLPDmkU\nQohh4/MHePadat4LldW66JR0rjg9C6v1yPtZ3T4Pj5Q+RfmuSgDmps5iUe4ljLEnDmnMQgghxMGE\nW0f27qEORAgx9DbWtPL4Ss3ujm4Avn1FMXPUxLDubepq5s9rH6TD10ly3FiW5F9JwXgpqyWEECJy\njmozm1LqFOBcYDJm3dgCYJ3WeucQxCaEGCTdvgCPvlHOZ+XmX9UFs6dy/knTSRnrDOv+DS2b+cfm\npwgYAU6ZfCLXqoVyxKwQQoiIC/dABDvwFLAI6AFigYeAHwOFSqnTtdY1QxalEGLAyut38cdn1/f+\n/ruLSpiVOyGsew3D4LGyZ1jTbN5/Sdb5nJ9x1pDEKYQQQhytcFdkfw2cB1wG/Atwh67fCqwAfgMs\nHvTohBAD5vb6+Z8X1lPT2A5A9tQxfHdRCYkue1j3l7ZV8M7WVejd1STHjWVR7iXMnjhjKEMWQggh\njkq4iewS4D+11q8ppXo/T9Ra1yul7gb+NCTRCSEGZGNNGw+8spnungBxsTaWnJvH/JLJYd3rD/p5\nseo1Pmj8GIDMMencXHQd453JQxmyEEIIcdTCTWTHAdWHaGsFwqucLoQYUkHD4PEVFXywsQmAgvRk\nvn9VCbEx4e1nbepq5sXKV6nYXUV8jIuvFS2maHz+UIYshBBCDFi4iWwp5taBtw7SdgFQNmgRCSEG\nJBAM8teXN7OuqhVnXAw3X5DP3PzwKhJ09HSytGo5nzevBWBqwmS+P/ubuGLDexhMCCGEiIRwE9nf\nAEuVUuOA1wADOE0pdT1wJ3B9uC+olJoG/A9wNmAFVgI/1FpvD7UvAf4bmA5sAL6jtf68z/05wP3A\nfGA38L9a6z/2abdh7um9CUgMjf9trXVzuDEKMdJsrGnjLy9vwucPMn5MHP95/RzGjXGEde/W9m38\nY/OTtHp3EWON4bLsCzhz2mlYLUc+3UsIIYSIpHDryL4cSlp/D1wauvxnoAUzSXw+nHGUUhbg9dB9\nC0KX/xczOZ6jlDoHeAT4DvAB8EPgLaVUnta6JVQ9YSWwDpgHzAIeUkrt0Vo/FBrvF8DXgBuBNuCv\nwFLMxFeIUcXnD/DQ8nLWVJhltU4rnsRVC3IYE3/kB7oMw+Cl6uW82/ABAFlJGdw+42sk2OOHNGYh\nhBBisIRdR1Zr/TTwtFJKAeOBvUC51jp4FK+XCpQD/0drXQ+glLoXWKaUSsYs5/WM1vrvobbbgbOA\nbwC/xSz/NQm4WWvdCZQppXJD9z0USnS/B3xXa/2v0BiLgTql1Kla69VHEasQUW3bzg7uvHcVgaCB\nBbj5woKwH+ja6W7h0dKn2drRSIzFxqXZF3BW2ulYLEc+3UsIIYSIFuHWkX0PeAJ4QWutB/piWusd\n9CnTFdpmcDvwOWZifBrmVoV9/YNKqVXA6aFLpwNrQknsPu8Dv1BKpQLpmNsJ3u8zRr1Sqj50rySy\nYlRYX9XKX5dtJhA0mD4xgTsXzWBC0pH3s3r8Xl6vfYv3tn0IQHZSBjcUXEOKa/xQhyyEEEIMunBX\nZAPAg8D9SqnlwD+BFVrrwEBfWCm1DLMu7W7MbQZjgXigsV/X7cCJoa+nHaIdIC3UziH6pA00ViGi\nRdAweOPjLby0qhaAy+dncun8zLDuLW3TPFH+HB09nSTZEzkv/SzOmHaqrMIKIYQYscLdI3uOUmoi\ncE3ov1eAXUqp54AntdafDOC178LcLvBzzEMW9iWr3n79uoF9T624MPfX9m8n1McFBLXWvsOMcUjJ\nyS5iwixTFK6UlMRBHS+SRstcRuo8drd7+fWjn1K5dQ8At18xg4vnZx3xvpauNu7/9DHKW8wKemdm\nnMItcxYTFxPewQjDZaS+LwczWuYyWuYBMhchRquj2SO7E7gPuE8pNR24GrgK+KZSqlZrnXc0L6y1\n3gS9e1gb2F/5IK5f1zigK/S15xDthPp4AKtSKkZr7T/EGIe0e7f7SF2OSkpKIi0tHYM6ZqSMlrmM\n1Hm8vaaB59+rxh8wmJDk4EfXzqYod+Jh59IT8LGs5g0+avwEvxEgLXEql2R9laLx+bTv7mb/vwEj\nb6S+LwczWuYyWuYBMpcjjSfESBZ2IttPHGDHLMNlAfqvgB5UaB/rAq31s/uuaa3dSqkaYApmstn/\naZUp7N8q0ACog7QT6hMb+npyqO/BxhBixNjd0c1Dr5VSEVqFPbkwlVsvLsRqPfx2gI0tpTxdsZQO\nXydWi5VFORezQB7mEkIIMcqEncgqpdIwH9RajFn2qgV4BviW1nptmMOkA88opaq11mtC4yZhJqeP\nYz6MdQbmg2UopazAV4B9pbU+BJYopVxa633LpwsArbXeqZTaC3SExngyNEYGkAGsCneuQkSDzXVt\n3PvcBgAmJDn49hUzSJ90+NWTnkAPT1cs5fPmdQAUjy/gpqLFOGPkYAMhhBCjT7hVC1YDJ2HuX30F\nc1/rWwN42GsNZn3Yh5VSt2Gu5P4eMyl+HKgBXlNKrQPexawjmwQ8HLr/ZczDGZ5WSv0cmIFZeuvb\nAFrrbqXUX4F7lFKtwE7MOrL/HuA+XiGGXYe7h0ffqGB9dSsA8wom8o1LCrFZD39AwU53C/8se566\n9i2kuiayKPdiOV5WCCHEqBbuiqwHuAVYqrUe8OacUDmthcA9wHLMB7DeBM4IldRaGUpw7wr1WQuc\np7VuDd3vUUqdDzyAWbJrJ/BfWuvH+rzMzzG3GDwZ+nUloURXiGgWDBo8/XYl7641d8FMSHJwzVm5\nnJA34bBbAoJGkGU1b/DOVvNDB5WcwzdLbsJui66HuYQQQojBZjEMI9IxRI2Wlo5B/cOQBwyiT7TO\nY3trF/ct3Ujzbg8AX52XxsKvZBMbc+hV2JSURN4r/4xn9Evs6d4LwDV5lzN/6skj7njZaH1fBmK0\nzGW0zANkLkcYTzbOixHtkCuySqk3gO9rrSv7XDsPWN33QAKl1DzgQ621LP8IcZQMw+CT0mYeWl4G\nwPTUBL67qIRxY45YLY5nN73KS2UrAJgxoZCr8y5jnCN5SOMVQgghosnhthacj3lIAQBKKRuwArPe\na9+HuyzA4BZfFeI40OX1cf/STeiGPVgssOTcPBbMnnrEygL+oJ8HNz1OWZt5yN71+VdxypQTD3uP\nEEIIMRodbfkt+QhCiEGwraWT3z35BZ7uAGMT7HzvyplHrEgAsKGllKfKX6DL7yY+1smP5nyHia4J\nwxCxEEIIEX0GWkdWCDFA769v5J8rzdXUufkTueXCAuLsh/9Qw+P38nrdW7zX8CEAJ0+ey20nL8az\nNzjk8QohhBDRShJZIYZR+ZbdvUnsuXPTuPac3MP2NwyDD7d/yrP6JQAcNgc3FF7NrJRiEuzxeBgd\nD7AIIQ7PMAwCHR0Y4+MjHYoQUUUSWSGGyasf1bHsgzoAbvyq4szZUw/bf0/3Xh4vfZbKPTUAnJ32\nFS7IPFsONxDiOBHweHCXleLR5XRu3IC/tRXf4qtxnnNhpEMTImocKZE9WDkqqdclxFHo7gnw+MoK\nPilrBuC7V5YwK+fQ+1oDwQBvbXmPFfXvEDACpCVM4aaia5kUnzpcIQshIiDo9dLduA13eRnuinK8\n1VUYfj8AVoeDhBPmMO6keXgiHKcQ0eRIiex9Sqn20Nf7HvT6q1Kq7+eZYwY/LCFGh7qmdv73xY3s\n7eoB4MfXzqYg/eAlsgzD4JMdX7C89k32dO8lxmLjvPQFXJh5LrFW+fBEiNHG19qCW1fg0RV46+vo\naWqCfbXdLRbi0qYTXzKT+KJiHJlZWGJiSEhJxDNKauIKMRgO99NxFebqa2yfa/8O/dr3mifUVwjR\nx/vrG3nyzUqChsGZs6ey6Iws4h2xB+3b2NnEo6VP09RlrtrOSpnBwpyLGe+UurBCjAaGYeDfvQtv\nbS3uslK6yjbjb23tbbc6nThzcolLS8OZq3DlF2BLPHIlEyGOd4dMZLXWZw5jHEKMGoZh8MRblby/\nzjxq9rL5mVx6WsZB68P2f5iraHw+l2dfyJSEScMasxBicBl+P94t9Xjr6vDoCjzVVQQ62nvbrS4X\n8bNm48ovwJmniJuWhsU6sk7kEyIayOeVQgwiwzB4+u0q3l/XSFysjR8tnkX21KSD9m3u2skT5c9T\n174Vq8XKtWoRp8rBBkKMSEYwiG9nM+7ycrpKN+EuL8Po7u5tjxk3joTZc4jLyMCVl48jO1sSVyEG\ngSSyQgySrc0dPPGmpmZ7Ow67jZ9cN5uMSQffQv7ZjrU8VfEi/qCf9DFp3FhwtTzMJcQIYgSD9IQe\nzOoqK6N7S/0BK66xqZNwFRTizM7BkZmFfZJ8yiLEUJBEVohB8Fl5Mw8vL8cfCDIhycEPrp7J5EPU\ne3xOv8yqxo8BWJhzMWelnX7EY2mFEJFl+P10b280y2HVVOOpKCfo2V8/ICZ5HIknnYwzJw9XUTH2\niRMjGK0Qxw9JZIU4BoFgkGffqeadL7ZhAa46M5sLTk4/aN+Onk6eqniBTa3lOGwObileQuF4NbwB\nCyHCEvR68FRX49YVeGuq8dbXYfT09LbHpqSQcMJcnHmK+KIiYsbKg5lCRIIkskIMUGXDHv74zDoC\nQQML8IOrZ1KcNf6gffWuah7Y+Ai+oJ9J8ancUXITE5wH7yuEGF5mRYHdeHQ5nqpKPDU19GxvPKAU\nln3KVJzZ2Thz83Dm5hE7ISWyQQshAElkhThqhmHw2ur63lO6cqYlcdP5+UyZ8OWtBEEjyEvVy3m/\n4SMMDE6YWMLXChcTI3VhhYgYw++ne9s23Locb001npoaAnv39LZb7HacuXk4srJx5RfgyMzCFi9H\nwwoRjeSnqRBHwdPt555n11HX1IHNamHx2bmcPWfaQftu7djGMxUvsbVjGwmx8VyafT6nTTlpmCMW\nQgS6u83TssrL8NTW4K2tOWCbgC1pLPGzZuPMzcOlCoibNg1LjPx4FGIkkL+pQoRpV7uXux/7nA63\nj6QEOz+5dvYhH+j6uGkNT5W/gIFB0fh8luRfRVKcFDcXYqgZhoFvRxOe2ho8WtO9rYGq7Y29R70C\n2CdPwZmbizNX4cxTxIwbJw9cCjFCSSIrRBh27HLz5xc30uH2MX1iAv/n+hNw2A/+1+ezHWt5qvwF\nLBYLS/Kv4uRJc+SHpBBDxDAM/G2teCorcVeU0VVWSmBPn20CMTHEZ2YQk5FNfGERjuxsbC7ZJiDE\naCGJrBCH4Q8EefrtKj7YsJ1A0ECljeU/Fs8ixvblQubN7hZeqnqNzW0VWLDwzZKbKZKqBEIMukBX\nF+6KMtwVFXRtXI+/ra23zZqQQOK8k3FkZuLKL8Q+ZQoTJ42lpaUjghELIYaKJLJCHEJZ/S6eebuK\nxtYuAC49LYPL5md+aXXVMAxWN33Gy9Wv4/F7GWNP5JslN5E+Ji0SYQsxqhiGQWDvXrxb63GXl+Mu\nKz2gooDV6SRhzlyc2Tk48wvkqFchjjOSyArRTzBocO/z6ymr3w3AKUWpXHlmDsmJcV/qu7e7g5er\nl/N58zpirbEszLmYBWnzsVrkB6kQAxH09dDdsA1vbQ3e2mrcWn+5okBOLq7Cot6KAvJglhDHL/nb\nL0Qf7e4e/vrSJiq37SUp3s43LimkMGPcl/oFjSDvb/uIpVWvATDGnsgdM29meuLBKxgIIQ5u3x5X\nd3kZnRs34C4rxeju7m23JY4hftZs4tKm99ZwtcbGRjBiIUQ0kURWiJBPynbw91fLAJiQ5ODnX5vL\nGJf9S/3aezp4oux5ynZpAOZPPZnLss7HFesa1niFGIkMw6Cnabt58EClxlNVhX/X/j2usamTiC8q\nMhPXPEXsxFR5WFIIcUiSyIrjnj8QZOm/a3jzswYAzp2bxqIzsrDH2r7Ut2ZPPfetfwhf0EeqayK3\nFl/PlIRJwx2yECNGsLub7m0NoWNe63FXlBFob+9tt7riSThhDs7cPOJnlGCfNDmC0QohRpphT2SV\nUqnAH4DzACfwKfAfWuvNofYlwH8D04ENwHe01p/3uT8HuB+YD+wG/ldr/cc+7Tbg18BNQCKwEvi2\n1rp5yCcnRpy6pnZ+9fgaABx2Gzd+VXFy0cET09XbP+OpihcBmD/lJK7Mu4xYOaFLiAMEPB666+tw\nV5TjLi/Du6UeAoHedlvSWBJPOtncJpCXj33yZFlxFUIM2LD+FFZKWYGXAQtwGdAJ/AJ4RylVCMwG\nHgG+A3wA/BB4SymVp7VuUUrZMRPTdcA8YBbwkFJqj9b6odDL/AL4GnAj0Ab8FViKmfgK0WvVhu08\ntqICgOTEOO7++jwSnF/ee9fctZM36t9mTfN6rBYrNxddxwkTS4Y7XCGikr+9HW99HZ6KcrpKN9PT\nuG1/o9VK3PR0nDk5ODIyiZuejn3yFElchRCDZriXk2YCpwCFWutyAKXUDcAu4CJgCfCM1vrvobbb\ngbOAbwC/BRYBk4CbtdadQJlSKhf4MWZCawe+B3xXa/2v0BiLgTql1Kla69XDN1URrTrcPTz/XjWf\nlJqL9OefNJ2rzsz+0g/XQDDAe9s+ZFn1GxgYjLEn8o0ZN5CVlBGBqIWIvEBnJ90NW+lu3Ia3rhZP\nZSX+3bt62y2xsThVPo7MLJw5uThVPjanM4IRCyFGu+FOZLcCFwO6z7Vg6Ndk4DTgzn0NWuugUmoV\ncHro0unAmlASu8/7wC9CWxbSMbcTvN9njHqlVH3oXklkj3Nrypu5++FPen//jUsKOeUgWwnK2jQv\nVL7CTk8rzhgHEF6mLAAAIABJREFU504/k3PTz5SyWuK44mtrxVtTQ/e2BrrKSuneUt9bvxVCFQVm\nlBCXnhHaKpCHNfbLD0gKIcRQGdZEVmvdBrze7/J3MffKrgHigcZ+7duBE0NfTztEO0BaqJ1D9JHq\n9Me5Nz/bynPvVmMBTimexOKzc7+0lcAwDD5pWsOTFS8AcMLEEhblXsLYuKQIRCzE8Aq4u3CXleKu\nqKChWuPZ1udbqc1mJqs5udinTsUxPZ3YSbK/VQgRWRF9UkUpdSnwO+BeYEvosrdft27AEfraBbQc\npJ1QHxcQ1Fr7DjPGISUnu4iJ+fKT6sciJSVxUMeLpJE8l6dWVvDcu9UA/PL2U5iVN/FLfXZ2tfGn\n1Q9Tvasem9XGf5x6G3OnRvde2JH8nvQncxl+3S0tdNbW01FRQXtpOZ01NRh+PwBWh4PkObNJmjkT\nV9o0EvMVMa6RW2JupLwn4RhNcxHiWEUskVVK3QQ8BDwL/ARzawFA/+OT4oCu0NeeQ7QT6uMBrEqp\nGK21/xBjHNLu3e5www9LSkriqDnfe6TOpdPj455n1rF1Zydj4u3cdctJjHfFHjCXQDDAuw0fsKzm\nDQCykjK4IudC0u0ZUT3nkfqeHIzMZXgEOjroKi/FU1GBu6wUX2ufdQGbjbhpaSTMmo2rsIhpc4pp\n22OuK/iB3V0B6IrOeR1JNL8nR2uw5yJJsRjpIpLIKqV+hlki637MB7MMpdQuzGSzfxHBKezfKtAA\nqIO0E+qz73PiyaG+BxtDHCc217XxwLJSPN1+HHYbP752Nvnp4w74IbChZTOv1Kyg2d1CnM3OOdPP\n4PyMs2UvrBgV/Hv34KmqwlNTjaeq8oA9rlaXi/hZs3GkZ+DIysaZk4s1bv86gXl6Vv8PyIQQIrpE\noo7sTzCT2P/WWv9q3/VQMrsaOAN4ItTXCnwFc+UW4ENgiVLKpbXet3y6wLxd71RK7QU6QmM8GRoj\nA8gAVg3x1ESU8PmDPPhqKWsrzdWm7Klj+NHi2cT1OeDAMAyW173Fyvp3ACgan8+NhdeQEBsfkZiF\nOFZGIEB3QwPeLfXmiVnVlfjb9p+Yhc2GMyeX+BklZmWBjEwstsHdSiWEEMNtuOvIlmCW0XoEs1xW\n38fFOzD3yr6mlFoHvItZRzYJeDjU52XgN8DTSqmfAzMwS299G0Br3a2U+itwj1KqFdiJWUf231rr\nTxCjXoe7h+/974cAOONsLD47l9NLphzQZ0NLKSvq36ahoxELFm4svIZ5k06IRLhCDJgRDNLTuA13\neTnuijI8VZUEPZ7edltCIvElM3Fk5+DMycWRmYXVLhUFhBCjy3CvyC4GbMDXQ//1dZfW+tdKqduA\nu4B7gLXAeVrrVgCttUcpdT7wAPA5ZqL6X1rrx/qM83PMLQZPhn5dSSjRFaPbvz5v4Jl3qgAYPyaO\n/7phLsmJ+z8q7Qn4uP+Tx1i15VPAXIW9IuciJsenRiReIY6GEQzib2vDrc39rV1lmwl27q9EGDsh\nhcQTT8KRkYkjMwv7tGlSUUAIMepZjD41AY93LS0dg/qHIQ8YDA+318+9z6+ndrt5fvu8gol845JC\nbNb9+1zdPjd/WvcgjZ1NJMeN5fqCq8gflxupkAdFNL8nR0vm8mVBn4/uLfW4y0rxVFXhra89YMU1\nJjkZV0EhTpWPq6CI2HHjjvk1+5L3JDoNwcNe8q8dMaLJQfFiRGtq6+L3T62lw+0jKd7O96+aSfqk\nA5/CXd+ymWcqltLp62JGquImtQRHzBGrsQkxrILd3bgryunatBFvfR092xp6S2EBxKZMxFVYhDNX\n4SooxD5FjnoVQghJZMWIFAwavPjvGt78bCuGAXPzJ3LbJYXE2PavwvqDfpZWLWdVo3mg2znTz+DW\nk66mre2IldiEGHK+XW1462pxV1Tgramme1sDBEMHHYZKYTmysnGpfFz5BdgSEiIbsBBCRCFJZMWI\n07zLzV3/+Ax/wPyhf+bsqdxwXt4Bq1N9V2EBvllyEzMmFGK1SlktMfwMw8DX2oK3qgp3RTmemip8\nzc297ZaYGBwZGTjz8kmYORtHZiaWGPn2LIQQRyLfKcWI4Q8EWfHpVpatqsUAHHYbP73uhAO2EvgC\nPp7WS/lsx1oAisfns1gtJNkxNkJRi+OVf88ePJWark0b6SovJbBnT2+b1eEgvmQmzpxcnLl5xGVk\nYI2VigJCCHG0JJEVUS9oGLz7xTZe/qAWT3cAgMVn53Lu3AOfyv6ieQPPVy6j09eFM8bBjQXXUJJS\nFKmwxXEm0NmJp6aa9upy2tZuwNe8o7fNlpBIwtx5ODIycBUWETctDYt8OiCEEMdMElkR1ba3dvG3\nV0rZ1mKWGcqZlsTNF+Qzefz+gwvcPjePlD5N+a5KADLHpPPtWV/HGeOMSMxi9DMMA/+uXXhra+gq\n24y3qoqeHU297Za4uN4arvHFM4ibni4PZgkhxBCQRFZErffXN/LPlRoAlTaWmy7MJzXZdUCfLe0N\n/M/av+EL+hjvSObGwsXkjM2MRLhiFDMMA9/OnbjLNtNVVoq3pppAe3tvuyUuDldBIY6cXKacPIfu\nCVPl1CwhhBgGksiKqPT025W8vWYbFuDkolRuvrDggIoEXT43T5Q/z6bWMgAWpM3niuyLsFkleRDH\nLujz0dPYiLe2GnelxlNddcAe15hx40g4YQ5x6RnEFxWbWwVCD2cljaKapUIIEe0kkRVRpdsX4A9P\nr6WuqYMEZyzfu7KE7KlJB/T5pGkNT5Q/D0Cczc51ahFzJ82ORLhilDCCQbq3bqVr43rc5WV462oP\nqOFqSxxDwpy5uAoKcRUWY584MYLRCiGE2EcSWRE1GnZ28pt/rqHHHyQ5MY6f3TCHcWP2H1xQvaeO\nV2tWULO3HpvFxtzUWVyrFhJri41g1GIkMoJBerY34qmtoWvjBjy6Yv+pWRaLWcM1OwdHejrOPEXs\nxFTZ4yqEEFFIElkRFVZvbuLh5eUAWCzwu9tOxh5rbhNo9ezin2XPUbO3DoDkuLHcNuNGpo+ZFrF4\nxcgScHfhra/HW1ONp1LjqanG6OnpbY9NmUjCCXOJL56Bq6gYm8t1mNGEEEJEC0lkRcSt2rCdx1ZU\nYLVYuOiUdC47PRNraPXrX1veZ1nNGwBMS5jC6VNP5rQpJ8nqmDgs/549uMtKzf2tVfqAwwcA7FOn\n4cjMxJGegaugCPukSRGKVAghxLGQRFZETNAweOPjLbzyobnS+r2rSpiRNR6Azp4uHt78BFV7agG4\nIucizko7HatFam+KLwt4PHiqKnGXl+Eu3UTP9u29bVan06zdmp6BIzMLV24etsTEw4wmhBBipJBE\nVkREjy/Ab5/4gq07O7EAt19a1JvE1u3dygMbHqHL7yYtYQpXq8vJSsqIaLwiugS7u/HW1dK1aSNu\nXUH3lnowDAAsdjuu4hm4VAGuoiLipk6TUlhCCDFKSSIrhp3PH+D3T61l685OnHE2fnKtecxsIBjg\nnYZVvFKzAjBLal2efSExVvnf9Hjn37MbT3U1nqpKvLU1eLdugYB5yhs2G46sbJx5ivjCIhw5OXLc\nqxBCHCckQxDDauWnW3n+vWoAxibY+f9uOpGkhDh2dDVz//p/sLvbrNV5Y8E1nDR5TiRDFRFiBAJ0\nN27DW12Fd8sWvHW19Gxv3N/BZiMubTqu3DxchUU48xTWuLjIBSyEECJiJJEVwyJoGLz7xbbeJLYk\nezy3XVKEyxFDfftW7lnzFwwMMsdMZ0nBVUyOT41wxGK4GIZBV109uz74FE9N1YGlsABLbKy5VSBP\n4cjJxZGZKSuuQgghAElkxTBo3uXm3ufX07LHi81qYcm5eZw5eypBI8iqbat5pWYlBgaXZV/AudPP\nlIoEo5xhGPhaW/BWVeGuKKerdDOBvftPzYpNmUjCnLk4c3JxZGZjT03tPTVLCCGE6Et+Ooghta2l\nk7sf/ZxA0CDBGcuPFs9iemoiHr+HBzc+TtWeWixYuCrvMs6cdlqkwxVDwPD78dbV4qmqNCsLVFZi\ndHt7222JiUw4/TRi8otx5uQSO35CBKMVQggxkkgiK4bM8tX1vLTKLJ912fxMLpufCcCOrmb+tO5B\nOno6Ge9I5vaSm5iaMDmSoYpBFOzuxrulHo+uwF1W+qXjXmNTJxGXloYzJxdnTh5x06czMTWJlpaO\nCEYthBBiJJJEVgw6wzC4b+km1le3ArD4rBzOmzcdX9DPQ5v+SVmbxsBAJefwzZKbscsRsyOaEQjQ\nvXVL7zYBb3XV/sR133GvOTm48vJx5uYSMzY5sgELIYQYNSSRFYPKHwjy0GtlrK9uZfyYOH54zSwm\nj4/n4+2f81rtSvb2dGC3xnJ13uWcPHmu7IcdgYLd3XhqqunesgW3LsdTVYnR3d3bHpc2HWd+Ac7s\nHFz5BdgSEiIYrRBCiNFMElkxaLY2d/DoGxVsae4gOTGO7181k5TkOB7e/CTrdm4E4MTU2SxWV+CI\ncUQ4WhEuIxike0s9XWWluMtK8VRX7a/hCtgnTzFXXPMLcRUUEjNmTASjFUIIcTyRRFYMikffKOeD\njU0ApKcm8pPrZlPXWcN9q5+jo6eTKfGTuDL3UtS4nAhHKo5kX+LqqarEU1ONu7yMoNvd2x43PR1X\nYRGOjAwc2bnEJstWASGEEJEhiaw4Jg07O/nzixvY1W5+tPydhTOYlTuBZTVv8G7DBwSNIPMmncBi\ntZA4m9T+jEbBnh68NdW9iau3tuaAOq4x48aTMGcu8QVFOAsKiEmUFVchhBDRQRJZMSD+QJBXP6pn\n+ep6AKalxHPnohLGj7HzWNkzrGleT3LcWBbmXswJE0siG6w4wL7E1a0r8FRqvLU1/aoKpJIw90Rc\neQpnTh4xEybIXmYhhBBRKaKJrFLqb0CM1vrWPtfOA/4AKKAK+KnWekWf9onA/cB5QA/wKPAzrbW/\nT58fAN8HUoCPgG9prauGfkbHh90d3fzq8c/Z09kDwHknpnH1WTlU76nh4TWv0djZxHjHOL498+uk\nxk+McLQi6PPRXV/Xm7h6qioxfD6zMVRVwJlfgEvl48jKlj2uQgghRoyIJLJKKQtwN3A78I8+1wuB\nV4FfAUuBJcAypdQJWuvSULelgAGcAUwFHgP8wM9CY9wSGvvrgAZ+A6xUShVqrfc/Wi0G5NOyZh58\n1XwrMicncstFhYxNsvKX9Q9Tsdv8t0LO2EzuKLlZHuiKEMPvx1Nb01vH1VNbc+DDWVOn4SoswqXy\ncebmYYuPj2C0QgghxMANeyKrlMrCTF6Lga39mr8HfKK1/k3o93cppeaHrt+mlDoFmA9kaa3rgA1K\nqR8D9ymlfhlKVH8C3Ku1fjH0etcBTcAi4Okhnt6oFQwaLPugllc/qscCzMqdwLeuKObdhlW8snEF\nBgZJ9kRuLrqOnLFZ8lH0MAp4PHjraumur8NTbR77avSYq+VYLGY5rJwcnHlm4hqTlBTZgIUQQohB\nEokV2VOBBuBa4Nl+bacDz/e79j6wuE/7llAS27c9EZillKoD8kLXANBadyql1oTulUR2AHbscvOd\nP62iy2vu3vjpkhMIxO/kd5//D01dzViwsCBtPpdnX0iMVbZdD7V9K65b3qyk9Yt1eLdsOWDFNXbS\nJOILi83tAnlK6rgKIYQYtYY969BaPwk8CaCU6t88DWjsd207kHaEdkJ9Qhv/DjuGOAprK1t4YNlm\nAkGDGJuF/7x+NuXez1mx4R2CRpD0MWncWHANk2Qv7JAJ+nx462rNrQLlZQc+nGWz4UhPx5mXjyMz\nE0dmNrHjxkU2YCGEEGKYRNvymQvw9rvWDTgO1a619imljFAfV+jy4cY4pORkFzExtqON+bBSUhIH\ndbzh0uML8MSKcpb9uwaARQtyuGTBFH676j4a2puwYOGbJ17PmZmnYLVYIxzt0Yn29yTo89FZVc2e\nDRvpqNC0l1cQ3HdylsVCfFYmiSqPsbNmkTSjmBiXM7IBD5Jof1+OxmiZy2iZB8hchBitoi2R9QBx\n/a7FAV2HaldKxQKWUB9Pn3sONcYh7d7tPlKXo5KSkkhLS8egjjkcmtq6uPe59bSFasNee3YOTKvi\nR28+QpfPzYwJBVyTdwXJjrG0tR7xjzWqRON7EuzuxlNVibu8FHd5OT3bGw8oh2WfPAVXQSHOPIWr\noLD34azx++bSFV3zGYhofF8GarTMZbTMA2QuRxpPiJEs2hLZBmByv2tT2L9VoAG48CDthPo0hL6e\nDFT361M+eGGOXh9tauIfr5t/VMWZ47jq3DReqHuOmvJ6AE6ZfCLX5S8acauw0cTw+/HW1eLeV1Wg\nprp3j6slJgb7tDScWdk48wtw5ubKAQRCCCHEIURbIvshZlmtX/W5tgBY1af9/yql0rTWDX3aO4D1\nWusepVRVaIwPAJRSCcBc4MFhiH/E2tvVw8OvlVJavxuA8+dNp7gkwANlD7C3p52kuERun3ET6WNk\nq/HRMoJBenbswFOlcZduxl1Rvv/I11BVAVdBAa6CIpx5CqtdTkATQgghwhFtiex9wBdKqbuBZ4Dr\ngJOAO0LtHwOfAM8ppe4EUjEPT7hXax2qN8S9wD1KqWpgM/BbzPJbLw3bLEaYf33ewOsf19PuNp+V\nu/1yRan/ff6ycQMARePz+e+zv0tra2cEoxxZfG2tdJVuxqMr6Nq0iaB7/xaMmHHjSTzpFFwqH1d+\ngVQVEEIIIQYoqhJZrfUmpdQVmMnpT4EK4BKtdXmo3Qi1P4C54toBPAz8ss8Yf1NKJWMmtGMwV3HP\n75PoipDungBP/auSDzc1AXDu3DQKS3w8VvZXeoI+JromcEHGOcybdILUhT2CQEcH7vIy3FUaT2Ul\nPY3bettikpOJnzkTZ1YOrqJi7BOlwoMQQggxGCyGYUQ6hqjR0tIxqH8Y0fyAQTBocNc/PqWpzU2i\nK5YlF6TzhfsdNreZ+2Pnps7iqtzLSLCbDxZF81yOxmDMwzAMfK0teHQF3dsa8FRW0t2wFUJ/lyyx\nsThVPvElM3HlF2CfPGVI/iEwWt4TkLlEo9EyD5C5HGE8WaUQI1pUrciK4dG8280DyzbT1OZm6oR4\nzjzXx3P1D+MNeLFg4eaia5mTOivSYUYV365d5jaBss24y8sI7NnT22aJicGZm4erqBhXQSFxadOx\nxsZGMFohhBDi+CCJ7HHEMAze+ryB5941CzrkZTiJzy3lpZoKAM6dfiYXZZ5LrE2SMP+ePbh1Oe6K\nctxlpfjb2nrbbImJJMyZi1Pl45ieTtz0dHlASwghhIgASWSPE92+AHc9/Cmte82zIs6ck0qF82Ua\n9raT4hzPrcU3MC1xyhFGGb38He14KivxVGlzy0BDQ2+b1RVP/MxZvXVc46alYbFK+TEhhBAi0iSR\nPQ78a00Dz79bTSBo4IqL4ZJzk/hg7yvs7W4nZ2wm3511Gzbr4J5oFu0CnZ24KzXu0k14qqsPeDjL\nEhODq6DQ3Cqg8olLz5DEVQghhIhCksiOYl1eHw++Usrmul0AzC4YgyOjgld3bgTg1MknsjD34uMi\niQ10ddFVuon2rbXs2lRGz/bG/Q9n2e29J2c5c/NwZOfIHlchhBBiBJBEdpSqadzLg6+W0rrXiz3G\nyoXnx/HmzuchtNXzpsJrOXHS7MgGOYT8e/fgravDW1tD1+ZNB1YVsNtx5uaZlQWKZ+BIz8ASI38V\nhBBCiJFGfnqPMp5uP/c8u466JrM8S2FOPN5pH/Hmzh0AqOQcbi2+AVesM5JhDirDMPDvasNdUYGn\n0tzj6mtt2d/BZsOZk4urqJipp56IZ0yKJK5CCCHEKCA/zUcRnz/ALx/7nObdHsDgpDPc1Po/oNPd\nRaprIgtzLqJ4QkGkwzxmhmHg27kTd3kp3ro6PJUV+Fr2J65Wl4v4kpnEpWfgyMzClaewOhwAJKYk\n4h0l9SSFEEKI450ksqNEty/Af/39E3Z3dDMpo5OEzFo2dm0H4OzpX+GyrAtG7F5Yw+/Hu6Ueb10t\nbl1Bd30d/t27e9utDgcJs+fgyMkhvrAI+5SpWGwjc65CCCGECJ8ksqNAZcMe/vjMOgLBIMl59ewd\nq9nbBVlJGdxSvISxcUmRDvGoBHt68NbX4S4vw1Op8dbXY3R7e9ttY8aQMGeu+YBWdg72qdOkqoAQ\nQghxHJJEdoR7bXU9r39cTyAYJHV2Oe2xW4m1xvK1wsXMSikekqNRB1uwpwdvbQ3e2hrc5eV4qjSG\n3282WizYJ08xKwpkZePMyyN2QkpkAxZCCCFEVJBEdoRqauvi4eVl1DV1YHF0kjRrHe2WLsY7kvnB\nCXeQ7Bgb6RAPKdDRYa64lpXirtT0NG7bn7gC9qnTcOUX4MovwJmnsMXHRzBaIYQQQkQrSWRHoHVV\nLdy3dBNY/SRmbiOQounBYHriNG4tvj7qkthAZyfuinK8dTV0bd78pcMH7FOnmSuu2dk4VT4xiWMi\nGK0QQgghRgpJZEeQoGGw7IM6lq+ux5bSgDOjCr+lB4AF0+azKPeSqNhKEHB34amsxF22GU919YE1\nXGNjcRUU4cjKMpPXvDyssfYIRyyEEEKIkUgS2RGi3d3D3Y9+zu5ON/asMmwTGgkAJ0+ay6XZF5AU\nlxix2ALuLjxa46muwlOl8dbV9SauvTVcC4tw5uTiyMrGGhcXsViFEEIIMXpIIjsC7O7o5j/+8hHW\nMW04534BliBWi5Vvzfw6BePyhj0e36423Js3462vw1NTfcBWAaxWHFnZuAoKcBUW48jMkuNehRBC\nCDEkJJGNcqV1u3jg1Y3EZm4iJqURgNkpM7gufxGuWNewxBDo6GDXlkp2frwG9+ZN9Oxo6m2z2O3m\nFoHcPFz5BbLiKoQQQohhI4lslAoGDZ59t4p3NlYRm7WRmDG7sdvs3Fp8PUXj84fsdc3jXnfhqazA\nU1WJp7bWXHHdt8fVbie+ZKa5VSBPETdlqhz3KoQQQoiIkAwkClU27OGR18tps1XjmFEONj8TXRP4\n8Zw7B30V1ggG6WnchluHEteaagJ79vS2W+x2nLl5jJ81A2NaJs7cXHk4SwghhBBRQRLZKPPRpiYe\neWcNsRml2MfswoqV89LP4qKs87Bajv30KsMw8LW04C7djFuX4y4vI9jV1dtuS0rqPe7VpfKJS5uO\nxWYjJSWRlpaOY359IYQQQojBIolslOjxBf5fe3ceJlV15nH8CzRbA0E2EQRFEF5Wl4hGUTZjNHFN\njM7DM46oE5cZFU10XBL3NYka1xnihsuoIc7EaFAjYlRwBRSVISCviiAgArIotA3N0j1/nFNQFN1d\n3aapqlv8Ps/TT3Wfe2/V+3qa26/nnnsuj7wwl+lfzKTFPh8A0KqklLMGnUrvdr2+9ftWbtjAuk8+\nZv2n86hYtJB17mwu21qQlrRrT+t996Ol9aW0T19KOnYsiCW8RERERLJRIVsAlq4q556/vMvKNu/Q\nvPcyAEZ2P4wT9z623qOwlRUV4VGvHznrPnLWz/tkm6dmlbRrR+vvHkBp/wGU9u1H0867qXAVERGR\nRFIhm0fLVpXzyvuLeWX+NEp2/5gmzdfTumkrRvcfxYAOVqf32FxWxrqPP6Lc57J+/qdUfLZgm8K1\nefc9KO3XP9yY1X0PStq3V+EqIiIiRUGFbB6sLd/AXU/PYOHGOTTptJhmPcMc1UO7fo+T+5xA08bV\nd0tVZSUbly8Lo61z57L+s/lsXLZs6w6NGoXCtW8/WvYxWvTqpce9ioiISNFSIZtjS1eVc/uk5ynr\n+AFNm24EGjGowwCO6jGSvdrusc2+VZWVVCxaSPncDymfM5v18z+lsrx8y/bGpa1o2bdfWMPV+oY1\nXJtpRQERERHZORRlIWtmTYAbgdOBNsBE4Dx3X1bbcTva23MW8+icP9Jk1+U0Ag7rejBH7/WDLY+X\nrdy4gQ2LF1M+d26Y5/qxU1lWtuX4prt2ptU++9KiZy9a9etP0926aJqAiIiI7LSKspAFrgVOA0YD\nK4GxwFPAYfkIpqqqijsmvsQnJa/SpP1mmlW25tKDz6Fz012oWLiQVZ+8Tvmc2ZR/NBc2b95yXEm7\n9rQecmh8+EBfmrZvn4/wRURERApS0RWyZtYMuBC4wN1fim2jgPlmNsTd38plPB9/sZz7Z/yJ8tIF\nlGyuYsianoxs3JkNYx9i3qfzMm7M6k6LXr1p2bsPLffurRuzRERERGpRdIUssB9hOsHkVIO7LzCz\nBcBQICeF7KbKzdz4p4com/8G+3y1kW5LN9N11UYaVX7J17DlxqyWvfvQstfetDSjpO0uuQhNRERE\npCgUYyHbLb5+ntG+BOieqyCe/+0vGTEvzIUFQuG6Z49YtPaltI/RpHXrXIUjIiIiUnSKsZAtBSrd\nfWNGewXQorYD27UrpaSkSYMEUdKyFct2/Q49DzyUHoMPoE2fPpS0btUg750vnTq1yXcIDaJY8gDl\nUqiKJZdiyQOUi0ixKsZCdh3Q2MxK3H1TWntz4JvaDly9ury2zfVyzM+voVOnNnz55Vo2AavXVcK6\ntVmPK1SpXJKuWPIA5VKoiiWXYskDlEu29xNJsvo9/zQZFsXXLhntXdl+uoGIiIiIJFQxFrIzgbXA\n8FSDmfUAegCv5SckEREREWloRTe1wN0rzGwscJuZrQCWE9aRneLuU/MbnYiIiIg0lKIrZKMrgabA\n4/F1InBeXiMSERERkQZVlIVsvMnr4vglIiIiIkWoGOfIioiIiMhOQIWsiIiIiCSSClkRERERSSQV\nsiIiIiKSSCpkRURERCSRVMiKiIiISCKpkBURERGRRFIhKyIiIiKJ1KiqqirfMYiIiIiI1JtGZEVE\nREQkkVTIioiIiEgiqZAVERERkURSISsiIiIiiaRCVkREREQSSYWsiIiIiCRSSb4DKDZm1gS4ETgd\naANMBM5z92X5jKsuzKw/MLuaTUPd/Q0zOxK4BTDgY+Ayd38hlzFmY2b3AiXufmZaW61xm9muwH8C\nRwIbgIedk/ZDAAAMqUlEQVSBK9x9Uy5jz1RDLtOBAzN2HZfap5ByMbPOhP/uRwItgWnAxe7+97j9\nFOBqYA9gJjDG3d9JO35vQi6HAauBu9391pwmsTWWbLksBzplHHaVu98YtxdELmbWDbgD+D5hIGMi\ncJG7L4nbk9Qn2XJJRJ9kMrODgTeAI9x9cmxL5DlMJBc0ItvwrgVOA0YDw4BuwFP5DKgeBgErgC4Z\nX9NikTsB+F9gf+AvwDNmNiBPsW7DzBqZ2fXAORntdYn7KWA3YDjhf0DOAK7LQdjVqiWXRsAA4BS2\n7Z+L0nYriFzMrDHwNNAHOAEYAnwNvGxmHczsCOAh4HfAd4FZwCQz6xSPb0YoTNYCBwGXAdea2VkF\nmEtnQsE0jG375Y54fEHkEn9/ngfaASMJvyNdgGfj9iT1SbZcEtEnmcysFfAY0CStLXHnMJFc0ohs\nA4onxwuBC9z9pdg2CphvZkPc/a28BpjdQGCOuy/N3GBmFwJT3f2m2HSVmR1GyPfsHMa4HTPrCYwj\nxL8wY3OtcZvZIYQRmZ7uPh+YaWaXAPeY2fXuXpGbLIIsufQESoG3a+ijQsplX+AQoL+7fxjjOxVY\nBRxDKMbHu/v9cds5wOHAWcDNwE8Jf5jPcPcyYI6Z9QYuAR7IYR51yeVzYBPh92xjNccXSi6dgQ+B\ny919AYCZ3U4oitrFeJLSJ9lyGUgy+iTT7cBiYO+0tkSdw0RyTSOyDWs/wnSCyamGeJJdAAzNS0T1\nM5Dwx6E6Q0nLK5pMYeQ1BFhEGFGen7EtW9xDgc/iH4D07W0I/ZlrteUyEFgHfFbDsYWUy0LgWMDT\n2irjazvgULb9d1IJvMa2/fJuLDJSJgO942hbLmXLZSAwr4aCCQokF3df6u6j0gq/boRR/3cII8yJ\n6ZPacnH31SSkT9KZ2dGE/zG6IGNT0s5hIjmlEdmG1S2+fp7RvgTonuNYvo2BQAszmwr0AP4O/Mrd\npxNyK8i83P1x4HEAM8vcnC3umrYT95nWYIHWQZZcBgJfAU+Y2XBgJWEu3J2x6CiYXNx9JeHSb7oL\nCPNL3wVaUX2sqfm/2XLJ2ZzzLLlMIkzt2GRmzwGDCXHf6e6PxX0LJpcUM3uGME1iNeHS/C4kqE/S\nVZMLxBHZpPSJmXUkXIk5g5BHukSdw0RyTSOyDasUqKxmFKACaJGHeOrMzFoSLl23JVxeO55wMpxi\nZv0Iua3POKzg8yJ73Nttj/1XReHlNgBoDbwIHAX8F2Ee3DVxe8HmYmbHA78mXDpNjSjXq1/idiig\nXOJUgwFAB0IhchRhLuPDZnZGPKQQc7kK+B7hpqKXCKN3kMw+2SYXM9ud5PXJfcAEd59YzbZiOoeJ\nNDiNyDasdUBjMyvJuFu0OfBNnmKqE3dfF+eWVaTmVJnZ6cABwLmE3JpnHFbweZE97u22m1lToBGF\nl9tooLW7fxV/nmVmbYErzOxaCjSX+Hv0APBH4FLC5XioZ7+k/VxIuUAYBWzm7mvjzzPNbE/CSO3D\nFGAu7j4LtszhXwT8S0ZcpP1c0H1STS6nkaA+MbPTCDdx7VPDLsV0DhNpcBqRbViL4muXjPaubH/p\np+C4+5r0GwPi5erZhMtTi0hmXtnirmk7FFhu7r4prYhNmUUYTWtLAeZiZlcQCod7gdHxd2oV4Q9s\novqlhlxw94q0gillFlsv/RZELmbWORZ7W7h7OTAvxpOYPsmSy+5J6ZPodML0gKVmVsbWudgvxCX4\niuYcJrIjqJBtWDMJy7kMTzWYWQ/CfNPX8hNS3ZjZAWa2xswOSGtrQrhZYDbhst3wjMNGUuB5kT3u\nN4CeZtY9Y/ta4IMdH17dmdlUM7sro3kwsCQWuAWVi5ldSlhT+Wp3H+PuVQDx9S22/XfSmLBUUnq/\nDDaz0rS3HBkO9+W5iD9dTbmYWYmZLTKzizIOGczWNZkLJZc9gfFmNjjVEEf0DZhDsvqktlw+SlCf\nQBgN70841+5HmAoBcCZhTd+iOYeJ7AiaWtCA3L3CzMYCt5nZCmA5MBaY4u5T8xtdVjMJqyvcZ2bn\nAWWEtRU7AncRlruZYWbXAeOBfybMS/v3vERbd/dQe9xvA1OBJ83sfEKetxDmP27IQ7y1+TNwvZnN\nAN4ERhD66MK4vWByMbN9CEs2PQQ8YGa7pW1eS5gr+6yZvQ+8Qrjk2xZ4MO7zNHAT8Aczu5KwisMl\nwHm5yWCrOuTyLGF6xyeEgvDHwKmEO9ChcHJ5F3gdeNDMzgY2Ar8BvgQeJYxmJqJPqD2XcYSCNgl9\ngrtvM2pqZqn5rp+7+3IzK6ZzmEiD04hsw7sSeIJw5/mrhBtbTsprRHUQ5/T+iHBZ61lgOmGdxWHu\nvjzOQ/sJIZcPCDeDHZdaV7NQZYs7jqz9hHCX8uuES8cPAtfnJeDa3Qr8ivA7NptQxP7C3R+Egstl\nFGFR938Fvsj4+kW8qeVs4GLgPcKI1JHuvgLCnG3gh8B3CMtD/YawgsYjuU0DyJJL/LoXuJvQL6cC\n/+Tuk6BwcolTIU4k/Dt4DpgCrAGGu3tZkvokWy4kpE/qosjOYSINrlFVVVW+YxARERERqTeNyIqI\niIhIIqmQFREREZFEUiErIiIiIomkQlZEREREEkmFrIiIiIgkkgpZEZF/kJk1yncMIiI7Iz0QQSTB\nzOwRwrPlazPF3UeY2WRgk7sfscMD+5bM7GTCerldgHHufm6eQ8rKzM4ABhLWXxURkRxSISuSbDcQ\nFn5PGQtsAi5Ia1sTX88FCn3h6HuA+YTnzy/Obyh1dgXhMaEiIpJjKmRFEszd5xEeLQqAma0hjLpu\n90hkd5+Ty9i+pQ7Afe4+Od+BiIhI4dOTvUSKSG3TBzK3mVkVcA4wDDgBWE8YEb0zfv0UWAc8Clwe\nH4WJmXUgPNLzBKANMAO4zN3fzBJbP+DXwBCglPA4zcvc/f/MbAThkc7p9nL3BdW8T2PgcuBnQDfC\nCO6t7j4ubr+a8CjfXd19TdpxVwK/BDq7e5mZDQJ+CwwFKoEXgYvcfXHcPxXT4YRR1yGE0e1HgCvc\nfbOZLQD2zBaziIjsGLrZS2TndhuwglCUPgdcB0wHygnPsv8zcGn8HjNrAbwMHEMoCk8CVgMvm9mB\nNX1ILBrfIcx9/TdgNNAReNPM+gPvAYcAm4Fx8fsvani73wPXEArK42LcD5jZmLj9caA54fnz6UYB\nz8Qitg/wJtAeOBU4GxgEvGZmbTOOGw9Mjjn/AbiMMPWB+BmLgb9miVlERHYATS0Q2bm95+4/BzCz\nmYQCbbm7nx/bXgFOIRRpTxGKvn2Ag9z93bjPC4Ti92bgBzV8ztXAN8Dh7v5NPG4SYVrEde5+MjDV\nzAAWVzc1Ih7TBzgLuMTdfxebJ5lZE+AGMxvn7p+a2ZuEwvXReNwgYADwH/GYa4Ay4Ah3L4v7TAE+\nBc4Hbkr72Pvc/cb4/atm9mPgWMLNaO+bWQXwZU0xi4jIjqMRWZGd27TUN+6+kjAimt5WRRhx3SU2\nfR/4HPjAzErMrIRwHnkOGGZmzWr4nGHAhFQRG9+7DJgAjKhHvIcDjYBnU58fY5gAtAUOivs9BhwR\np0FAKGqXAS+l5fEKsD7tPVbE3DOL8cwpE4uBVvWIWUREdhAVsiI7t7XVtH1TTVtKB8K81I0ZX9cA\nzQjTBarTHlhaTfsyQgFaV6nC1DM+/5XY3jW+/g9h3utJ8edRwHh335z2PqdUk8eItPdIKc/4uRKd\nO0VECoKmFohIfXwNfEiY41qdFTW0rwZ2q6a9Sy3H1PT5AMPZvsCEcOMX7r7azJ4DTjaz94CehLmz\n6e/zAnBXNe9RUY94REQkj1TIikh9TAF+BCxx9yWpRjO7gXD3fk0PZ5gCHGdmrdLmyLYi3Kw1uR6f\n/1p8be/uqe8xsxOBMwk3kq2MzY8RRmbPBD509xkZ8fQnzBGujO/RJO4/DZhVj5g2Z99FRER2BBWy\nIlIfDwNjgL+Z2c2E+bLHAhcRbtqqaT2/6wkF4stmdktsuxRoTXioQ53EpbrGAw+ZWU/gfcJTtW4C\nZrj7wrTd/0qYOvEzws1mmfFMBSaY2f2EaQVjCPNjf1/XeKKvgP3NbDgw3d3X1fN4ERH5ljTPS0Tq\nLN6gNZRQlN5OKBZ/CIxx92trOW5WPG4N8N+EgngFcEjcVh+nAXcTVhd4EbiEsGTX8RmfuQF4knCe\neyJj28wYT0nc9iRhTdyj3f1v9YzndsK0iReB/et5rIiI/AP0QAQRERERSSSNyIqIiIhIIqmQFRER\nEZFEUiErIiIiIomkQlZEREREEkmFrIiIiIgkkgpZEREREUkkFbIiIiIikkgqZEVEREQkkVTIioiI\niEgi/T+1U8+4jQck+AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124a71828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.plot(edata, np.arange(len(edata)), label='Real data')\n", "\n", "plt.plot(edata_td1, np.arange(len(edata_td1)), label='Small dead time')\n", "plt.plot(edata_td2, np.arange(len(edata_td2)), label='Large dead time')\n", "\n", "plt.xlabel('Time of event')\n", "plt.ylabel('Event number')\n", "plt.title(\"Modeled underlying data\")\n", "\n", "plt.legend(bbox_to_anchor=(1, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And plot the rates per unit time" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'Time')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WVg5oldAJgCah+ncjhPxA27QAafLZqk/oZKRoSuu+MJHQJDQLlIuT3/5kIHxN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\nDn/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UP2vtU8T8+AEYY/4rzg3RJaGn4n7dFAmIbSYUp+kdnKyF32Zgfp3LUisjQM4YsxE4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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x127626a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "h1, b1 = np.histogram(edata, np.arange(1000))\n", "plt.plot(tb.bin_edges_to_center(b1), h1, label='Real data', c='k')\n", "\n", "h2, b2 = np.histogram(edata_td1, np.arange(1000))\n", "plt.plot(tb.bin_edges_to_center(b2), h2, label='Small dead time', c='r')\n", "\n", "h3, b3 = np.histogram(edata_td2, np.arange(1000))\n", "plt.plot(tb.bin_edges_to_center(b3), h3, label='Large dead time')\n", "\n", "\n", "plt.legend(bbox_to_anchor=(1, 1))\n", "plt.xlim((0,400))\n", "plt.ylabel('Rate')\n", "plt.xlabel('Time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Can we use $n_1 = \\frac{R_1}{1-R_1 \\tau}$ to derive the relation and spread in the dist of R?\n", "\n", "Algerbra changes math to: $R_1=\\frac{n_1}{1+n_1\\tau}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use the small dead time" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 1097.694313\n", " Iterations: 8\n", " Function evaluations: 9\n", " Gradient evaluations: 9\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using ADVI...\n", "Average Loss = 8,491.2: 5%|▌ | 10102/200000 [00:03<01:09, 2740.60it/s] \n", "Convergence archived at 10200\n", "Interrupted at 10,200 [5%]: Average Loss = 65,144\n", "100%|██████████| 10500/10500 [00:16<00:00, 649.32it/s]\n" ] } ], "source": [ "# assume R1 is Poisson\n", "\n", "with mc.Model() as model:\n", " tau = deadtime1\n", " obsRate = mc.Uniform('obsRate', 0, 1000, shape=1)\n", " obsData = mc.Poisson('obsData', obsRate, observed=h2[:400], shape=1)\n", " realRate = mc.Deterministic('realRate', obsData/(1-obsData*tau))\n", " start = mc.find_MAP()\n", " trace = mc.sample(10000, start=start, njobs=8)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "obsRate:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 18.065 0.213 0.001 [17.647, 18.478]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 17.651 17.922 18.064 18.206 18.483\n", "\n" ] }, { "data": { "image/png": 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B4qz1M3PcUTBocPOD29l+sJ2zN8zjyvOWT7uqhmL2GEtSFaaUytRae0L/X4xZ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\nhlNdYCvTbsM5yS0kw+VkOoZ05wsX1Ri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kbcZtrbss7alz9OfK1ND5NZr2w75AD6AnsKUk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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x123f60160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mc.traceplot(trace, combined=True, varnames=('obsRate', ))\n", "mc.summary(trace, varnames=('obsRate', ))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The estimate of the real rate given that we know the dead time is: [ 11.64021164 19.78021978 29.88505747] 0.922378317014\n", "This compares with if we measured without dead time as: [ 11.975 20. 29. ] 0.85125\n" ] }, { "data": { "image/png": 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, "text/plain": [ "<matplotlib.figure.Figure at 0x127525470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(trace['realRate'].mean(axis=0), bins=10)\n", "plt.xlabel('realRate')\n", "plt.ylabel('Density')\n", "\n", "dt1_bounds = np.percentile(trace['realRate'], (2.5, 50, 97.5))\n", "print('The estimate of the real rate given that we know the dead time is:', dt1_bounds, \n", " (dt1_bounds[2]-dt1_bounds[0])/dt1_bounds[1])\n", "\n", "dat_bounds = np.percentile(h1[:400], (2.5, 50, 97.5))\n", "print(\"This compares with if we measured without dead time as:\", dat_bounds, \n", " (dat_bounds[2]-dat_bounds[0])/dat_bounds[1])\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use the large dead time" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 780.075528\n", " Iterations: 10\n", " Function evaluations: 11\n", " Gradient evaluations: 11\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using ADVI...\n", "Average Loss = 16,366: 5%|▍ | 9654/200000 [00:03<01:10, 2715.57it/s] \n", "Convergence archived at 9900\n", "Interrupted at 9,900 [4%]: Average Loss = 78,517\n", "100%|██████████| 10500/10500 [00:15<00:00, 672.93it/s]\n" ] } ], "source": [ "# assume R1 is Poisson\n", "\n", "with mc.Model() as model:\n", " tau = deadtime2\n", " obsRate = mc.Uniform('obsRate', 0, 1000)\n", " obsData = mc.Poisson('obsData', obsRate, observed=h3[:400])\n", " realRate = mc.Deterministic('realRate', obsData/(1-obsData*tau))\n", " start = mc.find_MAP()\n", " trace = mc.sample(10000, start=start, njobs=8)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "obsRate:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 6.642 0.129 0.001 [6.386, 6.891]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 6.391 6.555 6.641 6.729 6.896\n", "\n" ] }, { "data": { "image/png": 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a67R6qpRSyzGKLv1llF0PMrxaYBXQizFKI+um001Xsgp8k22sCVUqqRIqYNwJ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\n1mMJbeKcp7GyWozhq/FDC1Mxm0yUFDg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"text/plain": [ "<matplotlib.figure.Figure at 0x121066160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mc.traceplot(trace, combined=True, varnames=('obsRate', ))\n", "mc.summary(trace, varnames=('obsRate', ))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The estimate of the real rate given that we know the dead time is: [ 11.64021164 19.78021978 29.88505747] 1.28571428571\n", "This compares with if we measured without dead time as: [ 11.975 20. 29. ] 0.85125\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x121ac8a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(trace['realRate'].mean(axis=0))\n", "plt.xlabel('realRate')\n", "plt.ylabel('Density')\n", "\n", "dt2_bounds = np.percentile(trace['realRate'], (2.5, 50, 97.5))\n", "print('The estimate of the real rate given that we know the dead time is:', dt1_bounds, \n", " (dt2_bounds[2]-dt2_bounds[0])/dt2_bounds[1])\n", "\n", "dat_bounds = np.percentile(h1[:400], (2.5, 50, 97.5))\n", "print(\"This compares with if we measured without dead time as:\", dat_bounds, \n", " (dat_bounds[2]-dat_bounds[0])/dat_bounds[1])\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But this is totally broken!!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Output data files for each" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "real = pd.Series(edata)\n", "td1 = pd.Series(edata_td1)\n", "td2 = pd.Series(edata_td2)\n", "\n", "real.to_csv('no_deadtime_times.csv')\n", "td1.to_csv('small_deadtime_times.csv')\n", "td2.to_csv('large_deadtime_times.csv')\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "real = pd.Series(h1[h1>0])\n", "td1 = pd.Series(h2[h2>0])\n", "td2 = pd.Series(h3[h3>0])\n", "\n", "real.to_csv('no_deadtime_rates.csv')\n", "td1.to_csv('small_deadtime_rates.csv')\n", "td2.to_csv('large_deadtime_rates.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Work on the random thoughts" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Observed Bound distributions are not allowed. If you want to model truncated data you can use a pm.Potential in combination with the cumulative probability function. See pymc3/examples/censored_data.py for an example.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-23-2cccfddedfb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# we observe the following time between counts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlam\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'lam'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtime_between\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundedExp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tb_ob'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medata_td2\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 7\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_MAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnjobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/python3/lib/python3.6/site-packages/pymc3/distributions/distribution.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'observed'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m raise ValueError('Observed Bound distributions are not allowed. '\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;34m'If you want to model truncated data '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;34m'you can use a pm.Potential in combination '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Observed Bound distributions are not allowed. If you want to model truncated data you can use a pm.Potential in combination with the cumulative probability function. See pymc3/examples/censored_data.py for an example." ] } ], "source": [ "with mc.Model() as model:\n", " BoundedExp = mc.Bound(mc.Exponential, lower=deadtime2, upper=None)\n", " \n", " # we observe the following time between counts\n", " lam = mc.Uniform('lam', 0, 1000)\n", " time_between = BoundedExp('tb_ob', lam, observed=np.diff(edata_td2))\n", " start = mc.find_MAP()\n", " trace = mc.sample(10000, njobs=8, start=start)\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
phobson/seaborn
doc/introduction.ipynb
3
24808
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ ".. _introduction:\n", "\n", ".. currentmodule:: seaborn\n", "\n", "An introduction to seaborn\n", "==========================\n", "\n", ".. raw:: html\n", "\n", " <div class=col-md-9>\n", "\n", "Seaborn is a library for making statistical graphics in Python. It is built on top of `matplotlib <https://matplotlib.org/>`_ and closely integrated with `pandas <https://pandas.pydata.org/>`_ data structures.\n", "\n", "Here is some of the functionality that seaborn offers:\n", "\n", "- A dataset-oriented API for examining :ref:`relationships <scatter_bubbles>` between :ref:`multiple variables <faceted_lineplot>`\n", "- Specialized support for using categorical variables to show :ref:`observations <jitter_stripplot>` or :ref:`aggregate statistics <pointplot_anova>` \n", "- Options for visualizing :ref:`univariate <distplot_options>` or :ref:`bivariate <joint_kde>` distributions and for :ref:`comparing <horizontal_boxplot>` them between subsets of data\n", "- Automatic estimation and plotting of :ref:`linear regression <anscombes_quartet>` models for different kinds :ref:`dependent <logistic_regression>` variables\n", "- Convenient views onto the overall :ref:`structure <scatterplot_matrix>` of complex datasets\n", "- High-level abstractions for structuring :ref:`multi-plot grids <faceted_histogram>` that let you easily build :ref:`complex <pair_grid_with_kde>` visualizations\n", "- Concise control over matplotlib figure styling with several :ref:`built-in themes <aesthetics_tutorial>`\n", "- Tools for choosing :ref:`color palettes <palette_tutorial>` that faithfully reveal patterns in your data\n", "\n", "Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.\n", "\n", "Here's an example of what this means:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set()\n", "tips = sns.load_dataset(\"tips\")\n", "sns.relplot(x=\"total_bill\", y=\"tip\", col=\"time\",\n", " hue=\"smoker\", style=\"smoker\", size=\"size\",\n", " data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "A few things have happened here. Let's go through them one by one:\n", "\n", "1. We import seaborn, which is the only library necessary for this simple example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Behind the scenes, seaborn uses matplotlib to draw plots. Many tasks can be accomplished with only seaborn functions, but further customization might require using matplotlib directly. This is explained in more detail :ref:`below <intro_plot_customization>`. For interactive work, it's recommended to use a Jupyter/IPython interface in `matplotlib mode <https://ipython.readthedocs.io/en/stable/interactive/plotting.html>`_, or else you'll have to call :ref:`matplotlib.pyplot.show` when you want to see the plot.\n", "\n", "2. We apply the default default seaborn theme, scaling, and color palette." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, "outputs": [], "source": [ "sns.set()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "This uses the `matplotlib rcParam system <https://matplotlib.org/users/customizing.html>`_ and will affect how all matplotlib plots look, even if you don't make them with seaborn. Beyond the default theme, there are :ref:`several other options <aesthetics_tutorial>`, and you can independently control the style and scaling of the plot to quickly translate your work between presentation contexts (e.g., making a plot that will have readable fonts when projected during a talk). If you like the matplotlib defaults or prefer a different theme, you can skip this step and still use the seaborn plotting functions.\n", "\n", "3. We load one of the example datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, "outputs": [], "source": [ "tips = sns.load_dataset(\"tips\")" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Most code in the docs will use the :func:`load_dataset` function to get quick access to an example dataset. There's nothing particularly special about these datasets; they are just pandas dataframes, and we could have loaded them with :ref:`pandas.read_csv` or build them by hand. Many examples use the \"tips\" dataset, which is very boring but quite useful for demonstration. The tips dataset illustrates the \"tidy\" approach to organizing a dataset. You'll get the most out of seaborn if your datasets are organized this way, and it is explained in more detail :ref:`below <intro_tidy_data>`.\n", "\n", "4. We draw a faceted scatter plot with multiple semantic variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, "outputs": [], "source": [ "sns.relplot(x=\"total_bill\", y=\"tip\", col=\"time\",\n", " hue=\"smoker\", style=\"smoker\", size=\"size\",\n", " data=tips)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "This particular plot shows the relationship between five variables in the tips dataset. Three are numeric, and two are categorical. Two numeric variables (``total_bill`` and ``tip``) determined the position of each point on the axes, and the third (``size``) determined the size of each point. One categorical variable split the dataset onto two different axes (facets), and the other determined the color and shape of each point.\n", "\n", "All of this was accomplished using a single call to the seaborn function :func:`relplot`. Notice how we only provided the names of the variables in the dataset and the roles that we wanted them to play in the plot. Unlike when using matplotlib directly, it wasn't necessary to translate the variables into parameters of the visualization (e.g., the specific color or marker to use for each category). That translation was done automatically by seaborn. This lets the user stay focused on the question they want the plot to answer.\n", "\n", ".. _intro_api_abstraction:\n", "\n", "API abstraction across visualizations\n", "-------------------------------------\n", "\n", "There is no universal best way to visualize data. Different questions are best answered by different kinds of visualizations. Seaborn tries to make it easy to switch between different visual representations that can be parameterized with the same dataset-oriented API.\n", "\n", "The function :func:`relplot` is named that way because it is designed to visualize many different statistical *relationships*. While scatter plots are a highly effective way of doing this, relationships where one variable represents a measure of time are better represented by a line. The :func:`relplot` function has a convenient ``kind`` parameter to let you easily switch to this alternate representation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dots = sns.load_dataset(\"dots\")\n", "sns.relplot(x=\"time\", y=\"firing_rate\", col=\"align\",\n", " hue=\"choice\", size=\"coherence\", style=\"choice\",\n", " facet_kws=dict(sharex=False),\n", " kind=\"line\", legend=\"full\", data=dots);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Notice how the ``size`` and ``style`` parameters are shared across the scatter and line plots, but they affect the two visualizations differently (changing marker area and symbol vs line width and dashing). We did not need to keep those details in mind, letting us focus on the overall structure of the plot and the information we want it to convey.\n", "\n", ".. _intro_stat_estimation:\n", "\n", "Statistical estimation and error bars\n", "-------------------------------------\n", "\n", "Often we are interested in the average value of one variable as a function of other variables. Many seaborn functions can automatically perform the statistical estimation that is neccesary to answer these questions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fmri = sns.load_dataset(\"fmri\")\n", "sns.relplot(x=\"timepoint\", y=\"signal\", col=\"region\",\n", " hue=\"event\", style=\"event\",\n", " kind=\"line\", data=fmri);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "When statistical values are estimated, seaborn will use bootstrapping to compute confidence intervals and draw error bars representing the uncertainty of the estimate.\n", "\n", "Statistical estimation in seaborn goes beyond descriptive statisitics. For example, it is also possible to enhance a scatterplot to include a linear regression model (and its uncertainty) using :func:`lmplot`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"time\", hue=\"smoker\",\n", " data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _intro_categorical:\n", "\n", "Specialized categorical plots\n", "-----------------------------\n", "\n", "Standard scatter and line plots visualize relationships between numerical variables, but many data analyses involve categorical variables. There are several specialized plot types in seaborn that are optimized for visualizing this kind of data. They can be accessed through :func:`catplot`. Similar to :func:`relplot`, the idea of :func:`catplot` is that it exposes a common dataset-oriented API that generalizes over different representations of the relationship between one numeric variable and one (or more) categorical variables.\n", "\n", "These representations offer different levels of granularity in their presentation of the underlying data. At the finest level, you may wish to see every observation by drawing a scatter plot that adjusts the positions of the points along the categorical axis so that they don't overlap:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n", " kind=\"swarm\", data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Alternately, you could use kernel density estimation to represent the underlying distribution that the points are sampled from:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n", " kind=\"violin\", split=True, data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Or you could show the only mean value and its confidence interval within each nested category:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.catplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n", " kind=\"bar\", data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _intro_func_types:\n", "\n", "Figure-level and axes-level functions\n", "-------------------------------------\n", "\n", "How do these tools work? It's important to know about a major distinction between seaborn plotting functions. All of the plots shown so far have been made with \"figure-level\" functions. These are optimized for exploratory analysis because they set up the matplotlib figure containing the plot(s) and make it easy to spread out the visualization across multiple axes. They also handle some tricky business like putting the legend outside the axes. To do these things, they use a seaborn :class:`FacetGrid`.\n", "\n", "Each different figure-level plot ``kind`` combines a particular \"axes-level\" function with the :class:`FacetGrid` object. For example, the scatter plots are drawn using the :func:`scatterplot` function, and the bar plots are drawn using the :func:`barplot` function. These functions are called \"axes-level\" because they draw onto a single matplotlib axes and don't otherwise affect the rest of the figure.\n", "\n", "The upshot is that the figure-level function needs to control the figure it lives in, while axes-level functions can be combined into a more complex matplotlib figure with other axes that may or may not have seaborn plots on them:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "f, axes = plt.subplots(1, 2, sharey=True, figsize=(6, 4))\n", "sns.boxplot(x=\"day\", y=\"tip\", data=tips, ax=axes[0])\n", "sns.scatterplot(x=\"total_bill\", y=\"tip\", hue=\"day\", data=tips, ax=axes[1]);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Controling the size of the figure-level functions works a little bit differently than it does for other matplotlib figures. Instead of setting the overall figure size, the figure-level functions are parameterized by the size of each facet. And instead of setting the height and width of each facet, you control the height and *aspect* ratio (ratio of width to height). This parameterization makes it easy to control the size of the graphic without thinking about exactly how many rows and columns it will have, although it can be a source of confusion:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.relplot(x=\"time\", y=\"firing_rate\", col=\"align\",\n", " hue=\"choice\", size=\"coherence\", style=\"choice\",\n", " height=4.5, aspect=2 / 3,\n", " facet_kws=dict(sharex=False),\n", " kind=\"line\", legend=\"full\", data=dots);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The way you can tell whether a function is \"figure-level\" or \"axes-level\" is whether it takes an ``ax=`` parameter. You can also distinguish the two classes by their output type: axes-level functions return the matplotlib ``axes``, while figure-level functions return the :class:`FacetGrid`.\n", "\n", "\n", ".. _intro_dataset_funcs:\n", "\n", "Visualizing dataset structure\n", "-----------------------------\n", "\n", "There are two other kinds of figure-level functions in seaborn that can be used to make visualizations with multiple plots. They are each oriented towards illuminating the structure of a dataset. One, :func:`jointplot`, focuses on a single relationship:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "iris = sns.load_dataset(\"iris\")\n", "sns.jointplot(x=\"sepal_length\", y=\"petal_length\", data=iris);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The other, :func:`pairplot`, takes a broader view, showing all pairwise relationships and the marginal distributions, optionally conditioned on a categorical variable :" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.pairplot(data=iris, hue=\"species\");" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Both :func:`jointplot` and :func:`pairplot` have a few different options for visual representation, and they are built on top of classes that allow more thoroughly customized multi-plot figures (:class:`JointGrid` and :class:`PairGrid`, respectively).\n", "\n", ".. _intro_plot_customization:\n", "\n", "Customizing plot appearance\n", "---------------------------\n", "\n", "The plotting functions try to use good default aesthetics and add informative labels so that their output is immediately useful. But defaults can only go so far, and creating a fully-polished custom plot will require additional steps. Several levels of additional customization are possible. \n", "\n", "The first way is to use one of the alternate seaborn themes to give your plots a different look. Setting a different theme or color palette will make it take effect for all plots:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.set(style=\"ticks\", palette=\"muted\")\n", "sns.relplot(x=\"total_bill\", y=\"tip\", col=\"time\",\n", " hue=\"smoker\", style=\"smoker\", size=\"size\",\n", " data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "For figure-specific customization, all seaborn functions accept a number of optional parameters for switching to non-default semantic mappings, such as different colors. (Appropriate use of color is critical for effective data visualization, and seaborn has :ref:`extensive support <palette_tutorial>` for customizing color palettes).\n", "\n", "Finally, where there is a direct correspondence with an underlying matplotlib function (like :func:`scatterplot` and ``plt.scatter``), additional keyword arguments will be passed through to the matplotlib layer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.relplot(x=\"total_bill\", y=\"tip\", col=\"time\",\n", " hue=\"size\", style=\"smoker\", size=\"size\",\n", " palette=\"YlGnBu\", markers=[\"D\", \"o\"], sizes=(10, 125),\n", " edgecolor=\".2\", linewidth=.5, alpha=.75,\n", " data=tips);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In the case of :func:`relplot` and other figure-level functions, that means there are a few levels of indirection because :func:`relplot` passes its exta keyword arguments to the underlying seaborn axes-level function, which passes *its* extra keyword arguments to the underlying matplotlib function. So it might take some effort to find the right documentation for the parameters you'll need to use, but in principle an extremely high level of customization is possible.\n", "\n", "Some customization of figure-level functions can be accomplished through additional parameters that get passed to :class:`FacetGrid`, and you can use the methods on that object to control many other properties of the figure. For even more tweaking, you can access the matplotlib objects that the plot is drawn onto, which are stored as attributes:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "g = sns.catplot(x=\"total_bill\", y=\"day\", hue=\"time\",\n", " height=3.5, aspect=1.5,\n", " kind=\"box\", legend=False, data=tips);\n", "g.add_legend(title=\"Meal\")\n", "g.set_axis_labels(\"Total bill ($)\", \"\")\n", "g.set(xlim=(0, 60), yticklabels=[\"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"])\n", "g.despine(trim=True)\n", "g.fig.set_size_inches(6.5, 3.5)\n", "g.ax.set_xticks([5, 15, 25, 35, 45, 55], minor=True);\n", "plt.setp(g.ax.get_yticklabels(), rotation=30);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Because the figure-level functions are oriented towards efficient exploration, using them to manage a figure that you need to be precisely sized and organized may take more effort than setting up the figure directly in matplotlib and using the corresponding axes-level seaborn function. Matplotlib has a comprehensive and powerful API; just about any attribute of the figure can be changed to your liking. The hope is that a combination of seaborn's high-level interface and matplotlib's deep customizability will allow you to quickly explore your data and create graphics that can be tailored into a `publication quality <https://github.com/wagnerlabpapers/Waskom_PNAS_2017>`_ final product.\n", "\n", ".. _intro_tidy_data:\n", "\n", "Organizing datasets\n", "-------------------\n", "\n", "As mentioned above, seaborn will be most powerful when your datasets have a particular organization. This format ia alternately called \"long-form\" or \"tidy\" data and is described in detail by Hadley Wickham in this `academic paper <http://vita.had.co.nz/papers/tidy-data.html>`_. The rules can be simply stated:\n", "\n", "1. Each variable is a column\n", "2. Each observation is a row\n", "\n", "A helpful mindset for determining whether your data are tidy is to think backwards from the plot you want to draw. From this perspective, a \"variable\" is something that will be assigned a role in the plot. It may be useful to look at the example datasets and see how they are structured. For example, the first five rows of the \"tips\" dataset look like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tips.head()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In some domains, the tidy format might feel awkward at first. Timeseries data, for example, are sometimes stored with every timepoint as part of the same observational unit and appearing in the columns. The \"fmri\" dataset that we used :ref:`above <intro_stat_estimation>` illustrates how a tidy timeseries dataset has each timepoint in a different row:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fmri.head()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Many seaborn functions can plot wide-form data, but only with limited functionality. To take advantage of the features that depend on tidy-formatted data, you'll likely find the ``pandas.melt`` function useful for \"un-pivoting\" a wide-form dataframe. More information and useful examples can be found `in this blog post <https://tomaugspurger.github.io/modern-5-tidy.html>`_ by one of the pandas developers.\n", "\n", ".. _intro_next_steps:\n", "\n", "Next steps\n", "----------\n", "\n", "You have a few options for where to go next. You might first want to learn how to :ref:`install seaborn <installing>`. Once that's done, you can browse the :ref:`example gallery <example_gallery>` to get a broader sense for what kind of graphics seaborn can produce. Or you can read through the :ref:`official tutorial <tutorial>` for a deeper discussion of the different tools and what they are designed to accomplish. If you have a specific plot in mind and want to know how to make it, you could check out the :ref:`API reference <api_ref>`, which documents each function's parameters and shows many examples to illustrate usage." ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. raw:: html\n", " \n", " </div>" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3.6 (seaborn-dev)", "language": "python", "name": "seaborn-dev" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
ledeprogram/algorithms
class7/DecisionTrees-Validation.ipynb
1
20605
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import datasets\n", "from pandas.tools.plotting import scatter_matrix" ] }, { "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": [ "iris = datasets.load_iris() # load iris data set" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = iris.data[:,2:] # the attributes\n", "y = iris.target # the target variable" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import tree" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dt = tree.DecisionTreeClassifier()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dt = dt.fit(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now what? " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.33,train_size=0.66)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dt = dt.fit(x_train,y_train)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confussion_matrix=True):\n", " y_pred=clf.predict(X)\n", " if show_accuracy:\n", " print(\"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y, y_pred)),\"\\n\")\n", " if show_classification_report:\n", " print(\"Classification report\")\n", " print(metrics.classification_report(y,y_pred),\"\\n\")\n", " if show_confussion_matrix:\n", " print(\"Confusion matrix\")\n", " print(metrics.confusion_matrix(y,y_pred),\"\\n\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy:0.980 \n", "\n", "Classification report\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 16\n", " 1 0.95 1.00 0.97 18\n", " 2 1.00 0.94 0.97 16\n", "\n", "avg / total 0.98 0.98 0.98 50\n", " \n", "\n", "Confusion matrix\n", "[[16 0 0]\n", " [ 0 18 0]\n", " [ 0 1 15]] \n", "\n" ] } ], "source": [ "measure_performance(x_test,y_test,dt) #measure on the test data (rather than train)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(iris.target_names))\n", " plt.xticks(tick_marks, iris.target_names, rotation=45)\n", " plt.yticks(tick_marks, iris.target_names)\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_pred = dt.fit(x_train, y_train).predict(x_test) #generate a prediction based on the model created to output a predicted y" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[16 0 0]\n", " [ 0 18 0]\n", " [ 0 1 15]]\n" ] }, { "data": { "image/png": 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L5VLFVyTNiIh1c8L4KCJea2iwDSJpfuBwYJak/SPipTwyYbCkARFxv6RvA1tG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\nOYGamRXkBGpmVpATqJlZQU6gZmYF/T8L98jlffA5dAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feb34125b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = metrics.confusion_matrix(y_test, y_pred)\n", "np.set_printoptions(precision=2)\n", "print('Confusion matrix, without normalization')\n", "print(cm)\n", "plt.figure()\n", "plot_confusion_matrix(cm)" ] }, { "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": 0 }
gpl-3.0
Shinichi-Nakagawa/hatteberg
retrosheet_app/joey_votto.ipynb
1
111204
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/opt/pyenv/versions/py3.5.0_hatteberg/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "%matplotlib inline\n", "import seaborn as sns\n", "import pandas as pd\n", "from retrosheet_controller import RetroSheetDataController\n", "from retrosheet_util import RetroSheetUtil\n", "from analyze_batter import AnalyzeBatter\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Joey Votto VS Ichiro Suzukiお散歩対決\n", "client = RetroSheetDataController()\n", "analyzer = AnalyzeBatter()\n", "votto_df_atbat, ichiro_df_atbat = {}, {}\n", "votto_df_walks, ichiro_df_walks = {}, {}\n", "for year in range(2009, 2015): # 2009-2014シーズンまでが対象\n", " votto_df_atbat[year] = client.batter_event_by_at_bat('Joey', 'Votto', year)\n", " votto_df_walks[year] = client.batter_event_by_walk('Joey', 'Votto', year)\n", " ichiro_df_atbat[year] = client.batter_event_by_at_bat('Ichiro', 'Suzuki', year)\n", " ichiro_df_walks[year] = client.batter_event_by_walk('Ichiro', 'Suzuki', year)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# ボットVSイチローの年間四球数(月ごと&Total)\n", " \n", "graph_data_list = {}\n", "for df_walks in (\n", " {\"name\": \"Joey Votto\", \"data\": votto_df_walks}, \n", " {\"name\": \"Ichiro Suzuki\", \"data\": ichiro_df_walks},\n", "):\n", " graph_data = {}\n", " for year, walks in df_walks[\"data\"].items():\n", " monthly_walks, walk_counts = analyzer.monthly_walks(walks)\n", " graph_data[\"{year}({ball})\".format(year=year, ball=walk_counts)] = monthly_walks \n", " df = pd.DataFrame(graph_data)\n", " graph_data_list[df_walks['name']] = df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1069a82e8>" ] }, "execution_count": 4, "output_type": "execute_result", "metadata": {} }, { "data": { "image/png": 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LeuONV9Wixb2aNOk5OTk5qWfP3goK+rMyMjLk6empWrVqVfooOBnFn0SvIrYcGVXdR1DZ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\nRvkCAGAyyhcAAJNRvgAAmOz/A3i/uEXkUSzmAAAAAElFTkSuQmCC\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "# Ichiro Suzuki\n", "graph_data_list['Ichiro Suzuki'].plot(kind='bar', ylim=(0, 30), title='Osan-po({name})'.format(name='Ichiro Suzuki'))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x106a5de10>" ] }, "execution_count": 5, "output_type": "execute_result", "metadata": {} }, { "data": { "image/png": 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}, "output_type": "display_data", "metadata": {} } ], "source": [ "# Joey Votto\n", "graph_data_list['Joey Votto'].plot(kind='bar', ylim=(0, 30), title='Osan-po({name})'.format(name='Joey Votto'))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# ボットVSイチローのマルチ散歩対決(月ごと&Total)\n", " \n", "graph_data_list = {}\n", "for df_walks in (\n", " {\"name\": \"Joey Votto\", \"data\": votto_df_walks}, \n", " {\"name\": \"Ichiro Suzuki\", \"data\": ichiro_df_walks},\n", "):\n", " graph_data = {}\n", " for year, walks in df_walks[\"data\"].items():\n", " monthly_walks, walk_counts = analyzer.monthly_walks_multi(walks)\n", " graph_data[\"{year}({ball})\".format(year=year, ball=walk_counts)] = monthly_walks \n", " df = pd.DataFrame(graph_data)\n", " graph_data_list[df_walks['name']] = df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x106d46550>" ] }, "execution_count": 7, "output_type": "execute_result", "metadata": {} }, { "data": { "image/png": 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1a+3atUuSdPbsWV25ckXe3t5lSwcAwG2mTEe+HTt21MGDB/XXv/5VhmFo6tSp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\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "# Ichiro Suzuki\n", "graph_data_list['Ichiro Suzuki'].plot(kind='bar', ylim=(0, 10), title='Multi Osan-po({name})'.format(name='Ichiro Suzuki'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x106eefd68>" ] }, "execution_count": 8, "output_type": "execute_result", "metadata": {} }, { "data": { "image/png": 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AwCbVfk2NlJoqtbnGo6Jr2tWo9PCij6w7dgyuoKuRU4mPtTMy0n/5KPsqi8Ui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\n4QsAgMkIXwAATEb4AgBgsv8P2DvdyFl/D+MAAAAASUVORK5CYII=\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "# Joey Votto\n", "graph_data_list['Joey Votto'].plot(kind='bar', ylim=(0, 10), title='Multi Osan-po({name})'.format(name='Joey Votto'))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x107415dd8>" ] }, "execution_count": 9, "output_type": "execute_result", "metadata": {} }, { "data": { "image/png": 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PP4SJiQnmz38d77yzDkVFhXBycsGgQc/CxMQEY8eOx6xZQRACmD59Nho1agQnJ2dERUUA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\nxuJLREQkMxZfIiIimfFWI5J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8FGu5KFkAgFNcaVN4SpcubbVgJf5OFgAAayhZAAAsoWQBALCEkgUAwBJKFgAA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\nq00p8wAAAABJRU5ErkJggg==\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "# ボットさんの内野のポップフライ数を数える\n", "fly, pop_fly = {}, {}\n", "\n", "for year, atbat in votto_df_atbat.items():\n", " monthly_fly = analyzer._monthly_counts()\n", " monthly_pop = analyzer._monthly_counts()\n", " fly_cnt, pop_cnt = 0, 0\n", " for i, row in atbat.iterrows():\n", " month = int(str(row['game_dt'])[4:6])\n", " # フライっぽいアウト（と思われる）打球をカウント\n", " # event cdがアウトかつ、batted ballがラインドライブかフライ\n", " if row['event_cd'] == 2 and row['battedball_cd'] in ('L', 'F'):\n", " monthly_fly[month] += 1\n", " fly_cnt += 1\n", " # 内野に上がったフライの数\n", " if int(row['event_tx'][0:1]) < 7:\n", " monthly_pop[month] += 1\n", " pop_cnt += 1\n", " fly[\"{year}(Fly:{cnt})\".format(year=year, cnt=fly_cnt)] = monthly_fly\n", " pop_fly[\"{year}(Pop Fly:{cnt})\".format(year=year, cnt=pop_cnt)] = monthly_pop\n", " \n", "df = pd.DataFrame(fly)\n", "df.plot(kind='bar', ylim=(0, 40), title='Fly Count({name})'.format(name='Joey Votto'))\n", "df = pd.DataFrame(pop_fly)\n", "df.plot(kind='bar', ylim=(0, 5), title='PopFly Count({name})'.format(name='Joey Votto'))" ] }, { "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": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
maubarsom/ORFan-proteins
orfan_2016_annotation/20161129_summarize_results/.ipynb_checkpoints/filter_blast_hits-checkpoint.ipynb
1
15724
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "from glob import glob\n", "import requests\n", "from bs4 import BeautifulSoup\n", "import itertools" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "blast_folder_regex = re.compile(r\"(blast[np])_vs_([a-zA-Z_]+)\")\n", "cluster_id_regex = re.compile(r\"cluster([0-9]+[ab]?)_\")\n", "blast_cols = [\"query_id\",\"subject_id\",\"pct_id\",\"ali_len\",\"mism\",\n", " \"gap_open\",\"q_start\",\"q_end\",\"s_start\",\"s_end\",\n", " \"e_value\",\"bitscore\",\"q_len\",\"s_len\",\"s_gi\",\n", " \"s_taxids\",\"s_scinames\",\"s_names\",\"q_cov\",\"s_description\"\n", " ]\n", "\n", "blast_hits = []\n", "for folder in glob(\"blast_160427/blast?_vs_*\"):\n", " tool_id,db_id = blast_folder_regex.search(folder).groups()\n", " for blast_filename in glob(folder+\"/*.tsv\"):\n", " blast_hits.append( pd.read_csv(blast_filename,sep=\"\\t\", header=None, names=blast_cols) )\n", " blast_hits[-1][\"cluster\"] = cluster_id_regex.search(blast_filename).group(1)\n", " blast_hits[-1][\"tool\"] = tool_id\n", " blast_hits[-1][\"db\"] = db_id" ] }, { "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>query_id</th>\n", " <th>subject_id</th>\n", " <th>pct_id</th>\n", " <th>ali_len</th>\n", " <th>mism</th>\n", " <th>gap_open</th>\n", " <th>q_start</th>\n", " <th>q_end</th>\n", " <th>s_start</th>\n", " <th>s_end</th>\n", " <th>...</th>\n", " <th>s_len</th>\n", " <th>s_gi</th>\n", " <th>s_taxids</th>\n", " <th>s_scinames</th>\n", " <th>s_names</th>\n", " <th>q_cov</th>\n", " <th>s_description</th>\n", " <th>cluster</th>\n", " <th>tool</th>\n", " <th>db</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>GB3LKKR01DOS1W</td>\n", " <td>gi|935719420|emb|CEAZ01012945.1|</td>\n", " <td>86.4</td>\n", " <td>228</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>46</td>\n", " <td>271</td>\n", " <td>20508</td>\n", " <td>20283</td>\n", " <td>...</td>\n", " <td>144341</td>\n", " <td>935719420</td>\n", " <td>749906</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>83</td>\n", " <td>gut metagenome genome assembly P6C7-k21-2014-0...</td>\n", " <td>1073</td>\n", " <td>blastn</td>\n", " <td>env_nt</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>GB3LKKR01DOS1W</td>\n", " <td>gi|935719420|emb|CEAZ01012945.1|</td>\n", " <td>86.4</td>\n", " <td>228</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>46</td>\n", " <td>271</td>\n", " <td>107376</td>\n", " <td>107151</td>\n", " <td>...</td>\n", " <td>144341</td>\n", " <td>935719420</td>\n", " <td>749906</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>83</td>\n", " <td>gut metagenome genome assembly P6C7-k21-2014-0...</td>\n", " <td>1073</td>\n", " <td>blastn</td>\n", " <td>env_nt</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>GB3LKKR01DOS1W</td>\n", " <td>gi|935454047|emb|CEAX01018485.1|</td>\n", " <td>86.4</td>\n", " <td>228</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>46</td>\n", " <td>271</td>\n", " <td>12216</td>\n", " <td>12441</td>\n", " <td>...</td>\n", " <td>28297</td>\n", " <td>935454047</td>\n", " <td>749906</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>83</td>\n", " <td>gut metagenome genome assembly P6C90-k21-2014-...</td>\n", " <td>1073</td>\n", " <td>blastn</td>\n", " <td>env_nt</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>GB3LKKR01DOS1W</td>\n", " <td>gi|935344953|emb|CEBY01021626.1|</td>\n", " <td>86.4</td>\n", " <td>228</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>46</td>\n", " <td>271</td>\n", " <td>74375</td>\n", " <td>74150</td>\n", " <td>...</td>\n", " <td>86997</td>\n", " <td>935344953</td>\n", " <td>749906</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>83</td>\n", " <td>gut metagenome genome assembly P6C0-k21-2014-0...</td>\n", " <td>1073</td>\n", " <td>blastn</td>\n", " <td>env_nt</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>GB3LKKR01DOS1W</td>\n", " <td>gi|935324036|emb|CEBY01034087.1|</td>\n", " <td>86.4</td>\n", " <td>228</td>\n", " <td>27</td>\n", " <td>4</td>\n", " <td>46</td>\n", " <td>271</td>\n", " <td>12333</td>\n", " <td>12558</td>\n", " <td>...</td>\n", " <td>42116</td>\n", " <td>935324036</td>\n", " <td>749906</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>83</td>\n", " <td>gut metagenome genome assembly P6C0-k21-2014-0...</td>\n", " <td>1073</td>\n", " <td>blastn</td>\n", " <td>env_nt</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " query_id subject_id pct_id ali_len mism \\\n", "0 GB3LKKR01DOS1W gi|935719420|emb|CEAZ01012945.1| 86.4 228 27 \n", "1 GB3LKKR01DOS1W gi|935719420|emb|CEAZ01012945.1| 86.4 228 27 \n", "2 GB3LKKR01DOS1W gi|935454047|emb|CEAX01018485.1| 86.4 228 27 \n", "3 GB3LKKR01DOS1W gi|935344953|emb|CEBY01021626.1| 86.4 228 27 \n", "4 GB3LKKR01DOS1W gi|935324036|emb|CEBY01034087.1| 86.4 228 27 \n", "\n", " gap_open q_start q_end s_start s_end ... s_len s_gi \\\n", "0 4 46 271 20508 20283 ... 144341 935719420 \n", "1 4 46 271 107376 107151 ... 144341 935719420 \n", "2 4 46 271 12216 12441 ... 28297 935454047 \n", "3 4 46 271 74375 74150 ... 86997 935344953 \n", "4 4 46 271 12333 12558 ... 42116 935324036 \n", "\n", " s_taxids s_scinames s_names q_cov \\\n", "0 749906 NaN NaN 83 \n", "1 749906 NaN NaN 83 \n", "2 749906 NaN NaN 83 \n", "3 749906 NaN NaN 83 \n", "4 749906 NaN NaN 83 \n", "\n", " s_description cluster tool db \n", "0 gut metagenome genome assembly P6C7-k21-2014-0... 1073 blastn env_nt \n", "1 gut metagenome genome assembly P6C7-k21-2014-0... 1073 blastn env_nt \n", "2 gut metagenome genome assembly P6C90-k21-2014-... 1073 blastn env_nt \n", "3 gut metagenome genome assembly P6C0-k21-2014-0... 1073 blastn env_nt \n", "4 gut metagenome genome assembly P6C0-k21-2014-0... 1073 blastn env_nt \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_blast_hits = blast_hits[0]\n", "for search_hits in blast_hits[1:]:\n", " all_blast_hits = all_blast_hits.append(search_hits)\n", "all_blast_hits.head()" ] }, { "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", " <th></th>\n", " <th>cluster</th>\n", " <th>db</th>\n", " <th>tool</th>\n", " <th>query_id</th>\n", " <th>subject_id</th>\n", " <th>pct_id</th>\n", " <th>q_cov</th>\n", " <th>q_len</th>\n", " <th>bitscore</th>\n", " <th>e_value</th>\n", " <th>s_description</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>33</th>\n", " <td>1073</td>\n", " <td>env_nt</td>\n", " <td>blastn</td>\n", " <td>GB3LKKR01A150I</td>\n", " <td>gi|936108378|emb|CEAB01076172.1|</td>\n", " <td>98.180</td>\n", " <td>99</td>\n", " <td>276</td>\n", " <td>479</td>\n", " <td>1.000000e-131</td>\n", " <td>gut metagenome genome assembly P2E0-k21-2014-0...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1073</td>\n", " <td>metahit_cds</td>\n", " <td>blastn</td>\n", " <td>contig07331</td>\n", " <td>GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff...</td>\n", " <td>95.971</td>\n", " <td>96</td>\n", " <td>285</td>\n", " <td>444</td>\n", " <td>1.010000e-122</td>\n", " <td>GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff...</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>1073</td>\n", " <td>metahit_pep</td>\n", " <td>blastp</td>\n", " <td>contig07331</td>\n", " <td>GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff...</td>\n", " <td>97.727</td>\n", " <td>96</td>\n", " <td>92</td>\n", " <td>159</td>\n", " <td>3.240000e-48</td>\n", " <td>GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff...</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>113b</td>\n", " <td>metahit_cds</td>\n", " <td>blastn</td>\n", " <td>GB3LKKR01C5IFF</td>\n", " <td>GL0006538_MH0023_[Complete]_[mRNA]_locus=C1324...</td>\n", " <td>98.913</td>\n", " <td>100</td>\n", " <td>92</td>\n", " <td>165</td>\n", " <td>2.240000e-39</td>\n", " <td>GL0006538_MH0023_[Complete]_[mRNA]_locus=C1324...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>113b</td>\n", " <td>env_nt</td>\n", " <td>blastn</td>\n", " <td>GB3LKKR02F10X4</td>\n", " <td>gi|557595202|gb|AVOA01007617.1|</td>\n", " <td>87.360</td>\n", " <td>89</td>\n", " <td>98</td>\n", " <td>100</td>\n", " <td>3.000000e-18</td>\n", " <td>Gut metagenome Scaffold6187_1, whole genome sh...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cluster db tool query_id \\\n", "33 1073 env_nt blastn GB3LKKR01A150I \n", "3 1073 metahit_cds blastn contig07331 \n", "30 1073 metahit_pep blastp contig07331 \n", "0 113b metahit_cds blastn GB3LKKR01C5IFF \n", "5 113b env_nt blastn GB3LKKR02F10X4 \n", "\n", " subject_id pct_id q_cov q_len \\\n", "33 gi|936108378|emb|CEAB01076172.1| 98.180 99 276 \n", "3 GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff... 95.971 96 285 \n", "30 GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff... 97.727 96 92 \n", "0 GL0006538_MH0023_[Complete]_[mRNA]_locus=C1324... 98.913 100 92 \n", "5 gi|557595202|gb|AVOA01007617.1| 87.360 89 98 \n", "\n", " bitscore e_value s_description \n", "33 479 1.000000e-131 gut metagenome genome assembly P2E0-k21-2014-0... \n", "3 444 1.010000e-122 GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff... \n", "30 159 3.240000e-48 GL0080651_MH0011_[Complete]_[mRNA]_locus=scaff... \n", "0 165 2.240000e-39 GL0006538_MH0023_[Complete]_[mRNA]_locus=C1324... \n", "5 100 3.000000e-18 Gut metagenome Scaffold6187_1, whole genome sh... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#all_blast_hits[all_blast_hits.e_value < 0.001].groupby([\"cluster\",\"db\"])\n", "gb = all_blast_hits[ (all_blast_hits.q_cov > 80) & (all_blast_hits.e_value < 0.001) ].groupby([\"cluster\",\"db\"])\n", "reliable_fam_hits = pd.DataFrame( hits.ix[hits.bitscore.idxmax()] for _,hits in gb )[[\"cluster\",\"db\",\"tool\",\"query_id\",\"subject_id\",\"pct_id\",\"q_cov\",\"q_len\",\n", " \"bitscore\",\"e_value\",\"s_description\"]]\n", "\n", "sorted_fam_hits = pd.concat( hits.sort_values(by=\"bitscore\",ascending=False) for _,hits in reliable_fam_hits.groupby(\"cluster\") )\n", "sorted_fam_hits.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sorted_fam_hits.to_csv(\"filtered_blast_hits.csv\",index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
bt3gl/Machine-Learning-Resources
ml_notebooks/synthetic_features_and_outliers.ipynb
1
20176
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "synthetic_features_and_outliers.ipynb", "provenance": [], "collapsed_sections": [ "JndnmDMp66FL", "i5Ul3zf5QYvW", "jByCP8hDRZmM", "WvgxW0bUSC-c" ] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "JndnmDMp66FL", "colab_type": "text" }, "source": [ "#### Copyright 2017 Google LLC." ] }, { "cell_type": "code", "metadata": { "id": "hMqWDc_m6rUC", "colab_type": "code", "cellView": "both", "colab": {} }, "source": [ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4f3CKqFUqL2-", "colab_type": "text" }, "source": [ "# Synthetic Features and Outliers" ] }, { "cell_type": "markdown", "metadata": { "id": "jnKgkN5fHbGy", "colab_type": "text" }, "source": [ "**Learning Objectives:**\n", " * Create a synthetic feature that is the ratio of two other features\n", " * Use this new feature as an input to a linear regression model\n", " * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data" ] }, { "cell_type": "markdown", "metadata": { "id": "VOpLo5dcHbG0", "colab_type": "text" }, "source": [ "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n", "\n", "First, we'll import the California housing data into a *pandas* `DataFrame`:" ] }, { "cell_type": "markdown", "metadata": { "id": "S8gm6BpqRRuh", "colab_type": "text" }, "source": [ "## Setup" ] }, { "cell_type": "code", "metadata": { "id": "9D8GgUovHbG0", "colab_type": "code", "colab": {} }, "source": [ "from __future__ import print_function\n", "\n", "import math\n", "\n", "from IPython import display\n", "from matplotlib import cm\n", "from matplotlib import gridspec\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import sklearn.metrics as metrics\n", "import tensorflow as tf\n", "from tensorflow.python.data import Dataset\n", "\n", "tf.logging.set_verbosity(tf.logging.ERROR)\n", "pd.options.display.max_rows = 10\n", "pd.options.display.float_format = '{:.1f}'.format\n", "\n", "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n", "\n", "california_housing_dataframe = california_housing_dataframe.reindex(\n", " np.random.permutation(california_housing_dataframe.index))\n", "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", "california_housing_dataframe" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "I6kNgrwCO_ms", "colab_type": "text" }, "source": [ "Next, we'll set up our input function, and define the function for model training:" ] }, { "cell_type": "code", "metadata": { "id": "5RpTJER9XDub", "colab_type": "code", "colab": {} }, "source": [ "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", " \"\"\"Trains a linear regression model of one feature.\n", " \n", " Args:\n", " features: pandas DataFrame of features\n", " targets: pandas DataFrame of targets\n", " batch_size: Size of batches to be passed to the model\n", " shuffle: True or False. Whether to shuffle the data.\n", " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", " Returns:\n", " Tuple of (features, labels) for next data batch\n", " \"\"\"\n", " \n", " # Convert pandas data into a dict of np arrays.\n", " features = {key:np.array(value) for key,value in dict(features).items()} \n", " \n", " # Construct a dataset, and configure batching/repeating.\n", " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", " ds = ds.batch(batch_size).repeat(num_epochs)\n", " \n", " # Shuffle the data, if specified.\n", " if shuffle:\n", " ds = ds.shuffle(buffer_size=10000)\n", " \n", " # Return the next batch of data.\n", " features, labels = ds.make_one_shot_iterator().get_next()\n", " return features, labels" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VgQPftrpHbG3", "colab_type": "code", "colab": {} }, "source": [ "def train_model(learning_rate, steps, batch_size, input_feature):\n", " \"\"\"Trains a linear regression model.\n", " \n", " Args:\n", " learning_rate: A `float`, the learning rate.\n", " steps: A non-zero `int`, the total number of training steps. A training step\n", " consists of a forward and backward pass using a single batch.\n", " batch_size: A non-zero `int`, the batch size.\n", " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", " to use as input feature.\n", " \n", " Returns:\n", " A Pandas `DataFrame` containing targets and the corresponding predictions done\n", " after training the model.\n", " \"\"\"\n", " \n", " periods = 10\n", " steps_per_period = steps / periods\n", "\n", " my_feature = input_feature\n", " my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n", " my_label = \"median_house_value\"\n", " targets = california_housing_dataframe[my_label].astype('float32')\n", "\n", " # Create input functions.\n", " training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", " predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", " \n", " # Create feature columns.\n", " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", " \n", " # Create a linear regressor object.\n", " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", " linear_regressor = tf.estimator.LinearRegressor(\n", " feature_columns=feature_columns,\n", " optimizer=my_optimizer\n", " )\n", "\n", " # Set up to plot the state of our model's line each period.\n", " plt.figure(figsize=(15, 6))\n", " plt.subplot(1, 2, 1)\n", " plt.title(\"Learned Line by Period\")\n", " plt.ylabel(my_label)\n", " plt.xlabel(my_feature)\n", " sample = california_housing_dataframe.sample(n=300)\n", " plt.scatter(sample[my_feature], sample[my_label])\n", " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", "\n", " # Train the model, but do so inside a loop so that we can periodically assess\n", " # loss metrics.\n", " print(\"Training model...\")\n", " print(\"RMSE (on training data):\")\n", " root_mean_squared_errors = []\n", " for period in range (0, periods):\n", " # Train the model, starting from the prior state.\n", " linear_regressor.train(\n", " input_fn=training_input_fn,\n", " steps=steps_per_period,\n", " )\n", " # Take a break and compute predictions.\n", " predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n", " predictions = np.array([item['predictions'][0] for item in predictions])\n", " \n", " # Compute loss.\n", " root_mean_squared_error = math.sqrt(\n", " metrics.mean_squared_error(predictions, targets))\n", " # Occasionally print the current loss.\n", " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", " # Add the loss metrics from this period to our list.\n", " root_mean_squared_errors.append(root_mean_squared_error)\n", " # Finally, track the weights and biases over time.\n", " # Apply some math to ensure that the data and line are plotted neatly.\n", " y_extents = np.array([0, sample[my_label].max()])\n", " \n", " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", " \n", " x_extents = (y_extents - bias) / weight\n", " x_extents = np.maximum(np.minimum(x_extents,\n", " sample[my_feature].max()),\n", " sample[my_feature].min())\n", " y_extents = weight * x_extents + bias\n", " plt.plot(x_extents, y_extents, color=colors[period]) \n", " print(\"Model training finished.\")\n", "\n", " # Output a graph of loss metrics over periods.\n", " plt.subplot(1, 2, 2)\n", " plt.ylabel('RMSE')\n", " plt.xlabel('Periods')\n", " plt.title(\"Root Mean Squared Error vs. Periods\")\n", " plt.tight_layout()\n", " plt.plot(root_mean_squared_errors)\n", "\n", " # Create a table with calibration data.\n", " calibration_data = pd.DataFrame()\n", " calibration_data[\"predictions\"] = pd.Series(predictions)\n", " calibration_data[\"targets\"] = pd.Series(targets)\n", " display.display(calibration_data.describe())\n", "\n", " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n", " \n", " return calibration_data" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FJ6xUNVRm-do", "colab_type": "text" }, "source": [ "## Task 1: Try a Synthetic Feature\n", "\n", "Both the `total_rooms` and `population` features count totals for a given city block.\n", "\n", "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n", "\n", "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n", "\n", "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n", "the final RMSE should be.)" ] }, { "cell_type": "markdown", "metadata": { "id": "isONN2XK32Wo", "colab_type": "text" }, "source": [ "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**." ] }, { "cell_type": "code", "metadata": { "id": "5ihcVutnnu1D", "colab_type": "code", "cellView": "both", "colab": { "test": { "output": "ignore", "timeout": 600 } } }, "source": [ "#\n", "# YOUR CODE HERE\n", "#\n", "california_housing_dataframe[\"rooms_per_person\"] =\n", "\n", "calibration_data = train_model(\n", " learning_rate=0.00005,\n", " steps=500,\n", " batch_size=5,\n", " input_feature=\"rooms_per_person\"\n", ")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "i5Ul3zf5QYvW", "colab_type": "text" }, "source": [ "### Solution\n", "\n", "Click below for a solution." ] }, { "cell_type": "code", "metadata": { "id": "Leaz2oYMQcBf", "colab_type": "code", "colab": {} }, "source": [ "california_housing_dataframe[\"rooms_per_person\"] = (\n", " california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n", "\n", "calibration_data = train_model(\n", " learning_rate=0.05,\n", " steps=500,\n", " batch_size=5,\n", " input_feature=\"rooms_per_person\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZjQrZ8mcHFiU", "colab_type": "text" }, "source": [ "## Task 2: Identify Outliers\n", "\n", "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values. Ideally, these would lie on a perfectly correlated diagonal line.\n", "\n", "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n", "\n", "Do you see any oddities? Trace these back to the source data by looking at the distribution of values in `rooms_per_person`." ] }, { "cell_type": "code", "metadata": { "id": "P0BDOec4HbG_", "colab_type": "code", "colab": {} }, "source": [ "# YOUR CODE HERE" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "jByCP8hDRZmM", "colab_type": "text" }, "source": [ "### Solution\n", "\n", "Click below for the solution." ] }, { "cell_type": "code", "metadata": { "id": "s0tiX2gdRe-S", "colab_type": "code", "colab": {} }, "source": [ "plt.figure(figsize=(15, 6))\n", "plt.subplot(1, 2, 1)\n", "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "kMQD0Uq3RqTX", "colab_type": "text" }, "source": [ "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n", "\n", "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:" ] }, { "cell_type": "code", "metadata": { "id": "POTM8C_ER1Oc", "colab_type": "code", "colab": {} }, "source": [ "plt.subplot(1, 2, 2)\n", "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9l0KYpBQu8ed", "colab_type": "text" }, "source": [ "## Task 3: Clip Outliers\n", "\n", "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n", "\n", "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n", "\n", " clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n", "\n", "The above `clipped_feature` will have no values less than `0`." ] }, { "cell_type": "code", "metadata": { "id": "rGxjRoYlHbHC", "colab_type": "code", "colab": {} }, "source": [ "# YOUR CODE HERE" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WvgxW0bUSC-c", "colab_type": "text" }, "source": [ "### Solution\n", "\n", "Click below for the solution." ] }, { "cell_type": "markdown", "metadata": { "id": "8YGNjXPaSMPV", "colab_type": "text" }, "source": [ "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results." ] }, { "cell_type": "code", "metadata": { "id": "9YyARz6gSR7Q", "colab_type": "code", "colab": {} }, "source": [ "california_housing_dataframe[\"rooms_per_person\"] = (\n", " california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n", "\n", "_ = california_housing_dataframe[\"rooms_per_person\"].hist()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "vO0e1p_aSgKA", "colab_type": "text" }, "source": [ "To verify that clipping worked, let's train again and print the calibration data once more:" ] }, { "cell_type": "code", "metadata": { "id": "ZgSP2HKfSoOH", "colab_type": "code", "colab": {} }, "source": [ "calibration_data = train_model(\n", " learning_rate=0.05,\n", " steps=500,\n", " batch_size=5,\n", " input_feature=\"rooms_per_person\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "gySE-UgfSony", "colab_type": "code", "colab": {} }, "source": [ "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])" ], "execution_count": 0, "outputs": [] } ] }
gpl-2.0
msyriac/orphics
tutorials/Correlated maps.ipynb
1
388963
{ "cells": [ { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "from orphics import maps,cosmology,io,stats\n", "from enlib import enmap\n", "import numpy as np\n", "\n" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating theory Cls...\n", "Loaded cached Cls from output/clsAll_2000_20171130.pkl\n", "Loaded cached Cls from output/clphi_2000_20171130.txt\n", "Initializing CMB window..\n", "initializing power...\n" ] } ], "source": [ "lc = cosmology.LimberCosmology(lmax=2000,pickling=True)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Initializing galaxy window for ', 'g1', ' ...')\n", "('Initializing galaxy window for ', 'g2', ' ...')\n", "('Initializing galaxy window for ', 'g3', ' ...')\n" ] } ], "source": [ "lc.addStepNz(\"g1\",0.1,0.3,bias=2)\n", "lc.addStepNz(\"g2\",0.3,0.4,bias=2)\n", "lc.addStepNz(\"g3\",0.4,0.5,bias=2)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "ellrange = np.arange(0,2000,1)\n", "lc.generateCls(ellrange)\n", "clkk = lc.getCl(\"cmb\",\"cmb\")\n", "clk1 = lc.getCl(\"cmb\",\"g1\")\n", "cl11 = lc.getCl(\"g1\",\"g1\")\n", "cl12 = lc.getCl(\"g1\",\"g2\")\n", "cl22 = lc.getCl(\"g2\",\"g2\")\n", "cl33 = lc.getCl(\"g3\",\"g3\")\n" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "ps = np.zeros((4,4,ellrange.size))\n", "ps[0,0] = clkk\n", "ps[1,1] = cl11\n", "ps[2,2] = cl22\n", "ps[3,3] = cl33\n", "ps[0,1] = clk1\n", "ps[1,0] = clk1\n", "ps[1,2] = cl12\n" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "shape, wcs = maps.rect_geometry(width_deg=25.,px_res_arcmin=2.0)\n", "shape = (4,)+shape\n", "mg = maps.MapGen(shape,wcs,ps)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "imaps = mg.get_map(scalar=True)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4, 750, 750)\n" ] } ], "source": [ "print(imaps.shape)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "image/png": 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KQP6eoHsm4uTxTQqmx09MvE2iJJ9d+TTNTp5aecDuVhmr4PPs4h1evHsM870sSsJ4PgJd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AOd4A8aUIxZfHGX7T9dZjK2mSwmxH2b1Sd2uHk0y83uHJkUJLHO7oMp5ImNdWaIVQr5p8fPI5bG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2YgaRxRdXLyIijagQETVcxK0s8syAeN8h8kzMbYvEBrOjRM/zpxuYesLg5ijeiCSzoWBKcSdF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\nBRn3qJ3OkBoDfdJETTl0tRRJPDzPl59+AK+moa2oxDJ13Kk4TkPjQ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6IA1uT1GsVnAOXUy5oAg1aUuz994c0cjnf3/wFUajgJKXobVErQXkWhIVttgz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qsn5PqsIG6ZcU0TBzbGxTK+EmBJWlBEdY3ZtSjeh6RjAibFrZzBooGzNKRRHrKV1ahP\n9ml3i3RsB7XvUrtq0F/WJAc1g4MWlheS9S2cjth5BHOSHvH35aKw85wUWYwYJr/iUdyK2X7OJSmK\nf1Jv2aB5riTpmemcvG8T1zRdF4xdRyLhbYm+o5kU5aekho27JzKvqYsj7v6cBxMR+b6L6tkY57rM\nFQMebEygDE0wK8dAVFYEixn20KT2qsNoThOXFL1lj86HQ+y7PsU7NlFdCwe2J6oKFRiMFnK0KQzd\nyuoY89jTNJ/QOF3FxAWRt+UWhD/bYbdWhhwqtyzcXZO4nuPvGLhtTVwxsK95uF1Nf0kurGvv03n6\nJ53/fJTxox8z/zscYUO6lIqvSj821URINjECycggrf//7L1ZjKZXft73O+fdv32ptWvprt6b7G7u\nQw05i0amHVlOxpYRKXKEwBcJEiAIkovkIkauchHkKrkJkACGA8QwoCQCLMORIntGI8+MNENyyOHS\n3exm71Vd+/rty7uek4t/De0LQxhJbcgC8wJ1QbJQX/Gr7z3vf3me3yPker8nlJzpUkE2KwFlzXuA\nguOXFP01j9qGDO1RktDJnapYAPuKwXmYLuakczneWJw9aUPmfsaTEDK3lopEZduned8wczsjPNZM\nFhTDVc3RL6U4segJ8wjSusHvKfxnAcYXcK+75xMcaRoPwel68sE+J9Wr23GZHJd49ntroODwmxn9\ny+BdGbCw0GPUKTH4o3lUrBkvGbKaZbwiv3+475LcbTCdswxXFTu/KM/gyZLB7JRwpor4bELQN0Jt\nf6aJjg2D81LBeBNDVha7qVmOMbMp2UqKO4X51gDjSyxGPPKxJwEYUJlGtxKiI4V+tU82k+ONYPn7\nOf6zQA7Ua1OKQA5RZSVRQBk5EMZnCzo35IHn/7hK5/0FKrdDpqmHehYxs9Lj+s1ndF8q8MYCqza+\nhc0IThNOWw9y5j6Qv1F/zZUYmssTdAbTM5KTNFjzCTtWqtJdEcxPFiRlNehqggOX8FgkcsYXMEvc\nVrhTRfWRiz72cUay5JrOWR79pk/lXF9AM0caW82ZHJU5uDVPtOHhboR4I02ymNN7weL1tBg7You5\nMGW8BCcvKWzXJ+hA82FBsZBSu9DDGyqKmRTVSnEWJ5J6YKFzVSAz3sSgFmKyqpF5bl2R1hXTzxvg\nWMIDlyKE9PIUvy9dQtJUTBYNRajIQyUA7fT5dbrWqp/r6y/y+lIfpNbjCxCyOxSLYx7aUwKStHSV\nhyRXhgQAACAASURBVB7RnktWla1nZd1BTU6XEYmltGdFKzpvOXpVce53C879Toc8EslL+utdxi8m\n5PWCylOXM2dP6F0XMG8RyBxqulhQWeuT93zsS0OsK5Xy3lsuRSC/C4A1iuFZuHpzk+rlLkFHxPF5\nBHiWwpNcqSKyjM8owgsD9r8hRKe59xXlHYVXExpVcKJwTzyKtSnp4xr7G21qd3x0Bu1PNLMfSdW9\n+K6l8CG7LAinvCyJqNGBWEitJ5g8/Vqfs7+tySNN76IrlHxf0XhomCwoDt6U5dnCeznBg4joQYA+\n8hmdzzns1Pj2Wx/hfqXLueVjaKQSnuZYXLdg+pUx5pM6/r7LdN7y7D8UgLF1BCcYz1nqrx4TdBTx\nXEH37YTJosKWBcgcdBTXfu0+v/GrPyCrQu23quQVw8l6k53fWsMZa6ZLOcPzhksv7lBEAnyxDgxW\nXLrXNHlJRhDTr42wuyG1dUNw7LD/ljyoBudlrrr3VkjrniAY7SliMTySscjCBwWlXQcTiP5VmVPs\nXCJ/G//igOhQ4bViBvtVKk8keUCdZsQX8ynTcxlZw2AcS/snLn5PU9pVhMfyedJPI9IFiRapP5DX\nGp1x0J5hpdFj+soEf9eH44A8dQUCczXDG0NlG/bf1NR/GBEey+GsCxicN9izU1RQsPyNLcaXU+o/\nCtEJJDOG8fkMb6gp78nhO1xRjJae39Hyl2Fr/6U+SHUilPysYvEmEJzAwgeG5oOY0cWcuC2baKsB\nJZT54aWc2Q80lScunRc0SUORNCDoSZzHwes+u99qkTblw2+/0yZYDzj/23Lzh26Of+ycBqXB8EIh\nsJEgBc+Sxi5pW6KAnUTRvG8IesLorLXG5LMZT4/a9DfrBF1I2nJgkwvEOToUcpUqYLJb4eKlPbGE\n1jX9l1OyiUe8IMg0nYLpBuQzGc2lvlDvT91MTirtv9/PiQ4t3qOI0p5i+Q8N5T0R0XffSlm+dEhy\nY8K4FzGZdxkvKryhxRtIW9y/qImOLK1rJ+Rly7NfNxIaWBEZWbQrmVM/3L7Iqwvb/J3lD3F3AtTY\nwZlo0qMS2cAnnjEUJakSw1IqUp7ZBLtVwmo42moyfCXG72r0kU9yIUb3XWZuHDJeMfSSiP/jg7dx\nR3D8kiY8cPC7mt7XpDpununTvq14+tEKtSdadLt9+8XDcnAtA23JUwdvqJnMn8rAfElYaN/mNIkW\n4tMKrbwzZbhyOh4qG/a+6pA0LZVnwmqYLBenUGd5rexhDQwEgYhXZm4nBB1ovO9T2nTxtnzml7pE\nZ0a4U0VlLycvC0xnsmjp3ijQuSJsxBSNXB42CiZnLP69iJ3fWsN7WKK0p6g9kvfJLMeoRDN4KaHw\nFXMfyaK18bhg/gNDVlLUnmqWZ7t88/IjssKh+pmPdRW1bxxQVER9kqykpGVFZVtSZo33/O7TvwwV\n6Zd62XTjv/yfiY6l5bRaWpSfHZxFZFGZLHnixZxw32XmTsHxdUcydQKY/zAjqTsE3QJvlLPzTcl+\n772eSmsaO1htqT5xv8gd790Ueo8z0TTui86Pno+t5CjP4D0LKCLLzAvHdIcl7NMy7duW7jXB9ukU\n7GsDymFK53ELZ6rwhtI+JzMFNjSUWhPy3MG9VZF/3y6org4YDiJsoVCOhb5HfbVPb6+GM9I4U0Xt\nKUwW5WfpVCRJw/OG1m1F5yVD456MN0qHBl1Ypv9Jj+79FtffWOezn65RvtDH+90GaV2AzjOfDnjy\nH9QoQoNOBUgdHjgEXehfLQQ07Rmqt2RjbzW0bxwxmIRkqUs+dWnODul1KjBwqTxzGF7O8I8lKvmN\nK+t8urVMfhTi9+Tw8/uWwXlY/kHO8XW5m+M5w/XX17n1eAUnyikGPipVLF4+oh1NuPNoGeUbcYud\nBtklbSkXK1sCKBmeNzIXrwgkZvZj6LwonvKiYnBHmqyZo8KC+och1e0cv5dz9EpI0oCsajCNnGDb\nIzxWzH4yZeudiGQ+p3FHWo72vZjBakDx759graK30cC6AsNJ27I88w48QQYuFvKeuhDta5qPCvpr\nDo3HBZ2rDpOzubBMkQdj9lf6jE9KLH7PIQ8VR9/M8A49ghNFWj/VmFoYXM9wBg7Rvsa4As6uP4LB\nOxOUtihlSY4jFtZO2N9v4G/7FCFU18Xtd/KSdCnuUPPkv/2vn8uy6fXf/3s/1/f+9Ff+xz/x9U4D\nMv9X4B2gBTwB/t5p7tyf6/pSL5tQ0l7F7dNM9ErO0nc1g1VHDq1ctpOqlONOXQ5e1xjPUt5VhNuW\nwtd4E0P/vEde9nBSGJ2zOB33C46nTkUg371hsK5FlzPO/kPNyQ2XeAZK1YRx6tB+1wMtHEqdKQ52\nG5SeiFjdmxYk8xZdztHa4tyt0akZTDXHOg5O4hAeAtcnuG7BKwvbHE6rPLjo4+942NAwjT3mZgaM\nE5/kQV0qp49aqPmC5l3F8Vdz2m/vcryxSPs9D28sSLcL13d4NlmmvO0wXYC0ZqivGw5f9Xildci7\nQZNOXKIoG0bDkGpZEZ7IaGHnl+oYV/6/VazRU3HejJYU5Q2H6EjTvyTCcvc08vrgWQsda8rbMqcd\ndJs0HiuML4dkEXj4PdCJz4fZBVSUi4500CBuW5KWzCH7a7LgycuGaM/h1oNVgn0XnflMlzOsD7tb\nbfb8BmjwgpzkjKYIPfJGTrTlMV3NiKcuyWJO9Eyim7OqxVYK4pb3Lxm1mcNo1eJUM9RWRFqFvbcc\nrKPBCpFKx5qVpRN6ny2SVaAINDO3Cw5fcwi7Bje29NcC4rZi3KnghTn+woQid0izkGhhRDz1qT/y\nGKxZStsOWcVS3Rfn1GDVIavA0SvymcM1jJcslS1JJOWjOq1jwUCOzyiqd33csRXB/1AxeCHD7bl4\nxy7RgeiGvQlUdqQ7KfYj1FyCPQgoHWmOR3PoxRh3Ip1QEUkIX+2JvN7z5Cw/R/unC2wB30RgS78C\n/LZS6oa1duPP84O/1BXpS//5/0QeKfyBDOp7lzRBR7bU+VJC88eBeKMfywd9uCzVqC5EFD5zp6Dw\nFVlJKlplT4X912IYeCx/zzJaFB1nbcMwmRPkm3WFhZo2xbmkTxdOo8sp5JrZ9xxO3omxXZ/wSDSc\nRWAp72iJPK4bvL4mvySC+aIpUqO8UfDai0/5ZGMFdRhQvdijd1TBr6a4boHjGFxtGIwiipEHnkH3\nPAk9s8C5CcZoSh9HxG1L0FUEHYnyGC/JPDkWKzrRof2igk9mC5xWQtEJCPclIE3nIhWqP5GtedCz\nXywl3LEiOpTky/oHISq3DNdkBug/C0jXYuZmBozigPFhGaeaUf5pRNw6lXRVrCy3VjLCbY9rf+UR\nn3/vEuU9y8lrBXiGqB7j/biGygUXmLRFhWFcS2lPM1orCOYnxEcROtW4ixOyxMXdCQhOFHlF/PGT\npQJ3ogk6itHFjGhT8ql0pshrBZXHLtGRZXBe9L5BTwhP7tTSu6IkBE5LVVgE0Po8xzqKaC8maQds\nfttSu+sRdi1ZCZFLLeZ4HQnKK8oG1Uzh+F9JG28n6J2QomLQU014omh/ljNacr6AkQBfvD/lHct4\nWQkt7KnD+JUpznYon+VUOA3xckrUiDH3qhhXwOXJjNiUdSLz094VIfvrlTH5YYQyitK2JpkR27Qy\nYkYI9j0ajwwf/qPnI3965f/9736u7/3kb/wPf+rXU0rdBv57a+0//rP8fj+7vtQVqfEUacPijcEf\nGaobiu51uUndnUC0pIeiKZ0siKZwtGoJO4r6E0PvokNat5I937Jk9YLSpkv0eUhWs3QviSaw9szQ\nv6jJqha/q1j4ScLWX/PRrVQ2xBZG53KiZz7NB4bONYEN+0PN9GyKGrvC3Hx5QvRJCa6NyecciqFH\n45liiASoqSjn0w8u4pyZUigY32tS31XU/t0ThnHAeOqTjX38iswEHW0wc4r+KMJayPoBbl8OhvpT\ng5MYjKcYLrmn4A1LeCKtvxtDdzUXLqa2BLdLOFNZuPCNLqPNGv7ChPG0StAT+2IegfUNzpkpU11B\ndXziNngjRVEqcHd9uDbEe1jh+GAWlqZ49YSsFzBeMkT7WsDLqaL+1ND6XJNWLJ9+cgG3ZOm8naK0\npfxZyHTeJb5QEO45wmHV4EwU+vqIYTkC15LslyjtOsSzhuhHFYqz5jQmRubnacPiTDR5yeANHMJd\nDyeG2iNN/1qBKiTbS+fyvvSv5gQ9CTI8+LqAYlQJwhNQhWWyACcvuHhj6Fwty3vcF4PE8KxiuiTt\neNB1yUun0cqJQ1LRKM9CJcPZD7D7oeQqnWp8xxcyMYc0pTps3yuoPJuw/u0K8WJO2tQCMN90qD0r\nSFohOpMx0cJPEo5vBujUxyoff4rI11IIThyiYwF9x00l8rrzCeGnFZzXhpTChOG0TeM+dK9CeUeh\n11261wu85wfI/1MtkpRSP/1X/vHvW2v//p/wvfPAZf7kPLmf6/pSH6QgrdpwTTFYczDLMcGDiLwk\nGr+wYzj8qsVtTwk/qDC8IDda0IVgYEir0sYYR7bgzsTFBLI5b9wHNzbs/XJG+8c+hW+Z/4nh4Cua\nzV/2yasFTB2iXzxCK8tgr8Hld55wr3webwBRa8q0KFF66gvQ5Fgx8gPCE8uoGxLse3inlSzKoi5M\nUHslsNCqj+msl2BtQr8e0F+fZeHsCW+fWeeTkyWR/yjL4XENpS3VDyL6N1NKGx46h8EatO5B76KH\nO4HximXmU0v98z7DSzU6Vxyp5E8c/J5i7aubbJxpov+gybQFyWEF5YC+UwUlVtR0LcYWCjJNtlmm\nfCDhd0lLiFCqkWKSgPywhCqL79t9HGEuj0GBuzomjSvUH8l7v/u3U+zAR8eCtdOZIks05aceo8sp\nTk+cPEFHNuj9S4q0afDuV3AunibX9EOym2P8+2XGS5ainVG+HzC+mBHseuRVQ/sjTTzjMH4xgZFL\nfDbn7StPeNyboTeKGHzdwRpFeDcCxzI8a6k+E/+/k1gmizD4qoj2JdteXGjzHyT0z/sEXVns9Ncc\nvJ7DdE4MDToT8XvaMOi+h6nmMJEHamVL4Y4t/UtQXYc89LAaahuGg7cseeRgnDJBV5HO2VP5nlS4\ng3MOq9+ZcvBGhD+wrP9ND2UNJjA4I4fpxRQSh+pDUUhMF8QynX5zgDWKQIFOA4q7VYZhBf/KgOSw\njvGNdCiuwutrJrPP7x790zTN1trXf57vU0p5COjoH57Gyf+5ri/11t4bWWpPNHnlVCt5N6Lx0FB7\nIuLvg7cNXk+TH0dkVSjqEjesM8hKYtnLlxKmZwqmC0bC9O4UuGPJWrIa1MCjd02q1sJXoiNMoXnL\nQfmSn35yZxYKxe1b51CnEAkAfHO6QVcMr2WU1x28sWV2uUcyl+OkMLiS450d43kF7U8U0YHmcL1N\n1igovVdGlXMq8yM8bfj+5kVWqj0Go4ij/TrudoD3NBTcmwL9lR6jtVzYmfMOpQPLwh93WHivYLSk\nOXyzwf4vSKuatA3F+SmTl6doLNUwYfKNEe0391G+QSXCJzCOqArsxEVNXCqPPMrbMser33ewUcFo\nBWzXp2jmqFRRO9/DRIbsfIzvFxKVfKdKtpwynVUc33Rot0aE+w7lLS3Z70Nwe1LJBTs+lU3N+EpC\n/5KMXNyJwlRzdKaIPilRTFyKRk6jNiEvS0XpHvnoHPxaQrKQSQjcGSXpBkiUMbnmx7cvc9yt4nkF\nJtOEdyOmSwUzS33cKUxnZUYYzyjadwv8RxE2MDQudHDHYvgYLfkoI+wA4yq8kZwWqpCv+Xd7uBP5\n3a1rKT/y8Y8dgo6if0lwjvlSQvcFy3TR0nslw0mF5+BkkDRkxo9VlLYd4oWctGbl9XxNbVOkYdUN\nzdIPDLWHLkUtxznxZL66YshbOdmKpJlOj0okvZBso0Jts6B9pyBr50y2KwLHKRkqm6fSuCN1ajt9\nPtfz3torpTTwj4AU+C+ex+/4pa5Ix2cUSdsw/67CH+QcvOESt5TE6D7SuCN5e4pAWnnjeoQdhc4M\nOhcCUe2jkMEFcwqJFmlNVjMYT1Pdtsx8LM+qyQKMVjQqtyJHGjs0myOGd9qYlRgmLivftYznpWLp\n2grhSLSMtacW47gsfb/P/lt1lssjTqZtkpaVg10bskzmquowgIpEfQzXNJX6FEdZdo8b1H8Ycnuu\nDk2Dm0umvTeE3ssZauKQHNVpbCmxOeYWd2qYLleZthyqW4bClwwhbwh+TzPJIszZKQ++f0Fo9uWC\nE1Um2AgwroWllOmiI1rHZ65Q4g2Udw29yyLm1mMH94UBqtCUo4R+VGKaeNiw4OrSAes/OEfIqV3S\nL3BjmC4aOr0KxWqG3fbwxjI2KEqGogSt25rhWag2JwyNIm77VLYsOvcZX4+xmcbpuRSR4Wi7wcKN\nQw6Pa/hPIpKWxf+wghNAfKYgbVhKjwKRX52PKUcp9sM6Wc1hNOfw4oUdkmWXp3sz9O62sRdinI1Q\nnFZtib+ODixYl2GvTZgiiZwh5CVFVpKYmPZtSzLQlPZOo71LPuNVYTtYLaJ6ZSzjeYelG1sc317F\n3QnEgJCD1/dQtqC6nXN83UWncqCXN2RUk1UdwhOFNy7oXAloPkwYnQnwe5ajl13ihVwq/4kC5CET\n7Pi4U4+sJhleklMm2VHL358y++OQzouSNFp65lLeT3Fjl+4VTbz4r+UP/Zmu5+m1P42I/9+BeeBX\nrLXZ8/i5X+qDtAgt1aeauAVZSZZHnasO1Q3RQoYd0VIOjaZ0mFL4HlZbst/ocHBYo3bHIehZgq5G\n5fqL2VpWsWRVw+4vQjA/ova7FcGOIW2ZMtLCWSvzRnNaqfUuSZV8ctOCa3HO94ljj7mPXfoXPR7+\nVwH0Cj7/5CymZDDaUmpPSBKP4LMS+bUpxrOojo9tZixcO2T//hzGlxnfym8+Zf13LjBdMnhdB3V9\nwPAkImjE5JtlVCaLGSeB0VkIu7KECnuG3iUHvyfz5Lws6ZbVdY09LhG3LVRzvH2f8uyA7nkPfRDg\n366Qt4WHOlkTv3dWtqhcDmPUaYrrxMfxCpLMo1xKcLQh+qMqjw/P4idyo0YHGr1dovdShldJycYe\n1dkRxdMmxhcP/sz5DtPUo1vUsK7FyR0qn/vEMyI9S6uW9g8CBhfkb6+MwkYFk+/O4zUgaYmmN6sD\ntYzqRyE6h/4bCdovYCdCHUZc/1v3+cmD82Dg3p1VbLmAVLP40gHdcYR7GDFeloTR/V/QFPMp3o7P\n/AeGyYymupUzmXcxDrQ2C7Ky2DSnV2OKMMAdQzwXUN52iA7EJTRYcdC5EK9G766yuJ/TeVnjThyS\neQMzCYM4YnTWUHvMFwDq5uOc/jkJXZy+OaK6GREMDN0rAWkd/L6kEKQ1kY8lK7LwJBeFSo7Casvs\nJ7IsLXz5551vRBgXWnclPyyrwtErPlZJlR0eu/xrYzP+DNdz3of/b8A1BEr/J6UT/6muL3Vrn7YK\nSkfi/CkfFFQ2xtTWZQ6qc0tpP6NzXTE+m9O74HH8CznTOcX0/RlmfuRR3jN4Y0O8mMnSqQtZzcJc\nQnlb0/5YkxyUOPnlKYObKZNFy+BiQf9aQTKfk/2oTXlboScO9fUCnch80gaGcNdhfFDGHAcMlzwq\nm+CvC3SrdG5AZX4EFhrlKWo7IjqynFs8we86uGNFvTnm8NY80Z5GVzPyiuGzD9fwxhav6xD0FMlm\nBRU7lL9XwWpIZ0VcXToU66lxQWeGtKJxJxCdGAYXjeTWdzX+wJI0ZZGjuh5Zo2B4p03wVJYhadMQ\ndDUmsHjHsrDy+4rposWdWMp7hvK2IogyzFaZOPaYft5gOIpIGhJOGHQspR3F+GLK+JUpOsqpV6co\n3zDslGXJV7HYRkb/4xns+w1MWUAwaeqS1i2VTdCFGByKQPB83kBT3nSofxyQVSBtFyxePkI1UvyO\nRnV8BldyJgtC8yr/pIQuxEX24QeXv0gdtZ5lYbFLZX5EbxKRbFTJS+CvDamf74oaYuhS+JCWNcPz\n4MaFxNT0DEWgSGsyBip/FpIs5KR1y3DFFRpTp+DodVmAhV2DfyhQ74PXPbEsn0tFXL8XMriS48xP\nKe8VeGNLbdMQNxzhoHbB7JQYnHXoX9Qi1tfQujuk8SijuiHbf3KN03dofexQ2lc0HxgW38tJKxrn\nb4ipIjyWJWp5Vw5564A/gMq2OVUCyMP2eV3Pq7VXSp0F/jPgZWBfKTU6/frNP+/v+KWuSKuPXeKG\nHG7eyOH4Rk1igLcN0VGGE+dkdRedaobnINrymPs44+g/nTAt6owCRetewex7LqXDnO1vuZiSQR8E\nDK+njL0Cfz2kaIETFNjlAjuU9MrSnYjxWbnhF64dkt6aZ7wivEl6DurVPnRKOAOhtA8uSIVhKwWj\nwzLhrkeUg/ruLN7fGTKKqyTfXUaVpCLud8vYZk4+a2WWFyvsQsJ4EKFzy+TGlPKnEaoQJYJtZbTa\nQ6ZbMyQ1jZPCyQ1FeR9qGzHewYDBjRlMIJHJRazp3BSNbPuW4vAbBWEjJu9XSM7H2JHL0vljdp61\ncXsu9ccwfGdE9KMK+WJCdhgSDCyjVUvwcQ1enOI4hrSVUwoyqm8dcvRgBlMpcDsutZkxlTDh6JN5\netuh4P1KBXplTPRehWHgkjUM9Vc7jPfqlF4/prPdwFHQeSMDBeVHPtZRBF1L55UCt+eQN3N0OceO\nXY4+mcc0C/KyRc3FRH5OElcIw5TpVwteWDzgweEcZuTjHPmYSGAvR/dm0WemZP0AB5j/KIUPA9Z/\nPUKVhKBU3lakdWjdPGT6UZvSQcFwVazGo3MFwzWFNxCoeHkHhmeFgxu3XSobUD4wdK5qslqBvTgl\nP4nwTzRpG3o3c4JDh5lPNP3zZY5eFW2oE0PjcY5VDoMLEB5qlJUKNC+J4aJ/qULnRUXQld+DQuGO\nBeJSRELaGq7IEpLvz6B/YcjwnIvt+AQdh7ilThUEFqsUpQOLm4jc6nldz8u1ZK19Bv9mMkm+1BVp\n86EsVryBpnPTMlnNqf61fYarmsm8R14Syo07lBY3q1g2fhWmz6pCJp/PGa46xG3FcNklOlLij15I\naP3Eo/pBJGFomxGm41OrTmgt9bC50HEkCtqw+6zN8Kxg+lQBWJitjnEGDuVtqQzdsdDH9cBFl3LM\nCyPyquXwNU089oVAX7O4Y4j2NMHTgNKGx40L27xz4SFFM8eMhATlxApOgtPXl5/NwGWmNMFJhcg0\ncyvh3O9NOXwtoH8hpPPGLIUv8Rvhvkt4pJn5WNG+JXHEsz92UbeqVLYU4cOQcN9l51mb0oaH31ec\nvJ5TK8fo3GJzwc0ZFyqbEjNSDD3Cdyv4Ry6TgzKRJ4cfrsEsxQy3aoziQMYBrRx3bopXSSl2S+Ql\nCA8d5n6i6Nxvs/Z/WSYfzVBbHBIeK2p3fJyOR/ryiP7rsqCp3Xdxp4r6Zx7B57JxzysGDNj5BPdR\niZVmj1feeEz8rEr5jyvcXl/GPKyI42lTiFSALMm0zAm9gWbzr3ocfCXAqyW4Y3GDNZ7mOInFdwoO\n3lSMFxwW3h2TVSBcHLP28g552TL/04KsrGB1ynQ5E7F7IB3S4vsp1rfYzTLh/FiA2ENJTk0bhv2v\nC2jGOKce/gyOb7ocv1lQ2lXM3M7wBpaZ2zF5JASqYFAw/2HB3McxpR0HlSlmbhes/GFKcKxF+rdU\nMDpbCCLx9yo0vx+y9sIeSVtm1lnZMlqRtFk3kUhz9fxGpNif8+sv8vpSV6TTGckFVwaWv2foXXQZ\n31tgcqnAH2hGywFBT+Ab0wWZ9WEVfl/jjQ0q04xuJDAQeER14xQX988Cjl+C8u4pVX4uww1zrFVc\nn93jkVuQOXPizc4VeJZkpqC85fAf/93f56f9c3ywuUp4qOm/klJ67JO0DdOqfDqjKGO11eXxdgkn\nVpTvhKd80IKT6w5BT0YMQU/x+U/WeDxQVGLhaA5X7emcSzNdEKza9FpM492AzZNVKsdCiXeSgnjW\nP0WkScViNSz+KGfvLZfoEDovioto/n2xB9bWLQdfN0Q7p9T2dY/x5ZTyA5/GbY/qP66x8w2gUMRn\nU6zj0/rcMDmjaH7qELfl0Chtu3Q/X8KdtxS5h51PsJ4lyx3CQ4VaSyhyB2vAVAom9Rx/z2M6q6k+\nhY1/z8NWU/IHDYo1sVYufVfT6VdoPzHkoeL4Gwl+KWNwFOH1NfNnevhOwfb9efyHEd4I1j9Ywe8r\nzr2zzcZsm+heJB+cPZEbUSgoFI3ZEaP7Tawr0jfjW3htQHZUxjfQuqM4vi7vyfi9RRo7Foxl7+2y\nLMHWqwxNhdKsYv8rjqQiPItwHZlFN+8p+uccKruKyvwA97MGQ12haBXgCdXL1mVZFD1xOfftp6x3\nWqQfNijtWcpvdxntzDIwLkWoOHkhxBvBeEkxWPNEGXBzSjzUlB75xC1L4XuEJ5baVs7wjRxrFKXH\nIYPzosJIvrOMe8qT8MaK8aWU6DMfVVhQinju+ZGdrfm3H6P3pT5IR0uK6ADCE0tS18zeiuleDqg9\nlEiQ8VrOlX8wIW0EHL0SMDljcIaCGzt6Tdw7TpRhd32cWDGZF11nXhEQsjuWCtF4Hs7UZ1yNePdx\nA31mir0MwZFDeAK9lhyo/teP+V++88uy3V6MBXEWa/KXRtiph3YNtlDEo4CVlR47B2dJ6yLH6t3I\nMO+MyIYh/qcRWcOQti1exyFpG7yhonNNbpqs9LOMdZENTUcu8YyiuiEYNSc1HLxRIjo2VDczehd9\nui9IVHJw4pHOZTiJR2ULmg8zdGYYrgTozFJ54uJOZRaalyDYFrPAhd94yN0/uIzxLW7XpXK1S09V\nOA48mneh/ZNDjt6ewx+KMyc6MaR1YQCkBFR3Nep+jfGyoeiEqEKigFEWr5ziTGWmOfOBQ3ikieuW\nfDaj8VOfvKQ5vgF5xXDwNUv1sab8IGD6oiFaHBHdqnGw0CDY8bArKXGkiY0c7t7Ysv+Hy5hzjlrv\n1wAAIABJREFUGUUI7c8KnNTSP+dio4LKQ5/+uIGtFijrkJcN5S2HsargZKLBrG3EdF4MaH0Go2V5\nMDuJZXLGkJc1aVO2+1YZTGQobbnoHLIylHYU7tQQtxTjBc3oqIy6WrD4R4qjVx2yWkHtKZTf1ex8\nS5M0LHcerOCduGTLOd7IZXx3htaG5fgVi98T2HdpX+GfWEYrMgIYTyVkb7pgCHqCIow6lv03XdxN\nFy6OhQaVKSZnZJ68f28OjDi61EQ0u3tfV5S2ZZTxvK6/aCDJz3N9qQ9SfwD9VxOCH/m4sSW4v4te\nW6N9e8zRq2UqT1xOblRJG4q0ZjGBQWlN/aGm92JB+xOH0bhMEYHxITyG0Yo4o9IXpvjvC+dUFVC8\nMiQ7loqm6Ae0H8FwTWyTK8snTDOP4706WkMRGtytCKXA6zmcudRnnPocr7dwh5rLX93gQW+O8XIB\ntZz0jGTbD7dquDMxo5sxypF0TRNonIUp2awmqMSknzYpb5/Ceh3F4KWU2i2fwc2U/MWMbOox+0ce\nk0XLdF7hTH3Ku2JQmK4UTBo5cz/0sI545rOKQ9AxGAfSima8bDjzxxZ3bJjMuwQDw/Y7lrt/cJl4\nJUPFmsZdjXrSJDyjSNoFkwWH/m/Ok8zl+B0Hd6Q4ua4wnqWoFLj1lNGcplKbYgYR1dqUOPZ4eXmH\nTzZXMNslAJxqRhE4FIGFsQu+YfLNEZ5XkD+psXJzj2d7bUZnFbaZ4m2ETFs+zCh0UID1KT0M8MbQ\nfJCSlxVJTeMPofkHmoPXLQdvKsrbDs1HGW7sMlizLLwrWt/BqqL+zHJyDWY+knHR4GpB/wWX8vyA\no3IZZ6LJahKbHZ4dkD2qUdqR5Y/xILcwuZSw+jsOm3+7wF7NGO2VKO1APGspz0wYn5Q4+FsZztMI\nr5nQedMjve8THsjn0KumcOwS7blMFsVNlzSk1S8dWIZnJThw8GKOSjXVDzSTRR8nEetuZTfn6BUX\nZwrJSkKwGWCelNGXRiR7ZbyhZvpP5wkbinhOlk/VJw5JSxZNacNSnH9uC/HnvbX/N3J9qQ/S5BtD\nwrtVhmuW7NjBfXWFyYJCZ1KN+YOC45sCqyjtKfyhy2TRMFqxuAOH6YyiCMSTLoBb8C8OyO/V8Pyc\ntCZuE3eiOJkpEZ44ZGWJz5jMi0Uv6MHWThs9cKGeYR1NecNlfC5HVzLMxGXzs0VMZEQSdXHETr9O\n/1ldLIrPArzRz24QTdEroS9MsYchyrVUn2h6DQ/lCo/y7pkKjYcSbRKdGKzrM7xYoBxLNvYIt31O\nbp7GeaSK+EyOG7uEJ5bKW12OH7fpXRata2lfZDmsOAwvFzQ+09Qea3a/bvB7Mo9tfTamcbfGeMWi\nRw4mMvSvWsIDTXnbEq8WpA2NThQq1eRnY+rfCxm5CpMBOOShg3voE7aHxF5Bmrq4ruGnT88SPgyZ\nnk9RJz6XzxzQ/XbE8M4c1jOEWx7hiUfSAq5O2TpsoZS8dpL7NB9Y4qYj+MCRh04h7AiUOWkIhzSt\nGUxU4E4c/L7C7yt0YXEnBf7QwZ1qvImknPpDy2DVZe7jFJQif+xK27uoSHs1VNVQ2dSMVuRkcH9U\nR7uiKdUZ5NdHlN+rkIx9jGeo3QoYXHXwR4rxjRjtGlbrA7J/UOLopRL+EK59a5NnwyajVkB2q4k3\nBn27TNiR3CpVSFCiN5aRg9WK2hMEC/lCSn4S0ruGFAm5Q1aFzlWX8o6ld1UUC8mZDLfrkmcu1XXh\nRWQVmFxMKT/yGVxPWfp9h+Gy2IVRiuWZHuvP6T79y1CRfqmXTWeafYKTU2DIa1N2/6OEyXKOk1rc\n0wwbq4WS3nslQ2ew+CPZrNeeytPfnQidfbqW4o7B+XGdrGFoVCZkdcHfjVbUKehYIhzitrRXKBhc\nMIQbPrXHGu0Z5i6cUP/Ffbyug/84ovzUo/5AUV53KW+4pLGL/cMWyihmPlEs/CRjdCUlbin8PrQ/\nsxItvaEJjh3qGxmlJz5hOeVgVEWFBeW9FGWhc010ijpV2EzjH0jyZbA6knTR+Zz6PZfCg+4bGUdb\nTaJ9MQl4N3uMVsCdWmpbBfXPHfqXT5dlriXoQXnHcvxSlbSGBKdtyUPCmSgml1L6l6D+iY/KFfFC\nztkX9phtDbFKFnvhCdQfS7XNypTRNMBaRdINCf+wSvsHAcmMzOKmSwU7/ToHT2ek1cw0yXzB+Osj\npmdywjsR7pMQaxVZTTSk3sQy/+GI8FjR+MylKFk6r+fMf5ChjKXx0HD+nyS0PxblBAjdK60pupcC\n3NiS1SR9czLnkDQFXt274LPxazKL9keGpR+OZKFXKJzYUnv6M6AzJG1L0pao5fyg9MXr5IGkjPrH\nDkUA5WqMAh5vzNO56uG82aX/esKj7iwHd+cYDUM5xKzobn8W7RIdWMZLltGquN6KUDGdk8QGYzQ2\nKiiWYubPdVh+aY94MceJhULmjhVqL4RCEV7qU0xc8giiv35AVrVUHvhMFwxO1+XkRQd/YKk/UkQH\nit2PFp/fjWrVz/f1F3h9qSvSw3++zOillGDHp/XdkLilKP9Sh4OvNvD6LulaDH2PQ88FlRLPWGob\n0r44qTiOckmlYPhOyvCqQmWaaMfhwMzix1B6uUP/SZPSpoOTypa8sLIMUlZoRtaB3sspDD1oCfzZ\nG8qcyh3D4Osx8/80IA8VWTnEnVpmPlIMzyo6L7gEOwJNad637L1TEG1qnJ7IXEZLsuR4c2mTP/78\nMmrisP+mgDpKO+APLc0rHQaftokOFf1XErLDEno5xtkPpBo/sGQ1T2DHPTmERgcVGjsi6dHf7pK8\nP4M3VPSu55S2XIwLR1/L0UNHLKIOTOctJrCYekbl84DR5ZQxHtVrHdJemYMfLqEK8EtgXcPwKzEX\nF4+IMp+dpzPYSYizMuH8hQM2JmewGi7f2GL93VV0pjAbTZwZi7o0wr9fIW0awg8rhMhrF2WDsxeI\n7XYgvvBnv1LGPQVs1B9CZy1j698Jhfx/pBmcjYgOLbM/7TFeqzKZdUir0Hm1wP2xgwkMSUNT3TQM\nzktcCFrhdDzyEhx8RRMeVURf7ApA/OhV4XfqwlJ9Ct2XBIyjLIxekrFMz0SUN2C8WqCMYvqkRtHO\ncLriEEtuNbn+i0/57NZZ6hd7fGXxGbdnztAfRxTHJeKWKxEvQ1j+Fxndqx7j16eEdyLKuwJaSa9P\nyEoO5SDl8KiG/yxAr8VMzogV1e8r8rKh8sQl7tVRZUN6Y8L+szYs5LhPPRqfK5KGovmooPJ0yPEr\ndarbGQvvxTx5TvfpX4bW/ktdkQZdi3coyC8BRMBgEGFKBVndoLTk4YQd8Pc8aT+NxC8cv27IKtD9\nWsLwLOjPK1Ao6ncdmZcujUibhu5RFXckDqY8lAdn2JEMKBMV+JcHuBPwDzzwDQc7TZ4dtE+tgobJ\nokU/C3EnhqShKO0rkccglB9dQG3dUl0/XWL0Bbunc3uaFy9RF8thD+Ua6p/LeMEdiaBe5zD8uM2Z\nH+UkLWEDlDddZltDdC458d0XZUnhD6QFRYGeaomtPmPo9stMVzOSMxnBkcvkfMZ0zlKZGUvmfbvA\nXRsBUH2q8fYFxOKdpqr2umXs1GF6JqcIJcIFZFvraMPu41nckYMJLelJiFZW9KVzUx7eW+bc/zOi\nsimZTd5Qke6VSeYKbFRIe2tksRbtOFTXYeaWYbok/ICF93Om84YiOOVuTlzO/JHFugaVyYa6sp+D\nUlilaN2dUNssWPlnMDivqD2UBNnxksihmvcUOpVxj/FFZ5vWLeUtCfHrXlHUnmgxN1yQA9kqAabo\nuRjXLzATF5XLMtMdaebfg9mPIHoS4Pc0WQm++dc/ITeaYHHC5LMmP/iDlznuVpl2IvDEZDJdzYhO\nDM++rRhcyWFXuAqdG5beZU1vvclkvcbg3TlmvxegCoVJHBlVaZi5k9L8TEYD0ZGMepxHMo9e/V0Z\n7eShwhtDUtM8/G8C0roCA4evVZ7bfWqN+rm+/iKvL/VBOrgE5mzMwduG6bylvG2xPR8d5VSeadrf\nC6k/gf4lQ9Y0+EOYzDroVA4SnfJFtG58NkHlisElQ16yBN+voQpF/VOf+hNx+ag3+iRz+RciZhUV\nVMKEeNaQRxZ/18M7djG54vgbKaZSUFuHxgMYrkjPZ1yor+dExznuuCDaPwVrrCqOvp7hdzW1xwKt\nmC5Y+hc0eQT/53u/AF2f3ssZQU9J671f0D8vm/H+motOwJblYBlMQrJGgXt+hPXk4TG8UBDPKGY/\nSalsiD7S72ma3wtxuy7eoWDmwh2PoKOYjAMJwZuZoG5VMUsxWMlQan/lgLwkMO16YyLQ6liiifuX\nZbZnM82DT1bxuppoX+G0EprLfZ7uzqCHDtYq3JFm/VclCUBnEF+J8UaK1scOyhVAzGTB4o4V06UC\nZWH3WxDtOhgPjm96+ANNXrYcvC0P1s4Vh2jLI54vGFxPOb7ucfRanYM3NFlV/ntpZ0JlSyJDTl4x\nJE1RT3RuGNKGon0vJ7kQky5mqEIslFZDOlNgPHnoLf+LnHDPpfrYRU0d8oEPGyVeurJJ+sKEyaKl\nsgXTWU33qhzU8UJB0IUf/vOXuX97lXSrTFaRZaBej9BjB6cnVWtzYcDJTYnAjrZciopEzLRvC1Wq\n/akE8GUvTDh8qyBtGoGLLxjSuuXkmk/aEApa40lOcKKx2uIfOQyXHNKqYnAjJZ6RUUBwP2J4UWy2\nrQfJ87tR/xIISb/UrX14pPAfh2RlsS3GMwpbyXF3QvIQzIxi+Xs9pjMNjAdpFeI2KGOxizEnDZfS\nU4/SgcI6PvlSAloqyP4bCVE1xrtVw0ktzc8UHaeKaqYksxqdKlTH52DUonKgCU8s/b86wXENPKmQ\nlwx+16H12Zj+RVl+JXWNN7FEBzG7X6ugcxkVZDWBplTvClHfSSRQT88kZH3/NCNKopxN4TC8ktFc\nGHDQkMrXncLJOzGVjyPa73skDUW+rHDGmmxYwVYKpvOG8obwLNO6SxEBy1PcKCXtNSjt/iznyTJe\nlhueiUvShrgX0jqy5NcL4lkJ04tvz9N6BL1vJXhuwZtrG3y0fpWgp5nOWkxo8UoZat+j9soJxzt1\nmLhkkcPqQofLVw4ZZiGfv3uN3vWc9MRldCHH3QuYfe2AfX8eBi7lPctYCbx7rCS2efYDTdKA6az9\nwhYbHLiE+w61dcNwVQwYwbFDVhd50HROpHLR0w7N+hzW1Xhji9/VeAOHIpQ2OjxwaD7Imcw6VD51\nBeF3paC041BUDLX7Lo2nOZ1rLoPzLq17hu4VTbgnQGwsbPYb2MOQ1j0YLSuqWxasIugbnMwhGEje\nvU40QRd613PysqK6Didv5bhHEhc9Wx4ztC3mVzscj2ZwRg46U4yWQLVjpnMRpT0oOiXSuiWZz9Fe\ngd/18QcwvChVvVU+w8sGr2fJGgXhrsvorCXogFvKyS/npPuhHLRRQVZyCE6e30H6/y+b/i2/8hCS\nlsIEkiJqNdRuBZLf1LJkNcvWLzckMvjDhLO/26f5uUUnivCePP11LhvMyhbUfxJiYonedQ58Jp0S\n4zOK7lVp85yJovFeQLQ6xPgyHyU0jJdFJ1jsR1SihLxiuPDiLlZbNv5micEFxbStcWNppXa/ViGt\nW7yRxRtJ+6dSRdK2jF9M6F0FW80xqXMak6JYvHSEXZ5iSobG/JD+4yZnv7ZJ74bh6JsZ1koS6mRe\nUd02mIcVinpO/RGsfAfKm5qsirS4Glr3c9RmRPKgLhbWm+kXFPzGA1j+jqJ+xxOIxYZP3FKY7RLN\nNw5R58ZgIK0qWvUxvTsz3Pu/r9F8YFE55I2Cyrk++VFI+UYHkOp99kceo17Exdox3/34Bu8/WWOy\nKAoKdwJYuPDGJjsbM3gjRbTncPzVnOnZjP5liRKOjgxpTTz3TqJAW+Z+LHPc0oFlvCjwaIl2FheY\nCWTGG3YsT/7uPMMVzXgpwo0Nk2VDeCIPo9qmxEUPzro4KSy8J86lhR9LTEr5mUM8a9l922G8UhAd\nKIYroiP1xgIQSdqG7m6d0q5mOif0+jyUz1hWlsSC1iddoo4h6EJWk/yv2lOo7OV4Bx4zt2Ss8/Dh\nGfJWTufWLMGJpv4YrnzrCfnLI/R2yPhiRv9yIX9Xqwj2PaofRlQ3LKMVgyqkmh1dKPA6DioXmHRW\nE0ZCXobmdyPMYYg31GR16aqOXtM8+bXS87tR/xJUpF/qgzSel2RPZyqi/KAryyN3/C/jbeO2JCKG\nGx2UMQxXNdPlHOvI942XDTqF0YoE4oU7Hv8fe+8VY1uW3vf91tr55Ko6leveurFv7Bw4M80JHHI4\nJE1qSNoGTDmABgxCDpIMGdCD7QeZNgzYBmULki2I9oMeJFOABWaKcciZnp6enp4Ot8O93TffCrfi\nqZPP2XktP3zVTXpMDpvDBobkcAEHFc7eVSetb3/hH7ImFHM5lXse7esFtU3ZBN6FIcEXDlAvNTGh\nwT03Itz08cbC/565rjjamMHvaraOWoRHirxR4vegf8kyOKfpXxBBCHeqGJ0Wy5KiarC+JThSVN8J\nxMgs1zB0sanD9HzKNPUppy6V+QlnZzuYiuHWzRVqdx2qrZifuHJNstkMpvOavGWgEKfMpOngjS1h\nB0YnNHlFMjZ3rHCm4pLp73qkTUVeF9uNzuMOkzV7rKcpFya/p2n4Kdyr4saiDJ/+3jxlYBlcFYGY\n4cUCp5ExnQbU7zr0788wGEXYqUvnKXlMr+2v8Y++/1+gXZnYF/MiCKMKxa3NJbEaefpInFInzgeI\nARBMb9AzTJcN8WoBhaL2MCPsHPeUY2i/VR47I8jnJGtYybARokVlzzA441D6iuqW5ugZydiProqw\nd9CXc+OlEOPC8LSm9z0Z02VDZUdR21asPXLA8JwA8YMjaRPlNQhOj0Qta0X0bRe/keOP5TM4XhNf\npP3nZ5ksCJJgcjpHr8Z0ns/Z/EGHfD5ncFozPVEQ7bioTOyadSmDRa3kNavuKNwjl8ramOREhirB\nBJaiAllDAqjVlsY7Ps5YY07FOBdHBF3x08oaEviPHpeEoLJrRRltVv6+Xp1+hDtVfcjbd259VwfS\nxa8pll9Kad4v8KaQ1xVRR/pqSdtSzB5z8SeW7scWydoVWndFBDc+nVHdko05fWZK0BUYlCqOgc/3\nfJxUQOrtN8dMly3pgzoHN+clm+w6xMOQaM8S7csHvXfZYiPZsNlhRXQet1x0Dv7yhOKRKc7pMVnL\n4MbSO61vSO/NGWuylgSu0ZWMj1+5g5rJqLanXFjfI8k8nL5Lo5JwfW9ZvH72XManSp5Yeshv3r+M\nCQ2TEyVJG/RUU7/tYl2o7hdkTRnG+X259HcvuVT2xZJEIReVwUXxd1r/9AZ5zdB671ik+Bs5zTsQ\nDODutbVjC2KYnsoZXpVN7NRy8kYp2eXtCguzQ/JPDnFShd2q4B860CjwOi75i3P8j7d/BNMJyJZz\nFhcHGB+qJ0YoV+A4/W6N8ECx8Aqo2BE410LJ4Y+mdJ6Eyp5GJxqVa4KtHkWkKEIZJvbPOcQLx15e\nE7mgzr+icVPL+GxB2tLCBlp2iBctMysD/K4ITLtjGUKVgWK87LD8coo7Bd338Iaa5oMcDIx/eYlo\nX6yq85pldNpQ3bE0/nWd+toQ08wpKnB0xWP/e44tXJR4zBdVyZInJyzt1QH5xMPpuVjPoKYO8cmc\n6oZL6cvnabJqiQ4EGvXW1hpmo0o8b6nsK8wbTaq3fYraH/ouWQ31+5po12F0riTsKFbmBsTdiHRG\ngno2Yz/QgCjqhv4liztVtN7WeANFGH4kMp+yzIe8fQfXd3WPFAW9874I6Yai7u2kkp2ConJDAgkG\nDp8G+7rP8IzizD+7y0H/DN3HBYPp3Ik+MLDLT8fU6gner7VwMtE53f1EnfY1w2RVMz6bowYeyspg\nw03AGYh/+Pxr0PmREvdqTLpXJT+dkPV8wKHYqmIVFBVDfVszvJIz8kuq10PQku3lNUteBTcsuD+c\nhcOAJ87e56vvnuNvPfdlvtw+z4OjWfwX6+TPxRRpCA587esXUYUiPD0i7ocE9x2ylsUfCE40azhM\n1gzeWJHOHvcbTxXMvgvRfkLpV1GlReeaogZ7v7ROFEHvc1NMoelkIWUIyUpOe3XAfHXMuw+WcTo+\nZbXEhJafuPAWv/jq05RVg9936H1liaJmqVzqMxpGuPdEQjC61GfUr5C+OU+1p4jnNfvZLMHpMb5b\nkPsONgmpXgtE5+Bvdqn/bhs0zLyp6T3qE4w1WUNM8Py+ZfdzSxQVee/dKSx9cZ/us/P0LirCrkCn\nxmtCnzz1K5bOY6IQZhXkVc30jTmcpwbktxu4sSLaF9X7UvRNaN3NSWc9oqeOeBjNUtuA/sdSbOJg\nlYeTWmavSz+0/WaG/X+aOA2FzmWAlTyZkJUhpmKo35aJvthXW9I/aBPWITwAXSqSWcXMLQj6KXvP\nBahtD7+v6H9+Qv0PKjj3QrKVnNXVLmnh4gPDN+do3JVWS2OjxLiK7mWFP1LoRKB1nVEVr55Rjh3K\nQBEeKtwJtO4Y4jlN9zFD6YszRF63mNdnPrp9+tc90r/Ya7gudiFuAjPvClSlDKC+XeAmomCeNaS/\nWb+nmS4rapuWcrlNXlOY0FB7IB+qrAFFxfKp83c4PdMlryuypqL7RMnwcs5kVRMcWZEpiwUS444U\n4zXF3qcMR8/ndJ5QmEKRJh6EBltqcXE8Lv8qe5raPdGpdHsuzm6AKv6wNBSFc4heqzCcCh/9td+6\njNvxeJi2SEuXq0u7jB4pMV0fezKmuTZg4RsCD4oHIc03fZxYiAOTFcVkRTNZFNHfrGXwB5bajgDF\nd77X4f5/Kd5CxhNwedY0ZC3whha1FRHcCUnaEhDCXY/OZot3b69SfyegcQ+CdozVll+89hRLX3bw\n+lrwjxN5PYcHNZSyqBz0kUdyvUXjdVGBSmYtaMkIn1rdprvXJO2F+H1R+TceTL7exkkli3JjqN91\naL9V4g8UC6+mlKGi/0RO874hmROr4oNPLYBCgPNaREcaDwzhoQSR+qYhjxSTVU3YFcbQtBdRhlYs\noCuCytAp7D8bMDjlUQaW8fVZyorBH1mid0PmX5I8Jl3NmSxratuGdMbFn0jPPJ1VTFYVxcCntqGp\n3XUZnRfhmvZbBm+oRR3Kt/QfKwQTfLtkvOIwWhMrk7wuTg52o0I8Lwpj2i/5wtqbdPYaDCchjceP\n5DM5YxmvOHSvyGfeG1rq97XY6fQiiszBnSqScylYyZJHa/Ka6EzhDxVhV0p99RH2LK39cLfv5Pqu\nzkizlpjUNe+neN0pwZkGSdOhe8kjOhSleneqSJ+bUPv9KoPHc0rPp/NclXAPLv2jHvlcld3nKyx/\nNWXz8z5fuXeWKMooGlLiO7HGG2pUAaPTQCB9sdqmuJKWIYR74jxpFdjNgOJ0gtNzMXM55XJKul7i\nBwXprQbRrlAR229A/7wmXrCY1QT/diT2EosSXIJXm9QmMD5pcBLFFzceId6pca9WQCCK7gCjOy36\nP1AQ3fVlsHDKsPIVQ+tOycEzAcaF6VmD35Pj43lF+JMdnq73+No75wi8kumVhEmhUWMHWjmJ52Ed\nh8oOxMuWslngbnvkWiwr8vWU0XmFM9aUE59w38VcHjP8t2PM7Qa6kMFOUdHEi2AUpPOGykP9gXYB\nFtxUQaLobTd5uVclaCYszwzZGi/TuK3ofzxl9oWAvK7Qqdhoh13Z/OmsZed7A5LTKcGWT+8RRTZf\nUCSa6FBhtcLJZbgXz8O5XxhQhC365wToPnOzpLFV0j/j4Q0V7sTHnUobyCpL1lS07pakdWGCLX29\npHPVxR07YgGyK6W21eB0hZ7avaKoP1AkbQl4JoDqQwh6wm6afa+geV9Rv9XlwU/MkrZL0jZ4I83c\nqw57P5BDoWh/TdN8kFBUQqaXpd9ctHMaV4bEu02W2kP+6Zd+gNqGw/icYnCvyuxxRTH5ninO3Yiy\nYlBWU7rAwKOy46ALj3TO4oY54zMOup4TvhsxPSn20XnNkrWkveEkH+FG/UsAyP+uDqRFoyRpu+gi\noDwb0H1cmi3Nm8KjD44U8UpJ1S/wxhblG7LLMXNfCsFahpdnab6+R9iJOHwqQBeW4PUKuqxQLlrC\nTYU31ujc0n/UoBOFmjpEh6LEo3PBS4aHUi73L1pMsxCGU9UwPz/k6OYceqyYLJSEx8yirKEoPbHe\nTdZznP0AN4HxSgmOpXHDQxWSMdjFlKITcKnd4eabTezilOywQrTtMPUN4ckJzns14jOZyPotJwxO\nVyhCl7wuAsHhvgg9+32HogK7t+fp7yzhVy1TU5Xprm/wRprFCz12d5bIj5XUdQZz33BFn+DqkNAt\nGL07S/0+ZE1FOQ5YfCVnslWldwlsYEmaJXnVoazLBi20lqnz/ZLOow61gWX5pZLJgsP4pGLp9BFz\n0ZR3t5bYeWWFmTsCOrepw2RVmGNBH/JIBnTVbcvMDRmmuE/GcD0gXjJUNlxWvzzh9n8U4Ew0jdtK\njOQCy+RUnayhcKeWubdjjO8wOONT2y3xx5rBGUE1+EOxT2m/LT3C8YooiR09BbMnOozfmqP9Tsl4\n2WF8CsJDSCqGwQVFeKhRxhJ2LOHh8dAHS2NTMMOHj4fkdYhnZ2jeNXQjTXBhQPFmi9G6BOZoNqbz\nfEgyH0nV8zBg5oZlPPJhEfTYIf/FBewnMsZnJELpHKZLmiK0lIlLuZxDdjwAayMGkDX5LHgDxXQQ\ncOLsIXvdBmVoWfqyZnxCMT5VUN2Unn7a+ihT0r/4pb2y3+mc+Du0lFL2sb/9D5l7J6F3ISD9/JDJ\nYYX6LY+gJ4yfyYpM07FgvGNWUwaTE+JbYzx5j6V3aGjeUtIOaFmykxmq6zH/mpzb+8GYInUIaxmh\nnzPoVwjuhcJgmhdPc1UomusDzB/MUgYwPVFiA4PKNDoR8PnsDUPvosaNRWYtrxvcWFBH6Gd3AAAg\nAElEQVQGVsF02cJKIlbGgQy/srYMsPRUM3+xQ2k0n1t9j3/11Y9joxKVOOhYSX/yyCG42me81UDl\nUqLV72mG5wU833jHZ3g5p70yoPfuHHY5YXW+z85bSx+U/06qaFw6whhNf6eByrTgiTS4Q/EnSldF\nA8A8OcJ/qU4RwvRsRnTfp7JvGTwC3kB48WXFYH0DjmVxpc/+5izhjitC2IVAlIKBsLgaDzI2f8gX\nqFc3YPncITsP2oR7LkVk+fxnX+e3XngSdyKC2WUopWh0KDx4dwr9H5pS7kWSAVt5nRv37QeD4cFZ\nRfWhZXRKmFStu4b67REbP9aUAPeoovYAJieg/aZlvCxB1kmh8aBksuTQupszWXTleY7kgn3yNw2H\nj3lU9uUie/B8iZ5qwkN9bJZnqW3K8Ms64mePAmfo4MTy4JxYGGjpLPJeJApzMsEUCps5qExTfeAw\nXTa4E0W2mkOicccOZWSOoUSK1nvieqtLIW48/LcKSB2q913KQIZSzbuGvU8ZggMJnq3bhsp+xuhk\nwGhdcfNn/x72zwkCVUrZ9Z//nz/UsRs/8/f/3P/v213f1RmpP7QkbREmsd9owrr0n9KWYuGNhLwS\nMDgHyy8ZJkta2EIXE/RBgJPIh+n9zRUeSrBQJbRuWdL9gMHlkrTlUP2xPbi2iF8CeUD947sMVAWd\nweCCpayW1BbHjHdrDEcRUSj9vWjHYXrCYoMSUzNkocfRVU22kqGmDjY0eB1X7IpzGH1iitqOcO5E\nLH6jYPML0kvFQHDoUlQsxirizOPrR6fAQPurHllDMXwsY6Y9YtybZdSpUttyaHx2j8E0Ij9oHveI\nPTHHG7j0qlWsZ7GlYuv+PLUDyZCrD0WUZfzWHEXVQmio7GjSOeG6F2spXlDQCHJGriHSBm8swhrV\nOz5z7whYvWjlVLc8gud6DB60qN/yRJPg4QKhXBfIm8JDZ6DpnyxAQ9YMKAMD3YDKtsNOdYaZNx3i\nBTnnKw/P0DjfQ//KLEfPlqiwRFcyTK/O5Ozx0G7ks3LpgMN0kaImltzJnGK8bmi9pyhqhvG6EvHq\nbUXvvKb06tQ3LZX9nLCvSVoOdleTR+IXlbYNdiajuuMzuFQwuKKI2iPyUYBxfWxUcnTJp6hY3Fig\nWJUN2Z7WgegQBhcMw3OK6rbGuMJc6u00Ublk3a33YLIKgyuFMMMqhQyzY0cEU4bHPP1Zi21nmCXD\nhaVDVisDfv+tS+ipg4lK2i+7+BOLLgzGVRhHUbklWLB0TujCZSBWNAtfc4iOcrY/45K2FOCT1T5a\nhXz+Egg7f3cPm04J5GXwiKWyJ/YTVgvu8eEnQ8afmVDdUYzWHKIjy9ofjFn/BYfWTcjWMpnazhfC\nOa5Yeo8K/a93QfqYC18THUjnf2/jDwQZkM6V7L+6hH8/pKjI1D/cc0lvNlFW4WyHxFdjkrYh6Fr8\nrkO9PYGxJ723+RJSjXUsXj0lb5UUNUvahDDMKRfFh3zrBxXekYtdTfBmUvSVIQuvWbo32kz2qjx8\naZWg4zA6JSIsuu8SvzZHUTdED3ysht5Xlogf1MlrkpXXNiA6sCy8arm6toO3MqHdHkFQkjWtmPud\ngbxpyeYLVC7iyMXTI9zTY6xrcL0SpSyjhw3cnYDiRoPpkiLa03hjSGaF5KArBcOnU5I3ZvH7mnjR\nMjlRkp+Nyc7HRM8cUbvnEixOUYWitjDh5K9IVu6NFCqXjJNM03s2JzqQamJ8r8l4EtL8qYe4fYfG\nawHZYYW8Bu5EEzzXReWa3TvzuGPhx+cNg5OKolIRKvz+sQLW0pTJSclUi0iCy/CUj3EVh88Ko+nw\n4yWj04bGLU3tzZDDZ2DmLYfmDZf8Tp1gy6f1Hix90RVdgnbJ+ITGuKIQpks48TsjnNTSuqGxgcGd\nQOXAkr84hyoUxXyON5RgEx5ZFl5ycOdiqs0E7Rn02MXvuMzcgLAjQa79+wHuexVy42BQ+IeusLn2\nPPoX4OBpxWjVYbiuieeFQjtdL/AGUn2d/qU+rZtgHOhe8PCHIl7SeUJRORTfq49s/TUg/y/2qjzb\n4eiqonlLJqTTJcko0xlxRrSbVZzEktcgnlVkTZ/KtU3KQDH3oqjiY6QFYF2YPdGXaf+phMp+TnUn\nxx9C94JLsmCo39WoXDRMvQk07sPiKwarLd75IRwPUYL3IsIDcZzMG4bkRoto20FZJR5SYwdvNqE4\nipi9Jhz8vGEpCk1wP6TxwFK/51C/B+bIlyy0UyFtKPy+wplqYW+dSyQgTC1hR5MulNDMqW1Z0lnL\nye/fwLqW8kxC1gTrKqbLsmHeur9G9KU6/Tfb6IFHfUOYQmFX4Y4V0YZHeKQIBsfam1+v037ZPQ6k\nMLfeI58X1fl4Lcf40H8qY7ogfVV7FBDdDNCpYE4xoBON1ha1H5B9dU7K7kyshpNbTbZ+WED+tS1o\n3hJRDZU4UAhSYuamKLybhxGjNECXEC9ZKpsOKEsZWj67dgt/forKFEXN0rgPs2+Ld5EJLQuvx6QX\nYspABFSKupGL51VLMqeIugJoFLKCYFWDI006B9NVgztWx8LOMsj0RgovtsRtTTpjcUfShx6fUNS2\nYeHVFOOLt30wsGBgcKmkiBTjMwXuUKPGLkXNktcUvcuip/qDZ29SCTK+5/QDol3h6ReRorZlRL1r\nVZHOGe5tLHCrP489O4F2ihuDCd93CrUk85Z0RvCiTj3/IGL0LzXwYnlv/ZHFG0mFN/+6wWqZ4H9k\n668D6V/sleYeaPFwH6+X1LYV01WDNxQJu/BQ0dgsaN4z9J7L2fmkR3J5jawhfdL0XILXc/Emcqyj\nrfTZNgTzOF7zyWvi8z3zjiKdg6CncSdamDXziuEp2STxjoAOy1Cyv+LRMeVChk6lbNMFBPuOYEkb\nJVGYYz1D7xMptQ2NN1QEfoFxLINzMF0RQ7Joz6FMXCobLr3nU/IrU5oXumRzJc2XQ4rIMjgP6axB\nz6SoI590Vnqut6+dwEk01WqCk0qv0ASWwXmL6noMrpS4E0VtQzNdVCy/KLCY7PL0g/5l94oi/Eqd\n8dmCvK5w3ZKkHzJ6rU247dO4ByqVDK/+jk+yIL3nYGWCNxGY2OiUwZuI8lXR92lf7mBcGJ0v0Fsh\nyakMb6iwnmTxeU1gOChQsymfeew9xquK8aqwfGZuKPpD0f7MT0gG704VRbPkq/tnyFOXoKvJ2znT\nBSWe8IsW/0hz+GSEdzfCulasr0fS8w33NfFKyfbnLFtfMCgjkLrgSJPXLfFqgU4VjfsyqQ+GJd4Y\nwo4lGJTS370HM9dlCBb0oH/RcvBMwO73Vojbiv55zfpvWFSuGJ0yNJZGIiyzOMW4wrnXOYzPFHx5\n+yy9623Mce+pWE8YXCzxYgH+t+5KywUL6/UeeTfExC7JvKFxRxT7+xeFoVfbtJSRhZ2QeLmgsqMZ\nnNOkDZkJvN+VrO4b3EQkAZO5v85Iv2tW8xdqzL4D3lAR7jsMzhtMIFi85l1hD/XPeozXNJU7PpVd\nxf6zAcrC5370G9hC8/xn3qGIxKSs06mTzipUqdj8vMvRoxIIsqbg8oqqJV4pyE4nIlpyzMYo1hOc\nicYbK0xgiM+lFPsV9KFP/Z5m7m2LN7JEh1LaV+YnNKME5RtsIeIZyoD+vRnypiFdLHCniupDmJ7O\n8Q48picKbK6pVFLG0xBnqpkuW0xkqD8Q9EDwjpjpDa9mTE6U6ELU1EfbDWqf3WdySko764Kpl4Q7\njmhorhvyy1N2PuUzPpejtiJaN6F1t6SMxPu8/YroeGZvtVj5XSnfo33hqFe3HLJLU8pALkjjdUN6\nUEGV0oI29VKgZCksvOzA/92muDoBC4274B54ZLMGr56RziiStrBuep9OaH415J2fv0p0KK/RdEWG\ne8YqiqUMZ1e8uMwzQ7yuw9zPxDjbIcliSesNnzIUdSdzMiE7mZHOQmVXWjLBkaL0RTx57kbBid+2\nrP+6pXbDRxmFm1gqu5YysOhEyuOwZ6jsWQ6eckm/Z0z/Amx/1mF4BnqXofOUZeG1kvZbMaZZ4MQy\ndIzXc1QBm58XPLEJDcNOlYWvOeQj0Yd48sp9qucGrJzqMO5H6PUJ37i3TjJv8O9EzL8qvdvRaZEq\nnF5K8WoZb+2vEO26rP62xoSWpC192cZd0AOXoydEdyLaV4QHLrPv5cSrhWTpHuQ1RfutlHhGs/ec\nw/CMopj9KG1E/+ILO39XB9IikiuyLsAbCz5RFYqdT4pXz+LLQyoHhua9kul6zmTVYnzpC/36S08R\nbHt86c2LmMdGhOsjqu+EUpa6lvoDjfUE2G4d6UO6Y4WONTYVymIZijL/Tz36Kp/6zNuSjWpQjsVd\nnFLO5YxPWQ6fgv6jhjJUeF2H6X6VrbvznFw5ojE34ehpyW6MI49fhSVpu6QMFdFMTLmW0D7ZZ2Zh\nhO+WfOb0bS4/84C8ZXCaGb1LkgXHyyVF1eDvetTvOhjXMvh0jJ5NCd0CFR1nUfsa79Alb1qqO+JE\navaEAFC971FGotW6/XkppSerstl0CerSGONKJuaPLHFbkT414dzSIUVNAmt4oPH6mumSxRtIm8Cd\nwNILXXmOBtzrVWr3XKKOoZgrKGdz8l7A9Jxkp2lTY1KHyZoQK3QuGgXGs/hDy8rcAGulZPX7muD3\nG+QzJZs/tS6ZbK5IZ2To587HzLbGNGcmlL5lsgpYGD0imgvprKJ7yaV7waX3iHd8PnQeF856446U\n98GRpn/OYbqoSNcyrJHSXucSkEzFEPQ0O58UB9vmNalolr+sqN3yCLsWGxnRhjhwUZ7h4BMlXsel\nvqF49/fOM9yts1wdsr56BLeqqP0AEwkaYbqkyOoK41ryqqL+RgD3qxRvNwXT7CvCHZGJzGYM/auG\n2pZm7ppUHclTU8rAcvi4h9+RFkQyL3uiqDjM3EpElq9mcLsf3Rxb2Q93+06u7+qp/dGjsPCqZfnF\nKUXNA6S0VIWi/3jO+FRdLCFK8at3J5DMH/eQ9kXAGceSHVSoPnAoauANYfYdy9HfmBK8U5Veq5ZW\ngBND87ZieNahXEpxxyHu/ZB/7T9BkYulc1w12FyTJwHekYuTKMIjRXRoGJ4W+2NVKVFdj833FrGR\nIdxxyRqQzRqiA409FEqmP7QUrzUIgcnTOXGngq7lHDZqPOjNQCPH5BpbMbhTBzXW5C0Rtc6O7XRX\nZof0v7zEg1MeOnYYPpPQmpmQ3pxl/g3LwY+kmLFH/ZbL9OmY7CDASQThEO66ZOdjmq0Jnd0mtVse\n5maN/nlF5ZkOh7dnCTtieXHr7RM4DlR+dI/BfovZFwNad1J2PxYKLnEGhheaeLElOswZrwUYD4an\nHFZ+B4zn0HlCURpF8cSY+DAi3PKJ9qXHXd0zlHtitTH83oTeRpv1XwarC7oXXbImNG66QoltiZmd\nGwu0KrVV7K0q03MQXB4Q32vgxIrowCWZsxRPjeC9GnnNYl3LwtcVyZxm7oah+mBMvFbF7ErGdPC0\nxp0otF+i7lUwgaV5E5J5RdbQQhwIFaOTisVvpCRtj/AoZ+eHFO6Bx4lfVzz8Pim/dRrij2B02jBZ\nFa1aXcvZmzTYv7bIzF3oXVIEB5r5NzOm8y7dq4rFr1uKUJPXxO01mTeki4Zg4BB1LPXtgv1nPYrQ\nfiAdOXvdkNyLyE+kOHGIPTdBvVLDiRXuFDqPumQth4XH9+i8ukix/hEi8v8SIDS/7YxUKVVTSm0p\npezx7ae/xbG+UurvK6WuKaXGSqm+UuprSqmfUUr9qTm5UuoHlFK/ppQ6UEolSqm7Sql/pJRa/HYf\nPwhcpHtF8fDTVQ6e9JmuWvQjY/K6Idr0KBpSUtY2YPxIRlGVrFVn6lhwxqJiB78rfTKVQ+XA0Lre\nh/sVgr6USKUPyWJBvGyYLin8gSa4F1JcnMIjEx5b2SGqpMTLBSrVhJs+T1+6LwpTU+l97X6fEQ5z\nO8cahVpKsJWSaCbGfbLP9/zY29iFlMn5jKwltMW8ppiuF0xP5fynl77Cs1fvYkvFwbROknj4GwG2\n0AQHYi1hTiboRBPuO7h9l2jDk9LaAzzJPBl4jG/MgrLsfbrEFpqlF+T5m0IJZ79znIXn4N+KODqq\ngWNFCKZiMa6lu9USla0ji+n72MBQRpb9oya20AzOw9ZnhWhgPMFQZnVN74KmczWgebcULn2EDGoa\nijI0qFyRxx7h0oTsbMzolFzIDp6S4Z2TKMrYAaOYzrukLU3UEfGV4SMiuh0eKpo3YXyqpLprsA6M\nT4r1xmSrjjsRX6LpozFFoyTtRkJKAPyeQ/8RTdaE3ecVt366Tueqiz8o6F500IXgPN2NEJ2Khmj/\nglQTy18VGT7ssV12WrL3Cdj9WEDtPV96+G0H28pRhSWbseKAcKApGoboUOHfjnh4r03zttiN+8fW\nyv2zHkWoKNcSBmelL5/OG6ZrJZWHWvCqXUPYs8SzLumMoWiVhF1BQUwXRQD87NqhqEQlknnXNyXK\nNR4Ywo6i95UlsoUCU3x0xe5fhoz0z/Ns/wdg7U87SCnVAF4C/ifgcSQERcDHgH8G/KpS6k/MjJVS\n/w3wu8CPAnNACpwB/g7wtlLq6rf7BNIZ6SP6I6GLBl1Ful8h7GiK+vG0eaLoXzWE2z7xE1PSGUP9\nuUMu/uBtirMJNjSkSwWT56ZkLcvRjyTc+Q9mMKdipksiPRevlKz8vmLmupIp/EKJNwR9P6JWSXj1\nzXOMuxWhigLJiYy3XziPE8vwyx04eF2H6jZENwPcA4/g7Qpq4pImHtP7Dd74l49iJh565FLWDH5f\nSlMsuH2X3z68zLUXHiG8E7LbaWKNxr06BKPI5gzOp7t4tyK8kSJZKGnchfhsRiNIRAFr3yNtlzRv\nSslvNcy+5hLeDWSIs2/Qh74MhCLILsViSwI4uwFuWFA0Syp7GidVqIooqdc3C0Er8L6NiYVUU7Rl\nom8cKTOLmYLO8zmrLyQ0HxTsfUyTXYyp7AowfvrZMXPXpHcY3A+IDyvYvs/aUzvSCxwpmrchWSjQ\nI1dcYBcVw3UZlB0+qVj9omL9lw/xh/bYJkQxPqGpPFQkiwXZ02N0rqT/GVvcByHO2CHadqk/sKil\nhGy2RBnhv4eHmvp9zfRMznDdF1O6HPpPZvI6r2fkdUXRENjc9mddhs8k1DaPP5+zPrX74kTgjSzu\nxNK/bGHoEn//GOMLyy2vW/yOI6piE6Ecd580ZLOG2ra8V6N1OHq2ZHWhTxkK0sQdC+okfmaKdS1H\njzqMVzXjNcX8a9JG6j+ek9dkep8sl9y9sYLOFLNf8Zl9Nyc6LI5dRQVH3LxnCLc95l7yv91t+f9f\nf1V7pEqpp4D/Avj6hzj8/wSeBrrAjwE1oAL8NJAgAfK/+xP+z48gARvg54CWtbYJXAWuAfPAryil\ngm/neYwvZcRtRVGB+Sf3ufpj77F4tsP0bIY3kF5j8egYG5ZE+xbvVgXrWZLc5caXztFsTHB7roDe\nEV8btiLCA4XpBIRdRToL7khwgcaFC//HAbX74rhYRBbPMWDAO/Co7FncWBE89PEHYgeiM4VZTfAu\nDOlflJ6kGyvqW8esldTBjSXrUIWisiMY07kbBeGhcNuta7m9P0/ztjxvM/QwRjHdrlG77TH3mib/\n2ixlIEMaZ6qJPzfiwqldprlPdinGSRTNd13qDwu8oSboaoqqInz2iOmysK1YThmfFNuUqJKSPTOm\n+tDSvA3GHJf6DSt0VC1K+Zs/qqGVo1JBM5jMERhZx0OXUN82LL9oieZi1Ngla7gcPu5SNgvUdijA\n+OOsMGtIDzo7J57q1rM8uLeAv+mTtaQPXNkSvKYTK/Ka/UBbdu4dS2U3YePH50UwZEZRv6eZnBKg\nvzeTkg0C3LGmddPgjy1lRQD50YGInQRvV9BzKcaV96h4esToyYT6ex6jU+oDAoczcCXzLAVyV12a\nUHuuA6sxjD3iRWHHHV12KT41EPX6lmJw/nhfGIW6VqeyrRk+norXkidyfONTJXnDsHb+gLNPbHP0\nhCQEZc0QPXTZujdP/sgUbyyMvNqGg74XgVVkDcPwUk7eFOSHstB6w2PuusU4YCslOpWAWUaKyZLL\nw0+79K8UpDMCl8oritZtQzrz3QV/+jNTRJVSGgmgTwLPAq8f3/UfW2v/+Tcd++Qfuf8L1tpf/ab7\n/y7wvwExcMpae/BN919Dsthfstb+5Dfdtwa8iwTmv2Ot/cd/xudhn/xPfo7eFckc7NND4m7EZx97\nlz+4+QiMPYJDh7wu1M/hWRkW5U2DTkWDUbUy3Ach7Wf3OVHvk5UOb75+lmj3mNJ3Ycrcb4TE8xpv\nZJl9d8rgbIWkLVlb9flDlmojDqdV9jbmcPsO3kTgJNWHMnganYLaJsfmfAa7mOL6JXkn4vKVTa6/\newJnqjGehWaOH+WkYykF3bEMWNI58fSxDlR3LMPT4B73tsanSmxo0GMRQDaNAuUZgvsh1S37h+wf\nv6ReTYivzaIsJMs50ZYnXkkFJPOSYUYPHfLHxwSv14j2LaNTirADKAGon/q1ko0fFnhR1jSUzRI9\ndlj8Oux9XMzjWrczOo8GzNzK6T3iMTojfvBWSZnvjzju7wmV8uhjOdGGT9YyVLc0tR/a4+iVRbyh\nYnxOqLetdzRFRTRjs6WCYNfFOjD/umF0wiE8ElFv40sAUKUMlcJDsW12Uug8IdJ7xuXYYFASIScR\niu/C1xw6T4qxYdATum7RMIRLE5L9KlQFOaHHjpT1l7s42nJ0d5b6+oDxKGTh3wTsfcrgNHKCdyJ0\nDpNTJV5Ppv4rL1rcSUn3kmR8kzWDdcV9QRd8AIQ37QybOjQWxpiXZnASaTv1zx2Lz6wVLK0fcdir\no7XBdQ1xL6Jy32Ph9Zzdj3sfVBTWhWQtozobo19sks5Y8qahcdvBSQUFgRJ1f1OVz4uduGz+rT8/\nZVMpZU//w5/7UMfe/3v/1XeMIvrtZKR/G3gG+KfW2jf+lGP/5vHXm98cRI/XzwMDpNT/5kB5BQmi\nAP/LN59ord0GfuH4x3//wz30/+/yppbKnmZ6NiMehHhHLm8fLdOamVBfHZKdjSmb0jeza7F8UOYz\nll4pqW46KEcUenpfWeKV185z40vnxJ52xmIdi9qMRDA4konp4GyFwTlFfaukumPpvd3m7esn2duY\no3ZX7ClK31LfFCxeESqqW5DMKeITOeVsAQrMRpX2K5r3Xl0XbnxH+po2dikKoQOOzxQYH+InYpz4\nfYGTjHheUTQNxhVNS2cupXLPo7ahWbh0SNBICSq5DGkaCl3NCW8HeLcqaG0EWWDE+iPoC+axcd9w\n7l8lWC19u+jlGo0Hht5lWPvUFun3DelfKaCRc/CUT+Whpv7A4g809fc8/J6m9VaX+gPREBic9akc\nGoqqXJDqDzSNDXFMVRYaGwXprHhDFZGi/ZInWf1AlNu7oyppu2RyNSXcdXHHmt4zOdnHRuQzBuWX\npAsl2WzJ7o9npDPgjw3dp0oBvJ+XXnbYUVT3DJWD8ph0YT/QLG3clYxu+WsF67/WpXbfZbIq3vH1\nDSuqXh1FtDwm9EXM+ZlzD9ATBzuX4Z4dU/7BHP3rczjzCUWpqb0W0boxYOkFTXQtkgA2b4l2HBr3\nBZPaveAwOul9YIcTHcgWjg7kAqxyhb8+xiYOrTc9RlsNxhczxicsaVN9EBxVoTi6tsD8zIi8H5Lf\nq+MMpT2w9TmHMrDEp7JjaUBR6zevNwm7Fl0qaBQML5YMLlja1wuKisXO5JxY72BzjTN2vp0t+ccu\nZT7c7Tu5/kyBVCm1Cvz3wD7w336IU77v+Ovv/HF3Wmtj4CvHP372Tzh3wJ/cQvjt46/PKaX+zP6v\nB89Kk7x6x8ffE9/20Tfm6W+2GB7WMLmDv++RzVjKoc/i16H+WkhWFZC13Q9xp6Jn6vccsjlD5dBQ\n21I4iXiCp7PHfk2ZqPlUdyz9sw7xvKK2qQg6Do33XMYXMpxY+NvJrLiR6kIyLr8P1fse8y946IcC\nscrritqmJuiJsIcyoMKSciAuqG4jwxtZ/BsRi9/IRbD6gU+8aAgOHaIDCQa1r1ZILsdMThgOjhrk\n21WSbojViuEjBe5mSFmxLH5iB0dbilbB7HuGyo0QnVtat8V+5OFnqtSWxyJE3LYcPnFcbpYO8SCk\nuuHS/HpIfdOw+GpCMCxp3BcbZG8CB8/PEXQtte2MrK6o7mQkTdH7TFsQHWa07ohdc/+sizcQyND4\npKV1NxHnyk/sUV6akO5V8LsO3kOfMpC+pEod9Ft16ncdZl8IQFm8voPtBszeEHFiZ6wZXBHTQG/M\nB0GnendIGYL1LeknR4RdS+tuyuyNgsEpj96jLbKGlNbv9x/LQIJgMvWpBRmL5zq881sXsAqCeyHp\nQYXi2BuqGPqk9+tMn5vy4AszHDwn9iZlIC2C2fekh+pNBaZX+orxKfnd7I2C5k3F+KREktq2It2s\nEeyJLgLNHPfQo/7gDwV1VAmqmaHPjpmvTHAmmqCrqD3QhB1FcCRqX06loAzAhAY1dknnDNNFCdj1\n1pT6LQeVywDMGynCOwHbtxYIN31M+BFGtr8Epf2fFf70j4E68J9Zawff6sDjafzF4x+vf4tDbyB9\n0svf9Pv3f37XWvsnvSs33v93x//r1W/1mL55eSNN2rDUN8QQLZ4/VgMaa/wdh3ipxBvKpnAnLsNT\nMpTKa6KLacISZ/8PhYibdzTxjLhp1rYA6zC5nKL6HjoXvvjsDcvii106z85S38rYXPcpIgi3fRHh\nTSR7NKFh+MmM1hcjlLVML2ZkTZ+gq3AygaToDFZeinn4qYj00SnqIMQ2c2zPx9tz6F8yVM/1GR22\nSFvynHUmLQKspv5ch/G1NrYrQskC7lf4ex79pzJULBvEeLDz6jL5Uk7jhsd0QUDpkxWxvMgaoviU\nH1RhriTYdwl6UAaKwa+uELUkY0jnIGtpRidCgt4fql6d/Ddi2zFaV4xPBOR1y4ywYMkAACAASURB\nVN1/z8U/POaGG9h5PmJ6LqN5DerbJXvPSanvpNC9EOINIf6VRSqumPOFPcvR45bmLWEmDc/HJETY\nfYfxCUv9pkd9q6R7yaG6E+POeuQ1l/FJw+Iv+8Rti1WKYFAyuNLC78PcO4r+uTrNOxOOHqt8oD/a\nv6DQF8dkD6ssvmw5uqrEgqNioe/zsLOIuzLF1Cyzb4k1s8odkgXDyguWnU9pykZJ7fWKyDUaCWTh\noUWXUspXS0s865DOiI6DDQ21dzVZQ/rvMzfEzSBtykXcH8L4sQQ7dilaBYOKBg2rvwfjVYd8OySr\nGrbDJotXDjh8Y1H6+RPRQg06iuJhhH96JC6e1+sEXZisWiq7itHDBjyV4AYFR/M+zWuCesFKsHen\nH2FG+lcJ/qSU+jHgJ4AvWWv/xYc4pQFUj7/f+RbHvX/f8jf9fvmb7v9W5/5x5/+p6/3mf15RUoJf\nHRNd7GN84V3rXBEvy/Sz/XaJLsDvKbJZQ2XHfiDGXNm1OAk078U4OSx+cZdkTlF7aKlfC4RyWkJl\nB9zYsPfJWQ6fK+mf84n2JUtdeLXAyRSrLxQUEXg9B9ML6F8+7kENXfKZEncqWgDJUsH0fMb+0xFY\nsHshM9cVrVcDVr4kfUtVKNK3WvQvinZAGVnKupSvOoPDvSbGg8eeuM+Jq3uonidqSlZeF2oFk1MF\n8UpBfQP00CVrHUv1If1KVcjraFyItj3coZSFOhNtzqzO8WspX+sbhqVXEiZrEJ8owLNMlh2SWUWy\nUlB9KBz09TMHZAsFSVssYLwRRPd9wiPDwZMa//yQ6EAgSWVw7BvvCNc+a4ptht8TBfm8rpj/xQrV\nDYegDyYy+ENL2tJU9ixHV0LGKw7Dx1P+6x/+ZeI5Tf+KQRlL51GPrKbwRpasKhqd05WQIhLNgf4l\n6ZfWf6NG/a74NtU25fUPDyR4mdCQTXyKdk73UynrP3GPZMHgjRRHV0UCr3bLo7FRiqhzYEnmLOms\ncOJRirCTUd/KAOmbu10ZmhlHUd0XsfDeJUWlI+y8vAbNV4TB5owdTvyOpXndoYgUg+cSiprBXYgJ\nvILOoEY+n1PUS8zlMXNvCk3X7yu41qC8XcPvi3tEbeuYPr04IbgbUhxFAAyfTsnbOVhhliXzHyVF\n9K/I1F4pVQX+CZAD//mH/NvVP/J9/C2Oe99u8JtL8/fP/zDn/nHnf7CUUv/gj+BdrVJyjUvWcoan\nBbqRf++QhdaY4W5d6JuFAqPwuxo7m/HwCznjMzLhDTqCm3TGmnitpHdFlOj3n6uQV2Dj312hiODo\nh2JxZWy+H0gUnasu41MS5PyRJV60OLlsaquge8mjtmNov2VZ+oqirJU0P3YArRydaIbnS7I5g040\n1VsyjV56OWXhVehfsGRNePgj0oMtWjJl1Zlw+PPFnHDHpbYpGTWF4uRTD3l7a4WNrTZqMSE9kdH8\n/j2BWN0LcAeOQGsaimBtzOf/xivkaymHHy9JlgqS8wkXn9nAyaD+QGiEaNC5JTo8Lje3YLpkyVcz\nRic1R5dDMcM7cPA6LnFbMTktQ6HO8zn+ALbfWiJ86FHbkqDvjS2Ne4bpoiZbzskzl/iJmMaDkuGF\nkqAL00VL+OkOxrcMzxqS5ZLeJcGa7vxwThlIYJ8/2aP7qGTUvU9kBAOL1Yql3/X4v372x4m6ltY7\n4qDqjS3pnOLoeyWIhUeSJU6XxWiwuiV90f4FmH9jSjqj6T2fkrUs4ZFUNyjQfsknr9xCd3w2fukM\nugCsCKs0bwvLbXTCQZXgTRRlxRAdWoqaZbziEi8GbH+/TzJvKU8J2L2oKI4eg6Mrmt4VgTlN2xqd\nK7JzMVZLBRJ0NN0LQjbY+7QMgry+Jp/6/OSJayy2RjhRidd3yLohyYz0qt1YssvqjoisTJcNqrAM\nHs840z4ivziFQuEdeHgPfSozMfX1AdG+wvh/zbX/49bPAieB/9Vae+NPO/gv2rLW/gNrrfqjNwBK\noedFR4Zkr0rk5WIFcmpCslLgZAKMDu6G2EK84+PTGU4mIGoA1cgo6iXVbU1eFdX39lu5iD4bRbwo\nweR9ewy0ZLU6F5BztK/IqyJIMXPTUITQuyj4xqyqUKkmLzXeRoA7kYddWx1KILSS5d7/dxz2n7cE\nR5r2W4VAhwoRt5i5bXAyKGYLzpw8oKhZJicM8bLBGTvs9huE1yP8PQ9bKrxKxsOdWUwom3jlxZL5\na0aGG4nHr914FJs4eH0HPIs68nn32jpF1dJ/BKbLomalDPQuaPyPdeldtoIqGInAS9A3FJFAjmZu\nHE+aj4dFKnbQuWS7Tia3xZcHFBURGh4/kVCdjZn5zQozX5RebnAgdEVdKobX5nBihW1nWNdQ2ZUy\nt/lGQNaQ3TZ+eR5vpJi/VlB9J2ByLLy8/4M5/Qua8CiX8tmRbDavWcg0g0eO9amz44zcEdFjq9+v\nbgRKx9hD5wKhcmOF33FwtkJevHUOM5czOm1wx5rmHctoHTofK8guxUyXLWHXUts2BEcO41VFZUcx\nPA2HT4jgSnSgcO6HlBVD1oCyXhIdiFaCk4g9itVghz6j04Z8OaN11xz7NylmloeoXP6O2/H45//y\n82zvzVCpJhRVQ7TjMrhgiBcs6ZzFm1ic1OKPDctftXhTaL3hc+Otk5jDEJo5ebPEHSvSew3G95r8\nwH/4MgsXDj+y/ftXApCvlHoC+LvAFhJQP+ya/JHvo29xXOX46/hPOP/DnPvHnf+nLp1o8jMxh59P\n0bFm86U1okZC3gslUFgpT/MLU9pf9bC+RU0dRucKvJHwj5Uj72Bl3zB3vcQbgQkUqy8krPyij9/X\nx5hQS/vtlNUvxVR3LGtfLLFKyiV3amndFmhKZd/KlD2Do+cKnKmmd08gR+WpRKBOL88IVMmFxmbB\n/NccgkOHeMmQzAiPv39B03zX4eAZJarp+y77/y97bx5jWXbf933O3e/b36tXe3VV79M93T1Lz0IO\nR+RIFLV4k+zIC+QliBM7RowARgI4COIYAYIgQfyHgwQxHARIYCewgRixaJtSbMoiqRGXGc6+dE/v\n3dVd+/L25e7n5I/fmyHNkBQljiLZ4wMUCvVevf2+c3+/33cbVil8g1lIKG9JPnk0DAg+cyyKo5FL\nsVcifODhH1v4HcV4xWbnZwyTsxnWToAZelilnMIzWGMbd2ShwwLjGMIjRfWB8EG7TxWi1+6WJQyt\nUMy/LtlVx09D9/mMvc8aupdFeYQt4xQ7EuTdmUqbHrcU2nOYrEkF6W36TA7LHD2n6T0u/ErtGaIl\nYRRoVza1hfYQu5ITdA3xnMwPy7uK0oEhKxtq92C6YEtKrIKsYvA3xbZv9yd8pkuWbDy3c0q7EOy4\ntK4J9/XoSZelVzUrX+synRe/Bn0q4v4v+ZJXNLIk+WBJU7snai7rzJjmt3wYO5z/eyPSdkHSUGRN\nDa7h1NIxWGJwk9Qtwn1hhIw3xK/BmSrSutC+2u9qyls24YGcgEanNeUdReOuONy33zH4R7ZQkWwj\nlCc1o4v9sxZ+xyaryFgmaWtqbwZkbzUpb9lEJzLWf734aDY+PiGjhMmSzWTJRmlDHsCZS7tyXJdT\nVGYRHhqKZoY3sPjiK8/Rf+XHEh3+K+vjRO2VUi2l1BeVUhOl1EOl1J/97W/1268fpSL9HwEb+Bvy\nPFTlu3++6//82WUfbm5DvrMZrvyQ+//wur3vuXz3e67/Ybf9frf/bZfXt2j+VoAZiLNTVtekDyu4\nzRhOTkgXBSW274cMT0PYnhIc2qhC4Q1gcDnDFIpw22G8ZtG5bFM+KKi+vcfui2KlN3dNNpTKTko8\n5+CMEvISHDznMl0VL8t4Tsxzk9MJ4xOK2kNN4YE9dPB7Sr5gJxOCMAUFcVtTO9/7aMbrxIblb6XM\nv6noXxA0vv1egfal+nUnIvNLYhccgx67jC4nEpSXK6LEQ4dSjdTuWNhX+yRtTbSkmX5hTHVphPIl\n3/z8xW3OrR5KzrqBZCHHqWYsnO4wfKwgacGJr0aUthzspQg1dAnOD3BHMu+brAugZg0dwl0bZyJx\nJnOvObgji8ZNAZDCQ0X9jlRSg3MlMT1uGJRWeB2bYN9m8TX5HEt7Cu1LBv38WzK77vQrFAOXwxcK\nnAkMT8HodEH/opglF4GMQqxUJLz5RoyVQmXbiHHyvqZxNyetWERL6jsqoopQiKI5i/7lBpM1afed\n2yW0rwk7mqKiiRYUwbHFaEOx+rKm9NUKeahQ9ZTbf7EifqclCLdtbL9gt1/DXpsyuRwT/+SIvKyE\n+7knJ+F4XmNdHYhN3oqcLJOWonHNoQilwp8uCPC5/1MFecnQeE+6DFWA3zXkpe90UtGyWGu5AwFB\nk7ZIbsMtl+0/l30kM3ZHisojCHqa8bphsmQxPpfx4K01rEwRD32wDcaRGXp8MqG0bYvt3se1Pt7W\n/u8AKbCI0Cb/7oxq+WOtH2Uj3Zj9/j+A0ff5+XD9L7O/PwAwwvS/Mbvuhz3RD9H57x0ZfPj3xZkI\n4Ifd9rsf60deKz+xzeAcNK4LKGGqOeGehX2jAptllr8iCqTGLUSe+etVVA7lhzZpHeyRjbUbYKcw\nfjwBYLhh031xFTuD3FeExxknft3gTHOyssXgsRrdT2XYsRzEYg8nlY5JLZKWVKYfHshp01DatjFj\nh+RuDbU+xR1aRG/OEa0VDE657P18xsM/5ND5+Yh0Iaf7tGSTW/KUGDyek9ZFbdQ40QfLoCxDsRZT\nmZ+QPqjiHdskFyJKv7DPuFeCdkLl1IC4G2ArCVEbX0zZ/+IGd3YWUHMJ828CjsG5VSL51QW8jlQm\n2z8ZYqfgvlfGThT+r9Xx+99xlzI2LH3LsPRaMvPkFNVOaVfMkSu7Ap4MzlmoAo6e15R2FMvf0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EpYx46JM0NZ1LgaQNtF0q25rxio1/LJLZk18q0I5mcNpl8liCe+DBXErSCAlm3PA8EEDLDnM8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IatNxYxltSd45LUiKcvReS3ixO9CKlF545heCij87kxOlH0VyH1Nf6+Rr1UpX5RXHYzTzE+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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f08a0d189e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "io.plot_img(imaps[1])" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "# power spectra\n", "\n", "fc = maps.FourierCalc(shape,wcs)\n", "\n", "p2dkk, kkappa, _ = fc.power2d(imaps[0])\n", "p2dk1, kg1 = fc.f1power(imaps[1],kkappa)\n" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [], "source": [ "modlmap = enmap.modlmap(shape,wcs)\n", "bin_edges = np.arange(100,2000,80)\n", "binner = stats.bin2D(modlmap,bin_edges)" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "cents, eclkk = binner.bin(p2dkk)\n", "cents, eclk1 = binner.bin(p2dk1)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "image/png": 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bC9GJ5fzk4s6mvU8uTkaxs5h7v3ov5w04j9sW38HjW35KwHUK2nIuFTVw+7zV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\nK/FW249Zgp6j18Ds/fvhGml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"text/plain": [ "<matplotlib.figure.Figure at 0x7f08a4f49048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl = io.Plotter(yscale='log')\n", "pl.add(cents,eclkk,marker=\"o\",ls=\"none\")\n", "pl.add(cents,eclk1,marker=\"o\",ls=\"none\")\n", "pl.add(ellrange,clkk)\n", "pl.add(ellrange,clk1)\n", "pl.done()" ] }, { "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": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
cberzan/kaggle-caterpillar
exploration/bagging.ipynb
2
11075
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "\n", "from sklearn.metrics import mean_squared_error\n", "import pandas as pd\n", "import xgboost as xgb\n", "\n", "from soln import expert_params\n", "from soln.dataset import AllCategoricalsFeaturizer\n", "from soln.dataset import generate_xv_splits\n", "from soln.dataset import get_augmented_train_and_test_set\n", "from soln.experts import get_predictions\n", "from soln.experts import train_and_save_expert\n", "from soln.experts import xv_eval_experts\n", "from soln.utils import eval_model\n", "from soln.utils import train_model\n", "\n", "pd.set_option('display.max_columns', None)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.5 s, sys: 184 ms, total: 13.6 s\n", "Wall time: 13.8 s\n" ] } ], "source": [ "%time aug_train_set, aug_test_set = get_augmented_train_and_test_set()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 112 ms, sys: 24 ms, total: 136 ms\n", "Wall time: 138 ms\n", "(27270, 53) (27270,) (2943, 53) (2943,)\n" ] } ], "source": [ "from itertools import islice\n", "fold_number = 0\n", "%time X_train, y_train, X_test, y_test = next(islice(generate_xv_splits(aug_train_set), fold_number, None))\n", "print X_train.shape, y_train.shape, X_test.shape, y_test.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 42s, sys: 1.76 s, total: 2min 43s\n", "Wall time: 1min 42s\n" ] } ], "source": [ "# Baseline: Train a single model on everything.\n", "\n", "baseline_params = {\n", " 'objective': 'reg:linear',\n", " 'silent': 1,\n", " 'num_rounds': 1000,\n", " 'gamma': 0.0,\n", " 'eta': 0.02,\n", " 'max_depth': 8,\n", " 'min_child_weight': 6,\n", " 'subsample': 0.7,\n", " 'colsample_bytree': 0.6,\n", "}\n", "\n", "def all_get_indices(X):\n", " return np.ones(len(X), dtype=bool)\n", "\n", "baseline_featurizer = AllCategoricalsFeaturizer()\n", "%time baseline = train_model(baseline_params, all_get_indices, baseline_featurizer, X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on everything, test on everything:\n", "(27270, 53)\n", "(27270, 53)\n", "(2943, 53)\n", "train RMSLE 0.124960740984\n", "test RMSLE 0.227403087285\n" ] } ], "source": [ "baseline_train_results = eval_model(baseline['model'], all_get_indices, baseline_featurizer, X_train, y_train)\n", "baseline_test_results = eval_model(baseline['model'], all_get_indices, baseline_featurizer, X_test, y_test)\n", "print \"Train on everything, test on everything:\"\n", "print baseline['X_train'].shape\n", "print baseline_train_results['X_eval'].shape\n", "print baseline_test_results['X_eval'].shape\n", "print \"train RMSLE\", baseline_train_results['rmsle']\n", "print \"test RMSLE\", baseline_test_results['rmsle']" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train has 27270 rows and 7960 unique taids\n", "----- bag 0:\n", "this bag has 24486 rows (0.897909790979 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 3min 5s, sys: 1.08 s, total: 3min 6s\n", "Wall time: 2min 24s\n", "train RMSLE 0.123432578577\n", "test RMSLE 0.229418813724\n", "----- bag 1:\n", "this bag has 24586 rows (0.901576824349 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 55s, sys: 988 ms, total: 2min 56s\n", "Wall time: 2min 10s\n", "train RMSLE 0.122111587117\n", "test RMSLE 0.227541088433\n", "----- bag 2:\n", "this bag has 24717 rows (0.906380638064 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 32s, sys: 900 ms, total: 2min 33s\n", "Wall time: 1min 37s\n", "train RMSLE 0.121361805697\n", "test RMSLE 0.229519295813\n", "----- bag 3:\n", "this bag has 24601 rows (0.902126879355 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 31s, sys: 840 ms, total: 2min 32s\n", "Wall time: 1min 35s\n", "train RMSLE 0.12122519513\n", "test RMSLE 0.228024319671\n", "----- bag 4:\n", "this bag has 24556 rows (0.900476714338 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 38s, sys: 680 ms, total: 2min 39s\n", "Wall time: 1min 48s\n", "train RMSLE 0.123122970152\n", "test RMSLE 0.225918550374\n", "----- bag 5:\n", "this bag has 24489 rows (0.89801980198 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 21s, sys: 736 ms, total: 2min 21s\n", "Wall time: 1min 19s\n", "train RMSLE 0.120961715396\n", "test RMSLE 0.23123137468\n", "----- bag 6:\n", "this bag has 24612 rows (0.902530253025 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 20s, sys: 640 ms, total: 2min 21s\n", "Wall time: 1min 18s\n", "train RMSLE 0.121177518095\n", "test RMSLE 0.230244874745\n", "----- bag 7:\n", "this bag has 24573 rows (0.901100110011 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 19s, sys: 692 ms, total: 2min 20s\n", "Wall time: 1min 16s\n", "train RMSLE 0.120974039334\n", "test RMSLE 0.227374436674\n", "----- bag 8:\n", "this bag has 24593 rows (0.901833516685 of all) and 7164 (0.9 of all) unique taids\n", "CPU times: user 2min 18s, sys: 652 ms, total: 2min 19s\n", "Wall time: 1min 16s\n", "train RMSLE 0.1206663763\n", "test RMSLE 0.22666532693\n" ] } ], "source": [ "# Bagging:\n", "\n", "bag_params = {\n", " 'objective': 'reg:linear',\n", " 'silent': 1,\n", " 'num_rounds': 1000,\n", " 'gamma': 0.0,\n", " 'eta': 0.02,\n", " 'max_depth': 8,\n", " 'min_child_weight': 6,\n", " 'subsample': 0.7,\n", " 'colsample_bytree': 0.6,\n", "}\n", "\n", "all_taids = np.unique(X_train.tube_assembly_id.values)\n", "print \"X_train has {} rows and {} unique taids\".format(len(X_train), len(all_taids))\n", "\n", "n_bags = 9\n", "bags = []\n", "for i in xrange(n_bags):\n", " print \"----- bag {}:\".format(i)\n", " \n", " n_bag_taids = 0.9 * len(all_taids)\n", " bag_taids = np.random.choice(all_taids, size=n_bag_taids, replace=False)\n", " unique_bag_taids = np.unique(bag_taids)\n", " bag_is = X_train.tube_assembly_id.isin(bag_taids)\n", " bag_X_train = X_train[bag_is].reset_index(drop=True)\n", " bag_y_train = y_train[bag_is].reset_index(drop=True)\n", " print \"this bag has {} rows ({} of all) and {} ({} of all) unique taids\".format(\n", " len(bag_X_train), 1.0 * len(bag_X_train) / len(X_train),\n", " len(unique_bag_taids), 1.0 * len(unique_bag_taids) / len(all_taids))\n", "\n", " featurizer = AllCategoricalsFeaturizer()\n", " %time bag = train_model(bag_params, all_get_indices, featurizer, bag_X_train, bag_y_train)\n", "\n", " train_results = eval_model(bag['model'], all_get_indices, featurizer, bag_X_train, bag_y_train)\n", " test_results = eval_model(bag['model'], all_get_indices, featurizer, X_test, y_test)\n", " print \"train RMSLE\", train_results['rmsle']\n", " print \"test RMSLE\", test_results['rmsle']\n", "\n", " store_bag = {\n", " 'taids': bag_taids,\n", " 'is': bag_is,\n", " 'featurizer': featurizer,\n", " 'model': bag['model'],\n", " 'train_results': train_results,\n", " 'test_results': test_results,\n", " }\n", " bags.append(store_bag)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best bag RMSLE: 0.225918550374\n", "worst bag RMSLE: 0.23123137468\n", "mean bag RMSLE: 0.22843756456\n", "median bag RMSLE: 0.228024319671\n" ] } ], "source": [ "print \"best bag RMSLE:\", np.min([bag['test_results']['rmsle'] for bag in bags])\n", "print \"worst bag RMSLE:\", np.max([bag['test_results']['rmsle'] for bag in bags])\n", "print \"mean bag RMSLE:\", np.mean([bag['test_results']['rmsle'] for bag in bags])\n", "print \"median bag RMSLE:\", np.median([bag['test_results']['rmsle'] for bag in bags])" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean-combined RMSLE: 0.225020920942\n", "median-combined RMSLE: 0.22524214179\n" ] } ], "source": [ "y_pred_all = np.vstack([bag['test_results']['y_eval_pred'].T for bag in bags]).T\n", "y_pred_avg = np.mean(y_pred_all, axis=1)\n", "y_pred_median = np.median(y_pred_all, axis=1)\n", "print \"mean-combined RMSLE:\", np.sqrt(mean_squared_error(y_test, y_pred_avg))\n", "print \"median-combined RMSLE:\", np.sqrt(mean_squared_error(y_test, y_pred_median))" ] }, { "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": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Ykharo/notebooks
157 cosas de IPython que no sabías y nunca preguntaste (II).ipynb
2
40088
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "En la primera entrega vimos como usar la ayuda de IPython y como personalizar nuestras propias funciones m\u00e1gicas de ayuda.\n", "\n", "En esta segunda entrega vamos a hablar del uso de la historia dentro de IPython.\n", "\n", "IPython guarda la historia de los comandos que se usan en cada l\u00ednea/celda de cada sesi\u00f3n bajo un determinado perfil. Esta informaci\u00f3n se guarda en una base de datos sqlite. Por defecto, se guardan el inicio y fin de sesi\u00f3n, los comandos usados en cada sesi\u00f3n y algunos metadatos m\u00e1s. IPython tambi\u00e9n se puede configurar para que almacene los outputs." ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Historia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La base de datos con la historia se guarda en la carpeta que obten\u00e9is haciendo lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.utils import path\n", "path.locate_profile()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "'/home/kiko/.config/ipython/profile_default'" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y la base de datos se guardar\u00e1 en esa carpeta con el nombre *history.sqlite*.\n", "\n", "Una forma alternativa de obtener la ruta a la historia bajo el perfil actual es usando lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "get_ipython().history_manager.hist_file" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "'/home/kiko/.config/ipython/profile_default/history.sqlite'" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Acceso a determinadas celdas de la historia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para reusar comandos ya usados podemos hacer uso de la `historia` que guarda IPython en cada sesi\u00f3n.\n", "\n", "- Podemos usar las teclas de cursor hacia arriba o hacia abajo para recorrer los \u00faltimos comandos usados (esto no funciona en el notebook pero s\u00ed en la consola de IPython).\n", "\n", "- Si escribimos algo en la l\u00ednea de comandos y pulsamos el cursor hacia arriba nos mostrar\u00e1 solo lo que comience por lo ya escrito en la l\u00ednea de comandos (nuevamente, esto no funciona en el notebook pero s\u00ed en la consola de IPython).\n", "\n", "- En las sesiones interactivas, el input y el output se guarda en las variables In y Out. Poniendo el \u00edndice de la l\u00ednea usada nos volver\u00e1 a ejecutar esa l\u00ednea, en el caso de que usemos la variable In o nos mostrar\u00e1 el output en caso de que usemos la variable Out. In es una lista mientras que Out es un diccionario. En el caso de que no haya output para un n\u00famero de l\u00ednea nos dar\u00e1 un KeyError. Por ejemplo, veamos las siguientes celdas de c\u00f3digo:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "In?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos mostrar\u00e1 en pantalla lo siguiente:\n", "\n", " Type: list\n", " String Form:['', 'a = 3', 'a = 1', \"get_ipython().magic('pinfo In')\", \"get_ipython().magic('pinfo In')\"]\n", " Length: 5\n", " Docstring:\n", " list() -> new empty list\n", " list(iterable) -> new list initialized from iterable's items\n", " \n", "Mientras que si hacemos lo mismo para `Out` obtendremos la siguiente info:\n", "\n", " Type: dict\n", " String Form:{}\n", " Length: 0\n", " Docstring:\n", " dict() -> new empty dictionary\n", " dict(mapping) -> new dictionary initialized from a mapping object's\n", " (key, value) pairs\n", " dict(iterable) -> new dictionary initialized as if via:\n", " d = {}\n", " for k, v in iterable:\n", " d[k] = v\n", " dict(**kwargs) -> new dictionary initialized with the name=value pairs\n", " in the keyword argument list. For example: dict(one=1, two=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si ahora hacemos lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print(a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "Out" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite'}" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vemos que en `Out` se encuentra lo que hemos obtenido en las primeras celdas de c\u00f3digo. Si ahora queremos ver otro output podemos hacer lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "2" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "Out" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite',\n", " 7: 2}" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vemos que ya tenemos alg\u00fan valor para el `Out` y ya podremos acceder a ese valor por si lo quisi\u00e9ramos usar en alguna otra celda. Por ejemplo, de la siguiente forma:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "b = Out[7]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "print(b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como el `In` siempre lo tendremos, en lugar de ser un diccionario es una lista y podemos acceder al valor de la celda usando el \u00edndice de la misma. Por ejemplo:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for celda in In:\n", " print(celda)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "get_ipython().magic('pinfo In')\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[7]\n", "print(b)\n", "for celda in In:\n", " print(celda)\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tambi\u00e9n podemos acceder a los tres \u00faltimos outputs de las tres \u00faltimas celdas usando \\_, \\_\\_, \\_\\_\\_, que nos mostrar\u00e1 el output de la \u00faltima, pen\u00faltima o antepen\u00faltima celdas usadas, respectivamente." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('antepen\u00faltima:\\n', ___, '\\n\\n')\n", "print('pen\u00faltima:\\n', __, '\\n\\n')\n", "print('\u00faltima:\\n', _, '\\n\\n')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "antepen\u00faltima:\n", " /home/kiko/.config/ipython/profile_default \n", "\n", "\n", "pen\u00faltima:\n", " /home/kiko/.config/ipython/profile_default/history.sqlite \n", "\n", "\n", "\u00faltima:\n", " 2 \n", "\n", "\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos acceder a los inputs de las \u00faltimas celdas podemos usar algo parecido pero de la siguiente forma, \\_i, \\_ii o \\_iii para la \u00faltima, pen\u00faltima o antepen\u00faltima celda de input:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('antepen\u00faltima:\\n', _iii, '\\n\\n')\n", "print('pen\u00faltima:\\n', _ii, '\\n\\n')\n", "print('\u00faltima:\\n', _i, '\\n\\n')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "antepen\u00faltima:\n", " print(b) \n", "\n", "\n", "pen\u00faltima:\n", " for celda in In:\n", " print(celda) \n", "\n", "\n", "\u00faltima:\n", " print('antepen\u00faltima:\\n', ___, '\\n\\n')\n", "print('pen\u00faltima:\\n', __, '\\n\\n')\n", "print('\u00faltima:\\n', _, '\\n\\n') \n", "\n", "\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "An\u00e1logamente a lo visto anteriormente, podemos usar \\_*n* o \\_i*n* para mostrar, respectivamente, el output o el input de la celda *n*. Por ejemplo, para ver el input y el output de la celda anterior (en este caso ser\u00eda la 11) podemos hacer lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print('El input de la celda 11 es:')\n", "print(_i11)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "El input de la celda 11 es:\n", "for celda in In:\n", " print(celda)\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "print('Te he enga\u00f1ado. No existe output para la celda 11')\n", "print('Si intentas acceder al valor _11 obtendr\u00e1s un NameError ya que no existe la variable')\n", "print('pero te puedo ense\u00f1ar el de la celda 7:')\n", "print(_7)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Te he enga\u00f1ado. No existe output para la celda 11\n", "Si intentas acceder al valor _11 obtendr\u00e1s un NameError ya que no existe la variable\n", "pero te puedo ense\u00f1ar el de la celda 7:\n", "2\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo anterior es equivalente a usar `In[n]` o `Out[n]`. Una tercera alternativa, adem\u00e1s, ser\u00eda usar \\_ih[*n*] para los inputs y \\_oh[*n*] para los outputs." ] }, { "cell_type": "code", "collapsed": false, "input": [ "In[11]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "'for celda in In:\\n print(celda)'" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "_ih[11]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "'for celda in In:\\n print(celda)'" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Acceso a bloques de historia" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para acceder a toda la historia de la sesi\u00f3n actual podemos usar las funciones m\u00e1gicas `%history` o `%hist`, que es un alias. \n", "\n", "Podemos obtener toda la historia o solo una porci\u00f3n. Por ejemplo, el siguiente comando nos mostrar\u00e1 la historia desde la celda 1 a la 10 en la sesi\u00f3n actual:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist 1-10" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "In?\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[7]\n", "print(b)\n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si, adem\u00e1s de acceder a las celdas 1 a 10, queremos acceder a celdas sueltas podemos usar la siguiente notaci\u00f3n para acceder a las celdas 12 y 14 (adem\u00e1s de a las 10 primeras)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist 1-10 12 14" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "In?\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[7]\n", "print(b)\n", "print('antepen\u00faltima:\\n', ___, '\\n\\n')\n", "print('pen\u00faltima:\\n', __, '\\n\\n')\n", "print('\u00faltima:\\n', _, '\\n\\n')\n", "print('El input de la celda 11 es:')\n", "print(_i11)\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si ahora queremos acceder a todas las celdas donde hayamos usado, por ejemplo, un comando que incluya '`a = 1`' podemos hacer uso de la opci\u00f3n `-g` (similar a `grep`) de la siguiente forma:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist -g a = 1" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "29/129: a = 1\n", "29/133: a = 1\n", "29/136: a = 1.1\n", "29/138: a = 1\n", "29/142: a = 1.1\n", "29/145: a = 1\n", "29/147: a = 1\n", "29/149: a = 1\n", "29/151: a = 1\n", "51/7: a = 1\n", "185/1: a = 1\n", "187/19: %hist -g a = 1\n", "187/20: %hist -g [a = 1]\n", "187/21: %hist -g a = 1\n", "188/20: %hist -g a = 1\n", "189/2: a = 1\n", "190/1: a = 1\n", "190/3: a = 1\n", "190/4: a = 1\n", "190/10: a = 1\n", "201/1:\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a = 1:\n", " print(a)\n", " elseif a =2:\n", " print(a)\n", " else:\n", " print('kk')\n", " return None\n", "\n", "\"\"\"\n", "201/3:\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a = 1:\n", " print(a)\n", " elif a =2:\n", " print(a)\n", " else:\n", " print('kk')\n", " return None\n", "\n", "\"\"\"\n", "201/5:\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a = 1:\n", " print(a)\n", " elif a = 2:\n", " print(a)\n", " else:\n", " print('kk')\n", " return None\n", "\n", "\"\"\"\n", "201/14:\n", "a = 1\n", "b = exec(code)\n", "201/15:\n", "a = 1\n", "b = exec(code)\n", "print(b)\n", "201/16:\n", "a = 1\n", "b = exec(code)\n", "print(c)\n", "201/17:\n", "a = 1\n", "b = exec(code)\n", "print(b)\n", "201/43: def a():a = 1;return a\n", "201/45: def a():a = 1;b=2return a\n", "201/46: def a():a = 1;b=2;return a,b\n", "201/81:\n", "a = 1\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a == 1:\n", " print('a=1')\n", " elif a == 2:\n", " print('a=2')\n", " else:\n", " if a == 3:\n", " print('a=3')\n", " return None\n", "\n", "\"\"\"\n", "201/85:\n", "a = 1\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a == 1:\n", " print('a=1')\n", " elif a == 2:\n", " print('a=2')\n", " else:\n", " if a == 3:\n", " print('a=3')\n", " return None\n", "\n", "\"\"\"\n", "201/93:\n", "a = 1\n", "code = \"\"\"\n", "\n", "def hello():\n", " if a == 1:\n", " return('a=1')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "\"\"\"\n", "201/135:\n", "a = 2\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "hello()\n", "\n", "\"\"\"\n", "201/157:\n", "a = 2\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " \"\"\"Hola\"\"\"\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", " \n", "class A():\n", " \"\"\"Hola\"\"\"\n", " def __init__(self):\n", " \"Hola\"\n", " \n", " def _kk(self):\n", " 'Adios'\n", "\n", "hello()\n", "help(A)\n", "\n", "\"\"\"\n", "201/158:\n", "a = 2\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''Hola'''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", " \n", "class A():\n", " \"\"\"Hola\"\"\"\n", " def __init__(self):\n", " \"Hola\"\n", " \n", " def _kk(self):\n", " 'Adios'\n", "\n", "hello()\n", "help(A)\n", "\n", "\"\"\"\n", "201/159:\n", "a = 2\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''Hola'''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", " \n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", " \n", " def _kk(self):\n", " 'Adios'\n", "\n", "hello()\n", "help(A)\n", "\n", "\"\"\"\n", "201/174:\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''\n", " Hola\n", " '''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", " \n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", " \n", " def _kk(self):\n", " 'Adios'\n", "\n", "hello()\n", "help(A)\n", "\n", "\"\"\"\n", "201/219:\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''\n", " Hola\n", " '''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", "\n", " def _kk(self):\n", " 'Adios'\n", "\n", "print(hello())\n", "help(A)\n", "\n", "\"\"\"\n", "201/230:\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''\n", " Hola\n", " '''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", "\n", " def _kk(self):\n", " 'Adios'\n", " pass\n", "\n", "print(hello())\n", "help(A)\n", "\n", "\"\"\"\n", "201/233:\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''\n", " Hola\n", " '''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", "\n", " def _kk(self):\n", " 'Adios'\n", " pass\n", "\n", "print(hello())\n", "help(A)\n", "\n", "\"\"\"\n", "201/237:\n", "code = \"\"\"\n", "a = 1\n", "def hello():\n", " '''\n", " Hola\n", " '''\n", " # esto es un comentario de mierda\n", " if a == 1:\n", " # esto es otro comentario\n", " return('a=1 # esto no ser\u00eda un comentario')\n", " elif a == 2:\n", " return('a=2')\n", " else:\n", " if a == 3:\n", " return('a=3')\n", " return None\n", "\n", "class A():\n", " \"Hola\"\n", " def __init__(self):\n", " \"Hola\"\n", " pass\n", "\n", " def _kk(self):\n", " 'Adios'\n", " pass\n", "\n", "print(hello())\n", "help(A)\n", "\n", "\"\"\"\n", "202/2: a = 1\n", "202/3: a = 1\n", "202/4: a = 1\n", "202/5:\n", "a = 1\n", "print(a)\n", "214/20: %hist -g a = 1\n", " 20: %hist -g a = 1\n" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero esta busqueda no se restringe a la historia de la sesi\u00f3n actual sino que buscar\u00e1 en toda la historia almacenada por IPython bajo el perfil que estemos usando. El anterior output indica la sesi\u00f3n, el n\u00famero de l\u00ednea/celda de esa sesi\u00f3n y el c\u00f3digo usado en esa l\u00ednea/celda:\n", "\n", "*Sesi\u00f3n/celda: C\u00f3digo_introducido_en_la_celda*\n", "\n", "En este caso, pod\u00e9is ver que en la \u00faltima l\u00ednea no se indica el n\u00famero de sesi\u00f3n puesto que se refiere a la sesi\u00f3n actual:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si usamos la opci\u00f3n `-o` tambi\u00e9n obtendremos la historia con el output incluido. Pod\u00e9is ver el siguiente ejemplo para ver como funciona:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist -o" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "'/home/kiko/.config/ipython/profile_default'\n", "get_ipython().history_manager.hist_file\n", "'/home/kiko/.config/ipython/profile_default/history.sqlite'\n", "In?\n", "a = 2\n", "print(a)\n", "Out\n", "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite'}\n", "a\n", "2\n", "Out\n", "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite',\n", " 7: 2}\n", "b = Out[7]\n", "print(b)\n", "for celda in In:\n", " print(celda)\n", "print('antepen\u00faltima:\\n', ___, '\\n\\n')\n", "print('pen\u00faltima:\\n', __, '\\n\\n')\n", "print('\u00faltima:\\n', _, '\\n\\n')\n", "print('antepen\u00faltima:\\n', _iii, '\\n\\n')\n", "print('pen\u00faltima:\\n', _ii, '\\n\\n')\n", "print('\u00faltima:\\n', _i, '\\n\\n')\n", "print('El input de la celda 11 es:')\n", "print(_i11)\n", "print('Te he enga\u00f1ado. No existe output para la celda 11')\n", "print('Si intentas acceder al valor _11 obtendr\u00e1s un NameError ya que no existe la variable')\n", "print('pero te puedo ense\u00f1ar el de la celda 7:')\n", "print(_7)\n", "In[11]\n", "'for celda in In:\\n print(celda)'\n", "_ih[11]\n", "'for celda in In:\\n print(celda)'\n", "%hist 1-10\n", "%hist 1-10 12 14\n", "%hist -g a = 1\n", "%hist -o\n" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Otra cosa interesante es la opci\u00f3n `-p`, que coloca un *prompt* delante de cada l\u00ednea de la historia que se muestra. Esto puede ser \u00fatil para, por ejemplo, escribir doctests.\n", "\n", "En el siguiente ejemplo vamos a usar la opci\u00f3n `-p` junto con la opci\u00f3n `-o`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist -po 1-10" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ ">>> from IPython.utils import path\n", "... path.locate_profile()\n", "...\n", "'/home/kiko/.config/ipython/profile_default'\n", ">>> get_ipython().history_manager.hist_file\n", "'/home/kiko/.config/ipython/profile_default/history.sqlite'\n", ">>> In?\n", ">>> a = 2\n", ">>> print(a)\n", ">>> Out\n", "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite'}\n", ">>> a\n", "2\n", ">>> Out\n", "{1: '/home/kiko/.config/ipython/profile_default',\n", " 2: '/home/kiko/.config/ipython/profile_default/history.sqlite',\n", " 7: 2}\n", ">>> b = Out[7]\n", ">>> print(b)\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos guardar la historia o parte de la historia en un fichero para, por ejemplo, los doctests, podemos usar la opci\u00f3n `-f`.\n", "\n", "Con la siguiente l\u00ednea de c\u00f3digo vamos a guardar el input, el output y vamos a colocar la l\u00ednea del *prompt* de las 10 primeras celdas en un fichero llamado *kk.txt*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist 1-10 -pof kk.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si queremos acceder a la historia de una sesi\u00f3n anterior podemos usar lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist ~1/1-10" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "In?\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[7]\n", "print(b)\n" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "De esta forma accederemos a las 10 primeras l\u00edneas de la sesi\u00f3n anterior. Si queremos acceder a las 10 primeras l\u00edneas de la pen\u00faltima sesi\u00f3n podemos hacer:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist ~2/1-10" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "In?\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[9]\n" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si, adem\u00e1s, quer\u00e9is numerar las celdas usadas pod\u00e9is usar la opci\u00f3n `-n`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist ~2/1-10 -n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "213/1:\n", "from IPython.utils import path\n", "path.locate_profile()\n", "213/2: get_ipython().history_manager.hist_file\n", "213/3: In?\n", "213/4: a = 2\n", "213/5: print(a)\n", "213/6: Out\n", "213/7: a\n", "213/8: Out\n", "213/9: b = Out[9]\n" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Algunos de los comandos usados no son aceptados por un int\u00e9rprete Python cualquiera, como por ejemplo los comandos m\u00e1gicos que empiezan por `%`. Por ello, podemos obtener los comandos ya traducidos a c\u00f3digo Python ejecutable usando la opci\u00f3n `-t` de la historia:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%hist 1-10 -t" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "from IPython.utils import path\n", "path.locate_profile()\n", "get_ipython().history_manager.hist_file\n", "get_ipython().magic('pinfo In')\n", "a = 2\n", "print(a)\n", "Out\n", "a\n", "Out\n", "b = Out[7]\n", "print(b)\n" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "En la tercera l\u00ednea pod\u00e9is ver que en lugar de escribir `%pinfo In` ha escrito `get_ipython().magic('pinfo In')`." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Acceso a la historia de los directorios usados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`_dh` (tambi\u00e9n podemos usar `%dhist`) nos da informaci\u00f3n de los directorios recorridos. Por ejemplo, voy a recorrer varios directorios y despu\u00e9s veremos la historia de los directorios recorridos:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "cd /home/kiko/pyprojs" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/home/kiko/pyprojs\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "pwd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "'/home/kiko/pyprojs'" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "cd /home/kiko/pyprojs/ipython-master/nb/" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/home/kiko/pyprojs/ipython-master/nb\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "%dhist" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Directory history (kept in _dh)\n", "0: /home/kiko/pyprojs/ipython-master/nb\n", "1: /home/kiko/pyprojs\n", "2: /home/kiko/pyprojs/ipython-master/nb\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "_dh" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "['/home/kiko/pyprojs/ipython-master/nb',\n", " '/home/kiko/pyprojs',\n", " '/home/kiko/pyprojs/ipython-master/nb']" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si solo quiero saber el directorio del que part\u00ed en la sesi\u00f3n de IPython en la que me encuentro puedo hacer lo siguiente:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "_dh[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "'/home/kiko/pyprojs/ipython-master/nb'" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y esto es todo de momento. Pod\u00e9is combinar muchas cosas de las vistas aqu\u00ed con cosas como `%macro`, `%edit`, `%pastebin`,... Si da tiempo, algo muy caro \u00faltimamente, hablaremos sobre algunas cosas que se me ocurren en pr\u00f3ximas entregas.\n", "\n", "Saludos y hasta la pr\u00f3xima entrega." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-2-clause
ishank26/nn_from_scratch
.ipynb_checkpoints/mlnn-checkpoint.ipynb
1
292757
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import sklearn as skl\n", "import sklearn.datasets\n", "import sklearn.linear_model\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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Op3z22WdiMRYJsvlKBCUFkNqUlqncJLGmrdxggqRLly4XjT8rK0tmzZol//nP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R+VSs27777DggULin2uhIQEpk+fzq5du7GxsWHo0I9y00bIyBSV1atXs3r16ufakpOTCy2vrJzr\no8hxrp/+gv5TyXGur/6XtlWAhexc//oSHx9PRkYGzs7ORc4rJoR4qYwxY8Ywa9Ys/BXWGOgUXFQk\n0qxZM3bu3lUmxYTfZC5dukT16tVpXm8MzhVy/lwzs1LYsGckc+fO4eOPPy5jDfNGCIGenj7VfXrg\n7/3s1rLvxHT8qzuwa9euMtSuYOh0Oo4fP05aWhoNGzbMd53T0kAIwYMHDzA1NcXU1LTY5aemplK7\nVh0i70ThbBdAemYCD+KuMWfOHEaOHFns88nIQDl2rv+TWcAHkiQFS5LkC/wMGAFLASRJ+l6SpL/m\n6PoZ8JQkaZokST6SJA0Fev4pR+Y1Iz4+ns6dOmFnZ4erqytV/atw8uTJIsl8mdEVHR3N3Dlz6I4n\nY0R1Ppaq8rHOnz379nLgwIEizSnzT57+4jMyeJa/SU9thFqlz6NHj8pKrXwjSRKdOnUkPHIvjx7f\nRwhBVPQZomOv0rVr1yLLT01NZcaMGbRt25b33nuPw4cPF4PWL0ahUNCwYUNat25drowuyHmfHR0d\nS8ToAlixYgXhN8Jp03AiDQM+pFWDL6jk3oyvv/6GzMzMVwt4zdBoNKxdu5YPP/yQL774grCwsLJW\nSaaAlHjmeiHEWkmSbIBJ5BwZXgDaCCGeViK1B1z+0j9SkqQOwGxgBHAPGCiE+Huko8xrQFCvXpw5\nfIJgUQkT9AgJu0vrlq24HRlR7H4ely5dQqvT8dZfTqqrYY2hUs3Zs2dp0aJFsc73b+LproWxsXHu\nkX7t2rWxsLDkcvgWGgYMRqlQ8cetnWRmpf0jQW5GRgZr1qzh8uXL+Pr60qdPn3JRE2/27Nk0DWzG\nlgNfoK9vRFZWOu3bd2DgwIFFkpuZmUmzwEAuXriIn86CK6onrFy5kuXLlxeoDmlqaippaWnY2dmV\nSQWK14ELFy5gbemChalTbpubY13CIw8QFRVVYslkX8b9+/dJS0vD29u72HfZNRoNXbp0ZceO7Vhb\nupKe8YgZM2by++8b6dixY7HOJVOCFDTxV3l7ISdQLbeEh4cLQHxEFbFYai4WS83FHBoJlaQQCxYs\nKPb5wsLCBCAG4pc739fUEYBYv359sc/3b+H48eOiin9VAQilUil69eolkpKShBBCrFu3TqiUKqGv\nbyRMjCwFIEaPHv3c+Li4OOHr4yckSRKW5g5CkhTCw91T3L9/vyyW8w8yMjLEqlWrxPfffy/2798v\ndDpdkWX++uuvQkISE6ktFkvNxSKaibpUEBVsbMWTJ09eOT4lJUUEBwcLlUotAFHFv6o4fPhwkfV6\nE5k+fbpQqdSiZ+u5IrjLchHcZbmoWqmzMDQwFI8fPy41PR48eCBatmz1NKmm8PT0FocOHSrWOdat\nWycA0eytUSK4y3LxbsdFwqlCNeHi7Co0Gk2xziWTN0VJoCrXapQpMeLj4wGwwzC3zRQ1xko94uLi\nXjas0FSqVIluXbuyYss27upSMUDJQWUMvp6V6NSpU7HP92/g4cOHtGrVGiO9CjSpPYzMrGS2bv2d\n997ry7ZtW+nZsycB4QGsWrWK9PR0OnbsSIMGDZ6TMWXKFCIj79Kx6WQszZx5nBrD7mOTmThxIr/+\n+msZrewZBgYGxR6Ec+LECVxV5nhozQBQSBKNhD2n4i8SFRWFl5dXnuMHDRzEpk1bqe7TA2NDa65H\n7KZtm7aEhYfh5OSU59h/G8HBwXz33WR2HpmEl0sTUtPjuH3vKOPGjSux480X0avn21y4cJWGAYMx\n0DPhyo2ttGzRkqnTpvLhhx8WyxHw/v37sbJwxsW+JpBT/N3XozX7TswgIiICb2/vIs8hU/LI3sYy\nJUb16tWxMDNnF1FohQ6AIzwgWZNJ8+bNS2TOlatWMXz0J1ywzuCgaSJd332b/QdD0dPTK5H53nRW\nrlxJZmYWTd8ahbtTXXw9W1HLrw/bt28jKioKAE9PTyZMmMCUKVP+YXQB7Ngegot9HSzNnAEwM7HH\n3akBISE7S3UtpYmrqyuxIo00kZ3bFkEK+mq9V0Z7xsbGsm79Omr49sLfuz3uTm/R/K0xZGdrWb58\neUmr/lqh1WoZ/OFgkpMf8SQ7nUvhW4i4f5xBgwYxefLkQsvNyMjg3Llz3L9/P1/9r1+/zpGjh6lV\n+V28XBriVKE6gXVHkq3RMnr0GKr4V+XevXuF1ucptra2pGc8IlvzLMA/JS0WhUIh50Z7jZANL5kS\nw8jIiHkL5nNSimWs6iQTlWdYwnX69u1L48aNS2ROQ0NDpk+fzsP4OB49TmbpsmU4ODiUyFz/BmJj\nYzE0MENf/cwfy8wkJ/fx0x3NV2FhYUFm1vPO9ukZSUX28Xr8+DHz5s0jODiYr7/+ulgebMXFgAED\nUBsaMF15kf3iHmvEDbYq7jDoww8wMzPLc2x8fDw6nQ5zk2ffWz21EcZGlsTEvCwLz7+TLVu2sGnz\nJgLrDKd3+4X0af8z9jZ+7Nq5u9BJcJcsWYKDgyO1atXCxcWFHj16kpqamueYxMREAEyMnlVI0Fcb\no6c2xMejBXFxj5gwYUKh9Pkr/fr1Q6Ah9NRs7sac59qt3VwMX0+vXr3k3GivEbLhJVOi9O3blwsX\nLjBg5FA6DuzDli1bWLp0qewo/JrQrFkzUlLjuROdU5pJp9Ny7fZurKys810H8MPBH3A35jzn/lhL\nXOItLl7/ncj7J4iIiGDfvn2vFvACEhISqFO7LqNGjWZ3yAmmTZ2Bv38VLl269ML+Z86cYcqUKfz8\n888kJCQUas6C4OjoyIGDobjXr84KwjlhmsynYz9j9uzZrxxbsWJFKtjZExa5F50uJyP9vZgLJCU/\nIDAwsKRVf63Yu3cvVuZOuDnWAUCl0sfPsy1Rd+9w+/btAss7ceIEAwYMwNrMn3aNv6Je9ffZvi2E\nESNG5DkuICAACwtLrtzcjlabjRCCsMj9ZD1JxcOpHp5Ojdi2bUeh1vhXPD092bp1CwYmWRw4OZtz\n11bTvXsXfvnllyLLlilFCuoUVt5eyM71MjIlhlarFT269xCAsLF0EybGVkKhUIgVK1bkW4ZOpxN9\n+/YVEpIAhEKhFr4erYS9bWXh6uJWKKfgL7/8UujpGYouzaeJ4C7LRVC7n4SluZNo1ar1P+YeNmyY\nAIS+vpFQKJTC1NRMHD16tMBzFpbs7OwCO+yvXbtWKJVKYWpsLWytPAUg2rRpKztQ/40JEyYIQwMT\n8U7HRbmO9W9V6y8kSRKxsbEFlvfhhx8KczN70bfz0lx5AZXfFmq1nsjIyMhz7G+//SaUSqXQUxsJ\nY0NrAYiKbs1E387LhLdrE+Hh4VnYZf4DrVYrIiIiRGJiYrHJlCkYsnO9jIxMiaBQKFizdg0bN24k\nJCQEc3Nz+vXrR40aNfItQ5IkbG1tMTG2okntkZgY2aKvZ0x07BX2Hv+B69ev53v37Cn79x3A0bY6\n5qY5x3H6esZ4Ojfm0KFNz/U7cOAACxYsoHaVd/D1bE1WVgqHzsynX7/+hIeHlcrOq0pV8Ntsr169\nqFSpEosXLyYpKYlWrb6hd+/eKJXKEtDw9aV///5Mm/YDB0/PxdezDalp8VwMW0f3bt0LVTkhOTkZ\nA7UZkvTsMMhQ35zs7CdkZWXlWQEjKCiI6tWrM3bsWLZu3Yq7Yz0qugVy9eYObt87ynff/adQa3wR\nCoVCrlP7GiMbXjIyMnmiVCrp1atXkWr/mZmZka3JxNzUEZUyJ9AhM+tx7rWCUsHejhthF56rYpCc\n+gAbm+cfttu2bcPMxBY/zzZIkoShgTlVKnZi34mZ3Lhxo9RzPP0VIXLKWy1bspT0tDQ6d+vKyJEj\nMTIyAnKCU+bOnZtveTqdjn379nHs2DEcHR0JCgoq1Hv7OuHl5cXmzZsY/OEQ9h2fgSRJdOvWnV8X\nFy5atm3btqxZs4a7Medxsa9JZtZjrkfuoW6dt/5Rku5F+Pr6snnzZr755ht++OEHIg+dQKlUMWTI\nYD777LNC6STzBlLQLbLy9kI+apR5g9DpdGLLli1iwIABYsiQIcWeB6isuHnzplCp1MLFIUC0bTxR\nNKn9sTAxshQtWrQslLw9e/YIQLg71RPN3hol/L07CEmSxLRp057rN378eGFoaCbe7bQ49+ioUa0h\nAhBRUVHFsbRCM27cOAEIf4W1qI2dUCuUokmjxoU6TszOzhadOnUWgDAyNBeSpBB2dhXEH3/8UQKa\nF5xHjx6JGTNmiKCgIDFu3DgRERFRrPI1Go0ICwsTDx8+LJKcJ0+eiHbt2gtAmJnaCpVKLczNLQr1\nfElKShKnTp0q1JGnTPmnKEeNZW44FfUlG14ybxIfffSRAIS1hYswN60gADFz5syyVqtQ3LhxQ7z9\n9tvC0tJaeHtXFAMGDBA2Nra5CSYDA5uKmJiYQstfunSpsK/gIABhbGwiJkyYILRa7XN9rl69KhQK\nhfBwri86Nf1ONH9rlDA1thaBgU2LurwiERMTI9QqleiKR26y3zHUEIDYvn17geX9+uuvOe9pneGi\nb+dlonurWcLS3Ek0bdqsBLQvGPHx8cLbu5JQKtXC3tZXGBqYCmNjE3H69OmyVu2FaDQasXXrVjF2\n7Fgxe/ZsERcXV9YqyZRDimJ4lXiR7JJGLpIt86Zw4cIFatasSZ2q7+Hn2RohBGeurOTWvYPcv38P\nGxubVwspJ8THx+PvX4XMdB3uTg1JTY8j4t4xPv/8c3r06IGFhcUrk4jmB41GQ0xMDDY2Ni/1v1m5\nciUffTSUlJSco83ateuyadPGMk1Eun//flq0aMEU6mEv5RwtCiH4RHWMsV9PKHDqgR49enD00FXa\nNPwyty08cj8nLi4lLS0t9/iyLJg4cSI//DCT9o2/xczEnuzsDHYfm0zVGl4cOLC/zPSSyeH27dss\nWbKEhw8f0rRpU3r16oVarS5rtco95b1ItoyMTD4IDQ1FpVTj455TU1KSJPy82vDkSVaBC4tnZWUx\nbtw4rK1tUKv16NSxEzdu3CgJtV/IkiVLSExMonXDidTw7U6jgMH4e3dg7py5+Pr6FovRBTmO687O\nznk6Pb/77rs8eBBNaGgoFy9e5NSpEyVidGVmZrJ582aWL1/+ysSbnp6eAFwkDt2fP37vk0aKJgtv\nb2+EEMTGxr4yf9RTjI2Nyc5O468/pLOepKKnp18o5/7iJDT0IA42VXPzv6nVhng4N+LIkZIrGi6T\nP/bv30/lyv7MnDGH39fv5t1336Vt23Y8efKkrFV7o5ENLxmZckKFChXQaLNJTX+WmDQ59QEAdnZ2\nBZI1ePBgZs2ajb1lXar79ODwodM0aRxIcnJyser8Mm7cuIGlmRNGBha5bfY2lUnPSOfBgwelosNf\nMTY2JjAwkGrVqpVIJOOFCxfwcPeka9eu9OvXDzc3N+bMmfPCvjqdjiVLlqCnUrOGW4zgMP8Vl5ml\nukxFL2/s7e2pXq0GFSpUwNLSiuDgYFJSUvKcv3///iQm3+PUpeUkPb7LrbtH+ePWDt59950yr9rg\n6OhAavoDxJ/VKwCSU6Kxta2Qx6g3g0ePHjF16lS6du3KiBEjuHbtWlmrlIsQgiFDhmJp5kHXFrNp\n3+Q/tKw/lv3797F69eqyVu/NpqBnk+XthezjJfOGkJaWJhzsHYWluZNoFDBY1KveX5gYWYrateoU\nKA/UgwcPhEKhEHWqvpfrUN691SyhUCjETz/9VIIreMaCBQuEQqHMzbMV3GW5qOTeTFhYWIrMzMxS\n0aG00Ol0wtfHT9hYuosuzaeK3u1/Er6erQUgLl269I/+U6ZMERKSaIurGEoVUR0bAYi2bdqIs2fP\nCmMjY2Fn7S0a1x4qavn3Efp6RqJXz16v1GPOnDnCyNAo14euQ4eOIjk5uSSWXCAOHjwoJEkSLg61\nRGCd4aKyV9sXBkKUN5KSksTUqVNF9+7dxahRo0R4eHiBxicmJoqKFX2ESqUWjnZVhImRpdDXNxCh\noaEF1kWn04n58+cLT08vYWhoJFq1al3kZ150dHSuX+DTv9HgLsuFrZWnCA4OLh69JFgAACAASURB\nVJLsfwNF8fGSd7xkZMoJRkZG7Nu/F9/Kbhw5t5CTl5bRpGkDtmzdXKBdmnv37qHT6bC1fFYw18TI\nBhNjGyIiIkpC9X8QHByMh4cnu45O4uj5X9hz7HvCIw/w7bffoK+vXyo6lBZ//PEH18OuUd23J+am\njuipjant3xtDQzPWr1//j/7zZs8hEEfelrypLdkxnKo4qUyxsrJi9+7dZGdraf7WGDyc6uHv3Y6a\nfm+zfsN6Hj58mKceI0eOJPpBNIcOHeLmzZts27a1XKSTaNKkSc4OijKOg6fnczf2GF999RWffvpp\nWav2UhISEqhduy4TJkzk+OEw/vfzEqpWrcbMmTPzXZrqxx9/JDIiko6B39Gy/li6NJ+OuYkLY8YU\nfN3Tp09n+PDh8KQCfh6dOHv6Gk2aBBYqO/9TTE1NUanUpKTF5rZptdmkZya+Vv6kryOy4SUjU47w\n8/Pj2LGjxMbGkpCQwPbt2wpca9LHxwcjI2Nu3z2a2/Yw/jqPU2KpW7ducav8QkxNTTl+/CjDhn+E\nscVjKldzYsOGDa8svfI6kmsU/y1OSafVMnvWbGytrBk4cCAPHz7M8d1KiMeRZ87uCknCXmNA9P1o\nHj58iJGhBXpq49zrZiaOCCGIi4t7pS7m5uY0bty42HzoiougoCAi70Tw8OFD4uPj+Oabb1Aoyu/j\nZ/78+dyNukvHwMm0ajCOLs2nY2xQgc8+/Qw3NzdGjBiBTqfLU8aRI0eoYO2H2Z81N5VKPdwc3uLs\n2TNYWFhiaGhE7969X+kPqNVqmTbtByq5N6dRrSFUqdiBNg0moNMq+Omnnwq9RhMTE/r2fY8rNzbz\nx62d3Ht4kdDT88h6ksbAgQMLLVfm1cgJVGVkyiGFybr9FFNTU/7zn0mMGTOGxMe30VebEx13iQYN\nGtK5c+di1DJvbG1tmT59OtOnl9qUZYKfnx9+fv5cCFuHkaElhvrmXLz+O1lP0qj+xA5bDFm/fBXH\njx7jwqWLNGrQkKPHL9FQ64ChpOKBSOOKIonPmzWlRo0azJkzh3sx53G2r4lOpyUsYg92thXKNNnr\n34mNjeXnn3/m4sWLVKxYkY8++gg3N7c8xygUigL7KpYVhw4dxt7GPzcgQKXSx9s1kFOXl1PDpxfz\n58+nWrVqDBo06KUynJycOHHsPDqdFoUip+LAtdu7UCrUONs2RK0yZPu2PZw504w//rjyUl+8lJQU\nEhMT8PfwzW1Tqw2xNHPl1q1bRVrnggUL0Ol0rFixEq1Wg4uLGxs3bqBy5cpFkiuTN3I6CRmZN5Rt\n27axaNEiHj1KpkOH9gwcOJCrV69iZmZWYk7m/1YuX75Mu7btuR/99BhK4i3sGCzllEK6I1L4ltOs\nX78eFxcXmjdthuKJFkdhzC3xCC8vL46dPIG5uTmdOnUmJGQHtlaeZD5JJi09iZUrV9K7d++yW+Bf\nuHfvHm/VrUd8fAI2lt4kPo5ET0/JkSOHqVq1almrVyz07duXbVv207HpFBR/lg86cXEJdx+cp1fb\neew/MROPSuYcOnTwpTLOnTtH3bpvYWflg6dLIx7EXuH2vaM0ChiCp0sDAJKSo9gaOoG1a9e+tDKE\nEAIPDy90TyxpWmcEkqQgNT2erQe+4KtCpB55EY8ePSIpKQlXV1e5LFU+KUo6CXnHS0bmDaVjx450\n7NgRgN9//x1v74okJSUCULNGAL9v2vjKXQqZ/FG1alVuR9xiz549HDlyhKlTp9INj9zrrphgrNQj\nPDycHj16cPnqFRYuXEhkZCQf1q3LoEGDcv2xNm/exJo1a9i9ezcWFhYMGDCgQLUxS5pp06bx6FEq\nnZtNxcjQiifZaew8MokJX05g85bNZa1eoQgLC2PTpk2oVCp69uzJxx9/zKpVqzhwchaezg2JT7pF\neGQoNf16Ajk7YJmZWXnKDAgIYMuWzYwZ/SlHzy3E2NgEAFurirl9LMxc0NMzzNP3UpIkpk37nj59\n+hBy+BvMjB2JjruEg4M9Q4YMKYbVg4WFBRYWFq/uKFMsyDteMjJvOJGRkVSsWAlH22pUrdSFzKxk\nzlz9P3x8PTh56kRuv/T0dDQaTblwyH6diY6OxtXFha46dzpI7gBcFYnM5AIhISG0bdu2wDJ1Oh0L\nFixg0aJfSU1JpVPnjkycOLFMnKCrVa1OZooV9WsMyG27GLaJqJhQEpMSSl2fghAfH49CocDKyiq3\nbd68eYwcORI9tcGfUWdaRo0exdo1a4m6exchdEiSAm/XJrxVrR/RcVc4eGou303+D59//vkr5xRC\nkJqaSnJyMu7uHlSt2JlqPl0BiHpwltBTc9m/fz/NmjXLU86BAweYN28e9+5F07RpE8aMGYO9vX3R\n3hCZQiPveMnIyLyU1atXo1CoaBgwBLUqJ6JQq9MQemouYWFh2NjY8PHHH7N+/Xq0Wi2NGzdh4cKf\n8fPzK2PNyx4hBIcOHWLv3r1YW1vTp08fKlTIO/+Uo6Mjw4YPZ+7cudyQHmOgU3JeEU+TBo1p3bp1\nofQYNWoU8+fPx82xLvp6dvzyv8Xs3bOPc+fPlnqUqKOTI+fP3MwpffLncfWjx3dxcCxYEEhpcuvW\nLQYMGJh7NNiiRUsW/1lIe9SoUfh6tKKWf290QkvoqXlMnz4dB1t/6lYN5kH8H0RFn+LGnVAi7h1H\no81CkiSaN2+er7klScLU1BRTU1M+/XQM06ZNIybhGmqlPvdjL9O2bTuaNm36SjnNmjV7pXEm83og\nG14yMm84aWlpqJR6KJXPyoDo/xk1l56eTs+evTh54gw1/YJQqwy5cmknzZo15+bNG5iYmJSV2mWO\nEIL333+fZcuWYWxkQdaTNCZMmMjOnSE0atQoz7GzZs2iatWqLF+6jPT0dL7uPpxRo0YVKpIvNjaW\nH3/8LzV8e1K1UicAKroFsi10Ihs2bOCdd94p1Pogx19r9erVPH78mHbt2tGgQYNXjvnkk5G0a9eO\nQ2cW4OpQhwfxV7kTfZpfvv2l0HqUJNnZ2bRq2ZrEhHQa1PwAIXScPrmFNq3bMmLkcISAmpV7oVSq\nUaJGoVBiZuJAi/qfoZAU+Hg05+Dp+cQl3cLXoxWWZi4cv7iIdevWFThK+Pvvv6dGjRosW7aMzMws\nPv1iFoMHD5b9Lf9lyIaXjEwZ8/S4v6Ruvh07dmTy5MlcvbEdf+/2ZGsyuHRjM06OzigUCkJDDxBY\nZzhujnWAnAzzm/Z9yoYNG+jXr1+J6PQ6sGPHDpYtW0b9GgPxdm1CVnYqB0/P5f33BxIefj3Pz0uh\nUDBw4MBiCcu/efMmWq0G5wrVc9uszN0wNbHmjz/+KLTckJAQunbthhCgVhnw3XffMXz4cObNm5fn\nuLZt2/J///d/jB8/gcNn/4utjR2zZ88utykIdu3aRUTkbTo2/Q9W5jk+jeamjuw8/B+ioqIQQkd2\ndjpqVU7ZqbT0BGwsPXOd6gFsLL24//AiVSp2AEBfzyjf5Zz+iiRJ9O7du9wESsiUDeU3kYqMzBtO\ndHQ0QUFB6OsbYGxszMCBA3n06FGxz1OvXj3Gjh3L+WvrWL97GOt3jyQ5JYJly5fm5oayMHXO7W9i\nZI2enmGZlPYpT4SEhGBp7oC3axMkScJAz5TKXh24eTO81BLRAnh7e6NUqrj38GJuW8KjSFJSEwod\n9p+dnc2A9wdia+lDz1bz6NFqLrX9+zB//nyOHTv2yvHvvfcekZG3SUpK4kFMNJ988kmhfzgkJSWR\nlJRUqLH54Wni2af5tHL+neMbVbFiRUxNzThy/mfiEm8SE3+dzCfJ3Iu5QEZmzt+iRvuE23ePYWXu\njhA6wiMPkJQcnRu4IiNTUOQdr9ec7Oxsdu3aRVxcHIGBgbnFd2XKNxqNhubNWnD/3kOqVuyGVpfN\nqpVruHnzFqGhB4p992vatGkEBQUREhKCmZkZQUFB2NnZkZSUhIGBAWERe6lT9T0kSeL23aNkZaXT\nuHHjYtXhdcPExISsJ+nohBallHOrzMx6DOTUfiwt7Ozs+OijISxYsIBbdw9joG9Ocso9/CtXoUeP\nHoWSeenSJWIePqBto4Go1YYA+Hm14VpECCEhIfk6clQoFEWKhLtz5w4DBw5i3769ADRv3oJFi37B\nw8PjFSMLRpMmTQC4dmsnVSp2+vPfu1AoFLRq1Ypt27bSp/c7hByeBICbqxupaWlsDR2PnZUvicm3\nSctIQpIUrN8zgoyMx/Tv35/27dsXq54y/x5kw+s15saNG7Ru0ZLIu1G5bePHj+e7776TfQbKOSEh\nIYSFX6dD4LdYW+Q8aCzNXAg9NPev0TLFSkBAwD8ify0tLZkyZQqjR48mJuEqapUBcYm3eeedd/L1\n8C0q586d48SJE7i6utK2bVtUqvJzSwoODmbGjBkcOfcz/l7tSU2P42LYetq1a/9KB/viJCUlhePH\nc6JPdToNcYk3MDQ0ZPGSXwvtWG9qago8MyQBNJosnmRnYGpqSlZWFo8ePcLW1rZEMsxrtVpatWrD\nwwdJ1K+Rc0R55vRWWrVqw/XrfxTr96BixYqMGzeOadOmERl9DCEEjx4/4Ntvv8XFxQUXFxfuREVy\n+vRpVCoVtWrVIiYmhnnz5nH69BkqVuxFcHAw58+fJz4+nubNm9OoUSP5HitTaMrPXU6mwAzo35/M\n6ES+pS52GLKLKKZMmUKLFi3yHXEjUzbcuXMHhUKJpfmzPFq2ll6510rC8HoZo0aNIiAggOXLl5OR\nkUHnzpPp1atXiT5YtFot/fv3Z8WKFSgUCnQ6HT6VfNm3fy9OTk4A3L17l1u3buHn51eqhs5TKleu\nzIoVKxg69GN2HPoGgGbNmrNs2dIiy9ZoNJw8eRKdTke9evVQq9Uv7Tt//nwuXrxEu8ZfYWvlTVpG\nAnuOf8+UKVPYtGlToeavVKkS9es14OyVVQAYGJhzOXwLkpTz/bOysiY9PQ13Nw9mzZ5J27Zt2bZt\nG4mJibRo0QJvb+9XzJA3+/fv58aNMNo1/hpbq5zvvaWZCzsOfcO+ffto06ZNkeT/ne+//57mzZuz\nbt06FAoFQUFBz90jVSoV9evXz/2/o6MjU6dOfU5GafwQkfl3IBterykPHz7kyLFjfEBlXKScyLNO\nwp0TqjjWrVsnG17lnLp166LTaYm4dxwvl4YA3Iw6jEKhoHbt2qWmx+PHjzlx4gRWVlYsWrSo1H7F\nr1ixghUrVlC/xkC8XBuTlHyHg2fmMmLECH777Tc++OADli9fjhACpVLFyJEjmD59eqnW90tISECp\nVLJ48a/Y2Nhgb29fZIMD4OTJk/Tq3oO70Tk1+hwr2LNm/bqXRkru2BGCk111bK1y5jY2tMbbpSm7\ndm0pkh5r163h7V5BhJ7IcaavYGdPjx7d+fnnhVT2ao+1hTu37h6mR4+eWFpakJiYiCQpEELHV199\nxbffflvouZ/6XZmbPvO7Mjd1BCAmJqYIq3oxkiTRunXrV6bzyMzM5LfffuPs2bN4enoSHByMtbV1\nsesj8+9GNrxeU54+IHV/q8wroFwXn5XJoU6dOgQFBbF27f+IuHcEnU5DTHwYY8aMwdXVtVR0+PXX\nXxkxfATpGekA1Kheky1bN+Pi4lLic2/YsAF7Wz8qugUCYG3hgY97G7ZsWceUKVNYsWIldar0xcG2\nMhH3TjJr1ix8fX354IMPSlw3gGXLljF48BCysjIBcLB3ZPuObUWWm5mZSeeOnTBLyuZLaqFAYm3c\nLTp37MTd+/de6DtmaWHBzSdhz7WlZyRhZla0TOPOzs4cO36UsLAwUlJSqFatGvb2Dvi4tySgck75\nGheHWmza+xlpqel0bTEdI0NLrtzYxqRJk2jdujUNGzYs1NxPj+qu3dqVm0z02q1dSJJUZr6Fjx8/\nJrBJUy5cPI+VhTPJKQ/5/vupHDlyuNB1MtPS0lizZg1hYWFUq1aNHj16YGBgUMyay7xuyE/o1xQ7\nOzuaNglkqyqKWyKZVJHNBm7zUJNKUFBQWasn8wokSWLFihX88ssvVK/lSt0GPvz2229ML6WK0pcu\nXeKDDz7A0bYWXVtMo2X9sdy6dZe+fYMLJS8mJoYzZ87kO8RepVIjdJrn2nS6bBQKJUuXLsPTuTEV\n3Zpw/fYert7MMXg++WQUBw4cKJR+BeH27dsMHDgQZ7va9Gw9h07NJqN5YkCPHr3Q6XRFkr1nzx5i\n4+Por/XBSzLHQzJjoM6XpORH7Nix44VjPvjwAx7G3+DkpeXEJd7k6s0d3Ig6wIcfvrhAc1xcHGPH\njqVmzQBat27D5s15l/Hx8fGhdu3aSJJEUlJi7s4TgEJSYG7igLGhDWYmFVAp9aju0xVTE2s2bNhQ\n6PfB3d2dL774gothv7Pt4Hi2HRzPhesb+Pzzz8ssQGju3Llc/eMPOgROomPgFLq1mMGTTAWfffpZ\noeRFR0dTtWp1Bg0axMKflvLee+9Rt85bJRrBKfN6IBterzFLli3F0t2RyZxlBIfZpbjHf/7zn9wo\nHpnyjUqlYuDAgezYsYPNmzcTFBRUakd9q1atwsjQnHrV38fMxAFHuypU9+nJwYOh3L9/P99yMjMz\nCQ4OxsnJiTp16mBv78CcOXNeOe6dd/rwMOEGl8O3kJH1mHsxF7gesYugoCDS0tLRUxtx6vJKbkYd\nomqlzjSuNRQTA0fat2/PrVu3irL0V7JhwwYUChVvVeuHkaEVlmYu1PTrTUTELc6ePVsk2RkZGQAY\n/uWwweDPfz+99nc6d+7MzJkzuR93kpDDk7hwfT0DBw7gq6+++kfflJQU6tdvyPx5/yUlwYTLF6Lo\n2rUrP/744yt1U6vV1K3zFrfuHiJbk1OHMOnxPR7EXX0uFYMQAp1OV2gH+A0bNlC5sj9TpkzBycmF\nGgGV6N6zLTt37mTy5MmFklkc7AzZhaNtdawt3AEwMrTEy6UJe/bsKZS8CRMmEPswgc7NptKl+Qw6\nBE4iPPzmP3zHZP59yIbXa4y7uztXr19j7969rF69msg7kcVSqV7mzScrKwuVUg9JUua2qVWGudfy\ny4QJE1i9eg21/d+jfZNvcLZ9i1GjRhESEpLnuB49evDZZ59xMWwj63YOY//JWdSsWY05c2bTuXNH\nbt87xK2oQ1T37UY1ny54ONejRf2xgIpff/21UGvOLzqdDgnpOSM492i/iDteLVu2xEDfgHXcIkNo\nyBJa1nITtUqVp0P56NGjiYl5wPnz54mJecDChQtf6JC/bNkyIiJu07bRVzQM+IBW9b/A2zWQiRO/\nytfnOnvOLFIzotm0bwy7j05m+8GJGBgakPj4NrGJN0jPSOT0lZWkpScVKgnozp076dmzJymJat6q\n1h+FzpYDBw7QsWNH2rRpk+cPj1OnTvHuu+/RsEEjxowZQ3R0dIHnzwsrK0sys5L4a/3itPQEzC0s\nCyVv+/YduDs2zPVjs7Zwx8W+Ntu2bS8WfWVeX2TD6zVHqVTSokULevfujbOz86sHyMiQs4uSnBLL\n1Zs70Ok0pGUkcuXGZvz8/POdR0kIwS+/LMLHvRW+ni2xsfSkbrVgbK08X2kcSZLEDz/8QEREBBs2\nbODUqVMcOXoYKysrJk+ejIODHTqhxdzU6bkxaqUhISEhbN68GY1Gk8cMhadbt25ka7I4fWUVT7LT\nSEmL48K1dbg4uxY58MHKyopFvy7ijDKOTxRHGak4ynFFLD/9/PMrIzeNjY2pUaNGnoWxL1y4gLWl\nW+4OlSRJuDnVISkpMV87mQ0aNODy5csMHzGEZq0CmD17FseOHaWCvSU7D/+H9bs/4fa9Q8ybN+8f\nqUnyw/TpM6hgU4lmb43Gx6M5TeuMxMG2Mj/8MCPPcbt376ZBg4bs2HaA6Cgt//3v/6hdq84Lk/wm\nJCQwY8YMBg0axNy5c0lOTs6XboOHDOZhwg1OXFzKw/jrXA7fwo2oUD76aHCB1wlgZmZGZtbzc2c+\nScbczLxQ8mTeHGTnehmZfyFNmzblk08+Yc6cOVy+sQmN5gkWFpYsX76mQMedaWmpGBo8e5BIkoS+\n2izfDztXV9d/BBNUqFCBK1cv4+rqRnjEPhztqpKV9ZgdhyaRnplI2PUndO3alQYNGrJ7965iT2Za\nqVIlfvzxR0YMH0F45H4ArKysWbl0DbNmzWLPnj1YW9swePCH+Spu/HfeffddmjRpwsaNGxFC0LVr\nV9zd3YtFd29vbx4lryAjMzn3c4mJu4aRkTH29vb5kuHl5cW0adOea7t27SqHDx8mMTGRxo0bY2tr\nWyj9Im5HYGVWMfc7JkkS1hZeREbmfYQ7buzn2FpVpGW9sSgUStIzH7Et9Avmzp3L1KlTefz4MbNm\nzWLjxk2Eh4eh0WixtnBh6dJlzJ41h+MnjuHgkHcR744dOzJv3jwmTJjIjTsHUKv1GDr0I8aPH1+o\ntX744QeMG/d57lF+1IOz3Iu5yKQpiwslT+YNQgjxWr+AAECcPXtWyMjIFIwLFy6IGTNmiMWLF4vk\n5OQCj2/fvr0wN7UXPVrPEcFdlovWDb8QCoVSzJo1q8i6bdmyRSiVKmFibCUM9E2Fvp6p6Nzs+9x5\nVCo98d133xV5npdx//59sXjxYrFmzRoRHx8vatWqLZRKtXCuUENYWbgIQCxevLjE5i8MsbGxwsbG\nVpiZ2onqvt2Fl0tDIUmSmDBhQlmrJoQQok+fPsLMxE70bv+zCO6yXPTp8D9hYeYgunXr9tIxWq1W\nAKJe9f4iuMvy3JerQx3RqFFjsWLFCuHu7iHUan1hYmQjDPXNRc8/v4/dWs4QhgZm4uOPP863junp\n6eLKlSsiMTGxSGvVaDRi2LBhQqVSC0Do6xuIL7/8Uuh0uiLJlSkfnD17VpCTSCBAFNBukYQQL7fK\nXgMkSQoAzp49e7ZQW98yMjKFJywsjCZNAomPj8fE2JrHKbE0atSY3bt3YWhoWGT5ly9fZtGiRfz8\n80J8PdpRw7d77rVDZ37E1hFOnTqRpwyNRsPs2bP59dfFpKam0qlTR7799lvs7OzyrcfSpUsZMGAA\n7Rp/jY2lJ0IIjp5byOOMG9yPvoeenl6h11jchIeHM378ePbt3Y+1jTXDhn3MiBEjykWamWvXrlGv\nXn002WBr6UPCoxsIKZtjx45SrVq1l45zcXFDT3KhUUDOsZ9Wm83GvaPQaDPJzn4CQPsm3xB6ai4e\nzg2o5f8ssvvExaWgvk9Y+PUSXdvLiI+PJyIiAm9vbywtC+cvJlP++EuFkVpCiHMFGVv2f4kyMjKv\nLT4+PoSFXWf+/HkM+ag/GzZs4MCB/cVidAFUrVqVuXPnYm1tQ9aT51NVPMlOw9TU5JUyhg8fzuef\nf4Em3RpzgyosW7qKwCZNyczMydGl1WqZOXMmfn6VcXFxZejQocTGxj4n48SJE1hbuGBjmZPqQJIk\nPF0aEZ8Qx507d4plrcVFpUqVWL9+PUmPErl58waffPJJuTC6APz8/Dh37iwDBvbFzduQ4P59OH/+\nXJ5GF8AXX4zj9t2jHD77E9du7WLn0e/IzErFzDjHB9DY0BobS0/09UxJTX/+s0vLiMPWrnBHo8WB\njY0NderUkY0umVxkHy8ZGZkiYWFhwdChQ196/fr16xw6dIgKFSrQrl27Qu0ODRz4Pt9/Pw1zU0fs\nrf2IvH+C6NgrTHt/XJ7jYmJi+OWXX6jp1wt/75yixl4ujdgaOoGNGzfyzjvvMGLECH766Wc8nOph\nqu/K0iUrObA/lPMXzuUmu3RzcyM59SGZT1Iw0Mupc5jwKAK1Wq9AO2flgezsbJYtW8a2bdswNjam\nX79+r8zoXpx4eXkxf/78Ao356KOPUCgUTJs2nfPXT+Pp6UlCkg6FQoWpcQVS0+NISYujkntzTl5a\nypkrq3C0q0rUg7Pcf3iZ6bNXldBqZGQKQUHPJsvbC9nHS0amXKLT6cSoUaOe+kEIQLi7eYibN28W\nWFZmZqZ4772+QpIkAQi1Wk+MHz/+lf4yR44cEYDo1Gzyc/5BpsZWYsKECSImJkYolSoRUDko91qn\npt8JQKxcuTJXzoMHD4S5uYWwsnARdav2FZW92gqlUiWGDh1a4LWUJTqdTnTq1FlIkiTsbf2EtYWr\nAMT06dPLWrUCsW7dOgEIU2N74e0aKIwNrYWRgZWoVqmrsLbwEJKkEIAwN7cQM2fOLGt1Zd5AiuLj\nVT72n2VkZN44QkJCmD17Nv/f3n2HV1Wlexz/rpxUCBBCgIRAaKGHrlIEpIgiggEvBrExNkTUccTR\nOzoiysjYGHS8o8iIglhABBUQHEroRYgBCVIMICCEIjWkt7PuH4kZkZJCzjkJ/D7Ps58nWWevtd/D\n0eTN2mu/q0PLodw54H0G9nyJU6cyuP/+81dcvxg/Pz8++mg6+/fvZ+XKlRw6lMT48eOLfAKzadOm\neHv7cODwf5dgHD/1EylpJ2ndujV79uwhLy+X8Nr/vdVVvVoEVQJrsGPHjsK20NBQVq5cQZt2kcT9\n8DFJx7/l6aefKlax2PIkNjaW+fPn0eOqR7mh6zP07/E3WjS6gTFjxnD69Onz9jl58iR/+tOfaNSw\nMVFRrXnjjTfIy8tzc+Rnu/HGGwkMrILTmcPBo5vpcdWj1KrRlB0/LeL0mYM0a9aUH3/8kaNHjzB6\n9GiPxno5ys7O5uOPP+ahhx5i3LhxHDhwwNMhVSi61SgiLjFnzhyCg+rSKrI/xhiqV4ugVaMBrFw5\nhZMnTxIcHFziMevVq1eivSRr1qzJH//4GBMnTuRk8j78fKvw8+GNtG3TjkGDBpGcnFyYmFWvmj/u\n8VM/kZJ6gqioqLPGatu2LcuXLyMvLw8vLy+X7TKQlZXFggULOHToENdeey3t27cvs7HXrVtHQEBV\nIsLy65EZY2hSvyc7flrMli1buO666846Pzs7m57X9WLXrj3Ur9OF9OQ0nnzyz+zatYt33nmnzOIq\nqSpVqvDZZzMZMuQ2srIzWbT274RUb4QxlqrVqvDVV1+Ven9FubjMzEyuUIhyUwAAIABJREFUv74v\na9euIaR6fVLSjvHqq6+xZMliunbt6unwKgQlXiLiEl5eXlh7dqV3p80rfM1dXn/9dZo0acL7708l\nJeUIf3z8EZ555hl8fX3PTszO7MfPpwo/H9lAm9ZtGTx48HnHczgc520vC3v37qV3rz7s278XLy8H\nTmce9913H++9916Z/JvVrVuXzMwUUtOPU6Vy/oLzk8n5DweEh4efc/7cuXPZ+kMC/Xu8UPhgQXC1\n+vz73/9mzJgxRdbGcqVu3boxYcLrxMbGkpycTJUqVWjdOoaRI0dSp06dogeQUpk2bRrr1q3jxm5/\npXaNZmTnZBD77Ws89ugfid/0nafDqxB0q1FEXOL222/nVPIhNu+YTUbWGY4e38m23fO58cZ+BAUF\nuS0OLy8vRo4cSVzcBnbu3MFrr71G9erVWbt2LQ899BBHjx7lgQceoGYoePkf5rHHRrFi5XKPlIgY\nOfJhTp5MY2Cvv3PHzVPo3PYPfPDBB8yePbtMxo+JiaF2rVCWb5zAzr1LSfhxLnHbPqJ//5uJjIw8\n5/zt27dTuVJQYdIFEF67LXl5eSQmJpZJTKWxZcsWGjVqzKOPPsrXX39DbGwsISEhvPjiixdMurKz\ns/nggw+4/fbbGTVq1CXvu3mlWrp0KbVDmlG7RjMAfH0CaFq/D5s2x3PmzBkPR1cxaMZLRFyiT58+\njBs3jhdffJEfds0HIKpVa6ZMec/DkcHbb7/No48+SrWqofh6B3Ds5F6GDRvGJ598gjGG77//nvHj\nx5ORkcHAgQOL3EewLKSnp7N48SI6tfkD1avmb//VtEFv9hxYzRdffEFMTMwlXyMwMJCVq1bwyCOP\nsnTpdPz8/LnrrjuZOHHiec9v2bIlaemnOX7qp8Lk6+CR73E4HB69lXffvfeDszK39v0rAf7V2bVv\nOVOmTCE6OpoBAwacc35ubi79+9/MsmWx1ApuQkbWKSZPnszHH3/MsGHDPPAOKq6QkBDSM0/gdObh\n5ZU/+5uS/gsBAQGFTwHLxamAqoi4VFJSEmvXriU0NJRu3bp5vKZUcnIydcLqULd2Zzq1uQdjvNjz\n8xrWbv43q1atIjExkQcffJDKlarj7fDj9JnDPPzwwy5f05SZmUlgYCDtWwylZeN+QP5T5wtXj6H/\ngOuYPn16mV4vKysLh8OBt/eF//7Ozs7m6quuITFxNxFhncjOSWP/oTgefngkb7/9dpnGU1yHDx+m\nTp06dL9qFA3DOxe2f73yWW4d0o/33js3sf/888+JiYnh+i5PU6dWFE7rZHX8O6Rn7+PgwQPlqgBu\neRcfH88111xD3dodaNKgJ6eSD5CQ+CUjR44ocZmQikwFVEWk3AoPDycmJoYePXp4POkC2Lx5M+kZ\n6TRv2Bdj8uNpVK8rAf6BLFmyhD/+8XEa1evGoN7/YGDPV7g66i4mTZrEd9+5dv2Kv78/gwcPZvue\nBSQd3UJ6xim+3zmHE6d+5o477ijz6/n4+PDVV19x991388ADD7BixYpzzvH19WX5imU8PGoEuV77\nqRKcwRtvTOStt94q83iK69ckKScno7DNaZ3k5GZesHDv6tWrqV6tDnVq5T8w4WW8aFa/N8eO/cLu\n3btdH/RlpGPHjsyYMYMcDhO7fgIJiV8wfPjdvP76654OrcJw2U9BY0x1Y8wnxphkY8wpY8wUY8xF\nd7M1xkw1xjh/dyx0VYwicuX5dbPo5NSkwrb0zFNkZqWTnp5OenoarSL74+XlwBhDs0bX4+dbidjY\nWJfH9s477xAV1ZzYb//B7MWPs33PQsaOHUu/fv3K/Fr3338/t912G4sWruWLz7+hV69eTJgw4Zzz\ngoODmThxInv27CYhYQsjR45kxYoVrFy5kpycnDKPqyg1atSgf/+b2brrKw4c2UxyyiE2bPmQlNTj\n3HXXXeftExYWRlr6SbJz0grbTqUcxMvLq9QbfhdXbm4uCxYsYNKkSWzevNml13KXmJgYfv55H7t3\n7+bYsV+YMmWKbjOWREkLfxX3AL4BNgFXAV2BRODjIvpMBRYANYFaBUe1IvqogKqIlEivXr1tgH9V\ne3XUnfba9iNscFA9GxJSs7Dg6lWt7rDNGvSxjepea69pfbcFY6dMmeKW2JxOp92wYYP98ssvbVJS\nUrHO37Ztm01MTCz2BswbN24s2Hj6XntP9HR79y0f2haNbrS+vn722LFjF+y3dOlSGxJSs7Agbp2w\ncLthw4Ziv7eycuTIEdulc9fCOAICAuw777xzwfOTkpJs5cqBtmZwY9utw0O2fYsh1tfH395xx50u\njTMpKck2b9bCAoVFXe+++26bm5vr0uuK611KAVVXJV3NASfQ/jdtNwK5QOhF+k0FvijhtZR4iUiJ\nnDhxwt52223W4XBYwHbp0tUmJCRYp9NpGzZsZAFbyT/YBlerX1gp/+TJk54O+xwbN260kZFNCxOQ\nDu072l27dhXZb8KECdbHx8/edcu0wor9g6+fYAG7cOHC8/Y5ffq0DQysYuvUirIDer5k+/d4wdaq\n0cTWrh1qs7KyyvqtFcnpdNrvv//eLlmyxJ4+fbrI89evX287tO9oAevr62cffPBBm5aWdtY5+/bt\nsx9++KFdsGCBzc7OvuQYhwwZYgMrB9v+PV6wdw2caru0u98Cdtq0aZc8tnjWpSRernqqsQtwylr7\n23nVpQVBdgLmXqRvT2PMUeAUsAx4zlp70kVxisgVKDg4mFmzZpGamkpOTk7hBsYZGRmcOHGChuFd\nuLbjQ3gZL/YfimNl3P+xYcMGl9zyK62UlBRuvLEfPl7V6dPlz+Tl5fD9zln07z+AnTu3X3Q9XWho\nKDk5WaSmHaNqYG0ATqfk33q9UG2u+fPnk5qaQr+uD1IpIP/fq1ObPzB/+V9ZtmyZ2/9tjDG0bdv2\nnPa9e/eyZ88eWrVqddZ76dy5M/GbvuP06dP4+/ufc2vsb3/7Gy+88AJOZ37tuQb1G7J4ySKaNGlS\nqvjy8vL46quvaNtsSOEToU3qX8e+pHXMnj2b4cOHl2pcqfhctcYrFDhri3hrbR5wsuC1C/kGuAfo\nDTwNXAcsNK5+jltErkiBgYGFSRfAtm3bOHMmmRaNb8CrYOF9RNhVVKlcg5UrV3oqzPOaO3cup06d\npFuHRwiv1YaIsI50anMfu3b9yNq1ay/ad/DgwYSF1mFF3Bvs2r+Sbbv/w9pN7xIWVofY2FiOHz9+\nTp+srCwAvL39Ctu8Hf5nveZJWVlZDBt2B40aNaJv377Uq1eP0aNHFyZSvwoKCjon6Vq9ejXPP/88\nrRrfzO39JzOg5984fSqT4cP/UOp4jDEFRXBzz2p3OnPx9vYp9bhS8ZUo8TLGvHyexe+/PfKMMaUu\n7mKtnWWt/dpau81aOw8YAFwD9CztmCIixVWrVi0ATqccKmzLyk4hPfNM4aL88iI5ORkvLwcBflUL\n2yr552/DdKF9FwvPq1SJZctjad02kvXfv8+m7TPJzskgI83JX/7yDE2bNmPr1q1n9enfvz/e3j58\n98MMcnIzyc5JY9P2mVSuVJlevXqV/RssoZdeeonZs2fTue29DOrzOm2a3sobb7zB1KlTi+z7+eef\nU7VKTdq1GIKvTwDB1erTuslg1q9fx6FDh4rsfz5eXl4MHTqUnXsXcfDoFjKzzrA1cT5HT+zijjtU\nO+xKVtJbjRPIX4d1MT8BR8hfGF/IGOMAggteKxZr7V5jzHEgElh+sXOfeOIJqlWrdlbbsGHDVBxP\nRIotIiKCAQMGsnTJp2TnpOHvV5WdexdRuXIll5R0uBR9+/bF6cwjIXEebZvfirVOtiZ+hb9/AN27\ndy+yf/PmzVm1aiWvvvoqzz77V/p2fZbQkOZkZJ1h6fpXePzxP7Fs2X+f5AwLC+Pddyfx0EMPsTdp\nLdZaHA4HM2Z8StWqVS9yJfeYOnUajev2oGmD/CSwddOBHDuVyLRpH3L//fcXcxQLmN98fWnefPMN\n9uzew7L1/wDyk7Enn3ySIUOGXPLY4j4zZsxgxowZZ7UlJyeXejyXFFA1xjQHtgFX/brOyxhzA7AQ\nqGutLVbyZYypC+wHoq21X1/gHBVQFZFzWGuJjY1lzpw5eHt7c/vtt3PttdcW2S85OZlRo0Yx67NZ\n5Obl0r5dByb/+12uvvpqN0RdMuPGjWPs2LFUrhSE05lHZlYq7733XgkSDbj55pv5/rsDXN/lqcK2\nnXuXsjFhOtnZ2fj4nH1b7Oeff2bu3Ll4e3szePDgcjMTGFKjJmE1utCh5W2Fbavj36F6rRzi4y9e\ng23NmjV0796dqCYDaBV5M6npv7B287u0bNWItevWXFJc1lri4uI4cOAAV111FfXr17+k8aR8uJQC\nqi6rXF9Qf6sW8DDgC3wAbLTW3v2bc3YC/2utnVtQ42ssMIf8WbFI4FWgMtDGWnvegjFKvETkfJ5+\n+mlef/11gqqGYW0eySm/8Nprr/HUU08V3RlITU0lIyPD5XWeLlVcXBxffvklPj4+DB06lJYtW5ao\n/1133cU3C1Zxc4+XCrdF2rT9c/YeWs6ZM8nlouhtcdx33318NvMLenf6M9WrRnDk+HaWb3yDsWPH\n8NxzzxXZf/z48Tz//POFa8IaNmjE4iWLzruHpUh5TbyCgH8BA8kvLTEbeNxam/6bc/KAe621040x\n/sBXQDsgCDgELAKet9Yeu8h1lHiJyFl27txJixYtaN/iNqKaDAAs8ds+I3H/Ug4c+LlMZmmstXz4\n4Yf8e/J7nE4+Tf/+N/HMM89Qo0aNUo/pdDpZsmQJO3bsICoqit69e7s88Vm+fDm9e/emcUR3IiN6\ncPL0Pjbv/JzHHnvkgns4lkdHjx6l53W92PnjDgL8A8nITKVbt+785z/fULnyRWt3Fzpw4AArV64k\nJCSE66+//qLbKWVlZTF79mw2bNhAREQEw4cPL/dJupSdS0m8XFZA1V0HquMlIr/z7rvvWmO87J0D\n3i+sUzXkxrcsYGfPnl0m1xg7dqwFbL3Qdjay/nXWz6+Sbdmilc3IyCjVeKdPn7bXXN3JAtbb29cC\ntlu37jYlJaVM4r2YyZMn22rVgixgHQ6HHT58eKnfhydlZWXZWbNm2fHjx9sFCxa4rFBpampq4WcV\nHBRuvb19bfXqwTYhIcEl15PypzzW8RIR8ZiQkBCsdZKS/gtBVcIBSEk7CnDOrEROTg7x8fEEBATQ\npk0bilO9Jjk5mVdffZWoJgPo0DIGgFPJfZm/4jlmzZrFPffcU+KYx40bx5YtW7nh2meoXaM5h49t\nY+WGt3jllVd46aWXSjxeSYwYMYK7776bXbt2ERoaWvh0Z0Xj6+vLbbfdVvSJl2jSpEnEb9rETd2f\np2ZwZMEDCS/zxBOjWbp0icuvLxVbxbh5LyJSAjfffDPhdeqyOv5f7D24nj0/r2b99/+mZYtWdOvW\nrfC8JUuWUK9uBF26dKFdu3a0bt2WxMTEIsffs2cPmZmZRIR1LGyrXi2CoKqhJCQklCrmOXO+oEF4\nV0JDWmCMoU6tKOqHdWLWrNmlGq+kfk08K2rS5U6LFy8mLKQVNYPz138F+FUlMqIXy5bFnlM3TOT3\nlHiJyGXH39+fpbFLaNY8gtXxk1i7+T2uuqYN3/xnYeGaqV9++YXo6GgchHBT97Fc3+UpDh04QXT0\noF+XMVxQgwYN8PHx5dAvPxS2nUk9QnLKLzRr1qxUMRtjyM07uxBpTl4mx49fcImreEj16sFkZJ88\n67+TtIwTVK1arVgzpnJl061GEbksNW/enPXfruPIkSM4HI5zbjHOnj2brKxsuvV8GH/fKgAYcw9L\n1r3Cd999d9HyEcHBwTzyyCj++c+3OJN6mAD/6uw7tJb6EfVLXTswIqIeq1evoVZwU8JrteHAkc38\nfCgOyC+IGhQUVKpxi+Po0aPs37+fZs2anVMPUc41YsSDzJr1Ges2TyEyojvHTu3hx71L+fOfRyvx\nkiJpxktELmuhoaHnfdosLS0Nh8MbH++AwjY/30Agv5REUSZMmMCECa/jXekEx1PiuS1mEGvWriYw\nMLBUcUZGRuLrE8D6799n9uLH2ZAwjdo1mmOt85KKNV5MTk4ODz74IOHh4XTq1InQ0FBeeumlImf8\nrnR9+vRh8uTJnEzdxqK1fych8Qvuu+8PjBs3ztOhSQXgsnIS7qJyEiJSGtu2bSMqKorWTW+hbbNB\n5OXlsHbzZM5k7OXQoSQCAgKKHqQMzZw5k2HDhtG57X0E+FcjsFJN4rd9gl/lTHbvTnRJWYkXXniB\nl14aT/sWMdSu0Zx9SRvYtnsBM2fOZOjQoWV+vctNZmYme/bsISwsjODgYE+HI250KeUkdKtRRK5I\nrVq1Yty4cTz//PPs2h9LXl4uGMusWZ+5PekCGDJkCNOmfciiRR9QI6ge6ZmnyXNmM2/6XJfV8nrv\nvfeJrHcdLRv3A6BGUANOntnLlCnvK/EqBn9/f1q1auXpMKSCUeIlIlesMWPGEB0dzfz58wkICCAm\nJoa6det6JBZvb2++/no+c+bMYdmyZYSEhHDvvfe6tHJ6SsoZaoafvabLz6cqyclnXHZNkSudEi8R\nuaK1adOGNm3aeDoMID/5Gjp0qNtmm/r3v4mFXy+lZvVI8vKycTpzSfplM/eOeNYt1xe5EinxEhG5\nQo0bN475875m6frXCttCatTkscce82BUIpc3PdUoInKFmjlzJlnZ2XRt9wBDbvgnndoM5+Spk0yb\nNq3YYzidThYvXsz48eP55JNPyMjIcF3AIpcBzXiJiFyhpk//iIZ1uxJZvwcAzRr24eiJnUyf/hFP\nPPFEkf2zsrIYOPAWlixZXLgxdYP6DVm5agURERGuDl+kQtKMl4jIFSo7Oxtvh99ZbQ4vX7Kzs4vV\nf9KkScTGxtK702iG3PA2t/R+mRPHz/Dkk0+6IlyRy4ISLxGRciw5OZlFixYRFxdX5oVN/+d/bmVv\n0hqOHN+JtZakXxL4+chGhgz5n2L1nzt3HnVqtaZuaDuMMQRVCScyojfz539dpnGKXE50q1FEKrzc\n3Fy++eYbNm/eTJMmTRg8eDD+/v6eDuuSTZkyhcf/+DjpGekAtG3Tjvlfz6NevXplMv7YsWNZu2Yd\ni9f+HW9vX3Jzs+nevQdPP/10sfpXrlSJ3LzDZ7Vl56R7pA6aSEWhxEtEKrTU1FRuuOFG1q9fR6WA\naqRnJNMksikrV60gLCzM0+GV2pYtWxgxYgSN63UnqskAUtOPs2HrB9x1192sXLmiTK4RFBTE+m/X\nsWTJEnbu3Enr1q3p1atXsQu2Dv/DcBYsjGHzjtk0rnctv5zcTeL+WB55ZGSZxCdyOVLiJSIV2sSJ\nE4mL+44brn2G0JAWnDpzgNhvX+PZZ59l6tSpng6v1D799FMqBVSjc9t78fJyUDUwlLZNh7Bq1bsk\nJSURHh5eJtdxOBz069ePfv36lbjvkCFDGDNmDK+88ipbE+cBMGjQYMaPH18msYlcjpR4iUiFNm/e\nfOqFdiQ0pAUA1avWo3G965g/r2KvM8rKysLh8MGY/84++Xj7F75WHhhjGDduHI899hgJCQk0aNCA\nxo0bezoskXJNi+tFpEKrXLkyObnpZ7VlZacSUKlSicf69ttvGTBgIPUjGnDTTf1Zs2ZNsfvu2bOH\n8ePHM2bMGOLi4kp87d+Ljo7mTMoxtu1eiNOZS1rGCX7YPZcWLVrRsGHDSx6/LNWsWZM+ffqUKOly\nOp188cUXjBgxgqeeeooffvjBhRGKlCPW2gp9AB0AGx8fb0XkyjN16lQL2HbNb7XRvV+xndoMtw6H\nj33++edLNM63335rfXx8bY2gCNsqsr8Nqd7AOhzedtWqVUX2nTlzpvV2eFs/v0q2UkA1C9jnnnuu\ntG/JWmut0+m0o0ePtoD18fazxhhbvXqwjYuLu6RxywOn02ljYmIsYGsE1bOVKwVZh8NhP/vsM0+H\nJlIs8fHxFrBAB1vCvMXYMn482d2MMR2A+Pj4eDp06ODpcETEzay1/OUvf2HixDfIzc3BGMOdd97F\n++9PwdfXt9jjRN8SzZrVm7mp+4s4vLxxOvP4z5pxtL+qCYsXL7pgv7S0NMLC6lCjagu6tn8QLy9v\ntv44ly0/fskPP/xAq1atLun9JSQksHTpUmrUqMGtt95KlSpVLmm88mDRokX069eP7h0fpmHdLjid\nuayOn0Ra1j4OJh3Az8+v6EFEPGjTpk107NgRoKO1dlNJ+upWo4hUaMYYXn31VZKSDrJ8+XL27dvH\nRx9NL1HSBZCw9QdCa7TC4ZW/9NXLy0FYSFSRt8DWr19PSsoZ2jSNxtvhi5fxIqrpQHx8/Pjmm29K\n/b5+1aZNG0aPHs3w4cMvi6QLYPny5VQJrEGD8M4AeHl506JxP46fOMb27ds9HJ2Ia2lxvYhcFmrV\nqkWtWrVK3T8qqhXr12whz5lbOON15MQ22na4+IxV1apVAcjIOkNQQVt2dhp5eTmFr8nZQkJCyMxM\nJTsnHT/fygCkpB4FoEaNGp4MTcTldKtRRIT8hfXdu/egWmAYoSGtOXpiG6fOHGTZslh69OhxwX7W\nWqKi2nDo4HHaN78dH58AEhK/JDX9IPt/3o+1ltmzZ5Oamkq/fv0u+dbj5eDIkSNENo4kMCCcFo36\nkZGVTELiF/Tq3YMFCyr206hyZdCtRhGRS9S5c2dWrVrJNV2iSM5IoMPVzVi+fNlFky7Iv9U5f/5c\nGkfWZfnGN1m89mV8/TNYsHABW7dupX79BowaNYpn/vJXoqKiGDt2rJveUfkVGhrKfxb9h+ohDlbE\nvUXcDx9x0019+fjjjzwdmojLacZLRKQMWGtJTEwkIyOD1q1bA1C/fkPysgPp3vERfH0qsTVxHgk/\nfoV+XuWz1nLgwAECAwMJDg72dDgixaYZLxG5Ilhr2bNnDwcPHvR0KOcwxtCsWTPatWuHw+Fgy5Yt\nJCUdoG3TwQT4VcXh5U2bptEEBFRl7ty5ng63XDDGEBERoaRLrihKvESkQoiLi6NlyygiIyOpV68e\nPXv24sCBA54O64J+3aQ7JzezsM3pzCUvL0ebSItcwfRUo4iUe8nJydxww434eAXTq9MT5ORmsCl+\nNrcMjGbT5niMMZ4O8RwtWrSgfbsObNoxA4fDlwC/qiQkfoUzL4ehQ4d6OjwR8RAlXiJS7s2ZM4fk\n5GT+p+9YKgXk35by963C0vWv/3atRblijGHW559xyy2DWLLuFQCqVKnKJ59+Uu62/BER91HiJSLl\n3smTJ/H29sHf7791sSoHhABw4sQJT4VVpMjISLZt20pcXByHDx+mc+fO1K5d29NhiYgHaY2XiJR7\nffv2JScni+17FmGtxenM5YfdX1MpoBKdO3f2dHgXtX37dp54YjSDBg0iPDycmJiYcp0siohracZL\nRMq9tm3b8qc//Yk333yT3QeWkZeXQ3pGMlOmTCnX1eFTUlLo3bsPOVk+dG3/IDk56Sz4ej5Djt3G\n8uXLPB2eiHiAEi8RqRAmTpxIdHQ0c+fOxc/PjzvvvLOwXlZ5NXv2bI4d+4XB1/+DwEr5t0YrBQSz\nYsX/sX37dlq2bOnhCEXE3ZR4iUiFYIyhZ8+e9OzZ09OhFNuRI0fw9QmgcsB/61QFVQkH4PDhw0q8\nRK5AWuMlIuIiPXr0ICs7nZ8OrAXyC8Du/GkJAf4B5fJJzF9t376d6FuiqVYtiCZNmvKvf/2Lir7L\niUh5oRkvEREX6dq1K3fccQeffvoeiftjyclN5/SZI/zzn/8kKCjI0+Gd16FDh7j22m7g9KdRnT4k\npx7mscceIzk5mb/+9a+eDk+kwlPiJSLiIsYYpk+fTnR0NPPmzSMgIIB77rmH7t27ezq0C5o8eTIZ\n6VkM6jMeP99AIL9m2muvvc5TTz2Fr6+vhyMUqdiUeImIuJDD4SAmJoaYmBhPh1Isu3fvpnq1eoVJ\nF0DtkBbs+GkxJ06cICwszIPRiVR8WuMlIiKF2rVrx/FTP5GSdgzIX5e2/9BGQmuHUbNmTQ9HJ1Lx\nacZLREQKPfDAA7zz9iS+Wf084bU6kJp+hKMndvH+++/j7a1fGSKXSjNeIiJSqHr16ny7YT0PjxpB\nYHAK7a+OZMGCBdx3332eDk3ksqA/X0RE5Cy1a9dm4sSJng5D5LKkGS8RERERN3FZ4mWMedYYs9YY\nk2aMOVmCfuOMMYeMMenGmCXGmEhXxSgiIiLiTq6c8fIBZgGTitvBGPO/wKPACOAaIA1YZIxR4RgR\nERGp8Fy2xsta+yKAMWZ4Cbo9DvzNWvt1Qd97gKPAIPKTOBEREZEKq9ys8TLGNARCgdhf26y1Z4AN\nQBdPxSUiIiJSVspN4kV+0mXJn+H6raMFr4mIiIhUaCVKvIwxLxtjnBc58owxTV0VrIiIiEhFVtI1\nXhOAqUWc81MpYzkCGKA2Z8961QY2F9X5iSeeoFq1ame1DRs2jGHDhpUyHBEREbnSzZgxgxkzZpzV\nlpycXOrxjLX2UmO6+AXyF9e/Ya0NLsa5h4DXrbVvFHxflfwk7B5r7ecX6NMBiI+Pj6dDhw5lGLmI\niIjIuTZt2kTHjh0BOlprN5WkryvreNUzxrQF6gMOY0zbgqPyb87ZaYyJ/k23N4HnjDEDjTGtgenA\nQWCuq+IUERERcRdXbhk0DrjnN9//mhH2AlYVfN0EKLw/aK19zRhTCZgMBAGrgZustdkujFNERETE\nLVxZx+te4N4iznGcp+0F4AXXRCUiIiLiOeWpnISIiIjIZU2Jl4iIiIibKPESERERcRMlXiIiIiJu\nosRLRERExE2UeImIiIi4iRIvERERETdR4iUiIiLiJkq8RERERNxEiZeIiIiImyjxEhEREXETJV4i\nIiIibqLES0RERMRNlHiJiIiIuIkSLxERERE3UeIlIiIi4iZKvERERETcRImXiIiIiJso8RIRERFx\nEyVeIiIiIm6ixEtERETETZR4iYiIiLiJEi8RERERN1HiJSIiIuKcrpPuAAAGLElEQVQmSrxERERE\n3ESJl4iIiIibKPESERERcRMlXiIiIiJuosRLRERExE2UeImIiIi4iRIvERERETdR4iUiIiLiJkq8\nRERERNxEiZeIiIiImyjxEhEREXETJV4iIiIibqLES0RERMRNlHiJiIiIuIkSLxERERE3UeIlIiIi\n4iZKvERERETcRImXiIiIiJso8RIRERFxEyVeIiIiIm6ixEs8YsaMGZ4OQUpAn1fFo8+sYtHndeVw\nWeJljHnWGLPWGJNmjDlZzD5TjTHO3x0LXRWjeI5+yFQs+rwqHn1mFYs+ryuHtwvH9gFmAeuB+0rQ\n7xvgD4Ap+D6rbMMSERER8QyXJV7W2hcBjDHDS9g1y1p7zAUhiYiIiHhUeVzj1dMYc9QYs9MY844x\nJtjTAYmIiIiUBVfeaiyNb4A5wF6gMfAysNAY08Vaay/Qxx9gx44d7olQykRycjKbNm3ydBhSTPq8\nKh59ZhWLPq+K5Tc5h39J+5oL5zPnOdmYl4H/vcgpFmhhrU38TZ/hwBvW2hLPXBljGgJ7gD7W2uUX\nOOcO4JOSji0iIiJyie601n5akg4lnfGaAEwt4pyfSjjmBVlr9xpjjgORwHkTL2ARcCewD8gsq2uL\niIiIXIA/0ID8HKRESpR4WWtPACdKepHSMsbUBWoAh4uIqUTZpoiIiMglWleaTq6s41XPGNMWqA84\njDFtC47KvzlnpzEmuuDrysaY14wxnYwx9Y0xfYCvgERKkVGKiIiIlDeuXFw/DrjnN9//umqwF7Cq\n4OsmQLWCr/OANgV9goBD5Cdcz1trc1wYp4iIiIhblGhxvYiIiIiUXnms4yUiIiJyWbqsEq+CtWFT\njDE/GWPSjTG7jDEvGGN8PB2bnF9p9vQU9zLGPGKM2WuMyTDGfGuMudrTMcn5GWO6G2PmGWOSCva6\nvcXTMcmFGWOeMcZsNMacKSgc/qUxpqmn45LzM8aMNMZsMcYkFxzrjDH9SjrOZZV4Ac3J3+PxQaAl\n8AQwEhjvyaDkon7d03OSpwORcxljhgL/AMYC7YEtwCJjTIhHA5MLqQx8D4wiv66ilG/dgf8DOgHX\nk//zcLExJsCjUcmFHCC/lmkHoCOwDJhrjGlRkkEu+zVexpg/AyOttZGejkUu7FIK7YrrGGO+BTZY\nax8v+N6Q/8PnLWvtax4NTi7KGOMEBllr53k6Fimegj9ofgF6WGvXeDoeKZox5gTwZ2ttUTVOC11u\nM17nEwToFpZICRXcou8IxP7aVrB111Kgi6fiErmMBZE/U6nfWeWcMcbLGHM7UAlYX5K+5W2vxjJl\njIkEHgVGezoWkQooBHAAR3/XfhRo5v5wRC5fBbPJbwJrrLXbPR2PnJ8xJor8RMsfSAEGW2t3lmSM\nCjHjZYx5uWCh6IWOvN8vSDTGhJO/6fZn1toPPBP5lak0n5eIyBXuHfLXJt/u6UDkonYCbYFryF+b\nPN0Y07wkA1SUGa8S7RFpjKlD/qK3Ndbah1wZmJyXW/f0FJc5Tn5h49q/a68NHHF/OCKXJ2PMv4D+\nQHdr7QW3yBPPs9bm8t/fX5uNMdcAjwMPF3eMCpF4lWSPyIKZrmVAHHCfK+OS83P3np7iGtbaHGNM\nPNAHmAeFt0P6AG95MjaRy0VB0hUNXGet/dnT8UiJeQF+JelQIRKv4iqY6VoB7AWeBmrl/54Aa+3v\n16lIOWCMqQcE85s9PQte2m2tTfNcZFJgIjCtIAHbSH6JlkrANE8GJedXsBduJPlldQAaFfw/ddJa\ne8Bzkcn5GGPeAYYBtwBpxphfZ5eTrbWZnotMzscY83fylzD9DFQB7gSuA24o0TiXUzmJgpIEv1/P\nZch/GMvhgZCkCMaYqZy9p+evellrV52nXdzMGDOK/D9kapNfI+oxa+13no1KzscYcx2wnHNreH1o\nrdUdgHKmoOTH+X4J32utne7ueOTijDFTgN5AGJAMJACvWGuXlWicyynxEhERESnPKsRTjSIiIiKX\nAyVeIiIiIm6ixEtERETETZR4iYiIiLiJEi8RERERN1HiJSIiIuImSrxERERE3ESJl4iIiIibKPES\nERERcRMlXiIiIiJuosRLRERExE2UeImIiIi4yf8DynEaxP9WxvUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8aa8633e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate data\n", "X, y = sklearn.datasets.make_moons(300, noise=0.22)\n", "plt.figure(figsize=(7, 5))\n", "plt.scatter(X[:, 0], X[:, 1], s=15, c=y, cmap=plt.cm.Spectral)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "&nbsp;\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feedforward Neural Network" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# import feedforward neural net \n", "from mlnn import neural_net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<script type=\"text/javascript\" src=\"https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML\"></script>\n", "Let's build a 4-layer neural network. Our network has one input layer, two hidden layer and one output layer. Our model can be represented as a directed acyclic graph wherein each node in a layer is connected all other nodes in its succesive layer. The neural net is shown below-\n", "\n", "![neural network](nn.svg)\n", "\n", "Each node in the hidden layer uses a nonlinear activation function $f(x)$, which computes the outputs from its inputs and transfer these outputs to successive layers. Here we've used $f(x)= tanh(x)$, as our non-linear activation. Its derivative is given by- $f'(x)= 1-tanh(x)^2$. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our network graph can be represented as-\n", "![nn_graph](graph1.svg)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "| Layer No. | Notation | Value | Variable | \n", "|----------:|-----------:|---------------------------------------------:|----------:|\n", "|&nbsp;&nbsp;1 | &nbsp;&nbsp; X | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $X$| X|\n", "|&nbsp;&nbsp; 2 |&nbsp;&nbsp; W1(~)+b1 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; $W1*X+b1$|&nbsp;&nbsp; pre_act1|\n", "| &nbsp;&nbsp;2 |&nbsp;&nbsp; tanh | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; $tanh(W1*X+b1)$| &nbsp;&nbsp; act1|\n", "| &nbsp;&nbsp;3 |&nbsp;&nbsp; W2(~)+b2 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; $W2*(tanh(W1*X +b1))+b2$| &nbsp;&nbsp; pre_act2|\n", "|&nbsp;&nbsp; 3 |&nbsp;&nbsp; tanh | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;$tanh(W2*(tanhW1*X+b1))+b2)$| &nbsp;&nbsp; act2|\n", "| &nbsp;&nbsp;4 |&nbsp;&nbsp; W3(~)+b3 |&nbsp;&nbsp;&nbsp;$W3*(tanh(W2*(tanhW1*X+b1))+b2)+b3$ | &nbsp;&nbsp; pre_act3| \n", "| &nbsp;&nbsp;4 |&nbsp;&nbsp; softmax |$softmax(W3*(tanh(W2*(tanhW1*X+b1)+b2))+b3)$ </br>| &nbsp;&nbsp; act3| " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Backpropagation " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we formulate the backpropagation algorithm or backprop for training the network. For derivation of the backprop, please see Dr. Hugo Larochelle's excellent course on [neural networks](https://www.youtube.com/watch?v=_KoWTD8T45Q&index=13&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH). " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "$ \\large\\frac{\\partial L}{\\partial Pred} = \\frac{\\partial L}{\\partial L} * \\frac{\\partial L}{\\partial Pred} $\n", "\n", "$ \\large\\frac{\\partial L}{\\partial act3} = \\frac{\\partial L}{\\partial Pred} * \\frac{\\partial Pred}{\\partial act3} $\n", "\n", "$ \\large\\frac{\\partial L}{\\partial pre\\_act3} = \\frac{\\partial L}{\\partial act3} * \\frac{\\partial act3}{\\partial pre\\_act3}= \\delta4$ \n", "\n", "$ \\large\\frac{\\partial L}{\\partial act2} = \\frac{\\partial L}{\\partial pre\\_act3} * \\frac{\\partial pre\\_act3}{\\partial act2} $\n", "\n", "$ \\large\\frac{\\partial L}{\\partial pre\\_act2} = \\frac{\\partial L}{\\partial act2} * \\frac{\\partial act2}{\\partial pre\\_act2}= \\delta3$ \n", "\n", "$ \\large\\frac{\\partial L}{\\partial act1} = \\frac{\\partial L}{\\partial pre\\_act2} * \\frac{\\partial pre\\_act2}{\\partial act1} $\n", "\n", "$ \\large\\frac{\\partial L}{\\partial pre\\_act1} = \\frac{\\partial L}{\\partial act1} * \\frac{\\partial act1}{\\partial pre\\_act1}= \\delta2$ \n", "\n", "$ \\large\\frac{\\partial L}{\\partial W3} = \\delta4 * \\frac{\\partial pre\\_act3}{\\partial W3}$ \n", "\n", "$ \\large\\frac{\\partial L}{\\partial W2} = \\delta3 * \\frac{\\partial pre\\_act2}{\\partial W2}$ \n", "\n", "$ \\large\\frac{\\partial L}{\\partial W1} = \\delta2 * \\frac{\\partial pre\\_act1}{\\partial W1}$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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Uqb55i2Qn7XAuUoJXX4UPPvCVAEQi+wlYCzQpdr4J8H0pj/kO+CaE8EuRc7MA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\nqZJb8vLggQdg6lTvWd1/fzj9dF8JI1u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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8a7b195f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize tanh and its derivative\n", "x = np.linspace(-np.pi, np.pi, 120)\n", "plt.figure(figsize=(8, 3))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(x, np.tanh(x))\n", "plt.title(\"tanh(x)\")\n", "plt.xlim(-3, 3)\n", "plt.subplot(1, 2, 2)\n", "plt.plot(x, 1 - np.square(np.tanh(x)))\n", "plt.xlim(-3, 3)\n", "plt.title(\"tanh\\'(x)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be seen from the above figure that as we increase our input the our activation starts to saturate which can inturn kill gradients. This can be mitigated using rectified activation functions. Another problem that we encounter in training deep neural networks during backpropagation is vanishing gradient and gradient explosion. It can be observed from the derivative of our nth activation- $\\large\\frac{\\partial act\\_n}{\\partial pre\\_act\\_n}$ , is fairly large near zero. Let's assume that the weigths $< 1$, this will usually satisfy $|w_{i}*tanh'(x)| < 1$. The succesive product of such values in each layer will exponentially decrease the computed product leading to vanishing gradient. This is not a robust explanation of vanishing gradient problem. For more information refer to this [article](http://ieeexplore.ieee.org/document/279181/). \n", "\n", "Similarly if the weigths are large 100, 40.., we can formulate the gradient explosion problem." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss after iteration 0: 0.550828\n", "Loss after iteration 1000: 0.312276\n", "Loss after iteration 2000: 0.310907\n", "Loss after iteration 3000: 0.310098\n", "Loss after iteration 4000: 0.309545\n", "Loss after iteration 5000: 0.309141\n", "Loss after iteration 6000: 0.308830\n", "Loss after iteration 7000: 0.308582\n", "Loss after iteration 8000: 0.308379\n", "Loss after iteration 9000: 0.308209\n", "Loss after iteration 10000: 0.308063\n", "Loss after iteration 11000: 0.307935\n", "Loss after iteration 12000: 0.307822\n", "Loss after iteration 13000: 0.307721\n", "Loss after iteration 14000: 0.307629\n", "Loss after iteration 15000: 0.307544\n", "Loss after iteration 16000: 0.307465\n", "Loss after iteration 17000: 0.307390\n", "Loss after iteration 18000: 0.307319\n", "Loss after iteration 19000: 0.307250\n", "Loss after iteration 20000: 0.307183\n", "Loss after iteration 21000: 0.307116\n", "Loss after iteration 22000: 0.307048\n", "Loss after iteration 23000: 0.306980\n", "Loss after iteration 24000: 0.306908\n" ] }, { "data": { "text/plain": [ "{'W1': array([[ 0.11827437, 0.09296233, 0.10304653, 0.11684045],\n", " [-0.20240277, -0.38252876, -0.43947934, -0.4297747 ]]),\n", " 'W2': array([[ 1.73731779, -0.63048788],\n", " [ 1.83758017, -1.33090351],\n", " [ 0.09501323, -0.22903616],\n", " [-0.70044285, 1.49809302]]),\n", " 'W3': array([[ 1.73731779, -0.63048788],\n", " [ 1.83758017, -1.33090351],\n", " [ 0.09501323, -0.22903616],\n", " [-0.70044285, 1.49809302]]),\n", " 'b1': array([[-0.31322707, 0.10078616, 0.25341526, -0.24919222]]),\n", " 'b2': array([[-0.20185746, 0.04269616, -0.01080139, -0.14951606]]),\n", " 'b3': array([[-0.09947524, 0.09947524]])}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training the neural network\n", "\n", "my_nn = neural_net([2, 4, 2]) # [2,4,2] = [input nodes, hidden nodes, output nodes]\n", "\n", "my_nn.train(X, y, 0.001, 0.0001) # weights regularization lambda= 0.001 , epsilon= 0.0001" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QoZ4uvkA+W3CzQ3sIhzRJWOgkyCiSaAh1c135cnLM8XiDQb7NGPKxs44WXqcK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\nScaDKVP42t4aFEbCx9zkIhZFJRyyz1e3z8MN1atx+wKcRioKJL62t3B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mkbX4OlT6kY+c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gmbcz15lQ/xtJDP0kEvzzVUIQoQs3Ro1m2i6TWW4SFRESsOTUtOx4+DPUexDfSe1mZoslF5+hM+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\n413PMjnuBqy2PnJPrMFm6+e2x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li2neS4G9h+4EX257xGctz19PZ1cejYW/RbewjNWDbG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\n0W/tRdNsFFXsorRqLz2WOnTNzQS7hNOSv4eD/3yI1oqj4/xKL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BKyhBBCCCEcQEKWEEII\nIYQDSMgSQgghhHAACVlCCCGEEA4gIUsIIYQQwgEkZAkhhBBCOICELCGEEEIIB5CQJYQQQgjhAP8f\n/wYXxYWWnMMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8a793caf10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### visualize predictions\n", "my_nn.visualize_preds(X ,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Animate Training:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VjiYHff78eVtfwn2zWq36jz/+0GPGjNGzZs3SSUlJ9g7pvuzYsUN7uOfTzkYH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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8a7861da10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_, y_ = sklearn.datasets.make_circles(n_samples=400, noise=0.18, factor=0.005, random_state=1)\n", "plt.figure(figsize=(7, 5))\n", "plt.scatter(X_[:, 0], X_[:, 1], s=15, c=y_, cmap=plt.cm.Spectral)\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "'\\nUncomment the code below to see classification process for above data.\\nTo stop training early reduce no. of iterations.\\n'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "Uncomment the code below to see classification process for above data.\n", "To stop training early reduce no. of iterations.\n", "'''\n", "\n", "#new_nn = neural_net([2, 6, 2])\n", "#new_nn.animate_preds(X_, y_, 0.001, 0.0001) # max iterations = 35000 " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
theandygross/TCGA_differential_expression
Notebooks/Preprocessing/unify_methylation_probes.ipynb
1
2016
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pull common probes from all methylation arrays and create a new store. \n", "This is being done to allow for efficient selection of probes in a batch across tissues." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "store = pd.HDFStore(METH_STORE)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "395883" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = {}\n", "for c in store.keys():\n", " if c in ['/matched_tn','/codes']:\n", " continue\n", " idx[c] = store[c].index\n", "idx_common = reduce(set.intersection, map(set, idx.values()))\n", "len(idx_common)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for c in store.keys():\n", " if c in ['/matched_tn','/codes']:\n", " continue\n", " df = store[c]\n", " df.ix[idx_common].to_hdf(METH_STORE_MATCHED, c, format='t',\n", " append=False, complevel=9)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
hobson/pug-ann
pug/ann/pybrain_weather_predictor.ipynb
1
8420
{ "metadata": { "name": "", "signature": "sha256:b12c61e3ea20c2b54fe453299c08ec05a96abdbc12c366c26b7d997b7c1bbc44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pug.ann.data import weather\n", "df = weather.fresno\n", "print(df.describe())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Max TemperatureF Mean TemperatureF Min TemperatureF Max Dew PointF \\\n", "count 1461.000000 1461.000000 1461.000000 1461.000000 \n", "mean 79.261465 66.875428 54.007529 49.403149 \n", "std 16.350864 14.010400 12.265078 7.492898 \n", "min 40.000000 38.000000 27.000000 25.000000 \n", "25% 65.000000 55.000000 45.000000 45.000000 \n", "50% 79.000000 66.000000 53.000000 50.000000 \n", "75% 95.000000 80.000000 65.000000 55.000000 \n", "max 111.000000 95.000000 82.000000 67.000000 \n", "\n", " MeanDew PointF Min DewpointF Max Humidity Mean Humidity \\\n", "count 1461.000000 1461.000000 1461.000000 1461.000000 \n", "mean 44.780972 38.498973 74.214921 52.416153 \n", "std 7.452126 8.439846 15.742676 15.397896 \n", "min 18.000000 4.000000 28.000000 19.000000 \n", "25% 40.000000 33.000000 61.000000 40.000000 \n", "50% 45.000000 39.000000 76.000000 50.000000 \n", "75% 51.000000 45.000000 87.000000 64.000000 \n", "max 63.000000 58.000000 100.000000 95.000000 \n", "\n", " Min Humidity Max Sea Level PressureIn Mean Sea Level PressureIn \\\n", "count 1461.000000 1461.000000 1461.000000 \n", "mean 30.114305 30.056324 29.991485 \n", "std 16.621242 0.163034 0.160462 \n", "min 4.000000 29.670000 29.550000 \n", "25% 17.000000 29.930000 29.870000 \n", "50% 26.000000 30.040000 29.970000 \n", "75% 40.000000 30.170000 30.110000 \n", "max 89.000000 30.600000 30.540000 \n", "\n", " Min Sea Level PressureIn Max VisibilityMiles Mean VisibilityMiles \\\n", "count 1461.000000 1461.000000 1461.000000 \n", "mean 29.925079 9.595483 8.713210 \n", "std 0.162644 1.286902 2.210008 \n", "min 29.470000 2.000000 1.000000 \n", "25% 29.800000 10.000000 8.000000 \n", "50% 29.900000 10.000000 10.000000 \n", "75% 30.040000 10.000000 10.000000 \n", "max 30.500000 10.000000 10.000000 \n", "\n", " Min VisibilityMiles Max Wind SpeedMPH Mean Wind SpeedMPH \\\n", "count 1461.000000 1461.000000 1461.000000 \n", "mean 7.028747 13.415469 5.285421 \n", "std 3.296614 4.982216 3.117557 \n", "min 0.000000 4.000000 0.000000 \n", "25% 4.000000 9.000000 3.000000 \n", "50% 8.000000 13.000000 5.000000 \n", "75% 10.000000 16.000000 7.000000 \n", "max 10.000000 33.000000 17.000000 \n", "\n", " Max Gust SpeedMPH CloudCover \n", "count 1322.000000 1461.000000 \n", "mean 18.450832 2.850787 \n", "std 6.152202 2.395411 \n", "min 6.000000 0.000000 \n", "25% 14.000000 1.000000 \n", "50% 18.000000 2.000000 \n", "75% 22.000000 5.000000 \n", "max 45.000000 8.000000 \n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from pug.ann import util\n", "ds = util.pybrain_dataset_from_dataframe(df)\n", "print(ds)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "input: dim(2046, 3)\n", "[[ 1.19325827 -0.20421695 -1.60169216]\n", " [ 1.63790947 1.19325827 0.74860706]\n", " [ 1.63790947 0.17691266 -1.28408415]\n", " ..., \n", " [ 0.93917186 -0.14069535 -1.28408415]\n", " [ 1.38382307 0.74860706 0.11339106]\n", " [ 1.63790947 0.30395586 -1.02999775]]\n", "\n", "target: dim(2046, 1)\n", "[[ 50.]\n", " [ 51.]\n", " [ 55.]\n", " ..., \n", " [ 54.]\n", " [ 44.]\n", " [ 46.]]\n", "\n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "nn = util.build_ann(ds)\n", "print(nn)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "FeedForwardNetwork-26\n", " Modules:\n", " [<LinearLayer 'input'>, <LinearLayer 'hidden'>, <LinearLayer 'output'>]\n", " Connections:\n", " [<FullConnection 'FullConnection-24': 'input' -> 'hidden'>, <FullConnection 'FullConnection-25': 'hidden' -> 'output'>]\n", "\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "train = util.pb.supervised.RPropMinusTrainer(nn)\n", "print(train)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<RPropMinusTrainer 'RPropMinusTrainer-27'>\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "ans = train.trainUntilConvergence(ds, maxEpochs=10, verbose=True)\n", "print(ans)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('train-errors:', '[4139.9 , 3955.19 , 3783.37 , 3593.29 , 3361.81 , 3090.66 , 2774.69 , 2410.03 , 1998.28 , 1471.81 , 884.475 ]')\n", "('valid-errors:', '[4089.56 , 3908.32 , 3739.71 , 3553.19 , 3326.07 , 3060.06 , 2750.15 , 2392.57 , 1988.98 , 1473.55 , 894.674 , 423.093 ]')\n", "([4139.8960310591647, 3955.191433542142, 3783.3687206428003, 3593.2905941074878, 3361.8118032012608, 3090.6559403249894, 2774.687119950866, 2410.0314517727843, 1998.2758315700733, 1471.8059671912513], [4089.558608459422, 3908.3175470810306, 3739.7128566666015, 3553.1868896701071, 3326.0701954487186, 3060.0598032364178, 2750.1454518823525, 2392.5749056935315, 1988.9815833485743, 1473.5486603940292, 894.67433311603281])\n" ] } ], "prompt_number": 9 } ], "metadata": {} } ] }
mit
ersh24/manoelgadi12
manoelgadi12/hello.ipynb
1
642
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hello ():\n", " print (\"Hello World\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
M-R-Houghton/euroscipy_2015
cython/cy_tutorial.ipynb
1
264932
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015, Stefan Behnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See http://consulting.behnel.de/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import Cython\n", "#print(\"Python %d.%d.%d %s %s\" % sys.version_info)\n", "#print(\"Cython %s\" % Cython.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Building Cython modules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# simple setup without elaborate dependencies\n", "from distutils.core import setup\n", "from Cython.Build import cythonize\n", "\n", "setup(\n", " ext_modules = cythonize(\"*.pyx\"), # <- glob pattern\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# more complex case with per-module configuration\n", "from distutils.core import setup\n", "from Cython.Build import cythonize\n", "from distutils.extension import Extension\n", "\n", "ext_modules = [\n", " Extension(\n", " [\"mypackage/mymodule.pyx\"],\n", " ... # configure C build + libraries here\n", " )]\n", "\n", "setup(ext_modules = cythonize(ext_modules))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions and Coercion" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython\n", "\n", "def pyfunc(x):\n", " return x + 1\n", "\n", "def cyfunc(int x):\n", " return x + 1\n", "\n", "cdef int cfunc(int x):\n", " return x + 1\n", "\n", "cpdef cypyfunc(int x):\n", " y = cfunc(x + 1)\n", " return y * 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyfunc(2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cyfunc(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'cfunc' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-aad7450ab81e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'cfunc' is not defined" ] } ], "source": [ "cfunc(2)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cypyfunc(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Static typing and type inference" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "\n", "def sin(x):\n", " return math.sin(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.23.1 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" 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class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_e8d40d836d7eeffcab5c520a21184dd9.sin\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return NULL;\n", " __pyx_L4_argument_unpacking_done:;\n", " __pyx_r = __pyx_pf_46_cython_magic_e8d40d836d7eeffcab5c520a21184dd9_sin(__pyx_self, ((double)__pyx_v_x));\n", " int __pyx_lineno = 0;\n", " const char *__pyx_filename = NULL;\n", " int __pyx_clineno = 0;\n", "\n", " /* function exit code */\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "static PyObject *__pyx_pf_46_cython_magic_e8d40d836d7eeffcab5c520a21184dd9_sin(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_x) {\n", " PyObject *__pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"sin\", 0);\n", "/* … */\n", " /* function exit code */\n", " 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__pyx_n_s_cython_magic_e8d40d836d7eeffcab);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_sin, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">4</span>: <span class=\"k\">return</span> <span class=\"n\">libc</span><span class=\"o\">.</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-6 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>(sin(__pyx_v_x));<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_r = __pyx_t_1;\n", " __pyx_t_1 = 0;\n", " goto __pyx_L0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython -a\n", "cimport libc.math\n", "\n", "def sin(double x):\n", " return libc.math.sin(x)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython\n", "def local_variables(x):\n", " cdef int i = 5, ix = x\n", " print(i * ix)\n", " return (i + ix) // 2" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] }, { "data": { "text/plain": [ "3" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "local_variables(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise: optimise Python code into C code\n", "Optimise this code so that it compiles to plain C code but uses as few type declarations as possible." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "\n", "def py_circular_distance(radius, lon1, lat1, lon2, lat2):\n", " x = math.pi/180.0\n", " a = (90.0-lat1) * x\n", " b = (90.0-lat2) * x\n", " theta = (lon2-lon1) * x\n", " c = math.acos((math.cos(a)*math.cos(b)) + (math.sin(a)*math.sin(b)*math.cos(theta)))\n", " return radius*c\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { 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class=\"n\">double</span> <span class=\"n\">lon1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">lat1</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">lon2</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">lat2</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-96 '>/* Python wrapper */\n", "static PyObject *__pyx_pw_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_1cy_circular_distance(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n", "static PyMethodDef __pyx_mdef_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_1cy_circular_distance = {\"cy_circular_distance\", (PyCFunction)__pyx_pw_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_1cy_circular_distance, METH_VARARGS|METH_KEYWORDS, 0};\n", "static PyObject *__pyx_pw_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_1cy_circular_distance(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n", " double __pyx_v_radius;\n", " double __pyx_v_lon1;\n", " double __pyx_v_lat1;\n", " double __pyx_v_lon2;\n", " double __pyx_v_lat2;\n", " PyObject *__pyx_r = 0;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"cy_circular_distance (wrapper)\", 0);\n", " {\n", " static PyObject **__pyx_pyargnames[] = {&amp;__pyx_n_s_radius,&amp;__pyx_n_s_lon1,&amp;__pyx_n_s_lat1,&amp;__pyx_n_s_lon2,&amp;__pyx_n_s_lat2,0};\n", " PyObject* values[5] = {0,0,0,0,0};\n", " if (unlikely(__pyx_kwds)) {\n", " Py_ssize_t kw_args;\n", " const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n", " switch (pos_args) {\n", " case 5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", " case 4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", " case 3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n", " case 2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n", " case 1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n", " case 0: break;\n", " default: goto __pyx_L5_argtuple_error;\n", " }\n", " kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n", " switch (pos_args) {\n", " case 0:\n", " if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_radius)) != 0)) kw_args--;\n", " else goto __pyx_L5_argtuple_error;\n", " case 1:\n", " if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_lon1)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"cy_circular_distance\", 1, 5, 5, 1); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " case 2:\n", " if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_lat1)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"cy_circular_distance\", 1, 5, 5, 2); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " case 3:\n", " if (likely((values[3] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_lon2)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"cy_circular_distance\", 1, 5, 5, 3); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " case 4:\n", " if (likely((values[4] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_lat2)) != 0)) kw_args--;\n", " else {\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"cy_circular_distance\", 1, 5, 5, 4); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " }\n", " if (unlikely(kw_args &gt; 0)) {\n", " if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"cy_circular_distance\") &lt; 0)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 5) {\n", " goto __pyx_L5_argtuple_error;\n", " } else {\n", " values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n", " values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n", " values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n", " values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n", " values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n", " }\n", " __pyx_v_radius = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[0]);<span class='error_goto'> if (unlikely((__pyx_v_radius == (double)-1) &amp;&amp; PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " __pyx_v_lon1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]);<span class='error_goto'> if (unlikely((__pyx_v_lon1 == (double)-1) &amp;&amp; PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " __pyx_v_lat1 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]);<span class='error_goto'> if (unlikely((__pyx_v_lat1 == (double)-1) &amp;&amp; PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " __pyx_v_lon2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[3]);<span class='error_goto'> if (unlikely((__pyx_v_lon2 == (double)-1) &amp;&amp; PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " __pyx_v_lat2 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[4]);<span class='error_goto'> if (unlikely((__pyx_v_lat2 == (double)-1) &amp;&amp; PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " }\n", " goto __pyx_L4_argument_unpacking_done;\n", " __pyx_L5_argtuple_error:;\n", " <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"cy_circular_distance\", 1, 5, 5, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L3_error;}</span>\n", " __pyx_L3_error:;\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4.cy_circular_distance\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return NULL;\n", " __pyx_L4_argument_unpacking_done:;\n", " __pyx_r = __pyx_pf_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_cy_circular_distance(__pyx_self, __pyx_v_radius, __pyx_v_lon1, __pyx_v_lat1, __pyx_v_lon2, __pyx_v_lat2);\n", " int __pyx_lineno = 0;\n", " const char *__pyx_filename = NULL;\n", " int __pyx_clineno = 0;\n", "\n", " /* function exit code */\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "static PyObject *__pyx_pf_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_cy_circular_distance(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_radius, double __pyx_v_lon1, double __pyx_v_lat1, double __pyx_v_lon2, double __pyx_v_lat2) {\n", " double __pyx_v_x;\n", " double __pyx_v_a;\n", " double __pyx_v_b;\n", " double __pyx_v_theta;\n", " double __pyx_v_c;\n", " PyObject *__pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"cy_circular_distance\", 0);\n", "/* … */\n", " /* function exit code */\n", " __pyx_L1_error:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4.cy_circular_distance\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " __pyx_r = NULL;\n", " __pyx_L0:;\n", " <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "/* … */\n", " __pyx_tuple_ = <span class='py_c_api'>PyTuple_Pack</span>(10, __pyx_n_s_radius, __pyx_n_s_lon1, __pyx_n_s_lat1, __pyx_n_s_lon2, __pyx_n_s_lat2, __pyx_n_s_x, __pyx_n_s_a, __pyx_n_s_b, __pyx_n_s_theta, __pyx_n_s_c);<span class='error_goto'> if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_tuple_);\n", " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple_);\n", "/* … */\n", " __pyx_t_1 = PyCFunction_NewEx(&amp;__pyx_mdef_46_cython_magic_1bb61b3ca631ffd4895e85b4ddcdf8b4_1cy_circular_distance, NULL, __pyx_n_s_cython_magic_1bb61b3ca631ffd489);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_cy_circular_distance, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">4</span>: <span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">M_PI</span><span class=\"o\">/</span><span class=\"mf\">180.0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_x = (M_PI / 180.0);\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">5</span>: <span class=\"n\">a</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mf\">90.0</span><span class=\"o\">-</span><span class=\"n\">lat1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">x</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_a = ((90.0 - __pyx_v_lat1) * __pyx_v_x);\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">6</span>: <span class=\"n\">b</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mf\">90.0</span><span class=\"o\">-</span><span class=\"n\">lat2</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">x</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_b = ((90.0 - __pyx_v_lat2) * __pyx_v_x);\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">7</span>: <span class=\"n\">theta</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">lon2</span><span class=\"o\">-</span><span class=\"n\">lon1</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">x</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_theta = ((__pyx_v_lon2 - __pyx_v_lon1) * __pyx_v_x);\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">8</span>: <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">acos</span><span class=\"p\">((</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"n\">a</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"n\">b</span><span class=\"p\">))</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">a</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">b</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"n\">theta</span><span class=\"p\">)))</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_c = acos(((cos(__pyx_v_a) * cos(__pyx_v_b)) + ((sin(__pyx_v_a) * sin(__pyx_v_b)) * cos(__pyx_v_theta))));\n", "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">9</span>: <span class=\"k\">return</span> <span class=\"n\">radius</span><span class=\"o\">*</span><span class=\"n\">c</span></pre>\n", "<pre class='cython code score-6 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>((__pyx_v_radius * __pyx_v_c));<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_r = __pyx_t_1;\n", " __pyx_t_1 = 0;\n", " goto __pyx_L0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython -a\n", "cimport libc.math as math\n", "\n", "def cy_circular_distance(double radius, double lon1, double lat1, double lon2, double lat2):\n", " x = math.M_PI/180.0\n", " a = (90.0-lat1) * x\n", " b = (90.0-lat2) * x\n", " theta = (lon2-lon1) * x\n", " c = math.acos((math.cos(a)*math.cos(b)) + (math.sin(a)*math.sin(b)*math.cos(theta)))\n", " return radius*c\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.424943087932\n", "0.424943087932\n" ] } ], "source": [ "print(py_circular_distance(10, 1.2, 2, 2, 4.3))\n", "print(cy_circular_distance(10, 1.2, 2, 2, 4.3))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 8.66 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000000 loops, best of 3: 1.05 µs per loop\n" ] } ], "source": [ "%timeit py_circular_distance(10, 1.2, 2, 2, 4.3)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 14.79 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000000 loops, best of 3: 210 ns per loop\n" ] } ], "source": [ "%timeit cy_circular_distance(10, 1.2, 2, 2, 4.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calling C functions" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] }, { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.23.1 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n", " <title>Cython: _cython_magic_f4f6e6b93544e557bcf370d9ff8a211c.pyx</title>\n", " <style type=\"text/css\">\n", " \n", "body.cython { font-family: courier; font-size: 12; }\n", "\n", ".cython.tag { }\n", ".cython.line { margin: 0em }\n", ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n", "\n", ".cython.line 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".cython .vc { color: #19177C } /* Name.Variable.Class */\n", ".cython .vg { color: #19177C } /* Name.Variable.Global */\n", ".cython .vi { color: #19177C } /* Name.Variable.Instance */\n", ".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n", " </style>\n", " <script>\n", " function toggleDiv(id) {\n", " theDiv = id.nextElementSibling\n", " if (theDiv.style.display != 'block') theDiv.style.display = 'block';\n", " else theDiv.style.display = 'none';\n", " }\n", " </script>\n", "</head>\n", "<body class=\"cython\">\n", "<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.23.1</span></p>\n", "<p>\n", " <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n", " Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n", "</p>\n", "<div class=\"cython\"><pre class=\"cython line score-0\">&#xA0;<span class=\"\">1</span>: <span class=\"c\"># libc math functions</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">2</span>: <span class=\"k\">from</span> <span class=\"nn\">libc</span> <span class=\"k\">cimport</span> <span class=\"n\">math</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">3</span>: </pre>\n", "<pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">4</span>: <span class=\"k\">print</span><span class=\"p\">(</span> <span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">M_PI</span> <span class=\"o\">/</span> <span class=\"mf\">2</span><span class=\"p\">)</span> <span class=\"p\">)</span></pre>\n", "<pre class='cython code score-8 '> __pyx_t_1 = <span class='py_c_api'>PyFloat_FromDouble</span>(sin((M_PI / 2.0)));<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = 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"free(cmem)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "%%cython\n", "# dynamic CPython heap memory allocation\n", "from cpython.mem cimport PyMem_Malloc, PyMem_Free\n", "\n", "cdef int* pymem = <int*>PyMem_Malloc(22 * sizeof(int))\n", "if not pymem:\n", " raise MemoryError()\n", " \n", "try:\n", " pymem[:] = [1,2,3]\n", " print( pymem[0] + pymem[2] )\n", "finally:\n", " PyMem_Free(pymem)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Auto-wrapping C functions to Python" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.23.1 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n", " <title>Cython: 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0.23.1</span></p>\n", "<p>\n", " <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n", " Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n", "</p>\n", "<div class=\"cython\"><pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">1</span>: <span class=\"k\">from</span> <span class=\"nn\">libc</span> <span class=\"k\">cimport</span> <span class=\"n\">math</span><span class=\"p\">,</span> <span class=\"n\">stdlib</span></pre>\n", "<pre class='cython code score-11 '> __pyx_t_1 = <span class='py_c_api'>PyDict_New</span>();<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[1]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">2</span>: <span class=\"n\">py_sin</span> <span class=\"o\">=</span> <span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">sin</span></pre>\n", "<pre class='cython code score-6 '> __pyx_t_1 = __Pyx_CFunc_double____double____nogil_to_py(sin);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_py_sin, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[1]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">3</span>: <span class=\"n\">py_atoi</span> <span class=\"o\">=</span> <span class=\"n\">stdlib</span><span class=\"o\">.</span><span class=\"n\">atoi</span></pre>\n", "<pre class='cython code score-6 '> __pyx_t_1 = __Pyx_CFunc_int____const__char________nogil_to_py(atoi);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_py_atoi, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[1]; __pyx_lineno = 3; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython -a\n", "from libc cimport math, stdlib\n", "py_sin = math.sin\n", "py_atoi = stdlib.atoi" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "py_sin(1/2)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "123" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "py_atoi(b'123')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# External libraries" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "ename": "CompileError", "evalue": "command 'gcc' failed with exit status 1", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mCompileError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-23-df47a0dc49a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'cython'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'# distutils: include_dirs=/usr/include/luajit-2.0\\n# distutils: libraries=luajit-5.1\\n\\n## distutils: include_dirs=/usr/include/lua5.1\\n## distutils: libraries=lua5.1\\n\\ncdef extern from \"lua.h\":\\n ctypedef struct lua_State\\n lua_State *luaL_newstate ()\\n void lua_close (lua_State *L)\\n int luaL_loadbuffer (lua_State *L, char *buff, size_t sz, char *name)\\n void lua_settop (lua_State *L, int idx)\\n int lua_gettop (lua_State *L)\\n int lua_pcall (lua_State *L, int nargs, int nresults, int errfunc)\\n int lua_type (lua_State *L, int idx)\\n float lua_tonumber (lua_State *L, int idx)\\n enum:\\n LUA_TNUMBER\\n LUA_MULTRET\\n\\n\\ndef run_lua(code):\\n cdef int result_status\\n cdef float result\\n\\n if isinstance(code, unicode):\\n code = code.encode(\\'utf8\\')\\n elif not isinstance(code, bytes):\\n raise ValueError(\"code must be a string\")\\n\\n # init Lua runtime\\n L = luaL_newstate()\\n if not L:\\n raise MemoryError()\\n\\n try:\\n # compile Lua code\\n if luaL_loadbuffer(L, code, len(code), \\'<python>\\'):\\n raise SyntaxError()\\n\\n # execute code\\n if lua_pcall(L, 0, LUA_MULTRET, 0):\\n raise RuntimeError()\\n\\n # convert return value (Lua number == float)\\n assert lua_type(L, 1) == LUA_TNUMBER, \"not a numeric return value\"\\n return lua_tonumber(L, 1)\\n finally:\\n lua_settop(L, 0)\\n lua_close(L)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2262\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2263\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2264\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2265\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/site-packages/Cython/Build/IpythonMagic.pyc\u001b[0m in \u001b[0;36mcython\u001b[0;34m(self, line, cell)\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/site-packages/Cython/Build/IpythonMagic.pyc\u001b[0m in \u001b[0;36mcython\u001b[0;34m(self, line, cell)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[0mbuild_extension\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpyx_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mbuild_extension\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_lib\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib_dir\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m \u001b[0mbuild_extension\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\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 279\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_code_cache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/distutils/command/build_ext.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0;31m# Now actually compile and link everything.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_extensions\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 338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcheck_extensions_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/distutils/command/build_ext.pyc\u001b[0m in \u001b[0;36mbuild_extensions\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mext\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextensions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbuild_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/distutils/command/build_ext.pyc\u001b[0m in \u001b[0;36mbuild_extension\u001b[0;34m(self, ext)\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0mextra_postargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextra_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m depends=ext.depends)\n\u001b[0m\u001b[1;32m 497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0;31m# XXX -- this is a Vile HACK!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/distutils/ccompiler.pyc\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, sources, output_dir, macros, include_dirs, debug, extra_preargs, extra_postargs, depends)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextra_postargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpp_opts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[0;31m# Return *all* object filenames, not just the ones we just built.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/markhoughton/anaconda/lib/python2.7/distutils/unixccompiler.pyc\u001b[0m in \u001b[0;36m_compile\u001b[0;34m(self, obj, src, ext, cc_args, extra_postargs, pp_opts)\u001b[0m\n\u001b[1;32m 120\u001b[0m extra_postargs)\n\u001b[1;32m 121\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mDistutilsExecError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCompileError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m def create_static_lib(self, objects, output_libname,\n", "\u001b[0;31mCompileError\u001b[0m: command 'gcc' failed with exit status 1" ] } ], "source": [ "%%cython\n", "# distutils: include_dirs=/usr/include/luajit-2.0\n", "# distutils: libraries=luajit-5.1\n", "\n", "## distutils: include_dirs=/usr/include/lua5.1\n", "## distutils: libraries=lua5.1\n", "\n", "cdef extern from \"lua.h\":\n", " ctypedef struct lua_State\n", " lua_State *luaL_newstate ()\n", " void lua_close 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class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple_);\n", "/* … */\n", " __pyx_t_1 = PyCFunction_NewEx(&amp;__pyx_mdef_46_cython_magic_7464125faea6c1b99d5f1e14b64d3824_1ctuples, NULL, __pyx_n_s_cython_magic_7464125faea6c1b99d);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_ctuples, __pyx_t_1) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">03</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">a</span> <span class=\"o\">=</span> <span class=\"mf\">42</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_a = 42;\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">04</span>: <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">b</span> <span class=\"o\">=</span> <span class=\"n\">a</span> <span class=\"o\">/</span> <span class=\"mf\">5.0</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_b = (__pyx_v_a / 5.0);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">06</span>: <span class=\"k\">cdef</span> (<span class=\"nf\">int</span><span class=\"p\">,</span> <span class=\"kt\">double</span>) <span class=\"nf\">t</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">a</span><span class=\"p\">,</span> <span class=\"n\">b</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_1.f0 = __pyx_v_a;\n", " __pyx_t_1.f1 = __pyx_v_b;\n", " __pyx_v_t = __pyx_t_1;\n", "</pre><pre class=\"cython line score-5\" onclick='toggleDiv(this)'>+<span class=\"\">07</span>: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n", "<pre class='cython code score-5 '> __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_t.f0);<span class='error_goto'> if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " if (<span class='pyx_c_api'>__Pyx_PrintOne</span>(0, __pyx_t_2) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", "</pre><pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">08</span>: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">])</span></pre>\n", "<pre class='cython code score-8 '> __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_t.f1);<span class='error_goto'> if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " if (<span class='pyx_c_api'>__Pyx_PrintOne</span>(0, __pyx_t_2) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", "</pre><pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">09</span>: <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-3 '> __pyx_t_2 = __pyx_convert__to_py___pyx_ctuple_int__and_double(__pyx_v_t);<span class='error_goto'> if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " if (<span class='pyx_c_api'>__Pyx_PrintOne</span>(0, __pyx_t_2) &lt; 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">10</span>: </pre>\n", "<pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">11</span>: <span class=\"k\">return</span> <span class=\"n\">t</span></pre>\n", "<pre class='cython code score-1 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_2 = __pyx_convert__to_py___pyx_ctuple_int__and_double(__pyx_v_t);<span class='error_goto'> if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 11; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " __pyx_r = __pyx_t_2;\n", " __pyx_t_2 = 0;\n", " goto __pyx_L0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython -a\n", "\n", "def ctuples():\n", " cdef int a = 42\n", " cdef double b = a / 5.0\n", "\n", " cdef (int, double) t = (a, b)\n", " print(t[0])\n", " print(t[1])\n", " print(t)\n", "\n", " return t\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n", "8.4\n", "(42, 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Integers()\n", "x.add(2)\n", "x.add(3)\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
vadim-ivlev/STUDY
handson-data-science-python/DataScience-Python3/.ipynb_checkpoints/MeanMedianMode-checkpoint.ipynb
1
23771
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mean, Median, Mode, and introducing NumPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean vs. Median" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create some fake income data, centered around 27,000 with a normal distribution and standard deviation of 15,000, with 10,000 data points. (We'll discuss those terms more later, if you're not familiar with them.)\n", "\n", "Then, compute the mean (average) - it should be close to 27,000:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "27320.73747742573" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "incomes = np.random.normal(27000, 15000, 10000)\n", "np.mean(incomes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can segment the income data into 50 buckets, and plot it as a histogram:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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SJE0qfY2kR8R2lGkuD1Dmo2/NBmDHIR6b2mwfGUHtiKxevb6fp4str5Q9l3Wyf6Tx4c/c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\nF4me/xRP+QxlOb//vuC0CeH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"text/plain": [ "<matplotlib.figure.Figure at 0x11d5ec860>" ] }, "metadata": { "image/png": { "height": 248, "width": 372 } }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "# %config InlineBackend.figure_format='retina'\n", "# import seaborn as sns\n", "\n", "# sns.set_context(\"paper\")\n", "# sns.set_style(\"white\")\n", "# sns.set()\n", "\n", "import matplotlib.pyplot as plt\n", "plt.hist(incomes, 50)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compute the median - since we have a nice, even distribution it too should be close to 27,000:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "27428.566762737137" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(incomes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll add Donald Trump into the mix. Darn income inequality!" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "incomes = np.append(incomes, [1000000000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The median won't change much, but the mean does:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "27429.036458484166" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(incomes)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "127237.31216460519" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(incomes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's generate some fake age data for 500 people:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([21, 44, 52, 20, 68, 43, 65, 78, 69, 30, 77, 69, 24, 63, 65, 79, 47,\n", " 54, 33, 49, 53, 18, 65, 64, 65, 44, 86, 47, 87, 18, 31, 32, 27, 35,\n", " 88, 28, 79, 48, 45, 84, 88, 48, 73, 79, 28, 64, 52, 30, 76, 74, 24,\n", " 60, 82, 37, 53, 26, 19, 89, 28, 26, 82, 55, 72, 56, 22, 36, 30, 39,\n", " 53, 38, 29, 51, 59, 75, 41, 35, 75, 42, 53, 29, 26, 26, 54, 68, 38,\n", " 86, 86, 68, 72, 23, 19, 82, 35, 55, 78, 28, 18, 21, 83, 72, 89, 46,\n", " 66, 75, 34, 34, 74, 53, 26, 60, 43, 47, 73, 30, 75, 44, 70, 35, 76,\n", " 46, 73, 67, 72, 39, 48, 43, 34, 42, 43, 63, 80, 30, 28, 28, 40, 64,\n", " 53, 88, 77, 82, 72, 27, 45, 43, 67, 83, 26, 58, 81, 41, 70, 86, 89,\n", " 28, 88, 32, 57, 87, 63, 57, 41, 24, 20, 40, 42, 66, 76, 46, 37, 62,\n", " 61, 20, 48, 36, 49, 39, 68, 79, 69, 83, 62, 69, 62, 52, 58, 23, 71,\n", " 34, 27, 22, 27, 78, 51, 76, 56, 43, 66, 62, 47, 73, 45, 66, 82, 88,\n", " 48, 72, 24, 45, 42, 59, 35, 37, 60, 70, 37, 65, 37, 88, 28, 28, 39,\n", " 66, 52, 64, 50, 56, 55, 52, 30, 61, 36, 45, 61, 51, 77, 24, 44, 51,\n", " 42, 27, 77, 51, 27, 34, 65, 21, 72, 45, 61, 40, 46, 45, 77, 75, 24,\n", " 65, 87, 69, 63, 31, 25, 44, 45, 79, 82, 25, 89, 47, 63, 67, 60, 47,\n", " 82, 81, 27, 48, 35, 68, 38, 32, 40, 40, 59, 22, 34, 32, 88, 77, 42,\n", " 81, 20, 77, 51, 89, 34, 63, 20, 38, 55, 51, 41, 70, 60, 39, 62, 43,\n", " 34, 77, 83, 45, 81, 78, 66, 76, 71, 34, 65, 31, 27, 68, 43, 37, 71,\n", " 22, 27, 73, 61, 64, 58, 62, 24, 64, 60, 28, 60, 35, 78, 31, 35, 61,\n", " 85, 78, 68, 84, 83, 65, 79, 66, 70, 22, 73, 80, 61, 27, 57, 73, 33,\n", " 41, 61, 29, 65, 70, 80, 52, 46, 33, 19, 18, 58, 59, 72, 79, 82, 80,\n", " 65, 20, 71, 51, 51, 30, 27, 54, 87, 43, 35, 43, 53, 67, 51, 19, 49,\n", " 51, 32, 83, 65, 45, 61, 43, 70, 69, 18, 25, 78, 28, 44, 33, 46, 80,\n", " 86, 87, 69, 32, 82, 84, 39, 54, 40, 55, 29, 83, 67, 83, 35, 87, 77,\n", " 21, 52, 81, 70, 27, 71, 49, 83, 79, 50, 74, 86, 84, 58, 81, 78, 35,\n", " 59, 34, 54, 63, 61, 88, 41, 88, 60, 63, 30, 76, 35, 80, 76, 23, 18,\n", " 83, 53, 69, 61, 76, 88, 56, 59, 46, 77, 74, 89, 83, 68, 51, 62, 20,\n", " 87, 66, 29, 28, 70, 50, 43, 23, 42, 76, 20, 27, 73, 21, 26, 29, 48,\n", " 57, 34, 80, 71, 71, 51, 76])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages = np.random.randint(18, high=90, size=500)\n", "ages" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ModeResult(mode=array([27]), count=array([13]))" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "stats.mode(ages)" ] }, { "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": 1 }
mit
GlobalFishingWatch/vessel-maps
utilities/pipa_paper/.ipynb_checkpoints/density_pings_byday-v2-checkpoint.ipynb
1
1094726
null
apache-2.0